From 1a38f444e7acecff1ff614de5adfe91724776461 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 24 Apr 2024 16:00:21 +0300
Subject: [PATCH 01/27] Added Names.

---
 student.ipynb | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index d3bb34af..7afb869a 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,11 +7,14 @@
     "## Final Project Submission\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"
+    "* Student name: Winfred Kinya Bundi.\n",
+    "                Carol Mundia.\n",
+    "                Paul Mundia.\n",
+    "                Dennis Mwenda.\n",
+    "* Student pace: Full time Hybrid\n",
+    "* Scheduled project review date/time:2/05/2024 \n",
+    "* Instructor name: Mwikali Maryanne.\n",
+    "* Blog post URL:https://github.com/winnycodegurl/dsc-phase-2-projectgroup4\n"
    ]
   },
   {

From 4b44ff0f2076f4d06d9a8fd64f5adb66d4b07a73 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 24 Apr 2024 16:03:06 +0300
Subject: [PATCH 02/27] Second commit

---
 student.ipynb | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 7afb869a..9f5c5db4 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -7,10 +7,11 @@
     "## Final Project Submission\n",
     "\n",
     "Please fill out:\n",
-    "* Student name: Winfred Kinya Bundi.\n",
-    "                Carol Mundia.\n",
-    "                Paul Mundia.\n",
-    "                Dennis Mwenda.\n",
+    "* Student name:\n",
+    "1. Winfred Kinya Bundi.\n",
+    "2. Carol Mundia.\n",
+    "3. Paul Mundia.\n",
+    "4. Dennis Mwenda.\n",
     "* Student pace: Full time Hybrid\n",
     "* Scheduled project review date/time:2/05/2024 \n",
     "* Instructor name: Mwikali Maryanne.\n",

From 481e8fd1cd97fd6ed1340fa170c6e622934156c4 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Fri, 26 Apr 2024 09:38:22 +0300
Subject: [PATCH 03/27] update

---
 student.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/student.ipynb b/student.ipynb
index 9f5c5db4..eb615a39 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -15,7 +15,7 @@
     "* Student pace: Full time Hybrid\n",
     "* Scheduled project review date/time:2/05/2024 \n",
     "* Instructor name: Mwikali Maryanne.\n",
-    "* Blog post URL:https://github.com/winnycodegurl/dsc-phase-2-projectgroup4\n"
+    "* Blog post URL:git@github.com:winnycodegurl/dsc-phase-2-projectgroup4.git\n"
    ]
   },
   {

From f89641902007bc7ce96394e80f98c2d2f83589ae Mon Sep 17 00:00:00 2001
From: dennis2440 <dennismwenda2004@gmail.com>
Date: Fri, 26 Apr 2024 15:56:53 +0300
Subject: [PATCH 04/27] add introduction

---
 student.ipynb | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index eb615a39..e5cf6594 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -18,14 +18,21 @@
     "* Blog post URL:git@github.com:winnycodegurl/dsc-phase-2-projectgroup4.git\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## INTRODUCTION\n",
+    "\n",
+    "In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Your code here - remember to use markdown cells for comments as well!"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -44,7 +51,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,

From 1c382423ea8b0c14f486b15f1cb04a87f2eb0ef9 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Fri, 26 Apr 2024 21:16:12 +0300
Subject: [PATCH 05/27] Business understanding and  objectives

---
 student.ipynb | 107 ++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 103 insertions(+), 4 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index e5cf6594..f6d66f0f 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -28,11 +28,110 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BUSINESS UNDERSTANDING\n",
+    "\n",
+    "\n",
+    "\n",
+    "The provided dataset encompasses details on homes sold, encompassing their attributes and sale prices. This dataset holds significant potential for real estate agencies across various avenues:\n",
+    "\n",
+    "\n",
+    "\n",
+    "1. \n",
+    "Market Analysis: Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "2. \n",
+    "Property Valuation: By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "3. \n",
+    "Targeted Marketing: Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PROBLEM STATEMENT.\n",
+    "\n",
+    "\n",
+    "\n",
+    "In King County, people involved in real estate have trouble figuring out what affects property values and trends in the market.\n",
+    "This study wants to help by looking at things like what features a property has, where it's located, what buyers prefer,\n",
+    "and how things change over time. By understanding these things better, people in real estate can make smarter choices about\n",
+    "buying and selling property and how they position themselves in the market.\n",
+    "The main goal is to give them practical advice that helps them do well in King County's real estate market, \n",
+    "which is always changing.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## OBJECTIVES.\n",
+    "\n",
+    "Main OBJECTIVE\n",
+    "\n",
+    "\n",
+    "The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients \n",
+    "on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, \n",
+    "furnishing clients with valuable insights to facilitate informed investment decisions.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Specific Goals:\n",
+    "\n",
+    "\n",
+    "i). Identification of Key Influencing Factors on House Prices:\n",
+    "\n",
+    "\n",
+    "\n",
+    "Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price.\n",
+    "Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on \n",
+    "property prices.\n",
+    "\n",
+    "\n",
+    "\n",
+    "ii). Assessment of Model Performance:\n",
+    "\n",
+    "\n",
+    "Employ metrics such as mean squared error, R-squared values, and residual analysis to \n",
+    "evaluate the model's accuracy in predicting house prices effectively.\n",
+    "\n",
+    "    \n",
+    "\n",
+    "iii). Provision of Actionable Recommendations:\n",
+    "\n",
+    "\n",
+    "\n",
+    "Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. \n",
+    "Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {

From b593de5dd5d7a062da348bb6dccb78edea3e5903 Mon Sep 17 00:00:00 2001
From: dennis2440 <dennismwenda2004@gmail.com>
Date: Sat, 27 Apr 2024 15:30:01 +0300
Subject: [PATCH 06/27] clean Dataset

---
 student.ipynb | 641 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 641 insertions(+)

diff --git a/student.ipynb b/student.ipynb
index f6d66f0f..d9a7fa37 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -132,6 +132,647 @@
     "\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Understanding.\n",
+    "\n",
+    "\n",
+    "King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments.\n",
+    "\n",
+    "### Whereby the dataset contains:\n",
+    "\n",
+    "### Target Variable\n",
+    "\n",
+    "price: Sale price of the house .\n",
+    "\n",
+    "### Unique identifier\n",
+    "\n",
+    "id - Unique identifier for a house\n",
+    "\n",
+    "### Property Characteristics:\n",
+    "\n",
+    "bedrooms: Number of bedrooms.\n",
+    "\n",
+    "bathrooms: Number of bathrooms.\n",
+    "\n",
+    "sqft_living: Square footage of living space in the home.\n",
+    "\n",
+    "sqft_lot: Square footage of the lot.\n",
+    "\n",
+    "floors: Number of floors (levels) in the house.\n",
+    "\n",
+    "waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).\n",
+    "\n",
+    "view: Quality of view from the house, categorized into various types.\n",
+    "\n",
+    "condition: Overall condition of the house, categorized based on maintenance.\n",
+    "\n",
+    "grade: Overall grade of the house, reflecting construction and design quality.\n",
+    "\n",
+    "### Additional Features:\n",
+    "\n",
+    "sqft_above: Square footage of house apart from the basement.\n",
+    "\n",
+    "sqft_basement: Square footage of the basement.\n",
+    "\n",
+    "yr_built: Year when the house was built.\n",
+    "\n",
+    "yr_renovated: Year when the house was renovated.\n",
+    "\n",
+    "zipcode: ZIP Code of the property.\n",
+    "\n",
+    "lat: Latitude coordinate of the property.\n",
+    "\n",
+    "long: Longitude coordinate of the property.\n",
+    "\n",
+    "sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.\n",
+    "\n",
+    "sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.\n",
+    "\n",
+    "### TABLE OF CONTENTS\n",
+    "1.Data Preparation\n",
+    "\n",
+    "2.Data cleaning\n",
+    "\n",
+    "3.Exploratory data analysis\n",
+    "\n",
+    "4.Statistical Analysis\n",
+    "\n",
+    "5.Modelling\n",
+    "\n",
+    "6.Regression Results\n",
+    "\n",
+    "7.Conclusion\n",
+    "\n",
+    "8.Reccomendations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. DATA PREPARATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing necessary libraries for data analysis and visualization\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt  # for data visualization.\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns  # for enhanced data visualization.\n",
+    "from pandas.api.types import is_numeric_dtype  # Used to check if a data type is numeric.\n",
+    "\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor  # For calculating Variance Inflation Factor (VIF).\n",
+    "from statsmodels.graphics.regressionplots import plot_partregress_grid  # For partial regression plots.\n",
+    "from sklearn.model_selection import train_test_split  # Used to split data into training and testing sets.\n",
+    "from sklearn.preprocessing import PolynomialFeatures  # Generate polynomial features.\n",
+    "from sklearn.linear_model import LinearRegression  # Linear Regression model.\n",
+    "from sklearn.preprocessing import StandardScaler  # Standardizing/Scaling features.\n",
+    "from sklearn.feature_selection import RFE  # Recursive Feature Elimination for feature selection.\n",
+    "from sklearn.metrics import mean_squared_error, r2_score  # Evaluation metrics for model performance.\n",
+    "import statsmodels.api as sm\n",
+    "from scipy.stats import kstest\n",
+    "\n",
+    "# Statsmodels is used to create statistical models.\n",
+    "from scipy import stats  # Scientific computing library for statistical tests.\n",
+    "from scipy.stats import f_oneway  # One-way ANOVA statistical test.\n",
+    "from scipy.stats import ttest_ind  # Independent sample t-test for comparing means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None,\n",
+       "            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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset to inspect the initial state of the data\n",
+    "file_path = 'data/kc_house_data.csv'\n",
+    "housing_data = pd.read_csv(file_path)\n",
+    "\n",
+    "# Display basic information and the first few rows of the dataset\n",
+    "housing_data.info(), housing_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 3 columns with missing values.\n",
+      "waterfront      2376\n",
+      "view              63\n",
+      "yr_renovated    3842\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating function to check counts of missing values\n",
+    "def has_missing_values(df):\n",
+    "    missing_values = df.isnull().sum()\n",
+    "    num_missing_values = missing_values[missing_values > 0].count()\n",
+    "    if num_missing_values == 0:\n",
+    "        print(\"There are no missing values in the DataFrame.\")\n",
+    "    else:\n",
+    "        print(f\"There are {num_missing_values} columns with missing values.\")\n",
+    "        print(missing_values[missing_values > 0])\n",
+    "        \n",
+    "has_missing_values(housing_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are no duplicate rows in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#creating a function to check for duplicates.\n",
+    "def has_duplicates(df):\n",
+    "    num_duplicates = df.duplicated().sum()\n",
+    "    if num_duplicates == 0:\n",
+    "        print(\"There are no duplicate rows in the DataFrame.\")\n",
+    "    else:\n",
+    "        print(f\"There are {num_duplicates} duplicate rows in the DataFrame.\")\n",
+    "\n",
+    "has_duplicates(housing_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset contains 21,597 entries and 21 features. Here’s a brief overview of the data:\n",
+    "\n",
+    "### Columns and Their Data Types:\n",
+    "### Numerical:\n",
+    "id, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15\n",
+    "\n",
+    "### Categorical:\n",
+    "date (format object, should be datetime), waterfront, view, condition, grade, sqft_basement (format object, should be numeric)\n",
+    "\n",
+    "### Missing Values:\n",
+    "waterfront: 2,376 missing values\n",
+    "view: 63 missing values\n",
+    "yr_renovated: 3,842 missing values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. DATA CLEANING\n",
+    "\n",
+    "### A) Handle Missing Values:\n",
+    "#### waterfront and view: \n",
+    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
+    "yr_renovated: A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value.\n",
+    "\n",
+    "### B) Convert Data Types:\n",
+    "#### date:\n",
+    "Convert from object to datetime format.\n",
+    "sqft_basement: Convert from object to numeric, handling any non-numeric entries.\n",
+    "\n",
+    "### C)Encode Categorical Data:\n",
+    "One-hot encoding for categorical variables with no intrinsic ordering (waterfront, view, condition, grade).\n",
+    "This approach avoids any ordinal implications that could mislead the model.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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  datetime64[ns]\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     21597 non-null  object        \n",
+      " 9   view           21597 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  float64       \n",
+      " 14  yr_built       21597 non-null  int64         \n",
+      " 15  yr_renovated   21597 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: datetime64[ns](1), float64(7), int64(9), object(4)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None,\n",
+       "   waterfront  view  yr_renovated       date  sqft_basement\n",
+       " 0         NO  NONE           0.0 2014-10-13            0.0\n",
+       " 1         NO  NONE        1991.0 2014-12-09          400.0\n",
+       " 2         NO  NONE           0.0 2015-02-25            0.0\n",
+       " 3         NO  NONE           0.0 2014-12-09          910.0\n",
+       " 4         NO  NONE           0.0 2015-02-18            0.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A)Handle missing values\n",
+    "# For categorical data, impute missing values with mode or specific marker\n",
+    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
+    "view_mode = housing_data['view'].mode()[0]\n",
+    "\n",
+    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
+    "housing_data['view'].fillna(view_mode, inplace=True)\n",
+    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN\n",
+    "\n",
+    "\n",
+    "# B) Convert data types\n",
+    "from datetime import datetime\n",
+    "\n",
+    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
+    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
+    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric\n",
+    "\n",
+    "# Check transformations\n",
+    "housing_data.info(), housing_data[['waterfront', 'view', 'yr_renovated', 'date', 'sqft_basement']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The missing values have been addressed and data types for relevant columns have been corrected:\n",
+    "\n",
+    "#### Missing Values:\n",
+    "\n",
+    "waterfront and view had missing values which were filled using the mode of their respective columns.\n",
+    "\n",
+    "yr_renovated missing values were filled with 0, assuming that a missing value indicates no renovation.\n",
+    "\n",
+    "#### Data Type Conversions:\n",
+    "The date column has been converted to a datetime format.\n",
+    "\n",
+    "The sqft_basement column, previously an object due to some non-numeric entries, has been converted to numeric. Missing and non-numeric entries were replaced with 0, assuming no basement in such cases.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column               Non-Null Count  Dtype         \n",
+      "---  ------               --------------  -----         \n",
+      " 0   id                   21597 non-null  int64         \n",
+      " 1   date                 21597 non-null  datetime64[ns]\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   sqft_above           21597 non-null  int64         \n",
+      " 9   sqft_basement        21597 non-null  float64       \n",
+      " 10  yr_built             21597 non-null  int64         \n",
+      " 11  yr_renovated         21597 non-null  float64       \n",
+      " 12  zipcode              21597 non-null  int64         \n",
+      " 13  lat                  21597 non-null  float64       \n",
+      " 14  long                 21597 non-null  float64       \n",
+      " 15  sqft_living15        21597 non-null  int64         \n",
+      " 16  sqft_lot15           21597 non-null  int64         \n",
+      " 17  waterfront_YES       21597 non-null  uint8         \n",
+      " 18  view_EXCELLENT       21597 non-null  uint8         \n",
+      " 19  view_FAIR            21597 non-null  uint8         \n",
+      " 20  view_GOOD            21597 non-null  uint8         \n",
+      " 21  view_NONE            21597 non-null  uint8         \n",
+      " 22  condition_Fair       21597 non-null  uint8         \n",
+      " 23  condition_Good       21597 non-null  uint8         \n",
+      " 24  condition_Poor       21597 non-null  uint8         \n",
+      " 25  condition_Very Good  21597 non-null  uint8         \n",
+      " 26  grade_11 Excellent   21597 non-null  uint8         \n",
+      " 27  grade_12 Luxury      21597 non-null  uint8         \n",
+      " 28  grade_13 Mansion     21597 non-null  uint8         \n",
+      " 29  grade_3 Poor         21597 non-null  uint8         \n",
+      " 30  grade_4 Low          21597 non-null  uint8         \n",
+      " 31  grade_5 Fair         21597 non-null  uint8         \n",
+      " 32  grade_6 Low Average  21597 non-null  uint8         \n",
+      " 33  grade_7 Average      21597 non-null  uint8         \n",
+      " 34  grade_8 Good         21597 non-null  uint8         \n",
+      " 35  grade_9 Better       21597 non-null  uint8         \n",
+      "dtypes: datetime64[ns](1), float64(7), int64(9), uint8(19)\n",
+      "memory usage: 3.2 MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(None,\n",
+       "            id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       " 0  7129300520 2014-10-13  221900.0         3       1.00         1180   \n",
+       " 1  6414100192 2014-12-09  538000.0         3       2.25         2570   \n",
+       " 2  5631500400 2015-02-25  180000.0         2       1.00          770   \n",
+       " 3  2487200875 2014-12-09  604000.0         4       3.00         1960   \n",
+       " 4  1954400510 2015-02-18  510000.0         3       2.00         1680   \n",
+       " \n",
+       "    sqft_lot  floors  sqft_above  sqft_basement  ...  grade_11 Excellent  \\\n",
+       " 0      5650     1.0        1180            0.0  ...                   0   \n",
+       " 1      7242     2.0        2170          400.0  ...                   0   \n",
+       " 2     10000     1.0         770            0.0  ...                   0   \n",
+       " 3      5000     1.0        1050          910.0  ...                   0   \n",
+       " 4      8080     1.0        1680            0.0  ...                   0   \n",
+       " \n",
+       "    grade_12 Luxury  grade_13 Mansion  grade_3 Poor  grade_4 Low  grade_5 Fair  \\\n",
+       " 0                0                 0             0            0             0   \n",
+       " 1                0                 0             0            0             0   \n",
+       " 2                0                 0             0            0             0   \n",
+       " 3                0                 0             0            0             0   \n",
+       " 4                0                 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 36 columns])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# One-hot encoding for categorical variables without a natural order\n",
+    "housing_data_encoded = pd.get_dummies(housing_data, columns=['waterfront', 'view', 'condition', 'grade'], drop_first=True)\n",
+    "\n",
+    "# Display the columns after encoding to confirm the transformation\n",
+    "housing_data_encoded.info(), housing_data_encoded.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The categorical variables have been successfully encoded using one-hot encoding. This transformation results in binary columns for each category within the original features, which will help in preventing any misinterpretation of categorical data as ordinal by statistical models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering:\n",
+    "\n",
+    "Create additional features that might be informative for our modeling:\n",
+    "\n",
+    "**House Age**: Calculate the age of the house from the 'yr_built' column to the current year.\n",
+    "\n",
+    "**Renovation Age**: If a house has been renovated ('yr_renovated' > 0), calculate the years since the renovation.\n",
+    "\n",
+    "**Total Square Footage**: Sum up 'sqft_living', 'sqft_lot', 'sqft_above', and 'sqft_basement' for a total square footage feature.\n",
+    "\n",
+    "These new features could reveal deeper insights into the housing prices and help improve the performance of our statistical models.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8010.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>73</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>12382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>91</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11540.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>59</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8920.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11440.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   house_age  renovation_age  total_sqft\n",
+       "0         69             0.0      8010.0\n",
+       "1         73            33.0     12382.0\n",
+       "2         91             0.0     11540.0\n",
+       "3         59             0.0      8920.0\n",
+       "4         37             0.0     11440.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Current year for age calculations\n",
+    "from datetime import datetime\n",
+    "\n",
+    "current_year = datetime.now().year\n",
+    "\n",
+    "# Feature Engineering\n",
+    "housing_data_encoded['house_age'] = current_year - housing_data_encoded['yr_built']\n",
+    "housing_data_encoded['renovation_age'] = housing_data_encoded.apply(\n",
+    "    lambda x: 0 if x['yr_renovated'] == 0 else current_year - x['yr_renovated'], axis=1\n",
+    ")\n",
+    "housing_data_encoded['total_sqft'] = housing_data_encoded['sqft_living'] + housing_data_encoded['sqft_lot'] + \\\n",
+    "                                     housing_data_encoded['sqft_above'] + housing_data_encoded['sqft_basement']\n",
+    "\n",
+    "# Display the new features\n",
+    "housing_data_encoded[['house_age', 'renovation_age', 'total_sqft']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The new features have been successfully added:\n",
+    "\n",
+    "**House Age**: Represents the age of the house since it was built.\n",
+    "\n",
+    "**Renovation Age**: If renovated, this indicates the number of years since the last renovation; otherwise, it is 0.\n",
+    "\n",
+    "**Total Square Footage**: Sum of the living area, lot size, above-ground level area, and basement area\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 1e31ae60a222b788336e384e81f3f3134a5586de Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Mon, 29 Apr 2024 14:32:36 +0300
Subject: [PATCH 07/27] EDA

---
 student.ipynb | 1032 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 981 insertions(+), 51 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index d9a7fa37..4c88f6bd 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -10,7 +10,7 @@
     "* Student name:\n",
     "1. Winfred Kinya Bundi.\n",
     "2. Carol Mundia.\n",
-    "3. Paul Mundia.\n",
+    "3. Paul Muniu.\n",
     "4. Dennis Mwenda.\n",
     "* Student pace: Full time Hybrid\n",
     "* Scheduled project review date/time:2/05/2024 \n",
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
        " [5 rows x 21 columns])"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +485,7 @@
        " 4         NO  NONE           0.0 2015-02-18            0.0)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -561,26 +561,26 @@
       " 14  long                 21597 non-null  float64       \n",
       " 15  sqft_living15        21597 non-null  int64         \n",
       " 16  sqft_lot15           21597 non-null  int64         \n",
-      " 17  waterfront_YES       21597 non-null  uint8         \n",
-      " 18  view_EXCELLENT       21597 non-null  uint8         \n",
-      " 19  view_FAIR            21597 non-null  uint8         \n",
-      " 20  view_GOOD            21597 non-null  uint8         \n",
-      " 21  view_NONE            21597 non-null  uint8         \n",
-      " 22  condition_Fair       21597 non-null  uint8         \n",
-      " 23  condition_Good       21597 non-null  uint8         \n",
-      " 24  condition_Poor       21597 non-null  uint8         \n",
-      " 25  condition_Very Good  21597 non-null  uint8         \n",
-      " 26  grade_11 Excellent   21597 non-null  uint8         \n",
-      " 27  grade_12 Luxury      21597 non-null  uint8         \n",
-      " 28  grade_13 Mansion     21597 non-null  uint8         \n",
-      " 29  grade_3 Poor         21597 non-null  uint8         \n",
-      " 30  grade_4 Low          21597 non-null  uint8         \n",
-      " 31  grade_5 Fair         21597 non-null  uint8         \n",
-      " 32  grade_6 Low Average  21597 non-null  uint8         \n",
-      " 33  grade_7 Average      21597 non-null  uint8         \n",
-      " 34  grade_8 Good         21597 non-null  uint8         \n",
-      " 35  grade_9 Better       21597 non-null  uint8         \n",
-      "dtypes: datetime64[ns](1), float64(7), int64(9), uint8(19)\n",
+      " 17  waterfront_YES       21597 non-null  bool          \n",
+      " 18  view_EXCELLENT       21597 non-null  bool          \n",
+      " 19  view_FAIR            21597 non-null  bool          \n",
+      " 20  view_GOOD            21597 non-null  bool          \n",
+      " 21  view_NONE            21597 non-null  bool          \n",
+      " 22  condition_Fair       21597 non-null  bool          \n",
+      " 23  condition_Good       21597 non-null  bool          \n",
+      " 24  condition_Poor       21597 non-null  bool          \n",
+      " 25  condition_Very Good  21597 non-null  bool          \n",
+      " 26  grade_11 Excellent   21597 non-null  bool          \n",
+      " 27  grade_12 Luxury      21597 non-null  bool          \n",
+      " 28  grade_13 Mansion     21597 non-null  bool          \n",
+      " 29  grade_3 Poor         21597 non-null  bool          \n",
+      " 30  grade_4 Low          21597 non-null  bool          \n",
+      " 31  grade_5 Fair         21597 non-null  bool          \n",
+      " 32  grade_6 Low Average  21597 non-null  bool          \n",
+      " 33  grade_7 Average      21597 non-null  bool          \n",
+      " 34  grade_8 Good         21597 non-null  bool          \n",
+      " 35  grade_9 Better       21597 non-null  bool          \n",
+      "dtypes: bool(19), datetime64[ns](1), float64(7), int64(9)\n",
       "memory usage: 3.2 MB\n"
      ]
     },
@@ -596,30 +596,30 @@
        " 4  1954400510 2015-02-18  510000.0         3       2.00         1680   \n",
        " \n",
        "    sqft_lot  floors  sqft_above  sqft_basement  ...  grade_11 Excellent  \\\n",
-       " 0      5650     1.0        1180            0.0  ...                   0   \n",
-       " 1      7242     2.0        2170          400.0  ...                   0   \n",
-       " 2     10000     1.0         770            0.0  ...                   0   \n",
-       " 3      5000     1.0        1050          910.0  ...                   0   \n",
-       " 4      8080     1.0        1680            0.0  ...                   0   \n",
+       " 0      5650     1.0        1180            0.0  ...               False   \n",
+       " 1      7242     2.0        2170          400.0  ...               False   \n",
+       " 2     10000     1.0         770            0.0  ...               False   \n",
+       " 3      5000     1.0        1050          910.0  ...               False   \n",
+       " 4      8080     1.0        1680            0.0  ...               False   \n",
        " \n",
        "    grade_12 Luxury  grade_13 Mansion  grade_3 Poor  grade_4 Low  grade_5 Fair  \\\n",
-       " 0                0                 0             0            0             0   \n",
-       " 1                0                 0             0            0             0   \n",
-       " 2                0                 0             0            0             0   \n",
-       " 3                0                 0             0            0             0   \n",
-       " 4                0                 0             0            0             0   \n",
+       " 0            False             False         False        False         False   \n",
+       " 1            False             False         False        False         False   \n",
+       " 2            False             False         False        False         False   \n",
+       " 3            False             False         False        False         False   \n",
+       " 4            False             False         False        False         False   \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",
+       " 0                False             True         False           False  \n",
+       " 1                False             True         False           False  \n",
+       " 2                 True            False         False           False  \n",
+       " 3                False             True         False           False  \n",
+       " 4                False            False          True           False  \n",
        " \n",
        " [5 rows x 36 columns])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -731,7 +731,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -767,17 +767,947 @@
     "**Total Square Footage**: Sum of the living area, lot size, above-ground level area, and basement area\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sample data check."
+   ]
+  },
+  {
+   "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>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>...</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",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3012</th>\n",
+       "      <td>16000015</td>\n",
+       "      <td>2015-04-17</td>\n",
+       "      <td>219950.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>6601</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8741.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12202</th>\n",
+       "      <td>421000465</td>\n",
+       "      <td>2014-06-17</td>\n",
+       "      <td>269500.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1480</td>\n",
+       "      <td>7276</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>940</td>\n",
+       "      <td>540.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10236.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16335</th>\n",
+       "      <td>3528000290</td>\n",
+       "      <td>2014-06-09</td>\n",
+       "      <td>743700.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>33206</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>36</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38426.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14006</th>\n",
+       "      <td>4318200360</td>\n",
+       "      <td>2014-07-30</td>\n",
+       "      <td>286000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1170</td>\n",
+       "      <td>6543</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1170</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>111</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8883.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7932</th>\n",
+       "      <td>8856920110</td>\n",
+       "      <td>2015-05-04</td>\n",
+       "      <td>360000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>14092</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18392.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "3012     16000015 2015-04-17  219950.0         3        1.5         1070   \n",
+       "12202   421000465 2014-06-17  269500.0         2        1.5         1480   \n",
+       "16335  3528000290 2014-06-09  743700.0         4        2.5         2610   \n",
+       "14006  4318200360 2014-07-30  286000.0         2        1.0         1170   \n",
+       "7932   8856920110 2015-05-04  360000.0         3        2.5         2150   \n",
+       "\n",
+       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_3 Poor  \\\n",
+       "3012       6601     1.0        1070            0.0  ...         False   \n",
+       "12202      7276     1.0         940          540.0  ...         False   \n",
+       "16335     33206     2.0        2610            0.0  ...         False   \n",
+       "14006      6543     1.0        1170            0.0  ...         False   \n",
+       "7932      14092     2.0        2150            0.0  ...         False   \n",
+       "\n",
+       "       grade_4 Low  grade_5 Fair  grade_6 Low Average  grade_7 Average  \\\n",
+       "3012         False         False                 True            False   \n",
+       "12202        False         False                 True            False   \n",
+       "16335        False         False                False            False   \n",
+       "14006        False         False                False             True   \n",
+       "7932         False         False                False            False   \n",
+       "\n",
+       "       grade_8 Good  grade_9 Better  house_age  renovation_age  total_sqft  \n",
+       "3012          False           False         39             0.0      8741.0  \n",
+       "12202         False           False         46             0.0     10236.0  \n",
+       "16335         False           False         36             0.0     38426.0  \n",
+       "14006         False           False        111             0.0      8883.0  \n",
+       "7932           True           False         33             0.0     18392.0  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data_encoded.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 21597 entries, 0 to 21596\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column               Non-Null Count  Dtype         \n",
+      "---  ------               --------------  -----         \n",
+      " 0   id                   21597 non-null  int64         \n",
+      " 1   date                 21597 non-null  datetime64[ns]\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   sqft_above           21597 non-null  int64         \n",
+      " 9   sqft_basement        21597 non-null  float64       \n",
+      " 10  yr_built             21597 non-null  int64         \n",
+      " 11  yr_renovated         21597 non-null  float64       \n",
+      " 12  zipcode              21597 non-null  int64         \n",
+      " 13  lat                  21597 non-null  float64       \n",
+      " 14  long                 21597 non-null  float64       \n",
+      " 15  sqft_living15        21597 non-null  int64         \n",
+      " 16  sqft_lot15           21597 non-null  int64         \n",
+      " 17  waterfront_YES       21597 non-null  bool          \n",
+      " 18  view_EXCELLENT       21597 non-null  bool          \n",
+      " 19  view_FAIR            21597 non-null  bool          \n",
+      " 20  view_GOOD            21597 non-null  bool          \n",
+      " 21  view_NONE            21597 non-null  bool          \n",
+      " 22  condition_Fair       21597 non-null  bool          \n",
+      " 23  condition_Good       21597 non-null  bool          \n",
+      " 24  condition_Poor       21597 non-null  bool          \n",
+      " 25  condition_Very Good  21597 non-null  bool          \n",
+      " 26  grade_11 Excellent   21597 non-null  bool          \n",
+      " 27  grade_12 Luxury      21597 non-null  bool          \n",
+      " 28  grade_13 Mansion     21597 non-null  bool          \n",
+      " 29  grade_3 Poor         21597 non-null  bool          \n",
+      " 30  grade_4 Low          21597 non-null  bool          \n",
+      " 31  grade_5 Fair         21597 non-null  bool          \n",
+      " 32  grade_6 Low Average  21597 non-null  bool          \n",
+      " 33  grade_7 Average      21597 non-null  bool          \n",
+      " 34  grade_8 Good         21597 non-null  bool          \n",
+      " 35  grade_9 Better       21597 non-null  bool          \n",
+      " 36  house_age            21597 non-null  int64         \n",
+      " 37  renovation_age       21597 non-null  float64       \n",
+      " 38  total_sqft           21597 non-null  float64       \n",
+      "dtypes: bool(19), datetime64[ns](1), float64(9), int64(10)\n",
+      "memory usage: 3.7 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "housing_data_encoded.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. EXPLORATORY DATA ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next is EDA; Exploratory Data Analysis is a crucial step in data analysis. This process will involve examining and understanding the structure, patterns, and relationships within the dataset. It will aid us uncover insights, detect anomalies, and inform subsequent analysis and modeling decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a.) Univariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### #Distribution of prices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Set the style for seaborn\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
+    "\n",
+    "# Histogram\n",
+    "sns.histplot(housing_data_encoded['price'], bins=30, kde=False, color='skyblue', ax=axes[0])\n",
+    "axes[0].set_title('Histogram of House Prices')\n",
+    "axes[0].set_xlabel('Price')\n",
+    "axes[0].set_ylabel('Frequency')\n",
+    "\n",
+    "# Density Plot\n",
+    "sns.histplot(housing_data_encoded['price'], bins=30, kde=True, color='orange', ax=axes[1])\n",
+    "axes[1].set_title('Density Plot of House Prices')\n",
+    "axes[1].set_xlabel('Price')\n",
+    "axes[1].set_ylabel('Density')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interprtation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### #Distribution of House Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
+    "\n",
+    "# Histogram for house_age\n",
+    "sns.histplot(housing_data_encoded['house_age'], bins=30, kde=False, color='skyblue', ax=axes[0])\n",
+    "axes[0].set_title('Histogram of House Age')\n",
+    "axes[0].set_xlabel('House Age (Years)')\n",
+    "axes[0].set_ylabel('Frequency')\n",
+    "\n",
+    "# Density Plot for house_age\n",
+    "sns.histplot(housing_data_encoded['house_age'], bins=30, kde=True, color='orange', ax=axes[1])\n",
+    "axes[1].set_title('Density Plot of House Age')\n",
+    "axes[1].set_xlabel('House Age (Years)')\n",
+    "axes[1].set_ylabel('Density')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interpretation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### #Distribution of Bedrooms, Bathrooms and Floors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
+    "\n",
+    "# Box plot for bedrooms\n",
+    "sns.boxplot(x=housing_data_encoded['bedrooms'], ax=axes[0])\n",
+    "axes[0].set_title('Distribution of Bedrooms')\n",
+    "axes[0].set_xlabel('Number of Bedrooms')\n",
+    "\n",
+    "# Box plot for bathrooms\n",
+    "sns.boxplot(x=housing_data_encoded['bathrooms'], ax=axes[1])\n",
+    "axes[1].set_title('Distribution of Bathrooms')\n",
+    "axes[1].set_xlabel('Number of Bathrooms')\n",
+    "\n",
+    "# Box plot for floors\n",
+    "sns.boxplot(x=housing_data_encoded['floors'], ax=axes[2])\n",
+    "axes[2].set_title('Distribution of Floors')\n",
+    "axes[2].set_xlabel('Number of Floors')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interpretation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b.) Bivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "price_range\n",
+      "300K-600K    10782\n",
+      "600K-1M       4796\n",
+      "100K-300K     4531\n",
+      "1M-2M         1260\n",
+      "2M-5M          191\n",
+      "70K-100K        30\n",
+      "5M-8M            7\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the labels with ranges\n",
+    "labels = [\"70K-100K\", \"100K-300K\", \"300K-600K\", \"600K-1M\", \"1M-2M\", \"2M-5M\", \"5M-8M\"]\n",
+    "\n",
+    "# Cut the data into the specified ranges and assign labels\n",
+    "housing_data_encoded[\"price_range\"] = pd.cut(housing_data_encoded.price,\n",
+    "                                            bins=[70000, 100000, 300000, 600000, 1000000, 2000000, 5000000, 8000000],\n",
+    "                                            labels=labels)\n",
+    "\n",
+    "# Count the occurrences of each category\n",
+    "counts = housing_data_encoded['price_range'].value_counts()\n",
+    "print(counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>...</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",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>pricerange</th>\n",
+       "      <th>price_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11485</th>\n",
+       "      <td>3260200200</td>\n",
+       "      <td>2014-10-30</td>\n",
+       "      <td>580000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>1670</td>\n",
+       "      <td>7416</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>450.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10756.0</td>\n",
+       "      <td>300K-600K</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3075</th>\n",
+       "      <td>8651401270</td>\n",
+       "      <td>2015-05-04</td>\n",
+       "      <td>203000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>840</td>\n",
+       "      <td>6500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>840</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>55</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8180.0</td>\n",
+       "      <td>100K-300K</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14083</th>\n",
+       "      <td>1338800365</td>\n",
+       "      <td>2014-05-07</td>\n",
+       "      <td>1500000.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3560</td>\n",
+       "      <td>6480</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3560</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13600.0</td>\n",
+       "      <td>1M-2M</td>\n",
+       "      <td>1M-2M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19515</th>\n",
+       "      <td>3303850360</td>\n",
+       "      <td>2014-06-25</td>\n",
+       "      <td>1280000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>4660</td>\n",
+       "      <td>17398</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4660</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26718.0</td>\n",
+       "      <td>1M-2M</td>\n",
+       "      <td>1M-2M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8722</th>\n",
+       "      <td>5249804760</td>\n",
+       "      <td>2015-05-05</td>\n",
+       "      <td>479500.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>930</td>\n",
+       "      <td>5760</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>730</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>107</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7620.0</td>\n",
+       "      <td>300K-600K</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
+       "11485  3260200200 2014-10-30   580000.0         3       2.25         1670   \n",
+       "3075   8651401270 2015-05-04   203000.0         3       1.00          840   \n",
+       "14083  1338800365 2014-05-07  1500000.0         6       2.50         3560   \n",
+       "19515  3303850360 2014-06-25  1280000.0         4       3.50         4660   \n",
+       "8722   5249804760 2015-05-05   479500.0         2       1.00          930   \n",
+       "\n",
+       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_5 Fair  \\\n",
+       "11485      7416     1.0        1220          450.0  ...         False   \n",
+       "3075       6500     1.0         840            0.0  ...         False   \n",
+       "14083      6480     2.5        3560            0.0  ...         False   \n",
+       "19515     17398     2.0        4660            0.0  ...         False   \n",
+       "8722       5760     1.0         730          200.0  ...         False   \n",
+       "\n",
+       "       grade_6 Low Average  grade_7 Average  grade_8 Good  grade_9 Better  \\\n",
+       "11485                False             True         False           False   \n",
+       "3075                  True            False         False           False   \n",
+       "14083                False            False         False           False   \n",
+       "19515                False            False         False           False   \n",
+       "8722                  True            False         False           False   \n",
+       "\n",
+       "       house_age  renovation_age  total_sqft  pricerange  price_range  \n",
+       "11485         50             0.0     10756.0   300K-600K    300K-600K  \n",
+       "3075          55             0.0      8180.0   100K-300K    100K-300K  \n",
+       "14083        110             0.0     13600.0       1M-2M        1M-2M  \n",
+       "19515         21             0.0     26718.0       1M-2M        1M-2M  \n",
+       "8722         107             0.0      7620.0   300K-600K    300K-600K  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data_encoded.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Set the style of seaborn\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "\n",
+    "# Create a bar plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x=\"pricerange\", y=\"total_sqft\", data=housing_data_encoded, errorbar=None)\n",
+    "plt.title(\"Total Square Footage by Price Range\")\n",
+    "plt.xlabel(\"Price Range\")\n",
+    "plt.ylabel(\"Total Square Footage\")\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interpretation:\n",
+    "The bar plot illustrates the relationship between price range and total square footage. Each bar represents the total square footage of houses within different price range categories. \n",
+    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Creating a scatter plot for the relationship between bedrooms, bathrooms, and house price\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(housing_data_encoded['bedrooms'], housing_data_encoded['bathrooms'], c=housing_data_encoded['price'], cmap='coolwarm', alpha=0.5)\n",
+    "plt.colorbar(label='House Price')\n",
+    "plt.xlabel('Number of Bedrooms')\n",
+    "plt.ylabel('Number of Bathrooms')\n",
+    "plt.title('Relationship between Bedrooms, Bathrooms, and House Price')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interpretation:\n",
+    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both buyers and sellers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
+    "A house with a good balance of bedrooms and bathrooms tends to attract a wider range of potential buyers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Line plot for house age vs. average house price\n",
+    "average_price_by_age = housing_data_encoded.groupby('house_age')['price'].mean()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(average_price_by_age.index, average_price_by_age.values, marker='o', linestyle='-')\n",
+    "plt.xlabel('House Age (Years)')\n",
+    "plt.ylabel('Average House Price ($)')\n",
+    "plt.title('Average House Price by House Age')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Interpretation:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c.) Multivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "price             1.000000\n",
+      "bedrooms          0.308787\n",
+      "bathrooms         0.525906\n",
+      "sqft_living       0.701917\n",
+      "sqft_lot          0.089876\n",
+      "floors            0.256804\n",
+      "sqft_above        0.605368\n",
+      "sqft_basement     0.321108\n",
+      "yr_built          0.053953\n",
+      "yr_renovated      0.117855\n",
+      "zipcode          -0.053402\n",
+      "lat               0.306692\n",
+      "long              0.022036\n",
+      "sqft_living15     0.585241\n",
+      "sqft_lot15        0.082845\n",
+      "house_age        -0.053953\n",
+      "renovation_age    0.082779\n",
+      "total_sqft        0.119913\n",
+      "Name: price, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate the correlation Matrix\n",
+    "# Drop categorical variables\n",
+    "numeric_data = housing_data_encoded.select_dtypes(include='number')\n",
+    "correlation = numeric_data.corr()['price'].drop(['id'])  # Drop 'price' and 'id' columns from correlation calculation\n",
+    "print(correlation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "Inconsistent shape between the condition and the input (got (18, 1) and (18,))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[50], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plot the correlation matrix as a heatmap\u001b[39;00m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(correlation, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m, fmt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCorrelation Matrix\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
+      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:446\u001b[0m, in \u001b[0;36mheatmap\u001b[1;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[0;32m    365\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Plot rectangular data as a color-encoded matrix.\u001b[39;00m\n\u001b[0;32m    366\u001b[0m \n\u001b[0;32m    367\u001b[0m \u001b[38;5;124;03mThis is an Axes-level function and will draw the heatmap into the\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    443\u001b[0m \n\u001b[0;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    445\u001b[0m \u001b[38;5;66;03m# Initialize the plotter object\u001b[39;00m\n\u001b[1;32m--> 446\u001b[0m plotter \u001b[38;5;241m=\u001b[39m _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[0;32m    447\u001b[0m                       annot_kws, cbar, cbar_kws, xticklabels,\n\u001b[0;32m    448\u001b[0m                       yticklabels, mask)\n\u001b[0;32m    450\u001b[0m \u001b[38;5;66;03m# Add the pcolormesh kwargs here\u001b[39;00m\n\u001b[0;32m    451\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinewidths\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m linewidths\n",
+      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:115\u001b[0m, in \u001b[0;36m_HeatMapper.__init__\u001b[1;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;66;03m# Validate the mask and convert to DataFrame\u001b[39;00m\n\u001b[0;32m    113\u001b[0m mask \u001b[38;5;241m=\u001b[39m _matrix_mask(data, mask)\n\u001b[1;32m--> 115\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mmasked_where(np\u001b[38;5;241m.\u001b[39masarray(mask), plot_data)\n\u001b[0;32m    117\u001b[0m \u001b[38;5;66;03m# Get good names for the rows and columns\u001b[39;00m\n\u001b[0;32m    118\u001b[0m xtickevery \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:1933\u001b[0m, in \u001b[0;36mmasked_where\u001b[1;34m(condition, a, copy)\u001b[0m\n\u001b[0;32m   1931\u001b[0m (cshape, ashape) \u001b[38;5;241m=\u001b[39m (cond\u001b[38;5;241m.\u001b[39mshape, a\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m   1932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cshape \u001b[38;5;129;01mand\u001b[39;00m cshape \u001b[38;5;241m!=\u001b[39m ashape:\n\u001b[1;32m-> 1933\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInconsistent shape between the condition and the input\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1934\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (cshape, ashape))\n\u001b[0;32m   1935\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(a, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_mask\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m   1936\u001b[0m     cond \u001b[38;5;241m=\u001b[39m mask_or(cond, a\u001b[38;5;241m.\u001b[39m_mask)\n",
+      "\u001b[1;31mIndexError\u001b[0m: Inconsistent shape between the condition and the input (got (18, 1) and (18,))"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x800 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Plot the correlation matrix as a heatmap\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
+    "plt.title('Correlation Matrix')\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "housing_data_encoded['v']"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -791,9 +1721,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

From 00723573bc8e66149025222f4040c4a6e8267a2b Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Mon, 29 Apr 2024 15:33:23 +0300
Subject: [PATCH 08/27] EDA

---
 student.ipynb | 485 +++++++++++++++++++++++---------------------------
 1 file changed, 225 insertions(+), 260 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 4c88f6bd..c50d9507 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
        " [5 rows x 21 columns])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +485,7 @@
        " 4         NO  NONE           0.0 2015-02-18            0.0)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -619,7 +619,7 @@
        " [5 rows x 36 columns])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -731,7 +731,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -776,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -825,112 +825,112 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3012</th>\n",
-       "      <td>16000015</td>\n",
-       "      <td>2015-04-17</td>\n",
-       "      <td>219950.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>6601</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1070</td>\n",
+       "      <th>4864</th>\n",
+       "      <td>3293400010</td>\n",
+       "      <td>2015-03-04</td>\n",
+       "      <td>950000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3450</td>\n",
+       "      <td>35880</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3450</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>39</td>\n",
+       "      <td>False</td>\n",
+       "      <td>32</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8741.0</td>\n",
+       "      <td>42780.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12202</th>\n",
-       "      <td>421000465</td>\n",
-       "      <td>2014-06-17</td>\n",
-       "      <td>269500.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1480</td>\n",
-       "      <td>7276</td>\n",
+       "      <th>18353</th>\n",
+       "      <td>1370803445</td>\n",
+       "      <td>2014-09-09</td>\n",
+       "      <td>1140000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>3080</td>\n",
+       "      <td>6500</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>940</td>\n",
-       "      <td>540.0</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>1380.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>46</td>\n",
+       "      <td>True</td>\n",
+       "      <td>83</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>10236.0</td>\n",
+       "      <td>12660.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16335</th>\n",
-       "      <td>3528000290</td>\n",
-       "      <td>2014-06-09</td>\n",
-       "      <td>743700.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2610</td>\n",
-       "      <td>33206</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2610</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>1250</th>\n",
+       "      <td>1926049355</td>\n",
+       "      <td>2014-10-28</td>\n",
+       "      <td>399000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2620</td>\n",
+       "      <td>7030</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>1200.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>36</td>\n",
+       "      <td>59</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>38426.0</td>\n",
+       "      <td>12270.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14006</th>\n",
-       "      <td>4318200360</td>\n",
-       "      <td>2014-07-30</td>\n",
-       "      <td>286000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>6543</td>\n",
+       "      <th>10810</th>\n",
+       "      <td>6071800310</td>\n",
+       "      <td>2014-06-19</td>\n",
+       "      <td>558000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>10358</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>740.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>111</td>\n",
+       "      <td>62</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8883.0</td>\n",
+       "      <td>14478.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7932</th>\n",
-       "      <td>8856920110</td>\n",
-       "      <td>2015-05-04</td>\n",
-       "      <td>360000.0</td>\n",
+       "      <th>12222</th>\n",
+       "      <td>6918710340</td>\n",
+       "      <td>2014-08-22</td>\n",
+       "      <td>385000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2150</td>\n",
-       "      <td>14092</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2110</td>\n",
+       "      <td>8000</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>2150</td>\n",
+       "      <td>2110</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
@@ -940,9 +940,9 @@
        "      <td>False</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>33</td>\n",
+       "      <td>49</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>18392.0</td>\n",
+       "      <td>12220.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -950,38 +950,38 @@
        "</div>"
       ],
       "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "3012     16000015 2015-04-17  219950.0         3        1.5         1070   \n",
-       "12202   421000465 2014-06-17  269500.0         2        1.5         1480   \n",
-       "16335  3528000290 2014-06-09  743700.0         4        2.5         2610   \n",
-       "14006  4318200360 2014-07-30  286000.0         2        1.0         1170   \n",
-       "7932   8856920110 2015-05-04  360000.0         3        2.5         2150   \n",
+       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
+       "4864   3293400010 2015-03-04   950000.0         5       2.50         3450   \n",
+       "18353  1370803445 2014-09-09  1140000.0         4       1.75         3080   \n",
+       "1250   1926049355 2014-10-28   399000.0         5       2.00         2620   \n",
+       "10810  6071800310 2014-06-19   558000.0         4       2.25         2060   \n",
+       "12222  6918710340 2014-08-22   385000.0         3       2.25         2110   \n",
        "\n",
        "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_3 Poor  \\\n",
-       "3012       6601     1.0        1070            0.0  ...         False   \n",
-       "12202      7276     1.0         940          540.0  ...         False   \n",
-       "16335     33206     2.0        2610            0.0  ...         False   \n",
-       "14006      6543     1.0        1170            0.0  ...         False   \n",
-       "7932      14092     2.0        2150            0.0  ...         False   \n",
+       "4864      35880     2.0        3450            0.0  ...         False   \n",
+       "18353      6500     1.0        1700         1380.0  ...         False   \n",
+       "1250       7030     1.0        1420         1200.0  ...         False   \n",
+       "10810     10358     1.0        1320          740.0  ...         False   \n",
+       "12222      8000     2.0        2110            0.0  ...         False   \n",
        "\n",
        "       grade_4 Low  grade_5 Fair  grade_6 Low Average  grade_7 Average  \\\n",
-       "3012         False         False                 True            False   \n",
-       "12202        False         False                 True            False   \n",
-       "16335        False         False                False            False   \n",
-       "14006        False         False                False             True   \n",
-       "7932         False         False                False            False   \n",
+       "4864         False         False                False            False   \n",
+       "18353        False         False                False            False   \n",
+       "1250         False         False                False            False   \n",
+       "10810        False         False                False            False   \n",
+       "12222        False         False                False            False   \n",
        "\n",
        "       grade_8 Good  grade_9 Better  house_age  renovation_age  total_sqft  \n",
-       "3012          False           False         39             0.0      8741.0  \n",
-       "12202         False           False         46             0.0     10236.0  \n",
-       "16335         False           False         36             0.0     38426.0  \n",
-       "14006         False           False        111             0.0      8883.0  \n",
-       "7932           True           False         33             0.0     18392.0  \n",
+       "4864          False           False         32             0.0     42780.0  \n",
+       "18353         False            True         83             0.0     12660.0  \n",
+       "1250           True           False         59             0.0     12270.0  \n",
+       "10810          True           False         62             0.0     14478.0  \n",
+       "12222          True           False         49             0.0     12220.0  \n",
        "\n",
        "[5 rows x 39 columns]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -992,7 +992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1082,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1193,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1250,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1285,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1320,6 +1320,7 @@
        "      <th>sqft_above</th>\n",
        "      <th>sqft_basement</th>\n",
        "      <th>...</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",
@@ -1328,169 +1329,168 @@
        "      <th>house_age</th>\n",
        "      <th>renovation_age</th>\n",
        "      <th>total_sqft</th>\n",
-       "      <th>pricerange</th>\n",
        "      <th>price_range</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>11485</th>\n",
-       "      <td>3260200200</td>\n",
-       "      <td>2014-10-30</td>\n",
-       "      <td>580000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>1670</td>\n",
-       "      <td>7416</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1220</td>\n",
-       "      <td>450.0</td>\n",
+       "      <th>3834</th>\n",
+       "      <td>534000112</td>\n",
+       "      <td>2015-02-03</td>\n",
+       "      <td>348000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1270</td>\n",
+       "      <td>1242</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1270</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>False</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>50</td>\n",
+       "      <td>16</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>10756.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>3782.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3075</th>\n",
-       "      <td>8651401270</td>\n",
-       "      <td>2015-05-04</td>\n",
-       "      <td>203000.0</td>\n",
+       "      <th>14554</th>\n",
+       "      <td>6145601510</td>\n",
+       "      <td>2014-08-22</td>\n",
+       "      <td>412000.0</td>\n",
        "      <td>3</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>840</td>\n",
-       "      <td>6500</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>3844</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>840</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>900</td>\n",
+       "      <td>100.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>55</td>\n",
+       "      <td>96</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8180.0</td>\n",
-       "      <td>100K-300K</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>5844.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14083</th>\n",
-       "      <td>1338800365</td>\n",
-       "      <td>2014-05-07</td>\n",
-       "      <td>1500000.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3560</td>\n",
-       "      <td>6480</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>3560</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>13455</th>\n",
+       "      <td>1624049170</td>\n",
+       "      <td>2014-10-17</td>\n",
+       "      <td>446800.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>8712</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1260</td>\n",
+       "      <td>1150.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>110</td>\n",
+       "      <td>66</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13600.0</td>\n",
-       "      <td>1M-2M</td>\n",
-       "      <td>1M-2M</td>\n",
+       "      <td>13532.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19515</th>\n",
-       "      <td>3303850360</td>\n",
-       "      <td>2014-06-25</td>\n",
-       "      <td>1280000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>4660</td>\n",
-       "      <td>17398</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4660</td>\n",
+       "      <th>6065</th>\n",
+       "      <td>6911700066</td>\n",
+       "      <td>2014-06-04</td>\n",
+       "      <td>175000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>670</td>\n",
+       "      <td>2378</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>670</td>\n",
        "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>21</td>\n",
+       "      <td>105</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>26718.0</td>\n",
-       "      <td>1M-2M</td>\n",
-       "      <td>1M-2M</td>\n",
+       "      <td>3718.0</td>\n",
+       "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8722</th>\n",
-       "      <td>5249804760</td>\n",
-       "      <td>2015-05-05</td>\n",
-       "      <td>479500.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>930</td>\n",
-       "      <td>5760</td>\n",
+       "      <th>9734</th>\n",
+       "      <td>6071300030</td>\n",
+       "      <td>2014-06-24</td>\n",
+       "      <td>464500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>10466</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>730</td>\n",
-       "      <td>200.0</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
-       "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>107</td>\n",
+       "      <td>65</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7620.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>12766.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 41 columns</p>\n",
+       "<p>5 rows × 40 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "11485  3260200200 2014-10-30   580000.0         3       2.25         1670   \n",
-       "3075   8651401270 2015-05-04   203000.0         3       1.00          840   \n",
-       "14083  1338800365 2014-05-07  1500000.0         6       2.50         3560   \n",
-       "19515  3303850360 2014-06-25  1280000.0         4       3.50         4660   \n",
-       "8722   5249804760 2015-05-05   479500.0         2       1.00          930   \n",
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "3834    534000112 2015-02-03  348000.0         2       2.50         1270   \n",
+       "14554  6145601510 2014-08-22  412000.0         3       1.00         1000   \n",
+       "13455  1624049170 2014-10-17  446800.0         4       2.00         2410   \n",
+       "6065   6911700066 2014-06-04  175000.0         2       1.00          670   \n",
+       "9734   6071300030 2014-06-24  464500.0         3       1.75         1150   \n",
        "\n",
-       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_5 Fair  \\\n",
-       "11485      7416     1.0        1220          450.0  ...         False   \n",
-       "3075       6500     1.0         840            0.0  ...         False   \n",
-       "14083      6480     2.5        3560            0.0  ...         False   \n",
-       "19515     17398     2.0        4660            0.0  ...         False   \n",
-       "8722       5760     1.0         730          200.0  ...         False   \n",
+       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_4 Low  \\\n",
+       "3834       1242     3.0        1270            0.0  ...        False   \n",
+       "14554      3844     1.0         900          100.0  ...        False   \n",
+       "13455      8712     1.0        1260         1150.0  ...        False   \n",
+       "6065       2378     1.0         670            0.0  ...        False   \n",
+       "9734      10466     1.0        1150            0.0  ...        False   \n",
        "\n",
-       "       grade_6 Low Average  grade_7 Average  grade_8 Good  grade_9 Better  \\\n",
-       "11485                False             True         False           False   \n",
-       "3075                  True            False         False           False   \n",
-       "14083                False            False         False           False   \n",
-       "19515                False            False         False           False   \n",
-       "8722                  True            False         False           False   \n",
+       "       grade_5 Fair  grade_6 Low Average  grade_7 Average  grade_8 Good  \\\n",
+       "3834          False                False             True         False   \n",
+       "14554         False                False             True         False   \n",
+       "13455         False                False             True         False   \n",
+       "6065           True                False            False         False   \n",
+       "9734          False                False             True         False   \n",
        "\n",
-       "       house_age  renovation_age  total_sqft  pricerange  price_range  \n",
-       "11485         50             0.0     10756.0   300K-600K    300K-600K  \n",
-       "3075          55             0.0      8180.0   100K-300K    100K-300K  \n",
-       "14083        110             0.0     13600.0       1M-2M        1M-2M  \n",
-       "19515         21             0.0     26718.0       1M-2M        1M-2M  \n",
-       "8722         107             0.0      7620.0   300K-600K    300K-600K  \n",
+       "       grade_9 Better  house_age  renovation_age  total_sqft  price_range  \n",
+       "3834            False         16             0.0      3782.0    300K-600K  \n",
+       "14554           False         96             0.0      5844.0    300K-600K  \n",
+       "13455           False         66             0.0     13532.0    300K-600K  \n",
+       "6065            False        105             0.0      3718.0    100K-300K  \n",
+       "9734            False         65             0.0     12766.0    300K-600K  \n",
        "\n",
-       "[5 rows x 41 columns]"
+       "[5 rows x 40 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1501,33 +1501,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
-    "# Set the style of seaborn\n",
-    "sns.set_style(\"whitegrid\")\n",
-    "\n",
-    "# Create a bar plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(x=\"pricerange\", y=\"total_sqft\", data=housing_data_encoded, errorbar=None)\n",
-    "plt.title(\"Total Square Footage by Price Range\")\n",
-    "plt.xlabel(\"Price Range\")\n",
-    "plt.ylabel(\"Total Square Footage\")\n",
-    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
-    "plt.show()\n"
+    "# # Set the style of seaborn\n",
+    "# sns.set_style(\"whitegrid\")\n",
+    "\n",
+    "# # Create a bar plot\n",
+    "# plt.figure(figsize=(10, 6))\n",
+    "# sns.barplot(x=\"pricerange\", y=\"total_sqft\", data=housing_data_encoded, errorbar=None)\n",
+    "# plt.title(\"Total Square Footage by Price Range\")\n",
+    "# plt.xlabel(\"Price Range\")\n",
+    "# plt.ylabel(\"Total Square Footage\")\n",
+    "# plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
+    "# plt.show()\n"
    ]
   },
   {
@@ -1541,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1578,7 +1567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1597,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:"
+    "##### Interpretation:\n"
    ]
   },
   {
@@ -1620,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1659,40 +1648,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "Inconsistent shape between the condition and the input (got (18, 1) and (18,))",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[50], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plot the correlation matrix as a heatmap\u001b[39;00m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m sns\u001b[38;5;241m.\u001b[39mheatmap(correlation, annot\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoolwarm\u001b[39m\u001b[38;5;124m'\u001b[39m, fmt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;124m\"\u001b[39m, linewidths\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCorrelation Matrix\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
-      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:446\u001b[0m, in \u001b[0;36mheatmap\u001b[1;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[0;32m    365\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Plot rectangular data as a color-encoded matrix.\u001b[39;00m\n\u001b[0;32m    366\u001b[0m \n\u001b[0;32m    367\u001b[0m \u001b[38;5;124;03mThis is an Axes-level function and will draw the heatmap into the\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    443\u001b[0m \n\u001b[0;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    445\u001b[0m \u001b[38;5;66;03m# Initialize the plotter object\u001b[39;00m\n\u001b[1;32m--> 446\u001b[0m plotter \u001b[38;5;241m=\u001b[39m _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[0;32m    447\u001b[0m                       annot_kws, cbar, cbar_kws, xticklabels,\n\u001b[0;32m    448\u001b[0m                       yticklabels, mask)\n\u001b[0;32m    450\u001b[0m \u001b[38;5;66;03m# Add the pcolormesh kwargs here\u001b[39;00m\n\u001b[0;32m    451\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinewidths\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m linewidths\n",
-      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\seaborn\\matrix.py:115\u001b[0m, in \u001b[0;36m_HeatMapper.__init__\u001b[1;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;66;03m# Validate the mask and convert to DataFrame\u001b[39;00m\n\u001b[0;32m    113\u001b[0m mask \u001b[38;5;241m=\u001b[39m _matrix_mask(data, mask)\n\u001b[1;32m--> 115\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mmasked_where(np\u001b[38;5;241m.\u001b[39masarray(mask), plot_data)\n\u001b[0;32m    117\u001b[0m \u001b[38;5;66;03m# Get good names for the rows and columns\u001b[39;00m\n\u001b[0;32m    118\u001b[0m xtickevery \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
-      "File \u001b[1;32mc:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:1933\u001b[0m, in \u001b[0;36mmasked_where\u001b[1;34m(condition, a, copy)\u001b[0m\n\u001b[0;32m   1931\u001b[0m (cshape, ashape) \u001b[38;5;241m=\u001b[39m (cond\u001b[38;5;241m.\u001b[39mshape, a\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m   1932\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cshape \u001b[38;5;129;01mand\u001b[39;00m cshape \u001b[38;5;241m!=\u001b[39m ashape:\n\u001b[1;32m-> 1933\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInconsistent shape between the condition and the input\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1934\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (got \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (cshape, ashape))\n\u001b[0;32m   1935\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(a, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_mask\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m   1936\u001b[0m     cond \u001b[38;5;241m=\u001b[39m mask_or(cond, a\u001b[38;5;241m.\u001b[39m_mask)\n",
-      "\u001b[1;31mIndexError\u001b[0m: Inconsistent shape between the condition and the input (got (18, 1) and (18,))"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1200x800 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
-    "# Plot the correlation matrix as a heatmap\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
-    "plt.title('Correlation Matrix')\n",
-    "plt.show()\n"
+    "# # Plot the correlation matrix as a heatmap\n",
+    "# plt.figure(figsize=(12, 8))\n",
+    "# sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
+    "# plt.title('Correlation Matrix')\n",
+    "# plt.show()\n"
    ]
   },
   {

From f0a4c8a62e23a97ac7c62ddb383f7ed26ec4376c Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Mon, 29 Apr 2024 23:30:30 +0300
Subject: [PATCH 09/27] Finish EDA

---
 student.ipynb | 2141 ++++++++++++++++++++++++++++++++++---------------
 1 file changed, 1475 insertions(+), 666 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index c50d9507..3b8c5a8b 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 509,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,9 +249,16 @@
     "from scipy.stats import ttest_ind  # Independent sample t-test for comparing means."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *loading the King County House Sales dataset*\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 510,
    "metadata": {},
    "outputs": [
     {
@@ -290,40 +297,207 @@
     },
     {
      "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": [
-       "(None,\n",
-       "            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])"
+       "           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": 52,
+     "execution_count": 510,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,12 +508,214 @@
     "housing_data = pd.read_csv(file_path)\n",
     "\n",
     "# Display basic information and the first few rows of the dataset\n",
-    "housing_data.info(), housing_data.head()"
+    "housing_data.info()\n",
+    "housing_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *loading the column.md dataset*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 511,
+   "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>Column_name</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>* `id`</td>\n",
+       "      <td>Unique identifier for a house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `date`</td>\n",
+       "      <td>Date house was sold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `price`</td>\n",
+       "      <td>Sale price (prediction target)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `bedrooms`</td>\n",
+       "      <td>Number of bedrooms</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bathrooms`</td>\n",
+       "      <td>Number of bathrooms</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `sqft_living`</td>\n",
+       "      <td>Square footage of living space in the home</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_lot`</td>\n",
+       "      <td>Square footage of the lot</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `floors`</td>\n",
+       "      <td>Number of floors (levels) in house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `waterfront`</td>\n",
+       "      <td>Whether the house is on a waterfront</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `view`</td>\n",
+       "      <td>Quality of view from house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>* `condition`</td>\n",
+       "      <td>How good the overall condition of the house is...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `grade`</td>\n",
+       "      <td>Overall grade of the house. Related to the con...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>* `sqft_above`</td>\n",
+       "      <td>Square footage of house apart from basement</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `sqft_basement`</td>\n",
+       "      <td>Square footage of the basement</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>* `yr_built`</td>\n",
+       "      <td>Year when house was built</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `yr_renovated`</td>\n",
+       "      <td>Year when house was renovated</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>* `zipcode`</td>\n",
+       "      <td>ZIP Code used by the United States Postal Service</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `lat`</td>\n",
+       "      <td>Latitude coordinate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `long`</td>\n",
+       "      <td>Longitude coordinate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `sqft_living15`</td>\n",
+       "      <td>The square footage of interior housing living ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `sqft_lot15`</td>\n",
+       "      <td>The square footage of the land lots of the nea...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Column_name                                        Description\n",
+       "0              * `id`                      Unique identifier for a house\n",
+       "1            * `date`                                Date house was sold\n",
+       "2           * `price`                     Sale price (prediction target)\n",
+       "3        * `bedrooms`                                 Number of bedrooms\n",
+       "4       * `bathrooms`                                Number of bathrooms\n",
+       "5     * `sqft_living`         Square footage of living space in the home\n",
+       "6        * `sqft_lot`                          Square footage of the lot\n",
+       "7          * `floors`                 Number of floors (levels) in house\n",
+       "8      * `waterfront`               Whether the house is on a waterfront\n",
+       "9            * `view`                         Quality of view from house\n",
+       "10      * `condition`  How good the overall condition of the house is...\n",
+       "11          * `grade`  Overall grade of the house. Related to the con...\n",
+       "12     * `sqft_above`        Square footage of house apart from basement\n",
+       "13  * `sqft_basement`                     Square footage of the basement\n",
+       "14       * `yr_built`                          Year when house was built\n",
+       "15   * `yr_renovated`                      Year when house was renovated\n",
+       "16        * `zipcode`  ZIP Code used by the United States Postal Service\n",
+       "17            * `lat`                                Latitude coordinate\n",
+       "18           * `long`                               Longitude coordinate\n",
+       "19  * `sqft_living15`  The square footage of interior housing living ...\n",
+       "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
+      ]
+     },
+     "execution_count": 511,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_names_file = \"data/column_names.md\"\n",
+    "\n",
+    "with open(column_names_file, \"r\") as file:\n",
+    "    markdown_content = file.readlines()\n",
+    "\n",
+    "column_names = []\n",
+    "description = []\n",
+    "for line in markdown_content:\n",
+    "    parts = line.split('-', 1)\n",
+    "    if len(parts) == 2:  # Check if split produces two parts\n",
+    "        column_names.append(parts[0].strip())\n",
+    "        description.append(parts[1].strip())\n",
+    "\n",
+    "# Create DataFrame\n",
+    "data = {\n",
+    "    \"Column_name\": column_names,\n",
+    "    \"Description\": description\n",
+    "}\n",
+    "column_name_df = pd.DataFrame(data)\n",
+    "\n",
+    "column_name_df\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 512,
    "metadata": {},
    "outputs": [
     {
@@ -370,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 513,
    "metadata": {},
    "outputs": [
     {
@@ -399,14 +775,14 @@
    "source": [
     "The dataset contains 21,597 entries and 21 features. Here’s a brief overview of the data:\n",
     "\n",
-    "### Columns and Their Data Types:\n",
-    "### Numerical:\n",
+    "### Columns and their Data Types:\n",
+    "#### Numerical:\n",
     "id, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15\n",
     "\n",
-    "### Categorical:\n",
+    "#### Categorical:\n",
     "date (format object, should be datetime), waterfront, view, condition, grade, sqft_basement (format object, should be numeric)\n",
     "\n",
-    "### Missing Values:\n",
+    "#### Missing Values:\n",
     "waterfront: 2,376 missing values\n",
     "view: 63 missing values\n",
     "yr_renovated: 3,842 missing values\n"
@@ -416,123 +792,403 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. DATA CLEANING\n",
-    "\n",
-    "### A) Handle Missing Values:\n",
-    "#### waterfront and view: \n",
-    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
-    "yr_renovated: A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value.\n",
-    "\n",
-    "### B) Convert Data Types:\n",
-    "#### date:\n",
-    "Convert from object to datetime format.\n",
-    "sqft_basement: Convert from object to numeric, handling any non-numeric entries.\n",
-    "\n",
-    "### C)Encode Categorical Data:\n",
-    "One-hot encoding for categorical variables with no intrinsic ordering (waterfront, view, condition, grade).\n",
-    "This approach avoids any ordinal implications that could mislead the model.\n",
-    "\n"
+    "## 2. DATA CLEANING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a)Dropping columns:\n",
+    "We're dropping some columns during data cleaning to streamline our analysis and focus on the most relevant features. By removing unnecessary or redundant columns, we aim to simplify the dataset and improve the efficiency of subsequent analytical processes. This helps in reducing noise and enhancing the clarity of our findings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 514,
    "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  datetime64[ns]\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     21597 non-null  object        \n",
-      " 9   view           21597 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  float64       \n",
-      " 14  yr_built       21597 non-null  int64         \n",
-      " 15  yr_renovated   21597 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: datetime64[ns](1), float64(7), int64(9), object(4)\n",
-      "memory usage: 3.5+ MB\n"
-     ]
-    },
     {
      "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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>943</th>\n",
+       "      <td>8712100575</td>\n",
+       "      <td>8/28/2014</td>\n",
+       "      <td>915000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2750</td>\n",
+       "      <td>5589</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>9 Better</td>\n",
+       "      <td>1840</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1460</td>\n",
+       "      <td>4250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1346</th>\n",
+       "      <td>217500135</td>\n",
+       "      <td>4/21/2015</td>\n",
+       "      <td>450000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2040</td>\n",
+       "      <td>9565</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>1959</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1890</td>\n",
+       "      <td>8580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21296</th>\n",
+       "      <td>7708200670</td>\n",
+       "      <td>7/23/2014</td>\n",
+       "      <td>490000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>4349</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2510</td>\n",
+       "      <td>4314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5896</th>\n",
+       "      <td>3303980500</td>\n",
+       "      <td>9/5/2014</td>\n",
+       "      <td>1030000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>3780</td>\n",
+       "      <td>11200</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>11 Excellent</td>\n",
+       "      <td>3780</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3720</td>\n",
+       "      <td>11813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5809</th>\n",
+       "      <td>1922069071</td>\n",
+       "      <td>4/24/2015</td>\n",
+       "      <td>411000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>292288</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1963</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1550</td>\n",
+       "      <td>23798</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "(None,\n",
-       "   waterfront  view  yr_renovated       date  sqft_basement\n",
-       " 0         NO  NONE           0.0 2014-10-13            0.0\n",
-       " 1         NO  NONE        1991.0 2014-12-09          400.0\n",
-       " 2         NO  NONE           0.0 2015-02-25            0.0\n",
-       " 3         NO  NONE           0.0 2014-12-09          910.0\n",
-       " 4         NO  NONE           0.0 2015-02-18            0.0)"
+       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
+       "943    8712100575  8/28/2014   915000.0         5       2.50         2750   \n",
+       "1346    217500135  4/21/2015   450000.0         4       2.25         2040   \n",
+       "21296  7708200670  7/23/2014   490000.0         4       2.50         2510   \n",
+       "5896   3303980500   9/5/2014  1030000.0         4       3.25         3780   \n",
+       "5809   1922069071  4/24/2015   411000.0         4       1.75         2250   \n",
+       "\n",
+       "       sqft_lot  floors waterfront  condition         grade  sqft_above  \\\n",
+       "943        5589     1.5         NO  Very Good      9 Better        1840   \n",
+       "1346       9565     1.0         NO    Average        8 Good        1400   \n",
+       "21296      4349     2.0        NaN    Average        8 Good        2510   \n",
+       "5896      11200     2.0         NO    Average  11 Excellent        3780   \n",
+       "5809     292288     1.0         NO       Good     7 Average        2250   \n",
+       "\n",
+       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "943           910.0      1910           0.0           1460        4250  \n",
+       "1346          640.0      1959           0.0           1890        8580  \n",
+       "21296           0.0      2010           0.0           2510        4314  \n",
+       "5896            0.0      2002           0.0           3720       11813  \n",
+       "5809            0.0      1963           0.0           1550       23798  "
       ]
      },
-     "execution_count": 55,
+     "execution_count": 514,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# A)Handle missing values\n",
-    "# For categorical data, impute missing values with mode or specific marker\n",
-    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
-    "view_mode = housing_data['view'].mode()[0]\n",
-    "\n",
-    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
-    "housing_data['view'].fillna(view_mode, inplace=True)\n",
-    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN\n",
+    "housing_data = housing_data.drop(['long','lat','view', 'zipcode'], axis=1)\n",
+    "housing_data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b)Checking for placeholders\n",
     "\n",
+    "Placeholders are values used to denote missing, unknown, or invalid data within a dataset. Common examples include \"N/A\", \"-\", \"UNKNOWN\", \"NULL\", #, etc. It's important to identify and handle placeholders properly during data preprocessing to ensure accurate analysis and modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 515,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column 'sqft_basement': Found 454 occurrences of potential placeholder .\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a list of common placeholder values\n",
+    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
+    "\n",
+    "# Check for potential placeholders in the DataFrame\n",
+    "found_placeholder = False\n",
+    "for column in housing_data.columns:\n",
+    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
+    "    if placeholder_count > 0:\n",
+    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
+    "        found_placeholder = True\n",
+    "\n",
+    "if not found_placeholder:\n",
+    "    print(\"No potential placeholders found in the DataFrame.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 516,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "\n",
-    "# B) Convert data types\n",
-    "from datetime import datetime\n",
+    "# Convert the common placeholders to lowercase for case-insensitive matching\n",
+    "common_placeholders_lower = [placeholder.lower() for placeholder in common_placeholders]\n",
     "\n",
-    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
-    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
-    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric\n",
+    "# Replace any of the common placeholders with NaN\n",
+    "housing_data['sqft_basement'] = housing_data['sqft_basement'].replace(common_placeholders_lower, pd.NA)\n",
     "\n",
-    "# Check transformations\n",
-    "housing_data.info(), housing_data[['waterfront', 'view', 'yr_renovated', 'date', 'sqft_basement']].head()\n"
+    "# Drop rows with NaN in the sqft_basement column\n",
+    "housing_data.dropna(subset=['sqft_basement'], inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Counter-check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 517,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No potential placeholders found in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# confirm no more placeholders\n",
+    "# Define a list of common placeholder values\n",
+    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
+    "\n",
+    "# Check for potential placeholders in the DataFrame\n",
+    "found_placeholder = False\n",
+    "for column in housing_data.columns:\n",
+    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
+    "    if placeholder_count > 0:\n",
+    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
+    "        found_placeholder = True\n",
+    "\n",
+    "if not found_placeholder:\n",
+    "    print(\"No potential placeholders found in the DataFrame.\")\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The missing values have been addressed and data types for relevant columns have been corrected:\n",
-    "\n",
-    "#### Missing Values:\n",
     "\n",
-    "waterfront and view had missing values which were filled using the mode of their respective columns.\n",
     "\n",
-    "yr_renovated missing values were filled with 0, assuming that a missing value indicates no renovation.\n",
+    "### b) Handling Missing Values:\n",
+    "waterfront: \n",
+    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
+    "<br> yr_renovated: \n",
+    "A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 518,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For categorical data, impute missing values with mode or specific marker\n",
+    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
     "\n",
-    "#### Data Type Conversions:\n",
-    "The date column has been converted to a datetime format.\n",
+    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
+    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Convert Data\n",
+    "Convert date from object to datetime format.\n",
+    "sqft_basement: Convert from object to numeric, handling any non-numeric entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 519,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
     "\n",
-    "The sqft_basement column, previously an object due to some non-numeric entries, has been converted to numeric. Missing and non-numeric entries were replaced with 0, assuming no basement in such cases.\n"
+    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
+    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
+    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 520,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change waterfront to integer\n",
+    "housing_data['waterfront'] = housing_data['waterfront'].apply(lambda x: 0 if x == 'NO' else 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 521,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['7 Average' '6 Low Average' '8 Good' '11 Excellent' '9 Better' '5 Fair'\n",
+      " '10 Very Good' '12 Luxury' '4 Low' '3 Poor' '13 Mansion']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# checking \"grade\" column\n",
+    "unique_grade = housing_data.grade.unique()\n",
+    "print(unique_grade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Average' 'Very Good' 'Good' 'Poor' 'Fair']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# checking \"condition\"column\n",
+    "unique_condition = housing_data.condition.unique()\n",
+    "print(unique_condition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 523,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert grade and condition into representative numbers for easier Exploratory analysis.\n",
+    "housing_data['condition'] = housing_data['condition'].map({'Poor': 1,'Fair': 2,'Average': 3,'Good': 4,'Very Good': 5}).astype(float)\n",
+    "housing_data['grade'] = housing_data['grade'].map({'3 Poor': 1,'4 Low': 2,'5 Fair': 3,'6 Low Average': 4,'7 Average': 5,'8 Good': 6,'9 Better': 7,'10 Very Good': 8,'11 Excellent': 9,'12 Luxury': 10,'13 Mansion': 11}).astype(float) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 524,
    "metadata": {},
    "outputs": [
     {
@@ -540,103 +1196,209 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21597 entries, 0 to 21596\n",
-      "Data columns (total 36 columns):\n",
-      " #   Column               Non-Null Count  Dtype         \n",
-      "---  ------               --------------  -----         \n",
-      " 0   id                   21597 non-null  int64         \n",
-      " 1   date                 21597 non-null  datetime64[ns]\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   sqft_above           21597 non-null  int64         \n",
-      " 9   sqft_basement        21597 non-null  float64       \n",
-      " 10  yr_built             21597 non-null  int64         \n",
-      " 11  yr_renovated         21597 non-null  float64       \n",
-      " 12  zipcode              21597 non-null  int64         \n",
-      " 13  lat                  21597 non-null  float64       \n",
-      " 14  long                 21597 non-null  float64       \n",
-      " 15  sqft_living15        21597 non-null  int64         \n",
-      " 16  sqft_lot15           21597 non-null  int64         \n",
-      " 17  waterfront_YES       21597 non-null  bool          \n",
-      " 18  view_EXCELLENT       21597 non-null  bool          \n",
-      " 19  view_FAIR            21597 non-null  bool          \n",
-      " 20  view_GOOD            21597 non-null  bool          \n",
-      " 21  view_NONE            21597 non-null  bool          \n",
-      " 22  condition_Fair       21597 non-null  bool          \n",
-      " 23  condition_Good       21597 non-null  bool          \n",
-      " 24  condition_Poor       21597 non-null  bool          \n",
-      " 25  condition_Very Good  21597 non-null  bool          \n",
-      " 26  grade_11 Excellent   21597 non-null  bool          \n",
-      " 27  grade_12 Luxury      21597 non-null  bool          \n",
-      " 28  grade_13 Mansion     21597 non-null  bool          \n",
-      " 29  grade_3 Poor         21597 non-null  bool          \n",
-      " 30  grade_4 Low          21597 non-null  bool          \n",
-      " 31  grade_5 Fair         21597 non-null  bool          \n",
-      " 32  grade_6 Low Average  21597 non-null  bool          \n",
-      " 33  grade_7 Average      21597 non-null  bool          \n",
-      " 34  grade_8 Good         21597 non-null  bool          \n",
-      " 35  grade_9 Better       21597 non-null  bool          \n",
-      "dtypes: bool(19), datetime64[ns](1), float64(7), int64(9)\n",
-      "memory usage: 3.2 MB\n"
+      "Index: 21143 entries, 0 to 21596\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column         Non-Null Count  Dtype         \n",
+      "---  ------         --------------  -----         \n",
+      " 0   id             21143 non-null  int64         \n",
+      " 1   date           21143 non-null  datetime64[ns]\n",
+      " 2   price          21143 non-null  float64       \n",
+      " 3   bedrooms       21143 non-null  int64         \n",
+      " 4   bathrooms      21143 non-null  float64       \n",
+      " 5   sqft_living    21143 non-null  int64         \n",
+      " 6   sqft_lot       21143 non-null  int64         \n",
+      " 7   floors         21143 non-null  float64       \n",
+      " 8   waterfront     21143 non-null  int64         \n",
+      " 9   condition      21143 non-null  float64       \n",
+      " 10  grade          21143 non-null  float64       \n",
+      " 11  sqft_above     21143 non-null  int64         \n",
+      " 12  sqft_basement  21143 non-null  float64       \n",
+      " 13  yr_built       21143 non-null  int64         \n",
+      " 14  yr_renovated   21143 non-null  float64       \n",
+      " 15  sqft_living15  21143 non-null  int64         \n",
+      " 16  sqft_lot15     21143 non-null  int64         \n",
+      "dtypes: datetime64[ns](1), float64(7), int64(9)\n",
+      "memory usage: 2.9 MB\n"
      ]
     },
     {
      "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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>14733</th>\n",
+       "      <td>2525059077</td>\n",
+       "      <td>2014-05-20</td>\n",
+       "      <td>765000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>12100</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>1976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2240</td>\n",
+       "      <td>12100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5536</th>\n",
+       "      <td>2248000080</td>\n",
+       "      <td>2014-05-21</td>\n",
+       "      <td>385500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>7947</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1120</td>\n",
+       "      <td>420.0</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>7950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10495</th>\n",
+       "      <td>9290850060</td>\n",
+       "      <td>2014-10-22</td>\n",
+       "      <td>910000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3170</td>\n",
+       "      <td>32430</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3170</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>35610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15676</th>\n",
+       "      <td>5422560660</td>\n",
+       "      <td>2014-10-30</td>\n",
+       "      <td>407000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>6635</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>6635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14699</th>\n",
+       "      <td>714000210</td>\n",
+       "      <td>2014-09-03</td>\n",
+       "      <td>998000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3030</td>\n",
+       "      <td>6820</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2530</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>2070</td>\n",
+       "      <td>6820</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "(None,\n",
-       "            id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       " 0  7129300520 2014-10-13  221900.0         3       1.00         1180   \n",
-       " 1  6414100192 2014-12-09  538000.0         3       2.25         2570   \n",
-       " 2  5631500400 2015-02-25  180000.0         2       1.00          770   \n",
-       " 3  2487200875 2014-12-09  604000.0         4       3.00         1960   \n",
-       " 4  1954400510 2015-02-18  510000.0         3       2.00         1680   \n",
-       " \n",
-       "    sqft_lot  floors  sqft_above  sqft_basement  ...  grade_11 Excellent  \\\n",
-       " 0      5650     1.0        1180            0.0  ...               False   \n",
-       " 1      7242     2.0        2170          400.0  ...               False   \n",
-       " 2     10000     1.0         770            0.0  ...               False   \n",
-       " 3      5000     1.0        1050          910.0  ...               False   \n",
-       " 4      8080     1.0        1680            0.0  ...               False   \n",
-       " \n",
-       "    grade_12 Luxury  grade_13 Mansion  grade_3 Poor  grade_4 Low  grade_5 Fair  \\\n",
-       " 0            False             False         False        False         False   \n",
-       " 1            False             False         False        False         False   \n",
-       " 2            False             False         False        False         False   \n",
-       " 3            False             False         False        False         False   \n",
-       " 4            False             False         False        False         False   \n",
-       " \n",
-       "    grade_6 Low Average  grade_7 Average  grade_8 Good  grade_9 Better  \n",
-       " 0                False             True         False           False  \n",
-       " 1                False             True         False           False  \n",
-       " 2                 True            False         False           False  \n",
-       " 3                False             True         False           False  \n",
-       " 4                False            False          True           False  \n",
-       " \n",
-       " [5 rows x 36 columns])"
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "14733  2525059077 2014-05-20  765000.0         4       2.25         2560   \n",
+       "5536   2248000080 2014-05-21  385500.0         3       2.00         1540   \n",
+       "10495  9290850060 2014-10-22  910000.0         4       2.50         3170   \n",
+       "15676  5422560660 2014-10-30  407000.0         2       2.50         1700   \n",
+       "14699   714000210 2014-09-03  998000.0         4       2.50         3030   \n",
+       "\n",
+       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "14733     12100     1.0           0        4.0    6.0        1760   \n",
+       "5536       7947     1.0           0        3.0    5.0        1120   \n",
+       "10495     32430     2.5           0        3.0    8.0        3170   \n",
+       "15676      6635     2.0           0        4.0    6.0        1700   \n",
+       "14699      6820     2.0           0        3.0    7.0        2530   \n",
+       "\n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "14733          800.0      1976           0.0           2240       12100  \n",
+       "5536           420.0      1961           0.0           1910        7950  \n",
+       "10495            0.0      1989           0.0           3360       35610  \n",
+       "15676            0.0      1976           0.0           1700        6635  \n",
+       "14699          500.0      1947        2000.0           2070        6820  "
       ]
      },
-     "execution_count": 56,
+     "execution_count": 524,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# One-hot encoding for categorical variables without a natural order\n",
-    "housing_data_encoded = pd.get_dummies(housing_data, columns=['waterfront', 'view', 'condition', 'grade'], drop_first=True)\n",
     "\n",
-    "# Display the columns after encoding to confirm the transformation\n",
-    "housing_data_encoded.info(), housing_data_encoded.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The categorical variables have been successfully encoded using one-hot encoding. This transformation results in binary columns for each category within the original features, which will help in preventing any misinterpretation of categorical data as ordinal by statistical models."
+    "# Check transformations\n",
+    "housing_data.info()\n",
+    "housing_data.sample(5)\n"
    ]
   },
   {
@@ -658,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 525,
    "metadata": {},
    "outputs": [
     {
@@ -731,7 +1493,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 525,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,15 +1505,15 @@
     "current_year = datetime.now().year\n",
     "\n",
     "# Feature Engineering\n",
-    "housing_data_encoded['house_age'] = current_year - housing_data_encoded['yr_built']\n",
-    "housing_data_encoded['renovation_age'] = housing_data_encoded.apply(\n",
+    "housing_data['house_age'] = current_year - housing_data['yr_built']\n",
+    "housing_data['renovation_age'] = housing_data.apply(\n",
     "    lambda x: 0 if x['yr_renovated'] == 0 else current_year - x['yr_renovated'], axis=1\n",
     ")\n",
-    "housing_data_encoded['total_sqft'] = housing_data_encoded['sqft_living'] + housing_data_encoded['sqft_lot'] + \\\n",
-    "                                     housing_data_encoded['sqft_above'] + housing_data_encoded['sqft_basement']\n",
+    "housing_data['total_sqft'] = housing_data['sqft_living'] + housing_data['sqft_lot'] + \\\n",
+    "                                     housing_data['sqft_above'] + housing_data['sqft_basement']\n",
     "\n",
     "# Display the new features\n",
-    "housing_data_encoded[['house_age', 'renovation_age', 'total_sqft']].head()\n"
+    "housing_data[['house_age', 'renovation_age', 'total_sqft']].head()\n"
    ]
   },
   {
@@ -771,12 +1533,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Sample data check."
+    "##### *Sample data check.*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 526,
    "metadata": {},
    "outputs": [
     {
@@ -808,16 +1570,15 @@
        "      <th>sqft_living</th>\n",
        "      <th>sqft_lot</th>\n",
        "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>grade</th>\n",
        "      <th>sqft_above</th>\n",
        "      <th>sqft_basement</th>\n",
-       "      <th>...</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",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
        "      <th>house_age</th>\n",
        "      <th>renovation_age</th>\n",
        "      <th>total_sqft</th>\n",
@@ -825,174 +1586,166 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4864</th>\n",
-       "      <td>3293400010</td>\n",
-       "      <td>2015-03-04</td>\n",
-       "      <td>950000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3450</td>\n",
-       "      <td>35880</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3450</td>\n",
+       "      <th>19131</th>\n",
+       "      <td>6713700155</td>\n",
+       "      <td>2014-08-18</td>\n",
+       "      <td>352500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1470</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1470</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>32</td>\n",
+       "      <td>1953</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1470</td>\n",
+       "      <td>8400</td>\n",
+       "      <td>71</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>42780.0</td>\n",
+       "      <td>11340.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18353</th>\n",
-       "      <td>1370803445</td>\n",
-       "      <td>2014-09-09</td>\n",
-       "      <td>1140000.0</td>\n",
-       "      <td>4</td>\n",
+       "      <th>5032</th>\n",
+       "      <td>2558660190</td>\n",
+       "      <td>2014-10-30</td>\n",
+       "      <td>459000.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>1.75</td>\n",
-       "      <td>3080</td>\n",
-       "      <td>6500</td>\n",
+       "      <td>1730</td>\n",
+       "      <td>7807</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>1380.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1260</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>1976</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12660.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1250</th>\n",
-       "      <td>1926049355</td>\n",
-       "      <td>2014-10-28</td>\n",
-       "      <td>399000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2620</td>\n",
-       "      <td>7030</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1420</td>\n",
-       "      <td>1200.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>59</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>7650</td>\n",
+       "      <td>48</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12270.0</td>\n",
+       "      <td>11267.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10810</th>\n",
-       "      <td>6071800310</td>\n",
-       "      <td>2014-06-19</td>\n",
-       "      <td>558000.0</td>\n",
+       "      <th>7573</th>\n",
+       "      <td>7922720040</td>\n",
+       "      <td>2015-03-17</td>\n",
+       "      <td>680000.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2060</td>\n",
-       "      <td>10358</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2880</td>\n",
+       "      <td>9202</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1320</td>\n",
-       "      <td>740.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>62</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>1100.0</td>\n",
+       "      <td>1977</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>14478.0</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>10265</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14962.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12222</th>\n",
-       "      <td>6918710340</td>\n",
-       "      <td>2014-08-22</td>\n",
-       "      <td>385000.0</td>\n",
+       "      <th>12237</th>\n",
+       "      <td>7560000070</td>\n",
+       "      <td>2014-06-10</td>\n",
+       "      <td>710000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2110</td>\n",
-       "      <td>8000</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>3427</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>2110</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>450.0</td>\n",
+       "      <td>2000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>49</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>2601</td>\n",
+       "      <td>24</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8307.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6502</th>\n",
+       "      <td>4217402115</td>\n",
+       "      <td>2015-04-21</td>\n",
+       "      <td>3650000.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4.75</td>\n",
+       "      <td>5480</td>\n",
+       "      <td>19401</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3910</td>\n",
+       "      <td>1570.0</td>\n",
+       "      <td>1936</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3510</td>\n",
+       "      <td>15810</td>\n",
+       "      <td>88</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12220.0</td>\n",
+       "      <td>30361.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 39 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "4864   3293400010 2015-03-04   950000.0         5       2.50         3450   \n",
-       "18353  1370803445 2014-09-09  1140000.0         4       1.75         3080   \n",
-       "1250   1926049355 2014-10-28   399000.0         5       2.00         2620   \n",
-       "10810  6071800310 2014-06-19   558000.0         4       2.25         2060   \n",
-       "12222  6918710340 2014-08-22   385000.0         3       2.25         2110   \n",
+       "19131  6713700155 2014-08-18   352500.0         3       1.00         1470   \n",
+       "5032   2558660190 2014-10-30   459000.0         3       1.75         1730   \n",
+       "7573   7922720040 2015-03-17   680000.0         4       2.50         2880   \n",
+       "12237  7560000070 2014-06-10   710000.0         3       3.50         2440   \n",
+       "6502   4217402115 2015-04-21  3650000.0         6       4.75         5480   \n",
        "\n",
-       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_3 Poor  \\\n",
-       "4864      35880     2.0        3450            0.0  ...         False   \n",
-       "18353      6500     1.0        1700         1380.0  ...         False   \n",
-       "1250       7030     1.0        1420         1200.0  ...         False   \n",
-       "10810     10358     1.0        1320          740.0  ...         False   \n",
-       "12222      8000     2.0        2110            0.0  ...         False   \n",
+       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "19131      8400     1.0           0        4.0    5.0        1470   \n",
+       "5032       7807     1.0           0        3.0    5.0        1260   \n",
+       "7573       9202     1.0           0        3.0    6.0        1780   \n",
+       "12237      3427     2.0           0        3.0    5.0        1990   \n",
+       "6502      19401     1.5           1        5.0    9.0        3910   \n",
        "\n",
-       "       grade_4 Low  grade_5 Fair  grade_6 Low Average  grade_7 Average  \\\n",
-       "4864         False         False                False            False   \n",
-       "18353        False         False                False            False   \n",
-       "1250         False         False                False            False   \n",
-       "10810        False         False                False            False   \n",
-       "12222        False         False                False            False   \n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "19131            0.0      1953           0.0           1470        8400   \n",
+       "5032           470.0      1976           0.0           1800        7650   \n",
+       "7573          1100.0      1977           0.0           2500       10265   \n",
+       "12237          450.0      2000           0.0           2440        2601   \n",
+       "6502          1570.0      1936           0.0           3510       15810   \n",
        "\n",
-       "       grade_8 Good  grade_9 Better  house_age  renovation_age  total_sqft  \n",
-       "4864          False           False         32             0.0     42780.0  \n",
-       "18353         False            True         83             0.0     12660.0  \n",
-       "1250           True           False         59             0.0     12270.0  \n",
-       "10810          True           False         62             0.0     14478.0  \n",
-       "12222          True           False         49             0.0     12220.0  \n",
-       "\n",
-       "[5 rows x 39 columns]"
+       "       house_age  renovation_age  total_sqft  \n",
+       "19131         71             0.0     11340.0  \n",
+       "5032          48             0.0     11267.0  \n",
+       "7573          47             0.0     14962.0  \n",
+       "12237         24             0.0      8307.0  \n",
+       "6502          88             0.0     30361.0  "
       ]
      },
-     "execution_count": 58,
+     "execution_count": 526,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "housing_data_encoded.sample(5)"
+    "housing_data.sample(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 527,
    "metadata": {},
    "outputs": [
     {
@@ -1000,56 +1753,37 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 21597 entries, 0 to 21596\n",
-      "Data columns (total 39 columns):\n",
-      " #   Column               Non-Null Count  Dtype         \n",
-      "---  ------               --------------  -----         \n",
-      " 0   id                   21597 non-null  int64         \n",
-      " 1   date                 21597 non-null  datetime64[ns]\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   sqft_above           21597 non-null  int64         \n",
-      " 9   sqft_basement        21597 non-null  float64       \n",
-      " 10  yr_built             21597 non-null  int64         \n",
-      " 11  yr_renovated         21597 non-null  float64       \n",
-      " 12  zipcode              21597 non-null  int64         \n",
-      " 13  lat                  21597 non-null  float64       \n",
-      " 14  long                 21597 non-null  float64       \n",
-      " 15  sqft_living15        21597 non-null  int64         \n",
-      " 16  sqft_lot15           21597 non-null  int64         \n",
-      " 17  waterfront_YES       21597 non-null  bool          \n",
-      " 18  view_EXCELLENT       21597 non-null  bool          \n",
-      " 19  view_FAIR            21597 non-null  bool          \n",
-      " 20  view_GOOD            21597 non-null  bool          \n",
-      " 21  view_NONE            21597 non-null  bool          \n",
-      " 22  condition_Fair       21597 non-null  bool          \n",
-      " 23  condition_Good       21597 non-null  bool          \n",
-      " 24  condition_Poor       21597 non-null  bool          \n",
-      " 25  condition_Very Good  21597 non-null  bool          \n",
-      " 26  grade_11 Excellent   21597 non-null  bool          \n",
-      " 27  grade_12 Luxury      21597 non-null  bool          \n",
-      " 28  grade_13 Mansion     21597 non-null  bool          \n",
-      " 29  grade_3 Poor         21597 non-null  bool          \n",
-      " 30  grade_4 Low          21597 non-null  bool          \n",
-      " 31  grade_5 Fair         21597 non-null  bool          \n",
-      " 32  grade_6 Low Average  21597 non-null  bool          \n",
-      " 33  grade_7 Average      21597 non-null  bool          \n",
-      " 34  grade_8 Good         21597 non-null  bool          \n",
-      " 35  grade_9 Better       21597 non-null  bool          \n",
-      " 36  house_age            21597 non-null  int64         \n",
-      " 37  renovation_age       21597 non-null  float64       \n",
-      " 38  total_sqft           21597 non-null  float64       \n",
-      "dtypes: bool(19), datetime64[ns](1), float64(9), int64(10)\n",
-      "memory usage: 3.7 MB\n"
+      "Index: 21143 entries, 0 to 21596\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column          Non-Null Count  Dtype         \n",
+      "---  ------          --------------  -----         \n",
+      " 0   id              21143 non-null  int64         \n",
+      " 1   date            21143 non-null  datetime64[ns]\n",
+      " 2   price           21143 non-null  float64       \n",
+      " 3   bedrooms        21143 non-null  int64         \n",
+      " 4   bathrooms       21143 non-null  float64       \n",
+      " 5   sqft_living     21143 non-null  int64         \n",
+      " 6   sqft_lot        21143 non-null  int64         \n",
+      " 7   floors          21143 non-null  float64       \n",
+      " 8   waterfront      21143 non-null  int64         \n",
+      " 9   condition       21143 non-null  float64       \n",
+      " 10  grade           21143 non-null  float64       \n",
+      " 11  sqft_above      21143 non-null  int64         \n",
+      " 12  sqft_basement   21143 non-null  float64       \n",
+      " 13  yr_built        21143 non-null  int64         \n",
+      " 14  yr_renovated    21143 non-null  float64       \n",
+      " 15  sqft_living15   21143 non-null  int64         \n",
+      " 16  sqft_lot15      21143 non-null  int64         \n",
+      " 17  house_age       21143 non-null  int64         \n",
+      " 18  renovation_age  21143 non-null  float64       \n",
+      " 19  total_sqft      21143 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(9), int64(10)\n",
+      "memory usage: 3.4 MB\n"
      ]
     }
    ],
    "source": [
-    "housing_data_encoded.info()"
+    "housing_data.info()"
    ]
   },
   {
@@ -1077,19 +1811,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### #Distribution of prices."
+    "**#Distribution of House Prices.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 528,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
+       "<Figure size 1000x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1097,57 +1831,59 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
     "# Set the style for seaborn\n",
     "sns.set_style(\"whitegrid\")\n",
     "\n",
     "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
     "\n",
     "# Histogram\n",
-    "sns.histplot(housing_data_encoded['price'], bins=30, kde=False, color='skyblue', ax=axes[0])\n",
+    "sns.histplot(housing_data['price'], bins=\"auto\", kde=False, color='#003399', ax=axes[0])\n",
     "axes[0].set_title('Histogram of House Prices')\n",
     "axes[0].set_xlabel('Price')\n",
     "axes[0].set_ylabel('Frequency')\n",
     "\n",
     "# Density Plot\n",
-    "sns.histplot(housing_data_encoded['price'], bins=30, kde=True, color='orange', ax=axes[1])\n",
+    "sns.histplot(housing_data['price'], bins=\"auto\", kde=True, color='#339933', ax=axes[1])\n",
     "axes[1].set_title('Density Plot of House Prices')\n",
     "axes[1].set_xlabel('Price')\n",
     "axes[1].set_ylabel('Density')\n",
     "\n",
+    "# A common title\n",
+    "plt.suptitle('Distribution Analysis of House Prices.', fontsize=16)\n",
+    "\n",
     "# Adjust layout\n",
     "plt.tight_layout()\n",
     "\n",
     "# Show plots\n",
-    "plt.show()\n"
+    "plt.show();\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interprtation:"
+    "The histogram depicts the distribution of house prices, with most bars are clustered towards the left, suggesting that a significant number of houses are priced lower. The density plot illustrates a curve representing the density of house prices. Similar to the histogram, the curve peaks sharply on the left and gradually tapers off, indicating a right-skewed distribution. \n",
+    "<br>In summary, the majority of house prices are concentrated at the lower end, creating a skewed distribution.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### #Distribution of House Age"
+    "**#Distribution of Bedrooms, Bathrooms and Floors.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 529,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
+       "<Figure size 1800x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -1155,52 +1891,75 @@
     }
    ],
    "source": [
+    "\n",
     "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
     "\n",
-    "# Histogram for house_age\n",
-    "sns.histplot(housing_data_encoded['house_age'], bins=30, kde=False, color='skyblue', ax=axes[0])\n",
-    "axes[0].set_title('Histogram of House Age')\n",
-    "axes[0].set_xlabel('House Age (Years)')\n",
-    "axes[0].set_ylabel('Frequency')\n",
+    "# Common title for all subplots\n",
+    "fig.suptitle('Distribution of Bedrooms, Bathrooms, and Floors.', fontsize=16)\n",
     "\n",
-    "# Density Plot for house_age\n",
-    "sns.histplot(housing_data_encoded['house_age'], bins=30, kde=True, color='orange', ax=axes[1])\n",
-    "axes[1].set_title('Density Plot of House Age')\n",
-    "axes[1].set_xlabel('House Age (Years)')\n",
-    "axes[1].set_ylabel('Density')\n",
+    "# Box plot for bedrooms\n",
+    "sns.boxplot(x=housing_data['bedrooms'], ax=axes[0])\n",
+    "axes[0].set_title('Bedrooms')\n",
+    "axes[0].set_xlabel('Number of Bedrooms')\n",
+    "\n",
+    "# Box plot for bathrooms\n",
+    "sns.boxplot(x=housing_data['bathrooms'], ax=axes[1])\n",
+    "axes[1].set_title('Bathrooms')\n",
+    "axes[1].set_xlabel('Number of Bathrooms')\n",
+    "\n",
+    "# Box plot for floors\n",
+    "sns.boxplot(x=housing_data['floors'], ax=axes[2])\n",
+    "axes[2].set_title('Floors')\n",
+    "axes[2].set_xlabel('Number of Floors')\n",
     "\n",
     "# Adjust layout\n",
     "plt.tight_layout()\n",
     "\n",
     "# Show plots\n",
-    "plt.show()"
+    "plt.show();\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:"
+    "After analyzing the distribution of bedrooms, bathrooms and floors, an outlier was detected in the bedroom column. To ensure the accuracy of our analysis, the outlier value was identified and subsequently removed from the dataset. The box plot below displays the distribution of bedrooms after excluding the outlier value. "
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 530,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "##### #Distribution of Bedrooms, Bathrooms and Floors."
+    "# Identify the outlier value\n",
+    "outlier_value = housing_data['bedrooms'].max()  \n",
+    "\n",
+    "# Filter the DataFrame to exclude the outlier\n",
+    "housing_data = housing_data[housing_data['bedrooms'] != outlier_value]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 531,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1800x600 with 3 Axes>"
+       "Text(0.5, 0, 'Number of Bedrooms')"
+      ]
+     },
+     "execution_count": 531,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1208,65 +1967,48 @@
     }
    ],
    "source": [
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
-    "\n",
     "# Box plot for bedrooms\n",
-    "sns.boxplot(x=housing_data_encoded['bedrooms'], ax=axes[0])\n",
-    "axes[0].set_title('Distribution of Bedrooms')\n",
-    "axes[0].set_xlabel('Number of Bedrooms')\n",
-    "\n",
-    "# Box plot for bathrooms\n",
-    "sns.boxplot(x=housing_data_encoded['bathrooms'], ax=axes[1])\n",
-    "axes[1].set_title('Distribution of Bathrooms')\n",
-    "axes[1].set_xlabel('Number of Bathrooms')\n",
-    "\n",
-    "# Box plot for floors\n",
-    "sns.boxplot(x=housing_data_encoded['floors'], ax=axes[2])\n",
-    "axes[2].set_title('Distribution of Floors')\n",
-    "axes[2].set_xlabel('Number of Floors')\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show()\n"
+    "sns.boxplot(x=housing_data['bedrooms'])\n",
+    "plt.title('Bedrooms')\n",
+    "plt.xlabel('Number of Bedrooms')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:"
+    "### b.) Bivariate Analysis"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### b.) Bivariate Analysis"
+    "**#Total Square Footage of houses by Price Range.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 532,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "price_range\n",
-      "300K-600K    10782\n",
-      "600K-1M       4796\n",
-      "100K-300K     4531\n",
-      "1M-2M         1260\n",
-      "2M-5M          191\n",
-      "70K-100K        30\n",
-      "5M-8M            7\n",
-      "Name: count, dtype: int64\n"
-     ]
+     "data": {
+      "text/plain": [
+       "price_range\n",
+       "300K-600K    10560\n",
+       "600K-1M       4691\n",
+       "100K-300K     4433\n",
+       "1M-2M         1234\n",
+       "2M-5M          187\n",
+       "70K-100K        30\n",
+       "5M-8M            7\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 532,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1274,18 +2016,17 @@
     "labels = [\"70K-100K\", \"100K-300K\", \"300K-600K\", \"600K-1M\", \"1M-2M\", \"2M-5M\", \"5M-8M\"]\n",
     "\n",
     "# Cut the data into the specified ranges and assign labels\n",
-    "housing_data_encoded[\"price_range\"] = pd.cut(housing_data_encoded.price,\n",
+    "housing_data.loc[:,\"price_range\"] = pd.cut(housing_data.price,\n",
     "                                            bins=[70000, 100000, 300000, 600000, 1000000, 2000000, 5000000, 8000000],\n",
     "                                            labels=labels)\n",
     "\n",
     "# Count the occurrences of each category\n",
-    "counts = housing_data_encoded['price_range'].value_counts()\n",
-    "print(counts)"
+    "housing_data['price_range'].value_counts()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 533,
    "metadata": {},
    "outputs": [
     {
@@ -1317,15 +2058,15 @@
        "      <th>sqft_living</th>\n",
        "      <th>sqft_lot</th>\n",
        "      <th>floors</th>\n",
+       "      <th>waterfront</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>...</th>\n",
        "      <th>sqft_above</th>\n",
        "      <th>sqft_basement</th>\n",
-       "      <th>...</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",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
        "      <th>house_age</th>\n",
        "      <th>renovation_age</th>\n",
        "      <th>total_sqft</th>\n",
@@ -1334,208 +2075,229 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3834</th>\n",
-       "      <td>534000112</td>\n",
-       "      <td>2015-02-03</td>\n",
-       "      <td>348000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1270</td>\n",
-       "      <td>1242</td>\n",
+       "      <th>16202</th>\n",
+       "      <td>4331000130</td>\n",
+       "      <td>2014-10-17</td>\n",
+       "      <td>315000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>9685</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>1270</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>16</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1948</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3782.0</td>\n",
+       "      <td>1520</td>\n",
+       "      <td>11122</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13225.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14554</th>\n",
-       "      <td>6145601510</td>\n",
-       "      <td>2014-08-22</td>\n",
-       "      <td>412000.0</td>\n",
+       "      <th>16770</th>\n",
+       "      <td>1138010170</td>\n",
+       "      <td>2014-08-01</td>\n",
+       "      <td>350000.0</td>\n",
        "      <td>3</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>3844</td>\n",
+       "      <td>860</td>\n",
+       "      <td>7030</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>900</td>\n",
-       "      <td>100.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>96</td>\n",
+       "      <td>860</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5844.0</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13455</th>\n",
-       "      <td>1624049170</td>\n",
-       "      <td>2014-10-17</td>\n",
-       "      <td>446800.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>8712</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>1150.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>66</td>\n",
+       "      <td>1973</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13532.0</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8750.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6065</th>\n",
-       "      <td>6911700066</td>\n",
-       "      <td>2014-06-04</td>\n",
-       "      <td>175000.0</td>\n",
-       "      <td>2</td>\n",
+       "      <th>13526</th>\n",
+       "      <td>424000145</td>\n",
+       "      <td>2014-07-07</td>\n",
+       "      <td>230000.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>670</td>\n",
-       "      <td>2378</td>\n",
+       "      <td>1390</td>\n",
+       "      <td>6000</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>670</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>105</td>\n",
+       "      <td>1390</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1954</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3718.0</td>\n",
+       "      <td>1170</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>70</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8780.0</td>\n",
        "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9734</th>\n",
-       "      <td>6071300030</td>\n",
-       "      <td>2014-06-24</td>\n",
-       "      <td>464500.0</td>\n",
+       "      <th>16900</th>\n",
+       "      <td>1324079007</td>\n",
+       "      <td>2014-11-10</td>\n",
+       "      <td>425000.0</td>\n",
        "      <td>3</td>\n",
        "      <td>1.75</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>10466</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>144619</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>65</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1977</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12766.0</td>\n",
+       "      <td>2220</td>\n",
+       "      <td>144619</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>147839.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9417</th>\n",
+       "      <td>5149800040</td>\n",
+       "      <td>2014-09-18</td>\n",
+       "      <td>255000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2560</td>\n",
+       "      <td>12155</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>1210.0</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>11906</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17275.0</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 40 columns</p>\n",
+       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "3834    534000112 2015-02-03  348000.0         2       2.50         1270   \n",
-       "14554  6145601510 2014-08-22  412000.0         3       1.00         1000   \n",
-       "13455  1624049170 2014-10-17  446800.0         4       2.00         2410   \n",
-       "6065   6911700066 2014-06-04  175000.0         2       1.00          670   \n",
-       "9734   6071300030 2014-06-24  464500.0         3       1.75         1150   \n",
+       "16202  4331000130 2014-10-17  315000.0         3       2.00         1770   \n",
+       "16770  1138010170 2014-08-01  350000.0         3       1.00          860   \n",
+       "13526   424000145 2014-07-07  230000.0         3       1.00         1390   \n",
+       "16900  1324079007 2014-11-10  425000.0         3       1.75         1610   \n",
+       "9417   5149800040 2014-09-18  255000.0         4       2.00         2560   \n",
        "\n",
-       "       sqft_lot  floors  sqft_above  sqft_basement  ...  grade_4 Low  \\\n",
-       "3834       1242     3.0        1270            0.0  ...        False   \n",
-       "14554      3844     1.0         900          100.0  ...        False   \n",
-       "13455      8712     1.0        1260         1150.0  ...        False   \n",
-       "6065       2378     1.0         670            0.0  ...        False   \n",
-       "9734      10466     1.0        1150            0.0  ...        False   \n",
+       "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
+       "16202      9685     1.0           0        3.0  ...        1770   \n",
+       "16770      7030     1.0           0        3.0  ...         860   \n",
+       "13526      6000     1.0           0        3.0  ...        1390   \n",
+       "16900    144619     1.0           0        3.0  ...        1610   \n",
+       "9417      12155     1.0           0        4.0  ...        1350   \n",
        "\n",
-       "       grade_5 Fair  grade_6 Low Average  grade_7 Average  grade_8 Good  \\\n",
-       "3834          False                False             True         False   \n",
-       "14554         False                False             True         False   \n",
-       "13455         False                False             True         False   \n",
-       "6065           True                False            False         False   \n",
-       "9734          False                False             True         False   \n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "16202            0.0      1948           0.0           1520       11122   \n",
+       "16770            0.0      1973           0.0           1360        7500   \n",
+       "13526            0.0      1954           0.0           1170        6000   \n",
+       "16900            0.0      1977           0.0           2220      144619   \n",
+       "9417          1210.0      1960           0.0           1790       11906   \n",
        "\n",
-       "       grade_9 Better  house_age  renovation_age  total_sqft  price_range  \n",
-       "3834            False         16             0.0      3782.0    300K-600K  \n",
-       "14554           False         96             0.0      5844.0    300K-600K  \n",
-       "13455           False         66             0.0     13532.0    300K-600K  \n",
-       "6065            False        105             0.0      3718.0    100K-300K  \n",
-       "9734            False         65             0.0     12766.0    300K-600K  \n",
+       "       house_age  renovation_age  total_sqft  price_range  \n",
+       "16202         76             0.0     13225.0    300K-600K  \n",
+       "16770         51             0.0      8750.0    300K-600K  \n",
+       "13526         70             0.0      8780.0    100K-300K  \n",
+       "16900         47             0.0    147839.0    300K-600K  \n",
+       "9417          64             0.0     17275.0    100K-300K  \n",
        "\n",
-       "[5 rows x 40 columns]"
+       "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 533,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "housing_data_encoded.sample(5)"
+    "housing_data.sample(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 534,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
-    "# # Set the style of seaborn\n",
-    "# sns.set_style(\"whitegrid\")\n",
+    "# Set the style of seaborn\n",
+    "sns.set_style(\"darkgrid\")\n",
     "\n",
-    "# # Create a bar plot\n",
-    "# plt.figure(figsize=(10, 6))\n",
-    "# sns.barplot(x=\"pricerange\", y=\"total_sqft\", data=housing_data_encoded, errorbar=None)\n",
-    "# plt.title(\"Total Square Footage by Price Range\")\n",
-    "# plt.xlabel(\"Price Range\")\n",
-    "# plt.ylabel(\"Total Square Footage\")\n",
-    "# plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
-    "# plt.show()\n"
+    "# Create a bar plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\", legend=False)\n",
+    "plt.title(\"Total Square Footage by Price Range.\")\n",
+    "plt.xlabel(\"Price Range\")\n",
+    "plt.ylabel(\"Total Square Footage\")\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
+    "plt.show();\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:\n",
+    "\n",
     "The bar plot illustrates the relationship between price range and total square footage. Each bar represents the total square footage of houses within different price range categories. \n",
-    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
+    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. \n",
+    "<br>This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Relationship between bedrooms, bathrooms, and house price**\n",
+    "\n",
+    "Created a scatter plot to visualize the relationship.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 535,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1545,34 +2307,40 @@
     }
    ],
    "source": [
-    "# Creating a scatter plot for the relationship between bedrooms, bathrooms, and house price\n",
+    "# plot for the relationship between bedrooms, bathrooms, and house price\n",
     "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(housing_data_encoded['bedrooms'], housing_data_encoded['bathrooms'], c=housing_data_encoded['price'], cmap='coolwarm', alpha=0.5)\n",
+    "plt.scatter(housing_data['bedrooms'], housing_data['bathrooms'], c=housing_data['price'], cmap='winter', alpha=0.5)\n",
     "plt.colorbar(label='House Price')\n",
     "plt.xlabel('Number of Bedrooms')\n",
     "plt.ylabel('Number of Bathrooms')\n",
     "plt.title('Relationship between Bedrooms, Bathrooms, and House Price')\n",
     "plt.grid(True)\n",
-    "plt.show()\n"
+    "plt.show();\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:\n",
-    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both buyers and sellers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
+    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both the real estate companies(sellers) and buyers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
     "A house with a good balance of bedrooms and bathrooms tends to attract a wider range of potential buyers."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#House age and house price**"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 536,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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P58Of//xnnHLKKVi1alXE3/bv349bb70V69evx6WXXoqmpiYMDAxAEISIAq6vr0/+/+z9+t73voclS5bEfT0IgiCSQbZJgiCILNHT04N///vfOP3003HkkUdG/Ld06VJ8+ctfxptvvomuri6YTCaceeaZ+Oc//4m//vWvWL58uayglZeXY/bs2di1axfmzZsn/zdz5kzcf//9CS1nu3btwuDgIC6++GLMmDFDXmz+61//AiAVPyaTCUcccURMtP0//vGPiOCJJUuWoKOjA3V1dRHH8Z///AePPPJITAhHqixevBj//Oc/IxS2YDCIV155BfPmzYPVapUtlZ2dnfJ1hoaG8MUXX8j//utf/4qlS5eip6cHJpMJCxYswI9//GNUVlbi4MGDab2uRrNo0SK0t7dHqJBerxff/OY38cc//hGLFi0CAAwMDEQca39/P375y19GFNlqvPHGGxH/fuWVV+BwOHDYYYfJl5133nnYsWMHfvvb3+Loo49GU1OTYc+PFTCvvPJKzHEEg0EsXLgQgPRZV76nAGIsoRdccIE8e62urg7nnHMOLrroIgwPD8PlcmHJkiUYGxuDKIoRr9WOHTuwbt061TCVf/zjHxgcHMQFF1wQ810999xzccghh8jWySVLliAQCOAf//iHfHtRFLFhwwb539OmTUNdXR32798fcQxNTU24++67sXXr1lRfSoIgSgxS3giCILLEiy++iEAgENfq9pWvfAXPPfcc/vCHP+Cb3/wmzj77bDz66KPgeT7Gxvfd734XV155JW644QacddZZcqrkli1bcM0118Q9hqlTp6K8vBwPPfQQzGYzzGYzXnvtNdkixtImv/Wtb+HrX/86vvWtb+G8887DwYMH8ctf/hIA5D64c845B08++SQuu+wyXHXVVRg3bhzefvttPPzww1i1ahUsFkvarxkg9cb961//wsUXX4wrr7wSFosFTz75JNrb2/HII48AAFpbWzFu3DisW7cO5eXl4DgOv/rVryLUsiOOOAKCIODaa6/FlVdeCafTiVdffRUjIyOyqpnq62o055xzDp544glcffXV+Na3voWamhr87ne/g9/vx8qVKzFp0iScddZZ+J//+R8cOHAAc+fOxe7du3HPPfdg4sSJOOSQQxLe/9/+9jc0NTXh6KOPxltvvYVnn30W119/fURf4cKFCzF16lS89957uOeeewx9fjNmzMCKFStw3333we12Y/Hixdi2bRvWrl2LI488EscddxwA4KSTTsI//vEP3HnnnVi2bBk2bdoUkxC5ePFiPProo6ivr8eCBQvQ1dWFxx57DEuWLEFtbS1OOOEELF68GNdccw2uueYaTJ8+HR9//DHuu+8+HHfccbJlVMnzzz+Puro6LF26VPX4zzrrLNx333149913ceSRR+KYY47BD37wA/T29mL8+PH44x//iLa2Nvm7YjKZ8J3vfAe33XYbTCYTTjrpJAwPD+OBBx5AV1eXbD32+XzYunUrmpubaeYfQRDq5CwqhSAIosT48pe/LJ5++ulx/y4Igrhs2TLxuOOOk9MOzzjjDPGYY46JSD9kvP322+LKlSvF+fPniwsXLhQvvvhi8f3335f/ztImWWojY+PGjeI555wjzp8/XzzqqKPEyy+/XNy0aZO4YMEC8f/+7//k6/39738XzzjjDHHOnDniKaecIr7yyitiS0uL+Oijj8rX6e3tFb///e+LRx11lDh37lzx1FNPFR9++GExGAzGfZ4s8fD5559X/bvacW/dulW84oorxMMPP1xcsGCBeMkll0Q8V1EUxS1btohf+9rXxLlz54onnnii+Nhjj4n/8z//E5FWuGXLFvHyyy8XlyxZIs6bN08855xzxL/97W+6Xlc1WNqkFuJdV5k2KYqi2NnZKX73u98VFy1aJB5xxBHi5ZdfLm7btk3+u9/vF9euXSuefPLJ4pw5c8Tjjz9e/NGPfiQnRqrBXvs//OEP4iWXXCLOnTtXPOmkk8Tf/va3qte/8847xcWLF8vJqKk8L8ZJJ50kp02KoigGAgHxgQcekI//pJNOEn/xi1+IHo8n4jo///nPxaOPPlqcP3++uHr1ajn5kqVN+v1+8b777hOXL18uzp07VzzqqKPEH/zgBxGJm6Ojo+Idd9whHn/88eKcOXPEZcuWiXfffXfEYzE6OzvFQw89VLz99tvjPpf29naxtbVV/Pa3vy2KoigODg6Kt9xyi7ho0SLx8MMPF2+44Qbx9ttvFxcsWBBxu1deeUVcsWKFOHfuXHHJkiXiVVddJW7fvj3ifqM/BwRBEEo4UaThIgRBEEQkr7/+OpqbmyPCSHbu3IkzzjgDDzzwAE4++eQcHh2RDURRxOmnn45jjz0Wt956a64PJ285cOAAPvroI5x88skRiaLf+ta30N7ejvXr1+fw6AiCKDbINkkQBEHE8NZbb+Evf/kLbrzxRkydOhVdXV148MEHMW3aNBx77LG5Pjwig7hcLjz++OP45JNP0N7ejq9//eu5PqS8hud53HLLLTj55JNx3nnnwWQy4d///jf+9re/4c4778z14REEUWSQ8kYQBEHE4PF48Mtf/hKvvfYauru7UV1djeOOOw433HCDamw/UTwEAgGceOKJEAQB3//+93HmmWfm+pDyno0bN2LdunXYtm0bAoEApk+fjssuuwxnnHFGrg+NIIgig4o3giAIgiAIgiCIAoBGBRAEQRAEQRAEQRQAVLwRBEEQBEEQBEEUAFS8EQRBEARBEARBFABUvBEEQRAEQRAEQRQANCogh4iiCEGgvJh8g+c5el9KBHqvSwd6r0sDep9LB3qvS4NSep95ngPHcUmvR8VbDhEEEf39o7k+DEKB2cyjpsaJ4eExBAJCrg+HyCD0XpcO9F6XBvQ+lw70XpcGpfY+19Y6YTIlL97INkkQBEEQBEEQBFEAUPFGEARBEARBEARRAFDxRhAEQRAEQRAEUQBQ8UYQBEEQBEEQBFEAUPFGEARBEARBEARRAFDxRhAEQRAEQRAEUQBQ8UYQBEEQBEEQBFEAUPFGEARBEARBEARRAFDxRhAEQRAEQRAEUQBQ8UYQBEEQBEEQBFEAUPFGEARBEARBEARRAFDxRhAEQRAEQRAEUQBQ8UYQBEEQBEEQBFEAUPFGEARBEARBEARRAJhzfQAEQRAEQRCEMQiCiB3tgxgc9aLaaUPLpGrwPJfrwyIIwiCoeCMIgiAIgigCNrd146kNOzEw4pUvq6mwYeXymVjY2pjDIyMIwijINkkQBEEQBFHgbG7rxrr1n0YUbgAwMOLFuvWfYnNbd46OjCAII6HijSAIgiAIooARBBFPbdiZ8DpPb9gJQRCzdEQEQWQKKt4IgiAIgiAKmB3tgzGKWzT9I17saB/MzgERBJExqOetxKHGZoIgCIIobAZHExdueq9HEET+QsVbCUONzQRBEARR+FQ7bYZejyCI/IVskyUKNTYTBEEQRHHQMqkaNRWJC7PaCsldQxBEYUPFWwlCjc0EQRAEUTzwPIeVy2cmvM6Fy2dSWwRBFAFUvJUg1NhMEARBEMXFwtZGXLtibowCV1thw7Ur5lI7BEEUCdTzVoJQYzNBEARBFB8LWxuxYGYD7nxyM744OIy5U2vw7a8eToobQRQRpLyVINTYTBAEQRDFCc9zsFtNAIDyMisVbgRRZFDxVoJQYzNBEARBFC+BoNSzHgxS7zpBFBtUvJUg1NhMEARBEMVLQBCk/w0KOT4SgiCMhoq3EoUamwmCIAiiOAkEQsobpUYTRNFBgSUlDGtsfmD9J/hgZy+WzmnCFafPJsWNIAiCIAoYprxR8UYQxQcpbyUOz3OY2FgOAHBYzVS4EQRBEESBE+55I9skQRQbVLwRcNotAIBRjz/HR0IQBEEQRLoEAqS8EUSxQsUbgXJHqHhzU/FGEARBEIUO2SYJonih4o2A0yG1PrrcgRwfCUEQBEEQ6cJGBFDaJEEUH1S8EWSbJAiCIIgiwh8k5Y0gihUq3gg4HVS8EQRBEESxwIJKaEg3QRQfVLwRcNol26TbGySLBUEQBEEUMKIohtMmBTqnE0SxQcUbIdsmAWDMQ31vBEEQBFGoKK2SZJskiOKDijcCPM+hzCapb2SdJAiCIIjCRemgIdskQRQfVLwRAMKJk6OUOEkQBEEQBUsgSMobQRQzVLwRAMLWSRcpbwRBEARRsCiVN+pjJ4jig4o3AgAN6iYIgiCIYiDCNknKG0EUHVS8EQAU4wKoeCMIgiCIgkXZ50Y9bwRRfFDxRgAIjwtwUdokQRAEQRQsfoXyJogiRJEKOIIoJsy5PgAiP2A9b5Q2aQyCIGJH+yAGR72odtrQMqkaPM/l+rAIgiCIIidabQsKIswmOv8QRLFAxRsBgHrejGRzWzee2rATAyNe+bKaChtWLp+Jha2NOTwygiAIotiJDikJBkWYTTk6GIIgDIdskwQA5agAKt7SYXNbN9at/zSicAOAgREv1q3/FJvbunN0ZARBEEQpEF28BQRKnCTyF0EQsX3vADZu7cT2vQMQKGQnKaS8EQCUowKo5y1VBEHEUxt2JrzO0xt2YsHMBrJQEgRBEBkhEG2bpNASIk8hp1JqkPJGAKC0SSPY0T4Yo7hF0z/ixY72wewcEEEQBFFyxNgmSckg8hByKqUOFW8EgHDa5CgpbykzOJq4cNN7PYIgCILQS2zPG9kmifxCq1OJLJTqUPFGAAgHlri9AQTJH58S1U6bodcjCIIgCL3E2CZpAUzkGVqdSm37BrJ0RIUFFW8EAKDMHm5/JPUtNVomVaOmInFhVlshjQ0gCIIgiEwQG1hCxRuRX2h2Krl8GT6SwoSKNwIAYOJ5OGyUOJkOPM9h5fKZCa9z4fKZFFZCEARBZAyyTRL5jmanUrk1w0dSmFDxRshQ31v6LGxtxLUr5sqvJaO2woZrV8yl9CSCIAgio5Btksh3tDqVWifXZOmICgsq3ggZGtRtDAtbG3H+shnyv5fObsKaq4+mwo0gCILIOGpDugkinyCnUnpQ8UbIsHEBLire0ka588lxoB8ggiAIIivEjgog2ySRfzCnkt1qiricnErJoSHdhAzZJo3D7w/K/79/mEYDEARBENkhWmmjwBIiX1nY2ojP9vTjjQ8PAgDOOX4aTls6hTa8k0DKGyFDg7qNwxsI73Qmi8MlCIIgCKPwk22SKCBGRsNrzsYaBxVuGqDijZApt4eKNw8Vb+niDyiUtxEvRJFOngRBEETmiS7WyDZJ5DPKsQFehWuJiA8Vb4SMrLyRbTJtfP7wyTIQFDAyRgUxQRAEkXkosIQoJIYUs9yUayciPlS8ETKs540CS9LHF4j8Aeof8eToSAiCIIhSInZINy2IifxEFEUMjYaLN1LetEHFGyFDPW/G4Y/6AaLQEoIgCCIbxMx5I+WNyFPc3iD8is1uHxVvmqDijZCR57xRz1vaeKOUNwotIQiCILJBtNJGQ7qJfGVoNHJtRMqbNqh4I2TkUQFu6nlLF6a82ULzS/qHyTZJEARBZJ5AgIo3ojBQ9rsBgJd63jRBxRshw2yTY94ApVOlCet5a64pAyAlThIEQRBEpom1TdL5nMhPBqOVNx8pb1qg4o2QYcobAIxR4mRa+EKjAprrQsUbKW8EQRBEFoi2TUYXcwSRLwxHKW++ABVvWqDijZAx8TwcNsnmR+MC0sMfkv6bahwAqOeNIAiCyA7MNmk1S0s8ctIQ+cpgKGmSiQfU86YNKt6ICJx2Spw0AhZY0lwrKW8DI14INKibIAiCyDCBUI8b67mmnjciX2E9b42hjW4f2SY1QcUbEYGTEicNwR+S/huqHeA46eQ5POpLciuCIAiCSA/W42azhIo3sk0SeQpLm2yoloo3CizRBhVvRATlNKjbEHyhHyC71YTqchsAmvVGEARBZB5/IKS8WUh5I/KbGOWNet40QcUbEUF4UDf1vKUD+wGyWEyorWTFG4WWEARBEJmF9biFbZOkZhD5yVDIkdRYLbWYUM+bNqh4IyIg22T6iKIoB5bYzDxqK+wAKLSEIAiCyDyBKNskpU0S+UggKMguL6a80agAbVDxRkQQDiwh5S1VAkER7FRpMZtQUxFS3kZIeSMIgiAyCyvWyDZJ5DMsB8DEc6gNrZOo500bVLwREcg9b6S8pYzSs2218KitlJQ36nkjCIIgMg1T3uzMNklDuok8ZDDU71bptMoW30BQgECbDUmh4o2IINzzRsVbqrCwEp7jInaUSHkjCIIgMg1T3qykvBF5DEuarHJaZZUYoL43LVDxRkRAPW/pEw4r4cFxHClvBEEQRNaI7nmj4o3IR1jSZHW5DRYzDy50uY+Kt6RQ8UZEUE49b2nDwkqsZunrxdImh1w+Sv0iCIIgMopcvJFtkshjWNJkpdMKjuNkpZiUt+RQ8UZE4HRIPW+kvKWOL8CKN+mHqLLMChPPQRBFeaeJIAiCIIxGEESIIaHNZpGWeKS8EfnIkEtyI1WXWwGEP68+Ci1JChVvRAQsbXLME6Cm0RRhkr819EPE81x4UDeNCyAIgiAyhF+hstGoACKfYcpbVWh9RMqbdqh4IyJgypsIYMxL1slUiFbeANCgboIgCCLjKC2SNKSbyGdY2mSVM6S8Wal40woVb0QEJp6HwyZ9gShxMjX8isASBoWWEARBEJnGr1DZ5MASUt6IPGSYpU2GbJNsw5uKt+RQ8UbEwKyTNOstNXxRgSUA5HEBA2SbJAiCIDIEU97MJg4mnnreiPxEFMWwbdJJPW96oeKNiMFpp1lv6cBGBShtkzU0640gCILIMCxp0mTiYTJJ4etkmyTyjVFPQO7FrHJK6yMb9bxphoo3IgY5cZLGBaSErLyRbZIgCILIIsw2aTHxMPOh4o1sk0SewZImnXYzLCGXEvW8aSenxdu7776L1tZW1f9OPvlkAMD+/fvxjW98A0cccQSOPfZY3HvvvQgGI9/Y3//+9zj55JMxf/58rFy5Elu3bo34e7buo1god5BtMh3UlDc5sISUN4IgCCJDBGXljYMpVLwFyDZJ5BnRSZNAeM1EQ7qTk9PibcGCBXjrrbci/lu7di04jsM111wDv9+P1atXAwCeeeYZ/PjHP8bTTz+NdevWyfexfv16rFmzBtdffz1eeOEFTJw4EZdddhn6+/sBIGv3UUyQbTI9/KG0yYjAkgpJeRt2+WRbC0EQBEEYCbOimXkeJlOo543OOUQaCIKI7XsHsHFrJ7bvHTBkjNRQVNIkoLRN0uc1GeZcPrjVakVDQ4P877GxMdx5551YsWIFzj33XLz88ss4ePAg/vCHP6CqqgotLS3o6+vDmjVrcNVVV8FqteKhhx7CqlWrcNZZZwEA7rjjDixfvhzPPfccvvGNb+C1117Lyn0UE2SbTA+1wJKKMgvMJg6BoIhBlxf1VY5cHR5BEARRpLDNQbNZ2fNGyhuRGpvbuvHUhp0RYWs1FTasXD4TC1sbU77fsPIWXj9brSywhJS3ZORVz9tDDz0Et9uNm2++GQCwadMmzJkzB1VVVfJ1li5dCpfLhW3btqGvrw979uzBUUcdJf/dbDZj0aJFeP/997N2H8WGrLyRbTIl1GyTHMeFQ0uo740gCILIAAFF2qSZ0iaJNNjc1o116z+NSckeGPFi3fpPsbmtO+X7Hgz1vKkrb1S8JSOnypuS/v5+PP7447jhhhtQXV0NAOjs7ERzc3PE9RobpUq/o6MDZrN0+OPGjYu5zvbt27N2H4cddpj+JxzCbM6r+hkAUBn6Mo15A3l5fJmE2UzY/6aCP3TytNtMEa9fXaUdPYMeDI36Su51zUeMeK+JwoDe69KA3meAlWkWEw+rPKRbLLpzDr3XmUUQRDy9YWfC6zz9+k4sPrQJfKi3Ug8jY5I4UFtplz+bDpu0HvcHBPkyep/VyZvi7amnnkJFRQW+9rWvyZd5PB5UVlZGXM9mk9QLr9cLt9sNADG2RZvNBq/Xm7X7SBWe51BT40z59pmiuaECAODxB/Py+LJBZWXqtkaOk35kqiodEa9fc0M5tu8bhNsvlOzrmo+k814ThQW916VBKb/PNvswAMBuM6OmugyAtBAv1nNOKb/XmeSTz3vRn2Qubf+wFwcHPJg3o173/Y96pbac8Y0V8mezJtROIgAxn1d6nyPJm+LtxRdfxFe+8hXY7Xb5MrvdDp/PF3E9ViyVlZXJ11W7jsPhyNp9pIogiBgeHkv59plCDKVoDo14MTAwmuOjyS4mE4/KSgeGh90pN3m7xqTPStAfiHj9KkK7Svu7hkvudc1HjHivicKA3uvSgN5nYGhY2pCGKGLUJaUb+wNC0Z1z6L3OLO0dQ5qvN7FOf2HVOyh9Ti0c5M9mMGSXdI355MtK7X2urHRoUhnzonjbvn072tvbceaZZ0Zc3tzcjB07dkRc1t0teWybmppkq2N3dzemT58ecZ2mpqas3Uc6BAL592G0h3zHLrc/L48vGwSDQsrP3euTdpRMPBdxH9Whxty+IU/Jvq75SDrvNVFY0HtdGpTy++zzSQtgE8+BmdmCQvG+HqX8XmeSitDIKC3XS+X1Hwypek7F7c2hgB2PLxhzn/Q+R5IXJtJNmzahrq4Os2bNirh88eLF2Lp1K1wul3zZxo0b4XQ6MWvWLNTV1WHq1Kl499135b8HAgFs2rQJixcvztp9FBtsztuYJ2BIJGyp4QuwtElTxOU1oXEByawIBEEQBJEKfjmwhJfnvNGQbkIvLZOq5ZC1eNRW2NAyqVr3ffsDQYyFbJPVyrTJkHDANiCI+ORF8bZ161a0trbGXL58+XI0NDTg29/+NrZv344NGzbgF7/4BS6//HK5R+3yyy/HY489hvXr1+Pzzz/HrbfeCo/Hg/POOy+r91FMlNklQVYE5C8YoR25eLNEfr3YoO6BYRrUTRAEQRhPUFm8hexXIkAbsYQueJ7DyuUzE17nwuUzUworYTPezCYeZbawAZDSJrWTF7bJnp4eOWFSic1mwyOPPILbb78d559/PqqqqrBy5Upcc8018nXOP/98jIyM4N5778Xg4CDmzp2Lxx57DLW1tVm9j2LCbOJht5rg8QUx6vHLShyhDTajxBKlvNVWhgZ1j/nhDwiwFFn6F0EQBJFb2JBuk4mTlTdAsk7yvCnezQgihiNaGlDusMDljhwbVVthw4VpzHmTZ7w5reC48GeUFW8+skcmJS+Kt4cffjju36ZMmYJHH3004e1Xr16N1atX5/w+igmn3QKPLwiX24+mmlwfTWHhj6O8Oe1mWM08fAEBAyMeNNakHnZDEARBENGwOW8WhW1SulyEJS9WfESh0DPojijcqitsuPKM2WiZVJ2S4sYYdMUO6AbCayYv2SaTQlv/hCpOh/QrP+om26RemPIW3fPGcRxqQupb9NBLgiAIgkgXVryZTDxMJqXyRrZJQh9b9wwAABw2aS1j4oBZU2rSKtwAYHg0dkA3QLZJPVDxRqjCrJKjHn+SaxLRxOt5AyS7ASDNRyEIgiAII2G2SYuJB6+wpJVCzDphLFv3SsXbvGl1AKQUSCMIK2+RgSgssCQoiPImBKEOFW+EKk57qHhzU/GmF3+ctElAUbyNUGgJQRAEYSxh5Y0Dx3Fy/Dopb4QeBFHE9lDxdvhMaQi3UYoY63mrjqO8AYDPT8VbIqh4I1Rxysob2Sb1EAgK8klSLZCkOlS8bdszgO17BygBjCAIgjAMpryZQ0mTJl763wCdawgd7O92weX2w2Yx4dApUnhfIGiMIjbkkpxHlVE9b2YTJ6vFZJ1MDLWvEqo4Q+MColOGBEHEjvZBDI56Ue20pd24Wmz4FSlJtijb5Oa2bvzzgwMAJDvC1r0DqKmwYWUaqU0EQRAEwQgIkQOPw7PeSMkgtMP63VomVcvrQUDq6WcbA6kSVt4ibZMcx8Fq4eHxBeXsAEIdKt4IVdR63ja3deOpDTsjwjao+IiE9btxQMQP3Oa2bqxb/2nM9QdGvFi3/lNcu2IuvYYEQRBEWgQC4TlvAOTQErJNEnrYFrJMHjqlBmYTD7OJQyAowuMLosye3vgoeVRAeeycZJtFGlNFyltiyDZJqBLueZNsk6z4iE5JZMXH5rburB9jPhKe8cbL80sEQcRTG3YmvN3TG3aShZIgCIJIi1jbJFPe6PxCaCMQFLCjfRAAMPsQaVaUUUmQgihiWDHnLRp51hv1vCWEijdCFXlUgMdPxYcOwkmT4cbbHe2DSUcD9I945R9LgiAIgkiFYIxtkvW80WKY0Maug8Pw+oMod1gwsbEcAGC3SmuadBMnXW6/rAJXqhRvVhoXoAkq3ghVmPLmcvup+NCBUnljDI5qGwug9XoEQRAEoYY/yjYpp02S8kZohFkmZ02pkQNE5KIqzeJtODQmoNxhUe2dY1kBVLwlhoo3QpUym6S8Dbm82LqnX9NtqPhQjAlQKG/RTbnx0Ho9giAIglCDqRqs180UWiBTzxuhlW2hNd/sKTXyZbLylmZRxdaJav1uAClvWqHAEiKGzW3dePLvOwAAXr+Al9/Zq+l2VHwAvoD0g2NVKG8tk6pRU2FLqF7WVkjJnQRBEASRKmwD0RLd80a2SUIDXl8QXxwcBgAceki4eLMZpLwNudRnvEU/DqVNJoaUNyICFkzCvmBaoeJDgjXZKos3nuewcvnMhLe7cPlMGrlAEARBpAUbCUCBJUQq7Ng/iKAgoq7ShsZqh3y53SppPekqYixpsjLOZr/NypQ32mxIBBVvhIyWYJJ4UPEhIStvCtskACxsbcS1K+aipiLyB6u2wkZjAgiCIAhDCMTYJmlUAJEcQRCxfe8AXt+8HwAwa3KNnJgNhIuqdANLZOUtnm3STD1vWiDbJCGjJZgkmtoKGy6kOW8yTHlTBpYwFrY2YsHMBry7rRMP/3kbzDyHn111VNoDLwmCIAgCCM95C9smQ2mTNKSbiIPaDN+PPu/F5rZueW0Xtk0G0nqsIdbzRrbJtKBVIyGjNXDk0MnVAIC5h9RgzdVHU+GmQC2wRAnPc1hyaJM08FIQMeiikBeCIAjCGMLKW1TaJClvhArxZviOegIRM3yNCCwRBBEdvaPy/auNlwrbJvU/DlMPN27txPa9A0U9voqUN0JGa+DIhIZybNs3CLvNTFbJKNQCS6Ix8TyaaspwoHcUnX1jqK9yxL0uQRAEQWglEFSf80Y9b0Q0Wmf4LpjZkHZgSbS69+e39+CtTzqwMsq5ZU1ReVNTD2sqbDH3XyyQ8kbIsFTERNRW2OShjWwgNRFGLbBEjebaMgBAR99Yxo+JIAiCKA0C8QJLKG2SiELPDF+mvKVSvMVT9wZGvBHqHgDY5J437Z9XPfdfLFDxRshoTUVM50tc7MQLLImmuU4q3jr7qXgjCIIgjCEQUtjk4o1sk0QctLbKDI56w4ElOhUxreoeszhada4v9d5/sUDFGxGBllREuaE0QMVbNP4EgSVKxtUx5W0048dEEARBlAbBGNskjQog1NHaKlPttKVsm9Sj7gHQvb7Ue//FAvW8ETGwVMQd7YMYHPWi2inNcGP9bUxVojkcsWjpeQOA5lonAKCDlDeCIAjCIPwxtslQz1uRKQ9E+rBWmUTFD5vh++HOHgD6lTc96h6gSLXU+Dh6779YIOWNUIXnOcyaUoOls5sxa0pNRDBJuo2rxYwvSdokg/W8Dbl8cHvTi94lCIIgCCCssJmj0iZpVAARjdZWGZ7nwimQOtd9etQ9ALBaQj1vPm2fV733XyxQ8Uboxhb6cpFtMhatgSVldjOqQkMqqe+NIAiCSBdBFGWFLTykm5Q3Ij6sVcZpjzTiKVtlAMBukf6ut3jTGoTXMqkagP45b3rvv1ig4o3QjVWnrF1KaA0sAYBxtdT3RhAEQRhDUKGuWShtktDIwtZGnHD4eADAoVOq8b0LF8TM8E01sESPugconF0axQG9918sUPFG6Ca8MyJAEGk3T4nWwBIAaK4L9b3RuACCIAgiTQKKUBIKLCH00D3gBgAcNr0+plUGQMq2SUAqDmsrY9WxaHUP0K+8sftPFrRXbFBgCaEb5kkGAH9AkL9shDKwRLvy1knFG0EQBJEmyr42E40KIHTA2jeaQuuSaOwKx5UgiuA57UrWgR4X+oe94DngunPmweMPxgThMWRnl0+AqEMcYEF73//1O+gZ9GDquEr84OsLi05xY1DxRuhGaQn0+oNUvCkIB5YkV97G0aw3giAIwiCY8mbiOXlxLadNkvJGxEEQRXSFlDc2gzYaprwBkipmt2ovH97bJg3JnjetDofPbEh4XZapIIgiAkERFovmhwHPc/Ln3WTiirZwA8g2SaQAz3FyIIceabsUYK+HFuWN/Uh2DYxRPwJBEASRFkx5Y2obAJhDC9gAnWOIOPQPe+APCDDxHOqr7KrXsZp5sE+VHuukKIp4b7tUvC2Z3ZT0+kpxIJVQPH9oA93jLe61KRVvRErQrDd19ChvtZV2WM08AkERvUOeTB8aQRAEUcSw4s3Mh88/sm2SlDciDsz901jjkJWraDhOMS5Ax6Z9e7cLXf1jsJh5HD6jPun1zSZe7tNMpb+OzTn0+Ip7BBMVb0RKyOMCSHmLQE9gCc9xsr+c+t4IgiCIdGC2SbPi/BMe0k0brYQ6bP3RHKffjSEnTuooqt7d1gUAmD+tDg6bNqtlOonm/pBaV+zzc6l4I1LCmkIiUCmgJ7AECPe9UeIkQRAEkQ6y8qawTVJgCZGMrv5Qv1uS4s2us6gSRRHvh/rdFh+qPfExLA7o33CQbZO+oK7Ak0KDijciJWjWWyyCIMo7n1psk0D4x7Kzn2a9EQRBEKnDrJFK26SZRgUQSWDrD63Km1Y74+6OEfQOeWCzmHDY9OSWSflxUlxfspATQNqsYIVcMUJpk0RKKGe9ERLKHwqtylszKW8EQRAlhSCI2NE+iMFRb9zI9FRg/T4RtkkTs03mtnjL1HMm0ifZmAAGU9602ibfC1kmD5tRF5FWmYxUZr0BQCCqWPP4ghEBKMUEFW9ESjBliZS3MF5FMpJFo/I2rlYa1E3jAgiCIIqfzW3deGrDTgyMeOXLaipsWLl8ZtrDhINyYInCNsnSJoO522jN5HMm0sPnD6JvWHpf4o0JYFg1BpYIgojt7QP4zycdAIBFs/S9x6k6u/xRn3G3L4BKp1XXfRQKZJskUiJVWbuYYWElZhOveYAlsymMjPnhcvszdmwEQRBEbtnc1o116z+NKGIAYGDEi3XrP8Xmtu607l+e82ZSSZvMkfKW6edMpAeb7+a0m1HhSDxUTYvytrmtGzc9+DbuevojjHqk0JCn/75D1/tsS1EciHaCFfO4ACreiJRgtkCyTYYJh5Vo/1rZrCbUVtoAUOIkQRBEsSIIIp7asDPhdZ7esBNCGkUWU9csEXPe2JDu7J+rs/GcifRQWia5JJvOyUYFxC3UXT5dhbo1xbacGOWtiBMnqXgjUiKVeR/FDvuh0RpWwhhXy/reKLSEIAiiGNnRPhizqI2mf8SLHe2DKT9GeEi3clRA7pS3bDxnIj06+7SFlQCA3SJ1Wqkpb0YW6qmuL6MDSvSMNCg0qHgjUoLmvMXCfji0hpUwmuuo740gCKKYGRxNXMTovZ4azDZpMcfaJgM5KN6y8ZyJ9OjUOCYASJw2aWShztZQeou36MASdxEP6qbAEiIlUv1yFTMssERrWAmDZr0RBEEUN9VOm6HXU0NW3iICS5htMnHxlok0yGw8ZyI92KaxruLNH1sUGVmop5qpEKO8FbFtkoo3IiXINhkLCyzRrbwx26RByhtFMhMEQeQXLZOqUVNhS6hO1FZIv9epEh7SrWabjN8/lKk0yGw8ZyJ1RFHUVbwlCiwxslC3WVMb0u0LRB6Xu4htk1S8ESnBQjkosCRMKoElADAuZJvsGXAjEBQiTrx6oUhmgiCI/IPnOaxcPhPr1n8a9zoXLp+Z1kYbs00qzyHs/8dT3ljIRDQsDfLaFXNTPndk4zkTqTMy5ofbGwAHoLHGkfT6iWyTRhbqRilvFFhCEFHQqIBYWCGr1zZZXW6FzWqCIIroGXSn/PgUyUwQBJG/LGxtxLUr5sJpj903v+TU1rQ32MLKm8I2aYqvvGUjDZI95+j6rNJpTaswJNKHqW61lXZNw6ztCRxXrFBPhNZCPZxmToEl8aDijUgJtgNDgSVh/CHlzabTNslxXNg6mWLfG0UyEwRB5D8LWxux4oRpAIApzRWY1CA5Lw4YkDacyDapFliSrTTI1sk1YA9fWyHZ5s45fhoVbjlGtkwmGc7NsCWZ88YK9ejE7doKm65CPZHCl4joUQHF3PNGxRuREuHAErJNMrwpKm8A0FwrWRbe29aF7XsHdBdZFMlMEARRGHhDw4Mn1jvx1WUzAAD/2nIQw2O+tO5XzTYp97yp2CazlQa56+AwAKCpxiEv4Pd3u9K6TyJ99PS7AYmVN8bC1kbMnlIDADh2/jh878IFWHP10boKdVb8+QI657zFpE0Wr7hAPW9ESuRiVEC+B3H45Z43fcrb5rZubPm8DwDw3rZuvLetW3efGkUyEwRBFAZjIUWgzG7BnENqMaW5Ans7R7Bh036cc/y0lO9X3TYZ6nlT2RDMVhrkroNDAIBp4ysxuakcALCPirec09mnr3hjilgyO+JYaHNi7tRazAoVcnqgnrfkUPFGpIQ1yz1vhRDE4ZPnvGlX3oxqFqdIZoIgiMJg1MOKNzM4jsPpS6fggRc/xYZN7Zg+vhJuXyClDUqmrqkP6Y5VMbKVBrmrQ1Lepo2vwuSmCgBAe/cIRFEEx+XPBmypoVd5k4uqJMXbqMcPAHA6LCkdV6rrS1a8Oe1mjHoC1PNGENGwL3E2lLdCCeLQG1hiZJ8aOwkngiKZCYIgcs9YaHFbZpP2z49obUB1uRUeXxC//OPH+PVLW7Hm6Q9x04Nv6zq/sZ4fi1J5S2CbNDJkIh6iKGL3QVa8VWJcXRnMJg5ubxA9Q56U75dIj6AgyAFp2m2T0ufV6w9CFOOvS0bd0ue73J5a8aa1SIyGuZ8qyqwAAE8RD+mm4o1ICSvzPuv0JOulkII49AaWGNmnlo2TMEEQBJE+YduktBj+cEcPBl2x/W56NyiDKoElZoVtUm3BzUImHNbI85bekIl4dA24MeoJwGziMamxHGYTjwn1knWyvWskrfsmUqd30IOgIMJq5lFTqc2Rw4qqoCDK/ZVqMGVZLVVVz+Po7nkLff4ry6SisZhtk1S8ESlhY3PefIl3YNKlkII49AaWGN2ntrC1EeefND3mcqNOwgRBEET6uJlt0mY2dIOSJUpG2CYVKpxa3xsgnTtOXjhR/vfcabW6QybiwfrdpjSXy4Uk63vb20V9b7miI2SZbKwpA6/RusqGZwPxLY0+fzBsX0zRNskyFVK1TVY4JeWNAksIIgrmSRYhfWG0zAhJhUIK4tAbWJKJPrXaSnvEv2+84DDMmlxLihtBEESeoFTe9GxQJgt/CATi2yYBqXiLd3pSKik+v2DYOYMlTU4fXyVfJvW9dZDylkO6dI4JAAATz8Ni5uEPCPD4AihXKc6Y6sZznJxOqRe2ntQrDjClrjJkm/T6ghBEUXNxWkiQ8kakhE1RrOmVtvVQSEEcegNLMtGnxhqQGRPqy6lwIwiCyCPGQgtch81s6AYlS5uMDCwJ/3+1vjeGMqmvayC1eaNq7FL0uzEocTL36A0rYSTrR2P9biyMJxVsUeKAVmTlrSxcVOrtmysUqHgjUoLnOdkCkckvRyEFcbDwFq22yUz0qXVFFW/DY37NtyUIgiAyj1J5M3KDktkmI0cFKJW3+AthfzB8Hh9y+QwJe/D5g2gPFWjTxoWLt4kN5eAg9fSlO9uOSI3wmACHrtsxNc0Tx9KYbtIkECkO6LFOMuW5zGaWFedi7Xuj4o1IGXnWWyBzxVs6BY4giNi+dwAbt3amNPhaL2zXR2tgCRBuFo8uUFPtU+sacEf8m06MBEEQ+YM/EO4JKrNZDN2gZItXZWAJz3FgAkiikIloB0131LkkFfZ1uRAURFSWWVBXFbb0O2xmNNZIRUM79b3lhM4BVrw5dd2OzXrzxVPeQqpyeYphJUCUOODXr7xZLCY4Qkmuxdr3Rj1vRMpYLSaMegIZn/XGCpzf/rUNLndYSUo05y0Xc+F8AX2BJYyFrY1YMLMBf39/H5795xeoqbBhzdVH67Y7iqIo76ZVllkwPObHyCgVbwRBEPkCG2DMAbDbTOA5aYNSbd4nQ6sDIyDEFm/s3/6AkFh5Uyne2Ey2VAkP566KsdBNaqpA14Ab+7pHMGdqbVqPQ2hHEER8sqsPQ6F008ZqncqbJYny5k5feQMkcSAQFHStL8OjMnjYrSa43H54SHkjiEhSncWRCgtbG7HyS5EK3JVnzo5buOViLhyzTWoNLFHC8xwWhJ6Ly+1HKlZxl9uPMW8AHIDpE6TmcLJNEgRB5A9sxpvDZpaDFIxyYDBlTWmbBJSDupP3vLFjMqLvLTycuzLmb1NY3xspb1ljc1s3bnrwbfzyjx/Ll/3osfd0rYmY8ha35y3NMQEMawrrS7l1xczLyluxDuqm4o1ImVRncaSKL0o+39MZm1SVy7lwcmCJTuWNUVthA8dJJ9FUiq6ufsnmUltpQ10odXIki7bJbNtUCYIgCo3oGW+Mha2N+PnVR2PBzHoAwFFzmnTH9QdU5rwBiQd1M1jx1hTqgWLnk3T44kD84m1So6Tq7aPEyaxg1Ka2LZnyxnreUhzQHf04vlSUNzMv9+YVa88b2SaJlGFFSrbSfKJ3UFiKlRIjY5f1Et71SS0e12ziUV1uw8CIF71DblSFZpVohaVHNdWWyXNOhrNkm8yFTZUgCKLQGFPMeIuG5zlMbCjHhzt7YbeZdVvnw8pbVPGmGNQdD9a7PrGhHB19Y+hOU3kbGvWhb9gDDsDUcfGVt87+MXj9wYiQCsJYtG5qL5jZkPQzl1R5U6RNpoPs7NJTvAXCxVu45604i7eUlbehoSG8/vrrePrpp9Hf349du3ZldFgzkX+ElbdsFW/Sl5B5tNWKt1zOhQsHlqQuaNeHmrr7hjy6b8tsLk21ZagMReWOZME2mSubKkEQRKEhF29xFresV2jUrf+3OyiPCohnm0ze8zaxQQqwiA6/0gvrdxtf75QX0kqqym2odFohisD+HrJOZhI9m9rJsCcbFcBsk2n2vFlTGNTtV4xrklMxvWSblHnwwQdxwgkn4Nprr8X//u//oqOjA3feeSe++tWvYng4dkFNFCeyJ1lHGlA6MOVt1pRqcAD6hj0YilKWcjUXThRFRWBJ6juIdWkUb/LclpoyeUhlptMmc2lTJQiCKDTCtkn1xW25Qyp02CJYD8rABiWseEuUNhku3iRFbGjUl5bljG2uTlWxTDImN0qPRYmTmcXITW2bxlEB5QbZJvU4u9hn2EzKWyxPPvkk7r//flx22WX4wx/+IKttq1atQnt7O375y18afpBEfmLLkW2yutyG8fXS7iDb3WPkai6cMqlL65BuNZjy1puK8hbqUcimbdLIHT2CIIhihwWWqNkmgXCvUGrKm7QeM6mkTUp/T668VZZbUR5STdIZF6A2nDsalmZJfW+ZxchN7eRDupnyZoxtUk+mgmybNPFwWCmwJIInnngCV155Ja6//nrMmTNHvvyEE07At7/9bfzjH/8w9ACJ/CVXtkm71Szv5kVbJzMx+FoLyh8YS1rFm2QJ7RvWV7wJoij3KDTXOrJmm8ylTZUgCKLQiBdYwmB2M1cKxVs4sCTKNmlKnjbpUyx8WWhJ96D+4k0QRGzd04/P9w8CAA5JMG5gMkuc7CblLZMYualtT1IUGRVYkkrapLLnLWybJOUNAHDw4EEsWbJE9W/Tpk1Db29v2gdFFAbWFBpK04F5l+02k7ybp9b3trC1Eau+1BJzeaqDr7XAfjRMiuGSqcBSIvUqb4MjXvgCAkw8h7oqOypCtkmvP5hRZTRXNlWCIIhCxJ0gsAQIR6ynYpsMJLFNahkVYLWY0FRTBgDo6tcXWsKi6O965iP4Qyrg/S98ErfvmSlv+7tdZK3PIEZuasuBJcnSJg2Y85bocdSISJss8iHduleZ48aNw4cffqj6t08//RTjxo1L+6CIwsAqR7lmq+eNKW8mTAulV+3uGFb90Q+ELmP2D7uF1x27rAd5xluKYwIYysASPQFArN+todoBEy/tOjEFMJPjAnJlUyUIgihEmPLmiKO8sXOW2xtIGDASjSiKck9btG3SxDPbZKLiLZSWbOLRWBMaF6AjcTKV4KrGGgdsFhN8AUE+hxGZYWFrI67+ypyYy/VuaocDS2I3FwJBAe7QJrthc940Fm+iKCoCS0xwFPmoAN0rzfPOOw8PPfQQfvOb32DPnj0AgLGxMbz22mv41a9+hRUrVhh9jESeksrOSDowmd5uNWNCgxM2iwkeXxAdfaMx1/1oZw8A4EuLJkq39QsZPU7ZcpLimABGbaVUCHn9QV22GbZD2lwr7ZhyHCdbJzM5qDtXNlWCIIhCJNGoACDSTjmmQ31TqmrxbZPqxaCgKPwsFl5W3rT2vKUaXMVzHCY1smHd1PeWaarLpfWF3cLjv8+Yje9duED3pnaiwJIxRaFk1KgArXPelGE8NKRbhf/+7//GihUrcNddd+GMM84AAFx88cW4/vrrceKJJ+Ib3/iG4QdJ5CfWFIYopgP7EjqsJph4Hoc0S5aLaOvkqMePHe1SkMmRs5vkncxUEhy1IitvafS7AVLxV1UuWR71WCc75bASh3xZRZYSJxe2NuLaFXNRURZpk8ikTZUgCKIQSdbzZuLDC089G3gBRRhJtHXfnMQ2GVD2bCt63rSOC0gnuGpioxQ+tnFrF7bvHSD7ZAb5dFc/AGDe9HocNbcZs6bU6N5YTTTnjW02OGwmWe1NlWTz5KLxK7IXSqHnTXdpzHEc/vd//xeXX345Nm7ciMHBQVRUVGDx4sVoaYntMyKKF1vObJPSx3ba+Eq0tQ9iV8cwjjtsvHy9T3b1QRBFjK93orGmDHWVdrjcfvQOeTAxtMtnND5Fv0C61FfZMeTyoW/IozrcVA3ljDdGZShxciQLg7oXtjaC4zisfeETAMChh9TghvMPJ8WNIAhCQTLlDZAsZ25vQFffm1J5iFHe5FEB6udqZeCW1cKjsVo6jwyHxgWozWlTkmpw1ea2bry3TbJTfvxFHz7+og81FTasXD4zK5t+giBiR/sgBke9qHZK9v5iPmd9ulsq3uZOrU35PphtUk3RYgmp6YaVAOGNcK2OKWaZ5CB93u1FPiogJV1z37592LRpEy644AIAwBdffIHnn38eq1atwvjx45PcmigWUhmimA5h26T04xEvtGTL530AgMNn1AOQiqG9XSO6Exz1IPcLpKm8AVJoyRcHhnUpb8w2yewuAGQlLNPKG0Np8eGAoj4JEgRBpII8KiDBAtfpsKB3yKNrXAArzDhIdkQlJlPinje28OU5DiaeR5mdR0WZBSNjfnQPuDGlOX5iJJBacBXrkYuG9chl2rWxua0bT23YGaEYZrNwzDYutx97OqS10pw0irdEgSVGJU0CipEEOos3i5kHx3GKnjeyTQIAPvroI3zlK1/Bb37zG/my4eFhvPTSS1ixYgV27Nhh6AES+YteT3I6iKIoy+fh4q0KALC/xyX/LRAU8PEXkcVbnTw7LfWZNclg6mO6tklA/7iAQFBAz6B03Wal8hayTWZ6XABDafEZHs3OYxIEQRQKoiiGbZMJ1Cxm9WeLYS3IYwJCi1clydIm5c1HReCWnDipIbREb3BVqj1yRpFKuEqhs3VPP0QAExqcqA2lWqeCPUGEv1Ez3oBwkajV2aVMmgSg6HkrTuVN90rz7rvvxhFHHIH169fLly1YsACvv/465s+fjzVr1hh6gET+ks1RAV5/EOxnnNkmaypsqKmwQRSBPZ3SjtLO/UNwewOoKLPIylydIsExU7BZd0bYJvUeb++QB4IowmrhUR3qlwOy1/PGiCjesvSYBEEQhYI/IMj2xkSBDvK4ALeOwJLQ/UZbJgGF8ha3eIsdMRBOnEy+6ak3uCqdHrl0yXXhmCtYv1s6lklAUVQFhJjXyGWg8qZ3falU3oDwJn8gGE6hLCZ0F2+fffYZVq9eDbs9snK32Wy45JJLsGXLFsMOjshv9Mra6cAskxwXGccfbZ38aKc0Z3D+9Dr5RFGf4uw0PRirvOlTClnEcnNNWcSOa6UzNKg7Cz1vAOByhx9nZMxXdCc/giCIdGCqG8eFF5dqpDKomykPakERcmBJkp43a4TyFhrUrTHCnwVXOWyRz0stuCrVHjkjyGXhmCtEUcSnuyVH0typdWndl/JzG732C/e8GaC8sZ43jYElvpjiLXwMxai+6X6F7XY7urq6VP82MDAAPs2EGaJwyGZgibLfTVmgTBtfic1tPdh1cBiiKGLL51LxdviMBvk6spKVwZ43owNLAOl4RVGMscBEI/e7KSyTQNg2OZQlC6NLsUssitLCg4WmEARBlDrKsJJEv+tMudBjm2TKm1rfdXLbZKzyxs4nWhMnAamAa9s3iA2b92P+9Dp8eclk1RCQVHrkjCKXhWOuONAzikGXD1Yzj5ZJVWndl9nEg+c4CKIIjy8YEWbDAnbSHdANANYkw8Cj8UeNa+J5DjaLCV5/EG5fEBVliW5deOiutI477jjcd999aGtri7j8iy++wP3334/jjz/esIMj8ptsBpZEJ00y2LDuXR3DONg3hu5BN8wmHnOm1sjXYcXQyJhf8y6OXowMLGF+dLc3GDE3JR7s5BpdvFXIPW9ZUt6iHoeskwRBEGGSjQlghHve9KRNMuVNzTaZOG0y2nIGQDHrTd/wbKZqzZ1aGzeKXm+PnJHksnDMFSxlsmVyddqzaDmOixtaMpYPgSWKDQi7rXjHBehead54443gOA4rVqzAKaecggsuuACnnnoqzjzzTADA9773PcMPkshPlMqbKGbWIufxRoaVMA5prgQH6YTxTMjHPmtydUSRV2a3yLtDmVLfvAbaJm0WkzxgW0vfWzhp0hFxuTwqYMwPIcPvDwC4ohYaw1myaxIEQRQCYeUt8eI23POmP7BEVXnT2vOmWNiznrfhMT/cOha//SPSOStRKIbeHjkjyWXhmA6CIGL73gFs3Nqpex4es0zOS9MyybDHmcEmK29G2CZTLd4U1l+HtXgHdet+hRsaGvDnP/8ZL7zwAj744AMMDg6iqakJq1atwjnnnAOn05mJ4yTyEGvoh14QRQSCIizmzEXDR48JYHy6uw88zyEoiPhsj7S7tOvgMDa3dUd47Osq7djf40LvkAfj643/jPoNDCwBJKvn8Jg0m25yU+KYZrnnLUZ5kxYIgihizBOQd3MzhUvhdx/1BKh4IwiCUDDmZWMCEi+9Uul5Y0Eoaj1vyWyTPhXniMNmRmWZBcNjfnQNjOGQZm0zR/uGJeWtLkmiIeuRi47rB4CqcisOn1mv6fH0wgpHtTEFjEwVjqmSzlgDrz+IHe1DAIC509ILK2HY5FlvkUW93PNmhG1SIQ5oKVT9wdBnWKm8yeMCik95S6k8Lisrw6pVq7Bq1Sqjj4coIGzW8JfE6w8aYhmMh5ptMt6cmDFvIGZOTH2VVLxlSnmTe94Meg3qqhzY3TGSNGTF6wvKP+jRtkmziUeZzYwxr1RIZbJ4E0VR/uEeX+/Ezv1DVLwRBEEocGsY0A0A5Sn0vMmjAlTSJs28tjlv0eevxtoyDI8NoXvAral48wcE+Xe/pjK57XBhayMWzGyQB2U7rGY88vJWDLl8eOvjDpxw+ISk95EKrHB8+OWtET37tRU2XJhnc97SnYfXtm8QgaCAukpbzAZvqsSzTboMVd7Cn0Ut46j8/ljl2VHEg7o1vcJr167FV7/6VTQ1NWHt2rUJr8txHK699lpDDo7Ib0w8D7OJQyAoSl+uDBYH0cqb1rjfBTMbwPNcxscFsB8Xo5S3eo3Hy2bwlDssqsVZhdOKMW8g1PeWOVXc7Q3Ku7qseBuinjeCIAgZ1vPmSKq86R8VoJzzFo3c8ybE6XmLY7lsqnbg8/1DsjU/GQMhy6TFzKNC43qA5znMmhLuUT/rmKl4+vWd+NNbu3HUnGZd51RBEOVCsNppUw1LYSxsbcTf3tuHnQekpOqvnTQdX1o8Oa8UN73rnOjb7mgfxIZN7QCA2YfUJg0/04pdVt7ipE0aqLwB2qyTap9htl70FOGgbs3F2/HHH0/FGxGD1WxCIBjIeGhJdPGmJ+531pQa2cKRqUHdag3f6aD1eMNhJQ7Vv1eWWdDVL/UtZBI2JsBq4eVjH6FB3QRBEDJjGpU3tvgd8wYgCKKmgoLZJs1qgSXJ0iZVVAtAUt4A7YmT/SHLZG2FLeVC4cQFE/C399vRN+zB0xt2onVKddJCDEjNWtij2BxtrC3Lq8IN0L/OYai9Fh/s6MH86XWGqIpqypsgirJSbERgCc9xsJp5+AICPL4grEneGrU1WMkrb9u3b5f//9atW2kcACFjs5ow5g1kfFxAtG1Sb9xvfYbHBRg55w3QfrxdihlvarBxAZm2MLIxARUOixyUQmmTBEEQYVigQ9KeN8Xfx7za+pXDtskEPW/x0ibjKW/yoG5tyhs7XyUKK0mGxczj8Bl1eP2DA3hzy0G8ueUggMSFWCrWQp8/iEFX+ByVjdRsvaQy1iDeazHqiW0nSRW1wBKPNwiWi2aEbRKQ1DepeAvAakuswKpZf4tZedO90jzrrLPwz3/+MxPHQhQg7IuSbeVNb9xvXVVmB3X7MhBYAmiwTYaKt8Y4XvYKZ3bGBTDlzemwKObLUfFGEATBkEcFJFHeTDwvD7vWGlqSqHhjl8XreZNt/1Ex8uFxARqVt5DSU6uh3y0em9u68foHB2IuZ4XY5rbuiMu1WgujQy96BiOfU6bGCKWD3nVOqq+FXtSSINmYAIuZN2wdxPretLw34VEB4ccuZuVNd/HW0dEBh0PdokWUHuFxAZku3pjyJj2e3rhfpmQNuXzyl9xIDA8sCe1cjnoCCZOSOgfUkyYZbORA5m2T0v1HKG9UvBEEQci4PdrSJgHFoG7NxVvINqkSWJLUNhlHeWPjAkbG/LLlMxH9IeUtWdJkPFIpPtr2DWi2FirpGYzcGPVm2D2UCnrXOXpslulgU+l5M3JMAMMaJ9VSDTXbJClvCs4880w8/vjj6O7uTn5louixyt7nTNsmmfIm/TDonRNT7rDIQ8X7M2CdZMVrugMwGQ6bWbbKJFLfuvpDPW816hsq8qDuLNkmnQ4LKp3ScY+M+TI+/48gCKJQCCtvyW2QTp2Jk8FEtsnQZQGdPW8Om1keOfP65vak88XknrcUi7dUig+l9TER0RbE7mjlLQ9tk3rXOanYLFPBpmKbdHmMCyuRH8cS+zjxYMWbmXre1NmzZw82bdqEE044AdXV1Sgri9zx5zgOGzZsMOwAifzGFvqiZF55i53zFm9OjFrcL8dxqKu0o6NvDL3DnphY/XRhPxzKeNt0qau0w+WWZr1NbCyP+JsgiNjyRa+seDVUxQksyVL/GbNNljssqAo9ZiAowu0NoMyA5mWCIIhCZ0xjzxsAlOtMnPRnoOdtc1u37PxY/+/dABL3nvXLPW+p2SZTKT6qy62abhNtQewZyH/bJCCtc75+Sgue+NuOiMuddjMu/a9ZEe+DXptlqsiKlmLdJydNGni+t8ZJtVRDnvOmLN5oSHeYcePG4cwzz8zEsRAFCPtyeQMZLt5CJxB7VK9A9JyYRKlU9VUOdPSNZWRcgE/euTRGeQMkq+ferpGY0BK1JKkf/uZd1RNq9myT0vtT7rDAYjbBYTPB7Q1iaNRHxRtBEAQUypsW26TOQd3BNGyTPhXlLZUQkP7QqIDaitSUt1SKj9bJNaipsCVU7JTWQgZT3iqdVgyP+vJSeWOwc2hzrQMTGsqxua0HMyZUxbz+zGap97XQi5oilgnbZPhxtNsm1QNLSHnDnXfemYnjIAoUuectwzsbasobI3pOTDwyGVoSDiwxUHmrih0XoPeEmjXb5FhYeWOP6/a6MTzqw7i6zM2XIwiCKAREUdQ8KgDQb5tkM9xMqrZJbT1vLLAklfliY54A3KHeolSVt1SKD2YtVDsvMpTWQgYr3iY1OPHZqC9vlTcA2HVQmkU3+5BanHj4BGxu68Fnewbg8QXkVhIg9ddCL2qjAjKhvDEnkyblTa3nTbZN5u97myqaV5o+nw9/+ctf8Mgjj+Dvf/87fD4KIyAUylsObJN6CQ++Nn7Wm9GBJUBssZlKMzezTY55A3IaWSZgu8PlIaUvbNekWW8EQRC+gCAXT9qUN322yUBAum+Lqm2SpU3GsU36Iy1nqfSeMdXNaTdHFBR60NvjxVjY2ojVZxwac93aCpuqQigIInpZ8dZYASA/e94YuzqGAADTxldiQoMTjTUOBIICPtnVH3Pdha2NOHxGXczl8V6LVLCr2BHZxgT73BqBNYWeN+XnnyW2Jgp9K1Q0vcqdnZ24+OKL0d7eLgcQTJ48Gffffz9aW1szeoBEfiMrbxlIcFQSPectFVgCltG2SVEUDQ8sAZTFpnS8qQzsLLObwXMcBFHEyJg/aXJVqsjFW0h5q8rSfDmCIIhCgC1ueY6Tz5uJKNervAWZ8harqjArZdzAkqiet1R6z9INK2HE62Uvd1hwyZdb4xYf0bNOm2sd+MkVS1VVpv4RD4KCCBPPoblOul22izdBEDW1ewSCAvZ2ugAA08ZXgeM4LGxpwKvv7sPmtm4sntUYc/3PD0hK3TnHT0N9tV3TkHM9qNkmXQYO6I5+HC3Km09tSHcR97xpkgl+8YtfYHh4GD/72c/wyiuvYO3atRAEAT/60Y8MOYgXX3wRp512GubNm4fTTz8dr776qvy3/fv34xvf+AaOOOIIHHvssbj33nsRDEa+Eb///e9x8sknY/78+Vi5ciW2bt0a8fds3UcpYtUxhyMdDFXeDE6bDAqiPJzS6MASIKy8pXJC5TlOTgvLZCEVHhUgFW00LoAgCCKMst+N45IvouWeN822Sdbzlkh5i1O8RakWqfSeyWElBmwQLmxtxM+vPhrfu3ABZkysBAB8adHEhKpRZ2jmKdug7B/xAnFeZhZWUl/tUB04nWk2t3XjpgffxpqnP8SvX9qKNU9/iJsefDtmhh0A7O9xIRAU4LSb5VTpI1obAABbvuiDPypv4KOdUpBZVbkV/7V0MpbObsasKTWGFW5AksCSDKRNahoVoBK6o+x5K7bka00rzbfffhs33ngjzj77bEyfPh3Lly/Hj370I2zZsgUjIyNpHcCf/vQn/OAHP8BFF12EV155BWeccQa++93v4sMPP4Tf78fq1asBAM888wx+/OMf4+mnn8a6devk269fvx5r1qzB9ddfjxdeeAETJ07EZZddhv5+SU7O1n2UKmrDGo0mKAjyrko6xRuzIfaPeA21EPoUYxIyoby53H54fcGUk6TkvrcMJU6KoqgYFWAOPSYLSqHijSAIgg0x1tLvBqQw541FpasFlsg9b+rnPdn2H9p81DtfDFCElVSlp7wxWC/7vGn1AMLFWTw6+qS/Hza9DmYTD59fiNvfzvrdGqsdWVnDKGF969EumnhDyFm/29RxlXLRP3VcJWoqbPD6gti6ZyDi+v/6+CAA4Nh54+Si3WjUgkQyM+cthSHdijUY63kTkd+22FTQ9M4ODg5i6tSpEZfNnz8foiiis7Mz5QcXRRG//OUvcfHFF+Oiiy7C5MmTcfXVV+Poo4/Ge++9h9deew0HDx7EmjVr0NLSguXLl+O73/0ufvvb38o9dw899BBWrVqFs846CzNmzMAdd9wBh8OB5557DgCydh+lijULtknlFzcd22Sl0wqziYcoAoMq9kNBELFtTz/e/GA/tu3pTzjPRgkLK+E49RNnqpTZLfKckt5hT0onVADy3LVMFVJef1AuhmXbJClvBEEQMsw26dC4uNXd8yZoGBUQ55wWiFLeUuk96xsK2SYNtuaPD9kaWXEWj46+UQDAhIZy+TYHelyq11UWb3aV8I1MkUrfurJ4Y/AchyNmSurb5h098uX9wx58FuqDO3b+OMOOOxq112w0k3PeNLw3ARXbpNXMgw8VvO4iG9StqXgLBAKwWCLfkPJyae5UOsXL7t27ceDAgZjRA7/5zW/wjW98A5s2bcKcOXNQVVUl/23p0qVwuVzYtm0b+vr6sGfPHhx11FHy381mMxYtWoT3338fALJyH6WMLQu2SWaZNPFczBwaPfAch7pQClb0jhyzMdz55Ae46/ebceeTH8S1MUQTDisxabLD6EEZssLzHC48WX8zd6Xcf5aZ8BBmmTSbePnHNlvz5QiCIAqB8IBuncqb5p63RLZJNuctzqiA0AakRdGLx3rPojcM4wVfDISUt7o0e96iYWnFHf1jCa1vTJlrrivDhAbpNgd6RlWvy2yTDTWOrIWuAakFwbDibdr4yojrMevkRzt7ZUX1rY87IAKYNbkaTTXGzrJVwtImPb6g/J6E0yaNDyzRZJtUKd44jpNDS7TcRyFh3KucArt3S0Mfx8bGsHr1amzduhUTJ07E1VdfjWXLlqGzsxPNzc0Rt2lslH4wOjo6YDZLhz9u3LiY62zfvh0AsnIfhx12WArPXsJsYDphLnCETjD+oJCx58K8zHabOe3HqK92oGvAjQGXV76v97cnjt//5nnzY5qClbBdMquFN/w1aKh2oL3bhQGXD2YzH3dXq7bShotOaVU9zqrQENNRjz8j7xGL4a0os8gn/5rQCXxkNDOPaQQsUlstWpsoLui9Lg3y+X1mxUG5w6LpN7E6VDSNeQLgeS5pzxJbwFstppj7t4YW20FBVH1sVvjZbZG3PXJOMxYf2oTX3tuHpzfsRHW5Fb/45rHqISChwJKGGoehv/njG5ww8Ry8viCGx/xy+4PyvQ4EBXQPsPTIckxqqsA7n3XhYN+o6rH0hDZvm+vKUBY6p3r9mVvDMEY0WmBH3NJ5c9Ttl4vSmZOqI45v9tQalDsscLn9+OLgMGZNqcFbn3QAAE5cMCGjz4WtQ0RRsiRazLxsm6xy2gx7bJbK6vEFk36nw+vEyM+w3WrGqCeQ0TVqLki7eEtHaXC5JEn75ptvxnXXXYcbb7wRr732Gq655ho89thj8Hg8qKyM3G2w2aQfNK/XC7db+rJardaY63i90g9JNu4jVXieQ01NYc/Aqq2WdneCgpix59IdOik47ea0H2NCYwU+292PUW8QNTVOBAURT/19R8LbPL1hJ04+8hB59zKanhFJXbLb0j++mONtqsAHO3rg8gRQXV2Gl97eDAA487ipOGruePQPe1BbacfsaXVxj6+pXlLJPQEhI+/R7m5pd7Oq3Cbf/6TQYmB4zJf3n/HKSkeuD4HIEvRelwb5+D4LobVSdaVd029iReg5iAAsdqvsZogHF+pvqlK5/1qXVDSIovp5mqkW9bXlqn8//bjpeHrDTgy6fHA4bfLQaPm5CaIUEAJg2qRa1Bis+jTXOXGgx4VhbxAzoo6vstKB/d0jCAoi7FYTpk2uRf+oH8Dn6Ox3xzwfURTRE7JNzphSizJbqHjzBTN+rpo0rir5lULXq6lxYne35PxprivDlImxs2yPmjcOf39vH9746CA+PziC3iEPymwmLD9qqqZE01SpVNg67WU22G1m+TM0YVyVYdbJ2tDnyOsLJv1Os9aN6M9weZkFfcMemC2WvF+L6EFz8XbttdfGFDgAcNVVV0VYKjmOw4YNGzTdJ7vd6tWrsWLFCgDAoYceiq1bt+Kxxx6D3W6PsWWyYqmsrAx2u7QDo3Ydh0N6o7NxH6kiCCKGhxP7uPOdgE86KYy6/RgYULcopEt3r1TkW8182o9RYZd+0Nq7hjEwMIpte/qTjg7oHXTj3S37ceghtap/7+uXjsnMc4a/Bux493eN4M1N+9C2dwBWM48vLZyI6nIbJtZJn9HhofifI0uopusdGMvIe9TRLYUWOaym8P2HbDgeXxCdXcOyzSKfMJl4VFY6MDzsjjv/iCgO6L0uDfL5fe4LKUNmHpp/h+1WEzy+IA50DiFYm3it4QnZK72e2HPx6Kh0jvMHBNXHZqNu3GNeDAyoqxNsePYnO7pj+qoHXVIIGAeAE4KGn2eaax040OPCjj19mNooLcCV7/X2Xb2h65VhcHAM1aF+wfauEfT0jkRYSUfGfHL/oZ0HPG5pLRcICjHXNZrxNXbUVtjkQleN2kobxtfYMTAwio/bugAAhzRXqL6m9lDbysZPOwFI+RNBAXhz076EbiEjsFqkUJiu7hFZ0eI5Dl63Fz6PMe0S/pDV2OsLJP1Oe+N8htns3e4+FwYGMmclNYrKSocm54Cm4o0VVkbT1NQEAGhpaYm4fMaMGXjjjTewZMkS7NgRqYp0h3YimpqaZKtjd3c3pk+fHnEddt/Nzc0Zv490CGR4PlqmYWqP1x/M2HNhDds2qyntx6itkIr1ngE3AgFB89iAvmFP3MdmAyAtJt7w14AFf+w6OIy9nZL3/cQFE1But2h+LNY7MeTyZeQ9GnaFlFFH+JjMof5Ef+g1bqzOv51wRjAoFPz3kNAGvdelQT6+z6PukEPDov085rRb4PEFMeTyoj5JLxnrveY5lXVFSCgJqLwuoigqbsvFPbZJjeUYGPFi98FhTBsX6URiPWTVFTZANH5d01zLAkhGY+47GBRwMNTb1lxbhkBAQJXTCpvVBK8viAM9o5hQH1ZcOnqljc7qcit4joNZ4VgZc/tjVEWjuXD5TNU2DfnvJ8+EIIgQBBGf75eGc09trox53pvbuvHn/+yJub3XH8T9f/zYsIHc8bBbTPD5BanXLfQSltnNob5KY2L52Xvj8QUTfqdFUZSVPx6Rnz+2cTzq9ufdb0I6aCre7rzzzow8+Jw5c+B0OrFlyxYsWrRIvnzHjh2YPHkyFi9ejBdffBEul0sOSNm4cSOcTidmzZoFq9WKqVOn4t1335UDRwKBADZt2oSVK1cCQFbuo5TJRsyuEQO6GXVRs95Sjd9XwkYFWA22KWxu68bv/yZtGjCbB4CIE5EWKkJpk5kaFRA9oBuQFPjKMiv6hj0YGfXldfFGEASRaZjao6c4KHdIli8tiZNBeUi3SmCJKX7aZEARYmJJsOM/qbEcH3/Rh/bu2PFQzL1idNIkYxxLnOxVV/RYEiUbuM1xHCbWO/HFwWEc6HFFnDO7B6XrsnOS2cTDxHMICiK8fgFlxuatxMCCYB588VMo3w6bxYQrzjhULrhEUcSujlDSZFRYidbUygUzGwyd76bEZjUBY354/EH5s2dk0qT8GEg+ZFs5azc61K5YB3XntHvPbrfjiiuuwLp16/Dyyy9j3759ePDBB/Gf//wHl112GZYvX46GhgZ8+9vfxvbt27Fhwwb84he/wOWXXy5bOC+//HI89thjWL9+PT7//HPceuut8Hg8OO+88wAga/dRqrDiTTnrzGiMGNDNYOmN/cNeCIKYcvy+EjYkM50kzGjYLJjhsdgG58de3a4pBZMhp02O+TMyqFKteANoUDdBEARDOaRbK/K4AA2Jk/4AS5tUmfOWYFSAcshzonPY5KYKAEB7d2z8PrMB1hqcNMmQEyf74hRvodYFdj0AcRMnlUmTjGwmTgJSAcdmmB07TwrD4zhg3rQ6+Tq9Qx6MjPlh4jlMaSqPuH0qqZVGY7NIn02vLyiHlZQbmDQJKN6XJEmRfoWiFv0ZZutG5pAqFnKaNgkA11xzDRwOB+655x50dXVh+vTpuP/++3HkkUcCAB555BHcfvvtOP/881FVVYWVK1fimmuukW9//vnnY2RkBPfeey8GBwcxd+5cPPbYY6itlfqTbDZbVu6jVJHnvGVFeUu/eKsut8m7bIMuL2or7ViZzMagEr+vhFlOjGoQNnpXjRVv/oAAjy8oz44zinjFG7N8DtG4AIIgihhBELGjfRCDo15UO6XNvujfZll50/H7yyzvLg0phSxtUk09Y8OaAyo9Q2zhyyHxnNJJjVIBsb9nFEFBiBgA3R9ystRWZkZ5Y7bJ4TE/XG5/xLlGFEV0hpS3cYq+wAmhoK4DUWqdcsYbw241we0NZHTkkRJ/IAiPT3rdz182E9v2DqJv2IMPdvZg6WypmGMjAiY1lkcMngaAwVFtQXlar5cKdoUqJg+gN9hyajNrG0WlLN6iexbZesdNowKM57LLLsNll12m+rcpU6bg0UcfTXj71atXY/Xq1XH/nq37KEXYnLegICIQFDLS7BtW3tL/uPI8h5oKG3qHPOgdkpIaF7Y2YtGsBmza3hNx3YoyCy4+tTWpb5wVrkYpb3p21WZNiU2gisZmNcnNxSNjvgwWb5H3W1EWGg5OyhtBEEXK5rZuPLVhZ8Rvdk2FDSuXz4w4d6SmvIVmvWko3gIJbJOsKBNFQBBFeXAxEDkfK1F6eGO1AzaLCV5/EF39boxXWBHDxVtmlDeHzSwHpnT0jWLmxGr5byNjfox6AuAANCrUNKa87Y8a1J0PyttQ6JxoNnFw2s04Zl4zXvrPHrz1cUdM8RY93w0wpt0jXWzyoO6ArLw5HcauLVgQii8g4LPd/Zg5oUp1wzrRZ1guMktxSDdBxEPZ55Up9c1I2ySgGHwdOuH4/EFs3zsIAPjKcVMxbYL0Y3nmMYdoavgND+k25uuUiV01pXXSaMLFW6SFmNkmRzI0HJwgCCKXMHt79GYbmxGqtLenprwx22Ry1SA8pFvNNhk+N0UP6vYpFr6J4HkOE0NJj/ui+t76QuN8WCBYJhjP+t76IpOVD4aUtboqe8R6ZEKDpLz1DLgjirKw8hZW6ewWbb1VRjESOg9XlFnBcRyOmScF523bM4DeIen4dnfEL96MaPdIF7tsaQzKtl6ngcrb5rZu/O/j78v//r/ff4CbHnxbtWWEzXhTU52LVXmj4o1IC9bsC0hDLjOBkbZJAKgL7Q6yJuuNW7vgcvtRV2nH2cdNxYKW0AD2Pm1jHFjRalRgSSZ21SrKWCFlvAqWrOeNbJMEQRQbWu3tgiBCFMWUA0sAbT1vTHlTc7+YFAUds1cy/BqLNwCY1Bjqe+uKVLP6R6RzaV1V5pSeeH1v7N/KfjcAqCyzoNxhgai4js8fxKBLOh8pVTrmIMpk+4cSpryxc2RDtQOHTqmBCODtTzoRCArY2yUVyNPGx86G43kOK5fPTPgYydo90kUOE/GHe96cBvW8sU0R9l4x1DZFgMSfYVLeFPh8Pjz11FO47rrr8LWvfQ1ffPEFnn76aXz88cdGHx9RAGS6781I2yQQTpzsHfJAFEW89t4+AMDyRRNh4nlMbpZ2uuIlW0Wj5+SnhUzsqlUyC2MGCim5eCtT73kj2yRBEMWGHnu71x+EEAqLylTPW1h5U+t5UxZvkcqbX3aOJN98nBzqe1OGlgSCAoZDi+xMKm/j6lnxFrmpyv7NEikZHMdhYlRoCUttdtjMEYWG1Zpd2yQ7J1YpBq8fG1Lf3vqkA+3dLvgDAsps5ogiUwlLrYxeK9RW2DI+JgBQ2CZ9QdnWa0TapJ5NEYYvQWgcU948Raa86V4N9/f345JLLsGuXbswbdo0fP755/B4PHjjjTfws5/9DI8//jgWLFiQiWMl8hSrhYfbm7kfPqNtk8pxAZ/u7kdH3xjsVhOOmz8eQDhVK7rROR5Gjwpgu2rphKhEU+HMjG3S5w/Kz788akeZqX1UvBEEUWzosbePeaQFuInn5JRBLchpkxpGBcjKm8p5IaJ4C0YXb9p7tieFUg/3KYq3gREvREhFY0WZsYEVSlgYycFedeWtOap4A6TQku37BuXijVkmG6rtEb1R2bdNSudE5et1RGsDHH83oXfIg2f/8TmAkDooQp6jFs3C1kYsmNmQNCwnE9gVfYJMGY5eA6RCKj3/gYTKW8g2WerK25o1azA6Ooq//OUvWL9+vRw9ft9992HevHm47777DD9IIr/J9Kw3I+e8AeHdwfZuF9b/axcA4Lj54+VG8olN5eAg+dK1KFVs18eonjfA+F21ygzZJtmOsInn4LBFFq9yzxvZJgmCKDL02NtZWInDZk4YChINU9502SZVzkMcx8UdF6C15w0AJtZL58bhUR+GXNICW5k0qee56YUpb31DngiXDxu6rUyaZMihJb1SscnCSqLnjtqykJqtJNo2yY5hamj4OYv439M5ErfPi8HzHGZNqcHS2c2YNaUmK4UbEK28GRdYkkrPfyL3E1uXFFvPm+5X+p///CduvfVWTJkyBcFg+INus9lw+eWX45ZbbjH0AIn8h9ktMjXrTVbebOkrW5vbuvFkaPD18KhPVoWaa5WxwWY0VDvQPejGwZ5RVE5JPMvP6MAShpG7apmyTboUdonoEzezhIx6AhlLIiUIgsgFzN6eSCVg9vbPDwwB0Jc0CSh63pLYJgXFkOJ4v7NsRE70uAA9tn+b1YSm2jJ09o+hvduFqnIb+kNhJXUZSppkVJZZ4LSbMeoJoLN/DNMmVMHnD8pWyOieNwCYGAotiVHeoqyIWodBG4VsmywLry02t3Vj656BmOuyPq9sWCH1wApej0J5M2JUQCo9/wmLNxrSLeH1elFdXa36N5PJBL+fkuVKDZs1NIsjU8qb1xjbJGuCHVJRn574246I3S22Y3cwzlBQJUYHligxaletQlbBjP1+xgsrAaSFCtvtJeskQRDFhJ7QCHlMgM4xLayHaMwTkHvm1PArCjJTnHMEGyEQv+dN23KQzXtj1kmW2lybpE87XTiOU4SWSGrbwd5RiJBeVzXLJhtnMDDixZjHrzrjDci8eygaOW0ydF5Opc8r10Qob3LaZPrKWyo9/4nSJsOBJcWlvOku3ubNm4ennnpK9W9//vOfMXfu3LQPiigsMm05MMI2qffHcTxrdNbQ92Z0YEkmCI8KyIzypla88RwXnvWm8riCIGL73gFs3NqJ7XsH8urERBAEkYyFrY2YEuoDUxJtb3d79M94A8KLYRHhUQNqBBXFW7zzkGybjFbe2MJXQ2AJAExuigwt6Q8pj5ma8aZknDwuQDov7w+NLBhXV6Zq2Syzm+XB4Qd6R9EzKBWaubZNDkfZJvX0eeULrOdtzBOQ+8mMCCxJJUmTub7UPsP20IaJLyCoDqkvVHSvhq+//npceumlOPvss3HCCSeA4zi8/PLLuP/++/HWW2/hkUceycRxEnkMs01mOrDEkYbypvXHsW3fAI6uK8fEeukEdbAnt8qbUbAiKlM9b2rFGyAVjYMuX4zylmywrSCIOWnCJgiC0EogKKCzX1Jzjj9sHP61pQO1lTasueroiN+r8IBufYtbs4mHzWqS1Y14v7MBRQhJfOUtTs9b6Pyl1iunBhsXsC8UZa/secs0THk7GFLeDoQKSLWwEsaE+nL0D3vR3u1CbzLbZLaKt7FI22QmZrtmGvaaKc/hRo0KYD3/0WuE2gobLgytEZSENyDiK2+AtJYsd+TvJrsedL/SixYtwmOPPYa7774bjzzyCERRxOOPP47Zs2fjV7/6FZYuXZqJ4yTyGFk+z0DPmz8gyCebdGyTmn8cQ5HHE3Qob5nqeTMSOTzE7YcgiIYVQkmLN3lcQNiuyeyr0TBv/5eXTMK727rjFnYEQRD5wBcHhuD1B1FRZsFZx0zFv7Z0YGDEi6AggOfD56sx1hOk0zYJAOV2czgUokb9OuEZb1zc0BBznMAStvDVa5vs7B+Dzx+Ui7dM97wBasqbK3R5bL8bY0KDE5/s6sOnu/oRFESYeC5mpIFNMXA60wQFAa4o22QmZrtmGqa8sRl/DpspYhh8urCe/wde/AQf7OjFMXObcdlph6quXRK5n8wmHlYzD19AgMcbiLtWKTRSKpMXL16MZ555Bh6PB0NDQygvL4fTGf/LQxQ37Ec/E5YD5WwOWxrFm+Yfx3Lpx3RcvRMcpOJkeMwn2w7V0NszkAvYD5YoAi6PP+Hz0YPm4i2006jFvvrX99pjLsvXpm2CIEqXz0IBE7MPqUVNhU0O1DjYO4YpzRXy9UZTtE0CkhWtb9ibMHGSFW+mBKFQbGEdMyrAr8/2X11uRUWZBSNjfhzoHUVfKLCkJhvFW6iHrat/DEFBCNsmVZImGRNCt/lsTz8AoL7aEVMA2LI458015pfT/ytC50094Tf5gi3UxsJUX6cBYSXR8DyH8fXl+GBHL5xllribzsnGXditJql4K6LQkpRWmy6XC11dXbDb7aitrcWzzz6Ln/zkJ3j//feNPj6iAMhksy/7slnNfFq7OlqbYFsnS1ubNosJ9dXSySiZddJbALZJs4mXLQ1GWie1K2/SY2qxryYi35q2CYIoXbaGCoLZh9SA4zhZldrf44q4XqqBJYC2Qd3ygO4Ejgpmm4xJm0xgOVOD4zh5WPeO9kG4Q88t04ElAFBfaYfFzCMQFNE94JaVt0S2SZY4yTZZo/vdgOwGlrBZq+WKYiSVPq9cE72ZnsrGhBbYkG13gsCRZLkDrO+tmMYF6F4Nb9myBSeddBKefPJJAMBPfvITrFmzBi+99BIuueQSvP7664YfJJHfsC9xJkYFGDWgO5Ufxwmhvrdk1slCCCwBwn1v727tMiwgREvPGxAu3tL17Odb0zZBEKXJqMeP3R3DAIA5h9QCCBcK7d2RxVuqgSVAOAQi0biARDPeGLLyFidtUs/5i/W9bfm8F4BUlDpSKEz1wvMcmkMq29Y9A/D4gjDxHBpUCjLGuKjCzmLiYs59Ydtk5gMtosNKGEbPds009qjN6kwob4BiTpuW4i2O8uwowkHdur9t9957L6ZPn47zzz8fbrcbf/rTn7By5UrcdtttuO222/DQQw/h5JNPzsSxEnkKswtmRnkzbkC33ibY8fVOfPR5Lw4mKd7kId15rLxtbutG75DkTX/5nb14+Z29hvSRMe9+fOUtMm3SCM9+PjVtEwRRmmzfOwBRlIoDlrQ4MQPKW3mo4BtNkDYZVt4SFG9yYElkgRLu2dZ+/prUxJQ3aX5dNpImGePqytDe7cLm7dJon8YaR8IZop/s6gPPAaxe+2BnL2568O2Ic1/YNpl5ZUYu3lRaF4yc7ZppopU3I5Im1Qgrb/HXl8k2IFgB6Cki5U33L8mWLVtwzz33YNKkSdiwYQO8Xi/OPvtsAMBpp52Gl156yfCDJPKbTMbsGqW8MfT8ODKvfNLizZ/fPW/JAkLS2dWTlTeVGTtArG1Si7c/GfnUtE0QRGmi7HdjMOVtf5TVfixbypsp/iJfDiyJ7nlL0i+kBrNNstlz2UiaZLBwkm17ByL+rYbWc1/YNpkF5W1MXXljsNmu+U70mqw8l7bJJNZfe4JB3YWabK371eZ5Hjab9EX997//jcrKSsyfPx+A1Atnt2dvB4bID6xZ6HkzqngDtP84sgGfiWyTQSGchpmPypvW+XYLZjak9IOl1zbJ7KtqJ1Qt5FvTNkEQpcnW3VK/2xxF8TYhFHQ1POrD0KgPVaEF+piXpU3qVyfknjcNgSWJbZNx0iZTsE0215XBxHPyffEcZ2iKcSKYDZI99rh69X43Pec+5cDpTJNIeSskzCY+4jOQKeWtTEfPWzz12B7HeplsZFE+o1sqmDt3Lp577jl89NFH+Otf/4oTTzwRHMehr68PDz/8MA3pLkEyqryFvmz2LPjpo2muKwsnTsYJ+VD2+eVjz1smh38GguH0pnjFW1XUiAJAUj+/fmpLzHVrK2z48pJJCR8z35q2CYIoPXoG3egedMPEc2idXC1fbrOa5BliSuskU94cKSlvIdukO13bpPS3QJRtMpXi7aOdvZH//lyyIm5u69Z8H6kyPlppE6Hav63n3KdcwzA1MVOEe94KP7JeuameqZ63eIWXkqS2SWtsAchU2ejPCFNls/FZTgfdq82bbroJb7/9Ni644AKYTCZcffXVAIAzzjgDe/bswbe//W2jj5HIc2TlLZC/gSWpYLOY5EboeOqbP5DfxVsmh38y1Y3j4tuBysss4CCNKBhR2H78AekEOanBiSvPmo3vXbgAa64+Gucvm4lrV8yVRzYw8rVpmyCI0oPFzk8bXxkT1DEpZJ1kA6RFUUyz5y1km0ygvAU12CZNcWyTeueUskVvtIKXrUVv9Ln4lXf2qhaOes59LHxDRHh0QqZgaZOFrrwBkX1vRg3ojkZL2EjytEnW8ybdh1ZVNp+TrXWvNufMmYO///3vePbZZ7FhwwYccsghAIAf//jHePnll0l5K0FsltCctwxYDsKBJbmxJI5P0vfGjs/Ec9ixbzDvvuyZHP7Jijen3QI+zmBYE8/LdgrliIIPQifaYw8bj6WzmzFrSo2sqC1sbcRd1xwjv+eX/tcsrLn6aCrcCILIC9QskwwWWtIeUt48viCYmJOpnje/XLzFX9Kxv8W3TSY/x+Z60bu5rRu/eumzmMvVCkc95z6LJfy6ZXpcQLy0yULEpmgVKctY2qT0nfH6g3E/V3LfZtK0SWm9lklHUrZISSooLy/HYYcdhrKysNf41FNPRUNDg2EHRhQO2el5y75tEkhcvG1u68YdT34AQDohrnn6w6xZR7Sidb5dKn1ko0n63RjMOjkUatQeGvVh534ppWxhi/pvBs9z8jye6nIrWSUJgsgLBEGUwzJmT1Up3lhoSbd0zmALRhPPpRRqJRdvCdImmZqmTXmLtk0mXvgqyeWiV2/hqOfcx3McrJbMpWYrSRZYUkgoN9XLHZkNLAHip0UmVd6skcpbJh1J2UL3q33xxRcnvc7vfve7lA6GKExkv3iR2SaBcOJktFUjkwmORqIlICTVPrKRJGMCGJVOKw70jso7jh/u7IEIYOq4ioQR07WVduzrdqF/OH9/QAmCKB0EQcSbWw5g1BOA1cJjSigyX8nExtCGX98ogoIgF11OuxlcHIdCIsKjAvwQRFHV5cACS0wJCjB5SHc85c2SvHjL5aJXT+HInBx6zn12iwk+v5DR0BJRFDEyVhyBJUCk8papnjeLmYfFzMMfEDDmDagqfMnSJh1RQ7oz6UjKFrq3gURRjPlvdHQUH3/8MT7//HNMmzYtE8dJ5DHyjlVGbZO5V97EkPcl19YRvbD5dlVOY/vIWPqZluINCNtFPmjrAQAcEUd1Y9SE4qf7RzwpHZ+RCIKI7XsHsHFrp2EDzgmCKBw2t3XjpgffxhOv7QAghVXd8quNMU6LhmoHrBZpsdk94MZY6HfSkeLili1WRTF+aAMr3hKpZ8l63rQob7lc9KZSOOoZfJ1JBxFjzBuQw2WKI7AkvC7LVNokELYbe+L0vSVX3iJvn0lHUrbQvSJ+4oknVC8fGhrCf//3f1PxVoKElbfCGBWgh3GKxMmRMT8qnVbdO4D5wMLWRhw6pQbX3ftvAMB3vnoY5kytTcuOmGxAN6OiLDyoe8zjly1HyYrG2tCP60COlbdCjhMmCCJ99DgteI7DxIZy7Do4jPZul7ygTCWsBJAWpDaLCV5/EKNuv6rCwQoCU0LbJOt5U0+btGpQ3rTM6czUojfVwlHrbFfZWpfB4o1tYDpsJk09hvlONgJLAGnExpDLJytn0fg0Dulmt8+kIylbGBaPV1VVhSuvvBKPP/64UXdJFAhsxyoQFGNODOmS6+LNqpI4Wah+aYfNLO++jq93pv3DlGxAN6NKobxt+bwPQUHEhHonmmvV5/MwaiskS2V/GgO906XQ44QJgkiPVJwWymHd6QzoZrB+onh9bwENgSWssIsbWKJBeWOL3kRkatGbjlrCZrtGh2MpkTehM2ibLJYZbwzWw2niOew6OJwxR0qZI3HipF7lDQBaJ9dA7WNaKMnWhmeb9/X1GX2XRJ6j9D17fUYXb7m1TQKxoSWF6pfmOE5WyRLFTmsl2YBuRnhQtx+bd2izTAKQT9T9w7mxTRaaPZYgCONJJaRjYoN0ztjf7UprTABDHtQdJ3FSU/EWWqkGgvF63rRtkOqxIhpJpgvHbNgm5TEBRRBWsrmtG+9tkzYvMx3Yxobbx7MNJ9uAcMijAsK3f397NwRRGlf0vQsXRIwsyvfCDUjBNvn+++/HXBYMBtHZ2YkHHngAc+bMMeTAiMLBbOLAcZIn3xcIokz/xyouuVbeAGBCgxMffd4rK28tk6pR6bTGHdwN5K9f2umwYGjUF3cRoAfNxVvoRNU75EbfkFSILWxNXrzVhnreBka8EEUxpWb/dChEeyxBEMaSitNiUiNT3lw4pLkCQHrKW7JxAQENaZPhUQFxhnRrUN4YWq2IRsMKx2gbe22FDRemaWPPpm2y0JW3bAe2se9OPNukHFgSZwPCrpgVx9YSGz/rBAAcNXdcQZ6/df+afP3rX49ZRLEgh3HjxuHWW2815siIgoHjONgsJnh8QcN3reTizZa74i1aeRNEEXaLCcMJbpOvfulwcln82GmtaB0VwP7e0TcGAKirtMmLm0SwnV1fQEpsS/Y4RlOo9liCIIwjFafFhJBtsnfIg76QcyA95c0A26RKYEkgKEAIrd+09LwpYVbEbMMKxy8ODsEvcrBwIqaPr0r7fJtV22QBK29aHSkLZjYYtgZyJAksCWhU3gRRhC8gYCQ0rogDcOTsJkOOMdvo/jVRGwPAcRzKy8vR2toKnjfciUkUAKx48/mL0DZZJxVv+7pGsPGzTny6uw/dg25YzTwcNjOGFAqcETuAmYTt4BqhvI1oKN42t3Xjyb/tiLjM5Qnggx09SV8ji9mEijILRsb86B/2ZL14K1R7LEEQxpFKSEe5wyLfhs20zI7ypq/nza8Y76NHecs1PM/h0ENqUVPjxMDAqLx4TwdbNpS3IpjxlgtHCrMNq9kmg4Igf6bj9bzZLCZwAEQAHm8AG7d2AQBmTalJ2keZr+j+NVmyZIn8/91uN1wuF6qrq2GxFH7sKZE6mRhwKYpiXtgmO/slxc3jC+LXf94qX37C4RPwtWUzsm4dSYdkiwA9JFPe4lkrvL6gZmtFbYVdKt5GvJjcVJH2Meshl8lqBEHkB6km001sKMfAiBed/ZLjIB3ljf3GuuL0KgcEprxpSZuMU7ylMEC8mLBlo+dNtk0W7no5F44U2TapUrxp+QxzHAe7zQS3Nwi3L4h3QpbJpQWqugEpBpZs2rQJ559/PhYuXIjjjz8e8+fPx9e+9jVs3LjR6OMjCoRM/PD5/AJCjo6cFW+b27rxq5e2qv7t75va8eHOnqQpVvlEeZLGdyWJZpsph8+qFW9GhX3IfW85CC3JZbIaQRD5w8LWRnz91JaYyxOFdLBh3QxHOsqbPYnyFtAwpFu2TYYXu2zhazbxWe8pzjessm3SWPeQkmJQ3nLhSIkesq1E6wYEc2/taB9ER98YzCY+bx1SWtD9a/LBBx/g0ksvxaRJk3DNNdegvr4e3d3deOWVV3DFFVfgiSeewIIFCzJxrEQeI//wGVi8Mcskh8hEy2yRC293pnHKkdOJi7dks82UvRfsPpUYZa2QEydzNC6ANcj/9q9tEQVvvttjCYIwloYqaWRMbaUN5504PanTYlJDZF8vS8xLheQ9byHbmAbbpDJtks1mLXXVDVAGlqTfDx6PkdHCT5vMhSOFDapX63kLb0Bw4BNsQDhsZgyMePHPDw4AAA6fUZeWlTnX6D7ye++9F4sWLcJvfvMbmEzhBfV1112H1atX4/7778ejjz5q6EES+U8mlDdmmbRZTTnZFSzGtMGwbTL+CUpLktS4UB9gmc0s23GUGGWtqK0MzXozeFC3IIjYtqcf/t0DSRveF7Y2YsTtx+/+2gYAmDGhErdctLBgCnaCINKnI2R/PKS5EktnNye9/sTo4i2DPW8sQTLRkG6zypBueUA3FW+KNUzmlLehscJPm8zFgGtngrRJOWkyyWfYHmrt2ds1AgBYUsCWSSCF4u2TTz7B3XffHVG4AQDP81i1ahVuvvlmww6OKBzkpCYdP3yCICbsF8t1v1sxpg3Ktsk4yptWtfGKM2ZL9xfHu2+UtaK2go0LMM42mUxVVGNQcV0Tz1PhRhAlRmcoLbe5tkzT9ZvrysBzAHOGd/aNYdq4ypR+O9ji1RVHeVPaH+ORKLCElDdF8ZahtEmvPyjfdyErb0BmRzaoUZYgsMTvTz7qYnNbN/Z1uyIue/rvO8EBBeue0V28OZ1OBAJxpPtAQB4bQJQWegNLtCygc500WYxpg8l2cLWqjTv3DwKIH1ZilLXCaNtkqvNp+hQ9d0YMOCcIorBgwSPj6rQVb1s+75UcI6E10aN/2Yb1/96VcJMoHizsZHDEi+17B2I2OllBljiwJHZUABVvYVjaZKYCS0ZCYSVmE5/TADajyOasP4ccWKJim0yivMU957syM5MuW+j+xh5xxBH49a9/DbfbHXH52NgYfv3rX2PRokWGHRxROOjpeWNfpuiFPVtAb27rBgC4c6y8sQIkEYWWNliepHjTqiL2hWyM8Yo3o8I+mG2SDepOh3RCVJS2TSNm5BEEUViw4k2L8sbOccGo35Loc5wWNrd14xfPbQEgFRZrnv4QNz34dsR9aJvzppI2qdFyVgrYMpCYrYRZJquclqIJh2Gz/jId2FamIbDEbI5dJxoVnJaP6P7G3nDDDdi9ezdOPvlk3Hrrrbjnnntw6623Yvny5di9eze+853vZOI4iTzHZtbmF9fzZQorb7kp3ooxbVDZ+K5WDGlVEdkzTjR7jVkrogvgRAltMcdTLt3WHxDkuXKpoqeHMeZyhfI2RsUbQZQUbm9A/u1oTqK8GblgZEXgkMsXcXl0ERjQZZsMn6NZm4NVZeFbamTaNlkMA7pzBXMMJQosUevbTOecn+/o9qNNmTIFzz77LNauXYs333wTQ0NDqKqqwpIlS3DddddhxowZmThOIs+xWqUvTjLlTc+XKdzzlrtEoGx7uzMNK7aCgjRDzxE1e0ir3dFuM0XcXzzStVZYzDwqnVYMj/owMOxNq9E71R5GURQjbJtefxCBoJBwoUQQRPHQNSCpbpVlFjm2Px5GBV3pSTsO6LBNKtMm/UFKm2Rk3DY5Jm0+VhRwWEmuYOsUrz+IoCBEhKQlsv4WY24BI6VV8YwZM3DvvfcafChEIWPTaJvU82ViuyysUMgV2fR2ZxqrxQSLmYc/IMDl9scUb1qTpLZ80QcgefHG7jOdNM7aChuGR33oH/FgSnPqg7pT7WF0uf0Rs2QASbmsoh1UgigJOlhYSZ0zyTWNWzDqKQK12CbZ3yLSJv1km2RkWnkbIuUtZZRJrR5fEE57+PMqj7tQ+ewXY24BQ1PxdvDgQV13On78+JQOhihcrGZtu1Z6vkws3SuXyhsj3QIknyh3WDAw4sWox48GOGL+ztTGR17eqmqDLbOZ0dE7CkAqbARBzGghW1Nhw57OkbTHBaQaosIet8pphT8gYMwbwJjHT8UbQZQIepImjVow6ikCmZpmTvA7rBpYQj1vMpkYd6SE2SbpvKEfi9kEi4mHPyjA7Q1EqN+JlLdczKTLFppWxcuWLdPVYLlt27aUD4goTMKWg8Q9b3q+TB993gsgdz1vxYrTLg2rdCXoIVvY2oh3PuvEBzt6cczcZhwzbxze+awD//64E3c/+5Ecf/2399vx/vbulBLUtKIMLUmHVOfTsH632kobRsb8GPMGEs7JIwiiuOjQkTRp1IJRTxEoK28JijC1UQHhnjcq3tgaJiiIGbHFj4QCS8g2mRoOuxn+UV9M31uiDYhczKTLFpqKtzvuuCOieAsGg/jhD3+I6667DhMmTMjYwRGFA/vxT2ab5HkOXzluKh77y/a412FfplzPeStWmNUxUfEGhD3682fUY9aUGvSPePHvjzsR3WefLGY/XWor2biA9Ge9MVXx4T9vhU9hhUzUw8j63Wor7BAEoHfIQ+MCCKKE0KO8GbVg1FMEBiltMm2Y8gZI6pvRxVs4sCR5qwERi8NqwvAoMBY16y3ZuItiyy1gaCrezjnnnIh/s+LtpJNOwpw5czJyYERhocdywPoHTDwXcSKJ/jLles5bscIsB8nUI/lkU2aBIIh4/s0vEl6fNc8bvYslz3pL0zbJWNjaiD+/vQf7uqShnQ3Vdtx55VFxj7tPVt7sclQxJU4SpYAgiEXR65sOgijKgSVaZ7wZsWDUUwT6Q1ZIkybbpKLnTe4Xog1Ss4mX1yReXzBpMI1ehkOboVWkvKUE68/3RI0LCGiYVVhMuQUMWhUThsAsB74ktsnhUR/+8cF+AMA1K+aibd8g/vZ+Ow5prsAPL14U8WUi5S0zJBvUzRgeCzdYG5Wglgq1Fcw2mb7yBkjpkT2D4TmVQy5fePaBCkrb5IBLeg1cpLwRRc7mtu6Y4qOmwpZRi3Q+0j/kgT8gwGziUF8V2yMcDyMWjPGKwOj3IahBQVOzTcqqhYWUN0Baa4x6Ahnpe2OboRXU85YSrHiLHtTtk4u3xOvEYsotAFKY80YQajDbZLIfvVff3QufX8DUcZU4fEY9Fs2STj6DLm/MSY2Kt8zgdEg/gokKEH8gKP9IVjmtOY3cZbbJgREvhDQHdQOSHdTtDYKD9IPuCwgxc5SUMMWvrtIuz8kj5Y0oZth8segNm1SGTBc6rN+tqaZM9069EUOMF7Y24udXH42bLjxcLs6+de78iAKaBZaYEqVNhmyTAYXyJi98aewJACmNGTA+tCQQFOQ2BUqbTA1HnEHd/hL9DJfWsyUyRqIZKYIgYvveAfzzwwN4fZOkup197FRwHIcJ9VL08qDLF9ODRbbJzFCuQXljscZmEweHzZzTyN3qchs4SAsU1oeXDswCVVtpR1ONZIPqDl2mBuu1q6m0hS2npLwRRYqRQ6aLAT39bpmC5zkcOqVWHpXS0Tca8XdWkCVawCZS3qykvAFIPi6ArWU2bu3E9r0Dmr8DbG3DcUC5wXbMUsERGhnl1tnzVqzQqpgwBDYqIDqwRM16Y+I5+XoOmxn1VXb0Dnmwv9sVIWuT8pYZwgVIfPVoeDS8S8hxXE4jd80mHpXlVgy5fBgY8aQdtdzVL1kmm2rLYLeZ0dE3iq4BN1onx1oqgoKAwRGpkK2tCCtvxZQ2SX1NhJJcWqTzEaa8NWvsd8skExvK8fn+IezvCRdvgijKBZlJw5Bu5aiAQImqFvFItAmdjo1YtkyWWem3NUXi2SZZ6E6pJaZqKt5efPHFiH8LggCO4/DGG29g587YHbqvfOUrRhwbUUCEA0vClgxmvYkmKIh44MVwOuHEhnL0DnnQ3kPFWzbQkjYZDiuRCqVcR+7WVtgw5PKhf9iLQ5rTuy+mvDXXOuBwWIE2oHvArXrdIZcPgijCxHOoKrfKw0LHikR5o74mIppcWqTzkc6QypVL5Y3BnCoHelzyZcpijFkj1TCZYtMmZdukhc6xgPo6Boi/ltGatBx9PiX04wg5sDwxylsodIeKt1huueUW1cvvv//+mMs4jqPirQRhO1aBoCBbCbRYbxbMbMDExnJ89Hkv9ne7Iv4u2yZtJBAbiRbbpDKshJHLyN3aCjt2d4zI4SHp0DUQVt6cIZtnVxzbJOt3q6mwgee4sGrpLXzlLd0FCVGc5NIinY+EZ7w5c3wkwMQG6RiUypuyh81ijr95xgZ4BwVl2iQpb0rYRrEy0VCrjThR0nL4fEqWyVRx2BP3vCWacViMaFoVv/7665k+DqLAUUrWXn8QeztHNFtvJjWWA4g8IQmCKCdXkvJmLMz6l0h5GxqNLd6A3EXu1ihCS9Kluz/cw1IRSrKMp7yxfrfa0LiCsG2ysJU3IxYkRHGSS4t0vuH2BuQwo7xQ3hqkc2XfsAdubwAOmzmieDMlUt4UtklRFMFxnKxaUM+bBAssUaZmG2EjVrYhEKkR1zZJPW/xoUHcRDIsZh4cABFS35se682UJqkJ+0CvC4IogufCA7oBaTgjYRxMeRvzBOTXOxpm81DrL8tF5C4bF9CfZvEmimKE8lYViv7uHnDLCxol8oy3Kunxy+zh166Qob4mIh65tkjnE52hjZ4qZ9gynUvKHRZUl1sx6PLhQO8oZkyokpMmeY5L+J4w26QIqU/OxHGkvEVhV0mbNMJGTLbJ9GHrwLiBJSX2GS6tZ0tkDI7jwjG7AUGX9aaxxgGLmYfPL8jzt5htwcRzMJfYlzLTsDlvIuIXIfl2smHjAtK1TQ6N+uD1B8FxQGONA021ZeA46WTNnrMSZptkxSMbszDqCUA0YGxBrqC+JiIRC1sb8eUlk2Mur62wlZSdNh+SJqOZ2MCcKlKbAVPezAksk0DkAG/WJ+ejOW8RsJ435eaxETZitTYEQh/xhnSHE1NLa5OfvrGEYdhCJwCfLyhbbxLBrDcmnsf4UD8B63tThpVEqyFEephNvNyjGC/yfjiObTJXhAd1p1dMdIV20usq7TCbeFjMJtRVSvfdpWKdZMViXSWzTUqFbyAoyAufQoT6mohkMMtwfej70VBtx5qrjy6Zwg0AOvolK/+4PEiaZLDi7UC3dGxy8ZbAMglIY18YLLSkVFWLeFitoTWMQnnTs5aJR75thhYiydImS+0zXFrPlsgoygGXPM/h8Jn1Ca+vtN5MbJSKt3aV4o0wnvIkfW/5tlPITp7pDupWWiYZbFddLbREDiwJLWDtVpNsMy1k66QRCxKiePH5g9jyeR8A4IxjDgEgLZpKwSqpJB+Vtwmh0JIDvUx5k34PzQnGBACR/XDh4o31vNF5FgjbJpXKG7MRJyKZjTjfzqeFSLh4izzvsv7EUut5K61nS2QUmyU8623M48em7d0AwsMVGWrWm0lsNzEUWkIDujOLU06cTGKbzJOTTXWFFRwnLTpGVOyNWmHBJE01DvmyxtqyiL8piQ4s4ThO7n0p5EHdRixIiOLlk1198PqDqKu044iWBgDSRk/0HM9iJzzjLfdJk4ywbXIUoigqbJOJl3M8z4F9m4Oh25DyFolyDaOEJS2zfnGGVhtx+HxKaZOpIg/pjrZNBkuzeEtrZTwyMoLu7m5MmjQJJpMJJhPt3pQyVsWMlBff2o2RMT/G1ZXhR5cuxq6DwwnTCSeEEifbe0h5ywZy5L2K8hYICvIA73QHYhuFiedRXS4l4PWPeFFVnpqdj6lrTTXhnXRWyEUXbz5/ECNj0utTFwosAaTESZfbX9DKGxBekPz2r20xCuyEBmdJ2eOISN4PbbwtntUIp90Mq5mHLyBg0OVFY03+qFCZRBBEdPVLvwn5MKCbMa5O6tN1uf0YGvXJ/WvJbJOANMQ7EAwP9faVaFJfPBIN6V7Y2giX24/f/rVNvuzHly1BeVnigkwQRfk8QrbJ1GHKm88vICgIspIcKNE5byk923fffRdf/epXsWTJEpx55pnYuXMnbrjhBvzsZz8z+viIAkEQRASC0pfove1deH3TfgDAyuUtsFpMmDWlBktnN2PWlBrV3XymvPUMuOH1BRXKGxVvmUAe1K2iHrFdQhPP5UXCGoPZ/NIJLWGLsabasPLWFMc2yfrrbBYTyhSzBssSFL6FxsLWRpx+1BQAwNTmCnzjrNkw8ZIC/tnu/hwfHZELvArL5KJZjeA4LsK2XCr0DnsQCAowm3i57y8fsFpMcgF9oGdUVh5MSWyTQNg6GYhW3kps4RsPtcASJcNjkb/5zJmRiDFPQC6W88XJUog4FOdg5ftTqp9h3c/2nXfewerVq2G323HjjTfKiWuzZs3C7373Ozz22GOGHySR32xu68ZND76N9lAD9cbPuiACmDquEnOm1mq6j0qnFZVOK0QAB3pHFcpb/hQPxYQzwaBu5s+vKLOojhHIFcy6mOq4AFEU0T2YWHlTJkjKYwIqbRGhOcrEyWKAhbjMmVaLI2c3Y9kRkwAAf3zji7T6C4nC5JMvwpbJqeOkMS41aX73ChHW79ZU68g7+3B4WLdLtkBqsT6yvrigIEIQwgpcqS184xHPNskYdEV+/lk6djwEQcRHO3tD983n1fm00JACxqTPqVtx7i1V26TuZ3vvvffi5JNPxhNPPIFLLrlEXuxcddVVuOKKK/Dcc88ZfpBE/rK5rRvr1n+quiO7u2MYm9u6Nd+X8oREtsnMUs4KEJWet3xNxqphiZPDqS0gB10++PwCeI6LsEE21DjAQdrNG1HsrMpjAqJ23Z3yrLfCV94AoCO0SB1XK33/Tj96CuxWE/Z2jeC9bV3YvncAG7d2YvveAQgCFXPFzqa2sGWSbVrUGJT2Wkh09oWSJvMorIShHBfAAktMGoo35aBuvyIt12qm8ywQtk164hVvI9HFW3zljW1qP/qXbQCkdpKbHnxb15qIiESe9aZQ3uTAkhLr29T9bLdt24Zzzz0XAGIi3I855hgcOHDAmCMj8h5BEPHUhp0Jr/P0hp2aF3zyCanbRYElGYYVIGq2yaE8CythyLPeNFhV1GAKU321PWJ2oNVsku9b2fcWHVbCYFZSV46VN0EQDSmsOtgitV5apFaWWeUZX4/8eSvWPP0hfv2S9L+0+ChuvP4gPvpcUgoWzQr3PJaibbIzD8NKGBPqQ4mTPaPhwBIttsnQ715QEGXFAig91SIeTHnzxrFNsnMje/3jKW/xNrUHRrxYt/5T+g1NEXtU4mQpq8e6V8YVFRXo6elR/VtHRwcqKirSPiiiMNjRPpj0ZN4/4sWO9kHMmlKT9P6Uu4ns/9tttCOYCcoT2SbztniTdv9TtW6phZUwGmvK0DfsRdfAGGZMrJIeR57xln/K2+a2bjy1YWfE96+mwoaVy2fqChpxuf1yH4cyDr2hWrKSRteDbPFRSoOaS4lPvuiDzy9EWCaB3BRvgiBiR/tgwqCrTBJWpPNQeQsFfB3sHYUvFNhg1qG8BQRBtgaaeC7vbKG5gilvyWyTMydW4UDvqGrxpnVTe8HMBnrddRI9qLuUNyB0F28nn3wy7rnnHrS0tGD27NkAJAWus7MTDz30EE488USjj5HIUwZHtZ3ItV5vUmM4Arm+Slo8km0yM8jKm0rxJidj5VnxJi8gUwwsYTPeGhVjAhiNNQ5s2zsQMag7POMtUnlzhpS3XKVNsl3daFIprFhfT22lTVa5BUHEH9/8IuHtaPFRXLBC6a/v7QMALGptiHDW1MrFW+phQXowanMiFdhrsa97BID670Wuaax2wBJKAGVFpp7iLRgUS7ZXKBFyYIlK8SaIIoZc0sbmzInVeOOjg6rFm9Gb2kQYZpscCylvSutvqX2OdRdvN9xwA7Zs2YLzzz8f9fXSEObvfve76OzsxLhx4/Dd737X8IMk8pNqp7a4dq3XG18fjkDuDKkkZJvMDLLyliBtMt963sILSB8EQdRdODDbZJPKYoypcd2KxEmm8EX3vIVtk9lX3oze1e1Q6euhxUdpoVYovf1ZJ2ZMrJILpZrK7AWWGLk5kcpjR78W69Z/gou+1JJXSjPPcxhf58TerhHs7ZSKTN22yVCvkLXEFr2JCAeWCBBEMSJgxOX2yxa96RMqAQC9Q56Yc5HRm9pEGFl580rFNSveTDwXMYS+FND9bKuqqvDcc8/h9ttvx+LFi3H00UejtbUVN910E1544QXU1mpLFyQKn5ZJ1bIaEo/aCsnuogWL2SRbt/Z0SCckUt4yA0tMdKkEljBff77MeGNUl9vAc5y0A5rCoG55QLeKDaoxatabKIpy2mS0bbJctk1mX3nTU1hpQbaGKfp6aPFROsTrzRkZ80f05tSE5ioOu3xyj1UmMLqPWg/xXotBly8v+5RYwNceuXjTo7wJpLypYFOsN6KtkyyspKLMgvoqB0w8h6AgxnxejN7UJsKwzXw2qNvPLMMl+BlOSdawWq04//zzcf755xt9PEQBwfMcVi6fqbpLyrhw+UxdCsnEhnJ09I3JCwQq3jIDGxXg9gYiBl4C4VEB+Wab5HkOlU4LBl0+/Pvjg2iZWK25D0YQRXSHLC5qyhsr3rpC4wLc3oDctB69QcGUt1yMCjC6sJKVN8UQYlp8lAZ6VNwKp1VerA6P+mLUaKPIlepbiH1KE0J94cz6rkV5Y9cJCKJcnFgoaVLGaubBARAhpUPaFafAwZBlsrrcBp7nUF9lR9eAGz2D7oj0YrapnehzrGdTmwhTJgeWRCpvpZY0CaRQvK1duzbu33ieR1lZGaZMmYJjjjkGVmt+Lf4I41nY2ohrV8yNsZrUVthwYQr9CRMbnHh/e/jfZJvMDE7F8O1RTyDCIpmvgSWb27rlfrwX/70bgPY+mIFhL/wBASY+ckwAgwV0uL0BuNx++URd7rDIVhqGM4dDuo0urJjypkzUo8VHaaC3UKout6Fv2IP+EW/Girdcqb6FaBVmyhtD26iAkG2Set5U4TgOVqsJXl8Q3ijlbSgUVlJVLp0XG6odcvGm/ExkYlObkGABdixtspQ/w7pXxi+99BI6Ozvh8/lgNptRXV2NwcFBBAIBcBwnz32bMWMGfve735GNsgRY2NqIBTMbDEkGYylaDFLeMoOJ5+GwmeH2BjDq9svFW1AQ4MrDwJJ0+2BY0mR9tUPVG2+zmOSCpXvALe9m11bGFkFMtRzzBCCKYszIlExiZGHlDwTRMySpkeMVyhstPkoDvYVSTaVUvGUycTJXqm8hWoWZ8sbQoj7ItklBgD+091SKC99E2Cyh4i1qXABLmqwOWYjZhh/7DVXCNrUfe3V7hL0+1U1tQiLc8xYZWFKKfZu6n/H1118Pq9WKX/ziF/j444/x1ltv4ZNPPsHatWtRU1ODe++9F3/+85/BcRx+8YtfZOKYiTyE5znMmlKDpbObMWtKTcoLu0kNVLxlC6a+KQd1u8b8EAFwHFARKlJyjRF9MCxFUs0yyWhS9L3JYSUVsQoDs00KoigPk88WrLBKhNbCSrKISifE6EKdLT6iL6+tsNGYgCJBb6HE+t4yWbwZ3UetlUK0CleXWyMcFCZNgSWseAsrb6W48E2EzSK9HtHKW9g2GVbegPiDuhe2NuKo2U0AgHnTavG9CxdgzdVH029nGkQP6ZZtkyX4Gdb9jO+//358+9vfxmmnnQY+tIPNcRyWL1+Ob33rW/jlL3+JmTNn4qqrrsKbb75p+AETxU1dlV3+8QSkIaSZaE4nwomTynEBLAikwmHJG2XFiJCOcNJk/JlN4b63sbgz3gBpscN6R9TSOjMNK6yi3x+rmddVWDHL5Pi6MlX1cGFrI3582WL53zdecBgtPooIvYVSTRbGBRi5OaGHXBWN6cBxXIT6pi2wJGyb9IXSJksx7CERNotUECdX3qRzQ7xB3QBwoFfqKV5yaFNam9qEhCNqSLePijftdHR0YMqUKap/mzBhAg4cOAAAaGpqwtDQUHpHR5QcH+zoQSAYLtYeeukz3PTg23mX9FUMOFXGBeRjWIkRlqZw0mR85a1RHhfglos3Ndskx3GKQd25mfU2d2qdvKlx6uJJAKSEzNmHaLeps7CS5rr4BW1FmQVsuTGxoYIWH0WE3kKpNkuDutnmhHITjz1+plTfXBWN6TKhPvzdHXR5k250Kod0h5U3crcosVmTKW9Rtsk4xZsoimjvdgEIz7Al0sMeVbwFSjiwRPcznjFjBp577jnVv/3xj3/E1KlTAQB79uxBYyPt0BLaYX1NwagTEOtrogLOWNSUt3wMKzHC0sR63hIqb9XhxMl4A7oZuUycBID2HmlRUOW04vxlM9BU44A/KOKDHT2a7yOsvDnjXsfE8+G5djkIaDECQRCxbU8/3vxgP7bt6SclX0E8FVetUKoJqdDZmPW2sLURc6aGNyLOPuaQjKu+7LWIVuDy1Sq8ua0b724NnxM3ftaVdKOTOQaCQbGkLWeJsIcCqmICS0YjA0vqq6TzxciYXy4mlAyMeDHqCYDnuIhRLETqkG0yjO7Akm9+85u49tprsWLFCpxyyimoq6tDb28vNmzYgLa2Ntx3333YunUrfv7zn+Pcc8/NxDETRUghRjUXOnLPm1J5G82/sJJ0QzoEQZR3RxP2vNWGB3Uze4aabRLIbeIkAHkw75TmCnAch6PmNuPFf+/GO5914ph54zTdhxblDZCK/FFPoCCLN7Why1oTSkuFw2bUA6GgsQuXz8SkhnLVwClW1AxmoXgDImdQVobi2TMNC9+65aF30DvswfknTccpiyfn3Tkn1QCniCHdATYqoPQWvomwsuJNYZsURBFDIeWN9X6W2c0od1jgcvvRO+SJUdf2hzbYxtWV0WtsEDGBJXLaZOmpx7o/USeeeCJ+85vfoKysDGvXrsVtt92GBx54ABUVFfjtb3+Lk08+GZ2dnTj99NPx7W9/OwOHTBQjRg8fJpITVt7CiyRZeSvLn+ItXUtT37AHgaAIs4lLGHHOlLdRT0Ae0K0WWAKEC98xlR3XbLC3SyreJjdVAACOmtMMANi2Z0CTrU0QRXRqUN4AoLxM+pywMQ2FQryhy6TkR9I14IYgSgOKly+cGLc3RxlYIoiZVy9HQhbu6P+faXieA/MKz5yYWmpyJkknwCkibbKEVYtEsEHdSuXNNeaXHUHKjc1EfW/MMhmdoE2kTnTPWyl/hlMaorV06VIsXboUPp8PQ0NDqKurk8NLAGDZsmVYtmyZYQdJFD+FGNVc6KipRyywpCqPlDcgvXmCzDLZUO1IuBCzWU2oKrdiyOWDKEqJm9UV6q9DmT22XzCb7GPKW6h4a6h2YObEKuzcP4SNWzvxX0eq9yUz+oc98IXm3tVXJ57ZVeGQXoNcPddUICVfOwdCCsGEemfCsRdV5VZwkFSbkTF/xn8jlEpvtjcO2MLdlodpx+nMpJOLt6Aohz1Q2mQkdhXljYWVVJRZIoJhGqod2N0xolq87e+RnA3R8/iI1GHFmy8gIBAs7Q2IlIo3r9eLtrY2+Hw+iKKIPXv2QBAEuN1ubNq0CTfeeKPRx0kUOYUY1VzoqPa85WFgCYNZmu565kNs3zeIEw8fj1WntCZdfHf1M8tkYnsgADRVO2R7THW5TXUmHKA+ZiFb+AOCnGI2pTm8q3vUnGbs3D+Edz5NXryxfrem2rK4z5HhdEjPNZvqR7oU4tDlXHEgtMicUJ94kWk28agMbW4MjHgyWrwJohjxu5Rty65cvFnyr3hLZ6Mz0jZZugvfRFhVet6iw0oYiUJL9lNYieEoR0d5fEH4Stj6q7t4e/fdd3H99dfHTZJ0Op1UvBG6MXL4MKENtiiP7HnL3+INkCxN0ydUYfu+QXA8l7RwEwQR2/b2AwAsZg6CICa8TWNNGXbsl37b1JImGeFB3dlXow70uhAURDjt5oievMWHNuKpDTuwv2cU7d2uhIsGVryNS9LvBoSVt0LqeSMlXztsIyB66LMatRW2UPHmxSHNmTumUbcfSmdmNjcOBDEco5+PxVs6G51y2mRQUPS85d9zzCWsQPD4Y5W3+MVb5PgMf0CQf2MnavheEdowm3hYzDz8AQEebyC8AUFpk8m55557UFNTg/vuuw/Lly/HKaecgoceeggrV64Ex3F4+OGHM3GcRJFTqFHNhYw8KkAtbTKPet6iSRbRzNjc1o2bHnwbH+zoBQC8v70naRpbY024GLKY+LjJhLlMm4wOK2E47RYcNr0eAPDy23uwcWsntu8dUH0OLKxES/HGet4KqXgjJV874eItub2rOguDuoHYz5ori7ZJn2LRno/FWzoz6czKId0lvPBNBHvPfQrb5JArMmmS0VCl3vPW0TcKQZQ22JK9V4Q+mHVyTFm8WUrvM6z7Gbe1teG6667Dl770JZx00kno6OjACSecgP/5n//BeeedhwcffDATx0mUAIUW1VzolIf6tlyhAkQQRbm3JF+VN0B5wow/LDiVsIrNbd147b12+d/b9w3GLfbUkjqzxd4uyY7D+t2UsEHj72/vxq9f2oo1T3+o+hzCylvyBbtsry2gwJJCHLqcC3z+ILpDPaHJbJNAOMAn08VbdI/bSBY3Drwh1Y1Dfi4K09nojBjSTbZJVVKxTfYOeSJCfJTz3RL1kRL6YeMCPL5gOG2yBDcgdD9jQRDQ1NQEAJgyZQp27gw3hZ966qnYunWrcUdHlBwLWxvx86uPxvcuXIArz5qN7124IOPzfUoVprx5fUEEggJcbr98AqoIqS35CDth9g25VVPvUkljY8VetJIWr9iTw15yoLzt6worb0o2t3Xj1Xf3xVxf7TnoUt5UeiPzHZ7n8LVlMxJeh5R8qYgXRWkzQksPG5t7yOYgZgpmk2QF+MiY1F+fDdii3Woxgc/ThXeqG51qaZMUWBJJYttk5HekptIGE88hEBTkXmkgPCaALJPGoxzUXcp9m7p73iZPnoy2tjYsWrQIU6dOhdvtxq5duzBt2jQEAgGMjo5m4jiJEoLnuZIPEcgGZXYzOAAiJOsk290ud0QmauUb4ROmiMERb0z8v9awirZ9Azi6rjylZEJWvGW75y0oCPKurlJ50/McxrwBWdlori3O4g0A7Fbp9MZxiOif0pJQWiocVPS7aVEI5FlvrgwXb6HP2vi6MgyMeBEIivD4grJlKpOwlEFbHqpuSliA0472QQyOelHttKnO51NiUrNNluDCNxFqtsl4ypuJ51FXaUf3oBs9g275+0FjAjJHeFC3snjLP3tzptH9rT3zzDNx11134cknn0RtbS3mzp2L//f//h/+8Y9/YN26dZgxI/FuJ0EQ+QHPcXLvlsvtz/uwEoaJ5+UwEbW+N81hFaETctu+5LPRomcMluUobbKjbwz+gAC71YQGxcBxPemKbL5bbaVNLnASUajF28bPOgEAJy2YgJsuXCAvXL913nwq3ELs7w2NCdAYZ85mvfVnyTZZW2mHNVREZcs6qVTe8h220bl0dnPc+XxKlKMCSnnhmwibjsASQH3WGyVNZo7wrLdgSW9A6H7GV1xxBS644AJs2bIFAPCjH/0I27ZtwzXXXINdu3bhe9/7nuEHSRBEZmAL81FPQBFWkr+WSUa8lC9AR1hFyAIz6NKWZKcsCpnl1O0NZGVgMYOFlUxuLI+wdOlJVzzILJMaVDcgHFgy6gkgKAh6DjdnuL0BfLCjBwBw9NxxmDe9DofNaAAg9TISElrHBDCYbXJgxJNRGyOzTZaXWVDhsERclmlY8WbPwxlv6SL3vNGQ7rjY5J436fURRFE+N0bbJoHYAK2hUR+Gx/zgAIzX+L0itMOKN2XaZClaf3V7EHbv3o2bb75Z/ve8efOwYcMG2TpZXk47DQRRKDgdFmDAXVDKG8BOmAPoHYpV3rSOnWidLFlz1U7IaiiLQhZYIkIqFJiNMtPsDfW7TY7qd9OTrvjxF30AtIWVANJzle21nkBeJ5EyPtzZA19AQFONA1PHSa/VYTMb8EFbN7bu6ccpiyfl+AjzA93FW0h58PkFjGXwc8/CcSocVpSXWdE37M1aYI7Pl78z3tKFqc+BoCjPyCrFhW8iZNtkqIh3jfkRDPVHq50bo4s3pro11pYV5Wco1zhCbhHJNlm6c950P+OVK1fixRdfjLisvLwc8+fPp8KNIAqMcsW4gKGx/B8TwEg0LkBvGlvr5BrdyYRmEy/buUazaCfcx8YERCVN6klXPKgjrASQdutle22BJE6+81kXAGlwOevlOrxFUt7a9g0iECwMBTGTuL0B9A1LyrWWGW+AZCVkvxmZTJxkFsmKCOUtO589TwHZJvVipiHdSbEp0gyBsGWysky9FzzaBSInTWq0IhP6sNtCPW9eSpvUhcViQU0NhUkQRDHAFCSXpxCVt/jjAha2NuKqs+fEXK6WxpZq9Ha2EycFUcReFlYSpbzpeQ6dOsYEMAqp723I5cXWPdJg9qVzmuTLDxlXiYoyC7z+IHYdHM7V4eUNrIivclrl91cL2Zj1xiySFWVWOfk2W589ZpssRtUk3PNGtsl4KJU3URRlW32VSr8bANRH9bzJSZPU75YRypRpk/7S/Qzrtk1ef/31WLNmDUZGRjBr1iyUlcXu3o4fP96QgyMIIrOEB3UHMDya/zPeGPVxhqMqYf0GVguPS788C9Xl8dPYWPT2Uxt2RixKEyUTOu1mDIx4MZal4q1nwA2vLwiLmVdVzeI9ByWf7upDd+g1a1IEniSjvMyCrpC9Nt95d1s3RBGYPqESjTXh14nnOcw+pBbvbu3C1j39JT/jTbZM6lQIaitt2N/jynDxplDeQk6AbPW8Mdtkcfa8UdpkMljxFhREKdE4QVgJEN5IHBr1wesPhsNKaExARogYFRAs3c+w7uLtxz/+MYLBIG666aa419m2bZvm++vq6sLxxx8fc/mdd96Jc845B9u2bcNPf/pTfPrpp6itrcWll16Kiy++WL6eIAhYu3YtnnvuOYyMjGDx4sW47bbbMGlSuKchG/dBEIWIPKi7IHvewidMtV1ytjid3FiBpXOak96n3ujtMll5y05Bw/rdJjWWy8ED0ag9h4+/6MVf32vHAy9+GhGZ/5MnNmOlxsh85eck33knlDK5dHbsez5naqh42zuArxyX7SPLL+QxAfX6FpnMnts/rK56p4soivLnrMJhkVXBbNkmCyltUi/KUQE+Oeyh+J5nOtis4d9Wrz8Yd8Ybw2m3oMxmxpg3gK7+MVnRpqTJzBAxpLuEE1N1F28/+clPDD2A7du3w2azYcOGDRFzZioqKjAwMIDLLrsMy5Ytw+23346PPvoIt99+O5xOJ84991wAwAMPPICnnnoKP/vZz9Dc3Iyf//znuOL/t3fv8U3W5//4Xzn0fKDnFjmDUEBAChRhDgVEPvtN0AHu5xDcpqIoChNFxMlE3RCmRRTEAyowJ6cpB8dEUWTqREBaUVRooRzLoS09N22TNMn9/SO57yZtmibpncOdvp6Phw8huXP3DneS3leu631ds2Zh165dCA8P99s+iJRIyrzpG1Fj+2bbnWG9gRYTqUVUhBYNBhPKqvVOGy5c9LANOuDZjEGx5NRfZZPnWlnv1lzz51Bt60TZvDmgOLzb1VBfkdhx0l/ZD09ZLAJOFFXhTHENzhXXQq0Csge0fE7X9EoCAJy+WIMGg8kvc8OC1cUrnr8/gKbgzVeZN0Nj00VZIMom9SFcNqm1feljMlukdZ8dMWvhikathlajhslsgbHRLA3fbq1sErB+mXiupBY/ni6HySwgMlyD5E6RrW5P3uOQbiuPf3NNmTJF1gM4ceIEevbsibS0lr9o//GPfyAsLAzPPfcctFot+vTpg3PnzmHt2rWYNm0ajEYj1q1bhwULFmDs2LEAgJUrV2LMmDH49NNPMWnSJPzrX//y+T6IlComqqkRRdOogOAP3lQqFVITInG+RIcrVQ3OgzcPO+l5yt+DusXMW/P1bq5YLAL+9d9TLrdpPoDcmbgo62vC33Pt3JFXUNqiVFSjVuNkUVWLoDQ1IQppCVEorWpAwfkqDO2b4u/DDRoXyrx7f4gdJyt9NKhbzLCFaa1NgWKj/F02ab0gjAjhskm93QDqjnjh25aIMGvwpjc2Zd4SXXQlTk2IxLmSWhw5WQbAut7NnaH35LloBm8AvGhYAgBGoxGbNm3Cww8/jDvuuAOnTp3C5s2bcfToUY/3VVBQgD59+ji9Lzc3FyNHjoRW2xRjjho1CmfPnkVZWRny8/NRV1eH0aNHS/fHx8dj4MCBOHz4sN/2QaRUYknSleoGu3bIwT/nDXDdcRJoWjjubic9T/lzULcgCG5n3ux5MrzbFTHIr20IrsxbXkEp1uz4qcVzbDRbsGbHT8grKG3xmIE9rVlJsalJR6RraJQyCp7Oomqa9ebb4C0uOgwqlUrKvPl7SHdEWOhdEGpsXfkaDE2fWR3xwrctYuBuXzbZVuYNgNQIievdfEdci9pgNEulvx2x26THmbeKigr84Q9/kOa6FRYWQq/X44svvsDy5cuxYcMGZGVlub2/EydOIDExETNmzMCZM2fQo0cPPPjgg7jhhhtQXFyMfv36OWwvZuguX76M4mLr2obOnTu32Ea8zx/7SEnx/ttbLT84g4r4y03TQT4M4mPE9SvWX1DREVpE+WlmWXuJzSjKa/Qt3kcGo1nqRNkjI87p+6y951pcG9hgNDndv8UioOB8Jap0RiTEhiOze6LL7JYrZVUNqNOboFGr0L2V5+OMuxe8tQ2NLvcpXrjU6Z0/10CwWARs3nvS5TabPz+J7AHpDud6UJ9kfPH9JRw/Vxk0z8XfSiqtHUdTOkUizsMyafFCtbLW4JN/vwajNbCIjw6HVquWgkVdvevXKCDP57fRVk4YFakNudeHON5EDFBVKmugosQskS9/V0eGawEYYDJbpC85kjtFtvp6SE9ybCDV2u8c8lzz8yw2MGrQm6TS31B8r7bF4+DthRdeQF1dHXbv3o0uXbpg0KBBAIBVq1bh3nvvxapVq7B+/Xq39mUymXD69GlcffXVWLRoEWJjY/HRRx/h/vvvx/r166HX6xEe7viLJSLC+kFuMBjQ0GD9xt3ZNtXV1QDgl314S61WITGRs0CCUXy8+534lKxLs3FXifERinlN9uzSCQBQXdfY4phPnK8EYO0Q1qOr6zVs3p7rlCTrzzSahRY//5ujl7B2548or25q6pDcKRL3/2YwfjHEs268ZouAPYeLAFgHvyYnx0rlT23p1rmT29u5Ou8ZqdZsX4PRHDSvjx8Ly1DRVlaxxoBLlXoMvtr6BVt8fBRGX9sVa7b/iItldbCo1Uju1DHe6/Yqjlszkj2vcn3enYmwlTHW602Iio6Q1qDIxQzrEPnE+EgkJsZAE24rTzaYEBsX5VamqD2f37YCBCR1ig6a17pcEhKs1zsNhqZ1fUlJys4S+eJ3dbStIkUbHoYq23KCnl0TkZjg/Gf17JLg8Pf+vVJC7rUTaOJ5Fj8PGu1mdaamxEoNxDoKjz91//vf/+LPf/4zevToAbO5qW46IiIC99xzDxYtWuT+D9dqcejQIWg0GkRGWhd3Dho0CCdPnsQ777yDyMhIGI2OZTpisBQdHS09xmg0Sn8Wt4mKsp5of+zDWxaLgJqaeq8fT/LTaNSIj49CTU0DzB1gkK/Z6FjyFxsZhsrKugAdjWdibOUTF0trWxzz8VPWcuarUqJbfT7tPdcqi/UxVTV6h59xOL8Uqz9oWUJeXq3Hsn8cxtzbhyC7f9sdHsV9bdxTIAUpl8vqcM9zezDj/zLd2sdViZFIiotwGeQkxUfgqsRIl+ddZbENrK01BM3ro+hytdvb9UiLcTjXPTLicPZyLQ58fxHXD+nc9k5CzAlbyWh6guvz3prIcA30RjNOF1V4NC/QHcW2cueocA0qK+tgEQSoVSpYBAEXLlUhwcUwejk+v3W2Bj9mkzloXutyabA9NzFjodWoFfscffm7Osz25diZi5WwWASoAAgmk9N/q8P5pXj343yH2/7+7mHM/JV7n9HkWvPzbHJyrut0ehiCrKTfW/HxUW5lkz0O3gwGAxISEpzep9Fo0NjoWV16TEzLD/6+ffvi66+/RkZGBkpLHdcsiH9PT0+HyWSSbuvevbvDNpmZmQDgl320h8kU+gGCEpnNlg5xbsI0KunCCLCuM1HK8062XcSVVjWgsdHsUPpz3tbc46qUmDafj7fnWmxZrGtolB5vsQh4b0+By8dt3FOAa3snt1lCKa7naq6i1oDVHxx1q0skYB3O7Ww/0v039YXFIsBiEVrdJipcbGxjDJrXR5ybg6XjosKkizvxXA/okYizl2vx9dFLECC0ORYi1IizqDonR3t1PhPjInC5vB5llQ1IlTlzKXZHjYls+iyKjdKipr4RlbUGtwaKt+fzW2zmoVWrgua17ithWrXin6MvfleL2d3LZdYv1+OiwwCh5fVaa5/RlTrPPqOpbfbnOVyrlta7qVSAYBFgEpT9OvaUx0WigwcPxqZNm5zet2vXLqmM0h0nT57EsGHDcOjQIYfbf/rpJ1x99dXIzs5GXl6eQ4bv4MGD6NWrF5KTk9G/f3/ExsY6PL6mpgbHjh1DdnY2APhlH0RKpVKppGYUgDJmvImS4iOhAmBstKCm2Qyoi7ZOel19uHA82km3SbkahFgsAja1tZ5r70mXAZdIHN6d2CxjkRQX4fbFhTgqoE5vgtkSHL8k+3VLaPGcmkuKi3A6jFtc4P7z2Uqs/fcxvLD5CB5//RunDU5CjSAI0vvD0xlvImnWmw+altg3LBHF+nFQtyGERwU0L7dmsxLnxHMvrg11NqBbzs9o8ox9qXaYVq3INZvt5fE7909/+hP279+P2267Da+88gpUKhX+85//4IEHHsAnn3yChx56yO199enTB71798Zzzz2H3NxcnDp1CsuWLcP333+PBx98ENOmTYNOp8NTTz2FwsJCbN++HRs2bMDs2bMBWNepzZw5Ezk5Ofj888+Rn5+P+fPnIyMjAxMnTgQAv+yDSMnsv8lWUvAWZtfMoKxZx0lphpWPxgQAzue8VdW5dzHb1nZyBYGi4ZlpePHBX2Dh9Czcf+tALJyehRce/IXb3wqLzxXw31y7tqjVKtw5oa/LbaZP6Nsim5ZXUIp/7z/bYltx7l2oB3A1dUboGhqhUlkzb97w5aw3nS14i7UL3sQsqz9mvUnBWyiOCmhWjhXO4M0p8dyXVlh/rzjrNCn3ZzS5L8ruvdkRO00CXpRNjhgxAuvXr8eKFSvw9ttvQxAEbNiwAQMHDsSbb76JUaNGub0vtVqNN954AytWrMAjjzyCmpoaDBw4EOvXr5e6O7799ttYunQppkyZgtTUVCxcuNBh1ty8efNgMpmwePFi6PV6ZGdn45133kFYmPXDPjk52S/7IFKqmEhlBm8AkNopChU1BlypakAfWwMTXUMjqrxsg+4JccC53miGyWyBVqNGQozrTJCore3kCgLteTKAvDmNWo2YSC3q9Cbo6huDZhagmFX8554T0pB5wJpxmz6hb4vg1N1vy9uae6dUFouAAz9buygnxIRD6+WFjxS8+WDWmziOQpwtCNgPifdD8GYM3cyblpk3t4jnvszWcCrByYw3X3xGk3ui7DJv4SH4PnWHV22isrOzsWXLFuj1elRXVyM2Ntbp2jV3pKSkYNmyZa3eP2TIEGzdurXV+zUaDR5//HE8/vjjAd0HkVLZZ946BclFubtSE6JQUFTlMOvtkq0kLDk+0uFDXm7RdvuuN5gQHx0ulfK5+ka2tVI+e3IFgXKKjQqzBm9+mrflruGZaVCrVVi97Uckx0fg3lsGtrp+reB8pdvflnsb6Aar5sPMK3VGPP76N7jTSZDblsQ4a3Ovyhr/lE3G+bVs0jakOwQvCluWTYbec5SDOEtMXAvurGwyGD+jOwr73+sdNfPm8bP+zW9+gw0bNqCsrAyRkZFIT0/3OnAjosCLjmj6BV5Za1BUjX5qgvUiUpzpBtgP5/bt55JarUKU7d+u3lZK6G0pX3PtWc/lK7F+LF3zVJUtIOmWFof+PVqfpydmZNvcX4h9W97aMHNvS0V9WTbpNHiL8s+gbrOlqZtdRyibZObNuebZHGeZt2D8jO4oIu3LJjvoa9jjZ33VVVdhxYoVuPHGG3Hvvfdi165d0Ov1bT+QiIJOXkEp8k6USX9/77MTimrckGKbu1NW3ZR5u3jF1ozBx8Eb0FRyWmd3UTk8M63Vn52aEInBvZORf64SB48VI/9cpdNgWa4gUE7BHLyJjTOS4l1fTDm7CHO6XQh9W+6LxgoJtvLq0sr6Vl/D3jCZLWgwWL8IiYv2f9mkwdjUjCciLPQuCltk3jpo1qItzbOuzjJvwfgZ3VHYZ9462nBukcc1Ra+99hpqa2uxZ88e7N69G4sWLcKSJUtw880347bbbsPo0aM7ZOcXIqVptc2x7dt4JbQ5TrUFb/Zlk2Kzkq5edtLzRHSkFqh2bOJhaDSjpMLapWzWpAFQq1XQqNX4x8fHcaVKj0dWfy21IwesWQxnpWvDM9Mw+pp0HPi5xOH21tZz+VrTBXTwzdMpr7F+gZgcH+lyu8zuibKUtSqJJ40V3CkVzSsoxcbPTgCwDm1/YfORVl/DnhK/GFCpbO8tGzELp/Pxa09sVqJWqbxeDxjMNBrHa7PwEAxQ5RDZLOvqrGEJ0LTm1r4cGQjcZ3RHEdWs22RH5NWCkLi4ONx+++24/fbbUV5ejk8++QSffPIJ7rvvPqSkpODLL7+U+ziJSEah0rhBDN4qagwwmS3QqFVNbdD9mHlrPi7AZBaQHB+B0ddkSF9mnbpYjU8PFzkEboDrYFks85swvCt6d4kP6DwyMfNW1xAc3SbtVdgaCyS1EbyJ35a7nHsXYt+Wy9lYwddf+IiZtdioMKjtvgQWm5f4umzSKHWaDM3241p1s7LJEAxQ5dA8qHWVsR+emYasvqk4UVSFqjpDh5sZGQhRdks9OmrH1HY/6/LycpSVlaGmpgZmsxmdOnWS47iIyIdCpc1xfHQYwsPUEACUV+tRpTOiTm+CWqXyug26J5yNC/j5TAUA4JpeSdIFoMUi4HC+61LU5qVrBqMZJy9UAQDGD++KUQMzXK7n8rVYad1R8GXe3C2bBOSZe6ckcjVW8MdcKzGrG9escVKcv8ombcFbqHawa555CwvR59lekWFNeQ0V2u7CLHbyDfRndEcRFc6GJV5l3oqKivCf//wHu3fvRmFhIVJSUjBp0iT8/e9/R//+/eU+RiKSWai0OVapVEhNiMLFK3XW0knb78z0pCi/dFITB3XX2WXemoK3ZOk2b0rXCqQMXiTSE6PkPnSPiRfUOj+0a/eExSJI/7ZtlU2KxG/Ll2/MQ+HFGkwY3hW/uymwGTeLRfDJt/dydUCVu/zSGbFsMs6uAy7g+NoTBMFnWTF9CI8JALjmzV326x3josNCsoRWyZoP6e6IPA7epk2bhmPHjiEyMhI333wzFi1ahNGjR0NtS8f78oOViOQRSm2OUzvZgrdqvVT25Mvh3PbEzJvYbbKy1oCLZXVQARhgdwHrTbDsLIMXSGKJqE4fXMFbdZ0RZosAtUqFTm42JAGs35b3zIhH4cUaaLXqgAZuzdv4A62vhfSUXKWi/vjCx1mnSaAp62sRBNQbTA6zKeUkfn5EhmjwplKpoFappBb4XPPmXLjdmjdnzUoosOzLJjtq8Obxs05ISMDy5cvxzTff4IUXXsD1118PtVqN0tJSvPrqqxg/frwvjpOIZBRKbY5TpHEBDXadJn3frARoGtQtdps8dtYacPXsHOcwP8+bYPln274G9UqS5Vjbq6lpRHAFbxW2ZiWJceHQqD37lZbcKdJhH4Egdxt/Z8RS0eYBmielov74wkcsm4xtVjYZplVLTSR8+fqTyiZDcEyAyL50kpk35+yD94Q2fk+S/zmUTXbQ4M3jzNs777zj8Pf//e9/2LJlC7788kuYTCZ07dpVtoMjIt8IpcYN9h0nxYtwf2XeoputebPPltnztHStokaPS2V1UKmAAT2DY1h0sI4KEDtNJrpZMmlPLLMsrw5M8ObPxkFD+iRL69Fm3NwPXVJiPCrNlKv80pXaVsomAevrT280o7a+Eek++j5DDN5CtWwSsJZOiu/gMGbenLIPagVBgMUiKOJ3YUfh2G0ydN+rrnj1zq2oqMDatWsxYcIE3H///fj2228xZcoUvPfee9i7d6/cx0hEPhAqjRvE4K20ssGvnSYBx26TFkGQsmXX9HS8uvR0JpAYBPbuHO+zEjFPicFbvd4Es8XSxtb+U1Hj2Xo3e2LmrSxAmTd/Ng66VGYdXxEbFYbxw7p43FjBH3OtWiubtN5m6zjpw3EBhhBf8wY4rntj5q2lvIJSLN/0nfT3H09XKGr2aUfAId0eZt4OHjyIrVu3Yu/evTCbzRg+fDguXryINWvWYOTIkb46RiLykVBocywGbxdKdRAAaDVqpPmpwYd9t8miEh1q6xsREa5Bny4tu+62NhNIq1Fh9q3XOATLUhAYJCWTABATZX2uAqzPNz7a/fVlviRm3tzpNNmcGPBV64xoNFn8fiHgz8ZBRaW2+YepMV6voWztNQxYvw0f1Du5lUe6RyeVTToL3sRup74sm7R+KRHKwZt9841Q7arprVCYfdoRREew26RbwduGDRuwdetWnDlzBj169MCcOXMwZcoUREdHY+TIkUGxmJ6IvCO2OVaqFFv2RGxQflVytMdrn7wVY9dtUlzvNqB7YqvdyeyD5YtlOmz67CRMZkEKQAFrKZ2YeRvUq30Xw3LSqNWIidSiTm+Crr4xaII3sVQ2Kc7zzFtcdBjCtWoYTRZU1uqRluj78RL2/Nk46II4vD6tfetBm3/hExcVhg0f56O8xoB9eRfw/43q4fW+mzJvLV9bYimlTzNv0py30A1quObNuVCZfdoRsNukm2WTy5cvR3h4ON59913s2bMHDz74IDIyMhi0EVHARYRpEG/3TX1sdFi7Zk15Itqu2+RPtoBrYBtr1MRg+abh3TCiv/Vb3L15F6T7z5XUok5vQlSEBr2uivPRkXsnJgjXvbWnbFKlUkmDvf217s1iEZB/rhIHjxXDYhEcSoCckatxkBi8dZOhmY/9XKtreiXjN2N6AwB2HzyHBoP3Q9xdrXlrKpv0fcOSiBBeC+ZQNtlBL3ydCZXZpx2B/WDuylq9337fBxO33rm33HILzp07h9mzZ2POnDn47LPPYDJ5/wFNRCSXvIJShyHZx85W+m2Ngph5M5os0i91T0odbx7RDQBw8OcS1NgyCmIQOKBHkt8yiO6KC8LgrT1lkwCQbHtceY3vZxrmFZTi8de/wQubj2Dtv48hZ+v30myx1sjVOOhCqTyZN2dGX5OBzsnRqNObsOmzEzh4rBj55yo9uqiyCILUSdJp5i3a96+9jrHmrekzhcFbk1CZfRrq8gpK8eTag9Lf/3vkUodck+hW2eSKFSug0+mwa9cubN++HXPnzkViYiImTJgAlUrFDBwRBUSg1yhERmigUgGCAJgtApLjI5CR5H7pXZ8u8eiZEYezxbX48vtLmPyLnnYlk8Gz3k0UbB0njY1m6ViSvMi8AU1NS8p93LSktdeqSCxJbS6lU/vXb1bXGVFT3wgVgKt80IlVrVZhcO9kXC6vx/6firH/p2IAns2qq9ebpPljsa10mwT8lHnrKGWTDN4koTT7NFQF+vd9MHH7nRsbG4vp06fj/fffx65du3Dbbbdh3759EAQBf/7zn/HKK6+gsLDQl8dKRCRxd42CT0sqBCDC7gJoYM9Ej77MUqlUUvZtX14Rjpy8gpMXqgA4DvkOFsEWvFXYypwiwjRS8xhP+WNcgDuv1XCtGgt+NxT33zoQC6dn4bqB1ouQ7V+dbvfPF7NuaUnRPskq5RWU4tPDRS1u92RWnfiaiorQOA0qxGycrsEPa95COvPW9PkU3kHbrDsTSrNPQ1FQ/L4PIl597dK3b18sWrQIX375JVavXo3evXvjrbfewuTJk3HrrbfKfYxERC0Eeo2CWAKnb2xqm//diTKPyzeyB6QhOkKL6rpGrN72I2zJB7yw+UjQlYLEBtmgbvuSSW8rQKQ1bz7MvLnzWq3UGaFWqTBqYAb690jEb8b0hkatwo+ny5F/rlJaJ+dpOSLQ1Gmymw9GaMh1USU2IomLct4IR3zt+TTz1gHKJu2bKTHz1sQfozDIewXnK7km0Y53X1WKD9ZqcfPNN+Pmm29GWVkZduzYgR07dsh1bERErQrkGoXWyjfq9CaPyzd+KCxDvZMmD8FYCiKVrvkw++EJqdOklyWTQFO3Ul8Gb968VtMTozFmSGd88f0lvPSv72EyNwU/npQjAvJ1mnTGky9RXHW1FYMyZ2MCALtRAX5pWBK6wRsblrSutVEYSXERmO7B+43kV6Vz73dOR1mT2K7gzV5KSgruu+8+3HfffXLtkoioVYFaoyBnS2mltacWg7e6huBoWNXUadL7cyyWTVbU6GERBKh9sIbb29dqj4x4AJccAjfA88D+Qql8nSabk+tLFJ2LTpPW260ZOUOjGcZGs09mlHWINW8M3lwKhdmnoSgh1r3RNB1lTSLfuUSkSIFaoyBnuWagSz89FWu7gA6WzFu5DJm3hLgIqFSAySygps43z8ub16rFIuDf+8+4fIw75YgmswWXyusA+CbzJteXKFLZZCvzA6MiNFLg4as1lx1hSLfGfkg3gzen7Edh9O+RyMAtCGR2T+SaRDt85xKRIgVqjYKc5ZpKa08dF2Rr3iptwZs3M95EWo0aCbHiuADflE5681qVK7AvqaiHyWydJyd21pSTXF+itFU2qVKpfL7uzWC0ZpRDOnhj5o0UiGsSHfGdS0SKJa5RaH7xmBQX4bO1YnKWayqtPXWwDekWZ7MltRE8tEUaF+DDjpPDM9Nw36SBLW5v7bUqV2BfZFvv1iU1xicloXJdVDVl3pwHb0BTSaWvMr9i5i28wwzpDt0glUJPIH7fByvZ1rwREQWCv9coiJkGV1kRd8s35NyXP4gXz/V6E8wWS0CHiAuC0NSwpJ0ZpZT4SBSi2uez3mKirL9yO8WE4Y6b+rp8rcoV2F8otZZM+mK9m6i1Rg9x0WH4/f9lunVRVSuteWt9bYu1pLLOZ5k3o23NW2R46F4aid0mVQC0mo6RpaDQwTWJVqH7CUVEHYa4RsFfP+vOCX1dDlx2t3xDzn35gxh8CLB21oxvZX2SP9TpTTCarJmS9mbexDVzFdW+LU/NP1cFABjSJwWjBma43FauwN6XnSbt2V9Ubf78JIpKdbj1l73c/jZcDMhcZt58WLZrMltgtq0fjAjlzJstYAvTqr0er0EUSP78fR+sQvcTiojIR+Qs31BSKYhGrUZ0hDWAqwtw6aRY4hgfHdbu8q9kP4wLAIDj5yoBuDeAXa5yRCl482HmTSReVInPr7Siwe3H6mxlk62teQN8O6pCb5vxBsAnnSyDhVg2yfVuRMrFzBsRkRfkLN9QUilIbHQY6g0m1NY3onNy4I5DjhlvIrHhSZkP17zV6RtxvqQWANz+1ri1csSE2HDMuLlfm4F9nb5RGqfgj+BNlJEcDQAorqh3+zFS2aSLbK54ny/KJsWSSY1a5TDIOtSIpc5aBm9EisXgjYjIS3KWbyilFCQuKgyllQ0Bb1pSLkOnSZE4J67Ch5m3E+erIADISIqWulu6wz6w/+eeAlyuqMeEEd08mu+WHB+J6Ej//brvnCQGb3VubW+d3WYtgW1tzhvg27JJg7TeLXSzbkBT2STHBBApF9+9RETktmDpOFlhy0QltmNAt0gsm6w3mNBg8M0Ack9KJpsTA/sJI7oCAHLzS9163IUrtmYlPl7v1lyGLXgrq9Kj0WRuY+umTpNajcpl8CSVTdbLXzYpBm+hXDIJ2JdNhvbzJAplDN6IiMhtccESvMmYeYsM1yLGlpny1biA/PPW4K092dXhmWlQq1Q4W1yLksq2SxKLSsVmJTFe/0xvxMeEIypCAwFASWXb696ampWEu2yiIZVN+uC1Z7CteQvlGW8AoLH9+xobzcg/V9nmkHciCj4M3oiIyG2xMpauWSwC8s9V4uCxYo8vJOUsmwR827Skpt4oZcEyuyd4vZ/4mHAM6GkN/r493nb2zZ/NSuypVCop+1Zc3naQqZPGBLReMml/vy/WvImZt1AO3vIKSvHlD5cAWNd3vrD5CB5//RvkFbiXySWi4MA1b0RE5LZYmTJveQWlLRpxJMZF4M4Jfd1azyU24pCjbBKwBoHnS3Q+Cd4KzlcBALqmxrR7vMLIAWn4+UwFDh8vweRf9Gx1O4sgBCx4A4CMpBicuVzrVtOSWjc6TQJNa97qGhphsQiyNvQRB3RHhOiat7yCUqcjSSprDViz46eg62xLRK1j5o2IiNwmR/AmXkg2n2EmXki2lQkwmS2o0lkfK1vmzbYfX5RN5tvWu/Xv3v6GNMP6pUKjVuHClTpctAVnzlypaoCx0QKtRo30pKh2/1xPedJx0r5s0hVxvaV1zqC82bdQLpu0WARs2nvS5Tab955kCSWRQjB4IyIit8VGieuOvGsaIceFZJXOAEGwNl+Ij5FnULgvyyblWO8miokMw+De1hkNrkonxU6TXVJipPbw/tTUcVK+skmtpmnOoNylk01lk6F3WXSiqMrlsHfA2gDoRFGVfw6IiNol9D6liIjIZ6R27Q3edWWU40JSLJlMio+A2kWDC09ImTcPg7e21u1V1hpwubweKrRvvZu9kQOs5W3fHi+BILQMci0WAd+fLAMAxEVrA5JREde8XS6vd3qM9sSyybg2yibtt5G742Qor3mrqnP9fvN0OyIKLK55IyIit0mjAry8eJbjQlIa0B0nT8kkYJd586Bs0p11ewW2rFv39DjERLYdnLjj2qtTEKZVo6SyAV99fwkRERppsPuRk1ccjumnM5V4/PVv3F5LKJe0xCioADQYTKipb0QnFxlSMYsW68Z6wNjoMJT4YM6gVDYZgmveEmLcWxfq7nZEFFgM3oiIyG1iaVu93uRV0wg5LiTF7FiSTOvdgKbMW7XOCJPZulbMFXcbQDSVTCbIdqxREVp0T4vFqUs1+MeeAun2mEgt6vQtM6KBaEoRHqZBcqdIlFXrUVxe5zp4c7Ns0rqNrWzXZ2WToRe89euWgMS4CJcZ76Q4a/BPRMGPZZNEROS2mCjrd37eNo0QLyRdaetCUhzQndxJvkxBXHQYwrRqCHb7b4076/Y2fXYCx85W4IitfFHOC+O8glKculTT4nZngZs9fzelcLdpSVPDEpZN+oJarcKdE/q63Gb6hL6ydu8kIt9h8EZERG7TqJuaRnhTuibHhWRFtfxlkyqVSsrktVU66c66vUqdETlbvpcCk3/uKZBlnpY7gWNr/N2UIsPNpiU6aVSAe2WTgPyDusXgLTwEgzfAOuD9oSmDWnxxkhQXwTEBRArDskkiIvJIbHQY6g0m1NY3onOy548XLyRf3/kzLM2aWYzITG3zQrJcalgiX/AGACnxESipqJfW1LXGm8YOVTqjLKWL7gSOLo/Dj00pOts1LWmNsdEsZQyLK+rQOSnaZeAurhs8e7kG+ecq0a9bgiwZI3HNW2QIrnkTDc9MQ1bfVJwoqkJVnUFaJ8mMG5GyMHgjIiKPxEWFobSdTSMG9kySAre7/i8TtXUG7Pz6LI6eLkdlrcFlaWVlrTW4SpZpQLfI3aYl7WnssHnvSWT1TfX6grm9wZc/m1K0lXnLKyjFe5+dkP6+ZvtPLge15xWU4uOD5wAAhRdr8MLmIx4NdnfFGMJlk/bUapUsIyuIKHBYNklERB6JjrR+73f0VLnT9vjuOF9SC8Da7n9cVhdMvr4X+nSJh7HRgg+/PtPq4xoMJilTI3fmTdxfWRuZN3fW7bWmvaWL7Qm+/N2UIiM5BgBQVqWHyWxxuE9s+FKtc1y71tqgdnH75uv63B3s3hZ9iJdNElHoYPBGRERuyysoRcH5KgDAVz9cwgubj+Dx17/x+OL5bLE1eOuRHgfAuubs/x93tbTfr49edjo7TWwmEhWhRVSEvMUjYsfJtsom3Vm350p7smftCRz93ZQiITYcEeEaWAQBpZUN0u2eDmqXY7B7WwxGa3AZEc7LIiIKbvyUIiIit4jZD6PJMYviTfbjnC1465kRJ93Wt2sCenWOBwCs230ca/99zCE4tFgE/FBo7d4YEyn/8OkUD2a9Dc9Mw80junr1c9qTPXMncIyJdAxqA9WUQqVSOS2d9HRQu7vbizP1vCGWTUaGcTUJEQU3fkoREVGb3M1+uLueS8q8ZcRLt+UVlOLM5ZYt8MXg0H6OWVm1Xvbh01K3yRoDLIIAtcr18yizBXmjrknHkD7JiI8Kxzu7j/t8npbY8KX5gPCkuAhMn9A3qJpSdE6KxrniWofgzdNB7W5vr/N+fEBTt0l+p01EwY3BGxERtcmTbElbDREaDCaU2C7mxcybO8Fha+ud5MoqJcZFQKUCTGYLausbXQ6WbjCY8OPpCgDAr0Z2R3db+eedE/o6Hd4tkqt0sa3OgcHSlCJD6jhZJ93m6aB2t7ePbXvUQGvENW8RIdxtkohCA79iIiKiNnmaLXGlqFQHAdZgKd4WILWnBb5cw6e1GjUSYq2BQlulkz+eLofJbEFaYhS6pcVKt/tznpbYOXDUwAz075EYlC3fnQ3q9nRQu7vbZ3b3LmAVBAFGY8foNklEysfMGxERtcnTbIkrZ52sd2tPEw93M37uSIqPQGWtAQePFcPYaG615DA337q+b0RmGlTNyis5T6uJtObNbtabWq3C7Tf2wVv/Odbq4+wzlOI6P19lNBtNFoihP4M3Igp2DN6IiKhNYvZDjvVc54qt69rETpNA++ePyTF8Oq+gFEUlOgDA3twL2Jt7wekcMYPRjKOnywEA2f2dZ9I4T8sq3Ra81elNqK03Ii7ammltMFpLYNVqlUPWVFy31zxD2dY6v/ZkNMX1bgCDNyIKfgzeiIioTXJmP5qalTQFb+4Eh660N/gTO2k252xd3Y+ny2FstCClUyS6p8e2eAw1iQjTIDk+AuU1BhRX1CMuOhyCIOC/Ry4CAH47rg96pMW5laEUM5rPv5eH05dq8H8ju+G3Y69ud0bTYCuZDNOqO2R2lIiUhWveiIjILa2t5xJpNWrkn6t0Op9NpDeapBI6+7LJ9sxOa28HR0/niOXaRiKM6N+yZJJaampaYj3vJy9U4+KVOoSHqTFmcGeP1u2p1SqkJ1r3Fx8TLkuwJWbemHUjIiVg5o2IiNzmbD1XXkEpPv/uIlZtOwrBLl5zVnJ4vsTarCQhNhydYiNa7NtZaVxspBa6Zp0m7bW3g6MnnTR7XxWPH05ZSyZH+HlumlJlJMfg57OVUtOSL763Zt2uG5CO6Mgwj/cXE2W9dKlraP014QlDo21AN4M3IlIABm9EROSR5uu5qusM+Py7iw6BG+C85PBcidisJB7OtNbs48jJKz5Z7wR41knz5zMVMBjNSIqPQK/OcW0/iByaltTUG6VmL2Ozuni1v1hbwFenb5Tl+AwcE0BECsLgjYiIvGaxCPjXf0+53MZ+ePc5J+vdmnPW7MOXHRzdXS9XozMi74Q18BjeL5Ulk26yHxew/8fLMJkF9MyIQ6/OzgP4tkRH2jJvLrKxnjBIYwK4koSIgh+DNyIi8pqnw7vdCd5a46sOju40S1GpgC37CqW/HzxWgn7dEmSd2xaqOtsyb6WV9fjk4HkAwA1Dr/J6fzFRtsxbg8yZN5ZNEpEC8GsmIiLymiclhwajGZfK6wA4NisJNHeapTQvCa2tb8SaHT8hz9a8hFp36pJ1NIRFAGptAde/vz7j9b9djK/KJhm8EZECMHgjIiKveTK8u6hUB0EAOsWEIyG2fa395dZaJ822KiPtu1BSS3kFpXh9Z8sRDFU6o9fBr/wNS7jmjYiUg2WTRETkNU+Gd+/77gIA70om/aH5uroandGhVNIZ+5JQcuTuCAZxPaS7ZG9YYlvzFs7MGxEpADNvRETkNXdKDsVW/uJ6t2AqmWxOXFc3amAG4mPD3XqMu6WjHY0n6yE9Ia550xvNMJkt3h6eRMy8RTJ4IyIFYPBGRETt4mp49zU9E1uMCQjWzFtznpSEUkuerIf0RHREU9FQvaH9pZMsmyQiJWHZJBERtVvzksPa+kZs3nsS+eerUFJZj8TYCFwqsw5pbm3GW7DxpCSUWvJV8KtWqxAVoUWDwYS6hkbER7uXIW2NsZFlk0SkHMy8ERGRLOxLDm8e0Q1D+iTDbBGw86vT+ProZVgEAdGRWsRHhwX6UN3iSUkotSQGv654G/zGyDjrTW9kt0kiUg4Gb0RE5BNTxvQGABw6Xor3PjsBAKjXm7DwjQOKabHfWkloUlwEHpoyiHPeXPBl8CvnrDdjo3XdXCTLJolIAVg2SUREPlFW3eD09spaA9bs+EkxwU/zktCEGGu2iBm3tonB76a9Jx3KT5PiIjB9Ql+vz3+slHlrf/BmkMom+X02EQU/Bm9ERCQ7X7WJDxSxJJQ854vgN1oaFyBjwxKWTRKRAjB4IyIi2XnSJp5BUeiTO/iVs2zSwDVvRKQgrBEgIiLZ+apNPBEgb8MSjgogIiVh8EZERLLjjDTypRipbFK+NW/MvBGREjB4IyIi2fmyTTxRTJQt89bANW9E1LEweCMiItlxRhr5UqxMmTeLIEijAhi8EZESMHgjIiKf4Iw08pVomda8GW1ZN4Br3ohIGdhtkoiIfIYz0sgX5Oo2abBl3VQAwrX8PpuIgh+DNyIi8inOSCO52TcssQgC1CrvvgxoGtCtgcrLfRAR+RO/ZiIiIiJFEUcFCAKgN5jb2Lp1TTPeeDlERMrATysiIiJSlPAwjVTm2J6mJZzxRkRKw+CNiIiIFEda9yZH8MZOk0SkEAzeiIiISHHk6DhpNDJ4IyJlYfBGREREiiM1LWlHx0m9XcMSIiIlYPBGREREihMjQ+ZNLJuM5Jo3IlIIBm9ERESkOHLMemPZJBEpDYM3IiIiUpzYSPkalrBskoiUgsEbERERKU5MlK1sssH7skk9u00SkcIweCMiIiLFiZYh82Y0WgBwzhsRKQeDNyIiIlIcORuWRITxcoiIlIGfVkRERKQ4cgzpZtkkESlNUAVvZ86cQVZWFrZv3y7ddvz4ccycORNDhw7F+PHj8e677zo8xmKxYNWqVRgzZgyGDh2K++67D0VFRQ7b+GMfRERE5D+xMsx5MzJ4IyKFCZrgrbGxEQsWLEB9fb10W2VlJe6++250794d27Ztw0MPPYScnBxs27ZN2ua1117Dpk2b8Ne//hVbtmyBxWLBrFmzYDQa/boPIiIi8h9ZyibFUQFc80ZECqEN9AGIVq9ejdjYWIfb/vWvfyEsLAzPPfcctFot+vTpg3PnzmHt2rWYNm0ajEYj1q1bhwULFmDs2LEAgJUrV2LMmDH49NNPMWnSJL/sg4iIiPxLLJtsNFlgbDRDq/X8+2gDM29EpDBBkXk7fPgwtm7diuXLlzvcnpubi5EjR0KrbYoxR40ahbNnz6KsrAz5+fmoq6vD6NGjpfvj4+MxcOBAHD582G/7ICIiIv+KDNdArVIB8D77xuCNiJQm4MFbTU0NFi5ciMWLF6Nz584O9xUXFyMjI8PhtrS0NADA5cuXUVxcDAAtHpeWlibd5499EBERkX+pVCpES6WT3q17k4I3lk0SkUIEvGzymWeeQVZWFiZPntziPr1ej/DwcIfbIiIiAAAGgwENDQ0A4HSb6upqv+2jPbwp8yDf0WjUDv+n0MVz3XHwXIeu2Kgw6BoaoTeavTrPhkbrnLfoqDD+PlYQvqc7Bp5n5wIavO3cuRO5ubnYtWuX0/sjIyOlpiEiMViKjo5GZGQkAMBoNEp/FreJiory2z68pVarkJgY4/XjyXfi46MCfQjkJzzXHQfPdejpFBeB4op6QKORzq8n51nsNpmWEovERO9/n1Ng8D3dMfA8Owpo8LZt2zaUl5dLjUJES5Yswe7du5GRkYHS0lKH+8S/p6enw2QySbd1797dYZvMzEwA8Ms+vGWxCKipqW97Q/IbjUaN+Pgo1NQ0wGy2BPpwyId4rjsOnuvQFWkbrl1SVouamgSPzrPZYkGjybqdvt6ASgg+PVaSD9/THUNHO8/x8VFuZRkDGrzl5ORAr9c73DZx4kTMmzcPt956Kz788ENs2bIFZrMZGo21Hv3gwYPo1asXkpOTERcXh9jYWBw6dEgKvGpqanDs2DHMnDkTAJCdne3zfbSHyRT6L0YlMpstPDcdBM91x8FzHXqiI6yXMbV1jdLFnbvnud6uyYlWreJrQ4H4nu4YeJ4dBbSIND09HT169HD4DwCSk5ORnp6OadOmQafT4amnnkJhYSG2b9+ODRs2YPbs2QCs69RmzpyJnJwcfP7558jPz8f8+fORkZGBiRMnAoBf9kFERET+Fy0O6vaiYYnYrEStUkHLNTVEpBABb1jiSnJyMt5++20sXboUU6ZMQWpqKhYuXIgpU6ZI28ybNw8mkwmLFy+GXq9HdnY23nnnHYSFhfl1H0RERORf7RnUbZQ6Taqhso0cICIKdipBEFjkHSBmswUVFXWBPgyyo9WqkZgYg8rKOqboQxzPdcfBcx26Psstwua9J5HdPw1zbx/i0Xk+V1yLZzccRqfYcKx8+Jd+OFqSC9/THUNHO89JSTFurXljnQAREREpUqwMZZMc0E1ESsLgjYiIiBQpJspWNtngfdlkJIM3IlIQBm9ERESkSHI0LAkPZ/BGRMrB4I2IiIgUqT0NS/RGlk0SkfIweCMiIiJFiomyZt4aDCaYLZ41NDByzRsRKRCDNyIiIlIkMfMGOA7ddoeh0RrsMXgjIiVh8EZERESKpFGrERVhDb50De6ve7NYBFy8ogMA1BtMsFg4NYmIlIHBGxERESlWjNi0xM2Ok3kFpXj89W+w/6diAMAPhWV4/PVvkFdQ6rNjJCKSC4M3IiIiUqxoqWlJ25m3vIJSrNnxEyprDQ63V9YasGbHTwzgiCjoMXgjIiIixWrKvLkO3iwWAZv2nnS5zea9J1lCSURBjcEbERERKZbYcVLXRubtRFFVi4xbcxW1BpwoqpLr0IiIZMfgjYiIiBQrViybbGPNW1Wd68DN0+2IiAKBwRsREREplph5a6tsMiEmwq39ubsdEVEgMHgjIiIixRLXvLU1KqBftwQkxrkOzJLiItCvW4Jch0ZEJDsGb0RERKRYTd0mXZdNqtUq3Dmhr8ttpk/oC7VaJduxERHJjcEbERERKZbUbdKNUQHDM9Pw0JRBiLEFfKKkuAg8NGUQhmem+eQYiYjkom17EyIiIqLgFBslNixpO3gDrAHc5fI6bP/qDPp2jceUMX3Qr1sCM25EpAgM3oiIiEix3F3zZq+0Ug8AuKZXMvr3SPTJcRER+QLLJomIiEixmrpNmiAI7g3YLq6sBwCkJ0b77LiIiHyBwRsREREplrh+zSIIaDC4bloiKq2wBm8ZSQzeiEhZGLwRERGRYoWHaaDVWC9ndPVtl07W602osW2Xlhjl02MjIpIbgzciIiJStBhb05LaemOb25bYSiY7xYQjKoJL/4lIWRi8ERERkaLFik1L3Mi8lVSI692YdSMi5WHwRkRERIomrnurbXAn89YAAEjjejciUiAGb0RERKRoYsfJWncyb5VsVkJEysXgjYiIiBRNmvXmzpo3lk0SkYIxeCMiIiJFi7aVTba15k0QBJRUWMsm05l5IyIFYvBGREREitZUNuk681bb0Ih62yy4tARm3ohIeRi8ERERkaLFipm3BteZt1Jb1i05PgLhYRqfHxcRkdwYvBEREZGiuZt5K7atd0tLZMkkESkTgzciIiJStKhwa+atuLwex89WwGIRnG7HTpNEpHTaQB8AERERkbfyCkrxzz0FAICyqgYse+87JMZF4M4JfTE8M81hW3HGGztNEpFSMfNGREREipRXUIo1O35CTbMuk5W1BqzZ8RPyCkodbhfHBHBANxEpFYM3IiIiUhyLRcCmvSddbrN570mphFIQBJZNEpHiMXgjIiIixTlRVIXKWoPLbSpqDThRVAUAqNIZYWy0QK1SIaVTpB+OkIhIfgzeiIiISHGq6lwHbs23E0smUxIiodXw8oeIlImfXkRERKQ4CTERHm1XbCuZTOeYACJSMAZvREREpDj9uiUgMc51AJcUF4F+3RIANA3oTk9ip0kiUi4Gb0RERKQ4arUKd07o63Kb6RP6Qq1WAWia8cbMGxEpGYM3IiIiUqThmWl4aMqgFhm4MI0KD00Z5DDnrbiCnSaJSPk4pJuIiIgUa3hmGrL6puLUpWqUVOuxbtcxmC0CMrsnSttYLAKuVHFANxEpHzNvREREpGhqtQoDeiZhyti+6JYWC4sAHDlxRbq/vEYPk1mAVqNGUjzHBBCRcjF4IyIiopBx3cB0AMDh/FLpNnG9W1pilLQGjohIiRi8ERERUcjIHmBd53b8XCV0DY0AgJIKlkwSUWhg8EZEREQho3NyDLqlxcJsEfCdrXRSHNCdzmYlRKRwDN6IiIgopIzob82+5dpKJ0sqmXkjotDA4I2IiIhCSrYteDt21lo6WcIxAUQUIhi8ERERUUjJSIq2dZ0UcPh4Ccqq9QCANA7oJiKFY/BGREREIUfMvu3afxYWQUCYRo346LAAHxURUfsweCMiIqKQExWhBQBU1RkBAI1mCxa+cQB5BaWuHkZEFNQYvBEREVFIySsoxcbPTrS4vbLWgDU7fmIAR0SKxeCNiIiIQobFImDT3pMut9m89yQsFsFPR0REJB8Gb0RERBQyCs5XorLW4HKbiloDThRV+eeAiIhkxOCNiIiIQkaVzujednWuAzwiomDE4I2IiIhCRkJsuHvbxUT4+EiIiOTH4I2IiIhCRmb3RCTGuQ7MkuIi0K9bgn8OiIhIRgzeiIiIKGSo1SrcOaGvy22mT+gLtVrlpyMiIpIPgzciIiIKKcMz0/DQlEEtMnBJcRF4aMogDM9MC9CRERG1jzbQB0BEREQkt+GZacjqm4oTRVWoqjMgIcZaKsmMGxEpGYM3IiIiCklqtQr9eyQG+jCIiGTDskkiIiIiIiIFYPBGRERERESkAAzeiIiIiIiIFIDBGxERERERkQIweCMiIiIiIlIABm9EREREREQKwOCNiIiIiIhIARi8ERERERERKQCDNyIiIiIiIgVg8EZERERERKQADN6IiIiIiIgUgMEbERERERGRAjB4IyIiIiIiUgCVIAhCoA+ioxIEARYL//mDjUajhtlsCfRhkB/wXHccPNcdA89zx8Fz3TF0pPOsVqugUqna3I7BGxERERERkQKwbJKIiIiIiEgBGLwREREREREpAIM3IiIiIiIiBWDwRkREREREpAAM3oiIiIiIiBSAwRsREREREZECMHgjIiIiIiJSAAZvRERERERECsDgjYiIiIiISAEYvBERERERESkAgzciIiIiIiIFYPBGRERERESkAAzeiIiIiIiIFIDBG3VIVVVVePrpp3HDDTdg2LBhmD59OnJzc6X7Dxw4gKlTp+Laa6/Fr371K3z00UcBPFqSy5kzZ5CVlYXt27dLtx0/fhwzZ87E0KFDMX78eLz77rsBPEJqr507d+LXv/41Bg8ejFtuuQUff/yxdN+FCxcwe/ZsDBs2DL/85S/x8ssvw2w2B/BoyRsmkwmvvPIKxo0bh6ysLMyYMQPff/+9dD/f06HhzTffxF133eVwW1vn1mKxYNWqVRgzZgyGDh2K++67D0VFRf48bPKQs/O8b98+TJs2DVlZWRg/fjz+/ve/Q6/XS/cbDAY8++yzGD16NLKysvDYY4+hoqLC34ceMAzeqEN69NFHceTIEbz00kvYtm0bBgwYgHvvvRenT5/GqVOnMHv2bIwZMwbbt2/Hb3/7WyxcuBAHDhwI9GFTOzQ2NmLBggWor6+XbqusrMTdd9+N7t27Y9u2bXjooYeQk5ODbdu2BfBIyVsffvghnnrqKcyYMQMfffQRJk2aJL3XGxsbce+99wIAtmzZgmeeeQabN2/GmjVrAnzU5KnXX38d77//Pv76179i586d6NWrF2bNmoXS0lK+p0PExo0b8fLLLzvc5s65fe2117Bp0yb89a9/xZYtW2CxWDBr1iwYjUY/PwNyh7PznJubi4cffhg333wzduzYgSVLlmD37t149tlnpW2eeeYZfP3111i9ejX+8Y9/4PTp05g3b56fjz6ABKIO5uzZs0K/fv2E3Nxc6TaLxSJMmDBBePnll4W//OUvwu233+7wmEcffVS45557/H2oJKMVK1YIv//974V+/foJ27ZtEwRBEN544w3hl7/8pdDY2Oiw3cSJEwN1mOQli8UijBs3Tli+fLnD7ffcc4/wxhtvCLt27RIGDRokVFVVSfdt2bJFGDZsmGAwGPx9uNQOt956q7Bs2TLp77W1tUK/fv2EPXv28D2tcMXFxcLs2bOFoUOHCr/61a+EmTNnSve1dW4NBoOQlZUlbNy4Ubq/urpaGDJkiLBr1y7/PQlqk6vz/Nhjjwl//OMfHbbfsWOHcM011wgGg0EoLi4W+vfvL3zxxRfS/adPnxb69esnfPfdd357DoHEzBt1OImJiVi7di0GDx4s3aZSqaBSqVBTU4Pc3FyMHj3a4TGjRo1CXl4eBEHw9+GSDA4fPoytW7di+fLlDrfn5uZi5MiR0Gq10m2jRo3C2bNnUVZW5u/DpHY4c+YMLl68iMmTJzvc/s4772D27NnIzc3FNddcg06dOkn3jRo1CjqdDsePH/f34VI7JCcn47///S8uXLgAs9mMrVu3Ijw8HP379+d7WuF+/vlnhIWF4d///jeuvfZah/vaOrf5+fmoq6tz+P0dHx+PgQMH4vDhw357DtQ2V+f5nnvuwRNPPOFwm1qtRmNjI3Q6HfLy8gBYz72oV69eSE9P7zDnmcEbdTjx8fG48cYbER4eLt22Z88enDt3DmPGjEFxcTEyMjIcHpOWloaGhgZUVlb6+3CpnWpqarBw4UIsXrwYnTt3drivtXMNAJcvX/bbMVL7nTlzBgBQX1+Pe++9F6NHj8Zvf/tb7Nu3DwDPdSh56qmnEBYWhptuugmDBw/GypUrsWrVKnTv3p3nWeHGjx+P1atXo1u3bi3ua+vcFhcXA0CLz/m0tDTpPgoOrs7zwIED0b9/f+nvjY2N2LBhAwYNGoSkpCSUlJQgMTERERERDo/rSOeZwRt1eN999x2efPJJTJw4EWPHjoVer3cI7ABIf2fdvPI888wzyMrKapGRAeD0XIu/EAwGg1+Oj+Sh0+kAAE888QQmTZqEdevW4frrr8ecOXNw4MABnusQUlhYiLi4OKxZswZbt27F1KlTsWDBAhw/fpznOYS1dW4bGhoAwOk2PPfKZDKZsHDhQpw8eRJLliwBADQ0NLQ4x0DHOs/atjchCl179+7FggULMGzYMOTk5ACwfgA0D9LEv0dFRfn9GMl7O3fuRG5uLnbt2uX0/sjIyBbnWvzwj46O9vnxkXzCwsIAAPfeey+mTJkCABgwYACOHTuG9evX81yHiMuXL+Oxxx7Dhg0bMGLECADA4MGDUVhYiNWrV/M8h7C2zm1kZCQA6+9r8c/iNvzdrTw6nQ6PPPIIvv32W7z66qsYMmQIAOevA6BjnWdm3qjDeu+99zB37lyMGzcOb7zxhvQNXufOnVFaWuqwbWlpKaKjoxEXFxeIQyUvbdu2DeXl5Rg7diyysrKQlZUFAFiyZAlmzZqFjIwMp+caANLT0/1+vOQ98Xz169fP4farr74aFy5c4LkOET/88AMaGxsd1iwDwLXXXotz587xPIewts6tWC7pbBuee2UpLS2VRoC88847uPHGG6X7MjIyUFVV1SKA60jnmcEbdUhiK+EZM2bgpZdeckjBjxgxAt9++63D9gcPHsSwYcOgVvMtoyQ5OTnYvXs3du7cKf0HAPPmzcPSpUuRnZ2NvLw8h1lfBw8eRK9evZCcnBygoyZvXHPNNYiJicEPP/zgcPuJEyfQvXt3ZGdn49ixY1J5JWA91zExMQ7rKyi4iWueCgoKHG4/ceIEevbsyfd0CGvr3Pbv3x+xsbE4dOiQdH9NTQ2OHTuG7OzsQBwyeaG6uhp/+MMfUFFRgY0bN7Y4d8OHD4fFYpEalwDWNc8lJSUd5jzzSpQ6nDNnzuD555/HzTffjNmzZ6OsrAxXrlzBlStXUFtbi7vuugtHjx5FTk4OTp06hXXr1uGTTz7BrFmzAn3o5KH09HT06NHD4T/A2q0uPT0d06ZNg06nw1NPPYXCwkJs374dGzZswOzZswN85OSpyMhIzJo1C2vWrMF//vMfnD9/Hq+//jr279+Pu+++GxMmTEBqaioeeeQR5OfnY+/evXjppZdwzz33OF0/QcFpyJAhGD58OJ544gkcPHgQZ8+excsvv4wDBw7g/vvv53s6hLV1bsPDwzFz5kzk5OTg888/R35+PubPn4+MjAxMnDgxwEdP7lq2bBmKiorw4osvIikpSbo+u3LlCsxmM9LT03HLLbdg8eLFOHToEI4ePYpHH30UI0eOxNChQwN9+H6hEtj7nDqYN954AytXrnR635QpU7B8+XJ89dVXePHFF3H27Fl07doVc+fOxa9//Ws/Hyn5QmZmJpYtW4apU6cCAI4ePYqlS5fi2LFjSE1NxT333IOZM2cG+CjJW+vXr8d7772HkpIS9OnTB3PnzsWECRMAAOfOncOzzz6L3NxcdOrUCbfffjvmzp3LjLrCVFdX4+WXX8YXX3yB6upq9OvXT7p4A/ieDhWLFi3CxYsX8c9//lO6ra1zazab8dJLL2H79u3Q6/XIzs7G008/ja5duwbiKZAb7M+z2WxGVlZWq41HPv/8c3Tt2hX19fV4/vnnsWfPHgDADTfcgMWLFyMxMdGfhx4wDN6IiIiIiIgUgF83EhERERERKQCDNyIiIiIiIgVg8EZERERERKQADN6IiIiIiIgUgMEbERERERGRAjB4IyIiIiIiUgAGb0RERERERArA4I2IiPxq0aJFGD9+fKv3jx8/HosWLfLjEcnDYrFg7NixyMzMxE8//RSw41i3bh0WLFiA06dPY8iQIZg+fTqcjXS1WCz43e9+h+uuuw4lJSUBONImH3zwAe6///6AHgMRkRIweCMiIpLB/v37UVZWht69e2PLli0BOYZTp07hzTffxOOPP47evXtj7ty5+O6777Bp06YW27733ns4cuQInn76aaSnpwfgaJtMmzYNV65cwQcffBDQ4yAiCnYM3oiIiGSwfft2ZGVlYcqUKfjoo4+g0+n8fgwvvvgiJk2aJAVj99xzDwYPHowVK1bg8uXL0nYXLlzAypUr8etf/xq33HKL34+zOZVKhdmzZ+Oll16CXq8P9OEQEQUtBm9ERBTUzGYzNm7ciMmTJ2PIkCEYO3YscnJyYDAYpG3uuusu3HXXXQ6PO3ToEDIzM3Ho0CEA1jLBlStXYvz48Rg0aBDGjx+PFStWoLGxUXqMwWDACy+8gBtvvBGDBg3C5MmTsXv37jaPsbq6Gnv37sW4ceMwadIkNDQ04MMPP2yxnU6nw9NPP43Ro0cjKysL8+fPx4YNG5CZmemw3d69ezF16lQMHjwY119/Pf72t7+hvr7e5TGcOHECX3zxBSZNmiTdptFosGzZMhiNRjzzzDPS7UuWLEFMTAyWLFki3fb+++/jlltuwaBBgzB27FisXr0aZrPZ4We8//77mDp1KoYOHYohQ4bgtttuw8cffyzdv337dgwcOBDvv/8+rr/+eowcORKFhYU4f/48HnjgAVx33XW49tprcccdd+DLL7902Pe4ceNgMBiwbds2l8+TiKgj0wb6AIiIqGMymUxubff000/jww8/xH333YcRI0bg2LFjWLNmDY4fP463334bKpXKrf289dZb2Lx5M5544gl069YNP/zwA1auXImwsDDMmzcPgiDgoYcewnfffYd58+ahT58++OyzzzB//nwYjUb85je/aXXfu3btgtlsxuTJk5GamopRo0Zh69atmDFjhsN2c+bMwfHjxzF//nxcddVV2LRpE1asWNFiXwsWLMDkyZPxyCOP4OLFi1i5ciUKCwuxfv36Vp/vrl27kJqaiqFDhzrc3rdvXzz88MNYuXIl9u3bB71ej6+//hpr165FQkICAODNN9/EypUrMXPmTDz55JM4fvw4Vq9ejcuXL+P5558HAGzcuBF/+9vfMHfuXAwfPhzV1dV46623sGDBAmRlZSEjIwOANdhet24dli5disrKSvTq1QuTJk1CWloaXnjhBWi1Wrz77rt48MEH8fHHH6NHjx4AgIiICIwbNw67du1q8e9GRERWDN6IiMjvLl68iGuuuabN7QoLC/HBBx/gsccekxpaXH/99UhLS8PChQvx1Vdf4cYbb3TrZ3777bcYNGgQpk2bBgAYOXIkoqKiEBcXBwD45ptv8L///U8qJwSAMWPGoKGhATk5OZg0aRK0Wue/Nrdv344bbrgBqampAICpU6fi8ccfx3fffYdhw4YBAA4cOIBDhw5h9erVmDhxIgDghhtuwKRJk3Dq1CkAgCAIyMnJwZgxY5CTkyPtv2fPnvjjH/+IL7/8EmPHjnV6DAcPHsTgwYOdBnezZs3Cp59+imXLlkGv1+OOO+6Q/t1qa2vx2muv4Y477sDixYsBAL/85S+RkJCAxYsX4+6770bfvn1RVFSEe++9F3PmzJH226VLF0ydOhV5eXkO5ZcPPPCAdJxXrlzB6dOnMWfOHOlnDhkyBK+++iqMRqPDcQ4ePBi7d++GTqdDbGys0+dJRNSRsWySiIj8LjU1FR988IHT/8QACLAGXABarMu65ZZboNFopJJId1x33XXYv38/7rzzTrz99tsoLCzEzJkzcdtttwGwBlcqlQo33ngjTCaT9N/48eNx5coVnDx50ul+8/Pz8fPPP2PixImoqalBTU0NRo0ahejoaGzdulXa7uDBgwgLC8OECROk29RqtRQoAsDp06dRXFyM8ePHOxxDdnY2YmNjsX///lafX1FREbp27er0Pq1Wi2XLluHy5csIDw/HE088Id135MgR6PX6Fj9T7Agq/sxFixZhwYIFqKmpwffff48PP/wQGzduBIAWQdiAAQOkP6ekpODqq6/GX/7yFzzxxBPYtWsXLBYLnnzySfTt29fhcV26dIHZbEZxcXGrz5OIqCNj5o2IiPwuPDwcgwcPbvU+UXV1NQA4BHSANRhJTExEbW2t2z9z1qxZiImJwbZt25CTk4MXX3wRffv2xeLFizFq1ChUVVVBEAQpU9ZcaWmpQ1AiEjskPvnkk3jyyScd7vv444/x5z//GZ06dUJlZSUSEhKgVjt+b5qcnCz9uaqqCgDw7LPP4tlnn3V6DK3R6XSIiopq9f7MzEykpaUhOzsbMTExLX5ma636xZ95/vx5PP300zhw4ADCwsLQu3dv9O/fHwBajCKIjo6W/qxSqbBu3Tq8/vrr+Oyzz7Bz504piH322WfRqVOnFo/z5LwSEXUkDN6IiChoiRf2V65cQZcuXaTbGxsbUVlZicTEROm25s01mjf4UKvVmDFjBmbMmIHy8nJ8+eWXeOONNzB37lzs378fcXFxiI6Oxrvvvuv0WMS1WfaMRiN27dqFiRMnYubMmQ73XbhwAX/+85+xY8cO/PGPf0R6ejoqKythsVgcArjy8nLpz/Hx8QCAhQsXYuTIka3+eziTkJDgVdAj/sycnBz07Nmzxf0pKSmwWCy4//77ERYWhg8++AADBgyAVqtFYWGh08YszaWnp+OZZ57BkiVLkJ+fj08++QRvvfUWEhMTHZqmiMG6/XklIqImLJskIqKgJQYwH330kcPtH330EcxmM4YPHw4AiI2NbVFql5eX5/D33/3ud/jb3/4GwJrtmjp1KmbMmIGamhrodDqMHDkS9fX1EAQBgwcPlv47ceIE1qxZ47TByr59+1BVVSUNu7b/b9q0aejZs6dUOjly5EiYTCbs27dPerwgCNi7d6/09969eyM5ORkXLlxwOIb09HSsWLECx44da/XfqkuXLg7jANx17bXXIiwsDCUlJQ4/U6vV4qWXXsKFCxdQWVmJM2fO4Pbbb5fuA4CvvvoKgLWTZ2uOHDmCX/ziFzh69ChUKhUGDBiA+fPno1+/frh06ZLDtiUlJdBoNAGfO0dEFKyYeSMioqB19dVXY8qUKVi1ahUaGhqQnZ2N48eP49VXX8V1112HMWPGALC2md+3bx+WLVuG8ePHIzc3Fzt37nTYV3Z2NtatW4eUlBRkZWWhpKQE69evx8iRI5GUlIQbb7wR2dnZmDNnDubMmYM+ffrg6NGjWLVqFcaMGYOkpKQWx7dt2zYkJydj1KhRTo//1ltvxapVq3Do0CFcd911uP766/HUU0+hrKwMV111FT744AMUFBRITUY0Gg3mz5+Pp59+GhqNBuPGjUNNTQ1ee+01lJSUuGzycv3112PTpk0QBMHtDpyANcs1a9YsvPLKK9DpdLjuuutQUlKCV155BSqVCv3790dcXBy6dOmCjRs3IiMjA/Hx8fjf//4nZSkbGhpa3f/AgQMRGRmJhQsXYu7cuUhJScE333yD48eP4/e//73Dtnl5eRgxYoTL8k8ioo6MwRsREQW1pUuXokePHti2bRveeustpKWl4fe//z3mzJkjlR9OmzYN58+fx44dO7BlyxZkZ2dj1apVmD59urSfP/3pTwgPD8e2bduwZs0axMXFYfz48XjssccAWMsq165di1deeQVvvvkmysvLkZ6ejrvvvhsPPfRQi+MqKSnB/v378bvf/Q4ajcbpsd92221YvXo1tmzZguuuuw4rV67E8uXLsWLFCphMJtx0002YPn26Q6D529/+FjExMXj77bexdetWREdHY9iwYcjJyUG3bt1a/XeaOHEi1qxZg6NHj+Laa6/16N/4kUceQWpqKjZt2oS3334bnTp1wujRo/Hoo49K3Thfe+01LF26FIsWLUJ4eDiuvvpqvP7663j++eeRm5vbYs6eKCIiAuvWrcOKFSuwdOlS1NTUoGfPnnjuuecwdepUaTuDwYBDhw7hkUce8ejYiYg6EpXQfJUxERERye7ixYv4/vvvcdNNNyEyMlK6fd68eSgqKsKOHTva/TMeeOABJCYmYtmyZe3el7/t3LkTOTk52Lt3r8O/DxERNeGaNyIiIj9Qq9VYtGgRFi1ahK+//hoHDhzACy+8gE8//bRF+aC35s+fj08//bTFWrJgZ7FYsG7dOjz88MMM3IiIXGDmjYiIyE8OHjyINWvW4Pjx4zCZTOjTpw/uvvtuTJo0SbafsXbtWuTn5+Oll16SbZ++9v777+OTTz7BO++8E+hDISIKagzeiIiIiIiIFIBlk0RERERERArA4I2IiIiI33lFyAAAAE1JREFUiEgBGLwREREREREpAIM3IiIiIiIiBWDwRkREREREpAAM3oiIiIiIiBSAwRsREREREZECMHgjIiIiIiJSAAZvRERERERECvD/AMWwdF9DNRrcAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1583,12 +2351,12 @@
    ],
    "source": [
     "# Line plot for house age vs. average house price\n",
-    "average_price_by_age = housing_data_encoded.groupby('house_age')['price'].mean()\n",
+    "average_price_by_age = housing_data.groupby('house_age')['price'].mean()\n",
     "plt.figure(figsize=(10, 6))\n",
     "plt.plot(average_price_by_age.index, average_price_by_age.values, marker='o', linestyle='-')\n",
     "plt.xlabel('House Age (Years)')\n",
-    "plt.ylabel('Average House Price ($)')\n",
-    "plt.title('Average House Price by House Age')\n",
+    "plt.ylabel('Average House Price')\n",
+    "plt.title('Average House Price by House Age.')\n",
     "plt.grid(True)\n",
     "plt.show()\n"
    ]
@@ -1597,76 +2365,117 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Interpretation:\n"
+    "The scatter plot depicts the relationship between house age and prices; and reveals a lack of a clear linear trend. Instead, prices exhibit significant variation across different house ages. While the graph provides insights into how house prices fluctuate based on their age, no discernible pattern emerges. \n",
+    "In summary, the graph shows how house prices fluctuate based on their age, but no specific pattern emerges. This suggests that other factors beyond house age play a more influential role in determining house prices."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### c.) Multivariate Analysis"
+    "**#Prices of houses in relation to their respective Condition and Grade**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 537,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "price             1.000000\n",
-      "bedrooms          0.308787\n",
-      "bathrooms         0.525906\n",
-      "sqft_living       0.701917\n",
-      "sqft_lot          0.089876\n",
-      "floors            0.256804\n",
-      "sqft_above        0.605368\n",
-      "sqft_basement     0.321108\n",
-      "yr_built          0.053953\n",
-      "yr_renovated      0.117855\n",
-      "zipcode          -0.053402\n",
-      "lat               0.306692\n",
-      "long              0.022036\n",
-      "sqft_living15     0.585241\n",
-      "sqft_lot15        0.082845\n",
-      "house_age        -0.053953\n",
-      "renovation_age    0.082779\n",
-      "total_sqft        0.119913\n",
-      "Name: price, dtype: float64\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "# Calculate the correlation Matrix\n",
-    "# Drop categorical variables\n",
-    "numeric_data = housing_data_encoded.select_dtypes(include='number')\n",
-    "correlation = numeric_data.corr()['price'].drop(['id'])  # Drop 'price' and 'id' columns from correlation calculation\n",
-    "print(correlation)\n"
+    "# Create subplots with a shared y-axis\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6), sharey=True)\n",
+    "\n",
+    "# Bar plot for condition vs. price\n",
+    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], hue=\"condition\", palette='viridis', legend=False)\n",
+    "axes[0].set_title('Condition vs. Price')\n",
+    "axes[0].set_xlabel('Condition')\n",
+    "axes[0].set_ylabel('Price')\n",
+    "axes[0].grid(True)\n",
+    "\n",
+    "# Bar plot for grade vs. price\n",
+    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], hue=\"grade\", palette='viridis', legend=False)\n",
+    "axes[1].set_title('Grade vs. Price')\n",
+    "axes[1].set_xlabel('Grade')\n",
+    "axes[1].grid(True)\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show()\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 72,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "\n",
-    "# # Plot the correlation matrix as a heatmap\n",
-    "# plt.figure(figsize=(12, 8))\n",
-    "# sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
-    "# plt.title('Correlation Matrix')\n",
-    "# plt.show()\n"
+    "The visualization presents comparisons between house prices and their condition and grade ratings. On the left, the condition of houses, rated from 1 to 5, shows relatively consistent prices across different condition levels, with no significant price increase observed for better conditions. Conversely, on the right, the grade of houses, ranging from 1 to 11, demonstrates a clear positive correlation with prices, indicating that higher-grade properties command higher prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c.) Multivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **#Correlation matrix**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 538,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exclude the 'id', date and 'price_range' column from the correlation matrix\n",
+    "correlation_matrix = housing_data.drop(columns=['id', 'price_range', 'date']).corr()\n",
+    "\n",
+    "# Set up the matplotlib figure\n",
+    "plt.figure(figsize=(20, 20))\n",
+    "\n",
+    "# Create a heatmap using seaborn\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
+    "\n",
+    "# Add a title\n",
+    "plt.title('Correlation Matrix')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "housing_data_encoded['v']"
+    "Price has a moderate positive correlation with sqft living (0.70), grade (0.67), sqft above (0.61). This means that as the values of these features increase, the price of the house also tends to increase."
    ]
   }
  ],

