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    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Final Project Submission\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \n",
+    "# Group 1 Team Members:\n",
+    "#     1. Judy Koech                                     6. Joseph Mwangi\n",
+    "#     2. Eugene Marius                                  7. Harris Lukundi\n",
+    "#     3. Henry Rono                                     8. Sheila Mulwa\n",
+    "#     4. Esther Njagi                                   9. Phinidy George\n",
+    "#     5. Grace Ndura                                   10. Chris Laaria\n",
+    "# "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the Dynamics of Real Estate Market in King County: A Data Science Perspective"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Business Overview\n",
+    "The `US` real estate market has seen fluctuations over time, generally trending upwards with occasional declines. This trend is driven by various factors, including supply and demand dynamics, economic conditions, interest rates, demographics, and location-specific elements\n",
+    "\n",
+    "**Supply and demand** play a pivotal role in shaping house prices. Limited housing inventory compared to demand tends to drive prices higher, while an oversupply can lead to price decreases\n",
+    "\n",
+    "**Economic factors**, such as employment levels, GDP growth, and inflation, influence both supply and demand. Favorable economic conditions stimulate demand, pushing prices up, while economic downturns can dampen demand, leading to stabilization or decline in prices.\n",
+    "\n",
+    "**Interest rates** impact affordability, with lower rates boosting demand and prices, while higher rates may reduce demand and stabilize or lower prices.\n",
+    "\n",
+    "**Demographic shifts**, like changes in household formation and migration patterns, affect housing preferences and demand. Location-specific factors, including proximity to employment, quality of schools, and neighborhood amenities, also influence prices.\n",
+    "\n",
+    "Understanding these factors is crucial for navigating the real estate market effectively, whether buying, selling, investing, or developing properties."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1 PROJECT ALIGNMENT\n",
+    "\n",
+    "### 1.1. Project Scope\n",
+    "\n",
+    "Our project aims to equip Nara Real Estate(stakeholder) with the necessary insights and strategies to facilitate a successful entry into the King County real estate market. By leveraging data-driven analysis and market intelligence, we will provide actionable recommendations to navigate the complexities of the local market landscape effectively.\n",
+    "\n",
+    "### 1.2. Problem Statement:\n",
+    "\n",
+    "Despite its potential for growth and profitability, entering the King County real estate market presents Nara Real Estate with significant challenges stemming from the market's dynamic nature and diverse factors influencing supply, demand, and pricing. To ensure a successful market penetration strategy, Nara Real Estate requires a comprehensive understanding of local market dynamics, including the impact of economic conditions, demographic shifts, and location-specific elements on housing preferences and demand. Additionally, the company needs actionable insights and strategies derived from data-driven analysis to effectively identify lucrative market segments, optimize pricing strategies, and enhance client acquisition and retention efforts. Therefore, the overarching problem statement is to equip Nara Real Estate with the necessary tools, insights, and strategies to navigate the complexities of the King County real estate market and establish a strong presence while capturing market share effectively.\n",
+    "\n",
+    "### 1.3. Objectives\n",
+    "\n",
+    "Through our data analytics and market insights, we offer Nara Real Estate a strategic advantage by answering the following questions:\n",
+    "\n",
+    "**1. House features affecting the prices of houses in King County**\n",
+    "\n",
+    "Understanding home buyers' preferences can focus our campaign and help us guide clients in purchase of their new homes.\n",
+    "\n",
+    "**2. Seasonal impact on house sale prices**\n",
+    "\n",
+    "Understanding seasonal trends will influence when the campaign should be launched.\n",
+    "\n",
+    "**3. Market trends and property value**\n",
+    "\n",
+    "Using the dataset provided to create a model that predicts the market trend of the area and the property values.\n",
+    "\n",
+    "**4. Locations which have the highest average house prices**\n",
+    "\n",
+    "Understanding what locations to focus the advertising campaign on is key for our stakeholders.\n",
+    "\n",
+    "We have been provided with a dataset with house sale prices in King County, Washington State, USA from May 2014 to May 2015 to use for this project.\n",
+    "\n",
+    "### 1.4. Brief Conclusion\n",
+    "\n",
+    "Through our comprehensive analysis and strategic recommendations, we aim to empower Nara Real Estate to make informed decisions and successfully enter the King County real estate market. Our data-driven approach will help them achieve sustainable growth and enance their penetration of King county real estate market.."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 DATA UNDERSTANDING\n",
+    "### Dataset Description\n",
+    "\n",
+    "The data utilized for this project consists the following dataset:\n",
+    "\n",
+    "`data/kc_house_data.csv`: This dataset contains detailed information about individual properties in King County, including attributes such as square footage, number of bedrooms and bathrooms, location, and sale price.\n",
+    "\n",
+    "Here are the key columns in the datasets:\n",
+    "\n",
+    "`id`: Unique identifier for each house sale.\n",
+    "\n",
+    "`date`: Date of the house sale.\n",
+    "\n",
+    "`price`: Sale price of the house.\n",
+    "\n",
+    "`bedrooms`: Number of bedrooms in the house.\n",
+    "\n",
+    "`bathrooms`: Number of bathrooms in the house.\n",
+    "\n",
+    "`sqft_living`: Square footage of the living area.\n",
+    "\n",
+    "`sqft_lot`: Square footage of the lot.\n",
+    "\n",
+    "`floors`: Number of floors in the house.\n",
+    "\n",
+    "`waterfront`: Whether the house has a waterfront view (0 for no, 1 for yes).\n",
+    "\n",
+    "`condition`: Overall condition of the house.\n",
+    "\n",
+    "`grade`: Overall grade given to the housing unit, based on King County grading system.\n",
+    "\n",
+    "`sqft_above`: Square footage of the house above ground level.\n",
+    "\n",
+    "`sqft_basement`: Square footage of the basement.\n",
+    "\n",
+    "`yr_built`: Year the house was built.\n",
+    "\n",
+    "`yr_renovated`: Year the house was renovated.\n",
+    "\n",
+    "`zipcode`: Zip code of the house location.\n",
+    "\n",
+    "`lat`: Latitude coordinate of the house.\n",
+    "\n",
+    "`long`: Longitude coordinate of the house.\n",
+    "\n",
+    "`sqft_living15`: Average square footage of interior housing living space for the nearest 15 neighbors.\n",
+    "\n",
+    "`sqft_lot15`: Average square footage of the land lots of the nearest 15 neighbors.\n",
+    "\n",
+    "### Relevance of king County dataset from stakeholder\n",
+    "\n",
+    "The columns in the dataset provide crucial information about various aspects of the houses that could potentially influence their sale prices. Features such as number of bedrooms, bathrooms, square footage, condition, and grade are likely to have a significant impact on home values. We'll use these features to build regression models and identify which characteristics contribute most to home prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 DATA PREPARATION\n",
+    "\n",
+    "The approach taken shall involve the following steps:\n",
+    "\n",
+    "1. Data Mining\n",
+    "2. Data Cleaning\n",
+    "3. Data Analysis "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 DATA MINING\n",
+    "We shall import the necessary libraries for the whole data analysis approach we shall be taking as well as reading into the various documents that we shall be using. We shall display the first 5 results of each to get a better understanding of what is in each documents and give a summary of what we are observing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# libraries required for the data anaylsis\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import folium\n",
+    "import warnings\n",
+    "import plotly.io as pio\n",
+    "import plotly.graph_objs as go\n",
+    "import plotly.express as px\n",
+    "import scipy.stats as stats\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from scipy.stats import skew, kurtosis\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n",
+    "warnings.filterwarnings('ignore')\n",
+    "pio.renderers.default = 'notebook_connected'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Relevance of king County dataset from stakeholder**\n",
+    "\n",
+    "The columns in the dataset provide crucial information about various aspects of the houses that could potentially influence their sale prices. Features such as number of bedrooms, bathrooms, square footage, condition, and grade are likely to have a significant impact on home values. We'll use these features to build regression models and identify which renovations or characteristics contribute most to home prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1.1 Overview of kc_house_data dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2/25/2015</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>2720</td>\n",
+       "      <td>8062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  7129300520  10/13/2014  221900.0         3       1.00         1180   \n",
+       "1  6414100192   12/9/2014  538000.0         3       2.25         2570   \n",
+       "2  5631500400   2/25/2015  180000.0         2       1.00          770   \n",
+       "3  2487200875   12/9/2014  604000.0         4       3.00         1960   \n",
+       "4  1954400510   2/18/2015  510000.0         3       2.00         1680   \n",
+       "\n",
+       "   sqft_lot  floors waterfront  view  ...          grade sqft_above  \\\n",
+       "0      5650     1.0        NaN  NONE  ...      7 Average       1180   \n",
+       "1      7242     2.0         NO  NONE  ...      7 Average       2170   \n",
+       "2     10000     1.0         NO  NONE  ...  6 Low Average        770   \n",
+       "3      5000     1.0         NO  NONE  ...      7 Average       1050   \n",
+       "4      8080     1.0         NO  NONE  ...         8 Good       1680   \n",
+       "\n",
+       "   sqft_basement yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "0            0.0     1955           0.0    98178  47.5112 -122.257   \n",
+       "1          400.0     1951        1991.0    98125  47.7210 -122.319   \n",
+       "2            0.0     1933           NaN    98028  47.7379 -122.233   \n",
+       "3          910.0     1965           0.0    98136  47.5208 -122.393   \n",
+       "4            0.0     1987           0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "   sqft_living15  sqft_lot15  \n",
+       "0           1340        5650  \n",
+       "1           1690        7639  \n",
+       "2           2720        8062  \n",
+       "3           1360        5000  \n",
+       "4           1800        7503  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# reading the data into the king_county_df\n",
+    "king_county_df=pd.read_csv(\"data/kc_house_data.csv\")\n",
+    "king_county_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7 Average        8974\n",
+       "8 Good           6065\n",
+       "9 Better         2615\n",
+       "6 Low Average    2038\n",
+       "10 Very Good     1134\n",
+       "11 Excellent      399\n",
+       "5 Fair            242\n",
+       "12 Luxury          89\n",
+       "4 Low              27\n",
+       "13 Mansion         13\n",
+       "3 Poor              1\n",
+       "Name: grade, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df[\"grade\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21597 non-null  int64  \n",
+      " 1   date           21597 non-null  object \n",
+      " 2   price          21597 non-null  float64\n",
+      " 3   bedrooms       21597 non-null  int64  \n",
+      " 4   bathrooms      21597 non-null  float64\n",
+      " 5   sqft_living    21597 non-null  int64  \n",
+      " 6   sqft_lot       21597 non-null  int64  \n",
+      " 7   floors         21597 non-null  float64\n",
+      " 8   waterfront     19221 non-null  object \n",
+      " 9   view           21534 non-null  object \n",
+      " 10  condition      21597 non-null  object \n",
+      " 11  grade          21597 non-null  object \n",
+      " 12  sqft_above     21597 non-null  int64  \n",
+      " 13  sqft_basement  21597 non-null  object \n",
+      " 14  yr_built       21597 non-null  int64  \n",
+      " 15  yr_renovated   17755 non-null  float64\n",
+      " 16  zipcode        21597 non-null  int64  \n",
+      " 17  lat            21597 non-null  float64\n",
+      " 18  long           21597 non-null  float64\n",
+      " 19  sqft_living15  21597 non-null  int64  \n",
+      " 20  sqft_lot15     21597 non-null  int64  \n",
+      "dtypes: float64(6), int64(9), object(6)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Looking at the info printout\n",
+    "king_county_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       2014-10-13\n",
+       "1       2014-12-09\n",
+       "2       2015-02-25\n",
+       "3       2014-12-09\n",
+       "4       2015-02-18\n",
+       "           ...    \n",
+       "21592   2014-05-21\n",
+       "21593   2015-02-23\n",
+       "21594   2014-06-23\n",
+       "21595   2015-01-16\n",
+       "21596   2014-10-15\n",
+       "Name: date, Length: 21597, dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#conversion of date to dtype datetime to confirm timeframe of dataset\n",
+    "date_df=pd.to_datetime(king_county_df[\"date\"])\n",
+    "date_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2014-05-02 00:00:00'), Timestamp('2015-05-27 00:00:00'))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Timestamp of the dataframe\n",
+    "date_df.min(),date_df.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cursory Observation:\n",
+    "1. The dataframe has 21,597 entries with waterfront, view and yr_renovated having null entries\n",
+    "2. Datatypes range from int64, float64 and objects and will require further analysis\n",
+    "3. 21 columns in the dataset, further analysis to determine if all shall be used "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. DATA CLEANING\n",
+    "Data cleaning shall involve the following steps:\n",
+    "1. Check and resolve for duplicate values\n",
+    "2. Check and resolve for null values\n",
+    "3. Check and resolve for extraneous values\n",
+    "4. Perform further cleaning as needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#FUNCTIONS TO BE USED DURING DATA CLEANING\n",
+    "\n",
+    "#Function to get the number of duplictes\n",
+    "def get_duplicates(df):\n",
+    "    df=df[df.duplicated(keep=False)]\n",
+    "    return df\n",
+    "\n",
+    "# Function to get extraneous values i.e. values that look like placeholders or are exaggerated values\n",
+    "def extraneous_values(df):\n",
+    "    for col in df.columns:\n",
+    "        print(col, '\\n', df[col].value_counts(normalize=True), '\\n')\n",
+    "\n",
+    "# Function to calculate percentage of missing data in a column\n",
+    "def missing_data(df, column):\n",
+    "    length_of_df=len(df)                                                    #getting the length of the dataframe\n",
+    "    missing_data= column.isna().sum()                                       #total number of missing data in column foreign_gross\n",
+    "    percentage_of_missing_data = round((missing_data/length_of_df*100),2)   #percentage of missing data in the foreign_gross column\n",
+    "    return print(f\"Percentage of Missing Data: {percentage_of_missing_data}\"\"%\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.1 Check and resolve for duplicate values in King_county_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for duplicates in king_county_df\n",
+    "get_duplicates(king_county_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "795000620     3\n",
+       "1825069031    2\n",
+       "2019200220    2\n",
+       "7129304540    2\n",
+       "1781500435    2\n",
+       "             ..\n",
+       "7812801125    1\n",
+       "4364700875    1\n",
+       "3021059276    1\n",
+       "880000205     1\n",
+       "1777500160    1\n",
+       "Name: id, Length: 21420, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking unique identifiers for houses\n",
+    "king_county_df[\"id\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17588</th>\n",
+       "      <td>795000620</td>\n",
+       "      <td>9/24/2014</td>\n",
+       "      <td>115000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>6250</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5 Fair</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98168</td>\n",
+       "      <td>47.5045</td>\n",
+       "      <td>-122.33</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>6250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17589</th>\n",
+       "      <td>795000620</td>\n",
+       "      <td>12/15/2014</td>\n",
+       "      <td>124000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>6250</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5 Fair</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98168</td>\n",
+       "      <td>47.5045</td>\n",
+       "      <td>-122.33</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>6250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17590</th>\n",
+       "      <td>795000620</td>\n",
+       "      <td>3/11/2015</td>\n",
+       "      <td>157000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>6250</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5 Fair</td>\n",
+       "      <td>1080</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1950</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>98168</td>\n",
+       "      <td>47.5045</td>\n",
+       "      <td>-122.33</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>6250</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "17588  795000620   9/24/2014  115000.0         3        1.0         1080   \n",
+       "17589  795000620  12/15/2014  124000.0         3        1.0         1080   \n",
+       "17590  795000620   3/11/2015  157000.0         3        1.0         1080   \n",
+       "\n",
+       "       sqft_lot  floors waterfront  view  ...   grade sqft_above  \\\n",
+       "17588      6250     1.0         NO  NONE  ...  5 Fair       1080   \n",
+       "17589      6250     1.0         NO  NONE  ...  5 Fair       1080   \n",
+       "17590      6250     1.0        NaN  NONE  ...  5 Fair       1080   \n",
+       "\n",
+       "       sqft_basement yr_built  yr_renovated  zipcode      lat    long  \\\n",
+       "17588            0.0     1950           0.0    98168  47.5045 -122.33   \n",
+       "17589            0.0     1950           0.0    98168  47.5045 -122.33   \n",
+       "17590            0.0     1950           NaN    98168  47.5045 -122.33   \n",
+       "\n",
+       "       sqft_living15  sqft_lot15  \n",
+       "17588           1070        6250  \n",
+       "17589           1070        6250  \n",
+       "17590           1070        6250  \n",
+       "\n",
+       "[3 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# exploration of ID unique identifier 795000620\n",
+    "king_county_df[king_county_df[\"id\"]==795000620]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of houses sold more than once in a year:  177\n"
+     ]
+    }
+   ],
+   "source": [
+    "# difference in unique house identifiers and total entries\n",
+    "multiple_times_sold=len(king_county_df[\"id\"])-len(king_county_df[\"id\"].value_counts())\n",
+    "print(f\"No of houses sold more than once in a year: \", multiple_times_sold )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cursory Observations\n",
+    "\n",
+    "The length of the unique identifier ID is 21,420 which is less than the 21,597 entries seen from the info printout. This would indicate duplicates of the unique identifier but we can see that each entry is unique to the database.\n",
+    "We can conclude that there are 177 houses that have been sold more than once between May 2014 and May 2015. We can hence conclude that there are no duplicates and that there is flipping of houses. This can be confirmed by a report by ATTOM team (reference link in appendix) showing steady growth in house flipping in the US."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.2 Check and resolve for null values in king_county_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                  0\n",
+       "date                0\n",
+       "price               0\n",
+       "bedrooms            0\n",
+       "bathrooms           0\n",
+       "sqft_living         0\n",
+       "sqft_lot            0\n",
+       "floors              0\n",
+       "waterfront       2376\n",
+       "view               63\n",
+       "condition           0\n",
+       "grade               0\n",
+       "sqft_above          0\n",
+       "sqft_basement       0\n",
+       "yr_built            0\n",
+       "yr_renovated     3842\n",
+       "zipcode             0\n",
+       "lat                 0\n",
+       "long                0\n",
+       "sqft_living15       0\n",
+       "sqft_lot15          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#checking for null values in the king_county dataset\n",
+    "king_county_df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NO     19075\n",
+       "YES      146\n",
+       "Name: waterfront, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the counts for each value in the column waterfront\n",
+    "king_county_df[\"waterfront\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of Missing Data: 11.0%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "NO     21451\n",
+       "YES      146\n",
+       "Name: waterfront, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# percentage of missing data\n",
+    "missing_data(king_county_df, king_county_df[\"waterfront\"])\n",
+    "\n",
+    "# Replacing Nan Values in Waterfront column\n",
+    "king_county_df[\"waterfront\"].fillna(king_county_df[\"waterfront\"].mode()[0], inplace= True)\n",
+    "king_county_df[\"waterfront\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation: \n",
+    "\n",
+    "The Null values account for 11% of the dataset. While dropping these rows would be possible, we would lose quite a bit of data for this dataset and this may affect or skew the results. As the most common value is NO we assume that the NaN values are also NO and replace with it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0       17011\n",
+       "2014.0       73\n",
+       "2003.0       31\n",
+       "2013.0       31\n",
+       "2007.0       30\n",
+       "          ...  \n",
+       "1946.0        1\n",
+       "1959.0        1\n",
+       "1971.0        1\n",
+       "1951.0        1\n",
+       "1954.0        1\n",
+       "Name: yr_renovated, Length: 70, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the counts for each value in the column yr_renovated\n",
+    "king_county_df[\"yr_renovated\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of Missing Data: 17.79%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0       20853\n",
+       "2014.0       73\n",
+       "2003.0       31\n",
+       "2013.0       31\n",
+       "2007.0       30\n",
+       "          ...  \n",
+       "1946.0        1\n",
+       "1959.0        1\n",
+       "1971.0        1\n",
+       "1951.0        1\n",
+       "1954.0        1\n",
+       "Name: yr_renovated, Length: 70, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# percentage of missing data\n",
+    "missing_data(king_county_df,king_county_df[\"yr_renovated\"])\n",
+    "\n",
+    "# Replacing NaN values in yr_renovated column\n",
+    "king_county_df[\"yr_renovated\"].fillna(king_county_df[\"yr_renovated\"].mode()[0], inplace= True)\n",
+    "king_county_df[\"yr_renovated\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation: \n",
+    "Similar to the Waterfront column, dropping the missing data would greatly affect the dataset and hence we shall replace the Nan values with 0.0 which is the most common value in the column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NONE         19422\n",
+       "AVERAGE        957\n",
+       "GOOD           508\n",
+       "FAIR           330\n",
+       "EXCELLENT      317\n",
+       "Name: view, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the counts for each value in the column view\n",
+    "king_county_df[\"view\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentage of Missing Data: 0.29%\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "NONE         0.901923\n",
+       "AVERAGE      0.044441\n",
+       "GOOD         0.023591\n",
+       "FAIR         0.015325\n",
+       "EXCELLENT    0.014721\n",
+       "Name: view, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# percentage of missing data\n",
+    "missing_data(king_county_df, king_county_df[\"view\"])\n",
+    "\n",
+    "# Dropping NaN values in view column\n",
+    "king_county_df.dropna(subset=[\"view\"], inplace=True)\n",
+    "king_county_df[\"view\"].value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "The percentage of missing date is less than 1% of the dataset. We can drop these rows without causing much effect to the overall dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21534 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   id             21534 non-null  int64  \n",
+      " 1   date           21534 non-null  object \n",
+      " 2   price          21534 non-null  float64\n",
+      " 3   bedrooms       21534 non-null  int64  \n",
+      " 4   bathrooms      21534 non-null  float64\n",
+      " 5   sqft_living    21534 non-null  int64  \n",
+      " 6   sqft_lot       21534 non-null  int64  \n",
+      " 7   floors         21534 non-null  float64\n",
+      " 8   waterfront     21534 non-null  object \n",
+      " 9   view           21534 non-null  object \n",
+      " 10  condition      21534 non-null  object \n",
+      " 11  grade          21534 non-null  object \n",
+      " 12  sqft_above     21534 non-null  int64  \n",
+      " 13  sqft_basement  21534 non-null  object \n",
+      " 14  yr_built       21534 non-null  int64  \n",
+      " 15  yr_renovated   21534 non-null  float64\n",
+      " 16  zipcode        21534 non-null  int64  \n",
+      " 17  lat            21534 non-null  float64\n",
+      " 18  long           21534 non-null  float64\n",
+      " 19  sqft_living15  21534 non-null  int64  \n",
+      " 20  sqft_lot15     21534 non-null  int64  \n",
+      "dtypes: float64(6), int64(9), object(6)\n",
+      "memory usage: 3.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "king_county_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id               0\n",
+       "date             0\n",
+       "price            0\n",
+       "bedrooms         0\n",
+       "bathrooms        0\n",
+       "sqft_living      0\n",
+       "sqft_lot         0\n",
+       "floors           0\n",
+       "waterfront       0\n",
+       "view             0\n",
+       "condition        0\n",
+       "grade            0\n",
+       "sqft_above       0\n",
+       "sqft_basement    0\n",
+       "yr_built         0\n",
+       "yr_renovated     0\n",
+       "zipcode          0\n",
+       "lat              0\n",
+       "long             0\n",
+       "sqft_living15    0\n",
+       "sqft_lot15       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "1. The null values that were present in the dataset have now been resolved either by dropping the rows or replacing them.\n",
+    "2. The dataframe now contains 21,534 entries with no null values present."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.3 Check and resolve for extraneous values in king_county_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id \n",
+      " 795000620     0.000139\n",
+      "5430300171    0.000093\n",
+      "5083000375    0.000093\n",
+      "3395040550    0.000093\n",
+      "5101405604    0.000093\n",
+      "                ...   \n",
+      "3021059276    0.000046\n",
+      "880000205     0.000046\n",
+      "8165500110    0.000046\n",
+      "9492500170    0.000046\n",
+      "1777500160    0.000046\n",
+      "Name: id, Length: 21357, dtype: float64 \n",
+      "\n",
+      "date \n",
+      " 6/23/2014     0.006594\n",
+      "6/26/2014     0.006083\n",
+      "6/25/2014     0.006083\n",
+      "7/8/2014      0.005898\n",
+      "4/27/2015     0.005758\n",
+      "                ...   \n",
+      "1/17/2015     0.000046\n",
+      "11/30/2014    0.000046\n",
+      "11/2/2014     0.000046\n",
+      "5/15/2015     0.000046\n",
+      "8/3/2014      0.000046\n",
+      "Name: date, Length: 371, dtype: float64 \n",
+      "\n",
+      "price \n",
+      " 450000.0    0.007941\n",
+      "350000.0    0.007941\n",
+      "550000.0    0.007384\n",
+      "500000.0    0.007059\n",
+      "425000.0    0.006966\n",
+      "              ...   \n",
+      "870515.0    0.000046\n",
+      "336950.0    0.000046\n",
+      "386100.0    0.000046\n",
+      "176250.0    0.000046\n",
+      "884744.0    0.000046\n",
+      "Name: price, Length: 3613, dtype: float64 \n",
+      "\n",
+      "bedrooms \n",
+      " 3     0.454676\n",
+      "4     0.318798\n",
+      "2     0.127891\n",
+      "5     0.074115\n",
+      "6     0.012538\n",
+      "1     0.009102\n",
+      "7     0.001765\n",
+      "8     0.000604\n",
+      "9     0.000279\n",
+      "10    0.000139\n",
+      "11    0.000046\n",
+      "33    0.000046\n",
+      "Name: bedrooms, dtype: float64 \n",
+      "\n",
+      "bathrooms \n",
+      " 2.50    0.249094\n",
+      "1.00    0.178369\n",
+      "1.75    0.140940\n",
+      "2.25    0.094595\n",
+      "2.00    0.089440\n",
+      "1.50    0.066917\n",
+      "2.75    0.055029\n",
+      "3.00    0.034922\n",
+      "3.50    0.033900\n",
+      "3.25    0.027166\n",
+      "3.75    0.007198\n",
+      "4.00    0.006316\n",
+      "4.50    0.004644\n",
+      "4.25    0.003576\n",
+      "0.75    0.003297\n",
+      "4.75    0.001068\n",
+      "5.00    0.000975\n",
+      "5.25    0.000604\n",
+      "5.50    0.000464\n",
+      "1.25    0.000418\n",
+      "6.00    0.000232\n",
+      "5.75    0.000186\n",
+      "0.50    0.000186\n",
+      "8.00    0.000093\n",
+      "6.25    0.000093\n",
+      "6.75    0.000093\n",
+      "6.50    0.000093\n",
+      "7.50    0.000046\n",
+      "7.75    0.000046\n",
+      "Name: bathrooms, dtype: float64 \n",
+      "\n",
+      "sqft_living \n",
+      " 1300    0.006408\n",
+      "1400    0.006223\n",
+      "1440    0.006176\n",
+      "1010    0.005991\n",
+      "1800    0.005991\n",
+      "          ...   \n",
+      "4970    0.000046\n",
+      "2905    0.000046\n",
+      "2793    0.000046\n",
+      "4810    0.000046\n",
+      "1975    0.000046\n",
+      "Name: sqft_living, Length: 1033, dtype: float64 \n",
+      "\n",
+      "sqft_lot \n",
+      " 5000     0.016578\n",
+      "6000     0.013467\n",
+      "4000     0.011656\n",
+      "7200     0.010216\n",
+      "4800     0.005526\n",
+      "           ...   \n",
+      "35752    0.000046\n",
+      "937      0.000046\n",
+      "9133     0.000046\n",
+      "64438    0.000046\n",
+      "14321    0.000046\n",
+      "Name: sqft_lot, Length: 9760, dtype: float64 \n",
+      "\n",
+      "floors \n",
+      " 1.0    0.494242\n",
+      "2.0    0.381304\n",
+      "1.5    0.088372\n",
+      "3.0    0.028374\n",
+      "2.5    0.007384\n",
+      "3.5    0.000325\n",
+      "Name: floors, dtype: float64 \n",
+      "\n",
+      "waterfront \n",
+      " NO     0.993266\n",
+      "YES    0.006734\n",
+      "Name: waterfront, dtype: float64 \n",
+      "\n",
+      "view \n",
+      " NONE         0.901923\n",
+      "AVERAGE      0.044441\n",
+      "GOOD         0.023591\n",
+      "FAIR         0.015325\n",
+      "EXCELLENT    0.014721\n",
+      "Name: view, dtype: float64 \n",
+      "\n",
+      "condition \n",
+      " Average      0.649252\n",
+      "Good         0.262701\n",
+      "Very Good    0.078806\n",
+      "Fair         0.007894\n",
+      "Poor         0.001347\n",
+      "Name: condition, dtype: float64 \n",
+      "\n",
+      "grade \n",
+      " 7 Average        0.415529\n",
+      "8 Good           0.281090\n",
+      "9 Better         0.120925\n",
+      "6 Low Average    0.094316\n",
+      "10 Very Good     0.052475\n",
+      "11 Excellent     0.018436\n",
+      "5 Fair           0.011238\n",
+      "12 Luxury        0.004087\n",
+      "4 Low            0.001254\n",
+      "13 Mansion       0.000604\n",
+      "3 Poor           0.000046\n",
+      "Name: grade, dtype: float64 \n",
+      "\n",
+      "sqft_above \n",
+      " 1300    0.009845\n",
+      "1010    0.009706\n",
+      "1200    0.009473\n",
+      "1220    0.008870\n",
+      "1140    0.008498\n",
+      "          ...   \n",
+      "2665    0.000046\n",
+      "2601    0.000046\n",
+      "440     0.000046\n",
+      "2473    0.000046\n",
+      "1975    0.000046\n",
+      "Name: sqft_above, Length: 942, dtype: float64 \n",
+      "\n",
+      "sqft_basement \n",
+      " 0.0       0.594316\n",
+      "?         0.020990\n",
+      "600.0     0.010031\n",
+      "500.0     0.009706\n",
+      "700.0     0.009613\n",
+      "            ...   \n",
+      "506.0     0.000046\n",
+      "2050.0    0.000046\n",
+      "2130.0    0.000046\n",
+      "508.0     0.000046\n",
+      "784.0     0.000046\n",
+      "Name: sqft_basement, Length: 302, dtype: float64 \n",
+      "\n",
+      "yr_built \n",
+      " 2014    0.025913\n",
+      "2006    0.021037\n",
+      "2005    0.020804\n",
+      "2004    0.019968\n",
+      "2003    0.019458\n",
+      "          ...   \n",
+      "1933    0.001393\n",
+      "1901    0.001347\n",
+      "1902    0.001254\n",
+      "1935    0.001115\n",
+      "1934    0.000975\n",
+      "Name: yr_built, Length: 116, dtype: float64 \n",
+      "\n",
+      "yr_renovated \n",
+      " 0.0       0.965496\n",
+      "2014.0    0.003390\n",
+      "2003.0    0.001440\n",
+      "2013.0    0.001440\n",
+      "2007.0    0.001393\n",
+      "            ...   \n",
+      "1946.0    0.000046\n",
+      "1959.0    0.000046\n",
+      "1971.0    0.000046\n",
+      "1951.0    0.000046\n",
+      "1954.0    0.000046\n",
+      "Name: yr_renovated, Length: 70, dtype: float64 \n",
+      "\n",
+      "zipcode \n",
+      " 98103    0.027909\n",
+      "98038    0.027213\n",
+      "98115    0.026934\n",
+      "98052    0.026609\n",
+      "98117    0.025680\n",
+      "           ...   \n",
+      "98102    0.004830\n",
+      "98010    0.004644\n",
+      "98024    0.003669\n",
+      "98148    0.002647\n",
+      "98039    0.002322\n",
+      "Name: zipcode, Length: 70, dtype: float64 \n",
+      "\n",
+      "lat \n",
+      " 47.6846    0.000789\n",
+      "47.6624    0.000789\n",
+      "47.5322    0.000789\n",
+      "47.5491    0.000789\n",
+      "47.6711    0.000743\n",
+      "             ...   \n",
+      "47.2916    0.000046\n",
+      "47.1795    0.000046\n",
+      "47.3773    0.000046\n",
+      "47.4710    0.000046\n",
+      "47.2715    0.000046\n",
+      "Name: lat, Length: 5029, dtype: float64 \n",
+      "\n",
+      "long \n",
+      " -122.290    0.005340\n",
+      "-122.300    0.005062\n",
+      "-122.362    0.004830\n",
+      "-122.291    0.004644\n",
+      "-122.372    0.004597\n",
+      "              ...   \n",
+      "-121.947    0.000046\n",
+      "-121.721    0.000046\n",
+      "-121.743    0.000046\n",
+      "-122.460    0.000046\n",
+      "-121.849    0.000046\n",
+      "Name: long, Length: 750, dtype: float64 \n",
+      "\n",
+      "sqft_living15 \n",
+      " 1540    0.009148\n",
+      "1440    0.009055\n",
+      "1560    0.008870\n",
+      "1500    0.008312\n",
+      "1460    0.007848\n",
+      "          ...   \n",
+      "4890    0.000046\n",
+      "2873    0.000046\n",
+      "952     0.000046\n",
+      "3193    0.000046\n",
+      "2049    0.000046\n",
+      "Name: sqft_living15, Length: 776, dtype: float64 \n",
+      "\n",
+      "sqft_lot15 \n",
+      " 5000      0.019783\n",
+      "4000      0.016532\n",
+      "6000      0.013374\n",
+      "7200      0.009752\n",
+      "4800      0.006734\n",
+      "            ...   \n",
+      "8989      0.000046\n",
+      "871200    0.000046\n",
+      "809       0.000046\n",
+      "4907      0.000046\n",
+      "6147      0.000046\n",
+      "Name: sqft_lot15, Length: 8663, dtype: float64 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking for extraneous values in all columns\n",
+    "extraneous_values(king_county_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    " * There is a placeholder in the sqft_basement column i.e ? which needs to be replaced\n",
+    " * There also seems to be an extraneous value in the bedrooms columns i.e. 33 bedrooms\n",
+    " * All other columns do not seem to have any extraneous values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0       12798\n",
+       "?           452\n",
+       "600.0       216\n",
+       "500.0       209\n",
+       "700.0       207\n",
+       "          ...  \n",
+       "506.0         1\n",
+       "2050.0        1\n",
+       "2130.0        1\n",
+       "508.0         1\n",
+       "784.0         1\n",
+       "Name: sqft_basement, Length: 302, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the counts for each value in the column sqft_basement\n",
+    "king_county_df[\"sqft_basement\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "datatype for column: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking column datatype\n",
+    "print(f\"datatype for column:\", king_county_df[\"sqft_basement\"].dtype)\n",
+    "\n",
+    "# creating a variable to hold the dataframe with sqft basement with a datatype of float\n",
+    "king_county_df['sqft_basement'] = pd.to_numeric(king_county_df['sqft_basement'], errors=\"coerce\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Quick plot to check for outliers in the data set\n",
+    "sns.boxplot(y=king_county_df['sqft_basement'],color=\"r\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([   0.,  400.,  910., 1530.,  730., 1700.,  300.,  970.,  760.,\n",
+       "        720.,  700.,  820.,  780.,  790.,  330., 1620.,  360.,  588.,\n",
+       "       1510.,  410.,  990.,  600.,  560.,  550., 1000., 1600.,  500.,\n",
+       "       1040.,  880., 1010.,  240.,  265.,  290.,  800.,  540.,  840.,\n",
+       "        380.,  770.,  480.,  570., 1490.,  620., 1250., 1270.,  120.,\n",
+       "        650.,  180., 1130.,  450., 1640., 1460., 1020., 1030.,  750.,\n",
+       "        640., 1070.,  490., 1310.,  630., 2000.,  390.,  430.,  210.,\n",
+       "       1430., 1950.,  440.,  220., 1160.,  860.,  580., 2060., 1820.,\n",
+       "       1180.,  200., 1150., 1200.,  680.,  530., 1450., 1170., 1080.,\n",
+       "        960.,  280.,  870., 1100.,  460., 1400.,  660., 1220.,  900.,\n",
+       "        420., 1580., 1380.,  475.,  690.,  270.,  350.,  935.,  710.,\n",
+       "       1370.,  980.,  850., 1470.,  160.,  950.,   50.,  740., 1780.,\n",
+       "       1900.,  340.,  470.,  370.,  140., 1760.,  130.,  520.,  890.,\n",
+       "       1110.,  150., 1720.,  810.,  190., 1290.,  670., 1800., 1120.,\n",
+       "       1810.,   60., 1050.,  940.,  310.,  930., 1390.,  610., 1830.,\n",
+       "       1300.,  510., 1330., 1590.,  920., 1320., 1420., 1240., 1960.,\n",
+       "       1560., 2020., 1190., 2110., 1280.,  250., 1230.,  170.,  830.,\n",
+       "       1260., 1410., 1340.,  590., 1500., 1140.,  260.,  100.,  320.,\n",
+       "       1480., 1060., 1284., 1670., 1350., 2570., 1090.,  110., 2500.,\n",
+       "         90., 1940., 1550., 2350., 2490., 1481., 1360., 1135., 1520.,\n",
+       "       1850., 1660., 2130., 2600., 1690.,  243., 1210., 1024., 1798.,\n",
+       "       1610., 1440., 1570., 1650.,  704., 1910., 1630., 2360., 1852.,\n",
+       "       2090., 2400., 1790., 2150.,  230.,   70., 1680., 2100., 3000.,\n",
+       "       1870., 1710., 2030.,  875., 1540., 2850., 2170.,  506.,  906.,\n",
+       "        145., 2040.,  784., 1750.,  374.,  518., 2720., 2730., 1840.,\n",
+       "       3480., 2160., 1920., 2330., 1860., 2050., 4820., 1913.,   80.,\n",
+       "       2010., 3260., 2200.,  415., 1730.,  652., 2196., 1930.,  515.,\n",
+       "         40., 2080., 2580., 1548., 1740.,  235.,  861., 1890., 2220.,\n",
+       "        792., 2070., 4130., 2250., 2240., 1990.,  768., 2550.,  435.,\n",
+       "       1008., 2300., 2610.,  666., 3500.,  172., 1816., 2190., 1245.,\n",
+       "       1525., 1880.,  862.,  946., 1281.,  414.,  276., 1248.,  602.,\n",
+       "        516.,  176.,  225., 1275.,  266.,  283.,   65., 2310.,   10.,\n",
+       "       1770., 2120.,  295.,  207.,  915.,  556.,  417.,  143.,  508.,\n",
+       "       2810.,   20.,  274.,  248.])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Median value is selected as it is not affected by the outliers\n",
+    "median_value=king_county_df['sqft_basement'].median()\n",
+    "print(median_value)\n",
+    "\n",
+    "# replacing placeholder value with median_value derived above\n",
+    "king_county_df[\"sqft_basement\"].replace(np.nan,median_value, inplace=True)\n",
+    "king_county_df[\"sqft_basement\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0       13250\n",
+       "600.0       216\n",
+       "500.0       209\n",
+       "700.0       207\n",
+       "800.0       201\n",
+       "          ...  \n",
+       "588.0         1\n",
+       "1920.0        1\n",
+       "602.0         1\n",
+       "1245.0        1\n",
+       "1135.0        1\n",
+       "Name: sqft_basement, Length: 301, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df['sqft_basement'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3     9791\n",
+       "4     6865\n",
+       "2     2754\n",
+       "5     1596\n",
+       "6      270\n",
+       "1      196\n",
+       "7       38\n",
+       "8       13\n",
+       "9        6\n",
+       "10       3\n",
+       "11       1\n",
+       "33       1\n",
+       "Name: bedrooms, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Checking the counts for each value in the column bedrooms\n",
+    "king_county_df[\"bedrooms\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15856</th>\n",
+       "      <td>2402100895</td>\n",
+       "      <td>6/25/2014</td>\n",
+       "      <td>640000.0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1620</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98103</td>\n",
+       "      <td>47.6878</td>\n",
+       "      <td>-122.331</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>4700</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "15856  2402100895  6/25/2014  640000.0        33       1.75         1620   \n",
+       "\n",
+       "       sqft_lot  floors waterfront  view  ...      grade sqft_above  \\\n",
+       "15856      6000     1.0         NO  NONE  ...  7 Average       1040   \n",
+       "\n",
+       "       sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "15856          580.0      1947           0.0    98103  47.6878 -122.331   \n",
+       "\n",
+       "       sqft_living15  sqft_lot15  \n",
+       "15856           1330        4700  \n",
+       "\n",
+       "[1 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df[king_county_df[\"bedrooms\"]==33]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>10/13/2014</td>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>5650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>12/9/2014</td>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>1991.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>7639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2/18/2015</td>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1321400060</td>\n",
+       "      <td>6/27/2014</td>\n",
+       "      <td>257500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>1715</td>\n",
+       "      <td>6819</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1715</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1995</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98003</td>\n",
+       "      <td>47.3097</td>\n",
+       "      <td>-122.327</td>\n",
+       "      <td>2238</td>\n",
+       "      <td>6819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2414600126</td>\n",
+       "      <td>4/15/2015</td>\n",
+       "      <td>229500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>7470</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1050</td>\n",
+       "      <td>730.0</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98146</td>\n",
+       "      <td>47.5123</td>\n",
+       "      <td>-122.337</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>8113</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "0  7129300520  10/13/2014  221900.0         3       1.00         1180   \n",
+       "1  6414100192   12/9/2014  538000.0         3       2.25         2570   \n",
+       "4  1954400510   2/18/2015  510000.0         3       2.00         1680   \n",
+       "6  1321400060   6/27/2014  257500.0         3       2.25         1715   \n",
+       "8  2414600126   4/15/2015  229500.0         3       1.00         1780   \n",
+       "\n",
+       "   sqft_lot  floors waterfront  view  ...      grade sqft_above  \\\n",
+       "0      5650     1.0         NO  NONE  ...  7 Average       1180   \n",
+       "1      7242     2.0         NO  NONE  ...  7 Average       2170   \n",
+       "4      8080     1.0         NO  NONE  ...     8 Good       1680   \n",
+       "6      6819     2.0         NO  NONE  ...  7 Average       1715   \n",
+       "8      7470     1.0         NO  NONE  ...  7 Average       1050   \n",
+       "\n",
+       "   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n",
+       "0            0.0      1955           0.0    98178  47.5112 -122.257   \n",
+       "1          400.0      1951        1991.0    98125  47.7210 -122.319   \n",
+       "4            0.0      1987           0.0    98074  47.6168 -122.045   \n",
+       "6            0.0      1995           0.0    98003  47.3097 -122.327   \n",
+       "8          730.0      1960           0.0    98146  47.5123 -122.337   \n",
+       "\n",
+       "   sqft_living15  sqft_lot15  \n",
+       "0           1340        5650  \n",
+       "1           1690        7639  \n",
+       "4           1800        7503  \n",
+       "6           2238        6819  \n",
+       "8           1780        8113  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df[king_county_df[\"bedrooms\"]==3][:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "In the bedrooms column, from domain knowledge we know that it is not possible for a house to have 33 bedrooms in a living space of 1620 sqft. Compared to 3 bedroom houses, the former seems to be a typing error and we shall replace 33 bedrooms with 3 bedrooms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3     9792\n",
+       "4     6865\n",
+       "2     2754\n",
+       "5     1596\n",
+       "6      270\n",
+       "1      196\n",
+       "7       38\n",
+       "8       13\n",
+       "9        6\n",
+       "10       3\n",
+       "11       1\n",
+       "Name: bedrooms, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# replacing erroneous value with rationalized value\n",
+    "king_county_df[\"bedrooms\"].replace(33,3, inplace=True)\n",
+    "king_county_df[\"bedrooms\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>id</th>\n",
+       "      <th>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.153400e+04</td>\n",
+       "      <td>2.153400e+04</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>2.153400e+04</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.582351e+09</td>\n",
+       "      <td>5.400577e+05</td>\n",
+       "      <td>3.371645</td>\n",
+       "      <td>2.115712</td>\n",
+       "      <td>2079.827854</td>\n",
+       "      <td>1.509060e+04</td>\n",
+       "      <td>1.494126</td>\n",
+       "      <td>1788.557537</td>\n",
+       "      <td>285.244311</td>\n",
+       "      <td>1971.002275</td>\n",
+       "      <td>68.866722</td>\n",
+       "      <td>98077.939352</td>\n",
+       "      <td>47.560180</td>\n",
+       "      <td>-122.213948</td>\n",
+       "      <td>1986.299944</td>\n",
+       "      <td>12751.079502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.876779e+09</td>\n",
+       "      <td>3.660596e+05</td>\n",
+       "      <td>0.904144</td>\n",
+       "      <td>0.768602</td>\n",
+       "      <td>917.446520</td>\n",
+       "      <td>4.138021e+04</td>\n",
+       "      <td>0.539806</td>\n",
+       "      <td>827.745641</td>\n",
+       "      <td>439.334095</td>\n",
+       "      <td>29.376044</td>\n",
+       "      <td>364.314552</td>\n",
+       "      <td>53.506639</td>\n",
+       "      <td>0.138528</td>\n",
+       "      <td>0.140735</td>\n",
+       "      <td>685.121001</td>\n",
+       "      <td>27255.483308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000102e+06</td>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98001.000000</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.123212e+09</td>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98033.000000</td>\n",
+       "      <td>47.471200</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.904945e+09</td>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.617000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98065.000000</td>\n",
+       "      <td>47.571900</td>\n",
+       "      <td>-122.230000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7620.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.312175e+09</td>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068775e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>98118.000000</td>\n",
+       "      <td>47.678100</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10083.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.900000e+09</td>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>4820.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>98199.000000</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
+       "count  2.153400e+04  2.153400e+04  21534.000000  21534.000000  21534.000000   \n",
+       "mean   4.582351e+09  5.400577e+05      3.371645      2.115712   2079.827854   \n",
+       "std    2.876779e+09  3.660596e+05      0.904144      0.768602    917.446520   \n",
+       "min    1.000102e+06  7.800000e+04      1.000000      0.500000    370.000000   \n",
+       "25%    2.123212e+09  3.220000e+05      3.000000      1.750000   1430.000000   \n",
+       "50%    3.904945e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
+       "75%    7.312175e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
+       "max    9.900000e+09  7.700000e+06     11.000000      8.000000  13540.000000   \n",
+       "\n",
+       "           sqft_lot        floors    sqft_above  sqft_basement      yr_built  \\\n",
+       "count  2.153400e+04  21534.000000  21534.000000   21534.000000  21534.000000   \n",
+       "mean   1.509060e+04      1.494126   1788.557537     285.244311   1971.002275   \n",
+       "std    4.138021e+04      0.539806    827.745641     439.334095     29.376044   \n",
+       "min    5.200000e+02      1.000000    370.000000       0.000000   1900.000000   \n",
+       "25%    5.040000e+03      1.000000   1190.000000       0.000000   1951.000000   \n",
+       "50%    7.617000e+03      1.500000   1560.000000       0.000000   1975.000000   \n",
+       "75%    1.068775e+04      2.000000   2210.000000     550.000000   1997.000000   \n",
+       "max    1.651359e+06      3.500000   9410.000000    4820.000000   2015.000000   \n",
+       "\n",
+       "       yr_renovated       zipcode           lat          long  sqft_living15  \\\n",
+       "count  21534.000000  21534.000000  21534.000000  21534.000000   21534.000000   \n",
+       "mean      68.866722  98077.939352     47.560180   -122.213948    1986.299944   \n",
+       "std      364.314552     53.506639      0.138528      0.140735     685.121001   \n",
+       "min        0.000000  98001.000000     47.155900   -122.519000     399.000000   \n",
+       "25%        0.000000  98033.000000     47.471200   -122.328000    1490.000000   \n",
+       "50%        0.000000  98065.000000     47.571900   -122.230000    1840.000000   \n",
+       "75%        0.000000  98118.000000     47.678100   -122.125000    2360.000000   \n",
+       "max     2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n",
+       "\n",
+       "          sqft_lot15  \n",
+       "count   21534.000000  \n",
+       "mean    12751.079502  \n",
+       "std     27255.483308  \n",
+       "min       651.000000  \n",
+       "25%      5100.000000  \n",
+       "50%      7620.000000  \n",
+       "75%     10083.000000  \n",
+       "max    871200.000000  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "All extraneous and placeholder values have be catered for and we can see that all the values fall withing acceptable parameters which may include outliers\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2.4  Perform further cleaning as needed in king_county_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Further Feature engineering to know in which seasons a house was sold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+       "       'lat', 'long', 'sqft_living15', 'sqft_lot15', 'seasons'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# applying mapping to determine which season relates to each month\n",
+    "season_mapping={\n",
+    "    \"03\": \"Spring\", \"04\": \"Spring\", \"05\": \"Spring\",\n",
+    "    \"06\": \"Summer\", \"07\": \"Summer\", \"08\": \"Summer\",\n",
+    "    \"09\": \"Autumn\", \"10\": \"Autumn\", \"11\": \"Autumn\",\n",
+    "    \"12\":\"Winter\", \"01\":\"Winter\", \"02\":\"Winter\"\n",
+    "}\n",
+    "king_county_df[\"seasons\"] = date_df.apply(lambda x: x.strftime('%m')).map(season_mapping)\n",
+    "king_county_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Spring    6493\n",
+       "Summer    6320\n",
+       "Autumn    5042\n",
+       "Winter    3679\n",
+       "Name: seasons, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df[\"seasons\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
+       "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
+       "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
+       "       'lat', 'long', 'sqft_living15', 'sqft_lot15', 'seasons', 'house_age'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Age of the House\n",
+    "# Calculating the age of the house by subtracting the year built from 2015.\n",
+    "current_year = 2015\n",
+    "king_county_df['house_age'] = current_year - king_county_df['yr_built']\n",
+    "king_county_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counts of unique values in the 'renovated' column:\n",
+      "0    20791\n",
+      "1      743\n",
+      "Name: renovated, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Renovated Status for renovated, 0 for not renovated).\n",
+    "# This feature captures the potential impact of renovations on house prices.\n",
+    "# Creating a binary feature indicating whether the house has been renovated (1 for renovated, 0 for not renovated)\n",
+    "king_county_df['renovated'] = (king_county_df['yr_renovated'] != 0).astype(int)\n",
+    "\n",
+    "# Count the unique values in the 'renovated' column\n",
+    "renovated_counts = king_county_df['renovated'].value_counts()\n",
+    "\n",
+    "# Display the counts\n",
+    "print(\"Counts of unique values in the 'renovated' column:\")\n",
+    "print(renovated_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping columns that do not provide actionable insight\n",
+    "to_drop = [\n",
+    "'id',\n",
+    "'sqft_living15',\n",
+    "'sqft_lot15',\n",
+    "]\n",
+    "king_county_df.drop(columns=to_drop, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21534 entries, 0 to 21596\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   date           21534 non-null  object \n",
+      " 1   price          21534 non-null  float64\n",
+      " 2   bedrooms       21534 non-null  int64  \n",
+      " 3   bathrooms      21534 non-null  float64\n",
+      " 4   sqft_living    21534 non-null  int64  \n",
+      " 5   sqft_lot       21534 non-null  int64  \n",
+      " 6   floors         21534 non-null  float64\n",
+      " 7   waterfront     21534 non-null  object \n",
+      " 8   view           21534 non-null  object \n",
+      " 9   condition      21534 non-null  object \n",
+      " 10  grade          21534 non-null  object \n",
+      " 11  sqft_above     21534 non-null  int64  \n",
+      " 12  sqft_basement  21534 non-null  float64\n",
+      " 13  yr_built       21534 non-null  int64  \n",
+      " 14  yr_renovated   21534 non-null  float64\n",
+      " 15  zipcode        21534 non-null  int64  \n",
+      " 16  lat            21534 non-null  float64\n",
+      " 17  long           21534 non-null  float64\n",
+      " 18  seasons        21534 non-null  object \n",
+      " 19  house_age      21534 non-null  int64  \n",
+      " 20  renovated      21534 non-null  int32  \n",
+      "dtypes: float64(7), int32(1), int64(7), object(6)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "king_county_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categorical columns: ['date', 'waterfront', 'view', 'condition', 'grade', 'seasons']\n",
+      "Numerical columns: ['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'house_age', 'renovated']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Splitting the columns based on variable types\n",
+    "categorical_columns = []\n",
+    "numerical_columns = []\n",
+    "for i in king_county_df.columns:\n",
+    "    if king_county_df[i].dtype == 'O':\n",
+    "        categorical_columns.append(i)\n",
+    "    else:\n",
+    "        numerical_columns.append(i)\n",
+    "        \n",
+    "print(f\"Categorical columns:\",  categorical_columns) \n",
+    "print(f\"Numerical columns:\",  numerical_columns) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 2000x2000 with 15 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Outlier Analysis for numeric variables\n",
+    "fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(20,20))\n",
+    "\n",
+    "# Iterate over the columns and create the box plots\n",
+    "for i, column in enumerate(numerical_columns):\n",
+    "    sns.boxplot(y=king_county_df[column], ax=axes[i // 3, i % 3])\n",
+    "    axes[i // 3, i % 3].set_xlabel(column)  \n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "   1. Multiple numeric variables have outliers but given that this is real data, we will avoid removing outliers as it will not give accurate insight into the dataset.\n",
+    "   2. Numeric variables such as zipcode, latitude and longitude seem not to have an outliers at all but from our domain knowledge we know that they are data related to the location of the data hence we can assertain that all the information is for King County City\n",
+    "   3. We also see that variable floor and year built have no outliers showing that houses built range between approximately the years 1950 and 2000 while the houses are do not go above 3.5 floors indicating no highrises/skyscrapers were sold in that calendar year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4 EXPLORATORY DATA ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Univariate EDA\n",
+    "\n",
+    "We'll explore the distribution of individual variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>bathrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>...</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovated</th>\n",
+       "      <th>date</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>seasons</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.153400e+04</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>2.153400e+04</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</td>\n",
+       "      <td>21534.000000</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>mean</th>\n",
+       "      <td>5.400577e+05</td>\n",