From 9ecfc7070db07c631f7d799324bc3d30e52a79b1 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Tue, 30 Apr 2024 07:16:47 +0300
Subject: [PATCH 10/27] EDA Finish up

---
 .vscode/settings.json | 3 +++
 student.ipynb         | 2 +-
 2 files changed, 4 insertions(+), 1 deletion(-)
 create mode 100644 .vscode/settings.json

diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 00000000..642ff51b
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.REPL.enableREPLSmartSend": false
+}
\ No newline at end of file
diff --git a/student.ipynb b/student.ipynb
index d9a7fa37..0b1d7cba 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -791,7 +791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

From 789795ca553a821a8fd3d808f593a7dfdccbab7b Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Tue, 30 Apr 2024 10:57:16 +0300
Subject: [PATCH 11/27] Statistical analysis done

---
 student.ipynb | 1954 ++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 1532 insertions(+), 422 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 3b8c5a8b..29134053 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 509,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 510,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +497,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 510,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 511,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +684,7 @@
        "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
       ]
      },
-     "execution_count": 511,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 512,
+   "execution_count": 212,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 513,
+   "execution_count": 213,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 514,
+   "execution_count": 214,
    "metadata": {},
    "outputs": [
     {
@@ -850,133 +850,133 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>943</th>\n",
-       "      <td>8712100575</td>\n",
-       "      <td>8/28/2014</td>\n",
-       "      <td>915000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2750</td>\n",
-       "      <td>5589</td>\n",
-       "      <td>1.5</td>\n",
+       "      <th>16132</th>\n",
+       "      <td>3298400470</td>\n",
+       "      <td>2/4/2015</td>\n",
+       "      <td>437400.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>8925</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>9 Better</td>\n",
-       "      <td>1840</td>\n",
-       "      <td>910.0</td>\n",
-       "      <td>1910</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>2150</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>4250</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1100</td>\n",
+       "      <td>7875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1346</th>\n",
-       "      <td>217500135</td>\n",
-       "      <td>4/21/2015</td>\n",
-       "      <td>450000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2040</td>\n",
-       "      <td>9565</td>\n",
+       "      <th>14888</th>\n",
+       "      <td>1842300050</td>\n",
+       "      <td>7/2/2014</td>\n",
+       "      <td>600000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2190</td>\n",
+       "      <td>9072</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>1400</td>\n",
-       "      <td>640.0</td>\n",
-       "      <td>1959</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1110</td>\n",
+       "      <td>?</td>\n",
+       "      <td>1965</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1890</td>\n",
-       "      <td>8580</td>\n",
+       "      <td>1660</td>\n",
+       "      <td>8327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21296</th>\n",
-       "      <td>7708200670</td>\n",
-       "      <td>7/23/2014</td>\n",
-       "      <td>490000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2510</td>\n",
-       "      <td>4349</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2510</td>\n",
+       "      <th>4485</th>\n",
+       "      <td>2397101270</td>\n",
+       "      <td>1/26/2015</td>\n",
+       "      <td>716000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1420</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
+       "      <td>1904</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2510</td>\n",
-       "      <td>4314</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>3600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5896</th>\n",
-       "      <td>3303980500</td>\n",
-       "      <td>9/5/2014</td>\n",
-       "      <td>1030000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>3780</td>\n",
-       "      <td>11200</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>9132</th>\n",
+       "      <td>1523059100</td>\n",
+       "      <td>9/2/2014</td>\n",
+       "      <td>320000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1740</td>\n",
+       "      <td>27350</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>11 Excellent</td>\n",
-       "      <td>3780</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>5 Fair</td>\n",
+       "      <td>1740</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2002</td>\n",
+       "      <td>1958</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3720</td>\n",
-       "      <td>11813</td>\n",
+       "      <td>2760</td>\n",
+       "      <td>10749</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5809</th>\n",
-       "      <td>1922069071</td>\n",
-       "      <td>4/24/2015</td>\n",
-       "      <td>411000.0</td>\n",
-       "      <td>4</td>\n",
+       "      <th>16234</th>\n",
+       "      <td>7922900460</td>\n",
+       "      <td>12/5/2014</td>\n",
+       "      <td>660000.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>1.75</td>\n",
-       "      <td>2250</td>\n",
-       "      <td>292288</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>9032</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>NO</td>\n",
        "      <td>Good</td>\n",
        "      <td>7 Average</td>\n",
-       "      <td>2250</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>?</td>\n",
        "      <td>1963</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1550</td>\n",
-       "      <td>23798</td>\n",
+       "      <td>2350</td>\n",
+       "      <td>8937</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "943    8712100575  8/28/2014   915000.0         5       2.50         2750   \n",
-       "1346    217500135  4/21/2015   450000.0         4       2.25         2040   \n",
-       "21296  7708200670  7/23/2014   490000.0         4       2.50         2510   \n",
-       "5896   3303980500   9/5/2014  1030000.0         4       3.25         3780   \n",
-       "5809   1922069071  4/24/2015   411000.0         4       1.75         2250   \n",
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "16132  3298400470   2/4/2015  437400.0         3       1.75         2150   \n",
+       "14888  1842300050   7/2/2014  600000.0         5       2.00         2190   \n",
+       "4485   2397101270  1/26/2015  716000.0         3       2.00         1420   \n",
+       "9132   1523059100   9/2/2014  320000.0         5       1.00         1740   \n",
+       "16234  7922900460  12/5/2014  660000.0         3       1.75         2030   \n",
        "\n",
-       "       sqft_lot  floors waterfront  condition         grade  sqft_above  \\\n",
-       "943        5589     1.5         NO  Very Good      9 Better        1840   \n",
-       "1346       9565     1.0         NO    Average        8 Good        1400   \n",
-       "21296      4349     2.0        NaN    Average        8 Good        2510   \n",
-       "5896      11200     2.0         NO    Average  11 Excellent        3780   \n",
-       "5809     292288     1.0         NO       Good     7 Average        2250   \n",
+       "       sqft_lot  floors waterfront  condition      grade  sqft_above  \\\n",
+       "16132      8925     1.0         NO       Good  7 Average        2150   \n",
+       "14888      9072     1.0        NaN  Very Good  7 Average        1110   \n",
+       "4485       3600     1.5         NO       Good  7 Average        1420   \n",
+       "9132      27350     1.0         NO       Good     5 Fair        1740   \n",
+       "16234      9032     2.0         NO       Good  7 Average        2030   \n",
        "\n",
        "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "943           910.0      1910           0.0           1460        4250  \n",
-       "1346          640.0      1959           0.0           1890        8580  \n",
-       "21296           0.0      2010           0.0           2510        4314  \n",
-       "5896            0.0      2002           0.0           3720       11813  \n",
-       "5809            0.0      1963           0.0           1550       23798  "
+       "16132           0.0      1960           0.0           1100        7875  \n",
+       "14888             ?      1965           0.0           1660        8327  \n",
+       "4485            0.0      1904           0.0           1250        3600  \n",
+       "9132            0.0      1958           0.0           2760       10749  \n",
+       "16234             ?      1963           0.0           2350        8937  "
       ]
      },
-     "execution_count": 514,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 515,
+   "execution_count": 215,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 516,
+   "execution_count": 216,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 517,
+   "execution_count": 217,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 518,
+   "execution_count": 218,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 519,
+   "execution_count": 219,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 520,
+   "execution_count": 220,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 521,
+   "execution_count": 221,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 522,
+   "execution_count": 222,
    "metadata": {},
    "outputs": [
     {
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 523,
+   "execution_count": 223,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 524,
+   "execution_count": 224,
    "metadata": {},
    "outputs": [
     {
@@ -1263,104 +1263,104 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>14733</th>\n",
-       "      <td>2525059077</td>\n",
-       "      <td>2014-05-20</td>\n",
-       "      <td>765000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2560</td>\n",
-       "      <td>12100</td>\n",
+       "      <th>9602</th>\n",
+       "      <td>2917200085</td>\n",
+       "      <td>2015-04-08</td>\n",
+       "      <td>350000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>5395</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1760</td>\n",
-       "      <td>800.0</td>\n",
-       "      <td>1976</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>860</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>1940</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2240</td>\n",
-       "      <td>12100</td>\n",
+       "      <td>1664</td>\n",
+       "      <td>5363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5536</th>\n",
-       "      <td>2248000080</td>\n",
-       "      <td>2014-05-21</td>\n",
-       "      <td>385500.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>7947</td>\n",
+       "      <th>11877</th>\n",
+       "      <td>2025049114</td>\n",
+       "      <td>2014-05-29</td>\n",
+       "      <td>402000.0</td>\n",
+       "      <td>2</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>710</td>\n",
+       "      <td>1173</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1120</td>\n",
-       "      <td>420.0</td>\n",
-       "      <td>1961</td>\n",
+       "      <td>710</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1910</td>\n",
-       "      <td>7950</td>\n",
+       "      <td>1943</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1370</td>\n",
+       "      <td>1173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10495</th>\n",
-       "      <td>9290850060</td>\n",
-       "      <td>2014-10-22</td>\n",
-       "      <td>910000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3170</td>\n",
-       "      <td>32430</td>\n",
-       "      <td>2.5</td>\n",
+       "      <th>14592</th>\n",
+       "      <td>968000120</td>\n",
+       "      <td>2014-11-12</td>\n",
+       "      <td>395000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1470</td>\n",
+       "      <td>10125</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3170</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1470</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1989</td>\n",
+       "      <td>1962</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3360</td>\n",
-       "      <td>35610</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>10125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15676</th>\n",
-       "      <td>5422560660</td>\n",
-       "      <td>2014-10-30</td>\n",
-       "      <td>407000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>6635</td>\n",
+       "      <th>1990</th>\n",
+       "      <td>8562890370</td>\n",
+       "      <td>2015-04-14</td>\n",
+       "      <td>399950.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3110</td>\n",
+       "      <td>5868</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>1700</td>\n",
+       "      <td>3110</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1976</td>\n",
+       "      <td>2001</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>6635</td>\n",
+       "      <td>2950</td>\n",
+       "      <td>5924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14699</th>\n",
-       "      <td>714000210</td>\n",
-       "      <td>2014-09-03</td>\n",
-       "      <td>998000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3030</td>\n",
-       "      <td>6820</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>17687</th>\n",
+       "      <td>3575302562</td>\n",
+       "      <td>2014-11-13</td>\n",
+       "      <td>356000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2530</td>\n",
-       "      <td>500.0</td>\n",
-       "      <td>1947</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>2070</td>\n",
-       "      <td>6820</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1976</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1380</td>\n",
+       "      <td>7500</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1368,28 +1368,28 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "14733  2525059077 2014-05-20  765000.0         4       2.25         2560   \n",
-       "5536   2248000080 2014-05-21  385500.0         3       2.00         1540   \n",
-       "10495  9290850060 2014-10-22  910000.0         4       2.50         3170   \n",
-       "15676  5422560660 2014-10-30  407000.0         2       2.50         1700   \n",
-       "14699   714000210 2014-09-03  998000.0         4       2.50         3030   \n",
+       "9602   2917200085 2015-04-08  350000.0         2        1.0         1160   \n",
+       "11877  2025049114 2014-05-29  402000.0         2        1.0          710   \n",
+       "14592   968000120 2014-11-12  395000.0         3        2.0         1470   \n",
+       "1990   8562890370 2015-04-14  399950.0         4        2.5         3110   \n",
+       "17687  3575302562 2014-11-13  356000.0         3        1.5         1140   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "14733     12100     1.0           0        4.0    6.0        1760   \n",
-       "5536       7947     1.0           0        3.0    5.0        1120   \n",
-       "10495     32430     2.5           0        3.0    8.0        3170   \n",
-       "15676      6635     2.0           0        4.0    6.0        1700   \n",
-       "14699      6820     2.0           0        3.0    7.0        2530   \n",
+       "9602       5395     1.0           0        3.0    5.0         860   \n",
+       "11877      1173     2.0           0        4.0    5.0         710   \n",
+       "14592     10125     1.0           0        4.0    5.0        1470   \n",
+       "1990       5868     2.0           0        3.0    6.0        3110   \n",
+       "17687      7500     1.0           0        3.0    5.0        1140   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "14733          800.0      1976           0.0           2240       12100  \n",
-       "5536           420.0      1961           0.0           1910        7950  \n",
-       "10495            0.0      1989           0.0           3360       35610  \n",
-       "15676            0.0      1976           0.0           1700        6635  \n",
-       "14699          500.0      1947        2000.0           2070        6820  "
+       "9602           300.0      1940           0.0           1664        5363  \n",
+       "11877            0.0      1943           0.0           1370        1173  \n",
+       "14592            0.0      1962           0.0           1440       10125  \n",
+       "1990             0.0      2001           0.0           2950        5924  \n",
+       "17687            0.0      1976           0.0           1380        7500  "
       ]
      },
-     "execution_count": 524,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1420,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 525,
+   "execution_count": 225,
    "metadata": {},
    "outputs": [
     {
@@ -1493,7 +1493,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 525,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1538,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 526,
+   "execution_count": 226,
    "metadata": {},
    "outputs": [
     {
@@ -1586,119 +1586,119 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>19131</th>\n",
-       "      <td>6713700155</td>\n",
-       "      <td>2014-08-18</td>\n",
-       "      <td>352500.0</td>\n",
+       "      <th>3529</th>\n",
+       "      <td>2221000100</td>\n",
+       "      <td>2014-05-07</td>\n",
+       "      <td>310000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1470</td>\n",
-       "      <td>8400</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1840</td>\n",
+       "      <td>10723</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1470</td>\n",
+       "      <td>1220</td>\n",
+       "      <td>620.0</td>\n",
+       "      <td>1974</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1953</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1470</td>\n",
-       "      <td>8400</td>\n",
-       "      <td>71</td>\n",
+       "      <td>1590</td>\n",
+       "      <td>9820</td>\n",
+       "      <td>50</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11340.0</td>\n",
+       "      <td>14403.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5032</th>\n",
-       "      <td>2558660190</td>\n",
-       "      <td>2014-10-30</td>\n",
-       "      <td>459000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1730</td>\n",
-       "      <td>7807</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>20241</th>\n",
+       "      <td>8924100372</td>\n",
+       "      <td>2015-04-23</td>\n",
+       "      <td>1300000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>3590</td>\n",
+       "      <td>5334</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>1976</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3140</td>\n",
+       "      <td>450.0</td>\n",
+       "      <td>2006</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>7650</td>\n",
-       "      <td>48</td>\n",
+       "      <td>2100</td>\n",
+       "      <td>6250</td>\n",
+       "      <td>18</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11267.0</td>\n",
+       "      <td>12514.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7573</th>\n",
-       "      <td>7922720040</td>\n",
-       "      <td>2015-03-17</td>\n",
-       "      <td>680000.0</td>\n",
-       "      <td>4</td>\n",
+       "      <th>19223</th>\n",
+       "      <td>3832080610</td>\n",
+       "      <td>2015-04-06</td>\n",
+       "      <td>270000.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>2.50</td>\n",
-       "      <td>2880</td>\n",
-       "      <td>9202</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1780</td>\n",
+       "      <td>5015</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>1780</td>\n",
-       "      <td>1100.0</td>\n",
-       "      <td>1977</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>10265</td>\n",
-       "      <td>47</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>5250</td>\n",
+       "      <td>14</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>14962.0</td>\n",
+       "      <td>8575.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12237</th>\n",
-       "      <td>7560000070</td>\n",
-       "      <td>2014-06-10</td>\n",
-       "      <td>710000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>2440</td>\n",
-       "      <td>3427</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>18603</th>\n",
+       "      <td>9297300750</td>\n",
+       "      <td>2014-11-05</td>\n",
+       "      <td>355000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>4600</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>2000</td>\n",
+       "      <td>850</td>\n",
+       "      <td>910.0</td>\n",
+       "      <td>1926</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2440</td>\n",
-       "      <td>2601</td>\n",
-       "      <td>24</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>4800</td>\n",
+       "      <td>98</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8307.0</td>\n",
+       "      <td>8120.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6502</th>\n",
-       "      <td>4217402115</td>\n",
-       "      <td>2015-04-21</td>\n",
-       "      <td>3650000.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4.75</td>\n",
-       "      <td>5480</td>\n",
-       "      <td>19401</td>\n",
+       "      <th>4980</th>\n",
+       "      <td>9547201155</td>\n",
+       "      <td>2014-10-16</td>\n",
+       "      <td>567500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>3060</td>\n",
        "      <td>1.5</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>3910</td>\n",
-       "      <td>1570.0</td>\n",
-       "      <td>1936</td>\n",
+       "      <td>1440</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3510</td>\n",
-       "      <td>15810</td>\n",
-       "      <td>88</td>\n",
+       "      <td>1910</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>3570</td>\n",
+       "      <td>114</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>30361.0</td>\n",
+       "      <td>5940.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1706,35 +1706,35 @@
       ],
       "text/plain": [
        "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "19131  6713700155 2014-08-18   352500.0         3       1.00         1470   \n",
-       "5032   2558660190 2014-10-30   459000.0         3       1.75         1730   \n",
-       "7573   7922720040 2015-03-17   680000.0         4       2.50         2880   \n",
-       "12237  7560000070 2014-06-10   710000.0         3       3.50         2440   \n",
-       "6502   4217402115 2015-04-21  3650000.0         6       4.75         5480   \n",
+       "3529   2221000100 2014-05-07   310000.0         3       1.75         1840   \n",
+       "20241  8924100372 2015-04-23  1300000.0         4       3.50         3590   \n",
+       "19223  3832080610 2015-04-06   270000.0         3       2.50         1780   \n",
+       "18603  9297300750 2014-11-05   355000.0         2       1.75         1760   \n",
+       "4980   9547201155 2014-10-16   567500.0         3       1.00         1440   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "19131      8400     1.0           0        4.0    5.0        1470   \n",
-       "5032       7807     1.0           0        3.0    5.0        1260   \n",
-       "7573       9202     1.0           0        3.0    6.0        1780   \n",
-       "12237      3427     2.0           0        3.0    5.0        1990   \n",
-       "6502      19401     1.5           1        5.0    9.0        3910   \n",
+       "3529      10723     1.0           0        4.0    5.0        1220   \n",
+       "20241      5334     2.0           0        3.0    7.0        3140   \n",
+       "19223      5015     2.0           0        3.0    5.0        1780   \n",
+       "18603      4600     1.0           0        4.0    5.0         850   \n",
+       "4980       3060     1.5           0        4.0    5.0        1440   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "19131            0.0      1953           0.0           1470        8400   \n",
-       "5032           470.0      1976           0.0           1800        7650   \n",
-       "7573          1100.0      1977           0.0           2500       10265   \n",
-       "12237          450.0      2000           0.0           2440        2601   \n",
-       "6502          1570.0      1936           0.0           3510       15810   \n",
+       "3529           620.0      1974           0.0           1590        9820   \n",
+       "20241          450.0      2006           0.0           2100        6250   \n",
+       "19223            0.0      2010           0.0           2010        5250   \n",
+       "18603          910.0      1926           0.0           1150        4800   \n",
+       "4980             0.0      1910           0.0           1440        3570   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  \n",
-       "19131         71             0.0     11340.0  \n",
-       "5032          48             0.0     11267.0  \n",
-       "7573          47             0.0     14962.0  \n",
-       "12237         24             0.0      8307.0  \n",
-       "6502          88             0.0     30361.0  "
+       "3529          50             0.0     14403.0  \n",
+       "20241         18             0.0     12514.0  \n",
+       "19223         14             0.0      8575.0  \n",
+       "18603         98             0.0      8120.0  \n",
+       "4980         114             0.0      5940.0  "
       ]
      },
-     "execution_count": 526,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 527,
+   "execution_count": 227,
    "metadata": {},
    "outputs": [
     {
@@ -1816,12 +1816,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 528,
+   "execution_count": 228,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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4s+Vdkut3Z+DAgXrjjTe0ceNG1wR7nTt31v33359mWHJuP3bsWP3vf//Tt99+6+q2/uijj6p169bpfvCybds2SdI999yT4ocTDzzwgCwWi3799dd0990TCxcu1P79+1P9eu6555Jts337dknS/fffn+JztmrVym1fvFGvXj1JSWPKJ06cqG3btslmsykwMFDdunXT3Xff7VrXea/u1Fph77rrLrf1PHH06FGtWbPG7WvTpk2y2+166KGHtHDhQnXr1i3FbdPqBn495/FJ6UOSatWq6dtvv9WwYcPcavd0H+vUqSOr1aovvvhCvXv31rJly1zDNRo3bqx27dol++ACAHIbWq4B5FnO8crOFuzUjB07Vv3799cff/yh6dOna/r06SpUqJCaNWum9u3bq1GjRh6/ZlhYWIZvG1W6dOkUlztbcNObZMsfnK1SqdUiSWXKlNHu3btd6zo5u1zfyBnk0mtZvXDhgis4L1y4UJGRkW6PO89jZGSk7rvvvmTbp3bMna9/4zjQX375RUuXLtUff/yhf//91xV2nc/hybjRDh066LffftPKlStVv359xcfH64svvlBYWJhatmzpWq979+46cOCA1qxZo48++kgfffSRAgMD1bBhQ9dYZovFkurrtG7dWr///rsWLFig5cuXa/ny5QoICFCdOnX04IMPqn379ml2CZeundvUbqcVEhKiIkWK6MyZMzp//rwhs4On9/vnrN3Z6uqNoUOH6tixY9q0aZPmzJmjOXPmKDQ0VE2bNtUjjzziNn/CiRMnJCnFDwKul9ZcADdq27ZtqrfiSk96f8OcUmudT0lG9/Gmm27ShAkTNGrUKG3YsME1EV6lSpXUsmVLderUyesu+wCQUxCuAeRZzpbI9Lp1lypVSsuXL9f27du1bt06/fzzzzpw4IBWr16t1atX65lnntHw4cM9ek1PuoHfKCgoKM3HPbnvcUqzYmeEJ4HS+Ro3jjH19R7Uq1evdvUySG3WZimpu/G///6bbFK2jLz+a6+9pkWLFslsNqty5cp68MEHVaFCBdWqVUubNm3Su+++69HztG7dWuPGjdPXX3+tV155RRs3btTFixf1+OOPu4Vdq9WqiRMnql+/flq7dq1+/vln7dq1Sz/++KN+/PFHLVmyRB9++GGa43aHDx+uLl266Ntvv9WPP/6oX375Rdu3b9f27dv10UcfafHixWnet92Tc+v8AMQf44e9kV6NGa0vpf8PYWFhmj9/vvbs2aPvvvtOmzdv1u+//661a9dq7dq1uv/++/XOO++4bd+iRQvlz58/1ddxztGQ2Tz9u3LjjP5p8WYfW7Vqpbvuukvff/+9Nm7cqK1bt+qvv/7SX3/9pQULFuiDDz5Q7dq1Pa4BAHIawjWAPGv9+vWSkmY/To/JZFKDBg3UoEEDSUmTeC1fvlxTpkzRBx98oK5du2Zal8cbW4KdnLfCcbZgO99gp9QSfONtuzLK2YU6rVsMHT16VJLSHT+dUc7bg82dO9fVFfVGffr00fr16xUZGenq1ppR27Zt06JFi3TTTTdp7ty5yYLR2rVrPX6u/Pnz6/7779fKlSv1888/6+uvv5aUvFu7U4UKFdSvXz/169dPMTExWr9+vV577TX98ssv+uqrr1wzpKemdOnS6tatm7p16yabzabNmzdrzJgxOnz4sBYvXqz+/funuq3z3KY2ljwqKkrnz5+XxWJJ1r0/q5QoUUKHDx/W8ePHValSpWSPO3/3ihYt6lrm/FAlpSB95cqVVF+rRo0aqlGjhl588UVduXJFX331lcaOHatvvvlGO3bsUL169VSiRAkdOXJETz31lEd/P7KL4sWL6/jx4zp58mSKw1MiIyNVokQJRUREeL2PYWFhevTRR/Xoo49Kkv744w9NnjxZP/30k6ZOnaoFCxb4aW8AIPthzDWAPOnLL7/U33//rVKlSql58+aprnfw4EE9/PDD6tmzp9vyokWLqnfv3goPD5fD4XB1zfa1lTYlP/74Y7Jl//33n/bs2aN8+fKpWrVqkuQaW3vu3Llk66c2LtvTeqtXr66QkBDt3bvXFWSu9++//2rv3r0KDQ1VjRo1PHpOT/zxxx/6888/VbRo0TTf4DvfyK9YscLrccHOVvH77rsvWbBOTEzUli1bJHnW0isldQ2XpK+++kobNmxwtYBf/5xdu3ZV06ZNXV3PpaRu2A8++KBrRvK0uhYPHDhQDRs2dPvQw2q1qlmzZq6J89Lrmuwct/3999+n2LL5zTffyOFwqF69el71vPAHZ43ffPNNio9/9dVXkuT68Eu69v/h+vtCO93YA+LChQtq165dsvkNwsLC1LFjR9fs4c5j6aznxnuAO02YMEGPPvqoli5dmuZ+ZbW6detKSrnuQ4cO6ZVXXtG0adMkZXwf582bpxYtWrjmFnCqVq2ahg4dKulaV3MAyK0I1wDynG+++UajRo2SJI0cOTLNrqTly5fX6dOn9dNPP7laH51+//13HTp0SKGhoa4w5uzCHR0d7dFttjyxcuVKffvtt66fo6KiNGzYMCUmJqpz586u13ROauSc/dnp5MmTevvtt1N8bue+p9WSJyUFvo4dO8put2vw4MFugeX8+fMaNGiQ7Ha7R2N8M8I5kdmDDz6Y5tjje+65RwULFtSFCxeSnSdPObtOb9682W2StZiYGI0aNcp1n+G4uDiPnq9+/foqV66cVq9ercuXL7tNZCZJFotFYWFhOnPmjCZPnuzWwnrx4kVt3LhRUtKkbakpXry4Ll68qAkTJrh9qBAbG+v6nUnvw44GDRqoatWq+ueffzRmzBhXF3wp6Xd8woQJktxnuc9qHTt2VGhoqD777DN99tlnbo8tX75cq1atUmhoqNtM4s7/DwsXLnT7QGTBggXJ7kFduHBhJSYm6sCBA8laVo8dO6ZffvlFZrNZ1atXl5Q0W3ZoaKg+/vhjffHFF27rr1u3TgsXLtSff/7p1w+a/KFz584ymUyaNWuW9u7d61ruvB2cJNeHOhndx3Llyum///7T7Nmz3ca+OxwOrV69WpL773JMTIwOHTqkQ4cOZc7OAoAB6BYOIFe6cOGC221t7Ha7rly5ov379+vUqVOyWCwaNWqU2+RSKQkICNCYMWM0YMAAvfDCC6pWrZrKlCmjCxcuaOfOnUpMTNT//vc/hYWFSUq67UyBAgV0+fJlderUSWXLlk3xftcZUatWLT333HOqU6eOihcvru3bt+vChQtq0KCBnn/+edd6ZcuW1X333ae1a9fq8ccfV+PGjSUlzeZbsWJF3X777Tpw4IDbcztv6zNr1izt2rUr2cRN1xs0aJD27t2r7du3695773W1Em7btk1Xr15Vw4YNk91KyBfx8fH6/PPPJSnd2xkFBgbqwQcfVGRkpCIjI1O9D3VaHnzwQc2YMUMHDhzQvffeq9q1ays+Pl67du3SlStXVKlSJf311186e/asx8/Zvn17TZ48WRaLJcWu3SNGjNDOnTv14Ycf6rvvvlOVKlUUHx+vX375RVFRUWrVqpXrPKakf//++uGHH/T1119r586drvD322+/6dy5c6pXr166XcpNJpMmT56sp59+WpGRkVq/fr1q1aqlixcvaseOHUpMTFSvXr1SnCwuq5QsWVLjx4/XoEGDNGLECC1YsEC33nqrDh8+rD///FMhISGaMGGC24RnTz31lL7++mt98803euCBBxQeHq6//vpLhw8f1iOPPKJVq1a5vcZrr72mrl27aty4cVq6dKkqVKigqKgo7dy5U3FxcerVq5fr/8v19QwaNEgzZ87UbbfdphMnTriC+//+9z9VqVIly46RJ+rUqaMXXnhBU6dO1WOPPaZ69eopX7582r17t86dO6cmTZqoe/fukjK+j/fcc49atmypb7/9Vi1btlTdunWVL18+HThwQEeOHFGxYsXc7vv+22+/6amnnpKU8r3NASAnIlwDyJWio6O1Zs0a188mk0khISEqU6aM7r33XnXu3FkVKlTw6Lnuu+8+zZs3TwsWLNCePXu0f/9+FShQQM2aNdPTTz/tFn7MZrMmTpyo8ePHu7pQX7p0yad9GTx4sHbv3q3IyEj9/vvvrvG1zzzzTLJW94kTJ+r999/XmjVrtHnzZhUrVkydO3fWc88953rTfL0nnnhCf/75p2sCogoVKqQaroODgzV//nwtWrRIq1ev1pYtW2SxWFSpUiW1a9dOjz32mF+7DX/33Xe6dOmSSpcu7erOmpa2bdsqMjJSO3fu1F9//aWQkJAMvV7+/Pm1dOlSTZs2TVu2bNGGDRuUL18+Va1aVZ06dVKjRo1055136qeffpLNZvNoIjnnvYSbNWum4sWLJ3u8bNmyioyM1LvvvqutW7dq/fr1Cg4Odh1TZ9fy1BQuXFiLFi3S7Nmz9eOPP+qnn36S1WpV+fLl9cwzz+ipp57yaJKvW2+9VZ999pnmzJmj77//XuvWrVOBAgV011136amnnlKTJk3SfY7Mdt999+nTTz/VnDlztHXrVh06dEjFixdXhw4d9MwzzyT7/1yjRg19/PHHmj59unbt2qXTp0+revXqGj16tGw2W7JwXbt2bS1atEhz5szRL7/8onXr1ilfvnyqW7euOnXqpAceeCBZPcuXL9fcuXO1ZcsWrV+/XkWLFlWLFi3UvXv3VG9hZbR+/fqpatWq+vDDD7Vnzx7FxMSoTJky6tKli3r27On2fzgj++j8kGbBggX68ssv9csvvygxMVGlSpVS165d1adPnxT/DwBAbmJyeDp4DAAAZMgbb7yhjz76SLNnz07x3sIAACD3YMw1AAB+5JycbNu2bfr00091yy23pDlpHgAAyB3oFg4AgB+9/PLL+u6771whe8iQIYbNsg0AALIOV3sAAPzIOXtyqVKlNHr06GRjdQEAQO7EmGsAAAAAAHxEyzUAAAAAAD4iXAMAAAAA4CPCNQAAAAAAPiJcAwAAAADgI8I1AAAAAAA+IlwDAAAAAOAjwjUAAAAAAD4iXAMAAAAA4CPCNQAAAAAAPiJcAwAAAADgI8I1AAAAAAA+IlwDAAAAAOAjwjUAAAAAAD4iXAMAAAAA4CPCNQAAAAAAPiJcAwAAAADgI8I1AAAAAAA+IlwDAAAAAOAjwjUAAAAAAD4iXAMAAAAA4CPCNQAAAAAAPiJcAwAAAADgI8I14CWHw2F0CQAAwM9ywvU9J9QI5EWEa+RKI0aMUERERKqPR0REaMSIEan+nJ7vv/9ew4cP96nG3GD//v169NFHVb16dbVq1SrFdaZPn67w8PBUn6Nr167q2rVrZpWYKcLDw5N91axZU61bt9acOXNkt9vT3H7FihUKDw/XsWPHsqhiADBO165d3f5eVq5cWXXq1FG7du20cOFCJSQkGFLXsWPHFB4erhUrVkiSLl++rGHDhmnHjh0+Pe+IESOSXSOqVaumpk2baujQoTpx4oRrXW+ugTt37lTv3r19qtHpiy++UIsWLVS9enWNHj06xXXSqzE8PFzTp0/3Sz1ZYevWrcnOT+XKlVW3bl116tRJ69atS/c50nufibwrwOgCgOxgxowZyp8/v8frL1iwIPOKyUFmzpyp//77TzNnzlSRIkWMLidLdejQQY899pjr55iYGK1du1YTJ07U5cuXNXjw4FS3bd68uZYsWaISJUpkRakAYLiqVavqlVdekSQlJibq0qVL2rhxo8aNG6cdO3Zo6tSpMpuzts2nRIkSWrJkicqWLStJ2rdvn1atWqX27dv7/NzFixfXjBkzXD8nJCTo8OHDmjhxonbt2qXPP/9cwcHBXj33smXLdOjQIZ9rlKTXX39d5cuX11tvvaWSJUv65TlzitGjR6tatWqSknoCXLp0SfPnz1f//v313nvv6e6770512/79++upp57KqlKRgxCuASVd9JFxFy5c0O23357mBSi3KlWqlGrXru22rHHjxvr777/1ySefaMCAAbJarSluW6RIkTz3YQSAvC1//vzJ/mZGRETotttu09ixY/X555+rTZs2WVpTYGBgspoy87nr1asnq9Wq4cOH6/vvv1fr1q0z5bUz4uLFi2rSpIkaNmxodClZrmLFiimeo+bNm2vhwoVpvrdxfiAD3Ihu4YCSdwt3XuRr1qypRo0aaciQITp16pSkpO5R27Zt07Zt2xQeHq6tW7dKkk6fPq2XXnpJd999t2rWrKkOHTro+++/d3udqKgojR49Wo0bN1adOnX04osvasGCBW7dprt27aohQ4ZowIABql27trp37y4pqfvasGHD1LRpU1WrVk2NGzfWsGHDdOHCBbf9mDFjht588001bNhQderU0eDBg3X16lW9//77atasme644w49//zzbtulJL39CQ8P17Zt27R9+3a3bnX+8OWXX6pdu3aqU6eOmjRpotGjR+vSpUuux1PqjnVj9z5J+vDDD/XAAw+oRo0auuuuu/Tqq68qKirK9bjdbtf777+vli1bqnr16rr//vv10Ucf+VR79erVdfXqVVe94eHhmjFjhtq1a6eaNWtqxowZKXYL37Bhgzp16qTatWuradOmGj16tC5fvux6/L///tOgQYPUoEED1apVS08//bT27t3r9tpp/d4CQHbUpUsXlSxZUpGRkW7Lly1bptatW6t69epq3ry5pk+frsTERNfjI0aMULdu3bR8+XLdf//9ql69uh555BFt3LjRtY7dbteUKVMUERGh6tWrKyIiQpMmTZLNZpPkft3YunWrqyXyqaeeUteuXfXJJ58oPDxchw8fdqtt1apVqlKlilv3bk/VqFFDknT8+PEUH4+Li9PMmTNd16777rtP77//vmu40YgRI/TZZ5/p+PHj6V579+zZox49eqhhw4aqW7eu+vbtq7/++kvSta7RUlIvNH8OVUpvH6SUh+PdeG2MjY3Vq6++qmbNmql69ep64IEHNG/ePLdtLl68qNGjR+vOO+9UjRo11LFjR23evNnr2vPnz69bb71V//33n6RrxykyMlItWrRQ3bp1tWnTpmTvQxwOhxYsWKAHH3xQNWvWVMuWLTVv3jy3sfE7duxQly5dVKtWLTVo0EDDhw/X+fPnXY+n9/uKnIGWa+Rq3ozj2rlzp4YNG6b+/furfv36OnnypN5++20NHjxYH3/8sV555RUNHTpUkvTKK6+oYsWKOnv2rDp06KCgoCC9+OKLKly4sFasWKFnn31WEyZMcH0a379/f+3bt08vvviibr75Zi1atEiTJk1KVsNXX32lNm3aaPbs2bLb7YqJidFTTz2lwoUL65VXXlFYWJh27dqlGTNmKDg4WK+//rpr2/nz56tJkyaaMmWKfv/9d02aNEl//PGHSpQooTFjxujYsWMaO3asihUr5uqidyNP9mfJkiV67bXXXMchvU9xUzsXDodDJpPJ9fOsWbP0zjvv6Mknn9SLL76oo0ePatq0adq9e7eWLl3qcTe6zz//XG+//baGDx+u8PBw/f333xo/frxiYmI0fvx4SdKrr76qFStWqE+fPqpTp462b9+uN998U5cvX9azzz7r0evc6PDhw8qXL5+KFi3qWvbuu+9q8ODBuvXWW1W6dGnt2bPHbZsffvhB/fr10z333KOpU6fq4sWLmjBhgo4fP6558+bp/Pnz6tSpk0JCQjRq1CiFhIToww8/VOfOnfXpp5+qQoUK6f7eAkB2ZDab1bhxY33xxRdKSEhQQECA3nvvPU2ZMkVdunTRSy+9pH379mn69Ok6ceKE3nzzTde2v//+u06fPq0BAwYof/78mjZtmp5//nlt3LhRBQsW1Jw5c7R48WINHz5ct9xyi3799VdNmTJFVqtVAwYMcKujWrVqGj16tF5//XWNHj1aDRs2VIkSJTR+/HitWrVKAwcOdK27cuVKNW7cWDfddFOG99cZ1FO6ZjocDvXt21e7d+/Wc889p8qVK2vr1q2aOnWqjh49qjFjxqh///46f/689u7dqxkzZqR67d2yZYt69uyphg0b6s0331RcXJzee+89derUSUuXLlW1atW0ZMkSPf74465hTmkNVXI4HB69p/JkHzz15ptv6qefftLw4cNVrFgxbdy4URMmTFChQoXUvn17xcXF6emnn9bZs2f14osvqkSJElq+fLl69uypuXPnqnHjxh6/llN8fLyOHTummjVrui2fMWOGRo4cqdjYWNWpU0dr1qxxe3zChAn68MMP1b17dzVp0kR79uzRxIkTlZCQoD59+mj79u3q3r27GjVqpKlTp+rSpUuaNm2annrqKX366acKDg7O0O8rsi/CNXKt48ePu8bSZMTOnTsVHBys3r17KzAwUJJUqFAh7dmzRw6HQxUrVnSNz3Z2J5o9e7bOnz+vb775RqVLl5Yk3X333erWrZsmTJighx56SFu3btXWrVs1ffp03XfffZKkZs2a6aGHHko2dspqteq1115zvf6+fftUqlQpjR8/XrfccoskqVGjRvr111+1bds2t23z58+vKVOmKCAgQHfeeac+++wznTp1SsuWLVNYWJgk6ccff9Qvv/yS6jH44IMP0t2f2rVrJzsOaUnrXDRo0ECSdOnSJc2ePVsdO3Z0m1jl9ttvV+fOnbV8+XJ17tw53deSpG3btqlMmTLq3LmzzGazGjRooNDQUFeL8uHDh7V06VINGjTINTFM06ZNZTKZ9N577+nJJ59U4cKFU31+u93ueqPhcDh09uxZrVmzRuvWrVPPnj3dPjCoV6+eqweCpGThevr06apSpYpmzJjh2i4wMFDTpk3T2bNn9dFHH+nixYtavHix63w0a9ZMrVq10rRp0/TOO++k+3t7fT0AkJ0UK1ZMNptNFy9eVFBQkGbNmqXHH39cI0eOlJT0t7lQoUIaOXKkunfvrkqVKkmSrly5ohUrVrgCZmhoqLp06aItW7bo/vvv17Zt21S9enXXGOoGDRooJCTEdS28Xv78+VWxYkVJSd2Fnd+3bNlSq1ev1gsvvCCTyaSTJ09qy5Ytevvtt9Pdr+vDaFRUlPbs2aNx48apTJkyat68ebL1N27cqJ9//lmTJ092dRlv0qSJgoODXUGsUqVKKlKkSLpd2idNmqRy5crp/fffl8VicR3Hli1b6p133tG0adNc26c0zOlG27dv9+g9laf74Ilt27apSZMmrudp2LChQkNDXR9er1q1Sn/++aeWLl2qWrVqSUq6Nnbt2lUTJ07U8uXL03z+66/jCQkJOn78uGbNmqXz588ne6/x5JNP6oEHHkjxeS5fvqyFCxeqS5cursaXO++8U2fOnNH27dvVp08fTZo0Sbfeeqvee+891/moVauWWrdu7Xpvk5HfV2RfhGvkWsWLF9fs2bNTfKxfv36pble/fn1NmTJFDz30kO6//37dfffdatq0aZpjb7Zt26Y6deq4go9TmzZt9NJLL+nvv//Wli1bZLVade+997oeN5vNatWqVbJZNm+77TZXQJKkKlWqaNGiRbLb7Tpy5Ij++ecfHTx4UH///XeyT5Jr1qypgIBr/7WLFSum0NBQtz/OhQoV0oEDB3zaH+cbD099+umnKS6/vvV89+7dio+P10MPPeS2Tr169VS6dGlt27bN43DdqFEjLVmyRO3atdO9996ru+++Ww8//LArZG7ZskUOh0MRERFuxzAiIkKzZ8/Wzp073c7VjWbNmqVZs2a5LQsODtbjjz+u559/3m15lSpVUn2e2NhY7d27V88//7xbAG7VqpVrBvbNmzerSpUqKlmypKtWs9msZs2aafXq1ZK8+70FgOzA2XXWZDJp165dio2NTfFvsyRt2rTJFc6KFCni1nJbqlQpSUkTTEpJYWzSpEl68sknFRERoebNm6tLly4Zqq1Dhw76/PPPtWPHDtWvX18rV65Uvnz51LJlyzS3S+0D/lq1aun1119PsRfWtm3bFBAQkCzEtWnTRtOmTdO2bds8CqbR0dHas2ePnnvuOVeQk6QCBQqoRYsW2rBhQ7rPcaNq1aq5eqvdqEOHDn7fBynp/EVGRurkyZO6++67dffdd7v1Ktu8ebOKFy+uatWquf2utGjRQhMmTNClS5dUsGDBVJ+/W7duyZYVLVpUI0eOVLNmzdyWp3Ud3717txISElwNJ07OD4diYmL066+/qkePHm49AG655RZVqFBBmzZtUufOnf3y+wrjEa6RawUGBrrGNqX0WGrq1Kmj999/XwsWLNAHH3yg999/X8WKFVPfvn1TvRXFpUuXXC3K1ytWrJikpE81L1y4oEKFCiWbDfX67sNO+fLlS7bsgw8+0LvvvquLFy+qWLFiql69ukJCQnTlyhW39VKa9Tw0NDTV/fV2fzIqtXNx/b46W5Wdr3Pja9+4r2lp1aqV7Ha7Fi1apFmzZmn69OkqXbq0hgwZolatWunixYuSlOqEMumNVe7YsaM6duwoKekNYb58+VSmTJkUJzFL6/hfunRJDocjxd8Dp4sXL+qff/5JtdUgJibGq99bAMgOTp06peDgYBUqVMj1tzm1W02dPn3a9X1ISIjbY84PKJ1je3v27Kl8+fJp+fLlmjhxot5++21VqlRJI0eOVKNGjTyqrVGjRipTpoxWrlzpCtetWrVSUFBQmtvd+AF/YGCgSpUqlWbYu3TpkgoXLuwWiJ3PJcnja+CVK1fkcDj8ci11ypcvX6rX8ev5ax8k6eWXX1apUqW0evVqjRkzRmPGjFGdOnX06quvqnLlyrp48aLOnDmT6rXxzJkzaR7v1157zbWtxWJRwYIFdfPNN6fY0yut67jzdza1iUovX74su92uOXPmaM6cOcked/4u+eP3FcYjXAMpuOuuu3TXXXcpJiZGW7Zs0cKFC/XGG2+oVq1aycbhSFLBggV15syZZMudywoXLqySJUvqwoULstvtbgH73Llz6dazZs0avfXWWxo6dKjatWvn+gP+wgsvJOti7A+e7E9mcF4Ez549q9tuuy3ZazsDv8lkcpvYRkr6pP5GDz30kB566CFduXJFP/30k+bMmaOhQ4fqjjvuUIECBSQlTXqW0ocZN998c5q1lihRwqM3GunJnz+/TCaT26QmUtKEMFu2bFGtWrUUFhamBg0aaNiwYSk+h/PDooz+3gKA0RISErR161bVrVtXFovF9bd54sSJKl++fLL1UwqMqTGbzercubM6d+6sc+fOacOGDXr33Xf1/PPPa9OmTR49h8lkUtu2bfXRRx/piSee0OHDh13zdqQlrQ/4U1OwYEFduHBBiYmJbuHU+YGCp9fesLAwmUwmnT17NtljZ86cUaFChTJUV0ZkZB/Su44HBgaqX79+6tevn/777z/98MMPmjVrlgYPHqwvvvhCYWFhKl++vCZOnJhiLWXKlEmz1ltvvdUv13Hn7+z58+fd3rv8999/+vfff1W9enWZTCZ169YtxQ/0nR8Spff7mlbDELIPZgsHbjB+/Hi1b99eDodDISEhatGihYYPHy5Jrtkjb2x9rl+/vnbt2pVs9s/Vq1erePHiKleunBo0aKCEhAStW7fO9bjD4dB3332Xbk07d+5UgQIF1LNnT1ewvnr1qnbu3Ok2+6a/eLI/maFWrVoKDAzU559/7rZ8x44d+u+//1S3bl1JSZ+gX7hwQXFxca51du7c6bbNwIEDXd3HwsLC9OCDD6p///5KSEjQ6dOnVa9ePUlJtxOrUaOG6+v8+fOaNm2a65PozJYvXz5VqVJFP/zwg9vyjRs3qnfv3jp9+rQaNGigw4cPu94IOL9WrVqlTz/9VBaLxaPfWwDIbpYsWaIzZ87oiSeekJR0HbBarTp16pTb37uAgABNnjw5QzNad+rUSW+88YakpF5i7dq1U+fOnXX58mW3O0c43dja6tSuXTtdvnxZ48ePV4UKFVzje/3N+T7h66+/dlvuHP5zxx13SEr+HuRGoaGhql69ur766iu3AHvlyhWtX7/e9TyZwdN9yJ8/v06ePOm2zvXX8djYWN1///2aP3++pKQPvDt37qzWrVu7rmkNGjTQiRMnVLRoUbfflU2bNmnu3Lmpnk9/q1mzpqxWa7Lr+Pz58zVo0CCFhoaqatWq+vvvv93qrFSpkqZPn+6660xGf1+RPdFyDdygUaNG+uCDDzRixAi1adNGNptNc+fOVaFChVzdcgoUKKBdu3Zp8+bNqlq1qrp3767Vq1erW7dueu6551SoUCGtXLlSW7Zs0Ztvvimz2az69eurSZMmevnll3X27FndfPPN+vTTT7V///50J5uqWbOmFi9erLfeekstWrTQ6dOnNW/ePJ09ezbNLk/e8mR/MkOhQoXUu3dvzZw5U1arVS1atNCxY8c0bdo0VaxYUW3btpWUNJ7qo48+0ssvv6wOHTrowIED+uCDD9wupI0aNdIrr7yi8ePHq1mzZrp8+bJmzJih8uXLq3LlyrJarWrTpo1GjRql48ePq3r16jp8+LCmTJmiMmXKpNhiklkGDBigfv36adCgQXr00Ud19uxZTZ48Wffee69uv/12devWTatWrVK3bt30zDPPqHDhwvryyy+1dOlSvfTSS679Te/3FgCMEhUVpd27d0tK6rZ94cIF/fTTT1qyZInatGnjGq9auHBh9ezZU9OmTVNUVJQaNmyoU6dOadq0aTKZTKpcubLHr1m/fn3Nnz9fxYoVU506dXTq1Cl98MEHatCggYoUKZKspdQ5N8n69etVsGBB12vdfPPNuvPOO/XTTz9pyJAhfjgaKWvWrJkaNmyokSNH6tSpU6pcubK2bdumOXPmqG3btq65TgoUKKCzZ89qw4YNqlKlSoqzfA8ePFg9evRQ79699eSTT8pms+n9999XfHy813fD8Oc+tGjRQu+9957ee+891apVS+vWrdOWLVtczxMcHKxq1appxowZslqtrluiffbZZ7r//vslJX3o8fHHH6t79+7q27evbrrpJv3888+aM2eOunTpkuIwrcxQpEgRPfXUU1qwYIECAwPVoEED/frrr1q8eLGGDRsms9nsmjx18ODBatOmjRITEzV//nz9+uuv6t+/v6T0f1+RMxCugRvcfffdmjhxoubPn6/nnntOJpNJd9xxhxYuXOjqStW5c2f9/vvv6tWrl8aNG6eHH35Yixcv1qRJk/TGG2/IZrOpcuXKmjVrlu655x7Xc0+ZMkVvvfWWJk2apISEBN1zzz164okntHLlyjRratu2rY4dO6bly5dr0aJFKlmypO6++249+eSTGjVqlA4dOqQKFSr47RgUL17co/3JDM8//7yKFSumjz/+WEuWLFGhQoX0wAMPaODAga4xT02aNNHw4cP10Ucf6ZtvvnFdgDt16uR6nk6dOslmsykyMlKLFi1ScHCwGjdurKFDh7ouuOPGjdN7773nmjClaNGiatWqlQYOHJhln3hLSW8y3n33Xc2YMUPPPvusihQpoocfftg1MZrzHrCTJk3Sq6++qri4OJUvX15jx451TSTjye8tABhl7969evzxxyVdm6fi9ttv16uvvqrHHnvMbd2BAweqePHiWrRokebOnauCBQuqcePGGjRoUIZmTn7hhRcUGBio5cuXa+bMmQoLC1NERIQGDx6c4vqVKlXSQw89pE8++UQ//vijWy+q5s2ba/PmzXrkkUe82HvPOO9W8c4772jBggU6f/68ypQpo0GDBrndcaJdu3basGGDnn32WQ0YMCDF8emNGzfWBx98oHfeeUeDBg1SYGCg6tWrp/Hjx3s8oVhm7kOfPn10/vx5zZs3TzabTc2bN9fYsWPdJpx9/fXXNXXqVM2fP19nzpxR0aJF1aFDB73wwguSklroP/nkE02aNElvv/22rly5otKlS2vw4MF65plnMm0fUzJ06FAVLVpUkZGRmjt3rsqUKaNRo0a53pc0bdpU8+bN04wZMzRgwABZrVZVq1ZNH3zwgWum9oz+viJ7Mjmuv7s5gExz/Phx7d69W/fcc4/bLKEDBgzQ0aNH9dlnnxlYHQAASE3Pnj0VFBSkmTNnGl0KgGyMlmsgi5jNZo0YMUL33HOPOnToIIvFoh9//FFr167VuHHjjC4PAADcYObMmTp8+LB++uknLVq0yOhyAGRztFwDWWjLli2aOXOm9u3bp4SEBFWoUEHdu3dPdl9nAABgvPbt2+vff/9Vv379sryrMYCch3ANAAAAAICPuBUXAAAAAAA+IlwDAAAAAOAjwjUAAAAAAD4iXAMAAAAA4CNuxeWFXbt2yeFwyGq1Gl0KACAHs9lsMplMqlOnjtGl5DpcqwEA/pCRazUt115wOBzyZpJ1h8Oh+Ph4r7aFf3AOjMXxNx7nwFg3Hn9vrydIH9fqnItzYCyOv/E4B8by5VpNy7UXnJ+C16hRI0PbRUdHa9++fapYsaJCQ0MzozSkg3NgLI6/8TgHxrrx+O/Zs8foknItrtU5F+fAWBx/43EOjOXLtZqWawAAAAAAfES4BgAAAADAR4RrAAAAAAB8RLgGAAAAAMBHhGsAAAAAAHxEuAYAAAAAwEeEawAAAAAAfES4BgAAAADAR4RrAAAAAAB8RLgGAAAAAMBHhGsAAAAAAHxEuAYAAAAAwEeEawAAAAAAfES4BgAAAADAR4RrAAAAAAB8RLgGAAAAAMBHhGsAAAAAAHxEuAYAAAAAwEeEawAAAAAAfES4zmE6DFurDsPWGl0GAABIQ+SeSKNLAABksQCjC0DGRMcmGF0CAABIh81uM7oEAEAWo+UaAAAAAAAfEa4BAAAAAPAR4RoAAAAAAB8RrgEAAAAA8BHhGgAAAAAAHxGuAQAAAADwEeEaAAAAAAAfEa4BAAAAAPAR4RoAAAAAAB8RrgEAAAAA8BHhGgAAAAAAHxGuAQAAAADwEeEaAAAAAAAfEa4BAAAAAPAR4RoAAAAAAB9lq3D93nvvqWvXrm7L9u3bpy5duqh27dqKiIjQwoUL3R632+165513dNddd6l27drq1auXjh49mqHnAAAAnuFaDQBAyrJNuP7kk080depUt2UXLlxQ9+7dVbZsWS1fvlzPPvusJk6cqOXLl7vWmTVrlhYtWqQxY8YoMjJSdrtdPXv2VHx8vMfPAQAA0se1GgCA1Bkerk+dOqW+fftq4sSJKl++vNtjS5culdVq1euvv64KFSqoffv26tatm95//31JUnx8vObPn68BAwaoefPmqly5sqZMmaKTJ09q7dq1Hj1HTtB11DqjSwAA5GFcq9MXuSfS6BIAAAYzPFz/8ccfslqtWr16tWrVquX22I4dO9SgQQMFBAS4ljVq1EhHjhzR2bNn9eeff+rq1atq3Lix6/ECBQqoatWq2r59u0fPkRPExCUYXQIAIA/jWp0+m91mdAkAAIMFpL9K5oqIiFBERESKj508eVK3336727ISJUpIkk6cOKGTJ09Kkm666aZk6zgfS+85ihUr5lXdDodD0dHRGdomJibG7V9PBVrN6jzyO835352y2x2SlOHXRhJvzwH8g+NvPM6BsW48/g6HQyaTyciSPMK12gOJ0se7Pla78HaSpARbAtdqL/F3ylgcf+NxDozly7Xa8HCdltjYWAUGBrotCwoKkiTFxcW5djildS5duuTRc3jLZrNp3759Xm175MgRj9c1m82KiY5RbHyi/vrrL0VHX5Uk7d+/X3a73bWO83t4JiPnAP7H8Tce58BY1x//G69ROQ3X6qTr8IULF2Sz27Rf+yVJ58+f51rtI/5OGYvjbzzOgbG8uVZn63AdHBzsmuzEyXmRDQ0NVXBwsKSk8VzO753rhISEePQc3rJarapYsWKGtomJidGRI0dUvnx5V32eCAk9J5MlUZUqVVJo6DFJUnh4uOvxXm/+rDn/uzNDteRV3p4D+AfH33icA2PdePwPHjxodEk+41qd5Pc/f1eCPcF1fd6duNvtWr1i/wq1q9IuQ7XkVfydMhbH33icA2P5cq3O1uG6VKlSOn36tNsy588lS5ZUQkKCa1nZsmXd1nFe0NJ7Dm+ZTCavL/ghISEZ2tZsNslsTno9szmpS8L128fF231685EXZfQcwL84/sbjHBjLefxzQpfw9HCtThJgDZDs167PAdYAt+0dZgf/5zKIv1PG4vgbj3NgLG+u1YZPaJaW+vXra+fOnUpMTHQt27Jli2699VYVLVpUlStXVv78+bV161bX45cvX9bevXtVv359j54DAAB4j2s1AABJsnW4bt++vaKiovTyyy/r4MGDWrFihRYsWKA+ffpISur73qVLF02cOFHff/+9/vzzT7344osqVaqU7rvvPo+eAwAAeI9rNQAASbJ1t/CiRYtq7ty5Gjt2rNq2bavixYtr2LBhatu2rWudAQMGKCEhQSNHjlRsbKzq16+vefPmyWq1evwcAADAO1yrAQBIkq3C9VtvvZVsWc2aNbVkyZJUt7FYLBo6dKiGDh2a6jrpPQcAAPAM12oAAFKWrbuFAwAAAACQExCuAQAAAADwEeEaAAAAAAAfEa4BAAAAAPAR4RoAAAAAAB9lq9nCkbrgIIt6jdlgdBkAACAVAeYAffTrRwow8/YKAPIi/vrnIDHxCUaXAAAA0mBLtMnhcMhkMhldCgAgi9EtHAAAAAAAHxGuAQAAAADwEeEaAADAj777+zt9uvdTxSfGG10KACALEa5zoJAgi7qOWmd0GQAA4AaX4y5r79m9On7luKZvmW50OQCALES4zqFi4pjcDACA7OafS/+4vj919ZSBlQAAshrhGgAAwE+OXDzi+v5czDnjCgEAZDnCNQAAgB/EJ8br+JXjrp/PRROuASAvIVwDAAD4wZGLR2R32GUxWSRJZ2POyuFwGFwVACCrEK4BAAD84O8Lf0uSqhavKpNMik2IpWs4AOQhhGsAAAA/OH31tCSpXMFyKhRcSJJ06PwhAysCAGQlwjUAAIAf2BJtkqTggGAVCy0mSTp0gXANAHkF4RoAAMAP4u3xkqRAS6CKhhaVRMs1AOQlhGsAAAA/iE+4Fq6LhxaXJB08f9DIkgAAWYhwDQAA4CO7w64ER4IkyWq2ulqu/77wNzOGA0AeEWB0AQAAADldfGK86/tAS6BCzCGSpJiEGF21XTWqLABAFqLlGgAAwEfOcG02mWUxW2S1WBVoCZQknY0+a2RpAIAsQrgGAADwkTNcOwO1JIVaQyVJ56K51zUA5AWEawAAAB85w7XVbHUty2fNJ0k6F0O4BoC8gHANAADgI1e4tlwL186W6/Mx5w2pCQCQtQjXAAAAPrIl2iRJgeZr3cJdLdd0CweAPIFwDQAA4KOUWq5DrEkzhjOhGQDkDYRrAAAAH6U0oRljrgEgbyFcAwAA+CjennxCs9BAxlwDQF5CuAYAAPBRSt3CGXMNAHkL4RoAAMBHad3n+nzMeTkcDkPqAgBkHcI1AACAj5yzhV/fLTwkIGlCs0RHomISYgypCwCQdQjXOVyvMRuMLgEAgDwvpZZri9miQsGFJEnRtmgjygIAZCHCdQ4XE59gdAkAAOR5cYlxktzHXEtS0ZCikgjXAJAXEK4BAAB85OwWHmgOdFteNJRwDQB5BeEaAADARynNFi5da7m+arua5TUBALIW4RoAAMBHqYZrWq4BIM8gXAMAAPjINaHZjd3CnWOu4wnXAJDbEa5zqOAgCzOFAwCQDTgcjhRbrgPMATp0/pAkuoUDQF5AuM7BmCkcAADjxSXGySGHJPdbcUlSUECQJLqFA0BeQLgGAADwwfXB2Wp2H3MdEhAiSYpLiMvSmgAAWY9wDQAA4ANnuLaarTKZTG6PBQcES5JiE2OzvC4AQNYiXAMAAPjganzSeOobu4RL17qFJ9gTXPfCBgDkToRrAAAAHzhbrlMM15Yg1/eX4y5nWU0AgKxHuM7mmBEcAIDsbe3BtZKS3+NakkwmkytgX4m/kqV1AQCyFuE6m2NGcAAAsrfohNRbrqVr466vxBGuASA3I1wDAAD4wHmP69TCtXPcNd3CASB3I1wDAAD4wDlRWaA5lZZry/+3XNMtHAByNcI1AACAD1wt1wG0XANAXka4BgAA8EFcYpyk1FuuneGaMdcAkLsRrgEAAHzg6hae2oRmdAsHgDyBcA0AAOADmz0pXKd0Ky7p2mzhdAsHgNyNcA0AAOCDRHuiJMlitqT4ON3CASBvIFwDAAD4IMGRIEmymNIO17RcA0DuRrgGAADwgd1ul5R6yzVjrgEgbyBcAwAA+CDR8f/dwlNpuXaOuaZbOADkboRrAAAAH6QXrukWDgB5A+EaAADAB+lNaOZsub5qu+paFwCQ+xCuAQAAfOAK16m1XFuCXN8z7hoAci/CNQAAgA9c3cJTabm2mC2ympPugR0VH5VldQEAshbhGgAAwAfpjbmWGHcNAHkB4RoAAMAH6Y25lq51DSdcA0DuRbgGAADwQUZarrkdFwDkXoRrAAAAH3jSch1sSZoxnJZrAMi9CNcAAAA+yFDLNbOFA0CuRbgGAADwksPhkN1hl5TOmGu6hQNArke4BgAA8JLNbnN9n2bLNROaAUCuR7gGAADwUnxivOt7syn1t1XBAUljrukWDgC5F+EaAADASwn2BNf3ntyKKyo+KtNrAgAYg3ANAADgJWfLtUmmNFuuAy2BkhhzDQC5WY4I1wkJCZo2bZpatGihOnXqqHPnztq9e7fr8X379qlLly6qXbu2IiIitHDhQrft7Xa73nnnHd11112qXbu2evXqpaNHj2bxXgAAkHvl1Wu1LTFpzHVardbStQnNaLkGgNwrR4Tr2bNna9myZRozZoxWrlypW2+9VT179tTp06d14cIFde/eXWXLltXy5cv17LPPauLEiVq+fLlr+1mzZmnRokUaM2aMIiMjZbfb1bNnT8XHx6fxqgAAwFN59VrtnNAsrcnMJLqFA0BekCPC9XfffaeHHnpITZs2Vbly5TRixAhduXJFu3fv1tKlS2W1WvX666+rQoUKat++vbp166b3339fkhQfH6/58+drwIABat68uSpXrqwpU6bo5MmTWrt2rcF7BgBA7pBXr9Wetlw7u4UTrgEg98oR4bpo0aL64YcfdOzYMSUmJmrJkiUKDAxU5cqVtWPHDjVo0EABAQGu9Rs1aqQjR47o7Nmz+vPPP3X16lU1btzY9XiBAgVUtWpVbd++3YjdAQAg18mr12rnmOt0W67/v1t4XGKcK5ADAHKXgPRXMd7LL7+sF154Qffcc48sFovMZrOmT5+usmXL6uTJk7r99tvd1i9RooQk6cSJEzp58qQk6aabbkq2jvMxbzgcDkVHR2dom5iYGLd/PWG3O5L97Py6fllGa8mrvDkH8B+Ov/E4B8a68fg7HA6ZTCYjS/KbvHqtjopOaok2m8xKsCXIZrNJZslkdz+vZvu19owzl86oUHChDNWVl/B3ylgcf+NxDozly7U6R4TrgwcPKiwsTDNnzlTJkiW1bNkyDRkyRB9//LFiY2MVGBjotn5Q0P9/OhwX5zooKa1z6dIlr2uy2Wzat2+fV9seOXLEo/XMZrNiomPcTmZMTIwcdotMjmv7ExOXoP3798tut3tVT17k6TlA5uD4G49zYKzrj/+N16ecKq9eqw9FHZIkmRwmXbh4QVfjrspqtirQ6r4vtkSbgsxBirPH6dd9v6pEcAmv6spL+DtlLI6/8TgHxvLmWp3tw/WJEyc0ePBgLViwQPXq1ZMk1ahRQwcPHtT06dMVHBycbLKTuLg4SVJoaKiCg4MlJY3ncn7vXCckJMTruqxWqypWrJihbWJiYnTkyBGVL1/e49cOCT3n/nO8SSFBFgUHX3fqzIkKDw/PUC15lTfnAP7D8Tce58BYNx7/gwcPGl2SX+Tla/Wl45ek/ZI1wKrChQoryBYkq9kqq8Xqtl6CPUEFThXQmegzKlm2pCoXrZyhuvIS/k4Zi+NvPM6BsXy5Vmf7cP3rr7/KZrOpRo0abstr1aqljRs36uabb9bp06fdHnP+XLJkSSUkJLiWlS1b1m0dXwKpyWRSaGioV9uGhIR4vK3ZbEr2s/Pr+mXe1pJXZeQcwP84/sbjHBjLefxzS5fwvHytNluTunsHmAMUYA2QVdak7y03vMWyS2FBYToTfUYJ5gT+/3mAv1PG4vgbj3NgLG+u1dl+QrNSpUpJkvbv3++2/MCBAypfvrzq16+vnTt3KjEx0fXYli1bdOutt6po0aKqXLmy8ufPr61bt7oev3z5svbu3av69etnzU4AAJCL5eVrtWu28HQmNJOk/IH5JTFjOADkVtk+XNesWVN33HGHhg8fri1btujIkSOaOnWqNm/erN69e6t9+/aKiorSyy+/rIMHD2rFihVasGCB+vTpIympf3yXLl00ceJEff/99/rzzz/14osvqlSpUrrvvvsM3jsAAHK+vHytdt3nOp1bcUnXwvWVuCuZWhMAwBjZvlu42WzW7NmzNXXqVL300ku6dOmSbr/9di1YsEC1atWSJM2dO1djx45V27ZtVbx4cQ0bNkxt27Z1PceAAQOUkJCgkSNHKjY2VvXr19e8efNktVpTe1kAAOChvHyt9vRWXBIt1wCQ22X7cC1JBQsW1CuvvKJXXnklxcdr1qypJUuWpLq9xWLR0KFDNXTo0MwqEQCAPC2vXqsT7EnjxT1puQ4LDJNEuAaA3CrbdwsHAADIrmi5BgA4Ea4BAAC85JrQLANjrgnXAJA7Ea4BAAC85JrQzIOW67AguoUDQG5GuAYAAPCSs1u42Zz+WypargEgdyNcAwAAeMmb+1xfiedWXACQGxGuAQAAvOTNfa5puQaA3IlwDQAA4CVvWq4J1wCQOxGuAQAAvOS6FVcGW64dDkem1gUAyHqEawAAAC8l2BMkZazlOsGeoLjEuEytCwCQ9QjXAAAAXnK1XHsQrkOtoTKbkt560TUcAHIfwjUAAICXMtIt3GwyK581nyTCNQDkRoRrAAAAL7m6hXsQrqXrbscVx+24ACC3IVxnY73GbDC6BAAAkIbjl49L8qxbuCSFBYVJouUaAHIjwnU2FhOfYHQJAAAgDQkOzyc0k7gdFwDkZoRrAAAALyXaEyV53i2cMdcAkHsRrgEAALyU6Pj/cJ3Blusr8Yy5BoDchnANAADgpYy2XDPmGgByL8I1AACAl7xtuSZcA0DuQ7gGAADwkrPl2mz27C0Vt+ICgNyLcA0AAOAlZ8t1gCnAo/XDAukWDgC5FeEaAADAS66Wa1PGWq6v2q5mWk0AAGMQrgEAALzkGnPt4YRmjLkGgNyLcA0AAOAFh8Mhu8MuSQowe9gt/P9nC2fMNQDkPoTrXKTrqHVGlwAAQJ5hs9tc33vaLXzDkQ2SaLkGgNyIcJ2LxMQlGF0CAAB5hi3xWrj29FZczvWu2q66Wr0BALkD4RoAAMAL17dcezrmOiggSJJkd9gVbYvOlLoAAMYgXAMAAHjB2XJtksnjbuEWk8U1Ppuu4QCQuxCuAQAAvBCfGC/J81ZrSTKZTMwYDgC5FOEaAADAC85u4Z6Ot3YiXANA7kS4BgAA8IKzW3hGWq4lKSww6XZchGsAyF0I1wAAAF5wdQun5RoAIMI1AACAV+gWDgC4HuE6FwkOsqjrqHVGlwEAQJ7gTbfwAHOATl09JUm6EnclU+oCABiDcJ3LxMQluL4naAMAkHlcLdcZHHMdYLp2K67IPZF+rwsAYAzCdS4QHGRRrzEbki2/PmgDAAD/ysiY6wBzgD7941NJUqAlUFJSuHYGdABAzke4ziVi4gnSAABkpQR70rXX0zHXziAdFBAkiTHXAJDbEK4BAAC84Gy5Npsz9nbK2XJ9JZ4x1wCQmxCuAQAAvOAM184x1J66vls4ACD3IFwDAAB4wdktPKMt10EWuoUDQG6UsY9ake0FB1nUYdhaBQdlbOZSAACQMRmZ0Ox6zpbrM1fP+L0mAIBxCNe5UHRsghwOh0wmk9GlAACQa3lzn2vpWst1bEKs32sCABiHbuEAAABecN3n2suWa5vdJrvD7ve6AADGIFwDAAB4wTXm2uTdbOHSta7lAICcj3ANAADghYze59rJYrYowJw0Mi8uIc7vdQEAjEG4BgAA8IK3LdfStdZrWq4BIPcgXAMAAHjB21txSdcmNYtLpOUaAHILwjUAAIAX/NFyTbdwAMg9CNcAAABe8HbMtXSt5Zpu4QCQexCuAQAAvOBLy3VQAN3CASC3IVwDAAB4wZaYdJ9rn7qFE64BINcgXAMAAHjBp5ZrZ7fwBLqFA0BuQbgGAADwgk9jrukWDgC5DuEaAADAC36ZLZxwDQC5BuEaAADAC66Wa7MPs4XTLRwAcg3CNQAAgBdouQYAXI9wDQAA4AVuxQUAuB7hGgAAwAv+mC08LoFwDQC5BeEaAADACz7NFu4cc53ImGsAyC0I1wAAAF7wx5jrREcirdcAkEt4Fa4///xzxcfzSSsAAMi7/BGuJSkqPspvNQEAjONVuB42bJiaNGmiV199Vb/99pu/awIAAMj2XOHanPG3UyaTydU1/Er8Fb/WBQAwhlfhet26dXrmmWe0ZcsWPf7442rVqpXmzZunM2fO+Ls+AACAbMmXMdfStRnDabkGgNzBq3BdqlQp9evXT19//bU++eQT1atXT3PmzFGLFi3Ut29frV27VgkJCf6uFQAAINvwpVu4dG1SM8I1AOQOAb4+Qd26dVW3bl099thjmjBhgtavX6/169erWLFievrpp/XMM8/IYvHuE10AAIDsytdw7Rx3TbgGgNzBp3B9/PhxrVq1SqtWrdK///6rsmXLatCgQWrevLnWr1+vmTNn6uDBgxo/fry/6gUAAMgWfG65pls4AOQqXoXrZcuWadWqVfrll18UFBSkBx54QGPHjlW9evVc69x+++26cOGCIiMjCdcAACDX8XnMtXNCszgmNAOA3MCrcD1q1CjVqlVLr776qlq1aqX8+fOnuF54eLgef/xxnwoEAADIjmi5BgBcz6tw/fnnn6tixYpKTEx0jaeOjY2VzWZTWFiYa71HH33UL0UCAABkJw6HQ4mOREm+T2jGrbgAIHfw6mpQvnx5vfLKK+rYsaNr2S+//KLGjRtr/PjxstvtfisQAAAgu3G2Wku0XAMAknh1NXjnnXe0evVqPfTQQ65lVatW1ZAhQ7R06VLNnTvXbwUCAABkN9eHa4uZMdcAAC/D9Zo1azR8+HB1797dtaxQoULq1q2bXnzxRX366ad+K9Bp5cqVatWqlWrUqKHWrVvrq6++cj127Ngx9enTR3Xr1lXTpk01depUJSYmum3/ySef6J577lHNmjX15JNPau/evX6vEQCA7OLUqVNZ/pp56Vrtz5brq7arfqkJAGAsr64GFy5c0C233JLiY7fddptOnjzpU1E3WrVqlV5++WV17txZX3zxhR566CENGjRIu3btks1mU48ePSRJkZGRevXVV7V48WLNnDnTtf1nn32mCRMm6IUXXtCKFStUpkwZde/eXefPn/drnQAAZBctWrRQz5499eWXXyo+Pj7TXy+vXavdWq6ZLRwAIC/D9W233aZvvvkmxcfWrVuncuXK+VTU9RwOh6ZNm6annnpKnTt3VtmyZdWvXz/deeed2rZtm7755hv9999/mjBhgm6//Xbde++9GjRokD788EPXm4l3331XXbp0UZs2bVSxYkW9+eabCgkJ0bJly/xWJwAA2cm4ceNkt9s1ZMgQNW3aVK+99pr27NmTKa+VF6/VznBtkkkmk8mr53CGa8ZcA0Du4NVs4U899ZRGjBihixcv6t5771XRokV1/vx5/fDDD/rqq680btw4vxV4+PBhHT9+XA8//LDb8nnz5kmSXn31VVWrVk0FCxZ0PdaoUSNFRUVp3759KlOmjI4cOaLGjRu7Hg8ICFC9evW0fft29enTx2+1AgCQXTzyyCN65JFHdOrUKX322WdatWqVFi9erIoVK6pdu3Zq06aNihUr5pfXyovXal9vwyVd6xbObOEAkDt4Fa4fffRRXb16VbNmzdLatWtdywsXLqxRo0b59RZchw8fliRFR0erR48e2rt3r8qUKaN+/fopIiJCJ0+eVKlSpdy2KVGihCTpxIkTCghI2sWbbrop2Tp//vmn3+oEACA7KlmypPr27au+ffvqjz/+0FtvvaW3335bkydPdnUdr1Wrlk+vkRev1X4J1//fcn01/qrsDrtPzwUAMJ5X4VqSOnfurCeffFKHDx/WxYsXVaBAAd12220ym/17YYiKSuoqNXz4cD333HMaMmSIvvnmG/Xv318ffPCBYmNjVaBAAbdtgoKSLlZxcXGKiYmRJAUGBiZbJy4uzuu6HA6HoqOjM7SNsxbnv+mx2x0pLnN+pbWd3e5QSLBFnUd+pzn/uzNDdeZmGT0H8C+Ov/E4B8a68fg7HA6vuxRnxI4dO7Rq1Sp9++23unz5spo0aaLmzZtr/fr1euKJJzRs2DB169bN6+fPi9fqK9FJrc1mk1kJtmvjr202m2SWTPbUz6tzHedYbYccOnfpnPIF5stQrbkVf6eMxfE3HufAWL5cq70O15JkMpl02223+fIU6bJarZKkHj16qG3btpKkKlWqaO/evfrggw8UHBycbKIW54U4NDRUwcHBkpTiOiEhIV7XZbPZtG/fPq+2PXLkSLrrmM1mxUTHJDuRMTExctgtMjkCU9nSfZ2YuATt37+fe4/fwJNzgMzD8Tce58BY1x//GwOlv/zzzz9atWqVVq9erePHj6t06dLq2rWr2rVr52oh7tKli4YMGaLZs2f7FK7z4rX6WPQxSUljri9cvOBafjXuqqxmqwKtqZ/X69cxyyy77Pr1z19V2FrYq1pzK/5OGYvjbzzOgbG8uVZ7Fa7Pnz+vsWPHav369UlBzuHeimoymfx2+4ySJUtKkm6//Xa35RUrVtT69evVoEEDHThwwO2x06dPu7Z1voE4ffq0KlSo4LaO87m9YbVaVbFixQxtExMToyNHjqh8+fIevVkICT2XfFm8SSFBFgUHp37q3NYxJyo8PDxDdeZmGT0H8C+Ov/E4B8a68fgfPHgw017r/vvvV1BQkO69916NGTPGbTzz9W677Taf38DlxWu1+ZxZ2icFmANUuNC1UBxkC5LVbJXVYk112+vXCQoIUkxCjEreUlIVCldIdZu8hL9TxuL4G49zYCxfrtVehevXX39dP/zwg1q3bq1SpUr5vSv49apVq6Z8+fLp119/Vb169VzLDxw4oLJly6p+/fpauXKloqKilD9/fknSli1blC9fPlWuXFmBgYG69dZbtXXrVtcbi4SEBO3YsUNPPvmk13WZTCaFhoZ6tW1ISIhH25rNybsfmM0m11da213/5W2duZmn5wCZg+NvPM6BsZzHPzO7hI8aNUpt2rRRWFhYmuv1799f/fv39+m18uK1OuBK0lsos9msAOu1t1NWWRVgDlCAJfW3WNevExwQrJiEGCWYE/g/eQP+ThmL4288zoGxvLlWexWuN27cqP/97396/PHHvdk8Q4KDg9WzZ0/NnDlTJUuWVM2aNfXFF19o06ZNWrBggWrXrq2pU6dq4MCBGjJkiI4dO6bJkyfrmWeecTXfP/PMMxo7dqzKlSunGjVq6P3331dsbKw6dOiQ6fUDAGCEb775Ro0aNUoxXP/5558aOnSo1qxZ45fXyovXaueEZt7e49qJ23EBQO7hVbi2Wq265ZZb/F1Lqvr376+QkBBNmTJFp06dUoUKFTR9+nQ1bNhQkjR37ly99tpr6tixowoWLKgnn3zS7VP4jh076sqVK5o6daouXryo6tWr64MPPlCRIkWybB8AAMhsO3bscA3V2rZtm7Zv367z588nW++HH37Q0aNH/fraee1a7Y/ZwiUpMCDpwwVuxwUAOZ9X4bply5b6/PPPdeedWTcLdffu3dW9e/cUHytXrpzmz5+f5vY9evRQjx49MqM0AACyhWXLlmnVqlUymUwymUx67bXXkq3jDN8PPfSQ318/L12r/RWugy1Jk7nRcg0AOZ9X4bpq1aqaOnWqjh49qlq1arlm+XQymUx69tln/VIgAADwzMiRI9W+fXs5HA49/fTTGj16dLIJvcxmswoUKKBKlSoZVGXuYLPbJPmx5TqOlmsAyOm8ntBMkrZv367t27cne5xwDQBA1gsLC1ODBg0kSQsXLnRNNAb/81fLNWOuASD38Cpc//nnn/6uAwAA+GjlypW6++67VbhwYf3333/677//0lz/0UcfzZrCciG/TWgWQLgGgNzCq3B9vStXruj06dO65ZZbZLFYZLH4dpEBAADeGTFihJYuXarChQtrxIgRaa5rMpkI1z5wtVz7eDtSWq4BIPfwOlxv3bpVEydO1O+//y6TyaRly5Zpzpw5KlWqVLoXdAAA4H/ff/+9ihcv7voemYeWawDAjbz6uHXz5s3q0aOHgoODNWTIENfMo5UrV9bChQv1wQcf+LVIAACQvtKlS7vuG126dOlkXyVLllS+fPlcP8N7/h5zza24ACDn8+qKMHXqVN1zzz366KOP9PTTT7vCdd++fdWzZ08tW7bMr0UCAICMSUhI0IwZM7RmzRpJST3OmjRposaNG+vpp5/WpUuXDK4wZ2NCMwDAjby6Iuzbt0/t27eXlDRm63pNmjTR8ePHfa8MAAB47Z133tHs2bN1+fJlSdIbb7yhQoUK6aWXXtK///6rSZMmGVxhzua3cP3/3cK5FRcA5HxeXRHCwsJ05syZFB87ceKEwsLCfCoKAAD45osvvtCgQYPUuXNnHTp0SH/99Zf69eunp556Si+++KLWrVtndIk5mt/GXNNyDQC5hlfh+p577tGUKVO0Z88e1zKTyaSTJ0/q3XffVfPmzf1VHwAA8MLp06dVq1YtSdL69etlNpvVrFkzSVKpUqV05Qotpb7wd8t1XGKcbIk2n+sCABjHqyvC4MGDVbRoUXXs2NEVpAcNGqQHHnhAJpNJgwYN8meNAAAgg0qUKKFjx45JktatW6cqVaqoSJEikqRdu3apVKlSRpaX4/nrVlyBlkDX97ReA0DO5tWtuAoWLKhly5Zp5cqV2rJliy5evKiwsDB17dpV7dq1U0hIiL/rzHN6jdlgdAkAgBzsoYce0rhx47RmzRrt3LlTo0ePliSNHTtWixcvVt++fQ2uMGfzV8u12WRWoDlQ8fZ4XYm/osIhhf1RHgDAAF7f5zowMFAdO3ZUx44d/VkP/l9MfILRJQAAcrCBAwcqNDRU27dv1+DBg/Xkk09Kkvbs2aNnnnlG/fr1M7jCnM1fY64lKTAgUPHx8bRcA0AO51W4XrlyZbrrPProo948NQAA8AOTyaQ+ffqoT58+bssjIyMNqih38VfLtZQ0qVmUopgxHAByOK/C9YgRI1JcbjKZZLFYZLFYCNcAABjsypUr2rJli6Kjo+VwOJI9zrXae/4M185x17RcA0DO5lW4/v7775Mti46O1o4dOzRnzhzNnDnT58IAAID3fvzxRw0YMEAxMTEpPm4ymQjXPvBry3UAt+MCgNzAq3BdunTpFJdXqlRJNptNY8aM0aJFi3wqDAAAeG/SpEm67bbb9NJLL6lkyZI+z2oNd87bZvljzLXzXtdX4ukWDgA5mdcTmqUmPDxckyZN8vfTAgCADDh06JBmzZqlevXqGV1KruTvMdeSdDX+qs/PBQAwjl8/xo6Pj9enn36qokWL+vNpAQBABt18882KiqKbcWbx132upaTZwiVargEgp/Oq5ToiIkImk8ltmd1u14ULFxQXF6fhw4f7pTgAAOCdPn36aObMmapRo4bKlCljdDm5jj9vxeVsuWbMNQDkbF6F6wYNGiQL15KUP39+tWjRQnfeeafPhQEAAO+tWbNGp06dUsuWLVWkSBEFBwe7PW4ymfTdd98ZVF3OlxndwrkVFwDkbF6F67feesvfdQAAAD8qVaqUSpUqZXQZuVaiI1ESs4UDAK7xKlz/999/GVr/5ptv9uZlAACAl8aNG2d0CbmaP7uFO+9zzZhrAMjZ/DbmOi379u3z5mUAAICPDh06pE2bNun06dPq2rWrjh49qsqVKyt//vxGl5ajMVs4AOBGXoXrqVOn6pVXXlG1atXUpk0blSxZUhcuXNC6dev01VdfqV+/fqneCxsAAGQ+u92u0aNHa/ny5XI4HDKZTHrwwQc1a9Ys/fvvv/r444/pNu4Dv4brAO5zDQC5gVfhetWqVWrRokWysdetWrVS0aJF9csvv+i5557zS4EAACDjZs2apTVr1uiNN95Q8+bN1aRJE0nS0KFD9eyzz2rKlCkaP368wVXmXH69Fdf/dwu/Gn/V9UEIACDn8eqKsHnzZj300EMpPtasWTPt3LnTp6IAAIBvli9frgEDBqh9+/YqVKiQa3mVKlU0YMAAbdq0ybjicoHMuBVXoiNRMQkxPj8fAMAYXoXrwoUL69dff03xsc2bN6tkyZI+FQUAAHxz9uxZValSJcXHSpYsqcuXL2dxRbmLP7uFB5gDXCGd23EBQM7lVbfwDh06aPbs2YqJiVFERISKFCmis2fP6uuvv9bixYs1atQof9cJAAAyoFy5ctqwYYPuvPPOZI9t27ZN5cqVM6Cq3MOf4dpkMiksKEwXYy8qKj5KJUUjBQDkRF6F6/79++vKlStasGCB5s2bJ0lyOBwKCQnRiy++qE6dOvm1SAAAkDFPP/20Ro8eLZvNphYtWshkMumff/7R1q1bNX/+fI0YMcLoEnM0f4ZrScofmF8XYy8yqRkA5GBehWuTyaQRI0aof//+2r17ty5duqTChQurdu3a3NoDAIBs4LHHHtP58+c1e/ZsLVq0SJI0aNAgWa1W9ezZU0888YTBFeZs/hxzLUlhgWGSpKj4KL88HwAg63kVrp3y58+vEiVKSJJq166thIQEvxQFAAB816tXLz388MPatm2bAgICFBYWplq1arlNcAbvZEbLtUS4BoCczOtwvWrVKk2aNElnzpyRyWTSsmXLNH36dFmtVk2aNEmBgYH+rBMAAHjo888/V2RkpH799VfXB9/BwcGqW7eunnjiCd17770GV5jz2RJtkgjXAIBrvLoifPnllxo+fLgaNWqkyZMny263S5JatmypDRs2aNasWX4tEgAApC8xMVEDBw7UkCFDdPToUbVu3Vo9evTQM888o4iICB04cEDPP/884639ILNarpktHAByLq9art9991116tRJr776qhITE13L27dvr/Pnz2vp0qUaOHCgv2oEAAAeWLRokdauXauXX35ZXbp0kclkcns8MTFRkZGRevPNN1WvXj116NDBoEpzPteYa7N/xlzTcg0AOZ9XH7cePnxYLVu2TPGxWrVq6dSpUz4VBQAAMm7lypXq1KmTunbtmixYS5LFYlHnzp3VsWNHffbZZwZUmHv4u+U6LChpQjNmCweAnMurK0LRokV16NChFB87dOiQihYt6lNRAAAg4w4fPqxmzZqlu95dd92lAwcOZEFFuVOiPVEOOSQx5hoAcI1XV4RWrVrpnXfe0ddff634+HhJSbfn+v333zVr1iw98MADfi0SAACkLyYmRgULFkx3vcKFC+vq1atZUFHu5Gy1lrgVFwDgGq/GXA8cOFAHDhzQwIEDZTYn5fOuXbsqOjpa9erV0wsvvODXIgEAQPocDocslvTDntlslsPhyIKKcqfrwzUt1wAAJ6/CdWBgoObOnatNmzZpy5YtunjxosLCwtSgQQPdfffdKY7zAgAAyA0yI1w7x1wTrgEg5/IqXPfo0UM9e/ZUkyZN1KRJE3/XBD8KDrKo15gNio1P1EdjIowuBwCQyV599VXlz58/zXWioghwvvB3uA4wB2jL0S2SuBUXAORkXl0RfvnlF1qnc5CY+AQ55FDXUeuMLgUAkInq16+vfPnyyeFwpPmVL18+1atXz+hyc6zrZwr31/sh5y29LsZe9MvzAQCynlct13fddZdWr16tO+64Q1ar1d81IZPExCWkvxIAIMf66KOPjC4hT/D3bbgkKcgSJEmy2W1KsCcowOzVWzQAgIG8+ssdFBSk1atX66uvvlKFChUUGhrq9rjJZNKHH37olwIBAACyk8wI14GWQNf3UfFRKhRcyG/PDQDIGl6F65MnT6pOnTqun2+ccZQZSAEAQG6VGeHaYrbIarbKZrcRrgEgh/I4XK9du1aNGjVSgQIF6HYGAADyLGe49tc9rp2CLEGucA0AyHk8/sj1hRde0JEjR9yWzZkzR+fOnfN3TQAAANlWZrRcS1JQQNK4a2YMB4CcyeOrwo1dvRMTEzV58mSdPHnS70UBAABkV5kVrp3jrmm5BoCcyaerAmOrAQBAXpNZ4To4IFgS4RoAcir/XhUAAAByOZvdJsn/Y66dLddX4ukWDgA5EeEaAAAgA1wt12ZargEA1/h8VTCZTP6oAwAAIEfIrNnCXS3XTGgGADlShu5z/eyzzyowMNBtWd++fWW1Wt2WmUwmfffdd75XBwAAkM1k9mzhtFwDQM7kcbhu27ZtZtYBAACQI2RauLYQrgEgJ/M4XI8bNy4z6wAAAMgRMqtbOOEaAHI2JjQDAADIgMya0MzZLZzZwgEgZyJcAwAAZAAt1wCAlBCuAQAAMsCWmHSf68ya0IzZwgEgZyJcAwAAZIDNnhSu/d1yHRIQIimpW3iiPdGvzw0AyHyEawAAgAzIrNnCgwOCJUl2h51x1wCQAxGuAQAAMsDZLdxi9m/LtcVscY27vhh70a/PDQDIfIRrAACADMislmvpWus14RoAch7CNQAAQAZkZrgOsSaNuyZcA0DOQ7gGAADIANetuPzcLVy6NqkZ4RoAch7CNQAAQAY4ZwunWzgA4HqEawAAgAxwtVz7+VZc0rVu4ZdiL/n9uQEAmYtwDQAAkAHO2cIzZcw13cIBIMciXAMAAGRAZo65pls4AORchGsAAIAMyMwx17RcA0DORbgGAADIgKwYc024BoCch3ANAACQAZkarmm5BoAcK0eF68OHD6tOnTpasWKFa9m+ffvUpUsX1a5dWxEREVq4cKHbNna7Xe+8847uuusu1a5dW7169dLRo0ezunQAAPKEvHCtzswJzZxjrq/EX3GFeABAzpBjwrXNZtOQIUMUHR3tWnbhwgV1795dZcuW1fLly/Xss89q4sSJWr58uWudWbNmadGiRRozZowiIyNlt9vVs2dPxcfHG7EbAADkWnnlWp2ZE5oFBQS5Qju34wKAnCXHhOvp06crf/78bsuWLl0qq9Wq119/XRUqVFD79u3VrVs3vf/++5Kk+Ph4zZ8/XwMGDFDz5s1VuXJlTZkyRSdPntTatWuN2A0AAHKtvHKtzswJzcwmswoEFZBE13AAyGlyRLjevn27lixZorfeestt+Y4dO9SgQQMFBAS4ljVq1EhHjhzR2bNn9eeff+rq1atq3Lix6/ECBQqoatWq2r59e5bVDwBAbpeXrtWZOeZakgoFF5JEuAaAnCbbh+vLly9r2LBhGjlypG666Sa3x06ePKlSpUq5LStRooQk6cSJEzp58qQkJduuRIkSrscAAIBv8tq1OjPHXEuEawDIqQLSX8VYr776qurUqaOHH3442WOxsbEKDAx0WxYUFCRJiouLU0xMjCSluM6lS76NY3I4HG5jyjzhrMf5b1rsdkeqy51faW2b0jp2e8Zrzm0ycg7gfxx/43EOjHXj8Xc4HDKZTEaW5Bd57VrtDNeySwk290nHbDabZJZM9tTPa1rrJNgTFGYNkySdvnw6T163+TtlLI6/8TgHxvLlWp2tw/XKlSu1Y8cOrVmzJsXHg4ODk012EhcXJ0kKDQ1VcHDSjJvx8fGu753rhISE+FSbzWbTvn37vNr2yJEjaT5uNpsVEx2T4kmMiYmRw26RyRGYwpZprxMTl6D9+/fLbrd7VXdukt45QObi+BuPc2Cs64//jaEyp8mL1+ro2KTAezXqqi4kXnB77GrcVVnNVgVaUz+vaa1jS7RJ//9++uCxg9qX6F39uQF/p4zF8Tce58BY3lyrs3W4Xr58uc6dO6fmzZu7LX/llVf05ZdfqlSpUjp9+rTbY86fS5YsqYSEBNeysmXLuq0THh7uU21Wq1UVK1bM0DYxMTE6cuSIypcvn+4bhpDQcykvjzcpJMii4ODUT12q65gTfd7vnC4j5wD+x/E3HufAWDce/4MHDxpdks/y4rXatM8kxUsFCxRU4XyF3R4LsgXJarbKarGm+hpprZNgT5A51CydlQILBKpKlSoZqj834O+UsTj+xuMcGMuXa3W2DtcTJ05UbGys27L77rtPAwYMUJs2bbRq1SpFRkYqMTFRFkvSpCJbtmzRrbfeqqJFiyosLEz58+fX1q1bXRfsy5cva+/everSpYtPtZlMJoWGhnq1bUhISLrbms0pdz0wm02ur7S2TWkds9n7mnMbT84BMg/H33icA2M5j39u6BKeF6/ViY5ESVKgNVABVve3UlZZFWAOUIAl9bdYaa5jl4oHFpckRSVE5en/p/ydMhbH33icA2N5c63O1uG6ZMmSKS4vWrSoSpYsqfbt22vu3Ll6+eWX1bNnT/32229asGCBXnvtNUlJzfddunTRxIkTVaRIEZUuXVpvv/22SpUqpfvuuy8rdwUAgFwpL16rnbOFm82ZO6HZpTjucw0AOUm2DtfpKVq0qObOnauxY8eqbdu2Kl68uIYNG6a2bdu61hkwYIASEhI0cuRIxcbGqn79+po3b56s1tS7awEAAP/Ijddq54RmmX0rrnPRKQ8RAwBkTzkuXO/fv9/t55o1a2rJkiWprm+xWDR06FANHTo0s0sDAADK/ddqV8t1Jt2Kq1hoMUnSuRjCNQDkJNn+PtcAAADZhcPhkM2euS3XznB9IeaCEu2JmfIaAAD/I1znQV1HrTO6BAAAciTnZGZS5rVcO7uFJzoSdTH2Yqa8BgDA/wjXeVBMXILRJQAAkCM5u4RLksWcOS3XAeYAhQQk3X6HruEAkHMQrgEAADzknMxMyryWa0kKtSbdfuds9NlMew0AgH8RrgEAADzk1nKdSWOupWvhmhnDASDnIFwDAAB46PrJzEwmU6a9Tj5rPkm0XANATkK4BgAA8JCz5TrAnLl3Mw2xMuYaAHIawjUAAICHnGOurRZrpr4O3cIBIOchXAMAAHgoq1qu6RYOADkP4ToPCQ6yqNeYDUaXAQBAjuUcc201Z07LdYA5QJ/+8em1lmu6hQNAjkG4zmNi4rnHNQAA3sqKlmub3catuAAgByJc50HBQRZ1GLZWHYatNboUAAByFGe4zqox19G2aEXbojP1tQAA/kG4zqOiYxMUHUsrNgAAGeGc0Cyzx1xbzVbXazCpGQDkDIRrAAAAD2XVhGYmk8k1qRnjrgEgZyBcAwAAeMg5oVlmh2tJjLsGgByGcA0AAOAh15jrTJot/HqEawDIWQjXAAAAHsqqMdfStXtdM+YaAHIGwjUAAICHsmq2cEnKF5gUrmm5BoCcgXANAADgIeeY66zoFu5suT599XSmvxYAwHeEawAAAA9l1WzhkpQ/ML8k6Uz0mUx/LQCA7wjXAAAAHjIiXJ+KOpXprwUA8B3hGgAAwENGhOurtqu6Gn81018PAOAbwjUAAICHnLOFZ8WEZoGWQAWaAyXRNRwAcgLCdR4WEmRR11HrjC4DAIAcIytbriUpf1BS6zWTmgFA9ke4zuNi4hKMLgEAgBwjK2cLl651DSdcA0D2R7gGAADwkLNbeFa1XIcFhkkiXANATkC4BgAA8JCzW3hWjLmWrrsd11XGXANAdke4BgAA8JArXGdRt3Bny/Wpq9yOCwCyO8I1AACAh5zh2mK2ZMnr0XINADkH4RoAAMBDTGgGAEgN4RoAAMBDWX0rrrCgpG7h52POuyZTAwBkT4RrAAAADzkDblZNaBYSECKzySyHHDobfTZLXhMA4B3CNQAAgIeyuuXaZDLRNRwAcgjCNSRJXUetM7oEAACyvawecy1J+az5JEmf7fssy14TAJBxhGtIkmLiEowuAQCAbC+rW66la7fjuhR3KcteEwCQcYRrAAAAD7nuc51FY64lKV9gUst1VHxUlr0mACDjCNcAAAAeMqLl2jnm+mr81Sx7TQBAxhGuAQAAPOScLdyIcH0l/kqWvSYAIOMI13lccJBFvcZsMLoMAAByBCMmNCsYXFCSdNVGyzUAZGeEaygmnsnMAADwhJETmkXFR8nusGfZ6wIAMoZwnQ3RkgwAQPbk7Bb+0z8/ZdlrOm/FZXfYdTH2Ypa9LgAgYwjX2RAtyQAAZE/Olmu7sq4F2WK2KNQaKkk6ffV0lr0uACBjCNcAAAAecoZri8mSpa/r7BpOuAaA7ItwDQAA4CFnuDabsvYtlHPGcMI1AGRfhGsAAAAPOWcLt5iztuWacA0A2R/hGgAAwENGtVzTLRwAsj/CNQAAgIecs4Vn9ZhrWq4BIPsjXAMAAHgg0Z4ohxySDBhzHUS4BoDsjnANAADgAed4a8m4Mddnrp7J0tcFAHiOcA0AAOAB53hrybgx11dtVxUVH5Wlrw0A8AzhGgAAwAPXh+usHnMdaAlUoCVQEq3XAJBdEa4BAAA8cP1kZiaTKctf39k1/NTVU1n+2gCA9BGuAQAAPOBsuQ4wBxjy+swYDgDZG+EaAADAA84JzYwK185x16eiaLkGgOyIcA0AAOCB7NJyTbdwAMieCNcAAAAeiE+MlyRZLVZDXp9u4QCQvRGuAQAAPOAM10GWIENe39kt/GTUSUNeHwCQNsI1AACAB1zhOsCgcB3EmGsAyM4I1wAAAB6ITYiVZFzLtbNb+FXbVUXFRxlSAwAgdYRrAAAADzhbrgMtgYa8fqAl0NU1nHHXAJD9EK4BAAA8YHS3cEkqka+EJLqGA0B2RLgGAADwQFxCnCTjuoVLUsn8JSVxOy4AyI4I10im66h1RpcAAEC2E5eYFK6N6hYuSSXzJYXr7//+3rAaAAApI1wjmZi4BKNLAAAg2zF6zLV0reX6Utwlw2oAAKSMcA0AAOABV7dwA8dcl8pfSpIUFcds4QCQ3RCuAQAAPOCa0MzAMdfOCc2uxF8xrAYAQMoI1wAAAB7ITmOuo+Kj5HA4DKsDAJAc4Rpueo3ZYHQJAABkS9mhW7hzzLXNblNUPF3DASA7IVzDTUw8k5kBAJCS7DChWXBAsIIDgiVJJ6NOGlYHACA5wjUAAIAHnN3CjRxzLUkFgwpKkk5EnTC0DgCAO8I1AACAB7JDt3BJKhBUQJL035X/DK0DAOAuR4TrixcvavTo0WrWrJnq1q2rJ554Qjt27HA9vnnzZrVr1061atXSAw88oC+++MJt+7i4OL322mtq3Lix6tSpo8GDB+v8+fNZvRsAAORaeeFanR26hUvXwjXdwgEge8kR4XrQoEHatWuXJk+erOXLl6tKlSrq0aOH/v77bx06dEh9+vTRXXfdpRUrVuixxx7TsGHDtHnzZtf2r776qn766SdNnz5dH374of7++28NGDDAwD1KHROKAQByorxwrXaG690ndhtaBy3XAJA9BRhdQHr++ecfbdq0SYsWLdIdd9whSRo1apR+/PFHrVmzRufOnVN4eLhefPFFSVKFChW0d+9ezZ07V40bN9apU6e0cuVKvfvuu6pXr54kafLkyXrggQe0a9cu1alTx7B9SwkTigEAcpq8cq12jrmWydg6aLkGgOwp27dcFy5cWO+//75q1KjhWmYymWQymXT58mXt2LFDjRs3dtumUaNG2rlzpxwOh3bu3Ola5nTrrbeqZMmS2r59e9bsBAAAuVheuVY7x1wHmIxtm3BOaEbLNQBkL9m+5bpAgQK6++673ZZ98803+ueff/S///1Pn332mUqVKuX2eIkSJRQTE6MLFy7o1KlTKly4sIKCgpKtc/Kk95/4OhwORUdHZ2ibmJgYt39TYrc70nwOu93h+vLnOnZ70v44l2V033IKT84BMg/H33icA2PdePwdDodMJoObQf0gr1yrYxNiJUkmu0kJtpR7mtlsNsmctE5qvF0nwZ6g6OhohZpDJUkXYy/q3OVzCgkISX/HchD+ThmL4288zoGxfLlWZ/twfaNffvlFL730ku677z41b95csbGxCgx0n1jE+XN8fLxiYmKSPS5JQUFBiouL87oOm82mffv2ebXtkSNHUlxuNpsVEx2T5smLiYmRw26RyZH6ZCrerBMTl6C//vpLMdExio1P1P79+2W32z3boRwotXOArMHxNx7nwFjXH/+UrlE5XW69VkfFREmSoq9G64L9QorbXY27KqvZqkBr6ufV23VsiTb99ddfir4SLavZKpvdpp9/+1k3h9zs4V7lLPydMhbH33icA2N5c63OUeH6u+++05AhQ1S3bl1NnDhRUtKFNz4+3m09588hISEKDg5O9riUNCtpSIj3n/RarVZVrFgxQ9vExMToyJEjKl++fKqvHRJ6Ls3nCIk3KSTIouDg1E+dV+uYE1WpUiWFhJ6RyZKo8PDw9HcoB/LkHCDzcPyNxzkw1o3H/+DBg0aX5He5+Vrt+COpt1fhgoVVOLRwitsH2YJkNVtltVhTfQ1v10mwJ6hSpUr6LfE3FfivgM7FnFNoyVBVKV0lI7uY7fF3ylgcf+NxDozly7U6x4Trjz/+WGPHjtUDDzyg8ePHuz49uOmmm3T69Gm3dU+fPq3Q0FCFhYWpVKlSunjxouLj490+cTh9+rRKlizpdT0mk0mhoaFebRsSEpLqtmZz2l0OzGaT68uf64SGBOiFSdtdy7zdt5wirXOAzMfxNx7nwFjO458buoRfL7dfq212myQpKDBIAdaU30JZZVWAOUABltTfYnm7ToAC9OXhLxVgDVDB4II6F3NO5+PP59r/y/ydMhbH33icA2N5c63O9hOaSdKiRYs0ZswYde7cWZMnT3a78NarV0/btm1zW3/Lli2qW7euzGaz7rjjDtntdtdkKZJ0+PBhnTp1SvXr18+yfcgJmKkcAOCtvHCtdt6KK8BsXNuEM+A7Zww/EXXCsFoAAO6yfbg+fPiw3nzzTbVs2VJ9+vTR2bNndebMGZ05c0ZXrlxR165d9dtvv2nixIk6dOiQ5s+fr6+//lo9e/aUJJUsWVKtW7fWyJEjtXXrVv32228aNGiQGjRooNq1axu7cwAA5AJ54VrtcDhct+IyMlw7hQWGSZJOXCFcA0B2YfzVIR3ffPONbDabvv32W3377bduj7Vt21ZvvfWWZs2apbffflsffvihypQpo7ffftvtlh9jxozRm2++qeeee06S1KxZM40cOTJL9wMAgNwqL1yrna3WkmQxWQysJImz5ZrbcQFA9pHtw3Xfvn3Vt2/fNNdp1qyZmjVrlurjoaGheuONN/TGG2/4uzwAAPK8vHCtvj5cZ4eWa2e4Phnl/a3KAAD+le27hQMAABjN2SXcbDLLbDL+7ZMzXJ+LOacYG/fCBYDswPirAwAAQDYXl5AUrgMtgdlilvfggGBXwD5+5bjB1QAAJMI1AABAupzdwoMsQQZXck2ZAmUkSf9e+tfgSgAAEuEaAAAgXc5u4YGWwHTWzDplC5aVJB29dNTgSgAAEuEaAAAgXc5u4UEB2afl+pYCt0iSjl0+ZnAlAACJcI00dBi2Vh2GrTW6DAAADOfsFp6dWq6d3cJ3/LfD4EoAAFIOuBUXjBMdm2B0CQAAZAvObuHZacz1LQWTWq4vxF4wuBIAgETLNQAAQLqyc7fwqPgoxSbEGlwNAIBwDQAAkI7s2C28UHAh5bPmkyT9d+U/g6sBABCuAQAA0pEdu4WbTCZX13BmDAcA4xGuAQAA0pEdW66la13Dj14mXAOA0QjXAAAA6ciOY66lazOG03INAMYjXAMAAKQjO3YLl67NGE7LNQAYj3CNNIUEWdR11DqjywAAwFDZvVv4vjP7DK4EAEC4Rrpi4rjfNQAgb8uu4bp8ofKSpCvxV7gdFwAYLMDoApD9BAdZ1GvMBqPLAAAg23COuQ4OCDa4kiQB5gB99OtHCjAHKDggWLEJsTpy8YgqF6tsdGkAkGfRco0UxcTTWg0AgJNzzHV2arm2JdpkS7SpSEgRSdKRi0eMLQgA8jjCNQAAQDqya7dwSSoaUlSS9M/FfwyuBADyNsI1AABAOrLrrbgkuVquD188bHAlAJC3Ea4BAADSkV1vxSWJbuEAkE0QrgEAANKRnbuFO8P1v5f+VaI90eBqACDvIlwDAACkwxmus2O38AJBBWQxWRSfGK8TUSeMLgcA8izCNQAAQDqc95DOjt3CzSazCocUlkTXcAAwEuEaAAAgHdm5W7gkFQ5OCteHLzCpGQAYhXANAACQjuzcLVxixnAAyA4I1wAAAOlw3ooru7ZcM2M4ABiPcI0M6TpqndElAACQ5bLzrbgkqWhIUUnSofOHtPi3xQZXAwB5E+Ea6QoOsrhCdUxcgsHVAACQ9bJ7t/BiocVkNpl11XZV52PPG10OAORJhGt4hFANAMirHA5Htp/QzGK2uCY1OxdzzuBqACBvIlzDI8FBFvUas8HoMgAAyHLO23BJUnBAsIGVpK1YaDFJ0rlowjUAGIFwDY/FxNN6DQDIe6LioyRJFpNFIQEhBleTOle4puUaAAxBuAYAAEjDlfgrkqT8gfllMpkMriZ1tFwDgLEI1wAAAGm4EpcUrsOCwgyuJG3OcH0+5rwS7YkGVwMAeQ/hGgAAIA3OluuwwOwdrgsGFVSAOUCJjkQdu3zM6HIAIM8hXAMAAKTBOeY6u7dcm0yma/e7vnDI4GoAIO8hXAMAAKTB2S08f2B+gytJn7Nr+MHzBw2uBADyHsI1AABAGnJKt3BJKpGvhCRp35l9BlcCAHkP4RoAACANOWVCM0kqlb+UJGnvmb1yOBwGVwMAeQvhGgAAIA3OMdc5oVt48dDiMpvMOhdzTqeunjK6HADIUwjXAAAAachJ3cKtFqtrUrM/Tv9hcDUAkLcQrgEAANKQk7qFS1LJ/CUlSX+cIVwDQFYiXAMAAKQhJ7VcS1KpfEnjrmm5BoCsRbgGAABIQ065z7WTs+V639l9SrQnGlwNAOQdhGsAAIA05KT7XEtSkZAiCgkIUbQtWkcuHjG6HADIMwKMLgBJOgxbq+Agi9FlAACA6zgcDle38LUH12p7yHaDK0qf2WRWleJV9MuJX7T75G5VKFLB6JIAIE+g5TqbiI5NUExsgtFlAACA68QlxinBnnR9NpvMsiXaDK7IMw1LN5Qkbfxno8GVAEDeQbiGV7qOWufT4wAA5ATOLuFmk1lWs9XgajwXcWuEJGnb8W2uMeM3itwTmZUlAUCuR7iGV2LikreyXx+oU3ocAICc5vqZwk0mk8HVeO7WwreqcHBh2ew2bfp3k2v59YHaZs8ZrfAAkFMQruGV4CBLstZpAjUAILfJaZOZXa9S0UqSpAW7F7iWEagBIPMQruE1wjQAILdztVznkNtwXa9SkaRwfeTiEcUlxBlcDQDkfoRrAACAVLjucR2Y88J1yXwllT8wv2x2m7746wujywGAXI9wDa8FB1nUYdhadRi21uhSAADIFDm5W7jJZFKtkrUkSdO2TFNsQqzBFQFA7ka4hk+iYxMUzS3EAAC5VE7uFi5J1YtXV4HAArpqu6pFexYZXQ4A5GqEawAAgFQ4W65zYrdwSbKYLWpUppGkpInNjl0+ZnBFAJB7BRhdAAAAQHblGnOdQ1uuJSm8aLj2nt2rY5eP6bM/P1Op/KXUrFwzORwOo0sDgFyFcA0AAJAKZ7fw/IH5FR0fbXA13jGZTHok/BHtP7df3/39nWZun6mZ22cqJCBE+87uU+eanVW9RHWjywSAHI9wDQAAkIrru4Xn1HAtSQHmAL15z5tKsCcoxhaj3Sd3KyYhRt/+/a3WHV6nbrW7qdcdvRRg5q0hAHiLv6DZQK8xG4wuIdP0GrNBF67EKTjIIpNM+mhMhOuxrqPWuf0MAEB242y5/u3UbyqZr6TB1fjGbDKrVsla6lqrq+IT4zX558m6HH9Zaw+t1bxd87Txn41qXam1utTq4tomck+kOtXoZGDVAJBzMKFZNhATn3tn246JT5pNPCY2QTFx7vt5488AAGQ3zjHXua1FN9ASqNIFSuvNe95Uy9taymKy6K/zf2nl/pWKS4hzrWez2wysEgByFsI1MiQ4yOJRS7un6wEAkJ3dV+E+1SxZM0e1WgeYA/TpH596vF6lIpX08O0PK8AcoCMXj2jw2sHcExsAvEC4RoZ52tKem1vkAQB5Q996fTX/kfmyWqxGl5IhnrY4O9crW7Cs2lVuJ6vZqi3HtujFr19UjC0mM0sEgFyHcA2/y2iLdddR6zKpEgAAkJKUWrZvKXiLHqn8iEKtodr+33a98PULik+Ml5Q09hoAkDbCNfwuoy3WjL0GACBrpdayXTqstGa2mql81nz65cQvWvnnSl2MvcjYawDwQO6anQPZGuOwAQDI3gLMAdp/dr86VOmgJXuX6ETUCfVY1UMtyrcwujQAyPZouTZYbgqbnoTnmPgE11dqbuwmTrdxAICRPJkcLKfwZLIzm92mYvmK6fFqjyt/YH79c+kfRf4RqS3HtrjWubGbON3GAYBwbbjcNumXP/bn+m7ivcZsSLHbOIEbAJBVcluXaE/3p1hoUsCuUqyKYhNi9fyXz2vK5im6HHfZ7Tk+/ePTFJ+TwA0gr6FbOHwWkondva8P6x2GrZUkfTrhPsZpAwCQAZ7enutG+QPza26bueq9prf+OPOHPtnzidYcWKNbCtyiYqHFdFPYTToTfUZBliBJ0ke/fiRJ6lqra677UAIA0kO4hl9kRQt8dCyBGgAAb3kbdoMCgnTvbfeq1x299M7Wd/T3hb/1x5k/NOqHUW7rLfljiQoHF9bNYTfrrnJ3yeFw+KNsAMgxCNfIVrxtAe86ap0+GhPh52oAAIBT07JN1bhMY036eZL+Ov+X7A67zsWc08mok0qwJ+hczDmdizmngxcOauO/G1U0pKhCrCF6sNKDyh+YX5F7ItWpRiejdwMAMg3hGtmKty3gqXUTJ3QDAOA/FrNFtxS8RTeF3aSutbpKkhbv+b/27j4oqvve4/j7wO6yCqjRKBAfUxNFERUrWqJUkk6cRqEz9jZpMmhaCxlHUm2akpgMJmidaZPKhCgi0Y7KJK0Pk+glaS1qvNqk43V8SCxeRYMa4qiNEjBC5Bn33D8sW56VLOxB9/OaYZTf+Z3DdxfYD989T1uoqq8iKiyKP37yRy59c4nSqlLKqst4/cDrZB3OYtaDs9yHjrekpltE7hZqrqXHamsvdsvzu2/VPLdsup/53f+S+l8Duq5IERERH9Z4HrfD38GksElE3xdNjF8MfoYfxy4f43z5eb649gXvFd6cV3ClgFEDRjGw90BsfjZ62Xtx8quTfHHtC4b3HY5hGOz4bAcRfhFWPiwRkW/FZ5prl8vFmjVrePfdd/nmm2+Ijo7m1VdfZejQoVaXJi003tKrvb3Y1XUN/5lzm3usGz+vrrnRLTWLiIjnlNV3jsYLpLV3Hre/nz+T75vM9wZ/jzNXz1BeW86+4n2cKDnBiZITrebnn82nT0Afnhz3JDfqb0DbO7lFRHo0n7kV19q1a9m8eTMrVqxg69atuFwukpOTqaurs7o0n9XYIDsD/PnJi3uY+8r/uJfdzuHhHc0xMZvt4b6dq4u3d3uv9sZ/8uIe9xXMRUTEc8rqnqexibb52Xin4B22/N8W97LbuUBag9nA0L5Def3R1/lF1C9YFreMpKgk5oTPIWJgBKMHjCYsKAy7n52K2grWf7Ked068Q97FPMpry1ttr/H2XrUNtZwuPU3+mXx2n93N7z/+PRfKL3DD1fxN9HcK3nFfwVxEpLv5xJ7ruro6Nm7cSGpqKnFxcQBkZmYSGxvLnj17iI+Pt7ZAH9bYIFfVNGCaJr2crX8knR3c6qtpg95yb3XTPdy3VUttx7f9arn9tq5ernO8RUS+HWV1z9XYRNffqMc0Tez+9lZzOrrVV+Oyfs5+XK+9zsLohQDNGnXTNCksLeRM2RnOfX2OnV/uZN9/7yN2eCxTBk/BaXNSWlXKrrO7yPssj+Kvi7lhNm+kt5/ejr/hz8j+IwkLCsNpc3L+2nlsfjZKq0px+DsIsAVw6qtTTBk8hUBHIEP6DGFEvxH0c/bromdLRHyZTzTXp0+fprKykpiYGPdYnz59GDt2LEeOHFFg3wE62kvduKy9vdOdbbLhZuPc8vzujvZ+uw87b6cRb2/+rTTOa9rs3866avJF5E6jrL7zdbQn292gtzPHMAwiBkYwYdAEjl8+zonLJ7hQdYE95/aw51zbR4kF+Adwb+978Tf8qayv5HrddWpv1FJUVkRRWVGzuSe+an4o+kfnm/9N0M/Zj/uC7+Me5z04bU4Mw+BC+QVG9BtBX2df7u19LwN7DyQ4IJjahlquVl/lQsUFLpRf4NRXp8CA6vpqbH42BgUOoqahhhH9RhDkCGr1EWALwOZn4/DFw0wbNg1/P3/8DX/8/fzxM/yw+dmw+dlw2pxtftj8mv/53uBq4Hrdda7XXaeyrhKX6cLub8fh78Dh78Dud/P/dn87dj87hmFgYGAYRrvfLxH5dgzTB25CuGfPHhYtWkRBQQFOp9M9/qtf/YqamhrWrVvXqe19+umnN9+5tbd+57YjpmnS0NCAzWZzv6CVltcC4HKZGAa3fKG7nXm+PMc0zY6Xu0z6Bfljs9n4uuLmYYYmMKBvAF9dq2FgPydXy2tpaPG1/AAXzc+jaJzjbxjuZTdMk4H9nJSW17o/NwwDvyZfB3AvbzrW0tXyWvf2Sr6uAWDQPU53nY1z2tpG0zmNysprW81ra6w97X2tzmyrrd+BrtaZx+SLvPE9kPa1fP7r6+sxDINJkyZZXZrlenJWV9VXAeAyXe7GpD1365yu3JaJ2fFy08RhOHAZLirrK7lh3sDExO5nxzRNett702A2gEmrr3XDvIHLdGGaJiam+18DAxPTvX1/P3/qXfW4TBcu09Xh4+5p/t0ag4H78XXFNjs9t4NVbvdnqeXcltvuTF0dafc5amO4rbmNz3dX1XM7bvU3rdfq6IKfryYb87pARyCB9sBOreNJVvvEnuvq6moAHA5Hs/GAgADKy1ufz3Mr7heDTv7AG4bRqoaWDZB4z4AWz/2ge3q1Of5t3Or7ejvf96Z1hPTv5f5/Y50t5zTVdE6je9uY29bY7dTTltvZVlu/A12tM4/JF3njeyDta/n8G4b2HjXqyVkd6OjcH2bSdZx2vaaLiHd5ktU+0Vw3vgNeV1fX7N3w2tpaevVq3YTcSlRUVJfVJiIiIspqERG58/nE1cLDwsIAKCkpaTZeUlJCSEiIFSWJiIhIE8pqERG50/lEcx0eHk5QUBCHDh1yj1VUVFBYWEh0dLSFlYmIiAgoq0VE5M7nE4eFOxwO5s6dS0ZGBv3792fw4MGsXLmS0NBQZs6caXV5IiIiPk9ZLSIidzqfaK4BFi9eTENDA0uXLqWmpobo6Gg2bNjQ6auIioiISPdQVouIyJ3MJ27FJSIiIiIiItKdfOKcaxEREREREZHupOZaRERERERExENqrkVEREREREQ8pOZaRERERERExENqrkVEREREREQ8pOZaRERERERExENqrr3E5XKxevVqYmNjmThxIs888wwXLlywuiyfce3aNV599VW+//3vM2nSJJ566imOHj1qdVk+qbi4mKioKHbs2GF1KT4nLy+PWbNmERkZyezZs8nPz7e6JJ/S0NDAqlWrePjhh4mKiiIxMZF//vOfVpclTSirraWs7jmU1dZRVlvL06xWc+0la9euZfPmzaxYsYKtW7ficrlITk6mrq7O6tJ8wvPPP8+xY8d444032L59O2PGjCEpKYnPP//c6tJ8Sn19PampqVRVVVldis95//33SUtLIzExkZ07dxIfH+/+vRDvyMnJ4d1332XFihXk5eVx//33k5ycTElJidWlyb8pq62lrO4ZlNXWUVZbz9OsVnPtBXV1dWzcuJHFixcTFxdHeHg4mZmZXL58mT179lhd3l3v/PnzHDhwgGXLljF58mTuv/9+XnnlFQYNGsRf/vIXq8vzKVlZWQQFBVldhs8xTZNVq1bx9NNPk5iYyLBhw1i4cCEPPfQQhw8ftro8n7F3717i4+OZPn06w4cP56WXXuKbb77R3useQlltLWV1z6GstoayumfwNKvVXHvB6dOnqaysJCYmxj3Wp08fxo4dy5EjRyyszDfcc889rF+/nsjISPeYYRgYhkFFRYWFlfmWI0eOsG3bNl577TWrS/E5xcXFXLp0iYSEhGbjGzZsYMGCBRZV5XsGDBjA/v37uXjxIjdu3GDbtm04HA7Cw8OtLk1QVltNWd0zKKuto6zuGTzNajXXXnD58mUAwsLCmo0PGjTIvUy6T58+fZgxYwYOh8M9tnv3bs6fP09sbKyFlfmOiooKXnzxRZYuXdrq90C6X3FxMQBVVVUkJSURExPD448/zr59+yyuzLekpaVht9v5wQ9+QGRkJJmZmaxevZphw4ZZXZqgrLaastp6ymprKat7Bk+zWs21F1RXVwM0CwyAgIAAamtrrSjJp3366ae8/PLLzJw5k7i4OKvL8QnLli0jKiqq1bux4h3Xr18HYMmSJcTHx7Nx40amTZtGSkoKBw8etLg633H27FmCg4PJzs5m27Zt/PjHPyY1NZVTp05ZXZqgrO5plNXep6y2lrK6Z/A0q23dXJ8ATqcTuHk+V+P/AWpra+nVq5dVZfmkvXv3kpqayqRJk8jIyLC6HJ+Ql5fH0aNHdc6chex2OwBJSUnMmTMHgDFjxlBYWMimTZuaHQYr3ePLL7/kN7/5Dbm5uUyePBmAyMhIzp49S1ZWFmvXrrW4QlFW9xzKau9TVltPWW29rshq7bn2gsZDa1peZa6kpISQkBArSvJJf/rTn1i0aBEPP/wwb731FgEBAVaX5BO2b99OWVkZcXFxREVFERUVBUB6ejrJyckWV+cbGl9nRo0a1Wz8gQce4OLFi1aU5HMKCgqor69vdj4pwIQJEzh//rxFVUlTyuqeQVltDWW19ZTV1uuKrNaeay8IDw8nKCiIQ4cOuY/Xr6iooLCwkLlz51pcnW9ovLXKvHnzSEtLwzAMq0vyGRkZGdTU1DQbmzlzJosXL+ZHP/qRRVX5loiICAIDAykoKHC/EwtQVFSk8329JDQ0FIDPPvuM8ePHu8eLiooYMWKERVVJU8pq6ymrraOstp6y2npdkdVqrr3A4XAwd+5cMjIy6N+/P4MHD2blypWEhoYyc+ZMq8u76xUXF/O73/2ORx99lAULFlBaWupe5nQ6CQ4OtrC6u197e3wGDBigvUFe4nQ6SU5OJjs7m5CQEMaPH8/OnTs5cOAAubm5VpfnE8aPH893v/tdlixZQnp6OqGhoeTl5XHw4EG2bNlidXmCstpqymprKautp6y2XldktZprL1m8eDENDQ0sXbqUmpoaoqOj2bBhg/v8Cuk+u3fvpr6+ng8//JAPP/yw2bI5c+bodhPiE1JSUujVqxeZmZlcuXKFkSNHkpWVxdSpU60uzSf4+fmRk5PDm2++ycsvv0x5eTmjRo0iNzeXCRMmWF2e/Juy2jrKahFltdW6IqsN0zTNbq5TRERERERE5K6mC5qJiIiIiIiIeEjNtYiIiIiIiIiH1FyLiIiIiIiIeEjNtYiIiIiIiIiH1FyLiIiIiIiIeEjNtYiIiIiIiIiH1FyLiIiIiIiIeEjNtYiIyG1at24d8+bN6/R6eXl5zJo1i8jISGbPnk1+fn43VCciIiJWZrWaaxFpZd68eYwePbrZx7hx44iLi2P58uWUl5e3u+7FixcZPXo0O3bs8GLFIt3vz3/+M2+++Wan13v//fdJS0sjMTGRnTt3Eh8fz/PPP8+xY8e6vkgR8RnKapHWrM5qW6e/soj4hLFjx5Kenu7+vL6+npMnT/LGG29w6tQptmzZgmEYrdYbNGgQ27ZtY9iwYd4sV6TbXLlyhfT0dA4dOsSIESM6ta5pmqxatYqnn36axMREABYuXMjRo0c5fPgwUVFR3VCxiPgKZbXITT0lq9Vci0ibgoKCmDhxYrOx6OhoKisrWb16NQUFBa2WAzgcjjbHRe5UJ0+exG6388EHH5Cdnc2lS5eaLd+/fz9ZWVmcPXuWkJAQZs+eTUpKCg6Hg+LiYi5dukRCQkKzdTZs2ODNhyAidylltchNPSWrdVi4iHTKuHHjAPjXv/7FvHnzSE1NZfHixUycOJH58+e3eajZ559/zi9/+UumTJlCdHQ0CxYs4Ny5c+7ltbW1/OEPf2DGjBmMGzeOhIQE/va3v3n9sYm05ZFHHiErK4uhQ4e2Wvbxxx/z3HPP8cQTT/DXv/6V9PR08vPzeeGFFwAoLi4GoKqqiqSkJGJiYnj88cfZt2+fVx+DiPgWZbX4mp6S1WquRaRTGl+AGl+88vPzCQwMJCcnh+Tk5Fbzr1y5wk9/+lO++OILli1bxsqVKyktLeVnP/sZ165dwzRNnn32WbZu3cr8+fPJyckhKiqKX//61+Tl5XnzoYl02ltvvcUTTzzBk08+ybBhw5g+fTrLly9n165dXLx4kevXrwOwZMkS4uPj2bhxI9OmTSMlJYWDBw9aXL2I3K2U1SL/4c2s1mHhItIm0zRpaGhwf15eXs7hw4fdgdr4rrjdbmf58uU4HA7g5kVSmsrNzaWuro5NmzYxcOBAAMLDw3nqqacoKCjAZrPxj3/8g8zMTGbNmgVAbGws1dXVZGRkEB8fj82mlyrpmQoLCzl+/Djvvfeee8w0TQDOnTuH3W4HICkpiTlz5gAwZswYCgsL2bRpEzExMd4vWkTuGspqkVvzZlbrt0BE2nTkyBEiIiKajfn5+fHQQw/x29/+1n2BlO985zvusG7LJ598wsSJE91hDRAaGsr+/fsByMjIwDAMZsyY0ewPhEceeYQPPviAM2fOMGbMmK58aCJdxuVykZyc7A7jpgYOHMipU6cAGDVqVLNlDzzwAH//+9+9UaKI3MWU1SK35s2sVnMtIm2KiIhg+fLlABiGQUBAAGFhYQQFBTWbFxgY2OF2rl27xpAhQzpcbpomkyZNanN5SUmJAlt6rAcffJDi4mKGDx/uHjt06BBvv/02y5YtIyIigsDAQAoKCpg8ebJ7TlFRka7SKyIeU1aL3Jo3s1rNtYi0KTAwkMjISI+3ExwczNWrV1uNHzx4kCFDhhAcHEzv3r15++2321y/6QuhSE/zzDPP8Nxzz7FmzRpmz57N5cuXSUtLY8iQIe49QMnJyWRnZxMSEsL48ePZuXMnBw4cIDc319riReSOp6wWuTVvZrUuaCYi3Wry5MkUFBQ0C+2ysjKSk5P56KOPmDJlClVVVZimSWRkpPujqKiI7OzsZoefifQ0P/zhD8nMzGTv3r0kJCTwwgsvMH36dNasWeOek5KSwqJFi9znKu7atYusrCymTp1qYeUiIv+hrJa7mTezWnuuRaRb/fznPycvL4/k5GQWLFiA3W4nJyeH0NBQEhISCA4OJjo6mpSUFFJSUhg5ciTHjx9n9erVxMbG0r9/f6sfgojba6+91mrsscce47HHHutwvfnz5zN//vzuKktExCPKarmbWJnVaq5FpFuFhYWxefNmVq5cyUsvvYTD4WDq1KlkZmbSt29fANavX8+qVatYt24dZWVlhISEMH/+fJ599lmLqxcREbn7KatFuoZhNl6HXERERERERES+FZ1zLSIiIiIiIuIhNdciIiIiIiIiHlJzLSIiIiIiIuIhNdciIiIiIiIiHlJzLSIiIiIiIuIhNdciIiIiIiIiHlJzLSIiIiIiIuIhNdciIiIiIiIiHlJzLSIiIiIiIuIhNdciIiIiIiIiHlJzLSIiIiIiIuIhNdciIiIiIiIiHvp/3UtWFWxKv2IAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1876,12 +1876,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 529,
+   "execution_count": 229,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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mSSZfoK78dzJ5h13dgwepXsOGDfPiiy+mQYMG5XZcddVV88EHH+T666+fZmg/Zk3Tpk2nCU8qd4Lz+6/DZ9XBBx+cXXfdNa1bt06SVFRUZNFFF83vfve7vPnmm355VY3HHnssxxxzTLp27Zo+ffokmXwAnnpbrHxtX1m96trxjDPOyPHHH18e6qOioiKNGjXKUUcdleOOO86dZ9WoHEbg7LPPzuuvv55bb73VvhFqSXX7NarfT5177rm1XKvacd9992XAgAF58MEHa7sqdYpzpWk1atQoSbLffvtl++23T5KstNJKeeedd3LjjTfOl20ytfvuuy/bbbddlesU86tvvvkmRx99dG666aasscYaSZKOHTvmww8/zOWXX56rrrqqlmtYt1TXV04m95f1leuGNdZYI88//3xat26devXqJUmuuOKKbLDBBunXr18OOOCAWq7hL9es9Gedb9auWVlH2223XXr27FllNKkVV1wxPXv2zBNPPJEttthiXlV3vjQr5z+ORbVrVtZRXTkWzfUdfJW3in733XdVpn/33XdZbLHF5rb4+UqLFi2mOflYccUVM2jQoFqqUfEtvvji1W6bSWyfs6h+/frlcK/SiiuumCSG4a3GrbfemsMPPzy9evXKNddcU/6V2hJLLFHttti8eXNDnVZjeu3YsGHDKuP4J7bH6gwePDgPPfRQJkyYUJ5Wv379rLDCCvnuu+/sG6EWTG+/Nr+a2X5qftW3b9/8+OOP2WCDDdKlS5d06dIlSfLnP/85+++/fy3XrnY5V6qq8ng99ZDbK6ywQr788svaqFKd8t577+WLL75wJ+z/9/rrr2f8+PFVns2UJJ07d85nn31WS7Wqu6rrK48bNy5Dhw41dF0dsvDCC5cvqCaTfzi71FJLzbfHhXlhVvuzzjdrz+ycc0z9qJi2bdumVatWrq38TGb3/MexaN6bk3PUunAsmuuAr0OHDllggQXy4osvlqcNHz4877zzTrp16za3xc83Pvjgg3Tt2rVKOybJW2+9Nd2HODJz3bp1y8svv5yJEyeWp73wwgv59a9/nTZt2tRizYrjuOOOy957711l2ptvvpkkts2p3H777TnzzDOz22675eKLL65yK/0aa6yR/v37V1n+hRdeSNeuXVO//lzvin9RZtSOe+yxR/70pz9VWf7NN99Mo0aNstxyy83jmtZdP/zwQ3r37l1liK7x48fnnXfeSbt27ewbYR6b0X5tfjWz/dT8qk+fPnn44Ydz3333lf9LkiOOOCJnn3127VauFjlXmtYqq6ySFi1a5PXXX68yfeDAgfPNM9VmZMCAAWnTpk06dOhQ21WpEyqfhfX+++9XmT5w4EB96Gp069Yt3377bZXws/JcbvXVV6+tajGFu+66Kz169MioUaPK00aMGJFPP/10vj0u/Nxmpz/brVu3vPPOO+XhpJPJ55stWrSwX/4Zzc46uuSSS7LpppumVCqVp3355ZcZMmSIv6Gfyeye/zgWzXuzu47qyrForq8qN27cOLvvvnv69OmTxx9/PO+9916OOuqoLL744tlkk01qoo7zhXbt2mX55ZfPGWeckQEDBuSjjz7Kueeem9deey0HH3xwbVevsHbccceMGDEiJ510Uj788MP069cvN910Uw488MDarlphbLrppnn++edzxRVX5PPPP8/TTz+dE088MVtttdV8fQFuap988knOOeec/OY3v8mBBx6YH374Id9//32+//77/PTTT9ljjz3yxhtvpE+fPvnoo49yww035F//+td8/2v8qc2sHTfddNPcf//9ueOOO/LFF1/k4YcfzgUXXJD99tsvCyywQG1Xv86oqKhIz549c9ZZZ+Wll17KwIEDc8IJJ2T48OHZ+/+1d/dRNWf7H8DfRYWJCnk2cZlTVKcHKabLLbpTQwyJsYgIjSRUc6ciyW0YSQ09oDSYboyM8jDMzFozVq4m5enKw8TgTkx5PF1K96KH0/79YfX9OXoSKYf3a62zVt/93d/v3vv7bZ3zOXt/z96zZvG9kagFNfa+9rZq7H3qbdW9e3cYGRmpvACgS5cub/UT7/yuVFu7du0wd+5cJCQk4ODBg/jjjz+wadMmZGdnY/bs2a1dvVaXn58PY2Pj1q7Ga0Mul2PIkCEICgpCbm4url27hvXr1yMnJ4dTGQJQKpVQKBTSulMWFhawtraGv78/zp07h9zcXISFhWHChAlv9Xtxa3r2Ho0cORLV1dX47LPPcOXKFZw/fx5+fn7o3Lkz3NzcWrm2b57G4tmKigooFAppOkEnJycYGhpiyZIluHTpEn7++WfExMTAy8uLD7q9Ik29R3/9619x48YNhIeHo6CgACdPnoSfnx+sra0xYsSIVm7Nm6mx7z/8LGp9Tb1Hr8tnkYZ4eqj+BSmVSsTExCAjIwOPHz/G0KFDERYWhj59+jRHHd8axcXFiI6ORlZWFh48eIDBgwfj008/lebIp8YFBwfjxo0b+Mc//iGlnTt3DqtWrUJ+fj4MDQ3h5eUFDw+PVqzl662ua/jDDz8gKSkJv//+Ozp27Ihx48ZhyZIlb/30Yk/bvHkzvvzyyzr3TZw4EWvWrMHRo0cRFRWFa9euoU+fPvDz8+O85s94nuu4Y8cO7NixA4WFhdJ6kN7e3vwl5DPKysoQHR2Nn3/+GWVlZbCxsUFwcLA0pSnfG6kl1PWZ8rZ5nve1t1Vj71P0hLGxMb744ou3vsOS35Xqtm3bNqSmpuLOnTsYMGAA/Pz84OTk1NrVanXz5s2Drq5uve+/b6PS0lKsX78eR44cQWlpKWQyGQICAmBra9vaVWtxz8YnRUVFGD16tMp77X/+8x+sXLkSWVlZ0NHRgYuLC0JCQvgduIU8zz369ddfER0djXPnzkEIAXt7e4SEhEhLCVHzaSyenThxImbOnImUlBTY2dkBAK5fv46VK1fi1KlT0NPTg7u7O/z8/Pi9/RV5kXuUk5ODDRs24LfffoO2tjZGjx6NoKCgWsuiUPNp6PsPP4teD029R6/DZ1GzDPARERERERERERERERERUcvgYxNEREREREREREREREREaoQDfERERERERERERERERERqhAN8RERERERERERERERERGqEA3xEREREREREREREREREaoQDfERERERERERERERERERqhAN8RERERERERERERERERGqEA3xEREREREREREREREREaoQDfERERERERNRkQojWrgIREREREdFbiwN8RC1oxowZGDx4MM6fP1/n/lGjRiE4OLhF6hIcHIxRo0a1SFlNUVVVheDgYFhZWcHa2hq5ubm18hw/fhzGxsYqLzMzM4wePRpr167Fo0ePmqUucXFxMDY2bpZzERERUdMxdmpca8VO3377LSIjI6XtjIwMGBsbo6io6KXaQ0RERK8XxmONe554rKioqFY89vTL1dUVAGMqImqatq1dAaK3jVKpREhICDIyMqCtrd3a1XntZGVlYe/evViwYAHef/99DB48uN68YWFhMDU1BQA8evQIly5dQmxsLBQKBaKiolqqykRERPQKMXZqWGvFTps2bYKtre1L1Z2IiIjUA+OxhjUlHvPx8YGDg0Ot9Hbt2r3CGhLRm4oDfEQtrGPHjrhy5QoSEhLg7+/f2tV57ZSUlAAA3Nzc0Ldv3wbzDhw4EJaWltL28OHDUVZWhk2bNmHFihXQ1dV9hTUlIiKilsDYqWGMnYiIiOhVYzzWsKbEY++++65KPEZE9DI4RSdRCxs0aBAmTJiA5ORkXLhwocG8xsbGiIuLU0l7dtrI4OBgzJkzB2lpaXBycoJcLsfUqVNRUFCAzMxMjBs3DhYWFpg8eTIuXrxYq4y0tDQ4ODhALpfD09MT+fn5Kvtv3ryJgIAA2NrawsLColaemikGtm3bBhcXF1hYWCA9Pb3O9iiVSuzYsQPjxo2DXC6Hg4MD1q1bh/LycqktNdM6ODk5YcaMGQ1en7p06tSpVlpJSQnCwsLw/vvvw9zcHFOmTEFOTo5KnvLycnzxxRewt7eHlZUVQkJCpHrVCA4OhqenJ1asWAFra2uMGTMGSqUS5eXlSEhIgIuLC8zNzfHBBx8gKSkJ1dXVKsd///33cHNzg5WVFezt7REWFobS0lJpf1xcHFxcXPDTTz/B1dUV5ubm+Oijj3DmzBnk5eVh8uTJkMvlcHV1Van/48ePER4ejpEjR8LMzAwuLi746quvmnztiIiIXkeMnVo+drp06RIWLlyIYcOGwdTUFCNGjMDnn3+Ox48fA3gyFdeNGzewd+/eWlNInT17FlOnToW5uTkcHByQnJz8XG0/f/485syZAzs7O1hbW2P+/Pm4cuWKSr3u3r2LkJAQ/OUvf4FcLoe7uzsOHz6sksfY2BjffPMNgoODMWTIENja2kp1j4yMxLBhw2BnZ4dly5apxHrZ2dmYMmUKrKysMHToUPj4+ODf//53k68nERHRm4jx2KuNxxqTnZ2NadOmYciQIbCzs0NgYCBu3bqlkufatWtYtGgR7O3tYWlpiRkzZuD06dONtpl9SkTqjb/gI2oFS5cuRXZ2NkJCQpCenv7S0xucOXMGd+/eRXBwMMrLyxEeHg5vb29oaGhg0aJFaN++PVasWIFPP/0Uhw4dko67ffs24uPjERgYCF1dXcTHx2PGjBn47rvv0KtXL9y7dw9Tp05F+/btsXz5crRv3x5ff/01pk+fjj179mDAgAHSueLi4rBs2TLo6urCwsKiznqGhYVh//79mDdvHmxsbJCfn4+EhARcvHgRycnJWLBgAXr06IFNmzYhPj4e/fv3b7Dd1dXVqKqqAgBUVlbi4sWLSElJwYQJE6Qn0MvLy+Hp6Yni4mL4+/ujW7duSE9Px9y5c5GcnIzhw4cDAP72t78hKysL/v7+MDIyQlpaGr777rtaZZ46dQo6OjpISEjAw4cPoampifnz5yMvLw8LFy6EiYkJjh8/jvXr16OwsBAREREAgI0bNyI2NhbTpk2Dv78/CgsLsWHDBuTl5WH37t3SVAy3b9/GmjVr4O/vjw4dOiAiIgKLFi2ClpYW5s+fj549e0r7jxw5gnbt2mH16tX45ZdfEBQUhK5du+Lo0aNYu3Yt9PX1MWnSpOf9NyIiInptMXZqudjp7t27mD59OiwtLbFmzRpoa2vj6NGj2LZtG7p16wZvb2/Ex8fD29sbgwcPxoIFC9CtWzepjPDwcCxatAiLFy/G7t27ERUVhQEDBsDR0bHetufm5mLu3Lmws7PD6tWrUV5ejsTEREydOhW7d+/GgAEDUFxcDHd3d+jo6MDf3x8GBgbIyMiAr68v1q5di/Hjx0vnj4qKgqurK+Lj45GZmYmvv/4av/zyC0xMTLBu3Trk5eUhLi4O/fv3x9y5c1FYWIgFCxZg0qRJCAgIwIMHDxATEwNvb2/89NNP0NTkc7FERESMx5o/HquhoaGBNm3a1Jl/3759CAoKgqurKz755BPcv38fsbGx+Pjjj7F371506dIFV69exZQpU9CvXz+EhoZCS0sLKSkp8PT0xNatW1WmVX+2zexTIlJzgohajIeHh/Dw8BBCCHH48GEhk8lETEyMtN/R0VEEBQVJ2zKZTMTGxqqcIzY2VshkMmk7KChIyGQycfXqVSktLCxMyGQycezYMSntq6++EjKZTJSWlqocd/bsWSnP3bt3hVwuF2vWrBFCCBETEyPMzc1FUVGRlKe8vFyMHj1a+Pn5CSGEKCwsFDKZTCxdurTBtl+5ckXIZDKRmJiokr5v3z4hk8nEkSNHhBBCpKenC5lMJgoLC+s9V25urpDJZHW+Ro0aJW7fvi3lTUtLEzKZTOTl5Ulp1dXVYvr06cLNzU0IIcTly5eFTCYTO3fulPIolUoxZsyYOq/1rVu3pLQjR44ImUwmDh48qFLHhIQEIZPJxOXLl0VJSYkwMzMTy5cvV8lz8uRJIZPJRGpqqhDi/+/tP//5TylPYmKikMlk4ttvv5XSfvzxRyGTyUR+fr4QQghnZ2cRGhqqcu74+HiRmZlZ7zUkIiJSB4ydWj52ysrKEtOnTxdlZWUq53B1dRVeXl7S9rPXvqYeT8dTDx8+FKampmL16tUNtt3d3V2MGTNGVFVVSWmlpaXC1tZWLFq0SAghxNq1a4WpqanKtRVCCE9PT2Fvby+USqUQ4sn/wOTJk6X9VVVVwtLSUowaNUpUVlaqtMfHx0cIIcTBgweFTCZTuQ5nz54VMTExta4DERHR24bxWPPEYzVl1vUyMzOT8j19LqVSKezt7VViMCGEuH79ujA1NRWRkZFCCCEWL14s7OzsVOKWyspK4ezsLCZNmtRgm9mnRKTe+CgiUSsZNWoUxo8fj+TkZPz6668vdS49PT2VJ5C6du0KACpPH+nr6wMAHjx4IKX17dsXcrlc2jY0NISlpSVOnjwJAMjJycGgQYPQvXt3VFVVoaqqCpqamhg5ciSOHTumUodBgwY1WMcTJ04AAMaOHauSPnbsWLRp0wbHjx9/3uZKVq5ciT179mDPnj3YuXMnIiMjoaOjA3d3d9y8eVNqg6GhIUxNTaU2KJVKODo64sKFCygtLcWpU6cAPLknNTQ1NeHs7FyrTH19ffTo0UOlXW3btoWLi4tKvpqnyE+cOIG8vDxUVFTA1dVVJY+NjQ169+4tXZsa1tbW0t/Pcy/t7Oywe/duzJs3D6mpqSgsLISvr2+dizYTERGpK8ZOkLZfZez05z//GampqdDR0cHVq1dx+PBhbNq0Cffu3UNFRUWjZdjY2Eh/t2/fHl27dlW5hoBq2x8+fIjz58/jww8/VHlyvVOnTnB0dJSuw4kTJ2BlZYXevXurnGv8+PFQKBT4/fffpTQrKyvp7zZt2sDAwACmpqZo2/b/J7DR19dHWVkZgCf3veY6rFq1CllZWTAxMYG/vz/XJSQiInoK4zFI2y8ajy1cuFCKx2peu3btqjNvQUEBFApFrf6kd999F1ZWVipxkqOjo0rc0rZtW4wdOxYXLlzA//73Pyn92TazT4lIvXGKTqJWFBoaipycHGl6gxdVX8dDhw4dGjyuJnh6WpcuXaR5vEtKSnD9+nWYmprWefyjR4+eu6yateYMDQ1V0tu2bQsDAwOpg6Up+vfvD3Nzc2m7Zp0VJycnbN26FaGhoSgpKYFCoai3DQqFQqqbgYGByr5n6woA77zzTq12GRgY1JpKoebYsrIy6fx1Xe+uXbvWantd97N9+/Z11h8Ali1bhh49euDAgQOIiIhAREQErKysEB4eDhMTk3qPIyIiUjeMnV597FRdXY2YmBjs2LEDDx8+RM+ePSGXy6Gjo/NcZTwbs2hqakIIoZL2dNvLysoghGg0TiotLUXfvn3rzAOodvzVdX8but59+vRBamoqkpKSsGfPHqSkpKBTp06YNm0alixZAg0NjXqPJSIietswHnu5eKx3794q8VhDSkpKANTfn1SzrmBpaWm9eYQQ+O9//yulPdtm9ikRqTcO8BG1Ij09PYSHh8PX1xcbN26sM49SqVTZfvjwYbOVXxOoPE2hUKBz584AgI4dO8LW1hafffZZncc3Zb51PT096fxPP3ldWVmJ+/fv1xpce1G9evVC586dce3aNQBP2tCvXz+sW7euzvx9+vSRyi4uLkavXr2kfTWBVEP09PRw//59KJVKlUG+u3fvAngyaFjT9uLiYvzpT39SOV6hUNTZWdUU2tra8PHxgY+PD27evInMzExs3LgRgYGBKvPUExERqTvGTq8+dkpKSsL27duxcuVKfPDBB+jYsSMAwN3dvVnKe1bHjh2hoaGB4uLiWvsUCoX05L6enh4UCkWdeYDaD2o1lVwuR3x8PCoqKnD69GmkpaVh8+bNMDExwYcffvhS5yYiInqTMB5r/nisPjVxUH1xUk35enp69eYBnsRJNf1Uz2KfEpF64xSdRK3MyckJrq6uSEpKwr1791T26erq4s6dOypp//rXv5qt7IKCAvzxxx/S9q1bt3DmzBnY2dkBAGxtbVFQUCA97V3z2r9/P/bs2VPvAsB1qVnQ99ng4NChQ1AqlRgyZEgztAgoKirCvXv30K9fP6ncW7duoUuXLiptyM7ORnJyMtq0aYNhw4YBAH788UeVc2VmZjZanq2tLaqqqmode+DAAQBPnoy3sLCAtrY2Dh48qJLn1KlTuHnzpsqUnE31+PFjODs7Y+vWrQCedNJNnz4dY8eOlabaIiIiepMwdnq1sdPp06cxcOBATJo0SRrcu3PnDi5fvozq6mrpOE3N5vkq2aFDB5iZmeGHH35Q6QwsKyvDkSNHpHYOHToUZ86cwY0bN1SOP3DgAAwNDWFkZPTCddi+fTscHR1RUVEBbW1tDB8+HBEREQDAeIqIiKgOjMeaNx6rT//+/WFoaFirP6mwsBB5eXlSf9LQoUORmZmp8ks9pVKJQ4cOwdzcvN5BTfYpEak//oKP6DWwfPly5Obm1nraxsHBAYcOHYKFhQWMjIyQkZGB69evN1u5Ojo68PHxgb+/P5RKJTZs2AB9fX14enoCAGbNmoX9+/dj1qxZ8PLygoGBAb7//nvs3r0bISEhTSpr4MCBmDhxImJjY/Ho0SMMHToUFy9eRHx8POzs7DBixIgm1//q1avSdFFCCNy8eRMJCQnQ0dGBh4cHAMDNzQ2pqamYPXs25s+fj549e+LYsWPYsmULPDw8oKWlBSMjI3z88cf48ssvUVVVhUGDBmH//v347bffGq3DyJEjYWdnh9DQUNy5cwcmJiY4ceIEtmzZgokTJ2LgwIEAAG9vbyQkJEBLSwuOjo4oKirChg0bpOvyotq1awdTU1PEx8dDS0sLxsbGKCgowN69e+tcQ5CIiOhNwNjp1cVOcrkcGzduRFJSEiwtLXH9+nUkJiaioqJCZUqrTp06IT8/HydOnFBZB+dFBAYGYs6cOfD29sa0adNQWVmJpKQkVFRUwNfXFwAwe/ZsHDhwALNmzcLChQuhr6+Pffv2ITc3F6tXr36pAcdhw4Zh3bp18PX1hYeHB9q0aYNdu3ZBW1sbjo6OL9U2IiKiNxXjsReLx5pCU1MTAQEBCAkJQWBgIMaPH4/79+8jPj4eenp6mD17NoAn6/odPXoUM2fOhLe3N7S0tKT19JKTk+s9P/uUiNQfB/iIXgP6+voIDw/HwoULVdJDQkJQVVWFyMhItG3bFmPGjEFgYCBCQ0ObpdzBgwfD2dkZ4eHhKCsrw/Dhw7F06VJpWoPu3btj165diI6ORnh4OMrLy9GvXz+sWrXqhaZpWrVqFYyMjJCeno4tW7agW7dumDlzJhYsWPBCnTJ///vfpb81NTWhr68PS0tLREVFSU+hd+jQATt27EB0dDSioqJQVlaG3r17IzAwEF5eXtLxK1asQNeuXZGamorS0lKMGDEC8+fPx/r16xusg4aGBhITExEbG4vt27fj3r176NOnDwICAqRACwD8/Pyk86elpUFfXx8uLi5YsmRJo3O+P891WL9+PbZu3QqFQoEuXbrA3d0dixcvfqnzEhERva4YO7262OmTTz7B/fv3kZKSgoSEBPTs2RMfffSRFPM8ePAAnTp1gpeXF1avXo05c+Zg27ZtTa7L04YPH45t27YhNjYWAQEB0NbWho2NDSIjI/Hee+8BeLL2zTfffIPo6Gh8/vnnqKyshImJCTZu3IjRo0e/VPkmJibYvHkzEhISEBAQAKVSCTMzM2zdurXW9OpERET0BOOxF4vHmsrNzQ3vvPMOEhMT4evrC11dXYwYMQIBAQHS2oDvvfcedu7ciZiYGISEhEBDQwNyuRwpKSmwsbFp8PzsUyJSbxri2RXPiYiIiIiIiIiIiIiIiOi1xTX4iIiIiIiIiIiIiIiIiNQIB/iIiIiIiIiIiIiIiIiI1AgH+IiIiIiIiIiIiIiIiIjUCAf4iIiIiIiIiIiIiIiIiNQIB/iIiIiIiIiIiIiIiIiI1AgH+IiIiIiIiIiIiIiIiIjUCAf4iIiIiIiIiIiIiIiIiNQIB/iIiIi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",
       "text/plain": [
        "<Figure size 1800x600 with 3 Axes>"
       ]
@@ -1929,7 +1929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 530,
+   "execution_count": 230,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +1942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 531,
+   "execution_count": 231,
    "metadata": {},
    "outputs": [
     {
@@ -1951,13 +1951,13 @@
        "Text(0.5, 0, 'Number of Bedrooms')"
       ]
      },
-     "execution_count": 531,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1989,7 +1989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 532,
+   "execution_count": 232,
    "metadata": {},
    "outputs": [
     {
@@ -2006,7 +2006,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 532,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2026,7 +2026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 533,
+   "execution_count": 233,
    "metadata": {},
    "outputs": [
     {
@@ -2075,124 +2075,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>16202</th>\n",
-       "      <td>4331000130</td>\n",
-       "      <td>2014-10-17</td>\n",
-       "      <td>315000.0</td>\n",
+       "      <th>19150</th>\n",
+       "      <td>1073100065</td>\n",
+       "      <td>2015-02-17</td>\n",
+       "      <td>348125.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1770</td>\n",
-       "      <td>9685</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>8451</td>\n",
+       "      <td>1.5</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1770</td>\n",
+       "      <td>1400</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1948</td>\n",
+       "      <td>1953</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1520</td>\n",
-       "      <td>11122</td>\n",
-       "      <td>76</td>\n",
+       "      <td>1590</td>\n",
+       "      <td>8433</td>\n",
+       "      <td>71</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>13225.0</td>\n",
+       "      <td>11251.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16770</th>\n",
-       "      <td>1138010170</td>\n",
-       "      <td>2014-08-01</td>\n",
-       "      <td>350000.0</td>\n",
+       "      <th>3012</th>\n",
+       "      <td>16000015</td>\n",
+       "      <td>2015-04-17</td>\n",
+       "      <td>219950.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>860</td>\n",
-       "      <td>7030</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>6601</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>860</td>\n",
+       "      <td>1070</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1973</td>\n",
+       "      <td>1985</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>7500</td>\n",
-       "      <td>51</td>\n",
+       "      <td>1030</td>\n",
+       "      <td>6614</td>\n",
+       "      <td>39</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8750.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>8741.0</td>\n",
+       "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13526</th>\n",
-       "      <td>424000145</td>\n",
-       "      <td>2014-07-07</td>\n",
-       "      <td>230000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1390</td>\n",
-       "      <td>6000</td>\n",
+       "      <th>14615</th>\n",
+       "      <td>7211400760</td>\n",
+       "      <td>2014-05-28</td>\n",
+       "      <td>277000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1450</td>\n",
+       "      <td>6250</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1390</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1954</td>\n",
+       "      <td>990</td>\n",
+       "      <td>460.0</td>\n",
+       "      <td>1964</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>70</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>60</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8780.0</td>\n",
+       "      <td>9150.0</td>\n",
        "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16900</th>\n",
-       "      <td>1324079007</td>\n",
-       "      <td>2014-11-10</td>\n",
-       "      <td>425000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1610</td>\n",
-       "      <td>144619</td>\n",
+       "      <th>15714</th>\n",
+       "      <td>2450000275</td>\n",
+       "      <td>2014-07-16</td>\n",
+       "      <td>595000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>8113</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1610</td>\n",
+       "      <td>1350</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1977</td>\n",
+       "      <td>1959</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2220</td>\n",
-       "      <td>144619</td>\n",
-       "      <td>47</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>8113</td>\n",
+       "      <td>65</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>147839.0</td>\n",
+       "      <td>10813.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9417</th>\n",
-       "      <td>5149800040</td>\n",
-       "      <td>2014-09-18</td>\n",
-       "      <td>255000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2560</td>\n",
-       "      <td>12155</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>3091</th>\n",
+       "      <td>9264950940</td>\n",
+       "      <td>2014-08-05</td>\n",
+       "      <td>348000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>7458</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1350</td>\n",
-       "      <td>1210.0</td>\n",
-       "      <td>1960</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1989</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1790</td>\n",
-       "      <td>11906</td>\n",
-       "      <td>64</td>\n",
+       "      <td>2480</td>\n",
+       "      <td>7743</td>\n",
+       "      <td>35</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>17275.0</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>11578.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2201,37 +2201,37 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "16202  4331000130 2014-10-17  315000.0         3       2.00         1770   \n",
-       "16770  1138010170 2014-08-01  350000.0         3       1.00          860   \n",
-       "13526   424000145 2014-07-07  230000.0         3       1.00         1390   \n",
-       "16900  1324079007 2014-11-10  425000.0         3       1.75         1610   \n",
-       "9417   5149800040 2014-09-18  255000.0         4       2.00         2560   \n",
+       "19150  1073100065 2015-02-17  348125.0         3        1.0         1400   \n",
+       "3012     16000015 2015-04-17  219950.0         3        1.5         1070   \n",
+       "14615  7211400760 2014-05-28  277000.0         4        1.0         1450   \n",
+       "15714  2450000275 2014-07-16  595000.0         4        1.5         1350   \n",
+       "3091   9264950940 2014-08-05  348000.0         3        2.5         2060   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "16202      9685     1.0           0        3.0  ...        1770   \n",
-       "16770      7030     1.0           0        3.0  ...         860   \n",
-       "13526      6000     1.0           0        3.0  ...        1390   \n",
-       "16900    144619     1.0           0        3.0  ...        1610   \n",
-       "9417      12155     1.0           0        4.0  ...        1350   \n",
+       "19150      8451     1.5           0        3.0  ...        1400   \n",
+       "3012       6601     1.0           0        3.0  ...        1070   \n",
+       "14615      6250     1.0           0        3.0  ...         990   \n",
+       "15714      8113     1.0           0        4.0  ...        1350   \n",
+       "3091       7458     2.0           0        3.0  ...        2060   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "16202            0.0      1948           0.0           1520       11122   \n",
-       "16770            0.0      1973           0.0           1360        7500   \n",
-       "13526            0.0      1954           0.0           1170        6000   \n",
-       "16900            0.0      1977           0.0           2220      144619   \n",
-       "9417          1210.0      1960           0.0           1790       11906   \n",
+       "19150            0.0      1953           0.0           1590        8433   \n",
+       "3012             0.0      1985           0.0           1030        6614   \n",
+       "14615          460.0      1964           0.0           1440        4000   \n",
+       "15714            0.0      1959           0.0           1930        8113   \n",
+       "3091             0.0      1989           0.0           2480        7743   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  price_range  \n",
-       "16202         76             0.0     13225.0    300K-600K  \n",
-       "16770         51             0.0      8750.0    300K-600K  \n",
-       "13526         70             0.0      8780.0    100K-300K  \n",
-       "16900         47             0.0    147839.0    300K-600K  \n",
-       "9417          64             0.0     17275.0    100K-300K  \n",
+       "19150         71             0.0     11251.0    300K-600K  \n",
+       "3012          39             0.0      8741.0    100K-300K  \n",
+       "14615         60             0.0      9150.0    100K-300K  \n",
+       "15714         65             0.0     10813.0    300K-600K  \n",
+       "3091          35             0.0     11578.0    300K-600K  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 533,
+     "execution_count": 233,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2242,12 +2242,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 534,
+   "execution_count": 234,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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D7dq1U5kyZaxns7766itJuu/si/dTpkwZRUVF3XeWv5SUFJ07d05+fn6S7kyvn5ycrD179li3SUpK0tdff53meZ6enrp06VKaZXfPYpj6uFGjRnryySetQ9vSU7unp6fKly+vU6dOqVKlStb/Hn/8cc2cOTPDwyv/Lr39ERgYqL179+r27dvWbVJSUrRt2zZVqlTJuk9/P8uYnv0ODAzUkSNHrEFPunMW6MiRI9bHqWfboqKi0rwPkZGRmjFjxn0D892++OKLNI+3bdsmNzc3ValSxbrs1Vdf1a+//qrly5frqaeeSjPM8mGoXr26IiIi0oSX69evq3Pnzvryyy9Vo0YNxcbGyjCMNO/Br7/+qtmzZ6cZ6phew4YNk4uLi8aOHWsdsnj48GE9+eSTevbZZ63B7KefflJkZGS6P2eSVK1aNTk4OGj37t1plu/atcv676z++QaQ/XDmDAAykdlsVv/+/TV06FC9++67atasmaKiojRr1izlzp1bHTp0kHTnLIJ056A6d+7cKlWqlDw9PTVv3jw5OjrK0dFRO3futF5Pld7ZGp9++mk1bdpUU6dO1S+//KLnn39ePj4+unTpktauXatLly5p+vTpku6cKahXr56Cg4N17do1FS1aVKtWrdKFCxfSnAlo0KCB5s+fr/nz56tKlSr6/PPP75nev3Llytq6dasqVKigQoUK6fvvv9eCBQtkMpn+Z+39+/dX165dre9XSkqKlixZooiICPXo0SNd+/1P0tsfvXr10ldffaV27dqpa9eucnJy0qpVq3Tu3DktWrTI2p6Xl5e+//57HTp0SNWrV0/Xfrdr106rV69Wp06d1LNnT0l3prFPSkqyXrPl7++vZs2aWa8Lq1ixok6fPq1p06apaNGiKlmy5L/u565du1SwYEE99dRT2rdvn9atW6c+ffqkGX5YrVo1lSpVSgcPHtS0adP+0/v6INq3b6/Nmzerc+fO6tatm5ycnDR37lwVKlRIL730knLlyqXAwED16NFDPXr0kK+vr44ePaqwsDDVqVPHOlQzI4oWLapOnTppzpw5Wr58uTp37qzKlStrx44d+uCDD+Tr66sTJ05o7ty56fpZvVuxYsXUsmVLTZ06VUlJSSpbtqx2795tvYYuNcin9+f7yJEj8vHxeWg37QZgnwhnAJDJWrRoIQ8PD82fP189e/aUp6en6tSpo/79+1uvDXr88cfVtGlTrV69Wl9//bU++eQTzZkzR5MmTVKfPn3k4eGhcuXKadWqVerSpYvCw8PT3Hvs30yePFk1atTQxx9/bL0vk4+Pj55++mmNHz8+zfTnYWFhmjJlimbOnKmEhAQ1adJEjRs31tGjR63bdOvWTZGRkVq8eLGSkpJUv359jRs3Tt27d7duM2HCBI0ZM0ZjxoyRJJUsWVKjRo3Sxx9/rPDw8H+tt3bt2lq8eLFmzZql3r17y8nJSRUqVNDSpUsz5SbG6e2PNWvWWKfaN5lMqly5slasWGE9qyVJb7/9tubMmaMuXbpo+/bt6dpvLy8vrVixQuPGjdOgQYPk4eGhNm3ayM3NLc31d+PHj9f8+fOtITpv3rx68cUX1bdv33vOtP7d+++/r23btmnZsmXKnz+/3nvvPeu99O5Wv359RUZG/uvkIlmlcOHCWrNmjSZPnqwhQ4bI2dlZTz75pKZNm6bcuXNLkhYsWKAZM2Zo/vz5un79ugoWLKgOHTpYQ+2D6Nq1qzZv3qw5c+aoWbNmGjJkiJKSkjR9+nQlJiaqaNGi6t69u06ePKnPP//8niG8/2bYsGFyd3fXkiVLdPv2bdWqVUvdu3fX7NmzrX2b3p/v1q1bq3nz5powYcID7yuAR5/JeJCrzAEA2daQIUN08OBBff7557YuJVuIiIjQjRs3VK9ePeuy5ORk1a9fX02aNNHQoUMfSh2GYahJkyaqXbu23nvvvYfymtnZjRs39NVXX6lOnTpprkmcOHGiNm3axJBFAA+EM2cAAGShCxcuqF+/furZs6dq1KihuLg4rVu3Trdu3dJrr72W5a9/+/ZtLVu2TD/++KPOnTunN998M8tfMydwc3PTuHHjVK5cOb311ltyd3e33sS9W7duti4PwCOKcAYAQBZ64YUXdOPGDa1Zs0aLFy+Wk5OTqlSpolWrVsnX1zfLX9/V1VVr166VxWJRSEhImmGteHAuLi5atmyZpk+friFDhiguLk7FixfX4MGDuXcZgAfGsEYAAAAAsANMpQ8AAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoDZGrOIYRiyWJhrBQAAAMjJzGaTTCZTurYlnGURi8VQZGSMrcsAAAAAYEM+Ph5ycEhfOGNYIwAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAUdbFwAAAAAg85nNJpnNJluXkW1ZLIYsFiNT2yScAQAAANmM2WxSnjzucnBgoFxWSUmx6MaN2EwNaDYPZ5cvX1bdunXvWT5+/Hi1aNFCx48f17hx4/TTTz/Jx8dH7du3V7t27azbWSwWzZo1Sxs2bNCtW7cUGBio4cOHq1ixYtZtMqMNAAAA4FFhNpvk4GDW+5NX6/S5y7YuJ9spVaygxg1sK7PZlL3C2YkTJ+Ti4qI9e/bIZPrrtGuuXLkUFRWlDh06qGHDhho1apSOHDmiUaNGycPDQy1btpQkzZkzR2vWrNGECRNUqFAhTZ48WZ07d9bWrVvl7OycKW0AAAAAj6LT5y7rxO9/2roMpJPNw9mvv/6qkiVLqkCBAvesW758uZycnDR69Gg5OjrK19dXZ8+e1YIFC9SyZUslJiZqyZIlGjBggOrXry9JmjZtmurUqaNdu3apadOmWr9+/X9uAwAAAACyms0Hof7yyy/y9fW977rw8HDVqFFDjo5/ZciaNWvqzJkzunbtmk6cOKGYmBjVqlXLut7Ly0vly5fXoUOHMq0NAAAAAMhqdnHmzNvbW23bttXp06dVokQJde/eXXXr1tWlS5fk5+eXZvvUM2wXL17UpUuXJEmFCxe+Z5vUdZnRxoNydLR59gUAAEAOxEQgD0dmv882DWfJyck6deqUypQpoyFDhsjT01Pbtm1T165dtXTpUsXHx99zzZeLi4skKSEhQXFxcZJ0321u3rwpSZnSxoMwm03y9vZ44OcDAAAAsG9eXm6Z2p5Nw5mjo6MOHDggBwcHubq6SpIqVqyo3377TYsXL5arq6sSExPTPCchIUGS5O7ubn1OYmKi9d+p27i53XmjMqONB2GxGIqOjn3g5wMAAAAPysHBnOnBAfeKjo5TSorlX7fx8nJL9xk2mw9r9PC49+zS448/rn379qlQoUK6cuVKmnWpjwsWLKjk5GTrsuLFi6fZxt/fX5IypY0HlZz87x0FAAAA4NGVkmLJ1GN+mw5G/e233/TEE0/owIEDaZb/9NNPKlOmjAIDA3X48GGlpKRY1+3fv1+lSpVS3rx5VbZsWXl6eqZ5fnR0tI4dO6bAwEBJypQ2AAAAACCr2TSc+fr6qnTp0ho9erTCw8P1+++/a/z48Tpy5Ii6d++uli1b6vbt23r//fd18uRJbdq0ScuWLVO3bt0k3blOLCgoSKGhofrss8904sQJ9evXT4UKFVKjRo0kKVPaAAAAAICsZtNhjWazWfPmzdOUKVPUt29fRUdHq3z58lq6dKl1hsVFixZp3Lhxat68ufLnz69BgwapefPm1jZ69+6t5ORkBQcHKz4+XoGBgVq8eLGcnJwkSXnz5v3PbQAAAABAVjMZhmHYuojsKCXFosjIGFuXAQAAgBzI0dEsb28Ptek9VSd+/9PW5WQ7ZX2LaE1Yf0VFxfzPa858fDzSPSEIN0AAAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7YFfh7PTp0woICNCmTZusy44fP66goCBVrVpVDRs21IoVK9I8x2KxKCwsTHXq1FHVqlXVpUsXnTt3Ls02mdEGAAAAAGQluwlnSUlJGjBggGJjY63LoqKi1KFDBxUvXlwbN25Uz549FRoaqo0bN1q3mTNnjtasWaMxY8Zo7dq1slgs6ty5sxITEzOtDQAAAADIanYTzmbOnClPT880y9avXy8nJyeNHj1avr6+atmypdq3b68FCxZIkhITE7VkyRL17t1b9evXV9myZTVt2jRdunRJu3btyrQ2AAAAACCr2UU4O3TokNatW6cJEyakWR4eHq4aNWrI0dHRuqxmzZo6c+aMrl27phMnTigmJka1atWyrvfy8lL58uV16NChTGsDAAAAALKa4//eJGtFR0dr0KBBCg4OVuHChdOsu3Tpkvz8/NIsK1CggCTp4sWLunTpkiTd87wCBQpY12VGGw/K0dEusi8AAAByGAcHjkMfhsx+n20ezkaOHKmAgAC99NJL96yLj4+Xs7NzmmUuLi6SpISEBMXFxUnSfbe5efNmprXxIMxmk7y9PR74+QAAAADsm5eXW6a2Z9NwtnnzZoWHh2vr1q33Xe/q6nrPpBwJCQmSJHd3d7m6ukq6c91Y6r9Tt3Fzc8u0Nh6ExWIoOjr2f28IAAAAZDIHB3OmBwfcKzo6Tikpln/dxsvLLd1n2GwazjZu3Kjr16+rfv36aZaPGDFC27dvV6FChXTlypU061IfFyxYUMnJydZlxYsXT7ONv7+/JGVKGw8qOfnfOwoAAADAoyslxZKpx/w2DWehoaGKj49Ps6xRo0bq3bu3mjVrpi1btmjt2rVKSUmRg4ODJGn//v0qVaqU8ubNq1y5csnT01MHDhywBqvo6GgdO3ZMQUFBkqTAwMD/3AYAAAAAZDWbXilYsGBBlShRIs1/kpQ3b14VLFhQLVu21O3bt/X+++/r5MmT2rRpk5YtW6Zu3bpJunOdWFBQkEJDQ/XZZ5/pxIkT6tevnwoVKqRGjRpJUqa0AQAAAABZzeYTgvybvHnzatGiRRo3bpyaN2+u/Pnza9CgQWrevLl1m969eys5OVnBwcGKj49XYGCgFi9eLCcnp0xrAwAAAACymskwDMPWRWRHKSkWRUbG2LoMAAAA5ECOjmZ5e3uoTe+pOvH7n7YuJ9sp61tEa8L6Kyoq5n9ec+bj45HuCUG4AQIAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHHB33izZs3FR4eritXruj555/XjRs3VKpUKZlMpsysDwAAAAByhAcKZ3PnztX8+fMVHx8vk8mkypUra/r06YqKitKSJUvk5eWV2XUCAAAAQLaW4WGNq1at0syZM9WhQwetX79ehmFIkoKCgnTu3DnNmDEj04sEAAAAgOwuw+Fs5cqV6tq1q/r06aMKFSpYl9erV099+/bV559/nqkFAgAAAEBOkOFwduHCBdWoUeO+60qXLq1r167956IAAAAAIKfJcDgrXLiwfvjhh/uu++mnn1S4cOH/XBQAAAAA5DQZnhDk1Vdf1cyZM+Xq6qr69etLkmJjY7Vz507Nnz9fHTp0yOwaAQAAACDby3A469Kli86fP6/Q0FCFhoZKktq1aydJeumll9StW7fMrRAAAAAAcoAMhzOTyaTRo0erQ4cO2r9/v27evKlcuXIpMDBQfn5+WVEjAAAAAGR7D3wT6lKlSqlUqVKZWQsAAAAA5FgZDmdvvvmmTCbTfdeZzWa5u7urRIkSatWqlUqXLv2fCwQAAACAnCDDszUWK1ZMR44csc7YmC9fPplMJkVEROjQoUOKjIzUJ598opYtW+rYsWOZXjAAAAAAZEcZPnOWP39+PfbYY1qyZIkee+wx6/IrV66oa9euqlu3rrp166ZevXpp+vTpWrBgQaYWDAAAAADZUYbPnG3cuFF9+vRJE8wkqUCBAurevbvWrFkjBwcHtW7dWhEREZlWKAAAAABkZxkOZ3FxcXJycrrvOpPJpJiYGEmSu7u7EhMT/1t1AAAAAJBDZDicPfHEE5oxY4auXbuWZvn169c1e/ZsBQQESJIOHjyo4sWLZ06VAAAAAJDNZfias6FDh6pt27Z69tlnFRAQIB8fH12/fl1HjhyRh4eHpk6dqq+++kqzZ8/WyJEjs6BkAAAAAMh+MnzmrHTp0tq+fbs6dOighIQE/fzzz5KkLl266NNPP5Wvr6/y5MmjadOmqXXr1pleMAAAAABkRw90E2pvb2/16dPnH9dXrlxZlStXfuCiAAAAACCneaBwdvToUR04cECJiYkyDEOSZBiGYmNjdfjwYa1fvz5TiwQAAACA7C7D4Wz16tUaO3asNZTdzWw2q3bt2plSGAAAAADkJBm+5mzVqlWqW7euDhw4oI4dO+q1117TkSNHNGPGDLm4uKhZs2ZZUScAAAAAZGsZDmfnz59XmzZtlDt3blWsWFGHDx+Wq6urnn/+eXXt2lUrVqzIijoBAAAAIFvLcDhzcnKSq6urJKlEiRI6e/askpKSJEnVqlXTmTNnMrVAAAAAAMgJMhzOypUrp71790qSSpUqJYvFooiICEnSpUuXMrc6AAAAAMghMjwhSIcOHdSrVy9FR0crJCREzzzzjAYNGqRGjRpp69atqlatWlbUCQAAAADZWobPnD377LOaN2+efH19JUmjR49WyZIltXbtWpUuXVrDhg3L9CIBAAAAILt7oPuc1a9fX/Xr15d054bUS5Yssa5jaCMAAAAAZNwDXXN29OjR+64LDw/XCy+88J+LAgAAAICcJl1nzpYsWaLY2FhJkmEY2rBhg7766qt7tvvhhx/k7OycuRUCAADAZsxmk8xmk63LyNYsFkMWi2HrMmAH0hXOEhISNGvWLEmSyWTShg0b7tnGbDYrV65c6t69e+ZWCAAAAJswm03Kk8ddDg4ZHmyFDEhJsejGjVgCGtIXzrp3724NXWXLltX69etVuXLlLC0MAAAAtmU2m+TgYNbg2et06sJVW5eTLZV+LL8m9mwts9lEOEPGJwQ5ceKE9d9xcXG6ffu28uTJIycnp0wtDAAAAPbh1IWrOn7mgq3LALK9B5qtMTw8XJMmTdJPP/0kw7iT8CtXrqx+/fqpZs2amVogAAAAAOQEGQ5n33//vdq3b69ixYqpR48eypcvn65cuaJt27apc+fOWrlypQICArKiVgAAAADItjIczqZPn67q1atr8eLFcnBwsC7v1auXOnXqpJkzZ6a57xkAAAAA4H/L8NQ7P/74o9q1a5cmmEl3ZmsMCgr6x3ugAQAAAAD+WYbDmYeHh5KTk++7Ljk52XoNGgAAAAAg/TIczp544gktWLBAcXFxaZbHxsZqwYIFql69eqYVBwAAAAA5RYavOXv33XfVokULPfPMM6pfv77y58+vq1ev6osvvlB8fLzGjRuXFXUCAAAAQLaW4XBWokQJrV+/XjNnztSXX36pmzdvKnfu3KpRo4Z69eqlMmXKZEWdAAAAAJCtPdB9znx9fTV9+vRMLgUAAAAAcq4HCmeRkZFasmSJDh48qOjoaHl7e6t69epq37698ubNm9k1AgAAAEC2l+EJQS5duqTmzZtr+fLlcnFxUfny5eXo6KilS5fqlVde0eXLl7OiTgAAAADI1jIcziZPnixHR0dt375dK1eu1NSpU7Vy5Urt2LFDrq6umjZtWobau379ugYOHKiaNWsqICBAXbt21e+//25df/z4cQUFBalq1apq2LChVqxYkeb5FotFYWFhqlOnjqpWraouXbro3LlzabbJjDYAAAAAICtlOJzt27dPvXv3VrFixdIsL1asmHr27KmvvvoqQ+317NlTZ8+e1YIFC/Thhx/K1dVV7du3V1xcnKKiotShQwcVL15cGzduVM+ePRUaGqqNGzdanz9nzhytWbNGY8aM0dq1a2WxWNS5c2clJiZKUqa0AQAAAABZLcPhLCUlRd7e3vdd5+Pjo9u3b6e7rZs3b6pIkSIaO3asKleuLF9fX/Xo0UNXrlzRb7/9pvXr18vJyUmjR4+Wr6+vWrZsqfbt22vBggWSpMTERC1ZskS9e/dW/fr1VbZsWU2bNk2XLl3Srl27JClT2gAAAACArJbhcObv76+tW7fed92WLVvk5+eX7rZy586tKVOmWJ8TGRmpZcuWqVChQipTpozCw8NVo0YNOTr+NW9JzZo1debMGV27dk0nTpxQTEyMatWqZV3v5eWl8uXL69ChQ5KUKW0AAAAAQFbL8GyNPXr0UKdOnXTz5k29+OKL1ptQb9u2Tfv27VNYWNgDFTJs2DCtX79ezs7Omjt3rtzd3XXp0qV7wl6BAgUkSRcvXtSlS5ckSYULF75nm9R1mdHGg3J0zHD2BQAgWzKZTDKbTbYuI1uzWAwZhpGpbTo4cCzzsGT2e03fPRyZ/T5nOJw9/fTTmjBhgkJDQ9NcX5YvXz6FhIToueeee6BC3nrrLbVu3VqrV69Wz549tWbNGsXHx8vZ2TnNdi4uLpKkhIQExcXFSdJ9t7l586YkZUobD8JsNsnb2+OBnw8AQHaSYrHIwczBYlbiPX60eXm52boEPIDM7rcHus/ZK6+8opdfflmnTp3SzZs3lTt3bpUuXVom04N/I1amTBlJ0rhx4xQREaFVq1bJ1dX1nkk5EhISJEnu7u5ydXWVdOe6sdR/p27j5nbnjcqMNh6ExWIoOjr2gZ8PAEB24eBglpeXm4Yu+1CnLl2zdTnZUulC+TS+/auKjo5TSool09pN7TtkPfru0ZSefvPyckv3GbZ0hbOlS5fqpZdeUr58+azLTCaTfH190/Ui/yQyMlLfffednn/+ees1YWazWWXKlNGVK1dUqFAhXblyJc1zUh8XLFhQycnJ1mXFixdPs42/v78kZUobDyo5OfM+YAAAPOpOXbqmE+cv2rqMbC0lxcLxxyOKvns0ZXa/pSvCTZo0SRcuXLA+NgxDI0eO/M/XZF27dk39+/fXd999Z12WlJSkY8eOydfXV4GBgTp8+LBSUlKs6/fv369SpUopb968Klu2rDw9PXXgwAHr+ujoaB07dkyBgYGSlCltAAAAAEBWS1c4+/vFpRaLRevWrdP169f/04v7+fmpbt26Gjt2rA4dOqRff/1VQ4YMUXR0tNq3b6+WLVvq9u3bev/993Xy5Elt2rRJy5YtU7du3STduU4sKChIoaGh+uyzz3TixAn169dPhQoVUqNGjSQpU9oAAAAAgKz2QNecSfcGtgc1depUTZkyRf369dOtW7dUvXp1rV69Wo899pgkadGiRRo3bpyaN2+u/Pnza9CgQWrevLn1+b1791ZycrKCg4MVHx+vwMBALV68WE5OTpKkvHnz/uc2AAAAACCrPXA4yyy5cuXSyJEjNXLkyPuur1y5statW/ePz3dwcNDAgQM1cODAf9wmM9oAAAAAgKzEfKsAAAAAYAf+Uzj7L1PnAwAAAAD+ku5hja1bt75nWcuWLe9ZZjKZdOzYsf9WFQAAAADkMOkKZ7169crqOgAAAAAgRyOcAQAAAIAdYEIQAAAAALADhDMAAAAAsAOEMwAAAACwA4QzAAAAALADhDMAAAAAsAPpvs/Z3RITE/Xhhx/q22+/1dWrVxUSEqKDBw+qQoUKqly5cmbXCAAAAADZXobPnEVGRqply5YaN26czp49q6NHjyo+Pl5ffPGF3nzzTf3www9ZUScAAAAAZGsZDmeTJk1STEyMtm/fro8++kiGYUiSwsLCVKlSJYWFhWV6kQAAAACQ3WU4nO3du1d9+vRRiRIlZDKZrMtdXFzUsWNH/fzzz5laIAAAAADkBBkOZwkJCcqTJ8991zk4OCgpKem/1gQAAAAAOU6Gw1mlSpW0Zs2a+67bunWrKlas+J+LAgAAAICcJsOzNfbp00ft27fXyy+/rHr16slkMumTTz7RzJkztW/fPi1atCgr6gQAAACAbC3DZ86qV6+upUuXys3NTYsWLZJhGFq2bJmuXr2q+fPnq2bNmllRJwAAAABkaxk+c/bdd98pICBAa9euVXx8vG7evClPT095eHhkRX0AAAAAkCNk+MzZO++8o127dkmSXF1dVbBgQYIZAAAAAPxHGQ5nXl5ecnV1zYpaAAAAACDHyvCwxm7dumns2LE6ffq0ypYtK3d393u2CQwMzJTiAAAAACCnyHA4GzFihCRp2rRpkpTmRtSGYchkMun48eOZVB4AAAAA5AwZDmcrVqzIijoAAAAAIEfLcDirUaNGVtQBAAAAADlahsOZJB09elQHDhxQYmKiDMOQdGdIY2xsrA4fPqz169dnapEAAAAAkN1lOJytXr1aY8eOtYayu5nNZtWuXTtTCgMAAACAnCTDU+mvWrVKdevW1YEDB9SxY0e99tprOnLkiGbMmCEXFxc1a9YsK+oEAAAAgGwtw+Hs/PnzatOmjXLnzq2KFSvq8OHDcnV11fPPP6+uXbsyYQgAAAAAPIAMhzMnJyfrTahLlCihs2fPKikpSZJUrVo1nTlzJlMLBAAAAICcIMPhrFy5ctq7d68kqVSpUrJYLIqIiJAkXbp0KXOrAwAAAIAcIsMTgnTo0EG9evVSdHS0QkJC9Mwzz2jQoEFq1KiRtm7dqmrVqmVFnQAAAACQrWX4zNmzzz6refPmydfXV5I0evRolSxZUmvXrlXp0qU1fPjwTC8SAAAAALK7B7rPWf369VW/fn1Jkre3t5YsWZKZNQEAAABAjpPhcHbhwoX/uc1jjz32QMUAAAAAQE6V4XDWsGFDmUymf93m+PHjD1wQAAAAAOREGQ5nISEh94Sz2NhYhYeH68CBAwoJCcm04gAA+Duz2SSz+d+/JMSDs1gMWSyGrcsAgBwpw+GsRYsW913etm1bjR8/Xlu3brVejwYAQGYym03Kk8ddDg4Zns8K6ZSSYtGNG7EENACwgQeaEOSfNGzYUD169MjMJgEAsDKbTXJwMGvMho909uo1W5eT7ZTIn0/DWjWX2WwinAGADWRqOIuIiJCjY6Y2CQDAPc5evabfLl6ydRkAAGSqDCepoUOH3rPMYrHo0qVLOnTokF599dVMKQwAAAAAcpIMh7MDBw7cs8xkMsnT01NdunTR22+/nSmFAQAAAEBOkuFw9vnnn2dFHQAAAACQozHdFQAAAADYgSy5CXUqk8mkPXv2ZLgoAAAAAMhpMhzOmjdvrlWrVkm6E9QKFiyoGzdu6Ouvv9aFCxfUuHFjubq6ZnqhAAAAAJCdZTic3b59W6VKldLixYvl4eFhXZ6cnKwePXooV65cGj16dKYWCQAAAADZXYavOdu8ebO6du2aJphJkqOjo4KCgrRt27ZMKw4AAAAAcooHmhDkxo0b911+/vx5ubi4/Jd6AAAAACBHynA4q1+/vqZMmaKvvvrKuswwDO3evVszZszQSy+9lKkFAgAAAEBOkOFrzt577z116tRJXbt2lZOTk/LkyaOoqCilpKSobt26evfdd7OiTgAAAADI1jIcznLnzq0NGzboiy++0OHDh3Xz5k15e3urVq1aqlWrVlbUCAAAAADZXobDmXTn/mUNGjRQgwYNMrseAAAAAMiR0n3NWUpKinbu3Kljx45Zl/3xxx/q06ePmjZtqnfffVenT5/OkiIBAAAAILtLVziLiYnR66+/rr59+2rfvn2SpOjoaLVp00afffaZSpQooV9//VWvv/66/vzzzywtGAAAAACyo3SFs8WLF+uPP/7QrFmz1LFjR0nS8uXLdf36dY0YMUKzZ8/W5s2b5efnpzlz5mRpwQAAAACQHaUrnO3atUudO3fWM888I0dHR+syDw8PtWjRQpLk4OCg119/Xd98803WVQsAAAAA2VS6wtn58+dVsWJF6+OoqCidPHlS1atXl4ODg3V5wYIFdf369cyvEgAAAACyuXSFMwcHByUnJ1sfHz58WIZhqGbNmmm2i4qKkru7e+ZWCAAAAAA5QLrC2eOPP67Dhw9bH+/Zs0cmk0l16tRJs93OnTvl5+eXuRUCAAAAQA6Qrvucvfbaaxo+fLhMJpMsFou2bt2qJ598Ur6+vpKkhIQErVy5Utu3b9eoUaOytGAAAAAAyI7SFc5atGihCxcuaNGiRYqPj1eVKlU0ceJE6/r69evrxo0bevHFF9WqVassKxYAAAAAsqt0hTNJ6tWrl7p166Zbt27Jx8cnzbru3burTJkyeuqppzK9QAAAAADICdIdziTJycnpnmAmSe3atcu0ggAAAAAgJ0rXhCAAAAAAgKxFOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO5Cu2RobNmwok8mUrgZNJpP27Nnzn4oCAAAAgJwmXeGsRo0a6Q5nAAAAAICMS1c4mzBhQlbXAQAAAAA5WoZuQp0qISFBv/zyixITE2UYhiTJYrEoLi5O4eHhGjBgQKYWCQAAAADZXYbD2YEDB9SnTx/dvHnzvus9PDwIZwAAAACQQRmerXHatGny9vZWWFiYnn32WTVq1Ejz5s1TmzZtZDKZtHDhwgy1d+PGDQ0fPlx169bVE088oTfeeEPh4eHW9d99951atGihKlWqqHHjxtq2bVua5yckJGjUqFGqVauWAgIC9O677yoyMjLNNpnRBgAAAABkpQyHs19++UW9evXSc889pwYNGujixYuqV6+ehg0bpldffVVz587NUHv9+/fXDz/8oKlTp2rjxo0qV66cOnXqpFOnTun3339Xt27dVKdOHW3atEmtWrXSoEGD9N1331mfP3LkSO3bt08zZ87U8uXLderUKfXu3du6PjPaAAAAAICsluFhjRaLRQULFpQklShRQr/99pt13fPPP6/Bgwenu62zZ8/qm2++0Zo1a1StWjVJ0rBhw/T1119r69atun79uvz9/dWvXz9Jkq+vr44dO6ZFixapVq1aunz5sjZv3qx58+apevXqkqSpU6eqcePG+uGHHxQQEKDly5f/5zYAAAAAIKtl+MxZ8eLF9csvv0iSSpUqpbi4OJ06dUqSlJycrJiYmHS35e3trQULFqhSpUrWZSaTSSaTSdHR0QoPD1etWrXSPKdmzZo6fPiwDMPQ4cOHrctSlSpVSgULFtShQ4ckKVPaAAAAAICsluFw9tJLLyk0NFSrVq2Sj4+PKlasqDFjxujzzz/X7NmzVaZMmXS35eXlpXr16snZ2dm6bOfOnTp79qzq1KmjS5cuqVChQmmeU6BAAcXFxSkqKkqXL1+Wt7e3XFxc7tnm0qVLkpQpbQAAAABAVsvwsMbOnTsrKipKERERCgoK0ogRI9SlSxf16NFDnp6eGb7m7G7ff/+9hg4dqkaNGql+/fqKj49PE9wkWR8nJiYqLi7unvWS5OLiooSEBEnKlDYelKNjhrMvAOBfODjwe/VhyOz3mX57eOi7Rxd992jK7Pc5w+HMbDanua6sUqVK2rNnj06dOqXSpUvL09PzgQrZs2ePBgwYoCeeeEKhoaGS7gSkxMTENNulPnZzc5Orq+s966U7sy+6ubllWhsPwmw2ydvb44GfDwCArXh5PfjfP9gWfffoou8eTZndbxkOZ+3atdOIESPk6+trXebp6anKlSvrxIkTGjhwoLZu3ZqhNletWqVx48apcePGmjhxovVMVuHChXXlypU02165ckXu7u7KlSuXChUqpBs3bigxMTHN2a8rV65YJy3JjDYehMViKDo69oGfDwC4l4ODmQOYhyA6Ok4pKZZMa49+e3jou0cXffdoSk+/eXm5pfsMW7rCWXh4uAzDkCQdPHhQhw4duu99wPbu3atz586l64VTrVmzRmPGjNGbb76p999/XyaTybquevXqOnjwYJrt9+/fryeeeEJms1nVqlWTxWLR4cOHrZN+nD59WpcvX1ZgYGCmtfGgkpMz7wMGAMDDkpJi4W/YI4q+e3TRd4+mzO63dIWzDRs2aMuWLdaZFEeNGnXPNqnhrWnTpul+8dOnTyskJETPPfecunXrpmvXrlnXubq66s0331Tz5s0VGhqq5s2b68svv9Snn36qRYsWSZIKFiyoJk2aKDg4WCEhIXJzc9OIESNUo0YNVa1aVZIypQ0AAAAAyGrpCmfBwcFq2bKlDMPQW2+9peHDh98zK6PZbJaXl5cef/zxdL/4zp07lZSUpN27d2v37t1p1jVv3lwTJkzQnDlzNHnyZC1fvlxFixbV5MmT00yNP2bMGIWEhKhXr16SpLp16yo4ONi6/vHHH//PbQAAAABAVktXOMuVK5dq1KghSVqxYoXKly//wBN/3O3tt9/W22+//a/b1K1bV3Xr1v3H9e7u7ho7dqzGjh2bpW0AAAAAQFbK8IQgNWrUUGRkpEJDQ3Xw4EFFR0fL29tb1atXV/v27ZU3b96sqBMAAAAAsrUMT8x/6dIltWjRQsuXL5eLi4vKly8vR0dHLV26VK+88oouX76cFXUCAAAAQLaW4TNnkydPloODg7Zv365ixYpZl587d04dO3bUtGnTNGHChEwtEgAAAACyuwyfOdu3b5969+6dJphJUrFixdSzZ0999dVXmVYcAAAAAOQUGQ5nKSkp8vb2vu86Hx8f3b59+z8XBQAAAAA5TYbDmb+/v7Zu3XrfdVu2bJGfn99/LgoAAAAAcpoMX3PWo0cPderUSTdv3tSLL76o/Pnz6+rVq9q2bZv27dunsLCwrKgTAAAAALK1dIWzdu3aacSIEfL19dXTTz+tCRMmKDQ0NM31Zfny5VNISIiee+65LCsWAAAAALKrdIWzgwcPKiYmxvr4lVde0csvv6xTp07p5s2byp07t0qXLi2TyZRlhQIAAABAdpbhYY2pTCaTfH19M7MWAAAAAMixMjwhCAAAAAAg86X7zFnPnj3l7Oz8P7czmUzas2fPfyoKAAAAAHKadIez8uXLy8fHJytrAYCHxmw2yWzmOtmsYrEYslgMW5cBAMAjJUNnzipXrpyVtQDAQ2E2m5TH210OZkZ2Z5UUi0U3omIJaAAAZMADTwgCAI8qs9kkB7NZ03d8rPOR121dTrZT1Cev+r7QTGaziXAGAEAGEM4A5FjnI6/r9JXLti4DAABAUjpna2zevLm8vb2zuhYAAAAAyLHSdeZs/PjxWV0HAAAAAORoXA0PAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2wNHWBQCPMrPZJLPZZOsysjWLxZDFYti6DAAAgCxHOAMekNlsUh5vdzmYOQGdlVIsFt2IiiWgAQCAbI9wBjwgs9kkB7NZy77eoUs3I21dTrZUKLeP2td5QWaziXAGAACyPcIZ8B9duhmpc5FXbF0GAAAAHnGMxwIAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA7QDgDAAAAADtAOAMAAAAAO0A4AwAAAAA74GjrAiCZzSaZzSZbl5GtWSyGLBbD1mUAAAAA/4hwZmNms0ne3u4ymzmJmZUsFouiomIJaAAAALBbhDMbu3PWzKxPjuzW9duRti4nW8rr6aOmVZ+T2WwinAEAAMBu2VU4mz9/vvbt26eVK1dalx0/flzjxo3TTz/9JB8fH7Vv317t2rWzrrdYLJo1a5Y2bNigW7duKTAwUMOHD1exYsUytY2sdv12pC5HX3torwcAAADAvtjNWLrVq1dr+vTpaZZFRUWpQ4cOKl68uDZu3KiePXsqNDRUGzdutG4zZ84crVmzRmPGjNHatWtlsVjUuXNnJSYmZlobAAAAAJDVbH7m7PLlyxoxYoQOHDigkiVLplm3fv16OTk5afTo0XJ0dJSvr6/Onj2rBQsWqGXLlkpMTNSSJUs0YMAA1a9fX5I0bdo01alTR7t27VLTpk0zpQ0AAAAAyGo2P3P2888/y8nJSR9//LGqVKmSZl14eLhq1KghR8e/MmTNmjV15swZXbt2TSdOnFBMTIxq1aplXe/l5aXy5cvr0KFDmdYGAAAAAGQ1m585a9iwoRo2bHjfdZcuXZKfn1+aZQUKFJAkXbx4UZcuXZIkFS5c+J5tUtdlRhsPytHxf2dfBweb5+McI7Pfa/ru4aHvHk1Z8T7Tdw8Hn7lHF3336KLvHk2Z/T7bPJz9m/j4eDk7O6dZ5uLiIklKSEhQXFycJN13m5s3b2ZaGw/izhT5Hg/8fGQ+Ly83W5eAB0TfPZrot0cXfffoou8eXfTdoymz+82uw5mrq+s9k3IkJCRIktzd3eXq6ipJSkxMtP47dRs3N7dMa+NBWCyGoqNj/+d2Dg5mPowPSXR0nFJSLJnWHn338NB3j6bM7jeJvntY+Mw9uui7Rxd992hKT795ebml+wybXYezQoUK6cqVK2mWpT4uWLCgkpOTrcuKFy+eZht/f/9Ma+NBJSdn7kEJ/puUFAt98oii7x5N9Nuji757dNF3jy767tGU2f1m14NRAwMDdfjwYaWkpFiX7d+/X6VKlVLevHlVtmxZeXp66sCBA9b10dHROnbsmAIDAzOtDQAAAADIanYdzlq2bKnbt2/r/fff18mTJ7Vp0yYtW7ZM3bp1k3TnOrGgoCCFhobqs88+04kTJ9SvXz8VKlRIjRo1yrQ2AAAAACCr2fWwxrx582rRokUaN26cmjdvrvz582vQoEFq3ry5dZvevXsrOTlZwcHBio+PV2BgoBYvXiwnJ6dMawMAAAAAsppdhbMJEybcs6xy5cpat27dPz7HwcFBAwcO1MCBA/9xm8xoAwAAAACykl0PawQAAACAnIJwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcAYAAAAAdoBwBgAAAAB2gHAGAAAAAHaAcPb/LBaLwsLCVKdOHVWtWlVdunTRuXPnbF0WAAAAgByCcPb/5syZozVr1mjMmDFau3atLBaLOnfurMTERFuXBgAAACAHIJxJSkxM1JIlS9S7d2/Vr19fZcuW1bRp03Tp0iXt2rXL1uUBAAAAyAEIZ5JOnDihmJgY1apVy7rMy8tL5cuX16FDh2xYGQAAAICcwmQYhmHrImxt165deueddxQRESFXV1fr8j59+ig+Pl7z58/PcJuGYchi+d9vrckkmc1mxSTEymJYMvw6+N/MJrM8XNxlsViUmT/tqX13Ky5WKZaUzGsYVg5mB+Vyy7q+uxkbo+QUPneZzdHBrNzuHpneb9JffRd1O0bJKXzuMpujg4O8PTO/71L7LfLWbSXxmcsSTg5m+eTyzLK+u37zNp+5LOLo4KC8ubOu7yJv3FJyMn2X2RwdHeSTJ1e6+s1sNslkMqWv3Uyo7ZEXFxcnSXJ2dk6z3MXFRTdv3nygNk0mkxwc0tcJkuTh4v5Ar4P0M5uz5kRxLjf6LqtlVd/ldvfIknZxR1b1myR5e9J3WSmr+s4nl2eWtIu/ZFXf5c1N32W1LPvc5cmVJe3ijszuN4Y1StazZX+f/CMhIUFubm62KAkAAABADkM4k1S4cGFJ0pUrV9Isv3LligoWLGiLkgAAAADkMIQzSWXLlpWnp6cOHDhgXRYdHa1jx44pMDDQhpUBAAAAyCm45kx3rjULCgpSaGiofHx8VKRIEU2ePFmFChVSo0aNbF0eAAAAgByAcPb/evfureTkZAUHBys+Pl6BgYFavHixnJycbF0aAAAAgByAqfQBAAAAwA5wzRkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGQAAAADYAcIZAAAAANgBwhkAAAAA2AHCGWzm5MmTSkpKsnUZQI519OhRW5cAAADuQjiDTcyZM0dDhgwR90B/9O3cuVPXrl2zdRnIoHnz5ik4ONjWZeBfLFmyRLt27bJ1GUC2debMGVuXgAf0448/6vbt27YuI0sQzvBQpYaxiIgIFSlSRM7OzjauCP/FRx99pEGDBsnDw8PWpSCDzpw5o/z580uSkpOTbVwN/u6bb77RpEmTNHjwYO3Zs8fW5eBfbN++XVeuXLF1GcigqVOnqnHjxny+HkHz5s1Tq1attHnz5mwZ0AhneKhSUlKs/3ZxcbFhJcgMsbGxyp8/vxwdHWWxWGxdDjIgNjZWDg4OkiRHR0cbV4O/K1KkiMqVKydvb2+NHj1aH3/8sa1Lwn2sWbNG/fv319ChQ3X9+nVbl4MMOH36tCRp0KBB2rFjh42rQUZcvHhRkjRlyhR9+OGHio2NtXFFmYtwhocq9SDQMAzFx8fbuBo8qNQgZhiG9QAfj4a7r/M0mUySxPBiO1S0aFH5+/vLyclJL774oqZOnapPPvnE1mXhb/z8/OTk5KRvvvlG77zzDgHtEZD6+65Dhw4KDAxUmTJlNGDAAL4AeQSk9l337t1Vv359+fr6atKkSVq5cmW2OqYknOGhiIiI0I0bN6yPTSaTnJyc0mxz9wE/7FvqQX1sbKySkpLk4OAgs5lfJ/Zs3759kmT93Hl6elrPXt/9mWOIo20YhmHtB4vFIkdHR/Xv31/Ozs5ycHBQ7dq1NXHiRAKaHTEMQ35+fnrllVfUrFkz3bp1Sx07diSg2bnUv18lS5aUo6OjKlWqpF69emnIkCHaunWrjavDv0ntu1y5cilfvnwKDAzUyJEjNW3aNC1fvjzbBDSOppClDMPQ/v371bp1a23YsME6cYTZbJbFYlFERITi4uKUmJhoPbhP/fDB/mzevFmHDx+29pGzs7OcnZ3TBLOUlBTrQSZB2z58//336tWrl0aMGGFdFhsbq2vXrunPP//UuXPnrMsZ4mgbiYmJ1s+V2WxWcnKyvL299dxzz8nV1VVBQUF68sknNWHCBAKanTCZTPLy8lK5cuW0f/9+DRkyRM7OzurcuTMBzc5ZLBb5+PioZ8+e+uyzz1SuXDl16NBBQ4YM4fNl5ywWizw8PBQUFKQNGzaoaNGiGjFihKZNm6YVK1Zki4DGX2FkKZPJpJo1a6pDhw6aMWOGpDtDCVxdXbVz50598cUXcnBwUL58+VS2bFmVLFlSRYsWVd68eVWmTBkVK1bMxnuAVKdOndLSpUvl5uamIUOGqGrVqnJxcVF0dLTWrVun4sWLq0yZMnJyclKePHkkEbTtRfHixdW1a1d9+OGHGjVqlEaMGCE3Nzd9//33atq0qaQ7w+jy5csnX19fValSRYZhqGHDhvL09LRx9dnfnDlz9Omnnyo4OFilSpWyXscpSU888YQGDBig559/XoMGDdLkyZM1YcIEmc1mvfjiizauPGdLTk6Wo6Oj2rRpo/379+vLL79UcHCwBg8erC5dumjhwoXKmzevrcuEpE8++URVqlSRt7e3PD09ZTKZlJKSovLly6thw4Y6deqUevTooZiYGA0ePFiSrL8bYVubN29WwYIF5evrqwIFCshkMslisahs2bJq3769Pv/8cw0ZMkS3bt3S1KlTJUnt2rWTq6urjSt/cIQzPBSpv+ymTJmi3Llz6/bt26pcubIGDRqkgwcP6ty5czp27Ji+/PJLxcXFydPTU1u2bLFx1bhb6dKl1a1bN61bt06TJk1ScHCwvLy8FBkZqZCQECUnJyslJUV58uSxBmsfHx81aNBA1atXt3X5OVq+fPn0+uuvy2w2a/Xq1XJxcZGbm5uaNWumJk2a6Ny5c/rxxx917tw57dmzR6tWrZKjo6N2795NOMti4eHhmjdvnhITEzV06FBVrVpV1atX1xtvvCHDMFS7dm29/PLLmjNnjiZNmqTOnTvL0dFRgwcPloODg55//nlb70KOERYWppSUFD3zzDOqXLmyHB0drQGtfv36+uqrr+Tr66vp06erd+/e6tKlixYtWiQfHx9bl56jLV++XOPHj1eJEiUUGBiopk2bqmbNmnJwcJC7u7vKlCmjRYsWqXnz5ho5cqTMZrPef/99JSYmqkWLFrYuP0dbuXKlxo0bp4IFC6pkyZIKCgpSgwYNrF9e+fv7a/r06frzzz/VtWtXGYahmTNnKj4+Xt26dXtkJ54zGYw7QhbYunWrTp8+LZPJJD8/P+sBxPjx47V8+XI5Ozvrrbfe0rvvvmt9TkpKiuLi4nTp0iXlypVLBQsWtFX5uIvFYlFKSor1WqVdu3Zp2bJlcnR0VJ48efTnn39q3rx5unz5sn755RdduHDBGrhv3rypDRs2qEyZMjbei5xn8+bNOnfunK5du6aXXnpJ1apVU1xcnFauXKkNGzbo/PnzatWqlcaMGZPmeZGRkbp8+bK8vLxUpEgRG1Wfc1y+fFnr1q3TF198obi4ODVu3Fhr165VxYoV1bhxY7388sv65ZdfNG3aNAUHB6tkyZKKiIjQRx99pPbt26tkyZK23oUc4euvv1aXLl0kSU899ZRy586t999/X7ly5ZKLi4tu3bqlFi1aqGnTpurTp4+OHTumgQMHKjExUevWrSOg2dCHH36oyZMnKyEhQc8884x27typ119/XU899ZQaNmwoSerbt698fHwUHBysmJgYhYSEaO/evdqzZw9fUNnQwYMH1bdvX8XFxalOnTravXu3XnrpJQUEBOiNN96QJAUHB+vixYuaO3eunJ2dNXPmTK1atUqffvqpvL29bbwHD4Zwhkw3ZcoUbdiwQYGBgTpz5ozi4+Pl6+urefPmSbozhCcsLEzPPPOMJk6caP3Fl5SUdM8kIbCtZcuWKTw8XLGxsSpVqpT69OkjLy8v7d27V0uWLNGJEydUunRprVu3ToZhWIcxWiwWmc1m3bhxwzrEEQ/P5MmTtWnTJlWsWFExMTE6evSoXnvtNQUFBalo0aJasmSJVqxYobJly2rJkiWS7lzz5OzsnKYf8XBcuXJF69ev19atW1WnTh116dJFkyZN0qlTp2QymTR8+HCNHj1a/v7+Gj9+vKS/+gsPx7lz57RgwQJt2LBBzZo106VLl3Tq1Cm99NJLatiwoQIDA/Xtt99qyZIlGjBggMqWLauIiAiNGTNG06ZNY4i+DXz77beqUKGC3NzctGvXLg0fPlwvvviinnnmGS1atEi3b99W4cKFNXDgQH3//ff68ccfrSHt5s2bSkpKUr58+Wy9GzleeHi4unfvrmeffVZ16tTRl19+qSNHjih37tzq06ePLBaLPv/8czVv3lyVK1eWJEVFRT2ywUxiWCMy2YkTJ7Rjxw7NmDFDTz75pGJiYrR3715NmjRJrVu31tKlS9WjRw8lJCRo4cKFWrdunVq2bKk8efIQzOzMzJkztXr1ar322mtKTEzUtm3btG/fPg0YMEDPPfecHBwcNH36dF29elUnTpxQ2bJlZRiGLBaL9eA+d+7cNt6LnOfHH3/Unj17tGDBAlWqVEmStHbtWi1dulSXL19Wr1691LFjR5lMJq1cuVJjxozRsGHD5OzsrJSUFG6N8BB8+umnSklJUZMmTSRJBQoU0GuvvSbpTl/lz59fU6ZM0dmzZzV58mSNHTtWDg4O2rp1q1q2bKnq1asTzB6yYsWKqV+/foqJidFnn32mxYsX69ixY/r222/VpUsX61lMT09PnT9/XmXLllXFihW1Zs0a+soGRo0apYMHD2rlypVydnZWo0aNlJiYqGHDhql48eKaPn26fvvtN4WGhmro0KHy8fHRN998o2LFiqlLly787bKhw4cP6+rVqypQoIBKliyp6tWrKywsTN27d5ezs7O6desmR0dHhYSEaNasWYqJidHly5fl6upqDWePcjCTJBlAJjpw4IBRu3Zt4/r169ZliYmJxuHDh42GDRsarVu3ti6fMGGCUbFiRWPmzJnGzZs3bVEu7sNisRhXrlwxXnnlFWPXrl3W5XFxcUZQUJDx3HPPGR999JFhGIbx5ZdfGm3atDHatGljHDlyxEYV425Hjx416tata5w4cSLN8h07dhgvv/yy0bt3b+OPP/4wbt26ZcydO9do2LChMXjwYBtVm7OkpKQYCQkJRvv27Y1XX33V+OKLL9Ksv3r1qjFz5kzj6aefNiZOnGhdvnv3bmPKlCnGU089ZVy4cOFhl51jbdy40RgzZowxZMgQY+3atYZhGMatW7eMbt26GTVr1jQiIiKM+Ph4Y+vWrcYrr7xidOvWzfD39zcaNGhgXLlyxcbV51whISFGYGCgcezYMcMw7vxNMwzDSEhIMDZs2GCUL1/emDBhgnX7devWGWPGjDH8/f2NFi1aGDExMdbn4OGaPHmy0bBhQ6N+/fpG3bp1jUmTJhlxcXGGYRjGt99+a1StWtXo3bu3ERMTYxiGYXzzzTfGtGnTDH9/f6N+/fpGdHR0tug7whkyReqH4c8//zRq165tPXi/26FDh4zatWsbffv2tS4bM2aMUaNGDSMqKuohVYr0uHbtmtGwYUPrwWNCQoJhGIaRnJxsdOvWzWjUqJHx5ZdfGoZx56D/rbfeMpo2bWr8+OOPNqsZdxw+fNioUaOGER4ebhjGX31nGIaxbds2o2HDhkZoaKhhGHfCwNSpU40mTZpwMPkQJCcnG4ZhGD169DACAgKMDh06GHv27EmzTWpAq1OnjjF27Ng061IPSJD1Jk+ebDz99NPGsGHDjLZt2xqNGjUyRo8ebRjGnd+PPXr0MKpXr27s37/fMAzD+OOPP4zdu3cbr7/+uvHUU08Zly9ftmX5Odb48eONmjVrGsePH//HbdavX2+UL1/eGDlyZJrln332mXH69OksrhD/5KOPPjKefvpp44cffjBiYmKMkJAQ45lnnknzN+ybb74xqlatavTq1SvNceOhQ4eM8+fP26DqrEE4Q6aKiooyunXrZnTp0uWeX44JCQnG+vXrjaZNmxpHjx61Lr/7LBvsQ2JiotGoUSNj+PDh1mV3B7TXX3/daNKkiTWUb9myxejatWu2+uX4KOvWrZvRoEED48aNG4ZhpA1oy5cvNypUqGCcPHnSMAzDiIyMNCIjI21SZ07VunVro2PHjsbrr79uvPnmm/8Y0Bo0aGAMGzbMRlXmXF9//bXx7LPPGhEREYZh3Bk1MGHCBKNBgwbGuXPnDMMwjEuXLhnvvPOOERAQYA1oqfii4+GzWCzG6dOnDX9/f2Pp0qXW5SkpKcZHH31kTJ061VixYoXx22+/GYZxJ6BVqFDBCAkJsVHF+LupU6ca7733nvXx0aNHjaCgIGPmzJnGkiVLrJ/H1DNo/fr1My5evGircrMUN6HGf/Lxxx9rzpw5Gjt2rI4ePao8efKoT58+Onr0qBYuXKizZ89at3V2dladOnX0559/pln+yI8NziYMw1BycrIkycnJSd27d9fOnTu1cuVKSXf6LzExUQ4ODlq4cKEiIyO1cOFCSVKzZs00depUZvezga1btyosLEwzZ87Url27JElDhgyRl5eXunXrpps3b8rZ2VkJCQmS7tz/pWDBgvr+++8l3fn88Rl8OAzD0Pnz5xUbG6uBAwdq2LBhSkxM1PLly/XZZ59Zt0u99cELL7ygw4cPc0Pjh+zChQvy9PSUv7+/JMnV1VUtW7bUhQsXdPr0aUlSwYIFFRwcrNq1a6t379767rvvrM/Pnz+/TerOyUwmk0qWLKlu3bpp9uzZOn/+vCQpKChIH3zwgXbv3q3169erbdu2+uGHH9SqVSuNHj1ay5cvt94bC7ZlsVh06NAh/fHHH7p9+7aGDh2qixcv6vPPP9cnn3yi1157TTt37lStWrU0Z84cbd++XTNmzFBKSoqtS890TAiCB5Y6I1y5cuV0+/ZtrV27Vm3atFH37t01d+5cvfnmm5Kk9u3bWycmyJMnj/z8/OTu7m5th5nhbG/p0qU6fvy4Tp8+rcaNG6tWrVpq1qyZvv/+e61du1aurq5q1aqVnJ2dlZSUJHd3d/n5+enGjRvWNjw8PGy3AznU32dG3bJli7Zt26bQ0FD17dtX06ZNU+fOnbVgwQJrALt9+7bc3d3l5eVl4+pzHpPJJHd3dz3zzDNyd3dX8eLFNWDAAE2ZMkXLly+XJD3zzDOS7gS0jh07qlOnTkzD/pCZTCbdvHlTV65cUbFixZScnKxcuXLJyckpzd+rAgUKKDg4WAMHDtR7772nHTt2PNI3vn2UGf8/y2y7du30+++/Kzg4WMWLF1fRokXVr18/FSpUSL/88ovCwsI0ceJELVq0SE2bNpWTk5PKly9v6/KhO7eo2Ldvn5o3by4PDw/lz59fS5cuVf78+XX58mXNmTNHw4cPV6VKlVSrVi2tWrVKPj4+2XISK86c4YEcPXpUu3fv1sKFC7Vo0SKtX79egwYN0hdffKHhw4eraNGiWrVqlQ4dOqRp06Zp6dKlCg8P17Rp0/THH3+obNmytt4F/L8ZM2Zo3rx5KlKkiEqWLKmdO3eqb9+++u677zRo0CD5+/tryZIlWrZsmaQ7Z9XMZrNcXFyss5AZ3JHjobt7ZtSZM2dq7dq16tu3rw4fPqwuXbqoSpUqGjBggJKSktS0aVPt3LlTe/bs0cKFC3Xjxg1VrFjR1ruQI/n4+Khz584qXry4LBaLqlevrnfffVdJSUlavny59u7da902b968BDMbqFy5sooUKaK4uDhJkqOjo3U20/j4eEl//c4rUKCA5s6dqzVr1hDMbCg1NOfNm1evvPKKUlJS9PXXX+uZZ55R4cKFZTKZVLZsWTVp0kTnz59XTEyMnJ2d9dJLL8nX19fG1UOSatWqpcGDB2v48OEqUaKEmjVrZj0LXbBgQbVs2VKOjo66cuWKJKl69eoqXbq0LUvOMpw5wwMxDEOJiYlyc3OT2Xwn47dr107e3t5auHChxo8fr1GjRmnBggVavXq1li1bJldXV7m4uGjx4sV67LHHbLwHkKTr16/ru+++U0hIiPUb+x9++EHr169Xly5dNHfuXI0ePVrTpk3T7NmzFRERIT8/P124cEGHDx/WkCFDJHH20xaio6OVkJCgxx9/XNKdM5fPP/+8HnvsMQ0YMEADBgzQ4sWLVa5cOYWGhmrChAlydHRUrly5NH/+fIag2lDqWebUA/zUgJYatM1ms+rVq2fLEnMsi8Uif39/zZ49O80Ij4SEBJlMJmsAM5lMmjZtmm7duqXhw4en2Ra2kXr27Nlnn9W3336rkydP3vNFcNGiReXp6amkpCQbVYn7Sb03as2aNSXdGVp869atNNvkyZNHPj4+OeK2S4QzPJCkpCTFxcUpNjZW0l83RH3ppZdksVg0Y8YMLVmyRH369FFwcLASEhIUGxsrDw8P5cqVy8bVI1ViYqJ+++03JSYmWpcFBAQoX758cnJyUu/evTV//nwNGzZMdevW1bx583T16lV5enpq1apVKlWqlA2rz9mKFi0qSfrqq6/0yiuvSLpzVvOJJ57QpEmT1LdvXw0ePFgTJ07UhAkTdO7cOXl4eMjBwYF7+GSxPXv26KmnnrrngD05OVmOjo6Kj4+X2WxOc/+r6tWrq2fPnlq0aJHKlCnzsEvOsYy/3XQ99cvGu4f9WiwWXblyRSkpKdYzmWFhYVq6dKlWr179cAtGGnf3n8lksh6LDB8+XH379pWXl5diYmLk5OQkZ2dnffrpp3J3d+d3oB1IDWTSnc9d6u/HVB9++KHq1q2rxx9/XK6urtq4caPMZrMKFy5sq5IfGpPBeCQ8oK5du+rMmTP66KOP5OHhYf2lKEkLFy7UrFmz9Mknn6hYsWI2rhR3u/uPWUxMjHr37i0/Pz/17NlTnp6e1u3Onj2rOXPm6MSJE5o6dWqaoR939zUertQbRd+6dUsDBw6UYRjq169fmm+IExMTtWXLFq1YsULjx49XxYoV0/whRNZZt26dJkyYoAMHDqT5jKT22/nz5zV69Gj179//vsO74+PjGR73kCQkJMjFxeWeg8LUvkpdL90ZUfDGG29o9+7d2rp1q+bNm6c1a9YwPNgGUkffvPDCC5L++puW2m8XLlzQN998o1atWunEiRMaMGCA8ubNq9y5c+vAgQNatmyZypUrZ+O9yJlmzJihwoUL67XXXpP0V0C7u+927NihoKAgtWnTRhcvXlTevHlVuHBhHTt2TAsXLswRfcdfaqTLBx98oDNnzkiSdWacXr16ydXVVW+//bZiY2PTzAiXehF7eHi4rUrGP7BYLNYzZR4eHqpQoYI2bdqkQ4cOyWKxWLcrUaKEWrZsqZSUFP3++++S/hqGlROGFdibVatWadiwYercubN27dqlXLlyqWvXroqIiNDixYv/cWbU1M8twezhiImJUe7cueXg4GD9XWmxWOTg4KA///xTbdu2Vd68ea0zAf5dahhA1kodyh0ZGSlHR0frTLWpB4l//vmnOnfurBMnTki6cybNy8tLQ4cO1Zw5c7R69WqCmQ189913mjx5soYOHapPP/1U0l9nzFL77bXXXtOlS5ckSaVKlVJAQIAee+wxFS9eXGvXrs0RB/f26MiRI5o7d66GDx+uDz/8UNKdv0t3993rr7+uixcvysXFRRs3blSXLl1Uv3591atXTx988EGO6Tv+WiNd1qxZo379+uncuXPWmXHKly+vt99+Wzdu3FCXLl0UExNjPbC4ffu2PDw8mBHOzixfvlz9+vVTp06dFBoaKknq37+/qlevrmHDhungwYNppqWtUaOG3Nzc9M0330hSmuEjeHimTJmiOXPmyGQyyTAMffnll5KkJ554QhMnTtSOHTsUFhamH3/80fqc+82MiqyT+sVFXFycXFxc5ODgYP1daTabFRUVpQ4dOqh+/foKCQn5x88Qn62sFx8frz179ujatWsKCgrS9evX5ejoaD1IPH/+vNq0aSNfX19riM6VK5eio6N15MgRffjhh9YZiPFwPfbYYypfvrx8fHwUEhKiTZs2SbrzhdSFCxfUokULPfPMM+rVq5cMw5CLi4vGjBmj8ePHa8CAAQzFt6Hy5curXr168vb2VnBwsHWSMWdnZ128eFEvv/yyGjZsqPfee896HNKhQwe9++67atu2bY4ahcU1Z/hXqaecCxcurK+++so6TX6xYsXk6Oio5557TiaTSfPmzVOTJk00YsQImUwm/fDDD4qOjmZWRjsybdo0bdy4Ua1bt9b169e1atUqlS1bVk2bNlVYWJi6dOmivn37avTo0Xr66afl4eEhwzDk7e2t4sWL27r8HOuXX37RZ599prlz56pKlSpp1t2+fVv16tXTkiVLNGDAAEVGRqpu3bqqVKmSdu/ezcyoD1Hq0Kq/X8OUymw2q2vXrmrZsiUBzMZcXV3VoUMHffjhh4qLi9PLL7+sjz76SPnz59f169fVpk0b1a1b1/r3zGKxqECBAhozZoyeeOIJZvezoaJFi6pcuXIKDw/X888/r7CwMJlMJjVv3lw7d+5UixYtNGjQIOtn7O7h3P/02cTD4eTkpEqVKunatWtq27atJk2aJOnO7ZaOHDmitm3bqk+fPvftr5zWd1xzhnR5/fXXVaZMGZ06dUpXr17VkiVLrN9ipKSk6OzZs5o9e7bCw8OtM8KFhIRw/xA78euvv+qdd97RmDFjVKNGDV2/fl3t27fXO++8I39/f5UoUULJycl677339PXXX6t+/foqUqSIrly5ok8//VRr167NtlPW2ruff/5ZHTt21KJFi1SpUiVZLBa9//771pu5t2zZUkFBQYqKitKyZcv01VdfWWdGnThxYo4ZBmIrmzdvtn4bLElLlizRunXr9Omnn95zMJF6gMH1f7Z3/Phx9ezZU6+88op+/vln/fzzz9q8ebPc3d21e/duNWvW7J7+o98evrsPylPf/+vXr6tjx456+umndfv2bX3++ecaPny4GjVqdM9zYDup8eLu33kxMTFq3ry5GjZsKC8vL4WFhWnEiBF64403rM+h7zhzhnQ4e/asoqKi1KJFCxUsWFC9e/dWp06dtHjxYhUrVkwODg4qXbq0pkyZorNnz8rd3V1OTk7KkyePrUvH/4uOjlZcXJxKlCghSXJzc9OVK1c0fvx4Xbx4UWXLllWvXr00adIkrV69WkeOHNHu3btVrFgxrVixgmBmQwkJCbJYLNbr/Nq1aydnZ2fVrVtX165d0+bNm3X69GmNHj1aw4cPt86iysyoWe/y5csaM2aM1qxZowULFihPnjzWmWx3794tFxcXlSxZUkWKFJHZbE4zMxkePovFIpPJJJPJpHLlyqlt27bavXu3OnXqpJs3b+rVV1/Vpk2b9PLLL1uvPbsb/fbwJSQkWCfISZ3Rz8vLS88++6wMw9DLL7+suLg4jRo1ynoWlC9A7EPq3yHpr2vLPDw81LVrVx05ckTNmzdXTEyMRo0apaSkJLVr146++385e++RLsWKFVPlypWVN29eFSlSRJMmTVLu3LnVqVMnnTt3TtJfk4QUL15c+fPnJ5jZmQIFCsjLy0tXr16VJL333nsqWbKkhg8frg8++EBubm6aMGGCfvnlF7Vt21YTJ07UunXrNHXqVIbF2dgTTzyhSpUqKTg4WMeOHVOhQoUUGhqqzp07a8iQIerTp4++/vprHTlyRE5OTvLy8lKhQoUIZg9B/vz5tWjRIkVFRentt9/WrVu3ZDKZdOXKFY0ZM0Zvv/22Xn31VT377LNq1aqVRowYoZCQECZKesgOHjwo6c4Boslksk7+Ubt2bRUoUECenp4aOXKkcufOrVdeeUXXrl2Tg4MD98KysTlz5qhVq1bav3+/9cbDjo6OcnJyUvXq1bV69WoZhqHBgwerdu3amjx5srZu3SqJIG1rU6ZMUcOGDbV582YdP35ckqyz1/r7++vw4cO6du2aBg4cqE6dOikkJMR6Wwr6jmGN+B/+6RuM33//XUOGDNHNmzetZ9Bgn1KHCfz888+qUKGCpDs3eJRkvRl4YmKiGjdurAYNGmjYsGE2qxVppfbd119/renTp8tiscgwDK1du1YODg7Ws2kvvfSS6tSpo0GDBtm44pzj7ttJREREqE+fPvL19VXVqlUVERGh4OBgXbt2TSdPntTx48d18eJFnThxQomJiVq3bp31LDay1vz58zVt2jTVrVtXDRo0UMOGDVWwYEHr+iFDhujixYtavny5jh07puDgYN24cUNr165VgQIFbFh5znb48GF17NhRCQkJeuyxxxQQEKCAgAAFBQVZfy9OnDhRf/75p0JDQ3XmzBktX75cW7Zs0aRJk/Tiiy/aehdyrF9//VVdunTR5cuXVbFiRXl6eiogIECdOnWSm5ubHBwcNG/ePO3atUuLFi2S2WzW0qVLNX/+fI0aNUqtW7e29S7YHPEU/8psNsswDOuZsdT/+/r6asKECfLx8dGrr76q8+fP27JM/IvU8dupwSwxMVGPPfZYmmDm7OysKlWq6NatWzarE/dK7bsaNWro+eefV2xsrK5fv55mmGNSUpK8vLw42H9Ili1bpsGDB6tNmzZatmyZfvzxR1WpUkVhYWE6f/68Zs+erZs3b6pkyZKqXr26Xn/9dY0aNUoLFizQxo0b9emnn9JXD0lUVJT1b1NERIQOHTqk1q1ba+vWrdZbTwwfPlwJCQn65JNPVL58eQ0fPlyOjo7q0KGDUlJSxPfXtlG6dGl16dJFVapUkZOTk0qUKKG5c+eqU6dO+vDDD5WUlKRmzZopNjZW58+fl5+fn1q3bq3XXnuNa91taPPmzbp+/br69u2rUqVKKTIyUk2bNtWmTZvUuXNnzZw5U/Hx8WrZsqVKlSqlH3/8UXny5FHr1q3Vq1cvVatWzda7YBcIZ5Akffnll7p+/fo9y1NSUtIMA7l7DL6vr6/GjBmjcuXKpZl+HfYp9SAj9dv+27dvp3kcFxenQoUKpdkWD89333133/c9MTFRLi4ueuONN/Tqq68qOTlZLVu2VHh4uL799lvNmTNHv//+u2rWrGmDqnOW6dOna+7cucqXL5/8/Py0du1ajR07Vh9//LEqV66sSZMmqUKFCrpy5Yr1XoJ3/25kyPfD5e3trbfeektvvfWWoqOjrQf8EydO1MiRI7VkyRI5ODgoICBAx44dkyRVrlxZoaGhmjdvnhwcHJic4CHbtWuXli1bpq1bt6p8+fKqU6eOXF1dFRkZqc2bN8vHx0cffPCBWrVqpbi4OEVFRWnevHmS7vTdkCFDVLJkSdvuRA41duxYBQcHq0yZMnrhhRes98ANDw/X7t27Vbt2bX355Zdq3LixvvnmG125ckUbN26UdGcUT/fu3VWmTBkb74V9YFgjtGHDBk2cOFFffPGFPD09rcvvvhnn22+/rdDQ0PveODUpKYmbEtuJ5cuXq3r16tazZKlS+/L27dvWoaorV65UbGys/P399dNPP2nTpk3Mymgjy5Yt0+LFi/X111+nWZ7ab2fPntX8+fP19ttv69SpU1q+fLl+/PFH5c2bV7ly5dLo0aP5tjiLXb58Wb1791bPnj1Vt25dSVJ4eLjWr1+vAwcOqFevXmrVqpWOHj2q3r17q3DhwpozZ451Fkc8PFu2bNHly5fVtWtXSXeG4S9dulQffvih1q9fr3z58mnjxo1asmSJ6tSpI09PT23btk1z587VU089ZePqc67Q0FBt3rxZfn5+OnbsmEqVKqV69eopJSVFH3zwgd544w317NlT586d0+TJk/Xnn3/KwcFBP//8s/VvH2wjJCREW7Zs0bJly6wzBMfGxmr37t0aNmyYmjRpovHjxys5OVkTJkzQ6dOndf36dZ04cULTp09X48aNbbwH9oXZGqHr16+rYMGCcnNzS3OvntSbcbZr1061a9eWn5/ffZ9PMLMP3377rcaPH2+9QXGq1AP88+fP64033tC4ceNUtWpV6x+8YsWKycvLi1kZbejq1avy8fGRJCUnJ8vR0VEWi0UODg66cOGC2rVrp+rVq6t48eIqXry46tevr19++UU+Pj5ydnZW7ty5bbwHOcOZM2cUHR1tfVy9enXlyZNHrq6umj9/vjw8PPTiiy8qLCxMAwcOVFBQkD744AN5eXnZsOqcI/W75gMHDujLL7+Ut7e3WrVqJV9fX3Xu3FlJSUl67bXXtHjxYr3zzjt69dVXNWvWLN26dUsJCQnavn27AgMD+ZtmA9u2bdOOHTu0aNEilS1bVrGxsWrfvr0iIiI0cuRImUwmrVmzRrdu3dKQIUMUFhamzz//XEePHtWFCxdUpEgRW+9CjjVx4kRt3rxZGzduTDP/gLu7uxo1aiTDMDRx4kT16tVLs2bNUnBwsH744Qf99ttvWrhw4T1fJoNwlqOlHrSbzWa5uLikGb5hMpkUHR1tnWls1KhRDO+wc87OznJycpJhGGkmckk9+9muXTvVqVNHtWvXltlsVq9evdS5c2cZhiHDMOTu7m7jPci54uPjrQeEjo53fi2bzWZdu3ZNr776qp5//nkNHz5c0l+ThPj5+fGZzGJ3f1nl5OSkUqVK6cyZM0pMTJSTk5NMJpPKlCmjoKAgxcTEaO3atSpXrpwqV66scePGaezYsYqOjiacPSSpX2i4ubkpLi5OK1euVEJCgoKCglSyZEn16NFDJpNJnTp1UlhYmBo1aqRhw4YpKipKvr6+atq0KcHMRk6dOqXHH39c/v7+SkpKkru7u7p06aL+/fsrMTFRrVu3lmEYWrdunZKSkjRs2DA1bNhQDRs2VLdu3eTm5mbrXciRUlJSdOTIERUuXNgazJKSkjRz5kydPHlSrq6uKlGihAYPHqypU6eqe/fumjt3rnWCl1deecV6aQX+wjVnOVjq9WOp90VKvb4sVXJyssaMGaMxY8ZwEPgIMJlM99yXxzAMxcXFqVOnTqpdu7bGjRtnDW0Wi0Wurq5yc3MjmNlI6nVJku77GUs9kBw+fLh1/d//j6xjsVgk3XmvfXx8VLt2bc2fP18HDx60hjZJ1skITp06pd9++03SnbNq69atU9GiRW1Wf06T+vvvzJkzqlSpkvz8/LRu3TrrFN0lSpRQ9+7d1bx5c/Xu3VufffaZXF1dVbhwYetsm3i4Uj9DV69e1fXr12UymawB2cvLS8nJyYqNjVXevHn1+uuvq3Xr1tq7d6+GDh1qbYNgZjsODg4aOnSoLBaLpk2bJknq1q2bDh48qMKFC+vWrVvas2eP9u7dq/79++vIkSPq1KmT9fl8GXJ/hLMcaOfOnTp8+LD1ceqU3Hd/SCwWi3x8fPTcc89xEGjnUs+UxcTEKCUlRW5ubtYAZjKZ5ObmpkmTJt1z9pN7idjOjh07JP01GYuHh8c9wdpisShv3rzq2LFjmiCAh2PlypV699131bNnT40fP16JiYnq1auXmjRpon79+un7779P0y81atRQsWLF9O2331rbcHFxsVX5OZJhGIqMjFRMTIw6deqkvn37WiduuTugvf3222rZsqV69ux5zzBwPFypf5Oee+45JSQkWO+dKkm5c+eW2WxWQkKCJClv3rx688031aRJE/3666/W+3bCtvz8/NSsWTMdPHhQgwcPVt68eTV37lwNGzZMs2fPVtOmTXXu3DmVKVNG/fv318mTJ3X58mVJfMn4Tzg6y2FOnjypiRMnauHChfr+++8l3TkwjI+P1/r163X06FFdvXrVOpMf7Ne8efO0efNmmUwmmc1mOTs7y9HR8b5nzypXrmwdvgjb+v3339WvXz916dLFuiw2NlY3btxQRESEIiIidOvWrTQB++7/I+uFhYVpzpw5Klq0qLy9vfXZZ5/ppZde0nfffac+ffroqaeeUteuXbV3717rjYpTz0Rzpsx2TCaTvLy81KxZMxUpUkRFixZV9+7d5e/vf09A69Spk9q0aUN/2Yk6depowYIF1hmDpTszCjs5OVmvh5ek3bt3y8nJSQsXLlT+/PltVS7u4urqqmbNmil37tz6+OOPVbBgQeXJk0cWi0XOzs5q1aqVfv31V126dEkvv/yytm3bluZeg7gX15zlEKnXTpQpU0Z9+vTRihUrtGjRIvXu3Vvu7u46d+6cxo8fr7i4OJlMJhUuXFilS5fWY489pgIFCujJJ59UYGCgrXcD/+/27dv67bffNH36dLm4uOiFF16Qq6ur4uLiNHXqVHl5eal69ery8fFRkSJFrN9AwvYKFy6s0NBQjR07Vt26dbNOJHHmzBl17tzZOoQnd+7cKlKkiAICAmQ2m9W4cWNu9v4QXLlyRXv27NHo0aP13HPPSZKuXbumfv36aeDAgRo5cqTef/99zZgxQ3369NFrr72m3LlzKyoqSj///DM3cbcxR0dHvfbaa9ZJdcqUKaO3335b8+bN09q1a2UymdSmTRuVLl1aQ4cOZViVHbk7mEl3ZkhNTk5Wrly5ZDKZNH36dM2fP1+ffPKJdQIl2IeCBQuqX79+OnfunJo1ayaTyWQdWWAYhsqVKycvLy85OztzjVk6EM5yiNQLpSXp5Zdflslk0qJFi7RgwQLFxsaqTJkyWrJkic6cOaMzZ87o999/1/fff6/jx48rJiZGL7zwgo33AHfz9PRU37595enpqX79+skwDFWqVEmStHfvXhmGofnz58swDHl4eKhIkSLy8PDQSy+9pDfeeIOzMDZisVisM1iZzWYNGzZMffv2lb+/vxo3bqyOHTvq0qVL+vnnn3X69GmdP39ec+fOlSSmGn5IkpOTFRkZqXz58km6c8F7vnz5tHLlSnXs2FGjR49WWFiYxo0bJz8/Px0+fFjh4eF67LHHmPHUTqROqpP6ey41oC1cuFDz5s2Tk5OTWrVqRTCzc0lJSXJwcJCnp6dmz56tpUuXasOGDVwbaKf8/f21adMmubi46OLFi3J3d5fZbNaqVat09epVFS9e3NYlPjK4z1kOsHLlSh0+fFhJSUkqWrSo3n33XTk7O2v79u1asGCBLl68qPLly2vp0qXWM2ypDMPQzZs3uXGqnbh165YSExOVN29eSXdugzB16lRt3LhRr7zyin7//XfNnTtXnp6e+vXXXxUVFaWIiAj9+uuv+uOPPzR58uT73qsOWeuDDz7QyZMndf78eb388suqVq2aChYsqO3btyskJETXrl1T3bp1tWDBgjTPS0xM1OXLl+Xi4qICBQrYqPqcp0mTJqpWrZpGjx4t6U4/pH7b+8Ybb+jWrVv65JNPJEkJCQnWb4i5xsy+nThxQmvWrFGXLl04C23HUo9DduzYoZCQEFWrVk179uzR2rVrVbFiRVuXh//h+vXratasmSwWiwoXLqwbN25o1qxZ3IszAwhn2VxYWJg++OADtWzZUjdu3ND+/fvl4OCgUaNGqWbNmvrss880fvx4OTk5aerUqdabByYlJcnR0dF60MGZFtubNWuWvvzyS12+fFnFixfXG2+8oYYNGyolJUWTJ0/Wxo0b5eTkpK+++koeHh7WYQWp4uLimNXKBqZOnar169erQYMGunnzpsLDwxUYGKigoCDVqlVL27dv14wZM+Ts7KytW7dKShsGkPU++ugjnTp1SvHx8WrYsKF+++03rVu3Tu3atVPr1q0l/dUn58+fV1BQkDp16qQ333wzzW0rYP/4bD06jh8/rubNm8vFxcV6mwo8GsLDw3X06FEVKFBAAQEB3IcugxjWmE1ZLBZdvXr1H6+d6Nu3r0JCQvTMM8/IMAzNmjVLs2fPVpcuXVSlSpU0wz0IZra3ePFirVq1Su+++668vb21fv16zZs3TwcPHlT//v3Vv39/ubi4aMWKFQoPD1eDBg2sszhKd2ZmdHV1tfFe5DynTp3S3r17NWvWLFWvXl2StGfPHq1cuVJTpkxRr1699OKLL0qSgoODrdfGODs7W+9DiKw1efJkffjhh6pdu7YOHDigQoUK6YUXXtD333+vjz76SC4uLtZ78RiGofz58yt//vyKjIyUxKynjxqC2aOjVKlSatu2rdq0acNQxkdM9erVrX/zkHH8VcmmTCaT3N3d//HaiQoVKmjYsGGKiIjQs88+qw4dOujixYuaOnWqfvrpJxtXj1SGYSg+Pl4HDx5U9+7d1apVKz377LNasGCBGjdurIiICI0ePVomk0m9evXSq6++qu7du2v79u3WWRz/PusfHh4HBwddu3bNOqOfJD377LN65513lC9fPs2bN0+HDh3Siy++qLFjx+ro0aNq27at9bnIWkePHtXu3bs1f/58TZkyRfv27VOnTp302GOPqXv37vLw8NDmzZu1bNkySXc+Qy4uLvLx8bEOYWTwCZA1XF1dNWTIEIIZchzOnGVDM2fO1IULFzR+/Hi5u7tr8+bNCggIkIODg3VIx+LFi9WmTRu9//77+uSTT/Tyyy8rOTlZH330kTXMwfZMJpNcXV0VGxtrvS9I6hmVnj17ysPDQ1u2bNGsWbP07rvvqk+fPnJyclL//v3l6OioRo0a2XgPcq7UM5fu7u7/196dR1Vd538cfwICci9LmCgi2CikCK4lmpZiLqOTyzjZMTvJCMFxQfNUMmIuiJWWgsAoisvgQjKkOQJNRWY5M6XHBHGk0aOpCZlrKm7EZb+/P/hxJ7LFpvJe8fU4h3O83+/9fu/76/fcw/fFZ+PcuXNAfXdhR0dHevXqRV1dHcuXL7d013n00UepqqpixYoVnDt3jjZt2lj5Cpq+ixcvYjKZ8PHxAeq/W4mJiRw7dgwfHx9atmxJTU0Nb731FgcOHCAkJIRjx45RWFhoWQRXf/QQ+fVo0ha5G6nlrIlJSEhg5cqVfPbZZwCEhYWxf/9+tmzZAtR36aiqqgJg6dKllJWVkZGRAcDYsWNZvXr1TdPZivU0POB7eXlRWFiIyWSyhGyA8PBwQkND+fjjjzlw4ABeXl6Eh4cTHh6uWeOszM7Ojvbt29O/f39effVViouLcXR0tNy73r17M2HCBD744AOOHj2Ki4sLv/vd78jNzVUwu01cXV1xdHTkxo0bmM1mJk6cyKFDh+jYsSMXLlyguLiYixcvEhkZyeXLl3n77bc5e/Ysmzdv5je/+Y21yxcRkSZI4awJWbx4Mdu2beOFF16goqKCsrIyBg4cSEBAANnZ2eTm5gLcNHbiypUrlnO4urpaq3z5htLSUm7cuEFZWRn29vbExMRQUlJimT3umyH7ueeew93d3RLA77vvPmJiYggICLBa/XerrVu3smDBAuLi4tiwYQMAs2bNIjg4mLCwMC5cuICTk5Olm+Pw4cNp164dn3zyCQDOzs76Dt5G7du3x2Qy8eabb1JSUoKHhwdJSUnExsaSmprK0KFDuXbtGu3btyczM5PMzEzS0tIIDAy0dukiItJEKZw1Ea+99hrZ2dls2rSJ0NBQSkpKOH/+PH5+fkRHR2M0GsnKyiIzMxPQ2AlblpqayrPPPsuoUaN4/vnnycnJwcfHh7i4ON555x0WLFgA1Ae0hgk/HnroIW7cuGE5R8M6P3L7JCcnk5SUhNls5uzZs2RkZDB+/HguX77MrFmz8PX1ZezYsRw7dszSVaeqqgqDwaBp8q2kVatWzJs3j40bN7Jw4ULs7e3x9PQE6r9fTzzxBKdOneLf//43UP+90oQSIiLya9ITXBOQkJDApk2byM7OJjAwkMrKSjp06MCHH36Iv78/nTp1Yvbs2axdu9Yym98DDzygsRM2aN26dWRmZjJnzhwuX77MF198wezZszl16hTjx49n7ty5LF68mPLycuLi4jAajQCcO3eOe+65RzP8WcmXX37Jjh07WLp0KQMGDKC2tpZDhw4xb948oqOjWbJkCQsXLmTp0qWMHz+e6OhoDAYDX375JadOneKhhx6y9iXctYYOHcq0adNIT0+nc+fOlJeX4+7uDoCLiwtBQUG0bt3aylWKiMjdQuHsDldbW4u/vz+5ubl07NgRs9mMk5MTbdq0Yc+ePUyePBmA+++/n5iYGAoLC8nIyOCdd97BaDRq7IQNqa2t5eDBg0RFRTFq1Cigfm2yzp07s3DhQkwmE5MmTaJFixbExcUxceJE7r33XgwGA7t37yYrK0vBzErKy8u5fv067dq1A+pnWuzevTvp6elMmTKF2bNnk56eTnp6OklJSezYsYOysjJatmzJ+vXrLcfJ7efk5ERERARms5k1a9aQmJjIH/7wBzw9PcnOzqa4uFjrK4mIyG2jRaibgIZFohtupZ2dHXv37uVPf/oTixYtIjQ09KaFpGtra6mtrVUXHRtRV1dHRUUFY8aM4fe//z3Tpk1rtP/tt98mNjaWZ599lilTpnDlyhXS09O5cuUKLi4uPPXUU5pu2IoqKioYNmwY48aNs9y7hsWJz58/T3h4OF5eXrz++usAXLlyhebNm1NbW6sxZjaisrKSvLw8lixZgr29PW5ubtjb25OYmEhQUJC1yxMRkbuEWs6agIbQ9c3w5efnh4eHB/n5+Y3CWcMDo4ODg1pZbIi9vT0Gg4HBgwfz/vvvM3z48EZha+TIkVy7do1FixYREBDAkCFDiImJAf4bAuT22r17N9evX6eyspJhw4YxbNgwPvroI4KDgxk4cCD29vaYzWa8vb2Jj49n3rx5fPDBBwwZMgQPDw/dMxvTsNh0v379OH/+PM2aNcPb25sWLVpYuzQREbmL6OmgifL19WXixIls3LiRgwcPWh4U9UBoW3Jzc/nLX/5ieR0SEoKDgwNbt261rGsG9a2jo0eP5re//S179+61tHyCxgpaw5IlS5gzZw7r16/nxRdfZMWKFTzzzDPU1taSmZnJ3r17gf/em6CgIOrq6jhz5gyAvoc2rFWrVnTr1o2goCAFMxERue30hNCEPfbYYwwePJhly5Zx8uRJPcTbELPZjNlsZt++fWzYsIE333wTgEGDBjFo0CB27txJVlYW58+fB+of8t3c3DAajXz++eeNWj51X2+v7Oxs3n33XdauXcvGjRtZvHgx27dvx9vbm/nz53Pq1Ck2b97Me++9ZznG3d0dPz8/DAaDFSsXERERW6dw1oS5uroyevRovvrqKzIyMqipqbF2SfL/6urqsLOzw8XFBZPJxOuvv24ZjzR9+nSGDx/O3//+d9atW8fJkyctx9nZ2eHr66t7aUXHjx/nwQcfJDAwEFdXVzw8PDAajcTHx/Phhx/St29fSktLyczM5JVXXiEvL4+XXnqJI0eOaFZGERER+UEac9ZENYwxGzJkCBcuXGDAgAFa+8qGNLR6lZSU0LVrV7y8vNi6dSsAYWFhzJo1Cw8PD3bt2sWkSZPo0qULFRUVFBQUkJWVpXtpBQ0T7pw5c8bSWtkwwx9AWVkZ+/fvx93dHS8vL7p06UJubi779u3DaDSSkZGBn5+f1eoXERER26fZGpuwb8/QKLbDbDZz5coVoqOjiY6OpkOHDiQnJ3Ps2DHGjRtHWFgYAIWFhfznP//hwIED+Pj48MQTTxAQEGDl6u9uhw8f5sCBA4SFhXH69Gm2b9/OhAkTaNGiBWVlZbz22mucOHGClStX4unpyddff42Dg4O6NIqIiMiPUjgTsZKamhq2bt1Knz598Pf358SJE6xevZrPPvuMJ598kgkTJli7RLkFFRUVlmnxHRwcOHPmDIMHD2bNmjWEhoZauzwRERG5g2jMmYiVNGvWjHHjxuHv709dXR0BAQFMmTKFTp06sWXLFrKysqxdovyAhr9rNW/eHKjvqmo2m6mpqaFjx460adPGmuWJiIjIHUjhTMSKGsaONXQ/bQhoQUFBpKWlsW3bNmuWJz+g4Z6dPXuW/Px8SktLKSsrIycnB5PJhKenp5UrFBERkTuNZhUQsQHfHBsYEBBAREQEzs7O9OnTx4pVya24ePEiUVFRuLm50apVK65du0ZqaipeXl7WLk1ERETuMBpzJmKjqqqqcHJysnYZcgs+/fRTjh8/joeHB8HBwerSKCIiIv8ThTMREREREREboDFnIiIiIiIiNkDhTERERERExAYonImIiIiIiNgAhTMREREREREboHAmIiIiIiJiAxTOREREREREbIDCmYiIiIiIiA1QOBMREREREbEBzaxdgIiIyK0KCwsjPz+/0TZHR0datmzJo48+ynPPPYeHh8f3Hn/69GkGDx7Mq6++yuOPP/6r1dnwOd/m6OiIh4cHPXv2ZObMmbRv3/5Xq0FERO48CmciInJHCQoKYsGCBZbX1dXVHD58mKSkJI4cOUJWVhZ2dnbfeWyrVq3YsmUL7dq1uy21Tp06lYEDB1pem0wmDh8+zOrVq3nmmWd47733cHZ2vi21iIiI7VM4ExGRO4qrqys9evRotC0kJISvv/6a5cuXU1RUdNP+Bk5OTt+779fQrl27mz6vb9++GI1G4uPj+eSTTwgNDb1t9YiIiG3TmDMREWkSunTpAsDZs2eB+i6QMTExzJgxgx49ehAREcHp06fp1KkT27dvtxx38uRJpk+fTu/evQkJCWHy5Ml8/vnnlv2VlZUsXbqU0NBQunTpwqhRo3j33Xd/Vq3u7u43bSsoKCAyMpKQkBC6dOnCoEGDWLFiBXV1dQCW2vPy8pgxYwY9e/akd+/ezJs3j/Lycst5qqurSUxMZMCAAXTr1o3IyEhycnLo1KkTp0+ftrxv//79TJgwge7du9O7d29iY2MpLS39WdclIiI/j8KZiIg0CcXFxQD4+flZtuXl5WE0GklLSyMqKuqmYy5cuMCTTz5JSUkJ8fHxJCQkcOnSJSZOnMjVq1cxm81MmzaNN954g4iICNLS0ujZsyfPP/88OTk5P1pTXV0dNTU1lp+ysjL27NnDsmXLaNu2Lb169QLg6NGjhIeHc88995CcnExaWhq9evUiNTWVvLy8RudcsGABbdu2ZdWqVURGRrJt2zbS0tIs++Pi4ti0aRMTJkxg5cqVtGzZkvnz5zc6R0FBAeHh4TRv3pyUlBTmzJlDfn4+f/zjH6moqLjl/3MREfllqVujiIjcUcxmMzU1NZbX165dIz8/3xKcGlrQoH4CjoULF+Lk5ATQqOUIYOPGjVRVVbFhwwa8vLwACAwM5KmnnqKoqIhmzZrx8ccfk5yczGOPPQZA//79MZlMJCYmMnLkSJo1+/5fpXPnzmXu3LmNthkMBh5++GFiY2MxGo1AfTjr168fCQkJ2NvX/9304YcfZteuXezbt48RI0ZYjg8NDSU2Nhao7yK5Z88e/vnPfzJz5kxOnTpFdnY2sbGxREREWOq9dOkSu3fvtpxj2bJltG/fnjVr1uDg4ABA9+7dGTFiBH/72994+umnf/Q+iIjIL0/hTERE7igFBQUEBwc32mZvb0+/fv146aWXGk0G0qFDB0sw+y6FhYX06NHDEswAvL29+cc//gFAYmIidnZ2hIaGNgqEgwYN4q233uL48eN07tz5e88/ffp0Bg4ciNlspqCggJSUFEaMGEF8fHyjUDdmzBjGjBlDZWUlxcXFfPHFFxw5coTa2lqqq6sbnfPbY9i8vb05c+YMAPv27cNsNjN8+PBG7xk5cqQlnJlMJoqKioiMjGwUdP38/PD392fPnj0KZyIiVqJwJiIid5Tg4GAWLlwIgJ2dHc7OzrRp0wZXV9eb3tvQMvV9rl69iq+v7w/uN5vNPPDAA9+5/6uvvvrBcNa2bVu6du0KQLdu3fD09OTFF1/EwcHBcg0AFRUVvPzyy+Tm5lJTU4Ovry89e/akWbNmmM3mRud0cXFp9Nre3t7ynoYxY/fee2+j93zz9fXr16mrq2PdunWsW7fuppo1e6SIiPUonImIyB3FaDRaAs/P5ebm9p2TYOzduxdfX1/c3NwwGAxkZGR85/H33XffT/q8xx9/nB07dvDGG28wdOhQHnnkEQAWLVrEjh07SElJoV+/fhgMBqC+2+JP0bp1awAuXbqEj4+PZfs3r9FoNGJnZ0d4eHij7pINvh3+RETk9tGEICIictfq1asXRUVFjcLL5cuXiYqK4l//+he9e/emvLwcs9lM165dLT/Hjh1j5cqVjbo63qr58+fj7OzMK6+8YumyWFhYSJ8+fRgyZIglmB06dIjS0lLLbI234sEHH8TBwYGdO3c22v7+++9b/u3q6kpQUBAnT55sdE33338/K1asYN++fT/5mkRE5JehljMREblrhYeHk5OTQ1RUFJMnT8bR0ZG0tDS8vb0ZNWoUbm5uhISEEB0dTXR0NP7+/nz66acsX76c/v3706JFi5/8mb6+vkRGRrJq1So2bdpEVFQU3bp1Iy8vj6ysLPz9/Tl69ChpaWnY2dlhMplu+dx+fn6MHTuWpKQkqqurCQwMZOfOnZYxdA2TjbzwwgtMmjSJmTNnMnr0aGpra1m/fj1FRUVER0f/5GsSEZFfhsKZiIjctdq0acNf//pXEhISmD17Nk5OTvTp04fk5GQ8PDwAWLt2LX/+859Zs2YNly9fpnXr1kRERDBt2rT/+XMnTZpETk4Oq1atYvTo0cyePZvq6mpSUlKoqqrC19eXqVOncuLECXbt2kVtbe0tn3v+/PkYDAbWr19PWVkZffv2ZerUqaxcudLSKvfII4+Qnp5OamoqM2bMwNHRkeDgYDZs2HBbF+kWEZHG7MzfHmksIiIid6SrV6/y0Ucf0b9/fzw9PS3blyxZwvbt29VlUUTExqnlTEREpIlwcXFh0aJFdO7cmYkTJ2IwGDh48CCbN29m8uTJ1i5PRER+hFrOREREmpAjR46QkpLCwYMHMZlMtGvXjvHjx/P00083WgNORERsj8KZiIiIiIiIDdBU+iIiIiIiIjZA4UxERERERMQGKJyJiIiIiIjYAIUzERERERERG6BwJiIiIiIiYgMUzkRERERERGyAwpmIiIiIiIgNUDgTERERERGxAQpnIiIiIiIiNuD/AGHcXP7vRCUdAAAAAElFTkSuQmCC",