+       "      <td>3.371645</td>\n",
+       "      <td>2.115712</td>\n",
+       "      <td>2079.827854</td>\n",
+       "      <td>1.509060e+04</td>\n",
+       "      <td>1.494126</td>\n",
+       "      <td>1788.557537</td>\n",
+       "      <td>285.244311</td>\n",
+       "      <td>1971.002275</td>\n",
+       "      <td>68.866722</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.560180</td>\n",
+       "      <td>-122.213948</td>\n",
+       "      <td>43.997725</td>\n",
+       "      <td>0.034504</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>std</th>\n",
+       "      <td>3.660596e+05</td>\n",
+       "      <td>0.904144</td>\n",
+       "      <td>0.768602</td>\n",
+       "      <td>917.446520</td>\n",
+       "      <td>4.138021e+04</td>\n",
+       "      <td>0.539806</td>\n",
+       "      <td>827.745641</td>\n",
+       "      <td>439.334095</td>\n",
+       "      <td>29.376044</td>\n",
+       "      <td>364.314552</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.138528</td>\n",
+       "      <td>0.140735</td>\n",
+       "      <td>29.376044</td>\n",
+       "      <td>0.182523</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>min</th>\n",
+       "      <td>7.800000e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>5.200000e+02</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>370.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1900.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.155900</td>\n",
+       "      <td>-122.519000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</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>25%</th>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.040000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1190.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1951.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.471200</td>\n",
+       "      <td>-122.328000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0.000000</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>50%</th>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.617000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.571900</td>\n",
+       "      <td>-122.230000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>0.000000</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>75%</th>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.068775e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.678100</td>\n",
+       "      <td>-122.125000</td>\n",
+       "      <td>64.000000</td>\n",
+       "      <td>0.000000</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>max</th>\n",
+       "      <td>7.700000e+06</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>13540.000000</td>\n",
+       "      <td>1.651359e+06</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>4820.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.777600</td>\n",
+       "      <td>-121.315000</td>\n",
+       "      <td>115.000000</td>\n",
+       "      <td>1.000000</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>mad</th>\n",
+       "      <td>2.337691e+05</td>\n",
+       "      <td>0.732005</td>\n",
+       "      <td>0.614536</td>\n",
+       "      <td>697.780558</td>\n",
+       "      <td>1.381088e+04</td>\n",
+       "      <td>0.488435</td>\n",
+       "      <td>640.115529</td>\n",
+       "      <td>359.443545</td>\n",
+       "      <td>24.564997</td>\n",
+       "      <td>132.981148</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.114810</td>\n",
+       "      <td>0.115094</td>\n",
+       "      <td>24.564997</td>\n",
+       "      <td>0.066626</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>skew</th>\n",
+       "      <td>3.974402e+00</td>\n",
+       "      <td>0.550696</td>\n",
+       "      <td>0.515035</td>\n",
+       "      <td>1.469694</td>\n",
+       "      <td>1.310992e+01</td>\n",
+       "      <td>0.615262</td>\n",
+       "      <td>1.447844</td>\n",
+       "      <td>1.604663</td>\n",
+       "      <td>-0.469533</td>\n",
+       "      <td>5.101680</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.486295</td>\n",
+       "      <td>0.885081</td>\n",
+       "      <td>0.469533</td>\n",
+       "      <td>5.101163</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>kurt</th>\n",
+       "      <td>3.386074e+01</td>\n",
+       "      <td>1.800318</td>\n",
+       "      <td>1.261202</td>\n",
+       "      <td>5.239506</td>\n",
+       "      <td>2.870077e+02</td>\n",
+       "      <td>-0.489108</td>\n",
+       "      <td>3.409876</td>\n",
+       "      <td>2.809201</td>\n",
+       "      <td>-0.657305</td>\n",
+       "      <td>24.031241</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.675120</td>\n",
+       "      <td>1.054699</td>\n",
+       "      <td>-0.657305</td>\n",
+       "      <td>24.024096</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>mode</th>\n",
+       "      <td>3.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>1300.000000</td>\n",
+       "      <td>5.000000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1300.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>47.532200</td>\n",
+       "      <td>-122.290000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</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>missing</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nunique</th>\n",
+       "      <td>3.613000e+03</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>1033.000000</td>\n",
+       "      <td>9.760000e+03</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>942.000000</td>\n",
+       "      <td>301.000000</td>\n",
+       "      <td>116.000000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5029.000000</td>\n",
+       "      <td>750.000000</td>\n",
+       "      <td>116.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>371.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>14 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n",
+       "count    2.153400e+04  21534.000000  21534.000000  21534.000000  2.153400e+04   \n",
+       "mean     5.400577e+05      3.371645      2.115712   2079.827854  1.509060e+04   \n",
+       "std      3.660596e+05      0.904144      0.768602    917.446520  4.138021e+04   \n",
+       "min      7.800000e+04      1.000000      0.500000    370.000000  5.200000e+02   \n",
+       "25%      3.220000e+05      3.000000      1.750000   1430.000000  5.040000e+03   \n",
+       "50%      4.500000e+05      3.000000      2.250000   1910.000000  7.617000e+03   \n",
+       "75%      6.450000e+05      4.000000      2.500000   2550.000000  1.068775e+04   \n",
+       "max      7.700000e+06     11.000000      8.000000  13540.000000  1.651359e+06   \n",
+       "mad      2.337691e+05      0.732005      0.614536    697.780558  1.381088e+04   \n",
+       "skew     3.974402e+00      0.550696      0.515035      1.469694  1.310992e+01   \n",
+       "kurt     3.386074e+01      1.800318      1.261202      5.239506  2.870077e+02   \n",
+       "mode     3.500000e+05      3.000000      2.500000   1300.000000  5.000000e+03   \n",
+       "missing  0.000000e+00      0.000000      0.000000      0.000000  0.000000e+00   \n",
+       "nunique  3.613000e+03     11.000000     29.000000   1033.000000  9.760000e+03   \n",
+       "\n",
+       "               floors    sqft_above  sqft_basement      yr_built  \\\n",
+       "count    21534.000000  21534.000000   21534.000000  21534.000000   \n",
+       "mean         1.494126   1788.557537     285.244311   1971.002275   \n",
+       "std          0.539806    827.745641     439.334095     29.376044   \n",
+       "min          1.000000    370.000000       0.000000   1900.000000   \n",
+       "25%          1.000000   1190.000000       0.000000   1951.000000   \n",
+       "50%          1.500000   1560.000000       0.000000   1975.000000   \n",
+       "75%          2.000000   2210.000000     550.000000   1997.000000   \n",
+       "max          3.500000   9410.000000    4820.000000   2015.000000   \n",
+       "mad          0.488435    640.115529     359.443545     24.564997   \n",
+       "skew         0.615262      1.447844       1.604663     -0.469533   \n",
+       "kurt        -0.489108      3.409876       2.809201     -0.657305   \n",
+       "mode         1.000000   1300.000000       0.000000   2014.000000   \n",
+       "missing      0.000000      0.000000       0.000000      0.000000   \n",
+       "nunique      6.000000    942.000000     301.000000    116.000000   \n",
+       "\n",
+       "         yr_renovated  ...           lat          long     house_age  \\\n",
+       "count    21534.000000  ...  21534.000000  21534.000000  21534.000000   \n",
+       "mean        68.866722  ...     47.560180   -122.213948     43.997725   \n",
+       "std        364.314552  ...      0.138528      0.140735     29.376044   \n",
+       "min          0.000000  ...     47.155900   -122.519000      0.000000   \n",
+       "25%          0.000000  ...     47.471200   -122.328000     18.000000   \n",
+       "50%          0.000000  ...     47.571900   -122.230000     40.000000   \n",
+       "75%          0.000000  ...     47.678100   -122.125000     64.000000   \n",
+       "max       2015.000000  ...     47.777600   -121.315000    115.000000   \n",
+       "mad        132.981148  ...      0.114810      0.115094     24.564997   \n",
+       "skew         5.101680  ...     -0.486295      0.885081      0.469533   \n",
+       "kurt        24.031241  ...     -0.675120      1.054699     -0.657305   \n",
+       "mode         0.000000  ...     47.532200   -122.290000      1.000000   \n",
+       "missing      0.000000  ...      0.000000      0.000000      0.000000   \n",
+       "nunique     70.000000  ...   5029.000000    750.000000    116.000000   \n",
+       "\n",
+       "            renovated   date  waterfront  view  condition  grade  seasons  \n",
+       "count    21534.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "mean         0.034504    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "std          0.182523    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "min          0.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "25%          0.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "50%          0.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "75%          0.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "max          1.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "mad          0.066626    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "skew         5.101163    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "kurt        24.024096    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "mode         0.000000    NaN         NaN   NaN        NaN    NaN      NaN  \n",
+       "missing      0.000000    0.0         0.0   0.0        0.0    0.0      0.0  \n",
+       "nunique      2.000000  371.0         2.0   5.0        5.0   11.0      4.0  \n",
+       "\n",
+       "[14 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([\n",
+    "    king_county_df.describe().T,\n",
+    "    king_county_df.select_dtypes('number').apply(lambda x: x - x.mean()).abs().mean().rename('mad'),\n",
+    "    king_county_df.skew(numeric_only=True).rename('skew'),\n",
+    "    king_county_df.kurt(numeric_only=True).rename('kurt'),\n",
+    "    king_county_df.mode(numeric_only=True).iloc[0].rename('mode'),\n",
+    "    king_county_df.isnull().mean().rename('missing'),\n",
+    "    king_county_df.nunique().rename('nunique'),\n",
+    "], axis=1).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observations: \n",
+    "\n",
+    "* Sale prices (price) in the dataset range nearly 3 orders of magnitude, from a low of about 78,000 to a staggering high of 7,700,000.\n",
+    "\n",
+    "* Sale prices exhibit a positive skew, meaning that they display a right-skewed distribution where the mean is to the right of the median.\n",
+    "\n",
+    "* A home has sold 11 times (the maximum value of bedrooms) within the time span covered by the data, though the most common number of sales is 1.\n",
+    "\n",
+    "* The oldest home in the dataset is evidently more than 100 years old, with a recorded build date (yr_built) of 1900; the median build year is 1975.\n",
+    "\n",
+    "* The improved tax value (sqft_living) and land tax value (sqft_lot) both range from 370 to more than 13,540.\n",
+    "\n",
+    "* The largest property in the dataset covers over 1,651,359 sqft (sqft_lot), or more than 380 acres.\n",
+    "\n",
+    "* The smallest home in the dataset is recorded as having 370 sqft (sqft_living), less than the smallest first floor which has 370 sqft (sqft_above)⁠—worth flagging.\n",
+    "\n",
+    "* Considering the columns independently, homes in the dataset most commonly have 1 story (floors), 3 beds (bedrooms), and 2 baths (bathrooms).\n",
+    "\n",
+    "* The sales data includes 116 unique dates (yr_built), suggesting the dataset has comprehensive coverage of the full 20+ year span.\n",
+    "\n",
+    "* The dataset spans dozens of assessment areas (lat) and cities (long), as well as submarkets (lat) and subdivisions (long). Aside from sale numbers and subdivisions, the data have no explicitly missing (NaN) values; some columns may use a missingness indicator value (e.g., 0)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TlhRST9c+dfdbSRF19Se1bdu2ag9tXEG5x0HGPS4/7nH5cY8dkUikrjflzJs3T5dddlnmz15nKalyXaqAUrF2cHvPCWHJyMrKKG6lBpJ+ABBYvk76+X1nulSellSlaB3inenXFDaDduc0hKWmkFVv2qgzFdL0cHV/YamXNinMs/bUy1yZZ+2pl7nWyzxz0dbWJsmJdSZPnpx5vLOzMxP7tLW1KZlMqqura1BM1dnZqYMOOihzTUdHx7D3H/o+mzZtGvR8V1eXUqnUiLGY5GzoikajIz5XzwurA1lrfX0vku7QIrKKyekx358O1s/P7/e4FnCPy497XH7c4/oWCoUycdVAlexSBZRKX1qKWyMjq0kRK2OkhpDzeH/a+XcAQDD5Ounn8evOdKm8LakKbR1irVXf67slSQtmTVdbQ3jQ89O7OrSlKyHT0qbZUxuLHmcp1EubFOZZe+plrsyz9tTLXOtlnmOZMWOG2tratH79ei1cuFCSlEwm9fzzz+vMM8+UJB1wwAEKh8Nav369li9fLknau3evtmzZkrlmyZIl6unp0aZNm7Ro0SJJ0osvvqienp5MYnDJkiW6++67tXfv3kyCcf369YpGozrggAMqOm9UTibpZ2xmgSglo6SVIuTdAQA1Yvv27Tr33HMViUS0ePFiffjDH9bMmTMr2qUKKJU+6wRpMSOFjZSW1GCs+mTUbznXDwCCzNdJP7/vTJfK05Kq2NYhibSUtM6O+c5dO9Q7ZHdOUzokKaz/292hafG9BY2xVOqlTQrzrD31MlfmWXvqZa4jzbOWW1L19fVp+/btmT/v2LFDmzdvVktLi6ZNm6aTTjpJ69at0+zZszVr1iytW7dODQ0NOvbYYyVJzc3NOuGEE7R27Vq1traqpaVFa9eu1fz58zOLU3PnztWyZct000036ZxzzpEkffe739WRRx6ZWYQ6/PDDNXfuXH3rW9/SWWedpa6uLq1du1bveMc7Anc+MnKXyCT9pKjJtobqT0uR8NivBQAgCBYvXqwLLrhAc+bMUXt7u+6++25deuml+vrXv17RLlUjKUcHKj+ptw4elZpv3E36NYSc72WU3ayVtlI+392osPHW08+2nuYq1dd8mWttCvpcfZ30C8LO9HK2pCq0dUhPyvkaMVYRYzX0LSa5LT3bE8Y3C7710iaFedaeepkr86w99TLXepnnSy+9pCuuuCLz5zVr1kiSjjvuOF1wwQVasWKF4vG4br75ZnV3d2vRokVatWpVZkFIkj7+8Y8rHA7rG9/4huLxuA477DB97nOfG3RezUUXXaRbb71VV111lSTpqKOO0tlnn515PhQK6T/+4z90880367LLLlMsFtOxxx6rj370o+W+BaiipLtoFDEa1BoqnjaaUOVW8gAAlMIRRxyR+ff58+dryZIluvDCC/Xoo49mKvMq1aVqqHJ2oPKTeuvgUe757mvvl/bsU0tDVLNnT9eO9k41NaSlZFKhaFQtrbGc3ysaS2lG27SCx1JPP9t6mqtUX/NlrrUpqHOtetKPneml553n1zhK/20v6deRCmamGgAADHbooYfqxz/+8ajPG2N0+umn6/TTTx/1mlgsppUrV2rlypWjXtPS0qKLLrpozLFMmzZNl1xyyfiDRs1IDqj0M5GoGkJGfWmpNxTVpDx+2wjLyiYT418IAECVNTY2av78+dq2bZve+MY3SqpMl6qRlKMDlZ/US6cST6Xm+3qPkRSRSca1bds2pcNRpeJpSSH1xRPq2hfP+b0S4bC2bduW9xjq6WdbT3OV6mu+zLU2lXOulehCVfWkHzvTS6837XxtCo38F3JSxHm8KyWlrNO7GwAAACiEl/SLGquUjBJpK8lo476UdvTm/j5L28IaZc8aAAC+kkgk9Nprr+nv/u7vKtqlaiTl7EDlJ/XSwcNT7vn2uwUD0ZDzfaykkLs+mLJOs/ZcWRX3d62efrb1NFepvubLXGtTUOda9aQfO9NLb7xKv6aQs5M6JaOelNRa9b8FAAAACCJrB1f6DfyaskbKY8EIAAC/WrNmjY4++mhNmzZNHR0d+p//+R/19vbquOOOkzGmYl2qgFLp9870G1AIEM7EcFUYEACgZEj31KBeN+k3WqWfMVJzWNqXkrrTRq0sxgAAAKAAaUlW2TP9JClsnEo/FowAALViz549+uY3v6nOzk5NnDhRixcv1lVXXZVpz1WpLlVAqcTdLmGxAQUDYXd9MFWF8QAASoekXw3qcz+4R6v0k6QJYat9KaPuFDuwAQAAUJjkgDAym/Qb/hwAAEH2z//8z2M+X8kuVUApxN04rcFkAzavvWeaGA4AAo1jM2pQtr3n6J/SE9znutm+AwAAgAJ5ib2QbGahyNtVSJgJAADgT3F37XBQpR/tPQGgJpD0q0G9bqXfaO09JWlC2PnanTajXgMAAACMJTHkPD9pYKUfcSYAAIAf9bsxXGxApZ+7VKiUnHObAQDBRNKvBmUr/Ua/xqv060mxGAMAAIDCeIm9gUm/iLt4xC5xAAAAf/Iq/RpGqPSTjNIVHxEAoFRI+tWgvpwq/WjvCQAAgOJ47T2jI1b6VX48AAAAGF/cXTuMDYjhnH9l8xYABB1JvxqTtlLCDu/LPVSzl/SjvScAAAAKlMy098yuDEU4DwYAAMC3rB3Q3nNAwYAx2RafVPoBQHCR9KsxAxdXImPk8ya4P/mENZndPQAAAEA+RmrvOfA8GAAAAPhL0kpWIxcMhNm8BQCBR9KvxiTcD2Ujm1lwGUk0lD2sl2o/AAAAFCJb6Zd9zAw4D8ayYAQAAOArXpVfSFaRIc+R9AOA4CPpV2MGLryYcXJ5nOsHAACAYozU3nPgLxg0lAAAAPCXeDpb5Td07TDbsYECAQAIKpJ+NWakFkuj8Vp89qT4IAcAAED+Rqz0kyQ5T6TZJQ4AAOArcTc+axhhOTBEpR8ABB5Jvxoz0sLLaDKVfrT3BAAAQAESI2w4M0aZveFU+gEAAPhL3A3QYqHhmb2wYeMWAAQdSb8akxihxdJoaO8JAACAYoy24cz7JYP1IgAAAH/pdzdtxUaoAci29wQABBVJvxqTT6Vfs/vT76a9JwAAAArgxZ7RIRvOvNZQ7BIHAADwF6/Sr2HESj/nK+09ASC4SPrVGO9MvyjtPQEAAFBm41X60d4TAADAX+JjVfqR9AOAwCPpV2MKOdOvJyVZPswBAACQp+QIZ/pJzrl+EjEmAACA3/RnzvQb/pz3UEoUCABAUJH0qzHJPM70aw5JRlZpGfWxDRsAAAB5otIPAAAgWLz2nrER1g7D7mO0aAeA4CLpV2MSo+y2HknISE3euX60+AQAAECeRttwlk36EWMCAAD4Sb+7dtgwwqow7T0BIPhI+tWYfNp7SlKzd65fqkwDAgAAQM0aLfb02nuySxwAAMBfMpV+oREq/dyvLBMCQHBFqj0AlJa38BIdstvaRKIj9uNuDktKSD2KKj3K34awrGwyUeKRAgAAIOhG6zIRkpXTSB4AAAB+Enfjt9gIBQNU+gFA8JH0qzGj7bZOyWh9+/B9Ot1JIymkzT1p9SdH/kRf2hamJBQAAADDjHqmH5V+AAAAvuRV+o3U3tOL4ayM0jb7ZwBAcJDLqTHJPM70k6So8V5XpgEBAACgZo3WZSJ7ph8AAAD8wlop7oZtMTN6e0+JOA4AgoqkX41J5Hmmn5f0S6TZugMAAIDcWTvGmX7eNRUdEQAAAMaSsE4VnzRypZ8xXpt2WnwCQFCR9Ksxo+22Hk3UPbQ3wQc5AAAA8uA0jh/lTD/aewIAAPiOV+UXls2c3zcU5/oBQLCR9Ksxo+22Ho13HUk/AAAA5GNge/ihi0a09wQAAPCffrfTV2yMFWGvxWeq/MMBAJQBSb8aU+iZfmkZdvAAAAAgZ8kBO8VDQ9t7UukHAADgO3F3R1bDGB3C6NgAAMFG0q/G5FvpFx7QqzvJhzkAAAByNNZmM++XDCvOjQYAAPALr73nmJV+mfaexHEAEEQk/WpMvmf6Odc6X2nxCQAAgFwlxthslmnvSXwJAADgG3E3kRcbI58XdosDaO8JAMFE0q+GWDv24stoskk/dvAAAAAgN9kOE8MzeyH3Mc70AwAA8I+EG5xFQ6PvzMpW+lVgQACAkiPpV0OcHTj5nennXOt8iidYlQEAAECOxmor7z3EWhEAAIB/eJV+0THWDUMk/QAg0Ej61ZCBZ/LlVekXGv56AAAAYCzJMRaNvMUi2nsCAAD4h9chbOz2ng5qAwAgmEj61RAvaReWlaG9JwAAAMoo6a4EjXmmX8VGAwAAgPHE82rvyTohAAQRSb8aksihRH8kUa+9JzuxAQAAkKOk+3WkM/28DWiW+BIAAMA3vLXDsSr96NgAAMFG0q+GjHWuylgimUq/0o4HAAAAtWus2JNKPwAAAP+Ju/HbmGf6uacypyowHgBA6ZH0qyGZFktjlOiPxPugT1p2YwMAACA33pl+4yX9iC8BAAD8IeGuHcZyaO9JpR8ABBNJvxpSaKWfl/SzMuziAQAAQE4SY5zplz1f2oj1IgAAAH+I53A0EB0bACDYSPrVkLF2W4/FGCniLsck+EQHAABADrIbzoan9Qb+kkHSDwAAwB+8o3040w8AahdJvxqSKLDST5IiocHvAQAAAIxlrC4TzkPOBSwYAQAA+EPc3ewfHaO9J5V+ABBsJP1qSDJzGG/+KyteWX/CFpAxBAAAQN1JjtEeyhgWjAAAAPwkZaW0uzVrrEq/8IBjgNi8BQDBE6n2AFA6hZ7pJ3mJQkOlHwAANSiVSunOO+/UY489pvb2dk2ePFnHH3+8PvCBDygUclIz1lrdeeedevjhh9XV1aXFixfr7LPP1rx58zLvk0gktHbtWj3xxBOKx+M67LDD9MlPflJTp07NXNPV1aXbbrtNzzzzjCTp6KOP1sqVKzVhwoTKThplN17s6T1MeAkAAFB9A9f8xlo7HFghkhYVIwAQNPx3u4YUeqaflN2hnWRVBgCAmnPvvffqwQcf1Nlnn61vfOMbOuuss/STn/xEv/zlLwdd8/Of/1wrV67U1Vdfrba2Nq1evVq9vb2Za26//XY9/fTTuvjii3XllVeqr69P11xzjdLpbC3X9ddfr82bN2vVqlVatWqVNm/erBtuuKGi80VljHWmn8R5MAAAAH7itfaMGJuJ00ZijGRo0w4AgeX7Sj92pueumDP9Brf35BMdAIBasnHjRh199NE68sgjJUkzZszQ448/rpdeekmSE0vdd999OvXUU3XMMcdIki644AKdc845evzxx3XiiSeqp6dHv/rVr3ThhRdq6dKlkqQLL7xQ5513ntavX69ly5bp1Vdf1XPPPaerrrpKixcvliSde+65uvTSS7V161bNmTOnCrNHuSTdr6PFnrT3BAAA8A/vSJ+xWnt6QpJSIo4DgCDyfdLP25l+wQUXaO7cuXr55Zd14403qrm5WSeddFLmmp///Oc6//zzNXv2bN19991avXq1rrvuOjU1NUlydqY/++yzuvjii9Xa2qo1a9bommuu0bXXXptJHl5//fXavXu3Vq1aJUm66aabdMMNN+iSSy6pzuTzVNyZfs5rEnyaAwBQcw4++GA9+OCDmcTb5s2btWHDBn384x+XJO3YsUPt7e06/PDDM6+JRqM65JBDtGHDBp144ol6+eWXlUqlMgk/SZoyZYrmz5+vjRs3atmyZdq4caOam5szCT9JWrJkiZqbm7Vhw4YRk36JREKJRCLzZ2NMJn4zpr7PGvbm79f7kPK6TISMvCEaZdt6hty9ZNZmHxuLUeXn6vd7XAu4x+XHPS4/7jGGWrdunX74wx/qpJNO0ic+8QlJbEiH/8XzWDcMGecMwBR1AQAQOL5P+rEzPXdFnennbsXmTD8AAGrPihUr1NPTo8985jMKhUJKp9M644wzdOyxx0qS2tvbJUmTJk0a9LpJkyZp165dmWsikYhaWlqGXeO9vr29fdh7DL1mqHXr1umuu+7K/HnhwoW69tprNX369EKmWpNmzZpV7SGMbPceKZXWzOnTNKvZ+bViR3unWlrDkqRIvF9Kp9XQ1KSWxvC4bxeNpTSjbVpZhzwa397jGsI9Lj/ucflxjyFJmzZt0kMPPaQFCxYMepwN6fA7b6N/LIfDnsKSEqLSDwCCyPdJPz/vTJfKszu90F2ESWXP9Bv62oG7rkfilfanZGStBvX2Lueu63rZMck8a0+9zJV51p56mWu9zDNXv/3tb/XYY4/poosu0rx587R582bdfvvtmbbpnqH3y9rxdwPles1oP4tTTz1VJ5988rAx7Ny5U8lkcsTX1AtjjGbNmqXt27fndJ8rLZ6MSDLau3unbIfzWDocVde+lCTJpkKSjHp6+9SQw86yRDisbdu2lW/AI/D7Pa4F3OPy4x6XH/fYEYlE6n5TTl9fn2644Qade+65uvvuuzOPsyEdQRB3uzREc/gVyevYwJl+ABA8vk/6+XlnulTe3en57iIMdbVL/UnNmDJZsyc3DHpu4K7rkVhrFertU9pK0eZmNUWy234qseu6XnZMMs/aUy9zZZ61p17mWi/zHM/3v/99rVixQm9961slSfPnz9fOnTt1zz336Pjjj1dbW5skZc5P9nR2dmbio7a2NiWTSXV1dQ2KqTo7O3XQQQdlruno6Bj2/Qe+z1DRaFTRaHTE5+p5YXUga60v74XX7ikkK294VtnTob08b8rmdmK0VfV+5n69x7WEe1x+3OPy4x7j5ptv1hFHHKGlS5cOSvpVe0M6kAtvD1Ysl/ae7te0dbN/AIDA8H3Sz88706Xy7E4vdBdhT19YUkj7OvZqW9/g1w3cdT2aqELql1FHV49SA/KD5dx1XS87Jpln7amXuTLP2lMvcx1pnvW8O72/vz/TMsoTCoUy92bGjBlqa2vT+vXrtXDhQklSMpnU888/rzPPPFOSdMABBygcDmv9+vVavny5JGnv3r3asmVL5polS5aop6dHmzZt0qJFiyRJL774onp6ejKJQdQGayUv0h3tF4qQrCTDMhEAoCY88cQTeuWVV3T11VcPe66aG9Jr/XzkeuvgUc75JrxKv5AZt0NY2P1DesjjIym0Q1g9/Wzraa5Sfc2XudamoM/V90k/P+9Ml8q7Oz3fXYRJ98M7ouGvG7jrejRRI/VbKZ42suHs1ZXYdV0vOyaZZ+2pl7kyz9pTL3Otl3mO56ijjtLdd9+tadOmae7cudq8ebN+9rOf6e1vf7skJ5A96aSTtG7dOs2ePVuzZs3SunXr1NDQkOmu0NzcrBNOOEFr165Va2urWlpatHbtWs2fPz+zY33u3LlatmyZbrrpJp1zzjmSpO9+97s68sgj2ZleY5zzXZzYMzzK70Feu3jaQgEAgm7Xrl26/fbbtWrVKsVisVGvq8aG9Ho5H7neOniUY74vvNYtdfVqcusEzZ49YdBzQzuExVJxKZVSJBZTS8vI656eYjuE1dPPtp7mKtXXfJlrbQrqXH2f9GNneu68Mv1IgQnoaMhKaaMcjlwBAAABsnLlSt1xxx26+eab1dHRoSlTpujEE0/UaaedlrlmxYoVisfjuvnmm9Xd3a1FixZp1apVmZ3ikvTxj39c4XBY3/jGNxSPx3XYYYfpc5/73KBY7aKLLtKtt96qq666SpKTcDz77LMrN1lURGpAvDhq0s/9mi77aAAAKK+XX35ZHR0duuSSSzKPpdNpvfDCC/rlL3+p6667TlJ1NqTX+vnI9dKpxFPO+e7d53QIi/d0adu2zkHPDe0Qlk4aSSH19cfVZfvHfN9CO4TV08+2nuYq1dd8mWttKudcK9GFyvdJP3am5y6ZSfoV9hfRO8iXpB8AALWlqalJn/jEJ/SJT3xi1GuMMTr99NN1+umnj3pNLBbTypUrtXLlylGvaWlp0UUXXVTMcBEA2aSfVWiUa0zmCgAAgu0Nb3iDvvrVrw567Dvf+Y7mzJmjFStWaObMmVXbkF4v5yPXWwePcsw37rZfiJrxO4R5HRtyOZu52A5h9fSzrae5SvU1X+Zam4I6V98n/diZnrts0q+w12eTfhzSCwAAgNF5+8DDkkY75oD2ngCAWtHU1KT58+cPeqyhoUGtra2Zx9mQDr/zzvSL5bBuSMcGAAgu3yf92Jmem5SV0u5+6mjBST9nRYZKPwAAAIzF22w2WmtPicUiAEB9YUM6/C7uxm/RHDqEhQdU+gEAgsX3ST/kJjngQ7jQSr8I7T0BAACQg5S7U3zMpB+VfgCAGnb55ZcP+jMb0uF3ibRb6Tdab/YBspu36AYGAEGTw3/mEQRe0i8km1lgyZf3oZ+0UgBb1QIAAKBCUjm0lc+e6VdgcAoAAICSyVb6jX8tm7cAILhI+tWIYs/zk9wzWWQlGar9AAAAMKrsmX6jB40sFgEAAPiHt9YXy6G9Z8iN8WjTDgDBQ3vPGpF0WywVk/Qzxnl9wjr/xEo0NgAAANSWVE5n+rFYBAAAire3s0vpcLSgJpNhWdlkouRjCpq0za4dRnNp78mZfgAQWCT9akQpKv0kp8TfS/oBAAAAI8kl6Zdt7wkAAFC4RDqt9e2pgmKKpW1h2pxp8DpfLu09w+5XNm8BQPDwuVcjsgsvxS2rRN3XJyxnrwAAAGBkKTdWHLPSj/aeAAAAvuAl/cKyY8ZvHuI4AAgukn41IpnDbutceLt9EmzlAQAAwCgysecY13i/aPg5rEykpZc747IsaAEAgBoWT+fe2lMaHMcRJwFAsJD0qxEpt4FSsf1avQ9/2nsCAABgNCn361hdJoy7mczPC0W/6wzrxy916pU+ulwAAIDa5a3zxXIMeUKZ6wyt2gEgYEj61YhczlXJBe09AQAAMJ5UDudJ+73SL56W/s9N9rUniX0BAEDtiruxWzTHY4EGLhinRr0KAOBHJP1qROmSfs5XKv0AAAAwmlxiT7+3hdrSH8p0y+hLk/QDAAC1K+HGOrEcV4KNkUJujR/n+gFAsJD0qxHZhZfiPom9pF/S+nNxBgAAANWXcrtCjJn083lbqFf6sr8K9fq1HBEAAKAEspV+ub/Gi+VI+gFAsBR7BBx8IlnSSj8rK6OkpGiR4wIAAEDtyZzpN8Y1A8NSv60V9aSk7fHsCHvpWwUAgO+krHP+7twUH9TFSuTZ3lPyf6t2AMDIqPSrEV5romKzuMZkz2ZJ8KkOAACAEeTSZcIJKf3ZFmpzX0hWRjF3/L209wQAwHe29hu91BvSb3elMrEHChPPs72nlK30494DQLCQ9KsRpTrTT+JcPwAAAIwtly4TzlkwDr/tJdvc74xsSbMzst40re0BAPCbfSkn0EhYqSPJBp1iJApo7+l1dPBbHAcAGBtJvxpRqvae0sCkHwEVAAAAhst1w5n3tN/yaZ3uwuHCRmcZy8qo32+DBACgznWlsoHGbpJ+Rel3M3cNoTzae2bO9OPeA0CQFJz0a29vL+EwUCxv4SWSR2/u0Xj9van0AwCgfIilEGQpd/FnrDP9pIGLReUdTz6sleLueBpDUpObuaTFJwDUHuKtYBuY9OtJG/VRclawfjd2a8gj3Al5bdrLMSAAQNkUnPQ777zzdN111+mvf/1rKceDApWjvWfSR4szAADUGmIpBFnK/RoZJ/b0Y3tPZ2Obu/AVkiZEnVH2pkZ/DQAgmIi3gq3LDSAa3YBiT4INOoWKF1XpV4YBAQDKJlLoC//xH/9RDz30kJ588knNnz9f73nPe3TssccqFouVcnzIUUq57bbOhbvuoUTayH/NmAAAqA3EUgiy7IazsWNF44aTfjovz2vjGZZV2EgTIiHtUkp9xL4AUHOIt4LL2myl3+FtYT21J6W9SaPZMevEF8hLfzq74SlX3hoj+6IAIFgKrvQ77bTTdOONN+riiy9Wc3OzbrrpJn3qU5/SmjVrtH379lKOETko7Zl+zpvFWfMAAKBsiKUQZLl2mfBjpV/cXfSKuYNr8Sr9/DRIAEBJEG8FV7+Vkm5LyiWtIUlWKRm6UhXI2/QUy+NYICr9ACCYCq70k6RQKKTly5dr+fLl+r//+z/98pe/1IMPPqj77rtPy5Yt0z/8wz9o2bJlJRoqxlLaM/2cr0mf7coGAKDWEEshqHJN+nlPW/mnis5rb+UtemXae3KmHwDUJOKtYPKq/JpCVtGQUURSUs5aVbSqIwue1IAEaj6Vfn7cvAUAGF9RSb+B5s+fryOOOEJbtmzRpk2b9L//+7/64x//qIULF+qiiy7SnDlzSvWtMIJynOmXluGDHQCACiGWQpDk2lrejzvEvW4WXqXfBPdgwj4CXwCoecRbweEl/VrDzgd3xDgJv2Q1BxVQ8UyMYxXLY90wG8f5Z/MWAGB8RSf9Ojs79fDDD+uhhx7Srl27tGTJEv3zP/+z3vjGN+q5557T2rVrdeONN2r16tWlGC9GUcqkX8g4Z5ykZJTgMx0AgLIilkIQ5dpaPlvp5x+ZM228wSUTkqQeG1Y6kt8J2WFZWff1AAD/It4Kni73ILkW96M54uadkiSg8pZt7am8zkP0Kv040w8AgqXgpN+LL76o+++/X08++aQk6S1veYtOOukkHXDAAZlrjj76aIXDYX3lK18pfqQYk7fbulSlm9GQlEpLCXY8AwBQFsRSCLLshrOxF91CspLPukdkK/3soK/tcav17fmNdGlbuPBD0gEAZUe8FVxepV+LW+nnxBwmE4Mgd5kNT3kGLV6c56eODQCA8RWcI7r00kvV1tam97///XrXu96lSZMmjXjd9OnTddBBBxU8QOTG220dKtFRJFEj9UlKWM42AQCgHIilEGTZ86THvs7bTe6nc6Lj7sKX196qya0goMMFANQe4q3g2jck6efFHEk+r/PW7+5pagjld/M40w8AgqngpN8FF1yg5cuXKxIZ+y3mzp2rL3zhC4V+G+Qou/BSmugn6u6gYvEDAIDyIJZCUFmbx5l+7lc/LRYNPdOvye1RmpKRtfm1vQIA+BvxVnBlz/Rz/kzSr3BxO6S1eY78eDYzAGB8BSf9/v7v/76U40CRSnmmn+RU+knseAYAoFyIpRBUAxN4457p5y0WlW00+Ru6270xJMltQ5q02TgYABB8xFvBZK3U7Z3pFxla6ceZfvnyYp9YvpV+7j2npSoABEvBR1Dcc889uvXWW0d87tZbb9VPfvKTggeF/AzabV3ypB+rHgAAlAOxFIJq4MLPeLGn98uGr9p72sHtPY0xmYVENrwBQG0h3gqmnrSUlpGRVbMbTHidraj0y1/mTL88l/i8jg5+2rwFABhfwUm/Rx99VPPmzRvxuQULFujRRx8teFDIT2rAvxdcujlE1A2mEnyyAwBQFsRSCKps0s+O+8uEL9t7jrDbPUrLMACoScRbweS19pwQzlab0d6zcF5r87zP9PPOZpahxScABEjBSb9du3Zp9uzZIz43a9Ys7dy5s+BBIT/57LbOVdT9m8FuZwAAyoNYCkGVdL9GNP75d97zfq70k4a2DAMA1ArirWDykn4t4WwAEaHVZMG8Sr9YnqvAA89u9tMGLgDA2ApO+oXDYXV2do74XEdHh8x4KwAoGS/gMbKZXTjF8nY7p9yzTQAAQGkRSyGovNgzl7gzW+nnn7/P2TP9so9lulwQ9wJATSHeCqbMeX6hEZJ+Mr7aTBQE/V6lX55/3Y1x1holUekHAAFScNLvwAMP1MMPPzzicw8//LAOOOCAggeF/HgLL6Wq8pOcvxjeB3tvauxrAQBA/oilEFQptxoukkPs6V3il3Uia7OJvZgZvpDIZjcAqC3EW8HU78YajQNWLZ2qM871K0R2w1P+Ny6cSbYCAIKi4KTf+973Pv35z3/W5Zdfrt/+9rf661//qt/+9re64oor9Oc//1krVqwo5TgxhqS7nFKq8/wkZzePV+1H0g8AgNIjlkJQ5bPhzKsG9Mvu8IR1zqWRBre4IukHALWJeCuY+kc4f9eY7LoXn9f58dp7NhSwCpzp2sA9B4DAKDhPtGzZMp177rlas2aNvvnNb2Yeb25u1rnnnqtly5aVYnzIQTkq/SQn6Re3btIvPO7lAAAgD8RSCKps7Dn+6o/XOcIv60RxrzWp7KBKRS/UTcnIP6MFABSLeCuY4t4ZdEPWuSLGSfglR3gNRhfPtPcssNLPcpYiAARJUcVhJ5xwgpYvX66NGzeqs7NTEydO1JIlS9TY2Fiq8SEH5Ur6RYyVZNTLab0AAJQFsRSCyGsCkcuesOyZfv4w2k53L4HJLnYAqD3EW8GTSVINaUfpJaCSlk06uUpl7tfgLge5Gnw+M/ccAIKg6I6QjY2NWrp0aSnGggIl89htnQ+vvWcP7T0BACgbYikETT4bzozP2nt6i4hDKwf81oYUAFBaxFvB4n1eR4dV+jmb06k6y53XKtXIDot/cpE50497DgCBUVTSz1qrl156STt37lQ8Hh/2/HHHHVfM2yNH3gdvpOSVfs5XzvQDAKA8iKUQRN5u8ZzO9HO/+mWdKD7CGUFSdpyEvQBQe4i3gic+SmU+Z/DmL26zrVJNAeuGITmJVr90bQAAjK/gpN/WrVv15S9/Wdu2bRv1GgKnykjlsfCSj6gbXNHeEwCA0iOWQlDlU+nntwq60c4ICvtsnACA0iDeCqbMJh23ss9D0i9//ZkNT4W9nko/AAiegpN+t9xyixKJhD7zmc9o/vz5ikajpRwX8pDPuSr5iLrtQmnvCQBA6RFLIai80DCSQ/2et0znl3Wi/lHOCPLb2YMAgNIg3gqelJWSGvkMumzSj/PlcuUl/YbGPrny2wYuAMD4Ck76bdq0Seeee67e/OY3l3I8KEAyj93W+aC9JwAA5UMshaDKq9LP/eqXZNp4lX5WRmmbXeACAAQb8VbwxAckl0Y+049Kv3z0u93BGgqMbbwCA5YGASA4Ck76NTY2qrm5uZRjQYGyZ/qVNurxgquElRLpbLtPAABQvErHUnv27NH3v/99Pffcc4rH45o9e7bOO+88HXDAAZKc827uvPNOPfzww+rq6tLixYt19tlna968eZn3SCQSWrt2rZ544gnF43Eddthh+uQnP6mpU6dmrunq6tJtt92mZ555RpJ09NFHa+XKlZowYULF5oryyifplz07xsjaws6SKSVvIXFo5cDAP6aH/BkAEFysXQWP19ozauywTThh2nvmjUo/AKg/BSf93v72t+vxxx/XsmXLSjickbFINbZ8Fl7yETbOgb1pGfWS9AMAoKQqGUt1dXXpsssu06GHHqrPf/7zmjhxol5//fVBi2D33nuvfv7zn+v888/X7Nmzdffdd2v16tW67rrr1NTUJEm6/fbb9eyzz+riiy9Wa2ur1qxZo2uuuUbXXnutQiEnULj++uu1e/durVq1SpJ000036YYbbtAll1xS9nmiMvI5T3poMq3U7ejzNfiMoCxjJCObqfQTlX4AUBMqGW+hNLyq/JEq0yIBOV8uZUu/Rleo0boc5MqL5VKipSoABEXBSb958+bpiSee0LXXXqujjjpKra2tw6455phjihqcxCJVLlLuqkQ5FlEixtkR3Zs2msiHOwAAJVOpWEpyYqWpU6fq/PPPzzw2Y8aMzL9ba3Xffffp1FNPzXzPCy64QOecc44ef/xxnXjiierp6dGvfvUrXXjhhVq6dKkk6cILL9R5552n9evXa9myZXr11Vf13HPP6aqrrtLixYslSeeee64uvfRSbd26VXPmzCnJfFBd+ZwnPXB9yQ+RZKbF1Qib2cLGqRzw+0IiACB3pYq3HnjgAT3wwAPauXOnJGnu3Lk67bTTdMQRR0hiM3opZavyh38gZ5J+Pm7H/WJvSL/rDOtNrSkd1Fz9BuejnWecq7C7UYpKPwAIjoKTftdff70kaceOHfrDH/4w4jV33HFHoW+fwSLV+Mp1pp/ktPiMW6knzY4eAABKqVKxlCQ988wzOvzww/X1r39dzz//vKZMmaJ3vetdeuc735kZQ3t7uw4//PDMa6LRqA455BBt2LBBJ554ol5++WWlUqlMLCVJU6ZM0fz587Vx40YtW7ZMGzduVHNzcyaWkqQlS5aoublZGzZsGDGeSiQSSiQSmT8bYzKbtky1e0FWmTd/v92HTGv50PCxGQ1O9A2soBuvvadR+ecazyx8Od8r8/2MGXT+YK6jqMSYg86vf49rCfe4/LjHwVWqeGvKlCn6yEc+olmzZkmSHn30UX35y1/Wl7/8Zc2bN6/uN6OX0liVac6GI6ckP+XTpN8rfSFZGf1+X1gzolaTo9VdS+tPj77hKRe09wSA4Ck46feFL3yhlOMYlZ8XqfyiXO09JaeHumTUy4m9AACUVKViKcmJlx588EG9973v1amnnqpNmzbptttuUzQa1XHHHaf29nZJ0qRJkwa9btKkSdq1a5ckqb29XZFIRC0tLcOu8V7f3t4+7D2GXjPUunXrdNddd2X+vHDhQl177bWaPn16gbOtPd4Co1/E4vuk3n5Nntiq2TMHn5O0o71TLa2DawBDPb1KWalpQrOaIqOvOEVjKc1om1aWMXvSe/dISmv29Kma3RKV5I65ZYKifX2KJ60amprU0pBbD41KjLlW+O3vcS3iHpcf9zh4ShVvHX300YP+/OEPf1gPPPCAXnzxRc2dO7fuN6OXUn+m0m/4c8Y4C5lJORvgo5UcWA7SVtqVcBbn0jL6TUdY752azFQoVkO2tXlhr/ciIpYFASA4Ck76HXLIIaUcx6j8vEgllWd3er67CL1zVSJm9NcM3XWdq6j7op60KcuuxnrZMck8a0+9zJV51p56mWsQ5lmpWEqS0um0DjzwQH3kIx+R5CTW/va3v+mBBx7Qcccdl7lu6P2ydvwtvbleM9rP4tRTT9XJJ588bAw7d+5UMpkc971rmTFGs2bN0vbt23O6z5WyrycsKaTuffu0Ld0x6Ll0OKqufYOXhZwaOqOurh6lxsilJcJhbdu2rfQDHqAnHpFktG/PLm3b59zjUGOzurq6pbQTMXf39Ckaz+1+V2LMQefXv8e1hHtcftxjRyQSCdymnHLEW+l0Wk8++aT6+/u1ZMkSNqOX2Gjn73q8dtx+jBL3Jo2S1ihqrCJG6kiF9FxPTEcOX24clZG0t7OrZGPqK7K9J5V+ABA8BSf9PD09Pdq4caP27dunI444YlhirVh+XqSSyrs7PdddhLG+TqkvrimTJmr2jKYRrxlp13UuJoQS2rkvKTU0a/bs4b3vS6Vedkwyz9pTL3NlnrWnXuYahHmWO5aSpMmTJ2vu3LmDHps7d66eeuopSVJbW5skZxPU5MmTM9d0dnZmNkW1tbUpmUyqq6tr0Bg7Ozt10EEHZa7p6BicBBr6PkNFo1FFoyPv067nhdWBrLW+uhcpdzEurOHjshreEN6LpNMjPDfstWWcp7UDzgkyVoO+lbUKuTF/yube1L7cY64lfvt7XIu4x+XHPQ6uUsRbW7Zs0apVq5RIJNTY2KjPfvazmjt3rjZs2CCptjajV1PC3dgeG6EVt7FOMq3fOkmoXGZXyVbcOxNOeeKMqNXCCSE9vld6qdsqavM4288YHRVLl2zMPSnnfSaEx74PoxULeAvHKUkapVV7ofc4CBs1S6We5irV13yZa20K+lyLSvrddddduvfeexWPxyVJV199tVpaWnTllVdq6dKlev/731/0AP28SCWVZ3d6vrsIu3q93dYd2pZqH/GakXZd5yKdNJJC2t3Vq23bSrfTyFMvOyaZZ+2pl7kyz9pTL3MdaZ5+3J1eiVhKkg466CBt3bp10GNbt27N3I8ZM2aora1N69ev18KFCyVJyWRSzz//vM4880xJ0gEHHKBwOKz169dr+fLlkqS9e/dqy5YtmWuWLFminp4ebdq0SYsWLZIkvfjii+rp6cnEXAg+L6LMtbX8wLPyqilhJavsQuJQYe+MoMoOCwBQZqWKt+bMmaOvfOUr6u7u1lNPPaVvf/vbuuKKKzLP1+Jm9GoIJ/ZJPf2aNqlVs2c5bcS9VtySFEv2q7s/rWhDo1qax1/WrGQr7qde6ZQU1wFTWzQ7kpD2JhS3Rs0tEzKbi3KTKsnmxf5UWvHteyRJB+43Uw3h0dusj1YskEpbqbdPklFz6wSFR5hHsfc4CBs1S6We5irV13yZa20K6lwLTvrdf//9uuuuu/Sud71LRxxxhK655prMc0ceeaSefvrpkixU+X2Rqpy703PdRZj0zvQb43uOtOs6FxG3nUJP2pR553V97JhknrWnXubKPGtPvczVz/OsVCwlSe9973t12WWX6e6779by5cu1adMmPfzww/p//+//SXIWqU466SStW7dOs2fP1qxZs7Ru3To1NDTo2GOPlSQ1NzfrhBNO0Nq1a9Xa2qqWlhatXbtW8+fPz7Sqmjt3rpYtW6abbrpJ55xzjiTpu9/9ro488si6aUlVD7LnSef2/y1jJFmp2v9X9Kr8QrIjnq1D+yoAqD2ljLcikUhm8e/AAw/USy+9pPvuu08rVqyQVFub0aupvcvZ2N7fvU/btnUMbsVtray7Ob2nr19dqb5x369SrbitlbZ0Om3Em/o6FWuKKCSrtIz2dHarcfR822DGSE2NJdmk2Z6UpKiixmrPjtfHvHa0YgFnCE6r9s7ObkVHmEeh97heNqRK9TVXqb7my1xrUznnWokN6QUn/X75y1/q5JNP1llnnaV0evC+3dmzZ5fsA5VFqiwTiSo1QrG9F8KFIhGlR/mJFlqK6p3p11vtrdkAANSYSsVSkrRo0SJ99rOf1Q9+8AP9z//8j2bMmKGPf/zjetvb3pa5ZsWKFYrH47r55pvV3d2tRYsWadWqVZn2UJL08Y9/XOFwWN/4xjcUj8d12GGH6XOf+5xCoexv/xdddJFuvfVWXXXVVZKko446SmeffXbJ5oLqyyb9crveL5V+SbddWHSUcXvjpNIPAGpHOeMta60SiURNb0avhsyZfkPbiFsrq+wmnWSO7bgr1Yq7K+VsmDeymhpNS0ZqDEk9aak3bXI+U8+4Yy3F5sWupNfac/z3GrVYwDgxUlpOjDTSsmOx99jPGzVLrZ7mKtXXfJlrbQrqXAtO+u3YsWPQIcUDNTU1qaenp+BBDcQiVVZKRuvbhy9DdCWdHTdbulNq7x/5tUsnjxwAjieaCaaMEmmNuKMHAADkr1KxlOeoo47SUUcdNerzxhidfvrpOv3000e9JhaLaeXKlVq5cuWo17S0tOiiiy4qaqzwt8KTfm7JX5Uk3EXE0ZJ+YSr9AKDmlCre+sEPfqAjjjhCU6dOVV9fn5544gn95S9/0apVq+pqM3olZM7fHSVJ5tfP6x1xJ+KZErGKGidJ1hiy6kkb9VVp51O3d55fkWt5YePc75TP7jkAYGQFJ/2am5tHbDsgOUHVxIkTCx7UUCxSjc0LdEKFFfONKWSchZGEdXYnTSLpBwBASVQylgJKKel2nsj1Fwmv4US1N0gm3O8fGWURkfaeAFB7ShVvdXR06Fvf+pb27t2r5uZmLViwQKtWrcok7OplM3olxNPu+bujbdJxv/qtMn9nwhnwjFg2kGhwf7R96epsfOpOZyv9iuGXrg0AgNwUnPQ77LDDdO+99+roo49WLBaT5CTfUqmUHnzwwVF3UqH0vI/uMuT8JEnNYakjKfWkjCZFWAUBAKAUiKUQVKk8N5z5ZaHIOwd7/Pae1a1IBACUTqnirfPOO2/M5+t9M3op9Y9T6ZfdpOOvz+tdbtJvejQb8TS6c+ivWqWf87XYpF/YvdVU+gFAMBSc9PvQhz6k//iP/9C//Mu/6E1vepMkp1f65s2btWvXLn3mM58p2SAxNi92KFcRXibpV6WdSQAA1CJiKQSVt+ATMTmeTSOb+d9qSrhn+kVo7wkAdYN4K1jSNvt5HRtlkcv7vPZbAmqf20pzUjj7WKM71n7rzK0cHbrGUqr2npkNXD5LtAIARlbwf/ZnzZqlL37xi9pvv/10//33S5J+85vfqLW1VVdccYWmTZtWskFibF6rJFOm4KHZDVi6/dY7AQCAACOWQlDlfaafT5Jp41f6ORdUuyIRAFA6xFvBkhgQK4ze3tO5yE9LVIm0FLfDW2lGjDdeU5Vqv1K198wkWosdEACgIgqu9JOcQ4ZXrVqlRCKhffv2qaWlJdMuAZVTiUo/KRssAACA0iCWQtBY67W/zJ6pMx4vgqz2vvDEOBWKfq0cAAAUh3grOOLuAldEdtTNRX7ZTDRQd+YcQjuoQtEY51y/nrTUZ42aKhgNWSv1lKi9Z8g4iUs/3XMAwOiKSvp5otGopkyZUoq3Qp6slay7lFKuNgFe0q8nRdIPAIByIJZCUAzcpB68Sj9nIOOd6UelHwDUJuIt/4uP09pTym468lPVWdcYybXGkFVPuvKVfn1pKe2eU9xcovaefrrnAIDRFZz0u+uuu8a95rTTTiv07ZGjgeFEuVJyEzKVfmX6BgAA1CFiKQRRckDwmWvSz3+VfiM/75fkJACgdIi3gsWr9IuFRv8w9mNlfubsvBHaIDS6GbO+dGXPw/OqD5tDxRcJcO4xAARLwUm/O++8c9xrCJzKb2AertztPan0AwCgdIilEETeApuRzTn29EsFXfZMv7Hbe6ZlZG35zssGAFQO8VawxN2P6NHO85OyCSzro8/rLne9rGWEZGWj+1hfhQOh7hK19pT8E8sBAHJTcNLvjjvuGPZYV1eXnn76ad1333265JJLihoYcpPdZWPLFuh4Sb+4NUqkpWi5sosAANQRYikEkZc4C5vcF9n8UkE3bqXfgH9PK/czCwEA/kW8FSz9mbPxRr9m4OdzSiU6t6hIYyXYGty5xK0TC5XraJ7hY3KrD8eomsxVtrqystWKAIDClDR909LSohNOOEHHHnusbrvttlK+NUbhfdSWMw8XC2V3RPewrQcAgLIhloLfpdyzdkZLnI0k296zulvxk+mxz/RzHnZiXj+1DAMAlBbxln9lKv3GSFQZ43QckPzzed3lxhgtIyT9IkYKyUoymflVgtfec6SWo/nyywYuAEBuypIrWrRokf785z+X460xhJeDK3fxnbczqJsWnwAAlB2xFPwq6X7NZ/3ILy2hMpV+owTOxmTnVe2xAgDKj3jLf+JuoqphnEUuv50xN9aZfsY4m+ml7JmFlRxTcwnae4a9JGvR7wQAqISy5Io2b96sxsbGcrw1hrDeuSplzsV5LT69nUIAAKB8iKXgV6lxWmSOxItTbZUX5sY7009iJzsA1BPiLf+JZz6rx77Oy635IQmVslJvpqpu5ACiwY09+m3l1tQyLUdL0N6T+AgAgqXg1tePPvrosMcSiYS2bNmiX//613rb295W1MCQm0pV+nk7g3r8EFEBAFADiKUQRF7SLzxG4myokLs7vNrVc8kcWpOGJSXkj0VEAEDxiLeCxauEaxgnURVyj5bzQ3tPL7kWllXjKDFGLCQpVeFKvxK29/RTkhUAML6Ck3433njjiI9Ho1G97W1v00c/+tGCB4XcVarSL9Pek0o/AABKglgKQZQMcKVfIofqAW8RkZ3sAFAbiLeCJe5u0InlWOmXlvvBXUUDW3uOtjbX4D7uVPqVf7xJK/WNU32YDyr9ACBYCk76fetb3xr2WDQaVVtbWzHjQZ4qdqafG1H1cKYfAAAlQSyFIEq5i3HhPEJC353pl1N7z+ovIgIAike8FSzxHFpxS9k4xA+Vfl05JNe8ysVKVfrtTThjagzZTMKxGJkzFGVkbfkLDwAAxSk46Td9+vRSjgMFcnY1VaC9Z4j2ngAAlBKxFIKooEo/92s11+WsHXim3+jX0b4KAGoL8VawJNwEWmycRa6QsZKMLyrPvEq/ljGSfl7lYtztJhAqc9JsV9L5BtMitiQJuoE/jrSy8RIAwJ/KnStCmVWsvWeY9p4AAAD1zkuGhfNI4fmhJVRKktX4Z/qF3MoCPywiAgBQb3JpxS35a5OOd6bfhDHOIYwY74xjk5ljOe1yK/2mRktTWhgyknFjPz9UVwIAxlZwpd+HPvShnK81xuhHP/pRod8KY6hUe89m9xskrFE8Pf6uKwAAMDZiKQRRUCv9kgO++ZhJP/drtVuRAgBKg3grWBI5tvcM+am9Z6bSb/RrjHHW0frSUn9aaijzmtruhPMNpkVLd4NCcpKsfki0AgDGVnDS7x//8R/16KOPqq+vT0cddZTa2tq0d+9e/eEPf1BjY6OOP/74Eg4To/E+vs04AVGxoiEpZqzi1qiHpB8AAEUjlkIQFXumX7XOgcmc5yc7ZkstP50RBAAoHvFWcKSts9FcGn/NKeyDLgKe7hzO9JOkBiP1SYqX+dzgeFrqTHmVfqX7PhHjxEfESADgfwUn/ZqamtTW1qbLLrtMjY2Nmcd7e3v1xS9+UQ0NDTrllFNKMkiMzgtwKpGDaw5bxZNG3Smjtgif8gAAFINYCkFUUKVf5trqtYlPugtykfHOCHK/UukHALWBeCs4Blbl597es7wJtPGk7YD2nuMk/WIhK6WM+sscZHitPVvCVo0lXCwMu7c6yXIgAPhewf/5f+CBB3TKKacMCpokJ6A65ZRTdP/99xc9OIyvUu09JWmC+016UpzrBwBAsYilEETe7u5CKv2k6iXTEjkmK7PnDxLvAkAtIN4KjnhmU7sdN87wzuCtdtVZX9o5M9jIZo7FGU3MnVO8zDHG7qTz/tMipY26IpluCMRIAOB3BeeK9uzZo3B45IbV4XBY7e3thb418pCp9KvAZ663a6krzQc8AADFIpZCEGUr/XJfZRsYOVYr6eeNe7wzgrKVAwCAWkC8FRyZ1p45LDn5pb1nt7spvik0/rpcQ8gZbLkr/bzz/ErZ2lOSwj5JtAIAxldw0m+//fbTz372MyWTyUGPJ5NJ/exnP9N+++1X9OAwvsyZfhX4Xq1u0m9fcpwLAQDAuIilEESFVPoZIxk3arVVWijKvdLPubDai4gAgNIg3gqOhJsMi+awUumXTTq5nucnOWf6SU5FYznjIa+957QSJ/28GIr2ngDgfwWf6XfGGWfoK1/5ii688EK96U1vUltbm9rb2/X000+rvb1d//Zv/1bKcWIUlaz0yyT9aO8JAEDRiKUQREl3F36+v0SE5CzMVa/Szxn3uGcEZVpXlXlAAICKIN4KjkSOVfnSwHbcZRxQDjLn+YXGH0jE3QRlZRS32SRgKfWkpJ600250SqTElX7u12onWgEA4ys46XfkkUfq85//vH70ox/p/vvvl3W3qSxatEjnnXeeli5dWrJBYnRpt8avEmf6tbqf8CT9AAAoHrEUgshb6Mmn0k9yqv1U5p3tY8m50s/9Wq3kJACgtIi3giOe4wYdaXACylo3zqiCnkyl3/jXGiM1hJxzAPvSzr+X2k63ym9SxOZUMZmPcKbSzw3qAAC+VXDST5Le8IY36A1veIP6+/vV3d2tCRMmqKGhoVRjQw68BYlKJP1a3Eq/uDWKp6VYJb4pAAA1jFgKQZPMtPfMb7Gn2sm0XM/080vlAACgdIi3giGv9p6ZJJ9RWtkkYKV5Z/rl0t5TkppCVn1po9600aQyJM62xZ2bN6vErT2l7HnOdEMAAP8rSdrGuFtqIpGicogogLdb2uS58FKIaEhqDNHiEwCAUiOWQlCkcqyYG8q7vFrrRAmvLSmVfgBQt4i3/M2ryo/lsL7l/CSrfw5vPu09JanZDTR60+VZU/OSfrNjpY9kONMPAIKjqEjnz3/+s374wx/qpZdekiR96Utf0gEHHKCbb75Zb3jDG3TMMceUZJAYXSUr/STnXL++tNG+lDQ1WqFvCgBAjSKWQtCk3ORZvu09Q24nqGotzGUr/ca+zpuXlVHaVubcbABAeRFvBUMij/aexjjVfSn3n2otT3W7ybvmHEsNm9zkYE+q9G1Ju1LOBn0jq5mx0gdcnHsMAMFRcK7oz3/+s6666iolEgm9733vy/RFl6SJEyfqkUceKcX4MA5v4aRSCxKtYSr9AAAoBWIpBFEyqJV+7k65yHjtPQf8O9V+ABB8xFvBkchxg44nVOUkVMpKfen82ns2hiTJKiWTmW+peFV+06K2LMfxZJJ+qt4ZzQCA3BT8MXDHHXfoiCOO0Je//GWdccYZg55bsGCBNm/eXOzYkINKV/q1uLuXSPoBAFAcYikEkbewlneln/s1rerEkJlKv3GCZmMk44N2YQCA0iDeCo64u8AVy7FVpheLVOvz2mvtGZZVQx6JysZMi8/SjmdbvzOI2WWo8pMGtoozSpXlOwAASqXgXNHmzZv1zne+U1K2L7pn4sSJ6uzsLG5kyEn2TL/KfD+v0q8rSdIPAIBiEEshiJLu10ieNXumygtzuZ7pJ3GuHwDUEuKt4MinvafktPeUpFSVNhT1ZKr88luTa3aTmqU8189aaXsZz/OTnDmG3PiPc/0AwN8KTvqFQiGlUiPv7ejo6FBjY2PBg0LuqnGmn0SlHwAAxSKWQtBYW0yln/PCaq0R5Xqmn8SZNQBQS4i3giNo7T27U/m19vQ0uQt4PSVM+u1NGvVZo4ixmhYt3w2JECMBQCAUnCtatGiRfvOb34z43O9+9zstWbKk4EEhN9ZWL+nXneZDHgCAYhBLIWjSkqxyr5gbKFT1Sj/na3ScM/0kKv0AoJYQbwVH3u09vXbc5RrQOLz2nhNyHK+nyav0S5XubLxtcSfQmhm1eW/Myof33lT6AYC/FZwrWrFihZ5++ml95Stf0TPPPCNJ2rRpk2655RY99dRTWrFiRckGiZE5n7HOJ26oQoV3jSEpYqwkoy6aeAMAUDBiKQTNwA1fhZ/pVx15tfescoISAFA6xFvBkXd7z2pX+rmVes3hcS4cojHknB+ckslsSipWuVt7erKVfnT/AgA/i4x/yciWLl2qCy64QN/73vcygdMtt9yi5uZmnX/++Tr44INLNkiMbGBsUKmPW2Ocar+9SaN9KaNJEVZDAAAoBLEUgsbb1W1k8945mDnTr6Qjyp039vySfkbVa0gKACgF4q3gKLS9Z7U26RTa3jNkpIaQ1JeWetJSrMjWXWkr7Ui4lX6x8t4Mp7rSiBoAAPC3gpJ+6XRa27dv11FHHaU3v/nN2rBhgzo6OtTa2qqDDjqInugVkg1sbEWPLW4NS3uT3rl+LIQAAJAvYikE0cDz/EyBlX6lamOVryTtPQGg7hBvBUvC/eCN5tze01GtBFRPge09vdf0pY32JEJqixQXcexNGiWsUdRYTS7zxvwI7T0BIBAK2k9irdW//Mu/aOPGjYrFYnrDG96gY489VocffjhBUwUNPM8v34WXYrS4u5i6UpTzAwBQCGIpBFEyjxaZQ1UzkWZtftUDYTcxyPnVABBsxFvBkbZSUoW296zO2pTX3jPfSj9Jmh61MrLqShvtKzJr+bp7nt+MqC370T/VbqkKAMhNQUm/cDistrY22Wpt1YWkwUm/Smp1A5rOJEk/AAAKQSyFIPLWpPI8ukbS0JaZlZWU5DXDz6m9p/uVSj8ACDbireAYeLZdLADtPePp7BmEEwoIjGIhaYpblbc9HiqqE8LrCSdymVnm8/ykbNIvyZl+AOBrBeeLli9frkcffbSUY0GevKCgklV+kjLn+HVQ6QcAQMGIpRA0+ZyLN1Q1E2nJAd80vzP9yjMeAEDlEG8Fg5f0Cyv3arWwe9xMNdp7euf5NRhbUFwkSTNiViFZ9aaNtieMulL5xx7WZiv9ZkbLH7hEqPQDgEAo6Ew/Sdp///315JNP6oorrtAxxxyjtrY2mSHZp2OOOaboAWJ01ar0a4tk23smbO6tFwAAQBaxFIIme6Zf/is91UykZZOVNqfNclT6AUDtIN4KhoTbKjOaxwJXNVtNdrlBQiGtPT1RI02LWu1IGO1MhLQz4cQq8/Ko2GtPGsWtUcRYTa1A0s+LATnTDwD8reCk37e//W1J0p49e/T888+PeM0dd9xR6NsjB96iSbl7dg/VGJIajVWfNepMmooEFgAA1BpiKQRNcZV+zourkUjz2m/lf0ZQmQYEAKgY4q1giLufubm29pSqu0nHO+5mYhFJP8mpzouYtLpTRt1pp23mK/1hxXYldUzz+K/fnnDGMb0C5/lJ2UXkalRXAgByl1fS7/vf/77e8573aOrUqfrCF74gSUqlUgqHCznZA8WqVqWf5LT47EsYtZP0AwAgZ36JpdatW6cf/vCHOumkk/SJT3xCkmSt1Z133qmHH35YXV1dWrx4sc4++2zNmzcv87pEIqG1a9fqiSeeUDwe12GHHaZPfvKTmjp1auaarq4u3XbbbXrmmWckSUcffbRWrlypCRMmVHSOKL2UmzwLF5L0q2KlXyLPZGV2EdFIIs4FgKDxS7yF3Hmf1dE8uglUc5NOh5v0846/KZRxq/2mRa3SVtoeN9qVNNqwL63ZYaP5DWO//+tx9zy/aGVSn9kz/ZzWopU+bggAkJu8kn4//elP9eY3v1lTp07VIYcconQ6rQ9/+MO6+uqrdcABB5RrjIOwSJVl3YWXanzItkWsXk84rQQAAEBu/BBLbdq0SQ899JAWLFgw6PF7771XP//5z3X++edr9uzZuvvuu7V69Wpdd911ampqkiTdfvvtevbZZ3XxxRertbVVa9as0TXXXKNrr71WoZCz6HD99ddr9+7dWrVqlSTppptu0g033KBLLrmkIvND+QT2TL88FxI50w8Agq0c8da6dev09NNP67XXXlMsFtOSJUt01llnac6cOZlr6nVtqhQKae/pfV5bGaVtZbtgdbhn+k0suH/acCEjzWlwKvZ2JIzWd4U1L5Yedc3PSxJK0qxYZYKWbAxolJZEGh0A/KkaRWIFG2+RauXKlbr66qvV1tam1atXq7e3N3PN7bffrqeffloXX3yxrrzySvX19emaa65ROp1derj++uu1efNmrVq1SqtWrdLmzZt1ww03VGx++ap2pZ+U3d0EAAD8r6+vTzfccIPOPffcQQtH1lrdd999OvXUU3XMMcdo/vz5uuCCC9Tf36/HH39cktTT06Nf/epX+tjHPqalS5dq4cKFuvDCC7VlyxatX79ekvTqq6/queee06c+9SktWbJES5Ys0bnnnqs//OEP2rp1a1XmjNLxWjmFC6h+88eZfrldX81WpAAAf3r++ef17ne/W1dddZUuvfRSpdNprV69Wn19fZlr6nVtqhQKae85MOFUyc9sawdU+hXZ3nMk06NWESPtSRptjY9+Q/a45/lFjVMpWAkhIxk3TqINOgD4V2CSfixSDZdN+pX3kzZkjNKR2KB/JjY425naU6Fhz3n/mEi0rOMCAAD5ufnmm3XEEUdo6dKlgx7fsWOH2tvbdfjhh2cei0ajOuSQQ7RhwwZJ0ssvv6xUKjXotVOmTNH8+fO1ceNGSdLGjRvV3NysxYsXZ65ZsmSJmpubM+8zVCKRUE9PT+afgQtjxpi6/8dP98Fr7xkJjXGdNOI/Ayv9RrvGlGmuA8/0G+0ea8DYwwMSlGOOtYxjrrV//PT3uFb/4R5zjyt1D+rVqlWrdPzxx2vevHnaf//9df7552vXrl16+eWXJdX32lQpFNLe05jselglE1D9VopbpwX4xCLbe44kYqSDWp3I6X+7R6+l29rv/H9ydqwy5/l5vE1USZJ+AOBbJSxEL6+Bi1R333135vHxFqlOPPHEcRepli1bNu4i1cCWDQMlEgklEonMn40xmRZYhQbFA3+hGPS4+4/Huh+wzk6bHN63oNE455n8b3ty0GPOh3tYXSmrP+1NjxhgLG0LKzTOPRhtrrWGedaeepkr86w99TLXeplnPp544gm98soruvrqq4c9197eLkmaNGnSoMcnTZqkXbt2Za6JRCJqaWkZdo33+vb29mHvMfSaodatW6e77ror8+eFCxfq2muv1fTp03OdWs2bNWtWtYcgSXpxW7fU1auJE5o1e3bLiNfsaO9US+vwBapYykq9fbIymtAyYcT/b0ZjKc1om1bycb+6o1fq7FZrc6Nmz5448phbBrRMS6Slvn5ZY9TSOvI8PeUacy3yy9/jWsY9Lj/uMTw9PT2SlImLqr02FXQDN+jkI2ScTTqVTPp5VX4t4cJanufiDZPCeqEzpR2JkF6PG80coX3nNvc8v9mxbJ2jiUSVKnD1L9ffm8LGSdJS6QcA/pV30m/r1q2ZM1u89gOj7TYq1dk0fl2kksq7UDX0F4qhiyi79yWkRFINsahaWmNjvpcx8XEXLfJ9baS3V0lrFGpqVssIjdfzWQSpl1+emGftqZe5Ms/aUy9z9eM8qxFL7dq1S7fffrtWrVqlWGz0mGHoL/vWjv/bfK7XjLaQcOqpp+rkk08eNoadO3cqmUyO+Jp6YYzRrFmztH379pzuc7l1dIYkhdXf061t2/aNeE06HFXXvtSwx1PuhjFJ6tzXnammGygRDmvbtm2lG7Brd5cz7lR/n7Zt6x70nDFGocZmdXV1Z3bU9aedsSbTVl37usZ873KNuZb47e9xLeIelx/32BGJRAKzKaec8Za1Vt/73vd08MEHa/78+ZKquzZVjs3oleZV+sVCI2yA9/5sjMyQ//+FJSUlWY2+0d2ryi+VzlS2tedY7zt0437OjFFzxGhRk9XGXqONvWHNahgcWyXSzrl/krRfQ3Yczqb94XFYLt4wOZrTeL2F5JRMptVnofe4njZq1tNcpfqaL3OtTUGfa95Jv29/+9vDHhutt/gdd9yR/4iG8PMilVSeharRfqEYuojS1+80S0omEuraFx97HpOj4y5a5PvaBhNS0hrt7eqVRmhpkMsiSL388sQ8a0+9zJV51p56metI8/TLQlWlYynJac3Z0dGhSy65JPNYOp3WCy+8oF/+8pe67rrrJDkLTZMnT85c09nZmVl4amtrUzKZVFdX16DFqs7OTh100EGZazo6OoZ9/4HvM1Q0GlU0OnJL8Fr+O5oPa60v7oXXxilsRv/ZWPefoQZG02mNfMaAVXl+5gl3A3zEjHEfrc2M2xtrWm6LzzF+zyvXmGuRX/4e1zLucflxj4OjnPHWLbfcoi1btujKK68c9lw11qZqoWtCJL5P6unX1Emtmj2zedjzw6ryXdF4n/oTVtHGJrU0jtwKs9RV+c+/2iWpT3Mmjd75QBq9+0FuUlo+f4o2bmjXlv6QJk2fpuZINnp6saNfdsc+tcVCWjJvdkm+Z67FAk2puLr6UjLRmFpanBi+2Hvsx42a5VJPc5Xqa77MtTYFda55Jf3OO++8co1jVH5epJLKu1A19BeKoYso2TP9Rl5cGfZ+xYxlhMcaQ1bdaaPetNQ2ymtyvQf18ssT86w99TJX5ll76mWufptnNWIpSXrDG96gr371q4Me+853vqM5c+ZoxYoVmjlzptra2rR+/XotXLhQkpRMJvX888/rzDPPlOTsgg+Hw1q/fr2WL18uSdq7d6+2bNmSuWbJkiXq6enRpk2btGjRIknSiy++qJ6enkzMheDKJv3y//+UMZKRlZVReqzt+GWQPScot+uzbevNmJUDAAB/Kme8deutt+rZZ5/VFVdcoalTp2Yeb2trk1Sdtala6JrQ0R2WFFJv1z5tSw+e/0hV+RnpkCSj7p4+NSRGjk9KXZW/tcMZa6S/a9TOB9Lo3Q/GZYzU1Ch17tLUSEi7kyH9dvNOHToh28bzL273hZnhxKC5Ffw9lXuxgEk5BQhdfXF12X5Jhd/jetmQKtXXXKX6mi9zrU3lnGslNqTnlfQ7/vjjyzSM0bFINbpM0q9KKxEN7iaj/rRzgDEAABhbNWIpSWpqasq0n/I0NDSotbU18/hJJ52kdevWafbs2Zo1a5bWrVunhoYGHXvssZKk5uZmnXDCCVq7dq1aW1vV0tKitWvXav78+ZmzaebOnatly5bppptu0jnnnCNJ+u53v6sjjzyyZs+gqSfe2S2Fnl8TkpRSNoatlGQm6ZdbvOqEuE66b7SqRACAf5Uj3rLW6tZbb9XTTz+tyy+/XDNmzBj0/IwZM6q2NlULXRO8qvxojlX5Hq+mLaXRV6VKXZXf4bb3nBgee3PhaN0PxuO1MLXWanFTWrv3hbSxJ6S/a0rJGCfv+Vp/9jy/sYoF8pXLa71NVHHrbOfKfN8i7rHfNmqWUz3NVaqv+TLX2hTUuebd3rPSWKQanXUPOq7W7uPGkPMXvq/SKzcAAKDkVqxYoXg8rptvvlnd3d1atGiRVq1alTkTRpI+/vGPKxwO6xvf+Ibi8bgOO+wwfe5zn8ucmSNJF110kW699VZdddVVkqSjjjpKZ599dsXng9JLulFnoY2qQsZJHKYr/DtT3I2Zc630M8ZJ9KXljLfQJCcAoHbccsstevzxx/Xv//7vampqypyv19zcrFgsJmNM3a5NlUIizw06npBxNumkKhRbJK3U5RbSTRrhmJtSSadSSoejWtAiPdPlnCO4PR3TzAbppW5pX8qJT2Y0R51iR1clzp6KuT+jOGuBAOBbvk/65aJeF6kGtveshkb3G8etsyASZkEEAIDAuPzyywf92Rij008/Xaeffvqor4nFYlq5cqVWrlw56jUtLS266KKLSjVM+EgpKv2kKlT6ZaoHcn9NyDjJyUonKAEA/vTAAw9IGh4/nX/++ZnKwnpdmyoFb4NOLM8Yw9uIVKnYojNpJBnFjFVDGdfAUlZa356SlTQxbLQnGdJvdlstaEzrpV6npem0SFp/7RwcqCydPHLFZylF3b+qCetUHVYgzwgAyFMgk34sUjm8RYhqtfeMGGcXVsIa9aWlCYVu+wYAAIDvFXOmn+TGrFVIpGWqB0K5f+NqJSgBAP704x//eNxr6nVtqhTyPX/X420+r1Sln9fac1LEVizZNSNqtS9l1W+NXuwNycqowVhNj1ZnZ5KXmE3LKKWALiwDQI3jiIoAy1b6VW8Lslft15tmaw8AAEAtC2qlX8KtHshn3N6mOir9AAAoP69VZD4bdKTs53Wlkn7tSTfpF65cgBALSQc0pp3zDt1W6/s1pKtWYRcyUsRdh0ywOwoAfImkX4B5Z0hWs5S+iXP9AAAA6kLKTZ4V2tI9m0irbPBaSPWA18AiVfLRAACAgVLWqRqTimnvWZnYYlvc+T7TY5XdFdTgJv5aQlYzo2m1VLnTVnTAcT8AAP+hCjvAqn2mnyQ1ukk/p9KPT3sAAIBalQxspZ/zNZpHW9LBCUpiXAAAyiWeCQxsAe09nc/oSlT69aWlXQm30i5W+Z3vDSHpgCZ/7LiPGalX3lmMxEkA4DdU+gWYH5J+Te4370tnKw8BAABQe1KZM/0Ke33IXZirZMtMa6WkW1mYz0Ki1z7fH0trAADULq9aLGby72RVyXbc2+IhSUZtkbSaq1xpV23eRiraewKAP5H0CzAvqAlVsb1nzDiLIlZG/ST9AAAAalbS/RopcEd3NSr9kgOGGs3jNx/O9AMAoDLi6cJae0qVbce9td8Z4JwKt/b0o1imvWcVFyQBAKMi6RdgXphRzY9YY6RG929Rb5oPewAAgFpkbfGVft7LKrlU5rX2NLLKZ1M+Z/oBAFAZmUq/UP4RgrdJp9ztPa2Vtsadxa85VWjt6TeZSj/ynwDgSyT9AspaybpLJ9Ws9JOkJjcw6yPuAQAAqElpZWPPgs/0q0L1nLcYFcmzZRiVfgAAVIZXLVZMpV9a5T1ypj1p1Js2CstqJpV+mZ9VnHVAAPAlkn4BNfBztdo/RCr9AAAAatvAHfQFn+nnfq3k+lCigPP8pOqMFQCAeuQljmIFLG5lYxJT1s/s1+LON5oVswXHQbXE+1mlZNggBQA+VO18EQo0MJipdrzhVfr1psq7swoAAADVkRzQJrPQXyCqWenntaHKVXas1Y60AQCobfECP6slbz3MeV05W3z+rd9t7dnAdiDJSbaG3PseZx0QAHyHpF9A2QELL/m0KioHp9LPKiWTWRACAABA7Rh4nl+hsWe2eq5ywWvCXZuL5vlbD5V+AABURtztGlVIpZ8xg1t8lsPehNHOREhGVgtI+mV4Py9afAKA/5D0CyjvM9UPP8CQkRrctZtePuwBAABqTtI7z6+I96hGpV+ywPaeYVP+qgEAAJCtyo8VUOknZVt8lusze0Ovs/I2v8GqOTzOxXXEi60SdEUAAN/xQ84IBfAWS0I++Wz1Wnz2ca4fAABAzRlY6Vcorw1UZc/0c75yph8AAP7UX8SZflJ2XawcSb94Wnq5zxnYQc2p0n+DAPOStLT3BAD/IekXUN4ChF9SbI3u3yQq/QAAAGqP18I9UkzSL5Bn+pV4QAAAYJC4WykWKzDGyLb3LP0K2ct9ISWt0aSw1cwoQcFAXpK2n83/AOA7JP0Cyvq00q+XD3sAAICak630K3zBqxrVc17LqXyTlST9AACojESm0q+wD91yVfpZK210W3suaU4VfKZxrWrMdPyq8kAAAMOQ9AsoP53pJ0mN7taquOXsEwAAgFqTKPBsvIGqUunnBs15n+nnfqWRFwAA5VV8pV95zuHtTEntyZBCsjqwkczWUE3ugmTcGsW5PQDgK37JGSFPXtsCv/wAo0aKGCvJsMsHAACgxhR6Nt5A1an0c75G86we8BKUVibTYQMAAJSedyZcwUk/b1NRaYaT8Wq/E7nMjNmCzxusZRGTbZ/enqjyYAAAg/CxFVDeDmk/tRfwdvn00eITAACgpmQq5gpsvSVl49ZKJtKSBVYoDvwlif1sAACUh7XKVIn5rb2nl/Sb20AkMJpGN2DaS9IPAHyFpF9AebFMSP7ZetyYOdevygMBAACoATviRq91+2MVpZSVfpIqFsEWOm7ncufFnOsHAEB5pJTtZFV4e09HKZei+tPSjoQzIJJ+o2ty1wH3+CNcBQC4SPoFlLf4EPJRUZ1X6ddLpR8AAEBRulLS/XvC+sGLHb5onV6SM/0G/HulppRN+uWXuTOGc/0AACg3r8rPyCpSZHvPlC3dWtTWeEhWRm3htFrD419fr7ykH5V+AOAvJP0Cylso8dMP0Puw70uLs08AAACK8NeesNIySlnptf7qb6gqNHk2kDHOop5Uueo5b9yFLCR6m+uo9AMAoDziAyryCz2+phztPV91Y6/9GggCxuK192xPEC8BgJ/4KWeEPPjxTL+YcdqNWhn182EPAABQkHhaerE3G6b/ra/6IXv2TL/i3sd7ecUq/dwOFIWMO1ymM4IAAIAj7n5Ox4qILyLuhqJSVeanrfQa5/nlxFsHTEvqTPlogRIA6lz1VxBQkOyZfv5hTHaXDy0+AQAACvNib0gJa9TgVtW9FjdVTzyVor2nVPnquWIqFLPtPYlrAQAoB6/Sr6GITgKl3qSzO2kUt0YxYzU9ys6fsQxcB9yTIF4CAL+IVHsAKIwf23tKTovPnrTxxdkzAAAAQZO20gs9TrrpyNaU/rcnpq5kWtvjpqotpkrR3lOqbKWftVJyQNuwfFHpBwBAecWLqMj3lPrz2kteTY9ahaPRgjf/GD+15iojbx1wT9LogGoPBgAgiaRfYHm7o0M+iyEylX6U9QMAAORte9yoJ23UaKwObLLqjcb03O4+vdof0n4NpWpclb9EEcmzgUJGkq1MpV9aUlqFVyiGjZVU/SpLAABqlVfpFysivvCSfmkZpW3x62R7ks4bTI5YpRTS+vbC4q+lk6PFDSQgvHXAvUnWAQHAL0j6BZS3gOHHSj9J6k07u6sBAACQuw5349T0mFXYSLOizkLT3+JhHR0O53Wec1hWNpkoybhK1t7T/VqJSr/EgFg0UsC4vbFWL9UKAEBtS2SSfkW095TkHILjbNQpNum31630m0Jrz5x464C7E0bWKq9YFQBQHiT9AiqVqfTzVxDSGJKMrFIy6k5JE6s9IAAAgADpdHdJTww7Md6MBie26kkZPb0npabwWK8ebGlbuGQbxBJulq6Y9lvSwDP93JK/MvLGHJEtaAEwUzngr3AbAICa0e+294wVEV8Y4yT+Uu4/xdTXpW22Ym1KhHNrctEUcmKmuDXqSBm1RQicAKDa/FYohhx5oUce6z4VETJSQ+YQ3+qOBQAAIGg63Uq/ie6CSSRk1OzGVn3p6m2dDuKZfpnqxAJ/4+FMPwAAyitegko/qXSf2Z0pKSWjiLFq8duCm08ZI01zM6074pT5AYAfkPQLKC+QCfvw89Qr7SfpBwAAkJ99Qyr9JKnBja36q5R8StnizsYbyLiLepWYipeoLKS1p5TdXJeSDwNuAABqQKIElX5Sdm0sWWSAsTfhDGRypLAuAfVqeoPzdUeCmwYAfkDSL6DSmfae1R3HSJq8Sr94dccBAAAQJCkrdbklcBMHtEaKufFevEpdpoo9G2+gTKVfBbJ+xVYnht3XUekHAEB5ZCv9inufSKbSr7g32uNuvppMi8q8zIg5X3cmWGYGAD/gv8YBZK3Tp1zyX3tPiUo/AACAQuxLGUlGUWPVOGDNymudHi9yIatQmYq5As/GG6gq7T0LrfSjvScAAGXlbWiKhYps7+n2EEiNc9149mTO8+PDPx/TYpJktS9l1FvsDwEAUDSSfgHkhB5OIOLfSj+rvrTUw4c9AABATjqTzteJYSszIMbzzrmpWqVfuriz8QbyYtdKVPol3ftVaNKvkglKAADqkbehqdhKv1Js1LFW2uu2p5wSJemXj1hIanMTpbT4BIDqI+kXQNkgxvryBxgyUoP7Ge/tkgIAAMDYOlPueX5Ddpd7lX5JmapUnWXbZBb/XpWt9HO+RgusHqDSDwCA8spW+hX3PqU40683LfVZIyObSWAhdzPcRCktPgGg+vgvcQANbO1pfJpTy7b49OkAAQAAfKbT3Sw1MTx4oSlssm2r4lVN+hX/zStZ6VdsstJbQEzLyLL2BwBASVk78Ey/4j5oIyXYqLM3E4cVf4ZxPfKSflT6AUD1kfQLIG+RxI+tPT1N7mGDu5L8FQMAAMhFttJv+HPeDvhqtPgs9my8gbzzqFMqfyDrjbvQhbuBZ2fTsR4AgNJKWsmqNC3Es9X5hccXXtJvcoTG3oWYHnPu256EKariEgBQPDIyATSw0s+vmt1Kv10JdkYDAADkIpP0Cw8PnjLn+hWxmFWohLv2FSnJmX7OPCrRMjNRZPWAMVJIlRsvAAD1xKvyM7IaYb9TXryOCMVs0vE6LkyitWdBWkLOWmBaRjvjPq5SAIA6UOznKqogFYRKv5BkJPWljbrTUoufM5QAAABVFk87cZM0ctKvISQpJfVXpdLP+Vps6y2pNO23ctXv3s9izgkKG6fLBkk/AABKy9vIFDPFH13jxRfFVJiN1XEBYwsZo3Q0plkN0su90rZkVDMnjP86I2lvZ1fZxwcA9YaPsgBKu4FR2MdJv5CR2qLS3oRT7dcywuIVAAAAHN5CU1PIjtjiKubGffG0kVTZuKrYNpkDhSuY9PNaoTYUMW7vR0GjLwAASsvbyNRQgk4CpYgvxuq4gLGlZbS+Pal4ykgK6eUeq5AdP3oyko6aQZQFAKVGe88Ayrb39HcgMi3mfN2V4K8ZAADAWLyWUqMtNMVCXnvPig0pw6v0K8WZfl5UmJLK3gK+333/hlDh36iSSUoAAOpJn5vraSzic9rjfV6nVdgRMwM7LrSS9CuYt+G/J03sBADVRDYmgILQ3lOSpkWdr7sSPh8oAABAlXm7y1tHOUfGq1aL2/Iny4byzvSLlWBRLlstaMpePVeq9p6SlKrCWYoAANQyL8nWWIpKvwH/Xsi5fgM7LhQTN9S7WMhrB2/UXcwBiwCAotDeM4DS7nqLn9t7StlKvz0Jo7T1f5ISAIBatW7dOj399NN67bXXFIvFtGTJEp111lmaM2dO5hprre688049/PDD6urq0uLFi3X22Wdr3rx5mWsSiYTWrl2rJ554QvF4XIcddpg++clPaurUqZlrurq6dNttt+mZZ56RJB199NFauXKlJkzI4WCPOuYtjIy2uzxiJCMrK6O4La5lZb68Sr9StPc0ys4jZcsbz2bbhhVR6Sdn4Yp1KwAASiub9Ct+U5Exzmd2SkZJm3/MMl7HBeSuJWy1J2nUlTKaOMpmNgBAefk+6cci1XDZ9p7+NjEiRY1Vwhq1J42mRPmwBwCgGp5//nm9+93v1oEHHqhUKqUf/ehHWr16tb7+9a+rsbFRknTvvffq5z//uc4//3zNnj1bd999t1avXq3rrrtOTU1NkqTbb79dzz77rC6++GK1trZqzZo1uuaaa3TttdcqFHK2RV9//fXavXu3Vq1aJUm66aabdMMNN+iSSy6pzuQDottd+Jowyu5yY5xz/fqt04KqFOff5Mo70y9WiqSfcVqNpFTYTvxcJa2UkjPgYhKktPcEAHief/55/eQnP9Err7yivXv36rOf/aze9KY3ZZ6vt7WpYmXbe5bm/cLG+bwu5DM703GBpF/RWsLSnqTUVYVzqAEADt8XrXuLVFdddZUuvfRSpdNprV69Wn19fZlrvEWqlStX6uqrr1ZbW5tWr16t3t7ezDW33367nn76aV188cW68sor1dfXp2uuuUbpdLax0PXXX6/Nmzdr1apVWrVqlTZv3qwbbrihovPNRdpdePF75Zwx0lQ30UeLTwAAqmfVqlU6/vjjNW/ePO2///46//zztWvXLr388suSnEWq++67T6eeeqqOOeYYzZ8/XxdccIH6+/v1+OOPS5J6enr0q1/9Sh/72Me0dOlSLVy4UBdeeKG2bNmi9evXS5JeffVVPffcc/rUpz6lJUuWaMmSJTr33HP1hz/8QVu3bq3a/IOg211smjDGYpPXbipe4VaT2Uq/0izcVCKRFndDfCNb1FmEIZJ+AABXf3+/9t9/f61cuXLE5+ttbapYpaz0k4qLL/a5O5GoTCued65fX9qpugQAVJ7vk34sUg3nBTB+r/STpOlu0m8nST8AAHyjp6dHktTS0iJJ2rFjh9rb23X44YdnrolGozrkkEO0YcMGSdLLL7+sVCqlpUuXZq6ZMmWK5s+fr40bN0qSNm7cqObmZi1evDhzzZIlS9Tc3Jx5Hwxnbba951hJvwY36Rav9Jl+7vcrJnk2UKQCiTTvPL8G42xEK5QXb5f7/EEAgP8dccQROuOMM3TMMccMe64e16aK1et+uDaVOOmXLGBzFO09SydipEY3Zt2XYi0QAKrB9+09h8p3kerEE08cd5Fq2bJl4y5SDWwnOlAikVAikcj82RiTaYFlClxh8F439PXG/cdbdAgbq3y+QzEftYW81kialqn0C8mY4cslo8211jDP2lMvc2Wetade5lov8yyEtVbf+973dPDBB2v+/PmSpPb2dknSpEmTBl07adIk7dq1K3NNJBLJxGADr/Fe397ePuw9hl4zVDliqaDpS0tpGRlZTQibTJLKZP9FxtpMpV8inVtsZlSae5hwE2ixkBk3gebFq2PxFuXS1pnzoNeW6GfuVUM2hMZ+z6H3eKhsgnLwWDOvV/38PS0U/z0uP+5x+XGPMZ5qr00FUbbSrzTvFynwHF5rs+09qfQrjYkRq76EUUfSaDL3FAAqLlBJP78tUknOmYN33XVX5s8LFy7Utddeq+nTp+c7vWFmzZo16M872jvV0hqW7euTZNXS3KSWhtzq/YyJq6W1ZfwLS/jaaCylN0yZol+371FHymjStJlqjo4czQ2da61inrWnXubKPGtPvcy1XuaZj1tuuUVbtmzRlVdeOey5oYuZdoQkyFC5XjPaQmk5Y6mg2NqdkHZ2qDUa1n5zBs97R3unWlqcM3z6+lJSPK5UKKyW1sZx3zcaS2lG27Six5fauVuS1ZyZ0zStcexfH7x4dSwNqbj2pVKKNDSoZUL2/Uo1XknqbO+X9u5TS2NUs2eP/Xdp4D0eqrc3KcUTUiisltbmYc+Xcsy1jv8elx/3uPy4xxgNG6jy553p1xQuboOOZ2B7z4HvNt4Gnb60d36x1cTI4A1OuWxmGktBr81xviX9niV+7eSI1Y6E0zY1ZbObqIa/qH42VNTb5pF6mi9zrU1Bn2ugkn5+W6SSpFNPPVUnn3zysHHs3LlTyWRy3PcfiTFGs2bN0vbt2weNMR2OqmtfSol0SJJRf2+vuuK5vaedHFXXvq6CxlPoaxPhsDp2vq62SETtSaP1r+7QgsbB93y0udYa5ll76mWuzLP21MtcR5pnJBKpq0TSSG699VY9++yzuuKKKzR16tTM421tbZKchabJkydnHu/s7MwsPLW1tSmZTKqrq2vQYlVnZ6cOOuigzDUdHR3Dvu/A9xmqHLFU0Pxfn5EUUaOS2rZtW+ZxY4xCjc3q6uqWrFU6JUlh9SbTOcVmiXB40PsVwlqpPxWRZNS+a6cS4+w38+LVMd8zaSSF1NPXr6509pzuUozXs73HuacmGR/zPYfe46ESSUkKK55KjXjPSznmWlUvnzvVxD0uP+6xg1hqfGygyk0ybZXYvluStP/smWqMjF3uN9YGHU+TElIyqVA0qpbWWObx8Tbo/K3L2Xw1KRbW3JE2X42zmWk0xWy+l+LjzrfU37OUr22R9LdEn3qSUn+kUW3NYy0/p+pqQ0U9zVWqr/ky19oU1LkGJunnx0UqyWnXEI1GR3yu2F8GrLWD3sO6/6Tdh0JGIzQZGuP9ihlLga+x1mpmNK32ZFjb+43mN4x8IsrQudYq5ll76mWuzLP21Mtc62We47HW6tZbb9XTTz+tyy+/XDNmzBj0/IwZM9TW1qb169dr4cKFkqRkMqnnn39eZ555piTpgAMOUDgc1vr167V8+XJJ0t69e7Vly5bMNUuWLFFPT482bdqkRYsWSZJefPFF9fT0ZGKuocoZSwVFl3uOTHN4lL+v1spK8homJK2zazo0zqZDLxYrRspKXkP5iOxIebHh33Oc98yeuTP42lKM15M90y/H/wa493go7x6n7MjzKuWYax3/PS4/7nH5cY8xGjZQ5cc5yzgqI6s9O14fs334eBt0POmEs6morz+hrn3ZHfLjbdB5xd0oNGHI5ispt81Moyl4870x0uTouPMt6fcsw2snGqMehbS9O64Jqb6RX2iM1NRYFxsq6m3zSD3Nl7nWpnLOtRKbqHyf9PPzIlU1WOuc+SJlF0z8bmYsrQ29YW1PBGTAAADUmFtuuUWPP/64/v3f/11NTU2Z9lDNzc2KxWIyxuikk07SunXrNHv2bM2aNUvr1q1TQ0ODjj322My1J5xwgtauXavW1la1tLRo7dq1mj9/fuZsmrlz52rZsmW66aabdM4550iSvvvd7+rII4+sqTNoSq3bPUdmwjhn2oQlGVlZGSWs1FCB0Co+4PebaIm+nzfNVBl/T+zPnENY3Ptkzx8sckAAgJrGBqr89KYGnuc3/qYiSaNu0PF49XhJN1rKvExj34eOpBMstI6w+SqXzUxjKeS1mZae48y3lN+zHK+dFLHanpC6UlJilBaf3lzraUNFPc1Vqq/5MtfaFNS5+j7pxyLVYAP3F5XorOOymxlz/o/RngypL126Q5oBAEBuHnjgAUnS5ZdfPujx888/X8cff7wkacWKFYrH47r55pvV3d2tRYsWadWqVZkzYSTp4x//uMLhsL7xjW8oHo/rsMMO0+c+9zmFQtkP94suuki33nqrrrrqKknSUUcdpbPPPru8Ewy4bjdB1RIe+5cJY5zEW9w6iycNFRhbwm3SEDF2zF34+cicuSPn/Jxy6HfH3WCKe39vATElZ/NdQI90AACUQF9fn7Zv3575844dO7R582a1tLRo2rRpdbU2VSzvPL/GUOnigLD7mZ/vpqJ9bgJyYiR4i7p+1hCSmkJWvWmjvUmj6VHuLwBUiu+TfixSDebtMjay47Z08oumkDQpbNWRMtoRN5rfyAc9AACV9OMf/3jca4wxOv3003X66aePek0sFtPKlSu1cuXKUa9paWnRRRddVNA461W3u6trwjhJP0mKeUm/tJFyuL5YCesEnKWq8pOcBKJU3kq/uDvuhhJV+klGaWWTgACA+vPSSy/piiuuyPx5zZo1kqTjjjtOF1xwQV2tTRWr193w1FTCTeGZTUV5xhedbhw2sQJxVb2ZHLHqjTtrgW1hm2lVDwAoL98n/VikGsyr9AvagsPMWFodvWG9nghpfmNh/dABAABqUVeO7T0lKRqyUtoMartZTgn3+5Qy6RcqcFEuH5lKvyIXl4yyLVVTtvrt9ZNW2hY3ihppVozFSQCopEMPPXTMNap6WpsqVnkq/ZyvyTze0lqp0z1bmaRf6U2NWO1NOtV+r8ZD2r8hTdcEAKgA3yf9MJhX6ReUKj/PzJjVxl5pezxgAwcAACijRDpblZZrpZ+UTcaVWzKT9Cvhopz7tZzbwPrd4caKHLcxzniTKu94x5O20u/2hbW5L6SkNTKyet/UhNr4bQ4AEEB96YFn+pWGd2ZcWkZpm9u6WXfauT4kqwlB210fAMZI8xrSerE3pH0po50Jo2nR4HQuA4CgorA6YLwd0dXeZZyvWVFnG9fepMns6AIAAKh33W5cFDVWsVwq/dwYMJ6uTDDofZ9StmOKVKTSrzTtPaXsomG6igUAW+NGm3rDmYSfldHLvaxOAgCCqSyVfnKq86XcN0d5VX6t4eBtrg+KxpA00z3Pb3sipBd6Qnq132h3gvVBACgXkn4Bk5IThQTtV/ymsNQWTksy2hbnrx0AAIAkdadyr/KTpFgov8WsYpWzvad1d+KXmrUD23sW/w0KPSOolP7W78TPixpTetskp+bw5b6QLJ3IAAAB1FuGSj9jsvFKzkk/Nw5rjfCBWk7To1azomlFjVVKRnuSIb0WD2lDb1h/2Jus6sYqAKhFNIQJmKC295SkOQ1W7T3S1n6jhY3VHg0AAED1dedxnp80oNLPOsmtcp+Lkihbe08ruefklTquTcpp1SVJDSV470w7UmskVX5VylrpVTfpt6AxrdnNEUU7pZ600bZ0TLMacn+vsKxsMlGmkQIAkBuvwquphJV+khMnxa2UyPEze1+K8/wqwRhpRsxqetRqX0rqThv1pIy600bPtae1MxbW2yYlc+p6AQAYH0m/gMm29wxeQDInltbzPWFtjYdkbYrDewEAQN3rTudX6RcdUCWXUvmD+YR73mApK/28c/JS7j/R0r21JCnuLiSGZDOtRIsRMU6CslLVlUPtThr1po2ixmpWzMn0toTT2psM6dn2tOY15D6wpW1hWr0AAKquHGf6SVI0ZKW0USLHtpFee0+SfpVhjDQxIk2UlWS1N2G0NeFU/T3ZGdZxbdU8QRkAage/8wWMF7cE8Qc3M2YVkVVv2qg9ScYPAACg213baMlxsSlkvCRUNrlVTuU4c0cqb8vMgef5lWKTmXeeYbJK64Fea885sXTmvk1225B1JMvTIhUAgHKxtnzxRaHtPSfS3rMqpkSt3jMrIiOr/+sPa3NfEFc7AcB/qPQLmGylX3XHUYiwcRJ/r8WNXosbTY4SVAEAgPrWlWd7T0mKGScBVYnKs153fE0lXoMJuV23ypL0c98zVqLOGJE8FxBL7W/9zgAGVvRNCDktVxPWaF9KmsRvdQCAgIhbp2OBVIZKv8xn9vjtPVNW6nI3X1HpVz3TolZvaDVav096al9EM5qkxvD4r5Pya1uettIz+8J6NR7SnFhaCxrTmhW1dCEDUJP49TBggpz0k6Q5DWm9Fg9pa39Ih02owPZ0AAAAH/OSfi157DCPuu0m4xU4Y663TGfueL+EpFT6OfS7Y24o0UKit4CYrMKZfvuSUnsyJCOr/RqysbMxzgLl7qRRd8poEhUKAICA8Kr8osaWfG3LO4M4l406XSkn+RgxtuSbm5C7lJVsKqXGUEh9aaMHd1rNb0jnlIzLtW15Ii39piOi1+LO1Rt7w9rYG9ayCUktbWFtEkDt4WMtYNLubqig/uD2izkfpjsSufdYBwAAqEVJK/W4rShb89hh7rWbrEQs1euOrynHHde5Kmd7z/iA9p6lEMljAbHUvMWpGVE7bD7eAqX3MwIAIAjKdZ6f5HRDkHL7zM609gxT7VVtISPNi6UlWXWkjDpSpfuBJK30YLuT8AvL6uiWpA5odEo8/7c7on2KKR3J/R8TKfVp1ABQelT6BUzQK/1aw86ZNV0po21xowVN1R4RAABAdXgtpaLGqiGP2M5b0Cp3pd/AM3dKXekXcqsVg9DeM1vpV5K3y8uuhPPNZ8WGZ3ib3URxT9r5WbFgCQAIgnKd5ycNPoc3bd124qPoTOa/8Qrl0xSWZkatXk8YvdZvNCFkMz/PQlkrPdUZ1q5ESDFj9Y62pKbHrKyVutJh7YhLv96V1vzG3P8O5FpdCADVxH+nAsZdG1Kowq2FSsUYaa67aPFqP3/9AABA/cq09sxzh7mXzIqXORzst9kuE6XejV/OSr/+klf6OV/TZUpSjmVXwpnEtBHOwm4wzu8EViazgAoAgN9lugiUYUkoLMnI2Vg03madfV6lH+UQvjEjatUYskrJ6NV4SOki466NvSG91BeWkdVxbsJPctYmj54kSVbtqZC6U2O+DQAEDlmXgEkHqNIvZMyIpfD7NTv9oV6Nh5UMRbWjvVPpcJRyeQAAUFe8xabWPFtnxtwIPu5WeJWL134rVoYzd7Jn+pVe9ky/0tycsMluuKtktV9/Ott6bOoIST9jpGb370IPLT4BAAHR7X62NZeh0s+YbIX+eC0+vUq/iVT6+YYx0ryGtIys9qWMNveHBm24iqelnQmjLX1GG3pCemCntL4rpD2J4XHQpt6Qfr/PCbKPbElpdmzwz3lKTJrsnom8Pc7yOIDawn6WgAlSe8+0jNa3J4c9bq0UknNA71N7UprZFlbXvtSg2kXK5QEAQK0bWOmXD6e9p1VaRklJ5doq1eNm5MqxKBfKVPqVvkVpv3Ur/UoYL0eMU1mZsFJD6d52TLsT2bZjo1VaNoetutJGPWlpaoXGBQBAMTKbniLlSbZFM5/ZY8cYHSmSfn7UFJL2b0hrc39IXSmjF3tDagw566HdaUnKBng74tKOeETPdUvTo2ktbkoraqy2xUPa2Osk/A5oTOmQ5pFbIsyMWu1NWnWnjeLp7MY6AAg6kn4BkrRSyv1wiwQg6TcaY5zFi46UUWfKaGa1BwQAAFAF+wo8Sybk7mJPWGfHczTPSsFceZV+jWV4//K293S+lqrST8ouICbLfI7iQN55ftOio/fu9M5a7E1VblwAABSj091UVK5kW9Q9N3isSr++dLbN6KQyJR9RuNaIdKBJ65W+kOLWKD6gNcSEkHU3RFnNaAprW29ar/Ub7UyEtDMxOGt3+ISklk5Ij9pGPxaSJoScZGJ70mhGjL8LAGoDSb8A6XU/5IysyrS2UzETI1JHSupIBjh7CQAAUIR9bmyXb9JPcqrYEtY5v25CmRbNet1cU1MZKv0iboKqHO09+0p8pp8kRXJYQCy1sc7z83jtPfusk0ANQjcQAED9srbwTU+5ioYkpaTEGOfd7h1QTU91lz81h6WDmtPqSjlnKls5ieKBP68lE6SDG5LqSUkbesN6Pe78XMNG+rvmlOY2jP93bHLEqjtutDdpND2a3znbAOBXJP0CxDtYNmoU+A8hZ0eXVb816k2OEYkBAADUIGuz7T0LWfSKhayUNoqXMQnl7YBvKsNiWKhMlX7WZmPmfNumjiXX84FKxdoBlX5jVCBEQ05FQ8Ia9aallqDvDAQA1LTe/5+9O4+Pqrr/P/66s2SZJGRhCxECYQlKMazKt0iLe1u0ImqpK/IF/GHlC/j91ro0WtFiEeuCorVaqhTaWgFBbN1BawGtVC1GxYIIiGxCIAvZZ+be3x+TmWSSAFlmMpnJ+/l4YDJ3ztx77mFwzpzPOZ9j+jJYGVgkhekzq+4z+/ir4I/WBh7TtcqvQ3MYkHaCvyObYWA64khwwLBG+ddtnGi00TDqVnruq6kdnzR9wUYRkWinoF8U8e+rEhflAT/wzbpJtkGZCUervaRGukIiIiIi7aitg17+VWzVYZw7Fc6Vfv4BOY/lC3CFakJbpenbV9rACqyCCwV/av32mqtWbkKVZWDDIuMEK/3At9qvxAsVXiOkgU4REZFQ8+/nl2QP3+p0X3rPE0/UKaoN+mU4NAk9mpkYFBR7WvXavHTfrth2w7cwocRrUOwxcKkvJSIxQIvYo4g/6OcMw8BLJHSpna1zuDIciZ1EREREOq76g162Vgx6xdcOaFVb4ZsNFs6Vfr6gn4WFgSeEXVv/6kmXrXXtejzO2jbwhLG96/PvSZPusE46KOoPylaYMTAzUEREYlppG7IcNJd/YtGJsiEcrV1Nf7KJNdI5+Fd8FnsNLL0lRCQGKOgXRSrqpfeMBWkOCwOLco8VmEkuIiIi0hm0JbUnENjPpMYkbIMT/nlZiWEYmDOM5g3KtZS/XUO94q05qwZC6Yg/tWczBiP9M9LVnxYRkY7Ov59fl3YI+vmzCTTksaDE61/ppwiPQIod7Fh4LIMyrUsQkRigoF8UibWgn8OALrXprPybKIuIiIh0BsfaGJyKq10pZ2LQuqRGJ1e30i88A2JxgaBf6PqBZWHYzw/q0nu2V9DvcEuCfjYA375+bgX+RESkAztW+zkdzpV+DgMMLMBo8nO72GNgYZBgWGHJZiDRx6i3d6A/9auISDTTx1sUqQv6xc5MJH/+9CKPltCLiIhI59HWQS9b/ZVyYQj0eK26YFy4BsTiaoOJoax/+Fb6+X6aGJhh7rOaVl3asW7OkzeOzYCE2r+jCgX9RESkA/NPekoJ4wq7k2UT8H/GpjutkO0pLNHPn+KzxGvg1fikiEQ5Bf2iSKyt9APfEnqnDTwYgcEvERERkVh3LAR72sTX9gmrw7CXmz9VpA0rsCIv1OLCsHquzPQH/UJ3TvB9afKtGgj/ar8ij4EXA6dhBbJinIxL+/qJiEgHZ1mh6f80h3/CUoW38eeifyWXUntKfYk2iDN8+02XarWfiEQ5Bf2ihNeCqtrBF2cM/a0ZBnRPcABw1BNDNyYiIiJyAnUr0lp/jsBKuTCMWdWl9iRss+DrViqGMr1neFb61V814AnzGGH9/fya2/YnGtwUERHpCKoscFsGYJES4sk5Dfn3uy1voo9xtDagk66gn9RjGHXvCaX4FJFopyhLlPCn6jGwCHPfqN31dNkBi1KvQblW+4mIiEiMqzKhyvQNenVpy0q/2p58dRhSOlbW9snCtZ8fhD5oaVoE+pKhDvpB++3r15L9/Pz8g5uVJkqZLyIiHdKx2kBKkg3sYY6pJPlXwHuDPxctq95Kv2ak0JbOxb+vX5mJ9kkWkaimoF+U8M/adRrhm20dKS6HLZBW4UCNTQMVIiIiEtP8K7m62NuWwSG+dp/naiv0ncMq/0q/MM42q5/eMxT9v0oTLAxsWGHZh9C/r7YnDO1dX2Cln6P5o00Jhi8Vq4lBtfrSIiLSAbXHfn5+iTbfpHkvBiWeuuPFHgOPZWCn+Sm0pfOIt/kDxgZHtNpPRKKYgn5Rorxe0C8WZTotbFhUmAYlSkskIiIiMexoiGaYx9X25GvCsLqrIpDeM3wDc75+rW/vlFCsnvOn9kyygy0M3cn2WOlXY0Kxt+Ur/QxDKT5FRKRj8wf92pLloLkMw7eiEOBQTd3xXVW+g1nxVlj6ChL9utX2z4+4DUxNpBKRKKWgX5Twp/f0zzCONU4bdHf6V/sZVCjNp4iIiMSoI25fF7xrG2e6xxu+WezhWN3l30s6IYzfFgyjbrVfKFJ81gX9wtNfrtuDMCynB/wBYYMkm9XiVZb+FJ8VSkclIiIdkH/FXThScDfF/7l4uNr32LLqgn45CRp0kqZ1sUOc4Vslqr39RCRaKegXJQLpPWP4b6y70yLOsHBbBusL6wabRERERGKJP31j1xas5GqKYYArTKu7KmtX+rnCuNIP6qX4NNte/7Iw7ucHdaseK0NQ1+Op28+v5R1h/99VRRjrJyIi0hqWBYdqJz21ZCV7W/j39Ttcu9LvsNug3DRwGha942NzQr20nWHU9dEL3Ya2IBKRqBTDIaTY4v/yHqvpPcGXhiknwcRhWJR4YF2RQ7NqREREJKZUmVBe26/LCMGeNuFa3VXqCT5/uDhrB+RCudIvOUyBSv/KuxrLwBumZjnShgHRpNr6VZmhSZcqIiISKiVe36QZO1Ygy1O4uewAFmVeqPDCztpVftnxZiBlt0hTMhy+LYiqLYNSLQoVkSikoF+UKK/9kInV9J5+8Tbon2ASb4OjHht/PeLgH8V2vqiw8U2NodV/IiIiEtWO1q7kSrFbgT352iIcq7vcJoE9ltuagvRkwpHeM7mFaTGby2HU9cUrw9An9a2CaPl+fn4OAxJq3w9l2tdPREQ6kIM1vk5PjzgLezt9RNmNujTlH5bZ+SqQ2lMDS3Ji9nqr/fbX2MI22UtEJFwcka6ANE9nWOnnl2CD73eHLcUmX1Xb2F1tZ3d13fNxhm9mWE6CSZ94M6ZTnoqIiEhsOeLxB9NCM+DkT+9ZZYLXIiQDaUdq95VztWJfuZbyBz5rQpLe0x/0C9/ITKIN3LWrFUJ9nSMegyrTwGFYrU59lmK3qDINyryQrm96IiLSQRyoDfplxrVvwK1L7efiripfhybRZpEZpwiOnFwPp0WJx6LGMjhQYyglrIhEFX0VjAJeq242cWcJcKU4YHyahyNug51VNoo9BqUeX/71GstgX43BvhobDsPiNJfJEJeX+E7SNiIiIhK9jtamb8wIUWorp823+sxtGVSaoVnldqQNq81aKs4ITXpP06pLcRreoJ9FqdcIy0q/vdW+90ZWG1ZBpNgtDrvhmNfAsiyMTjBhUEREOjbTgm9qfB9Ivdo54NbTaTGkC3xV7uVgjY3Tk7zY9NkozWA3oHe8yc4qO0c9NrrYvXTRKLqIRAn97yoK+AYVDGx0nr8wm2FgOuJId8CoxLrjHhNKPLC3CnZX+AY0Pim3858KO0NS4LQkiLdZWB535CovIiIichx1K/1CN+jlslmUeA0qvKFZfVZYG/Tr6gz/bHx/ek+35RsUbO0crkK3gYVBnGGRGMaJYIm16TMrvQYQ2oHLfdW+xugd3/p2d9nAwMJjGVRZkKiBTRERibCjHt/kbadhhWQ/45YwDOidANkOL6DN2aRlku2+7BxHPDa+qraRjVLDikh06CwxpKjmNn3pleyG0Wlm65oYFBR7jvu8AfSLh1IvfOO2UWUafFwKW49ZjE41GBCHZm+JiIhIh1Jt1qWgDNVKPwCXHUq8UG6GJhB1xOOLmnVrh4E5h+ELUlkYHPNAeiv7b/tr04b1ijPD2l/2BxSra4OUoVLh9be7xSltSH1mM3wDVMe8vveaP0gpIiISKQdqV/llxlkap5Go0yvOosayOOY1+KraRtdjMCRBY44i0rEpIWIUSHdaXNHdzSU9I12TjsUwINUBg/x7+9WmtnqvGF4+6mB/tT6BRUREpOM4WDvolWK3QpqW3FUb2KnwgtXGGE9VvcBk13ZI72kYdfsSHqg+cdkT8Q8oZoV5vxWnDRyGBYQ2xee+2tSe3Zxt30cxpXa15zGP+sIiIhJ5ByO0n59IKNgM6BdvkuEwAYOPSuGVow4O1aifJSIdl4J+UaSzrPJrKcOAdIfF4ESTTKeJ04Aij411xU7WFzko0oCHiIiIdAA7q3zRnL5tSN/YlMTalI5ejDbvjeffz6+L3SKunb4pdKldUbi3qnWvrzHrUpL2aocBRf9qv0ozdH3MvbUDor1DUH9/itdy07c3uIiISKSUeuomPWUp6CdRyjDglDiLrDjfmONRj43Xipy8UeRbcNDWSXciIqGm9J4SM2wG9IizGNcVPi3xsq3Sxr4aG/uOGGTHW3zL5aWb01LwVERERNpdlVm3Z1v/xNAOetkMSLJBmQlFHoPMuNaPPLTnfn5+XewWB4BD1b4AXkuDjQdrfPv5dbFbJLdxlVxzJNp8KZ5CtdLPY8GBwH5+bR81ijcg3rCotgyOegy6t8OKzYYsC/bXGHxZacOLQUJlKammjdxELw71xUVEOo0t5XYsDE6JM0nVCKREMcPwZWQYmwFbSrx8WWnjYI3vT6LNIjveJMNpkWK3sOHbr9pjgccyan83+PJABbZKg65Oi2SbFneISPjoI1dijsthMDrDTq4HtpTAniqDPdUGe6ptpNghOxG6xvlSgybYfXvJ2PB92NqxsDzuSN+CiIiIxJjdVTZMDDIcJmlh2Cuvq9OkrNrOEbdBD2fr98w54q5LM9le4m0QZ1jUWAYHagz6JrTs2vX382sP/n3yyr2hmdm9vcKGB4Nku0V6CN4b/kGpfTUGhW6Dbo72nfRW6Db41zE7h931orfVNYCdT8ttDEvykpsY3r0XRUQk8orcBrtrsxyMSPZGuDYioeFyGPxXhp3TPfB5GXxZ4cv+sK3SDpUneXFZBf6h+BS7RU6CSb8EL2kanReRENP/ViTmmBgUFHsASLNDQiIcdhsUewyOeQ0+K2vqVb6ZOIl2gy52B13sFl0cFl3sFhlOK5DGSURERKLTv47ZOeo2yHBadHVYvv2A2/HzfVeV72L9E8ITmOpiJ7C/cYnXaFXwyLTqVvp1C0Ng8kS62C0KPQZfV9vom9CygcEDtUG/rBCnTT2eZDvY8AUp91VDdhu+UblN+LTcNyB6epI3ZIGwdIfFwZq690M4As1N2V5hY/MxOyYGdixyXb4gt+VM4NNiD2Veg/ePOTjogW+ngeMk/wY1IU9EJHr9u9yf1txLRgRWnYuEQ/0xxzhgcCKUeeGY16DaMqip7Y7aahcY2Gt/2gyLbvF2jtZYFLl95QvK7RSU20l3Qt9E6BHnW6Rgb6I/2Nw+kdeCUq/BMQ+4a1cZJtp82TDSHFaT5xaR2KOgn8S8BBv0ibfIivOlYjrmhSrToNr0fVj7GJhAuRfKvTYONDhHF7tFjziTnk6LnnEmSVqGLyIiElW+qTE46rHxTe135TjD4ltJXk5NDH/wr9QDh902DCz6hSnoZxiQ4bD4xm1wxN26oN8XlTaqLIMEw2r3wbkuDotCD+yrtmFa3mavVDzm8Q2aGFhktlOd7bVtXegx+OwYZKe3/lzbats8xW4xIITvDVvtar9v3AaH3Qap9vCu9vNasPmYnS8qfQO82fEmZ6Z4cNnBMAzsiTbKK70UenyrOb+qNPimyqJvgkn8Cf795aXZtQm9iEgU+qTcxt5qX99nuFb5SQyzGdDF4d+j+vh9UQPIy7BRcLSGTKcvMOdbnOBbFVsUiOdZOI26rGQ2w/fa9DgDp2XHYYDTBg7DV85jQY3pO0+xx6DU60t73xSH4RsbzY436ZPg259QRGKTgn5NeP3113nppZcoLi6md+/eTJ06ldNOOy3S1ZI2shu+WS2+ZfO+D2LLAhPfzHYT6Jtkp6zGQ2ntB2WJxzc7utRrUFppZ0ftUn2XzaKH08RlhwSbhaP2A9lu+D5E4w3fB36CRilERKQT6oh9qXGpXo54fekO91f5AkX/LnPwnwoYleqbXXuioEhrVxxZFnxwzNfl7hVn4QrjnnMZTotDbosK06CiheNrbhM+rp2Rn5fsbfdZwEk2iDOg2jI4WGOQ1Yy97SwLNte2bc84q11XbnZ3WhzxWByuMfimxqBnK/ZRrKm3yi8vqfmBzubqWvt+qDR9g0rpYQqKVnjh7yUOCt02wGJEspehrsbpO22Gr91cNouvqn3Bzh2VNvrEm3TRt1IRkUY6Yn/qZCzL158oqP18G57s1V5+Ig3YDV9WhnSHhceCktrMZBVe8OBbnedu0G0rrQRo3hcJp+HLXBZv841VVloGpR6DarNu+yPHMd/2R/1d0DPu+N+DlHVBJDrpo7eBd999l6VLlzJjxgwGDx7MunXr+NWvfsUjjzxCt27dIl09CTHfPn51S+czEwzM+OB/FtUmHKqGQzVwuBqOuKHCNNhdffIP2wTDolucRc84C6vcjWVxnPk2jXlrN/2tv+egiIhIR9dR+1JpDosuCQbHir30i4diry9YU2kabCyCf5dYZMaZJB3n4721K462lNvZW2PDXhsMCSenAal2i2Kvwe5qG9/yQGozX/tphZ0q07fiLDexfdJk1mcYvoGHHRWwqdTBhAz3cf8u/LZV2thXY8OGxRkp7buKwGnzDdYc9Rh8Um6nh9PTor5atQlvFTuosQy61O7pEmqO2iDbIbfB1zUGDiO0QT/Lgp1VNj48ZqfKMogzLL6T6uGUkwRsk+wwKMHkq2pbbZ/aRlevRWacUk6JiPh11P7UiRyqMfiwrG5P15HJHoYmBX++GQ4n3maPitS+BsCr1YISmxyGb6JWV6eFVTsO6K79aWIEFitkJdp8wUCzXhmTwMq/JDukOSHVAS67gVGvY2oYBluOeqg061YY1lgGOytgZ4UvSJhit0iy+xY5xBl1Y5DKuiASnRT0a+Bvf/sb5557Lueddx4AU6dO5eOPP+aNN97g6quvjnDtJNzq5+ZuyAlkxUGmE8pNqPQaeCzwAKZlkOww8JomXsu3iW+5aVBlGeytNthbDR8eK8FhOOjmtOjhtGr3VrHwWgYVJlR4fa+p8Pp+r7LqfUDjWzWYYPP9TLRZ9fKC+zrB/t9t+NI31X/OafheG2+r++mgbYFEf8fD8P/RII2IiBAdfSmjdnZtqt3isNvgkNv3GfxllZ1km0W60zc7ti0BCLfpC0p9UjvT/dtdvHRth/STWfEWVZUWVZbBm4VwbqpxwlSdpgVfVtnYWl43QBfqFWfNNTIVCqtNir023ip2cGG6p8nUj5YF+2oMPjzma9tRKd5WpTNtq+5OiyIP7K+x8XaJg7FdPM3K8lDkNthQaqfYYyPOsBiXGr427+m0qDF9bbq72kaPchh4nL1imstjwdfVNv5TYQsM7KY7TM5O9ZDSzG+XTptvf8v9tWl3j3gMij0W3Wv7yHHtOLpkWVBt+dL/V3qhpnaSns2A+Np+d6KtbW0mItJS0dCf8li+z7QDNb6VQ0c9vv9527EYleLlVFfjCS1eDAqKWxbA86VFDGOqBJEOwjB843fOwJG6/u3gFBsFRW7fwgUg3iBo4Z/HC4VeKGzivHnpTgwDXHZw2S16Oi0qTCjy+AKAbsvgqMfgaGA41Bf4sxtwxAMJhp0Emy846M905jB8+xTWbZpU76cBBgYVJTUcrTbAqtvf0FGbIc1u1D22oXFFkVBT0K8ej8fDzp07ufTSS4OO5+XlsW3btshUSjocmwEpdkix1x9cshie7sC06kYoPCYUeXyrAw/VwOEaqDZ9KasO1rTsmhYGlaYvmBgqdnzBv/qDTP7godPmyw0eV/thbOFbeVhjGVSZvvuoCtoT0ReYNADn4SPYLUdtDnJfh8D/4R0IEAY9tho/X/9Dv8Fz/sCmrXaVZv2f1NbT6/9pgdcy8BL8et99WoHz2hpes97vvvKNBxINw6C0uJqiKuNEaduD2vZkBxuWMWvPa1Hb1pbvd6ve/VCvHY93nfrlmlWv+s8bBpWlvo6aFaG919vnsgblJTUcrWr+v7EW16sVNxKOezcMg2O1793W/p22x9+Jy+4bUJfoEm19KZvhSwuZ7vCthjrqMSgzDcqqjcCEm8Taz8Tt5RBn2mo/I6zA54lZ+/9mr+XbH7jShFKPwdfVNmpqJ/Cc5vLSv51WzzkMyEk02Vlpo8Jr8LejTjIcJplxvrSK8TZffT2WL8XpwRqDotpBul5xJtnNSKsZLnE2ODfdwytHnRR5bKw87KR3vEWGw7fvolk7sWp/jUFxvTqfGoGVieALCJ2ZBv8qtthbbeOlQieZ8SZdHXXplCx8QaUqE8pNI7C3JPjeW+enecKWdhN8fZre8Raeaosyr8H7xfCxzUlWnEkXu0Wiva4f5X9fQ9372sQ3ya3ahPLameFHPAae2ve2w7DIS/JymstscVDMVlu3VIeX/dU2X2pXt8FBt3+imwWlEGfZfLPYa/uWDQeErAa/1G9Nf/09tf1Ct1Xbp/bW9a0b9mmPJ87wBf8S7VYgEOhP8283/NlDrEZ966Y0ddyiXrvX/v8k6BiN+7f+x2CQVHOMqkpbvf5ybb0Mq67P3KCerflm0dzXRPITPBxjhh2hT+x3ssvXfz5LK2ijUkftTx2qMfhPhW+VdoVpUO4laO8wA4uBiSbDkrxhTWcuIm1jGL6VgUl23x5/ZV4oq12IUGX6/l3X1A4+7auG5qYVbaS4lJaEHvzjg/4xuvpjeA3H9Hxjd1bwOCN1gcP6Y3p144ZWoH9ka3CutjAMSKGc8jIbRm0nwWhwbqNeWRocDypjNH5Nh2JA4ZEqiisNsDpkDUPCP+56+EgVdjekR2EELQqrHD6lpaWYpklqanAipNTUVIqLi5t8jdvtxu2uy21sGAaJiYk4HK1vWv8SbKfTiVXvG4Vlt5Ma37p/UE6HndRWDuK09rXNep1hkOiwYUtwUv/bUyTq29bX2h0OdpU2XiWY5IAcp8GQbgkUl1VRZUKFSWCwxOUAt7dutkz9mTMGdV/2/V/sPZaBt/bLf0a8UW9woG5gyaz96R8g8G3s65u5XOP1HTsZE6iqf6D2rRdnhzgg5YSvDp6e7Q9UtZp1nN/beq62OlgBxIfwhB3U/nI6xX0e6CT3CVHx3s2Kt8hytj6NT1OfpW35bJbm6Sh9qeM5UV+qe6Lvs7LU69tTo+E+Gl+15AuvAekJvuD1wAQv2Qk2Gn42Nus0hoHNbqNLg35Sc6TFQQU2DlWDiZ0jFhzx4utQ1BPnhFPiINdl0i/BwFZvbnFrtKW/GuewEW84ubgH/LvMTpkXqoEDJkGdl3inL/tCdrzJYJeBw2h9ndvSxgCDUmxkJ3j4sMxOuRcqgb1NtLOf0wm9nNAjzmKIy4vL3vL3eWvaOLU2pW2ZaVDlhXJ8mSua1SmszwFdHb73dlacSb8EiwSbf855007WxqlAVqIvCF1aG4zz+8bDCc/danZw2H39WX+fNs7wBZ7jDAvL8PWxayyobtB3toAKoMLiuH/P7ar2rVBW6cHXS6euv6u5M6EVhX3i3kkenCEck1Nfqn20tD/VXn0pm2VQabNj2CAJ35+42nTXmU6TnvEQZ5y4z9OqfoJh4LTbW/1ZHe+0Yzla+Vli2Np/HK4N9xt1Y2lt/LvtsGOdTenA95pe73d/ilEPvjHIbvE2qk3f9yT/JCovvt/r34ZV7/X+3w2j3mT2BhP0Tavl3dCW8nfVvECTuxKGqp9kwaGSGgL9sFh3uJJo6w+12uFKBrvi6OEM7bu1PfpT6rE1wWg4ffQ4xwDWrFnDqlWrAo/POuss5s6dS3p6epPlW6KpPO09Mlp/vp5tqFJrX9v81yWG7Jod+rXdXa0/uYiItFhH3fMk1nWUvlRT2tKXipSzTjzTpsNpaxt3BwaGpCbN15Y27gEMCFlNmnlNvY9FRGJec/tT7dWX6g7kheA8rf0M6xGermFYr9uW8aXWXrfDjoedQKTaOPxjnY1F272KSHTSXpz1dOnSBZvN1mjmVElJSaMZVn6TJk1i6dKlgT833HBD0Ayr1qisrOS2226jsrKyTeeJBp3lXnWfsaez3KvuM/Z0lnvtLPfZ0XSUvlSs0Ps4/NTG4ac2Dj+1cfipjaU9tbQ/Fet9qc72768z3a/uNXZ1pvvVvcamaL9XBf3qcTgc9O/fn4KCgqDjBQUFDB48uMnXOJ1OXC5X0B+ns41pkSyLXbt2BaX2jFWd5V51n7Gns9yr7jP2dJZ77Sz32dF0lL5UrND7OPzUxuGnNg4/tXH4qY2lPbW0PxXrfanO9u+vM92v7jV2dab71b3Gpmi/V6X3bODiiy9m8eLF9O/fn9zcXNatW0dhYSEXXHBBpKsmIiIi0uGpLyUiIiLSNupPiYiISGsp6NfA2LFjOXbsGC+88AJFRUX06dOHO+64g+7du0e6aiIiIiIdnvpSIiIiIm2j/pSIiIi0loJ+Tfje977H9773vYhd3+l0csUVV8RUOobj6Sz3qvuMPZ3lXnWfsaez3Gtnuc+OKtJ9qVih93H4qY3DT20cfmrj8FMbSySoP+XT2f79dab71b3Grs50v7rX2BTt92pY0ZqYVEREREREREREREREREQAsEW6AiIiIiIiIiIiIiIiIiLSNgr6iYiIiIiIiIiIiIiIiEQ5Bf1EREREREREREREREREopwj0hWQxl5//XVeeukliouL6d27N1OnTuW0006LdLVCauvWrbz00kvs2rWLoqIibrnlFs4888xIVyvk1qxZw+bNm9m3bx9xcXHk5uZy7bXXkpWVFemqhdQbb7zBG2+8weHDhwHo3bs3V1xxBSNGjIhwzcJrzZo1PPfcc0yYMIGpU6dGujohtWLFClatWhV0LDU1ld/97ncRqlH4HD16lD/+8Y9s2bKFmpoaevXqxU9+8hP69+8f6aqFzKxZswL/Puu78MILmTFjRgRqFD5er5eVK1eyYcMGiouLSU9P5+yzz+ayyy7DZtNcJ+n4OkvfoSOJ5c/zSOoMn6+RpM+70DvZd1TLsli5ciXr16+nrKyMQYMGMX36dPr06RPBWovErs4ybgSdr//XWceQILb7nZ1pHAk6V19XY0rR18dW0K+Deffdd1m6dCkzZsxg8ODBrFu3jl/96lc88sgjdOvWLdLVC5nq6mr69evHOeecw0MPPRTp6oTN1q1b+d73vseAAQPwer385S9/Yf78+Tz88MMkJCREunohk5GRwdVXX01mZiYA77zzDg888AAPPPBAzH4J3rFjB+vWraNv376RrkrY9OnTh7vuuivwOJo+3JqrrKyMu+66i29961v8/Oc/p0uXLnzzzTe4XK5IVy2kFixYgGmagcd79uxh/vz5fPvb345grcJj7dq1vPnmm8yaNYvevXuzc+dOfvOb3+ByuZgwYUKkqydyUp2l79BRdIbP80joLJ+vkaTPu9A72XfUtWvX8vLLL3PTTTfRq1cvVq9ezfz581m0aBGJiYkRqLFIbOss40bQ+fp/nXEMCTpHv7MzjCNB5+vrakwp+vrYCvp1MH/7298499xzOe+88wCYOnUqH3/8MW+88QZXX311hGsXOiNGjOgUM3jy8/ODHt90003MmDGDnTt3MmTIkAjVKvRGjx4d9Piqq67ijTfe4IsvvojJDltVVRWLFy9m5syZrF69OtLVCRubzUZaWlqkqxFWa9eupWvXrtx0002BYz169IhgjcKjS5cuQY9ffPFFevbsGVP/H/Lbvn07o0ePZuTIkYDv73Pjxo18+eWXEa6ZSPN0lr5DR9BZPs8jobN8vkaSPu9C70TfUS3L4pVXXmHSpEmMGTMG8M16v+GGG9i4cSMXXHBBe1ZVpFPoLONG0Pn6f51tDAk6T7+zM4wjQefr62pMKfr62LEZbo9SHo+HnTt3MmzYsKDjeXl5bNu2LUK1klCqqKgAIDk5OcI1CR/TNNm0aRPV1dXk5uZGujphsWTJEkaMGEFeXl6kqxJWBw8eZObMmcyaNYtFixbxzTffRLpKIffBBx/Qv39/Hn74YWbMmMGtt97KunXrIl2tsPJ4PGzYsIFzzjkHwzAiXZ2QO/XUU/n000/Zv38/ALt372bbtm2dZsBAYk9n6DtESmf5PI+Ezvj52t70ede+Dh06RHFxcdB3dafTyZAhQ/RdXURCrjP1/zrDGBJ0nn5nZxhHgs7d19WYUnTQSr8OpLS0FNM0SU1NDTqemppKcXFxZColIWNZFn/4wx849dRTyc7OjnR1Qm7Pnj3k5+fjdrtJSEjglltuoXfv3pGuVsht2rSJXbt2sWDBgkhXJawGDRrErFmzyMrKori4mNWrV3PnnXfy8MMPk5KSEunqhcyhQ4d48803ueiii5g0aRI7duzg2Wefxel0Mn78+EhXLyw2b95MeXk5Z599dqSrEhYTJ06koqKC//3f/8Vms2GaJldeeSXjxo2LdNVEWizW+w6R1Fk+zyOlM36+tjd93rUv//fxpr6rFxYWRqBGIhKrOkv/r7OMIUHn6Xd2lnEk6Nx9XY0pRQcF/TqgpqLksRg572x+//vfs2fPHu69995IVyUssrKy+PWvf015eTnvv/8+TzzxBPfcc09MddoKCwtZunQp+fn5xMXFRbo6YVV/Bkt2dja5ubnMnj2bd955h4svvjiCNQst0zQZMGBAIH1yTk4OX3/9NW+88UbMdtTefvtthg8fTkZGRqSrEhbvvvsuGzZsYM6cOfTp04fdu3ezdOnSwObLItEk1vsOkdKZPs8jpTN+vrY3fd5FRsPv5ZZlRagmIhKrOkv/rzOMIUHn6nd2lnEk6Nx9XY0pRQcF/TqQLl26YLPZGq3qKykpaTSjUKLLM888w4cffsg999xD165dI12dsHA4HIFNmAcMGMCXX37JK6+8wv/7f/8vwjULnZ07d1JSUsLtt98eOGaaJp9//jmvvfYaf/7zn2N2k+KEhASys7M5cOBApKsSUunp6Y2+VPTu3Zv3338/QjUKr8OHD1NQUMAtt9wS6aqEzR//+EcmTpzIWWedBfi+bBw+fJgXX3wxqjpoIp2h7xApnfnzvL10ts/XSNDnXfvy709UXFxMenp64Hhpaam+q4tIyHSm/l9nGEOCzt3vjNVxJOi8fV2NKZ0d2cq1gIJ+HYjD4aB///4UFBRw5plnBo4XFBRwxhlnRLBm0lqWZfHMM8+wefNm5s2bF9ObujZkWRZutzvS1Qip008/nQcffDDo2JNPPklWVhYTJ06M2Y4agNvtZt++fZx22mmRrkpIDR48OJCn22///v107949QjUKr7fffpvU1NTAhsSxqLq6utG/RZvNppn4EjU6c9+hvXTmz/P20tk+XyNBn3ftq0ePHqSlpVFQUEBOTg7g29Nm69atXHPNNRGunYhEO/X/YnMMCTp3vzNWx5Gg8/Z1NaYUPRT062AuvvhiFi9eTP/+/cnNzWXdunUUFhZywQUXRLpqIVVVVcXBgwcDjw8dOsTu3btJTk6mW7duEaxZaP3+979n48aN3HrrrSQmJgZWcbpcrpha1v/nP/+ZESNG0LVrV6qqqti0aROfffYZ+fn5ka5aSCUmJjbKqR8fH09KSkrM5dpftmwZo0ePplu3bpSUlPDCCy9QWVkZc2kKLrroIu666y5Wr17N2LFj2bFjB+vXr4+52YXgm03497//nfHjx2O32yNdnbAZNWoUq1evplu3bvTu3Zvdu3fzt7/9jXPOOSfSVRNpls7Sd4ikzvR5Himd6fM1UvR5F3on+446YcIE1qxZQ69evcjMzGTNmjXEx8dH3R4vItGis4wbQefr/3WWMSToXP3OzjKOBJ2zr6sxpejqYxtWtIUpO4HXX3+dl156iaKiIvr06cP111/PkCFDIl2tkPrss8+45557Gh0fP348s2bNikCNwmPy5MlNHr/pppuiaknwyTz55JN8+umnFBUV4XK56Nu3LxMnTiQvLy/SVQu7efPm0a9fP6ZOnRrpqoTUokWL+PzzzyktLaVLly4MGjSIK6+8Muby6wN8+OGH/PnPf+bgwYP06NGDiy66iPPPPz/S1Qq5jz/+mPvuu49FixaRlZUV6eqETWVlJc8//zybN2+mpKSEjIwMzjrrLK644gocDs11ko6vs/QdOppY/TyPpM7y+Rop+rwLvZN9R7Usi5UrV7Ju3TrKy8sZOHAg06dPj7lBW5GOorOMG0Hn6/915jEkiN1+Z2caR4LO19fVmFJ09bEV9BMRERERERERERERERGJcrGbOFhERERERERERERERESkk1DQT0RERERERERERERERCTKKegnIiIiIiIiIiIiIiIiEuUU9BMRERERERERERERERGJcgr6iYiIiIiIiIiIiIiIiEQ5Bf1EREREREREREREREREopyCfiIiIiIiIiIiIiIiIiJRTkE/ERERERERERERERERkSinoJ+ISBNmzZrFE088EelqiHQqW7du5f7772fmzJlMnjyZzZs3t/gclmXx0ksvMXfuXK6++mp+8pOfsHr16jDUVkSk41ixYgWTJ0+mtLQ0rNd54oknmDVrVlivISIiItIeQt1/+uijj1ixYkWTz02ePJnf//73IbmOiMjJOCJdARGRjuiWW24hMTEx0tUQ6VSqq6vp168f55xzDg899FCrzvHss89SUFDAddddR3Z2NhUVFWEfBBcREREREZHO7d///jevv/46kydPjnRVRKSTU9BPRKSempoa4uLiyMnJiXRVRDqdESNGMGLEiOM+7/F4+Mtf/sKGDRuoqKigT58+XHPNNXzrW98CYO/evbz55ps89NBDZGVltVe1RUSkHtM08Xq9OJ3OSFdFREREJOpVV1cTHx8f6WqISBRR0E9EYs6KFStYtWoVCxcu5IUXXuCTTz4BYNSoUUydOpUuXboAvhSeffr04ZxzzuGFF15g3759TJgwgWuuuYZZs2YxZMiQoBRW5eXlvPDCC2zevJmjR4/icrkYMGAAU6ZM4ZRTTgF8QYm1a9eyYcMGDh06RGJiIqNGjeLaa68NXFdEWuc3v/kNhw8f5uabbyY9PZ3Nmzfzq1/9igcffJBevXrx4Ycf0qNHDz788EPuu+8+AE4//XSuvfZakpOTI1x7EZHwO3LkCE8//fRx+z4A7777Li+//DJ79uwB4NRTT+Xqq69uNOHp73//O2vWrOHw4cP07NmTSy+9tNH1Dh06xP/8z/9wzTXX4PF4eOuttzhy5Ai33347w4cP54MPPmDNmjV89dVX2Gw2Bg0axI9//GNyc3ODzvOf//yHFStWsGPHDkzTpF+/flx22WWMHDkyqD6/+c1v+MUvfsHGjRvZvHkzXq+XM844gxkzZlBVVcUzzzxDQUEBcXFxjBs3jquvvhqHo+4r7xtvvMGbb77JwYMHMQyDjIwMzjzzTK6++uo2t72IiIhEp5P1n959913eeust9uzZQ3l5OT169GD06NFcfvnlJCQkAL4U6O+88w5A0Eq/xx9/nB49egQe/+Mf/wj0r3r16sWVV17JqFGjAs/7x7Puv/9+1qxZw6efforT6eTpp5+mpqaGVatWsWnTJo4ePUqXLl0444wzuOqqq0hKSgqcwzRN/vrXv/L2229z6NAhXC4Xw4cP56qrrqJr166BcvPmzePYsWPMnDmTZcuWsWvXLtLS0rjiiis455xz+Oijj3j++efZt28fPXv25LrrrmP48OGB15eWlvLcc8+xZcsWSkpKSExMJCsrix/96Efk5eWF8G9IRFpKQT8RiVkPPvgg3/72t7ngggv4+uuvA52V++67LzAAtGvXLvbt28dll11Gjx49jjt7qrKykl/84hccOnSIiRMnMmjQIKqqqvj8888pKirilFNOwTRNHnjgAT7//HMmTpxIbm4uhYWFrFixgnnz5nH//fcTFxfXnk0gEjMOHjzIpk2bePLJJ8nIyADgkksu4eOPP+btt9/m6quv5ptvvqGwsJB//vOf/M///A+mafKHP/yBhx56iLvvvjvCdyAiEn4n6/usXr2a559/nrPPPpvLL78cj8fDSy+9xC9+8QsWLFhA7969gboA2+jRo5kyZQoVFRWsXLkSt9uNzdZ4W/hXX32VXr16cd111+FyucjMzGTjxo089thjDBs2jLlz5+J2u3nppZeYN28ev/jFLzj11FMB336uv/zlL+nbty833ngjTqeTN954g4ULFzJ37lzGjh0bdK3f/va3nHnmmdx8883s2rWL5557Dq/Xy/79+xkzZgznn38+n3zyCWvXriUjI4OLL74YgE2bNrFkyRK+//3vc91112EYBgcPHmTv3r1h/lsRERGRjuxk/acDBw4wYsQIJkyYQEJCAvv27WPt2rXs2LEj8D3z8ssvp7q6mn/+85/Mnz8/cO709PTA7x999BFffvklkydPJiEhgZdeeokHH3yQRYsW0bNnz6A6PfTQQ4wdO5YLLriA6upqLMvi17/+NZ9++imXXnopp512Gl999RUrVqzgiy++YP78+YEsC0uWLGHdunV8//vfZ+TIkRw+fJjnn3+ezz77jIULFwZNBisuLuY3v/kNl1xyCV27duXVV1/lySef5MiRI/zzn/9k0qRJuFwuVq1axa9//WsWL14c+D6+ePFidu3axZVXXklWVhbl5eXs2rWLsrKysP1diUjzKOgnIjHrzDPP5NprrwVg2LBhpKWl8dhjj/Hee+/xne98B4CSkhIefvjhk6YCfPnll/n666+58847g2YsjRkzJvD7e++9x5YtW/jpT38adLxv377ccccd/P3vf+fCCy8M5S2KdBq7du3Csizmzp0bdNzj8QRW8VmWhdvtZtasWYF/0zfeeCO33347+/fvV8pPEYl5J+r7nHbaaaxcuZLvfe97TJs2LfCavLw85syZw8qVK/nf//1fTNPkueeeIycnh5/97GcYhgH4VgTOmTMnMNBTn9PpJD8/PzCpyjRN7rrrLrKzs7njjjsCgcKRI0cye/Zs/vSnP/HLX/4SgD/96U8kJyczb968wGz5UaNG8bOf/Yzly5fz7W9/O1AH/zmmTJkSqPv27dvZtGkTU6ZMCQT48vLy+Pjjj9mwYUPg2LZt20hKSgq699NPPz0ErS4iIiLR7GRjR5dffnmgrGVZDB48mFNOOYV58+bx1Vdf0bdvXzIzM0lNTQVolNHAr6amhrvuuovExEQAcnJymDlzJu+9916jjArjx48PWjG4ZcsWPv74Y6699louueQSwNff6dq1K4sWLeKdd97h/PPPZ9++faxbt44LL7wwqM+Tk5PDz3/+c15++WWuuuqqwPFjx46Rn59P//79ARgwYAAzZszgxRdf5LHHHgv0+9LT07n11lt5//33+cEPfgD4+lbnnnsu559/fuB8Z5xxRgtaXkTCpfE0TRGRGOEP7Pl9+9vfxm6389lnnwWO9e3bt1mBgC1bttCrV68Tpij48MMPSUpKYtSoUXi93sCffv36kZaWFnRdEWkZy7Kw2WwsXLiQX//614E/jzzyCP/93/8N+L6I2O32oH/T/lUrhYWFEam3iEh7OlHf5+OPP8br9TJ+/PigforT6WTIkCFs3boVgP3791NUVMS4ceOCgm3du3dn8ODBTV539OjRQWk0/ef47ne/G7QyMCEhgTFjxrB9+3aqq6upqqpix44djBkzJhDwA7DZbHz3u9/lyJEj7N+/P+ha9VNgAYEU6/VTgfqP1/9//8CBAykvL2fRokX861//orS09PgNKSIiIp3GycaOvvnmGx599FFuuOEGrrzySq666irmzZsHwL59+5p9nW9961uBgB9AWloaqampHD58uFHZ+hPJAT799FMAzj777EZ1jY+PDzzvr3PDcgMHDuSUU04JlPNLT08PBPwAkpOTSU1NpV+/fkETvfzfq+vXdeDAgbzzzju88MILbN++HY/Hc8L7F5H2o5V+IhKz0tLSgh7b7XaSk5M5duzYccscT2lpKd26dTthmZKSEsrLy4+7L0z964pIy/Tr1w/TNCkpKeG0005rsszgwYPxer0cPHiQzMxMgMBg8cn+/YqIxIIT9X1KSkoAuOOOO5p8rT/A50/J1FQfKS0trcmBqfqpq052jvT0dCzLory8HMuysCyr0evrn7Nh/6nhHq3+YGNTx2tqagKPv/vd7+L1elm/fj0PPfQQlmUxYMAArrzySu07IyIi0omdqP9UVVXFL37xC+Li4rjyyivp1asX8fHxHDlyhAcffDCor3EyKSkpjY45nc4mz9FU38putwel5gRf/y0tLS3QX/L/bKpvlZGR0agf17D/BL4+1PH6W263O3Ds5ptvZvXq1bz11ls8//zzJCQkBFZNNnesTUTCQ0E/EYlZxcXFQTOTvF4vZWVlQR2t+jPYT6RLly4cOXLkhGVSUlJISUnh5z//eZPP15/RJSKNVVVVcfDgwcDjQ4cOsXv3bpKTk8nKymLcuHE8/vjjTJkyhZycHEpLS/n000/Jzs5m5MiRnH766eTk5PDkk08ydepULMvi97//PXl5eUrtKSKdwon6Pv7+z//93//RvXv3457DP8hTXFzc5Pmb40TnKCoqwjAMkpKSsCwLwzAoKipqshzQaHCrLc455xzOOeecwL7MK1as4P777+fRRx89YZuIiIhI7DpR/+nTTz+lqKiIefPmMWTIkECZ8vLydq1jcnIyXq+X0tLSoL6RZVkUFxczYMAAoC6wWFRURNeuXYPOcfTo0SYDj63VpUsXpk6dytSpUyksLOSDDz7gT3/6EyUlJeTn54fsOiLSckrvKSIxa8OGDUGP33vvPbxeb1BHrbmGDx/OgQMHGqVCqG/UqFEcO3YM0zQZMGBAoz8KOoic2Jdffsmtt97KrbfeCsCyZcu49dZbef755wG46aabGD9+PMuWLWPu3Lk88MAD7NixI7CKz2azcdttt5GSksLdd9/NggULOOWUU7j55psjdUsiIu3qRH2fYcOGYbfb+eabb5rsp/gHi7KyskhPT2fTpk1YlhU41+HDh9m2bVuz6pGVlUVGRgYbN24MOkdVVRXvv/8+ubm5xMfHk5CQwKBBg9i8eXPQLHfTNNmwYQNdu3alV69ebWmSJiUkJDBixAguu+wyPB4PX3/9dcivISIiItGhOWNH9dOYA6xbt67ReZxOJ0CLVv81l38f4n/84x9Bx99//32qq6sDzw8dOhRofE87duxg3759YdvPuFu3bnz/+98nLy+PXbt2heUaItJ8WuknIjFr8+bN2O128vLy+Prrr3n++efp27cvY8eObfG5LrroIt577z0eeOABLr30UgYOHEhNTQ1bt25l5MiRDB06lLPOOouNGzeyYMECJkyYwMCBA7Hb7Rw5coTPPvuMM844gzPPPDMMdyoSG771rW+xYsWK4z7vcDiYPHly0IbmDWVkZHDLLbeEo3oiIh3eifo+/v+H/uUvf+Gbb75h+PDhJCcnU1xczI4dO0hISGDy5MnYbDZ+/OMf89vf/pZf//rXnH/++ZSXl7Ny5cpmp2qy2Wxce+21PPbYY9x///1ccMEFuN1uXnrpJcrLy7nmmmsCZa+++mp++ctfcs899/DDH/4Qh8PB66+/ztdff83cuXObnZXhZH77298SFxfHqaeeSlpaGsXFxbz44ou4XC4GDhwYkmuIiIhI9DlR/6myspKkpCR+97vf8aMf/Qi73c6GDRv46quvGp0nOzsbgBdffJERI0Zgs9no27dvo4Bha+Tl5TFs2DD+9Kc/UVlZyeDBg9mzZw8rVqwgJyeH7373u4Bv4tX555/Pa6+9hmEYjBgxgsOHD/P888/TtWtXLrroojbXBaCiooJ77rmHs846i1NOOYXExER27NjBli1bGu1HKCLtT0E/EYlZP/3pT1m5ciVvvPEGhmEwatQopk6d2qoOV2JiIvfeey8rV65k3bp1rFy5kuTkZAYMGMD5558P+Aa4br31Vl555RX+8Y9/sGbNGux2O127duW0004LdABFREREwuFkfZ9JkybRu3dvXnnlFTZt2oTH4yEtLY0BAwZwwQUXBM5z7rnnArB27VoefPBBunfvzqRJk9i6dStbt25tVl3GjRtHfHw8L774Io888gg2m43c3FzuvvtuBg8eHCg3ZMgQ7r77blasWMETTzyBZVn07duXW2+9lVGjRoWsbU477TT+/ve/895771FeXk5KSgqnnnoq//M//xPSFKIiIiISXU7Uf0pJSeGOO+5g2bJlLF68mPj4eEaPHs3NN9/MbbfdFnSecePG8Z///Ic33niDF154AcuyePzxx+nRo0eb62gYBj/72c9YuXIlf//731m9ejVdunThu9/9LldddVVglSHAjBkz6NmzJ2+99Ravv/46LpeL4cOHc/XVV4csvafT6WTgwIFs2LCBQ4cO4fV66datGxMnTmTixIkhuYaItJ5h1c+3IiISA1asWMGqVatYsmSJBnFEREREREREREREpFPQnn4iIiIiIiIiIiIiIiIiUU5BPxEREREREREREREREZEop/SeIiIiIiIiIiIiIiIiIlFOK/1EREREREREREREREREopyCfiIiIiIiIiIiIiIiIiJRTkE/ERERERERERERERERkSinoJ+IiIiIiIiIiIiIiIhIlFPQT0RERERERERERERERCTKKegnIiIiIiIiIiIiIiIiEuUU9BMRERERERERERERERGJcgr6iYiIiIiIiIiIiIiIiEQ5Bf1EREREREREREREREREopyCfiIiIiIiIiIiIiIiIiJRTkE/ERERERERERERERERkSinoJ+IiIiIiIiIiIiIiIhIlFPQT0RERERERERERERERCTKKegnIiIiIiIiIiIiIiIiEuUU9BMRERERERERERERERGJcgr6iUhM2LFjB5dffjm9evXCZrORlpYW0vP369ePfv36BR1bunQphmGwdOnSNp3bMAzOPvvsNp1DREREJFIi0Q8TERER6Wg++ugjLrzwQrp3745hGAwfPhyAqVOnYhgGu3fvjmj9RKRzcES6AiIibWWaJpMmTWL79u1cd9119O7dm4SEBIDAAJE6ViIiIiKhFy39sN27d5OTk8P111/f5glbIiIiIg2VlpYyYcIEKisrue666+jWrRuZmZmRrpaIdEIK+olI1Nu5cyeffvopN9xwA08//XS7XXfSpEn813/9F7169WrTeT7//HNcLleIaiUiIiLSfiLVDxMRERHpSDZv3sw333zDfffdx89//vNIV0dEOjEF/UQk6u3fvx+ArKysdr1uamoqqampbT7PqaeeGoLaiIiIiLS/SPXDRERERDoS9YlEpKPQnn4i0i527NjBjBkzGDBgAAkJCaSnp3Paaacxc+ZMjhw5ElT22LFj/N///V8gPdSpp57KQw89xM6dOzEMg6lTpwbKGobB+PHjAbjnnnswDCOwR55hGHz11Vd89dVXgeMNX98WDff0q6qqIi0tjR49euDxeJp8zYwZMzAMg5dffjnoHhru6Tdv3jwMw+Dvf/87q1at4swzz8TlcpGRkcGPf/xj9u7d2+T5//Wvf3HhhReSkpJCly5dOP/883nvvfeCziciIiKdSyz2w8DX91qwYAGnn346LpeLLl268J3vfIe//OUvQeXmzZtHTk4OAH/4wx+C6qNUnyIiItIWu3fvxjAMrr/+egD++7//u0X9jOeff57vfOc7pKamkpiYyNChQ/nVr35FVVVVk+U/+OADLrvsMnr06EF8fDx9+/blJz/5SSDoWJ9/L8GdO3eyaNEiTj/9dBITEwNjUJZl8cwzz/Dtb3+b7t27k5CQQFZWFueff36j/pSIRA+t9BORsNu/fz9nnnkmx44dY8KECVxxxRVUVVWxa9cu/vjHPzJ79my6du0KQHV1Needdx7/+te/GDZsGNdccw3FxcXMnz+fd955p9G57777bnbv3s0f/vAHxo8fH+i49OvXj7PPPptFixYBcPPNNwde499IOdQSEhL48Y9/zNNPP82rr77KD3/4w6Dnq6qqWLVqFT179uR73/tes875m9/8hpdeeolLLrmE8ePH8/7777NixQq2bNlCQUEB8fHxgbIbNmzgwgsvxO12c/nllzNgwAA++eQTzjnnHM4999yQ3quIiIhEh1jth9XU1HDhhReyYcMGhgwZwqxZs6ioqGDlypVcddVV/Pvf/2bhwoUAnH322RQXF/Poo48ybNgwLr300pDXR0RERDqntLQ07r77brZs2cLatWuZOHFioH9xsn7GbbfdxgMPPED37t255pprSEpK4pVXXiE/P5/XXnuNdevWERcXFyi/du1afvSjH2EYBldccQXZ2dl88MEH/Pa3v2Xt2rVs3LiR/v37N7rOnDlz2LhxIxdddBETJkzAbrcDcPvtt/PAAw+Qk5PD5MmTSU1N5cCBA/zrX/9i1apVXHnllSFrJxFpR5aISJg9+uijFmA98sgjjZ4rKyuzKioqAo/vu+8+C7Auu+wyy+v1Bo7v3LnTSk9PtwDr+uuvDzrH22+/bQHW3Xff3ej8ffv2tfr27dvme2jqPM8++6wFWM8++2zg2KZNmyzAuvzyyxud47nnnrMA6//+7/+CjgPW+PHjg47dfffdFmClpKRYBQUFQc9dddVVFmD95S9/CRzzer3WwIEDLcB65ZVXgso/+eSTFmAB1ttvv938mxYREZGoF6v9MH9dL774YsvtdgeOHzx40OrTp48FWBs2bAgc37VrV5P1FxEREQmFpsaI/K6//noLsHbt2hU4tnHjRguw+vbta33zzTeB426325owYYIFWPPnzw8cP3bsmJWRkWHZ7XZr06ZNQef/1a9+ZQHW+eef3+R1s7KyrJ07dzaqV3p6upWVlWWVlZU1eu7w4cPNvXUR6WCU3lNEws4wDABcLlej55KSkkhMTAw8fvbZZ7HZbDzwwAPYbHX/i8rJyWHOnDnhr2wbjR07lkGDBvHXv/6Vo0ePBj33hz/8ASCQ8qE55s6dy+mnnx507IYbbgB8qTz93n33XXbs2ME555zDD37wg6Dy/+///T9yc3NbdB8iIiISG2K1H/bMM89gGAYPPfQQDkddApuePXty1113BcqIiIiIdETPPvssAHfeeSc9evQIHHc4HDz88MPYbDZ+//vfB46/+OKLHD16lCuvvJKxY8cGneuWW26hb9++rFu3jq+++qrRtX72s58FUp3XZxgGcXFxQX0pv27durX63kQkshT0E5Gwu+SSS0hOTmbWrFn86Ec/4umnn+azzz7DsqygcseOHWPHjh2ccsopDBgwoNF5Gu5711Fdf/311NTUBOU/P3jwIG+++SYjRowgLy+v2ecaPXp0o2N9+vQBoKioKHDs3//+NwDjxo1rVN5mszXqEIqIiEjnEIv9sGPHjvHll19yyimnNDmx6fzzzwfgo48+au+qiYiIiDSLfxznnHPOafTc4MGD6d27N7t27aK4uPik5Z1OZ2CfZX+5+saMGdNkHa655hp2797Nt771LX7+85/z2muvUVJS0qr7EZGOQ0E/EQm7vn37snnzZi677DLeeOMNZs6cydChQ+nbty+PP/54oJy/Y9GzZ88mz5OZmdku9W2r6667DsMwAiv7AP74xz/i9XpbtMoPIDU1tdEx/wwsr9cbOHaytjvecREREYltsdgP89f1eHXq1atXUDkRERGRjqal/Zm29H+O95pHHnmERYsWkZSUxIIFC/jBD35At27duPTSS9m5c2cL7kZEOhIF/USkXZx22mk8//zzHDlyhA8++ID7778f0zSZPXt2IKWBP8D1zTffNHmOgwcPtlt92yI7O5tzzjmHzZs385///AeA5cuX43Q6ufrqq8NyzS5dugDHb7vjHRcREZHYF2v9MH9dj1enAwcOBJUTERER6Wha2p9pS//Hn+69Ibvdzty5c/n444/55ptveOGFF5g0aRJr167l+9//PjU1NS24IxHpKBT0E5F25XA4GDVqFLfddhvPPfccAGvWrAEgJSWFgQMHsm/fPr788stGr/373//e4uvZ7fagFXHtxb+i7w9/+AP//ve/KSgo4Ac/+AHdu3cPy/VGjBgBwMaNGxs9Z5om7777bliuKyIiItEjVvphKSkpDBgwgH379vHFF180ev7tt98GYOTIkUF1ASLSLxQRERFpyD+O01Qfa8eOHezdu5ecnBzS0tJOWt7j8QTGg+r3f1qiR48eXHbZZaxYsYJzzz2XL774gk8//bRV5xKRyFLQT0TCbvPmzU3OGvcfS0hICBz77//+b0zT5LbbbsM0zcDxXbt28dhjj7X42l27duXw4cNUVVW1ouatd/nll5OcnMwf//hHli5dCsDUqVPDdr2zzjqLAQMG8Pbbb/Pqq68GPff000+zffv2sF1bREREOq5Y7YdNmzYNy7L42c9+FhTIKyws5Je//GWgjF96ejqGYfD111+HvC4iIiIiLeXvp8yfP5/Dhw8Hjnu9Xm655RZM02T69OmB45deeikZGRk899xz/POf/ww616JFi9i5cyfnn38+2dnZzbp+dXU169evb7TPs9vt5ujRo0BwP1FEoocj0hUQkdj35z//mSeeeILx48czcOBA0tPT+fLLL/nrX/9KfHw8c+fODZT96U9/yosvvsgLL7zAyJEj+d73vkdJSQnPP/883/3ud3nppZdadO3zzjuPf/3rX/zgBz/gO9/5DnFxcQwbNowf/vCHob7NIElJSVxxxRUsXbqUJ598kq5du3LRRReF7Xo2m40lS5bw/e9/n0suuYTLL7+cAQMGUFBQwJtvvskPfvADXn31VWw2zfUQERHpTGK1H3bLLbfw6quvsnbtWoYNG8aECROoqKhg5cqVHDp0iFtvvZVx48YFyicnJzNmzBj+8Y9/cO211zJo0CDsdjuXXHIJeXl5ba6PiIiISEuMHTuWW2+9lQceeIChQ4dyxRVXkJSUxKuvvsqnn37KuHHj+NnPfhYon5yczDPPPMOPfvQjxo8fz49+9COys7P58MMPeeONN8jMzOSpp55q9vUrKys5//zz6devH2PGjKFv375UVVXx5ptv8vnnn3PxxRczZMiQcNy6iISZgn4iEnZXXXUV1dXVvPvuu3z00UdUVlZyyimncOWVV/LTn/6UoUOHBsrGx8ezbt065s2bx/PPP8+jjz5Kv379uPPOO5k0aVKLB5vuvPNOiouL+etf/8qGDRvwer1cf/31YQ/6gS/F59KlS3G73Vx11VXExcWF9Xpnn30277zzDnfeeScvv/wyAGPGjOHtt9/mT3/6E6C9bURERDqbWO2HxcXF8eabb/Lwww/z5z//mcWLF+NwOBg2bBiLFi3iqquuavSa5cuX87//+7+8+uqr/PnPf8ayLHr37q2gn4iIiETEwoULGTFiBI8//jjLli3D7XYzYMAA5s+fz09/+tNG40gTJ05k06ZN/OpXv+L111+npKSEzMxMbrzxRu666y6ysrKafe2kpCQWLlzI22+/zbvvvsuLL74YSKH+5JNPBmVMEJHoYlgN1/CKiHRQu3fvJicnJxBMk+Y766yzeP/99ykpKSEpKSnS1REREZEoo36YiIiIiIhIx6c8byIiMaKiooLi4uJGx5cuXcq7777L97//fQX8RERERERERERERGKU0nuKiMSIPXv2MGLECC644AIGDhyIx+Ph3//+Nxs3biQ9PZ2HHnoo0lUUERERERERERERkTBR0E9EOqV58+Y1q9yll17K8OHDw1qXUOnZsyfXXXcdGzZs4J133qGyspLMzEymTZtGfn4+/fv3j3QVRURERGKyHyYiIiIiItIRaE8/EemUDMNoVrlnn32WqVOnhrcyIiIiIp2I+mEiIiIiIiLhoaCfiIiIiIiIiIiIiIiISJSzRboCIiIiIiIiIiIiIiIiItI2CvqJiIiIiIiIiIiIiIiIRDkF/URERERERERERERERESinIJ+IiIiIiIiIiIiIiIiIlHOEekKxKqioiI8Hs9xn+/evTuHDx9uxxp1bGqPOmqLYGqPYGqPOmqLYGqPOsdrC4fDQXp6egRqJK1xsr5Ua+jfSTC1Rx21RR21RTC1Rx21RZ3O2hbqS0WXcPSloPO+/9uT2jj81MbhpzYOP7VxeIWrfdujP9Xhg34rVqxg1apVQcdSU1P53e9+B4BlWaxcuZL169dTVlbGoEGDmD59On369AmUd7vdLF++nE2bNlFTU8PQoUOZMWMGXbt2DZQpKyvj2Wef5YMPPgBg9OjRTJs2jaSkpFbV2+Px4Ha7m3zOMIxAGcuyWnX+WKL2qKO2CKb2CKb2qKO2CKb2qKO2iB0n6ku1ht4bwdQeddQWddQWwdQeddQWddQWEi1C3ZcCvf/bg9o4/NTG4ac2Dj+1cXhFe/t2+KAfQJ8+fbjrrrsCj222uqyka9eu5eWXX+amm26iV69erF69mvnz57No0SISExMBWLp0KR9++CFz584lJSWFZcuWcf/997Nw4cLAuR577DGOHDlCfn4+AE899RSLFy/m9ttvb8c7FREREREREREREREREWm5qNjTz2azkZaWFvjTpUsXwLfK75VXXmHSpEmMGTOG7OxsZs2aRXV1NRs3bgSgoqKCt956iylTppCXl0dOTg6zZ89mz549FBQUALB37162bNnCjTfeSG5uLrm5ucycOZOPPvqI/fv3R+y+RURERERERERERERERJojKlb6HTx4kJkzZ+JwOBg0aBBXXXUVPXv25NChQxQXFzNs2LBAWafTyZAhQ9i2bRsXXHABO3fuxOv1kpeXFyiTkZFBdnY227dvZ/jw4Wzfvh2Xy8WgQYMCZXJzc3G5XGzbto2srKzj1s3tdgelSzAMI7DC0L8MtCH/8eM939moPeqoLYKpPYKpPeqoLYKpPeqoLUREREREREREpLPq8EG/QYMGMWvWLLKysiguLmb16tXceeedPPzwwxQXFwO+Pf7qS01NpbCwEIDi4mIcDgfJycmNyvhfX1xc3OgcDcscz5o1a4L2HMzJyWHhwoV07979pPeWmZl50jKdidqjjtoimNojmNqjjtoimNqjjtpCREREREREREQ6mw4f9BsxYkTg9+zsbHJzc5k9ezbvvPNOYGVew9n8zdlcsbllTrZSYNKkSVx88cWBx/7yhw8fxuPxNPkawzDIzMzk4MGDUbkRZKipPeqoLYKpPYKpPeqoLYKpPeqcqC0cDkezJuWIiIiIiIiIiIhEow4f9GsoISGB7OxsDhw4wBlnnAH4Vuqlp6cHypSWlgZW7qWlpeHxeCgrKwta7VdaWsrgwYMDZUpKShpdq/55jsfpdOJ0Opt87mQDr5ZldfrB2frUHnXUFsHUHsHUHnXUFsHUHnXUFiIiIiIiIiIi0tnYIl2BlnK73ezbt4/09HR69OhBWloaBQUFgec9Hg9bt24NBPT69++P3W4PKlNUVMSePXvIzc0FfPv3VVRUsGPHjkCZL774goqKisB5RERERERERERERERERDqqDr/Sb9myZYwePZpu3bpRUlLCCy+8QGVlJePHj8cwDCZMmMCaNWvo1asXmZmZrFmzhvj4eMaNGweAy+Xi3HPPZfny5aSkpJCcnMzy5cvJzs4mLy8PgN69ezN8+HCeeuopbrjhBgCefvppRo4cSVZWVsTuXURERERERERERERERKQ5OnzQ7+jRozz66KOUlpbSpUsXBg0axH333RfYk2fixInU1NSwZMkSysvLGThwIPn5+SQmJgbOcf3112O323nkkUeoqalh6NCh3HbbbdhsdQsd58yZwzPPPMN9990HwKhRo5g+fXr73qyIiIiIiIiIiIiIiIhIK3T4oN/NN998wucNw2Dy5MlMnjz5uGXi4uKYNm0a06ZNO26Z5ORk5syZ09pqdmqmBZ9V2DhUY6OLw6Kbw6JvgonNiHTNRERERIKVeOCjYw5OT/LS1al9H0VEREREREQkdnT4oJ90bF4LNpQ42FPtWzW5r8Z3fH+Nl7FdvBgK/ImIiEgHsqvKxp5qG4k2i65Ob6SrIyIiIhJVikrLMO1OWjN1yo6F5XGHvE4iIiJSR0E/aTWvBeuKHHzjtmHD4vQkL1WmwfZKG19W2XHZYUSyBtNERETC7ejRo/zxj39ky5Yt1NTU0KtXL37yk5/Qv39/ACzLYuXKlaxfv56ysjIGDRrE9OnT6dOnT+Acbreb5cuXs2nTpkA69BkzZtC1a9dAmbKyMp599lk++OADAEaPHs20adNISkoKlCksLGTJkiV89tlnxMXFcdZZZzFlyhQcjo7R7fTWjlCZka2GiIiISFRymyYFxd5WBf3y0uzYTl5MRERE2kCftXJShsOJ6Yhr9GdnTRzfuG04DTi3m8HpaQ7OyLBzZppved8n5Xa+rNRbTEREJJzKysq46667cDgc/PznP+fhhx9mypQpuFyuQJm1a9fy8ssvM23aNBYsWEBaWhrz58+nsrIyUGbp0qVs3ryZuXPncu+991JVVcX999+PadaFxx577DF2795Nfn4++fn57N69m8WLFweeN02TBQsWUF1dzb333svcuXN5//33WbZsWfs0RjOYtSNUljJ7ioiIiIiIiEiMUURGTsqLQUGxN+jPx0VePirxjZZ1dZgcqqx7rtLtpYfTN0D4UZk9MKNeREREQm/t2rV07dqVm266iYEDB9KjRw9OP/10MjMzAd8qv1deeYVJkyYxZswYsrOzmTVrFtXV1WzcuBGAiooK3nrrLaZMmUJeXh45OTnMnj2bPXv2UFBQAMDevXvZsmULN954I7m5ueTm5jJz5kw++ugj9u/fD8DHH3/M3r17mT17Njk5OeTl5TFlyhTWr19PRUVFZBqoAX8IU90TEREREREREYk1HSPPkkSdY16otgxsWHR1Nh426+G0KPNChWnwZaWNXJeSaImIiITDBx98wLBhw3j44YfZunUrGRkZXHjhhZx//vkAHDp0iOLiYoYNGxZ4jdPpZMiQIWzbto0LLriAnTt34vV6ycvLC5TJyMggOzub7du3M3z4cLZv347L5WLQoEGBMrm5ubhcLrZt20ZWVhbbt28nOzubjIyMQJlhw4bhdrvZuXMnQ4cObVR/t9uN2123t4thGCQmJgZ+DxX/uUx8Py2MkJ4/2vjvvTO3gZ/aoo7aIpjao47aoo7aQkREREQ6MgX9pFUOu32LRDOcFvYmvuvYDBiSAh+UwKfldgYmmtj0nUhERCTkDh06xJtvvslFF13EpEmT2LFjB88++yxOp5Px48dTXFwMQGpqatDrUlNTKSwsBKC4uBiHw0FycnKjMv7XFxcXNzpHc8okJyfjcDgCZRpas2YNq1atCjzOyclh4cKFdO/evblN0CLxCYlQUU1CYiK9eqWE5RrRxL8iVNQW9aktgqk96qgt6qgtRERERKQjUtBPWqzCC+WmgYFFN8fxk2MNdMGnpRZlpsGuKhsDErXaT0REJNRM02TAgAFcffXVgC9o9vXXX/PGG28wfvz4QLmGKxKsZmxq19wy9c/d1MqHhmXqmzRpEhdffHGj1x8+fBiPx3PS6zeXYRhkZmZSXlEJ2CivrOTAgbKQnT/a+Nvj4MGDzfp7jmVqizpqi2BqjzpqizqduS0cDkfYJuWIiIiISGgo6CctVuz1Dcal2i3iTrArpMMGQ5K8fFTm4LMKG/0TTJQBRUREJLTS09Pp3bt30LHevXvz/vvvA5CWlgb4VuGlp6cHypSWlgZW5aWlpeHxeCgrKwta7VdaWsrgwYMDZUpKShpdv+F5duzYEfR8WVkZXq+3yVWC4Es16nQ6m3wuHIOpgT39rPCcP9pYlqV2qKW2qKO2CKb2qKO2qKO2EBEREZGO6AQhG5GmldUG/VKaETLOTTSxYVHssVHsUcRPREQk1AYPHsz+/fuDju3fvz8wE79Hjx6kpaVRUFAQeN7j8bB169ZAQK9///7Y7fagMkVFRezZs4fc3FzAt39fRUVFUFDviy++oKKiInCe3Nxc9uzZQ1FRUaBMQUEBTqeT/v37h/jOW8dbOz6rYVoRERERERERiTUK+kmLuE2oMg3AIsV+8uGyOBucEu8rt7tabzcREZFQu+iii/jiiy9YvXo1Bw8eZOPGjaxfv57vfe97gC8N2YQJE1izZg2bN29mz549PPHEE8THxzNu3DgAXC4X5557LsuXL+eTTz5h165dLF68mOzsbPLy8gDf6sHhw4fz1FNPsX37drZv385TTz3FyJEjycrKAmDYsGH07t2bxx9/nF27dvHJJ5+wfPlyzjvvPFwuV2QaqAF/78VU1E9EREREREREYozSe0qL+Ff5JdrA0cyFe/0STL6utrGrysbwJK9SfIqIiITQwIEDueWWW/jzn//MCy+8QI8ePbj++uv5zne+EygzceJEampqWLJkCeXl5QwcOJD8/HwSExMDZa6//nrsdjuPPPIINTU1DB06lNtuuw2brW7Szpw5c3jmmWe47777ABg1ahTTp08PPG+z2bjjjjtYsmQJd911F3FxcYwbN47rrruuHVqiebTST0RERERERERilYJ+0iLHvL6fzVnl59c73sSBRZnX4IjHoJtTw2wiIiKhNGrUKEaNGnXc5w3DYPLkyUyePPm4ZeLi4pg2bRrTpk07bpnk5GTmzJlzwrp069aN22+//eSVjhCrwU8RERERERERkVihoJ80m2XBsdqVfsktCPo5DV/gb3e1nd1VNro5veGqooiIiMgJBVb6KeonIiIitbZu3cpLL73Erl27KCoq4pZbbuHMM88MPG9ZFitXrmT9+vWUlZUxaNAgpk+fTp8+fQJl3G43y5cvZ9OmTYGsCTNmzKBr166BMmVlZTz77LN88MEHAIwePZpp06aRlJQUKFNYWMiSJUv47LPPiIuL46yzzmLKlCk4HBrCExERkZPTJmvSbJUmeDGwYeFq4TunX4IJwO4qmwbZREREJGJMpfcUERGRBqqrq+nXr99xMx6sXbuWl19+mWnTprFgwQLS0tKYP38+lZWVgTJLly5l8+bNzJ07l3vvvZeqqiruv/9+TNMMlHnsscfYvXs3+fn55Ofns3v3bhYvXhx43jRNFixYQHV1Nffeey9z587l/fffZ9myZeG7eREREYkpCvpJs/n380uyg62F+/KdEm/hNCwqTINCjzb1ExERkcgwMWp/ioiIiPiMGDGCK6+8kjFjxjR6zrIsXnnlFSZNmsSYMWPIzs5m1qxZVFdXs3HjRgAqKip46623mDJlCnl5eeTk5DB79mz27NlDQUEBAHv37mXLli3ceOON5Obmkpuby8yZM/noo4/Yv38/AB9//DF79+5l9uzZ5OTkkJeXx5QpU1i/fj0VFRXt1yAiIiIStRT0k2YrN32DZC3Zz8/PbkBWnG94bW+13nYiIiISGVrpJyIiIi1x6NAhiouLGTZsWOCY0+lkyJAhbNu2DYCdO3fi9XrJy8sLlMnIyCA7O5vt27cDsH37dlwuF4MGDQqUyc3NxeVyBc6zfft2srOzycjICJQZNmwYbrebnTt3hvU+RUREJDYoIbg0W2XtlPhEW+uGyfrEW3xVDV9XG4xIDmHFRERERJrJv8JP6cZFRESkOYqLiwFITU0NOp6amkphYWGgjMPhIDk5uVEZ/+uLi4sbnaM5ZZKTk3E4HIEyDbndbtxud+CxYRgkJiYGfg+lwPkMA6MVnSkjDHWKNf72UTuFj9o4/NTG4ac2Dq9ob18F/aRZPBZ4LAOwSGjlQr1T4k0MLIo9No55IEXvPhEREWlnWuknIiIirdFw4M9qRtCruWXqn7upAcaGZepbs2YNq1atCjzOyclh4cKFdO/e/aTXbo1DxaUkJye16rXOOC890rqFuEaxKTMzM9JViHlq4/BTG4ef2ji8orV9FXaRZqn0+n7GGb5Una0Rb4MeTotv3AZ7a2yc5tBuOiIiItK+zAY/RURERE4kLS0N8K3CS09PDxwvLS0NrMpLS0vD4/FQVlYWtNqvtLSUwYMHB8qUlJQ0On/D8+zYsSPo+bKyMrxeb5OrBAEmTZrExRdfHHjsDw4ePnwYj8fT0ts9IcMwsCW4KCsrb1XaBLfdzoEDB0Jap1hjGAaZmZkcPHiwWUFjaTm1cfipjcNPbRxe4Wxfh8MRtok5gWuE9ewSMypr9/NrbWpPvz7xJt+4bXxdbeM0l4bbREREpH0FVvrpe5GIiIg0Q48ePUhLS6OgoICcnBwAPB4PW7du5ZprrgGgf//+2O12CgoKGDt2LABFRUXs2bMnUCY3N5eKigp27NjBwIEDAfjiiy+oqKgIBAZzc3NZvXo1RUVFgQBjQUEBTqeT/v37N1k/p9OJ0+ls8rmwDQRbVquyJliEsU4xxrIstVWYqY3DT20cfmrj8IrW9lXQT5qlbj+/tp2nT7zJB2XwTY1BjQlxbTyfiIiISEsE9vQjOnPzi4iISOhVVVVx8ODBwONDhw6xe/dukpOT6datGxMmTGDNmjX06tWLzMxM1qxZQ3x8POPGjQPA5XJx7rnnsnz5clJSUkhOTmb58uVkZ2eTl5cHQO/evRk+fDhPPfUUN9xwAwBPP/00I0eOJCsrC4Bhw4bRu3dvHn/8ca699lrKyspYvnw55513Hi6Xq51bRURERKKRgn7SLK1Z6WczDExHXNCxJAekOqDEY7DXE0c/l28j50PFpZh2Z2CmmB0Ly+NudE4RERGRttCefiIiItLQl19+yT333BN4vGzZMgDGjx/PrFmzmDhxIjU1NSxZsoTy8nIGDhxIfn4+iYmJgddcf/312O12HnnkEWpqahg6dCi33XYbNlvdbOc5c+bwzDPPcN999wEwatQopk+fHnjeZrNxxx13sGTJEu666y7i4uIYN24c1113XbibQERERGKEgn5yUm4TampHxhLtzX+diUFBceP88XGGAdj4tNSktMY3zz45xU7ZMW9gAC4vzY4WAYqIiEioBVb6KeonIiIitb71rW+xYsWK4z5vGAaTJ09m8uTJxy0TFxfHtGnTmDZt2nHLJCcnM2fOnBPWpVu3btx+++0nr7SIiIhIExRXkZMqcgMYOAwLRwgyYXWx+0bZSr2GBtxERESk3ZiWFUjrqZ2FRURERERERCTWKOgnJ1VUm2Wzrfv5+blsvvSdJgblGnETERGRdmLWm2ykeUciIiIiIiIiEmsU9JOTOhoI+oVmeMwwoIujdrWfJwRLB0VERESawVsvxYCCfiIiIiIiIiISaxT0k5MqCnHQD5TiU0RERNpf0Eo/9T9EREREREREJMYo6CcnZFlQ4vH9Hh/Cd0uKHQwsaiyDKg26iYiISDvw1utzKMO4iIiIiIiIiMQaBf3khCpN/wCZRXwIM3HaDEi2+35Xik8RERFpD0rvKSIiIiIiIiKxTEE/OaFSry8gF2f49uILJX+KzxKvgn4iIiISfkrvKSIiIiIiIiKxTEE/OSH/KrxQpvb0S3VYgEWlaVDlUZItERERCS+t9BMRERERERGRWKagn5xQ3Uq/0A+NOQxIqn0HHqlW0E9ERETCy1uvu6Ggn4iIiIiIiIjEGgX95ISOecO30g/8q/3gSJU3PBcQERERqWVSf6WfoRSfIiIiIiIiIhJTFPSTEyr1+H7Gh2GlH9Tt63fMbeLWYj8REREJI2+DvoZifiIiIiIiIiISSxT0k+MyrfCv9IuzgcvmG3Irqb2WiIiISDh4GyztU9BPRERERERERGKJgn5yXBUmmBjYAGcY43Gptav9SjwK+omIiEj4mA2ifAr6iYiIiIiIiEgsUdBPjqu0NgiX7AAjnEG/2n39ykzwavRNREREwqRhP6NhEFBEREREREREJJop6CfHVVqbbrOLI7zXibdBot0ADI55w3stERER6bxMpfcUERERERERkRimoJ8cl38/v5QwB/0A0uPtQdcUERERCbWGK/0U9BMRERERERGRWKKgnxyXP71n+wT9bIFrWhqBExERkTBotNJPfQ4RERERERERiSEK+slxtVd6T4AucTZsWHgxqDTDfz0RERHpfLTST0RERERERERimYJ+0iTTgrLa/fXaY6WfzTBIsfuG3kqV4lNERETCwNtgaZ/mGYmIiIiIiIhILFHQT5pU7gULAzsWrnZ6l3TxbeunoJ+IiIiEhdlwpZ+W+omIiIiIiIhIDFHQT5pUZvoCb0l2MNopBtfFYQEWVaZBpbd9rikiIiKdh9J7ioiIiIiIiEgsU9BPmlTu9Qf92m84zGFAQu078lBNu11WREREOgmzwdI+Bf1EREREREREJJa0w25tobVmzRqee+45JkyYwNSpUwGwLIuVK1eyfv16ysrKGDRoENOnT6dPnz6B17ndbpYvX86mTZuoqalh6NChzJgxg65duwbKlJWV8eyzz/LBBx8AMHr0aKZNm0ZSUlK73mNHUFYb9Etux6AfQJLNt9LvUDXkONv10iIiIhLjGq/0M1DoT0RERERERERiRVQF/Xbs2MG6devo27dv0PG1a9fy8ssvc9NNN9GrVy9Wr17N/PnzWbRoEYmJiQAsXbqUDz/8kLlz55KSksKyZcu4//77WbhwITabb3nZY489xpEjR8jPzwfgqaeeYvHixdx+++3te6MdQHltes12D/rZLY544JtqILldLy0iIhKVVqxYwapVq4KOpaam8rvf/Q5o38lRhYWFLFmyhM8++4y4uDjOOusspkyZgsPRMbqc3gYr/Rru8SciIiIiIiIiEs2iJr1nVVUVixcvZubMmUGDS5Zl8corrzBp0iTGjBlDdnY2s2bNorq6mo0bNwJQUVHBW2+9xZQpU8jLyyMnJ4fZs2ezZ88eCgoKANi7dy9btmzhxhtvJDc3l9zcXGbOnMlHH33E/v37I3LPkeRf6ZfUzu+QZLvvZ7EHqsz2vbaIiEi06tOnD08//XTgz0MPPRR4zj85atq0aSxYsIC0tDTmz59PZWVloMzSpUvZvHkzc+fO5d5776Wqqor7778f06z7MH7sscfYvXs3+fn55Ofns3v3bhYvXhx43jRNFixYQHV1Nffeey9z587l/fffZ9myZe3TCM1gNuhbKOYnIiIiIiIiIrEkaoJ+S5YsYcSIEeTl5QUdP3ToEMXFxQwbNixwzOl0MmTIELZt2wbAzp078Xq9Qa/NyMggOzub7du3A7B9+3ZcLheDBg0KlMnNzcXlcgXO0xS3201FRUXgT/0BNMMwjvvnZM9H+k+56atjigMMWveH5parbQ8MA6cB8YZvCO6w2xbxdojEn47+3lB7qD06yh+1hdqjpW0Ry2w2G2lpaYE/Xbp0Adp3ctTHH3/M3r17mT17Njk5OeTl5TFlyhTWr19PRUVFZBqmgYYr/RT0ExEREREREZFY0jFyLZ3Epk2b2LVrFwsWLGj0XHFxMeBLY1VfamoqhYWFgTIOh4Pk5ORGZfyvLy4ubnSOhmWasmbNmqCUWjk5OSxcuJDu3buf9L4yMzNPWiYSTMui4uARAHJ6daeqopzkFHuLz2MYNSSnND9HZ3KybwVnulnDwUovZc5kevXqnDk+O+p7I1LUHsHUHnXUFsHUHnU6W1scPHiQmTNn4nA4GDRoEFdddRU9e/Y86eSoCy644KSTo4YPH37SyVFZWVls376d7OxsMjIyAmWGDRuG2+1m586dDB06tMm6u91u3G534LFhGIH07KEM1hqG0eSefjEeDz6u+gHyzk5tUUdtEUztUUdtUUdtISIiIiIdWYcP+hUWFrJ06VLy8/OJi4s7brmGHW7LOvnc7eaWOVFnftKkSVx88cWN6nH48GE8Hs9x65qZmcnBgwebVYf2Vu4FEycGFscKD4HDSdkxb4vPY6U7KTtWdvKChkFychJlZeVgWcSZBmDjy6JKhtiPtfwGolhHf2+0N7VHMLVHHbVFMLVHnRO1hcPhaNaknGgzaNAgZs2aRVZWFsXFxaxevZo777yThx9+uF0nRzVVJjk5GYfDEbYJVC21ZU9wvySja1d6JTtDfp1o0tkC5Ceitqijtgim9qijtqijthARERGRjqjDB/127txJSUkJt99+e+CYaZp8/vnnvPbaayxatAjwDTSlp6cHypSWlgYGntLS0vB4PJSVlQUNaJWWljJ48OBAmZKSkkbXr3+epjidTpzOpgeLTjbwallWhxycPeap3c/PDgYWJq1Pf9Wc1xn+NrAsLCDJ5ntc5IFqr0Vc1CShDZ2O+t6IFLVHMLVHHbVFMLVHnc7UFiNGjAj8np2dTW5uLrNnz+add94JrMxrr8lRTU2UCscEqtbwrfRLCjpWeOQIjmOd433SkCYL1FFb1FFbBFN71FFb1OnMbRGrE6hEREREYkmHD/qdfvrpPPjgg0HHnnzySbKyspg4cSI9e/YkLS2NgoICcnJyAPB4PGzdupVrrrkGgP79+2O32ykojfd8hAABAABJREFUKGDs2LEAFBUVsWfPnkCZ3NxcKioq2LFjBwMHDgTgiy++oKKiIhAY7CzKvb7BtmRbZL7AOG2QYodjXoPDboNT4jvXFykREZG2SEhIIDs7mwMHDnDGGWcA7TM5Ki0tjR07dgQ9X1ZWhtfrDdsEqpYyG5zONDtPcPh4OlOA/GTUFnXUFsHUHnXUFnXUFiIiIiLSEXX4NVSJiYlkZ2cH/YmPjyclJYXs7GwMw2DChAmsWbOGzZs3s2fPHp544gni4+MZN24cAC6Xi3PPPZfly5fzySefsGvXLhYvXkx2dnZg/5revXszfPhwnnrqKbZv38727dt56qmnGDlyJFlZWZFsgnZXVpvJM8keuS8w3WozuR52d/i3qIiISIfidrvZt28f6enp9OjRIzA5ys8/Ocof0Ks/OcrPPzkqNzcXCJ4c5ddwclRubi579uyhqKgoUKagoACn00n//v3Des/N5W0wOKuhWhERERERERGJJR1+pV9zTJw4kZqaGpYsWUJ5eTkDBw4kPz+fxMTEQJnrr78eu93OI488Qk1NDUOHDuW2227DZqsLKs2ZM4dnnnmG++67D4BRo0Yxffr0dr+fSCs369J7Rkq3ONhVCYVubY4uIiJyIsuWLWP06NF069aNkpISXnjhBSorKxk/fnzQ5KhevXqRmZnJmjVrjjs5KiUlheTkZJYvX37cyVE33HADAE8//XTQ5Khhw4bRu3dvHn/8ca699lrKyspYvnw55513Hi6XKzKN04C3QZRPQT8RERERERERiSVRGfSbN29e0GPDMJg8eTKTJ08+7mvi4uKYNm0a06ZNO26Z5ORk5syZE6pqRq0yf3rPDrDSr9BtYFlwgq2AREREOrWjR4/y6KOPUlpaSpcuXRg0aBD33XdfYM+d9pocZbPZuOOOO1iyZAl33XUXcXFxjBs3juuuu66dWuLkzAYr/cwI1UNEREREREREJByiMugn4VXeAYJ+aU6wYVFjGRzzQhe9U0VERJp08803n/D59pwc1a1bN26//fYTlomkRiv9tNRPRERERERERGKINkyTIJYF5f49/WyRGwmzG9DV6bt+ofb1ExERkRDQnn4iIiIiIiIiEssUTZEgVSZ4MQALVwT39APoVhv0O6x9/URERCQEzAZRPhP1MUREREREREQkdijoJ0HKTN/gl8vmW20XSd0CK/00ICciIiJtp/SeIiIiIiIiIhLLFPSTIP79/JIiuJ+fXzenCUCRx2g0SCciIiLSUt7AUj+r3n9FRERERERERGKDgn4SpCPs5+eXbIMEw8LE4KhW+4mIiEgbmbU/HbXdisj3dkREREREREREQkdBPwlSYfpX+kW4IoBh1Evx6VHQT0RERNrGv9LP381Rek8RERERERERiSWOSFdAOhZ/ek9XB0jvCb4Un3trbByuMTjNFenaiIiISDTzZ/e0G4CllX4iIiIiIp1RUWkZpt3Z4u8DdiwsjzssdRIRCRUF/SRIRW3eq46Q3hPqr/SzAd7IVkZERESimrd2aZ+9NoGAeYKyIiIiIn5er5eVK1eyYcMGiouLSU9P5+yzz+ayyy7DZvMl0bIsi5UrV7J+/XrKysoYNGgQ06dPp0+fPoHzuN1uli9fzqZNm6ipqWHo0KHMmDGDrl27BsqUlZXx7LPP8sEHHwAwevRopk2bRlJSUvvetEgMc5smBcXeFgf98tLsSpsnIh2e/j8lQepW+kW4IrX8Qb8yr0GVRuZERESkDfwr/RyG75eOMcVJREREOrq1a9fy5ptvMn36dB555BGuvfZaXnrpJV577bWgMi+//DLTpk1jwYIFpKWlMX/+fCorKwNlli5dyubNm5k7dy733nsvVVVV3H///Zhm3YDHY489xu7du8nPzyc/P5/du3ezePHidr1fERERiV4K+kmAaUFlB1vpF2eD1NpUo4Vu7esnIiIireetn94T7eknIiIizbN9+3ZGjx7NyJEj6dGjB//1X/9FXl4eX375JeBb5ffKK68wadIkxowZQ3Z2NrNmzaK6upqNGzcCUFFRwVtvvcWUKVPIy8sjJyeH2bNns2fPHgoKCgDYu3cvW7Zs4cYbbyQ3N5fc3FxmzpzJRx99xP79+yN2/yIiIhI9lN5TAnypPQ1sWCR0oHBwN6dJidfOYbeN3vFK8SkiIiKtE0jvWftYSQRERESkOU499VTefPNN9u/fT1ZWFrt372bbtm1cf/31ABw6dIji4mKGDRsWeI3T6WTIkCFs27aNCy64gJ07d+L1esnLywuUycjIIDs7m+3btzN8+HC2b9+Oy+Vi0KBBgTK5ubm4XC62bdtGVlZWo7q53W7c7ro9xgzDIDExMfB7KAXOZxgYrZg9ZYShTrHG3z5qp/Bpy/tY7+Hm0fs4/NTG4RXt7augnwRU+FN72qAjvZ+7OS2+rNJKPxEREWmbuvSevp9a6CciIiLNMXHiRCoqKvjf//1fbDYbpmly5ZVXMm7cOACKi4sBSE1NDXpdamoqhYWFgTIOh4Pk5ORGZfyvLy4ubnSOhmUaWrNmDatWrQo8zsnJYeHChXTv3r01t3pSh4pLSU5u3f6CzjgvPdK6hbhGsSkzMzPSVYhprX0f6z3cMnofh5/aOLyitX0V9JOActO/n1/HGgLz7+tX6DawrI4VkBQREZHoYFp1QT6l9xQREZGWePfdd9mwYQNz5syhT58+7N69m6VLl5Kens7ZZ58dKNdwRYDVjM5Gc8scb7XBpEmTuPjiixvV4fDhw3g8npOeuyUMw8CW4KKsrLxVHSm33c6BAwdCWqdYYxgGmZmZHDx4sFnvDWm5tryP9R5uHr2Pw09tHF7hbF+HwxG2iTmBa4T17BJVKmozZyZ1sKBfusPCjoXbMij1QqretSIiItJC9Xs3dsNqdExERETkeP74xz8yceJEzjrrLACys7M5fPgwL774ImeffTZpaWmAb6Veenp64HWlpaWBlXtpaWl4PB7KysqCVvuVlpYyePDgQJmSkpJG169/noacTidOp7PJ58I2EGxZrepHWYSxTjHGsiy1Vbi14n2s93DL6H0cfmrj8IrW9u1AO7dJpFWYdek9OxKbAV0Dq/06WOVEREQkKnjr9dMDK/1Q+gARERE5uerqamy24PEIm80WGAjs0aMHaWlpFBQUBJ73eDxs3bo1ENDr378/drs9qExRURF79uwhNzcX8O3fV1FRwY4dOwJlvvjiCyoqKgLnERERETkRrZmSgPLaPf062ko/8KX4POSGw26DAYmRro2IiIhEG7Pe7/banx2vxyMiIiId0ahRo1i9ejXdunWjd+/e7N69m7/97W+cc845gC8N2IQJE1izZg29evUiMzOTNWvWEB8fH9j3z+Vyce6557J8+XJSUlJITk5m+fLlZGdnk5eXB0Dv3r0ZPnw4Tz31FDfccAMATz/9NCNHjiQrKysyNy8iIiJRRUE/CfCn93TZOt4QWDenCdgpdGtGvoiIiLScWdu9MbACK/3MjtflERERkQ5o2rRpPP/88yxZsoSSkhIyMjK44IILuOKKKwJlJk6cSE1NDUuWLKG8vJyBAweSn59PYmLdzOXrr78eu93OI488Qk1NDUOHDuW2224LWkU4Z84cnnnmGe677z7AF3CcPn16+92siIiIRDUF/SSg3PSv9ItwRZrQrTa9Z5HHwGOBQ7E/ERERaQH/Sj8bBJJ6KuYnIiIizZGYmMjUqVOZOnXqccsYhsHkyZOZPHnyccvExcUxbdo0pk2bdtwyycnJzJkzpy3VFRERkU5MG6QJ4NvnprJ2NKwjrvRLskGizcLC4KhW+4mIiEgL+Vf12QwF/UREREREREQkNinoJ4A/4GdgwyKhA74rDKNutZ9SfIqIiEhLBa30U3pPEREREREREYlBHTC8I5FQ7vWNfrnsdQNhHY1vXz84rKCfiIiItJBW+omIiIiIiIhIrFPQTwCo8O/n1wFTe/rVrfTT21ZERERaxqwN9dkAW224r+P2ekREREREREREWk7REwGg3Ov76bJ33OGvrg4LsCg3DSq9ka6NiIiIRJOglX61S/2sjtvtERERERERERFpMQX9BIAKf3rPDvyOiLNBWm1QUik+RUREpCUCQT+U3lNEREREREREYlMHDvFIewqk9+zAK/1AKT5FRESkdfxJArSnn4iIiIiIiIjEKkekKyAdQ4Xp+5nYQfb0sxkGpiOu0fGuCbCjCgq9dkyHvcnX2rGwPO5wV1FERESiiBVI72lhq436mR2j2yMiIiIiIiIiEhIK+glQL71n03G0dmdiUFDsaXTct5efnUPVFh8XmYE9eerLS7NrCauIiIgECaz0Qyv9RERERERERCQ2KTYimBZU1q70c3WQlX7Hk2ADAwsTg+qOXVURERHpQPwr/exB6T21R7CIiIiIiIiIxA4F/YRq0zfoZWCR2MHfEYYBrto6+lcnioiIiJyMf6WfQV3Qz4xQXUREREREREREwqGDh3ikPVSYvqGvBBuBPW46MpfdN1W/QiN1IiIi0kxm/ZV+tf0dS1kDRERERERERCSGKOgnVNROfe/oqT39/PX0BytFRERETsasXd9nUNcBjo6ej4iIiIiIiIhI8yjoJ4Hgmcse4Yo0kz8FaZVZN2tfRERE5ETMJvf0ExERERERERGJHQr6SV3QL0pW+sXZwGFYgKEUnyIiItIs/i6DDaX3FBEREREREZHY5AjnyYuLi0lLSwvnJSQEAuk97dEz8uWyQakXKrwGyVFUbxEREb9w9ZPWrFnDc889x4QJE5g6dSoAlmWxcuVK1q9fT1lZGYMGDWL69On06dMn8Dq3283y5cvZtGkTNTU1DB06lBkzZtC1a9dAmbKyMp599lk++OADAEaPHs20adNISkoKlCksLGTJkiV89tlnxMXFcdZZZzFlyhQcjrB2O0/Kv9LPZoBRu8ZPPQgREZHopTEnERERkcbCutLvJz/5CYsWLeI///lPOC8jbVS30i/CFWkB7esnIiLRLhz9pB07drBu3Tr69u0bdHzt2rW8/PLLTJs2jQULFpCWlsb8+fOprKwMlFm6dCmbN29m7ty53HvvvVRVVXH//fdjmnXL6h977DF2795Nfn4++fn57N69m8WLFweeN02TBQsWUF1dzb333svcuXN5//33WbZsWcjusbXqVvpZgQ6wEgaIiIhEL405iYiIiDQW1jDP5Zdfzn/+8x/uvvtufvazn/HWW29RU1MTzktKK/hX+iVGSXpPqFuVWKnROhERiVKh7idVVVWxePFiZs6cGbTyzrIsXnnlFSZNmsSYMWPIzs5m1qxZVFdXs3HjRgAqKip46623mDJlCnl5eeTk5DB79mz27NlDQUEBAHv37mXLli3ceOON5Obmkpuby8yZM/noo4/Yv38/AB9//DF79+5l9uzZ5OTkkJeXx5QpU1i/fj0VFRVtaK22C17p5xM9PR8RERFpSGNOIiIiIo2FNeh3xRVX8Jvf/Ia5c+ficrl46qmnuPHGG1m2bBkHDx4M56WlBQIr/aIoTWaiDcDCbRm4FfgTEZEoFOp+0pIlSxgxYgR5eXlBxw8dOkRxcTHDhg0LHHM6nQwZMoRt27YBsHPnTrxeb9BrMzIyyM7OZvv27QBs374dl8vFoEGDAmVyc3NxuVyB82zfvp3s7GwyMjICZYYNG4bb7Wbnzp0tvqdQ0p5+IiIisUVjTiIiIiKNhX1zFZvNxtixYxk7dixfffUVr732Gm+++SavvPIKw4cP5/vf/z7Dhw8PdzXkONwWuK3oS+9pNyDBBlUmVJiQGkV1FxER8QtVP2nTpk3s2rWLBQsWNHquuLgYgNTU1KDjqampFBYWBso4HA6Sk5MblfG/vri4uNE5mlMmOTkZh8MRKNOQ2+3G7XYHHhuGQWJiYuD3UDFr1/fZDANb7XktjJBeI5r477uz3n99aos6aotgao86aos6aouORWNOIiIiIsHCHvSrLzs7mxEjRrBnzx527NjBJ598wr///W9ycnKYM2cOWVlZ7VkdASprU3s6DIu4KAucuWwWVaZBhWmQqgRdIiIS5VrbTyosLGTp0qXk5+cTFxd33PM3HJy0mrHMrbll6p+7qUHQhmXqW7NmDatWrQo8zsnJYeHChXTv3v2k126JBE8ZlFfRJSWJbklOKCrF7nDQq1dorxNtMjMzI12FDkNtUUdtEUztUUdtUUdt0fFozElERESknYJ+paWlrF+/nnXr1lFYWEhubi4333wzZ5xxBlu2bGH58uX85je/Yf78+e1RHaknkNozygJ+4KvzUaDCa6BdeUREJFq1tZ+0c+dOSkpKuP322wPHTNPk888/57XXXmPRokWAbxVeenp60HX9q/LS0tLweDyUlZUFrfYrLS1l8ODBgTIlJSVN1r/+eXbs2BH0fFlZGV6vt8lVggCTJk3i4osvDjz2BwcPHz6Mx+NputFaoazCDtioLCvjaLUFOKjxeDhw4EDIrhFNDMMgMzOTgwcPNiu4G8vUFnXUFsHUHnXUFnU6c1s4HI6QT8oJBY05iYiIiNQJa9Dviy++4PXXX+e9994D4Nvf/jYTJkygf//+gTKjR4/Gbrfz61//OpxVkePwBcyiaz8/P3+dK03fnjzKriIiItEkVP2k008/nQcffDDo2JNPPklWVhYTJ06kZ8+epKWlUVBQQE5ODgAej4etW7dyzTXXANC/f3/sdjsFBQWMHTsWgKKiIvbs2RMok5ubS0VFBTt27GDgwIGBe6ioqAgEBnNzc1m9ejVFRUWBAGNBQQFOpzPovupzOp04nc4mnwvlYKq39lQGYNROFrKs0F4jGlmW1enbwE9tUUdtEUztUUdtUUdtEXkacxIRERFpLKxBvzvvvJO0tDQuvfRSLrzwwuPO8O7evXtgsEjaV4Xp++myRd+XlXgDbFiYGFRZkKign4iIRJFQ9ZMSExPJzs4OOhYfH09KSkrg+IQJE1izZg29evUiMzOTNWvWEB8fz7hx4wBwuVyce+65LF++nJSUFJKTk1m+fDnZ2dnk5eUB0Lt3b4YPH85TTz3FDTfcAMDTTz/NyJEjA+myhg0bRu/evXn88ce59tprKSsrY/ny5Zx33nm4XK62NVgb+cdlbQb4uwzR1/sRERERP405iYiIiDQW1qDfrFmzGDt2LA7HiS/Tu3dv7r777nBWRY4jsNIvCtN7GgYk2qDc9N1HYhQGLkVEpPNqz37SxIkTqampYcmSJZSXlzNw4EDy8/NJTEwMlLn++uux2+088sgj1NTUMHToUG677TZstrpOwpw5c3jmmWe47777ABg1ahTTp08PPG+z2bjjjjtYsmQJd911F3FxcYwbN47rrruuTfUPhdptjLFRlx1ACzRERESil8acRERERBoLa9Dvu9/9bjhPLyEQ2NMvCtN7gq/e5aZBhQldI10ZERGRFghnP2nevHlBjw3DYPLkyUyePPm4r4mLi2PatGlMmzbtuGWSk5OZM2fOCa/drVu3oP0FOwqztqtjNyz8YUwzYrURERGRttKYk4iIiEhjYQ36vfjiixw9erTJwaNnnnmGbt26cckll5zwHG+88QZvvPEGhw8fBnwztK644gpGjBgB+PLor1y5kvXr11NWVsagQYOYPn06ffr0CZzD7XazfPlyNm3aFJi5PmPGDLp2rQsTlZWV8eyzz/LBBx8Avrzv06ZNIykpqc3t0JFFc3pPqKt3pddASbpERCSahKKfJM3nD/AZKL2niIhILFBfSkRERKSxsCZ1fOedd4KCb/X17duXd95556TnyMjI4Oqrr2bBggUsWLCAoUOH8sADD/D1118DsHbtWl5++WWmTZvGggULSEtLY/78+VRWVgbOsXTpUjZv3szcuXO59957qaqq4v7778c06+Z3P/bYY+zevZv8/Hzy8/PZvXs3ixcvbmMLdHyVtek9E+0Rrkgr+dOSVlng1cidiIhEkVD0k6T56lb6KegnIiISC9SXEhEREWksrEG/wsJCevXq1eRzmZmZgdV7JzJ69GhGjhxJVlYWWVlZXHXVVSQkJPDFF19gWRavvPIKkyZNYsyYMWRnZzNr1iyqq6vZuHEjABUVFbz11ltMmTKFvLw8cnJymD17Nnv27KGgoACAvXv3smXLFm688UZyc3PJzc1l5syZfPTRR+zfvz90DdLBWBZU1sY9o3U/PKcNnIYFGIF7ERERiQah6CdJ8xn4Or6+Pf18/R4zOrs/IiIigvpSIiIiIk0Ja9DPbrdTWlra5HMlJSUYhtHkc8djmiabNm2iurqa3NxcDh06RHFxMcOGDQuUcTqdDBkyhG3btgGwc+dOvF4veXl5gTIZGRlkZ2ezfft2ALZv347L5WLQoEGBMrm5ubhcrsB5YlGNBWbtXPfEsL4Twsu/2q/C27L3k4iISCSFup8kJ3ZBhpdbR3SjX2Ldnn6K+YmIiEQv9aVEREREGgvrnn4DBgxg/fr1jB07ttFz69evp3///s06z549e8jPz8ftdpOQkMAtt9xC7969AwG51NTUoPKpqakUFhYCUFxcjMPhIDk5uVGZ4uLiQJmG52hY5njcbjdutzvw2DAMEhMTA783xX880h3QqtqRrjjDwmE7fl3q733TUs16nb8dDAPDqht+a+41XXaLEq9BhWlgYPnqG6Wd+47y3ugo1B7B1B511BbB1B51oqktQtVPkpZTek8REZHop76UiIiISGNhDfr98Ic/ZMGCBcybN48LL7yQjIwMjh49yptvvsnWrVu54447mnWerKwsfv3rX1NeXs7777/PE088wT333BN4vuHAnmWdfAinuWVONmi4Zs0aVq1aFXick5PDwoUL6d69+0nPn5mZedIyoVJUWobbDM5/WVNpAh4SHQb2xKTjv9jrJTklscXXNIwaklOST16wVnJyXR1a8lqzxsuBozVUWgZJyUk440x6pHVrcX07kvZ8b0QDtUcwtUcdtUUwtUedaGiLUPWTpOX83btmdAdFRESkg1JfSkRERKSxsAb9hg8fzsyZM1m2bBmPPvpo4LjL5WLmzJkMHz68WedxOByBwbsBAwbw5Zdf8sorrzBx4kTAt1IvPT09UL60tDSwci8tLQ2Px0NZWVnQar/S0lIGDx4cKFNSUtLouvXPczyTJk3i4osvDjz2BwkPHz6Mx+Np8jWGYZCZmcnBgwebFXwMBdPupKDYG3SsyOPb3cbjtfjwUOVxX3t6upOyY2UtvqbV3NcZBsnJSZSVlQdG35r9WgALDGy4TYOjpeW4HXYOHDjQ4vp2BJF4b3Rkao9gao86aotgao86J2oLh8PRrEk57SVU/SRpOa30ExERiX7qS4mIiIg0FtagH8C5557L2LFj2b59O6WlpXTp0oXc3FwSEhJafU7LsnC73fTo0YO0tDQKCgrIyckBwOPxsHXrVq655hoA+vfvj91up6CgIJDyoaioiD179gTK5ObmUlFRwY4dOxg4cCAAX3zxBRUVFYHA4PE4nU6cTudx63my+2ivwVmLxgNb7toDTsM66aBXa2vZnNcFUnpawfVo7jUNAxJsUGlCuWn47jXKB73b870RDdQewdQeddQWwdQedaKlLcLRT5KTU9BPROT/s3fv8VGWd/7/X9c9hySThCScDBEiIASLGE5W+1W6KtZ2V91SdiurrVV/Aavigt19uNU2dasuFLHb6tdDu/hARelqLdQsbXVXqlZbcCtftZgqLRGBRkQIgRyYTJI53Pfvj8lMMiRAEjKZzOT9fDyiZOaamfv+5M7MJ9fnOohkBuVSIiIiIomSXvQDyM7Opry8vF+PfeaZZ5g9ezajRo2ira2NrVu38v7771NZWYkxhssvv5yqqirGjRtHcXExVVVVZGVlMW/ePCA6wmv+/PmsX7+e/Px88vLyWL9+PaWlpfFjGj9+PLNmzWLNmjXceOONADz22GPMmTOHkpKSgQnCEBTu6OlyD/1tj07KZzm02oZA5ORtRUREhpJTyZOkf2JbGTsYHKdzuU8RERFJP8qlRERERDolvejnOA4ffvghhw4dIhgMdrv/oosuOuHjm5qaeOSRR2hoaMDn83HGGWdQWVkZT+gWLFhAMBhk7dq1tLS0MGXKFCorK8nJ6dyD7vrrr8flcvHAAw8QDAaZMWMGd9xxB5ZlxdssX76cJ554gpUrVwIwd+5cFi9ePBAhGLJCmVT0c8HhMATsDDgZEREZNk41T5L+6ZotOMd8LyIiIulDuZSIiIhIoqQW/fbv38/9999/wv3VTpaA3XLLLSe83xjDokWLWLRo0XHbeL1eKioqqKioOG6bvLw8li9ffsLXyjThjgKZJwN6unKtaAWz1Y7OYPSm+HhEREROZiDyJOmfY4t+IiIikn6US4mIiIh0l9Si3+OPP04oFOKf/umfKC0tPe7ed5Ianct7pn93l8dEzyPsGI4EodiV6iMSERE5MeVJqdN1OU8bUNogIiKSfpRLiYiIiHSX1KLfrl27uOmmm/jMZz6TzJeRfoov72mduF06MAZ8FjRHoD4IxTknf4yIiEgqKU9Kna6pj6P1PUVERNKScikRERGR7pJa7snOzsbn8yXzJaSfHAciZM7yngC+jiU+67sv4y8iIjLkKE9KHS3vKSIikv6US4mIiIh0l9SZfpdccglbtmxh1qxZyXwZ6YdwvIfLyZglrXwuB0JQH0r1kYiIiJyc8qTUUdFPREQk/Q12LnXkyBF+8pOfsH37doLBIOPGjeOWW25h8uTJADiOw4YNG3jllVfw+/1MnTqVxYsXM2HChPhzhEIh1q9fz9atWwkGg8yYMYMlS5YwatSoeBu/38+TTz7JW2+9BcC5555LRUUFubm5g3KeIiIikt6SWvSbMGECW7duZfXq1cydO5f8/Pxubc4///xkHoIcR3xpT5O4r00681kADoGIoSUCuZlSzRQRkYykPCl1orlPdF1PR1U/ERGRtDSYuZTf7+euu+7i7LPP5tvf/jYjRozg4MGDCTMNN23axAsvvMDSpUsZN24czz//PCtWrODBBx8kJye6B8m6det4++23ue2228jPz+fpp5/mvvvuY/Xq1VhWdDGuhx56iMOHD1NZWQnAmjVrePjhh7nzzjsH5FxEREQksyW16PfQQw8BUFdXxzvvvNNjm+eeey6ZhyDHEZvplylLewJYBrItaLOhPmTIdakXT0REhi7lSalliJb9lC2IiIikp8HMpTZt2sSoUaNYunRp/LaxY8fG/+04Di+++CILFy6MFxpvvfVWbrzxRrZs2cJll11GIBDg1VdfZdmyZZSXlwOwbNkybrnlFqqrq5k1axb79u1j+/btrFy5kqlTpwJw00038Z3vfIf9+/dTUlIyIOcjIiIimSupRb/vfve7yXx6OQVhJ1rtc2dQ0Q+i+/q12YZDIYszsiOpPhwREZHjUp6UWhYQAexUH4iIiIj0y2DmUm+99RYzZ87khz/8ITt27GDkyJF8/vOf53Of+xwQLTw2NjYyc+bM+GM8Hg/Tp09n586dXHbZZezevZtIJBIv+AGMHDmS0tJSampqmDVrFjU1Nfh8vnjBD6CsrAyfz8fOnTtV9BMREZGTSmrRb/r06cl8ejkFnct7Ztb49lwLjgCHQhlWzRQRkYyjPCm1TMdUPy3vKSIikp4GM5eqq6vj17/+NVdccQULFy5k165dPPnkk3g8Hi666CIaGxsBKCgoSHhcQUEB9fX1ADQ2NuJ2u8nLy+vWJvb4xsbGbs9xbJtjhUIhQqFQ/HtjTHw5UTPA+7nEn88YTD+SKJOEY8o0sfgoTslzKtexruHe0XWcfIpxcqV7fJNa9IsJBALU1NRw9OhRZs+e3S3BkcGXict7Avg6lvQ8HDJEHHBl2PmJiEjmUZ6UGrEUQTU/ERGR9DYYuZRt25x55pl85StfAWDSpEl89NFHbN68mYsuuije7tjOQacXBYXetjlex2NVVRUbN26Mfz9p0iRWr17NmDFjTvq8/VHX2ExeXm6/HuvxRhhbOHqAjygzFRcXp/oQMlp/r2Ndw32j6zj5FOPkStf4Jr3ot3HjRjZt2kQwGARg1apV5OXlce+991JeXs6XvvSlZB+C9CBTl/f0muhX0DE0hA2jPerKExGRoUt5UurEUiAt7ykiIpK+BiuXKioqYvz48Qm3jR8/njfffBOAwsJCIDpTr6ioKN6mubk5PnOvsLCQcDiM3+9PKEw2Nzczbdq0eJumpqZur9/1eY61cOFCrrzyyvj3seLgoUOHCIfDfT3VEzLGYGX78Ptb+rVcQsjl4pNPPhnQY8o0xhiKi4s5cOBArwrC0nench3rGu4dXcfJpxgnVzLj63a7kzYwJ8ZK5pO/9NJLbNy4kUsuuYQ777wz4b45c+Ycd6NlSb5MXd7TGBjtjf5bS3yKiMhQpjwptWJJsIPyBRERkXQ0mLnUtGnT2L9/f8Jt+/fvj3fajR07lsLCQqqrq+P3h8NhduzYES/oTZ48GZfLldCmoaGB2tpaysrKgOj+fYFAgF27dsXbfPDBBwQCgfjzHMvj8eDz+eJfsaU9ITpDcKC/Op4YB/r3lYRjyrSvZP3s9JUYYxxH13CSY5zqY8j0L8U4PeM7GJI60+9//ud/uPLKK7n22mux7cRx1OPGjdPIiBTK1OU9IVr0298O9Sr6iYjIEKY8KbW0p5+IiEh6G8xc6oorruCuu+7i+eef54ILLmDXrl288sorfP3rXweiMwIuv/xyqqqqGDduHMXFxVRVVZGVlcW8efMA8Pl8zJ8/n/Xr15Ofn09eXh7r16+ntLSU8vJyIDp7cNasWaxZs4Ybb7wRgMcee4w5c+ZQUlIyYOcjIiIimSupRb+6ujpmzpzZ4305OTkEAoFkvrycQKzol2nLewKMic/0s4BISo9FRETkeJQnpZb29BMREUlvg5lLTZkyhdtvv51nnnmGn//854wdO5brr7+ez372s/E2CxYsIBgMsnbtWlpaWpgyZQqVlZUJM++uv/56XC4XDzzwAMFgkBkzZnDHHXdgWZ0LcS1fvpwnnniClStXAjB37lwWL148YOciIiIimS2pRT+fz9fjWuQQTc5GjBiRzJeX44g4YJOZe/oBjPICOPgjhtYI5LhSfUQiIiLdKU9KLRX9RERE0ttg51Jz585l7ty5x73fGMOiRYtYtGjRcdt4vV4qKiqoqKg4bpu8vDyWL19+SscqIiIiw1dS9/SbMWMGmzZtoq2tLX6bMYZIJMKvf/3r447IkuSKzfKzcHBlYNHPa0GhK3qSWuJTRESGKuVJqWU6UgQt7ykiIpKelEuJiIiIdJfUot8//MM/UF9fzz//8z/z9NNPA9E117/97W9z4MABvvzlLyfz5eU4Mnlpz5jRnuhJRpf4FBERGXqUJ6VWLEOwT9hKREREhirlUiIiIiLdJbUiUlxczL/9279x+umn89JLLwHw29/+lvz8fO655x5Gjx6dzJeX4wgNg6LfGG+s6JfBJykiImlNeVJqaXlPERGR9KZcSkRERKS7pO7pBzB+/HgqKysJhUIcPXqUvLw8vF5vsl9WTiDsRLu5PBlcDxvTMdPvcNhgO2Bl8LmKiEj6Up6UOsY4gNHyniIiImlMuZSIiIhIoqQX/WI8Hg8jR44crJeTE+hc3jNze7kKXA4e4xByDI1hw0hP5p6riIikP+VJgy82HkjLe4qIiKQ/5VIiIiIiUUkt+m3cuPGkbbTG+uAbDst7GhPd1++ToOFQSEU/EREZepQnpVZsjXtlCCIiIulJuZSIiIhId0kt+m3YsOGkbZSADb7hsLwnRJf4/CQI9SHDtFQfjIiIyDGUJ6WW9vQTERFJb8qlRERERLpLatHvueee63ab3+9n27ZtvPjii9x5553JfHk5jtAwWN4TYLTHBlwcCllAJNWHIyIikkB5UmqZjqqf9vQTERFJT8qlRERERLqzTt5kYOXl5TF//nzmzZvHk08+OdgvL3Td0y+1x5FsozuW9GyOGNq1YY+IiKQB5UmDRzP9REREMo9yKRERERnukjrT70SmTJlCVVVVql5+2HKczqJfpi/vmW3BCJdDc8RQHzKcnqVuPRERSQ99yZM2b97M5s2bOXToEADjx4/ny1/+MrNnzwbAcRw2bNjAK6+8gt/vZ+rUqSxevJgJEybEnyMUCrF+/Xq2bt1KMBhkxowZLFmyhFGjRsXb+P1+nnzySd566y0Azj33XCoqKsjNzY23qa+vZ+3atbz//vt4vV4uvPBCrrvuOtzulKWcxxVLg2wMKv2JiIhkFvU5iYiIyHA16DP9Yvbu3Ut2dnaqXn7Yii5yGe3cyvSZfhBb4pOOJT5FRETSQ1/ypJEjR/KVr3yFVatWsWrVKmbMmMH999/PRx99BMCmTZt44YUXqKioYNWqVRQWFrJixQpaW1vjz7Fu3Tq2bdvGbbfdxr333ktbWxv33Xcftt05Vf6hhx5i7969VFZWUllZyd69e3n44Yfj99u2zapVq2hvb+fee+/ltttu48033+Tpp58eoKgMLEvLe4qIiGQs9TmJiIjIcJXUYdevv/56t9tCoRC1tbX85je/4bOf/WwyX156EOrou3PRuZdNJhvjcdjdBodCw+BkRUQkrQxUnnTuuecmfH/NNdewefNmPvjgA8aPH8+LL77IwoULOf/88wG49dZbufHGG9myZQuXXXYZgUCAV199lWXLllFeXg7AsmXLuOWWW6iurmbWrFns27eP7du3s3LlSqZOnQrATTfdxHe+8x32799PSUkJ7777Lvv27ePHP/4xI0eOBOC6667jRz/6EVdffTU+n6/fsUoGLe8pIiKS3tTnJCIiItJdUot+P/rRj3q83ePx8NnPfpavfe1ryXx56UF8ac9hMvFtTMe+fvUhg+MMj0KniIikh2TkSbZt87//+7+0t7dTVlZGXV0djY2NzJw5M+H5p0+fzs6dO7nsssvYvXs3kUgkXvCD6OzB0tJSampqmDVrFjU1Nfh8vnjBD6CsrAyfz8fOnTspKSmhpqaG0tLSeMEPYObMmYRCIXbv3s2MGTP6fD7JpKKfiIhIelOfk4iIiEh3SS36PfLII91u83g8FBYWJvNl5QRCTrSLazgs7QlQ6HZw4xByDE0RKBx6WwqJiMgwNZB5Um1tLZWVlYRCIbKzs7n99tsZP348O3fuBKCgoCChfUFBAfX19QA0NjbidrvJy8vr1qaxsTHe5tjn6E2bvLw83G53vE1PQqEQoVAo/r0xhpycnPi/B0rsuWL/jy/vOcCvky6Ojcdwplh0UiwSKR6dFItOisXQoT4nERERke6SWgIZM2ZMMp9e+iE+088Mj3HtloFRHoeDIcOhkEWh2z75g0RERAbBQOZJJSUlfP/736elpYU333yTRx99lHvuuSd+/7Edk04vNrLrbZuuz91TB+ixbY5VVVXFxo0b499PmjSJ1atXJy2PLC4uBiCntRnag4woKGDc6JykvFY6iMVDFIuuFItEikcnxaKTYpF66nMSERER6U7znoaZWNFvuMz0g+gSnwdD0SU+pw7fPj0REclgbrc73vl45pln8uGHH/Liiy+yYMECIDoLr6ioKN6+ubk5PiuvsLCQcDiM3+9PmO3X3NzMtGnT4m2ampq6ve6xz7Nr166E+/1+P5FIpMdZgjELFy7kyiuvjH8fKxAeOnSIcDjc+yCchDGG4uJiDhw4gOM4tLe5AIvGxmY+CTUO2Ouki2PjMZwpFp0Ui0SKRyfFotNwjoXb7VahTURERGSIS2rR7x/+4R963dYYw09/+tMkHo0AhIZh0W+0xwZcHAoNo5MWEZEhL5l5kuM4hEIhxo4dS2FhIdXV1UyaNAmAcDjMjh07+OpXvwrA5MmTcblcVFdXc8EFFwDQ0NBAbW1tvE1ZWRmBQIBdu3YxZcoUAD744AMCgUC8MFhWVsbzzz9PQ0NDvMBYXV2Nx+Nh8uTJxz1Wj8eDx+M57nkMNMdxorMPO3bzs3GGXadtV7F4iGLRlWKRSPHopFh0UixST31OIiIiIt0ltej393//97z++uu0tbUxd+5cCgsLaWho4J133iE7O5uLL744mS8vPQgPsz39IDrTD6AxbAja4LVSfEAiIiIMXJ70zDPPMHv2bEaNGkVbWxtbt27l/fffp7KyEmMMl19+OVVVVYwbN47i4mKqqqrIyspi3rx5APh8PubPn8/69evJz88nLy+P9evXU1paSnl5OQDjx49n1qxZrFmzhhtvvBGAxx57jDlz5lBSUgLAzJkzGT9+PI888gjXXnstfr+f9evXc+mll+Lz+QY+gKcotuKo+mtFRETSk/qcRERERLpLatEvJyeHwsJC7rrrLrKzs+O3t7a28m//9m9kZWXxxS9+MZmHIMcYbnv6AeS4IM/l4I8Y6kOGkqzhc+4iIjJ0DVSe1NTUxCOPPEJDQwM+n48zzjiDysrKeMFuwYIFBINB1q5dS0tLC1OmTKGyspKcnM41r6+//npcLhcPPPAAwWCQGTNmcMcdd2BZnSNlli9fzhNPPMHKlSsBmDt3LosXL47fb1kW3/rWt1i7di133XUXXq+XefPm8bWvfe2UY5UMsfFP2u1XREQkPanPSURERKS7pBb9Nm/ezLXXXpuQfEE0MfviF7/I+vXrlYANsuG4vCfAGI+NPxJd4lNFPxERGQoGKk+65ZZbTni/MYZFixaxaNGi47bxer1UVFRQUVFx3DZ5eXksX778hK81evRo7rzzzhMf8BARK2cqKxAREUlP6nMSERER6S6pCx0eOXIEl8vV430ul4vGxsZkvrwcw3bA7hjX7hlmRb+xHUt8HgxpbU8RERkalCellpb3FBERSW/KpURERES6S2oF5PTTT+dXv/oV4XA44fZwOMyvfvUrTj/99GS+vBwjtrSnwUnuD34IGuuNnnx90GCrc09ERIYA5UmpFRv/pLRAREQkPSmXEhEREekuqct7Xn311Xz/+99n2bJlnHfeeRQWFtLY2Mi2bdtobGzkX/7lX5L58nKMrkt7mmE206/Q5eA1DkHHcCRsGO1RF5+IiKSW8qTU6iz6DbOkSEREJEMolxp89SHD2Gz1p4iIiAxlSS36zZkzh29/+9v89Kc/5aWXXsLpWD9pypQp3HLLLZSXlyfz5eUYsZl+w21pT4gWOcd6HPYFDQeDKvqJiEjqKU9KLasjH9IKACIiIulJudTgOhg0bG5wMb41TAGduZSIiIgMLUkt+gGcc845nHPOObS3t9PS0kJubi5ZWVnJflnpQdiJZmTuDE/MLGOw3d5ut4/Jhn1BOBh286njXPkuHJxwKMlHKCIiEqU8KXW0vKeIiEj6Uy41eFptgwE+CjjUGYszsm2yh9veMSIiImkg6UW/GNOxnqTbPWgvKcfoXN4zs7u3bAzVjeFut7dEAFx80ubwboPd4xKn5YWuYbffoYiIpJ7ypMGnop+IiEjmUC6VfBOzbfLd8Fqjm5aIYXebxVk5tmb8iYiIDDFJz4bee+89nn32WT788EMAvve97zF58mTWrl3LOeecw/nnn5/sQ5AOw3l5T4AcCwwOEQztDmQP0ziIiMjQoTwpdVT0ExERSX/KpQbXaI/DF0/3sPGjICHH0Bg2jNT2KSIiIkNKUic1vffee6xcuZJQKMTf/u3fxtdXBxgxYgSvvfZaMl9ejjFclvc8HsuAr+OKb4kM0yCIiMiQoTwptYz29BMREUlryqVSI8dlGO2Oxro+bHCUS4mIiAwpSS36Pffcc8yePZv777+fq6++OuG+M844g7179ybz5eUYw2V5zxPJdUXP3R9J8YGIiMiwpzwptayOOX7DNysSERFJb8qlUmekx8Hg0GYbAnaqj0ZERES6SmrRb+/evXzuc58DOtdXjxkxYgTNzc3JfHk5xnBf3hMgL17002g0ERFJLeVJqaXlPUVERNKbcqnUcRsojM32Cw3jTiYREZEhKKlFP8uyiER6nlLV1NREdnZ2Ml9eunCczqLfcF3eE6LLe1od+/q1aTSaiIikkPKk1Ir1DWoQkIiISHpSLpVasSU+myKGkPpXREREhgx3Mp98ypQp/Pa3v+XTn/50t/t+//vfU1ZWdtLnqKqqYtu2bXz88cd4vV7Kysq49tprKSkpibdxHIcNGzbwyiuv4Pf7mTp1KosXL2bChAnxNqFQiPXr17N161aCwSAzZsxgyZIljBo1Kt7G7/fz5JNP8tZbbwFw7rnnUlFRQW5u7qmEYUgIOuAwvPf0g+i+frkuOBqBoxFDjks9fSIikhoDkSdJ/2mmn4iISHpTLpVaOS7IsRxabcPRiGGkpaxKRERkKEjqTL8FCxawbds2vv/978cLabt27eLxxx/nzTffZMGCBSd9jh07dvCFL3yBlStX8p3vfAfbtlmxYgVtbW3xNps2beKFF16goqKCVatWUVhYyIoVK2htbY23WbduHdu2beO2227j3nvvpa2tjfvuuw/b7hyO9NBDD7F3714qKyuprKxk7969PPzwwwMYkdRp7Rj85sLBGsZFP4D8Lkt8ioiIpMpA5EnSf7EkWAPTRURE0pNyqdSL9a8c7XnCpYiIiKRAUot+5eXl3Hrrrfz5z3/mBz/4AQCPP/44W7ZsYenSpZx11lknfY7KykouvvhiJkyYwMSJE1m6dCn19fXs3r0biM7ye/HFF1m4cCHnn38+paWl3HrrrbS3t7NlyxYAAoEAr776Ktdddx3l5eVMmjSJZcuWUVtbS3V1NQD79u1j+/bt3HzzzZSVlVFWVsZNN93EO++8w/79+5MUocETW8pyOM/yi4nt69dig62BaCIikiIDkSdJ/2l5TxERkfSmXCr1ug6qVk4lIiIyNCRteU/btjlw4ABz587lM5/5DDt37qSpqYn8/HymTZvW77XVA4EAAHl5eQDU1dXR2NjIzJkz4208Hg/Tp09n586dXHbZZezevZtIJEJ5eXm8zciRIyktLaWmpoZZs2ZRU1ODz+dj6tSp8TZlZWX4fD527tyZsJxoOorN9FPRD7IMuI1D2DG02JDvSvURiYjIcJOsPEl6T8t7ioiIpC/lUkODzwILhwiGVht86l8RERFJuaQV/RzH4Z//+Z+54447mD17Nuecc86APOdTTz3FWWedRWlpKQCNjY0AFBQUJLQtKCigvr4+3sbtdscLhV3bxB7f2NjY7TmObdOTUChEKBSKf2+MIScnJ/7vnsRuP979ydDWUfTzGIf+vGp/j7RXj4vFwRhMl6FhpxKdEz3WmOhotIawwR8xjHAlvuZg/ly6H9vgXxtDmeKRSPHopFgkUjw6pUsskpEnSd90Fv2G9rUiIiIi3SmXGhqMgTwXNEeis/18Lg2nEhERSbWkFf1cLheFhYU4Azi///HHH6e2tpZ77723233Hdu715nV72+ZEHYdVVVVs3Lgx/v2kSZNYvXo1Y8aMOelzFxcXn7TNQHl79xHAxpflJS/f06fHGhMkLz/v5A1P8XF5ebmn/Jq9fexod5iGphAtjou8/M4RgB5vhLGFo/v1ugNpMK+NdKB4JFI8OikWiRSPTkM9FsnIk6RvYtmd9vQTERFJP8qlho48l0NzJDqoeqzWUBAREUm5pBX9AC644AJef/115syZc8rP9cQTT/D2229zzz33MGrUqPjthYWFQHSmXlFRUfz25ubm+My9wsJCwuEwfr8/YbZfc3Mz06ZNi7dpamrq9rpdn6cnCxcu5Morr4x/HysQHjp0iHA43ONjjDEUFxdz4MCBQUtQW4LRQp8TCuI/2t6nxzpFHvxH/X1+zV4/zhjy8nLx+1viG+v09zV7+1ivA2ARCMPhJj9ZHbtbhlwuPvnkk3697kBIxbUxlCkeiRSPTopFIsWj04li4Xa7ezUoZ7AMZJ4kfWdpTz8REZG0plxqaIjt69dig+105lgiIiKSGkkt+k2cOJH//d//5Z577uH888+nsLCw26y5888//4TP4TgOTzzxBNu2bePuu+9m7NixCfePHTuWwsJCqqurmTRpEgDhcJgdO3bw1a9+FYDJkyfjcrmorq7mggsuAKChoYHa2tp4m7KyMgKBALt27WLKlCkAfPDBBwQCgXhhsCcejwePp+eZcyfreHUcZ9A6Z1s7hrG7Tf/2runvUfbmcfElPR0nof2pROZkj3UZyLWiSWlTxDDGcuKPGwod5oN5baQDxSOR4tFJsUikeHRKh1gMRJ4k/ac9/URERNKbcqmhwWuiW8mEHENLBPKT2tMoIiIiJ5PUj+JHH30UgCNHjrBjx44e2zz33HMnfI7HH3+cLVu28M1vfpOcnJz4/no+nw+v14sxhssvv5yqqirGjRtHcXExVVVVZGVlMW/evHjb+fPns379evLz88nLy2P9+vWUlpZSXl4OwPjx45k1axZr1qzhxhtvBOCxxx5jzpw5lJSUDEQ4Uiq2p5/bUtdWTIHboSVoaAobxngUFxERGVwDkSdJ/6noJyIikt6USw0N0X39HBrCBr9tyFd2JSIiklIDXvT7yU9+wt/8zd8watQovvvd7wIQiURwuVz9er7NmzcDcPfddyfcvnTpUi6++GIAFixYQDAYZO3atbS0tDBlyhQqKyvJycmJt7/++utxuVw88MADBINBZsyYwR133IFlWfE2y5cv54knnmDlypUAzJ07l8WLF/fruIearjP9JGqEy2E/ELAh5IBHsRERkSQb6DxJ+s9oeU8REZG0o1xqaPJZ0AC0RgwaUiUiIpJaA170++Uvf8lnPvMZRo0axfTp07Ftm2uuuYZVq1YxefLkPj/fz372s5O2McawaNEiFi1adNw2Xq+XiooKKioqjtsmLy+P5cuX9/kYh7qIA+0dRT8Vtjp5LcixHFptQ3PYMEqz/UREJMkGOk+S/rM6OqTsFB+HiIiI9N5QyaWqqqp49tlnufzyy7nhhhuA6PLyGzZs4JVXXsHv9zN16lQWL17MhAkT4o8LhUKsX7+erVu3xgekL1myhFGjRsXb+P1+nnzySd566y0Azj33XCoqKsjNzR208+srX8eqUgE7OqDKqO9JREQkZayTN5F01xbvzXLQ2LdEBR0bTjdFlJGKiIgMJ1reU0RERPpj165dvPzyy5xxxhkJt2/atIkXXniBiooKVq1aRWFhIStWrKC1tTXeZt26dWzbto3bbruNe++9l7a2Nu677z5su3MY0kMPPcTevXuprKyksrKSvXv38vDDDw/a+fVHtgUGBxtDu5IrERGRlFLRbxhotaPdWh6j0VbHKnBHs1F/BMJKTEVERIaN+PKeqT0MERERSSNtbW08/PDD3HTTTQkz7xzH4cUXX2ThwoWcf/75lJaWcuutt9Le3s6WLVsACAQCvPrqq1x33XWUl5czadIkli1bRm1tLdXV1QDs27eP7du3c/PNN1NWVkZZWRk33XQT77zzDvv370/JOfeGMZDT0cPYqkHVIiIiKTXgy3vK0NOm/fyOK8uCbMuhzTY0hRUgERGR4SI+009VPxEREemltWvXMnv2bMrLy3n++efjt9fV1dHY2MjMmTPjt3k8HqZPn87OnTu57LLL2L17N5FIhPLy8nibkSNHUlpaSk1NDbNmzaKmpgafz8fUqVPjbcrKyvD5fOzcuZOSkpJuxxQKhQiFQvHvjTHk5OTE/z2Q4s9nDOaYJCrXcgjYhoANI4/3+CQcU6aJxUdxSp4TXccnfSz62fSGruPkU4yTK93jm5Si3/79+7Gs6BCf2BIFxxuRpP1rki82ykpFv54VuR0+CRqOqOgnIiKDQHnS0BD71NeefiIiIuklVbnU1q1b2bNnD6tWrep2X2NjIwAFBQUJtxcUFFBfXx9v43a7ycvL69Ym9vjGxsZuz3Fsm2NVVVWxcePG+PeTJk1i9erVjBkzpren1id1jc3k5XXfX7DIHeZQU4h24yIvP7vHx3q8EcYWjk7KcWWa4uLiVB9CRjvedXwyuob7Rtdx8inGyZWu8U1K0e/RRx/tdtvx1h9/7rnnknEI0kVrfKafhrL3pNDt8EnQodU2NIWgSLU/ERFJIuVJQ4MVW95T6ZGIiEhaSUUuVV9fz7p166isrMTr9R633bEzApxeJBq9bXO82QYLFy7kyiuv7HYMhw4dIhwOn/S5+8IYg5Xtw+9v6ZZEWTaAi5aQTXOzP55rdRVyufjkk08G9JgyjTGG4uJiDhw40KtrQ/ruRNfxyega7h1dx8mnGCdXMuPrdruTNjAn/hoD/YS33HLLQD+lnKKue/pJdx4D+S44GoHdAZjb94E+IiIivaI8aehwdfw/ghIkERGRdJGqXGr37t00NTVx5513xm+zbZs//elP/M///A8PPvggEJ2pV1RUFG/T3Nwcn7lXWFhIOBzG7/cnzPZrbm5m2rRp8TZNTU3dXr/r8xzL4/Hg8Xh6vC9pHcGO021fZI8BFw4RDK02+Fw9PCyZx5RhHMdRrJKth+v4pA9B13Bf6DpOPsU4udI1vgNe9Lv44osH+inlFMWKflre8/hGum2ORlzsCcBsHz2OSBMRETlVypOGjthnfST98ncREZFhK1W51DnnnMO///u/J9z24x//mJKSEhYsWMBpp51GYWEh1dXVTJo0CYBwOMyOHTv46le/CkSXGnW5XFRXV3PBBRcA0NDQQG1tbbxNWVkZgUCAXbt2MWXKFAA++OADAoFAvDA4VBkTLfQdjUDANvhcSrJERERSISnLe8rQElve06PlPY8r3xUdkRawDQeChpIsxUpERCSTuTqKfrY+8kVEROQkcnJyKC0tTbgtKyuL/Pz8+O2XX345VVVVjBs3juLiYqqqqsjKymLevHkA+Hw+5s+fz/r168nPzycvL4/169dTWlpKeXk5AOPHj2fWrFmsWbOGG2+8EYDHHnuMOXPmUFJSMohn3D8+y+FoxBCIAD1PPhQREZEkU9FvGNBMv5OzTHRvv8Nhw4dtFiVZkVQfkoiIiCSRq2MxH33ii4iIyEBYsGABwWCQtWvX0tLSwpQpU6isrCQnJyfe5vrrr8flcvHAAw8QDAaZMWMGd9xxB5ZlxdssX76cJ554gpUrVwIwd+5cFi9ePOjn0x85VjS/ivZDaWSViIhIKqjol+EcB9o6erNU9DuxIrfD4TDUtlkE8yN4rZM/RkRERNKTlvcUERGRU3H33XcnfG+MYdGiRSxatOi4j/F6vVRUVFBRUXHcNnl5eSxfvnygDnNQZXf0o7Q70dUUtHWKDCW2A/Uhg982hIIRwk7n6h8iIplERb8MF3YgjGb69UaOBSPc0Bw2/KXdYmqOnepDEhERkSRxdfzfxuA40X1oRERERKT/PCa6mkIEQ5sd3eNPZCiwHXi50c2BYGyEf4Qcy2Jytq3Cn4hkHM1lynCx/fzcRqNXTsYYmOyL/vvDVv1qiIiIZLKueZGG+YiIiIicOmM6Z/u12eqEkqHj3RYXB4IWLhyKvTZZVnQZ2r+0WdrjW0Qyjmb6ZbjYfn45qmH1ymQfvNvsUBeyOBqGfP2GiIjIEFdVVcW2bdv4+OOP8Xq9lJWVce2111JSUhJv4zgOGzZs4JVXXsHv9zN16lQWL17MhAkT4m1CoRDr169n69at8T1mlixZwqhRo+Jt/H4/Tz75JG+99RYA5557LhUVFeTm5sbb1NfXs3btWt5//328Xi8XXngh1113HW730PpQ7brclK2lfUREREQGRI7l0GJHZ/qJDAWftBv+2BLtGL2gIMLkHIcjlpcX9ofw24ZPgobTs1T5E5HMoVJQhosX/bSkQq/4XFDsjX7Qf9imoImIyNC3Y8cOvvCFL7By5Uq+853vYNs2K1asoK2tLd5m06ZNvPDCC1RUVLBq1SoKCwtZsWIFra2t8Tbr1q1j27Zt3Hbbbdx77720tbVx3333YdudPTYPPfQQe/fupbKyksrKSvbu3cvDDz8cv9+2bVatWkV7ezv33nsvt912G2+++SZPP/304ATjJBqa/dguD7bbi3F747eHXF5s94m/jNuTwiMXERERSQ+xmX6tmuknQ0DEgS3NbsAwNSfCpOzo3zZjsizOyIr++3DYEFKRWkQyiIp+GS62vGe26le9dmZHArC71cLRQB8RERniKisrufjii5kwYQITJ05k6dKl1NfXs3v3biA6y+/FF19k4cKFnH/++ZSWlnLrrbfS3t7Oli1bAAgEArz66qtcd911lJeXM2nSJJYtW0ZtbS3V1dUA7Nu3j+3bt3PzzTdTVlZGWVkZN910E++88w779+8H4N1332Xfvn0sW7aMSZMmUV5eznXXXccrr7xCIBBITYC6CNk21Y0Rqhsj/LEpgiH6Qf9eUyR++/G+IqjjSkRERORkcqxoftVmoz4VSbnaNotW2+CzHD6dH0m4b4Qbci0HMBwOK9cXkcwxtNZZkgHXGtHynn1Vmm3jOergtw0HQyY+809ERCQdxIpreXl5ANTV1dHY2MjMmTPjbTweD9OnT2fnzp1cdtll7N69m0gkQnl5ebzNyJEjKS0tpaamhlmzZlFTU4PP52Pq1KnxNmVlZfh8Pnbu3ElJSQk1NTWUlpYycuTIeJuZM2cSCoXYvXs3M2bM6Ha8oVCIUCgU/94YQ05OTvzfAyX+XMZgOnqgDBD7lD/ZK5kBPp5Ui51LJp1TfykWnRSLRIpHJ8Wik2IhcmJZFoBDBEPIAa9+VSSFalqjHaJTcyK4e7gWR3tsWtpdHA4ZxnqchC0ARETSlYp+GS420y/HBXbkxG0lym3gjCybXW0uPmy1KPYqcCIikh4cx+Gpp57irLPOorS0FIDGxkYACgoKEtoWFBRQX18fb+N2u+OFwq5tYo9vbGzs9hy9aZOXl4fb7Y63OVZVVRUbN26Mfz9p0iRWr17NmDFjenXOfVHX2ExeXuf+g1agFduBnFwfPveJR0h5vBHGFo4e8GNKteLi4lQfwpChWHRSLBIpHp0Ui06KhUjPLAPZBtqc6Gw/rwahS4o0huFgyMLgMDWn5/U7R7jAaxyCjqEhbBjl0cB/EUl/KvpluNga6tkWBFS76rUzc6JFv7+0W5znRPBopI+IiKSBxx9/nNraWu69995u9x07I8HpxXpLvW3T9bl7mvlwbJuuFi5cyJVXXtnt8YcOHSIcDp/09XvLGIOV7cPvb+my1pQFGPz+APZJlkIPuVx88sknA3Y8qWaMobi4mAMHDvTq55zJFItOikUixaOTYtFpOMfC7XYnZVCOZJ5sy6EtYmi1DSMYXr8nMnTUBKIJ/oQsB99xcn1jYLTHYX/QcChkGOl20ERuEUl3KvpluK4z/QKhE7eVTmM9Dvkuh6MRQ22bxZnHGREkIiIyVDzxxBO8/fbb3HPPPYwaNSp+e2FhIRCdhVdUVBS/vbm5OT4rr7CwkHA4jN/vT5jt19zczLRp0+Jtmpqaur3usc+za9euhPv9fj+RSKTHWYIQXWrU4/H0eF9SOlMdh64lPwAbTtod5STreFLMcZyMPK/+UCw6KRaJFI9OikUnxULk+HJc0BiBNrvrYuoigyfswIdt0Wy/LOfEsyCK3A6fBKOz/drs6PUrIpLONMk+w7XZ2tOvP4yBydnRpGB3m4InIiJDl+M4PP7447z55pv867/+K2PHjk24f+zYsRQWFlJdXR2/LRwOs2PHjnhBb/LkybhcroQ2DQ0N1NbWUlZWBkT37wsEAglFvQ8++IBAIBB/nrKyMmpra2loaIi3qa6uxuPxMHny5IE/+VMUG8SrrigRERGRgZNtRbOrVo2flhT5uN0Qcgx5lsM474mzfZeB/I5CX1NE0/xEJP1ppl8GszvWTwfI1iiVPpucbfNuCxwIGlptFU5FRGRoevzxx9myZQvf/OY3ycnJie+d5/P58Hq9GGO4/PLLqaqqYty4cRQXF1NVVUVWVhbz5s2Lt50/fz7r168nPz+fvLw81q9fT2lpKeXl5QCMHz+eWbNmsWbNGm688UYAHnvsMebMmUNJSQkAM2fOZPz48TzyyCNce+21+P1+1q9fz6WXXorP5xv84JyE6Rh8rokaIiIiIgMn1n8SdKJ9U5bqKDLI9rVHL8LSbLtXy3UWuByaI4bmiKFYQwJFJM2p6JfB2m1wiPZmZSvD6rN8N4xy2xwOW9S2WUzzaYiaiIgMPZs3bwbg7rvvTrh96dKlXHzxxQAsWLCAYDDI2rVraWlpYcqUKVRWVpKTkxNvf/311+NyuXjggQcIBoPMmDGDO+64A8vqHPWyfPlynnjiCVauXAnA3LlzWbx4cfx+y7L41re+xdq1a7nrrrvwer3MmzePr33ta0k6+1MTy470CS8iIiIycNwG3DiEiS6XeLz91ESSwXbg446i3/is3mX6+W4Hgg5ttqHdhiwN/BeRNKaiXwZr7VjaM9vSqKr+OiPb5rDf4i8q+omIyBD1s5/97KRtjDEsWrSIRYsWHbeN1+uloqKCioqK47bJy8tj+fLlJ3yt0aNHc+edd570mIaC2N/yGssrIiIiMrCyLfDb0W1nfC5lWzJ4DocMbY7BYxzGenp37bkN5FrQYkNzxDDG0jUrIulL4xYyWGzt9Bx9UPXbxOxoEA+GDK0n3vdXRERE0kznnn4aHSUiIiIykGL7+rVp/LQMsn3BaHf36V67T5MgCtzRa7Y5rL8NRCS9qeiXwWIz/bQXXf/luWC028bB8Jd2BVJERCSTxPb30J5+IiIiIgMru6MLpc1WAUUG17726DU3PqtvSf6IjhmpLTaE9feBiKQxVTEymGb6DYwzOmb7/aVNvy4iIiKZJPbJrgHoIiIiIgNLM/0kFVoi0BC2MDiU9HI/vxivFbtuDUcjKlaLSPpSFSODtXXZ00/674wuS3wGtMSniIhIxjAdu/lpeJSIiIjIwIr2RTmEMZo1JYPm445VukZ7nH71h+Z1FKtb1P8nImlM5aAMFtuDTjP9es8yBtvtTfjyZXkZ7QEw/CXk7XZ/7Mu4Pak+fBEREekDLe8pIiIikhyWAW9HrtWq2X4ySD4JRi+60739u+jyOpb49Gumn4ikMXeqD0CSJ76nnyvFB5JGbAzVjeFut3uMASx2HHVoD/ecOJQXulRFFxERSSOxP+XVDyUiIiIy8LItCEaiK1HluzTKSpLLceBgKNozd5q3f9dbrgvAIegYgnZ0yU8RkXSjt64MFugo+vk00++UFXTZzDeknkEREZGMEEuElSmJiIiIDDzt6yeDqamjwOzCYbSnfxm+y4Cv448EzfYTkXSlol8Giy2foOU9T53XihVPDU360BcREckIWt5TREREJHk6i37qR5HkOxiMdnOP8Tq4TuGSy+0y8F9EJB2p6JehQjaEnY7lPfVTHhAF7uiHfmNYyaqIiEgmiH2iq+YnIiIiMvCyO/qj2mwNspLkO9Cxn1+x59SqdV339dN1KyLpSOWgDBXo+HzzGAePfsoDIrbEZ8COrustIiIi6S2WIuljXURERGTgZRkwODgYgiqeSBINxH5+MblW9LoNObpuRSQ9qRyUoVptzfIbaJ1LfKIlPkVERDKAlvcUERERSR5jIKvLbD+RZBmI/fxiLO3rJyJpTiWhDBXo+FDyaT+/AVXYscRnk5b4FBERSXta3lNEREQkuXK0r58MgoHazy9G+/qJSDpT0S9DtXZ8KOW41I01kKJLfDpa4lNERCQDxPoDbNQJJSIiIpIMWR1plmb6STIdHKD9/GJyO4rVAc30E5E0pKJfhtLynsnhsaJre4OW+BQREUl3sTRJQ6REREREkkMz/STZHAfqOvbzG3uK+/nF+FwADkHHEFLBWkTSjEpCGSoQif5fy3sOvAIt8SkiIpIRtKefiIiISHJld/Q8tjsQUc4lSdBiQ8A2mAHYzy/GZTqv3YCKfiKSZlT0y1DxmX6uFB9IBtISnyIiIpnBdMzxU/+TiIiISHK4DbhwAENTKNVHI5noUMd+fiPdDu4BHJ8fW+KzRSt9iUiaUdEvQwU6in6a6Tfwui7x2ajZfiIiImkrlghrDI+IiIhIcpguM6Yaw6k9FslMdaFo39xALe0Z4+uYSBHQ0rQikmZU9MtQrR29Vzkq+iVFYccSnw1hoyXBRERE0pSW9xQRERFJvuyOvqlGzfSTJIgV/cZ4BnYoX2ymX6sNtv5eEJE0oqJfBgrZEHY6lvfUTzgpCt0OBod2x8QLrCIiIpJeYmN29Te8iIiISPLEZ/qp6CcDLGR3rsI1doD284vxGHAbBwejff1EJK2oJJSBYh9EHuPg0U84KVwmtrcfHNESnyIiImlJRT8RERGR5NNMP0mWQyGDgyHXcuLLcQ4UYzq39wloXz8RSSMqCWWgVluz/AbDyI4RRI1ho2n+IiIiacjq+Ntdn+MiIiIiyROb6RewoV0zpmQAHYrv55ecC8vXMeC/Rfv6iUgaUVkoA8VGn/i0n19S5VrgNQ42hiaN+BEREUk7muknIiIiknwuE12NCjqXYhQZCHWhaNf2mAFe2jMmtq9fIKJ9wEUkfbhTfQC9sWPHDn7xi1+wZ88eGhoauP322znvvPPi9zuOw4YNG3jllVfw+/1MnTqVxYsXM2HChHibUCjE+vXr2bp1K8FgkBkzZrBkyRJGjRoVb+P3+3nyySd56623ADj33HOpqKggNzd38E52AMT2mMtx6dMomYyBIrfDwZDhSEhJq4iISLpR0U9ERERkcGRbEIpAQ9hwmlfZl5w624H6UHL284vJscDgEMHQrstWRNJEWsz0a29vZ+LEiVRUVPR4/6ZNm3jhhReoqKhg1apVFBYWsmLFClpbW+Nt1q1bx7Zt27jtttu49957aWtr47777sO2O6d/P/TQQ+zdu5fKykoqKyvZu3cvDz/8cNLPb6Bpec/BU+R2AIcW22htehERkTQTW97TwWjkroiIiEgS5XTMmDqimX4yQBrDhpBj8BiHQndyknljwNfRv9qiVb5EJE2kRVlo9uzZXH311Zx//vnd7nMchxdffJGFCxdy/vnnU1payq233kp7eztbtmwBIBAI8Oqrr3LddddRXl7OpEmTWLZsGbW1tVRXVwOwb98+tm/fzs0330xZWRllZWXcdNNNvPPOO+zfv39Qz/dUBSLR/2t5z+TzWlDQsVHwzpbUHouIiIj0Tdc/25U1iYiIiCRPdkcfVYNWSpIBUtdxLY32OPHBfMmQ27GSWkD7UYpImkiLot+J1NXV0djYyMyZM+O3eTwepk+fzs6dOwHYvXs3kUiE8vLyeJuRI0dSWlpKTU0NADU1Nfh8PqZOnRpvU1ZWhs/niz9PuojP9HOl+ECGiVGe6Kf+7gAElQCIiIikDRX9RERERAZHbDWqhrDBVuIlA+BQfGnP5HbGxSZVaKafiKSLtNjT70QaGxsBKCgoSLi9oKCA+vr6eBu3201eXl63NrHHNzY2dnuOY9v0JBQKEQp1rutojCEnJyf+757Ebj/e/acqVvTzubq8FokdW33V38f26nGxOBiD6bK2ViqOtz+PzbMg2zi0OYYP21xMz+1/spHsayPdKB6JFI9OikUixaOTYiF9oaKfiIiIyODwGnAbCDuG5ggUpn2PpKRaXTBaSR6TpP38YnwdkyqCjqEtAr6kvpqIyKnLmI/YYzv3nF5szNLbNifqOKyqqmLjxo3x7ydNmsTq1asZM2bMSZ+7uLj4pG36ynEcWusOA3DGaWMYmR39ZKprbCYvv39T/4wJkpefd/KGp/i4vLzcU37NVD22xB1md3OIXUEP888sOuXO5mRcG+lM8UikeHRSLBIpHp0UC+kNY8Dg4NAx4ly1YhEREelBVVUV27Zt4+OPP8br9VJWVsa1115LSUlJvI3jOGzYsIFXXnkFv9/P1KlTWbx4MRMmTIi3CYVCrF+/nq1btxIMBpkxYwZLlixh1KhR8TZ+v58nn3ySt956C4Bzzz2XiooKcnM7+0zSkTFQ5IFDQTgSsih0a6kk6b+WCLTYBoOT9KKf20CWcWh3DIeCcIYnqS8nInLK0r7oV1hYCERn6hUVFcVvb25ujs/cKywsJBwO4/f7E2b7NTc3M23atHibpqambs/f9Xl6snDhQq688sr497Fiz6FDhwiHwz0+xhhDcXExBw4c6FXhsS+CNoTs6KdPy5E62js6r2yXB//RSL+e0yny4D/qT97jjCEvLxe/vwU64tHf10zVY30OeIyLhnabt/9ykNOz+vdzTea1kY4Uj0SKRyfFIpHi0elEsXC73b0alCPDiyE6y294/+aIiIjIiezYsYMvfOELnHnmmUQiEX7605+yYsUKfvjDH5KdnQ3Apk2beOGFF1i6dCnjxo3j+eefZ8WKFTz44IPxFaHWrVvH22+/zW233UZ+fj5PP/009913H6tXr8ayorOWHnroIQ4fPkxlZSUAa9as4eGHH+bOO+9MzckPoFjRryGskVZyaupC0d+XIreDZxA2r8p1ObSHDXUq+olIGkj7Pf3Gjh1LYWEh1dXV8dvC4TA7duyIF/QmT56My+VKaNPQ0EBtbS1lZWVAdP++QCDArl274m0++OADAoFA/Hl64vF48Pl88a9YIgfRUV7H+zrZ/f398nfUGbOMg4su90G/v0jy42KFProcZ39fM1WPtQyc2TG//88t1in9DJN1baTrl+KheCgWisdAxUKkJ7EuJ10hIiIicjyVlZVcfPHFTJgwgYkTJ7J06VLq6+vZvXs3EM09X3zxRRYuXMj5559PaWkpt956K+3t7WzZsgWAQCDAq6++ynXXXUd5eTmTJk1i2bJl1NbWxvur9u3bx/bt27n55pspKyujrKyMm266iXfeeYf9+/en7PwHSlFHseSIin5yig4Fo9dQsmf5xeRasdcdlJcTETklaVH0a2trY+/evezduxeAuro69u7dS319PcYYLr/88vhSC7W1tTz66KNkZWUxb948AHw+H/Pnz2f9+vX88Y9/ZM+ePTz88MOUlpZSXl4OwPjx45k1axZr1qyhpqaGmpoa1qxZw5w5cxKWaxjqWjpWR8h1qetqsJV1rLSxL2g42vMkTxERERlirNiqCEqdREREpJcCgQBAfDWpuro6GhsbmTlzZryNx+Nh+vTp7Ny5E4Ddu3cTiUTi/VAAI0eOpLS0lJqaGgBqamrw+XxMnTo13qasrAyfzxd/nnQWK/o1hAwakyenoi4UTeLHegfnQvJ19LMeCUJE166IDHFpsbznhx9+yD333BP//umnnwbgoosu4tZbb2XBggUEg0HWrl1LS0sLU6ZMobKyMmHW3fXXX4/L5eKBBx6Ir5t+xx13xJdPAFi+fDlPPPEEK1euBGDu3LksXrx4kM5yYLREoh96uf3bvk9OwQgPlHht9gctdra6ODe/f8upioiIyODRTD8RERHpC8dxeOqppzjrrLMoLS0FolvOAN22hykoKKC+vj7exu12J2w7E2sTe3xjY2OPW8x0bXOsUChEKBSKf2+MifeHxbagGSjx5zMG04+qXZEnup9ym2Nocwy+tJiKMLhiMR7on10mCdmdS8Se5nX6HKv+XMdZBtzGIewYjoStQSs2pitdx8mnGCdXusc3LYp+Z599Nj/72c+Oe78xhkWLFrFo0aLjtvF6vVRUVFBRUXHcNnl5eSxfvvyUjjXV4kU/Sx8+qXCWL8L+oMWuVotZeRHc6fm+ICIiMmyo6CciIiJ98fjjj1NbW8u9997b7b5jOwd7s8R8b9scr+OxqqqKjRs3xr+fNGkSq1evTtpe1nWNzeTl5fbrsTlZEUZlR6hvi0D+KMYVeAf46DJHcXFxqg9hyNp7NIhT18wIj8WU8eP69Rz9uY4LIu0cbrdpzR7BuNN8/Xrd4UbXcfIpxsmVrvFNi6Kf9J6W90ytEq9DnsvBHzHsarU4y2en+pBERETkBIwBHLTElIiIiJzUE088wdtvv80999zDqFGj4rcXFhYC0Zl6RUVF8dubm5vjM/cKCwsJh8P4/f6E2X7Nzc1MmzYt3qapqanb63Z9nmMtXLiQK6+8Mv59rDh46NAhwuGB3XvEGIOV7cPvb+lX8hRyucjHph6LXXUN+ALqMzmWMYbi4mIOHDigfcmP489+C3Ax0hXmk08+6fPj+3sdex0DWHx42M8ZdvffU+mk6zj5FOPkSmZ83W530gbmxF8jqc8ugy4208+n5T1TwjIw3Rdh21E3O1pclOXY8b2CREREkmXHjh384he/YM+ePTQ0NHD77bdz3nnnxe93HIcNGzbwyiuv4Pf7mTp1KosXL2bChAnxNqFQiPXr17N169b4UuhLlixJ6NDy+/08+eSTvPXWWwCce+65VFRUkJvbOUq2vr6etWvX8v777+P1ernwwgu57rrrcLuHZtoZW1VKXU4iIiJyPI7j8MQTT7Bt2zbuvvtuxo4dm3D/2LFjKSwspLq6mkmTJgEQDofZsWMHX/3qVwGYPHkyLpeL6upqLrjgAgAaGhqora2NtykrKyMQCLBr1y6mTJkCwAcffEAgEIgXBo/l8XjweDzHPe6kcJx+rZLgAEVumz1YHA4ZdVQfR0Ozn4jl7nOMXTg44dDJG6a5g8GO/fw89qldQ328jn0dq6rVBQ227ZCmq/4NKsdx9HueZIpxcqVrfIdm74v0W6zol6flPVNmSo7Nu34Hv234S5vFpBx1I4qISHK1t7czceJELrnkEn7wgx90u3/Tpk288MILLF26lHHjxvH888+zYsUKHnzwwfieL+vWrePtt9/mtttuIz8/n6effpr77ruP1atXx/dAfuihhzh8+DCVlZUArFmzhocffpg777wTANu2WbVqFSNGjODee+/l6NGjPProowAnXGI9lTqX9+yY8iciIiJyjMcff5wtW7bwzW9+k5ycnPj+ej6fD6/XizGGyy+/nKqqKsaNG0dxcTFVVVVkZWUxb968eNv58+ezfv168vPzycvLY/369ZSWllJeXg7A+PHjmTVrFmvWrOHGG28E4LHHHmPOnDmUlJSk5NwH2mhPNN86HFLF5HhCtk11Y6TPmWl5oYtM3ybRdqC+49oZ4xnc3D3HAhfQ7hiaI1CgXnURGaL09pRBbAcCWt4z5dwGPuWLsL3FzXsBi4nZtkb/iIhIUs2ePZvZs2f3eJ/jOLz44ossXLiQ888/H4Bbb72VG2+8kS1btnDZZZcRCAR49dVXWbZsWbzTadmyZdxyyy1UV1cza9Ys9u3bx/bt21m5ciVTp04F4KabbuI73/kO+/fvp6SkhHfffZd9+/bx4x//mJEjRwJw3XXX8aMf/Yirr74an2/o7X0R+4xOw8F7IiIiMkg2b94MwN13351w+9KlS7n44osBWLBgAcFgkLVr19LS0sKUKVOorKyMD7ACuP7663G5XDzwwAPxlRXuuOOO+AArgOXLl/PEE0+wcuVKAObOncvixYuTe4KDaKTbARxabENrBHK0UpX0QWPYEHIMbuNQ5B7cBN4yMMoLdUGoC1kUuDXIX0SGJhX9MkibHR2lbnDIzvShPUPcNJ/NewGHhrDF/qDh9Cz1JIqISGrU1dXR2NjIzJkz47d5PB6mT5/Ozp07ueyyy9i9ezeRSCRe8AMYOXIkpaWl1NTUMGvWLGpqavD5fPGCH0SXoPL5fOzcuZOSkhJqamooLS2NF/wAZs6cSSgUYvfu3cyYMaPb8YVCIUKhzmWIjDHxzjEzgKNm4s9lDKZLhS+WMjl0zvrr8fEDfDypFjuXTDqn/lIsOikWiRSPTopFJ8ViePrZz3520jbGGBYtWsSiRYuO28br9VJRUXHCFRDy8vJYvnx5v44zHXgtKHBBUwQOhw3jNWhd+qCuyyy/VGynM6aj6HcoaJiac/L2IiKpoKJfBonv52ehfeRSLMuCshybHQEXf/C7KPGGNdtPRERSIrb8VEFBQcLtBQUF1NfXx9u43W7y8vK6tYk9vrGxsdtz9KZNXl4ebrc73uZYVVVVbNy4Mf79pEmTWL16dVI2tq5rbCYvLzfhNk+4HSI23qws8nKPnxp7vBHGFo4e8GNKteLi4lQfwpChWHRSLBIpHp0Ui06KhUj/jfbYNEVc1IcsxmdFUn04kkYOpWhpz5gx3thxWICuXREZmlT0yyB+O/rBp6U9h4YZuRE+aLU4ErbYq739REQkxY6dkdCbzah726brc/c08+HYNl0tXLiQK6+8stvjDx06RDgcPunr95YxBivbh9/fkrCWZyRsAIu29nb8dttxHx9yufjkk08G7HhSzRhDcXExBw4cSMuNyQeSYtFJsUikeHRSLDoN51i43e6kDMqR4WeUx+HDts692UR6qy4YXadjrCc1fWyxol9TxNBmo5XWRGRIUtEvgwQ6Bpio6Dc0ZFvRwt8f/G7+0OKiNNvGpXxWREQGWWFhIRCdhVdUVBS/vbm5OT4rr7CwkHA4jN/vT5jt19zczLRp0+Jtmpqauj3/sc+za9euhPv9fj+RSKTHWYIQXWrU4/H0eF9SOlMdh67PGqtF2g6c6NWcZB1PijmOk5Hn1R+KRSfFIpHi0Umx6KRYiPTf6I5ZWodDBsdBKyNJrwQi0GJHtzVK1Uy/LBcUuGyaIhZ1QUNptj4HRGTo0XiEDBJb3jNXP9WUsIzBdnsTvqblu8mxwB8x7Gz3drvfdnsx7p47OkVERAbC2LFjKSwspLq6On5bOBxmx44d8YLe5MmTcblcCW0aGhqora2lrKwMiO7fFwgEEop6H3zwAYFAIP48ZWVl1NbW0tDQEG9TXV2Nx+Nh8uTJST3P/oqlTZqPLyIiIjI4itwOFg7tjsGvFRKll+pC0cy90O3gSWHfZ7E3Wug7EFQHrIgMTZrpl0FatLxnStkYqhu7L0M20m34OGjxTpODP2jjPSYnKC90qfouIiKnpK2tjQMHDsS/r6urY+/eveTl5TF69Gguv/xyqqqqGDduHMXFxVRVVZGVlcW8efMA8Pl8zJ8/n/Xr15Ofn09eXh7r16+ntLSU8vJyAMaPH8+sWbNYs2YNN954IwCPPfYYc+bMoaSkBICZM2cyfvx4HnnkEa699lr8fj/r16/n0ksvxefzDXJUeic2sFzZk4iIiMjgcJlo4e9w2FAftsh3a/iVnNyBYDRzPy1Fs/xiTvPa7Gx1cUDL04rIEKWiXwZp0fKeQ9JIt0ND2CFgR4t/E7NsLV0hIiID6sMPP+See+6Jf//0008DcNFFF3HrrbeyYMECgsEga9eupaWlhSlTplBZWUlOTk78Mddffz0ul4sHHniAYDDIjBkzuOOOO7CszqEpy5cv54knnmDlypUAzJ07l8WLF8fvtyyLb33rW6xdu5a77roLr9fLvHnz+NrXvpbsEPRb7DNZK7SJiIiIDJ7RHofD4egSn5OyU300kg5iM+uKvaktEsdm+jWGLe3rJyJDkop+GUTLew5NxsD4LJsPWi2ORgyNYUNRikcliYhIZjn77LP52c9+dtz7jTEsWrSIRYsWHbeN1+uloqKCioqK47bJy8tj+fLlJzyW0aNHc+edd578oIcIzfQTERERGXyjPA60Qr1mS0kvBCLQHInu5xcruqVKtgWFbpvGsMXBoOEM7esnIkOMykMZIuxAu6PlPYeqbKtz+YGPg4Z2rVwhIiIyJGhPPxEREZHBN9oTzb4OhwwRdWPJScRm+Y10O922zUmFWB+f9vUTkaFI70wZItCxtKfHOHg0SGpIGuNx8FkONobadgtbSa2IiEjKaXlPERERkcFX4IIs4xDBcFiz/eQkPunYzy/Vs/xiYkuMHgzq2hWRoUdFvwxxNNI5y0/7xQ1NxkBplo0Lh1bbsF+JgYiISMp1Lu+pz2URERGRwWIMnNZRwDmoop+cgOMMnf38YmLXbmPEonVoHJKISJyKfhmiKRxNkEa4UnwgckJeK1r4A4cjYYsjSmxFRERSKvZJrL/VRURERAbXaR0FnDotkSgn4I9Aix3dz2/sEJnpl21BkTs220/Xr4gMLXpXyhDNHTP9CtxD48NPji/fnbi/X117ig9IRERkGLO0vKeIiIhISozt6BupCxltgSLHFZvlN8YztLY0ii01ur99CB2UiAgq+mWM5vhMP2VJ6WCsx6HAZeNgeP1IdNSSiIiIDD5DNHdSBiUiIiIyuIrcDh7jEHIMDWEVTqRnQ20/v5jTO2aqfhy0NIBQRIYUFf0yRGym3wjN9EsLxsCELIdsy6Hdht80uglpXTEREZFB17mnn4iIiIgMJst0zvY7GFTRT7qzHdjfMdPv9CGyn1/MaV4HNw6ttorWIjK0qOiXAUI2BGzN9Es3loGJWTbZFjSELbY0uzUySEREZJDFkuGh1YUgIiIiMjzE9vU7GFIXpXRXFzIEHUOWcRjlGVqdZi7TOfvwYxWtRWQI0SdqBojN8ss2Dln6iaYVrwUXjQILh4/aLd7x6wcoIiIymIz29BMRERFJmdNi+/oFjfIx6ebj9o5Zfll2fC/uoaQkq2OJz3b154nI0KF3pAwQ389PS3umpTFe+D8jopv6vdfiYnt9W4qPSEREZPjQ8p4iIiIiqTPK4+A2Du2O4YiWSJRj7IsX/YZmtn56R9HvUMgQ1NIhIjJEqOiXAZoiWtoz3Z2ZY1OeGy38vfSRn33tSnRFREQGg4p+IiIiIqljGRgXWyJRfSHSxdFwtM/T4Ay5/fxi8l1Q4HJwMHyiJT5FZIhQ0S+NGLcH2+3t9tVkuwDI97p6vN92ezFGHzxD3czcCGdm2zjAaw0uDihZEBERSbrYMkG2qn4iIiIiKTG+o6CzL6huSukUux7Gehy8Q/jSiC3xuU9LfIrIEOFO9QFI70UwVDdGut1+sN0CDEfaI1R3vxuA8iJPcg9OTpkx8H8KIuDN4sPmEK82uvlcYZixXvVCioiIJItm+omIiIik1ulZNhyF+pCh1YYc1U6ExP38hrIJWTZ/Crj4qN0i4kRwaQy/iKSYPkbTnOMQXzM6Sz/NtOcysHDSCMZ5bcKO4ZVGt5a3EBERSSIV/URERERSy+eCkW4bMOzXbCkB2mziy2VOGOJFv7EehxzLIehoiU8RGRr0SZrmQg7YGMAhS58rackypnMpVpeHI81HuXiURXEWhBzDq40edrYfZ9lWt2ZwioiInIrY8p4OBkeVPxEREZGUGJ8VTcT2DdOBz44DIRuaw+APOcM+L61ts3AwFLltCob4OnWWgTM6CpN729TVLiKpN8TfNuVk2juSAK+JLg8p6cfGUN0YBqKzDfLyXfiPRhjtgja3oTFs8WYj7DxqU5Ll4O7ycy4vdKlyLyIicgq6pk/OMd+LiIiIyOA4PcumusXF/qCF7UTiA7My3ZGQ4cM2i71tFq127KRDuLDwuWCU2ybfNfz6/PZ0FM8mZQ/tWX4xZ2Tb/LlVS3yKyNCgol+aa+9ICLS0Z+axDEzwOmQZm4MhQ2PEoqXVocRrM2IYJnwiIiLJEN0Z2cHBEHQgW5+vIiIiIoNutNsh2zi0OYaDQcO4rMye6tYagf/nd7G3zZVwu6sjL41gOBqBoxEX2cZhXFa0+DcctETgYCialKdL0W+sx8FnOQRsw/52w4TszL5+RWRoU9EvzbV2fPblWPowyUTGwGlehzyXw0ftFkHH8Jd2F3lWNOETERGRU2MM5FgQsCEQMWQrpxIREREZdMbA+GybXa0udrdZjMuKpPqQkmZ3q8WbR12EHIPBYUKWw5k5EYo9Dl6XgWwfbxxoozFiOBwytDmGPW0uRrltxnmdjJ8FGV0i03CaxyY3TQqdxkRn+/0p4GJvu8WE7My9fkVk6NP8sDTXEol+0ueqgyqj5bqgLMdmrMfG4OC3DR+0WmxrjG5uLCIiIv3nc0XzqIA+U0VERERS5syOWV1/abcIZ2A3l+3AW0ddbGl2E3IMo9w2l48Mc3FhmAlZDp6OXlqXMfhcMM7r8CmfzSh3NC6Hwxa72ixCGZ6zptvSnjETO463ts0imF6HLiIZRkW/NBZyIOgYwMGXJiNfpP8sA8Veh2k5NgUuGzDUtMCmeg9/acvwYV4iIiJJFBs8FbD1eSoiIiKSKmM90ZWOwo6hti2zuiwjDrzW6GZHINqBN8MX4W9GhhnlOXF102Xg9CyHiVkRXDi02YZdbVbGDgA/EjIcCVsYHErTrOg32u1Q6LaJYNjVmlnXr4ikF70DpbFAx0zxbIM2iB1GvBacke0wOTtCoRvaHcPrTR62NrkIZeBIOBERkWTzdWTEbXa0Q0ZEREREBp8xcGbHsogfZlDRL+zAbxrd7AtauHD4bEGYOfmRPi3TOcINU3JsvMYh5Bg+bLXi/YKZ5E+B6M/9jCyb7DS7BIyBaTnRQuXOVheO/q4QkRRJs7dP6Sq2tGdsSSoZXvJc8Ddjo6PDDA4ftrn4nyNujmZg0iciIpJMHgs8xgGMlvgUERERSaHJHUWTT4KGlgzo34gV/PYHLdw4zC8K93vZyiwrWvjzWQ4RDLvbMqvw12p3Lu35KV96JuWTs208xuFoxPBJUDM0RCQ1VPRLY7ElqHL1Uxy2XAbm5Ef4fFGYbMuhIWzxwmEP+9uVWIiIiPSFL7bEZ0SfoSIiIiKpku+C0zzRLU0+TPMlEkMOvNro5pOghdtEC37jvKc2cN9tonvd+SwHO8MKfzUBCxvDaI/NmFOMU6p4rGjhD6Kz/USSqaHZj+3yYLu9ffoybk+qD12SzJ3qA5D+sZ3oCBjQTD+B07wOV44M8Vqjm/qwxSuNbubmRfiUz8ao71JEROSkcl3QFIkNqlJuJSIiIpIqU3JsDoYs/tzqYnqujTsN+zVCdrTgdzAULfhdWhjmtAEqZLk6Cn972yxa7Gjhr7+zB4eKiNNZJEvXWX4x03wRdra62NduaA5Hl2YVSYaQbVPdGOnzX6/lhS7NBMtw+vmmqVYbHAxu4+BNw+RHBp7PBV8YGebM7AgOhrf8brY0uwir31JEROSkOmf6of03RERERFJoUrZNnuXQZhs+SMPZfiEbXuko+HmMw2UDWPCLiRX+cjtm/O1ps6hrH9CXGFQftlq02Qaf5XBGVnoX/QrdcLrXxsGw3a/ZfiIy+NLvk1OALvv5WWgm1zBmGZM4Pdvj5TMjXXy6AAywp83Ffzd4aUZTuUVERE4k2wJDdH+UNhX9RERERFLGMjAjN7pm5fstLiJplJvFCn51HQW/zxWFk7ZUpWU6C6Q2hlcPw8E03EcuaMP2lmhx7OzcCFb6nUI3s/MigMPedheHQxlwQiKSVlT0S1P+jqJfrpb2HNZsDNWNkYSvPzZFaA9HmJQdwYVDQwh+edDht/WJ7SIo6RAREYmxDOR1DMT9S5ulmfIiIiIiKXRmjk2O5RCw02dvv2MLfpcVhRnjSW5SaRmY2FH4CzsdMwzTrPD3XouLNtswwuUwLSe9Z/nFjPQ48SVX39FsPxEZZOnxqSkJ2m3wd+w3U6CinxxHngumdiTJEQy17S5q2406MUVERI7jdK+NxzgEnejeKOk0qlxEREQkk7i6zPZ7t8VFcIjXgkI2vHxMwW90kgt+MbHC37gsCDuGVxrcHEiTwp8/AjsC0e7puXnhjJjlFzMrN4KFwydBi4/bM+jERGTIU9EvDR0JRz8o8lzg1U9QTsBrwZRsm7EeG3BoDFvUBCwawkb7FYmIiBzDa8HkbBs30T1k9Me5iIiISOpMzbHJdzm02oa3jg7d2VJtNvy60c2hkIV3kAt+MZaBi0dBidcmTLTw98kQz2UdB37f7MbGUOyxGZ+VWR1V+W6Y5otWq99odtM2xAvXIpI5VDJKM44DDR1Fv5FufVrIyRkDxV6HM7NtsoxDGMNH7RYvHSJtRn6JiIgMliwLzsjuGCwTsTgaSfURiYiIiAxPbgMXjAgDDrvaXOwfgkWs5jD89xEP9R0Fv8+loOAX4zJwSWGY0702EQyvNLqH9NKo7wUs9gctXDicNyKCGXo/3lM2Oy9CQUfh+n+b3RqAL6fEcSDkQEsEjoYhEHaIOOi6km7cqT4A6ZvmSHSqvguHEUN3kJMMQbkdy30eChnqQob6kGFzg4cxHpupOTZnZNl4hm4uKCIiMmhyXTDK7XA4bPi43aIsQ/YWEREREUk3p3kdzsqx+XOrizea3Vw+MoRviPSH7Ws3bGlyE3QMeS6HSwtDFKS4p9Vl4OLCML9rclPbbrG12U1jOMLsvMiQWjrzYNCwvWOvu/NGRCh0Z2bVwm1gXkGY/z7i5qN2iz+3WnzKp78t5ORsJ7raX10w2ofbFDEcDRvCdP1FDgEuLBy8BrIthxwX5FoOORYZWUiX3lHRL80cCUerMkUeZ0h9WEt6sEw0YR7pdohYLj5ocTgUsjgUsvg9DmM9DsVemyKPQ5HbwWeh60xERIalYq9DcyS6v59mxouIiIikzuz8CB8HLY5GDC83uvlCUZisFA5ajjjwrt/Fe4Fo0Wq02+aSwjA5Q6QY6TJwUUGYP3Qc4/sBF4dChnkFYfKGwDHWhwy/aXTjYJicHWFKdmYXwUZ5HGbnRXjb7+b/HXXjMWGmaFCh9CDswP726Apt+9ot2p2e/w41OLgMRByDA9gY2hxoixgaO1aqceGQ73LId0O+y8GtP2mHFRX90sjuAByNGMBhVIaOgJHB4bFgbiGU54T4sM1iV6uLoxHDgZDhQKgzczZER4bkuhxyLYdsK7rfUZYVHUES+7/Xcsgy0ftc+hAREZEM4DLRPVH+0u6iPmyx0w+fyk71UYmIiIgMPx4DnysM8T8NHhrDFq82uplfmJrC36Gg4X+PumjsGJQ/LSfCufmRIdcXYgzMyY9Q5Hb4/VEXdSGLXx72MDsvQlmOnbIB3nXB6LKjIccw2mNzfn5mLut5rOk+m5ZIpGPGarTyqsKfQHRP0I/bLWrbLfa3GyJdZvJ5TXSCxhivTaHbYYQrOkHDbcCyDK6cXLYdbCXoQNCGVtsQsA0tEYhgaIwXAR1yLSjoeA7JfCr6pYnmMGxrjP77NI+T0hFNkhksY8jO8nJ2FkwfEV0L+pN2qA9CQyh6zdkYAjYEbMOhXj6v20QLgT7LId/tkO+CEa7Y6JJocXA4JHQiIpL+Ctxwmm1zMGTx/5og27GYpD/ORURERAZdvhs+VxjmpQY3h0IWLxz28FeFg7d/XlMYqltc7GmzAEOWcTh/RJiJ2UO7A31Sjs1oj82W5mjcth11U9NqMycvwuleZ9D6Z2wHdgQstvtd2BhO89jMLwwPm21mjIFP50ewgZqOpWrrghE+PSKCR31kw87RMOwLWtS2WdSFDE6XQl+u5VCabTMhy2ZsL1b6cxnINpBtwQgcwMF2IGBHJw8djRjabEOLDS1Bw36gvg5KsyzOyLLJV3UoI+nH2oOXXnqJX/ziFzQ2NjJ+/HhuuOEGPvWpT6XseCIOvN7kJuxEf/HHpmhDYMksNobqxnC32/MtyM8CxxudVh5yIOhAyDFEnOj1mO+xaLeh3Y6OJAna0TYQ3XMy7EQLhfXdnx6vic4ezOqYORj/v4n+P9flkNsxcmWojZQTEZHeGWq51KkY63EIOzaHwxa/a3azLxhhbl5kyOwlIyIiIpkpk/KpgVLkcbisKMzrTW78EcP/HHEzzWdzti85uZntwCdBwwetLj5q7+yYPzM7wtz8CNlpUrDKd8MXisLUtEaLbtHZkhaFLpuzfDZnZNtJm1zgdMRwe4uL+o6VpcZ7bf6qMDzslhs0Bs7vuG6qWyx2tbk4ELI4JzfCmdmpm32ZLA3NfmyXh772YrtwcMKhpBxTqoQdOBA07G+34ksVd1XktpmQ5VCaZVPkPvVivGUgzwV5LodxOARtaIoYmsLRyR31IUN9yM07fihw2YzzOozz2pzmdfCmyfuanJiKfsd44403WLduHUuWLGHatGm8/PLLfO973+OBBx5g9OjRKTmmj9sNDWGLLAtKs2zNkpJBYUx0CQ0P4APo8jFdXmRR3ZD4Aew4EIF4YTDkQLttCHb5f8gxBB1DMAJETnYhRwt/RUcb8URc5FgO2R3LiGZZ0dmuLgMW4DIOFtEPNUPHV8d9sX+70P6EIiKDYSjmUqfCGCjxOozJgj+3OOxpc7G3zWKk22GkJ/q55LGc6GemAY+J7pfgtaKz37M6Rl0ORv4WiET/gPNHDEUeh9EeRyOHRURE0lCm5VMDaZTH4cqRIf632c1f2i3+FHBRE7A4I9umNCvaed3f2WOOA0cjUB+y2B807A9atNmdydT4LJuZuRFGpeFgfMvAWT6bidk277W4+KDVojFi8fujFtuOOozzOhR7bYq9DoVu55QGYTsONIYN+4KGvW0WDR1LoXqMw7n50T38hmvfpjEwKy9CsddmS0fx+n+b3VT7HSZ2XMOjejG7a7BEHDoG/Rvau/3bRL93THwygNOxx5wDOPVthCJOvH8u1m/nMg4uoktUukz0b6bo/6Pfzy50pXXBIuRAc9hwOGQ4HDbUhwyN4cTZfIbopJ4JWTYTsm3ykzyg1GvBGMthjMchZEO+10Vtq83BoKEpYtHUCn9udWE6thQb6XEY6XYocjsU6m/KtJTOv0NJ8atf/Yr58+dz6aWXAnDDDTfw7rvvsnnzZr7yla+k5JhKsx0uNSEcl4f6tpQcgshJGRN9Q0kcqeUk/N92YHK+i0Akmii0RYjPGGzr+D4QgZZIl6VFW8JEU4NT58bB3bH2daxTNtZJ6zadH799Sd9Nx39ixcbYbV0LkJaJjlSyTJckp0sbujw29g/TcSRdw2mMobmxnaY201Hs7Piio/DZ5ftjE8Ru53SSk+xtDJzj/Lvb98d5wuM9psfbnc5/GwNZbRGaw7EbuhR8Ox4TK/jCMfcd8/Pqjf7Eo88P7lvTBMZAW9im3Y7+gZWs1znuY5Nwji6DlrJOU0MxlzpVxsB5RYZJPvh/jdHC2uGw4XAPM9p74jEOBS6HXBfkWNGOKANYRP8Ijv47ccCK1eV2y0R/cyKOiQ+wseODbQxN4WjnVIud+M4W+6NtrNeJb97u6vj8cxtD8GiQI0EDTvf3yI5/9nC7c5zbe3gfPuY9+GR6eu936Hxf6+n7znMl/jkYi+UJn7zLTY6BlpBNayTxeLt+TvfmePtjqP39bICw7RBxevd5kunSKR7JPjwDhGyHcBrEItnSKRYutLVCusrEfGogeS34q4IwnwQN77a4OBSy2N3mYnebC3AocEGh28bXkXu5u/wtbhHNoUI20UHJHf0RRyOG5ogh5CT+0niNw5k5NlNyojNw0l22BefmRyjPjfBBq8XujqLcx0HDx8HoH2CG6L5feS7ItqKrNeW4olu5dA6y7sxPQ05sL7FosaMhnBhHt3GYkm1zdm6EXK2WAUCx12HB6BA1AYv3Ay5abMP7ARfvB1y4iBZdClwOeS6HHFf0OvR0GVgY+5uhp76QCLGB+Qa74+8Gm+jfEA7RWWdBx8R/B0JO4u9D14Je5JSzVdNDknLi5/xTALzGQ1bH6mDRQZTRwf/e+N8yiX17btP5+21i8enhbytDzzlTT7fF/t6yneh1HothpCN+7R2FznbbEIiA344Ovmyzez4/n+VwepbN6R3F9VTNqPNYUJYHZ2WHabejMxA/CVp80jEDsT587Mpt0UkZPld0X0CfyyHHir4feKzOa9NlOv92jb/fdvkZHE+3+8xxbk84omP/cfKf63H7LI/3HAaOBiMEbdKy6KmiXxfhcJjdu3fzpS99KeH28vJydu7c2eNjQqEQoVDnjCdjDDk5Objdxw+t6ci6PR4PTi//SpjoAcfl6pZ89JbH7aIgq3/JSX8f2+vHGUOO28LK9sT/akrF8abqsQmP6yEWyXjNVD52VI6bhubop0e2K/p1rNiswbBjMF4vgbYgYQfy3Ca+OW3QjiYtdkfSEktkoLNTsDciHV8D7kSfLKfiQADIGsAnTGMtzUB2qo9i6DjaRCbFoyTLYU5e3387T/QZe6LPZhkYg5VL9YcxBstlMaKXn7HHcrnd+ANhzsqL/qHc1vFHcaTjj2gbE/9civ1hHft/TFvHV8KNA8GCvCzIJ7qMi88d3XemreNX6LBDzwXKj1vQZ0qHDHsPPSXNjSgWXSgenRSLTmkSiy+MDA9oR5VyqcHR13xqsHKp2HOfSj7ldVsY4xmw4znDC2fkQUMYPmk3HAhZBDrynzagzaF3f/Bb4LZgpCfaQT3C3TlwqsgDVnyeUvKdSoz7El8PMCsLZgH+iENdyOJwyHAkFC0CAYQBP+B3Or7pzfG7YaQ7Gq3R3uhsptOzHDxm8GJ4MoMV45PxALO8cE4h1AXhk6BFXTC6ZQ7AUeBo9A+N5IuPegMv0a/8LndFizvRIpvXihZBvMaJ3+7tUlwnVvzxZLHnaAjHcaL9dhgcJ/q3VHwAI51bCUWcnk814TocqnV3C1xWdE/4AqLxGeGOzpqNfeVYHQ0H6PdgIK5jDzAlC6Z03N5qOxwJRQdANIej/2/v8kPp0/tqJvA3M83nZWrOwP4SDkY+pYyti+bmZmzbpqCgIOH2goICGhsbe3xMVVUVGzdujH9/4YUXctttt1FUVHTS1+vPkgxjR/b5IXGnnfyQBvyxfXtczoC8Zjo+tvvjcnpqNqCvma6PFRHpjeG+7FGqDHYu1R8X5p+8zfHo80tERESSra/51GDnUnBq+VQyjAHKUn0QA2wwYzwGmDR4LzdkDKXruBgoT/VBJMEU/VmedAN9HZcO7NNJigyNIRZDjOlh/YuebgNYuHAh69ati3/deOONCSOsetLa2sodd9xBa2vrgBxvulM8OikWiRSPRIpHJ8UikeLRSbEYGpKdS/WHro1EikcnxaKTYpFI8eikWHRSLGSw9DafGqxcCnT9DwbFOPkU4+RTjJNPMU6udI+vZvp1MWLECCzL6jZyqqmpqdsIqxiPx4PH07dp3Y7jsGfPnl4v7ZnpFI9OikUixSOR4tFJsUikeHRSLFJrsHKp/tC1kUjx6KRYdFIsEikenRSLToqFJFtf86nByqVA1/9gUIyTTzFOPsU4+RTj5Er3+GqmXxdut5vJkydTXV2dcHt1dTXTpk1L0VGJiIiIpAflUiIiIiKnRvmUiIiInArN9DvGlVdeycMPP8zkyZMpKyvj5Zdfpr6+nssuuyzVhyYiIiIy5CmXEhERETk1yqdERESkv1T0O8YFF1zA0aNH+fnPf05DQwMTJkzgW9/6FmPGjBmw1/B4PHz5y18etOUXhjrFo5NikUjxSKR4dFIsEikenRSL1BuMXKo/dG0kUjw6KRadFItEikcnxaKTYiGDQfnU8KUYJ59inHyKcfIpxsmV7vE1TrouTCoiIiIiIiIiIiIiIiIigPb0ExEREREREREREREREUl7KvqJiIiIiIiIiIiIiIiIpDkV/URERERERERERERERETSnIp+IiIiIiIiIiIiIiIiImnOneoDGI5eeuklfvGLX9DY2Mj48eO54YYb+NSnPpXqw+q3qqoqtm3bxscff4zX66WsrIxrr72WkpKSeBvHcdiwYQOvvPIKfr+fqVOnsnjxYiZMmBBvEwqFWL9+PVu3biUYDDJjxgyWLFnCqFGj4m38fj9PPvkkb731FgDnnnsuFRUV5ObmDt4J90FVVRXPPvssl19+OTfccAMw/GJx5MgRfvKTn7B9+3aCwSDjxo3jlltuYfLkycDwikckEmHDhg387ne/o7GxkaKiIi6++GL+7u/+DsuKjsHI1Hjs2LGDX/ziF+zZs4eGhgZuv/12zjvvvPj9g3ne9fX1rF27lvfffx+v18uFF17Iddddh9s9eB+JJ4pHOBzmpz/9KX/4wx+oq6vD5/Nxzjnn8JWvfIWRI0fGn2O4xONYjz32GC+//DLXX389V1xxRfz2TIqH9F5fc6odO3bw1FNPsW/fPoqKivjiF7/I5z//+YQ2v//973nuuec4ePAgp512Gtdcc81xr8ehpC+xePPNN9m8eTN79+4lHA4zfvx4rrrqKmbNmhVv89prr/GjH/2o22N/8pOf4PV6k3UaA6IvsXj//fe55557ut3+wAMPcPrpp8e/T9frAvoWj0cffZTXX3+92+3jx4/nhz/8IZC+10ZfPmu6PiYT3zP6GotMfs/oayyGw3uGDF/Jep+UTsl6z5FOvemj7Imu5d7pT3x1Hffd5s2b2bx5M4cOHQKiufiXv/xlZs+efdzH6Bruvb7GNx2vYfVgDbI33niDdevWsWTJEqZNm8bLL7/M9773PR544AFGjx6d6sPrlx07dvCFL3yBM888k0gkwk9/+lNWrFjBD3/4Q7KzswHYtGkTL7zwAkuXLmXcuHE8//zzrFixggcffJCcnBwA1q1bx9tvv81tt91Gfn4+Tz/9NPfddx+rV6+OF0QeeughDh8+TGVlJQBr1qzh4Ycf5s4770zNyZ/Arl27ePnllznjjDMSbh9OsfD7/dx1112cffbZfPvb32bEiBEcPHgQn88XbzOc4rFp0yZ+/etfc+uttzJ+/Hh2797Nj370I3w+H5dffnm8TSbGo729nYkTJ3LJJZfwgx/8oNv9g3Xetm2zatUqRowYwb333svRo0d59NFHAaioqBiMUAAnjkcwGGTPnj38/d//PRMnTsTv9/PUU09x//33c99998XbDZd4dLVt2zY++OADioqKut2XSfGQ3ulrTlVXV8eqVau49NJLWbZsGTt37mTt2rWMGDGCz3zmMwDU1NTw4IMP8g//8A+cd955bNu2jQceeIB7772XqVOnDvYp9lpfY/GnP/2J8vJyrrnmGnJzc/nNb37D6tWr+d73vsekSZPi7XJycvi///f/Jjx2KHfeQ/9z7QcffDAhPxkxYkT83+l6XUDf4/H//X//H1/96lfj30ciEf7lX/4l/jsSk47XRm8/a2Iy+T2jr7HI5PeMvsYiJlPfM2R4S8b7pCRKxnuOJOpNH+WxdC33Xn/iG6PruPdGjhzJV77yFYqLiwF4/fXXuf/++7n//vsTBsfH6Brum77GNyatrmFHBtW3vvUt57HHHku47Rvf+Ibzn//5nyk6ooHX1NTkXHXVVc7777/vOI7j2Lbt3HjjjU5VVVW8TTAYdK6//npn8+bNjuM4TktLi3P11Vc7W7dujbc5fPiws2jRIucPf/iD4ziO89FHHzlXXXWVU1NTE2+zc+dO56qrrnI+/vjj5J9YH7S2tjrLly933n33Xee73/2u8+STTzqOM/xi8ZOf/MS56667jnv/cIvHqlWrnB/96EcJt33/+993HnroIcdxhk88rrrqKufNN9+Mfz+Y5/3OO+84ixYtcg4fPhxvs2XLFucrX/mK09LSkozTPalj49GTDz74wLnqqqucQ4cOOY4zPONx+PBh56abbnJqa2udpUuXOr/61a/i92VyPOT4+ppTrV+/3vnGN76RcNuaNWucb3/72/Hvf/jDHzorV65MaLNixQrngQceGJiDTpKByC//6Z/+ydmwYUP8+9/85jfO9ddfP1CHOGj6Gov33nvPueqqqxy/33/c50zX68JxTv3aePPNN51FixY5dXV18dvS9droqjefvZn8ntFVb2LRk0x5z+iqN7HI9PcMkZiBep+U4xuo9xw5sWP7KHuia7n/ehNfXccD44YbbnBeeeWVHu/TNXzqThTfdLyGtaffIAqHw+zevZuZM2cm3F5eXs7OnTtTdFQDLxAIAJCXlwdERxs0NjYmnLfH42H69Onx8969ezeRSITy8vJ4m5EjR1JaWkpNTQ0QHTHp8/kSRkeWlZXh8/mGXPzWrl3L7NmzE84Hhl8s3nrrLSZPnswPf/hDlixZwje/+U1efvnl+P3DLR5nnXUW7733Hvv37wdg79697Ny5Mz59fLjFI2Ywz7umpobS0tKEZTJnzpxJKBRi9+7dST3PUxEIBDDGxEcUDbd42LbNww8/zBe/+MUeR10Nt3hI/3KqDz74oNvn8qxZs9i9ezfhcBiIXgPHtpk5c2b8OhqKBiK/tG2b1tbWeO4W09bWxtKlS7n55pu577772LNnz4AddzKcSiy++c1v8vWvf517772X9957L+G+dLwuYGCujVdffZVzzjmHMWPGJNyebtdGf2Tqe8ZAyJT3jFORie8ZIn3Vm/dJGRgnes+REzu2j7Inupb7rzfxjdF13D+2bbN161ba29spKyvrsY2u4f7rTXxj0uka1vKeg6i5uRnbtikoKEi4vaCggMbGxtQc1ABzHIennnqKs846i9LSUoD4ufV03vX19fE2bre724dE19g0NjZ2e45j2wwFW7duZc+ePaxatarbfcMtFnV1dfz617/miiuuYOHChezatYsnn3wSj8fDRRddNOzisWDBAgKBAP/0T/+EZVnYts3VV1/NvHnzgOF3fcQM5nn31CYvLw+32z0kYwPR5T6feeYZLrzwwnjRb7jFY9OmTbhcLv7mb/6mx/uHWzykfzlVTz/fgoICIpEIR48epaioiMbGRgoLCxPaFBYWDumf/0Dkl7/61a9ob2/n//yf/xO/raSkhKVLl1JaWkpraysvvvgid911F9///vcZN27cQJ7CgOlPLIqKivj617/O5MmTCYfD/Pa3v+Xf/u3f+O53v8v06dMB0vK6gFO/NhoaGti+fTvLly9PuD0dr43+yNT3jIGQKe8Z/ZHJ7xkifdWb90k5Nb15z5Hj66mPsie6lvunt/HVddw/tbW1VFZWEgqFyM7O5vbbb2f8+PE9ttU13Hd9iW86XsMq+qWAMaZXt6Wjxx9/nNraWu69995u9x17jo7jnPT5ettmqMSvvr6edevWUVlZecL9K4ZDLCA6WuLMM8/kK1/5CgCTJk3io48+YvPmzVx00UXxdsMlHm+88Qa/+93vWL58ORMmTGDv3r2sW7eOoqIiLr744ni74RKPYw3WefcUg6Eam3A4zIMPPojjOCxZsuSk7TMxHrt37+bFF19k9erVfT6mTIyHJOprTnW895kTPSZdfv79zS+3bNnChg0b+Jd/+ZeEPxTLysoSRjpOmzaNO+64g//+7/8e8ntc9iUWJSUllJSUxL8vKyujvr6eX/7ylyf8Ay5drgvo/7Xx2muvkZuby3nnnZdwezpfG32Vye8Z/ZWJ7xl9MRzeM0T6oj/vk9J7/X3PkagT9VEeS9dy3/U2vrqO+6ekpITvf//7tLS08Oabb/Loo49yzz33HLcwpWu4b/oS33S8hrW85yAaMWIElmV1G+HX1NTU4+yDdPPEE0/w9ttv893vfpdRo0bFb4+Ncjz2vJubm+PnXVhYSDgcxu/3d2sTe3xhYSFNTU3dXrfr86Ta7t27aWpq4s477+Tqq6/m6quvZseOHfz3f/83V199dfw4h0MsIDoS4tg3y/Hjx8dnbw2nawPgJz/5CQsWLODCCy+ktLSUv/qrv+KKK67gv/7rv4DhF4+YwTzvnkZZ+/1+IpHIkItNOBzmgQce4NChQ3znO99J2Cx4OMXjT3/6E83NzSxdujT+vnro0CGefvppbr31VmB4xUOi+pNT9fTzbW5uxuVyxWeJ9tRmqOdpp5JfvvHGG/zHf/wH//RP/9RtOZhjWZbFmWeeyYEDB071kJNmoHLtsrKyhPNMx+sCTi0ejuPwm9/8hs9+9rO43SceJ5oO10Z/ZOp7xqnItPeMgZIp7xkifdWb90kZeMe+50jPjtdH2RNdy33Xl/j2RNfxybndboqLi+OTKSZOnMiLL77YY1tdw33Xl/j2ZKhfwyr6DSK3283kyZOprq5OuL26uppp06al6KhOneM4PP7447z55pv867/+K2PHjk24f+zYsRQWFiacdzgcZseOHfHznjx5Mi6XK6FNQ0MDtbW18RGjZWVlBAIBdu3aFW/zwQcfEAgEhkz8zjnnHP793/+d+++/P/515plnMm/ePO6//35OO+20YRMLiI7wje1fF7N///74vjDD6doAaG9vx7IS33Yty4qPvhlu8YgZzPMuKyujtraWhoaGeJvq6mo8Hg+TJ09O6nn2Razgd+DAAe666y7y8/MT7h9O8firv/orvv/97ye8rxYVFfHFL36RyspKYHjFQ6L6k1NNnTq1W/t3332XyZMnx4saZWVl/PGPf+z2nCdb2z+V+ptfbtmyhUcffZTly5czZ86ck76O4zj85S9/6bZk3VAyULn2nj17Es4zHa8LOLV47NixgwMHDjB//vyTvk46XBv9kanvGf2Vie8ZAyVT3jNE+qo375My8I59z5FEJ+uj7Imu5d7rT3x7ouu47xzHIRQK9XifruFTd6L49mSoX8Mq+g2yK6+8kldeeYVXX32Vffv2sW7dOurr67nssstSfWj99vjjj/O73/2O2267jZycHBobG2lsbCQYDALRacSXX345VVVVbNu2jdraWh599FGysrLie5n5fD7mz5/P+vXr+eMf/8iePXt4+OGHKS0tjY8iHT9+PLNmzWLNmjXU1NRQU1PDmjVrmDNnTsIU21TKycmhtLQ04SsrK4v8/HxKS0uHVSwArrjiCj744AOef/55Dhw4wJYtW3jllVf4whe+AAyvawNg7ty5PP/887zzzjvU1dWxbds2fvWrX/HpT38ayOx4tLW1sXfvXvbu3QtE93vcu3cv9fX1g3reM2fOZPz48TzyyCPs2bOHP/7xj6xfv55LL700YSZdKuMRiUT44Q9/yO7du1m2bBm2bcffV2MbMA+neMTeP7t+ud1uCgsL4+eRafGQ3jlZTvXMM8/wyCOPxNt//vOfp76+nqeeeop9+/bx6quv8uqrr/K3f/u38TaXX3457777Lv/1X//Fxx9/zH/913/xxz/+kSuuuGLQz68v+hqLWOf9ddddR1lZWfw9JhAIxNts2LCB7du3c/DgQfbu3cuPf/xj9u7dy+c///lBP7++6GssXnjhBbZt28Ynn3zCRx99xDPPPMObb77JX//1X8fbpOt1AX2PR8yrr77K1KlTe9yfJV2vjRN91sDwes/oaywy+T2jr7HI9PcMGd6S8T4piZLxniOJTtZHCbqWT0V/4qvruO+eeeYZ/vSnP1FXV0dtbS3PPvss77//Pp/97Gfj9+sa7r++xjcdr2Hj9GbDGxlQL730Er/4xS9oaGhgwoQJXH/99UN2/dfeWLRoUY+3L126NL5PmeM4bNiwgZdffpmWlhamTJnC4sWLEzoSgsEgP/nJT9iyZQvBYJAZM2awZMkSRo8eHW/j9/vjU8ghWkRZvHgxubm5yTvBU3T33XczceJEbrjhBmD4xeLtt9/mmWee4cCBA4wdO5YrrriCz33uc/H7h1M8Wltbee6559i2bRtNTU2MHDmSCy+8kC9/+cvxkTeZGo/333+fe+65p9vtF110Ebfeeuugnnd9fT1r167lvffew+v1Mm/ePL72ta/h8XiSGIFEJ4rHVVddxT/+4z/2+Ljvfve7nH322cDwiUdsCc+ubr31Vi6//PKEzrNMiof03olyqkcffZRDhw5x9913x9vv2LGDp556io8++oiioiIWLFjQrUP697//PT/96U85ePAgxcXFXH311Zx//vmDeVr90pdY3H333ezYsaPbc3T9nVu3bh3btm2jsbERn8/HpEmTuOqqq9JipkpfYrFp0yZefvlljhw5gtfrZcKECXzpS1/qNpMpXa8L6PvvSSAQ4Otf/zo33HBDQs4Wk67Xxsk+a4bTe0ZfY5HJ7xl9jcVweM+Q4StZ75PSKVnvOdKpN32Uupb7rz/x1XXcdz/+8Y957733aGhowOfzccYZZ7BgwYL4oGZdw6emr/FNx2tYRT8RERERERERERERERGRNKflPUVERERERERERERERETSnIp+IiIiIiIiIiIiIiIiImlORT8RERERERERERERERGRNKein4iIiIiIiIiIiIiIiEiaU9FPREREREREREREREREJM2p6CciIiIiIiIiIiIiIiKS5lT0ExEREREREREREREREUlz7lQfgIjIQNmzZw/r1q1j7969tLa2cv311zNu3Dh27drFokWL+vx8P/vZz9i4cSM/+9nP4rfdfffdCf/vrbq6Ov7xH/+RpUuXcvHFF/f5WERERESGmsHIvXpry5YtNDU1ccUVV/T5sSKZZMeOHfziF79gz549NDQ0cPvtt3Peeef16Tkcx+GXv/wlr7zyCocOHaKgoIDLLruMv/u7v0vSUYuIZI433niDjRs3cvDgQUKhEOeeey5vvfVWv/IbEZH+UNFPRDLGj3/8Y9rb2/nGN75Bbm4uY8eO5ec//zkvvfRSvzqeerJkyZJ+Pa6oqIgVK1ZQXFw8IMchIiIikmqDkXv11pYtW/joo49U9JNhr729nYkTJ3LJJZfwgx/8oF/P8eSTT1JdXc3XvvY1SktLCQQCNDc3D/CRiohknubmZh5++GFmzZrFkiVLcLvdbNu2LdWHJSLDjIp+IpIxPvroIy699FJmz56dtNcYP358vx7n8XgoKysb4KMRERERSZ3ByL1EpG9mz559wt/JcDjMT3/6U373u98RCASYMGECX/3qVzn77LMB2LdvH7/+9a/5wQ9+QElJyWAdtohIRti/fz+RSITPfvazTJ8+HYDt27en5Fja29vJyspKyWuLSGqp6CciKdXc3Myzzz7L9u3baWpqIicnh5KSEq666irKy8txHIdf/OIXvPTSSzQ1NTF+/HiuvvpqNm3aBESX2Xzttdf40Y9+BMDmzZvZvHkzABdddBGvv/46QMJo80ceeYSxY8f263i7Lu8ZDoe5+eabmTlzJsuWLUto19LSwte//nU+//nPc/311/e4vGdsCasf/OAH/PznP+cPf/gDXq+X2bNnc8MNN+Dz+RKe7+mnn2bbtm2Ew2GmT59ORUUFy5Yt48tf/vKgj6YXERGR9JRuuZdt2/zyl7/kN7/5DXV1dfh8PmbNmsU111zDqFGj4se0Y8eObq+rZbREuvvRj37EoUOH+MY3vkFRURHbtm3je9/7Hv/+7//OuHHjePvttxk7dixvv/02K1euBOCcc87h2muvJS8vL8VHLyIydD366KPxPOjBBx/kwQcfZPr06fHiX1e9yW9iXn31Vf77v/+b/fv34/V6mT59Otdcc03CoPRHH32U3//+96xcuZKnn36ampoaJkyYwMqVK9mzZw/PPfccu3btIhAIkJ+fz8SJE/n617/e7bVEJDOo6CciKfXwww+zZ88err76akpKSmhpaWHPnj34/X4ANmzYwMaNG5k/fz6f+cxnqK+vZ82aNdi2HR95OmfOHFasWMF3vvMdPvOZz3DllVcCMGLECNrb2/n973/PihUr4q9ZVFQ0IMfudrv57Gc/y69//WsWL16cUKTbunUroVCISy655KTP84Mf/IALLriA+fPnU1tby7PPPgvA0qVLgWgyuHr1aj788EOuuuoqJk+eTE1NDd/73vcG5DxERERk+Ei33Gvt2rW8/PLL/PVf/zVz5szh0KFDPPfcc7z//vusXr2aESNGsGTJEtasWcPBgwe5/fbbTyE6IpntwIEDbN26lR//+MeMHDkSgC9+8Yu8++67/OY3v+ErX/kKBw8epL6+nt///vf84z/+I7Zt89RTT/GDH/yA7373uyk+AxGRoevv//7vmTJlCo8//jjXXHMNZ599Nj6fjzfeeKNb297kNwBVVVU8++yzXHjhhVxzzTX4/X42bNjAd77zHVatWsW4cePizxkOh1m9ejWXXXYZX/rSl4hEIrS1tbFixQrGjh3L4sWLKSgooLGxkffff5/W1tZBi42IDC4V/UQkpXbu3Mn8+fP53Oc+F7/t05/+NBCd3bZp0ybOO+88br755vj9EyZM4K677op3PI0YMSKeEBUUFCQso1lQUACQtKU1L7nkEl544QXeeOONhHN47bXXmDx5MqWlpSd9jvnz5/PFL34RgPLycg4cOMBvfvMbbrnlFowxbN++nT//+c8sWbKEz3/+8/F2brebZ555JinnJSIiIpkpnXKvjz/+mJdffpnPf/7zVFRUxG+fNGkS3/72t3nhhRfiI91zc3O1nLrISezZswfHcbjtttsSbg+Hw/FZfI7jEAqFuPXWW+O/8zfffDN33nkn+/fv15KfIiLHUVxczOHDhwEYN27ccXOS3uY3LS0t/PznP2f27NkJ79vTp0/ntttuY8OGDSxfvjx+eyQS4ctf/nLC4PPdu3dz9OhRbr755ni+B3DBBRcM2HmLyNCjop+IpNSUKVN4/fXXyc/P55xzzmHy5Mm43dG3ppqaGkKhEPPmzUt4zLRp0xgzZkwqDreb0tJSJk+ezGuvvRbvPNu3bx+7du1i8eLFvXqOc889N+H7M844g1AoRFNTE4WFhfHlqo5Nyi688EIV/URERKRP0in3ev/99wHiS6PHTJkyhdNPP5333ntv0I9JJJ05joNlWaxevRrLshLuy87OBqIzc10uV0JxL7aEXH19vYp+IiKnqLf5TU1NDcFgsFu70aNHM2PGDP74xz92e+7zzz8/4fvi4mJyc3P5z//8TxoaGpg+fXrCsqAikplU9BORlPrGN77B888/z6uvvspzzz1HdnY25513Htdeey1Hjx4FoLCwsNvjerotVS655BIef/xxPv74Y04//XRee+01PB4PF154Ya8ef+zeGB6PB4BgMAiA3+/H5XJ1axcbSS8iIiLSW+mUe8WOp6flQUeOHMmhQ4cG+5BE0trEiROxbZumpiY+9alP9dhm2rRpRCIRDhw4QHFxMQD79+8Hoh3NIiJyanqb35yoXVFRUXxp9pisrKyEbWcAfD4f99xzD88//zzPPvssLS0tFBUVcemll/J3f/d38YFfIpJZ9JstIik1YsQIbrjhBm644Qbq6+t56623+M///E+ampq4/PLLAWhsbOz2uMbGxiEz2+/CCy/k6aef5rXXXuOaa67ht7/9LZ/+9KcHbKP7vLw8IpEIfr8/4Tl7iouIiIjIiaRT7pWfnw9AQ0MDo0aNSrjvyJEj8ftFpFNbWxsHDhyIf19XV8fevXvJy8ujpKSEefPm8cgjj3DdddcxadIkmpubee+99ygtLWXOnDmcc845TJo0iR//+MfccMMNOI7D448/Tnl5uWb5iYgMgN7mN13bHauhoaHXeVBpaSnf+MY3cByHv/zlL7z22mts3LgRr9fLl770pVM4ExEZqqyTNxERGRyjR4/mr//6rykvL2fPnj1MnToVj8fDli1bEtrt3Lmz1yO7j501lwx5eXl8+tOf5re//S1vv/02jY2NCWuon6rp06cDdNv8uafNoEVERER6a6jnXjNmzADgd7/7XcLtu3bt4uOPP+acc86J3+Z2u5Oa74mkiw8//JBvfvObfPOb3wTg6aef5pvf/CbPPfccAEuXLuWiiy7i6aef5rbbbuP+++9n165d8Vl8lmVxxx13kJ+fz3e/+11WrVrF6aefzje+8Y1UnZKISEbpbX5TVlaG1+vt1u7w4cO899578efpLWMMEydO5IYbbiA3N5fdu3efwlmIyFCmmX4ikjKBQIB77rmHCy+8kNNPP52cnBx27drF9u3bOf/888nLy+Nv//Zvef755/mP//gPPvOZz3D48GE2bNjQ6yWmSktLAfiv//ovZs+ejWVZnHHGGQO+hMEll1zCG2+8wRNPPMGoUaMSOqFO1axZs5g2bRpPP/00gUCAyZMnU1NTw29/+1uAbvtxiIiIiPQk3XKvkpISPve5z/E///M/GGOYPXs2hw4d4rnnnmPUqFFcccUVCa+7bds2Nm/ezOTJkzHGcOaZZ/b5NUXS3dlnn83Pfvaz497vdrtZtGgRixYtOm6bkSNHcvvttyfj8EREhr3e5je5ubn8/d//Pc8++yyPPPIIF154IUePHmXjxo14PB6uuuqqk77W22+/zebNm/n0pz/N2LFjAXjzzTdpaWmhvLw8qecpIqmjop+I/P/s3Xl8lOW9///Xfc+SZDIhCTsIgSCCAkIAqxZpUaz1e6zVUlu1WqWN+qXCAW2//o626BGtfIVuWq2eLx4rCD1upVJta6sWV7Qt7lFRkE1ExBDIwmSb5b5+f0xmSEhCFmZJJu/n48EjzD3Xvcw1WT5zf67PdaWNx+Nh7NixvPzyy5SXlxOJRBg4cCDnn38+559/PgAXXXQR2dnZPP3007z00kscc8wxXHXVVfzpT3/q1DlmzpzJhx9+yDPPPMMf/vAHjDH85je/iQc7iXLiiScyYMAA9u/fzze/+c2EJuJio21Xr17NE088QTgcZvz48SxcuJDFixe3mrNdREREpC29Mfa68sorGTJkCM899xxPP/00Pp+PkpISLrnkkhbTWp1zzjns3r2bhx9+mLq6OowxR0x8iIiIiKRLZ+ObOXPmkJ+fz1//+ldeffVVvF4vEyZM4JJLLmHYsGEdnmfYsGH4fD6eeOIJKisrcbvdDB8+nPnz53P66acn8RWKSDpZxhiT7osQEemqJUuWtPjaF23YsIG77rqLn/70p4wfPz7dlyMiIiIZTLGXiIiIiIhIz6dKPxGRXmDDhg0cOHCAoqIibNtmy5Yt/OlPf+KEE05Qwk9ERERERERERERElPQTkb7HcRw6KnJ2uVwpuprOycnJ4dVXX+Xxxx+nsbGRgoICZs2axcUXX5zuSxMRERE5ot4Ye4mIiIiIiPRGmt5TRPqce+65hxdffPGIbbQGjIiIiEhiKPYSERERERFJDSX9RKTPKS8v5+DBg0dsc+yxx6boakREREQym2IvERERERGR1FDST0RERERERERERERERKSXs9N9ASIiIiIiIiIiIiIiIiJydJT0ExEREREREREREREREenllPQTERERERERERERERER6eWU9BMRERERERERERERERHp5ZT0ExEREREREREREREREenllPQTERERERERERERERER6eWU9BMRERERERERERERERHp5ZT0ExEREREREREREREREenllPQTERERERERERERERER6eWU9BMRERERERERERERERHp5ZT0ExEREREREREREREREenllPQTERERERERERERERER6eWU9BMRERERERERERERERHp5ZT0E5FeaevWrVxwwQUMGzYM27YpKChI6/UsWbIEy7J44YUX0nodIiIikrmSHf+MHj2a0aNHJ/SYIiIiItI9q1atwrIsVq1aldTztBUDpurcIpJ47nRfgIhIVzmOw5w5c9iyZQuXXXYZI0aMIDs7GyAepOzcuTN9FygiIiKSYIp/Ms8LL7zAGWecwc0338ySJUvSfTkiIiIiHVLcKdLzKeknIr3O9u3bee+997jqqqu477770n05IiIiIkmn+EdEREREkmH9+vXpvgQRSSAl/USk19mzZw8Aw4cPT/OViIiIiKSG4h8RERERSYZjjz023ZcgIgmkNf1EJCm2bt3KlVdeybHHHkt2djaFhYWccMIJzJs3j/3797doe/DgQX70ox/Fp6k6/vjj+eUvf8n27duxLIvvfe978baWZTFr1iwAbrnlFizLwrIsTj/9dCzL4uOPP+bjjz+Obz98/856/vnn+d//+38zYcIE+vXrR05ODhMnTuTmm2+mvr7+iPs++OCDTJ06lZycHAYPHkxpaSl79+5ts21siq7hw4fj9XoZPnw4l112GVu2bGnRbt68eViWxZNPPtnmcTZs2IBlWXz7299usb2uro7bb7+dkpIScnNz8fv9fPGLX+Thhx/uQm+IiIhIZ/T2+Cemurqaf//3f+eYY44hOzubCRMmcNddd2GMadV21apVXHDBBYwZM4acnBz69evHaaedxurVq4+6jwAefvhhzjjjDAoLC8nOzuaEE07gtttuo7GxsVXbWJ98/vnnlJaWMmTIEHJzc5kxYwYvv/wyAIFAgB/96EcUFRWRlZXFxIkTWbt2bbt90Z3zV1RU8L//9/9m2LBh8XP89re/bdH2e9/7HmeccQbQ8j3VGtEiIiI9y6mnnorL5Wp3Ostf/OIXWJbFL3/5y/i22Bp51dXVXHPNNYwaNQqPx3NU03n/+c9/ZsaMGeTm5lJYWMi3vvUtPvroo1btYvFhW9pbp68z6zq/8MILSYk7RSTxVOknIgm3Z88eTj75ZA4ePMg555zDt771LRoaGtixYwe/+93vWLhwIQMGDACgsbGRM888k9dee40pU6Zw6aWXUlVVxW233caLL77Y6tg333wzO3fu5MEHH2TWrFmcfvrpQDRAOf3007nzzjsBuPbaa+P7lJSUdPk1LF++nA8//JAZM2bwta99jfr6el555RVuvfVWnn/+eZ577jnc7ta/Qu+44w6eeeYZLrroIv7X//pfbNiwgZUrV/LCCy/wr3/9i0GDBsXb/utf/+Kss84iEAhw/vnnc8IJJ/DBBx/wP//zPzzxxBM8++yznHLKKUD0xtB9993Hgw8+yHnnndfqvLEba3Pnzo1vq6qqYvbs2bz11ltMnz6d0tJSHMfh6aef5pJLLuH999/ntttu63LfiIiISGuZEP8ABINBvvKVr1BVVcXFF19MMBjkD3/4A9dccw2bN2/mnnvuadH+6quvZsKECXz5y19m2LBhVFRU8Je//IW5c+fy4Ycf8n//7//tVh8BXHHFFTzwwAOMHDmSCy64gPz8fP75z39y0003sX79ep555hk8Hk+L66mqquK0004jLy+P73znOxw4cIBHHnmEs88+m1dffZWrrrqK6upqvv71rxMKhXjkkUe48MILefXVVzn11FNbHOtozu/1euOvb+3atVx55ZXYts33v/99AL7xjW8AtHpPgQ5vuomIiEjqzJ8/n7lz5/Lf//3fLF26tMVzxhjuu+8+srKyWtyPgWi8N3v2bCorKzn77LPx+/3d/hv/+OOP89e//pU5c+Zw+umn8/bbb/OHP/yB559/nldffZXx48d39+V12ujRo7n55psTGneKSJIYEZEE+/Wvf20Ac8cdd7R6LhAImLq6uvjjpUuXGsB885vfNJFIJL59+/btprCw0ABm7ty5LY7x/PPPG8DcfPPNrY4/atQoM2rUqKN+Ddu2bTOO47Ta/uMf/9gA5uGHH26x/eabbzaA8Xg85s0332zx3LXXXmsAU1paGt8WiUTM+PHjDWAeeeSRFu0feughA5hx48a16JNx48YZr9drKioqWrSvr683BQUFZvDgwSYUCsW3z5071wDmF7/4Rav2Z599trEsq9W1ioiISPdkQvwzatQoA5jTTjvNNDQ0xLfv37/fjBkzxgDmxRdfbLHP1q1bWx2noaHBnH766cbtdptPPvkkvr0rfbRy5UoDmG9961umvr6+RdtY3HX4cQADmHnz5rXo19WrVxvA5Ofnm3PPPbfF8V555RUDmG984xstjnU057/iiitMOByOb3///feNy+Uyxx9/fIv2R3pPRUREpGdoaGgwAwcONEOHDm1xz8UYY9avX28Ac8kll7TYHoupzjzzTBMIBLp97lg8Apg//elPLZ678847DWBmz57dYvusWbNMe7f8Y8dbuXJlq+s9PJbsSlsR6Vk0vaeIJFxsGgGfz9fqudzcXHJycuKPV65ciW3b/OxnP8O2D/1KKi4uZtGiRcm/2HaMGTOmzekQ/s//+T8APPPMM23ud9lllzF16tQW25YsWUJ+fj4PPfRQfCqoV199lc2bN3Paaadx0UUXtWj/ne98hxkzZrBlyxY2bNgQ33755ZcTDAZ55JFHWrR/4oknqKqq4tJLL41XH+7fv5/f/e53fOELX4hfc0x2djbLly/HGMNDDz3Ume4QERGRDmRC/BNz++23k5WVFX/cv39/brrpJiB67c21tQZMVlYW//7v/044HOa5556Lb+9KH/3617/G4/Hw3//932RnZ7doe9NNNzFgwAD+53/+p9VxfD4fP//5z1v06yWXXILb7aa6uppf//rXLY43Y8YMiouLefvtt1sc52jOf8cdd+ByueLbJkyYwGmnncaHH37IwYMHW+0jIiIiPVdWVhbf//732bt3b6slV1asWAHAD37wgzb3/cUvfkFubu5RX8Ps2bM599xzW2z793//d4499liee+45Pv7446M+h4hkDk3vKSIJd9555/GTn/yEBQsW8Oyzz3LWWWdx2mmnMWHChBaJtIMHD7J161ZGjhzZ5g2j008/nVtuuSWVlx5XW1vLr3/9a9atW8eWLVs4ePBgi3VsPv300zb3i62301x+fj4lJSW8+OKLfPDBB5SUlPDWW28BxNdyOdxXvvIVXn31Vd58802+/OUvA9Gk33/+53/y4IMPsmDBgnjbtqb2fO2114hEIgBtzhkfCoUA+PDDD9vtAxEREem8TIh/ANxuNzNmzGi1PTb9ZCyGidm1axfLly9n/fr17Nq1q9Xax81jps72UV1dHe+88w4DBw6MTyF1uKysrDbjmHHjxpGXl9dim8vlYsiQIdTW1jJmzJhW+wwfPpx//etfSTs/wMiRI4Ho9J9tPS8iIiI91w9+8AN+8YtfsGLFCr75zW8CUF5ezh//+EcmTJjAl770pVb7ZGVlMWXKlIScv617TS6Xi5kzZ7Jt2zbeeustRo0alZBziUjvp6SfiCTcqFGj2LhxI0uWLOFvf/sba9euBaI3O/7jP/6Df//3fweguroagCFDhrR5nKFDh6bmgg8TCoWYPXs2GzduZNKkSVx00UUMGjQovmbLLbfcEq/YO1xHryX2mmNf23uNw4YNa9EOov13xhlnsH79ej744ANOOOEEPv/8c5555hlKSkpaBJP79+8Hosm/1157rd3XGggE2n1OREREOq+3xz8xAwcObFGlFnN4LAOwfft2Tj75ZCorK/nSl77EV7/6VfLz83G5XPE1CJvHTJ3to8rKSowx7Nu3r8sJ0Pz8/Da3u93uIz4XDofjj5N1fiA+KEtERER6jzFjxnD22Wfz9NNPs2PHDoqLi1m5ciXBYJB58+a1uc+QIUPanEGqOzp7r0lEBEDTe4pIUpxwwgk8+uij7N+/n9dff51ly5bhOA4LFy6MTwsVuyny+eeft3mMvXv3pux6m3viiSfYuHEjc+fO5d133+W+++5j6dKlLFmypN1gLqaj1xJ7zbGv7b3Gzz77rEW7mFg134MPPgjA//zP/xAOh1stGB3b74c//CHGmHb/Pf/880d8PSIiItJ5vTn+iamoqGgzMXV4LAPwq1/9iv379/Pb3/6WF154gbvuuouf/vSnLFmyhLPPPrvN43elj6ZOnXrEOKb5LAyJlO7zi4iISM8zf/58jDH893//d/xrTk4Ol112WZvtE5Xwg87fawLiU5w3H9AUU1VVlbBrEpGeS0k/EUkqt9vN9OnTuf7663n44YcBWLduHQB5eXmMHTuWTz/9lG3btrXa94UXXujy+Vwu11GPoN66dSsAF1xwQavnXnzxxSPu29bz1dXVvP3222RnZ3PCCScAxNf9a+81xrZPmzatxfYLLriAvLw8fve73+E4Dg8++CBut5tLLrmkRbuTTz4Z27Z5+eWXj3i9IiIikni9Mf6JCYfDvPrqq622x66r+drFRxMzHamP/H4/EydO5P333+fAgQPdeh1HI1Xnj1VUqvpPRESk5/va177GqFGjeOCBB/jrX//Ktm3buPDCCyksLEz6uduKqyKRCBs2bABaxmex6/nkk09a7fP6668f9bUkMu4UkeRQ0k9EEm7jxo1tjkKKbcvOzo5v+/73v4/jOFx//fU4jhPfvmPHDu66664un3vAgAHs27ePhoaGblx51OjRowFaVcFt376d66+//oj7rlmzptVaN0uWLKG6uprvfOc7ZGVlAXDaaacxfvx4NmzYEJ/aKmbt2rW89NJLjBs3jpkzZ7Z4zufz8a1vfYtPP/2UX/3qV5SVlXHOOecwePDgFu0GDx7MpZdeyuuvv85Pf/rTNkd4bdu2jR07dhzx9YiIiEjn9Pb4p7kf//jHLablPHDgALfddhsQvfaY9mKmp59+mvvvv7/VcbvSRz/60Y8IBoOUlpa2OSq9srKSN998s/MvqotScf4BAwYAbd+UExERkZ7Ftm3mzZvH559/zpVXXglE1/pLheeee44///nPLbb95je/Ydu2bZxxxhkt1vM75ZRTAPjv//7vFu3Xr18fH2h1NBIdd4pI4mlNPxFJuIceeoh77rmHWbNmMXbsWAoLC9m2bRt/+tOfyMrK4pprrom3/T//5//wxz/+kT/84Q9MmzaNs88+m+rqah599FG+/OUv8+STT3bp3GeeeSavvfYa//Zv/8aXvvQlvF4vU6ZM4etf/3qnj/H1r3+dsWPHcscdd/Dee+8xdepUdu3axZ///Ge+9rWvsWvXrnb3PeecczjttNO48MILGTZsGBs2bGDDhg2MHj2aZcuWxdtZlsWDDz7IWWedxUUXXcT555/P8ccfz+bNm/njH/9IXl4eq1evjk/L0NzcuXNZuXIlP/nJT+KP2/Kb3/yGjz76iP/8z/9kzZo1zJw5kyFDhrBnzx4++OADXnvtNR5++GGKi4s73TciIiLStt4e/8QMGzaMYDDIpEmTOO+88wiFQqxdu5bPPvuM+fPn8+Uvfznedv78+axcuZILL7yQCy64gGOOOYb33nuPv/3tb1x44YU8+uij3e6j0tJS3njjDe69916OPfZYzj77bIqKijhw4AA7duzgpZde4vvf/z7/7//9vy6/xs5IxfnHjx/PMcccwyOPPILH46GoqAjLsrjsssta3LwTERGRnuGKK65gyZIlfPbZZ0yePJlTTz01Jec977zzmDNnDnPmzGHs2LG88847PPXUU/Tv35977723Rdvvf//7/OIXv+D222/nnXfeYcKECWzZsoW//vWvzJkzhz/84Q9HdS2JjDtFJEmMiEiC/fOf/zQ/+MEPzOTJk01hYaHJzs42xx57rPne975n3n333Vbtq6urzQ9/+EMzfPhwk5WVZcaPH29+8YtfmG3bthnAzJ07t0X7559/3gDm5ptvbnWsQCBgfvCDH5hjjjnGuFyuNvfvjF27dplLLrnEDB8+3GRnZ5sJEyaY5cuXm1AoZAAza9asFu1vvvlmA5jnn3/erFq1ykyZMsVkZ2ebgQMHmu9973tmz549bZ7nww8/NN/97nfN0KFDjdvtNkOHDjWXXnqp+fDDD9u9NsdxTHFxsQFM//79TWNjY7ttGxsbzd13322++MUvmn79+hmv12tGjhxpZs+ebe644w5TUVHR5b4RERGR1jIh/hk1apQZNWqUqaqqMvPnzzfDhw83Xq/XHH/88ebXv/61cRyn1T6vvPKKOeOMM0xBQYHx+/3mtNNOM+vWrWvzervaR8YY86c//cl87WtfM4MGDTIej8cMGTLEfOELXzCLFy82H3zwQYu2bcVoh7+2tsyaNcu099E4UeefO3euAcyOHTtabN+4caOZPXu26devn7EsKx5PioiISM80Z84cA5h777233TZHiju6YuXKlQYwK1euNH/+85/Nqaeeanw+n8nPzzff/OY3zebNm9vc7/333zfnnHOO8fv9Jjc318yaNcu88MILLY7X0fW21zZRcaeIJI9ljFYfF5GeaefOnRQXFzN37lxWrVqV7ssRERERSTrFPyIiIiI9k+M4HHvssezbt489e/bQr1+/dF+SiEgrWtNPREREREREREREROQIHnvsMXbu3Mnll1+uhJ+I9Fha009EREREREREREREpA233XYbBw4c4Le//S1+v58f//jH6b4kEZF2KeknIn3CkiVLOtXuG9/4BiUlJUm9FhEREZFUUPwjIiIicvRuuukmPB4PEydO5Je//CUjR47s1nFeeOEFXnjhhQ7bFRQUcO2113brHCIiWtNPRPoEy7I61W7lypV873vfS+7FiIiIiKSA4h8RERGRnmPJkiXccsstHbYbNWoUO3fuTP4FiUhGUtJPREREREREREREREREpJez030BIiIiIiIiIiIiIiIiInJ0lPQTERERERERERERERER6eWU9BMRERERERERERERERHp5ZT0ExEREREREREREREREenl3Om+gExVWVlJOBwGYNCgQezbty/NVyR6H3oGvQ89h96LnkHvQ+q43W4KCwvTfRnSSc1jqUTRz1vqqc9TS/2dWurv1FJ/p97hfa5YqndJRiwF+lnsDvVZ16nPukf91nXqs65Tn3VPrN9SEU8p6Zck4XCYUCiEZVnxx8aYNF9V36X3oWfQ+9Bz6L3oGfQ+iLQvFkslin7eUk99nlrq79RSf6eW+jv11Oe9X6JjKdD3RXeoz7pOfdY96reuU591nfqse5r3Wypoek8RERERERERERERERGRXk5JPxEREREREREREREREZFeTkk/ERERERERERERERERkV5OST8RERERERERERERERGRXk5JPxEREREREREREREREZFezp3uCxAREREREREREenJNm3axJNPPsmOHTuorKzkuuuu4+STT44/f+GFF7a533e/+13OO+88AJYsWcKmTZtaPD9jxgyuvfba+ONAIMDKlSt5/fXXATjppJMoLS0lNzc3wa9IREREMpGSfiIiIiIiIiIiIkfQ2NjI6NGjOeOMM/jlL3/Z6vn77ruvxeO33nqL//f//h+nnHJKi+1nnnkmF110Ufyx1+tt8fxdd93F/v37Wbx4MQArVqzg7rvv5oYbbkjUSxEREZEMpqSfiIiIiIiIiIjIEUydOpWpU6e2+3xBQUGLx6+99hoTJ05kyJAhLbZnZWW1ahuze/du3n77bZYuXcpxxx0HwLx587jxxhvZs2cPw4cPP6rXICIiIplPST8REREREREREZEEqaqq4q233mLBggWtnnv55Zd5+eWXyc/Pp6SkhG9/+9vk5OQAsGXLFnw+XzzhBzBu3Dh8Ph+bN29W0k9EREQ6pKSfJNTBMHzSaDMiy6GfvrtERESkh9hab7Fpd4DhBvJd6b4aERERyWQvvvgi2dnZLdb8A5g5cyaDBw+moKCATz75hIceeoiPP/6Ym266CYgmC/Pz81sdLz8/n6qqqjbPFQqFCIVC8ceWZcWTiJZlJegVHTp2Mo6bydRnXac+6x71W9epz7pOfdY9qe43pWUkYRodeKbSQ61j8XoAhngcTu0XJl/fZSIiIpJm2+pt9lY38KV8i3yXSffliIiISAZ7/vnn+dKXvtRqvb6vfOUr8f8XFRUxbNgwbrjhBrZv386YMWPaPZ4xpt0bhevWrWPt2rXxx8XFxSxfvpxBgwYd5ato39ChQ5N27EylPus69Vn3qN+6Tn3Wdeqz7klVvykdIwlhDPyjxk2tY+GxDGEDn4dsNlS7Oad/GCX/RUREJJ18dvRrnaOgRERERJLngw8+YM+ePVx77bUdti0uLsblcrF3717GjBlDQUEB1dXVrdrV1NS0WQEIMGfOHM4999z441hycN++fYTD4e69iHZYlsXQoUPZu3cvxmgQVWeoz7pOfdY96reuU5913VH1mcuD083z2gCRUEfNeqzm/eZyuZI6MAeU9JME2Vxvs6vRxsZwVmGYLMvwp/0e9odt9gQtjsnSL04REZFk2bRpE08++SQ7duygsrKS6667rtV0UjH33Xcff//735k7dy5f+9rX4ttDoRBr1qzhlVdeIRgMMmnSJK688koGDBgQbxMIBFi5ciWvv/46ACeddBKlpaXk5ubG21RUVHD//ffz/vvv4/V6Oe2007j88stxu9MbdvqaqvvqImm9DBEREclwzz33HGPGjGH06NEdtv3kk0+IRCIUFBQA0fX76urq2Lp1K2PHjgXgo48+oq6ujvHjx7d5DI/Hg8fjafO5ZN3ENsboBnkXqc+6Tn3WPeq3rlOfdV13+swByqq694F8coELOwPeo1R9n9kpOYtktIiBtwLRxXGm+SMM9Bjy3DDOF83dv1PrIgN+JkVERHqsxsZGRo8eTWlp6RHbbdy4kY8++ojCwsJWz61atYqNGzdyzTXXcOutt9LQ0MCyZctwnENj8e666y527tzJ4sWLWbx4MTt37uTuu++OP+84DrfffjuNjY3ceuutXHPNNfzrX/9i9erViXux3ZTbFPXWRlTpJyIiIl3X0NDAzp072blzJwDl5eXs3LmTioqKeJu6ujr++c9/Mnv27Fb77927l7Vr17Jt2zbKy8t58803ueOOOyguLub4448HYMSIEZSUlLBixQq2bNnCli1bWLFiBdOmTWP48OEpeZ0iIiLSu6nST47aZ0GLkLHIsQ0n+A7dGJzoi7C5zqYiZPNZ0GK4qv1ERESSYurUqUydOvWIbQ4cOMADDzzA4sWLWbZsWYvn6urqeO6551i4cCGTJ08GYOHChVx99dWUlZVRUlLC7t27efvtt1m6dCnHHXccAPPmzePGG29kz549DB8+nHfeeYfdu3fzX//1X/Tv3x+Ayy+/nHvvvZeLL74Yn8+XhFffOfFKv+7OJyIiIiJ92rZt27jlllvij2ODmmbNmsWCBQsAePXVVzHGMHPmzFb7u91u3n33XZ566ikaGhoYMGAA06ZN49vf/ja2fWhM/qJFi3jggQdYunQpANOnT+eKK65I5ksTERGRDKKknxy1XQ3R4LQoy2mxdl+OK1rt90Gdi/dqXQzPSuxc8iIiItI5juNw9913c9555zFy5MhWz2/fvp1IJBJP+AH079+foqIitmzZQklJCVu2bMHn88UTfhCdgsrn87F582aGDx/Oli1bKCoqiif8AKZMmUIoFGL79u1MmjSp1blDoRCh0KG5+S3LIicnJ/7/RMltinrrIlZCjyvti/Wz+js11N+ppf5OLfV36qnPW5s4cSKPPfbYEdt85Stf4Stf+Uqbzw0cOLBF0rA9fr+fRYsWdesaRURERJT0k6PiGPik8VDS73An+CJ8UOdib8iiwYFsTSgrIiKSck888QQul4t/+7d/a/P5qqoq3G43fr+/xfb8/HyqqqribfLz81vt21Ebv9+P2+2OtzncunXrWLt2bfxxcXExy5cvT/jC1v1CDlQcoM6xGDx0KC7dxEyZoUOHpvsS+hT1d2qpv1NL/Z166nMRERGR3kVJPzkq5SGLRmPhtQxDvK2n7/S7oNDtUBm2+bTR5tgczaklIiKSStu3b+epp55i+fLlXR6t35lFpo0xLY7b1jkOb9PcnDlzOPfcc1vtv2/fPsLhRM4SYGFbbhwDOz7dS64rgYeWNlmWxdChQ9m7d2/KFizvy9TfqaX+Ti31d+q11edutzvhg3JEREREJLGU9JMOWW4PEdq+UfdxIPp1RI4FHi/NU3ouDCYcYkSWoTIMnzZaHJuT/OsVERGRQz744ANqamqYP39+fJvjOKxevZqnnnqKe+65h4KCAsLhMIFAoEW1X01NDePHjwegoKCA6urqVsevqamJV/cVFBSwdevWFs8HAgEikUibVYIAHo8Hj8fT5nOJvLFrWZDnsakOOtSGwWfrpnGqGGN0kz6F1N+ppf5OLfV36qnPRURERHoXJf2kQxEsyqoirbYbA9vrbcAiEolQVtXy+ckFLmxghNfh3VoXnwZtHBPB1mxaIiIiKfPlL3+ZE088scW2pUuX8uUvf5kzzjgDgDFjxuByuSgrK2PGjBkAVFZWsmvXLi699FIgun5fXV0dW7duZezYsQB89NFH1NXVxROD48aN4/HHH6eyspLCwkIAysrK8Hg8jBkzJiWv90jiST8HVKcgIiIiIiIiIplGST/ptkYDIWNhYcg7whRZAzyGLMvQaCzKQxZD25gGVERERLqvoaGBvXv3xh+Xl5ezc+dO/H4/AwcOJC8vr0V7t9tNQUEBw4cPB8Dn8zF79mzWrFlDXl4efr+fNWvWUFRUxOTJkwEYMWIEJSUlrFixgquuugqA++67j2nTpsWPM2XKFEaMGMFvfvMbvvvd7xIIBFizZg1nnnkmPp8vFV1xRHme6OLCdRELUDwiIiIiIiIiIplFST/ptugNM/DZHLF6z7bgmCyH7Q0uPm20GeptXTUoIiIi3bdt2zZuueWW+OPVq1cDMGvWLBYsWNCpY8ydOxeXy8Udd9xBMBhk0qRJXH/99di2HW+zaNEiHnjgAZYuXQrA9OnTueKKK+LP27bNj3/8Y+6//35uuukmvF4vM2fO5LLLLkvEyzxqed7oKKU6R9MOiIiIiIiIiEjmUdJPuq2+aQG/nE6siXNMlmF7A+xutJmep6SfiIhIIk2cOJHHHnus0+3vueeeVtu8Xi+lpaWUlpa2u5/f72fRokVHPPbAgQO54YYbOn0tqRSr9KtVKCIiIiIiIiIiGcjuuIlI2+qbRsnnHGFqz5hjvA4WhuqIpRttIiIikhZ53qbpPVXpJyIiIiIiIiIZSJV+0i3GHKr083Wi0s9rQ3+3YX/YojxkU+xyknyFIiIiIocJNgJQ59g4bm+XdnVhMOFQMq5KRERERERERCQhlPSTbmkwYLCwMXg7OVh+kMewPwz7ghbF2cm9PhEREZHDZTUNVKqNGN6pdLC6UPA3ucClKTJEREREREREpEfTvQvplvpI09SeNp2+YTbIG73RVh7SlFoiIiKSetEpyQ1gEe54ogIRERERERERkV5FST/pltjUnjmuzt8xG+yJ7lQZtghpdk8RERFJMduy8DSNPQop6SciIiIiIiIiGUZJP+mWOid6x8zXhe+gXFd0/T+DRUVY1X4iIiKSekr6iYiIiIiIiEimUtJPuswYaIhV+tldu2MWq/bbF1TST0RERFLvUNJPsYiIiIiIiIiIZBYl/aTLGhwwWNgYvF28X3ZoXT9964mIiEjqeaxoLKJKPxERERERERHJNMq8SJfVN5va0+pi0m+wJ3qHbV/Iwuhmm4iIiKSYuyl2iSgOEREREREREZEMo6SfdFlj002yrC5O7QlQ6Da4LUPIWFRFNK2WiIiIpJbdFH44SvqJiIiIiIiISIZR0k+6rLGp0i+rG989tgUD3U3VflrXT0RERFIsFr44KA4RERERERERkcyipJ90WaMT/ZpldW+I/ICmKT73h3WzTURERFJLlX4iIiIiIiIikqmU9JMuMQaC8ek9u3eMgZ5o1nB/SEk/ERERSS2baCDjpPk6REREREREREQSzZ3uC5DeJWTAYGFh8HSQs7MtC8ftbbW9MBuohsqwTcjlxdXGcVwYTDiUmIsWERERaXJoek8RERERERERkcyipJ90SWNTlZ/XAquDpJ+DRVlVuNV2Y8CFTQSL1w5E8Lla7zu5wKUyVBEREUk4Te8pIiIiIiIiIplKeRXpkkYneqesu1N7QjRZGEv01Tma4lNERERSJxZ5qNJPRERERERERDKNkn7SJcGmO2Re++iGx+c07V+vO24iIiKSQi5V+omIiIiIiIhIhlLST7qk0TRV+h1lgV486RdRpZ+IiIikjtb0ExEREREREZFMpaSfdElj0x2yrKOs9PM1fec1GI20FxERkdQ5tCaxpRhERERERERERDKKkn7SaY6BYNPNsaOt9PPY4LYMYGmKTxEREUmZ5sGvQhARERERERERySTudF9AV61bt46HH36Yc845h+9973sAGGP4/e9/z/r16wkEAhx33HFcccUVjBw5Mr5fKBRizZo1vPLKKwSDQSZNmsSVV17JgAED4m0CgQArV67k9ddfB+Ckk06itLSU3NzclL7Gniqa8LOwMbgTMCunz4aaCNQ5FrkuDbUXERGR5LMtgOjAIxP9IiIiIiIiIiKSEXpVpd/WrVv5+9//zqhRo1psf+KJJ/jLX/5CaWkpt99+OwUFBdx2223U19fH26xatYqNGzdyzTXXcOutt9LQ0MCyZctwnENjvO+66y527tzJ4sWLWbx4MTt37uTuu+9O2evr6YJNXeW1m0+N1X2H1vU7+mOJiIiIdJbW9RMRERERERGRTNRrKv0aGhq4++67mTdvHo8//nh8uzGGp556ijlz5nDKKacAsGDBAq666io2bNjAWWedRV1dHc899xwLFy5k8uTJACxcuJCrr76asrIySkpK2L17N2+//TZLly7luOOOA2DevHnceOON7Nmzh+HDh6f+RfcwjSaa6cuyElOV52tK+tU5FtER9yIiIiLJZ1vRacu1pp+IiIh01qZNm3jyySfZsWMHlZWVXHfddZx88snx5++55x5efPHFFvscd9xxLF26NP5Ys1CJiIhIsvWapN/999/P1KlTmTx5coukX3l5OVVVVUyZMiW+zePxMGHCBDZv3sxZZ53F9u3biUQi8YQfQP/+/SkqKmLLli2UlJSwZcsWfD5fPOEHMG7cOHw+H5s3b2436RcKhQiFQvHHlmWRk5MT/7/VVBJnJaI0Lk2spn+xSr8su/MzYR2pnc8V/Ro0Fo4BV7PGFonts0x4HzKB3oeeQ+9Fz6D3QSQ9VOknIiIiXdXY2Mjo0aM544wz+OUvf9lmm5KSEubPnx9/7Ha3vO22atUq3njjDa655hry8vJYvXo1y5YtY/ny5dh2NEK566672L9/P4sXLwZgxYoV3H333dxwww1JemUiIiKSSXpF0u+VV15hx44d3H777a2eq6qqAiA/P7/F9vz8fCoqKuJt3G43fr+/VZvY/lVVVa2OcXibtqxbt461a9fGHxcXF7N8+XIGDRrUot3QoUPbPUZPV15Vgz/PRSTUCDj0y8nC7+v4W8eygvjz/Edsk9XQQKNjMFk5+LNc8e0eb4TBBQOP9tJb6c3vQybR+9Bz6L3oGfQ+iKSW3TTJgCr9REREpLOmTp3K1KlTj9jG7XZTUFDQ5nOahUpERERSoccn/SoqKli1ahWLFy/G6/W22+7wKgljOr6L09k2R6rAmDNnDueee26r69i3bx/hcBjLshg6dCh79+7t1Pl6IsflIXAwQn3IBiycYAOBTqzDZwo9BA4Gjtgm27JpxOJAoAF38FD/hFwuPvvss6O88kMy4X3IBHofeg69Fz2D3ofUcrvdrQblSN+kSj8RERFJhk2bNnHllVeSm5vLCSecwHe+8534APNkzULV0QxUiaSZSrpOfdZ16rPuUb91nfqs646mz2KzCXbrvN08Z0+R6u+1Hp/02759O9XV1S2mMXAchw8++IC//e1v3HnnnUC0Uq+wsDDepqamJh5YFRQUEA6HCQQCLar9ampqGD9+fLxNdXV1q/M3P05bPB4PHo+nzeea38A1xvTaG7qG6Ej4UNPle6zOr8DXUbsc21AdsahzLEyz1obOJWW7qje/D5lE70PPofeiZ9D7IJJah5J+WldYREREEmPq1Kl88YtfZODAgZSXl/Poo49y6623smzZMjweT9JmoersDFSJpJlKuk591nXqs+5Rv3Wd+qzrutNnsdkEuyNZswKmWqq+13p80u/EE0/kF7/4RYtt//Vf/8Xw4cM5//zzGTJkCAUFBZSVlVFcXAxAOBxm06ZNXHrppQCMGTMGl8tFWVkZM2bMAKCyspJdu3bF24wbN466ujq2bt3K2LFjAfjoo4+oq6uLJwb7sgixG2PRpF+i+FwGQlCnofYiIiLdtmnTJp588kl27NhBZWUl1113HSeffDIQjYseeeQR3nrrLcrLy/H5fJx44olccskl9O/fP36MUCjEmjVreOWVVwgGg0yaNIkrr7ySAQMGxNsEAgFWrlzJ66+/DsBJJ51EaWkpubm58TYVFRXcf//9vP/++3i9Xk477TQuv/zyVmvapJPdFMtoek8RERFJlNj9JoCioiKOPfZY5s+fz5tvvskpp5zS7n5HOwtVRzNQJZJmKuk69VnXqc+6R/3WdeqzrjuaPovNJtgdiZ4VMNWa95vL5Ur6LFQ95+5LO3JycigqKmqxLSsri7y8vPj2c845h3Xr1jFs2DCGDh3KunXryMrKYubMmQD4fD5mz57NmjVryMvLw+/3s2bNGoqKiuLTKowYMYKSkhJWrFjBVVddBcB9993HtGnTNGc6EGpKyrkx8RtliZDTNNQ+ZCzCBty9t0pXREQkbRobGxk9ejRnnHEGv/zlL1s8FwwG2bFjBxdccAGjR48mEAjw4IMP8rOf/Yxly5bF261atYo33niDa665hry8PFavXs2yZctYvnw5th39g33XXXexf/9+Fi9eDMCKFSu4++674zMyOI7D7bffTr9+/bj11ls5ePAg99xzDwClpaWp6IpOsTGApek9RUREJGkKCwsZNGhQ/CZlsmah6uwMVImkmUq6Tn3Wdeqz7lG/dZ36rOu602eG7s+zk6xZAVMtVa+hxyf9OuP8888nGAxy//33U1tby9ixY1m8eHF8DnOAuXPn4nK5uOOOO+Kj16+//vr4TSyARYsW8cADD7B06VIApk+fzhVXXJHy19MTxZbb89hHbtdVLgu8liFoLOojkJcR35EiIiKpNXXqVKZOndrmcz6fj5tuuqnFtu9///v85Cc/oaKigoEDB1JXV8dzzz3HwoUL4wOiFi5cyNVXX01ZWRklJSXs3r2bt99+m6VLl8bXmZk3bx433ngje/bsYfjw4bzzzjvs3r2b//qv/4pXEV5++eXce++9XHzxxfh8viT2Quep0k9ERESS7eDBg+zfvz++FI1moRIREZFU6JUpliVLlrR4bFkWF154IRdeeGG7+3i9XkpLS484ytzv97No0aJEXWZGCZnET+0Z47MNwaZ1/fK0ro6IiEjS1dXVYVlWPAm3fft2IpFIPOEH0L9/f4qKitiyZQslJSVs2bIFn88XT/hB9MaUz+dj8+bNDB8+nC1btlBUVNRi2tApU6YQCoXYvn07kyZNanUtoVCIUCgUf2xZVnzgViIXuY4fy7Lia/pF6/06uX+Cr6cvSPVi5X2d+ju11N+ppf5OPfV5aw0NDezduzf+uLy8nJ07d+L3+/H7/Tz22GOceuqpFBQUsG/fPh5++GHy8vLiU65rFioRERFJhV6Z9JPUi1X6ea3EJ+VyXFAVgXrHovtFviIiItIZwWCQhx56iNNOOy2e9KuqqsLtdreYagogPz+fqqqqeJu2ppXqqI3f78ftdsfbHG7dunWsXbs2/ri4uJjly5cnZY778qoa/P5csk0IwmFcHg/+PG+n9s2UhcPTIVWLlUuU+ju11N+ppf5OPfX5Idu2beOWW26JP169ejUAs2bN4qqrruKTTz7hpZdeora2lsLCQiZOnMi1116rWahEREQkpZT0k06JremX6Ok9IVrpB1CnhXVERESSKhwOc+edd2KM4corr+ywfWfmmzfGtKgCaKsi4PA2zc2ZM4dzzz231f779u0jHA53eP7OsiwLO9tHIFBLOAhg0xgMETgY7NT+vX3h8HQ4mkXepevU36ml/k4t9XfqtdXnbrc7KYNyeouJEyfy2GOPtft8bM3jI9EsVCIiIpJsSvpJp8Sm90xGpV+2DWAIG4uQk5zEooiISF8XDoe544472LdvH//5n//ZYn29goICwuEwgUCgRbVfTU1NfP2YgoICqqurWx23pqYmXt1XUFDA1q1bWzwfCASIRCJtVgkCeDwePB5Pm88l5cZuswRkxHR+joFMWTg8HbqzyLt0n/o7tdTfqaX+Tj31uYiIiEjvovSKdEpses9krOnnsiCr6biq9hMREUm8WMJv79693HTTTeTl5bV4fsyYMbhcLsrKyuLbKisr2bVrF+PGjQOi6/fV1dW1SOp99NFH1NXVxROD48aNY9euXVRWVsbblJWV4fF4GDNmTDJfYpccWtNP6xSJiIiIiIiISOZQpZ90KGIgHFvTL0lpYp/L0Bi2qHcs8rWun4iISJc0NDSwd+/e+OPy8nJ27tyJ3++nsLCQX/3qV+zYsYPrr78ex3Hi6+vF1tvz+XzMnj2bNWvWkJeXh9/vZ82aNRQVFTF58mQARowYQUlJCStWrOCqq64C4L777mPatGkMHz4cgClTpjBixAh+85vf8N3vfpdAIMCaNWs488wzW1QWppvdlOtzFHKIiIiIiIiISAZR0k86VBcBsLAwuJJ0jhwbKoF6x6LzE22JiIgIwLZt27jlllvij1evXg3ArFmz+Pa3v83rr78OwH/8x3+02O/mm29m4sSJAMydOxeXy8Udd9xBMBhk0qRJXH/99dj2oRE/ixYt4oEHHmDp0qUATJ8+nSuuuCL+vG3b/PjHP+b+++/npptuwuv1MnPmTC677LLkvPBusptiDU0wICIiIiIiIiKZREk/6VBtJPrVY4GVpFmwfHb05ltdBLRcgIiISNdMnDiRxx57rN3nj/RcjNfrpbS0lNLS0nbb+P1+Fi1adMTjDBw4kBtuuKHD86WTKv1EREREREREJBNpTT/pUF1T0s+bxGVvsm0AQwSLkG7AiYiISBLFAmBV+omIiIiIiIhIJlHSTzpUG45+9djJy8bZVizxB3W6AyciIiJJpKSfiIiIiIiIiGQiJf2kQ7UpqPSDQ1N8Rtf1ExEREUkOTe8pIiIiIiIiIplIST/pUPM1/ZIpp+m7sT6ipJ+IiIgkT/NKP60lLCIiIiIiIiKZQkk/6VA86ZfE6T3hUKVfnaMbcCIiIpI8dnx8kYVCDhERERERERHJFEr6SYfqUzS9Z7YdvfXmYHEwktxziYiISN/VPADWun4iIiIiIiIikinc6b4A6dlCDoSahsC7k5z0s6zoFJ91DuwPQoE3uecTERGRvsmyogONDFZ0XT/NLC4iIiIiItKrWW4PkS58uLOA8qoaHJcHG4MJh5J3cSIppKSfHFF90/B3G4MrBTfEcmxDnWOxPwjHKuknIiIiSWIDEVTpJyIiIiIikgkiWJRVdX76OAvw57kIHIxwYoFLUyJKxtD3shxRnRPN9HlSNAI+p+k78oAGVoiIiEgSxdb1c7Son4iIiIiIiIhkCCX95IjqItE7Ysme2jPG54reeTsQ0k04ERERSZ5YaKNwQ0REREREREQyhZJ+ckR1TXNeeazU3BLLsqJTiYYN1ES0wI6IiIgkhyr9RERERERERCTTKOknR1Qfm94zRd8plnVois/9ISX9REREJDlioY3W9BMRERERERGRTKGknxxRXdPap6la0w8gx44Oua9Q0k9ERESS5FCln+INEREREREREckMSvrJEcUq/dwpmt4TIMcV/bo/rJtwIiIikhyq9BMRERERERGRTKOknxxRXWx6zxTm33xNlX4HQhYRrbMjIiIiSWATDTKU9BMRERERERGRTKGkn7TLGKhPw/SeXit6PgeLKlX7iYiISBIcmt4zvdchIiIiIiIiIpIoSvpJu4IGIsSm90zdeS0LBnij/9+vdf1EREQkCTS9p4iIiIiIiIhkGiX9pF2xqT2z7EOj4VNlgCf6Vev6iYiISDKo0k9EREREREREMo2SftKu2NSeOWn4LlGln4iIiCSTKv1EREREREREJNMo6SftilX65bhSf+5YpV9l2CKiEfgiIiKSYKr0ExEREREREZFMo6SftKu+aei7Lw1JP58Lsi2DweKApvgUERGRBItFFw6KM0REREREREQkMyjpJ+2qi6Sv0s+yYIAnOvReU3yKiIhIoqnST0REREREREQyjZJ+0q7Y9J6+NH2XKOknIiIiyWITjTOU8xMRERERERGRTOFO9wVIz1UfiX7NcUFDOPXnH+BxAJeSfiIiIpJwqvQTERGRrti0aRNPPvkkO3bsoLKykuuuu46TTz4ZgHA4zCOPPMJbb71FeXk5Pp+PE088kUsuuYT+/fvHj7FkyRI2bdrU4rgzZszg2muvjT8OBAKsXLmS119/HYCTTjqJ0tJScnNzk/8iRUREkqzegYqQRYNj4bUgyzYMcBs8Kk9LGCX9pF2xSr8cF1Sm4fwD3NG7cNURi5CDfvBFREQkYWJhhZPWqxAREZHeorGxkdGjR3PGGWfwy1/+ssVzwWCQHTt2cMEFFzB69GgCgQAPPvggP/vZz1i2bFmLtmeeeSYXXXRR/LHX623x/F133cX+/ftZvHgxACtWrODuu+/mhhtuSNIrExERSb6QAzsbbGoihwp86gEiFvtDhhFZDvnKViWEulHaZEw06w7gS8OafrHz+mxDnWNxIGwxxKuh+CIiIpIYsY8ZSvqJiIhIZ0ydOpWpU6e2+ZzP5+Omm25qse373/8+P/nJT6ioqGDgwIHx7VlZWRQUFLR5nN27d/P222+zdOlSjjvuOADmzZvHjTfeyJ49exg+fHhiXoyIiEgKBR1YX0lTws+Q7zLkuyFs4EA4WvX3caOLIY6jHEACKOknbWpwwGBhYci20ze95gCPoa7RYn9IST8RERFJnFh4YxReiIiISBLU1dVhWRY+n6/F9pdffpmXX36Z/Px8SkpK+Pa3v01OTg4AW7ZswefzxRN+AOPGjcPn87F58+Y2k36hUIhQKBR/bFlW/HiWldj7ObHjJfq4mUx91nXqs+5Rv0UHdnbp1cf6yrKi+/bhvuus7nyfhRx4ttLF/jC4MIzJdloUGQ1wG/YGLfaFbT4P2eTYkVYVf739/Un1z6eSftKm+qapPbPtQzfF0mGgx+GTRpsKresnIiLSriOtMQNgjOH3v/8969evJxAIcNxxx3HFFVcwcuTIeJtQKMSaNWt45ZVXCAaDTJo0iSuvvJIBAwbE23RmjZmKigruv/9+3n//fbxeL6eddhqXX345bnfPCjs1vaeIiIgkSzAY5KGHHuK0005rkfSbOXMmgwcPpqCggE8++YSHHnqIjz/+OF4lWFVVRX5+fqvj5efnU1VV1ea51q1bx9q1a+OPi4uLWb58OYMGDUrsi2pm6NChSTt2plKfdZ36rHv6cr+VV9Xgz+v6lHV+fy4eb4TBBQM7bixA177Pnvr4IPvDjWTbML4wG38ba3j1A9w1QT6ri/BJ0EX/vCxy3IfaZcr7k6qfz55190V6jNjUntm2oYtjJBJqYNO6fhUhG4ik7TpERER6siOtMQPwxBNP8Je//IX58+czbNgwHn/8cW677TbuvPPO+EjwVatW8cYbb3DNNdeQl5fH6tWrWbZsGcuXL8e2o8F2R2vMOI7D7bffTr9+/bj11ls5ePAg99xzDwClpaWp6IpOiw2wM1gYc+ixiIiIyNEIh8PceeedGGO48sorWzz3la98Jf7/oqIihg0bxg033MD27dsZM2ZMu8c0xrRbHTBnzhzOPffc+ONYu3379hEOh4/mpbRiWRZDhw5l7969GE2X0Cnqs65Tn3WP+g0cl4fAwS7cP7Ys/P5cAoFaQi6bzz77LHkXlyG6+n22q8GirMoNGL7c3+Lz+joCDW23HQRU2zZ1jsUHBxo4LtuJf04PuVy9+v1p3m8ulyupA3NAST9pR6zSL6d14j2lBngMYAg4FvURyEnT+oIiIiI92ZHWmDHG8NRTTzFnzhxOOeUUABYsWMBVV13Fhg0bOOuss6irq+O5555j4cKFTJ48GYCFCxdy9dVXU1ZWRklJSafWmHnnnXfYvXs3//Vf/0X//v0BuPzyy7n33nu5+OKLW01vlU7NQ5z0DnESERGRTBEOh7njjjvYt28f//mf/9lh7FNcXIzL5WLv3r2MGTOGgoICqqurW7WrqalpswIQwOPx4PF42nwuWTf+jTF9NqnQXeqzrlOfdU9f7jfT9K+zrFg/GRPdt4/2W3d05vuswYF/1ETTTxN8DoOzXOytP8IOFozKcthSb1PvWBwIW/T3RM+RKe9Pql5DmlM60lPFKv1y7PT+MHltKHDFqv10O05ERKSrysvLqaqqYsqUKfFtHo+HCRMmsHnzZgC2b99OJBKJJ/wA+vfvT1FREVu2bAE6XmMm1qaoqCie8AOYMmUKoVCI7du3J/V1dlXzqEJTfIqIiMjRiiX89u7dy0033UReXl6H+3zyySdEIhEKCgqAaGxVV1fH1q1b420++ugj6urqGD9+fLIuXUREJOHeOOiiwbEocDtM9XeuAtNjw2BvNBewN2Th9P48X1qo0k/a1NBDKv0ABnoMVRGoCNuM1BSfIiIiXRJb/+Xw0eH5+flUVFTE27jdbvx+f6s2sf07s8ZMW238fj9ut7vddWhCoRChUCj+2LKs+JSjiVzk2mq+SLsxTSPfmmr8OjG9Z29fODwdUr1YeV+n/k4t9Xdqqb9TT33eWkNDA3v37o0/Li8vZ+fOnfj9fgoLC/nVr37Fjh07uP7663EcJx77xGKhvXv3smHDBqZOnUpeXh67d+9mzZo1FBcXc/zxxwMwYsQISkpKWLFiBVdddRUA9913H9OmTWP48OEpf80iIiLdURm22N4Q/dT9xbwILqvzg20HuA0VIUPIWOwLWQzxKvPXVUr6SZvqm3JrOa70/1AN9Bi2NsA+VfqJiIh02+E37TozrURn2zQ/dls3B4+0Ds26detYu3Zt/HFxcTHLly9Pyhz35VU1+P258cd2fT2OgexcX4tFwtuSKQuHp0OqFiuXKPV3aqm/U0v9nXrq80O2bdvGLbfcEn+8evVqAGbNmsW3v/1tXn/9dQD+4z/+o8V+N998MxMnTsTtdvPuu+/y1FNP0dDQwIABA5g2bRrf/va34+snAyxatIgHHniApUuXAjB9+nSuuOKKZL88ERGRhHnroAuDRVGWw6AuJu1sC4Z6DZ80RpN+Azzpz0/0Nkr6SZtia/pl94BKv0FNP9j7m0p6beX+REREOi02XVRVVRWFhYXx7c3XhikoKCAcDhMIBFpU+9XU1MSnkurMGjMFBQUtpqMCCAQCRCKRdtehmTNnDueee278cSw5uG/fPsLhcFdfbrssy8LO9hEI1EJTMtMyNmARqK0j0kHM09sXDk+Hri7yLkdH/Z1a6u/UUn+nXlt97na7kzIop7eYOHEijz32WLvPH+k5gIEDB7ZIGrbH7/ezaNGiLl+fiIhIT/B50GJ30MbCMNXfvc/0BS7DPtvQ4FgcUCFQlynpJ22qj0/vmf4PVPlug9uKlvTWRCwK3Om/JhERkd5i8ODBFBQUUFZWRnFxMRBdc2bTpk1ceumlAIwZMwaXy0VZWRkzZswAoLKykl27dsXbNF9jZuzYsUDrNWbGjRvH448/TmVlZTzBWFZWhsfjYcyYMW1en8fjwePxtPlcUm7sNi3SDtGBRBEDjul4wfdMWTg8HTqzyLskjvo7tdTfqaX+Tj31uYiIiHTFWwEXAMflOOR3M/tkWTDIbfgkaLE/3FQIlMBrzHRK+kmb6psm2e0JST/bis7l+3koWtKrpJ+IiEhLR1pjZuDAgZxzzjmsW7eOYcOGMXToUNatW0dWVhYzZ84EwOfzMXv2bNasWUNeXh5+v581a9ZQVFTE5MmTgc6tMTNlyhRGjBjBb37zG7773e8SCARYs2YNZ555Jj6fL8W90rHYeMHOri0gIiIiIiIiIm0rD1qUh2xsDJNzI0d1rHy34bOmtf121cMYb4Iusg9Q0k9aCRsImVilX5ovpslAj+HzEFSELI7LSffViIiI9CxHWmNmwYIFnH/++QSDQe6//35qa2sZO3YsixcvJifn0B/VuXPn4nK5uOOOOwgGg0yaNInrr7++S2vM2LbNj3/8Y+6//35uuukmvF4vM2fO5LLLLkt2F3SLbQEmPtuniIiIiIiIiHTT+3XRKr8x2Q4+19Edy7agv9tQHrL4MABj+ifgAvsIJf2klViVnwuDx+p4uqtUGORxABcVmsNXRESklY7WmLEsiwsvvJALL7yw3TZer5fS0lJKS0vbbdOZNWYGDhzIDTfc0PFF9wCxdKYq/URERERERES6ryoMnzTagGHiUVb5xQxwG/aFDBUhi4qQxUBPT8hU9Hw9pI5LepKGSFOVnys6f25PEPuBrgpbhHRnTkRERBIgFuYYekjAIyIiIiIiItILvV8bLe0bmWW6vZbf4Tw25LuieYGP6pXK6qyk9lRVVVUyDy9J0pPW84vxucBnGwzRxTtFRER6O8VJ6Wc3hRROzwl5REREJIEUb4mIiCRfvQM7GqKppkm+xFT5xfRvKgba2WAT1mf3Tklq0u/qq6/mzjvv5MMPP0zmaSTB6p2etZ5fzKCmH3BN8SkiIplAcVL6xSIKTSIgIiKSmRRviYiIJN/WehsHi4Fuh0HexGbmcu1oQVDIWOxu7GEJix4qqWv6XXDBBfz973/nH//4B0VFRfzbv/0bM2fOxOv1dvoYzzzzDM888wz79u0DYMSIEXzrW99i6tSpABhj+P3vf8/69esJBAIcd9xxXHHFFYwcOTJ+jFAoxJo1a3jllVcIBoNMmjSJK6+8kgEDBsTbBAIBVq5cyeuvvw7ASSedRGlpKbm5uYnoil4llvTL7kGVfgADPQ4fN9pUhGx0e05ERHq7RMRJcnRsDGD1iPWLRUREJPEUb4mIiCSQy0N5VQ2OyxP/HO0Y2FIf/f9xfhvH3fbfWKub64hZFozJgfcCsK3eZnS28gIdSWpq9Fvf+hb33nsv11xzDT6fjxUrVvCDH/yA1atXs3fv3k4do3///lxyySXcfvvt3H777UyaNImf/exnfPLJJwA88cQT/OUvf6G0tJTbb7+dgoICbrvtNurr6+PHWLVqFRs3buSaa67h1ltvpaGhgWXLluE4h75B7rrrLnbu3MnixYtZvHgxO3fu5O67705sh/QSPXF6Tzi0rt++kIXpWZcmIiLSZYmIk+ToaHpPERGRzKZ4S0REJHEcYHO9i7KqSPzfq/sj1EbAheFgMNLiueb/DN2fva/YF/26J2hRn9jZQzNSUiv9AGzbZsaMGcyYMYOPP/6Yv/3tbzz77LM89dRTlJSU8L/+1/+ipKSk3f1POumkFo+/853v8Mwzz/DRRx8xYsQInnrqKebMmcMpp5wCwIIFC7jqqqvYsGEDZ511FnV1dTz33HMsXLiQyZMnA7Bw4UKuvvpqysrKKCkpYffu3bz99tssXbqU4447DoB58+Zx4403smfPHoYPH56czumheur0ngM8BgtDvWNR50CuK91XJCIicnSONk6SoxP7yKGcn4iISOZSvCUiIpI8+8PRJEKh28QH1iZavic6C2BFyGZHg82EXFX7HUnSk37NFRUVMXXqVHbt2sXWrVt59913eeuttyguLmbRokUdJtccx+Ef//gHjY2NjBs3jvLycqqqqpgyZUq8jcfjYcKECWzevJmzzjqL7du3E4lE4gk/iFYPFhUVsWXLFkpKStiyZQs+ny+e8AMYN24cPp+PzZs3H/G6QqEQoVAo/tiyLHJycuL/j5Wtdrd8NR3ilX6uptcA3c7DH81+h/eZx4JCNxwIQ0XYxu/u/C263vg+ZCK9Dz2H3oueQe+DNHe0cZJ0nSr9RERE+hbFWyIiIokTdOBgU+Vdf09yP1iPyY4m/XYq6dehlCT9ampqWL9+PX//+9+pqKhg3LhxXHvttXzhC1/g7bffZs2aNdx7773cdtttbe6/a9cuFi9eTCgUIjs7m+uuu44RI0awefNmAPLz81u0z8/Pp6KiAoCqqircbjd+v79Vm6qqqnibw49xeJv2rFu3jrVr18YfFxcXs3z5cgYNGtSi3dChQ494nJ4ktP8A4DBy8ACG5Ubn6fXndb2szrKC+PP8HTdsg8cbYXDBwFbbR4UDHKhooN6bx7BhXV9vsTe9D5lM70PPofeiZ9D70LcdbZwk3RdLt+vjgoiISGZTvCUiIpJ4B8LRciG/bchO8qyBo7IcNh40VIRtAhHwaxbAdiU16ffRRx/x9NNP849//AOAL37xi5xzzjmMGTMm3uakk07C5XLx85//vN3jDB8+nJ///OfU1tbyr3/9i3vuuYdbbrkl/vzhFRKmEwu+dbZNR9UXc+bM4dxzz211Lfv27SMcDmNZFkOHDmXv3r2dOme6GQOBkBuwqK2s4LMacFweAge7PlmuKfQQOBjo1nVE3G4+La9otT0nHP26s6qeTz31rZ6HpoUqI6EW23rb+5Cp9D70HHovega9D6nldrtbDcpJp0TFSdJ9sc8k+ukTERHJTIq3REREksOYWNIP+nuSP5Q2xwVDPIbPQxa7VO13RElN+t14440UFBTwjW98g69+9attVtMBDBo0iPHjx7d7HLfbHa+COPbYY9m2bRtPPfUU559/PhCt1CssLIy3r6mpiZ+roKCAcDhMIBBoUe1XU1MTP2dBQQHV1dWtztv8OO3xeDx4PJ42n2t+A9cY0ytu6DY64DSNe8+2DMZEb4R198q7u18Ei7KqcKvtDQ6Ai31Bw9uVDm3lZCcXuLDb6eve8j5kOr0PPYfei55B70PflKg4SbpP03uKiIhkNsVbIiIiyVEdgbCxcFuG/BRV3RVlO3wesvm4UUm/I0lq0m/BggXMmDEDt/vIpxkxYgQ333xzp49rjCEUCjF48GAKCgooKyujuLgYgHA4zKZNm7j00ksBGDNmDC6Xi7KyMmbMmAFAZWUlu3btircZN24cdXV1bN26lbFjxwLR0WB1dXV9LuiLrefntQyuHrjEVJYFLgwRLBqcaIZfRESkN0pWnCSdFwt1DBaq9xMREck8irdERESS40AoOndOf7dpszAnGUZlObx2EPaFLOoi4FNuoE1JTfp9+ctfPupjPPTQQ0ydOpUBAwbQ0NDAK6+8wvvvv8/ixYuxLItzzjmHdevWMWzYMIYOHcq6devIyspi5syZAPh8PmbPns2aNWvIy8vD7/ezZs0aioqKmDx5MhAN7kpKSlixYgVXXXUVAPfddx/Tpk3rc4s41zvRn9Acu2fe+LKsaKIvEIE6xyLH1TOvU0REpCOJiJPk6MSm91Sln4iISGZSvCUiIpJ4DQ4EnOjg2f7u1H2g9rlgkMdhX8hmV6PN8T5V+7UlqUm/P/7xjxw4cIDS0tJWzz3wwAMMHDiQ884774jHqK6u5je/+Q2VlZX4fD5GjRrF4sWL4wm7888/n2AwyP33309tbS1jx45l8eLF5OTkxI8xd+5cXC4Xd9xxB8FgkEmTJnH99ddj24dWl1y0aBEPPPAAS5cuBWD69OlcccUVieiGXqU+0pT068FZcp9tCEQs6hwYkO6LERER6aZExElydCwr+uFEHxNEREQyk+ItERGRxNsfiuYQ8lzgtTtonGCjsqJJv48blPRrT1KTfi+++CLnnHNOm8+NGjWKp556qsPg6uqrrz7i85ZlceGFF3LhhRe228br9VJaWtpmkBfj9/tZtGjREc/VFzQ0/Zz01Eo/iCb9AOoimopLRER6r0TESXJ0Yp9NFE2IiIhkJsVbIiIiiRUxhspwNOk3wJ36pFtRtsPrASgPWTQ6kJXipGNvkNQuqaioYNiwYW0+N3ToUPbt25fM00s3HJreM80XcgSxuXobjUVYd+lERKSXUpyUfnbTugOa3lNERCQzKd4SERFJrP0NESJYeCxDXhpmC/S7oMDtYLD4tLEHJzHSKKm94nK5qKmpafO56upqrFSt8CidVt+UnM/uwZV+bgu8TdNx1UfSfDEiIiLdpDgp/WI93HOjHhERETkairdEREQS67O6MAAD3IZ0/RkdmRX9FL+7UX/H25LUpN+xxx7L+vXr23xu/fr1jBkzJpmnl27oDZV+0GyKT0c/2CIi0jspTkq/WLijVQBEREQyk+ItERGRxDkQhEDIYGHo70nf8NkR3uin+E+DNhGN4m0lqamdr3/967z33nssWbKEV199lQ8//JBXX32VW265hffee4/zzz8/maeXbqjvBWv6waEpPpX0ExGR3kpxUvrFRiWanh32iIiISDcp3hIREUmcLbXRr/kugzuNt+UHegzZtiFkLMqDyg8czp3Mg5eUlDBv3jxWr17Nr3/96/h2n8/HvHnzKCkpSebppRvqI72s0i8SvVGnGTlERKS3UZyUfqr0ExERyWyKt0RERBIj6MCO+uj/B6Sxyg+iuYBjvA7bGlzsDtoMy9IaYM0lNekHMHv2bGbMmMGWLVuoqamhX79+jBs3juzs7GSfWrrIMdBompJ+rp495D3bBgtDBIuggSwl/UREpBdSnJRedqzSD0uDiERERDKU4i0REZGjt73BJmwgx22R2wMKhkZmRZN+nzTanOSP6PN8M0lP+gFkZ2czefLkVJxKjkJD0zB3C9Pjk2i2Fa1GrHOiU3xm9fDpSEVERNqjOCl9moc75rDHIiIikjkUb4mIiHSfMbClPprpG5rjxnLCpPtu/DCvwcYQiFhUR6AgJZmu3iHpXWGMYdu2bezbt49gMNjq+VmzZiX7EqST6p1DU3v2hsy4zzbUORZ1ESjUD7WIiPRCipPSq/ngRIckL3YtIiIiaaF4S0RE5OiUhyyqwjYuCwbnuGioTfcVgceGIV7DZ0GLPY02BW4t3BGT1FTJnj17+NnPfsZnn33WbhsFVz1HfdPPRU4vqZrLdRkqwlDrWJD2sQUiIiJdozgp/aKDnKI1fo5K/URERDKO4i0REZGjF6vyK84Bt91zPjgP9zp8FrTZE7SZkKukX0xSk36//e1vCYVC/PCHP6SoqAiPx5PM08lRilX6ZfeSYe6+putscCBiwNVzft+IiIh0SHFSz2ATrfLT8CEREZHMo3hLRETk6NRH4OOG6I34cblQnebraW54luGNAHwetAgbcCs/ACQ56bd161bmzZvHqaeemszTSILUR6Jfe0uln8cGj2UIGYs6B/Jc6b4iERGRzlOc1DPEkn4aEygiIpJ5Ehlvbdq0iSeffJIdO3ZQWVnJddddx8knnxx/3hjD73//e9avX08gEOC4447jiiuuYOTIkfE2oVCINWvW8MorrxAMBpk0aRJXXnklAwYMiLcJBAKsXLmS119/HYCTTjqJ0tJScnNzj/o1iEjvZLk9RDqYlsQCyqtqcFyeFgMaXRhMOJTU65PMtrnehYPFII/DAK9NdSTdV3RIgcvElwArD1oMz+odeY1kS2pNV3Z2Nj6fL5mnkASKr+nXi5JnuU0JyrqI0vgiItK7KE7qGWLrGBt9NhAREck4iYy3GhsbGT16NKWlpW0+/8QTT/CXv/yF0tJSbr/9dgoKCrjtttuor6+Pt1m1ahUbN27kmmuu4dZbb6WhoYFly5bhOIeGH911113s3LmTxYsXs3jxYnbu3Mndd9+dkNcgIr1TBIuyqkiH/zbXu1pt6yhZKHIkEXNoas8TfD1vqKxlRaf4BNgT7CXTF6ZAUnvijDPOYMOGDck8hSRQPOnXSyr9AHxNCcpaR3/ARESkd1Gc1DPEguGe9/FFREREjlYi462pU6dy8cUXc8opp7R6zhjDU089xZw5czjllFMoKipiwYIFNDY2xs9fV1fHc889x+WXX87kyZMpLi5m4cKF7Nq1i7KyMgB2797N22+/zQ9+8APGjRvHuHHjmDdvHm+++SZ79uxJyOsQERHprB0NNg2Ohc82FGX1zE/Nseq+TxuVH4hJ6vSeI0eO5JVXXmH58uVMnz6dvLy8Vm3aCpYkPeqbfm57VdIvXukXHaFv6WdbRER6CcVJPYNlAUaVfiIiIpkoVfFWeXk5VVVVTJkyJb7N4/EwYcIENm/ezFlnncX27duJRCJMnjw53qZ///4UFRWxZcsWSkpK2LJlCz6fj+OOOy7eZty4cfh8PjZv3szw4cNbnTsUChEKHZq6z7IscnJy4v9PpNjxEn3cTKY+6zr1WWtW078jN7LiX61mH24s+k5fdqqfWuzQrM/oO/3UWcbAh3XRipvjfQ4u28Kh7e+zzupuDx/p/RmeZbAwVEds6hyL3B44i2Gqf68lNel31113AdHg580332yzzaOPPprMS5AuaGiqlsvuRZWwOTZYGBwsGg1k63eziIj0EqmMkyKRCL///e95+eWXqaqqorCwkNNPP51vfvOb2Hb0D39fXYdGlX4iIiKZK1XxVlVVFQD5+fkttufn51NRURFv43a78fv9rdrE9q+qqmp1jMPbHG7dunWsXbs2/ri4uJjly5czaNCgbr6ajg0dOjRpx85U6rOuU58dUl5Vgz+vc5kMv7/l5y6PN8LggoHJuKwepyv91Jzfn3tU/VRZEyDkdO8Tpce2Kezn77hhGuysCXLg8xrcFnypeBA5bpvyqhqob/191hmWFcSf173X2tH7MyxQxZ66MLU5hYwdkN2tc6RCqn6vJTXpd/PNNyfz8JJgvbHSz7LAZ0OtA7URi+xedO0iItK3pTJOeuKJJ3j22WdZsGABI0aMYPv27dx77734fD7OOeeceJu//OUvzJ8/n2HDhvH4449z2223ceedd8ZHi69atYo33niDa665hry8PFavXs2yZctYvnx5PHl41113sX//fhYvXgzAihUruPvuu7nhhhtS9nq74lDSr6nkT0RERDJGqu9LHT6C33SiCqKzbdqrDpgzZw7nnntuq2vYt28f4XC4w2N3hWVZDB06lL1793bqukV91h3qs9Ycl4fAwciRG1kWfn8ugUBti2lMQi4Xn332WZKvsGfoVD8116zPQi672/3kuDyUVXXhvM1MLnDRUHuwW/sm2wsHXIDN2JwIVfs+pwpwXF7A1er7rDNMoYfAwUC3rqWj7+NBls0eXGz6vIbBwcpunSOZmv9ec7lcSR2YA0lO+k2YMCGZh5cECjkQNrE1/dJ8MV2U6zLUOha1ERjgSffViIiIdE4q46QtW7Zw0kknMW3aNAAGDx7Mhg0b2LZtG9B6HRqABQsWcNVVV7FhwwbOOuus+Do0CxcujE9LtXDhQq6++mrKysooKSmJr0OzdOnS+LRU8+bN48Ybb2TPnj1tTkmVbrH7Z7qfICIiknlSFW8VFBQAxGdUiKmpqYlX7hUUFBAOhwkEAi2q/Wpqahg/fny8TXV1davjNz/O4TweDx5P2zdDkpUwMcYoGdNF6rOuU58dYuh4eGJ8qkVjWrQ1JO93QU/TmX5q7vA+624/dfW8rfbtge/PvqDF3qCNjWGiL9Ls83Lb32edlax+Gu51eKfWxWdBi4hjsHvobICpeq+TmvSLqaurY8uWLRw8eJCpU6e2mspA0i9W5ee2DJ5emPQjBLWORuiLiEjvk4o46fjjj+fZZ5+NJ9527tzJ5s2bmTt3LtA31qGx2lnjIhb2GI68voDWeOg6rceSWurv1FJ/p5b6O/Uyrc+THW8NHjyYgoICysrKKC4uBiAcDrNp0yYuvfRSAMaMGYPL5aKsrIwZM2YAUFlZya5du+Jtxo0bR11dHVu3bmXs2LEAfPTRR9TV1cUTgyIiIslWVhudJnVMttMj18g73ACPwWsZgsZif8hikLdv5wiSnvRbu3YtTzzxBMFgEIDbb78dv9/PrbfeyuTJk/nGN76R7EuQTqh3emeVH0CuDWAIGYugFuQREZFeJFVx0vnnn09dXR0//OEPsW0bx3G4+OKLmTlzJtB31qEpr6pptfaANxKESAR3lhd/bvtTBvSltTASTeuxpJb6O7XU36ml/k69TOjzRMVbDQ0N7N27N/64vLycnTt34vf7GThwIOeccw7r1q1j2LBhDB06lHXr1pGVlRWPt3w+H7Nnz2bNmjXk5eXh9/tZs2YNRUVF8UFVI0aMoKSkhBUrVnDVVVcBcN999zFt2rQeOWOCiGQ2y+0hcsShke1zYTDhUMcNpcc5ELL4NGhjYZiU271pS1PNtmCY1+HjRhefBm0GeXvHdSdLUpN+Tz/9NGvXruWrX/0qU6dOZdmyZfHnpk2bxsaNG5X06yF643p+MXbTun51Tev6iYiI9AapjJNeffVVXn75ZRYtWsTIkSPZuXMnq1atorCwkNNPPz3eLpPXobEsCzvb12rtgUjYAmwaGoIEnMZ29+9La2EkitZjSS31d2qpv1NL/Z16bfW52+1O+ho0iZbIeGvbtm3ccsst8cerV68GYNasWSxYsIDzzz+fYDDI/fffT21tLWPHjmXx4sXxGQwA5s6di8vl4o477iAYDDJp0iSuv/76+NrIAIsWLeKBBx5g6dKlAEyfPp0rrrjiaLpBRKRbIlhHtVZdumtL6h2oDls0ONFCEcsCtwXZtiHPZcADEAoAAQAASURBVPCn+wJ7qLcC0dK+UdkO/VIyT2RiDM8yfNwIe4IWJem+mDRL6tv2t7/9jXPPPZfvfve7OE7LEqxhw4bpxkkP0psr/SA6xWedY1GrSj8REeklUhkn/e53v+P888/ntNNOA6CoqIh9+/bxxz/+kdNPP71vrUNz2NoDsdDH4ciThPfUtRZ6A63Hklrq79RSf6eW+jv1enufJzLemjhxIo899li7z1uWxYUXXsiFF17Ybhuv10tpaSmlpaXttvH7/SxatKjT1yUivcPRVM11daplYyBkIGygvBGcsIUDREz0c42rKfmVY0cTYFnWobXOM0EgAuVBm4Bz2Itq+nN2MGKxLwRuDMPsEP1675+5hPsseKjKr6SXVPnFDPdG/87vD1k0OJDd9GG/uz97vblaNalJv/Ly8hZrwzSXk5NDXV1dMk8vXdDQ9EswuxdW+gHk2oZ9QECVfiIi0kukMk5qbGxsMYIcwLbt+E28vrwOTezDbS++nykiIiLt0H0pEekpjqpqrrD1IMmQgUYHGh2LRgeCxiLc2EBj2G6R4NjaAND+MgYAXsuQ7zYM9BiGeByGeg3eXlgYEnbg00aL/eFDK7f3c0WLRbKs6Ae+kLGojUQTf2EsPgmEsbCxXDApm175uhPFGHjzYLTKb3xO76ryA8h1QYHLoSpi81nQpjg7mgTs7s9eT6hW7a6kvnU+n6/N0d4QDbz69euXzNNLF9Q3fd/3xuk9AXwugOhinfURyO1oBxERkTRLZZw0ffp0Hn/8cQYOHMiIESPYuXMnf/7znznjjDOA6MjRvroOTfNKPxEREcksui8lIpkg6BgOhqHOsah3LOocCJu2Ch8MxBN+JlrN57Jw4eAiWuEH4Jho8qu+KVkYNBb7QtHqtw9wYWM4JstQnBtNBPWGKsBABNbvh+qmhF9/t8NgT1vJS8MAT7SSvTpisT/iojYM7x2ErQEPk/0RxuU42L3gNSfajgab/WEbj2WY7O9dVX4xw7MMVXWwp9GiODvdV5M+SU36TZo0iSeeeIKTTjoJr9cLRG8qRSIRnn322XZHW0nqxaf3dKX5QropOh8zNDhQHoTiIw9gERERSbtUxkmlpaU8+uij3H///VRXV9O/f3/OOussvvWtb8Xb9NV1aA59JBYREZFMo/tSItIbOQZqIxBwLAIRi3drDYbDb9oavBZk2ZBlGbJsyM/NJtxQj9sCF9Fk3eQCF/YR1koPG6gJW1SFLcpDFnuDNjURi08aLT5pBK9lM8hj6O82PTb5VxW2+HulmzoH3JZhpNchr4Osh2VBodswoiCLPVW17A+7OBix2HjQzYd1hmn+MCOzeu5rTrRGB95oWstvoi8SnxqztxnmddhU52Jv0MaYSJ95/w6X1KTfRRddxI9//GN+9KMfcfLJJwPR+dR37txJRUUFP/zhD5N5eumC+qbh7b210g+iU3w2OBafNyrpJyIiPV8q46ScnBy+973v8b3vfa/dNn11HRq72WhXERERySy6LyUivUUs+VYdsQhEwBy2BpnXMvhsQ46L6FebFtVoFuDPchEIdm1Ao9uC/h5Df49hTA5AhMqQxfYGm4/qXQSNxadBi4qQ4ZgsB38PKxjZH7J4ttJN0Fjku2GYx+nSFJ2WFd1vxgDYejDMOwEXNRGLF6o9DPY4TPdHGOQ9+g+LsXUWG5qqK0NN6yuGqiEbm1w7moQsSFNy9Y2DLuodi3yXYWJu750HZ7DXYGOodSwORuh1U5QmSlJf9tChQ/npT3/Kgw8+yNNPPw3ASy+9xMSJE1m4cCEDBw5M5umlC+KVfr00iw/gdxn2h+HzRsCf7qsRERE5MsVJPcOh6T0tVO8nIiKSWRRviUhP5pholVpV2CLgAM0SfR7L4HcZ/C74Qn8XW2tSN91ioccw3RNhUr6Ll/Y7lActGo3F9gYXhW6H4V4Tnyo0nWrCsL4p4TfQ43DGAJvNB7t3LNuC430OY7Id3qt18UGdTXnI5q+VNkM8Dif4IhyT1fnX7TQl+Godi7qIRW07U7JWhKB5isZrGYZ5DcXZXTvf0djTaLG1wQUYvtgv3CPe2+7yWDDIY/g8ZPFZ0Kafu/cmMI9G0nOdI0aMYPHixYRCIQ4ePIjf749PqSA9Q+yXEPTySr+mdf2qw9EFWXN72MgTERGRwylOSr/YKErTe0MgEREROQLFWyLS0zQ60Qq1yrBFpFmiL9s25LsM+W5DlnXos4ovTVkYjx1NoBS6DZ8HLfaHLSrDNrURQ1GWgy+N915rI/BspYcGY9Hf7fCVgjBu19H/bvfaMC0vwnhfhLcDLrY32Hwesvm8OrrW3TFZTrxPsu1oUi5somsKBp3oOon1TV8Pr9YEQ7YNXiua1LUsGJxl0xCOUBuJ9m/QWHzcaPFxo02WZTjeF2G8z0nadJv1DrxaE00Rjc9xGJyAqsZ0G+p1+DxkszdoM96npF9SeTwe+vfvn6rTSRc0xn8JmV47Xy9Ey9Fz7Ogvq8+CNmNz+uYPtYiI9D6Kk9LHbqruU9QgIiKS2RRvicjRstyeFkm6Lu1rRaftLA/aBJyWFX39m6Z1zErCfVnbsnDc3UuGWU1ZR7cFx2RFr3FXo03QWGxrsDnGG50WNNVCDjxX5abWsejnMpxZGMZrJ/YzXa4LTsuPMNUf4YO6aPKv3rHY2eBiZ0Nbe7TOgLow+FzRJal8LoPvsClZASbngx2OVnE6JpoM/rjRZkfT+d6pdfN+neH4HIdJuZEuTV3aEcfAS1Vu6hyLPJdhmj911aTJNMxreKcWPgtafXZwb1KTfmvXru2wzbe+9a1kXoJ0Qmxqz+w2fvH0NnkuQ71j8VnQYmxOuq9GRESkfYqTeoZY6KOkn4iISOZRvCUiiRTBoqyq64mR2ggcdFyUB2OJIUOeCwa4HfJcJHUNNweLsqpwt/adXOhp8TjXBcflOOxutKmJWOwOWjQ4DsO8qVuHzjHwUrWbyrBNtm34SmEoqctV+VwwPS/CNH+EipDFp0GbA2GL6rBFowMRE02s2hg8VrRaM9sGn6tltWZn2BYM8hoGeaPn+7jR5r1am8qwzXt1Lj6qt5nsjzAuxznqKTiNgdcOuvg8FK1gPKMghKcXFwM1N9BjcFuGoLE4ELY47Nu4T0hq0u/3v/99h20UXKVffQZM7RnjdxnKQ9FKP2MiaVn4VEREpDMUJ/UMtqb3FBERyViKt0QknYIO7A1aVEWi2RSLaFXfII9JaMVWKrktGJXl8HnIojxkUxG2CRnDyCwnJcUkrx108WnQxoVhdkEYf4qmGLWaJeQO57i9lFUldhipbUFxtsPoLIdPGi3eCripjli8dtDNB3WGqf4Io7Ocbt37jiX8NtdH1/GbmR+mIGXzQSafbcFQj2F3MFoYVNgHC4OS+nY++uijrbYFAgE2btzIU089xQ033JDM00snxSr9kjkqIlV8NrgsaHCi82Kno8RcRESkMxQn9Qyx8EeVfiIiIplH8ZaIpIMxcCAcTTg4TUsqHeuz8Bin1yb7mrMsGOo1ZNvRhFR1xCLcYDM6++gr0I7kgzq7RaJqYB+472tZUJRtGJEVYmu9zTu1LgIRi5er3WxyO0zLizCsC+vwRQz8o8bF9oZotvTkvAgjszKvH4d6HXYHbT4L2kxI98WkQcp/zfj9fmbPns3MmTNZuXJlqk8vbcikSj/bgiFN01R/FlSZn4iI9C6Kk1LPUqWfiIhIn6J4S0SSKeTA9gabT4M2DhY+2zA22+GL/e2MSPg1V+A2FGc72Bhqneg6f6Ekjabc3Wjx+sFoomqaP8Ko7L71Ac62YJzP4RsDQ5TkhnFbhv1hm2crPfz1gJtdDRZOB13yWaPFk/s9bG9wYWGY2S/M8b7MHP4aS4SWBy0ifetbBUhD0i9m7NixvPfee+k6vTRTHzm0pl8mGJYd/fpZMENekIiI9DmKk1JHlX4iIiJ9k+ItEUm0g2HYUm9T61hYGIZ7HY7NdvClaArKdPC74NhsB7dlaHAstjbYNCT4w9W+kMVL1W4MFsflRJiYoYmqzvBYMNnvMGdgiPE5EWwM+0I2L1R7+P0+D6/WuNhWb7MvaFEZstgbtHi/1uapA26erfJwMGKRY0enRh2Tk7n9WOA2ZNuGCBYVwXRfTeqlbbbWnTt3kp2dna7TSzOxSj+fKzPS3sOyol/3Bi1CDhmzCKmIiPQdipNSJzYvQGZEQSIiItJZirdEJFGMiSam9oYswCLbNozKcsjqI/ckc1wwNtthe4NN0Fhsq7cZE4TBCXj9VWFYX+kmbCyGex1OyYt0ax27TJNjwyn9IkzOjfBhvYstdTaNxmJrvYut9W3vY2EYl+Mw1R/JuKrTw0WnoHXY2eDis8Y0Vr6lSVKTfi+++GKrbaFQiF27dvH888/zpS99KZmnl05qcDKr0i/fDX6XIRCx2BO0+ly5t4iI9A6Kk3qG2GLzBgtj0AdIERGRDKJ4S0SSzTGwO2hRFY7eWB3gdhjmNfHPGX2F14axOQ47GmzqHYtnK2BWvsWIo1gvriYMf6/0EDQWAz0OswrCfa5fO5Ljgqn+CFNyI3wesvikwaYqYlETjk73mWVHC31GZjmMynLIyeCq08MN8xp2NsDeRhjuSffVpFZSk3733ntvm9s9Hg9f+tKXuOyyy5J5eumkuqakXyas6QdNC5xmOWyqc/FJo82o7Ei6L0lERKQVxUk9Q/MxTw7Qhz4DiYiIZDzFWyKSTBEDHzfYBBwLMBzjNQzwZMb91e5wWzAm2+HjRptAxOL5KjdT/dHpOLs6uPJAyOLvVW4aHIt8V3Q6So8Sfu2yrWiSa5i34/vglttDhO51pmWlp2rItiwct7dL+wzJAWpgfxCGuMHVh75/kpr0+81vftNqm8fjoaCgIJmnlS6KzbPsy5CkH8DIpqTf7kYbx0T61A+1iIj0DoqTeobmIULmREIiIiICirdEJHnCBnY22NQ5FjaGUdkOeRpBiMuC4iyHelxsrbN4M+CmIuRwar9wp2eZ+7QxuoZfyFgUuh2+UtD5faVjESzKqrpXJDO5MD1vhINFWVW4y/t5reiUs7UR6Je2he5SL6kvddCgQck8vCRAyEDIZNb0ngCDPIYsy9BoLMpDVnydPxERkZ5CcVLPYFnRtQ0M0elPujngUURERHogxVsikgwRA9sbbBocCxeG4mwHnxJ+cZYFp+TDQFeYjQdd7Gq0+bzCw0l5EcZkt1/1FzLw5kEXm+ujnTnY4zC7IJzx689J8vhdhgNhi0DEop+77wzz7UP5TWlLQ1NS34XJqBJp24IRWQ7bGlzsarAZluWk+5JERESkh7KBCNHpPUVERERERNoTMbCjWcLv2BwnowopEsWyYJzPob/H8GqNi6qwzSs1bspqDeNyIgzzGvq5DY6BmojFxw02H9VHq7IATvBFmObX7G1ydKJJPzgYiU7B21ckNel30UUXdbqtZVk88sgjSbwaaUt9bD0/F12eW7mnK2pK+n3