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t27dXwYIFzWejvv/+e0l66eyFL1OwYEGFhoa+dJa82NhYBQUFqVChQpKeTz8fExOjvXv3mteJjo7WDz/8kOB5rq6uCg4OTrDsj7MAxj+uXbu2KlasaB56lpjaXV1dVaxYMV27dk0lS5Y0//fGG29ozpw5SR7++GeJ7Y8333xT+/fv15MnT8zrxMbGavv27SpZsqR5n/58ljAx+/3mm2/q9OnT5iAnPT+Lc/r0afPj+LNloaGhCd6Hhw8fatasWS8NxH/03XffJXi8fft2OTs7q3Tp0uZlLVq00C+//KIVK1aoUqVKCYZBWkL58uUVGBiYIJw8ePBAXbp00YEDB1ShQgU9e/ZMhmEkeA9++eUXzZ07N8FQxMQaPny4HB0dNW7cOPOQwhMnTqhixYqqVauWOXidO3dODx8+TPTnTJLKlSsnW1tbffPNNwmW79mzx/zv5P75BpA6ceYLABLJxsZGffv2lZ+fn/r166fGjRsrNDRUAQEBypgxoz788ENJz88CSM8PmjNmzKh8+fLJ1dVVCxYskJ2dnezs7LR7927z9UyJne2wcuXKatiwoaZPn65Lly6pTp06cnd3V3BwsNatW6fg4GDNnDlT0vNv+n18fDRs2DCFhIQoZ86cWr16tW7fvp3gm/waNWpo4cKFWrhwoUqXLq19+/a9MP19qVKl9PXXX6t48eLKmjWrTp48qUWLFslkMv1j7X379pWvr6/5/YqNjdVnn32mwMBAde/ePVH7/VcS2x89e/bU999/r/bt28vX11f29vZavXq1goKCtGTJEvP2MmTIoJMnT+rYsWMqX758ova7ffv2WrNmjTp37qwePXpIej7Ne3R0tPmaqcKFC6tx48bm67JKlCih69eva8aMGcqZM6fy5s37t/u5Z88eeXl5qVKlSjp48KDWr1+vTz75JMHwwHLlyilfvnw6evSoZsyY8T+9r/9Gx44dtWXLFnXp0kVdu3aVvb295s+fr6xZs6pRo0ZKnz693nzzTXXv3l3du3dXgQIFdObMGc2ePVtVq1Y1D6VMipw5c6pz586aN2+eVqxYoS5duqhUqVLauXOnPv/8cxUoUEAXL17U/PnzE/Wz+ke5cuVS8+bNNX36dEVHR6tIkSL65ptvzNewxQf1xP58nz59Wu7u7ha7KTWA1xfhCwCSoFmzZkqXLp0WLlyoHj16yNXVVVWrVlXfvn3N1+a88cYbatiwodasWaMffvhB27Zt07x58zR58mR98sknSpcunYoWLarVq1fro48+0vHjxxPce+vvTJkyRRUqVNBXX31lvi+Ru7u7KleurIkTJyaYHnz27NmaNm2a5syZo8jISDVo0EB169bVmTNnzOt07dpVDx8+1NKlSxUdHa3q1atr/Pjx6tatm3mdSZMmaezYsRo7dqwkKW/evBo9erS++uorHT9+/G/rrVKlipYuXaqAgAD17t1b9vb2Kl68uJYtW/ZKbtKb2P5Yu3ateSp6k8mkUqVKaeXKleazUpL08ccfa968efroo4+0Y8eORO13hgwZtHLlSo0fP14DBw5UunTp1Lp1azk7Oye4/m3ixIlauHChOSR7eHiofv36+vTTT184U/pnQ4cO1fbt27V8+XJlzpxZQ4YMMd9L7o+qV6+uhw8f/u3kHcklW7ZsWrt2raZMmaLBgwfLwcFBFStW1IwZM5QxY0ZJ0qJFizRr1iwtXLhQDx48kJeXlz788ENzaP03fH19tWXLFs2bN0+NGzfW4MGDFR0drZkzZyoqKko5c+ZUt27ddOXKFe3bt++FIbZ/Z/jw4XJxcdFnn32mJ0+e6O2331a3bt00d+5cc98m9ue7VatWevfddzVp0qR/va8AUgeT8W+u9AYApEiDBw/W0aNHtW/fPmuXkioEBgYqLCxMPj4+5mUxMTGqXr26GjRoID8/P4vUYRiGGjRooCpVqmjIkCEWec3ULCwsTN9//72qVq2a4JpAf39/bd68mSGFAP41znwBAPAv3b59W3369FGPHj1UoUIFhYeHa/369Xr8+LHee++9ZH/9J0+eaPny5Tp79qyCgoLUrl27ZH/NtMDZ2Vnjx49X0aJF1aFDB7m4uJhvUt61a1drlwcgBSN8AQDwL9WrV09hYWFau3atli5dKnt7e5UuXVqrV69WgQIFkv31nZyctG7dOsXFxWnChAkJhp3i33N0dNTy5cs1c+ZMDR48WOHh4cqdO7cGDRrEvbsA/E8YdggAAAAAFsBU8wAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABbAVPP/gwcPHou5IgEAAIC0y2SSPDzSJ2pdwtf/wDBE+AIAAACQKAw7BAAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAXYWbsAAAAAAEljY2OSjY3J2mWkanFxhuLijFe6TcIXAAAAkILY2JiUKZOLbG0ZxJacYmPjFBb27JUGMKuGr2+++UY9e/ZMsKxOnTqaPXu2Lly4oJEjR+qXX35RwYIFNXr0aJUoUcK83rZt2zRz5kzdv39fVapU0dixY+Xu7i5JMgxD06ZN08aNGxUXF6cWLVqof//+srF5/gMaGhqqESNG6ODBg3Jzc9Mnn3yiJk2aWG7HAQAAgH/JxsYkW1sbDZ2yRteD7lq7nFQpXy4vjR/QRjY2ptQTvq5cuaIaNWpo7Nix5mWOjo569uyZfH191ahRI02aNEmff/65unbtqm+++UYuLi46c+aMhg4dqtGjR6tIkSIaP368/Pz8tHDhQknSsmXLtG3bNgUEBCgmJkYDBgyQh4eHOnfuLEny8/NTRESE1q9fr8DAQA0bNkz58uVTqVKlrPI+AAAAAEl1PeiuLl69Ze0ykARWPVd59epVFSpUSJkzZzb/lyFDBu3YsUOOjo4aOHCgChQooKFDhypdunTatWuXJGn16tWqV6+emjZtqiJFimjy5Mk6cOCAgoKCJEkrV65U7969Vb58eb311lvq37+/1qxZI0m6efOm9u/fr3HjxqlQoUJq2bKlGjdurLVr11rtfQAAAACQ+lk9fOXNm/eF5YGBgSpXrpxMpucXEZpMJpUtW1anT582t5cvX968frZs2ZQ9e3YFBgbq7t27unPnjt58801ze7ly5XTr1i3du3dPgYGBypYtm3LmzJmg/dSpU8mzkwAAAAAgKw47NAxD169f18GDB7Vw4ULFxsaqbt266t27t+7fv6+CBQsmWN/Dw0OXL1+WJN27d09ZsmR5oT04OFj379+XpATtnp6ekmRuf9lz795N+nhZExPMAAAAAKnaPx3zJyUTWC183b59W+Hh4XJwcNDMmTP122+/ady4cYqIiDAv/yMHBwdFRUVJkiIiIv6yPSIiwvz4j22SFBUV9Y/bTgoPj/RJfg4AAACAlMHNLd0r3Z7VwleOHDl05MgRZcyYUSaTSUWLFlVcXJwGDBigChUqvBCGoqKi5OTkJOn5pBwva3d2dk4QtBwdHc3/liRnZ+e/fG78tpPiwYPHMl7t1P8AAADA37K1tXnloQAvFxr6VLGxcX+7jsmU+JMyVp3tMFOmTAkeFyhQQJGRkcqcObNCQkIStIWEhJiHC3p5eb20PXPmzPLy8pIk3b9/33xdV/xQxPj2v3puUhmGCF8AAABAKvYqj/etNuHGDz/8oIoVKyo8PNy87Oeff1amTJnME2AY/91TwzB08uRJlS5dWpJUunRpnThxwvy8O3fu6M6dOypdurS8vLyUPXv2BO0nTpxQ9uzZlSVLFpUpU0a3bt1ScHBwgvYyZcok8x4DAAAASMusFr68vb3l6OioYcOG6dq1azpw4IAmT56sLl26qG7dunr06JHGjx+vK1euaPz48QoPD1e9evUkSR988IG2bt2qDRs26OLFixo4cKCqV6+uXLlymdunTp2qI0eO6MiRI5o2bZrat28vScqVK5eqVKmiAQMG6OLFi9qwYYO2bdumNm3aWOutAAAAAJAGWG3Yoaurq5YuXaoJEyaoefPmSpcund5//3116dJFJpNJCxcu1MiRI/XFF1+ocOHCWrRokVxcXCQ9D25jxozR7Nmz9fvvv6ty5coJbtTcuXNnPXjwQD179pStra1atGihjh07mtsnT56soUOH6r333lPmzJk1YcIEbrAMAAAAIFmZDIOrlv6tkBAm3AAAAIBl2dk9n3Cjde/punj1lrXLSZWKFMihtbP7KjT0qWJi/nnCDU/PxE24YdWbLAMAAABAWkH4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAW8NuHL19dXgwcPNj++cOGCWrZsqdKlS6t58+Y6d+5cgvW3bdumWrVqqXTp0urRo4cePnxobjMMQ1OnTtVbb72lChUqaPLkyYqLizO3h4aGqlevXvL29lbNmjW1devW5N9BAAAAAGnaaxG+tm/frgMHDpgfP3v2TL6+vipfvrw2b94sb29vde3aVc+ePZMknTlzRkOHDlXPnj21fv16PXr0SH5+fubnL1u2TNu2bVNAQIBmz56tr7/+WsuWLTO3+/n56fHjx1q/fr26deumYcOG6cyZM5bbYQAAAABpjtXDV1hYmCZPnqySJUual+3YsUOOjo4aOHCgChQooKFDhypdunTatWuXJGn16tWqV6+emjZtqiJFimjy5Mk6cOCAgoKCJEkrV65U7969Vb58eb311lvq37+/1qxZI0m6efOm9u/fr3HjxqlQoUJq2bKlGjdurLVr11p+5wEAAACkGVYPX/7+/mrSpIkKFixoXhYYGKhy5crJZDJJkkwmk8qWLavTp0+b28uXL29eP1u2bMqePbsCAwN19+5d3blzR2+++aa5vVy5crp165bu3bunwMBAZcuWTTlz5kzQfurUqWTeUwAAAABpmZ01X/ynn37S8ePH9fXXX2vUqFHm5ffv308QxiTJw8NDly9fliTdu3dPWbJkeaE9ODhY9+/fl6QE7Z6enpJkbn/Zc+/evZvk+v+bDQEAAACkUv90zJ+UTGC18BUZGamRI0dqxIgRcnJyStAWHh4uBweHBMscHBwUFRUlSYqIiPjL9oiICPPjP7ZJUlRU1D9uOyk8PNIn+TkAAAAAUgY3t3SvdHtWC18BAQEqUaKEqlat+kKbo6PjC2EoKirKHNL+qt3Z2TlB0HJ0dDT/W5KcnZ3/cdtJ8eDBYxlGkp8GAAAA/Gu2tjavPBTg5UJDnyo2Nu5v1zGZEn9Sxmrha/v27QoJCZG3t7ek/w9Iu3fvVsOGDRUSEpJg/ZCQEPNwQS8vr5e2Z86cWV5eXpKeD12Mv64rfihifPtfPTepDEOELwAAACAVe5XH+1abcGPVqlX6+uuvtWXLFm3ZskU1a9ZUzZo1tWXLFpUuXVqnTp2S8d89NQxDJ0+eVOnSpSVJpUuX1okTJ8zbunPnju7cuaPSpUvLy8tL2bNnT9B+4sQJZc+eXVmyZFGZMmV069YtBQcHJ2gvU6aMZXYcAAAAQJpktTNfOXLkSPA4Xbrnp07z5MkjDw8PTZs2TePHj9f777+vdevWKTw8XPXq1ZMkffDBB2rXrp3KlCmjkiVLavz48apevbpy5cplbp86daqyZs0qSZo2bZo6deokScqVK5eqVKmiAQMGaOjQoTp79qy2bdum1atXW2rXAQAAAKRBVp3t8K+4urpq4cKFGjlypL744gsVLlxYixYtkouLiyTJ29tbY8aM0ezZs/X777+rcuXKGjt2rPn5nTt31oMHD9SzZ0/Z2tqqRYsW6tixo7l98uTJGjp0qN577z1lzpxZEyZMUKlSpSy9mwAAAADSEJNhcNXSvxUSwoQbAAAAsCw7u+cTbrTuPV0Xr96ydjmpUpECObR2dl+Fhj5VTMw/T7jh6Zm4CTesfpNlAAAAAEgLCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFjAvwpfsbGx+u6777R8+XI9evRIgYGBevz48auuDQAAAABSDbukPuHOnTvq3LmzwsLC9Pvvv+udd97RkiVLdOrUKS1dulSFCxdOjjoBAAAAIEVL8pmvMWPGqFy5cvrhhx/k4OAgSZo+fboqVaqkcePGvfICAQAAACA1SHL4On78uDp16iRbW1vzMnt7e3Xv3l3nzp17pcUBAAAAQGqR5PDl5OSkBw8evLD8+vXrcnV1fSVFAQAAAEBqk+Tw9f7772vEiBH67rvvJD0PXZs2bdLw4cPVokWLV10fAAAAAKQKSZ5wo0ePHsqQIYNGjRql8PBw+fr6ysPDQx07dlTnzp2To0YAAAAASPGSHL4kqV27dmrXrp2ePXum2NhYpU+f/lXXBQAAAACpSpLDV0BAwEuXm0wm2dvbK0uWLKpatao8PDz+5+IAAAAAILVIcvi6fv26duzYoaxZs6pEiRIyDEM///yzbt++rTJlyujx48caN26clixZojJlyiRDyQAAAACQ8vyrYYctWrTQqFGjzNPNx8XFafz48Xr27JkmTpyoBQsWaNKkSVq3bt0rLRYAAAAAUqokz3a4b9++F+7zZWNjo7Zt22rXrl2SpAYNGujixYuvrkoAAAAASOGSHL48PT11/PjxF5afOHFCmTJlkiSFhIRwzy8AAAAA+IMkDzvs1auXhg4dqhMnTqhkyZIyDEPnz5/X9u3bNWLECF2/fl2DBg1SgwYNkqNeAAAAAEiRkhy+GjdurOzZs+vzzz/XunXrZGtrq4IFC2rlypUqU6aMzpw5o7Zt26pNmzbJUS8AAAAApEj/asKN8uXLq3z58i9tK1WqlEqVKvU/FQUAAAAAqU2Sw1d4eLjWr1+vK1euKDY21rw8KipKFy5c0M6dO19pgQAAAEg+NjYm2diYrF1GqhUXZyguzrB2GXhNJDl8DRs2TD/99JPefvtt7dq1S/Xq1dOvv/6qs2fPqmfPnslRIwAAAJKBjY1JmTK5yNY2yXOwIZFiY+MUFvaMAAZJ/yJ8ff/995o1a5YqVaqky5cvq2PHjipRooQmTZqky5cvJ0eNAAAASAY2NibZ2tpo0Nz1unb7vrXLSXXyZ88s/x6tZGNjInxB0r8IX5GRkcqbN68k6Y033tC5c+dUokQJtWrVSm3btn3V9QEAACCZXbt9Xz/fuG3tMoBUL8nnmAsUKKAff/xR0vPwdeLECUnS48ePFRkZ+WqrAwAAAIBUIslnvnr27KlPPvlEcXFxatKkiRo0aKCPP/5Yly5dUpUqVZKjRgAAAABI8ZIcvt555x3t3LlTcXFxypYtm9auXautW7eqbNmyat++fXLUCAAAAAApXpKHHfr5+cnNzU158uSRJBUpUkSDBg1Sq1atNHDgwFdeIAAAAACkBok683Xq1Cn9+uuvkqQtW7aoePHicnV1TbDOtWvXdPDgwVdfIQAAAACkAokKX87OzpozZ44Mw5BhGFqyZIlsbP7/pJnJZJKLi4v69++fbIUCAAAAQEqWqPBVpEgRffvtt5Kkdu3aKSAgQBkzZkzWwgAAAAAgNUnyhBurVq2SJB06dEhXr15VXFyc8uXLp0qVKsne3v6VFwgAAAAAqUGSw9fdu3fVrVs3Xb9+Xfny5VNsbKx+/fVXZc+eXcuWLZOXl1dy1AkAAAAAKVqSZzscNWqUPDw89N1332nz5s3aunWr9u/fr+zZs2v8+PHJUSMAAAAApHhJDl+HDx/WgAEDElzz5ebmpv79++vQoUOvtDgAAAAASC2SHL4yZsyo33///YXljx49SvI1X7/++qs6d+4sb29vVa9eXUuWLDG3BQUFqWPHjipTpozq16//wjT2P/74oxo2bKjSpUurffv2CgoKStC+fPlyVa1aVd7e3hoyZIjCw8PNbZGRkRoyZIjKly+vKlWq6LPPPktS3QAAAACQVEkOXw0aNNCwYcP0008/6cmTJ3ry5IkOHTqk4cOHq379+oneTlxcnHx9feXm5qYvv/xSo0eP1vz58/X111/LMAz16NFDnp6e2rRpk5o0aaKePXvq9u3bkqTbt2+rR48eatasmTZu3Ch3d3d1795dhmFIknbv3q2AgACNGTNGK1asUGBgoKZMmWJ+7cmTJ+vcuXNasWKFRo4cqYCAAO3atSupbwUAAAAAJFqSJ9z45JNP9ODBA3Xu3Nl83y87Ozu1bNlSAwcOTPR2QkJCVLRoUY0aNUqurq7Kmzev3n77bZ04cUKenp4KCgrSunXr5OLiogIFCuinn37Spk2b1KtXL23YsEElSpRQp06dJEkTJ05U5cqVdfToUVWsWFErV65Uhw4dVKNGDUnS6NGj1blzZw0YMECGYWjDhg1avHixihcvruLFi+vy5ctas2aN6tatm9S3AwAAAAASJcnhy8HBQZMmTdKQIUN048YNOTg4KHfu3HJxcUnSdrJkyaKZM2dKkgzD0MmTJ3Xs2DGNHDlSgYGBKlasWIJtlitXTqdPn5YkBQYGqnz58uY2Z2dnFS9eXKdPn1b58uV19uxZ9ezZ09xepkwZRUdH6+LFizIMQzExMfL29k6w7QULFiguLi7BzaP/icmUpF0GACDVsrExycQfxmRlGIbi4gxrl4F/iY9HyvVPfZeUvk1y+JKke/fuac2aNbp69apiY2OVP39+tWzZUnnz5v03m1PNmjV1+/Zt1ahRQ3Xq1NGECROUJUuWBOt4eHgoODhYknT//v2/bH/06JEiIyMTtNvZ2SlTpkwKDg6WjY2N3Nzc5ODgYG739PRUZGSkwsLC5O7unui6PTzS/5vdBQAg1YmNi5NtEr7ARNLxHqdcbm7prF0C/qVX3XdJDl/Hjx/XRx99pMKFC6tMmTKKjY3VsWPHtHr1an322WcqV65ckouYPXu2QkJCNGrUKE2cOFHh4eEJwpH0/IxbVFSUJP1te0REhPnxy9oNw3hpmyTz9hPrwYPHMvgCCgCQxtna2sjNLZ38lm/UteAQa5eTKuXP6qmJHVsoNPSpYmPjXtl24/sOyYt+S7kS03cmU+JPyiQ5fE2aNElt27ZVv379EiyfOnWqpkyZonXr1iV1kypZsqSk57MQ9u/fX82bN08wO6H0PBg5OTlJkhwdHV8ISlFRUcqQIYMcHR3Nj//c7uzsrNjY2Je2STJvP7EMQ4QvAAD+61pwiC7+dsfaZaR6HHukTPRbyvUq+y7J564vX76s5s2bv7C8RYsW+vnnnxO9nZCQEO3duzfBsoIFCyo6OlqZM2dWSEjIC+vHDyX08vJ6aXvmzJmVKVMmOTo6JmiPiYlRWFiYMmfOLC8vL4WGhiomJsbcfv/+fTk5OSlDhgyJrh8AAAAAkiLJ4StHjhw6c+bMC8sDAwPl6emZ6O389ttv6tmzp+7evWtedu7cObm7u6tcuXI6f/68eQihJJ04cUKlS5eWJJUuXVonTpwwt4WHh+vChQsqXbq0bGxsVLJkyQTtp0+flp2dnYoUKaKiRYvKzs7OPHlH/LZLliyZpMk2AAAAACApkjzssEuXLho5cqSuXbumUqVKSXoevFatWqW+ffsmejslS5ZU8eLFNWTIEPn5+enWrVuaMmWKPv74Y1WoUEHZsmWTn5+funfvrv379+vMmTOaOHGiJKl58+ZaunSpFi1apBo1amju3LnKmTOnKlasKElq3bq1RowYoUKFCilLliwaNWqU3nvvPTk7O0uSmjZtqlGjRmnChAm6d++ePvvsM/O2AQAAACA5JDl8NWvWTJK0evVqLVu2TI6OjsqXL5/Gjx+vevXqJXo7tra2mjdvnsaOHatWrVrJ2dlZ7dq1U/v27WUymTRv3jwNHTpUzZo1U548eTR37lxlz55dkpQzZ07NmTNHEyZM0Ny5c+Xt7a25c+eap7ht0KCBbt26pREjRigqKkq1a9fWgAEDzK/t5+enUaNGqUOHDnJ1dVWvXr1Uu3btpL4VAAAAAJBoJsPg8r9/KySE2Q4BALCzez7zWqtJC5hwI5kUyZlN6wd/rNDQp4qJeXWz5sX3XcuhAfr5xu1Xtl08VzRvdm0Y3zPZ+q117+m6ePXWK9su/l+RAjm0dnbfRPWdySR5eiZutsNEXeRUvXp1hYaGJlh27dq1BJNWAAAAAAD+WqLCV3BwsOLiEia+Fi1a6M4dvt0CAAAAgMT419P7MVoRAAAAABKPudUBAAAAwAIIXwAAAABgAYmean7p0qVycXExP46OjtbKlSuVMWPGBOv17Nnz1VUHAAAAAKlEosLXm2++qbNnzyZY5u3trYsXLyZYFn+fLQAAAABAQokKX6tWrUruOgAAAAAgVeOaLwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAW8K/C1+PHj7VmzRqNGzdODx8+1P79+3Xz5s1XXRsAAAAApBpJDl+//PKLateurU2bNmndunV6+vSp9uzZoyZNmujo0aPJUSMAAAAApHhJDl/jxo3TBx98oM2bN8ve3l6SNHHiRLVu3VqTJ09+5QUCAAAAQGqQ5PB19uxZNW3a9IXl77//vq5cufIqagIAAACAVCfJ4cvd3V3Xr19/YfnJkyfl4eHxSooCAAAAgNTGLqlP+OijjzRs2DB9/PHHMgxDhw8f1pdffqkVK1aoT58+yVEjAAAAAKR4SQ5f77//vrJkyaKlS5fKyclJkydPVr58+TR27FjVr18/OWoEAAAAgBQvyeFryZIlatiwodasWZMc9QAAAABAqpTka74WLFig6Ojo5KgFAAAAAFKtJIevhg0bav78+bpx44aioqKSoyYAAAAASHWSPOzw+++/1+3bt/Xll1++tP3nn3/+n4sCAAAAgNQmyeFr0qRJyVEHAAAAAKRqSQ5fFSpU+Mu2e/fu/U/FAAAAAEBqleTwde3aNU2dOlVXrlxRbGysJMkwDEVFRenhw4e6cOHCKy8SAAAAAFK6JE+4MXz4cD18+FCdO3dWSEiIOnXqpLp16+rJkycaP358ctQIAAAAACleks98nT17VuvXr1fRokW1ZcsW5c+fX23atFG+fPm0ceNGvfvuu8lRJwAAAACkaEk+82VnZ6f06dNLkvLnz2+e3bBSpUq6dOnSq60OAAAAAFKJJIcvb29vLV26VBERESpRooT27dsnwzB07tw5OTo6JkeNAAAAAJDiJXnYoZ+fn7p166ZcuXLp/fff18qVK1WhQgU9e/ZM3bt3T44aAQAAACDFS3L4KliwoPbs2aOIiAg5Oztr06ZNOnr0qDJlyqQyZcokQ4kAAAAAkPIlOXzdvn3b/O/Q0FBJUqFChcxt2bNnf0WlAQAAAEDqkeTwVbNmTZlMpr9sj5+AAwAAAADw/5Icvr799tsEj2NjY3Xz5k3NmTOHa74AAAAA4C8kOXzlyJHjhWW5c+dWhgwZNGDAAPn4+LySwgAAaY+NjUk2Nn89ugL/u7g4Q3FxhrXLAIA0Kcnh66+YTCbdvXv3VW0OAJDG2NiYlCmTi2xtk3wXFCRBbGycwsKeEcAAwAqSHL4CAgJeWPb06VPt2rVLlStXfiVFAQDSHhsbk2xtbTR2w5f69X6ItctJlfJk9tTwlu/KxsZE+AIAK0hy+Dpy5EiCxyaTSfb29mrSpIk+/PDDV1YYACBt+vV+iC7fCbZ2GQAAvHJJDl+rVq1KjjoAAAAAIFVLcvjasmVLotdt2rRpUjcPAAAAAKlSksPXpk2bFBgYKJPJpDx58sjBwUE3btzQ06dPlS1bNvN6JpOJ8AUAAAAA/5Xk8FW2bFk5OzvL399fbm5ukqRnz55pxIgRyp49u/r27fvKiwQAAACAlC7J8/muXbtWAwcONAcvSXJxcVH37t21evXqV1ocAAAAAKQWSQ5frq6uunDhwgvLjx8/Lnd391dSFAAAAACkNkkedti1a1cNGzZMR48eVdGiRWUYhs6ePaudO3dq8uTJyVEjAAAAAKR4SQ5f77//vrJly6aNGzdq7dq1cnJyUsGCBfXFF1+oSJEiyVEjAAAAAKR4SQ5fkuTj4yMfH59XXQsAAAAApFqJvubr9u3bWrx4sR49eiRJioyM1IQJE9SoUSO1b99e3333XXLVCAAAAAApXqLC1/nz59WoUSNt2rRJT58+lSQNGjRIa9euVfXq1VWlShUNGjRI+/btS9ZiAQAAACClStSww5kzZ6phw4YaPXq0JCkoKEi7du1S69at1a9fP0mSu7u7Fi5cqJo1ayZftQAAAACQQiXqzNepU6fUrl078+MDBw7IZDKpfv365mXlypXTpUuXXn2FAAAAAJAKJCp8xcTEyNHR0fz4xx9/VPr06VW2bFnzsujoaNnb27/6CgEAAAAgFUhU+CpatKgOHTokSXr48KEOHTqk6tWry8bm/5/+9ddfM9U8AAAAAPyFRF3z1bNnT/Xo0UMHDx7UpUuXZGNjo65du0qSLl26pM2bN2v16tUKCAhI1mIBAAAAIKVK1JmvypUra/Xq1cqZM6dq1aqljRs3qkCBApKkLVu26KefftK0adNUo0aNZC0WAAAAAFKqRN9kuUSJEipRosQLywcNGvRKCwIAAACA1CjRN1kGAAAAAPx7hC8AAAAAsADCFwAAAABYAOELAAAAACwgURNuJGUK+Z49e/7rYgAAAAAgtUpU+Dpy5EiiNmYymf6nYgAAAAAgtUpU+Fq1alVy1wEAAAAAqVqi7/P1Rz///LMuX76suLg4SZJhGIqKitKFCxc0evToV1ogAAAAAKQGSQ5fAQEBCggIkKenpx48eCAvLy+FhIQoNjZW//nPf5KjRgAAAABI8ZI82+H69es1evRoHTx4UNmyZdOqVav0448/qlKlSsqdO3dy1AgAAAAAKV6Sw1doaKiqVq0qSSpatKhOnTqlDBkyqE+fPtqxY8crLxAAAAAAUoMkhy8vLy8FBQVJkgoUKKALFy5IklxdXfXw4cNXWx0AAAAApBJJvuarZcuW6tu3ryZMmKBatWqpY8eOypIli3788UcVKVIkOWoEAAAAgBQvyeHr448/VtasWeXs7KxSpUrJz89P69atU6ZMmTRx4sTkqBEAAAAAUrwkh68tW7aofv36cnBwkPT8TFjLli317Nkzbdy4Ufny5XvlRQIAAABASpeo8PXw4UNFRERIkvz8/PTGG2/Izc0twToXL17U1KlT1b59+1dfJQAAAACkcIkKX0ePHtWnn34qk8kkSWrRooWk5zdX/qPGjRu/4vIAAAAAIHVIVPiqW7eu9u3bp7i4ONWqVUsbNmyQu7u7ud1kMsnZ2fmFs2H/5O7duxo/frwOHz4sR0dH1a9fX3379pWjo6OCgoI0fPhwnT59WtmzZ9eQIUNUpUoV83N//PFHTZgwQUFBQSpdurTGjx+vXLlymduXL1+upUuX6smTJ6pXr56GDx8uZ2dnSVJkZKRGjx6tPXv2yMnJSZ06dVKnTp2SVDsAAAAAJEWip5rPnj27cubMqYsXL6pkyZJyd3fX48eP9fvvvytDhgxJDl6GYah3794KDw/XmjVrNGPGDO3fv18zZ86UYRjq0aOHPD09tWnTJjVp0kQ9e/bU7du3JUm3b99Wjx491KxZM23cuFHu7u7q3r27+Uzc7t27FRAQoDFjxmjFihUKDAzUlClTzK89efJknTt3TitWrNDIkSMVEBCgXbt2Jal+AAAAAEiKJE+4ER0drSlTpmjt2rWKiYl5vhE7OzVq1EijR482T8TxT65du6bTp0/r0KFD8vT0lCT17t1b/v7+qlatmoKCgrRu3Tq5uLioQIEC+umnn7Rp0yb16tVLGzZsUIkSJcxnqyZOnKjKlSvr6NGjqlixolauXKkOHTqoRo0akqTRo0erc+fOGjBggAzD0IYNG7R48WIVL15cxYsX1+XLl7VmzRrVrVs3qW8HAAAAACRKkm+y7O/vr/3792v+/Pk6fvy4jh49qrlz5+r48eOaMWNGoreTOXNmLVmyxBy84j158kSBgYEqVqyYXFxczMvLlSun06dPS5ICAwNVvnx5c5uzs7OKFy+u06dPKzY2VmfPnk3QXqZMGUVHR+vixYu6ePGiYmJi5O3tnWDbgYGBiouLS+rbAQAAAACJkuQzX9u2bdOsWbNUsWJF8zIfHx85Ojqqf//+GjRoUKK2kyFDBlWtWtX8OC4uTqtXr9Zbb72l+/fvK0uWLAnW9/DwUHBwsCT9bfujR48UGRmZoN3Ozk6ZMmVScHCwbGxs5ObmluAMnaenpyIjIxUWFpbgWrZ/8t/5RwAASHH4G5Zy0XcpE/2Wcv1T3yWlb5McvgzDkIeHxwvL3d3d9fTp06RuzmzKlCm6cOGCNm7cqOXLl78wfNHBwUFRUVGSpPDw8L9sj58S/6/aDcN4aZsk8/YTy8MjfZLWBwDgdeDmls7aJeBfou9SJvot5XrVfZfk8PXWW29p6tSpmjp1qlxdXSVJjx490vTp0xOcDUuKKVOmaMWKFZoxY4YKFSokR0dHhYWFJVgnKipKTk5OkiRHR8cXglJUVJQyZMggR0dH8+M/tzs7Oys2NvalbZLM20+sBw8e60+z7QMA/iVbWxsOUCwkNPSpYmNf3VB7+s5y6LuUiX5LuRLTdyZT4k/KJCp8HTt2TN7e3rKzs9OQIUPUvn17Va1aVfny5ZMkXb9+Xbly5dL8+fMT9aJ/NHbsWH3++eeaMmWK6tSpI0ny8vLSlStXEqwXEhJiHkro5eWlkJCQF9qLFi2qTJkyydHRUSEhISpQoIAkKSYmRmFhYcqcObMMw1BoaKhiYmJkZ/d89+/fvy8nJydlyJAhSbUbhghfAIAUib9fKRd9lzLRbynXq+y7RIWv9u3b6+DBg/Lw8JCXl5e2bdum77//XteuXZOjo6Py5cunypUry8YmafN3BAQEaN26dZo+fXqCmQZLly6tRYsWKSIiwnw26sSJEypXrpy5/cSJE+b1w8PDdeHCBfXs2VM2NjYqWbKkTpw4YT4Td/r0adnZ2alIkSLPd9rOTqdPnzZPynHixAmVLFkyyfUDAAAAQGIlKnwZf4p79vb2euedd/TOO+/86xe+evWq5s2bJ19fX5UrV0737983t1WoUEHZsmWTn5+funfvrv379+vMmTOaOHGiJKl58+ZaunSpFi1apBo1amju3LnKmTOnOWy1bt1aI0aMUKFChZQlSxaNGjVK7733nvkmy02bNtWoUaM0YcIE3bt3T5999pl52wAAAACQHBJ9zZfpFU/R8u233yo2Nlbz589/YbjipUuXNG/ePA0dOlTNmjVTnjx5NHfuXGXPnl2SlDNnTs2ZM0cTJkzQ3Llz5e3trblz55prbNCggW7duqURI0YoKipKtWvX1oABA8zb9/Pz06hRo9ShQwe5urqqV69eql279ivdPwAAAAD4o0SHr+bNmydqWN63336bqO35+vrK19f3L9vz5Mmj1atX/2W7j4+PfHx8/tX2nZ2d5e/vL39//0TVCgAAAAD/q0SHrw8//FDp0zO1OgAAAAD8G4kKXyaTSQ0aNHjp/b0AAAAAAP8sUdP7/XnCDQAAAABA0iQqfL377rvmmxcDAAAAAJIuUcMOmYYdAAAAAP433FUYAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsIBE3ecLAFIaGxuTbGxM1i4j1YqLMxQXZ1i7DAAAUhTCF4BUx8bGpExuLrK14eR+comNi1NY6DMCGAAASUD4ApDq2NiYZGtjo5k7v9JvDx9Yu5xUJ6e7hz6t11g2NibCFwAASUD4ApBq/fbwga7fu2vtMgAAACQx4QYAAAAAWAThCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZgZ+0CgNeZjY1JNjYma5eRasXFGYqLM6xdBgAAgEUQvoC/YGNjUiY3F9nacII4ucTGxSks9BkBDAAApAmEL+Av2NiYZGtjo+U/7FTw7w+tXU6qkzWjuzpWrScbGxPhCwAApAmEL+AfBP/+UEEP71m7DAAAAKRwjKcCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWYGftAtICGxuTbGxM1i4jVYuLMxQXZ1i7DAAAAOAvEb6SmY2NSW5uLrKx4SRjcoqLi1No6DMCGAAAAF5bhK9k9vysl422nf5GD548tHY5qZKHq7salvmPbGxMhC8AAAC8tghfFvLgyUPdfRRi7TIAAAAAWMlrMRYuKipKDRs21JEjR8zLgoKC1LFjR5UpU0b169fXwYMHEzznxx9/VMOGDVW6dGm1b99eQUFBCdqXL1+uqlWrytvbW0OGDFF4eLi5LTIyUkOGDFH58uVVpUoVffbZZ8m7gwAAAADSPKuHr8jISPXt21eXL182LzMMQz169JCnp6c2bdqkJk2aqGfPnrp9+7Yk6fbt2+rRo4eaNWumjRs3yt3dXd27d5dhPB9ytnv3bgUEBGjMmDFasWKFAgMDNWXKFPP2J0+erHPnzmnFihUaOXKkAgICtGvXLsvuOAAAAIA0xarh68qVK3rvvfd08+bNBMsPHz6soKAgjRkzRgUKFFDXrl1VpkwZbdq0SZK0YcMGlShRQp06ddIbb7yhiRMn6tatWzp69KgkaeXKlerQoYNq1KihUqVKafTo0dq0aZPCw8P17NkzbdiwQUOHDlXx4sX1n//8R126dNGaNWssvv8AAAAA0g6rhq+jR4+qYsWKWr9+fYLlgYGBKlasmFxcXMzLypUrp9OnT5vby5cvb25zdnZW8eLFdfr0acXGxurs2bMJ2suUKaPo6GhdvHhRFy9eVExMjLy9vRNsOzAwUHFxccm0pwAAAADSOqtOuNG6deuXLr9//76yZMmSYJmHh4eCg4P/sf3Ro0eKjIxM0G5nZ6dMmTIpODhYNjY2cnNzk4ODg7nd09NTkZGRCgsLk7u7e6LrN3HrrtcOfZIy0W8pF32XctF3KRd9lzLRbynXP/VdUvr2tZztMDw8PEE4kiQHBwdFRUX9Y3tERIT58cvaDcN4aZsk8/YTy8MjfZLWR/Jyc0tn7RLwL9BvKRd9l3LRdykXfZcy0W8p16vuu9cyfDk6OiosLCzBsqioKDk5OZnb/xyUoqKilCFDBjk6Opof/7nd2dlZsbGxL22TZN5+Yj148FjGP9xWytbWhg+chYSGPlVs7KsbOkrfWcar7jeJvrMUPnMpF32XctF3KRP9lnIlpu9MpsSflHktw5eXl5euXLmSYFlISIh5KKGXl5dCQkJeaC9atKgyZcokR0dHhYSEqECBApKkmJgYhYWFKXPmzDIMQ6GhoYqJiZGd3fPdv3//vpycnJQhQ4Yk1WkY+sfwBcuiP1Im+i3lou9SLvou5aLvUib6LeV6lX1n9anmX6Z06dI6f/68eQihJJ04cUKlS5c2t584ccLcFh4ergsXLqh06dKysbFRyZIlE7SfPn1adnZ2KlKkiIoWLSo7Ozvz5B3x2y5ZsqRsbF7LtwMAAABAKvBapo0KFSooW7Zs8vPz0+XLl7Vo0SKdOXNGLVq0kCQ1b95cJ0+e1KJFi3T58mX5+fkpZ86cqlixoqTnE3ksXbpUe/fu1ZkzZzRq1Ci99957cnZ2lrOzs5o2bapRo0bpzJkz2rt3rz777DO1b9/emrsMAAAAIJV7LYcd2traat68eRo6dKiaNWumPHnyaO7cucqePbskKWfOnJozZ44mTJiguXPnytvbW3PnzpXpv1ONNGjQQLdu3dKIESMUFRWl2rVra8CAAebt+/n5adSoUerQoYNcXV3Vq1cv1a5d2yr7CgAAACBteG3C16VLlxI8zpMnj1avXv2X6/v4+MjHx+cv2319feXr6/vSNmdnZ/n7+8vf3//fFQsAAAAASfRaDjsEAAAAgNSG8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALSLPhKzIyUkOGDFH58uVVpUoVffbZZ9YuCQAAAEAqZmftAqxl8uTJOnfunFasWKHbt29r0KBByp49u+rWrWvt0gAAAACkQmkyfD179kwbNmzQ4sWLVbx4cRUvXlyXL1/WmjVrCF8AAAAAkkWaHHZ48eJFxcTEyNvb27ysXLlyCgwMVFxcnBUrAwAAAJBapckzX/fv35ebm5scHBzMyzw9PRUZGamwsDC5u7snajs2NpJhJO41vTJklr1tmny7k517Ojfzv22S4euEXO5Z5GBH371qXhn+/3OWHP0mSfmzeMnRzj55Np6G5XBP3r57I1tWOdnTb8khl6eH+d/J0XdFc2WVswN9lxzyeiVz3+XJJmdH+u5Vy5vV0/zv5Oi3IgVyyNnJ4Z9XRJLlyZHZ/O9/6juTKfHbNRlGYuND6rFlyxbNmjVL+/fvNy8LCgpSrVq1dODAAWXNmtWK1QEAAABIjdLksENHR0dFRUUlWBb/2MnJyRolAQAAAEjl0mT48vLyUmhoqGJiYszL7t+/LycnJ2XIkMGKlQEAAABIrdJk+CpatKjs7Ox0+vRp87ITJ06oZMmSskmui08AAAAApGlpMmk4OzuradOmGjVqlM6cOaO9e/fqs88+U/v27a1dGgAAAIBUKk1OuCFJ4eHhGjVqlPbs2SNXV1d17txZHTt2tHZZAAAAAFKpNBu+AAAAAMCS0uSwQwAAAACwNMIXAAAAAFgA4QsAAAAALIDwBQAAgFQjfjoDpjVIuVJz3xG+8EotWLBA+/fvt3YZQJp17tw5a5cAAFYVfx9Xk8mUqg/iU6OLFy9KSt19R/jCKxEXF6dff/1Vc+bMkbu7u7XLwf9ozZo15l+ASDmWLFmiESNGWLsM/IMvvvhCly9ftnYZ+AfLli3TgQMHrF0Gkuizzz7TBx98oEWLFklK3Qfxqc28efPUtGlTrV+/XlLq7TvCF14JGxsbeXh4yNXVVc+ePbN2OfiXDMPQqVOnNHbsWGXMmNHa5SCJHj16JBcXF0lSdHS0lavBy3z//fcaMWKE+vXrp+vXr1u7HPyFU6dOyd/fX/369dNPP/1k7XKQBPFfAE+fPl2zZ8+WlHoP4lMbBwcHSdKUKVO0fPlySamz7whfeCUMwzB/aJ48eWLlavBvmUwmubm5ycXFRZGRkdYuB0lkGIYiIiIkSXZ2dlauBi9TqlQp5cmTR7/88ou6deumq1evWrskvIS3t7eqVKmiJ0+eqGfPnpwBS0H+85//6L333lO1atW0ZMkSTZw4UVLqPIhPbWrVqqXq1aurYsWK+vzzz7Vw4UJJqa/vCF/4n5w8eVIxMTEymUyKjIyUi4uL+aAvNjZWUuq+aDI1cnZ2lmEYCg8Pp+9SgD+e4XJyclJcXJyk53+s8HqJjY1VpkyZ9Mknn6hJkyZKnz69OnTooCtXrli7NPxB/N+uzp07q0WLFqpUqZJ69Oihb7/91sqV4e/E/+5Lly6dcufOrdjYWE2fPl1ffvmlJk2aJCn1HcSnFvF9lzdvXuXOnVt37txRp06dtHnz5lQ5fJTwhX/tyJEjGjt2rBYuXKioqCilT59ejo6Ounv3riIiImRrayvp/w8C4z9ceP3cuHHD/O/4vrO3t5fJZKLfXmPr1q3T1q1b9fTpU0lS5syZX3rGMiYmxtKl4U9iY2PNvxPz5cunkJAQ9e7dWzVr1lSHDh04A/aaiIuLS9BPd+7ckY+PjwYNGqRPPvlE+/bts3KF+LNbt25Jen75Q/zfqy5duujZs2c6deqUJkyYoHXr1snf319S6jqIT+kePnwo6XnfxX+ROGjQIGXMmFH3799X27ZttW7dOi1ZskRS6uk7whf+tWLFiuntt9/W8ePHtWzZMhmGITc3Ny1atEgNGzbUkCFDtHjxYp04cUJhYWEcAL6mdu7cqSFDhuibb76RJHl6esre3l5hYWGSnv9S/KP4b4VhfcePH9fChQu1f/9+GYahdOnSKSQkRLt379b+/fv1+++/S2IIojX9+OOPevTokfmAXpKKFi2qypUra/Lkyfr4449VsWJFApiVffPNN7p8+XKC33dZs2ZV+/btNXfuXFWoUEHdunVT7969CWCvkYULF6pNmzZasGCBIiIiEnxZ2LNnTz169EhvvPGGJkyYoFWrVhHAXiOLFi1S79699cUXX0iS7O3tzW21a9dWZGSk6tSpo7Zt22r16tWpKoDxFxlJFh0drYiICKVPn149e/bU4sWL9d133ykmJkbR0dHKly+fypYtqyNHjmjv3r2KioqSYRhyd3dXw4YN1adPnxcO6GE9xYsXl4uLizZv3iw7OztVqVJFdnZ2mjFjhhwdHeXj46NcuXKpRIkSypw5c4KDSFjX1KlTNXLkSM2ZM8c8VPT333/XjBkz9Ntvv8nT01Ourq4qXbq0ypUrJxcXF9WuXZvPn4WsXLlSEyZMUP78+TVw4EDlyZNH+fLlkyS1adNG169f17Fjx+Tv76+ePXuqY8eOWr58uQoUKGDlytOWDRs2aPjw4XJ2dtbgwYNVpEgRlS5dWpJUrVo11atXTz/99JN69OihJ0+eqHfv3goICFD16tWtW3gad+/ePe3bt0/BwcGaOXOmLly4oJIlS6p58+Zyd3dX/vz5FRQUpKNHj6ply5aSpKFDhyo8PFyjRo1iaLYVXblyRatXr9a9e/d0/PhxHTx4UPXr19fbb7+tjBkzqly5clq8eLFKly6tTp06SZLWrl2riIgI9ezZM8X3nclI6fERFjV37lydOnVKQUFBatSokerUqaP8+fMrICBAx48f15kzZ9S/f3+1a9dOz549k2EYunr1qk6fPq3Lly+rbdu2Kly4sLV3A/8VFRUlBwcH3bp1S+PGjVNUVJQaNWqkWbNmKWfOnPLw8NCZM2d0+/ZtpU+fXu7u7sqWLZuaNm2qpk2bWrv8NOmHH35QXFycoqOjVatWLUnSuHHjdPz4cZUsWVLnz5/X1KlTFR4ersDAQJ09e1bXrl3TmTNn5Orqqq+++krZsmWz8l6kflFRUVq7dq1Wrlyp27dvq3LlynJxcVHlypX1/vvvS3oezr7//nvzN7o9evTQ999/r6+++soc0pB8DMNQbGys9uzZo6VLl+r8+fMqV66cPDw8lDVrVg0aNEi2trbas2eP5s+fr1WrVsnV1VVTp07VkiVLtGjRIlWrVs3au5EmnTp1St7e3vrpp5/01VdfKSgoSOnTp5ebm5sOHTqkESNGqGrVqjp37pz69++vefPmqUiRItqyZYumTp2qrVu3yt3dPcUfxKdkW7Zs0ddffy0nJyc9ePBAOXLkUFBQkIYPH66SJUvqm2++0cqVK+Xv7y9XV1etWbNGO3bs0KpVq5QxY8YU3XeELyTa8uXLtWDBAn366acKDw/X7t27lT59ejVq1EiNGjXSggULtGjRItWsWVPTpk174fl/vOYB1rVs2TL98ssvun//vpo1a6Y6deooLCxMQ4cO1bNnz3Tp0iVNnTpVVatW1b179/Ts2TOdPn1aJ0+e1NWrVzVy5EgVKlTI2ruR5vj7+2vr1q3KkiWL7ty5o+LFi6t///4qVqyY/P39tWzZMmXMmFFfffWVvLy8zM+Ljo5WcHCwHBwcEixH8goNDdXevXu1ceNGOTk5qUmTJvL391fFihXVuHFj1apVSx06dFC5cuXUu3dvRUdHa/DgwerVq5fy5s1r7fLTjNjYWH333Xdas2aNwsLC9PHHH2vevHlydHRU48aN1aZNG40ePVoRERHmmfMCAgJUr149zlJawdixY3XmzBktW7ZMrq6uOnDggHbu3Kk7d+7o448/1qVLl7Rx40bly5dP1apVU2hoqNKlS6c2bdooKipKUVFRcnV1tfZupEm3b9/W06dP9cYbb0iSNm3apAMHDih9+vSqXLmyzp8/r82bN6tBgwbKly+fHjx4oNKlS8vHx0chISGys7NTpkyZrLsTrwDhC4k2duxYZciQQZ988okk6dq1a9qwYYOOHz+uFi1aqFWrVpo3b56OHDmit99+W507d5a9vb1iYmK45uQ1Mm/ePK1cuVItW7bUo0eP9O2336patWpq3769smXLphEjRmj37t3q3bu3unfv/sLzIyMj5ejoaIXK07ZLly6pV69emjp1qvLnz6/Y2Fj16tVLsbGxat++verUqaN58+Zp+fLlGjdunCpXrqx06dJJej6JAEMNLePAgQOqXLmy+Xfew4cPtWfPHi1btkzNmzfXBx98oKlTp+rixYvKkCGDqlSpotOnT2vo0KHy9PS0cvVpx5YtW/Tzzz/LyclJZcuWlY+Pj/bt26eAgAAVLlxYEydO1KJFi3T48GHduXNHjRo10tmzZzV48GDlyZPH2uWnWRMnTtTGjRu1atUqFStWzLz8wIED2rRpk548eaLJkyfr8ePH+uabb7R06VJFRUUpQ4YM2r17t5ycnKxYfdo2Y8YM7d+/X8HBwSpatKhGjhyp/Pnza/369dq7d6+yZs0qPz8/nTt3Tt9884327dunW7duqVixYtq8ebO1y3+l+GuMfxSfz2/fvq1ffvnFvDx//vzq0KGDqlatqi+//FI7d+6Ur6+vvL29deTIEQUEBCg2Npbg9RqJiYnRxYsX1b9/f/Xr10+jR4/WlClT9PDhQ82YMUNXr17V+PHjVbt2bZ07d0579+5N8Fzp/2+CCMt6+vSpnjx5oixZssjV1VUZM2bUwoULlSNHDq1cuVLffvutunfvrkaNGmn69On69ttvzffcI3glv/jfk3379tXEiRPNM3e5u7urbt266tixo9auXatNmzZp9OjRmjp1qpycnLR06VLt3LlThw4dsmb5acqMGTM0Y8YMPX78WD///LMmTZqktWvXqmbNmurevbvOnDmjYcOGydfXV/PmzVPt2rX15Zdfav/+/dq6dau1y0+zZs+erZUrV2rLli3m4BX/ufPx8VH79u3l4OCggQMHSpJ8fX21ceNG1a1bVy4uLnrw4IHVak/rNmzYoI0bN8rPz08rV65UcHCw5s2bJ0lq1aqV6tatqxs3bmjSpEnKnz+/hg4dqoCAAL355pt69uyZ7t69a+U9eLX4i4x/FD+utk2bNvr111+1e/duc1vWrFnVsmVLlSxZUlu3btWDBw/UpUsXFShQQL/88osePXpkrbLxEnFxcbp+/bquXbtmXvb222+re/fucnZ21tKlSxUcHKwRI0YoLi5OmzZt0o4dOyT9/4x5KXmcdUoUf3CRN29e5cqVS4cPHzYvc3Z21pgxY5Q1a1YtW7ZMN2/e1PDhw/X2229r0qRJ+uGHH1L8rFApRfwsoAULFtSaNWs0btw483ufKVMm1a9fX127dtXKlSs1e/Zs5cqVS3PmzNHo0aP1wQcfqESJEtYsP8345ZdftHPnTk2dOlUTJkyQv7+/atWqpS1btujRo0eqXLmy+vTpo8DAQPXr109OTk7q06ePpk2bpr59+6pBgwbW3oU0KTQ0VNeuXVP9+vXNQwbj4uL05ZdfatGiRfrqq69UqlQpffrpp3JwcNCYMWN08eJF5cqVS8OHD9eGDRuUI0cOK+9F2nX37l01btxYb7/9tooUKaIuXbooY8aM+vHHHxUWFqbmzZurXbt2un79umbNmqWbN2+qaNGimjt3rj7//PNUN1ye8IW/dPDgQW3ZskWnTp3S3bt3VaFCBRUvXlw7duzQ8ePHzetly5ZNrVu31o0bN7Rv3z65urqqT58+Gj9+vNzc3Ky4B/gzBwcHNW/eXJcuXdKFCxfMy0uVKqUPP/xQkZGRWrNmjTw9PTVs2DDz0I34+0jBsqKjoxUeHi5JSp8+vfLnz6+vv/7aHJ4Nw5CTk5PGjRun8PBw8/UoI0eOVJMmTVS8eHHCsoXEfznh4OCgRo0a6ZtvvtGAAQPMASxjxoyqX7++PvroI23dulUzZsyQJNWoUUNDhw7l2iELiY6OlslkMr/fbm5u8vHx0fnz53Xz5k05OzurWrVq+uSTT3TlyhX17dtX0vPfkfFfLMLy3Nzc1LBhQ4WGhuqnn35SdHS02rZtqw0bNmjLli1avny5WrZsqdy5c6tnz55Knz69hg4dql9++UUuLi5c42Vljx8/1v79+xUVFaWIiAgtWrRIe/bsUY8ePdSpUydNmjRJ77zzjlq3bq3bt29r5syZCgoKUoYMGVLlcSThCy81depUDRo0SMuXL9fw4cM1YMAAnTp1Sn379lVoaKg+//xzHTt2zLx+vnz5VK5cOR07dkyxsbFKly6d3N3drbgHiHf+/HkdPnxYDx48UExMjKpWrapnz55p+/btunPnjnm90qVLq2PHjvrqq6/0008/KWfOnJo6daoGDhxovnYIljN37lx169ZN7777rgICAnTz5k0NGzZMISEhmjhxooKDg833O3F2dtaUKVN0/vx5c6geNGiQcufObeW9SFvu37+vyMhItWrVSitWrNCBAwc0aNCgFwKYr6+v1qxZYx52w9Bsy8mcObNu3rypn376SdLzM5YFCxZUhgwZzCM1HBwcVK1aNfXq1UsnT56Un5+fJIbvWkv856dWrVoqW7aspk6dqpkzZypXrlyaP3++Nm7cqClTpihjxowaPHiwSpQooebNm6tgwYKErtdEmzZt5OLioipVqqhOnTrKkCGD1q5dq71796pRo0Y6f/68Vq1apXr16qlmzZqKiopK1deW8xsfL7h48aJ27NihOXPmqGzZsjp37px27typ7t27a+rUqZo4caKGDh2qVatW6d69e2rQoIH5HkMeHh7MaPgamTJlirZt2yaTySQnJye99dZb6tq1q/r06aPBgwfLwcFBLVq0MA/HqFKlikqVKqXDhw/r7bffVtasWa28B2nT8uXLtWrVqgQzi546dUqtWrXSmjVr1LhxYw0dOlQDBw4037ohLi5O6dOn52DDiuLPmmTLlk05cuTQ0qVL1aVLFw0aNEj+/v4ymUzKmDGjateuLTs7O5UvX97aJacpcXFxypIli6ZNm6YsWbLIMAzZ2trKwcFBkZGRevjwoaTnB/sODg6qVauWHB0dmXnSykwmk3nSoJ49eyo4OFhLly5V7969zVOOFyhQQK1bt9aiRYv05MkT+fj46M0335SLi4u1y4eknDlzaubMmTpx4oS2b9+umjVrKleuXJKk9u3bKzg4WPv27VPHjh3Vpk0bNW7cWOnTp7dy1cmH8IUXODg4yMXFxTzzVokSJZQzZ05lyJBB3bt314IFCzR58mTNmzdPs2fP1uLFi+Xm5qZz585pzZo1Vq4e8Q4fPqytW7dq1qxZKlSokA4cOGCelGHGjBkaM2aMRo0apfDwcDVo0EAlS5aU9PwPXYYMGaxcfdoWFBSkDz74wHw/KB8fH23YsEELFy5UVFSUvvrqK3Xo0EGTJ09W5cqVVapUKX333XeKioriYMOKXF1d5evrKwcHB8XFxalUqVJasmTJCwHMzc1N7777LmdSLCz+/a5du7ZsbW3NQ3Lj4uJka2tr/uLQZDJpzpw55muaYX02Njbm29X07dtXz549U/Xq1RMMq86aNauePn2qsLAwubq68rvwNWJjY6PcuXMrd+7cCg4OTnDZg62trd58800dPnxYjx8/Vvr06VN18JIYdoiXsLe31927d3X27FnzskyZMqlDhw765JNP5Ofnp5s3b2rkyJGaNWuWfHx8VLNmTa1fv557P71GDMNQtmzZVKJECaVPn14NGzZUly5dVKxYMXXt2lW5c+fWpEmTdOnSJU2YMEHdunVTv379dPbsWdWoUcPa5adJcXFxkqRbt269dGZRHx8frVq1SsePH9eqVatUtGhR7dmzR35+fjpw4IBmzZrFdOUWEBwc/NLlcXFx5tlAbWxsZBiGOYAdPHhQPXv2NA+hInglv/gZJ//MxsYmwUF7/DWt8WeNZ86cqYULFyaYyhyWs3PnTl29evWF5fGfHQcHB40ePVrFihXTjRs3FBMTo8jISO3fv1/p0qXjy0MrOnDggCIjI19YHj9bclRUlLJmzaqgoCB9+eWX5r95J0+elKenZ9qZTdkADMN4+PBhgscLFy40qlevbhw7dizB8vv37xtjx441evXqZTx+/NiSJSKJjh07ZpQoUcL4+eefEyy/cuWK4efnZ7Rq1coIDg42fvvtN2PHjh3Gp59+avj7+xuXLl2yUsWI98MPPxgNGzY0du3alWD57du3jXHjxhldu3Y1goODjZiYGCMiIsK4c+eO8ejRIytVm7bMmTPHGDBggGEYhhEXF2deHhMTYxiGYYSGhho3btx44XknTpwwatasaQQHB1um0DRu4cKFxqJFi174OxXfTw8ePDACAwMNwzCMR48eGRUrVjT27NljzJkzxyhZsqRx9uxZi9cMw/j222+NwoULG7169TKuX79uXh4VFWUYhmHcvHnTaNu2rXHs2DHjwYMHRtu2bY1ixYoZrVu3NipXrmycP3/eSpXjyy+/NAoXLmyMGDHCiIiIMC+Pjo42DON533366afGsWPHjEGDBhnvvfeeUa1aNaNr167GW2+99cKxSmrGV2/Qnj17NGfOHN28edO8rF69eqpcubKmT5+u06dPS3r+rZOnp6eqVq2qU6dOKTQ01EoV469ERUVJev4NfPny5VW/fn1Nnjw5Qd8WKFBArVq1kru7u9atW6ccOXKoXr16mjFjhgYOHMjZSyv46quvNGvWLK1fv14XL15UmTJlVKJECe3evfsvZxbdv3+/bG1t5ejoqKxZs6b6YRqvi1OnTpm/nY3/1jZ+ONStW7fUqlUrnTt37oXnlS1bVjt27Eh1Uya/jq5evarp06dr1qxZ+vrrr81ntuKHF966dUvvvvuuLl68KOn5hCeenp4aPXq0lixZorVr1zL1v5VkzpxZ9vb22rNnj/z9/fXrr79Kej4iJygoSG3atFGePHlUvnx5ZciQQRMmTNCoUaPUqVMnrVu3jrOVVhT/u239+vUaOXKk+XjEzs7O3Hf29vYqX768hg0bpgEDBqhly5aqU6eO1q9fryJFilizfIsifEFxcXFat26d1q9fr+vXr0uScuXKpWbNmilHjhyaNGmSjhw5Yh6mUbJkSbm5uTH9+Gtm2bJlGjBggHr06KH169crLi5OzZo1k4ODgxYtWqRbt26Z1y1durQqVKigHTt2vHSIACxn+vTp8vf3188//6wvvvhCBw4ckKurq1q2bKmHDx9q/fr1L51Z9OjRo1asOu0x/nC/tPh7esVfIxR/QN+iRQtVqFBB9evXf+k2UvPsXa+TAgUK6L333lNMTIzGjx+vL774QhEREbKxsVFwcLCaNGkiHx8ftWzZ0jxb6Ntvvy0bGxutW7eO4GVFJUuW1IgRI+Tj46Pjx49ryJAhCgoKUnR0tGbPnq233npLY8eOlfT8oD5Xrlxq2bKl3nnnHeXMmdPK1adtb7/9tj799FNVr15de/fu1aeffqqYmBhFR0dr3Lhxqly5svz9/WUYhlxdXVW+fHn17NlT7777bpqbmZfwlYbFH0zEXyPy1Vdfac2aNbpx44ak59/Utm7dWoUKFdInn3yitWvXav/+/VqyZIl+//13Zc6c2Vql408CAgK0ePFilS9fXra2ttq6dasuX76sihUrqm7dugoKClJAQIA5XEtSxYoV5ejoqN9//92KladdhmEoKChI+/fv14wZM7RgwQItW7ZMXbt2lfT88zdw4EDdv39fK1eu1Pbt283Pi59ZFJYT/+VTpkyZEpxhlp73yTfffKP//Oc/GjNmDPdWs6L4YOzr66sOHTqoTp068vf314oVKxQREaHAwEA1b95cI0eOlMlkMvdV06ZNtW7dujT17fvrJDY21nxMUrBgQeXJk0cLFy7U48eP1a9fP92+fVv9+/fX5MmT+Xy9ZuI/c9LzLwednZ21YMECXb16Vb169VJUVJQGDBig8ePHJ/jMpWXMdpiGxX8ALl26pPLly+vjjz/WsGHDJElt27ZV3rx55e3trVy5cqlo0aL67LPPlD59esXFxWn+/Pkc/L0mHj16pB9++EHDhg1T/fr11bx5czVu3FgXL15U9uzZ1bRpU7m5uenzzz+Xn5+f+vXrJzc3N3399deKi4uTs7OztXchTTKZTHJ2dtazZ8/MZx9dXFw0Y8YMXb9+XVmyZFGTJk00depUBQQEMLOolezYsUOZM2fWm2++Ken5pAx3796V9P+TaphMJrVo0ULp0qXjwMKKjP9OHS89v6daVFSU3nrrLTVo0EDdu3eXjY2N3n//fdWpUyfBc0wmk4oXL26tstO006dPq0yZMgluUePt7a21a9fq888/15YtW9SoUSMNHjxYkydPNq8T32+wnkuXLqlw4cIJ+q5OnTrauHGjNm7cqIULF6pDhw7y8/PTtGnTmGToD3gnoCxZsihr1qyqVKmShg8frn379mn16tXmM2Cenp764IMPtGHDBq1Zs0arVq1iXPVrJDIyUvfv31emTJkkPT8gjIqK0oIFC9S4cWO9//77Klq0qAYMGKDChQvL19dXffr00a5duzR58mSuFbIiwzBkGIb5mrwuXbrozJkzSpcunR48eKB27drp9OnTGjVqFDOLWsGzZ880c+ZMzZ8/XydPnpQkOTk5KSwsTL/88ot+++0387e+rq6uHAxaybp163T48OEE73/69OnVsmVLTZ8+Xbly5VJAQICmTZumzz//PMFQa/rMehYuXKj3339fffr00YEDB8z3WZOksWPH6u7du/rhhx+0ceNGPXjwQAMHDlRQUJAk+s3aFi5cqCZNmmj48OG6evWqnjx5Ym4bNWqUnj17prCwMC1ZskQnTpxQnz59zDMeQjIZfxzIjjTp3r17iouLM99Q9/vvv9eoUaNUs2ZNtW/f3jwWN/4mh3j9jBo1St7e3mrSpIlmz56t7777TgMHDlRkZKRWrlyp69eva9u2bXJxcdH169fl6OgoR0dHzl6+BtatW6eZM2dq6NCh+vHHH/Xpp5/Ky8tLT5480apVq7Rp0ybNnz9fb7zxhrVLTZPu3r2rXr16ydXVVf3799fu3bu1cOFC5cmTR3fv3lXRokWVN29eFS5cWJUqVVJsbKyKFi1q7bLTjB07dqhv376SpJ49eypv3rxq2LChuX3ZsmW6ceOGRo8erc2bN2vIkCHq37+/2rZtKycnJ2uVDUlz587VnDlzZGdnp3fffVe//vqr+vXrpxIlSsjW1lbLli1TUFCQRowYoadPn6p58+ays7PTggULuL7Lis6cOaPt27drxYoVkqS6devKxcVFH330kfLly6fw8HBNmTJFGTJk0KeffqpffvnF3LZkyRLZ2THojiPpNOZlWTv+zFdcXJzi4uJUrVo1jRo1Svv27dOaNWvM1wkRvF4/8decDB48WE2aNJEk9e7dW4sWLdJbb70lHx8fzZo1S1myZNHq1atlGIby5s2r7NmzE7xeE7Vq1VK9evU0f/58HTlyxHyvIVdXV9WuXVuSzMPcYBk///yzrl69qmvXrsnLy0vz5s3TkydPtHTpUt2/f19lypSRv7+/Bg0apKJFi+rcuXNasmSJGjdurI8//pjrKC0of/78qlatmiTp119/1ZYtW+Tr66ugoCBFRUWpSpUqunr1qi5duqRmzZpp/Pjxmjp1qtavX2/lytOuy5cv6/r166pUqZImTZpkvj9e1apV1bFjR02ePFmHDx9Wy5YtdeDAAW3btk3p0qXTxo0b0859oF5T/v7+WrhwoRo1aiRfX1/lzp1bDg4O5hvHL168WMHBweratat5AqlChQpp4cKFCg4OVkhIiLV34bXAma80Yu3atapSpcoLM8rExMTIzs7OfFYrLi7OfEHkDz/8oN69e6tNmzb65JNPZG9vb6Xq8Wd/Nd49KioqwR+n+P7s1q2bcuXKpaFDh1qyTPzBjRs3lDdv3pe2nT9/XsuWLdO2bdu0YMECVa9eXdLz/mvZsqW6d++ud955x3LFpmGTJ0/W9u3b5ezsrJCQEHXu3FkdOnRQZGSkunXrptOnT6ts2bJau3at+TnR0dF69OiRTp8+rTfeeCPNzdxlDWfOnFH+/Pnl6uqqK1euaPTo0QoKCtL8+fM1a9YshYaGqnjx4vrkk0+0atUq/fjjj1qxYoXs7e319ddfq2jRoipYsKC1dyPNmTFjhr799luFh4fLxcVFtWvXVtasWTV8+HCtWrVKrq6uWr16tQ4dOqTmzZvLMAydP39eY8aMkZeXF9d6WdHEiRO1bt06bdy4UW+88YYuX76sTZs2ac+ePZo0aZIiIyO1fPlyhYaGqmHDhoqJidG9e/fUv39/OTk5vXB8kqZZ7pZisJaffvrJKFKkiHH//v0Ey+NvNnnr1i1j8ODBRlhYmLkt/uahhw4deukNQ2EdO3bseOnNreP7Ml5ISEiCx/369TOmT59uxMXFJbgxLCxjwYIFRocOHQzDSHhj3vgbhz569Mj4/PPPjcGDBxvFixc3li1bZmzfvt2YMmWKUbFiRePWrVvWKDvN+fHHH43KlSsbx48fN27evGns3r3bqFmzpjFgwADj2rVrxuPHj40PP/zQqFGjBjfhtZLY2Fjj/v37RsWKFY3FixcbT58+NQzj+c3jmzRpYnzwwQeGYRjG/v37jV69ehkVKlQw5s6da7z77rvGd999Z83S07x169YZVatWNQIDA41jx44ZX3zxhVGmTBlj48aNRkBAgFG4cGHjxx9/NAzj+U3J3333XaNp06YJlvP3yzomTJhgvPnmmwlufG0YhnHnzh1jzJgx5t+JERERxs6dOw0fHx+jatWqRrFixV54DrjJcprg4eEhJycnhYeHJ5gaOf7eNB988IFcXFyUMWPGF55bqVIl5cmTx9Il4yV27NihIUOGvHCvoPgbvN69e1eLFy/WkydPtHv3btWvX1/+/v4aPHiwvvvuOzVu3JhpXq3kypUr5utL/nhj3vgbhzZv3ly2trbq27ev/Pz8tG7dOq1YsUKnTp3SsmXLlD17dmuWn2aEhIQoS5YsKleunHLlyqXatWtrypQp+u233zR79mw9fPhQM2fOlIeHh2bOnKlTp05Zu+Q0xzAMeXp6KlOmTJo5c6bWrFmjqKgoFShQQNOmTdOjR4/03nvvycfHR7Nnz1afPn104sQJXbhwQdu3bzd//mB5V69eVc2aNVWqVCmVL19e77zzjsqUKaMrV67I19dXH330kT788EN98803Klu2rFauXKmuXbuqZs2aypYtmyQm2rCG4OBgHTx4UPXr1zeP3oiOjtahQ4d09epVFStWTM2aNVPv3r118uRJ1a1bV1u3blXHjh1VqlQp+uwlCF9pQPxwwujoaPN1WyaTSY8ePVKdOnXk4+NjnmI+Hh+W10+6dOnMp+7jZw2Ki4szh+jmzZvr8ePHcnV1VbVq1VSyZEndvXtXtra2+vzzz1WgQAEr70HaEz8T3h+vl3zZjXkrVqyoli1bKnPmzGrTpo3Wr1+vNWvWaNGiRUzeYAHGf0ff58mTRxERETp9+rS5rWzZsho8eLDu3LmjBQsWKEOGDOZrwPz9/XXmzBkrVZ02xX9+MmXKpIoVK2ratGlatGiROYDNmjVL4eHhatq0qQzD0Pvvv69Ro0YpICBAH3/8MdcuW0H85+v+/fsJrvlxd3dXnjx5dOjQIUlSt27d1LVrV/Xq1Ut79uwxX/c6Z86cvxyyjeTn7u6u1q1b68GDBzpy5Iik5zPzTpo0ScOHD9fkyZP1yy+/qH79+ho6dKi+//57ZcyYUe3bt9fy5cv5Av8l+C2USv3xZrqS5OzsbL5mK/6AMEOGDJo2bZpGjx5N2EoBbGxszDeijD8AsbGx0cOHD1WvXj298847+vTTTyVJOXPmlL+/v6ZPn66xY8cyU56V/PFAMf7GvMYfLrM9deqU6tSpozFjxpiXGYahDBkyyM7OTunSpbNswWlU/O8/Ly8veXh4aNeuXQoNDTW3lypVSv3799e+ffu0cuVKZc6cWbNmzZKTkxM3m7cw47+3Znj06JEmTZqkuXPnKiAgIEEAmzlzpiSpWbNmMgxDuXLlUq1atZQ/f37rFp9GxX++GjZsqKCgIN29e9d8HJI9e3bz70QXFxd17dpV3bp1U+/evbV3717Z2NgkuI8ULM/BwUF16tSRg4ODvvzyS/n7+ytjxoxauHChVqxYoQkTJuiHH34wf+nRp08fHTlyRHZ2di+M1MFzzPeYCu3du1dLly7Vhx9+qNq1a8vd3V2GYSgoKEi5cuV64YZ48cMwCGCvn4cPH8rd3V3S8/6xtbWVvb29TCaT+cJjOzs7+fn56f333zf34R9vC0C/Wt62bdsUFRWld99913wz5cePH5v7Jb7vGjZsqAYNGiToI/rLcpYvX67r16/rzp07ateunSpUqKDevXurW7duypQpkzp27CgnJycZhqHy5curT58+WrZsmerXry8vLy8tXbqUA0MLM5lMypIli+rXr6+oqCi98847mjp1qvr37y9J8vX1VYECBTRjxgx17txZbdq0STA5CqznnXfeUYkSJeTh4WH+PWdra2v+eyY9D2AVKlRQjhw5lC9fPmuWiz/w9PRUjx495Ovrq0ePHmno0KHm4fC5cuVSz549deLECTVt2lRPnz4137oIL0f4SoWyZMkiLy8vbd68WXZ2dqpSpYrs7e01f/58rVy5UpUrVzbPyJU9e3aGYbymvvzyS/3444/q3LmzihQpIk9PT8XGxurevXvKlSuX+Y9X+vTp9cEHH5hv2GsymRL0KQfzlhUZGakvv/xS0dHRsre3V6NGjZQxY0ZFRETo+++/V/bs2ZU5c2a5ublJon+sZd68eVq5cqXee+89PXz4UFOmTFHRokX16aefavr06fL19VVsbKxatWolT09PSc/PjLm6uppvB0Dwsg4nJyf5+vrKwcFBsbGx5vt6/TGA5c+fX8uWLaOPXjNeXl4JHj979kxPnz41fwk8Y8YMLVy4UIcOHeJ2KK+Z/Pnza9y4cRo2bJi8vb0TtNnZ2enBgwcqWbKkihcvznHlPyB8pTKGYahUqVLq1q2bFi1apDVr1ujhw4dKly6d0qVLp5iYGK1cuVJBQUFKnz69smfPrkyZMqlNmzbmewrh9WBnZ6erV6/qiy++UNu2bZUhQwZFRkZq6dKlcnZ2VrVq1ZQrVy5lyZJFDg4OHMS/JhwdHTV58mRNmDBBX3zxhZydneXg4KDLly9r2rRpunLlinLnzq08efIob968qlatmmxsbFSpUiVrl55mxMTE6MyZM+rXr59atmwp6fmIgW3btmngwIGaOHGiFi1apEGDBik0NFRVqlRRyZIldezYMfP1s9yg17rip6yOP2sSH8AGDx6siIgI9e7dm+uEXmN//KLQ1tZWTk5OmjNnjlauXKkNGzYQvF5Tb731lrZs2SJXV1eFhYUpU6ZMkp7fizL+S0Z+N/4z7vOVSsQPn7l9+7bat2+vsmXLKjQ0VDNmzFBISIhOnjypefPmqWrVqgoJCdHt27d1/fp1/fDDD7p9+7ZGjx7NdUGvoW+++UYLFy5UsWLF9NZbb2no0KEqUaKE7t27p7CwMD1+/Fh58uRRkSJF5OHhoffee0+FChWydtlp0okTJ2QYhlxcXFSsWDE9fvxYo0aNUmRkpGxsbHTp0iUtWLBAV65c0a+//qrvv/9e9+/f1/Xr15UxY0bt3LnTPMQUyevp06dq0aKFmjVrpo8++si8/MSJE1q3bp0ePHigiRMn6tatW1qxYoVOnjyp9OnT6/Hjx+bPI14f8YcxJpNJmzdv1qRJk7Rr1y4+T6+x+PC1Zs0abdy4Ud7e3tqwYYM+//xzlShRwtrl4R/cuXNHLVq0UK5cuZQuXTqdO3dOK1asUJEiRaxdWopA+EoF/jh85vr167p586aKFCkiX19fpUuXTv7+/tq5c6f69u0rX1/fF54fGRnJRZGviY0bNyokJERhYWFq166dcuTIoYMHDyogIEAeHh4KDAzU0qVL9cYbb+jnn3/WtWvXdPHiRf30008KCwvTkiVLuKjcCqZMmaKtW7fKzc1Nv/76q9577z198MEHypYtm0aMGKF9+/bJy8tLW7duTXCTyejoaAUGBip79uxMJ28B169fl7Ozs7y8vLR27Vpt2bJFkyZNSjAT6LFjx7Rq1Sq5ublp5MiRevr0qcLCwvT7778ra9as5iGIeL38MYA9efLEPDQUr7fAwEC1atVK9vb2Wr9+PV9spBBRUVHat2+fjh8/Li8vL9WqVYtr9JKA8JWCxcXFyTAMffzxx6pdu3aC4TPbt29XSEiIhgwZIi8vL40ZM0YRERFq0aKFatWqJen50Bs7O0aevi5mzJih9evXq1KlSrpx44b5fjXt2rXTmTNnNG7cOF2+fFkBAQHmPowXGxurZ8+eKX369FaqPu0KDAxU7969NW3aNBUpUkQXL17U7Nmz5eLiovbt26t8+fIaO3asfvzxRw0cOFC1a9eWyWQy358NljF16lQdOHBAoaGhKlmypCpVqqSDBw8qf/78at++vfk+QtLze+rNmDFDAQEBKly4sBWrRlLEn02J/z9ef+Hh4Zo2bZpat27NF4dIM7giLgU7e/asbG1t9dtvv+n33383L69Vq5batm2rbNmyacqUKXr06JH69u2rdOnSaePGjdqxY4ckEbxeI7///ruOHDlinh5+8+bNeu+99/TDDz9o0qRJKlKkiMaPH6/ChQvryJEjunjxovm5MTExsrW1JXhZyaNHj+Ti4qJSpUrJ1dVV5cuX15AhQ+Tq6qqlS5fq5MmTGjp0qMqUKaN169Zpx44d5vuzwTK2bdumLVu2aPTo0Ro4cKBatmypdu3aqXHjxgoMDNSGDRsUFBRkXr9+/fpKnz69du7cacWqkVTxgYvglXI4Oztr8ODBBC+kKYSvFGrOnDlq1aqVbty4oSZNmmjXrl26evWqub1cuXJq2bKlXF1dtWzZMuXMmVNdunSRJO3Zs0dPnz61Vun/196dR0VZt38cfw+7LDIs40FMyaUk08TMo6SGYaXZpiUuuCQdo5KtNAql5TE1Nbcks0VNNDtmaJl5PDUirrhVoqIZLqmp2BEtU1AGYeb3h4f7ZPo8j/0enQH9vP5h+M5931z3QQ9z3d/v97rkCmw2GwcPHjR6QcHFil09e/bk8OHDvPfee9x+++2MGDGCHTt2sGjRInbv3g0oiXa1xo0bY7PZWLdunTEWGRnJCy+8QGBgIJ999hmnTp3izTffJDg4mOzsbFauXOnCiG8+xcXFNG/enDZt2vD4448TExPDnj17aNy4MY0bN2bv3r3Mnz+fffv2ARdnUIKCgqhXr56LIxe58elvmNxs9C++FhozZgyLFy/GbDZTXFzMww8/zLZt21i8ePEly2fatWtHSUkJ06ZNY+/evUYZ5aCgIDVvrWHq1avH/fffT15eHm3btjU2ij/11FPY7XaWLl3KkiVLGDBgADabjSlTpuDt7c1tt912yR4icY7qBqHu7u5YLBbatWuH1WolIiLCKFzTtGlThg4dymuvvcaiRYsYPnw4o0aNYsqUKbRq1cqV4d80/roM7dy5c8Zyz4EDB3L27FnKysoICQnBZrMRFhZGamoqrVu3pry8nMLCQjIzM119CyIicoPRzFctM2HCBJYtW8Z3331H9+7dWb58OREREXTp0oXt27fzxRdfUFxcbBz/9+UzkZGRl/XZENf47bffOHjwoPF9TEwMBw4cIC8vj/PnzxvjcXFxtG/fnjlz5lBaWsqDDz5IRkYGAwcOVOLlAh999BHp6en069ePnJwcfv/9dxITE9mzZ89ly9datGjBsGHDWLRoEcXFxYSEhDB27FgV13CS6uVnMTEx7Nixg6VLl7JhwwaCgoKYNWsW06dPp3fv3pSUlODl5UVKSgrnz58nODiYBQsWaCmUiIhcc5r5qkUmTpzIggULyMnJISwsDIvFwpo1awCIj4/HZrNhtVo5e/Ys/fv3p2nTpsbyGSVcNcvkyZNZs2YNv/32G23atOG+++5j0KBBHDhwgOzsbHx9fenSpQu+vr4ApKamsnTpUqxWK08++SRdunRx7Q3cpGbPnk12djYvvPAC/v7+fPvttyxbtoyRI0cyfvx4UlNTsdvt9O7d2yi5GxYWRnh4uNH7RM0nnS8yMpLU1FTmzp1LaGgorVq1on79+tSvX58mTZpw7Ngxjhw5wiuvvEK3bt20H09ERK4bJV+1RHl5OQEBASxZssSovhUXF8eiRYv45ptveOyxx0hISKBu3bqsXr2a5ORkoqKiOH/+vJbP1DArVqzgyy+/ZMqUKfj5+ZGbm4vVaqWwsJCJEydSVlbGzJkzOXPmDN27d8dsNhtP46sbGopr7NmzhyFDhjB48GAAtm/fzrJly0hJSWHatGlkZWXxxhtvcOrUKe69917uueceVqxYgc1mUxEAF+vTpw8lJSV8/fXXl/TC8/f3x2w2s3HjRlWAFRGR605/ZWoJHx8fnnvuOdzd3Y1+Jr6+vrRs2ZKffvqJxx57DLi4RygqKooff/yR/Px8QkJCtHymhikrK6N169ZER0cDF5/Kr1u3jtmzZ5OWlsb06dOpW7cuy5YtY+3atURFRVFcXMyxY8fUCNuFKioqOHHiBGFhYcZYVFQUwcHBeHp68sorr/Duu+8ydepUPv/8c6ZPn05gYCA2m42srCyCgoJcGL0EBwfz/PPPc+bMGebNm0eTJk2Ii4vDbrdz6tQpQkJClHyJiMh1pz5ftZzVauWll15i4cKF3HXXXa4OR65CdnY2c+fOZe3atcZYVVUVmzdv5v3336dZs2a89dZb5Ofns379egoKCrBYLCQnJ6t7vAscP34cDw8PLBYLy5cvZ8KECcyYMYOoqCjjmIMHD5Kdnc3x48cZP348ISEhnD59mrNnz+Lv76/EqwY5ffo0n376KR9++CHNmjXDx8eHQ4cOkZ2dzR133OHq8ERE5Aan5OsGMGrUKGw2GyNHjiQ0NFQNJmugnTt3EhERQWBgIKWlpQwePJioqChef/1143dVWVmJ1Wpl4cKF9OnTx5jNrKiowM3NTU/kXWDatGls2rSJ4uJiOnfuzP3338+aNWsoLS0lJSXlkpnI77//nsmTJ5OSkkKnTp1cGLVcjZ9//pnCwkKjR1vDhg1dHZKIiNwEtPP7BhATE8OJEyfYtGkTFy5cUOJVg9jtdk6ePEliYiJLliyhtLQUPz8/+vXrx/79+5k1a5ZxrIeHB926deOWW25h1apVxriXl5cSLxeYN28eixcvJj09nbS0NA4dOsSff/5Jz549uXDhArNnz76k2XW7du3w8fHBarW6MGq5WpGRkcTFxfHII48o8RIREadR8nUD6NatG3feeSfjxo2jsLDQ1eHIXzgcDkJDQzGbzUydOpWFCxdiMpno0aMHbdq0YcOGDXz44YfY7XbgYt+oDh06cOTIEc6dO+fi6G9ue/fupXfv3rRr1464uDhuvfVWCgoK8Pb2JiwsDG9vb7KysigoKDDOCQoKMvrsiYiIiPydHqfXctVLDDMyMjh58qTRnFdqhuqS1WazmQYNGjBlyhTKy8tJSUlh6NChzJkzh40bN3L48GFGjx7NuXPn2LVrF4GBgZrtchGHw0FVVRW//vor/v7+wMU9efn5+fj4+LB161bKyspo1KgRHTt2JDExkXvuuYfKykq2bdtGcnKyi+9AREREaip9uqvlTCYTdrsdNzc3Jk+e7Opw5G8cDgdHjhzhzJkzvPfee+zcuZOkpCRMJhPJyck8++yzrFmzhnnz5tGxY0fCw8M5ceIEn3zyiRoou4jJZMLDw4O0tDR2794NwO7du7n99tv517/+hcPhYN++fYwYMYKYmBiysrLIy8vDz8+PjIwMmjZt6uI7EBERkZpKBTdErrPy8nJmz55Nr169aNCgAcuXL+fll18mOTnZSMQA8vLyqFu3LuHh4YSHh7s4avm78vJyo1EywMcff0xBQQEffPCBC6MSERGR2kQzXyLXmY+PD4mJiXh5eVFVVcWjjz4KwMsvvwzAM888g6+vL7Gxsa4MU/6N6pnlvyZeAJ6enpw8eVK9oUREROSq6RODiBNULyE0mUw4HA4jAcvIyKCiooJhw4Zd9uFeagY3t4t1iU6cOMHJkydp0aIFACUlJYSGhir5EhERkaumTwwiTuTm5obD4TASsIqKCiZMmMCQIUOUfNVg1Xv3nn76aZo1a0ZAQAD79+9n7ty5+r2JiIjIVdOeLxEXqP5vZzKZKC0tNarqSc1lt9vZunUrhYWFBAYG0qFDBxo1auTqsERERKQWUfIl4iLVbQKqv4qIiIjIjU3Jl4iIiIiIiBO4uToAERERERGRm4GSLxERERERESdQ8iUiIiIiIuIESr5EREREREScQMmXiIiIiIiIEyj5EhERERERcQIlXyIiIiIiIk7g4eoARERErkZsbCzHjh0zvvfw8KBhw4b069ePIUOGXPGco0eP0rVrV1atWsUtt9xyzWLZsmULgwcPvmTM09OTevXq0atXL1JSUq7ZzxIRkRuHki8REak1Ro0aRY8ePQCorKxk8+bNZGZmYjab6dmz52XH169fnw0bNhAcHHxd4tmwYYPx+vz586xatYqJEyfSsGHDK8YjIiI3Ny07FBGRWiMgIACLxYLFYqF+/fr06tWL6OhorFbrFY93d3fHYrHg7u5+XeKpjsVisdCoUSMSEhKIjo5m5cqV1+XniYhI7abkS0REajUPDw88PT0BGDRoEGPGjKFr16506dKFoqIimjdvztGjRwE4deoUL774InfffTcdO3Zk6tSpOBwOAI4fP87zzz9P69atiY2NZcaMGVRVVf3jeLy8vC5J9nJycujevTstW7akffv2jB492rhuRkYG48eP58UXX6R169bExMSwdOlS49zy8nIyMzNp27YtnTt3JicnhxYtWhj3c61iFhER59CyQxERqZUuXLjA6tWryc/P5+233zbGv/zyS+bMmYOXlxd+fn6XnJOUlIS7uzsLFiygrKyMl156iXr16jFgwACSk5OJjIzkq6++oqSkhDfeeAOTyURSUtJVxVNVVUVubi75+fm88847AGzdupWxY8cyadIkWrRowa5du0hPTyc6OpqHHnoIgM8++4y0tDRGjBjB/PnzefPNN+natSsBAQGMHTuWgoIC5syZQ2VlJZmZmUZy5XA4/ueYRUTEuUyO6kd+IiIiNVhsbCwlJSV4eFx8blheXo6Pjw/x8fGkp6cDF2e+QkNDmTZtGnBpwY3S0lKeeOIJcnNzadiwIQC5ubmcO3cOi8XC8OHDyc/Px83t4qKQvLw8Ro4cyZYtWy6Lpbrghq+vrzFms9kIDw8nISGBAQMGALBr1y4OHTrEo48+ahzXt29f7rvvPpKSksjIyGDfvn0sWbIEgNLSUtq2bcvChQtp3rw57du3Z9asWURHRwOwfv16hg4dyqpVqzhy5Mg/illERFxPM18iIlJrpKamGjNG3t7eV9zP1aBBgyuee/DgQcxms5F4ATzwwAMALFiwgNOnT9O2bVvjPbvdTnl5OX/88QdBQUFXvGb1EsEDBw4YM1bViRdAy5Yt8fHxISsri/3791NUVMThw4fp1KmTccytt95qvPb39wcuFhP55ZdfuHDhAq1atTLeb9OmjfH6wIED/6+YRUTEdZR8iYhIrRESEkJERMR/PMbb2/uK49X7wq6ksrKSJk2aMHPmzMveCwgI+LfnVccSERGB2WxmwIABhIWFkZCQAFycqUpKSqJnz5507tyZpKQkRo8e/V/jcjgcxgzf38f/15hFRMR1VHBDRERuChEREZw+fZrjx48bY/Pnz2fYsGE0btyY4uJigoODiYiIICIigqNHj5KVlYXJZLqq6999993Ex8fz7rvvGv3IcnJyeOqpp3jrrbeIi4ujadOm/Prrr1zNiv9GjRrh6enJrl27jLG/vr4WMYuIiHMp+RIRkZvCbbfdRocOHcjMzKSoqIgtW7bw8ccf07FjRzp16kSDBg1IT0+nqKiIH374gddff506der8ozL1aWlp+Pr6MmHCBADMZjMFBQUUFRWxb98+MjIyKCkpoaKi4r9ey8/PjyeffJJx48axY8cOtm/fzrhx4wAwmUzXLGYREXEeJV8iInLTmDRpEnXq1KFv376MGDGCvn37Eh8fj7u7Ox988AF2u50+ffqQkpJCTEwMr7322j+6ft26dRk+fDhWq5WNGzeSnJxMSEgIffv2JSEhAW9vb/r378+ePXuu6nqvvvoqzZs3Z8iQIaSkpBiFOzw9Pa9ZzCIi4jyqdigiIlJD5ebmEh0dbZTM37lzJ/Hx8RQUFPzHPWwiIlIzqeCGiIhIDTVjxgxWr15NYmIiZWVlTJo0idjYWCVeIiK1lGa+REREaqj9+/czZswYdu7ciZeXF7GxsYwaNUrVDEVEaiklXyIiIiIiIk6gghsiIiIiIiJOoORLRERERETECZR8iYiIiIiIOIGSLxERERERESdQ8iUiIiIiIuIESr5EREREREScQMmXiIiIiIiIEyj5EhERERERcYL/A8jl4PvmK64IAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2292,12 +2292,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 535,
+   "execution_count": 235,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -2335,12 +2335,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 536,
+   "execution_count": 236,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2378,12 +2378,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 537,
+   "execution_count": 237,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x600 with 2 Axes>"
       ]
@@ -2440,12 +2440,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 538,
+   "execution_count": 238,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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j44OXlxeurq4W+5w/fx5fX99CzYsCGCIiIiIiIiIiIiIiAkDDhg2Jj4/n7Nmzudu2b98OQP369bGxsSE0NDR3W45t27bRoEGDQs2LAhgiIiIiIiIiIiIiIv+jMjMzSUhIyF3bok6dOoSGhvLiiy+yf/9+wsPDeeedd+jRo0fuCIthw4axdOlSfv31VyIiIhg3bhxHjhxh6NChhZo3BTBERERERERERERERP5HxcXF0bx5c5YtWwaAjY0NEyZMIDAwkKFDh/LCCy/QsmVL3nvvvdw0zZs356OPPuK3336jZ8+ehIeHM3HiRIKDgws1b1rEW0RERERERERERETuKhsHm+LOwv+sTz75xOxzYGAgx44dM9tWqlQpxo8ff8vj9OjRgx49ehR29sxoBIaIiIiIiIiIiIiIiFgdBTBERERERERERERERMTqKIAhIiIiIiIiIiIiIiJWRwEMERERERERERERERGxOgpgiIiIiIiIiIiIiIiI1bEv7gyIiIiIiIiIiIiIyP8WW3ub4s6C3AM0AkNERERERERERERERKyOAhgiIiIiIiIiIiIiImJ1FMAQERERERERERERERGrowCGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6tgXdwZERERERERERERE5H+LjYP61svt6SoRERERERERERERERGrowCGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6iiAISIiIiIiIiIiIiIiVkcBDBERERERERERERERsTr2xZ0BEREREREREREREfnfYmtvU9xZkHuARmCIiIiIiIiIiIiIiIjVUQBDRERERERERERERESsjgIYIiIiIiIiIiIiIiJidRTAEBERERERERERERERq6MAhoiIiIiIiIiIiIiIWB374s6AiIiIiIiIiIiIiPxvsXGwKe4syD1AIzBERERERERERERERMTqKIAhIiIiIiIiIiIiIiJWRwEMERERERERERERERGxOgpgiIiIiIiIiIiIiIiI1VEAQ0RERERERERERERErI59cWfgf93ChQt54403OHbsWHFnpUg4B/jScu8SdvZ+hksbtt9y37L9u1D5jRG4Vgoi5UwMJz/7iZjpf5jt41m/FtU/fRXP+rUwXEsietpCjr8/gayMjCIsxX+35cgpJixeT0TcBbxLuDGgRX0ebhuGjY1NvvunZRj4cfkmlu04xOXEZKoG+PBU5xY0q1Epdx+jMYv5m/cwd+Nuoi9cwdvDjftrV2FE5xa4uzjdraL9a1vPnuf7rUeIuHSdUq5O9K1dkSH1ggs8J1FXEukxbbXF9mBvD+YObgOAMSuLGXsiWHjgDOcTUyhX0p2HQyvTuVpQkZalsDhVq41n1wHY+wdhvH6VxA0rSVyzON99XRu1wnvwMwUe69L070jevh4Ah6BKePUcgkO5YLJSk0natp5ry+ZCZmaRlKMwbd+zn19mzuV0ZDQlvTzp2bk9A7p3KfA6Afh7wxamzfud2HPn8fMpw6Be3ejUpqXZPstXr2f2oqXExJ2jlLcXndq0YnCf7tjb2RV1kf6zrQdPMGHhX5yKPY93CXf6t2nMkI7NCzwn6RkGpq/cxJItu4m/dBXfkp50alKHRzu3wsH+RjPgdNx5vp63gl1HT2NnZ0v9qhV5qX9nAn2871bR/rWth04y4fe/ORV3Hm8PN/rf34ghHZrd+pz8tZkl4Xuzz0kJOjWqw6OdWuBgb0/shct0eePLAv9et6b1GDOsV1EVp1Ds3L2HX6fP5GxkJCW9vOjWpTN9ena/5b2zZv0GZs2ZR1z8OXx9fBjQtxcd2rYpcP8ffp7EwkWLWbXkjyIoQeFzrFoLjwf6Ye8bQGbiVZK3/E3y+mW3TONUrS5u7Xvi4B+EMSmR1AM7SFw+l6yMNOxKlqbMm18XmDZ5xwauzf2pkEtReOwrVce1dQ/sypTFmHSNtJ3rSA1fdcs0DpVr4dKiK3Y+AWSlJJF+dDfJa/+AjHTLnR2d8HziHVI2LCF9/9aiKUQh23LkNBOWZLfXPNwY0DKUh9vcrr22mWU7b26vNadZdfP22vS125m/eS/nrlynfJmSPNKuMV0a1rxbxfpPdu7ey+TpszgTGUVJLy+6d3mAvj273aYu2ciMOfOJy34OD+jTk45t7zfbp/8jw7lw8ZJF2oUzfsXTs0Shl6Mwbd97gJ9mzud0VAzeXiXo+UA7BnbvdMtzkuP4qTMMf20Ms78bh79PGbPv1mzZzqzfl3I2Jg4PN1ca1K7JU0P64e3lWVRFKTT2Favj0qobdqX9TfXJrg2kbf+7wP1tS5bB86kxFtszE2K59ssH2Z9scApri1Noc2w9SmK8cpG03etJ27W+iEpRuHbs2cfPM+ZwJrcN24H+Pbrepg27melzfyf23DlTG7Z3dx5o08psn43hO5g2dyFRMbF4e3nR4f4WDOrdAweHe+PVTkpKCtMm/8TWLRtITUmhRq3aPPbE0wQElrtluiuXLzH55x/Ys2sHmZmZ1G8YxrDhT+PtXSp3n1UrlvLd+C8s0nbu2p0nnn6+0MtSGHSdWNq29yA/zVqQW8f2eqAtA7s9cId17Fkef/195kz4JN86duYfy2+qY2swYnDfe6KO/f/O1v72/7Yi1l97/T/XuXNnWrRoUdzZKBLOgX6ELZ2Eg9ftf4T49exA3Wmfc/rbaSSs3Ihf93bUnfwpxrR04uaaXi64VAyk0YpfuRy+l90DX8C9WjAhY1/EwduLg8+8W9TF+df2n45h5MR5dAytzjNdW7InIpqvFq3BYDTyWIcm+aZ5b9YyNhw4yXPdWlHex5s/tx1g5MS5/PLcIEIrm17G//p3ON8tWc/Qto1pFFKes+cv8d2SDZyMTWDiswPu6AFfXA7EXeKFxeF0qBLAiMbV2Bt3ifGbDpFpNDKsQdV80xxLuAbADz2b4mJ/4yWzs8ON/58YfpSpu07wVONq1PQtyaYz5xj9125sbWx4ICSwaAv1HzlWqELpJ18nefcWri6dg1Olanh2H4SNnS3XVy2y2D/10G7Of/GWxfaSA5/ExtmV1MO7AbAr5UOZZ0eTfuY4lyZ/hb1fAJ5dB2Dr6s6VOT8Xebn+i0PHTvD6h5/RplkTHnuoL/uPHGPi1N/IzDQyuHe3fNOs27KdsV99R5+uD9AotDYbt+3k4/ETcXSwp22LpgDMW7ycbydNp3XTMEYMfYgr164xedZ8Tp4+ywevv3g3i/iP7Y+I5LlvptEx7D6e7tmevSfO8PW8FRgyjTzapVW+acb9toSlW/Yw/ME21KwYwOEzMfz05xriLlzhvUd7AxB/6QrDPvqR8n5l+PjJ/qSmZ/DdwlU8/eVk5r7/PM6ODnezmP/I/ogonvt2Bh0b1uLpHm3Ze+IsXy/4C4PRyKOdWuabZtzsZSwN38vwrq2pWSH7nCxZR9zFK7z3SE9Ke3ow9fUnLNLNXbeNlTsO0qN5/aIu1n9y+OgxRr//Ia1aNOORwQ9x8PARfv51KpmZmQzo2zvfNBs3b+GTz7+iZ7euNAgNZUv4Nj77ajwO9g7c38qynbL/4CF+/3NJURel0DiUC6bksFdI3RfO9ZXzcaxQFY/OA7CxtSNpbf6BYqfq9fB65EVSdm0icdkc7H0DcO/UD1t3D67O+p7Ma1e4+K1l+8O1aXuc6zQmZfu6Ii7Vv2cXUBGP/s+SfngnKev+xL5cZVza9gJbW1K3rMw3jUOV2rj3HUH6gXCS1/6OXWl/XO/vgY2rB0l/TDLb18bZFfd+T2PnVfpuFKdQ7D8dw8gfs9trXVqy51Q0Xy1aiyHzVu215Ww4eJLnHsxur20/wMiJ8/hl5EO57bXvl23k17/DebpzC2qV92fjoQjenLYYWxsbOjWocTeL+I8dPnqct8Z+TOvmTXlk8EAOHj7CT1Omk5mZycC++QdxN2zeykdffEOvB7vQsH5dNodvZ9zXE3BwcKBNy+YAXL16jQsXL/HksIepVaO6WXp3d7ciL9d/cfDYSV796EvaNmvE4wN7s//ocX6YPodMYyZDej14y7SnzkYz6sMvycynA8nfm8J578vv6d7hfp4Y1IeLl6/yy28LeO7dT5j02RicHB2Lqkj/mV3ZCqa64cguUjYsxj4wGJc2PcDWlrTwv/JP42Nql1+f9TVZNwdAb+qU5tK2F85hbUnbvYH04/uw9SqNS8sHsfUsRcqahUVZpP/s0LETvP7BOO5v3oTHBvXjwOFjTJw6i8xMI4P6dM83zfot2/jgywn06foAYaFD2LhtBx9/8wMO9g60bWlqw+7Yu5/Rn3zJ/c2b8MTDAzkTGc1P03/jyrXrvPDEsLtZxH/ty3EfcPzoER5+9AlcXV2ZM2sab7/+Mt/+MBl3D49802RmZvL+O6+TnJzMU8++gMGQyfQpP/PeW6/y5bc/Yp/dIef0qZMEBAbx3EuvmaX3KmmdnXF0nVg6ePwkr378FW2bhjF8YE/2HznB99PnkpmZyZBeXW+Z9lRkNK989FWBdey7X02ke4fWPPFQby5ducrPvy1k5LufMvmz96y6jhUREwUwipmzszPOzs7FnY3CZWND4JAeVP/0NbjDd+ghY18ibv4KjrzyMQAXVm3CoaQnIe89nxvACB41HMP1JHb2epqsjAwSVmwgMyWVWt+M5uQnE0mNiiuqEv0n3y/dSLVAXz4aanrh2qxGMBmZRib9tYVBrRtYvBiMuXiFZTsO8UbfDvRvaXpJFla1AntPxTBn4y5CKwdhNGbx66pw+jSrx/PdWwPQuFpFPN1ceO3XRRyOjKdmef+7Ws5/YuK2o4SU8WRsR1P5mlbwxWA08uuOEwysG4yzvWUv+OMXruLr7kxYUBmL7wBSMgzM2hvBwLrBuUGQsKAyHD1/hdn7Tll9AKNE575kRJ/m8vQJAKQd2YeNnT0e7Xtyfd0ysx90AMbE66QnXjfb5t6qE/Z+gSR8+TbG7O882nXHmJbChZ/GmUZcHN5DVnoaXn0f4/pfC8m8fPHuFPBfmPzbfKpUrMDbLz4NQKPQOhgMmcyYv4i+XR/AycmyofnzjDm0btqIkY8NASCsXh2uXU/il1nzaNuiKZmZRqbO/Z0Gde7j/VdfyE1XtVJFHnn+NXbsPUDDuvfdlfL9GxP/WE21cv58MLwfAM3uq4oh08jkpet4qH1Ti/rkSmIyC9fv4Pk+HRma/TK/UY3KAIyfv5Ln+nTEu4Q7E/9YjbuLMxNfeRSX7PNatnRJXhg/ncNnogmtWvEulvKfmfjnGtM5eawPAM1qVcGQmcnkZRt4qG2T/M/Jxp0837sDQzuaXqY1qh4MwPiFq3iudwe8PdyoHWw+cuvw2RhW7jjIyJ7tqFel/F0o2b83beZvVK5UkddfNgXkGtYPxWAw8Nvc+fTs1hUnJ8tRepOnzaBl86aMGP5Ydpp6XE+8zpQZsywCGCkpKXz+9XhKl/Im4YL11iE3c+/Qm4zYM1ydPRGA9GP7wc4OtzbdSNq4AgyWIzk9ug0mdf/23FEU6RGHwdYW1+YdwMERMtLJiIwwS2MfUAHnOo25vmIuGWeOF33B/iXXlg+SGR9F0qJfAcg4dQhs7XBp1onU7WvyPR+u7fuSfmQ3SYunAmA4cwxsbHEOawP2DrlpHKrUxrVjf2wc76327ffLNpnaaw+bXkI3q1GJjMxMJq3aWnB7bech3ujbnv4tQwEIq1qevaeimbNxN6GVg0hJz2DG2h0Mat0gNwjSKKQCR6LimbV+p9UHMKbOmk3lShV542VTz+Ww+vUwZGYya95CenXrkm9dMmn6LFo1a8LTw00vyRqG1uP69USmzPgtN4Bx8vRpAJo3aURZf7+7VJrCMXnOQqpULM/o558CoHFobdPL1AWL6delY75tk4wMA/OXrWLS7AU4OuTfIWD6gsU0Ca3DqKduvFwsF+DPk6+PYcvOvdzfNKxoClQIXFp0JfNcFMk5dcOpw9jY2uHStCNpO9fmW5/Y+QZivHYZw9n860kbFzecGrQmbe8mklfOzt2edf0ybr2fIm3vZoyXzhVNgQrBr7PmZbdhnwWgUWhdDJkGZsz/gz4Pdsq/DTt9Nq2bNuLZx4cCEBZah+vXE5k0a27ui+nlq9fhW6YUb7/4LHZ2tjSsW5vLV64yd9FSnn10SO6LfGt19MghdmzbyugxH1O/YSMAatSqzZPDHmL50kX0HTA433SbN67jVMRJvp04maByFQCoFBzMcyMeY/PGdbS6vx0Ap09FULlKCCHVrLtuzaHrxNKk2X9QtWJ53nn+SQAa16uNITOTaQuX0K9Lh4Lr2OWr+GX27wXWsdMWLKFJaG1effKR3G3lyvrxxBtj2bJrH/c3aVgk5RGRwqM1MApRSEgIM2fOpF+/ftx33308+OCDrF59Y8qbb7/9lsGDB/Piiy8SGhrK2LFjWbhwISEhIbn7JCUlMXbsWJo3b069evUYPHgwBw8ezP1+9+7dDBo0iNq1a9O6dWvGjBlDYmLiXS3n7ZSoHUKt78YQPeMP9j7y6m33dykfgHtIReIXmU9bELdwJW5VKuBa2fSiqEz75pxfvt5suqj4BSuwsbOjTIfmhVuIQpKeYWDnyUja1Akx296+XghJqensiYi2SFOmhDuzRj1Cl7AbUwvY2tpgZ2tDWoapN0Fiahpdw2rSqYH59AMVfU1DaKMuXC7sohSadEMmu6Ivcn+weYClbeWyJGUY2Bub/8uwYwlXqVq64OGdjnZ2/Nq3BYPrBZttt7e1Jd1g/O8ZL0r29jhVrknKfvNp1pL3hmPr4opTpWq3PYSthycluvQnadNfpJ89mbvduXodUg/tMZsuKmVvODa2tjhXr1toRShs6RkZ7D14hBaNzRuTrZuGkZySwv4jltPuxZ1LICo2jpaNG1ikiYk7R1RsHJevXuXa9USaNqxntk+l8kF4lvBg6849hV+YQpKeYWDnsVPcH2p+37drUIuk1DT2njhjkSYpJZU+rcNoVde8h2sFf1MgMCbhMllZWazedYjuLernBi8AalYMZNVXb1h18CI9w8DO46e5v555+drVr2k6JyfPWqRJSkmjT6uGtMpTL984J5ZTmmRlZfHJzCVU8i/DoPZNC7EEhS89I4P9Bw7SrEljs+0tmzUlOSWFg4ePWKSJP3eO6JhYmjU2T9OiWVNi4+KIjok12/7T5KmULFmSDu3aFn4BioKdPY7B1Uk7uMtsc+r+Hdg6u+BYMcQiiX3Z8tiX9iV5s3nv4eRNK7nwycv5T5kElOj5CIbzMSRvWF54+S9sdvbYl69K+jHz+i79yC5snFywD6psmcQ3CDtvH9J2rjHbnrZjDVe/ezv3BaWNkwvufUdgiDzB9d/GF10ZCllue622+SjQ9nWrmdprpwpqrw01mwrK1F6zJc1gAMDR3o5pLw3h4TbmL5/t7e1y97FW6RkZ7DtwiOaNzfPesmkTklNSOHD4qEWa+HPnTXVJk0bmaZo1ISYunuhYU10SceoMri4u+Pv5Fl0BikB6RgZ7Dh6lZSPzUXj3N2lIckoq+4/k/zJ+6+59/Dr3d4b07saIIf0tvjcajTSoXZNuHVqbbS8fYGorx8SfL5wCFAU7e+zLVSH92D6zzenH9pjqk8Dg/JP5BmI4Z3lf5bD19sXG1o6MEwfMtmecPY6NrS0Owdb7gtrUhj1Mizz3TqumjbPbsJb3Tty580TFxlmmadY4+94xddRLT8/A2ckZO7sbr3FKeLiTYTCQnJJaBKUpXHt27cDZ2Zm6oTfa6p6eXtS8rw67dmwrON3unQQEBuUGLwCCylUgMKhcbrqsrCzOnD5FxWDLZ5g10nViKT0jgz2HjtIyLNRs+/1NGpCcksq+owXXsZPnLuLhXl0ZMaSfxfdGo5GGdWrSvX1rs+33RB0rIrkUwChkn3/+Od27d2fRokW0atWKZ599lt27d+d+v2PHDkqXLs2iRYsYMmSIRfoXXniBDRs28PHHH/PHH38QFBTEo48+ytWrVzl69CjDhg2jRYsW/Pnnn3z++eccOnSIRx99lKysrLtZzFtKiYxjXbX2HBn1CZnJt39AulczNWyT8rx8S44wvXhyr1oRW2cnXCsEknTitNk+6Rcuk3H1Ou5W+oIt+uIVMgyZlM8zh3y5MiUBOHPe8mW9o4M9Ncv74+HijNGYRfzla4ybv4roC1fo29z00rWEqzOv9+1AvWDzUQVr95se6sH++Y9SsAYx15LJMBop7+Vutj3I0zRlwNnL+QfkjidcJTnDwLC5G2jy3WI6/LKC8ZsPkZFpCk7Y2dpQpbQnpd2cycrK4mJyKr/uPM72qAT61K5QpGX6r+xL+WLj4EDGefNRRIaEeNP3vmVve4wSnftBVhZXl9zopYaDA/alfDCcN3/5aEy8jjElGXuf2x+3uMTGnyfDYCCorHnPzEB/08uOyDwvVAHORseY9ilrHhwLyO7dGRUTh7ubK3Z2dsSfv2C2z/XERBITk4g9Z70N2OiES6b6xK+U2fYgH9PnM/EXLNIElPHmzSHdc1/O51i3+zD2dnaU9ytN7IXLJKak4l+qJB9PX0SrkWNp9MQ7vDB+OucuXS26AhWC6AvZ58T3n5yTkrw56EEq+OU5J3uOmM6Jr+WUNyt3HODA6WhGDeiMna11N53i4uPJMBgICDC/v8tm3xdR2ffJzSKjTC+RAvOkCfA3pYmOuZFm1569/L1mLaNeGImtFU9VeDO7Uj7Y2DtgSDCvYzMvZtexZSxHLDqUNXWeyDJk4DXsZXw/mozPmIl4dBsMdvn3XHSu0xjH8pW5vmgGWFG7LC9br9LY2DuQedG8vjNeTgDArpTlS2U7P9OIpCyDAff+z1DytW/xevlLXNv3MzsfWRnpXJ34Hkl/TiEr2bo62NzKbdtr5ywDm44O9tQsl6e9tuBvs/aana0tVQN8KF3C3dQ2uZbEpL+2su3YGfo3D7U4pjWJiz9HhsFgWS9kP5dvrhdynM2pS8rmqX9ynsPRpmf3ydNn8PBwZ8zHn9Gt/xC69B3E2E+/4OIl6+2AAxB7ztQ2KZenbRKQ0zaJzX80ePXKFZk/8UuG9umGXT5rbdna2jJy2EO0CDMPjGzcbgq6ViwXUBjZLxI59YnxUt76xPQ5v/oEwN4nEBtHJzyGvILXqG/wHPkxLq27Q/YzNivFVH/Yepo/3+28yuT+XWuV24YNMH+25LRho27Rhs2bJiBPu7dn5w5Ex8Ux+/fFXE9M4tCxE8xfvJzG9etRwsP8t5U1io6KxNevrMV94O8fQExMVMHpIs9SNsByNL1/2QBiok3p4uNiSUlJ5uTxYzw9/GF6P9iep4c/zNrV+U9jVtx0nViKPZeQ7++/AL+c8sXnm6565Uos+OFzUx2bTzvd1taWkY8MpEWewMiG7ab3dBWDrLeOFZEbrHfs2D2qV69eDBo0CIBXXnmF7du3M2PGDEJDb1SWzz33HB7Z8zveHNw4deoUGzZsYNKkSTRvbhpR8N5771GiRAkuX77MpEmTaNasGU89ZRqyXKFCBb744gvatWvH9u3badTIvLdTccm4fJWMy3f+0sve0/QQNVwz/6FruJ5k+r6EOw6eHvnuk7OffQnrfBAnpqQB4O5sPtTRNXvIfVJq/j04c/y6aivjF5sWquvdtC6Nq1UocN/9Z2KYvCqcVrUqU6Ws9QYwEtNMvTTdHM2rH9fsz0nplj0SL6ekcT4plcysLJ5rVgN/D1e2RycwdedJziWm8mFH8x98K4/H8NZK04++5hV8rX4Rb1sXVwCyUlPMtmelmT7bOrveOr17CdzCWnF9zWKyUpJvbM9OZ8xz3JxtNs4u/ynfRSkp2VQON1fzPLq4mD4nJ1uWqaA0ri7O2d+n4OzkRJvmjfl92V9ULBdIy8YNuHz1GuN/mYadnR2pqWmFXpbCkpjdY8otz7SDrtn1S1LKneV9za5DLN6yh/5tGlPCzYXIc6aX/N/MW0GtSoF88uQALl1P5Nv5fzF83C/MGTPSbGSGNUlMNpX5P5+T3YdZvHUv/e9vRAk3y/ti2srN1K1cjgYh1hksv1lSUs59YF5vuObcOzfVEXnTuOZJc+N+S87eL4kvxk/g4UEDCQy4d37s2WbXdTl1ao6sNNM9lV9daOtuaneUHPoCKXu2kLxhGQ5BlXDv0Btb9xJcnfW9RRq31l1IP32M9FOWo1ysSU55s9ILOB9O+ZwPV1M7y73vU6Qf3EFq+Crsy1bApeWD2LjdtAaGMdOqp3UpyI32mvmUSK7ZdV/SbZ4Nv/4dflN7rQ6NQypY7LNi1xFen/onAC1qBlv9It436oW8z1TT56R/9Bw2f3ZHnDrNhYuX6NKxPb26dyUyKpopM+fw4huj+fGbz3Gx0ul1E5NM+c8pT46b2xn5KVPqn8+/HxN/ju+mzqZKxXI0Ca3zj9PfLTZOprJb1iemeya/qeRsXNywLVESbG1JWfsHxquXsK8QgnPj9tiUKEnyn1MwXjpPRtRJnFt0wXj9Mhlnj2HnVRrXToPIMmRg42A5fZm1yLkP8l4nLre6d3Kurdvcb6G1azGwZzd+mDKTH6bMBKBKpQqMfnlkIZag6CQnJ1m0NcB0blKSLdsnN6fLL4Dh4uKam+70KdMI9HPxcQx7fAT29vasXf0X33zxCRkZ6XR44NbrJ9xtuk4sJeY8QwqqY1MKqmNL/uO/FR1/nu+mzcmuY2v/4/QicvcpgFHI8gYR6tWrx+bNm3M/lypVKjd4kdfx46be83Xr1s3d5uTkxBtvvAHA4cOHOXv2LPXq1bNIGxERYTUBjH/K5ja9WbOMxtzeOAXvY509HY236YF5u4W2W95XhbrBgeyJiObH5ZtIzcjIXUvjZnsiohk5cR4BpTx5f3CX/5Tnona7yZzyOyUuDvZ816MJ5bzcKVvC1OitH1gaRztbvt96lMcbVqWi9437qqZvSX7u3YwTF67xQ/hRRv6xlZ96N7Pehc1vl6+sW581t6ZtwdaWxPXL8hz3Nj3FrbiH8G3vnXzqBONt6gFbW9N5fvmpx3Cwd2Dcdz/z6YSfcHJ0ZFDvbiSnpOJspS/q4b/XJwCrdx3kzR/nUrdKeV7o9wAAGQbT9GKlPN354plB2Gaf2yCfUgz9cCLLtu6ld2vrnHv79tfJHZyT3Yd48+f51K1cjhf6dLD4fu/JSI5ExvLlMw/963zeTbcbkWmbT72QdZs6xiY7zfc/T6JM6dL07mH5HLJq/6YuzB5VkHpwJ4nL5gCQHnEEbGzx6NyfxL8WknnhRk9Ah/JVcAisyOUpXxZatovKbeuK/K6H7PORcXRv7uK5hrPHwcYG1za9SNmw2KIH9r3kP7fXalWmbqUAU3ttxWZSMwy5a2nkqFXen8nPP8TxmAS+W7qBp3+Yy6TnHrLatsnt6oX8RmDdrj2eUye/NHIEdrZ2VKtqmuqlds0aVChXjudfe4tVa9bRrfMD/zLXRet29eudPHPuxNnoWF58fxx2trZ8MGpk7nPZKt2mfs3vnGVlpHP9t/EYL5/HeNU0uskQdQIyDbi06kbq5hUYL8aTtPBnXDs9hHtv01z4xpRkUtb+jkuLLuYLf1uZLOPt7p182rC3vd9Mab78YRLLVq/l4X49Ca1di/jzCUyZPZ9RYz7mq7Fv45zPujTFxWg0Wvz7G29xbm5VF96qbsm572rUqs1b737IfXXq4pwdqK9XvyFXr1zht+lTaN+xi1XVt7pOLN3uGVJYI3/PRsfy4tjPTXXsK89adx37P8LGwXruTbFeCmAUsrwLImVmZppViLdasPt2iykZjUYefPDB3BEYN/P2/uc9e6yF4appsWF7Dzez7TmjKgxXE3NHXuTdJ2e/nGNYG3eX7JEWaeaN7JyefB4ut2485IykqF+5HAajkR+WbmTkg63w976xFsSKXYd5Z8ZSyvt48/3T/fFyv3Vv/eLmnjPSIsN8pEXOyAt3R8uFt5zt7Whczsdie/MKfny/9SjHL1w1C2AEebkR5OVGaEBp3BzteXfVHvbEXiQ0wDqHmxuze0Tn9GLLkdNL1phPj+mbudRtROrRfbkLd+fISjWls82vd7Gzi9loDWvjnt07K+88rTm9N91dLcvk7pZ/mpzeSDk90l1dnHl95BM89/jDnEu4gG+Z0ri6OLNk1drcIdjWyCO791Fynp7AOaMM3F1vXZ/M+GsTX81ZToNqFfny2SE4ZS9y55rd47jZfVXNnle1g8vh7uLM0UjLIezWIqcOtTgn2Z/dXW7di3fGqi18NW8FDUIq8uUzD+Wek5v9vesQJVxdaF6raj5HsD4513neUUq5PaPdLJ8Rbm6mZ2tKnp5tyTelCd++g3UbNvHdV5+TlZVFZmZm7kvfzMxMbGxsrPYHYE5dmHdkQc5nY6plXZgzGiHtiPk6EWnH9uHRuT8OAeXNAhjOtcMwJieSdsR8HnhrlDPaL2/P6Nye1PmM2ss5H+kn95ttz4g4BG16Ye9XjvR7OIBxo72Wf13yj9tryzYxsmtLs/ZaUJmSBJUpSf3K5XB3duTtGUvZHRFF/crlCrMohSanLkmxeKbeqi7JeQ7nrX/Mn8M1q1muO1OrRjXc3FyJOG25dpG1yBlZUnDb5L+3wXcfPMJb48bj4uzE+PffyJ06xVrljGwrsD5Jy6fHtCEDwxnL+f0zTh7EpVU37HwCMF6MJyv5OkkLfiTZyQUbd0+MVy6A0YjrAwPJSk0q/MIUkoLug5zrxC2fkZ7ud5Am4eIlFv+1msF9evDYoBtrqVSvUpmhI19h2d9r6dXFeoJ/c2ZNY86saWbbmjZvydUrllPFJack4+pW8IwKrm5upOTzu8U0osPUhvHyKknDRk0s9qkf1oh9e3dx5fJlSlrROxNdJ5Zy27CpRVvHvvnZBFydnfh2zGsE+lm+YxAR62SdvzTvYQcOmC80tmfPHmrWvLMh4sHBwRbHMBgMtGnThhUrVlClShVOnjxJ+fLlc/8zGAx8/PHHxMXlP+fqvSDxuGldC9fg8mbb3bI/Jx6NIDMpmZToeIt9HMt441DCncSjEXcns/9QUOmS2NnaEJVg3lCLzP5cMc+87QCxl66ycMs+0vK84K8eaPoBk3D1xjRaU//exutTFlG7YgCTXxhEGU/rnErrZoGebtjZ2BB9xfyHR1T255sDETkirySy4MAZrqdlmG1Py+45XtLFicvJaSw5EsmlZPOXD9V8vABISLLeBcsMF86RlZmJfRnz+T5zPhviLeeZzmHrWRLHoEqk7Nlq8V1WehqGyxexL21+XFv3Eti6uJJxi8UTi1tZPx/sbG2JiTOf6zQ6+3P5QMvpa3Lmg82bJibunFmaLTt2c+DIMVxdnKlYLhBXF2cuX7lKwsVLVK1UobCLUmgCfbyxs7UlKs/aOTmfK/nn3wDPysri05mL+WL2MjqE3ceEFx/B7aaXcUE+3tjY2JCekWmRNtNoxCmfoKK1yD0neRbejjpv+lypgPWAsrKy+PS3pXwxdzkdGtZiwvNDcHPO/wXlxv3HuL9edRzsLectt0Zl/f2wtbUlNk+7IDZ7bvZygZZTMORMBxWTN03253JBgWzYvIX09HSGP/McD3TvzQPdezNz9lwAHujem8+/+bbQy1JYDBfPk5WZaTEXu31p0+fMc5ZBOkN2cMLGLs/1b2sKwmdlmD+PnKrXJfXgLjBa3kfWJvNyAlnGTOxKmtcZtt6mzzlrg9wsZ3SF5fkw3RfW3Bv6Ttxor10x257bXvMroL22NZ/2WpDpmZtwNZFL15NZvO0AF6+bt3mqZe9z/qr1rhOSU5dY1gum66NckGVdEpS9XkZsnudwTl1SPiiQxKQklq9azemzkWb7GI1GDBkGvDxLFFoZCltATtsk3nyatOj4nHbGf1tbbNXGrbz0/jh8Snnz48fv/Ofj3Q3G7PrEtqT58zbnszGf+sS2ZBkc6za3nK4uuxNBzvo5DtXrY1cmgKy0FNNxMg3Y+QZiY2tLZnzB6yUUt7J+vtltWPPrJOaWbdiy2fvkubZuSnMu4QJZWVnUqm7eoaJCuUA8PTw4HWld7fqOnbry+dc/mP0XEBDEufh4i5EYcbExBAYVHMwNCAwiLtby91BcbCyB5UzvBw4d3M+av1da7JOelo6trS3uBcyCUVx0nVgK8CuDna0t0XnLl73IdvlAyzXL/olVG8N5cezn+HiX5MePRt8TdayI3KAARiGbOnUqixcv5vTp03z66accO3aMoUOH3lHaihUr0qFDB8aMGUN4eDinT59m9OjRpKWlERYWxqOPPsrhw4cZM2YMERER7Nmzh5dffpkzZ85QoUKFoi1YEUqOiCT5VBT+vTqabffr2YHE46dJOWtqrFz4ezM+nVtje9PLNL9eHTEaDFxYG35X83ynnBzsCa1cjtV7j5kNof177zE8XJyoVcHyoRl36SpjZi1jzb5jZtu3Hj2Ng70dFXxNPUfmbdrDl3+soUO96vzwdP/c3tnWzsnejnoBpVgTEWd2TtZExOLuaE9NXy+LNBeSUvlo7T5WnTBvuP51IgY3R3uq+3iRmpnJu6v28Mch85574WdNDZ4qpaz3BzGGDNIijuBSx3waONc6jTEmJ5F+9mSBSR0rVAEg7dSxfL9PO7of51qhcNMIL5e6jcjKzCTt+MFCyHzRcHJ0pHbNamwI32F2nazfugN3V1eqVw22SBPo74e/bxnWbdlutn391u0EljV9B7Bo5Wq+z54PNse8xcuxtbWlaQPrXVTVycGB0KoVWL37kNk5Wb3rEO4uztSsaPkyCeDbBX8xe/VWBndozkdP9Mchz2g/V2cnQqtWYM3uQ6Tf9CJu2+GTpKSlE1qlQpGUpzA4OTgQWqU8q3cfzv+cVMh/nYZvf1/F7DXhDG7flI8e72txTnJcTUom8vxF6gRbZw/p/Dg6OlK7Vk02bQk3Oycbt2zFzc2VkKqWI0kCyvrj5+vLxs1bzLZv3LKVgLJl8fP15eGHBjDhq8/N/uvcsT0AE776nIcfGlC0BfsvDBmknz6K830NzDY73dcQY0oS6VGWnSAyTh3FmJaKcz3z3pzONUPJyjSQfvZE7jYbFzfsy/iTceZ40eS/sGUaMESewLGa+ZSkjtVCMaYmY4g5bZEkI/IEWempONZsaJ6mah2yMjMxxJwq0iwXNScHe0KDg1i9L097bV92e618Qe215azZZ/7vfqO9Voq0jAzenrGUP7but9gHoGpZ6+35aapLarBxyzazc7JhSzhubq5Uq1LFIk1AWX/8fX3YsNm8U8XGLeHZ9YwPjg4OfDvxF36bt9Bsny3bdpCWnk7d+2oVTYEKgZOjI3VqhLA+fKfZOVm3dSfurq7UqFLpXx976659fPDNj9QKqcL3H779r9bNKBaZBgyRJ3EMqWu22TGknqk+iT1jkcTW3RO3Tg/hUM28zeVYvT5ZqSlkxpuCWy7NOuHc1HxqR6ewNhhTk8mItN761tSGrc6G8O152rDbcHdzpXr21Gk3M7VhfVi/2fw37YYt27LbsD4E+vthZ2vL/sPmo1cio2O5ev06ZX2ta7SOd6nSVK4aYvZf3dAGpKQks2fXjtz9rl69wuGD+6kb2qDAY9UNbUB0VCRRkWdyt0VFniE66ix165nSHdy/l/Fffpq7qDeYAqNbNq+nWvWaOOQzyrY46TqxlFvHbttlXseG78Td1eU/1bFbdu1j7PifuC+kMj98+Na/WjdDRIqXppAqZAMGDGDKlCkcP36catWqMWnSJKpVq3bH6T/66CPGjRvH888/T3p6OnXq1GHSpEl4e3vj7e3NL7/8wjfffEPPnj1xdXWlSZMmvPbaazg6Wu+87XnZe7jhXqMyyRGRpF8w9Ww78eF31Jn0CRmXrnBu8Rp8u7WlbL/O7H7ohdx0EZ//Qtn+XWi45BdOf/0rblUrEDL2JaJ+mUtqlPWOQBnesSlPTviNUZP/oEfj2uw9Hc3U1eE83+1+XBwdSExJ41T8BQJLl8Tbw5V6lYJoHFKBT+atIjE1naDSXmw4eJI5G3YzoksLSri6cOFaIp8v+JuypTwZ0Ko+R6LMezflHMtaPdawKk//voXXlu+ke41y7Iu7xLRdJxnZrAYuDvYkpmVw+tJ1Aj3dKOnqRN2ypQgLKs3XGw+RZsikkrcHm86cY/beU7zUohYeTg54ODnQvUY5ftl+DHs7W6qV8WRPzEWm7DpB9xrlqGTNAQzg+soFlH5mNN6Pvkhy+FocK4bg3vZBrv45i6yMdGycXXDwC8RwId5sqiiHsuXIykgn80L+i6ZeX70I1/rNKD3iTRLXLMHexx/PBweStOVvMi9fzDeNtXi4b09eevcj3v3sGzq3bc3Bo8eZ/ccSnhwyAGcnJ5KSkzkTFUOAn29uj81H+vXi429/xNPDnWZh9dm0fSdrN4fz7is3Fq3r3aUjr4z5hG8nTadZWCi79h9ixoI/eajXg1Y9hRTA4w/ez1OfT+bVH36je/P67DsZydQVG3muT0dcnBxJTEnlVOx5Ast4413CnWORsUxZvoGaFQNp37AWB06Z91isVNYHdxdnRvbuyPBxPzPy6ykM6diCS9cS+Wb+Cu6rFESretWLqbR35vEurXnqqym8+uMcujcLZV9EJFP/2sxzvdrfdE4SCPTxxtvDjWORcUxZsYmaFQJoX78WB06Z90SrVLZM7tRTJ6LP5W67lzzUvy+vvf0uYz/5jAfat+XwkaPMW/gHjw0dgrOz6d45GxlFWX8/vDxNU9wMHtiPz7/+lhIeHjRpFMaW8O2s37iZt159BQA/X1/88vzo3bbd9AIipIrlj2xrk/T3Iko+8Tpeg0eSvGM9jhWq4taqC4nL50BGOjZOLtj7ljWN1ki6TlZ6Gol/LaDEg4MwpiSRdmAnDhWq4Na6K8mbVpKVdKMetvcPAsBwvuDRctYmZdMyPAa9gHuvJ0jbtxn7wGCcm7QnZc3vYMgAR2fsyvibelcnJ0JGGinrF+Pavi9ZqcmkH92DfWAlnJt2JHX76twe0/ey4R2b8uR3s29qr8UwdfU2nu/W+tbttfmrSExNI6h0STYcym6vdW5OCVdnSrg606NxbX5csdnUNgn0ZXdEFJNXhdOzSW2C/a1zasscg/v1YdToMbz/6Rc80K4Nh48eY+7CRTw+dPBNdUk0Zf19b9QlA/ry2TffZdclDdmybTvrNm3h7VdfAkyBkQF9ejJ11hy8vDxp1CCU02cimfbbXJo2aki9OvcVZ5Fva2jf7rzw3qeM/nwCXdq25ODRE/y2aBlPDe6X3TZJ4XRUDAF+PpS8w9EkaenpfPL9JFxcnBnauxuno8zrEp9S3viUtt6ARuqW5bgPfA63Ho+Ttn8L9oGVcGrcjpS1i27UJ6X9MF6+QFZKIoaoCDJOH8W1bS9sHBzIvBCHQ3AtnBq0JmX1gtxpp1J3rsX1gYE4J8RhiD6FY436ONUMI2nFb5BmvSOrAR7u15OX3vmQd8d9Ted2rTl09Dizf1/CEw8PLLANO7R/Lz4ZP5ESEyeZ2rDbctqwzwHg5VmCPt06Mfv3JQA0qFObcwkJTJm9AD+f0nTt0KbYynunat5Xh1q16/LVZx8x9NEn8ChRgtkzp+Lm5k6nzjfW14qKPENGRgaVgk2B0uYtWzN/zkzeH/06Q4YNB2D6rz9TvkIlmrdsDZhGfKxY+icfjnmLhwYPw8nZmeVLFxF55gwfjfv6bhf1jug6sfRInwd5fsxnjP7iO7q0acmBYyeYtWg5Iwb3vVHHRscQ4PsP69gffsXVxZmHez/I6eh86th7JWgs8j/MJut2q5HJHQsJCeHjjz+mV69exZ2Vf2Spg+U8tIXFu2UYTVZPZ2vbIVzasN1s277HXid62u+5+5Yb3p9KLz6Kc5A/yaeiiBj3EzEzF5kdr2Sz+lT/9FVK1KlO+oXLxMxcxPH3xpNlMB++/190yThG6qophXY8gNX7jvHD0o2cOX8JH08P+rcMZWhbU2/7HcfP8vj4Wbw/uAvdG9cGTHMuT1y2ib/3HSPhaiLlypRk8P1h9GpaB4Dft+7jvZnLCvx7Nx+rMDi3f4TE714ttOOBacTFj+HHOHs5ER93Z/rWrsiQUNNLsJ3RF3hy4WbebVePbjVMPZ8T0zL4afsx1kbEcSEplUBPNx6qG0zPWjemFcvINDJt9wmWHIki7loKvh4u9KpVniGhlQtt0a8c7s+MI3pkv0I9pnPthpTo3A8Hn7JkXr1E4saVJK4xNT6dKtegzPPvcWnGdyRvW5+bxqvfY7jUaUTcW08UeFzH4Gp4dh+MY2AFMhOvk7xjA9eWzi30qU4Cv53LuSO7CvWYG8J3MPm3+UTFxFG6VEl6durAgB6mher3HDjM86M/4I2RT9KpbavcNItWrmb2H0tIuHAJf18fBvfuRsf7W5gd9+8NW5g273fiziXg61Oang+0p3dX81FghcG3en2SNy8o1GOu2XWIiYtWcyY+AR+vEvRr05iHHzCVb+fRUwwf9wtjHu1Nt+b1+f73Vfy8eG2Bx/r51cdpUM3Um2nvybN8t/AvDp6KxtnRgdb1avBS/0545LPeyH/h2qw3yRvmFuox1+w+zMQ/13Dm3AXTObm/EQ93aAbAzmOnGf75ZMY80pNuzUL5ftFqfl6yrsBj/fzKozQIqQjAXzsO8NpPc1n4/nNULGA6qsLg2rIfkSeOFOoxN20JZ9qs34iOjqFUqVJ069KJvr16ALBv/wFeeXM0r7wwko7t2uamWbJ8JfMW/kHChQv4+/kyoG9v2re5v8C/MW3mb0z/bQ6rlvxRqHkHKFelOvGjBhfqMZ1qNcC9Qy/sy/iTefUyyVtWkbxhOQCOlarjPeItrs75kZSdG3PTuDRoiWurTtiX9iPz2mVSwteStG6J2cLfzrUb4TVkJAnjRpGZUDQdKvw+m8GlD54s1GM6hNTFpeWD2JXyxXj9Cmk715G67W8A7MtXpcSQl0n8cwrp+2/0pnes0xTnRu2w8/bBmHiVtN0bSd2yErD8OWHrWQqvkR9ZHKOweL/9I6l//Vqox1y97xg/LNuU3V5zp3+Lm9prJ87y+PjfeH9QZ/P22vLN/L33GAnXctprDenVpE7uMTMMmUxZvY0/tx0g7vI1/Lw86N2sLkPbNMK2kBZ9zuHcYRjRhTy6ctPWbUyZNZvo6FhKl/KmW5dO9OtpetG498BBXn7zXUY9/wwPtLvxUmzx8r+Y9/sizl+4iL+fLw/16Un7Nq1zvzcajSxZ8ReLlq4gNv4cJTzcadu6JUMH9sOpkBeXDaxai4RD2wr1mOvDdzJ5zu9EZrdNej3QjoHdOwGm+dWfe+dj3nx2OJ3btLBIu2zNRj6a8DPzJn6Bv4/pubLrwGGef/eTAv/esH49eGxA4f2+LFOzEZc/frrQjgfgULUOzi26muqG61dJ272etO2rAbAvVwWPQS+StGQa6Qeye447OuPSvDMOIXWxdffEeDmB1B1rSN9nPhLQqcH9ODVoha2bJ5mXzpG67W8yDu8s1LwDlHzje+KP7rn9jv/Ahq3b+fW3+UTFmO6dnp070L9HVwD2HDjEC2+P5fXnnqJT29a5af5c8Xd2G/Yi/r4+DOrTnY73t8z9Pisri/mLl/Pnir+JO3eeUt5eNKhbm+GDBxT69Gt+1epxJKLwA/OJ168z+efv2Ra+mSxjFtVq1OSxJ54mIPDGaNe3XnuR8+fi+XnKb7nbEhLOM+nHCezdswt7O3vqhjbg0Seextv7xhR/cbExTJ/yM4cOHiAlJZkqVUIYNPQxatQsnMBo9eAAXSd5+FWrx4WDhfuMX79tF5Nm/05kbDxlvEvSq1MbBna7UceOfPdT3nzmMbrkU8cuXbORj76bxPwfPjOrY597b1yBf+/Rft15rH/PQsl76VqW67DI7a2tUuf2O4mZ+09Y/7p7hU0BjEKkAMb/D0URwLjXFUUA415XFAGMe11RBDDudUURwLjXFUUA415XFAGMe11RBDDuZUURwLjXFUUA415XFAGMe11RBDDudUURwLjXFUUA415XVAGMe1lRBDDudUURwLiXKYDx76yvXre4s3DPaXVkb3Fn4a7TGhgiIiIiIiIiIiIiImJ1tAZGITp2LP9FdEVERERERERERERE5J/RCAwREREREREREREREbE6CmCIiIiIiIiIiIiIiIjVUQBDRERERERERERERESsjtbAEBEREREREREREZG7ysbOprizIPcAjcAQERERERERERERERGrowCGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6iiAISIiIiIiIiIiIiIiVkcBDBERERERERERERERsTr2xZ0BEREREREREREREfnfYmtnU9xZkHuARmCIiIiIiIiIiIiIiIjVUQBDRERERERERERERESsjgIYIiIiIiIiIiIiIiJidRTAEBERERERERERERERq6MAhoiIiIiIiIiIiIiIWB374s6AiIiIiIiIiIiIiPxvsbG1Ke4syD1AIzBERERERERERERERMTqKIAhIiIiIiIiIiIiIiJWRwEMERERERERERERERGxOgpgiIiIiIiIiIiIiIiI1VEAQ0RERERERERERERErI59cWdARERERERERERERP632Nipb73cnq4SERERERERERERERGxOgpgiIiIiIiIiIiIiIiI1VEAQ0RERERERERERERErI4CGCIiIiIiIiIiIiIiYnUUwBAREREREREREREREatjX9wZEBEREREREREREZH/LbZ2NsWdBbkHaASGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6iiAISIiIiIiIiIiIiIiVkcBDBERERERERERERERsToKYIiIiIiIiIiIiIiIiNWxL+4MiIiIiIiIiIiIiMj/Fhtbm+LOgtwDbLKysrKKOxMiIiIiIiIiIiIi8r9jW5NGxZ2Fe06jrduKOwt3nUZgCKmrphR3FqyKc/tHWOoQUtzZsCpdMo6R+N2rxZ0Nq+L+zDguf/x0cWfDqpR843siTxwp7mxYlXJVqnNt96rizoZVKRHanlMREcWdDatSKTiYMyePF3c2rEqFylVJXfJDcWfDajh3HUHSz28XdzasitvwD0hdPa24s2FVnNs+zJW964o7G1bFq25r1a95VKhcldQFXxV3NqyKc+8X1YbNo1yV6mw9cq24s2FVmlQvQcSpU8WdDasSXKkSMccPFHc2rEZA1fuKOwsi/29pDQwREREREREREREREbE6CmCIiIiIiIiIiIiIiIjVUQBDRERERERERERERESsjtbAEBEREREREREREZG7ytbOprizIPcAjcAQERERERERERERERGrowCGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6iiAISIiIiIiIiIiIiIiVkcBDBERERERERERERERsTr2xZ0BEREREREREREREfnfYmNnU9xZkHuARmCIiIiIiIiIiIiIiIjVUQBDRERERERERERERESsjgIYIiIiIiIiIiIiIiJidRTAEBERERERERERERERq6MAhoiIiIiIiIiIiIiIWB374s6AiIiIiIiIiIiIiPxvsbFV33q5PV0lIiIiIiIiIiIiIiJidRTAEBERERERERERERERq6MAhoiIiIiIiIiIiIiIWB0FMERERERERERERERExOoogCEiIiIiIiIiIiIiIlbHvrgzICIiIiIiIiIiIiL/W2xsbYo7C3IP0AgMERERERERERERERGxOgpgiIiIiIiIiIiIiIiI1VEAQ0RERERERERERERErI4CGCIiIiIiIiIiIiIiYnUUwBAREREREREREREREatjX9wZEBEREREREREREZH/LbZ2NsWdBbkHaASGiIiIiIiIiIiIiIhYHQUwRERERERERERERETE6iiAISIiIiIiIiIiIiIiVkcBDBERERERERERERERsToKYIiIiIiIiIiIiIiIiNWxL+4MiIiIiIiIiIiIiMj/Fhtbm+LOgtwDFMCQIrHlyCkmLF5PRNwFvEu4MaBFfR5uG4aNTf4VU1qGgR+Xb2LZjkNcTkymaoAPT3VuQbMalXL3MRqzmL95D3M37ib6whW8Pdy4v3YVRnRugbuL090q2n/iHOBLy71L2Nn7GS5t2H7Lfcv270LlN0bgWimIlDMxnPzsJ2Km/2G2j2f9WlT/9FU869fCcC2J6GkLOf7+BLIyMoqwFIVj69nzfL/1CBGXrlPK1Ym+tSsypF5wgddI1JVEekxbbbE92NuDuYPbAGDMymLGnggWHjjD+cQUypV05+HQynSuFlSkZSks9hWr49KqG3al/TEmXSNt1wbStv9d4P62Jcvg+dQYi+2ZCbFc++WD7E82OIW1xSm0ObYeJTFeuUja7vWk7VpfRKUoXDt37+HX6TM5GxlJSS8vunXpTJ+e3Qu8TgDWrN/ArDnziIs/h6+PDwP69qJD2zZm+6z8ezXzFy4iNj4enzKl6dalMz0e7HLL41qL8P1H+H7OYk5Fx1HKswR9OrRgcJe2d5T3Y6ejGDr6MxZ+9S5ly5Qy+27djn1M+n0FZ2PPUcqrBJ2bh/FIjw442Ft/U2HX7t1MnTqVyMhIvLy86Nq1K7179brlOVm3bh2/zZ5NfHw8vr6+9O3bl/bt2pntc/z4cX6ZNIkTJ07g6upK+3btGDRoEA4ODkVdpP9s1+7dTJk2nbPZ5+TBLl3o06vnLc/J2nXrmTVnDvHZ907/vn1o366t2T4bNm5i3oIFREXH4ObmRr26dXjskaGULFmyqIv0n205dpYJy7cQEX8Rbw9XBjStw8OtQ2/dNlm1jWW7j3I5MYWqZUvzVIfGNKtWAYAdJ6N4/IcFBf69ER0a81THxkVRlEKx9Uw83208yKmL1/B2daJfvcoMaVD1juoSg9HIsFlrcLa35+cBrc2++/PgGabvOEbUlUTKuLvwYM0KPNq4Gva21j/we8vhU0z4cx0RcQmm9mvLBjzcrtGtr5FlG1m24yCXrydTNdCXp7q0oFmN4Hz3P3f5Gr0/+ImvnuxLw6rli7IohSZ832Emzv6DU9GxeHuWoE/H1gzq2v4OnzmRDHvrY+Z/PZayPqXNvjsTE8+EmQvYdfg49ra21KtRleeH9CHAt0xRFaXQqH61tOVEFBP+2k7E+ct4u7swoHFNHm5e587qk0wjD//4Oy4O9kwa3t3su0PR5/ly+VYOxSTg7uRIt9AQRrRtgIO9XVEVpdCoDZu/1JRk5k77lp1b15CWmkJIjXoMfOxF/AMq3PExJnz6Gk7OLgx//j2z7VlZWaxYNIN1K3/n4oVzlC7jR7uu/WnXuV/hFqKQpaSkMHnyZLZs3kxKSgq1atXiiSefJDAw8JbpLl++zM8//cSuXbvIzMykYcOGDH/iCby9vXP3yczMZObMmfy9ahXXr1+ncuXKPPb441SrVq2oi/Wv7di9l8nTf+NMZBQlvbzo3qUj/Xp2u+U1vnr9JmbMmU/cufP4+ZRhYJ+edGzb2myfyKgYfpwynX0HDmFnZ0ftWjUY8dhQyvr5FnGJRKQwWP9bidsICQnh448/plevXvfEcf8X7D8dw8iJ8+gYWp1nurZkT0Q0Xy1ag8Fo5LEOTfJN896sZWw4cJLnurWivI83f247wMiJc/nluUGEVja9fP7173C+W7KeoW0b0yikPGfPX+K7JRs4GZvAxGcHWH2jzTnQj7Clk3DwKnHbff16dqDutM85/e00ElZuxK97O+pO/hRjWjpxc5cB4FIxkEYrfuVy+F52D3wB92rBhIx9EQdvLw4+825RF+c/ORB3iRcWh9OhSgAjGldjb9wlxm86RKbRyLAGVfNNcyzhGgA/9GyKy00/Wpwdbvz/xPCjTN11gqcaV6Omb0k2nTnH6L92Y2tjwwMht24AFje7shVw7zuC9CO7SNmwGPvAYFza9ABbW9LC/8o/jY+pTNdnfU1WRvqNL24KYLm07YVzWFvSdm8g/fg+bL1K49LyQWw9S5GyZmFRFuk/O3z0GKPf/5BWLZrxyOCHOHj4CD//OpXMzEwG9O2db5qNm7fwyedf0bNbVxqEhrIlfBuffTUeB3sH7m/VAoBlK1fx1bff0a93T+rXq8vR48eZ+MtkUlJTeKhf37tZxH/swInTvDhuIu2bhPJU367sOxbBt7MWkZlp5JHuHW6Z9mRULC+M+4HMTKPFd9v2H+HVr36hfeNQnh3QjYjoeL6f/SdXricyaph1/+g7cvQo7733Hi1btODhIUM4dOgQkydPxpiZSb9++ed906ZNjPvsM7p3706D+vXZsnUrX375JQ4ODrRu1QqAuLg43nzrLapXq8Ybb7xBVFQUU6dO5fr164wcOfJuFvEfO3L0KO+MGUurFs15ePBgDh0+zKRfp2DMzKR/Adf4xs2b+fTzL+jR7UHTOQkP5/Ovvs4+Jy0BWLd+Ax+P+4zOnR7gkYeHcOnyFaZNn8Grb77Fd998jaOj490s5j+y/2wcIyctomPdqjzzQBP2nI7lq6UbTW2Ttg3zTfPe3L/ZcPgUz3VuRvkyJflz52FGTlrELyP6EFopgOqBPkx/rr9FugnLt3Ao6hwPhIYUdbH+tf2xF3l+4SY6VAtiRPNa7I25wDfr95NpzGJYo9u/1Jiy7SiH4i9TP9D8ZfOsXSf4fO1e2lUN5IVWtbmcksYPmw9xPOEKn3dvWlTFKRT7T8cw8oc5dKxfg2cebMWeiCi++mO16RrpmH/e35uxlA0HTvBc99aU9y3Fn+H7Gfn9HH55YTChlcuZ7Rt/6RojJvzG9ZS0u1GcQnHg+Cle/nQC7Zo24Mn+3dh39CQTZi4kM9PI0B4P3DJtRGQML306Id9nzrkLlxj+zjjKl/Vl7HOPk5aWzsQ5i3juw2+Y+fk7OFtxXaL61dL+yHOMnLacjvcF80z7huw5E89XK8IxGLN4rFW926afvH4Ph6ITaFDR32x79KVrPDl5CbXL+fLZwPacOn+FCau2czUljdE9WhZVcQqF2rAFm/jl20QcP0i/h0fi4urGojm/8OnbI/jw2zm4ud/6d7LRaOS3yV+xc+samt3fxeL7OVPHs2rJbHoOfJJKVWqyf9dmZvz0GfZ29rTuaL3vdcZ9+ilHjx7l0ccew9XVlVkzZ/L6a6/xw8SJeHh45JsmMzOTd0aPJjk5mWdHjsRgMDDl11956623+Pbbb7HP7oD0808/sXLlSoYNG4avry+///47b735Jt9OmEDZsmXvZjHvyOGjx3lr7Ce0bt6UYYMHcODwUX6aMoPMTCMP9e2Zb5oNm8P56Itv6PVgZ8Lq12NT+HY+/XoCDg72tGnZHIDzCRd47rW3CAwoy9ujXiA1LY3JM2bz6jtjmfTtFzg53RsdYkX+l93zAQyxPt8v3Ui1QF8+GtoNgGY1gsnINDLpry0Mat0AZ0fznqsxF6+wbMch3ujbgf4t6wMQVrUCe0/FMGfjLkIrB2E0ZvHrqnD6NKvH891bA9C4WkU83Vx47ddFHI6Mp2Z580av1bCxIXBID6p/+hrcYYwlZOxLxM1fwZFXPgbgwqpNOJT0JOS953MDGMGjhmO4nsTOXk+TlZFBwooNZKakUuub0Zz8ZCKpUXFFVaL/bOK2o4SU8WRsR9O/d9MKvhiMRn7dcYKBdYNxzqdX1fELV/F1dyYsKP+eeSkZBmbtjWBg3eDcIEhYUBmOnr/C7H2nrD6A4dKiK5nnokhePBUAw6nD2Nja4dK0I2k714LBclSNnW8gxmuXMZw9nu8xbVzccGrQmrS9m0heOTt3e9b1y7j1foq0vZsxXjpXNAUqBNNm/kblShV5/eUXAWhYPxSDwcBvc+fTs1vXfBuak6fNoGXzpowY/lh2mnpcT7zOlBmzcn/8/TZ3Pi2aNWX4sKEAhNatQ3RMLIsWL7P6H38/zV9KSIVA3n/GlPemdWtgyMxkyqK/GNCpdb4vfTIMBuasWM+P85fi5JD/Y3/x+nD8SpXk/WeHYmdrS6Pa1bl09Rqzlq3lxSG9sbfino4zZswguFIlRo0aBUCDBg0wZGYye84cunfvnu91MmXqVFo0b86TTzwBQP369Um8fp3p06fnBjDmzZ+Pi4sL77zzDg4ODoQ1bIiTkxM//PAD/fv3x8fH5+4V8h+aPnMWwZUq8eorLwPQsEF9DIZMZs+dR4/u3Qo4J9Np0bwZTz0xHIAG9UO5fv06U2fMyH3BNnvuPMIaNOD5Z5/JTRcUEMDzL7/Ctu07aNG82V0o3b/z/YqtVAsow0cPmV66NqtWgYzMTCat3sGglvVwznNvxFy6yrLdR3mj5/30b1YHgLDKQew9HcuczfsIrRSAu7MTtfO0PdYdjGDbiSg+f7gLFcpYb6/piVsOUc2nJB90bgRAs4p+GDKNTN52hIGhVcw6B+R1/PwVJm87Smk3Z7PtmcYsft56mMblfRnX7UaHlWo+Jek39S/Cz5yjcQXr7eX4/ZINVAvy46NHTD3Am9XMbr+u3Myg+xsW0H49yBv9O9K/VQMgu/0aEc2cDbtyAxhGYxaLt+3ny4Wrybq7RfrPfp63mJCKQYx59lEAmtStZXrm/LGc/p3bFPjMmbt8LT/N+xPHAkar/Tx/Ce6uLkx4+0WcnUzHKOtTilc++54jEWepV71K0RXqP1L9aun71Tuo5l+aj/qZRpQ0q1qODKORSet2M6jpfRb1682OxV1g0vo9lPZwtfhu8vo9uDo58M3gB3Cwt6NFSHlcHOz5ePEmHm9dD3+v/F/sWgO1YfN38uh+9u7YyEujv6Z2fdM1XbVGPUY92Z3Vy+fTre+jBaaNOnOCGT9/xukTh3F0tDx/CediWfnnLIYMH0WbTn0AqFG7IZcunOPAnnCrDWAcOXKEbdu2Meb992nY0NSholbNmgwbNoylS5YwYODAfNNt3LiRiIgIJk6cSLnyphF9wZUqMWLECDZu2MD9bdqQkJDA0qVLeeqpp+jStSsAoaGhPD58OPPmzeP555+/O4X8B6bMmkPlShV48+XnAAirX4/MTAOz5i2kd7fO+d47v0yfRatmTXhm+DAAGobW5fr1RH6dMTs3gDH1t7m4ubry+dh3cXY2HcPf14e3P/iUYycjqF2zxl0qoYj8W9Y/llvuKekZBnaejKRNHfNeh+3rhZCUms6eiGiLNGVKuDNr1CN0CauZu83W1gY7WxvSMjIBSExNo2tYTTo1qGmWtqKvaQqUqAuXC7sohaZE7RBqfTeG6Bl/sPeRV2+7v0v5ANxDKhK/aJXZ9riFK3GrUgHXyqYGSpn2zTm/fL3ZdFHxC1ZgY2dHmQ7NC7cQhSjdkMmu6IvcH2z+0qdt5bIkZRjYG3sx33THEq5StbRngcd1tLPj174tGFzPfNoGe1tb0g2Wvf+sip099uWqkH5sn9nm9GN7sHFywT4w/6ko7HwDMZyzvKdy2Hr7YmNrR8aJA2bbM84ex8bWFodg622opWdksP/AQZo1MZ9+pWWzpiSnpHDw8BGLNPHnzhEdE0uzxuZpWjRrSmxcHNExsQB88N7bPPHoULN9HOztSU9Px5qlZ2Sw6/BJWjesY7a9TaN6JKWksu9oRL7pNu85xC8LlzOseweeHdgj333SMgw4Ozlid9MUL54ebmQYDCSlphZaGQpbekYG+/fvp2lT897RzZs3JyUlhUOHDlmkOXfuHDExMTTJJ01sbCwxMTEA7Nq1i7CGDc2mi2revDlGo5Fdu3cXQWkKh+mcHLC4d1o0z7l3DlukMd07MTRrYj5KskWzZsTGxhETE4vRaKRevbp0eqCj2T5BQabgcGyc9QbN0w0GdkbE0KZWZbPt7WtXISktnT2nYizSlCnhxqwXBtKl/o3RCKa2iS1phsx8/05qhoFP/lhHi+oVaV/Hel/Aphsy2RWVwP1VAsy2t6saSFK6gb0xFwpMm5FpZPTy7QwIrUz5kuYvDy8lp3I1NZ0WeZ7vlct44uXiyMZTVnyNZBjYeeJsPu3Xatnt1yiLNGVKuDPrtWF0CauVu83W1gY7O1vSMgy5247HnOOD35bTtdF9fJjduedekJ6Rwe7Dx2nV0LwHfZtG9UlOSWXf0ZP5ptuy5yC/LFjCIz068exDli8Ms7KyWLttNw+2bpobvACoHlyBpRPHWXXwQvWrpXRDJjtPxdKmZkWz7e1rVSIpLYM9ZwrOe4Yhk7fnrWVgk1pUyKeNv/VEFC1CyptNF9WuViWMWVlsOWF5T1oLtWELdmBPOE7OLtSqe6OcJTxLElIzlP27Nt8y7U9fv4vRaGT0uF/x8PS2+H73tnU4ODjSop15Pfv0qI8Z+fq4wilAEdi1axfOzs6EhobmbvP08uK+++5jx86dBabbvWsXgYGBucELgHLlyxMUFMSOHTsA2Lt3L5mZmWZtXgdHR8LCwtiZvY81Sc/IYN+BQzRv3Mhse8umTUhOSeHA4aMWaeLPnSc6JpbmTcLM0zRrTExcPNGxcWRlZbFhSzgPtG+TG7wACKlSmXlTf1bwQuQe8f8igHHq1CkGDBhArVq16NSpE8uXLzf7fu3atfTq1YvatWvTvn17vv76a7OHfHx8PCNGjKBevXq0bNmSxYsXm6X/9ttvGTx4MC+++CKhoaGMHTsWgD179vDwww9Tv359GjVqxBtvvMHlyzdepKempvL111/Ttm1b7rvvPrp3787KlStzv1+4cCHt27dn9uzZtG7dmjp16vDcc89x7tw5Xnnlldz8zJ8/PzfNmTNneOyxx6hfvz716tXjscce49ixY4V6Pv+L6ItXyDBkUt7HvFFRLrsX4pnzli+nHR3sqVneHw8XZ4zGLOIvX2Pc/FVEX7hC3+amH00lXJ15vW8H6gWb96Jfu9/U8zzY33rny02JjGNdtfYcGfUJmcm3fxHoXs30sjrpxBmz7ckRZ03fV62IrbMTrhUCSTpx2myf9AuXybh6Hfeq5j8irEnMtWQyjEbKe7mbbQ/ydAPg7OXEfNMdT7hKcoaBYXM30OS7xXT4ZQXjNx8iI3tqAjtbG6qU9qS0mzNZWVlcTE7l153H2R6VQJ/aFYq0TP+VrVdpbOwdMF46b7bdeNn02a5U/j1W7X0CsXF0wmPIK3iN+gbPkR/j0ro7ZL+EzkoxnUtbT/O1Duy8yuT+XWsVFx9PhsFAQID50OayZU0vxqKiLV84RkaZgjmBedIE+JvSRGe/mC4fFISfry9ZWVlcu36dZStXsWrNOh7s0qnQy1GYYs5fJMNgoJy/ec//oOz5ws/Gnc8vGTWCy7No/Bge7fkAdnb5P/b7dmhJVHwC05f8zfWkZA6cOM3s5etoVrcmnu5uhVuQQhQfF4fBYCAgwPxFbNk8/+Y3i4yMBCAwTxr/7GH00dHRpKWlcf78eYvjenl64urqSnR0wYHD4hYfl3Pv5D0nOeWzPCdRUaYXQQXebzHR2Nra8uTjj9E0zwuZLVvDAShf3ny6HGsSffEaGZmZlM8zIqJcaS8AziRYdoJwtLenZpAvHi5O2W2T64z7Yx3RF6/St8l9+f6dmRv2cP5qIq/2aFXoZShM0VeTyMg0Uq5knudw9uczl64XmPanrYcxZBp5qmlNi+/cnRywt7Uh7mqy2fZrqelcT80g5mr+z3drEH2hgParT3b79VxB7deyN9qvl64xbt5fRCdcpm+LGy+i/L09WTLmaUb1aW8xisOaxZy7kO8zJ9DP9MyJjM1/BGf14PL8MeEjhvXqnO8zJy7hIonJKfiVKcW4SbNo/+iLtBj8DK989j3nLlpvhyRQ/Zqf6EvXyMg0Ur6UeQCiXPbnMxeuFpj2xzW7MGQaebqd5TR+qRkGYq8kUj5PYMPb3QV3J0fOJBR83OKmNmzB4qJPU8Y3AFs781F+vv6BxMecvWXaJ154n7c+/oWgCvkHOSNPH8fXP4hjh/bw7kuDeax3Y14e3o11K617ytyoqCj8/Pywy3NO/MuWJeYW7c3IqCiLugigbNmyuddLVGQkLi4uZmti5Oxz8eJFUlJSCqEEhScu/hwZBgOBAeYdIQLK+gEQlR3Iu9nZnHunbN57JztNdAzx586TlJSMb5kyfPPDz3Qf+Agdew3k7Q8+IeFC/p0nRcT6/L+YQmrq1Km89dZbfPzxxyxatIgXX3yRoKAgatWqxYYNG3jhhRd44403aNq0KZGRkYwdO5bTp0/zzTffYDAYePzxx3F3d2fGjBmkp6czZozlorg7duzg4YcfZtGiRWRmZrJ//36GDBlC//79effdd0lISOD999/nscceY968edjZ2fHSSy9x+PBh3nvvPcqXL8+SJUt4/vnnmTBhAu2yFwqNjY1lxYoV/PTTT8TFxfH0008THh7OiBEjePrpp5k8eTLvvfcebdu2pWTJkrz00ktUq1aNBQsWYDAY+PTTT3n22WdZtWqVRZ6LQ2L2vL7uzuZDyl2zh/olpd66d8ivq7YyfrFpceHeTevSOHuhzPzsPxPD5FXhtKpVmSplrTeAkXH5KhmX77yRbe9peoFguGb+Q99wPcn0fQl3HDw98t0nZz/7Eu4W261FYpppxIibo3n145r9OSndYJHmckoa55NSyczK4rlmNfD3cGV7dAJTd57kXGIqH2ZPRZVj5fEY3lq5C4DmFXytfhFvGyfTNBxZ6eaNyKw00/1k4+hsmcbFDdsSJcHWlpS1f2C8egn7CiE4N26PTYmSJP85BeOl82REncS5RReM1y+TcfYYdl6lce00iCxDBjYO1jvXZ1KS6QWYm6v5lAKuLi4AJKckF5jGNU8al5w0yeZpjhw9xvOjXgegapXK9OlpvnCktUlMNl0fbi7m14OrS3b9mpJ/gNTH2+u2x25YsyoPP9iO8TP/YPzMPwAIqRDIByMf+df5vRuSkvP/N8/5nPff/JZpbrpOkpKS8t0HTNdTfse1FknJ+efd1TX/+wD++b2TIzYujp8nTSa4UiXCGjT4bxkvQompBbVNTJ9v2zZZu4Pxy7YA0LtxLRpXtXyZmGHIZNbGPXSsG5IbGLFWOc9hdyfzl+k3nsOWUxYCHIq7xPQdx/hlwP045jOtnIuDPe1Dgpiz9ySVSpegTZUALiWn8dmaPdjZ2pCSkf/IFWuQe424mD8X77j9+tcWxv+5DoDezerSuNqNjiSebi54urkUYm7vjhvPHPO8u2Y/g5IKePHl433rqdMuXzMFyL6btZAawRUY+/xwLl+7xve//cHT73/JjE/fxsXZOtsnql8tJWbfGxb1a3awLikt/3vnYPR5pm7ax+Th3fKtT3KP62Q5TZmrk0OBx7UGasMWLDk5ERdXy44xzi5upKQk3TJtUIXKt/z++tXLXLmUwI9fjaZH/+H4B1Zg28a/mPKDaUpma51CKjkp6V+1N5OTkgjIZw2Lm9MlJScXeGwwXVcuLtbzfLrtvXOLdr2ba95nVU6aFK5cM62l+fPUGVSrUpm3R73AlavX+GXaTF56811+Gv85Ls6Wv7fl7rGx/X/Rt16K2P+LAMZDDz3EgAEDAHjhhRcIDw9nypQpfP7550ycOJF+/frlfl+uXDnGjBnD0KFDiY6O5vTp05w4cYJVq1ZRrpzpB+nHH39Mjx49LP7Oc889l7uI0gsvvEBISAijR48GIDg4mC+//JLu3buzadMmAgMDWb16NRMnTqR169YAjBw5kqNHjzJx4sTcAIbBYGD06NEEBwdTtWpVqlWrhoODA8OGmebvGzZsGPPmzePMmTOULFmSyMhImjZtSkBAAA4ODnz00UecOnUKo9GIrRXc9MasW8/ue7uFtlveV4W6wYHsiYjmx+WbSM3IyF1L42Z7IqIZOXEeAaU8eX+w5QJe97LbVd5ZRmNuD/uC97HeWZZvN5lTfpeIi4M93/VoQjkvd8qWMDVo6geWxtHOlu+3HuXxhlWp6H1jKouaviX5uXczTly4xg/hRxn5x1Z+6t3Mehd6t7nNv2c+91VWRjrXfxuP8fJ5jFcvAWCIOgGZBlxadSN18wqMF+NJWvgzrp0ewr33kwAYU5JJWfs7Li26mC/8bWXyK/PNbPM5Z1lZt766bPKk8fHx4fOPPyD+3DmmTJ/FC6Ne5/uvvzQbWmxN/mv9eiufTJrNn+vCebTnA4TVqkpswiV+nr+M5z75ju/fes5smg9rkmW89b+5bT7n5HZpbGxt7+D6s9K6BNN8+7eSX96Nt7l38rvfIqOieHP0O9jZ2fH2m69bRRukILc7J7dtm9SoRN0KZdlzOpYfV20jNcOQu5ZGjlX7T3DhejKP3F+/gKNYj39zfacZMnln+XYeql+FWv6WU3fkeKt9fRztbBm7cifvr9yJs70dj4RVIzndkO/6VtbiP18jtatQNziIPRFR/Lhso+kaeeTeeKFYkNtdJ3mfqXcqI3sKNm/PEnz68lO5dUegrw+Pj/6UFZu20bOddS7QrPrV0r9pm6RlGBg9by2Dmt7HfUH5jzIuyjZPUVMb1sRoNFqU61a/Uf9tnZLDYMjg+rUrPPvapzRo0gYwrYFx8UI8f8z5xSoCGKZzkmWxrSC3us5vdY/k1EW3bfNa2X10u/oyv2vkdu89bGxtMGRP61jSy5Mxb47KrVMD/P14dtSbrF63ka4PtP+XuRaRu+X/RQCjfn3zH4t16tQhPNw05Pbw4cPs37/fbBqmnIdGREQEJ0+exNPTMzd4AVC9enWc80RgS5UqlRu8ADh+/DjNmpkvplatWjU8PDw4duxYbu/NvHlr2LAhX375pdm2m/+2q6sr/v43hszlLFKUM+XViy++yEcffcSsWbMICwujRYsWdO3a1Woatjk91/L2iknK7tnm4XLrRlXOSIr6lcthMBr5YelGRj7YCn/vG8OHV+w6zDszllLex5vvn+6Pl7tlr4J7meGqqWeavYd575ScURWGq4m5Iy/y7pOzX84xrJF7Tg/PDPORFjkjL9zzmV7B2d6OxuUsF8xtXsGP77ce5fiFq2YBjCAvN4K83AgNKI2boz3vrtrDntiLhAZY55RJWWmmXox5R1rkjsxIy6eXoyEDwxnLeUAzTh7EpVU37HwCMF6MJyv5OkkLfiTZyQUbd0+MVy6A0YjrAwPJSr11T6fi5Jbbg9687Lm9bNws73s3N9P9kHc4dHIBaUqX8qZ0KW/q3FcLfz8/Xn79LTZu3kL7tvcXTiEKmXtOT6Ls+jRHzsgLd9d/14Pq/KUr/L5mC8O6d2BEP9MCf/WBmsHl6T/qQ/5ct5V+Ha1zSpycf/PkAv7NXd0s68jbpXFzdc3trZbf0Prk5OR8j2stcq7zvHlPutU5cf1n986+/Qd4/8OPcHFx5tOPP8ydsstaFdg2yf7s4XLrAF0Vf9Ozo35woKltsjKckZ2a4l+yRO4+q/afINivFCFWPCI0R87Ii7wjHpPSDGbf3+z7TQfJAoY3roEh+4VIVvaS1AajETsbG2xsbHB1tOfdBxoyqk094q4l4V/CDVdHe/44cIogL+sdHZp7jeStX++4/Wpqo9Svkt1+XbKBkd1am7Vf7zU5z5TkPOsg/ddnjmv2C9YmdWua/X65r2ol3F1dOH7Getc2UP1qKWfkRVKa+cit3Po1nw4QE1btwJiVxRNt6mPIngo2512sIdOIna1N7siL/EZaJKWlW4z4sCZqw5osmvMLi+b8bLatQdO2XMvueHWzlOQkXF3/2zPC2cUNGxub3MXBc9xXrwkH94Rz9cpFPL1KFZD67pg1axazZs4029a8eXOuXLlisW9KcnLudZEfN1fX27ZT3dzcCtwn53trklNf5m2j3/recS0gTfYoQlfX3FFyYfXrmT13alSripubKydOmU/LLSLW6f9FACPvy/vMzEwcHU2NGqPRyOOPP07Pnj0t0pUpU4aIiIh8o9729uanJm9Ao6CeFVlZWWYLfub3fd5j593/VsGIQYMG8cADD7B+/Xq2bt3K+PHj+eGHH/jjjz8oXbr4X84GlS6Jna0NUXnmk47M/pyz6PbNYi9dJfzoGbo0rImTw41zUz3Q1CMn4Wpi7g/AqX9v46tFa2hQpTxfDe+Fh8v/v6F+icdND1DX4PJc23tjkTe3YNMCXYlHI8hMSiYlOh7X4PJmaR3LeONQwp3EAhbztQaBnm7Y2dgQfcX85XlU9uebAxE5Iq8ksiPqAh2qBuBx04uVnIVUS7o4cTk5jc1nz9G0vC/erjdeNFTz8QIgIcl6FyI2Xk4gy5iJbUnzF185n40X4y3S2JYsg335EDKO7DIPcGTXJ1nJpiCXQ/X6GC/Ek5kQk7ufnV85bGxtyYy33pcEZf39sLW1tVi4MjbW9LlcYKBFmpw1DWLi4qgcXOlGmuxjlAsKJCUlha3bthNStSoBZW+8FKiSvf/FS5Y/qqxFoG9p7GxtiY5PMNue87liQP69GG8n/sIlsrKyqB1SyWx7pUB/PN3dOBVtvYuH+vv7Y2trS1ys+Zy4sdmfywVZTh8XmH3txMXGUjk42DJNuXK4uLhQqlSp3G05rly5QkpKSr7HtRZls89JgfdOvuckIHcfs3OSfYygm9KsXbeez7/6mqDAQD4Y8x6lSxfvy4A7EVTK09Q2uXDFbHtk9ueKPpYjCmIvXSP8RCRdQqvlaZuYXlQnXEvKDWBkZGay5dhZht1vvdO83CzQyx07Gxui8qw5FXXF9Dm/5/Dfx6OJu5ZMs/G/W3wX9uUC3nugId1qVWBDRCwlnB2pG1Ca4Oy56y8lpXLuegrVfG89tVBxCipTUPvV9Eyo6GfZxo69eJXwo6fpElbL/BoJMs27fXP79V4U4FsGO1tbouLN11eKzv5cIcDvXx030K8MNjY2ZGRYThmamWnEyYrXCVH9ainIu4Tp3rloPl1uZPbnitnt8Jv9fTCC2CuJNHlvksV39Uf/xPu9W9O9fjV8SrgRdfGa2fcXE1NISsugUhnL41oLtWFNWnfsSd2Gzc227d62noN7wi1mjzgfF4V/YIX/9Pd8/YPIysoi05ABjjd+C2Zmmn4vOjreOhB9N3Tq1ImwMPPFprdu3cquXbsszklsbKxZ/ZBXYGAgERGWv/ljY2MJCQkBICAwkOTkZK5euYKnl1fuPnGxsfj4+OR2lrUWAf6+2NraEhNn/ts353P5IMs1P4Ky142JiYvPvRcAYnPTBOJZwuM2zx3rDYiKyA3W0W3/Pzp06JDZ5927d1OlimlxpypVqnD69GnKly+f+198fDzjxo0jKSmJ6tWrc/36dU6cOJGb/syZMyQm3nqhwZCQEHbt2mW27ejRoyQmJhIcHJz70Mi7z86dO6lc+dbzNxbk4sWLvP/++2RkZNCrVy8+++wz/vzzTxISEti+ffu/OmZhc3KwJ7RyOVbvPWYW5Pl77zE8XJyoVcFynsa4S1cZM2sZa/aZL0a+9ehpHOztqOBrerEwb9MevvxjDR3qVeeHp/v/vwxeACRHRJJ8Kgr/Xh3Ntvv17EDi8dOknDUtynXh7834dG6N7U0/9Px6dcRoMHBhbfhdzfM/4WRvR72AUqyJiDO7RtZExOLuaE9NXy+LNBeSUvlo7T5WnTBf9O6vEzG4OdpT3ceL1MxM3l21hz8OmS8AF37W9EO7SqkSWK1MA4bIkziG1DXb7BhSD2NqMobYMxZJbN09cev0EA7VQs3TVK9PVmoKmfGmhYpdmnXCuWkHs32cwtpgTE0mI/J4oRajMDk6OlK7Vk02bQk3u042btmKm5srIVWrWqQJKOuPn68vGzdvMdu+cctWAsqWxc/XFzs7O7789jvmLTR/Cbdzz14AKlYwDwpaEydHB+pVq8zaHXvN753te3F3daFmcIV/ddwgP9NLqr15Ap9nYs9xNTGJAJ/iD44XxNHRkftq1WLzli1m52TT5s24ublRNZ/rpGzZsvj5+bFp0yaz7Zs3byagbFl8fU2BoNDQULZv3056xo1epZs2b8bW1pY6deoUUYn+u4LPyRbc3NwIqWq5+GXO/bFx82az7Zs2b8n9DmD7jp2M++JLalSvxheffXpPvFyD7LZJpQBWHzhp3jbZfwIPZ0dqlbN8ERt3+Rpj5v7NmgMnzbZvPRaJg50dFW5aEPxk3EVS0w3Uy6eNY42c7O2oF1iaNSeizc7H6uPRuDs5UDOfKaK+7tmcGYPbmv1XzdeLar5ezBjclpbBppdp8/ed4qt1+8zSztx9AjtbG1oEW29P8gLbr3tu036duZQ1e/O0X4+cMmu/3qucHB2oW70K67bvMTsna7ftNj1zKle8ReqCuTo7U696FdZu32NWv+44cISUtDTqVst/gV5roPrVkpODPaEV/Fl96LT5vXPotKl+DbQcQT3+4U7MerqX2X/Vy5ametnSzHq6F62qVwCgSZVANhw7S7rhxvo5qw+ews7WhrBgyxeZ1kJtWJOS3mWoWLmG2X+16jYiNSWJg3u25u537epljh3eQ626jW9xtNvLGXmxbeNfZtv3bt9AUIUquPzHER6FoVSpUlStWtXsv9DQUFJSUszeG129coWDBw8SGhpa4LFCQ0OJiooi8uyN376RZ88SFRVFvex09erVAzBr82akp7N9+/ZbHru4mO6dGmzasi3PvROOm5sr1arkV8f64+/rw4bN5u8/NmwJJ7CsP36+Pri4uFC7ZnU2btlm9tzZvW8/qamp3FezetEVSkQKzf+LERhTpkyhXLly1KlTh9mzZ3P8+HG++OILAIYPH84LL7zAhAkT6NKlC/Hx8bz11lsEBgZSpkwZSpUqRZ06dXj11Vd59913sbOzY+zYsbedkmnYsGE89NBDjB07loceeogLFy4wduxYatSoQZMmTXBwcOD+++9nzJgx2NjYUL58eZYuXcrq1av5+uuv/1U5PT09WbduHZGRkbz88su4u7uzcOFCHBwcqFWr1r86ZlEY3rEpT074jVGT/6BH49rsPR3N1NXhPN/tflwcHUhMSeNU/AUCS5fE28OVepWCaBxSgU/mrSIxNZ2g0l5sOHiSORt2M6JLC0q4unDhWiKfL/ibsqU8GdCqPkeizKPyOce6F9l7uOFeozLJEZGkXzD1/Dvx4XfUmfQJGZeucG7xGny7taVsv87sfuiF3HQRn/9C2f5daLjkF05//StuVSsQMvYlon6ZS2qU9faYBnisYVWe/n0Lry3fSfca5dgXd4lpu04yslkNXBzsSUzL4PSl6wR6ulHS1Ym6ZUsRFlSarzceIs2QSSVvDzadOcfsvad4qUUtPJwc8HByoHuNcvyy/Rj2drZUK+PJnpiLTNl1gu41ylHJmgMYQOqW5bgPfA63Ho+Ttn8L9oGVcGrcjpS1i8CQAY7O2JX2w3j5AlkpiRiiIsg4fRTXtr2wcXAg80IcDsG1cGrQmpTVC3JHW6TuXIvrAwNxTojDEH0Kxxr1caoZRtKK3yDNekelADzUvy+vvf0uYz/5jAfat+XwkaPMW/gHjw0dgrOzE0nJyZyNjKKsvx9enqZeroMH9uPzr7+lhIcHTRqFsSV8O+s3buatV18BTA3jAX16M23WbLw8PalT+z5OnT7D9N9mE1q3DmENrHv++kd7duSZjybwxjeTebB1Y/YfP830Jat5dkA3nJ0cSUxO4XRMPIG+pSlZwrIXdX5KlvBgQKfWTF/yNwCN7qtG3IVL/LJgOf6lvenRpmlRFuk/GzBgAG++9RYfffwxHdq358iRIyxYsIBhjzyCs7MzScnJREZG4u/vn3udPDRwIF9+9RUeJUrQuFEjtoaHs2HjRl5/7bXc4/bt04f169fzzujR9OzZk5iYGKZMnUqnTp3w8bF8IWNNHhrQj9ffGs2HH39Kxw7tOHzkKPMXLuTRR4YWeE4GDRzAF19/QwkPD9M52baNDRs38cZrrwKmqSy/Gv8tri4uDOjfj8jISLO/Wbp0acpYwUjQggxv14gnf1zAqGnL6BFWg71n4pi6bhfPd25uapukpnHq3CUCS3ni7e5KvYoBNK5Sjk9+X0diWjpBpbzYcPgUczbvY0THxpRwvdGJ4kTcBQAq+d0bLxwBHm9SgxFz1/Pa4nC616rAvtiLTNtxjJEt78t9Dp+6eI0gL3dKujpRpYzlSAK37BF/NfxuvKgfGFqZZ+Zv5PO1e2kVXJbtkef5ddtRHgkLseoppACGd2rOk+NnMuqXhfRoWoe9p6KZ+vdWnu/eJk/71QtvDzfqBQfRuFpFPpm7ksTUNILKlGTDgRPMWb+LEV1bUuJfTrFkTR7t1ZlnP/iaN7/6iQfvb8b+4xHMWLyKZwb2vPHMiY4j0K/MHT9zAJ4e2IMRY77kxU++ZVDXDly6eo0JsxZSs3JFWjSw3gAxqH7Nz/D76/Pk5MWM+m0VPepXY29kPFM37uX5jo2y69d0Tp2/TKB3CbzdXaiST13plj3CuuZNAY9hLeqyYt9Jnp6ylCHN63D2whW+/Ws7vRtWx9/rzq+34qA2bP5CaoZSrVZ9fvzqHfoNHYm7hyd/zP4ZVzcP2nTqnbtfTNQpDBkZlK8UcsfHrn5ffeo2bMGsyV+RlpZKQLlgNq9dyomj+3jujc+LojiF4r777qN27dp8Nm4cjz72GCU8PJg5cyZubm507nJjrc/Is2fJyMggOLszbMuWLZkzZw6j33knd/3UX3/9lQoVKtCypWkdIV9fX9q1a8dPP/1EWno6AQEB/P777yQmJtKnb9+7X9g7MLhfb0aNfp8xn35Bp3ZtOHT0GHMW/snwoYNuuneiKevvm3vvDBnQl3HffEcJDw+aNmrA5m07WLdpC6NffTH3uI8PHcRLb7zLG+99SL+e3bh85So/T51B9ZAqNA27N0bQivyvs8m63SpTVi4kJIRXXnmF5cuXc/z4cSpXrsyrr75K06Y3XrgsX76cH3/8kZMnT+Ll5UWbNm145ZVXKFHC9ELz8uXLfPDBB6xduxZnZ2eefPJJfvrpJ15++WV69erFt99+y++//86aNWvM/vbWrVv5+uuvOXz4MO7u7rRr146XX34Zr+zhecnJyXz55ZcsX76ca9euUbVqVZ566inatzctELRw4ULeeOMNjh270XNryJAhBAQE8MknnwAQHR1N27ZtmTZtGo0aNSIiIoJPP/2Uffv2kZKSQvXq1Xn++efNyvtPpa6a8q/TFmT1vmP8sHQjZ85fwsfTg/4tQxnathEAO46f5fHxs3h/cBe6N64NmOYYnrhsE3/vO0bC1UTKlSnJ4PvD6NXU9CPm9637eG/msgL/3s3H+q+c2z/CUoc7byz9E94tw2iyejpb2w7h0obtZtv2PfY60dNu9KgpN7w/lV58FOcgf5JPRREx7idiZi4yO17JZvWp/umrlKhTnfQLl4mZuYjj740ny2A5PPK/6JJxjMTvXi3UY66JiOXH8GOcvZyIj7szfWtXZEioqUG2M/oCTy7czLvt6tGthmmNmMS0DH7afoy1EXFcSEol0NONh+oG07PWjd5GGZlGpu0+wZIjUcRdS8HXw4VetcozJLRyoS+66/7MOC5//HShHtOhah2cW3TFztsH4/WrpO1eT9r21QDYl6uCx6AXSVoyjfQD2T1MHJ1xad4Zh5C62Lp7YrycQOqONaTvM++95dTgfpwatMLWzZPMS+dI3fY3GYd3FmreAUq+8T2RJ47cfsd/YNOWcKbN+o3o6BhKlSpFty6d6NurB2CaJ/qVN0fzygsj6diubW6aJctXMm/hHyRcuIC/ny8D+vamfZsbcwJnZWWxZPlK/lyyjNj4eDw9S9CmVUsefmhA7vSDhaVclepc272qUI+5dsc+fpq3lLNx5ynj7Unf9i0Z3NVU/l2Hj/PU2PG889RgHmxl2ZNt8fpw3p84g0Xjx1C2zI0XCFlZWfy2fB0LV28i9vxFSnuVoFHtajzd/8F/9FLqTpQIbc+pfIa8/xebt2xhxowZREdHU7p0abp27UrvXqbFGvfv389rr7/OSy++mPv8BVi2bBkLFi4kISEBPz8/+vfrR9u2bc2Oe/DgQX6ZNIlTp05RokQJ2rZpw5AhQyymgvyvKgUHc+Zk4Y6I2rxlK9NnziI6OppSpUrxYNcu9Ollmk5z3/4DvPrGm7z8wvN0aN8uN83S5cuZv/B3EhIu4O/nR/9+fWjXxrQQ5t59+3jtzbcL/HuDHxrIkEEPFVr+K1SuSuqSHwrteACrD5zkh5XhnDl/GR9PN/o3q8PQ1qYXPjtORvH4Dwt4v397uofVBCApNZ2Jf4Xz94GTJFxNolwZLwa3rEevRuadRn5ds5Ovl25i+yfPmk0lVJicu44g6eeCz/+/seZEDBM3H+Ls5ev4uLvQr24wQxqa2j87I8/zxNz1uVND5Wf47HUA/Dygtdn2FUci+SX8CLFXk/Ar4Uq/usEMCC38XvVuwz8gdfW0Qj3m6r1H+WHJRs6cv2hqv7aqz9B2prp0x/GzPP71DN4f0pXuTUzt06TUNCYu3cjfe49mt1+9GdwmjF7N6uZ7/Jxj/PLCYBpWLfze0s5tH+bK3nWFesx12/fw87zFnI09RxlvL/p0aM2gB0116f+xd9fRUVx/G8CftbiHeAIJIQKE4MHd3d0ptLi0xaoU2lLsR4sV9+LF3Z3gLsECcQ9xWXv/2CSw7Ia0kJDJ2+dzTs/p3p073JnMzr0z3ys3HgRh1Iz/4fuRg9C+se6zyIEzlzDzz/XYvegXOL8zmu9u0HP8uXUPHjwLhpGhARrVqIJxA7rDXM8c5x/Dqkpj3l/f4V7OG5l/Lyi0/QHAyQfB+PPkNbyMfQ17C1P0qu2HQQ00v5NrL8IxbNX+vKmh9PlspeYZZ/XwTlrpN4Mj8b8jlxEUGQ8rEyO0r+qNUc1rQCaRFGr5jbpNZBv2HaW9yuPyo+SCN/yX0lKTsWXNAty8chZqtQpevpXR57OJcHJxz9tm1rdfIC4mEvNX7tO7j6+Gd4SvXzUMHz9dKz07Owt7t63E5TOHkZz8Gi5uHujYcxiq125cKGWvU94Cz1+8KJR9vS0lJQUrV65E4OXLUKlUqFChAj7/4ou8aU8BYMrkyYiOjsa69evz0mJjY7F82TLcunULEokE1apVw+dffAEbmzcdC+TZ2Vi7di3OnDmDjIwMlPPywmeffQZfX/2/xX/Ls2xZhD+5Vyj7ynX+8hWs37wNoWERKGVrg07tWqNnl44AgNv37uPLb6Zj8vjRaN38zW9j/+Fj2L57H2Li4uHs6IA+3bugZVPtNfzuP3qMNRu34FHQUxgaGqJ+7QCMGDoQZmaFtxaIi3elQtvXf8m99sJZv6ekqHTgdHEX4ZMr8QEM+nhFEcAoyYoygFFSFUUAo6QrigBGSVcUAYySrigCGCVdUQQwSrqiCGCUdEURwCjJiiKAUdIVRQCjpCuKAEZJVxQBjJKuKAIYJV1RBDBKuqIKYJRkRRXAKMmKIoBRkjGA8WEYwPj3/osBjP8Xa2AQEREREREREREREdH/LwxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCY60uAtARERERERERERERP8tYomouItAJQBHYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgSIu7AERERERERERERET03yISi4q7CFQCcAQGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCY60uAtARERERERERERERP8tIjH71lPBeJUQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4EiLuwBERERERERERERE9N8iEouKuwhUAnAEBhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQmOtLgLQERERERERERERET/LSKxqLiLQCUAR2AQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4EiLuwBERERERERERERE9N8iEouKuwhUAnAEBhERERERERERERERCQ4DGEREREREREREREREJDgitVqtLu5CEBEREREREREREdF/x5M+rYu7CCWO95YjxV2ET45rYBBSl0wu7iIIitnoOTwn7zAbPQcHZT7FXQxBaScPwovB7Yu7GIJSdt0BRD+6UdzFEBSH8tWRtvzb4i6GoJh+8Qsigu4WdzEExdnHH9eDEou7GIJSw8caUZP6F3cxBMNx7iZETOxT3MUQFOcFW5BxZktxF0NQjBv3QeqyacVdDEExGzELYU/uF3cxBMXV2w9J88YXdzEExfLrP9iGfYdD+eqYt0tV3MUQlK+7ihH8/FlxF0NQPDzLIe7+5eIuhmCU8qtT3EUg+n+LU0gREREREREREREREZHgcAQGEREREREREREREX1SIjH71lPBeJUQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4EiLuwBERERERERERERE9N8iloiKuwhUAnAEBhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQmOtLgLQERERERERERERET/LSKxqLiLQCUAAxhERERERERERERERP+PqVQqLF68GDt27EBKSgpq1qyJH374AW5ubjrbLlq0CIsXL9a7n65du2LWrFkAgCFDhuDSpUta3wcEBGDjxo2FVm4GMIiIiIiIiIiIiIiI/h9bunQpNm/ejN9++w2Ojo6YO3cuhg0bhv3798PAwEBr26FDh6J3795aaWvXrsWWLVswePDgvLSgoCBMnz4dzZs3z0uTyWSFWm4GMIiIiIiIiIiIiIiI/p/Kzs7GmjVr8PXXX6Nx48YAgAULFqBBgwY4duwY2rdvr7W9qakpTE1N8z4/fPgQGzZswMyZM+Hj4wMAiI+PR3x8PCpXrgw7O7siKzsX8SYiIiIiIiIiIiIi+n/q8ePHSEtLQ506dfLSLCwsUKFCBVy7dq3A/DNmzECNGjXQpUuXvLSgoCCIRCJ4eHgUSZlzcQQGEREREREREREREZHANWvW7L3fnzx5Um96VFQUAMDJyUkr3d7ePu+7/Jw+fRq3bt3Cnj17tNKfPHkCc3NzzJgxAxcvXoSJiQlat26NUaNG6UxJ9TEYwCAiIiIiIiIiIiKiT0ok5uRAn0pGRgYA6AQWDA0NkZSU9N68a9euRZMmTVC+fHmt9CdPniArKwv+/v4YMmQIHj16hDlz5iAiIgJz5swptLIzgEFEREREREREREREJHD5jbAoiJGREQDNWhi5/w8AWVlZMDY2zjdfREQErly5ghUrVuh8N2PGDEyZMgWWlpYAAG9vb8hkMkycOBGTJ09GqVKlPqis72KYi4iIiIiIiIiIiIjo/6ncqaNiYmK00mNiYuDg4JBvvhMnTsDGxgb16tXT+U4qleYFL3J5eXkBQIHTUv0bDGAQEREREREREREREf0/5evrCzMzM1y5ciUvLTk5GQ8fPkTNmjXzzXf9+nUEBARAKtWdyGnAgAGYNm2aVtq9e/cgk8ng7u5eaGXnFFJERERERERERERERP9PGRgYoH///pg3bx5sbGzg4uKCuXPnwtHRES1btoRSqURCQgLMzc21pph6+PAhunXrpnefrVq1wq+//gp/f3/Ur18f9+7dw5w5c/DZZ5/BzMys0MrOAAYRERERERERERER0f9j48aNg0KhwHfffYfMzEzUrFkTq1evhkwmQ1hYGJo1a4ZZs2aha9eueXliY2NhZWWld3/9+/eHSCTCxo0b8euvv8LOzg6DBw/G559/XqjlZgCDiIiIiIiIiIiIiD4pkVhU3EX4T5FIJJg0aRImTZqk852rqyuCgoJ00u/cufPeffbr1w/9+vUrtDLqwzUwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHCkxV0AIiIiIiIiIiIiIvpvEYlFxV0EKgE4AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhKcEjeFlI+PD2bNmoWuXbt+8D4iIiJw69YttGvXDgDQtGlTdOnSBWPHji2sYv7nXX4Vg6WXH+F5QgpsTQzRw98DA6p6QiTSPzQs9HUqOm84qZPuaWOO7f2bAgBUajU23XqOXfdeIiY1A6WtzTCwWjm09XUr0mMpDDwf+TNycUDD2wdwvdtoJJy7+t5tnXu1Q7lpI2FS1g0ZL8PxbO4KhG/co7WNZXU/lJ89GZbV/aBITkPYhl14MmMx1HJ5ER5F4TCuWBXW3QbAwKU0lMmvkXzyIJKO7Na7rVn9ZrAfNjHffcWs/B9SL54CAMicXGHTcwiMfStBrVQiM+g+4reugiI2ukiOozBdvXUXq/7ajuCQMFhbWaJL2xbo3aldvr8dADhx7hI27NiNiOgYONrboV/XjmjTtKHWNodPnsXWvQcRHhkNWxsrtGnaCP27d4JUIinqQ/pol19GYcnFB3gRnwwbE0P0rOKJAdW98z0nIYmp6Lz2iE66p60Fdgxq+cH7FZJrt+5g9cYteBkSCmsrS3Ru1xo9O3d4b9lPnr2Ajdv/RmR0NBzt7dGnW2e0btZYa5uQsHAsX7cRt+89hEQiRmW/Chg5dBCcHR2K+IgKR2ZGOrasX4Jrl04jMzMDvhWroP9nE+DsWuYf7+P336bByMgYIyb8oJUeHxeDLWsX4f6da5DLs+FXuSb6DB4NR+fShX0YhcbA2w/mrXtC6uACZWoS0i+dQPrZQ+/NY+hbBaYtukDm5AZVWioy711D6uHtUMuzILEuBbtvfs83b/q1c0jevqKQj6LwGPpUgnnbXpA6ukKVkoS0C8eQdubg+/NUqArzll0hcyoNVXoKMu5eRcrBbVBnZ+VtY1yzIcyatIe0lAOUyYlIv3oOqSf2ACpVER/Rx7v08BkW7zmFFxExsLEwQ6/GNTGwRd187yXZcgU2HL+EA4F3EJWYDAdrC7QNqIShretDJn3zmHXq9iOsPHgOL6PjUMrCDO1qVcZnbbS3EarLr6Kx9OIDPI/PacNWLosB1b3e34Zde0wn3dPWAtsHNtdJj05JR8+NJzG/Q23UcLMr9PIXhes3b2PNxs05dY4VOrVrjR5dOr63zjl19jw2bduJyJy2Se/uXdCqWROtbXoNHo64+ASdvLs2rYWlpUWhH0dhkpbxgWGD9pDYOkKdnoKsW+eRff10vtuLrUrBfNj3OunKuEikrvsNErdyMOuV/7N55sXDyLqs27YRErZh9ZNnpeHqkfkIvn8c8ux0OLlXR+3202Bl5/HefKe3Tcaz2/t10pv1/R1lK7XSSQ88OBtx4Q/Q/vMNhVb2onDj5k2sW78BISEhsLKyQof27dCta9f3Xienz5zBlq3bEBUVBQcHe/Ts0QMtmuveXwEgPT0dI0ePQb++fdCyRYuiOoxCdeX2fazY/DeCQ8NhY2WBrq2boU/H1v/omeTJi1cYNnUGti3+DU722nXKqUtX8deew3gVHglzUxPU8K+Akf17wMbKsqgOhYgKkfBbzUVgypQpcHFxyQtgUOG6F5mACfsD0dLLBSNr++J2ZAIWXngApUqFITW89eYJik0GAPzZpS6MpW8aX0ayN/+/LPAx1t94ihG1fVHRwRoXXkbj+2M3IRaJ0NrHtWgP6iPwfOTPyNURAQdXQ2ZV8EOZY5eWqLJhHoIXbUDs0fNw7NQcVdbMhiorG5HbNS+gjD1cUevIWiQG3sbNPhNg5usJn5kTIbOxwv3RPxb14XwUQ08fOE78AalXzyNx9yYYeVWATc8hgESCpIM7dbZPv3MN4TO/0km3GzIOYmNjpN+9DgCQ2JSC87dzII8MR/SyORAbGMK66wA4fT0TYd+NgVqeXeTH9qEeBD3F1F/momm9Ovisbw/cfRSEZeu3QKlUoX+3jnrznLl0FTMXLEH39q1Rq5o/zl+5jlkLl8FAJkWzBnUBADv2H8ai1RvRuG4ARg7qi9fJyVizeSeeBb/Cz1PzDwoJwd2IeIzfcxEtfdwwsm5F3I6Iwx/n7kGpUmNIgK/ePE9iXwMAlnVvCKN87icfsl+hePj4Cb6ZOQtN6tfF0H69cO/RYyxftwlKpRJ9u3fRm+fspUD88r+F6NahLWpWq4KLgVcx+48lMJDJ0LRhPQBATGwcxk75Dm4uzvju6/HIysrGmr+2YNIPM7Fm0XwYGhp+ysP8IEvm/4BnQQ/Qe/BomBibYtfW1fjlu9GYs3gzTM3ef99VqVT4a/UfuHbpNBo0bav1XWZmBmZ9PxYiETBk5GTIZDLs3rYWM6eNxOzFm2FmLrwHQVlpT1gP+RqZdwKRcnQnDNy9Yd62N0RiCdJO674QAQDD8lVhNXgiMm5cQOqhbZA6uMCsTU+IzcyRtHkplMmvEb9It24xqdsCRpVrI+PqmSI+qg8nK1MONsMmI+P2ZaQc3gEDDx9YdOgLkUSC1JP79OYxrFgNNkO/Qsa1c0g+sBVSRxdYtOsFsakFXm9aDAAwbdgall0GIeN2IJL3/QWxmQXM2/SAzLk0Etf9/gmP8N+7+yIU4xZvRqsafhjdsQluPQvB77uOQ6lSYWjrBnrzzNl+GAcC7+Lzdg1RsYwLHr6KwPIDZxCZkITpAzsBAC4/fI6vlm1Dqxp+GNelOZ5HxGDRnpN4nZqOqX3a6t2vUNyLTMCEPZfQ0tsVI+tWwO3weCw8fz+nbvDRmycoJgkA8Ge3+jCWvXnUfLv+yRWVko4xuy4iNUv4nUxyPXz8BN/OnIXG9eticP8+uP/wEVas2wilUok+PfR3sDt38TJ+nf8HunZoh5rVNXXOnN8XQyaToWnD+gCApKRkxMUn4IshA+FXobxWfjMz0yI/ro8hcSoDk66fQ/74FrIuHITEpSyMGnWESCxB1tUTevOI7V0AAKnbFgOKN23R3A5HyuhQpP71P518RvXbQeJYGvLHN4rgSAoP27D5O7VtEmJC7qBWm68hMzTFzZNLcXDlIHSfuB+Gxvm3H+IjH8GzcjtUrNtfK92ylLvOtnfPr8W9C+vg5FGzsItfqB49fowfp/+Ehg0aYNCA/rj/4CFWr1kLpVKJXj176s1z4cJFzJk7D507dUT16tVx+XIg5v9vAWQyGRo3aqS1bUpKCn6aMRPR0cLvtJbr/pNnmDxrAZrVDcDwPl1w99FTLN24HUqlEgO6tn9v3hchYfj61wVQKpU63524EIgfFyxDp5aN8Xnfbkh4nYSVW3Zh7I+zsWbudBgaGBTVIRFRIflPBjCoaC278hg+dpaY2ao6AKCuuwMUKhXWXnuKPlU89T7APIlLgoOZEQLy6XmVIVdg8+3n6FPFM++lf4CbHR7HvMbWOy8E/cKe50MPkQiuAzqj/OwpwD/s3O0z80tE7jyCR1/PAgDEHb8AmbUlfKaPzwtgeE4aDkVKGq53HQW1XI7YI+egzMiE3x/f49lvy5AZGllUR/TRrDv3Q9arF4hdoXlYy7h3EyKJFNbteyL52D6dQIMqJRlZKclaaRbNO0Dm7IqInydBlfOddee+UKWnI3Lud3k9ZOWx0XAc/z0MPbyQ+eTBJzi6D7Nmy054ebjju4mjAAC1qlWGQqHEpp170aN9axga6jY0V27ahsZ1a2HsZwMAAAFVKyM5JQ2rNu9AswZ1oVSqsH77btSoXAkzJk/Iy+dd1gODx0/Btdv3ULNKpU9yfB9i2eWH8LW3ws9tAgAA9TwcoVCqsObqY/Sp6qUVlMgVFPsaDmbGCChtX6j7FYq1W7ajXFkPfPPlOABAQPWqUCiU+GvHbnTr0FZvoGH1xs1oVK82Rg8brMlTrQqSU1Ox5q+teQGMdVu2w9TEBPNm/gCjnH04Odjj219mI+jZC/hXLK+zXyF5+vgebl69gEk//g9VqmtefPhUrIIJw7vi+KG/0bnnkHzzhgQ/xfoV/8OLpw9hYKB7/q5ePIXI8FeYvXgzXEuXBQC4lfHEhOFdceXiKTRrrT9wVJzMWnaDPOIlkrYuAwBkB90FJBKYNu2ItPNHAIXuC1Tzjv2Refdq3iiK7OcPAbEYJvVbAjIDQJ4NechzrTxSF3cYVa6NlCPbIX/5pOgP7AOZt+4OefhLvP5rKQAg6/EdQCKBWfNOSD13GNAzatGy0wBk3rmC11uXAwCynz2ASCyGaYPWEMkMoFbIYd6yKzKD7iJx/R95+eRhwbCfMhfp3pWQ9eTepznAD/Dn/jPwdXPCL0M1L6Hr+XlBoVRh9eHz6Nu0NowMZFrbv05Nx9/nb2B8lxYY3Epz36hVXvN7+GP3CYzr0hw25qbYe+kWHG0s8cvQrpCIxahTwRMJKWnYeOIyvurZCjIB95pedvkhfOytMLON5iVgXXdHKFRqrL0WhD7Vyulvw/6DOkelVuPAwxD8fk6410N+1m/einJlPTDtq/EAcuocpRKbd+xC147t3lPn1MGo4Zr7bs1qVZGSkop1m7bkBTCeBQcDAOrXqQVnJ8dPdDSFw7BuGyhjwpBxeBMAQPHyMUQSCQxrtUDWzbN6768SOxeokhOhDH2qf6fZWVBGvtJKknr6QVrGB2n71kCVGFvox1GY2IbVL/rVLYQ8Oo3Wg5fDzUczssTRvQa2zm2Bh4FbULXJCL35FPIsvI59Cb96g+BQukq++09OCMOVQ7Px6tFpGBiZF8UhFKqNm/6CZ9mymDzpawBAjRo1oFQqsG3bdnTu1Env/WTt+vVoUL8+vvj8c02e6tWRkpKCDRs3aQUwLgcG4s9ly5GRkfFpDqaQrN66B94eZfDD+C8AALWr+kOhVGLDrgPo2a6l3t+OXK7AzsPHsWrrbhjIZDrfA8CGvw+gTjV/TP5icF5aaWdHfD5tJi7duIMmdYQd7CKiEroGxosXL9C7d2/4+fmhTZs2OHz4cN53KpUKy5cvR6tWreDn54dq1aph2LBhCAkJAQAMGDAAV69exe7du9G0adO8fLGxsRgzZgyqVKmCWrVqYdasWXmR2127dqFFixb4+eefUb16dYwapWmIPH/+HCNGjECtWrVQvXp1jBs3DuHh4Xn7VCqVWLduHVq1aoVKlSqhVatW2LJlS973V65cQYUKFXD8+HG0atUK/v7+GDhwICIjI/Hzzz+jRo0aqFOnDv7888+8PPHx8Rg3bhxq1aoFf39/9O7dG1evvn/anU8pW6HEjbB4NPF00kpvVs4ZaXIFbkfE680XFJsE71L597gwkEiwtkcD9K/qqZUuFYuRrRDulAQ8H/pZ+PvAb8lPCNu0B7cHTy5we+MyLjDz8UDU3uNa6ZG7jsLUyx0m5TTTodi1qI+Yw2e1pouK+vsIRBIJ7FrWL9yDKExSKYx9KyHt5mWt5LTrFyE2NoGRd4UCdyGxsIJNtwFIPnUYWS/evDQzrVEXKeePa03vkf3yGUImDhJ08CJbLsft+4/QoLZ2Y7Jx3QCkZ2Tg7qMgnTyR0bEIjYhEw9o1dPKER0YjNCISiUlJSE5JRd2aVbW2KVvGDZYW5rh8/VbhH0wh0dxPYtGknItWenNvV6RlK3A7Ik5vvqCY1/C2tyr0/QpBtlyOO/ceoEHtAK30RvVqIz0jA/cePtbJExUdg9DwSDSoXUs7T906CI+MQlhEJNRqNc5dvoI2zZvmBS8AwMfLEzvXrRB88AIA7t4MhKGRMfyrvDlOC0trlPeritvXL70375+/z4BKpcRPc1fBwspa5/satRvhx9kr8oIXACCVah4Y5dkCHNUlkcLAszyy7mv32M28ew1iI2MYeOj2JJc6l4G0lAPSL2pPhZN+4SjifvsKyGf0mkWXwVDEhCP93GG93wuCRArDchWQee+aVnLmnSsQG5nA0EN31JXUxR1SO0eknT+qlZ527ghifpkAtTwbYnMriE3NkfXgptY2iqgwKFOTYVhB+74rJNlyBa4/eYmmVbWPvXn1CkjLzMatZyE6edIys9C9YQ00rqx9/bg7lgIAhMcl5u3b2MAAEvGbxy5LU2PIFUqkZ2ZBqDR1QxyaeDprpTfzctbUDeH51DmxSfC2e/8orKexSZh18hbaVyiNGa1rvHdbIcmtc+q/U+c0rFvnvXVOWHgE6tXRrnMa1sutcyIAAM9fvISJsTGcSsgUhXkkEkjdvKB4qh2Mkj+5A5GhEaQuZfVns3eFMjZc73d6SWUwbtYN8ucPoHhy52NKXOTYhs1f2NOLkBqYwMWrXl6asZkNnDxqIDToXL75EqOfQq1SwNb5/e2vwIO/ISnuFdoNWwtbJ2GPIM6Wy3Hv7l3UrVtHK71+/fpIz8jAgwcPdfJERUcjPDxcT556iIiIyHsXlZqaipk//wL/SpXwy8yZRXcQhSxbLsetB4/RMKCaVnqTOjWQnpGJO4/1dwy5fPMO1mzfi4Fd22PkAN2RKyqVCjUrV0SnFo210su4aN7RhEfFFM4B0AcTicX871/+919UIo96/fr16Ny5M/bv349WrVph4sSJuH//PgBgw4YNWL16NaZOnYqjR49iyZIlePnyJX777TcAwKJFi1C1alW0adMGO3e+mZZl586dqFmzJvbv349JkyZh3bp12L37zdzzISEhiImJwZ49ezBx4kSEh4ejV69eMDAwwPr167FmzRrExsaif//+SE1NBQD89ttvWLp0KcaMGYP9+/ejX79++OWXX7Bu3bq8/SqVSvz555+YN28e1q9fj8ePH6NTp06QyWTYsWMHevfujd9//x1BQZqGzvTp05GVlYVNmzZh//798PDwwKhRo5Cenl7Up/0fCU9Oh1ylQhkrM610N0vN0OdXial68z2JTUK6XIEh28+hzpL9aLnqCBZefAC5UvMyXiIWwauUJUqZGkGtViM+PRNrrz/B1dBYdPd3L9Jj+hg8H/plhETijG8LPJr0G5TpmQVub+arCdSkPX2plZ7+XNMzy8zbA2IjQ5i4uyLtabDWNtlxiZAnpcDM+/3zqhYnmZ0jRDIZ5FHaD3LyaM1Drcyx4BE11l36Qa1WIXHXxrw0aSkHSEzMoIiPge2AESizeAvcV+6Cw7jvILG2LdyDKGQRUTGQKxRwc9buhejqpHmwDwmP0MnzKkxz/lydtQOGLjk9GUPDI2FmagKJRIKoGO0XLympqUhNTUNEtHAbsGFJaZArVShtrd2jzC3n/vIyIUVvviexSUjPlmPwllOo/ccutFi2HwvP38u7n3zofoUgMioacoUCrs7aL9je/M3fc5245HedRCAqOgZpaelwsC+F35etQse+g9GyW198+/NsxMbpDzwLTXjYS9g7OEP8Tu9uBydXRIbrvox928iJP+LH2StQ2sNL7/cmpmbwLu8PAFDI5QgJfoplv8+AuYUVatVvVjgHUIgktvYQSWVQxGqPwlPGRwEApHZOOnlkzprAuFohh9WQr+Dw6xrY/7QM5h37AxL9A5iNKteGQZlySNm7CVCrC/koCo8093zEaJ8PRZxmigmJvZ7z4fLmfNgMmwSn2evh+PNKWHQemHc+1BlpUCsVkFhrjx4VGZtCbGwKqW3+PfKLW1hcIuQKJcrYa9eNpe1sAACvonVf1ruUssa3fdvnBSxynb79GFKJOG9fvRoHICQmHuuPXURyegbuvgjFXycDUd/PC5amJkV0RB8vPKduKGP9Ths2p27Ivw37WtOG3XoGdRbuQcvlB7Hw/P28OgcAHC1MsGdIS3zZyF/vKA6hyqtzXN6pc3LaKmHhui/kX4WGAYBOPZU7yiI0TFNPPQt+CXNzM/w0ay469hqAdj36Yebs+YhPSCz04yhMYstSEEmlUCZqt5+UOSMkxDb6f/cSexeIDAxh2mcCLCbMg/nImTBs0AHI56WMYbVGEJlZIvP0rsI9gCLANmz+Xsc8h4WNK8Ri7d+9hW0ZvI4NzieXZvooAAi6tgObfm2A1d/5Y9/y/ogJ0Q5m1WgxDt3G7xX81FEAEBUZCblCARcX7Q5Ezk6aayAsPEwnT2hIKADo5sm5v4TlXEeGhoZYvuxPfP3Vl7AQ+Po5b4uIjtX723FxzP3tROnNV75cWfz95zwM6t5Rq7NALrFYjLGD+6DBO4GRc1c1HS483Fx08hCR8JTIKaT69u2L3r17AwAmTJiAwMBArFu3DvPmzUPp0qUxe/ZsNGmiWRTNxcUFrVu3xpEjmkW+rKysIJPJYGRkBBsbm7x9tmzZEoMGDQIAuLm5YcOGDbh//z66d++et82oUaPg5qZZIHnu3LkwMTHBvHnzYJAzX97ChQvRrFkz7N27F506dcKWLVswdepUdOjQAQDg7u6OsLAwrFixIu/fAoDx48ejUiXNcM/atWvjzp07mDx5MkQiEb744gssXboUT58+hY+PD0JCQuDt7Q03NzcYGRnh22+/RYcOHSARyPDz3DlsTQ20Ly2TnM9p2QqdPIkZWYhJy4RSrca4ehXgZG6Cq2GxWH/9GaJTM/FLztRLuY4+Cce3RzW9KOu7Owh60WqeD/3kiUmQJyb94+2llpqHZUWy9sOyIiVN872FGWSW5nq3yd1OamGmky4UYhNNQEudoR2IVGVqhvyKjY3fn9/cEmb1miLpyG6o0tPy0iU589Db9BiMrOAniPlzDiQWlrDpPgjOU2Yh7IexWiMzhCQtJyhraqJ97MY55yI9XXc4dH55TIyNcr7PgJGhIZrWr43dh47Bo7QrGtaugcSkZCxctQESiQSZAu4Nm3s/Mcv3fqI7RUNiRhZiUjOgVKkwvqG/5n4SEoN114MQnZKOX9rW+qD9CkVaWn5/c83nNH3XSW4e4/yuk3S8TtZMwbZi/V/w9SqH7ydNxOvXSVi5cTMmfjsdK/+YC2Mjo8I9mEKWkZYGYxPdedONjE2QkZGmJ8cbpd3L/eN/Z97PX+PerSsQicUYPvYbWNuUKjjTJyY20vyt1Vna14M6SxNAFxnp3mPFZpo6xXrQBGTcuoT0c4cgcysLs5bdIDazQNLmpTp5TBu3Q3ZwELJfPCrsQyhUImPNS3OVzvnIqXP0nA9JzpopNkO+RPrNi0g9cxAGpT1h3qo7xGaaNTDU8mxk3AqEaYOWkEeFIfPeNYjNLGDZZRCgUkKkZzoyoUjN0FwLpsbaZTQxMsj5/p/VDaduPcL+wNvo3TgAFqaa8xjg64HBLethwd/HseBvzUhSXzdHzBrWrbCKXyRSc9qopu9MnVVwnZMJpUqNcQ384GRhgqshsVh//QmiUzPwS85UVJZGBrA0KnnzjefWHyb/ps7Jt22i3Z55/iIYcfEJaNeqBbp2ao+Q0DCs+2sbJk77Hsv/mCfYOkdkqClX7v00T077UmSgW26RsSnE5laAWIzMs/ugSk6AtIw3DGs2h9jcChmHNmpnEEtgUK0h5I9vQfVauKNCc7ENm7/szFTIDHWfyWSGJpBn5d82iY/QjG6SZ2egae/5yEp/jdtnVuLAqsHoNHIrbJ00I+FsHPWvLylEuX9zExPtQHbuZ30dVNNynvNM382Td21p8shkMri5CnxKaT1S0wtoo+czHZadre5o4YKERcVgyYZt8PIojTrV/P91fiL69EpkAKN6de0XuJUrV0ZgYCAAoGnTprhz5w7++OMPBAcHIzg4GM+ePYODw/uH47q7u2t9trS0RFZWVr7bPHnyBH5+fnnBCwCws7ODh4cHnjx5ghcvXkAul+uUNSAgAOvXr0d8/JsenGXKlMn7fxMTE7i6ukIk0iwMYJTTWM3OmZJhzJgxmDRpEo4ePYrq1aujfv36aN++vWAWEy1o8iKRnvUOjGVSLOlcB6WtzOBsoamMq7uWgoFEjKWXH2NYTW942LzpGVzRwRoru9XD07hk/Bn4GGP3XMaKbvXyzpmQ8HwUjoKGyKlVqnx7bL3ZRri9YfVeCG9RF9CT16JRS4jEYiQdf2fhVanmFq9Mfo3oRb/m9QiWx0TC5fv5MKvTGClnj767O0FQFXDM+q4JVQF/Y7FYc56/GvEZZFIZ5ixZidmLV8DQwAD9unVEekYmjPTMqyoUahRwfHquI2OpFEu7NdDcT3JGflV3s4NMKsbSiw/wWa3yH7RfoSjoOsn9m2vnef+dWSwSQ6HQvLiztrTEjGlfQ5xzvbk4OWL05G9x4sx5dGjd4gNLXfhUKhXU7xzX+45TLCq8Abhdeg1B+679cenMUaz442eolSo0bql/gdJiU9Dx6ruOckYVZN6/jtRD2wAA2c8fASIxzNv2QuqxXVDGvekJKCvjBZmrBxLX6S46KzgF/abfcz4y7l1DygHNdKjZzx4CIhEs2vdBytGdUMZGIWnHKs2olV7DIerzBVTZWUg9uQ9iIyOo5cJ9ufYh95J3nbz5ENNW/42qnqUxodub+8Mvmw9gz8VbGN62IQJ8PRAR/xrLDpzB6D82YfmXA2Es0IVDC6yH9SxiZiyTYknXetp1jqudpg176SGGBfjAw7bk9AZ+17v32Xfpqy8Lan+Kcq6tL8eOhEQsga+3JoDsX7EC3EuXxvgp3+L4qTPo2Lb1B5a6iH3A/UQtz0bajqVQJsZCnZwAAFCGPQcUChg1aI+swGNQJbxZdFjmXQViM0tkXTtZqEUvKmzDaqj1tE3e9xt637Nrxbr9Ubp8E7h5v5kS2NmzNrbPb43bp5ehWd8FH1/gT0ytev/9RN/5+Kf3k5KqoOMrrGeSV2ERmDhzHiRiMX7+ekxeO5+IhK1EBjDevcEolcq8QMKKFSuwZMkSdOnSBXXq1MHgwYNx8uRJHDx48L371DeC4d2XhkZv9XzJ74WiSqWCTCZ77/cAIJW+OfVv/z+ge3xva9GiBc6fP4/z58/j0qVLWLt2LRYvXozt27fDy0v/VA+fUm4P3jS59siC3JEGZga6iyoZSSWorWehv/rujlh6+TGexCVpvbB3szKFm5UpqrmUgqmBFD8ev4VbEfGo5iK8Xp88H4VDkaSZwkZqrt2bOHdUhSIpNW/kxbvb5G6Xuw8hUuX0NhEZafemEef2kk1/f29p0xr1kH7/Vt7C3bnUOSM40u/e0HqAzHoeBGV6KgzLeEKoZ8Ust/dRhnaPvtxea2Ymuj2EzUz158ntEZnbW8nE2AhTx36OccMGIjo2Dg52pWBibIQDx0/DxUm4c0/n3i/eHbmVlpVzPzHUcz+RSVC7jO4xNfBwwtKLD/A0NgnlSln86/0KhWne31y7R9abnoy607OY5nNt5fbqMjU1yeslGVC9qladXMHXG6amJnj2Iv9pDorD7q2rsWvraq20gLpNkfw6QWfbjHT9IzM+lE+FKgAAv8o1ERsTiT071gkugKHOzLnHGmrfN3I/qzJ1eznm9ibOeqQ9p3hW0B2Yt+0FmUsZrQCGkX8AVOmpyHok7LnZgTd1g9hQu2d07kgUVYa+86HJk/VQe32LzEd3YNG+D2Qu7lDGRkGdnYWkbSuQvHs9JDZ2UCbEQp2dBZPaTfKmqBIis5zenemZ2mubpOX0aM79Pj8bT1zGgp3HUMPbHQtG9YZhziKi0YnJ+Pv8DXzWugFGd3qz9p6fuwu6/bQUey/eQu8mtfLbbbHKq3PeWdA9rw2rr86R6q9z6ns4Yumlh5o2bAkOYOTWHxk67YycOkfPlGD511PabZOKvrpr8fhV8IWpqQmeB7/S+U4o8kayvTPS4s3IDD09phVyKF7prgMhf/EQRg3aQ2Ln/E4AozKUcZFQxepOvSREbMNq3Dy1FDdPLtFK8/BrhYxU3ak45Zlp711028rOA1Z22tMBGxpbwKFMNcRH6V5LJYGJqaYt9u4i2+l59xPdtppJge3ewmvfFYe8Nnpmfr+dj5928eb9R/hm7mKYGBli0U9T4Ooo3OktiUhbiQxgPHjwAM2bN8/7fPPmTfj6ahZpWrZsGUaPHo3PP/887/vVq1cX2IP53/Lx8cG+ffuQnZ2dFzyJi4vDq1ev0LdvX3h6ekImk+HGjRsoX/7NYlPXr1+HnZ0dLC3fv7idPtnZ2Zg/fz46deqEtm3bom3btsjMzES9evVw5swZQQQwXC1NIRGJEPZa+4VraM7nt1+85wp5nYproXFo6e0C87cehrIUmkXUrY0NkZiehYuvolG3jANsTN6MNvHNWZg2Nq3gdRSKA89H4Uh9onlZaOJZBsm330zNYeqpGb2U+vg5lGnpyAiLgolnGa28BnY2kFmYIfXx809X4H9JERsJtVIJmYP2vLcye818pvJI3TlQc0msbGHoXg5JK3R7/cpjIqFWqSCS6b5kEIklUAlxsd0czo72kIjFCI/Unus0LOdzGVfduUrdchdii4yCd1n3vPTwyGitPJeu3YS5mSkqlfeBR2nN8OrE10mIjU/Qyic0rlZmkIhECH2tPU1a7me995PEFFwNjUUrb1eYvzVdR2bO/cTKxOCD9isULo4OEOu5TnI/l3HTHT5fOmfu8vDIKHh5eujmcXWBpYU5RCIR5HLdKVKUSiUMBNbLsWmrzqhas75W2vUrZ3H3ViBUKpVWECY6Mgwubu4f9e89f/oQsdERqF2/uVa6u6cPnj6+l0+u4qOIj4FaqYTEVvvljrSU5rMyWvelmCInOCGSvHP/FOes9/DOtWFYvgoy798AVMrCKnaRUcRFa85HKe05pqU5nxXRuvP4K2JzfmNS7fMhyukAlHs+DCtUhTojDdnBT6CI0tRdYjMLSCxtIA8TVuDvbW521pCIRQiJ0Q765X72cNTfKUStVmPOtsPYcvoqWtf0w8zBnSF7q2NSVEIS1GqgSrnSWvk8ne1hZWqM5xGxhXwkhcfVKr827PvqnFRcC41By3fqnLfbsCWZs5NjTp2jvX5MRE79UVpPneOWU+dEREbBy7PsW3k0+yjj5orUtDScvxQIX28veJR5c62oVCoo5ApYCXgOe9XrOKhVSoittX8jYivNZ2WCbuBSbGUHaWkvZAfdAt4KcIhy7i+qt6c5FIsh9fBF1tWSMfoCYBs2l29AT5T2bayV9vLhCYQ9vaB5PnmrbZIc/wpWdvoXfAeA53cPwdDIEq7e9bTSFfJMGJna5JNL2JydnCAWixER8c79JELTJsmduvxtudNCRUREoJynZ156ZM4+3EoLfyrp93FxtINELEZYpPZ9Iyxnke0yrrprdP0bx88H4ufFK1HG2Qnzv/vqg6aeIqLiUyLHSuUusP3ixQv8+uuvePLkCYYPHw4AcHJywsWLF/Hs2TO8ePECCxYswLFjx/KmYAI00ezw8HBERelfBOif6NOnD9LS0jBp0iQ8fvwYd+/exfjx42FtbY127drBzMwMvXr1wsKFC3HgwAG8evUKf/31FzZv3oyhQ4d+0PQ+BgYGuHfvHr7//nvcvn0bYWFh2LVrF9LT01G1atUPPpbCZCiVoKqLLU49j9QKGp16HgEzAykqOljp5IlLy8Svp+/g+FPtB+ZjT8NhaiBFeXsrZCqV+PH4Lex5oN0DKfCVpjLzEmhvLp6PwpH+PATpL0Lh1LWVVrpjl5ZIfRKMjFeacxV34iLs2zaG+K2RLY5dW0GlUCDudOAnLfO/oZbLkRl0H6bV62qlm9aoC2V6KrJePMk3r6GnZq7XzKcPdfeblYnMJw9gWr1O3nRSAGBUvjLERsbIfPKgkI6g8BkaGMC/oi/OBV7T+u2cvXwNZiYmKO/tqZPH1ckRTg52OHPpqlb62ctX4eqs+Q4A9h49iaXr/tLaZsf+wxCLxahbQ3txNyExlEpQ1bUUTj0L1zonJ5+GwcxQhoqOug9wsWmZ+PXETRx/oh0EOxYUCjMDKSrYW3/QfoXCwMAAlSuWx/nLV7TKfu7SFZiamuRNxfE2F2cnODnY4+yly1rp5y4FwtXZCY4O9jA2NoZ/zn6z33pRfePOPWRmZsG/Qvl3d1usrG3tUNarvNZ/larUQmZGOu7eenPvS05KxOMHt1Gpysf1+L5z4zKWzv8R8bFvHjBVSiUe3r0Ot3+xfsYno5AjO/gxjCrV0Eo2rFQTqow0ZIfqBrjlLx5DlZUJo6p1tNKNKlaDWqlA9quneWkiY1NI7Zwgf5n/vVpQFHJkv3gMY3/tRU6N/AOgykiDPET3fGQ/fwRVViaMq2rXU0Z+1aFWKvKO3bRuc1h07Ke1jWnDNoBahcwH2qM3hMRQJkM1rzI4deuR9n3w5iOYGRvCz0P/Ap+L9pzEltNXMaB5Hcz6rJtW8AIA3OxtIBGLcPOpdnvtZVQcXqdlwKWUcF+evKkbIrTbsE8jcuoG3bLHpWXi15O3dduwT8Ly2rAlmYGBAfz9KuD8pXfrnEBNnaOnM1lunXPuonadc/5SIFxy6hwDmQyLlq3Clh3aC1RfunINWdnZqFLJr2gOqDAoFVCGPYfMS3sOeZl3Zagz06GM1B09IjKzgHHLXpB5V9HO41sV6qwMKKND89LEpZwhkhlCES7cAOi72IbVMLWwh52rn9Z/rl71IM9KQ9jTC3nbZaQmIPLlDbh41ct3X4+ubMeFPdOhVLx5p5OWFI3oV7fgXDagSI+jqBgYGKCSnx8uXrqkdZ1cuHgRpqam8PHWXc/D2dkZjo4OuHDholb6hYsX4eLsDMcCpk0XOkMDA1Su4IOzV25onZMzgddhZmKMCl75B7kKcunGHcxcuAKVfMrhz1++ZfBCYERiEf/7l//9F5XIERijRo3Cxo0b8f3336NcuXJYsWIFPDw0vSjnzJmDGTNmoFu3bjA1NUXlypXx008/Yfr06YiIiICzszN69+6NKVOmoGPHjrh8+XIB/5p+rq6u2LRpE+bOnYtevXrBwMAA9erVw9y5c2FhoXl5PG3aNFhbW2PevHmIi4uDu7s7fvjhB/Ts2fODj33BggWYNWsWRo4ciZSUFJQtWxbz5s1DjRo1Cs78iXxW0xujdl/ClMPX0alCadyJTMCGG88wtl4FGMukSM2SIzghBa6WprA2MUQVZ1sEuJXC7+cfIEuhRFkbc1x4GY2tt1/gywZ+MDeUwdxQhk4VSmPV1SBIJWL42lniVng81t14ik4VSqOsgF/Y83z8e1JzU5hVKIf05yHIjksEADz9ZQkqr/4N8oTXiN5/Cg4dm8G5Z1vc7DshL9/zeavg3Ksdah5YheDf18LU2x0+M79E6KrtyAyNzOdfE4bE/dvgNOln2I+eipRzx2HkVR6WbboiYcd6qLOzIDIyhoFLachjIrWmijJwdYdKnv2md+w7Enash/PUWXD68ie8PrwLEksr2PQYjMznj5F+68qnOrwPMrBHF3z546/4ce4faNusMe4/foKtew7giwG9YWRoiLT0dLwMDYeLo0Ne78TBPbti1qLlsDQ3Q72A6rhw9TpOXwzEj1+Pzdtvt3at8PVPv2HR6o2oF1ANN+4+wKa/96Fv1w6CG37/rmG1ymPkznOYciAQnfzccSciHhuuP8HYBpXy7icv4pPhZmUGaxNDVHUphYDS9lhw7q7mfmJrgfPBkdh66xm+bFw5r4dsQfsVsv49u+HrH2bip9n/Q5sWTfHgURC27d6H4QP75V0nr0LC4OzkAKuc0Y8De3fH7D+WwsJ8JeoF1MDFK9dw5sJlfD9pQt5+hw/si4nfTMfUn35Fry4dkfj6NVas+wvlvb1QN0A4dW5+yvtVRflK1bB0/nT0GTwaZuaW2LVlFUxMzdC8bde87cJCgqGQZ8PdU3f6kvw0a9UZJ4/sxryZX6Fr788gkclw4tDfCH31HFN+WlgUh/PR0k7shfXnU2HVfyzSr52Fgbs3TBu1Q+rhbYA8GyJDY0gdnDWjNdJSoM7OQuqxv2HRoR9UGWnIuncdMncvmDZuj/QLR6FOezMBn9RJ0+NREaM7ckGoUo7thu3Ib2A9aDzSr5yBgYc3zJq0R8rBrVDnng9HFyjjoqHKOR8ph3fAsvMAqDPSkHH3KgzcvWHWtCPSzh2BKud8pJ0/AtsR38Ci8wBk3r8BQy8/mLfojJSTe6GMjynmo36/4W0b4ovfN2DSih3oXK8q7jwPxfrjFzGuS3MYGxggNSMTLyJj4WpnAxtzUzwOjcTaoxdQ0d0ZLapXwL1g7UBxWSc72Jibol+z2thw7BIAoE4FT0TEv8byA2fhZGuJrg2E9cLxXZ8F+GDU3xcw5eBVdKpYBncic+qG+n7627Autghws8Pv5+7ltGEtcCE4CltvPceXjfy1RmWUVP17dsek73/CjNnz0bp5Uzx8HITtu/Zi2KD+MDLSX+f0790Dc/9YAgtzc9SpVROXrlzFmQuX8N3kLwFoXmT27t4F6zdvg5WVJWrVqIbglyHYsGU76taqiaqVKxXnIRco8/IxmPYcBZMOg5F9/wokzh4wqNkUmef2Awo5YGAIia2jZrRGRhqUYS+geBUE48adIZLKoIyPgqxsRRhUa4jM03u0RmVI7DQ9rlXxH97xsDiwDaufk0dNOJUNwOltkxHQ5isYmljh5oklMDQyR4XavfO2S4x+BqUyG6WcKwAAqjUdiUOrh+LYxjHwqzsAWRlJuHlyCYxMLOHfYEhxHc5H69O7N6Z9+y1+mTULrVq0xMNHj7Dz710YOngwjIyMkJaejpCQEDg5OeXdT/r26YP/LfgdFhbmqF2rFi4HXsG58+cxbcqUYj6awjG4eweM/2kuvp+/BO2aNsS9oKfYvPcwRvbvkfPbyUBwWDhcHOxh/Q9Hp2VlZ+O3P9fCxNgIA7t1QHCYdnvN3tYG9rbC7bBFRBoidWHPrUQlTuqSyYW+z1PPI7A8MAivElNhb2aEHv4eGFBN0yvzelgcvth1ET82r4qOFTTDpFOz5FhxNQinn0ciLi0Trpam6FvFE1383kwHJFeqsOHmUxx4FIrI5Aw4mBujq18ZDKhWrlAXmTUbPafQz0lJPh+A5pwclP3zl1v/hk3DANQ5uRGXmw1AwrmrWml3PpuKsA2787YtPbwXyk4cCiM3J6S/CMXzOSsQ/tderf1Z16uO8rMnw6JyeWTHJSL8r714Mn0h1Art+f0/Vjt5EF4Mbl+o+zSpVgfWXfrCwNEVisR4JJ86iKQjmuM38q0E56mzELNqAVIvvBlGbztgJExr1EXI+AH57tewnC9sug2EYVlvqLOzkHYzEAnb1hS4tsa/VXbdAUQ/ulGo+zwXeA1rtuxEaHgkStlao0ublujduR0A4Na9hxj//c+YNvYLtGnWKC/P3qMnsXXPAcTGJcDJwR79u3VEqyYNtPZ74twlbNixG5HRsXCwL4UurVugW3vtET6FwaF8daQt/7ZQ93nqaTiWXX6IV4kpsDczRs/KnhhQQ9NL63poDD7fcQ7TW9VAx4ruAHLuJ4EPceppuOZ+YmWGftW80KWSxz/eb2Ey/eIXRATdLdR9nr98Bes2b0doeARK2dqgc9vW6NmlAwDg9r0HmPjtdEwZPwqtmzXJy7PvyHFs370PMXHxcHa0R9/uXdCySSOt/d5/FITVm7bgUdBTGBoaon7tmhg5ZCDMzAp3jmFnH39cD0os1H0CQFpqMjat/gPXA89BrVbBu7w/+n82Ac6ub+qSn78ZidiYSPyxao/efYwf1hnl/aphxIQftNJjosKxZd0SPH5wC5kZ6fD0qYge/T7PWxPjY9XwsUbUpP6Fsq9chn41YNayK6R2TlAmJSL90nGknzsMADAoWx42I79F0rblyLh+Pi+PcY2GMGnUBtJSjlAmJyIj8DTSzhzQWlfIyL8WrAaMReycSVDGFk2w3HHuJkRM7FOo+zSqVAPmrXtAau8EZVIC0i4cR9oZzZpxBp7lUWrMD0jc/Ccyrp3Ly2Mc0AhmjdtBaueoOYeXTyH11D6t82FctS7MWnbRrIGRGIf0i8eRdv5ooZYdAJwXbEHGmS2Fus9Ttx7hz/2n8TI6HvZW5ujVOAADW2hGnVwLCsbw/63HT4M6oVPdqli67xRWHDyX775WfjkINX08oFar8dfJQOw8dx3h8a9RytIMdcp7YkznZrDRs27XxzBu3Aepy6YV6j5PPQvH8suPNG1YUyP0qOKJAdU1Iw2uh8bii53n8WPL6uhYMWdazyw5VgQ+wulnEW/asNXK6dQ5uXL3sbx7A9RwsyvUsgOA2YhZCHtyv1D3eeHyFazbvBVhYZo6p2O7NujZRbP2z+179/HVNz9i0vjRaN38zbon+w8fw47dexETFw8nRwf07d4FLZo2zvtepVLhwJFj2HvwCCKiomFhboZmjRtiUJ+eMDQs3Km3XL39kDRvfKHuU1rOH0b12kBsbQ916mtk3b6A7OunAQASt3Iw6zUW6Yf/gvxBzggDA0MY1W0DmZc/RKYWUL2OQ9aNM5Df0x4xbVCzKYwbdULSgq8AZeG25d9m+fUfbMO+w6F8dczb9f6Fpj9EVkYSAg/OxssHJ6FWq+BYpipqt5+mtcbFgRUDkZIYjj5T3jzzhD+7jBsnlyAhMggikRhu3vUR0OYrmFk56/13DqwYCABo//mGQiv7113FCH7+rND2BwAXL13Cxk1/ITwsDLalbNGhfXt066rpaHLn7l1MmToNX06cgJYtWuTlOXjoMP7etQuxsbFwcnREz5490bxZU737j4qOxuAhQ3X2UVg8PMsh7v6HdQrOz9krN7B6626ERETBzsYaXds0RZ+ObQBo1rAY++NsfDP6M7Rr2kAn78FT5/HrktXY+edcONlr6pQb9x5i3PQ5+f57Q3t2wme9uhRK2Uv51Sl4I9IROqpbcRehxHFb+ndxF+GTYwCDiiSAUZIVRQCjpCvKAEZJVRQBjJKuKAIYJV1RBDBKuqIIYJR0RRXAKMmKIoBRkhVFAKOkK4oARklXFAGMkq4oAhglXVEEMEq6oghglHRFFcAoyYoigFHSFUUAoyRjAOPDMIDx7/0XAxglcg0MIiIiIiIiIiIiIiL6/40BDCIiIiIiIiIiIiIiEhxhr8xJRERERERERERERP/viMTsW08F41VCRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgiMt7gIQERERERERERER0X+MSFTcJaASgCMwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHCkxV0AIiIiIiIiIiIiIvpvEYlFxV0EKgE4AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER1rcBSAiIiIiIiIiIiKi/xaRmH3rqWC8SoiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwpMVdACIiIiIiIiIiIiL6bxGJRcVdBCoBOAKDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBEda3AUgIiIiIiIiIiIiov8WkZh966lgvEqIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER6RWq9XFXQgiIiIiIiIiIiIi+u+ImtS/uItQ4jjO3VTcRfjkuIg3IWxsz+IugqC4LtqOxFmjirsYgmI9bSleDG5f3MUQlLLrDuCgzKe4iyEo7eRBSLp5oriLISiW1Zoj8+Cy4i6GoBi1G4HYh1eLuxiCYlchAA+fRRR3MQSlQjlnPB/YrriLIRieGw7iWv3axV0MQal5IRCZW2YXdzEExajPFGQeX1fcxRAUoxaDEfbkfnEXQ1Bcvf3Yrn9H2XUHkHzzeHEXQ1AsqrXArquq4i6GoHQNEOPVs6DiLoaglCnng5SrB4u7GIJhHsC2K1FRYQCDiIiIiIiIiIiIiD4pkVhU3EWgEoBrYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgSIu7AERERERERERERET03yISi4q7CFQCcAQGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCY60uAtARERERERERERERP8xYvatp4LxKiEiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBkRZ3AYiIiIiIiIiIiIjov0UkEhV3EagE4AgMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhxpcReAiIiIiIiIiIiIiP5bRGL2raeC8SohIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwZEWdwGIiIiIiIiIiIiI6L9FJBYVdxGoBOAIDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsHhGhhU6Ax9/WHZvjekTm5QpSQh9dxRpJ7ar3dbk1qNYNN/dL77Sti4BOlXzwIAZG5lYdVlAGSlPaHOTEfalbNIPrQdUCqL5DgKk9SjPIwbdYSklBNUacnIunEOWVdP5Lu92NoOliN+0klXxkYgedXPOZ9EMAxoBsNq9SE2t4bqdTyybp5F1o2zRXQUhcu4YlVYdxsAA5fSUCa/RvLJg0g6slvvtmb1m8F+2MR89xWz8n9IvXgKACBzcoVNzyEw9q0EtVKJzKD7iN+6CorY6CI5jqJg5OKAhrcP4Hq30Ug4d/W92zr3aody00bCpKwbMl6G49ncFQjfuEdrG8vqfig/ezIsq/tBkZyGsA278GTGYqjl8iI8isIRePcR/ty2Dy/CImFjaYEeLRuiX7tmEIkKniczKDgUg7+fg78XTIeznS0AICI2Hp3H/ZBvnvaNauOHEQMKrfxF4VLQKyw+dBHPo+JhY26C3vUqY2Dj6vmekyy5AsuPBeLQzcdITM2At7MdRrSqjXq+7lrb1J22BAqVSiuvsYEMgb+NKcrDKRRXb9/Dir92IDgkHDZWFujSpjn6dGr7j66TJy9eYvjk6di6dC6c7O3+9fdClpGRgQ1rlyPw4jlkZmaggl9lDB0+Ci6upd+b73ViAtauWoqbN65BpVSiWo1aGDJ8FGxsbPO2UavV2LtrO44d2Y+42BjY2TuiXccuaNu+S1Ef1gcz9qsKm+4D8+qdpBMHkXR4l95tzes3h/3n76l3VvwPKRdOAtDUO7a9hsK4vKbeyXh8D/FbVkMRG1Ukx1FYLGoGwPXzETDyKAtFQgJidu9E1JbN+WeQSODYpy/s2nWErFQpZIWFInLjBiSc0m7PWDVoCOfBQ2HkVhryhHjEHz2CyI3roVYoiviIPt6lZ+FYfOoGnsckwsbMGL1rlsfAun7/6F6iUKowcPUBGMukWD2krdZ3e289xfpL9xCWmAJHS1P0qlkefWtV+Ef7LW6XHr3A4v1n8TwyDjYWpujdoDoGNgt4f51z+AIOXXuAxNR0eLvYY0TbBqhXoWzeNiqVGjsv3sL28zcRFvcaNuamaOLvhZFtG8DM2PBTHdoHu37zNtZs3IyXIaGwtrJCp3at0aNLx/f+PU+dPY9N23YiMjoGjvZ26N29C1o1a6K1Ta/BwxEXn6CTd9emtbC0tCj04yhMbNfrCrz7CEu37ceLsEjYWlqge8sG6P8v2rCDvp+LXQt+zGvD5jpz7Q5W7z6CVxHRsLWyQNv6ARjcuSVk0pLxaicrMw1Hts7H/evHkZ2ZDnef6mjffxrsnDzem+/V01s4un0Bwl8+gKGhCfwCWqNVjwkwNDbN2yYu+hWObf8dL5/cQHZWBhzdvNG86xiUq1inqA/rg12/eQvrNmzEq5AQWFlZoWO7dujetfN7r5PTZ85i87btiIyKhoO9PXr16IaWzZtpbXPs+Ens2L0bkZFRsLezQ4f2bdG5Q/sSUe8E3gvC0h2H8Dw8CrYW5ujRvB76t22cb9mz5QpsOnwGBy9cR3T8a9jbWKJN3WoY3KGZ1u/ixJXb2HDwNF5GxsDcxBgBFb0wpld72Fqaf6pDI6KPUDJquSL0+vVrTJgwATdu3ICPjw927NiBPXv2oGHDhrC1tS14BwCaNm2KLl26YOzYsdi1axemTZuGoKCgAvP9m21LCgN3L5T6YirSb15C0sFtMCzrC8tO/SCSiJFyfK/O9pkPbiJm/rc66dZ9voDIyASZD28CACS29rAb8z2yXz5BwpoFkDq6wLJ9b4hNzPB628oiP66PIXF2h1mPkch+dAMZ5/ZD6uoJ46adAbEYWYHH9OexdwUApGz+HWp59psv3nrhbNysK4wCmiHr5jlkP7kDsVUpGDfsALGlLTJO6X8hIxSGnj5wnPgDUq+eR+LuTTDyqgCbnkMAiQRJB3fqbJ9+5xrCZ36lk243ZBzExsZIv3sdACCxKQXnb+dAHhmO6GVzIDYwhHXXAXD6eibCvhujfS4FysjVEQEHV0NmVfCDqmOXlqiyYR6CF21A7NHzcOzUHFXWzIYqKxuR2w8BAIw9XFHryFokBt7GzT4TYObrCZ+ZEyGzscL90T8W9eF8lHtPg/HlnD/Rok41jOjRHreDXmDR5j1QKlUY1Knle/M+D43AxDl/QqnUfiFfysoCq2d8rbP9zmNncfzyTXRsLNwHHAC4+zISY1ftQasq3hjdui5uBYdjwYHzUKhU+KxZgN4807cdx7mHLzCuXX2UsbPCvmsPMXbVHqwa1R3VymruNc+i4qBQqfBrv9Zws7XKyysuAQuq3Q96hsm/zEezerUxrE933H0UhD83bINSqcKAbh3em/fFq1BM+nk+lPkEwgv6XugWzPkZQUEPMWjIFzA2McH2Levx/bQvsXDpWpiZ639YUyqVmPnjVKSnp2HE6IlQKhXYuG4lfvpuEuYvXAFpzoPg+jXLcXDf3+jdbwi8fHxx89oVrPxzIaQSKVq2ef95Lw6Gnj5w+vJHpF45j4S/N8HYuwJsew2BSCLB6wM7dLZPu3MVYT99qZNu/9k4iIxNkHbnGgBNvePy/VxNvbN0DkQGhrDpPgDOk2ci9JvRgq13TCtWhNec+Ug4eQJhq1bA3L8yXEeOASQSRG3aqDePy9BhcOo/EBHr1iDl7h1YN2wEzxk/Q61UIvHsaQCARY0AlPvlNyScOoGwP5fC2MMDriNGQmpphZDf53/KQ/zX7obGYOzm42jl54HRTarhVkg0Fhy/prm/NqhcYP41F+7iQUQcapRx1ErfdSMIP+2/iMH1KqGupwvuhcVi/tGryMhWYFjDgvdbnO4Gh2Pssh1oVa08RrdviFvPw7Bg7ynNOWmpv76cvvkQzt17hnEdG6GMvQ32XbmHscu2Y9W4fqhWzg0AsPZEIJYcOItBzWqjlk8ZvIpJwJID5/AsIhbLxvQW9Au2h4+f4NuZs9C4fl0M7t8H9x8+wop1G6FUKtGnR1e9ec5dvIxf5/+Brh3aoWb1KrgYeBVzfl8MmUyGpg3rAwCSkpIRF5+AL4YMhF+F8lr5zcxM9e1WMNiu13XvaTAmzlmW14a9E/QcizbvhVKpwuAC2rDPQiMwQU8bFgCu3H2EyQtWoUXtahjTuyOeh0Vh6dZ9eJ2SiklDehbV4RSqbUsnIeTZHbTp/TUMjU1xcvdSrPx1ECb+th/GppZ680SGBGH1b0PhWaE2+o1biJTEGBzZ/j/ERQVj6ORVAIC0lESs/GUgTMys0L7/NBgam+Ha6R1YM3sYhk1bi7Ll9beTi9Ojx4/xw08z0ahBfQzq3w/3Hz7CqrXroFQq0btnd715zl+8hN/m/Q+dO3ZAzerVcCkwEPMW/AGZTIYmjRoCAA4fPYYFCxejZ7euqF6tKh4HBWH5ytXIzMhAn17Cvk7uPXuJCfNXoUXtKhjRvTVuBwVj4bYDUKpUGNyhmd488zbtxqELNzCscwtUKOuGh8GhWLn7GCLjEvHD8N4AgKOXb+HbpRvRtWkdjOrRFnFJyVi28whGzlqKjTO+hKGB7FMeJhF9gP98AGPfvn24fv06Nm/eDAcHB1y7dg1Tp07FyZMnP2h/bdu2RYMGDQp925LCom0PyMOCkbhxMQAg69EdiCRSmLfogpQzh7RewAOAKjUF2akpWmlmjdpA6uiK2P99B1XOd+bNO0GVlYG4FXM0Iy4e3oI6OwtWPT5DyrFdUCbGf5oD/ADGDdpDGR2K9P3rAQCKFw8hEktgXLcVsq6fBhS6veAlDq5QJSdC8eqJ3n2KjE1hWKMxsm5fQPrRrXnp6pREmHYbgazbF6FKEG7PJOvO/ZD16gViV/wPAJBx7yZEEims2/dE8rF9Og8kqpRkZKUka6VZNO8AmbMrIn6eBFXOd9ad+0KVno7Iud9BnZ0FAJDHRsNx/Pcw9PBC5pMHn+DoPpBIBNcBnVF+9hTgHz63+8z8EpE7j+DR17MAAHHHL0BmbQmf6ePzAhiek4ZDkZKG611HQS2XI/bIOSgzMuH3x/d49tsyZIZGFtURfbQVOw/Cx90VP40eDACoU6UiFEol1u09il5tGsPIwEAnj1yhwPYjZ7B850EYyHQbogYyGSp5affwevQiBMcv38So3h1RxbdckRxLYVl69DJ8Xezxa782AIB65d0hV6qw+sQ19GtQDUYG2tV6eEISDt18jGldm6BXPc2LsoBypXE7OALbLt7NC2AEhcdCKhajRWUvGJSQHny51mzdBS+PMvh+wggAQO1q/lAoldj49z70bN8KhoZ6rhO5AjsPHcPqLX/DQPbvvy8JHj96gGtXL+G7n35D9Rq1AAAV/PwxYmgfHD64Fz1699eb79L5M3jx/CkW/rkWbqXdAQAeZcth/KihuHj+NBo1aYGY6Cjs37MDw0eMQ+t2nQAA/pWrIS4uBrduXhNkAMOmq6beiVmueYmece8GIJHCukNPJB3d+4/qHcsWHSBzdkP4jDf1jk2XflClpyNi9rd59Y4iLgqOE34QdL3jMnQ40p8+QfDPmtGeyVcCIZJK4TxgMKK3b887lreVatce8cePIWLtagBAyo3rMPXxhX237nkBjFLt2iE7OhovZkwHVCokX78KmY0NHHr1Qeii36EWcDBw6Zlb8HWywa9dGwEA6nm5Qq5SYfX5u+hXuyKMZPnfG4Oi4rH6/B2UMjPW+W7V+TtoUcEdE1vUBADUKuuMV/FJ2HL1oeADGEsPnoevqwN+HdQRAFCvgqemzjl2Cf0a14DROy98wuNf49C1B5jWoyV6NawOAAjwdsftF+HYdv4GqpVzg0qlxtrjgeheryrGd2oMAKjt6wFLU2NMWbsXD0OiULGM0yc9zn9j/eatKFfWA9O+Gg8ACKheFQqlEpt37ELXju1gaKg7gmT1xs1oVK8ORg0fAgCoWa0qUlJSsW7TlrwAxrPgYABA/Tq14OzkqLMPIWO7XlduG3bG6EEAgLpVKuS0YY+h93vasNuOnMXynQdhmM/9Zv/ZQDjaWmPGmEGQiMWo5V8eCUnJ2HzoNCYO6AapVFKkx/WxXj29hUe3TmPw18vhU1nzst3dpwbmftkCgSe2oEmnEXrzXTyyHsamlug3/g9IpW/O3c6V3yA2Mhh2Th64eWEv0pITMWr6dljaOAAAvPzqYuE3nXHu0BpBBjA2/LUFnmXLYsrXmg4TNWtUh1KhwNbtO9ClUwe995O16zeiYf16GPn5MABAjerVkJKSivWb/soLYGzZtgMN6tXFsKGDAQBVq1RGWHgE9u4/KPgAxvJdR+FTxgUzR/QDANT1Lw+FUom1+06gd6sGOr+d1ylp2H06EGN7tcPAdk0BAAEVvQEAi7cdxNhe7WFtYYa1+0+gXuXy+GZIj7y87o72GPzTHzh/+yGaBwi7Pv5/T8zVDahg//mrJDk5GXZ2dvD394eDgwPUavVH7c/IyAh2dv9siol/s22JIJXCsFxFZNzVnvIm/XYgxMYmMCzrW+AuxOaWsGjXC2kXjiH71bO8dKPylZH54JbWdFEZtwMhEothVL5KoR1CoZNIIS3theygO1rJ2UG3IDI0htTVU382B1coosPy3a3YxgEisQTyp/e00uWvnkAkFkPmWeHjy15UpFIY+1ZC2s3LWslp1y9CbGwCI++Cyy6xsIJNtwFIPnUYWS/eBHlMa9RFyvnjWi9esl8+Q8jEQYJ+yAEAC38f+C35CWGb9uD24MkFbm9cxgVmPh6I2ntcKz1y11GYernDpFwZAIBdi/qIOXxWa7qoqL+PQCSRwK5l/cI9iEKULZfj5sOnaFyzilZ6s1pVkZaRiTuPn+vNd/HWA6zadRiDO7XCmD6dCvx31Go15q7dBg9XR/Rp27Qwil5kshUKXH8WhqaVtO8bLSp7IS0rG7eCw3Xy2FmYYvPEPmhX/U1vTrFYBIlEjCz5m+lcHofHwt3eusQFL7Llcty6/wgNa9XQSm9SJwDpGZm4+0j/CMfLN29j7bbdGNCtI0YO7PWvvy8Jbt+8BiMjI1Sp+ubcWFpaoaJfZdy8Hphvvls3r8HF1S0veAEAbqXd4epWGjevXwEABF46D5nMAM1attHK+/XUHzHl2xmFeyCFQSqFsa8/0m68U+9cu/Dv6p3uA5F88hCyXry5rkxr1EXyuWNa9U5W8DO8Gj9QsPWOSCaDedVqSDx3Ris98fQpSExNYe6v/yFebGAAZXqaVpoiKQlSC8u3tjGEKjMDeGs6OkVyEsQGBhCbCLcXebZCiesvI9HUt4xWeosK7kjLluNWSP6dQuQKJb7bfQ59alWAu61u7+HF/VrmBS9yySRiZCmEG8wBNFNyXH8WgqaVfbTSW1T1QVpmNm49122n2lmYYfOkwWgXUDEvTSwWQSIWIUuuOd7UzCy0D6iINjUqauX1cNCMfA+NSyzsQyk02XI57tx7gPq1tV+ENqxbB+kZGbj38LFOnqjoGISFR6BenVraeerVQXhkFMIiIgAAz1+8hImxMZwcHYruAIoC2/U6suVy3Hj4DI1rat9Lm/7DNuyQTi0xpk9nvdtkyRUwMjSA5K2XfZbmppArFEjLzCy0YygqT+9dhIGhCbwq1ctLM7OwgYdvDQTdOZdvvhbdx2Pw18u1ghcSqSaAqsi5PixtHFC/zaC84AUAiMUS2DqWQUJMaGEfykfLlstx9+491KtTWyu9Qf16SM/IwP2HD3XyREVHIyw8HHXfzVOvLiIiIhEerrmf/Dz9Bwz/bIjWNlKpFNnZwh21BGjqnRuPnqFJjUpa6c0CKiMtMwu3g4J18qRlZKJb0zpoWM1PK93dSXMdhMfEQ6VSoZafN7o00R456O5sDwAIi4krzMMgoiLy/yKAcfbsWXTt2hWVK1dGnTp1MHXqVCQlJQEArl+/jp49e8Lf3x8dO3bEoUOH4OPjg7CwMEydOhWLFi1CREQEfHx8sGvXLgwcOBAA0KxZM+za9e+n4dm1axd8fDQN/alTp6JHjx5a34eHh8PX1xeXLl3S2hYAfHx8sHPnTgwePBj+/v6oX78+Fi9erJV///79aNOmDSpVqoQePXpgw4YNWvsoTlJbB4hkMshjtHt0584BLXVwLnAfFm17Amo1kg68GVUAmQxSW3soYiK0tlWlpkCVkQ6pfcH7LS5iq1IQSWVQJcRopasSNZ8ltvofUqT2rhAZGMJ8wNewmvQHLMfOgnHjTnmRaXVGqmb/ltrTnEms7PL+XaGS2TlqrpMo7Zet8mjN31fm6FrgPqy79INarULirjfTXEhLOUBiYgZFfAxsB4xAmcVb4L5yFxzGfQeJ9T+bDq44ZYRE4oxvCzya9BuU6QU/gJj5al5ipz19qZWe/vyV5ntvD4iNDGHi7oq0p9qNvey4RMiTUmDm/f65ZotTeEw85AoFSjvZa6W7Omiu8VeR+l8mVfAsgz0LZ2Bol9aQSgqu4o5fvoH7z17iy4HdtR4GhSgsPglypRJl7Ky10kuXsgIAvIzRfeljIJWiopsjzI0NoVKpEZWYgjm7zyAsLgk96vrnbRcUEQuJRIwvlv2NWlMXocG3SzFj+wmkZQr7QSciKkZznThr91Z1yXloCYnQvwZB+XJlsXP5Agzq0QkSPddJQd+XBGGhr+Dg6AyJRLs3pqOzC8LD83+QDwt9BWdnN510RycXhIdp8gUHP4OTswse3L+Lr8Z9ju4dm+PzIb1x7LD+9a6Km8zeKZ96R9NekTn9g3qnaz+oVSok/P1OvWNqBkVcDEoNHAn3pVvhsWo3HCd8L+h6x9DZBWIDA2SFaF8HmeGaF9JGpfWvkRK9fRtKtW4Di1q1ITYxgU2LVrCsVRvxRw+/2WbXThi6usGxT19IzMxgWrEiHHr0xutLF6F8p8e1kIQlpkCuVKHMOwGI0jaa6RxfxiXlm3f52dtQKNUY1aSa3u/L2lnBxdocarUaSelZ2HUjCPvvPEevmgV37ClOYfGvIVcoUcbeRiu9dE4d9DJGd/SzgUyKimWcYG5slFPnJGPOzuMIi3uNHvWrAgAsTIwwtUdLVPXU/t2dvqt5ce3pJNzOXZFR0ZArFHB10X72cMmpg8LCdTsSvArV/K5cnbXz5I6yCA3TtH+fBb+EubkZfpo1Fx17DUC7Hv0wc/Z8xCcIN6ADsF2vT35tWLe8NmyMvmyo4FkGexf+hKFdWufb9ujRsiFCo2Kx8cAJpKSl497TYGw9fAb1qlSEpcCnGgOAmIjnsLF3hVis3TaxdSiD2Ejdl9O5LG0c4FRa884jOzMdz+5fwtEdC1DGuxqcymjupf612qB1L+2pyTLSkhD8+DocXIQ3wjoqMkrv/cTZSTMCLSxM934Skns/cXHRzuOsyROacw8qXdoNjjmdc5NTUnD46DGcOHUaHdppr88kNJrfjhKlHbXrATcHzbuNV1G6vx0Xe1tMHdwd7u/83s7euAepRILSTnYQi8WY2LcTGlfXDnKcuXEfAODpUrJGvRH9V5WsrpZ6JCQkYMyYMZg6dSoaN26MqKgoTJ48GXPmzMHnn3+OoUOHomPHjpg1axYePXqEmTNn5uX99ttvYW1tjUOHDmHnzp0wMjKCmZkZxo4dix07dsDb2/ujyta1a1cMGDAAISEhKJ3zMLh//344Ojqidu3a2LNnj06e2bNn47vvvsPMmTNx8OBBLFiwALVq1ULNmjVx+vRpTJkyBV999RWaNm2KwMBAzJo166PKWJjExiYAAHVmhla6OkvzWWxk8v78ZhYwDWiElFP7oc5If5Oek0/1zn5z00RGusP1hUJkaAQAUGe/e040PUVEBka6eYxNIbawBsRiZJzeA1VSAqTuPjCq3QIiC2uk71sHVUIM5KHPYNSgHVQpiZC/CoLEqhRM2vSDWiGHSCbcBRBze2C+/TcG3vx9xcbv/3uKzS1hVq8pko7shuqtnqASc81LB5seg5EV/AQxf86BxMISNt0HwXnKLIT9MFbvlBhCIU9Mgjwx/xck75JamgEAFMmpWumKFM05kVqYQZazINm72+RuJ7Uw+9DiFrnUdM31YGqs/RsxyVncMy1Df5DH3sbqX/07mw6cQGWfsqhe4ePu959CaoYmmGBmpD102iRniqS0rPcHG9aeuoaFhy4CALrV9kNtb029pFar8TQiDmqo0bWWHz5vUQv3Q6Ox/GggXkTHY83onoJdCyP3OjEx0b5vmORcN2npuvUGANjZ2uhN/6fflwTpaWkwNtGtd42NTZCRnq4nR06+9DQ4Oeu+cDI2NkF6Tr7kpCQkxMdhwdxf0LvvILi4lcaFs6fw52LN9CFCm0Iqt32i0ql30rW+z4/E3BLm9Zsh6fA79U7OyAPbXkOQ9eIJopfOhsTCCrY9BsF52iyEfSfMekeS85Lr3dEUypy/r8RU/0uwqG1bYepXCT7zf89Liz2wD1Fb/sr7nHLjOqI2b4Lb6HFwGz0OAJAWFIQXP/1QmIdQ6FJzgrVmhtpTIpnkTJGUlqU73ScA3A+PxfpL97FmSFsYFDB1y92wWAxcfQAAUNG5FAbWqfTe7Ytbaobm2tWtc3Lq4QIC3GuPX8bC/WcBAN3qVkFtX/d8t737MhxrjgeikV85eDkLN4CRlqb5jejWOZrP+uqctJzflWk+edJz8jx/EYy4+AS0a9UCXTu1R0hoGNb9tQ0Tp32P5X/Mg7GR7jODELBdr6so27A1K3pjYIfmWPjXHiz8aw8AwMfdFT+PHfzB5f2UMtNTYWis+/xhaGSCrIw0PTm0qdVqzBxVFwp5FkzMrNBxgO5amrlUKhV2rf4BWRmpaNjus48qd1FIy7neTd5pq+XeX9L1tNXS0tK0tsllbKw/z6PHQZjwtWZkv7dXOXTr2vnjC16EUjPy+e0Y5f52/tlv/vT1uzhw4Tp6Nq8HC1P9bbyw6Dj8vmUfvMu4oF7l8nq3ISJhKfEBjOjoaGRnZ8PZ2RkuLi5wcXHBsmXLoFQqsW3bNpQqVQrTp0+HVCqFp6cnoqOjMWfOHACAubk5TExMIJFI8qZysrTMaSzZ2MDoIxuKNWvWhJubG/bt24cxY8YA0AQwOnXqBHE+PX07d+6MTp00056MGDECq1evxs2bN1GzZk2sXr0arVu3xmefaSpgDw8PvHz5EuvWrfuochaaghbcU+suRPY207rNALEYqWcPvbPfAnq/fuS0X0WqgLLrm7JMLc9GypaFUCXGQJWUAABQhD4FlAoYN+qIzItHoIqPQtqulTBp0xdm3b4AoHkxk3F6N4wbtBP0onYFXScFTeNm0aglRGIxko7v0/4iZ+obZfJrRC/6Ne+6kMdEwuX7+TCr0xgpZ49+eLkFRlTAaAG1SlXgXJJqlXB/OwVdB+KC7gv/wN0nL/A4OBRzv/r8o/f1KagKOCcF3YIbViyLKh7OuBUcgeXHApEpV+DXfm2gVgN/fNYR1mbGKOeo6eFU3dMVpcxN8M1fR3Ap6CXqlxfmaB11AfWKkBeCLUwqlUrnN/O+6+V950X1nvtCbiBLIZcjOTkJk7/5CXXqaeZb9q9cDXGxMdi2ZYPgAhgf244wb9wKIrEYr4/t1d7tW/VO1MJf3tQ70RFw/fF/MKvbBClnjnx4uYuIqMC2ie7vSiSTofzSZZDZlsLLub8h89UrmPlVgtOgIVBlZCDkjwUAgDJfT0apdh0QsW4Nkm9ch4GjE1yGDoP3/N8RNH4MVFnCfOH4IffXLLkC3+8+j361K6CSa8Ev3Z0szbB6cBuEJ6Zi8akbGLj6ALZ+0QnGBsJ8HCv4nLz//tqwkheqeLri1vMwLD98AZlyed5aGm+79TwMY5ftgIutJWb0b/dRZS5qBdU5Yj3npKC2lijnvvrl2JGQiCXw9db0FPevWAHupUtj/JRvcfzUGXRs2/oDS13E2K7X8bG/nff5bfVW7DsTiKFdWiPAzxsRsQlYufMQxv22BEu/HQcjPet+FRdN20T7N/O+35DoH3SWUSkVGDhxCRTybJzZvwLLfxmAEd/9lTcKI5dSIceOFd/g/rVj6DjwO7h5+uezx+LzvvYWoL+uLuj39O615WBvh3m//YqoqGis27gJE76ejKV//A4jI2F2dizonOi7x77r1LW7+O7PTaji7YFxvfW3R19GRGP0nOWQiMWYM3ZQvu/miEhYhNli/hfKly+P9u3bY8SIEbCzs0O9evXQuHFjtGjRAs+ePUOFChUgfWtO74CAT7d4k0gkQufOnbF//36MGTMGDx8+xLNnz7B06dJ883h6as9tbm5uDnnO3PUPHjxAy5Yttb6vWbOmYAIYuT0bc0cd5ModIfFuz8d3GVephczHd/IW7s6lzu0hqWekhdjIWKfHj5Dkjj55d6RF3siMLD29gxVyKF7qzqErf3Yfxo06QmLvAlV8FNTpKUj7eznSDY0hMrOE6nUcoFLBpHUfqDML7sFSXFQ5PUNE74zIyeshm/7+spvWqIf0+7fyFvjLlTvyJ/3uDa2XUVnPg6BMT4VhGU9oX1klmyJJczRSc+2esrmjKhRJqXkjL97dJne73H0IUW7Pm3fn883ttWZq8vE9EU9euQULUxPUq+JX8MYCYGacM9IiU7sncG4vWPMCHka8nN4EJxRKFf48ehlj29aDk7UFapbTnTKoQQVN0CIoIlawAQzTnF5r6e/0Zszt0WpmKtwReoVp+5YN2LZ5vVZanXqNkPQ6QWfbjPQ0mJjmP/rK1NQUGXrq1Yz0dJjk9LQ1NjGBSCRC9ZraczBXrR6AWzev4XViAqyshTOKRZXTq/PddsQ/rXfMauqvd1Q5PQXT71zXrXfSUmFYpqwg6x1FWs40lO/0+pTk/H2Vqbrnw7pxE5h4eSNowlgkX78GAEi5fQuKtDS4fzUJsfv2QpGSAruOnRG5cT3CV63Iy5v26AEqbdqKUu06IGbXzqI6rI+SO8ogLfud+2vOyAtzI92XgotP3YRKrcbnjapAodS8lMu9ChRKFSRikdbLJHsLE9hbmKCGO+BqbY6h6w7hxMNgdKjiVfgHVAjMcnuLvzO6Ly1TE4QyNy6gzskZSVG9XGkoVCr8efA8xnZoBCebN9N0HbnxED9sOogy9jZYOqoXrMzePxqquOXWORnv1Dl5oyz09PTNTUvPyHgnT4bWPiv66k4J7FfBF6amJnge/OojS1502K7XZZbbGz5TO2Cb24Y1M/mwtklMwmvsPnUJQzq1xMie7QEA1QFU9CyDXpN+wb4zl9GzVaMPL3ghO7VnKU7uXqKV5lezFVKTdKefy8xIg5GxeYH7lEhleetnePjWwOyJzXHx2EZ0H/5L3jYZacnY9Mc4BD++hg4Dv0OdFv0+8kiKhmnOaEfde8N77id59yDtPLntXtN3RlDa2trC1tYW/pX84OjkgK+nfIPzFy+iRTNhrvuX+9tIz+f5z8z4/c9/fx0+iz+27EP18uUwb8IQGBrIdLa5/ugZJv+xFsZGhlj+zSi4Ogh36m0i0lbiAxgAMH/+fIwePRrnzp3DpUuXMGnSJFSvXh2GhoZQqbSj/AYGn7ZXQpcuXbB48WLcu3cPhw4dQrVq1VCmTJl8t9dXvtxIu1Qq1TkeIVHERUOtVEJqpz2HYO5nRZTuPI65xJbWMHAri4Qzi3W+U2dnQZEYD2kp7f2KzSwgNjaB/D2LXRc3VWIs1ColxNbaPfNyP6vidednF1vbQVrGB/JHN7QDHDJNBaxO17x4kJWvDlVcFJSx4XnbSRxLQyQWQxklvIXKciliI6FWKiFzcNJKl+WsZSKPzP/vKbGyhaF7OSSt+J/Od/KYSKhVKohkug0VkVgClcAXLfu3Up9o5ok18SyD5NuP8tJNPTX3l9THz6FMS0dGWBRMPLXvOQZ2NpBZmCE1n0UEhcDVwQ4SsRhhUbFa6bmfPQphrtILN++jUQ1/SAuY9kMo3GytIBGLEBr3Wis9JOezh4PuC+OIhGQEPglBu+q+MJS9qfLLu2rmiY1NSoNELMb5h8Go61sGTtYWedvkLvJtnc/QayFwcbSHRCxG+DtrooTlfC7j6qIv2/87LVu3R40A7YUJr1y+gNs3r0GlUmn1LIuMjICrm/41DgDA2cUNwS+e6qRHRobDy1vTw9HJ2QVqtRoKhVyr3aJQaq4ZA0Nh9exTxORT7+SszZUdkX+dKbHW1Duvl8/X+e699Y5EItjRkFnh4VArFDBy0Z4qzMhV8znz1UudPIYOmntuyt27Wumpt28BAIw9yiIrOgoisRip97S3yXz5EvLXr2HsUbawDqHQuVmbQyISITRB+5VoSILmpapHzlpDbzvx8CUiklJR59eNOt9Vn7kOMzo1QIsK7jgTFAI/FzuUtn1zfy3vpJnDPyZFuJ1w3EpZa+qcWO01GEJyPucuuv22iIQkBD5+iXY1K75T52jWJYpNSs0LYKw/cQUL9p5CDa8yWDC8K8wLeDElBM5OjhCLxQiP1F7vLyJS054v7aY7/Z5bzvz2EZFR8PIs+1YezT7KuLkiNS0N5y8FwtfbCx5l3tyfVSoVFHIFrCwtIFRs1+tydShVQBv2wxZqj4pLgFqthr+P9r20rKsTLM1M8SIsMp+cxSOgSU/4Vmmslfbwxgk8vXdBp20SH/0Kds751xGPbp6GkYkZPHxr5qUZmZjD1sENyYlv1kVISojC6tmfITE2DH1Gz0elWgIduYQ395OId+8nEZrPpd10Oxe55rRrIyIiUe6tjq+5+yjt5oqMjAxcvnIVPt5ecHlr7R2vnO3j43U7twiFq70tJGIxQqO1F9XO/eyez29HrVZj3sbd2Hb8AlrVqYrpn/eBTKr7qvPI5ZuYvnwL3J3tsfDr4f966mEqOv9kBBZRiR8rdefOHfz6668oW7YsBg8ejBUrVuDXX39FYGAgnJyccP/+fWS/1cC5f//+e/dX2NNNuLi4oFatWjh69CgOHz6Mrl27fvC+fH19cefOHa20W7dufWwRC49Cjqznj2BcuZZWsknl2lClpyH71bN8sxq4a3qfZb0I0vt91uO7MPKrljecGNCM2FArlch68v6/abFSKqAIeQYDnypayQY+VaHKTIci4qVOFrGZJUzb9IXMV3sxSIPy1aHOzIAyKgQAYFyvDYzqao/IMQxoClVmOuQhTwr1MAqTWi5HZtB9mFavq5VuWqMulOmpyHqRf9kNPTXrFGQ+fai736xMZD55ANPqdbSuE6PylSE2MkbmkweFdATCkP48BOkvQuHUtZVWumOXlkh9EoyMV5qAYdyJi7Bv2xjit3qgOHZtBZVCgbjTgZ+0zP+GoYEMVXzL4fS1O1rDpU9dvQ0zE2NU9HT/qP0npaYhNCoG/j6eBW8sEIYyKaqVdcXJe8+0zsmJu09hbmQIv9K6QZ3IxGT8tP04Tt3Tvv9eDnoFmUQCd3trKFUqzNhxAjsv39Pa5uitJ5CIRahWVrhBAEMDA1Su4IOzgde1zsmZwGswMzFBBS/hvjAtTDa2pVDOy0frvyrVaiAjIx23bl7L2y4p6TUe3r+DKlVr5LuvKtVqICw0BKEhL/PSQkNeIiz0FapU0+SrXkMz8uLC2VNaea9duQR3j7J5IzWEIq/eqfFuvVMPyrT31ztGnppe0fnWO7n7fbt9UiGn3gkSZr2jzs5Gyp3bsG7UWCvdunETKFJSkPZQt9wZIZoe4OaVK2ulm/lrpuXIighHVngY1AoFzPy1tzFyKw2ZlRWyIvLvyFLcDGVSVCvjiJOPXmrfXx+9hLmhAfxcdKeIWti3OTYP76j1X3knW5R3ssXm4R3RyMcNErEIP+27gHWXtO+vl55rzoW3nsCzUBjKpKhWrjRO3g7SPie3g2BubAg/d2edPJEJSfhp8yGcuqPdnr/8OBgyqQTuOce748It/G/PKbSsWh5/jupVIoIXgKajmb9fBZy/dEXrnJy7FAhTUxP4eumOpnFxdoKTgz3OXbyslX7+UiBcnJ3g6GAPA5kMi5atwpYdu7S2uXTlGrKys1GlknBHirJdr8vQQIaqvuVw+trtQm3DujlqOvfcfqcD0suIaCSlpsHFXlg9yS2s7eFa1k/rP69K9ZCVmYan9y7kbZeanICXQTfyRlboc+HIeuxZ9xNUKmVeWlJCFGLCn8PJLec6ykjFqllDkJwYg6FTVgs6eAFo7ieV/Cri4qXLWtfJhYuXYGpqCh8967G6ODvD0cEB5y9e0kq/cPFS3ncSiQQLFi7Cjr93a21z46bmvVFZD/fCP5hCYmggQ1Wfsjh9/Z72b+faXZiZGMGvrP4OOEu2H8S24xfQr00j/Dyyv97gxYXbD/Hjss3w93LHqu/HMnhBVAKV+BEYZmZm2Lx5M2QyGXr27ImsrCwcOnQI7u7uGDVqFI4cOYJp06Zh5MiRCA8Px4IFC967v9xFlB4/fgxra2udYXgfokuXLpgxYwZUKhXatGnzwfsZPnw4vvjiC/j7+6NJkya4ceMGNm3a9NHlK0wpR/9GqdHfw2boRKQHnoaBhw/MmnVA0r7NUMuzITIyhszRFYq4KK2pomTOpaGWZ0MZF61/vyf3wqR6PZQa+Q1STx2A1N4Jlh36IO3SCSgTdYehCknmpcMw6zMOpp2HIevuJUhdy8KwdnNknN4LKOSAgREkpRyhSoyDOiMVitDnkAc/hkmzrhDJZFDGRULm6QfDGo2RcfLvvNEWmddPw6R1HxjFRkIR9gIGFarDsGIA0o5sAbL0Lw4nFIn7t8Fp0s+wHz0VKeeOw8irPCzbdEXCjvVQZ2dBZGQMA5fSkMdEag0pN3B1h0qeDUWs7sgVAEjYsR7OU2fB6cuf8PrwLkgsrWDTYzAynz9G+q0rn+rwioTU3BRmFcoh/XkIsuM0vR+f/rIElVf/BnnCa0TvPwWHjs3g3LMtbvadkJfv+bxVcO7VDjUPrELw72th6u0On5lfInTVdmSGCqun1ruGdmmNMb8uwrQ/VqNj4zq4++QFNh04gdG9O8HI0ACp6RkIDo+Cq0MpWFsUPOz8bc9DIgAUzkiOT2l4iwB8sexvTNpwEJ0DKuL2y0isP3Md49vVh7GBDKmZWXgRlQDXUpawMTNBVQ8X1PYujd92n0ZqZjbcSlni3INgbLt4ByNb14GFiREsTIzQKaAi1p2+DkOZFJXLOOFWcDhWnbiG3vWqwN3eurgP+70G9eiECdNn4/u5i9CuWSPcD3qKLXsOYcSAnjAyNERaegaCQ8Ph4mgPawH3Yi1sFf0qw69SFfw+9xcMHPoFzM0tsG3zOpiamqF1205524WGvIRcLkdZT82Lt/oNm+Dv7X9h5g9TMWDwcADAxnUrUca9LOo1aAIA8POvgpoBdbFm5VJkZmaitLsHzpw8hscP72Pa9z9/+oP9BxL3boXTlF/gMGYaUs4dg6FXeVi17YqE7esKqHfKQJWdDUWM/nonfsd6uEz7DU5f/YSkw7sgsbCGTa/ByHz2GGk3hVvvRKxfC5/fF8Fz5i+IO3gAZn6V4NinH8KWLYUqKwtiExMYe3ggKzwcitev8frCeaQ+uI+yP0xH+OpVyHz1EqYVK8J54BAknj+HtMeakYDRO7bBsW9/AEDytauaNTCGfIasyEjE7t/7viIVu+ENK+OLDUcwacdpdK7qhduhMVh/8R7GN68BYwMpUjOz8SL2NVxtzGFjagwvPcEH05zOAhVd3rxIHNqgMv48fRM2pkao6e6EJ9EJWH7mNmqXdUZ9L90e+0IyvFVdfLF4Cyat2YPOtf1xOzgM608GYnzHJpo6JyMLL6Li4FrKGjbmJqha1g21fdzx247jOXWOFc7df4Zt525iZLsGsDAxRlxyKub9fQLOtpbo3ag6HoVq/7Zy9yVU/Xt2x6Tvf8KM2fPRunlTPHwchO279mLYoP4wMjJEWno6XoWEwdnJAVY5ayv2790Dc/9YAgtzc9SpVROXrlzFmQuX8N3kLwFoXmT27t4F6zdvg5WVJWrVqIbglyHYsGU76taqiaqVhb3gO9v1uoZ2aYXRvy7GtD/WoEPj2rj7JBgbD5zEmN4dP7gNa21hjt5tGmPjgRMAgFqVfBEZl4BVfx+GUykbdG5at4A9FD8P35ooWz4A2/6cjDa9v4KJmRVO7FoCIxNz1G7WO2+76PBnUMqz4exeAQDQtPNIrJn9GbYs/hI1m/REWnICTu/9E8amFqjfdggA4MTfixAX9RLNuoyBRCJFyLPbefuTSg3y9iUkfXv3xNRvf8DPs2ajVcsWePjoEXbs2o3PBg/Mu5+EhITAycnpzf2kT2/M+/0PmJubo06tAFy+cgVnz1/AN1MmAdDcT3r16I6Nf22BlZUlKvtXwosXL7Fpy1ZUrVIZNWtUL85DLtBnnVpg1OxlmLpoAzo2CsDdpy+x8dAZjOnZTvPbycjU/HbsS8HawgxBr8Kx/uBpVCjrhuYBlXH/ufaUex4ujpBJJPh59XaYGBliaMfmCA7XvufY21jBgQENIsEr8QEMT09PLFq0CIsXL8bmzZshFotRu3ZtrFy5Eg4ODli/fj1++eUXdOnSBa6urujRo8d716Dw9vZGo0aNMGHCBHz55ZcYOnToR5exVatWmDFjBpo3bw4zs/znnS5Iw4YNMWPGDCxfvhzz58+Hn58f+vTpI6ggRtaTB4hfPR8WbXvCdtgkKJMSkLR3E1JPHQAAGLh6wG78dCRsWoL0K2fz8knMLd+7RoYiOgKxS3+GZaf+sP3sSyhTU5By+iCSD24v8mP6WIpXT5C2ayWMGrSHWbcvoEpJQsap3ci6ehIAIHV0g3m/iUg7sAHZ9wIBqJG6awWM67eFYc2mEJtZQpUYi/TDm5F9501vi+zbFyGSGsCwRiMY1WkFZUI0Uveugfzh9WI60n8u89FdRC+eBesufeE47jsoEuORsH0tko5oeooYupeD89RZiFm1AKkXTublk1hYvXcu3aznjxExexpsug2Ew5hpUGdnIe1mIBK2rSlwEXmhs6haEXVObsSdz6YibIPmPIVt+Vwz7wABAABJREFU2A2xoQHKThwK18HdkP4iFLcHT0bkjsN5+dKCXuBKm6EoP3syqm1biOy4RAT/sQ5Ppi8srkP5x2r6+eC3icOwcsdBTJq/AnY2lhjXtzP6tW8OAAh6GYqRM//ADyP6o32jOgXsTVt8kuYB2kLA0yPpU8urNOYP6oA/j17GhDX7YW9piokdGmJQY83DyKOwGAxbuhMzerdEp4CKEItF+N/gDlh2LBBrTl1DbFIaSttZ4fsezdG19psend91bwpXW0scuP4IK49fgYOlGUa1roPBTfLvqS8U1f0r4ufJ47Bm6y5889vvKGVrjVGDeqNPp7YAgKAXLzHu+1/xzdjhaNu0YTGX9tOa8u0MrF21FOtXL4NarYZvBT98PfVHmJm/eVmyfOnviImOwoq1WwEAMpkBpv88D6uWL8bSxfMhlUhRpVoNDBk+GhLJm+nWvp72I7ZtXo99e3YgOek13Eq7Y8q3M1CzljBfoGQ8uovoRb/Cuks/OI7/HorEeMRvXaNV77h88xtiVixAyoUTefkkltbvr3eePUb4rGmw7T4QDmO/0dQ7NwIRv3W1oOudlJs38Oy7aXAZOhzlfp2N7LhYhC5djOitmwEApj6+8F20FC9+mYn4wwcBlQpBE8fB9fORcB48BFJzC2RFRCBiw1pEb92St9/QJYuQHRMDu85d4Ni7L+Tx8Ui6dgXhK5ZBmZpaXIf7j9Qq64z5vZriz9O3MGHrSdibm2Biy5oYVFfz8vhRZDyGrT+MGZ0aoFPVf75uxecNK8PaxAhbrz7Ehkv3YW1ihO41fDCycdVCH/ld2Gr5uGP+sK748+B5TFj5N+wtzTGxc1MMaqYZbf0oNArDFm7GjP7t0Km2v6bOGd4Vyw5dwJrjlxGblIrSdtb4vk8bdK2rGZlz/sFzZMoViIhPwpAFus8wufsSqqqVK2H6tElYt3krfvxlNkrZ2uDzIQPRs4tmgfKnz1/gq29+xKTxo9G6uWae+dbNm0IuV2DH7r04fOIUnBwdMHXiWDRp8KbHef9e3WFlaYG9B49g/+FjsDA3Q/s2LTGoT89iOc5/g+16XTX9fDB74jCs2HEQk+avzGvD9m/fDICmDTti5kL8MKI/OjSqXcDe3hjfrwvsbayx6+QF/HXwFEpZWaCWvy9G9eoA8xLSpu0/fiEO/jUbh7bMg1qtQhmvqug7dgGMTd+sj7N33QwkxoVjygLN9eJZoRaGTlmNE38vwuaF4yGWSOHtXx+te30Fc0tNwPj+9eMAgJO7F+Pkbu1pqa1KOeftS0iqVq6M77+Zio1/bcZPM3+Bra0thg8djO5duwAAnj17jknTvsXXE8ajZQvNtdOyRTNkK+TYuWs3jh4/ASdHR0z+aiIaN2yQt99+vXvBytISew8cxM5de2BpaYH2bVpjQL8+gq93alb0wpxxg7B811F8/fsa2FtbYnzvDujftjEA4PHLMIz4dSl+HN4bHRoG4PT1u1Cr1Xj4IhRDftJ9vl32zSioVWrEvdY8+42Zs1xnm+FdWuKLrsIesUNEgEj99tis/4ArV65g4MCBOHnyJFxdhd3r6V1Xr15FqVKlULbsmykxli1bhp07d+LEiRPvyfl+YWOF3zD+lFwXbUfirFHFXQxBsZ62FC8Gty/uYghK2XUHcFCmu+Dif1k7eRCSbn74vej/I8tqzZF5cFlxF0NQjNqNQOzDq8VdDEGxqxCAh88iirsYglKhnDOeD2xX3MUQDM8NB3Gt/j9/yfVfUPNCIDK3zC7uYgiKUZ8pyDy+rriLIShGLQYjTMjTzRYDV28/tuvfUXbdASTfPF7cxRAUi2otsOuqsANFn1rXADFePdM/5fV/VZlyPki5erC4iyEY5gFsu36IhJ+/KO4ilDg23+kG4/6/K/FrYPyXXLhwAZ999hkCAwMRERGBkydPYv369ejUqVPBmYmIiIiIiIiIiIiISpASP4VUUZoxYwZ279793m2WLFmCunU/zXQJY8aMQXp6OiZPnoyEhAQ4OTlh8ODBGDZs2Cf594mIiIiIiIiIiIgKg0jEvvVUsP9cAKNWrVoICvpnw/7GjBmDQYMGvXcbe3v7wijWP2JgYIDvvvsO33333Sf7N4mIiIiIiIiIiIiIisN/LoDxb9jY2MDGxqa4i0FERERERERERERE9J/DcTpERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwzUwiIiIiIiIiIiIiOjTEouKuwRUAnAEBhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQmOtLgLQERERERERERERET/LSIx+9ZTwXiVEBERERERERERERH9H3v3HRbF0ccB/EuvAqJ0sIACKjZQLGCJvffeSzRGYzTFHo0lxq6x94pixxJ7RaWpgF0QxUJHVECqwB3vHwfIcQdYDlnefD/Pw/PI3s4yc87OzO5vdpYEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBEe1tDNARERERERERERERP8tSspKpZ0FKgP4BAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJjmppZ4CIiIiIiIiIiIiI/mOUOLeeisdaQkREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERIKjWtoZICIiIiIiIiIiIqL/FiVlpdLOApUBfAKDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBEcpOzs7u7QzQURERERERERERET/He//+bW0s1Dm6E1eWdpZ+OZUSzsDVPpigwJKOwuCYlLDCWFPg0o7G4JSqXoN1pMCTGo4ITHwUmlnQ1D0HdvgtJpdaWdDUDpnPkHo8+elnQ1BsbG2xstnIaWdDUGpUs0Wz0JflHY2BKWaTVXEPbpZ2tkQDKNajfA+8GJpZ0NQ9Bzb4kXos9LOhqBUtamGiJCHpZ0NQbG0dcDz0NDSzoagWNvY4N39G6WdDUExrNMMYeN6lXY2BKXSJg8EhUaWdjYEpYaNBfudAqraVOP1Xz6dM5+UdhaI/m9xCSkiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgEhy/xJiIiIiIiIiIiIqJvSklJqbSzQGUAn8AgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwVEt7QwQERERERERERER0X+MMufWU/FYS4iIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwVEs7A0RERERERERERET036KkrFTaWaAygE9gEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERIKjWtoZICIiIiIiIiIiIqL/GCXOrafisZYQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4KiWdgaIiIiIiIiIiIiI6D9GWam0c0BlAJ/AICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHD4Em8FSUhIwOTJkxEQEAA7OzscPnwYx48fR/PmzVGhQoVPOkarVq3Qs2dPTJw48ZP2j4+Px6VLl9C3b9+vybrC3bpzH9v2HcKLsAiUN9BHz05tMaB7ZygpFf5inkvXfbDn8DFExb6GqbERBvfqho6tmkvtc/byNRw4cRqR0bGoYGiAjq1aYEif7lBVUSnpIn01/8A72Om2D6/CwlDewADdOndCn57di/xOrly7DveDhxEdEwsTY2MM6NsL7Vq3ktrn/KXLOOJxAlExMTA2qohunTuhR9eiv2uhYD2R5Xc/CBsPnsTziGgY6uuhb7vmGNy59Sf9fz55EY4Rs5fi6Kq5MDeStDlRcW/R4+c5habp0qIx5owbqrD8lyRNCxM0v3sK/r0n4N31W0Xua96/M6rN+BHa1lZIexmJZ8u2INLtuNQ++k4OqLFkKvSdHJD1PgURezwQMn8dsjMzS7AUipOWloYdO3bAx9sbaWlpcHBwwNgffoClpWWR6eLj47F1yxYEBARAJBKhYcOGGDN2LAwNDeXuf/LECXh4eGDX7t0lUQyFCQgMxK49bngVFgYDAwN07dwZfXr1LPLcuep5De4HDyImp43t37cP2rZpLbXP9RteOHz0KMIjIqGjo4P69epi9IjhKF++fEkXSSHS0tKwc8d2ePt4Iz0tDQ4OtTFm7FhYWloVmS4+Ph7btm5BQIA/RCIRGjRsiDFjxsLQUP545uTJEzjmcRQ7d+0piWIozK27D7Bl3xG8CI+EoYEeenZog4HdO35SGxvy/CXGTJuHA+uXwszY6LM/FyK/+0HYcPBfPI+IRgV9PfRp1wxDPqPPGT57GTxW/ZnX5+TyvH0P24+dw6uoWFQw0EMnV2eM6NEOaqrCv+wICAzErt17EJbblnTpjN69ehXTlnhi/4GDiImJgYmJMfr17Yu2bdpI7XP9xg0cPnIUEREROW1JPYwaOaJMtCX+gXexw80dL8PCUd7AAN07d0Dfnt2KGcPewN6DRxCdM14b0Kcn2rf+Tmqf/iPG4M3bdzJpPfbuhL6+nsLLoUgBgYHYvXt3Xj3p0qVLsfXE09MT+w8cyKknJugrp56EhIRg2/btePr0KbS1tdG2TRsMHjwYampqJV2kr3bz3iNs3n8Mz8OjYKivh94dvsOgru0+sT0Jw+gZC3F4zUKYGVcsdL9/dh3AwdOX4Ht4myKzXmI0a9SFfvfBUDO3gvh9ApKunUPSxRNy99Vp8h0qDC/8HsDbXWuQ4ucJANBybAK9dj2hZmoBcWoK0oPvI+GYG8RJiSVRDIVLS0vDnh1b4OtzHelpaajpUAejx46HhWWlItMlxL/Djq0bcSfgNkQiEZwaOmPkmPFSY5OL505j/ZoVMmk7demOseMnKbwsilBS/U6u1NRU/DjhJwweNBDt2rYtqWKUCF7/EVF+wr+SKCNOnjwJf39/uLu7w8TEBLdv38b06dNx+fLlEvubS5cuRUREhKACGI+ePMX0hcvQyqUJRg/qi/tBT7Bp936IRGIM6d1NbhpPn1tYsGo9+nTpgEaOdXDjpj8WrdkEdTVVtG7WFABw+N+zWLvdDS2bOuPH4YOQ8P49drgfwbMXr/DX9F++ZRE/2+PgJ5g9fyFaNHPBiCGD8PBxELbu3A2RSIQBfXvLTXPD2weLl69Cz25d0MDRET5+N7Fs1RqoqarhuxbNAABnzl/EqrXr0a93TzjVr4fgkBBs2rYDaelpGNRPOHVCHtYTWQ+evsCvSzeibRNHjOvbBXefPMda9+MQicQY3r1dkWlDw6Pwy9KNEInEUtsrGuhh+/zfZfY/cuEaLvoGolvLJgotQ0nRtDSF8+ntUDMo/oaGac92qLdnOV6s3YO48zdg2r0N6u1YAvGHDEQfOgMA0KpqiUbndiLe7y4CB06Grr0N7Bb8AjVDAzyc8GdJF0chli5ZguDgYIwaPRra2tpw37cP06dNw8ZNm1CuXDm5aUQiEebMno3U1FT8NHEisrKysGvnTsyaNQtr166FaoGbi9c8PbF169ZPDsKXlqDgYMyZtwAtmrli2JAhePT4Mbbv3AWxSIT+hbSFN7y9sWT5CvTo1hUNnJzg4+eH5av+gZqaGlq2kARFPa9dx6Kly9CpYweMGDYU7+ITsMdtL6bOnIX1q/+Burr6tyzmF1m6dDGeBAdj5ChJPdnvvg8zpk/Dho2bi6wnf875A6mpqZjw00SIskTYtWsH/pg1C2vWrpOtJ9c8sW3rFsHXk4dPnmHq3yvR2qURvh/YG/eDQ7DR7SBEYhGG9upaZNrnryIwZeFKiESiL/pciB48fYFflm7K63PuPQnFWvcTEInEGFFMn/MsPAqT5fQ5AHDzfhCmrtqGto0d8dOAbgiNiMGGAyeRkJSMKSP7lVRxFCIoOBh/zp2H5s2aYfjQIXj46DG279gJkUiE/v3k593LyxtLly1Hj+7d4OTkBF9fP6xYuSqnLWkBAPC8dg2LlyxFp44dMWL4MMTHx2PPHjdMmzET69asFnRb8jg4BLMWLEJL16YYMWQgHj4OwpZdbhCJRBjYt5fcNNe9ffH3itXo1bUzGjrVg7ffLSz9Zx3U1NTQqrkrACAx8T3evH2HH0YOg0PNGlLpdXV1SrxcXyMoOBhz585F82bNMGzoUDx69Ag7duyAWCRCv0LriReWLluG7t27S/ocX1+sXLlSqp5ER0dj5qxZqGFvjxkzZiA8PBy7d+9GUlLSJ09uKy0PQ0Lx+6I1aNO0Icb074H7wU+xfu8RiEQiDOvZqci0oWGR+H3R6mLbzzuPQ3DoTMldVyuaelVbGE2YiVR/bySe3A+NavYw6DkUSsrKeH/+mMz+aQ8CELNkusx2wyHjoaylhbSHgQAA7QYuqPj9b0i6fh6JJ/ZBRa889LsNhPEv8xDz9xQgS/g3Ylcu/QshwUEYNmostLW1cdB9D/6Y/hvWbtwB3SLGJvPnTEdqairG/TQZWVkiuO3airmzpmLl2s15Y5MXz5/BwtIKP/86TSq9QXn5E3VKW0n1O7mSkpIwb/4CxMbGfoviKBSv//5blJS4OBAVjwEMBXn//j2MjIxQp04dAMDLly9L/G9mZ2eX+N/4XDv2H0H1qlXwxy/jAQCNHOsiK0uEvUdOoG+XDtDQkL1I27r3IFo2bYSJoyUzwZ3r18X7pBRscz+M1s2aQiQSY/ehY2hQtzbmT52cl87WuipGTJqG23cfoGG92t+kfF9iz779qGZdFdN/k9xAb+jkiKysLOw/dAQ9u3WBhoaGTJode/aiuWtT/DhmdE6a+khKTsKuve55AYz9h46gmUtTjBk5HADgWK8uIiKjcOLfM4IPYLCeyNpy5DTsqlhi3oQRAIAm9WohSyTCrhPn0b9jS2jKucGRmZWFQ+c8sfnIaajLmZ2nrqaG2tWrSm0Leh6Gi76BGD+gG+rZVyuRsiiMkhIsh/ZAjSXTgE98qMhuwa+IPnIOQb8vAgC8uegFtfL6sJs7KW8AazNlDLKSUuDfazyyMzMRd+46RGnpcFg9G88Wb0J6eHRJlUghgoKCcPPmTcybPx8NGzYEADjUqoWRI0fi9KlTGDBwoNx0N27cQGhoKDZt2oRKlSsDAGysrfHjjz/ixvXr+K6V5AmvhIQEuO3Zg7NnzxZ6k1tI3Pa5w8baGlN//w0A0LCBE7KyRDhw6DB6dO8mt43dtdsNzVxdMG7sGABAAydHJCUlYffevXkBjAOHDsO5QQNM+mlCXjorCwtM+u133Lx1G81cXb5B6b5cUNBj3Lp5E/PmLUCD3Hri4IBRI0fg9OlTGDCgsHpyHaGhodi4aTMqVZLUE2sba4z/cRxu3LiO777LV0/cduNcGaknOw56oHrVypg9aRwAoLFjHckNkKP/ol/n9nL7nczMLBw5cxHbDxyV28YW97mQ5fY58ydIxhBN69XM6XMuYEARfc7Bc9ew+chpaKjJv4T495ofTCuUx/yfhkNFWRmN6tTAu8T3cD9zFb8M7Q1VVeE+Dem2d5+kLZkiCfw3aNAAIlEWDh48hB7du8ttS3bu3o1mrq74YexYSRonJyQlJWGP2968G0kHDh5Cw4YN8PPEn/LSWVpYYPKvv+HmrVto5ur6DUr3ZXa7H0A166qY8Ztk5rKzU31kiURwP+yBXt06y/1Otru5o4VLE4wfMxIA0NCxPpKSkrFr7/68AMazFy8AAK5NGsHczPQblUYx9u7dCxtra0yZMgWApJ5kiUQ4cPAguhdST3YVqCdOTk5ITkqCm5tbXj05fOQItLS0MGfOHKipqcG5YUNoaGhg48aN6N+/P4yNjb9dIT/TtkMnYVu1Ev78+XsAQJP6DsgSibD72Bn069QGmoW0r4fPXcbWgyeKbT9T09KxcMNOGBka4PXb+BIpg6Lpd+mPjPAXeLtrDQAg/fEdKKmoQq9DbyRdOY3szAyp/cXJ75GR/F5qm+53naBmZoHYpTMhzvlMr0NvpD0IQLz75rz9MmMjYTp9CbTqNEBaoG8Jl+zrBAc9wu2bvpg9bxGcGjYCANR0qIMfRg7C2dMn0HfAELnpvG944nnoM6zdtANWlaoAAKxtbPDzj6PhfcMTLb6TPH3w4nkoqlW3g519zW9Qmq9XUv0OAPj6+WHjps1IS0v7NoVRFF7/EZU4sViMdevW4fDhw0hKSkLDhg0xZ84cWFnJf0r/5MmTeeOe/C5fvpy3AsTZs2exdu1aREREwNraGtOmTUOTJoqdMMswVz7Xrl1Dr169ULduXTRp0gTTp09HYqLkUUx/f3/069cPderUQbdu3XDmzBnY2dkhIiIC06dPx9q1axEVFQU7Ozt4eHhg2LBhAIDWrVvDw8Pji/Jz584dDBs2DE5OTmjUqBFmzJiB+HjJoG369Ok4duwYbt26BTs7O8V8AV8pIzMTdx8GoVnjhlLbWzZ1RmpaGu4HPZFJEx0bh/CoaDRv3EAmTWR0LMKjohGfmIj3Sclo2rC+1D7Wla2gr1cOvv53FF8YBcnIzMT9Bw/h0qSx1PbmLk2RmpaGh4+DZNLExMYiIjIKLo2l0zRzaYqo6GhEREYBAP6a+wfGjhoutY+aqioyMqQHxELDeiIrIzMTgY+fomXDelLbWzeqj5S0dNwLDpWbzvvOI2zzOIsR3dvjp4Hdi/072dnZWLbzIKpammJgp1bF7l/a9OrYwWH9PETsPY67I6YWu79WZQvo2lVFzImLUtujPc5Dp3oVaFeT3Iw1auuK12evST0uHHP0HJRUVGDUTrg3knIFBARAU1MTjo6Oedv0DQxQu3Zt3Pb3LzRdYEAALC0t84IXAFCpcmVYWVnh9u3bedsOHjiAgIAAzPrjDzRq1KhkCqEgGZmZuH//gUwb28w1t419LJNG0sZGwqXAgKqZiwuioqIRGRkFsViM+vXroWOH9lL7WFlJBmhR0cK/yAnMqSf189cTfQM41K4N/3z/3zLpAnPqSaV89aSSpJ7kT3fo4AEEBgRg5qw/4FwG6smdh8Fo3shJavt3TRoiNS0d94NC5KbzDbyHnYeOYWjvbvhxaP/P/lyoMjIzEfD4GVo2rCu1vdUn9jkju7fDTwN7yN3nQ2YWNDXUoaL88RJDv5wOMrOykJKerrAyKFpGZiYe3L+Ppk2l2wVXV1ekpqXh0SP5bUlkZKScNC6IiopCZGQkxGIxHOvXR6cOHaT2yb1AjBZwW5KRmYl7Dx7BtbGz1PbmTZsgNS0NDx4Hy6SJiX0tGcM2kW4Tmrs0QWR0DCKiJGPY0Ocvoa2lBTNTk5IrQAmQ9Dn30bRpU6ntrq6uSEtLw6NHj2TSxObUkyZy0uTWE0DStzs3bCi1XJSrqyvEYjECAgNLoDSKkZGZicBHT9DCWXr8/V1jJ0n7GvxUbjqfOw+w/fC/GN6zEyYMkf9Eeq51bodhaKCHzi2FPXEgj6oqNG0dkHb3ptTm1EBfKGtpQ6NajUISfqRcTh8G3QYh+fp5ZLzM+Q6VlJAedB/JXhek9s2MkdQh1YrCDwbeCbgNTU1N1HP8eE2nr2+AWrXrIuD2zcLTBfrDwtIqL3gBAFaVqsDSqlJeuuzsbLx88RxVbQQ+QStHSfU7AJCcnIwFfy1Endq1sXDBgpIrRAng9R9RyduwYQPc3d2xYMECHDhwAGKxGN9//32h9xKfPHkCZ2dneHl5Sf2YmZkBAPz8/DBlyhQMGDAAx44dQ5MmTTB27FiEhsq/pvhSDGDkePfuHX766Sf07t0bZ86cwbp163D79m0sXboUr169wqhRo2Bra4tjx45h7NixWJCvI5g1axZGjRoFU1NTeHl5oW3btli7di0A4PDhw+jUqehHZ+W5f/8+hg4diurVq+PQoUNYvXo17t27h9GjR0MkEmHWrFno2LEj6tevDy8vL4V9D18jKuY1MrOyYGUuPXiyNJNcnITl3HjP71WEpJO1NDeT2m6RMxsrPDIaujraUFFRQczrN1L7JCUnIzk5BVGxrxVWBkWLjolBZlYWLCzMpbab55Q3PKf8+YWFRwAALAukschpHCJyBiaVraxgamKC7OxsvE9KwpnzF3Hxiie6du6o8HIoEuuJrMjXb5GZlYVKZtKz6yxNJOuov4qW/9hvTZvKOL5mPkb17ABVleKb84u+AXj47CV+HdZH6uaSUKWFRcPTvi2CpiyGKLX4G1+69jYAgJSnL6W2p4a+knxuWxXKmhrQrmKJlKcvpPbJeBOPzMQk6NpKP7EiROHh4TA1NYVKgfe6mJmbIzIiotB0YeHhsLCwkNlubm6e164AQKfOnbFt+3a4uAj/RkFMdG4bK10uczNJ+xkhp40NDw8HgMLb5cgIKCsr44fvR6NpgcCIj68fAKBy5aLXaRaCwuqJuZk5IiMLryfhYeEwl1NPzMzNEZGvfnXs1Blbt+2Ai4vwL/qiYiX9TqUC/Y5Fbr8TJf8mco1qVXFk00oM79NN5nv8lM+FqrA+xyqvz5HfX9a0qYwTa+ZhVM8OUCmkz+nbrjnCY+LgduoSklJS8eDpCxw46wmXerWgL+ClgWKiowtpS3LHXrLnTHhYbltSII35x/ZHWVkZY8d8LzMDzcdXMku6cr5AodBEx8QiMytLdjyacx7l7zdyvcodw5oXaF9zx2sRkjHesxcvUa6cLuYtWoZu/Yeic9/BWLBkBd6+E/bs+pjoaGQVWU/kjOvDwgBInrrJzyyvnkTgw4cPeP36tcxxDfT1oa2tLdX2Ck1UbFxO+yodjLI0lbQvr6Lkj2Fr2FSBx/rFGNG7C1SUC28/b917hLPXffHH+JFQVhb+O/4AQLWiCZTU1JAZK31Nkxkn6WtUTczlJZOi33UAkJ2NhBPuHzdmZyPh6C6k3ZOehKBdTxJkzIwO+8qcl7yI8DCYmJrLjmHNLBAZGV54urBXMLeQfc+bmbkFIiMk6WKio5CWlopnIU8wfsww9O7aFuPHDMPVyxdk0glBSfU7AKChoYHNmzbi999+hZ7A3ylUEK//iEpWRkYGduzYgZ9//hktW7aEvb09Vq1ahZiYGFy4IL+9DAkJgZ2dHYyMjKR+ctvyrVu3ok2bNhg2bBhsbGwwbdo01KpVC7sV/B5NLiGVIzY2FhkZGTA3N4eFhQUsLCywadMmiEQiHDx4EBUrVsTcuXOhqqoKGxsbxMbGYunSpQCAcuXKQVtbcvPUyEhy8aevrw8AMDQ0hKam5mfnZ8eOHbCzs8Ps2bMBADY2Nli5ciW6d+8OLy8vtGjRApqamlBTU8v7m6UtJTUVAKCjrSW1XUtL8ntqquzji4Wl0dbSzPk8DZoaGmjl2hjHzlxA1UqWaN64AeIT32PNtj1QUVFBevoHhZdFUVJScsunLbVdO/c7SUstNI12gTQfv0fpNEHBTzBpimTNVNvq1dCnZ/Ez8UsT64ms5Jwy62hJtxXaWpLHhlPS5A/ejA0NPuvv7D11CXXtrOFU0/bzM1kKMuMTkRn/6S8kVNXXBQBkvU+W2p6VlCL5XE8Xavrl5O6Tu5+qnu6XZvebSU1JkWkfAMk5VLB9KJjOwlz2orlgusIeHRWilFTJ/23B70NbW357CXx+G5srKjoaW7fvgI21NZwbNJC7j5CkpH5hPUlNgbnF/1c9SU6RtLG5fW+u/H2IPEYVil4zu7jPhaok+5yGtWwxrGsbrNl3HGv2HQcA2FWxxF8TR3xxfr+F3HGGbFsi+V1uW5LT/hQ6xiuiLdm2bTtsrK3RsKFw25KPbWXB80byu7zzpvDxmvQYL/T5C7x5+w6d27dFr+5dEBYegV37DuKXGbOxefVyaH3BtdO38GX1pJA0+epJSor8vgwovs0ubbntSaHtayHL1xhXKP4F9skpqfh7426M6d9dJgAtZMpakmCtOF267Nk5vytryv4/S6Uvpw+dxi2RdOkksuVcK+anWtEEBr2HIyPsOdIfCvdJnVypRYxN0oodm8gGMLS0tPPSvXj+DAAQGxONkd//CFVVVVy9fAGrVyxGZmYG2nXooqBSKEZJ9jtqamqwspT9vsoCXv8Rlazg4GCkpKRITa7R09NDzZo1cfv2bXTpIttWPnnyBK1ayV/BQywWIzAwENOnS7/HqVGjRoUGRL4UAxg5atSogS5dumDcuHEwMjKCi4sLWrZsibZt2+LZs2eoWbOm1IsrnZ2dizja1wsJCZGZ/Wpvb49y5crhyZMnaFHgBU1CIC7mnRxKcmZ8i8VFp8mdafPbuNFQU1XD0vVbsWTdFmioq2Nw725ITUuXu66qUBT3nhJlOS8rys6WfSlmfgVfcGRsbIzli/5CTGwsdrm5Y/KU6djwz0poasqumSkErCeyvqSefK77Ic8R/CIcy34b+9XHEip5dSe/bLEYKHYfYb1bSCwWy9QPsbjwNkJJqfDZiUWde8pFpBOyYtsGOeUSF9PGyjvfwsLDMXP2HKioqOCPmdOhLLAnmOTVk6LqcpH1pIh0ZWX2a0HFtbFKZbRcX6rYfvgr2oPF2w/gpKcfRvXsAGcHW0TFvcPWI2fw8+L12DDrZ8H2xdlFtKuA/O+kuP5CXr0KDw/HzD9mQ1lFBbNmzRRcW5JfceNRee3rp34nv078ESrKKrC3lSz1UqdWTVSpVAmTps3CxSue6NapQ1GHKTXF1RP530kxdUtZ+RPGgcJto4prT74m7//sOgDjiuUxoHPbLz5GqSiuzMWcW7oubaCkrIykK6eL3E/VxALGk+YAYjHebFkGCOz9mIocwxY5pslpV2o61MGsPxeidt160NSU3NCv79QQiQkJ2O+2C23bd/6q/k3RvlW/8//u//H6j+hTtG7dusjPL1++LHd7TEwMAOQt/5TL2Ng477P8EhMTERsbC39/f7i7uyM+Ph516tTBlClTULVqVbx//x6pqakwNTX9pON9DQYw8lmxYgUmTJiA69evw8fHB1OmTIGTkxM0NDRkOlt1OS83VKTCBrLZ2dlSa6MKiW7ubIECM/dyZ1vpFpiNBQC6OvLT5M7qyp1doK2liekTx+Ln74chNu4NTIwqQltLE6cuXs1bAkKIdPJmUEjPwMmboaYjOwNFR0cya6fgC7dSC0lTsYIhKlYwRN3aDjAzNcVv02fhhrcP2rb+TjGFUDDWE1m5s2ALrg+eOwtWR/vrZyJevnkHejracKnn8NXHEqqsxCQAgGo56WVKcmfVZCUm5828KbhP7n65xxAKd3d3uO/bJ7XN1dUVCQkJMvumpabmtR/y6Ghry32RX2pqKrSLSCdkue1hwXLlzWqTUy4d7c9rY+/df4D5C/+GlpYmlixamPdov5Dsd98Hd3fpeuLi6oqEBNnlWFLTiqknOkXUE+0yWk9yn8gptN8pejbs/xvd3JmaBZ5MzO1z5PXDn+L1uwQcu+KDkd3b4cd+ktlbTgBq2VRG/ykLcdLTF/3aC28CDvCxrSi8XZCt+9p5Y5NCxngFzpd79+9jwV8LoaWlhaWLFwmyLckvd2yVJjP2KmoMW9h3Ij1eq2Uv+/4+h5r20NHRRuiLV1+Z85KTWw8Kli+1qD6nmDQ62tp5M67LYh+tW0j7mlLEuP5TeAXcw0Xv29i55A+Is7MhFonyAuxZIhGUlZQEGwDMfWpCWVO67LlPXoiLeapC27EJ0h/fy3txtzwatrVQ8YepyP6QjtiVc5D1Rv5SXaXpoPseHHTfI7WtqWtzJBYyNtHWKXwWvLaODtLkfG+SJzok54eBQXk0bCT7wlgn50a4dzcACfHxKG8onCcnv0W/81/w/3j995/0Hwy+lZbcNqfgPW0NDY28d0Dn9/Sp5D1M2dnZWLRoEdLT07Fx40YMGjQI//77L7Kysgo93ocPil0FhQGMHPfu3cPp06cxc+ZMWFtbY8SIEXlvWh8wYACuXr2KjIyMvP+Uhw8fFnm8r43u29nZISAgQGpbcHAwkpOTYWNjo5C/oWjmpsZQUVZGZLR0lC0i5/fKlrJraltZSC7eIqNjYGtdJW97ZM6a/7lpfG4HopyuDmrXsEPVSpLHIeMTEhH39p1UOqExNzOFsrKyzAtfo3LW264k59HO3DVyI6OjUc3G+mOanGNUsrJEWloafG/egp2tLSzyvReies7+b9+9U2xBFIj1RJaliRFUlJURERMntT3396oWX//YvFfgQ7RoUAeqqmVnnfbPlRwiWddU26Yy3t8NytuuYyNZYzw5OBSilFSkRcRA20Z63XF1I0Oo6ekiuZCX15aWjh07yjzx5+vri4CAAIjFYqmL96ioqCKX9bG0tJT7Iq2oqCjY2cneTCoLzM3Mim5j5XwfljntRVRUNKrl9KfAxzY2/3d41fMalq/6B1aWlvhr3lxUrFhB4WVQhA4dO8HZWfqlub6+PggMCJSpJ9FR0UXWEwtLSzyXU0+io6Jha1c2lp8ryCK334mRvsETEZPbhxS/Hvn/E0uTisX0OV8W8I958w7Z2dmoY2cttd3a0gz6ujp4HiHcF1bntSVRBdsSyRr28s6Z3OU5oqKipNqS6JxjWFXK35Z4YsXKVbC0tMRf8+ehYsWKCi+DouWOYSMLtq8547VKVrJjWKuc5eeiomPyxqSS3yXHqGxlieSUFNzw8YO9bXVUzfc+IbFYjKzMLBgIeL12s5x6Eh0l/W6D3Hoiv8+RfE/RBepJXppKlaClpYUKFSrkbcuVkJCAtLQ0uccVCgsT45z2RPrdObm/V7H8skDdVd8AZGRmYvCvf8p81mzAD+jUoilm/zTqi45d0jLjYpAtEkHVSHr8rmos+T0zpvB3mqgYGEK9kjXeXj5V6D7aDVxRYcREZMZEIm7dXxAlCPO6r33HLmjoLB1QuOnrhTsB/nLGJpGwtCr8/WIWllZ4Hir7QvjoqChUt7MHADx6eB+xMdFo1aa91D4ZHzKgrKwM3XLlvqY4ClfS/c5/xf/j9R/RpyjsCYvi5L7iICMjQ+p1Bx8+fMhbUjm/Bg0awNfXF+XLl8+7B71u3Tq0bNkSHh4e6Nu3b97x8ivseF9DmNMWSoGuri7c3d2xbNkyvHr1CiEhIThz5gyqVKmC8ePH48OHD5gxYwaePXuGa9euYdWqVUUeL3cmTe76Yp9r5MiRePLkCRYsWIDQ0FDcvHkTv//+O2rWrJm3Vpm2tjZev36d90LS0qahro46texx3e+21BMk13xvQ1dbGzVsbWTSWJqZwszECJ4+t6S2X/O9BUtzyWcAcOL8ZWzYJT279PC/Z6GsrIymDRxLoDSKoa6ujjoOteDl4yf1ndzw8YWOjjbsbGVvBlmYm8HUxAQ3vH2ktt/w8YWFuTlMTUygoqKClWvX47DHMal9/O/cBQBUrSLcl0KynsjSUFdDPftquHr7ntR3cuXWXehqa6GWTZWvOn5icgrCY16jjp3sd/v/JDU0DKnPw2HWS/rCxbRnOySHvEDaK8mL7d5c8oZxp5ZQVv/4NJtpr/YQZ2XhzVW/b5rn4lSoUAG2trZSP46OjkhLS5MKcicmJODhw4dwdCy8njs6OiI8PBxhrz7Obg179Qrh4eGoX0Q6IVNXV0dtBwd4+/hInTte3j7Q0dGBnW11mTS57egNb2+p7V7ePnmfAcCt2/5YumIlatawx4plSwQbvAAk9aS6ra3Uj6OjE9LSUhGYv54kJuDhwweo7+hU6LEcHZ0k9SQsXz0Je4Xw8DA41i88nZBpqKujbk07XPPzl6onnr7+0NXWRs3q1kWk/v+joa6G+vbVcPX2XYX2OVamkmD83QI3Al5GxSIxOQUWxsK9aV94W+Kd05bIjtfMzc1hamoCL6+CbYl3gbbkNpYtX4EaNWpgxfJlZSJ4AeSOYWvihs9Nqe/kuo8fdHS0YV9dXvtqBjMTY1z39pXafsPHL2d8awx1NTWs3bQN+w97SO3jc/M2PmRkoF5t4T4pWlw9sS20npjCy8tLart3Tj0xyaknjo6OuHXrFjIyM6WOq6ysjLp165ZQib6ehroa6tWwxbWbgdLt681A6GproWa1L3s57vf9umHH4j+kfrq3bg4A2LH4D3zfr5tC8l8isjLx4eljaNdvLLVZu34TiFNTkPFC9kZ8LvUqkvPqQ2iQ3M81HRxRYeQkfHj+BLHLZwk2eAEAhhUqopqtndRPPccGSEtLxZ2Ajy8iT0xMwOOH91HPsfB3AtVzbICI8DCEh73M2xYe9hIR4a9Qr74k3cP7d7Fm5ZK8l3oDksCoj/c12NeoJbhVLEqy3/kv+X+8/iMqSblLR71+LT3x4PXr13ljkoIMDQ2lJtBraWnB0tISsbGxMDAwyLs3/anH+1IMYOSwsbHB2rVr4efnhx49emDgwIFQUVHB1q1bYWJigt27d+P169fo2bMnFi9enBdlKoytrS1atGiByZMn4+DBg5+dn7p162Lbtm14+PAhevTogcmTJ6N+/frYuXNnXufbo0cPpKWloUuXLoiNFcZjo8P69sTjkFD8uWw1/ALuYtu+Qzhw/BSG9OkOTQ0NpKSm4tGTp0hI/PhI7Ih+vXDV2w8rN+3AzcB7WLFpO656+2H0oI/fce/O7fHoyTOs3e6GwAePsHXfIew9ehL9u3cS9NJAADCof18Eh4RgweJluOUfgF1u+3DY4zgG9u0DTU3Jd/I4+AkS8j2uNWRgP1y74Y01GzbhdkAgVq/fhGs3vDFiyCAAkgHPgD69ceb8Rexy24c79+7j6PGTWLF6LRzr1YVzA2HfZGI9kTWqZwc8evYSM1Zvh8/dR9h06F/sPXUJI7q3h6aGOpJT0/Dg6QvEv//8R1xDwyQzeRTxJIeQqJbTgUGjulCv+PFFkE8Xrod5v05wWPsnjNo1g8O6uTDv1wkhc1fn7RO6fBs0jCug4altMO7UElUnj0DN5TMQvu0Q0sOFO0M4V+3atVGnTh0sW7oU586dg4+3N2bOnAkdHR106tw5b7+wV68Q+uxZ3u/NmzeHhYUFZs+ZA09PT3h6emL2nDmoUqUKmjdvXhpFUYhBA/oh+EkIFi5agtv+/tjtthdHPDwwoF9faGpqIiU1FUHBwVJt7OCBA3D9hhfWrt+A2/4BWLN+A67f8MKwoUMASGaQrFqzFtpaWhjQvx/CwsIQFByc9xP35k1pFfeTOdSujdp16mDZsiU4f+4sfHy8MWvmDOjo6KJTp3z1JOwVQkNl68mc2bPh6XkVnp5XMWf2bFSpUgXNynA9Gd63Ox4/fY7Zy9fBN/Aetrofwf4TZzC0d9ecficND588Q3xi4Ut2/D8Z1bM9Hj57hRmrd8D77iNsPHQKbqcuY2T3dl/c55TXK4cBHVvC7dQlrD9wEv6PQvDvNT9MWrwBZhUN0aNV0xIs0dcbOGAAnjx5goWLFuH2bX/s3uOGI0c9MKBfv0LbkkEDB+L6jRtYt349/P39sXbdely/cQPDhg4FIGlL/lm9BtraWhjYv3+Za0uG9OuD4JCnmL9kBW76B2Ln3v045HECg/r2zjeGDZEeww7oC08vH6zesAW3Au7gnw2b4enlg5FDBgLIHcP2xOVrN7Bh204E3L2HI8f/xdJ/1qFpo4aoX7d2aRX3kwzIqSd/L1qE27dvY8+ePTh69Cj6f1Y9WYfrN25gaE49AYC+ffogITERc2bPxs2bN+Hh4YEtW7agY8eOMDY2Lo2ifrIRvTvj0bMXmLVyE3zvPMDmA8ex7+R5DO/V6WP7GhKK+M9YpsXMuCJq2FSR+qloqA8AqGFTBWYCDogCQOLZw1CvUh0Vx/wOzVr1od91IMq17Y7Ec0eRnZkBJU0tqFe1hbKu9BNHahaVkZ2ZIX9JKFU1GA4Zj+z0NLw/exRqZlZQr2qb96NiINyJFrlq1a4Lhzr1sGrZ37h47jT8fG7gz5m/Q0dHFx07fQxKhYe9lHriwrV5S5hbWGL+7Om47nkZ1z0vY/7s6ahcxRquzVsCkDzxUb68IRbOmwWva1dx+6Yv/po7E2EvX2LE6HHfuqifpCT6nf93/9XrPyJFsbe3h66uLm7evJm37f3793j8+DEaNmwos//BgwfRqFGjvOXtACA5ORkvX75EtWrVoKSklDcJI7+bN2+iQYPCA9NfQim7uLeGkVw3b97EsGHDcPny5bxHg8uq2KCA4nf6DNf9bmPH/iMIj4xGxQrl0bNjOwzoIblZcufBY0ya/RdmTPwBHVt/XAf5xPnLOHD8FOLevIOZiTGG9O6G9t81kzrupes+2HP4GKJj42BiXBE9O7RF7y7SkXZFMKnhhLCn8me9fCkvHz/scd+PiIhIVKhQAd06d0TfXj0ASNZX/33mbPw+eSLat/n4Ip5TZ8/jsMdxxL15AzNTEwzo2xttW318r0V2djZOnT2Pk6fOIComBvr6emjVojmGDRqg8He0VKpeg/WkAJMaTkgMvKTQY169fRdbD5/Gq+jXMDLUR9+2zTG4SxsAQMDjEPy4YDXmjBuCLi1k13c9dc0X8zftxfE182FuJH0Bc9E3ALPW7MCh5bNRpQSDGPqObXBarWSWIjJs7owml93g23oo3l2/JbXt3ujpiNjz8WmkSmP6w/qXUdC0MkPq83CELt2CyH0npI5X3sUJNZZMhV7dGsh4E4/IfScQMncNsnPWcFSUzplPEPr8uUKPCQBJSUnYunUr/Hx9IRaLUbNmTYz94Qep/mja1KmIjY3Frt2787bFxcVh86ZNuHPnDlRUVODo6IixP/wAw0LWBF65YgXu378vdYyvZWNtjZfPQhR2PADw9vGF2z53REREoEKFCujapTP69OoJQNLGTp0xE79NnoR2bdvkpTl99iyOeBxDXNwbmJmaon+/PmjTqhUA4O69e5g2849C/96QQQMxdPAgheW/SjVbPAt9obDj5UpKSsK2rVvg6+eLbLEYNWvWwpixY2Fp+XGJgenTpiA2NhY7d31cpzouLg6bN2/E3bx64oQxY8fC0FD+zZGVK5fjwf37Usf4WtVsqiLu0c3id/wM1/z8sePgMYTl9Du9OrTBwO4dAQCBD4Pw85xFmPnTGHRq1Uwm7ZkrN/D3uq04vGkFzIyNPvvzr2VUqxHeB15U6DGv3r6HLQX6nCFdJOOQgMchGLdgDeaMG4KuLRrLpP33mh/mb9qLE2vmSfU52dnZ2H/WEx6XvRD1+i0qGuihUR17jO/fFeX1FLt8h55jW7zIF3xTBG8fH7jt3YfIiAhUqFgBXbt0Qe9evQBI3mExbfoM/PrLZLRr+/GlwqfPnMVRDw/ExcXBzNQU/fr1Q5vWOW3J3XuYPnNmoX9v8KBBGDpksMLyX9WmGiJCil7m9nN5+d7ELvcDiIiIQsUKhujWuSP69ZTcaLz74CF+m/knpkyagA5tWuWl+ffsBRw+dgKv37yFmakJBvXpibatWuZ9LhaLcercBZw4fQ5RMbHQK6eL1i2bY/jAftDQ0FBo/i1tHeQui/c1vH18sHfvXkRERKBixYrokq+e3L9/H9OmT8evv/yCtvnqyZkzZ/LqiampKfr36yfzAs6HDx9i2/bteP78OfT09NC6VSsMHToUqqqKXfXZ2sYG7+7fUOgxPW8GYtuhEwiLioWRoQF6d/gOg7pKxt+Bj4IxYe5y/DF+JDp/5yKT9vRVb/y1YSc81i8uMjCx7dAJbD/8L3wPb1No3gHAsE4zhI3rpdBjatVrBP0u/aFmYgFRwlskXTuHpEsnAUjeYWHy6wK83b0WKb5X89KUHzgW2vUaIXLaaJnjadg5wOSX+YX+vcRTB5F46vMnTham0iYPBIVGKux4uZKTkrBj6wbc9PNGtjgb9jVrYfTY8bCw/LiE1Kxpv+B1bAy27tqfty0u7jW2b16Hu3cCoKqiinqODTBq7HipsUl0VCTcdm3Fo4cPkJaWiurV7TB4+GjUrKWYwGgNGwvB9zsFxcTGYsTIUTLHUJSqNtV4/ZdP58wnCjvWf0nK1sKvu0g+nTF/fXHaVatW4cCBA/j7779hYWGBZcuWISIiAqdOnYKysjLevXuHcuXKQVNTE9HR0ejWrRsaNWqESZMmIT09HStXrszbX0NDA15eXhg7diymTJmC5s2b4+jRo9i3bx88PDzyXoGgCAxgfCEGMP5/lUQAo6wriQBGWVcSAYyyriQDGGVVSQUwyrKSCGCUdSUVwCjLSiKAUZaVRACjrCuJAEZZVxIBjLKuJAIYZV1JBDDKupIIYJR1JRXAKMtKIoBR1pVkAKMsYgDjyzCA8fm+JoAhEomwcuVKeHh4ID09HQ0bNsScOXNgaWmJiIgItG7dGosWLUKvnGDqo0ePsCJnYmN2djZcXFwwY8aMvOWoAOD48ePYsGEDYmJiUK1aNUyZMiXv9QeKwpd4fwPz58/HsWPHitxn/fr1aNpU2I/WExERERERERERESmCkjLfbvAtqaioYMqUKZgyZYrMZ5aWlnjyRDoQV6tWLezYsaPIY/bo0QM9evRQZDZlMIDxhRo1aiTzn1qYn376CcOHDy9yH6GvbUpERERERERERERE9C0xgPENGBoaFrq+OBERERERERERERERyeJzOkREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDd2AQERERERERERER0belpFTaOaAygE9gEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeColnYGiIiIiIiIiIiIiOg/Rplz66l4rCVERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOKqlnQEiIiIiIiIiIiIi+o9RUirtHFAZwCcwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHBUSzsDRERERERERERERPTfoqTMufVUPNYSIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhzV0s4AEREREREREREREf3HKHFuPRWPtYSIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgERyk7Ozu7tDNBRERERERERERERP8dae6LSjsLZY7WoBmlnYVvTrW0M0ClL9X7aGlnQVC0XXrjfeDF0s6GoOg5tkXK5lmlnQ1B0flhIdJPbyrtbAiKZudxCH3+vLSzISg21tY4rWZX2tkQlM6ZT/DyWUhpZ0NQqlSzxdk7maWdDUHpWF8NaW5/lXY2BENr6B9IXj+1tLMhKLoTluLCvYzSzoagtKurjtTtc0o7G4KiPXo+noeGlnY2BMXaxgap1w6UdjYERbvFADwLfVHa2RCUajZV8c9JznXNb3I3JV7rFGBjbY0Xoc9KOxuCUdWmWmlngej/FpeQIiIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHD4Em8iIiIiIiIiIiIi+qaUlDi3norHWkJERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCo1raGSAiIiIiIiIiIiKi/xhlpdLOAZUBfAKDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBEe1tDNARERERERERERERP8xSpxbT8VjLSEiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBUS3tDBARERERERERERHRf4ySUmnngMoAPoFBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgqNa2hkgIiIiIiIiIiIiov8YZc6tp+KxlhARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgqJZ2Buj/j+/Dp1jncQHPo17DUE8X/Vs1xtD2rlBSUpK7f0ZmFtzOe+GUTyBi3iXCpLw+Ojapi1GdWkBN9WMVfRH9Gv8cPoeA4BdQUVGGk21V/Nq/EyyNDb9V0b6Y3/0gbDj4L55HRKOCvh76tGuGIZ1bF/qd5PfkRTiGz14Gj1V/wtyogtRnnrfvYfuxc3gVFYsKBnro5OqMET3aSX1vQuX7MgbrvR/h+dv3MNTWQL96NhjqZFvodxIWn4weO8/JbLepoIfDw9t98XGFxOfJK6w7443QmLcwLKeNAS51MaylU6F5/5CZhc0X/HAmMBjxyWmwNTfCuPaN4WJfRWqfpjPWI0sslkqrpa4Gv8U/lWRxFCYtLQ07duyAj7c30tLS4ODggLE//ABLS8si08XHx2Prli0ICAiASCRCw4YNMWbsWBgaym8zTp44AQ8PD+zavbskiqFwmhYmaH73FPx7T8C767eK3Ne8f2dUm/EjtK2tkPYyEs+WbUGk23GpffSdHFBjyVToOzkg630KIvZ4IGT+OmRnZpZgKRQjIDAQu/a44VVYGAwMDNC1c2f06dWzyPP+quc1uB88iJiYWJgYG6N/3z5o26a11D7Xb3jh8NGjCI+IhI6ODurXq4vRI4ajfPnyJV0khfiQnop/3Vfi3s2L+JCeCpsaDdBj2FSYmFctMl34i8c4c3AtwkIfIjtbDKuqNdFl0C+wqlozb5/s7GxcPbULPpcPI+FtDMpXNEfzDoPRrP3Aki7WF/MJjcI6z7t4HpcAQx1N9G9gh2GNa35S/5AlFmP4rnPQVFXF9mHtpD578SYR/1wOhP+rWKgoK8Gpsgl+a+MEy/LlSqooCuP76jU2+AYh9F0SKmhroG+dqhha36bQ7yQ8IRk99lyW2W5jWA6HhrQCAIizs+Hx8CUO33+JyPcpMNTSQAtrU/zQyB66GmolWh5F+JCeihN7V+LuzUv4kJ6KajWd0Gv4J5w3zx/j1MG1CHv2COJsMSpZ10S3QZNhZf3xvPG5fBT7N8+VSdus/QD0Gz1L0UVRCN8XMVh34z6ev3kvOW/qV8PQhnaffN6M2HsZmmoq2DawldRnJx+8wJ7bwQiPT4aRrha6OlTF6CY1oKos/Pl1AYGB2L17N8Jy+pwuXbqgd69eRX4nnp6e2H/gAGJiYmBiYoK+ffuibZs2UvuEhIRg2/btePr0KbS1tdG2TRsMHjwYamrCP298Hz3DuuOX8TwqDoZ6Ouj/nTOGtm1a9PXfRR+c8r2HmPhEmJTXQ8dGdTCqg6vUdczVO0HYevoaXsa+RUU9XXRuXAejOjYrE9c6gGQMu3PHdnj7eCM9LQ0ODrUxZuxYWFpaFZkuPj4e27ZuQUCAP0QiERo0bIgxY8bC0LCC3P1PnjyBYx5HsXPXnpIohkJlfkiB7+kVeP7gAjIzUmFu3QBNu05HeWPrTz7Gi4eXcW73BHQbtxsWNo3yticnxsL31FKEP/GCWJwF08r14NxhMoytapdEURSmJK91RCIR9u3bh0sXLyIpKQnVqlXD6O+/h729fUkX64sFBAZi1+49eW1s1y6di21jr3p6Yv+BgzltrDH6yWljc6WmpuLHCT9h8KCBaNe2bUkVgz6HkvD7fip9ZaPnF6gHDx5g6tSpCA8Ph62tLWxtbbF48eLSzlapuh8ahp9X70F759oY37Mt7j59iX8On0OWSIxRnVvITbN0/ymc9rmDMV1boVZVCzx+GYktJ68g+k0C5o7qDQCIeZeAkX9vRmVTIyz6oT/SMzKx3uMixq/cgUPzJ0FTXbgD+wdPX+CXpZvQtokjxvXtgntPQrHW/QREIjFGdG9XZNpn4VGYvHQjRCKxzGc37wdh6qptaNvYET8N6IbQiBhsOHASCUnJmDKyX0kVRyHuR73FpOPeaGdnhR+b1sLdqDdYff0BROJsjHSWP5gKiUsAAGzq0xyaqip52zXVPv77S44rFPdfRmPituNoX88WEzo0xZ0XkVh16gayxGKMbu0sN83cgxdx/fFz/NzZFZWNDHDy9mNM3HYc28b3gaO1ZMD7LOYNssRi/D24A6wqGOSlVVYWfkAn19IlSxAcHIxRo0dDW1sb7vv2Yfq0adi4aRPKlZN/g1AkEmHO7NlITU3FTxMnIisrC7t27sSsWbOwdu1aqBa48L3m6YmtW7eiQgX5F4ZCo2lpCufT26FmoFfsvqY926HenuV4sXYP4s7fgGn3Nqi3YwnEHzIQfegMAECrqiUanduJeL+7CBw4Gbr2NrBb8AvUDA3wcMKfJV2crxIUHIw58xagRTNXDBsyBI8eP8b2nbsgFonQv19fuWlueHtjyfIV6NGtKxo4OcHHzw/LV/0DNTU1tGzRHADgee06Fi1dhk4dO2DEsKF4F5+APW57MXXmLKxf/Q/U1dW/ZTG/yJ61U/Hq6X10HfQrNLV1cP7IRqxfMArTlx2Htq6+3DRxMWFYN28ELKvWxMAf5gNKSrh6ahfW/DkMvy8+nHcT9+S+Fbh+di869vsJlWxqI+jOdRzduRAqKqpo2kb+916a7kfE4eeDV9G+ZmVMaFkXd8Je45/LgRCJszHKxaHY9Du9H+JR1Fs4VTKR2h6TmIIRu8+hcgV9LOrpivRMEdZ73sWP7pdxeGwXaKoJd6j9IPodJv/rh3bVLfBjY3vcjX6HNV6PIBKLMbKBrdw0T+LeAwA29mwKrUL64t0BT7HRNxhDHavB2aoiXiWkYJNfEELfJmF9jyaCn1Cwa/U0vHx6H92H/AJNLV2cPbIRa+aNxqwVx4o8b1bPHQkr65oY9OM8AEq48u8urJozHNOWHso7byJeBsPYvAqGjP9LKr2eQcWSLtYXuR/1Bj8fvYH29lYY71obdyPf4B/Pe8gSZ2NU4xrFpt/pF4RHMe/gZGUktd3dPwTLrtxBGztLTG5ZD/Gp6djo9RAhcQlY0cOlpIqjEEHBwZg7dy6aN2uGYUOH4tGjR9ixYwfEIhH69ZM//vby8sLSZcvQvXt3SZ/j64uVK1fm9DmS66Po6GjMnDULNeztMWPGDISHh2P37t1ISkrCxIkTv2URP9v95+H4eZ072jeohfHdW+HuszD8c/Si5PqvYzO5aZYePIvTfvcwpnML1KpigcevIrHl32uIfpuAucN7AAD8Hofit00H0a5BLfzcqy1Co15j7bFLiE9OxfSBnb9hCb/c0qWL8SQ4GCNHScaw+933Ycb0adiwcXORY9g/5/yB1NRUTPhpIkRZIuzatQN/zJqFNWvXyY5hr3li29YtZWYMe9H9d8S+uocmnX+HuqYu/C+ux8nNwzHgt1PQ0JbfxuaXnhKPa0dlx6Yf0pJwfP0gZGakwbnDJOhXrIznDy7gxMah6DZuD0wq1SmJ4ihESV7rbN2yBefPn8fIkSNhYmKCY8eOYdbMmVi7bh3Mzc2/ZTE/SVBwMP6cOw/NmzXD8KFD8PDRY2zfsRMikQj9C21jvbF02XL06N4NTk5O8PX1w4qVq6Ta2FxJSUmYN38BYmNjv0VxiEiBhHtVVQZs3rwZampqOHPmDCZMmFDa2RGETccvw76SGf4aI+lcXGrbIkskxo7TnhjUtqlMoCEhORUe125jUp/2GN5RctOoUc1qAIA1R87j5z7tYaini03HL0NXSxObfh8FLQ3JTSPziuUxeY0bHr+MgKNt0bPiStOWI6dhV8US8ycMBwA0rVcTWSIRdp24gAEdW0JTzk2wzKwsHDx3DZuPnIZGITc//r3mB9MK5TH/p+FQUVZGozo18C7xPdzPXMUvQ3tDNd+NBaHZ5PsY9sYG+Kuj5Ma8S1VTST25FYyB9atL3QjJ9SQuASa6WnCuZKzQ4wrFhvO+sLcwxt+DOwIAXGpUQaZIjO2XbmNwM0doqkvXg8h3iTgTGIwZvb5Df5e6AADnapVw90UUDnrfzwtgPImMg6qyMtrWrQ71MjJbLb+goCDcvHkT8+bPR8OGDQEADrVqYeTIkTh96hQGDJQ/2/vGjRsIDQ3Fpk2bUKlyZQCAjbU1fvzxR9y4fh3ftZLMBE1ISIDbnj04e/ZsoRcIgqKkBMuhPVBjyTTgE+8B2i34FdFHziHo90UAgDcXvaBWXh92cyflBTBspoxBVlIK/HuNR3ZmJuLOXYcoLR0Oq2fj2eJNSA+PLqkSfTW3fe6wsbbG1N9/AwA0bOCErCwRDhw6jB7du0FDQ0Mmza7dbmjm6oJxY8cAABo4OSIpKQm79+7NC2AcOHQYzg0aYNJPH/t3KwsLTPrtd9y8dRvNXIV9k+1FyF08CvDE2GkbUbO+5AaSjb0T5k9sD6+LB9Cu5w9y010/txdqGpoYO20DNDS1AQDVazlj/sT2uHHOHX1GzcLb15HwPL0HvUfOhGu7AQAAW4dGiH8bg+D73oIMYGy8fg/2puWxsIcrAMDFxgJZ4mxs936IQc72RQYansS+w3bvh6ioqyXz2abr96CroY7Ng9tAK+cYFga6mHzoKh5Hv4VjgYCHkGy6GQw7I30saO8EAGhaxQRZYjF23n6KgfVspCYL5Ap5kwgTXU04F7ghnUucnY3dAc/Qy6EyJrpInjxoVAkw0FTHjHP+CHqdgJomwn2C6UXIXTwM8MS4GRtQK/e8qeGIuRM64MaFg2jfa6zcdNfO7IO6hibGTV+fd97YOjhj7oQOuHbWPe/pisiXT1DZxgFVbet+mwJ9pU1ej2BvYoC/ujQGALhYm0nGVX6PMcipetHnzet47PALQkUdTantIrEYW3weoXEVEyzr/rEdrWFSHn13noffyxg0rmJaMgVSgL1798LG2hpTpkwBADRo0ABZIhEOHDyI7t27F9Ln7EYzV1f8MFZSf5ycnJCclAQ3N7e8m2uHjxyBlpYW5syZAzU1NTg3bAgNDQ1s3LgR/fv3h7Fx4ePf0rbp5FXYW5nir9GSiWcuDtWRJRJhx9kbGNS6sfzrvxsBmNSrDYa3l7TJjWpIZt+v8biEn3u1hWE5HZzwuQNTQ30sHN0bKsrKaFzTBm/fJ2PvJV/81rcD1AR8rQMAQUGPcevmTcybtwANcsewDg4YNXIETp8+hQEDChvDXkdoaCg2btqMSpUkY1hrG2uM/3Ecbty4ju++yzeGdduNc2VlDAsg5uUdvHp8FZ1Gb0Fle8l4y6xqA+xd1AYPfffDqfW4Yo9x/dh8KKvItj3Bt48iKT4SPca7w6yqIwDAytYF6Snx8Dm5CD1/2q/YwihISV7rxMXF4fTp0xg3bhw6d+kCAHB0dMT3Y8bg8OHDmDRp0rcp5Gdw27tPMq6f8jsASRsrEmXh4MFD6FFIG7uzQBvbwMkJSUlJ2OO2VyqA4evnh42bNiMtLe3bFIaIFIrP6XyFxMRE1KhRA5UqVYKBgUFpZ6fUZWRmwf/Jc3znWEtqe5sGDkhJ/4C7T1/KpElJS0efls5oUU96FlcVM8mFcWRcPLKzs3E54BG6N3PKC14AQK2qlri4aoaggxcZmZkIePwMLRtKX6i2alQfKWnpuBccKjed951H2OZxFiO7t8NPA3vI3edDZhY0NdShku9Re/1yOsjMykJKerrCyqBoGVkiBETE4btqFlLb29haIiUjC3ej3shN9+R1AmyNDRR+XCHIyMqC/7MItKptI7W9bd3qSPmQgTsvImXSGOnpwP2Xgejs9PHcUVZWgoqKMj5kZuVtC46MQxXj8mUyeAEAAQEB0NTUhKOjY942fQMD1K5dG7f9/QtNFxgQAEtLy7wBPQBUqlwZVlZWuH37dt62gwcOICAgALP++AONGjWSdyhB0atjB4f18xCx9zjujpha7P5alS2ga1cVMScuSm2P9jgPnepVoF1N8v0YtXXF67PXpJaLijl6DkoqKjBq56rYQihQRmYm7t9/AJcmjaW2N3NtitS0NDx8/FgmTUxsLCIiI+HSpIl0GhcXREVFIzIyCmKxGPXr10PHDu2l9rGykgQGo6KFG9DJFXzPG+oaWrCv2zRvm66eIarVbICgOzcKTWdibo1WXUbk3YQFAA1NbRhUMMGb2HAAwIPbl6GmroHG3/WSSjti8gqM+vUfxRZEATKyRPB/FYtWdpWktrepUQkpGZm4E/660LSZIhFmn/DBQGd7VDaUfuIpOzsbl4LD0KOuTV7wAgBqmVfAxcl9BB28kPSZb/GdjZnU9tbVzJGSmYW7UW/lpnsSlwjbioXPkE3JyEIne0t0sJNe9qJKeV0AQERi6lfmvGQF3fWBuoYWauQ7b8rlnDePijpvLK3RqmvR5012djaiwkJgUUXYT4TmysgSwT/8Nb6rLv1/2cYuZ1wVWfi4KlMkwuzTNzHAqToqG0rfWH2b8gGJ6RloZiM987eakQEMtDRwIzRKcYVQMEmfcx9NmzaV2u7q6oq0tDQ8evRIJk1sbCwiIyPRRE6aqKgoREZKxngBAQFwbthQarkoV1dXiMViBAQGlkBpFCMjMwv+IS/xXX3pa7k2TrUk13/PwmTSpKR/QJ/mDdCirvS5UMX04/Vf7rG11NWkrnUMdLWRmSVC6ocPii6KwgXmjGHr5x/D6hvAoXZt+Ocbi8qkC8wZw1bKN4atJBnD5k936OABBAYEYOasP+BcBsawABAe4gVVdW1Y2X4MXmrpGsLcuiHCgq4Vm/7Z3TOICPFBk86/y3wW/zoUGlr6ecGLXBY2jRDz6g4+pCZ+fQFKQEle69y9excikUiq/VFTV4ezs3ORdbC0ZGRm4sH9+2jaVHqM7urqitS0NDx6JH9cHxkZKSeNi1Qbm5ycjAV/LUSd2rWxcMGCkisEEZUYBjC+UKtWrXDr1i0cP34cdnZ2eQ1jrtDQUIwbNw6NGjWCk5MTfv75Z6l9RCIRdu3ahfbt26N27dpo37499u//OCvg5s2bqFmzJrZs2YJGjRqhV69eEIvFOH78ODp37ozatWujWbNmWLhwITIyMr5ZuYsSEfcOmVkiVDaVfnzVyljy+8sY2QsdCyNDzBzaPS9gkcsz8DFUVVRQ2bQiot7EIzktHWYVymOR2wm0mLgAjcbOweQ1boh9J8yBSK7I12+RmZWFSmbSs6asTCTlfRUt/6ZJTZvKOLFmHkb17AAVFfmnad92zREeEwe3U5eQlJKKB09f4MBZT7jUqwV9XR3FFkSBIhJTkCkSo1KBtcGtDCQ3N16+S5KbLiQuEakZmRix/woar/ZA203/Ys2NB8jMWV7rS48rBBFvE5EpEqGykfSs1EoVDQAAL1/Hy6RRV1VFLStTlNPSgFicjZj4JCw95omIN4no2/TjI9JPouKgoqKMHzYdRaPpa9Fs1gbMP3QJKenCaDeKEx4eDlNTU6ioSM+yMzM3R2RERKHpwsLDYWFhIbPd3NwcEfna4k6dO2Pb9u1wcRH2bPpcaWHR8LRvi6ApiyFKLT5QqWsvCYqlFAggp4a+knxuWxXKmhrQrmKJlKcvpPbJeBOPzMQk6Ao4SBwTHYPMrCyZ/2tzM8mNsYgI2eBfeLjkZqKFhfTNM3NzyY3c8MgIKCsr44fvR6NpgcCIj68fAKByZekb4UIUG/kcFUwsoawsfe5UNKmE19EvC03n2m4AWnUdJbUtLiYM0eHPYGYpqU+Rr4JR0bQSQoP8sXx6X/w6uB7m/dQOPpcOK7wcihCRkIxMkRiVK0gHIHL7i1dv3xeadvP1+8gSi/Fjc9kZ81EJyUj+kAkzfV38ffYmmi8/COdF+zD50FXEvk9RbCEULPJ9KjLFYlTO6SNzWelLxg+v4pPlpguJS0RqZhZGHrqOJuv/Rbtt57DG+1FeX1xOQw1TW9RBPfMC7+x6Lgn6WVcQ9izhmMjnqCjnvDEyrYTXUS8LTdesXX+06TZSaltcTBiiwp/BzFLyZPGb2HCkp6UgLPQhFkzqikkD62PBpK64ee2kwsuhCLnjqoIBCKuc86aocdUWn8fIEmfjRznLs5XTVIOqshKiE6XPkffpGUhKz0BEgnDPnZjoaGTJ7XMk/UdEpGyfExYmuYFvWSCNmXluPxWBDx8+4PXr1zLHNdDXh7a2NiKKGO+Utog38ZLrP5MC139GkjX45V7/VSyPmYO7oIqp9NJpnneDJNd/Ocfq19IZYa/fYc8FbySlpuH+83Dsu+QHV4fq0NfRljmu0BQ2hjU3M0dkZOH/p+Fh4TCXM4Y1MzeXqgsdO3XG1m074OIi3IkmBcW/fg49Q9k2Vr9iJSTEvSgklURq0hvcODYfLt1nQltP9ilATZ3yyPyQIhOoSHwrOQffvxPmeVSS1zrhYWHQ0tKSef+fubk53r59K7gnEWKiowsZ1+e2sbLfR3hY7ri+QBpz6WsBDQ0NbN60Eb//9iv09ItfgpeIhKdsTskVgCNHjmD8+PEwNTXFrFmzpB6/i4yMRP/+/dG0aVPs3r0bHz58wOLFizFkyBD8+++/0NXVxeLFi3HixAnMnj0btWvXxvXr17Fw4UJ8+PABI0aMACAJcly7dg0HDx5EWloaQkJC8Mcff2D58uWoU6cOQkND8dtvv6F8+fIYP358KX0THyWnSW6m6WhKPyqurSl5aiIl7dNmylwJeIR/fe6gf6vG0NPRQlisZOC7+vA5OFhbYvEPA/AuKRlrj1zAmKXbcHDeRKknM4QkOVUyKNDRKvCdaEkefUxJk38D0tjQoNhjN6xli2Fd22DNvuNYs+84AMCuiiX+mjjii/P7LSR/kMzw1i2wJJJ2zu8pGbIvDI5P+4DXyWkQicWY1LwOzMpp41bYa+zyf4LYpFQs7NToi44rFMlpkmCCrqZ0PdbOqdcpH4oONuy8chtrzngDAHo3dkBjW8nN1ezsbDyNeoNsZKNXIweMbdsID8Njsfm8H57HvsWOCf0E/y6M1JQUaGvLXqRqaWkhNbXwmbypKSmwkLOua8F0VlZFv0RRaDLjE5EZ/+mBW1V9yc3JrPfSNyOzkiQ3iFT1dKGmX07uPrn7qerpymwXipRUSTkK1hFtbclSP/LqSEpKqtw0WlqFpwEkT11s3b4DNtbWcG7Q4Osy/g2kpyVDU0v2/05DSwfpqfJvTsuTkZEO9w0zoaamjmYdBgMAkt/HI/Hda7itm44OfcbD2Lwq7vicxaFt8wBAcEtIJecEbHUKLGOinfNC6dz+o6CHUW+wx+8xtg9rD3U5S5W8S5WMa1ZfCUQt8wpY3LMZ4lPTsebKHXzvdhGHxnSGlkDf0ZVbZp1C+8wsmTTxaR/wOiUdouxs/OxSU9IXR8Rht/8zxCanY2HOUlQFPYh5h13+T9G8qimqVRD2TYP0VPnnjaaW9mefN27rZ0FNTR0tOg4CIHn/BQC8fR2JnsN+h4qKKm5dP4m962chKzMDLm36KKYQCpL8Ife8KaSOFHLePIp+iz23grF9UCu5542Wmira2VfCwTvPYFNRH61sLfEuJR3LrtyBirIS0jNl655QpKTK7z9yf5fb5xSWJl+fk5Iivy8Dih/vlLaP13/Sy7rkXf+lf+L1350g/Ot7D/2/c4aejuS7cbavihHtXbDqyAWsOnIBAGBvZYa/vxfWuVKYlNQvHMOmpsDc4v9vDAsAGWlJUNeUbWPVNHSQ8aHo4OW1I3NgUrke7Jy6IzL0psznto7dcO/aTpx3mwTX7rOgo2+CV0HXEOx/DACQmSGsm/W5SvJaJyU1tdBjA5L2J/ffQvBlbayk3ugU0cYCgJqaGqyKeSk6EQkbAxhfyNDQEGpqatDU1ISRkZFUxNzd3R3a2tpYvnx53ks+16xZg9atW+PEiRPo3r079u/fj+nTp6Nr164AgCpVqiAiIgJbtmzB8OHD8441atQoVKlSBQBw6dIlKCkpwcLCAubm5jA3N8f27duhqyuMm0vi7OwiP/+UlzZeDniImZsPoV71ypjcrwMAIDNLBACooK+LFRMGQznnMWIr4woYvnATzvjeRe+W8l9yXNoU8Z0UZvH2Azjp6YdRPTvA2cEWUXHvsPXIGfy8eD02zPoZmgIN6mSj6O9EWc53oqWqig29m6GSgS7Mc2aHOlkZQU1VGRu8H2F0oxpfdFyhKL6eFJ2+eS1r1KtqjjsvorD5gh/SM7Pw9+COyM4GVo/uhvK6WqiWM8vNycYSFctpY+a+c/B58hKuNYQzu14sFiO7wHchFsu+wD5XUedPUd+pkOuCoikpF/2gZbZYDBS7T9H1szSJi8mbvP9rcXbhdUqSRvb7CAsPx8zZc6CiooI/Zk7P64eEQnLuSJcru6hz5xPzn56Wgu3Lf8ar0IcY+ctKGBpJLpRFWZlISYrHyF9Xoa5zWwC578CIxvmjGwUXwCiujZVXTz5kiTDnpA8GN6qB2hbyX7CclfPUgaGOJlb2bZl3HKvy5TBs1zmcfvgCfRzlvwy7tBV9Fsjvd7TUVLG+RxNJX6wnuVHgZFkR6irK2OAbjO8b2qJqgdn6d6PeYvK/N2Gup40/29RXUO4VQ+55U0T78DnnzdZlk/Dq2UOM/nVF3nlTrYYTfpi2DtVrNcxbaqpGPRckJb7DmUPr0bR1b0G94Ly4pl9eXj9kiTD7zC0MamALB7PCXyg8q50T1FSUMf/cbcw7dxuaqioY0agGUjIyBf3i+6LaVUB+W1JcGiVlZZmxz6ccVygUcv0X+Bgztx1FvWqVMLl327ztC/edwgnvO/i+c3M0srdG1NsEbPrXExPWuGHzL8MFNYFN3hi2qPFTkWPYItIJfeJRftmf28YW8Z0E+x9D9IsA9P/930L3MTSphk4jN+La0Tk4uEJyj8XIshac202E14mFUFPXLDTtt/Ktr3WKbX8E1rZ8SX6Lu05RKkPnzH8a/5/oEwh3hFiGhYSEwMHBIS94AQBGRkaoWrUqQkJC8Pz5c2RmZsLJSXqmmrOzM3bv3o23bz+uO5wbvACAZs2aoX79+ujTpw8sLS3h4uKC1q1bw8FB9vHs0lAu5ymD1AIzbXKfvNDVln3hUn57L3hh1cGzaGBfFSt/GgqNnDVgtXNm9LjUtpW6aVTHphJ0tTQRHCbctXJ1cyP/Mt+JZLaSrvaXzXh4/S4Bx674YGT3dvixn+SFXE4AatlURv8pC3HS0xf92rco+iClRDdnNmrB2Z0pHyS/62rIzlbVVFNB48qya4k3q2qGDd6P8DQuEdUq6n32cYVCVyt3lpr0bMbcZZ7KaRZ97lQ3+xicyBKJsfG8LyZ2coFZeT00rCY7O6tZTUnQ4klUnKACGO7u7nDft09qm6urKxISEmT2TUtNhY5O4Uul6Whry30sOjU1FdpFpPt/k5UoWeJDtZx0mXOfqshKTM578qLgPrn75R5DiHRylpAo+H+dN4NLzv+1jraO3DS5M7R0CixLce/+A8xf+De0tDSxZNHCvMfYheT80Y04f3Sj1La6jdohKVH2PQbpacnQ1C5+4kP8m2hsXToBr6NfYvikZajdoFXeZxpa2lBSUkLNes2l0tjXdUXwPW8kJbxBOQP5N/1LQ+7TbakFnsTLnUGuqynbP6z3vAtxdjbGutZGVt4FteQiOUsshoqSErQ1JMNoFxsLqRuMdSyNoKuhhicxssv/CUXu04opBWa75/ahunKeHNFUVUHjSrIvEnatYooNvsEIeZMoFcC4EBKJuRcDUam8LtZ2bwIDLeHcbASAc0c24ewR6fOmXuO2eC/vvElNgdYnnTcx2LRkAl5HvcTIyctQp+HH86acfgU4OMmOzWo5NseTB35ISnwLPQGdN+Vyxk2pMuOqnPNGzrhq/Y0HyM7OxtgmtfLOm9x7bLnnjZKSErTV1TC3ozOmtq6P6PepMNPThra6Go7ffw4rA+EuM5Y77kgtpP+Q2+cUk0ZHWztvdnFZHLfkXf8VeFo498kLXa1irv8u+mDVkQtoYFcFK8cPzLv+ex3/Hh43AjC6YzNM6N46b/9aVSzQZ+56nPC+gwGthPPeh/3u++DuLj2GdXF1RUKCbD+QmlbMGFaniDGstnDrQkH+l9bD/+J6qW3WddojLVm2jc1MT4a6pvxzPzkhBt4n/kbTrtOhpWMIsSgr70Z3tlgMsViUtySVlZ0rBs+4jKR4ybJBeoaWCLp1FACgoW2gqKJ9sW99raOjo1PoPrmfC0luvgsfo8vmVztn3F6wjU3Ja2OFVUYi+nIMYJSAwmbRiMViqKmpFfk5AKjme9muhoaG1L/37NmDx48fw8vLC15eXhg3bhx69OiBRYsWKbAEX8bS2BAqysoIfy09KMn93dpM9qIXkHxfS91P4cBlX3RoVAfzR/eBWr7vwMrYEEpKSsjIFMmkFYnF0BDo8gwAYGlSESrKyoiIiZPanvt7VYsve8FnzJt3yM7ORh07a6nt1pZm0NfVwfMI4b5g1tJAFypKSghPkF6KIff3grM3ASAsPgm3wuPQ3tYS5fIts5Se83SOgbb6Fx1XKKwqGEBFWQnhbxKktofl/F7VxFAmTdS79/ALCUNnJ3to5JutWMNScp7FJaZARVkZNx6/QFP7yjAr/3HZjtyXfJcX2PrBHTt2hLOz9NNUvr6+CAgIgFgslgpgRkVFFfnovKWlJUJDQ2W2R0VFwc7OTnGZFrjkEMl6wto2lfH+blDedh0byQv/koNDIUpJRVpEDLRtKkulVTcyhJqeLpKDZb9HoTA3M4OysrLMS7WjoiS/V5JTRywtLfL2qWZj8zFNzjHy16urntewfNU/sLK0xF/z5qJixcJnFJempq37opaj9I3RB/5XEHzfW+bceRMTDhNz64KHkBIVFoJNi35AZsYH/DhzC2xqSC+ZZWRaGdnZ2RBlZUJN/eM4RSyS3NgUwizH/KzKl4OKkhLC4qWDcWE5a/hXrSD7UuqLQa8QnZiCJksPyHzW4O99mNe1KdrWqAQlSF5YXJBInA0NNdnlc4TCUl8HKkpKMu8bCM/5XW5fnJCM2+Fv0M7WIu/mNiCZdQ8A5fPdqNwT+AxrvB7BybIilnd2ltpfKFza9JEJKNy/fRnB93xkzpu42DCYWBR/3qxfOA6ZGemYMGszqtWUPm+eBQXgbWwEGrXsLrU9M+MDlJVVoK1b+MvRS0PeuCpe/rjKWs5yYJeehCP6fSqa/nNU5rOGyw9jXkdndKtdFdefRUFPUw31LI1gk/NS+Hcp6YhNSkUNk/IyaYXCLKfPiY6SnjwVlfO7/D5HsmRJdFSUdJ+Tm6ZSJWhpaaFChQp523IlJCQgLS1N7nGFwtKofCHXf+8AANYF3nOYKzs7G0sPnsWBKzfRwbk25o/oIXX9F/0uEdnZ2ahrI/3OKRtzYxjoaCO0kPcIlpYOHTvB2Vk6oOLr64PAgECZ9iQ6KrrIMayFpSWeyxnDRkdFw9ZOmE/1yVOzUT9UrtFSatuLR5cR/sQL2WKx1FNtiW/DUN5Yfhsb8dQHGelJ8Dw8C56HZ0l99u+WkShX3hxDZl5BUnwUIp76wNaxG/QMPy4V9CbyETS09VGuvOz7Ir61b32tY2FpidTUVCQmJEDfwCBvn+ioKBgbG0vdaxKCvHF9VMFxvaRtlPd95C4LFVWgjY3OOYZVJeG2n0T0eYS1BsL/CTs7Ozx48EDq5dpv3rzBq1evYGNjAxsbG6ipqSEgIEAqnb+/P4yMjKCvL/8C5tq1a1i3bh1q1qyJsWPHYs+ePfj5559x5syZEi3Pp9JQU4OjbRVcDnwkFaS5HPAIulqaqFVV/pqDa49ewIHLvhjSzhV/j+0vNXgFJE9gONpWwZXAR8jIN1Pw5uNnSPuQAcfqVUqkPIqgoa6G+vbVcPX2Xanv5Mqtu9DV1kItmypfdFwrUyOoKCvjboEbiy+jYpGYnAILY+HM4CtIQ1UF9S0r4sqzSOl68jQCuhpqqGUqe7M+LiUdf18KxMUQ6Rd3XXgSDl11VdQ0Lv9FxxUKDTVVOFpb4vKDZ1J5v3T/KcppasChkqlMmuj495h36CKuPHgmtd33ySuoqaiginF5iMRizD98CUd8H0jtc/5OCFSUleBoXfoD+fwqVKgAW1tbqR9HR0ekpaVJtZeJCQl4+PAhHB0dCz2Wo6MjwsPDEfbqVd62sFevEB4ejvpFpPt/kxoahtTn4TDr1V5qu2nPdkgOeYG0V5IZam8uecO4U0so5wsIm/ZqD3FWFt5c9fumef4c6urqqO3gAG8fH6lzx8vbBzo6OrCzrS6TxsLcHKYmJrjh7S213cvbJ+8zALh12x9LV6xEzRr2WLFsiWCDFwCgb2iMSjYOUj/2dZriQ1oKgu99LGfy+3cIDfKHfZ2mhR4r/k00Niz8HkpQwqR5bjLBCwCoUa8ZACDQ56zU9of+njCvZPtJT3h8SxqqKnCsZIwrwWHS/UNwGHQ11OAgZ4moNf2/w75RnaR+apgaooapIfaN6oQW1S2hra4Gx0omuBwchoysj0GMmy+ikZaZBUcr+RM3hEBDVQX1LSrgSmi09PgkNAq66qqoZWIgk+ZNSjr+vnoPF59Kv6j4wtNI6KirooaxJM3RBy+x2usR2la3wLruTQQZvAByz5taUj/2dZoiPS0FQfnOm6T37/DscUAx500M1i0YAyUo4ZcFbjLBCwB4+ugW9m74Q+pl4GKxGHf9LqCqXV2oqgrre9JQVYGjlREuP42QPm+e5IyrzGTHVat7N8PeoW2lfmqYlEcNk/LYO7QtmleTLKd15O4zrPS8J5V2X4BkbNLMRnZNd6EovM/xho6ODmxtZW8um5ubw9TUFF5eXlLbvb29YWFuDpOcPsfR0RG3bt1CRubHJ8W8vL2hrKyMunXrllCJvp6Gmhocq1fG5TtB0vUk8LHk+q+K/LHm2mOXcODKTQxp2wR/j+4tc/1nlTMx7s6zV1LbX8a8QUJKKiwrCivQVaFCBVS3tZX6cXR0QlpaKgLzj2ETE/Dw4QPUd5T/ziAAcHR0koxhw/KNYcNeITw8DI71C08nNDr6JjC2qi31Y2XrgswPKQgL+Xg+pCW/Q9Rzf1jausg9TpWa36H3z4elfpr3mgsAaN5rLjqO3JhznLfwPPyH1PsxUt/H4end06hSs5Uglkv61tc69etLlm7M3/5kZmTg1q1bRR67tBTXxtoV2saawMur4LjeW2pcT0RlH5/AKAEDBw7E/v37MWXKFPz444/IyMjAkiVLUL58eXTu3Bm6urro378/1qxZAwMDA9SuXRteXl5wd3fHr7/+WmjnqqamhvXr10NXVxetW7dGYmIiPD098zomIfi+63cYt3wHpm7cj+6uTrj3LAy7z93Az33aQ0tDHclp6Xge9RqWRoYw1NPFk7Ao7Dp7HbWqWqJtQwc8eB4udTxrc2PoamliYu/2GLN0Kyb+swtD2zfDu/fJWH3kHGpbW6FF/RqlVNpPM6pne0z4ex1mrN6Bri0b437IC7iduoyfBnSDpoY6klPT8CIyBpYmFVFe79OeEiivVw4DOraE26lLAIBGte0R/eYdth09C7OKhujRqvCLbCH4vlEN/HjkOqad8kN3hyq4F/UWe/xDMLFZbWipqSL5Qyaev30PKwNdlNfWQH2LinCuZIxV1+/jQ5YI1hX0cONFNA7ceYZfW9bNeyqjuOMK2Zi2zvhh01FM2XMaPZxr4e7LaOz29Mekzq7QUldDcvoHPI95B8uK+jDU1Ub9qhZobFsJi49dRXJ6Bqwq6uP6oxc46H0PP3ZoAj1tTehpa6K7cy3suuoPDTVV1K1shjsvIrHt0m0McKmHKsbCuviTp3bt2qhTpw6WLV2KUaNHQ69cOezbtw86Ojro1Llz3n5hr14hMzMTNtWqAQCaN2+OgwcPYvacORg5ciQAYOfOnahSpQqaN28u92/9P1AtpwPdmtWQGhqGjDeSZQueLlyPutsXI/NdAmL/vQKTbq1h3q8TAgdNzksXunwbzPt3RsNT2/Din53Qsa0CuwW/InzbIaSHC/eJLgAYNKAfps+ajYWLlqB9uzZ4HBSMIx4eGDViODQ1NZGSmoqwsDCYmZnBIGeCwOCBA7Din9XQK1cOjRs1gu/Nm7h+wwszpk0FAGRkZGDVmrXQ1tLCgP79EBYWJvU3K1asCKOKwg0UA4BNjQaoVrMh9q6bhq6Df4WOrgHOHdkALR09uLTtn7dfTEQosjIzYFlV0pd67F6E5MR36Pv9HKSnJePl0483GTW1dGFqaYPqtZxRy6kljrstRcaHNJhZVcPt6yfxIuQORv++9puX9VOMca2NH/ZdwhSP6+hRtxruRcRht+8j/NzKMaffycDzuERYli8HQx1NVJfTPmrnBPhqmX8MZv3cqj6+d7uAnw5cwbDGNfE2JR2rrwSitkVFtLAV9ssiRze0xfhjPph21h/da1bCveh32BPwDBNdaub1xS/eJcFSXwfltTVQz7wCnK0q4p8bjyR9sWE5eL2MxYG7z/FrMweU01DDm5R0rLjxEOZ62uhXtyqCXydI/c3cYwlVtZoNUL1WQ+xZMx3dh/wKnXIGOHN4A7R1yqFZu355+0XnnDdWOefNkZ2LkJT4Dv3HzEZ6ajJehOQ7b7R1YWZpA5c2feF14RA2L5mITv0nQENDCzcuHEB0+DNMmrfrWxf1k3zfpCbGHfTE1JM+6F7bGvci32D3rWD83KJOvvFaIiwNdGGorYnqRgYyx8h96Xf+gMdAJ1uMP3wNyy/fQYtq5rgV9ho7/IIwopE9rMoLKwBa0IABAzBz1iz8vWgR2rVti6CgIBw9ehQjR4wotM8ZNHAgVq5ahXJ6epI+x88P12/cwPRp0/KO27dPH1y7dg1zZs9Gz549ERkZiV27d6Njx44wNhZuMBQAvu/cHONW7cHUzYfQ3cUR956HYfcFH/zcs83H67/oOMn1XzkdPAmPxq7z3qhVxQJtnWrhwQvpCUrWZkYwLKeDQa0bY895yU3JxjVsEP0uAZv/9YRZBQP0aib8G/kOtWujdp06WLZsCUaNGo1yenpw37cXOjq66NQp3xg2LGcMa/NxDHvo4AHMmT0bI3LGsLtyxrDNyvgY1ty6IcxtnHHZfQoad/4dmtoG8L+4Dhpa5eDQZGDefu9in0GUlQEji5rQ1CkPTR3pPjkzQ7I8kIFxVVQwkzxtYGTpANMqjrjuMQ9NOk+BsrIqbp77B8rKqmjYbuK3K+RnKslrHRMTE7Rp0wZbtmzBh4wMWFhY4NixY0hOTkafvsJ6X1mugQMGYMasWVi4aBHat22Hx0FBOHLUA6OKbWP/gZ5ezrje7yau37iBGfnaWCIq+5Syi3trGBVq6NChsLCwwOLFi6X+DQCPHz/GsmXLEBgYCHV1dbi4uGDq1KkwN5fMKsrKysKmTZtw5MgRvHnzBlWqVMGwYcPQr5/k4ujmzZsYNmwYLl++nPfoMQB4eHhgx44dCA8Ph6amJlq0aIHp06fD0PDLZ5eness+5v01rgQ8wqYTl/EyJg7GBnro16oxhnWQzNb0D36OMUu3Yd6o3ujm6oQNxy5i679XCz3W1qnfo4G95HHSu89eYb3HBTx8HgFNdTW0rF8Tv/bviHJf+B6Jwmi79Mb7wIsKPebV2/ew5fBpvIp+DSNDffRt2xxDukjWcw14HIJxC9Zgzrgh6NqisUzaf6/5Yf6mvTixZh7MjT7eNMnOzsb+s57wuOyFqNdvUdFAD43q2GN8/66fHAj5VHqObZGyeVbxO36GK08jscn3MV7FJ8FYVwv96tpgaAPJrAr/8NcYe/g65rZvgG61qgAAkj9kYovfY1x5Gok3KemwNNDFYMfq6Fm76icfV5F0fliI9NObFHrMy/efYeN5X7x8HQ9jfR30d62H4S0lF2i3n4Xj+w1HMH9AO3R3rgVA8o6MTRf8cOn+U8QlpqCSkQGGNHdEr8Yf34uTkZWFXVcDcMo/CNHx72Gir4tejWtjxHcNFP4iQM3O4xD6/LlCjwkASUlJ2Lp1K/x8fSEWiyVPof3wg1TbOG3qVMTGxmLX7t152+Li4rB50ybcuXMHKioqcHR0xNgffii0vVy5YgXu378vdYyvZWNtjdNqJbNklWFzZzS57Abf1kPx7votqW33Rk9HxJ5jeftWGtMf1r+MgqaVGVKfhyN06RZE7jshdbzyLk6osWQq9OrWQMabeETuO4GQuWuQnSW9/vnX6pz5BC+fhSj0mN4+vnDb546IiAhUqFABXbt0Rp9ePQFI3mExdcZM/DZ5Etq1bZOX5vTZszjicQxxcW9gZmqK/v36oE0ryXr1d+/dw7SZfxT694YMGoihgwcpLP9Vqtni7J3M4nf8TKnJiTjutgwP/K8gWyxGVbv66DFsKkzMP7aba+eNwLu4KPy57gKysjIxdXhDiEXy/89tajTAxD93AZAse3Pu6AYE3DiF5KR4mFrYoF3vcajTsLXctJ+rY301pLn9pZBj5boSHIaN1+/h5dv3MC6njf4N7DCscU0AwO2XMRiz9yLmdW2K7nVt5KYfvecCAGD7sHZS2++Gv8Y6z7t4GPkGmmqq+M7OCr+0cYKepuLe+aA19A8kr5+qsOPluhIahc1+T/AqPhnGuproW6cqhjpKbo74R7zBDx7e+LNNfXSrKVnGJflDJrbceoKrodGSvlhfB4Pq2aCng2QJuhOPXmH+5buF/r38x/pauhOW4sK9jOJ3/EypyYnw2LMM929fQbY4G9b29dBruPR5s3ruSLyLi8K89eeRlZWJ34Y6F3reVKvZAJPm7gQAxMWE4aT7PwgNCkB6Wioq29RCl4E/w8ZeMTNh29VVR+r2OQo5Vq4rIRHY5P0QL9/ljKvqV8MwZ3sAgH/Ya4w5cDVvaSh5vt9/BQCwbWArqe1nH7/CNt/HiEpMgZmeNvrWr4aBToofr2mPni93OZ6v4e3jg7179yIiIgIVK1ZEly5d0LtXLwDA/fv3MW36dPz6yy9o2/bjC6nPnDmDox4eiIuLg6mpKfr364fWraXby4cPH2Lb9u14/vw59PT00LpVKwwdOlRqeWFFsLaxQeo12eXxvsaVO0HYdPIqXsa+kVz/tWyIYe0kM+r9n7zAmBW7MG9ED3RrWh8bTlzB1tPXCj3W1t9GoIFdVWRnZ8P9sh+OXPNH5Nt4VNQvhyY1bTChR2sYynlv19fQbjEAz0JfKPSYgGQMu23rFvj6+SJbLEbNmrUwZuxYWFp+XNZm+rQpiI2Nxc5de/K2xcXFYfPmjbibN4Z1wpixY2FoKP9p0JUrl+PB/ftSx/ha1Wyq4p+Tir9V9CE1Ed7/LsaLR5eBbDFMqziiadfpUktIndg4FEnxkRgy84rcY0SG3sTJTcPRbdxuWNh8XLorNekNvE8uQkSIN7KRDQubRmjU8RcYGCnmnX+TuymVuWudzIwM7Ny5E56enkhLS0O16tUxevRo2NvbKyTvNtbWeBH6rPgdP4O3jw/c9u5DZEQEKlSsgK752th79+9j2vQZ+PWXyWiXr409feZsXhtrZmqKfv36oU3rVnKPHxMbixEjR8kcQxGq5gQi6fOkH19T2lkoczR7/FzaWfjmGMAghQcwyrqSCGCUdSURwCjrSiKAUdaVVACjLCvJAEZZVRIBjLKupAIYZVlJBDDKspIKYJRlJRXAKMtKIoBR1pVEAKOsK4kARllXUgGMsqykAhhlWUkFMMqykghglGUMYHyZ9BPrSjsLZY5m959KOwvfHN+BQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERIKjWtoZICIiIiIiIiIiIqL/GCWl0s4BlQF8AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER7W0M0BERERERERERERE/zHKnFtPxWMtISIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFRLe0MEBEREREREREREdF/jJJSaeeAygA+gUFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCo1raGSAiIiIiIiIiIiKi/xglzq2n4rGWEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeColnYGiIiIiIiIiIiIiOg/Rplz66l4rCVERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOKqlnQEiIiIiIiIiIiIi+o9RUirtHFAZwCcwiIiIiIiIiIiIiIhIcJSys7OzSzsTRERERERERERERPTfkX5+e2lnoczRbD+6tLPwzXEJKULq9UOlnQVB0W7eD89DQ0s7G4JibWODqCf3SzsbgmJuVwdxj2+VdjYExaimM14+CyntbAhKlWq2/E4KqFLNFqfV7Eo7G4LSOfMJ60kBVarZIib4TmlnQzBM7esjOvhuaWdDUMzs67GOFGBqXx8hoWGlnQ1BsbWpxDFsAeZ2dXjuFGBqXx+vnj0p7WwISuVqdvxOCqhczY7nTgE8d6RVrsZrHKKSwiWkiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiweESUkRERERERERERET0bSlxbj0Vj7WEiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISHAYwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBEe1tDNARERERERERERERP8xSkqlnQMqA/gEBhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQmOamlngIiIiIiIiIiIiIj+Y5Q5t56Kx1pCRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgqNa2hkgIiIiIiIiIiIiov+WbCWl0s4ClQF8AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER7W0M0BERERERERERERE/zFKnFtPxWMtISIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFRLe0MEBEREREREREREdF/jBLn1lPxWEuIiIiIiIiIiIiIiEhw+ARGPtnZ2Th+/DiaN2+OChUqfPFxEhISMHnyZAQEBMDOzg5HjhxRYC6lXb16FVZWVqhWrVqJ/Y3P5fvoGdYdu4Tn0a9hWE4H/b9rhKHtXKCkpCR3/4zMLLhd8MYpv7uIeZcIk/J66NioLkZ1bAY1VVVEvYlH5xkrC/173ZrWx7yRvUqqOAoREBiI3bt3IywsDAYGBujSpQt69+pV6HcCAJ6enth/4ABiYmJgYmKCvn37om2bNlL7hISEYNv27Xj69Cm0tbXRtk0bDB48GGpqaiVdpK92+849bHfbj5dh4ShvoI8enTugX4+uRX4nl695we3QUUTHxsLU2BgDe/dAh9YtpfYJi4jE5l1uuPvgMVRUlFHXoSZ+HDUc5qYmJVwixbh19wG27DuMF2GRMDTQQ8+ObTCwe6civ5dcIc9fYszUuTiwYRnMjI0++3MhCggMxK49bniVc+507dwZfXr1LPL7uOp5De4HDyImJhYmxsbo37cP2rZpLbXP9RteOHz0KMIjIqGjo4P69epi9IjhKF++fEkX6avxOymcpoUJmt89Bf/eE/Du+q0i9zXv3xnVZvwIbWsrpL2MxLNlWxDpdlxqH30nB9RYMhX6Tg7Iep+CiD0eCJm/DtmZmSVYCsVgPZF1+849bN17EC/DIlDeQB89O7VD/x5divxOLl33htuhY4iKjYWpsREG9+6ODq1aSO1zw+829hzyQHhkFAwNDNDuu2YY3LsH1NSEPcy+fecetu09kPd99OjUvtjv4/J1b7gd8sj7Pgb17pH3fUTHvsbAsRMLTduhVQtMnzRe4eVQJNYR+dLS0rBrx1b4+HghPS0NtRxq4/uxP8LS0qrIdPHx8di+dRMCA25DJBKhQUNnjB4zDoaGH6+z4t+9w163XbgTGICkpPewsLRC79790KxFyxIu1ZdT9Bg2JvY1Bo6ZUGjaDq1bYtqkwj8XgpI6d85e9sTB46cQGR2LCobl0aFVCwzp2wOqKiolXaSv5h94R6of7ta5M/r06vEJ/fAhROf1w73RrkA/fOHiZRw+dgzR0TEwNjJC1y6d0KNr0d+1UJTUd3L9hhcOHfXIG5s41quL0SOGcWxSRvsdnjtEJI/wW69v6Pbt25g+fTouX778Vcc5efIk/P394e7uDhOTkrtpGhkZiXHjxmHPnj2CCWDcDw3Hz2v3on1DB4zv0Rp3n77CP0cvIEssxqiOzeWmWXrgDE773cWYLi1Rq4oFHr+MxJZTnoh+m4C5I3qion457J4+VibdIc+bOH/7IXq4OpV0sb5KUHAw5s6di+bNmmHY0KF49OgRduzYAbFIhH79+slN4+XlhaXLlqF79+5o4OQEH19frFy5EmpqamjZIudGQXQ0Zs6ahRr29pgxYwbCw8Oxe/duJCUlYeLEwm8gCMHj4BDMXLAI37k2xajB/fEgKBibd+2FSCTCoD495aa55uOHhSvXoHfXTmjoWA/efrewZPV6qKupoVVzFwDA67g3mDjtD1hZmOOP3yfhw4cM7Ni3H1PmLMCOtSugoaHxLYv52R4+eYapC1egtUtjfD+wD+4HPcHGPQchEokxtHfXItM+fxWOKX+tgEgk+qLPhSgoOBhz5i1Ai2auGDZkCB49foztO3dBLBKhf7++ctPc8PbGkuUr0KNbV8m54+eH5av+yTl3JG2Q57XrWLR0GTp17IARw4biXXwC9rjtxdSZs7B+9T9QV1f/lsX8LPxOCqdpaQrn09uhZqBX7L6mPduh3p7leLF2D+LO34Bp9zaot2MJxB8yEH3oDABAq6olGp3biXi/uwgcOBm69jawW/AL1AwN8HDCnyVdnK/CeiLr0ZOnmP7XUnzn2gSjB/fDg8dPsGm3O0QiMQb36S43zTWfm/hr5Tr06dIBzo5DcePmbSxavRFqqmpo3bwpAOD23fuYvXglvnNtgrHDBuJlWAS2uO1HwvskTB478lsW8bM8ehKCGX8t+dgPPw7G5t37IBKJMLhPD7lpJN/HWvTu0hHOjnXhddMfi1dvgJqqKlo3d0EFw/JYv3SBTLrjZy7gqpcPOrVtVcKl+jqsI4VbvvRvPAkOxohR30NbWxv73fdi1vTfsX7jNuiWKyc3jUgkwtw5M5GWmorxP01CVpYIu3dtw5xZ0/HP2o1QVVVFZmYG/pwzAynJKRg8dDgMDSvA2/sGli5ZiMysTLRq3fYbl7R4JTGGNTQsj/VLF8qkO37m3H/63Dny7xms3bYHLZo2wrgRg5GQmISd+w8h9OUrLJj+67cs4mfL3w8PHzIYDx8HYdvOXRCJRBjQr4/cNDe8fbB4+Ur06NYVDZ0cc/rh1VBTU8N3Of3w2fMXsGrNOvTr3QtOjvUR/OQJNm/djvS0NAzsL/+6UihK6ju5eu06Fi1djs45Y5P4+HjsdtuHqTP/wPrVqzg2KWP9Ds8dIioMAxj5ZGdnK+Q479+/h5GREerUqaOQ4xVGUflVpE0nr8C+khn+Gi3pXFwcqiNLJMKOM9cxqHUTaKpLPxmQkJwKjxv+mNS7HYa3dwUANKphAwBY43ERP/duB8NyOqhjIz3D6/GrSJy//RATe7ZB/eqVv0HJvtzevXthY22NKVOmAAAaNGiALJEIBw4eRPfu3eXeVN+1ezeaubrih7GSwI2TkxOSk5Lg5uaWF8A4fOQItLS0MGfOHKipqcG5YUNoaGhg48aN6N+/P4yNjb9dIT/Tzv2HUM26Kmb++jMAwNmpPrKyRNh3+Bh6d+0k9zvZ7uaOFi6NMeH7EZI0jvXwPjkZO/YdyAtg7Np/CDra2li+YA40c45hZmKMWQuX4Mmz56hTq8a3KeAX2nHAA9WrVsbsyeMAAI0d6yBLJILb0ZPo16U9NDRkB+CZmVk4cuYCtu8/CnW1z/9cyNz2ucPG2hpTf/8NANCwgROyskQ4cOgwenTvVsi544Zmri4YN3YMAKCBkyOSkpKwe+/evJuwBw4dhnODBpj008fZjFYWFpj02++4ees2mrm6fIPSfRl+J3IoKcFyaA/UWDIN+MQJVHYLfkX0kXMI+n0RAODNRS+oldeH3dxJeQEMmyljkJWUAv9e4//H3n2GRXE0cAD/0zsoqHREkSoqoNh771FjNxp7YhKNxm4sMabYSzRq7L1HY4vGXiiiggULqKj0piJSjnLcvR8ODpc7QOMhx5v/73n4wO7OMjPs7szuNEhzc5F8+gryRFnwXDUHTxauR1Z0fFml6IPxOlG0dc9BONdwxOxJ3wAAGvl4QZwnxq5Df6Fvjy5Kn68bd+5D66aN8M3ozwEADX3qIS0tHZv3HJB/JDh1/hIsq1pg9qRvoKWlCV+vukh5nYoDR0/im5FDoa2tnlXtbXsOwrlGDXz/Vn7k5eVh96G/0LdHV6X5sWnnXrRu2vit/PBCWlo6tuw5gHYtm0FXRwe1XV0EYcKfPMVFvwCM/mwQ6nq4lX3CPgCvEeXCHj7A9aBrmDf/ZzTwbQgAshEYI4bi5MljGDBwiNJwflcv42nEE/y+fhMcHGR19ZpOTvhm3Bj4Xb2M1m3a4cb1IDx7+hTLVq6Bi4srAMDbpz6Sk5Lw56EDatmAURZ1WF0dHXi4Fb13ImT3ztDBqOOh3vXXsrh38vIk2L7/MBp41cGP0yfJw7k41cCICVNx4/Zd+HqV7bv2h9ixey+catbE9CmyhhbfBvWRJxZj34GD6P1JD6XXydbtO9GyeTOMGzsaQEE5nI7tu3bLP8Lu3X8QLZo1xeiRwwEA3l71EBMbh6PHT6r9R9iyypPCuknhCD87W1t8O3kqrl2/gZasm1Socof3DhEVp8KvgdGnTx/89NNP8t/PnTsHV1dXnD59Wr5t4cKFGD58OB49eoQvvvgCvr6+8PT0RLt27bBlyxYAQFBQEIYNGwYAaNeuHQ4fPgwACAkJwZAhQ1C3bl20bt0a8+fPR3p6uvzcbdu2xaJFi9C1a1c0atQIQ4cOxerVqxEXFwdXV1ccPnwYq1evxmeffYZJkybBx8cHCxbIeqfdunULw4YNQ/369dGoUSPMnDkTKSkpgnNv3rwZ48ePh7e3Nxo1aoSffvoJYrEYMTExaNdONiRu2LBhWL16dRnl8LvLyRXj5qNnaOMtrGS3r18bGVnZuP0kUiFMhigbfVv5olU9V8F2R2vZ9Daxya8UwkilUizcfQI1ratiSIemKkyB6uXk5uLu3bto2lQYz+bNm0MkEuH+/fsKYRITExEbG4smSsLExcUhNjYWABAcHIyGvr6C6aKaN28OiUSC4JCQMkiNauTk5uJO6H20aNxQsL1Vs8bIFIkQ+iBMIUxCYhKiY+PRonEjYZimTRAbn4CYuHhIpVJcCQxCl/Zt5Y0XAODq7IRD2zaofeNFTm4ubt17iJaNGgi2t2nSEJmiLNx9GK40XGDIbWzdfwRDP+2JccMGvPd+dSW7d0LRrEljwfYWzZsiUyTCvQcPFMIkJCYiJjYWzZo0EYZp1gxxcfGIjY2DRCKBt7cXunTuJDjG3t4OABAXr74fpZknypnWdYXn7/MRs+sv3B4+rdTjDarbwti1BhKOnhVsjz/8D4ycHWFYS/ahrWqH5kg6dVkwXVTCn6ehoaWFqh2bqzYRKsTrRFFObi5u33ugWO40lZU7dx8qljvxiUmIjotXWlYVlDsAkJOTC309fWhpFVapTU2MkSsWI1OUVQap+XAF+dG8sa9ge6umjWTlcAn5oRCmWSNBfrxNKpVi5R+bUd3eDv16dlNtIlSM10jxQoJvQl9fH94+hSOezcwqwbNOXQTfKH6qvpCQm7C1s5c3XgCAg0N12Nk74GZ+OENDI3Tu0g3OzsKP93b29kiIj1NxSj5cWdVhi5JKpVi1Xnbv9P2P3jspr1/jTVo6mjTwERxTs7o9zExNcO3mLdUnRkWKL4eblVoONy0apllTeTkMAD/9MBdjRgl70GtrayMnJ0fFqVCtssoTiUQCH28vdO3cUXCMQ37dJD4+QcUpUR2WO4p47xBRSSp8A0abNm3g7+8v/z0gIAAaGhoICgqSb7t06RLatGmDkSNHolKlSti3bx9OnDiBzp07Y9GiRXj48CG8vb3ljQAHDx5E165dERYWhhEjRqBFixY4duwYli5divv372PkyJGC0Q+7du3C7NmzsWnTJqxduxYjR46ElZUV/Pz80LVrVwCy6amqVKmCo0ePYujQobh79y6GDh0KZ2dnHDhwAKtWrcKdO3cwatQowRQvq1atgq+vL44dO4Zp06Zh165dOHHiBKytrXHw4EEAwOrVqzFy5Mgyzed3EfPiFXLFeahuKVw/xL6a7PfnCS8UwthWrYxZQ3rA0Uo4H/+lWw+hraWF6pZVFML8cyMUoc9iMHVgV2hpqvclnBAfD7FYDFtbW8F2G2trAEBMfmPE26KiogDIeo68zdrGRhYmJgbZ2dlISkpSOG8lMzMYGhoiJiZGZWlQtfiEROSKxbDLT08BW2srAEB0rOILa2SMLJ/sbK2LDZOQmISMjExYVquCles3oefg4ej46WB8/9MiJL94WRZJUam4hCTkisVwsLESbLe1lk1DFxWnvALuXqsmDv2xAp/3+0RQSX3X/eoqIT4BuUrvnYL7QPHeiY6OBgDY2gqvLRsb2XUTHRsDTU1NfDF6lEIlNyDwGgCgenUH1SSgDDBPlBNFxeOSWwc8nLoQeZmlv5QZu8lG+WU8fi7Ynhkha2Q3dqkBTX09GDraIePxM8ExOS9SkJuaBmOXGqqJfBngdaKo4PlqX6QMsct/vpZU7hQNI38m54fp3bUjYuLjse/IcaSlZ+B++GMcOn4Kjet7w9TEWOVpUYWCclgxbbLyJ0pJfkQVmx/Fh7lwNQAPHz3BN6M+V/vyh9dI8aKjo2BlZQ2tImsOWFvbICa2+PpmTFS0wnMIAGxsbBAbI3vmeHn74OvxEwXzj4vFYty8HiRo+FAXZVWHLeri1QA8fPQY34weoZDv6qas7h1jYyNoaWkhMVn4/piWno709AzEJSSpLA2qVlAO2xUtUwve/5SUw1HRsnup6PtfYTksC+PgYA8rS0tIpVK8SUvDqX/O4NyFi+jRravK06FKZZUnxdVN/Fk3qZDlDu+d/y6phgZ/3vPnv0h9x469o7Zt22LNmjWIj4+HtbU1/P390a5dO3kDRlRUFJ49e4b27dsjOzsbQ4YMgZGREQBgwoQJ2LRpE8LDw+Hu7g4zMzMAgLm5OfT19bF582Y0a9YMX34pm87F0dERy5YtQ/v27XH9+nU0aiTrSdOqVStBD3tDQ0NoaWmhalXhR/kJEybAJH+e2IkTJ8LV1RVz5swBADg5OWH58uX45JNP4Ofnh1b50wQ1b95cPjLE3t4eO3fuREhICHr16gVzc3MAgJmZmTxN5Sk9MxsAYKSvL9huqC8b+pghyn6n81wIeYDjgbcxoE0jmBoZKOzf8Y8/vGo5oIGr+n5AKpCRmQlAdk28reD3zPz97xTGwEAeJiMjQ+kxAGBgYKD0vOoiI0MWNyND4f+2IH0ZmaLiwxgUDaOfHyYTr9+8AQBs2L4bbs61MGfqJLx+nYqNO/dg0vc/YOOqJTAocm2qk/T8dBsq5EtBGhXzBQCqWpiXeN7S9qurjEzl13hB/ii9dzKU3zsGBsWHAWQ9xzdu3gKnmjXRsEEDpceoA+aJcrkpqchNSX3n47XNZC9u4jfpgu3iNFn+apsaQ8fMROkxBcdpm6rvyx+vE0XycrVIGWJQYrlT3DNZGManricG9e6Jddt2Y9223QAA55qOmDNZfdeiSi8lPzKV5Ed6qdeIYpj9fx2Hp7srvOvU/vBIlzFeI8XLzMyAgdL6piFEJdQ3MzIzYKOkAcPAwLDEeurWLRsRFxeLmd/P/XcRLkNlVYctat+Ro/B0d4XXf/je0dfTQ9vmTXDk5D9wdLBDy8a+SHn9Bqs3bYeWlhaystW3F/m/K4czBMcUKK4cfhgWjolTZKNOXZxr4dM+vT484mXoY+RJAVndZCucatZAwwbqu1Ymyx1FvHeIqCTq3R3qHdSuXRuWlpbw9/dHXFwcYmJi8MUXXyAiIgLJycm4dOkS3N3dYWtri8GDB+PEiROYN28eRowYgdatWwMAJBKJ0nM/ePAAV65cgbe3t/ynZ8+eAICIiAj5cdWrl95DyMLCQt54AQCPHj2Cj49wSKybmxtMTEwQHl44VYyTk5PgGBMTE+S+NZ2FOpGUsiaHhmbprYTnQ+5j5saD8KrlgIl9Oyrsv/0kCg+j4jCsk/pO3/E2aTHXVgFNJS2npYXR0NQsdf0TZedVF6VdJ5pKrhOJtLR81IRYLAYAVDYzw48zp8DXux46tGmJH6Z9h9j4BJy7dPXfR/ojkJaSRg01/p+WBYnk/a/xd7lOioqKjsa0mbOgpaWF2bNmQFONR3UxT1RDo5T0SCUSoNRj1G8NqgK8ThSVXhYrxv1d82T5us3Ye+QYhvXvjZU/zcGMCV8iLT0dU+f/iqzsd+u48bGVdv0qK29Kq3cUDXPvYTgeRTzDwN493j+C5YDXiIxEIkFeXp7gp7j3JKDkuklJearsOSyVSrF180Yc++sw+nzaH02btXi/yH8EZVWHfdu9h+F4HPEMA3orX8BX3ZTlvfPduNHo0Lo5lqzZgO5DRmP0pBmo7eYCF6caSufBVxellcMaSvLkfZ+xltWqYunCXzBl4rd49SoFE6dMQ1aWej1P3vYx8gSQ9cafOvN7aGlpYQ7rJhWi3Hkb7x0iKkmFH4EBCKeRqlOnDurWrQtLS0sEBQXh8uXLaNeuHZKTkzFgwACYm5ujbdu2aN68OerUqSMf6aCMRCJBjx495CMw3lYw+gEA9N+hV3fRY4p70EqlUsGaBrq6igs3qePi3QBgYiCrSGYWKQAy8n83Nig5n3adDcCKg6fRwLUGln89GHpv5UOBc8H3YWpogOaeLkrOoH4KRsZkioQ9KAp6AhgqGTlTWhgjQ0N5rwSRSLFnRmZmptLzqgsjo/zRJ0XinvFW+hTCFIxYKTJnZ0b+OYyMDOW9LBrW9xZUVj3cXGBkZIgnT4VTwaib4tJY0KvVWMlopP9nBddJ0Ws8o6R7x9BIaRj5vWMkvLbu3A3Fjz//AgMDfSz69Wf58GR1xTxRDXFqGgBA20SYXwWjKsSp6fKRF0WPKTiu4BzqiNeJouLKnYLnq5GS56vxO4RJfvkKx8+cx2d9e2HUkMI1htyda+Hz8VPw97mL6NOts+oSoiKF10hx5Y2ScriY66rgGika5nJAEEyMjdC4vrdqIl3GeI3I7NuzC3v37BRsa9a8BV6/fq1wbKYos8QR4IZGRhCJFHvLZmZmyJ85BXJzc7By+RJcuXwJfT7tjxGjxvy7BJSxsqrDvu1yQKDs3mnw3753ANkolenjv8T40cORmJwMy6pVYWigj5NnL8in4FJHxb3LZRRTpgKF14niM7YgT4T3jIWFBSwsLFC3jiesrC0xZfosXPX3R4d2bVWTCBX7GHkiq5v8Cn0DfSxm3aTClDtv471DRCVR3ybp99C2bVsEBgYiMDAQTfIXoGzSpAkuXLiAoKAgtGvXDidOnMDr16+xd+9efPXVV+jQoQNSU2VTThQ0CBRtnXV2dsaTJ09QvXp1+Y9YLMavv/6K+A9crNLV1RXBwcGCbWFhYUhPT1cYdVEcdeuRbVfNHFqamogusvB2dJLs95rWVZUFg1QqxaK9J7HswCl09PXEmm+Hwkhfea+aq3fD0cbbHTra6j0fbAFra2toamoiPk44h2Vc/u8O9vYKYezs8hcdKy6MgwMMDAxgYWEh31bg9evXEIlESs+rLmytLKGpqYnYIouqFfxePX/Rtbc55M+DWWwYO1vYWllCQ0ND6QilvLw86OopNgaqE1uratDS1ERsfKJge0z+79XtFKdh+H9mk3/vFF0YOC5/cTrl946t4Bh5mPxz2L8V5uKly5g1Zy6qVqmClUuXqvU9U4B5ohrpj2SNmYZOwtGTRvm/p4dFIC8jE6KYBIVjdKuaQ8fUGOlhEVBXvE4U2VhZKn2+vl2GFGUvL3eKPpMLwyQmv4BUKoWnu7BThaODHcxMTPAsSj3Xo7IppRx2KDE/ioZJVBom8GYImjfyhbZ2xegrxWtEplOXrli+co3gx9bWHokJCQojMeLjYmFnX/z88nZ29gr1VFm4ONg5FD43MjIy8P3MafC7egVjxo5T28YLoOzqsG+7diMEzRo1/M/fOwAQcCMYoQ/DYWigjxoO9jA00EfK61Qkv3wFFydHVSdFZWysrVReDjvY20EkEuHCpcuILXJfOed/O3j5UvgOrk7KKk8KXLx0GTPnzEOVKhZYtXSJYJ+6YrmjiPcOEZXk/6IBo0mTJsjOzsaZM2cEDRinTp1C1apV4eHhASsrK4hEIpw+fRpxcXHw8/PDd999BwDIyckBUDjXXlhYGDIyMjBy5Eg8ePAA8+fPR0REBG7duoXJkyfj+fPncHR0/KA4jxgxAuHh4ViwYAEiIiIQFBSEKVOmwMPDQ56G0hTE99GjR0hLK//eoHo6OvBxro7zIQ8Eo0TOB9+HsYE+ajsq/wC7+shZ7LtwDZ91aIpfRveDTjEV9tSMTEQlvUQ9J/VdjKsoXV1d1PH0hH9AgCBP/Pz9YWRkBBcXxZEkNjY28kXg3+bv7w9bGxtYWsoW6fLx8cH169eR89YHez9/f2hqaqJevXpllKIPp6uri3q13XE1MEiQJ1cCgmBkZAg3l1oKYWxtrGFtWQ2XAwIF268EXIOdjTWsLKvBwMAAdfPP+3aeBN8JRVZWNup6uJddolRAT1cX9TxccfnaTUG+XLp2A8aGhvBwrlmOsfv4ir93AmBkZARXF2eFMLY2NrCytMTV/BF5b4cp2AcA12/cxOJly+Hh7oZlSxahShWLsk2MijBPVCMzIgqZT6Nh3aeTYLtV745If/QMokjZYn8vzvmjWtfW0NQtHA1o1acTJGIxXly89lHj/D54nSjS09VF3druuHLtuiBPLgcGwdjIEO5Kyh07aytZueMv/F9fCQiCnY1sn521FbQ0NXH3QZjgmKiYOKSmpcEmP9/UjV5+OVw0P64EysrhkvMjSLBdlh+yMrrAm7R0xMTFw9PdtewSoWK8RmQsLKrA2cVV8OPtUx8iUSZCgm/Kj0tNfY3790Lh7VP8/PLePvUREx2FqKhI+baoqEhER0fB21u2Zk5eXh4W/DAHjx+FY9qM79GzV5+yS5wKlFUdtsCbtDTeO/n3DgAcO30Oa7fuEhxz8Pjf0NTURJMGwqmY1YmsHK4N/4DAYsphxfe/wnI4QLD97XJYS0sLK35bjYN/HhEcExxyCwBQs4aj6hOjImWVJ4CsbrJo2Qp4uLthOesmFa7ceRvvHSIqScXo2lEKXV1dNG3aFH5+fvDy8gIga8CQSCRo21Y2FKxz5864f/8+Fi5ciPT0dNja2qJfv344f/48QkNDMWjQILi4uKBVq1aYOHEivvvuO4wcORKbNm3CqlWr0Lt3bxgaGqJJkyaYPn260qmd3ke9evWwadMmrFy5Er169YKxsTHat2+PyZMnC6aQKknlypXx6aefYvHixYiMjMTs2bM/KE6qMLpba3y5Yhum/bEfnzTzwZ2IKGw/448JfTrAQE8X6aIsPI1Lhl01c5ibGCE8Kh7bTvuhtqMtOtT3ROhTYY+AmjZV5VNPPY5JlG+rSAYOHIhZ33+PX379FR07dMDDhw/x559/YsTw4dDX10dGZiaioqJgbW2NSvkLyQ8eNAjLV6yAiakpGjdqhMBr13Dl6lXMmD5dft5+ffvi8uXLmDtnDnr37o3Y2Fhs274dXbp0QbVq1YqLjlr4rP+nmDJ3AeYvWo4uHdri/sNw7D9yDGOGDYG+nh4yMjMRGRUDG2tLeZ4MG9gXi1athanJRjRr2AD+QTdwyS8Qc6ZOlJ93zLDBmDTrB8yY/wsG9O6JlNevsWHbbri7OKNpQ/VdYLbA5/0+wcQfFmHOktXo1q4V7oU/xt6//saXQ/vn54sIz6JjYWtVDZXNTMs7umVu8MD+mPH9HPz86yJ06tgeDx6G4dDhwxg5/PNi750hgwZi2cpVMDUxkd07QUG4ctUPM6fLFmvLycnBit9Ww9DAAAMH9EdUVJTgb1apUgVVq1T56Gl9V8yT96dtYgRjj1rIjIhCzosUAMDjn39Hvc0LkfvqNRKPX4Blz3aw6d8VIYMnysNFLN0EmwHd4HtiE56t3AojF0e4LvgO0ZsOICv6w0ZhljVeJ4qG9e+N7+b+jHmLV6Jr+9a4H/YI+46cwNhhg+TlzvPoWNhaWaJS/vP18wF9sPC39TBdvxnNGtaHX9BNXPS/hnlTJgAAKpmZom/PLth35AQAoEG9ukhMTsa2fX/CqloVdO+ovtMRDO3fB5Pn/oQfFq9A1/ZtcC/sEfYdOV4kP2Jga2Ulz49hAz7Fot/WwXS9MZo2bAD/oJu46B+IuVO+FZz7aaTs2nCsAD1g38ZrRDnPOnVRp249LFuyEMNHjoapqSn27N4JIyNjdO1auMZJVFQkcnNz4eQk++jWomUrHNi/Fz/MmYXPR4wCAGzfuhmOjjXQoqVsCt+Tx4/i/v1QdO7SDRZVqiAs7IHgb7u5eXykVL67sqrDAm/dOw68dwDg0+6dMeWHX7Fm03Y0bdgAIXfvYfehoxjcp6daTyEFFJTDc/HTr4vQqWMHPHj4EAcPH8Go4cOgr6+ntBz+bNBALF25CiYmJmjSqCECg4Jw+aofZk2fCkD2zWNAv77YuXsvKlUyQ726dfD06XPs2rsP3l714KvGC1YDZZMnOTk5WP7bGhgaGGDQgH6sm6Dilzu8d/6jlKxvQlSUhlRdF1SgjybzygGVnu9CyAOsP3YBzxNfoFolU/Rv0wjDOjYDANwMf4YxS7dg/vDe6NnMB2uPnsfGE5eKPdfGKSPRwLUGAODMjVBM33AAh3+cgBrFTEelCoYt++NphGqnB/EPCMCuXbsQExODKlWqoHv37vi0j6yX2d27dzF9xgx8N2kSOnToIA/z999/48/Dh5GcnAwrKysM6N8f7dq1E5z33r172LR5M54+fQpTU1O0a9sWQ4cOVfmw85pOTogLv6vSc14NDMK2PQcQHRuHKhbm6NW1M/rnL/Z5O/Q+Jn3/A6Z/+xU6t2sjD3Ps9FkcOHIMSS9ewsaqGgb37Y2ObYTr2Nx7GI7Nu/biYfhj6OnpoXljX4wbMQzGxqpdF8TGtS6SH1xX6TkB4PK1m9iy7zCiYuNRxaIy+nRpj0GfdAUAhNx7iAlzfsGs8WPQtW1LhbB/X7iCX1ZvxME/lsO6muI9Utr+D1XVoyGeP3mk0nP6BwRi5+49iImJgYWFBXp074a+fXoDkM11O23mLEye+C06dmgvD3Py1CkcOnwEyckvYG1lhQH9+6J9fmP27Tt3MH1W8Y29nw0ehKFDBqss/o61XJgnRTjWcsFJnbLpXWresiGanN+JwHZD8erKdcG2O6NmIGZHYc8rhzEDUHPSSOjbWyPzaTQiFm9A7O6jgvNVblYf7oumwbSeO3JepCB291E8+uE3SMVilca7W244r5MiHGu5ICHslsrOBwBXAq9j695D8nKnd9eOGNCrOwDgVuh9TJy9ADMmfIku7VrLwxw7fQ77/jqB5BcvYW1ZDUP6foJObQqfv1KpFIeOn8Kx0+cQn5gEC/NKaOBVF2M+Gyj/2KAKVm7eiA+7rbLzAcDVwOvYuvfgW+VwRwzoJSuHb4Xex6TZP2L6hHFF8uMs9v91QlYOW1bDkL690LGNsDy66BeI+UtWYvvvy8t0+kNrNy9eI0VYuXnjUURU6Qe+p/S0NGzauB7XrgVAKpHA3aM2Ro8dBzu7wuk8Zk6fjKTERGzeVthjPjk5CRv/WIvbt0KgpaUNb5/6GD32S5iby3pIz5j6He7fDy327x7/++wHx93FyaHC1GEv+gXgx8UrsH3tSqVTuamKjWvdCnHvAMC5K/7YeeAI4hOTYFmtCnp16YhPu6t+/n4rN29EPglX6Tn95OVwLCwsLNCze1dBOTx15veYMvFbdOxQ+H534tRpQTk8sH9ftG9beB1JpVKcPHUaR0+cRHx8AszMTNG2VSsMHTLogztYFlW9lqva58mtO3cwfdacYv/eZ4MHYpgK6ybVa7lWiHvnY5c76n6dAB/v3qleq+KMoFMnqv4m+V9g2LJ/eUfho2MDBvFhUURZNGBUdGXRgFHRlVUDRkVWFg0YFV1ZNGBUdGXZgFFRlUUDRkVXFg0YFVlZNGBUdGXRgFHRlVUDRkVWFg0YFV1ZNGBUdGXxEbaiK4sGjIquLBowKjreO0JswPh3+E3y/f0XGzA4ToeIiIiIiIiIiIiIiNQOGzCIiIiIiIiIiIiIiEjtsAGDiIiIiIiIiIiIiIjUjmpX+iUiIiIiIiIiIiIiKo2GRnnHgCoAjsAgIiIiIiIiIiIiIiK1wwYMIiIiIiIiIiIiIiJSO2zAICIiIiIiIiIiIiIitcMGDCIiIiIiIiIiIiIiUjtswCAiIiIiIiIiIiIiIrWjXd4RICIiIiIiIiIiIqL/GE32rafS8SohIiIiIiIiIiIiIvo/JpFI8Ntvv6FFixbw8vLCmDFjEB0dXezxjx8/xtixY9GoUSM0adIEEyZMQFxcnHx/Xl4e6tatC1dXV8HP6tWrVRpvNmAQEREREREREREREf0fW7t2Lfbs2YMFCxZg3759kEgkGD16NHJychSOTUlJwYgRI6Cvr4+dO3di48aNePXqFUaPHo3s7GwAwPPnz5GdnY2jR4/Cz89P/jNy5EiVxpsNGERERERERERERERE/6dycnKwZcsWTJgwAa1bt4abmxtWrFiBhIQEnDlzRuH4c+fOITMzE4sXL4aLiws8PT2xZMkSREREICQkBAAQHh4OY2NjuLm5oWrVqvIfIyMjlcadDRhERERERERERERERP+nwsLCkJGRgSZNmsi3mZqawsPDAzdu3FA4vkmTJli7di309fXl2zTz1yx58+YNAFkDhpOTUxnHnIt4ExERERERERERERGpvXbt2pW4//z580q3JyQkAACsra0F26tVqybf9zY7OzvY2dkJtm3YsAH6+vrw9fUFADx69AhisRijRo1CWFgYLC0t8fnnn+OTTz555/S8CzZgEBEREREREREREdFHJdXQKO8o/GeIRCIAgK6urmC7np4eUlNTSw2/c+dO7Nq1C7Nnz4a5uTkA2SLfEokEEyZMgJWVFS5fvoyZM2ciNzcXffv2VVnc2YBBRERERERERERERKTmihthUZqCqaBycnIE00JlZ2fDwMCg2HBSqRSrVq3CunXrMG7cOAwdOlS+78SJE8jLy5OveeHm5oa4uDhs3rxZpQ0YXAODiIiIiIiIiIiIiOj/VMHUUUlJSYLtSUlJsLS0VBomNzcXU6dOxfr16zFz5kxMnDhRsF9fX19hwW4XFxelU1J9CDZgEBERERERERERERH9n3Jzc4OxsTGCgoLk2968eYMHDx7I17Qoatq0aTh9+jSWLVuG4cOHC/a9efMGDRs2xOHDhwXbQ0ND4ezsrNK4cwopIiIiIiIiIiIiIqL/U7q6uvjss8+wdOlSmJubw9bWFkuWLIGVlRU6duyIvLw8vHr1CiYmJtDX18fhw4fx999/Y9q0aWjYsCGSk5Pl5zIxMYGpqSkaN26MFStWwMLCAtWrV8eZM2dw7Ngx/PHHHyqNOxswiIiIiIiIiIiIiIj+j02YMAFisRizZ89GVlYWfH19sXnzZujo6CAmJgbt2rXDr7/+ij59+uDEiRMAgMWLF2Px4sWC8xQc88svv2D16tWYN28eXr58CScnJ/z2229o0aKFSuPNBgwiIiIiIiIiIiIi+rg0uLrBx6SlpYWpU6di6tSpCvvs7OwQHh4u/33Lli2lns/Y2BgzZ87EzJkzVRrPoniVEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdphAwYREREREREREREREakdNmAQEREREREREREREZHa0S7vCBARERERERERERHRf4tUg33rqXS8SoiIiIiIiIiIiIiISO2wAYOIiIiIiIiIiIiIiNQOGzCIiIiIiIiIiIiIiEjtsAGDiIiIiIiIiIiIiIjUDhswiIiIiIiIiIiIiIhI7WiXdwSIiIiIiIiIiIiI6D9GQ6O8Y0AVAEdgEBERERERERERERGR2mEDBhERERERERERERERqR0NqVQqLe9IEBEREREREREREdF/R3rQ8fKOQoVj3KhHeUfho+MaGISoxw/LOwpqxcHZHc+fPCrvaKgVx1ouuBmeUt7RUCsNXCvjwZO48o6GWvGoZYMnEc/KOxpqpZZTDZy6lVve0VArXbx1+IwtwrGWC07quJZ3NNRKt9xwXAtLLe9oqI3GbmYIfPimvKOhVpq4m+L1rQvlHQ21Usm7Lfb4sW/a2wY312B9rQiPWja4+zipvKOhVuo6V0PE06flHQ214lSzJrJOrCvvaKgV/e7j8DgisryjoVacnarz/e8ttZxqlHcUiP5vcQopIiIiIiIiIiIiIiJSOxyBQUREREREREREREQflVSDfeupdLxKiIiIiIiIiIiIiIhI7bABg4iIiIiIiIiIiIiI1A4bMIiIiIiIiIiIiIiISO2wAYOIiIiIiIiIiIiIiNQOGzCIiIiIiIiIiIiIiEjtaJd3BIiIiIiIiIiIiIjoP0ZDo7xjQBUAR2AQEREREREREREREZHaYQMGERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdrRLu8IEBEREREREREREdF/jAb71lPpeJUQEREREREREREREZHaYQMGERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdrRLu8IEBEREREREREREdF/i1RDo7yjQBUAR2AQEREREREREREREZHaYQMGERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdrRLu8IEBEREREREREREdF/jAb71lPpeJUQEREREREREREREZHaYQMGERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdrRLu8IEBEREREREREREdF/ixQa5R0FqgA4AoOIiIiIiIiIiIiIiNQOGzCIiIiIiIiIiIiIiEjtcAopUrmbIbewdeduREZFoXKlSujZrSv69v4EGhrFDwu7cPkK9uw/iPiERFhWq4aB/fqgY7u2xR6/buNmHD56HGdP/FUGKVC94JAQbNuxE5FRUahUqRJ6dOuGvn16l5gnFy9dxp79+5GQnycD+vVFh/btBMdcueqHg3/+ieiYWBgZGcHbqx5GDf8clStXLuskqUSWKBN7t/+OGwEXkZUlglttL3w2aiJs7Kq/8zlWLpwJfX0DfDlxrmD7yxdJ2Lt1Ne7duYHc3Bx41vPFoOFfw8rGQdXJUCmRSIQdW//ANf8ryMoSwcOzHkaO+Qq2diXH+3XKK2zdtBYhwTcgycuDT4NGGDHmK5ibW8iPkUqlOHr4AM6cPo4XyUmoWs0K3Xr2Rtfuvcs6WR9EJBJh65bN8A/wR5ZIBE/POhgzdizs7OxLDJeSkoJNGzcgOPgm8vLy0MDXF2PGjBXkyduOHTuKI4f/xNZtO8oiGSqVnZWJ43uW407QWWRnZcLJvQF6DZsGS5saJYaLfvYAf+9fjaiIe5BKJbCv4YHugyfBvoaH/BipVIqLJ7Yh4PxBvH6ZgMpVbNCy8xC06DSorJP1r/EZWzx9W0u0vH0CNz/9Gq+uXC/xWJsB3VBr5jgY1rSH6HksnizZgNidfwmOMavvCfdF02BW3xPiNxmI2XEYj35cA2lubhmmQnWyRJk4sH0NbgZeQFaWCK61vTF45CRYv0e5s3rhDOgbGGDMt/ME2y+d+Qtbf/9F4fh2Xfth2BdTPzjuZSFLlIkDO1bjZuAFZGeJ4OrhjUGjJsHa1vGdz7Fm0XTo6RtgzLc/CLZLpVKcProLl/45gpcvElGlqhXadx+A9l37qzYRKnTtzgOs338MT2PiYG5mir4dW2FI9/YlPksKhD+LxojZC3FoxY+wqSYrZ+KSXqL3hNnFhunWqgnmjhumsviXlZysDJw9tAwPg88gJzsT1V0aoNPAGahiVbPEcNFPbuH84eWIf34fuvpG8GjQCW17T4SegbH8GHFuDi4fW4O7144jM+0VLCwd0aL7l6jt26Wsk/VBWF9TJBJlYve29bjmf1mWJ7Xr4fMx498pT7ZvWoPbIUHIy8uDT4PG+Hz0N6hsXkV+zMsXydi5dS1uBwdBkpcHF7faGDRsLJyc3co6WR9EJBJhy5YtCPD3h0gkgqenJ8Z+8QXs7OxKDJeSkoKNGzYgODgYeXl58PX1xZixY2Fubi4/Ji8vD7t378a5s2eRlpaGWrVqYdTo0XBzU988CQiPxJpTAYhIeAlzE0MMbFoPw1r7FPuMzc4V44+zQfg7JAwp6SK42FTBlx0bo5mbo+C4u5HxWHXSH/eiE2Coq4tmbtUxqXsLWJgYfoRUfTiRSIRtWzbBP8APWSIRanvWwZixX77ju856hMjfdRpi9JgvBM+TlFevsHPndtwKCUZa2hvY2tnh00/7o2Wr1mWcqg/D9z8iKoojMFTs8OHDcHV1lf/etm1brF69GoCsMnrkyBG8fPlS6bH/Dx6EhWPOjz/D3s4W82bNQNvWrbBx63bsP3S42DBX/QOwcOkK1Pf2wg/fz0S9Op5YsuI3XLx8Venxd+/dx5FjJ8oqCSr3MCwMc+cvgL2dHebMmoW2rVtj89ZtOHDwULFhrvr7Y9HSZajv7Y15s79H3bp1sHTFSly6fEV+zKXLV/DzwkWoVasW5syageHDhuLOnbuYNut75OTkfIykfbDfl83Fdf8LGPD5Vxg3cS5SXibj59lfIyP9TalhJRIJdm5cgRsBFxX2ZWWJ8Ouc8Yh89ggjxk3D15Pn49XLZCyYOQ7paallkRSVWbH4JwT4XcbQ4WMx4buZePUyGXNmfof0tLRiw+Tl5WHBvBl4FP4QX349CV98PRFhD+9h/uypEIvF8uO2b/kDu3dsQrsOXTF7/kL4NmyCjet+w5lTxz9G0v61xYsXws/vKoYPH4HvJk/By5cvMHPGdKSVkifz5s5GeHgYvv5mPL7+ejwePniA2d9/L8iTApcvX8KmjRvKMhkqtWP1NNy+dgbdB03CkK9/QeqrRPy+YCQy04u/vpMTorBm/nDkZIsw6IsfMXjczxCLc/HbvGFIjHsmP+7Y7mU4uW8VGrfpg7HT18HTpxX+3PozAs4d/BhJe298xhZP384KDf/eAp1KpqUea9W7I7x2LEXyOX/c/PRrvLxyHV5bFsG6f1f5MQY17NDo9FbkibIRMmginq7YghoTR6D2yuI/0Kqbdcvm4HrAefQb9g3GTvwBKS+TsHD2uHcud3ZvWo6bgReU7o969hjWttUxZ/FmwU+XXkNUnQyVWb98Nm4EnEe/od9gzLc/IOVVMha9V34sKzY/9m//DYd2rUWL9j3x3ZyV8PJtgV0bluDSP8XXCctT6OOnmLx4LarbWGLRd1+gc3NfrNlzBDuOnSk1bER0LL5b/Dvy8iSC7VUqm2LTgqkKP52bN4S2lhZ6tmlaVslRqT83TMGDm6fR/tPv0HvUQqSlJGL74s8hyii+zEmMDseOZSOgp2+E/l/9hnZ9JuH+jdM4uO5bwXFHNk7FjYt70LzrWAyasA5WDm449Md3eBKq/D1AXbC+pmjVkh8R6HcRQ4Z/gfHffY9XL5Mxf9YEpKeXlCdi/PLDFDx+9ABjv56CMV9NRtjDUCyYM1meJxkZ6Zgz/WuE3r6JQUPHYPKsn1DN0gZzZ3yDx+EPPlby/pXFixbB7+pVDB8xApOnTMHLly8xY3rpddi5c+YgPDwc34wfj6+/+QYPHjzA90XqsBs3bMCRw4fRt29fzJgxA1paWvh+1izExcV9jKS9t7uR8Ri/+Sgcq1XG8uHd0c3HDStOXsWWCzeLDfPDgXPY738HI9o0wKqRPWFfpRLGbz6KkKex8mMeRCdi9NpDMNTTwYrhPTCxWzMEhkdi0lb1vl/etmTxr/Dzu4Lhw0fhu8nT8PLlS8yaMbXU58kPc7/Ho/BwfP3Nt/jq6wl4+OA+5nw/U36d5ObmYO7cWbh9KwSfDR2G72fPg7OzCxYv+gUXzp/7WMn7V/j+R0RFcQRGGTt06BD09PQAADdu3MCMGTNw/vx5AEDXrl3RokWL8oyeyu3YvRe1atbAjMmTAAC+9X0gFoux98Ah9O7ZXZ4Xb9uyYxdaNm+KcWNG5YfxRlp6Grbt2oM2rYT5IxKJsHTlb6hiYY7kFy/LPkEqsHP3HjjVrIlpUyYDAHwb1IdYnId9Bw6i1yc9lebJtu070aJ5M3w5dgwAoEF9H6SlpWH7rl1o3aolAGDfgYNo2KABvv3ma3k4e1tbfDt5CoKu30CL5s0+Qur+vcdhoQi57oep85bDq77sBd61thcmjumDs3//iV79RxQbNurZY2zfsBxPHz+Arq5i/l33v4D42EgsWrMHdg6y3oH21Z0wcUwfBPlfQLvO6tmDLezhfdy4HoDZ8xeifoNGAAAPz7r4cuQgnDp5FP0GfqY0XMDVS3ga8Ri/rdsKewdHAECNmrXw7Vcj4X/1Ilq16YCkxAQc/+sgxnw5AZ27fQIAqFvPBy9eJOFWyA107NLjYyTxvT18+ADXg4Iwf/4CNPD1BQB4enpi5IjhOHnyBAYOVD4q4OrVK4iIiMC69X/AwUHWs7qmU018Ne5LXL16BW3ayEZ4vX79Gjt3bsfpU6dgYmLycRL1gZ49uo37wZcwdvo6eHjLnpFObvXx4/hO8Du7Dx17f6E03JXTu6Cjp4+x09dCT1/WI825dkP8OL4Trp7eg74jv8fLpFhcOrkDn46YheYdBwIAXDwbIeVlAsLu+qNp+34fJ5Hvgc9YJTQ0YDe0F9wXTce7ronnuuA7xB86jYdTfgUAvDjrB53KZnD94VvEH/gbAOA0dQzEaRm42ecrSHNzkXz6CvJEWfBcNQdPFq5HVnR8WaVIJZ6E3cXtG1fx3dyVqJdf7rh4eGHK2F44//ch9Ow/stiwUc8fY9eGpXhWTLkDAFHPHqGGswdqudYpk/irmjw/5qxE3fqy69nFwxtTv/gE508dQs9+xedH9PPH2LVxSbH5kZwYh3+O7cHQMVPRtktfAIBHXV+8epGI0FvX0LpTn7JJ1AfYePAEXB3tMf8bWf2jiVdtiMUSbPvrNAZ0aQN9XV2FMLliMQ6cvoQNB49DV0dHYb+ujg7qOAtHKTx8GolzgcEYN/ATeLnVKpvEqFD0k1t4dOciBk/cAOc6suejg0sDrJreHjcu7kXL7l8qDXft7DYYGJmh/1e/QUu7MO+Obp2FFwlPUcWqJiIf3cSD4H8E567h3gSvkqLwOPQKatVRz/ck1tcUhT+8h+Dr/pj1wxJ4N2gMAHCrXRdfjxqAf04ewacDlI80CvS7hGcRj7F87Q7YO8hGkTrWrIXJX3+OwKsX0KJNR1w8exLJifFYsPh3uHnUBQDU8/bFmzevsX3Tavy0ZN3HSeR7evjwIYKCgjD/xx/hW1CHrV0bI0aMwMkTJzBwUHF12KuIiIjA+vXr4VBdVod1qlkT48aNw9UrV9CmbVskJyfj5MmT+PLLL9Gte3cAgI+PD0aPGYODBw/i22+/VXru8rT2dCDcbKvil8GdAQDN3ByRm5eHzedvYEhLb+jrCD9Nxb5Kxd8hYZjZuw0GNKsHAGhYyx63n8Vhv/8d+NS0BQCsOOEHN9tqWDWiJzQ1ZZUeI309LP7rEmJepsLOwuwjpvL9yd51ruGH+T+hgW9DAEBtT0+MGjEMJ08ex4CBg5WG87t6BRERT7B2/ca33nWc8PW4sbh69TLatGmHG9ev49nTp1i+cjVcXGQdZ7196iM5KQmHDu1H23btP04i3xPf/4hIGY7AKGPm5uYwMjICIBuB8TZ9fX1UrVq1PKJVJnJyc3E39B6aNWks2N6yWVNkikS49+ChQpiExETExMahWWNhmBbNmiIuPh4xscIeJBu2bEflypXRscg0H+oqJzcXd++GKuRJi+YFeaLYa0iWJ7Fo1qSJMEyzZoiLi0dsbBwkEgm8vb3QpXMnwTH29rLhyHHx6v0RCQDuhlyDnr4B6no1km8zNasMd09v3L4ZUGLYdSt/hESSh/lLNsG0kuJULg0at8K8RRvkjRcAoK0t+7CQq8Y9p2+H3IC+vj68vBvIt5mZVUJtz3oIuXmt2HC3Qm7A1s5e/jIMAPYOjrCzd0DIzSAAwLWAq9DR0UW7jsIpGabMmIfp3/+o2oSoUEhwMPT19eHt4yPfZmZWCZ516uDmjRvFhwsJhp2dnbzyCgAODtVhb28vCHdg/z6EBAdj1vez0bBRI2WnUjthd/yhq2cAt3qFPXeNTc1Ry6MBHt4qvseqpU1NtO0+XN54AQB6+oaoZGGJF4nRAIDQG+eho6uHxm2EHxeHT1yGkd+tVG1CVIDPWOVM67rC8/f5iNn1F24Pn1bq8QbVbWHsWgMJR88Ktscf/gdGzo4wrCW7j6p2aI6kU5cF00Ul/HkaGlpaqNqxuWoTUQZCb8nKnTpFyh232j64E1xyubNx5Q+QSCSYs2QLTCuZK+yXSqWIfv4EDjVcVB7vslKQH55ehfePqVlluNb2wd1g/xLDblg5T5Yfi7fCxEwxP0KCLkFHRxct2vcUbP9q6q8YP2OxahKgQjm5uQh58BitGnoJtrdt7I1MURbuhEUoDRdw6x42/XkSw3t1xjeDe5X6d6RSKZZs2YcadtYY1K1i1GUj7vtBR88QTrULG22NTMzh6OKLJ6GXiw3XpvdEDPl2g6DxQiu/LibOldXFHtz8B5WrOcgbLwBAQ0MDI2fuRZfB36s6KSrD+pqiOyHXZfV6b1/5NjOzyvDwrIdbNwOLDXc7JAg2dg7yxgsAsHeoAVv76vK8jImOhJGxibzxooBnXR+EP7xX4giP8hScX4f1ebsOW6kS6tSpgxs3ix91EBKcX4et/lYdtrqsDnsjvw57+/Zt5OXloUnTwrqgjq4uGjZsWGL9uLzkiMW4GRGLtp7CRtsOdZ2RkZ2DW2+NqChQ1dQIeyYOQrf6hVNiaWpqQEtTE9niPADA6wwRbkbEoH+zuvLGCwBoX7cWzswdrfaNFwAQEnwz/12nvnyb7F2nLm7eKH7qz5CQm8W86zjIwxkaGqJzl25wdhbWTezs7ZGgxnVZvv/990g1NPnznj//Rf/3IzAyMjKwfPly/PPPP8jIyEDt2rUxY8YMeHp64tatW1ixYgXu378PbW1ttG3bFtOmTZPPbd22bVsMGTIEt2/fhp+fH3R1ddGjRw/MmDED2tqyrDt79ix+++03PH/+HHXq1EHTpsKh4G3btkXv3r3RsGFDDBsm63nSrl07/PqrrIfjzJkzER4eDkDWErxq1SpcuHABKSkp8PDwwKRJk9Ao/6G6evVqBAcHo2nTpti1axdSUlJQr149zJ8/H05OTh8lP0sSn5CAXLEYtrY2gu02NtYAgOiYWNT39hLsi4qOAQDYFQljay0LExMbK98XfOs2zl24iHW/LceFS1dQESTEF+SJrWC7jbUsTTExsajv7S3YFx0t+5BYbD7GxsDW1gZfjB6l8PcCAmUV/erV1XudBwCIjXmOapY20NTSEmy3tLaD/6V/Sgw7btI8ODgW32vR0MgYLu6ylxxxbi7iYp5j95bfYGJaCY2aq+8Hg5joSFha2UCrSJ5Y2djiyqXih/nGREfCxkZxPlAra1vExsiup2fPnsDaxhb3793Fzq0bEPn8KcwtqqBv/yFq25sPkN0PVlZWCnliY22DS5cUpw+Th4uKhk2R+w4ArG1sEBMTI/+9S9duGDlqNLS1tREUVPxHB3WSGPsUFpZ20NQU5kkVSwcE+58sNlzBiIq3JSdEIT76CVxqy8qZ2MgwVLFyQMTDmzi+ZwXioh/DrHI1dOg1Ri1HX/AZq5woKh6X3DogKzYR5i0blnq8sZusDpHx+Llge2ZEpGy/Sw1kxSTA0NEOGY+fCY7JeZGC3NQ0GLuUvP6KOoiLfo6qlrYK5U41azsEXj5dYtixE+fDvoRyJykhBlmiDDx7/ADTx/VFcmIsqlraoke/EWjetptK4q9q8THPlOaH5Tvlx48l5kfUs0ewtLZH+P1bOLhjNWIin6CSeTX06DtcLUdfxCa9QK5YDAfraoLtdpayjkZRcYloVNddIZy7kyP+Wv0TzIyNcOJS8R9pC5wNvIn7T55j7ZxJ0NKsGC+fL+KfonJVxTKncjUHhAYVP0WLaWVLmFa2BADkZGciJuI2zh9eAftaPrCyl32QTIx+iGo2zgi9dhxXTqzDy8RIWFhWR7s+38HNRz17BgOsrykTExMJSytrxTyxtoPf5bPFhAJioyNhrTRP7BAXGwUAMDU1Q5YoE+npaTA2LuwtnRAv++idlBAH41rqNy1zcXVYaxsbXLpYfB02KjpaoV4DADY2NoiJlaU5OioKBgYGgjUxCo55+fIlRCIRDAwMVJAK1Yh5+Qa5eXmoXlXY8cyhSiUAwPPkFDRxFa5Fpautjdr2smeIRCJFUmo6dlwORszLVMzo3RoA8Cj+BSRSKSobGWDmrlO49OAppFKgXZ1amN67FUwN9Ms6aR8sJjoaVkruHWtrG1y+pHyKRkB2DdjYKq6lYm1jg9j8dx0vbx94efsI9ovFYty4fl3wkV/d8P2PiJSpGDXnDzBx4kRcuXIFv/76K/766y/Y29tj5MiRuHPnDoYOHQpnZ2ccOHAAq1atwp07dzBq1Cjk5eXJw69atQq+vr44duwYpk2bhl27duHECdn6CyEhIRg/fjw6deqEY8eOoXfv3tiwQfkcet7e3vK1MA4ePIiuXbsK9ufl5WHkyJG4efMmlixZgsOHD8PFxQWjRo3C3bt35cfdvHkTwcHB2LBhA/bs2YOXL19i/vz5qs62fyUjIxMAYGQoXCzLML/ylCnKLDaMYZEwBRWuzMzM/OMysOy3NRg2ZBDslBRK6iojMwOAYvoMDYXpE4R5xzwpKi4+Hhs3b4FTzZpo2KCB0mPUiSgjAwaGRgrb9Q0MIRJllBi2pMaLopb+NAUzvx2K+6HBGDRCuCCgusnMyIBBkf87ABgYGEJUzP8dADIziw9XcL28SU3Fq5cvsGLJz2jfsSvmLlgML+8GWLdmuVrPqZyRmaFwLwCy+6G4ewGQ5cm7hLO3t5c3SFcUWaJ06L+1AGoBPQMjZGWmv/N5cnKysGftLFkv6c6yOfrT36Qg9VUSdq6ZgcZtP8WXM/+AW92mOLBpvlqugcFnrHK5KanIik185+O1zWTXk/iN8PoRp8nyV9vUGDpmJkqPKThO21TxmlQ3osz0f13ulPSxHgCinj4CIJs6adDIbzFp9nLUqOWOjavm49KZv/51nMtSZrH5YfTB+ZGWmoLXr5Lxx4o5aNn+E0yetxqeXo2wbd2varkGRnqmCABgVORjl2H+7xkikdJw1cwrwcxYMQ+Ls+v4WdR1dUL92hVnpE6WKA16+krKHH0jZJdynQD5o06+bYKdy0YiJzsTXQYXrpmTkZaC2Gd3ce7QMjTvOhZDJm5AFaua2L92vFqvgcH6mqLMDOXPEwNDQ2RmFn+dlFRfE+WHa9GmIzQ0NLHsl9mIjnyGjPQ0XL14BpfOyaY3zM7KUlEqVCsz41/WYd8hXEZmZrHHAMXXZcpLelY2AMBYXzgVn6Ge7PeMrJJHyG+9eAOdftqM3Vdvo3ej2mjsIutIkpIuezbP238WejraWDm8B77r0QKXHzzF+E3HFGbAUEfFvesYquhdp6itWzYiLi4W/Qcon4ZJHfD9j4iU+b++a58+fYorV65g8+bNaN5cNrXBDz/8AFNTU2zatAmurq6YM2cOAMDJyQnLly/HJ598Aj8/P7Rq1QoA0Lx5c/nICXt7e+zcuRMhISHo1asXdu3aBR8fH3zzzTcAgBo1auDRo0fYsWOHQlx0dXVhZiYbwmhubg59feELkp+fH+7fv4/jx4/DxUX2UjN//nyEhoZi8+bNWLVqFQBZi/nixYvl5xo4cCCWLFmi0nz7t0qrIGgqGeYklUqUHFlIIz/M2o2bUbVKFXzaq2eJx6sbiaS0PFGcnFxSSp4oy8eo6GjMmjMXWlpamD1rBjTVrFefRCJR+F+XlE5lafy3eg8Yge59PkPApX+wYdVPkOZJ0Lpj+V9HsjwRXh+SEu4hDSXXSuG5ig9XMJxanJuLN29SMW3WfDRpJpuqoW49H7xITsL+vTvUolefsjyRlpC2D82TikDZvSOVFH/vaLzjvZ8lysDmpRMQGXEPIyYth3lV2WiEPHEuMtJSMOK7FajXsAOAgjUw4vHPn+vUbhQGn7GqUdp1I5VIgFKPUa+PBMrLnRKeCx9Y7rjW9sak2cvgXqcB9PRlH5Dq+DTBm9QUHN7zB1p1+KTEZ1ZZU/4sKen5+mH5IRbnIu3Na3wzfREaNJHNOe1R1xcvXyTgr/2b1G4URmnX77s+W0tyNzwC4c+isXiK8jUj1IH0fcucd7imJXliDBy/FuLcbPj9vQHbFn2GETP3wMreDXl5uUhPTcbYuX/CunptAEAN98ZYP68XLh//XS3WwGB9TZHS58m/fL6+S53G3qEGZsxdiD/WLMF3X8vey2vWcsWAz0Zhyx+roKtf/r3slV4n//LeKbmskoUrKd9KO395KK2+Vlp8W3rUhJejDW49i8MfZ4OQlSvGL4M7Ize/46mHnSV+GCCrtzZycYCJgR5m7DqFwEdRaOqqPiMNlL/r/Mvr5D3fdaRSKbZu2YSjfx1Bn0/7oWkz9Zj6k+9/RPSu/q8bMB49kvWG8/Lykm/T09PDzJkz0bVrVzRrJlyA083NDSYmJggPD5c3YBSdmsnExAS5+XM/P3r0SOEc3t7eShsw3iWuJiYm8sYLQPZwbtCgAfz8/OTbqlSpIm+8KBqf8lYw8iIzU9hLLSO/tdvISLE1vGB9EFGRnm2Zb4W5dv0GLl3xw+8rlkIqlSIvL09escvLy4OGhobafkwqSHPR9BXkiaGRYk8lI8PS8+Rtd+6G4seff4GBgT4W/fozbPKn31InR/ZtxuF9mwXbGjZtizevXykcK8pUPjLj33L18AIAeNbzRXJSPP46uE0tGjAO7N2B/Xu2C7Y1adYKqcXkiaFR8b2bjYyMIFIywkmUmQnD/Lw0MDSEhoYG6vsK1wrwrt8Qt0Ju4HXKK1SqrDiP+ce0d89u7NmzW7CtWfPmeP06ReHYTFGm/PmhjJGRocI9BMjuI0MVXl9l7Z8/1+GfP4WLU9Zr1BFpqS8Vjs0SpUPfsPRe8Ckv4rFx8ddIin+Oz79dgjoN2sr36RnIrhMPr5aCMG71miPsjj/SXr+ASSX1GcXEZ6xqiFNl84drmwjzq2BUhTg1XT7yougxBccVnENdHN2/CX/t2yTY5tu0Ld68VnLvZGbA4B3unZKYVjKHl6/ix9Z6DZrh/p3rSH39EpUql9+9c3T/Jhzdv1GwrUHTdniTWkyZ84H5oW9gBA0NDfni4AXqeDfBvVvXkPr6JcwqWXzQ31Al44JRW6JswfYMkaxnt7EKpiG5EHQLpkaGaObl+cHnKiuXj/+Oy8d+F2zzqN8JGW8U75vsrHToG5a++KmWto58/YzqLr5YNb0dgs7uwCcjf4GenhGMzarKGy8AQFNTCzU9miD48v4PTI1qsL6m6NDebTi4d6tgW+NmrZGaUlyeFF/vMjQyfqf6Wj2fhvh98wEkJcrm7be0ssGFM7JpM9+eVqq87NmzB3t2C+uwzZs3x+vXrxWOFWWWUoc1LKEOmx9Odi0pP6ZgvzoxNtADAGRkC0daFPxuYqCrEOZtztay8rO+kx3EEgnW/XMN47s0hVH+CI6WHsJpLJu5yRotwmKT1KoBY++eXdi7Z5dgW7PmLZCi5Dop/V2n+Gug6LtObm4OVixfiiuXL6HPp/0wctSYf5eAMsD3PyJ6V//XDRglDQsrrpeIVCqFjo6O/HddXcXCtCCshoaGQs+Kt8O+j5Li83Y6lMVHXdhYW0FTU1NhcdO4ONnvDnaKczQWTAcVGx+PWk6FCy4XnMPB3g479uxDTk4Oxnw9QSF8508+RYd2bTBt0rcqS4cq2Vhbl5wn9opzvtrZ2cqPqfVWA1rBOezfCnPx0mUsXbES9nZ2+Gn+D6hSRX0+Brytbade8PYV9vK4GXQZd29dg0QiETRAJcbHwNbe8YP+XsTjB0hOjEPj5sL5kx2dXPE4LPSDzq0qHTt3R4OGwkWEgwL9cDvkhkKexMfHwc6++Dn3bWzt8ezpY4Xt8fGxcHaRzTNtbWMLqVQKsThX8BwR54kBALp6eh+UHlXo3KUrGjYULqQWGBiAkOAQxTyJixfcC0XZ2tnhaYTioqvxcfFwca04U3c0bdcPtX1aCbaF3ryAsLv+CnnyIiEaljY1i55CIC7qEdb/+gVyc7IxbtYGOLkLp0KqalVd1lAszoWObuE1IcmTNZTr6JZ/L8e38RmrGumPZOtaGDpVx5vbD+XbjZxkL/3pYRHIy8iEKCYBhk5F5qiuag4dU2OkF7PIcXlp3bE3vBoIy53goMsIvRWkWO4kxMDmA8ud8Pu3kJwYi+Ztuwu25+RkQ1NTC8bG5buQaOtOveFVpBwOCbqMe0rK4aT4aFjbOX7Q37O0tpc/S/DWs6RgmlZd3fIvc95ma1kVWpqaiE5MEmyPSUgGADjafnjDpV9IKFr61oO2tlbpB5eT+i37w6Vea8G2sJDzeHLfD1KJRDAS5VVSFKpYF1/mhN++AH0DE1R3LVzUWd/QBJWr2iMtVZbP5pbVkZoSD6lUKuhVm5cnhraOepQ3rK8pat+5J+o3FK77eP3aVdwJua6QJwlxsbC1K/4DcnF5khAfg1ousnVnkpMScff2DbRs0wmWVoXrVz2NeARjE1NUsyz/jgVdunRBw4bCNacCAwMRHByskCdxcXEl1mHt7OwQoaQOGxcXB1dX2VoftnZ2yMzMROrr1zCrVEl+THxcHKpVqwY9NbhO3mZvYQYtTQ1Ev3gt2B6V/3uNaoqNcnGv3uDa4yh083GDnk7h9xB3O9laRclvMuRraOSI8wRhxXmybzRvh1MHnbt0Q8OGwsbJa4H+CCnmOinpeVL8u04cXFwL14TJyMjA/HmzERb2EGPGjsMnvXqrICWqw/c/InpX6tltXUUKRk+EhhZ+sBSLxWjbti2eP3+O4OBgwfFhYWFIT09/5wWx3dzccOvWLcG2e/fuFXt8ScPdXF1dkZaWJh81AsgaL4KDg1Gr1rvP91+edHV1UdezNvwCrgkaZK4GBMLIyBCuLoqFhq2NNawsLXHVP0Cw/WpAIGxtbGBlaYlhgwdizYqlgp+unWRDRNesWIphgxUXplUXurq6qOPpCf+AAEGe+PkHwMjICK4uzgphCtJ91d9fsN3PP0C+DwCu37iJxcuWw8PdDcuWLFLrD2uVLaqiprO74KeOVyNkiTJx91bhwllvUlMQdv826ng1KuFspbsTHIi1y+bhZXLhHPCSvDw8uHuz1Hm7PxZziyqo5ewq+PHyaQCRKBO3Qm7Ij0tNfY0H9+7Ay7v4Ofe9fBogJjoK0VHP5duio54jJjoSXj6ycPUbyCrLfpeFi8HdCAqAY42aatErxcLCAs4uLoIfH5/6EIkyEfLW8zo19TXu3QuFt0/9Ys/l41Mf0dHRiIqKlG+LiopEdHQUfLyLD6duzMyrwcHJU/DjVrcpskUZCLtT+IxIf/MKEQ9vwq1u02LPlfIiHmt/Hg0NaODb+TsVGi8AwN1L1oM8JOCUYPu9m5dg4+DyTiM8PiY+Y1UjMyIKmU+jYd2nk2C7Ve+OSH/0DKJI2aKhL875o1rX1tDULeysYdWnEyRiMV5cVK9FECtbVEUNZw/Bj6d3I2SJMhBapNwJv38Lnh9Y7jwMDcbGVT8iIbbwmSORSHAj4AJqudWB9r/s4KIqlc2rokYtD8GPp5csP+7dKlx8+k1qCsIf3IKnV+MSzla6gpEXQVfPCLbfvn4F9o7OHzziRdX0dHXg5V4Ll67fFjxLLgbdgrGhAWrXcvyg86emZyA6IQn1XN7tHaO8mFS2hI1jHcGPU+1myMnKwJP7hSPCM9JeIfLRTfnICmWund2Ok7vmQyIp/LD45lUCkuMjYGkn+7jmXKclROmv8fR+4fM4T5yDiHtXUd1FPcpq1tcUmVtUgZOzm+CnnrcvRKJM3Am5Lj8uNTUFD+7fQT0f32LPVc+7IWKjIxEd9Uy+LTrqGWKjI1HPW9Yg8CY1Bet/W4T7d0Pkx6SkvIT/lXNo0KiZWkyXZGFhARcXF8GPj48PRCKR4JtD6uvXuHfvHnx8fIo9l4+Pj6wOG/lWHTYyEtHR0fDOD+ft7Q0AgpkacnNycP369RLPXV70dLThU9MW50OfCJ6x5+4+hom+LjwdrBTCxKe8wfwD53Ah9Ilge2B4FHS0tOBYtTJqWprDxtwU/9wOF5z30v2nAACfGuq1dqaydx1v+bvOTflxqamvcf9eKHxKeNfx9qmP6Ogope863vnvOnl5efjxhzl49Cgc02bMUrvGC4Dvf5RPQ5M/7/vzH6ReTdIqVqNGDXTs2BHz58/HDz/8AEtLS2zYsAHZ2dnYt28fBg8ejAULFmDw4MF48eIFFixYAA8PDzRp0qT0kwMYOXIk+vXrh0WLFqF///4IDQ3Frl27ij2+YEGhsLAwVK5cWbCvefPmcHd3x+TJkzFnzhxYWFhg165dePToEebNm/fvM+EjGzygH6bPnocFC5egc4d2ePAwDAcP/4VRnw+Fvr4eMjIzERkVDRtrK1TKnwrrs0H9sXTlapiamKBJo4YIuHYdl6/64/tpUwAAVpaW8g9KBYKuy14YXJ3V42N0SQYP7I8Z38/Bz78uQqeO7fHgYRgOHT6MkcM/h76+PjIyMxEVFQVra2t5ngwZNBDLVq6CqYkJGjdqhMCgIFy56oeZ06cBAHJycrDit9UwNDDAwAH9ERUVJfibVapUQdUq6jPNizLunt5wr+ODtct+wKDhX8PYxAyH926CoZEx2nctnBs7JuoZxLk5cHRyLeFsQu069cL500ewdMFk9Bk4Clo6Ojj395+IjozA9Pm/lUVyVKK2Zz141vHCyiU/Y9jIL2BiYor9e7bByMgYnbt+Ij8uOuo5cnNzUdNJ9nG2ecs2+PPAbiyYOwNDh8uGBO/cthHVHWuiWYs2AADPul7wbdgUWzauRVZWFhwca+DS+TMIe3APM+f89PET+44869RBnbp1sWTJIowcOQompqbYs3sXjIyM0bVrN/lxUVGRyM3NhZOT7JnQsmVLHNi/D3PnzMHwESMAANu2boWjoyNatGyp9G9VFE7uDVDLwxe71kxHjyHfwci4Ek4fWgsDI1M06zBAflxCTATEuTmwqyHrwXh4+69IT32FfqPnIkuUjueP78iP1TcwhpWdE5xrN0Tt+q3x187FyMkWwdq+Fm5cOYZnj25h1JTVHz2t74LP2PenbWIEY49ayIyIQs4L2RD9xz//jnqbFyL31WskHr8Ay57tYNO/K0IGT5SHi1i6CTYDusH3xCY8W7kVRi6OcF3wHaI3HUBWdHwxf019uNX2gZtnffyxfC76f/4NjE3M8Ne+jTA0MkHbLp/Kj4uNegqxOBfVa757udOmc29cOP0nVvw0GX0GfwE9fX2cP/UnYiMjMOuXP8oiOR/MtSA/VsxF/8/HF58f0U8hzn2//HCvUx9evi2wZ8sKZGdnwdbBCf4XT+Jx2B1MmLm0LJLzwUb27oJvfv4Ns1ZuQo/WTXD30VPsOnEWXw/qBX09XaRnivAsNh52llVR2fT9pqyJiJI1AtawK/+e4u+ruqsvHF0b4vCGqejQbwoMjCvh8tE10Dc0QYPWhYvAJsc9gTg3B9bVPQAALXuMw85lo3Bo/STUb9kfGekpuHJ8LQwMTdGko6xcrtO4B65f2IXDG6ei3affwbSyFYLO7cCblAT0G7eyPJL7TlhfU+Th6YXadbyxaumPGDpiHIxNzHBwzxYYGRmjY9fCj6bRUc8gzs1FDSdZx7amLdvi8MGd+GXeVAwZLlsfZve29XBwrImm+XlSs5YrXD3qYOPaZRg68itoaWlh746N0NLSQv/Boz5+Yt9RnTp1ULduXSxZvBgjR42CqYkJdu/eDSMjI3Tt9lYdNjK/DlursA67f/9+zJk7FyPy67Bb8+uwLfPrsJaWlmjfvr3s20ZODmxtbXHkyBGkp6ejbz/1Wq+swJj2jfDFH39i6o6/0auhB24/j8f2S8H4tmtzGOjqID0rG08TX8HOwgzmxobwrmGLxs4OWHjkEtKzc2BvUQlXHjzFfv87GNepMUwNZaO0vuveAlN3nsS0nX/j08Z1EJH4EmtOBaB93Vry0RrqzLNOXdSpWw9LlyzCiJGjYWJq8ta7TuGoTsV3nVY4uH8v5s35HsNHyO6DbVs3w9GxBlq0lI3ePnH8GO7fv4fOXbqhSpWqCAt7KPjbbm7uHymV74fvf0SkjIa0tJWXK7i0tDQsXrwYZ86cQU5ODurVq4cZM2bAzc0NgYGBWLlyJR48eABjY2O0b98ekydPRqX8YZht27ZF7969MX78ePn5hg4dCltbWyxcuBCAbGjokiVL8PjxYzg7O6NLly5YunQpwsPDFc6Rk5ODb775BgEBAfjuu+9QqVIlzJw5U37sq1evsGjRIly8eBE5OTnw9PTEt99+C19fWa+V1atX48iRI7hwobBHzuHDhwXn+DeiHj8s/aD34BdwDTv27EVMTCwsLCzQs1sX9OvTC4BsLvEps+ZgysTx6NS+nTzMiVP/4ODhv5D84gWsrSwxsN+n6NC2TbF/Y8fuvdi5dz/OnvhLpXEHAAdndzx/8qj0A9+Df0Agdu7eg5iYGFhYWKBH927o20dWmb9zNxTTZs7C5InfomOHwimPTp46hUOHjyA5+QWsrawwoH9ftG8rm6v+9p07mD5rdrF/77PBgzB0yGCVxd+xlgtuhivOQ/mhMtLfYNfmVbh57QqkUglc3Ovis1ETYfPWUPOfZo1DclI8Vm36S+k5vh3dC+6ePvhy4lzB9qSEWOzd9jvC7t9CligTTq610W/IWPmaGB+qgWtlPHgSp5JzvS09LQ1bN61FUKAfpFIp3Dw8MXLMV7C1KxxCPHvGRCQlJmDD1n3ybS+Sk7DpjzW4c/smtLW04eXTACPGfA1z88Je4zk5Odi/ZzsuXzyLN6mvYe/giP6DhqFRE9Us4uZRywZPIp6VfuB7SktLw6aNGxB4LRBSiQQeHrUxZuxY2NkVDiGeMX0qEhMTsXVb4RpEycnJ+OOPdbh96xa0tLTg41MfY8aOFeTJ25YvX4rQu3cF5/hQtZxq4NQt1a9TlJmeir92LkHozQuQSiSo4eqNXsOmwdKmcA7g1fOH41VyHOatOQOxOBfTPveFJH8KiqKc3Btg/LxtAIDcnGyc/nMtgq+eQHpaCqxsndDx0y9R17ed0rDvq4u3Dp+xRTjWcsFJnXf/OPw+zFs2RJPzOxHYbiheXbku2HZn1AzE7DgiP9ZhzADUnDQS+vbWyHwajYjFGxC7+6jgfJWb1Yf7omkwreeOnBcpiN19FI9++A1SsfJr69/qlhuOa2GpKj0nICt39mxeiZCgy5BKJXB2r4fBIyfB+q1y59fvv8SLpHgs23hU6Tkmj/kEbp4+GPOtsINJYnw0Du74HeH3byNLlIEazh7o+9k4uKig3GnsZobAh28++DxFZaS/wd4tKwrzw60eBo2aBGtbR/kxv37/RX5+HFN6jsljeubnxw+C7Tk52Ti6fyMCL53CmzevYWtfAz37j0b9xq1VEvcm7qZ4fetC6Qe+h0vXb2PjoROIjEtEVXMz9O3YGkO6y54bwfcf4asFKzDny2Ho3lqxs9OJS4FYsH4Hjvz2E2yqCcuZc4HB+H7VJuxfNg+Otoo9jVWlkndb7PFT/audKCMVZ/YvRNit85BKJbCv5YNOA2egilXhFFLbFg/F6xexmLi48H/yLOwaLv21GgnRYdDU1EatOs3Rvu8UmJlbC859/vAKhIWcRXZWBqwdPNDu0+9Q3aX4kQzvY3BzDdbXivCoZYO7j5NKP/A9paenYfum1bgReBUSqRRu7nXw+ZjxgjyZN2M8kpMSsHbLQfm2F8mJ2LrhN9y9fQNaWtqo5+2L4WPGo7J5YUeB1ymvsG3Taty9dQNSqRS163hj8OdjYWNb/BQ776OuczVEPH2qknO9LS0tDRs3bsS1wEBIJBJ4eHhg7BdfwO6tqZWnT5uGxMREbNteuNZKcnIy/li/HrfkdVgfjP3iC5ibF061lJuTg61bt+LSpUsQiUSo5eyMUaNGwc3NTSVxd6pZE1kn1pV+4Hs4H/oE6/65hudJKahmZoQBzerh89aynvE3nkRj9Lo/8eOADvikoWxdnIysHKw/cw3nQp8gOTUDDlUr4bOW3ujTSLiW0OUHT/HHmSA8jn8BM0N9dPVxwzddmkC3hCnF/w397uPwOCKy9APfU3paGjZt/AOB1wIglUjg7lEbY8Z+WeRdZwqSEhOxZdtO+bbk5CRs+GMdbt8KgZaWNrx9fDBm7Jfy58n0qd/h/v3iZwg58feZYve9K2en6nz/e0stpxqlH0QKXt++VN5RqHAqebUu7yh8dP/3DRhUOlU3YFR0ZdGAUdGVVQNGRVZWDRgVWVk1YFRkZdWAUZGVRQNGRVeWDRgVVVk1YFRUZdWAUZGVRQNGRVdWDRgVWVk1YFRkZdWAUZGVVQNGRVYWDRgVXVk1YFRkZdWAUVGxAePfYQPG+/svNmD8NyfOIiIiIiIiIiIiIiIitcYGDCIiIiIiIiIiIiIiUjv/14t4ExEREREREREREZH6kWpolHcUqALgCAwiIiIiIiIiIiIiIlI7bMAgIiIiIiIiIiIiIiK1wwYMIiIiIiIiIiIiIiJSO2zAICIiIiIiIiIiIiIitcMGDCIiIiIiIiIiIiIiUjva5R0BIiIiIiIiIiIiIvpvkWqwbz2VjlcJERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdphAwYREREREREREREREakd7fKOABERERERERERERH9x2holHcMqALgCAwiIiIiIiIiIiIiIlI7bMAgIiIiIiIiIiIiIiK1wwYMIiIiIiIiIiIiIiJSO2zAICIiIiIiIiIiIiIitcMGDCIiIiIiIiIiIiIiUjva5R0BIiIiIiIiIiIiIvpvkWqwbz2VjlcJERERERERERERERGpHTZgEBERERERERERERGR2mEDBhERERERERERERERqR02YBARERERERERERERkdphAwYREREREREREREREakd7fKOABERERERERERERH9t0ihUd5RoAqAIzCIiIiIiIiIiIiIiEjtsAGDiIiIiIiIiIiIiIjUDhswiIiIiIiIiIiIiIhI7bABg4iIiIiIiIiIiIiI1A4bMIiIiIiIiIiIiIiISO1ol3cEiIiIiIiIiIiIiOi/RarBvvVUOl4lRERERERERERERESkdtiAQUREREREREREREREaocNGEREREREREREREREpHbYgEFERERERERERERERGpHQyqVSss7EkRERERERERERET035F8P6i8o1DhVK3dqLyj8NFpl3cEqPwlTP2svKOgVqyW7ELWiXXlHQ21ot99HK+TIqyW7ELEsG7lHQ214rTjJCsfRVSt3QiinT+VdzTUisHQ2UgIu1Xe0VArVm7euBaWWt7RUCuN3cxwUse1vKOhNrrlhuPp8O7lHQ21UnPbCby+daG8o6FWKnm3hWjXL+UdDbVi8NksPH/yqLyjoVYca7kg+qtPyzsaasV+7Z94cS+wvKOhVqp4NsG60+UdC/UyrjPwJOJZeUdDrdRyqoFXd6+WdzTUhnndFuUdhYpJQ6O8Y0AVAKeQIiIiIiIiIiIiIiIitcMGDCIiIiIiIiIiIiIiUjtswCAiIiIiIiIiIiIiIrXDBgwiIiIiIiIiIiIiIlI7bMAgIiIiIiIiIiIiIiK1o13eESAiIiIiIiIiIiKi/xYp+9bTO+BVQkREREREREREREREaocNGEREREREREREREREpHbYgEFERERERERERERERGqHDRhERERERERERERERKR22IBBRERERERERERERERqR7u8I0BERERERERERERE/y1SDY3yjgJVAByBQUREREREREREREREaocNGEREREREREREREREpHbYgEFERERERERERERERGqHDRhERERERERERERERKR22IBBRERERERERERERERqR7u8I0BERERERERERERE/y1SDfatp9LxKiEiIiIiIiIiIiIiIrXDBgwiIiIiIiIiIiIiIlI7bMAgIiIiIiIiIiIiIiK1wwYMIiIiIiIiIiIiIiJSO2zAICIiIiIiIiIiIiIitaNd3hEgIiIiIiIiIiIiov8WKTTKOwpUAXAEBhERERERERERERERqR02YBARERERERERERERkdphAwYREREREREREREREakdNmAQEREREREREREREZHaYQMGERERERERERERERGpHe3yjgARERERERERERER/bdINdi3nkrHq4SIiIiIiIiIiIiIiNQOGzCIiIiIiIiIiIiIiEjtsAGDiIiIiIiIiIiIiIjUDhswiIiIiIiIiIiIiIhI7XARb1I5XRdPmHTuD21LW+SlpyIz4BwyL/9dYhg9Ny8YdegNHWt7SDLSkRV6A+mnDkCamw2tylVQddbKYsNm3riCNwc2qDgVqhUQHok1pwIQkfAS5iaGGNi0Hoa19oGGhobS47NzxfjjbBD+DglDSroILjZV8GXHxmjm5ggAuPEkGqPX/Vns3xvXsTG+7NS4LJKiMrxOFBl4esO87zDo2jog781rpJ47idRTh5Uea9K8PaqNnVTsuZI2LEea33kAgI61HSwGjISBex1I8/IgCgvFy72bIU5OKJN0qNr126HYsPsQnkXHwrySKXp3bo9Bn3Qp9v5526OnzzFm+nzs+30xrKtVfe/96iggIg5rLt3G0+TXMDfSx4AGrhjW2OOd8kMskeDzbaehr62NzcM6CvY9e5GKledDcDMyEVqaGqhf3RKT29eHXWWTskqKyty4dQcbd+3H86gYVK5kht5dO2JAr+4l5sm5K/7YeeAI4hITYVWtKoZ8+gk6t20lOObqtRvYceAwomPjYF6pEjq2aYEhn/aCjk7FqD5liTJxYPsa3Ay8gKwsEVxre2PwyEmwtqv+zudYvXAG9A0MMObbeYLtl878ha2//6JwfLuu/TDsi6kfHPeypG9riZa3T+Dmp1/j1ZXrJR5rM6Abas0cB8Oa9hA9j8WTJRsQu/MvwTFm9T3hvmgazOp7QvwmAzE7DuPRj2sgzc0tw1R8OIPa3qj86VB5mfPm/Emknj6i9Fjj5u1QbXQJZc7G5Uj3vwBAVuaY9x8BAzdZmZMVfg8v922CODmxTNKhStfuPMD6/cfwNCYO5mam6NuxFYZ0b/9Oz9fwZ9EYMXshDq34ETbVLAAAcUkv0XvC7GLDdGvVBHPHDVNZ/MtCQEQs1ly8JStzjA3yy5za717mbP0b+jra2Dyss2Bfx5UHkZSWqRDm4uQBqGyor7L4l4XgkBBs27ETkVFRqFSpEnp064a+fXqXmCcXL13Gnv37kZCQCMtq1TCgX190aN9OcMyVq344+OefiI6JhZGREby96mHU8M9RuXLlsk7SB9Nzr4dKPQdD29oekjevkX7lNNLOHVN6rGHjNrAY9k2x53q5fTUygy4Jtmno6cPq++VI/fsgMq9dVGXUy0zQ7XvYsOdPef21T+d2GNSz8zvWXyMxesaP2L9moUL99ELAdez+6xQiY+NhYmSIBnU9MO6zfjCvZFZWSVGpnOwM+B1biid3ziA3OxO2Tg3QsvdMmFvWfOdzRISew/FNX+PTb3bA3rmRfPu9wIM4t0/xmVuvxRC06TtXJfEvCyKRCFu3bIZ/gD+yRCJ4etbBmLFjYWdnX2K4lJQUbNq4AcHBN5GXl4cGvr4YM2YszM0tlB5/7NhRHDn8J7Zu21EWyVCZoDv38cfeI3gaLSuLP+3cBoN7dHzHsjgKo2b+jIO//QzralUE+3p+MRXJr1IUwpzavAKVTNX/fYfov65ivIH/h7Vt2xa9e/fG+PHjyzsq70THwQmVR0xB1p1rSPvnEHQdXWDSdSA0NLWQcfG40jB67t6oNHwSRMF+SP97P7QtbWHcpT80jU2Qumct8t68xsvV8xTCGTbtAP16jSG6fqmMU/Vh7kbGY/zmo+jk5YKvOzfBrWdxWHHyKsQSCUa181Ua5ocD53DlwVNM6NoM1atWxrGbDzB+81FsGtcXPjVt4W5XDTsnDFAIt+ZUAO5HJ6Kzj2tZJ+uD8DpRpOfkCuvv5iE96Cpe/bkLBi4esBgwAhpaWnh94qDC8Rl3riNm/ncK26uNmgANA0Nk3LkBANAyrwLbOUuQGx+LxLWLoaGrB/O+Q2EzbQGiZ30NaW5OmaftQ9wLf4JpvyxHu2aNMHrQp7gb9gjrdu5HniQPQ/v0KDHs08gYTP15OfLy8v7VfnV0NyYZE/ZfRCeP6vi6dT3cikrCyvMhyJNIMbKZZ6nht/rfw/24l6jvYCnYnpCageHbT6O6hRl+7d0cWbl5+P3SbYzbcx4Hx3aHvhp/sL8f/hgzflqMNs2bYNSQ/gh9EI712/cgL0+CIX0/URrmckAQflq+Bn27d0ZDn6G4GnQDv65aBx1tHbRr2RQAcOP2XcxZuBxtmjfB2GGD8DwqBht27sXrN2mYOHbEx0ziv7Zu2RxEPLqHAZ+Ph4GhEf7atxELZ4/DL2v2wcjYtMSwEokEe7esxM3AC2jetpvC/qhnj2FtWx2jvxV+EDCrpPylWV3o21mh4cnN0KlUcvoBwKp3R3jtWIpnq3cg+Z+rsPqkPby2LIIkOwfxB2QN7gY17NDo9FakXLuNkEETYezmBNcFk6BjXgn3vlYsk9SFnpMrrCbNRfr1q0g5sgv6zh4w7z8C0NJC6slDCsdn3rmB2AWTFbZXHTEBmgYGyLx7E4CszLH5frGszFm/GJq6eqjcZyispyxAzOxv1LrMCX38FJMXr0X7JvXxRf8euBP+BGv2HEGeRILPP+lUYtiI6Fh8t/h35OVJBNurVDbFpgWKDXqH/rmMc4HB6NmmqUrToGp3Y5IxYd8FdKrtiK9be+NWdCJWngvOL3PqlBp+q3+orMypLixzUjKzkJSWiUnt68PbXrjPRF9XpWlQtYdhYZg7fwFatWiOYZ99hvsPHmDz1m2Q5OVhQP9+SsNc9ffHoqXL0KtnDzSoXx8B165h6YqV0NHRQetWLQEAly5fwa+Ll6Brl84YPmwoXqW8xo6duzBt1vf4fdVK6Oqqb77oOjqj6riZyAwOQOrxvdB1codZr6GAphbSzig2imbdC0bi4hkK280/+woa+gbIuh8i2K5hYIQqX86AdhVLhTDq6t6jJ5j26wq0a9oQYwb1xt2Hj7F25wHk5eVhaJ/uJYZ9GhWDKb+sUFo/Ped3DfNWrMcnHVtj7OBP8ep1KjbuPYzx8xZhy5IfoKfG10mB0zsmI/75HTTvORV6+sa4dnoN/lwzDENnnoS+YemNMKKMFJzfr7x8TY59iMrVaqDjkIWC7YYmVZQery4WL16I8LAwjBg5CoaGhti7ZzdmzpiOtev+gImJ8g/reXl5mDd3NjIzM/H1N+ORJ87Dtm1bMPv77/Hb6jXQ1hbW3S9fvoRNGzfAwkK962n3HkVgyq+/oX1TX4wZ0At3wx7j912HkJeXh2G9u5YYNiIqFlN+XaX03nn9Jg3Jr1LwzdB+qOdWS7DP2MhQpWmg9yd9h8YpIvX9IkEVknHHT5Eb9xyp+9YDAHLC7wJaWjBq2xMZV08DYsXeiCY9P0PW3evy3vE5EQ8ATU0YNu8I6OgCuTnIjYoQhNG2dYR+vcZIO30Auc8flX3CPsDa04Fws62KXwbLep41c3NEbl4eNp+/gSEtvRU+DMa+SsXfIWGY2bsNBjSrBwBoWMset5/FYb//HfjUtIWxvh7qVrcWhLt0LwJBj6OxdFg3OFZV755avE4UmfcZguzIp0j6YxkAQBQaDGhpo3KP/kj956jCRx9J2htkp70RbDPr0AM6NvaI/XEqJPn7zHsPgSQzE3GLvoc0JxsAIH6RAKuJc6FXwxlZj+5/hNT9e1v2H4ZzjeqY8+2XAIDGPnUhFudh55/H0b9bJ+jpKb6o5eaKcejvs9i870/o6ui89351tu7KHbhZVcbPvZoDAJo52UIskWKz/z0MbuhWYkNDeOIrbPa/hyrGBgr71l+5A2M9XfwxpD0M8s9hW8kYEw9cxIP4l/BxUN+PBlv3HIRzDUfMniTrzdnIxwviPDF2HfoLfXt0UXqNbNy5D62bNsI3oz8HADT0qYe0tHRs3nNA3oBx6vwlWFa1wOxJ30BLSxO+XnWR8joVB46exDcjhyq8GKqbJ2F3cfvGVXw3dyXq1ZelycXDC1PG9sL5vw+hZ/+RxYaNev4YuzYsxbPHD6Crq6f8mGePUMPZA7VcS/+IqRY0NGA3tBfcF00H3vEdyXXBd4g/dBoPp/wKAHhx1g86lc3g+sO38gYMp6ljIE7LwM0+X0Gam4vk01eQJ8qC56o5eLJwPbKi48sqRR+kci9ZmZO8YTkAQBQaAg0tbVTu3h9vzhx7pzLHtH0P6NjYIe6nwjKncq/BkGRmIn7JbHmZk5ucCKtv56h9mbPx4Am4Otpj/jeyBsomXrUhFkuw7a/TGNClDfSVfBjMFYtx4PQlbDh4XGl5oqujgzrOwl7FD59G4lxgMMYN/AReRT6iqJt1l2/DzcocP/dqAQBoVssW4jwpNvuFYnBD95LLnIRX2OwXqrTMCU94BQBo6+oAe/PSGxPVyc7de+BUsyamTZE16Pk2qA+xOA/7DhxEr096Qk9P8Zm5bftOtGjeDF+OHQMAaFDfB2lpadi+a5e8AWPfgYNo2KABvv3ma3k4e1tbfDt5CoKu30CL5s0+Qur+HdPuA5ET/Qyvtv8GAMh6cBsaWlow7dQH6RdPKj5P0t8gJ134PDFu3RXaVrZIWvo9JG/t06/TAJX7j4KGnuJ1pM427/sLLjWqY+63XwAAGnvXhTgvDzsOn0D/bh2Lr7+eOotN+44UWz/d8ecJNPGpi2lfDJdvc7CxwtiZCxAQfAdtmijvHKcu4p7dwtN7F/HJFxtQw0M26tXGqQG2zm+Hu3570LDjuFLPceHgfGhqKX/2JMc8hKVDHVg7eqky2mXq4cMHuB4UhPnzF6CBr+z/5+npiZEjhuPkyRMYOHCQ0nBXr15BREQE1q3/Aw4OspG1NZ1q4qtxX+Lq1Sto06YtAOD169fYuXM7Tp86VWxjiDrZdOAYXGo4YN6E0QCAJt6eEOflYfuRv9G/a3voF3PvHDx9Hhv3Hy323nn0PBoA0KqhN+ysqpVdAoiozHANDFIdLW3oOrkj+16wYHPW3RvQ1DeAbg3FUQHaNtWhXcUSmf5nBNsz/f7Bi4WTgWJ66pn2Hg5xUiwyr5xSXfzLQI5YjJsRsWjrKXxB7VDXGRnZObj1NFYhTFVTI+yZOAjd6rvJt2lqakBLUxPZYuU9xbNyxVj41yW0cK+BDvWcVZsIVeN1okhbGwZudZERHCjYnHHDD5oGhtB38Sj1FFqmlWDedxjenP8b2U/D5duNGjTFmytn5B+SACD72RNEfjtMrT8kAUBObi5u3QtDy0b1BdvbNPFFpigLdx8qb5QKDLmDrQeOYOinPTFuqOJIpdL2q6sccR5uRiairauDYHt7dwdk5OTiVnRSsWFz8/Iw52gABjV0Q/UiH4ukUinOhUWhVz0neeMFANS2scDZiX3VuvEiJzcXt+89QIvGDQXbWzVtjEyRCHcfhimEiU9MQnRcvGKYZo0RG5+AmDjZB+ecnFzo6+lDS6uwqmRqYoxcsRiZoqwySI1qhd66Bj19A9TxKpxawdSsMtxq++BOcECJYTeu/AESiQRzlmyBaSVzhf1SqRTRz5/AoYaLyuNdVkzrusLz9/mI2fUXbg+fVurxBtVtYexaAwlHzwq2xx/+B0bOjjCsJftYULVDcySduiyYLirhz9PQ0NJC1Y7NVZsIVdHWhoFbHWSEFClzbvq/X5nz6VC8uXAK2U8Ln8VGDZoi7epZQZmT8/wJoiZ9rtZlTk5uLkIePEarhl6C7W0beyNTlIU7YRFKwwXcuodNf57E8F6d8c3gXqX+HalUiiVb9qGGnTUGdWtX6vHlSVbmJKCtW5Eyx6P6O5Y5fhjU0B3VLRQbKMITX8FIV6dCTFH4tpzcXNy9G4pmTYTTtLZo3hSZIhHuPXigECYhMRExsbFo1qSJMEyzZoiLi0dsbBwkEgm8vb3QpbNwpI+9vR0AIC5ePRtCAQDa2tB3rg3RHeF0fKJbgdA0MISuk1sxAQtpmpjBrMcgpF85g5znj+XbNQwMUeWLach+fB/JaxaoPOplJSc3F7fuh6FlQx/B9jZNGuQ/T4qvv245cBTD+nTHuKH9FfZLJBL41quNTzq0Fmyvbivr1BabUPw9qS4iw/ygo2uI6m6F5aOhsTlsa/ni2YPLpYYPD/kbUeEBaN5TcWSbVCrFi7hwVLV1V2mcy1pIcDD09fXh7VN4vZiZVYJnnTq4eeNG8eFCgmFnZydvvAAAB4fqsLe3F4Q7sH8fQoKDMev72WjYqJGyU6mNnNxchNwPR6uG3oLtbRrXl737hT1WGi7gVig2HzyOz3t3xdeffar0mMfPo2FooA9by4oxZTARKVLv7oNq7NWrV1iwYAGuXr0KLS0t9OvXD3fv3oVvfqt5UFAQqlatisuXL6N3796YM2cODh48iB07diAyMhKamprw8PDAzJkzUaeOrPdiWloafvrpJ5w/fx7a2tr44osvFP5uSEgIli1bhtDQUJibm6NNmzaYPHkyjI2NP2r6ldGyqAYNbR2Ik4WV7LyXsnn2tataI+fxPcE+HRtZgSsV56LSiMnQc64NaW4ORMF+SDu5D8gTK/wd/XqNoVu9Fl6t+xmQSssoNaoR8/INcvPyUL3IiAiHKpUAAM+TU9DEVTgXua62NmrnD6eXSKRISk3HjsvBiHmZihm9Wyv9O7uv3EJSajo2fKm8wFYnvE4U6VSzhoaODnIThA1auYmyPNKxtoPo/u0Sz1G5zxBIJRK8+nOnfJt2FUtoGRlD/CIJVYaNg3HjVtDQ1YPoXgiSt69FXspLladFleISk5ArFsPBxkqw3dZadn9ExcXD10tx2iT3WjVwaP1ymJoY4+8LV997v7qKeZ2O3DyJwscgh/wPQJEv36BJTRulYf+4chdiiQTjWtbDuD3nBfviXqcjPTsX1mbG+OVUEE7ff46sXDGaOtlgZueGsDQ1KpsEqUBcguwasbcVjkizy79GomPj4OtVV7AvMkZ2nxUNI7+uYuNgZ2ON3l07YtqPC7HvyHF069AWUbFxOHT8FBrX94apSfmXuaWJi36Oqpa20NTSEmyvZm2HwMunSww7duJ82DsW3zM8KSEGWaIMPHv8ANPH9UVyYiyqWtqiR78RSqebUgeiqHhccuuArNhEmLdsWOrxxm5OAICMx88F2zMjImX7XWogKyYBho52yHj8THBMzosU5Kamwdilhmoir2I6Va2KKXPiZPut3qHM6T0EUqkEKYeLlDmGxhC/TILF0C9h3KgVNPT0IAoNwYud69S6zIlNeiErb6yFvTLt8j90RMUlolFdxY9i7k6O+Gv1TzAzNsKJS4EK+4s6G3gT9588x9o5k6Clqd79yGJS0mRljnlxZU5qCWXOHVmZ08oL4/acVdgfnpACUwNdTDl0CUHP4pEnkaKFsx2mdvRFVRP1ncojIT4BuWIxbG1tBdttrGX5EBMTi/rewg9v0dGyXr+2tsK8srGRlUHRsTGwtbXBF6NHKfy9gMBrAIDq1R0U9qkL7SqW0NDRgTgpTrA9N0lWr9extEV22N0Sz2HWfQAglSL1+B7BdmlODhJ+nAhxUhy0zCvOR8e4xGRZ3aRo/dWqoJ6RgIb1lNVfa+LPdUthaojmJQAAAQAASURBVGKMk0rqp5qamhg/XLE3/pXrsim3atjbKuxTN68SI2BmYQdNTWHdpFIVB4QFK59GuEDGmxe4eGg+WveZBSNTxesh9UUUcrIzkBgViu0/d0LqixiYVbGDb4dx8GjYS5XJUKno6GhYWVlBq0h9zcbaBpcuFb/eS3RUNGxsFf/n1jY2iImJkf/epWs3jBw1Gtra2ggKuqa6iJeBgnvHwUbYeapgxERkXCIa1qutEM7dyRGHf18IMxNjnLzor/Tcj59HwdTYCLOWrcONuw8hkUjQ1KcOJo4YiCqVK6k8LUSkeupdc1ZTEokEX3zxBSIjI7Fp0yZs2bIFt2/fxvXrhT1Pbty4gSpVquDo0aMYOnQozp49ix9//BGjR4/GqVOnsG3bNmRnZ2P27MJFpiZOnIi7d+9i/fr12Lp1Ky5duoTY2MKXy7CwMIwYMQItWrTAsWPHsHTpUty/fx8jR46EVA0+0Grqy4b2SrNFgu3SbFlPVQ19xaG/msayl6DKn0+EODEGKZuXIOPicRg2bguzAWOV/h2j1t2Q8ywcOU8fqjL6ZSI9S9YD0bjIfL6G+UMfM7JKngt668Ub6PTTZuy+ehu9G9VGYxfFF5hccR72XL2FTl6u8oYRdcbrRJGmgexlXSISLmYpycoU7C+OlokZTJq3w5tzJyDJzCjcbiqbR9ZiwAhoV7ZA4tpFSN7yG/SqO8Fm5q/QKGZqGHWRniG7RgwNhNeEoYFscc+MTJFCGACoamFe4gfm0varq/T854WRrnBotKGe7Pf0bOULBt+Le4Ed1x5gfo+m0NXWUtj/KlP2nFp1IQRJaZlY2LsF5nVvgofxrzB651mIctR3IeKMTNk9UvQaMcj/Xdk1klFwXRkWva6EYXzqemJQ755Yt203ug8Zha+mzUElM1PMmVwx1qQSZabDwFCx8UnfwBAiUYaSEIVKarwAgKj8HvfJiXEYNPJbTJq9HDVquWPjqvm4dOavfx3nspSbkoqs2HdfRFrbTPaMEL9JF2wXp8nyTtvUGDpmJkqPKThO21Q9nzOa+deFVKHMkV37mgYlT9WiaWIG42ZtFcscE1mZY95vOLQrWyBp3WK8KChzpqt3mZOef98bGQgXj5aXNyLl5U0180owM373Rt5dx8+irqsT6tdW/9FLBWWKkd6/KHMC72N+z2ZKyxxANgIjOS0T7tYW+G1gO0zu6IvgyASM2vGPmpc5suvd0FBYLysoTzIzFRclz8jIVBqmoJxSFgaQjbrYuHkLnGrWRMMGDT4s4mVIU7+gDlu0Xi/7XVm9XhDe2BSGjVoj7fIphWcS8sQKDSMVQXr+/9SouPprMc+TqhaV37t+GpOQhN937IdzDQc08albeoByliNKg66+Yhp19Y2Qk1Vy3eT8/jmwdvSGu28vpfuTY2XvfKmvYtDikxnoOXY9qtl74szu6QgNOPDBcS8rGZkZCs8HQPaMKO75AACZ7xjO3t5e7ac9LVBQFhf77ldcWWxRGWal3DuPn0cj+dVruNasjqUzx2PC5/1x68EjfDVvCURZ2SWGJSL1UDGeZGrm+vXruHv3Lk6dOoWaNWXz2q5cuRJt27YVHDdhwgT5PIPJycn4+eef0bNnTwCAra0t+vbtix9//BEA8PTpU/j5+WHbtm1okF9JXbZsGdq0aSM/3+bNm9GsWTN8+aVsLnhHR0csW7YM7du3x/Xr19GovIcEapTSHqaskSV//sqsezeR/vd+AEBOxENAQxMmXQcg/cxh5L1IkB+uU90ZOnY1kLJtucqiXZYkkpIbljRKWayopUdNeDna4NazOPxxNghZuWL5WhoFzt59jBdpmRjepn4xZ1EzvE4U/Zs8eYtJ607Q0NTE6zNHhafNr6zmvXmNhN8KR6LkJsbBbt5yGDdtg7RLJffELk+lNcxqaP63FvuSlJIfmkqeJ9niPMw9FoAhjdxRx1b5Aobi/EVnzY30sbxfa/l57CubYNi20zh57xn6+qjnxzapRFLifk0l95ZE+m5hlq/bjL/PX8Sw/r3hU9cTCUnJ2LbvEKbO/xUrFsyGvpJ5zsuLRCKBtEi6SrpelOXL+3Ct7Y1Js5fBvU4D6OV/nKrj0wRvUlNweM8faNXhk1LLN3WnUUrveKlEApR6TPl3LlGqlP9Nac9e01YdoaGpidSzx4Q73ipzElf/UljmJMXDds4yGDdpjbTL//z7eJeh0v5XpV0P7+JueATCn0Vj8ZQvP/hcH8O/LnOO+mFIIw/UsS2+x/zc7k2gpakJTxtZueTjYAmnqpUwYtspHL8bgf4NSp92qDyUVq9XlifvWua8LSo6GrPmzIWWlhZmz5oBTXUerVNa3Eq5joyatYeGpibSL55UYaTKV2nPE2XXyb8RGROHSQuWQktTEz9N+UbtrhOpkrpJSeVLSfWGB9ePIPZpMIbNOFHsMbZOvug5Zj3snRtBR0/2Yd/RvQVEaa8QeOo3eDbpV+51E1l9TZgHJV0vJcW3pOeRZgV9R/o35c67mvHF59DS0oRHLdnoWC93F9Swt8GXcxbh1OUA9OnUppQzEFF5YwPGv/DgwQOYmZnJGy8AoEqVKqhRo3CqAAsLC8EiSb6+voiIiMDvv/+Op0+fIjIyEuHh4ZDkf3x59EjWm7FgOqmCc9rb2wv+bmRkJLyLDE0GgIiIiHJvwJDm9xYvushawe8FvckFYfJ73Wc/vCXYnh1+ByZdB0DHtrrgw7R+3YaQZKYj++Edlca9rBgbyD5wZWQLR1oU/G5ioLgI1ducrWUvdvWd7CCWSLDun2sY36UprCsXDuc/e/cxnKws4GpTMYZW8zpRJMnvDa1ZpJeafGRGZsk9kox9myHz3i35IqqF55X1Usm8c1PwApkdEY68jHToVa+JtA+OfdkxKujRWGS9gcz83jnGSnod/T8rGMmVWaR3akZ+L1hjfcVF636/dBsSqRRjm9eBWP6xX3YtiCUSaGlowFBPVhVo5mQreDGoa1cVxno6CE9IUXVSVMbISHYNZBbpkVVwjRgZKfb8NH6HMMkvX+H4mfP4rG8vjBpSuE6Ku3MtfD5+Cv4+dxF9ugkbk8vT0f2b8Ne+TYJtvk3b4s1rxSl7sjIzYGD4YSMDTCuZw8u3hcL2eg2a4f6d60h9/RKVKitvMKsoxKmyp6O2ibB3fcGoCnFqunzkRdFjCo4rOIe6keT3zNTQFz5D37XMMWqgvMyR5o/gyLwbrFjmZKZDr7qT2pY5xvLyRtgLMyO//DEuMjLj37gQdAumRoZopmTqQ3VknD/SotgyR09JmXMxRFbmtKhbWObkXwoFZY6Ghgbq2SkuoOptXw3Gejp4lKj+ZY6oSPkhHw1opPgsMMof8VQ0TEEP6YJzFrhzNxQ//vwLDAz0sejXn2FjLZzuUN0UjB7W1BfeI4UjM4rvQQ4Aht5NkPXwjmDh7orOKL9+mplVdvXXkHsPMWvJGhjq62H1/OlquSjxtX9+R9DpNYJtzl6dkJn2QuHYnKwM6OkrXxMn7XUCLh3+Ga16zYCBsTkkeWJ5BxapRAKJJA+amlowNLFATU/Fj9COtVsh6lEAMtNeKJ166mPau2c39uzZLdjWrHlzvH6t+NzLFGXCSMkzpYCRkaHCcwWQPVsMlYzArQiMi3n3y5DfOyWP6CpJHVcnhW313JxhbGiAx5ExSkLQxyRFxWx0o4+LDRj/gpaWlrzhoTj6RSpxx48fx4wZM9CjRw/4+Phg4MCBePTokXwERkHretHzvj3cTyKRoEePHvIRGG8zN1dcZPNjE79MgjQvD1oWwjkLtavIfs9LVBwCLM7/6KyhVeQlSFOW7rcXxQQAPXcvZN0LBiTKF7NWN/YWZtDS1ED0i9eC7VH5v9eopvh/i3v1BtceR6Gbjxv03lpQ1z3/ZS/5TYa8ASM3Lw8B4ZEY0UZ9h5YXxetEkTgpHtK8POhYCl9SdSxl8yXnxEUXG1arsgX0HGvh9R/LFPblJsVDKpFAQ0fxI4OGlhakxSx+ri5srapBS1MTsQnCaV9i8n+vbqd87u3/V/aVTaCloYGoFOEnwKhXst9rWJgphDn7MBLxqRlosnifwr4Gv+zG/B5N0cHdARqQPU+KypNIoaejfAoQdWBjZSm7RuKF10hsvOyZUd1OcW5g+/x5yGPjE+FSs7DjQcxbYRKTX0AqlcLTXTjyxNHBDmYmJngWpV4vOq079oZXA+GC0cFBlxF6KwgSiUTQKzMxIQY29o4f9PfC799CcmIsmrftLtiek5MNTU0tGBsrXosVTfoj2boWhk7V8eZ24VSERk6yNZnSwyKQl5EJUUwCDJ2KrGVV1Rw6psZIL2bh5/ImTi6mzKkmuzdy44u/vrUqycqc1A2KIxxLLHM0tSDJUd8yx9ayKrQ0NRGdKFwENyYhGQDgaPvhH5H9QkLR0rcetIuZVknd2JubysqcV0XLHNmH5hpKpi2VlzmL9ijsa/DzTszv2QxtXR1wPiwSnjZVUKta4RpxEqkU4jwJKht+eGNRWbGxtoampqbCotpxcbLfHd7qeFbALr8ciouLRy2nwg9oBed4u7PaxUuXsXTFStjb2eGn+T+gShULladB1cTJCZDm5UG7qvAe0a4qW/9BnFDC88TMHLoONfFye/G96isiWyvZ8yQmvmj9VfZ8qW73Yc+Ts1ev4ac1G1HdxhrLZk9GVYvKpQcqB3Wa9kfN2q0F2yJCzyHyoZ+srHirbvI6ORKVrRQ/MANAVHgAckRpOLv3e5zd+71g3+G1w2FibotR8y4gNuImUl9Gw6Nhb8Ex4txsaGhqQd+w/Osmnbt0RcOGwk6ngYEBCAkOUaivxcfFC54PRdna2eFphGI9Iz4uHi6u6jlyujS2lrJ3v5iEomWx7HfHf3nvpGdk4mJQCDxq1YCTQ+G7gUQiQa44D5VMlTeeEZF6Ua9xhhWEm5sb0tLSEPFWgZGSkoLIyMhiw2zYsAF9+/bFwoULMWTIEPj6+soXdZNKpXB3ly0MGBISIg/z5s0bREVFyX93dnbGkydPUL16dfmPWCzGr7/+ivgiFelyIc5FzrMw6NcRfkzXq+MLiSgDOdGKBWzu0zBIsrOg791EsF2/tg+keWLkRD6Wb9MwMIJ2VWvkPn9UNvEvA3o62vCpaYvzoU8Ew0XP3X0ME31deDpYKYSJT3mD+QfO4ULoE8H2wPAo6GhpwfGtBcGfxL9EVo4Y3o4V6EMurxMF0txcZIXfg1GDpoLtRg2aIS8jHdlPi0+LvpMrACDr8QPF82ZnFZ73rcZQA4960NQ3QFb4fRWloGzo6eqinocrLl+7Kbh/LgXehLGhITyca5YQ+v+PnrYWfByq4UJYlCA/zodFwVhPB55Kpoj6bUAb7B7ZVfDjbmUOdytz7B7ZFa2c7WCoqwMfB0ucD4tCjriwESPoWTxEuWL42Ktfr74Cerq6qFvbHVeuXRfkyeXAIBgbGcLdRXEtBztrK1hbVsNlf+FChlcCgmBnI9tnZ20FLU1N3H0QJjgmKiYOqWlpsLEUNsCWt8oWVVHD2UPw4+ndCFmiDITeKkznm9QUhN+/BU+vDxux+TA0GBtX/YiE2MJ6j0QiwY2AC6jlVgfaSj5gVzSZEVHIfBoN6z6dBNutendE+qNnEEXK1ih7cc4f1bq2huZba9NY9ekEiViMFxfVc7FMeZlTv2iZ0xR5mSWXOXpOso8ixZY5j+7DqH4TQZmj755f5jxS3zJHT1cHXu61cOn6bcGz5GLQLRgbGqB2LccPOn9qegaiE5JQz0X5Bzp1pKetBZ/qloplzsOSypx22D2qm+BHXuaM6oZWznbQ1dbCwlNB2OwfKgh7KTwaWeI8+Doq1o3Vha6uLup4esI/IECQJ37+ATAyMoKri7NCGFsbG1hZWuKqv3BhWT//APk+ALh+4yYWL1sOD3c3LFuyqEI0XgAAxLnIfvIABkXKFQPvxpBkZiDn+eNiAgK6Nf7H3n1HR1H9fRx/p/eQhJKEJLTQe++9Se8dREBAFEHQH4piQ0QFVKSoKEWadEE6FnqX3ntLDx1Cetnnj00CyyaAGsjy+Hmdwzlkdu7k3snsnTvzvcV4vuIvnM50n+dRevt17wHT9uue/bg6O/2r9uuuA0cYM/lHyhQrzPdjR1ls8ALANYc33vnKmPzLV6w2CfHRXD59f5HymHs3Cb2wn/zFamV4nEKlG9D9rWUm/xp2GQ1Awy6jaTvgewCCz+3h959HcuvqpfS0hpQUzh/+jbwFK2Bj++iZD56FnDlzUqRoUZN/FStWIjY2hoMHDqTvd+fObY4fP0aFiplPEV2xYiWCg4MJCrrfFgsKukJwcBAVKzwnU0s/xHgvLsrWvQdNvzt7Dxq/O4ULPiJ15uzs7Phq5gLmrlhnsn37/iPEJyRQqVSxf5VvEXk2NALjH6hWrRrlypXj7bff5oMPPsDR0ZEJEyYQGxuLlZVVhnM7+vr6cvDgQU6cOIGbmxubNm1i/vz5ACQkJJAvXz6aNWvGJ598gr29Pbly5eLrr78m4YHeav369aNnz56MHj2aXr16cffuXUaPHk1cXBwFChR4VsV/pOg/V+I5cCQevYYQs28r9gWK4lKvJffWL4bEBKwcnLD1zmvshR8dhSEhnnu//4J7656kxEYTf2w/dgWK4FK/FTE7fsMQfb/Xl62vsQdC0tXQzH69RRrQuBqv/PALI+auo13Vkhy+HM6cLQd4o0VtnOztuBcXz8XIm/jnzIGXqzMVCvpRvUg+vlixhXvxCQTk9GDbyYss3nmEV1+ojvsDPdPOhRuH4BbyeU4eclLpOjF3a+UifN8Zi/fr7xK17XccipTAo0UHbi6ZjSEhHitHJ+z98pF4Ndxk2g57//ykJCSQdDUiw+PeWDoHv3e/wPet0dxZvxwbd0+8uvYh7vxpog/ufVbF+8de6tyWYR+P44Mvp9KyUV2Onz7HwpXrGNSrC44ODkTHxHIpOBQ/nzx45nB//AGfcwNql+GVn/9kxPJttCtXmCMh15iz+wRDG1bEyc6We/EJXLx2B39PN7xcHCmSx/zB1jn1RWupvPfrjaENK9B/3u+8vmgTvauX5EZ0HJM2HaSMXy7qFfV/ZuX7J3p3ac+bH47lo/Hf0KJxfU6cPsuiFWsY2Lt76jUSw+XgUPx8vPFIvUZe6tqBLyZPw33aTGpVrcSOvfvZvHMPH/1vKAAeOdzp1KY5i1YYe4VWLleWyGvXmL3oF3zy5KJV04aZ5sdSFC9VkeKlK/HD1x/S5aXXcXXLwa+LpuPs4kbD5h3T9wsNukhSUiL5Cz35w1uDZu3ZtOEXJn76Fh16vIKDoyMb1/9C6JULvPfZD0+jOE+drZsLriULE3MhiITrxqkczo39lnIzvyDx5m0iV2/Cu00j8nZpwcEew9LTXfhyBnm7tqTKmhlc+uYnXIoWoNiYNwmesYS4YAvoXJKJW6sX4zviU/IMHknUtj9wLFKCHM07cHPpnMfccwqQkphA0rWM7zk3l84h78jP8X1zNLfXL8cmhwdenfsQd+E0MYcs+57Tr31zXh87mfe+mUHr+jU4evYi89f8weDu7XB0sOdeTCyXQsPx986N59/srXkhyNgmKfgve14/awNql+WV+b8z4pettCtfmCPB15iz+zhDG1XK+J7jncE9xyHtnnM/4NG3Vhm+33qYnC5O1C7sx7mrt/hh2xHqFw2gakHLPkc9unVh5KgPGPv5OF5o2piTp06zbPly+vV5CUdHR6JjYggKCsLX1xePHMYe3z27d+Orbybh7uZG9WrV2L13L9u27+Ddd94GjM+CEydPwdnJiW5du5h0YAPjtMK5c1nutHx31y8j99CPyNn/LaJ3bcK+UDHcGrflzsr5GBITsHJ0ws7Hn6TrkSZTRdnlzYchMYHk65GPOPrzqU+n1rwxegIffPUtLRvW5diZcyxYuZ5Xe3W+334NCcXP+8nbr/EJCXzx/U84OznSu2NrLoWYPuvkyelFnpzZPyvDo/gXroJ/4apsmDeCOm1G4OjswZ4NU3BwcqNs7e7p+92IOE9yUgJ5/Evi5OKJk4tp3ZIQb5yazDNPQXLlNbZfytTsxtGdi1g1fRDVm7+Bnb0TR3cs4Eb4WToNNZ22yZKULlOGMmXLMmHCOPr1exk3d3cW/DwfFxdXWrRomb5fUNAVEhMTCQw0ds6pW7cuSxYv4sMPPqBP374AzP7pJwoUKECdunWzpSxZoU/Hlgwd8zWjvp5G64a1OXrmAj+v+o3XenZ44LsTlvrdebJ7sYO9HS+2a86MJSvxyuFOjYpluBAUwswlq6lbpTyVy5R4yqUSkaygAMY/NGXKFD755BP69OmDg4MDPXr04OLFi9jZ2ZkEHdJ88MEHfPjhh/Tq1Qt7e3uKFy/O+PHjGT58OMeOHaNy5cqMGzeOcePGMXz4cFJSUujatSs3b95MP0b58uWZMWMGkyZNon379jg7O1OjRg3eeecd7O2zv0cBQMKFk9yeNxnXph3w7DOc5Du3iFq7kJht6wGw8yuA16ujuLP4B2L3G3texGxbjyEmGud6zXGuWp/ku7e49/svRG8xHU5skzolxePmZrY01YoE8NVLrfj+tz0M+2kNeXK4MLxVHV6qb+wZcSrkKv2//4VPujahbdVSWFtb8XWfVkz7fQ+zNu3n2p1o8uX24IPOjehQzXTe5BtRxsabu5PlLCb7JHSdmIs9dZTIKZ/h2b4nPm98QNKtG9xYNIs7G1YA4FCgMH7vfcHVHycStePP9HQ2OTwfWdb486cJ/fxdcnbqjfeQ9zAkxBN9YA83Fs2ExywsaQkqlSnJpyOGMGvxCt77YhK5cnryWu9udG/bHIAzFy8z9MPPee/1AbRoaD4n//83VQv68lWneny/7QjDl24hj5szwxtXonf1kgCcCr/JgPl/MLp1TdqWe/KevuX8czO9VxOmbjnM/5ZtxdHOlgbFAhjeuBI2FrYo5MMqli3NJ+8M56eFy3j/s6/IldOLV/v0pGs74/RGZy9cYtj7Yxg5dBDNG9UHoHmj+iQmJrHo1zWs/3MLvt55eG/YazSsc79H+qt9epE7Z05WbfiTJb+uJaeXB5XLl2VAr264uT4fcwsPfXccC2Z+w+LZUzAYUihSohyDR3yOi+v9lyVzfxjP9avhfDV95RMfN4dHTkZ9/iNL537L/OlfERcbTcEiJXlnzLcEFns+5vd/mHuFUtTYOI8jL48kZK6x3g2ZuwJrB3sKDe+Hf5+OxFwM5nCftwlfuj49XfSZi+xt3o8S496m4uLJJFy/xaVJszn78eTsKsoTiTt1lMipn+PZvgc+Q98n6dYNbi75yeSek3fk51ydMZF7Ozamp7Nx93j0PefCacLGvYtXx954v/6u8Z5zcA83F8+y+HtO5dLF+WL4QKYvW8PbX/1Abq8cDOnZgZ6tGgNw5lIwr42ZyAeDetOqfo3HHM3UzdT1UNxc/v3c989S1YK+fNW5Pt9vPcLwJZtT7zmV6V2jFJB6z5n3G6Pb1KJtOfMRb5kZUKcsns4OLN5/hqUHzpDDyYFOFYsxqF65p1WULFO+XDk+eO9d5v28gNFjxpIzZ0769+tLpw7GaWvOn7/A2+++x1vD3qBpE+O107RJYxKTElm2fAW//fEnvj4+jHhrOPXrGtstJ0+dSn/me+/9D81+Z68e3XmxZ49nVMK/L/7scW5Mn4B7q27keuUdku/c5M6KuURtXA2AfUAh8gz/hBtzpxKzZ3N6usfVJ8+zSmVKMnbE68xctIJ3x00mt5cng3t3oXub++3XIR+N473BL9PyCduvx8+c58at2wAM/+RLs8/7dWnLy13bm223NK1ensq2FV+wfeV4DIYU8hasSIs+35hM8bRp6Wju3gzl5Y82PfFxXdxz0WXoz+xc8xVbf/mUhPhovPOVocPg2fgWsOy6ZdSoD5gx/UdmzpqJISWFkiVLMfLd90zWVP3u26lERkby0+y5ANjZ2fPp2M/54YfvmTplMjY2NlSsWIkBAwdiY/N8TFWYkcplSvDZW68yY8lK3hn/Lbm9PHj9xU70aG0cEXvm0hUGf/wl77/Wl5YNMh61k5G+HVvi6e7KL79tZvnvW8jh5kr7pvV4uXObp1UUEcliVoaMhgvII928eZMjR45Qu3Zt7FKnSUhISKBatWp89NFHtGvXLnsz+DdFjOiV3VmwKD4T5hO35vvszoZFcWz1qq6Th/hMmM+F3i0fv+N/SODctVw7Ydk9bJ+13KWqETvv0+zOhkVxevF9Ik4fyu5sWBSf4hXYc/pOdmfDolQvnoO1dhrSn6Zl4hku9mn1+B3/QwrNXsPtQ0/+cuu/wKNCQ2Lnf5bd2bAoTr3e4/L552da0WehQOGiBL/W8fE7/ocEfPcL14/vzu5sWJRcpWvw/YbszoVlebUZnL9w6fE7/ocUDizIzaPbH7/jf4RX2f//HeqehpCzx7M7C88d/6LPZ6exf0MjMP4BW1tbhg8fTrdu3ejevTuJiYnMnDkTe3t76j7Hw/VEREREREREREREngWDlWWP+BfLoKvkH3B3d2fatGkcPnyYdu3a0bVrV65fv87cuXPx8rLseSdFRERERERERERERJ4HGoHxD1WvXp1FixZldzZERERERERERERERP5f0ggMERERERERERERERGxOApgiIiIiIiIiIiIiIiIxVEAQ0RERERERERERERELI7WwBARERERERERERGRZ8qAVXZnQZ4DGoEhIiIiIiIiIiIiIiIWRwEMERERERERERERERGxOApgiIiIiIiIiIiIiIiIxVEAQ0RERERERERERERELI4CGCIiIiIiIiIiIiIiYnFsszsDIiIiIiIiIiIiIvLfYrBS33p5PF0lIiIiIiIiIiIiIiJicRTAEBERERERERERERERi6MAhoiIiIiIiIiIiIiIWBwFMERERERERERERERExOIogCEiIiIiIiIiIiIiIhZHAQwREREREREREREREbE4ttmdARERERERERERERH5bzFgld1ZkOeARmCIiIiIiIiIiIiIiIjFUQBDREREREREREREREQsjgIYIiIiIiIiIiIiIiJicRTAEBERERERERERERERi6MAhoiIiIiIiIiIiIiIWBzb7M6AiIiIiIiIiIiIiPy3GKzUt14eT1eJiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTgKYIiIiIiIiIiIiIiIiMWxze4MiIiIiIiIiIiIiMh/iwGr7M6CPAc0AkNERERERERERERE5P+xlJQUJk+eTJ06dShfvjwDBgwgODg40/1v3brFW2+9RZUqVahatSqjR48mNjbWZJ/169fTokULypYtS7t27di9e3eW51sBDBERERERERERERGR/8e+++47FixYwJgxY1i0aBEpKSn079+fhISEDPcfOnQoV65cYfbs2UyaNImtW7fy8ccfp3++Z88eRowYQbdu3VixYgU1atRg4MCBXLhwIUvzrQCGiIiIiIiIiIiIiMj/UwkJCcyaNYuhQ4dSv359ihcvzsSJE4mIiOD333832//QoUP89ddfjBs3jlKlSlGjRg0++eQTVq5cSWRkJADTp0+ncePG9O7dm8DAQN555x1KlSrFnDlzsjTvCmCIiIiIiIiIiIiIiPw/dfr0aaKjo6lRo0b6Nnd3d0qWLMm+ffvM9t+/fz+5c+cmMDAwfVvVqlWxsrLiwIEDpKSkcPDgQZPjAVSrVi3D4/0bWsRbRERERERERERERMTCNWrU6JGfb9y4McPtERERAPj6+ppsz5MnT/pnD4qMjDTb197eHg8PD8LDw7l79y4xMTH4+Pg80fH+DQUwBJ8J87M7CxbHsdWr2Z0Fi6PrxFzg3LXZnQWLk7tUtezOgsVxevH97M6CxfEpXiG7s2BxqhfPkd1ZsDgtE89kdxYsSqHZa7I7CxbHo0LD7M6CxXHq9V52Z8HiFChcNLuzYHECvvslu7NgcXKVrvH4nf5jXm2W3TmwPIUDC2Z3FiyOV9k62Z0Fec4ZrKyyOwv/GWmLb9vb25tsd3Bw4M6dOxnu//C+afvHx8cTFxeX6fHi4+OzKtuAAhgC3Pz0lezOgkXxev8HoqfrheODXAZ8Stjw7tmdDYuSd+JC9tWunt3ZsChVduzh7sE/sjsbFsW9YhPufft2dmfDorgOHk/46cPZnQ2L4lu8PLtP3c3ubFiUGiXcudinVXZnw2IUmr2GtXbFsjsbFqVl4hlOXQjN7mxYlBKBfsQtHp/d2bAojl3f5vL5s9mdDYtSoHBRrgxsl93ZsCj5f/yVqP0bsjsbFsWtcjOm/5ndubAsAxrDpQvnszsbFqVgYGHu7V2d3dmwGK7VWmd3FuQ/IrMRFo/j6OgIGNfCSPs/QHx8PE5OThnun9Hi3vHx8Tg7O+Pg4JB+vIc/z+h4/4bWwBARERERERERERER+X8qbTqoq1evmmy/evUq3t7eZvv7+PiY7ZuQkMDt27fJkycPHh4eODs7P/Hx/g0FMERERERERERERERE/p8qXrw4rq6u7N27N33b3bt3OXnyJFWqVDHbv0qVKkRERHDlypX0bX/99RcAlSpVwsrKiooVK6ZvS7N3714qV66cpXnXFFIiIiIiIiIiIiIiIv9P2dvb06tXL7788ku8vLzw8/NjwoQJ+Pj40LRpU5KTk7l58yZubm44OjpSrlw5KlasyPDhw/n444+JiYnhww8/pF27dukjLPr27cvAgQMpWbIkdevW5ZdffuHUqVOMHTs2S/OuERgiIiIiIiIiIiIiIv+PDR06lE6dOvH+++/TvXt3bGxsmDlzJnZ2doSHh1O7dm3WrVsHgJWVFVOnTsXf35+XXnqJYcOGUbduXT7++OP049WuXZvPPvuMhQsX0r59e/bs2cO0adMIDAzM0nxrBIaIiIiIiIiIiIiIPFMGg1V2Z+E/xcbGhhEjRjBixAizz/z9/Tlz5ozJtpw5czJ58uRHHrNdu3a0a9cuK7NpRiMwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTgKYIiIiIiIiIiIiIiIiMVRAENERERERERERERERCyObXZnQERERERERERERET+WwzqWy9PQFeJiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTgKYIiIiIiIiIiIiIiIiMWxze4MiIiIiIiIiIiIiMh/iwGr7M6CPAc0AkNERERERERERERERCyOAhgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4thmdwZERERERERERERE5L/FgFV2Z0GeAxqBISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTgKYIiIiIiIiIiIiIiIiMVRAENERERERERERERERCyOAhgiIiIiIiIiIiIiImJxbLM7AyIiIiIiIiIiIiLy32LAKruzIM8BjcAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTi22Z2BZ+H27dsMGzaMAwcOUKxYMZYuXcqvv/5K3bp1yZkzZ5b8juXLl/Puu+9y5syZLDne88y2UAmc67fDJndeUqLvEr9/C3F7/nhkGrvCpXGq0wqbPH4YYqNJOH2QmM2/QmKC+c72DuQY+CGx29aQcHT30ylEFtt9OYJvtx/n4o27eDk70KVCYV6sXBQrK6vHpk1KSaHvgk042toyvVt9k89WHb/MvH1nCL59j9yuTrQuVYB+1Ytja235sUmHYmVwa9EVWx9/UqLuEL3jd6K3rH10mpIVcGvaATvffKTERBF79C+i1i7GkBCfvo9Tlbq4NmiFbS5vku/eIuavbdz781dISXnKJfr33KtUxX/gIBwLFiLp5k2urlhGxMIFmSewscGnew9yt2yDXa5cxIcEEz5vLjc3/Wmym0eduuTt0w/HgHwk3rzBjd82ED5vDoakpKdcon9vz9FTfLd4NRdDwsmZw51OTevQq2WjJ/runLkUzEsfTGD5xI/Im9u0rt+y7wgzV2zgSlgkOT3caVG7Kn3aNcXO1vJvi7uvXOW73ae4cDOKnM4OdC5bkBcrBGZ6ToJv36Pd3I1m2wO93FjSqyEAKQYDy49fZunRy4TejcbLyYF6hXx4pVpxXB3snmp5ssK+Q0eYMX8Rl4NC8PTIQbsWL9C1XatHXicbt+1k3pLlhEVG4pMnNz06tqNZw3oAhEdepfvAIZmmbdawHiPfeC3Ly5HV4mJjWDJ3Cvt3byI+LpZiJSvQ/eXh+PoVeOJjTB33Dg6OTgx442OT7QaDgQ0r57PltxXcuB5Jrtw+NG7VlcYtumRtIbKQU6kKeHZ8EXu/fCTfvc3djWu5s2FFhvu61m5Env7DMz3W1elfc2/nJgDsfP3x6tIXp+JlMCQnE3fmODcWzSDpWuRTKcfT4OjnTd3Da9jfcTA3t/31yH3zdm1J4XdfxblQALGXQzk/4UdC5/1qsk+OSqUpMe5tclQqTdLdaELmLufsJ1MxJCY+xVJkjdjYWObO+pHdu7YRFxtLydJleXnga/j553tkutu3bjJr+vccOrCP5ORkKlWpSt8Br+Hldf/+88eGtXw7+SuztC1atWXga29keVmywq7zIUz98wAXrt3Cy8WJblVL0rtW6Sdrwyan0HvGapzsbJnZr6XJZysPnWXOzmOE3IzCJ4crXauVoEe1kk903Ox24OBBZs+dx5WgIDw8PGjdsiWdOrR/ZN43b9nKgsWLiYiIxDtPHrp27kSTxo1M9tm2fQdLf/mF4JBQXFxcqFC+HC/3eQlPT8+nXaR/zbFkeTza9cQubz5S7t4mavM67v6xMsN9XWo0JFffoZke6/pPk4jevTl9X/embbHN7UPyzetEbVlH1KZHPy9Yij1HT/Pd0jVcCIkgZw43OjepQ68WDTK9ThISk5i/bhNrt+8j8uZt8nh50LxmJfq0aWzSPj15MYhvFqzk1MUgXJwcaV23KgM7Nn8u2rAACXHRbFv5JWcP/U5ifAz+hSvToNO7eHkXeuJjnD/yJ7/+OJgub8wlX9Fq6dujbkWwdcV4rpzZRXJSAvmK1aBeuxF45inwFEqSNQ4cPMjsOXMJSqtPWrWkY4cOj6lPtrBw0WIiIiLw9s5Dl86dadK4cYb7xsTE8Org1+nZoztNmzR5WsXIUruPneG7Zeu5EBpJTndXOjeuxYvN6z36u7N+K2t27ifyxm28vTxoVrMCfVs1NPle/PHXEeau3czl8Gu4OTtStVQRhnRpSc4cbs+qaJIJA5Z/75fs93zc5f6lVatWsX//fhYsWIC3tzf79u1j5MiRbNxo/kJH/h0bv4K4dX2dhJP7id2yCtt8hXFq1AGsrYnb9VuGaeyKlMW186skHNtDzOYV2OTyxblBO6yc3Yj+dabJvlaOzrh2eQ0bj1zPojhZ4mjYDd5YvoOmxQN4tXZpDodeZ9LWoySnGOhbrfhj08/ee5oTEbeo5J/bZPuCA+f4cvNhGhf1Z1i9styKjef7nSc4e+02X7at+bSKkyXs8hfGq//bxB7eTdT6pdgXLIZ76x5Y2dhwb+OqDNM4lKqIV7+3iN23jbtrFmHr44d7y65Yu7hze/5UAFzqNiNH+5eIPbyHu6t+xtrVHbfmnbHLm49bs795hiX8+1xKlaLI+K+4ufFPQmb8iFvZcvi/+jrY2BAxf16Gafz69ce3V2/CZs8i6ugRPOvWI/CTTzEkJ3Nrq/HBz71yVQqP/YKbm/4k5PvvcCpYEP9Br2Kbw4Ogb8xfoliSY+cuMXz8NJrUqMigzq04cuYCUxasJDk5hT5tmz4y7fngMIaN/57kZPPA1d6jp3h74gyaVK/I693acCEkgu8WreJ21D1G9LXcl68Ax8JvMmz1HpoW8ePV6sU5HH6TyTtOkJySQt/KRTNMc+baXQC+b18TJ1ub9O2Odvf/P+fAOb7ffZoXKxamakAurtyOZtqeU1y4EcW37WpY9AulE2fO8u6n42hQuyb9enbl2MnT/DDnZ5KTk+nZqV2Gabbu2sunX0+hY6vmVK1Yjh179/PFpO+ws7WlUd1a5PTy5NvxY8zS/brudzbv2EWLJg2fcqmyxrSv3+fC2eN06T0EJ2cXVi6ewbj3X2XslMW4uLo/Mm1KSgoLZ01k/+5N1GrQ0uzzxXMm88eaRbTv/gqFipTi6IGdzP9xArY2ttR/ocPTKtI/5hBYDJ/hH3Lvr+3cWjEfxyIl8erSF2xsuLN2mdn+MUf2ETrmLbPtufsOxdrJiZij+wGw8cpF3lHjSQwPJXLaeKztHfDs8CK+/xtDyPuvY8ioI4aFcfT3oeramdh5PPqaAPBp35Tyc7/k0pS5XPttOz5tG1N+1jhS4hMIX7IOAKeC/lTb8BO39hzmYPdhuBYPpNiY4dh5eXB88EdPuzj/2tfjP+Xs6VP07jcQZ2dnFi+Yy/sj32LK97Nwdcv4JUdycjKffDiSmJgYBr0+jKSkZObNns7Ho97m6yk/YJv6AuXSxfP4+Qcw9M13TNJ7eHo99XL9E0eDrzLk5z94oXRBBjeqxKErEUz84y+SUlJ4uW65x6aftf0IJ0KvU7mAj8n25QfOMHrlDvrULkPNQH+OhVzlqw17iY1PpH+98k+pNFnj1OnTfDh6DPXq1KZ3r16cOHmSmT/NJiU5ma5dOmeYZvvOnYz78ivatWlN5UqV2LVnD19O/AY7Ozvq16sLwJat2/h8/ARaNG9Gn94vcvPWbebOm8/b743i20nfYG9v/yyL+bfYFyxKntdHEb1/J7dXLsCxcAk8Or4ENjbc3bDcbP/YY/sJ//xts+05ew/G2smZ2GMHAHCt3ZicvV/nzoblxJ08jH3Bonh27oeVgxN315vX25bk2LnLDPvyR5pUr8CgTi05fOYikxeuIjk5mT5tMn6J/OXcX1i3cz/92zWlZKF8nLwUzPTlGwi/fpMPB/YAIOTqdV77/DvKFi7A50P7cDk0ku+WruXOvRjee7nrsyziP7Z29luEXTpCvXYjsHdyZfe6qSye1Ju+76/F0TnHY9PH3rvF7wvN7yUJ8TEsndIHsKJx14+xtbNn9/rvWDSxF33eX4OTi0eWl+XfOnX6NB99PJq6derw0ou9OH7iJDNn/URycjJdu2T8TLJjx07GT/iSdm3bUKlSJXbv3sNXX09MrU/qmewbFRXF6E/GEBn5/HSoOHb+CsO+nkXTauV4tWMzDp+9xOTFa0lOTqFv64zb31/O/5W1Ow/Qv20TShUK4OSlYH789Q8irt/mw/7G8/jbnkO8993PdGxQndc6NefGnSi+/2UDg76YxvzRw3Cwt/wOWyL/df+JAMbdu3fJnTs3ZcuWBeDy5cvZm6H/x5zrtiY5IpjolT8BkHjxBFjb4FSrOXF/bYIk8553zk06k3DqINGr5wCQdPkMWFnjWLUh2Nqlp7ErUhbnF7piZe/47AqUBabtOkHxPJ582sLYO6RWQR+SklOYtfcU3SsWMXmJ+LCzV28za+9pcrmYljk5xcD03Sepnt+b8W1qpG8vnseTLnN+Z8/lSKoX8H46BcoCbs06kRh6mds/fwdA/OkjYGODa+O23Nu2HjLooZmj7YvEHdnL7UU/AJBw/gRW1ta41GmGlZ09hqRE3Jp2IO7MUW7NmZSeLjHkEnnemUBM0TLEnz32bAr4D/j1G0DMubNc+nQ0AHf37sHK1pa8L/YhcskSk1EmaXK1bMWNP34n7CdjoC/qwH5cihUnT8dO6QGMXC1bkhAZycVPPoaUFO7u/ws7Ly+8u3YneMo3GJKTn1UR/7Yfl62lWAF/Phn8EgA1y5ckKTmZ2St/p1vz+jhm8DCfmJTE4g1b+WHZWhzsMr7Frd66B5+cnnzy+kvYWFtTrWwJbt65y4J1mxn+YkdsbTP/Tma3aXtPUyx3Dsa8UAmAmgW8SUpJ4ad95+hePhDHDPJ+9vodvF0dqRqQ2+wzMI6+mHPgPB1K52dIrZIAVMsHHo72vLthP6eu3qakt+X2/py9YClFChZk1PDXAahWsTzJycn8vOxXOrVugYOD+XUyY95C6teszuv9jddW1YrliYq6x6wFS2hUtxb2dnaUKmYaEDpz/iKbd+yif6/ulC35+OBzdjt/+iiH923nzQ++oWylWgAULVmBEa+0ZeP6ZbTp3C/TtMGXzzF/+gQunTuJvb2D2efXIsP4bdUCXhwwgobNOwFQsmwVbl6P5NihPRYZwPBs15P4Kxe59uPXAMQeO4iVjS2erbpw9/dVZoGGlKi7xEfdNdnm3rg1dnn9Cft0BCmpn3m260FKTAzhE95Pr6cTr0Xi88YHOBQsQtzZE8+gdP+QlRX+L7ajxLh3eNKOb8XGvEn4sg2c+t/nAFz/Ywd2njko9vEb6QGMwBEDSIqKZn+H1zAkJnJtwzaSY+MoPekDzn8xjbjg8KdVon/t9KkT7Nu7mw9Gf06lKsZ2W8nSZXmlbw/Wr11J5269Mky3c/sWLl44z5RpswjIVwCAQoGBDH31ZXZu30K9BsaesZcuXqBwkWIUK17yGZTm3/tu00GK++Tks471AahVxJ/ElBRmbj9CzxqlcMzkPgtwJuIGM7cfIZerk9lnM7YdoUmpAgxvWhWAaoF5uXLjLgv3nrT4AMa8nxcQWKgQb//PGOCsUrkSSUnJLFqylHZt2+DgYF5nzp4zjzq1azFo4AAAKleqSFRUFHPmz08PYCxaspSqlSvzxuuD09MF+Pnxxlv/Y+9f+6hTu9YzKN0/49GmOwnBl7gx6xsA4k4cAhtbcjTvRNTGNeb16727JNwzrV/dGrbEztefiHEjSUn9zL15J6IP7OT28rnG454+ip13XtwatrD4AMYPv6ynWAF/xrz2IgA1y5UgKTmZn1b+Qbdm9czasLejolmxeTdDurWmdyvjyJyqpYsBMHXRaoZ0a4OnuytzVm/ExdGBr97qj52tLbXLl8LRwZ7xs5fRr20TfHJZZjA0TdjFQ1w4tpkOr/1IoVLGl+3+gZWZ/mEjDm9bQPVmrz72GH8uHo2NjXndc/bQb9yMvESfUWvIlbcIALnyFmX6h404e3AD5ep0y9rCZIF583821icj/gdA5cqVSU5OYvHiJbRr2zbD+uSnOXOoU7s2rwwcaExTqRJRUVHMnTffJICxe88evp/2A7Gxsc+mMFlk2vLfKJY/L2MGGYN2NcsWN353Vm+k+wt1cHwo0HA7KprlW/YytEsLerdsAEDVUsa//5Ql6xjSpQWe7q7MWr2JWuWK817fTulp8/vmps/oKWw/fJLGVR8flBeR7GX588yk2rp1Kx06dKBcuXLUqFGDkSNHcufOHQD2799Ply5dKFu2LG3atGHdunUUK1aMkJAQRo4cyZQpUwgLC6NYsWIsX76c3r17A9CoUSOWLzfvFZKRsLAwhg8fTo0aNShVqhR169ZlwoQJpDw0Lc2SJUuoU6cO5cqVY9CgQYSGhqZ/FhcXxzfffEOjRo0oU6YMbdu25bffjKMSoqOjqVChAgsWmE4XM3XqVOrXr09KSgoGg4Hp06fTqFEjypUrR9u2bVm1KuPe6tnCxhbb/EVJOHPIZHPCqQNYOThhG1DYPIl3ADZeeYjfv8lke/y+Tdz59v304IWVgxOunV8lKegcUQsnP70yZLGEpGQOBF+jQRE/k+2Ni/oTnZDE4dDrmaZNTE7hg/V/0a1iYfJ7mvb4uxkTx524BOoE+ppsL5w7Bx5O9my/aLkvB7CxxaFwSeKO7TPZHHdkL9aOzjgUNH8xaOtXANvcPkRvNx3FE71tA1fHDsOQmIC1mwfWLm7Enzhosk9SRAjJ9+7iULJC1pcli1jZ2eFWoSK3tm0x2X5r8yZsXFxwK5txg8ra3p7kmGiTbUl37mDrnuOBfRxIiYs1mUIr6e4drO3tsXZ2ybpCZLGExEQOnDxP/SqmZW9YrQLRsXEcOX0hw3Q7D51gxvL19G3blNe7t8twn/jEJBwd7LF5YKq1HG4uJCYlER0Xl2VlyGoJSckcCLlBg4e+940K5yU6MYnDYTcyTHfm2h2K5sq8R1t0QhItivvTrJi/yfYCnq4AhNyJ+Zc5f3oSEhM5fPwktatXMdler2Y1YmJjOXbqtFma8MirBIeFm6epVY3Q8AhCwszrT4PBwDc/zCR/gD+d25iPRrBExw7twcHRidLlq6dvc8/hSbFSFTl6YOcj0/74zUekpKTwwfifcMth/jLk4N4t2NnZU6dxG5Ptr434nCEjx2dNAbKSrS1OxcsQfdB02sno/TuxdnLGsejjXybbuHvg1fFF7m5aT/zFs+nbXSrXJGr7HyZB5oTL5wka/pJlBy8A97LFKP3taELm/8rhPua9oR/mlN8P12IFiVhpOi1o+PLfcClSAOfC+QHI3aQ2V9dvNZkuKuKXDVjZ2JC7ae2sLUQWO3RgH