SaHOy0W08ERHpWRQn9Ry2Ff3w7vSdzwAiIiJ9guItEUmkSLMpPV0Yxijh16GBHsO5/cO8X2fzfq2LgxGLNwLtpyT8LsOpeWGGZ+nDmRw9f1MFbqOxCDng6SM/r0lN+l1wwQW8+OKLNDQ0MH36dAoKCqisrOTNN98kOzub008/PZmnl06ob8qF5WTQen4xw7IMbgy1jkVFyGJ4ui9IRESkGcVJPUcs7tcQIRERkcyieEtEEsUx8MoBqG1aw6842yGnjyQQjpZtwYm5Dsf7HHbU2+xosKkMW/GqvmzbMMAdrQA8JstgZ1hhiqSP24JCD1SGoj+7BRmYA2lLUpN+OTk5FBQUcNNNN5GdnR3fXl9fz09/+lOysrI477zzknkJ0oF4pV8G/pFyWzAy22FHg4vtDRaT031BIiIizShO6jnsppk+VOknIiKSWRRviUgiGAOvH3SxqyG6HvhoreHXLZ6mKT/H+aLDLRsccFlk1JJTXWVbFo7b2619XRhMOJTgK8o8Q7KiSb9ABAr6yGJ3SU31PPPMM5x33nktAiuIBl3nnXceTz/9dDJPL51wKOmXmXe5xmRH/4jsqLeJ6E6eiIj0IIqTeg5V+omIiGQmxVsikgib620+rI9m+UZmmfiUgXJ0su2+nfADcLAoq4p061+EPt55nTQkK9pPgUjf6a+kJv0OHDiAy9X2b0GXy0VVVVUyTy+dUB+Jfs3UpN8wryHHNjQai201wXRfjoiISJzipJ4jNn2MY/rOhwAREZG+QPGWiBytPY0Wrx2M/h6Z2g8K3Jl5D1UkUw3JAjAEm9b16wuSmvQ75phj+POf/0w4HG6xPRwO8+c//5ljjjkmmaeXTohX+mXoCBXbOlTt996BxjRfjYiIyCGKk3qOWKpPH99FREQyi+ItEWmL5fbguL0d/qvGy0vVHgwWxTkwMS/dVy5ySGxq0K7+s6y+NdjVa1vxpc0CTt947UmdxfTiiy/m5z//OQsXLuTkk0+moKCAqqoqNm7cSFVVFf/f//f/JfP00gn1TdntTK30AxiT4/B+nYutNUGmZkFW3/jZFhGRHk5xUs9hWwawNL2niIhIhlG8JSJtiTRNqXgkjoGtDTZBY+GzDbmWA5YHOPJ+IqkSnRo03HHDw0wu9CThano2v8tQ71gEIlDYB9b1S+pLnDZtGj/5yU945JFHePrppzEmmlgaO3YsV199NZMnT07m6aUTDq3pl+YLSaJCt6G/23AgbLGt3maCT3+cRUQk/RQn9RzxNf0ydwyUiIhIn6R4S0S669OgRYNj4cIwKsuJLwkgIr1Prm3YB9RGLPrCHD9Jz2ueeOKJnHjiiTQ2NlJbW0tubi5ZWVnJPq10gmOgoQ9U+gGM8zn8s8bFh3U2x+dE9IdaRER6BMVJPUM86ZfWqxAREZFkULwlIl11IGRRGbYBQ1G2gyeDiyVE+oJcF8TW9Qs64M3wn+mUFTPG5op1u7t2ynXr1rFx40Y+/fRTvF4v48aN47vf/S7Dhw+PtzHG8Pvf/57169cTCAQ47rjjuOKKKxg5cmS8TSgUYs2aNbzyyisEg0EmTZrElVdeyYABA+JtAoEAK1eu5PXXXwfgpJNOorS0lNzc3KN56T1WowFDNLudneHf6MfmOLxd6yYQgd2NFkXZmZ3kFBGR3qW7cZIkRmwwkCr9REREMpfiLRHpjPpItMoPYIjHkOdK8wWJyFFzWeCzoc6BQMSif4YXQCU90nnvvfd4+OGH2bZtGwD/9//+X8aMGcP999/PiSeeyCmnnHLE/Tdt2sTZZ5/NscceSyQS4ZFHHuG2227jV7/6FdnZ2QA88cQT/OUvf2H+/PkMGzaMxx9/nNtuu40777yTnJwcAFatWsUbb7zBNddcQ15eHqtXr2bZsmUsX74c245mvO666y7279/P4sWLAVixYgV33303N9xwQ7K6J2Ust4cILcvb6kLRr1m2BR5vu6PbM2FxT7cFJQOy+Wd5PR/WuSjK7vp8xyIiIol2tHGSJIYq/URERDJXquKtBQsWsG/fvlbbv/rVr3LllVdyzz338OKLL7Z47rjjjmPp0qXxx50ZsC4iyRMx8HGjjcHC7zIM9mR2YkCkL8l1Geoci4AD/dN9MUmW1Pqu9957j6VLlxIKhfj6178enzsdoF+/frzwwgsdHmPx4sWcfvrpjBw5ktGjRzN//nwqKirYvn07EK3ye+qpp5gzZw6nnHIKRUVFLFiwgMbGRjZs2ABAXV0dzz33HJdffjmTJ0+muLiYhQsXsmvXLsrKygDYvXs3b7/9Nj/4wQ8YN24c48aNY968ebz55pvs2bMn8Z2TYrEFapv/21QdXdvOwrR6rvk/Q+9P+gFMG5SNhWFvyKYylBmvSUREeq9ExEmSGKr0ExERyUypjLduv/127rvvvvi/G2+8EYAvfvGL8TYlJSUt2vz4xz9ucYxVq1axceNGrrnmGm699VYaGhpYtmwZjqOhSSLJZkx0drCgsfBYhqIshwyogxCRJn5XNAaojViYDP/sn9Sk36OPPsrUqVP52c9+xsUXX9ziuVGjRrFz584uH7Ourg4Av98PQHl5OVVVVUyZMiXexuPxMGHCBDZv3gzA9u3biUQiLRZo7t+/P0VFRWzZsgWALVu24PP5OO644+Jtxo0bh8/nix8n04RM09QWGfYHzLYsHLe35T+Xh4a6Wopyoi/2/QZP6zZuL5bbk+arFxGRviIZcZJ0TywUcjJkoJOIiIhEpTLe6tevHwUFBfF/b775JkOGDGHChAnxNm63u0Wb2L0t6NyAdRFJnsqwRXWkaR2/LCfj7peK9HW5drT4KWQsghme9Evq9J47d+7khz/8IdB6ish+/fpRU1PTpeMZY3jwwQc5/vjjKSoqAqCqqgqA/Pz8Fm3z8/OpqKiIt3G73S2CqVib2P5VVVWtjnF4m7aEQiFCoVD8sWVZ8SlFLcuKv+50T5FpQavbWOGmb26P1XEtX3ev/mhedXf3dbB4t+qw6TstC7/fRXQSU5vtdQavccg6LO09ucCFrWE8SdNTfh5E70VPofehb0t0nNSRAwcO8Lvf/Y63336bYDDIsGHDuPrqqxkzZgzQt9dIjlX6ZfpoPxERkb4m1fFWTDgc5uWXX+ZrX/tai/Nu2rSJK6+8ktzcXE444QS+853vxO9FdTRgvaSkJCnXKiLQ6Bxax2+ox5CrdfxEMo7dtK5fbdO6flkZvK5fUpN+tm0TiUTafK66ujq+Jl9n/fa3v2XXrl3ceuutrZ47PHgznbhr09k2R7oZu27dOtauXRt/XFxczPLlyxk0aFCLdkOHDu3wXMlUXlWD/7CVZ8trghCK4Mvy4s9rv8LNsoL48/ztPp/o/ZK57+D8XPZFGqkKOlTiZWyet8XzHm+EwQUDu3Ve6bx0/zzIIXovega9D31TouOkIwkEAtx0001MnDiRn/zkJ/Tr14/PP/8cn88Xb9OX10i2icaEmjhLREQks6Qy3mpu48aN1NbWcvrpp8e3TZ06lS9+8YsMHDiQ8vJyHn30UW699VaWLVuGx+Pp1ID1tnQ0GD2RNGix69RnXZeKPmteHOEY2BVbx8+OruN3pDOno8ChU/vG+suysA67552Oa7ZI/fd9W0UvR96hWZ/R/evt8nnb2D/V+x71O9PG91myz5uIff0uQ61jURuBgR1M+JfI7+FU/y1IatJv7NixvPTSS3zhC19o9dw///lPxo0b1+ljPfDAA7zxxhvccsstLUaTFxQUANFKvcLCwvj2mpqa+GipgoICwuEwgUCgRfBUU1PD+PHj422qq6tbnbf5cdoyZ84czj333Pjj2Bu3b98+wuEwlmUxdOhQ9u7d26kkY7I4Lg+Bgy0D3fpGC7AxoSCBg43t7msKPQQOBrp8zu7ul5R9LQu/P5dAoJaBtqEKF5/Xh+lPEG+zar+Qy8Vnn33WrfNKx3rKz4Povegp9D6kltvtbjUoJ50SGSd15IknnmDAgAHMnz8/vm3w4MHx/x++RjLAggULuOqqq9iwYQNnnXVWfMqphQsXxkegL1y4kKuvvpqysjJKSkriayQvXbo0PmX6vHnzuPHGG9mzZw/Dhw9P2GtKpPiafum9DBEREUmwVMZbzT3//POUlJTQv3//+LYZM2bE/19UVMSxxx7L/PnzefPNN+PxV1s6+pzQ2cHoiaRBi12nPuu6ZPZZ8+KInQdD1Dth3BYcPyCHLFf7N+V7YoHD4fz+ljOspOuau1tYUVkTINTddUwjEfx5OV3eze/PPapCkLaKbTorHe/P0Z4TWn+fpeK8idh3UGOEzyuD1BqbXH/2EZNwySgOStXfgqQm/c4//3yWLl3Kz3/+c2bNmgXA1q1bef755/nXv/7FzTff3OExjDE88MADbNy4kSVLlrS4QQXRG1YFBQWUlZVRXFwMRKdR2LRpE5deeikAY8aMweVyUVZWFg+yKisr2bVrV7zNuHHjqKurY+vWrYwdOxaAjz76iLq6unhisC0ejwePp+20cPPAzBiT1hu6pulfc83X9Ovoyrp75UfzihO5b3zkgTH4XOC3DQHH4vOQxYgs02I/3XhPvnT/PMghei96Br0PfVMi4qTOev3115kyZQq/+tWv2LRpE/379+erX/0qX/nKV4CO10g+66yzOjXlVEdrJLeV9EvV6HTrCCNfYx/PHNP+6MF0jFTt7TSyPbXU36ml/k4t9XfqZUqfpzLeitm3bx9lZWVcd911R2xXWFjIoEGD4gOPOzNgvS0dDUZPJA1a7Dr1Wdelos9ixREHI/Bpgw1YHOONEKqrJXSE/XpUgcPhmhU8NF+3IF3X3N3CCsfloayq7QrtjpzY1ett1mcRt4tPyyu6dV4sm8DB+m7tmo7352jPCbT6PkvFeROxr2XAwibkWOyvqSXbbn+/RBYHNf+95nK5kj4gPalJv8mTJ7NgwQIefPDB+Louv/3tb/H5fMyfP5/jjz++w2P89re/ZcOGDfzHf/wHOTk58SkNfD4fXq8Xy7I455xzWLduHcOGDWPo0KGsW7eOrKwsZs6cGW87e/Zs1qxZQ15eHn6/nzVr1lBUVBS/cTVixAhKSkpYsWIFV111FQD33Xcf06ZN67Gj0o9WqOnn0m31vaBjiNch0ODiQNhikMe0WttPREQk2RIRJ3VWeXk5zz77LF/72teYM2cOW7duZeXKlXg8HmbNmpXWNZJTOTq9vKqm7RGJIQcaGjGW1e7oQU0B3n0a2Z5a6u/UUn+nlvo79Xp7n6cy3op5/vnnyc/PZ9q0aUdsd/DgQfbv3x+ftaozA9bb0tnB6ImkQYtdpz7rumT2mSF6X3RXYzTh19/tkO/uXAFCTylwOFzzgofD26bjmrtbWNFW4UpX9++s5n0WwaKsqnsDJSYX2j32+yIZ54weoPX3WbLPm4h9rS6s65eM4qBU/R1IWtLPcRz27t3L9OnTOfXUU9m8eTPV1dXk5eUxfvz4Ts+b/swzzwCwZMmSFtvnz58fnxv9/PPPJxgMcv/991NbW8vYsWNZvHhxfJQ4wNy5c3G5XNxxxx0Eg0EmTZrE9ddfH19/BmDRokU88MADLF26FIDp06dzxRVXHEUv9GzheNIvvdeRDrkuyHMZDkYs9gZtRmVrQi8REUmdRMVJXTnfscceyyWXXAJEE2uffPIJzzzzTHzUO6RnjeRUjU63LAs729fmiMRGB8BFxDHtjh7UFOBdp5HtqaX+Ti31d2qpv1OvrT7vaVOldyTV8VbsnC+88AKzZs3C5To01VtDQwOPPfYYp556KgUFBezbt4+HH36YvLw8Tj75ZKBzA9ZFJDGMgU8bLcLGIssyDPfqb4tIXxFb1y8QgQEdrOvXWyUt6WeM4Uc/+hHXX389U6dO5cQTT+zWcR577LEO21iWxYUXXsiFF17Ybhuv10tpaSmlpaXttvH7/SxatKhb19nbOAacpgmsPH0w6Qcw1OtwsN6mOmJRFwFf96ZeFhER6bJExUmdVVhYyIgRI1psGzFiBP/617+A9K6RnPLR6W2MSIyFQg7tjx7UFODdp5HtqaX+Ti31d2qpv1OvN/d5quMtgHfffZeKigrOOOOMFttt2+aTTz7hpZdeora2lsLCQiZOnMi1117b5QHrInL0dtZDdcQGDCOznPga3yKS+fwuw+ehaKVfdIByuq8o8ZKW9HO5XBQUFPTa4DDTxab2tDD01dAxx4ZCt6EybPFZ0GaMqv1ERCRFUh0njR8/nj179rTYtmfPnvho/XSvkZxuh4J8C8egD/0iIiIZIB33paZMmdLm4HWv18vixYs73L8zA9ZF5OjUReC1quj/h3iMigBE+pgcO5oTiWDRYCAnAz//JzXfM2PGDF588cVknkK6KTa1p8ciI7PZnTXEY7CIlvQe7N4asSIiIt2Syjjpa1/7Gh999BGPP/44e/fuZcOGDaxfv56zzz4boMUayRs3bmTXrl3cc8897a6R/O6777Jjxw7uvvvudtdI3rJlC1u2bGHFihU9fo3k5gGxhquJiIhkDt2XEpHmjIFXatwEDeTYhsEeRf8ifY1tQW7TTYDaSGYmRpJW6QcwevRo/vGPf3DLLbdwyimnUFBQ0Go9l1NOOSWZlyDt6Mvr+TXntWGgx7AvFK32c0ySM+EiIiJNUhknjR07luuuu46HHnqIP/zhDwwePJi5c+fypS99Kd6mL6+RHO11Q6zSz9XH4yMREZFMoftSItLc5nqbz4I2LmBkltOnCyFE+jK/yxBwLAIRi4EZmPxPatLvnnvuAeDAgQNs2rSpzTaPPvpoMi9B2hEy0b9qfT3pBzDYYzgQMjQai+11MC4r3VckIiJ9QarjpOnTpzN9+vR2n+/LayRbVnTQj9P0T0RERDKD7kuJSExNGN44GJ3Lc2o+BMNpviARSRu/y0AIaiPRCuBMGwCQ8KTf7373O/7t3/6NAQMGcPPNNwMQiURwuTRBck9yaHrPzMtkd5XLgsFew2dBi3dqYMxAJUNFRCQ5FCf1XLYFjon+ExERkd5L8ZaIHM4xsKHGTQSLoV6H8bk271an+6pEJF1ybLBj6/o5kJNhIULCk35/+tOfOPXUUxkwYAATJkzAcRy+853vcPvttzNmzJhEn066KaTpPVsY4DZUhAz1jsV7tS5K/FrgT0REEk9xUs8Vm6BUlX4iIiK9m+ItETnce7U2FSEbj2U4rV8Yy/Km+5JEJI0sC3JdcDACAccix5VZo3+1fFkfFdb0ni3YFgzzRm/zvVdrc1Al/iIiIn2Kkn4iIiIiIplnf8jindpoGc8peRFyM6yiR0S6x9+U6AtEMi9BoqRfH6XpPVvLd8HQLHCweD2Q1OUuRUREpIexm+J8Te8pIiIiIpIZIgY2VLswWBRlORRna4ifiETl2tEP/7F1/TKJkn59lKb3bM2y4KR8sDB80mjzaaM6R0REpK+I/dV30N9/EREREZFM8FbARXXEJts2nNovjKVQP2Vsy8Jxe7v8z9KbJCkSW9fPwaI+w8YDJKWcac+ePdh2NJ/oOE58W1s0n3rqGdO80i+919LTFHjgeJ/DB3Uu/lXj5usDQniUGhcRkQRSnNQzxSr9Mm2En4iISF+keEtE9gYtNtVFfw/M6BcmW/f3UsrBoqyq6+snTS70JOFqRFprvq5frWPhy6B1/ZKS9Lvnnntabbv77rvbbPvoo48m4xLkCCJAdDy7UaVfG0pyI+xqsAk4Fm/XuvhCXiTdlyQiIhlEcVLPZGMAS2v6iYiIZADFWyJ9W9CBV6rdgMXYnAgjsjLnZr6IJI7fZTgYsQhELAZ5Muf3RMKTfldffXWiDykJFmq6m+W2UFl7Gzw2fLFfmL///+zdeXyU1d3//9d1zZJ9g7BFCHtQpGziUsVKsda6tGqrfK27LNXCrdjfbasteoveIFq1WlFbrAuK1gUUsaKAWjfQStVqbkXZkV0IZLIns1zn98ckAyEBkpDMZCbv5+ORB+Sac22fmcycOZ+z+Dx8XWnTJ8mhizdx/uhFRCR2VE9qv7Smn4iISGJQfUskflhuD6EDpte3gF2+UhyXh0NVzV0YTDDQ6GOflbuocCzSXYbj09WZX0Qad+C6fomSK2n1pN+YMWNa+5DSyrSe3+HlJRn6J4dYX+1iRambczoHNBWqiIgcMdWT2q+62X400k9ERCS+qb4lEj9CWBT66iflLCA9w0V5WeiQSb+h2S4am7Fzh99iTZULCE/rqWV7RORgDlzXL9UV6ytqHXrb64ACJpy9UhLr0EZlhEi1DaUhi3+XJshfvIiIiDRKI/1EREREROJbwIGPSsNjXApSQnTXzF0icgh16/pBeF2/RKGkXwcUrP2881j64DuUJBtGZwUBw7pqF5uqE+cPX0REROrTSD8RERERkfj2n3IX5SGLNNtwnKb1FJEmSHeFcyTlocRp+1fSrwPS9J5N191r+F5auPnvo1I3ZcEYX5CIiIi0ibpqkZJ+IiIiIiLx5zu/xTe103p+X9N6ikgTHbiuXyLQ218HpOk9m2dYWohcj0PAWLxX4o6MlBQREZHEUTe9pzGqIImIiIiIxJOggQ9rp/UckBwiL0mNdyLSNAeu65cIlPTrgDS958HZloXj9tb7wePl1E42STbsDdp8XO5tUMZxe7HcnlhfvoiIiLSQpvcUEREREYlPn5e7KAtZpNiGURma1lNEmi4R1/Vzx/oCJPo0vefBOVgU+hqfwzPPCxurbdZXWtQEHTp56idNh2a7lEUXERGJU3Uj/Rz1iRIRERERiRt7AxZfV4Zb5L6fGcSrxjkRaaZ0l6EsZFEesujiif9GAb0NdjDG7D/SL7bXEm8yXNCt9o9+m9+iSh2HREREEoZN+DNeI/1EREREROKDMfCvMhcGi95JDj01raeItECireunpF8HE074WYDRSL8W6OoxZLgMBotva2yt7yciIpIgNNJPRERERCS+rKmyKQrYeCzD8RmNz9wlInI4ibaun5J+Hcz+U3taSvo1m2VBryQHj2XwG4stNXZCZP9FREQ6Oq3pJyIiIiISP6pC8Fl5eCGu4ekhUl0xviARiVuJtq6fkn4djKb2PHJuC3onOViE5/rdHVAwRURE4p1G+omIiIiIxI9PSyBgLDq5HQalqOueiByZdFe4MaA8FP9t/Ur6dTABE37RKul3ZFJdcJQ3/EawM2BRpvX9RERE4ppG+omIiIiIxIeyEGyqAjCclBmKdOATEWmpRFrXzx3rC5Do2je9Z5y/ctuBTh5DheNQHLTZXG0zPAQZsb4oERERaZG6ac8NFsZoGnQRERERiQ3L7SFE8yujLgwmGGiDK2pfHAPbasJd9galOOR61MYpIkcuxQ6/j4YSYF0/Jf06GE3v2bqO8hqqHEO1Y/HBHjgzB1yKrYiISNzZf/oLAy1oZhEREREROXIhLAp9zZ9Sami2q0NM6VYUsPAbi2QbRqRr6i0RaR116/qVhuJ/is+O8Fkg+6mb3tMd36/bdsOuXd/PhaEoAJ+WadVgERGReLR/pTjOO/WJiIiIiCSkgIFdgXCj5sgs8KplW0RaUVrtun4VTnwnT/TW2MEEIiP9NPS9tSTZ0Csp3Dz4TZWLjVX6sxIREYk3lgUW4fqRo2qSiIiIiEi7853fwsEixTb0TYn11YhIoknfb12/eG4XUHaigwnWdl3X9J6tK9MNQ9LD//+o1IUvqACLiIjEm7qKsUb6iYiIiIi0L1Uh2Fvb3pbndbQGt4i0uuTadf0cLPbG8RKpWtOvAzEGgrUr1HiU7m11QzOhyO+w02/zrs/NOZ0CirOIiEgcsS0Imfju0SciIiKx8eKLL7JgwYJ627Kysvjb3/4GgDGG+fPn8/bbb1NeXs7AgQOZMGECvXr1ipQPBALMmzePFStW4Pf7GTJkCBMnTqRz585RvReR9sYY2O63AYssl0OaVtcRkTaw/7p+39VA1zgdUaykXwcSiDRgGfTZ2PpsC07NCrJ4j4fSkMWHpS5+kBVSzyMREZE4UfeRrZF+IiIi0hK9evXi1ltvjfxu2/t6Ai9atIjFixczefJkevTowcsvv8yMGTN44IEHSEkJtyrOnTuXTz/9lKlTp5KRkcHTTz/NXXfdxd13313vWCIdTWkovMaWhaGHVz30RKTtpLkMpSGL72rge0r6SXsXjKznhxJRbSTFhtOygiwpdvNtjYuvKw2D09R0KCIi7cvChQt57rnnOPvss7nqqquA1ut9Xl5ezpNPPsknn3wCwKhRoxg/fjxpaWlRvceWsC1AI/1ERESkhWzbJjs7u8F2Ywyvv/46F1xwASeeeCIAU6ZMYdKkSSxfvpwzzjiDyspK/vnPf3LdddcxdOhQAK677jp+/etfU1hYyPDhw6N4JyLth2Nghz+c9O7iMXiV/xaRNlS3rt9uf/j9x47DPIreJjuQwH5JP2k7XbyGURkhAD4td7Hbr4CLiEj7sW7dOt566y169+5db3td7/Px48cza9YssrOzmTFjBlVVVZEyc+fOZeXKlUydOpU77riD6upq7rrrLhxnXweXBx98kE2bNjFt2jSmTZvGpk2bmD17dtTu70jUzYTgoM9uERERab6dO3dyzTXXMGXKFB544AG+++47AHbt2oXP52PYsGGRsh6Ph8GDB7N69WoANmzYQCgUiiT8ADp16kR+fj5r1qyJ7o2ItCN7ghZ+Y+G2DF086p0nIm2rbl2/gNm3jmi8UdKvAwmY2vX84vO1GleOTnHokxTCYLG81L3f1KoiIiKxU11dzezZs7nmmmvqjbw7sPd5fn4+U6ZMoaamhuXLlwNEep9fccUVDB06lL59+3LdddexefNmCgsLAdi6dSuff/451157LQUFBRQUFHDNNdfw2WefsX379pjcc3O4autIIX1ui4iISDMNHDiQKVOmMG3aNK655hp8Ph+33HILZWVl+Hw+ILzG3/6ysrIoKSkBwOfz4Xa7SU9Pb1Cmbv/GBAIBKisrIz/7d9iyLKvVf9rquIn80+yYQct/2sH9Hum9UhszLAvHwK7azvQ9PAa31Ur328L4cgTPTVvvu3/c4uWaY33O/WMWb3GKZYzrYhYP99qSfW2LyLqh3/ntVv8siAZN79mB7Bvpp5astmBbFo7bG/n9hE6waxeUhSw+rfByQnbj+7kwmGAgOhcpIiId2mOPPcaIESMYOnQoL7/8cmT74Xqfn3HGGYftfT58+HDWrFlDamoqAwcOjJQpKCggNTWV1atXk5eXF50bbSGXZQBLST8RERFpthEjRkT+n5+fT0FBAddddx3vvfdepG50YGOfMYevdByuzMKFC1mwYEHk9759+3L33XfTpUuX5lx+s3Tv3r3Njp2omhOzXb5S0jNchy94AI83RNfs3GbvF0uHutf09DS+LQsQIkiK26JXTmq9v6Ejud+Wxtiy/KRnpB++YAz3TU+vv6xCPFxzrM+Znp4Wd3E6kn2P9JzQ8HUWjfNGc9/OdhBPTZCunbLo0Tm5RedtTLQ+P5X060D8tTNveTS+s004WBT6gvW2dfPAxpCLNRUQCIbIaOQvbmi2S0NuRUSkza1YsYKNGzcya9asBo8dqvd5UVFRpMzhep/7fL4GxziwzIECgQCBwL7OL5ZlkZKSEvl/a7H27/l6kMazfdN7HtCLsfb3aPXKSxTR7s3Y0Sne0aV4R5fiHX2K+ZFLTk4mPz+fHTt2cPzxxwPhulJOTk6kTGlpaaTulJ2dTTAYpLy8vF59q7S0lEGDBh30PBdccAHnnntu5Pe652z37t0Eg8GD7dYilmXRvXt3du7c2aSEpbQsZo7LQ3lZqNnnCrhc7Nixo9n7xVKj92pZpKenUVxawfbK8Nibbq4gFeX1X89Hcr8tjbHJ8VBeVt6ic7b5vrVxKy+vgP1ea+36mmN9zv1iZrLdcRWnI9n3SM8JNHidReO80dw3w8DoLi5sfzGt8ba6/2eBy+Vq0445oKRfh6LpPaMvwwWd3Q57gjZb/TaDXE5cLv4pIiLxraioiLlz5zJt2jS8Xu9By7VF7/O6MgdrNIxm7/RdvtJD9khMIQDBILbbQ3pG/TjFY8/p9kKjAaJL8Y4uxTu6FO/oU8xbLhAIsG3bNo455hi6du1KdnY2hYWF9O3bF4BgMMiqVau49NJLAejXrx8ul4vCwkJOPvlkAIqLi9m8eXOkTGM8Hg8ej6fRx9oqMWeMUdKvmZoTM1P70+xz0HbPeVtp7F7rOuh95w93sE+1DRmuhuWO5H5bGuO6fVuqLfeNdGw0ptFYtdV522LfaJ1z/5hF87ztYd8jfqdo5HXW1ueN6r5W27ynRus9Wkm/DkTTe8ZGd6+hNGQIGIudfou8JMVfRESia8OGDZSUlHDzzTdHtjmOw9dff82SJUt44IEHgCPvfZ6dnR1Zl2Z/+x/nQNHqnW5ZFnZy6iF7JIYCFmBT7Q9QXuav91g89pyONY0GiC7FO7oU7+hSvKOvsZi73e4275kez55++mlGjRpFbm4uJSUlvPTSS1RVVXHaaadhWRZnn302CxcupEePHnTv3p2FCxeSlJTE6NGjAUhNTWXs2LHMmzePjIwM0tPTmTdvHvn5+fWmVxfpCKqDDnuD4e8F3b0OGnQsItJ0Svp1EMbsn/SL7bV0NC4LjvI6bKpxURS0yHEbUpo/bbiIiEiLfe973+Pee++tt+0vf/kLeXl5nHfeeXTr1q1Vep8XFBRQWVnJunXrGDBgAABr166lsrLyoNNSRb13+iF6JNZNtx3C4sBS8dhzur3QaIDoUryjS/GOLsU7+hTzptu7dy9//vOfKS0tJTMzk4EDBzJz5sxIovS8887D7/fz2GOPUVFRwYABA5g2bVpkWnOAK6+8EpfLxf3334/f72fIkCHcdNNN2LYWBZGOZXN5EINFum1IVxuaiEizKOnXQVQ7EF6NxijpFwOZbsgKOpSEwtN8DkhWLyUREYmelJQU8vPz621LSkoiIyMjsr01ep/37NmT4cOHM2fOHCZNmgTAo48+ysiRI8nLy4viHbeMq3Y2hJDaNkVERKSZbrjhhkM+blkW48aNY9y4cQct4/V6GT9+POPHj2/lqxOJH1UO7K4Or7fX3evE+GpEROKPkn4dREXt2rQeCyWbYiTPayirMlQ5FnuDFp09alEUEZH2o7V6n19//fU88cQTzJw5E4DjjjuOCRMmRP1+WsJVW0dS0k9EREREJDZ2+sPfLbJchlSN8hMRaTYl/TqIyv2SfhIbHju8vt92f3htvyy3wa3nQ0REYmT69On1fm+t3ufp6elcf/31rXWZUbVvek8REREREYm2ihCUhvat5SciIs2nScE7CCX92ofObkOyZQgRTvyJiIhI+6GRfiIiIiIisWHMvlF+XVNcJKvVWkSkRTTSr4PYl/RTK1YsWRbkJTlsqHaxN2jRya3nQ0REpL2oS/oZLIzRlOgiIiIiEnvGQNBAoLYJyWWFO/XbB9RVbcvCcXtbdA4XBhMMHOGVHpnyEFQ4FhaG/HQ3gUp/TK9HRCReKenXQUSSfuolE3PpLsh2OfhCNtv9NkZ5PxERkXZh/yVDQqiiLCIiIiKxETBQErQoC1mUh8Kd0vZnYUizIdNtyHEbXBY4WBT6gi0639BsV0yngzMGdgbCV9DZbUhy2cQ2BSkiEr/UltFBVGh6z3alh9dQWmWodCw2VMLApFhfkYiIiFgW2BgcLEIGrb0rIiIiIlFVHLT4qgw2VtoHJPpMpE0vaMJJwHIHyv0WO/2GXI/haCd+e5WXhCyqHAsbQzdv/N6HiEh7oKRfB6HpPdsXjw1dPYadAYvPSqF3Z/BqFKaIiEjMuSxwjNb1ExEREZHoqQzBf8pdrK+uaxyySLEN2W5DhsuQZO2bet4YqDFQFrLYG7CoMRa7AhaLvzN084RnmIon4bX8wjeX6zHqeCcicoSU9OsAjIEqjfRrd3I9huKgocax+LzcxQmZoVhfkoiISIfnAgKEp/cUEREREWlLjoHVVTb/KXcRNOFGu/xk8BIi9SDJO8uCZAuSbUOu21Aagh1+m4qQxYaQiy4eh+4eEzfrU+8NWviNhQtDF4963omIHKm4SPqtWrWKV199lY0bN1JcXMyNN97ICSecEHncGMP8+fN5++23KS8vZ+DAgUyYMIFevXpFygQCAebNm8eKFSvw+/0MGTKEiRMn0rlz50iZ8vJynnzyST755BMARo0axfjx40lLS4vezbaBagccYP+pACT2bAvykhw2VrtYXWUzMMUhR5UbERGRmLItwIQbYERERERE2kppEJaXuimqXcuui8dhVHqIzqkeCn1NO4ZlQZYb0l0ONZaLdRWwO2Djdxx6JZlw3bYdCzrwXSB8kd284bUJRUTkyMTFhII1NTX06dOH8ePHN/r4okWLWLx4MePHj2fWrFlkZ2czY8YMqqqqImXmzp3LypUrmTp1KnfccQfV1dXcddddOI4TKfPggw+yadMmpk2bxrRp09i0aROzZ89u8/traxVO+BPTvd9UANI+ZLjCPbgMFh+XuTBqYBQREYmpuoaGkFGlSURERERanzGwrsrmtb0eigI2HstwUkaQn+QE6dLC9excFpyUY9PL62BhKAnZbKy22/2U9asrIGgsPJahk7t5F2tbFo7b26IfSw2kIpLA4mKk34gRIxgxYkSjjxljeP3117ngggs48cQTAZgyZQqTJk1i+fLlnHHGGVRWVvLPf/6T6667jqFDhwJw3XXX8etf/5rCwkKGDx/O1q1b+fzzz5k5cyYDBw4E4JprruGWW25h+/bt5OXlRedm20Dden5efZ61S8dlwfZqw65AuELWL8U5/E4iIiLSJlwYwNL0niIiIiLS6vwO/KvMxabq8Nyd3T0Op2QFSWuldfhyPAaPbdhUbVPhWGyqtumb7LTLEX81DnxVFv5/N0/zRyU6WBT6gi0699AcT4v2ExGJB3Ex0u9Qdu3ahc/nY9iwYZFtHo+HwYMHs3r1agA2bNhAKBSKJPwAOnXqRH5+PmvWrAFgzZo1pKamRhJ+AAUFBaSmpkaOE6/qRvppas/2Kc0N30sPNy1+Wu7Cr5yfiIhIzOwb6Rfb6xARERGRxLLbb/HaXg+bql1YGEakB/lRTusl/Oqku6BfsoONiST+2uPU9V9VuPAbSLIMOc0c5SciIgcXFyP9DsXn8wGQlZVVb3tWVhZFRUWRMm63m/T09AZl6vb3+XwNjnFgmcYEAgECgUDkd8uySElJify/brh4LIeNV4bqkn6Gll5FtPdr9X3r4m9ZWIeZQzPa12wBx6YZ1lUZykIWhRUujs9MzMxfe/h7kDA9F+2DngeR9kdJPxEREZH2xXJ7CLWwtcaFwQQDhy/Yhowx/F+5zX/KbQwW6bbh1KyWT+XZFKku6JvssLHaptyx2Fpj0SvJtJtlfypD8HVleCxKd6/Tbq5LRCQRxH3Sr86BDaamCYujNbXMoRpjFy5cyIIFCyK/9+3bl7vvvpsuXbrUK9e9e/fDnqutBKtLodJPRkoS6WnNf8oty096RvrhC7bSfm25b3p6WkzOeygeb4iuXXP5SZqf+RtK+abSxcn5nclNSZg/zwZi+fcg9em5aB/0PIi0H3XTYGh6TxEREZH2IYRFoa9ltbOh2a6YTnNWEYLn15XybXl4OF+fpBAnZYbwRuGi0lzQuzbx5wvZJAUcurVhorE5CitchLDI9UJmK490FBHp6OI+q5CdnQ2ER+rl5OREtpeWlkZG7mVnZxMMBikvL6832q+0tJRBgwZFypSUlDQ4/v7HacwFF1zAueeeG/m9LkG4e/dugsEglmXRvXt3du7c2aQkY1vYXe4GLIy/mvIWDCAzOR7Ky8qjtl+b7GtZpKenUV5eEV4xOVrnbYKAy8WOHTtIBXoludhSY/Pq+r2c2SnULudcPxLt4e9BwvRctA96HqLL7XY36JQjcqB9I/0sQH+XIiIiItIym6stPip1U2MCuC3DCRkh+idHd1RbhguO8hq2+S2+C9gk2SGyY9wa7AtarK0KZz1HZMJ3VbG9HhGRRBP3Sb+uXbuSnZ1NYWEhffv2BSAYDLJq1SouvfRSAPr164fL5aKwsJCTTz4ZgOLiYjZv3hwpU1BQQGVlJevWrWPAgAEArF27lsrKykhisDEejwePp/HFX/dvwDXGxKxBt7y2M5TXbnnTVbT3a+19I1N6GnPY40b7mg37XiujMoLs8HvYFbD5vNwwIj0xxxnE8u9B6tNz0T7oeRBpP1xW+G+xPa57IiIiIiLtX8DAJ2Uu1laFh7B1T3FzUloVma7YVDA7eww1jkNR0GZLjY3XckiN0eg6Y+DfZS4MFvlJDt2SbCX9RERaWVwk/aqrq9m5c2fk9127drFp0ybS09PJzc3l7LPPZuHChfTo0YPu3buzcOFCkpKSGD16NACpqamMHTuWefPmkZGRQXp6OvPmzSM/P5+hQ4cC0LNnT4YPH86cOXOYNGkSAI8++igjR44kLy8v+jfdSvwO+E3dmn4xvhg5rAwXfD8zxAclbv6vwkVXj8NRSWp1FBERiZbISL/YXoaIiIiIxKGigMXyEjelofCsEUPSHM4a0Jld31UdauKpNtfDa6gxhrKQxaYamwHJTlSmGD3QNr/FDr+NjeG49CDgjf5FiIgkuLhI+q1fv57bb7898vvTTz8NwGmnncaUKVM477zz8Pv9PPbYY1RUVDBgwACmTZtGSkpKZJ8rr7wSl8vF/fffj9/vZ8iQIdx0003Y9r5PuOuvv54nnniCmTNnAnDccccxYcKEKN1l2ygPhVuukux9jVjSvvVNdtjlD7G6ysUHJW7OzAmS41HiT0REJBrqOj2H9NErIiIiIk3kd+DzchffVNmARYptGJ0VJC8JXO1g7RbLgvwkh/VVNtVmX+IvmpcWMvBJWbgp+phUhww3tGAVIhEROYy4SPode+yxvPjiiwd93LIsxo0bx7hx4w5axuv1Mn78eMaPH3/QMunp6Vx//fVHdK3tTd3UnulaFDeujMoIsTdosTtg82axmx93CsR8znUREZGOYN+afrG9DhERERGJD5urLVaWual0whXJfskhRmWESLYBYp/wq+OyoE+yw9oqm2rHYkuNRX6Sidoag19V2JSGLJJtw/fSNK+GiEhbURohwZXVjvRL1zPdbtmWheOuP52BBfwwF94qgr0BizeLvfywM3Q6YNYDFwYTDETvYkVERBJcXdLPIbzmSLQaQURERESkfbHcHkKHSNqVBOA/pbC1Ovx7ugtOzIYeyS5c2O2yvcZrhxN/G6ptSkI2uwMOXb1t39utNAiFFeERCaPSQzGZWlREpKNQKijB1U3vme4C1GO9XXKwKPQFG32suwcqgzZVjsUbuw09vabeVJ9Ds12oniQiItJ69n2uWjjsm+5TRERERDqWEBaFvoYj0moc2B2w2Bu0CHfbNnTxGLp5DLurYXd1+26vSXNBntewzW+xMxAeedeWjIGPy9w4WPTwOvRN1qSeIiJtSUm/BFe+30i/qvbXwUgOw21BvxSHLTU2ZSGLLX6Lcschz2u0RqOIiEgbsC2wMBgsQkZrIouIiIhIOHFV4cCegEVJqC7ZB5kuQ3evUzuVZ/zo7DFUOw57gjaba2xKApDTRvXeddU2O/w2NoYTM4KaSUNEpI0p6Zfg9h/pp6RffHJb0CfJ4buAxa6ARXHQpixk6OlVzygREZG24LIgaLSun4iIiEhHZgxUhqA0ZOELWvjNvmxVhsvQ1eOQFsfTQuR5DdWOocKxeHcPnNWJVk9elgbh32XhIA1PD5GplmgRkTant9oEZgyU185CkO6G3bG9HDkClgXdvYYMl2FLjY3fWGyqcWEXw/FpkBRnPcpERETaMxcQBBpO5iQiIiIi7ZEx4TWZndpOW3V9tyqC4A6FZ3NwWeF63oEjzRwTnrKzyrEoC0FJ0KIoaLM7ADXOvqyejSHbbejsMaQkQDuMZUHvZIe1VeHZpd7xuTkjJ4i7lUbiOQY+KHETNBbdPA6DU9V5XUQkGpT0S2DVDrULDhtSNTdVQkhzQUGKw06/RVHQYkOlxY5qD9/PDNIzScMRREREWoMrXH3SSD8RERGRdihkoCIEFY5FtWNR40DAgKFh29c3VQDeetsswkumWLXHchrZr46NIcMFmW5DlstgJ1jzmtuCvskOm6pd7A7YfFDi5rSsYKvc56flLvYEbbyWYXQrHVNERA5PSb8EVje1Z6qt9WgSiW1BXpIhy23YFXRRFrT4p89D/+QQJ2aGWq1HloiISEdV1yDhmNrsn4iIiIjEVMiEp9ksDlq1s1odrPGjfurPwsLB1CtvsAg2qOIZkizIcBsyXYYctyE3xc22Cifhk1XJNozpDG8VhWeXWlHq4pTM0BHd9+pKm68rw6Mkv58ZjOtpUEVE4o2SfgmsrDbpl+GqX7mRxJDmgnM6QaEvxNeVNuurXZSGLMZmBzXdp4iIyBFw1TYMaXpPERERkdiqCsGqCvi60q43Is9rGdJdhmQbkm2D1wqPWjswUTU024UVCGAIJw5DhKedDJlw1y67dj9vI/s6bthR2dZ32D50TYIfZAV5r8TNxmoXxsDorJYl/rbVWKysXcdvRHqQ3snqRCciEk1K+iWwyHp+Ln24Jiq3BaMyQvRKcnjH52Z3wGbJ3vAc7KnqRSUiItIidTMkaHpPERERkdgIOPBlpYtVFXZtRywLrxUegZftNs3q7GzVTuVpW+Bpm8tNCPnJhtMI8n6Jm001Lvw+i1OzmtexfFO1xfISNwaL/skhhmgdPxGRqNN4oARWN72nkn6Jr5vXcGanICm2oSRk85bPjV/1KhERkRapmyo7oCqUiIiISNRtqrZ4ZY+H/6twEcIi1wO9k0IMSnHo5m1ewk+aJz/ZcFp2EBeG7X6bxXs97A0cfrifMbCqwub9EjcOFr2THE7KDGFp4jERkajTSL8EVhZJ+sX4QiQqctyGn3QKsGSvB1/Q5h2fmx/lBLWeo4iIsHDhQlauXMm2bdvwer0UFBRw2WWXkZeXFyljjGH+/Pm8/fbblJeXM3DgQCZMmECvXr0iZQKBAPPmzWPFihX4/X6GDBnCxIkT6dy5c6RMeXk5Tz75JJ988gkAo0aNYvz48aSlpUXvho+QJ5L005p+IiIi0jRNqW89/PDDvPfee/X2GzhwIDNnzoz83pT6Vryy3B5Ch1h+pjoEK32wuTr8e7oLRmZBfgoU+qJyiQL0SjL8pFOQ93xuykMWi/e6GZjiMCwtREojbYx7AxYfl7nYHQhnYwelhDg+48jWBBQRkZZT0i+BldYm/bLcaqzqKDJccHp2kKXFbr4LhBdfPlU9q0REOrxVq1Zx5pln0r9/f0KhEM8//zwzZszgT3/6E8nJyQAsWrSIxYsXM3nyZHr06MHLL7/MjBkzeOCBB0hJSQFg7ty5fPrpp0ydOpWMjAyefvpp7rrrLu6++25sO/wl/8EHH2TPnj1MmzYNgDlz5jB79mxuvvnm2Nx8C3itcN0poFHzIiIi0kRNqW8BDB8+nMmTJ0d+d7vrN801pb4Vr0JYFPoaXzW5PASba2yCtZ2uunrCP74ayE/1QJyttmxbFo7biwXs8pXiuDxN7kpmtYNGnM4ewzmdA/yr1M23NTZrqlysrbLp6jF08xpclsHvWGzzW/iC4del2zKMSA9xdIqjdigRkRiK79qCHFSNA1VO+BM2U9N7diidPIYxWUEsDJuqXXxVqT9zEZGObtq0aYwZM4ZevXrRp08fJk+eTFFRERs2bADCo/xef/11LrjgAk488UTy8/OZMmUKNTU1LF++HIDKykr++c9/csUVVzB06FD69u3Lddddx+bNmyksLARg69atfP7551x77bUUFBRQUFDANddcw2effcb27dtjdv/N5an96PSrCiUiIiJNdLj6Vh232012dnbkJz09PfJYU+pbicYY+M5vsaE6nPBLsgwDkx26e01cjxRzahOchb4Qq6tckf835cccYjRkNCXZcFp2kDNzAuR6HAwW3wVsCitc/KfczVeVLnxBGwtD76QQ53UOcEyqEn4iIrGmkX4JqiQY/oRNtQ1eG9RRPTHV9Rw7UDc3HG9gZQl8Vu4mKwmOSq5fxoXBBANRulIREWlPKisrASKNTLt27cLn8zFs2LBIGY/Hw+DBg1m9ejVnnHEGGzZsIBQKMXTo0EiZTp06kZ+fz5o1axg+fDhr1qwhNTWVgQMHRsoUFBSQmprK6tWr601v1Z55axsqHCxCBk2VLSIiIs12YH2rzqpVq5g4cSJpaWkcc8wx/PKXvyQrKwugSfWtAwUCAQKBfd/tLcuKzNLQ2iPG6o7X0uNatT91Qga21NiU1M5U1cntcNRBkn0tvRPXQdpNmsKybKwjGGFohQ9SdzAs0/QeZS293yPZ72DPa/ckOCcpRFkwxDa/HVnjz8LQ1WvomVS3zmLzz37gayK8sekxO5JXeLRj3Ob7HiJu7faaY33O/WIW1fO2g32P+NOhme9prXHeaO97qPfFZh/rCD8/m0tJvwRVoqk9O4Rwz7Fgo48ZA53cFnuDNu/tMQxMceotdj0026WhviIiHZAxhqeeeoqjjz6a/Px8AHw+H0CkwalOVlYWRUVFkTJut7tBw1VWVlZkf5/P1+AYB5Y5ULQaqqxmNB64rHDnmBAWAQNuq3Ur/B1FtL/YdHSKd3Qp3tGleEefYn5kGqtvAYwYMYLvf//75ObmsmvXLl544QXuuOMO7rrrLjweT5PqWwdauHAhCxYsiPzet29f7r77brp06dIm9wbQvXv3Fu23y1dKekZ4UTh/yLCquIaKUHhcW/9MD91SG2+mtCw/6RnpjT52OI7lZ0OwZUm/fp6Wn/fAa05Pb/r61i293yOJk8cbomt27iHL9AAKWnT0g9v/NXGgw8XsSO43FjGO1r4Hxi0erjnW50xPT4u7OB3Jvkd6Tmjee1prnTfa+zblfbG5Wvr52VxK+iWoupF+2Ur6dViWBXleQ7VjqHQsNlXbDEhxNFpBRKSDe/zxx9m8eTN33HFHg8cObNgzTei519QyB2s0jGZD1S5faZO/nCTXVFMRNLiSUkhPdrVJhb+jiNYXGwlTvKNL8Y4uxTv6FPOWOVh96+STT478Pz8/n/79+zN58mQ+++wzTjzxxIMe71D1rQsuuIBzzz038ntdnWv37t0Eg413Em4py7Lo3r07O3fubFId8ECOy0N5WYgaBzZU2/iNhRtDn2SHtFCI8rLG9zM5HsrLylt0zTHf17JIT0+jvLwi3Du7Dc97JNcbcLnYsWNHi/Y9EnWviXqaGLNYPLcxfz0dykHi1q6vOdbn3C9mJtsdV3E6kn2P9JxAs97TWuu80d63Nd8X9//8dLlcbdoxB5T0S1h1Sb8srefXodkW9E52WFdlU2MsNtfY9EnS/OoiIh3VE088waeffsrtt99O586dI9uzs7OB8Ei9nJycyPbS0tLIyL3s7GyCwSDl5eX1ep+XlpYyaNCgSJmSkpIG593/OAeKVkOVZVnYyalN/nLiMjZgUVpZjTdgYtYQEs+OtGFQmkfxji7FO7oU7+hrLOZut7vNG6kSwcHqW43JycmhS5cukTpGU+pbB/J4PHg8nkYfa6u/F2NMi45tgIoQbKy2CWHhtQx9k8OzEh3uaEdyJ7HcNzLDhDHNOlZLz3sk+8Xi/dXQ8JqbE7NYPLft9bV4qLi112uO9Tn3j1k0z9se9j3iv/Zmvqe1xnmjvW9bvC9G631WSb8EFUn6aaRfh+exoHeSw/pqm7KQxXcBi+5evS5ERDoSYwxPPPEEK1euZPr06XTt2rXe4127diU7O5vCwkL69u0LQDAYZNWqVVx66aUA9OvXD5fLRWFhYaSXenFxMZs3b46UKSgooLKyknXr1jFgwAAA1q5dS2VlZftpqGrilxOPZQALv9nXGKGG5pZpacOgtIziHV2Kd3Qp3tGnmDfd4epbjSkrK2PPnj2RTldNqW/Fsx3V4RF+DhYpdjjh51anZBERkValpF8CChgod5T0k31SXdAzybClxmJXwCbZdmJ9SSIiEkWPP/44y5cv53e/+x0pKSmRNWFSU1Pxer1YlsXZZ5/NwoUL6dGjB927d2fhwoUkJSUxevToSNmxY8cyb948MjIySE9PZ968eeTn5zN06FAAevbsyfDhw5kzZw6TJk0C4NFHH2XkyJHk5eXF5N5bylu78G1AH5kiIiLSBIerb1VXV/Piiy9y0kknkZ2dze7du3nuuefIyMjghBNOiJQ9XH0rXn1bbfFBCThYpNuG3slafkRERKQtKOmXgEprR/klWYZkO8YXI+1GjttQ7TjsDthsqbEo8kNXvT5ERDqEZcuWATB9+vR62ydPnsyYMWMAOO+88/D7/Tz22GNUVFQwYMAApk2bRkpKSqT8lVdeicvl4v7778fv9zNkyBBuuukmbHvfB8r111/PE088wcyZMwE47rjjmDBhQtveYBvw1DZCBYxFK0x+IiIiIgnucPUt27bZsmUL77//PhUVFeTk5HDsscdyww03NLu+FW++rbZ4v8SNIbwMTa8kB1sJPxERkTahpF8CqpvaM1uj/OQA3T2GasdQFrL4ZxGcmWOR49HrREQk0b344ouHLWNZFuPGjWPcuHEHLeP1ehk/fjzjx48/aJn09HSuv/76Fl1nexKe3hP8+pgUERGRJjhcfcvr9TJt2rTDHqcp9a14si/hZ9E3BdItB0sJPxERkTYTv92E5KBKQpraUxpnWZCf5JBqG/wG3ix24wvG+qpERETaH29kpF9kXXcRERERaYb9E379kkN8Pwcl/ERERNqYkn4JqG6kn5J+0hiXBX2THTp5oNpYLNvrYW9AtW4REZH9uS2wMIBFQFUqERERkWY5MOF3cmZIU3qKiIhEgZJ+CchXl/RzqYVKGuey4PTO0MnthBN/xW52K/EnIiISYVn7r+sX22sRERERiSdK+ImIiMSO1vRLMEEDZaHw/7WmnxxKkgt+nBPkbZ+b3QGbN4vdjM0O0t2r142IiAiEk35+AwGjVioRERERgOLSchyXh4O1HGyugg9KwAB9U+CkHBdYLhzCa0hL4rHcHkK07LnVa0JEpPUp6Zdg9gQsDBYptiFF4zjlMLw2/CgnyDs+Nzv9Nm8XuxmTHeSoJCX+REREPLYBx8LvxPpKRERERNqHgONQ6As1mvQrCcK3NTZgke1ySLcMX5bse3xojidalylRFMKi0Bdq0b56TYiItD6lhRJMUe0UjV08RosjS5N4LDg9O0hPr0MIi3d8br6t1otHRETEq+k9RURERJrkwIRfryS1S4mIiMSCRvolmN2BcB4316Mu6dJ0LgvGZAdZXuJiU42L90vcnGxC9E/R60hERDquujX9/FGe3jNooDhoURywKAtZVDrgdywcwJjwdFkO4d57tgUuwp/ltmVw1W2zwGXbBIxF0LDvx6He7yEDHhuSbUh3QZYn3HmsiyuAWw11IiIi0gRK+ImIiLQfSvolmP1H+okcim1ZOG5vvW0ndwaXD9ZXWqwodROwoSCt4b4uDCYYiM6FioiIxEiyHa5PVYbCyba25AvCt9U2O/w2u2una4+WagfKgN0AVQAWNh66eQ15Xoc8ryHbrcY7ERERaUgJPxERkfZFSb8EUhGCSsfCwtBJST85DAeLQl+wwfZUoLPbYk/QZqUPtlQ6DZLIQ7NdmhtYREQSXqoNNoYQFnsC0LWVP/zKQ7Cp2mZjtU1xsP7Bk21DJ7ch02Pj8zu4LbAsalOB4ZSgwcKY8Kg/U/ez3++dk2z21DiREYH7/jX7fgdChEf9+R2Lagf82FSGLHb4LXb4bT4F0mxDrySH/GSHrp7w/iIiItKx+YKwWQk/ERGRdkVJvwRSN8ov220i01GJNJdlQZ7XYFsOuwPhEQch49BN60SKiEgHY1mQ4YKSEGyrhq6pR37MGic8om9jtc13gX2JPhtDntfQM8mhh9cho7aW7ri9FPoO1pnr0J28hmbbFBaHmnF14eN9LwvKavzs8Ntsr7HZ6beocCy+qXLxTZULrxVOAPZKcshLMpoGVEREpAPyBS0211iARbbboZdXbQbxrLHZoJrK0hMvItKuKOmXQOrW89PUnnKkLAt6eA0uHHYGbHYFbBzj0EOVeBER6WAyXIaSkMX2ahjRwqSf4/KwqcpiYxXsqA6PxKvT1Qt9UyE/xSLJtqB2LH1dmVg0olgWZLsh2+1wTKpD0MCOGovNNTZba2xqjMX6ahfrq124MOQlhZOAPZMckjUVgIiISMIr3i/hl+N26Km2grh3sNmgmmJojqeVr0ZERI6Ekn4JROv5SWvrWjvib7vfpihoE8LhKK9eXyIi0nFkuMKfe3sC4bXvmpPU8gVhbZWL9VUW/v0+PpNtQ7YrvE6e14aqAKw+yFK5sWhEObCntw0c5YGj0sExsNsPW6pgSzVUhCy21FhsqbGxCNcdenlD9Ep2yNQ3DRERkYSzoTykhJ+IiEg7pq/iCcIxsKc26ZfrcQ5TWqTpcj0GG4etfovioE2NYygIQXqsL0xERCQKPHY4SVftWGyvsemXcuh6ljHwbY3NN5XhkfKR41iGHHc40dfeR8M1pae3F+iXFE6EloYsSkIW1Y7Fd36L7/xuPimHLh6H01L9pKq/kIiISELYUGWxvCSEEn4iIiLtl5J+CWJPwCKEhdcyZLpifTWSaDp5DG7LsKXGptKxeH0XnJBh0zvJUQVfREQSXoYrnPTb5rfol9J4GWNgc43FFxUufMFwVs/C0DPJMCDdpqgq8T4zLQtSXJDiMnTD4Hcg3etiS6XDroDF7oDNgg2ldHK7OT4jSDfNFiAiIhK3NlbZLC91YYBO7vAsQIlWtxEREUkESvoliC014calHt7Ea1CS9iHTDQNsh2+rbaodi/dL3PT0OoxID5GjKWVFRCSBZbgMuwOwtcamOODU+9wzBrbUJvuKa5N9HstwTKpDQUqIVBc4bi97qmN19dHjteHodBicHKQqBF9XuVhT5WZvEJYWe+iTFGJURjgmIiIiEl/SXQaXBf3SbJKdEKjtSUREpF1S0i9B1CX98pOUfJG2k2TDgBQHy+XiqzLDVr/N1r02vZIcBqWE6O412Kr4i4hIgkmzobMnPLPC0mIPP+gc/kzc7YdvyqG0diZMjxVOeh2TbuG1XYALB7A6UI+suvUAk9wwIgmGdnbzyZ4AaypgU42LbX4XI7NgQCoNOqq5MJjgQRY3FBERkZjq4jWc2zlITrqXz3YHUOuTiIhI+6SkXwIoCUJJyMLGcFSS1vOTtmVbMDQT+noDfFHu4tsamy21P6m2oXeyw1Feh27ecC9AERGReGdZMLaLxeKdDpWOxVtF9R+3MXT2GLp4DJYD35TWf3xojid6Fxtj+68HaAHpGSmkUM3AZNjqt6lyLD72wZelhp5JDkn7rW84NNtFO1/uUEREpEPLcneszkwiIiLxSEm/BLC5dpRfd6/Bq5YSiZJsN5yWHcIXDPFNpYtN1eH1/r6udPF1pQu3ZejhNRzldeiZ5GgqLxERiWtJtkXfZIctNTaloXAl2mNDttvQya2OLoeT4oIByQ5FQYudfosKx2JNlU03jyHXo5kCRERERERERFqDkn4JYHN13dSeGuUn0VE3dReE1/o7IRmOM7Ctet9PtWOxpcYKTz1bBjkeOCoJ+u8qJdNo+n8REYk/Lgv6JDsY03BqSjk8y4IuHkOWy7C1xqbcsdgZsNgbNPTwhuMqIiIiIiIiIi2npF+cqwjBnqANGHop6SdRsv/UXQdKs2BgMlQ5UBayKAtZVDpQHLAoDsCX5X6SLDdHJTkclWTI89af2ktERKS9U8LvyHht6Jvs4AtZ7PBb+I3FtzUuSnbB91Jt8pMdjZwUERERERERaQEl/eLcptpRfl08hhRNnyjthGVBqgtSXYZuGIIGyoIWZSGoNDY1jsWGahcbqsHC0NUTXguwd5Kj17GIiEgHYFmQ4zZkugy7AxZFAQtf0OKDUjfeMkOfZIdeSeE1gt1KAIqIiIiIiIg0iZJ+cSxkYFVlOEMyIFmj/KT9cluQ4zF08sCILh52lFaxtdpia41FScjmu4DFdwGblWWGbp5wQ1++EoAiIiIJz2WF16XO9RjcLhdrKwxVjsWaKhdrqlzYGDp7DF08hlyPQxePIdXWaEsRERERERGRxijpF0cst4fQfiuhra0IT6GY6oI+GW6cQzR+WGoZkfbCceia4qZLCowAyoOwuSr8UxSw9ksAQrckyE+B/GRIdoELgwkGYn0HIiIi0srcFgzNhKEpAXb6LTZV2+zw21Q4FrsDFrsDAOHeQCn2viRgN084KWirqisiIiIiIiKipF88CWFR6AsBYAx8U2UDFtkuh69KzCH3HZrjicIVihxeyEChL8SBr9g8L+S6oSQUnt6ryrHYWQM7a2AlhnQbjs206O2BZK0BKCIiknBsy8LxeOnuge5p4fpueQh2+6Go9qc4AFWOxeYai8014QqB24JuXkM3T4huXkNnt5KAIiIiIiIi0jEp6RenioMWAWPhxtDJfeiEn0i88NrQpbb3vt8BX9CiJBROAJY78LEPPsZDJ7ehu9fQ3evQ1WPwKgkoIiIS9xwsCn3BRh9LBnp6Ic8TnumiImRR6VhUhCBoLLbVWGyrCX+1cVvh9YK7e8MjATt5DC4lAUVERERERKQDUNIvDvkd2OEPt1zkajojSVBeG7p6DV0x1DhQErTwY7M3YLE3aLE3GF7T0iLco79bbRKwi5KAIiIiCcu2IM0FaS4DGIyBageyklx8V+3wnd/Cbyy2+y22+8MVAhtDtjs8DWjn2n+z3UoEioiIiIiISOJR0i/OOAa+rbEJYZFiG3I9GuUniS+pNgE4NBuqa/zs9NvsDFjs9NuUhyyKghZFQfiq0gW1DXtdPeGfTh6HTBdKjouIiCQgy4IUFwzOsDg63cYY8AXhu5rwzy4/1Dj7Ogytrd3PBrI90NkLXTyGTnaQbE0LKiIiIiIiInFOSb844hjY5g9PdejC0DvJUcOEdCi2ZZGc5KVPEvSp3VYRDK/7950fdtVAee2agL4grKkKl3HVJgJz3IYstyHdZUhzQbrLkGSFGwxFREQkfjU2NWiOC7KTIWDCU4JWOhZVIYsqJ7xW9t4A7A3AWizAg014KtDO7nC9Id1lyKitM2hUoIiIiIiIiMQDJf0asXTpUl599VV8Ph89e/bkqquu4phjjonpNVWF4L1iKA7agCE/ydEUhtLhHGqtn3QL0pMh4EDlfmv9VNc27O0JWuxpZFe3ZUi3ww16KS5Dig0ptiHZ3v//4FFyUESkydpjXUo6JssCrxWeNjyLfVOC+g1UOeEEoMu22eM3BIxFUcCiKHDgUQyptXWCFBek2ib8f3tf3SG1tr6gDnkiItJaVJ8SERGRllDS7wAffvghc+fOZeLEiQwaNIi33nqLO++8k/vvv5/c3NyYXFNRwOIdn5sqJ7wmSa8khww9cyKN8tiQZUOWe1/DXv9MF3v9UByAsiCUh8IjBKscCBoLX8jCF2rCsS2Dx6r91w43Iu7/f7cVTiJ6av/vsUztv/sec1nh0QK2BS7UOCgiiac91qVE9mdZkGRBkm3IBobnWIQcKAvBXj/sqasvBMPbQsaisnakII33PaoVTvyl2Ka2jgBeu67usO//dXUCd22dYP/f635cqLORiEhHpvqUiIiItJRSRwd47bXXGDt2LKeffjoAV111FV988QXLli3jkksuick1JVmGkIEsN3TzOCRrhJ9Ik1kWpLttNpQFsIBMO/yDJzxlbqC2t7/fsQgaan8sArX/N4T/DxAwdf+3oAlJwiZdH7WJQIgkBF212zxWOIm5L9kIntoGQ29tA6FtgUV4bSLL2vcvhO/P1P7rRP61Ir/vLKqiuMImZMLvMSZ8Z1iNHRMOOJc5zOONlD3gGg+2n4jEt/ZYlxI5FAeL/yvZl83zAJ1c4R9jwh/5fofausG+OkLAWHhsi6qQodoJ1xmqHah2jvzDzGL/TkS1HYns/f4f6VS0X13Asti6q4qyChsLU/9zN/J/0/Cz/sDfG/y/8WMduP/+/xcRkSOj+pSIiIi0lJJ++wkGg2zYsIHzzz+/3vahQ4eyevXqRvcJBAIEAvvmALIsi5SUFNxud+R3AI/HgzGmRdfVyQM/7WaR5nGxsaJl36I9bhdZSS07f0v3jcU5D7qvZZHitrGTPeHWm2idtw33i8t9LQuPy0XmYZ6H1j7vkdzrgEw3a0qC+yXNwo1/xoR7/u+/PZJkI7w9yYaQEx4UUJdQNLVlmsIA/tqfyIZWSjYCUFQNeFvxgK2jrl9Do42L1r4yVuTHtFkDY7NeNS15iVkW7o1lBEPJh/ybCDe3to6DnaVlfyFtp6vHYXCa06rHrPtslrbTFnWp1mJZFrbLjvpnUDzu22rnbGL9q9XPG8V9CzJdGGPCHYkcqHb2TxCGtwWB4H7bHAOh2v+HapOKISf8/0O96zVaLzjgsrfsbR+f7ZHP6P2Sg1j7trfFTAcHHrKxU1iHeby553NvLCUYTI48DVZLP00PcTGJlkNtaX3DwsK9qaw23jGotbS3ilITfT8rhLuFL6LG2jJUl4qO5tanolWXqjt2LOpT8VhHiOzbzPrQkZw35vfaWvuqDa9l+x4kbu36mmN9zv1iFm9xOpJ9j/ScaTTvPa21zhvtfb1uG8vytOicB9q/XuVyuVrlmIeiGtt+SktLcRyHrKysetuzsrLw+XyN7rNw4UIWLFgQ+f2UU05h6tSp5OTk1Ct3pNMvdKn9N+8IDtMt5/BlWnvfWJzz0PumxOi87e+csdy3aweKsYhIR9KWdanWckpGy/eNx8/c9vG5efj6V9ucNzr7iogkMk0lGX3NrU9Fuy4FsalPxWMdof6+Ta8PHcl528e9tta+asNr2b4N49b+rznW50yJ0Xljt+8RnRNo7ntaq5w3Ab7vRatepYkiG2E1MmSksW0AF1xwAXPnzo38TJo0qV4Pq6qqKm666Saqqqra7Hrl8PQ8tA96HtoPPRftg54HSVStWZdqLfp7iz7FPLoU7+hSvKNL8Y4+xTz2mlqfilZdCvS6aAnFrPkUs5ZR3JpPMWs+xaxloh03jfTbT2ZmJrZtN+g5VVJS0qCHVR2Px4PHc/BhnsYYNm7c2OKpPaV16HloH/Q8tB96LtoHPQ+SaNqiLtVa9PcWfYp5dCne0aV4R5fiHX2Keew0tz4VrboU6HXREopZ8ylmLaO4NZ9i1nyKWctEO24a6bcft9tNv379KCwsrLe9sLCQQYMGxeiqREREROKD6lIiIiIiR0b1KRERETkSGul3gHPPPZfZs2fTr18/CgoKeOuttygqKuKMM86I9aWJiIiItHuqS4mIiIgcGdWnREREpKWU9DvAySefTFlZGS+99BLFxcX06tWL3//+93Tp0qVFx/N4PFx44YVRm2pBGqfnoX3Q89B+6LloH/Q8SCJq7bpUa9HfW/Qp5tGleEeX4h1dinf0KeaxpfpU4lDMmk8xaxnFrfkUs+ZTzFom2nGzjCZgFREREREREREREREREYlrWtNPREREREREREREREREJM4p6SciIiIiIiIiIiIiIiIS55T0ExEREREREREREREREYlzSvqJiIiIiIiIiIiIiIiIxDl3rC8gkS1dupRXX30Vn89Hz549ueqqqzjmmGNifVlxaeHChaxcuZJt27bh9XopKCjgsssuIy8vL1LGGMP8+fN5++23KS8vZ+DAgUyYMIFevXpFygQCAebNm8eKFSvw+/0MGTKEiRMn0rlz50iZ8vJynnzyST755BMARo0axfjx40lLS4veDceJhQsX8txzz3H22Wdz1VVXAXoeomnv3r0888wzfP755/j9fnr06MGvf/1r+vXrB+i5iIZQKMT8+fP54IMP8Pl85OTkMGbMGH7+859j2+F+NXoeRNoH1cuab9WqVbz66qts3LiR4uJibrzxRk444YTI43p/a12q70bXsmXLWLZsGbt37wagZ8+eXHjhhYwYMQJQrNuavke0vRdffJEFCxbU25aVlcXf/vY3QPHuyA73+e7z+Xj22WcpLCykoqKCY445hvHjx9OjR49ImY722jjSmJWXl/Piiy/yxRdfsGfPHjIyMjj++OO5+OKLSU1NjRxHMav/OqtjjGHWrFl8/vnnDY6TSDGD1ovbmjVreO6551i3bh0ul4s+ffrwhz/8Aa/XCyRW3FojZj6fj3nz5lFYWEh1dTV5eXlccMEFnHTSSZEyiRQzfe9ovtaIWTQ/CzTSr418+OGHzJ07l5///OfcfffdHHPMMdx5550UFRXF+tLi0qpVqzjzzDOZOXMmt9xyC47jMGPGDKqrqyNlFi1axOLFixk/fjyzZs0iOzubGTNmUFVVFSkzd+5cVq5cydSpU7njjjuorq7mrrvuwnGcSJkHH3yQTZs2MW3aNKZNm8amTZuYPXt2VO83Hqxbt4633nqL3r1719uu5yE6ysvLufXWW3G73fzhD3/gT3/6E1dccUW9Dwk9F21v0aJFvPnmm0yYMIH777+fyy67jFdffZUlS5bUK6PnQSS2VC9rmZqaGvr06cP48eMbfVzvb61L9d3o6tSpE5dccgmzZs1i1qxZDBkyhD/+8Y9s2bIFUKzbkr5HRE+vXr149NFHIz/33Xdf5DHFu+M61Oe7MYZ77rmHXbt28dvf/pY//vGPdOnShf/93/+t93nU0V4bRxqzvXv3snfvXi6//HLuvfdepkyZwhdffMFf/vKXesdSzOq/zuosXrwYy7IaPU8ixQxaJ25r1qxh5syZDBs2jDvvvJNZs2bxk5/8pF4MEylurRGz2bNns337dm666SbuvfdeTjjhBO6//342btwYKZNIMdP3juZrjZhF9bPASJv4/e9/bx599NF622644Qbz7LPPxuiKEktJSYm56KKLzFdffWWMMcZxHDNp0iSzcOHCSBm/32+uvPJKs2zZMmOMMRUVFebiiy82K1asiJTZs2ePGTdunPnPf/5jjDFmy5Yt5qKLLjJr1qyJlFm9erW56KKLzLZt29r+xuJEVVWVuf76680XX3xhbrvtNvPkk08aY/Q8RNMzzzxjbr311oM+ruciOmbNmmUeeeSRetvuuece8+CDDxpj9DyItBeqlx25iy66yHz88ceR3/X+1vZU342+q666yrz99tuKdRvS94joeeGFF8yNN97Y6GOKt9Q58PN927Zt5qKLLjKbN2+ObAuFQubqq682b731ljFGr42WxKwxH374ofnlL39pgsGgMUYxO1jMNm7caK699lpTXFzc4DiJHDNjWh63P/zhD+a555476HETOW4tjdlll11m3nvvvXrHuvrqq83bb79tjEnsmBmj7x0t0ZKYNaatPgs00q8NBINBNmzYwLBhw+ptHzp0KKtXr47RVSWWyspKANLT0wHYtWsXPp+vXsw9Hg+DBw+OxHzDhg2EQiGGDh0aKdOpUyfy8/NZs2YNEO4Nk5qaysCBAyNlCgoKSE1N1XO3n8cee4wRI0bUiyXoeYimTz75hH79+vGnP/2JiRMn8rvf/Y633nor8riei+g4+uij+fLLL9m+fTsAmzZtYvXq1ZGpwfQ8iMSe6mVtQ+9vbU/13ehxHIcVK1ZQU1NDQUGBYt2G9D0iunbu3Mk111zDlClTeOCBB/juu+8AxVsOLhgMAuHXQx3btnG73XzzzTeAXhsHakrMGlNZWUlKSgoulwtQzBqLWU1NDX/+858ZP3482dnZDY7TkWIGTYtbSUkJa9euJSsri1tuuYVJkyZx22231YtrR4pbU19rRx99NB9++CHl5eWRemEgEGDw4MFA4sdM3zuaryUxO9hx2uKzQEm/NlBaWorjOGRlZdXbnpWVhc/ni81FJRBjDE899RRHH300+fn5AJG4NhbzkpKSSBm32x35Y9y/TN3+Pp+vwTEOLNPRrVixgo0bN3LJJZc0eEzPQ/Ts2rWLN998k+7duzNt2jTOOOMMnnzySd577z1Az0W0nHfeeZxyyin85je/4Ze//CU33XQTZ599NqNHjwb0PIi0B6qXtQ29v7Ut1XejY/PmzVx++eVccskl/O1vf+PGG2+kZ8+einUb0feI6Bo4cCBTpkxh2rRpXHPNNfh8Pm655RbKysoUbzmovLw8unTpwt///nfKy8sJBoO88sor+Hy+es+7Xhv7NCVmByorK+Oll17ijDPOiGxTzBrG7KmnnmLQoEEcf/zxjR6nI8UMmha3us4d8+fP5/TTT+cPf/gDffv25Y477mDHjh1Ax4pbU19rv/nNbwiFQowfP55LL72URx99lN/+9rd0794dSOyY6XtH87U0Zgdqy88Cd5NLSrM1Nt/0weaglqZ7/PHH2bx5M3fccUeDxw6MrzHmsMdrahk9d1BUVMTcuXOZNm1aZPHfxuh5aHuO49C/f/9Io0nfvn3ZsmULy5Yt47TTTouU03PRtj788EM++OADrr/+enr16sWmTZuYO3cuOTk5jBkzJlJOz4NI7Kle1jb0/tY2VN+Njry8PO655x4qKir4+OOPefjhh7n99tsjjyvWrUffI6KvbuYJgPz8fAoKCrjuuut47733Ir3HFW85kNvt5r//+7/5y1/+wvjx47Ftm+9973v1Xk8H01FfG82NWWVlJXfddRc9e/bkwgsvPOzxO2rMPvnkE7788kv++Mc/Nvv4iRgzaFrc6v4Of/SjH/HDH/4QCLcXffnll7zzzjuNdrzZf99Ei1tT/z6ff/55KioquPXWW8nIyODf//43f/rTn7jjjjsiSZ3GJELM9L2j+VojZm39WaCkXxvIzMzEtu0G2deSkpJGM7XSdE888QSffvopt99+O507d45srxvm7/P5yMnJiWwvLS2NxDw7O5tgMEh5eXm9XgilpaUMGjQoUqax7Pv+x+nINmzYQElJCTfffHNkm+M4fP311yxZsoQHHngA0PMQDTk5OfTs2bPetp49e/Lxxx8D+puIlmeeeSYy2g/CDSq7d+/mlVdeYcyYMXoeRNoB1cvaht7f2o7qu9HjdrsjPbj79+/P+vXref311znvvPMAxbo16XtE7CUnJ5Ofn8+OHTsio2YUb2lMv379uOeee6isrCQYDJKZmckf/vAH+vXrB+i10ZjDxaxOVVUVd955J8nJydx444243fuaZRWz+jH78ssv+e6777jqqqvq7XffffdxzDHHMH369A4XMzh83Ore0w9sLzrqqKMoKioC9Fo7MGY7d+5kyZIl3HffffTq1QuAPn368M0337BkyRJ+9atfJWzM9L2j+Y4kZnWi8Vmg6T3bgNvtpl+/fhQWFtbbXlhYGHnRS/MYY3j88cf5+OOP+Z//+R+6du1a7/GuXbuSnZ1dL+bBYJBVq1ZFYt6vXz9cLle9MsXFxWzevJmCggIgPEduZWUl69ati5RZu3YtlZWVeu6A733ve9x777388Y9/jPz079+f0aNH88c//pFu3brpeYiSQYMGRdaRq7N9+3a6dOkC6G8iWmpqarDt+h+ltm1HevLoeRCJPdXL2obe31qf6ruxZ4whEAgo1m1A3yNiLxAIsG3bNnJycvQalyZJTU0lMzOTHTt2sH79+kiyWK+NgztYzCA8qmPGjBm43W5+97vfNRj1rJjVj9n555/PPffcU+9zA+DKK69k8uTJQMeNGRw8bl26dCEnJ6dBe9GOHTvIzc0FOm7cDhYzv98PNByhtX/bTqLFTN87mq81YgbR+yzQSL82cu655zJ79mz69etHQUEBb731FkVFRfXmaJWme/zxx1m+fDm/+93vSElJifTWT01Nxev1YlkWZ599NgsXLqRHjx50796dhQsXkpSUFFlbKzU1lbFjxzJv3jwyMjJIT09n3rx55OfnRxYd7dmzJ8OHD2fOnDlMmjQJgEcffZSRI0eSl5cXk3tvT1JSUhoMa09KSiIjIyOyXc9DdJxzzjnceuutvPzyy5x88smsW7eOt99+m1/96lcA+puIkuOOO46XX36Z3NxcevbsyaZNm3jttdci02joeRBpH1Qva5nq6mp27twZ+X3Xrl1s2rSJ9PR0cnNz9f7WylTfja6///3vjBgxgs6dO1NdXc2KFSv46quvmDZtmmLdBvQ9IvqefvppRo0aRW5uLiUlJbz00ktUVVVx2mmn6TXewR3u8/2jjz4iMzOT3NxcNm/ezNy5czn++OMZNmwY0DFfG0cas6qqKmbOnElNTQ3XXXcdVVVVVFVVAftmpVDM6scsOzs7MnJmf7m5uZHG9kSLGRx53CzL4mc/+xkvvvgiffr0oU+fPrz77rts27aN/+//+/+AxIvbkcYsLy+P7t2787e//Y3LL7+c9PR0/v3vf1NYWMhNN90EJF7M9L2j+VojZtH8LLBMUyZalRZZunQpr776KsXFxfTq1Ysrr7ySwYMHx/qy4tK4ceMa3T558uTIulnGGObPn89bb71FRUUFAwYMYMKECfW+XPr9fp555hmWL1+O3+9nyJAhTJw4MdLbBaC8vDwyVBfCjfoTJkwgLS2t7W4wjk2fPp0+ffpEplzQ8xA9n376KX//+9/ZuXMnXbt25ZxzzuFHP/pR5HE9F22vqqqKF154gZUrV1JSUkKnTp045ZRTuPDCCyPD8/U8iLQPqpc131dffVVvfbM6p512GlOmTNH7WytTfTe6/vKXv/Dll19SXFxMamoqvXv35rzzzos0UijWbU/fI9rWAw88wNdff01paSmZmZkMHDiQiy++ODLlm+LdcR3u8/3111/nH//4R2SKsh/84Af1vt9Ax3ttHGnMDrY/wEMPPRRJYilm9V9nBxo3bhw33ngjJ5xwQmRbIsUMWi9ur7zyCkuXLqW8vJzevXtz2WWXcfTRR0ceT6S4tUbMduzYwbPPPsvq1auprq6me/fu/PSnP+UHP/hBpEwixUzfO5qvNWIWzc8CJf1ERERERERERERERERE4pzW9BMRERERERERERERERGJc0r6iYiIiIiIiIiIiIiIiMQ5Jf1ERERERERERERERERE4pySfiIiIiIiIiIiIiIiIiJxTkk/ERERERERERERERERkTinpJ+IiIiIiIiIiIiIiIhInFPST0RERERERERERERERCTOKeknIiIiIiIiIiIiIiIiEueU9BORuLZx40Zuu+02rrzySsaNG8fixYv57LPPePHFF9v0vO+++y7jxo1j/fr1bXoeERERkUNp7brQww8/zOWXX97KV5nYli9fzuLFi2N9GSIiIhLn2qKtadeuXYwbN4533323wXl27doV2ab6jEjicMf6AkREjsRf/vIXampquOGGG0hLS6Nr16689NJLLF26lHHjxsX68kRERETalOpCsbd8+XK2bNnCOeecE+tLEREREaknJyeHGTNm0L1790OWU31GJHEo6ScicW3Lli2cfvrpjBgxItaXIiIiIhJ1qguJiIiItG81NTUkJSXF5Nwej4eCgoKYnFtEYkNJPxGJutLSUp577jk+//xzSkpKSElJIS8vj4suuoihQ4dijOHVV19l6dKllJSU0LNnTy6++GIWLVoEwPTp03n33Xd55JFHAFi2bBnLli0D4LTTTuO9994DqNe7/aGHHqJr166Hvbb169fzj3/8g7Vr1+Lz+cjOzmbgwIFceumldOnSpUH5iooKHnnkEf79738TDAY59thjufrqq+nWrVu9cv/85z9544032L59O16vl8GDB/PLX/6Snj17ArB48WKeeuopHnzwwQa9r5555hkWL17MnDlzyMzMBKCwsJBXXnmF9evXEwqF6Nu3L+PGjeN73/tek54DERERiZ32XBeqs2XLFp588knWrl2L1+vl5JNP5rLLLqvXYLVkyRI++ugjtm3bRk1NDV27duUHP/gB55xzDm73vq+aGzdu5IUXXmDdunVUVlaSkZFBnz59+NWvfkXnzp0BMMawbNky3nrrrUh9aciQIVx22WX16lXTp0+nrKyMa665hqeffpqNGzeSnZ3NhRdeyA9/+EM+++wzXnjhBbZt20a3bt24/PLLGT58eL1727FjBy+++CL/93//R2VlJd26dePMM8/kJz/5SaTMV199xe23387111/Pli1bePfdd6murmbAgAFMmDCBvLy8yPWsWrWqQbzbeqp5ERERia6vv/6a2267jeuvv57Ro0fXe+y9997j4Ycf5s4772Tp0qX861//YubMmTz99NOsWbOGXr16MXPmzCafqyltTVOmTGHw4MFMmTKl3r7Tp0+v9++uXbv4r//6LyZPnsyYMWMaPZ/qMyKJRUk/EYm62bNns3HjRi6++GLy8vKoqKhg48aNlJeXAzB//nwWLFjA2LFjOemkkygqKmLOnDk4jhNpYBk5ciQzZszglltu4aSTTuLcc88FIDMzk5qaGv71r38xY8aMyDlzcnKadG27d+8mLy+Pk08+mfT0dHw+H8uWLeP3v/89f/rTnyJJtzp/+ctfGDp0KFOnTqWoqIgXXniB6dOnc++995KWlgbAwoULee655zjllFP45S9/SXl5OfPnz+eWW25h1qxZ9OjRg1NPPZVnn32Wd999l4svvjhyfMdx+OCDDzjuuOMi537//fd5+OGHGTVqFFOmTMHlcvHmm28yc+ZMpk2bpsSfiIhIO9ee60IAoVCIWbNm8aMf/Yjzzz+f1atX8/LLL7N7925uvvnmSLnvvvuOU045ha5du+J2u/n22295+eWX2bZtG5MnTwagurqaGTNm0LVrVyZMmEBWVhY+n4+vvvqKqqqqyLEeffRR3n33Xc466ywuvfRSysvLeemll7jlllu45557yM7OjpT1+Xw88sgj/OxnP6Nz58688cYb/OUvf2HPnj3861//4oILLiA1NZUFCxZwzz33MHv2bDp16gTA1q1bueWWW8jNzeWKK64gOzubzz//nCeffJKysjIuuuiierF47rnnGDRoENdccw1VVVU8++yz3H333dx///3Yts3EiROZM2cO3333HTfeeGOTYywiIiLx5ZhjjqFv374sXbq0QdJvyZIl9O/fnwEDBrB06VKCwSB33303Z5xxBueffz6hUKhZ52pKW1NrUn1GJLEo6SciUbd69WrGjh3Lj370o8i2448/Hgj3Zlq0aBEnnHAC1157beTxXr16ceutt0YaujIzMyNJsKysrHpTFWRlZQG0aPqCk046iZNOOinyu+M4jBw5kkmTJrF8+XLOPvvseuX79+/Pr3/96wbXuXTpUn7+859TUVHBSy+9xIgRI5g6dWqk3ODBg5k6dSrz58/n+uuvJzMzk5EjR/Lee+8xbtw4bNsG4IsvvqC4uJgf/vCHQHhKiLlz5zJy5Eh++9vfRo43YsQIbrrpJp577jkl/URERNq59lwXAggGg5x77rmRes/QoUNxuVw8//zzfPPNNxx99NEAXHnllZF9HMfhmGOOISMjg0ceeYQrrriC9PR0tm/fTllZGddee23kHgFOPvnkyP/XrFnD22+/zRVXXBFJXkK4cW3q1Km89tprXHbZZZHtZWVlTJs2jX79+gHh+tjEiRN55ZVXePDBByMJvpycHH73u9/x8ccfc9ZZZwHw1FNPkZKSwh133EFqamrk/oLBIK+88gpnnXUW6enpkXP17NmT66+/PvK7bdvcf//9rFu3joKCAnr27ElaWpqmzhIREekAzjrrLB555BE2bdpEnz59AFi3bh3r16+vN+IuFApFZiFoicO1NbU21WdEEosd6wsQkY5nwIABvPfee7z00kusWbOGYDAYeWzNmjUEAoEGvaYGDRrU6PSara26uppnnnmG6667josvvpiLL76YK664gpqaGrZt29ag/MGu86uvvgLC9+P3+xtMoZCbm8uQIUP4v//7v8i2H/7wh+zZs6fetnfeeYfs7OzIOj2rV6+mvLyc0047jVAoFPkxxjB8+HDWr19PdXV1a4VDRERE2kB7rgvVOfXUU+v9Xnc9dXUcCE/beffddzN+/HguvvhifvnLX/LQQw/hOA47duwAoHv37qSlpfHss8+ybNkytm7d2uBcn332GZZlceqpp9ar32RnZ9O7d+/IdFN1cnJyIgk/gPT0dLKysujTp08k4QdEplHfvXs3AH6/ny+//JLjjz+epKSkeucaMWIEgUCAtWvX1jvXqFGj6v3eu3dvAIqKipoQRREREUkkp5xyCllZWSxZsiSybcmSJWRmZtbr0ARw4okntvg8h2trEhE5FI30E5Gou+GGG3j55Zf55z//yQsvvEBycjInnHACl112GWVlZQD1pnCq09i21vbnP/+ZL7/8kl/84hf079+flJQULMti1qxZ+P3+Jl1TdnZ25D7q/m1sSq2cnJzINF4QHq2Xk5PDO++8w7BhwygvL+fTTz/lrLPOioz8KykpAeBPf/rTQe+hvLyc5OTkpt+0iIiIRFV7rgsBuFwuMjIyGj133fUVFRXxP//zP+Tl5XHVVVfRtWtXPB4P69at4/HHH4/Um1JTU7n99tt5+eWXee6556ioqCAnJ4fTTz+dn//857jdbnw+H8YYJk2a1Oj1HLhW8v4j8eq43e4G2+vWFQwEAkC4jhQKhViyZEm9xrr91d1fnQPjUHfMxuqFIiIiktg8Hg8/+tGPeO2117j88ssJhUJ89NFHnHPOOXg8nki5pKSkyIwCLXG4tiYRkUNR0k9Eoi4zM5OrrrqKq666iqKiIj755BOeffZZSkpKItNI+Xy+Bvv5fL427eFeWVnJZ599xoUXXsj5558f2R4IBOol5w68psa2de/eHdjXUFRcXNygXHFxcb2GJNu2OfXUU3njjTeoqKhg+fLlBAKBetNB1JUfP348AwcObPSaotUgKCIiIi3TXutCdUKhEGVlZfXqKXXXU7dt5cqV1NTUcOONN9a7pk2bNjU4Xn5+PjfccAPGGL799lveffddFixYgNfr5fzzzyczMxPLsrj99tvrNZjVaWxbS6SlpWHbNj/4wQ8488wzGy3TtWvXVjmXiIiIJKYf//jHvPLKK7zzzjv4/X5CoRA//vGPW/Uch2trgnD9qK5j0/4OrMOJSMej6T1FJKZyc3P5yU9+wtChQ9m4cSMDBw7E4/GwfPnyeuVWr14dmZrpcOoahlrSA9sY06Bh6e2338ZxnEbLH+w6Bw8eDITX0vF6vXzwwQf1yu3Zs4cvv/ySIUOG1Nv+wx/+kEAgwIoVK3jvvfcoKCjgqKOOijx+9NFHk5aWxtatW+nfv3+jP3U90EVERKT9a291oToH1l3qrufYY48FwLKseueCcD3q7bffPugxLcuiT58+XHXVVaSlpbFhwwYARo4ciTGGvXv3Nlq3yc/Pb/F97C8pKYljjz2WjRs30rt370bP1ZJGMrfbrZF/IiIiHUROTg7f//73Wbp0KW+++SbHHXccubm5rXqOw7U1AXTp0oXNmzfXK7d9+3a2b9/eonOqPiOSONQyLCJRVVlZye23384pp5zCUUcdRUpKCuvWrePzzz/nxBNPJD09nZ/+9Ke8/PLL/PWvf+Wkk05iz549zJ8/v8kj2Ooahl555RVGjBiBbdv07t37sMmw1NRUjjnmGF599VUyMjLo0qULq1at4p133iEtLa3RfdavX1/vOp9//nk6deoU6T2elpbGL37xC5577jkeeughTjnlFMrKyliwYAEej4eLLrqo3vGOOuooCgoKWLhwIXv27OFXv/pVvceTk5O5+uqrefjhhykvL+ekk04iMzOT0tJSvv32W0pLSw86NZaIiIjEXnuuC9Vxu9289tprVFdXM2DAAFavXs3LL7/MiBEjOProowEYOnQobrebP//5z/zsZz8jEAiwbNkyKioq6h3r008/ZdmyZRx//PGRUXQff/wxFRUVDB06FAh3avrRj37EX/7yFzZs2MAxxxxDUlISPp+Pb775hvz8/FbrQX/11Vdz66238j//8z/8+Mc/pkuXLlRVVbFz504+/fRTbrvttmYfMz8/n5UrV7Js2TL69euHZVn079+/Va5XRERE2p+zzz6bP/zhDwBMnjy51Y9/uLYmgB/84AfMnj2bxx57jBNPPJHdu3fz6quvkpmZ2aJzqj4jkjiU9BORqPJ4PAwYMIAPPviAXbt2EQqFyM3N5bzzzuO8884D4P/9v/9HcnIyS5cu5f333+eoo45i0qRJ/OMf/2jSOUaPHs0333zDsmXLeOmllzDG8NBDDzVpuqapU6fy5JNP8swzz+A4DoMGDeKWW27hrrvuarT8r3/9a95//33+/Oc/EwgEOPbYY7n66qvrrSlzwQUXkJWVxRtvvMGHH36I1+tl8ODBXHLJJfTo0aPBMceMGcOjjz6K1+ttsBA0hCt2ubm5vPrqqzz66KNUVVWRlZVFnz59GDNmTJNiJCIiIrHR3utCEF7T7+abb+bJJ5/k5Zdfxuv1cvrpp3PZZZdFyhx11FH893//N88//zz33nsvGRkZjB49mnPPPZc777wzUq5Hjx6kpqayaNEiiouLcbvd5OXlMXny5Hr1ll/96lcMHDiQt956i6VLl2KMIScnh0GDBjFgwIAmXXdT9OzZk7vvvpuXXnqJ559/npKSEtLS0ujRowcjRoxo0THPPvtstm7dynPPPUdlZSXGGF588cVWu2YRERFpXwYMGECXLl3wer1873vfa/XjN6WtafTo0RQXF/Pmm2/yzjvvkJ+fz8SJE1mwYEGLzqn6jEjisIwxJtYXISLSFNOnT6/3r4iIiEhHorqQiIiISOx9++23/Pa3v2XChAkHXSdYRCRWNNJPREREREREREREROQQdu7cSVFREc899xw5OTmabUlE2iUl/USkQ3Ach8MNbHa5XFG6GhEREZHoUl1IRERE5Mi89NJLvP/++/Ts2ZPf/OY3JCUlNWk/YwyO4xyyjG3bWJbVGpcpIh2cpvcUkQ7h4Ycf5r333jtkGc1VLiIiIolKdSERERGR2Hj33Xd55JFHDlnmtttu49hjj43SFYlIIlPST0Q6hF27dlFWVnbIMv3794/S1YiIiIhEl+pCIiIiIrFRVlbGrl27DlkmLy+PlJSUKF2RiCQyJf1ERERERERERERERERE4pwd6wsQERERERERERERERERkSOjpJ+IiIiIiIiIiIiIiIhInFPST0RERERERERERERERCTOKeknIiIiIiIiIiIiIiIiEueU9BMRERERERERERERERGJc0r6iYiIiIiIiIiIiIiIiMQ5Jf1ERERERERERERERERE4pySfiIiIiIiIiIiIiIiIiJxTkk/ERERERERERERERERkTinpJ+IiIiIiIiIiIiIiIhInFPST0RERERERERERERERCTOKeknIiIiIiIiIiIiIiIiEueU9BMRERERERERERERERGJc0r6iYjIYc2dOxfLspg7d26sL0VERETamTFjxmBZVqwvQ0RERKTdUT1JRKJNST8RkTjVp08f+vTpE+vLEBEREWkVqtuIiIiIiIgcGSX9REREREREREREREREROKckn4iIiIiIiIiIiIiIiIicU5JPxFpkpNOOgmXy8WmTZsaffzee+/Fsizuu+++yLa6KZpKSkqYOnUqvXv3xuPxMH369Gade//15BYvXswPfvADMjMz682JHgwGeeSRRzjppJPIzMwkNTWVESNG8NBDD+E4Tr3jbdq0CcuyuOqqq9i0aRMXX3wxubm5JCcnc9xxx/Hqq682eh3V1dXMmjWL733ve6SmppKZmcmpp57K888/X6/cRx99hGVZ/PznPz/oPQ0cOJCkpCT27t0LgN/v56GHHuLss8+md+/eJCUlkZOTw+mnn87ixYvr7fvuu+9iWRbffvst3377LZZlRX6uuuqqemW/+eYbrrrqKnr16kVSUhLdunXjkksuYfXq1Y1e17p167jooovIyckhLS2Nk08+mddee+2g9yEiIiLSmLaq24iIiIgkAsdxeOSRRzj++ONJT08nLS2NUaNG8cgjjzRoxwKwLIsxY8ZQVFTEr371K3r06EFSUhLHHnssjz/+eKPnqKmpYfr06fTr14+kpCT69u3LLbfcQk1NTeR4IpJ43LG+ABGJD5MnT+bKK6/kb3/7GzNnzqz3mDGGRx99lKSkJK688sp6j9XU1DB27FiKi4s588wzSU9Pb/FaLfPnz2fJkiWcffbZXHvttWzcuBGAQCDAT3/6U5YuXcrRRx/NJZdcQnJyMu+88w7XXXcd//rXv3jmmWcaHO/bb7/lhBNOoF+/flx++eXs3buXF154gfPPP58333yT008/PVLW7/fz4x//mA8++IDBgwczZcoUKisrmT9/Pr/85S/5z3/+w9133w3A97//fQoKCnjttdfYs2cPnTt3rnfeDz/8kHXr1vGLX/yCTp06AbB3716mTp3KySefzBlnnEGXLl3YsWMHixYt4txzz2XOnDn86le/AsLJ1Ntuu40HHngAgBtuuCFy7OHDh0f+v2TJEn7+858TDAY599xzGTBgAFu3buXll19m8eLFvPPOO4wcOTJSfu3atXz/+99nz549nHXWWQwfPpx169Zx/vnnc/bZZ7foORMREZGOqS3qNiIiIiKJ4pJLLuGFF14gPz+fiRMnYlkWCxcuZMqUKbz//vsNOpgD+Hw+TjnlFLxeLxdeeCHV1dUsWLCAiRMnYts2V199daSsMYZf/OIXLF68mIEDB/Jf//VfBAIB5s6dy1dffRXNWxWRaDMiIk1QXV1tcnNzTffu3U0gEKj32Ntvv20Ac8kll9Tb3rt3bwOY008/3ZSXl7f43E8++aQBjGVZ5o033mjw+G233WYAM3XqVBMMBiPbg8GgGT9+vAHMwoULI9s3btxoAAOY6dOn1zvWkiVLDGB+8pOf1Ns+c+ZMA5hzzz233v3v3LnT9OrVywDmgw8+aFB+9uzZDa73mmuuMYB59dVXI9uqq6vNli1bGpTdu3evOeaYY0xOTo6prKys91jv3r1N7969G+xTt192drbJzc01X3/9db3HvvzyS5OWlmaGDx9eb/sZZ5xhAPPAAw/U2/7KK69E4vXkk082ej4RERHpuE477TRz4FfL1q7biIiIiMSjxupJzz77rAHMqFGj6rWXlZeXm5EjRxrAPPPMM/X2qWuXmTBhQr22r6+++sq4XC5z9NFH1yv/9NNPG8CceuqppqamJrK9uLjYDBo0yADmtNNOa8U7FZH2QtN7ikiTJCUlcfXVV7Nz584G01/OmTMHgGuvvbbRfe+9917S0tKO+Bp+9rOf8ZOf/KTeNsdxeOihh+jRowf33XcfLpcr8pjL5eK+++7DsiyeffbZBsfr06cPt9xyS71tZ555Jvn5+fz73/+ut/2JJ56ITF/qdu8bJN2tWzduvfXWSJk6l19+ObZt89RTT9U7Tk1NDS+88AJdu3blrLPOimxPSkqiZ8+eDa4xJyeHCRMmUFxc3OCaDuXpp5/G5/Nx++23c/TRR9d77Nhjj2XSpEl8/vnnkd5dW7du5c0336Rv377813/9V73y5513HqeddlqTzy0iIiLS2nUbERERkURR1340a9aseu1laWlp3HXXXQCNTtmZmprK/fffX6/ta/DgwZxyyil88803lJWVRbbXtUfNmDEDr9cb2Z6dnR1pxxKRxKTpPUWkya699lruvfde5syZE1mvbteuXbzyyisMHjyYU089tcE+SUlJDBs2rFXOf+KJJzbYtmbNGvbs2cPAgQP53//930b3S0lJ4Ztvvmmwffjw4fUqSnV69erFRx99FPm9rKyM9evX07NnTwoKChqU/9GPfgTAZ599Vu8YY8eO5a233mLVqlUMHjwYgFdffRWfz8dvfvObeslDgK+++op77rmH999/nx07dlBdXV3v8W3btjV6f42pu/7PP/+80TUU16xZA4TX/Dv22GP5z3/+A8Do0aMbjcmYMWN47733mnx+ERERkdas24iIiIgkiv/85z/Ytt1oB+sf/vCHuFyuem1MdQoKCsjIyGiwvVevXkB4+s+6x+vOcfLJJzcoP3r06CO9BRFpx5T0E5Em69evH2eeeSZLly5l48aN9O3blyeffBK/388111zT6D7dunXDsqxWOX/37t0bbNuzZw8QXo/u9ttvP+i+5eXlDbZlZWU1WtbtdtdbNLmkpOSg5wfo0aNHvXJ1rrzySt566y2eeuqpyHp/dT2tDlz78F//+hdjx44lGAxy+umn87Of/YzMzExs2+bzzz9n0aJF1NTUHPT+DlQXl7/97W+HLFcXl7pr79atW6PlDnbvIiIiIo1p7bqNiIiISKIoKSmhU6dOeDyeBo+53W5yc3PZtWtXg8cO1Y4FEAqFGpzjwA7ncPC2HxFJDEr6iUizTJ48mSVLlvC3v/2NmTNn8re//Y2UlBQuv/zyRsu3VsLvYMeqq/BccMEFvPzyy612rsbOsXPnzkYf37FjR71ydX7+858zefJknnnmGe6880727NnD0qVLGTZsWIPRjzNmzKCqqop33nmHMWPG1Hts1qxZLFq0qEXX/MUXXzB06NAml//uu+8affxg9y4iIiLSmNau24iIiIgkiqysLPbu3UsgEGiQ+AsGgxQVFZGZmXlE58jMzGTv3r0Eg8EGib+Dtf2ISGLQmn4i0iznnHMOvXv35oknnuCNN95g/fr1jBs3jpycnJhcz9FHH012djb/+te/CAQCbXKOjIwM+vfvz7Zt21i7dm2Dx9955x0ARo4cWW97amoqF154Idu3b+ett97i2WefJRgMNhjlB7Bu3To6derUoFEMOOi0mi6Xq14vrv2ddNJJAHzwwQeHvLc6I0aMAGD58uWNHvPdd99t0nFEREREoPXrNiIiIiKJYsSIETiOw/vvv9/gsffff59QKNSgjaml5/jwww8bPLZ8+fIjOraItG9K+olIs9i2zTXXXMN3333HxIkTgfBaf7Hidru57rrr2LFjB9dffz1VVVUNyuzYsYNVq1Yd0XnGjx+PMYbf/va39RqjioqKImsJjh8/vsF+V111FQBPP/00Tz/9NG63m0svvbRBuT59+rB3714KCwvrbX/88cdZunRpo9fUuXNndu/e3WB9HICrr76a7Oxsbr/9dlauXNngccdx6iXyevbsyRlnnMHGjRt56KGH6pVdtGiR1vMTERGRZmntuo2IiIhIoqhrP/r9739PZWVlZHtlZSU333wzABMmTDiic1xxxRUA3HLLLfj9/sj2kpKSSDuWiCQmTe8pIs02YcIEpk+fzo4dOxg6dGhkVFms3HrrrXzxxRf89a9/5R//+Adjx47lqKOOYteuXaxdu5YVK1Ywc+ZMBg8e3OJz3HjjjbzxxhssWrSIYcOGcfbZZ1NZWcn8+fPZtWsXv/vd7xpdCPnUU0+lb9++zJ8/n0AgwE9/+lO6du3aoNwNN9zA0qVLGT16NOPGjSMrK4tPPvmE5cuXc+GFF7JgwYIG+5x++un8+9//5qyzzuLUU0/F6/UybNgwfvrTn9K5c2cWLFjABRdcwEknncTpp5/Osccei23bbN68mY8++og9e/bUa1R7+OGH+f73v88NN9zAsmXLGDZsGOvWrWPhwoX89Kc/5R//+EeL4yciIiIdS2vXbUREREQSxSWXXMKiRYt48cUXOfbYYzn//POxLItXXnmFjRs3Mm7cuEY7jDfHFVdcwfPPP8+SJUsYMmQIP/vZzwgEArz00kuMGjWK1atXY9saDySSiPSXLSLN1rVrV8455xwgtqP86ng8Hl555RWefvppBg0axGuvvcZ9993HkiVLcByH//3f/z3iypLX6+XNN99k5syZAMyePZunnnqKgQMH8ve//52777670f0sy+KKK66ITD3a2NSeAD/5yU/4xz/+weDBg3nhhRd4/PHHSUpK4p133onE+kC33HIL1157LWvXruXOO+/k1ltv5aWXXoo8fvrpp1NYWMjkyZPZtGkTf/3rX3nsscf48ssvGTt2LM8//3y94w0cOJB//etf/OIXv2DFihX8+c9/ZsuWLbzyyiv8/Oc/b3bMREREpONqi7qNiIiISKJ47rnnePjhh+ncuTNz5szhr3/9Kzk5OTz00EM899xzR3x8y7JYuHAht956K4FAgNmzZ7No0SKuvPJKHn74YSC8tqCIJB7LGGNifREiEl8cx6F///7s3r2b7du3H/HiwiIiIiIiIiIiItL23nzzTX784x9z8803M2vWrFhfjoi0Mo30E5Fme/HFF9m0aRNXXHGFEn4iIiIiIiIiIiLtzPbt2xts27NnT2TdwF/84hfRviQRiQKN9BORJpsxYwZ79+7l8ccfx3EcVq1aRa9evWJ9WSIiIiIiIiIiIrKfiy++mC+++IKTTz6ZLl26sHXrVt544w327t3L5MmTI9N8ikhiUdJPRJrMsiw8Hg/HHnss9913H2PHjm3Rcd59913efffdw5bLzs7mhhtuaNE5REREREREREREOqr58+czZ84cvvrqK/bu3UtSUhJDhgxhwoQJTJgwIdaXJyJtREk/EYm66dOnc/vttx+2XO/evdm0aVPbX5CIiIiIiIiIiIiISJxT0k9EREREREREREREREQkztmxvgAREREREREREREREREROTJK+omIiIiIiIiIiIiIiIjEOSX9REREREREREREREREROKcO9YXkKiKi4sJBoOteswuXbqwe/fuVj1me6F7i0+JfG+Q2Pene4tPurcj43a7ycnJadNzSOtpi7pUrCXy3/CRUmwOTfE5OMXm0BSfQ1N8Dq6x2KguFV8SsS7VEvo7bxrF6fAUo6ZRnJpGcWqaRIxTNOpTSvq1kWAwSCAQaLXjWZYVOa4xptWO2x7o3uJTIt8bJPb96d7ik+5NOprWrkvFml7nB6fYHJric3CKzaEpPoem+BycYpMYEq0u1RJ6LTeN4nR4ilHTKE5Nozg1jeLUcpreU0RERERERERERERERCTOKeknIiIiIiIiIiIiIiIiEueU9BMRERERERERERERERGJc0r6iYiIiIiIiIiIiIiIiMQ5Jf1ERERERERERERERERE4pySfiIiIiIiIiIiIiIiIiJxTkk/ERERERERERERERERkTinpJ+IiIiIiIiIiIiIiIhInFPST0RERERERERERERERCTOKeknIiIiIiIiIiIiIiIiEueU9BMRERERERERERERERGJc+5YX4A0XXFpOY7Lg2nBvi4MJhho9WsSERERERERiRbL7SGE1aJ99b1YRKJF71UiIhIrSvrFkYDjUOgLtSjpNzTbpWGdIiIiIiIiEtdCWBT6Qi3aV9+LRSRa9F4lIiKxos8QERERERERERERERERkTinpJ+IiIiIiIiIiIiIiIhInFPST0RERERERERERERERCTOaU0/ERERkQS3atUqXn31VTZu3EhxcTE33ngjJ5xwQr0yW7du5dlnn2XVqlUYY+jVqxe/+c1vyM3NBSAQCDBv3jxWrFiB3+9nyJAhTJw4kc6dO0eOUV5ezpNPPsknn3wCwKhRoxg/fjxpaWnRu1kRERERERERkQ4q5km/hQsXsnLlSrZt24bX66WgoIDLLruMvLy8SBljDPPnz+ftt9+mvLycgQMHMmHCBHr16hUp01oNUUVFRTz22GN89dVXeL1eTjnlFK644grc7piHSkRERKRFampq6NOnDz/84Q+57777Gjy+c+dO/ud//oexY8cybtw4UlNT2bZtGx6PJ1Jm7ty5fPrpp0ydOpWMjAyefvpp7rrrLu6++25sOzx5xIMPPsiePXuYNm0aAHPmzGH27NncfPPN0blREREREREREZEOLObTe65atYozzzyTmTNncsstt+A4DjNmzKC6ujpSZtGiRSxevJjx48cza9YssrOzmTFjBlVVVZEyc+fOZeXKlUydOpU77riD6upq7rrrLhzHiZR58MEH2bRpE9OmTWPatGls2rSJ2bNnRx53HIdZs2ZRU1PDHXfcwdSpU/n44495+umnoxMMERERkTYwYsQILr74Yk488cRGH3/++ecZMWIEl112GX379qVbt26MHDmSrKwsACorK/nnP//JFVdcwdChQ+nbty/XXXcdmzdvprCwEAiPFPz888+59tprKSgooKCggGuuuYbPPvuM7du3R+1eRUREREREREQ6qpgn/aZNm8aYMWPo1asXffr0YfLkyRQVFbFhwwYgPMrv9ddf54ILLuDEE08kPz+fKVOmUFNTw/Lly4HWa4j64osv2Lp1K9dddx19+/Zl6NChXHHFFbz99ttUVlbGJkAiIiIibchxHD777DN69OjBzJkzmThxIn/4wx9YuXJlpMyGDRsIhUIMHTo0sq1Tp07k5+ezZs0aANasWUNqaioDBw6MlCkoKCA1NZXVq1dH74ZERERERERERDqodjdnZV1yLT09HYBdu3bh8/kYNmxYpIzH42Hw4MGsXr2aM84447ANUcOHDz9sQ1ReXh5r1qwhPz+fTp06RcoMGzaMQCDAhg0bGDJkSIPrDQQCBAKByO+WZZGSkhL5f2uJHMuysIxp/v6tfD2tqe662uv1HQndW/xK5PvTvcUn3Zu0ldLSUqqrq1m0aBH/7//9Py699FI+//xz7rvvPm677TYGDx6Mz+fD7XZH6md1srKy8Pl8APh8vsjIwIOVaUy06lKxptf5wSk2h6b4HJxic2iJGh+r9qfF+x4Ql0SLT2tQbERERETiV7tK+hljeOqppzj66KPJz88HiDQSHdiIlJWVRVFRUaRMazRENVYmPT0dt9t90MaqhQsXsmDBgsjvffv25e6776ZLly5Nuufm2OUrJT097fAFG+HxhuiandvKV9S6unfvHutLaDO6t/iVyPene4tPujdpbXVToY8aNYpzzz0XgD59+rB69WqWLVvG4MGDD7qvaUJHJGPMIRsNo1mXag/0Oj84xebQFJ+DU2wOLdHis8tXSnqGq0X7Nva9ONHi05oUGxEREZH4066Sfo8//jibN2/mjjvuaPDYgY1FTW1kakqZ/Y/dWKPUoRqrLrjggkgD2f777969m2AweNjzN5VlWdjJqZSXV0ALRvoFXC527NjRatfTmizLonv37uzcubNJz1k80b3Fr0S+P91bfNK9HTm3252wiaQjkZmZicvlomfPnvW2H3XUUZFpObOzswkGg5SXl9frZFVaWsqgQYMiZUpKShocv7S0tNGOV3WiVZeKtUT+Gz5Sis2hKT4Hp9gcWqLGx3F5KC8LtWjf/b8XJ2p8WsPBYqO6lIiIiEj7126Sfk888QSffvopt99+O507d45sz87OBsKj8HJyciLb929Aaq2GqOzsbNatW1fv8fLyckKh0EEbqzweDx6Pp9HH2uSLgzG05KiGNrqeVmSMaffX2FK6t/iVyPene4tPujdpbW63m/79+0fWOK6zY8cOcnPDoyH69euHy+WisLCQk08+GYDi4mI2b97MpZdeCoSnTa+srGTdunUMGDAAgLVr11JZWRmpjzUm6nWpGNPr/OAUm0NTfA5OsTm0RIuPqf1p8b4HxCLR4tOaFBsRERGR+BPzpJ8xhieeeIKVK1cyffp0unbtWu/xrl27kp2dTWFhIX379gUgGAyyatWqSCNTazVEFRQU8PLLL1NcXBxJMBYWFuLxeOjXr1/bB0NERESkDVRXV7Nz587I77t27WLTpk2kp6eTm5vLz372M+6//36OOeYYhgwZwueff86nn37K9OnTAUhNTWXs2LHMmzePjIwM0tPTmTdvHvn5+ZE1lXv27Mnw4cOZM2cOkyZNAuDRRx9l5MiR5OXlRf2eRURERFrLsmXLWLZsGbt37wbC9Z4LL7yQESNGAOG2rfnz5/P2229TXl7OwIEDmTBhAr169YocIxAIMG/ePFasWIHf72fIkCFMnDixXsf38vJynnzyST755BMgPP36+PHjSUtr2VIvIiIi0vHEPOn3+OOPs3z5cn73u9+RkpISWTsvNTUVr9eLZVmcffbZLFy4kB49etC9e3cWLlxIUlISo0ePjpRtjYaoYcOG0bNnTx566CEuu+wyysvLmTdvHqeffjqpqanRD46IiIhIK1i/fj2333575Penn34agNNOO40pU6ZwwgknMGnSJF555RWefPJJ8vLy+O///m+OPvroyD5XXnklLpeL+++/P9JQddNNN2HbdqTM9ddfzxNPPMHMmTMBOO6445gwYUKU7lJERESkbXTq1IlLLrkkss7he++9xx//+Ef++Mc/0qtXLxYtWsTixYuZPHkyPXr04OWXX2bGjBk88MADpKSkADB37lw+/fRTpk6dSkZGBk8//TR33XUXd999d6Q+9eCDD7Jnzx6mTZsGwJw5c5g9ezY333xzbG5cRERE4k7Mk37Lli0DiPQkrzN58mTGjBkDwHnnnYff7+exxx6joqKCAQMGMG3atEjFCVqnIcq2bX7/+9/z2GOPceutt+L1ehk9ejSXX355G929iIiISNs79thjefHFFw9ZZuzYsYwdO/agj3u9XsaPH8/48eMPWiY9PZ3rr7++xdcpIiIi0h6NGjWq3u+//OUvWbZsGWvXrqVnz568/vrrXHDBBZx44okATJkyhUmTJrF8+XLOOOMMKisr+ec//8l1110X6Zx+3XXX8etf/5rCwkKGDx/O1q1b+fzzz5k5cyYDBw4E4JprruGWW25h+/btmjlBREREmiTmSb/DNUBBeBHpcePGMW7cuIOWaa2GqNzcXPWgEhERERERERGRBhzH4aOPPqKmpoaCggJ27dqFz+dj2LBhkTIej4fBgwezevVqzjjjDDZs2EAoFIok/CA8ejA/P581a9YwfPhw1qxZQ2pqaiThB+FlaFJTU1m9evVBk36BQIBAIBD53bKsSCd5y7Ja+/bjSt39xyIOVu1Pi/eN4jXHMk7xQjFqGsWpaRSnplGcWi7mST8REREREREREZH2bPPmzUybNo1AIEBycjI33ngjPXv2ZPXq1QBkZWXVK5+VlUVRUREAPp8Pt9tNenp6gzJ1y9z4fL4GxziwTGMWLlzIggULIr/37duXu+++my5durTkNhNS3bSs0bTLV0p6hqtF+3q8Ibpm57byFR1eLOIUbxSjplGcmkZxahrFqfmU9BMRERERERERETmEvLw87rnnHioqKvj44495+OGH662ZfOBIBGPMYY/Z1DKHGuVwwQUXcO655za4jt27dxMMBg97/ERmWRbdu3dn586dTYp1a3JcHsrLQi3aN+BysWPHjla+ooOLZZzihWLUNIpT0yhOTZOocXK73W3eMUdJPxERERERERERkUNwu92R0Qb9+/dn/fr1vP7665x33nlAeKReTk5OpHxpaWlk5F52djbBYJDy8vJ6o/1KS0sZNGhQpExJSUmD8+5/nMZ4PB48Hk+jjyVSI+mRMMZEPRam9gfAMeH/u5o4Q50hNs9dLOIUbxSjplGcmkZxahrFqfnsWF+AiIiIiIiIiIhIPDHGEAgE6Nq1K9nZ2RQWFkYeCwaDrFq1KpLQ69evHy6Xq16Z4uJiNm/eTEFBARBev6+yspJ169ZFyqxdu5bKysrIcSS6jAn/HAm/A99U2XxV6eKrCpsNVTYhtV2LiEgb0kg/ERERERERERGRg/j73//OiBEj6Ny5M9XV1axYsYKvvvqKadOmYVkWZ599NgsXLqRHjx50796dhQsXkpSUxOjRowFITU1l7NixzJs3j4yMDNLT05k3bx75+fkMHToUgJ49ezJ8+HDmzJnDpEmTAHj00UcZOXIkeXl5Mbv3jqo0CB+UuPEbi+9nBunubX6mzjHwbY1N0ISH+IWwKHdgb9Cii0eZPxERaRtK+omIiIiIiIiIiBxESUkJDz30EMXFxaSmptK7d2+mTZsWSdidd955+P1+HnvsMSoqKhgwYADTpk0jJSUlcowrr7wSl8vF/fffj9/vZ8iQIdx0003Y9r5JuK6//nqeeOIJZs6cCcBxxx3HhAkTonuzwuZqixWlbgK1ybplxR4Gp4YYmR7CbuIUnQDb/RZVjoULQ/8Uh9Kgxc6AzZ6ARa7bcIilGkVERFpMST8REREREREREZGD+PWvf33Ix/9/9u4+PK7yvvP/+z5nZiSNRpbkJ2xjC9vYMsHGT1CcEFIekrTblJZl29C0IdAVsCRmbbL7yxZahyaweMHtlZAfhOyaHwFi90oCYXHJNmzbBFICJA0FQgQ4WBjbGGMbybaeRqPRzJxz//6YB1u2JI9GI2lm9Hldly57Zs6Zuc+t0Zl77u/5fm9jDFdffTVXX331sNuEQiFaWlpoaWkZdptIJMLGjRsLbqeMXWfK8Fx3AIthdtBnWsCyu99lZ8zFNbAm4uX1PPv74VjKASxN1T7VDoSClo6kJWENPR7Ua1ZWRETGgdb0ExERERERERERkSnvnX4Hi2FuyOd3GlNcNM3jo9NSALzR5/BB4vTpeSkLr3Sn/z87aKlz0/93DEzPlPU8mtSUrIiIjA99woiIiIiIiIiIiMiU5lvYG09PlS6rOV7K8+wan7OrPSyGF7oDJPyRn+fNPoc+D4LGMvuktftmBCxgifqG/tM8j4iISCEU9BMREREREREREZEp7YPMGnwhYzmzanCw7rfqPCKupc83/LLXHfY5oh680Zd+fG7InrIGYMiBaZndu1Na1E9ERIpPQT8RERERERERERGZ0vZksvzOqvZxhwjWfWxaCoNlb9xlT/+pU6q+hV/0BPAwzA5BvWtP2QYgkrm/31fQT0REik9BPxEREREREREREZmyPAv7B9LTpIurh667OStkOa82/dgve12i3uDHX+9zOJRwcLFc2ABmmJhejZMN+oEdOi4oIiJSMAX9REREREREREREZMo6MGBIWkPYOXUdvhOtrPWYGfRJWsNPOoMcSRpsZi3AX2fKen54mkdDcPjXqnEALClrSCroJyIiRRaY7AaIiIiIiIiIiIiITJb2ZDovoqnKHzZDD8Ax6TKf/9gZpMcz/N9jAaqd46U6l9R4nF3jM3Su4PHnqHYg7qez/UJKyRARkSLSx4qIiIiIiIiIiIhMWceS6aDdjBGy/LLqAvCHM5IsrPawGPp9Q8hYzqnxuLDOO+3+cGKJT63rJyIixaVMPxEREREREREREak4nT1RfDfISKE8a+FYKv3/huoAfma21MViU8kh96ly4LfrPZZU+6QsnFllcUcRvws70AnEfAMjtk5ERGR0FPQTERERERERERGRipP0fVq7vBHDagM+JK2LwbI/6vNeJni3ssE9bYm0eVWFBexymX5eOug4UklRERGR0VB5TxEREREREREREZmS+jML8FU7Exd8q3bAYPEwJJXoJyIiRaSgn4iIiIiIiIiIiExJ2XX1stl3E8Ex6cAfQMyfsJcVEZEpQOU9RURERERERDJMIIhHYakeI63/JCIipel40G/w/Y4x+IFQQc9p8kgZrHEs/b6h3zc0aF0/EREpEgX9RERERERERDI8DK1dXkH75rP+k4iIlA5r0+vqwamZfj6G1q5UQc+7sjF42m2yQca4b0BBPxERKRIF/UREREQq3M6dO/nhD3/I3r176ezs5Etf+hIXXnjhkNs++OCD/OQnP+G6667j93//93P3J5NJtm/fzosvvkgikWDFihXccMMNzJgxI7dNNBrlkUce4eWXXwbgggsuoKWlhdra2vE9QBERERGRAqQsmexumyu3OVGqMkHGAZX3FBGRIpr0oN/pJqGuvvrqIfe75ppr+MM//EMAvvrVr7Jz585Bj1900UV88YtfzN3OZxLqyJEjPPTQQ7z55puEQiE++tGPcu211xIITHo3iYiIiBRsYGCAhQsXctlll/G1r31t2O1eeukl3n77bRobG0957NFHH+WVV17hlltuoa6ujm3btnHPPfewZcsWHCc9Q3Lfffdx9OhRNm3aBMDWrVu5//77ue2228bnwERERERExqA/E3CrMul19iZSKPN6SZvOOMyjIqiIiMhpTXo063STUA8++OCg27/61a/4X//rf7Fu3bpB93/84x/nT/7kT3K3Q6HBNbdPNwnl+z53330306ZN484776S3t5cHHngAgJaWlrEfqIiIiMgkWbNmDWvWrBlxm2PHjvHwww+zadMm7rnnnkGPxWIxnn32WTZs2MDKlSsB2LBhA1/4whdobW1l9erVHDhwgNdee43NmzezdOlSAG666Sa+/OUvc/DgQebNmzc+ByciIkJ6whw0aS4io5Nbz8+d+PKaQQNgsRiS9ngQUEREZCwmPeh3ukmohoaGQbf/7d/+jeXLl3PGGWcMur+qquqUbbPymYT69a9/zYEDB/if//N/Mn36dACuvfZavvWtb/GZz3yGcDhc+EGKiIiIlDDf97n//vv5wz/8QxYsWHDK43v27MHzvFzAD2D69Ok0NTXR1tbG6tWraWtrIxwO58ZaAM3NzYTDYXbt2qWgn4iIjAvfQmfK0JE0JCw4pCfOzwj5THMVBBSRkeWCfpOwIKsx6fNVwqZ/QqffRURE5LQmPeg3Gl1dXfzqV7/i5ptvPuWx559/nueff576+npWr17Npz/9aWpqagDymoRqa2ujqakpF/ADWLVqFclkkj179rBixYoh25RMJkkmk7nbxpjc65oifrvIPZcxGDv6q49MkdtTTNl2lWr7xkLHVr4q+fh0bOVJxybj6amnnsJ1XX7v935vyMe7uroIBAJEIpFB99fX19PV1ZXbpr6+/pR9T9xmKBM1lppsep8PT30zMvXP8Marb0zmp+B9S+R3VanvnRN/P3Ef9sYdEvb4MfpA3MK7Ay4Rx9JU5RN0Ttj3pH6ptP4pBvWNTCWJzBRblZn4TD+AkAMJDxK+gUnINhQRkcpTVkG/5557jurq6kFr/gFcfPHFzJ49m4aGBt577z2++93v8u6773L77bcD+U1CDbVNJBIhEAiMOFG1Y8cOnnjiidztRYsWsWXLFmbNmlXgUQ6vvauHSKT29BsOIRjymN0ws8gtKq45c+ZMdhPGjY6tfFXy8enYypOOTYptz549PP3002zZsmXUk3s2jwuRrLUjPu9EjqVKgd7nw1PfjEz9M7xi9017Vw+ROregfUvxe1elvXeyv59YymfPsQGSFoIOzK8NMqPawbfQ3u/xfl+KqG/Ymwhw3owqgo4Z8vdTaf1TTOobqXTWQiKzpl9oEjL9IB1sjGJywUcREZGxKqug309/+lM+9rGPnbJe3yc+8Ync/5uampg7dy633XYbe/bsYfHixcM+38mTUENNSJ1uouqqq67iiiuuOOU5Ojo6SKVSpz+oPBljcKrDRKN9xxcrGIWk63Lo0KGitaeYjDHMmTOHw4cP5zV5WE50bOWrko9Px1aedGxjFwgEKjaQNBa/+c1v6OnpYf369bn7fN9n27ZtPP300zzwwAM0NDSQSqWIRqODsv16enpYtmwZkC7J3t3dfcrz9/T0DHnxVdZEjaUmWyX/DY+V+mZk6p/hjVff+G6QaK9X0L6l9L2rUt87vhvkaLfH7rhDyhqqHcvZ1T4B3yMZS28zA4jUwDtxh34P3jjSz+Jqf9Dvp1L7pxiG6xuNpaTSeICfyR2erPX0ssHGAX9yXl9ERCpP2QT9fvOb33Dw4EG++MUvnnbbRYsW4bouhw8fZvHixXlNQjU0NLB79+5Bj0ejUTzPG3GiKhgMEgwGh3xsXL44WEshz2oZp/YUkbW25NtYKB1b+ark49OxlScdmxTbb//2b3PeeecNum/z5s389m//NpdddhkAixcvxnVdWltbueiiiwDo7Oxk//79fPaznwXSpdNjsRi7d+9myZIlALz99tvEYrFcYHAoEz6WmmR6nw9PfTMy9c/wit03NvNT8L4l9nuqtPdOysK7A8cDfourfVxz6u8s5MCiap93+h1ivuG9AYfVnPr7qbT+KSb1jVS6bJZfwFicyQr6ZcqKpssU6+9NRETGrmyCfs8++yyLFy9m4cKFp932vffew/M8GhoagPwmoZqbm3nyySfp7OyksbERgNbWVoLB4IjZgiIiIiKlLh6Pc/jw4dzt9vZ29u3bRyQSYebMmdTV1Q3aPhAI0NDQwLx58wAIh8NcfvnlbN++nbq6OiKRCNu3b6epqYmVK1cCMH/+fFavXs3WrVu58cYbAXjwwQdZu3Zt7nlERETG6vVe6PcNLpZFVT6BESbqqx1YWO3zTtyh2zO82w+Lhr7ORESmoGxJzcnK8jvxtRPK9BMRkSKZ9KDf6SahAGKxGP/6r//K5z73uVP2P3z4MC+88AJr1qyhrq6OAwcOsH37dhYtWsQ555wD5DcJtWrVKubPn883v/lNrrnmGqLRKNu3b+fjH/844XB4vLtBREREZNy888473HHHHbnb27ZtA+CSSy7h5ptvzus5rrvuOlzX5d577yWRSLBixQpuvfVWHOf4AigbN27k4YcfZvPmzQCcf/75XH/99UU8EhGR0uYYgx8InX7DIbhYbCpZ5BZVlo6E4c3e9P/PrPIJ5rEGV60Ls4OW9qTh37pg7ox0MFBEJOFnS3tOXoZdtrynh8Gz4E5iAFJERCrDpAf98pmE+vnPf461losvvviU/QOBAK+//jpPP/008XicGTNmsHbtWj796U+PahLKcRz+8i//koceeojbb7+dUCjExRdfPGSgUURERKScLF++nMcffzzv7R944IFT7guFQrS0tNDS0jLsfpFIhI0bNxbURhGRSuBjaO0qbD3SlQ0uikUNz7fwi14XCzS4Pg2jmM2YHbR0pyxx3/Byr8vF9YWt2SgilSWX6TeJJ1/XpC/68DAkfKhxJ68tIiJSGSY96JfPJNQnPvEJPvGJTwz52MyZMwcFDYeTzyTUzJkzue222077XCIiIiIiIiIycXb3O3SlHEIG5lWNLivHMbCgymd33GVP3OWcsM+swhIyRaSCHM/0m9x2VDkQ89NByJrJbYqIiFQAXUgoIiIiIiIiIiUr6cNrfen0l5XTGHEdv+GEXVicmU1/NepiJ6+an4iUiOOZfpN7QsiWFx3wVdtTRETGTkE/ERERERERESlZb8Rc4r5hmmtpri38eVZOAwfL4YTDwYQm10WmMmshmQ36TfLpIFteNKGLEUREpAgU9BMRERERERGRktTvwc6+9NTF2kgKZwyT85EALAv7ALza62KV7icyZSUtWAwGS3Cyg36Z108o009ERIpAQT8RERERERERKUk7Yy4ehplBnwWjXMtvKOfVegSN5VjK8HZ3oggtFJFylM2qCxowkx30y5QXVaafiIgUg4J+IiIiIiIiIlJy4j7s6k9PW6ys9YoyMV/twDmZbL9ffNCvtf1EpqhsVt1kl/Y8sQ1Ji85JIiIyZoHJboCIiIiIiIiIyMl+E3NJWcP0gM+ZoeLNhJ8T9tjZ53AoluJwlWFOEZ9bKtOOHTt46aWXeP/99wmFQjQ3N3PNNdcwb9683DYPPPAAzz333KD9li5dyubNm3O3k8kk27dv58UXXySRSLBixQpuuOEGZsyYkdsmGo3yyCOP8PLLLwNwwQUX0NLSQm3tGBa0lFNks+qyWXaTKZAJ+lkM3uQ2RUREKoCCfiIiIiIiIiJSUhI+vBVLZ/mdV6Qsv6waB5aGfd6Kubze5zAn5BfvyaUi7dy5k9/93d/l7LPPxvM8vv/973PXXXfx9a9/nerq6tx2q1evZv369bnbgcDgabdHH32UV155hVtuuYW6ujq2bdvGPffcw5YtW3Cc9Pv9vvvu4+jRo2zatAmArVu3cv/993PbbbdNwJFOHYnMn30pZPo5BgLGkrKGpE5HIiIyRirvKSIiIiIiIiIl5a2YQ9IaGlyfpiKs5Xey5bU+DnAo4XAkWQKz/lLSNm3axKWXXsqCBQtYuHAh69ev58iRI+zZs2fQdoFAgIaGhtxPJBLJPRaLxXj22We59tprWblyJYsWLWLDhg3s37+f1tZWAA4cOMBrr73G5z//eZqbm2lubuamm27i1Vdf5eDBgxN6zJUuYTPlPUtkZjR4QolPERGRsSiRjzYREREREREREUj6sDPmAnBerV/ULL+siAvnTq8CYGefpkZkdGKxGMCgoB6kMwJvuOEGbrnlFv7X//pfdHd35x7bs2cPnuexcuXK3H3Tp0+nqamJtrY2ANra2giHwyxdujS3TXNzM+FwmF27do3nIU05ufKepjSibMeDfroIQURExkblPUVEREREREQKYC1EPYj6hj7P8EHKx/cNEdcScRiXYNVU0NbvkLCGOtdyVvX41bq7cHYNbxwb4N0Bh6jnEXHH7aWkglhr+c53vsM555xDU1NT7v41a9bwkY98hJkzZ9Le3s5jjz3GnXfeyT333EMwGKSrq4tAIHBKoLC+vp6uri4Aurq6qK+vP+U1T9zmZMlkkmQymbttjKGmpib3/6ksd/zGYOzx4J61kMoF/WCkXhpLD45m36CxgCFp0/tN5O8u+1pT/f0yEvVRftRP+VE/5Uf9VDgF/URERERERERGKenDgYRDr3d8IiI2AODQkYRpruXMkE9QSWSjkrLwZi7Lz8MZx3me2TUB5oZ8DiUc3oq5XFDnjd+LScX49re/zf79+7nzzjsH3X/RRRfl/t/U1MTZZ5/N+vXrefXVV1m3bt2wz2ft6TPNrLXDTnru2LGDJ554Ind70aJFbNmyhVmzZp32eaeC9q4eIpHaQfcNeBZicQAaptUO27fGJIjURYZ87HRGu2+tSXI0moJAkGDIZXbDzIJedyzmzJkz4a9ZbtRH+VE/5Uf9lB/10+gp6CciIiIiIiIyClEP3o07eBgMloaApdaB+bUuu3o9ulKGHs8Q7XdYWO0rg2wU3u53iPuGWseyeByz/LKW16aDfm/3O6ys9UpmfS8pTQ8//DCvvPIKd9xxBzNmzBhx28bGRmbNmsWhQ4cAaGhoIJVKEY1GB2X79fT0sGzZstw2J5YEPXGboTIAAa666iquuOKK3O1sAKujo4NUKjW6A6wwxhic6jDRaF86vS8j5gG4BIylL9o37P62MUi0N1rQa492X5s0gENsIEUy4eXeNxPBGMOcOXM4fPhwXkHoqUh9lB/1U37UT/mp1H4KBALjfmGOgn4iIiIiIiIieer3YF/cwcdQ7ViaqnyqM4GiJbWGWMIyK2g5MOAQ8w174w6Lq31qFfg7Lc/Cm33pjloxzll+WfNClnrXp9tz2N3vcG7t+AcapfxYa3n44Yd56aWX+OpXv8rs2bNPu09vby9Hjx6lsbERgMWLF+O6Lq2trbmswM7OTvbv389nP/tZIL1+XywWY/fu3SxZsgSAt99+m1gslgsMniwYDBIMBodttwDWcmJPZNfzCxo4XQ+NpQdHs2/ASW+dtOn9JuN3Z63Ve+Y01Ef5UT/lR/2UH/XT6CnoJyIiIiIiIpKHhA97B9IBv1rHsqjaHzIwVe3A4mqffQMOUe944C+swN+I3ulPB0rDjmVJzcQE34yBc8M+v+h1+E3M5Zzw0L9Tmdq+/e1v88ILL/AXf/EX1NTU5NbXC4fDhEIh4vE4jz/+OB/+8IdpaGigo6OD733ve9TV1XHhhRfmtr388svZvn07dXV1RCIRtm/fTlNTEytXrgRg/vz5rF69mq1bt3LjjTcC8OCDD7J27VrmzZs3KcdeiVI2/UceLKG/9WxbkprXFhGRMVLQT0REREREROQ0rIV3BxxS1lBlLAuHCfhlOQYWVvnsjTv0+Yb9Aw5La3zcEppkLiW+hTcyWX7La70J7adFNT6/itrM78mwsFqz7jLYP//zPwPw1a9+ddD969ev59JLL8VxHN577z1+9rOf0dfXR2NjI8uXL+eLX/wiNTU1ue2vu+46XNfl3nvvJZFIsGLFCm699VYc53hd2Y0bN/Lwww+zefNmAM4//3yuv/768T/IKSQbWAuY0vlbzwb9fAxJH6omtzkiIlLGFPQTEREREREROY2OpKHfN7ikM/zyCUo5BhZW+7T1OySs4f2EoamqdCaZS8meuEPUT5dMXTpBWX5ZAQPNYZ/WPpedMZeF1VN7HTQ51eOPPz7i46FQiE2bNp32eUKhEC0tLbS0tAy7TSQSYePGjaNuo+QvdUJ5z1LhGnCw+BhiHlRNcNs6e6L4brCgcqYuFptKFr1NIiJSGAX9REREREREREYQ9+GDZHoGdl7IEnJOs8MJXANNVT7vxB26Ug51rk9jQIG/E/kWXs9k+Z0b9ghMwkT8shqPN/ocjiQdOhKGWSH9jkQqVTJT3nMyzjUjCRoYsBDzoHGCZ2yTvk9rl1dQ0G9lg8soPhZFRGSc6ZwsIiIiIiIiMgxr4cCAg8VQ51oaCgjY1bowO5je7+CAyWWZSNq+uEOvly6bumyCs/yyatz0OowAO2NafFGkkiVzmX6ldTLOZh72T85pUEREKoSCfiIiIiIiIiLD6PYMMd/gYDkz5GMKzAw5I2ipdiwehsOJEksvmUTWwut96amJD4U9gpM4S3FubXqmff+AodebvHaIyPhK5db0m9x2nCzopBsW0/lHRETGQEE/ERERERERkSFYSy5ANys4urKeJzMG5oXSQaVjKUO/JnWBdICt23MIGcs54clNb2kIWOaFfCyGt5TtJ1KRrC3NNf3geHsU9BMRkbFQ0E9ERERERERkCMdShoQ1uFhmBsdeBi7iQr3rA4b3Ew62tCrLTThroTWzlt85YX9MQdViOTecnm1/u98hoRJ7IhUnHfAzgC29TD8F/UREpAgmeFlYEREREZloO3fu5Ic//CF79+6ls7OTL33pS1x44YUApFIpvv/97/OrX/2K9vZ2wuEw5513Hn/2Z3/G9OnTc8+RTCbZvn07L774IolEghUrVnDDDTcwY8aM3DbRaJRHHnmEl19+GYALLriAlpYWamtrJ/aARUSKwLfwQTI9A3tGyOIWaXJ4bsjS02+J+YYeD+qn8LfyvXGHzpRD0Fg+FC6NWe65IUuD69PlObzd77C8VpE/kUqSPKG0Z6HlmsdLdo1BBf1ERGQsJv3rxUiTUAAPPPAAzz333KB9li5dyubNm3O3izUJdeTIER566CHefPNNQqEQH/3oR7n22msJBCa9m0REREQKNjAwwMKFC7nsssv42te+NuixRCLB3r17+aM/+iMWLlxINBrlO9/5Dn/zN3/DPffck9vu0Ucf5ZVXXuGWW26hrq6Obdu2cc8997BlyxYcJ52acd9993H06FE2bdoEwNatW7n//vu57bbbJu5gRUSK5FjKkLKGoLFMDxQvJS/kpEuFticNHyQcprmFrxNYzjwLv4qms/xW1HpUlUCWH6SDAB+q9flFj8NvYi4fCvs4U/D3I1KpkiVa2hOOrzGooJ+IiIzFpEezRpqEylq9ejXr16/P3T45CFeMSSjf97n77ruZNm0ad955J729vTzwwAMAtLS0FP24RURERCbKmjVrWLNmzZCPhcNhbr/99kH3/cf/+B/5q7/6K44cOcLMmTOJxWI8++yzbNiwgZUrVwKwYcMGvvCFL9Da2srq1as5cOAAr732Gps3b2bp0qUA3HTTTXz5y1/m4MGDzJs3b3wPUkSkiKyFjkyW3+ygLXrQZ2bQciRpiVtDt2doKGJQsVy8FXPo8w1hx/KhSV7L72SLq31+FU1nY7474LCourTaJyKFS9n0Cb3USnsCuRLHcT+dba4LDkREpBCTfi3dmjVr+MxnPsO6deuG3SYQCNDQ0JD7iUQiuceyk1DXXnstK1euZNGiRWzYsIH9+/fT2toKkJuE+vznP09zczPNzc3cdNNNvPrqqxw8eBCAX//61xw4cIANGzawaNEiVq5cybXXXsszzzxDLBYb304QERERKSGxWAxjDOFwGIA9e/bgeV4u4Acwffp0mpqaaGtrA6CtrY1wOJwL+AE0NzcTDofZtWvXxB6AiMgYdXmGZGYtv8ZxCMgFTDrbD+Bwwky5tf3iPryeWctvdcQrucl318CymnSqzc4+rb0oUkmOZ/qV3h+2CxgyJT4n8VoD38LRpOFwIv0TVeahiEhZmfRMv3zs3LmTG264gdraWj70oQ/xp3/6p9TX1wOnn4RavXr1aSeh5s2bR1tbG01NTYPWrlm1ahXJZJI9e/awYsWKIduWTCZJJpO528YYampqcv8vltxzGYMp4BuHKXJ7iinbrlJt31jo2MpXJR+fjq086dhkoiQSCb773e/y0Y9+NBf06+rqIhAIDLrwCqC+vp6urq7cNtnx2XDbDGWixlKTTe/z4alvRqb+Gd649c0JWX6zgqNfyy/fzWdlsv0SmWy/Yn9nK+X3zmtRh4RNZzieXWNH1UZD/n085L4n9ctwr31OreX1PsvRlMORlMPsUOkFCMZLKb93RMYqVcLlPY1JXxSStNDvGSLu5Jx33k8YOlPH80Q6kpalNT7Vk546IiIi+Sj5oN+aNWv4yEc+wsyZM2lvb+exxx7jzjvv5J577iEYDBZtEmqobSKRCIFAYMSJqh07dvDEE0/kbi9atIgtW7Ywa9aswg54BO1dPUQitaffcAjBkMfshplFblFxzZkzZ7KbMG50bOWrko9Px1aedGwynlKpFN/4xjew1nLDDTecdnubx4VI1o48mTuRY6lSoPf58NQ3I1P/DK/YffPqwS7ifgrHQFNjmOAo6qsZkyBSFzn9hhnznSTvRlMc8VwCwQBnzC7+d7ZSe++835ek7XA3AL+/qIEzI8FR7d/e1UOkzi3otV03gRMKDXoupzo85La1wJK6FG29PjvjQZoaq2iclv/vthKU2ntHpBiSJVzeE9LByKSF/knK9OtMZQN+6fVs+31Dv2/YH3dYUqM1TkVEykHJB/0uuuii3P+bmpo4++yzWb9+Pa+++uqIJUELmYQaakLqdBNVV111FVdcccUpz9HR0UEqlTptG/JljMGpDhON9lFIbZGk63Lo0KGitaeYjDHMmTOHw4cP5/V7Kyc6tvJVycenYytPOraxCwQCFRtIKoZUKsW9995LR0cHf/3Xf53L8gNoaGgglUoRjUYHXWjV09PDsmXLctt0d3ef8rw9PT1DXnyVNVFjqclWyX/DY6W+GZn6Z3jj1Tevd6WDUNNdn4G+PgZGsa9tDBLtjea9fcSCg0MsBe/2JvHjxfvOVorvHd/Cj44GAMPZ1T6B3iMc6h3lc7hBor2F1ZpLNQZ5/Wh/+oYxRCK1I37HNj6Ay7sxnw+iceJ9o2xsmRruvaOxlFSCUi7vCceDkTHfABPbxgEf3h84vp7tnJAlaS1vxxzi1nAoYTizqjT7TUREjiv5oN/JGhsbmTVrVi6AVaxJqIaGBnbv3j3o8Wg0iud5I05UBYNBgsGhr0wcly9W1hb0kW8Zp/YUkbW25NtYKB1b+ark49OxlScdm4yHbMDv8OHDfOUrX6Gurm7Q44sXL8Z1XVpbW3MXZHV2drJ//34++9nPAunS6bFYjN27d7NkyRIA3n77bWKxWG5MNpQJH0tNMr3Ph6e+GZn6Z3jF7JvuFBwaALDMCBb+3StfroHpAcuRlOHNXpjfWPzfcSm9d3b2ORxLGULGsrYuVdBaeZaxTYNn980tmzHCd+wqB+pcS69n+E0U1kVKox8nSim9d0SKJVves3Qz/SyQzq6baIcSDj6GWsdyRmbd2aCBBVU+ewdcjqYcZgQ9lfkUESlxZXea7u3t5ejRozQ2NgKDJ6GyspNQzc3NwOBJqKyTJ6Gam5vZv38/nZ2duW1aW1sJBoMsXrx4Ig5NREREZFzE43H27dvHvn37AGhvb2ffvn0cOXIEz/P4+te/zp49e9iwYQO+79PV1UVXV1cu0y4cDnP55Zezfft2Xn/9dfbu3cv9999PU1NTbl3l+fPns3r1arZu3UpbWxttbW1s3bqVtWvXMm/evMk6dBGRUXkrli4bWeemAz4TYWYwHcb6IAFHkiU6C10EnSnDr6Lp/j0/4lFTJrMRM4PpGnvvxCAxSeX2RKQ4rC3tNf3geLtihSU0F6zfs3RnXnNelc+JRc/qAjAts75gZ6pEO05ERHImPdMvHo9z+PDh3O3sJFQkEiESifD444/z4Q9/mIaGBjo6Ovje975HXV0dF154ITB4Eqquro5IJML27duHnYS68cYbAXjwwQcHTUKtWrWK+fPn881vfpNrrrmGaDTK9u3b+fjHPz6ovJWIiIhIuXnnnXe44447cre3bdsGwCWXXMKnP/1pXn75ZQD+4i/+YtB+X/nKV1i+fDkA1113Ha7rcu+995JIJFixYgW33norjnN81nbjxo08/PDDbN68GYDzzz+f66+/flyPTUSkWBI+vBNPn9OygZ6JEHKgMWDpTBne6HO5tKE8ShubQBCP/CZ/PQsvHAMfOLMKlta54JVHBC3iQLWxxK2hrd9hRW15tFtETpUO+KXLZpZqpl+2XROd6bc76gOGsGOHvCijMeDT47l0pgxzgpYRVkISEZFJNulBv5EmoW688Ubee+89fvazn9HX10djYyPLly/ni1/8IjU1Nbl9ijEJ5TgOf/mXf8lDDz3E7bffTigU4uKLL+Zzn/vceHeBiIiIyLhavnw5jz/++LCPj/RYVigUoqWlhZaWlmG3iUQibNy4saA2iohMtj1xh5Q1TAukAz0TaVbQ0pmC/QOGnhRMm/Rv6qfnYWjtyi8V5VDC0Jl0cLFMc3x845ZN2SFj0tmYBxKGt/oDnDMNnFFMdrtYbCo5fg0UkbwlTyjtWapBq+xag7EJvL7AWmjLrJXaGBi6pG+dmz6fpawh6qWz/0REpDRN+in6dJNQmzZtOu1zFGsSaubMmdx2222nfT0RERERERGpHNbCrkxpz2W1MDDByXbVDpxZDe/HDW/GXD4ybYLruo2j7hR0JNMhvvlVPsFyifadoCFgOZJKl9t7/qg/7KT4UFY2lE+AU6TSeSW+nh+QO0f2exPXyI6koTsJDpaGYc5vjkmfC4+mDJ0pQ90ozoMiIjKxNPYUERERERGRKe2DpKHbMwSMZfEkre6wPJL+951+h/4KifkN+PDeQKZkasCnPnPZsWMMfiBU0I+ZhPQcx8CySPp1OxIGq7lukbKUsum/40nPgBhBNiA5YE0uSDne3u5Pn6frAxZ3hFNs9oKHbm/i2iYiIqNXyp9zIiIiIiIiIuOuLZae8Fxc7RN03Elpw6wQzAr6dCQdfhNzWVtX3pE/z8K7cQc/s0bUnNDxGWIfQ2tXYemUKxuDxWriqDRH4PWe9Np+PR65AKaIlI/sWSdgSjdi5ZLO0PCBfh8i4/yR5Ft4N56O9E0/TfZejQNVxjJgDT0pQ2OwdPtRRGQqU6afiIiIiIiITFn9PuzPZKMtrZnARZROYgwsD6cDfW39DsnJa8qY2UzAL27T2ZNnVfmjWgevFFU5hhmZCe72pKNsP5EylCqD8p7GQE0m0DcRJT6PJA1Ja6hyoPY0s8TGpLMBAXrL+7oUEZGKpqCfiIiIiIiITFnv9Kez0WYG/FxQZ7LMr7LUuZaENbwTL8+v69bCgYQh6hscLAvLdB2/ocwKWgyWft9owlukDJVD0A8gnAn6xSbg4o+DifQJel6NQz7VkyNuuhOjvkodi4iUqgoZeouIiIiIiIiMjrXQ1p+eXW0OT35qnWPgQ5lsv9/EXPwynFDtSBo6Uw5gaaryc5PXlSBgYEZA2X4i5Sq7pt9I69aVgprMbG2/P/4NPZhIv8aZNfm9VtgBgyVlDXGdA0VESpKCfiIiIiIiIjIlHUwYop4haCwLqyc/6Adwdo1PyFh6PcOBgRKfmT5JZ8pwOJnJGglZplXgunfZbL+Yb4iWxltGRPJ0PNOvtKNV2fKesXEu7zngw9FkNuiX3xSxY46vMxidgPKjIiIyegr6iYiIiIiIyJSUzfI7u9ovmXJvQQPNmbUFd8bKJ00u6pELUs4M+syc5FKp4yXowPRstl9CUyoi5aRcynvm1vQb5wsLDicMFkN9wFI7ik7JlvjsVdBPRKQkFTxC7erqKmIzRERERORkGm+JiIyf2AlBquZwaS3Qdk7Yw8HSnnQ4kiz9SdW4D+/GnfTksWuZW6EBv6xstl+fb+grrbeOnERjKTmRVyZBv+Nr+o1vQ9/PrucXGl10sS4T9OvzKMsy1CIila7goN8XvvAFvvGNb/DWW28Vsz0iIiIikqHxlojI+Hm7Px2kOiPo01BiZSjDLrlyoztjpZ1NlrKwL+7gYQg7lgVVPqbEJ9THKuRAYybb74Nkaf9+pjqNpSTLt+CTPjmVetDv+Jp+4/ca1sKhgfQLnVk1ushdlUmXSLUYYipzLCJScgr+avNHf/RH/OQnP+EXv/gFTU1N/N7v/R4XX3wxoVComO0TERERmbI03hIRGR++hbczpT2X1pTmjOW5YZ89cZd34w7RiJdbQ6mU+JmAX8IaQpl1EZ0Sn0wvltlBy7GUJeoZYt7xzBwpLRpLSVa2tKfBlvxaRxOxpl+vB32+wcFyRmh0QT9jIOJYujyjEp8iIiWo4M+5P/7jP+Zb3/oWt9xyC+FwmK1bt/L5z3+ebdu2cfjw4WK2UURERGRK0nhLRGR8HBgwxHxDlbGcVV2aQb/pQcuckI/F8FYJru1nLRxMpPvRIR3wK/XsmWJStl950FhKsk5cz6/Us5GzQb+ENbmSpMXWkTlvzQjags7d2QtR+hT0ExEpOWMqYuI4DhdddBEXXXQR7777Lv/4j//Ij3/8Y55++mlWr17Nv/t3/47Vq1cXqakiIiIiU4/GWyIixbcrk+W3pMbHLeH5ynPDHocTDm/3O6yq9QiWUGyprQ+OpRzAclaVT3UJtW2izA5aOlOWXs/Q7x2fqJfSorGUwPGgXymf87NCBlwsXqZ8Zt04nFs6MuvFzipwDdbazLp+/X56rcQp+BEgIlKyinZObmpqYs2aNTQ1NWGt5fXXX+fuu+/mtttu4+DBg8V6GREREZEpS+MtEZGx60rBoYSDwbIs7E12c0Z0ZsgyzbUkraGtv3SmVA8nDC93p/8/J2ipK7E1ESdKlQMNmYnvdmX7lQWNpaaulC2P9fwgnYmYvYigf5wy6Y4H/QrLdg8ZCJBe1+9oopgtExGRsRrzqLSnp4cdO3bwn//zf+ZrX/sajuPwxS9+ke985zv8t//23+jv7+db3/pWMdoqIiIiMiVpvCUiUjy7MqUy51fZklwn70TGwPLadGByZ8wdtzJvoxH14LmuABZocP2Cs0QqxezMWljdniFempViBY2l5MTynuVxzqpxjmfSFVvSQlcqHfSbWeA53Jjja5l2KOgnIlJSCr4e7+233+af/umf+MUvfgHARz7yET71qU+xePHi3DYXXHABruvyt3/7t2NvqYiIiMgUo/GWiEhxJXx4J56+9vWcmtLO8staXO3zWtTS7xv2xB2W1kxeZCll4addAQasYXoQ5gVtya+NNd6qHah3Ld2eoT1haKouj4DCVKGxlGSlMv+WS2JyOBP0i/kGKO555WjSYDGEHUvtGC5+qXUtPZ6hPQHUFK15IiIyRgV/1n35y1+moaGBf//v/z2/8zu/Q319/ZDbzZo1i2XLlhXcQBEREZGpSuMtEZHi2hN3SFlDvWuZEyqP4IxrYHnY4+VogDf7XM6u9nEmKdD2cq9LZ8qh2lgumW54Jzo57Sg1s4M+3Z5Ll2c4w7dUqdJnySjWWGrHjh289NJLvP/++4RCIZqbm7nmmmuYN29ebhtrLT/4wQ945plniEajLF26lOuvv54FCxbktkkmk2zfvp0XX3yRRCLBihUruOGGG5gxY0Zum2g0yiOPPMLLL78MpIOSLS0t1NbWjrU7pjQvl+k3ue3IVzaLLjYO5T3HWtozqzYTmDySAGuZ8heBiIiUioKDfjfffDMXXXQRgcDITzF//ny+8pWvFPoyIiIiIlOWxlsiIsVjLbyVKe25LOyV1eTk0hqf1r50RsV7A4azJiGb7N24oa0/3X8X16eoDQQnvA2lqsaFOtfS6xk6kob5VeURUJ4KijWW2rlzJ7/7u7/L2Wefjed5fP/73+euu+7i61//OtXV1QA89dRT/OhHP2L9+vXMnTuXJ598krvuuotvfOMb1NSk06AeffRRXnnlFW655Rbq6urYtm0b99xzD1u2bMFx0tHi++67j6NHj7Jp0yYAtm7dyv33389tt91WjC6ZssppTT8Y3/KeHZk1SMdanrnaAYNlwDd0e9BQLmmUIiIVruDrz377t3/7tIMmERERESmcxlsiIsVzKGHo8QxBYzm7urwWXws6cE443eZf97nYCY4pRT34RU/682h52GOeglqnmJ3JmOlMGRLl9faqaMUaS23atIlLL72UBQsWsHDhQtavX8+RI0fYs2cPkM7ye/rpp7nqqqtYt24dTU1N3HzzzQwMDPDCCy8AEIvFePbZZ7n22mtZuXIlixYtYsOGDezfv5/W1lYADhw4wGuvvcbnP/95mpubaW5u5qabbuLVV1/l4MGDYz6Oqaz81vRL/9vvFzdKaS0cSWQz/cbWF46BcKadHQmlOIuIlIqCz8h///d/z8MPPzzkYw8//DA//OEPC26UiIiIiGi8JSJSTLsyWWqLq32CZTg3+aGwR9BYulIO+wcmLlXFt/B8d4CENcwM+KyJlMdaiBOt1k2XurOYXOk8mXzjNZaKxWIARCIRANrb2+nq6mLVqlW5bYLBIOeeey67du0CYM+ePXiex8qVK3PbTJ8+naamJtra2gBoa2sjHA6zdOnS3DbNzc2Ew+Hc80hhUuVW3jO7pl+RT7lRD+LW4GCZPsagH6TX9QNo13lPRKRkFPxV57nnnhtUl/xEZ511Fs8991zBjRIRERERjbdERIol6sGBTKDsnHB5Bq2qHPhQNtsvOnHZfq19Lh1Jh6CxfKw+NWnrCZaD2aH07+dYypAsj2SiijceYylrLd/5znc455xzaGpqAqCrqwvglDUD6+vr6e7uzm0TCARygcITt8nu39XVNeS6gyduc7JkMkksFsv99Pf35x4zxkz5HwCLGRT0M+T3Q57bFXtfA4QzCar9fnH740gqPRU8PWgJOIP7CWNG3c7sun7tSWfSf9cT8V6a7DaUw4/6Sf2kfjr9MY23gmscHDlyhLlz5w752Jw5c+jo6Ci4USIiIiKi8ZaISLHsirlYDHNDPvUlWjXZMQY/EBpxm3OmwW9i0OU57E2GWBhO3+9isalk0dt0OGFo7UtPEH94mkddifZdqYg46eycmG84kjTMDSnyN9nGYyz17W9/m/3793PnnXee8tjJk3k2j+h8vtsMN1G4Y8cOnnjiidztRYsWsWXLFmbNmnXa550K2rt6qKkNY/viADTU1eLmefWCMQkidZHTb1jkfYMhj0XTp8ORY+ks6zPmECzSFRdvvd8H9NNUH2bu3OPta+/qIRKpHfXzVfuWve1xej3DtJlnUFuOqfSjMGfOnMluQllQP+VH/ZQf9dPoFTxkd12Xnp6eIR/r7u7OO2q5c+dOfvjDH7J37146Ozv50pe+xIUXXghAKpXi+9//Pr/61a9ob28nHA5z3nnn8Wd/9mdMnz499xxf/epX2blz56Dnveiii/jiF7+Yux2NRnnkkUd4+eWXAbjgggtoaWmhtvb4B9qRI0d46KGHePPNNwmFQnz0ox/l2muv1Vo6IiIiMikmYrwF6YmkH/zgBzzzzDNEo1GWLl3K9ddfP+jK+GQyyfbt23nxxRdJJBKsWLGCG264gRkzZuS2yWe8JSIy0ZI+tPWnJyGX1ZRulp+PobUrddrtpgcMHyQdXuqydA34OAZWNriFl/EZRtxPl/UEw5Jqj0Vltg7iZDAmvbbfvgGXo0kz5vWyZOyKNZbKevjhh3nllVe44447Bo2BGhoagHSmXmNjY+7+np6eXOZeQ0MDqVSKaDQ6KNuvp6eHZcuW5bbJZgae6MTnOdlVV13FFVdckbudPaaOjg5SqdOfUyqZMQanOkx3NAY4GCz9fX15728bg0R7owW99lj2Tboux9o/wCWAh2Hv+4eLdtHF/i4XcKhKRDl0qBc43k/RaB+FpJE3BFy6UvD6gXbOqq7M854xhjlz5nD48OG8AvVTlfopP+qn/FRqPwUCgXG/MKfg7wVnn302zzzzzJCPPfPMMyxevDiv5xkYGGDhwoW0tLSc8lgikWDv3r380R/9EVu2bOH/+X/+Hw4dOsTf/M3fnLLtxz/+cR588MHcz3/6T/9p0OP33Xcf+/btY9OmTWzatIl9+/Zx//335x73fZ+7776bgYEB7rzzTm655RZ++ctfsm3btryOQ0RERKTYJmK8BfDUU0/xox/9iJaWFu6++24aGhq46667BpWHevTRR3nppZe45ZZbuPPOO4nH49xzzz34/vFJ4NONt0REJkNbv0PSGqa5lgVV5T9hMDNoCRhLwhqOpsanRJC18GJ3gH7fUO9afmta6QZLS02dC9WOxcdwVGtcTbpijaWstXz729/ml7/8JX/913/N7NmzBz0+e/ZsGhoaaG1tzd2XSqXYuXNnLqC3ePFiXNcdtE1nZyf79++nubkZSK/fF4vF2L17d26bt99+m1gslnuekwWDQcLhcO6npqZmULun+g9Ayk//GzBgyf+HUWxbzH1tZu9weila+rzi/C5939KZOS81Bk7tJ6wtqK2zq9K7f5Awk/77Hu/30mS3oRx+1E/qJ/XT6Y9pvBUc9PuDP/gD3njjDb761a/y85//nLfeeouf//zn3HHHHbzxxhtceeWVeT3PmjVr+MxnPsO6detOeSwcDnP77bdz0UUXMW/ePJqbm/mP//E/smfPHo4cOTJo26qqKhoaGnI/4XA499iBAwd47bXX+PznP09zczPNzc3cdNNNvPrqqxw8eBCAX//61xw4cIANGzawaNEiVq5cybXXXsszzzyTW6BZREREZCJNxHjLWsvTTz/NVVddxbp162hqauLmm29mYGCAF154AYBYLMazzz7Ltddey8qVK1m0aBEbNmxg//79uYmrfMZbIiITzbPwm1h61nR5rccELaMxrlwDczIZZB8kjq9TVUw7Yw7vJxxcLL/dkCJYAf02UdLZfulfypGkIaUEyUlVrLHUt7/9bZ5//nluueUWampq6Orqoquri0QiAaSzET71qU+xY8cOXnrpJfbv388DDzxAVVUVF198MZCe47r88svZvn07r7/+Onv37uX++++nqamJlStXAjB//nxWr17N1q1baWtro62tja1bt7J27VrmzZs3Pp00BZy4nl85qcmsl9fvF6fh/T7ErcFgaQgU78NjVqYydUeizDpYRKRCFZwcvnr1am666Sa2bdvG//v//r+5+8PhMDfddBOrV68uRvtOEYvFMMYMCuoBPP/88zz//PPU19ezevVqPv3pT+eubmprayMcDrN06dLc9s3NzYTDYXbt2sW8efNoa2ujqalpUNnQVatWkUwm2bNnDytWrBiX4xEREREZzkSMt9rb2+nq6mLVqlW5+4LBIOeeey67du3ik5/8JHv27MHzvNyEFMD06dNpamqira2N1atX5zXeEhGZaHvjDjHfUONYFldQecrGgOVIyhL3DR8UeZK1I2l4NZoOlP5WnUdjESeGp4p61xI0lqQ17OmHc6omu0VTV7HGUv/8z/8MpJeXOdH69eu59NJLAbjyyitJJBI89NBD9PX1sWTJEjZt2jQo8+66667DdV3uvffeXLn0W2+9Fcc5fk3+xo0befjhh9m8eTMA559/Ptdff30BRy9ZKZs+T5Zv0K84z3cskx1e79qi9kU26Hc0lb4Qpdz6WUSk0oypIvTll1/ORRddRFtbGz09PUybNo3m5maqq6uL1b5BEokE3/3ud/noRz86KOh38cUX50opvPfee3z3u9/l3Xff5fbbbwfSNdWHqn1eX19PV1fXsNtEIhECgUBum6Ekk0mSyeMLphtjcgO60daGH0nuuYzBFJAGaorcnmLKtqtU2zcWOrbyVcnHp2MrTzq2qWu8x1vZcc7J46D6+vpcZYWuri4CgcCg9Wey24w0ljp5m6FM1Fhqsul9Pjz1zcjUP8M7Xd9YC2/2pYNX54Z9Ak5+fWgyPwW3awL2NQbODPm8E3c5mjIcScAZrjlpm9G/dxKZdfwshoXVPs1hO+z+5dBPI+5bwHfs0fx+ZgUtBxOGt6JwTpUpqyzTSjvvFGMs9fjjj592G2MMV199NVdfffWw24RCIVpaWoYtuQ7puaiNGzfm3TY5PS+X6VdeFzFky3vGvOL8LR7LlvYs8nqjtS6EHUvMNxxJGuaEyqufRUQqzZiXga2urh501fd4SaVSfOMb38Bayw033DDosU984hO5/zc1NTF37lxuu+029uzZM2J9dmsHf4EZakB78jYn27FjB0888UTu9qJFi9iyZcu4LMbY3tVDJFJb0L7BkMfshplFblFxzZkzZ7KbMG50bOWrko9Px1aedGxT00SMt04e7+RTaz7fbUplLFUK9D4fnvpmZOqf4Q3XN7/pHKDb66XKNXxs8Syq3fxWt2jv6iFS5xbUFmMSROoip9+wCPtGgJ6uBB1xj593wY3nziE4RGBzpPdOZ0+UZGZtVmstP29PEfUsdQG4+IyqkQOlnkekrmb4x0cwkf10un3z/Y492tet9i0fdMTpSUGsdgZL6kOjamspqKTzzkTNXUlpSmX+LezMPnmKnenXmUp/Dk4vcgZ3uqyxz74Bl/aEgn4iIpNtTEE/ay3vvPMOHR0duTrmJ7rkkkvG8vQ5qVSKe++9l46ODv76r//6lNKeJ1u0aBGu63L48GEWL15MQ0MD3d3dp2zX09OTuyK9oaFh0ELJANFoFM/zhrxqPeuqq67iiiuuyN3OTmp1dHSQSqWG223UjDE41WGi0b70JaujlHRdDh06VLT2FJMxhjlz5nD48OEJW8xyoujYylclH5+OrTzp2MYuEAiUZSBpvMdbDQ0NQDpTr7GxMXf/yeOkVCpFNBodlO3X09PDsmXLctucbrw1lIkaS022Sv4bHiv1zcjUP8MbqW+shX85GgAM59Sk6Gz/IO/n9d0g0V6voDbZxiDR3uiE7TvbgU7j0JM0/N+32/mtacdnhvN57/hukNau9LEeThg+SDoYLHMCPq8fGbkPzpvgYy36vsYQidTm/R27kNedHjB0JB1eONBFbayw99RkGO69o7GUlKtyXdMvnLlWJVakNf2y5T2LHfQDmBWy7BuA9qQDVE45bRGRclRw0O/gwYP8zd/8zYiBpGIMnLIBv8OHD/OVr3yFurq60+7z3nvv4XlebhKrubmZWCzG7t27WbJkCQBvv/02sVgsN1HV3NzMk08+SWdnZ27Cq7W1lWAwOGK2YDAYJBgMDvnYuHwpt5ZCntUyTu0pImttybexUDq28lXJx6djK086tqllIsZb2RLpra2tLFq0CEiPv3bu3MlnP/tZABYvXozrurS2tnLRRRcB0NnZyf79+3Pb5DPeGsqEj6Ummd7nw1PfjEz9M7yh+mZf3KE7ZQgay4dqvFFdN2kzPwW3ZwL3dQ3MD6WzK3bGHOaEfOZXnRwAHf69kz3WzlQ64AdwZshS4+bXlnLpp6H2zZX0HMV37NG+7oyA5UgSDiccjiQ8ZhS5pN54q4TzzkTNXUlp8zJr+rllFvSrcTOZfnmW9zSBIN4whYiTPmSvZ6mvDuKfkPZoALyxXZgwO3N+60gafAt5VtQWEZFxUHDQ79vf/jbJZJL/8l/+C01NTcNO1pxOPB7n8OHDudvt7e3s27ePSCRCY2MjX//619m7dy+33norvu/n1oTJrrd3+PBhXnjhBdasWUNdXR0HDhxg+/btLFq0iHPOOQeA+fPns3r1arZu3cqNN94IwIMPPsjatWuZN28eAKtWrWL+/Pl885vf5JprriEajbJ9+3Y+/vGPnzazUERERGQ8TMR4a+bMmXzqU59ix44dzJ07lzlz5rBjxw6qqqq4+OKLAQiHw1x++eVs376duro6IpEI27dvp6mpKVcqK5/xlojIRLAWfh1NB7DODXuE8qvqWbamBaA5AG19hp91B/jdxtSogks9KTgwkJ6dnRX0mV5mgalSFnLgrBrY1w87Yw4fqy+fbL9KUayxlJS345l+5XV+C2fKe8byTJzzMLns7ZP1eQAuQWNp6x38hAZYOX1sxU8bA5agsSStoTNlyu4iBxGRSlJw0G/37t3cdNNNfPjDHx5TA9555x3uuOOO3O1t27YB6SutPv3pT/Pyyy8D8Bd/8ReD9vvKV77C8uXLCQQCvP766zz99NPE43FmzJjB2rVr+fSnP43jHP92t3HjRh5++GE2b94MwPnnn8/111+fe9xxHP7yL/+Shx56iNtvv51QKMTFF1/M5z73uTEdn4iIiEihJmK8dfPNN3PllVeSSCR46KGH6OvrY8mSJWzatImamuNrNV133XW4rsu9995LIpFgxYoV3HrrraMab4mITIS9cYduz0ln+YWnRomxC+qhN+lzKOHwbFeAfzc9ybQ8vu3vi8G+AQcw1Ls+czRJW3QfiqSDfvviDmsjHrXltqhYmSvWWErKm5c5tZVdpl9mmJ20hqSF4Bja358pEVo9ThfCOCad7fd+wvBBQkE/EZHJVHDQr7q6uigZcMuXL+fxxx8f9vGRHgOYOXPmoEms4UQiETZu3Hja57rttttO+1wiIiIiE2GixlvGGK6++mquvvrqYbcJhUK0tLTQ0tIy7Db5jLdERMaTZ+FX0XRUZUVt5Wf5ZTkGLqlP8X+PBej2HJ4+GuSSBo+5w2zvW3i9z6G1D8DQ4PosqLKYMpsQLwczQnBG0OeDpMNbMZfz65TtN5GKNZaS8laua/oFDQSwpDD0exAseBYX4plrYGqc8QvGzQn5vJ9wOJxwOLd2alx0IyJSigr+CnTZZZfxwgsvFLMtIiIiInICjbdEREbnrZhDn28IO1Mnyy8r5MAnG1PMCPgMWMOPO12eORClN3V8G8/C+wOGp48F+HVfAAtMDyjgN97ODacDfW39Dsmp9bacdBpLiW8t2VB7uWX6GQM1mezgbKZeobL7j2/QL/3cH2TW9RMRkclR8DUiCxYs4MUXX2TLli2cf/751NXVnbLNunXrxtQ4ERERkalM4y0RkfwN+PB6X3p2dHXEK7uMjmIIu/C701P8a4/LnrjLv3XE+TcC1Lvptax6PUPCpjsmZCy/1WDoGVDAb7zNr7LUuen+3xN3WDbFAtKTSWMpSfiQXrVuDJOgkyjspM8dMd8AhUXSrD2e6Tde5T0hva5fyFgS1nAsZZipEp8iIpOi4M+7++67D4D29nZeffXVIbd57LHHCn16ERERkSlP4y0Rkfy19rkkrKEh4LO4euoGVQIGPjrNY1GNZU+qhr29Sbo9yE56VzuWs6p8VtZ6VFWFaE1ManOnBGNgWY3Hy9EAbf0OzTW+Aq0TRGMpiWfS/BzK8wKHbGZe/xgqAw9YsBgcLKFx7APHwOyQ5cCA4XBCQT8RkclScNDvK1/5SjHbISIiIiIn0XhLRCQ/Xal0aU+ACyIeThlO7BaTMensst9aWE/be4eIeuk1rUIGZgZtrn+mbmh04p1d4/Nq1NKZcjiSNMwKaTJ8ImgsJfFMnclyzf7OlveMjaG8Z7a0Z7XDuAc+5wR9Dgyk1/VboXX9REQmRcFBv3PPPbeY7RARERGRk2i8JSJyetbCS70BLIYFVT7zqhRMOVFdACKu+mSyVTmwsNpnT9ylrd9hVmgMaTuSN42lZCDzp1auQb9wJtMvNoZTRjZLcDzX88vKruvXnlnXb6pfhCMiMhnGXMk5Fovx2muv8fzzzxONRovRJhERERE5gcZbIiLD2z9gOJxwcLBcEElNdnNEhrWsJp31si/uMKAEmAmlsdTUNZDJ9HPLNPiUvWijbwyZfvHMvjXjuJ5fVmPAUmUsKWs4kizTThcRKXNjWsP2iSee4KmnniKRSC8CcPfddxOJRLjzzjtZuXIl//7f//titFFERERkytJ4S0RkeAOez0s96dpny2t96jLfcE0giEdhk42mHBd9krIwM2hpDPh0phz2xB0+FFbkbyJoLDW1xXOZfuWZ8VybKe8Z9Qr7bLIW+jOnmuoJyPQzBuaEfN4dcHk/4TBbWc0iIhOu4Gs8/umf/oknnniCyy67jNtuu23QY2vXrh12gWQRERERyY/GWyIiI3vuYIyYb6hzLefVHp9Y9DC0dnkF/dgCg4Uip2MMNGey/dpiLrY8YxBlRWMpyWX6TXI7CpXN9Ov3DV4B54ykJXMRjKV6AjL9IL2mLMCBAX2eiohMhoIz/f7xH/+RK664gmuuuQbfH3x12ty5czl06NCYGyeDWX0jEBERmVI03hKRqex02XodA/DqkTgA6xoNTjBE9kypbD0pVYuqfV6JWro9wwdJk1v/SsaHxlISL/M1/apMOksxZQ19Hkwb5UxuPJvlZyZufb0zq3wMls6UQ593PFtRREQmRsFBv/b2dlatWjXkYzU1NcRisYIbJYOlLLzc41ATV0q8iIjIVKLxlohMZdlsvaH4Ft7udwBDY8Cnvd/S3n/88ZWNwYlppMgohZx04O/tfpe2mMMclb4bVxpLSbzM1/QzBmqd9IUCUc8wLTC6CwX6M+v5VbsTd4FBtZMuZ9yRNBwYcFimUsYiIhOq4MTucDhMd3f3kI+1t7czbdq0ghslgx0aMLT1u/y6yyeq7wMiIiJThsZbIiJD60gaBqwh6MA8ZUpJmcmW+Nw/4OTW2pLxobGUDJT5mn4AkTGs65cN+tVMUGnPrAVV6ZPbgYEJfmERESk86LdixQqeeuop4vF47j5jDJ7n8eMf/3jYK6lk9BZUW5ZkvhS8O+CQKt9xioiIiIyCxlsiIqeK+9CeTE9iLqoLlm3JNpm6ZgQtMwI+PoZ3+jUhPp40lpJyz/SD4+v69fmjP4hsec8aZ2InE7Pr+h1KGJKaxxQRmVAFjy7/5E/+hCNHjvBf/+t/Zdu2bUC6Vvpf/dVfcfjwYf74j/+4aI0UuLDOoz4IKWt4b8DRgt8iIiJTgMZbIiKDWZvOGrAY6lzLzGotFCTlqTlT7u7tflff78eRxlIyUOZr+gHUZoJ+o63+5VlI2Ex5zwm+vqDetURci4/h0EAZd76ISBkq+JQ/Z84c/vt//++ceeaZ/NM//RMAP/vZz6irq+OOO+5g5syZRWukQNCBS2cFMFh6PaMSICIiIlOAxlsiIoMdTRlivsHBMj/kY4wmEqU8LazyCZj09/uOpN7H40VjqanNtzCQmT+rhEy/0Zb3zGb5BY2d8KCnMTA/dLyUsYiITJzAWHaeP38+mzZtIplM0tvbSyQSIRQKFattcpIZVQ51LvR46Q/68AQuwisiIiKTQ+MtEZG0hA+HE+lZyzkhS0hziFLGgg6cVeXzTtzlnbjD7NAoU3gkbxpLTV2JE6bNxjQBOsmya/r1jTLoN1nr+WUtrPZ5q99l/4BDynplnW0pIlJOinLaDwaDTJ8+XYOmCTCWOt4iIiJSvjTeEpGpzFp4P+HgYwg7lhkBXQAp5e/smnQWzL64Q0pv6XGnsdTUk83yc7CUc2J4trxnzE+X7MxXtkpY9QSv55c1K2iJODa3VJGIiEyMgi90eeKJJ067jWqjF18k80Hd56W/+JbzoEVERERGpvGWiEhaV8rQ6xkMlvlVvr4HSUU4IzMhHvUN++MOi2u0jkexaSw1tQ1kLpgv9wyzagMuFg9DzIO6PGdz47lMv8kJ+hkDi2p8Xu9z2Rt3WFStc5yIyEQoOOj3gx/84LTbaOBUfNXOCR/0PtRq3XoREZGKpfGWiAgkLRzMlPU8I2ipVrKAVAhjYHGNT2tfusSngn7Fp7HU1JZd067cg37GpEt8dmeW+6nLI9vd2uPHP1nlPQEWVXu83udycMAw4EOVPsNFRMZdwUG/xx577JT7otEoL730Ek8//TS33XbbmBomQzMmHejr8dK1vGu1rp+IiEjF0nhLRAQODjh4GKody6ygvv9IZTm72qO1z+VQIp3BE9aFvUWlsdTUli3v6ZZ50A/Sy/10e4aob4DTfxbGLVgMDpbgJB5/QwAaAz6dKYd34w7NYV3cICIy3op6fUUkEuHyyy/n4osv5pFHHinmU8sJsuv6RUe5gK+IiIiUP423RGQq6U5Bt5ee4FwQUllPqTx1AZgV9AHDvrhSYCaCxlJTx/HynuV/wUjtKOcC4162tOfkLw2ULev5js5xIiITYlzOtkuWLOGNN94Yj6cWjgf9+nzwy3/cIiIiIgXQeEtEKt2AD+8n0l9ZZwctNcqAkgqVnRDfe9KEuAkE8QOhgn5MIDgZh1JWNJaqfPHMnFnBZc5KSG4u0Mtv+/5MQl31BKzn5xgz4vloYSSAATqSDl3oXCUiMt7G5XNv3759VFdXj8dTC1B1wgK+/VrXT0REZErSeEtEKt0r3ZCyhipjma2ynlLBFlb7/Fuv5WjKoTsF9ZmZGg9Da1eeM/wnWdngjs9V3hVEY6nKl830q4Tyntm5v3wz/fr945l+483H0NqVGnGbiOvQ6xn+9ZjPnNDxz3Sdq0REiq/goN9zzz13yn3JZJL9+/fz05/+lI997GN5Pc/OnTv54Q9/yN69e+ns7ORLX/oSF154Ye5xay0/+MEPeOaZZ4hGoyxdupTrr7+eBQsWDHrd7du38+KLL5JIJFixYgU33HADM2bMyG0TjUZ55JFHePnllwG44IILaGlpoba2NrfNkSNHeOihh3jzzTcJhUJ89KMf5dprryUQKK1rgk5c1y/ma10/ERGRSlWs8VY+PM/jBz/4Ac8//zxdXV00NjZy6aWX8h/+w3/AcdJfxYs1LhMROZ2DA4Y9MQDL/CofpwImbEWGU+3AvJDl/YRhb9xldaSwQJ+caiLHUlJ6smv6BSrgM6QuM/fXk0fQz1qIZ469ZgIy/fLRGPDp9Vw6U4YzgnbSS46KiFSygqNZ3/rWt4a8PxgM8rGPfYzPfe5zeT3PwMAACxcu5LLLLuNrX/vaKY8/9dRT/OhHP2L9+vXMnTuXJ598krvuuotvfOMb1NTUAPDoo4/yyiuvcMstt1BXV8e2bdu455572LJlS26S6r777uPo0aNs2rQJgK1bt3L//ffnFm32fZ+7776badOmceedd9Lb28sDDzwAQEtLy+g6ZwJUO5Yez+Q+xEVERKTyFGu8lY+nnnqKH//4x9x8883Mnz+fPXv28K1vfYtwOMynPvWp3DbFGJeJiIwkZeFfe9JfVWcErCqb5CFbWu1EBmjv6sF3gww35Ws06zruhvrdDGVhLbyfgD1xl/PqXYzR76cYJnIsJaUnO2fmVsCafvUBC1jifrrq10gZfAmbzhQ2WKpKZPg9zU1XLUtaQ9RLr2cqIiLjo+BT7De/+c1T7gsGgzQ0NIzqedasWcOaNWuGfMxay9NPP81VV13FunXrALj55pu58cYbeeGFF/jkJz9JLBbj2WefZcOGDaxcuRKADRs28IUvfIHW1lZWr17NgQMHeO2119i8eTNLly4F4KabbuLLX/4yBw8eZN68efz617/mwIED/M//+T+ZPn06ANdeey3f+ta3+MxnPkM4HB7VcY23bE3udKmC8h+8iIiIyKmKNd7KR1tbGxdccAFr164FYPbs2bzwwgu88847QPHGZSIip/N6n0vUN4RdBpUAk+ENVVrNAJE6l2ivN+w3xpWNWktpvOVT9g7At+DgEPUMvzzmEXb1+ymGiRxLSenJlveshEy/oIE6F3o96EwaaqqG/3yMnVDas1Qy5R0DDQHL0ZThWMpQF9Dnu4jIeCn4eo9Zs2ad8lPsQVN7eztdXV2sWrUqd18wGOTcc89l165dAOzZswfP83ITSwDTp0+nqamJtrY2ID2JFQ6HcwE/gObmZsLhcO552traaGpqygX8AFatWkUymWTPnj1FPa5iqM785uJ+Om1fREREKs9EjLeyzjnnHN544w0OHjwIpNe52bVrV+7irGKNy0RERtKdgjf70l92LqivjHWYRPLhmOPl+7pSeuMXy0SOpaT0VFJ5T4DGTKCs8zTniFimQnC4REp7Zk0PHC9RmiqtpomIVJSSTqbu6uoCoL6+ftD99fX1HDlyJLdNIBAgEomcsk12/66urlOeI59tIpEIgUAgt81QkskkyWQyd9sYkytvVcxSHLnnMgZjLVUGwOKT/qAMnealTJHbU0zZdpVq+8ZCx1a+Kvn4dGzlSccm4+3KK68kFovxX/7Lf8FxHHzf5zOf+QwXX3wxULxx2ckmaiw12fQ+H576ZmRTqX+shZd6XXwMZ1b5NFU7dA+MsMNJ35FOeXgMbamIfU/TP+PymuW0b579U4zXzXe/hoCl24NuzzAv06axvGah542pdN4ZjZ07d/LDH/6QvXv30tnZyZe+9CUuvPDC3OMPPPDAKWsILl26lM2bN+du57P2cTQa5ZFHHuHll18G4IILLqClpYXa2tpxPsLK4lsYyPxpV8oFJNMDPvsHnNMG/foymX61bmlF1mrcdOWyuG/oShlmBkurfSIilaLgoN+f/Mmf5L2tMYbvf//7hb7UKQNNm8eAPN9tTnzuoQa0J29zsh07dvDEE0/kbi9atIgtW7Ywa9as077+aLV39RCJHB/k1QzE6fcsprqGSNXIC10EQx6zG2YWvU3FNGfOnMluwrjRsZWvSj4+HVt50rFNLRM53vr5z3/O888/z8aNG1mwYAH79u3j0UcfpbGxkUsvvXTQ65xorOOyiRxLlQK9z4envhnZVOifnZ0DHPqgl4CBPzh7Bon+PiJ1p1/Q78TvSFnGJIjURYbY+vQqbd+h+me8X7Oc9h2pf4rxuqPZr8Za3muPk7QGqsMYkyr4WIsxB1AJ551ijqUGBgZYuHAhl112GV/72teG3Gb16tWsX78+dzsQGDztls/ax/fddx9Hjx5l06ZNAGzdupX777+f2267Le9jkfS6dtmweUlnPIxCPpl+nj2+lmG4RNbzO9H0gOVgwtCpoJ+IyLgp+HPvj/7oj3juueeIx+Ocf/75NDQ00NnZyauvvkp1dfWgyaFCZUsudHV10djYmLu/p6cnd5V5Q0MDqVSKaDQ66Krynp4eli1bltumu7v7lOc/+Xl279496PFoNIrneUNmCWZdddVVXHHFFbnb2Ymwjo4OUqnT1+3PlzEGpzpMNNqXq+cZwqEfQ1dfnGBi5A/KpOty6NChorWnmIwxzJkzh8OHD+c1cVhOdGzlq5KPT8dWnnRsYxcIBMoukDQR462sv/u7v+PKK6/kox/9KABNTU10dHTw93//91x66aVFG5edbKLGUpOtkv+Gx0p9M7Kp0j8JH358JAAYVtR69B9rx3eDRHu94XcyhkikdtB3pCzbGCTaGy2oLRWz7wj9M26vWU775tE/xXjd0e43zTF0eQ6HevpZGQ4UfKxjmQMY7rwz1cdSa9asyZU9H04gEBi2fGg+ax8fOHCA1157jc2bN+eWqLnpppv48pe/zMGDB5k3b17e7Z3qsqU9Q046sbcSPkEbM0Gy7pTBs0NnMMZ8AEPQWIIlGPRrCFgOJSz9vqF/hI94EREpXMFBv5qaGhoaGrj99tuprq7O3d/f389//+//naqqKv7wD/9wTI2bPXs2DQ0NtLa2smjRIgBSqRQ7d+7ks5/9LACLFy/GdV1aW1u56KKLAOjs7GT//v25bZqbm4nFYuzevZslS5YA8PbbbxOLxXITUM3NzTz55JN0dnbmJrJaW1sJBoMsXrx42DYGg0GCwaEX1x6XL+XW5gYqVY4Fz6TX9TvdbuPVniKy1pZ8GwulYytflXx8OrbypGObWiZivJU1MDCQu8I8y3Gc3O+kWOOyk034WGqS6X0+PPXNyCq9f16LuvT7hjrXsjzsYW3mO8wI++RKMp7wHelEY+mtStj3dP0zHq9ZTvvm2z/FeN3R7FcfsHR50OWZ9N/9GF5zrOeMSjjvTORYCtIlQG+44QZqa2v50Ic+xJ/+6Z/mLo463drHq1evpq2tjXA4nAv4QXq+KhwOs2vXriGDflOlVPpoJWz62KscRlXG92STUYp4uPK8EReCxpK0hl7P0Bg8db9+L1Pa07H5v34B5Y6HanM+ggamudDtpTMWS3k5ohOp7HF+1E/5UT/lR/1UuIKDfv/8z//MNddcM2jQBOkB1R/+4R+yffv2vAZO8Xicw4cP5263t7ezb98+IpEIM2fO5FOf+hQ7duxg7ty5zJkzhx07dlBVVZVbXyYcDnP55Zezfft26urqiEQibN++naamptyj/xhfAACFuElEQVRAav78+axevZqtW7dy4403AvDggw+ydu3a3IBp1apVzJ8/n29+85tcc801RKNRtm/fzsc//nHC4XCh3TSuqjPzcnHfUBnXLImIiMiJijXeysf555/Pk08+ycyZM5k/fz779u3jH/7hH7jsssuA9EC7GOMyEZGTHUsa3oqlv9ysq0tVzNpLIoWoc8EhPal/JDHZrSl/EzmWWrNmDR/5yEeYOXMm7e3tPPbYY9x5553cc889BIPBvNY+7urqGrLa1EjrI0+1Uun56u0agGO9VLkm7zK+J5usUsQjlec9I9rFgb4UfqSRudMHv6/bu3qImxTgMz1cRaR2NNO+iQnrp3khj+7OBF2egxMMMnd2aS9HdKJKKHs8EdRP+VE/5Uf9NHoFB/2OHTuG6w69voLrusMORk72zjvvcMcdd+Rub9u2DYBLLrmEm2++mSuvvJJEIsFDDz1EX18fS5YsYdOmTbmrlgCuu+46XNfl3nvvzS2EfOuttw66Wn3jxo08/PDDuQWUzz//fK6//vrc447j8Jd/+Zc89NBD3H777YRCIS6++GI+97nP5d0nE63KSQf64n66GomC3iIiIpWlWOOtfLS0tPDYY4/x0EMP0d3dzfTp0/nkJz/JH//xH+e2Kda4TKRSmEAQr4Dr+F0sNpU8/YZTgLXwr70uFsNZVR7zqnQxo0xtjoE619LtGd7rtxWzFtlkmcixVLbKAaTLpJ999tmsX7+eV199lXXr1g27X77rIw+X6TBVSqWP1uGYAQJUO+Rdxvdkk1WKeKTyvLXWAVz2dnQzY6Bz0GOeE6QnYQGDm4wT7c3zBY2BxuCE9VPQQsA4pKzh3Z4kxEtzOaITTZVy62OlfsqP+ik/ldpPE1EuveDx45lnnsk//MM/sHr16kELE6dSKf7hH/6BM888M6/nWb58OY8//viwjxtjuPrqq7n66quH3SYUCtHS0kJLS8uw20QiETZu3DhiW2bOnFlWCyNXGQCLjyFl0ynyIiIiUjmKNd7KR01NDX/+53/On//5nw+7TbHGZSKVwsPQ2jX6BWlWNrgoDJ62u9/hSNIhYCy/VafFfUQgXeKz24P34rAwpAt8x2Iix1Ina2xsZNasWbngTT5rHzc0NNDd3X3Kc524hvLJplqp9Hylq2JBtWtGVcb3ZJNRinik8ryNbvr+YylzyjZdKfAxGCzVTv6vX0i546HanDcDDa7lSMqwrx+aguXzPq2EsscTQf2UH/VTftRPo1dw0O8zn/kMf/u3f8uGDRu48MILaWhooKuri5deeomuri7+23/7b8VspwzBMRAykLAQtzD0EE9ERETKlcZbIlLJ4j68Ek1n4Kyu9QgPnYwjMuXUuWCw9KYMA0GoVtCvYJM5lurt7eXo0aM0NjYC+a193NzcTCwWY/fu3SxZsgSAt99+m1gslgsMSn4G/BPW9Kuga0oaMwGyztSpJ4YD8fS/tU7pXyxQH7AcScH7cfDqUGlvEZEiKjjot3btWv7qr/6K73//+/zTP/1TLtq6ZMkSvvCFL2jdlglS7UDCSw9m6lxFvEVERCqJxlsiUslejbokrKEx4HNO2J/s5oiUDNdAxIVeD7pThuqQvusXqphjqXg8zuHDh3O329vb2bdvH5FIhEgkwuOPP86HP/xhGhoa6Ojo4Hvf+x51dXVceOGFQH5rH8+fP5/Vq1ezdetWbrzxRgAefPBB1q5dy7x584rVLVNCPPOxUu0aUhUU9GsIWAyWuG/oScG0zMyutbAndnybUhd2IGjS65e+P2Boqi79NouIlIsxlYc/77zzOO+88xgYGKCvr4/a2lqqqqqK1TbJQ7Vj6fFMbjAjIiIilUXjLRGpRB0Jw+7+dGrfujoPR1f4iwwyzbX0eoYez3DGmAoMSrHGUu+88w533HFH7va2bdsAuOSSS7jxxht57733+NnPfkZfXx+NjY0sX76cL37xi6Ne+3jjxo08/PDDbN68GYDzzz+f66+/vtDDn7IG7PFMv0pa2TBoYG7IcjDzObo2Uxr7WCodBDRY6ssg6GcM1GdKfL474NBUXUGRWRGRSVaUNaGziwSfWB9dJkZV5stxwjeMrdK4iIiIlDKNt0SkUvgW/rU3HfBbUu0xW1lMIqeoD1jeT0C/b0j4ENJCoGM21rHU8uXLefzxx4d9fNOmTad9jnzWPo5EImzcuLGgNspxAydk+vVNblOKbmmNx8GEw+64w+pI+sKZPfH0SWKaa8umVGa2xOd7Aw4p6xEok3aLiJS6Mc0avfHGG3zve9/jnXfeAeB//I//weLFi3nooYc477zzWLduXVEaKcMLOekvyAP6niwiIlKRNN4SkXJnAkE8js/kvRWFzlR6ffLVDS6+O/RifqbUFyQSGUcBA7NC0JGAHs8w09GX/kJpLDU1DVrTr8IsqLJUO+kSn+8NGBZUWfZlgn6NZZDllxV2oNaFPs9wUCU+RUSKpuCPvjfeeIPNmzeTTCb5gz/4g1xddIBp06bxL//yL8Von5xGKPM9OGnT9btFRESkcmi8JTL+TCCIHwgV9KOgVH48DK1dHq1dHi8f83i1O30umxX0aev1co+d/GNR/8rUtqAm/TfQk9LfQqE0lpq6jmf6TW47xoNjYEl1+gB39bv8JubQ7xuqnPR6oOXCGFhQnf7//oGJjc6OevznBmnv6gE3OKHtFBEpRMGZfo899hhr1qzhL/7iL/A8jx/+8Ie5x8466yx++tOfFqWBMrKASdfrthgS9ni5TxERESl/Gm+JjL9sQKoQKxs18TNaBxMOPoawY5leRtkIIpNhfjW82g19PniWsinZV0o0lpqafHu8IlZVhS4au7TG442Yy+GEw+FEOmC2qGYM2R2TZEENvNUH7w84+Hbi1vgd7fjPAJE6l8UuuiRJREpewZ8F+/bt4xOf+ARwatmVadOm0dPTM7aWSV6MOZ7tl/Anty0iIiJSXBpviUgl6UmlyxSC5cwqHyVKioxsWtAQMumLfKOFXZsw5WksNTUlLGRDM5WY6QdQF4AFVemJwFrHsqo2xZr6SW5UAWaFIGQsA9ZwJDlxA4POJBxLGg4lDH06v4pIhSk4089xHDxv6LNid3c31dXVBTdKRifkwIAHCZv+Ai0iIiKVQeMtEakUnoX3M5kIs4KWmnJLRRCZJNNcy5GUoccz1Cs7dtQ0lpqasqU9g8biVPAVJhfXp4imDA0BizHgl+GhOgbOrPLZG3d5b8Bhdmj8I3Cv9rq8EYNsLsyRpGVhtU9dhQaIRWTqKfir1pIlS/jZz3425GP/+q//SnNzc8GNktGpMumBf0LjfxERkYqi8ZaIVIoPEoakNQSN5YygvriI5GtaJtDXmzJY/emMmsZSU9NAJvpVVeEXmAQNNAZt2WfOz69Kn9wOTMC6fnv6Hd6IpaN7tY4l7KSzqffFHWVUi0jFKPhseuWVV/LSSy/xt3/7t7z88ssA7N69m29/+9v88pe/5MorryxaI2VkocxvMVGOl/SIiIjIsDTeEpl81qbLUu6PG3b2Obze59Da59DW79DaY3PZBDK8Ywk4kkp/Vzkz5E/Yej0ilSDsgIMlhaFf55tR01hqaopn/laqHUXKy8GZIR+Dpdsz9KTG73WOJg2/6EkH/JZH4Owan8XVPnVuOvD3btzB01tGRCpAweU9V65cyc0338x3vvOd3MDp29/+NuFwmPXr13POOecUrZEyslAm029AH0wiIiIVReMtkckV9eD9AYcBe2qUKu5Da4/F4HBG0DKrAq60Hw++hV92ARjqXcu0gr+BikxNjoE6F7q99JqYYVdf/EdDY6mpKfu5XemZfpUi5MAZIcvhhOHAgMO5geJf4WAtvNjj4mE4M+SzaprDG93pc+xZVT5v96fHe8dShlmqSCAiZa6gr1y+73P48GHOP/98PvzhD7Nr1y66u7upq6tj2bJlqok+wY5n+qU/xDTZICIiUv403hKZPL6FgwnDsVR6oO1iaQikf4KZsXbUM6SMw+EBw+GkoceznFXt5x6XtLdiDkeT6UyleSGlKYkUos7NZMB4hjloMjpfGktNXdks/Cp9JpeNBVU+hxNOOuhXW/zxwv4BQ1fKIWgsH61P4ZhQ7jHHpNcbPpAwHEkaZgZ0IZeIlLeCrnmx1vJf/+t/pa2tjVAoxHnnncfFF1/MqlWrNGiaBKHMB5GPQeWnRUREKoPGWyKTI+nDnriTCfhZpgd8loV9zqyy1LrpC+5CDkwPWj4+07Ag5ONgifmGvf0OKc3H53Sn4FfRdBmtuSFLUBkXIgXJrusX9w1JnWPyprHU1JVd00/lPcvH/MyFQR8kDYkix/yshda+9HjkQ2Gf6iHGIw0BSwBL0hq6PEX8RKS8FfS1y3VdGhoasFpFuiQ4BgKZEp/F/mAUERGRyaHxlsjEG/Bhd9wh5htcLIuqfeZXWQLDzP0YY2gMWpbW+ASMJW4Ne7UeDJDOlnyxJ4CHYW4VTA+oU0QKFTBQkwleRFOajM6XxlJTVy7TTxeblI26ANS7PhbD+4ni/uL2Dxg6M1l+HwoPnS7hGJiRKet5JGnQaUNEylnBZ9GLLrqI5557rphtkTHIZvslhlhvRERERMqTxlsiEyfuwztxh6Q1hIxlSY1PnZvfvlUOLK72cbH0+4b9A86UnyzaGXM4kkxPsH24QUsQiIxVJLOWX6/K+4yKxlJTU9wq068cza9K/77eGyjeoOHELL9zwv6IgeAZQYvJjOViSqoQkTJW8DLqCxcu5Be/+AV33HEH69ato6GhAXPSN7l169aNuYGSnyonXVJImX4iIiKVQ+MtkYkRz5T0TFlDtbEsrvGHze4bTrUDi6p93ok79HqGoynDzODUnGzsSsFrmbKev1XnURso+GuniGTUuZaOJPR6Bmu13lS+NJaamso9088xBj8QOv2GJzn5vV1uFlT5vBlzOTjg4FsPpwiH055MZ/kFGD7LLytgoN61dGXWUK11p+Y4TkTKX8Hfvh544AEAjh07xs6dO4fc5rHHHiv06WWUspl+A/o8EhERqRgab4mMv5gHe7MBP8eyuHr0Ab+ssJteu+5gwnAoYQg7lnCe2YKVwrfwYncAH8OZIZ+zq330FUVk7MIOOFg8DP0+U+7cUiiNpaam7Jp+VWUaA/MxtHalRr3fysbgOLRm4swMWqqMZcAa2pOGOaGxjyB2xdKR30U1Q6/ld7JpAejyoCdlmFuE1xcRmQyjCvr93d/9Hb/3e7/HjBkz+MpXvgKA53m4rkabky2U+eBK+Ab0tVpERKRsabwlMnESPvy0i1xJz7EE/LJmBCxRz9LjGd4bcFha4xflSvVy8Uafw9GUQ8hYPjIthTH6diJSDI6BiAs9HkQ9Q1gZKMPSWEriuUw//Z2UE8fA/Cqfd+Iu7w04zAmNrZ5xvwfvDqQnTJfV5Fcarc5Nl/gcsIa4T16BQhGRUjOqoN//+T//hw9/+MPMmDGDc889F9/3+dM//VPuvvtuFi9ePF5tlDyETHogk9B4RkREpKxpvCUyMTwL/9IdoDMJAWNZVISAH6TXrptf5dMWcxiwhiNJw+wpcqX4BwnDrzPr5lxY5ykTSaTI6tz0BQW9nmG2wunD0lhqavMtJHJr+k1yY2TUskG/AwMOF0S8MZUyfrvfwWKYFfSZnmfJdddArQvRTLZf9RQZw4lIZdHHX4XIlvdM2vQitSIiIiIiMjRr4cVul8MJh4CBhVV+Udf9CRiYW5UelH+QnBrrbsd9+Fl3AIthcbXHouopcNAiEyySye7r89MXLojIqQZyHz+2bNf0m8rmhSwull7P0JkqPOLnW2jrT199lG+WX1Z95lzb402hUg0iUlH08VchAgYMFjAkNfgXERERERnWy1GXfQMuBssl08dnbawG11LrWCyG9xOV/bXLWnihO0C/b6h3LevqxnZlvogMrcrJVvkx9I2t6p1IxYqfsJ7fVCqvXSmCDszLXDiVLc1ZiAMDhphvqDKWs0Z5IdK0TNAv5qM5VhEpS6Mq7zlZbr75Zjo6Ok65/3d+53e44YYbeOCBB3juuecGPbZ06VI2b96cu51MJtm+fTsvvvgiiUSCFStWcMMNNzBjxozcNtFolEceeYSXX34ZgAsuuICWlhZqa2vH6ciKxxgImnR5z4SF0GQ3SERERMrOsWPH+Lu/+ztee+01EokEc+fO5Qtf+EKuFJa1lh/84Ac888wzRKNRli5dyvXXX8+CBQtyz5HPmEtkMr3Z5/CbWDrK99FpHnOrA3TEi/86xsCZVT5v9zv0eoZeD+rKuNylCQTxGHr29I1eOJhIl8T62AyDGwxx4vSaUQRQpGgiruVYyhD1DNMCmo0WOVk882eh0p7l66wqn/cGHN6NO6yuLexCol2ZLL8lNT7uKPcPOlDjWPp9Q2/K5F0aVESkVIw66Hfw4EEcJ/3J6ft+7r6hFKtW+t133517LYD9+/dz11138ZGPfCR33+rVq1m/fn3udiAw+NAeffRRXnnlFW655Rbq6urYtm0b99xzD1u2bMkdz3333cfRo0fZtGkTAFu3buX+++/ntttuK8pxjLdQNujnG9Ci3iIiImVrMsZb0WiU22+/neXLl/NXf/VXTJs2jQ8++IBwOJzb5qmnnuJHP/oR69evZ+7cuTz55JPcddddfOMb36CmpgbIb8wlMln29Du8Ek1/Tzg/kmJxjc94FqGsdmBGwHIkZTiUcIhU+2WbAedhaO06NbWoz4N34g5gmBv02d9n2X/SNisbgxPSRpGpIOLCsRREfQNa129YkzGWktKQzfSrdvT3Ua4WVPk4pNcw7UoZGkcZdOtJwaGEA1iaawpLi65zM0E/D6ZrGCMiZWbUQb8HHnjglPvuv//+Ibd97LHHRt+iIUybNm3Q7b//+7/njDPO4Nxzz83dFwgEaGhoGHL/WCzGs88+y4YNG1i5ciUAGzZs4Atf+AKtra2sXr2aAwcO8Nprr7F582aWLl0KwE033cSXv/xlDh48yLx584pyLOMp5FjwVd5TRESk3E3GeOupp55ixowZgy6imj17du7/1lqefvpprrrqKtatWwekqzHceOONvPDCC3zyk5/Ma8wlMlkODhh+3pO+6vtDYY9zwxOz5tzskOVYyhL3DV2eobGCMnNSNlt6y9AQ8Cvq2ERKVXZdv7hvSNn0Uh9yqskYS0lpyK7pp/X8ylfQgTOrLO8NGN4dcGgMji5wl13L78yQpa7AGncR19KehD7fYK0t24u2RGRqGtWp7wtf+MJ4tSNvqVSK559/nt///d8fVCZm586d3HDDDdTW1vKhD32IP/3TP6W+vh6APXv24HlebvIJYPr06TQ1NdHW1sbq1atpa2sjHA7nAn4Azc3NhMNhdu3aVRZBv2CmOxL6ri0iIlK2Jmu89fLLL7Nq1Sq+/vWvs3PnTqZPn87v/M7v8IlPfAKA9vZ2urq6WLVqVW6fYDDIueeey65du/jkJz+Z15hLZDIcTRr+pTuAj2FhlccFkYlbcy5gYHbQcjhpOJxIr3lXCayF/QMOKZteL+fMkCbERCZCwEC1scStIepBQ1ks2jKxSmHuSiaPMv0qw4klPleNosRnysLu/nTEd1m48MVPww4YLClrGLBQozGOiJSRUQ0PL7300nFqRv5eeukl+vr6BrVlzZo1fOQjH2HmzJm0t7fz2GOPceedd3LPPfcQDAbp6uoiEAgQiUQGPVd9fT1dXV0AdHV15YKEw20zlGQySTKZzN02xuTKWxVz7YrccxmDsUMPXLJXMSV9gzmpzIcpcnuKKduuUm3fWOjYylclH5+OrTzp2KaOyRpvtbe38+Mf/5jf//3f56qrrmL37t088sgjBINBLrnkktx46OTxUn19PUeOHAHIa8x1sokaS002vc+HN95905uCZ7oCpKxhTsjn4gYf54TXMpmfQuWz76yg5WjKkrSGYykzqrF5qbx3Tu6n9mR6TTGD5axq/7TZRoW2fsT9TvMdabx/ryW/bx7fIYv+muW0b579U4zXLfaxRlxLPGXoGyF7eCxzAKVy3ilUKcxdyeSJZzL9tKZfeZufKfHZ7Rk6R7Gu3t64Q8Iaah3LvFDhgV/HpAN/fT70eYYaBZFFpIyU3TVhP/3pT1m9ejXTp0/P3XfRRRfl/t/U1MTZZ5/N+vXrefXVV3Plp4Zi8xjYp1O4hx/o7tixgyeeeCJ3e9GiRWzZsoVZs2ad9rlHq72rh0ikdtjH/YQHAwlSxiFSFx70WDDkMbthZtHbVExz5syZ7CaMGx1b+ark49OxlScdm4wX3/c5++yz+bM/+zMgPaZ57733+Od//mcuueSS3HYnj4vyHU8NZyLHUqVA7/PhjUff9CY9/r6tm7jvM6va5TPN06l2B88Ctnf1EKlzC3p+YxJE6iKn3xBoCqR4pydJR8rBBILMnT26sflkv3dO7KfuhMfhvgQAZ08LMSs88tfK0fRTIfsN9R2p0NesxH1H+g5Ziu2d6H1H6p9ivO54HOusoMeRrgR91iVSVz3kvsWYA5js845IIQYymX5VCtKUBccY/EDolPsDwIIaeLcf3ooH+XDNqfu6WGzq+MWDvoU3+9JjlWVhD2eM1y1EXEufn86qnql1/USkjJRV0K+jo4PW1la+9KUvjbhdY2Mjs2bN4tChQwA0NDSQSqWIRqODrjzv6elh2bJluW26u7tPea6enp4hMwCzrrrqKq644orc7exEWEdHB6lUKv+DOw1jDE51mGi0L11LZwgpH8Al7vn09kQHpb4nXTfXH6XGGMOcOXM4fPhwXhOH5UTHVr4q+fh0bOVJxzZ2gUCgYgNJxdDY2Mj8+fMH3Td//nx++ctfAuTWTu7q6qKxsTG3zYljpXzGXCebqLHUZKvkv+GxGq++GfDhH48F6E4Z6lzLpXVxOtvjp2znu0GivYWVf7KNQaK90by2DVsIGYeEb3izM4kZyG9sXirvnWw/pSzs6k+v49cY8Amn4kR7R953NP00qv2MIRKpHfI7UqGvWVH7jtA/4/aa5bRvHv1TjNcdj2NNVwl2iHtwrDtKaIiMprHMAQx33tFYSsqBMv3Ki4+htWvoMX86kdnlnZglxKlVBVY2uJz4a94/YOjxDCFjaa4Z+9rNta6FE9b1ExEpF2UV9PvpT39KfX09a9euHXG73t5ejh49mpuQWrx4Ma7r0tramssK7OzsZP/+/Xz2s58F0uv3xWIxdu/ezZIlSwB4++23icViw05SQXotm2Bw6Ms9xuUDwVqGe9b0h58FDAkLoRM+DO14taeIrLUl38ZC6djKVyUfn46tPOnYZLwsW7aMgwcPDrrv4MGDucm92bNn09DQQGtrK4sWLQLSay3v3LkzN57KZ8x1sgkfS00yvc+HV8y+Sfrwk64AXal0OaZPNCapcYae17eZn0Llu6/JrO13IGF4IwrNVZbgKCYkJ/u9Y0lfQf/uCev4zQtZMPn1QaEtH2m/XEnGYb4jTcTvtZT3PV3/jMdrltO++fZPMV632MfqGKhxoN+HqGdoHCKjqRhzAJN93hEpRC7oZ/TeLXdhJ702Y9w3HEsaZp9UrvPELEFr4fVY+v5lEUMgFGKksF8+5YtPXtevrtADERGZYGUT9PN9n3/5l3/hkksuwXWPl9+Jx+M8/vjjfPjDH6ahoYGOjg6+973vUVdXx4UXXghAOBzm8ssvZ/v27dTV1RGJRNi+fTtNTU2sXLkSSF/Jvnr1arZu3cqNN94IwIMPPsjatWuZN2/exB9wAYxJB/oSFpIWTk2OFxERERna7//+73P77bfz5JNPctFFF7F7926eeeYZ/tN/+k9A+ovxpz71KXbs2MHcuXOZM2cOO3bsoKqqiosvvhjIb8wlMt48C//SHeBI0iFkLJ9oTFFg9c6iawxY2pOWAd/wVr/DebVjvwp9InWctI6fW57LfYlUhIhr6fcNUR8aT7+5yJSRLe+pTL/yZwzMDKQvmDqaMswK2kFVzU7MEuxJQWfSxcHieT6tXSM/98rG09frdAzUOhDNXGBR2osmiYgcVzZBv9dff50jR45w2WWXDbrfcRzee+89fvazn9HX10djYyPLly/ni1/8IjU1xws+X3fddbiuy7333ksikWDFihXceuutOM7xUcDGjRt5+OGH2bx5MwDnn38+119//cQcYJEEM0G/hDXUjul6QhEREZlKlixZwpe+9CW++93v8r//9/9m9uzZXHfddXzsYx/LbXPllVeSSCR46KGH6OvrY8mSJWzatGnUYy6R8eJbeL47wKGEQ8BYPt6QojFQOmNiY+CMoOW9hOHNPpdlNf6QZflKUfsAHE6mZ9rODFlNpopMsohr6UimJ6KtHTwRLjJVWQsDmY/99Jp++sModw0By6GEJWkN3Z6hYYhxnW/hUCI9MJketKeUAR2LWtcSzazrJyJSLsom6Ldq1Soef/zxU+4PhUJs2rTptPuHQiFaWlpoaWkZdptIJMLGjRvH1M7JFnLSi8wmyuuiYRERESkB559/Pueff/6wjxtjuPrqq7n66quH3SafMZfIkNzgiGWYRtwVi59M8osel/0DDg6WS+tTzAqVTsAvqyFg6fahJ2V4K+awMlL6A/e4D88fAzA0BPySCqSKTFW1mbJzSZte3qNKsQ0RBixYlOlXSRwDM4KW9qTh/QFDrWsJnnS+O5wwDFhDwFhmB4s7RtG6fiJSjsom6Cf5ya7jl9TnkIiIiIiUER9o7SrsMurz6l1ejbq8E3cxWD5Wn2JeVWkOiI2BlXXwQie8GXM5J1za2X7WwgvdAfp9qDKWM0PKKBIpBY5JrzfVlyk7VzXEun4iU012Pb+QsTj6rKoYs4OWHi+9tt+BAYeFVX5uLBL14EgqPZCaH/KLmuUHJ63r51nNpItIWSjhr5dSiOzVLglfoxsRERERmRpe74XfxNIL9100zeOs6tKe/D6rBhpcn6Q17IyVyIKDw3gj5nAw4eAatI6fSImJuOlzncrOiaTFtZ5fRXIMLKjyMVh6PcP7CUPch9/0WvbFM2U9Az7TxiEg5xioybyfepKlX51BRAQU9Ks4oczVfYnSnucQERERESmKjqShtTf9/9+qS3F2TelPyBgDqyLpWfrfxJxcZkKp+SBheC2aDkr+Vr0mUUVKzfGgn0FV50RgIPN5qszXylPjwJxM2fZjKYe2fpdXui0+hrBjmTuOJd3DmfdTr9ZSEpEyoaTkCnNieU9rUekdEREREalYx5KGQ4l0JGp1bYoPhctnMqapytIY8OlMOezsc1lbV1qpOnEfftYdwGJYXO1xdtjl9e7JbpWInKjGAQeLhyFuoUbf/8fVzp07+eEPf8jevXvp7OzkS1/6EhdeeGHucWstP/jBD3jmmWeIRqMsXbqU66+/ngULFuS2SSaTbN++nRdffJFEIsGKFSu44YYbmDFjRm6baDTKI488wssvvwzABRdcQEtLC7W1tRN3sGVKmX6VbVbQUm08jqQcej2ocgwzAz7TA+NberzWtRxJKdNPRMqHPgYrTLq8p8ViSOnCJhERERGpUF0pOJBIz/CcG4Hl9QH8QGjUP2aSrpIzBlbXpgN9b/U79JfQPNLxdfwM9a5lXZ2niwlFSpBjoDZTITjq6Y90vA0MDLBw4UJaWlqGfPypp57iRz/6ES0tLdx99900NDRw11130d/fn9vm0Ucf5aWXXuKWW27hzjvvJB6Pc8899+D7xz8E7rvvPvbt28emTZvYtGkT+/bt4/777x/346sE2cz5amX6Vay6ACyq9lke9vkPcw0zguO/1nA4c56NpSxK9hORcqCgX4Ux5oR1/TTGEREREZEK1OvBewMOYJge8FlTb3i926O1a/Q/lsmbKJ9fZZkR8ElZw5t9pbO2X24dPyy/3ZAiqG+NIiWrNhPc6FPQb9ytWbOGz3zmM6xbt+6Ux6y1PP3001x11VWsW7eOpqYmbr75ZgYGBnjhhRcAiMViPPvss1x77bWsXLmSRYsWsWHDBvbv309raysABw4c4LXXXuPzn/88zc3NNDc3c9NNN/Hqq69y8ODBCT3ecqRMv6nDNeBO0BVJQQMhkz7XdiQm5CVFRMZE5T0rUMiky3smrQEU+RMRERGRyhH34d24g8VQ7/qcGbKTlq03VsbA6ojHM10Ou2IOy8MeNZMc+ztxHb8Lp3k0BvR9QqSURVwLSejztMTHZGpvb6erq4tVq1bl7gsGg5x77rns2rWLT37yk+zZswfP81i5cmVum+nTp9PU1ERbWxurV6+mra2NcDjM0qVLc9s0NzcTDofZtWsX8+bNO+W1k8kkyWQyd9sYQ01NTe7/U8mAPR70M8YcP35jMAUufDmWHpyMfQvabwr202j3rXUhkUoH/RYEp9bf1Whk/+am2rlntNRP+VE/FU5BvwoUdCz4RinnIiIiIlJRUhb2xR18DLWOZUHV+Jd0Gm/zQpaZQZ8jSYc3Yi6/NYlr+528jt+San2hECl1g9b185n0Cwemqq6uLgDq6+sH3V9fX8+RI0dy2wQCASKRyCnbZPfv6uo65TlO3uZkO3bs4IknnsjdXrRoEVu2bGHWrFkFHk35stFuIMmc6fXMnV4NQHtXD5FIYeshGpMgUhc5/YYlsu9YXhMSU6afCtl3upuisyfJkaRh7ty5Bb3mVDJnzpzJbkJZUD/lR/00egr6VaCQynuKiIiISIXxMwG/hDWEjOWsah+nzAN+cHxtv59ksv3ODXu5NbomktbxEylPxqTXm4p60OcbalxNBEymk7MRbB5ZU/luM1ymw1VXXcUVV1xxShs6OjpIpVKnfe5K0hMPAIb+ni4ODaT7zKkOE432pT/oRsk2Bon2Rgtqy2TsW/BrGgONwSnTT4XsG/AN4NA+YHn/4KGKGIOOB2MMc+bM4fDhw3md26Yq9VN+KrWfAoHAuF+Yo6BfBcoG/VTeU0REREQqgbVwYMAQ8w0uloXVPoEKmmyZG7LMDvq0Jx3e6HNZN23is/1e79M6fiLlKuJYop6hzzPMDGoOYDI0NDQA6Uy9xsbG3P09PT25zL2GhgZSqRTRaHRQtl9PTw/Lli3LbdPd3X3K85/4PCcLBoMEg8EhH6ukSdJ8xDMJ6lXGDj52awueHRtLD07GvoXslyvpOYX6abT7VhmLa8CzcDSJzrWnYa2dcuefQqif8qN+Gj19latAwcxC3irvKSIiIiKVoD1p6PIcwNJU7VNdYd9ismv7Abzd79A7wYkZBwcMr/VpHT+RclWbye6LegUl6UgRzJ49m4aGBlpbW3P3pVIpdu7cmQvoLV68GNd1B23T2dnJ/v37aW5uBtLr98ViMXbv3p3b5u233yYWi+WeR4Zm7fGgX7WjPwQpLmNgWig9AG1PVNCVZyJSkZTpV4FOLO+phbxFREREpJz1pOCDZHqS5cyQpa5C16uaE7LMDfkcSji8HA1wWcPERP6iXnodPzAsqfFYWqMrB0XKTY0DJrOu34CFas0BjIt4PM7hw4dzt9vb29m3bx+RSISZM2fyqU99ih07djB37lzmzJnDjh07qKqq4uKLLwYgHA5z+eWXs337durq6ohEImzfvp2mpiZWrlwJwPz581m9ejVbt27lxhtvBODBBx9k7dq1zJs3b+IPuowkLFjSb/5KuzhISkNd0KFzwKcj6QAaL4lI6VLQrwIFDYDFYvDQL1lEREREylPCh/cG0jN3MwI+Myq8lNJv1Xn8n6OG9wYcDg4Y5lWN7/F6Fp7rCpCwhhkBn3V1E19WVETGzjFQ60DUhz7PKMtpnLzzzjvccccdudvbtm0D4JJLLuHmm2/myiuvJJFI8NBDD9HX18eSJUvYtGkTNTU1uX2uu+46XNfl3nvvJZFIsGLFCm699VYc53iUauPGjTz88MNs3rwZgPPPP5/rr79+go6yfGWz/IKZMowixZbL9EsaJVmISElTPKgCOSYd+Eva9ERJoEKvhhYRERGRyuVbeHfAwcNQ41jmhip/ErshYDkn7PObmMu/9Qb4g1ByXCcuf9nrcjTlUGUslzSkNEkqUsZqXUvUN0Q9mDH08m4yRsuXL+fxxx8f9nFjDFdffTVXX331sNuEQiFaWlpoaWkZdptIJMLGjRvH1NapqN9Pf4iFleUn4yQSdHBIv9eiPhVbfUJEyp8+CitU8IQSnyIiIiIi5eZwwtDvG1wsZ1X5OFMkILWq1qPaWLo9w5ux8fu69nbMYXe/C1g+Vp8iookrkbKWXdevzzda10+mpH4vW9pTfwAyPlxjmJ65qKIjoSl1ESldOkNVqJBJD3IS/hSZHRERERGRitGTgiOp9FeVBVU+oSn0rSXkwPmZMpu/jrp0Jov/Gh8kDL/sTUf51kS8cS8jKiLjL5xZ1y9ljS7+lSmpP1Pes8bVH4CMn1lV6X/bk5pvFZHSNYW+Pk8t2YmRpMY6IiIiIlJGkhYOnLCO37QpuCDB4mqf+VU+PoYXugN4fnEG9SYQpNOG+GlXEB/Dgmo4d1oAPxA67Y/RwjUiJc0xx8saRj39vcrUE8tc9F6jmU4ZR7ND6X8V9BORUjYFv0JPDcfLexpAkT8RERERKQ/vDzikMFSbqbGO31CMgY/UpXgqEeRYyvD8oRjLijCJGfUM/9RhSVhD2LHUOz6vd+e378pGLRImUupqXUufb+jzJrslIhMvns30U3lPGUezMkG/rpQh4TOlqlGISPnQqalCHS/vOckNERERERHJU3cKerz0RWsLptA6fkOpceEj01IA/Gt7P3v6x9YZ/R785Ej6osCQsSysntr9K1KJtK6fTGXK9JOJUONCnWsBQ4ey/USkROmjsEIFTyjvqcG+iIiIiJS6AR/eT6QHsbODlhp3khtUAs6qtqyoTafsvNjtcqTAyaV+H37cGaA7BUFjWVTtE9A8lUjFqXUALEmrbD+ZeuKZ97wy/WS8zQqmMyzak5pWF5HSpLNThQplvsT7GDTWFxEREZFS92o3pKyhylhmBzVhl7U24rNkWggfw086A3QkRhet60wanj4apMtzqHHS6wVW6VugSEU6cV2/DwYmty0iEy2X6aeLhmScZcepox2TiYhMFH3dq1COgYBKfIqIiIhIGdjXm2B3LP3/+VO8rOfJjIE/WBhhZtAnYQ3/3BngvYHTd5C1sKff4f92BujzDXWu5ZOzUMBPpMJlS3y2Jya5ISITyLPp8tWgTD8Zf7Mza04fSRp8vd1EpAQFJrsBp/P444/zxBNPDLqvvr6e/+//+/8AsNbygx/8gGeeeYZoNMrSpUu5/vrrWbBgQW77ZDLJ9u3befHFF0kkEqxYsYIbbriBGTNm5LaJRqM88sgjvPzyywBccMEFtLS0UFtbOwFHOT5CBlI2XeJTREREJF87duzge9/7Hp/61Kf48z//c6B4Yy6Rk6Us/OP+KAAzAj61ukL/FFWuw+80ejzXlS6B+tOuIIurPdZGPMJD9Fdn0vBvUZfDmXKpc0I+l9SnCAZCE9twEZlwtY6lg0ymX91kt0ZkYvRnLnZ3sFTpwiEZZ/WuJWQsCWs4ljLMVIUKESkxJR/0A1iwYAG333577rbjHL889amnnuJHP/oR69evZ+7cuTz55JPcddddfOMb36CmpgaARx99lFdeeYVbbrmFuro6tm3bxj333MOWLVtyz3Xfffdx9OhRNm3aBMDWrVu5//77ue222ybwSIsraNILy2avdhIRERE5nd27d/OTn/yEs846a9D9xRpziZzstV6HroRP2IU5oakxaeIYg59nAM4A7V09uMEgl8yEV7otu/pgT9zl3bjDGSHLGSEfBxjwDe8nDJ2p9N+bg+W8Wo/zatPZkyoAIlL50hdOWKJeel0/XUghU0E8U9qz2klnyIuMJ2NgVtDyfsLQnlDQT0RKT1nMvjiOQ0NDQ+5n2rRpQPqK86effpqrrrqKdevW0dTUxM0338zAwAAvvPACALFYjGeffZZrr72WlStXsmjRIjZs2MD+/ftpbW0F/v/27jw+qurg//jn3lmyMFnZAkJkS1CWCGrrAhbFWiuPfSzVUpe2+iCWCnVpH6tV3B9Rsf251KXVqqViqyIStXWtUrDiWkWjoiAii0JIAkz2ySz3/v6YZMgkgWyTzEzyfb9eA5k798w958y9M+eeFb766is++OADfv7zn1NYWEhhYSHz58/n/fffZ8eOHXFLd3c1Td2j6T1FRESkI3w+H3fffTfz58+Pmu0gVmUukZYqAgbr68KF1qOywNFPKuosDEq8oQ4/NtQ7KPGG+LgyxDeyDWblBhnssghhsMNvsq7GyXs1Tj6uc7A3aGJgk59icdrAAId5NF2qSH/iMGCQy+bIfvSdKlIXCv+vqT2ltwxxhytbywNJUbUuIv1MUnwzlZaWMn/+fBYuXMidd97Jrl27ACgrK8Pr9XLYYYdF9nW5XEyYMIENGzYAsHnzZkKhEEVFRZF9cnNzyc/PZ+PGjQBs3LiR9PR0CgoKIvsUFhaSnp4eeZ9k5G4s4DdopJ+IiIh0wIMPPsjUqVOjyk0QuzKXSHOWDW9WObAxODTbzYi0eMcoeQxy2Xw3J8j3cgMc6QkyKjXEmNQQ49NCHJMZZM7gAMdnB8lIinldRCTWhrttDvGERz2J9AdNI/3SNLJVesngxtF9ZQEDW23NIpJgEv42sKCggIULFzJ8+HC8Xi8rV67k6quv5vbbb8fr9QLhNf6ay8rKoqKiAgCv14vT6cTj8bTapym81+tt9R4t99mfQCBAIBCIPDcMIzLFlRHDOQUi72UYGB38NUlp7OHkt8LTAsUyPrHUFK9EjV93KG3Jqy+nT2lLTkqb9LS1a9fy5Zdfcsstt7R6LVZlrrb0Vlkq3nSet/ZJrcneoEmKYfPtER68e/fSndxJtrAdDtfiPqCpXG8YkOuGXLcNtLw/aPvdjc4cdz/hezvsAcO1c4+UbGmNedgO3kMmTHx7O2wX7rF7/HqP8TG7+puj3yxJNnVNjX4a6Se9ZJDLxsCm3jKosSBDDc4ikkASvtFv6tSpkb/z8/MpLCzkoosuYs2aNZGReS0LonYHCuwd3ae9Qm5xcTErVqyIPB89ejRLlixh8ODB7b5/Z5V5q/B4BrS/YyNXyAJfAwHbwOFykTdkUMzjFEt5eXnxjkKPUdqSV19On9KWnJQ26QkVFRUsXbqURYsW4Xbvf52xnihz9WZZKhHoPA/b7QtSsssLwEn5GQxwmdS6XXgyUrv0fobhx5PhaX/HBAnblXBN9wEud4gh2V0r15d5q/B0sVYqkfOprXukZDsnejLsge4hEzG+vR22o/fYvXm9xyJsd74rmug3S5JFfeOyNmka3Sq9xGnAQKdNRdCg3G+Skaa1lUQkcSR8o19Lqamp5Ofns3PnTr7xjW8A4Z7lOTk5kX2qqqoiPdGzs7MJBoPU1NRE9Tyvqqpi/PjxkX0qKytbHav5++zP7NmzOfXUUyPPmyrDysvLCQaDXUxla4ZhYKamU1NTS0fHjds2GJjYGFTWB7B9O2MWn1gyDIO8vDxKS0s7VHmYTJS25NWX06e0JSelrfucTmefbUjqrs2bN1NZWclvfvObyDbLsvj000958cUXufPOO4Hul7na0ltlqXjry9dwZ9k2vLjHQcg2OchtMbDBC+QR8Aeoqa7v2nvmuKiprkmasJ0KZxh4PAMi9wEBh4OdO7tWrrccLmqqQ10Km5D51CJvYnHMPhX2APnTY8dMprAdyJ9YHDdeae3Od8X+frNUlpJEVa+RfhIHg902FcHwFJ9jNE29iCSQpGv0CwQCfP311xx66KEMGTKE7OxsSkpKGD16NADBYJD169dzzjnnADBmzBgcDgclJSUce+yxAOzdu5dt27ZF9iksLKSuro5NmzYxbtw4AD7//HPq6uoOWEkF4fVsXC5Xm6/1SIWObbeavGe/jPC6fg02VAUhw5nYhR/btvtsJZjSlrz6cvqUtuSktElPmDx5Mr/73e+itv3hD39g+PDhnHbaaQwdOjQmZa629HpZKs50nsOGOpOygInTsDkqM8i+yes6Uc5tQ7KF7Wi4yLSDjfcBNl2/NtqaCLSz4Xs77IHCtcybWB2zr4RtL3964pjJFLaj+ROL48br2unu741+syRZ1Df2Z1Gjn/SmIS6LT3FQFtBUyCKSWBK+0e+RRx7hyCOPZNCgQVRWVvLUU09RX1/PjBkzMAyDWbNmUVxczLBhw8jLy6O4uJiUlBSmT58OQHp6OjNnzmTZsmVkZGTg8XhYtmwZ+fn5FBUVATBixAimTJnC/fffzwUXXADAAw88wOGHH87w4cPjlvZYcJvQEIKaEEnwaYuIiEg8pKWlkZ+fH7UtJSWFjIyMyPZYlLlEakPwfk14esmpnhAerX8iIiIi3RQZ6adyhfSiIa5wI7M3aOC3wnWwIiKJIOGbgfbs2cNdd91FVVUVmZmZFBQUsHjx4siUEqeddhp+v58HH3yQ2tpaxo0bx6JFi0hL2zeu+txzz8XhcHDHHXfg9/uZNGkSV1xxBaa579v44osv5uGHH2bx4sUAHHHEEZx//vm9m9ge4DZswKAmCKTEOzYiIiKSrGJV5pL+y7bh7SonAdtgsMtivNY+ERERkW6ybfBF1vTTSD/pPWkOyHDYVIcMygMGB6Xo/BORxJDwjX6XXnrpAV83DIM5c+YwZ86c/e7jdruZO3cuc+fO3e8+Ho+Hiy++uKvRTFhNvUy6uHSHiIiI9FPXX3991PNYlbmk/9rSYPKV38TE5pjMIKZmQhIREZFuarDBomlNvzhHRvqdwS6L6pCDsoDJQSmqfBWRxKCfwz4uPNKP8Eg/EREREZE48FnwblV4zq3JA0JkJ3zXQxEREUkGTVN7ug0bhzoUSS9rmuKz3K+TT0QShxr9+riUxk9YjX4iIiIiEi//qXbgsw2ynRaTBmhaTxEREYmN+sbBVZraU+JhiDt83lUEDCydgiKSINTo18e5Gzua+G1oUP2KiIiIiPSyrxsMNvscgM0xmSH1whcREZGYqWsc6ZfuiHNEpF/Kcti4DZsgBnuCKuSKSGJQo18fZxrgbJriM6QfHxERERHpPQEL3qoKz+V5aLrFYJe6QIuIiEjs1DaO9BugkX4SB4ZBpHxbpik+RSRBqNGvH2ga7VetRj8RERER6UXrahzUWgYeh80UTyje0REREZE+pqmDu8ehRj+JjyHu8NRqZQFVs4tIYnDGOwLS89ymTZ1lUK16FhERERHpJaV+g8/qw3NtHZ0RxKX+ZzFhGgaW092lsIahD0FERPqW2sbpPQdoek+Jk7zGkX6l/vC6fqaKWyISZ2r06wdSmkb6aW5pEREREekFARveaJzWsyAtxPAU9b6PFQuDEm+wS2GLclwxjo2IiEh81YaaGv1U1pD4GOiycRk2fju8rt8gTWcvInGmRr9+IKVxdHmlpvcUERERkR5kOF2EMHjfCzWhcK/7qdkOLHP/3e8NoMxbBYYJaGoKERER6Rjb3remn6b3lHgxDchz22xvMNjpV6OfiMSfGv36gRQj/GNTpZF+IiIiItKDQhi8sTvEZl+4kW+IK8RnVQcOYwCeDAejnV0bvSYiIiL9U70VHgFvYJOu5dQkjoa5LbY3mOz0m0weYMU7OiLSz+knsR9oGunXYBv49LsjIiIiIj0kYMH2hnDhc6DTIkPr64iIiEgPaZraM83UOmoSX8Pc4QrXMr9BUAP9RCTO1OjXD5jGvgWNKzXaT0RERER6yPtVELANXIZNnls1HiIiItJzaqxwHZem9pR4y3RAumljYVDmV92riMSXGv36iazGiVw1xaeIiIiI9ISvGww+rw3/PTLFwqFip4iIiPSgpvX8BqjRT+LMMPaN9tvpV3W7iMSX1vTrJzKdsKMBKkOqfRERERGR2KoPwdqq8K3FQKeFR9N6iohIP7N8+XJWrFgRtS0rK4s//elPANi2zZNPPsmrr75KTU0NBQUFnH/++YwcOTKyfyAQYNmyZaxduxa/38+kSZOYN28eAwcO7NW0JIuaUNNIvzhHRPoN0zAIOd1tvpaXBl/4YEfAwVRn65PSgY0dDPR0FEVE1OjXX2S6wv9rek8RERERiSXbhjeqnPgsg2wnDNO0niIi0k+NHDmSa665JvLcNPeN+HnmmWd47rnnWLBgAcOGDWPlypXcdNNN3HnnnaSlpQGwdOlS3nvvPS655BIyMjJ45JFHuPXWW1myZEnUe0lY05p+A0yVPaR3hDAo8QbbfC1gAzjYG4D39oRwtbhki7IdmnJPRHqFvmv6icj0nhrpJyIiIiIx9Gmdydd+Ewc203PD60mLiIj0R6Zpkp2dHXlkZmYC4VF+zz//PLNnz+aoo44iPz+fhQsX0tDQwOuvvw5AXV0dq1at4qc//SlFRUWMHj2aiy66iG3btlFSUhLPZCWspuk9taafJAKXAWmNDdDVqn8VkThSo18/kdnY6FcTgpDKQiIiIiISA7sDBu/XhKcvOiIjRLYrzhESERGJo9LSUubPn8/ChQu588472bVrFwBlZWV4vV4OO+ywyL4ul4sJEyawYcMGADZv3kwoFKKoqCiyT25uLvn5+WzcuLF3E5IEbBtqrMaRfmr0kwSR2XguatCFiMSTpvfsJ1JNcBs2ftugKmSQ41SBSERERES6LmDD65VOLAxGpFiMT7NQCVNERPqrgoICFi5cyPDhw/F6vaxcuZKrr76a22+/Ha/XC4TX+GsuKyuLiooKALxeL06nE4/H02qfpvBtCQQCBAL71gkzDCMyXahh9N2GB78NQbtxTT+nQVtJjaTfMDDsrpVSupOD8QjbpXD9MJ86HbbZCXagcJkOm12B8KAL246eAcOgb1+TsC99fT2d3aV86hjlU9ep0a+fMAzIdNpUBAyqgpCjT15EREREuuHdageVIYM00+bYzCCGgRr9RESk35o6dWrk7/z8fAoLC7noootYs2YNBQUFQOuKS7sDDSzt7VNcXMyKFSsiz0ePHs2SJUsYPHhwZ6KfdErrglDmJd1pMHL4sP3uV+atwuMZ0KVjGIYfT4an/R0TJGx3jgn+fpNP3QlrGMYBww2wbbb4fQQsAysljcwUR+Q1lzvEkOxBXYpvssnLy4t3FJKC8qljlE+dp6affiTLYVMRgMqggapkRERERKSrvqg32VTvAGymZwZJ1aIBIiIiUVJTU8nPz2fnzp184xvfAMKj+XJyciL7VFVVRUb/ZWdnEwwGqampiRrtV1VVxfjx4/d7nNmzZ3PqqadGnjc1LJaXlxMMBmOapkSyzWcATtKMEDt37mxzH8MwMFPTqampDQ+76iQ7x0VNdU2X4hePsF0+pmFAjqvf5FOXwhoGHs8AbNtuN1yGabDHMtlV7cPp35efAYdjv+dqX2EYBnl5eZSWlnaoU0N/pXzqmL6aT06ns8c75qjRrx/JapzSs1LzSouIiIjIfhhOF6EDTFy0NwBvVYX/LsowGDrAhdUUVlOviIiIAOFpN7/++msOPfRQhgwZQnZ2NiUlJYwePRqAYDDI+vXrOeeccwAYM2YMDoeDkpISjj32WAD27t3Ltm3bIvu0xeVy4XK1vahuX6okbammsW7LY3Ygnbbd5a7v3cnBeITtSrjIlJ79KJ86G7b5tKfthctw2OwJhtf1G050uL58TTZn23a/SWt3KJ86RvnUeWr060eaFpMNj/QTEREREWkthEGJN9T2azZ8Xm8SwiDDYWOHLEq8+14vymm70lFERKSve+SRRzjyyCMZNGgQlZWVPPXUU9TX1zNjxgwMw2DWrFkUFxczbNgw8vLyKC4uJiUlhenTpwOQnp7OzJkzWbZsGRkZGXg8HpYtW0Z+fj5FRUVxTl3iaWr0G+BQRbAkFo8DDGz8toHPQjNiiEivU6NfP5LjCheEvEEDq8VisiIiIiIiB2LbsL3BxG8buAybkSkWGtgnIiIStmfPHu666y6qqqrIzMykoKCAxYsXR6bwOu200/D7/Tz44IPU1tYybtw4Fi1aRFpaWuQ9zj33XBwOB3fccQd+v59JkyZxxRVXYJpqNWjJ29ihPVONfpJgHEa44a86FB54kerWOSoivSvhG/2Ki4t55513+Prrr3G73RQWFvLjH/+Y4cOHR/a59957WbNmTVS4psJVk0AgwLJly1i7dm2k4DRv3jwGDhwY2aempoY///nP/Oc//wHgyCOPZO7cuQwY0LWFbBONxwS3Ee5p4g0a5Lr0oyMiIiJhHSlz2bbNk08+yauvvkpNTQ0FBQWcf/75jBw5MrJPR8pckpwqggZVIQMDm4NTLJxq8BMREYm49NJLD/i6YRjMmTOHOXPm7Hcft9vN3LlzmTt3boxj1/c0zWKV7VTdliSeLIdNdcigMmQwtFuTkIqIdF7CdxVav349J598MosXL+bqq6/GsixuuukmfD5f1H5TpkzhgQceiDyuvPLKqNeXLl3KO++8wyWXXMKNN96Iz+fj1ltvxbKsyD6///3v2bJlC4sWLWLRokVs2bKFu+++u1fS2RsMA3IaC0N7NMWniIiINNORMtczzzzDc889x9y5c7nlllvIzs7mpptuor6+PrJPR8pcknxqQrDTHy4/DnfbpDviHCERERHpt/wW1Flq9JPElem0ARufZdCg2yAR6WUJ3+i3aNEijj/+eEaOHMmoUaNYsGABFRUVbN68OWo/p9NJdnZ25OHxeCKv1dXVsWrVKn76059SVFTE6NGjueiii9i2bRslJSUAfPXVV3zwwQf8/Oc/p7CwkMLCQubPn8/777/Pjh07ejXNPalpdN+egBr9REREZJ/2yly2bfP8888ze/ZsjjrqKPLz81m4cCENDQ28/vrrQMfKXJJ8AhZsazABg2yHRa4q10RERCSOmkb5pZs27oSv2ZT+yNk4xSfsO19FRHpL0v001tXVAUQ16kG4d/q8efO45JJL+OMf/0hlZWXktc2bNxMKhaIWPs7NzSU/P5+NGzcCsHHjRtLT0ykoKIjsU1hYSHp6Ohs2bOjJJPWqXI30ExERkQ5oWeYqKyvD6/Vy2GGHRfZxuVxMmDAhUlbqSJlLkotthxv8grZBimEzIsXWOn4iIiISV03r+WWpI5IksKzG9SYrQyo8i0jvSvg1/ZqzbZu//OUvHHLIIeTn50e2T506lWOOOYZBgwZRVlbGE088wY033sitt96Ky+XC6/XidDpbNRRmZWXh9XoB8Hq9ZGVltTpm833aEggECAQCkeeGYUQWYTZiWCMSeS/DwLA7X6gxGt9joKt5o5+REJU2TWmLZX4lCqUtefXl9CltyUlpk97UVpmrqTzUsryUlZVFRUVFZJ/2ylwt9VZZKt6S6Tw3Gh8AOwMGtZaBic2oVAtHB6Lf6RQ2y5Pu5E6yhe1wuDbuA5Itrd0Je8Bw7dwjJVtaYx62g/eQCRPf3g7bhXvsHr/eY3zMrv7mJNNvlvRP3pCm9pTEl+W0+dpvU68pPkWklyVVo99DDz3Etm3buPHGG6O2H3vssZG/8/PzGTt2LAsWLOD999/nqKOO2u/72R0o2Nu2fcCCbnFxMStWrIg8Hz16NEuWLGHw4MHtvndnlXmr8HgGdCmsyx1iSPYghto2z+/ZTdA2SM0dQm5q4izIkpeXF+8o9BilLXn15fQpbclJaZPesL8yF7SuAOxoeWp/erMslQiS4Twv81bhyXBQXh+kvDbcIFuQncKgDpQbDcOPJ8PT7n5thzW6EbY7x+39sF0J13QfkGxp7U7YjoZr6x4p2dLak2EPdA+ZiPHt7bAdvcfuzes9FmGb6gC6Ixl+s6R/ahrpl+1Qo58kLqcBHhNqrH3nrIhIb0iaRr+HH36Y9957jxtuuIGBAwcecN+cnBwGDx7Mzp07AcjOziYYDFJTUxPV87yqqorx48dH9mk+JWjzfdoaAdhk9uzZnHrqqZHnTRVh5eXlBIPBjiewHYZhYKamU1NTG55nqZMCDse+/HA6qAiYfLajnNFp8S8gGYZBXl4epaWlHao4TCZKW/Lqy+lT2pKT0tZ9TqezzzYkxdL+ylzZ2dlAeDRfTk5OZHvzslJHylwt9VZZKt6S6Rq2HC7KvCE2+cLr+A1xWaQG6qkJtBsUO8dFTXVN5w5oGHg8A7Btu/Nhu3PcOIbtVLjG/Gm6D0i2tHYnbLvhWuRNLI7Zp8IeIH967JjJFLYD+ROL48Yrrc3rADprf79ZKktJomhaI00j/STRZTttavwGe4NGV6pzRUS6JOEb/Wzb5uGHH+add97h+uuvZ8iQIe2Gqa6uZvfu3ZEKqTFjxuBwOCgpKYmMCty7dy/btm3jnHPOAcLr99XV1bFp0ybGjRsHwOeff05dXd1+K6kgvJaNy+Xab9xjzrbpyrva7ItPrtOmIgC7AwajUhNnfLlt2wlfCdZVSlvy6svpU9qSk9ImPaW9MteQIUPIzs6mpKSE0aNHAxAMBlm/fn2kPNWRMldLvV6WirNkOM/rQ7ClwcTGIMNhM9TVufJnZ1PXfFq97uRMsoXtaLhI/jS7D0i2tHYn7IHCtZU3sThmXwnbXv70xDGTKWxH8ycWx43XtdPd35tk+M2S/sdvQZ2lNf0kOWQ7bXb4bfy2QZkfhiXOhGsi0oclfKPfQw89xOuvv87ll19OWlpaZD2Y9PR03G43Pp+P5cuXc/TRR5OdnU15eTmPPfYYGRkZfPOb34zsO3PmTJYtW0ZGRgYej4dly5aRn59PUVERACNGjGDKlCncf//9XHDBBQA88MADHH744QwfPjwuae8puc7m6/qJiIiItF/mMgyDWbNmUVxczLBhw8jLy6O4uJiUlBSmT58e2be9MpckNsuG1/ZCwDZwGzb5KVZCrAEtIiIiAvumSUw3bdxmnCMj0g7TCDdO7w0afFEHwzLiHSMR6Q8SvtHv5ZdfBuD666+P2r5gwQKOP/54TNNk+/btvPbaa9TW1pKTk8PEiRO59NJLSUtLi+x/7rnn4nA4uOOOO/D7/UyaNIkrrrgC09xXQrj44ot5+OGHWbx4MQBHHHEE559/fs8nspflusKNfnsD4aHlqsgRERGR9spcAKeddhp+v58HH3yQ2tpaxo0bx6JFizpd5pLE9W61gzI/mNiMSrVwqJwoIiIiCaRpak+N8pNkkeu02RuEbfUQGAAu3RaJSA9L+Ea/5cuXH/B1t9vNokWL2n0ft9vN3LlzmTt37n738Xg8XHzxxZ2OY7LJdtqY2Phsg5oQZCT8WSAiIiI9rb0yF4TX+JkzZw5z5szZ7z4dKXNJYvq83mRDfXjOofwUi1RVSIiIiEiC8Ya0np8kl3QT3EZ4is+tDSbj0hJnqSUR6ZvU3NMPOQ0Y6LIpDxiUBkwynPqxEREREenPyv0Gb1eFG/yKMgAVD0VERCRBGE4XIcKNfd5QeFuW24HlPPACaQZAKNSzkRNph2GER/uVBgw+qzMZm6rp80WkZ6nRr5/Kc9uUB2CX36Agrf39RURERKRvqg3B6konFgb5KRaTM0w+qox3rERERETCQhiUeEPYNuxqMAGDCl+IusCBwxlAUe6BGwZFekOuy6Y8CHuCJqUBg2FujVQVkZ6jSXv6qaGucPftUr+Jrd8ZERERkX7Jb8GrXif1lkG2w2JaZlA9j0VERCQh1VtgYWBik6YaTUkiTgPGpof//qRWDdEi0rP0E9lPDXGH1/Wrs8Lr+omIiIhI/xKywyP8vEGTNNNmZk4Ql+4OREREJEHVWuGeSQMcqJOSJJ1DPWBgs8NvsjfQcydwwIZSv8H2BoOtPoM61fuK9Dua3rOfchowyGVTpnX9RERERPod24Y3qxyU+k2chs2J2UE86nQsIiIiCawmFG4o8Tg0ZZUknwwn5KdYbG1w8EmdyfSs2LbG7Q4YbKgz2dJgErSbNyra5LltJqSHGJGia0ekP1CjXz821G1TpnX9RERERPqdD2odbPY5MLA5PitIrksVACIiIpK4bDu8DjGAx1S5RZLTxAHhRr/NPpND0i0GdbAMbjhdhGh7dGCDBR9WwcbafdvSHZBmggXsDRiU+g1K/Sbj00IcmRHCoZGyIn2aGv36sTyXxUc4Gtf1C2lqBBEREZF+4LM6k48a1xI5JjPEcPX4FRERkQTXtJ6fA5tUTUcuSWqQy2ZMaojNPgfvVDk4Jbdj62mHMCjxth4ZWBWErxpMgo0NglmOcENiurlvCtwZuQ42VIf4tM7BhnoHFQGDE3OCuo5E+jBd3v3Y4Gbr+lVrfmcRERGRPm9jnck71eF+f4cNCDIuTVO8i4iISOJrmtpT6/lJsjvcE8Jl2FQETTb5ulY1b9nwdYPBlgYHQQxSjHBj4sGpdqtrxOOEb2SEmJkdIMWw2R00eXWvE79uA0T6LDX69WNOAwY3DiP/qkGngoiIiEhftrHO5K3GBr8J6SGKBuhOX0RERJJDrdXU6KcZCiS5pTvgsAHh0RfvVzs6PRDDZ8GmepPdwXBd7kCnRUGa1e763CNSbE7Obdbw53US0OUk0ieppaefOzg1XNmzpYs9S0RERETkwAynC8vhosxbheVwYTndHX4YTle3j2/bUFKzr8Hv0PQQR3g0tbuIiIgkh5DW85M+5pB0i1ynRYNt8OpeF74O9MWzbSgPGHxeb+KzDZzYjEoJcVCKjdnBcn22E76dE8Rt2JQHTNZWOrF1SYn0OVrTr587OMXi3erwkPLqIGTojBARERGJqRAGH3lDeDIc1FSH6Mx9dVG2o1u99EI2vFPt4PP6cNffSekhpqrBT0RERJLIlrrwen4uQ+v5Sd9gGjAzO8jze1xUhQz+5XVyQvb+19mr8MMmn0l944jXDIfNiBQLVwfK9KZhYDndkec5TjjehFcqYFuDybp6N1My2w7rwMYOBjqbPBGJMzXx9HNpDshz2+z0G2xpMJns1DRPIiIiIn1BTQhe8zqpCJqAzTczQhySrrKeiIiIJJeNteH/BzptdVySPiPdER5198IeJ+UBk2cqXBzuCTEy1SLVDE/jWeY3+KzeQakfwMCBzTC3TU4nrgULgxJvsNX2g9wG2/0mH1dDpd8ix9m6a2J3OyCKSHyo0U8YlWqx02+yxWcyWWu7iIiIiCQ12w6v8/FejQO/beA2bKZlBRmZorl7REREJLlUBAx2B8DAJtelsoz0LdlOm5NygrxR5cAbNHmz2smb1eAybAL2vlY9A8h2WuS57Q6N7uuIHJeNz7YoD5h81RC+ZxjQzrqAIpIc1Ogn5KdYvI3N3qCJNxie31lEREREuseyYW/QYE8D7PAb4PXjC5iRdTNMA5xG+MY91QxPV+U26HIPdtuGnX6DdTUOdgfDfXIHOS2+lR3Eoxt4ERERSUIb6sJlmiyHjVOj/KQPGuSyOTU3yKd1Jp/VOai1jEiDX6bD5qAUi/EZDjbXxL7RO89l02DZVIUMtvpMxqVZuDW0TyTpqXlHSDFhuNvmK7/BF/UOjsgIxTtKIiIiIkmpwYJtPpOv/CalfqNZD10TAiHC/XSbM1o8Czf+NTUC7vTBQJP9ru9h2+ANGnzdWI6rDIXfz2XYHDYgPJ2nqQoyERERSUI+C7b4woWggRrlJ32YacDEARYTB1gEbKgNGaSZNimN9wCWs2d68BkGjEyx+MJn4rMMvvSZjE2zeq2BPWSDSdc7PYpI29ToJwAUpof4ym+yod5k4oCQFkYWERERacZwugi1arALs23Y5YcNNfC1D5pPlu42IMcNwZCFJ9WN5fdH2vksGwKND59l4LPAxqDegnorvNPO3QBuUgybdEe4ITC8Qh/UW1ATMqKm/nEaNuNSLSYPCJGe4iKEk85O3m7orltERETizLbhjUonIQxyXZCueirpJ1xGeNrP3uIwYFSKxSafSYMdbvgbk2rh6OYtQcv7J8uGMj9sr4edDVAfCt8HmUCGE3JcMDINRqbYOK1A9w4u0s+p0U8AOMhtk+u02BM0WV/r4HCN9hMRERGJCGFQ4o0uH1mNo+wqggY+a98Nbappk+WwyXDYpJlwWK6Lj/aG8Hhc1FQ30PYtvI1tg98O92r3WQb1loGNQXXIpsE2aAi2feftxGaoOzz1z5jUfVPytBXnjijKcXU6jIiIiEgsra8Lz5xgYnN0tsFXdfGOkUjf5TZhTKrFF/Um9ZbBFp/JqNTOdh2M1nQvYttQFYJSf7hRsSULqAyGH1vqwWEYjEl1cEiaRY5G+Ip0iRr9BAgPoy4aEGJ1pcln9SYTNNpPRES66UAjo6L2A8q8VVgOV6QxxIGNHVTvPklMQRt2Bwx2Bw2CjTeuBja5TpuBLrvLZSjDgBQjPPV6FjZgU5TtIOQPUB0KjwD0WUbkOkkzbTwOG4+DbvfEFREREUkU23wG79eEpzP8RkaIXLdTjX6S9EzDwHK6uxS2N2biSDVhdKrFZp9JrWXwRb1JYSZkdPH9bBuqg1AaMCOzmDiwyXTaZDbOYOIwwlN8+i2osQwqgwZ+2+Dzegef1zsY6rI4JD3EyBRbSxaIdIIa/SRiZIpNjtNib9Dkk1qt7SciIt3T0VFGBuDJcFBTHYo0ZhRlO1DfE0k0PgsqAgZ7g+EReBBeO2+g0ybXZffY2hcuE3LNpqtDvV1FRESkbwpY8F6Ng4314Qa/USkhCtMslX6kT7AwKPEGuxS2t2biSHeER/xtaTDx2QYvlsGxmQYHpXTuKtzlN3i/Bsr84WvZxGaQy2awy27VWdHZ2OkxA5s8l01euoON1RbbGgx2BUx2VZqkmzaFaSEK0i3SVFEg0i41+kmEYcBhjaP91teZjEixGOpW0UpERET6L9uGHX6D9V7Y2eCIbE8zwzeu2Q5bC8+LiIiIdJFlQ3nAYLPPZIvPjKxVPCE9xFRPCMNQlyeR3pTugHGpFl/6wiP0XvW6GJMa4jBPiAzH/sPZNuz0G/z7i0o2V4WbHAzCHSSHuDvWQdIwYGgKDHMEqQ3BxnoHn9eZ1FkGH9Q6Kam1GZVqcUi6xSBN/SmyX2r0kygjU2zGpIbY7HPwWqWTUwcG1INCRERiwrbDC3U32OC3DIJ2eP5+24YUAgT9Bk4jPHKqKgjZ8Y6w9Gs+CzbXm3xe76Ay1HSHapPpgMEui3QTNfaJiIiIdILhdBG0DbxB2NUQfpQ2hO8RmmQ44JvZMCzVATiw6J2pDUVkH7cJ49IsQoaDz2ptNvscbPaZDHfbjEixyHHapJo2IdugJgRlAZPtDSbVIQMIYGAzNt3Aae9bb7yzBjhgqidE0YAQW3wmG+pMKoJmY1wcDHJajE+3yE+xcKnuWiSKGv0kimHAUZkhdgdMKkMGr3mdzMwO6stTREQ6LWRDbQhqQga1lkF9iP2v8RcMQrMJPYOVMDOrd+Ip0sRqHNW3qd7BVw0GVuP56jRsxqUb2JZFispEIiIiIh1m2+ANGpQGwo8dPrvVPYEDmwxneH3kASaU+8KPJr01taGI7OMwYGo2jE4J8kGNg51+kx1+gx3+/d8QuQybyQPTOJhqBqS4KPF2/rgt1z40gNEuGJ0BFX7YUANb66EiaFJRZfIWkJcKI1Ph4FSbFDvQ+YOK9DFq9GvDSy+9xLPPPovX62XEiBGcd955HHroofGOVq9xGTAjO8jzu53sCpi8sDfc8Oc5wBBuERERy4Y9QYNSv0Gp32RXAEJ2yx8PmxQj3HPQadg4ANMAp8uNz+8nYBsEbMjqqcXRpFckU1nKZ8GOBpOvGsI3sH5737k30GkxLs1idKqF0+3u0k2riIiISFckU3kKwh3+qkIGlUGoDBrhR8igKmi0aOQzMLFJd4DHtPE4bNI0g4JIwhrssjkpJ0h1EL7wOdjduMZ50A43DKaYNkNc4Ud+qk3+QR527qwm1MXjtbf2YYYJh6TDnoDBnqCB3zb42gdf++BtDAa5nAxz2wxzh6cAbbmGoEh/oEa/Ft544w2WLl3KvHnzGD9+PK+88go333wzd9xxB4MGDYp39HpNttPmpNwgq71OvEGT53a7mDwgRGG61aE5mEVEpO8LWOH1N8oDBmUBk4qAEVmDo4kDG48DBjhs0k2bVDPcyNecAXgyXNRUN2A3rthRlOWArq1xLnGWyGUpq7EyanfAoCLQ+H/QgGYVUSmGzZg0i3GpFjnN1omw4hBfERER6Z8SuTzlt6AyZEQ17FUGw1P82fuZ1cNphBsEhqaaVPtDauQTSRLNR90NcEJRalt7GVF/lXmrsByuHp2W12nAELfNYJdNgx3uaFAVMqi3DMoDJuUBKKl14MRu3C/cADjQFa6TEOnr1OjXwj/+8Q9mzpzJiSeeCMB5553Hhx9+yMsvv8zZZ58d59j1rsEum1m5Af7ldbInaPKfGicf19qMTLU4qLG3hApqIiJ9W8CCegvqLYPqUPs39i7DJs9tk+e2GJLmZFuNpd+JfiZRy1JrvA6+bjAJtlEZleO0GJFiR8o3LRum46nl9DadofVvREREklMilqe2+gzeqXZSb+2/fOEyIMsJma7w/1lOyHLBAIeBaRgYhsGHe3sx0iLSLe2Numsp3KHXQU11iMm9MC2vYUCqAalum6HYjPU4KK0PstNvUNpg4rONVlOSZjhscpw2GY7wI9MZHnGcYoQbE3ULJX2BGv2aCQaDbN68me9///tR24uKitiwYUN8IhVnAxwwKzfIZp9JSa2DmpDB5/UOPq8PT9fmNGzSTXCbNm4jXNnrMvaN4mjqO28Y+/p9RPenD7/msWupqTGxmy3e3BcobcmrK+mLZzZ09tgeK5y23j5uLLX1uRgGpIdqqKvt/HmZ6J+fAaQHa6itjV23NJvwNDwhOzw1R8gOD64L2RCwDXwh2mwgac5j2gx2WwxunM4j27mvwcRywnYVmPuVRC5LWRgEMXAaNgOd4V6euU6boW6LAQk8hXlnb7Sb0/o3IiIiySdRy1NOg0iDn9MIV5CnmjYpZnh6v9QWFeahEOwJwZ6Gfe+hsomI9KQMl8EAp5OxNK0nCrsaYLcfKgJQHYTqULhDc1tMGr/TDBtnY/22aRBZlsQkXN/RvI67+f8YbdR7N/u/6fvRAAY01l213K+t+qGObGtZB9bq9Xbew27xR5uvNfu7rfdrK1ebb7Mb42ljYBOeTSf8vNmjxXNnpRd/wIlF+IWmsE0PWuxPi/dpM/526/33/W80/hvu4l40IESRJ/nm/VGjXzNVVVVYlkVWVlbU9qysLLxeb5thAoEAgcC+BUINwyAtLQ2nM7ZZaxgGpsMkM9XVdk13O9xOE8PoeuHqUDcckgnlASjzG1QETGpD0RdNqPHha+sNDhRlG8qq/EDXerEnNKUtefXx9O2u7qNps2FPTYA+mTbAW9sDaWtRInU0PtzAgMZtpgGpJqQ5wNPskeGElAM1lhgmWSkdaPUzDNKcJmaz37ju/m61Jda/zdJaIpeljsmBYx0O0hytp5jtkI6ez21wOR1kptLqPO9o2KyUrnVL6GrYXj9m43dAPNIar7CdCtfiOzLZ0tqdsO2Ga+P3o7vH7FNhD5A/PXbMZArbgfyJxXHjldbulKWaRoq7XC7sZnmjslTv6Gx5qrfKUiOc8INUGOBysKW2axWgvX4dGQYuh6PLdWkJ8V3VG8fsZ/nUpbD9sLzapbDNflvj8bvpcDr5siq6w2SaA0akhR8hG3w2+C2DgAV+wv8H7f1XXdvEcOWRZgfZnah1Vz3dcbsjVUQ0bwS1cbniV/7ITDVxuWLb6Ncb5SmV2NrQ1lRI+5seqbi4mBUrVkSeT5s2jUsuuYScnJweidu0jB552w4bAkyMbxRERCSJDOnUz2FaT0VDelkilqUGx+A9Onc+RxsaCdv583xoTI6b+MeEtDgdN1nyeN+5k2xp7U7YjoVr+7pKtrT2XNgDf+8kXnx7O2zHv5eT6zu1++K9dlx/19HyVG/WSw1r/P+gbpwa8biOYlOGS46wyqeeDtu/yqtdD5vWreMm6++mSKLQ0pXNZGZmYppmq55TlZWVrXpYNZk9ezZLly6NPC644IKoHlaxUl9fzxVXXEF9fX3M3zvelLbk1JfTBn07fUpbclLaJBkkclkq3nSe75/y5sCUP/unvDkw5c+BKX/2T3kTX50tT/WXslRX6FzuGOVT+5RHHaN86hjlU8con7pOjX7NOJ1OxowZQ0lJSdT2kpISxo8f32YYl8tFenp61MPliv0c5bZt8+WXX0ZNrdFXKG3JqS+nDfp2+pS25KS0STJI5LJUvOk83z/lzYEpf/ZPeXNgyp8DU/7sn/ImvjpbnuovZamu0LncMcqn9imPOkb51DHKp45RPnWdpvds4dRTT+Xuu+9mzJgxFBYW8sorr1BRUcFJJ50U76iJiIiIJDyVpURERES6R+UpERER6So1+rVw7LHHUl1dzVNPPcXevXsZOXIkV155JYMHx2IlGBEREZG+TWUpERERke5ReUpERES6So1+bTj55JM5+eST4x2NKC6XizPOOKNPTtGgtCWnvpw26NvpU9qSk9ImySQRy1LxpvN8/5Q3B6b82T/lzYEpfw5M+bN/ypvEoPJU9+lc7hjlU/uURx2jfOoY5VPHKJ+6zrA1KaqIiIiIiIiIiIiIiIhIUjPjHQERERERERERERERERER6R41+omIiIiIiIiIiIiIiIgkOTX6iYiIiIiIiIiIiIiIiCQ5Z7wjIO176aWXePbZZ/F6vYwYMYLzzjuPQw89NN7ROqDi4mLeeecdvv76a9xuN4WFhfz4xz9m+PDhkX3uvfde1qxZExWuoKCAxYsXR54HAgGWLVvG2rVr8fv9TJo0iXnz5jFw4MBeS0tLy5cvZ8WKFVHbsrKy+NOf/gSAbds8+eSTvPrqq9TU1FBQUMD555/PyJEjI/snYroAFi5cSHl5eavt3/nOd5g3b15SfWbr16/n2Wef5csvv2Tv3r1cdtllfPOb34y8HqvPqaamhj//+c/85z//AeDII49k7ty5DBgwIG7pCwaDPP7446xbt46ysjLS09OZPHkyZ599Nrm5uZH3uP7661m/fn3U+x577LFceumlcU1fe59drM7DREzbnDlz2gz34x//mP/+7/8GEvdz68j3frJfdyJtKS4u5rHHHmPWrFmcd955QMeu5ZZeeeUVXnvtNbZv3w7AmDFjOOussxg3blyPxLu3xCp/3n77bYqLiyktLSUUCpGXl8f3vvc9vvWtb/VU1HtcrPKmubVr13LXXXdx5JFHcvnll8cyur0uVvmzevVq7rvvvlbbH330Udxud8zi25tiee7U1tby2GOP8c4771BbW8uQIUP4yU9+wuGHH94TUe8VscqftspcAFOnTuXKK6+MWXx7UyzPneeee46XX36ZiooKMjMzOeqoozj77LOT9rqSxLdy5Uref/99tmzZgtPpZOnSpVGvb9myhaeffpoNGzZQVVXFkCFDOOmkk5g1a1Zkn08++YTnnnuOTZs2UV9fT15eHv/93//Ncccd16E4BAIBrrrqKrZu3cptt93GqFGjYpjC2IhXPpWVlfHUU0/x8ccf4/V6yc3N5bjjjuMHP/gBTmfiVT/H83xKpvvZWOST3+/nT3/6E5s3b+brr7/m8MMP71A5dceOHTz66KNs2LCBYDBIfn4+P/rRj5g0aVKsk9lt8cwngPfff58VK1awdetWUlNTOfTQQ7nssstimcSYiHc+QXJ8j8dC4n3rSpQ33niDpUuXMm/ePMaPH88rr7zCzTffzB133MGgQYPiHb39Wr9+PSeffDJjx44lFArx+OOPc9NNN3H77beTmpoa2W/KlCksWLAg8rxlQWDp0qW89957XHLJJWRkZPDII49w6623smTJEkwzfgNVR44cyTXXXBN53jwuzzzzDM899xwLFixg2LBhrFy5kptuuok777yTtLQ0IHHTdcstt2BZVuT5tm3buOmmmzjmmGMi25LlM2toaGDUqFGccMIJ/L//9/9avR6rz+n3v/89u3fvZtGiRQDcf//93H333fzmN7+JW/r8fj9ffvklp59+OqNGjaKmpoa//OUv3Hbbbdx6661R+5544on86Ec/ijxveZMej/S199lBbM7DREzbAw88EPV83bp1/PGPf+Soo46K2p6In1tHvveT/boTaWnTpk288sorHHzwwVHbO3otN7d+/XqmTZvG+PHjcblcPPPMM5FrqHmHjWQSy/zxeDz84Ac/YPjw4TidTt5//33uu+8+MjMzmTJlSk9Ev0fFMm+alJeXs2zZsoTvHNgRsc6ftLQ07rrrrqhtydowEcu8CQaD3HTTTWRmZvKrX/2KgQMHsnv37qj7tWQTy/y57LLLCAaDkefV1dX8+te/jro3SiaxzJt///vf/O1vf+PCCy+ksLCQnTt3RhrXmxoTRWItGAxy9NFHU1hYyKpVq1q9vnnzZjIzM7nooosYOHAgGzZs4IEHHsA0Tb773e8CsGHDBvLz8znttNPIysri/fff55577iEtLY0jjzyy3Tg8+uij5ObmsnXr1pinL1bilU87duzAtm1+9rOfkZeXx/bt27n//vvx+Xz89Kc/7dE0d0U8z6dkup+NRT5ZloXb7eaUU07h7bff7vCxb731VoYNG8a1116L2+3mueeeY8mSJdx9991kZ2fHKokxEc98euutt7j//vs566yzIg2i27Zti03CYiye+dQkGb7HY0GNfgnuH//4BzNnzuTEE08EwgXoDz/8kJdffpmzzz47zrHbv6YfriYLFixg3rx5bN68mQkTJkS2O53O/X5R19XVsWrVKi666CKKiooAuOiii7jwwgspKSmJawWPaZptxtu2bZ5//nlmz54duUlauHAhF1xwAa+//jonnXRSQqcrMzMz6vnTTz/N0KFDk/Izmzp1KlOnTm3ztVh9Tl999RUffPABixcvpqCgAID58+dz9dVXs2PHjqgRTr2ZvvT09KhGaYD/+Z//4aqrrqKioiKqw0BKSsp+P894pe9AaWvS3fMwUdPWMk3vvvsuEydOZOjQoVHbE/Fza+97vy9cdyLN+Xw+7r77bubPn8/KlSujXuvotdzcxRdfHPX85z//OW+//TYfffQRM2bMiFm8e0us82fixIlRz2fNmsWaNWv47LPPkq7RL9Z5A+Gb39///vfMmTOHTz/9lNra2lhHu9f0RP4YhpFwlUNdEeu8WbVqFTU1Nfzf//1fpAPV4MGDYx7v3hLr/PF4PFHP165dS0pKCkcffXTM4txbYp03GzduZPz48UyfPh2AIUOGMG3aNDZt2hTzuIs0aRqVunr16jZfnzlzZtTzoUOHsnHjRt5+++1IZfEPfvCDqH1mzZrFhx9+yDvvvNNuo9+6desoKSnhf//3f1m3bl0XU9Hz4pVPU6ZMiSqTDR06lB07dvDyyy8nZKNfvPIp2e5nY5FPqampXHDBBUC4obQj5dSqqipKS0u58MILI51VzjnnHF5++WW2b9+ecOW6eOVTKBRi6dKl/OQnP4k6RqKdR03ilU9NkuV7PBa0pl8CCwaDbN68mcMOOyxqe1FRERs2bIhTrLqmrq4OaH3jtH79eubNm8cll1zCH//4RyorKyOvbd68mVAoFKn4BcjNzSU/P5+NGzf2TsT3o7S0lPnz57Nw4ULuvPNOdu3aBYSnM/B6vVGfmcvlYsKECZHPLJHT1VwwGOTf//43J5xwAoZhRLYn62fWXKw+p40bN5Kenh4pqAEUFhaSnp6ecNdoXV0dhmGQnp4etf3f//43559/Pr/61a945JFHqK+vj7yWyOnr7nmYyGlr4vV6WbduXatCDyTH59bye78/XnfStz344INMnTo16nxty4Gu5QNpaGggGAy2Kjsli57MH9u2+eijj9ixY0dUx6Rk0RN5s2LFCjIzMzt9niWinsgfn8/HggUL+PnPf86tt97Kl19+Gavo9qpY5817771HQUEBDz30EBdccAH/+7//y8qVK6Nm/kgmPf29vGrVKo499tikHAkZ67w55JBD2Lx5c6SRb9euXaxbty6pp4WVvqmurq7dslRH9vF6vdx///384he/SNqR4gcSq3yKRZhEFot86g/3s7H43DMyMjjooINYs2YNPp+PUCjEP//5T7KyshgzZkyMYhpfscinL7/8kj179mAYBpdffjk/+9nPuPnmmyNLRvQFsfoe6evf4y1ppF8Cq6qqwrIssrKyorZnZWXh9XrjE6kusG2bv/zlLxxyyCHk5+dHtk+dOpVjjjmGQYMGUVZWxhNPPMGNN97Irbfeisvlwuv14nQ6W13Y8U5/QUEBCxcuZPjw4Xi9XlauXMnVV1/N7bffHolXW59ZRUUFQMKmq6WmdT2OP/74yLZk/cxaitXn5PV6W71Hy30Sgd/v529/+xvTpk2LavSbPn06Q4YMITs7m+3bt/O3v/2NrVu3RkYJJmr6YnEeJmramluzZg2pqalRa/5BcnxubX3v97frTvq2tWvX8uWXX3LLLbe0u+/+ruX2/PWvfyU3N5fJkyd3NZpx01P5U1dXx/z58wkGg5imyfnnn99uBXai6Ym8+eyzz1i1ahW33XZbrKIZNz2RP8OHD2fBggXk5+dTX1/P888/zzXXXMNvf/tbhg0bFquo97ieyJtdu3ZRXl7O9OnTufLKK9m5cycPPfQQlmVxxhlnxCrqvaKnv5c3bdrE9u3bufDCC7sTzbjoibyZNm0aVVVVkfJnKBTiO9/5Dt///vdjEWWRmNi4cSNvvvnmAdfgfOutt/jiiy/42c9+tt99bNvmvvvu46STTmLs2LGUlZX1RHTjJlb51FJpaSkvvPBCQo7y64pY5VNfv5/tSD51hGEYXHPNNdx2222ce+65GIZBVlYWixYtSsi1DzsrVvnUNAjlySef5Kc//SlDhgzh73//O9dffz133XVX0je6xyqf+vr3eFvU6JcEmo+yOtC2RPXQQw+xbds2brzxxqjtxx57bOTv/Px8xo4dy4IFC3j//fcPuH6Abds9FteOaD41X35+PoWFhVx00UWsWbMm0lOn5efTkTjHO10t/etf/2LKlClR6wgl62e2Pz31Odm2nTDXaDAY5M4778S2bebNmxf12re//e3I3/n5+QwbNozf/OY3bN68+YA9p+Kdvp48D+Odtub+9a9/cdxxx7XqgZQMn9v+vvehf1x30rdVVFSwdOlSFi1a1KEegvu7lg/kmWeeYe3atVx//fVJ1wuxJ/MnNTWV3/72t/h8Pj766CMeeeQRhg4d2mrqz0TVE3lTX18fmbKv5TTtyaanzp3CwkIKCwsjz8ePH88VV1zBCy+8wNy5c7sd797QU3lj2zaZmZnMnz8f0zQZM2YMe/fu5dlnn02qRr/e+F5etWoVI0eOZNy4cd2Jaq/rqbz55JNPWLlyJfPmzaOgoIDS0lL+/Oc/k52dnVTnjsTf8uXLWbFixQH3ueWWWxg7dmyn3nf79u3cdtttnHHGGfvtIPTJJ59w7733Mn/+fEaOHLnf93rhhReor69n9uzZnYpDLCVDPjW3Z88ebr75Zo455pjIckW9IdnyqbnevJ+NZz51lG3bPPjgg2RlZXHDDTfgdrtZtWoVt956K7fccgs5OTndev+OSJZ8gvBUs03TjzfNbvHmm29y0kkndev9OyIZ8ikRvsd7mxr9ElhmZiamabbq6VFZWdlmr5BE9PDDD/Pee+9xww03MHDgwAPum5OTw+DBg9m5cycQXlsgGAxSU1MT1TOhqqqK8ePH92i8OyM1NZX8/Hx27tzJN77xDSDcc6f5D1BVVVXkM0uGdJWXl1NSUsJll112wP2S9TNrmvu7u59TdnZ21LSSbb1PPAWDQe644w7Ky8u59tprW03t2dLo0aNxOByUlpYyZsyYhE9fk66ch4metk8//ZQdO3Zw6aWXtrtvon1u+/ve7y/XnfR9mzdvprKykt/85jeRbZZl8emnn/Liiy/yt7/9DdMMz6DfmWu5ybPPPktxcTHXXHNNZP2KZNKT+WOaJnl5eQCMGjWKr7/+mqeffjppGv16Im+aRmotWbIksq3p5v/MM8/kzjvvjORZouvpa6uJaZqMHTuW0tLSWEW9x/VU3mRnZ+N0OiNhAQ466CC8Xi/BYDCyzl+i6+lzp6GhgbVr1/KjH/0o1lHvcT2VN0888QTf+ta3IpX5+fn5+Hw+HnjgAX7wgx9EnVMiB/Ld736XadOmHXCfzq41+tVXX3HjjTdy4okncvrpp7e5z/r161myZAnnnntuu2snf/zxx2zcuJGzzz47avtvfvMbpk+fzi9+8YtOxa8rkiGfmuzZs4cbbriBwsLCTo0MjIVkyKdEuJ+NVz51xscff8x7773Hn//850hd1pgxYygpKWHNmjW9MrI8GfKpqZ5lxIgRkW0ul4uhQ4dGZlTqacmQT4nwPd7bkqMU3085nc7IF1rz6TVKSkoijUuJyrZtHn74Yd555x2uv/56hgwZ0m6Y6upqdu/eHakMHjNmDA6Hg5KSksjInr1797Jt2zbOOeecHo1/ZwQCAb7++msOPfTQyJR7JSUljB49Ggg3vKxfvz4S52RI17/+9S+ysrLaXZMhWT+zWH1OhYWF1NXVsWnTpkiv388//5y6urq4N3I2NfiVlpZy3XXXkZGR0W6Y7du3EwqFIoWGRE5fc105DxM9batWrWLMmDGMGjWq3X0T5XNr73u/P1x30j9MnjyZ3/3ud1Hb/vCHPzB8+HBOO+20qIrOzlzLEG7we+qpp1i0aFGne0Imip7Mn5Zs2yYQCHQnur2qJ/Jm+PDhrd7z8ccfx+fzcd555zFo0KCYxb+n9da5Y9s2W7du7VIv/HjpqbwZP348a9euxbKsyHvs3LmTnJycpGnwg54/d958802CwSDHHXdcrKLca3oqbxoaGlqNSDFNM2FneJHElZmZGdOR6tu3b+fGG29kxowZnHXWWW3u88knn3DrrbdyzjnnRM2isj9z587lzDPPjDzfu3cvixcv5tJLL41al60nJUM+wb4Gv9GjR7NgwYJe7wCQDPmUCPez8cinzmpoaABodQ4ZhtFraw8nQz6NGTMGl8vFjh07OOSQQ4BwPUt5eXmnG9q6KhnyKRG+x3tb8pTk+6lTTz2Vu+++mzFjxlBYWMgrr7xCRUVFrwzP7Y6HHnqI119/ncsvv5y0tLTIaMX09HTcbjc+n4/ly5dz9NFHk52dTXl5OY899hgZGRmRBs709HRmzpzJsmXLyMjIwOPxsGzZMvLz8+O6fssjjzzCkUceyaBBg6isrOSpp56ivr6eGTNmYBgGs2bNori4mGHDhpGXl0dxcTEpKSlMnz49odPVxLIsVq9ezYwZM3A4HJHtyfaZ+Xy+qB7cZWVlbNmyBY/Hw6BBg2LyOY0YMYIpU6Zw//33c8EFFwDwwAMPcPjhhzN8+PC4pS8nJ4fbb7+dL7/8kiuuuALLsiLXoMfjwel0Ulpayuuvv87UqVPJyMjgq6++YtmyZYwePTpSUIhX+g6UNo/HE5PzMBHT1lQxW1dXx1tvvcVPfvKTVuET+XNr73s/Vt+P8bzuRADS0tKi1igGSElJISMjI2r7ga5lgHvuuYfc3NxIb8NnnnmGJ554gosvvpghQ4ZErqHU1FRSU1N7JjE9oKfyp7i4mLFjxzJ06FCCwSDr1q3jtddeazV1dSLribxxu92t3rNpnZOW2xNdT507Tz75JAUFBQwbNiyypt+WLVs4//zzey4xMdZTefOd73yHF198kaVLl/Ld736X0tJSiouLOeWUU3ouMT2gp/KnyapVq/jGN77RoU50iaan8uaII47gueeeY/To0ZHpPZ944gmOPPJIjfKTHlNRUUFNTQ0VFRVYlsWWLVsAyMvLIzU1le3bt3PDDTdQVFTEqaeeGilLmaYZqZBuaqA55ZRTOProoyP7NF9XfNOmTdxzzz1ce+215ObmtupA01Quy8vLa3dGq3iIVz7t2bOH66+/nkGDBvHTn/6UqqqqSJyaOqgmknjlU7Ldz8YinyA8cqtpVh+fzxd5n6aOJi3zqbCwEI/Hwz333MMZZ5yB2+3m1VdfpaysrN0BCvEQr3xKT0/npJNOYvny5QwcOJDBgwfz7LPPAkSm+0wk8cqnZPsejwU1+iW4Y489lurqap566in27t3LyJEjufLKK3uttb6rXn75ZQCuv/76qO0LFizg+OOPxzRNtm/fzmuvvUZtbS05OTlMnDiRSy+9lLS0tMj+5557Lg6HgzvuuAO/38+kSZO44oor4nojsWfPHu666y6qqqrIzMykoKCAxYsXRz6T0047Db/fz4MPPkhtbS3jxo1j0aJFCZ+uJh999BEVFRWccMIJUduT7TP74osvuOGGGyLPH3nkEQBmzJjBwoULY/Y5XXzxxTz88MMsXrwYCN8A90Yl0oHS98Mf/pD//Oc/AFx++eVR4a677jomTpyI0+nko48+4vnnn8fn8zFw4EAOP/xwfvjDH8Y9fQdK2wUXXBCz8zDR0rZw4UIA3njjDWzbjjSENZfIn1t73/sQu+/HeF13Ip1xoGsZwjc8zUdJvPzyywSDQW6//fao/c444wzmzJnTo3GNh87mT0NDAw8++CC7d+/G7XZz0EEHcdFFF0Wt89pXdDZv+pvO5k9tbS0PPPAAXq+X9PR0Ro8ezQ033JB0a7N1RGfzZtCgQVx99dX85S9/4de//jW5ubmccsopvTJlVjx05drasWMHn332GVdffXVvRDFuOps3p59+OoZh8Pjjj7Nnzx4yMzM54ogjYtYjX6QtTzzxBGvWrIk8b7rXbbrHffPNN6mqquL111/n9ddfj+w3ePBg7r33XgBWr15NQ0MDTz/9NE8//XRknwkTJkTuYxoaGtixYwfBYLDnE9UD4pVPJSUllJaWUlpays9//vOoOC1fvrwnktot8Tyfkul+Nhb5BOE13crLy1u9T9O50TKfMjMzueqqq3j88ce58cYbCYVCjBgxgssvv7zLs4T0pHjlE8CPf/xjTNPknnvuwe/3M27cOK699tqoJVMSRTzzqb8xbM2/ICIiIiIiIiIiIiIiIpLU4j+sSERERERERERERERERES6RY1+IiIiIiIiIiIiIiIiIklOjX4iIiIiIiIiIiIiIiIiSU6NfiIiIiIiIiIiIiIiIiJJTo1+IiIiIiIiIiIiIiIiIklOjX4iIiIiIiIiIiIiIiIiSU6NfiIiIiIiIiIiIiIiIiJJTo1+IiIiIiIiIiIiIiIiIklOjX4iIgnmpZdeYvXq1T3y3nPmzGH58uU98t4iIiLSv6xevZo5c+ZQVlbWqXAbNmxg+fLl1NbW9lDMRERERBJHV8tMIiJdoUY/EZEE8/LLL/dYo5+IiIhIvG3YsIEVK1ao0U9ERERERCTG1OgnInHX0NDQ5bB+vx/btmMYGxERERERERERERGR5OOMdwREpG/49NNPue6667j44ouZPn161Gtr1qzh3nvv5eabb+all17irbfeYvHixTzyyCNs3LiRkSNHsnjx4naPsXr1au677z4WLVrE2rVree+996iurubRRx/F7Xbzxhtv8Nxzz7Ft2zYADjnkEM4++2xGjx4deY97772Xt956i9/+9rf8+c9/Zv369Xg8Ho455hjOOussXC5XZN+amhoef/xx3n33Xaqqqhg4cCDTpk3jjDPOiOx3+eWXk5qayo033hgVV8uyuPDCCykoKOCyyy4D4Mknn2TdunXs3LkTy7LIy8vj5JNP5oQTTsAwDAAWLlxIeXk5EJ6KE2Dw4MHce++9ANTV1bFixQrefvtt9uzZQ2ZmJscccwxnnnkmqampkePX1dXxyCOP8M477xAIBDjkkEP4n//5nw58kiIiIiJdU1JSwosvvsjmzZuprq4mNzeXyZMnc+aZZ5KZmQnA8uXLWbFiBQC/+MUvImGvu+46Jk6cGJd4i4iIiPS2VatW8cILL7Bjxw7cbjcTJkzgrLPOYsSIEZF9OlOHtXv3bpYuXcqHH36IaZocfvjhzJo1i6uuuooFCxZw/PHHxyGVIhIPavQTkZg49NBDGT16NC+99FKrRr8XX3yRsWPHMm7cOF566SWCwSBLlizhpJNO4vvf/z6hUKhTx/rDH/7A4YcfzkUXXYTP58PpdLJy5UqeeOIJjj/+eE4//XSCwSDPPvss1157LbfccktUoSkUCrFkyRJmzpzJqaeeyqeffspTTz1Feno6Z5xxBhAeQXjDDTdQWlrKnDlzOPjgg/n00095+umn2bJlC1deeSUAxx9/PEuXLmXnzp0MGzYscowPP/yQvXv3RhWqysvL+fa3v82gQYMA+Pzzz3n44YfZs2dP5LiXXXYZt99+O+np6Zx//vkAkUJcQ0MD119/Pbt372b27NkcfPDBbN++neXLl7Nt2zauueYaDMPAtm1++9vfsnHjRk4//XTGjRvHZ599xi233NKpfBYRERHpjNLSUgoLC5k5cybp6emUl5fzj3/8g2uvvZbf/e53OJ1OTjzxRGpqanjxxRe57LLLyM7OBogqq4mIiIj0ZcXFxTz22GNMmzaNs846i5qaGp588kmuvvpqbrnllqj6pY7UYfl8Pm644QZqamo455xzyMvL44MPPuDOO++MUwpFJJ7U6CciMXPKKadw3333sWXLFkaNGgXApk2b+OKLL1i4cGFkv1AoxBlnnMEJJ5zQpeNMmjSJn/3sZ5HnFRUVPPnkk5x88snMnTs3sr2oqIiLL76YJ598kl/+8peR7cFgkDlz5nDMMccAMHnyZL744gtef/31SIFpzZo1bN26lV/+8peR/YqKikhNTeWvf/0rJSUlFBUVcdxxx/Hoo4+yevVqzjrrrMgxVq9eTVZWFlOnTo1sW7BgQeRvy7KYOHEitm3zwgsvcPrpp2MYBqNHj8btdpOWlkZhYWFUul944QW2bt3KzTffzNixYyNxz83N5fbbb+eDDz5g6tSpfPjhh3zyySecd955zJo1KxJ3p9PJ448/3qU8FxEREWnPd77zncjftm0zfvx4Jk6cyIIFC/jggw848sgjGThwYKQD1KhRoxgyZEi8oisiIiLS62pra3nqqaeYOnUql1xySWT7hAkTuOSSS3jyySe5+OKLI9s7WodVWlrKVVddxZQpUwA47LDDaGho4JVXXum9xIlIQtCafiISM9OmTSMrK4sXX3wxsu3FF18kMzOTY489Nmrfo446qsvHOfroo6Oef/jhh4RCIWbMmEEoFIo8XC4XEyZMYP369VH7G4bBEUccEbXt4IMPjkyrCfDxxx+TkpLS6lhNI/c++ugjADIyMjjiiCNYs2YNlmUB4WlB//Of/zBjxgwcDkfUe/7f//0f5557LmeeeSZnnXUWy5cvp7q6msrKynbT/d5775Gfn8+oUaOi0jllyhQMw+CTTz6JHAfguOOOiwrfcgSmiIiISCxVVlbywAMPcOGFF0bKOk2dnr766qs4x05EREQk/jZu3Ijf72813eagQYOYNGlSpL6pSUfqsNavX09aWlqkwa+J6oFE+ieN9BORmHG5XHz729/mH//4Bz/5yU8IhUK8+eab/Nd//VfUPOMpKSmkp6d3+ThN00A1aWowa5pys6Wm9fKauN1u3G531Dan00kgEIg8r6mpITs7u1XYrKwsHA4H1dXVkW0nnHACb7/9NiUlJUyZMoW1a9cSCASiCnCbNm3ipptuYuLEicyfP5+BAwfidDp59913WblyJX6/v910V1ZWUlpaGjWisLmmONXU1OBwOMjIyIh6vWW+iYiIiMSKZVncdNNN7N27l9NPP538/HxSUlKwbZtFixZ1qKwjIiIi0tc11d3k5OS0ei0nJ4eampqobR2tw8rKymr1fm1tE5G+T41+IhJT3/nOd3j66af517/+hd/vJxQKRU31FAstG+KaGrd+9atfMXjw4Jgcw+Px8Pnnn2PbdtTxKisrCYVCZGZmRrZNmTKFnJwcVq9ezZQpU1i9ejUFBQVRa9OsXbsWh8PBFVdcEVVYe/fddzscp4yMDNxuNxdeeOF+X2+KeygUorq6Oqrhz+v1dvhYIiIiIp2xfft2tm7dyoIFC6I6PpWWlsYvUiIiIiIJpqmeZu/eva1e27t3b6sO3B3h8XjYtGlTq+2qBxLpnzS9p4jEVE5ODscccwwvvfQS//znPzniiCMi67b0lMMOOwyHw8GuXbsYO3Zsm4/Omjx5Mj6fr1Wj3Jo1a4DwuoJNTNPkW9/6Fu+++y6ffvopX3zxRav1Cg3DwOFwYJr7vnb9fj+vvfZaq2M7nc42e8MfccQR7Nq1i4yMjDbT2LQmTlPc/v3vf0eFf/311zuTBSIiIiId1tRJqvnsDgD//Oc/W+3btI9G/4mIiEh/U1hYiNvtblVns3v3bj7++OOo+qaOmjBhAvX19axbty5q+9q1a7sVVxFJThrpJyIxN2vWLK666iqAyDouPWnIkCHMmTOHxx9/nF27djFlyhQ8Hg9er5dNmzaRmprKnDlzOvWe3/rWt3jppZe49957KSsrIz8/n88++4zi4mKmTp1KUVFR1P4nnHACzzzzDL///e9xu92t1jA8/PDD+cc//sHvf/97vv3tb1NdXc3f//73VhVjAPn5+bzxxhu88cYbDBkyBLfbTX5+PrNmzeLtt9/muuuu47/+67/Iz8/Htm0qKir48MMP+d73vkdBQQFFRUUceuih/PWvf6WhoYGxY8fy2WeftSpQioiIiMTK8OHDGTp0KH/729+wbRuPx8N7771HSUlJq33z8/MBeP755zn++ONxOBwMHz6ctLS03o62iIiISK8aMGAAp59+Oo899hj33HMP06ZNo7q6mhUrVuByufjhD3/Y6fecMWMGzz33HHfffTdnnnkmeXl5rFu3jg8//BBoPWOWiPRtavQTkZgbN24cgwcPxu12M3ny5F455uzZsxkxYgTPP/88a9euJRgMkp2dzdixYznppJM6/X5ut5vrrruOxx57jL///e9UVVWRm5vL9773vTYLYMOHD2f8+PFs2LCB6dOnt1qzcNKkSVx44YU888wzLFmyhNzcXE488UQyMzP54x//GLXvnDlz8Hq93H///dTX1zN48GDuvfdeUlNTueGGG3j66ad55ZVXKCsrw+12M2jQICZPnhyZ2tQ0Ta644gr+8pe/8OyzzxIMBhk/fjxXXnkll156aafzQkRERKQ9TqeTK664gqVLl/KnP/0J0zSZPHky11xzTatOYBMnTuT73/8+a9as4dVXX8W2ba677jomTpwYp9iLiIiI9J7Zs2eTlZXFCy+8wBtvvIHb7WbChAmcffbZDBs2rNPvl5qayrXXXsvSpUt59NFHMQyDoqIi5s2bxy233MKAAQN6IBUikqgM27bteEdCRPqWrVu38utf/5rzzz+fk08+Od7RERERERERERER6VdWrlzJE088wX333cfAgQPjHR0R6SUa6SciMVNaWkpFRQWPPfYYOTk5HH/88fGOkoiIiIiIiIiISJ/24osvAuGZqEKhEB9//DEvvPACxx13nBr8RPoZNfqJSMw89dRTvPbaa4wYMYJf/vKXpKSkdCicbdtYlnXAfUzT1BzkIiIiIiIiIiIiLbjdbp577jnKy8sJBAIMGjSI0047jdNPPz3eURORXqbpPUUk7lavXs199913wH20zouIiIiIiIiIiIiIyP6p0U9E4q66upqysrID7jN8+HDS0tJ6KUYiIiIiIiIiIiIiIslFjX4iIiIiIiIiIiIiIiIiSc6MdwREREREREREREREREREpHvU6CciIiIiIiIiIiIiIiKS5NToJyIiIiIiIiIiIiIiIpLk1OgnIiIiIiIiIiIiIiIikuTU6CciIiIiIiIiIiIiIiKS5NToJyIiIiIiIiIiIiIiIpLk1OgnIiIiIiIiIiIiIiIikuTU6CciIiIiIiIiIiIiIiKS5P4/EascJWAgnw4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1800x1500 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define a list of numerical columns\n",