46OjpSvWDl9W44cHpQqU44D+/Zmnu7gfvz8A9KDFwAB+QrgH5AvPZ3BYODypYsUDDRvD1uihKRk9l8Op2GJ/Cbbm5QqSHR8IoeuZN6jNzEpmfd/2Ub3aqUokMH9Z2qvpunBizR2NtbEJ1lupwow3nOOHj1GrRrVTbbXqV2TmNhYjp88aZYmIjKSkNBQatWoYZqmVi3CwsIJDQ0jJSWFChXK07zZCyb7BAQY78th4Rbcrre1xbFoaWIO7THZHHNgF9ZOzjgULvHYQ1i75cCjbU+itm4g4dK59O1Xp3zKrWVzTPY1JCVhZWu5o1HAOJ3NgVPnaFC5jMn2RlXLER0Xz+EzF83SRMfG0bFRTepWLG2yvYCvsUNa6FXjM+Oeo6epVb6kybQ4jaqWJ8VgYPdR8zaPpbl0agd29s4UKHH/XuDs5oV/kSpcPLH1selPH1jHldO7qNt+hNlnRco1pvtbC9ODFwA2NsaX3UlJ5h3BsltCYiLHjh6lZk3TuqF27drExMZy4kTG9UloaGgGaWoRFhaW/u7p3r17jPl0LGXLlGHsGPMRxZYqITGJA6cv0KDSQ9+dKmWN352zl8zSRMfF0bFBdepWLGWyvYBvHgBCrt0gJSWFaqWK0KF+9Yz3uZrxM5SIWJbnIoBx8+ZNXn/9dTp27Mi6deuYOnUq+/btY/z48Vy5coV+/fpRtGhRVqxYwcCBAxnzQCU9atQo+vXrh4+PDzt27KBJkyZMmTIFgKVLl9KiRYsnysOrr75KVFQUP/30Exs2bKBfv37MmDGDTZtMX7zPmzePSZMm8fPPP3Pr1i0GDx6MwWAA4M033+TXX3/lgw8+YNWqVTRu3Jg33niDP//8ExcXF5o1a8aaNWtMjrd69Wratm2LtbU1EydOZOHChXzwwQesXr2a3r178/HHH/Pzzz//m9ObZaw9cmFla0fyjasm21NuXQPAJqf5iAAbnwDA2Bh17ToYz3em4PHW1zg36QIP9KwwJCZwZ9rHRK+ajSHm3lMsRdYKuRNNYnIK+VJfBKYJSP358s2oTNP+uPskSckpDKpZyuwzVwc7bK2tCH/oxeLduASi4hIJvWO558g2Zx6sbO1Iumr6MJZ03fggbJPH1yyNnZ/x4dmQlIhX/xH4jpuDz6fTcW/XO/06McRGY0hOwsbTtJe5lZML1k4u2ObM8zSKkyUc8vphbW9PfFCwyfa40BAAHPNlPO925JLF5GrWHPdq1bF2dsaryQvkqFadG7+tv7/P8mU4+Afg070HNq6uuJQqhXfnbtzetZPkh3oXW5LQqzdITEoin6/p3y3A2/j3vRJ+NaNklAzMz8rJo+nXvhk2Nhnf4jo3rUtwxDXmrfmTqOgYjp27xKL1W6hVvhQ5XC03qBN6N4bElBTyezxUn+Qw5vnKrYy/92ev3SEmMYm+S7ZR49vVNJ2xgck7T5CYOr2Wm4Mdb9crS/m8D60TkhoILZTTcueFDY+IJDEpiQA/03rDz9c4XUlQaJhZmqAQ48Pd30mzafsuTp09z+svv5TpdWVpwkMukdvbD2sb01E53r7+RIReeWTagcM+YdTnMwgoUCTDz4MuncXbN4AzJw7x0Zu9eLljdd4a0IYtvz1Zp5BnzS63D1Z2diRGhJpsT4w0/q3tfPwzSmbCs31PDIYUbi2/P6WfbS5vbJxdSbpxlZwvDiL/1IUUmL4c76HvY+OZNWusPU2xQeFsKd6EUyO+IDnm8cFb1+KBAESfu2yyPeaC8XpyLVoQa0cHnAv4E33O9CVDwvVbJN6JwrVowazJ/FMSEhyEt09ebB763vj6+hEaGpxJKggJukJeP/PryDevH6EhxnQR4WHExsZw/uwZXhvQm46tm/DagN5s3vh71hYii4TciiIxOYX8DwUg8nkZpxq7fONOpml/2HKIpJQUXmtYMcPPC+X2wM/TDYPBwJ2YeJYfOMPqI+fpWvXxL7uzU0R4BIlJSfj5mXZMyuubF4CQkFCzNMHBxr+/n19e0zR5jfeg4NAQrK2teaX/y9R8KDCya7cxKJA//6PXX8lOtrlS69dI03tn0jVjG8LOxy+jZCY82nQHg4Hbv5p25EuKCEl/rrR2dsW1dmNca9Tn3tb1GR3GYoRevU5iUrJ5G9Yn8zasX56cjOzbhQJ5TZ+Xtx44iq2NDfl88xCXkED49Ztmx/V0d8XFyTHTtrEluRlxgRy5/LG2Nq1jPXPn41ak+cvpB0Xfvc7GxaNp0Ok9XNzNRxU7OLnhV8hY5yQnJXAt9DTr543EydWTYhWbZ10hskhEeHgm9YmxbghJfQ58UHBQWn3yUJq8pnWQg4MDP0z7nv+99SbuOR4/PaSlMD7/JZPfx/TvG+BtnD48w+9O7py826djejAizZaDx7G1sSG/T26sra15s0cb6lcyDRBuOXAcgEJ+ptMciohlei6mkIqMjCQhIYG8efPi5+eHn58f06ZNIzk5mcWLF5MrVy4+/vhjbG1tCQwMJDIykvHjjT0A3dzccHZ2xsbGhty5jRVhjhzGhriXlxeOjo+fjiguLo62bdvSvHlzfFNvKH369GH69OmcOXOGxg8smDRhwgSKFzf2IB83bhwvvPACu3fvxtvbm40bNzJt2jTq168PwJAhQzh9+jTTpk2jcePGtG/fnt69exMaGoqfnx9Hjx7l8uXLdOjQgZiYGGbPns3XX3+dnj5fvnyEhoYyc+ZMevbsmSXn+t+wcjQOETckmA5TNMQbH4qtHMyHkFs7G1/EuXYeRMLxfcTt+QPbvAVwqtsaK5cH1sBISSbl5vMzd2Oae/HG3ocPL4LrbG/86kUnZLyY5Ynwm8zbd4YZ3Rpgn8GUME52tjQpFsDiw+cplMudhkX8uBkTz4RNh7CxtiI20XJ7sFk5OQOQEv/wdWL82drR/DqxSZ2r3avvm8Qc3Mm9LWuxzxeI2wudsHY1roFhSEwg9tAeXOo0JTEihLhj+7B2dSdH+5cgJRmrDKZBsRQ2qS/NHx5NkRxjDFDZuGT8Uj1i8SJcSpeh2FffpG+7tmYVEQvvBzWjDuwnYsF8AgYPJWCwccHE6DNnuDj6w6wsQpa7F2O8HlycTOtoZyfj3zE6NuOXbXm8PB577CqlitK7dWMm//wrk3/+FYBiBfz5dEiff5zfZyGtPnGxN711369PzBdlvxUbz9XoOJINBobWKomvmzN/hVxjzv7zRN6LY2zqVFQPOxZxk9n7z1G3oA+Fc1rug8+91O+Is5NpveGU+nNMjPmw+XvRqWmcnZ84zeJfV1O6RDEqlDEPKFuqmJh7OGUwysrRyYXY2OgMUtwXUODRPcSj7tzi9s1r/DDxA9p1HYCvfwH2bv+d2d8bpxWytCmk0kabGWJNg/4pcan3HSfz+45JerccuNZqyJ0NK0h5oJ62cUttT3buQ/yls1z9fjw27jnw6vQSed/5nJAPh2Q4/Z+lSLx1h8Rbmb+EfphtDmObLemuabA0Kcp4TmzdXbFLXQjz4X3S9rN1dzXbbkliYqLN6gYw1g+xMZmPRouJic4wgOHk5Jye7tLF8wBERoTTt/+r2Nrasnnj70z66gsSExNo2qxVFpUia9yLM077Y96GNf4cHZfx+i7HQ68xZ9dxZvVrmWEb9kFHg6/Se4ax81apvLnoXbPMI/fPbtGp3/+HrxFn57T7h/k1Ev3Ye07G11VYeDjTZ84isFAhqlaunOE+lsA6tV1viMu4frVyNP8+maR3y4FLjQZE/bESQyb3JvtCxfAdOQ6A+MvnuPt7xouDW4p7qQFhszas46PbsA/bvO8Ia7bvo0uTOri7OHP9trHjkauT+fsLF0eHJz5udoqPjcLB0fw+YO/oQnzco9smvy/4AN+CFShVrR1BZzMfEQewYtqrXD61Aysra17oORbXHJbXkS06JuO6Ie3nDOuT1DrI5eE0D9UndnZ2BPg/vnOGpbkXm/bdMX1uv//debI21ab9x1iz4wBdGtfC3SXjOig48jrfLFpDsXx5qV3O8qeGFZHnJIBRokQJWrVqxaBBg8idOze1atWifv36NGnShPPnz1OyZMn0xfEAqlat+oij/X2Ojo706tWLDRs2cPToUa5cucKZM2e4fv26yRRSLi4u6cELgAIFCpAjRw7Onj3L7du3AahUyfRlUZUqVfj666/T/+/v78+aNWt45ZVXWLVqFRUrViR//vwcPXqU+Ph43nrrLawfmPYkKSmJhIQE4uLinigY8zQ9dpFXg/liumm95xNPHyZ2k7HnZtKVs2BlhXPDDsRuW03KTcvvTZKZtNE3mbHO4JzFJyXz4fq/6FGpCKV9M5/HdFSTStjbWDPmt/188tt+HG1t6FO1ODEJSRnOg28xHnudZHDOUq+T2GP7iFqzEICE8yfBygr3Vt2J+m0ZydciuLN0BoakRDy6DsCq+yukJMRzb+MqrB0dMSRa7kskK6tH9+g2ZPDdsbKzo8R307DLmYvLE74g7soVXEuXwfelvqTExhI0aSIA+f/3NrlatiZs9izuHtiPvY8vfv36U/SrbzjzxuukxFvmeUl5zHfn3ywq/cXMRazasod+7ZtRtXRRwq7dZPqydQz94lu+GzUUxwzWTLAEGdSgJjI6JU52tnzbrgb5PFzJ625swFfyz4W9jTXf7T5N/ypFKehlOsLicNgNhq3eS153Zz5qbLlTrwEYUv7+dfK4evnhNMdPneHshUt8+t7//n4GnxHjNJOmV8ijzs3j6pzHSUpKJOrubV5/ZxyVaxgXVCxZtgo3rkfw6+IZFhfAeNx953HXhHu9plhZW3Pnj4em7UxteybfvU3klM/S71+JV8Px++ArXGvUJ2rrbw8f7rllZf2Ye1VKCjx2n0ef62cpbXrWh7dl5lH3nUd+36yN6UqWLsuoj8ZSplx5HFM7a1SoVIU7t2+zcN5smrzQ8l/d27LaP7kPxycm8cHybfSsXooy/hmvu/QgXw9XZvZtQejtKKZuPEDvGatZNKgdTvaW+Yia8pjrN6N2fUpGzz8macy/M0HBwbz3wYfY2Njw/nsjTZ79LM0/ev57gGvtJlhZW3N345pM90m+cZWIL0dhm9Mbj3Y98Bn5BeGfvokhIeMgWnZ73Hcno+vkYZv2HeH9b+dSvmghhnY3Ttf4d9sv2c2QUdvkEWV4VP6P71lB6IUD9Hk/8+vkQdWbvUqVxv05tW8VG+a/i8GQTJmanZ8s48+I4RH3G8ikDfu4dq+1ZV0Df1dWPP9t2neMUdN+pnzRArzRNeNpXy+FXWXw+B+xsbZm3JDeFl3H/lcYDM/3tSvPhmW2DjPw1VdfMXjwYLZt28auXbsYMWIElSpVwsHBwexhwz6DhV3/jZiYGHr16kVcXBzNmjWjffv2lC1b1mzUw8PDzcH4IPSo/BgMhvTgi5WVFe3atWP16tX079+f9evXM2zYsPT9AL755hsKFSpkdpysLvM/YUjrafPQIttWDo4mn5ukSR2dkXD+qMn2xAsnoGEHbH3ykfAcBzDSeq093DM6Oj7J5PMHfbfjOAZgQPWSJKVe2waMf/+klBRsrKywsrLC2d6Wj5pVYUTDCoTfjcbX3QVne1t+PXaRAA/L7eGYdh1YOzx0naQ+zKfEmvc2SRudEX/SdH2LuFNHcG/VHTu/AiRfi8CQEM+dxT9yd8UcbLxyk3zzGoaEeJyrN0ifosoSJUUbe6paP9Sbxia113DyPfMeSZ71G+BcpChnhg3h7n7jeiJRhw+RFB1NgbdGcG3VSpKiosjdph3h8+YQOuPH9LTRp05QZv4icrVszdXllrkIomtaT6I40wBLWu8yV+dH95jOzNWbt1mxaRd92zbl1S7G3q6VgFKB+ek6Yiyrtuymywv1Hn2QbOKaNtIi8aH6JLV+cbU3r08cbW2ons+811ntAj58t/s0Z6/fMQlg/H42lI//OEg+T1emtK2Bh1P231sexSW1V1XsQ70O00ZRuGbQ6+p+mtiH0sRkmGbrrr24ubpQvZLlBnNWLp7BysXTTbZVrtmIu3dumu0bGxONs/O/u0c4OrlgZWWVvjh4mjIVanD80B7u3L5BDg/LmUIpJfVv+3BP4LSewykxj+716VK5FjHHD6Uv3J0m7X4Wc/SASfA9/sIZkmPu4ZA/kMwninz+JN0xlsbWzXRkT9qoiqQ799JHXjy8T9p+acewBIsXzGXxgrkm22rWrsud27fM9o2JjcHZJfPvjbOLC7EZtF+MIzqM58LDw5Mq1WqY7VOpajWOHD7A7Vu38PSynAV4XR2N9X90vOlo4bSf3RzN7w9TNx4gxWBgYP3yJKVOU5j21UhKTsHG2srkBVQedxfyuLtQGV/8Pd3oN2sdf568ROvyGU9fl90yu3+k96TOYMSsS+rfP7N7jstD95wjR4/xydjPcHJyZNznY9Onk7FUae32h0faW6fWtxm16x/kXKkGsScPpy/cnZHkO7dIvnOLeE6QdD0CnxGf4VyxJtF7tvy7zD8lrs7GZ5yYh9omT9qG/Xn9Zib9vJJKJQvz5fD+OKS279JGdGS0Xlt0bNw/bhs/LbvWf8vudVNNthWt8AIxUeZrQMbHRuPglPGUpVG3Iti8bCz1O4zE2dWLlOSk9Jf/hpQUUlKSzaak8i9sHLWUv3gN7twMZc+GaRYXwEirLzKvG8zrE+fU+iImkzrIxYLXN3wSrunX+MPPf8af075bmfl5wza+WbiaSiUC+eqNPunfnQftP3WeEZPn4OTgwA/vvpo+PZWIWL7nIoBx5MgR1q5dy3vvvUehQoXo06cPq1atYsSIEXTr1o3NmzeTkJCQ/hL/+PHjjzze3+2dsGPHDk6cOMHOnTvJlctYwd2+fZsbN26Y9CK4e/cuQUFB5Eudr/7MmTNERUVRtGhRPD09AThw4AANGjRIT7N//34KF74/VUP79u2ZOnUqixYtIjo6mubNjfM1FipUCFtbW8LCwkzSz507l/Pnz/PJJ5/8rTI9Dcm3rmFIScbGMw8PPupYexlfniXfiDBLkza6wsrmoZtLaiPEkGiZPWuelL+HKzZWVgQ/NDd98G3jzw/3fAb482wI4XdjqDV5hdlnVb/+hY+bVaFN6QJsuxCGu6M95f1yEZg6P/HN6Dgio2Ip7u35FEqTNZKuR2JITsYml+lck7apPydFms8fnHQt9dqxNb1OrGzSrhPjFedQsgKG2GgSLp0lKcI4b6i1qzs2ObxIDHn0vKrZKT40FENSEo4PTT/hmDr0N+7KZbM0Dt7G8xV11DT4d+/wIQCcChYiPjICK2tr7h0z3Sfu8mUSb9/GqaB5MNRS+HvnwsbampCIaybb034u6Ge+ps6TiLh+E4PBQNlipmUv5O9LDlcXLoZY7kKZ/jlcsLGyIuS26cvW4NSfM6pPgm7fY1/wdZoW9cPtgYBp2kKpng8M0Z578DyTd5ygkn8uvmxZ1WR/S5XXxxtra2tCw03vL2k/5/M3n3s7IHUe8tDwCIoUKvhAmsgM0+zef5Da1aqYjPS0NPVfaE/5KqaLIx/cu5Xjh/aQkpJi0rPsangwvv4F/tXv8/YNwGAwkJyUCA9Mz5ecbLyu7C1syr6ka+EYkpOx8zZ9EWiXx3gtJIabzzOdxsYjJw4FCnPnx6/NPku8Go4hJQUrO/PvipW1DSkW2jv4n7qXunimc2B+7h4+lb7dJdC4TtW90xdIjo4hNiQC50DThZ/tc3th5+7KvdMXnl2GH+OF5q2oUtU0oLB39w4OHdhv9r0JDwvFPyDzdQj8/AO4eOGc2fbwsDCKFDOOzj5x/CiREeE0bGy6UHNCfALW1ta4ulnWekMBnm7YWFsRfNP0xXJQ6s8Fc3uYpfnz5GXCbt+jxqdzzT6rNPonPmlfhyYlC7LlTBCl/XKT74EpCkv4Gp+xrt599Avv7JTX1xdra2uzRbXDwow/5wsIMEvjn3pPCQsLp3Bg4P00qccIeCDN5i1b+XLiNwT4+/Pp6I/JlctyAsGZSbwWYaxf8/jy4CtV29Q17R5dv3rhkC+Q63+uNvvMysERp3JVjW36a/fv8QlBF9PTWir/PMY2bHCk6Yv6tJ8fXucijcFg4Mu5y1n8+zZeqFGRjwf1NFms29nRgTyeOQiJMD3uzTtRRMfFUzCT42aXcrW6EFi6vsm280f/5PKpHcZ75wN17O1rV/DyCSQjV07vIj42it9+HsVvP48y+WzplD64e/kxcMwmwq8c5c71EIpXMl3j1DugFGEXD2VNobJQen0S9nB9YlxPJiCD+iRtWqiwsDCT+iQ89RgB+czTPE/88+Q0Pv9l8t0pmDfjqcAMBgMT5q9k8R87eKF6BUYP7Gry3UmzYfchPvpxEQXy5mHKW/3J45Ujg6OJiKV6LsZKubq6smDBAiZMmMCVK1c4e/Ys69ato0CBArz22mvEx8fz7rvvcv78ebZu3crEiRMfeby0eQVPnz5NdPSje90B+PgYXxSuWrWK0NBQ9u/fz2uvvUZiYiIJDzycWltbM2zYMA4fPszhw4d5++23qVq1KpUrVyYwMJAGDRowevRotmzZwqVLl5g6dSobN26kX79+6cfw8/OjWrVqfPXVVzRu3BhXV2NvLzc3N7p168akSZNYuXIlwcHBLFu2jAkTJpAnj4XM6ZicRFLQOeyLm/ZStS9ekZS4GJJCzV8gJwadw5AQh32pKqZpipbDkJxMUujFp5rlp83B1oYK/rnYdC7EJNi18WwIrg52lMpgiqhv2tdmfq9GJv+Ke3tQ3NuD+b0aUTfQ+ECw7MhFJm45YpL254PnsLG2ok6gBffWSkok4eJpnMqa/s0dy1YlJTaaxCDzlxsJF06REh+HU4WapmlKV8KQnETi5bMAuNRsjHsb05FRLnWbgyGFuBOmozcsiSEhgagjh/GsV99ku2f9BiRFRRF98oRZmtgg46KpbuXKmWx3LVsWgPiwUOJDQzAkJeFa1nQfx4B82Hl4EB9mHiyyFA72dlQoXpjN+w6bfHc2/XUYV2cnSgUW+EfHDfDJjY21NYcfeol2OSySO/ei8ctjub1wHGxtqOCXk00Xwk3PyYUwXO1tKeXtYZbmenQcn20+wh/nTP/Wv58LxcXelhJ5jGl+OXaZSTtO0KSIH1Pb1ngughcADvb2lCtVgm17/jI5J9t278XFxZkSRc3XcvD39cHXOw9bd5rOn7xt11788/ri633/nno36h4hYeGULlHs6RUiC3h65aZg4ZIm/0qXr0ZcbDTHD+1O3+/unVucOXmI0uWrP+Joj5c28mLvdtPFhw//tY2AAkVw+pcjPLKaITGRuDPHcalkeg9xqVyT5Jh7xF88m2lah8CiAMSdO2l+3Pg44s6ewKVSjfTppAAcS5TD2tGJuLPmdffzLOZCEDEXg/HtYPoC3qd9U+6dvUTsFWM9c/3PneRpUR/rB3o++nR4gZSkJK5v3vNM8/woXjlzUbhoMZN/5StWJjY2hkMH9qXvd+fObU4eP0r5ipmvQ1C+YmVCgoMIDrqcvi046DIhwVcoX8GY7vjRw0z+elz6ot5gHKm9a+dWipcohV0GgbDs5GBnS8X8Pmw8edmkfv3z5CXcHO0p7Wc+RdTknk1Y8Eobk38lfHNSwjcnC15pQ71i+bCxtmL0yu3M3mnauWLXBeP1U9THcl9M29vbU6Z0aXbu2mVyTnbs3IWLiwvFipqPHPHLmxcfb2+279xpsn3Hzl3pnwH8tW8/47/6mpIlivPVhHHPRfACgKRE4s+dwLmC6X3FuWINUmKiSbicef1qX9BYv8ZfOG32mSE5mZy9B+P+QnuT7Y4lywOQEHLlX2b86TG2YQPZvO/oQ23YI7g6O1H6oQBvmm8Xr2Hx79vo2bw+nw7uneEL2Gpli7P90AkSHhiNu2nfEWysralSyrJGLrl6eOOTv4zJv/zFa5MQF82lU9vT94uJuknI+f0UKF4rw+MElmlAr7eXmfxr0m00AE26jab9oO8BuHRiG+tmj+DurfsBgZSUZILO7iG3n+W14zKvT3am1idFzdLkzZsXHx9vdux4uD7ZaVKfPK8c7O2oUKwgm/YfN/3u7D+Kq7MjpQpl3JFg6tL1LP5jBz2b1WXsqz0y/O7sOHKKD39YSLki+Zn5/mAFL0SeQ5bbnfABgYGBTJkyhalTp7JgwQKsra2pXr0606dPx9vbmzlz5jB27Fjat2+Pv78/nTt35rvvvsv0eEWLFqVevXoMGzaMN9980ySAkJGyZcvy7rvvMnv2bL755hu8vb1p0aIFvr6+HDt2LH0/Ly8v2rZty2uvvUZsbCwNGjTg/fffT//866+/5uuvv2bUqFHcvXuXokWLMmXKFJo0aWLy+zp06MCePXvo0MF0Dul3330XT09PJk2axNWrV/H19WXo0KH079//75zOpyp2xzrceg7DtcNA4o/sxNY/EMcaTYjdtAKSEsHeEZvcvqTcuoYh5h4kxhO7dTXOTTpjiIsh4fQhbP0L4VjzBeL+2mjc5znXv0ZJXl2ylXdW76Ft6QIcCbvB3H1nGFK3DE52ttyLT+TijbsEeLji6exAkdzmN1OX1Ifakg881HWvWJjBy7bz5ebD1AvMy19BV/lp72n6VC1m0VNIAUT9voKcr76H50tvELN3C/YFi+LaoBVRaxdhSEzAysEJWx8/kq9HkhIdhSEhnqj1S8nR7kUMsdHEHv0L+wJFcW3YhuhtG0iJNk5JEb19AzkHvYd7uxeJO34AhyKlcWvSjqiNK0m+YdlTkYXN+Yli30whcMxYrq9dg2vpMvh070nItO9IiY/H2tkZp4IFiQ8NJen2bW7v2M69E8cp9OHHhM6cQdyVy7iUKkXe3n25tX0b0aeNvWMjly7Gp0cvAO7u+8u4Bkbfl4kPD+faasteBLFf+xcY/NlU3p00i9b1q3P07CXmrdnI693a4Ohgz72YWC6FRuDvnQtP9yfruerp7ka35vWZt+ZPAKqVKU749ZvM+GU9vrm8aNew5mOOkL1erlKU11bs4p31+2lbMh9Hwm8y98B5htQqmV6fXLoZhX8OFzydHSifNydVA3LxzfYTxCclU8jLjR2XI1l0+CJv1imNm4Md16Pj+Gr7cfK6O9OlXEFOX71t8jvTjmWpXuzSgbc+/JSPx0+kReMGHD99lkUrVjOwd3ccHRyIjonhcnAIfj4+eOQw9vbt3bUj4yZ/j/s0V2pWrczOvfvZvHM3H/7vDZNjX7wSBECBgOdvIcRipSpSvHQlfpj4IV1eGoKrWw5+XTQdZxc3GjbvmL5faPBFkhITyV/oyR/uS5SpRPkqdVgwayLx8XH45Qtk5+a1nDt9hKHvfvk0ivOv3Vq9GN8Rn5Jn8Eiitv2BY5ES5GjegZtL52BIiMfK0Ql7v3wkXg03mSrK3r8AKYkJJj2AH3Rz6Rzyjvwc3zdHc3v9cmxyeODVuQ9xF04Tc+jRi4xaOls3F1xLFibmQhAJ143TKp0b+y3lZn5B4s3bRK7ehHebRuTt0oKDPYalp7vw5Qzydm1JlTUzuPTNT7gULUCxMW8SPGMJccGWO8oNoFSZcpQuW56JEz7jpX4DcXN3Z9HPc3BxcaV5izbp+wUHXSYxMZFCgcYXhrXr1mfZ4p/55IORvNh3AADzfppO/gKFqF23PmAc8bFh7SrGjh5Fj159cXB0ZP3alQRdvsxn47951kV9IgPqleeVOesZsWQT7SoU5XDwVebsPMYbjavgZG/LvbgELl67jb+XG14uThTxNg8+uKQGxEs9EPDoV6cc328+iJeLE1UK+nI24iY/bDlE9UJ5qV3EsuvbHt26MHLUB4z9fBwvNG3MyVOnWbZ8Of36vISjoyPRMTEEBQXh6+uLRw5jm75n92589c0k3N3cqF6tGrv37mXb9h28+87bACQkJDBx8hScnZzo1rULQUFBJr8zV65c5M5luR0s7qxdSp7ho8n1ygju7dyIQ6HiuDdtx+0V8zAkJGDl6ISdbwBJ1yJMpoqy98uPIbP6NSmRu+t/IUfrbiTfvUP8mWPYBRTAo1U3Yk8eJu74gWdYwr/v5XZNee3z7xg5eTZt6lXj6LlLzFu7ide7tk5tw8Y90IZ15czlEOas2UjJQvloXK0Cx8+bBmgK+vng6uzIS60a8fuugwwdP42ezetzJeIa3y1ZQ/uGNfDJZbnBvzQBRaoQUKQq62aPoG67ETi5eLBr3RQcnN0oX7d7+n7Xw8+TnJSAd0BJnFw9cXI1nV0gId44UsvTu2B6cKJc7W4c2bGIFd+/Qo0Wr2NjY8fhbQu4HnqWTkNmPrtC/g3du3Xj3VGjGPv557zQpCknT51i2S/L6denT6b1SY/u3fl64je4u6fWJ3v2sm37dt59551sLk3WeLltY14b9yPvTJ1H27pVOHLuCnPXbWVIlxY4OdhzLzaOS6GR+OfJafzuXAllztrNlCoUQJOq5Th+wbT+LOjnjZ2NDWNmLsXZ0YF+bRpzKdR0auk8Xjnw9vJ4hqUUkX/CyvC41aCeQ3v37qV3795s3LgRf3/LbgRbgpufvpKlx7MrVh6nuq2xyelNStRt4vdvIW6v8WWhbf6iuL/4FvdWzSbh6P1eofblauJYrTE2XnlIuXeH+IPbidv1G2B+eVrnyInHkM/MjpFVvN7/gejp7z9+x79h07lQpu08wZVbUeRxdaJL+UBerGJsbO0PusrAJVvTp4bKyIBFWwCY3q2+yfYNp4KYsecUYXei8XF3pkv5QLpVzPreNy4DPiVsePfH7/g3OJapjFuzztjm8SX5zk2id/xB9Ja1ANgHliDX6x9ya8H3xO7blp7GqWo9XOu3xDa3D8l3bhGzexP3Nq0ymXvcqUJNXJu2N66Bces6MTv/IHp71i+imnfiQvbV/ne9mB/mUbcefv0G4JgvHwnXr3F1+S9ELloAgFuFihSf8h0Xx47hxnrjebJ2dsZ/4Kt41q+PrZs78WFhXP9tHZGLFmJIut8zy7tzV3K3a4+Db14Sb9zgzr69hP44jaTbt7M0/1V27OHuwT+y9Jib9x3hx6VruRJ+ldxeOejcpC69WjUC4MDJswwaM5kPB/WidT3zv8XqrXv4ZNp8Vk4eTd7c93syGgwGFq7fwvKNOwi7eoNcHu5UK1uc17q2fuJAyJNyr9iEe9++naXH3HQhjB/2nOHKrXvkcXWkc9mCvFjRONJgf8h1Xlm+k48aV6BNSWMvpXvxifz41xk2XwjnenQc/jlc6FE+kPaljT0AV564wicbD2f6+x48VlZwHTye8NOZ/75/Yvvuv/hp4VKCQ8PIldOLdi2a0rVdawAOHTvB8Pc/4Z2hr9K8Uf30NKs2/MHiX9dw9foN8nrnoWendjRtUNfkuJt37Gb0hG+Y8+3X5M9gOqqs4lu8PLtPZT7/9z8Vfe8uC2dN5ODerRgMKRQpXo7uLw/H169A+j6fj3qF61fD+Wr6qgyP8daANhQvXZEBb3xssj0hIZ6Vi6eze8t67t69jV9AQdp06U+l6vWzJO81SrhzsU+rLDlWGueKNfBs3wN7H3+Sbt3g7qa13NlgnK7RsXgZ8o78nKszJnJvx8b0NDlffBWXyjUJeuPFTI/rULg4Xh1741CoKIaEeKIP7uHm4lmPXVvj7yg0ew1r7Z5eD1KvulWpsXEeuxu9yM1tf5lsO/LySELm3p/WMt+ArhQa3g/HAF9iLgZzYfyPhP5sGhD3rFWJEuPexr1cCRKu3yL055Wc/Xiyyb3p32qZeIZTF7J+JOG9qChmTf+OvXt2YkgxULxkKV4e+Bp+/vfrwVHvDOdqZATTZy9M33bt2lVm/jCVw4cOYGtjS/mKlek38DW8vO7ff8LDQpk3ezonjh8jNjaGIkWK0fOllylZqkyW5L1EoB9xi8dnybHSbDx5me83H+Ty9TvkcXeha9USvFTLmN99l8Lp/9M6Pmlfh7YVzHsLA7w8y9hmmdnv/mKqBoOBpftPs2jvSUJuRuHp4kjzMoG82qACDnZZ27/OsevbXD6f+SiAf2Lnrt3M+3kBISEh5MyZk9atWtKpg3GkwJGjx3j73fd4a9gbNG3SOD3N2vXrWbZ8BdeuXcfXx4euXTrRuGFDAA4fOcI772X+7NGrR3de7Nkjy/JfoHBRrgxsl2XHA3AqXw2PNt2x8/Yj6fYNorasJ+oPY73gULQ0Pv/7lOs/TSZ696b0NF49XsG5QnVCRvTN9LiudV/ArUELbHP7kHLvLtF7t3F79SJjx7gslP/HX4navyFLj7l53xF++GU9V8KvksfTg85NatOrpfFvvv/kOQaNncpHA3vQul41pi1bx4wVmT+vTBv1OpVLGp/xDp2+wKQFKzkbFIqHqwstaldhUKcW2Nqar8f5b7hVbsb0P7P0kADExdxh8y9fcP7InxgMKfgVqkiDTu/i5X1/etdF37zI3RuhDByzKcNjBJ3dy5JJvenyxlzyFa2Wvv329WC2/TqB4HP7SIyPwbdAWWq1eiN9TYx/a0BjuHThfJYcK83OXbuYN/9nQkNCyJkrJ61btaJjakfWI0eP8s7Id3lz+DCaPtDpde269fyyfDnXrl3D18eHLl260LhRwwyPHxEZSZ++/cyOkVUKBhbm3l7zaeD+jU37j/HDit9Tvzs56Ny4Ji82rw8Y17B45fNpfDSgK23qVOH7XzYwY2XmF+oP7w4ixWDg1S9+yHSfge2a8MpDo0v/KddqrbPkOP81J8+HZXcWnjslC+fN7iw8cwpgSJYHMJ53TyOA8bx7GgGM593TCGA8755GAON59zQCGM+7pxHAeN49rQDG8+xpBDCeZ087gPE8eloBjOfZ0whgPO+eRgDjefc0AhjPu6cRwHjePa0AxvPsaQQwnndPI4DxPFMA4585cd6yR+haolKFLXja+KfkuZhC6mn65JNPWLHCfLHkB3377bfUrGnZ04qIiIiIiIiIiIiIiPx/8v8ygFGtWjXOnDnzRPu+/vrrvPTSS4/cx2IWyRYRERERERERERER+Y/4fxnA+Du8vLzw8rL8Ba9ERERERERERERERP5LrLM7AyIiIiIiIiIiIiIiIg9TAENERERERERERERERCzOf34KKRERERERERERERF5tgxYZXcW5DmgERgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFsc2uzMgIiIiIiIiIiIiIv8tBqyyOwvyHNAIDBERERERERERERERsTgKYIiIiIiIiIiIiIiIiMVRAENERERERERERERERCyOAhgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLY5vdGRARERERERERERGR/xaDwSq7syDPAY3AEBERERERERERERERi6MAhoiIiIiIiIiIiIiIWBwFMERERERERERERERExOIogCEiIiIiIiIiIiIiIhZHAQwREREREREREREREbE4ttmdARERERERERERERH5b0nBKruzIM8BjcAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFkcBDBERERERERERERERsTi22Z0BEREREREREREREflvMWCV3VmQ54BGYIiIiIiIiIiIiIiIiMVRAENERERERERERERERCyOAhgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHNvszoCIiIiIiIiIiIiI/LcYDFbZnQV5DlgZDAZDdmdCRERERERERERERP47Dp69kd1ZeO5ULJozu7PwzGkEhhD3+0/ZnQWL4ti0L3Eb52Z3NiyKY6PexG5ZmN3ZsChO9bsTt3BcdmfDojh2f4dLF85ndzYsSsHAwvx+JCG7s2FRmpazJ+L0oezOhkXxKV6B24c2ZXc2LIpHhYY6Jw/wqNCQUxdCszsbFqVEoB9r7YpldzYsSsvEM+w7czu7s2FRqhTzIOzM0ezOhkXJW6wsUfs3ZHc2LIpb5WZcPn82u7NhUQoULsrNYzuyOxsWxatMbYLOncrubFiUfEVK6Jw8IF+REtmdBZH/t7QGhoiIiIiIiIiIiIiIWBwFMERERERERERERERExOIogCEiIiIiIiIiIiIiIhZHa2CIiIiIiIiIiIiIyDNlwCq7syDPAY3AEBERERERERERERERi6MAhoiIiIiIiIiIiIiIWBwFMERERERERERERERExOIogCEiIiIiIiIiIiIiIhZHAQwREREREREREREREbE4ttmdARERERERERERERH5bzEYrLI7C/Ic0AgMERERERERERERERGxOApgiIiIiIiIiIiIiIiIxVEAQ0RERERERERERERELI4CGCIiIiIiIiIiIiIiYnEUwBAREREREREREREREYtjm90ZEBEREREREREREZH/FgNW2Z0FeQ5oBIaIiIiIiIiIiIiIiFgcBTBERERERERERERERMTiKIAhIiIiIiIiIiIiIiIWRwEMERERERERERERERGxOApgiIiIiIiIiIiIiIiIxbHN7gyIiIiIiIiIiIiIyH+LwWCV3VmQ54BGYIiIiIiIiIiIiIiIiMVRAENERERERERERERERCyOAhgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHNvszoCIiIiIiIiIiIiI/LekZHcG5LmgERgiIiIiIiIiIiIiImJxFMAQERERERERERERERGLowCGiIiIiIiIiIiIiIhYHAUwRERERERERERERETE4iiAISIiIiIiIiIiIiIiFsc2uzMgIiIiIiIiIiIiIv8tBoNVdmdBngMKYMhTsevUJaau2cqF8Ot4ubnQrW5FejesipVVxhVTfGISP6zfybr9J7h1L4aifnkY1KI2tUoUSt8nJcXAvM1/sWznYSJvR5E/tyd9GlenZZVSz6pY/9iukxeZumoLF8Kv4eXuQre6lenduNqjz8e67azbd5xbUTEU9fdmUMs61CoZmOH+kbfu0vHTH5n4SmeqFM3/NIuSZXadPM/UXzdxMewqXu6udK1fhd5NamZ6ThISk5j7xy7W7DlCxK27eHu606JqGfo1q42d7f2qbNPhU0xfu43LkdfJ5e5Ky2rleLm56T6Watf5UKZuOsCFq7fwcnWiW5US9K5ZOtNz8qCk5BR6z1yDk50tM/u2MPls5aFzzNl1jJBbUfjkcKFrlRL0qFbyiY6b3Q4cPMjsOXMJCgrCw8OD1q1a0rFDh0fmffOWLSxctJiIiAi8vfPQpXNnmjRubLLPtu3bWbrsF0JCQnBxcaFC+fL069sHT0/Pp12kLBEfF8PK+V9zeO+fxMfFULhkJTq89DbeeQs+Ml3wxZOsWTyFoPMnSDGkkK9QSdr0GEZAoZLp++za+AsLf/jYLG2dF7rR5eVRWV2ULLHv0BGmz1/M5aAQPD1y0L5FU7q2a/XI6+TPbTuZt2QFYZGR+OTJTc+ObWnWsJ7JPtv37GPukuUEh4bh5eFB0wZ16NmxHXZ2ll+f7DlykmmLV3ExJAyvHO50alqPnq0aP9H3/sylYPq+/wXLJn5C3jw5AQi7eoP2Q9/PNE3LejX48NXeWZb/p0HnJGOxsbHMnfUju3dtIy42lpKly/LywNfw88/3yHS3b91k1vTvOXRgH8nJyVSqUpW+A17Dyytn+j5/bFjLt5O/MkvbolVbBr72RpaXJas4+nlT9/Aa9ncczM1tfz1y37xdW1L43VdxLhRA7OVQzk/4kdB5v5rsk6NSaUqMe5sclUqTdDeakLnLOfvJVAyJiU+xFFknLjaGRXOmsm/XZuLiYileqjw9Xx5OXv8nb3NO+mIkjo7OvDLsQ5PtN65HsvCnKZw4so/ExARKlatC9z6v45P30ddfdtp36Agz5y3kclAwnh45aNeyGV3atX5kXbJx6w7mLfmF8MhIfPLkoXvHdjRrVB+AiMirdB8wONO0zRrV5503Mv/cEuw5eprvlq7hQkgEOXO40blJHXq1aPDIdv38dZtYu30fkTdvk8fLg+Y1K9GnTWOTNvvJi0F8s2Alpy4G4eLkSOu6VRnYsflz0a4/cPAgs+fO40paG7ZlSzp1aP+YNuxWFixeTEREJN558tC1cyeaNG5kss+27TtY+ssvBIeEprZhy/Fyn5eeizbs3sPH+WHhCi4Gh+Hl4U7HFxrQo80LT3YfvniFl98dy9Ipn+GbJ5fJZ2s372DBqt8IibyGTy4vOr7QkM4tGj0Xzzr7Dx7ip3k/cyUoCE8PD9q0bEGn9m0fmfdNW7exYPFSwlOvk26dO9C0UcNM9/9++kyWr1zNH2t+fQolyHpP65z89udGli1fSVhEBHly56JNyxa0a93yubhORMQCAhi3b99m2LBhHDhwgGLFirF06VJ+/fVX6tatS86cOR9/AKBhw4a0b9+eIUOGPOXcWp6wsDAOHTpEy5Ytszsr6Y5eCmXID0t5oWIJBresy6GLIUxcuZmk5BReblojwzQfL1jPtuPnGdq6HvnzeLHqr2MMmbaUGUN6ULFwAADfrdvOT3/u4bUWdSid35ftJy7w3tzVWFtZ0bxyyQyPawmOXgplyPeLeaFSSQa3rsehC8FM/HUjSSkpvPxCzQzTfDx/LduOnWNo2/rk987Jqj1HGfLdYmYM60XFwqYPdBE37/Lq1IVExcY/i+JkiaMXgxk6dQEvVC7N4DYNOHQ+iG+W/0FySgr9mtXJMM34JetZs+coA1vWpVR+P05eCeOHNVsIv3mHj3u3BWD3yQu8NW0xL1QuzdD2jbkQdpUpv27k9r0YRnZvkeFxLcXR4KsMWfAHL5QuyOAGFTkUFMnEP/YZr5M65R6bftaOo5wIu07l/D4m25cfOMPo1TvpU6sMNQP9OBZyja9++4vYhCT61338cbPTqdOn+ejj0dStU4eXXuzF8RMnmTnrJ5KTk+napUuGaXbs2Mn4CV/Srm0bKlWqxO7de/jq64nY2dlRv57x5fSWrVv5Ytx4WjRvTp+XenPr1i3mzp3HO+++x9TJk7C3t3+WxfxHZk96h8vnjtK213AcnVxZv+x7Jo9+mVFfrcDZNUeGaa5FBDHp474EFCpJj1dHA1ZsWj2biR++xDvjl6QHP0IunyZP3gL0eu1Tk/TuHrkyOGr2O3HmHCM/HU+D2jV4uWcXjp08w7Q5C0hOTqFnp7YZptm6ay+ffj2VTq2aUbXii2zfu4/PJ32Pna0djeoa6+V9h4/ywRdf06B2DQb27s7loBB+nLeQ23ejGDaw77Ms4t927NxF3hr/HY1rVOKVLq05cuY8UxesIDklhZfavvDItBeCQ3lz/LckJ6eYbM/l6c6MMSPM9l/221b+3H2ANg0yvp9ZCp2TzH09/lPOnj5F734DcXZ2ZvGCubw/8i2mfD8LVze3DNMkJyfzyYcjiYmJYdDrw0hKSmbe7Ol8POptvp7yA7apLxcvXTyPn38AQ998xyS9h6fXUy/XP+Xo70PVtTOx83B/7L4+7ZtSfu6XXJoyl2u/bcenbWPKzxpHSnwC4UvWAeBU0J9qG37i1p7DHOw+DNfigRQbMxw7Lw+OD/7oaRcnS3z71YdcOHOcbn1ex8nJhRWLZvDZ+68xbupCXFwffZ5SUlL4eeY37Nu1mToNTZ9X4uJi+eKDIVhZWdH31bextbPn18WzGPPuIMZNXYirW8b3s+x08vRZ3hvzOQ1q16Rfz64cO3WaH2bPJzk5mR6d2meYZuuuPYz9ejIdW7egSsXy7NzzF+MmfYu9nR0N69bCy8uTb8ePNUv367oNbN6xixZNMn8xaQmOnbvMsC9/pEn1Cgzq1JLDZy4yeeEqkpOT6dOmSYZpvpz7C+t27qd/u6aULJSPk5eCmb58A+HXb/LhwB4AhFy9zmuff0fZwgX4fGgfLodG8t3Stdy5F8N7L3d9lkX8206dPs2Ho8dQr05tevfqxYmTJ5n502xSkpPp2qVzhmm279zJuC+/ol2b1lSuVIlde/bw5cRvUtuwdQHYsnUbn4+fQIvmzejT+0Vu3rrN3Hnzefu9UXw76RuLbsMeP3uB/30xmcY1qzCgWzuOnj7Ht/OXkZySQu/2j35OuxAUwv8+n0RycrLZZ6v+3Mbn0+bQq20zqpYrxYlzl5g8ZzExcfH06Wg570gycvL0GT74ZCz16tSiT68eHD95iuk/zSE5OZlunTtmmGb7zl188eVE2rdpReWKFdm1Zy8TJk7GztaOBvXMn6OPHj/BilVrnnZRsszTOifrfvuDiVO+pUvH9lSqUJ7TZ88ybcYsYuNi6ZHJd1JELEu2BzBWrVrF/v37WbBgAd7e3uzbt4+RI0eycePG7M7ac+Gdd97Bz8/PogIY363bQXF/bz7r3RqAWiULkZiczMw/dtOzfmUc7e1M9g+9cZt1+0/wbucmdK1bEYCqRfNz+GIIi7cfpGLhAGITEpm/eR8961dOD4JUK1aAU8ERLNi636IDGN+t2UbxAB8+62N8kVarVCCJySnM/G0nPRtUyfh87DvOu11foGu9ygBULVqAwxdCWLztQHoAIyXFwOq9R/l6+UYMz7ZI/9r3q7dQPMCXsf06AFCrdBGSklOYuX47PRpWNzsnt+/F8Mv2A7zRvgl9XqgFQLXU0TmTVvzJ0PaN8XJzYeWuQ/h45WBsvw7YWFtTo2QgN6Oimffnbt7q8gJ2NjbPtJx/x3dbDlHc14vPOhhfstcq4k9iSgoztx+lZ/VSOD6ix/eZiBvM3H6EXK5OZp/N2H6EJiULMLxJFQCqFcrLlRt3WPjXSYsPYMyb/zOBhQrx9oj/AVC5cmWSk5NYvHgJ7dq2xcHBwSzNT3PmUKd2bV4ZONCYplIloqKimDtvfnoAY9HiJVSpUpmhQ15PT+fv58ewN99i719/Uad27WdQun/u0tnDHD+whUHvfkepCsZGeWCJinw8uBnbf1/MCx0GZphu67qfsXdwZNDIb3FwdAagaOmqfDy4GVvXL0gfXRF6+Qz5A0tTsKhlXx9pflqwlCIFC/D+cOPfs1rF8iQlJzF/2a90at0cBwfzh/np8xZRv2Y1Xu//EgBVK5YjKuoeMxcsSQ9grN+4Be/cOXl/+OvY2FhTpXxZbt2+w5KVa3m934vpL2gt0fSlayhWIIDRrxsDLTXKlyIpKYXZv26ga/MGOGbwgiMxKYklG7bw49LV2NvZmX1ub2dHmSKFTLaduniFP3cf4NVubSlfvPDTKUwW0TnJ2OlTJ9i3dzcfjP6cSlWqAVCydFle6duD9WtX0rlbrwzT7dy+hYsXzjNl2iwC8hUAoFBgIENffZmd27dQr4Fx1NulixcoXKQYxYpbbjstnZUV/i+2o8S4d+AJO2MWG/Mm4cs2cOp/nwNw/Y8d2HnmoNjHb6QHMAJHDCApKpr9HV7DkJjItQ3bSI6No/SkDzj/xTTigsOfVomyxLnTxzj013ZGfDSRcpWM9WOxUuUZPqA9f677hbZdMg/oBl06x9wfv+LiuZPY25vfs//auYnw0Ct8MXUh/vmM36WA/IEMH9CevTs30qhZh6dTqH/hp4VLKFyoIO+9ORSAqpUqkJSUzM9LV9CxdYsM2yYz5y2gXq3qDO7fx5imYnnu3rvHrJ8X0bBuLezt7ChZvKhJmjPnL7B5xy76v9iDMiVLPPVy/Rs//LKeYgX8GfPaiwDULFeCpORkflr5B92a1TOrX29HRbNi826GdGtN71bG0QVVSxcDYOqi1Qzp1gZPd1fmrN6Ii6MDX73VHztbW2qXL4Wjgz3jZy+jX9sm+OSy3EDovJ8XGNuw/3sLgCqVK5GUlMyiJUtp17ZNhtfJ7DnzqFO7FoMGDgCgcqWKREVFMWf+/PQAxqIlS6lauTJvvH5/RE6Anx9vvPU/9v61jzq1az2D0v0zMxavpGiBfHw01Fi+GhXKkJSUzJzla+nSojGOGbTXEhOTWLp+I9MX/5rhfRhgzvK1NKheicEvGl9CVylbkuDwCJat32jxAYy5Py+kcKGCjHxrOABVKlUkKSmJhUuW0b5Nqwyvk1lz51O3dk1eHfByapoKRN2LYvb8BWYBjNjYWL78ZjK5cnpx7fqNp1+gLPC0zsnCJcuoU6smA/oa2/4Vy5cjJDSMlavXKYAh8pzI9kW87969S+7cuSlbtize3t4YDM/bq1h5UEJiEvvPB9GwrGkjvEn54kTHJXDoYohZmtzuriwY8ZLJVFDW1lbYWFsTn5QEgL2tDXPffJHeDauapLW1tUnfxxIlJCax/9wVGpYrZrK9SYXU83Eh2CxNbndXFrzTl5ZVS6dvs7a2wsbGmvjE+2U9GxrJpwvX06paGca+1ObpFSKLJSQmsf/sZRpWKG6yvXGlksZzcj7ILE10XDyd6lam/kPnsYCPsUd46PVb6cd2srfHxvp+1ZbDxYnEpGRi4ix3hEpCUjL7L4fTsLjpVAxNShYgOiGRQ0GRmaZNTErm/RXb6F6tJAVymvdUnNqzaXrwIo2djTXxSeY9mCxJQmIix44epWZN01FbtWvXJiY2lhMnTpqliYiMJDQ0NIM0tQgLCyM0NJSUlBQqVqhAi2bNTPYJCDCO9AoPt+wXSQCnDu/C3sGJEuXu9+52c/eicMnKnDi0PdN03v6FaNi6T3rwAsDB0RmPnN5cjzTWRQaDgbCgs/gVKJ7ZYSxKQmIih4+fpE5103tDvZrViYmN5eip02ZpwiOvEhwWbp6mVnVCwyMICTNeAwkJiTg6OGJjc78+cXdzJTEpiZjYuKdQmqyRkJjIwZPnqFe1vMn2htUrEBMbx5HTFzJMt+vQcWb8spY+7Zrxeo92j/09BoOBCbMWUdDfl+4tGz12/+ykc5K5Qwf24ejoSPmKldO35cjhQaky5Tiwb2/m6Q7ux88/ID14ARCQrwD+AfnS0xkMBi5fukjBQMsP5AC4ly1G6W9HEzL/Vw73efux+zvl98O1WEEiVv5hsj18+W+4FCmAc2HjPT13k9pcXb/VZLqoiF82YGVjQ+6mlh0wBzh6cA8Ojk6UKV8tfZt7Dk+Kl67A4f27Hpl22jejSUlJ5uMJM3H3MH/ZXLl6PT4cNz09eAFga2t8SZmYkJBFJcg6CYmJHDl2IsP7R0xsLMdOmt9zIiKvEhwaTp3q1UzT1Kxhcs95kMFgYNK0meQP8KdTG8t+AZuQmMSBU+doULmMyfZGVcsRHRfP4TMXzdJEx8bRsVFN6lYsbbK9gK83AKFXrwPGaalqlS9pMl1Uo6rlSTEY2H3U/FxbioTERI4ePUatGtVNttepXZOY2FiOn8y4DRsSGkqtGqZt2Dq1ahEWFk5oaBgpKSlUqFCe5s1MRw0GBPgDEGbBbdiExEQOnjhDvWoVTbY3qFGZmNg4jp4+l2G6XYeOMnPpKl7q0JLBvTpluM9X773BkN6mI7PtbG1JsPAp+hISEzl67LjZdVK3Vtp1csosjfE6CaNW9YeurVo1CQsPJyQ0zGT7j7Pm4OnpSdPGz0eb5Gmek08/fp+B/V4y2cfO1pYEC7zXiEjGsiSAsXXrVjp06EC5cuWoUaMGI0eO5M6dOwDs37+fLl26ULZsWdq0acO6desoVqwYISEhjBw5kilTphAWFkaxYsVYvnw5vXsb5wpu1KgRy5cvf+I8XLt2jf79+1OmTBkaNmzIzz//bPL50qVLad26NWXLlqV8+fL06NGDY8eOpX9+9OhRevToQYUKFahSpQpDhgwhLOz+DSAyMpLhw4dTuXJlqlWrxqBBg7h8+XL65yNHjuTtt9/m008/pXLlylStWpXJkydz4cIFevToQdmyZWndujVHjhxJTxMVFcUHH3xA9erVqVSpEr179zbJ05QpU+jTpw8//vgjdevWpUyZMvTq1YsLF4wP2i+++CJ//fUXK1asoGFDyxhaHHLjNolJyeTPY/qQki+3cU7Oy5E3zdLY29lSKp8vbk6OpKQYiLh1l/G//EnI9dt0rl0BABtra4r65SGXuysGg4Ebd6OZ+ftu9p65TNfaFc2OaSlCrmdyPvKknQ/znhD2draUyp/3/vm4eZfxS38n5NotOte5X1ZfrxysGf0aIzo1MRuxYMlCrt9KPSemU8Tly208R1cir5ul8cvlyagerdIDFmk2Hz6NrY11+rG61q9K0NUbzPl9J3djYjl6MZifN+6hduki5HBxNjuupQi5FUVicgr5HwpA5PMyTstw+fqdTNP+sPUwSckGXmuQ8fegUG4P/DzdMBgM3ImJZ/mBM6w+coGuVSz7BXVEeDiJSUn4+fmZbM/r6wtASKh5MDQ4yPgS3ixN3rzGNCGhWFtbM3BAf2o89IC4a/duAPLns/w1ZCJCL5LL2x9ra9MRRbl98nE17HKm6eo07UrjNqY9Za9FBBEWfB5ff+MLxuuRwcTFRhN04Thj3mjNG90rMOaN1uzduirLy5EVwiKukpiURICfr8l2/9SXIMEPPcgBXAkJBTBL45eaJig1TfsWTQkJD2fRitVE3YvmxJlzLFu9nuqVKuDu5prlZckqoVevk5iURD7fPCbb/b1zAxAUlnFAtERgAX6d8il92zfHxvrxo9X+2L2fE+cvM7x3Z5OgsSXSOclcSHAQ3j55sXlohKKvrx+hoeadLNLTBV0hr5+/2XbfvH6EhhjTRYSHERsbw/mzZ3htQG86tm7CawN6s3nj71lbiCwSGxTOluJNODXiC5JjHh+kdC1uXJcs+txlk+0xF64YPy9aEGtHB5wL+BN97pLJPgnXb5F4JwrXoo9et8gShIVcJo+3H9YPXSPevv6Eh155ZNpXh3/Mh+Omk69gkQw/d3ZxpWiJsgAkJSYSdOkcP3zzCW7uHlSr3TjDNNkpPCKSxKQk/FPbFWn8fI3Tdz7qnuNvds/JPM3m7bs4dfYcr/fva/bdtDTG+jXZrH4N8DHWr1fCr5ql8cuTk5F9u1Agr7fJ9q0HjmJrY0M+3zzEJSQQfv2m2XE93V1xcXLM8LiWIiI8IpM27P326MOCg9PasKbXVt68xusmODQEa2trXun/MjUferm7a/ceAPLnt9x1Y8Iir6Xeh03/5v4+xr/vlbCIDNOVCCzI8u/G0adjK5MOJQ8q4J8X3zy5jM86UfdY9ec21m/dRYcXGmRtIbJYeETadZLJ3zyD6yQo2Pj84+/3cB2U9nx0P82BQ4f5c9NmRgwbgvVzssbD0zwn+QMC8EntMH03Kop1v/3BH5u20Lpl8ywvh4g8Hf96/oObN2/y+uuvM3LkSOrXr09ERARvv/0248ePZ+DAgfTr1482bdrw+eefc+rUKcaMGZOedtSoUXh6erJu3TqWLVuGo6Mjrq6uDBkyhKVLl1K0aNFH/GZTS5YsYdiwYYwaNYodO3YwduxY8uTJQ5MmTfjjjz/45JNP0oML165dY8yYMbz//vusXLmS5ORkXnnlFbp06cK4ceO4e/cuH374Ie+99x6zZ88mJiaGF198kVKlSjF//nysra356aef6NKlC6tXr8bb23gjXrduHT179mT58uWsWbOGSZMmsXr1akaOHIm/vz+jRo1i9OjRLF++HIPBwIABA3B0dOSHH37A1dWVlStX0r17d5YsWULJksah9vv378fBwYEff/yRxMRE3n77bUaPHs3cuXOZMmUKgwYNwsfHhw8//PBRp+eZuZe6DoOro+nQPufUIaHRj+kF/9Ofe5i8eisAHWuWo3qxAmb7bDhwipFzjC/T6pQKtOhFvO+lltfV6eHzYfw5Ou7REf+fft/F5FVbAOhYqzzVi99/0M3h4kQOF/MpgyzdvdSeyy4PnxNH+9TPn2ykxKZDp1i95zDd6lfFPfU8VC1ekD5NazHxlz+Y+IuxR2TxAB8+75/xfJmW4l7qdeDqYBqIck4NTEXHZ9yD6HjoNebsOs6svi2wt330A+7RkGv0nmmc/7RU3lz0rlHmkftnt+iYGACcnU0DT2k/x6R+bpomGgCXh9M4OWWaBow91mbMmElgoUJUqVI5w30sSVzMPRydzF+gOzo5Exdz74mPk5AQx7xvR2FnZ0+95qnzTV829mi8cTWU9r3/h42NLX9tW8X8b0eRlJhArcYZ937LLunXiZNpXeiU+nN0TKx5mmjjNmdn0zTOD6WpWLY03du34fvZP/P9bGOniCKFCvDBW5a93ta91Py7ODmabHdO/Tk61vycAOTx8vhbv2f+6j8oWyyQSqWevK2WXXROMhcTE21Wz4LxOxSbSZ2Zli6jAIaTk3N6uksXzwMQGRFO3/6vYmtry+aNvzPpqy9ITEygabNWWVSKrJF46w6JtzLvMPAw2xzGejjprmm9mxRlvBfZurtil8Mtw33S9rN1t9xgaJqY6Hs4ZXiNuBAbG/3ItAEFnnz0zVefvsWxQ3uxsrZmwJBReHpZ3rpL0dHGa9vlMfePDNM4PZwmtf7J4Hu2aMVKSpcoRvkylvuMk+ZeTFq7/qH6NfVZMPoJRyxu3neENdv30aVJHdxdnLl++y4Arg8dF8DF0eGJj5sd0tqj5m3YzNujadfJw2mcnqANO33mLAILFaJqZcttw6bdh83bXmnfg4z/nnlyPvnC5MfPXmDgKON0fiUCC9C9ddN/ktVn5n59kslzS+w/v06io6P5avJUevfsjv9DgTRL9jTPSZpTp8/wxoiRABQtUphO7TNeL0+eLcOTzt0p/2n/OoARGRlJQkICefPmxc/PDz8/P6ZNm0ZycjKLFy8mV65cfPzxx9ja2hIYGEhkZCTjx48HwM3NDWdnZ2xsbMid29hLI0cOYw9kLy8vHB3NGyyZady4MYMGDQKgYMGCHD58mFmzZtGkSRM8PDwYO3YsbdoYp9nx8/OjU6dOfPLJJwDcu3ePW7dukSdPHvz8/AgICOCbb77hxg1j7/i1a9dy9+5dJkyYkD7n9dixY9m7dy9LlixJXzzcw8ODd955B2tra/r06cOkSZNo0aIFjRoZh+x16NCBzz77DIA9e/Zw+PBh9uzZg4eHBwBvvvkmBw8eZO7cuXzxxRcAJCUlMX78+PTz0q1bNyZMmJD+++zs7HB0dMTLyzLmAE15zBRgVo+J/tctXZjyhfw4dCGEHzbsJC4xKX0tjTSl8/sy640enA29xrdrt/Ha90uYObTHY4+dHVJS/uX5KFuE8oEBHLoQzA/rthvPR5/n+yb7uGvE2vrxf8eNB0/y7sxfqBCYj2Ed7y8OOHbBGn7deYgBLepStXhBwm7cZtqaLQyeNJ8f3uyNk4UubPf47435tvjEJD5YsZ2e1UtSxj/3Y3+Hbw5XZvZpTuite0zddIDeM9ew6JW2ONlb5jz+hpSUR36e0XfH8LjvWwbXVnBwMO+9/wHWNjaMGvUe1hbWazolJQWDwfRcPPzzg6yeMP9xsdFMn/AGV84f5+U3v8Irt7HXUuESlXjlnakUKVUlfaqpEuVrEXXnJuuWfEvNRh0tqq593HVibWV+PlIecf4eTPP19zNZt3Ezvbu0p2LZ0kRcvcbsRcsYMfpzJo55H8cM5uC1BI//Hvz7a/zomQucuRTM+P8N+tfHehZ0ToyM9YnBbFtmHvVdf9Q5TatrS5Yuy6iPxlKmXHkcHY0vEipUqsKd27dZOG82TV5oaVH1yd/1uOvGkJICj93HsqbO/bv3nIzq2H+qXdd+tOzwIru2bODHSWNISU6hflPLmiL1n7Rhn/Sek+b4qTOcu3CJMe89fhozS/DYc/IE3/FN+47w/rdzKV+0EEO7G//mj5tW2pLrjsc9/2V0Tv7udQIQFBzMex98iI2NDe+/N9Li2rAPeuw5eYLnv8fxyZ2Tb0e/TfjVa/yw8FcGjvqcORM+tNz22mO/O+Z/z0fVxwBWqWm+mz6T3Lly0bGdZdWhj/M0z0maPHny8OXnnxIRGcnseQsYNmIk333zNY6OlnmdiMh9//rNVYkSJWjVqhWDBg0id+7c1KpVi/r169OkSRPOnz9PyZIlTRa6rFq16iOO9s9VqlTJ5Ody5cqxdauxJ3+VKlW4cOEC3377LRcvXuTKlSucOXMm/aEtR44c9O/fnzFjxjB58mSqV69OvXr1aN7cOJzs5MmT3LlzhypVTOeRj4+PT5/OCcDf3z+94ZAWAU6bVx3A0dGRxNS5GE+cOIHBYKBBA9OhjQkJCcTH3++BnitXrvTgBRiDPokWPJ9j2kiD6HjTXvRpIy/cnB59YyiS1/gitlLhfCSlpPD9uh0MaVUXX6/75yAgtycBuT2pVDgfro72vD9/LQcvBFOpsOUNm00/H3H/9HwYh9VWKpJ6PtZsY0ib+ibn43mT1pMq5qHRJ9Hpo1UeHbic9+duJi77ncpFCzDxtW44pC7oFnnrLr9sP8DLzeowuO39KdVKF/Cj4+jvWLnzEN0aVMvssNnKNXX0SXSC6Xc7beSFm6N54GXqpoOkGAwMrFeepGRjXZbW5EtKTsHG2srk4S6PuzN53J2pXAD8Pd3oN3sdf568ROvyGU/rkN2cXVwA4+JzD0rrReOS+rlpmtTRGQ+lSevd6OJsmubI0aOM+XQsTk5OjP/i8/TpqSzJhmXTWL/se5Nt5as34e4d8+nn4mKicXJ+fG/eW9cjmDZuMFfDLtN32ATKVrn/fXHLkZPSleqZpSlVsS5nju0h6s4N3D0sp1esSyZ/85i0HvcZjFJzfYI0127cZPXvG+nVqR0v9+yavk+JIoV5acj/WPfnZjq0NF1HxVK4pvXwfGg0W1pv1cfVsU9i095DuLs4U6t86cfvbAF0TowWL5jL4gVzTbbVrF2XO7dvme0bExuDs0vm9YmziwuxGfSENI7oMNa1Hh6eVKlWw2yfSlWrceTwAW7fuoWnhXTA+SeS7kQBYOtmem9JG1WRdOde+siLh/dJ2y/tGJZixaKZrFg0w2Rb1ZoNuXvbfArY2JhonJzNy/VPFStZHoDS5apw/Wo4K5f+ZHEBjMzuOffbGeYjVdK2Pbx2UtrIL5eHpjjdums3bq4uVK9cIWsy/ZS5Oqe2683Kl1q/Opvfhx/08/rNTPp5JZVKFubL4f1xSB19nDaiIzrOvGd+dGzcY4+bndL+pg+3YdNHjWbQhk1ro2be7jW9To4cPcYnYz/DycmRcZ+Ptcg27INcXdLuww9dJ6ltr6z4e+b28iS3lyeUKkZe79y89uF4Nu0+QIv6NR+fOBuk1w0xmdQnGUx/7PLY5yNn9vy1jy3bdvDtxC8xGAwkJyenBxqTk5OxsrKy2GDX0zonD8qV04tcOb0oV6Y0vj4+vDVyFNt37qJJI8ueckxEsiCAAfDVV18xePBgtm3bxq5duxgxYgSVKlXCwcHBrGeX/VPqAf1wJZySkpL+u9KmcWrdujUVK1akW7dunD17Nn0EBsD//vc/evTowdatW9m9ezdjxoxhxowZ/Prrr6SkpFCwYEG+/970JRKYDlWzszNfhyCzm0NKSgqurq4ZrvPx4Dl6WufraQnI5YmNtRXB126bbA+6Znw4LuiT0yxN2M077DlzmZaVS+Fgd/+SLBFgnBv22p17ONjZsfPkBWqWLETOBx4Ci6fuc/XOk0+b8iwF5E47H6YvB4KuGR8EC/qYvwgMu3GHPacv0bJq6UzPx/McwEg7J0FXTR+G037O6JyAsUfG+MXrWbj5L5pVKc2YPu1MFvWLuHkHgwHKPxTICsybBw8XJy6EXcvikmSdAE83bKysCL5p+iIj6KZx+HzBXB5maf48eZmwO/eo8dk8s88qjZnNJ23r0KRkAbacCaK0X27y5XRP/7yEr/F7eDUq8+lBslteX1+sra0Je2hxy7S1iR4MDqcJ8PdP36dwYGD69vDUYwTku59m85YtfPX1RPz9/fn0k9HkymU5L+UfVKtxJ7OAwtF9Gzl9ZBcpKSkm95hrkUF4+xV6+BAmwoLO8u3YQSQmxDF41A8ULmk63cD5Uwe4ERlCtfqmI70SE+KxtrbB2dWy6p68Pt7YWFsTGm66hkFouHEu5fz+5sPmA1LnyA0Nj6RoofvT8oU8kCby2nUMBgOlS5hOBVQgnz853Ny4FGS+Boul8PPOjY21NcGRpvODh0QY68ACfv/+JceOg8eoW6Ucto+Zus5S6JwYvdC8FVWqmgYU9u7ewaED+83qk/CwUPwDMu8Y4ucfwMUL5ouuhoeFUaSYcY2lE8ePEhkRTsPGpgvOJsQnYG1tjaub278pTra7d9a4roVzYH7uHr6/sKhLoHEtpXunL5AcHUNsSATO/8fefYdFcfRxAP/Se1XpTVC6SLP33nvvPVFjLEmM3Vhi7DVqrLH33k2MFaTYABUFFFE6iCjS671/HJwed2ADOV6/n+fheWRvZ5gdb3Zn97czYyO+vpJyNX0oaWsirYQF5CtKy3bd4VZHfGHxu/7XcT/AT+I7khAXBRPzL1vDI/zJI7xMiEX9YutdWNnY4UnIgxJSVRxTI0PIy8uLrjFFRNccc8lp1SxE15x41LSpLpmm2HXK7/Y9NKpXV+wFQFlmZlC18PwqvoZd0e/F17koIhAIsGL3cRz69wbaNXDHvLGDxPr16qoqMNDTQXS8eL7JKalIz8pG9RLylQWiPmxc8T6s8HcLKX1Ys8LvQWxsnFgftiiP9/u9V69dx4rVa2BuZobf589D1aqS99eyxtTQAAry8oiOL34dFv7+udfhjMwseN0JhGON6jB/b30Nu+rCc27S6zefV+CvwMTYqPTviZnk+aRoOqiYuDjUsHnX5y/Kw8LcDLv3H0ROTg7G/DBRIn37br3QplUL/DplUpkdR1kqrzrJzMyEr/8t2NnawtTk3XetZuH+r5Ilg/REJHu+OPQaFBSEP/74A9bW1qIFp//44w/4+fnB2NgYDx8+RE7OuzetHz58WGp+nzscNDg4WOz3u3fvomZN4ZvFW7ZsQe/evbFkyRIMGjQIderUES2UJRAI8OzZM/z222+oUqUKBgwYgHXr1mHbtm0IDw9HSEgIbG1tERsbCy0tLVhaWsLS0hImJiZYuXIlbt++/VnltbW1RVpaGnJzc0V5WlpaYuvWrbh8+fJn5SkLVJQU4W5jjstBoWJDAP8LCoWWmgqcLU0k0sQlp2D+/gu4EhQmtt03JAJKigqwMqyC7NxczN57Did970vsAwC2JuILvMkKFSVFuNewwOXAYvURUFgfViXUx75zuBIYKrbd9/GzwvqovG8rAoCKkhLca1riSsBjsTq5fO8xNNVU4Fxd+jydf568jANXb2FI6wZYPKqX2E0OAJgb6ENBXg73nogvKPk8Pglv0jNhWvXj51D92lSUFOFuaYTLj5+Lf08eP4eWijKcTSWniFo3sDX2j+kq9uNgXAUOxlWwf0xXNLMzh4K8HOaf9sZOH/GHAD7hwsXMbGX4u6SsrIxazs646eMjVifeN29CQ0MDdlLWSDIxMYGRkSG8vW+Kbfe+eROmJiYwKlyv6Nbt21i+YiUcHBywcsVymQ1eAICOvgEsbJzEfuxdGiIrMx2Pg94dZ+rbZDx9dBf2LiW/ZfY6KR7rF46BHOQwZeEeieAFADwJvoW9G2eLLQZeUFCAQL9/Ud2uNhQVJQP1FUlFWRkuTg644XdL7Hty3dcfmhrqcLCVnH/dzNgIxoYGuH7TT2z7DR9/mJkIPzMzNoKCvDzuPwoR2ycyOhYpqakwMZTdBycqykpwdaiBa7cCxerkqn8ANNXV4FTD6ovyT0lLR1R8Imrb2nx4ZxnBOhHSr1IVNWztxH5c3T2RmZmBgLvv+rMpKW/w6OF9uLqXPJ+6q7snoqMiERX5XLQtKvI5oqNewNVNmO7h/UCsW7VUtKg3IDyf+Ny8DnsHJ6kv/lQmGeGRyHgWBeOe4gEaox5tkRYWgcwXwmtt0n83YdCxOeSV3x2vUc92KMjLQ9JV8fNQRdOrUg3WNR3Efmq51kNWZgYeBLwr69uU1wgJDkQt1y8b2Xr/rg/+WjkXr16+C0IX5Ocj+P7dT1o/42tRVlZGbScHePn6i51Lbvj4Q0NDHfZSrjmmJsbCa46Pr9j2Gz5+MDMxhpHhu3uYt6mpiI6Ng7ODXfkdRBlTUVaCm70Nrt6+L1YnV24FQVNdDc7FgndFNhw6i0P/3sCgDs3x+w9DJfr1AFDPxR5eAcHIyc17l+/tICjIy6OOk2yOIAZK68P6FPZhJcte1E/1ulm8D+tTrA97B8tWroKjgz1WLl9aKYIXQOF12NEW1/3vitXJNb+70FRXg2PNzwuGKigoYPFfu7Dv9D9i228FCZ8N1bCUfOAtK5SVleHi7ARvHz+xOvHy8YWGhrrUex1TE+PC74mP2HYvH1/R92TowP5Yv3qF2E/HdsIpl9evXoGhA/uX74F9gfKqEwUFBaz6cwOOHD8hts+dgEAAQHUr6ecpIpItX/xqh6amJvbv3w8lJSX07dsX2dnZOH/+PKysrDB+/HhcvHgRM2bMwLhx4xATE4PVq1eXml/RiIaQkBDo6elJnSZEmnPnzsHe3h7NmzfHf//9h0uXLmHXrl0AAGNjY9y7dw/BwcHQ0tLClStXsHfvXgDCKZv09PRw7tw5ZGVl4bvvvoO8vDxOnDgBHR0dWFtbo3r16tiyZQsmTpyIqVOnQlNTExs3bsSNGzcwadLnRa+bNGkCBwcHTJkyBbNmzYKxsTH279+P48ePY/v27R+dj4aGBmJiYhAfHw8jI6PPKktZG9OuIb7fcBBT/z6J7vVdEBgRg12X/TGpa3OoKSshLTMbz+KTYFZVD/pa6nCzNkd9OyssOXoJaVnZMK+qhxvBT3Hoxj2M69gY2uqq0FZXRff6Lth88SYUFeRhb2aIe+FR+PuSH3o0cIGNsew+gBzToTG+X7cPU7cdR/eGtRH4LBq7/vPFpG4ti9WHLvS1NOBmY4769tWx5PA/wvqopocbD57g0PW7GNe5KbRleMj0xxrTsSm+X7MbU7ccQfdGbggKj8KuSzcxsUdrqCkrIy0zC8/iXsKsmj70tTQQEhWHHf94w8nKBG08HPEgQvztZ2vjatDX0sCgVvWx+19h56WBow1iX73B5rPXYVxFBz2buFfEoX60MU1r4/vdFzH1yFV0d6uJwKhE7Lr5AJNae0JNWRFpWTl49vINzPS1oK+hhppSgg8ahQ9HnEzftYeRTWrjr6v3oK+hijpWxghLSMbma4Gob22CxjVlt1MPAAP698eMWbOwaPFitGvTFo8eP8bRY8cxcvhwqKqqIj0jA5GRkTA2NoZu4VR7AwcMwKrVa6CtrYX69erB188fN7y8MGPaNADCc/6ateugrq6GAf36ITIyUuxvVq1aFdVkOKABADUcPVHTqQ52r5uOboN/goaWLs4f2Qh1DS00adtXtF9cdDjycnNgXt0BAHB0x2KkpiSj35g5yMpIQ0RYkGhfVXVNGJvZoFHrPvD+9zA2L/0RHfv9ABUVNXj9exBxUU8xaf7Or32oH2Vo3x74ae4i/LZsDTq2bo7gkDAcPHEW3w0dAFUVFaRnZOB5VAxMjQyhqyMciTSsX08sWbcJ2pu2o1FdD3j738HVm3747Rfh22q6Otro3bUDDp4QLnzvWdsFCS9fYufBYzAyqIrObVuWWB5ZMLJHB0xYtA4z12xDl+YNcD/sGfaevYQfBnSHqooy0jIyERETBzPDatDT/rS34MMjhQ9lq5vJ9nQVxbFOpHOqVRvOLq5YvfwPDBv5HbS0tXFw3y5oaGiiQ8d30/dERT5Hbm4urG2ED94aN22Oo4f2YcGc6RgyYgwAYM+OrbC0skbjps0BCEd8XDx3Govmz8LAwSOgoqqKC+dOIfL5c/yxbM3XPtQvpqilAU3HGsgIj0ROknBk7ZNFG1B7+xLkJr9BwpkrMOzaCiZ9O+LewMmidOErtsGkXyfUObsNEWt2QMPWCnYLf0LUtsPIioor4a/JDntnNzjUcsfGlb+h//AJ0NTSwYkDW6GuoYnWHXuK9ouJfIbc3FxY2Xz8w/eW7Xrg8sUTWLnwJ/ToPxqKSkr47/xRRL94imnz15XH4XyxwX174Ze5CzF/6Sp0aNMSwY9DcejEaYwZOkh0zXkRGQ0TY0NR32Ro/95YunYjtLW2olFdT9z0v41r3r6YM3WyWN7PXgj7JFYWst0/K25U97YYv3gjpq/bia7N6uH+kwjsOXcFE/p1KTy/ZiEiJh5mhlWhp62J0OfR2HX2MhytLdC6nhsePhV/+ai6qRE01VUxrHMr/OtzDxOXbcKgDs3xIv4lNh4+ix4tG8Coquy+hAMAA/v3xfRZc7Bo8VK0a9sajx6H4Ojx4xg5fFiJfdhBA/pj5Zq10NYq7MP6++OGlzdmTBOuh5KTk4PV6/6Eupoa+vfrW+n6sMN7dcbEBSsxa+Vf6NKyCe6HPsW+0/9g/KBehW0nExHRsTA1NICezsddh1WUlTC0R0dsO3wKetpa8HC2x5PnUfj7yGnUcXFEA7da5XxUX2Zgvz6YNvs3LFyyHO3btMKjxyE4cvwkRg0bAlXVovNJFEyMjUTfk8ED+mLFmj+hraWFBvXqwsfvFq573cSsX38BABgZGooCXkX8bwlfUrCrKXuB4eLKo06UlZXRv3cv7N5/ELo6OqjtUgvPIp5jz4GDcHetjbqeHqUViYhkhJzgQyvlfISrV69i/fr1ePbsGeTl5VG/fn1MmzYNFhYWCAkJwaJFixAYGAgzMzO0b98eGzduxOXLl2FmZoY///wTJ06cwJUrVwAIL8wTJkyAj48PfvrpJ4wcOfKDf79ly5bo3LkzAgICEBAQAFNTU0ycOBGdOnUCIFykde7cuQgMDISysjLs7e3Rr18/TJkyBfv27YOnpycCAgKwcuVKPH78GPn5+XB1dcXUqVPh5OQkymPZsmXw9fVFfn4+nJyc8NNPP8HdXfhQdPr06YiJicGePe+mc7Gzs8PixYvRs6ewc3/8+HHMmDEDoaHCN+uTk5OxfPlyXL16FZmZmbCxscGECRPQsqXwwUjxupGWx7Vr1zBt2jQIBAL4+vpCQeHTpy7I+nfHJ6f5kMtBofjrvDeeJybDQEcT/Zq4Y1gr4Vtat5+8wOh1B7BgUEd0q+8CQLj+waYLN/FfYChevk2DRTU9DG5RBz0b1BblmZuXj52X/XHa/wHiXr+Fka4WejVyxbCW9cpk4a8iqm1HIOvy7g/v+AkuB4bgr7NeeJ74CgY6WujXzAPDWtcHANwOe4HRa/ZiwZDO6FZ4vOlZ2dh0zgv/BYbgZUoaLKrpY3DLuujZyFVq/kV5bJs8GHVsy/4NAtVWQ5F57UCZ5nkl4DH+OnMVzxNewUBXC/2a18XQNsK3x2+HRmDMql2YP6wbujV0w8bTV7Dl3I0S89r60zDUsasOgUCAfZf9cPTGHcS8eoOqOppo4GCDCd1bQV/K/NNfQq35AGQdWFqmeV5+/Bx/XQ3A81cpMNBSR7+6DhjWUNjxvh0Rh9G7LmBBtybo5ib9rbNRO84DALaP6CjaJhAIcOROKA7eeoTo16nQU1dFh1rWGNfcTWyKsrKgOmAaIsKflmmeN318sGfvPsRER6NK1Sro0rkzehWeU4Pu38e06TPw05TJaNvm3WLu585fwLHjx/Hy5UsYGxmhb9++aN1KeF4NDAzC9JkzS/x7gwYOxJDBg8qs/NVtauDfoJwP7/iJMtJScHz3cty/fQWCAgGs7V3Rc9ivMDR59wbb2nkjkPwyFvM3/IO8vFz8PKQuCvLzpOZXw9ETk+YJrwUv4yNxev8ahD++i6zMDFjaOKHzgImwsS+bIGDb2sqIDwkok7yK3PC9hR0HjiIqJhZVq+ijR8e26Ne9MwAg4EEwJs9eiOkTx6JDq+aiNKcv/oeDJ8/iZdIrGBsaYFDvbmjXoqnoc4FAgKNnLuD0xf8Ql5CIKvq68HR1wZjB/UWBkLJiZO+GNwFXPrzjJ7h2KxBbj57Fi9gEVNPXQe+2zTGos3CalrvBYRi/cDXmjB2Kzs0l1yg4e80XCzftxol1v8PEQPztzv9872LW2m04tPI3WJmW34sTum4tWSfv0XVriceFo+fKWlpqKv7euhH+fjchKBDA3tEJo74bD1Ozd1NIzZo2BYkJ8di6811f4OXLRGzfvB6BAXehqKAIV3dPjPxuPPT139VPXGwM9uzciuCHD5CZmYGaNe0waNgoODp9+UMlBxtTnFMqnzfV9ZvWRYPLe+DbagiSb9wS2xY0ajqid797i9NiTD9YTxkJVXNjZDyLQviyLYjZd0osP71GHnBY+iu0azsgJ+k1YvadQti8dRDkST8nf65OuaG4HfqmTPMEgPS0t9i7fQ3u+t2AQFAAWwcXDBo1BSZm7/qcv88ch6TEOKzZdlJqHpNHd4eDszu+nzxXbHtifAwO7lyPkOAAZGVmwMbOCb0HfS9aE+NL1bHTRWzo/Q/v+Am8fP2xc/9h0TWne8f26NujCwAg8EEwpsyah2mTxqP9e3Oqn754CYdPnEZi0iuYGBlgYO8eaNtCfIrIq94+WLBsNXZtXAMLKVMglhUTOxek3rlYpnlevR2Ezccu4EVcIgz0dNGnTWMM7iTse9159ARjF63Hb98NRJdm9bDp6HlsO/FPiXltmjUBno7Cvm5ASDjW7j+FsMgY6GpqoGPjOhjbu2OZT9en5dkez5+GfXjHT3DTxxd79u1HdHQ0qlSpgi6dO6F3zx4AhGtY/DpjJn6ePAlt27ybQu3chQs4evwEXr5MgrGREfr17Y3Whc8GAoOCMG3m7BL/3uCBAzBk0MAyK79VDVskP/Aus/wA4Jr/PWw7dAqRsfGopq+LXu1bYmBX4Si2ew9D8MO85Zj9wwh0atFYIu25q974fcMOHN+4FMYG7wI1AoEAJ/69jmMXLyM64SX0tLXQtnE9jOrbTbSmSlnRr9UYkU8ef3jHT+Dt44fd+w8gOjoGVapUQddOHdCnZ3cAwu/JLzPn4JfJP6Jd61aiNGcv/IMjx0/iZVISjI0M0b9PL7RpWfIaDrv3HcCeA4dw6ezJMi07AFjUdKgUdSIQCHD2wj84ffY8YuPjoaOjjZbNmmLowP5lOm27RU2HMsvrW3LzkWxOCS/LGjl+eO3L/zdlEsD4FP7+/hg6dKgogEEVrzwCGJVZeQQwKrvyCGBUduURwKjsyiOAUdmVVwCjMiuPAEZlVx4BjMquPAIYlVl5BjAqq/IMYFRW5RXAqMzKI4BR2ZVHAKOyK48ARmVXHgGMyq48AhiVXXkEMCozBjA+DwMYn+5bDGB88RoYREREREREREREREREZa1s5w8pYwsWLMCJEydK3WfDhg1o2LDkRUuJiIiIiIiIiIiIiKjy+eoBjHr16onWb/iQCRMmYNiwYaXuY2BgUBbFIiIiIiIiIiIiIiIiGSLTIzD09fWhr69f0cUgIiIiIiIiIiIiIqKvTKYDGERERERERERERET0/0cAuYouAlUCXMSbiIiIiIiIiIiIiIhkDgMYREREREREREREREQkcxjAICIiIiIiIiIiIiIimcMABhERERERERERERERyRwGMIiIiIiIiIiIiIiISOYoVnQBiIiIiIiIiIiIiOjbUiCo6BJQZcARGEREREREREREREREJHMYwCAiIiIiIiIiIiIiIpnDAAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyR7GiC0BERERERERERERE3xYB5Cq6CFQJcAQGERERERERERERERHJHAYwiIiIiIiIiIiIiIhI5jCAQUREREREREREREREMocBDCIiIiIiIiIiIiIikjkMYBARERERERERERERkcxRrOgCEBEREREREREREdG3RSCQq+giUCXAERhERERERERERERERCRzGMAgIiIiIiIiIiIiIiKZwwAGERERERERERERERHJHAYwiIiIiIiIiIiIiIhI5jCAQUREREREREREREREMkexogtARERERERERERERN8WgaCiS0CVAUdgEBERERERERERERGRzGEAg4iIiIiIiIiIiIiIZA4DGEREREREREREREREJHMYwCAiIiIiIiIiIiIiIpnDAAYREREREREREREREckcxYouABERERERERERERF9WwogV9FFoEqAIzCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyR04gEAgquhBERERERERERERE9O24/CCrootQ6bSqpVrRRfjquAYGITrsYUUXQaaY2TrjTeC1ii6GTNF1bY60TTMquhgyRXPsYmRd2lnRxZApqm2G83xSjJmtMzK2z63oYsgU9VELEBYeWdHFkCm2NhbY7833Sd43sLEcMvf+UdHFkBlqg2ci69Cyii6GTFHt9ytuh76p6GLIlDp2ujinZFfRxZApnXJDcT04o6KLIVOaOakj698dFV0MmaLadgTiQgIruhgyxdjeFY27XK/oYsgU7zPNcCM4vaKLIVOaOmkgNvR+RRdDZpjYuVR0EYj+b3EKKSIiIiIiIiIiIiIikjkcgUFEREREREREREREX5VAIFfRRaBKgCMwiIiIiIiIiIiIiIhI5jCAQUREREREREREREREMocBDCIiIiIiIiIiIiIikjkMYBARERERERERERERkcxhAIOIiIiIiIiIiIiIiGSOYkUXgIiIiIiIiIiIiIi+LQJBRZeAKgOOwCAiIiIiIiIiIiIiIpnDAAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyhwEMIiIiIiIiIiIiIiKSOYoVXQAiIiIiIiIiIiIi+rYIIFfRRaBKgCMwiIiIiIiIiIiIiIi+YdnZ2Zg/fz4aNGgANzc3/Pzzz0hOTi41zb179zBkyBB4eHigSZMmmDVrFt68eSP6PCEhAXZ2dhI/x48f/+hyMYBBRERERERERERERPQNmzdvHry9vfHnn39i165dePbsGSZOnFji/hERERg1ahTs7Oxw+PBhrF69Gvfv38ekSZNE+4SEhEBFRQVeXl7w9vYW/XTs2PGjy8UppIiIiIiIiIiIiIiIvlEJCQk4efIkNm3aBE9PTwDAqlWr0L59ewQEBMDNzU0izcmTJ2FgYIBZs2ZBTk44Hdhvv/2GQYMGISoqCubm5ggLC4OVlRUMDAw+u2wcgUFERERERERERERE9I26e/cuAKB+/fqibdWrV4ehoSFu374tNU3Xrl2xdOlSUfACgOjfKSkpAIDQ0FDY2Nh8Udk4AoOIiIiIiIiIiIiISMa1atWq1M8vX778WfkmJCRAT08PKioqYtsNDAwQHx8vNY20wMTWrVtRrVo12NnZAQDCwsKgp6eHQYMGISIiApaWlhg3bhyaNm360WVjAIOIiIiIiIiIiIiIvqoCQUWX4NsRHR1davBj0qRJUFZWltiuoqKC7Ozsj/obS5cuxbVr17B+/XooKSkhLy8Pz549Q40aNTB9+nRoamri3Llz+O6777Bjxw40aNDgo/JlAIOIiIiIiIiIiIiISMZ97ggLQ0NDnD9/vsTPr1+/jpycHInt2dnZUFNTKzXv3NxczJ07FydPnsTChQvRunVrAICioiL8/f2hoKAAVVVVAICzszOePHmC7du3M4BBRERERERERERERPStU1JSKnUtitDQULx58wY5OTliIzESExNhaGhYYrq0tDRMmDABd+7cwapVq9ChQwexzzU0NCTS1KxZE97e3h9ddi7iTURERERERERERET0jfLw8EBBQYFoMW8AiIiIQEJCAurUqSM1TU5ODr7//nvcv38f27dvlwhePHnyBO7u7vD39xfb/vDhQ9SoUeOjy8YABhERERERERERERHRN8rQ0BCdOnXC7Nmz4e/vj/v37+Onn35C3bp14erqCkAYsHj58qVoqqnNmzfj7t27WLhwIaytrfHy5UvRT05ODmxsbGBtbY0FCxbgzp07CA8Px+LFixEYGIhx48Z9dNk4hRQRERERERERERER0Tds4cKF+OOPPzBhwgQAQNOmTTF79mzR5wEBARg6dCh2796NevXq4ezZsxAIBPjpp58k8iraZ9OmTVi5ciUmT56Mt2/fwtHRETt27ICtre1Hl4sBDCIiIiIiIiIiIiL6qgQCuYouAr1HXV0dv//+O37//Xepn9erVw+hoaGi3//5558P5lm1alUsXrz4i8rFKaSIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyhwEMIiIiIiIiIiIiIiKSOQxgEBERERERERERERGRzGEAg4iIiIiIiIiIiIiIZI5iRReAiIiIiIiIiIiIiL4tAkFFl4AqA47AICIiIiIiIiIiIiIimcMABhERERERERERERERyRxOIVWOhgwZAlNTUyxZsqRM8jh+/DhmzJiB0NBQAMDr16/x33//oU+fPmVV5DJz514g/t6zH88jo6Cnq4tundqjT4+ukJOTKzHNlete2HvoKOISEmFkUA39e/dAu1YtxPbpN3wMkl4lS6Q9vncHdHS0y/w4yopf0CNsOngSz6Jjoa+jjd7tmmNQ5zal1keR0IhIjJi1GEfXLISJQVWxz57HxGP9vmO4+ygMivLycHO0xaQhvWFqWK28DqXM+L5IwMabwQh/lYoq6iroU9saQzxqllgnUW/S0H3HvxLbbapo4/DQ1hLbE1Iz0HfPZazsUh+e5rJfHwDg8/gZ1p+5jvC4JOhra6B/Ew8MbVW3xDrJzs3D5gveOH87GK/TMmBraoCxHZugkaO1aJ+CAgGO3gzAYa97iE56A30tDbRwqYlxHZtAU03lax3aZ+O5RJJvRDzWe93Hs6S30NdQRT+3GhhSx+6jzid5BQUYvvcyVJUUsG1AS7HPTj+IwO7bIYh6nYZqmmro4lwdoxo4QFG+crzrkJmZiZ1/b4WPjzeyMjPh5FwLo78bBzMz81LTvX79Gtu3bsK9u7eRn58Pzzp1MWrMWOjrV3m3T3Iy9u7ZiYB7d5Ga+hamZubo1asvmjRrXs5H9WVystJx6ehKPL77L3KyM2Bp64l2/aejqpF1qemingbg8vFViHseDGVVDTh6tkPLHpOhoqYp2icvNwfXT6/Hfb8zyEhNRhVDKzTpPBZOdTqU92F9Np/wGKy/GoBnL99AX1MN/TztMLS+00e3nWE7zkNVSRHbh7YX+6ztmiNITM2QSHP1537QU1cts/KXB5+n0Vj/312Ev3wNfQ019K/riKGNnD+uTvILMHTbGagpKWL7yE5in50KCMOumw8QnZwKIx1N9KvngIH1HD8q34qWlZmBg7vW47bPVWRlZcLeyRWDRk2BiZnlR+exdsl0qKqq4/vJc8W2v0pKwIEdfyI46DZyc3PgVLsOBgyfACMTi7I+jDKnamqIpoFncafXD0i+cavUfU36dUKNGeOgbm2OzOcxeLp8C2L2nBTbR8fDGQ5Lf4WOhzPy3qYjevdxhC1YD0FubjkeRdnJyszA8T1rcc/vMrKzMlDT0R19R/wCI1OrUtO9CH+MUwc24PnTYAgKBLCwcUDPwRNhaeMg2ic/Pw9nD22Gz9UzSEtNgaWNA3oPmwJr21rlfFSfz+dxBNafLezDammgf1N3DG35oT7sTZy/834ftjEaOYj3YfdcvYWjNwOR8CYVltX0MLx1fXSq4/S1DuuL3A4Iwra9B/E8Mhp6ujro3rEd+nXvXOp58PKNm9hz+DhiExJgZFANA3t1R/uWzQAAcQmJGPDdjyWmbd+yGaZPGl/mx1GeJoy0hl0NLfw4M+iD++rpKuHHUTao564PBQU5+N1Jxp/bw/HqdY5oHwV5YMQAK3RoZQgdbSWEPk3Dhr/D8SgstTwP44tlZWbg2J61uOd3RXQ+6Tfi5w+eT97317KpUFFTx8gf54ttf/0qEUd3r8HDAB8U5OfD2q4WegycAKsajmV8FGXndkAQtu85UHj/p4Pundqjb/cupbed697Yc/gY4hISYGRggAG9uqN9q+YAgPiERAwY80OJadu3ao5pk0r+nIhkAwMYMu7PP/+EgoKC1M+WLVuG6OhomQtgPAoJw6yFi9G8cUMMHzwADx89xpade5Cfn48BfXpKTXPjpi/+WLkWPbt0Qh0PV9z0u4Vla9ZDSUkJLZs2BgCkpLxF0qtkfD9iKJwdHcTSa2pqlPtxfa4HYc/w89L1aN3QE9/364qgkKdYv+848vMLMKx7+1LThkfG4Kel65GfXyDxWUJSMsbMXQZLE0MsnDga2dk52HToFCYuWot9K+ZCVVm5vA7piz2IS8bkkz5oa2uGcQ0dERjzCuu8HiK/QIARde2kpglNTAEA/NWrMdSU3p26VBUl20d8agYmHL+JtOzKcRMMAPcjYvDjpiNo5+6AHzo3RUB4NFafuoK8ggKMattAapp5+8/jxoOnmNi1GSwN9HHa/wF+3HQY2yYOgnsN4UPbHf/5YcPZ6xjWqj7q2VniRWIyNpy9gaexL7FpQn+ZfpjEc4mk+7FJmHjMC+3szTG+cS0ExiRhzbUg5BUIMLK+wwfT7/B7jOD4ZHgUC+rtvxOG5VcC0NrODJObu+J1Rhb+8n6IsJdvsLJ7o/I6nDK1YtkfCA0JwfCRo6Guro4D+/di1vRfsOGvbdDU0pKaJj8/H/PmzkRmRgbGT5iEvLx87Nq5DXNnTceaP/+CoqIicnNz8NvcGUhPS8egIcOgr18FN296YdnSRcjNy0XLVm2+8pF+vGNbfkH0syC06f0LVNQ0cf30BuxaNgzjF56FmoaO1DQJUaHYvXIErB3qo+/4dUh9k4j/jq3Cq/gIDP5pu2i/E1unIvzRTbTq9TOqGFrivu8pHN38E1RUNVGjVpOvdYgf7X70S0w8eAXtnKzwQ3M3BEQlYM1/d5FfIMDIRh9+KLjj5gMEx76Ch6Wh2PbXGVlITM3AlNYecDMX/0xLVXavwwBwPyoRP+67hHbO1fFDKw8EvIjH6ku3hNedprU/mP5vryAExyTB08pIbPvxu6GYf8obwxvXQkMbMzyITsTKi/7IzM7F6Gau5XQ0ZWfDyrkID32I/sMnQE1NAycObsMfs8dj6foD0NAsPcBdUFCAfdvX4LbPVTRpKR7UycrKxJI5P0JOTg4jxv0KRSVlnDz0NxbOGIul6w9AU0t6m5QFqmZGqHtuO5R0PxzgN+rRFq67VyDiz914+Y8XjLq1huvfS1GQnYO4w+cBAGrVzVDv4g689gvEvQGToWlvA7uFU6Ckr4uHP/xW3odTJratnoGIsAfoNXQyVNU1cPbQZqycOwbz1h4r8XuSGBeJFXNGw9LGAUPH/wY5OTn8e2o3ls0aiTkrD4geVh7ZsRLel0+i5+CJqGJggktn9mL1vLGYs/IADIxlL9h1PyIGP24u7MN2aoqAZ9FYfeoq8vJL68NewI2HTzGxS2Ef9tYD/LjpCLb9OFDUh9143gs7/vPD+I5N4GxpDK/gcMzcfQbycnLo4Cm7D18BIDg0DDN+X4oWjRti5KB+ePAoBJt37UN+fj4G9e4uNc11H3/8vupP9OrcAXXda8Pb/w6WrN0IJUVFtGraCFX09bBh2UKJdCfP/4ur3j7o2KallFxlV//uZujfwxwBD958cF8FeWDFvFrQUFPEio1hUFSUx9hh1bFqQS2MnHwP+fnCCfQnjLZB5zbG2LTrGeITstGvuxnWLHTBiMl3EROXVc5H9Pm2rZ6JZ2EP0GvoJKipa+DMoS1YMfc7zF979KOuO4d3rMQ9v8to0KKL2GcZ6alYOmskcrIy0X3AeBgaW+Cu32Usnz0avyzcguo1ncvzsD7Lo5AwzFy4+F3beRyCzTv3Ij8/HwN795Ca5rqPHxatWodeXTqijrvw/m/p2g1QVlJCy6aNoK+vhw3LFkmkO3n+YqVsO0TfKgYwZJyurm6JnwlkdKWbXfsPooZ1dcz4eRIAoK6HG/Ly87H/yHH07NoJKiqSb31v37MfzRo1wPgxIwAAddzdkJqahp17D4geOj6NiAAANG5QDybGRhJ5yKqtR87Arro55k8YCQBo4OqMvPx87Dx5Af06tpQaaMjNy8PhC1ex5chpKCspSc/36Floqqth/ewpUFUR5mFiUAW/LN+Ix+Ev4OZQs/wO6gtt8n0EOwNdLOxQBwDQ0MoIeQUC7LgdigHuNaQGJcJevoGhphrqWhiUmG+BQICzjyKx5saDcit7edl4zgv2Zob4Y1hXAEAjRxvk5hdg+78+GNTcE6rK4t+DmFdvcP52MGb0aYt+TT0AAHVtrRD4LAaHvO7CvYY5CgoE2HHJD70buWFSt+YAgPr21aGjoYZpO07hUWQ8nCyNv+pxfgqeSyRt8g6GvaEufu9cHwDQyNoYefkF+NvvEQZ61ISqUsmX9dDE1/jb7zGqaoi/EZ5fUIAtPsGob2WI5d3eBSscDPXQZ8c/8Hsej/pWsl1PIY8f4Za/H36bvwiedeoCgHAExoghOHfuNPr1HyQ1nbfXdTwLf4oNm7bBwkL4drW1jQ0mjBsDb6/raN6iFW7f8kfEs2dYuWY9bG2FAVY3dw+8TEzEsaOHZTaAEfU0AGFBVzFw8hbUrNUUAGBh64m101rj9tUDaNp5rNR0fpd2Qk1DB33Hr4OC4rvr06kdM5EU/wxVjazxIuwOHt39Ryzv6g4NkJwYiScPbshkAOOv64GwN9LHou7CsjWqYYq8fAG2ez/AwLoOpbed+GRs936AqppqUj8DgJZ2FjDXl+3RW8VtvHIP9kZV8Eev5gCARjXNkFtQgO1eQRjUwOkDdfIK272CpNbJthtBaONkhSlthW2xno0JXrx6iwP+j2Q+gPEk5AECbnlh6m+rUdujIQDAzskVU8b0wH/nj6Fb3xElpo2MeILdW1bi2ZNHUFaWvD7dunkFcTEvsGT9AZhZCN8yN7e0wZQxPeB/8zJatZcemK9QcnIwG9IdDkunAR/5voPdwp8Qd/QiHv+yGACQdMkbSno6sJs3SRTAsJk6Bnmp6bjTczwEubl4efEG8jOz4Lx2Dp4u2YSsqLjyOqIyER4ahPt3buDH2X+ilruwb1HTwQ0zx3XGtYuH0an3aKnprpw7AGUVVfw460+oqArbjn2tupgxtiOunD+IgWOmIzkpHtf+OYr+o6aiefu+AABH1waY80M3XDyxE0PHz5Wad0XaeN5b2IcdKnx42sjRGrn5+dh+ybfkPuydYMzo0wb9mroDAOraWiLwWTQOed2Dew1zZObkYu/V2xjU3FMUBKlnZ4XHUfHYf/2OzAcwdu4/gprVq2PWlAkAgHrursjPz8e+oyfRu0tHqKhI3v9t23MAzRvWx4TRwwAAdd1dkZqahr/3H0arpo2grKQEJztbsTShT5/hqrcPRg8eABdH+/I/sDJgbKiKCSOt0aheVaSm5X1UmhaNq8HORguDx9/G8yjhiMcnz9Kwe70nWjauhkvXE2FQVQU9OphgzZanOHlBeA65FZCMA5vrYlAvCyxbH1Zux/QlwkODEHTnBibO/hO13IX98JoO7pjxgfMJAEQ/D8OBbcvw/OkjKCtLjvi8eeUUXiXGYtqiv1HDwRUA4OhaH2lv3+DQ3ysxffGOcjmmL7HjwGHUsK6OmT9NBFB4/5eXj31HTqBXl46l3P/Vxw+jhwvTuLvibVoa/t53EC0L246jffG2Ey5sO0MGopbjh18CI6KKVznmhfgK/vjjD7RuLT4NTWpqKlxcXLBv3z60adMGv//+Ozw8PDB+/McPzUxPT8fPP/+M2rVro3Hjxli3bh0KCoRv0/v7+8POzg7R0dGi/YtvGzJkCKZPny6R7/Tp03HixAncunULdnbS31ivCDm5uQh6EIzG9euKbW/asAEyMjPx4FGIRJr4hEREx8SiUYN64mkaNUBMXDyiY2MBAOHPnkNdTQ3GRoYSeciqnNxc3HsUhmZ13MS2t6zngYzMLASFPJWazifgIbYdO4vh3TtgwkDJG1qBQICr/vfQpXlDUfACABxsrHBu0zKZDl7k5OXjbnQSWtiYiG1vVdME6Tl5CIxJkpou9GUKbKuV/nbik5cpWHw5AJ0dLbCgvWeZlbm85eTm4c7TSLSsLd6W27jZIT0rBwHh0RJpqmlrYv/U4ehU990wenl5OSjIyyE7Nx8AkJaVjc51ndDBU3yofXVD4dQ4UUmvy/pQygzPJZJy8vJxJyoRLWqaiW1vbWdWatsBgNz8fMw554/+HjVhqS8+GuFVejZSsnLQpFibrFFNF7pqKvAKjy27gygn9+7egaqqKtzcPUTbdHR04VzLBXdvlzzVyb17d2BqZi4KXgCAhYUlzMwtcKcwnbq6Btp36ISaNcVvfMzMzREfJ7t1Ex7sDSUVddg4vQtKaWjpw8q2Dp4+uF5iuhY9JmPQpC1iwQsFReHDp7xc4TQNj+78Az0DC1HwAgDk5OQwcsYBdBg4q6wP5Yvl5OXjzot4tLQXf3O5taMl0nNyERCVWGLa3Px8zDnljQF1HWBZRTJAEZqQDA1lJZjpSR/lI6ty8vJx53kcWjqIT4vUxqk60rNzEfAiocS0uXn5mH3sBgbUc4JVVcnr8vrBbUXBiyJKCvLIzssvm8KXo/v3/KCiqoZaru+uI9o6erB3dkPgHZ9S025aMx8FBfmYt3w7tHX1JT73rN8Mc5duFQUvAECxsG3l5uRI7C8LtF3s4LxhPqL3nkTg8F8/uL+apSk07aoj/tQlse1xx/+BRk0rqNcQft+qtWmMxAvXxaaLij92EXIKCqjWtnHZHkQ5CA7whYqqGpxqvxtdoKWjj5qOHnh417vEdEZm1dG221BR8AIAVFTVoFfFEC/jowAAIfdvoSA/D2713r0RrKSkDBfPpnh4r+S8K4qoD+sifo1s42ov7MM+K6kPO0xsKihhH1Ye2XnCB9rKigrY/dMQDG0pfi5RVFQQ7SOrcnJzEfjwERrXryO2vVnDesI+7GPJPmxcQiKiYuMk0zSqV9iHlQzqCQQCrNm8HZbmZujTtZPE57Lqx9E2MDNRw6RZQXgakfZRaeq66+NFdIYoeAEAz6My8CI6Aw08hedbj9q6UFSUxw3fd/3h3DwBfG6/Eu0ji96dT+qLtmnp6MHW0QMP7t4sNe32dXNRUFCAGUt2QktHT+LzuOgIqGtqi4IXRexreSI8NAjpaW/L5BjKStH9X5Ni93/NGtUv9f4vKiYOTeqL3/81a9ig1LazdpOw7fSuRG3n/1kB5PjziT/fIgYwCvXs2RNRUVG4c+eOaNv58+ehra0NZWVlREZGIjExESdPnsSUKVM+Ot9///0Xenp6OH78OH799Vfs2LEDu3fv/uLyzpo1Cx06dICbmxu8vWWnMxsXn4DcvDyYmYo/CDM1Eb69Gx0TI5HmRZSwY2tmIp6m6M3oqGjhA6KnEc+hpaWJ+YuXo2u/IejUZxAWLl2JV8my+xA2JiEJuXl5sDAWHzVgZiScviUyVvoDAgcbS5xc/wdG9OwIBQXJZhr38hXSMjJhVK0Klm3fjzYjp6DJ4B/wy/KNSHglu/UBADEp6cjNL4ClnqbYdnNd4e8vXkvvyIa9fIOM3DyMOHgNDdadRNvN57DO6yFy35tey0hbHSdHtMVPzVykjuKQVdGv3iA3Lx+WBuKda4tqwo7o88RXEmmUlRThZGkMLTVVFBQIEP/6LZYdvYTopDfo01gYMNNWV8X0Pm3hZiP+wPvqfeEbSDbGsrs2CM8lkqKL2k6xAIR54YPT58klz++7xecR8goEGNdIcqi4lqoSFOXlEJeSLrb9bVYOUrNyEP0mXSKNrImKioSRkbHElIvGxiaIjpF8eFIkOjIKpqamEttNTEwQEy18mOTq5o4ffpwsNt1aXl4e7tzyFwt8yJqkuGfQq2YGeXnxOtEzsEBSfESJ6bT1DGFoLgym5mRn4NkjH1w+vhrmNdxhZC58uzMh6jEMTGrigd8ZbJjdEQvGOGHD7I4Iufdf+R3QF4h+nVrYdsQDEBaFbefFq5QS026+EYS8ggKMK2HkQGj8a2irKeOXo9fQeNl+NFiyD78eu46XUtbEkCWiOikWgLAorKPnpdXJtQDkFRRgfEt3qZ9bV9OFqZ4WBAIBUjKycfxuKM4EPUW/urL/hmNs9HMYGJpCvti5xNDYDHExL0pNO27KPMxduhUW1aW/RKKuoQlbBxcAQF5uLiIjnmDzmgXQ0tZFvcaSa3nJgszIOFyzb4PHU5cgP+PD069o2tsAANKfPBfbnhEurDtN2+qQV1WBupUZ0p+In4dykl4jNyUVmrbVy6bw5Sg+OgJVDc0kvicGxuaIjy35e9K8fV+06z5MbFtiXCRiIsNhYi6su7joCKiqaUBHT3zdu2rG5niT/BJZmbJ1bvlgHzZBcr0xZSVFOFkU68Me+0+sD6sgLw9bUwNU1daEQCDAq7fp2P6vL/xDn6NfY+nnHllR1Ic1NxUf5Wxa2B+NjJF8+SEyWtiv/ZQ0V7x88DjsKSaMGib1flFWbd0TgWE/3kVQcMnXmeKszNQRFSP53Y+OzYSFqbpon/SMPCS/EZ9CODouE9WqqEBNVTbrKC46AlWlXHcMjM2RUMr5BABGTVqIaX/8DTMrW6mfa2rpIjszQyJQkRgv7BsnJUjeT1Uk0f1fsXu5onYQJaUdvChsO2YltB1paa56+eBx2BNMGD2ixOnaiUj2cAqpQvb29nBycsLp06fh6Sl8c/vEiRPo2rWr6KQ2fvx4mJuXvhhocY6Ojpg9ezYAwMbGBuHh4fj7778xfPjwLyqvlpYWVFVVoaSkhGrVZOchZHq6sGOhri4+pYC6mvD39IxMyTQZwjQaJaTJKEwT/iwCSa+S0aldG/Ts1hmRUdHYue8QpsyYg81rV0BNVfYWykwrLLuGWvFjE5Y1PVOyPgDAQF/yDYr3vX4rfFC5Yf9xONpYYeGkMXj99i02HjiJ8QtWYe/S2VBTlc0FmtNyhG9NaRQbTq6uLDwdpedIrlvxOjMbiWlZyC8QYGITZxhrq+NW5EvsuhOGhLRMLCqcikpHVRk6Mj7nuDRpmdkAAM1iZVcvHCKbnlX6m5k7Lvli3RnhG9W9Grqivr1Vifvefx6Dvy/5oZlzDdQ0kZ1zR3E8l0hKyxZ+DzSUxS/dorZTwpovwXGvsPtWCLYPbAllKYE9NSVFtLW3wKGAp7CpqoOWtmZITs/C8isBUJCXQ1aubL/pCAAZGelQU1eX2K6mpo7MjJIf9qRnpMNESgBDTU0dGaWk2/H3VsTGxmDGLNmbyqNIVmYqVFQ1JbarqGogO/PDQSmBQIDlkxogLzcbapq66DBwtuiz9NTXeJXwAnEvgtGy52Ro6lTDnasHcGjjjxg0aYvMTSFVtB6Shkqx607h7yWtl/QwNgm7fYOxfVh7qW0HEI7AeJmaAQd3Wwyq54hnSSn461oARu3+B4fGdIaasvRpICtaWuF1RbN4nRSWt6TrzsOYl9jl8xB/j+xUYp0UuR+ViKHbzgIAnEyqYmhD2V2AuEhGeloJ5xINZH6g3Zhb1fjov7Py95/xIMAfcvLyGPPjLOjpV/1wogqQ+zoFua8//iGjoo7wnJP3VvxllLxUYd0pamtCSUdL6j5F+ylqS563ZE1mRhpU1STXzFJV00BW5se9UQ4AOdlZ2LFuLpSUlNGyY//CvFOl560q/F5mZaZBVU3yO1pR3vVhxe871AtHiKdnZZeafsd/fu/1YWujvp2VxD4X7z7G9F2nAQBNnGxkfhHvtML+g3qx+z+1Yv1RsTSifq/6R6c5dPIMnB3s4FZLtuujuIjITw/CaWgoIipWsg4yMvOhoa4g2ic9Q3KkX0amcJu6uiIyP3BPVREyM9Kgpialv6amjswPnE/MLEufdaF+s064dHovNi2fiv6jfoVeFQPcv+MFnyvC9pSdLf1ZREUpuv8r6V5O6v1fUZqSnrdI6c8fPHEKzg52cK1kbYfoW8cAxnt69eqFNWvWYPbs2YiLi0NAQAAWLVqEoKAgAICVldUn5+nh4SH2u4uLCzZt2oS3b2VruF5ZEQgkF5t+n7yUBYMFBaWv5SEnL0zz04/joCCvAHtb4Q2ii5MjrCwsMGnaLFy6cg1dO5a+IHZF+NA6JXJyn/cmSG7hNAz6OtpY+vNYyMsL8zEzNMDoOUtx0dsfPVo3LS2LClPwoTqRMhxOTUkRG3o2goWuJkx0hDd1HmbVoKwgj40+jzC6rh2qS5nao7L4YJ18YKHtprVqwtXGDAHh0dh8wRtZubmitTTeFxAejR83HYFpFR0sGCzbw2V5LpH0gcOT+j3JzsvHnPO3MNDTFs7GVUpMO6utB5QU5LHg4m3Mv3gbqooKGF7PAek5uaXOg18RCgoKJM6tRVMzSlNa+xGUlk5e8vwsEAiw8+9tOH3yOHr26ouGjWTjQb2goECizZR6bB84pwBAQX4e+v+4EXm52fA+vwU7lw7GiBn7YWRuj/z8XKSlvMR3c4/B2FJ481fdoT42/dYd189skLkAxofOsdLOJ9l5+Zh7yhuD6jmilmnJwd65nRtAQV4ezibCB9DuFoawqaaLETsv4Mz9cPT1lM05yT/nupOdm4c5x29gUH0n1DL7cADcWFcT20d0RMybVKy/fBdDt53BwbHdoaYsG+eUAmntppRrj/xn9tmk6d5vJDr1HAKfaxexZe1CFOQXoHlbyet2ZSPtvPk+QUEB8MF9ZGuNP2nfk4JSvicf27fPykzHxiU/IeJpMMZOXYYqBsI3jsvr3qG8fHEf1rkGXK1NhX3YizeRlZsnWkujiLOlMf6eNBBhMS+x4dwNjP/rMLZPHPhR17KK8MH+qLQ+7CfW48PHoQgLj8DvM3/59AJ+JXJygHyxQ80vvXtfouL5vK+oukvbB5CNc4v0607J5frS9m5ibo0JM9dgz1+/Y97kPgAASxsHdO0/Dge3L4OKimy9tPXB/pqU/+TSzseA5LX74eNQPAmPwMKZH54WkYhki2zcQciILl26YOnSpbh69SrCwsLg4uICGxsbUQBD9TPeypUv1kkvKCiAnJwclEpYmDk/X/bnBy6NRuFbI5mZ4sPMRW9Ga0i+MVS0LaPYaISiCHtRnk72kmt9ODvaQ0NDHeERpQ+vrCiahW8PZGQVq4/C+tFUl1z88mOoF77l1MDVSew7VsvWGprqagh7HvVZ+X4NmkVvd+aKv/GaXjgyo/jboACgqqiA+paS6xU0rm6EjT6PEJaUUqkDGJpqhSMtssXfCip6a01LrfTRNEUjKTxqWCCvoAB/nfPCj12awVj/3dQgF+8+wty952BpoI+N4/tBV1N23t6ThucSSVqFbSMjR3xERNHIC2ltZ4PXAwgEAnzXwAl5hQ+0i+4N8goKoCAnBzk5OagrK2Feh7r4tZUb4t5mwFhbHerKSjh5/xnMdWVrbv+D+/fiwP49YtsaNW6CN2/eSOybkZkBDQ3JN1mLqGtoIFPKdBwZGenQUBdPl5ubgzWrluPG9Wvo2asvRowa83kHUA6un9mA66c3iG1z9GiH9LeS089lZ6VBVf3D/6cKikqi9TMsbetg7bRW8L+0G91G/gEVFQ1o6lQTBS8AQF5eAdaODXD3+qEvPJqypylqO8WuO6W1nav3UCAQ4LsmLqK2Ayltp7aZgURaN3MDaKooISxBdqelKxrxV3zkVtHvWlJGM66/fFdYJ81dkZdf7HySXwAFeTmxh2wG2how0NaAJ4xhpqeFkX+fx3+PItDFVTbW6TpxcDtOHNwmtq1uw5Z4+0ZyypvMjHSoqZd8LvlUdo6uAADn2nWQlBiHU0d2/F8EMPJShCOEFbXE66poVEVeSppo5EXxfYr2K8pDVpw9vAVnD28W2+beoDVS30ieX7My0qCm/uERJMlJ8Vi/aCLiY1/gu5+XwLVuC9FnauqayMqSvC5lFY4A+pj8v6Z3fVjxkRaf3Yc9740fOzcV68OaV9ODeTU9eNSwgKaqMmbvPYd74VHwqGFRUrYVqqg/WrwPWzSKQrOUPmxmsT5s0WjQ4mmu+/hDS1MD9T3E11mUJSP6W2LkQCuxbY27lLwGV2nSMvKgriY56k9DXQHp6XmF++RL30dNUfR5RTt7eAvOHN4its2jQWu8lXo+SS+T9u7k2gCLN51FUqJwKqVqhqbwvnwSAKCuWfr6kl9byfdyRSMzpLQd9aI0xZ+3ZIrlWeS6j6+w7XjKbtshIukYwHiPtrY22rRpg0uXLiEkJASDBg364jyDg4PFfr979y7MzMygpqYmCmKkpb0bGvj8+fOPzlsW3zoxMTaCvLw8YuLEF0uKjYsHAFiYm0mkMS+c4z42Lh41bazfSyPMw9LcDGnp6fDy8YO9bU1Ut3zXWS0oKEBebh50dWTz4bWpYTUoyMsjKl58gdDowt+tTI0+K18zo2qQk5NDrpSpXfLzC6Aio1NWAICZrgYU5OQk5tWPeiNsB9X1JR+sRb5Ow+2oRLS1NRN7qFK0IKjeB26OZJ15VT0oyMsh6qX4w67Iwt+LFt1+X2xyCvxCnqNTHSeovPeGvIOZMNDzMiVNdPO36z9/rD51BZ41LbF6TE9oqcnW2zbS8FwiyUxXEwpycogqtk5MUduxlhLE+y80CnFvM9BwzTGJz+qsOIL5Heqia63quPE0FtqqSnA1qwabwjnxk9OzkJCaAQfD0qe0+9radeiIOnXFF+rz8/XBvbt3UVBQIBbUjYuNgZl5yQ84zMzMER7+VGJ7XGwsatq9C3Slp6dj/m+zEBryGGO+G4eu3XuWwZGUHY+mfWFbu7nYtpB7l/E02BuCggKxt6KTEyNR1dgaJQkNvAJVNS1Y2r1bSFRVXQt61cyRmiK8dukbWiLldRwEAoFYXyQ/Pw+KSrJ3fjHX14aCnBwii60TE5ksHA1bvaquRJpLj18gLiUdDZbul/jMc9EezO/aCC3tLHA55AWcTaqihsG7dlIgECAvvwB66rJXF0XM9bSE151k8RHBojqppiuR5r9HzxH7Jg0Nfpdcy81j/g4s6NEEbRyr41poJJxNq8HivXOSg7FwhEriW9mZv79lu+5wqyO+YPRd/+u4H+AncS5JiIuCifmXrc0Q/uQRXibEon6x9S6sbOzwJOTBF+UtK9LChOtaqNtY4m3gY9F2DRvhekFpIeHIT89AZnQ81G3E1xBSrqYPJW1NpIWEf70Cf4SmbXvCxVN8VFngrWt4FOgr8T1JjI+CsVnp35PoF0+wdsF45OZkY/LcjbB1Eh+tb2hiiayMNKSmJENL5926EolxUahSzRjKMvbG9Ls+7Bux7aI+rFEJfdjQ5+jkWawPay68L3qZkgYVJSXcfBSOho7WqPJesMu+cJ/ElI+fqutrMzEyLOzDxottL/rdwkxy6sqiPmxMXDxqWld/L02C1DS+d+6hcb06UFSU3cc5p/6Jw83bkg/mP0dkdCZsbSQf5psaq+HxE+G1PTImA5oaitDVVsKbt++C82YmqohLyEJOzmcO/yhDTdv2goun+CwJAbeuIvgzzycf8uplHB4F+aNBs06oZvjuOxT5LAQamjqoamBSSuqvz/QDbcdSyv2fxfttx6a6ZJpibcfv9j00qldXptsOEUknW2NQZUCvXr1w6dIlREZGolOnL59i5d69e1i+fDnCw8Nx5MgR7N+/H+PHjwcA2NraQl1dHVu2bEFkZCS8vLywY8eOj85bXV0diYmJiIqSnbftlZWV4eLsCC8ff7HhkDd8/KChoQ77mpJv3ZmaGMPY0AA3bvqKbffy8YOpiTGMDA2grKSEPzdtw4Ejx8X28fG/jeycHLjWklyYVhaoKCvB1aEmrt0KEKuPq/73oKmuBqcan9cpUVdVhZtDTVy9FYCc90Yy3H7wGJnZ2XC1l423G6VRUVSAm1lVXHkaK1YnV57EQlNFCU5Gkg9Lk9Kz8MflQFx6Ir7Q2L9h0dBQVoSDgW55F7tcqSgpwr2GBS4HhorVyX+BodBSU4GzlWTnMi45BfP3n8eVoFCx7b4hEVBSVICVofCm94h3AFadvIK2bg74a3y/ShG8AHgukUZFUQHu5tVw+Um0WJ1cDo0Wth1jfYk0a3s1wd4hbcR+HAz14GCoh71D2qBpDeF362jgU6y6FiSWdt/dMCjIy6GJjWzd3FSpUhU1be3EftzcPZCZmYF7d++I9ktJeYPghw/g5u5RYl5u7h6IjopEZOS7kTeRkS8QFRUJNzfhelj5+flYOG8OnoSF4tfps2QueAEAWnqGMLGqJfZj49QIOVnpeBrsLdovPTUZL8LuiEZWSON3aRfO7Z2PgoJ3byq+TY7Hy7hwGJoJgzo1azVFZtobPAu+KdonPy8H4Q+9YGlbcn1XFBVFBbhbGuJKSKR423kcCU0VJTibSq4/sK5fK+wb1Unsx8FIHw5G+tg3qhOa1TSDsqICllzwx/ab4g+fr4VGISsvH3WsPu8lha9BRUkR7pZGuPzoufh151EEtFSV4Sxl2qx1g9pg//ddxX4cjKvAwbgK9n/fFc3sLKAgL4f5p7yw8+Z9sbQ+4cLrt62R5HmqouhVqQbrmg5iP7Vc6yErMwMPAvxE+71NeY2Q4EDUcq1XSm4fdv+uD/5aORevXiaIthXk5yP4/t1PWj9DlmWERyLjWRSMe7YT227Uoy3SwiKQ+UL4PUj67yYMOjaH/Hsv3Bj1bIeCvDwkXfWDLNHVN4BVDSexH8fa9ZGVmY7gQB/RfqkpyXjy6B4cazcoMa/kpHisnjcWcnJy+PWPHRLBCwBwrF0fAHDX9z/RttzcHNy/6wVH15LzrigqSopwtzHH5aBifdigwj6sZUl92Au4EhQmtv1dH7YKsnNzMXvvOZz0vS+xDwDYmkiOfpMVKsrKqO3kgBt+t8T7sL7+0NBQh4OtZHs3MzaCsaEBrt/0F9t+w8cfZoX92yJvU9MQHRsHZwfJEcWy5FVyDkKfpon9fK7bAa9haaYOK/N3b9NbmaujuoUGbge8Fu0DAM0bvbumKynKoWGdKrgdIDmyriLo6leDVQ1HsR8n0fnk3f1LasrrwvNJ/S/6e6kpr7F74wKEPLwt2pbyOgm3vP5B7TpNZe6FWOXCtuPlW/z+T9h27KW0naL7v+s+4vd/N3z8YFZ4/1fkbWpqpWg73yKBgD+f+vMtYtixmAYNGkBPTw/u7u7Q1v7yN3H79OmD58+fo0ePHtDX18fPP/+Mnj2FDz80NTWxfPlyrFixAh07doS9vT2mTZuGH3744aPy7t69Oy5duoTOnTvj33//haGh5BQ7FWFw396YOmc+FixdifatW+JRSCgOHz+F0cMGQ1VVBekZGXgRGQ0TY0Po6gjf9B3cvw+Wr90AbS0tNKhXBz7+t3DN2wezf/0JgPBi1r93D+zafwi6ujqo5+mOiOeR2H3gMBrWqwO32rK7MOTInh0x4fc1mLl6C7q0aIT7YeHYe+YSfhjQA6oqykjLyEREdBzMjKpBT/vjp2oZP6A7xs1fhSlL/sSgzm2RnPIW6/cfh1ON6mjiWbscj+jLjaprh/HHvDHt3C10c7JEUNwr7L4Thh8bO0NNSRFp2bmISE6FmY4G9NRV4GpaBXXNq2HNjQfIzsuHtb42vCPicTAgHD81c5E61UVlM6ZdQ3y//gCm/n0S3eu7IDAiGrsu+2FS1xZQU1ZCWmY2nsUnwayqHvS11OFmbY76dlZYcuQS0rJyYF5VFzcePsWhG/cwrlMTaKurIeltGlYc+w8mVXTQv5kHHkeJv81SlJes4rlE0ugGjhh76Bp+Pe2DbrWsERSThF23QjCxmYuo7Tx7lQIzXU3oq6uippS3qIsW/X4/4DHAwxbjj1zHissBaFbDBLciE/G332MMr2cPcz3Zmq5CGudaLqjlUhsrly/B8JGjoa2tjf379kBDQxMdO76bSzsy8gVyc3NhYyO8AWrStBkOHzqAeXNmYtiIUQCAXTu2w8qqOpo0bQYAOHfmFIKDH6B9h06oUrUqQkIeif1te3vHr3SUn8bSrg6s7Ori+JapaNPnF6hp6uL6qfVQVdeCZ/MBov1exj5FXm4OjC2Fx9G0yzjsWTkKRzdNgUfTvkhPe40bZzZCTV0bDdqOAADUqt8Ft67sxfGtU9Gq10/Q1jOC/3+78fZ1PPqMW1MRh/tBYxq74Pu9/2Lqsevo7loDQVEvscv3ISa28ihsOzl49jIFZnpa0NdQRU0pI4+KFv12Mnn3cGREo1r463ogqmiooXENUzxJfI3NN4LQ3NYcdasbf7Xj+xxjmrni+10XMPXwFXR3s0VgVCJ23XyASa3rQE1ZEWlZOXj28g3M9LWgr6GGmoaSwYeihdGd3gt4jGxSG39dvQd9DTXUqW6MsPhkbL4WgPrWJmhcU/LtSVli7+wGh1ru2LjyN/QfPgGaWjo4cWAr1DU00brju+BlTOQz5Obmwsrm4x+CtGzXA5cvnsDKhT+hR//RUFRSwn/njyL6xVNMm7+uPA6n3ClqaUDTsQYywiORkyR8ePhk0QbU3r4EuclvkHDmCgy7toJJ3464N3CyKF34im0w6dcJdc5uQ8SaHdCwtYLdwp8Qte0wsqLiSvhrssPWyQO2zp7YvmYWeg2ZBE0tXZw5tAlq6lpo1r6PaL/YqHDk5ebCwlq4Fs7BbcuQmpKMQd/PQlZGOp6Fvns4r6quARNzG1QxMEGDFl1weMdK5OZkw9DEEpdO70VmeiradR/21Y/1Y4xp1xDfbzj4Xh82Brsu+2NS1+al92GPXkJaVjbMq+rhRnBhH7ZjY2irq0JbXRXd67tg88WbUFSQh72ZIe6FR+HvS37o0cAFNsayufB9kSF9e+Lnub9j3rLV6Ni6BR6GhOHgiTP4bugAqKoI+7DPo6JhamQkGv07tF8vLF33F7Q3aaJhXU/c9L+Dqzd9MfeXSWJ5P3sRCQCwkvI2+v8LK3N1KCnJ48kzYdDjslcihvS1wIp5tbBp1zMAwNhh1ngakYYrXsLRoQkvs3H+cjx+HF0DKsoKiIrNQL9uZtDUUMS+47Lzwmdxtk4esHP2xLY1s9B7yCRoaOngzKHNUFPXQnOx88kz5OXmiM4nH8PSxgE17F2xb8ti9B46GQoKCjixbwPkFRTQtf/Y8jicLza4by/8Mnch5i9dhQ5tWiL4cSgOnTiNMUMHidpO8fu/of17Y+najdDW2opGdT1x0/82rnn7Ys7UyWJ5i9qOxf9v2yH6fyYn+NCKUd+Y9PR0NG7cGBs2bEDDhg0rujhfRXTYwzLP09vXHzv3H0R0dCyqVtFH104d0LeHcG7fwAcP8fPM3zB10g9o37qlKM2ZC//iyIlTSEx6BWMjQwzs3QNtWjYXfV5QUICzF//FqXMXERufAG0tTbRq3hTDBvSFikrZTSFkZuuMN4HXyiw/ALh2KwBbj5zBi9gEVNPXRe+2zTGoSxsAwN3gUIxfsApzxg1D5+aS37mz13yw8K9dOPHnIpgYiHfW74eG46+DJxH8NAKqKspo5umKiUN6Q0vK3KpfQte1OdI2zSjTPK88jcFm38d48ToNBhqq6ONqgyEewrfq70S9xPdHvfBbWw90dSqcdiA7F1v8HuPq01gkpWfBTEcDA91roEct6aNYivLY3LsJPM0/vNjop9IcuxhZl3aWaZ6Xg0Lx1zkvPE9MhoGOFvo1dcewVsI3Pm+HvcDodfuxYHAndKvvAkA4v/Cm8974LygUL1PSYFFND4Nb1EXPhsIA1gnfIMzbd77Ev/d+XmVBtc3wMj+fVOZzCSA8n2Rsn1umeV4Ji8ammw/xPDkVBppq6OtWA0PrCm9m7kQmYszBq6KpoaQZfeAKAGDbgJZi2y88eoFtvo8Qm5IOY2119HGrgQEetmVadgBQH7UAYeGRZZ5vWmoqtm3dBD8/HwgKCuDg6ITR342DmZm5aJ8Z035GYkICtu/cK9r28mUitm7eiMCAe1BQUISbuwdGfzcW+vrCaS+mT/0JwcElT+9y5vylLy67rY0F9nuXfXcsMz0F/x5agpCAyxAICmBewx3t+k9HVaN3U0jtXDYEb5JiMHnZFdG2iBA/XDv5J+KjQiAvr4gatRqjde9foKNvLJb35eOrEXLvErKz0mFs4YhWvX6Cpa1nmZR9YGM5ZO79o0zyKnIl5AX+uh6E569SYKCljn6e9hjaQLiOx+3n8Riz5x/M79oI3WpLfxt+1O6LAIDtQ9uLthUIBDh6NxSH7oQi+nUqdNRU0NHZGmOb1YaqUtm9J6Q2eCayDi0rs/yKXH70HH9dvYfnSSkw0NZAv7oOGNZIGMi9HRGH0TvOY0GPJujmJv1cMOrvcwCA7SPfjVoWCAQ4cicEB/0fITo5FXoaquhQywbjWriJTRfzpVT7/YrboW/KLL8i6WlvsXf7Gtz1uwGBoAC2Di4YNGoKTMzeTXn0+8xxSEqMw5ptJ6XmMXl0dzg4u+P7yeLn/8T4GBzcuR4hwQHIysyAjZ0Teg/6XrQmxpeqY6eLc0rl82apftO6aHB5D3xbDUHyjVti24JGTUf07hOifS3G9IP1lJFQNTdGxrMohC/bgph9p8Ty02vkAYelv0K7tgNykl4jZt8phM1bB0Ge5PSoX6JTbiiuB5f91GXpaW9xZMdKBN66igKBADXsa6PviF9gZGol2mfFnNF4lRiLxZvPIy83FxMGNkRBvvTjs3XywC8LhWuy5Obm4PiedbjldQHZWRmwtHFAr6FTYG1bNi9ZNHNSR9a/Hz/q/2NcDgrFX+e9C/uwmujX5L0+7JMXGL3uABYM6ijeh71wE/8FhuLl26I+bB30bPDuJazcvHzsvOyP0/4PEPf6LYx0tdCrkSuGtawndTHfL6HadgTiQgLLNE8v31vYceAIomKEfdjuHduiX3fhSxUBD4IxZfYCTJs4Dh1aNRelOX3xEg6dPIvEpFcwMTTAoN7d0baF+JRDV719MX/5GuzasEpiepyyZGzv+tlrVnysP/8Q/n//ODNIYruRgSr6jH43IsWgqgomjbFBHVc95OULcCvgNf7cFo5Xr9+tIaikKIexw63RpqkB1NQUEPo0FRt3PMOjsLJZW8f7TDPcCE7/8I6fKD3tLQ7vWInAW9cgEAhgY18b/Ub8LHY+WT5nDF4lxmLJ5nNS85j+fSfYOnti5I/zxba/ffMKh3asxKNAPwgEAtg5e6LH4AkwMrGUms+nauqkgdjQ+x/e8RN4+fpj5/7D77Wd9ujbQ9h2Ah8EY8qseZg2aTzat3q3ftDpi5dw+MRpYdsxMsDA3j3QtkUzsXyvevtgwbLV2LVxjdSp3MqCiV3Z3Vt/S87cLdtr/7egi8e3Nx6BAYxCKSkp8PPzw4ULF/D06VOcOXNG5obUlZfyCGBUZuURwKjsyiOAUdmVRwCjsiuPAEZlVx4BjMquvAIYlVl5BTAqs/IIYFRm5RXAqMzKK4BRmZVnAKOyKq8ARmVWHgGMyq48AhiV3dcIYFQ25RXAqMzKI4BRmTGA8XkYwPh032IA49s74hLk5+dj1qxZ0NfXx5o1a0oNXmzduhUbN24sNb+ZM2eiT58+pe5DRERERERERERERETSMYBRSF9fH3fu3PnwjgD69u2Ltm3blrpPlSpVyqJYRERERERERERERETfJAYwPoOOjg50ChcMIiIiIiIiIiIiIqJPIxB8G9P305eRr+gCEBERERERERERERERFccABhERERERERERERERyRwGMIiIiIiIiIiIiIiISOYwgEFERERERERERERERDKHAQwiIiIiIiIiIiIiIpI5ihVdACIiIiIiIiIiIiL6thQIKroEVBlwBAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyhwEMIiIiIiIiIiIiIiKSOQxgEBERERERERERERGRzFGs6AIQERERERERERER0bdFIKjoElBlwBEYREREREREREREREQkcxjAICIiIiIiIiIiIiIimcMABhERERERERERERERyRwGMIiIiIiIiIiIiIiISOYwgEFERERERERERERERDJHsaILQERERERERERERETfFgHkKroIVAlwBAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyhwEMIiIiIiIiIiIiIiKSOQxgEBERERERERERERGRzFGs6AIQERERERERERER0belQFDRJaDKgCMwiIiIiIiIiIiIiIhI5jCAQUREREREREREREREMocBDCIiIiIiIiIiIiIikjkMYBARERERERERERERkcxhAIOIiIiIiIiIiIiIiGSOYkUXgIiIiIiIiIiIiIi+LQJBRZeAKgOOwCAiIiIiIiIiIiIiIpnDAAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmyAkEnG2MiIiIiIiIiIiIiL6eI34FFV2ESqdP/W9vPAIX8Sa8DPav6CLIlGpO9fD8aVhFF0OmWNWwRXTYw4ouhkwxs3VmnRRjZuuMZ+HhFV0MmWJtY8M6KcbaxgaxofcruhgyxcTOBY+exlZ0MWSKYw0TXovfY1XDlvVRjFUNW55LijGxc8H14IyKLoZMaeakjnNKdhVdDJnSKTeUfZNi2F+TZG1jg9Q7Fyu6GDJFy7M97jRrUNHFkCme133Zdt5jbWNT0UUg+r/FAAYRERERERERERERfVWcF4g+xrc35oSIiIiIiIiIiIiIiGQeAxhERERERERERERERCRzGMAgIiIiIiIiIiIiIiKZwwAGERERERERERERERHJHAYwiIiIiIiIiIiIiIhI5ihWdAGIiIiIiIiIiIiI6NtSIJCr6CJQJcARGEREREREREREREREJHMYwCAiIiIiIiIiIiIiIpnDAAYREREREREREREREckcBjCIiIiIiIiIiIiIiEjmMIBBREREREREREREREQyR7GiC0BERERERERERERE3xaBoKJLQJUBR2AQEREREREREREREZHMYQCDiIiIiIiIiIiIiIhkDgMYREREREREREREREQkcxjAICIiIiIiIiIiIiIimcMABhERERERERERERERyRzFii4AEREREREREREREX1bBIKKLgFVBhyBQUREREREREREREREMocBDCIiIiIiIiIiIiIikjkMYBARERERERERERERkcxhAIOIiIiIiIiIiIiIiGQOAxhERERERERERERERCRzFCu6AERERERERERERET0bSkQVHQJqDLgCAwiIiIiIiIiIiIiIpI5DGAQEREREREREREREZHMYQCDiIiIiIiIiIiIiIhkDgMYREREREREREREREQkcxjAICIiIiIiIiIiIiIimaNY0QUgIiIiIiIiIiIiom+LQCBX0UWgSoAjMIiIiIiIiIiIiIiISOYwgEFERERERERERERERDKHAQwiIiIiIiIiIiIiIpI5XAODytytwAfYsu8oIqJioK+rjR7tW2NAtw6Qk/vwvHZhz55jzLT5OLhhGYwNqol9dsXnFvafOIcXMXHQ0lCHp4sTxg7pC31dnfI6lDJz99497Ny9By8iI6Grq4sunTqhd88epdbJ1WvXsf/QIcTHJ8DQwAD9+vRGm9atxPa54eWNI8eOISo6BhoaGnBzrY1Rw4dBT0+vvA/pi925F4i/9+zH88go6Onqolun9ujTo2updXLluhf2HjqKuIREGBlUQ//ePdCuVQuxffoNH4OkV8kSaY/v3QEdHe0yP46yxDqRdPfePezatQuRhW2nc+fO6NWzZ6l1cu3aNRw4eBDx8fEwNDREnz590KZ1a7F9wsLCsG37djx58gTq6upo07o1Bg0aBCUlpfI+pC/GOpF0OyAI2/ccKGw7OujeqT36du9Sap1cvu6NPYePIS4hAUYGBhjQqzvat2oOAIhPSMSAMT+UmLZ9q+aYNqnkz2VFZmYmdu/YDL+bN5CVlQlH59oYOWY8TM0sSk335nUydmzbiHt3b6MgPx/unvUwYsx46OtXEe0jEAhw6vhh/HvxDJJeJqKagRE6de2Bjp17lPdhfTZei8WxPiTxXCJdVmYGju9Zi3t+l5GdlYGaju7oO+IXGJlalZruRfhjnDqwAc+fBkNQIICFjQN6Dp4ISxsH0T75+Xk4e2gzfK6eQVpqCixtHNB72BRY29Yq56P6cqqmhmgaeBZ3ev2A5Bu3St3XpF8n1JgxDurW5sh8HoOny7cgZs9JsX10PJzhsPRX6Hg4I+9tOqJ3H0fYgvUQ5OaW41GUDfZNJLFOJPndD8HGI2cRHh2PKjpa6NOmCQZ3bFFineTk5mHv+Ss453UbCclvYKCviw4NPTC8a2soKb57lPXoWSTW7D+Fx88ioaGmii5N6+K7Xh3E9pFV2p51YTrme6haWSPvdTISTxxDwqH9JSdQUIBRv4Go2qkLlKpURXZ0FOL27cbrq5cBAMpGRnA5dKLE5EkXzuL5kkVlfRhlqrzaTpGMjAyM/+EHDBo4EG3atCmvwyCiMib7Z/T/U7GxsQgICECnTp0+O4/jx49jxowZCA0NLcOSfZmHoU/x6x+r0KpRPYwe0Av3Q8Lw155DyC/Ix5CeXUpN++xFNKYuWoX8/HyJz/7z9sO8VRvRrW0LfDeoN169TsG2A8cw8bcl2L58PlSUlcvrkL7Y45AQzJ2/EM2aNMbQwYMR/OgRtu/YiYL8fPTr20dqGq+bN7F0xUp079oFnh4e8PHzw4rVa6CkpITmzZoCAK5dv4HFy5ajY4f2GD50CJJfv8HuPXvx68xZ2LB2DZRluE4ehYRh1sLFaN64IYYPHoCHjx5jy849yM/Px4A+PaWmuXHTF3+sXIueXTqhjocrbvrdwrI166GkpISWTRsDAFJS3iLpVTK+HzEUzo4OYuk1NTXK/bi+BOtE0uOQEMybNw9NmzTB0CFDEBwcjL///hsF+fno27ev1DTe3t5Ytnw5unXrJmw7vr5YtWpVYdtpBgCIi4vDzFmz4GBvjxkzZiAqKgq7du1Camoqfvzxx695iJ+MdSLpUUgYZi5cjBaNG2LkoH548DgEm3fuRX5+Pgb2lv4w/bqPHxatWodeXTqijruw7SxduwHKSkpo2bQR9PX1sGGZ5M3dyfMXcdXbBx3btCzvwyoTq5f9jtDQRxg24nuoqavj8IFdmDPjJ6zbuAOaWlpS0+Tn52Phb9ORkZGOsT9MQX5+Hvbs3Ir5s6di5botUCx8GLDr7804d/oY+g8agZp29rh32x9b/1oHRQVFtO1Q+vW+IvBaLI71IYnnkpJtWz0DEWEP0GvoZKiqa+Dsoc1YOXcM5q09Bg1N6S9CJMZFYsWc0bC0ccDQ8b9BTk4O/57ajWWzRmLOygOi4MeRHSvhffkkeg6eiCoGJrh0Zi9WzxuLOSsPwMC49GBrRVI1M0Ldc9uhpPvhF0GMerSF6+4ViPhzN17+4wWjbq3h+vdSFGTnIO7weQCAWnUz1Lu4A6/9AnFvwGRo2tvAbuEUKOnr4uEPv5X34XwR9k0ksU4kPXjyHJNXbEGb+m4Y27sTAkOfYd2B08jPz8fwrtIfIq/YfQznb97B6O5t4WhtgUcRUdh6/CLikpIx97uBAIDoxCSMX7wRLjWssHjicDyPScDGI+eQkpaBmaP6fc1D/GQajk6osWQFXl/9DzHbt0KzlgvMxv4AOQUFxO/fIzWNyfBRMB40FLG7/kbag/vQa9IMNvN+x9P8fLy5cQ25r17h8bjREukMevSCXovWSDp3prwP64uUV9spkpqaigULFiAhIeFrHA4RlSEGMCrItGnTYGpq+kUBDFn096HjqFndEnMmjQUA1Hd3QV5ePvYcO4O+ndpBRUXyxjU3Nw9Hz1/C9oPHoFzCmyN7jp1BA/famDp2hGibhakxvp8+Hz53AtGiYd3yOaAysGfffthYW+PXX34GANTx9EBeXj4OHj6C7t26QkVFRSLNzl170KRxI4z9bgwAwNPDHampqdi1d6/oIcHBw0dQ19MTkya8e3PP3NQUk37+Bf63bqNJ40Zf4eg+z679B1HDujpm/DwJAFDXww15+fnYf+Q4enbtJLVOtu/Zj2aNGmD8GOF3oI67G1JT07Bz7wHRw/qnEREAgMYN6sHE2OgrHU3ZYJ1I2rt3L2ysrTF16lQAgKenJ/Ly83Hw0CF069athLazC00aN8b3330HAPDw8EBaair27Nkj6sAeOXoUampqmDt3LpSUlFC3Th2oqKjgr7/+Qr9+/WBgYPD1DvITsU4k7ThwGDWsq2PmTxMBFLadvHzsO3ICvbp0LKXt1McPo4cL07i74m1aGv7edxAtmzaCspISHO1txdKEPg3HVW8fjB4yELWKBQNlUcjjYNy+5YPZ85fAw7MeAMDR2QVjRw7AhXOn0Kf/YKnpfLyu4Vn4E6z7awfMLawAANWta2DS+JG46XUVzVq0QWJCPM6cPIIxYyeifaduAACX2u5ISkpEwL3bMhnA4LVYHOtDEs8l0oWHBuH+nRv4cfafqOUu7FvUdHDDzHGdce3iYXTqLfmgDACunDsAZRVV/DjrT6ioqgEA7GvVxYyxHXHl/EEMHDMdyUnxuPbPUfQfNRXN2wsfTDm6NsCcH7rh4omdGDp+7tc5yE8hJwezId3hsHQa8OHB5QAAu4U/Ie7oRTz+ZTEAIOmSN5T0dGA3b5IogGEzdQzyUtNxp+d4CHJz8fLiDeRnZsF57Rw8XbIJWVFx5XVEX4x9E0msE0mbj12AnZUZFo4fAgBoWNsBefn52HHqEvq3bwbVYgHuN6npOHHVFz/274KhnYUj/eo62wEA1h88gx/7d4WetiZ2nbkMDVUVrPx5NJQUFdHY1QmqKspYtvMoRnZrA6Oq+l/3QD+ByYjRyHgShohFCwAAb2/5QU5REcaDhyHh6GEIcrIl0lTt2BnJl/9F3K6/AQCp9+5A3c4OBj17482NaxDk5iL9UbBYGnVbO+i1aI2YrZuQ9uB++R/YFyivtgMAfn5++GvTJmRmZn6dg6GPJhBUdAmoMuAaGFRmcnJzEfAwBE3reYhtb9GgDjIys3D/cZjUdL73grDj8AkM6dUV44ZIviVRUFAATxcndG3bXGy7pakxACAmPrFsDqAc5OTm4v79B2jUoL7Y9iaNGyIjMxMPHz2SSBOfkIDomBg0atBAPE2jRoiNjUNMTCwKCgrg5uaKDu3bie1jbm4GAIiNk92bnJzcXAQ9CEbj+uJBp6YNGyAjMxMPHoVIpIlPSER0TCwaNagnnqZRA8TExSM6NhYAEP7sOdTV1GBsZFh+B1AOWCeShG3nPho2bCi2vXHjxsjMzERwcLBEmoSEBMTExKCBlDSxsbGIiYkBANy9exd169QRG2rfuHFjFBQU4O69e+VwNGWDdSKpqO00KdZ2mjWqX2rbiYqJQ5P64m2nWcOitiN5/hQIBFi7aTsszc3Qu2vlePEg8N5tqKqqwtXNU7RNR0cXTs61ce+OX4npAu7dhqmZuSh4AQDmFlYwM7fAvTv+AAA/Hy8oKSmjVdsOYml/mf4bps1aULYHUgZ4LRbH+pDEc0nJggN8oaKqBqfa7/7vtXT0UdPRAw/vepeYzsisOtp2GyoKXgCAiqoa9KoY4mV8FAAg5P4tFOTnwa3eu5EoSkrKcPFsiof3Ss67Imm72MF5w3xE7z2JwOG/fnB/NUtTaNpVR/ypS2Lb447/A42aVlCvYQkAqNamMRIvXBebLir+2EXIKSigWtvGZXsQZYh9E0msE0k5uXm4+/gJWniKTw3Xqm5tpGdlIzD0mUSa9Mws9GrVEE3dncW2WxkL72liEpMACKelauTqKDZdVKu6rigQCOB7X/LcLSvklJSg5eqON17Xxba/vn4VChoa0HJxkZpOXlkF+ekZYtvyUt5CUbvkabUtJv+CrOcRSDhy8MsLXo7Ks+2kpaVh4e+/o1atWvh94cLyOwgiKjdfLYDxxx9/oHWxOehSU1Ph4uKCffv2oU2bNvj999/h4eGB8ePHf1SeQ4YMwZw5c9CnTx94enri9OnTAIBjx46hQ4cOcHFxQYcOHbBr1y4UFBQAAKKjo2FnZ4d//vkHffr0gbOzM1q2bIlDhw6J5X3y5El07doVLi4uaNmyJTZu3Cia2mjIkCGYPHmy2P63b9+GnZ0dXrx4gYKCAmzevBnt2rWDs7Mz3N3dMXr0aERGRorS37p1CydOnEDLlsIOe05ODpYvX44mTZrAzc0Nffv2hbe3eMf90qVL6NKlC2rVqoWBAwcitvABpayITUhEbl4eLEzE3/I2LexkREq5kQMAhxrVcXTTKgzr3RUKCgoSn8vLy+PHEQPRpK54YMTr1l0AQHUL07IofrmIj4tHbl4eTE3Fy2hibAIAiI6OkUgTFSW8qTM1NRFPYyIM2ETFRENeXh7fjx6FhsUePvj4Ch9KWVrK7pD7uPgE5OblwazY8ZkWfm+iYyTr5EVUNADAzKRYnRSOKIiKFraFpxHPoaWlifmLl6NrvyHo1GcQFi5diVfJr8v8OMoS60RSfFwc8qS2HWE7kFYnRedYs2JpjE2K2ls0srOzkZiYKJGvro4O1NXVER0dXWbHUNZYJ5JEbadYOzAtagcxktfJF4XnXbPCIPjHpLnq5YPHYU8wYfQIqdcpWRQd9QKGRiYS5TUyMUVMTFSp6UxMzCW2GxmbIiZamC4i4imMTUwR/PA+fp74HXp3bY3vRvTHvxdkc1oCXovFsT4k8VxSsvjoCFQ1NIN8sfIaGJsjPvZFiemat++Ldt2HiW1LjItETGQ4TMxtAABx0RFQVdOAjl5Vsf2qGZvjTfJLZGWKP6STBZmRcbhm3waPpy5BfkbWB/fXtBcea/qT52LbM8KFdadpWx3yqipQtzJD+pMIsX1ykl4jNyUVmrbVy6bw5YB9E0msE0kxiUnIzcuHhbH4CBFzI+Galy/iJF9INDWogukj+sLKRPwlrOt370NRQQEWxgbIyslBXFKyRL562prQUFOVmq+sUDExgbyyMrKiIsW2Zxf+P6qYW0pNl3D0EKq0aw/tuvUhr64O/dZtoVO3Hl79e1Hq/notW0PTyRlR69cAhc/EZFV5tR0AUFFRweZNm/DLzz9DR0f211AlIklfLYDRs2dPREVF4c6dO6Jt58+fh7a2NpSVlREZGYnExEScPHkSU6ZM+eh8jxw5gqFDh2L//v1o0qQJDh06hGXLlmHChAk4d+7A4HXIAAEAAElEQVQcJk+ejK1bt2LFihVi6RYvXoyxY8fiwoULaN68OebNmye6Odu5cyfmzJmDfv364fTp05g0aRK2b9+OJUuWiI7l6tWrSEtLE+V3+vRpuLu7w9LSErt378b27dsxffp0/PPPP9iwYQOeP38uSv/nn3/Czc0NHTp0wNGjRwEAM2bMwM2bN7FixQqcOHECHTp0wNixY3Ht2jUAwL179/Djjz+iXbt2OH36NHr06IEtW7Z8+n9EOUpLFw7FU1dTE9uurqYKAEjPkD5Ur1oVfWhraX7S34qJT8CGXQdRs7oFGrjX/ozSfh3pGekAAHV1dbHt6urCOsrIkLwxSy98o6J4GjW1ktMAwjcbt27/GzbW1qjr6Sl1H1nw7viKf0+Ev0v7nqQXHrNGCWkyCtOEP4tA0qtk1Kxhg9/nzsC4UcMQ9PARpsyYg8ysD99kVhTWiaSi45NsO8LfpbadktK813bS06W3SUDYxkpqX7KAdSKpqO2U1A6ktp2iNCVeqySP9+CJU3B2sINrLacvL/RXkpGeDjWp/6fqyCzl/zQjo+R0Rd+FtykpSH6VhNXLF6F1246Yu3AZXN088df6VTIZxOC1WBzrQxLPJSXLzEiDqprkmlmqahrIykyTkkK6nOws7Fg3F0pKymjZsX9h3qnS81YVfs8+Jf+vJfd1CrJiPn7udEUd4T1O3lvxY8lLFbZDRW1NKOloSd2naD9F7U+7T/qa2DeRxDqRlFYY7NMoPD8WUVcVTgeUnvlx9yRXbwfhrNdt9GrVCNoa6qJ8NYvlCwAaqiofnW9FUNAQtuv8Yv9v+YWBWwUN6WsVJhw+iLQH92G7fDXcL1yG9Zz5SPrnAhIO7pO6v1H/QUi9H4TUwIAyLH35KK+2AwBKSkowMzMr2wIT0Vf11dbAsLe3h5OTE06fPg3PwhuYEydOoGvXd2/djx8/Hubmkm/9lcbBwQFduryba3njxo0YN26caG0Jc3NzpKWlYf78+Zg0aZJov+HDh6NVK+FcilOmTMG+ffsQFBQEMzMzbN26FYMHD8agQYMAAFZWVnjz5g2WL1+OiRMnol27dliwYAH+++8/dO/eHTk5Ofjnn39E8/RZWFhg6dKlaNGiBQDA1NQU7du3x8WLwqi4rq4ulJSUoKqqCn19fbx48QJnz57FyZMn4eAgnAt3xIgRCAkJwfbt29G8eXPs3bsX7u7umDBhAgCgevXqCAsLw+7duz+pvsqT4AMT18nJf+REsR/wIjoWUxYsg4K8PH6f+iPk5WV3JrSCgtLrRF5Osk4KBKW/GSEvJ3m8kVFRmDlnLhQUFDB75nSZrhPBB49Psk4EH6jHou/WTz+Og4K8AuxtawAAXJwcYWVhgUnTZuHSlWvo2rH9Z5a6fLFOJAk+8IaQ9DopPY2cvPwHz1PS8pUVrBNJBR8qu5TrzqeeYx8+DsWT8AgsnPnhqUIqSkFBgcT/Y2l1I1fK/2lp162i+szLzcXbtyn4deZ8NGgkXPvApbY7kl4m4tCB3TK3BgavxeJYH5J4LhESnkvEj6u045ST8v8uTVZmOjYu+QkRT4MxduoyVDEQvhn7wXuHj8xflsl94HsvKCgAPriP7E4Ozr6JJNaJpA+eYz+i7FduB2H2ht1wtbXGxAFdAXzMOUR26wQfOr9J+U7IKSnB/s+/oFSlKp6vWIqsyBfQdK4F4yHDUZCZgag/14jtr+FUCxp29ngqw9ed95VX2yGi/w9fdRHvXr16Yc2aNZg9ezbi4uIQEBCARYsWISgoCIAwUPCpLC3fDa1LTk5GfHw8Vq1ahbVr14q2FxQUIDs7G9HR0aJFf2xsbESfa2kJ33rJzc1FcnIykpKS4OEhPl1R3bp1kZubi2fPnqF27dpo3749zpw5g+7du+P69evIyclBhw7CuaBbtmyJoKAgrF27FhEREYiIiMDTp09haCh9DvpHhXMNDxw4UGx7bm4utLW1AQBhYWFo1Eh84UM3NzeZCmAUvbWWUexNh6I3wTWlvC3yqe49fIxZy9ZBTVUF6xbMgKmMz+uvoSE85uILRYneFJDyZoWGuobUNEVvDxTlWSTo/gMsWPQH1NRUsXTxItEQS1mloV5UJ+Lfk/QSju/9bRkS9ZgplqeTvZ1EWmdHe2hoqCM8ouRpDioa60SSRmHbKH58GaW1nQ+k0VBXF72dI23xtoyMDKn5ygrWiaSS28G745NIU/QWV/H2VphH8fZ23ccXWpoaqO/pVjaFLgeHD+zGof27xLY1aNQMKW+SJfbNzEiHukbJb/NqaGggU8q0LZkZGVAvvD6pqatDTk4OHnXEpwpy86iLgHu38eZ1MnT1ZGfRTF6LxbE+JPFcInT28BacPbxZbJt7g9ZIffNKYt+sjDSoqX94ZEByUjzWL5qI+NgX+O7nJXCt20L0mZq6JrKyJM83WZnpos8ru7yUVACAopZ4uyoaVZGXkiYaeVF8n6L9ivKQReybSGKdSNJUF46QkDxfFo6gKDb6rbh9F65i7b5T8HCsgRVTRkNFWbgGSNGIjnQpo8rTM7M+mG9Fyk8XtnuFYtcXhcLrbdHn79Nr1gLqNW0R+tNEpN69DQBICwpAfnoaLKdMxcuzp5EV8W49Eb3mLZD39i1S/HzK6zDKVHm1HSL6//BVAxhdunTB0qVLcfXqVYSFhcHFxQU2NjaiAIaqquTQvw95P03ROhczZsyQWPgHAIyNjZGYKJwHUVlZWeJzgUBQYhS/KG/FwsWhevbsiWHDhiEpKQlnzpxB69atoakp7Ihu2bIFGzZsQI8ePdCgQQMMHz4cly9fxrlz56TmXfQ39+3bJzoBFyl6W01OTk5UhiLvL94lC0yNDKAgL4+YePFh1dGFv1uamUhL9tEuefli0Z9bYGlqghWzf0a1KrLzcKQkJsbGkJeXl1i4MrZwPRALKSOOzMxMRfvUeC/QVpTH+6OUrl67jhWr18DczAy/z5+HqlWrlPkxlDUTYyPIy8sjpnidxMUDACzMJYd2mhfOuR0bF4+aNtbvpRHmYWluhrT0dHj5+MHetiaqvzfPdkFBAfJy86Cro13mx1JWWCeSjAvbTlyxtX6K1v6R3naE9RQXGyvedorSWFhATU0NVapUkVhD6M2bN8jMzJSar6xgnUgyNTIsbDvxYtuLfreU0nYsCttOTFw8atpUl0xjJj6Hrt/te2hUr67o+i+L2rbvDM+64osr+/t6I/DebRQUFIi9+R4XFwsz85LXIjAxNUfEsycS2+PiYlDT1h4AYGxiCoFAgLy8XLH+VF5+HgBAufBlEVnBa7E41ocknkuEmrbtCRfPJmLbAm9dw6NAX4lzSWJ8FIzNSl+bIfrFE6xdMB65OdmYPHcjbJ3EXxAzNLFEVkYaUlOSoaXzrl+fGBeFKtWMoazy6feGsiYtTLiuhbqNJd4GPhZt17ARvoSXFhKO/PQMZEbHQ91GfM575Wr6UNLWRFpI+Ncr8Cdi30QS60SSmUFVKMjLIyohSWx70e/F17koIhAIsGL3cRz69wbaNXDHvLGDxBbrVldVgYGeDqLjxfNNTklFelY2qpeQryzIjo2BIC8PKqbi1xfVwt+zXjyXSKNsKFxjKe3BfbHtqUGBAAA1q+piAQzdBo3wxvsGBIVrucq68mo7JPtkeKAhyZCvOp5KW1sbbdq0waVLl/DPP/+gZ8+eZZp/lSpVoK+vj6ioKFhaWop+goODsWbNmo/Ko2rVqqhatSru3r0rtv3OnTtQUlISnQA9PT1hamqKU6dO4dq1a2LHsmnTJvzwww+YN28e+vXrB1dXVzx//rzE4EjNmjUBAC9fvhQr9/Hjx3H8+HEAwim4AgLE5y18+PDhRx3T16KirIzajna47ndH7Fiv+d6Bpro6HGtal5K6dL53g/D72s1wtquJjYtmV4rgBSAMlNVydsZNHx+xOvG+6QMNDQ3Y2daUSGNqYgIjQ0N43bwptt37po/oMwC4dfsOlq1cBUcHe6xcvrRSPCAAhHXi4uwILx9/sTq54eMHDQ112NeUVifGMDY0wI2bvmLbvXz8YGpiDCNDAygrKeHPTdtw4MhxsX18/G8jOycHrrWcy+eAygDrRFLJbecmNDQ0YGtrK5HGxMQERkZG8Pb2Ftt+8+ZNmJqYiEbBubu749atW8jJzRXLV15eHrVry+6aOqwTScrKyqjt5AAv3+Jtx1/YdgqnTntfUdu57iPedm74+MGssO0UeZuaiujYODg7SI5kkiX6VaqiRk07sR9Xd09kZmYg4N5t0X4pKW/w6GEQXN1KXovA1d0T0VGRiIp8LtoWFfkc0VEv4OouTOfhKRx54X39ilja2/4+sKpuLRqpISt4LRbH+pDEc4mQrr4BrGo4if041q6PrMx0BAe+e4M3NSUZTx7dg2PtBiXmlZwUj9XzxkJOTg6//rFDIngBAI61heeSu77/ibbl5ubg/l0vOLqWnHdlkhEeiYxnUTDu2U5su1GPtkgLi0DmC+GitEn/3YRBx+aQV373gppRz3YoyMtD0lW/r1rmT8G+iSTWiSQVZSW42dvg6u37YnVy5VYQNNXV4FwseFdkw6GzOPTvDQzq0By//zBULHhRpJ6LPbwCgpGTm/cu39tBUJCXRx0nyeuZrBDk5CD1fiD0mjYT267brDnyUlOR/viRRJqsSOHoeS0X8f9rTWcXAEB23LsH/wpa2lA1t5AIdsiy8mw7RFT5ffVXgHr16oVx48ZBIBCI1qkoK3JychgzZgxWr14NExMTNG3aFKGhoZg3bx5atWolddSFNKNGjcLq1athbm6ORo0a4f79+1i/fj369esnmm5KTk4O3bt3x4YNG6Cvr4/69d9No2BsbIybN2+iZcuWkJeXx6lTp/Dvv/+iatWqon00NDQQExOD+Ph41KxZEy1atMBvv/2GuXPnombNmrh48SI2b96MxYsXAwBGjhyJPn36YOnSpejbty8ePHiAvXv3lmHtlY1hfbph8rylmLNiPTq1aoqHIU9w4NR5jB3cF6oqKkjPyEREVAxMjQyg95Fvf2fn5GDJxu1QU1PFsF5dEREVI/a5QRV9GFSV3YDGwP59MX3WHCxavBTt2rbGo8chOHr8OEYOHwZVVVWkZ2QgMjISxsbG0NXRAQAMGtAfK9eshbaWFurXqwdff3/c8PLGjGnC+StzcnKwet2fUFdTQ/9+fREZGSn2N6tWrYpq733fZM3gvr0xdc58LFi6Eu1bt8SjkFAcPn4Ko4cNhqqqCtIzMvAiMhomxoaiOhncvw+Wr90AbS0tNKhXBz7+t3DN2wezf/0JgLDD0793D+zafwi6ujqo5+mOiOeR2H3gMBrWqwO32rUq8pA/iHUiqX///pg5axb+WLwYbdu0wePHj3Hs2DGMGD68xLYzcMAArFq9Glra2sK24+eHG15emD5tmijfPr174/r165g7Zw569OiBmJgY7Ny1Cx06dICBgUFJxZEJrBNJg/v2wi9zF2L+0lXo0KYlgh+H4tCJ0xgzdFDhdUey7Qzt3xtL126EttZWNKrriZv+t3HN2xdzpk4Wy/vZC+G51cqi8i365+RcG861XLFm+SIMHfk9tLS0cWj/TmhoaKJ9x26i/aIinyM3NxfWNsKb/MZNW+DY4X1YOHc6hgwfAwDYs3MrLK2s0aiJcOoXZxdX1KnbEH9v3YisrCxYWFXHtcv/IuTRQ8yY8/vXP9iPwGuxONaHJJ5LpLN18oCtsye2r5mFXkMmQVNLF2cObYKauhaate8j2i82Khx5ubmwsBaO1Dq4bRlSU5Ix6PtZyMpIx7PQdw/RVNU1YGJugyoGJmjQogsO71iJ3JxsGJpY4tLpvchMT0W77sO++rGWBUUtDWg61kBGeCRykl4DAJ4s2oDa25cgN/kNEs5cgWHXVjDp2xH3Bk4WpQtfsQ0m/TqhztltiFizAxq2VrBb+BOith1GVlRcCX9NNrBvIol1ImlU97YYv3gjpq/bia7N6uH+kwjsOXcFE/p1gaqKMtIyshAREw8zw6rQ09ZE6PNo7Dp7GY7WFmhdzw0Pn4pPfVvd1Aia6qoY1rkV/vW5h4nLNmFQh+Z4Ef8SGw+fRY+WDWAkw88IACBu907YrloH6/mLkHT+DDSdXGDUfxBitmxEQXY25NXVoWZVHdkxMchLeYM3N72QFvwQ1Wf/htgd25AV+QIaDk4wHjIcr71vICPk3SgvNWvhaITMFxEVdXifpbzaDhFVfnKCD618VMYEAgFatGgBd3d3rFq1CgBw/PhxzJgxA6GhoZ+U15AhQ2BqaoolS5aIbd+3bx/27NmD6OhoVK1aFZ07d8bEiROhrKyM6OhotGrVCrt370a9evVEaezs7LB48WLRSIq9e/diz549iImJgZGREfr27YtRo0aJFhwHhMPSWrVqhe+++w5TpkwRbQ8ODsaCBQsQEhICDQ0N1K5dG82aNcO8efNw5coVmJiY4Nq1a5g2bRoEAgF8fX2FN32rV+P8+fNISUmBhYUFRo4ciV69eony9fX1xfLly/HkyRPUrFkTHTp0wIoVKz653op7Gez/RemLu+53B38fOoHImDhUraKHnu1bY0A34fog9x4+xsS5izFzwhh0bNlEIu35K174Y/1WHNm0EsYG1QAAdx88wqTflkjsW2RE3+4Y1b/sRvNUc6qH50/Dyiw/ALjp44s9+/YjOjoaVapUQZfOndC7Zw8Awnmif50xEz9PnoS2bVqL0py7cAFHj5/Ay5dJMDYyQr++vdG6ZUsAQGBQEKbNnF3i3xs8cACGDBpY4uefyqqGLaLDynbEj7evP3buP4jo6FhUraKPrp06oG8P4YJsgQ8e4ueZv2HqpB/QvnVLUZozF/7FkROnkJj0CsZGhhjYuwfatGwu+rygoABnL/6LU+cuIjY+AdpammjVvCmGDegrWv+mrJjZOrNOijGzdcaz8LKd5uCmjw/27t0rdj7vVXievn//PqZNn46fpkxBmzZtRGnOnz+PY8eP4+XLlzAyMkK/vn3RqlUrsXwfPnyIbdu349mzZ9DW1karli0xZMiQMp/aw9rGhnVSjLWNDWJDy/ZtMC9ff+zcfxhRMcK2071je/TtIVxIOvBBMKbMmodpk8ajfat3c6+fvngJh0+cRmLSK5gYGWBg7x5o20L8Lbir3j5YsGw1dm1cA4ti08GUJRM7Fzx6GvvhHT9RWmoqdmzbCH9fbwgEAtg7OmPkmPEwNXs3nH729MlITIjHlh0HRduSXiZi2+b1CAq8A0UFRbi6e2LEmB+gr//ubfqcnBwc2r8L169ewtuUNzC3sELfAUNRr0HjMim7Yw0TXovfY1XDlvVRjFUNW55LijGxc8H1YMk1Jb5UetpbHNmxEoG3rqJAIEAN+9roO+IXGJlaifZZMWc0XiXGYvHm88jLzcWEgQ1RkJ8nNT9bJw/8snAbAOGIi+N71uGW1wVkZ2XA0sYBvYZOgbVt2bxk0cxJHeeUymfki37TumhweQ98Ww1B8o1bYtuCRk1H9O4Ton0txvSD9ZSRUDU3RsazKIQv24KYfafE8tNr5AGHpb9Cu7YDcpJeI2bfKYTNWwdBnvR6/FydckPZNymG/TVJ1jY2SL1zsUzzvHo7CJuPXcCLuEQY6OmiT5vGGNxJeA258+gJxi5aj9++G4guzeph09Hz2HbinxLz2jRrAjwdhS9fBISEY+3+UwiLjIGupgY6Nq6Dsb07QlFRocT0n0PLsz3uNCvb0WG6TZrBZMRoqJpbIDfpJRJPHEPC4QPCv+fqBru1GxGxeCFeXTwPAJBXV4fpmLHQa9ocilrayI6Lxat/LiDh8AGxc4Vei1awmfc7Hg7pLxq5UR48r/tWmrZTJCEhAcNHjJDIoyxYvzeNFX28ndcqugSVz/DmFV2Cr++rBzDS09PRuHFjbNiwQeo6FfT1lXUAo7IrjwBGZVceAYzKrjwCGJVdeQQwKrvyuCGu7MojgFHZlVcAozIrjwBGZVYeAYzKrjwCGJVdeQUwKrPyDGBUVuURwKjs2F+TVB4BjMquPAIYlV15BDAqMwYwPg8DGJ/uWwxgfLUppFJSUuDn54cLFy7A1NQUDRrwxE9ERERERERERERERNJ9tQBGfn4+Zs2aBX19faxZswZycnIl7rt161Zs3Lix1PxmzpyJPn36lLoPEREREREREREREcmerzsvEFVWXy2Aoa+vjzt37nzUvn379kXbtm1L3adKlSqlfk5ERERERERERERERJXXVwtgfAodHR3o6OhUdDGIiIiIiIiIiIiIiKiCyFd0AYiIiIiIiIiIiIiIiIpjAIOIiIiIiIiIiIiIiGQOAxhERERERERERERERCRzZHINDCIiIiIiIiIiIiL6/yUQVHQJqDLgCAwiIiIiIiIiIiIiIpI5DGAQEREREREREREREZHMYQCDiIiIiIiIiP7H3l3GRZX9YQB/GLq7ERFUDCzE7sDu7tY1Vzes1S11ddfd1V117Q7s7rULVMQuDFQ6VEBgiKn/iwF0mAEMcC7/fb6fDy+4c8/l3Mu55565vxNEREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDh62s4AEREREREREREREf23yBXazgGVBByBQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERIKjp+0MEBEREREREREREdF/i0Kh7RxQScARGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4etrOABERERERERERERH9t8jl2s4BlQQcgUFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCo6ftDBARERERERERERHRf4tCoe0cUEnAERhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkODoKBdd7JyIiIiIiIiIiIqLPZ9kxbeeg5BnTRts5+Pz0tJ0B0r7EeWO1nQVBsZ6+FBm7F2o7G4Ji1P0rJP8xUdvZEBTLb/9G2JAO2s6GoHiuP4TXty9oOxuCYlO1EcTntmk7G4Ji0qQPYh/e0HY2BMWpQg3cfhyv7WwIStVyDogY213b2RCMUkt348WoLtrOhqCUXrkPKdf4jfdd5n5tkPHvOm1nQ1CMWg1F2NOn2s6GoHh6eeGwvre2syEo7SWheHP9hLazISgWvv7YdUWu7WwISo86IjwNC9N2NgTFy9MTyddPajsbgmHp21LbWSD6v8UppIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcLuJNRERERERERERERJ+VXKHtHFBJwBEYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDh62s4AEREREREREREREf23KBQKbWehBNLRdgY+O47AICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsHR03YGiIiIiIiIiIiIiOi/RaHQdg6oJOAIDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhw9LSdASIiIiIiIiIiIiL6b5HLtZ0DKgk4AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER0/bGSAiIiIiIiIiIiKi/xaFQts5oJKAIzCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgEhwEMIiIiIiIiIiIiIiISnBK9iHdSUhImTZqEkJAQeHt7Y+fOndi3bx8aN24MW1vb9zpG8+bN0bVrV0yYMAF79uzB9OnTERoaWmi6D9n3Y6xYsQIXL17Epk2bVLbPnDkTO3fuVNnm6uqK06dPF0s+PoZemYowbtIJunbOkKe9QWbIeWRePZnv/iJre1iO/lltuywhGm9Wz8n+TQeGtVvA0LchRObWkCe9Qub1c8gMOVdMZ1G0Ah9HYMm/V/E0PhE2ZsboU7cyBjWsBh0dnULTSmVyDFqxF8b6elgzsrPKZ/ci47HgaBDuRSXAzNAAnXy9MaaFH/T1dIvrVIqMXmlvGDbqAF1bJyjEKci8cQFZ187ku7/Iyg7mI75X2y57GYPU9b9Ct1RZmPWekG/6jEtHkRl0rEjyXlyMK9eAdfeBMHB1h+xNEt6cOozkY3s17mvWsAUcRnyV77HiVy1A6iVlvaDv7AabXkNhXKEKFDIZMkLv4tW21ZAmxBXLeRSlK7fuYcXWvQiLiIaNpQW6t2mGfh1bvde9E/osHMOn/4Kdi36Bs4Ndvvv9tX4bth8+iaCdq4sy68Um6N4TLNl3CmHRCbCxMEXvZrUx0L9+vtckSyLFphOBOBR0C7GJyXC0tkDbOlUxrE1D6Ou9bQacufEAqw6fw/O4V7CzMEP7ulUxrG0jlX2EKvjGLazavB3PwyNhbWWJru1aoXeXDgWWk5PnL2HTjr2IjouDk4M9+nfvjDbNm6jsc/TUWWzfdwhRMXGwtbFGm+ZNMKBnF+jpCr+OBYD0dDG2rF+Oy5fOISMjHZUqV8PgkRPg6uZeYLqkxNfYsHoJbl6/AplMBl+/uhg8Yjysbd7eR69eJmDTuqW4GXIFcpkM5StURt9Bo+BVrkJxn9ZHM6xYDVad+kHPuRTkb5KQev4YUk4e0LivSd1msB00Pt9jvdqwGOIrZ1W26RgawWnGAiQf2Qnx5fyfZ0JiVKk6rLr0h76LO+RvkpBy5gjenNivcV/Tes1hN/TLfI/1ct3fSAs6k7uvRavO0LN3guz1S6ScPYKU04eL5RyK0uXbD7F05yE8jYyFraU5evo3woB2zQqsXzcfOY3DF4IR9zoJDjZWaFu/JoZ0aqlSd94PC8dfAfvxICwcpsZG6Ni4NkZ1b1si6tfAB8+w5NA5PI15CRtzU/Rp7ItBzWvne00yJVKsOHoJR67dQ2KqGOVdHTC6XUM0qOiZu49crsCmM1ex69JNxCWloLS9NYa0rIv2tSp/rtP6JCHXr2PDhg0IDw+HlZUVOnTogO7duhX4zDl79iy2btuG2NhYODo6omfPnvBv2VJln0ePHmH1mjV4/PgxTExM4N+yJfr37w99ff3iPqUiY+TqiMY3D+Fa93F4ff5qgfu69G6PstPHwMSzFNKfR+HJ7ysRtWmfyj6WNX1Q8bcpsKzpA+mbNERu3INHs5ZAIZEU41kUjcu3H2Dp9oMIi4yBraUFerRqhAHtW7xnGzYCg7//HXsW/ggXe9X3GmeDb2HN3mN4ER0HWysLtGtYG0O6tCoR9QkAZGak4fj2P3Hv2glkZYjh4V0T7fpPh71zmQLThT++gX93LkTU83swMDRBldpt4N9jEgyNTXP3iY96gmPb/sCLxzego6ODCtWbonXvb2BuZV/cp/VJ0tPTsXbtWgReuoT09HT4+Phg1BdfwM3NrcB0iYmJWLVyJUJCQiCTyVCrVi2MHDUKNjY2ufvIZDJs2bIFJ0+cQEpKCsqWLYvhI0agQgXhttcu336AZdsPICwyBjaWFujZqjH6f8C9M+T7+di98CeN987avUdz7522DWtjSJfWJebeIfqvK9F36oEDB3Dt2jUEBATA0dERwcHBmDZtGk6dOvVRx2vXrh0aNWpU5Pt+qC1btuCvv/6Cn5+f2mehoaEYPXo0BgwYkLtNV0AvUnRdPGDWcwyyHoQg/fxB6Ll5wbh5F0AkQublfzWncVA+mFMC/oJCkvX2g3capsYtusGodgtkXj+PrEe3ILKyg3HjjhBZ2iL99J7iPKVPdjs8DhM2HkXrKl4Y518LN57HYuGxy5DKFRjepEah6deeu4F7kQnwK+Ossj3y9Rt8sfYQqro74ve+/giLT8KSE1eRnJ6J77s0Lq7TKRK6zqVh0m0UJA9vIPPiYei6esKoSSfoiHTzDXaJHFwBAKnblwDSt+Uk5wuMLC4CqVsWqKUzatgeuk7ukDwMKYYzKTqGXt5w+uoHpF69gMS9m2FUrhJseg0FdHWRfHiX2v7iW8GImv2N2nb7oV9CZGwM8e1rAABdGzu4zJgPSUwU4pbPh8jAENbdBsL529mInDle9Z4TmLuPnuLbeYvQsn4tjOzdBbcfPsY/m3dBJpNhUNd2BaZ9Gh6Fb+f9DZlMVuB+N+4/wo4jH/fM0IbbYRH4ckkAWvtVxtjOzXHzSTj+2n0CUpkcw9pqfibN334Uhy/fwsj2TVDZwxX3X0Rh5cFziHmVhJ8GdwEAXL7/FN8s345WfpXxZTd/PI2Ox+K9J5GYKsa0vu0/4xl+uHuhjzFtznw0a1gPw/v3wp37oVi+IQAymRz9e3TWmOZc4BXMWbAEPTq0QW3fgbhwJRjz/l4GfT19tGhcHwCw6+ARLF69EU3q18HoIf2RlJyCdVt34OnzF5g97evPeYof7e/fZ+Fx6D0MGDoGJiam2BmwDj9/9yUWLN0EMzNzjWlkMinm/vQtxGIxRo37FlKpFFs2LMfs77/B/L/XQE9PD2lpqfh+6jhkZqSj78CRcHJxw5VL5/DDtPH4ae4ilPOu9JnPtHAGHuVgP2Y6xCGBSD64FQZeFWHZZSAg0kXKv+qB4oy7IYibP01tu82AsdAxMkbGvesq23WMTWE3ehr07ByL7RyKmkGZ8nAYPwNp1y4haX8AjMpWhFX3wYCuLt4cU29bpd+5hph5U9S22w4aB5GxCdLvKJ+zZg1bwnbQeCQf24OM+zdhUKY8rHsOg46hMd4cVX+eCcWdx88x6Y+V8K9bA6N7tMfN0DAs2noAMpkMQzr5a0zzx8bdOHLpGkZ0aYVKnu64/ywCq/YcQ8zL1/hhVD8AQGT8S4ydtxRVy3pg3pdD8DwqDkt3HkZyqhjfDe/9OU/xg91+FoUJK3aitW9FjGvfGDfCIrFw/xlIZXIMb1VPY5qfAo7i/N0n+LJjE5R2sMGBq3cwYflOrJ7QD75lSwEAlh65gHUnL2Nsu0bwKe2MC/ee4ruNByHS0UFbP+HVH+968PAhfvrpJzRu1AiDBg7EvXv3sHbtWshlMvTq1UtjmosXL2L+77+jc+fO8KtZE4FBQViwYAH09fXRtIkycB4TE4PvZsxAxQoVMH36dERERGDDhg1ISUnBhAn5d9AREiM3J9Q+vAb6VhaF7uvUtRWqb/wDzxZvRMLxC3Dq3BLV1/4GeWYWYnYcAQAYl3FDnWPrkHj5Jq73nQSzCl7wnv0V9G2scHfcj8V9Op/kzuNn+Gr+cvjX88Xonh1wK/QpFgfsh0wmx5DOrQpM+yQiGpPmL4NMJlf77MrtB5iycDX86/pifJ9OeBoZi6XbDiApJRWTh2ouf0KzY9lkRDy5hTZ9voWhkSlO71uKNfMGY+K8gzA2tdSYJjY8FGt/GwbPSnXRb8IipCTF4/iOBUiIeYahU5QdkN4kxmP1vCGwdSiFnqN/gyQrAyd2LsTa34Zh/Ow90NUTbiBw/m+/4eHDhxg2fDhMTEwQsGULpk2dimXLl8PcPL/2mgw/fP89xGIxxk+YAKlUivXr1mHGjBlYvHgx9LJfyq9auRLHjx/H0KFD4ejoiL1792LGd99h8ZIlcHFx+Zyn+V7uPH6Gr+cvy713boaGYXHAPshkcgwu5N55GhGNrwq4d6YuXAX/ur4Y16czwiJjsu+dtBJz7/w/kyu0nQMqCUp0AOPNmzewt7dH1apVAQDPnz//pOMZGRnByMioyPd9X3Fxcfjxxx9x5coVeHh4qH2uUCjw5MkTjBo1Cvb2wuxFYNyoA2RxERAf3AAAkIbdh45IF8b1WyPz2hlAqt5bRtfRDfI3iZC+eKTxmDrGpjD0a4rMmxchPr4td7siJRGm3Ucj8+YlyF8Ltyf50lPBqOBsh7m9WgAAGpR3h0Qux5qz19G/fhUY6ed/G4bGvMSaczdgZ26i9tnaczdgYqiPvwe0gb6eLhp5l4axvh7mHbyIEU1rwNlKc2NHCAzrt4UsPhLpRzcDAKTPH0JHVxeGdfyRef2c5nJi7wr5m0TIIh5rPmhWJmQxL1Q26Xn5QK+0N9IOrIU8MaHIz6MoWXfpj8wXYUhYqQzCpN+5Dh1dPVh36IU3/x5QCzTIU94gM+WNyjaLlh2h7+KG6DmTIc/+zLpLP8jFYsT8PhOKrEwAgCQhDk4Tv4dhmXLIeHTvM5zdx1m94wDKl3HHj1+OAADUq+EDqUyGDXuPoFe7ljAyNFBLI5FIsfPYKazavh8GhfRYFKdn4Jel62BvY4X4V4nFcg5FbfmBM6hQyglzhncHADTwKQepTIa1Ry+gX4u6MDJQPeekVDH2XAjBxG4tMbh1QwBAnexesIv2nMSX3fxhY26K/YE34GRjiV+Gd4euSIS6lbzw6k0qNp8Mwjc92wh6VNe6gJ0oV8YDM79S9pav41sdUpkUm3ftQ4+ObWGooZys2rQNTevXwfgRgwEAtX2rISUlFWsCdqBF4/qQyeTYsH0P/KpXwaypb0c6lfcqg6FfTkbwzduoVb3q5znBjxT64C5Crl7Cdz/9jhp+dQEAFSpXxbjhvXH88F507z1IY7qgi2fx7OljLFi6EaXclb0hPTzL4ptxgxF04TQaNWuFMycOIyEuBrPn/4MKlZTXoVqNWnjzJgkbVi/GnN+XfZ6T/AAWHfogK+IZXm9YBADIuH8TOrq6sGjdDalnDqvXsalvkJWqWseaNW0HPSdXxP8xA/J3PjOq4gfrXsOhY2hc/CdShKw69UVWxDO8WvsXACDj3g1AVw+WbXsg5dSh97om5s3bQ9/ZDbG/Tcu9JhZteyAt5BKS9mxUHvfhbeg7usC8eTtBBzBW7D4Kbw83zB47EABQv1pFSGUyrNt/An3aNIGRgWpdkpSShr1ngjChT0cM6qBs49X28QYALNl2EBP6dIK1hRk2HDwFUyND/PnNCOjr6aFh9cowMjTA/PW7MKyzP5zsbCBUS49cRAU3R8wd1BEA0KCSJyQyGdacCEL/pn5qz5yoV0k4cu0epvf0R+/GvgCA2uVL42ZYJLZfuA7fsqWQniXB5jPB6N/ULzcIUsfbAw8iYhFw7prgAxibN2+Gl6cnJk+eDADw8/ODVCbDtu3b0blzZxgaGqqlWb9hAxo1bIgvRo0CANSsWROpKSnYtGlTbgBj565dMDY2xg8//AB9fX3UrlULhoaGWLZsGXr37g0HB4fPd5IfSkcHbgO7oOJvU4HCO0gDALxnf42YXcfw4Nt5AICXJy5C39oS3j9NzA1geE0eCWlKGq51GwuFRIKEY+chS8+Az9/f48mvy5EREVNcZ/TJVu46DG8PN8wap2xn1K9eCVKZDOv3/4s+bZuq1ScAIJFKsf3YOazYdRiG+Xw/PHjuMpxsrTFr/GDoikSoU7UiXie/QcCRM/hqYHfoCbi9BihHUTy8cQaDvlkB72rKDnce3n744xt/XD61Fc06jdaY7tLxDTA2tUS/L/+Gnt7ba7d71XdIiHkGe+cyCD67E5npKRj41VKYmFsDAEzNbbBm3mCE3b+CclUbFv8JfoQHDx7gypUr+HnWLNSqVQsA4FO5MoYOHYrDhw6hT9++GtNduHABT58+xfLly+FeujQAwMvTE2PGjMGF8+fRrHlzJCQk4PDhwxg9ejTad+gAAPD19cWIkSOxc+dOTJw48fOc5AfIuXd+HjcEAFCveuXse+c4ehdw7+w4dhYrdh3O9/tfzr3z8/gh79w7KQg4crpE3DtEn1NmZiZ+/fVXHDt2DBkZGWjevDlmzJihMrorr2XLluGvv/5S2/7urEVbtmzB2rVrkZCQAB8fH8ycOROVKr1/u0/ra2CcO3cO3bp1Q7Vq1VCvXj1MmzYNycnJAIBr166hV69eqFq1Kjp16oQjR47A29sbkZGRmDZtGhYvXozo6Gh4e3tjz549GDRI+WW8RYsW2LPnw3vl79mzB97eyi8e06ZNQ8+ePVU+j4qKQoUKFRAYGKiyLwB4e3tj165dGDJkCKpWrYqGDRtiyZIlKukPHjyItm3bokqVKujZsyc2btyocox79+5BX18fBw4cQLVq1dTyFx4eDrFYDE9PT7XPBEFXD3ru5ZAVektlc1boDegYGkPPzUtzMkc3SOMi8z2syMYROiJdSB7fUdkuefEIOiIR9L2E+0UnSyrDtbBoNK+sOiTW38cTaZkS3Hief8NbIpVh5s4z6FvPBx526r1Rgh5HoJF3aZUXiy19PCFXKBD4OKLoTqKo6epCr1Q5SPP+Px/dgo6hEfRcNZdvXQc3yBKi3v/v6OnDuEV3SJ7eg/TRrcL31yY9PRhXqIK060Eqm9OuXYLI2ARG5Qsv47oWVrDpPhBvTh9FZtjbYKCpX32kXDiRG7wAgKznTxD+1WBBBy+yJBJcvxeKJrVVRyk1q1sT4vQM3H6oOZAVeOMO1uw8iMFd22HcgO4F/o0lm3bCxsoC7Zs2KLJ8F6csiRTXHj1HsxoVVba3rFkZaRmZuPkkXC1NWkYmejT2Q5NqqsPEPZyUQfCohMTcYxsb6ENX9LZZYGVmAolUBnFmJoQqSyLBzbv30ahubZXtTerXhTg9HbcfPFRLExMXj4joGPU0DeoiKiYWkdExSExKwpuUVNTz81XZx7N0KVhamOPytRtFfzJF7Nb1qzA0MkbVGrVyt1laWqOSTzXcuBaUb7qb16/Axc09N3gBAKXcy8C1VGlcv3YZABAZ8QKmZua5wYscPlV9EfrgLlJTU4r4bD6Rnh6MylVG+i3V6UzSbwRBZGwCA6/Cp1EQmVvCsmNfpJ7/F1nP39Y/OsYmsPtiCjIf30PCktlFnvVio6cHo/I+EN+4rLJZHBIIkbEJDMtWzCfhWyJzS1h17o+Uc8eQ9eztNYlfPAeJuzao7KuQSqGjp/7SQSiyJFKEPHiMZn5VVLa3qF1NWb+GhqmlSUvPQPcW9dHY10dlu4ezchROVPxLAMppqRpUr6QyRUWL2tUhVygQdFu9jhKKLIkU156Eo3nV8irb/atXQFpGFm6Eqbfd7S3MEDB5sMpUUCKRDnRFImRKpQAAAz1dbPx6IAY1V62D9fR0c/cRqiyJBLdv30b9+vVVtjds2BDp6em4d0+9XRUXF4eoqCjU05AmOjoaUVHKtm1ISAhq16qlMl1Uw4YNIZfLEXJddcSX0FhU9YbPPz8jcvM+3ByiPkorL+PSrjDzLoPY/SdUtsfsOQ7Tch4wKat8GWvv3xDxR8+pTBcVu/sYdHR1Yd9KmC+jAWU5Cbn/BE1rqX6fb16nBtLSM3Dr4VON6S7duIfVe45iaOdWGN+3i8Z9MiVSGBkaqLTXLM1NIZFKkZaRUWTnUFwe37kEA0MTlKvytu1tamEDjwp+eHTrfL7pWnafiMHfrFAJXujqKu8VqUTZTq3boi9GztiSG7wAkDvqQiIRbls2JCQERkZG8PV92+a0tLJClSpVEHztWr7proeEwM3NLTd4AQDupUujVKlSCA4OBgDcvHkTMplMpf7RNzBA7dq1cS17HyHJkkhw/f5jNK1VXWV7i/e8d4Z0bo3xfTWPvs6SSEr0vUP0Of3000+4ePEiFi9ejA0bNiAsLAxffpn/NLKAMlDRuXNnXLx4UeUnx969ezF//nxMnDgRe/bsgZubG4YOHYrXr1+/d760GsB4/fo1xo8fj+7du+PIkSNYsmQJgoODMX/+fLx48QLDhg1D+fLlsXfvXowaNQqzZ7/9YjhjxgwMGzYMTk5OuHjxIvz9/bF48WIAwM6dO9GuXcHTixSmW7duuH37NsLD374QOnjwIJycnFC3bl2NaX777Td07doVhw8fxoABA7B48eLch8eZM2cwdepU9OjRAwcOHEC3bt3wxx9/qKRv3rw5Fi9ejFKlSmk8/qNHypeSmzZtQvPmzdGyZUvMmjULKSnCeFEgsrKDjp4+5K/jVbbLE5W/69pqnlZBz8ENOgaGMB/4Lawm/w3LCfNg3LQzkP1wUaSnKo9vqTqHoW72XJYiq/zntte2yNdvIJHJUdpWNQDhnv3785fJ+aZdcToEUpkcY1vWUvssQyJFdFIqSucJbNiYGcPM0ADPE/I/rraJLO2go6cHWaJqOZFlj5AQ2WjuYabr4AodA0OY9p0Ei0l/wHzMbBg26phbTvIy9G0CHTNLZJwR9hRjAKBv7wQdfX1IYlUDNJK4aOXnTgXPfwoA1l37Q6GQI3HP23Vz9OwcoWtiBumreNgOHI3SS7bCY9UeOH45E7rW77dOkLZExyVAIpXC3UW13nBzUpaPF9GaR11V9PLAnn9+xZDuHaAryr8nzdVb93D0fBBmjh0Kkeg9uwtqWeTLREikMpR2VP3flbJX9oR4HvtSLY2rnTW+698BHk6q9eTZmw+gp6ube6xeTWsjPP41Nv57CSnidNwOi8CWk5fR0KccLE3VR4AJRXRsPCRSKUq5qk6x55b9AjEiKlotzYtI5X2WN41rdprwqGiYmZlCV1cXcQmq1zQlNRWpqWmIjlWtv4QoMvIFHJ2c1aaZdHJ2Q3RU/kHuqIgXcHZRb4co0ynbRBYWlshIF6sFKmJjlNc2Plb9umuTnp0jdPT1IY1XzZckPhYAoO/oWugxLDv0BhQKJB8MUNmuyMpC7KxJeL1xicqoDKHTs8t+7sSpXhNpgrJjhb5T4dfEqlNfQKFA0j7VayKNjYTslfIeEZmYwaxhS5jVa4rUc0eLKPdFLyr+JSRSGdydVdsgpbKDvS9i1O95VwdbTBvaCx55nlPnQm5DT1cX7s4OyMjKQszL12rHtbYwg6mxkcbjCkXkqyTlM8dBtbedu73y5eDzOPUvnAb6eqjs7gxzYyPI5QrEJr7B/N0nEfkyCT0bKjsk6IpEKO/qADsLMygUCrx6k4Y1/wbhSuhz9G7oq3ZMIYmNiYFUKoWrq+r94eKsfJ5ERql3tMn5LumWJ41z9tQtkZGRyMzMRHx8vNpxrSwtYWJigsjI/Dt6CUF6eAzOVvDHg8m/QiYu/EWgWQVlp7a0x89VtoufKkdSm5UvA5GRIUw83JD2+JnKPlkvEyFJToFZ+YLXS9CmqPhXyjZs3vrEMf/6BAAqeZXG/kU/Y1jXNtDV1fz9pmerxoiITcCmQyeRkibGncfPsO3oWTSoXhmWZqYa0whJQvRTWDu4QZSnjW7rWBovY57lkwqwtHGEk7uy42dWphhP7gbi310LUbqcL5zdlZ0QTC1s4OapDChLsjIR/uQmDm6YDRsHd5WAidBERETAyclJrb3m7OKCqALu/fCICLU6AwBcXFxy66KI8HAYGxur9Zp2cXHBq1evkJ6eXgRnUHTyu3fccu8dzd//KnmVxr5FszCsaxvo5XPv9Mi+dzar3DtnSsy9Q/S5xMXFYd++fZg5cyb8/PxQtWpVLFiwAMHBwbhxI/9OfI8ePUKlSpVgb2+v8pNj+fLlGDBgADp16oSyZcti7ty5MDY2VlvjuSBanUIqLi4OWVlZcHFxgaurK1xdXbF8+XLIZDJs374ddnZ2+Omnn6CnpwcvLy/ExcVh/vz5AABzc3OYmJhAV1c396JYWipf5trY2Hzy9E61atVCqVKlcODAAYwfr5yW4uDBg+jcuTNE+bww7dKlCzp3VkZ8R48ejTVr1uD69euoVasW1qxZgzZt2mD48OEAgDJlyuD58+dYv379e+fp0aNHEIlEcHBwwPLlyxEeHo758+fj8ePH2LBhQ775+lx0DJXXXJGl+iBUZPfe1TFQ/5/oGJtCZGENiERIP7MP8uTX0PPwhlFdf+hYWEN8YD3kr+MhiXgCo0btIU9JhORFKHSt7GDStj8UUgl09NWHagtFaoZy+gUzI9VehybZQ+7TMjWvP3A3Mh4bLt7C2pGdYKBhOGPucTVMjWJiqJ/vcYUgt5xk5vmCk1VIOTG3AkQiZJw7APmb19ArXR6GtVpCZG6F9COqi91DpAsD38aQPLwBeZL6S12hEZkoG02KdLHKdnmG8l4SGRc8LYnI3BJmDZoj+dheyMVpudt1zbPrxJ5DkPnsEeKXzYeuhSVsegyGy9R5iPxhgsrIDCFJFSvP3STPuZsYK8tHWj4Nbgdba43bVY6dJsbcZRswsndnuLs4fWJOP5/UdOU9Y2qkWueZZNcvaRnv9788feMBDgbdQu9mtWFhqry+tSuUwZDWDbBw179YuEu5XlGFUs6YO6JHUWW/WKSJlfdM3nJinP17mli9nKSlZZctk7xl620aI0NDNG9YD3sPH4eHuxsa162FxKQ3WLx6A3R1dZGRt/4SIHFaKoxN1L+QGZuYQPxOPaGWTpwGZxf1oKmxsTHSs9M1atYKB/dux59zZ2LYF5NgY2uH68FBOHtSOe1HpsB6somMlEE4eXre9onydx2jQupYMwuY1GmKlFMH1eppyKRqgZGSQGSsvCaKDM3PHR2jggOXInNLmNZrhpQT+6FI11yeDDy94TztNwBA5vPHePOv5sXBhSA1+6WrqbFqG8Qku75NS3+/Mn0m+BYOXQhGL/9GsDA1wcskZVDLzFi9bWNqZPjex9WG1HTlM8Us7zPH8P2eOetOXsaig+cAAN3rV0Ndbw+1fY6FPMC0DQcAAI0qewl+Ee/cZ46J6v2R87tYLH7/NNnPHLFYjLS0NI37AMq6V9NxhUSSmAxJ4vt3ntKzNAMASN+kqmyXpiivg56FGfQtzTXuk7OfnoXZx2a32OW0YdXqE+OC6xMHG6tCj12rcnkM6tgSi7bsw6It+wAA3h5umDNhyEfn93PKSE+FkZH6/87QyASZ+TxL3qVQKPDL2PqQSjJhYmaFDgNnaNxvycwueBn7HPoGRuj/5WLoa/h+KRTitLSPuvfFaWlw1bCGxbvp0sTifI8NKOsf40K+Z35OxXvveGNgR38s2rIXi7Yo1z7z9iiF2ROGfkKOif7/hIQo17R7t+N+mTJlctedrlFDfR3frKwsPH/+PN/Zgl69eoXnz5+jXr2366fp6enBz88PwcHB+OKLL94rb1oNYFSsWBEdOnTA6NGjYW9vjwYNGqBp06bw9/fHkydPUKlSpdzFhwCgdu3aBRytaOno6KBLly44ePAgxo8fj/v37+PJkydYunRpvmm8vFSnSDI3N4cke8jrvXv30KqV6qJDtWrV+qAAxpgxY9CvXz9YWytf0JUvXx729vbo1asX7ty5o3Haqc9Kp+AAikKhvjKPQpKFlK2LIE+MhzxZ2ZNLGvEYkElh3KQTMi4dg/xVLNL2rIJJ234w664s2PJ0MdLP7IVxo/aCXoRYruGc36Wjo97zO1Mixfc7z6B//SqoUkrzqJWPOa5gFJa3fMpJ2s6lkCUmQPFGWU5kkU8BqRRGjTog8/K/Kuug6JevDpGZJTKDS8jizIVcE033zrssmrSCjkiE5BMHVD/Irj9lb5IQt3hu7rWVxMfA9fs/YVavKVLOHf/4fBejwsq46BPK+F/rt8HBzhp92mtelFWoiuK+P3X9Pr5bvRvVy7pjUve35//LlkPYf+kGRrRvjDoVPBH9KgnLD57FuEWbsOKrwTDWECwVAoVcfZG+d4k0PJfkivdL8/WYEdDX18PvS1Zi/uIVMDQwQL/unSFOT9c4x7k2yeVyKPKcV0H1hqbrkpuugGuqk91RopR7GUz74VesWPI7vh6nnL7Ts6w3eg8YjrUr/oZBEa8R9skK6+BRyL1l2qAldEQipJ45XISZ0q5C64tC7hOzhv7QEYnw5tShfPeRvYpH7B8zoGfrCKsu/eA07VfEzPkaiizhtduK4plzOvgWZv6zEdXLe+LLvp0AFP78FnJ77VOfOY19yqK6pytuPI3EimOXkCGR5q6lkcOntDPWTuyHR1EJ+OfweYxdtgNrvuwn2OtS+DNHPd+FpdERiQotJ5/S5hEinULqZIVcXmi9rRDwiq/F+T3t1zXbcODsZQzr2ga1fcojOuE1Vu06gi9//QdLZ3ypcX04bdHcNimojVH4dZHLpBj41T+QSLJw/uBKrJo7EKNmbskdhZGj46DvoVAoEHRiMzYuHINBXy0TxBoYymuiUNuWn4LKSkHlLKfOKLT+EVjdUnhd+PEddn9dsw0HzwZhWNc2qOXjjZiEV4K9d4jeR4sWLQr8/NSpj3sXFhcXB2tra7Xvuw4ODoiNjdWY5smTJ5DJZDh+/Dh++eUXZGZmolatWpg8ebJKOmdn1RkQHBwc8PDh+0+nqvVFvP/880+MGzcO58+fR2BgICZPnoyaNWvC0NBQrTI30LBgT3Hq2rUrlixZgjt37uDIkSPw9fVF6XfmGMxLU/5yKmE9Pb0CH07vQyQS5QYvcpQrVw4AEBsbq/UARm5Pxjw9HN72uNfQY1oqgfS5eoGVPLkL4yadoOvgCvmrWCjEKUjbvQJiQ2PomFkqe9XL5TBp0xeKjMJ7a2hLzsiLtEzVRalzRkiYa3hQLjkRDLlCgVHNa0IqU5aZnGe5VCaHrkgnd+SFppEWaZlZaiM+hCRn5MUHl5MXoWqbJWH3YdSoA3TtXfIEMKpB9jIG8oSS0StWnt1LJm+P15wesvICeksDgKlfA4jv3shduDuHIrsnrfh2iMoLusynoZCJU2FY2gvCmIBOnVl273hxnp42OT3qzUw+rrfQxZBbOHEpGOt+mwm5QgG5TAZ59pdgqUwGkY6O1kez5cc8uzeSOM99n9ML1sy44Jfqm08EYuGuf+Hn7YEFY/vCMHue7fjEN9hzIQTD2zbCuM5vG0KVPVzR46d/sP/SDfRpXqcoT6XImGZPbyXO07NenNODy1S9nJi9ZxoTYyNMnTAaE0YMQVxCAhzt7WFibITDJ07D1VlYI3d2bV2PnVvXqWyr26ApkhPVp3hJF6fBxDT/ofImpmYapxQQi8UweWdERzXf2vhnzQ7ExymnHHJ0csHpf5Uv+M3MzD/qPIqLPHvUhChPYOXtyIyCezeb1KiHjAe3StQUUYXJOee8C4+/9zWpWQ/p928WeE1kyYmQJSciE/cgfRkLp8lzYeJbH2mXz35a5ouBmUl2/Zr3mZP9e2HPnC1Hz+DvLftRs1JZ/PHVCBhmj7TN6UWqaX7ttPSMj36WfQ45z5S0POsg5TxzzAt55pRzUY6Qr1nWHVK5HMuOXMSEDo3hbPN2+tNS9tYoZW+NmmXdYWZkgJmbD+P60wjULOtelKdSZEyz607150f2KAsNdWthaUxNTHJ7SOdb9xZQZ5dE0mRl61PPXPW8ckZVSJNTc0de5N0nZ7+cYwiRWU7v9jyjlN63PslP/Osk7D0diKGdW2FML+WCzDUBVPYqjd6Tf8GBs0Ho1brJx2e8iJ3ZtxSn9/2jss2nVmukJr9S2zczPQ2GxoW3HXT19FHWRzkdVBlvP/z+dUsEHt+E7iN/UdmvrI9yzQfPSnWwaHpHnD+8WhABjICAAARs2aKyrWHDhkhKSlLbN10szq0/NDE1MSm0zjA1Nc13n5zPhSS/Z2bOvWNq8nEdZOJfJ2Hf6UsY0rk1Rvd6G0iv5OWBPpPn4MDZQPRq3fTjMk1FopDYFRWhyMjIAoMfEydO1Phu29DQEJn5rI2Zs9yBsbEx/v77b7x69QoLFizAoEGDsG/fvtx6KO9xCzqmJloNYNy6dQuHDx/Gd999B09PTwwZMgQHDhzA5MmT0adPH5w5cwZZWVm5J3n37t0Cj1fUEWRXV1fUqVMHx48fx9GjRzFu3LiPPlaFChVw65bqQsIFzR+myZQpUxAfH68yauPOHeVCyGXLlv3ovBUVeWICFHIZRNb2Kttzfpe/Uo/WiaztoVfaG5IHIaovrrNfrCnEysarfsWakL+MhSwhKnc/XSd36IhEkMUKd8HqUjYW0BXpIOKV6rDq8OzfyzhYqaU5efcpopNSUe+nNWqf1fx+JWZ1b4rONSvAwcIUEa9UXxy8Sk1HWqYEnvbqxxUKedLL7HKiOid/zlomsncCEW8/s89eIP4G8E450clemE3+7pBjkQh6ZSog82oJGX0B5ZzjCpkM+o6qEWl9B+WwYElM/vOf6lrZwtCjLJJXLlD7TBIfA4VcDp13FoTMoSPShVyAvWBzuDo6QFckQmSetQZyfvdwc9aUrFBngkKQJZGg/9c/qn3WqM8XaNekPr4fP+yjjl3c3OytoSsSISJe9YtfRLzyJbWns72mZFAoFJi//Si2nb6CNrWrYNaQLiqLyca8ToZCoUA1L9UXRl4uDrAyNcFTAc/R7uLkCF2RCFF55sSNilE+b0q7qc8NXMrVJXufOJT3fDuHdmSeNIHBITA3M0OVit4o465cEyIxKRkJr16jvJdHkZ/Lp2jZphNq1lZdIPbq5Qu4df0q5HK5SlAuNjoKrm75d8ZwcS2FZ2GP1bbHxkSibHnlws4J8XG4fTMYjZu1hqPT2+kLwp4+gpm5BRwcP+7+LC7ShFgoZDLo2avmS89eGYiSxhZQx1rawMDdE6825D/SoCSSZF8TfQdnvPtqQ89BeY0Kfu7YwNDdCy9PHlT7TMfQCMbVaiPr2SNIE962+7LCw3LTCpGbg52yfo1TnXYy5/e861zkUCgU+GPjHmz/9zxa1/PFT6P7q9SvJkaGcLC2RGSeNYpeJ6cgLSMTZfI5rhCUsrNWtmETklS2hyckAgDKOKmvpRX9OhmXQ5+jvV9lGOq/vQ4VSynvtYTkVBjq6+PS/aeoX8kTtu+8nK6QvU98svqUQULh7OwMkUiEmGjVDjLR2b+7a1jH0M1NOSVfTHQ0yr4zWj83jbs7jI2NYWtrm7stR1JSEtLT0zUetyRLfaRc68DEqzTe3HyQu93US/lsSn34FLI0MdIjY2Hipfq8MrC3gb6FGVLzWcxXCNwc7bLbsAkq23N+L+P6cfd97MvXUCgUqOqtOkWHp5szLM1MERYZ83EZLia1mvWCd/WmKtseXD+Jx3cuqrVNXsW9gIOL5qlHAODBjTMwMjZDmQpv14Y0MjGHjUMppCQp26lh969AIsmAd7W3QRxdXT04liqPuEj1do02tG3bVm1WkaCgIISEhKhdk+jo6HzXRgWUdcvTp+r3QXR0NLy9leuFuLq5QSwWIzkpCZZWVrn7xERHw8HBQXAjit0c7Qu5dz6uA1HOvVOthNw7RO/jY0dYODo64siRI/l+fu7cOWRpeE+UmZmZ75RzXbp0QePGjVXW2ylXrhwaN26M06dPw91d+Z4h73ELOqYmWu1mamZmhoCAAPz+++948eIFHj16hCNHjsDDwwNjx45FZmYmpk+fjidPnuDcuXNYuHBhgcfL6b3y8OHD3LlEP1XXrl0REBCApKQktG3b9qOPM3LkSBw7dgzr1q3D8+fPsXv3bmzevPmDjtG6dWsEBQVhyZIlCA8Px7lz5/Ddd9+hQ4cOatNXaYVMCmn4Exh4V1fZbOBdA/IMMaTRz9WSiMwsYdq2H/QrqC7aZ1CxJhQZ6ZDFKhe+M27QFkb1VafgMqzdHPIMMSThj4r0NIqSob4efD2ccereM5UhkSfvPYO5kQF83NQXrF40qC0CxnZT+anoYoeKLnYIGNsNTSp6AADqlXPD+dAXyJLKctOeuhsGXZEOansVvvim1sikkEU+hX65qiqb9ctXgyJDDFnMC7UkOmYWMG7VG/rlq6umqVADisx0yOLeBrFEdi7Q0TeENCr/heCERiGRICP0Lkxrqr6ANPWrD5k4FZlh+ZdxQ6/yAICMx/fVj5uZgYxH92Bas17udFIAYFSxGkRGxsh4dK+IzqDoGRroo3rF8jh35brKvXP2ynWYmRijUtmPW7xxRK9OWPvrTJWfzi0aAwDW/joTI3p1KpL8FwdDfX34liuNUzceqFyTU9fvw8zYCJU9NN/3i/eexLbTVzDAvx7mDu+u8nINAEo52EBXJMKNJ6r33vPYl0hKE8PNrvB1RbTF0MAAVStXxPnLV1WuybmgKzAzNUHF8urBfTdnJzg7OuDcpcsq288HXoGbi/IzADhw7CSWrlN9Tu88eAQikQj1/IS10KyNrR28ylVQ+alWoxbS08W4df1q7n7JyYm4f+8WqvnWyvdY1WrURlTEC0SEv61DI8KfISriBarVUH7pfpOciOWLfsO929dz90lMfIVL50/Cr04DwU1JAKkEmU/uw7i66kgi4xp1IRenIet5/i82DMooR7pmPn3/4c0lglSCzMf3YFKjrspmE9962dck/+eOQRnlc0fTNVHIZLAdNA4WrbuqbDeqVB0AkBWp/owXAkMDfdSo4IUzwbdV6pLTV2/BzMQYPl6ag37/bD+E7f+eR/+2TTFn3CC1+hUA6lStgAs37iFLIn173OBb0BWJUKtyuaI/mSJiqK8HX69SOHUrVLUNeysU5saG8CmtPvd6zOtk/BxwFKdvqZafoIfPoK+nCw9HW2RKJJi5+TD2Bd1W2wcAyruot42FwsDAAFV8fHApMFDlmly8dAmmpqYoX768WhoXFxc4OTnh4sWLKtsvXboEVxcXODoqX2b7+vri6tWryJK8HbV98dIliEQirY+yL2rip+EQh0XAuVtrle1OXVsh9dEzpL9QLkD88uQlOLRrCpHB2444Tt1aQy6V4uUZ1We4kCjrk7I4E3wzT31yE2Ymxqj8kZ0gSjkpX+7ezBO8eR4dh+TUNLg62OWTUjssrB3g5umj8lPWpwEyM9Lw+M7b+yHtzWs8Dw3JHVmhSeCxDTiw4WfI5W+/9ya/jkVC9FM4lVLedzcuHcCuFdNU1tLITE9D+JObcCrlXQxn+OFsbW1Rvnx5lR9fX1+kp6fnzjsPAMlJSbh79y58ffNvb/r6+iIiIgLhL94+V8NfvEBERARqZKfLmav+3fpHkpWFq1evFnhsbTE00Ef1CmVxJvhWMd07T1S2vxDovUNUnPT19eHl5ZXvj5OTE5KSktSCDfHx8bltFk3eDV4AyumhrKysEBsbmzt1VHy8asfIwo6Zl1ZHYHh5eWHx4sVYsmQJAgICIBKJULduXaxatQqOjo7YsGEDfvnlF3Tt2hVubm7o2bNngWtQlC9fHk2aNMGkSZPw9ddfY9iwT+9F27p1a8yaNQstW7aEmdnHLxbWuHFjzJo1CytWrMCff/4JHx8f9O3b94OCGC1atMBff/2FlStXYtWqVTA3N0fHjh0xadKkj85XUcsIPAqzvl/CtMsIZN4OhJ6bJwzrtkT6mf2AVAIYGEHXzgnyxJdQpKdCGvEUkmcPYdKiG3T09SF7GQN9Lx8Y+jVF+qnduaMtMq6dgUmbvjBKiIE0MgwGlWrCsHJtpB3bCgh8MdWRzWrii7UHMXnrCXSpWQE3w2Ox4cJNTGxdB8YG+kjNyEJYfCLcbCxgY2aMchp6tJkaKhvuld8JeAxtVB3Hbj3B2PWHMbBhNbx4mYTF/15F91oV4WwlrOk78soI+hemvcbCpOMQZN29Al2XMjCo1RwZ5w9mlxND6No6KUdrpKdBFhkG6YtQGDftAh09fchexULfszIMfBsj48w+lVEZutk9bDWN+BGyxIPb4Tx5DhzGTUPK+RMwKlcRlm274fXODVBkZULHyBgGru6QxMeoTBVl4OYBuSRLpafru17v3ACXafPg/PXPSDq6B7qWVrDpOQQZTx9CfOPK5zq9jzKke3t8OXsBZixYjo7NG+J26FNsOXAcY/t3g5GhIdLE6XgWGQ1XRwdYW75fmXd2sINznkbqpRDl6LiKAutVr8mI9o0xeuFGTFmxA50b+OJWWDg2/BuIL7u2hLGhAVLTMxAWkwA3exvYmJsiNCIG649fQmUPV/jXrIw7z1R7VXs628PG3BT9WtTFxuOXAAB1K3oh5nUSVhw8C2dbK3RrVFMLZ/r+BvXqiq9/+AU/zv8L7Vo2xb2Hj7Bt7yGMGtQ3u5yI8TwiCq5OjrCytAAADO7dDb8uWg6L5WvQoHZNXLxyDWcuXcaP336Ze9zuHdrg25/mYcnqDahf2w/Xb9/Fll370a9bJ8FNIaVJJZ/qqFylBv7+YxYGDh0DM3NL7AxYC1NTM7Rq9/blckT4M0glEpTJDobWb9wce3ZuwtwfJ6P/kNEAgC3rl8PdwxP1GzUDoFzvwrtSFaxa+icGDhsLXV1dbN24Crq6uujVb/jnP9n38OboLth/+SNsR3yDtMDTMPD0hnnLzkjevxkKSRZ0jIyh7+QG6cs4lWmR9F3coZBkQfZSfXRgSZd8eCccvvoZdl9MRuqlUzD0rACLVl2QtHcTFFnZ18S5FKQJsSrXxMC1NBT5PXekErw5uhuWHftA9iYZmaF3oF/KA1Yd+iD9/k1k3A1RTyMQw7u0wth5SzFt0Xp0alIHtx8/w6bDpzG+d0cYGRogVZyBZ1GxcHO0g7WFGUKfR2LDoVOo5OmOlnVq4G6eIHAZVyeYmRhhcIcW+DfwOr6cvxz92zbFi9gELN1xCF2b14OTnTBHpOQY2bo+vvhnGyav3Ycudavi5rMobDh1BRM7NVW2YdMzERb7Em521rAxN0ENz1Ko6+2BX3edQGpGJkrZWeP8vSfYfv46xrRrCAsTI1iYGKFL3apYcewS9HRFqODmiOtPI7D2xGV0rVcVXs7CfpHUp08ffDdjBubOm4dW/v548OABdu/ejaFDhsDIyAhpYjHCw8Ph7OwMK0vldFn9+vbFgoULYW5hgbp16iDo8mWcv3AB06ZOzT1uzx49cO7cOfzw/ffo2rUroqKisH7DBrRt2xYODsIN6rwPPXNTmFUqC/HTcGS9VI7gefzLP6i25ldIXich7uBpOHZqAZde7XC936TcdE//WA2X3u1R69BqPPtrHUzLe8B79teIWL0DGRHC7jE9rGtrjJu7BNP/XouOTevi9qNn2HToFMb36ZRdn6S/U5+8XxvW2sIcfdo2xaZDJwEAdapUQMzL11i9+yic7WzQpXn9Qo6gfWUq1EKZirWxc/kUtO79DUzMrHB67z8wMjFHnRZ9cveLj3oCqSQLLh6VAADNOo/BuvnDse2fr1GraS+kpbzGmf3LYGRqgYZtlYswN2o3DHevHsOmhWPQsN0wyCRZOH94DbLS09Ci68fPpFHcqlSpgqpVq+L3+fMxbPhwWJibY8uWLTA1NUW79u1z9wt/8QISiQRe2TNvNG7cGNu3b8f3P/yAoUOV12DdunXw8PBA48bKzlmOjo5o2bIlVq5cicysLLi6umLv3r1ITU1Fj549P//JvodhXdtg/NzFmP73GnRqWg+3H4Vh86GTGNen8yfeO81y753aVSoi9uUrrMq9d/IPnhH919SsWRNyuRwhISG5i24/e/YMcXFxqFVLcye4hQsX4tixYzh27FhuR7bIyEgkJiaibNmysLW1RZkyZXDlypXcY0qlUly7dg39+vV777zpKApbKUdArly5gkGDBuHUqVO5w3FLiqtXr8LOzk5lVfbly5dj165dOHnypBZzBiTOG1ukx9MvX025LoGNA+Qpyci8fi53Oh8993Iw7/8V0g5tRNad7J4zBkYwbtgO+t7KhZfliQnICD6NrFuBKsc19GsGQ78mEJlaQvY6DhlXTkJy/1qR5h0ArKcvRcbugkf7fKhT955h2algPE9IgoOFKXrX9cHgRsreVMFhURix+mDu1FCaDF+1HwCwZmRnle3Xn8VgwbEghMa8gpWJETrUKI+xLf2gr6tbpPk36v4Vkv+YWKTH1CtbFUYN2kJk7QBFahIyb15E1rUzAADdUmVh1nsCxEe3QHIvu/ewgSGM6reFfrmq0DG1gDzpJTJDzkJyR7UHlkGt5jBu0hnJC78BZNK8f7bIWH77N8KGdCjSY5r41oN1134wcHKDNPEV3pw+jORjewEARhWqwGXaPMSvXojUi2+HC9oOHANTv/oInzgw3+Malq0Am+6DYOhZHoqsTKRdv4zX29cWurbGh/Jcfwivb18o0mOevXIdq3fsR3h0HOxtrNC9TTP066jssXf93kOM++kPzBw7FO2bqTc8D5+5hDlL12HPP7+qBS3etXrHfqzZeRBBO1cXad4BwKZqI4jPbSvSY56+8QDLD5zB87iXcLCyQK+mtTColfL8r4U+w8g/1+PnIV3QqX4NLN1/GqsOn8v3WKu+GQI/7zJQKBQIOHUZu85dQ9SrRNhZmqNeJS+M69ICNhrmn/4UJk36IPbhh02hWJjzQVexbusuRERFw87WBl3btULvLsr788ade5g0czamfTkabVs0zU1z4NhJbNt3CAkvX8HZ0QH9e3RG62aNVY578vwlbNqxFzFx8XB0sEOXtq3QvUObIs07ADhVqIHbj4t+qq7U1BRsWL0YwUEXIFcoUKFiFQweOQGubm+nC/tx2gQkxMdi6dqdudteJsRh3cpFuH0zGLq6eqhWoxaGjJwAa5u391FS4musX70Yt28EQ6FQoHKVGug3eBRcXItm7vqq5RwQMbZ7kRwrh3G12rDo0Af6Di6QJb9G6rmjSDmlnAbJsFxlOHw1C682LoH48pncNNZ9RsK4Wh1ETx9R6PF1bezhMme52jGKQqmlu/FiVJciPSYAGFevA6tOfaHv6App0iuknD2KlBPKNodheR84fTsHL9ctQlrQ6dw0Nv2+gEmNuoicPDTf45o1bg3zZu2gZ+8EeeobpF05j6SD25SdFIpI6ZX7kHLtWJEdDwDOBN/Cit1H8SImHg7WVujp3xAD2jcHAFy7/xijf1mCH0f1Q8cmdbB81xGs3ns832MtnzEefpWUIyxuPHyKvwP241F4FKzMTNGuYS2M7tEOenpF214z92uDjH/XFb7jBzh1KxTLjlzE8/jXcLA0Q+9GvhjcQjmaKfjxC4xYtBWz+rdD57rKkbVpGZlYfvQSTt4MRcKbVLjbW2NAs1roVu/tKAKJVIb1p67gwJU7iEl8Aycrc3RvUB2Dm9eB6D0W8v0QRq2GIkzDNCuf4lJgIDZv3ozIyEjY2dmhQ4cO6N6tGwDg9u3bmDptGr7+6iv4+/vnpjly5Ah279mDhIQEODk5oXevXmpzUN+9exer16xBWFgYLCws0KJ5cwwcOBB6Gkb2fApPLy8c1i+eHuk2jWuj3qlNCGoxEK/PX1XZdmv4NERu3Ju7r/vI3vD8ahiMSjlDHBaBp/NXImrLfpXjWTeoiYq/TYFFtYrIepmIqC378einRVBIi7aN314SijfXTxTpMc8E38LKnYfxIiYe9jaW6OnfGAM6KP/nIfcfYfTsRfhh9AB0bFJXLe3Bc5cxa/lm7F/0M1zs33ZuUygU2Hr0LPacuojo+Fews7JAnaoVMLZ3x/d+mfu+LHz9sevKp63hqUl6WjKOBPyG+yGnoFDIUbpcDbTrPx32zm9HV6+eOwiJL6MwecHb7zxh96/g5J7FiA1/CJGuHspVaYjWvb+Ble3b6SGjn9/HvzsXIjLsDuQyKTwq1ELrXl/B0U19dNTH6FFHhKdhYUVyrHelpKRg1apVuBwUBLlcjkqVKmHUF1+ovPOaOmUK4uLisH7DhtxtCQkJWLF8OW7cuAFdXV34+vpi1BdfqPSElmRlYd26dTh79izS09NRtlw5DB8+HBUqaH7v8KG8PD2RfL1o32edCb6JVXnunf4dWgJQ3jtjZv+NH0YPQIcm9dTSHjoXhFnLN2Pfollq9862o2fy3DsVMaaI7x1L35ZFdqz/kj/3lZjX0oLxTZfiG/H+zTff4ObNm5g7dy6MjY3x448/wszMDJs2bQKgnAoqOTkZlpaWMDAwwN27d9GnTx90794dQ4YMwcuXLzF37lwYGRkhICAAOjo62LFjB3755Rf89NNPqFKlClauXInz58/jyJEjaqM38sMAxmeyYMECHDx4EPPmzYO7uzsePHiAmTNnol+/fpgwYYJW81bUAYySrjgCGCVdcQQwSrriCGCUdMURwCjpiiOAUdIVRwCjpCuuAEZJVhwBjJKsuAIYJVlxBDBKuuIIYJR0xRHAKOmKM4BRUhVHAKOkK64ARklWXAGMkqw4AhglGQMYH4cBjA9XnAEMsViMuXPn4vhxZUedxo0bY+bMmbC2Vk4tnfNufuPGjahTR9mpJSgoCH///TdCQ0NhYGCAFi1aYOrUqbDMHo0KAGvWrMHGjRuRlJQEHx8fzJw5ExUrVnzvfGl1CqniNGvWLOzdu7fAff755x/Ur/95hlqOHz8eYrEYU6ZMwevXr+Hs7IwhQ4ZgxIjCe/QRERERERERERER/T9RyBnA+HDFF8AwMTHBnDlzMGfOHI2f16lTB6GhoSrb6tWrlzs9VH6GDx+O4cM/frrhEhXA0HSR8jN+/HgMHjy4wH0+53yiBgYGmDlzJmbOnPnZ/iYRERERERERERERUUlVogIYH8LGxua959EiIiIiIiIiIiIiIiJhEWk7A0RERERERERERERERHkxgEFERERERERERERERILDAAYREREREREREREREQnO/+0aGEREREREREREREQkTHKFtnNAJQFHYBARERERERERERERkeAwgEFERERERERERERERILDAAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHg6Gk7A0RERERERERERET036JQaDsHVBJwBAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJjp62M0BERERERERERERE/y1yuULbWaASgCMwiIiIiIiIiIiIiIhIcBjAICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHD0tJ0BIiIiIiIiIiIiIvpvUSi0nQMqCTgCg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiARHT9sZICIiIiIiIiIiIqL/FoVC2zmgkoAjMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwdBQKrvdORERERERERERERJ/P7K1SbWehxPm+r562s/DZ/ffOmNTEPryh7SwIilOFGgh//EDb2RAU93IVEfcgRNvZEBTHijXx5voJbWdDUCx8/RE+upu2syEo7sv34MnTZ9rOhqCU9SqDF09CtZ0NQSld1htPw8K0nQ1B8fL0xMu7QdrOhmDY+dRDyrVj2s6GoJj7tcHzJ4+0nQ1B8ShbHjEPb2o7G4LiXKE6wp4+1XY2BMXTy4tt2DwsfP1xWN9b29kQlPaSUNx9EqvtbAiKT1kn1id5eHp58fvfO9yX79F2Foj+b3EKKSIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHi3gTERERERERERER0WelkGs7B1QScAQGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCY6etjNARERERERERERERP8tCoVC21mgEoAjMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhw9LSdASIiIiIiIiIiIiL6b5HLtZ0DKgk4AoOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLBYQCDiIiIiIiIiIiIiIgER0/bGSAiIiIiIiIiIiKi/xaFQqHtLFAJwBEYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDh62s4AEREREREREREREf23yBXazgGVBByBQUREREREREREREREgsMABhERERERERERERERCQ4DGEREREREREREREREJDgMYBARERERERERERERkeAwgEFERERERERERERERIKjp+0M0P+f4Bu3sGrzdjwPj4S1lSW6tmuF3l06QEdHJ980J89fwqYdexEdFwcnB3v0794ZbZo3UdnnwuVgbNyxBxFR0bCxskKrZo3Qv3sX6OsLvxhfu34D6zZtwYvwcFhbWaFT+3bo0bVzgdfk9LnzCNi+EzGxcXB0cECfnt3QqkVzlX2OnzyFXXv2Izo2Fg72dujUvh26dGxf4HGF4uqN21i9ZQee5ZYTf/TpXHDeT54PxMadexEdF68sJ906oW3zxir7HD11Dtv2H0ZUTBxsbazQtnkTDOjRGXq6usV9Sp/s8u0HWLr9IMIiY2BraYEerRphQPsW7/X/DH0WgcHf/449C3+Ei72tymdng29hzd5jeBEdB1srC7RrWBtDurSCvp7w7x2jitVg2bk/9F1KQf4mCSnnjiHlxH6N+5rWawbbwRPyPdar9YuQdvksAMDYtx4sWnWFvpMr5OI0ZDy8jaS9myBPSS6O0yhy6enpWLd2DS4FXkJGejp8fKpg5KhRcHMrVWC6xMRErF61EiEh1yCTyeBXqxZGjhwFGxtbjfsfOLAfe/fsxrr1G4vjNIrMtes3sH7jJrwID4eVlRU6tW+PHt26FHjvnDl7DgHbd+TWsb17dkerli1U9vn3xCns3LsXMTGxcLC3R8cO7dClY8HPMyFJT0/H2rVrEXjpEtLT0+Hj44NRX3wBNze3AtMlJiZi1cqVCAkJgUwmQ61atTBy1CjY2Njk7iOTybBlyxacPHECKSkpKFu2LIaPGIEKFSoU92l9tCs372JlwG48i4iCjZUFurVpgb6d2rzX//NR2AuMmDYL25f8CmcHe5XPTgdexZZ9R/EiKgbmpibwq1oJYwb0hI2VZXGdSpG4fPshlu48hKeRsbC1NEdP/0YY0K5ZvtcjSyLF5iOncfhCMOJeJ8HBxgpt69fEkE4tVZ4n98PC8VfAfjwIC4epsRE6Nq6NUd3blohnTsj16yp1Scf27dGjW9f3qEu2Iza3LukB/zx1yfkLF7Fz925EREbB1NQUNapXw/Ahg2FtbV3cp/TJgm/cwurN23Lb9V3atS60XX/q/CVs2rEnt13fr3uX3HZ9TFw8+o7K/1ndpnkTTJs4tsjPoyiFXL+ODRs2IDy7nHTo0AHdu3Ur8JqcPXsWW7dtQ2xsLBwdHdGzZ0/4t2ypss+jR4+wes0aPH78GCYmJvBv2RL9+/eHvr5+cZ/SJ2MbNn9Gro5ofPMQrnUfh9fnrxa4r0vv9ig7fQxMPEsh/XkUnvy+ElGb9qnsY1nTBxV/mwLLmj6QvklD5MY9eDRrCRQSSTGeRdFJTxdj87oVuHzpPDIy0lHRpyqGjhwPVzf3AtMlJb7G+tX/4EbIVchlMtTwq4OhI8fDOrsNu3jBPJw9dSzf9MvWbYeDg1ORnktRKK76JIdYLMbYcePQv18/+Pv7F9dpFCl+//vvUcgV2s4ClQAl58lfjJKSkjBp0iSEhITA29sbO3fuxL59+9C4cWPY2mp+qZNX8+bN0bVrV0yYkH/l+a7ExEScPHkSPXv2VPssJCQEAwYMwIMHD1S2HzhwAJMnT1bb/9SpU4W+jPhc7oU+xrQ589GsYT0M798Ld+6HYvmGAMhkcvTv0VljmnOBVzBnwRL06NAGtX0H4sKVYMz7exn09fTRonF9AEDwzdv4/tcFaNawHkYN6ovn4ZFYuWkrkt6kYNKooZ/zFD/Y/Yeh+H7WL2jSqAGGDOiHu/cfYNW6DZDJZOjTs7vGNBcuBeLXPxaia6cO8PP1ReDlK/h94SLo6+mjWZNGAIAjx09g4eJ/0Kt7V9SsUR0PHz3C8tVrkZ6Rjn691MuVkNwLfYxpv/yO5g3qYXi/nrj9IBTLN2yFTCbHgO6dNKY5G3gVsxf+gx4d2qCOb1VcuHIN8xYth4G+Hlo0UpaTnQePYvGaTWhavzbGDO6HpDdvsDZgF548e4E50776nKf4we48foav5i+Hfz1fjO7ZAbdCn2JxwH7IZHIM6dyqwLRPIqIxaf4yyGRytc+u3H6AKQtXw7+uL8b36YSnkbFYuu0AklJSMXlor+I6nSJhUKY87Md9B/G1S0g+sBWGZSvAqutA6IhEeHN8r9r+6XdCEPvbNLXtNgPGQmRsjPS71wEAJn4NYDfiG6ScP47k/Vuga2ENy0594fDVz4idOxmQCv8L4Pz5vyL04UMMHTYcJiYm2BqwBdOnTcXSZStgbm6uMY1MJsOPP8yEWCzGuPETIJPKsH79WsycMQOLFi+BXp6XAefOncXqVSvf+zmoLQ8ePsQPP89Gk0YNMXhAf9y9/wCr161X1rG9emhMo6xjF6BLp46oVdMXgZcv44+Ff0NfXx/NmiiDokeP/4uFi5agV/duqOlbAw9DQ7Fi1RpkpKejb29h3zs55v/2Gx4+fIhhw5XlJGDLFkybOhXLli8vsJz88P33EIvFGD9hAqRSKdavW4cZM2Zg8eLFueVk1cqVOH78OIYOHQpHR0fs3bsXM777DouXLIGLi8vnPM33cvfRE0yZtxAt6tfGyL5dcfvBYyzdtAMymQwDu3UoMG1YeCS+nbsQMplM7bOTFy/jx4XL0blVU4zq1x2vk5KxauseTPjxN6z9/ScYGhgU1yl9kjuPn2PSHyvhX7cGRvdoj5uhYVi09QBkMhmGdNL8guOPjbtx5NI1jOjSCpU83XH/WQRW7TmGmJev8cOofgCAyPiXGDtvKaqW9cC8L4fgeVQclu48jORUMb4b3vtznuIHe7cuGTRgAO7dv48169ZDLpOhdz7tqguXLuG3P/5El04d4VezZnZd8hf09fXRNLsuOXvuPObN/x3t2rbBkEED8ToxCRs3bcaU72bgn7//goFAywgA3At9hOlzfkOzhvUxrH9v3Ln/ECs2bIFMJkP/Hl00plG26xeje4e2qO1bDRevXMOvfy+Fvp4eWjRuAFsba/wzf7Zaun1H/sWZi4Fo599cw1GF48HDh/jpp5/QuFEjDBo4EPfu3cPatWshl8nQq5fmZ8PFixcx//ff0blzZ2U5CQrCggULsstJdmAnJgbfzZiBihUqYPr06YiIiMCGDRuQkpLy3t8vtYVt2PwZuTmh9uE10LeyKHRfp66tUH3jH3i2eCMSjl+AU+eWqL72N8gzsxCz4wgAwLiMG+ocW4fEyzdxve8kmFXwgvfsr6BvY4W7434s7tMpEn/Nn41HofcxcOhomJiYYsfW9fhx+iT8tXQDzPJtm0gx58cpSBeL8cW4ryGTSbF5/UrMmvkNfl+0Gnp6eujZdxBat1P9HpmSkoI/5/2IylWqw97e8XOc3gcprvokR0pKCmbNmoW4uLjPcTpFgt//iCg/DGBAGRi4du0aAgIC4OjoiODgYEybNg2nTp0qtr85f/58REZGqgUwQkJCMHbsWMjl6o240NBQ1K5dGwsWLFDZ/m6PSG1bF7AT5cp4YOZX4wEAdXyrQyqTYvOufejRsS0MDdW/pK3atA1N69fB+BGDAQC1fashJSUVawJ25AYwjp46C0d7W8z8ajx0dUWoVb0qEpOSsWP/YYwfNlDtxZuQbNyyFWU9y2DaN8oX6LVq+kIqlWLrjl3o2qkDDA0N1dKs3bgZjRvWx5iRw7PT1EBKagrWbw7IDWBs3bELjRrUx8ihyuvmW70aIqOisf/gEcEHMNZu3ZVdTpQ97Or4VoNUKsPmXfvRs0MbzeVk83Y0rV8HE4YPBADUrlENb1LSsDpgJ1o0qg+ZTI4NO/bCr1oVzJoyKTddec8yGDJxKoJv3kGt6lU+y/l9jJW7DsPbww2zxin/n/WrV4JUJsP6/f+iT9umMNLwgkMilWL7sXNYseswDPMZiXTw3GU42Vpj1vjB0BWJUKdqRbxOfoOAI2fw1cDu0NMT7sgUyw69kRXxDK/WLwIAZNy/AR1dPVi06Y6U04ehkGSp7C9PfYOs1Dcq28yatYO+syvi5n8HefZnFm26I/1OCBIDVuTuJ4mLgtO032Bc1Q/p14OK+cw+zYMH93H1yhX8/PNs+NWqBQDw8fHBsKFDcPjwIfTp01djugsXzuPp06dYtnwF3N1LAwA8vTwxdsxoXLhwHs2aKV8aJSUlYdOmDTh29Gi+L7mFZOOWrfDy9MTUb78GANTyqwmZVIptO3aia+eOGuvYdRs2oXHDBhgzagQAwK+mL1JSUrFh85bcAMbW7TvRqEF9jBg2BABQI7eOPVwiAhgPHjzAlStX8POsWaiVU04qV8bQoUNx+NAh9OmbXzm5gKdPn2L58uVwL60sJ16enhgzZgwunD+PZs2bIyEhAYcPH8bo0aPRvoPy5b+vry9GjByJnTt3YuLEiZ/nJD/Amm37UL5Mafww8QsAQN0aVSGVybBxzyH0at9K43NHIpFi19ETWL1tLwzy6QG9cfch1POtiilfDMnd5u7ihFHTZyMw5Baa1atVLOfzqVbsPgpvDzfMHqt8ptavVhFSmQzr9p9AnzZN1J45SSlp2HsmCBP6dMSgDsrRBbV9vAEAS7YdxIQ+nWBtYYYNB0/B1MgQf34zAvp6emhYvTKMDA0wf/0uDOvsDyc74bRZ89q0JQBenp6Y8u03AJR1iVQqw7YdO9GlcyeNdcn6DZvQqGEDjB41EkBOXZKCDZs35wYwtu3Yidp+fpg4flxuulKurpj4zbe4cjUYjRo2+Axn93HWB+xEuTJlMOOddr1MJsOWXfvQo2M7jffN6k1b0bR+3Xfa9dWRkpKKtQE70KJxAxjo66Oyd3mVNKFPwnDmYiBGDOiLqpWEO4oLADZv3gwvT8/cTmV+fn6QymTYtn07OnfunE852YBGDRvii1GjAAA1a9ZEakoKNm3alPvCceeuXTA2NsYPP/wAfX191K5VC4aGhli2bBl69+4NBweHz3eSH4htWA10dOA2sAsq/jYVeM9Bm96zv0bMrmN48O08AMDLExehb20J758m5gYwvCaPhDQlDde6jYVCIkHCsfOQpWfA5+/v8eTX5ciIiCmuMyoSoQ/u4trVQMz4+Tf4+tUFAFT0qYqxw/rg2OF96NFnoMZ0gRfO4tnTx/hr2QaUcvcAAHh4lsNXY4cg8MIZNG7mDydnVzg5u6qkm//L9zAzN8ekyTMFOXq2uOoTALh8+TKWLV+O9PT0z3MyRYTf/4goP1wDA8CbN29gb2+PqlWrwtHREQpF8Q9fyvs3pFIp5s2bh8GDB8PV1VVjmkePHsHb2xv29vYqP7oCmRonSyLBzbv30ahubZXtTerXhTg9HbcfPFRLExMXj4joGPU0DeoiKiYWkdHKRlhWlgRGhkbQ1X1bZC3MzSCRSiFOzyiGsykaWRIJbt+5iwb16qpsb9ygPsTp6bh7/4Famti4OERGRaNBXdU0jRrUR3RMDCKjogEAc36aiVHDBqvso6+nh6ws1Ye60CjLyQM0qqv6Uqdp/drZ5SRULU1MXAIiomPQuK6fWpqomDhERMcgMTkZb1JSUb9WDZV9PEuXgqWFOYKu3Sj6kykiWRIJQu4/QdNa1VS2N69TA2npGbj18KnGdJdu3MPqPUcxtHMrjO/bReM+mRIpjAwNoCt6e+9YmptCIpUiLUO49w709GBU3gfpN6+obBZfD4LI2ASGZSsWegiRuSWsOvVD6vnjyHr+WLlRRwcZD24j9eK/KvtKYqOUf9ZOeEPL87oeEgIjIyPU8PXN3WZpaQWfKlVwLTg4/3TXQ+Dm5pYbvAAAd/fSKFWqlEq6Hdu34XpICL6bMRO169QpnpMoIlkSCW7fvqNWxzZq2CC7jr2vlkZZx0ahft40DeojOjoGUbl17A8YOVx1hJ9eCahjc4RklxPfd8uJlRWqVKmC4GvX8k13PSS7nJR+p5yUVpaT4OxycvPmTchkMtSrXz93H30DA9SuXbvAMqgtWRIJbtx7iMa1fVW2N6vnB3F6Bm49fKQxXdD1W1i7Yz8GdeuAMQPVg1ZyuRy1qlVGZ/+mKttLuzoDAKJi44vmBIpYlkSKkAeP0cxPNajfonY1pGVk4mZomFqatPQMdG9RH419fVS2ezgre7RGxb8EoJyWqkH1SirTu7SoXR1yhQJBt9XbgUKRf11Sv9C6pEG9eqppGjTIrUvkcjlq1KiOtm1aq+xTqpRy5HR0jHBfNua06xvmaa81qV8H4vR03CmgXa+WpkEdlXb9uxQKBf5asQalS7mhZ6f2RXsSRUxZTm6j/jt1HwA0bNgQ6enpuHfvnlqauLg4REVFqdSXOWmio6MRFaVsf4SEhKB2rVoq00U1bNgQcrkcIdevF8PZFA22YTWzqOoNn39+RuTmfbg5ZEqh+xuXdoWZdxnE7j+hsj1mz3GYlvOASVnlM9nevyHij55TmS4qdvcx6Ojqwr5Vw6I9iWJw83owjIyMUa3G2zrC0tIKlXyq4fq1ywWmc3Fzzw1eAEApdw+4liqdb7qQ4Mu4EngeQ0eOh6mZ8DrkFGd9kpqaitlz5qBKlSqYM1t9xJtg8fsfERXg/yaAce7cOXTr1g3VqlVDvXr1MG3aNCQnK+eyu3btGnr16oWqVauiU6dOOHLkCLy9vREZGYlp06Zh8eLFiI6Ohre3N/bs2YNBgwYBAFq0aIE9e/Z8VH5u3LiBQYMGoWbNmqhTpw6mT5+OxMREAMC0adOwd+9eXL16Fd7eyt5rYrEYwcHBWL16NQYMGKDxmKGhofDy8vqo/HwO0bHxkEilKJX9xT2HW/aX24jsl0LvehGpfGjkTeOanSY8O03Xdq0QGRODbXsPIiU1DfdCH2PXwaOoW7MGLMzNivxcikpMbCwkUilcXVWn03BxUZ5vRPb5vys8IhIA4JYnjauzMk1kdsOkdKlScMoOuL1JScGR4ydw4vRZdGzftsjPoyjllhMX1YaCW57/+btyyombS95yojxGRFQMzExNoKuri9jslyg5UlJTkZqahug4Yb5IAoCo+FeQSKVwd1btXVfKUTnP+osYzXmv5FUa+xf9jGFd26gE997Vs1VjRMQmYNOhk0hJE+PO42fYdvQsGlSvDEsz06I9kSKkZ+cIHX19SOJUy4MkQfnyQ8+x8ClqLDv2ARQKJO0PeLtRoUDS7vVIv6X6ktWkujKIKokJ/8ScF7+IiAg4OTmpBa9dnF0QFRWZf7rwCLhoCJA7u7ggMvJturbt2mPV6rVo0ED4X4RjY5R1bN760iWnviywjlW9Frn1cnYd6+6uWscePf4vTp4+g47t2xX5eRSH/MqJs4sLoiLzLyfhEREaO1K4uLjkPn8iwsNhbGysNgLUxcUFr169Elxvv+i4BI3PHVennOdOrMZ0Fct6YveyPzC4RyeVF2g5RCIRJgzpi0Z5AiPnrypfNpYppblDirZFxb+ERCpTf+Y45f/McXWwxbShveDhojoFx7mQ29DT1YW7swMysrIQ8/K12nGtLcxgamyU77NMCHLqkrxl38VZWbdoqksiIiIAIP82XlQkRCIRvhgxXC1gGhikfOFWunTBc75rU0xsnMZ2fU7bS1N7LTzfdn3+aU5fCMSDR08wfvjgfNsyQhEbEwOpxnKi2kZ/V3i4sl2R95nj7JJTtiKRmZmJ+Ph4teNaWVrCxMRE5RktNGzDapYeHoOzFfzxYPKvkIkLD7aYVVB+x097/Fxlu/jpC+Xn5ctAZGQIEw83pD1+prJP1stESJJTYFa+TNFkvhhFRryAg5OzWtvEycUV0VER+aaLingBFxf1KbOdnV0RHameTqFQYOOapahcpTrqNWz6yfkuDsVVnwCAoaEhVixfjm+/+QaWlsJej+td/P5HRAUR7rw7H+D169cYP348pk2bhqZNmyI2NhZTpkzB/PnzMWrUKAwbNgydOnXCvHnz8ODBA8x+Jwo9Y8YMWFtb48iRI9i1axeMjIxgZmaGCRMmYOfOnShfvnwBf1mz27dvY+DAgejduzd+/PFHJCQkYNasWRg+fDh27tyJGTNmICMjA7GxsVi8eDEAwMLCIjdYoilokpycjLi4uNyprhITE1G1alVMnjwZZcoIo7GSJhYDAEyMjVW2G2f/niZWf6GRlqbcZmKimsYkTxrfqj7o27UTlq3fgmXrtwAAynl64PtvhD0nbFqa8pqYmpiobM85P3G6ON80JnnS5FxHsVg1zYOHoZg4WTnvY/lyZdGjq+a1RoQip5yYmmguJ2JN5SSfNCbGRtmfp8PI0BDNG9bF3iP/ooy7GxrX9UNi8hssWr0Rurq6yMjILPJzKSqp2edsmn0+OUyMlcOG0/IZZeRgY1XosWtVLo9BHVti0ZZ9WLRlHwDA28MNcyYM+ej8fg4iY+UXU3mGanlQZP8uMjJRS6OS3twSpnWbIuXkASg03Gfv0rNzhFX3wcgKD0PGXeH2csyRJk5Tqx8A5T2Ut354l1icBhdX9YZ/3nSlShW8ELiQpInTAKjXlznPFE3XIy0tTWWfHAXVsZO+VfaeLF+uLLp36/LpGf8MxGkfWU7S0uCqYQ2Ld9OlicX5HhtQXkPjPG0BbUrNeYYY5/MMySfgYm/74QssR8bG45+N21GujDvq+Vb94PSfQ2r2yzS1Z45Rwc+cvM4E38KhC8Ho5d8IFqYmeJmknKbBLM9xAcDUyPC9j6sNH1eXfFh7LUd0TAxWrVkLL09P1Pbz07iPEKQW0q7X1F5LLfSaqKfZvu8gfCp6o0aVyp+e6WKW+11HrZwof9dYTvJL8045eftc+vA6W9vYhtVMkpgMSeL7LwysZ6nskCd9k6qyXZqiLBt6FmbQtzTXuE/OfnoWwu3Ul0PZNlEPPhkbmyA9ux7WJE2cBmcNAQxjYxOINaS7diUQkREvMOyLLz8tw8WouOoTANDX1xfMGqkfgt//iKggwu7m8p7i4uKQlZUFFxcXuLq6ombNmli+fDkGDhyI7du3w87ODj/99BO8vLzQoUMHjBgxIjetubk5TEyUPbft7e1hbm6eG6W2sbGBkZH6l7DCrF27Ft7e3vj+++/h5eWFunXrYsGCBbh37x4uXrwIc3NzGBkZQV9fH/b29u91zMePlcPfFAoF5s2bh7/++guZmZno168fXr58WUjqz0OhYd2Od4l01IubXPF+aRYsW4Otew9gUK+u+GvO95j25WikpKZi8s/zkJEp3BfThU1HpumaKAq5Jjp50jg4OOCPeXPw7aQJeP06EZMmTxP0y3p5IddER0MvV7m8kOsoUs5p+s3o4fBv0hDz/1mF9gNGYfhX38GnQnmU9yoDIw3zNAtFodfkE+Zs/XXNNmw8eBLDurbB8u+/xA+jB+BNqhhf/voPMjIFPBVOYedcyH1i1qAldEQipJw+XOB+eo6ucPh6FiCX4+XK34HPMIXgh5DL5ZDJZCo/igLuh4LKSkH3Uc49VNIUVjfkrS+BwuvlvNfQ0cEef/w6F99OmqisY7+dIrg6VlM50bSWVo4Cy0kB10eUna6w573Q5pku6J4B3p7Xp3oRGY0vf/wVuiIR5nw7HiINzzMhKOyZ8z7X43TwLcz4ZyOql/fEl32Vi6Z+6L0lJIW2MzTk/X3bsO8Kj4jAlOnfQVdXFzO/mybYMgIUft9o+n9+aBm4+yAUj54+Q5+uHT88g1pQ+HcdDdeksPpSJHqP7wsCvnfYhi0Smr7/vEshlwOF7iPENqxU5aeg77ma2mw5CrqPNF27o4f2oIxnWVSrIdwgcXHVJyUav//9ZykU/PnQn/+i/4sRGBUrVkSHDh0wevRo2Nvbo0GDBmjatCn8/f3x5MkTVKpUSWWR59q1axdwtE/36NEjNGiguiBfhQoVYG5ujtDQUDR5Z3Gl9+Xn54egoCBYW1vnNgSXLFmCpk2bYs+ePRiVvYiTNpmaZvcWyNOTMae3lampem9Ms/dIk/DqNQ7+ewoDenTB8P69c/epWK4sBk/4FkdOnkG39m2K7kSKkGluDwrV88sdUWCq3ovA1FTZ8yDvFBzifNLY2drAztYG1ar4wNnJCd9Mm4ELlwLh36JZ0ZxEETPLuSZ5emTlXCMzk4LKiWqanBE6OdfZxNgI0yaMwpcjBiEu4SUc7e1gYmyEQyfO5E5LJkRmOb1m8rwUzem1pumavI/410nYezoQQzu3wpheyoV2awKo7FUavSf/ggNng9Cr9YfXR59DTq8ZkZHquef0vJEX0qvGxLceMu7fyl24TRPD8pVh98UUKDIzELfgB0hfxn1irove1oAtCAjYorKtQcOGSEpKVNtXnC7OrT80MTU10Ti1j1gs1tgbriTIOd+8z5AC69js+kK9js157qheC1tbW9ja2qJqFR84OTvi26nf4cKlS/Bv0bxoTqIIBAQEIGCLajlp2LAhkpKS1PZNFxdSTkwKKCfZ6UxNTfPdJ+dzIcl9Fmfk99wpuEff+7h+9wG++30JTIwMsfjnqXBzEu6Cu2Ymys45as/U93zmbDl6Bn9v2Y+alcrij69GwNBAOWd/Tg9sTXPTp6VnfPSz7HPIqSvyluvc3q4ayrSpyYe1127dvoNZv8yFsbERfpv3S+40IUL19prkc99obMPmV7+KNaY5F3gF5mamqFtTdf0yocrvmSMuqJwUksbUxCS3N3Vhda8QsQ1bNKTJKQAAPXPV/3XOqAppcmruyIu8++Tsl3MModi5dQN2BKxX2VavQRONbdh0cVqB5dzE1BTpGtr+YnFabl2cIyXlDe7evoH+g7X/fqQgxVWflGT8/kdEBfm/CGAAwJ9//olx48bh/PnzCAwMxOTJk1GzZk0YGhqq9UI0MCjentj59aJRKBQqC7N9qLxzTRsbG8PNzQ1xccKodF2cHKErEiEqRjU/UTHKuaVLu6nPBV0qezqTqJg4lPd8OxVW5Dtp4hJeQqFQwKei6nReHu5usDQ3x7Nw4c4L6+LsBJFIpLZIY3T2IobuGoZ25sxpGRUTg7Jenm/TZB/DvZQb0tPTEXTlKrzLl4frO+tClMve/9Xr10V7IkXIxckhu5yozjkeWWA5yV4QNSYW5T09crfnlLWcNIHB12FuZooqFb1Rxl15bROTkpHw6rVKOqFxc7SDrkiEyNgEle05v5dx/bjgS+zL11AoFKjq7amy3dPNGZZmpgiLFO7ioZKEWChkMujZq85Zr+eg/F0Sm/99r2tlAwN3T7w6dSjffUz8GsJ2yARIYqOQsGQOZEnCvGfatG2H2rVVF9IOCgrE9ZDrkMvlKr13Y6JjCpz+ydXNDWFP1RfTjImOQXnvD58uUQgKrWM1XA+37PoiOjoGZd9ZV0pzHVtOZTqlctn7v3olrPLStm1btc4ZQUFBCAkJUSsn0dHRBZYTNzc3PNVQTnLWCgOUZUksFiM5KQmWVla5+8RER8PBwQGGhoafeEZFy9XJXlnH5mmfRGYvsl3a7dNeJJ+4cBlzlqxCaRdn/Dnzm4+aeupzcnNQPnMi4lRH8Ob8nnedixwKhQJ/bNyD7f+eR+t6vvhpdH+VxbpNjAzhYG2JyFjV475OTkFaRibK5HNcIXBxdi7yuuTd++zM2XP4Y+FfKOXmhjk//wQ7O9siP4ei5uLkCJGG9lrO7+4FtutjUe6ddn1Oey1vmqBr19GwTi2VjmZC5pxdTmKiVednj87+XXM5UbZHY6KjVctJThp3dxgbG8PW1jZ3W46kpCSkp6drPK5QsA1bNFIfKde1MPEqjTc3H+RuN/VSLt6d+vApZGlipEfGwiR7Ww4DexvoW5ghNZ8F07XFv01H1KxdT2Xb1aCLuHk9WL0NGxMFt1Kl8x4il6urO8LCHqttj42JQtnyqgs73wy5AplMhnqNmn7aCRSz4qpPSjJ+/yOigpTwMWZKt27dwty5c+Hp6YkhQ4Zg5cqVmDt3Li5fvgxnZ2fcvXsXWVlvh5nevXu3wON96hB3b29vhISEqGx7+PAhUlNTcxfh/tC/sX37dtSpU0dlLsTU1FQ8f/4cZcuW/aT8FhVDAwNUrVwR5y9fVQninAu6AjNTE1Qsr55PN2cnODs64NylyyrbzwdegZuL8jM3ZyfoikS4ff+hyj7hkdFITkmBi6NwvxAbGBigqk9lXAy8rHJNLgQGwdTUBN4a1lhxdXGGk6MjLlwKVNl+ITAIri4ucHJ0hK6uLhYs/gc79+xV2efajZsAgDIe+TcAtU1ZTirg/OXgPOUkGGYmJqhYXn2hemU5scfZwKsq288FXc0uJ8qp2PYfP4Wl61V7Ie88eBQikQj1/VQXWRUSQwN91KhQFmeCb6pck9NXb8LMxBiVvTw+6rilsl/a3czzheZ5dBySU9Pg6mD3KdkuXlIJMh/fh0kN1YVPTWrUg1ychqxn6l9ichh4lAMAZD59oPFzIx9f2A6diMywUMT9MUPQjVdbW1uUK19e5cfXtybS08W4/s5zJjk5CXfv3kEN35r5HsvXtyYiIiIQHv4id1t4+AtERITDt0b+6YTMwMAAVXwq41JgkMq9c/FSIExNTfOpY1001rEXLwWq1LELFy3Gzt2qdWzI9RsAAM8yHkV/Mp/A1tYW5cuXV/nx9fVFenq6SnskOSkJd+/eha9v/vWhr6+vspy8eKecvHiBiIgI1MhOV6OGsrf0xYsXc/eRZGXh6tWrBR5bWwwNDFCtkjfOXQlRKSdnL1+DmYkxKpXzLCB1wQJDbmH2opWo4l0Wy36ZIfjgBZDzzPHCmeDbeZ45t2BmYgwfL81tiH+2H8L2f8+jf9ummDNukErwIkedqhVw4cY9ZEmkb48bfAu6IhFqVS5X9CdTRJR1iQ8uBQbmU5eo5/1tXXJJZfu7dQkAXA2+hvl/LkClihXw5++/lYjgBZB932ho158PugLTQtv1V1S2K9v1znB2fDsy6U1KKiKjY+BT0bv4TqKI5V9OLsHU1FTj2okuLi5wcnJSqS8B4NKlS3B1cYFjdjnx9fXF1atXkSWRqBxXJBKhWrVqxXRGn45t2KIhfhoOcVgEnLu1Vtnu1LUVUh89Q/oL5YLOL09egkO7phAZvO0U6dStNeRSKV6eUf0+rW02tnYoW66Cyk8131pITxfj5vW33+mSk5Pw4O7tAqd7qubrh6iIF4gIf567LSL8OSIjXqC6r2q6Rw/vw9bOHg4OThCy4qxPSix+/yOiApSM7i6FMDMzQ0BAAPT19dGrVy9kZmbiyJEj8PDwwNixY3Hs2DFMnz4dY8aMQVRUFBYuXFjg8XKG8T58+BDW1tYfPBXC0KFD0a9fP8yePTt3jYrZs2ejUqVKqFevXu7fiI+PR0RExHstmNq4cWP88ccfmDJlCiZOnIiMjAwsWLAANjY26Nat2wflrzgN6tUVX//wC36c/xfatWyKew8fYdveQxg1qC+MDA2RJhbjeUQUXJ0cYWVpAQAY3Lsbfl20HBbL16BB7Zq4eOUazly6jB+/VS66ZWVpgR6d2mLbXmU03a9aVcQlJGD9tt1wcrBDh1bCmcZDk369e2LqzB8x+9ff0ca/Be4/eIide/Zh+OCBMDJSXpMX4RFwcXaCVfb6KwP69sIffy2Ghbk56tWpjcDLV3HuwiXMmPItAGWDp0+P7tgYsA1WlpaoVrUKwp49x6at2+BbvRpq+wn7ZeSgnl3x9Y9z8ePvf6Ndi6a4+/ARtu07hC8G9sm3nAzp1Q3zFq+ApbmZspxczSknbxdy796+Nb79+VcsXrMJDWr7IuT2PWzefQD9unUU9BRSADCsa2uMm7sE0/9ei45N6+L2o2fYdOgUxvfpBCNDA6SK0/EsKhZujnawtjB/r2NaW5ijT9um2HToJACgTpUKiHn5Gqt3H4WznQ26NK9fnKf0yZKP7oTDxJ9gN/JbpAaegqFnBZj7d0bSvs1QSLKgY2QMfedSkCbEqgwV1nctDYUkS/OQYD192AwYC0VGOt4c3Q19Z9X6V5b4CrKkV8V9ap/Ep0oVVKlaFb///huGDRsOcwsLBGzZDFNTM7Rr1z53v/DwF5BIJPDyUr5katy4MXZs34Yfvv8eQ4YOBQCsX7cOHh4eaNS4sVbOpSj069ML02b8gDnzfkPrVv64/+ABdu7Zi+FDBuXWseHh4XB2dn6nju2DP/76G+bZdWzQlSs4d+Eivps6GYCyju3dswc2bdkKK6vsOjbsOTZv3YYa1auhlsDrWACoUqUKqlatit/nz8ew4cNhYW6OLVu2wNTUFO3av1NOXmSXk7Jvy8n27dvx/Q8/YGh2OVmXXU4aZ5cTR0dHtGzZEitXrkRmVhZcXV2xd+9epKamokfPnp//ZN/DkB4dMfHn3/H9n/+gffPGuBP6GAH7j2LMgJ7Zz510PIuMgqujA6yznzuFyczKwq/L1sHE2AiDunfEs8golc8dbG3gYGuTT2rtGt6lFcbOW4ppi9ajU5M6uP34GTYdPo3xvTtmP3My3nnmmCH0eSQ2HDqFSp7uaFmnBu4+eaFyvDKuTjAzMcLgDi3wb+B1fDl/Ofq3bYoXsQlYuuMQujavByc7YV6LHMq65Hv8Mu83tG7VEvcfPMSuPXswbMhgGBkZaaxL+vftgz//+hsW5uaoW6cOgq5cwfkLFzF96hQAQFZWFhYuWgwTY2P06d0L4eHhKn/Tzs4O9nbCfRE7sFc3fPPDHPw0fyHatWymbK/tPZinXR8JVyen3PbaoN7d8duiZbBYbob6tf1w6co1nLkUhB++nahy7LAXymvhUapkLTTbp08ffDdjBubOm4dW/v548OABdu/ejaFDhuRbTvr17YsFCxfC3MJCWU4uX8b5CxcwberU3OP27NED586dww/ff4+uXbsiKioK6zdsQNu2beHgINwp6QC2YT+GnrkpzCqVhfhpOLJeKqdUevzLP6i25ldIXich7uBpOHZqAZde7XC936TcdE//WA2X3u1R69BqPPtrHUzLe8B79teIWL0DGRHCH5VS2acaKlepjr9/n4OBw0bD3NwC2wPWw8TUDK3bdcndLyL8OSSSLHh6KV/iN2jcHLt3bMGcH6ZgwBDl1FCb16+Eu4cn6jdSnTb5xfMwuLl7fK5T+iTFVZ+UZPz+R0T50VEUtmpYCXHmzBksWbIEYWFhEIlEqFu3LqZOnQp3d3c8fPgQv/zyC27evAk3Nze0adMGS5cuxalTp+Dm5obFixdj7969OH36NADll43x48cjMDAQX3/9NYYNG1bo32/evDm6du2KCROUL1ODgoLw119/4f79+zAzM0PLli3xzTffwCp7qoU7d+5g3LhxSE5Oxr///qsSLd+zZw+mT5+O0NBQlb9x7949/Pnnn7h9W9ljrkGDBpg+fTqcP3EO3diHNz4pfV7ng65i3dZdiIiKhp2tDbq2a4XeXZRzl964cw+TZs7GtC9Ho22LprlpDhw7iW37DiHh5Ss4Ozqgf4/OaN3s7Qs1hUKBXQeP4sCxk4iJi4etjRX8qlfFyAF9cr8wFRWnCjUQ/lhz5P5jXQy8jI0BWxEZGQVbW1t0at8WPbt1AaCcE/nb777Ht5MmoHXLFrlpDh09jp179iHh5Us4OzmiT8/u8G/+toGmUChw6OhxHDh0BNGxsbC0tEDzJo0xqF+fIp8mzb1cRcQ9CCl8xw9w/nIw1m7dhYioGNjZWqNr21bo00X5Uu3GnfuY+P0cTJ/wBdq2eDu/7f7jp7LLyWs4OzpgQPdOaN2skcpxT54PxMadexETlwBHBzt0beOP7h1UezMVBceKNfHm+okiPeaZ4FtYufMwXsTEw97GEj39G2NAB2WZCLn/CKNnL8IPowegY5O6amkPnruMWcs3Y/+in+Fi/7Z3p0KhwNajZ7Hn1EVEx7+CnZUF6lStgLG9O773l8j3ZeHrj/DRRRtQNa5eB5YdekPf0RWypFdIOXcMKScPAFDOYer49Wy82rAYaUFnctNY9x0Fk+p1EDV1uNrxDL194PjVrHz/XvKh7Ug+tL3I8u++fA+ePH1WZMfLkZKSgtWrViLochAUcjkqVaqMkaNGwc3tbYN82tTJiIuLw7r1G3O3JSQkYMWKZbh54wZ0dXXh61sTI0eNgo2N5h7BCxb8gTu3b6sc41OV9SqDF09CC9/xA1wMDMKmLQFv69gO7dCjW1cAyjp28vQZ+HbSRLTyf7eOPYZde/YiIeElnJ2c0KdXD7TMU8cePnoM+w8dRkxMTh3bBAP79y3yOrZ0WW88DQsr0mMCynKyatUqXA4KglwuR6VKlTDqiy9ypyAAgKlTpiAuLg7rN2zI3ZaQkIAVy5fjRm458cWoL75QmcpSkpWFdevW4ezZs0hPT0fZcuUwfPhwVKhQoUjy7uXpiZd3g4rkWDnOXQnBmm17ER4dC3sba3Rr2xx9O7UFoFzDYsKPv+G7ccPRvnkjtbSHT1/A3H/WYNey3+HsoBz1F3LnPr78aX6+f29Yr84Y3rtrkeTdzqceUq4dK5Jj5TgTfAsrdh/Fi5h4OFhboad/Qwxor+wUcu3+Y4z+ZQl+HNUPHZvUwfJdR7B67/F8j7V8xnj4VVL2frzx8Cn+DtiPR+FRsDIzRbuGtTC6Rzvo6ekWaf7N/drg+ZNHRXrMS7l1SSRsbW3RsUN7lbpkyvTv8M2kiWjl3zI3zeGjR1Xqkt69eqBlc+V1vHnrFqZ+NzPfvzegX18M7N+vyPLvUbY8Yh7eLLLjAcCFoKtYt3Vnbru+S7tW6N1Fuej2jTv38NXMWZj65Zg87foT2L7vEOJfvoKLowP69+iCVs1UA+VnLgbh59//woZ/FmicPrSoOFeornH6xE9xKTAQmzdvRmRkJOzs7NChQwd0z+5Qdvv2bUydNg1ff/UV/P39c9McOXIEu/fsQUJCApycnNC7Vy+0aNFC5bh3797F6jVrEBYWBgsLC7Ro3hwDBw4s8im2PL282IbNw8LXH4f1i2c0kE3j2qh3ahOCWgzE6/NXVbbdGj4NkRvfjvZ0H9kbnl8Ng1EpZ4jDIvB0/kpEbdmvcjzrBjVR8bcpsKhWEVkvExG1ZT8e/bQICqkURam9JBR3n8QWvuMHSk1JwfrVS3A16CIUCgW8K/lg6MjxcHV7O/3RD9MmIj4uFsvXvW2Tv0yIx9oVi3Dr5jXo6eqhmm8tDB05HtZ52rATRw9C6TJe+Hrqj0Wed5+yTiWmPskRFxeHIUOHqh2jqHh6efH73zvcl+8pkuP813y3JrPwnUjF3OHCmrL3c/i/CWB8iCtXrmDQoEG5AYz/uqIOYJR0xRHAKOmKI4BR0hVHAKOkK44ARklXXAGMkqw4AhglXXEFMEqy4ghglGTFEcAo6YojgFHSFUcAo6QrjgBGSVccAYySrjgDGCVVcQUwSrLiCGCUdMURwCjJGMD4ONNWZWg7CyXOryONtJ2Fz+7/Yg0MIiIiIiIiIiIiIiL6//J/sQZGcZo1axb27t1b4D7//PMP6tcv2fNwEhEREREREREREREJyX8ygFGnTh219SXyM378eAwePLjAfYS+sBoRERERERERERERUUnznwxgfAgbGxuVRSuJiIiIiIiIiIiIiKj4cQ0MIiIiIiIiIiIiIiISHI7AICIiIiIiIiIiIqLPSqFQaDsLVAJwBAYREREREREREREREQkOAxhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJjp62M0BERERERERERERE/y0KubZzQCUBR2AQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4OhpOwNERERERERERERE9N8iVyi0nQUqATgCg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiARHT9sZICIiIiIiIiIiIqL/FoVCoe0sUAnAERhERERERERERERERCQ4DGAQEREREREREREREZHgMIBBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOHrazgARERERERERERER/bfI5QptZ4FKAI7AICIiIiIiIiIiIiIiwWEAg4iIiIiIiIiIiIiIBIcBDCIiIiIiIiIiIiIiEhwGMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsHR03YGiIiIiIiIiIiIiOi/RaHQdg6oJOAIDCIiIiIiIiIiIiIiEhwdhYKxLiIiIiIiIiIiIiL6fCYtTtV2FkqcvyaYaTsLnx2nkCI8eBql7SwISkUvVwQ9eKPtbAhKvYoW+GOPXNvZEJRvu4mw5yqvybu61RaxPsmjopcr/jrAfgLvmtRJBy+ehGo7G4JSuqw3Mg4t03Y2BMWowxgsO6btXAjHmDbAqpPazoWwjGwJvL5zUdvZEBSbKg3RsOM5bWdDUC4ebIKUa6xM3mXu1wa7rrAN+64edUS4+yRW29kQFJ+yTjis763tbAhKe0ko3oQc13Y2BMWiZmvcexKj7WwIRuWyztrOAtH/LU4hRUREREREREREREREgsMABhERERERERERERERCQ6nkCIiIiIiIiIiIiKiz0oh55TLVDiOwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhwGMAgIiIiIiIiIiIiIiLB0dN2BoiIiIiIiIiIiIjov0WuUGg7C1QCcAQGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQUREREREREREREREgsMABhERERERERERERERCY6etjNARERERERERERERP8tCrlC21mgEoAjMIiIiIiIiIiIiIiISHAYwCAiIiIiIiIiIiIiIsFhAIOIiIiIiIiIiIiIiASHAQwiIiIiIiIiIiIiIhIcBjCIiIiIiIiIiIiIiEhw9LSdASIiIiIiIiIiIiL6b1HIFdrOApUAHIFBRERERERERERERESCwwAGEREREREREREREREJDgMYREREREREREREREQkOAxgEBERERERERERERGR4DCAQURERET0P/buOiyqrI8D+Fe6QZQOKcUAlbS7u3t17c5V1651XXPttbuxu1tRDMAGFEQaxACJoYZ5/xgYHWcAV0GGd7+f5+HZ5c45l3OO95575v7uOZeIiIiIiIgUjkpxF4CIiIiIiIiIiIiI/luyRcVdAioJOAODiIiIiIiIiIiIiIgUDgMYRERERERERERERESkcBjAICIiIiIiIiIiIiIihcMABhERERERERERERERKRwGMIiIiIiIiIiIiIiISOGoFHcBiIiIiIiIiIiIiOi/RZQtKu4iUAnAGRhERERERERERERERKRwOAODioxAIMCubZtw985NpAkEqOxUFYOGjoSFpXW++RI+fsC2zevh7/sAQqEQbh6eGDBkJAwNy0jSXDp/Bv+s/lsmb+u2HTB05LhCr0thSBOk4uCuNXh49yrS0wRwrOyCXoMmwMzC5pv3sXbxFKhraGLIuLlS20UiEc6f2IPrF47h/bs4lDUyRdO2PdC0dffCrUQhy0xPwf3zfyP02SVkZqTCzMYNNdtOg4GRbb75rnn9juBHp2S2N+m9EnbOLWS2+5xZjHdRz9F26K5CK3tRSU9LwfkDf+PZw0vISEuFjaMb2v4yDUZm+bdJ2Ct/XDi4AlFvnkNdXQtOni3Rott4qGtqS9K8iwvDxYMr8ealLzLSBTC1qoCmnUfDoUqtoq7WD2FfIiszPQV3z/yN108vIjMjFeZ27qjdbipKG9t98z5Cn13B+Z2j0H74TljY15BsT06Mw93TSxARdBvZ2VkwLVcdni3Hw9jKuSiqUige+vljx67dCAsPh4GBAdq3aYOunTuiVKlSeea5dv0G9nkdRExsHEyMjdGjWxc0b9pEKs3NW7dx8MhRRERGQVtbG67Vq2FQ/34oXbp0UVfph90JCsPac3cQEvsehrpa6Fm7Gvo1dM2zTdIzs7Dx0j2c9QvEx2QBKpiXxfDmNVGnoo1UuidhMVh1xhvPImKhpaaGOhXLYULbeiijq/UTavXjMtJTcPvkMgQ/vojM9FRY2LujfqdpMDT59nMn5OllnNoyCl1G74JV+c/nzrO7h3D5wEyZ9NXq9UGjrrMLpfxFISMtBTdPLMNLf3GbWDq4o1HXf9cmwY8v4/imUeg+bhesK3xuk6SPsbhxbAnCgu5AmJUBa8daaNBxMkob2xRBTX7cvUfPsHH/MbyOiIahgR66tGiE3u1b5NuX5Ap6HYZB0xbg0Jq/YGZcVuqzM9duY9/JC4iMi4dpWUN0adEY3Vo3+ab9KprRA+3g6KCLMdMfF5i2tIEqxgyyRw1XQygrl4LPww9YszUE7z9mSNIoKwEDetmgVRMT6OupIig4Gf9sC8GLl0lFWY0f4vMkEOsOnUZIZCzK6OuiW7N6+KV1ozz/PTMys7Dn7FWcufUAcR8SYGxogFa13dC/fVOoqnz+Ov7idThW7juBgNfh0NbUQLv6nhjapZVUGkWWnpaCC15/4/kXY9jWfQoew4a/8sfFQ+IxrJq6Fpw9W6JZV+kx7NuoYJw/sAxhr/xRqlQpVKzeEC16TISugVFRV+uHCASp2LN9I3y8byItTYBKTlUxYMjobxrD7tjyD/x97yNbKISLew0MGDIapXPGsGuWL8T1K+fzzL9+uxeMjU0LtS6FScPCBPUfncbDLqPw4eb9fNOa92gDh2kjoGVnBcGbKAQv3YSo3cel0ui7OaHS4t+h7+aErE8piNx1FC//WAtRZmYR1qJw+DwJwLqDZ/A6MgZl9HXRtVk9/NKm8bddd95E4NdZf+Po8lkwNyoj9dmpG/ew58xVRMbFo6yBPto28MSADs2hoqxcVFUpVAJBKnZv3wQf7xtISxOgslM1DBgy6pvOne1b1knOHVf3Gug/ZJTk+9+a5Qtx7cqFPPNv2H5Aoc8dov+yEjEacnR0xMKFC9G5c+fiLgr9C8uX/ImXgQHoN3AotLS04LVvF2ZOnYg167dBR1dXbh6hUIg/Zk9Famoqho8ej6wsIXbv2Iy5M37H8jUboZIzgA99HQwLSyuM/W2KVH6D0oZFXq/vtWH5TIS8fIbu/cZAU0sbJ7y2YPHMEViwxgvaOnr55s3Ozsb+bSvw8O5V1GnURuZzr52rcen0AXTqNQx25avgia839mxaChVlFTRsobjnzVWvyXgb/hg1Wk2Cqro2/K6sw5nNv6LrhFNQ19TPM9/7mADYV2uDKrV/kdquX9ZGJu2TW9vx9PYOmNl6FHbxi4TXuskID36MVj0nQV1TG1eOrcPmv37FhEWnoKktv01iwoOwddFA2FeuiT5jVyPp41ucP7gc72JDMfD3LQCAlKSP2LygH7R0DND2l2lQ19TBg2uHsG3xYAyeth12lTx/ZjX/FfYlsi7tm4S4sMeo1WYS1DR08PDSPzi58Vf0nHga6lp5nzu50lI+4saROTLb0wVJOP5Pb2RmCODZchz0y5bD66cXcWJ9X7Qfvgsm1lWLojo/JCAwELPnzUeDenXx6y998OxFALZs3wGhUIie3bvKzXPL+w4WLVuOju3bwcPNFXd8fLBsxSqoqqqiUYP6AIBrN25i4ZJlaNOqJfr364uPHz9i5+69+H36TPyzagXU1NR+ZjX/lSdhMRiz9QRaVK+AUS1rwT80GivO3EJWdjYGNZHfF849eBk3X7zG2NZ1UM6oNE4+fIExW09gy4iucLWzAAC8iIjD4HWHUaOCNVb0b4f4xGSsPuuN8PgE7Brb42dW8bud3zURMW8eo277yVDX0IHP+bU4srYf+k47A41vOHcEKR9xxUv23AGA+KgAlDa2RfM+i6S2a+mWlZteUZzZMRHRoY/RoONkqGnq4O7ZtfBa1Q8DZn5jmyR/xMX9sm2SkZ6KQ2v6AyiFpj3mQkVVDXfPrcOBFb+g/8zT0NQ2KPS6/IhnL0MwadFqNK3tgSE9O+JJ4Cv8s+cwhNnZ6Nepdb55Q8IjMWnhKgiFQpnPTl6+iYUbduKXDi3hWa0Knr8KxeqdXkhNS0f/LrJjOkXWs6Mlenaygv/ThALTKisBy+Y6Q1tTBcvWvYSKihKG/2qL5X84Y+B4PwiF4uUiRg+2R9tmZtiw8zVi49LRo6MlVs6vigHjfREVk1bENfr3nr56g/HLNqFZTRcM79oGj4JeY/X+kxAKhejfvpncPMt2HcFZ74cY3LE5KttZ40VoBDYfPY+Ydx8we2hvAEDk23cYuXAdqjrYYOHY/ngTFYd1h84gMTkV0weVjP714PrJiAh+jJY9J0FdQxtXj6/D1oW/YtzCvMewseFB2LZ4IOwq10TvMauRlPAWFw4uR3xMKAbkjGE/fXyLLQv7o4yxFboNX4zMjDRcOrQC2xYPxOj5R6Gsovozq/mvrFwyHy+DXqDvgOHQ0tLGwf07MGfaeKxctzOfMWwW/pzzOwSpqRg26jcIhVnYs2MT/pg5EUtXb4GKigq69eqHFq3bS+VLSkrC3wvnoIpzdRgZmfyM6n0XDUtTeJ7ZClWD/L//AoBpp+aovmsZQtfsQvyFWzDt0BTVty1GdnoGYg6eBQBo2lqixvnt+OjzCH69xkOnoj0c50+AqqEBno2Sf71WFE9fhWLC0k1oVssFw7u1xuOg11iz/ySE2dl59ie5giOiMX7JRgiF2TKf7T93Hct3H0UTz+oY27sDEj4lY+Phs3gVFo0lEwYVVXUK1Yolf+Jl0Av0GzAMWlra8Nq/E7OnTcCqdTvyPXfmz5kCQWoKho/6DVmSc2cSlq3eLDl3mn917iQnJWHZwrkKf+4Q/deViAAGlTyBAc/x4N5dzJq3EG4e4ifxKjtVxbABvXHuzAl06/mL3Hzet67jdUgw1mzYBitrGwCAnb09xo4YBO9b19GgUVMAQOjrEDiUd4Rjxco/oTY/LjjwCR49uIXfZq1EVbc6AIAKlV0weVgHXDl3GO27Dcwzb8SbV9izeSlCX72Ampq6zOfxcdG4cHIf+g6ZjMatxDfrKlf1wId3cXjq76OwAYy4MH+EB1xDy/4bYeUovmFoauOOA0ub4YXPfrg0Gi43X1ZmOhLi38Cpzq8wsa6e5/4/fYjEvbOLERZwDWoa8gc5iibslT8C/K+h/6SNcKwmbhMbR3cs/a0ZfC7vR6MO8tvE+/xOaGrro8+4VVBR+XxT9fDm6YiPCYWRmS38bp9AyqePGDn3IPQNxQOz8k61sXp6R9w8u01hAxjsS2TFvvFH2ItraD1oE8pVFB8nZrbu2LOwKZ7d3Q+3JvKPky/dPPYHlJRlhwCBD44g6WMUOo7cBzNbVwCAVYU6SEv5iDsnF6LT6P2FW5lCsGvvftjb2WHKpN8AAB7ubhBmZeHAwUPo1KEd1NVl+83tO3ejft06GDF0MADA3c0VSUnJ2LlnrySAceDgIXi6u2Pc6JGSfJYWFhg3cTJ87j9A/bp1fkLtvs+683dR0cIIf/VuCQCoU9EGmUIhtl55gD71XaChKv1vH/UhEWf9AjGtUyP0qFMNAODpYIVHodHw8n4sCWCsOH0bFS2MsWpAeygpiZ8M1NZQx5Lj1xH5PhGWZQq+2V2cokP98frZNXQYtgm2lRsAAMzt3bF9XhM8ub0Pns1HFLiPq4fmyT13ACA+MgAm1s4ws6lemMUuUtGv/RHy9Bo6j9wEuyriNrG0d8fm2U3w6OY+1GxZcJtc9poHZTlt8tL/Aj7EhaL/jNMoa14eAFDWvAI2z26Cl37nUa1ez8KtzA/a4nUCFWysMWfsEABALRdnZGUJsfPoGXRv3RQa6rJBy8zMLBw6dwWbvY5DTVX+TdSdR8+gUU03jOrbDQDgUbUyImJicfjclRITwDAz0cDogXaoU6MskpKzvilPo7pGcLTXxS8jH+BNRCoA4NXrZOxa647GdY1w6cZbGJdVR6dW5li5KRjHz8UAAO77f8D+jZ7o08UaS9a+LLI6fa+NR87B0cYS80f2BQDUrlYJWUIhtp+4hJ4tG0Djq+B2QlIKjl27izE926FfW/EsP08nRwDA2gOnMKZne5TW08HOU1egraGOvycOhqqKCupWrwINdTUs2XEYAzs0g2lZxX6wIvyVPwL9r6HfROkx7LKJzeBzZT8atc9jDHtBPIbtPVZ6DHvkizHsg+uHkC5IQt8J66ClK54Bqa1riK0Lf8XrF/dQvmrdoq/gdwgKeIaH9+9gxrzFcHWvCQCo5FQVIwf2xPkzx9G1Z1+5+e7cuo7QkFdYuX6nZAxrY1ceE0b2x51b11C/UTOYmlnA1MxCKt+SBbOgo6uL8ZNnKubsrlKlYNm3IyotngJ8Y/Ec5/+GmMPnETBpIQDg3aXbUC2tD8e54yQBDPvJQ5CVlIKHnUdClJmJ+PM3IRSkwWnVLAQv2oC0iJiiqtEP23RY3J/8MbIfAKB2tcrIEgqx48RFuf0JAGRmZcHrwk1sPHwW6qqy115hdja2HjuPGs6OWDT+8z0GR1tL9JqyCPeeBqKGc8Wiq1QhCAp4jof372DmvMVwdRd//6vkVBUjCjx3biA05BVWrd8hOXds7RwwfuQAeN+6hgZ5njuzoaOriwmTZyjmuUNEAPgODCoi/r4PoKGhgequ7pJt+voGqOJcDb4P7uWdz+8hLCytJBccALCytoGllbUkn0gkwpvQ17C1dyiy8he2p/4+UNfQhFP1mpJtevql4VjFFU98vfPNu2nlHGRnZ2PWku3Q1Zf98uJ37zpUVdVQr6n0kwQjJy/EmKlLCqcCRSDylTdU1LRgUf7zTUBNHUOY2bojIuhmnvk+xr2CKDsLZcwr5bt/nzOLkPguDG0Gb0cZM8UepOV69dQbaupaKO/8uU109AxhW9EdQY/zbpNmXceh/6SNUl/8cp9Gy8pIBwDoG5qgbqtfJcELAFBSUkYZ03L48DaisKtSaNiXyIp4eRsqalqwqiB97pjbeSA84EaB+YMfnUXkyzuo1WaSzGcf34ZAXVNfErzIZWFfA7Fh/khPTfzxChSijMxMPHnyFHVq1ZTaXq9uHaQKBHj24oVMnti4OERGRaH213nq1EZ0dAyioqKRnZ0NV5fqaN2yuVQaaytLAEBMTGwh16TwZGRl4WFIFBo7SR/XzaqWR0p6BvxfR8nkMdLTxr7xvdDG7XNfqaRUCspKSkjPEj9RnpAiwMOQSHSvU1USvACAplUdcHH2YIUPXgBAWOBtqKppoVzFzze7tHQMYeHggdAXBZ87QX5nER50B3XbT5b5TCQS4V10EIws8r82KZrQAHGb2FT6ok10DWFZ3gOvnxfcJoG+ZxEWeAf1O8m2SflqTdFr4n5J8AIAlJVzrk1Z6YVQ+sKTkZkJv+dBaFBDuu9rVMsdqYI0PAl8JTffHf8n2HroJH7t3AajfpE/4+vv6eMwpp/0kp6qKirIKAFLm+QaM9geluaaGDfjMYJDk78pj6erIcIiUyXBCwB4E5GKsMhU1HIXj2fdqhlARUUJN+++k6TJzBLhzoP3kjSKJCMzC74Br9DIXXpJxSae1ZCSlo5HQa9l8qQI0tClSW3Ud3WS2m5jJh6PRb0V193nSSDqVK8stVxUE8/qyBaJcPdJYGFXpdDJG8Nq6xnCpqI7XuYzhm3aZRx+nfjVGDa3n8gU9xM1m/TCkBl7JcEL4PM4NzNTsfqSLz3yewANDU1Uc/k881Ff3wCVnarB76FPvvnMLa1lxrAWVuXyzOf7wAf37tzEgCGjoa2jmA9u6VV1hNM/8xC55zge9f+9wPSa5Syg42iL2BOXpLbHHL0A7fI20HIoBwAwalYXb8/dkFouKvbIeZRSVoZRc8UMbgHi645vwCs09JCe3dzYszpSBOl4LKc/AQBv/xfYcvQcBnRohtG92st8/iExCYnJqajrIt3nOFiZw0BXG7f9nxdeJYqIv999aGhooJqL9Pe/yk7V4fsw7+9/j/zu5/H9rxz88sjn+8AHPnduYsCQUQp77hCRWIkJYISGhqJ///5wdnZGvXr1sHHjRqnPr1+/ju7du8PFxQV169bFwoULkZb2edqxo6Mjjh49KpXny20CgQAzZsxAnTp14OzsjI4dO+LixYuStCKRCJs3b0aTJk1QrVo1dOjQASdPnvxXdUhMTMTMmTNRr149VKlSBbVq1cLMmTMhEAgkaZ49e4Y+ffqgWrVqaNKkCU6ePInKlSvj3r3PN9x+tBw/Q2REOExMzaH81RqLZmYWiIrK+2ZpZHgYzC0sZbabmVsgKlKcLzYmGgJBKoJfBmHkkH7o0q4ZRg7ph2tXLsrkUxQxkaEwMrGA0lftYWJmidiosHzzDh3/B2Ys3AIrm/JyPw8PfQkTMysEPffHnN9+waAuNTFxSHtcv3BUbnpFkfA2BHqGllBSkm4TvTLlkBAfmme+9zEBAICgB4ew56962DqzKk5u/AVvw6XXYnZvNhZdxp0oMUtHAcDb6BAYGsu2SRmTcoiPybtN9A1NYGYtfpovIy0Vwc/u4MKhFShXwRVm5cQ3JKvWaIWWPSZK5ROkJCI08CFMLBT3Bj77Elkf376We+7ol7XO99wBgNSkd7h17A/U6TAdWnqy60ZraJdGZnqKTKAi8X04APHMJkUSGxOLzKwsWFqYS203NzMDAERGyt6sD48Q18HSQvrpK3NzcZ6IqCgoKSlh2OBBMkEO77viGwflyuW//m5xinz/CZlCIcoZSb+nw7qsAQDgTfxHmTxqKiqoYmUCXU11ZGeLEPsxSTKrolst8Y26lzHvkC0SobS2JqbtOYda0/9BzWn/YMa+C/gkULxlXuT5EBcC/TKy545BWWt8fJv/uZPy6R2uHZ6Hhp2nQ1vOuZP4LhwZ6SmIC3+KnQtaYPWEKti5oAVe3D9emFUodB9iQ6BfVrZNShtZ42NcwW1yxWseGnWV3ybqmrqwsBMHBIRZGYiPCsS53VOhqVMajq6tCq8ShSA6Lh6ZWVmwNpNeOsLS1BgAEBYtP2hZyd4WR9ctRv8ubaGsLP9rlY2lOcyMy0IkEiExKRknL9/EuRt30LlFo8KtRBHavDsUv47xxePn3x7EtrHUQkRUqsz2yGgBrC20JGlSUrPwIUE6mBMZI4BRGXVoaijWV9Wot++QmSWEtZmx1HYrU/HxHxbzViaPhXEZTB3QHTbm0sfWDd8nUFFWhrWZMdIyMhDz7oPMfkvr6UBbU0PufhVNfHQISucxhn1XwBjWNHcMmy4ew148vALlyrvCzFo8htXWM4SlnfhmbGZGOsKDH+HUzvkwNLaWCpgomsiIMBibmsmMYU3NLRCdzxg2KiIM5uZyxrBmFoiOlM0nEomwa+s6VHGujlp1G/5wuYuKIDwG1ys2Q8DkRRCmFjxu0KloDwBIefVGantqiPi7s04FWyhpqEPLxhIpr6SPsYx3H5GZmASdCvm/f6U4Rb19L+5PTL/qT0xy+pNo+ed9ZXtrnFg1FwM7toCykuz7LHS1NKGsrISYdx+ktn9KTkVSigBRb98XUg2KTp7f/8wtEB0Vnm8+M3Mrme2mZhaIjpTNJxKJsHPrelRxrobaCnzu/BeIRCL+/Muf/6ISs4TUnj17MGfOHMyfPx+nTp3C8uXLUbVqVdSqVQuXLl3C2LFjMWbMGCxevBivX7/G3LlzERERgXXr1n3T/letWoWgoCBs2rQJenp6OHToECZMmIALFy7A0tISK1aswOnTpzF79mzY2dnhwYMHmDt3LpKSktCnT59v+htTp05FXFwc1q5dizJlysDPzw/Tp0+Hg4MD+vfvj7i4OPz6669o0qQJ5s2bh6ioKMydO1dqPd3CKMfPkJqaAi0t2Rd6ampqQpAq+2Xmy3zybjpqampJ8oW+DgYAxMXGYMDgEVBRUcG1Kxex6u9FyMzMQPOWbQupFoUnNTUZmlraMts1NLUhEKTkm9fKJv+by0mJH5HwIR4bV8xCxx5DYGZpg3u3LmLHevFUW0VdQiojLRmq6joy21XVtZCZnnebvI8WP4WWmSFA455/Iz01AY+ub8bpLf3RYcQBlDETfwkyNK1QNAUvQmmpyVDXlG0TdQ0tpBdwnADiC//8kbWRlZkOLR0DtO87I8+02dnZOLp1NtIFyajfRnHXQmVfIitDkAQ1DXnnjjYy8jl3AODG4dkwKVcdjm4dEBUi+yRSBdf2eHxjOy7sHoe6HWZAW98EYQE3EPjwGADxeadIUlLF9f36GNHS0gQApMo5RlJSUqTS5NLUzDsPAETHxGDz1u2wt7OFp7vbjxW8CCWniZ9G1dGQXnZAK2f5m5S0DJk8X9p+7QFWn70DAOhS0wk1K4iDNR+Txf/2c7wuoW5FG6zs3w5h7xKw+qw3It8nYsfobgo/7T6vc0dNQxsZafmfO1e8ZsHMxgWVPDoi4pXsuRMfJQ6uJ36IRL0OU6GkrIKAB8dxce8UCLMy4Fy7u0weRZAuSIJ6Hm2SXkCbXNw3C2a2LqhSoyPCX+b9RCQAHNswAm8CbqNUKSW06LMAOvrG+ab/2ZJTxcf31/2ClqYGACAlj5ttxmVKy90uz7OXIRg6Qzw2q2Rvg17tmheQQ3GEhud9vc2LtrYKIqJlrxmpAiG0tZQlaVJSZd8bkioQb9PSUoGggD7rZ0rOOQ60c46LXFoa4qUKU74xmHvtwWOcvvUA3ZvVg562Ft4lfAIA6Hy1X0C8TN+37rc4pQmSoSGnL/k3Y9gFX4xh2+Yxhl07syPexb6BqpoG+oxdA1U12TZTFKkpKdCS8/1PPBbNu01SUlNgJieAoamphVQ5+R7eu4PIiDAMHDb2xwpcxDI/JiLz47cHQVX0xcdT1ifpWV9ZSeI2UNHTgaq+rtw0uelU9GSPSUWRe92R6U808+9PjA0N8t2vhroamtV0xaGLN2FvaYqG7tXw4VMS/t51BMrKShCkK06fmpfUlGRoftf3v2SYmVvIbNfU1JQ7vn+Qc+4MGjbmxwpMRD9FiQlg9O7dGx07dgQAjBw5Etu2bcOzZ89Qq1YtbNq0Cc2aNcPIkeJ1qm1tbSESiTBq1CgEBwfDwaHgp4vDw8Ohra0NKysr6OnpYdy4cfDw8IC+vj5SU1OxY8cOLF++HA0bNgQAWFtbIyoqClu3bv3mwEGdOnXg4eEBR0fxDVZLS0vs2bMHL1+K13f18vKCrq4uFixYAFVVVTg4OGDmzJmSehVWOQpbdna2TAQwO1v2ZVK58rvBIcrOO5JYKmfJispOVTFjzgI4V6sODQ3xF00XNw8kJiRg/+4daNaiTbHeRBG3h3T9861XqR97uiwrKxNJnxIwespiuNdqDED8Doz372Jx3GuLQgQwRPLaRPR9x0iV2r/AulIjWFX4PCXY3L4mDv7dEo+ubUCT3it+vMA/gdzjJL82USr4mM4WZqHfhH+QlZmB66c2YeOCvhg+c69kFkYuYVYmDm2ajmcPLqJ9v5mwsleMFzOzL5FVmOdO4MNjiAn1RY9Jp/JMY2jigNYD1uPGkdnw+rsdAMDIsgo8m4/B7RMLFO5GQXY+/86A/P61oCdW5LVheEQkps2aDWVlZcyaPhVKSor1VPCXCm6T/I/p+pXtUN3GHP6h0dh46R7SMrPwV++WyMx5mKKypQnm9hC/WLJGBWvoaqpj6p5zuPsyHLUdyxVOJQqB/HMnv2tx3u3y4v4xRL32Rb+pp/NMY2HvgfZDNsCqfA2oquc8XV6pHgRJH3D33Go41Sr+AE9htskzn2OICvFF/5l5t8mXarYcAY+mgxHw4CTO75kGkUgI59rdvq3gP0FB543SN1yDC2JqVAb/zPsdMW/jsXH/cQydsRA7l86Ghpz39BSXUqWAr6sq5/2w3yS/Jstt7oKaNb9reXHILuD6ofQN5/jVB48x859dqF7BDmNzln/5nutScSqqMWzfCf8gMzMDN09twua/+mLozL2SWRi52vWbBZFIhLuX9mDXihHoN2G9QrwD41+3ST7f/0T5jX3ljD/OnT4KWzsHqeV2/h/Iq+uXRNnZQIFpFKsP+VJB/cm3nDd5mTaoB9RUVfDn5gOYv2k/1NVU8Wv7pkhNS4eGmmK99F7+ufN9Y5P8ruXyruPnTh/7vzx3iP5flZgAho2NjdTvenp6SE8XP2X48uVLtGkj/RI8T09PyWffEsAYMmQIhg8fjlq1aqFq1aqoU6cO2rVrB11dXTx58gTp6emYOHGi1E2LrKwsZGRkIC0tDRoaBd/U6d27N65evYpjx47hzZs3CA4ORmRkJOzs7AAAL168gJOTE1S/eAmgh8fn5W+Cg4MLpRyFzWvfLnjt2yW1rXbd+khMkF2mIlWQCi3tvJ+E0NLWhkAgGx0XP4UtfoLFwKA0PGrUkknj5lkDjx/5IuHjR5Q2LL51c094bcEJr81S29xrN8GnxA8yaQWpKdDS+rEnQzQ0tVGqVCnJy8FzObvUwjN/HyQmvIe+QZkf+hs/yu/qOvhd+Udqm61TCwiSZaewZqal5PvSbQMjWxgYSU8HVtfUg0k5V7yPDSqcAv8EV4+vw5Vj0m3i5NECyYmybZImSIGGZsFrciqrqEqm0ttWdMfiCU3hfXE3ug5ZIEkjSPmEPavGIjTwAdr1m4lazRRn5hb7ElkPL/+Dh5ekjxO7qnmdO8l5njvJCbHwPvEXarebCk1tQ2QLsyRfkEXZ2cjOFkqWfbByrIs+064g6aN4+SU9Q0sE3D8CAFDXMiisqhUKbW3xv2WqQPop35Scp6y0tWWf3tLOeaJL8FWe1Nwn4bSln5Z8/OQp/liwEBqaGliycIFkeSpFpZP75N5XT9jl/q6rKftCyC+VNysLAHCzt0RWdjbWX/DBmFa1oZ0zg6N+Zen+t05FcdAiMOqtQgUwfC78g3vn10ptK1+9BVKT3smkzUhLgXoe505SQiyuH12ABh2nQlMn73NHS7cM7JxklwSyqdIA4S/vIDXpndxlln6mO+f+wd2z0m1SwUV+m6QLUqCex3Un6WMsrh1egIadp0Irnzb5kqWD+OZAuYq1kPghCj7nNyhUAENHO2cG1ldPvKbk9As6X83M+B5GhqVhZFgaqOIIcxMjjJy9BFfv+qJ1w9o/vO/CMqBnOQzsbSO1rW67gt+FIk9yaha0NGWXN9HWUkZKSlZOGqH8NJoqks8ViY6W+HuWzHGS83tBx8nec9ewau8JuFV2wLIJg6GecyMx9wnslDTZJ65TBGmFcvwVpmvH1+Hq8W8bw+bXl3xJWUUVDk45Y1hHdyz9rSnuXNiNLl+MYQHAwUl8vthVroHV09rh5pktChHAOLR/Jw7u2yG1rVadBkiQM4YVpKZAS1t2Zkau/Maw2l/N6EhK+oRnT/zR59eh31dwBZaVmAQAUNGVrnPurIqsxGTJzIuv0+Smy92HIso9r1PT8uhP5MzI+lZaGuqYNbQ3Jvbrgph3H2BW1hBaGuo4cc1HskSVoji4fycO7tsptS2vcyc19Vu+/8mZ+ZeaKnPP5fO5M+Q7S05EP1uJCWB8vf4d8DkyKy9Cm/vUroqK/CpmZWVJ/e7i4oIbN27A29sbd+/exfHjx7F+/Xps2bJFsjTFypUrJcGGL6mp5X8zILc8w4YNw6tXr9C2bVu0bt0aVapUwaxZs6TqmN/Txrn1/JFyFIUWrdrCw1P6JuC9u7fh7/sQ2dnZUsGWmOgoWFrlvXa4haUVXofIvigxJjoa5R3FT+A8f/YEcbExaNy0hVSajPQMKCkpQUe3eF++1LBFJ1T3kB5I+927gWf+PjLt8TYmAmaWNj/090zMrCASiSDMygTUPj/Fl7v0mJpa8T/ZV9GzO6wrNpTa9ubFZUS+ug1RdrbUEzaf3ofBwEj2+M4V8uQs1DX0YVlBOmCTlZkGDW3Fe+FjXjwbdUfF6g2ltr3wvYxXT2/LHCfv48JgZJ53mwT4XYOGlg5sK34OeGpo6aKMiRU+ffy8fmrih1hsXTwIH+Mj0WvU33Cu0bLwKlQI2JfIqlyjO8pVaii1LfT5FUQEyZ47ie/DUdpY/nES+eoOMtKScP3QDFw/JL0sw6lNA6Bb2hy/TL+KpI/RiHx1BxVc20PP8PPyBe+inkNdSx+6pWWnZRcnczNTKCkpITomRmp7dLT4d2sr2XVwLS0tJGkc7O0/54nJzfO53teu38DSFatgZWmBBfPmomzZ4g0GfwurMvpQViqFiHcJUtvDc363NZbtJ6M/fILPq3C0ca0IddXP46ZKluJlfuI/pUjeoZGRJX1DMSvn8ewv8ykC59rdYVelodS2kKeXERYge+4kxIehtKk95AkPuoMMQRIu7Z+BS/ulz52j6/pD19ACg+ZcRVTIQyS+j0Blz05SabIy01FKSRkaWsX/kvNqdbrD3qmh1LbgJ5fxJo82McyjTcIC7yBdkIQLe2fgwl7pNjm0pj/0DC0wdP5VxIQ9QeK7SFR0ay2VxsSqCqJf+xdOpQqJhYkxlJWUEBkrveZ47u82Ft8XuEwVpOHWw0eo7GALqy/er+FoKw72vfuY8H0FLiInLsTA+0HhrI8eHilABXvZG00WZpoIeCW+qRgelQodbRUY6Kki4dPn92BYmmsgJi4NGRnfOf2jiFgal4WykhIi4qSDfrm/f/2ei1wikQjLdh2F18WbaFHLFXOH95F6WbeWhjqMS+sjMlZ6vx8Sk5CSlg7bPPZbXDwadYfjV2PYAL+8x7DG+Y1h/a9BQ1N2DGtobIWkBPH59/rFPWRmpsGxWgNJGmVlFZhYVUBcpOxYrzg0a9kObl+NYe/fvY1Hfg9kx7AxUbC0yjvgb2FhjdevZesVGxMFhwqVpLY98r0HoVCIWvUa/lgFFFDyS/F7LbTsy+HTowDJdm17cdslB4ZAmJIKQWQstOyl21PNyBCqejpIDgz5eQX+l3L7k6/P+9zfbS1Mv3vft/yeQU9bC9Uc7WBvKb5+fUhMwtsPCXC0kV2erDg1b9kO7t947sTGFPD9z8JKskTwl2JjolC+gvRsLn/f+xAKhaj9f3juEP2/Utw1EP4FR0dH+Pn5SW17+PAhAMA+5+aEqqoqkpM/r40YFib94uTVq1fD19cXTZo0wcyZM3HhwgVYWVnhwoULsLOzg4qKCqKjo1GuXDnJz40bN7B169ZvWkoiICAAN2/exKpVqzBp0iS0b98e1tbWCA8PlwQmKlasiBcvXiAz8/MA3t//8xe8wihHUTAsUxYOFRylfqq7ukMgSIW/7wNJusTEBLx49gTVXfOeolfd1R2REeGICH8j2RYR/gaREWGonjO179mTR1i9fLHkRbyAOEB0x/sGKlaqIjWDpTiUNjSCrUNlqR+n6jWQJkjBM/+7knSfEj8i6IU/nKrXzGdvBcudeXHvlvSLhx/dvwkrm/LQ/MEZHoVBW88YRpZOUj+W5esgMz0Fka9uS9IJkj8g5o0vLMrn/UK+gHsHcfv4XAizPj9dnJIYh7gwf5jbeRZpPQqTXmljWNo5Sf2Ud66D9LQUvHr6uU2SP33AmyDffF9SePv8ThzfMQ/Z2Z9vLCZ+iMXbqBCYWYnfBZImSMaWhQPw6eNbDJyyVeGCFwD7Enm09U1gbOUs9WNVQXzuhL+UPneiXz+UCezlsqncCF3GHpL6qd95LgCgfue5aDVgfc5+3uP6oZlS78dI/RSPV4/OwKZyY4VbxkJNTQ3OTlXgfeeu1MMMt73vQFtbG44VZN+FY2FuDlMTE9zyviO1/bb3HclnAHD/wUMs/nsFKleqiOVLF5eI4AUgDiS42lngytNgqTa5/OQVdDXU4GQt+4U45uMnzDt4GVefSn/puxsUDlVlZdgYlYadiSHMDfVw4VGQ1H6vP38NAHC1Vazglo6+CUysnaV+rB3rIiM9BW8Cb0nSpSZ/QFTIQ5RzlH/u2Dk1Qq+Jh6V+GnefBwBo3H0eOgwRnzsRr3xwce9UqZeBi7KzEfzoAsxtXaCsUjwPmXxJx8AEpuWcpX7KVayLjLQUhAZ80SZJHxAZ/BA2FeW3ib1zI/zy+2Gpn2Y9xW3SrOc8dBoubpPQ5zdxdsdkfPr4OcCYnS1E+EsfGFk4FmFN/z11NVVUr1wBN+75Sh/fPr7Q0dJE5fLf9yJYZWVlLFy/E3tPXpDafv/xcwCAQznFupH0/kMGgoKTpX6+1wP/jyhnqQUbq88z4WystGBrrY0H/h8laQCgYZ2ykjSqKqVQ26MMHvjLzlwubupqqnCpaI9rD55IHSdX7z+GjpYmnOzl35T+x+s0vC7eRJ9WDfHnqH5SwYtcNapWxC3/58jI/PyQ3dUHj6GspASPKuULvzI/QN4Y1sFJdgybkjOGzZ1ZIc+d8ztxcqfsGDY+OgSmOWNYf++TOLxxqtS7NNIFKQgPfgRTK8XoSwzLlIVD+YpSP9VcPSAQpOKR331JusTEBAQ8e5LvkjXVXN0RFREmfwz71dj3ZeALlClrBGPj77/ZrahSQ8KR+joCZp2lHzQy7dQcyS9DIQgTzxR+d9kbxq0bQumLpZFMO7dAdlYW3l3z+all/jc+9yePpfuTB4+go6WJKnn0J9/i6BVvrNp7XGrb/nPXoaRUCvVcq3z3fotC/ufO19//HqOai0ee+6rm6oHIPM6daq7S+f6fzx2i/1eK9bjcdxo8eDDGjRuHdevWoVWrVnjz5g3mz5+PRo0aSQIY1atXx6FDh+Dh4QGRSISFCxdKzViIiIjAyZMnMX/+fFhbW+Px48eIjo6Gi4sLdHV10bNnT6xatQo6OjpwdXXFvXv3sHTpUgwbNuybyli2bFmoqKjg3LlzMDQ0REJCAjZs2ID4+HhkZIhvxPbu3Rvbt2/HrFmzMGTIEMTFxWH+/PkAxGv9FUY5fpYqztXgVLU6Viz9C78OHApdPT0c2LsT2to6aNW6vSRdRPgbZGZmws5ePDCvW78hDnvtxR+zpqLvAPF0vt3bN6OcjR3q1m8IQPyU9vkzJ7Fg3gz0/mUA1DU0cO7MCYS/eYO/lqz82VX9Jo5VXFHRyQ0bV8xG91/HQEdXH8cPbIaWti4at+oiSRcV8RpZmZkoZ/ftg/FKzm6o7lEP+7atQHp6Giys7eF97QxeBT7G2GnLiqI6hcLM1gNmdp645vU7PFtNhLqWAfwu/wN1DV1UrtlTku5jXDCEwgyUNa8MAHBtPAJntw7Exd2j4VS7L9IFifC78g80tPRRtd6A4qpOobCt6AG7Sp7wWv87WvWcCC0dA1w++g80tHRRs8nnNomLCoYwMwPmNuI2adxxBLYtHoT9a3+DR6PuSPn0AddOrIemth7qtha3yeUja/Au9g2adBoNZWUVhAc/kuxPRUVNsi9Fw75ElrmdB8ztPXFl32TUbDMJGloGeHhpLdQ1deFUq5ck3Ye4YAizMmBkURka2qWhoS39stnMDPHyBAbGtihjJu5zjCydYGrjiptH56FWm8lQUlLBvfMroaSkAo/mivmCu949u2PqjNn4c+FitGjeDC8CAnDo6DEM6t8PGhrqSElNRXh4OMzMzGCgL34K/pdePbFs5Sro6uqiVg1P3L13Dzdu3cb0KZMBABkZGVi+ei20NDXRq0c3hIeHS/3NsmXLwqhsWZmyKIohTWtg2MYjmLzrLDp6VsajNzHYed0X41rXhaaaKpLT0vE67gMsy+jDUEcLLrYWqFneGouOXUdyegasyhjg5ovX8PJ+jBEtakIvZ9mU39rWw+TdZ/D77rPoUtMZIXHvsfbcHTSt6iCZraHILB08YOngifO7J6Ne+8nQ0DKAz/k1UNfURdW6n8+d97Hic8fYsjI0tUtD86tzJyNdfO6UNrZFWXPxueNcuyeeeB/Ayc3DUbPVOKiqaeLJ7X14H/MSXcfu/XmV/JesynvAqrwnzu6YjPodJ0NT2wB3zq6BupYuqtf/3CbvYsRtYmJVGZo6paGpk0ebmNhKghPV6vbE49sHcGz9MNRqPRrKyqp4dHMf3kW9RNcxW39eJb9R/y5tMfaPvzHj7/Vo17gengQFY+/JCxjZpws01NWRkipAaGQ0LEyMUVr/22boqaupol+n1thy8ARK6+nCzakiXr2JwLZDJ+FRtTJquTgXca1+HhsrLaiqKuHVa3HQ48qtt+jb3RrL5jpjw05xoHP4r3YIDk3G1VviJ+vj4tNx9kosxgx2gLqaMiKiU9GjgyV0tFWw92hEnn+rOA3q2BwjF67D1NU70L5BDTx5FYrdZ65idI920FBXQ3JqGkKjYmFpUhal9XQQ9CYSO09fQWU7azSt4YJnwdIP0dlamEJHSwO/tm2Ci3f8MHbJBvRp1RBhsfFYd/A0OjWuBdOyij/D2LaiB2wreeLQht/Rood4DHv1mHgMW+OLMezbqGBkfTGGbdRhBLYvGYQD//wGj4bdkZIkHsNqaOuhbivxGLZe64F4dv88dq8YgbqtB0KYmYGbZ7YiQ5CCJp1GFUt9v0UVp2qo4lwdq5b+ib4Dh0NXVw9e+3ZAS1sHLVp3lKQTj2EzYGcvDtjUqd8YRw7uxZ+zf8cv/cVLQ+3ZsQnWNnaoXU96qcKwN69haW3zs6pUpFR0taFT2QGpIeHIeCcObr5a8A+qbV2EzA8JiDt1FSbtm8C8e2v49R4vyReybAvMe7SBx+ktCF25HdoVbOA4/zdEbDmItIiYPP6aYhjYqQVG/fUPpq3ajnYNa+LJy1DsPn0Vo3vm9ieCL/qTb58Z3qNFfYxZtB7Ldx9FfVcnPHj+EjtOXkK/dk1hqWBLSMlTxakanJyrY+XSP9F34DDo6upLzp2WrTtI0sl+/2uEowf3YP7sKeibc+7s3rEJ5WzsUOermRbhb17D6v/k3Pl/UNC7yIgAoJSooLeGKQBHR0csXLgQnTt/fhlx48aN0alTJ4wZI76hcvbsWaxfvx6hoaEwNDRE27ZtMXbsWMk7IYKDgzF37lw8fvwYxsbGGDduHNasWYMRI0agc+fOSE5OxuLFi3Ht2jUkJCTAwsICffr0Qb9+/QCIl5zasGEDjh49irdv38LMzAzdu3fH4MGDv/mJ1FOnTmHNmjWIiYmBkZERGjZsCFVVVVy9ehWXLl0CAPj5+eGvv/5CYGAgTE1N0atXLyxZsgQHDhyAi4tLoZTjawEhUd+VryDJSUnYtnkd7vl4Q5QtQsXKVTBo6EhYWH6e9jdjygS8jYvF5h37Jdvi499i68a1eOTvCxVlFVR3dcfAoSNhaPj56deY6Cjs3rEZz589hUCQivLlHdHn10GoXOXHvwhWsrfA3YBPP7yfr6Ukf8L+bSvgd+8GRKJslK9YDb0GTYCZhY0kzcIZw/DubQz+3nxS7j4mDmmPik6uGDJurtT2jIx0nPDajLvXz+HTpwRYWNmifffBcKvZsFDKXquSHpYdLfxp/OmCRPicWYw3z69AJMqGaTkX1Gw7TeodF6c39UPSxyj0mnJFsi0q+C58r/yDDzFBKFVKCVYV6sKz1UToGJjL/TunN4nP47ZDd8n9/HtM6qyEo/cLv00EKYk4s3cxnvuK26RceRe0/WUajMw+t8mmBf3w8V0Upqz43CYhL+7h8pE1iAkPhJKyCipUrYuWPSbCoIx42vCi8Y2R+F7+IN6grLnUvr5XZ0+lIulPSmpfAoj7k5UnC/8ym56aCO9TixD6/AogyoapjStqt5sqtYTUifV9kfQxCr9Mvyp3H1Eh93Byw69oP3wnLOxrSLanJr2D98mFiHzpDRFEsLCvgRqtJsi8e+Z7jW9fCmHBhfu+mtt37mL33n2IjIxCmTJl0L5ta3TtLF7K5/GTp5g8bQYmjR+H5s2aSPKcPnceh48eQ3z8O5iZmqJn965o2lh8Y8D/8WNMmT5L7t8CgF9690S/Pr0LrfzlHByRdnp9oe0PAK48Dcb6Cz548/YjjPW10aNONfza0A0A8CA4AoPXH8EfPZqhg6f4SbyUtAxsuOiDy0+DEZ+YAmsjA/xS3wWdazhJ7ffGi9fYePEeXsW8g76WBlq7VsToVrWglseSnd9Lo+0IrD9fqLsEAKSlJuLmsUUIeXoZIlE2zG1dUb/TNBiafD53Dq3pi08fojBojvxzJ+LVPRxZ2w9dRu+CVfnP505CfBi8T/+NqOCHyEhPgYm1M2q3GQ8L+x9/OeSIlsDmyz+8G7nSUhNx7cgiBD8Wt4mFnSsadZVukwMr++LT+ygMnS+/TcJf3sPBVf3QfdwuWFf4ok3eReDm8aWIePUAmempMLOpijptx0neifEjhjQFPnzxtHdhuH7PD1u8TiA8OhZGhgbo0rIxercXP/3r9ywQo+YuxcxRA9Cmkeya+2eu3caf/2zH0XWLYWb8OcApEolw7OINHDl/BZFx8Sitp4vmdWtgUPcOkncgFBZD57rf/c6Kb7Xmr2oAgDHTH8tsNzXWQLfBn2fwGZdVx7gh9vCoXhpZQhHu+3/Emi0heP/x8yxaVZVSGN7fDs3qG0NTUxlBwUlYt/01XrwsnLXrb59qgKSHhduZXHvwGBuPnENYzFsYlzZAt2Z18UubxgCAhy9eYfiCtZgztDfaNaiBDYfPYsuxC3nua8OM0XCvLL755h8YglX7TuBleBQMdLTRuq4HhndtDRUV2aWUf4Sue0scvlc0Y9iz+xbjxRdj2NZ9pMewW/4Sj2EnL/887nz94h4uH12D2JwxbHnnumjxxRgWAKLfvMDFQysQ+fopsoVZsKnogRbdJ8DEUnam5ffoWkMJz4JjC2VfX0pOSsKOLWtx/+5tiEQiOFZ2woAho6XGsLOnjsPbuFhs2O4l2fYu/i22bVyNx48eQkVZBdVcPTBgyGiUNpSeDTpueD+Us7XHb1PmFHrZnRxMcUa1aGa4GNb3RK0ru3G3SV98uHlfatvjQVMRueuYJK31kB6wmzAQGlZmSH0dgZAlmxC194TU/krXcUOlxb9Dr1olZLz7iKi9J/By7mqIvlo2/Ee1yQzCJ9+8z+fvce3BY2w6fA5hMXEwKm2Abs3rSfoT3xevMPzPNZg9rA/aNaghk/fUjXv4Y+NenFg1B+ZG0sfGhTu+2HrsAqLj38OsrCG6NquLHi0ayOzjR+m5tcDz4MIPFCUnJWH7ln8k507Fyk4YMGSU1LkzK+fc2fjVubN145ovzh13DBgyWur7HwCMHf4rytnaYWIhnztVHBT7XXmKashfhbN85X/J5uklY3WAwlQiAhj/FcHBwUhMTISbm5tkm5+fH3r16oXr16/DrIheHFpUAYySqqgCGCVZUQUwSrKiCmCUZEUVwCjJiiqAUZIVRQCjpCuKAEZJV1QBjJKqKAMYJVVRBDBKup8RwChpiiKAUdIVVQCjJCuqAEZJVpQBjJKqKAIYJV1RBTBKKgYwvg8DGP/efzGA8X/xDoz/F7GxsejXrx+OHz+OqKgo+Pv7Y+HChfD09Cyy4AURERERERERERERkSL6v3gHRnHbvHkz1q1bl2+a6dOno1u3bvmmqVu3LmbMmIGNGzdi1qxZ0NXVRePGjTFp0qTCLC4RERERERERERERkcJjAKMQdO/eHc2bN883TZky3za9p3fv3ujdu/DW1SYiIiIiIiIiIiIiKokYwCgE+vr60NfXL+5iEBEREREREREREZUIfDUzfQu+A4OIiIiIiIiIiIiIiBQOAxhERERERERERERERKRwGMAgIiIiIiIiIiIiIiKFwwAGEREREREREREREREpHAYwiIiIiIiIiIiIiIhI4agUdwGIiIiIiIiIiIiI6L9FlC0q7iJQCcAABhERERERERERERHRf1h6ejoWLVqE8+fPIy0tDY0bN8aMGTNgaGgoN/3UqVNx7NgxuZ+NGTMGo0ePBgA0b94cYWFhUp936tQJixYt+qZyMYBBRERERERERERERPQfNnfuXDx8+BBr1qyBmpoa5syZg7Fjx2LPnj1y08+YMQMTJ06U2rZw4ULcv38f3bp1AwCkpqYiIiICGzduRJUqVSTpNDQ0vrlcDGAQEREREREREREREf1HxcXF4fjx49iwYQPc3d0BAMuXL0fLli3h7+8PFxcXmTy6urrQ1dWV/H716lWcPXsWO3fuhImJCQAgODgY2dnZcHFxgb6+/neVjS/xJiIiIiIiIiIiIiL6j/L19QUA1KxZU7LN1tYWJiYmePDgQYH509PTsWDBAnTp0gU1atSQbA8KCkLZsmW/O3gBcAYGEREREREREREREZHCa9KkSb6fX7ly5bv2GxcXh9KlS0NdXV1qu7GxMWJjYwvMf+jQIbx79w7jx4+X2h4UFAQtLS2MHTsWfn5+KF26NLp06YJ+/fpBSenb5lYwgEFEREREREREREREP5UoW1TcRfjPiIyMzDf4MW7cOKipqclsV1dXR3p6er77zs7Oxs6dO9GtWzcYGRlJffbq1St8+vQJLVq0wKhRo+Dr64ulS5ciMTER48aN+6ayM4BBRERERERERERERKTgvneGhYmJCc6ePZvn5zdu3EBGRobM9vT0dGhqaua7bz8/P4SHh6NXr14yn23evBnp6emSd2U4OjoiOTkZ69evx5gxY75pFgYDGERERERERERERERE/6dUVVVhb2+f5+dBQUFISEhARkaG1EyMt2/fSl7InZdLly6hcuXKcvevpqYmM7OjQoUKSE1NRWJiIkqXLl1g2fkSbyIiIiIiIiIiIiKi/yg3NzdkZ2dLXuYNAKGhoYiLi4OHh0e+eR88eIBatWrJbBeJRGjatCnWrl0rtf3p06cwMjL6puAFwAAGEREREREREREREdF/lomJCdq0aYOZM2fi3r17ePLkCX777Td4enqievXqAICMjAzEx8dLLTUlFArx8uVLVKxYUWafpUqVQrNmzbB161acPXsW4eHh8PLywpYtWzB27NhvLhuXkCIiIiIiIiIiIiIi+g+bP38+/vrrL4wePRoAUL9+fcycOVPyub+/P/r164ddu3ahRo0aAICEhARkZmbCwMBA7j4nTpwIHR0dLF++HLGxsbC0tMSMGTPQvXv3by4XAxhERERERERERERE9FNli0TFXQT6gpaWFv7880/8+eefcj+vUaMGgoKCpLaVKVNGZtuXVFRUMGrUKIwaNeq7y8UlpIiIiIiIiIiIiIiISOEwgEFERERERERERERERAqHAQwiIiIiIiIiIiIiIlI4DGAQEREREREREREREZHCYQCDiIiIiIiIiIiIiIgUjkpxF4CIiIiIiIiIiIiI/ltE2aLiLgKVAJyBQURERERERERERERECocBDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBQOAxhERERERERERERERKRwVIq7AERERERERERERET03yISiYq7CFQCcAYGEREREREREREREREpHAYwiIiIiIiIiIiIiIhI4ZQSca4OEREREREREREREf1E/WbFFHcRSpxd882Kuwg/Hd+BQYgN9C/uIigU04ouCHn9uriLoVDs7ewQGhJc3MVQKLb2DggLDiruYiiUcg6OPE6+YmvvwP7kK/Z2drzufMW0ogtehYQVdzEUSnn7cggOCS3uYigMB3tb9q9fsbV3QPirgOIuhkKxLl8JN5+nFHcxFEr9Ktp42KBWcRdDobjfuMuxyVfs7ezwOiSkuIuhUOzs7fHJ90JxF0Oh6Lm1wBlVx+IuhkJpkxnE8ckXbO0dirsIRP+3uIQUEREREREREREREREpHM7AICIiIiIiIiIiIqKfKjubbzaggnEGBhERERERERERERERKRwGMIiIiIiIiIiIiIiISOEwgEFERERERERERERERAqHAQwiIiIiIiIiIiIiIlI4DGAQEREREREREREREZHCUSnuAhARERERERERERHRf4soW1TcRaASgDMwiIiIiIiIiIiIiIhI4TCAQURERERERERERERECocBDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBSOSnEXgIiIiIiIiIiIiIj+W0QiUXEXgUoAzsAgIiIiIiIiIiIiIiKFwwAGEREREREREREREREpHAYwiIiIiIiIiIiIiIhI4TCAQURERERERERERERECocBDCIiIiIiIiIiIiIiUjgqxV0AIiIiIiIiIiIiIvpvEWVnF3cRqATgDAwiIiIiIiIiIiIiIlI4DGAQEREREREREREREZHCYQCDiIiIiIiIiIiIiIgUDgMYRERERERERERERESkcBjAICIiIiIiIiIiIiIihaNS3AUgIiIiIiIiIiIiov+W7GxRcReBSgDOwCAiIiIiIiIiIiIiIoXDAAYRERERERERERERESkcBjCIiIiIiIiIiIiIiEjhMIBBREREREREREREREQKhwEMIiIiIiIiIiIiIiJSOCrFXQAiIiIiIiIiIiIi+m8RiUTFXQQqATgDg4iIiIiIiIiIiIiIFA4DGEREREREREREREREpHAYwCAiIiIiIiIiIiIiIoXzn3kHhq+vL0QiEdzd3REZGYkmTZpg165dqFGjRnEX7f/OA//H2LzHC2/CI1HaQB+dWjdHj45tUapUqTzzXL7pjd0HjyE6Lg6mxkbo06UDWjZuIJXmls8D7Dp4FBFR0TA0MEDzRvXQp0tHqKqWjMNYIBBg27ZtuOPtDYFAACcnJwwdNgyWlpb55vv48SM2b9oEX19fCIVCeHh4YMjQoTA0NJSkEQqF2Lt3Ly5fuoSkpCQ4ODhg0ODBqFixYlFX67v5+vlhx85dCA8Ph4GBAdq1bYMunTvne5xcu34d+w94ITY2FiYmxujerRuaNW0qN21qaipGjBqNPr17oXmzZkVVjUL10M8fO3btRlhOm7Rv0wZdO3csoE1uYJ/XQcTExsHE2Bg9unVB86ZNpNJcvHQFh44dQ0xMLIyNjNCubWt0bJf/OakoeJzIx/5EGq878gkEAuzYtgXed24jTSBAFSdnDBk6HJaWVvnm+/jxI7Zs3gA/34cQCoVw9/DE4CHDYGhY5nOaDx+we/dO+Pv5IinpEywsLdGlS3fUb9CwiGv1YwQCAbZv2wrvO95IEwjg5OSMIUOHfmObbIKvpE08MGTIUKk2+dLJkydw7OgRbN+xqyiqUSjYv8p66OeP7bv3Iiw8HKUNDNC+TWt07dQh3za5euMm9nkdklyHe3brjOZNGueZfv3mrTh64hQunT5eBDUoGmmCVBzZvQp+PleRnpaK8pVd0WPARJha2HzzPtYvmQx1TS0MHDNPavvH929xeNdKPPO/g2yhEHaOzujUezRsHCoXci0Kj567JyyGDIOGjR2yPn7A22NHEOe1L+8Mysow7dEbZdu0g2qZskiPjEDM3l34eO0KAEDN1BRVvY7lmf3dudN4s2hBYVej0HFsIs3Xzw87d+6U9LFt27YtsI+9fv069h84kNPHmqBbAX3syFGj0Kd3bzQrIX2sz5MArDt4Bq8jY1BGXxddm9XDL20af9N3kqA3Efh11t84unwWzI2kr72nbtzDnjNXERkXj7IG+mjbwBMDOjSHirJyUVWl0GlYmKD+o9N42GUUPty8n29a8x5t4DBtBLTsrCB4E4XgpZsQtfu4VBp9NydUWvw79N2ckPUpBZG7juLlH2shyswswloUDo5PiEie/8wMjN69eyM8PBwAYGZmhtu3b8PFxaWYS/X/53nQK0z9cwmsLc0xf9pvaNagLjbs3Id9R07mmefGnXv4c/laeFR3xoJpk1DdqTIWrlqPKzfvSNI8ePQEsxYth6W5GeZPm4hObVpg75ET+Gf77p9RrUKxZPFi3L51C/0HDMDESZPw/v17TJ0yBUlJSXnmEQqFmD1rFoKCgjB6zBiMGj0aL168wIwZM5CVlSVJt3nTJhw7ehRdu3bF1KlToaysjBnTpyM6OvpnVO1fCwgMxJy582BlaYlZM6ajUcOG2LptOw4eOpRnntu3vbFk6TK4ubpg9qyZqOpcFX8vX4HrN27IpE1KSsLsOXMRFxdXlNUoVAGBgZg9bz6sLC0xe/o0NG7YEFu274DXoSN55rnlfQeLli2Hq4sL5s6cjmpVnbBsxSpcu3FTkubchYtYtnIVari7Y/6cWWjWpBE2bt6KAwfzbmtFweMkb+xPPuN1J29LlyzE7ds30b//IPw28Xe8f/8e06dORnIBx8nc2TPwMigIo0aPw8hRYxHw4jlmzZgmOU4yMzMwe/Z0PPL3wy99+2HGzDkoX74Cliz+C1evXP5Z1fsuS5Yswu3bt9C//wD8NnES3r9/h2lTCz535syeiaCgQIwaPQajRo1BwIsXmPnVuZPrxo3r2LJ5U1FW44exf5X1IjAIs/5YACtLC8yZPhWNGzbA5u074XX4aJ55xNfhFXBzqY65M6ahmrMTlq5YjWs3bslN/+TZcxw7ebqoqlBktqyYDt87l9H5lzEYOPYPJLx/i2WzhyIl+VOBebOzs3Fg61L4+VyR+Sw1JQmLZwxEwON76NhrJEZMXoqyxhZYOnMwQl89K4qq/DDtylXgsGgZ0sLDEDJrGt5fugDL4aNg2rtvnnnM+w+CxeBheH/xPIKn/47kp09gP/dPGNRvCADIfP8eASMGy/y8v3gO2ZmZeHfm1E+q3Y/h2OSzgMBAzJ07F1aWlpg5YwYaNWyIbdu24VC+fextLFm6FK6urpg9axacnZ2xfPnyPPvYOXPmlKg+9umrUExYugk25sZYMmEQWtZxx5r9J7HzVMHjhuCIaIxfshFCYbbMZ/vPXccfG/fC1twESyYMxtAurXDq+j1MX72jCGpRNDQsTeF5dhtUDfQKTGvaqTmq71qG+MveeNhlFN7fvI/q2xbDrHtrSRpNW0vUOL8dQkE6/HqNx+sV22A7fgCqrJxZlNUoFByfEFFeSsYjhIVMWVkZRkZGxV2M/0vb9x1CeVsbzJwwGgBQw7U6soRZ2HP4OLq2awV1dTWZPJt3H0DD2jUwevCvAABP12pISkrG1n0H0aR+bQDAuSvXYWJUBjMnjIayshI8qlfFx4REHDxxBqMH9oWKimIfygEBAbh37x7m/fEHPDw8AABOVapgwIABOHP6NHr26iU3361btxASEoINGzbAulw5AIC9nR1GjBiBWzdvolHjxoiPj8eZM2cwfPhwtGnbFgDg6uqKwUOG4NChQxg3btzPqeS/sHvPXtjb2eH3yZMAAO7u7hAKs+DldRAdO3SAurq6TJ7tO3eiXt26GDZ0qDiPmxuSkpKwa/ceNGzw+anpuz4+WL9hIwQCwc+pTCHZtXc/7O3sMGXSbwAAD3c3CLOycODgIXTq0C6PNtmN+nXrYMTQwQAAdzdXJCUlY+eevWjUoD4AYL/XIdSrUxuDB/YHALhUr4bIqGicOHUGvXp0/zmV+048TuRjfyKN1x35AgJe4P49H8yd9yfcPTwBAFWcnDBoQD+cOXMKPXr2lpvv9q2bCAkJxroNm2FtLT5O7OztMWrEUNy6dQONGjXBg/v3Efr6NZavXIMKFRwBAC6uboh/+xaHD3uhcRP5T7wVN3Gb3MO8efPhnnvuODlh4ID+OHPmNHr2zOvcuYmQkBCs37Dxizaxw8gRw3Hr1k00aiR+2j4hIQG7d+/E+XPnoKur+3Mq9Z3Yv8ratXc/HOxsMXXiBACAh5srsrKysP/gYXRq31Zum2zbtQf169bGiCGDcvK4ICk5CTv27EOjBvWk0goEAixbuRplyxgi/t37oq9QIQkJeozHD29i7Mw1cHatAwAoX8kV00a0xfXzB9Gm6+A880a+eYn9W5bgTfALqKlpyHzuffUE3r+NxpQF2+BQqToAoHL1mkj+lACvbX9j6sLtRVKnH2E+YDBSX71E6II/AACf7vuglIoKzH75FXGHD0KUkS6Tp2zrtvhw5SJidm4DACT5PYSWoyOMO3dFws3rEGVmIuXFc6k8WhUcUbpRU0Rt3oDkp0+KvmI/iGMTaXv27IG9nR0mT54MQNzHZgmFOODlhQ559LE7vupj3dzckJyUhN27d0v1sT4+Pli/YUOJ62M3HT4HRxtL/DGyHwCgdrXKyBIKsePERfRs2QAaarLjtcysLHhduImNh89CXc7sV2F2NrYeO48azo5YNH6gZLujrSV6TVmEe08DUcNZcWfpoFQpWPbtiEqLpwDfODHecf5viDl8HgGTFgIA3l26DdXS+nCcOw4xB88CAOwnD0FWUgoedh4JUWYm4s/fhFCQBqdVsxC8aAPSImKKqkY/jOOT/yZRtqi4i0AlwL+ageHo6IjVq1ejUaNGqFu3Lt68eYOMjAwsXboU9erVg4uLC7p3747bt29L8hw9ehTNmjWT/NfJyQmdO3eGr6+vJE1aWhpWrlyJJk2awNnZGR06dMCFCxcAACkpKXBxccG+fdLTcteuXYuGDRsiOzsbiYmJmDlzJurVq4cqVaqgVq1amDlzpqRjcnQUf7meNm0apk6disjISDg6OuLevXsAxE9+7NixAy1atICzszNatGiB/fv3S/7WvXv3ULlyZdy4cQNt27aFk5MTWrZsicuX/91ThpcvX0a3bt1QvXp1ODs7o3Pnzrh16/MTWkKhECtWrEDdunVRvXp1jB07FgsWLEDfvp+f6AkJCcGQIUPg4uKCunXrYuLEiYiPj/9X5SgqGZmZePTsBerV9JTa3qB2TaQKBHgSECiTJybuLSKiY2Tz1KmJqJhYREaLL64ZGZnQUNeAsvLnQ1ZPVweZWVlIFaQVQW0Kl6+vLzQ0NODq6irZpm9gAGdnZzx4+DDPfH6+vrC0tJQM6AHAulw5WFlZ4cGDBwCAR48eQSgUolbt2pI0qmpq8PT0xMOcNIokIzMTT588Qe3ataS2161bF6kCAZ4/fyGTJzYuDlFRUXLy1EF0dDSioqIAAMnJyZj/5wJUdXbGgvnzi64ShSwjMxNPnjxFnVo1pbbXq1sHqQIBnr2Q3yaRUVGo/XWeOrURHR2DqCjxE2l/zp2NIYMGSKVRUVFBRkZGIdeicPE4yRv7k8943cmbn+9DaGhowMXVTbJNX98ATs5V8fBB3ksT+Pk9FB8n1l8cJ9blYGVlLcmnpaWFlq3aoHz5ClJ5La2sEBujuF+K/XLOHZcvzx19Azg5O+d7fPv5+ebRJlZS+Q56HYCfry+mz5gJTwVeopT9q6yMzEw8efpM5jpcv07tnOtwgEwe8XU4GnVqyrkOx8QgMkr6yfBN23aidOnSMss8Krrn/nehrqGJKtU+11NXvzQqVHbDU1/vfPNuXT0b2dnZmLZoB3T1S8t8HhMZCi0dPUnwIldFZ3eEBD3+phkeP1MpVVXoVndFwi3pp3o/3rgGZW1t6FatKjefkpo6hCmpUtuyEj9BRU8/z79lPX4S0t6EIu7QgR8v+E/Ascln4nH9E9T+oryAuI8VCAR4/vy5TJ64nD62lpw8sn3sn3B2dsafJayP9Q14hYYe0udIY8/qSBGk43HQa7n5vP1fYMvRcxjQoRlG92ov8/mHxCQkJqeirouT1HYHK3MY6Grjtr9sWysSvaqOcPpnHiL3HMej/r8XmF6znAV0HG0Re+KS1PaYoxegXd4GWg7i88ioWV28PXdDarmo2CPnUUpZGUbN6xZuJQoRxydElJ9/vYTUvn37sHr1aqxduxY2NjaYNm0avL29sWzZMhw7dgytWrXC8OHDcf36dUmemJgYHDhwAEuXLsWxY8egqamJqVOnQiQSR9l+++03HD9+HLNmzcLJkyfRtGlTjBs3DpcvX4a2tjZatmyJ06elp1ufOnUKHTp0gJKSEqZOnYoXL15g7dq1uHDhAqZNm4bjx4/Dy8sLACQBlenTp2PGjBkydVq0aBHWrVuH0aNH49SpU+jTpw8WLFiAHTt2SNIIhUIsXboUM2bMwOnTp1GhQgVMmTIFKSkp39Ruz549w5gxY9CmTRucOnUKBw8ehKGhIX7//XfJjcRly5bBy8sLc+bMwZEjR2BkZITduz8vVREXF4fevXujXLlyOHz4MDZs2IDk5GT06NEDqampef3pnyY69i0ys7JgZWEmtd3SzAQAEBElO8U3LFJ8Qfk6j0VOnvCcPJ1aN0dkTAwOHDuFpOQUPA96hcOnzqGmmwv0dHUKvS6FLSIiAqamplD+ah1OM3NzREVG5pkvPCICFhYWMtvNzc0RmXMxjggPh6amptQ6sblp3r9/r3BPGMTGxCAzK0umXuZm4mMgMkq2PSLCIwBANo+5uThPznGkrq6OjRvWY9LE36CnX/AUXEURGxOLzKwsWFqYS22XtElO/b4UHiFuJ0uZNhHnicg5PqytrWBqYgKRSIRPSUk4d+EiLl+9hnZtWkOR8TjJG/uTz3jdyVtkRARMTc1kjxMzc0TJOX9yRYSHw9xCdr3yL4+v6i6uGD1mnNRaxFlZWXhw/77UTX5Fk9e5Y15gm0TAXM65Y2ZujsgvzrlWrdtg85ZtqFNHcW8OAOxf5YmJjc1pk6+uw7nX1Hyvw9J5LCTt+DmPr/8jXL56DZPHj4FSCXj/1JdiIkNR1sQCSl+dN8ZmVoiLDss376Bx8zHlr22wtKkg93MdXQOkC1JlAhVvY8Vt+y5Ott2Lk7q5OZTU1JAWES61PT2nH1C3kt//xR32QpkWLaHnWRNKWlowbNoc+p418P7iebnpSzduCp0qTohYuxLIll0yRxFxbPJZbEwMsvLtY+X0JzlLXX89rjeT9LE5x5i6OjZu2IBJEydCXz/vAJiiiXr7HplZQlibGktttzIRr4oRFv1Wbr7K9tY4sWouBnZsAWUl2fdZ6GppQllZCTHvPkht/5SciqQUAaLeKvZsN0F4DK5XbIaAyYsgTC344RidivYAgJRXb6S2p4aI+2KdCrZQ0lCHlo0lUl6FSqXJePcRmYlJ0KlgWziFLwIcnxBRfv71+gcdOnSAs7MzACAsLAynT5/G8ePHUalSJQDAgAEDEBgYiK1bt6Jhw4YAgMzMTMybN08qzahRoxAfH4+kpCRcuXIFGzZskKQfM2YMAgMDsWHDBjRt2hSdOnVCv379EBUVBQsLCzx58gRv3rxB586dAQB16tSBh4eHZKaFpaUl9uzZg5cvXwKAZLkoXV1d6OrqIjExUVKf5ORk7N+/H1OnTkW7du0AADY2NoiMjMSmTZvw66+/StKOHz8etWqJI7sjR47EhQsX8PLly296l4aysjJmzZqF3r0/L9nQr18/DBkyBO/fv4eBgQH27duHadOmSV7CNXPmTPj7+0vS79+/H6amppg58/PahStXrkTNmjVx/vx5SXsUl5ScIIqWpqbUds2c31NSZQeXKSnibVpa0nm0vsrjWtUJvTq1x/ode7F+x14AQHk7G8yaOKYQa1B0UlNSoKWlJbNdU1Mz3+BTakoKLMzNZbZ/mS8lNTXPfQPil1RpfvVvUpwkx8lXZc79XV57pKSKA4XaX+f5oo4AoKqqCqsCXhSoiHLrJ9sm0vWTypOSIpUml6am/DwBgUEYP0n8ZE+F8g7o0rnjjxe8CPE4yRv7k8943clbSqr840SroOMkNUXuzfqCjq/t2zYjOjoK02fM/r4C/wR5tUmB505qCswt8j93AMDKKv8XgSsK9q+yUnKejs+zfgJ512H57fj1dTglJQV/r16Lfn16ydycLAkEqcnQ1JQN2qprakEgSM43r2W58vl+XrNBG1w6uQcblk5Gz0G/o3QZYzx5eAt3rorfYZSerlg3ppW1xe0g/OocEeYcH8ra2nLzxR08AJ0qTqiwdIVkW/yZU4g7sFduetOefZD05DGSHvnL/VwRcWzy2ff1sXnkkdPHFvRSdEWUnDO20taUXkpOS1O8HFBKHjNbjQ0N8t2vhroamtV0xaGLN2FvaYqG7tXw4VMS/t51BMrKShCkK/aM88yPicj8mFhwwhwq+uI+KOuTdN+blSS+Rqvo6UBVX1dumtx0KnqK+xAOxydElJ9/HcAo98X0zhc5y5p8eVMeEAcs9PSko5r29vaS/89dFzgzMxNBQUEAxGs8fsnDwwPLly+X/L+lpSVOnz6NYcOG4eTJk3B1dZWUpXfv3rh69SqOHTuGN2/eIDg4GJGRkbCzsyuwPq9fv0ZmZqbM3/f09MTOnTvx/v3nqP2X+9PR0ZHU4VtUqlQJ+vr62LRpE16/fo2wsDAEBoqXthAKhQgJCUFaWhqqV68uyVOqVCm4ublJ0r148QKvXr2SCZikp6cjJCTkm8pRlEQFPCGkVEp2wk+26NvyLF+/FWevXEO/7p3gWtUJsW/jsePAYUyetxAr5s+Ehpy1EItLdna2ZHbRl9vyUiqfJ/GyRXmvBZj7BF9B7Z7f/ovD95S3oDURSykpVh3/reyC6ifn3Pn6GJPNI90mJsZGWLboL8TGxmHH7j0YP+l3rFu1EhoainPufInHiRj7k/zxuiMm7zjJr23yPU7yOY+U5JxDIpEI27dtwYnjx9C5SzfUVpDZB/LbJO+6FWabKDr2r7IKuqbK60tEBfQludfudZu3wqhsWXTpKLv8iaIRnzfS9cqvbeSNT/4Ncys7jJ6+ErvX/4m547sBAMrZV0L7niNwYOsSqKvLvjejWBVUXznnVilVVVRcsx6qZcrizbLFSAsPg46TM8z69ke2IBURa1ZKpdeu4gxtx4oInl7wcjLFhWOT/BU8NpHXxxZQR6UfO9eKW37/zsCPXUOmDeoBNVUV/Ln5AOZv2g91NVX82r4pUtPSoaGm+t37VUQFHQei7GygwDSK+64Bjk+IKD//OoChofF5IJk7cNm7dy+0v3riROmrjlNNzkuZ8hsQi0QiyQsyS5UqhY4dO+LUqVMYPHgwzp07h/HjxwMQD5aGDRuGV69eoW3btmjdujWqVKmCWbNmfVN98ipD7iDsy5d0/ts6fOn+/fsYNGgQGjZsCDc3N7Rr1w4CgQCjRo2S+jv57S87Oxs1a9bEnDlzZD5ThJdFamvnRMa/msabmvvEhbbskzE635An/v0HnLp4Bb907YhBfXpI0lQq74Bfx0zC2cvX0LlNy8KryA/at28f9u2VfqKqbt26SEhIkEkrSE2VOXe+pK2lJXdadGpqKrRy8mlra+eZJvdzRZJb7q/LnF95tfI4TnKf0tDWUqw6/lu5dc6zftqyT53lPmUi24655450m5QpUwZlypRBVWcnmJqZYNKU6bjl7Y1mTRoXTiUKGY8TMfYn+eN1R2z/vj3Yv2+P1LY6devho5zjJFVQwHGSzzGg9dU5lJmZgRXLl+Hmjevo3KUbBg4a8n0VKAL79+3Fvn3S506dunWRkPBRJm3BbZLPuVMC+xX2r7K0JU93/ovrcIHtqAWf+w9w/eZt/LNiGUQiEYRCoeRGnlAoRKlSpWS+MxWn0wc34dTBTVLb3Go1xacE2WVY0lJToKn140/yVqleCws3nMa7t+Ll+4xMLHD7ynEAgJaOYi2RI0wRP9Gs/NWTvso5x3/u518q3aARtMpXQNBvY5HkK35fQ/JjfwhTklFuwmTEnz6JtNDP6/+XbtgIWZ8+IdHnTlFV44dxbJK/vMb1ueXVklPegvJ8/XR5SaOTO6s8TXqmRe7MCx3N7w9WammoY9bQ3pjYrwti3n2AWVlDaGmo48Q1H8kSVf8vshKTAAAqutLHUO6siqzEZMnMi6/T5KbL3Yci4viEiPLzrwMYXypfXjwtOD4+HpUrV5ZsX7FiBZSUlDBu3LgC95G77JOvry8aNWok2f7w4UM4ODhIfu/UqRPWrl2LAwcOICUlBa1atQIABAQE4ObNmzh48CCqVasGQDwrIjw8/Jum8tvb20NVVRW+vr6SJa5y/76RkVGhrS25bds21KhRA2vWrJFsy32/hUgkQrly5aChoYFHjx5JlePx48dQz3nKs3z58jh79izMzMwkwZSEhARMmTIFAwYMQM2vXiL4s5mbmkBZSQlRMXFS26NiYgEA5Sxlp81b5SzJEBUThwp2n9djjPwiT1z8O4hEIjhVkl4/18baEvq6uggNz3td1eLQqlUreHpKvxz27t278PX1RXZ2ttQX1ejo6HyPU0tLS7mza6KjoyXnjoWlJVJTU5GYkAB9AwNJmpjoaBgbG0uOH0VhbmYGJSUlREdLv+g1Olr8xVVee+RO94yOjobDF7O5YnL2YWVdMpbtyIu5mam4TWK+bhPx79Zy2sQy53yKjo6RapPcfVhbWUIgEODuvftwrFBeanp++Zz0799LrxerSHiciLE/yR+vO2ItW7WBp6f0GMDnrjf88jhOLK2s89yXhaUlXss5TmKio1Eh5zgBxMvizJszE4GBARgydAQ6dOxUCDUpPC1btYanp/SLtO/evQM/Xz+ZNomJjsn33Mm7TWJQwVH+2v6KjP2rrAKvw3KWnchdDioqJgYO9p9naX95Hd617wAyMjIwZNRYmfwtO3RBsyaN8PuEgr8v/Sz1m3dBVff6Utv871/D80d3Zc6bt7ERMLP8sbXU38fH4MXje6jVoA2MTD731+GvA6Gto4+yxrJLCxWn9OgoiLKyoP7Ve4I0cn5PC3sjk0fNxBQAkPz0idT2pMePAACaNrZSAQyDWnWQcPsmREJhIZa8cHFskj+znD42Jlr6PVy5faz8cb34GIr5qo+V5LHO+7pdElgal4WykhIiY99Jbc/93dbC9Lv3fcvvGfS0tVDN0Q72luJ3JXxITMLbDwlwtPn/WjIo+aX4vRZa9uXw6VGAZLu2vXhlkuTAEAhTUiGIjIWWvfQ7edSMDKGqp4PkwOJfuSMvHJ/8dynyzCBSHD/0yE/58uXRqFEjzJkzB1evXkVERAQ2b96MjRs3fvNF1t7eHo0aNcK8efNw/fp1hIaGYu3atbhy5QoGDhwoSWdhYYEaNWrg77//RtOmTSVLOJUtWxYqKio4d+4cIiIi8PTpU4wfPx7x8fGSl2MD4nXzQkJC8PGj9JN3Ojo66NGjB1avXo3Tp08jLCwMe/fuxb59+zBw4MBCm5JqZmaGoKAgPHz4EJGRkThy5AhWrVoFAMjIyICmpib69u2L1atX4/LlywgNDcXixYvx+PFjyT569+6NpKQkTJo0CYGBgQgMDMSECRPw9OlTVKhQ/F+g1dXUULVKJdz0uS81k+TG3XvQ0dZCpQoOMnkszUxhZmKMG94+Uttv3rkHS3PxZ5ZmplBWUsKTF4FSacIjo5GYlARzE5OiqdB3KlOmDCpUqCD14+rqCoFAAF9fX0m6xIQEPHv2DK6urnnuy9XVFREREQgP+/ySxPCwMERERMAlJ1/ukmK5L6sHgMyMDNy/fz/f7W1/mAAAq2lJREFUfRcXNTU1ODs5wfvOHanj5La3N7S1teEo51g2NzeHqakJbt/2ltp+29sbFubmMFWwY+DfErdJFXjfuftVm9zJs01y633LW/oJvdvedySfKSsrY8XqNTh05JhUGl8/8ZrKdrY2hV+ZQsLjRIz9Sf543RErU6YMyleoIPXj4uoGgSAVfr4PJekSExPw/NlTuLq65bkvF1c3RESEIzz8i+MkPAwREeFwcRHnEwqF+GPuLLx8GYTfp05XuOAFIL9NXCVt8sW5k5iAZ8+ewiWfNnF1dROfO3LaxNUl73yKiv2rLDU1NVR1qoLbd3yk2uTWnbvQ1tbK4zpsJvc6fOvOXUmb9OvdE2tXLJP6ad1C/K67tSuWoV/vnkVbsX/JwNAINg6VpX6qVKuJNEEKnj+6K0mXlPgRr174oXK1H3t4KinxI3at+wOBzx5ItiV+fIf7ty6gmkd9xVsaKCMDSU8eoXT9BlLbDRo0RFZSElICXsjkScvpN3SrVpParuNUFQCQHvP5Jreyrh40rKxlgh2KhmOT/BXUx8r73i7uY02l6ggA3jl9rEkJ72PV1VThUtEe1x48lmqTqw8eQUdLE1W+utn+bxy94o1Ve49Lbdt/7jqUlEqhnmuV796vIkoNCUfq6wiYdW4htd20U3MkvwyFIEz8wup3l71h3LohlL5YQsu0cwtkZ2Xh3TXp8a8i4fiEiPLzQzMwAPFsixUrVmD27NlITEyEtbU1FixYgE6dvv3L7PLly7F8+XLMmDEDnz59QoUKFbBmzRrJy6xzde7cGT4+PlIvqzYxMcGiRYuwZs0a7N27F0ZGRmjYsCH69++Pq1evStINHDgQW7ZsQUhIiNRLsAFg2rRpKF26NJYtW4Z3797BxsYGs2fPRvfu3b+zVWSNHTsW7969w/DhwwEADg4O+OuvvzB58mQ8ffoU9vb2GDduHDIzMzFz5kwIBAI0atQITZo0QXp6OgBxxHnPnj34+++/0atXLygrK8PV1RW7du2CoaFhoZX1R/Tr3gm/zV6AOUtWonXThnge+BIHjp3G0H69oKGujpTUVLyJiIKFqQkM9MXvSfm1R2csWr0Behu2oo6nG27fe4hr3j6YM0n8tJqBvh66tm+FA8dOAwDcq1VFXHw8dhw4AlPjsmjbXDGXwPmSs7MzqlatiqVLlmDgoEHQ09WVLL3Wuk0bSbrwsDBkZmbCPmf2Uf369eHl5YVZs2djwIABAIDt27fDxsYG9euLn5AzMTFB06ZNsWnTJqRnZMDCwgLHjh1DcnIyunbr9vMr+w169eyJaTNmYMHChWjRrDleBATg8JGjGNi/PzQ0NJCSmorw8HCYmZnBIGcWVO9evbB8xUro6emiZo0auOtzDzdv3cK0KVOKuTaFo3fP7pg6Yzb+XLgYLZo3w4uAABw6egyD+veDhoa63Db5pVdPLFu5Crq6uqhVwxN3793DjVu3MX3KZADiQWCPbl2xe+9+GBjoo1pVZ7x+/QZ79h+AS/Vq8HBX7BtvPE7kY38ijdcd+Zycq8K5ajUsW7oYAwYOhq6eLvbt3QNtbR20bt1Wki48POc4sc89ThrgkNd+zJk1A/0HDAIA7Ni+FTY2tqiXc9Pu9KmTeP78GVq2aoOyZY0QGBgg9bcrVqwEReTk7AznqlWxdOliDBw4CLp6el+0yRfnjkyb1MdBrwOYPWsW+uecOztyzp169evL/VuKjv2rrN49umHKzDmYv2gpWjZrghcBgTh09DgG/dpXch0OC4+AuZnpF9fh7li2cg30cq7Dd3zu48Ytb8z4fRIAwNTERObmyb374pv1juVlA6yKqEIVNzg6uWPLyhno2ncctHX1ccprIzS1dNGw5efrQnTEa2RlZsDaruI377ucfSU4VKyOvZsWomu/8VBWVsaxvf9ASVkZ7XsOL4rq/LCYXTtQYflq2M1bgHdnT0GnSlWY9uyDqE3rkJ2eDiUtLWja2CI9KgpZiQlI8L6F5OfPYDtzDqK3b0FaeBi0K1WBWd/++Hj7JlK/6D817cRPDwvCQouret+NYxNpPXv2xPQZM/DXwoVo3qwZAgICcOTIEQwosI9dAV09vZw+1gc3b93C1P+TPnZgpxYY9dc/mLZqO9o1rIknL0Ox+/RVjO7ZDhrqakhOFSA0KhaWJmVRWu/bl8fu0aI+xixaj+W7j6K+qxMePH+JHScvoV+7prAs4UtIqehqQ6eyA1JDwpHxTvwg7qsF/6Da1kXI/JCAuFNXYdK+Ccy7t4Zf7/GSfCHLtsC8Rxt4nN6C0JXboV3BBo7zf0PEloNIi4jJ468pBo5PiCgvpUTf+hIHKnKXLl2Cm5ubVDBi4MCBMDU1xV9//VVkfzc20L9Q93fz7n1s338YEVHRKFvGEJ1aN0ePjuKbJf5Pn2P8zPmYOnY4WjVpKMlz8vxlHDh+GvHv3sPMxBh9unZAi0afbwqIRCIcPnUOJ89fRkzcW5QxNIB79aoY8ktPyQ2pwmJa0QUhr18XnPBfSkpKwubNm+FzVzwNv3Llyhg6bJhkyjAATPn9d8TFxWHHzp2SbfHx8di4YQP8/f0lQauhw4ZJHSeZGRnYvn07rl+/DoFAAIfy5TFo0CBUrPjtXyLzY29nh9CQ4ELZVy7vO3ewe89eREVGokzZMmjXti265AQnHz95gilTp+G3CePR/ItA5pmz53Dk6FHEx8fDzNQU3bt3R9M83uEQGxeH/gMGyuyjsNjaOyAsOKhQ93n7zl3s3rsPkZFRKFOmDNq3bY2uncXB4MdPnmLytBmYNH4cmjdrIslz+tx5HD56DPHx72Bmaoqe3buiaePPy/GJRCKcOXceJ06fQUxMLPT19dC4QQP07dNL7nt9fkQ5B0ceJ1+xtXdgf/IVezs7Xne+YlrRBa9CwgpO+C8lJyVhy+aNuOtzB6LsbFSqXAVDhg6HpeXn6fRTp0zC27g4bNuxW7ItPv4tNm1cj0f+flBWVoGLqyuGDB0OQ8MyAIApk3/D8+fP8vy7p89e/OGyl7cvh+CQwr+Jl5SUhC2bN+Guz12IsrNRuXIVDBk69Ks2mYy4uDhs37FLsi0+Ph4bN67HI8m544YhQ4dK2uRry5cvw9MnT6T28SMc7G3Zv37F1t4B4a8CCk74L9y+44Nd+/Z/vg63aYVunTsCEF+HJ02fhUnjx6BF0y+vwxdw6OhxxL97BzNTE/Ts1gXNvrgOf23X3v3Yvd8Ll04fL9SyA4B1+Uq4+Tyl0PebkvwJB7f/jUf3r0MkEsG+YjX0GDARphY2kjRLZw3B+7fRWLTxjNx9TB3WBhWc3DFwzDyp7Z8S3sNr+9948Ug8+8XRyR2dfhkNU/PvfyL7S/WraONhg1qFsq9cBvUawHzAYGhYWSPzXTzeHjuCuIP7AQC61V3guGodQhfOx/vzZwEASlpasBgyHKXrN4SKrh7SY6Lx/sI5xB3cD1FWlmS/pRs1gf3cP/Gsb0/JzI2i4H7jLscmX7G3s5O7VOCP8L5zB3v27EFkZCTKli2Ltl/0sU+ePMGUqVPx24QJUg9tnj17VtLHmpqaokf37mjSpInc/cfFxaH/gAEy+ygsdvb2+OR7oVD3ee3BY2w6fA5hMXEwKm2Abs3r4Zc24muI74tXGP7nGswe1gftGtSQyXvqxj38sXEvTqyaA3Mj6WvvhTu+2HrsAqLj38OsrCG6NquLHi0ayOzjR+m5tcAZVceCE34Hw/qeqHVlN+426YsPN+9LbXs8aCoid32eUW89pAfsJgyEhpUZUl9HIGTJJkTtPSG1v9J13FBp8e/Qq1YJGe8+ImrvCbycu1qqzykMbTKDOD75gq19yXg4QdF0HVf416T/d4dX2RWc6P8MAxgKpGfPntDU1MTvv/8OHR0dXLlyBUuWLMG2bduK9P0WhX0jqaQrqgBGSVYUAYySrigCGCVdUQQwSrqiCmCUZEURwCjpiiqAUZIVVQCjpCqKAEZJVxQBjJKuqAIYJVlRBDBKuqIKYJRkRRHAKOmKIoBR0hVlAKOkKooARknGAMb3YQDj3/svBjB+eAkpAtzd3SHM50VrZcqUweXLlwvcz7Jly7Bo0SL0798faWlpcHBwwKpVq4r95dxERERERERERERERD8bAxiF4OjRo8hvIouysvI37cfS0hJr164trGIRERERERERERERKaRsUXZxF4FKAAYwCoG1tXVxF4GIiIiIiIiIiIiI6P+KUnEXgIiIiIiIiIiIiIiI6GsMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBQOAxhERERERERERERERKRw+BJvIiIiIiIiIiIiIvqpRNmi4i4ClQCcgUFERERERERERERERAqHAQwiIiIiIiIiIiIiIlI4DGAQEREREREREREREZHCYQCDiIiIiIiIiIiIiIgUDgMYRERERERERERERESkcFSKuwBERERERERERERE9N8iyhYVdxGoBOAMDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBQOAxhERERERERERERERKRwGMAgIiIiIiIiIiIiIiKFo1LcBSAiIiIiIiIiIiKi/xaRSFTcRaASgDMwiIiIiIiIiIiIiIhI4TCAQURERERERERERERECocBDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBSOSnEXgIiIiIiIiIiIiIj+W7Kzs4u7CFQCcAYGEREREREREREREREpHAYwiIiIiIiIiIiIiIhI4TCAQURERERERERERERECocBDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJRKe4CEBEREREREREREdF/iyhbVNxFoBKAMzCIiIiIiIiIiIiIiEjhMIBBREREREREREREREQKhwEMIiIiIiIiIiIiIiJSOAxgEBERERERERERERGRwiklEon4thQiIiIiIiIiIiIi+mnaDnlR3EUocU5vrlzcRfjpVIq7AFT83j27W9xFUChlnWoh6uXT4i6GQrGo4Mzj5CtlnWoh6f6Z4i6GQtH1bIMzqo7FXQyF0iYzCKEhwcVdDIVia++AsOCg4i6GQinn4IjgkNDiLoZCcbC3xYcnt4q7GArDsGo9JN87VdzFUCg6Ndoh/FVAcRdDoViXr4TooCfFXQyFYu5YFa9DQoq7GArFzt4eiX6Xi7sYCkXftSnCh3cu7mIoFOsNR/E8OKa4i6FQqjiYcVz/FVt7B37/+0KbTH7H+R4iUXZxF4FKAC4hRURERERERERERERECocBDCIiIiIiIiIiIiIiUjgMYBARERERERERERERkcJhAIOIiIiIiIiIiIiIiBQOAxhERERERERERERERKRwVIq7AERERERERERERET03yLKFhV3EagE4AwMIiIiIiIiIiIiIiJSOAxgEBERERERERERERGRwmEAg4iIiIiIiIiIiIiIFA4DGEREREREREREREREpHAYwCAiIiIiIiIiIiIiIoWjUtwFICIiIiIiIiIiIqL/FlG2qLiLQCUAZ2AQEREREREREREREZHCYQCDiIiIiIiIiIiIiIgUDgMYRERERERERERERESkcBjAICIiIiIiIiIiIiIihcMABhERERERERERERERKRyV4i4AEREREREREREREf23ZIuyi7sIVAJwBgYRERERERERERERESkcBjCIiIiIiIiIiIiIiEjhMIBBREREREREREREREQKhwEMIiIiIiIiIiIiIiJSOAxgEBERERERERERERGRwlEp7gIQERERERERERER0X+LKFtU3EWgEoAzMIiIiIiIiIiIiIiISOEwgEFERERERERERERERAqHAQwiIiIiIiIiIiIiIlI4DGAQEREREREREREREZHCYQCDiIiIiIiIiIiIiIgUjkpxF4CIiIiIiIiIiIiI/ltE2dnFXQQqATgDg4iIiIiIiIiIiIiIFA4DGEREREREREREREREpHAYwCAiIiIiIiIiIiIiIoXDAAYRERERERERERERESkcvsSbCt29R8+wad8RhEZEwdBAD51bNkGv9i1RqlSpAvO+fB2GwVP/gNfaRTAzNpL67Oqd+9h7/BzComKgq60F96qVMeKXbjA00C+qqhSaB36PsG33frwJj0BpAwN0aNMC3Tu1z7dNrty4jT1ehxET9xamxkbo1bUTWjRpKJUmPCIKG3fsxuOnz6GsrIyqTpUxYtCvMDc1KeIa/TgeJ7J8ngZh3aGzCImKRRk9XXRrWge/tG6YZ5tkZGZhz7nrOHP7IeLeJ8DYUB+taruif7smUFX53L1fvvcIu85cw5uYt9DV0oRnlfIY3aMtyujr/qyq/TANCxPUf3QaD7uMwoeb9/NNa96jDRymjYCWnRUEb6IQvHQTonYfl0qj7+aESot/h76bE7I+pSBy11G8/GMtRJmZRViLwuHr54cdO3chPDwcBgYGaNe2Dbp07pzvuXPt+nXsP+CF2NhYmJgYo3u3bmjWtKnctKmpqRgxajT69O6F5s2aFVU1CtVDP3/s2LUbYTlt0r5NG3Tt3LGANrmBfV4HERMbBxNjY/To1gXNmzaRSnPx0hUcOnYMMTGxMDYyQru2rdGxXdtv6qcUgUAgwPZtW+F9xxtpAgGcnJwxZOhQWFpa5Zvv48eP2LJ5E3x9H0IoFMLdwwNDhgyFoWEZuelPnjyBY0ePYPuOXUVRjUJz7/FzbNx/DK8jomGor4cuLRuhd7vm3/TvGRQajkHTFuDQ6gUwMy4r9Vn7YZMR/+GjTJ5zW1fAQE9x+9m7T4Ow7vA5hETFoYyeDro1rYO+rRoUcM25gdPe4muOiaEBWtZ2wYC2jaWuOZfuP8655sRDV0sDnlXKY0z3NiXimvPQzx/bd+9FWHg4ShsYoH2b1ujaqUO+x8jVGzexz+uQpC/p2a0zmjdpLJXmwuUrOHz0BKJjY2FsVBbt27RGx3ZtSkRf8sD/MbZKxrD66NimJbp3bFfgGHb3wSOIiYuDqbExenXpiJY5Y9jYuLfoNWRUnnlbNmmIKePy/lwR+Pr5YefOnZLrcNu2bQu8Dl+/fh37DxzIuQ6boFsB1+GRo0ahT+/eaFZCrsM+TwKw3uskXkfGwFBfD92a10efNk2+sX+NQP9ZS3BkxVyYG0lfZ64/eIxtx84hLDoOZQz00KquJ/p3bCHV5ygqjUrVoN+hD1TNrZD9KQFJN84j6dIJuWm1azVCmV/H5Lmv9ztWI8XnOgBA07UW9Jp3gqqpBbJTU5AW+AQJx3YjOymxKKpR6ASCVOzevgk+3jeQliZAZadqGDBkFCwsrfPNl/DxA7ZvWQd/3/vIFgrh6l4D/YeMkoxN1ixfiGtXLuSZf8P2AzA2Ni3UuhQGjuvzxu9/RPQlxb/y/4Do6Gj4+/ujTZs235T+2rVrsLKygoODwzel79u3LywsLLBo0aIfKea/Mm/ePBw/fhxKSkq4cOECwsLCIBKJ4O7u/tPKkJ9nL4Px+8IVaFLbE0N6dcKTgFdYt/sghEIh+nZum2/e1+GRmPTXCgiFQpnPLt/2wZwVG9CheUMM7d0FHxISsXn/UYyZsxjbls6FuppaUVXph70IfIkZ8xehYd3aGPBLTzx9EYhNO/ZAKMxG726d5Oa56e2Dv/5ehc7tWsPTzQW3fe5j8cq1UFVVQeP6dQEAb+PfYeyUGbC0MMfMyeORlp6ObXsO4PfZ87F1zd9QV1f/mdX8V3icyHoa/Abj/96CZjWrY3jXlngUFIrVXqchzM5G/3ZN5OZZtucYzt72xeCOzVDZzgovQiOw+dhFxLz7iNlDegIALtz1x4x1u9G5cS2M7NYa7xI/YcPh8xixcB12//Eb1NVUf2Y1v4uGpSk8z2yFqoFegWlNOzVH9V3LELpmF+Iv3IJph6aovm0xstMzEHPwLABA09YSNc5vx0efR/DrNR46Fe3hOH8CVA0N8GzUnKKuzg8JCAzEnLnzUL9ePfza9xc8e/4CW7dth1AoRI/u3eXmuX3bG0uWLkPHDu3h5uaGu3d98PfyFVBVVUXDBg2k0iYlJWHeH/MRFxf3M6pTKAICAzF73nw0qFcXv/7SB89eBGDL9h0QCoXo2b2r3Dy3vO9g0bLl6Ni+HTzcXHHHxwfLVqyCqqoqGjWoDwA4d+EiVqxei+5dOsPN1QWBQUHYuHkr0gQC9Oohv60VzZIlixAUGIgBAwdBS0sL+/ftxbSpU7Bu/Ubo6sq/mSwUCjFn9kykpqZi1OgxEGYJsWPHNsycMQOr16yFylc3jW7cuI4tmzehTBn5wQ1F8exlCCYtXI2mtT0wpEdHPAl8hX/2HIZQKES/Tq3zzRsSHoVJC1fJve4kfEpC/IePGN23G6pVlB4/6mhrFWodCtPT4DCMX74NzWtUw4guLfHoZShWe52BUJiNAe0ay82zbM9xnPH2xeAOzVAl55qz6fglxL5LwOzB4nPigo8/pq/biy6NamJk11Z4n5iE9UfOY/iiDdgzb7xCX3NeBAZh1h8L0KBeHfT/pTeevQjA5u07xX1Jty5y84j7khXo1L4t3F1dccfnHpauWA1VFVU0alAPAHD2wiWsWPMPunfpBDeX6gh8+RIbtmyDIE2A3t27/cwq/msvAl9i+vyFaFS3Ngb26YGnAYHYuGMPhEIheneVP4a9cccHC5avRpd2reHhWh3ePvexeNU/UFNVReP6dWBoWBr/LFkgk+/42fO4dvsOWjeTf/wpioDAQMydOxf169VDv7598fz5c2zbtg3ZQiG653kdvo0lS5eiQ4cOcHdzw527d7F8+fI8r8N//PFHiboOP30Vit+WrEezWq4Y3q0tHgW9xpp9xyEUZuPXDs3zzRsSEY0JS9ZDKMyW+ezekwBMWbEZzWq6YlTPDngdGYN1B04iISkFkwco9nVYzbYCjEZNR+pDbySe3A91h4ow6NQXpZSU8OnCMZn0gqe+iF08VWa74S8joaSpCcEzPwCAlnsdlB08EUk3LyDxxF4o65WGfvteMJ4wD7F/TQayFP9G7Iolf+Jl0Av0GzAMWlra8Nq/E7OnTcCqdTugk+fYJAvz50yBIDUFw0f9hixhFvbs2IQ/Zk7CstWboaKigm69+qF56/ZS+ZKTkrBs4VxUca4OIyPFe8CP4/q88fvff4soW1TcRaAS4P86gDFlyhRYWFh8UwAjKioKw4cPx65du745gPGzBQYGYt++fZg3bx7q1q2LsmXLok6dOli4cKHCBDC2HjiOCrblMHvcMABATZeqyBIKsevoaXRv0xzq6rI3kDMzs3D43CVsOXAMaqryv9juOnIatVyr4vdh/SXbrM1NMXTafNzxfYxGtTyKpD6FYcc+LzjY2WD6xLEAAE83FwiFWdh36Ci6tG8tN9CwZfc+NKhTC6OGDAAAeLhWR1JSMrbvOSAJYOzcfxDaWlpYNn8ONDTE+zAzMcbMPxcjKDgEVatU/kk1/Pd4nMjaePQCHMtZYP7wPgCA2lUrIUsoxPaTl9GzRT1ofBV8SUhKwbFrPhjTow36tRF/2fesUgEAsNbrDMb0aIvSejrYfuoy6lSrhOkDPt8ksTE1Rv95q3Dr0Qs09az2k2r4HUqVgmXfjqi0eArwjQ+pOs7/DTGHzyNg0kIAwLtLt6FaWh+Oc8dJBrD2k4cgKykFDzuPhCgzE/Hnb0IoSIPTqlkIXrQBaRExRVWjH7Z7z17Y29nh98mTAADu7u4QCrPg5XUQHTt0kNufbN+5E/Xq1sWwoUPFedzckJSUhF2790h90bnr44P1GzZCIBD8nMoUkl1798Pezg5TJv0GAPBwd4MwKwsHDh5Cpw7t8miT3ahftw5GDB0MAHB3c0VSUjJ27tkrCWDs9zqEenVqY/DA/gAAl+rVEBkVjROnzpSIAEZAwAvcv3cP8+bNh7uHuO9zcnLCwAH9cebMafTs2Utuvlu3biIkJATrN2yEtXU5AICdvR1GjhiOW7duolEjcX+TkJCA3bt34vy5c3kGQxTJloMnUcHWGnPGiv/Na7k4IUsoxM5jZ9G9dVNo5HHdOXT+CjZ7ncjzuvPyTQQAoIGnCyxNjYuuAoVsw9ELcCxnjvnDewMAaletKL7mnLqCXi3qQeOrQENCUgqOXr+Hsd1bo1+bRgAAzyrlAQBrDp7FmO6tUVpPB9tOXUWdahUxfcDn4GE5MyP0n7dG4a85u/buh4OdLaZOnAAA8HBzRVZWFvYfPIxO7dvK7Uu27dqD+nVrY8SQQTl5XJCUnIQde/ZJAhj7Dx5GvTq1MWTArwAAV0lfclbhAxjb9x+Eg50tpv/2eQyblSXE3kPH0KWd/DHs1t370KBOTYwa3F+cx7U6PiUnY9veA2hcvw7UVFVRuWIFqTxBwSG4dvsOBvftDefKlYq8Xj9iz549sLezw+TJkwGIr8NZQiEOeHmhQx7X4R1fXYfd3NyQnJSE3bt3S12HfXx8sH7DhhJ3Hd50+AwcbSwxb1R/AECt6lWQJRRix4kL6NGqocwYFgAys7Jw8Px1bDx8Js/+9dQNH5iWKY15o/tDWUkJNapWwofEJOw7exUT+naBiopyUVbrh+i37YGMiFC837EaAJD2wh+llFWg17ILkq6egSgzQyp9dvInZCR/ktqm06g1VM0sELdkOrJzPtNr2QWCp774uG+jJF1mXBRMpy6GZlV3CPzuFnHNfkxQwHM8vH8HM+cthqt7DQBAJaeqGDGwJ86fOY6uPfvKzXfn1g2EhrzCqvU7YGVtAwCwtXPA+JED4H3rGho0agZTMwuYmllI5VuyYDZ0dHUxYfIMhZzxxnG9HPz+R0R54DswcohEih/x+/RJPHCpU6cOLC0ti7k0sjIyM+H/PBD1PV2ltjeq5Y5UQRoeB76Um++u32NsO3gC/Tq3xYi+sjeFsrOz4VGtCjo0ayi1vZyFGQAgKvZt4VSgCGRkZuLx0+eoW7OG1Pb6tWshVSDA0xeBMnli494iMioadWt5SuepUxNRMbGIjI6BSCTCzTs+aNmssSR4AQCO5R1waOdmhQ5e8DiRlZGZBd+AYDRyd5ba3sSzGlLS0vEo6H/s3XlcjdkfB/DPbZNCVGijFC2iFFJ2yr7HZE9k3zODEsaSomgiDQnZEjFEGMbakC1lVColSxtZGksLpfv8/kh3XLcw8+Oeq+f7fr3m9eLc5+Zzn7k92znne+5LvKew+A2GdrdDZ+sWYu0G2uWji3KePIdQKES7FsYY0s1OfBud8ods2U+efc2P8dXVsTBBi6DlyN4Tib9cFnx2+5r6uqhl0gSPj5wWa3906BRUmxlApWn5w9j6PTriye/RYtOFH/92EgJ5edTv2fHrfoivqKS0FIkJCWjfXvz/Z8eOHVFUXIzbt5Ml3vM4Lw85OTmVvKcDcnNzkZOTAwAoKCjASq9VsGjZEqtWrvx2H+IrKyktRUJCIjrY2Yq1d+rYAUXFxUhKrnyfZOfkoP3H7+nQHrm5j5CTkwsA8Fq2FJNcx4tto6CggJIS8YcOsio+Lg7Kysqwsv7nWKumVhctWrbEjdjYqt8XHwc9PT1R5wUANG6sj0aNGom9L2L/PsTHxWGR52LYtGtX2Y+SGSWlpYi/fQddbKzE2rvZtkZR8RskpKZX+r7LNxOx7UAUxg3pixljKh+Bn/4gCyo1laHbsH6lr8uiktJ3iEvNQLfWH51z2lqUn3PSKjnnvHmDod1s0dnaXKzdQPv9+eTp+3OOeTM4drWtfJsnz7/mx/iqSkpLkZCYJHEs6dyh/ftjSYrEe8qPJbnoYFvJseTRI2SLjiWLMXnCOLFtFL+DY0nFNWwnW/Hr0S4dbD95DZuV8widPrru7dLeTnQN+zGO47B+8zboN9LDsIFfNmuelfJzTgLat28v1t6xY0cUFxfj9u3bEu/Je38etqvkPZLnYS+0bNkSXt/ZeTg+OR1d27YSa7dvZ4XC4je4lZpR6ftibt7G1kO/w2VQL8wcOajKn61cQwnycv88slCrrYrSd+9Q+ObNV/sMX52CApSNW6D4r2tizUXxVyBXUwU1mn6+k06uthrqDhyFgj9PoeTB+3OUQIA3KQkouPSH2Lalj8u/Qwqaslce6WM3469DWVkZllb/DL5UU6uL5i1aIe7GtSrf91f8dejqNRJ1XgBAo8YG0Gukj/gq3hcXexVXL/+J8ZNmQLWW7A20oOv6ytH9HyGkKtW2A2Ps2LG4fv06Dh8+jO7du+PNmzcICAiAvb09WrZsiUGDBuHUqfIaidnZ2bC3Ly/R4uzsjMDAQADAmTNn8MMPP6BVq1Zo2bIlHB0dcfHixf+c6cGDB3B1dUXr1q1hZWUFV1dX3LlzR/R6Xl4eZsyYASsrK3Tp0gUHDhxA9+7dcejQIRw6dAhjx5aPSHBwcIC7uztMTEwAAB4eHnB3l5xyKm25eU9R+u4dGumIXzzpvl+PITPncaXvM2tqiN82rcW4YQPFLlAryMnJYZbLSHT66IH3n9fLp9I2aaQr8R5Z8ehxHkrfvYPe+4foFXTf76Os9ze3H3qYlQ0A0NPREX+P9vv3ZOfgcd4TFBYWoWH9+li/KQSDRrqgl+NILPZajafPZPcBAUDfk8rkPHmO0ndlaKwl/gCsUcPyOusPK+l80W2gAXeXYaIHQxWi4xKhIC+Pxtr1IScnB7dRg9C1tXgnx4W4JACAka5s3+gUZz7CBdMeSJm/GmVFn79RrWVqBAAoTH8g1l6U8bD8deMmkFOuARUDPRSmiz+gK3n2N0pfvkYt4yZfJ/w38PjRI5S+ewddXfHvso52+fElOydb4j1ZmeWjwyXe8/74kp1dfqNTo0YNBG/ehJ9+nIc6ap+fqi0rHj96/P4YK368FO2T95/vQ5kVx1iJfVL+nqz3N3+NGzeCVsOG4DgOr16/xu+n/sCZc+cxoN+nyw3JiqysLGhpaUFeXnyEqo62DnIq+a6I3peZBR1dyeOlto4OsrP/eV+fvv0QsnU7OnSQ/Zu+ivNOYx3x8hEVMyYe5lZeWsHMyACHglbDZWh/yMtVPtI3/UEm6tRSxaJ1m+DgPAvdx8zAYv/NePb3i6/6Gb6minOOflXnnEeVnHPqa8DDZajEOedCfBIU5OWhr1V+zpk3amCV5xxDGT7nPHr8+P3x9aNjScVx4ZPHko+v1yqOyeXv0W8kfiw5ceo0Tp+7gAH9+nz1z/E1ia5hq7oerewa9v1+krju/cR7zl+8jJS0dMycOF7ieCVrHj96hHefPA9X8j3JzAQgec7RFp2Hy79H5efhzfjpxx+hpib767ZVKD+evEPjj44Neu87dR8+qvz42txIH5EbVmDCkN5QkK/8kcSwnp2R9fgp9hw7g9eFRUhMv499v59Hh1bmUKul+nU/yFekoNkQAkVFlOaJf99Ln5Z34Ck01KnsbWLUBowAOA4vjuz9p5Hj8OK3HSi+JT4IQaVVeSdj6aPM/zP5t5edlYmGWjoSv+vaOrrIzak6f3ZWJrR1JNfv0tLWRW625Ps4jsPObZtg3tIS7Tt2/b9zfwt0XV85uv8jhFSl2nZgBAYGwsrKCn369MHBgwcxb948REZGYsmSJTh69CgcHBwwZ84cnDlzBtra2jhw4IDofRMmTEBSUhJmzZqFfv36ISoqChEREVBXV8eCBQv+84ipefPmoWHDhvjtt99w4MAByMnJYebMmQCA0tJSTJgwAc+ePcPu3buxbt067NixA0+elN9E9u3bV9SxcuDAAXh6euLSpUsAgEWLFsHT0/P/3WX/t4KiIgCAas2aYu0qNZUBAIVVTF+sr1EPdWrX+lf/VvbjJwjatR/NmjSGnbXFf0grHYWF7/eJingdbJX3+6jo/T4Te0/FflT5eD9WvKcYL97PxgnZuQfPnudj8fy5+GnWNKRn3Me8RT+jWIZHJdH3RFLB+8+s+n4fVFB5P7umsPjtF/2c8zcScOzSDQztboc6VdRez857hoDwozDW10UHS9ku01D690u8yfnymq0KauXfj3evCsTa370uLH+9Ti0ovl9E9uNtKrZTqPPvvmPSVHFsUPn4ePL+75UfT8o/++eOQYqKimgkgzP7Pqfi80nuk08cYwsLxbapULOK43JK6h0MGzEav2zYiCYG+hjqOPirZP/WCosKJfYLUP45K9svFYq+8H2NGjWSWA9DVhUUlR9jVf7leaeBRj2ofea8k/4gC0/zX8DEUB9rPWZh9jgn3ExOw/Sf/VD85suO3dJWUFx+jaBaU7w0xb8955y7kYhjl+I+ec7JynuGgH3HYNJYBx0tTf+P1N/WZ6/Xiis7llR+TP7UsWToyLH4JTAITQz0MWxI5aPOZcU/+6Ty69HCIsnfG9F7qvpdq+TYs+/wEbQwM0GrluYSr8ma/3YeruI9lZyHZXGG/edUHF8lrmFrVhxPKr8naaBe97OdEG3NTTB2QA9sCDsM+4nz4bp0LerVqY2Vs8Z/8n2sydUs/1zCN+K/I9z7v8spf3p9JLnaalC17YrXF06Aq+TY8yEFzYaoO3QcSjLv4c37dTJkWVFhAWpWcY1R/Mlrk4J/dU0Te+0ysrMeYtjwyktSyQK6rq8c3f8RQqryfdx5/gd169aFoqIilJWV8ffff+Ps2bPYvHkzunbtCgCYNWsWUlNTsXnzZjg4OEBdXR0AoKamBlVVVcjLy2PJkiUYNWqU6Gc6Oztj0qRJeP78ObS1tSv7Zz8pMzMT7du3h66uLhQVFeHt7Y179+5BKBTi8uXLuHv3Lk6dOgUDAwMAwOrVqzFsWHkNYWVlZdFoHHV1ddSuXVtUc/rDP7P0uYV35L5S3cmH2blwW7kW8nJy8PppJuQqGY0vK4Sc5IJ0HxIIJLN/bj8K5AR4V/oOAFCvrhqWL5ov2ge62lqYOX8Rzl64iP69e/zH1N8WfU8kCb/CPjkXm4DFm/aglXETzB4xoNJtHuTmYYZvMOTl5OA7a5xM75P/QvCZz8MJhcBnt5HdcoKc8HPHE8nvyZccT75nn/vdqfQY+5mSkR/vx4YN6mPtam88fpyHHbv3YO5PC/Dr+gCx8n2sCYVCic/1qf/3n6oD/al9Kvedfl+En/l//v+cd9ynjIO8vByaNy0fvdfKzBhNGulg6pI1+D36Mhx7dfvPP/tb+dz++JI64ediE+G5OQytjA0wZ3jlZX/u5z7BDN8tkJeTw5pZzjJ9zvnccUGu0mPJv7vGa9CgAdb6eOFxXh527N6LufPd8WuAv0wdSz702d+bSo4Hn7vu/Xg/JqXcQXrGfaxc9PkyIbLgc+fhyo4lnz13y/DvxZf4L787X2r1tn2IunAFE4b0RtsWJnj09DlCDp7A7NVB+NVzdqVrF8mEzx1DP/N7UquDAwRycnh97vgnt1NoqIsGc5YCQiGebfEDZKwkdvm1ifhn/dT35Wtem/x+7DCaGDYVK1Ula+i6/uuo7vd/hJB/VNsOjA9VlGlq3bq1WHvbtm3h7+9f6XvMzMygpqaGLVu24N69e3j48CFSU8trvZaVlf2nHG5ubvD29sbevXthY2ODTp06oX///pCTk0N6ejrU1NREnRcA0LJly0pHGsiqipEARR+N/i96PzKn1lf4LPFJKVjktxEqyjUQuHyhzC+YqapSPgKn6KPRnaJZFpWMWKxok3zP+xFOKiqikcM2ra3EHgg0NzWGqqoK0u9J1q+WFfQ9kVSrYrT4R/ukYtRarY9GtX0s7PdorA8/itZmTbF27njUUJJcDPFGyl0sWB+Kmso1ELxoOvTelwqpTt69fA0AUKgtPqKvYlTNu5cFopE3H29TsV3Fz5BFKqrlmT9ejK9IdDyR/EwqVR5PKkbWym4Jhi9R8Zn/1TH2/TFGcj8Wi/3MChoaGtDQ0IBFyxbQ0m6InxYuwsWYGPSw7/51PsRXEL43DHv3hom1dejYES9e/C2xbVFxUaXflQqqqiqVLvhYVFQEle/0+yI6xn40ErhQdN6pKfGeL9XSxEiizdK0GWqp1ET6w6pLdbFUcU4p/GiGSMXMi1oqnznnnPwTAeFRaG1mhHVzXKo858zfsBM1a9RAsMc0UXkqWSW6Nin6N9drnzsmi79HU0MdmhrqsGzZAtpaWvjR3RMXYy6jh73sdXIBn7oerXy2yodtEr9rFTNNP9on0ZevoHYtVdi2EV+fRlZVdc6p+H+uUsmx9XPvqWw/fk9URceTyq9hVT9zPKnKk/wXiDwXA5dBvTDV6Z+BOc2NDDBivheOXrgMp15d/1vob6xi1oScsvi5pWLmhfAzsypUrO3wJvmWaOHuytQwNofmlAXg3r5Bnv9SvHv25SPWpSUifCci9u4Ua7Pr0KXya5OiIqioVj0KXkVV9RPXJuLve/36FZISbmL0uEn/Mbl00HX911Hd7//44nODQggBeNKBURWO46osf3D9+nW4urqia9euaN26NQYMGIDi4mLMmDHjP/97o0ePRu/evREdHY0rV65gw4YN2LRpEyIjIwGUj1L4mJKSjI4sqYSuVn3Iy8kh+6Nap9nv6/fr6/37WSsfOn3xKrw2hkBfRxvrFv+I+hr1/q+fJw262g0hJyeHnEfi6zpU/F2/knUZGr2vpZzz6DGaGRmK2nNF79GDWp3aEAgEKH0/E+NDZWVC1JDh7w19TyTpNdCAvJwcsvLEF9Wu+LuBbsPK3gaO47B292HsP30JveyssGzySChWckw7eSUey4LDYaDTABt+moQG6nW/+meQBQXvF55VMdLHq7/+WXBV1ah88baC1AyUFRahOPsxVIz0xd6rVF8dinVqoaCKxSZlgY62NuTk5JD70SKoubnlNZYbNZKsDVwxfTw3NxdNjf550Pro/c9o1FjyPd8THW2t8n3y6ON9Uv73xpXsEz09XdE2H+6Tip/RuJEeiouLceXadZgYN4PuB/Xfm73f/vnz/K/7Qf5Pvfv0hY2N+KK5V65cRnxcPIRCoVhH96PcR5V+Vyro6unhXobk78Gj3EcwNjH+eqGlSLdhg/LzzkfrCVX83eA/nncKCotw/lo8mjdtAqPG/5zPhUIhSt+VoW4d9rNjK1Nxzsmu4pzTRKfyTn+O4+C350j5OcfWCssnD6/inHMTP2/ZBwOdBgj8cSIaqMt+Pf/PHksqKcVRsaZBzqNHaCp2vVbZscQYujr/fM8qru+e58vWseRDulqfu4aV3CeNxa5hm0i+R0/8uvdqbDw6tLP5bsrRab8/Dz/KFV/boOI8XPk5p3w/PfroPCx6T+PG3yquVOg1fH9d//ipWHvF35v8x7VvHj/LB8dxsDQxFGs31NOGWi1V3MuWXBBeVpQ+fQyurAwK9cU/u0KD8r+XPq66c1u+rjqUGhvi+dljVW6j0qYjNFxmofRxDp5u9ELZC9k8jvTsPQBtbMQXm75+5RL+io+VuDZ5/CgHeo2q/l3Q1W2E+/fuSrQ/fpSDZsbi5Qlvxl1HWVkZ2nfq+v99gG+Mruu/jup+/0cI+cf3PWf1C1Usdh0XFyfWfuPGDTRt2hSA5BS97du3o127dggMDISLiws6dOiAR+9vSD43VbYyz58/x4oVK1BaWgpHR0f4+fnh6NGjePr0Ka5fvw5zc3O8fv0a6enpovdkZWXhxYsX//rfYqWGkhIsm5sg+lqc2D66cPUGaqnURPNmhp9496ddjruFlRu2oKVJU2xa5fldPJQGyjugLFo0x6XL18T2ycXLV6GqqgLTZs0k3qOrow3thg3wZ8xVsfY/L1+Fno42tBo2QM2aNWFhboaLl6+hpLRUtE38rQS8efMGLc1ld20D+p5IqqGkCCsTQ5y/kSi2T87FJqCWijJaGFZ+QR8UcRz7T1/C6D5d4DVtTKUPki79lYyfN++FRTMDbF0yq9p2XgBAUUYmiu5lQduxl1i71pCeKEi7j+KH5QvbPTsTgwZ9u0Lug1HDWo69IHz3Ds/Oi//eyRIlJSW0bNECMZcvi31PLsXEQFVVFSbGkg+XdXR0oKXVEJcuxYi1X4qJga6ODrQaVt459r0o3yfmiLl85aN9crnKfVLxuS/GXBZrvxRzWfSavLw8ftkQiAO/HRbbJi7+JgDAsInB1/8w/wcNDQ00MzYW+8/aujWKi4sQ/8G1z8uXL5CUlAgr69ZV/ixr69bIyspCZuZDUVtm5kNkZWXC2qrq98myGkqKaGVmjOhr8eLnnWvx5eedpv9t8UZFRUWs27YXuw6fEGu/eOMW3paUoLW5yf+V+1spP+c0wbkbSeLnnBvl5xzzKs45Gw/8Xn7O6d0Zq6aNqvyccysFS4PDYdlMH9sWz/guOi+Aius1c1y6fPWj67UrUFVVqeJYol3pseTi5StixxL/wCAcOCR+LLlx8y8AQBMD8YcpskRJSQmW5ma4eEX8GvbPy9fKr2GNm0q8p+IaNvryFbH2D69hK7x6/RrZuY/Qwkw2f08q87nzsHGV52Et0dqFFWLen4cbfufn4RpKimhl2hTnY2+JH0+u/4VaKjVhbmTwn35uo/cDnv5KFX9o/TA3Dy8LCqHbQIZndb0rxdv0ZKhY2Yo1q1jZQVhUiJL76VW8EVAyKL83fJuRUunryi2soTF+Dt7eu4O8tZ4y23kBAOoammjazFTsP0vrtiguLsJf8f8sRP7y5QskJ92CpVXbKn+WpXVbZGc9RFbmA1FbVuYDZGc9hKW1+PvSUpOhoVkfDRr8t84zaaHr+q+jut//EUL+8X0Md/mPVFVVkZOTA1VVVXTr1g3Lly+HQCCAvr4+jh8/jrNnzyIgIADAP4slpaWloXnz5tDW1saZM2dw48YNaGlp4dq1a1i/fj0A/KdFvNXU1HDhwgVkZmbixx9/RK1atXDo0CEoKiqiRYsWaNSoEaysrDB//nz8/PPPUFJSwsqVKz/7c1VUVJCRkYG///4b9eqxf1jrMmwA5iz3w5J1QejXvTMS76Rj75HfMW3MD1CuUQOFRcW4n50D3YYNUE+tzhf9zLclJVi9KRQqNZXhPHQA7mfniL3eQEMdDTTUv8XH+SrGOA3F/CUrsHzNOvRx6I7bqXew/9BRTBo3GsrKNVBYVISHmdnQ0W6Iuu/XORk74gf4rg9Cndq10b5dG8Rci8WFS5exZIGb6OdOHDca8zx+hseyVXAaMhB/v3iJkJ17YGbSDO1tZLfeJ0Dfk8q4DuqB6Ws2wz1wFwZ2sUFC+gPsPnEBM536QbmGEgqK3+B+zmPoNdBEvTq1cOdhDnYeP4/mho3gYGOJpIyHYj+via4WFOXl4bUtAirKNTBhoAPu54iPomygXhcNv+MODYXaqqjVvCmKMjJR8qx8Onr6qiBYbluN0vwXyIs6h4YD7aHj1Bfxo+aK3pexdit0hvdD22NbcT8gFKrGBjBZOQ9ZWyPwJkt2R/QBwMgRI+Dh6YlVPj7o1aMnklNScPC3Q5jg4gJlZWUUFhUhMzMT2traouPJqJEj4f9LAOrUqQ3bdu1w5eo1/HnxIjwWLmT8ab6OUSOc4O65FF4+a9CrZw8kp6TgwKHDcHVxFh1jP94nY0aOwNqA9ahduzbs2tngyrVriL54CYsWzgdQflM5/Idh2B0Wjrp11WBp0RL37j3AnvB9sGplibZtZP9BfouWLdHSwgJ+fmswYYIratepg71he6CqWgt9+/6zZkFm5kOUlpbCyKj8YWTnzp0RsX8fli5ZApfx5Qul7ggNhYGBATp17szks3wNLkP7YfZKf3j6b8aA7h2RcCcDYUdPYfpoxw/OO7nvzztfNnOihpIixg7ug60RR6CuVgd21i2RkZmNbRFR6Ny2Fdq0lN3BBK6DHDB9zRYs3Lgbgzq3xa30h9h1IhqznPqipuickwe9Bhpi5xxzw0boYWOJpIxMsZ/XRLchFOXlsXLbgQ/OOeIzLRuoq8n0OWfU8B+wcPHPWLnaD7172CM5JRUHDkXCddzYD67XsqCjrfXBscQJawMCUef9seTy1euIvhgDzwU/ASg/lowYNhS79u5DXbX3x5L7D7A7fB+sW1nCRsaPJWOchuKnpSuxfI0/+vTojtspd7D/8FFMch79/vdG8hrWecQwrFn/K+rUDkEHm4pr2CtYMn+u2M++97D8O2TQ+PtaaHbEiBFY5OkJbx8f9OzRAykpKfjtt98w/rPn4V9Qu06d9+fhq/jz4kW4V5Pz8IQhvTHTOxAe67dhYFc7JKTdw55jZzBjxKDya9ii4vJr2IaaqPeFM9Pq1amNEX26YfexMwAAm5ZmePzsOUJ++x3amuoY3L3Dt/xI/7eXvx9AgznLoDnpJxRcPosahqao3WMQXkTuAVdaAoFyTShqN8K7p4/FSkUp6uqDKy2pvCSUgiLUx0wH96YYr37/DYra4qPty/5+jrIXz7/1R/u/mLewRIuWrRDg54WxE6agdm017N+7AyqqtdC77yDRdlmZD1BaWgpDo/IOnY6du+FQxB6sXLoQY10mAwB279gCfQNDdPhopkXmg3to1NhAWh/p/0LX9f8eH+//CCHlqnUHxogRI7Bw4UIMHDgQFy5cgL+/Pzw9PfHq1SsYGxsjMDAQPXqUL3Rcr149DB06FL6+vnj48CFmz56NZ8+eYerUqQCApk2bwtvbG/Pnz0diYiKMjCRrHn+KgoICQkJCsGbNGri4uKC4uBhmZmbYsmWLaOrw5s2bsWrVKkyYMAE1a9bE5MmTcfPmzU/+3AkTJmDr1q3IyMjA5s2b/8Ne+rpat2yOVfNnYtu+w/BYswH11ethhrMTRg7sAwC4c+8BZv28BotmuKJf905f9DOT7tzF879fAADcVqyVeH2C0yC4Dh/y1T7D12Zt2RLLPH7Czr37sXSVLzQ11DFl/Fg4DRkIAEjPuId5i5ZhwZwZ6O1QXge5t0M3lJaWIuLwUfx+5hx0tBrC3W0WunX652Ld3NQE67yXYfvucCzzWYsaNWqgo60Npk5whry8PJPP+qXoeyKprXkz+M4eh+BDp/BTwHY0qKeGOSMGYEzfrgCA1AfZmOr9K36eNAIDOtvg/I0EcByH5HtZGL98g8TP27xoOjghh2cvym+KZvoGS2wzaUhPTHHs/U0/17dUx8ocdmd345arO7J3lY9uzd51GHI1lGDoNgF6LkNRdC8Lf7kswKMDv4veV3jnHq71mQCzNQtgvX8DSp79jfvrdyBtmeR+lDWtWlliseci7N4ThhUrV0JDUwMTXSdgqKMjAODu3btY6O6BeW5z0fP9+a1njx4oLX2H3w4dwqk/TkNbSws//fgjunT5fh9Gf8jK0hJLFrljd9heLF+5ChoaGpg0wQXDHMt/3+/ezcB8D0/8NHcOevawBwD07GGPknelOHjoME6dPgNtLS0s+NENXTv/c7wZPWI46qqp4cix4zh4KBJqanXQv09vjB098osWOZYFnp5LsDVkC7Zt3wZOKETz5uZw91iE2rX/eYD0a9BG5OXlIXTHLgCAoqISvFb5IDh4EzYGboC8vDysrVtj0uTJMn9u+ZQ2Lc3g/eM0bI04goW+QaivXhczxw7DqAHlI/bu3H+IGcvWYvH08ejX7csfjI0f2g/16tTCb6fO49AfF6BWuxaG9OwC1x8GfquP8lXYNG8G31nOCD78B35cv+P9OacfxvbpCqD8nDPFZzN+njQcAzu1xbn3MwRv38uCy4pAiZ8X7DEVQu6fc84M3y0S20we3ANTPhohKUusLC2w1GMhdu0NxzIvn/Jjyfhx+MFxMIDyY8lPi5bgp7mz0Muh/FjSy8EepaXvcOBQJE6ePgttrYZYMG8OunbuKPq5o0c4QU1NDUePncCBw0dExxLnUSNk/lhibdkSy91/xI69EVjy/hp2qstYOA0pX5MgPeM+3DyXYeGc6ej9fi2P3vbdUFL67v017HnoaDWAh9tMdO8k/nv194uXAIDataquey+LWrVqBU9PT+zZswcrVq6EpqYmXF1dRefhjLt3sdDdHfPc3ET3mT169EBpaSl+O3QIf/zxB7RE5+EuLD/KV9O2hQlWu01EyIHjmL9uC+qrq2H2qMEY3d8BAHDnQRamrVyPpVPHoH8Xu8/8tH/MHj0EDdTr4tDZSwg7fg6adeugnYUZpg0fgNqVrEsjS97eScKzLX5Q6z8c9ae6o+zFc7w4tAuvzxwFACg1NkTDeSvxfGcgCq+cF71Pvk5dCIsKK/2ZNYxMoFC3fDBWgzk/S7z+8th+vDy2/xt8mq9rgedKhG4Nwq5tm8FxHEybt8BP7j+j1gfXJlt+/QVP8h4jOLT88ygqKuFnr3XYFhyITRvXQkFeAZbWbTB+0kzIy4s/0nrx4m/oN/nvM/qlia7r/z0+3v8RQsoJuP9SD4lIjYmJCXx8fOD4/iT2LTxLuvL5jXhEs4UdctISWceQKbrGLel78hHNFnZ4ff046xgypbZNPxxX/H5KQUhDv9I7uJ8hWbOXz5oYNcXDu3dYx5Ap+k1NcDfjPusYMqWpURPkJ1xkHUNmqFt0QsG1KNYxZEqtdgOQmV55mRW+atzMDLl3EljHkCk6JhaVruvDZ4ZGRngZf4Z1DJmiZu2AzKnf7n77e9R48yHcvksj0z9k3lSbrus/0sSoKd3/faBfKd3j/Bf2I66zjvDdObvPhnUEqavWMzAIIYQQQgghhBBCCCGEyB6hkMbVk8+jDoyvICQkBL/++usnt1m0aBF++OEHKSUihBBCCCGEEEIIIYQQQr5v1IHxFTg5OaFnz56f3EZDQ+M//ew7d2gKGiGEEEIIIYQQQgghhBD+oQ6Mr0BNTQ1qamqsYxBCCCGEEEIIIYQQQggh1YYc6wCEEEIIIYQQQgghhBBCCCEfow4MQgghhBBCCCGEEEIIIYTIHCohRQghhBBCCCGEEEIIIUSqOKGQdQTyHaAZGIQQQgghhBBCCCGEEEIIkTnUgUEIIYQQQgghhBBCCCGEEJlDHRiEEEIIIYQQQgghhBBCCJE51IFBCCGEEEIIIYQQQgghhBCZQx0YhBBCCCGEEEIIIYQQQgiROQqsAxBCCCGEEEIIIYQQQgjhF07IsY5AvgM0A4MQQgghhBBCCCGEEEIIITKHOjAIIYQQQgghhBBCCCGEECJzqAODEEIIIYQQQgghhBBCCCEyhzowCCGEEEIIIYQQQgghhBAic6gDgxBCCCGEEEIIIYQQQgghMoc6MAghhBBCCCGEEEIIIYQQInMUWAcghBBCCCGEEEIIIYQQwi8cJ2QdgXwHaAYGIYQQQgghhBBCCCGEEEJkDnVgEEIIIYQQQgghhBBCCCFE5lAHBiGEEEIIIYQQQgghhBBCZA51YBBCCCGEEEIIIYQQQgghROZQBwYhhBBCCCGEEEIIIYQQQmSOAusAhBBCCCGEEEIIIYQQQviFE3KsI5DvAM3AIIQQQgghhBBCCCGEEEKIzKEODEIIIYQQQgghhBBCCCGEyBzqwCCEEEIIIYQQQgghhBBCiMyhDgxCCCGEEEIIIYQQQgghhMgc6sAghBBCCCGEEEIIIYQQQojMUWAdgBBCCCGEEEIIIYQQQgi/cEIh6wjkO0AzMAghhBBCCCGEEEIIIYQQAgBYunQp3N3dP7tddnY2pkyZAmtra3Ts2BEBAQEoKysT2yYsLAz29vawsLDAqFGjkJyc/K+yUAcGIYQQQgghhBBCCCGEEMJzQqEQ/v7+2L9//2e3LS0thaurKwBg3759WLZsGcLDwxEUFCTa5vDhw/D19cWcOXNw6NAh6OnpYfz48cjPz//iTNSBQQghhBBCCCGEEEIIIYTwWEZGBkaNGoUDBw5AR0fns9ufOnUKubm58PX1hbGxMRwcHDBv3jzs3LkTJSUlAIDNmzdjzJgxGDhwIJo2bQpvb2/UrFkTBw4c+OJc1IFBCCGEEEIIIYQQQgghhPDY1atXYWRkhGPHjkFPT++z29+4cQPm5uZQU1MTtdna2qKgoAApKSl4/vw5Hjx4ADs7O9HrCgoKaNOmDWJjY784Fy3iTQghhBBCCCGEEEIIIYTIOHt7+0++fvbs2f/8s0ePHv2vtn/8+DG0tLTE2ho0aAAAePToERQUyrsetLW1JbZJTU394n+HOjAINFvYfX4jntE1bsk6gsyh74mk2jb9WEeQOf1K77COIHOaGDVlHUHm6Dc1YR1B5jQ1asI6gsxRt+jEOoJMqdVuAOsIMqdxMzPWEWSOjokF6wgyx9DIiHUEmaNm7cA6gsxpvPkQ6wgyx7yp9uc34hm6rpdE93/k/3UpqgvrCN+dz/RfVCk7O/uTnR9XrlyBurr6v/qZb968QZ06dcTaatSoAQB4+/YtiouLAQBKSkoS27x9+/aL/x3qwCCEEEIIIYQQQgghhBBCZNx/nWHRsGFDnDhxosrXPywD9aWUlZVFa11UqOiYUFFRgbKyMgBUuk3NmjW/+N+hDgxCCCGEEEIIIYQQQgghpJpSVFSE0VeemaqlpYW0tDSxtidPngAo7zCpKB315MkTsX/7yZMnaNiw4Rf/O7SINyGEEEIIIYQQQgghhBBCvljbtm2RnJyMgoICUdvVq1ehqqoKU1NTaGhooEmTJrh27Zro9Xfv3uHGjRto27btF/871IFBCCGEEEIIIYQQQgghhJAqlZSU4OnTp6KSUA4ODqhfvz7mzp2L1NRUnDlzBv7+/pgwYYJo3YsJEyYgNDQUhw8fxt27d7Fo0SK8efMGw4YN++J/lzowCCGEEEIIIYQQQgghhBBSpZs3b6Jjx464efMmgPLFuLdu3QqhUAgnJycsX74co0aNwvTp00XvcXJywuzZsxEQEIChQ4ciJycHoaGh/2rBcAHHcdxX/zSEEEIIIYQQQgghhBBCCCH/B5qBQQghhBBCCCGEEEIIIYQQmUMdGIQQQgghhBBCCCGEEEIIkTnUgUEIIYQQQgghhBBCCCGEEJlDHRiEEEIIIYQQQgghhBBCCJE51IFBCCGEEEIIIYQQQgghhBCZQx0YhBBCCCGEEEIIIYQQQgiROdSBQQghhBBCCCGEEEIIIYQQmUMdGIQQQgghhBBCCCGEEEIIkTnUgUEIIaTaevr0KesIRAZs3LgRxcXFEu0FBQVYtWoVg0SEfH9KSkpYR5B5HMexjiBz6DxMCPmv6LxDKuPh4YGCggKJ9hcvXmD69OkMEhFCpEGBdQBCYmNjkZGRgf79++Px48cwMDCAggJ9NQEgPz8f169fh7m5ORo1asQ6DjNv3ryBnJwclJSUkJGRgQsXLsDKygrW1tasoxEZYGZmhpiYGKirq4u1Z2dnY8CAAbh58yajZGyUlJRg+/bt6NOnD/T19eHp6YkTJ07A2toaa9euRb169VhHlIqMjAzk5+cDAIKCgmBqago1NTWxbdLS0hAREQFPT08WEYkMo2uTf4SHhyMkJASPHz/GqVOnsHXrVjRs2JC3Dwns7e3x22+/oW7dumLteXl5GDhwIK5du8YmGEN0Hib/VkJCAvLy8tC0aVM0adKEdRypys3Nhba2NgQCgagtPz8fhw4dwuPHj2FsbIzBgwdDSUmJYUq26Lwj6c2bNzh58iQyMjLg6uqKtLQ0NGvWjDfX9XFxccjKygIAREZGwtzcHLVq1RLbJiMjA1euXGERjxAiBfy8EyMyoaCgABMnTsRff/0FgUCADh06YO3atcjKysL27dvRsGFD1hGlLi0tDbNmzYKXlxdMTEwwcOBAPHv2DEpKStiyZQtsbW1ZR5S62NhYzJgxA+vXr4eRkRF++OEHyMnJobi4GGvXrkWfPn1YR5QqU1NTsRueDykqKkJLSwuDBg3C9OnTq9yuOjh48CCOHj0KoHzE64wZM6CoqCi2zZMnT1CnTh0W8Zhau3Ytjhw5gk6dOuHPP//E4cOHMXv2bFy4cAG+vr7w8fFhHVEqsrKyMHXqVNHvwcyZMyvdbujQodKMJVPu3buHFStWID4+HqWlpRKvp6SkMEjFFl2biIuKisK6deswbtw4bN26FQBgZGSEtWvXQllZGRMmTGCcUDpOnDiBixcvAgBycnKwYsUK1KhRQ2ybnJycan3e/RidhyV96hrtY3w5vtrb2+PgwYOih6wFBQWYPn06rl+/DgAQCAQYMGAAvLy8ePPA3t7eHpcuXYKGhgYA4MGDBxg1ahSEQiH09PQQGRmJbdu2YdeuXbw75wB03qnMs2fPMHz4cDx//hwlJSVwcnLC9u3bkZSUhJ07d8LIyIh1xG9OIBDA3d1d9GcvLy+JbVRUVODq6irtaIQQKaEODMKMv78/AOD06dMYOHAgAGD+/Pn46aef4Ovri3Xr1rGMx8SaNWugr68PQ0NDHDt2DO/evUN0dDT27duHgIAA7Nu3j3VEqfP394e9vT1atmyJiIgI1KpVC6dPn8Zvv/2G4OBg3nVgLFq0COvWrcPIkSPRpk0bAMDNmzcRFhaGkSNHQk1NDbt27YKSkhImTZrEOO234+DggLi4ONHftbS0oKysLLZNxQg2vjl58iT8/f1hbm6On3/+GTY2Npg6dSo6duxYrb8TH+vatSvOnTsHoVAIBwcHHDhwQGx0sEAggIqKisQoaj75+eef8fz5c/z000+oXbs26zgyga5NxG3fvh2enp4YMmQItm/fDgBwdnaGiooKQkJCePMgycrKCvv27ROViMrNzRV7WF9xPFmzZg2riFJH52FJ3t7eog6MnJwchISEYPjw4bCysoKioiISExMRFhaGadOmMU4qPTk5ORAKhaK///LLL8jLy0NERASaNWuGhIQEeHp6Yv369Zg/fz7DpNLzcak5Pz8/NG/eHBs3boSysjIKCgowa9YsrF69Gr/88gujlOzQeUfS6tWr0axZMxw7dgzt27cHUP7cYO7cufDz88PmzZsZJ/z2DAwMkJqaCqC8szgmJkbUCUgI4QmOEEa6du3KxcXFcRzHca1ateIyMzM5juO4+Ph4zs7OjmU0ZqysrLi7d+9yHMdxU6dO5RYsWMBxHMdlZmZylpaWDJOxY2FhIfpujB8/nluyZAnHcRyXnZ3NtWzZkmU0JpycnLh9+/ZJtB88eJAbMWIEx3Ecd+bMGa5nz57SjsaMu7s79/r1a9YxZEbLli25R48ecRzHcfb29tyWLVs4juO4rKwsrlWrViyjMZOdnc0JhULWMWROy5YtuaSkJNYxZApdm4iztLQU7YMP90dmZiYvz8Ecx3FjxozhXrx4wTqGTKHzsKTRo0dzBw4ckGg/cuQI5+joyCARGyYmJtyzZ89Ef3dwcODOnj0rts2FCxe4Tp06STsaMx/vk44dO3I3btwQ2yYhIYFr06aNtKPJBDrvSOrQoQN3+/ZtjuPE90lKSgrXtm1bltGkpk2bNlxubi7HcXTOIYSvaAYGYSY/Px/169eXaK9Tpw6KiooYJGKvYp2Hd+/e4fr161iyZAkAoLCwUGJUG1/UrFkTJSUlePv2LeLi4vDDDz8AKJ9Ky8cRwykpKZWWEmvTpg2WL18OAGjevDkePXok7WjM+Pj4gOM4/Pnnn0hLS4OCggKaNWsGW1tbyMvLs44ndY0bN0ZiYiKeP3+O7OxsdOrUCQBw5swZ6OnpMU7Hhra2No4ePSoqlcR9NPqRL2W1PlavXj2Jki98R9cm4jQ1NXH//n2Jdbhu3ryJBg0aMErF1u7du1lHkDl8PYZ+SkJCAlatWiXRbmFhgbt37zJIxMbHJbWEQiEaN24s1mZoaIjXr19LMxZTAoFAbL/UrVsXNWvWFNumZs2avCpJ9yE670gqLCyEiopKpa+9e/dOymnYKCsrw6VLl9C+fXtERkZizJgxVa7/oaOjI+V0hBBpoA4MwkzLli3x+++/Y/LkyWLtYWFhaN68OaNUbLVq1QrBwcFQV1fH27dv0blzZ+Tl5cHf3x+tWrViHY+Jdu3awc/PD2pqapCTk0OnTp2QkpICLy8vtGvXjnU8qdPT08P58+fh4uIi1n7+/HloaWkBADIzMyUW0qzOXrx4AVdXV9y+fRu1a9cGx3EoKCiAubk5QkNDeVV/GwAmTpyIefPmQU5ODra2tjA1NUVQUBCCgoLg7e3NOh4T3t7eCAsLg6mpqcSCf3w2ZswY+Pv7Y+3atbRf3qNrE3HDhw/HihUr4OHhAaB83ZRLly4hICAA48aNY5yODVo7phyt9/Bp+vr6OH78uMSiw/v370fTpk0ZpZI+juOwZ88emJubw8jICJ07d8bZs2fF9sHRo0d5tZA3x3Fwc3ODiYkJjIyMYGJigu3bt2Pt2rUAyhdr3rBhA1q2bMk4KRt03pHUtm1bhIeHi/YJAJSWlmLTpk2wtrZmmEx6Bg0ahCVLlojOO8OGDZPYhuM4CAQCXp5zCOEDAffxMERCpCQ+Ph4TJkxAx44dER0djQEDBiAjIwO3b9/Gtm3bePlw+uHDh3Bzc0NWVhbc3NwwatQorFy5EhcuXEBISAgMDQ1ZR5S6/Px8/Pzzz8jKysLMmTPh4OCA1atXIzExEQEBAZWOlK3OoqKi4O7ujt69e8PKygpCoRC3bt3CyZMnsWLFClhbW8PV1RUODg5YtGgR67hS4eHhgYSEBKxbtw6mpqYAgNTUVMyfPx/W1taimSl8cufOHWRlZaFz585QUlLCn3/+CUVFRdjZ2bGOxkS7du3g7u6OIUOGsI4iU8aPH48bN26grKwMGhoaEguonj17llEydujaRJK/vz927tyJt2/fAgAUFBQwYsQILFq0CHJycozTSd/YsWPx/PlzjBgxotKZoHw5zhw6dOiLOzD4sk8+dPr0acyePRutW7dGy5YtIRQKcfPmTaSkpCAkJKTS2bTV0eLFi5GWlob09HS8efMGHMdBXl4ely9fhpqaGsaPH4+rV68iICAAvXr1Yh1XKqKionDnzh2kpaXhzp07yMvLg0AgwJUrV1C3bl3ReWbHjh0wMzNjnJYNOu+Iy8jIwOjRo6GtrY309HS0a9cO9+7dw+vXr7Fnzx7R/U91d+fOHbx8+RLOzs4IDAyEmppapdvZ2NhIORkhRBqoA4MwlZqaiu3btyM5ORlCoRDNmjXDhAkTYGlpyTqazMjPz4eamhovS+FUpaSkROJBG5+cP38e27dvx+3bt6GgoAATExNMnjwZnTp1QmxsLC5duoSZM2fypjSMra0tNmzYIHGxeu3aNcybNw8xMTGMkrExbNgweHl58eZm5ktYWVkhKiqKtyW0qrJx48ZPvj5z5kwpJZEtdG0iqbi4GHfv3gXHcTA0NOT1jB0LCwuEh4fD3NycdRQi4+Lj47Fnzx6kp6cDAMzMzDBhwgTenp8zMzORlpaGtLQ00cyUhQsXolevXujevTvjdOy8evUKaWlpaNOmDQAgIiICnTt3Fs2s5is674jLy8tDeHg4UlJSRNcmo0aN4uW17eHDh9GvXz9ePw8ghI+oA4Mw9fr1a/z999+iWqh//PEHbGxsULduXbbBGHr58iUePHiAkpISidfatm3LIBF76enpSE9Pr3SfDB48WPqBiExp06YNIiIiJGYoZWRkwNHREbdu3WKUjI127drhwIEDEjWm+Wz27Nlo164dRo8ezToKId+V7t27Y/DgwRgyZIhEPXK+6tKlC0JCQmBsbMw6ClMeHh7w9PRErVq1xMqafEwgEPC2fCEh5N+LjY2ttF0gEEBRURFaWlpo2LChlFMRWRIZGfnF29KzAkKqD1oDgzBz+/ZtTJgwAY6Ojli4cCEAYPXq1SgpKcH27dt5eWP422+/Yfny5ZU+qOdrPcfNmzcjICCg0tcEAgEvL0pu3LhR5WLEfBwxbW5ujvDwcHh6eoq1h4eH83Lq/cSJE+Hp6QlXV1c0btwYysrKYq/zcWG7Vq1awc/PD1euXIGRkZHE7CQ+/t5USEpKwrZt25CWlgYFBQU0bdoU48aNg4WFBetoUvO5mSgf4tt3xdHREceOHRPV2XZ0dETv3r2hqqrKOhoztHZMuezsbAiFQtGfiaTo6Ghs27YN9+7dw/79+3Ho0CE0btwYgwYNYh2NEJnl4uIiOrZU3Od8XK7OxsYGgYGBvFnnbuzYsZWW7PuwU2fQoEG8Gey4YcMGPHv2DCUlJVBRUYGioiJevnwJgUAgdm/M12cFhFRXNAODMDN27Fjo6elh+fLloul/7969w5IlS5CXl4ft27czTih9Xbp0QefOneHi4iLx0BEAdHV1GaRiq3379hgxYgSmTJmCGjVqsI7DXFBQkOiC/eMHJwKBgJc162/evAlnZ2eYmpqKFrKLi4tDamoqtm7dyps60xXMzc1RVlYGQPyGj88L232qNAVff28A4Pr165gwYQKMjY3Rpk0bCIVCxMfHIy0tDTt37kTr1q1ZR5SKj78fjx49gqKiIho1agQFBQVkZmaitLQULVq0wL59+xilZCshIQFHjx7FyZMnUVhYiJ49e2LIkCG8O74CtHYM+TIxMTGYOnUq+vXrhxMnTuD48eM4ePAgQkJC4O3tTQ/VCKlCVFQUfvnlFyxevFj0QD4+Ph5eXl4YNWoULC0tsXr1apiammLFihWM00qHt7c3du/eDTMzM1GpsVu3buHWrVtwcHBAcXExrl27hvXr18Pe3p5x2m/v4MGD2LFjB1avXo0WLVoAALKysrBgwQL06dMHzs7OjBMSQr4F6sAgzFhZWeHo0aMSJQnu37+PYcOGIS4ujlEydqysrHD48GEYGBiwjiIz2rZti4MHD0JfX591FJnQsWNHjB07FlOmTGEdRaYkJCRg+/btSE9PB8dxMDExwfjx43k1irzC9evXP/k6LWxHKowcORLGxsYSC90vX74cd+/exe7duxklY2fHjh24cOEC1q1bBw0NDQDl9ckXLFgAY2NjzJs3j3FCtoRCIcLDw+Hv74+ioiJedojS2jGScnNzP/k6H2f+jRgxAr1794aLi4vYPU9ISAiOHj2KqKgo1hGloqqR45XZtWvXN04jG5o3by4xg7oqfDzG9ujRA0uXLkWnTp3E2i9fvoxly5bhjz/+wM2bNzFr1ixcunSJUUrpmj17Nho0aIDFixeLta9ZswZ5eXnw9/fHjh07cPz4cRw4cIBRSunp1q0b1q1bJxq4VuH27duYMmUKb74XhPANlZAizKiqqiIrK0uiA+PJkye8XZDJwcEB0dHR1IHxgUGDBiEiIgLz589nHUUmvH79Gv3792cdQ+ZYWFhUWWqMbz7soMjPz4eCggJvpth/CsdxuHjxoqhUUrNmzWBrawt5eXnW0ZhJTk6Gl5eXRPuYMWMwbNgwBonY27JlC7Zv3y7qvACAOnXqYN68eRg7dixvOzAePXqEqKgoREVFISMjAzY2NnB0dGQdiwk+dlB8Tvfu3T/5kJqPD2Hv3LkDX19fifbevXv/q7J137uOHTti/fr1aNKkCS8HlVRm9+7dmD59OvT09DBmzBjWcWTO06dPK+30bNiwIR4/fiz68+vXr6UdjZmLFy/i0KFDEu3Dhw/HkCFDAAD29vZYv369tKMx8fLlS9SsWVOivbS0FEVFRQwSEUKkgTowCDO9evXC8uXLsWzZMtEFbWJiIlasWIEePXowTsfG/PnzMWDAAJw6dQqNGzeWuBn08fFhlIydiRMnYtCgQThx4gT09PQk9glfRmtVsLa2xs2bN3lZTuxDVLP+03bt2oUtW7bg+fPnAABNTU24urrCxcWFbTBGXrx4AVdXV9y+fRu1a9cGx3EoKCiAubk5QkNDedvBU69ePfz9998S7fn5+bwdSFDVze/z58+/eBRxdbJv3z4cO3YM8fHx0NXVFS3ozccR9R9KTU3Fzp07cf/+faxfvx5nzpxBs2bNeDvD7eNrsbKyMty/fx87duyAu7s7o1Rs1a5dG0+ePEHjxo3F2u/evQs1NTVGqaRvypQpqFWrFtatW4fg4GDo6emxjsRc69at8euvv2LcuHHQ09PjzboFX6pFixbYunUrvLy8RINMysrKsHXrVpiamgIon23Mp+9SrVq1cO/ePTRp0kSs/e7du6IH+YWFhZWWoK6O7OzssHz5cvj5+YkGw2ZkZPD6ORIhfEAdGISZH3/8EZmZmRg/frzYQ4EePXpgwYIFDJOx4+XlhcLCQpSUlCAnJ4d1HJlQceNrYWEBFRUVxmnY69+/P1auXImkpCQYGhpKPGTkS03lykYhVUYgEPCuA2Pfvn3w8/PDqFGj0LZtW3Ach9jYWPj7+6NWrVq8HFm/Zs0avHnzBpGRkaKb39TUVMyfPx/r1q2TKKHEF926dcPKlSvh7+8PIyMjAOU3w15eXp9cN6Q66969O5YsWYKlS5eiRYsW4DgOcXFxWLlyJfr168c6ntStWbMGvXv3xpw5c+gh23tJSUkYOXIkWrVqhaSkJJSUlCAlJQU+Pj4ICgpCly5dWEeUuso6buzs7NCoUSMEBgby8ngyYMAAeHt7w9vbGwKBAIWFhfjzzz+xcuVK9O3bl3U8qRo9ejQuXrwIX19fbNiwgXUcmdC6dWuMHj0aa9aswcGDB1nHkSnu7u5wcXHBtWvX0KJFCwiFQiQnJ+PVq1fYunUr4uPjsXjxYolyStWZo6MjlixZgvz8fFhaWkIoFOLWrVvYsGEDBg0ahL///hu+vr68OU8vXboUEyZMQM+ePVGnTh1wHIfXr1/D0tISnp6erOMRQr4RWgODMHfv3j2kpaVBUVERRkZGvC6f1KpVKwQGBkrU/OQzS0tL7Nq1C5aWlqyjyISKh6+V4esCzV/qypUrsLa2rvaLwffu3RtjxoyRKEsQFhaGffv28abu9odsbW2xYcMGiYds165dw7x58xATE8MoGVsvX77E+PHjkZKSgtq1awMoL1NnamqK7du3o169eowTSl9BQQHmzJmDmJgY0eAKjuPQu3dv+Pr68m5mSlFREQ0e+IiLiwssLS3h5uYmtraBj48P4uLi6GHkB7KystCvXz8kJCSwjiJ1paWlcHd3x/HjxwGUX6NxHIeuXbsiICCANyOlKzx58gS3b99Gt27dWEeRGWVlZSgqKhKdf8k/njx5gn379iE5ORkKCgowNTXFqFGjoK6ujoyMDDx+/BgdOnRgHVNqhEIh/Pz8EB4ejrdv34LjOCgrK2Ps2LGYM2cOLly4gK1bt8Lf3583MyTfvXuHmJgY3L17F0D52jJ2dnaMUxFCviXqwCBEhnTr1g1bt24VjYQl5Q9j/f390bx5c9ZRyHfO2toaR44ckVh3p7qxsLDAsWPHJMpWZGZmon///rx8kNSmTRtERETA0NBQrD0jIwOOjo64desWo2TsCYVCXLx4Eenp6eA4DiYmJujYsSPk5ORYR2OqYnCFnJwczMzMqv1x41POnj2L4OBg0foxTZs2haurK2/LNLRp0wYHDhxAkyZNxDowMjMzMWjQINy8eZN1RKmrbBHvgoICBAcHIyUlBSdOnGCQSjY8fPgQKSkpEAqFMDY2RtOmTcFxHC9L0pF/5+nTp6hfvz7rGESGvHnzBhkZGZCXl4eBgQHvOkKrkp+fj+vXr6NFixa8Ki1GCN9QCSkiVWZmZrh06RI0NDRgampKC/59ZOrUqVi1ahWWLFmCxo0b83px2Qo//fQTli5dirlz56Jx48ZQUBA/bPFllAn5//Glv15HRwdJSUkSHRiJiYnQ1NRklIotc3NzhIeHS0wrDw8Ph5mZGaNUskFOTg5dunThZdmbTzE0NBTr8CopKUFiYiJat27NMJX0/fHHH5gzZw7s7e3Rr18/UUm6OXPmIDAwEPb29qwjSp2ioiIKCgok2h89elTpoqJ8UNki3hzHQUVFBX5+foxSsWVvb4/ffvsN+vr60NfXF7Xn5eVh4MCBuHbtGsN0RFaYmZkhJiYG6urqYu3Z2dkYMGAALztE3759i/379yMtLQ1lZWWi9pKSEiQlJeHUqVMM07Hz7t07vHz5UjQ79vnz56Jrk4EDBzJOJ11paWmYNWsWvLy8YGJigkGDBuHp06dQUlLCli1bYGtryzoiIeQboA4MIlXe3t6iabJ8XJD6c7Zt24bc3Nwqa+PysVNnzpw5KCsrw8SJE8VujitGr/Fhn1DHH/k3RowYgeXLl+PFixewtrYGAMTFxWHDhg1wdnZmnI6NuXPnwtnZGX/99ZfYPklNTcXWrVsZp5MuOp58WlJSEpYsWYK0tDQIhUKJ1/m2T3799VfMmDFDbC0hFxcXbNy4EZs3b+ZlB4aDgwMCAgLwyy+/iNoyMjKwatUqdO3alV0whj5exBso7+gxNjaGqqoqg0RsnDhxAhcvXgQA5OTkYMWKFRJlK3Nycng7++JT5xxFRUVoaWlh0KBBmD59erXeRwcPHsTRo0cBlN/PzJgxA4qKimLbPHnyBHXq1GERjzkvLy9ERkaiefPmSExMhJWVFR4+fIjnz5/DxcWFdTwmLl26hIULFyI/P1/iNWVlZd51YKxZswb6+vowNDTEsWPHUFpaiujoaOzbtw8BAQHYt28f64iEkG+AOjCIVA0ZMkT059u3b8PZ2VlilDCfTZs2jXUEmRMaGso6AnPU8Uf+DWdnZ+Tk5MDb2xtlZWXgOA4KCgoYMWIEb48xVlZWCAsLQ2hoKC5duiQqlbR06VJYWFiwjidVdDz5NB8fH8jLy2Px4sXw8fGBu7s7MjMzERYWBl9fX9bxpO7evXsYMGCARHv//v0REhLCIBF7CxcuxMSJE2FrawuhUAhHR0cUFBTA1NQUCxYsYB2PiQ/XFyooKICiomK1X2+qMlZWVti3b59oxmdubq7Yg2mBQAAVFRWsWbOGVUSmFi1ahHXr1mHkyJFo06YNAODmzZsICwvDyJEjoaamhl27dkFJSQmTJk1inPbbcXBwQFxcnOjvWlpaEqWAjI2NMXjwYCknkw1nz56Fj48P+vfvjx49emDlypVo1KgR3NzcUFpayjoeExXllCvWvFi7di1yc3OxYcMGXl7L3bx5EwcOHICGhgYuXryILl26oGHDhnB0dKRnB4RUY9SBQZg5fPgwb0dRVOXDDh5S7sOb4vz8fCgoKPBuRNKH3wstLS1aoIx8kpycHDw9PTFnzhzcu3cPQHk5nFq1ajFOxo6XlxecnZ3FRkzz1YfHE4FAgL59+0osTF1UVISIiAhpR5MJycnJ2LlzJywsLHDo0CEYGxtj1KhR0NLSQkREBPr06cM6olQ1aNAADx8+FCuBA5TX9efrwrO1atXCvn37cOXKFSQnJ4vWNujUqROv147ZuXMnQkNDkZeXB4FAAD09PUyfPp1XD2G1tbVFs1HGjh2LjRs3Qk1NjXEq2XH8+HEsWrQIw4cPF7U5ODjA0NAQBw8eRHh4OJo1awZfX99q3YFRt25dsYfOnp6evL5G+9irV69Es2WbNm2K5ORkGBoaYsqUKZg7dy4WL17MOKH03b17F97e3jA1NYWZmRlUVFQwduxYqKioYNu2bXBwcGAdUark5OSgpKSEd+/e4fr161iyZAkAoLCwkNYFIaQaow4MwkyXLl2wZ88ezJw5ky7aPkCLZUratWsXtmzZgufPnwMANDU14erqyssOsAkTJkBbWxuDBw/GkCFDeL2wLKlaYWEhoqKiRMeRZs2aoW/fvrw91lKH+T/y8/Px5s0bAICHhweaNWsmqqdcISUlBf7+/rzcZ0KhULRoqr6+PtLS0tCmTRvY29sjODiYcTrp69+/P5YtW4aff/5ZtP5HXFwcli9fXmW5S76ws7OjAQXvBQYGYvv27XB2doalpSWEQiFu3LiBZcuWoaCgAGPGjGEdUep2795d5WuPHz+GlpaWFNPIhpSUlEpr07dp0wbLly8HADRv3hyPHj2SdjRmfHx8wHEc/vzzT7FrNltbW96uhaiuro7nz59DR0cHBgYGSEtLAwDUq1cPz549Y5yODXl5edGggYprEzs7O9ja2vJyRlerVq0QHBwMdXV1vH37Fp07d0ZeXh78/f3RqlUr1vEIId8IdWAQZp4+fYoTJ05g586d0NDQkJhqfvbsWUbJ2KHFMiXt27cPfn5+GDVqFNq2bSvaJ/7+/qhVqxaGDRvGOqJUnTlzBlFRUTh27Bg2bdoEa2trDBkyBH369OFVnWlStdzcXIwZMwbPnz9HkyZNIBQKERERgc2bN2Pv3r28fGhCHeb/+PPPP+Hu7g6BQACO4yo9hnIcx9tFvfX19REXF4f+/fvD0NAQiYmJAIDXr1+jpKSEcTrpmzZtGtLS0jBlyhRRTXqO49C1a1fMmzePcTo2EhISsHz5cqSnp1dazoRv66QAQEREBFauXIn+/fuL2hwcHGBkZIRNmzbxsgMjKysLa9asEVuImOM4lJSUID8/H8nJyYwTSp+enh7Onz8v0Tl+/vx50bVJZmamxILW1dmLFy/g6uqK27dvo3bt2uA4DgUFBTA3N0doaCjvZp0DQOfOnbF8+XL4+PigdevW8Pb2Ro8ePXDixAleXsMCQLNmzXDu3DmMHTsWhoaGiIuLw7hx4/D48WPW0ZhYsmQJ3NzckJWVhUWLFkFdXR0rV65ERkYGb8tbEsIHAq6iSCchUrZx48ZPvv7hgpF8MXjwYDg4OEh89o0bNyI6OhoHDhxglIyd3r17Y8yYMRI3v2FhYdi3bx+ioqIYJWMvOTkZUVFROHnyJF68eIGePXvychTOl7KyssLRo0er/ayV2bNn49mzZ9iwYQM0NTUBAM+ePcPcuXPRsGFDrFu3jnFC6Rs7dixiY2MhEAiowxxAbGwshEIhxo0bh8DAQLESJxU12o2NjSUWFeWDAwcOwMvLC6tWrYKZmRkGDRqEH374AfHx8dDU1MS2bdtYR2QiIyMDaWlpovVjjIyMWEdiZuDAgahRowYcHR0rLVXBx3KgrVq1wuHDh9GkSROx9nv37mHIkCG4desWo2TsTJo0CQ8ePEDv3r0RGhqKCRMm4P79+zh9+jRWrFgBJycn1hGlLioqCu7u7ujduzesrKwgFApx69YtnDx5EitWrIC1tTVcXV3h4OCARYsWsY4rFR4eHkhISMC6detgamoKAEhNTcX8+fNhbW0tmpnCJ69evYK7uzs6dOiAUaNGYfLkybh48SIUFBSwZs0a9OvXj3VEqTtz5gxmz56NpUuXokuXLujVqxdsbW1x584dWFpaYsOGDawjMpefnw81NTWxmUu//fYbevfuTYP8CKkmqAODEBliYWGBqKgoiVrTDx48wKBBg3h5A2hhYYFjx45JLPaemZmJ/v37IyEhgVEy2XD79m2cPHkSe/fuhUAgwI0bN1hHkrrIyMhP1vGvGOm3cuVKzJw5U6JcTnXTpk0bbN++XWJx6oSEBEyaNAnXrl1jlIwd6jCv3PXr12FtbQ0FBZqQ+6EzZ86gbt26aNOmDaKiohASEgJtbW0sWbIEenp6rOMxceXKFaSnp0NJSQnGxsai+uR8ZGlpiYMHD6JZs2aso8iMH3/8ERoaGhIPndetW4cHDx4gMDCQUTJ2WrdujV9//RXt2rXD4MGDsWLFClhYWOCXX37B3bt3ERQUxDoiE+fPn8f27dtx+/ZtKCgowMTEBJMnT0anTp0QGxuLS5cuYebMmbzpQLe1tcWGDRvE1vwDgGvXrmHevHmIiYlhlEx2cByHlJQUaGpqokGDBqJ2vj2cvn37NuTl5WFqaorr168jNDQU2tramD17NurWrcs6nkyytrbGkSNHqv3gNUL4gu5YCVN//fUXdu/ejbS0NMjLy8Pc3BwuLi68vSmkxTIl6ejoICkpSaIDIzExUTS6nG+ysrIQFRWFqKgoPHz4EO3atcPSpUvRq1cv1tGk5kvq+CcnJ4vV8a9Y4K26k5eXR82aNSXaa9SowasSOE5OTggKCkL9+vWhp6dXaScXH23cuBGurq6oWbMmrl+/juvXr1e5LR87dioWfK845wwYMAADBgxgnIqd3NxczJw5E8nJyVBTU4NQKMTr169ha2uLgIAAXj40adGiBXJycnh7rVrBw8ND9OfS0lLs3r0bN27cQOvWrSEvL4/bt28jPj4eP/zwA8OU7JSUlIiOI02aNMGdO3dgYWGBwYMHY+zYsYzTsdOtWzd069at0tfatm2Ltm3bSjkRW+/evav0fkZTUxMFBQUMEskegUCA5s2bS7SvWrUKNjY2vOnAMDc3F/3ZxsZGotMLAHr27IkdO3ZAR0dHmtFkFo3VJqR6oQ4Mwsy5c+cwc+ZMtGzZEh06dEBZWRlu3rwJR0dHhIaGok2bNqwjSh0tlilpxIgRWL58OV68eCEa8RkXF4cNGzbA2dmZcTrpc3JyQmJiIvT09EQLefPxIpXq+FfN2toav/76K3x9fUUjGEtLS7F582ZejZpOTU3FkydPUL9+fXh4eKBz5868qqtdlUOHDmH06NGoWbMmDh06VOV2AoGAlx0YtOC7uBUrVkBJSQmnTp0SDa64c+cOFi5cCC8vL6xdu5ZxQulbsWIFpk+fjlu3bqFRo0aQk5MTe33w4MFsgklZdna22N8rrttTU1NFbdbW1rh3755Uc8kKXV1dpKWlQVtbG02aNBGtjSIUClFYWMg4HTs3btxAfHw8SktLJR4u8vGcY25ujvDwcHh6eoq1h4eHw8zMjFGq7wM9nJb09OlT0Zo7hBBS3VAJKcLMgAED0LVrV/z4449i7WvWrEF8fDz279/PKBk7b9++hZubG86dOyexWGZAQECltZarO6FQCB8fH4SHh6OsrAwcx0FBQQEjRozAokWLJB4cVHceHh5wdHTk3Qi1ylAd/8plZGRgxIgRUFVVRYsWLQCUz1gqLCzEnj17RDWWqzsXFxfRugW5ubnQ1tau8njBtzUwSNXmzZuHBg0a0ILv77Vq1Qp79+6VGP36119/YcKECYiPj2eUjJ3AwMAqy/8IBAJeLuL9pfhU8iU4OBjbt2+Hr68v6tWrB2dnZ8ycORMxMTEoLi7Gvn37WEeUuqCgIAQGBqJOnToSx1eBQMDLc/HNmzfh7OwMU1NTsYFaqamp2Lp1K2xtbRknlF18Wdvu36B9Io72ByHVC83AIMw8fPgQQ4cOlWgfPnw49u7dyyARezVq1MCvv/5Ki2V+QE5ODp6enpgzZ45oFJ+hoSFvHyz5+PiwjiATPiwP5OPjAzs7O95+Jz5mZGSEyMhI7N27F+np6eA4DgMGDMDIkSOhq6vLOp7UrF+/HkePHsXLly+xceNG3jw0I/+fp0+f4sSJE9i5cyct+A6gTp06KC0tlWgXCASVlqrjgz179mDu3LkYN24cb/fBf8Wnki+TJ09GjRo1wHEcLCwsMH36dGzatAna2trw8/NjHY+J8PBwuLm5YcqUKayjyAwrKyuEhYVh+/btuHTpkujeb+nSpRJrmRFCCCF8Rh0YhBkzMzNcuXIFBgYGYu1JSUm8rivMcRxycnKQk5MDBQUFqKmpwcDAAPLy8qyjMVNYWIioqCikpaVBQUEBzZo1Q9++fXnzwNrMzAyXLl2ChoYGTE1NRbNzKsOXkZ8flgdatGgRL0tFfYquri7mz5/POgZTampqojrj169fx4wZM3hzzPiUzx1DPsSX48mH2rVrh3bt2rGOITNmzZqFpUuXYu3ataJrs6ysLKxcuRJTp05lnI4NoVCIfv36UefFf8Cnif/Z2dli5egmT56MyZMnswskA16/fo3+/fuzjiFzLCwsEBAQwDoGIYQQItOoA4MwM3DgQKxduxb37t1Du3btoKCggMTEROzcuRMjRoxAZGSkaFu+1BN+8eIFXF1dcfv2bdSuXRscx6GgoADm5uYIDQ1FnTp1WEeUutzcXIwZMwbPnz9HkyZNIBQKERERgc2bN2Pv3r3Q0tJiHfGb8/b2Fi3i7u3t/cUPH6sza2trjBw5EpqamuA4DkOHDuV1eaAPF1P9HD7O4klPT8fDhw/FFkDkqw+PITk5OQgJCcHw4cNhZWUFRUVFJCYmIiwsDNOmTWOcVLYIBAIoKiri6NGj6Ny5c7VevPrjTi6O4zBw4ECoqqpCXl4er169gkAgwOPHj3m5GPGgQYOwd+9eLFy4kHUUIsN69OiB1q1bw9HRkWYAvmdtbY2bN2/yajZoZTZu3PjF2/JxXRBCCCGkMtSBQZhZuXIlAGD37t3YvXu32Gtbt24V/VkgEPCmA2PNmjV48+YNIiMjRXXqU1NTMX/+fKxbtw7Lly9nnFD6Vq9eDS0tLUREREBTUxMA8OzZM8ydOxd+fn5Yt24d44Tf3pAhQ0R/dnR0ZJhEdlB5IHFpaWkoKSlB3bp1kZubCy0tLd6tD/Mp6urqeP36NesYMuHDY8iYMWOwZMkSDBs2TNTm4OAAIyMj7Ny5E66uriwiMhUbG4vY2FgoKiqiSZMmAMpLXr558wba2tp48eIFatSogV27dlXb2aLUUf5pr1+/xvHjx3Hs2DE0atQICgrit1O7du1ilIzIkt27d+Po0aPw9fWFl5cXevTogSFDhsDOzo51NGb69++PlStXIikpCYaGhlBSUhJ7nS/3e4cOHfqi7QQCAXVgEEIIIe/RIt6EyBBbW1ts2LABNjY2Yu3Xrl3DvHnzEBMTwygZO23atMH27dsl6sAmJCRg0qRJuHbtGqNk0vOlI7UEAgFmzJjxjdPIHg8PD3h6evK6PFDLli1x4sQJNGrUCKampoiJiYGGhgbrWDJjzZo12Lt3L7p06QJ9fX2JdQ34+oDAwsICUVFR0NfXF2t/8OABBg0ahFu3bjFKxs66devw119/ISAgQPQ79Pfff+Onn35Cq1atMHXqVCxduhT5+fkIDg5mnJaw8LkZb3yc5fal+LigamlpKS5cuICoqChER0dDQ0MDgwcPxuzZs1lHk7qKwVmVEQgEvCxb+KWuXLkCa2triesXPuPj8eRzrK2tceTIEdon723duhXDhw8XVTIghHzfaAYGITLk3bt3olkGH9LU1ERBQQGDROzJy8tXWme6Ro0aKCkpYZBI+v7NSC0+dmDMmjULr169wqtXrz67rY6OjhQSSZ+enh5mzZoFMzMzAOULpVZ2kysQCODt7S3teMydOnUKGhoaSEpKQlJSkthrfB7hqK+vj+PHj2P69Oli7fv370fTpk0ZpWLr4MGD2L59u1gHYL169TB//nyMHz8es2bNgqurK0aOHMkwpXSdPXsWaWlpKCsrE7WVlJQgMTERoaGhDJOxQR0U5N9QVFQUlZOKjIxEUFAQgoODedmBkZqayjrCd2vGjBm8fDBdUlIiMVOnwowZM6p1Ocf/ojqvmflfyuVOnDjxW8UhhDBAHRiEyBBzc3OEh4fD09NTrD08PFz0YJJvrK2t8euvv8LX1xeKiooAykezbd68GdbW1ozTSce5c+f+9Xv4NFKre/funy13wnFctR7d5+fnh+DgYOTk5EAgECA3N1f0+0L+2+8QH8yePRuzZ8/G5cuX0bJlSwiFQty8eRMpKSkICQlhHY+Jd+/eobS0VKL97du3ePPmDQBASUkJQqFQ2tGYWLt2LbZu3QpNTU08f/4cDRs2xLNnz1BWVoZ+/fqxjsfMkydPEBERgfv372PRokWIjY2FsbExDA0NWUcjMqSoqAinT59GVFQUrl69Cl1dXbi6uoqVBiXkS/CtaEZ4eDhCQkLw+PFjnDp1Clu3bkXDhg3FBlzw7eF0cXEx0tLSUFpaKvF9aNu2LYDyMpjVVXZ2NusIhBDGqAODEBkyd+5cODs746+//hI9nI+Li0NqaqrYuiB88tNPP2HEiBHo0aMHWrRoAQBITExEYWEh9uzZwzid7OLTSC1/f394eXlh4sSJsLGxgZKSEhITExEQEAAnJye0a9eOdcRvrkWLFggMDARQ3qGzadMm1KtXj3Eq2RMbG4uMjAz0798fjx8/hoGBgUT9ej7p0aMHwsLCsGfPHly6dAkAYGZmhhUrVnyy1Ed11rFjRyxfvhz+/v6i0lr379+Hl5cXOnbsiLKyMoSHh8PExIRxUumIiorCokWL4OzsjC5dumDv3r1QUVHBjBkzeHF+qczDhw/h5OSEWrVqIS8vD3PnzsWJEyfg4eGBHTt2wNLSknVEIgPc3Nxw4cIFCAQC9O7dGzt27ECbNm1Yx5I6MzMzXLp0CRoaGjA1Nf3kgJPqOsiE/DtRUVFYt24dxo0bJ7r/NTIywtq1a6GsrIwJEyYwTih9Z8+ehbu7OwoKCiQ6L6rzAK0PfbxmKiGEf2gNDEJkTEJCAkJDQ5GWlgaO42BiYoLx48dLrAHBJ7m5uQgLC0N6erpon4wcORK6urqso8ksPtWFHTx4MCZPnoy+ffuKtUdHR8PX1xfHjx9nlIzIioKCAri6uuLWrVsQCAT4448/sGrVKmRmZiI0NBQNGzZkHZHIiPz8fEyZMgVJSUmoU6cOOI7D69evYWlpicDAQNy+fRtubm4IDg6WWK+qOmrRogVOnjwJPT09TJ48GY6Ojujduzdu3LgBT09PnDp1inVEqZs2bRrU1dXh5eUFa2trHD16FNra2li4cCGePHnCy4csM2fOhJubG4yMjD65HZ/qkY8dOxZDhw5Fr169Ki2FyheHDx9Gv379oKSkhMOHD39yW5qZUjU+XdcPGTIEzs7OGDJkiNjnPnjwIEJCQnh53unTpw+aNWuG6dOnV3r85OM98bt37/D8+XNReUuO40TlLQcOHMg4HSHkW+DvsENCZJCXlxecnZ3xyy+/sI4iMypuiufPn886CpFR9+/fr7TEmp6eHk03JgDKZ+kIBAKcPn1adFMzf/58/PTTT/D19cW6desYJ2RDKBQiKioK8fHxlZYk4GOtf3V1dURERODatWtISUmBvLw8TE1NRZ0VlpaW+PPPP3nxABYA6tSpg6KiIgBA48aNcffuXQDl6wnl5eWxjMZMfHw8wsLCxEaSKygoYPr06XBycmKYjJ2rV69+UclKPpV8qejIKigoQEJCAhQVFdGoUSPUqlWLcTLp+rBTQktLC3Z2dgzTkO/B/fv3K52t1K5dO6xYsYJBIvays7MRHByMxo0bs44iEy5duoSFCxciPz9f4jVlZWXqwCCkmpJjHYAQ8o/Dhw9DTo5+LT/0pTfFhL9MTU0RGhoqVpP+3bt32Lx5M5XyIACA8+fPY8GCBWIjF42MjLB06VJcuXKFYTK2vL294e7ujoSEBGRlZSE7O1vsP74SCASwtbXF+PHj4ezsLDbTol69erzpvADKHxitXbsWeXl5sLS0xMmTJ5Gfn49Tp05BXV2ddTwmhEJhpWugFBYWVusFVD9lyJAhWLt2LdLT01FSUsI6jkzgOA4+Pj6ws7PD8OHDMWTIELRv3x6rVq3i3XoGFSZMmIDu3btjw4YNyMrKYh2HyChNTU3cv39fov3mzZto0KABg0TsGRgY4PHjx6xjyAx/f380b94cwcHBUFZWxsaNG7Fo0SLUqlULfn5+rOMRQr4RmoFBiAzp0qUL9uzZg5kzZ/JuhFZVKm6KZ8yYAX19fSgpKbGORGTM/Pnz4erqipiYGJibm0MoFCIpKQnFxcW8LOVBJOXn56N+/foS7R+OLuejqKgoeHt7U9kOUqUFCxZg2rRp+P333zFq1CiEhoaiQ4cOAAB3d3fG6djo2LEjgoODxR6SvHjxAn5+frC1tWWYjJ3o6GhkZmZWWdqFD/XZP7Z582b89ttvmD9/PmxsbCAUChEbG4ugoCA0bNiQV7NRKpw5cwZRUVE4duwYNm3aBGtrawwZMgR9+vSBqqoq63hERgwfPhwrVqyAh4cHAODevXu4dOkSAgICMG7cOMbp2Pjpp5+wcuVKuLm5wdDQUOJ+WEdHh1EyNu7evQtvb2+YmprCzMwMKioqGDt2LFRUVLBt2zY4ODiwjkgI+QZoDQxCZMjYsWMRGxsLgUAADQ0NiZkHZ8+eZZSMnZ49eyIzM7PKRf/4eFP8JfhUKxcAMjMzsX//frHyJtOmTePtSC0ibsyYMejcuTMmT54s9ruxYsUKpKamYu/evawjMmFlZYWoqCjo6emxjkJk3Nu3b1GjRg0UFxfj0qVLaNiwodjaXFeuXIG1tTUvZkzm5eXB2dkZr1+/xosXL2BoaIicnBzUrVsXe/bs4WUt8rCwMCgrK1c5i5iPnaTdu3eHm5sbBgwYINYeFRWFwMBA/PHHH4ySyYbk5GRERUXh5MmTePHiBXr27Ik1a9awjiWz+HZd7+/vj507d+Lt27cAysv0jRgxAosWLeJltQJzc3PRWg8f3hNzHMebRbw/ZGVlhWPHjkFXVxceHh4wNTXFuHHjkJOTgyFDhuD69eusIxJCvgGagUGIDGnXrh3atWvHOoZMmTZtGusIRMa9evUKISEhGDNmDJo2bQpXV1eEh4fj6tWr2LJlC29u9kjV5s2bhwkTJiAhIQHv3r3Dpk2bkJGRgdu3b2Pbtm2s4zHTqVMnREdHY/To0ayjEBlX0TFRs2ZN9OjRQ+L1GTNm4MiRI7w43jZs2BCRkZE4duwYUlJSIBQKMXLkSAwaNIi3s2d9fX2hpqaGgQMHwtHREYaGhqwjMff8+fNKy1haWlri0aNHDBLJlubNm4PjOCgoKGDv3r28HKQFAJGRkejbt6/EiPqioiJERETAxcUFAODo6Mir48u8efMwbdo03L17FxzHwdDQkFef/2OhoaGsI8iUZs2a4dy5cxg7diwMDQ0RFxeHcePGUZktQqo5moFBCCHVEJ9Ganl4eODGjRvYvHkz0tPTsWDBAqxatQonTpyAgoICAgMDWUckMiA1NRXbt29HcnIyhEIhmjVrhgkTJvB6nZTt27djw4YN6NixI4yMjKCoqCj2+syZMxklI98bPp1zPlZaWorU1FQYGhrytgxOQUEBjh8/jsjISPz111+wsLCAo6Mj+vXrx9uHjoMGDcLIkSMxYsQIsfbw8HDs3LkTJ0+eZJSMraysLERFRSEqKgoPHz5Eu3btMHjwYPTq1QvKysqs40lFfn4+3rx5AwCwt7fHwYMHUa9ePbFtkpOTMW/ePCQkJLCIyFxubi7q1KmDWrVq4erVq/jjjz9gbW2N/v37s47GXH5+PhQUFFCnTh3WUZg5c+YMZs+ejaVLl6JLly7o1asXbG1tcefOHVhaWmLDhg2sIxJCvgHqwCCEscjIyC/edvDgwd8sh6ziOA6HDx9GUlIS3rx5I7bwoUAggLe3N8N00velI7VWrlyJmTNnStwQVUft27dHUFAQrKys4O7ujhcvXmDz5s1IS0vD6NGjERsbyzoiYexLf2/4pnv37lW+JhAIeDsilvx7fOrAePToETw9PTF37lwYGxtj2LBhyMjIQJ06dbBjxw6YmZmxjsjUw4cPERUVhVOnTiErKwsODg4YNmwY79YHiYyMxNKlSzF27FhYW1sDAOLi4hAWFoYFCxbwcuabk5MTEhMToaenh8GDB2PIkCG8q90PlH833N3dIRAIRCWAPsZxHLp06YLg4GAGCdk6ffo03NzcEBwcjEaNGqFv375o1KgRHj16hPnz5/PydwcAdu3ahS1btuD58+cAyhc7d3V15e017O3btyEvLw9TU1Ncv34doaGh0NbWxpw5c6CmpsY6HiHkG6ASUoQw9vEimBUXs8rKylBQUEBBQQHk5eVRr149XnZgrFmzBjt27ICJiQlvR5p8OFLLw8MDzZo1q3Sklr+/v+gidsmSJdKOyUxRURG0tbUBADExMZg0aRIAQFlZWVQvlvDPf/m94Ztz586xjkDId8fHxwevX7+Guro6fv/9d+Tk5CAsLAyHDh2Cn58ftm/fzjoiUzo6OjAxMcHDhw+RlZWFuLg4nDt3Drq6uvDz84OpqSnriFIxePBgvHjxAlu3bhWVKtTU1MTcuXN5+wDWyMgI8+fPR9u2bVlHYWrw4MHQ1dWFUCjEuHHjsGHDBrEHrgKBACoqKjA2NmaYkp1ff/0Vrq6usLOzw6ZNm6Cjo4Pjx4/j5MmTCAwM5OXvz759++Dn54dRo0ahbdu24DgOsbGx8Pf3R61atTBs2DDWEaVq48aNcHV1Rc2aNQEANjY2sLGxQUFBAdavXw9PT0/GCQkh3wJ1YBDCWGpqqujPx44dw7Zt2+Dj4yO6wXvw4AEWLlzI2ymzkZGR8Pb2hqOjI+sozPz5559iI7Uqu0itGKnFR0ZGRrhw4QK0tbXx9OlTdO7cGQAQEREBIyMjxukIK/R782U4jsPFixeRlpYGBQUFNGvWDLa2tpCXl2cdjRCZdPXqVezcuRN6enpYu3YtOnfuDGtra9SrV4/X1yrx8fE4cuQITp48ibdv38LBwQGbNm2CnZ0dCgsLRbNW+FQ6ycXFBS4uLsjPzwfHcdDQ0GAdiSkfHx/WEWSCk5MTgoKCUL9+ffj4+MDOzo63pdYqk5GRgY0bN0JOTg4xMTHo0qUL5OTk0KpVK+Tk5LCOx8SOHTuwcOFCjBkzRtTWo0cP6OvrY+fOnbzowMjIyEB+fj4AICgoCKamphIzLdLS0hAREUEdGIRUU9SBQYgMWbt2LdavXy82Os3AwACLFy/GtGnTMHbsWIbp2Hj79i3vFzankVqfNnv2bMyaNQulpaXo378/DAwM4OPjg7CwMAQFBbGORxih35vPe/HiBVxdXXH79m3Url0bHMehoKAA5ubmCA0N5e2sN0I+pbS0FGpqauA4DleuXMG8efMAAEKhEAoK/Ly16tGjB7Kzs9G8eXPMmTMHAwYMQO3atUWvq6qqok+fPoiJiWGYUvpycnJw69YtlJSUSLzGl1nVZmZmuHTpEjQ0NGBqalppuaQKKSkpUkzGTmpqKp48eYL69etj0aJFvB9I8bE6derg9evXeP36NRISEkQzqzMzM1G3bl224RjJzc0VDdD6UKdOnbBmzRoGiaQvKysLU6dOFR1DqlqnbejQodKMRQiRIn5eZRMio169eoUaNWpItAuFQlEpFL7p2LEjzp8/LzbihG9opNandenSBdHR0cjLyxN1/vXr1w9OTk40A4PnKspU7Nq1C9bW1rx9uFiVNWvW4M2bN4iMjBT97qSmpmL+/PlYt24dli9fzjghIbKnefPmOHjwIOrXr49Xr16hS5cuKCkpQUhICG/KI32se/fucHR0hImJSZXb2NnZ4dSpU1JMxVZERASWL19eaSlLgUDAmw4Mb29vUWeWt7f3Jzsw+MLa2hojR46EpqYmOI7D0KFDIScnV+m2fFyLqkuXLli6dClUVVVRu3ZtdOjQAZcvX8ayZcvQtWtX1vGY0NHRQVJSEho3bizWnpiYCE1NTUappKtr1644d+4chEIhHBwccODAAairq4terxiYxNdOLkL4gBbxJkSGTJs2DS9fvoSvry/09PQAlE+XnD9/PgwNDbF27VrGCaVj48aNoj8/efIEhw4dQq9evaCvry9xgV/V6IvqxMLCAuHh4TA3N4eZmRliYmLELtgIIZ+XmpqKtLQ0CIVCAOWlk0pKSpCYmAgvLy/G6diwtbXFhg0bYGNjI9Z+7do1zJs3j3ejpcl/x6dFvBMSEjB16lT8/fffmDRpEubNm4dly5bh+PHj2Lp1KywtLVlHJDKge/fu6NatG9zc3GjQCRHz8uVLHD16FC9fvsTGjRsxfvx4qKqqVrotH+5zPvbmzRsEBAQgKysLkyZNQqtWrRAYGIisrCz8/PPPVe6r6mzHjh3YtGkT5syZA2trawBAXFwcNmzYAGdnZ8yYMYNxQunKycmBjo4OdYgSwjPUgUGIDMnLy4OrqysyMjJQp04dcByH169fw8LCAlu2bJGo81hdde/e/Yu2EwgEvBiZ5OLigvj4eGhqaiI3Nxfa2to0UouQfyE0NFQ0xb5iTYyKP7dp0wa7d+9mGY+ZNm3aICIiAoaGhmLtGRkZcHR0xK1btxglI7IiMjISffv2hZKSklh7UVERIiIi4OLiAgBYuXIlZs6ciXr16jFIKX1CoRAFBQWiMmv3799HvXr1aOQnEWnZsiV+//130YAkvvpwUNKnCAQC3j2EBQAPDw94enpSJxf5JKFQCB8fH4SHh6OsrAwcx0FBQQEjRozAokWLqrwvrM7Onj2L4OBg0RpuTZs2haurK3r06ME6GiHkG6EODEJkTFlZGS5fvoz09HQIBAKYmprC1taWRhhUQigU8uKCjUZqEfL/cXBwQJ8+fTBz5kx069YNhw8fxosXL/Djjz9i2LBhooewfDNu3DgYGxtLLHbo5eWFpKQk7Nu3j1EywlJ+fr6obKW9vT0OHjwo0TGRnJyMefPmISEhgUVEmVQxo6t169asoxAZ4OTkhOnTp/O25E0FGpT0abm5uV+8rY6OzjdMIjsiIyM/+Tpfyq9VpqCgAPfu3QMAGBoa8rbj648//sCcOXNgb2+Ptm3bguM4xMbG4vz58wgMDIS9vT3riISQb4A6MAiRQbm5ucjIyEDbtm1RWFgIDQ0N1pGYsbe3x2+//SYxqjEvLw8DBw7EtWvX2ARjhEZqEfLvtWjRAidOnEDjxo0xYcIEjBo1Cg4ODrh06RJWr16NY8eOsY7IxM2bN+Hs7AxTU1OxkgSpqanYunUrbG1tGSckLERGRsLd3V00W6myARQcx6FLly4IDg5mkJCtpKQkLFmyRKwk3Yf4shAx+bQ//vgDq1evxvjx42FoaCgxi6lijSYi6cqVK7C2tq50XcDq5nMLmwMQHYf5cmypai2hGjVqQEtLizdr6cTGxsLKygoKCgqIjY395LZ8O54MHjwYDg4OEgP3Nm7ciOjoaBw4cIBRMkLIt0QdGITIkJKSEixcuBC///475OTkcOrUKaxZswaFhYUIDAzkzUPrEydO4OLFiwCAw4cPo2/fvhI3MTk5OUhLS8PVq1dZRGSGRmoR8u/Z2Nhg//79aNKkCZYtWwYtLS1MnToVjx49Qt++fXHz5k3WEZlJSEjA1q1bkZGRAY7j8PDhQ4SHh8PCwoJ1NMJQbGwshEIhxo0bh8DAQLESlhULZRobG0NRUZFhSjZGjx6Nt2/fYujQofDx8YG7uzsyMzMRFhYGX19f9OnTh3VEIgM+taA7nx5G/xfW1tY4cuQIL9bUOXHiBLy8vDBx4kTY2NhASUkJiYmJCAgIgJOTE9q1ayfa9uP1qviirKwMDx48wLJlyzB8+HD079+fdSSpMDU1RUxMDDQ0NEQdXZU9uuPj8cTCwgJRUVHQ19cXa3/w4AEGDRpEJVAJqaYUWAcghPxj06ZNSE1Nxc6dOzF16lQAwNixY+Hh4YG1a9di2bJlbANKiZWVFfbt2ye6SMvNzRV7SFLx8KSipj2fdO/enUZqEfIvWVlZYcuWLVi6dCmaN2+OgwcPYvLkyYiLi+PlYpAVbt++jUmTJsHR0REbNmwAUH6MmT59OkJDQ9GsWTPGCQkLTk5OCAoKQv369eHj4wM7OzveDKD4EsnJydi5cycsLCxw6NAhGBsbY9SoUdDS0kJERAR1YBAAtCbZ/4NP4yu3bNmCxYsXo2/fvqI2Y2NjaGpqwtfXF7NmzWKYTjbIy8vDyMgIHh4emDNnDm86MM6ePSsq30jHE3ENGjTAw4cPJTowHj58iNq1azNKRQj51qgDgxAZcvz4cSxbtkxstE27du2watUqLFiwgDcdGNra2ti1axeA8g6cjRs38mYB88/x9/f/4pFahJByP/30E8aPH4+wsDCMHDkSmzdvho2NDYqLi+Hq6so6HjOrV69G9+7d4ebmJmo7ffo0Fi9eDB8fH2zfvp1hOsJKamoqnjx5gvr162PRokXo0qUL60gyRSgUon79+gAAfX19pKWloU2bNrC3t+dlSS1SOV1dXQDls6uzs7PRuHFjcBzHy1lLpGr379+HmZmZRLuenh6ys7MZJJJdcnJyePLkCesYUlNxDAHKSyNVVkL4xYsXWLRoEX799Vdpx2Oqf//+WLZsGX7++WfRulNxcXFYvnw5DSIgpBqjDgxCZEheXh4aN24s0a6trY2XL18ySMTe7t27q3zt8ePH0NLSkmIa9mikFiH/no+PD6ZMmQIHBweoqqoiIiICx44dg5aWFnr37s06HjNJSUnw9vYWq80uLy+PyZMnY9iwYQyTEZasra0xcuRIaGpqguM4DB06FHJycpVuy8dRofr6+oiLi0P//v1haGiIxMREAMDr169RUlLCOB2RFRzHYd26ddi9ezdKS0tx6tQp/PLLL6hZsyaWLVtGHRkEQHmZoNDQUCxbtkx0nH337h02b94MS0tLxunYqGwR74KCAkRERPCqvGVcXByysrIAlO8Tc3NziQ6MjIwMXLlyhUU8pqZNm4a0tDRMmTJFVJmA4zh07doVP/74I+N0hJBvhTowCJEhRkZGuHLlCn744Qex9uPHj6Np06aMUrGVlZWFNWvWIC0tDWVlZQDKL1BKSkqQn5+P5ORkxgmli0ZqEfLvGRsbY+/evfD29oaZmRm6d+8OBweHT9Yo5wNVVVVkZWVJ1Bl/8uSJxIKzhD/Wr1+Po0eP4uXLl9i4cSN69+7N61JrHxs7diw8PT0BAL169cKgQYOgrKyM+Ph4tGrVim04IjN2796NI0eO4Oeff8aKFSsAAA4ODli+fDk0NTXFZr4R/po/fz5cXV0RExMDc3NzCIVCJCUlobi4+JODuKozd3d3iTYFBQVYWVnxphoBUF4yuWJfCAQCeHl5SWyjoqLCy5nEffv2xcGDB5Gfn4+0tDRwHAcTExPUqlULXbp0wbVr11hHJIR8A7SINyEy5Pz583Bzc8Pw4cMRHh6OiRMn4v79+6JRW7169WIdUeomTZqEBw8eoHfv3ggNDcWECRNw//59nD59GitWrICTkxPriFI1fPhwmJiYSIzU8vDwQF5enqj0FiFEUmZmJi5cuIDo6GjExsZCU1MT9vb2ooeRfLNy5UpcunQJy5YtE41qTExMxIoVK9CmTRvRQzfCXx4eHpWWreC7M2fOoG7dumjTpg2ioqIQEhICbW1tLFmyBHp6eqzjERnQr18/zJ07Fz169ICVlRWOHj2KRo0a4fTp0/Dx8cG5c+dYR5RZH+4vPsjMzMT+/ftx9+5dAICOjg6mTZuGBg0aME5GZIWpqSkuXboETU1N1lGYOXHiBC5evAgAOHz4MPr27YsaNWqIbZOTk4O0tDRcvXqVRURCyDdGHRiEyJg///wTwcHBSE5OhlAoRLNmzTBp0iRedl4AQOvWrfHrr7+iXbt2GDx4MFasWAELCwv88ssvuHv3LoKCglhHlKobN27A1dUVmpqalY7UMjY2Zh2REJn2/PlzXL16FefOncPvv/8OjuN4u+B9UVER5syZg4sXL4qm4ANAjx494O3tTQ+tCXJzc794Wx0dnW+YhJDvi6WlJY4fPw49PT2xB/JZWVno27evqPQYkcSnDoxXr17Bz88PY8aMQdOmTeHq6oqrV6+iSZMm2LJlCy/2QVUyMjKQlpYGRUVFGBkZoUmTJqwjEYYePXqEhQsXguM4xMbGolWrVmKl+AQCAVRUVDBy5Ehau4uQaopKSBEiYzp37ozOnTuzjiEzSkpKROuCNGnSBHfu3IGFhQUGDx6MsWPHMk4nfRWjPT8cqdWtWzcaqUXIJ/zxxx+4du0arl27hoyMDDRo0AB2dnbw9vZG+/btWcdjRkVFBSEhIbh//z7S0tKgoKAAIyMjGBgYsI5GZET37t3FOrcqw3EcBAIBrzoCz5w5g9DQUKSnp0NJSQnGxsaYPn062rRpwzoakRG6urpITEyUmJHz559/8vqhNBHn4+ODGzduwMXFBadPn0Z8fDz8/Pxw4sQJ+Pr6IjAwkHVEqXv79i1+/PFHnDlzRtQmEAjQrVs3BAQE8LLE5du3b7F//36xkspA+X1yUlISTp06xTCddGhra4sqDYwdOxZBQUGoU6cO41SEEGmiDgxCZExycjJ27NghdlM8adKkShf35gNdXV2kpaVBW1sbTZo0ET0gEQqFKCwsZJxO+l69eoWQkBCxkVrh4eG4evUq70dqEVKV2bNnQ05ODvb29li9ejVatGjBOpJMadKkCY1sJJXy9/eHl5cXJk6cCBsbGygpKSExMREBAQFwcnJCu3btWEeUurCwMHh7e6NPnz7o3bs3ysrKEBcXB2dnZ6xbtw59+vRhHZHIAFdXVyxfvhxPnz4Fx3G4cuUK9u/fj927d1da458PIiMj0bdvX4kH0EVFRYiIiICLiwsAwNHRkTczAKOjoxEUFAQjIyOEhISgffv2GDBgAExMTDB69GjW8Zj45ZdfkJCQgKCgINjY2EAoFCI2NhZeXl4IDAzk5SLNXl5eiIyMRPPmzZGYmAgrKys8fPgQz58/F/3e8Alf14chhO+ohBQhMuTkyZNwc3NDq1atYGlpibKyMsTHxyM9PR1btmyBra0t64hSFxwcjO3bt8PX1xf16tWDs7MzZs6ciZiYGBQXF2Pfvn2sI0qVh4cHbty4gc2bNyM9PR0LFizAqlWrcOLECSgoKPBypBYhn3PmzBlcuXIFMTExyM7OhoWFBezs7GBnZ4dWrVpBQYHGcxBSmcGDB2Py5Mno27evWHt0dDR8fX1x/PhxRsnYsbe3x/jx4zFmzBix9i1btuDw4cP4/fffGSUjsmb//v3YtGkTHj9+DABQV1fHpEmTMH78eMbJpCc/Px9v3rwBUP67c/DgQdSrV09sm+TkZMybNw8JCQksIjLVqlUrnDx5ElpaWujUqRMmTZoEZ2dnZGZmYvDgwYiPj2cdUeo6duyIlStXolu3bmLt58+fx/Lly3HhwgU2wRhq3749Fi1ahP79+6NHjx4IDg5Go0aN4ObmBi0tLSxevJh1REII+ebojp0QGbJhwwZMnToVc+bMEWv39vaGr68vDh06xCgZO5MnT0aNGjXAcRwsLCwwffp0bNq0Cdra2vDz82MdT+popBYh/56DgwMcHBwAlNfQvXz5Mq5cuYKtW7dCTk4ON2/eZJyQENl0//59mJmZSbTr6ekhOzubQSL2nj59ik6dOkm09+jRAxs3bmSQiMiiY8eOoXfv3hg+fDjy8/PBcRw0NDRYx5K6P//8E+7u7hAIBOA4DsOGDZPYhuM43tasNzIywoULF6CtrY2nT5+KyghHRETAyMiIcTo2CgsLYWhoKNHepEkT5OfnM0jE3qtXr2BtbQ0AaNq0KZKTk2FoaIgpU6Zg7ty51IFBCOEF6sAgRIZkZWVh8ODBEu0jR47k3UyDCgKBQGxq7OTJkzF58mR2gRgrKiqCtrY2ACAmJgaTJk0CACgrK4vVRCWESMrLy8OVK1dw+fJlXL58GXJycujQoQPrWITILFNTU4SGhmLZsmWQk5MDALx79w6bN2+GpaUl43RstGvXDqdOnZK4Frlw4QKsrKwYpSKyZsWKFdi7dy/U1NSgrq7OOg4zgwcPhq6uLoRCIcaNG4cNGzZATU1N9HrFwrvGxsYMU7Ize/ZszJo1C6Wlpejfvz8MDAzg4+ODsLAwBAUFsY7HhLGxMU6ePIkpU6aItf/++++8LXeprq6O58+fQ0dHBwYGBkhLSwMA1KtXD8+ePWOcjhBCpIM6MAiRIebm5rh+/Tr09fXF2m/duoWmTZsySsUerQvyDxqpRci/t2rVKly+fBn37t1DgwYN0K1bN6xevRp2dna8XAySkC81f/58uLq6IiYmBubm5hAKhUhKSkJxcTGvalB/OLNCW1sbAQEBSEpKgrW1NeTl5XH79m0cO3YMrq6uDFMSWVLxkJHP1+8A4OTkhKCgINSvXx8+Pj6ws7PjzfoWX6JLly6Ijo5GXl4eTE1NAQD9+vWDk5MTb6/rp02bhunTpyMlJUU06yAuLg6nT5/GunXrGKdjo3Pnzli+fDl8fHzQunVreHt7o0ePHjhx4gS0tLRYxyOEEKmgNTAIYSwyMlL054yMDOzatQujRo1C69atIScnh9u3byM0NBQzZszg5Y0xrQsiLjo6WjRSq1+/fli7dq3YSC2+TsEn5FMcHR3RvXt3dO/eHc2bN2cdh5DvSmZmJvbv34+7d+8CAHR0dDBt2jQ0aNCAcTLp6d69+xdtJxAIcPbs2W+chnwPli5dioMHD8LU1BQGBgaoUaOG2Os+Pj6MkkmXhYUFwsPDYW5uDjMzM8TExPB6Rgr5MqdPn0ZISAjS0tLAcRxMTEwwceJE9OzZk3U0Jl69egV3d3d06NABo0aNwuTJk3Hx4kUoKChgzZo16NevH+uIhBDyzVEHBiGMVYy2+RyBQICUlJRvnEb29O3bF7169ap0XZAbN27wcl2Qv//+W2ykVkJCAlRVVXk7UosQQsi38erVK/j5+WHMmDFo2rQpXF1dcfXqVTRp0gRbtmxBo0aNWEeUWVeuXIG1tbXEg2vCD2PHjv3k63yZweTi4oL4+HhoamoiNzcX2traonJ0H6POP0K+DMdxSElJgaamJq8GExBC+I06MAghMq1ly5Y4duyYRFmt+/fvY9CgQUhISGCUjBBCCKnePDw8cOPGDWzevBnp6elYsGABVq1ahRMnTkBBQQGBgYGsI8osa2trHDlyhDp5yCf99ttv6N27N1RVVVlH+SZevnyJo0eP4uXLl9i4cSPGjx9f5WedOXOmlNMRWXXt2jUkJSXhzZs3+PhxFR+/J927d8fgwYMxZMgQOqcQQniL1sAghMg0WheEEEIIYSM6OhpBQUEwMjJCSEgI2rdvjwEDBsDExASjR49mHU+m0Rgx8iVWrVoFGxubatuBoaamJpqNkpOTgxkzZtAaGOSTtmzZAn9/f9SuXRu1a9cWe00gEPCyA8PR0RHHjh3Dpk2bYG1tDUdHx2rd8UkIIZWhDgxCiMz5cF2Qtm3bwsvLC/fu3at0XRBCCCGEfBtFRUXQ1tYGAMTExGDSpEkAAGVlZZSVlbGMRki1wKeOrlmzZuHVq1d49erVZ7fV0dGRQiIii/bs2YM5c+Zg2rRprKPIjJkzZ2LmzJlISEjA0aNH8csvv8DLyws9e/bEkCFDeLcmJCGEn6iEFCFE5tC6IIQQQgh7Q4cOxQ8//ABtbW1MmTIFJ0+ehIGBAdauXYtr167hwIEDrCPKLCsrKxw9epTKfZBP4tP3xNTUFAKB4JPbcBxH1/c8Z2FhgRMnTkBPT491FJklFAoRHh4Of39/FBUV0e8LIYQXaAYGIUTmpKamso5ACCGE8N7s2bMxa9YslJaWon///jAwMICPjw/CwsIQFBTEOh4h5Dvi7+8PLy8vTJw4ETY2NlBSUkJiYiICAgLg5OSEdu3asY5IZEDr1q1x8+ZN6sCoxKNHjxAVFYWoqChkZGTAxsYGjo6OrGMRQohUUAcGIYQQQgghREKXLl0QHR2NvLw80ezIfv36wcnJCUZGRozTEUK+J1u2bMHixYvRt29fUZuxsTE0NTXh6+uLWbNmMUxHWPqwfHDLli2xbNkypKenQ19fH/Ly8mLbDh48WLrhZMC+fftw7NgxxMfHQ1dXV7SgN5VaI4TwCXVgEEIIIYQQQipVr1491KtXT/R3CwsLhmkIId+r+/fvw8zMTKJdT08P2dnZDBIRWeHu7i7RtmXLFok2gUDAyw6MNWvWoHfv3pgzZw7atm3LOg4hhDBBHRiEEEIIIYQQQgj5ZkxNTREaGoply5ZBTk4OAPDu3Tts3rwZlpaWjNMRlqh88KfFxMRARUWFdQxCCGGKOjAIIYQQQggh5AtERkaib9++UFJSEmsvKipCREQEXFxcAACOjo6oVasWg4SEyKb58+fD1dUVMTExMDc3h1AoRFJSEoqLi7F7927W8QiRWSoqKjh79iyCg4ORlpYGBQUFNG3aFK6urujRowfreIQQIhUCjuM41iEIIYQQQgghRBbl5+fjzZs3AAB7e3scPHhQrKwWACQnJ2PevHlISEhgEZHImJkzZ8LNze2za8Vs3boVw4cPR+3ataWUjK3MzEzs378fd+/eBQDo6Ohg2rRpaNCgAeNkhMiuP/74A3PmzIG9vT3atm0LjuMQGxuL8+fPIzAwEPb29qwjEkLIN0cdGIQQQgghhBBShcjISLi7u0MgEIDjOAgEAoltOI5Dly5dEBwczCAhkTVt2rRBZGQk9PT0WEeRGa9evYKfnx/GjBkjGj1+9epVNGnSBFu2bEGjRo1YRyREJg0ePBgODg6YOXOmWPvGjRsRHR2NAwcOMEpGCCHSQx0YhBBCCCGEEPIJsbGxEAqFGDduHAIDA6GmpiZ6TSAQQEVFBcbGxlBUVGSYksiKVatW4enTp5gxYwb09fUlSo7xkYeHB27cuIHNmzcjPT0dCxYswKpVq3DixAkoKCggMDCQdURCZJKFhQWioqKgr68v1v7gwQMMGjQIt27dYpSMEEKkh9bAIIQQQgghhJAqODk5ISgoCPXr14ePjw/s7OxofQvySdHR0cjMzMSpU6cqfT0lJUXKidiLjo5GUFAQjIyMEBISgvbt22PAgAEwMTHB6NGjWccjRGY1aNAADx8+lOjAePjwIW/KzxFCCHVgEEIIIYQQQkgVUlNT8eTJE9SvXx+LFi1Cly5dWEciMm7cuHFQVlaGnJwc6ygyo6ioCNra2gCAmJgYTJo0CQCgrKyMsrIyltEIkWn9+/fHsmXL8PPPP6N169YAgLi4OCxfvhx9+/ZlnI4QQqSDOjAIIYQQQgghpArW1tYYOXIkNDU1wXEchg4dWuWD6bNnz0o5HZFFvr6+UFNTw8CBA+Ho6AhDQ0PWkZgzMjLChQsXoK2tjadPn6Jz584AgIiIiM8udk4In02bNg1paWmYMmWKaA0mjuPQtWtXzJs3j3E6QgiRDloDgxBCCCGEEEKq8PLlSxw9ehQvX77Exo0bMX78eKiqqla67ceLrBJ+KigowPHjxxEZGYm//voLFhYWcHR0RL9+/Xhbfiw6OhqzZs1CaWkp+vXrh7Vr18LHxwdhYWEICgqimU2EfEZGRgbS0tLAcRxMTEyo448QwivUgUEIIYQQQgghX8DDwwOenp68fQhN/r2HDx8iKioKp06dQlZWFhwcHDBs2DDY2tqyjiZ1f//9N/Ly8mBqagoASEhIgKqqKj2IJeQLPHv2DKWlpfj4EZ6Ojg6jRIQQIj3UgUEIIYQQQgghXyA3N/eLt6WHSgQASktLceHCBfzxxx84ffo06tWrh5cvX0JXVxd+fn6ih/mEEFKZ+Ph4eHh4IDMzU6yd4zgIBAKkpKQwSkYIIdJDHRiEEEIIIYQQ8gVMTU1FNcirQg+VCFD+0PHIkSM4efIk3r59CwcHBwwdOhR2dnYoLCyEp6cnUlNTcfLkSdZRCSEyzNHRESoqKhg/fjxq164t8bqNjQ2DVIQQIl20iDchhBBCCCGEfAF/f394eXlh4sSJsLGxgZKSEhITExEQEAAnJye0a9eOdUQiA3r06IHs7Gw0b94cc+bMwYABA8QePKqqqqJPnz6IiYlhmJIQ8j1IT09HZGQklVojhPAadWAQQgghhBBCyBfYsmULFi9ejL59+4rajI2NoampCV9fX8yaNYthOiIrunfvDkdHR5iYmFS5jZ2dHU6dOiXFVISQ75G2tjYKCwtZxyCEEKbkWAcghBBCCCGEkO/B/fv3YWZmJtGup6eH7OxsBomILPLw8Phk5wUA1KlTB+rq6lJKRAj5Xk2bNg3e3t64c+cOSktLWcchhBAmaA0MQgghhBBCCPkCw4cPh4mJCZYtWwY5ufKxYO/evYOHhwfy8vKwa9cuxgkJIYRUJz179kRubi7KysoqfZ3WWyKE8AF1YBBCCCGEEELIF7hx4wZcXV2hqakJc3NzCIVCJCUlobi4GLt374axsTHriIQQQqqRw4cPf/L1IUOGSCkJIYSwQx0YhBBCCCGEEPKFMjMzsX//fty9excAoKOjg2nTpqFBgwaMkxFCCCGEEFL90BoYhBBCCCGEEPIFXr16hZCQEAwcOBC//vor3r59i/DwcIwbNw5ZWVms4xFCCKmGoqOj4ezsjI4dOyInJweBgYE4cuQI61iEECI11IFBCCGEEEIIIV/Ax8cHV69ehYKCAk6fPo34+Hj4+fnBwMAAvr6+rOMRQgipZmJiYjBz5kzo6Ojg1atXEAqForWXIiMjWccjhBCpoA4MQgghhBBCCPkC0dHR8PX1hZGRES5cuID27dtjwIABcHNzw9WrV1nHI4QQUs0EBgbixx9/xOrVqyEvLw8AcHNzg5ubG7Zt28Y4HSGESAd1YBBCCCGEEELIFygqKoK2tjaA8lGx7du3BwAoKyujrKyMZTRCCCHV0J07d9C9e3eJ9t69eyMzM5NBIkIIkT4F1gEIIYQQQggh5HtQMfNCW1sbT58+RefOnQEAERERMDIyYpyOEEJIdVO7dm08efIEjRs3Fmu/e/cu1NTUGKUihBDpog4MQgghhBBCCPkCs2fPxqxZs1BaWor+/fvDwMAAPj4+CAsLQ1BQEOt4hBBCqpkBAwbA29v7f+3dIa4iQQBF0deTTyBhD2AIQbEDEjQG2AOLwJPgESyFDbAEBPh2KBSKhP76i3EzdE3mHFvmqTI33ZX9fp+qqvJ8PnM+n7Pb7bJYLNqeB/ARVdM0TdsjAAAA/gWPxyP3+z2TySRJcrlc0u/3fYEBwB/3er2y3W5zOp2SJFVVpWmazOfzHA6HdLvdlhcC/H0CBgAAAAAUpq7rDAaD1HWd6/Wa9/ud8Xic0WjU9jSAjxEwAAAAAKAws9ksx+Mx0+m07SkArfnV9gAAAAAA4KdOp5OvL8/XAv83tyAAAAAAFGa9Xmez2WS5XGY4HKbX6/04X61W7QwD+CC/kAIAAACAwkwmk9+eVVWV2+32wTUA7RAwAAAAAACA4ngDAwAAAAAAKI6AAQAAAAAAFEfAAAAAAAAAiiNgAAAAAAAAxREwAAAAAACA4ggYAAAAAABAcQQMAAAAAACgON91T706jE5gWAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 2000x2000 with 2 Axes>"
       ]
@@ -2477,6 +2477,1116 @@
    "source": [
     "Price has a moderate positive correlation with sqft living (0.70), grade (0.67), sqft above (0.61). This means that as the values of these features increase, the price of the house also tends to increase."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4.STATISTICAL ANALYSIS.\n",
+    "\n",
+    "Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:\n",
+    "\n",
+    "\n",
+    "1. Descriptive Statistics\n",
+    "2. Correlation matrix\n",
+    "3. Distribution Analysis\n",
+    "4. Inferential Statistics using Hypothesis Testing and Analysis of Variance\n",
+    "5. MultiColinierity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **a.) Descriptive Statistics**\n",
+    "\n",
+    "Initial insights into the central tendency, dispersion, and shape of the data distribution.\n",
+    "<br>Understanding the characteristics of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.581107e+09</td>\n",
+       "      <td>2014-10-29 08:57:27.772206848</td>\n",
+       "      <td>5.405060e+05</td>\n",
+       "      <td>3.371157</td>\n",
+       "      <td>2.116096</td>\n",
+       "      <td>2080.942531</td>\n",
+       "      <td>1.508757e+04</td>\n",
+       "      <td>1.493615</td>\n",
+       "      <td>0.006716</td>\n",
+       "      <td>3.409848</td>\n",
+       "      <td>5.658310</td>\n",
+       "      <td>1789.104437</td>\n",
+       "      <td>291.838095</td>\n",
+       "      <td>1971.024359</td>\n",
+       "      <td>68.259720</td>\n",
+       "      <td>1987.302478</td>\n",
+       "      <td>12739.322202</td>\n",
+       "      <td>52.975641</td>\n",
+       "      <td>0.955681</td>\n",
+       "      <td>1.924945e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000102e+06</td>\n",
+       "      <td>2014-05-02 00:00:00</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>0.000000</td>\n",
+       "      <td>1.000000</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>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.013000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.123049e+09</td>\n",
+       "      <td>2014-07-22 00:00:00</td>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.043000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1200.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1952.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.820000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.904940e+09</td>\n",
+       "      <td>2014-10-16 00:00:00</td>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.620000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7626.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.159350e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.309100e+09</td>\n",
+       "      <td>2015-02-18 00:00:00</td>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.069575e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>560.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10087.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.546000e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.900000e+09</td>\n",
+       "      <td>2015-05-27 00:00:00</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>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>4820.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "      <td>124.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.653959e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.876357e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.680831e+05</td>\n",
+       "      <td>0.902213</td>\n",
+       "      <td>0.768545</td>\n",
+       "      <td>918.563816</td>\n",
+       "      <td>4.121013e+04</td>\n",
+       "      <td>0.539252</td>\n",
+       "      <td>0.081680</td>\n",
+       "      <td>0.650422</td>\n",
+       "      <td>1.174272</td>\n",
+       "      <td>828.413341</td>\n",
+       "      <td>442.504364</td>\n",
+       "      <td>29.322166</td>\n",
+       "      <td>362.774103</td>\n",
+       "      <td>685.671655</td>\n",
+       "      <td>27169.859971</td>\n",
+       "      <td>29.322166</td>\n",
+       "      <td>5.824659</td>\n",
+       "      <td>4.156724e+04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id                           date         price  \\\n",
+       "count  2.114200e+04                          21142  2.114200e+04   \n",
+       "mean   4.581107e+09  2014-10-29 08:57:27.772206848  5.405060e+05   \n",
+       "min    1.000102e+06            2014-05-02 00:00:00  7.800000e+04   \n",
+       "25%    2.123049e+09            2014-07-22 00:00:00  3.220000e+05   \n",
+       "50%    3.904940e+09            2014-10-16 00:00:00  4.500000e+05   \n",
+       "75%    7.309100e+09            2015-02-18 00:00:00  6.450000e+05   \n",
+       "max    9.900000e+09            2015-05-27 00:00:00  7.700000e+06   \n",
+       "std    2.876357e+09                            NaN  3.680831e+05   \n",
+       "\n",
+       "           bedrooms     bathrooms   sqft_living      sqft_lot        floors  \\\n",
+       "count  21142.000000  21142.000000  21142.000000  2.114200e+04  21142.000000   \n",
+       "mean       3.371157      2.116096   2080.942531  1.508757e+04      1.493615   \n",
+       "min        1.000000      0.500000    370.000000  5.200000e+02      1.000000   \n",
+       "25%        3.000000      1.750000   1430.000000  5.043000e+03      1.000000   \n",
+       "50%        3.000000      2.250000   1910.000000  7.620000e+03      1.500000   \n",
+       "75%        4.000000      2.500000   2550.000000  1.069575e+04      2.000000   \n",
+       "max       11.000000      8.000000  13540.000000  1.651359e+06      3.500000   \n",
+       "std        0.902213      0.768545    918.563816  4.121013e+04      0.539252   \n",
+       "\n",
+       "         waterfront     condition         grade    sqft_above  sqft_basement  \\\n",
+       "count  21142.000000  21142.000000  21142.000000  21142.000000   21142.000000   \n",
+       "mean       0.006716      3.409848      5.658310   1789.104437     291.838095   \n",
+       "min        0.000000      1.000000      1.000000    370.000000       0.000000   \n",
+       "25%        0.000000      3.000000      5.000000   1200.000000       0.000000   \n",
+       "50%        0.000000      3.000000      5.000000   1560.000000       0.000000   \n",
+       "75%        0.000000      4.000000      6.000000   2210.000000     560.000000   \n",
+       "max        1.000000      5.000000     11.000000   9410.000000    4820.000000   \n",
+       "std        0.081680      0.650422      1.174272    828.413341     442.504364   \n",
+       "\n",
+       "           yr_built  yr_renovated  sqft_living15     sqft_lot15     house_age  \\\n",
+       "count  21142.000000  21142.000000   21142.000000   21142.000000  21142.000000   \n",
+       "mean    1971.024359     68.259720    1987.302478   12739.322202     52.975641   \n",
+       "min     1900.000000      0.000000     399.000000     651.000000      9.000000   \n",
+       "25%     1952.000000      0.000000    1490.000000    5100.000000     27.000000   \n",
+       "50%     1975.000000      0.000000    1840.000000    7626.000000     49.000000   \n",
+       "75%     1997.000000      0.000000    2360.000000   10087.000000     72.000000   \n",
+       "max     2015.000000   2015.000000    6210.000000  871200.000000    124.000000   \n",
+       "std       29.322166    362.774103     685.671655   27169.859971     29.322166   \n",
+       "\n",
+       "       renovation_age    total_sqft  \n",
+       "count    21142.000000  2.114200e+04  \n",
+       "mean         0.955681  1.924945e+04  \n",
+       "min          0.000000  2.013000e+03  \n",
+       "25%          0.000000  8.820000e+03  \n",
+       "50%          0.000000  1.159350e+04  \n",
+       "75%          0.000000  1.546000e+04  \n",
+       "max         90.000000  1.653959e+06  \n",
+       "std          5.824659  4.156724e+04  "
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Price Distribution:**\n",
+    "\n",
+    "The price of houses in the dataset varies widely, ranging from $78,000 to $7,700,000, with an average price of $540,506. The standard deviation of $368,083.1 indicates a significant dispersion around the mean, suggesting a diverse range of housing prices.\n",
+    "\n",
+    "**Bedrooms and Bathrooms:** \n",
+    "\n",
+    "The average number of bedrooms is approximately 3.37, while the average number of bathrooms is about 2.12. The standard deviations for both variables are relatively small, indicating less variability compared to other features.\n",
+    "\n",
+    "**Square Footage:**\n",
+    "\n",
+    " The average square footage of living space is around 2,080, with a standard deviation of 918.56. Similarly, the average lot size is approximately 15,087 square feet, with a larger standard deviation of 41,210.13, suggesting more variability in lot sizes compared to living space.\n",
+    "\n",
+    "**Floors:**\n",
+    "\n",
+    "On average, houses have 1.49 floors, with a standard deviation of 0.54. This indicates some variability in the number of floors, although most houses seem to have either one or two floors.\n",
+    "\n",
+    "**Waterfront Property:**\n",
+    "\n",
+    " Only a small percentage (0.7%) of the houses are waterfront properties, based on the average value. This feature is likely represented as a binary variable (0 for no waterfront, 1 for waterfront), with most houses being non-waterfront properties.\n",
+    "\n",
+    "**Condition and Grade:**\n",
+    "\n",
+    "The average condition of houses is approximately 3.41, with a standard deviation of 0.65, suggesting some variability in the condition ratings. Similarly, the average grade is around 5.66, with a standard deviation of 1.17, indicating variations in the overall quality of houses.\n",
+    "\n",
+    "**Year Built and Year Renovated:**\n",
+    "\n",
+    "The houses in the dataset span a wide range of construction years, from 1900 to 2015, with an average year of construction around 1971. The standard deviation of 29.32 indicates some variability in the construction years. Additionally, the average year of renovation is approximately 68.26, with a standard deviation of 443.5, suggesting that most houses have not been renovated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **b.) Correlation Analysis with House Prices**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Correlation analysis was performed to examine the relationship between various features and the target variable, 'price'. \n",
+    "<BR>Here are the correlation coefficients between each feature and the price:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price             1.000000\n",
+       "bedrooms          0.316573\n",
+       "bathrooms         0.525899\n",
+       "sqft_living       0.702340\n",
+       "sqft_lot          0.087940\n",
+       "floors            0.256372\n",
+       "waterfront        0.265970\n",
+       "condition         0.035264\n",
+       "grade             0.667751\n",
+       "sqft_above        0.605167\n",
+       "sqft_basement     0.325003\n",
+       "yr_built          0.054471\n",
+       "yr_renovated      0.116721\n",
+       "sqft_living15     0.586441\n",
+       "sqft_lot15        0.083196\n",
+       "house_age        -0.054471\n",
+       "renovation_age    0.082356\n",
+       "total_sqft        0.118225\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drop_var = ['id', 'price_range', 'date']\n",
+    "\n",
+    "correlation = housing_data.drop(drop_var, axis=1).corrwith(housing_data['price'])\n",
+    "correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These correlation coefficients indicate the strength and direction of the linear relationship between each feature and the price of the houses.\n",
+    "<br> Features with higher positive correlation coefficients, such as 'sqft_living', 'grade', 'bathrooms', and 'sqft_above', have a stronger positive linear relationship with the price, indicating that as these feature values increase, the price tends to increase as well. Conversely, features with low or negative correlation coefficients, such as 'condition', 'yr_built', 'sqft_lot', and 'house_age', have weaker or negative linear relationships with the price.\n",
+    "<br>However, it's imperative to underscore that correlation does not imply causation. There could be an underlying third factor driving changes in both features, underscoring the need for thorough investigation beyond correlation analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **c.) Distribution Analysis**\n",
+    "\n",
+    "Distribution analysis involves understanding the distribution of data, such as whether it follows a normal distribution and skewed distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "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>condition</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>price_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>12.309987</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>7.074117</td>\n",
+       "      <td>8.639588</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.074117</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.201171</td>\n",
+       "      <td>8.639588</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.988571</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>13.195616</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>1.178655</td>\n",
+       "      <td>7.852050</td>\n",
+       "      <td>8.887791</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.682943</td>\n",
+       "      <td>5.993961</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>7.596894</td>\n",
+       "      <td>7.433075</td>\n",
+       "      <td>8.941153</td>\n",
+       "      <td>73</td>\n",
+       "      <td>3.526361</td>\n",
+       "      <td>9.424080</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>12.100718</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>6.647688</td>\n",
+       "      <td>9.210440</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.647688</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.908755</td>\n",
+       "      <td>8.995041</td>\n",
+       "      <td>91</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.353661</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>13.311331</td>\n",
+       "      <td>1.609438</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>7.581210</td>\n",
+       "      <td>8.517393</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.791759</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.957497</td>\n",
+       "      <td>6.814543</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.215975</td>\n",
+       "      <td>8.517393</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.096163</td>\n",
+       "      <td>600K-1M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>13.142168</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>7.427144</td>\n",
+       "      <td>8.997271</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.427144</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.496097</td>\n",
+       "      <td>8.923191</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.344959</td>\n",
+       "      <td>300K-600K</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 2014-10-13  12.309987  1.386294   0.693147     7.074117   \n",
+       "1  6414100192 2014-12-09  13.195616  1.386294   1.178655     7.852050   \n",
+       "2  5631500400 2015-02-25  12.100718  1.098612   0.693147     6.647688   \n",
+       "3  2487200875 2014-12-09  13.311331  1.609438   1.386294     7.581210   \n",
+       "4  1954400510 2015-02-18  13.142168  1.386294   1.098612     7.427144   \n",
+       "\n",
+       "   sqft_lot    floors  waterfront  condition  ...  sqft_above  sqft_basement  \\\n",
+       "0  8.639588  0.693147         0.0   1.386294  ...    7.074117       0.000000   \n",
+       "1  8.887791  1.098612         0.0   1.386294  ...    7.682943       5.993961   \n",
+       "2  9.210440  0.693147         0.0   1.386294  ...    6.647688       0.000000   \n",
+       "3  8.517393  0.693147         0.0   1.791759  ...    6.957497       6.814543   \n",
+       "4  8.997271  0.693147         0.0   1.386294  ...    7.427144       0.000000   \n",
+       "\n",
+       "   yr_built  yr_renovated  sqft_living15  sqft_lot15  house_age  \\\n",
+       "0      1955      0.000000       7.201171    8.639588         69   \n",
+       "1      1951      7.596894       7.433075    8.941153         73   \n",
+       "2      1933      0.000000       7.908755    8.995041         91   \n",
+       "3      1965      0.000000       7.215975    8.517393         59   \n",
+       "4      1987      0.000000       7.496097    8.923191         37   \n",
+       "\n",
+       "   renovation_age  total_sqft  price_range  \n",
+       "0        0.000000    8.988571    100K-300K  \n",
+       "1        3.526361    9.424080    300K-600K  \n",
+       "2        0.000000    9.353661    100K-300K  \n",
+       "3        0.000000    9.096163      600K-1M  \n",
+       "4        0.000000    9.344959    300K-600K  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import skew\n",
+    "\n",
+    "# Select numerical variables only\n",
+    "numerical_data = housing_data.select_dtypes(include=['number'])\n",
+    "\n",
+    "# Compute skewness for each numerical variable\n",
+    "skewness = numerical_data.apply(lambda x: skew(x.dropna()))\n",
+    "\n",
+    "# Select variables with skewness above a certain threshold (e.g., 0.5)\n",
+    "skewed_variables = skewness[abs(skewness) > 0.5].index\n",
+    "\n",
+    "# Log transformation for skewed variables\n",
+    "df_log = housing_data.copy()  # Create a copy of the original DataFrame to preserve the original data\n",
+    "df_log[skewed_variables] = housing_data[skewed_variables].apply(lambda x: np.log1p(x))\n",
+    "\n",
+    "# Check the distributions before and after transformation if needed\n",
+    "# For example, you can use histograms or density plots to visualize the distributions\n",
+    "\n",
+    "# Print the first few rows of the transformed data to verify\n",
+    "df_log.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x2800 with 19 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Selecting numerical columns\n",
+    "numerical_columns = housing_data.select_dtypes(include=[np.number]).columns\n",
+    "\n",
+    "# Calculate the number of rows and columns for subplots\n",
+    "num_cols = len(numerical_columns)\n",
+    "num_rows = (num_cols + 2) // 3  # Calculate the number of rows needed, rounding up\n",
+    "\n",
+    "# Plot histograms for numerical variables\n",
+    "plt.figure(figsize=(12, num_rows * 4))  # Adjust the height based on the number of rows\n",
+    "for i, col in enumerate(numerical_columns, 1):\n",
+    "    plt.subplot(num_rows, 3, i)\n",
+    "    sns.histplot(housing_data[col], kde=True, edgecolor='black')\n",
+    "    plt.title(col)\n",
+    "    plt.xlabel('')\n",
+    "    plt.ylabel('Frequency')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Conclusion**\n",
+    "\n",
+    "In conclusion, the log transformation has effectively addressed skewness in the distribution of numerical variables within the dataset. Prior to transformation, variables such as house price, bedrooms, bathrooms, and various square footage measurements exhibited skewed distributions with long tails. However, after applying the log transformation, these distributions appear to be more symmetric and closer to a normal distribution. This transformation has enhanced the suitability of the data for statistical analysis by reducing skewness and improving the interpretability of the variables. It's important to acknowledge that while the log transformation has provided valuable improvements, it alters the scale and interpretation of the variables, necessitating careful consideration in subsequent analyses. Overall, the transformed variables are now better suited for further statistical modeling and analysis in the context of predicting property values and understanding real estate market trends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **d.) Inferential Statistics.**\n",
+    "\n",
+    "*We used one-way ANOVA approach*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. \u001b[1mbedrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "2. \u001b[1mbathrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "3. \u001b[1msqft_living\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "4. \u001b[1msqft_lot\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
+      "5. \u001b[1mfloors\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "6. \u001b[1mwaterfront\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "7. \u001b[1mcondition\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "8. \u001b[1mgrade\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "9. \u001b[1msqft_above\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "10. \u001b[1msqft_basement\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "11. \u001b[1myr_built\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "12. \u001b[1myr_renovated\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "13. \u001b[1msqft_living15\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "14. \u001b[1msqft_lot15\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
+      "15. \u001b[1mhouse_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "16. \u001b[1mrenovation_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "17. \u001b[1mtotal_sqft\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n"
+     ]
+    },
+    {
+     "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",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F-statistic</th>\n",
+       "      <td>1.841471e+00</td>\n",
+       "      <td>3.728667</td>\n",
+       "      <td>7.702589</td>\n",
+       "      <td>0.732713</td>\n",
+       "      <td>1.662592e+00</td>\n",
+       "      <td>1.787176e+00</td>\n",
+       "      <td>1.051111</td>\n",
+       "      <td>7.29306</td>\n",
+       "      <td>5.098114</td>\n",
+       "      <td>1.750817e+00</td>\n",
+       "      <td>1.236748e+00</td>\n",
+       "      <td>1.046716</td>\n",
+       "      <td>5.209978</td>\n",
+       "      <td>0.709841</td>\n",
+       "      <td>1.236748e+00</td>\n",
+       "      <td>1.171737e+00</td>\n",
+       "      <td>0.775778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P-value</th>\n",
+       "      <td>1.765957e-139</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.808184e-95</td>\n",
+       "      <td>1.258742e-125</td>\n",
+       "      <td>0.026289</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.406487e-116</td>\n",
+       "      <td>2.417229e-17</td>\n",
+       "      <td>0.037943</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.417229e-17</td>\n",
+       "      <td>2.430304e-10</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  bedrooms  bathrooms  sqft_living  sqft_lot        floors  \\\n",
+       "F-statistic   1.841471e+00   3.728667     7.702589  0.732713  1.662592e+00   \n",
+       "P-value      1.765957e-139   0.000000     0.000000  1.000000  1.808184e-95   \n",
+       "\n",
+       "                waterfront  condition    grade  sqft_above  sqft_basement  \\\n",
+       "F-statistic   1.787176e+00   1.051111  7.29306    5.098114   1.750817e+00   \n",
+       "P-value      1.258742e-125   0.026289  0.00000    0.000000  1.406487e-116   \n",
+       "\n",
+       "                 yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "F-statistic  1.236748e+00      1.046716       5.209978    0.709841   \n",
+       "P-value      2.417229e-17      0.037943       0.000000    1.000000   \n",
+       "\n",
+       "                house_age  renovation_age  total_sqft  \n",
+       "F-statistic  1.236748e+00    1.171737e+00    0.775778  \n",
+       "P-value      2.417229e-17    2.430304e-10    1.000000  "
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "from scipy.stats import f_oneway\n",
+    "\n",
+    "# List of features of interest\n",
+    "features_of_interest = ['bedrooms', 'bathrooms', 'sqft_living',\n",
+    "                        'sqft_lot', 'floors', 'waterfront', 'condition', \n",
+    "                        'grade', 'sqft_above', 'sqft_basement', 'yr_built', \n",
+    "                        'yr_renovated', 'sqft_living15', 'sqft_lot15', \n",
+    "                        'house_age', 'renovation_age', 'total_sqft']\n",
+    "\n",
+    "# Create an empty DataFrame to store ANOVA results\n",
+    "anova_results = pd.DataFrame(index=['F-statistic', 'P-value'])\n",
+    "\n",
+    "# Perform ANOVA for each feature\n",
+    "significant_features = []\n",
+    "\n",
+    "for i, column in enumerate(features_of_interest, 1):\n",
+    "    groups = [housing_data[column][housing_data['price'] == category]\n",
+    "              for category in housing_data['price'].unique()]\n",
+    "\n",
+    "    # Perform ANOVA\n",
+    "    f_statistic, p_value = f_oneway(*groups)\n",
+    "\n",
+    "    # Store results in the DataFrame\n",
+    "    anova_results[column] = [f_statistic, p_value]\n",
+    "\n",
+    "    # Print interpretation\n",
+    "    if p_value < 0.05:\n",
+    "        significant_features.append(column)\n",
+    "        print(f\"{i}. \\033[1m{column}\\033[0m: Reject the null hypothesis. There is a statistically significant relationship.\")\n",
+    "    else:\n",
+    "        print(f\"{i}. \\033[1m{column}\\033[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\")\n",
+    "\n",
+    "# Display ANOVA results\n",
+    "anova_results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Conclusion**\n",
+    "\n",
+    "The features listed under \"Reject the Null Hypothesis\" have a statistically significant relationship with housing prices.\n",
+    "\n",
+    "These features are important predictors of housing prices in the given dataset.\n",
+    "\n",
+    "On the other hand, features listed under \"Fail to Reject the Null Hypothesis\" do not show a statistically significant relationship with housing prices based on the ANOVA test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **e.) Multicollinearity.**\n",
+    "\n",
+    "**Assessing Multicollinearity with Variance Inflation Factor (VIF)**\n",
+    "<br>In this analysis, we utilize the Variance Inflation Factor (VIF) to investigate multicollinearity among predictor variables in our regression model. Multicollinearity occurs when predictor variables are highly correlated with each other, which can lead to unreliable coefficient estimates. By computing the VIF for each predictor variable, we identify potential multicollinearity issues among property characteristics. \n",
+    "<br>High VIF values indicate a strong correlation between a predictor variable and the other variables in the model. Hence, it's crucial to examine the VIF values to ensure the reliability of our regression analysis and to address any multicollinearity detected.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  vif = 1. / (1. - r_squared_i)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1mVariance Inflation Factor (VIF):\u001b[0m\n"
+     ]
+    },
+    {
+     "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>Feature</th>\n",
+       "      <th>VIF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>bedrooms</td>\n",
+       "      <td>1.688154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bathrooms</td>\n",
+       "      <td>3.366371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sqft_living</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sqft_lot</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>floors</td>\n",
+       "      <td>1.934470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>waterfront</td>\n",
+       "      <td>1.028593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>condition</td>\n",
+       "      <td>1.218261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>grade</td>\n",
+       "      <td>3.235515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sqft_above</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sqft_basement</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>yr_built</td>\n",
+       "      <td>94.556823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>yr_renovated</td>\n",
+       "      <td>4.226966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sqft_living15</td>\n",
+       "      <td>2.763013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sqft_lot15</td>\n",
+       "      <td>2.130997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>house_age</td>\n",
+       "      <td>7.743224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>renovation_age</td>\n",
+       "      <td>4.109794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>total_sqft</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Feature        VIF\n",
+       "0         bedrooms   1.688154\n",
+       "1        bathrooms   3.366371\n",
+       "2      sqft_living        inf\n",
+       "3         sqft_lot        inf\n",
+       "4           floors   1.934470\n",
+       "5       waterfront   1.028593\n",
+       "6        condition   1.218261\n",
+       "7            grade   3.235515\n",
+       "8       sqft_above        inf\n",
+       "9    sqft_basement        inf\n",
+       "10        yr_built  94.556823\n",
+       "11    yr_renovated   4.226966\n",
+       "12   sqft_living15   2.763013\n",
+       "13      sqft_lot15   2.130997\n",
+       "14       house_age   7.743224\n",
+       "15  renovation_age   4.109794\n",
+       "16      total_sqft        inf"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# Compute Variance Inflation Factor (VIF) to detect multicollinearity\n",
+    "\n",
+    "X = housing_data[['bedrooms', 'bathrooms', 'sqft_living','sqft_lot', 'floors', 'waterfront', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15', 'house_age', 'renovation_age', 'total_sqft']]\n",
+    "# Calculate the correlation matrix\n",
+    "correlation_matrix = X.corr()\n",
+    "\n",
+    "# Calculate VIF for each feature\n",
+    "vif_data = pd.DataFrame()\n",
+    "vif_data[\"Feature\"] = X.columns\n",
+    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
+    "\n",
+    "# Print VIF for each feature\n",
+    "print(\"\\n\\033[1mVariance Inflation Factor (VIF):\\033[0m\")\n",
+    "vif_data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Variance Inflation Factor (VIF) Analysis**\n",
+    "<br>The Variance Inflation Factor (VIF) was calculated to assess multicollinearity among the features in the dataset. A VIF value greater than 5 is typically considered indicative of multicollinearity. The results revealed that most features exhibited low levels of multicollinearity, with VIF values below 5. \n",
+    "<br>However, 'yr_built' displayed a remarkably high VIF of 94.56, indicating strong multicollinearity with other features. Additionally, 'sqft_living', 'sqft_lot', 'sqft_above', 'sqft_basement', and 'total_sqft' exhibited infinite VIF values, suggesting perfect multicollinearity. \n",
+    "<br>Addressing multicollinearity in this case may require further investigation, such as feature selection, dimensionality reduction techniques or applying regularization methods to mitigate multicollinearity effects and improve model performance. \n",
+    "<br>Overall, understanding the VIF values can help refine the regression model and ensure the reliability of the coefficient estimates.\n"
+   ]
   }
  ],
  "metadata": {