+    "numerical_columns = ['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', \n",
+    "                     'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long']\n",
+    "\n",
+    "# Plot histograms for numerical features\n",
+    "fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(18, 15))\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, column in enumerate(numerical_columns):\n",
+    "    sns.histplot(king_county_df[column], ax=axes[i], kde=True, color='skyblue', bins=30)\n",
+    "    axes[i].set_title(f' {column}')\n",
+    "    axes[i].set_xlabel(column)\n",
+    "    axes[i].set_ylabel('Frequency')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Bedrooms and Bathrooms**:\n",
+    "\n",
+    "Most houses have around 3 bedrooms and 2 bathrooms.\n",
+    "These features could be relevant for predicting house prices. More bedrooms and bathrooms might lead to higher prices.\n",
+    "\n",
+    "**Square Footage Variables**:\n",
+    "\n",
+    "Sqft_living (square footage of the home), sqft_above (square footage apart from the basement), and sqft_basement all show right-skewed distributions.\n",
+    "Smaller living spaces are more common, which could impact house prices. Larger square footage might correlate with higher prices.\n",
+    "\n",
+    "**Floors**:\n",
+    "\n",
+    "The histogram for floors indicates that single-story homes are most common.\n",
+    "The number of floors might influence house prices. Single-story homes could have different pricing dynamics than multi-story ones.\n",
+    "\n",
+    "**Year Built and Year Renovated**:\n",
+    "\n",
+    "Yr_built shows that many houses were built in recent decades.\n",
+    "\n",
+    "Yr_renovated has a large spike at zero, indicating that many homes have not been renovated.\n",
+    "These features could impact house prices. Newer homes or recently renovated ones might command higher prices.\n",
+    "\n",
+    "**Latitude and Longitude**:\n",
+    "\n",
+    "The histograms for latitude and longitude could indicate clustering by location.\n",
+    "Geographical location might play a significant role in house prices. Certain neighborhoods or regions could have higher or lower prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**UNDERSTANDING SALES PRICE**\n",
+    "\n",
+    "\n",
+    "The core challenge in this dataset is to understand the effect that many factors can have on the price a home sells for, including across geography and season.\n",
+    "\n",
+    "We are going to start by plotting the overall distribution of sale prices\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    2.153400e+04\n",
+       "mean     5.400577e+05\n",
+       "std      3.660596e+05\n",
+       "min      7.800000e+04\n",
+       "25%      3.220000e+05\n",
+       "50%      4.500000e+05\n",
+       "75%      6.450000e+05\n",
+       "max      7.700000e+06\n",
+       "Name: price, dtype: float64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df['price'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "                            \n",
+       "var gd = document.getElementById('8f2c713e-e834-43cd-b232-a1b01436a404');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_histogram_with_median_line(df, x_column):\n",
+    "    \"\"\"\n",
+    "    Plot a histogram of a column in a DataFrame with a vertical line representing the median value.\n",
+    "\n",
+    "    Parameters:\n",
+    "        df (DataFrame): The DataFrame containing the data.\n",
+    "        x_column (str): The name of the column to plot as the x-axis.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    # Plot histogram\n",
+    "    fig = px.histogram(df, x=x_column, marginal=\"box\", nbins=200, title=\"Distribution of \" + x_column)\n",
+    "    fig.update_xaxes(title_text=x_column)\n",
+    "    fig.update_yaxes(title_text='Count')\n",
+    "\n",
+    "    # Adding a vertical line to represent the median value\n",
+    "    median_value = df[x_column].median()\n",
+    "    fig.add_shape(\n",
+    "        type='line', line=dict(dash='dash'),\n",
+    "        x0=median_value, x1=median_value, y0=0, y1=1, xref='x', yref='paper'\n",
+    "    )\n",
+    "\n",
+    "    # Show the figure\n",
+    "    fig.show()\n",
+    "plot_histogram_with_median_line(king_county_df, \"price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observations: \n",
+    "1. The curve has a sharp peak on the left side, indicating that a significant number of items fall within a lower price range.\n",
+    "\n",
+    "2. The positive skewness (longer tail on the right) implies that there are some outliers with much higher prices.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skewness: 3.9741247585096726\n",
+      "Kurtosis: 33.85260123948197\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate skewness and kurtosis\n",
+    "skw = skew(king_county_df['price'])\n",
+    "kurt = kurtosis(king_county_df['price'])\n",
+    "\n",
+    "# Print the results\n",
+    "print(\"Skewness:\", skw)\n",
+    "print(\"Kurtosis:\", kurt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation: \n",
+    "in the case of right-skewed data like this, we might consider applying a transformation like the\n",
+    "logarithmic transformation to make the distribution more symmetric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+    "plot_histogram_with_median_line(king_county_df, \"price\")"
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+  },
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\"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Distribution of log(price)\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"log(price)\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x\", \"showgrid\": true, \"showticklabels\": false, \"title\": {\"text\": \"log(price)\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 0.8316], \"title\": {\"text\": \"Count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.8416, 1.0], \"matches\": \"y2\", \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\", \"title\": {\"text\": \"Count\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('4cf723d6-c2c9-42a3-8127-d91c3c7d5507');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_histogram_with_median_line(df, x_column):\n",
+    "    \"\"\"\n",
+    "    Plot a histogram of a column in a DataFrame with a vertical line representing the median value.\n",
+    "\n",
+    "    Parameters:\n",
+    "        df (DataFrame): The DataFrame containing the data.\n",
+    "        x_column (str): The name of the column to plot as the x-axis.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    # Plot histogram\n",
+    "    fig = px.histogram(df, x=np.log(df[x_column]), marginal=\"box\", nbins=200, title=\"Distribution of log(\" + x_column + \")\")\n",
+    "    fig.update_xaxes(title_text=\"log(\" + x_column + \")\")\n",
+    "    fig.update_yaxes(title_text='Count')\n",
+    "\n",
+    "    # Adding a vertical line to represent the median value\n",
+    "    median_value = np.log(df[x_column]).median()\n",
+    "    fig.add_shape(\n",
+    "        type='line', line=dict(dash='dash'),\n",
+    "        x0=median_value, x1=median_value, y0=0, y1=1, xref='x', yref='paper'\n",
+    "    )\n",
+    "\n",
+    "    # Show the figure\n",
+    "    fig.show()\n",
+    "\n",
+    "\n",
+    "plot_histogram_with_median_line(king_county_df, \"price\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observation:\n",
+    "\n",
+    "It's clear that the sale prices are heavily right-skewed—that is, the mean of the distribution tends to be to the right of the median, with a very long tail of higher-priced homes.\n",
+    "However, the prices do conform more closely to a normal distribution on the log scale.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**UNDERSTANDING SQUARE FOOTAGE**\n",
+    "\n",
+    "Now let's see what role the size of the home plays by looking at the square footage. I'm going to plot the distribution of the square footage features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Assuming king_county_df is your DataFrame and it has been properly cleaned and preprocessed\n",
+    "features = ['sqft_living', 'sqft_lot', 'sqft_above', 'sqft_basement']\n",
+    "titles = ['Square Footage of the House', 'Square Footage of the Lot',\n",
+    "          'Square Footage of Above Ground Living Space', \n",
+    "          'Square Footage of the Basement']\n",
+    "\n",
+    "for i, feature in enumerate(features):\n",
+    "    plt.figure(figsize=(10, 5))\n",
+    "    \n",
+    "    # Plotting histogram\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    sns.histplot(king_county_df[feature], kde=False, bins=30)\n",
+    "    plt.title(titles[i])\n",
+    "    \n",
+    "    # Plotting boxplot\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    sns.boxplot(x=king_county_df[feature])\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Square footage of the house**\n",
+    "\n",
+    "Most houses have a square footage between 0 and 4000 sqft, as indicated by the peak in the histogram.\n",
+    "The rapid decline in counts as square footage increases suggests that larger houses are less common.\n",
+    "Understanding this distribution is crucial for modeling because it helps us identify the typical size of houses in the dataset.\n",
+    "\n",
+    "**Square footage of the lot**\n",
+    "\n",
+    "There are several outliers on the higher end of the square footage (sqft_lot) scale. These represent houses with exceptionally large lots.\n",
+    "Handling these outliers is crucial for building an accurate predictive model.\n",
+    "may choose to remove them if they are extreme or erroneous.Or consider transforming the sqft_lot feature (e.g., log transformation) to mitigate the impact of outliers.\n",
+    "\n",
+    "**Square footage of Above Ground living space**\n",
+    "\n",
+    "Most houses have between 0 to 4000 sqft of above-ground living space.\n",
+    "There’s a rapid decline in the number of houses as we move towards higher square footage values\n",
+    "Outliers (represented by dots beyond the whiskers) are houses with unusually large square footage\n",
+    "\n",
+    "**Square footage of the Basement**\n",
+    "\n",
+    "Most houses have smaller basements, as indicated by the high count near the 0 sqft mark.\n",
+    "This could imply that smaller basements are more common or that many houses don’t have basements at all.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 Bivariate EDA\n",
+    "\n",
+    "Exploring the interplay among variables.\n",
+    "\n",
+    "Our bivariate EDA encompasses scrutinizing the connections between various features and the price."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Relationship with the features**\n",
+    "\n",
+    "Now, let’s delve deeper by employing scatter plots to visually assess the linear relationships between individual features and the target variable. This exploration will provide a more granular understanding of how each feature contributes to the predictive dynamics of the target variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Relationship with  numerical features**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x2000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_numerical_vs_price(df, numerical_columns):\n",
+    "    \"\"\"\n",
+    "    Plot numerical columns against the price column in a DataFrame.\n",
+    "\n",
+    "    Parameters:\n",
+    "        df (DataFrame): The DataFrame containing the data.\n",
+    "        numerical_columns (list): A list of numerical column names.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    num_cols = len(numerical_columns)\n",
+    "    rows = (num_cols + 2) // 3  # Adjust for odd number of columns\n",
+    "    plt.figure(figsize=(15, 5 * rows))\n",
+    "    for i, column in enumerate(numerical_columns):\n",
+    "        plt.subplot(rows, 3, i+1)\n",
+    "        sns.scatterplot(x=df[column], y=df['price'])\n",
+    "        plt.title(f'{column.capitalize()} vs Price')\n",
+    "        plt.xlabel(column.capitalize())\n",
+    "        plt.ylabel('Price')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_numerical_vs_price(king_county_df, numerical_columns)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**Bedrooms vs Price**:\n",
+    "\n",
+    "There’s an increase in price with the number of bedrooms, but it’s not linear.\n",
+    "Homes with around 5-6 bedrooms have higher variability in price\n",
+    "\n",
+    "**Bathrooms vs Price**:\n",
+    "\n",
+    "Similar to bedrooms, more bathrooms generally correlate with a higher price.\n",
+    "However, there’s significant spread in the data, indicating other factors at play.\n",
+    "\n",
+    "**Sqft_living vs Price**:\n",
+    "\n",
+    "A clear positive correlation; larger living spaces are associated with higher prices.\n",
+    "\n",
+    "**Sqft_lot vs Price**:\n",
+    "\n",
+    "The correlation is less clear. Lot size (sqft_lot) might not be as influential on price\n",
+    "\n",
+    "**Floors vs Price**:\n",
+    "\n",
+    "Houses with more floors tend to have a higher price, but the relationship isn’t strong or linear.\n",
+    "\n",
+    "**Sqft_above vs Price**:\n",
+    "\n",
+    "Similar to sqft_living, more above-ground space correlates with higher prices.\n",
+    "Include sqft_above in your model\n",
+    "\n",
+    "**Sqft_basement vs Price**:\n",
+    "\n",
+    "There’s some positive correlation, but it’s weaker than sqft_living or sqft_above.\n",
+    "Consider it as a secondary feature.\n",
+    "\n",
+    "**Yr_built vs Price**:\n",
+    "\n",
+    "Newer houses tend to cost more, but there’s considerable variation in prices of older homes.\n",
+    "Year built is relevant but not the sole determinant\n",
+    "\n",
+    "**Yr_renovated vs Price**:\n",
+    "\n",
+    "Recently renovated houses can command higher prices.\n",
+    "Many old houses haven’t been renovated yet still have high values due to other factors like location or size.\n",
+    "\n",
+    "**Zipcode vs Price**:\n",
+    "\n",
+    "Certain zip codes (areas) have considerably higher median house prices.\n",
+    "Location plays a crucial role in determining house price.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Relationship with categorical features**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def plot_categorical_vs_price(df, categorical_columns):\n",
+    "    \"\"\"\n",
+    "    Plot categorical columns against the price column in a DataFrame.\n",
+    "\n",
+    "    Parameters:\n",
+    "        df (DataFrame): The DataFrame containing the data.\n",
+    "        categorical_columns (list): A list of categorical column names.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    num_cols = len(categorical_columns)\n",
+    "    rows = (num_cols + 2) // 3  # Adjust for odd number of columns\n",
+    "    plt.figure(figsize=(15, 5 * rows))\n",
+    "    for i, column in enumerate(categorical_columns):\n",
+    "        plt.subplot(rows, 3, i+1)\n",
+    "        sns.boxplot(x=df[column], y=df['price'])\n",
+    "        plt.title(f'{column.capitalize()} vs Price')\n",
+    "        plt.xlabel(column.capitalize())\n",
+    "        plt.ylabel('Price')\n",
+    "        plt.tick_params(axis='x', labelrotation=60) \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "categorical_columns=[\"waterfront\",\"view\",\"condition\",\"grade\", \"seasons\"]\n",
+    "plot_categorical_vs_price(king_county_df, categorical_columns)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "**Waterfront vs Price**:\n",
+    "\n",
+    "Homes with waterfronts tend to be significantly more expensive than those without.\n",
+    "Having a waterfront view can be a key determinant in pricing.\n",
+    "\n",
+    "**View vs Price**:\n",
+    "\n",
+    "The quality of the view impacts the price. Homes with excellent views command higher prices.\n",
+    "Enhancing views could potentially increase a property’s value.\n",
+    "\n",
+    "**Condition vs Price**:\n",
+    "\n",
+    "The condition of the home moderately influences the price. \n",
+    "Very good and excellent conditions yield slightly higher prices, but not as significantly as other features like waterfront or view.\n",
+    "\n",
+    "**Grade vs Price**:\n",
+    "\n",
+    "There’s a strong correlation between grade and price. \n",
+    "Higher-graded homes (especially those rated as Mansion or Luxury) fetch higher prices.\n",
+    "\n",
+    "**Seasons vs price**:\n",
+    "\n",
+    "The peak season for home sales typically occurs during the spring and summer months\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Identifying the peak and off-peak seasons for house sales.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define a dictionary mapping each season to its corresponding months\n",
+    "season_to_months = {\n",
+    "    \"Spring\": [\"Mar\", \"Apr\", \"May\"],\n",
+    "    \"Summer\": [\"Jun\", \"Jul\", \"Aug\"],\n",
+    "    \"Autumn\": [\"Sep\", \"Oct\", \"Nov\"],\n",
+    "    \"Winter\": [\"Dec\", \"Jan\", \"Feb\"]\n",
+    "}\n",
+    "\n",
+    "# Group by season and calculate the mean price\n",
+    "seasonal_avg_price = king_county_df.groupby('seasons')['price'].mean()\n",
+    "\n",
+    "# Create a bar plot for the average price per season\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(seasonal_avg_price.index, seasonal_avg_price.values, color=('skyblue',\"pink\",\"yellow\",\"orange\"))\n",
+    "plt.xlabel('Season')\n",
+    "plt.ylabel('Average Price')\n",
+    "plt.title('Average Price Distribution Across Seasons')\n",
+    "\n",
+    "# Add text annotations for the months corresponding to each season\n",
+    "for season, months in season_to_months.items():\n",
+    "    month_text = ', '.join(months)\n",
+    "    plt.text(season, seasonal_avg_price[season], f\"({month_text})\", ha='center', va='bottom')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Peak Home Sales Season**\n",
+    "\n",
+    "The peak season for home sales typically occurs during the spring and summer months.\n",
+    "Specifically, the busiest home selling months are March,April, May, June, July, and August.\n",
+    "Buyers are actively searching for properties, and there’s typically increased demand.\n",
+    "\n",
+    "The slowest months for home selling activity are November, December, January, and February.\n",
+    "Demand tends to be lower during these months.\n",
+    "\n",
+    "factors Influencing Seasonality:\n",
+    "\n",
+    "**Weather** Warmer weather encourages more people to explore the housing market.\n",
+    "\n",
+    "**School Year**: Families often want to move before the start of the school year, which aligns with the spring and summer months.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5 MODELLING"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #Pairplot for visualizing relationships between numerical features\n",
+    "# sns.pairplot(king_county_df, vars=['price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors'])\n",
+    "# plt.suptitle('Pairplot of Numerical Features', y=1.02)\n",
+    "# plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**checking for Correlations**:\n",
+    "\n",
+    "\n",
+    "There is a **positive correlation** between the number of bedrooms, bathrooms, and the overall living space (sqft_living) with the price of houses. This makes sense since larger houses with more bedrooms and bathrooms tend to be more expensive.\n",
+    "\n",
+    "The sqft_lot feature does not show a strong correlation with the price. This suggests that the size of the lot (land area) may not be as significant a predictor of house prices.\n",
+    "\n",
+    "The distribution of house prices appears to be right-skewed, meaning there are few very high-priced houses in the dataset. To improve model performance, you might consider applying transformations or scaling to normalize this feature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Plot the correlation matrix of the selected numerical variables in the DataFrame.\n",
+    "\n",
+    "Parameters:\n",
+    "- df: DataFrame containing numerical variables.\n",
+    "\"\"\"\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "correlation_matrix = king_county_df.corr()\n",
+    "heatmap = sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", annot_kws={\"size\": 10})\n",
+    "plt.title('Correlation Matrix of Selected Numerical Variables', fontsize=14)\n",
+    "plt.xticks(fontsize=10)\n",
+    "plt.yticks(fontsize=10)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Extract the colorbar from the heatmap\n",
+    "cbar = heatmap.collections[0].colorbar\n",
+    "cbar.set_label('Correlation', fontsize=12)  # Set label for the colorbar\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Observations:** \n",
+    "\n",
+    "**Variables Related to Price**\n",
+    "\n",
+    "sqft_living (0.70) and bathrooms (0.53) have a strong positive correlation with price. This means that as the living area (square footage) and the number of bathrooms increase, the house price tends to increase.\n",
+    "\n",
+    "bedrooms (0.32), floors (0.26), and sqft_above (0.61) also positively correlate with price, although not as strongly.\n",
+    "\n",
+    "There is a weak negative correlation between zipcode (-0.05) and price. This suggests that the specific location (zipcode) may not significantly impact the house price.\n",
+    "\n",
+    "**Strongly Related Variables**\n",
+    "\n",
+    "sqft_living has a strong positive correlation with both bathrooms (0.76) and sqft_above (0.88). This indicates that these variables are related and might contain similar information for predicting house prices.\n",
+    "\n",
+    "Additionally, there’s a notable positive correlation between ‘bedrooms’ and ‘bathrooms’ (0.53).\n",
+    "\n",
+    "\n",
+    "**Negative Correlation**\n",
+    "\n",
+    "The year built (yr_built) has a negative relationship with the zipcode (-0.35). This means that older houses tend to be located in certain zipcodes.\n",
+    "\n",
+    "**Twin Variables**\n",
+    "\n",
+    "Given their high correlations, ‘sqft_living’ can be considered twins with ‘bathrooms’ and ‘sqft_above’. This implies that these variables share similar predictive power for house prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**CHECKING FOR MULTICOLLINEARITY**\n",
+    "\n",
+    "How does each independent variable relate with the other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "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>cc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pairs</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>(yr_built, house_age)</th>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(yr_renovated, renovated)</th>\n",
+       "      <td>0.999968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(sqft_above, sqft_living)</th>\n",
+       "      <td>0.876553</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(bathrooms, sqft_living)</th>\n",
+       "      <td>0.755277</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 cc\n",
+       "pairs                              \n",
+       "(yr_built, house_age)      1.000000\n",
+       "(yr_renovated, renovated)  0.999968\n",
+       "(sqft_above, sqft_living)  0.876553\n",
+       "(bathrooms, sqft_living)   0.755277"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def high_correlation_pairs(df, threshold=0.75):\n",
+    "    \"\"\"\n",
+    "    Find pairs of variables with high correlation in the DataFrame.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - df: DataFrame containing numerical variables.\n",
+    "    - threshold: Threshold value for correlation. Default is 0.75.\n",
+    "\n",
+    "    Returns:\n",
+    "    - DataFrame containing pairs of variables with correlation above the threshold.\n",
+    "    \"\"\"\n",
+    "    # Calculate absolute correlations\n",
+    "    df_corr = df.corr().abs().stack().reset_index().sort_values(0, ascending=False)\n",
+    "\n",
+    "    # Zip the variable name columns in a new column named \"pairs\"\n",
+    "    df_corr['pairs'] = list(zip(df_corr.level_0, df_corr.level_1))\n",
+    "\n",
+    "    # Set index to pairs\n",
+    "    df_corr.set_index(['pairs'], inplace=True)\n",
+    "\n",
+    "    # Drop level columns\n",
+    "    df_corr.drop(columns=['level_1', 'level_0'], inplace=True)\n",
+    "\n",
+    "    # Rename correlation column\n",
+    "    df_corr.columns = ['cc']\n",
+    "\n",
+    "    # Drop duplicates\n",
+    "    df_corr.drop_duplicates(inplace=True)\n",
+    "\n",
+    "    # Filter pairs with correlation above the threshold\n",
+    "    high_corr_pairs = df_corr[(df_corr.cc > threshold) & (df_corr.cc < 1)]\n",
+    "\n",
+    "    return high_corr_pairs\n",
+    "\n",
+    "high_correlation_pairs(king_county_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observations:\n",
+    "\n",
+    "The mentioned pairs demonstrate the highest correlation with each other.\n",
+    "\n",
+    "Hence, incorporating all of these variables would introduce multicollinearity into the model, prompting us to eliminate some of them"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Preparing data for modelling**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The above pairs are the most highly collerated to each other.\n",
+    "* Therefore adding all those variables will bring about multicollinearity in the model so we will drop some of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>view</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>64</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>NONE</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  sqft_living  sqft_lot  floors waterfront  view  \\\n",
+       "0  221900.0         3         1180      5650     1.0         NO  NONE   \n",
+       "1  538000.0         3         2570      7242     2.0         NO  NONE   \n",
+       "2  180000.0         2          770     10000     1.0         NO  NONE   \n",
+       "3  604000.0         4         1960      5000     1.0         NO  NONE   \n",
+       "4  510000.0         3         1680      8080     1.0         NO  NONE   \n",
+       "\n",
+       "   condition          grade  sqft_basement  zipcode      lat     long  \\\n",
+       "0    Average      7 Average            0.0    98178  47.5112 -122.257   \n",
+       "1    Average      7 Average          400.0    98125  47.7210 -122.319   \n",
+       "2    Average  6 Low Average            0.0    98028  47.7379 -122.233   \n",
+       "3  Very Good      7 Average          910.0    98136  47.5208 -122.393   \n",
+       "4    Average         8 Good            0.0    98074  47.6168 -122.045   \n",
+       "\n",
+       "   house_age  renovated  \n",
+       "0         60          0  \n",
+       "1         64          1  \n",
+       "2         82          0  \n",
+       "3         50          0  \n",
+       "4         28          0  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df.drop(['date','yr_built','sqft_above','seasons','yr_renovated', \"bathrooms\"], axis=1, inplace=True)\n",
+    "king_county_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>cc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pairs</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [cc]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "high_correlation_pairs(king_county_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One hot encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define one-hot encoding categorical functions\n",
+    "def one_hot_encode(df, catcols):\n",
+    "    '''Returns df with dummy vars and drops original column'''\n",
+    "    \n",
+    "    # Create DataFrame with above columns\n",
+    "    dfonehot = df[catcols].astype('category')\n",
+    "    \n",
+    "    # Get dummy variables and drop first one to not create dependency\n",
+    "    dfonehot = pd.get_dummies(dfonehot, drop_first = True, dtype=int)\n",
+    "    \n",
+    "    # Recreate DataFrame with one-hot encoded variables\n",
+    "    df = pd.concat([df,dfonehot], axis=1)\n",
+    "    \n",
+    "    # Drop columns where we have done one-hot encoding\n",
+    "    df = df.drop(catcols, axis = 1)\n",
+    "        \n",
+    "    return df\n",
+    "\n",
+    "columns=[\"waterfront\",'view','condition', \"grade\"]\n",
+    "\n",
+    "# Apply one_hot encoding to king_County_df\n",
+    "king_county_transform = one_hot_encode(king_county_df, columns)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21534 entries, 0 to 21596\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   price                21534 non-null  float64\n",
+      " 1   bedrooms             21534 non-null  int64  \n",
+      " 2   sqft_living          21534 non-null  int64  \n",
+      " 3   sqft_lot             21534 non-null  int64  \n",
+      " 4   floors               21534 non-null  float64\n",
+      " 5   sqft_basement        21534 non-null  float64\n",
+      " 6   zipcode              21534 non-null  int64  \n",
+      " 7   lat                  21534 non-null  float64\n",
+      " 8   long                 21534 non-null  float64\n",
+      " 9   house_age            21534 non-null  int64  \n",
+      " 10  renovated            21534 non-null  int32  \n",
+      " 11  waterfront_YES       21534 non-null  int32  \n",
+      " 12  view_EXCELLENT       21534 non-null  int32  \n",
+      " 13  view_FAIR            21534 non-null  int32  \n",
+      " 14  view_GOOD            21534 non-null  int32  \n",
+      " 15  view_NONE            21534 non-null  int32  \n",
+      " 16  condition_Fair       21534 non-null  int32  \n",
+      " 17  condition_Good       21534 non-null  int32  \n",
+      " 18  condition_Poor       21534 non-null  int32  \n",
+      " 19  condition_Very Good  21534 non-null  int32  \n",
+      " 20  grade_11 Excellent   21534 non-null  int32  \n",
+      " 21  grade_12 Luxury      21534 non-null  int32  \n",
+      " 22  grade_13 Mansion     21534 non-null  int32  \n",
+      " 23  grade_3 Poor         21534 non-null  int32  \n",
+      " 24  grade_4 Low          21534 non-null  int32  \n",
+      " 25  grade_5 Fair         21534 non-null  int32  \n",
+      " 26  grade_6 Low Average  21534 non-null  int32  \n",
+      " 27  grade_7 Average      21534 non-null  int32  \n",
+      " 28  grade_8 Good         21534 non-null  int32  \n",
+      " 29  grade_9 Better       21534 non-null  int32  \n",
+      "dtypes: float64(5), int32(20), int64(5)\n",
+      "memory usage: 4.1 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "king_county_transform.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7 Average        8948\n",
+       "8 Good           6053\n",
+       "9 Better         2604\n",
+       "6 Low Average    2031\n",
+       "10 Very Good     1130\n",
+       "11 Excellent      397\n",
+       "5 Fair            242\n",
+       "12 Luxury          88\n",
+       "4 Low              27\n",
+       "13 Mansion         13\n",
+       "3 Poor              1\n",
+       "Name: grade, dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_df[\"grade\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #one hot encoding waterfront,view and condition\n",
+    "# king_county_transform = pd.get_dummies(king_county_df, columns=[\"waterfront\",'view','condition'], dtype=int)\n",
+    "# king_county_transform = king_county_transform.drop([\"condition_Poor\",'view_NONE','waterfront_NO'], axis=1)\n",
+    "# king_county_transform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The reference categories for view will be None, for waterfront will be No and for condition will be poor condition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Label Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #Convert grade column to numeric using label encoding\n",
+    "\n",
+    "# label_encoder = LabelEncoder()\n",
+    "# king_county_transform['grade'] = label_encoder.fit_transform(king_county_transform['grade'])\n",
+    "# king_county_transform['grade'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21534 entries, 0 to 21596\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   price                21534 non-null  float64\n",
+      " 1   bedrooms             21534 non-null  int64  \n",
+      " 2   sqft_living          21534 non-null  int64  \n",
+      " 3   sqft_lot             21534 non-null  int64  \n",
+      " 4   floors               21534 non-null  float64\n",
+      " 5   sqft_basement        21534 non-null  float64\n",
+      " 6   zipcode              21534 non-null  int64  \n",
+      " 7   lat                  21534 non-null  float64\n",
+      " 8   long                 21534 non-null  float64\n",
+      " 9   house_age            21534 non-null  int64  \n",
+      " 10  renovated            21534 non-null  int32  \n",
+      " 11  waterfront_YES       21534 non-null  int32  \n",
+      " 12  view_EXCELLENT       21534 non-null  int32  \n",
+      " 13  view_FAIR            21534 non-null  int32  \n",
+      " 14  view_GOOD            21534 non-null  int32  \n",
+      " 15  view_NONE            21534 non-null  int32  \n",
+      " 16  condition_Fair       21534 non-null  int32  \n",
+      " 17  condition_Good       21534 non-null  int32  \n",
+      " 18  condition_Poor       21534 non-null  int32  \n",
+      " 19  condition_Very Good  21534 non-null  int32  \n",
+      " 20  grade_11 Excellent   21534 non-null  int32  \n",
+      " 21  grade_12 Luxury      21534 non-null  int32  \n",
+      " 22  grade_13 Mansion     21534 non-null  int32  \n",
+      " 23  grade_3 Poor         21534 non-null  int32  \n",
+      " 24  grade_4 Low          21534 non-null  int32  \n",
+      " 25  grade_5 Fair         21534 non-null  int32  \n",
+      " 26  grade_6 Low Average  21534 non-null  int32  \n",
+      " 27  grade_7 Average      21534 non-null  int32  \n",
+      " 28  grade_8 Good         21534 non-null  int32  \n",
+      " 29  grade_9 Better       21534 non-null  int32  \n",
+      "dtypes: float64(5), int32(20), int64(5)\n",
+      "memory usage: 4.1 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "king_county_transform.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade_11 Excellent</th>\n",
+       "      <th>grade_12 Luxury</th>\n",
+       "      <th>grade_13 Mansion</th>\n",
+       "      <th>grade_3 Poor</th>\n",
+       "      <th>grade_4 Low</th>\n",
+       "      <th>grade_5 Fair</th>\n",
+       "      <th>grade_6 Low Average</th>\n",
+       "      <th>grade_7 Average</th>\n",
+       "      <th>grade_8 Good</th>\n",
+       "      <th>grade_9 Better</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>60</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>64</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>82</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  sqft_living  sqft_lot  floors  sqft_basement  zipcode  \\\n",
+       "0  221900.0         3         1180      5650     1.0            0.0    98178   \n",
+       "1  538000.0         3         2570      7242     2.0          400.0    98125   \n",
+       "2  180000.0         2          770     10000     1.0            0.0    98028   \n",
+       "3  604000.0         4         1960      5000     1.0          910.0    98136   \n",
+       "4  510000.0         3         1680      8080     1.0            0.0    98074   \n",
+       "\n",
+       "       lat     long  house_age  ...  grade_11 Excellent  grade_12 Luxury  \\\n",
+       "0  47.5112 -122.257         60  ...                   0                0   \n",
+       "1  47.7210 -122.319         64  ...                   0                0   \n",
+       "2  47.7379 -122.233         82  ...                   0                0   \n",
+       "3  47.5208 -122.393         50  ...                   0                0   \n",
+       "4  47.6168 -122.045         28  ...                   0                0   \n",
+       "\n",
+       "   grade_13 Mansion  grade_3 Poor  grade_4 Low  grade_5 Fair  \\\n",
+       "0                 0             0            0             0   \n",
+       "1                 0             0            0             0   \n",
+       "2                 0             0            0             0   \n",
+       "3                 0             0            0             0   \n",
+       "4                 0             0            0             0   \n",
+       "\n",
+       "   grade_6 Low Average  grade_7 Average  grade_8 Good  grade_9 Better  \n",
+       "0                    0                1             0               0  \n",
+       "1                    0                1             0               0  \n",
+       "2                    1                0             0               0  \n",
+       "3                    0                1             0               0  \n",
+       "4                    0                0             1               0  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_transform.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price                  1.000000\n",
+       "bedrooms               0.315229\n",
+       "sqft_living            0.701587\n",
+       "sqft_lot               0.090338\n",
+       "floors                 0.257052\n",
+       "sqft_basement          0.319082\n",
+       "zipcode               -0.053620\n",
+       "lat                    0.307868\n",
+       "long                   0.022417\n",
+       "house_age             -0.054273\n",
+       "renovated              0.117668\n",
+       "waterfront_YES         0.259220\n",
+       "view_EXCELLENT         0.304674\n",
+       "view_FAIR              0.093147\n",
+       "view_GOOD              0.183962\n",
+       "view_NONE             -0.358311\n",
+       "condition_Fair        -0.051633\n",
+       "condition_Good        -0.032003\n",
+       "condition_Poor        -0.019963\n",
+       "condition_Very Good    0.057674\n",
+       "grade_11 Excellent     0.358257\n",
+       "grade_12 Luxury        0.284765\n",
+       "grade_13 Mansion       0.212890\n",
+       "grade_3 Poor          -0.005177\n",
+       "grade_4 Low           -0.031754\n",
+       "grade_5 Fair          -0.084908\n",
+       "grade_6 Low Average   -0.209960\n",
+       "grade_7 Average       -0.316713\n",
+       "grade_8 Good           0.005135\n",
+       "grade_9 Better         0.237301\n",
+       "Name: price, dtype: float64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_transform.corr()['price']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Sqft_living has the strongest positive correlation with price\n",
+    "* Sqft_basement, bedrooms and view_EXCELLENT has low positive correlation with price\n",
+    "* Grade, Age and condition have weak negative correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 REGRESSION MODELLING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before beginning the modelling step, it is important that we consider what our goals are and what metrics of evaluation we will use. \n",
+    "\n",
+    "As a starting point, we are looking to establish the following models, each meeting different criteria.\n",
+    "\n",
+    "**Model A**\n",
+    "\n",
+    "This model will be generalisable. We will aim where possible to ensure it can be used as a basis towards creating a model for another area, so avoid features specific to King County such as exact `zipcode`. Provided we achieve a decent $R^2$, we will try and avoid interactions and/or polynomial regression. We will also try and limit the number of features if possible.\n",
+    "\n",
+    "**Model B**\n",
+    "\n",
+    "This model will be the more accurate whilst avoiding unecessary complexity.\n",
+    "\n",
+    "**Model C**\n",
+    "\n",
+    "This model will be our more accurate than model B and most likely complex. \n",
+    "\n",
+    "**Model D**\n",
+    "\n",
+    "This model will be our most accurate and most likely complex. We will aim for the highest adjusted $R^2$ value and lowest Root Mean Squared Error (RMSE) for model C.\n",
+    "\n",
+    "For all models, we only wish to have statistically significant features (p-value below 0.05)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1 Simple linear regression-Model A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will begin with a simple linear regression model, using the single feature of `sqft_living` which looked to be a good predictor based on satisfying the linearity assumption and being positively correlated with price."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define X_train and y_train. As single feature need to reshape X_train1 into column vector\n",
+    "X_train1 = np.array(king_county_transform['sqft_living']).reshape(-1,1)\n",
+    "y_train1 = king_county_transform['price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.492</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.492</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.087e+04</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Fri, 03 May 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:48:29</td>     <th>  Log-Likelihood:    </th> <td>-2.9912e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td> 21534</td>      <th>  AIC:               </th>  <td>5.982e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td> 21532</td>      <th>  BIC:               </th>  <td>5.983e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th> <td>-4.215e+04</td> <td> 4404.521</td> <td>   -9.570</td> <td> 0.000</td> <td>-5.08e+04</td> <td>-3.35e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>x1</th>    <td>  279.9321</td> <td>    1.938</td> <td>  144.473</td> <td> 0.000</td> <td>  276.134</td> <td>  283.730</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>14582.265</td> <th>  Durbin-Watson:     </th>  <td>   1.981</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>516142.289</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 2.781</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>26.331</td>   <th>  Cond. No.          </th>  <td>5.63e+03</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.63e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.492\n",
+       "Model:                            OLS   Adj. R-squared:                  0.492\n",
+       "Method:                 Least Squares   F-statistic:                 2.087e+04\n",
+       "Date:                Fri, 03 May 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:48:29   Log-Likelihood:            -2.9912e+05\n",
+       "No. Observations:               21534   AIC:                         5.982e+05\n",
+       "Df Residuals:                   21532   BIC:                         5.983e+05\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const      -4.215e+04   4404.521     -9.570      0.000   -5.08e+04   -3.35e+04\n",
+       "x1           279.9321      1.938    144.473      0.000     276.134     283.730\n",
+       "==============================================================================\n",
+       "Omnibus:                    14582.265   Durbin-Watson:                   1.981\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           516142.289\n",
+       "Skew:                           2.781   Prob(JB):                         0.00\n",
+       "Kurtosis:                      26.331   Cond. No.                     5.63e+03\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 5.63e+03. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create model in OLS\n",
+    "def  modeling_function(X,y):\n",
+    "    X_int = sm.add_constant(X)\n",
+    "    model = sm.OLS(y, X_int).fit()\n",
+    "    summary = model.summary()\n",
+    "    return model, summary, X_int\n",
+    "model_A=modeling_function(X_train1, y_train1)\n",
+    "model_A[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As one might expect, using a single feature did not provide a strong model. \n",
+    "\n",
+    "The $R^2$ metric indicates that only 49% of the variance can be explained by our model.\n",
+    "\n",
+    "Let us evaluate it by computing the RMSE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([264588.73740414, 281006.60140913, 248466.74475835, 283185.23488167,\n",
+       "        245037.59908383, 248515.39972768, 248475.17382312, 258382.33354171,\n",
+       "        261821.3018611 , 267529.86457734]),\n",
+       " 260700.8991068073,\n",
+       " 12958.457701152043)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Function to get the RMSE for the regression performed\n",
+    "def rmse_function(x,y):\n",
+    "    # Create linear regression object\n",
+    "    linreg = LinearRegression()\n",
+    "\n",
+    "    # Fit on training data\n",
+    "    linreg.fit(x, y)\n",
+    "\n",
+    "    # Evaluate using rmse\n",
+    "    scores = cross_val_score(\n",
+    "                linreg, \n",
+    "                x,\n",
+    "                y,\n",
+    "                cv=10,\n",
+    "                scoring=\"neg_mean_squared_error\"\n",
+    "    )\n",
+    "    rmse_scores= np.sqrt(-scores)\n",
+    "    return rmse_scores,rmse_scores.mean(),rmse_scores.std()\n",
+    "\n",
+    "rmse1=rmse_function(X_train1, y_train1)\n",
+    "rmse1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can interpret the RMSE as the mean error in USD, which is approximately USD 260,700. This means that, on average, the actual price will deviate by around USD 260,700 from our predicted price. Despite using just one feature, this level of error is reasonable. Additionally, the standard deviation of approximately USD 12,958 indicates that our model doesn't appear to be overfitting.\n",
+    "\n",
+    "The coefficient of `sqft_living` is 279.93, suggesting that for every additional square foot of living area, the price increases by approximately USD 279.93. In other words, if house A has a living area 1 sqft larger than house B, all other features being equal, house A will cost around USD 279 more.\n",
+    "\n",
+    "One advantage of simple linear regression is its visual interpretability. Let's create a visualization illustrating the data points alongside our regression line.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create visualisation of simple linear regression\n",
+    "\n",
+    "def millions(x, pos):\n",
+    "    \"\"\"The two arguments are the value and tick position.\"\"\"\n",
+    "    return f'${x*1e-6:1.1f}M'\n",
+    "\n",
+    "linreg = LinearRegression()\n",
+    "# Fit on training data\n",
+    "linreg.fit(X_train1, y_train1)\n",
+    "# Get predictions\n",
+    "y_pred = linreg.predict(X_train1)\n",
+    "# Plot data points\n",
+    "fig,ax=plt.subplots(figsize=(8,8))\n",
+    "plt.scatter(x = X_train1[:2000], y = y_train1[:2000], alpha = 0.3, label = 'Data Points')\n",
+    "\n",
+    "# Plot regression line\n",
+    "plt.plot(X_train1, y_pred, color = 'red', label = 'Regression Line')\n",
+    "ax.yaxis.set_major_formatter(millions)\n",
+    "plt.legend()\n",
+    "plt.title('Simple linear regression using sqft_living feature')\n",
+    "plt.xlabel('Living area (sqft)')\n",
+    "plt.ylabel('Price (USD)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that for smaller living areas our model looks decent but as the `sqft_living` value increases our model's performance declines, signifiying that `sqft_living` is not a good enough predictor for larger houses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will want to keep track of our models so let us build a table which will have the name of the model and key metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "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>Model</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Num Features</th>\n",
+       "      <th>R-squared</th>\n",
+       "      <th>Adj R-squared</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>RMSE sd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Model-A</td>\n",
+       "      <td>Sqft_living</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.492</td>\n",
+       "      <td>0.492</td>\n",
+       "      <td>260700.0</td>\n",
+       "      <td>12958.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Model  Description   Num Features  R-squared  Adj R-squared      RMSE  \\\n",
+       "0  Model-A  Sqft_living            1.0      0.492          0.492  260700.0   \n",
+       "\n",
+       "   RMSE sd  \n",
+       "0  12958.0  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define table as a dataframe with specific columns. Each column's values will be a list that we will add to.\n",
+    "evaluationtable = pd.DataFrame({'Model': [], 'Description':[], ' Num Features' : [], 'R-squared':[],\n",
+    "                                'Adj R-squared':[], 'RMSE': [],'RMSE sd':[]})\n",
+    "\n",
+    "# Add data for simple linear regression\n",
+    "evaluationtable.loc[0] = ['Model-A', 'Sqft_living', model_A[0].df_model, round(model_A[0].rsquared,3)\n",
+    "                          ,round(model_A[0].rsquared_adj,3), int(rmse1[1]), int(rmse1[2]) ]\n",
+    "\n",
+    "# View our evaluation table\n",
+    "evaluationtable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2 Multiple Linear Regression - Model B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For our next model, we will add more features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th>  <td>   0.727</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.727</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1978.</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Fri, 03 May 2024</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>11:48:29</td>     <th>  Log-Likelihood:    </th> <td>-2.9243e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td> 21534</td>      <th>  AIC:               </th>  <td>5.849e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td> 21504</td>      <th>  BIC:               </th>  <td>5.852e+05</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>    29</td>      <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>               <td> 9.125e+06</td> <td> 2.71e+06</td> <td>    3.361</td> <td> 0.001</td> <td>  3.8e+06</td> <td> 1.44e+07</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>bedrooms</th>            <td>-1.683e+04</td> <td> 1892.295</td> <td>   -8.896</td> <td> 0.000</td> <td>-2.05e+04</td> <td>-1.31e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_living</th>         <td>  158.8588</td> <td>    3.184</td> <td>   49.892</td> <td> 0.000</td> <td>  152.618</td> <td>  165.100</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_lot</th>            <td>   -0.0603</td> <td>    0.033</td> <td>   -1.816</td> <td> 0.069</td> <td>   -0.125</td> <td>    0.005</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>floors</th>              <td> 3.599e+04</td> <td> 3325.502</td> <td>   10.821</td> <td> 0.000</td> <td> 2.95e+04</td> <td> 4.25e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sqft_basement</th>       <td>    2.7315</td> <td>    4.104</td> <td>    0.665</td> <td> 0.506</td> <td>   -5.314</td> <td>   10.777</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>zipcode</th>             <td> -641.0210</td> <td>   31.469</td> <td>  -20.370</td> <td> 0.000</td> <td> -702.702</td> <td> -579.340</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>lat</th>                 <td> 6.231e+05</td> <td> 1.02e+04</td> <td>   60.895</td> <td> 0.000</td> <td> 6.03e+05</td> <td> 6.43e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>long</th>                <td>-2.011e+05</td> <td> 1.24e+04</td> <td>  -16.278</td> <td> 0.000</td> <td>-2.25e+05</td> <td>-1.77e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>house_age</th>           <td> 1899.6369</td> <td>   66.662</td> <td>   28.497</td> <td> 0.000</td> <td> 1768.975</td> <td> 2030.299</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>renovated</th>           <td> 7.672e+04</td> <td> 7530.261</td> <td>   10.188</td> <td> 0.000</td> <td>  6.2e+04</td> <td> 9.15e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>waterfront_YES</th>      <td> 5.233e+05</td> <td> 1.95e+04</td> <td>   26.821</td> <td> 0.000</td> <td> 4.85e+05</td> <td> 5.62e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view_EXCELLENT</th>      <td> 2.002e+05</td> <td> 1.46e+04</td> <td>   13.759</td> <td> 0.000</td> <td> 1.72e+05</td> <td> 2.29e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view_FAIR</th>           <td> 4.883e+04</td> <td> 1.22e+04</td> <td>    3.991</td> <td> 0.000</td> <td> 2.48e+04</td> <td> 7.28e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view_GOOD</th>           <td> 6.976e+04</td> <td> 1.06e+04</td> <td>    6.611</td> <td> 0.000</td> <td> 4.91e+04</td> <td> 9.04e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>view_NONE</th>           <td>-7.885e+04</td> <td> 6523.209</td> <td>  -12.088</td> <td> 0.000</td> <td>-9.16e+04</td> <td>-6.61e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition_Fair</th>      <td>-7989.4259</td> <td> 1.49e+04</td> <td>   -0.535</td> <td> 0.593</td> <td>-3.73e+04</td> <td> 2.13e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition_Good</th>      <td> 2.839e+04</td> <td> 3322.475</td> <td>    8.545</td> <td> 0.000</td> <td> 2.19e+04</td> <td> 3.49e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition_Poor</th>      <td>-5.762e+04</td> <td> 3.59e+04</td> <td>   -1.607</td> <td> 0.108</td> <td>-1.28e+05</td> <td> 1.27e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>condition_Very Good</th> <td> 7.598e+04</td> <td> 5274.609</td> <td>   14.405</td> <td> 0.000</td> <td> 6.56e+04</td> <td> 8.63e+04</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_11 Excellent</th>  <td> 2.521e+05</td> <td> 1.14e+04</td> <td>   22.099</td> <td> 0.000</td> <td>  2.3e+05</td> <td> 2.75e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_12 Luxury</th>     <td> 6.839e+05</td> <td> 2.19e+04</td> <td>   31.294</td> <td> 0.000</td> <td> 6.41e+05</td> <td> 7.27e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_13 Mansion</th>    <td>   1.9e+06</td> <td> 5.44e+04</td> <td>   34.901</td> <td> 0.000</td> <td> 1.79e+06</td> <td> 2.01e+06</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_3 Poor</th>        <td>-2.537e+05</td> <td> 1.92e+05</td> <td>   -1.323</td> <td> 0.186</td> <td>-6.29e+05</td> <td> 1.22e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_4 Low</th>         <td>-4.197e+05</td> <td> 3.81e+04</td> <td>  -11.007</td> <td> 0.000</td> <td>-4.94e+05</td> <td>-3.45e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_5 Fair</th>        <td>-4.343e+05</td> <td> 1.54e+04</td> <td>  -28.278</td> <td> 0.000</td> <td>-4.64e+05</td> <td>-4.04e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_6 Low Average</th> <td>-4.111e+05</td> <td> 9547.227</td> <td>  -43.061</td> <td> 0.000</td> <td> -4.3e+05</td> <td>-3.92e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_7 Average</th>     <td>-3.629e+05</td> <td> 7872.608</td> <td>  -46.098</td> <td> 0.000</td> <td>-3.78e+05</td> <td>-3.47e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_8 Good</th>        <td>-2.928e+05</td> <td> 7177.486</td> <td>  -40.788</td> <td> 0.000</td> <td>-3.07e+05</td> <td>-2.79e+05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>grade_9 Better</th>      <td>-1.687e+05</td> <td> 7042.823</td> <td>  -23.949</td> <td> 0.000</td> <td>-1.82e+05</td> <td>-1.55e+05</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>14127.438</td> <th>  Durbin-Watson:     </th>  <td>   1.984</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>704825.278</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>           <td> 2.533</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>       <td>30.566</td>   <th>  Cond. No.          </th>  <td>2.07e+08</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.07e+08. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  price   R-squared:                       0.727\n",
+       "Model:                            OLS   Adj. R-squared:                  0.727\n",
+       "Method:                 Least Squares   F-statistic:                     1978.\n",
+       "Date:                Fri, 03 May 2024   Prob (F-statistic):               0.00\n",
+       "Time:                        11:48:29   Log-Likelihood:            -2.9243e+05\n",
+       "No. Observations:               21534   AIC:                         5.849e+05\n",
+       "Df Residuals:                   21504   BIC:                         5.852e+05\n",
+       "Df Model:                          29                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "=======================================================================================\n",
+       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "---------------------------------------------------------------------------------------\n",
+       "const                9.125e+06   2.71e+06      3.361      0.001     3.8e+06    1.44e+07\n",
+       "bedrooms            -1.683e+04   1892.295     -8.896      0.000   -2.05e+04   -1.31e+04\n",
+       "sqft_living           158.8588      3.184     49.892      0.000     152.618     165.100\n",
+       "sqft_lot               -0.0603      0.033     -1.816      0.069      -0.125       0.005\n",
+       "floors               3.599e+04   3325.502     10.821      0.000    2.95e+04    4.25e+04\n",
+       "sqft_basement           2.7315      4.104      0.665      0.506      -5.314      10.777\n",
+       "zipcode              -641.0210     31.469    -20.370      0.000    -702.702    -579.340\n",
+       "lat                  6.231e+05   1.02e+04     60.895      0.000    6.03e+05    6.43e+05\n",
+       "long                -2.011e+05   1.24e+04    -16.278      0.000   -2.25e+05   -1.77e+05\n",
+       "house_age            1899.6369     66.662     28.497      0.000    1768.975    2030.299\n",
+       "renovated            7.672e+04   7530.261     10.188      0.000     6.2e+04    9.15e+04\n",
+       "waterfront_YES       5.233e+05   1.95e+04     26.821      0.000    4.85e+05    5.62e+05\n",
+       "view_EXCELLENT       2.002e+05   1.46e+04     13.759      0.000    1.72e+05    2.29e+05\n",
+       "view_FAIR            4.883e+04   1.22e+04      3.991      0.000    2.48e+04    7.28e+04\n",
+       "view_GOOD            6.976e+04   1.06e+04      6.611      0.000    4.91e+04    9.04e+04\n",
+       "view_NONE           -7.885e+04   6523.209    -12.088      0.000   -9.16e+04   -6.61e+04\n",
+       "condition_Fair      -7989.4259   1.49e+04     -0.535      0.593   -3.73e+04    2.13e+04\n",
+       "condition_Good       2.839e+04   3322.475      8.545      0.000    2.19e+04    3.49e+04\n",
+       "condition_Poor      -5.762e+04   3.59e+04     -1.607      0.108   -1.28e+05    1.27e+04\n",
+       "condition_Very Good  7.598e+04   5274.609     14.405      0.000    6.56e+04    8.63e+04\n",
+       "grade_11 Excellent   2.521e+05   1.14e+04     22.099      0.000     2.3e+05    2.75e+05\n",
+       "grade_12 Luxury      6.839e+05   2.19e+04     31.294      0.000    6.41e+05    7.27e+05\n",
+       "grade_13 Mansion       1.9e+06   5.44e+04     34.901      0.000    1.79e+06    2.01e+06\n",
+       "grade_3 Poor        -2.537e+05   1.92e+05     -1.323      0.186   -6.29e+05    1.22e+05\n",
+       "grade_4 Low         -4.197e+05   3.81e+04    -11.007      0.000   -4.94e+05   -3.45e+05\n",
+       "grade_5 Fair        -4.343e+05   1.54e+04    -28.278      0.000   -4.64e+05   -4.04e+05\n",
+       "grade_6 Low Average -4.111e+05   9547.227    -43.061      0.000    -4.3e+05   -3.92e+05\n",
+       "grade_7 Average     -3.629e+05   7872.608    -46.098      0.000   -3.78e+05   -3.47e+05\n",
+       "grade_8 Good        -2.928e+05   7177.486    -40.788      0.000   -3.07e+05   -2.79e+05\n",
+       "grade_9 Better      -1.687e+05   7042.823    -23.949      0.000   -1.82e+05   -1.55e+05\n",
+       "==============================================================================\n",
+       "Omnibus:                    14127.438   Durbin-Watson:                   1.984\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           704825.278\n",
+       "Skew:                           2.533   Prob(JB):                         0.00\n",
+       "Kurtosis:                      30.566   Cond. No.                     2.07e+08\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "[2] The condition number is large, 2.07e+08. This might indicate that there are\n",
+       "strong multicollinearity or other numerical problems.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Redefine X_train and y_train\n",
+    "X_train2 = king_county_transform.drop(['price'], axis = 1)\n",
+    "y_train2 = king_county_transform['price']\n",
+    "\n",
+    "# Calling on fucntion to provide model summary\n",
+    "model_B=modeling_function(X_train2,y_train2)\n",
+    "model_B[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We note that all p-values are below our threshold of 0.05. Compare to single linear regression, our model's accuracy has improved considerably.The R-Squared is now 68%\n",
+    "\n",
+    "Let us investigate if the residuals are normally distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy.stats as stats\n",
+    "# QQ-plot of residuals\n",
+    "residuals = model_B[0].resid\n",
+    "fig = sm.graphics.qqplot(residuals, dist=stats.norm, alpha = 0.3, line='45', fit=True)\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There appears to be some issues with the residuals not being normally distributed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us check homoscedasticity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatterplot of predictions\n",
+    "\n",
+    "def millions(x, pos):\n",
+    "    \"\"\"The two arguments are the value and tick position.\"\"\"\n",
+    "    return f'${x*1e-6:1.1f}M'\n",
+    "fig,ax=plt.subplots(figsize=(8,8))\n",
+    "\n",
+    "plt.scatter(model_B[0].predict(model_B[2]), model_B[0].resid, alpha = 0.3)\n",
+    "plt.plot(model_B[0].predict(model_B[2]), [0 for i in range(len(X_train2))])\n",
+    "ax.yaxis.set_major_formatter(millions)\n",
+    "ax.xaxis.set_major_formatter(millions)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whilst not ideal, there is no strong evidence of heteroscedasticity. As such we might not need to consider a log transformation of the target variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now evaluate the model using sci-kit learn's `cross_val_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([196810.31701434, 211004.3512284 , 184790.53497195, 203925.210456  ,\n",
+       "        174246.24274081, 186708.33230201, 192134.64315246, 189112.84023086,\n",
+       "        189596.80332984, 200901.60526937]),\n",
+       " 192923.08806960372,\n",
+       " 10032.626897883789)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rmse2=rmse_function(X_train2, y_train2)\n",
+    "rmse2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The RMSE has reduced and the mean error is now around $207,000. Our standard deviation remains low."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us add this model to our evaluation table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "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>Model</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Num Features</th>\n",
+       "      <th>R-squared</th>\n",
+       "      <th>Adj R-squared</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>RMSE sd</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Model-A</td>\n",
+       "      <td>Sqft_living</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.492</td>\n",
+       "      <td>0.492</td>\n",
+       "      <td>260700.0</td>\n",
+       "      <td>12958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Model-B</td>\n",
+       "      <td>Limited one-hot encoding</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0.727</td>\n",
+       "      <td>0.727</td>\n",
+       "      <td>192923.0</td>\n",
+       "      <td>10032.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Model               Description   Num Features  R-squared  Adj R-squared  \\\n",
+       "0  Model-A               Sqft_living            1.0      0.492          0.492   \n",
+       "1  Model-B  Limited one-hot encoding           29.0      0.727          0.727   \n",
+       "\n",
+       "       RMSE  RMSE sd  \n",
+       "0  260700.0  12958.0  \n",
+       "1  192923.0  10032.0  "
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add metric to evaluation table\n",
+    "evaluationtable.loc[1] = ['Model-B', 'Limited one-hot encoding', model_B[0].df_model, round(model_B[0].rsquared,3)\n",
+    "                          ,round(model_B[0].rsquared_adj,3), int(rmse2[1]), int(rmse2[2])]\n",
+    "\n",
+    "# View our evaluation table, sorted by Adj R-squared\n",
+    "evaluationtable.sort_values(by = 'Adj R-squared', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "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>price</th>\n",
+       "      <th>bedrooms</th>\n",
+       "      <th>sqft_living</th>\n",
+       "      <th>sqft_lot</th>\n",
+       "      <th>floors</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>zipcode</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>...</th>\n",
+       "      <th>grade_11 Excellent</th>\n",
+       "      <th>grade_12 Luxury</th>\n",
+       "      <th>grade_13 Mansion</th>\n",
+       "      <th>grade_3 Poor</th>\n",
+       "      <th>grade_4 Low</th>\n",
+       "      <th>grade_5 Fair</th>\n",
+       "      <th>grade_6 Low Average</th>\n",
+       "      <th>grade_7 Average</th>\n",
+       "      <th>grade_8 Good</th>\n",
+       "      <th>grade_9 Better</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>221900.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>5650</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98178</td>\n",
+       "      <td>47.5112</td>\n",
+       "      <td>-122.257</td>\n",
+       "      <td>60</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>538000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2570</td>\n",
+       "      <td>7242</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>98125</td>\n",
+       "      <td>47.7210</td>\n",
+       "      <td>-122.319</td>\n",
+       "      <td>64</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>770</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98028</td>\n",
+       "      <td>47.7379</td>\n",
+       "      <td>-122.233</td>\n",
+       "      <td>82</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>604000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>98136</td>\n",
+       "      <td>47.5208</td>\n",
+       "      <td>-122.393</td>\n",
+       "      <td>50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>510000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>8080</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>98074</td>\n",
+       "      <td>47.6168</td>\n",
+       "      <td>-122.045</td>\n",
+       "      <td>28</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      price  bedrooms  sqft_living  sqft_lot  floors  sqft_basement  zipcode  \\\n",
+       "0  221900.0         3         1180      5650     1.0            0.0    98178   \n",
+       "1  538000.0         3         2570      7242     2.0          400.0    98125   \n",
+       "2  180000.0         2          770     10000     1.0            0.0    98028   \n",
+       "3  604000.0         4         1960      5000     1.0          910.0    98136   \n",
+       "4  510000.0         3         1680      8080     1.0            0.0    98074   \n",
+       "\n",
+       "       lat     long  house_age  ...  grade_11 Excellent  grade_12 Luxury  \\\n",
+       "0  47.5112 -122.257         60  ...                   0                0   \n",
+       "1  47.7210 -122.319         64  ...                   0                0   \n",
+       "2  47.7379 -122.233         82  ...                   0                0   \n",
+       "3  47.5208 -122.393         50  ...                   0                0   \n",
+       "4  47.6168 -122.045         28  ...                   0                0   \n",
+       "\n",
+       "   grade_13 Mansion  grade_3 Poor  grade_4 Low  grade_5 Fair  \\\n",
+       "0                 0             0            0             0   \n",
+       "1                 0             0            0             0   \n",
+       "2                 0             0            0             0   \n",
+       "3                 0             0            0             0   \n",
+       "4                 0             0            0             0   \n",
+       "\n",
+       "   grade_6 Low Average  grade_7 Average  grade_8 Good  grade_9 Better  \n",
+       "0                    0                1             0               0  \n",
+       "1                    0                1             0               0  \n",
+       "2                    1                0             0               0  \n",
+       "3                    0                1             0               0  \n",
+       "4                    0                0             1               0  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "king_county_transform.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 21534 entries, 0 to 21596\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   price                21534 non-null  float64\n",
+      " 1   bedrooms             21534 non-null  int64  \n",
+      " 2   sqft_living          21534 non-null  int64  \n",
+      " 3   sqft_lot             21534 non-null  int64  \n",
+      " 4   floors               21534 non-null  float64\n",
+      " 5   sqft_basement        21534 non-null  float64\n",
+      " 6   zipcode              21534 non-null  int64  \n",
+      " 7   lat                  21534 non-null  float64\n",
+      " 8   long                 21534 non-null  float64\n",
+      " 9   house_age            21534 non-null  int64  \n",
+      " 10  renovated            21534 non-null  int32  \n",
+      " 11  waterfront_YES       21534 non-null  int32  \n",
+      " 12  view_EXCELLENT       21534 non-null  int32  \n",
+      " 13  view_FAIR            21534 non-null  int32  \n",
+      " 14  view_GOOD            21534 non-null  int32  \n",
+      " 15  view_NONE            21534 non-null  int32  \n",
+      " 16  condition_Fair       21534 non-null  int32  \n",
+      " 17  condition_Good       21534 non-null  int32  \n",
+      " 18  condition_Poor       21534 non-null  int32  \n",
+      " 19  condition_Very Good  21534 non-null  int32  \n",
+      " 20  grade_11 Excellent   21534 non-null  int32  \n",
+      " 21  grade_12 Luxury      21534 non-null  int32  \n",
+      " 22  grade_13 Mansion     21534 non-null  int32  \n",
+      " 23  grade_3 Poor         21534 non-null  int32  \n",
+      " 24  grade_4 Low          21534 non-null  int32  \n",
+      " 25  grade_5 Fair         21534 non-null  int32  \n",
+      " 26  grade_6 Low Average  21534 non-null  int32  \n",
+      " 27  grade_7 Average      21534 non-null  int32  \n",
+      " 28  grade_8 Good         21534 non-null  int32  \n",
+      " 29  grade_9 Better       21534 non-null  int32  \n",
+      "dtypes: float64(5), int32(20), int64(5)\n",
+      "memory usage: 4.1 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "king_county_transform.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3 Multiple linear regression-Model 3\n",
+    "This model uses th dfnehot dataframe that had been further encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create copy to work with\n",
+    "dfgeo = king_county_transform.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Assessing the geographic distribution of our house sales.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+      ],
+      "text/plain": [
+       "<folium.folium.Map at 0x2541582e1f0>"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def plot_zipcode_locations(df, lat_column='lat', long_column='long', zipcode_column='zipcode'):\n",
+    "    \"\"\"\n",
+    "    Plot the locations of zip codes on a map using Folium.\n",
+    "\n",
+    "    Parameters:\n",
+    "        df (DataFrame): The DataFrame containing the data.\n",
+    "        lat_column (str): The name of the column containing latitude values. Default is 'lat'.\n",
+    "        long_column (str): The name of the column containing longitude values. Default is 'long'.\n",
+    "        zipcode_column (str): The name of the column containing zipcode values. Default is 'zipcode'.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "    # Group the data by zipcode and calculate the mean latitude and longitude\n",
+    "    zipcode_data = df.groupby(zipcode_column).agg({lat_column: 'mean', long_column: 'mean'}).reset_index()\n",
+    "\n",
+    "    # Create a map centered at the mean latitude and longitude of all the zipcodes\n",
+    "    m = folium.Map(location=[df[lat_column].mean(), df[long_column].mean()], zoom_start=10)\n",
+    "\n",
+    "    # Add markers for each zipcode\n",
+    "    for _, row in zipcode_data.iterrows():\n",
+    "        folium.Marker(location=[row[lat_column], row[long_column]], popup=row[zipcode_column]).add_to(m)\n",
+    "\n",
+    "    # Display the map\n",
+    "    return m\n",
+    "\n",
+    "plot_zipcode_locations(king_county_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "tuple indices must be integers or slices, not str",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [92]\u001b[0m, in \u001b[0;36m<cell line: 23>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m df\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Add neighbourhood feature\u001b[39;00m\n\u001b[1;32m---> 23\u001b[0m dfgeo \u001b[38;5;241m=\u001b[39m \u001b[43mneighbourhood_feat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdfgeo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     25\u001b[0m dfgeo\n",
+      "Input \u001b[1;32mIn [92]\u001b[0m, in \u001b[0;36mneighbourhood_feat\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m'''Adds neighbourhood feature using reverse_geocoder'''\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Define coord column with lat and long\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoord\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m, df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Use reverse geocoder\u001b[39;00m\n\u001b[0;32m     10\u001b[0m results \u001b[38;5;241m=\u001b[39m rg\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;28mlist\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoord\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n",
+      "\u001b[1;31mTypeError\u001b[0m: tuple indices must be integers or slices, not str"
+     ]
+    }
+   ],
+   "source": [
+    "import reverse_geocoder as rg\n",
+    "#Define function to add neighbourhood\n",
+    "def neighbourhood_feat(df):\n",
+    "    '''Adds neighbourhood feature using reverse_geocoder'''\n",
+    "    \n",
+    "    # Define coord column with lat and long\n",
+    "    df['coord'] = list(zip(df['lat'], df['long']))\n",
+    "    \n",
+    "    # Use reverse geocoder\n",
+    "    results = rg.search(list(df['coord']))\n",
+    "    \n",
+    "    # Define neighbourhood list\n",
+    "    neighbourhoods = [results[i]['name'] for i in range(0, len(results))]\n",
+    "    \n",
+    "    # Add neighbourhood feature\n",
+    "    df['neighbourhood'] = neighbourhoods\n",
+    "    \n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "# Add neighbourhood feature\n",
+    "dfgeo = neighbourhood_feat(dfgeo)\n",
+    "\n",
+    "dfgeo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Bryn Mawr-Skyway',\n",
+       " 'Shoreline',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'White Center',\n",
+       " 'City of Sammamish',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Federal Way',\n",
+       " 'White Center',\n",
+       " 'Maple Valley',\n",
+       " 'Eastgate',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Kenmore',\n",
+       " 'City of Sammamish',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Duvall',\n",
+       " 'Seattle',\n",
+       " 'Auburn',\n",
+       " 'Federal Way',\n",
+       " 'Shoreline',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Lea Hill',\n",
+       " 'Lea Hill',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Auburn',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Lake Forest Park',\n",
+       " 'Redmond',\n",
+       " 'Issaquah',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'Lakeland North',\n",
+       " 'Newcastle',\n",
+       " 'City of Sammamish',\n",
+       " 'Normandy Park',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Seattle',\n",
+       " 'Fairwood',\n",
+       " 'Duvall',\n",
+       " 'Fife Heights',\n",
+       " 'Eastgate',\n",
+       " 'Seattle',\n",
+       " 'Klahanie',\n",
+       " 'Normandy Park',\n",
+       " 'Renton',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Medina',\n",
+       " 'Seattle',\n",
+       " 'Covington',\n",
+       " 'Covington',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Newcastle',\n",
+       " 'Normandy Park',\n",
+       " 'Normandy Park',\n",
+       " 'Normandy Park',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Newport',\n",
+       " 'Lakeland North',\n",
+       " 'Covington',\n",
+       " 'Clyde Hill',\n",
+       " 'Redmond',\n",
+       " 'Kingsgate',\n",
+       " 'Lake Forest Park',\n",
+       " 'Maple Valley',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Ames Lake',\n",
+       " 'West Lake Sammamish',\n",
+       " 'White Center',\n",
+       " 'Shoreline',\n",
+       " 'Black Diamond',\n",
+       " 'Ravensdale',\n",
+       " 'Maple Valley',\n",
+       " 'Boulevard Park',\n",
+       " 'Fairwood',\n",
+       " 'Lake Forest Park',\n",
+       " 'Seattle',\n",
+       " 'Lea Hill',\n",
+       " 'Renton',\n",
+       " 'Renton',\n",
+       " 'White Center',\n",
+       " 'Fife Heights',\n",
+       " 'Seattle',\n",
+       " 'Federal Way',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Mercer Island',\n",
+       " 'Lakeland North',\n",
+       " 'Fife Heights',\n",
+       " 'Hobart',\n",
+       " 'Tanner',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Cottage Lake',\n",
+       " 'Newport',\n",
+       " 'Fairwood',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Des Moines',\n",
+       " 'Cottage Lake',\n",
+       " 'Sammamish',\n",
+       " 'Issaquah',\n",
+       " 'Shoreline',\n",
+       " 'Maple Valley',\n",
+       " 'Boulevard Park',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Boulevard Park',\n",
+       " 'Seattle',\n",
+       " 'Woodway',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Shoreline',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Redmond',\n",
+       " 'Bellevue',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Fife Heights',\n",
+       " 'Seattle',\n",
+       " 'Lea Hill',\n",
+       " 'Issaquah',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Issaquah',\n",
+       " 'Seattle',\n",
+       " 'Federal Way',\n",
+       " 'Federal Way',\n",
+       " 'Brier',\n",
+       " 'Lake Forest Park',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Eastgate',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Newcastle',\n",
+       " 'Duvall',\n",
+       " 'Seattle',\n",
+       " 'Fall City',\n",
+       " 'Des Moines',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Federal Way',\n",
+       " 'Eastgate',\n",
+       " 'Seattle',\n",
+       " 'Fairwood',\n",
+       " 'Newcastle',\n",
+       " 'Federal Way',\n",
+       " 'Enumclaw',\n",
+       " 'East Renton Highlands',\n",
+       " 'Boulevard Park',\n",
+       " 'Kingsgate',\n",
+       " 'City of Sammamish',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Mirrormont',\n",
+       " 'Kingsgate',\n",
+       " 'Des Moines',\n",
+       " 'Eastgate',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Shoreline',\n",
+       " 'Burien',\n",
+       " 'Fife Heights',\n",
+       " 'Shoreline',\n",
+       " 'Yarrow Point',\n",
+       " 'Eastgate',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Enumclaw',\n",
+       " 'Shoreline',\n",
+       " 'Lea Hill',\n",
+       " 'White Center',\n",
+       " 'Kirkland',\n",
+       " 'Maple Valley',\n",
+       " 'Redmond',\n",
+       " 'Kirkland',\n",
+       " 'Fife Heights',\n",
+       " 'City of Sammamish',\n",
+       " 'Seattle',\n",
+       " 'Redmond',\n",
+       " 'Kingsgate',\n",
+       " 'Fairwood',\n",
+       " 'Mercer Island',\n",
+       " 'Shoreline',\n",
+       " 'Lake Forest Park',\n",
+       " 'Fife Heights',\n",
+       " 'White Center',\n",
+       " 'White Center',\n",
+       " 'Fall City',\n",
+       " 'Hobart',\n",
+       " 'Newcastle',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Seattle',\n",
+       " 'Fife Heights',\n",
+       " 'Fairwood',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'Burien',\n",
+       " 'Shoreline',\n",
+       " 'Maple Valley',\n",
+       " 'Yarrow Point',\n",
+       " 'City of Sammamish',\n",
+       " 'Lakeland North',\n",
+       " 'Mercer Island',\n",
+       " 'Riverton',\n",
+       " 'Mercer Island',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Shoreline',\n",
+       " 'City of Sammamish',\n",
+       " 'Des Moines',\n",
+       " 'Bothell',\n",
+       " 'Kent',\n",
+       " 'Maple Valley',\n",
+       " 'White Center',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Fife Heights',\n",
+       " 'Kent',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Burien',\n",
+       " 'Newcastle',\n",
+       " 'Federal Way',\n",
+       " 'Fall City',\n",
+       " 'Seattle',\n",
+       " 'Yarrow Point',\n",
+       " 'Ravensdale',\n",
+       " 'Seattle',\n",
+       " 'Duvall',\n",
+       " 'Federal Way',\n",
+       " 'Federal Way',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Auburn',\n",
+       " 'Seattle',\n",
+       " 'Fife Heights',\n",
+       " 'Sammamish',\n",
+       " 'White Center',\n",
+       " 'Auburn',\n",
+       " 'Shoreline',\n",
+       " 'Lakeland North',\n",
+       " 'Maple Heights-Lake Desire',\n",
+       " 'Tanner',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Lea Hill',\n",
+       " 'Shoreline',\n",
+       " 'White Center',\n",
+       " 'Fairwood',\n",
+       " 'Riverbend',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'SeaTac',\n",
+       " 'City of Sammamish',\n",
+       " 'Seattle',\n",
+       " 'Vashon',\n",
+       " 'East Hill-Meridian',\n",
+       " 'White Center',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Fife Heights',\n",
+       " 'Clyde Hill',\n",
+       " 'Cottage Lake',\n",
+       " 'Shoreline',\n",
+       " 'Federal Way',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Seattle',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Fairwood',\n",
+       " 'Kenmore',\n",
+       " 'Maple Valley',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Kent',\n",
+       " 'Riverton',\n",
+       " 'Seattle',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Kenmore',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Woodinville',\n",
+       " 'Kingsgate',\n",
+       " 'Newcastle',\n",
+       " 'Eastgate',\n",
+       " 'Normandy Park',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Lakeland North',\n",
+       " 'Newcastle',\n",
+       " 'Seattle',\n",
+       " 'City of Sammamish',\n",
+       " 'Covington',\n",
+       " 'Newcastle',\n",
+       " 'Seattle',\n",
+       " 'SeaTac',\n",
+       " 'Sammamish',\n",
+       " 'Lakeland North',\n",
+       " 'Shoreline',\n",
+       " 'Ravensdale',\n",
+       " 'Burien',\n",
+       " 'Seattle',\n",
+       " 'Woodway',\n",
+       " 'Sammamish',\n",
+       " 'Seattle',\n",
+       " 'Newcastle',\n",
+       " 'Seattle',\n",
+       " 'Snoqualmie',\n",
+       " 'Seattle',\n",
+       " 'West Lake Sammamish',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Maple Heights-Lake Desire',\n",
+       " 'Shoreline',\n",
+       " 'Lea Hill',\n",
+       " 'Seattle',\n",
+       " 'Burien',\n",
+       " 'Redmond',\n",
+       " 'Issaquah',\n",
+       " 'Pacific',\n",
+       " 'Fife Heights',\n",
+       " 'White Center',\n",
+       " 'White Center',\n",
+       " 'Cottage Lake',\n",
+       " 'Seattle',\n",
+       " 'Burien',\n",
+       " 'Newport',\n",
+       " 'Yarrow Point',\n",
+       " 'Des Moines',\n",
+       " 'Enumclaw',\n",
+       " 'Seattle',\n",
+       " 'Mercer Island',\n",
+       " 'Maple Valley',\n",
+       " 'Woodinville',\n",
+       " 'Seattle',\n",
+       " 'Shoreline',\n",
+       " 'Renton',\n",
+       " 'Redmond',\n",
+       " 'Newcastle',\n",
+       " 'Shoreline',\n",
+       " 'Maplewood',\n",
+       " 'Fall City',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Maple Heights-Lake Desire',\n",
+       " 'Lea Hill',\n",
+       " 'Mountlake Terrace',\n",
+       " 'Lake Marcel-Stillwater',\n",
+       " 'Mercer Island',\n",
+       " 'Des Moines',\n",
+       " 'Shoreline',\n",
+       " 'Lakeland North',\n",
+       " 'Riverton',\n",
+       " 'Maple Valley',\n",
+       " 'Eastgate',\n",
+       " 'Redmond',\n",
+       " 'White Center',\n",
+       " 'Ames Lake',\n",
+       " 'East Renton Highlands',\n",
+       " 'Maple Valley',\n",
+       " 'Kirkland',\n",
+       " 'Newport',\n",
+       " 'Tanner',\n",
+       " 'Burien',\n",
+       " 'City of Sammamish',\n",
+       " 'City of Sammamish',\n",
+       " 'Renton',\n",
+       " 'Renton',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Fife Heights',\n",
+       " 'Renton',\n",
+       " 'Ames Lake',\n",
+       " 'Lake Forest Park',\n",
+       " 'Maple Valley',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Bothell',\n",
+       " 'Cottage Lake',\n",
+       " 'Duvall',\n",
+       " 'Sammamish',\n",
+       " 'Newcastle',\n",
+       " 'Mercer Island',\n",
+       " 'Woodinville',\n",
+       " 'Fairwood',\n",
+       " 'White Center',\n",
+       " 'Esperance',\n",
+       " 'White Center',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Lake Marcel-Stillwater',\n",
+       " 'Clyde Hill',\n",
+       " 'Mercer Island',\n",
+       " 'Kenmore',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Eastgate',\n",
+       " 'Seattle',\n",
+       " 'Fairwood',\n",
+       " 'Seattle',\n",
+       " 'Buckley',\n",
+       " 'Shoreline',\n",
+       " 'North Bend',\n",
+       " 'Maple Valley',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Lea Hill',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Maple Valley',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Klahanie',\n",
+       " 'Cottage Lake',\n",
+       " 'Kirkland',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Kingsgate',\n",
+       " 'Ames Lake',\n",
+       " 'Shoreline',\n",
+       " 'Duvall',\n",
+       " 'Ames Lake',\n",
+       " 'Shoreline',\n",
+       " 'Newport',\n",
+       " 'Newcastle',\n",
+       " 'Fairwood',\n",
+       " 'Seattle',\n",
+       " 'Maple Valley',\n",
+       " 'Burien',\n",
+       " 'Yarrow Point',\n",
+       " 'Kingsgate',\n",
+       " 'Fairwood',\n",
+       " 'Covington',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Bothell',\n",
+       " 'Newport',\n",
+       " 'City of Sammamish',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'White Center',\n",
+       " 'Pacific',\n",
+       " 'Lakeland North',\n",
+       " 'Yarrow Point',\n",
+       " 'Hobart',\n",
+       " 'Des Moines',\n",
+       " 'Cottage Lake',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Kingsgate',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Brier',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Bothell',\n",
+       " 'Maple Valley',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Mercer Island',\n",
+       " 'Kenmore',\n",
+       " 'Woodinville',\n",
+       " 'Klahanie',\n",
+       " 'Renton',\n",
+       " 'Covington',\n",
+       " 'Eastgate',\n",
+       " 'Covington',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Federal Way',\n",
+       " 'Shoreline',\n",
+       " 'Carnation',\n",
+       " 'Shoreline',\n",
+       " 'Covington',\n",
+       " 'Boulevard Park',\n",
+       " 'Maple Valley',\n",
+       " 'Tukwila',\n",
+       " 'Sammamish',\n",
+       " 'Lake Forest Park',\n",
+       " 'Sammamish',\n",
+       " 'City of Sammamish',\n",
+       " 'Issaquah',\n",
+       " 'Seattle',\n",
+       " 'Lake Forest Park',\n",
+       " 'White Center',\n",
+       " 'Kirkland',\n",
+       " 'Eastgate',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Bellevue',\n",
+       " 'White Center',\n",
+       " 'City of Sammamish',\n",
+       " 'Fife Heights',\n",
+       " 'Eastgate',\n",
+       " 'Ames Lake',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Redmond',\n",
+       " 'Renton',\n",
+       " 'Covington',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Des Moines',\n",
+       " 'Seattle',\n",
+       " 'Fall City',\n",
+       " 'Fall City',\n",
+       " 'Shoreline',\n",
+       " 'Covington',\n",
+       " 'Kirkland',\n",
+       " 'East Hill-Meridian',\n",
+       " 'SeaTac',\n",
+       " 'Seattle',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Kirkland',\n",
+       " 'Cottage Lake',\n",
+       " 'East Renton Highlands',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Shoreline',\n",
+       " 'Kent',\n",
+       " 'Kirkland',\n",
+       " 'Seattle',\n",
+       " 'Maple Heights-Lake Desire',\n",
+       " 'Fairwood',\n",
+       " 'Renton',\n",
+       " 'Shoreline',\n",
+       " 'Renton',\n",
+       " 'White Center',\n",
+       " 'Bellevue',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Ames Lake',\n",
+       " 'Lake Forest Park',\n",
+       " 'Redmond',\n",
+       " 'Maple Valley',\n",
+       " 'Lea Hill',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Des Moines',\n",
+       " 'Black Diamond',\n",
+       " 'Fairwood',\n",
+       " 'Kingsgate',\n",
+       " 'Seattle',\n",
+       " 'Covington',\n",
+       " 'Renton',\n",
+       " 'Normandy Park',\n",
+       " 'Renton',\n",
+       " 'Lakeland North',\n",
+       " 'City of Sammamish',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Redmond',\n",
+       " 'West Lake Sammamish',\n",
+       " 'White Center',\n",
+       " 'Fairwood',\n",
+       " 'Riverton',\n",
+       " 'Mercer Island',\n",
+       " 'Seattle',\n",
+       " 'Lake Forest Park',\n",
+       " 'Renton',\n",
+       " 'Kenmore',\n",
+       " 'East Renton Highlands',\n",
+       " 'Mercer Island',\n",
+       " 'SeaTac',\n",
+       " 'Sammamish',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Snoqualmie',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Shoreline',\n",
+       " 'City of Sammamish',\n",
+       " 'Newport',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Lake Marcel-Stillwater',\n",
+       " 'Ravensdale',\n",
+       " 'Ames Lake',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Fairwood',\n",
+       " 'Hobart',\n",
+       " 'Kent',\n",
+       " 'Duvall',\n",
+       " 'Kenmore',\n",
+       " 'Fife Heights',\n",
+       " 'Snoqualmie',\n",
+       " 'Federal Way',\n",
+       " 'White Center',\n",
+       " 'Kingsgate',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Tanner',\n",
+       " 'Seattle',\n",
+       " 'Sammamish',\n",
+       " 'Maple Valley',\n",
+       " 'Seattle',\n",
+       " 'Enumclaw',\n",
+       " 'Fairwood',\n",
+       " 'Ravensdale',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Boulevard Park',\n",
+       " 'White Center',\n",
+       " 'North Bend',\n",
+       " 'Fife Heights',\n",
+       " 'Mercer Island',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Black Diamond',\n",
+       " 'Redmond',\n",
+       " 'Issaquah',\n",
+       " 'SeaTac',\n",
+       " 'Seattle',\n",
+       " 'Shoreline',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Kenmore',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Issaquah',\n",
+       " 'City of Sammamish',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Lake Forest Park',\n",
+       " 'Redmond',\n",
+       " 'City of Sammamish',\n",
+       " 'Riverbend',\n",
+       " 'Lea Hill',\n",
+       " 'East Hill-Meridian',\n",
+       " 'White Center',\n",
+       " 'Auburn',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Seattle',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Seattle',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Enumclaw',\n",
+       " 'Seattle',\n",
+       " 'Newcastle',\n",
+       " 'Lake Forest Park',\n",
+       " 'Seattle',\n",
+       " 'Redmond',\n",
+       " 'Auburn',\n",
+       " 'SeaTac',\n",
+       " 'Milton',\n",
+       " 'Seattle',\n",
+       " 'White Center',\n",
+       " 'Lakeland North',\n",
+       " 'Kenmore',\n",
+       " 'Seattle',\n",
+       " 'Snoqualmie',\n",
+       " 'Maple Valley',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Des Moines',\n",
+       " 'White Center',\n",
+       " 'Burien',\n",
+       " 'Cottage Lake',\n",
+       " 'Riverton',\n",
+       " 'White Center',\n",
+       " 'Snoqualmie',\n",
+       " 'Redmond',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Yarrow Point',\n",
+       " 'Sammamish',\n",
+       " 'Bellevue',\n",
+       " 'Tanner',\n",
+       " 'Issaquah',\n",
+       " 'Lake Forest Park',\n",
+       " 'Shoreline',\n",
+       " 'Cottage Lake',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Federal Way',\n",
+       " 'Yarrow Point',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Shoreline',\n",
+       " 'Newcastle',\n",
+       " 'Seattle',\n",
+       " 'Fairwood',\n",
+       " 'Kirkland',\n",
+       " 'North Bend',\n",
+       " 'Riverton',\n",
+       " 'White Center',\n",
+       " 'Fall City',\n",
+       " 'Fife Heights',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Ravensdale',\n",
+       " 'White Center',\n",
+       " 'Shoreline',\n",
+       " 'Newport',\n",
+       " 'Fife Heights',\n",
+       " 'Fall City',\n",
+       " 'Lake Forest Park',\n",
+       " 'Cottage Lake',\n",
+       " 'Renton',\n",
+       " 'Mercer Island',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'East Hill-Meridian',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Des Moines',\n",
+       " 'Covington',\n",
+       " 'Bellevue',\n",
+       " 'Seattle',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Newport',\n",
+       " 'Seattle',\n",
+       " 'Riverton',\n",
+       " 'Federal Way',\n",
+       " 'Kingsgate',\n",
+       " 'Lake Forest Park',\n",
+       " 'East Renton Highlands',\n",
+       " 'Enumclaw',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Lakeland North',\n",
+       " 'City of Sammamish',\n",
+       " 'Federal Way',\n",
+       " 'Bothell',\n",
+       " 'Kent',\n",
+       " 'Lake Forest Park',\n",
+       " 'Lake Forest Park',\n",
+       " 'Lake Forest Park',\n",
+       " 'Black Diamond',\n",
+       " 'Maple Valley',\n",
+       " 'Shoreline',\n",
+       " 'Bellevue',\n",
+       " 'Redmond',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Fairwood',\n",
+       " 'Des Moines',\n",
+       " 'White Center',\n",
+       " 'Fairwood',\n",
+       " 'Cottage Lake',\n",
+       " 'Renton',\n",
+       " 'Riverbend',\n",
+       " 'Duvall',\n",
+       " 'Lake Forest Park',\n",
+       " 'Lea Hill',\n",
+       " 'Fairwood',\n",
+       " 'Shoreline',\n",
+       " 'Eastgate',\n",
+       " 'Yarrow Point',\n",
+       " 'Lakeland North',\n",
+       " 'Kent',\n",
+       " 'Shoreline',\n",
+       " 'Cottage Lake',\n",
+       " 'Cottage Lake',\n",
+       " 'Shoreline',\n",
+       " 'Bothell',\n",
+       " 'Seattle',\n",
+       " 'Kenmore',\n",
+       " 'Shoreline',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Shoreline',\n",
+       " 'White Center',\n",
+       " 'Seattle',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Medina',\n",
+       " 'Federal Way',\n",
+       " 'Newcastle',\n",
+       " 'Seattle',\n",
+       " 'Bellevue',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Ames Lake',\n",
+       " 'Seattle',\n",
+       " 'Duvall',\n",
+       " 'Maple Valley',\n",
+       " 'Newcastle',\n",
+       " 'City of Sammamish',\n",
+       " 'Bellevue',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Federal Way',\n",
+       " 'Fairwood',\n",
+       " 'Renton',\n",
+       " 'Woodway',\n",
+       " 'Shoreline',\n",
+       " 'Eastgate',\n",
+       " 'Sammamish',\n",
+       " 'Riverton',\n",
+       " 'Seattle',\n",
+       " 'Lea Hill',\n",
+       " 'Seattle',\n",
+       " 'East Renton Highlands',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Sammamish',\n",
+       " 'Fife Heights',\n",
+       " 'Shoreline',\n",
+       " 'Mirrormont',\n",
+       " 'Federal Way',\n",
+       " 'White Center',\n",
+       " 'Redmond',\n",
+       " 'City of Sammamish',\n",
+       " 'Newcastle',\n",
+       " 'Boulevard Park',\n",
+       " 'Riverton',\n",
+       " 'Shoreline',\n",
+       " 'Ames Lake',\n",
+       " 'Kirkland',\n",
+       " 'Fall City',\n",
+       " 'Cottage Lake',\n",
+       " 'Seattle',\n",
+       " 'Eastgate',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Bothell',\n",
+       " 'White Center',\n",
+       " 'City of Sammamish',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Newport',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Redmond',\n",
+       " 'Seattle',\n",
+       " 'Renton',\n",
+       " 'City of Sammamish',\n",
+       " 'Mercer Island',\n",
+       " 'Black Diamond',\n",
+       " 'City of Sammamish',\n",
+       " 'Kent',\n",
+       " 'Newcastle',\n",
+       " 'Enumclaw',\n",
+       " 'Pacific',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Seattle',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Riverton',\n",
+       " 'Shoreline',\n",
+       " 'Riverton',\n",
+       " 'Redmond',\n",
+       " 'City of Sammamish',\n",
+       " 'Shoreline',\n",
+       " 'East Renton Highlands',\n",
+       " 'Fife Heights',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Lakeland South',\n",
+       " 'Federal Way',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Lakeland North',\n",
+       " 'Lea Hill',\n",
+       " 'Riverton',\n",
+       " 'Shoreline',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Enumclaw',\n",
+       " 'Shoreline',\n",
+       " 'East Renton Highlands',\n",
+       " 'Seattle',\n",
+       " 'Bothell',\n",
+       " 'Redmond',\n",
+       " 'Fife Heights',\n",
+       " 'Issaquah',\n",
+       " 'Ravensdale',\n",
+       " 'White Center',\n",
+       " 'Kenmore',\n",
+       " 'Pacific',\n",
+       " 'East Renton Highlands',\n",
+       " 'Maple Valley',\n",
+       " 'Renton',\n",
+       " 'East Renton Highlands',\n",
+       " 'Covington',\n",
+       " 'Boulevard Park',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Kingsgate',\n",
+       " 'Seattle',\n",
+       " 'Pacific',\n",
+       " 'Maple Valley',\n",
+       " 'Renton',\n",
+       " 'Issaquah',\n",
+       " 'Mercer Island',\n",
+       " 'Lea Hill',\n",
+       " 'Seattle',\n",
+       " 'Eastgate',\n",
+       " 'Kirkland',\n",
+       " 'City of Sammamish',\n",
+       " 'West Lake Sammamish',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Kingsgate',\n",
+       " 'Federal Way',\n",
+       " 'Boulevard Park',\n",
+       " 'Lake Forest Park',\n",
+       " 'White Center',\n",
+       " 'Lea Hill',\n",
+       " 'Yarrow Point',\n",
+       " 'Seattle',\n",
+       " 'Medina',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Kingsgate',\n",
+       " 'Woodinville',\n",
+       " 'Union Hill-Novelty Hill',\n",
+       " 'Seattle',\n",
+       " 'Covington',\n",
+       " 'White Center',\n",
+       " 'White Center',\n",
+       " 'Eastgate',\n",
+       " 'Mercer Island',\n",
+       " 'Cottage Lake',\n",
+       " 'Kirkland',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Mercer Island',\n",
+       " 'Cottage Lake',\n",
+       " 'Renton',\n",
+       " 'White Center',\n",
+       " 'Fairwood',\n",
+       " 'Lake Forest Park',\n",
+       " 'Maple Valley',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Seattle',\n",
+       " 'White Center',\n",
+       " 'Auburn',\n",
+       " 'Sammamish',\n",
+       " 'Lakeland North',\n",
+       " 'White Center',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'East Hill-Meridian',\n",
+       " 'White Center',\n",
+       " 'Fairwood',\n",
+       " 'Des Moines',\n",
+       " 'Lake Marcel-Stillwater',\n",
+       " 'SeaTac',\n",
+       " 'Mercer Island',\n",
+       " 'White Center',\n",
+       " 'Yarrow Point',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Bryn Mawr-Skyway',\n",
+       " 'Shoreline',\n",
+       " 'Maple Heights-Lake Desire',\n",
+       " 'Riverton',\n",
+       " 'Kingsgate',\n",
+       " 'Maple Valley',\n",
+       " 'Lake Morton-Berrydale',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Maple Valley',\n",
+       " 'Shoreline',\n",
+       " 'Covington',\n",
+       " 'Seattle',\n",
+       " 'Mercer Island',\n",
+       " 'White Center',\n",
+       " 'Lake Forest Park',\n",
+       " 'Seattle',\n",
+       " 'Burien',\n",
+       " 'Federal Way',\n",
+       " 'Bellevue',\n",
+       " 'North Bend',\n",
+       " 'West Lake Sammamish',\n",
+       " 'Mercer Island',\n",
+       " 'Lake Forest Park',\n",
+       " 'Kirkland',\n",
+       " 'Seattle',\n",
+       " 'Ames Lake',\n",
+       " 'Seattle',\n",
+       " 'Seattle',\n",
+       " 'Maple Valley',\n",
+       " 'Shoreline',\n",
+       " 'Mercer Island',\n",
+       " 'White Center',\n",
+       " 'Sammamish',\n",
+       " 'Seattle',\n",
+       " 'Enumclaw',\n",
+       " 'Cottage Lake',\n",
+       " 'White Center',\n",
+       " 'Lakeland North',\n",
+       " 'Sammamish',\n",
+       " 'Inglewood-Finn Hill',\n",
+       " 'Yarrow Point',\n",
+       " 'Seattle',\n",
+       " 'Des Moines',\n",
+       " 'Lake Forest Park',\n",
+       " 'Issaquah',\n",
+       " 'West Lake Sammamish',\n",
+       " 'City of Sammamish',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Fife Heights',\n",
+       " 'Federal Way',\n",
+       " 'Newcastle',\n",
+       " 'Renton',\n",
+       " 'Eastgate',\n",
+       " 'Fall City',\n",
+       " 'Shoreline',\n",
+       " 'Shoreline',\n",
+       " 'Seattle',\n",
+       " 'Enumclaw',\n",
+       " 'Boulevard Park',\n",
+       " 'White Center',\n",
+       " 'City of Sammamish',\n",
+       " 'Newport',\n",
+       " 'Shoreline',\n",
+       " 'Lake Forest Park',\n",
+       " 'Federal Way',\n",
+       " 'White Center',\n",
+       " 'Ravensdale',\n",
+       " 'Algona',\n",
+       " 'Seattle',\n",
+       " 'Maple Valley',\n",
+       " 'Lakeland North',\n",
+       " 'Seattle',\n",
+       " 'East Hill-Meridian',\n",
+       " 'Federal Way',\n",
+       " 'Lake Forest Park',\n",
+       " 'Lea Hill',\n",
+       " 'Shoreline',\n",
+       " 'Fall City',\n",
+       " 'Federal Way',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "location_names=dfgeo[\"neighbourhood\"].tolist()\n",
+    "location_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'LOCATION_DATA' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [81]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cords, name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[43mLOCATION_DATA\u001b[49m, LOCATION_NAMES):\n\u001b[0;32m      2\u001b[0m     folium\u001b[38;5;241m.\u001b[39mMarker(location\u001b[38;5;241m=\u001b[39m[cords[\u001b[38;5;241m0\u001b[39m], cords[\u001b[38;5;241m1\u001b[39m]],\n\u001b[0;32m      3\u001b[0m                   popup\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLattitude:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcords[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m<br>\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      4\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLongitude:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcords[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m<br>\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      5\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mName:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      6\u001b[0m                   )\u001b[38;5;241m.\u001b[39madd_to(folium_map)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'LOCATION_DATA' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# for cords, name in zip(LOCATION_DATA, LOCATION_NAMES):\n",
+    "#     folium.Marker(location=[cords[0], cords[1]],\n",
+    "#                   popup=f\"Lattitude:<br>{cords[0]}<br>\"\n",
+    "#                         f\"Longitude:<br>{cords[1]}<br>\"\n",
+    "#                         f\"Name:<br>{name}\"\n",
+    "#                   ).add_to(folium_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'Map' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [83]\u001b[0m, in \u001b[0;36m<cell line: 24>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     21\u001b[0m     folium\u001b[38;5;241m.\u001b[39mMarker(location\u001b[38;5;241m=\u001b[39m[row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlat\u001b[39m\u001b[38;5;124m\"\u001b[39m], row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlong\u001b[39m\u001b[38;5;124m\"\u001b[39m]], popup\u001b[38;5;241m=\u001b[39mrow[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzipcode\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39madd_to(m)\n\u001b[0;32m     23\u001b[0m m\n\u001b[1;32m---> 24\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m cords, name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mzip\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocation_names\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m     25\u001b[0m     folium\u001b[38;5;241m.\u001b[39mMarker(location\u001b[38;5;241m=\u001b[39m[cords[\u001b[38;5;241m0\u001b[39m], cords[\u001b[38;5;241m1\u001b[39m]],\n\u001b[0;32m     26\u001b[0m                   popup\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLattitude:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcords[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m<br>\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     27\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLongitude:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcords[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m<br>\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     28\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mName:<br>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     29\u001b[0m                   )\u001b[38;5;241m.\u001b[39madd_to(folium_map)\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'Map' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\"\"\"\n",
+    "Plot the locations of zip codes on a map using Folium.\n",
+    "\n",
+    "Parameters:\n",
+    "    df (DataFrame): The DataFrame containing the data.\n",
+    "    lat_column (str): The name of the column containing latitude values. Default is 'lat'.\n",
+    "    long_column (str): The name of the column containing longitude values. Default is 'long'.\n",
+    "    zipcode_column (str): The name of the column containing zipcode values. Default is 'zipcode'.\n",
+    "\n",
+    "Returns:\n",
+    "    None\n",
+    "\"\"\"\n",
+    "# Group the data by zipcode and calculate the mean latitude and longitude\n",
+    "zipcode_data = king_county_df.groupby(\"zipcode\").agg({\"lat\": 'mean', \"long\": 'mean'}).reset_index()\n",
+    "\n",
+    "# Create a map centered at the mean latitude and longitude of all the zipcodes\n",
+    "m = folium.Map(location=[king_county_df[\"lat\"].mean(), king_county_df[\"long\"].mean()], zoom_start=10)\n",
+    "\n",
+    "# Add markers for each zipcode\n",
+    "for _, row in zipcode_data.iterrows():\n",
+    "    folium.Marker(location=[row[\"lat\"], row[\"long\"]], popup=row[\"zipcode\"]).add_to(m)\n",
+    "\n",
+    "\n",
+    "for cords, name in zip(m, location_names):\n",
+    "    folium.Marker(location=[cords[0], cords[1]],\n",
+    "                  popup=f\"Lattitude:<br>{cords[0]}<br>\"\n",
+    "                        f\"Longitude:<br>{cords[1]}<br>\"\n",
+    "                        f\"Name:<br>{name}\"\n",
+    "                  ).add_to(folium_map)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# One-hot encode neighbourhood\n",
+    "dfgeo = one_hot_encode(dfgeo, 'neighbourhood')\n",
+    "\n",
+    "\n",
+    "# Drop coord, lat, long, zipcode columns\n",
+    "dfgeo = dfgeo.drop(['coord', 'lat', 'long', 'zipcode'], axis = 1)\n",
+    "dfgeo = dfgeo.replace({True: 1, False: 0})\n",
+    "# Check \n",
+    "dfgeo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define X_train and y_train\n",
+    "X_train3 = dfgeo.drop('price', axis = 1)\n",
+    "y_train3 = dfgeo['price']\n",
+    "model_C= modeling_function(X_train3, y_train3)\n",
+    "model_C[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the adjusted $R^2$ has improved significantly and is now 0.819"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us now evaluate the model using sci-kit learn's `cross_val_score`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse3= rmse_function(X_train3, y_train3)\n",
+    "rmse3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add model to evaluation table\n",
+    "evaluationtable.loc[2] = ['Model-C', 'Further one-hot encoding', model_C[0].df_model, round(model_C[0].rsquared,3)\n",
+    "                          ,round(model_C[0].rsquared_adj,3), int(rmse3[1]), int(rmse3[2]) ]\n",
+    "# View our evaluation table\n",
+    "evaluationtable.sort_values(by = 'Adj R-squared', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.4 Log Transformation - Model D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "df_log = dfgeo.copy()\n",
+    "\n",
+    "df_log[\"log(sqft_lot)\"] = np.log(df_log[\"sqft_lot\"])\n",
+    "# df_log[\"log(sqft_living)\"] = np.log(df_log[\"sqft_living\"])\n",
+    "\n",
+    "df_log[\"log(sqft_basement)\"] = np.where(df_log[\"sqft_basement\"] == 0, 0, np.log(df_log[\"sqft_basement\"]))\n",
+    "# Visually inspect raw vs. transformed values\n",
+    "x_df=df_log[[\"sqft_lot\", \"log(sqft_lot)\", \"log(sqft_basement)\", \"sqft_basement\"]]\n",
+    "x_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4))\n",
+    "\n",
+    "# Plotting histogram for sqft_lot\n",
+    "ax1.hist(df_log[\"sqft_lot\"], color=\"blue\", alpha=0.7, label=\"sqft_lot\")\n",
+    "ax1.set_xlabel(\"sqft_lot\")\n",
+    "ax1.legend()\n",
     "\n",
-    "Please fill out:\n",
-    "* Student name: \n",
-    "* Student pace: self paced / part time / full time\n",
-    "* Scheduled project review date/time: \n",
-    "* Instructor name: \n",
-    "* Blog post URL:\n"
+    "# Plotting histogram for sqft_basement\n",
+    "ax1.hist(df_log[\"sqft_basement\"], color=\"green\", alpha=0.7, label=\"sqft_basement\")\n",
+    "ax1.set_xlabel(\"sqft_basement\")\n",
+    "ax1.legend()\n",
+    "\n",
+    "# Plotting histogram for log(sqft_lot)\n",
+    "ax2.hist(df_log[\"log(sqft_lot)\"], color=\"orange\", alpha=0.7, label=\"log(sqft_lot)\")\n",
+    "ax2.set_xlabel(\"log(sqft_lot)\")\n",
+    "ax2.legend()\n",
+    "\n",
+    "# Plotting histogram for log(sqft_basement)\n",
+    "ax2.hist(df_log[\"log(sqft_basement)\"], color=\"red\", alpha=0.7, label=\"log(sqft_basement)\")\n",
+    "ax2.set_xlabel(\"log(sqft_basement)\")\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Define X_train and y_train\n",
+    "X_train4 = df_log.drop(['price','sqft_lot'], axis = 1)\n",
+    "y_train4 = df_log['price']\n",
+    "model_D= modeling_function(X_train4, y_train4)\n",
+    "model_D[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rmse4=rmse_function(X_train4, y_train4)\n",
+    "rmse4"
    ]
   },
   {
@@ -20,13 +7403,153 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
+    "# Add model to evaluation table\n",
+    "evaluationtable.loc[3] = ['Model-D', 'After Log Transformation', model_D[0].df_model, round(model_D[0].rsquared,3)\n",
+    "                          ,round(model_D[0].rsquared_adj,3), int(rmse4[1]), int(rmse4[2]) ]\n",
+    "\n",
+    "# View our evaluation table\n",
+    "evaluationtable.sort_values(by = 'Adj R-squared', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the above table you can see hour our model has improved by the R-squared Increasing while the RMSE reducing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6 CONCLUSIONS AND FINDINGS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.1 Summary of Findings and Recommendations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1 House attributes**\n",
+    "- **We recommend targetting the campaign towards houses with a higher bedroom count**.\n",
+    "However for a given house depending on its square-footage, note that adding an additional bedroom does not necessarily result in a a sale price increase.\n",
+    "* We can see that square foot living has the highest influence on the price of the house. \n",
+    "* The variables that have a major influence on the price of the house are; square foot living, age of the house,good condition of the house,if the house is on a waterfront and has an excellent view.\n",
+    "* The variables that has the least influence on the price of the house are; grade,number of bedrooms,sqft lot,sqft basement and sqft lot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2 Time of the year**\n",
+    "\n",
+    "The peak season for home sales typically occurs during the spring and summer months.\n",
+    "Specifically, the busiest home selling months are March,April, May, June, July, and August.\n",
+    "Buyers are actively searching for properties, and there’s typically increased demand.\n",
+    "\n",
+    "The slowest months for home selling activity are November, December, January, and February.\n",
+    "Demand tends to be lower during these months.\n",
+    "\n",
+    "**Factors Influencing Seasonality**\n",
+    "\n",
+    "**Weather** Warmer weather encourages more people to explore the housing market.\n",
+    "\n",
+    "**School Year**:Families often want to move before the start of the school year, which aligns with the spring and summer months."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3 Model**\n",
+    "\n",
+    "A model was developed using linear regression and it provided details such how the square footage of living space affects pricing levels, thus influencing market trends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4 Location**\n",
+    "\n",
+    "Waterfront living is key, with the median house price for a house with a waterfront view being almost double that of one that does not have this feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7 RECOMMENDATIONS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Future Work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following data would provide additional insights and improve our model's performance.\n",
+    "\n",
+    "**Commuting time**\n",
+    "Time it takes from the house location to downtown Seattle could be a good indicator, with better connected properties potentially being valued higher.\n",
+    "\n",
+    "**Median Income per zipcode**\n",
+    "Understanding income distribution amongst zipcodes would also be an indicator of which neighbourhoods are more affluent and should be the focus of the campaign.\n",
+    "\n",
+    "**Longer time span**\n",
+    "Having data beyond the one year of May 2014-May 2015 would let us examine whether there are any trends in location. For instance some neighbourhoods may be experiencing a price increase due to recent infrastructure development. Which areas are up and coming?\n",
+    "\n",
+    "**School rankings**\n",
+    "Proximity to a good school is often a key requirement for wealthy parents and likely to drive a house price up.\n",
+    "\n",
+    "**House Architectural Shape**: Additionally investigate certain features, such as constructional/architectural values of the house, to see what trends we could discern from that. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, further work on our model would include the following:\n",
+    "- investigating Principal Component Analysis to tackle multicollinearity\n",
+    "- considering other algorithms beyong linear regression\n",
+    "- consider regression methods to deal with under/over fitting\n",
+    "\n",
+    "Finally with input from our stakeholders we could develop a more tailored model, focusing on houses of a certain value or in a certain neighbourhood."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Appendix:\n",
+    "1. Trends on house flipping in the US\n",
+    "\n",
+    "https://www.attomdata.com/news/market-trends/flipping/attom-year-end-2022-u-s-home-flipping-report/"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -40,7 +7563,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,