From 59b7d1065f06a834422f4407f993012225463605 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Tue, 30 Apr 2024 22:56:53 +0300
Subject: [PATCH 12/27] Modelling

---
 student.ipynb | 2074 ++++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 1631 insertions(+), 443 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 29134053..41fdf478 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +497,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +684,7 @@
        "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
       ]
      },
-     "execution_count": 211,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -850,133 +850,133 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>16132</th>\n",
-       "      <td>3298400470</td>\n",
-       "      <td>2/4/2015</td>\n",
-       "      <td>437400.0</td>\n",
-       "      <td>3</td>\n",
+       "      <th>3102</th>\n",
+       "      <td>5151600195</td>\n",
+       "      <td>5/4/2015</td>\n",
+       "      <td>283200.0</td>\n",
+       "      <td>4</td>\n",
        "      <td>1.75</td>\n",
-       "      <td>2150</td>\n",
-       "      <td>8925</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>12540</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Good</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>2150</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1100</td>\n",
-       "      <td>7875</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1130</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>1958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>12540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14888</th>\n",
-       "      <td>1842300050</td>\n",
-       "      <td>7/2/2014</td>\n",
-       "      <td>600000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>2190</td>\n",
-       "      <td>9072</td>\n",
+       "      <th>18253</th>\n",
+       "      <td>2558160220</td>\n",
+       "      <td>12/10/2014</td>\n",
+       "      <td>385000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>11375</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Very Good</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
        "      <td>7 Average</td>\n",
-       "      <td>1110</td>\n",
-       "      <td>?</td>\n",
-       "      <td>1965</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>1969</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1660</td>\n",
-       "      <td>8327</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>9160</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4485</th>\n",
-       "      <td>2397101270</td>\n",
-       "      <td>1/26/2015</td>\n",
-       "      <td>716000.0</td>\n",
+       "      <th>17466</th>\n",
+       "      <td>1525079056</td>\n",
+       "      <td>5/2/2014</td>\n",
+       "      <td>284000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1420</td>\n",
-       "      <td>3600</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>23103</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Average</td>\n",
        "      <td>7 Average</td>\n",
-       "      <td>1420</td>\n",
+       "      <td>1800</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1904</td>\n",
+       "      <td>1968</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1250</td>\n",
-       "      <td>3600</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>18163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9132</th>\n",
-       "      <td>1523059100</td>\n",
-       "      <td>9/2/2014</td>\n",
-       "      <td>320000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1740</td>\n",
-       "      <td>27350</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>16157</th>\n",
+       "      <td>8081900101</td>\n",
+       "      <td>5/28/2014</td>\n",
+       "      <td>960000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>4560</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>5 Fair</td>\n",
-       "      <td>1740</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1958</td>\n",
+       "      <td>Very Good</td>\n",
+       "      <td>9 Better</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>610.0</td>\n",
+       "      <td>1929</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2760</td>\n",
-       "      <td>10749</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>5100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16234</th>\n",
-       "      <td>7922900460</td>\n",
-       "      <td>12/5/2014</td>\n",
-       "      <td>660000.0</td>\n",
+       "      <th>3439</th>\n",
+       "      <td>6300500475</td>\n",
+       "      <td>9/2/2014</td>\n",
+       "      <td>412000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>9032</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1553</td>\n",
+       "      <td>1991</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>?</td>\n",
-       "      <td>1963</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1553</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2350</td>\n",
-       "      <td>8937</td>\n",
+       "      <td>1509</td>\n",
+       "      <td>2431</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "16132  3298400470   2/4/2015  437400.0         3       1.75         2150   \n",
-       "14888  1842300050   7/2/2014  600000.0         5       2.00         2190   \n",
-       "4485   2397101270  1/26/2015  716000.0         3       2.00         1420   \n",
-       "9132   1523059100   9/2/2014  320000.0         5       1.00         1740   \n",
-       "16234  7922900460  12/5/2014  660000.0         3       1.75         2030   \n",
+       "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "3102   5151600195    5/4/2015  283200.0         4       1.75         1830   \n",
+       "18253  2558160220  12/10/2014  385000.0         4       2.50         2030   \n",
+       "17466  1525079056    5/2/2014  284000.0         3       1.75         1800   \n",
+       "16157  8081900101   5/28/2014  960000.0         4       2.25         2410   \n",
+       "3439   6300500475    9/2/2014  412000.0         3       2.50         1553   \n",
        "\n",
        "       sqft_lot  floors waterfront  condition      grade  sqft_above  \\\n",
-       "16132      8925     1.0         NO       Good  7 Average        2150   \n",
-       "14888      9072     1.0        NaN  Very Good  7 Average        1110   \n",
-       "4485       3600     1.5         NO       Good  7 Average        1420   \n",
-       "9132      27350     1.0         NO       Good     5 Fair        1740   \n",
-       "16234      9032     2.0         NO       Good  7 Average        2030   \n",
+       "3102      12540     1.0        NaN       Good     8 Good        1130   \n",
+       "18253     11375     1.0         NO    Average  7 Average        1330   \n",
+       "17466     23103     1.0        NaN    Average  7 Average        1800   \n",
+       "16157      4560     2.0         NO  Very Good   9 Better        1800   \n",
+       "3439       1991     3.0         NO    Average     8 Good        1553   \n",
        "\n",
        "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "16132           0.0      1960           0.0           1100        7875  \n",
-       "14888             ?      1965           0.0           1660        8327  \n",
-       "4485            0.0      1904           0.0           1250        3600  \n",
-       "9132            0.0      1958           0.0           2760       10749  \n",
-       "16234             ?      1963           0.0           2350        8937  "
+       "3102          700.0      1958           NaN           2020       12540  \n",
+       "18253         700.0      1969           0.0           1500        9160  \n",
+       "17466           0.0      1968           0.0           1410       18163  \n",
+       "16157         610.0      1929           0.0           2150        5100  \n",
+       "3439            0.0      2014           0.0           1509        2431  "
       ]
      },
-     "execution_count": 214,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1263,133 +1263,133 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>9602</th>\n",
-       "      <td>2917200085</td>\n",
-       "      <td>2015-04-08</td>\n",
-       "      <td>350000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1160</td>\n",
-       "      <td>5395</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>860</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>1940</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1664</td>\n",
-       "      <td>5363</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11877</th>\n",
-       "      <td>2025049114</td>\n",
-       "      <td>2014-05-29</td>\n",
-       "      <td>402000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>710</td>\n",
-       "      <td>1173</td>\n",
+       "      <th>8401</th>\n",
+       "      <td>2324800110</td>\n",
+       "      <td>2014-06-12</td>\n",
+       "      <td>699000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3280</td>\n",
+       "      <td>27441</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>710</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3280</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1943</td>\n",
+       "      <td>1996</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1370</td>\n",
-       "      <td>1173</td>\n",
+       "      <td>3200</td>\n",
+       "      <td>26960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14592</th>\n",
-       "      <td>968000120</td>\n",
-       "      <td>2014-11-12</td>\n",
-       "      <td>395000.0</td>\n",
-       "      <td>3</td>\n",
+       "      <th>295</th>\n",
+       "      <td>9510920070</td>\n",
+       "      <td>2014-07-10</td>\n",
+       "      <td>879000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>22111</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1470</td>\n",
-       "      <td>10125</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1470</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3360</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1962</td>\n",
+       "      <td>1994</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>10125</td>\n",
+       "      <td>3150</td>\n",
+       "      <td>11374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1990</th>\n",
-       "      <td>8562890370</td>\n",
+       "      <th>3958</th>\n",
+       "      <td>4113800300</td>\n",
        "      <td>2015-04-14</td>\n",
-       "      <td>399950.0</td>\n",
+       "      <td>600000.0</td>\n",
        "      <td>4</td>\n",
        "      <td>2.5</td>\n",
-       "      <td>3110</td>\n",
-       "      <td>5868</td>\n",
+       "      <td>2420</td>\n",
+       "      <td>7744</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3110</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2420</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2001</td>\n",
+       "      <td>1994</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2950</td>\n",
-       "      <td>5924</td>\n",
+       "      <td>2820</td>\n",
+       "      <td>11129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17687</th>\n",
-       "      <td>3575302562</td>\n",
-       "      <td>2014-11-13</td>\n",
-       "      <td>356000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>7500</td>\n",
+       "      <th>7966</th>\n",
+       "      <td>3122069029</td>\n",
+       "      <td>2014-06-19</td>\n",
+       "      <td>120000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>990</td>\n",
+       "      <td>39964</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>990</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1945</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>8990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6996</th>\n",
+       "      <td>2767601100</td>\n",
+       "      <td>2014-10-27</td>\n",
+       "      <td>513000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>4000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1976</td>\n",
+       "      <td>1480</td>\n",
+       "      <td>610.0</td>\n",
+       "      <td>1951</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1380</td>\n",
-       "      <td>7500</td>\n",
+       "      <td>1510</td>\n",
+       "      <td>5000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "9602   2917200085 2015-04-08  350000.0         2        1.0         1160   \n",
-       "11877  2025049114 2014-05-29  402000.0         2        1.0          710   \n",
-       "14592   968000120 2014-11-12  395000.0         3        2.0         1470   \n",
-       "1990   8562890370 2015-04-14  399950.0         4        2.5         3110   \n",
-       "17687  3575302562 2014-11-13  356000.0         3        1.5         1140   \n",
+       "              id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "8401  2324800110 2014-06-12  699000.0         4        2.5         3280   \n",
+       "295   9510920070 2014-07-10  879000.0         4        2.5         3360   \n",
+       "3958  4113800300 2015-04-14  600000.0         4        2.5         2420   \n",
+       "7966  3122069029 2014-06-19  120000.0         2        1.0          990   \n",
+       "6996  2767601100 2014-10-27  513000.0         4        2.0         2090   \n",
        "\n",
-       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "9602       5395     1.0           0        3.0    5.0         860   \n",
-       "11877      1173     2.0           0        4.0    5.0         710   \n",
-       "14592     10125     1.0           0        4.0    5.0        1470   \n",
-       "1990       5868     2.0           0        3.0    6.0        3110   \n",
-       "17687      7500     1.0           0        3.0    5.0        1140   \n",
+       "      sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "8401     27441     2.0           0        3.0    7.0        3280   \n",
+       "295      22111     2.0           0        3.0    8.0        3360   \n",
+       "3958      7744     2.0           0        3.0    7.0        2420   \n",
+       "7966     39964     1.0           0        2.0    2.0         990   \n",
+       "6996      4000     1.0           0        3.0    5.0        1480   \n",
        "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "9602           300.0      1940           0.0           1664        5363  \n",
-       "11877            0.0      1943           0.0           1370        1173  \n",
-       "14592            0.0      1962           0.0           1440       10125  \n",
-       "1990             0.0      2001           0.0           2950        5924  \n",
-       "17687            0.0      1976           0.0           1380        7500  "
+       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "8401            0.0      1996           0.0           3200       26960  \n",
+       "295             0.0      1994           0.0           3150       11374  \n",
+       "3958            0.0      1994           0.0           2820       11129  \n",
+       "7966            0.0      1945           0.0           1560        8990  \n",
+       "6996          610.0      1951           0.0           1510        5000  "
       ]
      },
-     "execution_count": 224,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1420,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1493,7 +1493,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1538,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1586,119 +1586,119 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3529</th>\n",
-       "      <td>2221000100</td>\n",
-       "      <td>2014-05-07</td>\n",
-       "      <td>310000.0</td>\n",
+       "      <th>5606</th>\n",
+       "      <td>4024101434</td>\n",
+       "      <td>2014-08-08</td>\n",
+       "      <td>318000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1840</td>\n",
-       "      <td>10723</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>7200</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1220</td>\n",
-       "      <td>620.0</td>\n",
-       "      <td>1974</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1948</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1590</td>\n",
-       "      <td>9820</td>\n",
-       "      <td>50</td>\n",
+       "      <td>7663</td>\n",
+       "      <td>76</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>14403.0</td>\n",
+       "      <td>9220.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20241</th>\n",
-       "      <td>8924100372</td>\n",
-       "      <td>2015-04-23</td>\n",
-       "      <td>1300000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>3590</td>\n",
-       "      <td>5334</td>\n",
+       "      <th>18192</th>\n",
+       "      <td>9471200065</td>\n",
+       "      <td>2014-10-15</td>\n",
+       "      <td>1860000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>5570</td>\n",
+       "      <td>9600</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>7.0</td>\n",
-       "      <td>3140</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>2006</td>\n",
+       "      <td>3860</td>\n",
+       "      <td>1710.0</td>\n",
+       "      <td>1952</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2100</td>\n",
-       "      <td>6250</td>\n",
-       "      <td>18</td>\n",
+       "      <td>3170</td>\n",
+       "      <td>10400</td>\n",
+       "      <td>72</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12514.0</td>\n",
+       "      <td>20740.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19223</th>\n",
-       "      <td>3832080610</td>\n",
-       "      <td>2015-04-06</td>\n",
-       "      <td>270000.0</td>\n",
+       "      <th>12922</th>\n",
+       "      <td>3222049044</td>\n",
+       "      <td>2014-06-12</td>\n",
+       "      <td>835000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1780</td>\n",
-       "      <td>5015</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>2790</td>\n",
+       "      <td>12523</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1780</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>1190.0</td>\n",
+       "      <td>1977</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>5250</td>\n",
-       "      <td>14</td>\n",
+       "      <td>2990</td>\n",
+       "      <td>11476</td>\n",
+       "      <td>47</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8575.0</td>\n",
+       "      <td>18103.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18603</th>\n",
-       "      <td>9297300750</td>\n",
-       "      <td>2014-11-05</td>\n",
-       "      <td>355000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1760</td>\n",
-       "      <td>4600</td>\n",
+       "      <th>8840</th>\n",
+       "      <td>2817900180</td>\n",
+       "      <td>2015-04-11</td>\n",
+       "      <td>380000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>51212</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>850</td>\n",
-       "      <td>910.0</td>\n",
-       "      <td>1926</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1510</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>1989</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>4800</td>\n",
-       "      <td>98</td>\n",
+       "      <td>2690</td>\n",
+       "      <td>40820</td>\n",
+       "      <td>35</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8120.0</td>\n",
+       "      <td>55392.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4980</th>\n",
-       "      <td>9547201155</td>\n",
-       "      <td>2014-10-16</td>\n",
-       "      <td>567500.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>3060</td>\n",
-       "      <td>1.5</td>\n",
+       "      <th>4657</th>\n",
+       "      <td>9547205225</td>\n",
+       "      <td>2014-08-14</td>\n",
+       "      <td>540000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1630</td>\n",
+       "      <td>6120</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1910</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>980</td>\n",
+       "      <td>650.0</td>\n",
+       "      <td>1918</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>3570</td>\n",
-       "      <td>114</td>\n",
+       "      <td>1630</td>\n",
+       "      <td>4080</td>\n",
+       "      <td>106</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5940.0</td>\n",
+       "      <td>9380.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1706,35 +1706,35 @@
       ],
       "text/plain": [
        "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "3529   2221000100 2014-05-07   310000.0         3       1.75         1840   \n",
-       "20241  8924100372 2015-04-23  1300000.0         4       3.50         3590   \n",
-       "19223  3832080610 2015-04-06   270000.0         3       2.50         1780   \n",
-       "18603  9297300750 2014-11-05   355000.0         2       1.75         1760   \n",
-       "4980   9547201155 2014-10-16   567500.0         3       1.00         1440   \n",
+       "5606   4024101434 2014-08-08   318000.0         3       1.00         1010   \n",
+       "18192  9471200065 2014-10-15  1860000.0         5       3.25         5570   \n",
+       "12922  3222049044 2014-06-12   835000.0         3       3.00         2790   \n",
+       "8840   2817900180 2015-04-11   380000.0         3       3.25         2090   \n",
+       "4657   9547205225 2014-08-14   540000.0         4       1.75         1630   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "3529      10723     1.0           0        4.0    5.0        1220   \n",
-       "20241      5334     2.0           0        3.0    7.0        3140   \n",
-       "19223      5015     2.0           0        3.0    5.0        1780   \n",
-       "18603      4600     1.0           0        4.0    5.0         850   \n",
-       "4980       3060     1.5           0        4.0    5.0        1440   \n",
+       "5606       7200     1.0           0        5.0    4.0        1010   \n",
+       "18192      9600     2.0           0        5.0    7.0        3860   \n",
+       "12922     12523     2.0           1        4.0    6.0        1600   \n",
+       "8840      51212     1.0           0        3.0    6.0        1510   \n",
+       "4657       6120     1.0           0        5.0    5.0         980   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "3529           620.0      1974           0.0           1590        9820   \n",
-       "20241          450.0      2006           0.0           2100        6250   \n",
-       "19223            0.0      2010           0.0           2010        5250   \n",
-       "18603          910.0      1926           0.0           1150        4800   \n",
-       "4980             0.0      1910           0.0           1440        3570   \n",
+       "5606             0.0      1948           0.0           1590        7663   \n",
+       "18192         1710.0      1952           0.0           3170       10400   \n",
+       "12922         1190.0      1977           0.0           2990       11476   \n",
+       "8840           580.0      1989           0.0           2690       40820   \n",
+       "4657           650.0      1918           0.0           1630        4080   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  \n",
-       "3529          50             0.0     14403.0  \n",
-       "20241         18             0.0     12514.0  \n",
-       "19223         14             0.0      8575.0  \n",
-       "18603         98             0.0      8120.0  \n",
-       "4980         114             0.0      5940.0  "
+       "5606          76             0.0      9220.0  \n",
+       "18192         72             0.0     20740.0  \n",
+       "12922         47             0.0     18103.0  \n",
+       "8840          35             0.0     55392.0  \n",
+       "4657         106             0.0      9380.0  "
       ]
      },
-     "execution_count": 226,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1816,12 +1816,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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5+9RyKfNnS5Ief/xxvfrqq1q3bp3j9YiNjVXHjh3zDVEtWrSQJL366qsaPny4Vq9e7Zj+fscdd+i2225TSEhIvvVu3rxZknTzzTfnOmJ96623ymQyaceOHQW23RUff/yx/vjjjzz/DB06NMc5W7ZskaQco7x2t912m1NbCsP+vbjvvvs0ceJEbd68Wenp6QoMDFS/fv2c1tLbrzXeunXrXB/Lft+Crkme1cGDB/XFF184/fnpp59ktVp1++236+OPP3b6wC6runXruvQc9tcntw9PGjZsqNWrV+u5555zqt3VNjZp0kQBAQFauXKl+vfvr4ULF+ro0aOOx+jevXuODzQAoDRjpBsAJMd66IoVK+Z7v1dffVVDhw5VXFycpkyZoilTpig8PFzt27dXz549HcHHFYXZDTyv0SH7G9j81jMXF/tz5DdSdemll2rHjh056rHPKMjOHvAKmmJ/5swZrVmzRlJmYMu+cVp8fLykzM3mcgsT5cuXz/WDFfvzZ79s2i+//KKFCxfqt99+07///qukpCRJFz+cyesya1n17NlTO3fu1PLly9W0aVOlpaVp5cqVqlixotM6/wceeED79+/XsmXLNGfOHM2ZM0eBgYFq1aqVOnfurC5duuQ5ei9lhs3du3fro48+0pIlS7RkyRKZzWZdd911uvXWW9WzZ0/HCGhe7N+vvC77FRoaqsjISJ04cUKnT5/2ym7lBf382Wu3j1YXxrPPPqvDhw/rxx9/1MyZMzVz5kyFhobqhhtuUNeuXZ2+b//9958k5foBQVb20OmKO++8M89LhhUkr39j2dlfn6yzRfLibhsvueQSvf766xo1apR++ukn/fTTT5IyP4SKiYnRvffey07qAMoUQjcASI6Ry9xGQbOqXr26Fi1apG3btunbb7/Vhg0b9Mcff2j58uVavny5BgwYkOeO4dm5Mp08u7xCkz38ubKetqApxgWxP1d+swLs98m+hrWo19D+/PPPHbMSfv311zzvt379eh06dChHeHTn+V9++WXNnz9fJpNJ9evX16233qratWurUaNG+vHHHzVjxgyXHqdz584aN26cvv76a40cOVJr1qxxTNXN+voEBARowoQJGjRokFatWqUff/xR27dv17p167Ru3TotXLhQH330Ub7rgp999ln16dNH33zzjX788Udt27ZNmzdv1ubNmzVv3jzNnz9f4eHheZ7vyocIeX1vS0pBP3/u1pfbv4dy5crpgw8+0O+//65Vq1bpp59+0q5du/TNN9/om2++0W233ea4qoH9/A4dOqhcuXJ5Po99DwhPc/X3SvYrDOSnMG287bbb1K5dO61Zs0Zr167Vzz//rL1792rv3r363//+p9mzZ6thw4Yu1wAA/ozQDQDKvAyYlLkbsyuaNm3qWAd56tQpLV68WG+99ZY+/PBD9enTp9AbOBUkr5Fs+yV77CPe9jfeuY0c20eDC8s+QpXfeuaDBw9Kypz6Xpzs07Kzb3CV1cCBA7V27Vp99tlnThuKucO+idQll1yiDz/8ULVq1XI6/vXXX7v8WOXKlVPHjh21bNkybdy4UatWrZLkPLU8qyuuuEIDBw7UwIEDlZycrB9++EEvv/yytm7d6tK64OrVq6tv377q27ev0tPTtXHjRo0ZM0b79+/XZ599poEDB+Z5bkHf28TERJ0+fVomkynf8O5JVatW1d9//61Dhw7lGmTttWffd0HKPWDn9+/h6quv1tVXX63HH39cCQkJWrlypcaOHasVK1aob9++aty4sapWrap//vlH999/v8u/P3xBlSpVdPjwYR09elRXXnlljuMLFixQtWrV1KFDh0K3sVy5curatatjs8Hdu3dr0qRJWr9+vd555x19+OGHxdYeAPBlrOkGUOatWLFCf/31l6pXr64bb7wxz/v99ddf6tKlix566CGn2ytVqqSBAweqXr16slqtjus3F3VUNzc//vhjjtsOHz6sXbt2qXz58rr66qslybGZWtbr+Nr98ssvuT62q/U2aNBAISEh+u233xzhOqt///1Xu3fvVmhoqBo0aODSY7pi9+7d2rNnjypVqpTn2lJJjh2+Fy9e7BgVd5d9FP2WW27JEbgtFovjklGujAxLmVPMpcyftXXr1qlu3bpOr43VatX999+vtm3bOq1rDwkJUadOnRyhxT7NNzdPP/20WrVq5XTN5ICAALVr186x+V5+50sX1zJ/9913uY6Efv3117JarWrWrFmhZmoUB3uN33zzTa7HV6xYIUlOSz3s/x5Onz6d4/7ZZ0ycO3dO3bt3z7F/Qvny5XXXXXc5Puyxv5b2evLaR+DNN99U9+7d892l3RvsHxquW7cux7H9+/frpZdecly73N02fvTRR+rQoUOOy9xde+21evbZZyW5N90eAPwdoRtAmWWz2bRy5UqNHDlSkjRixIh8p6RefvnlOnnypNavX59jpHPXrl3av3+/wsLCHDsb26eCJyUluXQ5MFcsW7ZMq1evdnx97tw5Pfvss7JYLE7Tle27qNt3o7b777//cr18mXRxOm5CQkK+NYSEhOiuu+6S1WrV008/7RRk7JcWslqt6tGjR4Ebd7nDvoFap06d8l3bfNNNN6lChQo6efKk02vlDvvGbhs3bnRsriZlbrQ2cuRIx3WSU1NTXXq85s2b64orrtDy5cuVkJCQYxM4o9Go8uXL68SJE5o0aZLTiOzZs2cdwSi/6biVKlXSmTNn9PrrrzttcpaSkuJ4HQqaztuiRQtdc801OnDggMaMGeP0ocWuXbv0+uuvS5LTDvol7a677lJoaKiWLl2aI9QtXrxYy5cvV2hoqONyctLFfw8ff/yx0wclH3zwgX777Tenx6hQoYKsVqv+/PNPzZ492+nYoUOH9Msvv8hoNDo+NLn77rsVGhqquXPn6quvvnK6//fff6+PPvpIu3fvLtYPoIpDbGysDAaDpk2b5lheI2XOZrBfetD+YY+7baxZs6aOHDmi6dOnO62tt9ls+vzzzyU5/ywmJydr//792r9/v2caCwBexvRyAKXemTNnnNZZW61WnTt3Tnv27NGJEydkMpk0cuRIxcTE5Ps4JpNJr7zyih599FE9/vjjuvbaa3XppZfqzJkz2rZtmywWi0aOHOlY8xgZGakKFSro3Llzuueee3TZZZflGXhd1ahRIw0dOlRNmjRRlSpVtHnzZp09e1YtWrRwujb1ZZddpltuuUWrVq3S3Xff7RgZ3rRpk+rWratatWrleINrv/zQtGnTtH37dnXr1s1pw6isnnrqKf3222/asmWLbr75Zseo4ubNm5WYmKhWrVq5vLbdFWlpafryyy8lqcDp1UFBQbr11lv12Wef6ZNPPnHsZu2OTp06aerUqfrzzz918803q3HjxkpLS9P27duVkJCgOnXqaO/evTp58qTLj9m9e3dNmjTJcf3u7J5//nlt3bpV//vf//Ttt9/q6quvVlpamn755RedP39eXbp0yXejvsGDB+v777/X119/rW3btjkC0M6dO3Xq1Cm1aNFCt99+e741GgwGTZo0SX379tWCBQv0ww8/qFGjRjp79qy2bt0qi8WigQMH5riMWkmqVq2aJkyYoKeeekrPP/+8PvroI1155ZX6+++/tWfPHoWEhOiNN95w2mjt/vvv18qVK/XNN9/o1ltvVb169fTnn3/qn3/+Ubdu3bR8+XKn53j55ZfVu3dvjRs3Tp999plq1aql8+fPa9u2bUpNTdUjjzyimjVr5qjnqaee0rvvvqurrrpK//33n+NqAsOHD3fMQvEVjRs31lNPPaU333xTPXr0UPPmzRUSEqIdO3bo1KlTateune6//35J7rfxpptuUkxMjFavXq2YmBhdd911CgsLc7zmVapUcbqE3s6dOx3Pldu12QHA3xG6AZR6SUlJ+uKLLxxfGwwGhYSE6NJLL9Utt9yi2NjYHFOI8xITE6MPP/xQs2fPVlxcnP744w9VqFBB7dq10wMPPKCWLVs67ms0GjVx4kRNmDDBMRW7qOupn376af36669asGCBdu3apRo1auiBBx5Q//79c4zST5w4Ue+//74+//xzbdy4UZUrV1bv3r01dOjQXEcq7733Xu3Zs0ffffed1q1bp1q1auUZuoODgzVr1izNnz9fn3/+uX7++WeZTCbVqVNH3bt3V69evYp1+vG3336r+Ph41ahRQ9ddd12B97/jjjv02WefafPmzdq/f3+Bu3ZnV65cOX322WeaPHmyNm7cqHXr1jkuzdarVy+1atVKbdq00fr165Wenu50LeK82Kfztm/f3uk64XaXXXaZFixYoBkzZmjTpk364YcfFBwcrDp16qhHjx45RsezCw8P17x58zR9+nT9+OOPWr9+vQICAnTllVfqoYceUu/evV2q88orr9TSpUs1c+ZMfffdd1qzZo0qVKigG264Qffff7+uv/76Ah/D02655RYtWrRIM2fO1KZNm7R//35VqVJFPXv2VP/+/XP8e46KitK8efM0ZcoUbd++XcePH1eDBg00evRoJScn5wjdjRo10vz58zVz5kz98ssvWrNmjcLCwtS0aVPdc8896tixY456Fi9erA8++EA///yzfvjhB1WqVEk33nij+vfv7/R7wZcMHDhQV199tT766CPFxcUpOTlZNWvW1P3336/+/fs7/Rt2p432D29mz56tFStW6JdffpHFYtEll1yi+++/X4888ojTmnsAKO0MNlcXpAEAgEJ79dVXNWfOHL333nv57h0AAABKF9Z0AwDgIfZN0TZv3qxFixapZs2aateunZerAgAAJYnp5QAAeMiLL76ob7/91hG+n3nmGa/t+g0AALyDnh8AAA9p2LChAgICVL16dY0aNUq33nqrt0sCAAAljDXdAAAAAAB4CCPdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdgBtsNpu3SwAAAB7iD/28P9QIwBmhG6XGsGHDFB0dnefxPn36qE+fPnl+XZBt27bp4YcfLlKNpcHRo0fVu3dvRUVFqXXr1kpOTs5xnyVLlqhevXo6dOhQro8xZcoU1atXz9OlFqthw4apXr16Tn+uvfZatW3bVs8++6z++++/Ah+jXr16mjJlSglUCwC+oU+fPk6/N+vXr68mTZqoe/fumjNnjiwWi1fqOnTokOrVq6clS5ZIks6dO6fnn39eW7duLdLjutNXFPS+JTf79u3TvffeW6Qa7TZt2qSOHTuqQYMGevDBB3O9j7vvrfxBdHR0ju9RVFSUYmJi9Oabbyo1NTXf8zdt2qR69epp06ZNJVQxSgOztwsAvOWll15y6/4LFy7Uvn37PFSN//jf//6n7du364033lC1atUUEhLi7ZJKTJUqVTR16lTH1xkZGfr77781ceJEbd++XV9++aWCg4PzPP/TTz9V9erVS6JUAPAZ11xzjaPPtVgsio+P19q1a/Xaa69p27Zteuutt2QwGEq0pqpVq+rTTz/VZZddJkn6/ffftWzZMnXv3r3Ij13UviI/K1eu1Pbt24tcoyRNmDBBVqtV77//vipVqlQsj+kv2rdvr8GDBzu+Tk1N1aZNmzRt2jQdPnxYkyZNyvPca6+9Vp9++qlq165dEqWilCB0o8zil2XhnD17VlWrVtVtt93m7VJKXGBgoBo3bux0W7NmzRQQEKDnn39e3333nTp37pzn+dnPBYCyoFy5cjl+/0VHR+vKK6/UuHHjFB0dra5du5ZoTbn9PvfkY7vTV5SUs2fPqnnz5mrTpo23SylxkZGROb5HLVu21NGjR7VkyRINGzZMVatWzfXc3H6egYIwvRxlVvYpURs2bNDdd9+tJk2aqHnz5ho8eLD++usvSZnTq5YuXarDhw87TUdLSEjQuHHjdPPNNysqKkq33367Fi1a5PQ86enpmjhxotq1a6eGDRvqwQcf1LJly5ymXw8bNkx9+/bVSy+9pGbNmunOO+9URkaGTp8+rZdfflkdOnRQgwYN1KJFCw0ZMsRp2nafPn00atQoTZ8+XTfccIMaNWqkAQMG6OTJk1q8eLFiYmLUpEkT9evXL8/p3nYFtSc6OlpLlizRkSNHin2qdFxcnB588EG1bNlS1113nR555BHt3bvXcTyv6VzZv4+7d+9W37591bRpU0e7d+zY4XTO1q1b1bt3bzVq1EgtWrTQ888/r9OnTxe69qioKEnS4cOHJeX9/cz+mp06dUovvPCC2rRpoyZNmig2Nlbbtm1zHLePQMTExKhBgwbq2LGj5syZ4/TcBw8e1KBBg9SyZUs1atRId999t9auXVvotgBASenTp4+qVq2qBQsWON2+cOFCde7cWQ0aNNCNN96oKVOmKCMjw3F82LBh6tevnxYvXuyYHt21a1en331Wq1XvvPOOoqOj1aBBA0VHR2vSpElKT0+X5Dy9fNOmTbr//vslSffff7/69OmjefPmqV69evr777+davvqq69Uv379AvvT3GTvK7KzWCyaN2+eunTpooYNG+rGG2/UxIkTHdOdp0yZ4hhBL6gP/ueff/TYY4/p+uuvV+PGjdWnTx9H/2Jv++HDhx3vR4pzqvSKFSvUvXt3NWnSRNdff71GjRql+Ph4x/G8lphlb9OKFSvUtWtXNWzYUK1atdIzzzyj48ePO51T0M+Kuxo0aCCbzeZYBhAdHa3XXntNffv21XXXXadRo0bl+n5k165deuihh9S0aVO1atVKTz75pNNSgrNnz2rUqFFq06aNoqKidNddd2njxo1Oz53f+1D4P0I3Sp2MjIxc/+S38Yg9uFx77bWaPn26Xn31Vf31118aOHCgrFarBg8erPbt26tKlSr69NNPdeONNyolJUX33XefPv/8c/Xv31/Tpk1T06ZN9eKLL2rGjBmOxx41apT+97//qXfv3nr33XdVuXJljRw5MkcNW7du1YEDBzRlyhQNGTJEJpNJDz/8sH766Sc9/fTT+vDDDzV48GBt2LBBo0aNcjr3q6++0oYNGzR27FgNHz5cGzZsUO/evTVnzhw9//zzevHFF7Vjxw698soreb4GrrRn6tSpTq9Dr1698v1eWK3WXL8XVqvV6X4///yz7r33XlmtVo0dO1avvvqq/vvvP91zzz3av39/vs+R1fnz5/XQQw8pIiJCkydP1ltvvaXk5GQ9+OCDSkhIkCRt2bJF/fr1U3BwsN5++2298MIL2rx5s+6//36lpKS4/FxZ2d+U2acpSjm/n2az88SipKQk3XPPPdqwYYOefvppTZ06VWFhYXrooYccbR49erQmT56srl27asaMGbr11lv12muv6d1335WU+fo+/PDDSkpK0uuvv65p06YpPDxcgwcP1oEDBwrVFgAoKSaTSa1bt9bOnTsdQem9997TyJEj1bp1a82YMUOxsbGaOXNmjn5v165d+vDDD/XYY4/p3Xffldls1mOPPeYIdzNnztS8efM0ZMgQzZo1S/fee68++OADp/7Z7tprr3U8/qhRo/TSSy+pS5cuCgoK0vLly53uu3TpUrVo0UKXXnqp2+3Nra/IatSoUXrttdcUHR2t6dOnKzY2VnPnztXgwYNls9nUq1cv9ezZU5Ly7YP37dun7t276+DBgxoxYoQmTpwog8Ggvn37avPmzY6p9VWqVFH79u316aef6tprr823dlffW02bNk1PPvmkGjVqpMmTJ2vIkCH65ptv1KdPH7f62G3btumZZ57RLbfcopkzZ2r48OH6+eef9fTTTzvu4+rPijvs36OaNWs6brN/ADNlyhR169Ytxzl79uzRvffeq+TkZI0fP16vvPKKfvvtN/Xv31/p6elKTU1V37599d133+nJJ5/U1KlTVb16dT300EOO4F3Q+1D4P6aXo1Q5fPhwvh1HixYtcr19586dSklJ0cMPP6xq1apJki655BJ99913SkpK0mWXXabIyEinKWPz58/Xn3/+qfnz56tp06aSpBtuuEEZGRmaNm2a7rnnHp07d05Lly7V888/rwceeMBxn5MnT2r9+vVONWRkZOjll1/W5ZdfLkk6duyYQkJC9Pzzz6tZs2aSMqc+HTp0KMeoQHp6uqZOnaqKFStKklavXq3169fr22+/dXQcv//+e443D1ktWbKkwPZcc801OV6H/MTExBR4H0l68803VbNmTX3wwQcymUySpLZt2yomJkZTpkzR22+/7dLj7Nu3T6dPn1afPn0cbbjqqqu0YMECnT9/XuXLl9ebb76pK6+8Uu+9957juRo1aqTOnTtr8eLFio2Nzfc5sn6Cfv78ecXFxWncuHGqUaOG2rdv73S/rN/P7JYuXaqDBw9q2bJlql+/vqTM6Yd33HGHtmzZIqPRqM8++0xPPfWUBg4c6HhNDAaD3nvvPd13333KyMjQ/v379cgjjzieu2HDhpo6dWqBG8EAgC+oXLmy0tPTdfbsWQUFBWn69Om6++67NWLECEmZv/fCw8M1YsQIPfDAA6pTp46kzJlZS5YscQTY0NBQ9e7dWz///LM6duyozZs369prr1WPHj0kZfb/ISEhKleuXI4aypUr51hyVrt2bcffY2Ji9Pnnn+vxxx+XwWDQ8ePHtWHDBr322msFtsvVvsJu3759WrRokZ544gkNGjRIknT99deratWqeu6557Ru3Tq1b9/esS9Ifn3w1KlTFRAQoI8//ljly5eXJN144426/fbb9cYbb2jhwoVq3LixAgMDc51mnZ2r763i4+M1ffp09erVy2nfnLp16yo2NlZLlizRfffdl+9z2W3btk1BQUEaMGCAgoKCJEnh4eGKi4uTzWbT+fPnXf5ZyY3NZnP6Hp06dUrr1q3TggUL1KlTJ0VGRjqOVa1aVcOGDZPRmDlWmX1WwLRp01SxYkXNmjXLUWv16tX1xBNP6I8//tBvv/2mPXv26LPPPlOjRo0kSe3atVOfPn00ceJELV68uMD3obn93MK/ELpRqlSpUkXTp0/P9Vh+G6c1atRIQUFB6tmzp2677Ta1b99ezZo1U8OGDfM8Z/PmzapRo4Yj3Nl17dpVixYt0o4dO3T8+HHZbDbdeuutTve5/fbbc4Tu4OBgp0+/q1Wrpo8//liSdOTIER04cED79+/XL7/84pgeZ1erVi1H4La/DpGRkU6f1IaHhztGewvbntzeKORn+vTpqlKlSo7bP/vsM3322WeSMkd84+LiHKP7dhUqVFCHDh3cmipdp04dRUZGatCgQerUqZPat2+v1q1b67nnnpMkJScna8eOHXrwwQedOtyaNWuqVq1a+umnn/IN3Xm98WjUqJFeeeUVp03lsn8/s9u6dasuvfRSR+CWpKCgIK1cuVKS9Mknn8hmsyk6OtrpjYF9BGTbtm266aabVLt2bY0cOVIbNmxQu3bt1LZtWw0fPtzFVwwAfIPBYND27duVnJyc6+89Sfrpp58cQSoyMtLpd6w9jNqvqNGyZUu9+eabuu+++xQTE6N27dqpd+/ebtXUs2dPffnll9q6dauaN2+u5cuXKzg4WB07dsz3PHf6CrvNmzdLkrp06eJ0e+fOnTV8+HBt2rTJ5T548+bN6tChgyNwS5LZbFbnzp317rvvKjExUWFhYS49luT6e6tff/1VaWlpOdrQrFkz1ahRQ5s2bXI5dDdv3lxvvfWWunTpok6dOjn6N/tr4M7PSm6WLVumZcuWOd1mNpsVExOj0aNHO91eq1YtR+DOzbZt29S+fXtH4JYyPwBfs2aNJOnDDz9UlSpVdO211zrV2qFDB73++uuKj48v1PtQ+BdCN0qVwMBAx5qp7PLrYC699FLNnTtX77//vj777DPNnj1bFSpU0H333afHH38811+28fHxqly5co7b7bedO3fOsU44+66guZ1XqVKlHLu3fv7555o0aZL+++8/hYeHq379+rnueJrbJ6Du7iruSnvcVbdu3Vyn4P3www+OvyckJMhms+X53Pl9UJBdWFiY5s2bp+nTp2vFihVasGCBQkJC1LVrV7344os6d+6crFarZs6cqZkzZ+Y4P2uHmZvsbzwCAwNVvXp1pw887HL7fmZ19uzZfHeLPXv2rCTludnOsWPHZDAYNGvWLE2fPl2rV6/W0qVLFRAQoJtvvlmjR49WeHh4vu0BAG87duyYgoODFR4e7vi9Z5/dk13W9bzZ+zj771v7VNyHHnpIYWFhWrx4sSZMmKDx48erbt26euGFF9S6dWuXamvVqpUuvfRSLVu2TM2bN9eyZcvUqVOnAvtXd/oKO/u0+OwfVJvNZkVERLjVF+bXn9tHid0J3a6+t7K3oTj68yZNmuj999/X7Nmz9eGHH2rGjBmqUqWKBgwYoL59+7r1s5KbDh06aMiQIZIyf3ZCQkJUo0aNXN9j5daerFzpz0+cOJHnbIETJ06odu3abr8PhX8hdAMX2KflpqWladu2bfr00081Y8YM1atXL9eduitWrJjrutkTJ05IkiIiIhzXHz116pQuueQSx31OnTpVYD1bt27V888/r969e+vBBx90fIr/+uuvO222VVxcaY8nlC9fXgaDQSdPnsz1ue3BMfsbKrvsn9hfddVVeuONN2SxWLRz504tX75cn3zyiS699FLdd999MhgM6tevX65htqA3Uvm98XBX+fLlc92IZ/v27SpXrpwqVKggKfMSbbm9Ofq///s/SZkzIkaPHq2XXnpJe/bs0ddff62ZM2eqYsWKevnll4ulVgDwBIvFos2bN+u6666TyWRy/N6bOHGirrjiihz3Lyj8ZGU0GhUbG6vY2FidOnVKa9eu1YwZM/Too49qw4YNLj2GwWDQnXfeqY8//lixsbHat29fvnuj2BWmr7AH8hMnTjh9WJ2enq4zZ8641QdXrFgxzz5V8lx/bm/DyZMnVatWrRzPbZ99Z+/PLRaLY4ZbYmJijse74YYbdMMNNyg5OVk///yzPv74Y7322mtq3LhxkX9WwsPDi7U/z20z1rVr16p+/foqX768rrjiCk2cODHX8+3fb3ffh8K/8LEJIGn27NmKjo5WWlqaAgMD1bp1a40ZM0aSHLtPZv+UsXnz5jp8+HCOAPz5558rICBADRs2VNOmTWUymbRq1Sqn+2T/Ojfbt2+X1WrVY4895gjcFovF8WahuDfWcKU9nhAaGqoGDRpoxYoVjg8ppMwR8B9++MEx3d0+mp91N9D4+Hinjda+/vprtWrVSidOnJDJZFKTJk00evRoVahQQUePHlW5cuV0zTXX6K+//lJUVJTjT506dTR16tRi3b21IM2aNdPBgwf1xx9/OG5LS0vTo48+qs8++0zNmzeXJJ05c8ap1rNnz+rtt9/W2bNntX37drVp00Y7d+6UwWDQ1VdfrSeffFJ169bV0aNHS6wtAFAYCxYs0PHjx3XvvfdKypx+HRAQoGPHjjn93gsICNCbb77p1o7h99xzj1599VVJmTOPunfvrtjYWCUkJOj8+fM57p91eVNWPXr0cFzZ44orrsixBKu42NdFf/HFF063f/XVV7JYLI7ndWXEs3nz5vr++++dRpYtFou++uorRUVFKTAwsBgrv6hRo0YKDAzM0YatW7fqyJEjuu666yTl3p//8ssvTudMmDBBPXv2lM1mU0hIiDp06KDnn3/ecV5x/qwUVbNmzfTjjz8qLS3Ncdsff/yhgQMHKi4uTi1atNB///2nSpUqOdW6ceNGx142rrwPhX9jpBtQ5hSyiRMnasiQIerdu7dMJpMWLFigwMBAdejQQVLmGuOTJ09q7dq1uvrqq9W9e3fNnz9fQ4cO1WOPPaaaNWtqzZo1Wrx4sYYOHaoKFSqoQoUK6tGjh+MyJfXr19fq1av1/fffS8q/87SH3FdeeUU9evTQuXPnNHfuXO3Zs0eSin1jDVfa4ylPP/20HnzwQT300EPq3bu30tPT9f777ystLU1Dhw6VlHkpkUsuuURTp05V+fLlZTQa9f777zuNTl933XWyWq0aMmSIBg4cqLCwMK1cuVIJCQm65ZZbJMmxMdnTTz+trl27ymKxaNasWdqxY4dj85qS0L17d82ZM0eDBg3S448/rsjISM2bN08pKSnq06ePLrvsMnXt2lUjR47U4cOH1aBBA/3999966623dOmll+qKK65QRkaGgoOD9dxzz+nRRx9V5cqVtWHDBv3++++Oy98AgLedP39ev/76q6TMD4zPnDmj9evX69NPP1XXrl0dv58jIiL00EMP6Z133tH58+fVsmVLHTt2TO+8844MBoPTHhgFad68uWbNmqXKlSurSZMmOnbsmD766CO1aNFCkZGRSkpKcrq/ff3zDz/8oIoVKzqe65JLLlGbNm20fv16Pfnkk8XwauSudu3auvPOOzV16lSlpKSoZcuW+v333zV16lS1bNlSN9xwgyQ5+uIvv/xSjRo1ctq7xW7o0KFat26d7r//fg0cOFCBgYGaO3euDh48qA8++MBjbQgPD9fAgQMdG7nddNNNOnTokN555x3Vrl1b3bt3lyS1b99e48aN08iRIzVgwAAdPXrUcQUPu9atW+ujjz7SsGHD1LVrV6Wnp+uDDz5QeHi4WrVqpfDw8GL7WSmqwYMH6+6773ZMfU9LS9M777yja6+9Vu3atVNGRobmzp2rBx54QI888oguueQSbdiwQTNnzlTv3r0VEBDg0vtQ+DdCNyCpfv36mjFjht5991099dRTslgsatCggWbNmqWrrrpKUmZIWrt2rYYMGaLHHntMAwcO1Jw5c/Tmm29q8uTJOn/+vK666iqNHTvWcUkPSRo5cqRCQ0M1a9YsnT9/Xq1bt9agQYP07rvvKjQ0NM+aWrZsqVGjRumjjz7S119/rcqVK6tly5aaOnWqhgwZ4ti4o7iEhIS41B5PsHeukydP1lNPPaXAwEA1a9ZMEyZMcGyEYjKZNHnyZL322mt66qmnVLlyZfXt21d//fWX4xIfVatW1QcffKB33nlHL774opKTk1WnTh1NmTJFrVq1kpS5u+mHH36oqVOn6rHHHlNAQICuvfZaffTRRy7tyF5cypUrp7lz5+r111/X2LFjlZGRoUaNGmnOnDmOzYHGjRun9957TwsWLNDRo0dVqVIl3XbbbXriiSdkMplkMpk0a9Ysvfnmmxo7dqzOnTunK664Qq+88orjzQ0AeNtvv/2mu+++W1Lmh82VKlXSlVdeqfHjx+fYdOuJJ55QlSpVNH/+fH3wwQeqWLGiWrduraeeesppY7CCPP744woMDNTixYv17rvvqnz58oqOjna65FRWderU0e2336558+bpxx9/1Jdffuk41qFDB23YsEF33HGH+413w9ixY3X55Zdr8eLF+vDDD1W1alX16dNHQ4YMcXxIf8stt2j58uUaNmyYevbsmWPTL3tb5s+fr0mTJumFF16QwWBQw4YN9fHHHzuuhuIp9g+A586dq4ULFyo8PFy33nqrnnjiCceH5FdeeaUmTJig6dOna+DAgapVq5bGjBnjGNmVMnf3njhxombNmqWhQ4fKYDCoadOm+vjjjx3LzorrZ6WorrnmGsf7pyeffFJhYWFq3769nnnmGQUGBiowMFDz5s3Tm2++qTfeeEMJCQmqUaOGnn76afXv31+Sa+9D4d8MtvwuXgygSM6ePat169bphhtucFpDNWHCBC1ZsqREpzMDAAD3DRgwQCaTKddrfAOAKxjpBjwoJCREY8eO1dVXX62+ffsqNDRUv/zyi+bMmaNHHnnE2+UBAIA8vPvuu/r777+1bt06zZ0719vlAPBjjHQDHvb777/r7bff1q+//qrk5GRddtlluueeexQbG5vvJaUAAID39OjRQwcOHNAjjzyihx56yNvlAPBjhG4AAAAAADyES4YBAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BAuGZaF1WpVRkaGjEYju0oDALzCZrPJarXKbDbLaOSz8bzQZwMAvM3VPpvQnUVGRobi4uK8XQYAAIqKilJgYKC3y/BZ9NkAAF9RUJ9N6M7C/ulEVFSUTCaTW+daLBbFxcUV6lxfQjt8C+3wLbTDt5TWdti/ZpQ7f/TZtMPX0A7fQjt8S2loR25tcLXPJnRnYZ+eZjKZCv3DUJRzfQnt8C20w7fQDt9SWtvBlOn80WdfRDt8C+3wLbTDt5SGduTWhoL6bD5GBwAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXT7mD4j13i7BAAA4IIFcQu8XQIAwA8Qun1McmqGt0sAAAAuSLeme7sEAIAfIHQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQugEAAAAA8BBCNwAAAAAAHkLoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAP8WroPn36tGJiYrRp0ybHbTt27FCvXr3UpEkTRUdHa+HChU7nLF26VDExMWrcuLG6d++u7du3O45ZLBZNmDBBbdq0UZMmTTRo0CAdP368xNoDAEBpRZ8NAEDheC10b9u2TXfffbf+/fdfx23x8fEaOHCg7rjjDm3ZskVjx47VuHHjtHPnTknSpk2bNGbMGI0fP15btmxR165dNWjQICUnJ0uSpk+frp9++kmLFy/Wjz/+qODgYI0YMcIr7XNHn5FrvF0CAAB5os/OtCBugbdLAAD4Ia+E7qVLl+qZZ57Rk08+6XT7qlWrFB4ertjYWJnNZrVu3VpdunTRvHnzJEkLFy5U586d1bRpUwUEBKhfv36KiIjQihUrHMcHDBigSy65ROXKldOLL76odevW6eDBgyXeRnckp2Z4uwQAAHJFn31RujXd2yUAAPyQV0J327ZttXr1at12221Ot+/du1d169Z1uq127dras2ePJGnfvn15Hk9ISNDRo0edjleuXFkVK1bUH3/84aGWFI/gIBOj3QAAn0SffZHZaNacHXM0Z8ccb5cCAPAjZm88aZUqVXK9PTExUSEhIU63BQcHKykpqcDjiYmJkqTQ0NAcx+3HXGWxWNy6f9ZzCnOuzZY52m2xWGSzFe4xiktR2uFLaIdvoR2+hXb4luzt8LX20GdfZLPZlJaR5jjXZrPRZxcD2uFbaIdvoR2+I7c2uNoer4TuvISEhCghIcHptpSUFIWFhTmOp6Sk5DgeERHh6Njta8VyO99VcXFx7pZe6HONRqOSEpOUkmbRrl27lJiYqJ07d8pqtTrdJ+vXJaEor4EvoR2+hXb4FtrhW/ytHWWtzzYajTp5+qQS0xJls9m0a9cunTh+wqnPNhozJxDSZxcO7fAttMO30A7fUZg2+FTorlu3rn766Sen2/bt26c6depIkurUqaO9e/fmON6uXTtVrFhR1apVc5rOduLECZ09ezbH9LaCREVFyWQyuXWOxWJRXFxcoc4NDTsto9miBg0aKCzsmBo2bOh0vO9La/W/l9u79ZiFVZR2+BLa4Vtoh2+hHb4lezvsX/u6sthn/777d4WlZ34o0KBBA+2w7VDDqIt99ry4zPXssY1j3aqnsErrvwF/RTt8C+3wLaWhHbm1wdU+26dCd0xMjN544w3Nnj1bsbGx2rZtm7744gtNmzZNktSzZ08NGTJEnTp1UtOmTTVv3jydOnVKMTExkqTu3btr+vTpioqKUkREhF577TW1aNFCl112mVt1mEymQv8wFOZcgyHzj8lkcvw/q5S0jBL/4SzKa+BLaIdvoR2+hXb4Fn9rR1nssw0GgwwGg+Ncg8HgdH6GNcNxrCT5289OXmiHb6EdvoV2+I7CtMGnQndERIRmzZqlsWPHavLkyYqMjNSIESPUqlUrSVLr1q310ksvafTo0Tp27Jhq166tmTNnKjw8XJI0ZMgQZWRkKDY2VomJiWrZsqXefvtt7zUIAIBSij4bAADXeD10Z9+lNCoqSgsW5H0dzG7duqlbt265HgsICNAzzzyjZ555plhrBAAA9NkAABSGVy4ZBgAAAABAWUDoBgAAAADAQwjdAAAAAAB4CKEbAAAAAAAPIXQDAAAAAOAhhG4AAAAAADyE0A0AAAAAgIcQun1EcJBJA8as9XYZAACgAGajWYt2L/J2GQAAP0Ho9iHJaRneLgEAALgg3Zru7RIAAH6C0A0AAAAAgIcQugEAAAAA8BBCNwAAgJtOJZ3SugPrdD7tvLdLAQD4OEK3jwkOMqnPyDXeLgMAAORjzd9rtP3odo1aM8rbpQAAfByh2wclp7KhGgAAvio1I1WHEg5Jko4lHvNyNQAAX0foBgAAcMOB+AOy2qySpFPJp7xcDQDA1xG6AQAA3PD32b8dfz+TfEYZVmaoAQDyRugGAABwkdVm1YGzBxxfW2wWHTp3yIsVAQB8HaEbAADARcfOH1OKJUXBpmBVDasqSdp3ep+XqwIA+DJCNwAAgIsOnjsoSboi/ApVDc0M3ftP7/dmSQAAH2f2dgEAAAD+IjE9UZJUKbSSAowBkqT9ZwjdAIC8EboBAABclG5JlyQFmgIVGRIpiZFuAED+mF4OAADgojRrmiQpwBSgyqGVJWVOOU/NSPVmWQAAH0boBgAAcFHWke6wgDAFm4NltVn1z9l/vFsYAMBnEboBAABc5AjdxkAZDAbHFPOs1+4GACArQjcAAICL7NPLA02BkqRygeUkSaeSTnmtJgCAbyN0AwAAuCjr9HJJCg0IlSSdSiZ0AwByR+gGAABwUZrFeaTbHrpPJ5/2Wk0AAN9G6AYAAHCB1WaVxWaRdDF0hwWESWJ6OQAgb4RuAAAAF9hHuSWmlwMAXEfoBgAAcIE9dBsNRpmMJkmMdAMACkboBgAAcEH29dzSxZHuMylnZLVZvVIXAMC3EboBAABc4AjdxouhOyQgRAYZZLVZlZKR4q3SAAA+jNANAADgAnvoDjAFOG4zGoyKCImQJCWlJ3mlLgCAbyN0AwAAuCD7NbrtIkMiJRG6AQC5I3QDAAC4ILeRbkmqFFJJEqEbAJA7QjcAAIALclvTLUmVQjNDd3J6conXBADwfYRuHzdgzFpvlwAAACSlWXPuXi5lGenOYKQbAJATodvHJadleLsEAACgfKaXhzK9HACQN0I3AACAC3K7TrfEmm4AQP4I3QAAAC6w714eYHQe6Wb3cgBAfgjdAAAALshzpNu+kVoGG6kBAHIidAMAALggrzXdlUMrS5JSMlJksVpKvC4AgG8jdAMAALggr5HuCkEVZDRkvqVitBsAkJ3Z2wUgp+Agk3o+t0rBQSZvlwIAAC7I7TrdZqNZS35bolBzqM6nn2ddNwAgB0a6fVRSSoaSU7hcGAAAviKv6eXp1nSFBIRIYqQbAJAToRsAAMAF9t3Ls08vl6Rgc7AkKS0jrURrAgD4PkI3AABAAWw2m9KsuY90S1KQOUiSlGpJLdG6AAC+j9ANAABQgDRLmqw2qyTnNd12wabMkW5CNwAgO0I3AABAAbJukJbvSHcGoRsA4Izdy72IHcoBAPAPi39bLClzt3L75cGysq/pZqQbAJAdI91exA7lAAD4h8T0RElSgDHnKLfESDcAIG+EbgAAgAI4rtGdy87lEmu6AQB5I3QDAAAUwH65sNzWc0vsXg4AyBuhGwAAoAD2MJ3bzuVSljXdTC8HAGRD6AYAACiAfXp5QSPd9vsBAGBH6AYAACiAfXq5K2u67dfzBgBAInQDAAAUKN2aGbrNxtyvtmof6Zak82nnS6QmAIB/IHQDAAAUwGK1SMo7dJuNZsexc6nnSqwuAIDvI3QDAAAUwGLLDN1GQ95vnYJMmaPdjHQDALIidAMAABTAPtJtMpjyvI99vTcj3QCArAjdAAAABXBppPvCum5CNwAgK0I3AABAARwj3ca8R7qZXg4AyA2hGwAAoAD2ke78ppfbQzcj3QCArAjdAAAABXCE7vxGui9ML09ITSiRmgAA/oHQDQAAUAD79HJXdi9npBsAkBWhGwAAoAAuTS+3j3SnMdINALiI0A0AAFAAq9UqybU13UwvBwBkRegGAAAoQIYtQ5JkNDK9HADgHkI3AABAAVwa6WZ6OQAgF4RuAACAAriypjvQFCiJ6eUAAGeEbgAAgAJkWF2YXm6+OL3cZrOVSF0AAN9H6AYAACiA1Vbw9PJgU7CkzFHx5IzkEqkLAOD7CN0AAAAFsF+n22TMO3SbjWbHdbyZYg4AsCN0AwAAFMCVNd0Gg+Hium42UwMAXEDoBgAAKIAroVu6uJna+bTzHq8JAOAfCN0AAAAFsE8vt08fz4v9Wt2EbgCAnU+G7t27dys2NlbNmjVT27Zt9eqrryotLU2StGPHDvXq1UtNmjRRdHS0Fi5c6HTu0qVLFRMTo8aNG6t79+7avn27N5oAAECZUBb6bJvNdnGkO5813RKXDQMA5ORzodtqterhhx9Wx44dtXnzZi1atEjr16/XzJkzFR8fr4EDB+qOO+7Qli1bNHbsWI0bN047d+6UJG3atEljxozR+PHjtWXLFnXt2lWDBg1ScjI7iAIAUNzKSp9tD9xSwSPdTC8HAGTnc6E7Pj5eJ06ckNVqdVzj0mg0KiQkRKtWrVJ4eLhiY2NlNpvVunVrdenSRfPmzZMkLVy4UJ07d1bTpk0VEBCgfv36KSIiQitWrPBmkwAAKJXKSp+dZklz/L2gNd1MLwcAZOdzoTsiIkL9+vXThAkTFBUVpfbt2+uKK65Qv379tHfvXtWtW9fp/rVr19aePXskSfv27cv3OAAAKD5lpc9Ot6Q7/u7q9HJCNwDAzuztArKzWq0KDg7WyJEj1bNnTx04cEBDhw7V5MmTlZiYqJCQEKf7BwcHKykpSZIKPO4qi8VS8J3yOMedc222i3/yO2azFa6mwihMO3wR7fAttMO30A7fkr0d/tSestJnp6SnSJIMF/6TTY6R/axsNpsjdJ9LPefx76U//szkhnb4FtrhW2iH78itDa62x+dC9+rVq/XNN9/o66+/liTVqVNHQ4YM0dixY9WlSxclJDhvTJKSkqKwsDBJUkhIiFJSUnIcj4iIcKuGuLi4Qtfv6rlGo1GJiedltZgla2CO40lJybJaTJI1UMmpGdq5c6esVmuh63JXUV4DX0I7fAvt8C20w7f4YzvKSp99Jv2MpMz13MePH1eAMUCBATn77sTURKUmpUqSDh07pF9//bXQtbnDH392ckM7fAvt8C20w3cUpg0+F7r/++8/x66ndmazWQEBAapbt65++uknp2P79u1TnTp1JGV29nv37s1xvF27dm7VEBUVJZMp/+lj2VksFsXFxbl1bljYfwoJMikkOOe3ITTd6DhmMFnUsGFDt+oprMK0wxfRDt9CO3wL7fAt2dth/9oflJU++9/4f6WdktloVtWqVRVgDJDZlLPvDksP0ynjKSleMoeZ1bhxY7fqcldp/Tfgr2iHb6EdvqU0tCO3NrjaZ/tc6G7btq3efPNNzZgxQwMGDNCRI0c0ffp0denSRTExMXrjjTc0e/ZsxcbGatu2bfriiy80bdo0SVLPnj01ZMgQderUSU2bNtW8efN06tQpxcTEuFWDyWQq9A+DO+caDBf/5HfMYFCJ/3AW5TXwJbTDt9AO30I7fIs/tqOs9NkWXbxGt8FgkAzK/H82BoNBQebMjdQS0xNL7Pvpjz87uaEdvoV2+Bba4TsK0wafC921a9fWe++9p7ffflsffPCBypcvr65du2rIkCEKDAzUrFmzNHbsWE2ePFmRkZEaMWKEWrVqJUlq3bq1XnrpJY0ePVrHjh1T7dq1NXPmTIWHh3u3UQAAlEJlpc+2b6RW0CZqEruXAwBy8rnQLUlt2rRRmzZtcj0WFRWlBQsW5Hlut27d1K1bN0+VBgAAsigLfXa69ULoLuByYRK7lwMAcvK5S4YBAAD4Evt1uhnpBgAUBqEbAAAgH47p5W6MdCelJynDmuHRugAA/oHQDQAAkA/H9HIXRrrtoVuSEtMSPVYTAMB/ELoBAADy4Zhe7sJIt8loYoo5AMAJoRsAACAf9mnirox0S1K5wHKSCN0AgEyEbgAAgHy4M9ItEboBAM4I3QAAAPlwZyM16WLoTkhL8FhNAAD/QegGAADIhzsbqUmMdAMAnBG6AQAA8uHuSHf5oPKSCN0AgEyEbgAAgHw41nQz0g0AKARCNwAAQD7su5cbDa69bSJ0AwCyInQDAADkwz7SbTaaXbo/oRsAkBWhGwAAIB/2jdQY6QYAFAahGwAAIB+OjdTcXNOdkMolwwAAhG4AAIB82Ue6zQamlwMA3Efo9pIBY9Z6uwQAAOCCvaf2SpKMRqaXAwDcR+j2kuS0DG+XAAAAXGDfvdzl63QHcp1uAMBFhG4AAIB8WKwWSVynGwBQOIRuAACAfFhsF0K3iyPd9tCdakl1bMIGACi7CN0AAAD5cDd0hwWGOf7OaDcAgNANAACQD3enl5uNZoUGhEoidAMACN0AAAD5cnekW8pyre40rtUNAGUdoRsAACAf7o50S2ymBgC4iNANAACQj6KMdBO6AQCEbgAAgHww0g0AKApCNwAAQD6KtKY7lTXdAFDWEbr9SJ+Ra7xdAgAAZU5hRrqPnT8miZFuAACh268kp2Z4uwQAAMocq80qSTIaXH/bZDaaJRG6AQCEbgAAgHzZR7rtQdoVQaYgSYRuAAChGwAAIF+FWdMdaA6UROgGABC6AQAA8pRhzZBNNknuTS9npBsAYEfoBgAAyEOG9eJ+Ku5MLw80MdINAMhE6AYAAMhDmiXN8Xe3RrrNjHQDADIRugEAAPJQ6NB9YXp5QhrX6QaAso7QDQAAkAf79HKTwSSDweDyeVmnl9tsNo/UBgDwD4RuPxIcZFKfkWu8XQYAAGWGfaTbZHR953JJCgsIk5QZ2lMtqcVeFwDAfxC6/UxyakbBdwIAAMUi3ZIuyb3LhUkXR7ol1nUDQFlH6PYDwUEmDRizNsftjHoDAOBZ6dYLodvFkW6z0axFuxfJYDA4gvenuz71WH0AAN9H6PYTyWk5R7gZ9QYAwLMKM9JtD+r2zdSS0pOKvzAAgN8gdAMAAOShsGu6pYtTzFMzWNMNAGUZoRsAACAPjunlbq7pli5eqzvrZccAAGUPoRsAACAPhd1ITbo4vZzdywGgbCN0AwAA5ME+0m00uv+WyTG9nNANAGWa2dsFwD3BQSb1fG6VgoPc/8QdAAC4xz413Gxw/y1TsDk48zEymF4OAGUZodsPJaVkyGazyWAweLsUAABKtQxr5pVCCrORmj10M9INAGUb08sBAADyYF/TbTQwvRwAUDiEbgAAgDw41nQXInQ7di9nejkAlGmEbgAAgDw4ppezezkAoJAI3QAAAHmwh+7C7F5uD91cpxsAyjZCNwAAQB4cobsI08sZ6QaAso3QDQAAkIdimV6eQegGgLKM0A0AAJCH4hjpTrOkyWazFWtdAAD/QegGAADIQ5FC94WRbptsSs5ILta6AAD+g9ANAACQh6KEbrPRLIMMkqSE1IRirQsA4D8I3QAAAHkoyppug8HgmGJ+Pu18sdYFAPAfhG4AAIA8FGWkW7o4xTwhjZFuACirCN0AAAB5KMp1uiUx0g0AIHQDAADkpbhGugndAFB2EboBAADyUJQ13RKhGwBA6AYAAMhTkUe6mV4OAGUeoRsAACAPxbaRGpcMA4Ayi9ANAACQB0a6AQBFRegGAADIA2u6AQBFRegGAADIQ1FHugPNgZK4TjcAlGWEbgAAgDwUNXQHm4IlMdINAGUZoRsAACAPjtBtLNpIN6EbAMouQjcAAEAeirqmm5FuAAChGwAAIA9FXtNtYqQbAMo6QjcAAEAeiuuSYUnpSY7HAgCULYRuAACAPBQ5dF+4ZJgkJaYlFktNAAD/QugGAADIQ1HXdJuMJpmNZklcNgwAyiq3Q/emTZs8UQcAAIDPKepIt3RxtJt13QBQNrndgzz22GO6+eab9e677+rIkSOeqAkAAMAnFPWSYRKbqQFAWed2D7J+/Xo9++yz2rVrlzp27Kj+/fvryy+/VFpamifqAwAA8JqiTi+XLm6mRugGgLLJ7dAdEBCgjh07avr06Vq7dq1uvvlmzZo1S23bttXLL7+sPXv2eKJOAACAEsf0cgBAURW6Bzl16pS++OILLVu2TPv27VPLli0VFBSkfv36acaMGcVZIwAAgFcUR+i2Ty9PSGUjNQAoi9zuQb766isNHDhQ7du31+LFi3Xbbbfphx9+0JQpUzRs2DC99dZbev/994tU1NmzZ/Xcc8+pZcuWat68uQYPHqzjx49Lknbs2KFevXqpSZMmio6O1sKFC53OXbp0qWJiYtS4cWN1795d27dvL1ItAAAgb6W5z7barLLarJKKONLN9HIAKNPc7kFefvll1ahRQwsWLNDnn3+ufv36KTIy0nH8yiuvVL9+/YpU1KOPPqqkpCStXr1a33//vUwmk0aOHKn4+HgNHDhQd9xxh7Zs2aKxY8dq3Lhx2rlzp6TMndXHjBmj8ePHa8uWLeratasGDRqk5OTkItUDAAByV5r7bPsot1TENd1MLweAMs3s7gnr16/XwYMHVa1aNUnSr7/+qvLly6tWrVqSpOrVq+uxxx4rdEG7du3Sjh07tGHDBpUrV06SNGbMGJ04cUKrVq1SeHi4YmNjJUmtW7dWly5dNG/ePDVs2FALFy5U586d1bRpU0lSv3799Omnn2rFihXq0aNHoWsCAAA5lfY+O2voLpbp5VynGwDKJLd7kO+++0533HGH/vnnH0nS9u3b1atXL61du7ZYCtq5c6dq166tzz77TDExMWrbtq0mTJigKlWqaO/evapbt67T/WvXru3YvG3fvn35HgcAAMWntPfZxRW6GekGgLLN7ZHuqVOnatq0aWrQoIEk6YEHHlDt2rX1xhtvqH379kUuKD4+Xn/88YcaNGigpUuXKiUlRc8995yef/55Va5cWSEhIU73Dw4OVlJSkiQpMTEx3+OuslgsbtdtP8fVc2025z/5Hc/rWHCQSb1HrNH/Xi76627nbjt8Fe3wLbTDt9AO35K9HcXZnoMHD6pmzZrF9njZlfY+OzU91fF3gwyy2Wyy2WySTZn/zyavY8HmYEnS3lN7i/X7W1r/Dfgr2uFbaIdvKQ3tyK0NrrbH7dD933//6YYbbnC6rW3btnryySfdfahcBQZmTsF68cUXFRQUpHLlyumJJ57QXXfdpe7duyslJcXp/ikpKQoLC5MkhYSE5Ho8IiLCrRri4uIKXb8r5xqNRiUlJikpKUVWi0myBua4T1JSskvHklMztHPnTlmt1kLXnJuivAa+hHb4FtrhW2iHb/FEOzp16qQmTZqoZ8+e6tixo4KDg4v18Ut7n30m7YykzMB96tQpSVJiaqICjAEKDMjZP+d1LC0pTZKUkJxAn50P2uFbaIdvoR2+ozBtcDt016hRQz/++KNT8N64caP+7//+z+0nz03t2rVltVqVnp6uoKDM6Vj2zunqq6/W/Pnzne6/b98+1alTR5JUp04d7d27N8fxdu3auVVDVFSUTCb3NkyxWCyKi4tz+dzQsNNKyTApJMikkOCc34bQdKNLxwwmixo2bOhWrflxtx2+inb4FtrhW2iHb8neDvvXxWHt2rVavny5PvzwQ40ZM0adOnVS9+7d1aRJk2J5/NLeZx9JOCLFSSajSZUrV5YkhaWHKcAYILMpZ/+c17GUwBTpP8lmtNFn54J2+Bba4Vtoh+/IrQ2u9tluh+6BAwdqyJAhuuWWW1SjRg0dOXJEq1ev1oQJE9yvPBdt2rRRzZo19cILL2jcuHFKTU3VW2+9pZtvvlm33367Jk+erNmzZys2Nlbbtm3TF198oWnTpkmSevbsqSFDhqhTp05q2rSp5s2bp1OnTikmJsatGkwmU6F/GFw912Bw/pPf8YLO9cQPblFeA19CO3wL7fAttMO3eKIdlSpVUv/+/dW/f3/99ttv+uqrrzRs2DAZjUb16NFD3bt3d7oCibtKe59t1cXLhRkudMgGg0EyyPF1Vnkds08vT7Ok0Wfng3b4FtrhW2iH7yhMG9zeFaRLly6aOXOmAgICtHv3bgUHB2vWrFnq2LGjuw+Vq4CAAM2ZM0cmk0kdO3ZUx44dVb16db322muKiIjQrFmz9PXXX6tly5YaMWKERowYoVatWknK3Bn1pZde0ujRo9WiRQt99dVXmjlzpsLDw4ulNgAA/FFGRoaOHDmiI0eO6NSpUwoJCdGOHTt0yy23aOnSpYV+3NLeZ9s3UivKJmrSxet0p1pSc10LDgAo3dwe6Zakli1bqmXLlsVdi0O1atX01ltv5XosKipKCxYsyPPcbt26qVu3bp4qDQAAv/Hrr79q+fLlWrlypQwGg7p06aK5c+eqfv36kqTVq1frxRdf1J133lno5yjNfbY9dBflGt3Sxd3LrTarUi2pjpFvAEDZ4HboPnbsmKZPn65//vknx0YgH3/8cbEVBgAAiiY2NlZt27bVyy+/rOjoaAUEBDgdv/rqqxUdHe2l6nxfcY1026/TLWVeNozQDQBli9uhe/jw4Tp58qQ6dOiQo/MGAAC+Y86cObruuuty3L5u3Tq1a9dOl156qcaPH++FyvxDcYVug8GgQFOg0ixpOp92XpVDKxdHeQAAP+F26I6Li9M333xTpI1XAACA5z300EP65ZdfnG47f/68Hn/8cW3fvt1LVfmP4grdUuYUc3voBgCULW6H7vLlyzuuywkAAHzLgQMH1LlzZ1ksFtlsNl199dU57pPb6DdyKq413dLFKeaEbgAoe9wO3YMHD9bw4cM1YMAAxzUr7YrrWt0AAKBwLr/8ci1cuFDnzp3TwIEDNXPmTKfjQUFBqlu3rpeq8y+OkW5j0Ue67aE7ITWhyI8FAPAvbofuESNGSMrc8VTKXKdks9lkMBj0+++/F291AADAbfbR7S+//FI1a9b0cjX+q7inl0uMdANAWeR26P7uu+88UQcAACgmo0eP1ujRozVt2rQ87zNu3LgSrMg/FWfoZno5AJRdbvciNWrUUI0aNRQfH6/du3erSpUqCg4OVo0aNTxRHwAAcJPNZvN2CaVCca7pDjJnjnQnpDG9HADKGrdHuk+dOqUhQ4Zo165dCggI0KJFi9SzZ0/NmjVLTZo08USNAADADS+//LIkRrOLipFuAEBxcLsXee2111S3bl1t2bJFZrNZtWrV0sCBA/X66697oj4AAFBIJ0+e1GuvvSZJ2rp1q9q0aaPbb79d+/fv93Jl/sEToZuN1ACg7HG7F/n55581fPhwhYSEyGAwSMq8Dui+ffuKvTgAAFB4L7/8svbv3y+bzaaxY8fqtttuU4cOHfTKK694uzS/UKzTy9lIDQDKLLenlwcEBCglJUUhISGONWOJiYkKCwsr9uIAAEDhxcXFacWKFTpx4oT27NmjWbNmqXz58mrZsqW3S/MLnrhkWGJ6YpEfCwDgX9zuRaKjo/Xss8/qn3/+kcFg0KlTp/Tyyy+rffv2nqivVBowZq23SwAAlAHJyckKDg7Wxo0bVbduXUVERCglJUVms9ufuZdJTC8HABQHt3uRp59+WqGhobr11lt17tw5tW3bVsnJyXrmmWc8UV+plJyW4e0SAABlQMOGDTV69Gi9//77iomJ0cmTJ/XCCy+oRYsW3i7NLzC9HABQHNz+qDssLEyTJ0/W6dOndejQIVWvXl1Vq1b1RG0AAKAIxo4dq0mTJqlZs2Z6+OGH9dtvvyktLU2vvvqqt0vzC8U60m2+MNLNJcMAoMxxO3Rv2bLF6esDBw7owIEDkqTmzZsXT1UAAKDIqlatqvHjxzu+btSokWbMmOHFivxLcYZu+0h3YlqirDZrsTwmAMA/uB26+/Tpk+M2o9GoSy65RN99912xFAUAAIouMTFR8+fP1z///COr1ep0jGt4F8wTa7ptsikpPUnlAssV+TEBAP7B7dC9Z88ep69Pnz6td999VzVq1Ci2ogAAQNENHz5c27dvV8uWLRUQEODtcvxOcYZuk8Eko8Eoq82q82nnCd0AUIYUefvSyMhIPfvss+rYsaP69+9fHDUBAIBisGnTJi1atEg1a9b0dil+qTg3UjMYDAoyBSk5I5nN1ACgjCmWBUXx8fFKTU0tjocCAADFJCgoSNWqVfN2GX6rOEe6JS4bBgBlldsj3cOHD3f6Oj09Xdu2bVObNm2KrSgAAFB09913n8aPH6+hQ4cqMjLS2+X4HUfoNhZv6GakGwDKliJPLw8KClKfPn109913F0c9AACgmHz22Wc6cuSIPvnkkxzHfv/9dy9U5F+Ke6Sba3UDQNnkduhmt1MAAPxD1suFwX3FuaZb4lrdAFBWuR26p06d6tL9hg4d6nYxAACg+LRo0UJS5t4rBw8e1DXXXKOMjAwFBgZ6uTL/4Kk13Yx0A0DZ4nbo3rt3r1atWqX69evryiuv1NGjR/XLL7/ommuuUVhYmKTMHToBAIB3JSYmatSoUfrqq68UHBysJUuW6IEHHtBHH32kq666ytvl+TxPTS9nIzUAKFvcDt1Go1HDhw/X/fff77ht+fLl+v777/X2228XZ20AAKAIXn/9dSUlJWnlypW66667VLNmTXXo0EFjx47Vhx9+6O3yfB4j3QCA4uB2L7J27VrFxsY63Xb77bdr48aNxVYUAAAouu+//17jx4/XlVdeKYPBoICAAA0bNkxxcXHeLs0vFPuabkI3AJRJbofuyMhIbdmyxem2H3/8UdWrVy+2ogAAQNFZrVbH+m2bzZbjNuSP3csBAMXB7enlDz/8sAYOHKiOHTvq//7v/3Tw4EF9//33mjJliifqAwAAhdSqVSu98sorGjVqlGO/lbffftuxwRryx3W6AQDFwe1epFevXpoxY4ZMJpN+++03hYeHa8GCBWrbtq0n6gMAAIU0fPhw/fXXX2revLkSEhLUpEkTbdmyRc8//7y3S/MLHttIjUuGAUCZ4vZItyS1adNGbdq00enTpxUZGVncNQEAgGIQHByswYMHKy4uTrVq1VKVKlXUpEkTmUzFs0a5tPPUdboZ6QaAssXt0J2enq6pU6dq7ty5slgs+uKLL/TEE09o+vTpqlq1qidqBAAAbvrggw80depUpaamOtZzh4WF6amnnsqxISpyx+7lAIDi4HYvMnXqVP3888965513FBAQoEqVKql69eoaO3asJ+oDAABuWrhwoWbMmKEXX3xR69at065du7R27Vo988wzeuedd/TNN994u0S/4Knp5SkZKY7HBgCUfm6PdH/xxRf65JNPVK1aNRkMBoWGhmrcuHGKiYnxRH0AAMBN8+fPz9E3V6tWTffee68qVqyoOXPmqGPHjl6s0D94aqRbyhztDg8OL5bHBQD4Nrd7kaSkJMc6bvt0teDg4GLb2RMAABTNP//8ow4dOuR67Oabb9Zff/1VwhX5p+Je0200GBUaECpJSkhlMzUAKCvcTsqNGzfW1KlTJclx+ZE5c+YoKiqqeCsDAACFYjAYZDbnPpktMDBQKSkpJVyRfyruS4ZJUrnAcpJY1w0AZYnb08tfeOEF9evXT0uXLlViYqJuu+02JSYm6qOPPvJEfQAAAF6Rbk2XVHwj3VJm6D6eeJzQDQBliNuhu3Llyvrqq6/0ww8/6PDhw6pevbpuvPFGlStXzhP1AQAAN2VkZGjZsmV5HrdYLCVXjB8r7jXd0sWRbq7VDQBlh9uh+/bbb9fnn3+uTp06eaIeAABQRJUrV9bkyZPzPF6pUqUSrMZ/eTJ0M9INAGWH26FbkpKTkxnZBgDAR61Zs8bbJZQKngjd5QPLS2IjNQAoS9wO3S1btlSvXr3Url07Va1a1enY0KFDi60wAAAAb7HZbMW+e7nESDcAlEVuh+5Dhw6pZs2a+vvvv/X33387brfvZA4AAODvLLaL696LdaQ7KHOkm9ANAGWHy6H7wQcf1Icffqg5c+ZIklJSUhQcHOyxwgAAALzFPsotsaYbAFA0Lvci27dvd/q6Xbt2xV4M3BccZNKAMWvVZyTr9wAAKC6eCN1mo1l/nvxTEqEbAMqSQvciNputOOtAESSnZcgmG8EbAIBikjV0m4zFt6bbbMqcZLjv9L5ie0wAgG8rdOhmDbfvSU7NKPhOAACgQFlDt0HF954nyBQkSUrNSC22xwQA+LbiW6QEAABQSmS9XFhxDjQEmS+EbguhGwDKCpc3UsvIyNCyZcscX6enpzt9LUl33HFHMZUFAADgPZ64RreUZaSb0A0AZYbLobty5cqaPHmy4+uIiAinrw0GA6EbAACUCp64Rrd0MXSnWdJks9lYrgcAZYDLoXvNGjbpAgAAZYPHRrovTC+32qxKyUhRSEBIsT4+AMD3sKYbAAAgG0+F7gBjgGNjNi4bBgBlA6EbAAAgG0+FboPB4BjtJnQDQNlA6AYAAMjGU2u6pYvruhPSEor9sQEAvofQDQAAkI1jpNtY/G+VAk2BkhjpBoCygtANAACQjaeml0tSsDlYkpSQykg3AJQFhG4AAIBsPBm6GekGgLKF0A0AAJCNJ9d020e6Cd0AUDYQugEAALJhpBsAUFwI3QAAANmkW9IlSSaj53YvJ3QDQNlA6AYAAMjGkyPd9ut0c8kwACgbCN0AAADZpFsvjHR78jrd7F4OAGUCoRsAACCbErlkGCPdAFAmELoBAACycazp9sBId0hAiCTpbMrZYn9sAIDvIXQDAABk47hkmAc2UgsxE7oBoCwhdAMAAGRjX9Ptienl9pHuc6nnHOEeAFB6EboBAACysVgtkjy7plvKDN4AgNKN0A0AAJCNJ3cvNxqMjuDNFHMAKP0I3QAAANk4di83euatEqEbAMoOQjcAAEA2nty9XGIzNQAoSwjdAAAA2XjyOt0SoRsAyhJCNwAAQDaeXNMtScEBTC8HgLKC0A0AAJCNp9d0M9INAGUHoRsAACAb1nQDAIoLoRsAACAbT6/pZno5AJQdhG4AAIBs2EgNAFBcCN0AAADZODZSM3p2enl8SrxHHh8A4Dt8NnRbLBb16dNHw4YNc9y2Y8cO9erVS02aNFF0dLQWLlzodM7SpUsVExOjxo0bq3v37tq+fXtJlw0AQJlTGvts+0g3u5cDAIrKZ0P31KlTtXXrVsfX8fHxGjhwoO644w5t2bJFY8eO1bhx47Rz505J0qZNmzRmzBiNHz9eW7ZsUdeuXTVo0CAlJyd7qwkAAJQJpbHPtm+k5unp5YnpiY7nAgCUTj4Zujdu3KhVq1bplltucdy2atUqhYeHKzY2VmazWa1bt1aXLl00b948SdLChQvVuXNnNW3aVAEBAerXr58iIiK0YsUKbzUDAIBSr7T22Z4e6Q4yBTkeOz6VKeYAUJr5XOg+deqUXnzxRb355psKCQlx3L53717VrVvX6b61a9fWnj17JEn79u3L9zgAAChepbnPtq/p9tRIt8FgUMXgipKYYg4ApZ3Z2wVkZbVa9eyzz+qBBx5Q/fr1nY4lJiY6deiSFBwcrKSkJJeOu8NisRT6HFfOtdku/t/+J7f7uHvMZitc7Vm50w5fRjt8C+3wLbTDt2Rvh7+0p7T32RmWi7uX27J0uDabTbLJ6bbCHLPZbKoYVFGnk0/rVOIpXVnxSo+0wx/QDt9CO3wL7fAdubXB1fb4VOh+7733FBgYqD59+uQ4FhISooSEBKfbUlJSFBYW5jiekpKS43hERITbdcTFxbl9jqvnGo1GJSUmyWAwKCkpWVaLSbIG5rhfYY4lp2Zo586dslqtha7friivgS+hHb6FdvgW2uFb/K0dpb3PPp98XpKUEJ+gk+knHbcnpiYqwBigwICc/bM7x9It6QqwBEiSdvyxQ+bjhX9L5m8/O3mhHb6FdvgW2uE7CtMGnwrdy5cv1/Hjx9WsWTNJcnTI3377rZ577jn99NNPTvfft2+f6tSpI0mqU6eO9u7dm+N4u3bt3K4jKipKJpN7a7gsFovi4uJcOjc07HTm/9ONCgkyKSQ457ehMMcMJosaNmzoVt3ZudMOX0Y7fAvt8C20w7dkb4f9a19X2vts0x8mKVWKjIhU5fKVHbeHpYcpwBggsyln/+zOsQxrhv7P+n/6M+FPhVcPV+OrG7vVBlfb4Q9oh2+hHb6FdviO3Nrgap/tU6H766+/dvrafumR8ePH68yZM3rjjTc0e/ZsxcbGatu2bfriiy80bdo0SVLPnj01ZMgQderUSU2bNtW8efN06tQpxcTEuF2HyWQq9A+DK+caDBf/b/+T233cPWYwqNh+iIvyGvgS2uFbaIdvoR2+xd/aUdr7bMdGakaTDFk6XIPBIBnkdFthjhkMBkWEZI7sn0s7V6Tvvb/97OSFdvgW2uFbaIfvKEwbfCp05yciIkKzZs3S2LFjNXnyZEVGRmrEiBFq1aqVJKl169Z66aWXNHr0aB07dky1a9fWzJkzFR4e7t3CAQAoY0pDn501dHtKeHC4JDZSA4DSzqdD9/jx452+joqK0oIFC/K8f7du3dStWzdPlwUAALIpbX22p3cvl+TYvfx08mmPPQcAwPt87pJhAAAA3ubp63RLUuXQzLXihG4AKN0I3QAAANmkWzw/0m0P3aeSTnnsOQAA3kfoLqX6jFzj7RIAAPBLNptNFlvmtVc9uaa7UkglSdLJpJMF3BMA4M8I3aVUcmqGt0sAAMAv2aeWS54d6a4Umhm6E9ISlJqR6rHnAQB4F6EbAAAgi6yh25NrussHlnc8Puu6AaD0InQDAABkYd+5XPLsSLfBYFBoQKgkppgDQGlG6AYAAMjCvoma5NnQLUlhAWGSpFPJbKYGAKUVoRsAACAL+/Rys9Esg8Hg0ecKC8wM3Yx0A0DpRegGAADIwj693Gw0e/y57NPLuWwYAJRehG4AAIAs7CPdAcYAjz8Xa7oBoPQjdAMAAGSRdXq5p9mnl7OmGwBKL8/3JigxwUEm9XxulYKDPHd5EwAASjvHSLfJcyPdZqNZi3Yvcmykxkg3AJRejHSXMkkpGUpOyVBwkEl9Rq7xdjkAAPgd++7lnh7pTremq3xgeUnSv/H/evS5AADeQ+guxZJTM7xdAgAAfscb08uT0pNks9k8/nwAgJJH6AYAAMjCG7uXW21WxafGe/z5AAAlj9ANAACQRUnuXm42mhVkCpLEZcMAoLQidAMAAGRhX9PtyY3UsrJvpsYO5gBQOhG6AQAAsijJNd0S1+oGgNKO0A0AAJCFfU13SUwvl6SQgBBJTC8HgNKK0A0AAJBFSY90c61uACjdCN0AAABZeCt0H088XiLPBwAoWYRuAACALEo8dF+4VveJpBMl8nwAgJJF6AYAAMiixHcvD2SkGwBKM0I3AABAFiU90l0uoJwk6UTiCdlsthJ5TgBAySF0AwAAZFHSu5fbLxmWbk3X2ZSzJfKcAICSQ+guxYKDTOozco23ywAAwK/Yp5eX1Ei3yWhyBO9jicdK5DkBACWH0F3KJadmeLsEAAD8in16eUmt6Zacp5gDAEoXQjcAAEAWJb2mW7q4mRoj3QBQ+hC6AQAAsnCMdJfQmm5JKhfISDcAlFaEbgAAgCzsG6mV5Ei3PXRz2TAAKH0I3QAAAFl4Y3q5fU03oRsASh9CNwAAQBb23ctLdCM1+/TyJKaXA0BpQ+gGAADIwqsbqZ1nIzUAKG0I3QAAAFnY13R7YyO1xPREJaUnldjzAgA8j9ANAACQhTdGugNNgQo0BUpiXTcAlDaEbgAAgCwclwwrwTXdEpupAUBpReguI/qMXOPtEgAA8AveGOmWLk4x/+KPL0r0eQEAnkXoLiOSUzO8XQIAAH7Bvnt5SYdu+2ZqZ1POlujzAgA8i9ANAACQhbdHuhPTEkv0eQEAnkXoBgAAyMIbu5dLF0P3+fTzJfq8AADPInQDAABk4bWN1OyhO43QDQClCaG7lAsOMmnAmLXeLgMAAL/hrTXdFYMrSiJ0A0BpQ+guA5LT2EQNAABXOUa6S3h6efnA8pKkpPQkRw0AAP9H6C5hjDoDAODb7IF37T8l22eHmENkNGS+NTuZdLJEnxsA4DmE7hLGqDMAAL7NvpGa1WYt0ec1GAyOdd3HE4+X6HMDADyH0A0AAJCFfaTbaCz5t0mEbgAofQjdAAAAWdg3UjMZTCX+3IRuACh9CN0AAABZOEa6DSX/Nsm+mRqhGwBKD0I3AADABTabzbGmm5FuAEBxIHQDAABcYLFZHH/35kj3icQTJf7cAADPIHQDAABckPX62CYjI90AgKIjdAMAAFyQNXR7Y6Q7a+i22Wwl/vwAgOJH6AYAALjAvnO55N013enWdJ1NOVvizw8AKH6EbgAAgAvsI90mg0kGg6HEn99kNCnEHCKJKeYAUFoQugEAAC6w71xuNpq9VoNjM7UkNlMDgNKA0A0AAHCBfaQ7wBTgtRrCAsMkSUfPH/VaDQCA4kPoBgAAuMC+ptsXRrqZXg4ApQOhGwAA4ALHSLfReyPdXDYMAEoXQjcAAMAFPrGmOyhzpPvY+WNeqwEAUHwI3QAAABekWdIkSYGmQK/VYB/pPpZI6AaA0oDQDQAAcIE9dAeZg7xWgyN0nz8mm83mtToAAMWD0A0AAHBBakaqJCnI5P3QnWpJVXxqvNfqAAAUD0I3AADABamWzNDtzenlZqNZEcERkljXDQClAaEbAADgAl+YXi5J1cpVk8S6bgAoDQjdAAAAF9inl3tzpFuSqoVlhm4uGwYA/o/QDQAAcIFjpNuLa7olqWpYVUlMLweA0oDQXUb1GbnG2yUAAOBzfGFNt3RxevnGgxs1Z8ccr9YCACgaQncZlZya4e0SAADwOY7dy728prt6ueqSpHNp55RuSfdqLQCAoiF0AwAAXGCfXu7tkW779PLzaee9WgcAoOgI3QAAABf4yppu+0ZqiWmJstlsXq0FAFA0hO4yaMCYtd4uAQAAn2Rf0+3t6eX2kW6LzaKUjBSv1gIAKBpCdxmUnMZ6bgAAcuMrlwwLMAUoNCBUElPMAcDfEboBAAAu8JU13ZJUPrC8JCkhLcHLlQAAioLQDQAAcIF9enmwOdjLlUgVgypKInQDgL8jdAMAAFzgUyPdQZkj3UwvBwD/RugGAAC4wFfWdEsXR7rPpZ7zciUAgKLwydC9Z88ePfDAA2rRooWuv/56Pffcczp9+rQkaceOHerVq5eaNGmi6OhoLVy40OncpUuXKiYmRo0bN1b37t21fft2bzQhV+waDgAoTUpjf22fXv7zoZ+9XIlUIaiCJEa6AcDf+VzoTklJ0UMPPaQmTZpo/fr1+vLLL3X27Fm98MILio+P18CBA3XHHXdoy5YtGjt2rMaNG6edO3dKkjZt2qQxY8Zo/Pjx2rJli7p27apBgwYpOTnZy63KxK7hAIDSorT21/bp5QabwcuVXAzdjHQDgH/zudB95MgR1a9fX0OGDFFgYKAiIiJ09913a8uWLVq1apXCw8MVGxsrs9ms1q1bq0uXLpo3b54kaeHChercubOaNm2qgIAA9evXTxEREVqxYoWXWwUAQOlSWvtre+g2GU1eruRi6E61pCoxLdHL1QAACsvnQvdVV12lDz74QCbTxc7um2++0bXXXqu9e/eqbt26TvevXbu29uzZI0nat29fvscBAEDxKK39tX1Nt9lo9nIlUpA5SEGmIEnSf+f/83I1AIDC8n6Pkg+bzaa3335b33//vebOnauPP/5YISEhTvcJDg5WUlKSJCkxMTHf466yWCxu12o/J79zbbacX9v/5Hbf4jxms12szX48t1pdaYc/oB2+hXb4FtrhW7K3wx/b463+Wir+Ptu+pttkMMmWS0drs9kkmzx6zGazyWKxyGazqXxgeaUmp+pw/GFdWfFKl9vhT2iHb6EdvoV2+I7c2uBqe3w2dJ8/f17Dhw/X7t27NXfuXNWrV08hISFKSHC+VmVKSorCwsIkSSEhIUpJSclxPCIiwq3njouLK3TdeZ1rNBqVlJgkg+HiGrGkpGRZLSbJmnOH1OI+lpyaoV27dmXeJzFJKWkW7dy5U1ar1a12+Bva4Vtoh2+hHb7FX9vhzf5aKv4+Ozktc115/Ol4mVJyTjFPTE1UgDFAgQE5++DiOpZuSddvv/2mkydOKlCZt237Y5vKnS7ncjv8Ee3wLbTDt9AO31GYNvhk6P733381YMAA/d///Z8WLVqkyMhISVLdunX1008/Od133759qlOnjiSpTp062rt3b47j7dq1c+v5o6KinKbLucJisSguLi7fc0PDTjt/nW5USJBJIcE5vw3FfSwkVHpl7jEFB5kUGhYqo9mihg0bFqod/oB2+Bba4Vtoh2/J3g771/7A2/21VPx9dsYvmZueVq1SVeEh4TnODUsPU4AxQGZTzj64OI/tMexR5SqVVSmhko4kH5GxolGNGzd2uR3+hHb4FtrhW2iH78itDa722T4XuuPj49W3b1+1atVKY8eOldF4cdl5TEyM3njjDc2ePVuxsbHatm2bvvjiC02bNk2S1LNnTw0ZMkSdOnVS06ZNNW/ePJ06dUoxMTFu1WAymQr9w5DfuQZDzq/tf3K7b3EfS07NkGRTSLBZBoPybWNRXgNfQjt8C+3wLbTDt/hbO3yhv5aKt8/OsGbIYsucKmg2mZ1mp9kZDAbJII8fy7BlyGAwODZTO5p4NM92+tvPTl5oh2+hHb6FdviOwrTB50L3kiVLdOTIEa1cuVJff/2107Ht27dr1qxZGjt2rCZPnqzIyEiNGDFCrVq1kiS1bt1aL730kkaPHq1jx46pdu3amjlzpsLDw73QEgAASq/S2F/bdy6XMtd0+4LygeUlSf8lsJEaAPgrnwvdDzzwgB544IE8j0dFRWnBggV5Hu/WrZu6devmidIAAMAFpbG/zhq6fWH3ckkqH3QhdLN7OQD4LZ+7ZBgAAIA32C8XZjQYc50G7g32ke7TyaeVkpFSwL0BAL6I0A0AAKCLlwvzlVFuSQoyBSnAGCCJKeYA4K8I3QAAALo4vdxX1nNLctpM7XDCYS9XAwAoDEI3AACALk4v96WRbkmqGFxRknQw/qCXKwEAFAahGwAAQFlGuo2+M9ItSRWDLoTuc4RuAPBHhG4AAABdXNPtS9PLpYuh+9C5Q16uBABQGIRuAAAA+fD08iCmlwOAPyN0l3F9Rq7xdgkAAPgEn51efmFN9+GEw8qwZni5GgCAuwjdZVxyKp03AABSlkuGGXxrpDssIExBpiBZbVYdPX/U2+UAANxE6AYAANDF6eW+NtJtMBhUo0INSUwxBwB/ROgGAADQxenlvramW5JqVqgpiR3MAcAfEboBAADku7uXS1LNihdCNyPdAOB3CN0AAADy3Y3UpIsj3Vw2DAD8D6EbAABAvnvJMCnLSDfTywHA7xC6AQAAlGWk2xenl18Y6T587rAsVouXqwEAuIPQXcYFB5m4VjcAAMpyyTAfHOmuGlZVJoNJ6dZ0LhsGAH6G0A2u1Q0AgHx7TbfJaFJ4cLgk6UD8Ae8WAwBwC6G7jAoOMmnAmLXeLgMAAJ/huE63j00vNxvNWrR7kSqFVJIk/XP2H+8WBABwC6G7DEtOY4QbAAA7X55enm5NV2RIpCTp7zN/e7kaAIA7CN0AAADy7enlkhyhm+nlAOBfCN0AAAC6GLrNBt8b6ZYuhm6mlwOAfyF0AwAASErJSJHkuyPdESERkqTTyacVnxLv5WoAAK4idAMAACjLSLcPrumWpEBToMoFlpPEaDcA+BNCNwAAgLKs6fax3cuzigxmijkA+BtCNwAAgLJcMsxHp5dLrOsGAH9E6AYAAJBvXzLMzr6um9ANAP6D0A0AACDfv2SYJEUEE7oBwN8QupGD0ciPBQCg7LFPL/fVS4ZJF6eXH044rNSMVPpsAPAD/KZGDmPmHPJ2CQAAlKgMa4YsNosk3x7pDg0IVYWgCrLarJq+bbrWnFjj7ZIAAAUgdCOHpJQMb5cAAECJsk8tl3x793KDwaBaEbUkScfPH3eMzgMAfBehGwoOMqnnc6vU87lV3i4FAACvyBq6fXkjNbPRLKvNKkk6nXLay9UAAFxB6IakzNFtRrgBAGWVfcQ4wBggg8Hg5WryZ99M7XQyoRsA/AGhGw4hQSY9PHa9z7/ZAACguCWlJ0mSQgJCvFxJwaqGVZVE6AYAf0HohpPkNEa7AQBlT0JagiSpfGB5L1dSsEqhlSRl1pxhpd8GAF9H6AYAAGXe+bTzkqTyQb4fukMDQhUaECpJOpdxzsvVAAAKQugGAABlXkKq/4x0S1Ll0MqSpHPphG4A8HWEbgAAUObZp5eXCyzn5UpcUzmE0A0A/oLQDQAAyjz79HJ/Cd32dd2EbgDwfYRuAABQ5jmml/vBmm6J6eUA4E8I3QAAoMzzp93LJalKaBUZZFCKNUUnEk94uxwAQD4I3QAAoMzzt5HuAFOAIkIiJEm/nfzNy9UAAPJD6AYAAGWev63plqSqoVUlSb+dIHQDgC8jdAMAgDLP36aXS1K1sGqSCN0A4OsI3QAAoMzzt+nlklQlrIqkzOnlVpvVy9UAAPJC6AYAAGWefXq5P410R4ZEyiijzqed18H4g94uBwCQB0I3AAAo8xzTy/1opNtkMCk8MFyStPvEbu8WAwDIE6EbAACUaakZqUqzpEnyr43UJCkyMFKStPs4oRsAfBWhu4QMGLPW2yUAAIBc2Ee5jQajVv650svVuCciMPOyYXHH47xcCQAgL4TuEpKcluHtEgAAQC7s67nDAsKUYfOv/rpSYCVJ0u8nfteppFNergYAkBtCN/LUZ+SaIh0HAMAf+OPO5Xah5lBdU/ka2WTT2gN5z6pbELegBKsCAGRF6EaeklNzftqfNWjndhwAAH9jn17ub+u57dpf0V6S9MM/Pzhum7NjjubsmOP4Ot2aXtJlAQAuIHQjT8FBphyj2QRtAEBp4xjp9qPLhWV14+U3SpJ+PvSzY6p8uiVd6RaCNgD4AkI38kXIBgCUdv54ubCsrgi/QpEhkbLarFr/73pvlwMAyIbQDQAAyjT76LC/jnRLUu3I2pKk1ftXe7kSAEB2hG4AAFCm2aeX++uabkm6uvLVkqS1B9bq9xO/e7kaAEBWhG7kKzjIpJ7PrVLP51Z5uxQAADzC36eXS1Ll0MqO4D1l8xQvVwMAyIrQjQIlpWQoKYW13QCA0snfN1Kzu77m9TIZTNp8eLP+jf/X2+UAAC4gdAMAgDLNsabbj0e6JalicEVFVYuSJK36a5UOnjvo5YoAAJJk9nYBAAAA3pT1Ot32UW9/1apGKxkNRv3y3y/6au9XSslIUfMazXU25axsNpsMBoO3SwSAMoeRbgAAUKbZR7r9eSM1uyBzkKbeNlV1IuvIarNq9V+r9dqPr+l/O/6nbgu66du/vvV2iQBQ5jDSDQAAyrTSsqbbLtAUqJuvvFkNqjRQlXJVtPXIVv169FcdSTiiYd8OU6fanfTCDS8oJCDE26UCQJlA6Pawns+tUnCQydtleJS9jSGBZqWkWTRnTLTTMUla9Pot3ioPAIB82Ue6fzzwo8ICw7xcTfEwGAy6pPwl6n9dfw1sOlCzfpmlFEuKPtr+kVbuW6m4Y3HqfnV33d/4fsc5C+IW6J6oe7xYNQCUTkwv97CklAwll/Kdv+1tTE7LUHJqRo5j7HwOAPBVGdYMpVvTJUlmY+kdiwgwBWhw88G669q7FGAM0KGEQ1r420IlpiU67mN/HQAAxYvQjRxCgsx6eOz6Au8XHGTSgDFrS6AiAAA8w2w065Fmj2hw88EKMgd5uxy3BZoCteT3JQXez2w0a9HuRbq0wqXqWrerAk2B+u/8fxq6YqhjpB8A4BmEbuQqOc210WlX7wcAgK/q36S/+jfp7+0yCs3VEWr7/aqVq6bu9bsryBSkuONxGvzVYJ1LPefJEgGgTCN0o1AKM8LdZ+QaD1QCAADys2j3ohy3VQ2rqh5X91DFoIr67cRvGvzVYCWnJ0uS5uyYozk75pR0mQBQahG6USiFGeHOvt4bAAB4Xl4j4VXCqmjG7TMUHhyuPSf3aMmeJTqReELplnSlW1jfDQDFpfTuGAKvYJ03AAD+wWw0a8fRHepxdQ8t2LVAJ5NO6oHlD+jGy29UZEikt8sDgFKDkW4PKo3h05VQnZyW4fiTl9ymmjP9HADgLblNwfZ39s3T8pNuTVd4cLjuaXCPwoPDdfT8US3Zs0R/n/nbcZ8FcQtynJfbbQCA3BG6Pai0bjJWHO3Kbao5088BAN5SWi+X5Wq7woPDddc1d6lx9cZKs6Rp5f6Vmrhhos6lnsv1MUrr6wUAnkDohktCPDxtPOtjZx3xZvQbAADXuTK6nZeQgBDNuH2GGlZtKElasGuB7lhwh9YdWKeNBzfqYPxBvb/1fc3fOd9xjn3Em5FvAMgba7rhMk+O3Gd97Kwj3ox+AwDgnqKMQpuNZrW9rK0uq3iZfj/5u/af2a/tR7fr0ZWPOt2nQlAF/XP2H6VkpOimxJsY+QaAfDDSjRJXlBFzRr4BAPC8yypepvk95uuOenfomirXqEb5Ggoxh0iSMqwZOp18Wsv/WK5v9n+jTvM6ac7OOZq7c67T9b4Z/QaATIx0o8QVZcQ8r5HvPiPXaM6Y6EI/LgAAcGYymlQrspYuD79cfRr1kSTN3DpTyRnJOpNyRuHB4fpm/zc6kXhCp5NP6+2f39aMrTPU4+oe6tOoT56j3wviFuieqHtKsikA4FWEbnhd9pHv4CCTej63SsFBJsdtBYXq3MK40chEDgAAisui3YtkMpoUHhyuiJAI9WnUR1XDqspitSjueJwOxB/QvtP7NC9unhbsWqBLK1yqAFOALq94uSJCImQ2mhUWEKbkjOQcj02fDaA0I3TD63Ib+U5KyZDNZpPBYMi8TyFGuMfMOaSFbzQsvkIBACjD8hq5NhlNalC1gcbfPF4bDm7Q+PXj9d/5/3Qg/oAmbpiY6zlf7f1KbWq20e11b9fPB3/WiRMn1FD02QBKp1L3seKpU6c0ePBgNWvWTC1bttTYsWOVkcFmXN5k3/k86zW+7aPZvUd+l++5BV0X3Cab0/Gez61Sz+dWScoM7nnJa214fmvGWU8OAMWLPtu3ZN35PK+/53fu3J1z9W/8v7rr2ru0+K7Fan1pa7W/vL2uirhKYQFhCjGHyGTInMX2z9l/ND9uvu5bfJ8+/+NzHU06muvjztkxR3N2zHG6zWaz5btenLXkAHxNqRvpfuKJJ1StWjX9+OOPOnnypAYNGqTZs2froYce8nZpZZp9NDvrqLZ9NDskOPPHMK+AnfUce1iXpEWv3+I4bj83v6Dt9JhZRs6zjpbbb89tBD37aHtJrSNnyh2A0oo+2/dkHc3O6+9S7kE83ZLumKV2efjlalOzjQwy6M2Ob2rWL7NkNpplNpqVnJGs/xL+0+4Tu7Xv9D79E/+P/tE/emrVU7rz6jvV6tJWCjYHK8OaoaPnj+pk0km9/fPb+vPUn4o7FqeUjBQFGAO08LeFujLiSiWnJ+v2urcrIiRCweZgHTp3SPtO71OQKUhB5iAFmgL1+Z7PFdswViajSZ5Enw0gN6UqdB84cECbN2/WunXrFBISopo1a2rw4MF644036MD9RF6brGUN5HkFa/u5IUEmPTx2vWNqekFssuVYQ+7KpcpK6nJmTJMHUBrRZ/u/vKabZw3k2e9jMBgUGhCqWpG1VCuyls6lnNPaA2v15+k/9dPBn/TTwZ9kNpoVZApSqiVVGdbc+9o0a5oOxB/QgfgDkqRNhzc5HV+wO+do9+TNkxVkCtJlFS/TFeFX6IrwK1SjQg1FBEcoLDBMBhlkMBhkNBgVGhCqyqGVVT6wvNP7icS0RB08d1D/xv+rIwlHlGZJk81mU1hgmHYe3anUxFTdXeluVQiuoHKB5Rx/gkxBLr8vAVD6lKrQvXfvXoWHh6tatWqO22rVqqUjR47o3LlzqlChgherQ1FlDeQhBUw7z3rf/EakswZ5+6h79qnwZxJS893UzR7YQwLNSkmz5Ppc9nMKqsV+vn00/9NxNzl9yDBgzFqdSUiVdHGkP7825na7t3Z659N/AFnRZ5du9rBd0NT0CsEVdNOVN+mKgCsUWTlSX+39SglpCY6wHWAMUOXQyrrh8huUkJqgSiGVFBYYpnRLus6knNHZlLM6nXxap5NPKyUjRRarRWnWNAUYA5RmSVNyRrKsNqvj+VItqdp7eq/2nt7rUjuCTEGqFFpJUmbgjk+NL/Ccn77+KcdtZqNZweZgmQwmpVvTFRYQJpPRJJPB5Ph/QmqCKoVWUpA5SMHmYJ1KOqVakbUUbA52/AkxhyjYHCybbEpMS9T5tPNKTL/w/7TEzNfMFKBAU6ACjJn////27j+o6frxA/hzP2MChSa6/KTnpYSnoiEgKaSgIZ2ChpCeJqZX+QNPkgLlNDM1Uc4UP+qlFnbcFel1FGpkIlcqGjpE8Ud28jE/X0Klkx8TFBAGe+/7B58tEDQ2GO9tPB93u9vev/Z8b8Bzr/feG0qZ0jRNguaDChKJpNVBhpbXDYIBd+/exeXLlyGVStvMN15/dFuPuy6RSGAwGNAkNLW66A16SCVSKKQKKGQKKKSKVutI0HyQwniwwjjdyDi/5TItCYKA4nvFqPhvBaRSKQwwAGj+eILxulHLaY+b39Kj9/e4LC0zt9yvltMe9eh9G/ej8v8qTa+lHs3T3nrtLdfeMu3pyHodzWAkCAJKKktw+z+327wm7O7svRS9EDQ4CE5ypw5tsys41KC7trYWKpWq1TTj7bq6un8scOOTpNPpIJOZd/qRXq9vs66rSg59kwCnp6RwUrZ9qG1zngxKqRxOTjLgf38InrSemFnlsubH+PH74YT4HWfhopIhbtuvqH7QgKeUMjg9JYe721OI2/araRuP3l/L7SpkEihkgOv/1jFuz7gN43y5DKZ5Rsb7NE5XyJp/RlZsP4cd778MAKbrchlM8xUyCZyekiJ+x1n0dlWafq6al2meF7MlFzvefxmrdp03rddSy+kt76+9ZVsyLrtq13kAQEOjvk3WR5dtb16r50Wvx87Mu/hs2D//bj1pO12xnqXbB5r3QyKRtPo9X5J0BgCwd3Vgh+/bnAydyfs47e2HrWvvcWi5Hx/8+3yXP07m6OzPFfB3fxhvt/eCxpHYWmcroPh7vkQPuUQOeTsvk2xpngIKqOQqyCGH9JGv6bF0m1bJKgBKifKJ+/Evl39hwDMDEDMmxjRQNn7+WyFTQKVQoVHf+l3zpxVPY8gzQyCXtpNF0JtOJZdJZahvrIdEIsED3QOU1ZahuqEa1fXVeKB7gPqmeuj0zb0oGAQYYIBOr0NDUwNgALS1WtN2naROcHNyg0qhwjPKZ+DxrAf+rPoTjUIjanW1qGuog6vKFXWNdaiqr0KDvsG0blNTE5rQfDDhgf5Bu499TUNNq9t3qu+0u1y3+Eu8u+5SxWIH6CLFYgfoIiViB2im9dUianiUWeu01x0d7WyJwYFaPScnBx9++CE0mr9PMSoqKsL06dNRUFAAV1fXJ66v0+lw9epVa8ckIiL6R15eXlAqlWLHsBp2NhEROYp/6myHeqfbw8MDVVVVqKioQN++fQEAN2/ehFqt/sfyBgC5XA4vLy9IpVJ+7oaIiERhMBggCALkcoeq6DbY2UREZO862tkO9U43AMydOxdqtRobNmzAvXv3sHTpUoSGhmL58uViRyMiIqIW2NlERNQTONygu6KiAhs2bIBGo4FUKsXrr7+O+Ph4u/ncIhERUU/BziYiop7A4QbdRERERERERLaC/8OHiIiIiIiIyEo46CYiIiIiIiKyEg66iYiIiIiIiKyEg24iIiIiIiIiK+GguwtUVlYiJiYGvr6+8Pf3x6ZNm9DU1CR2LItptVqEhIRAo9GIHcUi169fx8KFCzF27FgEBARg5cqV0Gq1Yscy29mzZ/HGG29gzJgxCAgIwMaNG1FfXy92LIvo9XpER0cjMTFR7CgWO3r0KIYPHw5vb2/TJSEhQexYZquqqsLKlSvh7+8PPz8/xMTEoKysTOxYZjly5Eir58Hb2xsjR47EyJEjxY5mlmvXruHNN9+Er68vAgMD8cknn0Cn04kdy+Gxs20LO9v22Htns69th6P0NdD5zuaguwusWLECvXr1wunTp5GRkYGzZ88iLS1N7FgWuXDhAmbPno2SkhKxo1ikvr4e77zzDry9vXHmzBlkZWWhqqoKq1evFjuaWbRaLRYvXow5c+agoKAAmZmZyM/Px+effy52NIvs3r0bBQUFYsfolKtXr2LGjBkoLCw0XbZu3Sp2LLMtX74cdXV1yMnJwYkTJyCTybB27VqxY5ll+vTprZ6HY8eOwc3NDZs2bRI7WocJgoDFixcjNDQU+fn5yMjIwJkzZ/DFF1+IHc3hsbNtBzvbNtl7Z7OvbYcj9DXQNZ3NQXcn/fnnn8jPz0dCQgJUKhUGDhyImJgYpKenix3NbJmZmYiPj0dcXJzYUSxWWlqKYcOGYdmyZVAqlejduzdmz56N8+fPix3NLH369EFeXh5mzpwJiUSCqqoqNDQ0oE+fPmJHM9vZs2dx/PhxTJkyRewonXL16lW7PDLb0m+//YbLly9jy5YtePrpp+Hi4oKNGzciPj5e7GgWMxgMSEhIQFBQEGbMmCF2nA6rrq5GeXk5BEGA8T93SqVSqFQqkZM5Nna2bWFn2x5H6Gz2tW2y174GuqazOejupBs3bsDNzQ39+/c3TRsyZAhKS0tx//59EZOZLzAwEDk5OZg6darYUSz2wgsvIDU1FTKZzDQtOzsbI0aMEDGVZVxcXAAAEydORHh4ONzd3TFz5kyRU5mnsrISa9aswbZt2+x6MCEIAq5du4aTJ08iODgYEyZMwNq1a1FdXS12NLNcuXIFQ4cOxbfffouQkBAEBgYiOTkZ7u7uYkez2OHDh/HHH3/Y3WmQvXv3xoIFC5CcnAwvLy9MnDgRgwcPxoIFC8SO5tDY2baFnW1bHKGz2de2y177Guiazuagu5Nqa2vb/GEy3q6rqxMjksXc3d0hl8vFjtFlDAYDUlJScOLECaxZs0bsOBY7fvw4cnNzIZVKERsbK3acDhMEAQkJCVi4cCGGDRsmdpxO0Wq1GD58OEJDQ3H06FEcPHgQxcXFdvcZserqahQVFaG4uBiZmZk4dOgQ7t69i1WrVokdzSKCIGDPnj1YsmSJ6QWvvRAEAU5OTli7di0uXbqErKws3Lx5Ezt37hQ7mkNjZ9sudra4HKWz2de2yZ77Guiazuagu5N69eqFhw8ftppmvO3s7CxGJAJQU1OD2NhY/PDDD/j666/h6ekpdiSLOTk5oX///khISMDp06ft5mjtvn37oFQqER0dLXaUTuvbty/S09MRFRUFlUqFAQMGICEhAbm5uaipqRE7XocplUoAwJo1a+Di4oK+fftixYoVOHXqFGpra0VOZz6NRoOysjJERUWJHcVsOTk5yM7Oxty5c6FUKuHh4YFly5bhwIEDYkdzaOxs28TOFp+jdDb72jbZc18DXdPZHHR3koeHB6qqqlBRUWGadvPmTajVari6uoqYrOcqKSlBZGQkampqkJGRYZflffHiRbz22mutvhVRp9NBoVDYzSlfhw8fRn5+Pnx9feHr64usrCxkZWXB19dX7Ghmu379Oj799FPT53iA5udDKpWaitEeDB06FIIgoLGx0TRNEAQAaLVv9iI7OxshISHo1auX2FHM9tdff7X51lO5XA6FQiFSop6BnW172Nm2wVE6m31tm+y5r4Gu6WwOujtp8ODB8PHxQVJSEmpqanDr1i189tlndnskx95VV1fjrbfewpgxY7B//367/BITAPD09ER9fT22bdsGnU6HO3fuIDk5GVFRUXZTGseOHcPFixdRUFCAgoIChIWFISwszC6/EdXNzQ3p6elITU1FU1MTSktLsXXrVkRERNjN8wEA48ePx8CBA7F69WrU1tZCq9UiJSUFr776ql2e7nXhwgX4+fmJHcMigYGBKC8vx969e6HX63Hr1i3s2bMH4eHhYkdzaOxs28LOth2O0tnsa9tkz30NdE1nc9DdBXbu3ImmpiZMnjwZs2bNwiuvvIKYmBixY/VI33//PUpLS/HTTz/Bx8en1f8FtCfOzs5ITU3FjRs3EBAQgOjoaIwfP97u/o2Ko1Cr1di3bx9+/vlnjB07FpGRkfDy8sJHH30kdjSzKBQKfPXVV5DJZAgNDUVoaCjUajWSkpLEjmaR27dvo1+/fmLHsMjQoUOxb98+/PLLL/D398f8+fMxadIku/4manvBzrYd7Gzqauxr22TPfQ10TWdLDPZ4jgIRERERERGRHeA73URERERERERWwkE3ERERERERkZVw0E1ERERERERkJRx0ExEREREREVkJB91EREREREREVsJBNxEREREREZGVcNBNREREREREZCUcdBMREXWSVqtFSEgINBpNh9fJzs5GWFgYXnrpJYSEhCAjI8OKCYmIiAgQp7Pl5oYkop5h0qRJKC8vh1ze/GfCYDDAxcUF4eHhSEhIgFTa+phdaWkppk2bhh9//BEDBgwQIzKRKC5cuIDExESUlJR0eJ1z584hMTERO3bswIQJE6DRaPDuu+/ixRdfxKhRo6yYlogcETubqGPE6my+001Ej7V+/XoUFhaisLAQly5dwv79+3Ho0CHs3r27zbIDBgxAYWEhy5t6lMzMTMTHxyMuLq7NvLy8PERFRcHX1xfTpk3DkSNHTPPS0tIwf/58TJw4ERKJBC+//DK+++47DBo0qDvjE5EDYWcTPZmYnc1BNxF1mKenJ/z8/PD7778jOjoaiYmJCA4ORlBQEIqKiuDp6Ynbt28DAG7duoUlS5bAx8cH48aNw8cffwydTgcAKCkpwZIlS+Dv74/g4GCkpKSY5hHZk8DAQOTk5GDq1Kmtpl+/fh1Lly7FokWLoNFosHHjRiQlJeH06dMAgCtXrsDNzQ2LFi2Cv78/ZsyYgZKSEri5uYmwF0TkiNjZRK2J2dkcdBNRhzQ2NkKj0eDcuXMICAgA0HxU8ODBgzhy5AicnZ1NyzY1NeHtt9+Gu7s7cnNzkZWVhUuXLmHXrl2oq6vDggUL4OHhgdzcXHzzzTfIy8vDrl27xNo1Iou5u7ubTuds6eDBg5g8eTKmTJkCmUyGMWPGYNasWUhPTwcAVFdXY//+/Vi6dCl+/fVXLFu2DHFxcbh8+XJ37wIROSB2NlFbYnY2P9NNRI+1fv16JCUlmW6r1WosXLgQ8+bNw/HjxzFhwgT0798fAHD//n3TchcvXsSdO3ewevVqqFQqODs7Y/fu3RAEASdPnoROp8P7778PiUSC5557Du+99x5iY2PxwQcfdPs+ElnDnTt3cO7cOfj6+pqm6fV606loSqUSkZGR8Pb2BgBMmTIF48aNQ3Z2NkaPHi1KZiKyb+xsIst0R2dz0E1Ej7Vu3TrMnDnzsfP79evX7vTy8nL07t0bKpXKNO35558H0Pztj1qtFn5+fqZ5BoMBjY2NqKysxLPPPttF6YnEo1arERERgQ0bNpimlZWVwWAwAACGDBnS5vRMvV5vmk9EZC52NpFluqOzeXo5EVlMIpG0O12tVuPevXt4+PChaVpBQQHS0tKgVqsxaNAgFBQUmC6nTp1CVlYW+vTp013RiawqKioKWVlZOHPmDARBQHFxMebNm4cvv/wSADBnzhwcOHAAeXl5EAQB2dnZ0Gg0CAsLEzk5ETkqdjZR+7qjsznoJqIuN2rUKAwePBjJycl4+PAhKioqsHnzZmi1WgQHB6O2thapqanQ6XS4f/8+Vq1ahbi4uMe+ICCyN6NHj8b27duxfft2+Pn5Yd68eZg0aZLpdMzIyEisW7cOmzdvho+PD3bt2oWUlBSMGDFC5ORE1NOws6mn647O5unlRNTlFAoF9u7di6SkJAQFBUEulyM8PByxsbGQy+VIS0vDli1bkJqaCkEQ4O/vjz179ogdm6hTioqKWt0OCgpCUFDQY5ePiIhARESElVMRET0ZO5t6ou7ubImBHyAjIiIiIiIisgqeXk5ERERERERkJRx0ExEREREREVkJB91EREREREREVsJBNxEREREREZGVcNBNREREREREZCUcdBMRERERERFZCQfdRERERERERFbCQTcRERERERGRlXDQTURERERERGQlHHQTERERERERWQkH3URERERERERWwkE3ERERERERkZX8P7TtnW1/OhGNAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -1876,12 +1892,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1800x600 with 3 Axes>"
       ]
@@ -1929,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +1958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1951,13 +1967,13 @@
        "Text(0.5, 0, 'Number of Bedrooms')"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1989,7 +2005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2006,7 +2022,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2026,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2075,124 +2091,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>19150</th>\n",
-       "      <td>1073100065</td>\n",
-       "      <td>2015-02-17</td>\n",
-       "      <td>348125.0</td>\n",
+       "      <th>4614</th>\n",
+       "      <td>2114700115</td>\n",
+       "      <td>2015-04-07</td>\n",
+       "      <td>291700.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1400</td>\n",
-       "      <td>8451</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1970</td>\n",
+       "      <td>4120</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1400</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1953</td>\n",
+       "      <td>1230</td>\n",
+       "      <td>740.0</td>\n",
+       "      <td>1927</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1590</td>\n",
-       "      <td>8433</td>\n",
-       "      <td>71</td>\n",
+       "      <td>1470</td>\n",
+       "      <td>4080</td>\n",
+       "      <td>97</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11251.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>8060.0</td>\n",
+       "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3012</th>\n",
-       "      <td>16000015</td>\n",
-       "      <td>2015-04-17</td>\n",
-       "      <td>219950.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>6601</td>\n",
+       "      <th>1011</th>\n",
+       "      <td>865100055</td>\n",
+       "      <td>2014-06-12</td>\n",
+       "      <td>900000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2460</td>\n",
+       "      <td>44431</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1070</td>\n",
+       "      <td>2460</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1985</td>\n",
+       "      <td>1957</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1030</td>\n",
-       "      <td>6614</td>\n",
-       "      <td>39</td>\n",
+       "      <td>2830</td>\n",
+       "      <td>44431</td>\n",
+       "      <td>67</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8741.0</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>49351.0</td>\n",
+       "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14615</th>\n",
-       "      <td>7211400760</td>\n",
-       "      <td>2014-05-28</td>\n",
-       "      <td>277000.0</td>\n",
+       "      <th>7116</th>\n",
+       "      <td>8563020380</td>\n",
+       "      <td>2014-05-20</td>\n",
+       "      <td>519900.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1450</td>\n",
-       "      <td>6250</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>9350</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>990</td>\n",
-       "      <td>460.0</td>\n",
-       "      <td>1964</td>\n",
+       "      <td>1820</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>60</td>\n",
+       "      <td>1967</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>9150.0</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>2260</td>\n",
+       "      <td>9299</td>\n",
+       "      <td>57</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12990.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15714</th>\n",
-       "      <td>2450000275</td>\n",
-       "      <td>2014-07-16</td>\n",
-       "      <td>595000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>1350</td>\n",
-       "      <td>8113</td>\n",
+       "      <th>4142</th>\n",
+       "      <td>461002551</td>\n",
+       "      <td>2014-10-04</td>\n",
+       "      <td>330600.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>580</td>\n",
+       "      <td>1799</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1350</td>\n",
+       "      <td>580</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1959</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>8113</td>\n",
-       "      <td>65</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10813.0</td>\n",
+       "      <td>1908</td>\n",
+       "      <td>2005.0</td>\n",
+       "      <td>1260</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>116</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2959.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3091</th>\n",
-       "      <td>9264950940</td>\n",
-       "      <td>2014-08-05</td>\n",
-       "      <td>348000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2060</td>\n",
-       "      <td>7458</td>\n",
+       "      <th>20907</th>\n",
+       "      <td>7852070210</td>\n",
+       "      <td>2014-05-27</td>\n",
+       "      <td>1150000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>5940</td>\n",
+       "      <td>11533</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2060</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1989</td>\n",
+       "      <td>4950</td>\n",
+       "      <td>990.0</td>\n",
+       "      <td>2004</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2480</td>\n",
-       "      <td>7743</td>\n",
-       "      <td>35</td>\n",
+       "      <td>4240</td>\n",
+       "      <td>12813</td>\n",
+       "      <td>20</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11578.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>23413.0</td>\n",
+       "      <td>1M-2M</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2200,38 +2216,38 @@
        "</div>"
       ],
       "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "19150  1073100065 2015-02-17  348125.0         3        1.0         1400   \n",
-       "3012     16000015 2015-04-17  219950.0         3        1.5         1070   \n",
-       "14615  7211400760 2014-05-28  277000.0         4        1.0         1450   \n",
-       "15714  2450000275 2014-07-16  595000.0         4        1.5         1350   \n",
-       "3091   9264950940 2014-08-05  348000.0         3        2.5         2060   \n",
+       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
+       "4614   2114700115 2015-04-07   291700.0         3       2.50         1970   \n",
+       "1011    865100055 2014-06-12   900000.0         4       2.25         2460   \n",
+       "7116   8563020380 2014-05-20   519900.0         4       2.00         1820   \n",
+       "4142    461002551 2014-10-04   330600.0         1       1.00          580   \n",
+       "20907  7852070210 2014-05-27  1150000.0         4       3.00         5940   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "19150      8451     1.5           0        3.0  ...        1400   \n",
-       "3012       6601     1.0           0        3.0  ...        1070   \n",
-       "14615      6250     1.0           0        3.0  ...         990   \n",
-       "15714      8113     1.0           0        4.0  ...        1350   \n",
-       "3091       7458     2.0           0        3.0  ...        2060   \n",
+       "4614       4120     1.5           0        3.0  ...        1230   \n",
+       "1011      44431     1.0           0        4.0  ...        2460   \n",
+       "7116       9350     1.0           0        4.0  ...        1820   \n",
+       "4142       1799     1.0           0        3.0  ...         580   \n",
+       "20907     11533     2.0           0        3.0  ...        4950   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "19150            0.0      1953           0.0           1590        8433   \n",
-       "3012             0.0      1985           0.0           1030        6614   \n",
-       "14615          460.0      1964           0.0           1440        4000   \n",
-       "15714            0.0      1959           0.0           1930        8113   \n",
-       "3091             0.0      1989           0.0           2480        7743   \n",
+       "4614           740.0      1927           0.0           1470        4080   \n",
+       "1011             0.0      1957           0.0           2830       44431   \n",
+       "7116             0.0      1967           0.0           2260        9299   \n",
+       "4142             0.0      1908        2005.0           1260        4000   \n",
+       "20907          990.0      2004           0.0           4240       12813   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  price_range  \n",
-       "19150         71             0.0     11251.0    300K-600K  \n",
-       "3012          39             0.0      8741.0    100K-300K  \n",
-       "14615         60             0.0      9150.0    100K-300K  \n",
-       "15714         65             0.0     10813.0    300K-600K  \n",
-       "3091          35             0.0     11578.0    300K-600K  \n",
+       "4614          97             0.0      8060.0    100K-300K  \n",
+       "1011          67             0.0     49351.0      600K-1M  \n",
+       "7116          57             0.0     12990.0    300K-600K  \n",
+       "4142         116            19.0      2959.0    300K-600K  \n",
+       "20907         20             0.0     23413.0        1M-2M  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2242,12 +2258,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  grouped_vals = vals.groupby(grouper)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "KeyboardInterrupt\n",
+      "\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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t27dXwYIFzWejvv/+e0l66eyFL1OwYEGFhoa+dJa82NhYBQUFqVChQpKeTz8fExOjvXv3mteJjo7WDz/8kOB5rq6uCg4OTrDsj7MAxj+uXbu2KlasaB56lpjaXV1dVaxYMV27dk0lS5Y0//fGG29ozpw5SR7++GeJ7Y8333xT+/fv15MnT8zrxMbGavv27SpZsqR5n/58ljAx+/3mm2/q9OnT5iAnPT+Lc/r0afPj+LNloaGhCd6Hhw8fatasWS8NxH/03XffJXi8fft2OTs7q3Tp0uZlLVq00C+//KIVK1aoUqVKCYZBWkL58uUVGBiYIJw8ePBAXbp00YEDB1ShQgU9e/ZMhmEkeA9++eUXzZ07N8FQxMQaPny4HB0dNW7cOPOQwhMnTqhixYqqVauWOXidO3dODx8+TPTnTJLKlSsnW1tbffPNNwmW79mzx/zv5P75BpA6ceYLABLJxsZGffv2lZ+fn/r166fGjRsrNDRUAQEBypgxoz788ENJz88CSM8PmjNmzKh8+fLJ1dVVCxYskJ2dnezs7LR7927z9UyJne2wcuXKatiwoaZPn65Lly6pTp06cnd3V3BwsNatW6fg4GDNnDlT0vNv+n18fDRs2DCFhIQoZ86cWr16tW7fvp3gm/waNWpo4cKFWrhwoUqXLq19+/a9MP19qVKl9PXXX6t48eLKmjWrTp48qUWLFslkMv1j7X379pWvr6/5/YqNjdVnn32mwMBAde/ePVH7/VcS2x89e/bU999/r/bt28vX11f29vZavXq1goKCtGTJEvP2MmTIoJMnT+rYsWMqX758ova7ffv2WrNmjTp37qwePXpIej7Ne3R0tPmaqcKFC6tx48bm67JKlCih69eva8aMGcqZM6fy5s37t/u5Z88eeXl5qVKlSjp48KDWr1+vTz75JMHwwHLlyilfvnw6evSoZsyY8T+9r/9Gx44dtWXLFnXp0kVdu3aVvb295s+fr6xZs6pRo0ZKnz693nzzTXXv3l3du3dXgQIFdObMGc2ePVtVq1Y1D6VMipw5c6pz586aN2+eVqxYoS5duqhUqVLauXOnPv/8cxUoUEAXL17U/PnzE/Wz+ke5cuVS8+bNNX36dEVHR6tIkSL65ptvzNewxQf1xP58nz59Wu7u7ha7KTWA1xfhCwCSoFmzZkqXLp0WLlyoHj16yNXVVVWrVlXfvn3N1+a88cYbatiwodasWaMffvhB27Zt07x58zR58mR98sknSpcunYoWLarVq1fro48+0vHjxxPce+vvTJkyRRUqVNBXX31lvi+Ru7u7KleurIkTJyaYHnz27NmaNm2a5syZo8jISDVo0EB169bVmTNnzOt07dpVDx8+1NKlSxUdHa3q1atr/Pjx6tatm3mdSZMmaezYsRo7dqwkKW/evBo9erS++uorHT9+/G/rrVKlipYuXaqAgAD17t1b9vb2Kl68uJYtW/ZKbtKb2P5Yu3ateSp6k8mkUqVKaeXKleazUpL08ccfa968efroo4+0Y8eORO13hgwZtHLlSo0fP14DBw5UunTp1Lp1azk7Oye4/m3ixIlauHChOSR7eHiofv36+vTTT184U/pnQ4cO1fbt27V8+XJlzpxZQ4YMMd9L7o+qV6+uhw8f/u3kHcklW7ZsWrt2raZMmaLBgwfLwcFBFStW1IwZM5QxY0ZJ0qJFizRr1iwtXLhQDx48kJeXlz788ENzaP03fH19tWXLFs2bN0+NGzfW4MGDFR0drZkzZyoqKko5c+ZUt27ddOXKFe3bt++FIbZ/Z/jw4XJxcdFnn32mJ0+e6O2331a3bt00d+5cc98m9ue7VatWevfddzVp0qR/va8AUgeT8W+u9AYApEiDBw/W0aNHtW/fPmuXkioEBgYqLCxMPj4+5mUxMTGqXr26GjRoID8/P4vUYRiGGjRooCpVqmjIkCEWec3ULCwsTN9//72qVq2a4JpAf39/bd68mSGFAP41znwBAPAv3b59W3369FGPHj1UoUIFhYeHa/369Xr8+LHee++9ZH/9J0+eaPny5Tp79qyCgoLUrl27ZH/NtMDZ2Vnjx49X0aJF1aFDB7m4uJhvUt61a1drlwcgBSN8AQDwL9WrV09hYWFau3atli5dKnt7e5UuXVqrV69WgQIFkv31nZyctG7dOsXFxWnChAkJhp3i33N0dNTy5cs1c+ZMDR48WOHh4cqdO7cGDRrEvbsA/E8YdggAAAAAFsBU8wAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABbAVPP/gwcPHou5IgEAAIC0y2SSPDzSJ2pdwtf/wDBE+AIAAACQKAw7BAAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAXYWbsAAAAAAEljY2OSjY3J2mWkanFxhuLijFe6TcIXAAAAkILY2JiUKZOLbG0ZxJacYmPjFBb27JUGMKuGr2+++UY9e/ZMsKxOnTqaPXu2Lly4oJEjR+qXX35RwYIFNXr0aJUoUcK83rZt2zRz5kzdv39fVapU0dixY+Xu7i5JMgxD06ZN08aNGxUXF6cWLVqof//+srF5/gMaGhqqESNG6ODBg3Jzc9Mnn3yiJk2aWG7HAQAAgH/JxsYkW1sbDZ2yRteD7lq7nFQpXy4vjR/QRjY2ptQTvq5cuaIaNWpo7Nix5mWOjo569uyZfH191ahRI02aNEmff/65unbtqm+++UYuLi46c+aMhg4dqtGjR6tIkSIaP368/Pz8tHDhQknSsmXLtG3bNgUEBCgmJkYDBgyQh4eHOnfuLEny8/NTRESE1q9fr8DAQA0bNkz58uVTqVKlrPI+AAAAAEl1PeiuLl69Ze0ykARWPVd59epVFSpUSJkzZzb/lyFDBu3YsUOOjo4aOHCgChQooKFDhypdunTatWuXJGn16tWqV6+emjZtqiJFimjy5Mk6cOCAgoKCJEkrV65U7969Vb58eb311lvq37+/1qxZI0m6efOm9u/fr3HjxqlQoUJq2bKlGjdurLVr11rtfQAAAACQ+lk9fOXNm/eF5YGBgSpXrpxMpucXEZpMJpUtW1anT582t5cvX968frZs2ZQ9e3YFBgbq7t27unPnjt58801ze7ly5XTr1i3du3dPgYGBypYtm3LmzJmg/dSpU8mzkwAAAAAgKw47NAxD169f18GDB7Vw4ULFxsaqbt266t27t+7fv6+CBQsmWN/Dw0OXL1+WJN27d09ZsmR5oT04OFj379+XpATtnp6ekmRuf9lz795N+nhZExPMAAAAAKnaPx3zJyUTWC183b59W+Hh4XJwcNDMmTP122+/ady4cYqIiDAv/yMHBwdFRUVJkiIiIv6yPSIiwvz4j22SFBUV9Y/bTgoPj/RJfg4AAACAlMHNLd0r3Z7VwleOHDl05MgRZcyYUSaTSUWLFlVcXJwGDBigChUqvBCGoqKi5OTkJOn5pBwva3d2dk4QtBwdHc3/liRnZ+e/fG78tpPiwYPHMl7t1P8AAADA37K1tXnloQAvFxr6VLGxcX+7jsmU+JMyVp3tMFOmTAkeFyhQQJGRkcqcObNCQkIStIWEhJiHC3p5eb20PXPmzPLy8pIk3b9/33xdV/xQxPj2v3puUhmGCF8AAABAKvYqj/etNuHGDz/8oIoVKyo8PNy87Oeff1amTJnME2AY/91TwzB08uRJlS5dWpJUunRpnThxwvy8O3fu6M6dOypdurS8vLyUPXv2BO0nTpxQ9uzZlSVLFpUpU0a3bt1ScHBwgvYyZcok8x4DAAAASMusFr68vb3l6OioYcOG6dq1azpw4IAmT56sLl26qG7dunr06JHGjx+vK1euaPz48QoPD1e9evUkSR988IG2bt2qDRs26OLFixo4cKCqV6+uXLlymdunTp2qI0eO6MiRI5o2bZrat28vScqVK5eqVKmiAQMG6OLFi9qwYYO2bdumNm3aWOutAAAAAJAGWG3Yoaurq5YuXaoJEyaoefPmSpcund5//3116dJFJpNJCxcu1MiRI/XFF1+ocOHCWrRokVxcXCQ9D25jxozR7Nmz9fvvv6ty5coJbtTcuXNnPXjwQD179pStra1atGihjh07mtsnT56soUOH6r333lPmzJk1YcIEbrAMAAAAIFmZDIOrlv6tkBAm3AAAAIBl2dk9n3Cjde/punj1lrXLSZWKFMihtbP7KjT0qWJi/nnCDU/PxE24YdWbLAMAAABAWkH4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAW8NuHL19dXgwcPNj++cOGCWrZsqdKlS6t58+Y6d+5cgvW3bdumWrVqqXTp0urRo4cePnxobjMMQ1OnTtVbb72lChUqaPLkyYqLizO3h4aGqlevXvL29lbNmjW1devW5N9BAAAAAGnaaxG+tm/frgMHDpgfP3v2TL6+vipfvrw2b94sb29vde3aVc+ePZMknTlzRkOHDlXPnj21fv16PXr0SH5+fubnL1u2TNu2bVNAQIBmz56tr7/+WsuWLTO3+/n56fHjx1q/fr26deumYcOG6cyZM5bbYQAAAABpjtXDV1hYmCZPnqySJUual+3YsUOOjo4aOHCgChQooKFDhypdunTatWuXJGn16tWqV6+emjZtqiJFimjy5Mk6cOCAgoKCJEkrV65U7969Vb58eb311lvq37+/1qxZI0m6efOm9u/fr3HjxqlQoUJq2bKlGjdurLVr11p+5wEAAACkGVYPX/7+/mrSpIkKFixoXhYYGKhy5crJZDJJkkwmk8qWLavTp0+b28uXL29eP1u2bMqePbsCAwN19+5d3blzR2+++aa5vVy5crp165bu3bunwMBAZcuWTTlz5kzQfurUqWTeUwAAAABpmZ01X/ynn37S8ePH9fXXX2vUqFHm5ffv308QxiTJw8NDly9fliTdu3dPWbJkeaE9ODhY9+/fl6QE7Z6enpJkbn/Zc+/evZvk+v+bDQEAAACkUv90zJ+UTGC18BUZGamRI0dqxIgRcnJyStAWHh4uBweHBMscHBwUFRUlSYqIiPjL9oiICPPjP7ZJUlRU1D9uOyk8PNIn+TkAAAAAUgY3t3SvdHtWC18BAQEqUaKEqlat+kKbo6PjC2EoKirKHNL+qt3Z2TlB0HJ0dDT/W5KcnZ3/cdtJ8eDBYxlGkp8GAAAA/Gu2tjavPBTg5UJDnyo2Nu5v1zGZEn9Sxmrha/v27QoJCZG3t7ek/w9Iu3fvVsOGDRUSEpJg/ZCQEPNwQS8vr5e2Z86cWV5eXpKeD12Mv64rfihifPtfPTepDEOELwAAACAVe5XH+1abcGPVqlX6+uuvtWXLFm3ZskU1a9ZUzZo1tWXLFpUuXVqnTp2S8d89NQxDJ0+eVOnSpSVJpUuX1okTJ8zbunPnju7cuaPSpUvLy8tL2bNnT9B+4sQJZc+eXVmyZFGZMmV069YtBQcHJ2gvU6aMZXYcAAAAQJpktTNfOXLkSPA4Xbrnp07z5MkjDw8PTZs2TePHj9f777+vdevWKTw8XPXq1ZMkffDBB2rXrp3KlCmjkiVLavz48apevbpy5cplbp86daqyZs0qSZo2bZo6deokScqVK5eqVKmiAQMGaOjQoTp79qy2bdum1atXW2rXAQAAAKRBVp3t8K+4urpq4cKFGjlypL744gsVLlxYixYtkouLiyTJ29tbY8aM0ezZs/X777+rcuXKGjt2rPn5nTt31oMHD9SzZ0/Z2tqqRYsW6tixo7l98uTJGjp0qN577z1lzpxZEyZMUKlSpSy9mwAAAADSEJNhcNXSvxUSwoQbAAAAsCw7u+cTbrTuPV0Xr96ydjmpUpECObR2dl+Fhj5VTMw/T7jh6Zm4CTesfpNlAAAAAEgLCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFjAvwpfsbGx+u6777R8+XI9evRIgYGBevz48auuDQAAAABSDbukPuHOnTvq3LmzwsLC9Pvvv+udd97RkiVLdOrUKS1dulSFCxdOjjoBAAAAIEVL8pmvMWPGqFy5cvrhhx/k4OAgSZo+fboqVaqkcePGvfICAQAAACA1SHL4On78uDp16iRbW1vzMnt7e3Xv3l3nzp17pcUBAAAAQGqR5PDl5OSkBw8evLD8+vXrcnV1fSVFAQAAAEBqk+Tw9f7772vEiBH67rvvJD0PXZs2bdLw4cPVokWLV10fAAAAAKQKSZ5wo0ePHsqQIYNGjRql8PBw+fr6ysPDQx07dlTnzp2To0YAAAAASPGSHL4kqV27dmrXrp2ePXum2NhYpU+f/lXXBQAAAACpSpLDV0BAwEuXm0wm2dvbK0uWLKpatao8PDz+5+IAAAAAILVIcvi6fv26duzYoaxZs6pEiRIyDEM///yzbt++rTJlyujx48caN26clixZojJlyiRDyQAAAACQ8vyrYYctWrTQqFGjzNPNx8XFafz48Xr27JkmTpyoBQsWaNKkSVq3bt0rLRYAAAAAUqokz3a4b9++F+7zZWNjo7Zt22rXrl2SpAYNGujixYuvrkoAAAAASOGSHL48PT11/PjxF5afOHFCmTJlkiSFhIRwzy8AAAAA+IMkDzvs1auXhg4dqhMnTqhkyZIyDEPnz5/X9u3bNWLECF2/fl2DBg1SgwYNkqNeAAAAAEiRkhy+GjdurOzZs+vzzz/XunXrZGtrq4IFC2rlypUqU6aMzpw5o7Zt26pNmzbJUS8AAAAApEj/asKN8uXLq3z58i9tK1WqlEqVKvU/FQUAAAAAqU2Sw1d4eLjWr1+vK1euKDY21rw8KipKFy5c0M6dO19pgQAAAEg+NjYm2diYrF1GqhUXZyguzrB2GXhNJDl8DRs2TD/99JPefvtt7dq1S/Xq1dOvv/6qs2fPqmfPnslRIwAAAJKBjY1JmTK5yNY2yXOwIZFiY+MUFvaMAAZJ/yJ8ff/995o1a5YqVaqky5cvq2PHjipRooQmTZqky5cvJ0eNAAAASAY2NibZ2tpo0Nz1unb7vrXLSXXyZ88s/x6tZGNjInxB0r8IX5GRkcqbN68k6Y033tC5c+dUokQJtWrVSm3btn3V9QEAACCZXbt9Xz/fuG3tMoBUL8nnmAsUKKAff/xR0vPwdeLECUnS48ePFRkZ+WqrAwAAAIBUIslnvnr27KlPPvlEcXFxatKkiRo0aKCPP/5Yly5dUpUqVZKjRgAAAABI8ZIcvt555x3t3LlTcXFxypYtm9auXautW7eqbNmyat++fXLUCAAAAAApXpKHHfr5+cnNzU158uSRJBUpUkSDBg1Sq1atNHDgwFdeIAAAAACkBok683Xq1Cn9+uuvkqQtW7aoePHicnV1TbDOtWvXdPDgwVdfIQAAAACkAokKX87OzpozZ44Mw5BhGFqyZIlsbP7/pJnJZJKLi4v69++fbIUCAAAAQEqWqPBVpEgRffvtt5Kkdu3aKSAgQBkzZkzWwgAAAAAgNUnyhBurVq2SJB06dEhXr15VXFyc8uXLp0qVKsne3v6VFwgAAAAAqUGSw9fdu3fVrVs3Xb9+Xfny5VNsbKx+/fVXZc+eXcuWLZOXl1dy1AkAAAAAKVqSZzscNWqUPDw89N1332nz5s3aunWr9u/fr+zZs2v8+PHJUSMAAAAApHhJDl+HDx/WgAEDElzz5ebmpv79++vQoUOvtDgAAAAASC2SHL4yZsyo33///YXljx49SvI1X7/++qs6d+4sb29vVa9eXUuWLDG3BQUFqWPHjipTpozq16//wjT2P/74oxo2bKjSpUurffv2CgoKStC+fPlyVa1aVd7e3hoyZIjCw8PNbZGRkRoyZIjKly+vKlWq6LPPPktS3QAAAACQVEkOXw0aNNCwYcP0008/6cmTJ3ry5IkOHTqk4cOHq379+oneTlxcnHx9feXm5qYvv/xSo0eP1vz58/X111/LMAz16NFDnp6e2rRpk5o0aaKePXvq9u3bkqTbt2+rR48eatasmTZu3Ch3d3d1795dhmFIknbv3q2AgACNGTNGK1asUGBgoKZMmWJ+7cmTJ+vcuXNasWKFRo4cqYCAAO3atSupbwUAAAAAJFqSJ9z45JNP9ODBA3Xu3Nl83y87Ozu1bNlSAwcOTPR2QkJCVLRoUY0aNUqurq7Kmzev3n77bZ04cUKenp4KCgrSunXr5OLiogIFCuinn37Spk2b1KtXL23YsEElSpRQp06dJEkTJ05U5cqVdfToUVWsWFErV65Uhw4dVKNGDUnS6NGj1blzZw0YMECGYWjDhg1avHixihcvruLFi+vy5ctas2aN6tatm9S3AwAAAAASJcnhy8HBQZMmTdKQIUN048YNOTg4KHfu3HJxcUnSdrJkyaKZM2dKkgzD0MmTJ3Xs2DGNHDlSgYGBKlasWIJtlitXTqdPn5YkBQYGqnz58uY2Z2dnFS9eXKdPn1b58uV19uxZ9ezZ09xepkwZRUdH6+LFizIMQzExMfL29k6w7QULFiguLi7BzaP/icmUpF0GACDVsrExycQfxmRlGIbi4gxrl4F/iY9HyvVPfZeUvk1y+JKke/fuac2aNbp69apiY2OVP39+tWzZUnnz5v03m1PNmjV1+/Zt1ahRQ3Xq1NGECROUJUuWBOt4eHgoODhYknT//v2/bH/06JEiIyMTtNvZ2SlTpkwKDg6WjY2N3Nzc5ODgYG739PRUZGSkwsLC5O7unui6PTzS/5vdBQAg1YmNi5NtEr7ARNLxHqdcbm7prF0C/qVX3XdJDl/Hjx/XRx99pMKFC6tMmTKKjY3VsWPHtHr1an322WcqV65ckouYPXu2QkJCNGrUKE2cOFHh4eEJwpH0/IxbVFSUJP1te0REhPnxy9oNw3hpmyTz9hPrwYPHMvgCCgCQxtna2sjNLZ38lm/UteAQa5eTKuXP6qmJHVsoNPSpYmPjXtl24/sOyYt+S7kS03cmU+JPyiQ5fE2aNElt27ZVv379EiyfOnWqpkyZonXr1iV1kypZsqSk57MQ9u/fX82bN08wO6H0PBg5OTlJkhwdHV8ISlFRUcqQIYMcHR3Nj//c7uzsrNjY2Je2STJvP7EMQ4QvAAD+61pwiC7+dsfaZaR6HHukTPRbyvUq+y7J564vX76s5s2bv7C8RYsW+vnnnxO9nZCQEO3duzfBsoIFCyo6OlqZM2dWSEjIC+vHDyX08vJ6aXvmzJmVKVMmOTo6JmiPiYlRWFiYMmfOLC8vL4WGhiomJsbcfv/+fTk5OSlDhgyJrh8AAAAAkiLJ4StHjhw6c+bMC8sDAwPl6emZ6O389ttv6tmzp+7evWtedu7cObm7u6tcuXI6f/68eQihJJ04cUKlS5eWJJUuXVonTpwwt4WHh+vChQsqXbq0bGxsVLJkyQTtp0+flp2dnYoUKaKiRYvKzs7OPHlH/LZLliyZpMk2AAAAACApkjzssEuXLho5cqSuXbumUqVKSXoevFatWqW+ffsmejslS5ZU8eLFNWTIEPn5+enWrVuaMmWKPv74Y1WoUEHZsmWTn5+funfvrv379+vMmTOaOHGiJKl58+ZaunSpFi1apBo1amju3LnKmTOnKlasKElq3bq1RowYoUKFCilLliwaNWqU3nvvPTk7O0uSmjZtqlGjRmnChAm6d++ePvvsM/O2AQAAACA5JDl8NWvWTJK0evVqLVu2TI6OjsqXL5/Gjx+vevXqJXo7tra2mjdvnsaOHatWrVrJ2dlZ7dq1U/v27WUymTRv3jwNHTpUzZo1U548eTR37lxlz55dkpQzZ07NmTNHEyZM0Ny5c+Xt7a25c+eap7ht0KCBbt26pREjRigqKkq1a9fWgAEDzK/t5+enUaNGqUOHDnJ1dVWvXr1Uu3btpL4VAAAAAJBoJsPg8r9/KySE2Q4BALCzez7zWqtJC5hwI5kUyZlN6wd/rNDQp4qJeXWz5sX3XcuhAfr5xu1Xtl08VzRvdm0Y3zPZ+q117+m6ePXWK9su/l+RAjm0dnbfRPWdySR5eiZutsNEXeRUvXp1hYaGJlh27dq1BJNWAAAAAAD+WqLCV3BwsOLiEia+Fi1a6M4dvt0CAAAAgMT419P7MVoRAAAAABKPudUBAAAAwAIIXwAAAABgAYmean7p0qVycXExP46OjtbKlSuVMWPGBOv17Nnz1VUHAAAAAKlEosLXm2++qbNnzyZY5u3trYsXLyZYFn+fLQAAAABAQokKX6tWrUruOgAAAAAgVeOaLwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAW8K/C1+PHj7VmzRqNGzdODx8+1P79+3Xz5s1XXRsAAAAApBpJDl+//PKLateurU2bNmndunV6+vSp9uzZoyZNmujo0aPJUSMAAAAApHhJDl/jxo3TBx98oM2bN8ve3l6SNHHiRLVu3VqTJ09+5QUCAAAAQGqQ5PB19uxZNW3a9IXl77//vq5cufIqagIAAACAVCfJ4cvd3V3Xr19/YfnJkyfl4eHxSooCAAAAgNTGLqlP+OijjzRs2DB9/PHHMgxDhw8f1pdffqkVK1aoT58+yVEjAAAAAKR4SQ5f77//vrJkyaKlS5fKyclJkydPVr58+TR27FjVr18/OWoEAAAAgBQvyeFryZIlatiwodasWZMc9QAAAABAqpTka74WLFig6Ojo5KgFAAAAAFKtJIevhg0bav78+bpx44aioqKSoyYAAAAASHWSPOzw+++/1+3bt/Xll1++tP3nn3/+n4sCAAAAgNQmyeFr0qRJyVEHAAAAAKRqSQ5fFSpU+Mu2e/fu/U/FAAAAAEBqleTwde3aNU2dOlVXrlxRbGysJMkwDEVFRenhw4e6cOHCKy8SAAAAAFK6JE+4MXz4cD18+FCdO3dWSEiIOnXqpLp16+rJkycaP358ctQIAAAAACleks98nT17VuvXr1fRokW1ZcsW5c+fX23atFG+fPm0ceNGvfvuu8lRJwAAAACkaEk+82VnZ6f06dNLkvLnz2+e3bBSpUq6dOnSq60OAAAAAFKJJIcvb29vLV26VBERESpRooT27dsnwzB07tw5OTo6JkeNAAAAAJDiJXnYoZ+fn7p166ZcuXLp/fff18qVK1WhQgU9e/ZM3bt3T44aAQAAACDFS3L4KliwoPbs2aOIiAg5Oztr06ZNOnr0qDJlyqQyZcokQ4kAAAAAkPIlOXzdvn3b/O/Q0FBJUqFChcxt2bNnf0WlAQAAAEDqkeTwVbNmTZlMpr9sj5+AAwAAAADw/5Icvr799tsEj2NjY3Xz5k3NmTOHa74AAAAA4C8kOXzlyJHjhWW5c+dWhgwZNGDAAPn4+LySwgAAaY+NjUk2Nn89ugL/u7g4Q3FxhrXLAIA0Kcnh66+YTCbdvXv3VW0OAJDG2NiYlCmTi2xtk3wXFCRBbGycwsKeEcAAwAqSHL4CAgJeWPb06VPt2rVLlStXfiVFAQDSHhsbk2xtbTR2w5f69X6ItctJlfJk9tTwlu/KxsZE+AIAK0hy+Dpy5EiCxyaTSfb29mrSpIk+/PDDV1YYACBt+vV+iC7fCbZ2GQAAvHJJDl+rVq1KjjoAAAAAIFVLcvjasmVLotdt2rRpUjcPAAAAAKlSksPXpk2bFBgYKJPJpDx58sjBwUE3btzQ06dPlS1bNvN6JpOJ8AUAAAAA/5Xk8FW2bFk5OzvL399fbm5ukqRnz55pxIgRyp49u/r27fvKiwQAAACAlC7J8/muXbtWAwcONAcvSXJxcVH37t21evXqV1ocAAAAAKQWSQ5frq6uunDhwgvLjx8/Lnd391dSFAAAAACkNkkedti1a1cNGzZMR48eVdGiRWUYhs6ePaudO3dq8uTJyVEjAAAAAKR4SQ5f77//vrJly6aNGzdq7dq1cnJyUsGCBfXFF1+oSJEiyVEjAAAAAKR4SQ5fkuTj4yMfH59XXQsAAAAApFqJvubr9u3bWrx4sR49eiRJioyM1IQJE9SoUSO1b99e3333XXLVCAAAAAApXqLC1/nz59WoUSNt2rRJT58+lSQNGjRIa9euVfXq1VWlShUNGjRI+/btS9ZiAQAAACClStSww5kzZ6phw4YaPXq0JCkoKEi7du1S69at1a9fP0mSu7u7Fi5cqJo1ayZftQAAAACQQiXqzNepU6fUrl078+MDBw7IZDKpfv365mXlypXTpUuXXn2FAAAAAJAKJCp8xcTEyNHR0fz4xx9/VPr06VW2bFnzsujoaNnb27/6CgEAAAAgFUhU+CpatKgOHTokSXr48KEOHTqk6tWry8bm/5/+9ddfM9U8AAAAAPyFRF3z1bNnT/Xo0UMHDx7UpUuXZGNjo65du0qSLl26pM2bN2v16tUKCAhI1mIBAAAAIKVK1JmvypUra/Xq1cqZM6dq1aqljRs3qkCBApKkLVu26KefftK0adNUo0aNZC0WAAAAAFKqRN9kuUSJEipRosQLywcNGvRKCwIAAACA1CjRN1kGAAAAAPx7hC8AAAAAsADCFwAAAABYAOELAAAAACwgURNuJGUK+Z49e/7rYgAAAAAgtUpU+Dpy5EiiNmYymf6nYgAAAAAgtUpU+Fq1alVy1wEAAAAAqVqi7/P1Rz///LMuX76suLg4SZJhGIqKitKFCxc0evToV1ogAAAAAKQGSQ5fAQEBCggIkKenpx48eCAvLy+FhIQoNjZW//nPf5KjRgAAAABI8ZI82+H69es1evRoHTx4UNmyZdOqVav0448/qlKlSsqdO3dy1AgAAAAAKV6Sw1doaKiqVq0qSSpatKhOnTqlDBkyqE+fPtqxY8crLxAAAAAAUoMkhy8vLy8FBQVJkgoUKKALFy5IklxdXfXw4cNXWx0AAAAApBJJvuarZcuW6tu3ryZMmKBatWqpY8eOypIli3788UcVKVIkOWoEAAAAgBQvyeHr448/VtasWeXs7KxSpUrJz89P69atU6ZMmTRx4sTkqBEAAAAAUrwkh68tW7aofv36cnBwkPT8TFjLli317Nkzbdy4Ufny5XvlRQIAAABASpeo8PXw4UNFRERIkvz8/PTGG2/Izc0twToXL17U1KlT1b59+1dfJQAAAACkcIkKX0ePHtWnn34qk8kkSWrRooWk5zdX/qPGjRu/4vIAAAAAIHVIVPiqW7eu9u3bp7i4ONWqVUsbNmyQu7u7ud1kMsnZ2fmFs2H/5O7duxo/frwOHz4sR0dH1a9fX3379pWjo6OCgoI0fPhwnT59WtmzZ9eQIUNUpUoV83N//PFHTZgwQUFBQSpdurTGjx+vXLlymduXL1+upUuX6smTJ6pXr56GDx8uZ2dnSVJkZKRGjx6tPXv2yMnJSZ06dVKnTp2SVDsAAAAAJEWip5rPnj27cubMqYsXL6pkyZJyd3fX48eP9fvvvytDhgxJDl6GYah3794KDw/XmjVrNGPGDO3fv18zZ86UYRjq0aOHPD09tWnTJjVp0kQ9e/bU7du3JUm3b99Wjx491KxZM23cuFHu7u7q3r27+Uzc7t27FRAQoDFjxmjFihUKDAzUlClTzK89efJknTt3TitWrNDIkSMVEBCgXbt2Jal+AAAAAEiKJE+4ER0drSlTpmjt2rWKiYl5vhE7OzVq1EijR482T8TxT65du6bTp0/r0KFD8vT0lCT17t1b/v7+qlatmoKCgrRu3Tq5uLioQIEC+umnn7Rp0yb16tVLGzZsUIkSJcxnqyZOnKjKlSvr6NGjqlixolauXKkOHTqoRo0akqTRo0erc+fOGjBggAzD0IYNG7R48WIVL15cxYsX1+XLl7VmzRrVrVs3qW8HAAAAACRKkm+y7O/vr/3792v+/Pk6fvy4jh49qrlz5+r48eOaMWNGoreTOXNmLVmyxBy84j158kSBgYEqVqyYXFxczMvLlSun06dPS5ICAwNVvnx5c5uzs7OKFy+u06dPKzY2VmfPnk3QXqZMGUVHR+vixYu6ePGiYmJi5O3tnWDbgYGBiouLS+rbAQAAAACJkuQzX9u2bdOsWbNUsWJF8zIfHx85Ojqqf//+GjRoUKK2kyFDBlWtWtX8OC4uTqtXr9Zbb72l+/fvK0uWLAnW9/DwUHBwsCT9bfujR48UGRmZoN3Ozk6ZMmVScHCwbGxs5ObmluAMnaenpyIjIxUWFpbgWrZ/8t/5RwAASHH4G5Zy0XcpE/2Wcv1T3yWlb5McvgzDkIeHxwvL3d3d9fTp06RuzmzKlCm6cOGCNm7cqOXLl78wfNHBwUFRUVGSpPDw8L9sj58S/6/aDcN4aZsk8/YTy8MjfZLWBwDgdeDmls7aJeBfou9SJvot5XrVfZfk8PXWW29p6tSpmjp1qlxdXSVJjx490vTp0xOcDUuKKVOmaMWKFZoxY4YKFSokR0dHhYWFJVgnKipKTk5OkiRHR8cXglJUVJQyZMggR0dH8+M/tzs7Oys2NvalbZLM20+sBw8e60+z7QMA/iVbWxsOUCwkNPSpYmNf3VB7+s5y6LuUiX5LuRLTdyZT4k/KJCp8HTt2TN7e3rKzs9OQIUPUvn17Va1aVfny5ZMkXb9+Xbly5dL8+fMT9aJ/NHbsWH3++eeaMmWK6tSpI0ny8vLSlStXEqwXEhJiHkro5eWlkJCQF9qLFi2qTJkyydHRUSEhISpQoIAkKSYmRmFhYcqcObMMw1BoaKhiYmJkZ/d89+/fvy8nJydlyJAhSbUbhghfAIAUib9fKRd9lzLRbynXq+y7RIWv9u3b6+DBg/Lw8JCXl5e2bdum77//XteuXZOjo6Py5cunypUry8YmafN3BAQEaN26dZo+fXqCmQZLly6tRYsWKSIiwnw26sSJEypXrpy5/cSJE+b1w8PDdeHCBfXs2VM2NjYqWbKkTpw4YT4Td/r0adnZ2alIkSLPd9rOTqdPnzZPynHixAmVLFkyyfUDAAAAQGIlKnwZf4p79vb2euedd/TOO+/86xe+evWq5s2bJ19fX5UrV0737983t1WoUEHZsmWTn5+funfvrv379+vMmTOaOHGiJKl58+ZaunSpFi1apBo1amju3LnKmTOnOWy1bt1aI0aMUKFChZQlSxaNGjVK7733nvkmy02bNtWoUaM0YcIE3bt3T5999pl52wAAAACQHBJ9zZfpFU/R8u233yo2Nlbz589/YbjipUuXNG/ePA0dOlTNmjVTnjx5NHfuXGXPnl2SlDNnTs2ZM0cTJkzQ3Llz5e3trblz55prbNCggW7duqURI0YoKipKtWvX1oABA8zb9/Pz06hRo9ShQwe5urqqV69eql279ivdPwAAAAD4o0SHr+bNmydqWN63336bqO35+vrK19f3L9vz5Mmj1atX/2W7j4+PfHx8/tX2nZ2d5e/vL39//0TVCgAAAAD/q0SHrw8//FDp0zO1OgAAAAD8G4kKXyaTSQ0aNHjp/b0AAAAAAP8sUdP7/XnCDQAAAABA0iQqfL377rvmmxcDAAAAAJIuUcMOmYYdAAAAAP433FUYAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsIBE3ecLAFIaGxuTbGxM1i4j1YqLMxQXZ1i7DAAAUhTCF4BUx8bGpExuLrK14eR+comNi1NY6DMCGAAASUD4ApDq2NiYZGtjo5k7v9JvDx9Yu5xUJ6e7hz6t11g2NibCFwAASUD4ApBq/fbwga7fu2vtMgAAACQx4QYAAAAAWAThCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZA+AIAAAAACyB8AQAAAIAFEL4AAAAAwAIIXwAAAABgAYQvAAAAALAAwhcAAAAAWADhCwAAAAAsgPAFAAAAABZgZ+0CgNeZjY1JNjYma5eRasXFGYqLM6xdBgAAgEUQvoC/YGNjUiY3F9nacII4ucTGxSks9BkBDAAApAmEL+Av2NiYZGtjo+U/7FTw7w+tXU6qkzWjuzpWrScbGxPhCwAApAmEL+AfBP/+UEEP71m7DAAAAKRwjKcCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWQPgCAAAAAAsgfAEAAACABRC+AAAAAMACCF8AAAAAYAGELwAAAACwAMIXAAAAAFgA4QsAAAAALIDwBQAAAAAWYGftAtICGxuTbGxM1i4jVYuLMxQXZ1i7DAAAAOAvEb6SmY2NSW5uLrKx4SRjcoqLi1No6DMCGAAAAF5bhK9k9vysl422nf5GD548tHY5qZKHq7salvmPbGxMhC8AAAC8tghfFvLgyUPdfRRi7TIAAAAAWMlrMRYuKipKDRs21JEjR8zLgoKC1LFjR5UpU0b169fXwYMHEzznxx9/VMOGDVW6dGm1b99eQUFBCdqXL1+uqlWrytvbW0OGDFF4eLi5LTIyUkOGDFH58uVVpUoVffbZZ8m7gwAAAADSPKuHr8jISPXt21eXL182LzMMQz169JCnp6c2bdqkJk2aqGfPnrp9+7Yk6fbt2+rRo4eaNWumjRs3yt3dXd27d5dhPB9ytnv3bgUEBGjMmDFasWKFAgMDNWXKFPP2J0+erHPnzmnFihUaOXKkAgICtGvXLsvuOAAAAIA0xarh68qVK3rvvfd08+bNBMsPHz6soKAgjRkzRgUKFFDXrl1VpkwZbdq0SZK0YcMGlShRQp06ddIbb7yhiRMn6tatWzp69KgkaeXKlerQoYNq1KihUqVKafTo0dq0aZPCw8P17NkzbdiwQUOHDlXx4sX1n//8R126dNGaNWssvv8AAAAA0g6rhq+jR4+qYsWKWr9+fYLlgYGBKlasmFxcXMzLypUrp9OnT5vby5cvb25zdnZW8eLFdfr0acXGxurs2bMJ2suUKaPo6GhdvHhRFy9eVExMjLy9vRNsOzAwUHFxccm0pwAAAADSOqtOuNG6deuXLr9//76yZMmSYJmHh4eCg4P/sf3Ro0eKjIxM0G5nZ6dMmTIpODhYNjY2cnNzk4ODg7nd09NTkZGRCgsLk7u7e6LrN3HrrtcOfZIy0W8pF32XctF3KRd9lzLRbynXP/VdUvr2tZztMDw8PEE4kiQHBwdFRUX9Y3tERIT58cvaDcN4aZsk8/YTy8MjfZLWR/Jyc0tn7RLwL9BvKRd9l3LRdykXfZcy0W8p16vuu9cyfDk6OiosLCzBsqioKDk5OZnb/xyUoqKilCFDBjk6Opof/7nd2dlZsbGxL22TZN5+Yj148FjGP9xWytbWhg+chYSGPlVs7KsbOkrfWcar7jeJvrMUPnMpF32XctF3KRP9lnIlpu9MpsSflHktw5eXl5euXLmSYFlISIh5KKGXl5dCQkJeaC9atKgyZcokR0dHhYSEqECBApKkmJgYhYWFKXPmzDIMQ6GhoYqJiZGd3fPdv3//vpycnJQhQ4Yk1WkY+sfwBcuiP1Im+i3lou9SLvou5aLvUib6LeV6lX1n9anmX6Z06dI6f/68eQihJJ04cUKlS5c2t584ccLcFh4ergsXLqh06dKysbFRyZIlE7SfPn1adnZ2KlKkiIoWLSo7Ozvz5B3x2y5ZsqRsbF7LtwMAAABAKvBapo0KFSooW7Zs8vPz0+XLl7Vo0SKdOXNGLVq0kCQ1b95cJ0+e1KJFi3T58mX5+fkpZ86cqlixoqTnE3ksXbpUe/fu1ZkzZzRq1Ci99957cnZ2lrOzs5o2bapRo0bpzJkz2rt3rz777DO1b9/emrsMAAAAIJV7LYcd2traat68eRo6dKiaNWumPHnyaO7cucqePbskKWfOnJozZ44mTJiguXPnytvbW3PnzpXpv1ONNGjQQLdu3dKIESMUFRWl2rVra8CAAebt+/n5adSoUerQoYNcXV3Vq1cv1a5d2yr7CgAAACBteG3C16VLlxI8zpMnj1avXv2X6/v4+MjHx+cv2319feXr6/vSNmdnZ/n7+8vf3//fFQsAAAAASfRaDjsEAAAAgNSG8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALIHwBAAAAgAUQvgAAAADAAghfAAAAAGABhC8AAAAAsADCFwAAAABYAOELAAAAACyA8AUAAAAAFkD4AgAAAAALSLPhKzIyUkOGDFH58uVVpUoVffbZZ9YuCQAAAEAqZmftAqxl8uTJOnfunFasWKHbt29r0KBByp49u+rWrWvt0gAAAACkQmkyfD179kwbNmzQ4sWLVbx4cRUvXlyXL1/WmjVrCF8AAAAAkkWaHHZ48eJFxcTEyNvb27ysXLlyCgwMVFxcnBUrAwAAAJBapckzX/fv35ebm5scHBzMyzw9PRUZGamwsDC5u7snajs2NpJhJO41vTJklr1tmny7k517Ojfzv22S4euEXO5Z5GBH371qXhn+/3OWHP0mSfmzeMnRzj55Np6G5XBP3r57I1tWOdnTb8khl6eH+d/J0XdFc2WVswN9lxzyeiVz3+XJJmdH+u5Vy5vV0/zv5Oi3IgVyyNnJ4Z9XRJLlyZHZ/O9/6juTKfHbNRlGYuND6rFlyxbNmjVL+/fvNy8LCgpSrVq1dODAAWXNmtWK1QEAAABIjdLksENHR0dFRUUlWBb/2MnJyRolAQAAAEjl0mT48vLyUmhoqGJiYszL7t+/LycnJ2XIkMGKlQEAAABIrdJk+CpatKjs7Ox0+vRp87ITJ06oZMmSskmui08AAAAApGlpMmk4OzuradOmGjVqlM6cOaO9e/fqs88+U/v27a1dGgAAAIBUKk1OuCFJ4eHhGjVqlPbs2SNXV1d17txZHTt2tHZZAAAAAFKpNBu+AAAAAMCS0uSwQwAAAACwNMIXAAAAAFgA4QsAAAAALIDwBQAAgFQjfjoDpjVIuVJz3xG+8EotWLBA+/fvt3YZQJp17tw5a5cAAFYVfx9Xk8mUqg/iU6OLFy9KSt19R/jCKxEXF6dff/1Vc+bMkbu7u7XLwf9ozZo15l+ASDmWLFmiESNGWLsM/IMvvvhCly9ftnYZ+AfLli3TgQMHrF0Gkuizzz7TBx98oEWLFklK3Qfxqc28efPUtGlTrV+/XlLq7TvCF14JGxsbeXh4yNXVVc+ePbN2OfiXDMPQqVOnNHbsWGXMmNHa5SCJHj16JBcXF0lSdHS0lavBy3z//fcaMWKE+vXrp+vXr1u7HPyFU6dOyd/fX/369dNPP/1k7XKQBPFfAE+fPl2zZ8+WlHoP4lMbBwcHSdKUKVO0fPlySamz7whfeCUMwzB/aJ48eWLlavBvmUwmubm5ycXFRZGRkdYuB0lkGIYiIiIkSXZ2dlauBi9TqlQp5cmTR7/88ou6deumq1evWrskvIS3t7eqVKmiJ0+eqGfPnpwBS0H+85//6L333lO1atW0ZMkSTZw4UVLqPIhPbWrVqqXq1aurYsWK+vzzz7Vw4UJJqa/vCF/4n5w8eVIxMTEymUyKjIyUi4uL+aAvNjZWUuq+aDI1cnZ2lmEYCg8Pp+9SgD+e4XJyclJcXJyk53+s8HqJjY1VpkyZ9Mknn6hJkyZKnz69OnTooCtXrli7NPxB/N+uzp07q0WLFqpUqZJ69Oihb7/91sqV4e/E/+5Lly6dcufOrdjYWE2fPl1ffvmlJk2aJCn1HcSnFvF9lzdvXuXOnVt37txRp06dtHnz5lQ5fJTwhX/tyJEjGjt2rBYuXKioqCilT59ejo6Ounv3riIiImRrayvp/w8C4z9ceP3cuHHD/O/4vrO3t5fJZKLfXmPr1q3T1q1b9fTpU0lS5syZX3rGMiYmxtKl4U9iY2PNvxPz5cunkJAQ9e7dWzVr1lSHDh04A/aaiIuLS9BPd+7ckY+PjwYNGqRPPvlE+/bts3KF+LNbt25Jen75Q/zfqy5duujZs2c6deqUJkyYoHXr1snf319S6jqIT+kePnwo6XnfxX+ROGjQIGXMmFH3799X27ZttW7dOi1ZskRS6uk7whf+tWLFiuntt9/W8ePHtWzZMhmGITc3Ny1atEgNGzbUkCFDtHjxYp04cUJhYWEcAL6mdu7cqSFDhuibb76RJHl6esre3l5hYWGSnv9S/KP4b4VhfcePH9fChQu1f/9+GYahdOnSKSQkRLt379b+/fv1+++/S2IIojX9+OOPevTokfmAXpKKFi2qypUra/Lkyfr4449VsWJFApiVffPNN7p8+XKC33dZs2ZV+/btNXfuXFWoUEHdunVT7969CWCvkYULF6pNmzZasGCBIiIiEnxZ2LNnTz169EhvvPGGJkyYoFWrVhHAXiOLFi1S79699cUXX0iS7O3tzW21a9dWZGSk6tSpo7Zt22r16tWpKoDxFxlJFh0drYiICKVPn149e/bU4sWL9d133ykmJkbR0dHKly+fypYtqyNHjmjv3r2KioqSYRhyd3dXw4YN1adPnxcO6GE9xYsXl4uLizZv3iw7OztVqVJFdnZ2mjFjhhwdHeXj46NcuXKpRIkSypw5c4KDSFjX1KlTNXLkSM2ZM8c8VPT333/XjBkz9Ntvv8nT01Ourq4qXbq0ypUrJxcXF9WuXZvPn4WsXLlSEyZMUP78+TVw4EDlyZNH+fLlkyS1adNG169f17Fjx+Tv76+ePXuqY8eOWr58uQoUKGDlytOWDRs2aPjw4XJ2dtbgwYNVpEgRlS5dWpJUrVo11atXTz/99JN69OihJ0+eqHfv3goICFD16tWtW3gad+/ePe3bt0/BwcGaOXOmLly4oJIlS6p58+Zyd3dX/vz5FRQUpKNHj6ply5aSpKFDhyo8PFyjRo1iaLYVXblyRatXr9a9e/d0/PhxHTx4UPXr19fbb7+tjBkzqly5clq8eLFKly6tTp06SZLWrl2riIgI9ezZM8X3nclI6fERFjV37lydOnVKQUFBatSokerUqaP8+fMrICBAx48f15kzZ9S/f3+1a9dOz549k2EYunr1qk6fPq3Lly+rbdu2Kly4sLV3A/8VFRUlBwcH3bp1S+PGjVNUVJQaNWqkWbNmKWfOnPLw8NCZM2d0+/ZtpU+fXu7u7sqWLZuaNm2qpk2bWrv8NOmHH35QXFycoqOjVatWLUnSuHHjdPz4cZUsWVLnz5/X1KlTFR4ersDAQJ09e1bXrl3TmTNn5Orqqq+++krZsmWz8l6kflFRUVq7dq1Wrlyp27dvq3LlynJxcVHlypX1/vvvS3oezr7//nvzN7o9evTQ999/r6+++soc0pB8DMNQbGys9uzZo6VLl+r8+fMqV66cPDw8lDVrVg0aNEi2trbas2eP5s+fr1WrVsnV1VVTp07VkiVLtGjRIlWrVs3au5EmnTp1St7e3vrpp5/01VdfKSgoSOnTp5ebm5sOHTqkESNGqGrVqjp37pz69++vefPmqUiRItqyZYumTp2qrVu3yt3dPcUfxKdkW7Zs0ddffy0nJyc9ePBAOXLkUFBQkIYPH66SJUvqm2++0cqVK+Xv7y9XV1etWbNGO3bs0KpVq5QxY8YU3XeELyTa8uXLtWDBAn366acKDw/X7t27lT59ejVq1EiNGjXSggULtGjRItWsWVPTpk174fl/vOYB1rVs2TL98ssvun//vpo1a6Y6deooLCxMQ4cO1bNnz3Tp0iVNnTpVVatW1b179/Ts2TOdPn1aJ0+e1NWrVzVy5EgVKlTI2ruR5vj7+2vr1q3KkiWL7ty5o+LFi6t///4qVqyY/P39tWzZMmXMmFFfffWVvLy8zM+Ljo5WcHCwHBwcEixH8goNDdXevXu1ceNGOTk5qUmTJvL391fFihXVuHFj1apVSx06dFC5cuXUu3dvRUdHa/DgwerVq5fy5s1r7fLTjNjYWH333Xdas2aNwsLC9PHHH2vevHlydHRU48aN1aZNG40ePVoRERHmmfMCAgJUr149zlJawdixY3XmzBktW7ZMrq6uOnDggHbu3Kk7d+7o448/1qVLl7Rx40bly5dP1apVU2hoqNKlS6c2bdooKipKUVFRcnV1tfZupEm3b9/W06dP9cYbb0iSNm3apAMHDih9+vSqXLmyzp8/r82bN6tBgwbKly+fHjx4oNKlS8vHx0chISGys7NTpkyZrLsTrwDhC4k2duxYZciQQZ988okk6dq1a9qwYYOOHz+uFi1aqFWrVpo3b56OHDmit99+W507d5a9vb1iYmK45uQ1Mm/ePK1cuVItW7bUo0eP9O2336patWpq3769smXLphEjRmj37t3q3bu3unfv/sLzIyMj5ejoaIXK07ZLly6pV69emjp1qvLnz6/Y2Fj16tVLsbGxat++verUqaN58+Zp+fLlGjdunCpXrqx06dJJej6JAEMNLePAgQOqXLmy+Xfew4cPtWfPHi1btkzNmzfXBx98oKlTp+rixYvKkCGDqlSpotOnT2vo0KHy9PS0cvVpx5YtW/Tzzz/LyclJZcuWlY+Pj/bt26eAgAAVLlxYEydO1KJFi3T48GHduXNHjRo10tmzZzV48GDlyZPH2uWnWRMnTtTGjRu1atUqFStWzLz8wIED2rRpk548eaLJkyfr8ePH+uabb7R06VJFRUUpQ4YM2r17t5ycnKxYfdo2Y8YM7d+/X8HBwSpatKhGjhyp/Pnza/369dq7d6+yZs0qPz8/nTt3Tt9884327dunW7duqVixYtq8ebO1y3+l+GuMfxSfz2/fvq1ffvnFvDx//vzq0KGDqlatqi+//FI7d+6Ur6+vvL29deTIEQUEBCg2Npbg9RqJiYnRxYsX1b9/f/Xr10+jR4/WlClT9PDhQ82YMUNXr17V+PHjVbt2bZ07d0579+5N8Fzp/2+CCMt6+vSpnjx5oixZssjV1VUZM2bUwoULlSNHDq1cuVLffvutunfvrkaNGmn69On69ttvzffcI3glv/jfk3379tXEiRPNM3e5u7urbt266tixo9auXatNmzZp9OjRmjp1qpycnLR06VLt3LlThw4dsmb5acqMGTM0Y8YMPX78WD///LMmTZqktWvXqmbNmurevbvOnDmjYcOGydfXV/PmzVPt2rX15Zdfav/+/dq6dau1y0+zZs+erZUrV2rLli3m4BX/ufPx8VH79u3l4OCggQMHSpJ8fX21ceNG1a1bVy4uLnrw4IHVak/rNmzYoI0bN8rPz08rV65UcHCw5s2bJ0lq1aqV6tatqxs3bmjSpEnKnz+/hg4dqoCAAL355pt69uyZ7t69a+U9eLX4i4x/FD+utk2bNvr111+1e/duc1vWrFnVsmVLlSxZUlu3btWDBw/UpUsXFShQQL/88osePXpkrbLxEnFxcbp+/bquXbtmXvb222+re/fucnZ21tKlSxUcHKwRI0YoLi5OmzZt0o4dOyT9/4x5KXmcdUoUf3CRN29e5cqVS4cPHzYvc3Z21pgxY5Q1a1YtW7ZMN2/e1PDhw/X2229r0qRJ+uGHH1L8rFApRfwsoAULFtSaNWs0btw483ufKVMm1a9fX127dtXKlSs1e/Zs5cqVS3PmzNHo0aP1wQcfqESJEtYsP8345ZdftHPnTk2dOlUTJkyQv7+/atWqpS1btujRo0eqXLmy+vTpo8DAQPXr109OTk7q06ePpk2bpr59+6pBgwbW3oU0KTQ0VNeuXVP9+vXNQwbj4uL05ZdfatGiRfrqq69UqlQpffrpp3JwcNCYMWN08eJF5cqVS8OHD9eGDRuUI0cOK+9F2nX37l01btxYb7/9tooUKaIuXbooY8aM+vHHHxUWFqbmzZurXbt2un79umbNmqWbN2+qaNGimjt3rj7//PNUN1ye8IW/dPDgQW3ZskWnTp3S3bt3VaFCBRUvXlw7duzQ8ePHzetly5ZNrVu31o0bN7Rv3z65urqqT58+Gj9+vNzc3Ky4B/gzBwcHNW/eXJcuXdKFCxfMy0uVKqUPP/xQkZGRWrNmjTw9PTVs2DDz0I34+0jBsqKjoxUeHi5JSp8+vfLnz6+vv/7aHJ4Nw5CTk5PGjRun8PBw8/UoI0eOVJMmTVS8eHHCsoXEfznh4OCgRo0a6ZtvvtGAAQPMASxjxoyqX7++PvroI23dulUzZsyQJNWoUUNDhw7l2iELiY6OlslkMr/fbm5u8vHx0fnz53Xz5k05OzurWrVq+uSTT3TlyhX17dtX0vPfkfFfLMLy3Nzc1LBhQ4WGhuqnn35SdHS02rZtqw0bNmjLli1avny5WrZsqdy5c6tnz55Knz69hg4dql9++UUuLi5c42Vljx8/1v79+xUVFaWIiAgtWrRIe/bsUY8ePdSpUydNmjRJ77zzjlq3bq3bt29r5syZCgoKUoYMGVLlcSThCy81depUDRo0SMuXL9fw4cM1YMAAnTp1Sn379lVoaKg+//xzHTt2zLx+vnz5VK5cOR07dkyxsbFKly6d3N3drbgHiHf+/HkdPnxYDx48UExMjKpWrapnz55p+/btunPnjnm90qVLq2PHjvrqq6/0008/KWfOnJo6daoGDhxovnYIljN37lx169ZN7777rgICAnTz5k0NGzZMISEhmjhxooKDg833O3F2dtaUKVN0/vx5c6geNGiQcufObeW9SFvu37+vyMhItWrVSitWrNCBAwc0aNCgFwKYr6+v1qxZYx52w9Bsy8mcObNu3rypn376SdLzM5YFCxZUhgwZzCM1HBwcVK1aNfXq1UsnT56Un5+fJIbvWkv856dWrVoqW7aspk6dqpkzZypXrlyaP3++Nm7cqClTpihjxowaPHiwSpQooebNm6tgwYKErtdEmzZt5OLioipVqqhOnTrKkCGD1q5dq71796pRo0Y6f/68Vq1apXr16qlmzZqKiopK1deW8xsfL7h48aJ27NihOXPmqGzZsjp37px27typ7t27a+rUqZo4caKGDh2qVatW6d69e2rQoIH5HkMeHh7MaPgamTJlirZt2yaTySQnJye99dZb6tq1q/r06aPBgwfLwcFBLVq0MA/HqFKlikqVKqXDhw/r7bffVtasWa28B2nT8uXLtWrVqgQzi546dUqtWrXSmjVr1LhxYw0dOlQDBw4037ohLi5O6dOn52DDiuLPmmTLlk05cuTQ0qVL1aVLFw0aNEj+/v4ymUzKmDGjateuLTs7O5UvX97aJacpcXFxypIli6ZNm6YsWbLIMAzZ2trKwcFBkZGRevjwoaTnB/sODg6qVauWHB0dmXnSykwmk3nSoJ49eyo4OFhLly5V7969zVOOFyhQQK1bt9aiRYv05MkT+fj46M0335SLi4u1y4eknDlzaubMmTpx4oS2b9+umjVrKleuXJKk9u3bKzg4WPv27VPHjh3Vpk0bNW7cWOnTp7dy1cmH8IUXODg4yMXFxTzzVokSJZQzZ05lyJBB3bt314IFCzR58mTNmzdPs2fP1uLFi+Xm5qZz585pzZo1Vq4e8Q4fPqytW7dq1qxZKlSokA4cOGCelGHGjBkaM2aMRo0apfDwcDVo0EAlS5aU9PwPXYYMGaxcfdoWFBSkDz74wHw/KB8fH23YsEELFy5UVFSUvvrqK3Xo0EGTJ09W5cqVVapUKX333XeKioriYMOKXF1d5evrKwcHB8XFxalUqVJasmTJCwHMzc1N7777LmdSLCz+/a5du7ZsbW3NQ3Lj4uJka2tr/uLQZDJpzpw55muaYX02Njbm29X07dtXz549U/Xq1RMMq86aNauePn2qsLAwubq68rvwNWJjY6PcuXMrd+7cCg4OTnDZg62trd58800dPnxYjx8/Vvr06VN18JIYdoiXsLe31927d3X27FnzskyZMqlDhw765JNP5Ofnp5s3b2rkyJGaNWuWfHx8VLNmTa1fv557P71GDMNQtmzZVKJECaVPn14NGzZUly5dVKxYMXXt2lW5c+fWpEmTdOnSJU2YMEHdunVTv379dPbsWdWoUcPa5adJcXFxkqRbt269dGZRHx8frVq1SsePH9eqVatUtGhR7dmzR35+fjpw4IBmzZrFdOUWEBwc/NLlcXFx5tlAbWxsZBiGOYAdPHhQPXv2NA+hInglv/gZJ//MxsYmwUF7/DWt8WeNZ86cqYULFyaYyhyWs3PnTl29evWF5fGfHQcHB40ePVrFihXTjRs3FBMTo8jISO3fv1/p0qXjy0MrOnDggCIjI19YHj9bclRUlLJmzaqgoCB9+eWX5r95J0+elKenZ9qZTdkADMN4+PBhgscLFy40qlevbhw7dizB8vv37xtjx441evXqZTx+/NiSJSKJjh07ZpQoUcL4+eefEyy/cuWK4efnZ7Rq1coIDg42fvvtN2PHjh3Gp59+avj7+xuXLl2yUsWI98MPPxgNGzY0du3alWD57du3jXHjxhldu3Y1goODjZiYGCMiIsK4c+eO8ejRIytVm7bMmTPHGDBggGEYhhEXF2deHhMTYxiGYYSGhho3btx44XknTpwwatasaQQHB1um0DRu4cKFxqJFi174OxXfTw8ePDACAwMNwzCMR48eGRUrVjT27NljzJkzxyhZsqRx9uxZi9cMw/j222+NwoULG7169TKuX79uXh4VFWUYhmHcvHnTaNu2rXHs2DHjwYMHRtu2bY1ixYoZrVu3NipXrmycP3/eSpXjyy+/NAoXLmyMGDHCiIiIMC+Pjo42DON533366afGsWPHjEGDBhnvvfeeUa1aNaNr167GW2+99cKxSmrGV2/Qnj17NGfOHN28edO8rF69eqpcubKmT5+u06dPS3r+rZOnp6eqVq2qU6dOKTQ01EoV469ERUVJev4NfPny5VW/fn1Nnjw5Qd8WKFBArVq1kru7u9atW6ccOXKoXr16mjFjhgYOHMjZSyv46quvNGvWLK1fv14XL15UmTJlVKJECe3evfsvZxbdv3+/bG1t5ejoqKxZs6b6YRqvi1OnTpm/nY3/1jZ+ONStW7fUqlUrnTt37oXnlS1bVjt27Eh1Uya/jq5evarp06dr1qxZ+vrrr81ntuKHF966dUvvvvuuLl68KOn5hCeenp4aPXq0lixZorVr1zL1v5VkzpxZ9vb22rNnj/z9/fXrr79Kej4iJygoSG3atFGePHlUvnx5ZciQQRMmTNCoUaPUqVMnrVu3jrOVVhT/u239+vUaOXKk+XjEzs7O3Hf29vYqX768hg0bpgEDBqhly5aqU6eO1q9fryJFilizfIsifEFxcXFat26d1q9fr+vXr0uScuXKpWbNmilHjhyaNGmSjhw5Yh6mUbJkSbm5uTH9+Gtm2bJlGjBggHr06KH169crLi5OzZo1k4ODgxYtWqRbt26Z1y1durQqVKigHTt2vHSIACxn+vTp8vf3188//6wvvvhCBw4ckKurq1q2bKmHDx9q/fr1L51Z9OjRo1asOu0x/nC/tPh7esVfIxR/QN+iRQtVqFBB9evXf+k2UvPsXa+TAgUK6L333lNMTIzGjx+vL774QhEREbKxsVFwcLCaNGkiHx8ftWzZ0jxb6Ntvvy0bGxutW7eO4GVFJUuW1IgRI+Tj46Pjx49ryJAhCgoKUnR0tGbPnq233npLY8eOlfT8oD5Xrlxq2bKl3nnnHeXMmdPK1adtb7/9tj799FNVr15de/fu1aeffqqYmBhFR0dr3Lhxqly5svz9/WUYhlxdXVW+fHn17NlT7777bpqbmZfwlYbFH0zEXyPy1Vdfac2aNbpx44ak59/Utm7dWoUKFdInn3yitWvXav/+/VqyZIl+//13Zc6c2Vql408CAgK0ePFilS9fXra2ttq6dasuX76sihUrqm7dugoKClJAQIA5XEtSxYoV5ejoqN9//92KladdhmEoKChI+/fv14wZM7RgwQItW7ZMXbt2lfT88zdw4EDdv39fK1eu1Pbt283Pi59ZFJYT/+VTpkyZEpxhlp73yTfffKP//Oc/GjNmDPdWs6L4YOzr66sOHTqoTp068vf314oVKxQREaHAwEA1b95cI0eOlMlkMvdV06ZNtW7dujT17fvrJDY21nxMUrBgQeXJk0cLFy7U48eP1a9fP92+fVv9+/fX5MmT+Xy9ZuI/c9LzLwednZ21YMECXb16Vb169VJUVJQGDBig8ePHJ/jMpWXMdpiGxX8ALl26pPLly+vjjz/WsGHDJElt27ZV3rx55e3trVy5cqlo0aL67LPPlD59esXFxWn+/Pkc/L0mHj16pB9++EHDhg1T/fr11bx5czVu3FgXL15U9uzZ1bRpU7m5uenzzz+Xn5+f+vXrJzc3N3399deKi4uTs7OztXchTTKZTHJ2dtazZ8/MZx9dXFw0Y8YMXb9+XVmyZFGTJk00depUBQQEMLOolezYsUOZM2fWm2++Ken5pAx3796V9P+TaphMJrVo0ULp0qXjwMKKjP9OHS89v6daVFSU3nrrLTVo0EDdu3eXjY2N3n//fdWpUyfBc0wmk4oXL26tstO006dPq0yZMgluUePt7a21a9fq888/15YtW9SoUSMNHjxYkydPNq8T32+wnkuXLqlw4cIJ+q5OnTrauHGjNm7cqIULF6pDhw7y8/PTtGnTmGToD3gnoCxZsihr1qyqVKmShg8frn379mn16tXmM2Cenp764IMPtGHDBq1Zs0arVq1iXPVrJDIyUvfv31emTJkkPT8gjIqK0oIFC9S4cWO9//77Klq0qAYMGKDChQvL19dXffr00a5duzR58mSuFbIiwzBkGIb5mrwuXbrozJkzSpcunR48eKB27drp9OnTGjVqFDOLWsGzZ880c+ZMzZ8/XydPnpQkOTk5KSwsTL/88ot+++0387e+rq6uHAxaybp163T48OEE73/69OnVsmVLTZ8+Xbly5VJAQICmTZumzz//PMFQa/rMehYuXKj3339fffr00YEDB8z3WZOksWPH6u7du/rhhx+0ceNGPXjwQAMHDlRQUJAk+s3aFi5cqCZNmmj48OG6evWqnjx5Ym4bNWqUnj17prCwMC1ZskQnTpxQnz59zDMeQjIZfxzIjjTp3r17iouLM99Q9/vvv9eoUaNUs2ZNtW/f3jwWN/4mh3j9jBo1St7e3mrSpIlmz56t7777TgMHDlRkZKRWrlyp69eva9u2bXJxcdH169fl6OgoR0dHzl6+BtatW6eZM2dq6NCh+vHHH/Xpp5/Ky8tLT5480apVq7Rp0ybNnz9fb7zxhrVLTZPu3r2rXr16ydXVVf3799fu3bu1cOFC5cmTR3fv3lXRokWVN29eFS5cWJUqVVJsbKyKFi1q7bLTjB07dqhv376SpJ49eypv3rxq2LChuX3ZsmW6ceOGRo8erc2bN2vIkCHq37+/2rZtKycnJ2uVDUlz587VnDlzZGdnp3fffVe//vqr+vXrpxIlSsjW1lbLli1TUFCQRowYoadPn6p58+ays7PTggULuL7Lis6cOaPt27drxYoVkqS6devKxcVFH330kfLly6fw8HBNmTJFGTJk0KeffqpffvnF3LZkyRLZ2THojiPpNOZlWTv+zFdcXJzi4uJUrVo1jRo1Svv27dOaNWvM1wkRvF4/8decDB48WE2aNJEk9e7dW4sWLdJbb70lHx8fzZo1S1myZNHq1atlGIby5s2r7NmzE7xeE7Vq1VK9evU0f/58HTlyxHyvIVdXV9WuXVuSzMPcYBk///yzrl69qmvXrsnLy0vz5s3TkydPtHTpUt2/f19lypSRv7+/Bg0apKJFi+rcuXNasmSJGjdurI8//pjrKC0of/78qlatmiTp119/1ZYtW+Tr66ugoCBFRUWpSpUqunr1qi5duqRmzZpp/Pjxmjp1qtavX2/lytOuy5cv6/r166pUqZImTZpkvj9e1apV1bFjR02ePFmHDx9Wy5YtdeDAAW3btk3p0qXTxo0b0859oF5T/v7+WrhwoRo1aiRfX1/lzp1bDg4O5hvHL168WMHBweratat5AqlChQpp4cKFCg4OVkhIiLV34bXAma80Yu3atapSpcoLM8rExMTIzs7OfFYrLi7OfEHkDz/8oN69e6tNmzb65JNPZG9vb6Xq8Wd/Nd49KioqwR+n+P7s1q2bcuXKpaFDh1qyTPzBjRs3lDdv3pe2nT9/XsuWLdO2bdu0YMECVa9eXdLz/mvZsqW6d++ud955x3LFpmGTJ0/W9u3b5ezsrJCQEHXu3FkdOnRQZGSkunXrptOnT6ts2bJau3at+TnR0dF69OiRTp8+rTfeeCPNzdxlDWfOnFH+/Pnl6uqqK1euaPTo0QoKCtL8+fM1a9YshYaGqnjx4vrkk0+0atUq/fjjj1qxYoXs7e319ddfq2jRoipYsKC1dyPNmTFjhr799luFh4fLxcVFtWvXVtasWTV8+HCtWrVKrq6uWr16tQ4dOqTmzZvLMAydP39eY8aMkZeXF9d6WdHEiRO1bt06bdy4UW+88YYuX76sTZs2ac+ePZo0aZIiIyO1fPlyhYaGqmHDhoqJidG9e/fUv39/OTk5vXB8kqZZ7pZisJaffvrJKFKkiHH//v0Ey+NvNnnr1i1j8ODBRlhYmLkt/uahhw4deukNQ2EdO3bseOnNreP7Ml5ISEiCx/369TOmT59uxMXFJbgxLCxjwYIFRocOHQzDSHhj3vgbhz569Mj4/PPPjcGDBxvFixc3li1bZmzfvt2YMmWKUbFiRePWrVvWKDvN+fHHH43KlSsbx48fN27evGns3r3bqFmzpjFgwADj2rVrxuPHj40PP/zQqFGjBjfhtZLY2Fjj/v37RsWKFY3FixcbT58+NQzj+c3jmzRpYnzwwQeGYRjG/v37jV69ehkVKlQw5s6da7z77rvGd999Z83S07x169YZVatWNQIDA41jx44ZX3zxhVGmTBlj48aNRkBAgFG4cGHjxx9/NAzj+U3J3333XaNp06YJlvP3yzomTJhgvPnmmwlufG0YhnHnzh1jzJgx5t+JERERxs6dOw0fHx+jatWqRrFixV54DrjJcprg4eEhJycnhYeHJ5gaOf7eNB988IFcXFyUMWPGF55bqVIl5cmTx9Il4yV27NihIUOGvHCvoPgbvN69e1eLFy/WkydPtHv3btWvX1/+/v4aPHiwvvvuOzVu3JhpXq3kypUr5utL/nhj3vgbhzZv3ly2trbq27ev/Pz8tG7dOq1YsUKnTp3SsmXLlD17dmuWn2aEhIQoS5YsKleunHLlyqXatWtrypQp+u233zR79mw9fPhQM2fOlIeHh2bOnKlTp05Zu+Q0xzAMeXp6KlOmTJo5c6bWrFmjqKgoFShQQNOmTdOjR4/03nvvycfHR7Nnz1afPn104sQJXbhwQdu3bzd//mB5V69eVc2aNVWqVCmVL19e77zzjsqUKaMrV67I19dXH330kT788EN98803Klu2rFauXKmuXbuqZs2aypYtmyQm2rCG4OBgHTx4UPXr1zeP3oiOjtahQ4d09epVFStWTM2aNVPv3r118uRJ1a1bV1u3blXHjh1VqlQp+uwlCF9pQPxwwujoaPN1WyaTSY8ePVKdOnXk4+NjnmI+Hh+W10+6dOnMp+7jZw2Ki4szh+jmzZvr8ePHcnV1VbVq1VSyZEndvXtXtra2+vzzz1WgQAEr70HaEz8T3h+vl3zZjXkrVqyoli1bKnPmzGrTpo3Wr1+vNWvWaNGiRUzeYAHGf0ff58mTRxERETp9+rS5rWzZsho8eLDu3LmjBQsWKEOGDOZrwPz9/XXmzBkrVZ02xX9+MmXKpIoVK2ratGlatGiROYDNmjVL4eHhatq0qQzD0Pvvv69Ro0YpICBAH3/8MdcuW0H85+v+/fsJrvlxd3dXnjx5dOjQIUlSt27d1LVrV/Xq1Ut79uwxX/c6Z86cvxyyjeTn7u6u1q1b68GDBzpy5Iik5zPzTpo0ScOHD9fkyZP1yy+/qH79+ho6dKi+//57ZcyYUe3bt9fy5cv5Av8l+C2USv3xZrqS5OzsbL5mK/6AMEOGDJo2bZpGjx5N2EoBbGxszDeijD8AsbGx0cOHD1WvXj298847+vTTTyVJOXPmlL+/v6ZPn66xY8cyU56V/PFAMf7GvMYfLrM9deqU6tSpozFjxpiXGYahDBkyyM7OTunSpbNswWlU/O8/Ly8veXh4aNeuXQoNDTW3lypVSv3799e+ffu0cuVKZc6cWbNmzZKTkxM3m7cw47+3Znj06JEmTZqkuXPnKiAgIEEAmzlzpiSpWbNmMgxDuXLlUq1atZQ/f37rFp9GxX++GjZsqKCgIN29e9d8HJI9e3bz70QXFxd17dpV3bp1U+/evbV3717Z2NgkuI8ULM/BwUF16tSRg4ODvvzyS/n7+ytjxoxauHChVqxYoQkTJuiHH34wf+nRp08fHTlyRHZ2di+M1MFzzPeYCu3du1dLly7Vhx9+qNq1a8vd3V2GYSgoKEi5cuV64YZ48cMwCGCvn4cPH8rd3V3S8/6xtbWVvb29TCaT+cJjOzs7+fn56f333zf34R9vC0C/Wt62bdsUFRWld99913wz5cePH5v7Jb7vGjZsqAYNGiToI/rLcpYvX67r16/rzp07ateunSpUqKDevXurW7duypQpkzp27CgnJycZhqHy5curT58+WrZsmerXry8vLy8tXbqUA0MLM5lMypIli+rXr6+oqCi98847mjp1qvr37y9J8vX1VYECBTRjxgx17txZbdq0STA5CqznnXfeUYkSJeTh4WH+PWdra2v+eyY9D2AVKlRQjhw5lC9fPmuWiz/w9PRUjx495Ovrq0ePHmno0KHm4fC5cuVSz549deLECTVt2lRPnz4137oIL0f4SoWyZMkiLy8vbd68WXZ2dqpSpYrs7e01f/58rVy5UpUrVzbPyJU9e3aGYbymvvzyS/3444/q3LmzihQpIk9PT8XGxurevXvKlSuX+Y9X+vTp9cEHH5hv2GsymRL0KQfzlhUZGakvv/xS0dHRsre3V6NGjZQxY0ZFRETo+++/V/bs2ZU5c2a5ublJon+sZd68eVq5cqXee+89PXz4UFOmTFHRokX16aefavr06fL19VVsbKxatWolT09PSc/PjLm6uppvB0Dwsg4nJyf5+vrKwcFBsbGx5vt6/TGA5c+fX8uWLaOPXjNeXl4JHj979kxPnz41fwk8Y8YMLVy4UIcOHeJ2KK+Z/Pnza9y4cRo2bJi8vb0TtNnZ2enBgwcqWbKkihcvznHlPyB8pTKGYahUqVLq1q2bFi1apDVr1ujhw4dKly6d0qVLp5iYGK1cuVJBQUFKnz69smfPrkyZMqlNmzbmewrh9WBnZ6erV6/qiy++UNu2bZUhQwZFRkZq6dKlcnZ2VrVq1ZQrVy5lyZJFDg4OHMS/JhwdHTV58mRNmDBBX3zxhZydneXg4KDLly9r2rRpunLlinLnzq08efIob968qlatmmxsbFSpUiVrl55mxMTE6MyZM+rXr59atmwp6fmIgW3btmngwIGaOHGiFi1apEGDBik0NFRVqlRRyZIldezYMfP1s9yg17rip6yOP2sSH8AGDx6siIgI9e7dm+uEXmN//KLQ1tZWTk5OmjNnjlauXKkNGzYQvF5Tb731lrZs2SJXV1eFhYUpU6ZMkp7fizL+S0Z+N/4z7vOVSsQPn7l9+7bat2+vsmXLKjQ0VDNmzFBISIhOnjypefPmqWrVqgoJCdHt27d1/fp1/fDDD7p9+7ZGjx7NdUGvoW+++UYLFy5UsWLF9NZbb2no0KEqUaKE7t27p7CwMD1+/Fh58uRRkSJF5OHhoffee0+FChWydtlp0okTJ2QYhlxcXFSsWDE9fvxYo0aNUmRkpGxsbHTp0iUtWLBAV65c0a+//qrvv/9e9+/f1/Xr15UxY0bt3LnTPMQUyevp06dq0aKFmjVrpo8++si8/MSJE1q3bp0ePHigiRMn6tatW1qxYoVOnjyp9OnT6/Hjx+bPI14f8YcxJpNJmzdv1qRJk7Rr1y4+T6+x+PC1Zs0abdy4Ud7e3tqwYYM+//xzlShRwtrl4R/cuXNHLVq0UK5cuZQuXTqdO3dOK1asUJEiRaxdWopA+EoF/jh85vr167p586aKFCkiX19fpUuXTv7+/tq5c6f69u0rX1/fF54fGRnJRZGviY0bNyokJERhYWFq166dcuTIoYMHDyogIEAeHh4KDAzU0qVL9cYbb+jnn3/WtWvXdPHiRf30008KCwvTkiVLuKjcCqZMmaKtW7fKzc1Nv/76q9577z198MEHypYtm0aMGKF9+/bJy8tLW7duTXCTyejoaAUGBip79uxMJ28B169fl7Ozs7y8vLR27Vpt2bJFkyZNSjAT6LFjx7Rq1Sq5ublp5MiRevr0qcLCwvT7778ra9as5iGIeL38MYA9efLEPDQUr7fAwEC1atVK9vb2Wr9+PV9spBBRUVHat2+fjh8/Li8vL9WqVYtr9JKA8JWCxcXFyTAMffzxx6pdu3aC4TPbt29XSEiIhgwZIi8vL40ZM0YRERFq0aKFatWqJen50Bs7O0aevi5mzJih9evXq1KlSrpx44b5fjXt2rXTmTNnNG7cOF2+fFkBAQHmPowXGxurZ8+eKX369FaqPu0KDAxU7969NW3aNBUpUkQXL17U7Nmz5eLiovbt26t8+fIaO3asfvzxRw0cOFC1a9eWyWQy358NljF16lQdOHBAoaGhKlmypCpVqqSDBw8qf/78at++vfk+QtLze+rNmDFDAQEBKly4sBWrRlLEn02J/z9ef+Hh4Zo2bZpat27NF4dIM7giLgU7e/asbG1t9dtvv+n33383L69Vq5batm2rbNmyacqUKXr06JH69u2rdOnSaePGjdqxY4ckEbxeI7///ruOHDlinh5+8+bNeu+99/TDDz9o0qRJKlKkiMaPH6/ChQvryJEjunjxovm5MTExsrW1JXhZyaNHj+Ti4qJSpUrJ1dVV5cuX15AhQ+Tq6qqlS5fq5MmTGjp0qMqUKaN169Zpx44d5vuzwTK2bdumLVu2aPTo0Ro4cKBatmypdu3aqXHjxgoMDNSGDRsUFBRkXr9+/fpKnz69du7cacWqkVTxgYvglXI4Oztr8ODBBC+kKYSvFGrOnDlq1aqVbty4oSZNmmjXrl26evWqub1cuXJq2bKlXF1dtWzZMuXMmVNdunSRJO3Zs0dPnz61Vun/196dR0VZt38cfw+7LDIs40FMyaUk08TMo6SGYaXZpiUuuCQdo5KtNAql5TE1Nbcks0VNNDtmaJl5PDUirrhVoqIZLqmp2BEtU1AGYeb3h4f7ZPo8j/0enQH9vP5h+M5931z3QQ9z3d/v97rkCmw2GwcPHjR6QcHFil09e/bk8OHDvPfee9x+++2MGDGCHTt2sGjRInbv3g0oiXa1xo0bY7PZWLdunTEWGRnJCy+8QGBgIJ999hmnTp3izTffJDg4mOzsbFauXOnCiG8+xcXFNG/enDZt2vD4448TExPDnj17aNy4MY0bN2bv3r3Mnz+fffv2ARdnUIKCgqhXr56LIxe58elvmNxs9C++FhozZgyLFy/GbDZTXFzMww8/zLZt21i8ePEly2fatWtHSUkJ06ZNY+/evUYZ5aCgIDVvrWHq1avH/fffT15eHm3btjU2ij/11FPY7XaWLl3KkiVLGDBgADabjSlTpuDt7c1tt912yR4icY7qBqHu7u5YLBbatWuH1WolIiLCKFzTtGlThg4dymuvvcaiRYsYPnw4o0aNYsqUKbRq1cqV4d80/roM7dy5c8Zyz4EDB3L27FnKysoICQnBZrMRFhZGamoqrVu3pry8nMLCQjIzM119CyIicoPRzFctM2HCBJYtW8Z3331H9+7dWb58OREREXTp0oXt27fzxRdfUFxcbBz/9+UzkZGRl/XZENf47bffOHjwoPF9TEwMBw4cIC8vj/PnzxvjcXFxtG/fnjlz5lBaWsqDDz5IRkYGAwcOVOLlAh999BHp6en069ePnJwcfv/9dxITE9mzZ89ly9datGjBsGHDWLRoEcXFxYSEhDB27FgV13CS6uVnMTEx7Nixg6VLl7JhwwaCgoKYNWsW06dPp3fv3pSUlODl5UVKSgrnz58nODiYBQsWaCmUiIhcc5r5qkUmTpzIggULyMnJISwsDIvFwpo1awCIj4/HZrNhtVo5e/Ys/fv3p2nTpsbyGSVcNcvkyZNZs2YNv/32G23atOG+++5j0KBBHDhwgOzsbHx9fenSpQu+vr4ApKamsnTpUqxWK08++SRdunRx7Q3cpGbPnk12djYvvPAC/v7+fPvttyxbtoyRI0cyfvx4UlNTsdvt9O7d2yi5GxYWRnh4uNH7RM0nnS8yMpLU1FTmzp1LaGgorVq1on79+tSvX58mTZpw7Ngxjhw5wiuvvEK3bt20H09ERK4bJV+1RHl5OQEBASxZssSovhUXF8eiRYv45ptveOyxx0hISKBu3bqsXr2a5ORkoqKiOH/+vJbP1DArVqzgyy+/ZMqUKfj5+ZGbm4vVaqWwsJCJEydSVlbGzJkzOXPmDN27d8dsNhtP46sbGopr7NmzhyFDhjB48GAAtm/fzrJly0hJSWHatGlkZWXxxhtvcOrUKe69917uueceVqxYgc1mUxEAF+vTpw8lJSV8/fXXl/TC8/f3x2w2s3HjRlWAFRGR605/ZWoJHx8fnnvuOdzd3Y1+Jr6+vrRs2ZKffvqJxx57DLi4RygqKooff/yR/Px8QkJCtHymhikrK6N169ZER0cDF5/Kr1u3jtmzZ5OWlsb06dOpW7cuy5YtY+3atURFRVFcXMyxY8fUCNuFKioqOHHiBGFhYcZYVFQUwcHBeHp68sorr/Duu+8ydepUPv/8c6ZPn05gYCA2m42srCyCgoJcGL0EBwfz/PPPc+bMGebNm0eTJk2Ii4vDbrdz6tQpQkJClHyJiMh1pz5ftZzVauWll15i4cKF3HXXXa4OR65CdnY2c+fOZe3atcZYVVUVmzdv5v3336dZs2a89dZb5Ofns379egoKCrBYLCQnJ6t7vAscP34cDw8PLBYLy5cvZ8KECcyYMYOoqCjjmIMHD5Kdnc3x48cZP348ISEhnD59mrNnz+Lv76/EqwY5ffo0n376KR9++CHNmjXDx8eHQ4cOkZ2dzR133OHq8ERE5Aan5OsGMGrUKGw2GyNHjiQ0NFQNJmugnTt3EhERQWBgIKWlpQwePJioqChef/1143dVWVmJ1Wpl4cKF9OnTx5jNrKiowM3NTU/kXWDatGls2rSJ4uJiOnfuzP3338+aNWsoLS0lJSXlkpnI77//nsmTJ5OSkkKnTp1cGLVcjZ9//pnCwkKjR1vDhg1dHZKIiNwEtPP7BhATE8OJEyfYtGkTFy5cUOJVg9jtdk6ePEliYiJLliyhtLQUPz8/+vXrx/79+5k1a5ZxrIeHB926deOWW25h1apVxriXl5cSLxeYN28eixcvJj09nbS0NA4dOsSff/5Jz549uXDhArNnz76k2XW7du3w8fHBarW6MGq5WpGRkcTFxfHII48o8RIREadR8nUD6NatG3feeSfjxo2jsLDQ1eHIXzgcDkJDQzGbzUydOpWFCxdiMpno0aMHbdq0YcOGDXz44YfY7XbgYt+oDh06cOTIEc6dO+fi6G9ue/fupXfv3rRr1464uDhuvfVWCgoK8Pb2JiwsDG9vb7KysigoKDDOCQoKMvrsiYiIiPydHqfXctVLDDMyMjh58qTRnFdqhuqS1WazmQYNGjBlyhTKy8tJSUlh6NChzJkzh40bN3L48GFGjx7NuXPn2LVrF4GBgZrtchGHw0FVVRW//vor/v7+wMU9efn5+fj4+LB161bKyspo1KgRHTt2JDExkXvuuYfKykq2bdtGcnKyi+9AREREaip9uqvlTCYTdrsdNzc3Jk+e7Opw5G8cDgdHjhzhzJkzvPfee+zcuZOkpCRMJhPJyck8++yzrFmzhnnz5tGxY0fCw8M5ceIEn3zyiRoou4jJZMLDw4O0tDR2794NwO7du7n99tv517/+hcPhYN++fYwYMYKYmBiysrLIy8vDz8+PjIwMmjZt6uI7EBERkZpKBTdErrPy8nJmz55Nr169aNCgAcuXL+fll18mOTnZSMQA8vLyqFu3LuHh4YSHh7s4avm78vJyo1EywMcff0xBQQEffPCBC6MSERGR2kQzXyLXmY+PD4mJiXh5eVFVVcWjjz4KwMsvvwzAM888g6+vL7Gxsa4MU/6N6pnlvyZeAJ6enpw8eVK9oUREROSq6RODiBNULyE0mUw4HA4jAcvIyKCiooJhw4Zd9uFeagY3t4t1iU6cOMHJkydp0aIFACUlJYSGhir5EhERkaumTwwiTuTm5obD4TASsIqKCiZMmMCQIUOUfNVg1Xv3nn76aZo1a0ZAQAD79+9n7ty5+r2JiIjIVdOeLxEXqP5vZzKZKC0tNarqSc1lt9vZunUrhYWFBAYG0qFDBxo1auTqsERERKQWUfIl4iLVbQKqv4qIiIjIjU3Jl4iIiIiIiBO4uToAERERERGRm4GSLxERERERESdQ8iUiIiIiIuIESr5EREREREScQMmXiIiIiIiIEyj5EhERERERcQIlXyIiIiIiIk7g4eoARERErkZsbCzHjh0zvvfw8KBhw4b069ePIUOGXPGco0eP0rVrV1atWsUtt9xyzWLZsmULgwcPvmTM09OTevXq0atXL1JSUq7ZzxIRkRuHki8REak1Ro0aRY8ePQCorKxk8+bNZGZmYjab6dmz52XH169fnw0bNhAcHHxd4tmwYYPx+vz586xatYqJEyfSsGHDK8YjIiI3Ny07FBGRWiMgIACLxYLFYqF+/fr06tWL6OhorFbrFY93d3fHYrHg7u5+XeKpjsVisdCoUSMSEhKIjo5m5cqV1+XniYhI7abkS0REajUPDw88PT0BGDRoEGPGjKFr16506dKFoqIimjdvztGjRwE4deoUL774InfffTcdO3Zk6tSpOBwOAI4fP87zzz9P69atiY2NZcaMGVRVVf3jeLy8vC5J9nJycujevTstW7akffv2jB492rhuRkYG48eP58UXX6R169bExMSwdOlS49zy8nIyMzNp27YtnTt3JicnhxYtWhj3c61iFhER59CyQxERqZUuXLjA6tWryc/P5+233zbGv/zyS+bMmYOXlxd+fn6XnJOUlIS7uzsLFiygrKyMl156iXr16jFgwACSk5OJjIzkq6++oqSkhDfeeAOTyURSUtJVxVNVVUVubi75+fm88847AGzdupWxY8cyadIkWrRowa5du0hPTyc6OpqHHnoIgM8++4y0tDRGjBjB/PnzefPNN+natSsBAQGMHTuWgoIC5syZQ2VlJZmZmUZy5XA4/ueYRUTEuUyO6kd+IiIiNVhsbCwlJSV4eFx8blheXo6Pjw/x8fGkp6cDF2e+QkNDmTZtGnBpwY3S0lKeeOIJcnNzadiwIQC5ubmcO3cOi8XC8OHDyc/Px83t4qKQvLw8Ro4cyZYtWy6Lpbrghq+vrzFms9kIDw8nISGBAQMGALBr1y4OHTrEo48+ahzXt29f7rvvPpKSksjIyGDfvn0sWbIEgNLSUtq2bcvChQtp3rw57du3Z9asWURHRwOwfv16hg4dyqpVqzhy5Mg/illERFxPM18iIlJrpKamGjNG3t7eV9zP1aBBgyuee/DgQcxms5F4ATzwwAMALFiwgNOnT9O2bVvjPbvdTnl5OX/88QdBQUFXvGb1EsEDBw4YM1bViRdAy5Yt8fHxISsri/3791NUVMThw4fp1KmTccytt95qvPb39wcuFhP55ZdfuHDhAq1atTLeb9OmjfH6wIED/6+YRUTEdZR8iYhIrRESEkJERMR/PMbb2/uK49X7wq6ksrKSJk2aMHPmzMveCwgI+LfnVccSERGB2WxmwIABhIWFkZCQAFycqUpKSqJnz5507tyZpKQkRo8e/V/jcjgcxgzf38f/15hFRMR1VHBDRERuChEREZw+fZrjx48bY/Pnz2fYsGE0btyY4uJigoODiYiIICIigqNHj5KVlYXJZLqq6999993Ex8fz7rvvGv3IcnJyeOqpp3jrrbeIi4ujadOm/Prrr1zNiv9GjRrh6enJrl27jLG/vr4WMYuIiHMp+RIRkZvCbbfdRocOHcjMzKSoqIgtW7bw8ccf07FjRzp16kSDBg1IT0+nqKiIH374gddff506der8ozL1aWlp+Pr6MmHCBADMZjMFBQUUFRWxb98+MjIyKCkpoaKi4r9ey8/PjyeffJJx48axY8cOtm/fzrhx4wAwmUzXLGYREXEeJV8iInLTmDRpEnXq1KFv376MGDGCvn37Eh8fj7u7Ox988AF2u50+ffqQkpJCTEwMr7322j+6ft26dRk+fDhWq5WNGzeSnJxMSEgIffv2JSEhAW9vb/r378+ePXuu6nqvvvoqzZs3Z8iQIaSkpBiFOzw9Pa9ZzCIi4jyqdigiIlJD5ebmEh0dbZTM37lzJ/Hx8RQUFPzHPWwiIlIzqeCGiIhIDTVjxgxWr15NYmIiZWVlTJo0idjYWCVeIiK1lGa+REREaqj9+/czZswYdu7ciZeXF7GxsYwaNUrVDEVEaiklXyIiIiIiIk6gghsiIiIiIiJOoORLRERERETECZR8iYiIiIiIOIGSLxERERERESdQ8iUiIiIiIuIESr5EREREREScQMmXiIiIiIiIEyj5EhERERERcYL/A8jl4PvmK64IAAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2292,12 +2325,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -2335,12 +2368,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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88EHYd/nKK6/A7/djzpw50t8WL16MoqIi/PrXv4bNZsO3v/1t3d5fUVERpkyZgg0bNoSJ6L6+Prz11lvS+2f7vDwco6enJyz04+WXX8YJJ5yA9vZ2GI1GzJgxA7fddhtKS0tx5MiRpH8v//73vzF9+nQsWrQo6rf6gx/8AD09PXjppZdy7vdCEER+QxUygiCIFHjuuefg9/sVV+kB4KyzzsI///lPrF27Ftdeey2WLVuGv/zlLzAajVi9enXYfa+55hr89Kc/xbXXXoslS5YgEAjgr3/9Kz799FNcfvnlMbdh/PjxcDgceOyxx2AymWAymfDKK6/gmWeeASBY8gChWnfRRRfh5z//Oc455xwcOnQIf/rTnwCEqhbLly/H//f//X/40Y9+hMsuuwwjRozABx98gDVr1uDCCy+E2WxO+TMDgB/96EdYv349fvSjH+FnP/sZKioq8Pzzz2Pjxo24++67JVH5rW99Cy+++CKmTZuG8ePH47nnnsO+ffuk56mrq8Pw4cNx5513or+/H2PGjMEXX3yBt99+G//zP/8DAFixYgXWrVuHFStW4Mc//jEqKiqwYcMGrF27FjfddJMu70cNxxxzDL7zne/gvvvug9vtxuTJk/Hee+/htddew4MPPgij0Yhf/vKX+PWvfw2j0Yj58+ejt7cXjz76KI4ePRrTFsdwOp246qqrcNFFF2Hv3r344x//iG9+85uYO3eudB+73Y7TTz8d//rXv3DuuefCZrPp+h6vvfZa/OQnP8Ell1yCCy+8ED6fD48//ji8Xq/UOzlx4kSMGDECDz/8MEpKSmAwGPD444+HVbW+8Y1vIBgM4sorr8RPf/pTFBcX46WXXkJfX58kIrX+Xj777DPs2LEjZujMwoULUVZWhqeeegpnnXWWrr+X/fv3o7OzE9OnT9ftsyYIIo/gCYIgiKT57ne/y59++ulx7/Od73yHnzt3Lu/xeHie5/mlS5fyJ5xwAu/z+aLu+8EHH/Dnn38+P23aNH7mzJn8D3/4Q/6jjz6Sbn/22Wf5pqYmvrW1NexxGzdu5JcvX85PmzaNnzt3Lv/jH/+Y//jjj/kZM2bw9957r3S/1157jT/jjDP4yZMn89/+9rf5F198kW9qauL/+te/SvdxOp38TTfdxM+dO5efMmUKv3jxYn7NmjV8IBCI+R5bW1v5pqYm/tlnn1W8fePGjXxTUxO/ceNG6W/79+/nf/GLX/DHH388f9xxx/HnnXce//rrr4c9rr29nf/5z3/OT58+nT/++OP5X//61/zatWv5pqYm6T5tbW38jTfeyJ900kn85MmT+VNPPZVfvXp12Pbu27eP//nPf87PmjWLnzZtGr9kyRL+6aefjvl+4n3WWu574YUX8hdeeKH0b4/Hw99///38Kaecwk+dOpVfsmQJ/9JLL4U95sUXX+TPOussfsqUKfzs2bP5yy67jN++fXvc158/fz5/55138rfeeis/ffp0fvbs2fxtt93Gu1yuqPs2NzfzTU1N/H//+9+k3xdj1apVYd8FzwvfNduHjz/+eP6yyy7jd+zYEXafTz/9lD/vvPP4KVOm8N/61rf4v/3tb/yvfvWrsM/q008/5X/84x/zs2fP5qdOncovX76cf/XVV8OeJ9HvRc6vf/1rftKkSXx7e3vM9/vrX/+ab2pq4r/66iue5/X7vaxcuTLqcyIIgmBwPJ9gsAlBEASRF7zxxhsYPnx4WKWlpaUFZ5xxBh599FEsXLgwi1tHZIrbbrsNn3zyCV544YVsb0pOQ78XgiAyBVkWCYIgCoT33nsPGzZswHXXXYfx48fjyJEjWL16NSZMmICTTjop25tHpJn/+7//w+7du/Gvf/1L1zCPfIV+LwRBZAoSZARBEAXCypUrYbPZsHr1arS1taG8vBwnn3wyrr322pgx4UT+8PHHH+Pdd9/FRRddpGr2WKFDvxeCIDIFWRYJgiAIgiAIgiCyBMXeEwRBEARBEARBZAkSZARBEARBEARBEFmCBBlBEARBEARBEESWIEFGEARBEARBEASRJUiQEQRBEARBEARBZAmKvU8DHR19oOzK3IHjgKqqEvpeCgD6rgsD+p4LB/quCwf6rguDQvue2ftNBAmyNMDzKIidbKhB30vhQN91YUDfc+FA33XhQN91YUDfczhkWSQIgiAIgiAIgsgSJMgIgiAIgiAIgiCyBAkygiAIgiAIgiCILEGCjCAIgiAIgiAIIkuQICMIgiAIgiAIgsgSJMgIgiAIgiAIgiCyBAkygiAIgiAIgiCILEGCjCAIgiAIgiAIIkuQICMIgiAIgiAIgsgSJMgIgiAIgiAIgiCyBAkygiAIgiAIgiCILEGCjCAIgiAIgiAIIkuQICMIgiAIgiAIgsgSpmxvAEEQBEEQBBGbQJDH1oM9cPZ7Ue2wYHpdGYwGLtubRRCETpAgIwiCIAiCyFGaW5y4v3kn2vq90t9qHBZcu6ABCxqrs7hlBEHoBVkWCYIgCIIgcpDmFidWrv8qTIwBQFu/FyvXf4XmFmeWtowgCD0hQUYQBEEQBJFjBII87m/eGfc+f3xzFwJBPkNbRBBEuiBBRhAEQRAEkWNsPdgTVRmL5GifB1sP9mRoiwiCSBfUQ5aHUPMvQRAEQQxtnAnEmNb7EQSRu5AgyzOo+ZcgCIIghj7VDouu9yMIInchy2IeQc2/BEEQBJEfTK8rQ00CsVVbYsX0urIMbRFBEOmCBFmeQM2/BEEQBJE/GA0crl3QEPc+18yvp5YEgsgDSJDlCdT8SxAEQRD5xYLGaty75NioSlltiRX3LjmWWhEIIk8gQZYnUPMvQRAEQeQfCxqrsf7SOagrswEAvj2xGusumU1ijCDyCBJkeQI1/xIEQRBEfmI0cLAYhUu2aoeVbIoEkWeQIMsTqPmXIAiCIPIXXzAIAPAHqBecIPINEmR5AjX/EgRBEET+4vWLgozCuQgi7yBBlkdQ8y9BEARB5CdMiPnFShlBEPkDDYbOMxY0VmNefRV+/uzn2Ly/G8unDccNCxupMkYQBEEQQxifaFX0kWWRIPIOqpDlIUYDhwnVxQCAEpuZxBhBEARBDHG8AbIsEkS+QoIsTym1CcXPXrcvy1tCEARBEESq+EmQEUTeQoIsTymTBJk/y1tCEARBEEQqBII8mFORCTOCIPIHEmR5SqnNDADoIUFGEARBEEMan0yEUYWMIPIPEmR5SpldrJANkmWRIAiCIIYychFGgowg8g8SZHkKVcgIgiAIIj8Iq5CRZZEg8g4SZHlKGYV6EARBEERe4A1QhYwg8hkSZHkKS1kc9AXh9dNqGkEQBEEMVaiHjCDyGxJkeYrDagIbP9brIdsiQRAEQQxV/FQhI4i8hgRZnmLgOJRYhSpZDwV7EARBEMSQxRvWQ0aCjCDyDRJkeUyZXQj2oFlkBEEQBDF08YWlLFIbAkHkGyTI8phSCvYgCIIgiCGPPFnRRxUygsg7SJDlMUyQUfQ9QRAEQQxdvBTqQRB5DQmyPKbMRpZFgiAIghjq+CjUgyDyGlO2N4BIH1KFjEI9dCEQ5LH1YA+c/V5UOyyYXlcGI4uyJAiCIIg0ES7IqIeMIPINEmR5DFXI9KO5xYn7m3eird8r/a3GYcG1CxqwoLE6i1tGEARB5Ds+SlkkhhC0gK0dEmR5DIV66ENzixMr138V9fe2fi9Wrv8K9y45lkQZQRAEkTZ8QeohI4YGtICdHNRDlsew2HsK9UieQJDH/c07497nj2/uQoBOkARBEESaiOwh43k65xC5B1vAlosxILSA3dzizNKW5T4kyPIY6iFLna0He6IOLJEc7fNg68GeDG0RQRAEUWjILYsAaBGQyDloATs1SJDlMWWSZZEqZMniTCDGtN6PIAiCILQSOXuMbItErqF2AXvLAVrAVoIEWR5TSqEeKVPtsOh6P4IgCILQSmSFjAQZkWuoXsB20QK2EiTI8pgyu1AhG/AFog7mhDqm15WhJoHYqi2xYnpdWYa2iCAIgig0oipklLRI5BiqF7CLaQFbCRJkeYzDagILGaVgj+QwGjhcu6Ah7n2umV9Pca4EQRBE2oiukNEiK5FbqF3AnjGKFrCVIEGWxxg4jqLvdWBBYzXuXXIsHBZj2N9rS6wUeU8QBEGkHS/1kBE5Di1gpwYJsjxHEmSDVCFLhQWN1fjBzDrp32dOrsW6S2aTGCMIgiDSTmRFLNLCSBC5AFvALjLTArZWaDB0nlNmN6O1202WRR2QnwCNBo5WeQiCIIiMQCmLxFBhQWM13m5px4Zt7QCAW7/diDMmD6drpgRQhSzPIcuifnj8oRXKo32eLG4JQRAEUUh4qYeMGEJ0DISuOSdUFZMYUwEJsjyHRd9ThSx13P6A9N9t/STICIIgiMzgp9h7YgjR4QoJskFfIM49CQYJsjynjCpkukEVMoIgCCIbUOw9MZSQzxob9FE1Vw0kyPKckGWRKmSpIhdk/Z4AXF76TAmCIIj0E21ZJEFG5Ca+QBDdg6EigJsqZKogQZbnlDHLIqUspow7YpWHqmQEQRBEJogUYJFzyQgiV+gcCHdkyds9iNiQIMtzSu1ChayHLIsp44k4qLSRICMIgiAyQPRgaKqQEbmJ3K4IkGVRLSTI8hwW6kGWxdRxi5ZFlhVEFTKCIAgiE9BgaGKo0BElyKhCpgYSZHkOhXroB+shG1FqBUCCjCAIgsgMlLJIDBUiK2TUQ6YOEmR5ThlVyHSDCbIxFUUASJARBEEQmSE6ZZFsYERuElkhc/tpX1UDCbI8h6UsurwBOoCnCFvlGVtpB0CCjCAIgsgMLGXRKHrmqUJG5CpMkLFh0GRZVAcJsjzHYTVJPU80HDo1QhUyQZC19Xnj3Z0gCIIgdIEJMLvFGPZvgsg1mCAbKbZ3UKiHOkiQ5TlGA4cSmkWmC+4IQXa0zwOep5MiQRAEkV5YyqLdLAoycrwQOQrrIasrF66VqIdMHSTICgAK9kgdnuelCtloUZAN+ALo99CBhiAIgkgvLGVREmRUISNyFFYhGy0JMlo8UAMJsgKARd+TZTF5PLKm1HK7WRK51EdGEARBpBtWESsiQUbkMDzPSxWyUeU2ANRDphYSZAUAC/boGaQKWbLIU4KsJiNqSsTo+34SZARBEER68UkVMuGyzR8gQUbkHn0ev7Sv1pWRINMCCbICoJR6yFKGVchMBg4mA4faEppFRhAEQWQGlrJoowoZkcOw6lipzSS5syj2Xh0kyAqAcjubRUYVsmRhgsxqEn4yJMgIgiCITMDzvCTAiqSURbrIJXIP1j9WVWSRqrkU6qEOEmQFgGRZpApZ0rADCgkygiAIIpPIq2GsQhY5KJogcgFWIatyWKR9lWLv1UGCrABgZWOyLCYPq5CxAwwJMoIgCCITeGUR9xTqQeQyHS7BiVVVZJYSQamHTB0kyAoACvVIHbdfuULWRoKMIAiCSCPyapgU6kGCjMhBnP1Chay62AqbKbSv0ty8xJAgKwDKqEKWMlKFTMGySMOhCYIgiHTBLmaNHGAxGsL+RhC5RMeAaFksDlXIAAr2UAMJsgKgzE6DoVMlUpANc1ilv1NvHkEQBJEu2FBok9EAk5EDQBUyIjdhPWTVDgvMRg7i7kq2RRWQICsAaDB06rBJ81aTUfx/AyrE9ErqIyMIgiDShU+shpmNHEwGsiwSuYs8ZZHjOAr20AAJsgKA9ZC5vAGyOSSJR+whs5lDPxnqIyMIgiDSDeshsxgNMBk48W90Lidyjw5ZhQyATJBRhSwRJMgKgBKrCWLVGL0eqpIlgztiDhlASYsEQRBE+vGJM8dMBg5m0QMWoAoZkWN4/UEpq6CqSBBkNItMPSTICgCjgUOJWCXrHSRBlgyRg6EBEmQEQRBE+mEVMrOsQkaWRSLXYIEeZiMnObNYsIebLIsJIUFWIISGQ1OwRzK4pVCPUGoQCTKCIAgi3TB7omBZpB4yIjdhkfesfwwIXTORZTExJMgKBAr2SA1WbpdXyGpIkBEEQRBphgkyk5ELpSwGSJARuUVk/xgQ6rsf9JMgS4Qp2xtAZAZWIaPo++SIZ1ls6ydBRhAEQaQHZcsiWcCI5AgEeWw92ANnvxfVDgum15XBaOASPzABUuR9cUiQkWVRPRmvkK1fvx4zZswI+9+UKVMwZcoUAMCnn36K733ve5gxYwYWLFiAp59+Ouzxzz33HBYtWoTp06dj+fLl2LJli3RbIBDAvffeixNPPBEzZszA5Zdfjra2Nun2jo4OXHHFFTj++OMxZ84c3HXXXfD7QxWjRK89lCmTBBlVyJJBmkNmjrYsttFwaIIgCCJNhCyLHPWQESnR3OLEkjWbcNnaz3Drhu24bO1nWLJmE5pbnCk/txR5HybIxAoZWRYTknFBtmTJEmzZskX638svv4zy8nLcdddd6OnpwU9/+lMsW7YMH330Ee666y7cc889+OyzzwAAmzZtwh133IHf/e53+Oijj7BkyRJcfvnlGBwcBACsXr0a77//Pp599lm8++67sNlsuPXWW6XXvvrqq1FUVIR3330XzzzzDD788EM88cQTAJDwtYc6ZWRZTInQHDKZZdFhAQdhaGfXIFUeCYIgCP3xyQZDG8UeMh9ZFgmNNLc4sXL9V2gTe70Ybf1erFz/VcqizKkgyGxUIVNNVnvIeJ7H9ddfj29961tYunQpXn31VZSXl+OCCy6AyWTC3LlzceaZZ+If//gHAODpp5/G6aefjpkzZ8JsNmPFihWoqKjAhg0bpNsvvfRSjBgxAg6HA7fccgveeecdtLa2Yt++fdi8eTOuv/562O12jB49GldccYX03Ilee6gjWRZJOCSFNIdMJshMRoN04KE+MoIgCCIdsNh7syz2nipkhBYCQR73N++Me58/vrkrpXEKShUyds1EFbLEZLWHbN26ddi5cyceffRRAEBLSwuamprC7tPQ0IBnnnkGALBz506cffbZUbdv374dfX19OHLkSNjjq6urUVZWhq+//hoAUF5ejtraWun2+vp6HDp0CL29vQlfWwtc6lZc3Smzhypkubh96YS931TetyfALIuGsOepKbHC6fKird+DY4eXpLCVhB7o8V0TuQ99z4UDfdeywdAmg0yQBfPuM6HvOn1sPdgTVRmL5GifB1sP9uD4MeVJvQYTZMMcFuk7LLIIFTKPPxj1/RbK96z2fWZNkAWDQaxevRqXXXYZHA4HAMDlcsFut4fdz2azYWBgIOHtLpcLAFBUVBR1O7st8rHs3+zx8V5bC1VVuXdhPqpG+IzdQR7V1bm3fZkgle8lII7WHlZZHPb5jakqxldH+uAKcgX7ueYiufgbJPSHvufCoZC/a6u9AwDgsFtQVVEMAOC5/D3nFPJ3nS48B3rV3c9gSHq/6hJbYupHlkvPUVUmXFfzxujnpe85nKwJsk2bNqGtrQ3nnHOO9De73Y6+vr6w+7ndbhQXF0u3u93uqNsrKiokMcX6ySIfz/N81G3s38XFxQlfWwsdHX3ItYwHTiwXO3vdcDr7Etw7v+A44YefyvfSL1o9vYPesM+vwiqs/uw63FNwn2suosd3TeQ+9D0XDvRdA129wrVK0B+Aq1+4BvJ4A3l3zqHvOn1YVaZyWoPBpParIM+jTWzdMPn90nMEfYJI6+oLXXsW2vfM3m8isibIXnnlFSxatCisotXU1IT3338/7H47d+5EY2MjAKCxsREtLS1Rt59yyikoKytDbW0tdu7cKVkP29vb0d3djaamJgSDQXR3d8PpdKK6uhoAsGvXLgwfPhwlJSUJX1sLPI+c28lKrWwwtD/nti1TpPK9sDlkFqMh7Dnks8gK9XPNRXLxN0joD33PhUMhf9deMeXXbORg5EI9ZPn6eRTyd50upteVocZhiWtbrC2xYnpdWVKffc+AX+o/q7CbpeewSoOhg1HPS99zOFkL9fjkk08wa9assL8tWrQITqcTTzzxBHw+HzZu3IgXXnhB6hs755xz8MILL2Djxo3w+Xx44okn0NHRgUWLFgEAli9fjtWrV6O1tRX9/f24++67MXv2bIwZMwbjxo3DzJkzcffdd6O/vx+tra149NFHpQpdotce6rBQjx4K9UgKpdh7IBR9T6EeBEEQRDrwK8whY1H4BKEGo4HDtQsa4t7nmvn1Sc8jYwmL5XYzzMaQtGCx924K9UhI1ipkBw4cQE1NTdjfKioq8Ne//hV33XUXVq1ahcrKStx666044YQTAABz587Fb37zG9x22204evQoGhoasGbNGpSXlwMArrzySvj9flxwwQVwuVyYM2cOHnzwQen5V61ahd/+9rdYuHAhDAYDli1bhiuuuELVaw91WOy9yxuAPxCEyZjVgM0hh9JgaECIvgcgleoJgiAIQk9YyqLJwEkXu6mk4RGFyYLGaty75Fjc/MJXkE9NqC2x4pr59VjQWJ30c4cSFs1hf7fLKmREfLImyOQDneVMnToVTz31VMzHLV26FEuXLlW8zWw247rrrsN1112neHt1dTVWrVoV87kTvfZQpsQW+qr7PH5UFFni3JuIxK0Qew/IhkP3exHkeRgKJTaIIAiCyAhev5iyKKuQUew9kQyTh5eEibHJwx34yw9mJF0ZY7AKWXVx+LWlnc0h81OFLBFUJikQjAYOJbI+MkI9PM+HLIsRgqzaYYWBE06Ona74kbIEQRAEoRV/MNRDZqI5ZEQKfLS/K+zfFqMhZTEGKM8gA4RRQQDNIVMDCbICQhoOTYJME74Aj2BEgyrDZOCkFSHqIyMIgiD0xqfQQ+anHjIiCT7a3w0AmCiOQhrQyUoYq0LG+u7dZFlMCAmyAoKCPZKDVceA6B4yAKgtsQEAjiYYukgQBEEQWvEGWIUsJMgCvBA1ThBq4XleEmTz6qsA6Fe5ilUhY5ZFqpAlhgRZAVFmF5otqUKmDY/ofTZwgmUkkhqH8Lm+vdOJT1q7qdmaIAiC0A1fQGZZNIQu2/wBOtcQ6tnXOYj2fi8sRg5zxlUAAAa8+gil2D1koZRFnhYQ4pK1UA8i85SxCpk7vEIWCPLYerAHzn4vqh0WTK8r08VTnC+4ZQmLXERoR3OLEx/uFTzZG75qw4av2lDjsODaBQ0pJRYRBEEQBBDqFzMbDWGLgv4gD4rnItSyWayOTasrQ4W4QJ/uCplNbPMI8IL11mKia8tYkCArIEpt0RWy5hYn7m/eGTYskARFOG4p0CO8f6y5xYmV67+Kun9bvxcr13+Fe5ccS58hQRAEkRLSYGgDJ1kWARb2YYzxKIIIhwV6zB5TDrtF2G8GvELlKnKxWSvOmJbFUEXX7Q/AotD2QQjQJ1NARPaQMUERObmdCYrmFmfGtzEXUZpBFgjyuL95Z9zH/fHNXWRfJAiCIFLCJ6uQGQ3hFTKCUEMgyOOT1h4AwKwx5SgSe7t4hPfJJ4PbF4BLtD5GWhZNsr5HmkUWHxJkBYS8h4wEhXrYhHm5INt6sCdKyEZytM+DrQd70rptBEEQRH7jl/WQcRwniTLqISPU8nVbP/o8fhRbjDimtkSKoweAgRRti6w6ZjUZUGyJrthSsIc6SJAVEGWy2HsSFOqRZpCZQwcap8pERbX3IwiCIAglvLLYewA0HJrQDEtXnDm6HCYDBwPHSXbCVIM95P1jStZHmyzYg4gNCbIColQW6kGCQj1uBctitUNdK7Xa+xEEQRCEEvKURYAEGaGdzfuE/rFZY8qlv+lVueqIkbAY+To0iyw+JMgKCIdFEGRHej3oGFAntEhQhGLvbTJBNr2uDDUJPpvaEium15WlddsIgiCI/MYXo0Lmo+HQhAo8/iA+PdQLIFyQFcmCPVIhVqAHg107DfqpQhYPEmQFQnOLEze+ICQCdg368MBbuxM+hgSFgMcXXSEzGjhcu6Ah7uOumV9P4wMIgiCIlIiskDFhRhUyQg2fH+qFxx9EVbEFE6qKpL/rVblSWyGjUI/4kCArAFiaYseAL/GdZZCgEAhZFsObVRc0VuPeJcdGVcpqS6wUeU8QBEHogpSyaKAeMkI9QrJiN5767wEAwMxRpWE9XixpUa9Qj6pis+Lt1EOmDppDlueoSVM0cID8uF5bYsU18+tJUIiEQj2i1y8WNFZjXn0Vnti0H499sA9jKmxYu2IWCVmCIAhCF1iFzMIsi0aWskgVB0IZpRmzH+7tRnOLU7q2Y7PIUu8hExb7E1fISJDFgypkeY6aNMUgDxw3shQAcNqkGqy7ZDaJMRmsh8waY6Ch0cBhYdMwAICz3wfSYgRBEIResB4yE4V6ECqINWO2z+MPmzErVchS6CELBHns7xoAAHQN+BRHJdko1EMVJMjyHLUpiSyBsbzITNWdCNwKPWSRjCq3wWjgMOALoJ2SKQmCIAidCPWQMcsi9ZARymiZMZtqhay5xYklazahtdsNAHjkvb1YsmaTJPgYUqhHEq/DbJevbGvDJ63deT0blyyLeY7alEQ2NDrVie35iNIcskhMRgNGldmwr2sQezsHUFNizdTmEQRBEHlMyLIYUSGjwdBEBFpmzKZSIWNVuEja+r1Yuf6rsD76ZEM9lGyXNQ4Lrl3QkJcuLqqQ5Tlq49nHVdoBkMdXCbdC7L0SYyuF9KK9nYNp3yaCIAgi/+F5XmZZFM5BLG3RH6QFVCIcLTNm7UmGemipwgGQBlBrCfWIZbtkgi+yCpcPkCDLc9TGsxeLM8ooljQaj8JgaCWYqN3XOZD2bSIIgiDynwAPsDqY2UA9ZER81Lqiqh0WFFmSsxJqqcIBsh4ylXPItAq+fIEEWQGgJp6dYkljI1kWVVbI9nWRICMIgiBSRz782SKeg4xsDhlZFokI1LqipteVhSpkGi2LWqpwQEiQqV3w1yr48gXqISsQWDz71oM9cPZ7Ue2wYHpdmRTgERoQSIIsklCoR+weMgAYWyFUyMiySBAEQeiBXJBRhYxIBHNFKfV3MdiM2WR7u7RU4QDArjHUQ6vgyxeoQlZAGA0cZo4ux+JJNZg5ujwsTTFUUibLYiQs9l5pDpkcViE72udJKUaWIAiCIIBQ5D0A6ZwdEmR0viaiYa6oEmt4zUXuigKSHwytpQoHaF/w1yr48gUSZASA1GJJ8x23yh6ycrsZFWJa5X6yLRIEQRApIk9Y5LhwQeYjyyIRgwWN1ThzSi0A4ISx5Xjs3GlRM2al2HuNC8hqswnYAoLWBX+tgi9fIEFGAEg+lrQQcEs9ZPEti4A82INsiwRBEERqMNHFZpDJ/5ssi0Q8WruE65CT66ujXFFA8hUyQBB87HpHTmQVDgi5i9Qu+GsVfPkC9ZARAEChHnFQm7IIAGMqi7DlYC/2UtIiQRAEkSI+0ZZokl18Ug8ZoYZ9oiAbqyCcgOQrZIDQmsH65e8+YxKCQT4qm0B6nSQW/JntMnIOWW2JFdfMr8/LOWQkyAgAMo8v9ZBFwUSqGkE2jmaREQRBEDrh8wuiyyI7/4QGQ9P5mlDGHwjiYI8bQChwLJIiSShpF2Rv7GgHAEyvK8WiicPi3jeZOWRAKIxu4cPvw+ULYtaYcjx09tS8q4wxyLJIAADsoh3P4w8iyNOqmxwp9t6swbJIPWQEQRBEirAKmVleITNShYyIz8EeNwJBHjaTATUlVsX7MKGUjGXx1e2CIFs0sSbhfUOhHtoXEIwGDqxV0mYy5K0YA0iQESLyBMFkfjT5jBbLIquQ7e8aJGFLEARBpATrITMZ5RUy6iEj4sPsiqMr7DBwyiKmSLQs+gJ82HiFRBzsGcSXR/pg4ICFTYmtg/LQOF7jdRHP89I1mCvP06tJkBEAwsUGJS2G8Ad56aSnRpCNKLXBbOTg8QdxpNeT7s0jCIIg8hivlLKoYFkkQUbEYD/rH6soinkfu8z1o+W67/WvnQCAb4wuR1Vx4uh55i7iEVrgVos/yIPt5STIiIKA4zhpFcPtz++dXgse2WdhUyHIjAYOo8vZgGiyLRIEQRDJ45dSFqNDPbRUNYjCYp94/REr0AMQ0jrZfqVlduqr29sAIGHvGEMu/LQ6sOQCbsDr1/TYoQYJMkKCou+jkR881FTIAHmwBwkygiAIInmY6GI2RSAkzgJUISNiwCyLY2IEejCKNF737escwI52F4wGDgsa1CUdGg0cLOI+q3XB3ytbdMj3ChmlLBISdrMBXYOAhyyLEvL+MS6GDzuSsTSLjCAIgtABybJoklfIcqOHLBDksfVgD5z93piR50R2kCyLlbEti4CwEN/j9qsO9njtayHMY/aYcpQXmVVvj81shDfg17zg7/WTICMKECtVyKKQEhZVVseAUIWMkhYJgiAKg3SJEya6zLIKmZSyGMieIGtucUbNiKpxWHDtgoa8nBE1lOj3+NHhEr6XWJH3DLWzyNj+/dxnhwGoC/OQYzMZ0AvtGQXyUUwefxC+QDBsSHo+QYKMkLCnMJMiX2HldbV2RSC0IkWzyAiCIPKfdIoTybKo0EPmD2Zn8bS5xYmV67+K+ntbvxcr13+Fe5ccS6IsizC7YmWRGQ5r/Mt8ZlmMVyFT2r//9/19KLGZVX/PyV5feiNCQFzeAMrt+SnI8vNdEUkRCvWgChnD41M/g4zBVqQ6XF70ufO7CZUgCKKQYeJEfrEKhMRJc4szpef3ilUwecqiMYspi4Egj/ubd8a9zx/f3EX9bVlkfxcL9IhvVwQSV8hi7d/tLm37tzSLTOP1pTciuEZL+MhQgwQZIUEVsmiSqZA5rCZUi1GwZFskCILITzIhTliFLDxl0SDelnnRs/VgT9TFeSRH+zzYerAnQ1tERML61xPZFQF5qEf0dZ+e+zebdevWeH0ZGZPvyuOkRRJkhIQ9yR9MPpNMDxkAjKNgD4IgiLwmE+IkFHsfnbKYjQqZM8H71Xo/Qn/YdUeihEUgdN2nZFnUc/+2JWtZjKiQuTz5e31KgoyQYKEeWudE5DPylEUtjKXoe4IgiLwmE+LEq1ghU9dDFgjy+KS1G69sa8Mnrd262AirHYkHAWu5H6E/WiyLRZbYQknP/TvZsUoeX3QPWb5CoR6EhF0UHWRZDMHEqdWkvocMoFlkBEEQ+U4mxIkvyZTFdAWNTK8rQ43DErdyUltixfS6sqRfg0ieIM+HIu9VVcjEUA9vtFDSc/9O1oEVVSEjyyJRCNiSbLrMZ9hnwfzPatFzFlk6VjkJgiCI1GDiJB6pihOfn1XIZIIswRyydAaNGA0crl3QEPc+18yvp3lkWaK93wu3PwijgUNdmS3h/eP1kOm5f9tMyTmwonvI8rdgQIKMkGArGFQhC+FJItQDCFXIWrsHU/L5N7c4sWTNJly29jPcumE7Llv7GZas2ZRychdBEASRGpkQJ1KFTNGyGH1uyUTQyILGaty75FgYI95WjcNCkfdZZp/oyqkrs8GkYl4Xsywq9ZDpuX/bkry+jK6Q5e/1KQkyQkKKJSVBJuFOsoestsQKq8kAf5DHoR53Uq+d7jhlgiAIIjWYOCm2RNvaL/vmWN3mkCkJMl8gutqQqRTEmaPKwByTbNvuWzqZxFiW2afBrgjIertiCB22f1sjxF1tiVWT+E42xTuqQuYhyyJRALA+KbIshgilLGrrITNwnJRwlEwfGc16IQiCGBosaKzGWdNGAABOHF+BkydUAgA+Pdib8nOHBJm6lMVMpSB+eaQPADC63IbjRpYCAFqcrpSek0gdqX9MRaAHEL9CxljQWI1jhzsAAOdOH4nHzp2GdZfM1iS+k51DRpZFoiAhy2I0rFqotUIGhFaoXt2uvfeLZr0QBEEMHdjA2ikjSnHN/HoYOODDvV34+mh/Ss/rU4i9j9dDlqkUxC8PC4Js8ohSNNUIF+s72lJ7r0TqMMuimsh7IHGFjNEnxs2fUl+FmaPLNdtw2eggzaEefhoMTRQgtizE3ud6YIUnyVCP5hYnPtzbBQB4ZXu75t4vmvVCEAQxdOgTrVQlVhNGlduxaOIwAMATm/en9LySIDNEWxaVzpeZCBoBgC+OCNW/qSNK0DSMBFmuIFkWK9UJMhbqEa9CBgC9bh8AoNSeXDh7srH3kWMf8jllkWLvCYlMV8jSFcurJ6E5ZOoti6z3KxLW+6XGd02zXgiCIIYOckEGACtmj8Er29vx+g4nTvryCEwGA6odFkyvK9NUXZAsiyal2Pvoi1sWxKB0DmKkGjTC83xYhYz1F+1odyHI8zBwlLCYDbz+IA6LPetjK9RZFu1x5pDJ6XEL+3epLTnZkGyoB7sGq7Cb0dbvRT9VyIhCwJ7BCtlQCazQGuqhV+9XplY5CYIgiNTpFwWZQxRkDcOKMalWqBzd9vKOpFNyfeLwZ6UKWawEXxbEYDenFsQQi9ZuN3rcfliMHJqGFWNcpR0WIweXN5B0iBWROq3dg+ABFFuMqCwyq3qMVCGLI3Q8/qAkjEqt6p43kmR7yFiFrKpYuB5yeUiQEQWA5PH1p3eHH0qBFSz23qZSkOnV+0WzXgiCIIYOfWIFocQmXHg2tzixTaF/TOuio9YeMsaCxmp8d1KN9O+zjxuhOYghFl8cFuyKE2scMBsNMBkNqK8uBkC2xWyyTxbowamsUtotiStXfaJd0cABxVZtAWeMlCtkosDMZ8siCTJCwpakx1crQymwwqOxQqZn7xdb5Sy1hlsE9FrlJAiCIPRBblnUc9GRWRYtirH38R/vibhdrwU8ZlecMqJU+hvrI/uaBFnW2C8GeqiNvAfkg6GDCPLK+xOzK5ZYTUnbUZMdq+SVWRYBCvUgCgS2gpHuOWRDKbCC2TeZWE2E3r1fCxqrsfy44dK/zQZOt1VOgiAIQh/6ZYJMz0VHJrrkQ36lHrJg/MVTeUIdi0PXgy+OMEFWIv1NSlpsp+j7bKE10AMICSUgdrtKryjIyuzJ2RWB5Bf8QxUy0bJIgowoBOQe31grJXowlAIrtFbI0tH7daA75Mn3BfmoyfUEQRBE9nD7AvCKwqnEZtJ10THeYOh4lkUgXJAd6NZHkHn8QcmWOFkmyCbWCJZFqpBlj32dwnc8RmWgByBc27A9K1bSopSwmGSgBxAKjdNcIRP3/0qZZZFP4/VpNiFBRkjIV0oih/HpyVAKrHBr7CFLR+/XvoiVzc6B7FcOCYIgCAFWHTNwggVMz0VHViGzhFXIxB6yRJZF2Xn8SK9Hl/P612398Ad5VNjNGFlqk/7eMKwYHID2fi+do7LE/i7tlkWO46Th0LFmkaWasAgANlNyC/5sUaFSrJAFee3BIEMFEmSEhLwKlE7bYiqiJdNzy6Q5ZBpi71nvV6ToTKb3i+d5tIqCjC2Qdg34VD+eIAiCSC9saG6J1QSO43RddGQpiyaFlEUeyrPIGB6Zm4IHcLAn9SoZC/SYMqIkLDii2GLCaFEIULBH5uke9EnCSe1QaIY9wSyyXnf4SIdkCFvw12BbZH2QpTYT2E8gX22LJMgICQPHSaIs3cEeTLSURCT21DgsMUVLc4sTS9ZswmVrP0s6QlgrWi2LjAWN1Vh/6RxcedI4AEBdmS2p3q/2fi/c/iCMBg4TxBSrThJkBEEQOUNfROS9nk4J5ZTF0OPi2RYjK2KtOvSRKQV6MEIDoqmPLJMEgjxe2dYGQAi/kO8rakhUIWMpi2W2VHrIQtukJWmRJV1bTQZpO12e/ExaJEFGhBGapp7+FYgFjdW4ePbosL/d9p2JMcVYNuaWsUqh1az9p2I0cDiloQoA0OP2JZVwxRqx68psqHFYAQBdZAchCILIGSKHQgP6OSVCKYvKgswXp6eY2b3KxTCG1u7UZ4SxCpm8f4zRRH1kGYctVN/35i4AQNegT/NCdaIKmR6WRfmCvxbLoVe2KF5sEV6fKmREQWBL4geTCpGVOKW5LdmaWxbkealRW20PWSQjRI99vycgzanRAvOEj6mwS3M4Mlkhy7RFlCAIYqjRLx7bHREXrMwpMXtMOYDkZoGFUhZDIkxeAYlfIRMuXBuqhZCHVCtknQNeHOr1gAMweXi0IJsoJS2SIMsEei1UFyWYEcYsi6UppCwCoesobRUycUHCZEAxq5Dl6Syy5OUukZckOysiWdhMCavJAI8/KMXpytESITxzdLlu2ya3e1g19JDJsZuNKLeb0T3ow+FeN0psDk2PZ4EeYyrs0qpopnrImlucuL95Z9hnX+Ow4NoFDRS7TxAEIaJUIWMYDRzGVxVh8/5ulNpMmp0SSimLRgMHDkJfmBrLYn11MT5u7cH+FJMWvxDtiuOqiiR7phwWfb+vcxCDvkBY3xChL2oXqufVVyXc5+yi0Ik146tXsiymJhnsZiN63H5NgowtiodVyDxUISMKgNAsssxUyNgBYMYoobn5S9EOISdbc8vkjadae8jkjCgVrIaHe7XbRfbLBBmbw5GJBKtsWUQJgiCGGiFBpixAWO9Nz6D2lX0lyyIgm0UWx7LIBFmD2H98IMUKGTs/T1GojgFAdbEFVcUW8AB20jyytKLnrLuiBK0qvTpYFgH5gr92y6LFGKqQxbJWDnVIkBFh2DLYQwaEvMDHjy6HkRMu+Nv6PGH3ydbcMhZ5bzZySfV/MZht8XCvJ8E9o5ELMjaHI90VsmxZRAmCIIYi/RGhHpGwC1lWaVBLIMiDJdubDRGCTMUsMjbDqWGYIMiO9qUWff/F4eiB0JE0DaM+skyg50K11EOWMPY+RctiErPI5KEexeKCRz9VyIhCwJ7AS6w3Az7hh15ZZJZSBCNti9maW+ZOMmExkpAg01Yh8wd5HOwRHjOmokjqIesaTK8g03PljSAIIt+JZ1kEgFK78PcejX3EcrEl7yEDAJMo0GIJMp7nJfFVW2KFw2oEj+QHRAd5Hl+K5+bJCgmLjCbqI8sIei5USymLMcS6NBg6hdh7QL7gr25RwC9bkJBXyPK1h4wEGRGGfHhfJmArMsUWo7Tq9sWhcNtiOoYtqyGZGWRKMMvioR5tguxwjxuBIA+ryYBhDgsq7cyymF5Bli2LKEEQxFCkzx2aQ6YEsyz2ahRk8gTFKMsiq5DFGA4dCPJgWs1qMmB0uTCbKplgj0CQx4tfHoXLG4DZwGFcZVHM+7Jgj68p+j6t6LlQLaVrK1TIAkFeqkixhYVk0brg7/WHt41QyiJRUNiTKCmnAvthFVmMmDJcWHVTCvZY0FiNs6eNiPp7MsOW1ZLsDLJIRpQJFbIjGi2LcruigeOkCln3gFfTpHutZMsiShAEMRTp8wiLZCUxemzKkrQsygVZZIWMhXz4g8qLp/JFVYtRJsg0VshYrPpvX9khbFOQx/K/bI7ZR8wsi7ucrrh2SiI19FyoLorTm9Unm/mVaoVM61gluSCz0BwyotCwJdF0mQoDkiAzSXNNth3pUzyQt7uEisxxIwXhVmozJTVsWS3SDLKULYvJhXrsk0XeA5AEWYDXvtKqhWxZRAmCIIYifZ74FbLSJEM9WOS90cDBwEVaFuP3kHkD4dWF0RXaBVky4U6jK+ywm4XU5H2dA6pfi9DOgsZq/OKU8VF/17pQHa9Cxq41ii1GmDQOnI5Eur5U6cDyyBJGDRxHoR5EYcHseRnrIZNVyMZXFaHYYoTbH8RuZ7jdwe0LYNO+LgDA/3xzLADhQJHOFTjJsphidC/rIetx+zV5n+UVMkCYPcOaw9MZ7JEtiyhBEMRQpD9RD5l43B7wBeIOco7EKyUsRh9r2cVxLMtiKJ2OA8dxmi2LyYY7GTgOjcOojyxTsGugqSNKcOdpx+Cxc6dpXqguijMYWuofSzFhEZDNuVV5fenxhyeMFlsp9p4oIDIf6hHqITNwHI4V43QjbYub93fD4w9ieIkVx48ul7bzaJ/25EK16BXq4bCapIOZlqTFSEEGABV2Nhw6vf1bCxqrce+SY6MOwum0iBIEQQxF+mIMhmaU2ExgkkqLu4GJLbNCZcKYoEIWOn8JF9usQrZfpSBLJdyJ2RZf2daOT1q7KZE3jXy4V1io/s6kWiyeVIOZo8s1L5ayOWRK1316JSwCcsuiukUJb8Q1mINCPYhCQmtJORX8wVAKFFuhYcEekfPI3t3VAQA4pb4KHMdheJLJhVpgcau2FAUZAAwvEW2LGoI9WiVBFmqgzlT0PSCIsh/NGS39+5T6yrRaRAmCIIYaPM8nTFk0cJws+l79xSSrkJkULrBDlkXlc7VUIRPPX2PECllbv1dVhSLZcKfmFide2d4OAHh/TycuW/sZlqzZlLHZlYEgj09au/HKtra8F4Murx+fiiFoJ46vSPp5iuLE3utZIdO64O+JmMEn9ZDlaahH6p8wkVdkMtRjQLbKwbzBk1mwx+FQhSzI83h3dycA4OT6SgBCX9aejgFNAkcreoV6AMDIMht2tLtUV8jcvgCOiNU/diIFIBsOnX5BBoRWfgHhooJsigRBECE8/qBUpYolyADhgrbH7UePhrElPvF5IxMWgcQ9ZJHnrzK7CQ6rEf2eAA70uKVh0bFIJtyJ9ZxFwnrO0u2uaG5x4v7mnWGVvRqHBdcuaMjLhcSP9/cgEOQxqtyGUbLrBK3YLbGFUu+gPkOhAflgaG2hHmwfppRFoqDIZOw9W42xGDnJD88qZHs6BiRf/raj/ehweVFsMeIbo8oByGZ7pdOy6NNPkGmt6B3oFu5XajOhTBY1K80iS7NlkSGfm5OJqhxBEMRQglXHjFxoQVMJKdhDk2UxFGoQCZtD5ovVQyY+1iqeW7X2kWkNd0q250wvkgkgGeps3CssVJ8wNvnqGJCgQubRT5BZTclZFi2SIMvvChkJMiIMWwZ7yFyyhEVGVbEFI0ut4AF8JfaRvSPaFU8YVyH9MCVBloEKWaqhHkAoafGISkG2X5awyMnStSozNBya0S17nXT3rREEQQw1mCBzWE1hx+pISpOIvpcsiwoVslDsfaIestBjWT+ymuHQWsOdUuk5S5Vsi8FssVEMOjthXGVKzxOvt6tX1x4ybQ6syH242Eqx90QBobXpMhXkCYtyJo8QbItfioJM3j/G0CpwkkHPHrKRooA8pNKyuE8h0APIvGWxJ0yQUYWMIAhCDrN1x5pBxiizax8Ozapf8S2L6nrIAEgVMrXBHizcyRZR+VMKd0q250wPsikGs0Vr1yAOdLthMnA4fkxqI2jkc8j4iBmnbAGhTEfL4qBfpWUxooeMWRbdMptwPqH5SrOzsxNPPPEE7rrrLvT39+PNN99Mx3YRWcKW0R6yUMKiHGZb/OJwHw73utHS7oKBA04cH1oFGqFR4CRDZEpVKrDtVV8hUxZklVm0LLq8gYwNDCcIghgK9CeYQcZIZji0T0pZVIq9FwVZDMuiR0mQJTGLbEFjtTT786ypw2PGqifTc6YX2RSD2YKlKx5XVyoJlWRhQikQ5KMssKEKmZ49ZKlZFoHwDIJ8QZMg+/LLL/Gd73wHL7/8Mp555hl0dXXhF7/4BZ599tl0bR+RYbQ2XaaCS3yNoghL4GQWfX+4V6qOHTeyFOX2UMl8RJkgcNr7PZLPXm8ky6IOFbIRZUJFr3PAp+qzVUpYBEI9ZJmqVnVHWCMzZZUkCIIYCvR6hGOiI4EgYxe0WnrIfFIPmVKFTJxDFmswtML5S+ssMgarPi1sGhYzVl1rz5meZFMMZgu9+seA0HUfED2LrGdQP8ui1pYYlrLI9mGz0SDN5FPqdxvqaLrSvOeee3DjjTfiqaeegslkwujRo/HII4/gL3/5S7q2j8gwmQ31EH7okZbFiTUOGDhBdDyxqRUA8M0J4R7pyiIzLEYOQR442p+eKhkTTnqEepRYTdLqjpqkRalCFpGcVGkXTiiZCNjgeV6yLLIF2k5X/qwwEgRBpEqfW12FTAr1GNQgyEQ7ojlu7H38HjK53ZFVyNRG3wPCeeCoeM6qFVsFlNDac6Yn2RSDqZBsRL8vEMTHrd0AgLkp9o8BwnfHrnMixZKug6GTnEMm34dZNbC/0AXZjh07sHTpUgCQmldPPvlkHD16VP8tI7JCJkM9YlkWP9jbBYO4f7WLAuDJTw6GpSQZZLPIjqTJthgK9UhdkHEcFwoiSWBb7HX7pErU6KgeMuGk3ufxS6un6WLQF4RXtC+w7eigPjKCIAiJ/gQzyBgsLTc5y2LsHrJY5wEpZVG2oFhmM0nbyZJ8E9HvCUhVEzZPMxas50xJHJkNHKaJ1ke9yaYYTJbmFieWrNmEy9Z+hls3bNc0r+3Tg70Y9AVRWWRGY0388QVqiZW0qK9lUVtLjJLtNp+DPTRdaVZWVmL37t1hf9u9ezeqq/NvvkOhIvf4RjZ36o1LIdSDRddGrvp1DPiiomtZsMehNCUtKqVUpQLb3kSCjNlJhjksUdXDEptJOqmku0rWI144WIwc6soEQUYVMoIgiBDylMV4sAqZtlCP2LH37G+xqipKoR4cx0mLa/tV9pEd6RPOV2U2k6rE4QWN1Vh/6Rw8du403HnaMVj9vamYPNwBX5DHXzfuV/WactRWkZgYjByirRRAkm1Sjehn/WMnjKuQFq9TxW5h1auQWOJ5XhZ7r0fKojYHVuQcMiAkHPMx+l6T5D3//PPxP//zP7jsssvg9/uxYcMGrF69Guedd166to/IMKwaxENYndAj8j0WAxGx92qja+fVV8FoSH+FTM9QD0AW1Z9ge2MlLAJCZbDCbobT5UXXgA81CVYsU4H1j5XZzVKYCCUtEgRBhOhTOaepTOoh06tCFr+HLFZK8OhyG7460qe6j+yoOOuzVsO5xmjgMHN0ufTvq07hcNnaz/Dsp4cwbWQJOHCodlgwva4sbtVK66DnBY3VKDIb0CsGrdyyqBFnThmeU5Uxrdc5kY/derAHr25vAwDMHluu23YpVcgGfAFJAOuRssj2RY8/qMqe6QkoWBat+TscWtMn/MMf/hBGoxH/7//9PwSDQaxatQrnnnsuVqxYkabNIzKNTSY+3L5MCTLhNbRE184cXS5FyasdtqwVj0+/UA8gFESSaHZarIRFRkWRIMg6B9NbrWL9Y+V2MyqLWdw+VcgIgiAY/VmqkCVOWVSOzJeCPdRWyMQFRLYAmgwzR5ejaVgxdrS78KsNX0t/jyeuWBUpElZFUqp69bn9khgDgPFVRTklxgDt1zkMJXH68Dt7YTebdKn+hUYehT4/tq9ajJwuTiF5eIiathilChlrcSn4lEUAOPPMM/Hvf/8bW7duxSOPPIKzzjoLBgONM8sXjAZOSrFROysiWVjKYrH4I9UaXTtcpQUwWdzi+7fq0EMGyC2L8StkbOVydLmyIAtF36fZsig2n5fZTFQhIwiCUCA0hyz+4iWroLm8AdXJwGp6yGLNIWMVssgLaSn6Po0VskiaW5zY0e6K+nssi16yg54P9IS/p0z0wmslmYj+WBZHp0udxVENRRZhP5GnLPbKEhbjDT1Xi9VkAHsWNYJKmkOmIMjysUKm6Upz48aNmDdvHrZt2wYAeOGFF7B48WJ89tlnadk4IjtonRWRLJEVMq3RtSNVWgCTJRR7r7dlUW2FrEjx9kwNhw63LFKFjCAIIpI+laEeJVaTdDHaqzKQQKqQKVR5jAlSFpUuZoGQ80J1hUwUZIkCPWKRjLjaciC5Qc+RQSUDab6GSQat1znJilOtSBUymdDp0TFhERB6GKVZt97E343UNqKQsujyFLgg+8Mf/oCbb74Z06dPBwBcffXVWLlyJe6+++50bBuRJWwKpet0EBl7rzW6llXIjvR5Uj4YKeHROdSDCUinyys9dyQ8z0uCbGwMy2KmhkOzg3G5vIfMRRUygiAIhtqURaOBQwkbDq0y+t4XTNxDFjnIlxHr/DVKdF6093tVneNTrZBpsegxnCrDoyKrTQciRGYm5qlqRet1TjKfXzIwQSYXsXomLDLYAveAT0WFTCllUbxe7C90y+LevXvxve99L+xvy5cvx86d8dU7MbRgPVPuNFsWI2PvtUbXDnNYYTRwCAR51QdwLeg5hwwQYo/ZZ8tOcnICQR5vtjgx4AuAQ0hwRlJhz4x9sFtuWaQeMoIgiCj6xJX6RD1kgHw4tLpjd7zB0KyvLHaoh7IgK7ebUSJGh6/dcjDh/KujoqMj1vkoEclY9KqLkxv0fDCiQpaLlkWt1znJfH7JwBbG5RUyPRMWGSz6Xs1gZ0VBZlWO588HNF1pVlVVRdkTv/jiC4q9zzPsGof3JYtS7H2sOSZK0bVGAyet2iUKykgGPeeQAeIsshjBHmwmycoXBDswD+Dsv36k6A1n9sH095CFLItVYoWsx+1X3f9AEASRz/A8r9qyCMiGQ6sM9gj1kMUbDB2rhyw6oQ4QzjXs3P7wu3vjzr8KBHmpOpNshUyrRQ8AZoxKbtAz6yFjn1euXrQvaKzG+d+oi/q7w2qMus5J5vNLhlCFTN5Dpq9lEZA5sFR8Nyxl0WZSsCzm6HebCpo+5QsuuAA//elPcd5556Gurg6HDh3C2rVr8bOf/Sxd20dkAZvG4X3JEhl7z1jQWI159VXYerAHzn5v3HjckaVWHOpx43CfG9NRFnV7svA8L1th1C9pckSpFXs6BsL6yLSmSVVIARvprVZ1y1IWy+xmGDggyANdgz4Mc6Qvbp8gCGIoMOgLxXeXqLhoZdHhaodDx01ZNMRPWVQaDK31XNM54IU/yMPAAdVJHvOZRS+e7S5SXLEqktK2MpQGPbMesnGVRWhpd6W9Dz4lxE0/pb4SpVYT/vNVGyZUFkUlJibz+SVDUZyURT0FmVKvWiy8CosKoVCPArcsXnzxxbjhhhuwZcsW/O1vf8Nnn32Gm2++Geeff366to/IArZMhXpEpCzKYXNMFk+qwczR5TGja1kU7+EefYM9vAEe7DSnV+w9EB3skUzDbsZSFsWDsSDGOJTbqY+MIAiCwapjRgOn6jxRKgkybaEekVUuADAZE80hCxdkyZxrmLW+utgSNXBZLVoteowFjdX41eKmqPvGGvTs9QfRJm5v47BiAOHVnlzji8N9AIAFjcNw2UnjAQCfH+6Lar9I9vPTitJgaLafluloWWQL/mq+G0+cHrJ8DPXQLHuXL1+O5cuXp2NbiBxBaR6F3vA8r2hZ1MqINEXfe2T9c3oKMhbscUhMhkxmJglLWewa9IHneV3iaJWQ5pCJFxFVxRZ0DvjQQX1kBEEQYXZFNcdhdmHLjq2JYJZFU9zYe3WCLJlzjR4zyIBQK0LkHK2qYgtuWKg8hwwARpWHv+43x1Xg/rOmKIqPQ71u8BB6lOrE1oBM95Cxwc2JnD3+QBBft/UDACaPKEFtiRVTRpTgi8N9eGenE8uPGxl2//kNVagutkSJtdoSK66ZX6/LHLIihd4uvVMWAXmFTH3svWLKYqFaFm+77TbcdtttuOmmm2Le55577tFto4jsEgr1SF+FzBvgpZW41ARZeoZDs+qgkVM+GSaLlAwpbm8yDbvMsujxBzHgC0gHKL2Rx94DmavMEQRBDAX62Qwyq7pzWCjUQ2PKosJFfSJBFhmIkMy5Ro8ZZAx5K8JtL32NI30e3HhqA77VEFtM7O0UesJYeFePxx+zEsQCPUaV2zOyqByJ0uDmWIOvdzpd8PiDKLGapDEE8xuq8cXhPrzZ0hElyLYe7IXT5YXNxOGeM4+FyxOIK/iSIV6FLC0pi2p6yOKGehSoZZHn9Y8UJ3KXTBzM5D8mfQSZvpbFUKCHfv1jADBSXLk7JIZ6JNOwazcbpaSidIkjty8gCfJySZBR0iJBEASDVcjUJCwCQKl4LNXDssj6ynwxQpYie6CTOdekOoMsEtaKMG1kKQBgf2f8WWj7OgcAALPHlAMAdjsHEIxxPcoi7+vKbBkLJmPEGtwca/A1sytOHl4Cg1hZ/ZYo2j5q7Y7qMVz3+WEAwOJjanHShKqErRzJwHrIBmTzwdLTQybsy2quL70KSaEFXyG7/fbbAQD19fX4wQ9+gOLi4pRetLu7G3fffTfefvttBINBzJo1C7fddhtqamrw6aef4s4778TOnTtRUVGByy+/PCxq/7nnnsOjjz6K9vZ2TJgwAb/61a8wY8YMAEAgEMB9992HdevWYXBwECeccAJuv/121NTUAAA6Ojrwq1/9Cps3b4bRaMSSJUuwcuVKmEzCx5DotQuFTIR6sB+T3WyQDkjJMKIsVHEK8nxKzyVH7xlkjOGyWWS+QDDpht2KIgsGe9zoHPBJc2X0hK3gGg2c5NlmlbkO6iEjCIKQBJnaC9ZkQz1MiqEe8XvIIu1eyZxr9KyQyRlXVQQA2CMKrljsEwXbSROq8HFrNwZ8ARzp9UgLm3IO9GSnQqa2N29efZUkoL44IgqyESXSfcZU2FFfXYRdzgG8t7sTpx1bC0CYc/f6DkHQLZk6PB1vAYDyQnyvZFnUs4dMQ6iHwnDzImkOWSCtLRvZQNPV5uOPPw6bLTUvMQBcddVVGBgYwGuvvYY333wTRqMRv/rVr9DT04Of/vSnWLZsGT766CPcdddduOeee6So/U2bNuGOO+7A7373O3z00UdYsmQJLr/8cgwOCj/a1atX4/3338ezzz6Ld999FzabDbfeeqv0uldffTWKiorw7rvv4plnnsGHH36IJ554AgASvnYhkYlQj1gJi1qpdVhh4AQLpNJcrkCQx8f7u7Fu60F8vD/+vBU5bAab3oKsqsgMq8mAIC+c7JJt2E33cGgp8t4W6o2oogoZQRCEhNqh0IxQD5nW2PvUe8iSOdccSXEGWSzGVwqCbG8CQcZubxhWhHHiY3Y5XYr3ZRWyUeW2kP0uA1WUZAY3f3m4F4BQIZMzX7RvvimrqL2yvQ0efxDjq4owdUT4/fWECZ2BtKcsqp9DJu3DCimLgWAoCTtf0HS1efLJJ2PNmjVoa2tL+gW/+OILfPrpp/jd736H0tJSOBwO3HHHHbjuuuvw6quvory8HBdccAFMJhPmzp2LM888E//4xz8AAE8//TROP/10zJw5E2azGStWrEBFRQU2bNgg3X7ppZdixIgRcDgcuOWWW/DOO++gtbUV+/btw+bNm3H99dfDbrdj9OjRuOKKK6TnTvTahYTdpL6knCyRQ6GTxWQ0SBHsR3qVZ3tdtvYz/OKprXHnrUQiWRZ1jLwHhFlkzP7BGqYbqouhtMYTK00KSP9w6Mj+MQCoLKYeMoIgCIZmy2KSFTKLUoVM/FsggWVRXl3QMucTSGOFTBRXezoGYrbEePxBydo/rrIIE6riCzKph6zMrskWlypae/P63H6pN25KhMCaL37+H+7tkrZ93edHAABLpwxPazUoMo7e4w9KbQv6piym1kMmb3HJN9uiJtn7ySef4MUXX8Sf/vSnqNu2bdum6jk+++wzNDQ0YO3atXjyyScxODiIk08+GStXrkRLSwuamsJjThsaGvDMM88AAHbu3Imzzz476vbt27ejr68PR44cCXt8dXU1ysrK8PXXXwMAysvLUVtbK91eX1+PQ4cOobe3N+Fra2GoV1DZ6pLbH0zbe2GrMEUWY8qvMaLUiqN9HhzudWOq6E1v3hF/3srvlxyLBU2xm4mllRmzQffPYESZDfu6BnG41w2OA/68cR94ACeNr8CFs0bD6fKiutiCGaNiN+xWFoeSFtPxHbGVsQq7WXr+quJQhSxX93G2Xbm6fYQ+0PdcOOTyd90vRm+X2Eyqtq/MHgr1UHN/KdTDGH0eYoLMH+SjbgsEealyZos4hy1sqsa3Gqrw5CcH8ODbe1BdbMb6S2dHnWu8/qC04Dei1Kbr5z+20g4DJ1xQdwx4pUVV+Xd9oHsQPAQRW1lkRsOwYryyvR27OlxR2xLkeRwUh0KPqrBJ4mfQF0j7fqOlN4/jgK+OCnbFujKbdB5nNNUUo67MhoM9bny4txOjyu3YdrQfJgOH0yfXpPW9FMsqZBwH9HuE797AAQ5b6tdpjCLJgRX/uwnbh02hfdjICW0ULm8gI9+vHqjdRk2C7Pe//30y2xJGT08Pvv76a0yZMgXPPfcc3G43brjhBqxcuRLV1dWw28P7YWw2GwYGhLK1y+WKebvLJayaFBUVRd3Obot8LPs3e3y819ZCVVX6ysqZYFiF8BkGOQ7V1el5L8ZDwkGprMiS8muMG+bA1oO96A0A1dUlCAR5PPD25pj35wA88PZunH3CuJiCx3JYiKQtsZt1/wzG15Rg494udPt5dAc5vLJNqDivPH0ypo5SN9yxrkro4xwMIi3fkd/UCQAYVmaTnn+CRxCpXW5/2vYLvRjqv0FCHfQ9Fw65+F37RG/D8MpiVcdEo10QHi5vAGUVxYpWRDm8+PzVCs9f5RQDMQyGqNvkoVkja0sVWwN+eEoDHnx7D5wuH4pK7SiJqILs6xCum6wmA+pHV+henRlTWYS9HQPo8gOTIt9bVQk2HhTOwfU1DgwbVooZ46uBd/dib5c76v0e7hmEN8DDZOAwZXw1vhZFjyfAp/1ctajSgRGv7MCRHjeUan0cgOFlNiyaPgpGA4e9nwkVr2+Mq1TcttOPG4nH39mN9V+2wSBenyw6tgaNY6rS+C5C+6bHH0RFpQOdYvGpzG5GzbBS3V6nSry+HPD64/6m5XbTkcPD9+ESmxkubwDmImvOX4toQbUg6+/vh8PhQENDAywWdSsCSrDH3nLLLbBarXA4HLj66qtx7rnnYvny5XC7w21nbrdbChGx2+2Kt1dUVEhiivWTRT6e5/mo29i/i4uLYbfb0dfXF/O1tdDR0YehHEzpE+0UPS4PnM6+BPdOjiMdwsHWwiHl16i0CSsuOw/1wOnsw8f7u3G4J3YMPg/gcI8br209gOPF9KZI2ruEk5GB53X/DMrMwkH23a+P4sMdbQjywCn1VRhhM6h+LRsn7GCHOl1p+Y4OtgvPaTdw0vMbvMJ+0dnvQVt7r24BKnrCccLJfKj/Bon40PdcOOTyd90mVmUM/oCq47C8h3nvwS5ppmQs2KymwX531PO7+oVz3KDHH3Vbt8xW3tc9gIEYC48s5GPT10ejgqO27e8GINgVO8TztZ6MLrdhb8cAtu7pQFN5qELGvuvP93UAAEaVWOF09mGYRXgPu9r7ceRoT9g4ms9ahW0dXmpFd5cLHpdgtXQpfDbp4JfzJuAGBUcOIFxv/HLeBHR1Cp/hpl1Cy0RjpU1x28y8sPD5/q4O6W8f7urA2g/2xHX1pIpX1o/Vergb+9qFayCHxajrZxgQK28D3kDc37R8Vl9vxD5sMwn/feBoL4Zb9e3zTwdsv06EKkH2+eef48c//jH6+/tRW1uLNWvWoLGxMakNa2hoQDAYhM/ng9Uq/AiDQWFHmDRpEv75z3+G3X/nzp3SazU2NqKlpSXq9lNOOQVlZWWora3Fzp07Jethe3s7uru70dTUhGAwiO7ubjidTlRXCzv1rl27MHz4cJSUlKCpqQnvv/9+zNfWAs8j504cWmB9U25/MG3vg01ZL7IYU36N4SWh6Hue1+bpjvXaLC7Xakp9++Q0tzjxz08OAgC2HOiV/j6jrlTT61TYmX3Ql5bvqFtsOi+zm6XnZ31rAV444Se6mMgmQ/03SKiDvufCIRe/a3kPmZptM3AcSqwm9Hn86B7wo9we/xgqpSwauKjnD4V6RJ+nWe+PycDBwEU/ltFU40Bbfye+PurCcSPDBRnrca4tsablcx9fWYR3d3WKfWTht/F8aAbZ2Moi8LyQUGw3GzDoC2J/lxvjq0JuqANdof4xng/vgw8G05/EN1/szbvxha/C3ku53YSbFjVhfmO1uP/y+FKKvI8+5ze3OPHIu3ujnr970I8b1n8Vs6dcD0wGTpr3NuANSIKo1GbW9fuXvhtvIO5vWpoFa+BgjNiHWfR9vyeQc8eEVFAlLe+55x4sW7YM69evx4knnog//vGPSb/giSeeiNGjR+Pmm2+Gy+VCZ2cnHnjgAZx66qk444wz4HQ68cQTT8Dn82Hjxo144YUXpL6xc845By+88AI2btwIn8+HJ554Ah0dHVi0aBEAYPny5Vi9ejVaW1vR39+Pu+++G7Nnz8aYMWMwbtw4zJw5E3fffTf6+/vR2tqKRx99FOeccw4AYNGiRXFfu5DIRENsKGUx9dCMkWKU/KHe5Gd7RRIK9dBv9YXNKlGaQfOnd/aoChthVKQ5ZbFblrLIMBkN0r/TFSZCEAQxVAgNhlbf/REaDp34GOoVUxZNGlMW1Y5taRwmOIB2tEdXwI7qPIMskrGV8aPv2QyycZWC+8nAcZggWvUjgz0OiJXKunLhWoAFRwT50GeYbubVV4F5FqfXCRa/qSNKwwTU4V4POgd8MBo4NA0Ld1+pjc9XmxStFY7jZLPIAmlJWATUh3pEjm2QE+p3y6/h0KquNrdv344bbrgBjY2NuPbaa/H5558n/YJmsxl///vfYTQasXjxYixevBjDhw/H3XffjYqKCvz1r3/Fyy+/jDlz5uDWW2/FrbfeihNOOAEAMHfuXPzmN7/BbbfdhtmzZ+PFF1/EmjVrUF5eDgC48sorMW/ePFxwwQWYN28ePB4PHnzwQem1V61aBb/fj4ULF+Lcc8/FySefjCuuuAIAEr52IWHLwFBFSZCZU/+xs0jeI71u8DwvzVuJh9JsLzken76x93ofbEOx9+kRRmx1rNwe3ldAw6EJgigEAkEen7R245VtbfikVXlkSqhCpn5hMZS0mPhi0h9nMDQTaX4FwaE0UFeJpmEOAEBLe3Ry4ZE+YYFT74RFhhR93xEtyHielyLvmXADgPpq5aTFA2LCYp04n4wlBgKZib4HBIHNQ+gZW7lQcFZ9sKcTTlfoXPmFGHffNKxYus5iJBOfrzfyxfj0CTJ1c26VEhYZxeICCHNa5QuqP2mzWbgwq6qqgsfjSelFa2tr8cADDyjeNnXqVDz11FMxH7t06VIsXbo05jZed911uO666xRvr66uxqpVq2I+d6LXLhTsGRgMzVIWU429B0LDlgd9QfQM+lFeZMa1CxoUUxYZSrO95DDLR+RBM1m0HGxnji5P+HzMLtg96EMgyMd9L8mgFHsPCNH3ezqBThoOTRBEntLc4sT9zTvDjtk1DguuXdAQVvGQ5pBpuGhlx1Q1giw0h0xpMHScClkcISenqUYQZLucLviDvPScgKxCpvMMMgazHDpdXvR7/GGjA9r6vRj0BWE0cBglGwJdXy1WyCJEXGgGmVBNMxo4WE0GePxBDPoDKId+se2xYOfEMruQCDllRAm+ONyHl746iotmjQYAfMkGQg+P7ifSGp+fDuSzyNhoBj0j74FQxavD5cXH+7sxvU45TVqqkCkIMrad+RZ7n9Tyfz5NxiaikXrI0lghc+loWbSaDFIk+2FxVW9+Q5VilazCblblw1Zr+VCL3gdbVrkK8upn2mihR7xYiKyQsd61DqqQEQSRhzBreeQCGhuZwqzlPM9LFTItlsUyTZbFUC9YJPEti+ocHqPKhb4sjz+I1q7w0DN5D1k6cFhNqBbP25EDotm/R5XZwuya9TEsi2xe2ajykHizZWCeqhx2TmTulTOnDAcAvPDlUWnWGusfmzIiOrVQj1aLVAnNIgtK1wB6VsiaW5y49vkvAQgunHizYeNVeR2SICtAyyJRWEgVMn8g5tDGVNGzhwwARoqreCxd8cO9XWjr96LIbMADZ03GseIAxp+eOEZVU6xbZ8ui3gdbk4FLaz9Xj0IPGQBU0XBogiDyFC3W8gFfAEwLaeshE46hPQkqZDwfmsOkZNtigsynMBjaG8fuJcfAcWioFqpkO9rC+8hCPWS2qMfpxbiq0IBoOczGOK4yfIxRvdh3daB7UDpH97n90mdZVxYaXcSuLTJlWWTnRCbIvj1xGKwmA/Z0DODLI33wB4LYLn7Gk0dEV8j0aLVIlfAKmSjI7PpUyNhCR0fEtUPkQgeDuZSUqrzMWZVvlkVVV5uDg4NYuHCh9L++vr6wfy9cuDDd20lkkEw0xLI5KXpYFoGQbfGwuKr3pJhkuGzaCJxcX4WTG4cBCCU3JULvUI90HGxZP5fe4sgXCEoVzCjLIvWQEQSRp2ixlveJF6wm0R6nFqmHbDD+cVte+TIbop+fzTCLF+qh5vzVVBMd7NHv8UvngNo0WRYBYFyFIKCiK2ShhEU5VUVmlNlMCPLAPvE+LNCjssgctsCbiV54OZ1ShUw4RzqsJmnx94UvjmKn0wWPPwiH1YgxFfaoxxsNHK5d0BD3NRK1WqRKqEIWsiyWalhsiEUyPfTxFhVYyqIrQ9XPTKHqk7777rvTvR1EDiHvmxr0BXSrEsnR07IIACNYhazXjZ1OFzbu64KBA86bUQcAaGBeeYUGYiUky6JOPWTsYJtKX1skFUViP5fO4oitNhq46JVftvpHKYsEQeQbWqzlrLem1GbS1MahtofMK6t8ae4hU1khAyCl/e2QBXscEatjZTZTWECG3oyPUSHb1xWesMjgOA711cX474Ee7OpwYWKtQwr0YP1jDLbdA5myLIo9ZJXFoYXXM6fU4qVtbXh521GwXWR0uU2IalfYZRaI8fmR/Yu1JVZcM78+bZH3jCKzQoVMB8tiMj30oZTF6A9K6iHz5JdlUdUnfdZZZ6V7O4gcwmTgYDZy8AV4wRagU8lajt6WxRGyCtmTnxwAIMwGGSk2BDfVChaB3c7oNCkl3Dr3kAH6H2zTlbTIAj1KrKYogcjCRDpcVCEjCCK/0GItl88g00KZypRFn8ydYlZMWRSOzYEgD54Pn7WlpQeaBXvILYtHRadJTZr6xxjMkhhZIQtF3hdFPUYSZOK5PBToEW6tLMpAOJmcrogeMgCYObocFXYTugb9ePbTwwCAbUddWLJmU1RADGNBYzXm1Vdh68EeOPu9qHZYYgZf6I2d2Tx1FmTJ9NCH9uHoa8Ria36GeuibZ0nkDTaTEb6AP23BHlLKog6x90Co8fjTgz1So+cPvlEn3c4qZJ0DPnQP+qLCKiJhTdF6ziED9D3YMnHUmcD6opVYkfdAqIeMKmQEQeQbzFoebzWfWcvf39MJQFv/GKB+DhmLvDdwUDw/yIM+AkFeEmhAqLqQKGUREAQOB+GY7nR5UV1swVExHCtdM8gYrEJ2sMcNrz8Iq9mAfo8fR/uEz39sZbS1LxR9L4i2g92hodBy1M670ovOiB4yAHhrZwe6BqOFN+ubihUwZjRwqtKW9UZpDpkeKYvJ9NCrsizmmSCjUA9CEXmwRzrQs0LW3OLEna/uACDY7fxB4WQlr+IUW02SrTHSHqFEOgZDM9jBdvGkGswcXZ70yle6hkP3xIi8B+R9a960Bb4QBEFkAy19PH1JDIUG1Id6eKXIe+VzkEnWVxZpW9RSIbObQz1NLWIfGbMspithkVFdbEGxxYggD+wXK117ROtkZZFZ+qzkRCYtRg6FZjBxMejPTg9Ztgc9JwOrkPV7/FIFuNSe+qJ5Mj30HlWhHvllWSRBRiiSzoZYnud1E2QsuSeyYuMP8lj5wraw5J4J4oF8d0di2yKrDOrVQ5YO0mZZjBF5L39Nb4DPu9UpgiCIBY3VmKKQgldbYg2raCRrWQwNho5/3GbpiUr9Y0B4hSwVQQbIbYvCuTE0gyx9CYuA0BM2LmJA9C5RFEYGejAmiBWyI30e9Hv8CXvIMmVZjKyQ5cKgZ60wESvfbj1CPZIJLAnNIYve/x1ihSxT/YGZggQZoUg6Z3gM+oJgp49UUha1rkCxA/luZ3YrZHohWRZ1FmSxIu8BQaiz70ypjywQ5PFJazde2daGT1q7pc8+1t8JgiByiUCQx35xJtdxI4V5UTNHl2HdJbPD7GXSDDKbtnNYuVj16fcEFAM5GD7xNqWERQBhFsXI6Pt41QUlGsVgD6lCluYZZHKk6PvOcEEWGejBKLWZpWrL1239aBPFY11ZuHi0iS6fTFgWeZ4PVcjEUI9cGPSsFSZij/QKIrfYYgybA5cKrIc+slIWudDBiBdMU5Snsfeape/atWvx97//HW1tbXjuuefwu9/9Dvfccw+Ki4vTsX1ElpBWl9JQ7meR9wYutdAMtStQWw704Ds1pagXD/y7OxMLMrfKwZrZpNKeHstid5weMkCwSrq8AXQO+DC2MvT35hZnVGBJjcOCxcfU4JXtbVF/j9XUTBAEkS22H+1Dr9sPh9WIH84ejWuf/xKdA74oa3l/EkOhAcAhW+jqc/ukhbVIElXIDBwHIwcE+OgKmTdOIIISMStkGRBk4yMqZLtFy6JSoAdjQnUx2vq9eGdXB3gILRby3i1ANocsA1UUlzcghbBUiOfNXBj0rBXWqsK+f637diJYD/0lT27FF0f6cNGsUbjypPGKbRuhClnsUI8BXwCBIJ+RwJNMoOlq84knnsBf/vIXXHTRRQgEAiguLkZbWxvuueeedG0fkSVsaUwokkfea4kLjkT1CpRYyRnPLIsqkhZDFbLctSxWpCmCnvU2KPWQAeF9ZAxmHY0UyG39Xvz94wOKf1caBkkQBJFNNu3rBgAcP7ocx4hCZX/ngHROYLAeMq2WRZOBg0O8oIzXR+ZL0EMGQKpexLYsqju/suj7fV0DGPQFpAvydM4gY0QmLSayLAKhPrJ3dnUAEOyKkdcS0kytDAgy5hYpthildo9cGPSsFSZie3RMWIzEaOCkfr9hDktMMRW/hyy0XZn4fjOFJkH25JNP4tFHH8W5554Lg8GAsrIyrFq1Cm+++Wa6to/IEun0XzPfb1GK/VmqV6BECwFLdGJJi/HQ6sHPBkwYubyBqIuFVIhnWRReVxBqHaIQVGMdjUWuNTUTBFHYbNrXBQCYM7YCwxwWlNpMCPChCg6jL8kKGRAK9ogXfZ+oQgbIZpEFIgRZnOqCEtXFFlTYzQjywEf7u+EP8jBwwLDi9Fdv2Hl5X9cg/IGgtGAay7IIhJIWWf9YpF0RkAuy9Id6KCUs5sKgZ61EzpwrTcPII0AIWQME224s4i0qWIyctO/nUy+7pqvNrq4ujB8/HgCkhLWqqir4/fmVdEKkN9SDebrlqxzJoHYFasYoYQWqyGLESHHFL1GwBxOirFKYizisRumgpKdtMV7sPQBUiSfpTnFVUI11NBa51tRMEEThMuAN4LNDvQAEQcZxHBqqhWrMzghnRbKWRUA+iyz2wmBIkMWpkMUYDh0vMlwJjuPQVCO8z3fFqlN1sUW3/qF4jCyzwWzk4PEH8d8DPUL8vcmA4SWxA0Xqq8NbZCIDPYCQ/S4TFRR2/o20n2rtm8o2kYvksRZlU0VNSqI3ToWM47jQc3jzR39o+rUdc8wx+Ne//gUAUnl4w4YNaGxs1H/LiKySzlAPl04Ji8msQE0QD+Txou/9gSDYgmMuV8g4jkOFGEm74aujuoVldMeJvQdCq4BsVTDVpuRcamomCKJw2XKgB/4gj5GlVmnQcCjwIlyQ9Ymr+44kLlrLVFXIElsWjZIgUw710HL+ahwm2DPf2y3MV6uNI4j0xGTgMFoUVG+2CGJwTIU9buWIVdUYgWAw6tyXUcuiQoWMsaCxGusvnYPHzp2GO087Bo+dOy0qICZXsEdck6XDsgjIBFmc6pY0Sy/GPlych8Eemj7tlStXYsWKFVi3bh0GBgZw6aWXYuvWrfjzn/+cru0jskR6Qz30m0HGVqAiwyRqS6y4Zn591EFvfGUR3tvdGTdpUf6e1Vo+skFzi1MaOrn6/X0A9AnL6IkTew/I0x2FzzvVpuRcamomCKJwYXbF2WJ1DAhVY3Y6+8Pum5plUXhMPOt8KGUxsWXRF2FZlAIRNFS4WIWM9VxnImGRMb6qCLs7BvD2TqGneGxFbLsiAHy4t0sKNAGAJ/97CG/scIad+zJqWRQ/s6oYFs9sDXrWSmSFTGkOnB6wvst4gixR0rVge/TkVYVM05Fk8uTJ+M9//oMXXngBkyZNwvDhw3H77bdj5MiR6do+IkukM9SDpSymEnkvhyX3bD3YA2e/F9UOC6bXlSmusEnR93Esi0yQcRC8yrkIC9GIhIVlJGuH8Ad5qVm9LMZAyKqIChmzjiZjW8y1pmaCIAoXef8Yg1XIdkYs4jHLYjJzmkKzyOJUyMTzkDlOlcscI9TDnUSFrEmskDGGZyDQg8GCPdg5JF7CotpznyTIMtBj1CUK64o09VxliqgKmc4piwyHbAB1LFRXyBS+30CQV3U9mGto/rSrq6txySWXgOd5vPPOO3A6nSTI8pBQqIf+q0t6WRblqF2BCg2Hjl0h88gi71NJgUwXauevzauv0nwQ6nP7pBlxsVbHKiMqZMw6qnSSTESuNTUTBFGYtPV5sLtjAByA48eUS3+fUFUMDkKSXueAF5VFFgT50MJVMpZFFpYQV5CJNkQ1FbJIy6LWHjJASDU0GwB2yvf6gxmLFB8fIcDGVSkLMi3nPnsGY+9ZymJlBkJQ0kl0hSxNlkUNFbJYs/RYBkHkc8QavzMUxuxoapBpbm7GySefDABYvXo1rrrqKlx00UVYu3ZtWjaOyB62NPqvJctiiimLyRCWtBgjLp6J0FztH1M7fy2ZsIyewZANxxTjRFwphXqEPr8FjdU457gRUfetLbHiouNHDZmmZoIgCpPN+4Xq2DG1jjC7dpHFKPWT7RT7yAa8AWnhKn2hHmpi72OkLCZRIRNmeoWO+Wu3HsKSNZsyMpoksiI24PUr9kNrOfdlMtSDuUWqFHrIhhKRIWZpS1lUUyFLsA8XKVTI4o3fGQpjdjRdca5evRpXX301gsEg/v73v+Ohhx7CP/7xD6xZsyZd20dkiXSGekix9ymmLCaD3SxLWuxUti1K3uUsCEY1qJ6/loSFMBToEfu7YY3LA75AmKX1iDi75swptWHNyz+fNwHrL52D+5YeK933XxfPJDFGEETOwOaPye2KjPqIpEXWP2Yxckkt3LFQD7YApgRLWYxnmzcZlC2LiS5mI2EXspHPk6kLWTaDjHH3azsVxaCWc19RBkM9YqUsDjUMHCcJWSB9KYtaesgSh3oIvyG11dNcHrOj6Uiyf/9+nHvuudi+fTvcbje++c1vYsqUKXA6c1t1EtpJZ6iHS4q9z47gYUmLsYI9mOfcHwzqllyoJ6rnryURltHjjh95DwjfG7tIYCuD/R6/1H9x4fGjsHhSDWaOLpfsLkYDh1PqqySh36HzMGuCIIhk4XkemxX6xxhSH5lYIUt2KDSDWcF6VFTI4kXPx4q9Z7b7WHYvOdm+kG1uceLWDduj/q4kBrWc+9iCqjfAR30+eqM0h2yoIp9Flu6URTU9ZLEWFSIti+l0DmUKTYLMbrejo6MDzc3NmDlzJkwmE7Zv346KiugDGDG0YRfO6Qn10L+HTAsTqliwR7Qga25x4sb/CL1QHS4fLlv7WcZsG2pRO38tmbAMqUIWJ12J47ioPrIP9nTCF+AxtsIe1Q8gf1yNmNzVJlbTCIIgss1OpwudAz7YTAZMG1kadXuDGHgRWSFLxq4IqAz1CGjoIQtExN6LYk5NhSybF7JaxaCWc59cWKTjOkb+3EwUxEpZHErIr8vSlbLIesgGvAEEeWWxLFV5Y/WQWcPnkKXTOZQpNAmys88+G8uWLcOaNWtw0UUX4YsvvsCKFSvw/e9/P13bR2SJdEbGZl+QsVlk4ZZFZtvojrCR5Jr/OJn5a2phFpryOJZFINRH1iH2kb0pfjbzG6vjBqGwKOWjJMgIgsgygSCPT1q78X8fHQAATK8rVbRINVaHwqACQT40FDrJCoKWOWTxgjnMxgQVMhWCLJsXslrFoJZzn8XIgbk902lbZAmLZiOXNdePnsiFbNosi+LnxCP2d+NWaVlk15PpdA5lCk2f9lVXXYVZs2bBZrNh+vTpOHz4MH7729/i29/+drq2j8gSUuy9P/dj77UyXqFCls7kwnTA5q/d+3qLZJcAYs9fUwuz0MQaCs0IDYf2wu0L4P09wjDR+QleN5cE2VCNxiUIInWU0ti+ONyH5hZn1PGzrtwGm8kAtz+I1u5BqUKWrGWR9ej2efwxkwxZymKscCXhtvg9ZLFmOMnJ5oVsMmJQ7exRjuNgMxvh8gaki/Z0wGaQVRZZcjKVWSus986cZH+kGqwmA0wGDv4gD5cnINkP5Uix9zFTFsNDPdSM38n1MTuajyYnnHCC9N8jRozAiBHRyWrE0MeWxgpZOmLvtRCZtFheZNa0UpcrAx4XNFbjmBoHlv55MzgOePScqZgxqjwlUcEsi/F6yICQIOsa8GHTvi4M+oKoLbFiUq0j7uMky2J/dgXZUI7GJQgiNWLNsur3BhTnOBo4DvXVxfjySB92trvQ5xHOYclaFktkVrA+tx/lCr1HXn9yKYs8z8OrorrGyOaFbLJiUO3s0SKLIMjSMb6HkU/9Y0BoMd5qMuC/B3rSslDJcRwcNhO6B3zo9/pRg+iZd4mCaaQeMnFxxGjg8PNTJij2IzJyfcyOpqPJMcccE3MFYNu2bbpsEJEbhOaQ6b+y5Mpi7D0gJi2W2XCox41dHS7MLCofsv5jdhLgeeCY2pKUDzbMspjIqiDvIdvfIlQaE9kVgdyokKVrqDZBELlPsm6IhmGCIGtxumASj3PJCjKTQbC3ubwB9Lh9ioKMzRaLn7IYPYfMIwviUlPhUDNHMl0XsqmIQTWzR+0ZSFpkfdSVQzxhERDOjVsP9gIA+j0BXLb2s7QtVDqsgiBzeaK/G/miQkxBJvaQ9cuqn2wfNXCAvGicqnMoU2g6mvzf//1f2L87Ozvx97//HUuXLtV1o4jsIw/14Hle11L8gJSymPnYe8aEqiIc6nFjT8cAZo4uH7L+Y5vZCKvJAI8/iB63L2kLDSMUe5+gQib2kB3t8+CTVsHfP7+xKuHzZ1uQDTVrKkEQ+pKsG6JB7CPb1e7CyDJhLlkqx9symwkubyBmH1myKYteWcBHrECESNTaAPUm3WKQXccMpFWQ5UeFLNMLlUKVeFAK5ZDjlVV8Y1V52YK+3I768rY2AELS84njK4dcO4Kmo8ns2bOj/jZz5kysWLEC5557rm4bRWQftrIU4IUTg8WkoyDzZdeyCAiC7L3dnVIfmZDKZIhr0cxV/3GZzYS2fi96Bv1IdfPUxN4DoQGYG/d2we0PosJuxnEjE794rSO7KYtD0ZpKEIR+JOuGYNH3LU6XFOZRYk3+HFZmN+NQryemIPOqSFlkF5k+2QUsq5AZufhiLhK1NkC9SacYZNcY6UxZZIJsKM8gy8ZCZUmcWWQeWXZB7JTF8Mf3DPqkXvbTjq2VZgcOJVIuUZSWluLo0aN6bAuRQ8iHA7r9AVVedDX4g7x0wsiuIBN+rJ8e7MEr29rQ1u9O2C+Xq/7jUpsZbf1e9MaZaaOWbsmyGF+QMcHGkpBOqa9U9dmwClmP2w+3L5Dx4dtD1ZpKEIQ+JOuGYBd4h3rcUoUs2ZRFIPEsMmkwdNyUxehQj0QDdeOhxgaYDuRi0GMwwBoM6iIG2fklrRUyMdSjqnjoVsiysVDpEPd/pVlkrH/MwCHmPuCwhMfev7GjHf4gj6ZhxUNSjAEaBdnzzz8f9m+fz4c33ngDkyZN0nObiBzAZDTAaOAQCPIY9AVRatPneQdk5elsRsR2iL7vHe2usCbQpmHF6B70ZdS2kSossatnMHaEshqCPC+Junix980tTtz7ekvY397e1YETFdLJInFYjVIl8kifB+NizCxLF0PVmkoQhD4k27dUbjdLj/vikNBnk2wPGRCa8dQTo0LGRBZLUlQiXg+Z1TS0ItiNBg7HjylHdXUJnM4+xBhPpYmiNIaTMToHmWVx6J4zsrFQ6YhXIZMlLMZql2EtL74AD68/KNkVvzOpRrdtzDSajiarVq0K+7fRaER9fT1+85vf6LpRRG5gNxvQ7wnoWu5nfl+zkYubHpVOmluceOidPYq37Wh34Z4zJ6HCbh4y/uNEJ3Y58aLe+z1+qRE2Vg9ZLJ9596Bflc+c4zjUllixt3MQbVkQZPkQjUsQRPKk0rdUX12Mtn6v5AzQo0LWO6hcIWNVArOaUI9AdA9ZvDCQQoE5fdJqWRQrZBVDuIcsGwuV7LejFOrBEkbjhdLIHVY7nS5sOdgLDsDiYwpEkDU3N6drO4gcxGYyioJMv9WlbCcsqvFKP/jWbqy7ZHZOizA5ZQmsL4xEUe/MrlhsMSqKZb185kyQZSPYI5uJYgRB5Aasb+nuV3eELWQlckM0DivGh3u7pH+nUiFjx+2YoR7i6lisOUxAqEcszLLoix8XXkjYFIIf9Ib1kFUN4QpZNhYqJcuiUqhHgsh7QDiXs9mA//7sMABg5phyabTOUETzL/aLL77AbbfdhksvvRQ333wzPv7443RsF5EDsNUlPSNjQwmL2RFkWrzSQwVWzYp1YgdCla3I984SlJpbnOhhCYsxVn31+uxqHNmdRcYuxhwRDfm1JVaKvCeIAmFBYzUumjUKADBlRAkeO3ca1l0yO+7vv2FYeG9KSimLduZsUF5I8wc0VMjkgiwwNC2L6SDdlkV/kJfOm5VDuIeMLVTGQ++FSinUQ6FC5hZDPRL1QbJgj1dEu+J3h3B1DNAoyN577z2cf/756O7uxsSJE9Hf348f/ehHeP3119O1fUQWYatLbr/+gqwoS5H3+RjqIFXIYlhf1Fa22DyVWHZFvT67dEXfB4I8Pt7fjXVbD+Lj/d0IBGM3ISxorMYZk2ulf58wriLhxRhBEPlFa5cbADB3XAVmji5PeMHZWB0++H630xX3OBOPUKhHrJTF5GLvUwn1yDfSPYese9AHHkL4RKIgrFyHLVTWRNgS07VQGeohU4q9D/WQxSIQ5MHWKtz+IMwGYEHT0D5/a+4hu/fee/Hd735X+ttLL72ERx99FKeeeqruG0dkF5tJ++pSvB4lAHBlOfI+H0Md2IkgVoVMbWXrc7FRPZYg0+uzS4cgS2THVEIuHK1iiA1BEIXDvi5h7MnYCnW9rHs6XWH/XvnCtqQH55YmOG77VPSCmaTY+9A5Wo3dq1CwW9IryFj/WLndnBfnj0yOPigR9/9+BTtpoh4ypfO9wWDA5v3dQ3pRVdMvds+ePVi8eHHY3xYvXoy9e/fquU1EjiA1xKqskDW3OLFkzSZctvYz3LphOy5b+xmWrNmE5handB+WspgtQca80vEYaqEOieKT1Va2jor3i2VZ1OuzYx7vtj59qpBq7JhKyAWhHiMDCIIYWuztHAQAVeFCzS1O3Pyf7VF/T3SciUWJeA480uvGJ63RFX1W9TLHSVlUir1n1QW1Q6HzmXS0XcjpkmaQDe3qmBw2+mDxpBpVVeNkccQJ9QjZbqP34Vjne48/mNTvMJfQ9IstLy/Hjh07wv62fft2DBs2TNeNInIDmwb/tdqL4mz3kGXDK51uEvWQqa1s8WLOcKyh0Hp9dnpWyNTaMZVsRUfkgkxhFgpBEPlL96AP3aLNe0ylPe59UznOKNHc4sRN/9kGQAiFUFq8ZJUuk8YeMjdVyCTSbVlk43OGcuR9tmA9ZPFCPSIti3r/DnMNTb/Y733ve7j88svx1FNP4b333sM///lPXHnllTjnnHPStX1EFlEbGavlR5LtlEUg817pdFOaoIdMbWXLJp7AY1kWAX0+OybI+jz+lNOvkg0a8QeCYZXDPhUjAwiCyB/2dQp2xdoSq3ThHgs9w6DY4mXHQPjxOnLxUl3KIou9j7YsUg+ZXJClJ9SDJSxW5lGFLFOEKmTR515PjFCPfAxlk6Oph+zSSy+Fx+PB//7v/8LpdKKurg4XXnghfvSjH6Vr+4gsIoV6JDiYafmRhEI9spsAlUmvdLqRV8iCPA9DxCBFtVHvbLBirAoZI9XPzmE1odhihMsbEGaRVSU/iyzZoJF2lxfyNTQ1M9wIgsgf9nUJdsWxFfGrY4B+gUZaRodoSVkMKIR6UIUsE5ZFqpAlC+shUxwMHaOHLB9D2eRoEmQcx+Gqq67CVVddla7tIXIIVjFJdDDT8iPJtmVRDvNKD3VYzxcPodKjVOFila3fbNguWVoYI0qtOGVCJf7134NhzxePVD+7mhIr9nQM4GiKgizZoJEjvYJdscxmQo/bD48/CK8/SKvKBFEgsAqZmv4xvQKNtCxeJpuySKEeIdJvWaQKWbKwlMUBbyBqITlWymI+hrLJUSXInn/++YT3WbZsWYqbQuQaag9mWn4koZTF7MTe5yNmowFFZiMGfAH0xhBkgCDKnh1Zgs37e3D2tBGYPbYcd726A4d7Pfjrpv043CtEQB/t8wiRsmmsFtY6REGW4iyyZAdasv61+upibDnQAx5CH1m1aWgeyAmC0MY+MdBjbIL+MUC/wblaFi99KqK/TWLghy9APWRK2NM8GFqqkBXTeUMrJbKF5AFvIGymX6xFhWwMsM4kqq6KV61aJf33kSNHMHz48LDbOY4jQZaH2KSUxfiWRS0/kn9tOQQg+5bFfKPUZsKAL4Aetw+jEfsCo2tQsObNa6zC3HGV8AZ4/GrDdqz5cL90n1Xv7MFT/z2YVJSzWvQK9lBrx4wUl0dE8Tm81IoSmwm9bj963T5U04mVIAqCvWKFbKyKClmyx5lItCxeSimL8SyLrIcsKOshUyHkCgW7yraLZOl0UYUsWawmYdQMyxaQC7JYKYt6/Q5zFVW/2ObmZul/xcXFYf9ubm7GG2+8ke7tJLJA6GAWf3XJaOBw0oSquPdhPxIWe58LlsV8glXFEvVCdbjCPe+xZtwkG+WsFj2TFpkdM/IgHC9ohL3u8BKrFIpCwR4EURj4A0Ec6BEWZdT0kAH6BBqpDVg6bmSpVCUwx7m4VB4MrRyIUIjI55CxFGE96aQesqThOA4Oq/D9RA6HjpWyCORfKJsczb4xjhuaypPQjtpQD18giA/2dAIAHFYj+mVzJWpLrLhmfr30IxnIgZTFfCRR0iIgNH6zmOeqIrPQYP7mrrjPyxrM9V5xqikRDqZtOg2H/lZDFYwcwPa8MRU2rF0xK+Z2M0FWW2KV4ndjjQ0giHwiEOTzIswoFQ70uBEI8rCbDdJcRDWkGmikdoUfHCeFDpnjpiyKc8gC0T1kNhJkUqgHDyHsxKbjdQfP85SymCIOiwk9g/6oWWSeBEmh+RTKJocaeYiYqA31WP/FERzp86C62IJnfzwLj72/B0/+9xCOrXXgr+fPCPuRuHIkZTHfKLPFn0UGCIOjgzzAASgvsmhqMNc7/ETPChkgCDuv7KKkw+VDvGMzm0FWW2qTxCwJMiLfaW5x4v7mnWG/+xqHJa325FyE9Y+NqSiKSqVNRKqBRmyFP973IHelxBVkihUysiwybKbQdcagL6CrIOvz+KXPvYIqZEnBnFKRs8jUBNPkSyibHPrFEjFRM8PD6w/ib5taAQArZo9GkcWIhU3CoHCnyxu1YpFLKYv5RJk9cYWM+d3L7GaYDFxWI2RrS2wA9BNkrd3CBdbwEis4ThD+3XE+izZZhayUiVkaDk3kMWz+VeQiTLrtyblIKGFRnV1RbxY0VmP9pXOw+ntTYRVt4w+cNUUSxV7ZXLFYtnIgviCjUA/hop19DgMxFpYDQR6ftHbjlW1t+KS1W/VQYXY+dViN9FknSTGzLEZWyGL0kOU7VCEjYhIK9Yg+kDHby4tfHsXRPg+GFZuxbNoIAEJyHSCc6HsGfWGpfwOUspgWylRUeTokv7vwfWQzQpZZFl3eAPo9/rCG3mRoFWcKNQwrhsHA4VCPG63dbsWVy0FfQOq1C+8hiy3gCGIoo2X+1VC3/ahhX5cY6FGR/MiNVDEaOBw/pgJNNSX4/HAv9nQMoKnGASA8NTHe98EEmS+gEOpRYBezsbCbjfD4g4oLy6lUjDsHqX8sVRzidaCWHrJ8RtVV0IIFC6Tesb6+PixcuDDqPhTskX/YTcqhHkoHsUF/EO/v6cSCxmo4rCaMLLXiUK8HO50uqazM8zxZFtMEq/L0xBEVnRERvdmMkC22mKR+w7Z+T8qCbH+X0KA/usKOAARBdqB7ENNGlkbd96g4g6zYYoTDasrLHjLqEyLkZNOenIvs1RB5n24ahhXh88O92NXhkv7mkw2Fjte3z+yMShUy6iETKDIb0D2ofB2j1MvHKsaJAiIoYTF1pApZxFiCQl1UUHUVRIOgCxMpZVEWex/rINbvCYQdxBqGOQRB1h4SZN4AL9kByLKoL5JlMY6oYCeQKvEEku0I2doSK/o9wnDoCVXFKT0XsyyOKbcDRgM+3N2B/WLVLBJ5oAeAvOshoz4hIpJs2pNzkX0aIu/TTYPoKNnZLhdkwnkyUYUgbg9ZgV3MxsKmMItMj4oxJSymTrFYIeuPaBeQZulRhSyas846K93bQeQgVnN4qIeWg1hDdRHe2dWBFmfoJDMgK0vbKWVRV6QKWbweMoUTSKwG88h0zHRQ47Bil3NAl6RFZlkcXWGDQTzIH+hWFmRH+kIzyICQIOvLgx6yVFd9ifwkm/bkXKN7wCctXKmNvE8nzOK/S3au9IlzxUwJFsOkOWQyyyL1kIVTZInuhdejYswSFiuoQpY0zBkTVSEr0EUFauQhYhIZ6qHlINYwTPDCy08y7EdnEwcCEvqhrodM2WKRrQhZvZIWA0EeB3pEQVZuh8km7K+t3W7F+0dWyEokMTu0BRn1CRGxyKY9Oddg/WPDS6y6pu4lCxNkh3o9cHn9KLaY4POzodDaK2Q0GDocmzk0i4yhR8WYLXBWUYUsaZhTKjLUQ03KYj5SWO+W0ATzoAeCPPyBoKaDWINs1S8oDmQcoP6xtFGmpofMFd5DJodFyC6eVIOZo8szcsHO5v+09aVmkzra54EvwMNs5DC81IZx1YINKVaFLFKQlUkVsqEd6qFlwYQoLJg9OR7ptCfnEnulhMXs2xUBoNxuRrV4TN7tFLaNVcjiJSwCgMkQOkczQj1kdJ4FQjNP5YJMj4pxF1vgLKYKWbJIgiwi1KNQUxYL690SmpDbCgd9QU0HsdEVdliMHAZ9QRzqESoVFHmfPlgPWb8nELZaKodZLHJlRU+vChmzK9aV2WA0cBgjXmj1uv2K0fdHxFCP4WL0fr6EelCfEBGPBY3VOGFsedTfa0usBWVl3ZdDgR6MenERaafoKGE9ZKZkKmQFaveKhd0cPU+VVYzjkahi3OFilsXcOJ8ORYqtoesWOYWaslhY75bQhNnIgS3Quf0BTK8rS+iXZgcxk4GTghpaxGZlF0Xepw1muwNix7eHUhZzY0VPL0G2vztkVwSE/WuYeLJVqpLFC/XgeXUzaHIR6hMi4uEP8vi6TTgWnzShAgAwvrII6y6ZXTBiDAhVyHIh0IMR2UfmlaUsxoP1kMlj790FaveKhZJlUY+KcciymBvn06GIwxqjQlagiwqa3+3atWtx5plnYs6cOTh06BB+/vOfw+VyJX4gMeTgOE52MAuC4wBHguqW/CBWPyw8PYosi+nDZOCkg5tSL1SQ56UKWa6kQtU6RMtivz4VstGyBn0mzlojBBnP8wqCTDih+oN8WKLoUEOPVV8if9lyoBtdgz6U2Uy4eNYYAMJFaiHYFOXsE48XuRDowWiIEGR+lSmL5ogKGc/zVCGLoMgcHeoBhAKtmGWdobZi3JVj59OhCJtD1h8j9r7QUhY1vdsnnngCf/nLX3DRRRchEAiguLgYbW1tuOeee9K1fUSWka8u/efLo2jtdsNqMkied4bSQayRxfk6mSAThAJZFtNDvFlkvW6/1GeQK3NTasWUQzYcOlmkyHu5IBP/+0BXeLBHj9sviS7Ww2Y3h0JmhrJtkfqEiHi8/rUTAPCtxmqMKBPsuu0ub1j/Ub7jCwRxUDxe5EoPGRCqkO10DoDneanixXrEYsFuD/LCopsvwIN9m4V2MRsLybIYcdEPCKLsRyeMkf5dZjOpqhi7fQEMiBU3SllMnlCoh3KFjCV9FwqavGNPPvkkHn30UdTX1+O+++5DWVkZVq1aRbH4eUogyIMTD+8f7u3EPz8+AAC47Jvj8INv1CVM5WuIEGTSUOgcSLbKR8psJhzqUZ5FxuwVpTZTwuSuTGE3G1FqM6HX7ceRPg8akhwOzeaNsaqY8N/CBWdkhYxVxyqLzJKlh+M4lFpN6Br0odftkypnQxG26nvHy19HrTqW2kw4cVxFlraMyCb+II/mFkGQLWoahqpiCwyccIzvGvCi2jF093ktHOx2I8AL56BhOWTdnVBVBA5A96APnQM+zZZFQKiqeWXWRbIsCtgVLIty2HxOQDh3+gJBGA3xr1E6xPOp1WSgBeYUKFaIvQ+r8ubItUqm0PRuu7q6MH78eACQei2qqqrg9w/dVWVCmeYWJ5as2QSneLB65N296Br0Y5jDgvNmjFSVytcgWhZbuwaFFSWyLKYVlrTYq1AhYyedXKmOMWqYbTHJPjJ/kMdBMTRGXiEbFcOyyAI9IkVXPg2HXtBYjW81VEn/versKahxWNDr9uPprYeyvHVENvhvaze6RbvizDHlMBk4yeVwtIBCXkL9Y3ZwXO5Uim1mo1TV3+l0SRZEtYOhgXDLNYfEYq5QSCTInK7wc8/h3vjnokCQx4d7OgEIwr6ACsy645BSFgNSGrc/KKvyFtiigqZ3e8wxx+Bf//oXAEgHsw0bNqCxsVH/LSOyBhsuqxSh3d7vxbu7O1U9T1WxBZVFZvAAdnUMkCBLMyxpUamHTGkodC6QarDHkV43AkEeVpNBsiACIXHG+ssYkf1jDGk4dB4IMgDYJ1o1T504DHPHVeKyb44DAPxt0368u6sDr2xrwyet3QVlVytkXt/RDgCY31gtXcSz30u7DoPZhwqsf2xMDvWPMSZUCRbKXU5XyLKYMPZeLsiCYf1juSQ4s0nkPNVIIlNnD/Yoj0sBQgvV976xCwDQNejDkjWbpOozoY1imSuGXR96ZH3cVCGLw8qVK/HHP/4R3//+9zEwMIBLL70Uv/3tb3H99dena/uIDKN2uKzaCznJG9/eL3muqcSfHkrjVMg6crQBubYktQrZflnkvUF2AcIqZD1uf9jnEUuQleRRhYzneakSMF7skznt2FrUOCzo8wRwzfNf4tYN23HZ2s/oYqIA8Ad5vNnSAQA4tWmY9PdhOoXqDCVybQaZHHmwhzegbjC03JniC/AFO1A3HnZL/ApZuzifk40/OdjtVrxfrIXqtn4vVq7/io6jSWAxctKiAusj9xRwlVfTr3by5Mn4z3/+g4ULF+J73/sejj/+eKxbtw7HHXdcuraPyDB6D5dtHBZqVpZ6yCj2Pi2wtCjFHjLxpFOVI5H3jJoS0TaVpCBrjbHiXWQxoqqYRd+HTrBH+4T/Hl5qC7u/JGZTCBfRg0CQxyet3SlVsDoHfOjz+GHgQuEmb+/qUPxd08VE/vNJhF2RwRI5j6Y4mF0LeuzfqRCaQZaDgkx2rmQVskSDoTmOC5tF5iFBFoXSHDI5rEJ2XF0pAEgWeDl6L1QTAhzHyYZDC98P64MsxCqv5ivj2tpaXHrppenYFiIH0Hu4rLxCxoQYWRbTQ6ldTFmMY1nMtUSoVC2Lrd3RgR6M0eU2dLi8aO0axLHDS8JeJ8qyaGWWReUZbpmgucWJ+5t3hgmnGocF1y5o0DQnilUBRpbZYDUZVF9MzKuvogTGPCIQ5LH1YA+e2LQfAPCthqowi5tUnc5QhUyv/TsZAkEeWw50Y6ezHwAwptyW4BGZp16c27nb6YLXL/SAJhoMDQi2RX+Qhz8YhCdQmGEI8YjXQ+b2BdAnLsIdN7IU7+3uVBRkWhaqZ44uT32jCwiH1YQet18SZIW8qKBJkB1zzDGKitVkMqGyshLz58/HjTfeCJst9w52hDr0Hi7LKmQt7S7pv4spZTEthCpkCqEeOWpZrEnRNrVfYQYZY1S5HVsP9oYFe7BQj+ExLItK1cVMwOwwkbAKlpq5OIxIWxZdTBQeSuLn7V2dOLHFKe1H7LfXngFBpuf+ncxrR34Wv3z+S1yXASGohVEVdliMHNz+IPaKxzWzigUSk5ED/ELKoscvXNQW4sVsLJggG1CIvXe6QmmJE2sdAJR7yPReqCZChCpkwrm3UBMWAY2WxRtvvBHHHHMMHnvsMbz44ot4/PHHMXXqVFx88cW47bbbsGvXLtx3333p2lYiA+g9XHZ8ZREMnHChyy6eqUKWHsrsrIcsWlR0uHI/1IMlt2pBaQYZg/3tgHifQJCXLj5zKdRDbzvMXmbLqhAEGV1MFBaxel26B31h9tRhol042f5NtWTT7hXrs2jPQauuycBhvFgl2360D4C6i1I2i8wf5OH1C58hCbIQTJC5/dGhHuyYV11sQV2ZcL442O2OOhfpvVBNhGDBHv2e8ApZIQ421/SO165di0cffRTz5s3DhAkTcPLJJ+Ohhx5Cc3Mz5s+fjwcffBAvv/xyuraVyAB6D5e1mY2SnYydFEmQpQepQjYYu0KWaz1kTBgN+oJY9/kRTX0l/kAQh0V7iZJlMRR9L9ynw+VFgBf28aqIweZS7H0Wesj07tvc2yEGelQJ758uJgoHLeInVJ32JrUYoha992+1DMW+n/pqYRFlj/gbTpSyCCCih4wqZJHE6yFjFbLqYgtGlFrBQRBu7HzJ0HuhmggRORya9ZAV4mBzTe/46NGjqKysDPtbWVkZDh8+DACorKyE262cUEMMHdhw2cgDUG2JNSlrCbMqMihlMT2E5pCFiwqeF4a/ArlXIftgbxfYJcddr7VoSv472CMMebWZDIpDXqXh0GJl9ohYCahxWKIWFEqssauL6UbvClakZZEuJgoHLeKHpSx6/MG0WnWzVaHNlhBMBZa0yDRiopRFQCbIAtRDpgSrkPkCPPyB8CoZS1gc5rDAbDRIC4SRfWR6L1QTIRwRw6ELuYdM0zueMWMG7rjjDng8woWNx+PBvffei+nTp4PnefzrX/9CfX19WjaUyCwLGqux/tI5eOzcabjztGPw2LnTsO6S2Ul57lmwB4NSFtMDq/IM+AJSShcgHOhYjHIuDYZmdqLI9Wm1yX9SoEeF8pBXViHrGvSh3+OPGegBhKqL2Qj10LOCNeANSMKTJcnRxUThoEX8WE0GVIg253TaFrNVoR2KVt3Ic2WilEUgFA3uD/Lw+ArX7hULuSMnchaZZFkUFyfqxEU8pT4ytlBdYg2/fkl2oZoQiNlDVoD7sKZ3fPvtt2Pr1q2YOXMmTj75ZMycORNbtmzBbbfdhk2bNuGBBx7AypUr07WtRIYxGjjMHF2OxZNqMHN0edIXbJEVMrIspocSm0mqNslXvFn/WLHFCFuOBKroYSeSAj0U7IqAsPLGBOiB7sG4giybc8j0rGDt7xKqYxV2M8rtIfGtd9WbyE20ih9WWW5PoyjJVoV2KFp1IwWZugqZrIdMXIizFeDFbCzMRoN07TIQYVt0uoRzQrVoYa8rEwVZjFlkCxqr8e2JwrHypPGVKS1UEwLFlvAeMnnsfaGhqVRRV1eH9evXY8uWLTh69ChGjhyJ4447DhzHYfjw4fjwww9hMBTeh0jEpyFCkO1o60fF2ApakdcZA8eh1CZEyPYM+qSTTChhMXeqY3ok/7XGSVhkjCq3o3PAh9ZuN470CifZ2pLoFFgp1MPjR5Dnw4ZMpxtWwVJKoWOorWDtYXbFqug5SwsaqzGvvgrPfXYI976xC5VFZqy7ZDb9DvMIJn7i/bbk4qemxIod7S4cTWPSop77txa0fha5QI3DghKrSYpiN6lNWQRLWSzci9l42M0G9HsCUX1kbCGCLUxIwR4K0feMXWJ/37cnDaNUWh1wWMMrZCx8hXrIVODxeFBXV4fp06ejpqYG+/fvx2uvvQar1UpijFBk+9F+yE8rv/j3F6r7hAhtKCUtduZg/5gediIpYTFGhQwI9ZElrJCJNpQgrxyPnG4WNFbjju8eE/V3s5HTGHkvfCbjKpU/E6OBwwnjhD5gty9IYizP0GpPlWaRpTlpkVVoI/tC0lmhHYpWXY7j0FAdWkxRl7JIg6ETURRjFpk81AOQVchiCDKe59HS7gIANFY70rKthQarkEmDoQt4UUHTO3722Wcxd+5czJ8/HwsXLsTChQvxne98B7fffnu6to8Y4jS3OHHjC9uS7hMitFGqkLTY4RIrZMW5I8j0sBOprZABgr2RCbLhpdGCzGY2Shcx2bAtAsCIMmG7ymwmXDtf6MX1BXhMFodaq4ElLLJADyVY+MuALyBdwA0lAkEeH+/vxrqtB/HxfvWpnIUCEz+R7UdK4kdKWkyzIGPbNW1EaF+++dTGtNu9hqJVd7ysun2oZzDh/h0SZMGQ3asAqwvxsMUSZFIPmSjIWA9Zd3QPGSAEQ7m8AZgMHMbGWPQitCFVyCIsi4W4qKDJsvjYY4/h6quvRnFxMT766CNcfPHF+MMf/oBvfvOb6do+Ygijtk9oXn1VTq1SDmWUkhZDFbLcsSymaify+oNSeEU8Qcb6yxJVyAChSubxe9Hr9mFkWeaH228/2g8AmDqyFN//Rh2ad7Rjy8FevLKtDT+cPVrVc0QmLCrhsBph5IAALwj3mhifRy6iNOS3xmHBtTk25DfbfHN8JViS/fUL6lFfXYzpdWVRx9lM9JDJ6ZYdl0aUWTNy3GdW3W+v/hC9bj9uWdSIM6cMz8lzTnOLE6993S79+5//PYTXdzjj7t/KFbLc6BXOFUIVstAClNsXkKyhw4rFUA/xuN/e74XHH4wSBaw6Nr6qSFV/H5EYFurRL1oWPWRZVEd7ezsuvvhizJ07F/v378fkyZNx99134+mnn07X9hFDmKEYOzzUkSpksrRAJsiqcsiymKqd6GCPG0FeONFWxRGaTKzt6RiQeuliCbLSLAZ7AMD2NkGQTawRrDDfPbYWALBh21FVc6L8QV4KOoknyDiOk6ytPVlIlUyWWEN+qdoezb7OAQQhLDJ8b/rImKFMTIyns4dMTresct89mLnfmdHAwS8mzaYSUJVO2P7d5wmv4iTav43ihasvIBdkuff+sok0i0xmR2d2RavJIFVpyu1mFJmN4AEc7o22Le4UBVlDRPgKkTxS7L2HLIua3nFVVRV8Ph9GjBiBPXv2AABGjhyJjo6OtGwcMbQZirHDQ53QhbasQiZZFnOnQgakZifa3xU/8p4xSrSgsM/DZjJIEfeRyIM9ssHXoiA7RhRkpzYNg8XIYZdzADvEC4F4HOpxwx/kYTUZFG2Zcth+0q0wRDwXGYpDfrPJrg5hf6mvLor7+6jNoGWR5/mw/a0rg/sez/OSXY1dnOcSqezfZpllkSpkyihZFiW7YrFF+o1wHCeLvo8WZFL/2DASZHoRGXsvBdNQhSw+06ZNw69//Wu43W6MGzcOTz75JJ577jmUl5enafOIocxQjB0e6ij1kOViqAeDzbtjVpErTxqnqq9EmkFWHt9aWGozhwmw2hJrzAtUFuyRjQqZxx/EbrH/65haQZCV2Ew4ub4KALDhq6MJn2OP+PixFfaEKZHlkiDLjvjUClXbtbHbKewLkTHqkQwrEY4JLm9AuiBKFy5vAL5ASFBkcjHA4w9Kfcz2HBy7ksr+HRoMzRd0dSEebNROmCBzhScsMuJF3+90CotmkcnRRPJEhXoECrfKq+lXe9NNN6GzsxMulwvXX3897r//fvzqV7/C1VdfnabNI4Yy2Zo/U8go9ZB15GDsvRyjgZNWHIssxoR2okCQx5YD3QCEyOdEVRF5j1ksuyIAlCokVGaKXU4XAkEeZTZT2DZ+d1INAOCV7e3wJ3if+8T+sfEKkfeRMEHWM0QqZFRt18Yup7CSPyHBvlBsMUkr1O196f3sIgVY90Dm9j23rHfIloPVo1T2b5MxNIeMUhaVsSn0kLVHJCwyRpYpD4d2+wKSM6ORLIu6USzF3gcQKPA+SE2/2o8++ggPPfQQampqcPzxx2Pjxo346KOPcOaZZ6Zr+4ghzFCMHR7qlCn1kIknnqocSlmMZGSCuGFGc4sTS9Zswju7OgEAL29rTzhCYZQsoMNkiC3gSrNYIWP9Y8fUOsIqeCeOr0SZzYQOlxcf7++K+xws0GNsnP4xBttPhoplkart2mCzkhJVyIDM9ZF1RQiwTFoW2UBgq8mQk+ebVPbvsFCPQOEGIsRDKfbeKe7v1Y7wRTo2i+xQxLloT+cAgrywmJXL59KhhsMScrAM+gIFPRha0zu+/fbbw2aNmUwm2O0U/UnEZijGDg9lyuzhomLAG5AGLeaiZZEhDeRUsIkwkgl1aG5x4t3dndK/P9jbFVPAlUg9ZJkXKV8fZYEe4RH3ZqMBiyYOAwD8/aMDeGVbGz5pVY56Z4JsvApBVj7Eesio2q6eQV9AuphMVCEDMtdHFlUhy+C+xy7EbTl6kZfK/q2UsliIF7PxkEI9lCyLEeIqVg9ZS5sY6DGsOG5fJqENi8kAszijo9/jD9luC3BRQVPs/dSpU7FhwwYsXbo0XdtD5CEsdnjrwR44+72odlgUI5iJ1Cm1hVvRWP+YzWSQfPS5CDsJHlJItgLUN71/q6FK+jcTcJEwARe5IJArFbJImIVx8/5ubN7fDSA66p3nedlQaA2WxSwlSmqFVduVvk8GVdsFWC9iZZEZFSoWYWrEPrK2dFfIxGOS1WSAxx/MsGVRuBDP1WNgKvs3E2S+QFC6mCXLYjhKoR7tETPIGKyH7FCPGzzPS+KrxckGQpNdUW+KLSZ0D/rgki0gUw9ZArq7u7Fy5UpMmzYNCxYskIZDL1y4MF3bR+QJRgOHmaPLsXhSTc7GDucDrELGLrQ7xFXAXBoKrYS8kVop4l1t0/uWA0LTezKpZaX27AgyfyCIne3hCYuM5hYnHnlvb9RjIquCHQM+9Hn8MHDx57Ix2H4yVCpkgLCw87OTx0X93WoyULVdhtQ/pvLCcZhYIUv3LDImwNiCQSYti6x3iF2Y5yLJuknMSj1kBVhdiAezLA54lVMW5YwotYGD0NPUIws9YsdoCvTQH2kWmbxCVoCLCpoqZBdeeGG6toMgCB1goR4efxBuX0CavRVvVlcuMKJUEGQDvgC6B31RK/uqm95FAbrlgPrUspmjywEApVbhM+rLsCDb0zkAb4BHscUoVQoBbYPVWaDHyDKbqtXxoRbqwWCiYdaYMpxyTC3uf3UHTAbglPqqBI8sHJggq1dhVwRkPWQZsiyOq7Tj67Z+9Lp9CAT5jCzODUiR97kryIDk3CRyy2Iooa7wLmbjwSyL8nCXUMpieA+Z1WTAMIcFbf1eHOwZRHmRGTzPS5H3TSTIdEeaReYNyPbh3P6tpgNNguyss86S/ruzsxOVlZW6bxBBEMlTbDHCyAEBXqiS5XLkvRyryYAa6STojhJkqpvexdVOdrJNhFzolUiDoTMrUtj8saYaR1hcvZYobBZ5r8auCAxNQeYPBPHq9nYAwIXHj8YZx4/Bmnd2o9ftx/ajfZgyojTLW5gb7NYQ6AFkroesazC8QhbkhcWP8gwsFrlzeAZZJMxNohaTMRR7Tz1kytgjYu/dvoA0bzKyQgYAdeV26Vw0eUQpnC4vetyCA2F8FQkyvQnNIgsUdJVX0zv2+/144IEHMHPmTCxYsACtra04++yz0d7enq7tIwhCAxzHSX1kvW5fzg6FViLe/Be1Te8zRglN70onWSXkQo/NcOvN8GDo7UeV7YpaorBZoIdaQcYqqUNlDhkAbNrXja5BHyrsZswZVwGjgcPx4oXr5n3dWd22XGK3ysh7RqiHLDOx99XFFmnmX6Yss4NDpEKWDCbFwdCFdzEbD7spXJCxBTuryQCHNXqfqItI/WXVsbEVRfTZpgFJkHn8spTFwmtr0bRnPfTQQ9i4cSP+9Kc/wWw2o6qqCsOHD8edd96Zru0jCEIjUh/ZoB8dQ6RCBgAjy8W4YYVgD60jFGaM0p5axgRZvyeQcLaZnnwdI9BDSxR2SJCpS71lFbIBX2hFMtd5aZswHPvbxwyTLkJnjy0HAGxOMBKgUOhz+yVhpbZCxixb3YO+tO4LLPa+osiMCrEqlqk+MtZDls+CLBDkKdQjBqEKmfD5sMWuYQ6LYmLiyIjFwZ3toYRFQn+KxQWaflmFrBBTFjW94xdeeAGrVq3CSSedBI7jUFRUhHvuuQcbN25M1/YRBKGRsAqZNBQ69wVZvAoZIPRX3HX6MVF/V2p6T2YGHktZBCDZWdJNkOclQTYxokKmJQpbS8IiADisgrUVGBq2RZfXj7d2dgAAvntsrfT3OWMrAACfHeoNS1ArVHZ3CBeOtSVWqS8jEWU2k3QB357GpEVWDSu3m6UKbeYE2dCxLGrFJI4i8gV4ePyheWtECCbEWS9hrKHQjLqI4dBSwiIJsrTgkFfICnhRQdM7HhgYkPrGWBKazWYLm01GEER2kYb+uv2yodBDyLIongSVYEOPi80G3HHaRDx27jSsu2S2YgKZ1tQyk9EgpXFlKthjf9cgBn1BWE2GqIHOakTlVSePh8cflAIZ1AoyjuNQJkXf574ge6ulAx5/EGMq7DhWVkkcVW7DiFIrfAEeWw/2ZHELcwMp0KNa3X4ACPsC+42kM/peLshYhYwsi6nDesg8/gACYmG/EKsL8QiFeoRbFquLrYr3j7QsShUyirxPC8WyUI+Q7Tb/fquJ0BTqMX36dDz88MP45S9/KZV5//73v2Pq1Klp2TiCILRTKl5o9w76pFCPihxPWQSiT4JKsAvOptoSfGdSbcz7MbSmlpXYTBjwBTLWR8YGQjcNK5asR3KYqLy/eWdYj4+BE0IRPj3UA5dX2NYSq1F1VQQAyuxmdA74hkT0PbMrfndSTZjFiOM4zB5TgXVfHMHmfd2YO66wg6Z2OQXr6gSNwQM1JVa0drvR1peePjKvPwiXGDleUWRGORu7kKFZZIVgWXTJIt0LsboQD3tE7L1TXHiIZQuvE+3zR/s8GPAGsEe0hFOFLD2EQj0Ku4dMkyC7+eabsWLFCjz33HNwuVw47bTT4HK58Le//S1d20cQhEZYhUxIWRx6lsWjfR74A0GYFFZ5tUZ6A9pSy0ptJhzt82QsaXF7DLuiHCVR6fYHcPW/v8TTWw9L9+vzBLBkzaawgdHxYH1kuRrsEQgKVa/dHS4ptOM7k2qi7jd7bLkoyKiPbFeH9goZANRIs8jSUyFjot9o4FBiNaHcLhyPMm9ZzENBJh4n5YKMUhbDYd+72x9EkOdDkfcxLItVRWZpgPnGvZ0IBHk4rEbUlihX1IjUKLaE+re9lLKojjFjxuDFF1/EjTfeiF/+8pe44oor8OKLL2LChAmaXnTDhg049thjMWPGDOl/119/PQDg008/xfe+9z3MmDEDCxYswNNPPx322Oeeew6LFi3C9OnTsXz5cmzZskW6LRAI4N5778WJJ56IGTNm4PLLL0dbW5t0e0dHB6644gocf/zxmDNnDu666y74/aGLkUSvTRBDAdaf0d7vkU7SVUNAkFUVW2A1GRDkgSMxIrhZBUBtYIFWWLBHpiyLsfrHIokcrO7xK4eORA6Mjock3HOwQtbc4sSSNZtw2drP8Ps3doEHYDZw2CFah+QcP6YcALCj3YWugfQmBeY6u5P8fbBgj3TNIuuS2RU5jsu8ZVE8DtrysodMqCSw6o/ZyIWNzyCAIktIiHv8QWmeYawKGcdx0gIh611trC5WDAAhUoclXfa4fSHbbQEuKmh6x3fccQf27duH0047DZdccgnOOOMMOBzxLySU+Pzzz7F06VJs2bJF+t8f/vAH9PT04Kc//SmWLVuGjz76CHfddRfuuecefPbZZwCATZs24Y477sDvfvc7fPTRR1iyZAkuv/xyDA4KPSerV6/G+++/j2effRbvvvsubDYbbr31Vul1r776ahQVFeHdd9/FM888gw8//BBPPPEEACR8bYIYKrCURTabymzkFKN9cw2O46LSrSIJ9cikR5CxOO7eDAgyXhboEZmwGA+1A6MTJUWGKmS5JciaW5xYuf6rqBh2X5BXFJuVRRbJSvTR/u5MbWbO0TngRdegDxyA8Sp7CRm1aY6+Z9ZEZlXMvGUxjytkkmVROGaRXTEa+Wcy4A1IKYvxxqMwQfbe7k4AQMMw7de6hDpYhaxTdjwoxD5ITe+4o6MD5513HpYvX44nn3wSfX19Sb3o559/jilTpkT9/dVXX0V5eTkuuOACmEwmzJ07F2eeeSb+8Y9/AACefvppnH766Zg5cybMZjNWrFiBiooKbNiwQbr90ksvxYgRI+BwOHDLLbfgnXfeQWtrK/bt24fNmzfj+uuvh91ux+jRo3HFFVdIz53otQliqMBSFvd1CQsVlUXK0b65SLxgj36PX6qcqZ2xpBVpFlkGBNnhXg963X6YDJymnh8tA6PjkYuCLFmxOUuskhXyPDK2WFFXboNNo/CoSfNwaLaPVYj7XIVoWcxYhUy0QRXltSATRGchXsgmwsBxsImibNAXCFkWHbEtiKyPjCXuUuR9+mALxl0yQVaICwuaesgefPBB9PX14YUXXsBzzz2He++9F4sXL8Y555yDWbNmqXqOYDCIL7/8Ena7HX/+858RCAQwb948XHfddWhpaUFTU1PY/RsaGvDMM88AAHbu3Imzzz476vbt27ejr68PR44cCXt8dXU1ysrK8PXXXwMAysvLUVsbCgKor6/HoUOH0Nvbm/C1tTBErn0LBvZ9FMr3wlafWVpRVZF5yLx3JsgO9Xqitpk1Vg9zWFAeI6Qk1e+a2T37PP6o5wgEeWw50AOny4vqYgtmjIodDpKIQJDHhq+EoIoRpVaYjJzqbWYXE2ruF+85mSDrdUe/12yhRWwurimVtnvO2Ar885OD2LSvCwA/ZBYg9GR3R8iuqPXt15aGesjS8dF1u9kMMgs4DmFzyBK9nh7Hb5auZ7cYc2Zf1wuzmLLo8oi2TJNhyL7HdJ6riyxGuP1B9Lh9ksiqKbHEfK2RZeFirbG6aMh+rrlG5PcsWRbFBRqLkYMhyXNrLqJ2v9EkyACgpKQE559/Ps4//3x8+OGHuOWWW7B+/Xps27ZN1eM7Oztx7LHHYvHixVi1ahW6urqwcuVKXH/99Rg2bBjs9vDBpjabDQMDwonG5XLFvN3lElYHi4qKom5nt0U+lv2bPT7ea2uhqqpE82OI9FMo38tYb/hw1+EVRaiuHhrvvamuHNhyCM5Bf9Q2H90thDZMGlmW8P0k+10PFytVXiDsNV7+4jBuf+ErHJYlQI4os+E3Zx6L70wZoek1Ip+rtduNZX/5SPVz1Y9UJ8jqR5bH/ZxG1Qi3uQJ8zuwfngO9qu7n5oTVU/Y9n1pih3ndlzjS54GLM2JcAcZTH+wXLmamjq7Q/H0eYxEEktPlRXlFsWKgTip4IFyRsGPRBINwAdY96ENVlUOVgE7l+O0VK6rDqx05s6/rRWW5YHtmM7bsVtOQf4/pOFcX20xCqqxofrCZDRg7slxx33v5i8P426bWsL/d9OJ23L5ksubjPREb9j2PFo/nzPfw/7d359FN1fn/+J9ZmqV7S6GFCqhtWVzZFzek2OHzUUQGUfzIMOL8xg10VGRRYUZcUJgZFRmVYQBBvzjOETeWUUEtKAcpUBBQEWgrSynQNt3TNm2W+/sjubdJtyRN0twkz8c5c86Y3CY3eSchr7xfizZKFfKv4a7wOiCrr6/Hl19+ic8++wxHjx7FzTffjBdffNHjv09JSXFJA9Tr9Zg/fz7uvvtuTJ06FSaTa+2IyWRCTEyMdGx71yclJUnBlFhP1vrvBUFoc5343zExMdDr9W1SMJ3v2xsVFXUQOi/foG6kUNjf+JGyLoLJ9Qt7rFoBg6Fr6cXdLdHR6vbXsro253zktL24ul+8tsPH4+taq6z2LzVl1Y3SfeSeNGDBlmNtjr1QY8LDGw/hr5OvQPYA910N/XVbl8dFoVesptOdpNQ4LS6Pi+p03dWOhkblNY2yeX1obTb3BwHQCfbjnNf56t7xOHSuBqtzT+LK3vE+72KGmp/PVQMA0qLVXq+nYBOgUipgtQk4cbbS793kShzdH3UKAQZDHQRHel2TxYZzF2qg13ScSuiPz2+j45f35oYm2bzW/aWx3vU7kVoROp/3rQXy32qtY17uz2ftNWEpMRpUVBjbHNfRZ3RpbZPXn/fUvtbr3NyqGZNGGbqv4faIj9cdrwKyp556Crm5uUhLS8Ndd92FN954A8nJyaisrPT4No4fP45t27bhqaeekn6ZaG5uhlKpxDXXXIN3333X5fjCwkJkZWUBALKyslBQUNDm+ptuugkJCQlITU1FYWGhlHpYXl6O6upqDBgwADabDdXV1TAYDEhJsb+ZioqKkJaWhri4OAwYMAB79uzp8L69IQiIiC/+oSZS1kWsIRMlR2tC5nH3cZpF1vqcC6UZS9FuH09X19q5qYcg2FML/+6mpunVnUW4KaOH2y/+/rotpcI+MHphO18aRHPHZ0CpUHT6HIivk5pGs2xeH0PSEzwKNoekJwBwXecejgL9Dw6dB3AeANArVuPxGIBQJggCfhVb3veI8Xo9lQoFesZocLGuCaW1TVJNmb+0NPWwfxbp1EpoVAo0WwVUNpjRx4PaLl8+v8U5ZLoolWxe6/7S+rNCo1KG/GMMxL/V4nDos47a6p4xbf9d9OfnPbknrnN0lGsoolGH/mu4K7zKS1Cr1VizZg2++OIL/OEPf0BNTQ3+8pe/IDs72+PbSExMxPvvv4+1a9fCYrHg/Pnz+Nvf/obf/va3mDhxIgwGAzZs2ACz2Yy8vDxs3bpVqhubNm0atm7diry8PJjNZmzYsAEVFRXIyckBAEydOhWrVq1CcXExjEYjXn75ZYwaNQr9+vXDpZdeiuHDh+Pll1+G0WhEcXEx3n77bUybNg0AkJOT0+l9E4UKnVop1RUAQHInnaTkpk+8PSCrNVnatJ7/NcAdFgGntveOGgN/NdDw922JA6N7tWrbnBqnxfLJV4TsHDKV0h5sdmbu+Iw2X4ZyCwz46kR5m2O9GQMQysqMzTA2WaFSKtA/We/+D9rRKy5ws8ha2t7b318KhaJbm8q0dFkMv0YBrQfKa8PwMfqD2Oim2NHBt0dM2x8d/PkZTZ7TOH6gEUViQw/Ayx2y5cuXAwDy8/Oxbt06fPvtt8jKypJmiHkiLS0Nq1evxmuvvYZVq1ZBq9Xitttuw/z586HVavHOO+9g6dKlWLlyJZKTk7F48WKMGTMGADB27Fg899xzWLJkCUpLS5GZmYk1a9YgMTERADBnzhxYLBbMmDED9fX1GD16NFasWCHd98qVK/HCCy9gwoQJUCqVmDJlCmbPng0ASEpK6vS+iUKFQqFAgi5Kav7Qo4MGGHIUrVEhOToKlQ1mnK8xYaDO3mq4sqEZlQ2Olt4B6rAIAHFOu0YApPbI7nhynD9vC2h/YPSQdM9T9MQvxA1m+zBOucx9EYPNZV8VuAwOTo3TYu74jDbBpqedGceF8a/aYofFfkl6RHWx/ksM7ksD0Ppe3CFLcvosSoq274QGeji0TRBgcjQ4Cs+2967rHYkDdT0hdtg8W9XSHKo1f39Gk+diNGo0S009IvM17HFAZrPZ8OWXX2L9+vUoKCiAxWLB6tWrceONN3p9p6NGjcJ//vOfdq+7+uqrO7wOAO644w7ccccd7V4XFRWFefPmYd68ee1en5KSgpUrV3Z42+7umyhUxOvUUkCWHAJDoZ2lJ+hQ2WBGSU0jBjrmczm39A7kl6qEVjtkHQ0Obc2T4/x5WyJxYHRXxGpVUCkAq2AfyNlZC+julp2VArPVhsX/PY5+iXo8+5usDoNNb37V7upzJWdWm4BvC+07gEl6NayOejBviTtkgWh939L2vuW13V2zyEzmlrrE8AzIWu2QyeSHFbkRh4IbHd0o25tBFojPaPJMjFYl/TgTqa9hjx71u+++i5ycHPztb39DTk4Odu3ahdjY2DZt4olIHhL0Lb9EJ8eEzg4Z4FpHJipy1I9leDGvqyvEGrJGsw1mq02qaeqMc01TZ/x5W/6gUCik14mcZpGJxMBgcFoshvdN7DDIiORftXMLDJi8Zh8+OXoRAHDoXC0mr9nXpRTNQM0iswkCahxt753HVXRXyqKYrqhAeH7Rc05PByCbnW65iW7VOKa9oEpun9GRRBwODUTua9ijR/3KK69g3Lhx2L59Ox588EEpRZCI5Cle2/KPT3FVY5tBunKW3m5AJtaPBS5dEQBitS3/KNSaLB7VNN14eTIA4GBxNbb/UoaDxdXtPt9drY8KJDkHZKWOwMBdx79I/VU7t8CAhVuOtdkd7GrdnPj8nCgzdvga7oraRgvEm0rUtby/xIAs0CmLYkCmi1JCGYaDpJiy6JnWu6PtpSzK8TM6UsQ6fWeJ1JRFjx71n//8Z+zbtw/jxo3D66+/jtLS0ogcvEkUCnILDNh3plr673mbj3X5V/NgSE+wNyVod4cswPOlVEqFtEsmNhXJzkrB0tsGtTlW/Adky0+luO1feXj4w6NY/PlxPPzh0Q6f7+ysFEy+KrXN5d404/An8QtyjYwae4haAjJdp8dF4q/antbNeRpU5RYY8GpuEQDgTFVjp69hb4nBfpxW7TLfTKwnC3TKYktDj/BLVwQAlYopi57QtVr/lHaaegD+aZhE3nPeIYvU17BHNWQzZszAjBkzsHfvXmzcuBE5OTmwWq3Yu3cvbr/9dqhU4flBRxRqxF/NWxN/NQ+Ff1DSE+1fwM87AjKXlt7dMPA3TqdGXZMFtU0tQYrYUj1Oq8L8CZnoFavF1b3j8bv/dxCnKhtRUe86P6uz57vS8QV06jVpGHZJotfNOPxJzjtkF2vtAVlafOc7ZOKv2u7GAITTr9r+rJsL9GeGuAOW1Kq5UFK3pSyGb0MPoG0NWaSme7kT7cEOmcjXhknkvRinlNJIDci8etRjx47FW2+9hS+++AKzZs3CsmXLcOONN2LZsmWBOj8i8pC/fzUPFjFl8XyNCVabgNK6JtQ3W6FWKtAvqWstvb0RL80ia/mimHe6CgBw/eU98L+DU6WaplpHgXhHWj/fzRYb8s9WAwDuvLYPJg7u1Wl9VKB1Z+txb4k7ZGkeDCmOtF+1/VU31x2fGWJAltBqPmJ3pyxGSkAWqV9m3XEeeaBTK10CgPaIDZOC/RkdKZzXgymLXkhPT8f8+fPx3XffYe7cudi/f7+/z4uIvBQuM1R6xmqhVipgsQkoNzZJ6Yr9k7ve0tsb4iyyWqc5aPvO2AOyMf2TpMsOl9Sgot675/vo+VqYLDYkR0chs2fgd/vcEXfIakzySlk0ma3SF3V3NWSi7KwUbHlgNFLj7EHZn266DJv/OCqowZjVJritLewKf9XNdcdnRnWD/fZb75CJDT4C/WOAKYxnkAEMyDzlHJCnxGpYdiMzzvXbkbrL69UcstY0Gg2mTZsmDVcmouAJl25zKqUCveO1KK42oaTG1NLQI8AdFkXScGhHkFLdYMbxUiMAYHT/ROm4rjzfeWJgd2mSLBoMyHWHTNwd00cppfXwhEqpwGU9YlBa14wEXVRQf9W212UVugQ8vWI1eCo70+cgUayb6yyY8qRurjs+M8TB40n61imLGsf1gX3tNUhNPcJzh6z1j1QMyNrnHJD1bKflPQUXUxa7uENGRPITTt3mnBt7FBq6r34MsNeQAS07ZPvPVkEAkJkSgxSnWV1deb73OVIfRzvttAWTNAtKZgHZRSldUef1L9m9HTVnF2pNbo4MHH93QGzNX93guuMzQ0pZ1LdOWWx5n1mstjZ/5y9iDVnrGqJw0aaGLELTvdxx3SGTz8xFsovRsqlHZD5qojAUTt3mxMYeLjtkAW55L4p31LqITT3EdMXWQZS3z3dVQzOOl9l32kbJJCAT63pqZBaQedryvj1pjq6MFwIw4NgT3VXLmZ2Vgpdubdv905u6ue74zKjqIGUxXhcFMZQIZMqsyantfThSs8uiR7TqlufJahNkX0sdaVhDxoCMKGyE0wwVsbFHcVUjTld2T8t7UbzU9t4MQRCkhh5jLk10Oc7b51scRZDVMwYpMkmZEVMWZReQOTosprrpsNgesStjaZB2yLqzlvMSR5ObOK0KL906CP+8+xqv6ua64zOjpoOURZVSIaWjBrKxR/g39WDKoju5BQYs+u9xl/8OpVEwkSCWO2QMyIjCSbh0mxMDsgNnq9FsFaBVK9EnofN5VP4ipizWmCw4XdmIMmMzNCpFu7sEHT3fsVpVm+dbrB8be6k8dscA5xoyeTX1uFhnD6Y86bDYWu94xw5ZbXB2yLqzlrOw3L7jOjg1rsvd4Dp6DQPAqH6JPn9miMFWYqsdMqB7ZpE1NEdW2/tI/TLbETF9uLLVa8xf6cPkH9wh87GpBxHJTzjMUBGDL7G26fIe0d3WBCPBqamHmK44JD2hw6YAzs/3F8fKsPmni4jTqDAuo4d0jCAIsqsfA4AERx1Pg9mKZotNNt2tfEpZFHfI6ppgE4Rub57SnbWcBeX2dF5fO3a2/syobjTj7zuLcLC4GsVVjejrw7gJMWUxUd9OQKaPwmk0BrSGUeqy6KbNeahSKRVQABAT8OTyHpYDT9OHx2X0CKl/H8NRrIZdFiPzUROFuVCfoSI29RBldlO6IuDU1KOpJSAb42ZXS3y+52VnIEGnxoW6ZuwuqpCuL6pogKG+GVq1EtfKqIYvVquGWIJSY5JP2qKnQ6Hb0zNWC5UCsNgEt2MJ/MW5vb3NJrQbfDjzVy2n2PAmyw8jFJw/M6YPS8d1lyXBKgBr9p7p8m0KgiDVh7VOWQRaGn10S8piGH/Jc64j4w5Zi3AZBRMJnGs8xRmkkYY7ZEQkO3E6NeK0KtQ5Bi9r1EpYbUK3BJbxWvuXxMr6Zlx01CF5uquli1JhyjW98e7+YvznhxLc7Ej3EuvQhvdNkNUXJqVCgQR9FCobzKhuNKOnDLqPCYLg0mXRW2qlAj1jtbhY14QLtU0Bf0zttbd3xx+1nIIgoNCxQ5aVEuvTbbXn4esvxfenqvDFL2UY2S8RGpXS6932RrMNTRZ7ymDrph7OlwVyh0zsshiuKYsAEKVUwmy1f1ZqIzTdqz3hMgom3OUWGPC3b1p2MjfsL8bnx0r9MiIklPCdS0Syk1tgkL5IAcDHRy50WxF2vL6lhqzR7P0Q52nX9oZKARwsrkGBo8ZHjumKopZOi/KoI6sxWaQv8b26kLIItLS+vxjgxh4dtbcXtTdDrVesxiWdtasM9c2oMVmgUgCX9vB/B9LBqXG4qnccAOCF7Sex+PPjePjDo169D6sa7c+LVq2Erp0fIqQaxgDWkIV7Uw/AdYcsUtO92hNOo2DClfgZaqgPzIiQUMJ3LhHJivgBbWmVstBdH9Ct5xWN6pfoVR1SWrwO47N6AgD+c6gEJrMVPzhSYtylPgaD3GaRiR0Wk6OjurybmOpo7HExgI09PKlP0amVeGva1Xjp1kF4fcqViNOqUGZsxpe/lPl8/2L9WL/k6IDsuuYWGPDThbo2l3vzPhQDrUR9VLvz5BK7M2UxTGvIANfGHlp1+D5Ob4XTKJhw1F0jQkIFAzIiko1gf0DnFhhw73sHXS77/nSV10HgPcP6AAC+OFaK9fvOosliQ6JOjX6JXW+OECgJ+sCnjXlD7LDYlYYeou4YDu1JfUqZsRkqpQITB/fCDRk9MGtUPwDAv74/DbOPw5DFdMVA1Ff6631Y3UHLe1H3pCyKO2Th+3XHOSDTqEKrXjiQwmkUTDj64Rxr/JyF7ycUEYWcYBZhd5R+VmuyeL0zd02feKQn6GC2Ae/sKwYAVJssuGPtftmlYIgBmVyaevjSYVGUJu6QBXA4dFfqU+4e2gc9YjQ4X9uEld/9iu2/lOFgcXWXfmAo8GNDj9b89T4UUxbba3kPOI9dCGSXxfCvIXMOyHTcIXMRLqNgwlHrNMUOj4uQGj829SAi2QhWEba/2yPvLKxASU3b3Rkx3UtOXwTkNouspcNi1+fOifPLApmy2JX6FF2UCjdenozPfryI/xw6D+A8AHtdmbcF7IHcIfPX+7DKKWWxPeLOWVUg55A5dsg6GlsRDtROjTxYQ9ZWOIyCCUcpMazxc8Z3LhHJRrCKsP25MxfstEtvdccuhTdaOiz6krIoDocOXMpiV+pTcgsM+OzHi22O87Y+0my14VRlA4DA7JD5633oLmXR+bUnCIF5P4hzyFrXhoYTl5RFBmTtCvVRMOFo6CWs8XPGdy4RyUawirD9uTMXarNvxKYeNTIJyMSUxa7MIBOJf1vfbEWdKTA7f97Wp/gzUD9d2QCrTUCsVuVTamdH/PU+rHakLLbX8h5oCcgsNgH1zdYunKl7kVRDplIqXIIzIjljjZ+r8P2EIqKQE6wPaH/uzIXa7Bux7b1sdshqfW/qoY9SIcHRcl5sEhII2VkpeOnWQW0ub68+xZ+BujQQOiWm3e6FvvLX+1BMRUzoYIdMF6WSAqVAvP4sNgHNVkG6r3Alpiy2N1qASM5Y49eCNWREJCviB3TrYbupcVrMHZ8RkA9ocUegsy/Mnu7MhdrsG3GXQg47ZBabIBV6+5KyCNjTFmtMRlyobUJWT/8PThaJuz+JOjWeys5Az1htu/Up/gzUpfqxAD6ujt6HPWM1mOdhvZsYZHWUsgjYX3+N5iZUNZhxiZ+7kIrpikBkNPXQcCg0hSDW+NkxICMi2enuD2hxR2DhlmMdHuPpzpw/g7vuIAVkAUrt84bB2ASbYP+CmexhwXdH0uK1OF5mDPhw6PziagDAdZcn438Gp3Z4nD8D9QIpIPN//Zgz5/fh01uPobrRgpf+dxCG9Uv06O89Dcgu1DYFZBaZmK6oUoR3O3gxIAvEPDqi7iDW+EUyvnuJSJa6uwjbX6kToZYXn+CoIatvtqLZ4ttsLF+JXRF7xWm9Gsbdnt7dMBwaAPLP2lMMR7j5MuHP+kgxZTEQHRZbE9+Hg3rFAQCKaxo9/lsxyOqoyyIQ2FlkjY6W97ooVUBSO+VC2iFjQEYUsrhDRkTk4K+duWCkXXZVrFYNlQKwCvZZZD1j/d8kwlP+6LAoSpOGQwcuIKtvtuDYxVoAwAg3u0b+2oWtbjCj3PGaykiJ9u6EfdA/WY+8M1U4XelZQGa22mBssu9QdTSHDHDqtBiA1vctDT3CN10RAKIcqYrcISMKXQzIiIic+Ct1IlTy4pUKBRL0UahsMKOm0RLcgMyRXuhLh0VRy3DowKUsHi6phVUA+iTopB25znQUqAPA4zdd5lGgLu6OpSfoEKPpvn/C+yXZg78zjnb77og1iSoFEK/r+DzFgCwgKYvN4d9hEWDKIlE4YEBGRBQgoZIXn6CzB2TB7rQotrz3Ryv33vGBHw598Gw1AGCkF2vcOlD/5Oh5HDpX63FAUiB2WAxw/Vhr/ZPtDTfOVHm2QyY+ngR9VKfpp4Gcg9doiYwdMrWKTT2IQh3fvUREEU6cRRbsgMyvKYuO2zDUNwesNk5s6DG8n3cNWpzrI6cPTQcAfHWi3KPhyIXlRgDdUz/m7NJk+w5ZSY0JFqv759Ndy3tRUiADMkcNWdgHZNwhIwp5fPcSEUW4BKnTokx2yDxI/3MnUR8lfUEVb9ef6kwWnCizB0fuGnp05rrLkhEdpcKF2ib8dKHO7fGFBnvKYHfvkPWK1UAfpYTVJuBcjfs0UE86LAKBbephipAaMrGBZHWjGQeLqz0aLk5E8sKAjIgowiX4cZfCahNwsLga238p8/rLoT9TFhUKRUvaYgDqyA6dq4FNAPon6X2qu9NFqXBTZg8AwI4T5Z0ea7UJKDIEfgZZexQKhVd1ZFJA1klDD8CphiwATT0aHDVkujCuIcstMCC3oAIA8EupEQ9/eBST1+xDboEhyGdGRN4I308pIiLySEsdj2+zyHILDJi8Zh8e/vAoFn9+3Ksvh/XNFtQ6ZqH5I2URaGnsEYhOi2K6orvuip74zcCeAICvT5R3GsCeq25Ek8UGnVqJ9ATfdxG91T/JUUfmQadFMcDqrOW98/WBSVm0B2TRmvDcIcstMGDhlmMwtUrJLTM2Y+GWYwzKiEIIAzIiogjnjy/F4pfD1t0DPf1yKO6OxWpViNX6p9+UGNgFYjj0QbF+zA9NW8ZcmoQ4rRqG+mYcLqnp8Dixw+LlKTFB6dbZ0tjD/Q6ZJzPIgJYdtEDMwTOFcQ2Z1Sbg1dzCTo95bWcR0xeJQgQDMiKiCJfgaEte08WAzB9fDkulhh7+2/kJ1HDoqoZmFJTbg6Phfb1r6NGeKJUS47PsaYtfdZC2aLUJ+K7QnpqWpFMH5Yu22NjDkx2yGg9ryMQ5eID/d8kaHDtkOnX4BWSHS2ra/PjRWmldU6cBPhHJBwMyIqII5+sOmT++HIpBkz/qx0TScGgvm3q4q4M7dM7+ODJSopEcrfHLuf5mYC8AwDcnDbC0uj8xFfTzX8oAAHtOVwWlTqi/WEPmQet7T3fIxDl4zn/jLy2DocPvq47BzfvN2+OIKLg4h4yIKMKJX5q7ukPmjy+HUst7PwyFFqXFe5+ymFtgaDO4uVesBk9lZ0qDm/Md88d86a7Y2vB+iUjSR6Gq0YwPDhajV6wOKbEaVDWY8cy2X9ocL6aCLp98hUcDpf2hnyNlsbrRPrOus2BLqiFz09QDsL/+AjEHzxTGNWQpsZ79EODpcUQUXAzIiIgiXEvb+6419fDHl0N/dlgUiSmLpXVNsAlCpwOKgZY6uNbE4OeV2wcjSR+FXYX2nalhl/ierihSKxUY2CsGeWeqsfK709Ll7krFXttZhHEZPbqlpkwfpUKvWA3KjM04U9mAxPSOH7+nbe8BRx1ZBVDt506L4hwyXRjWkA1JT5DWoiOpcVoM6WSNiEg+wm8fn4iIvCIOhu5qYwXxy2Fn3H05LHXsYvkzIOsZq4VSAZitAirrO9/F86QObtG2X/Dwh0dhqLcHDn/PLfJb2mBugQF5Z6rbXO6uVKy764T6J7tPW7QJQksNmQc7ZIEaDt0QximLKqUCT2VndnrM3PEZQWn+QkTeC79PKSIi8opzY4WuDIf2x5fDQKQsqpUKaUaYu9b3ntTBtQ6Oyuv9017ck2CwM91ZJ+RJY4/qBjOsjueqyFDvtgFJvKOpTH5xtV8HG4f7YOjsrBQsn3xFmx9DUuO03ZrKSkS+Y8oiEVGEUyoUiNfZ65dqGi1dGnScnZWC5/93IJ774kSb6walxnb65dAmCCgLQJdFAOgdr0VpXRMu1JpwdZ/4Do/zJajxNW3Qk2CwM91ZJyTOIjvbQev73AID/vp1gfTfj338U5savNbHbz9u7yy5q7ACuworOj3eG41h3PZelJ2VgnEZPXC4pAYGYzNSYjUYkp7AnTGiEMMdMiIiklrff3Wi/c6CnhDTDZP0UXjp1kFY8j8DoARwvNQoze1qT1WDGc1WAQrAbeqjt9Kc6sg640tQ42vaoC/BYHfXCUmzyNrZIRNr8Cpa1YJ1NItOPL6+2erR8d5qDPMdMpFKqcDwvomYOLgXhvdNZDBGFIIYkBERRbjcAgNKauw1XO/sK8bDHx7tUlv146VGAMC16fGYOLgXbrsyDVOv7Q0AeOPbX5F/tqrdVvJiumJKrAZqlX//Weod71nKoid1cJ3xJajyJRjs7johsYasuLrRpT2/t7PoumOwcTi3vSei8MJPKSKiCCbuUphbffHtyi7FL6V1AIDBqXHSZX8c2x8alRK/lBrxyKYfsfjz4y4Bn9Um4Ptf7QOPYzUqvw88TosTA7LOW997UgfXGV+CKk+CwdYxV7DqhFLjtNCqlbDYBJyvaXlOvZ1F5+nxP5zr+s6jGJCFY5dFIgovrCEjIopQnu5SeFofJe6QDUqNlS47cr4Wzda2nRvFgC9Bp5ba7Z+qbMTkNfv8Uj8k8jRlEbDX4wzrm4BDxa5BgFLRebdDX9MGxWCwvZb7oqWT7C33g10npFQo0C9Jj4LyepypbEA/R02Zt7PoPD7eTXfMzog1ZNEMyIhI5hiQERFFKG92NYa7GYJc32zBWUcr9MGOgMyTgK/17DN/DzwWZ5G52yEDALPVhoKyegDAU+MzkKSP6nQ4s8gfaYNix7zWQ6lT47SYOz5DVh3z+idF2wOyqkbc6LjM21l0Hh8f07WdR7PVJu22hnsNGRGFPgZkREQRyttdjc6cKDNCgD2ASIq2f4n2pXugvwYei230jU1WGJssiNV2/M/egbPVqGuyoEeMBncN6eNy30qlIuDBUqh0zGtp7NHSadHbQcWeHj+0i8O3G5wahbCGjIjkjgEZEVGE8nZXozNiuuJgp3RFXxpdeLoz544+SoV4rQq1TVZ8fOQCruod12GQk3vSXi83PrNtINhdwZLYMU/OpIDMaTi0SqnAkzdfjme2He/w75x3Ej1J0/Rl51GsH1MrFX5vFENE5G8MyIiIIpS3uxqdEQOygb1aAjJf52P5Y+BxboEBDY5aojd3nwKAdudcWaw27Cq0B2QTBvRs97ZCIVjqDv2TxOHQrrPIlEp74KNQAIJTzV1HO4mBTNM0ifVjGqYrEpH8MSAjIopQ/tylaNkha+mw6EnA1xlfAzqxg2Rr7dWpHTxXgxqTBUn6KAzpYppcpBB3yCobzKgzWRDnmGH38eHzAICZIy7BdZcle7STKO48Tlm3Hxdrm/DEuMtwz7BLfN55bLQ4OiyquTtGRPLHTyoioggm7lK013Y9LU6LMf2TcLC4ut35YaKGZitOO3ZLnDss+tJK3tfOhd7OuRLTFW/O6gG1zGq25CZGo0ZPx+vlTJV93c9WNWL/2WooANx5bR+vBhWrlAqkxtpr/XrH6/ySBirWkLGhBxGFAu6QERFFuNb1URq1Aq98VYCLdU3433/mocHc0iChvXS/k46GHr1iNejRqiteR2lpzu3u2+Nr50JvOkgOSU/ATse8tQlZ7acrkqv+SXqUG5txprIRV/WOxydHLgAArrssGX0SdF7fXrxjl62z14Q3xJRFBmREFAoYkBERUZv6qJ8vGvHu/mKXYAxoP93vlzJx/lgc2tNRQ4xviyoC1rnQmw6Sh0tqUNVoRoJOjeF9ma7oif7J0cgvrsGZqgY0WWzY9vNFAMDUa3t36fbi9VEAgFo/BWRiUw89a8iIKAQwICMiIhdWm4AvjpV2eoxzW/rjpXUAXNMVW2uvIUYgOxd600Hy6xPlAIBxmT3Ykc9D4kDo05WN+OZkOWpMFqTGaXH9Zcldur0Exw5Zrcnsl/OTAjK2vCeiEMCAjIiIXHg7MPqXdlreeypQnQs9aSjSK1YDs9WGL4+XAQBultHwZbkTA7KfL9TidIW9juy316R1OZj2d8piS0DGHTIikj/+dERERC68SfdrNFul9ueDenkfkAWKJw1FjM1WPPbxTzA22b+8L/uqALmOWjLqWG6BAS/tOAnAnsJ6yrH+KdFd74oZr/N3yqK9hkzHgIyIQgADMiIicuFNut/JMiNsApASo0GKo1OeXHTUQVLsoih24hOJ9XEMyjomjhKoqG+bWviSDwFtvNa+Q1bn55TFaAZkRBQCmLJIREQuvBkY/ZFj9lRn9WPB1LpOLTk6Cs99eQLlnTw25/o4auHpKIGuPHfx+kClLPJ3ZyKSP35SERGRC0/S/cS29GKHxa7Uj3UXsU5t4uBeUCoVnQZjQEt9HLnyprbQW/5PWXQMhuYOGRGFAAZkRETURmcDox+6rr/Ulr6lw2L7Le/lxpv6OHIVyOfO/10WOYeMiEIHUxaJiKhdrdP9vjpRjm+LKvD9qSr8f2P6ocliwylHhz0575A586Y+jlwF8rkTuyw2mm1ottigUfv2e7FJqiHj785EJH8MyIiIqEPObemH901A3pkq/HihFrt/rUSiPgo2AegRo0FPmTX06Ig39XHkKpDPXaxWDQUAAUBtkwUpat8CYqYsElEo4U9HRETkkZRYLe4Zlg4AeHv3KWz/xT6/q3e8FlabEMxT85g39XHkKpDPnVKhQJxO7LToex1ZQzNTFokodDAgIyIij/1+5CXQqZUoqmjAh44Oiz9dqMPkNftCpl18R/VxqXFaLJ98hVQfR20F8rmL92MdmcnCtvdEFDqYskhERB7LL66ByWJrc7k4wytUAprW9XEpsRoMSU/gzpgHAvXc2TstmvzS+r4lZZG/OxOR/DEgIyIijwRyDlUwONfHkXcC8dz5c4eMXRaJKJTwpyMiIvJIIOdQEbW0vvfDDlmzOBiaARkRyR8DMiIi8ghneFEgicOhfU1ZFARBSlnUaxiQEZH8MSAjIiKPcIYXBZK/uiw2WWwQe37qWUNGRCGAn1REROQRcQ5VZzjDi7oqwU81ZCZzS9MZnZo7ZEQkfwzIiIjII5zhRYEkNvXwNWWxwZGuqFUr+VokopDAgIyIiDzGGV4UKGINma9NPaT6MTb0IKIQwbb3RETkFc7wokDwX8qiGJDxN2ciCg0MyIiIyGuc4UX+5r8dMnsNmY47ZEQUIvjzEREREQWdc5dFmyC4ObpjDUxZJKIQw4CMiIiIgi5eaw/IBADGpq7vkokpi9FMWSSiEMFPKyIiIgo6jVop1X35krYoNvVgyiIRhQoGZERERCQLYh2ZL63vxRoypiwSUahgQEZERESyEO+HTouN7LJIRCGGn1ZEREQkC1JA1uh7yiJ3yIgoVDAgIyIiIlmQWt/70NSDKYtEFGoYkBEREZEs+DdlkQEZEYUGBmREREQkCwlSQObDDlmz2GWRX3GIKDTw04qIiIhkwT9dFsU5ZNwhI6LQwICMiIiIZKGlqYcPKYsW1pARUWhhQEZERESyIAZkdT409TBxMDQRhRgGZERERCQLYkDmS8piQzPnkBFRaAnqp5XVasXMmTPx9NNPS5cdOXIEd911F4YOHYrs7Gxs2rTJ5W8+/fRT5OTkYMiQIZg6dSp++OEHl9tbvnw5rrvuOgwdOhSPPPIIysrKpOsrKiowe/ZsjBgxAqNHj8bSpUthsVg8vm8iIiIKHKntvQ8BmbhDFq3hDhkRhYagBmRvvvkm8vPzpf+uqanBgw8+iClTpuDAgQNYunQpXnnlFRw9ehQAsG/fPrz44otYtmwZDhw4gMmTJ+ORRx5BY2MjAGDVqlXYs2cPPv74Y+zevRs6nQ6LFy+Wbv+JJ55AdHQ0du/ejY8++gh79+7Fhg0bPLpvIiIiCqwEp7b3giB06TbEOWRMWSSiUBG0gGzv3r3YsWMHfvOb30iX7dixA4mJiZgxYwbUajXGjh2L22+/He+//z4AYNOmTbjtttswfPhwREVFYdasWUhKSsLnn38uXf/AAw+gd+/eiI2NxaJFi/Ddd9+huLgYZ86cwf79+zF//nzo9Xr07dsXs2fPlm7b3X0TERFRYIk7ZGarAJOjOYe3WuaQMWWRiEKDOhh3WlFRgUWLFuHtt9+WdqgAoKCgAAMGDHA5NjMzEx999BEAoLCwEHfeeWeb648fP466ujpcvHjR5e9TUlKQkJCAEydOAAASExORmpoqXZ+RkYHz58+jtrbW7X17Q6Hw+k8ogMT14LqEP651ZOA6h69ojRJqpQIWm4BakxkxWvsul6drbRNaArnoKBVfIyGE7+vIEGnr7Onj7PaAzGazYf78+bj//vsxaNAgl+vq6+uh1+tdLtPpdGhoaHB7fX19PQAgOjq6zfXida3/Vvxv8e87u29v9OgR5/XfUOBxXSIH1zoycJ3DU2J0FAzGZqj0WmmNPV3reqfujH17J0LPOrKQw/d1ZOA6u+r2gGz16tXQaDSYOXNmm+v0ej3q6upcLjOZTIiJiZGuN5lMba5PSkqSgimxnqz13wuC0OY68b9jYmLc3rc3Kirq0MXUdwoAhcL+xue6hD+udWTgOoe3WI0KBgBnLtSgl0bh1VpX1DcDABQAjDX1qI+Un+HDAN/XkSHS1ll8vO50e0C2efNmlJWVYcSIEQAgBVhff/01FixYgD179rgcX1hYiKysLABAVlYWCgoK2lx/0003ISEhAampqSgsLJRSD8vLy1FdXY0BAwbAZrOhuroaBoMBKSkpAICioiKkpaUhLi4OAwYM6PS+vSEIiIgXWajhukQOrnVk4DqHJ3sdWSNqTBZpfT1da7HlvS5KCUDB10cI4vs6MnCdXXV7xeuXX36JQ4cOIT8/H/n5+Zg0aRImTZqE/Px85OTkwGAwYMOGDTCbzcjLy8PWrVulurFp06Zh69atyMvLg9lsxoYNG1BRUYGcnBwAwNSpU7Fq1SoUFxfDaDTi5ZdfxqhRo9CvXz9ceumlGD58OF5++WUYjUYUFxfj7bffxrRp0wDA7X0TERFR4ImzyGobzV7/bUtDD6YqElHoCEpTj44kJSXhnXfewdKlS7Fy5UokJydj8eLFGDNmDABg7NixeO6557BkyRKUlpYiMzMTa9asQWJiIgBgzpw5sFgsmDFjBurr6zF69GisWLFCuv2VK1fihRdewIQJE6BUKjFlyhTMnj3bo/smIiKiwGtpfe/9LDKx5T0DMiIKJQqhq4M+qEMGQ2TkxYYKhQJISYnjukQArnVk4DqHt9d2FuGDQyX4/ci++NO4y7xa631nqvDoRz8iMyUGH9w3PPAnS37D93VkiLR1Fh+vOxzSQURERLIR59ghq2vyPmXRxBlkRBSC+IlFREREsuFLymKDWWzqwZRFIgodDMiIiIhINuxdFoEaH2rIohmQEVEIYUBGREREsuFLl0WT2bntPRFRaOAnFhEREcmGTymLzWx7T0ShhwEZERERyYaYssi290QUKRiQERERkWyIXRYbzFZYrDav/lbqsqhhQEZEoYMBGREREclGnFYt/X9vdsmsNgHF1Y0AgEpjE6y2CBhyRERhgQEZERERyYZKqZCCMk8DstwCAyav2Ye9p6sAAJ/9VIrJa/Yht8AQsPMkIvIXBmREREQkK2KnxRqT+06LuQUGLNxyDGXGZpfLy4zNWLjlGIMyIpI9BmREREQkK/Eedlq02gS8mlvY6TGv7Sxi+iIRyRoDMiIiIpKVBA+HQx8uqWmzM9ZaaV0TDpfU+O3ciIj8jQEZERERyYrYabHOTUBmcBOMeXscEVEwMCAjIiIiWWlJWey8hiwlVuPR7Xl6HBFRMDAgIyIiIllJkJp6dL5DNiQ9Ab3cBFupcVoMSU/w27kREfkbAzIiIiKSlXhHDZm7HTKVUoGnsjM7PWbu+AyolAq/nRsRkb8xICMiIiJZkVIWG93PIcvOSsHyyVcgRqNyuTw1Tovlk69AdlZKQM6RiMhf1ME+ASIiIiJn8R6mLIqys1LwzYky7DhhwMSBPfHba3tjSHoCd8aIKCQwICMiIiJZEVMW65o8C8gAoLjaBAC4ZWBPDO+bGIjTIiIKCKYsEhERkay07JB1XkMmEgQBZ6saAQB9k/QBOy8iokBgQEZERESykuA0h8xmE9weX9VoRn2zFQoAlyQyICOi0MKAjIiIiGQlzpGyaBM8S1ssduyOpcVroVXzqw0RhRZ+ahEREZGsaNVK6ByBVU2D+7TFM2K6InfHiCgEMSAjIiIi2RHryKobm90eK+6Q9WP9GBGFIAZkREREJDtip8WaRvc7ZGzoQUShjAEZERERyY60Q+ZBymJxtT0g658UHdBzIiIKBAZkREREJDstKYudB2Q2trwnohDHgIyIiIhkJ0FMWWzovIas3NiMJosNKqUCfeK13XFqRER+xYCMiIiIZMfTlEWxoUd6gg5qFb/WEFHo4ScXERERyU6chymLZ6saALDlPRGFLgZkREREJDtxWhUA4PjFWuSfrYbVJrR73NkqEwC2vCei0MWAjIiIiGQlt8CAf+45AwD4qaQWD394FJPX7ENugaHNsdIOGQMyIgpRDMiIiIhINnILDFi45RhqTBaXy8uMzVi45ViboExsec8dMiIKVQzIiIiISBasNgGv5hZ2esxrO4uk9EWLTcC5aqYsElFoY0BGREREsnC4pAZlxs7b3JfWNeFwSQ0A4GKtCRabAI1KgdQ4trwnotDEgIyIiIhkweAmGGt9nJiueEmiHkqFImDnRUQUSAzIiIiISBZSYjVeHXe2kvVjRBT6GJARERGRLAxJT0AvN0FZapwWQ9ITALChBxGFBwZkREREJAsqpQJPZWd2eszc8RlQKe3piWeqGJARUehjQEZERESykZ2VguWTr2izUxarVWH55CuQnZUiXVbsCMg4g4yIQpk62CdARERE5Cw7KwXjMnrgcEkNvvm1Epvyz+GSBJ1LMGa22nCh1tHyPpEBGRGFLu6QERERkeyolAqM6JeIhf8zCEoFcLysHuccNWMAUFJtgk0AoqNU6BHjWTMQIiI5YkBGREREspUSq8XwvokAgNyTBunys9Ut6YoKtrwnohDGgIyIiIhk7ZaB9lTFr0+WS5edZUMPIgoTDMiIiIhI1rKzUqBUAL+UGqW0RTb0IKJwwYCMiIiIZC0pWiOlLX7jSFs8W9UAAOjPgIyIQhwDMiIiIpK9Wwb2BAB840hbFFMW+7LDIhGFOAZkREREJHvjM3tA5UhbLDTUo8zYDIA1ZEQU+hiQERERkew5py1u2HcWAJCgUyNBHxXEsyIi8h0DMiIiIgoJExxpizuO29MWk6OjYLUJwTwlIiKfMSAjIiKikBCltM8bE0OwU5WNmLxmH3ILDB3/ERGRzDEgIyIiItnLLTDghe0n21xeZmzGwi3HGJQRUchiQEZERESyZrUJeDW3sNNjXttZxPRFIgpJDMiIiIhI1n44VyN1VexIaV0TDpfUdNMZERH5DwMyIiIikjVDfefBmHScm6CNiEiOGJARERGRrKXEaDw7Ltaz44iI5IQBGREREcna0EsS0MtNsJUap8WQ9IRuOiMiIv9hQEZERESyplIq8FR2ZqfHzB2fAZWjLT4RUShhQEZERESyl52VguWTr2izU5Yap8XyyVcgOyslSGdGROQbdbBPgIiIiMgT2VkpGJfRA4dLamAwNiMlVoMh6QncGSOikMaAjIiIiEKGSqnA8L6JwT4NIiK/YcoiERERERFRkDAgIyIiIiIiChIGZEREREREREHCgIyIiIiIiChIGJAREREREREFCQMyIiIiIiKiIGFARkREREREFCQMyIiIiIiIiIKEARkREREREVGQMCAjIiIiIiIKEgZkREREREREQcKAjIiIiIiIKEgYkBEREREREQWJOtgnEI4UimCfATkT14PrEv641pGB6xw5uNaRg2sdGSJtnT19nApBEITAngoRERERERG1hymLREREREREQcKAjIiIiIiIKEgYkBEREREREQUJAzIiIiIiIqIgYUBGREREREQUJAzIiIiIiIiIgoQBGRERERERUZAwICMiIiIiIgoSBmRERERERERBwoCMwsrx48dx//33Y9SoUbj++uuxYMECVFZWAgCOHDmCu+66C0OHDkV2djY2bdoU5LMlX1mtVsycORNPP/20dBnXObxUV1djwYIFGD16NEaOHInZs2ejrKwMANc63Pz888+YMWMGRowYgRtuuAEvvfQSmpubAXCtw0FlZSVycnKwb98+6TJ36/rpp58iJycHQ4YMwdSpU/HDDz9092lTF7S31tu3b8cdd9yBYcOGITs7G2+++SZsNpt0fcSvtUAUJhobG4Xrr79eeOONN4SmpiahsrJSeOCBB4SHHnpIqK6uFkaNGiVs3LhRMJvNwvfffy8MHTpUOHLkSLBPm3ywYsUKYdCgQcLChQsFQRC4zmHod7/7nTBnzhyhpqZGqKurEx599FHhwQcf5FqHGavVKlx//fXCu+++K1itVuHChQvCxIkThTfffJNrHQby8/OFW265RRgwYICQl5cnCIL7z+u8vDxh6NChQn5+vtDc3CysX79eGD16tNDQ0BDMh0JutLfWP/74o3DNNdcIubm5gtVqFQoLC4Xx48cL69atEwSBay0IgsAdMgob58+fx6BBgzBnzhxoNBokJSVh+vTpOHDgAHbs2IHExETMmDEDarUaY8eOxe233473338/2KdNXbR3717s2LEDv/nNb6TLuM7h5aeffsKRI0ewbNkyxMfHIzY2Fi+++CLmzZvHtQ4zNTU1KC8vh81mgyAIAAClUgm9Xs+1DnGffvop5s2bhyeffNLlcnfrumnTJtx2220YPnw4oqKiMGvWLCQlJeHzzz8PxsMgD3S01iUlJbjnnnswfvx4KJVKZGRkICcnBwcOHADAtQaYskhh5PLLL8fatWuhUqmky7Zv344rr7wSBQUFGDBggMvxmZmZOH78eHefJvlBRUUFFi1ahFdffRV6vV66nOscXo4ePYrMzEx8+OGHyMnJwQ033IDly5ejZ8+eXOswk5SUhFmzZmH58uW4+uqrMW7cOFx66aWYNWsW1zrE3XDDDfjqq69w6623ulzubl0LCwu57iGmo7WeOHEinnnmGem/TSYTdu3ahSuvvBIA1xpgQEZhShAEvP7669i5cycWLVqE+vp6ly/uAKDT6dDQ0BCkM6SustlsmD9/Pu6//34MGjTI5Tquc3ipqanBiRMncPr0aXz66af47LPPUFpaioULF3Ktw4zNZoNOp8Of//xnHD58GNu2bUNRURFWrlzJtQ5xPXv2hFqtbnO5u3XluoeejtbamdFoxJw5c6DT6TBr1iwAXGuAARmFIaPRiD/96U/YunUrNm7ciIEDB0Kv18NkMrkcZzKZEBMTE6SzpK5avXo1NBoNZs6c2eY6rnN40Wg0AIBFixYhNjYWKSkpeOKJJ/Dtt99CEASudRj56quvsH37dtx7773QaDTIysrCnDlz8MEHH/B9HabcrSvXPfz8+uuvuOeee2CxWPDee+8hNjYWANcaYEBGYebs2bO48847YTQa8dFHH2HgwIEAgAEDBqCgoMDl2MLCQmRlZQXjNMkHmzdvxv79+zFixAiMGDEC27Ztw7Zt2zBixAiuc5jJzMyEzWaD2WyWLhO7cg0ePJhrHUYuXLggdVQUqdVqREVF8X0dptyta1ZWFtc9jHz77be46667cOONN2LdunVISEiQruNaMyCjMFJTU4P77rsPw4YNw7p165CcnCxdl5OTA4PBgA0bNsBsNiMvLw9bt27FnXfeGcQzpq748ssvcejQIeTn5yM/Px+TJk3CpEmTkJ+fz3UOM9dddx369u2LZ599FvX19aisrMTrr7+OW265BZMmTeJah5EbbrgB5eXl+Oc//wmr1Yri4mKsWrUKt99+O9/XYcrduk6bNg1bt25FXl4ezGYzNmzYgIqKCuTk5AT5zMlbhw8fxpw5c/DMM89g4cKFbdIaudaAQhDbGRGFuPXr12PZsmXQ6/VQKBQu1/3www/48ccfsXTpUpw8eRLJycmYPXs2pk6dGqSzJX8RZ5AtW7YMALjOYaa0tBTLli3DgQMH0NTUhOzsbCxatAjx8fFc6zDz/fffY8WKFfj1118RFxeHyZMnS11zudbhYeDAgXjvvfcwevRoAO4/rzdv3oxVq1ahtLQUmZmZWLx4Ma699tpgnT55wXmtH374YezatatNndjw4cOxdu1aAFxrBmRERERERERBwpRFIiIiIiKiIGFARkREREREFCQMyIiIiIiIiIKEARkREREREVGQMCAjIiIiIiIKEgZkREREREREQcKAjIiIiIiIKEgYkBEREREREQUJAzIiIupW2dnZ+OSTT9pc/sknnyA7OzsIZ+ReQUEBBg0ahEceeaRb7u/IkSN48MEHcebMGQwbNgxvvvlmm2N27tyJq666CocOHeqWcwKArVu34qWXXuq2+yMiigQMyIiIiNzYuHEjpk6dij179uDUqVMBva/m5mYsXLgQCxcuRP/+/fHCCy/g7bffdgm8ysrK8Mwzz+Cpp57CsGHDAno+zm6//XYcO3YMe/fu7bb7JCIKdwzIiIhIlk6cOIEHHngAo0aNwk033YQlS5agrq4OQPu7aTNnzsQ//vEPAPYdrRkzZmDkyJEYP348Fi5cCKPRCMAe8LzxxhuYMGECRo0ahQceeABnzpzp8Dzq6uqwZcsWzJgxAzk5OVi/fr3L9VarFStWrMD111+P6667Ds899xzuueceaRfQaDTihRdewLhx4zB27Fg8+eSTMBgMHd7fpk2bcMkllyAjIwMAMGnSJPz2t7/FvHnzYDQaIQgCFi5ciJEjR+L++++HIAh47733MHHiRIwYMQL33nsvfvrpJ+n2ioqK8NBDD+Hmm2/GNddcg1tvvRU7d+4EAJw7dw4DBw7EsmXLMHLkSDz//PMoLS3FH//4R+l5f/TRR1FWVibd3u9+9zu8+uqrnS8eERF5jAEZERF1u+effx4jRoxw+d/zzz8vXV9VVYXf//73yMzMxHfffYePP/4Yp06dwoIFCzy+/bFjx2L//v34+OOPcezYMWzatAkA8Prrr2PXrl3YsGEDdu/ejWuvvRZ/+MMf0NTU1O5tffzxxxgwYACuvPJKzJw5E5s3b0ZlZaV0/bp167Blyxa8++672LVrF+Lj4/HDDz9I1z/77LM4c+YMPvnkE3z99deIjY3Fo48+CkEQ2r2/f//735g0aZLLZYsXL4Zer8df//pXbNy4ESUlJXjllVek49evX4833ngDe/fuxdSpU3H//fdLQd9jjz2GAQMG4KuvvkJ+fj5uuOEGLFmyxOX26+vrsWfPHjz55JN47bXXkJaWhj179uDzzz9HQ0MD/vWvf0nHZmdno6ioCD/++KNHa0FERJ1jQEZERN3uueeeQ35+vsv/nnvuOen6b775BlFRUZg3bx50Oh169uyJP//5z8jNzUV5ebnb29dqtdi9eze+/PJLKJVKbN68WdpN+s9//oO5c+eib9++0Gq1mDNnDsxmM3bt2tXmdgRBwAcffID77rsPADBkyBAMHDgQ//73v6VjPvroIzz44IPIzMyERqPBE088gZ49ewIAKioqsH37dixatAg9evRATEwMnn32Wfz444/4+eef29yfwWBAYWFhmzREvV6PFStWYMuWLfjHP/6BlStXIjY2FgDw/vvv46GHHsKgQYMQFRWFadOmISMjA1u2bAEArF69Go899hgEQUBJSQni4+NRWlrqcvtTpkyBRqNBfHw8tFotDh48iP/+97+or6/H2rVrsXjxYulYnU6HQYMGMW2RiMhP1ME+ASIiotYqKirQp08fqFQq6bJLLrkEAFBSUuL271esWIF//OMfeP311zF37lwMGzYMS5YsQXJyMhoaGvD4449DqWz5TdJsNrd7u9999x1Onz6NJUuWSDt4JpMJxcXFeOCBB6DVanHhwgWkp6dLf6NSqdCnTx+Xc7377rtdblelUuHcuXO46qqrXC4/f/48ACA1NbXNuWRlZSEnJwcAMGjQIOnykpISLF++HH//+9+lyywWi3Tbx48fx+zZs1FeXo6MjAwkJye32Z3r1auX9P8XL16M1atXY926dXj66acxaNAgLF68GCNGjJCOSUtLw8WLF9ucIxEReY8BGRERyU56ejrOnz8Pq9UqBWVnz54FAPTs2ROnT59Gc3Ozy99UVVUBAGw2G44dO4bHHnsMzz77LC5cuIBXXnkFTz/9NDZt2gStVot33nkHQ4YMkf72119/bTcIev/99zF9+nTMnj1busxsNmPq1Kn47LPPMH36dPTp00cKpAD7rtqFCxcAtARWX3zxhbRrBgCFhYXo27dvm/sTg0Sbzdbu8+IcoIrS0tLwpz/9Cbfddpt02dmzZ5GYmIjS0lI8/vjjePPNN6Wau+3bt2PHjh0ut6FQKKT/f+zYMUyfPh2PPfYYKisr8dZbb+HRRx9FXl6edIzVanUJaImIqOv4aUpERLIzbtw4AMDf//53mEwmlJeXY+nSpRgzZgzS09ORkZEBg8GAvLw8CIKAzZs3o6ioCIA9qHnppZewYsUKNDU1ITk5GVqtFklJSVAqlZg2bRpeffVVXLx4ETabDZ9++ikmTZrUprHH2bNn8d133+H//u//kJaWJv2vb9++uOOOO7B+/XoIgoDp06fjnXfewalTp9Dc3Iy33npLaoKRmpqKm2++GUuXLkVVVRXMZjNWrVqFadOmoba2ts3jFnfWWqcUdubuu+/GqlWrpMe/e/du3HbbbThw4ADq6+thtVqh1+sB2APBt956CwDaBLSif/7zn3jxxRdhNBoRHx8PvV6PpKQkl2PKysqkcyUiIt8wICMiItmJi4vD+vXrcfLkSYwbNw6TJk1Ceno63njjDQDA1VdfjUceeQRPP/00Ro0ahby8PEycOFH6+xUrVqCoqAg33HADrrvuOtTV1eHFF18EACxcuBDXXnst7r33XowYMQIbNmzAypUrccUVV7icw/vvv4+BAwdi8ODBbc5v+vTpOHXqFHJzc3HfffchOzsb99xzD26++WZUV1cjLS0NUVFRAIC//vWviI+Px5QpUzBmzBh8++23WLt2rcuOmSg5ORlXXHEFDh486PFzNWvWLEyZMgWzZ8/G0KFDsXTpUvzlL3/BhAkTcPnll2PBggWYP38+hg8fjscffxx33nknoqKicPLkyXZv74UXXoDNZsOECRMwcuRIHDlyRHreAaCpqQk///wzbrzxRo/PkYiIOqYQOmrzRERERG4dOXIE6enpSElJAWBPWRwzZgxee+01XH/99V7f3saNG7Fr1y6sXbvW36fqF9u2bcN7772HDz/8MNinQkQUFrhDRkRE5IOtW7diwYIFqKurg8VikeaUOdeoeePuu+/GmTNnUFhY6Mez9J/33nsPc+fODfZpEBGFDQZkREREPnjiiSeQkpKCnJwcjBo1Cjt37sS6desQExPTpdvTaDRYvnw5li9f7ucz9d3mzZtx5ZVXYsyYMcE+FSKisMGURSIiIiIioiDhDhkREREREVGQMCAjIiIiIiIKEgZkREREREREQcKAjIiIiIiIKEgYkBEREREREQUJAzIiIiIiIqIgYUBGREREREQUJAzIiIiIiIiIguT/B8IXCigIYXTBAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2378,12 +2411,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Rectangle.set() got an unexpected keyword argument 'legend'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[51], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(nrows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m6\u001b[39m), sharey\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Bar plot for condition vs. price\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m], hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      6\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition vs. Price\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2763\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2528\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2519\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l, b, w, h, c, e, lw, htch, lbl \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[0;32m   2520\u001b[0m     r \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle(\n\u001b[0;32m   2521\u001b[0m         xy\u001b[38;5;241m=\u001b[39m(l, b), width\u001b[38;5;241m=\u001b[39mw, height\u001b[38;5;241m=\u001b[39mh,\n\u001b[0;32m   2522\u001b[0m         facecolor\u001b[38;5;241m=\u001b[39mc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2526\u001b[0m         hatch\u001b[38;5;241m=\u001b[39mhtch,\n\u001b[0;32m   2527\u001b[0m         )\n\u001b[1;32m-> 2528\u001b[0m     r\u001b[38;5;241m.\u001b[39m_internal_update(kwargs)\n\u001b[0;32m   2529\u001b[0m     r\u001b[38;5;241m.\u001b[39mget_path()\u001b[38;5;241m.\u001b[39m_interpolation_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m   2530\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_props(\n\u001b[0;32m   1220\u001b[0m         kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{cls.__name__}\u001b[39;00m\u001b[38;5;124m.set() got an unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1221\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{prop_name!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
+      "\u001b[1;31mAttributeError\u001b[0m: Rectangle.set() got an unexpected keyword argument 'legend'"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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3fq/67Gc/Ww0NDVUvvvhidf3111d/93d/d8p5//3f/11dccUV1aOPPlq98cYb1Xe/+93qyiuvrP73f/+3Aasem850Fo8++mjV3d1d7dy5szp27Fj11FNPVd3d3dWWLVsasOqx50zncMLQ0FB1ww03VDNnzqz+9V//dRRXOvad6Sx2795dXXXVVdW3v/3t6tixY9V//dd/Ve9973urhx9+uAGrHpvOdBaPP/549b73va/as2dPVVVVtWXLlmrWrFnVSy+9NNpLHrOefPLJasGCBW97zfGaff7ZP5XB3qkc9k/lsH8qg71TWUZr/9Swd+7s3bs3BgYGYuXKldHS0hLTp0+Pvr6+0/5NxubNm6O7uzsWLFgQzc3NsXDhwpg7d27cd999DVj52FPPLF5++eW4+eabo7OzM5qamqKrqyt6enpix44dDVj52FLPHE648847Y8GCBaO4ygtDPbP45je/Gb29vXHjjTdGU1NTzJo1K771rW/FnDlzGrDysaeeWTz//PNRVdXJj/Hjx8dFF10Uzc3NDVj52LN58+a4/fbbY8WKFb/wPK/Z54/9Uxnsncph/1QO+6cy2DuVZTT3Tw2LO7t27YrJkyfH1KlTTx5rb2+Pffv2xaFDh2rO3b17d8ycObPm2OWXXx7PPvvsqKx1rKtnFh//+Mfjk5/85Mnbg4ODsWPHjpg9e/aorXesqmcOERH3339/7N27N2655ZbRXOYFoZ5ZPPPMM/Erv/Ircdttt0VPT09cd911MTAwEJdddtloL3tMqmcW119/fbS1tcXChQvjPe95T9x6661x1113xbRp00Z72WPS+9///nj00Udj4cKFb3ue1+zzy/6pDPZO5bB/Kof9UxnsncoymvunhsWdw4cPR0tLS82xE7eHhoZ+4bmXXHLJKedxduqZxc975ZVX4uabb47Zs2fHDTfccF7XeCGoZw579uyJu+++O770pS/F+PHjR22NF4p6ZvHqq6/GPffcE4sXL44f/OAHsXr16vjiF78YW7ZsGbX1jmX1zOKNN96IWbNmxT/8wz/E008/HatXr45Vq1bFc889N2rrHcsuu+yyM/qbPK/Z55f9Uxnsncph/1QO+6cy2DuVZTT3Tw2LO62trXHkyJGaYyduT5gwoeZ4S0tLHD16tObY0aNHTzmPs1PPLE54+umnY+nSpfHOd74z/uZv/sZb90bAmc7hJz/5SaxYsSI+//nPxzve8Y5RXeOFop7nxMUXXxy9vb3xgQ98IJqbm2Pu3LmxZMmSePjhh0dtvWNZPbP4whe+EB0dHXHllVfGxRdfHB/5yEeis7MzNm/ePGrrxWv2+Wb/VAZ7p3LYP5XD/qkM9k45jcRrdsPiTkdHRxw8eDD2799/8tiePXti2rRpMXHixJpzZ86cGbt27ao5tnv37ujo6BiVtY519cwiImLTpk3xiU98In7/938/vvSlL8XFF188mssds850Dj/84Q/jhRdeiFWrVkV3d3d0d3dHRMSnP/3puOOOO0Z72WNSPc+J9vb2+OlPf1pz7M0334yqqkZlrWNdPbPYt2/fKbNobm6Oiy66aFTWynFes88v+6cy2DuVw/6pHPZPZbB3ymlEXrPP9Ts/n4vf+Z3fqVasWFG99tprJ7+Ld39//ynn7d69u7riiiuq7373uye/c/QVV1xRPf/88w1Y9dh0prPYsmVL9Z73vKf6/ve/34BVjn1nOof/z097GHlnOot/+Zd/qX7t136tuv/++6tjx45VAwMDVWdnZ/W9732vAasem850FnfffXfV09NT/fu//3v15ptvVg8//HB1xRVXVP/5n//ZgFWPbW93zfGaff7ZP5XB3qkc9k/lsH8qg71Tmc73/qmhceeVV16pli1bVr33ve+tfv3Xf7266667quHh4aqqqqqzs7P6zne+c/Lc73//+9XixYurzs7O6vrrr68ef/zxRi17TDrTWdxwww3VrFmzqs7OzpqPP/7jP27k8seMep4TP8/mZOTVM4vHH3+8+q3f+q2qq6ur6u3tre69995GLXtMOtNZvPHGG1V/f3/1wQ9+sLr66qurG2+80f+ZOk/+/zXHa/bosn8qg71TOeyfymH/VAZ7pzKd7/1TU1V57xsAAABAVg37njsAAAAAnDtxBwAAACAxcQcAAAAgMXEHAAAAIDFxBwAAACAxcQcAAAAgMXEHAAAAIDFxBwAAACAxcQcAAAAgMXEHAAAAIDFxBwAAACAxcQcAAAAgsf8De6fiXWDI/iIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1400x600 with 2 Axes>"
       ]
@@ -2440,12 +2491,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x2000 with 2 Axes>"
       ]
@@ -2506,7 +2557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2793,7 +2844,7 @@
        "std          5.824659  4.156724e+04  "
       ]
      },
-     "execution_count": 239,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2852,7 +2903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2879,7 +2930,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2911,7 +2962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -3116,7 +3167,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3146,12 +3197,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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A0tJS5syZ0275oUOHaGhooLy8vM32BQUF5OTkcPjwYQByc3MZMGBAcPmFF17IiRMnqK+vp0+fPiG332AIva/xEGhforezu9S/5Jbq/YPk6WO02qeglIiIiIiIJKx+/fp1+Lrdbsdms7V5zWq10tTU1OVyu90OQEZGRrvlgWWf3jbwe1NTU1hBqUQcDtmRZGlnd6l/yS3V+we9o48dUVBKRERERESSjs1mo6Ghoc1rTqeTzMzM4HKn09lueV5eXjDA5HA4Otze5/O1Wxb4PbD/UCVLnZhEb2d3qX/JLdX7B8nTR9WUEhERERERaTVy5Eh2797d5rXS0lKKiooAKCoq4siRI+2WT5kyhZycHAYMGEBpaWlwCF9lZSW1tbWMHDkSr9dLbW0tVVVVFBQUAPDBBx8wcOBAsrPDeyhLljoxydLO7lL/kluq9w96Rx87Yox3A0RERERERMJVXFxMVVUVmzZtwuVysWfPHnbs2BGsIzV37lx27NjBnj17cLlcbNq0ierqaoqLiwGYPXs269evp6ysjMbGRlavXs24ceMYPnw4559/PmPHjmX16tU0NjZSVlbGE088wdy5c+PZZRGRlKNMKRERERERSTp5eXls3LiRVatWsXbtWvLz81m+fDkTJkwA/LPxPfDAA6xcuZKKigoKCwvZsGEDubm5ACxevBi32838+fOx2+2MHz+exx9/PLj/tWvX8n//7//l85//PEajkRtvvJHvfOc7ceipiEjqMvh8vTFBrGeqqsIb62kwQEFBdtjbJRP1Mfmlev9AfTzXuhIZoX6uesPnsKd0jkKj8xQanaeunX2OQPeGSEv0z16q/xtR/5JbqvcPkqeP0Xp20PA9ERERERERERGJOQWlREREREREREQk5hSUEhERERERERGRmFNQSkREREREREREYk5BKRERERERERERiTkFpURERBKYz+ejqakJTZYrIiLJzOv1curUKU6dOoXX6413c0QkQZjj3QCRaJo8ZQLlJ092ud7AQYN4/dU9MWiRiEh4HA4H617azx3FnyEjIyPezREREemWqqoqnvzbfgC+/fnP0L9//zi3SEQSgYJSktLKT55k6dOvdLneI7dOjUFrRES6x5JujXcTREREeiwzJz/eTRCRBKPheyIiIiIiIiIiEnMKSomIiIiIiIiISMwpKCUiIiIiIiIiIjGnoJSIiIiIiIiIiMScglIiIiIiIiIiIhJzCkqJiIiIiIiIiEjMJXRQqqamhuLiYvbu3Rt87cUXX2TWrFlceeWVTJs2jXXr1uH1eoPLt2/fTnFxMVdccQWzZ89m3759wWUej4eHH36YSZMmMWbMGBYtWsSpU6di2icREREREREREQFzvBtwLm+//Tb33nsvH3/8cfC19957j6VLl/L4448zdepUjh49yre+9S0yMjL4xje+wd69e3nwwQfZsGEDo0ePZvPmzSxatIiXX34Zm83G+vXr2b17N8899xzZ2dmsWLGC5cuX8/Of/zyOPZWAyVMmUH7yZJfrDRw0iNdf3RODFomIiIiIiIhItCRkUGr79u2sXbuWu+++m//+7/8Ovn78+HG++tWv8rnPfQ6ACy+8kOLiYkpKSvjGN77B1q1bmT59OmPHjgVgwYIFPPvss+zcuZM5c+awdetWlixZwqBBgwBYtmwZkydPpqysjGHDhsW+o9JG+cmTLH36lS7Xe+TWqTFojYiIiIiIiIhEU0IO35s8eTIvvfQSX/7yl9u8fv3113PfffcFf3c6nfzv//4vl112GQClpaWMHDmyzTaFhYUcOnSIhoYGysvL2ywvKCggJyeHw4cPR7E3IiISSR0N7d6/fz/z5s1jzJgxTJs2ja1bt7bZRkO7RUREREQST0IGpfr164fZ3HkSV2NjI4sXL8ZqtbJgwQIA7HY7NputzXpWq5WmpibsdjsAGRkZ7ZYHloXKYAj/p7vbJdNPT/sYjfcg0vtM9fcx1funPna8bjJ5++23ufnmm9sM7a6rq2PhwoXceOONlJSUsGrVKh566CEOHDgAEBzavWbNGkpKSpg5cyaLFi3C4XAAtBna/dprr2G1Wlm+fHlc+hfg8/loamrC5/PFtR0iIiIiItGUkMP3uvLhhx9y55130rdvX371q1+RlZUFgM1mw+l0tlnX6XSSl5cXDFYFHkLOXp6ZmRnW8fv2ze5Wu7u7XU9devkoTpw4EdK6gwcP5v333u32sXrSR4PRQEZGekjrFRSEdpxo7DNe72OsJHv/Qv289/SznuiS/X3syLmGdu/atYvc3Fzmz58PwMSJE5kxYwabN29m9OjRSTm02+Fw8NNd73D7tSOxWm1dbyAiIiIikoSSLij1yiuv8D//8z/cdNNNfO9732uTUVVUVMSRI0farF9aWsqUKVPIyclhwIABbYb4VVZWUltb227IX1eqqxsI58trg8H/gBjudpFy/JPjIdVqAn+9pqqqhrCPEYk++rw+mpqaQ1ov1DZGcp/xfh+jLVX619nn3WAAmy0dh6OZh7/evc96ogvnfQysmywmT57MjBkzMJvNbYJSR44c6XDo9rZt2wD/fWDOnDntlocytDucoFSomWdnZ7N1to7BYODJl9/nP6ddetZrITcnqYVyjkTnKVQ6T13TORIRkXhJqqDUO++8w+LFi1m5ciVz585tt3zu3LksXryYL33pS4wdO5bNmzdTXV1NcXExALNnz2b9+vWMGjWKvLw8Vq9ezbhx4xg+fHhY7fD56NZDe3e3i7WetDFWfYzGMULdZ7K8j92Vyv0L9OvTf6aiVHwf+/Xr1+HrnQ3d7mp5JId2hxvg62z9piYTVlsaxswM+vbNxmZLo6AgG5vNhsPhwGazYegFT4/JFDSNJ52n0Og8dU3nSEREYi2pglI/+9nPcLvdrFq1ilWrVgVfHzt2LE899RQTJ07kgQceYOXKlVRUVFBYWMiGDRvIzc0FYPHixbjdbubPn4/dbmf8+PE8/vjj8emMiIhEhM1mo6Ghbdbb2UOzYzW0O9Qsw1Cy2ZqamnA6WjAYPVRXN+BwtLRm9jXw0137+e4XPtMukJZKUiVzM9p0nkKj89S1s88RKDglIiKxk/BBqbNnxvvZz37W5fqzZs1i1qxZHS6zWCwsWbKEJUuWRKx9IiISXyNHjmT37t1tXistLaWoqAiI3dDucLPTOlv/7Nc/ndlnSbemZCZcR3pLP3tK5yk0Ok9d0/kREZFYS8jZ90REREJVXFxMVVUVmzZtwuVysWfPHnbs2BGsIzV37lx27NjBnj17cLlcbNq0qcOh3WVlZTQ2NnZ7aLeIiIiIiIQn4TOlREREOpOXl8fGjRtZtWoVa9euJT8/n+XLlzNhwgQADe0WEREREUlQCkqJiEjSOXtoN8CoUaPYsmXLOdfX0G4RERERkcSj4XsiIiIiIiIiIhJzCkqJiIiIiIiIiEjMKSglIiKSoHw+Hw6HQzNiiYiIiEhKUlBKREQkQblbmvnFq//C43bFuykiIiIiIhGnoJSIiEgCs6RZ490EEREREZGoUFBKREQkgZ1sgr0nWzhd34hP4/hEREREJIUoKCUiIpKgfD7YXwPHGrx8/y+HcDod8W6SiIiIiEjEKCglIiKSoE63gMPj/3uD1xTfxoiIiIiIRJiCUiIiIgnqRNOZv9c2e+PXEBERERGRKFBQSkREJAH5fL42Qam6Zp9qSomIiIhISjHHuwEiIiLSnt3t/wl8e+TyQkWjK65tEhERERGJJGVKiYiIJKBmjz8rymqG7DT/ax/WOOPYIhERERGRyFJQSnodn8+Hx6shMCKS2FytJaQsBsix+P/+gYJSIiIiIpJCNHxPegWny8P7FY18UGWnzuEGIC/DwsUDsrhkQFacWyci0l4gKGU2Qk4aYIejp5s5PzuuzRIRERERiRgFpSTFGTh4soG3ympp8bTNjqppcvHG0dOUVtoxpGXEqX0iIh1znxWUymrNlKpodHF+tiV+jRIRERERiSAFpSRl1TpcZH35e7zx0WkA8jMsjBqczeA+VgA+qnHwVlktpxpbyPry93C4PNgspng2WUQkyNU6zNhiAGvrpanG4QYUlBIRERGR1KCaUpKSjtU08Y3f7MMy/DOYDAYmnZ/HV0YPZGS/LLLSzWSlm7l8UDYzLxtAmsmAeeBIfvzyB/FutohI0NnD9wJBqVqHG69PNfFEREREJDUoU0pSzttltSz90/vUO9146iuZM3kU+ZlpHa6bn5lG8UX9eP79U/zh3XJuuGwAnxmSE+MWS28zecoEyk+eDGndgYMG8fqre6LcIklEgeF7FiOkG8EA+ACnW0EpEREREUkNCkpJSnn+YAU/2PVv3F4flw/K5vWnF5N//Y5OtxmcY6X5X6+QfslUHv5bKb++5UqMBkOMWiy9UfnJkyx9+pWQ1n3k1qlRbo0kqsDwPbMBDAawmsHhBoeCUiIiIiKSIjR8T1KC1+fjidePsvKFw7i9Pq4bWcD6eaPxOepD2t6xZwuZaSaOVNp542hNlFsrItI111mZUgA2kz9Y7nApKCUiIiIiqUFBKUl6jc1ulvzhIL/cWwbAgnHDWHXDJVjDKFrua27kK6MHAbD5rU+i0k4RkXCcPfsegNXcGpQKLBARERERSXIKSklS+6DKzjd+8w6vfVhDutnI//3yRSy+9oJuDb+7ecxgTEYDb5XVcaiiIQqtFREJ3aczpaytA+6VKSUiIiIiqUI1pSQpub0+nikpY8Obx3B5fPTPSuPRWZdx6cDsbu2vscnO5LEXkfn575BWNJF59zyMY/cz7dYbMnQIr/7vGz1tvohIl86uKQWQjj9KZW/xxKtJIiIiIiIRpaCUJB3z4Iv5xm/28a+KRgAmj8hn2RdGUnCOGfZC4fN6Wfr0K3x82sGLhyrJu/KLLP7WN9tlXD1622d70HIRkdC5P50p1Toi2elRppSIiIiIpAYFpSQp+Hw+yuubefuTOrJnLuNfFY1kpZv43ucuZPqlAzBEaLa8ITlW0s1GHC4vJ+uaGZJrjch+RUTC5WpXU8r/p2bfExEREZFUoaCUJDSHy8ORSjuHTzVS63AD4PO4uWnscL4xfhgFWekRPZ7JaOCCvhkcqmiktMquoJSIxIXP5ztTU6o15h7MlFJQSkRERERShIJSknB8Ph/H65wcOmXnWE0TrWVVMBsNFPbLZM+Pbmfp0n9G7fgXtgalPj7twOfztcnCamxs5MLC80Laz8BBg3j91T3RaqaIpDDnWTPsmdsN3/PX1RMRERERSXYKSkmnTtQ5OVrTRLPLQ47NwkX9s8hKj9LHxpzO++UNvHuygXqnO/hyQWYaFw/I5MK+maSZjbzZWB2d47cakJ2OxWjA6fZSbXdRkHWmVpW3tfZUKB65dWq0migiKa7prGLmpta4uNHtxEA6PgzUNLnokxWnxomIJJCDBw+yevVqDh8+jNVq5Ytf/CJLly4lLS2N/fv384Mf/IDS0lLy8vJYtGgR8+bNC267fft2nnjiCSorKxkxYgQrVqxgzJgxAHg8Hn74wx/yxz/+EYfDwYQJE/j+979P//7949VVEZGUpKCUdOjvR6p4+h9lvF/e0OZ1A/CZIX2YeflArruoHzaLqcfHOtXQzO/eOUHO1x5n99HTAKSZDBQWZHLxgCz69qCAeXeYjAYG51g5dtpBWa2jTVBKRCQW7C3+TCmLEQLJmgYDpBmh2Qt1ZwXuRUR6K6/Xy7e//W0WLlzIM888w6lTp1iwYAF5eXl87WtfY+HChdx5553cfPPNlJSUsHjxYi666CJGjx7N3r17efDBB9mwYQOjR49m8+bNLFq0iJdffhmbzcb69evZvXs3zz33HNnZ2axYsYLly5fz85//PN7dFhFJKQpKSRt2l4fPfOeHpF04DvDXb/LUnoAWB4bMfEx9+vHO8XreOV7Pj17+gC9fOoCbxwzm/L4ZYR/rUEUDv3n7OLsOV+Lx+jBas+iTbubyQdmM7J+JxWSMdPdCNjTXH5T6pNbJmKE5cWuHiPRO9hZ/0Mn8qTkcAkGpBgWlRESoq6ujsrISr9eLz+cf1mw0GrHZbOzatYvc3Fzmz58PwMSJE5kxYwabN29m9OjRbN26lenTpzN27FgAFixYwLPPPsvOnTuZM2cOW7duZcmSJQwaNAiAZcuWMXnyZMrKyhg2bFh8OiwikoIUlJKgFreXgq8sJ23opRgM8JnBfbh8UDY2y4jgOo3Nbkqr7Ox599/Ycwaw9Z0TbH3nBJMuyOPbnyvikrx0DJx7Jjyny8Nf/13JHw6Us/9EffD1MUNzeO2p7/PNlT/CGKGZ9HpiaK4NOE1FYzMtbi9p5vgFyESkd/H5fNQ0NgH+TKmzBX6vb1ZQSkQkLy+PBQsW8PDDD/PII4/g8Xj4/Oc/z4IFC1izZg0jR45ss35hYSHbtm0DoLS0lDlz5rRbfujQIRoaGigvL2+zfUFBATk5ORw+fDjsoFQC/Ne2U2dn5MbiOIG/x+q8xKp/8aL+Jb9k6WO02qeglADg9fn467+rsA69lDSTgS9e0p8B2e1ntstKN3PFkBx23X83m/+6j2f/eZzXP6zhjaOneePoPxjcJ51rL+zL6MF9GJxjxWo2Ud/s4qPqJv75SR27j9bQ2OyvlWIyGrhuZAH/MXYolw7MpnD52wkRkALoYzXTx2qm3ummvKGZ4Xm2eDdJRHoJh8PBH94+Cpj4dDzcYvQBBuqbPR1tKiLSq3i9XqxWKytWrGDu3LkcO3aMO+64g7Vr12K327HZ2v7/zWq10tTkD/p3ttxutwOQkZHRbnlgWTj69s0Oe5t4iHY7PZ4mbLa01mNlUVAQ2/OSLO9Dd6l/ya839LEjCkoJAG+X1XG8zonX5WT6qPNDqKPkY/x5eYw/L4+y0w62vnOCHQcrOFHfzLP7TvDsvhPn3HJwjpUbRw3khssG0C+rfeArUQzMTqfe6aZCQSkRiTGfKQ3wYOlg+B5o+J6ICMBLL73Eiy++yAsvvABAUVERixcvZtWqVcyYMYOGhra1UZ1OJ5mZmQDYbDacTme75Xl5ecFglcPhOOf24aiubsCXwJOmGgz+h+Fot7O6uhGHoyX4d5Mp/PIf3RGr/sWL+pf8kqWPgXZGmoJSQkVDM+8c9w+lq3lhHQVT1oa1/bA8G9+bdiErbrycnW+Xseej0xw+1UhlYwvNbi+Z6SaG5tq4dEAWE8/PZ/SQPgmTEdWZAdnp/LvSTnl9c7ybIiK9jMvr/x9J+0wp/58aviciAidPnqSlpaXNa2azGYvFwsiRI9m9e3ebZaWlpRQVFQH+ANaRI0faLZ8yZQo5OTkMGDCA0tLS4BC+yspKamtr2w0JDIXPR0I/aAZEu51n7zse5yRZ3ofuUv+SX2/oY0cUlOrlvF4fr31QA8DIfpl8fPj1bu8rI83MZ4sKmFpYEHxt8pQJfHjyJO8Cf+li+0Z7Y7ePHQ0D+/izuCobW/B4fZiMiR9IE5HU4PJPvtdBTSn//1QanBq+JyIyefJkfvSjH/Gzn/2Mb33rW5w4cYL169czY8YMiouLefTRR9m0aRPz58/n7bffZseOHTzxxBMAzJ07l8WLF/OlL32JsWPHsnnzZqqrqykuLgZg9uzZrF+/nlGjRpGXl8fq1asZN24cw4cPj2eXRURSjoJSvdzBigZOO1ykm42MPy+Xv0Z4/+UnT7L06VdCWnfFnCsjfPSeybGaSTcbaXZ7qba30L+DGlsiItHgbg1KfTpTKk2ZUiIiQYWFhTz55JM8/vjjPPXUU2RnZzNz5kwWL15MWloaGzduZNWqVaxdu5b8/HyWL1/OhAkTAP9sfA888AArV66koqKCwsJCNmzYQG5uLgCLFy/G7XYzf/587HY748eP5/HHH49fZ0VEUpSCUr2Yy+PlnU/8w/auHp6L1WKKc4sSi8FgYEB2Oh+fdlDe0KyglIjETDAo9akEzUCNqQYFpUREAJg0aRKTJk3qcNmoUaPYsmXLObedNWsWs2bN6nCZxWJhyZIlLFmyJCLtFBGRjiX0PPc1NTUUFxezd+/e4Gv79+9n3rx5jBkzhmnTprF169Y222zfvp3i4mKuuOIKZs+ezb59+4LLPB4PDz/8MJMmTWLMmDEsWrSIU6dOxaw/iea9kw043V76pJu5qF/4RRt7gwHZ/oLvpxpUV0pEYsfTWlDg06OGgzWlNHxPRERERFJAwgal3n77bW6++WY+/vjj4Gt1dXUsXLiQG2+8kZKSElatWsVDDz3EgQMHANi7dy8PPvgga9asoaSkhJkzZ7Jo0aLgzBnr169n9+7dPPfcc7z22mtYrVaWL18el/7Fm8vj5d2T/hlJxg7Lwah6SR3ql+nPjqqyu+LcEhHpTVrrnLcLSqW11pTS8D0RERERSQUJGZTavn07S5Ys4b//+7/bvL5r1y5yc3OZP38+ZrOZiRMnMmPGDDZv3gzA1q1bmT59OmPHjsVisbBgwQLy8vLYuXNncPm3vvUtBg0aRFZWFsuWLePVV1+lrKws5n2MtyOVdprdXrLTzYwoiM10rMmob6YF8A+VaQ6MpxERiTJPa1DKdM5MKQWlRERERCT5JWRQavLkybz00kt8+ctfbvP6kSNH2k3DWlhYyKFDhwDaTNv66eUNDQ2Ul5e3WV5QUEBOTg6HDx8Oq30GQ/g/3d0uEj+f5vP5gllSlw/KxtjRSt04D+d6LRq62/dwWS0mstL9tbaqm1qCx450OxPlJxnbHM57fnYf49nXcEXzfZTEFMyU+tTrgaBUi8eH06UhfCIiIiKS3BKy0Hm/fv06fN1ut2Oz2dq8ZrVaaWpq6nK53W4HICMjo93ywLJQ9e2bHdb6Pd2upwxGAxkZZ4p0H62yU+90k242csV5+aR9anqns9ftbJ8FBe378+k+fvrYXenJsc+1bqjHP9d6A/vYKK1spK7Znylls4W2v3DamUji9TmNlFDec5stPa7vTzify+62M9nfx97uXMP3zAYwAD782VKaoEJEREREkllCBqXOxWaz0dDQ0OY1p9NJZmZmcLnT6Wy3PC8vLxisCtSX6mj7UFVXN9BagzYkBoP/ATHc7SLF5/XR1HSmUPf+j08DUNQvE3eLC3dL2/XPXrezfVZVnXkvztXHTx+7K905dlfrhnr8c62Xa/U/9J2s9Qc/HY7mkN7HcNqZCOL9OY2Uzt5zg8EfkHI4muP6/oTzuQy3neG8j4F1JfGca/iewQBpJmj2+INSmhVURERERJJZUgWlRo4cye7du9u8VlpaSlFREQBFRUUcOXKk3fIpU6aQk5PDgAED2gzxq6yspLa2tt2Qv674fHTrob2720WSvcXNx6f9gbmL+2f1aF8d9SVWfYzleSzI9M/AV9XYEvax4/1+d0cifE6jJdCvT/+Z6JL1eiPdd65MKYA0o4Fmj486pyZgEBEREZHklpA1pc6luLiYqqoqNm3ahMvlYs+ePezYsYM5c+YAMHfuXHbs2MGePXtwuVxs2rSJ6upqiouLAZg9ezbr16+nrKyMxsZGVq9ezbhx4xg+fHg8uxVT/z5lxwcMyE4nL8MS7+YkhYIsf1CqzunGYLHGuTUi0ht4WyOKHQalWkfsqdi5iIiIiCS7pMqUysvLY+PGjaxatYq1a9eSn5/P8uXLmTBhAgATJ07kgQceYOXKlVRUVFBYWMiGDRvIzc0FYPHixbjdbubPn4/dbmf8+PE8/vjj8etQjPl8Pv5d6a+fdXH/8IYs9mY2iwmbxYjD5cXSd1i8myMivYCns0wpk7+qVL0ypUREREQkySV8UOrTM+ONGjWKLVu2nHP9WbNmMWvWrA6XWSwWlixZwpIlSyLaxmRRZW+h3unGZDRwft+MrjeQoLwMC466ZiwFvSerTkTip9Phe8qUEhEREZEUkVTD96RnjrRmSZ2fZyPNpLc+HPk2/xA+BaVEJBaChc47WJbWGqmqdSgoJSIiIiLJTZGJXsLr8/FhtX/2uMJ+GroXrkD9LUtfBaVEJPo6z5Tyv9jYrKCUiIiIiCQ3BaV6iZP1zThcXtLNRobmqFh3uIJBKWVKiSSsgwcPMn/+fK666iomT57MD37wA1pa/LNm7t+/n3nz5jFmzBimTZvG1q1b22y7fft2iouLueKKK5g9ezb79u2LRxeCOgtKWVrv3Bq+JyIiIiLJTkGpXuKj1iyp8/NtGDt6ypFO5dn8QSlzdl+cLk+cWyMin+b1evn2t7/N9ddfzz/+8Q+2bdvG66+/zoYNG6irq2PhwoXceOONlJSUsGrVKh566CEOHDgAwN69e3nwwQdZs2YNJSUlzJw5k0WLFuFwOOLWn64LnStTSkRERESSn4JSvYKBj2r8D1cX5KvAeXekmY1ktVYXPu3QjFciiaauro7Kykq8Xi8+nz+iYzQasdls7Nq1i9zcXObPn4/ZbGbixInMmDGDzZs3A7B161amT5/O2LFjsVgsLFiwgLy8PHbu3BmXvnh9PlpjUpg6Ckq13rkbFJQSERERkSSnoFQvYBpYSJPLg8VkYLCG7nVbYAjf6SYFpUQSTV5eHgsWLODhhx9m1KhRTJ06lfPPP58FCxZw5MgRRo4c2Wb9wsJCDh06BEBpaWmny0NlMIT+09n67sDYPdpnShkMYGl9saHZHdYxk+0n3HPaW390nnSeonGOREREYsUc7wZI9KVdcBUA5+XZMEVo6F5jk53CovPavGYwGvCd9TAF0GhvjMjxEkGuzUJZrZNaZUqJJByv14vVamXFihXMnTuXY8eOcccdd7B27Vrsdjs2m63N+larlaYm/7DmrpaHqm/f7Iis7zGeuY5m2tKC122z0QcmEwZjGtCCvcVDQUF4x0w24Z7T3krnKTQ6T13TORIRkVhTUCrF+Xw+LCOuBuD8CA7d83m9LH36lTavZWSk09TU3Oa1FXOujNgx4y23ta5UnaZhF0k4L730Ei+++CIvvPACAEVFRSxevJhVq1YxY8YMGhoa2qzvdDrJzPTPRGqz2XA6ne2W5+XlhdWG6uoGfL6u1zMY/A9+51q/vKo++PdmZ0swa8HV0ozBZMZnCVyLXFRW1mNIwbSGrs6R+Ok8hUbnqWtnnyNQcEpERGJHQakUd+hUI6bsfpiNBoblauheT+TY/P9cap3KlBJJNCdPngzOtBdgNpuxWCyMHDmS3bt3t1lWWlpKUVER4A9gHTlypN3yKVOmhNUGn4+wHnjPtX5La5VzI+2H0fh8YPGXt8Pt9eFwebEFXkhB4Z7T3krnKTQ6T13T+RERkVhTTakU9/KRKgCG5Voxm/R290QgU6qx2YPb441za0TkbJMnT6ayspKf/exneDweysrKWL9+PTNmzKC4uJiqqio2bdqEy+Viz5497Nixgzlz5gAwd+5cduzYwZ49e3C5XGzatInq6mqKi4vj0peW1uvLuUZbmw1nCqA3OJW5KSIiIiLJS5lSKS4QlIrk0L3eymo24nU2YrRmUe90k5+ZFu8miUirwsJCnnzySR5//HGeeuopsrOzmTlzJosXLyYtLY2NGzeyatUq1q5dS35+PsuXL2fChAkATJw4kQceeICVK1dSUVFBYWEhGzZsIDc3Ny59cQUypc4RlDIYDGSlm6lzumlodtM/Oz2GrRMRERERiRwFpVLYsZomPqpx4PO4GZ5n63oD6ZTBYMBVc5z0wRdRq6CUSMKZNGkSkyZN6nDZqFGj2LJlyzm3nTVrFrNmzYpW08Li6iJTCiAr3eQPSilTSkRERESSmMZzpbDXP6wBwH3yEGlmvdWR4Dp9HEAz8CUYn89Htb0Fp8sT76aI9FigppSps6BUmr+OVEOzglIiIiIikryUKZXCXvuwGgDXsX3Al+PbmBThrjkB+Ge9ksTw71ONPPr3Ut457p+xLOvLS3C4PCld/FlSW1c1pQCy0/23bwWlRERERCSZKX0mRdU7XbzzSR0Aro/2xbk1qcNV8wkAdQ49CCaCf1U08I3fvhMMSAFYhn+G7QfKadTDuiQp11mz751LVro/6KrPuYiIiIgkMwWlUtSbR0/j8cEFfTPwNlTGuzkpw336JAD1ehCMuyp7C0v+cJBmt5erhuWw41vj+O2tY/HUnsTe4uHVD2rwaW5rSUIh1ZRqHb5Xr5pSIiIiIpLEFJRKUYGhe9eOyI9zS1KLu64CgGa3l2a3N86t6d1+8sqHnGps4YL8DB6ddRkD+1gpLMik8S8/xmQwcLzOyaFT9ng3UyRsLV3MvgdnMqU0fE9EREREkpmCUinI7fXxxtHTAFw7om+cW5NafC4nNov/n41mvYqfgyfreeFfpzAA//fLF5GVfqY8nreunKuG5wDwz7I6PF5lS0lycYVQ6DxQU0rD90REREQkmSkolYL2H6+jodlNjtXMqMF94t2clBN4GNQQvvj5yatHAZh+2QAuHpDdbvllA7PJsJhocnkorVK2lCQXDd8TERERkd5CQakU9NoHNQBcMyIfU2dPNdItfaytQSk9DMbFwfIG9n1Sh9lo4D+vOb/DdUxGA6MG+4NV+4/Xq7aUJBWXN/The8qUEhEREZFkpqBUCjpTT0pD96IhEJTS8L342PLP4wB84eJ+DMhOP+d6F/fPwmIyUOd0U17fHKvmifSY3en/vIYyfK+h2ROLJomIiIiIRIWCUinmWE0TH592YDIamHB+Xrybk5LODN9zxbklvU9lYzMvHfbPJvnVK4d0um6a2ciIvhkAHNEQPkkigZpSnd2gA8P3Gpy6DomIiIhI8lJQKsW8/qF/6N6VQ3PaFH+WyMlWplTc7Hz/FB6vj9GD+3BJB7WkPq2oIBOAD6ubcKvguSSJkGpKBWffU6aUiIiIiCQvBaVSTHDo3oUauhctgeF7jc0evAp0xIzP5+P5gxUAzLx8QEjbDOyTTmaaCZfHx8enHdFsnkjEhFJT6uzZ97yqmSYiIiIiSUpBqRRS73Txzid1AFw7Ij/OrUldGRYTJoMBH9DYomypWPlXRSNHa5pINxv5/Mh+IW1jMBgobM2WOlrdFM3miURMcPheJ0GpzNbhez7ArmwpERGRDnm9Xk6dOkVFRQVerzfezRGRDigolULePHoajw8u6JvB0FxbvJuTsgwGQ3AIn2bgi52d7/uzpD5b2Desoann5fv/LXxS68CjzDZJAoFMqc4KnaebjaSb/bfwBs3AJyIi0qGqqip+9tf9/OiPJVRVVcW7OSLSARUdSiG9Zda9xiY7hUXnhbauvTEqbehjNVHrcOlhMEa8Ph9/+7f/PxJfvKR/WNv2z0rDZjHicHkpb2gmO8sajSaKRExLCJlSPp+PrDQTzW6vrkMiIiKdyMzJx2ZLi3czROQcFJRKEW6vjzeOngZgyoWpPXTP5/Wy9OlXQlp3xZwro9KG4Ax8ypSKiXdP1FNlbyEzzcS44eHNKmkwGBiWa+PflXaO1TRRNCgnSq0UiYxQhu85nQ5aXP6Z9xoVlBIRERGRJKXheyli//E6Gprd5FjNXD6oT7ybk/L6WC2AZuCLlb8f8WdJTR6RT5o5/MtWYAjfx6cd+FQUWhJccPa9LtYLDN9TcFxEREREkpUypVLEax/UAHDNiHxMnX29LhERrCmlDIUemTxlAuUnT3a5Xt7XfwIZ+UwLscD5pw3JsWI0QEOzhzqHCyVwSyILZfY9gLTWolMaviciIiIiyUpBqRTRW+pJJYo+Zw3f8/l8GAwKBHZH+cmTXQ7FrLa38PsD5aSbjUw6P7yhewEWk5H+WemUNzTzyWkHI/JUV0oSV2D4XmeFzgEsrSto+J6IiIiIJCsN30sBx2qa+Pi0A7PRwIRuPrRLeLLT/dOxuzw+mt2aXjaaymqdAFw9PBerxdTt/QzO8QeiymqaItIukWgJpaYUQFrrChq+JyIiIiLJSkGpFPD6h/6he1cOzSErXclvsWA2GcloDZBo6Ex0fVLrAGDi+T0r4D8kJx2AMtWVkgQX7vA9ZUqJiIiISLJSUCoFBIbuTb5QQ/diKVhXSlkKUdPi9lLe0AzApAt6lgXYLysds9GAw+WhpskVieaJREXIw/da7+C6BomIiIhIslJQKsnVO12880kdANeO6FkmiYSnj4JSUXe8zonPB57akwzNtfVoXyajgYF9/NlSJ+qckWieSFR0lSnl8/lwOByYfB4AapuaY9U0EREREZGIUlAqyb159DQeH1zQN6PHD+0SnrOLnUt0lLUO3XOVHYjI/oa01pU6rqCUJDCXx1+n7lw3aHdLM7949V/BoFS9o4WmJtVKExEREZHko6BUktOse/ETGL6nei7R4fP5+KS1yLnr4/0R2Weg2Hl5fTMYu180XSSaQil0bkmzBofv2Vs02YKIiIiIJCcFpZKY2+vjjaOnAZhyoYbuxVpW6wx8jc2eOLckNZ1ucmFv8WAyGHCfOBSRffbNsGC1GHF5fZj6XRCRfYpEWmD4Xqg1pRpbdA0SERERkeSkoFQS23+8joZmNzlWM5cP6hPv5vQ6gZkOG1vcms0tCspas6QG56SDJzKFyQ0GA0PzMgCwDLksIvsUibRQMqWgbVBK1yARERERSUbmeDegOw4ePMjq1as5fPgwVquVL37xiyxdupS0tDT279/PD37wA0pLS8nLy2PRokXMmzcvuO327dt54oknqKysZMSIEaxYsYIxY8ZEvc2XXj6K458c73K9gYMG8fqre0La52sf1ABwzYh8TF09vUjEZVpMGACvDxwuLxlpGg4WSYF6UkNzbUSmopTfsDwbpacaMQ+5NIJ7FYmcUINSaa1BKafbR2NTE5mZmVFumYiIiIhIZCVdUMrr9fLtb3+bhQsX8swzz3Dq1CkWLFhAXl4eX/va11i4cCF33nknN998MyUlJSxevJiLLrqI0aNHs3fvXh588EE2bNjA6NGj2bx5M4sWLeLll1/GZotukfATJ06w9OlXulzvkVunhrxP1ZOKL6PRQEaaCXuLh4Zmt4JSEeT2eKlo8M8oNizXGtF9D2vNlDIPKKTZ7SXdrIRRSRw+n6/L2fcCzv7oNrZ4GBDFdomIiIiIREPSPY3V1dVRWVmJ1+sNDlcwGo3YbDZ27dpFbm4u8+fPx2w2M3HiRGbMmMHmzZsB2Lp1K9OnT2fs2LFYLJZgMGvnzp3x7FK3HKtp4uPTDsxGAxPOz4t3c3qt4BA+FTuPqIqGFrw+yEwz0cca2dh5boYFm8WIwZzGwfL6iO5bpKc83jPD8Lq6QRsNYG4NXNlVV0pEREREklDSZUrl5eWxYMECHn74YR555BE8Hg+f//znWbBgAWvWrGHkyJFt1i8sLGTbtm0AlJaWMmfOnHbLDx0Kr4iyIcyRcoH1DQYIpexHKPt//ah/6N6Vw3KCs8DFU7h9TEYd9TE73URFw7mLnYf7WYmns/sXbyfq/fWkBvVJx2Aw0Nhkp7DovJC2bbQ3nnOZweCvKzWoj5UPq5vY90kdY4flRqLJURXOexLO+5gI77W05T47KBXC+2MxgtsDjZqBT0RERESSUPyjGWHyer1YrVZWrFjB3LlzOXbsGHfccQdr167Fbre3G4ZntVppamoC6HJ5qPr2ze5W22229C7XMRgNFBR0vf89H9cC8KXRg7tc32A0kJHR9bEDQl23o/U66mOsjh2rfZ7dx7ysdKhqwun1tds+1Pcy0XT3890d5/pslje0AHBBv2wyMtLxeb2s3BparbXvfemyLt/L8woy+bC6iQPljXF5j8L5N9ndz1Es30eJnEA9KQg9KOXwKFNKRERERJJT0gWlXnrpJV588UVeeOEFAIqKili8eDGrVq1ixowZNDQ0tFnf6XQGi7/abDacTme75Xl54Q1/q65uCCsbKJCN4HA0d7mdz+ujqqqh03XqnS5KWjOlxgzI7HJ9n9dHU1NzyO0Ndd2z1zMY/MGajvoY7WPHap8dvY/prU+NtfbmdtuH8l4mEoPBH8gI9/PdEx19Nls8XipaM6X62kzB5ZF4zwOf0wKb/9L39rHTnCivIy3GdaXC+TcZ7uconPcxsK4kDrf3TMZTKIlswRn4zpGtKSKS6mpra1m9ejWvvPIKXq+Xq6++mpUrV9K/f/8eTYDk8Xj44Q9/yB//+EccDgcTJkzg+9//Pv37949XV0VEUlLS1ZQ6efIkLS0tbV4zm81YLBZGjhzJkSNH2iwrLS2lqKgI8AewOlseKp8v/J/AdpHY/xsfnsbjgwv6ZjAkxxbSsaMt3D4mo476mNVa3PxcD4Td+azE8yfWbe5IRX0zPvxDI7PTIxs3Dxwzx2rG21RHs9vLwZMNcTnP4bY7Wu+jJJZAppSB0IZXBoNSGr4nIr3Ud7/7XZqamnjppZd4+eWXMZlMrFixgrq6OhYuXMiNN95ISUkJq1at4qGHHuLAAf+cvoEJkNasWUNJSQkzZ85k0aJFOBz+2X/Xr1/P7t27ee6553jttdewWq0sX748nl0VEUlJSReUmjx5MpWVlfzsZz/D4/FQVlbG+vXrmTFjBsXFxVRVVbFp0yZcLhd79uxhx44dwTpSc+fOZceOHezZsweXy8WmTZuorq6muLg4zr0Kj2bdSxyBQucNKnQeMWfqSUV21r2zGQwG3Cf9teTe/qQ2ascRCZc7xJn3AtJa7+IavicivdF7773H/v37WbNmDX369CErK4sHH3yQJUuW9HgCpK1bt/Ktb32LQYMGkZWVxbJly3j11VcpKyuLZ5dFRFJO0g3fKyws5Mknn+Txxx/nqaeeIjs7m5kzZ7J48WLS0tLYuHEjq1atYu3ateTn57N8+XImTJgAwMSJE3nggQdYuXIlFRUVFBYWsmHDBnJzc+PbqTC4vT7eOHoagCkX5se5NZKV7s+Ucnl8NLu9pMd4GFgqOlHnH9Y2OCd6QSkA94l/kXbheN4uq+ObE8693uQpEyg/eTKkfQ4cNIjXXw2t9pVIR4JBqRDXD2RKNSgoJSK90IEDBygsLOR3v/sdv/3tb3E4HFx77bXcc889HDlypNsTIDU0NFBeXt5m+4KCAnJycjh8+DDDhg0Lq52JPrFIrCa7OXv//slnonu8Tx8nVseMtUSarCgaUr1/kDx9jFb7Ih6U2rt3L+PHj4/0btuYNGkSkyZN6nDZqFGj2LJlyzm3nTVrFrNmzYpW06Ju//E6Gprd5NosXD6oT7yb0+tZTEasZiNOt5fGZjfp5rR4NympNbu9VNv9w3MH9wm9mH13uE74M6UOnKjH5fFiMXUcBig/eZKlT78S0j4fuXVqxNqXamJxb0gFLo9/GF6oN31LMFNKw/dEJDFF8/pfV1fH4cOHufzyy9m+fTtOp5OlS5dyzz33UFBQ0O0JkOx2OwAZGRntlgeWhSNZ6jdGu50eTxM2W1rrsbJiMtmMx9OE1eo/Zn5+bI4ZL8nyOeuuVO8f9I4+diTiQak777yT7OxsvvKVr/CVr3yFwYMHR/oQvdprH/gLnF9zQR6mUMd3SFRlpZtxultobHbTN1NBqZ4or3fiw1/zKTPC9aQ+zXv6OLk2C7UOF++XN/CZITlRPV5vp3tDaMIdvnemppQypUQkMUXz+p+W5v9/17Jly0hPTycrK4u77rqLm266idmzZ3c4wVEoEyAFglWB+lIdbR+OWE4i0x2xmuymuroRh6Ml+HeTKaOLLSJzTKezBas1jZqa2Bwz1uIxWVEspXr/IHn6GK1JkiI+1uj111/n7rvv5r333uP666/nG9/4Bn/+85/bFSeX7gnUk5qselIJIzCET7Nf9dyJev/QvUFRHroXcOVQfyDq7bK6mByvN9O9ITTu1kypcIfv6fojIokqmtf/wsJCvF4vLpcr+Jq3dRbTSy65pNsTIOXk5DBgwABKS0uDyyorK6mtrW03JDAU8Z7IJpEmu4nXOUmW90H96539S6Y+RkPEg1IWi4Xrr7+e9evX88orr3DdddexceNGJk+ezPe//30OHToU6UP2Gsdqmvj4tAOz0cCE8/Pi3RxpFSh23qhi5z12os7/jWW0h+4FjB2WC0BJWW1Mjteb6d4QmkCmlIbviUiqiOb1f9KkSQwbNoz7778fu91OTU0Njz32GNdddx033HBDjyZAmj17NuvXr6esrIzGxkZWr17NuHHjGD58eETOi4iI+EWtKnN1dTU7duzgD3/4A6WlpYwfP5709HQWLFjAz372s2gdNqW9/qF/6N6VQ3OCgRCJv0CmlAoN94zT5aGmyf9NZzRn3jvbuPNyAX+tNqdL718s6N7QOZfHH5QKd/Y9Dd8TkUQXjeu/xWLhmWeewWQycf3113P99dczcOBAVq9eTV5eHhs3buSFF15g/PjxLF++/JwTII0bN47nn3++zQRIixcvZurUqcyfP5+pU6fS3NzM448/HqGzISIiARGPbDz//PP88Y9/5I033mDEiBHMnj2bn/3sZ+Tn+2eKmzp1KosXL+Y///M/I33olBcYunfthRq6l0iy0pQpFQknW4fu5dosZKSZYnLM8/Js9M9K41RjC/uP1zNeGYhRo3tDaNzebg7fU1BKRBJUtK//AwYM4LHHHutwWU8mQLJYLCxZsoQlS5Z0q10iIhKaiAelvv/97zN9+nS2bNnC5Zdf3m75BRdcwIIFCyJ92JRX73Txzif+ujeTR+THuTVytmwN34uIWA/dAzAYDIw7L48/H6xg77HTCkpFke4NoQk3U+rs4Xs+nw9Dos8lLCK9jq7/IiLSmYgHpV5//XXKysoYMGAAAO+88w7Z2dlceOGFAAwcOJA777wz0odNeW8ePY3HBxf0zWBorq3rDSRmAsP3HC4vbq8Ps2ZF7JZAkfPBMSpyHjDuvFz+fLCCf3xcG9Pj9ja6N4SmuzWlXF4fzW4vVktssgxFREKl67+IiHQm4jWl/va3v3HjjTfy0UcfAbBv3z7mzZvHK6+8EulD9SrBoXuadS/hpJuNwUCUXdlS3dLU4qHWEagnFbtMKYCrh/uzow6faqTarpngokX3htAEglKh3pzNZwWvlK0pIolI138REelMxINS69at44knngim595222385Cc/4Uc/+lGkD9VruL0+3jh6GoApF2roXqIxGAzBbClNy949J+v9Q/fyMywxz/QoyEzjkgFZALxxtCamx+5NdG8IjcvTWlMqxEwpg+FMtlS9glIikoB0/RcRkc5EPCh18uRJrr322javTZ48mRMnTkT6UL3GwZP1NDS7ybGauXxQn3g3RzoQmA2xQQ+F3XKiLj5D9wKuucAf7A3McBlpLo+XFrc3KvtOFro3hCbc4XtwZga+BqeuPyKSeHT9FxGRzkQ8KDVkyBBee+21Nq+9+eabDB48ONKH6jXe/MifJTXuvDxMqleUkLJU7LxHTtTHvsj52QKTB+w9djqYqRIJ//yklu9ue5fP/nQ3k3/yOnM2lrDz/YqI7T+Z6N4QGrcnvNn34KwZ+JSpKSIJSNd/ERHpTMQLnS9cuJDFixfzhS98gSFDhnDixAleeuklHn744UgfqtcIBKUmfmpmsMlTJlB+8mSX2zfaG6PSLjkjOzB8T9Oyh83e7Kbe6cYADOwTn0ypSwZmk59hoabJxb5P6hh3Xs9m4XN7ffz45Q/Y+k7bb4E/Pu3ggb8cJvO6xXh9Poy9aKa0WNwbamtrWb16Na+88gper5err76alStX0r9/f/bv388PfvADSktLycvLY9GiRcybNy+47fbt23niiSeorKxkxIgRrFixgjFjxkSsbaEK1pQK46NxZvieKwotEhHpGT0biIhIZyIelJoxYwb9+/fnD3/4AwcPHmTQoEFs3LiRK6+8MtKH6hVqm1z8q7wBgAmfCkqVnzzJ0qe7LhK5Yo7OfbRlpSlTqrsCs+71zUwj3Rzx5M2QGA0GJo/I50/vVfD3I1U9Cko1u73c/+d/8eoH/skJvjJ6IP/flUPpYzWz471yfv7mMSicwD+O1bb7N53KYnFv+O53v0tOTg4vvfQSRqOR++67jxUrVvDII4+wcOFC7rzzTm6++WZKSkpYvHgxF110EaNHj2bv3r08+OCDbNiwgdGjR7N582YWLVrEyy+/jM0W29lOXZ7uB6UanAqKi0ji0bOBiIh0JuJBKYDx48czfvz4aOy619l77DQ+oKhfJv2y4jO0Sbp2ptC5glLhOlHXOnQvJ76f7+KL+vGn9yr427+rWPK5CzGbuhcge+ivR3j1g2rSzUZWTb+YqYUFwWULxg9ncI6VZc8f4t2TDQzJsTIsL7ZBj3iK5r3hvffeY//+/bzxxhtkZfkL1z/44INUVlaya9cucnNzmT9/PgATJ05kxowZbN68mdGjR7N161amT5/O2LFjAViwYAHPPvssO3fuZM6cOVFp77kEa0qFsc2Z4Xu6/ohIYtKzgYiInEvEg1IVFRWsX7+ejz76CK+3bW2WX/3qV5E+XMp785h/6N6EHg4nkugK1pRq8fS6YVk9daaeVHyG7gVcNTyPPJuF0w4XJWW1TDw//Jku0y//As8frMBogB/OupQJHezjCxf353urH8P6mS+x59hphuRYMfaCWnHRvjccOHCAwsJCfve73/Hb3/4Wh8PBtddeyz333MORI0cYOXJkm/ULCwvZtm0bAKWlpe2CT4WFhRw6dKjH7QpXuLPvAVgMPsBAvVPD90Qk8ejZQEREOhPxoNR9991HVVUVn/vc57BYLJHefcprbLJTWHRe8PecW36KMTOXJ/7PYtaeeL/tuqoVlTAy0kwYDODzQVOLJxikks4ZswtobPZgAAbEqch5gNlo4PMjC9i2/yQvHqoMOyh1vM6JbdJ/AHDnlBEdBqQCHG9vJ3fsdGodbv51qpHLBmb3qO3JINr3hrq6Og4fPszll1/O9u3bcTqdLF26lHvuuYeCgoJ2w/CsVitNTU0A2O32TpeHKtRYdGC9jtb3dFFTqqNtTD43YKG2qTmsWfsSWWfnSM7QeQqNzlPXonmO9GwgIiKdifiT87vvvsuLL75Ifn74WQYCPq83WCeq2t7C7w+UYzYa+K9VT7SbeU+1ohKH0WAgM81EY7MHu4JSITMPvgSAfllppHVzuFwkffGS/mzbf5K/Ha7kfz47gj7W0P7z3OB087d/V2EwmvjSJf35j7FDOt+gxcGVw3J44+hp3j3RwKUDsjCk+NNStO8NaWlpACxbtoz09HSysrK46667uOmmm5g9ezZOp7PN+k6nk8zMTABsNluHy/PywstQ7ds3vOBiR+ub0/2fOYvZhM3W9jpiNvrAZMJibvu5zEj3QSO4MFBQkFoBznDPaW+l8xQanaeuReMc6dlAREQ6E/En5+zs7ODDgfRMWe2ZWjufDkhJ4slON9PY7KGh2c2AbNX/CoV58KUADM6J79C9gNGD+1DUL5MjlXb+9F4FX7tqaJfbuDxedh2upNntxX3qQ+6/85qQAkwX9cvkrY9raWh2U1brZHiK15aK9r2hsLAQr9eLy+UiPd3/7y8wTOSSSy7hN7/5TZv1S0tLKSoqAqCoqIgjR460Wz5lypSw2lBd3YDP1/V6BoP/wa+j9Rsa/dd9r8eDw9G2cLmrpRmDyYzb1Hb4Cx4XkEZVvZOqqoaw2pyoOjtHcobOU2h0nrp29jmCyAan9GwgIiKdiXhqwne+8x3uu+8+Dhw4wIkTJ9r8SHg+qXUAMDQntR9WU0WwrpSKDYfE5/NhGeLPlBoU56F7AQaDgZuuGAzA1n3Hg0OpzsXn8/HqBzXUNLmwWYw0vvgTrBZTSMcym4yM7O8vyP1+eWoEEjoT7XvDpEmTGDZsGPfffz92u52amhoee+wxrrvuOm644QaqqqrYtGkTLpeLPXv2sGPHjmAdqblz57Jjxw727NmDy+Vi06ZNVFdXU1xcHFYbfL7Qf861vruL4XsdPVCbW9dtbPaE1YZE/wn3nPbWH50nnadonKNI0rOBiIh0JuKZUsuXLwfgpZdeAvwPeT6fD4PBwL/+9a9IHy5lub0+TjU0AzAkNzGySKRzWWmBGfg0LXsoPj7twJjVF6MBBiZQZtkXL+nPuteOcqK+mecPVnS67v4T9XxY3YTBAJ8fWcAz9pqwjnXpgCzeO9lAWa2TphYPGWmhBbSSUbTvDRaLhWeeeYY1a9Zw/fXX09zczLRp01i2bBl9+vRh48aNrFq1irVr15Kfn8/y5cuZMGEC4J+N74EHHmDlypVUVFRQWFjIhg0byM3N7XG7wuXy+J8Gw/nGKM3o36axRdceEUk8ejYQEZHORDwo9be//S3Su+yVKhub8fjAZjGSY1V9omSgTKnw/OPjWgAGZKdjToB6UgFWi4kF44fzk1c+5P+9fhTSOs5U/KDKTsnHdQBMOj+PQd2YPTDHZqF/VhqnGlv4qKaJS1O44Hks7g0DBgzgscce63DZqFGj2LJlyzm3nTVrFrNmzYpW00Lmbh1yGE6JsUCmVIOuPSKSgPRsICIinYn4k+CQIUMYMmQIdXV1HDx4kH79+mG1WhkypIvCv9LGyXp/ltSgPtaUL4CcKrLS/VkuDcqUCsk/jp0GYEiC1JM6281jBnNeno2aJhcZ13wd36fGMhyraeJ/S6sBf7bTJQOyun2sC/pmAPBhdXgzvSUb3RtCE8yUCuOyb2nNlLK3ePBGetyNiEgP6fovIiKdiXhQqrq6mq9+9avcdNNN3HPPPZSVlXHdddexb9++SB8qpZW3BqUGJkitHena2ZlSnw5iSFser4+3y/xZRokYlLKYjCz9fCFGA6RfNJndR0/T4vbi8nj5Z1kdLx2uwuuDC/JtTLwgr0eB4/Pz/UGp8vpmHK7UDWjq3hCaYE2pMLaxtK7s9UGThvCJSILR9V9ERDoT8aDU6tWrGTlyJCUlJZjNZi688EIWLlzII488EulDpSyv10dFaz2pQQlUa0c6FwhKub0+mt3eLtbu3Q6daqSh2Y2vuYmCrMSckWfceXks+8JIAP5V0civ3/qEp0s+4e1P6vABF/XPZFpRAcYeZjL2sZrpm2nBBxyrcfS84QlK94bQuDz+a0c4mVImw5n1NXxYRBKNrv8iItKZiAel9uzZw3333YfNZgtmD9x+++2UlpZG+lApq8regtvrI91kJC/DEu/mSIjMRgO21pQFDeHrXEnr0D3XiX/1OKgTTTMvH0jjiz8hx2rG0zojUZ90M58r7Mu1I/IxhhM56MR5ef66VcfrnBHZXyLSvSE0gUypcD9ZgWypeqeCUiKSWHT9FxGRzkS8grbFYsHpdGKz2YJDmOx2O5mZmZE+VMoqbzgzdE/1pJJLdroZh6tF2QpdCBQ5d3/yHvCVuLalK66jbzH3ikE0ON2YjAYy00wR/3c5JMfGPz+p53idMzgjUarRvSE07m7UlAJ/UKrZo2LnIpJ4dP0XEZHORDxTatq0adx999189NFHGAwGqqur+f73v8/UqVMjfaiUdVL1pJJWYAifHgzPzenysP+4v56U6/jBOLcmNEaDgRybhax0c1QCRv2z0rAYDTS7vVQ3uSK+/0Sge0NoArPvdScoBRq+JyKJR9d/ERHpTMSDUt/73vfIyMjgi1/8IvX19UyePBmHw8GSJUsifagUZaC83j+EZ5CCUkkn+6xi59KxAyfqafH46JeVhrf2ZLybkxCMRkMwCH28NjWH8OneEBpXN4fvpbVGsTR8T0QSja7/IiLSmYgP38vMzGTt2rXU1NTwySefMHDgQPr37x/pw6QsS8FwWjw+LEYDfTMTswC0nFtWugmARtWUOqfA0L1xw3P5d3ybklCG5Fopq3VyvM7JZ4b0iXdzIk73htC0tM7A6PN6AFPI2wUypZSlKSKJRtd/ERHpTMSDUiUlJW1+P3bsGMeOHQPg6quvjvThUk76sMsBGJCdntAFoKVj2Rq+16V/tBY5v3p4Hr+Oc1sSyeA+VgAqGprx+nwp9+9f94bQBAuda/ieiKQIXf9FRKQzEQ9K3XLLLe1eMxqNDBo0iL/97W+RPlzKSR96KaB6UskqK8rD9yZPmUD5ya6HvA0cNIjXX90TlTb0RJW9hX9VNAIw/vy8OLcmseRlWLCYDLg8PmqaXBSkWKak7g2hcXt6VlNKw/dEJNHo+i8iIp2JeFDq0KFDbX6vqanh//2//8eQIUMifaiUlD5oJODPlJLkExi+1+LxYUjLiPj+y0+eZOnTr3S53iO3Jmbx0DeP1gBwyYCslAu69JTRYKB/VjrH65ycamhOufOje0NogplS+MLazmI0AD5lSolIwtH1X0REOhPxQueflp+fz913383TTz8d7UMlvaYWD+Y+/QAoyEqtB9LewmIyYjX7/1kZswvi3JrE80ZrUOqaC/Lj3JLENCDb/+++oqE5zi2JPt0bOhYodN7dTKkG1bMTkQSn67+IiJwt4plSHamrq6O5OfUfsnrqVKP/HOXZLKSZoh4vlCjJSjfjdLdgzOob76bExTmHGBpN5N76BIb0DH689Bs8euoDGu2NsW9gAuvfmiFZ0dAS55bEhu4N7XkCQakwt0trrYne4HRFtkEiIlGg67+IiAREPCh13333tfnd5XLx9ttvM2nSpEgfKuWcan0Q7Z+tLKlklp1uosoOxux+8W5KXJxriOHJOid/fv8UVrOR2x/5BQaDgRVzroxDCxNX/yx/UKqh2U1Ti4eMtNBnX0t0ujeExuXpWaFzZUqJSKLR9V9ERDoT9Uyp9PR0brnlFm6++eZoHyrpBTKlAg+mkpwCxc6N2b0zU+pcPq51ADA014ohxWaWi5R0s5E8m4XTDheVjc2clx/5umSJQveGjrm7mSllaR3vp5k/RSTR6fovIiJni3hQ6qGHHor0LnsFr89Hld2fKdVP9aSS2pmgVO/MlDqXstNOAIbn2eLcksRWkJXGaYeLKntLSgWldG8IjaeHNaVU6FxEEo2u/yIi0pmIB6XWrVsX0np33HFHpA+d1OqdblweH16Xk7wMS7ybIz2Q3ToDnwqdn9HQ7Oa0w4UBGJqroFRnCjLTOFJpp8qeWrWBdG8ITaDQeXeH79lbPLi9PszhRrVERKJE138REelMxINSR44cYdeuXVx88cVccMEFlJeX889//pNLL72UzMxMAA3d6UAgS8pV+RFGw8g4t0Z6IpgplaWgVEDZaf/Qvf7Z6aSbVcS/MwWZ/qB0tT21ip3r3tA1n8/Xg+F7Z/7e2Owm16YvN0QkMej6LyIinYl4UMpoNHLffffx9a9/PfjaH//4R15++WUef/zxiByjtraW1atX88orr+D1ern66qtZuXIl/fv3Z//+/fzgBz+gtLSUvLw8Fi1axLx584Lbbt++nSeeeILKykpGjBjBihUrGDNmTETa1ROBB9CWU0fj3BLpqexAUMqWnXLFqrvrWI0/KDU8zxrnliS+/Ez/8F17i4emltQpWh2Le0OyCwzdg/AzpYwGAzaLEYfLq6CUiCQUXf9FRKQzEU9ZeOWVV5g/f36b12644QbefPPNiB3ju9/9Lk1NTbz00ku8/PLLmEwmVqxYQV1dHQsXLuTGG2+kpKSEVatW8dBDD3HgwAEA9u7dy4MPPsiaNWsoKSlh5syZLFq0CIfDEbG2dVd161CdlooP49wS6ak0s5E0k/+J8mS9M86tiT+ny8Px1vNwfgrVSIqWNJORHKs/sJlK2VKxuDckO/dZQanu3JyzWgPg9U7VlRKRxKHrv4iIdCbiQan8/HxKSkravPbaa68xcODAiOz/vffeY//+/axZs4Y+ffqQlZXFgw8+yJIlS9i1axe5ubnMnz8fs9nMxIkTmTFjBps3bwZg69atTJ8+nbFjx2KxWFiwYAF5eXns3LkzIm3rLp/PF3z4dFUqUyoVBLKlFJSCj2oc+HyQn2FR9kaIClqzpapSKCgV7XtDKnB5zgpKdWMkS5/WYGaDglIikkB0/RcRkc5EfPjet7/9bRYuXMj111/P4MGDKSsr4+WXX+anP/1pRPZ/4MABCgsL+d3vfsdvf/tbHA4H1157Lffccw9Hjhxh5Mi29ZgKCwvZtm0bAKWlpcyZM6fd8kOHDkWkbd3V1OLB6fZiAFxVH8e1LRIZWelmqptcnKxvjndT4u5odRMAI/oqSypUBVlpfFDdlFKZUtG+N6QCt9cb/Ht3qqsEJlmoc6ZWkXwRSW66/ouISGciHpSaN28eQ4YM4U9/+hPvv/8+w4YNY8uWLVx00UUR2X9dXR2HDx/m8ssvZ/v27TidTpYuXco999xDQUEBNlvbmb2sVitNTf6HYrvd3unyUIVb6yOwvsEAPl/75VVN/geIXJuFY+7kfAjtqo+pIJw+np0pFa/anT35nEbK2UP3LkiAoNSn+xiN9yYS+8xvnYGzpvXaEM4+w+lbLD+b0b43pIJAppQBX7fem8B1R8P3RCSR6PovIiKdiXhQCmDSpElMmjSJmpoa8vPzI7rvtDT/sJZly5aRnp5OVlYWd911FzfddBOzZ8/G6Ww7XMrpdAZn9rDZbB0uz8vLC6sNfftmd6vtNlt6h683VNgBGJDjLwKdkdHxeh0Jdd1Y7bOjPiZzfzpyrvfxbHnZ6VDeQI3TQ0FB9z4vHTEYDSG102A0dPu43f18n33sQBs/PFGHz+fP/BncN6vD9ePxntts6SGfo1DPeWDdSOxziMkEVFLvdGOwpHXrvezp+xgN0bw3pILgzHvdDBb2UVBKRBKUrv8iInIuEQ9KuVwu1q1bx69//Ws8Hg87duzgrrvuYv369fTv37/H+y8sLMTr9eJyuUhP9z/UeVuHPFxyySX85je/abN+aWkpRUVFABQVFXHkyJF2y6dMmRJWG6qrG8LKBgp84+1wNHe43claf6ZWTuvQi6am0Id8hbputPdpMPgf9DvqYzL2pyNdvY9ns7ZWazta2UhVVUNIxw+Fz+sLqT8+ry/s4xoM/kBGuJ/vjo4daOPhE/UAnJ9nO2e7Y/met/mchniOQj3ngXUjsk+fj3SzkWa3F2PO4LDey3Dex8C6sRDte0MqcHn897LuJrAFhu8pKCUiiUTXfxER6UzEC52vW7eOPXv28JOf/ASLxULfvn0ZOHAgq1atisj+J02axLBhw7j//vux2+3U1NTw2GOPcd1113HDDTdQVVXFpk2bcLlc7Nmzhx07dgTrSM2dO5cdO3awZ88eXC4XmzZtorq6muLi4rDa4POF/xPYriOBmff6thY3TkZd9TEVhNPHwDCa47WObn1eOvschdPe7n5OI9HGRBu6B+3fw0if80jt02AwBIfwmfoOi+r7GCvRvjekgp5mSmVbA5lSqiklIolD138REelMxDOlduzYwW9/+1sGDBiAwWAgIyODhx56KOzAz7lYLBaeeeYZ1qxZw/XXX09zczPTpk1j2bJl9OnTh40bN7Jq1SrWrl1Lfn4+y5cvZ8KECQBMnDiRBx54gJUrV1JRUUFhYSEbNmwgNzc3Im3rjha3l4Zm/7fafTM0M1mqCMyCVed00+B0Bx8We5PeOOteY5OdwqLzul7P3tjlOvkZFk7WN2PKGxqJpsVdtO8NqaAnQSmfz4fV4AH81x0RkUSh67+IiHQm4k/KTU1NwbHivtav4a1WK0Zj5JKyBgwYwGOPPdbhslGjRrFly5Zzbjtr1ixmzZoVsbb0VGB2raw0E1aLKc6tkUixmIx4m+owZuRwvM7BxdbEq+8Tbf+u9NdKu7AgMbKkYsHn9bL06Ve6XG/FnCu7XCc/w585aeqbGkGpWNwbkp27B8P33K5mdh/6GEijLoyhriIi0abrv4iIdCbid4MrrriCdevWAf4hKADPPPMMo0aNivShUkJ1kz8olcxD96Rj3vpTAByvc3axZuqpc7ioaGjGABT1y4x3c5JSXmD4Xv6wOLckMnRv6FpPh+/ZWicCqXO4gg9+IiLxpuu/iIh0JuKZUvfffz8LFixg+/bt2O12vvzlL2O32/nlL38Z6UOlhKpgPaneMbypN/HUn8I8sIjjtb0vKBXIkhqaayUzrfcNXYyEQFDKmJlHrcOV9EMgdW/omsvTGpTq5vZprRuWNzTjcDjIyOg9WYoikrh0/RcRkc5E/GmxoKCA559/nv/93//l+PHjDBw4kM9+9rNkZXU8HXxvFxi+p0yp1BPIlPqkzhHnlsSYwci/T/mDUsqS6r40k5HMNBP2Fg/HaprIHZIT7yb1iO4NXXO3ziRr6GamlKU1KOXyRqhBIiIRoOu/iIh0JuJBqRtuuIE//elPfOlLX4r0rlOO1+ej1tGaKZWhoFSqCQ7f62WZUpbzr6TJ5cFmMXJ+vjI1eiLXZsHe4uGjmiY+k+RBKd0buhbMlOpmUCqttSyhxwfNbi/61yciiUDXfxER6UxUKgw6HL0sM6Sb6p1uvD4wGw1kpavIearprTWl0i/7PAAX9c/C1N2nawEg1+b/3uBYTWpcU3Vv6FywplQ3tzcbzhRJD8zqKiKSCHT9FxGRc4l4ptT48eOZN28eU6ZMoX///m2W3XHHHZE+XFI73eTPksq1WYKFHyV1eBoqASivd+L2eDGbUn+WmY+qm7AMvRyAiwcoLb+nclrrSH1U0xTnlvSc7g1dc7XOvtfdWK7B4B/C1+KFeqcngi0TEek+Xf9FRKQzEQ9KffLJJwwbNoyjR49y9OjR4OsKurR3unXoXqCgsaQWn72WdLORZreX8oZmhuba4t2kqNv89icAnJdnIztdBc57KlDc/Njp5P+GWfeGrgUypXpyRgJBKWVKiUii0PVfREQ6E7Gnxm9+85v84he/4JlnngHA6XRitVojtfuUVNuaKZWX5LNqybn4GJpr5YOqJo6ddqR8UKra3sLO9ysAGDU4O86tSQ25Vv8l+nitA5fHiyUJs+10bwhdcPheD57T0oxgR0EpEYk/Xf9FRCQUEXvC2bdvX5vfp0yZEqldp6xAplSuMqVS1vA8f6nhj1Mg06Urv9t3nBaPD3dFKQOz0+PdnJSQkWbC1+LA44NPkrRgvu4NoXP3cPge+INSAPXNGr4nIvGl67+IiIQial+7+3y+aO06JXh9PuocypRKdcPz/NlRH6dATaDO1DlcPLvvBADOd55XSn6EGAwGPLUngdSoKwW6N3QmOPse3T9Hlta7ujKlRCTR6PovIiIdiVpQSg+lnWtwuvH4wKSZ91LaeYGgVIpnSv3mn8ext3go6peJ6+jb8W5OSgkEpY6lSFBK94ZzC9aU6kmmVOvtpEGFzkUkwUT7+u/xeLjlllu49957g6/t37+fefPmMWbMGKZNm8bWrVvbbLN9+3aKi4u54oormD17dpvsLo/Hw8MPP8ykSZMYM2YMixYt4tSpU1Htg4hIb5R8BUpSRHDons2MUQ9pKWt4LwhKnW5q4dl/Hgfg9onnQQ+yPKQ9byBTKoU/Q+LX09n34Ozhe8qUEpHeZd26dbz11lvB3+vq6li4cCE33ngjJSUlrFq1ioceeogDBw4AsHfvXh588EHWrFlDSUkJM2fOZNGiRTgc/vvt+vXr2b17N8899xyvvfYaVquV5cuXx6VvIiKpLGKFzt1uN3/4wx+Cv7tcrja/A9x4442ROlzSq9XQvV7hvNaaUuUNzThdHqyW1MuK2/Dmx9hbPFzcP4vPFvaNd3NSTrJnSuneEDpXoNB5D/YRCErVORWUEpH4iuX1/80332TXrl184QtfCL62a9cucnNzmT9/PgATJ05kxowZbN68mdGjR7N161amT5/O2LFjAViwYAHPPvssO3fuZM6cOWzdupUlS5YwaNAgAJYtW8bkyZMpKytj2LBhEWm3iIhEMChVUFDA2rVrg7/n5eW1+d1gMOjB4yynAzPvqch5SsuxmeljNVPvdFNW66CoX1a8mxRRH1U38fv9/lpS/zV1hLL+osBT6z+/H9U04fP5km74m+4NoYtEofPAaPBah4JSIhJfsbr+V1dXs2zZMp544gk2bdoUfP3IkSOMHDmyzbqFhYVs27YNgNLSUubMmdNu+aFDh2hoaKC8vLzN9gUFBeTk5HD48GEFpUREIihiQam///3vkdpVr3Bm+J6CUqnMYDAwPM/Geycb+Ph09IJSTS0e3j1ZT43dhdfnwzpmBpWNzfTLit4seD6fj0f/XorHB9eOyOeq4blRO1Zv5q2rwAA0NnuoaXLRNzMt3k0Ki+4NoQsWOo/A8L1ANq6ISLzE4vrv9Xq5++67ue2227j44ovbLLPb7dhstjavWa1Wmpqaulxut9sByMjIaLc8sCwcif59UqB90W7n2fs3GGJzXs4+TqyOGWuxev/iJdX7B8nTx2i1L2JBKQmd1+ejtsn/LbaG76W+QFDqWE10agIdrW7ildLq4NAfANv4m5j3y7e4c+oIvjJqYFSya3YdquQfH9eSZjLwP5+7MOL7l1YeF4NyrJyoc/JRTVPSBaUkdJEYvhfIlDqtTCkR6QWefPJJ0tLSuOWWW9ots9lsNDQ0tHnN6XSSmZkZXO50Otstz8vLCwarAvWlOto+HH37Zoe9TTxEu50eTxM2W1rrsbIoKIj+efF4mrBa/cfMz4/NMeMlWT5n3ZXq/YPe0ceOKCgVB43NHjw+HyYDZFv1FqS68/P937IdjUJNoI9qmvjbv6vwAQWZaVwyIAuvz8cre9/C3v9CHnrpCEdONbJkWiGmnqRffMrpphZ+/L8fAPCNCcMZmmvrYgvpifPzbZyoc3Kspomxw3Lj3RyJkkgUOg8EpeqdbjxeX0T/3YuIJJo//vGPnDp1iquuugogGGT661//ytKlS9m9e3eb9UtLSykqKgKgqKiII0eOtFs+ZcoUcnJyGDBgAKWlpcEhfJWVldTW1rYbEhiK6uoGfAk8D4zB4H8YjnY7q6sbcThagn83mTK62CIyx3Q6W7Ba06ipic0xYy1W71+8pHr/IHn6GGhnpCkiEgeBoXs5Notq8PQCI/r6v1H7sCr8dO/O1Dlc/P1INT6gsCCDqYV9g5+nP2//Pv/n16+x7rWjbNt/EnuLh5VfuqjDz9vkKRMoP+kvpm0wGvB5O74SDhw0iNdf3YPP52P1S0eoaXJxYUEGt1ylugrRdn5+Bm8cPc0xzcCX0twRHL7nA+qcLvIzlFknIqnrhRdeaPP7vffeC8CaNWs4ffo0jz76KJs2bWL+/Pm8/fbb7NixgyeeeAKAuXPnsnjxYr70pS8xduxYNm/eTHV1NcXFxQDMnj2b9evXM2rUKPLy8li9ejXjxo1j+PDhYbfT5yOhHzQDot3Os/cdq3Ny9nGS5X3oLvUv+fWGPnZEQak4CBY519C9XuHCAv83Mh/VNOH2+jBHIHPB6/Pxygc1eLw+BvVJbxOQAsDn4+vjhjE4x8rynYf4y79OYbOYuPe6wnZD+cpPnmTp068AkJGRTlNTc4fHfOTWqQBsf7ec/y2txmw08P0vXUyauSeDjSQU5+Wf+QxJ6nJ5/ZlSPblCGA3+wFSL13+vUVBKRHqrvLw8Nm7cyKpVq1i7di35+fksX76cCRMmAP7Z+B544AFWrlxJRUUFhYWFbNiwgdzcXAAWL16M2+1m/vz52O12xo8fz+OPPx6/DomIpCgFpeIgUIA2VzPv9QqDc6xYzUacbi+f1DqCw/l64t+n7FQ0NGMxGtoHpM5y3UX98Pp8LH/+EL8/cBKrxchdU0d0u8bUvyoa+OHfSwFYdM35XNQ/tWYTTFTn5fmHR34UpbpkkhgiUegcIN1soKXFF/wCRESkt1izZk2b30eNGsWWLVvOuf6sWbOYNWtWh8ssFgtLlixhyZIlEW2jiIi0pRSHOAgEpZQp1TsYDQYu6OsPREViCJ/b4+Wfn9QBMHZ4DtnpnceWv3Bxf5Zf769/8Ju3j/PkG8e6dVxDZh5L/nAQl8fH1Av7csvVQ7u1HwlfIChVXu+kxe2Nc2skWiJRUwrA2lpXqqappYctEhERERGJLgWlYszn81EXrCmlRLXe4sICf12pD6p7Pvzq/YpG7C0eMtNMXDogtEJzMy8fyN3T/DPk/WLPxzz9j7Kwjul0ecj+8t2camzhgr4ZPPDFi6Iyo590rG9mGjaLEa8PTtQ7u96gl/N4PNxyyy3B2iIA+/fvZ968eYwZM4Zp06axdevWNtts376d4uJirrjiCmbPns2+ffti3WzcEZh9DyDd5P+3GfgCREREREQkUSkoFWNOt5eW1iEafbrIcJHUEQhK9ThTymji3RP+6Y2vHJoT1sxaN40Zwh3XXgDAuteO8rt9x0ParqnFw58PnsLUdxh9M9P4yezLNWtkjBkMhuAMh2Uqdt6ldevW8dZbbwV/r6urY+HChdx4442UlJSwatUqHnroIQ4cOADA3r17efDBB1mzZg0lJSXMnDmTRYsWtZsKPNoilSkVCErVaPieiIiIiCQ4BaViLJAllZVuwmzS6e8tAsXOP6jqWaaU5bwxNLk82CxGivplhr39reOG8Y0J/lljHv37Bzzx+lH/3J7ncKqhmT+8W85phwuv/TQ/mzeaQX2s3W6/dN/w1iF8ZbUKSnXmzTffZNeuXXzhC18IvrZr1y5yc3OZP38+ZrOZiRMnMmPGDDZv3gzA1q1bmT59OmPHjsVisbBgwQLy8vLYuXNnTNt+pqZUz6ZdSW8dvqdMKRERERFJdIqKxFid0w1AjlX1pHqTwtZMqY9PN+F0ebq9n/TLPg/ARf2zwsqSOtt/TjqPW8cNA+CXe8vIvvEBTtY58Z01/2hDs5s3jtbwp/cqsLd4yLGaafjTKs7v2/Mi7dI9gUypT2o1fO9cqqurWbZsGT/60Y+w2WzB148cOcLIkSPbrFtYWMihQ4cAKC0t7XR5qAyG0H86Wj84fM9w7lhxKK+nm89kSoXTpkT7Cfec9tYfnSedp2icIxERkVjRGJwYq1U9qV6pIDONvplpVNtb+HelndGD+4S9j4+qm7AMvRwDcPGA7s96ZzAYuOPaCygsyOShl47QNOBC/vz+KTLTTORmpNHU7Ob0WRkWI/pmcO2IfB6vq+j2MaXnhuX6M9Q0fK9jXq+Xu+++m9tuu42LL764zTK73d4mSAVgtVppamoKaXmo+vYNrcbbudb3tT4JpqdbsFrBYm775YXZ6AOTqcvX+2AC3DS6PBQUhNemRBPuOe2tdJ5Co/PUNZ0jERGJNUVGYqzOoUyp3shgMHDJgCxe/7CG98sbuhWUeu7AScA/jKurGfdC8cVL+nPVsBym/dcaMi+fhr3Fg73lTMBjUJ90rhjSJ5ihI/E1TMP3OvXkk0+SlpbGLbfc0m6ZzWajoaGhzWtOp5PMzMzgcqfT2W55Xl5eWG2orm7AF8LIO4PB/+D36fUdzf77g6fFhdPgw21qO9Oiq6UZg8nc5esGTxoAp+qdVFW17XeyONc5krZ0nkKj89S1s88RKDglIiKxo6BUjAWG7+UqU6rXuXRgdjAoFS6Hy8OfD5YDcEkPsqQ+rSArnaZXN/KfC27hVGMLXqMRn9tDfoaFTBXiTyjDWoODJ+uduDxeLKpJ18Yf//hHTp06xVVXXQUQDDL99a9/ZenSpezevbvN+qWlpRQVFQFQVFTEkSNH2i2fMmVKWG3w+QjrgffT6wcKnRsM595PKK8HakqdbnIl/QN4uOe0t9J5Co3OU9d0fkREJNb0VBNDXp+Pemfr8D1lSvU6lw70f+v4r4rwg1K7Dp2isdmDp66CobmRLzRuNhkZnGNl5IBshuXZFJBKQAWZaVjNRrw+OFGnulKf9sILL/DPf/6Tt956i7feeosbbriBG264gbfeeovi4mKqqqrYtGkTLpeLPXv2sGPHDubMmQPA3Llz2bFjB3v27MHlcrFp0yaqq6spLi6OaR9cgZpSPdyPtbWmVL3THQx0iYiIiIgkIgWlYqix2YPXByYDZAa+ypZe49LWDKdjNQ4aW4fphMLn87H1Hf/Qveb3/45BFUh7JYPBEBzCp2Ln4cnLy2Pjxo288MILjB8/nuXLl7N8+XImTJgAwMSJE3nggQdYuXIl48aN4/nnn2fDhg3k5ubGtJ3u1gBSN+cwCEozgrl1J9X2lp42S0REREQkapQOEUN1rcWj+1gtGBVY6HXyMtIY1Cedk/XNHKpo5KrhuSFtd7C8gcOnGkkzGag99CpwT1TbKYlraK6NI5V21ZUKwZo1a9r8PmrUKLZs2XLO9WfNmsWsWbOi3axOuTxnZt/rCYPBQL7NzCm7i7Kqegb2iXx2pYiIiIhIJChTKoYC9aQ0817vddlAf4Hzd47XhbzNtndOAFB8UT98zY1RaZckB83Al9pc3tZMqQjsq2+G/z5T3eTqYk0RERERkfhRUCqGAplSqifVe109PAeAko9rQ1q/1uHipcOVAMy9YnC0miVJIlDsXJlSqSlSmVJwJihVZVdQSkREREQSl4JSMaRMKbl6uH+K+QMn6nG4PF2uv+O9clo8Pi7un8VlAzU9c293pqaUglKpKFI1pQDyM/xfflSpppSIiIiIJDAFpWJImVIyNNfKwOx03F5fl0P4vD4fz+33Fzife8UgFTgXhrZmSp2ocwYDGJIaPF4frYlSEbkxFyhTSkRERESSgIJSsWKy0Njiz4xRplTvZTAYuLq1wHnJsdpO133zo9Mcr3OSlW7i+ov7R79xkvD6ZaWRbjbi8cHJ+uZ4N0ciyO31Bf8eifhzfiAo1aRMKRERERFJXApKxYixzwAA0kwGrGad9t5s3Hn+IXyvH63B5/Odc73Nb30CwIzLBmK1mGLSNklsRoOBoYFi5xrCl1JcZ2W+9XT4ns/nI9vk358ypUREREQkkSk6EiOm3IEA5NosGobVy11zQT5pJgNHq5s4fKrj2fQOVTRQ8nEtJgP8f2OHxLiFksiCxc41A19KcXvOBKh7emN2tzTz8sFjAFQrKCUiIiIiCUxBqRgx5gwCoI9VQ/d6u2yrmamFBQA8//6pDtd5+h/+LKnrLurHoD7WmLVNEp9m4EtNLq8/s8lkNERk+F6WNR2AxhZPSJMqiIiIiIjEg4JSMWLK8WdK5dhU5Fxg+qX+4Zwv/utUu4LV756o56//rgTg61cPi3nbJLENDc7A54xzSySSXK2ZUuZITL0HmA1gat1VZaPqSomIiIhIYkraoJTH4+GWW27h3nvvDb62f/9+5s2bx5gxY5g2bRpbt25ts8327dspLi7miiuuYPbs2ezbty9m7TUGglLKlBJg/Pl59M1M47TDxe/eORF83evz8aOXPwBgxmUDGNk/K15NlAQ1TDWlUlKgppQlQkEpgwFsraXoKhtVFF9EREREElPSBqXWrVvHW2+9Ffy9rq6OhQsXcuONN1JSUsKqVat46KGHOHDgAAB79+7lwQcfZM2aNZSUlDBz5kwWLVqEwxGbBzvjWTWlRMxGA/856TwAntx9jIoG/0Pj2leOcrC8gQyLie9ce0E8mygJKjB873ids82MbZLcXN7IZkoBWFuDUlXKlBIRERGRBJWUaTtvvvkmu3bt4gtf+ELwtV27dpGbm8v8+fMBmDhxIjNmzGDz5s2MHj2arVu3Mn36dMaOHQvAggULePbZZ9m5cydz5syJantrHS6M1mxANaXkjJmjBvKn9yp492Q9Czbvo6hfJm9+dBqAe64rpCAzrdv7bmyyU1h0Xmjr2jsuti6JqX92OmkmAy0eH+X1Toa2BqkkuQWG8VpMkQtK2cxAM5xSppSIiIiIJKiki5BUV1ezbNkynnjiCTZt2hR8/ciRI4wcObLNuoWFhWzbtg2A0tLSdsGnwsJCDh06FHYbwi1CG5glKzPNhMWUtMlpnQqcE4MBfCmavNGdPnb2WTEZDKz80kju+v17lNU6qbL7sxnumjqC6ZcN6FFbfV4vS59+JaR1V8y5sk1bO+tfsk8ceXYfz/4z0Z3dTpPBwNBcGx9WN/FJrYNhebYO1w2lb8nS/94g0jWl4MzwvUAmpoiIiIhIokmqoJTX6+Xuu+/mtttu4+KLL26zzG63Y7O1fTizWq00NTWFtDwcfftmh7V+9bE6APIy08jISO9y/VDWCXfdWO3TZmv/WjL3pyMd9bEjBqOBgoLOPysFBdm89L0Ctr5VRr3TTfGlAxg54NzbGIyGqJ+jc/UvlP4E1kv099xmS0+a/nTUzgsHZPNhdROn3b5z9iHc65TEV2D2vUh+cZHReoc/Wa+glIiIiIgkpqQKSj355JOkpaVxyy23tFtms9loaGho85rT6SQzMzO43Ol0tluel5cXdjuqqxvCygZ6v6wGgOw0E01NXT8chLJOuOtGe58Gg/9B3+FobndukrE/HQlklXTUx474vD6qqhq6XhH4UlHf4N8728bn9UXtHHX2HgaOHUp/wmljOO2MxD7P7mNDYyM5ufld7qvR3hjX/nR03gdk+GvT/euT2nbLDAZ/QCqU61RgXYm/aGRKWU0+wEB5vWZqFBEREZHElFRBqT/+8Y+cOnWKq666CiAYZPrrX//K0qVL2b17d5v1S0tLKSoqAqCoqIgjR460Wz5lypSw2+HzhTdE7ViNf/heji2pTndYAucjVYfuQff6mEznI5T+JVN/OnJ2H0Md4hgY3hgvjU12LixsWx8s7dJpZE65jad//2ee+NaPARg4aBCvv7onuE641ymJL3drUCqSNaXSfC1AuoJSIiIiIpKwkipK8sILL7T5/d577wVgzZo1nD59mkcffZRNmzYxf/583n77bXbs2METTzwBwNy5c1m8eDFf+tKXGDt2LJs3b6a6upri4uKotzsYlLJq5j1pb/KUCZSfPBnSuipK3vt0FDw7Xudk5/un6HfR1Sz+qn/ZI7dOjUfzJEJcrYXOI1tTyh/oqm/20NTiISPNFLF9i4iIiIhEQlIFpTqTl5fHxo0bWbVqFWvXriU/P5/ly5czYcIEwD8b3wMPPMDKlSupqKigsLCQDRs2kJubG/W21TT5C1jn2hSUkvbKT54Muyi59G6BWTwbmt14fT6Mqlie9FzeyGdKWYz+H5fXX+z8gr4ZEdu3iIiIiEgkJHVQas2aNW1+HzVqFFu2bDnn+rNmzWLWrFnRblY7/zV1BEtX/B/6TLwv5scWkdSTmWbCaACvDxqbPcEglSSvM5lSkZ2h1WbyB6XKG5wKSomIiIhIwons/36lQ9MvG0Dzey/GuxkikiKMBkMwEFXvdMW5NRIJ0agpBWAz+/dXrhn4RERERCQBKSglIpKE+rTWqKt3uuPcEokElzfyNaUAMlqT6MobFJQSERERkcSjoJSISBIKZErVKSiVElxRzpQ6XmPHp+kYRURERCTBKCglIpKEcoLD9xSUSgXu1kLnka4pFciU+mdZDQ6HI6L7FhERERHpKQWlRESSkGpKpZZAoXNLhIfvBTKlHIpdioiIiEgCUlBKRCQJnV1TyqthWUkvUOjcHOHhe4FMqSa3T58TEREREUk4mkdcRCQJZaWbMBrA6wN7iyfezZEeChQ6txgNRDKpKd3oC35OappcZGVGcOciIpLUvF4vVVVVGAyQn68bhIjEh4JSIiJJyGgwkJ1ups7pVl2pFBAodI7PQyQTmrzuFtIMJpw+AxUNLQzvF7l9i4hIcquqquLJv+0HYFnfLEymjDi3SER6Iw3fExFJUsEZ+ByqK5XsAjWlDnxciccT2fczw+IfEljR2BLR/YqISPLLzMknMyc/3s0QkV5MQSkRkSSVYztTV0qSW2D2PYs58gnMGSb/n6cUlBIRERGRBKPheyISksYmO4VF53W9nr0xBq0ROHsGPgWlkl0gUyrCk+8BYGu90ytTSkREREQSjYJSIhISn9fL0qdf6XK9FXOujEFrBCAnMHxPQamkF6gpFY305cAMfKcaFJQSERERkcSi4XsiIkmqj9U/fK/B6QKikGIjMRMMSkXhbQwM31OmlIiIiIgkGgWlRESSVFa6CYMBPD4wZObFuznSA25vYPhe5KNSgeF7qiklIiIiIolGQSkRkSRlNBjok+6POJhyBsa5NdITUc2Uag1K1TndOFyeyB9ARERERKSbFJQSEUligWLnxpwBcW6J9EQ0C51bjP4fgIr65sgfQERERESkmxSUEhFJYoGglElBqaTm8kYvUwogw+zfcXmDMzoHEBERERHpBgWlRESSWE5rsXNlSiU3dxQzpQAyLK1BKWVKiYiIiEgCUVBKRCSJBTOl+igolcyCNaWitP9AptTJBgWlRERERCRxKCglIpLEcmz+TClDRm58GyI90hyjTKmKeg3fExEREZHEoaCUiEgS62M1c/XwHBz/2BrvpkgPNLv9QSlT1GtKKVNKRETia19FC//1/Ec0ON3xboqIJAAFpUREktwVQ3Jo+dfL8W6G9EAwKBWlVKlgUEo1pUREJI7sLi+Ha1yU1jgp+fh0vJsjIglAQSkREZE4a3Z7gChmSgWG7zU04/X5onMQERGRLnxQ6yZwFzpSaY9rW0QkMSgoJSIiEmctUR6+ZzP761W5vT6OV9XhU2BKRERizOP18WHtmSF7pVUKSomIglIiIiJx5fX5aGmdfc8Upbuy0WCgb4a/KP66vx/E4XBE50AiIjF26NAhbrvtNsaNG8c111zD0qVLqampAWD//v3MmzePMWPGMG3aNLZubVt/cfv27RQXF3PFFVcwe/Zs9u3bF1zm8Xh4+OGHmTRpEmPGjGHRokWcOnUqpn1LNf+qdOBwn/lSREEpEQEFpUREROIqkCUF0cuUAhiQneY/HpboHUREJIacTie33347Y8aM4fXXX+fPf/4ztbW13H///dTV1bFw4UJuvPFGSkpKWLVqFQ899BAHDhwAYO/evTz44IOsWbOGkpISZs6cyaJFi4JB+/Xr17N7926ee+45XnvtNaxWK8uXL49nd5NeRaMLgNx0/yPoJ7VOmlo88WySiCQABaVERETiyBmjoFT/LH9QqtGloXsikhpOnDjBxRdfzOLFi0lLSyMvL4+bb76ZkpISdu3aRW5uLvPnz8dsNjNx4kRmzJjB5s2bAdi6dSvTp09n7NixWCwWFixYQF5eHjt37gwu/9a3vsWgQYPIyspi2bJlvPrqq5SVlcWzy0mtusk/dC/XaiTfZgbgA2VLifR6CkqJiIjEUWDmPbPRgMEQvahUX6v/lm9v1hTcIpIaRowYwVNPPYXJZAq+9uKLL3LZZZdx5MgRRo4c2Wb9wsJCDh06BEBpaek5lzc0NFBeXt5meUFBATk5ORw+fDiKPUpt1Q5/plSG2cD5uemAhvCJCJjj3QAREZHeLBCUSjdH93uifpn+YXt2ZUqJSAry+Xw8/vjjvPzyy/z617/mV7/6FTabrc06VquVpqYmAOx2+zmX2+3+QElGRka75YFl4Yji9w09EmjXp/+MlkCmVIbZwHl56fzzpJ3SKntUj2swtO1for4XPRGr9y9eUr1/kDx9jFb7FJQSERGJo2a3v55GejTH7gGDshWUEpHU1NjYyH333cfBgwf59a9/zUUXXYTNZqOhoaHNek6nk8zMTABsNhtOp7Pd8ry8vGCw6tOTQpy9fTj69s0Oe5tY8HiasNnSgr9Hu521Lf4vYXIyLRQNyob3a6hxuikoiN5xPZ4mrFZ/H/Pzs6J6rHhL1M9ZpKR6/6B39LEjCkqJiEhKOHToEA8//DAHDx7EYrFwzTXXcO+995Kfn8/+/fv5wQ9+QGlpKXl5eSxatIh58+YFt92+fTtPPPEElZWVjBgxghUrVjBmzJiYtDuQKWUxGfBFMV40sLXQud3lwxfNA4mIxNDHH3/Mt771LQYPHsy2bdvIz88HYOTIkezevbvNuqWlpRQVFQFQVFTEkSNH2i2fMmUKOTk5DBgwoM0Qv8rKSmpra9sN+QtFdXVDVK/v3VVd3YjD0RLMfoh2OyvqWwAweT1YPP6sqZOnHVRVNXS2WY9UVzfidLZgtaZRU9OIyZTR9UZJxmDwBzMS9XPWU6neP0iePgbaGWmqKSUiIkkvmjMwRVsgKNXkbMHjdkXtOANah++5fVDrUF0pEUl+dXV13HrrrVx55ZX84he/CAakAIqLi6mqqmLTpk24XC727NnDjh07mDNnDgBz585lx44d7NmzB5fLxaZNm6iurqa4uBiA2bNns379esrKymhsbGT16tWMGzeO4cOHh91Ony9xfwLti3Y7XR4ftc4zw/dyrf7ciJqmlpj2M1V/1L/k/0mWPkaDglIiIpL0ojkDU7QFZt8zRXn4XprZSOtkR5yob47qsUREYuH3v/89J06c4C9/+Qtjx45lzJgxwZ+8vDw2btzICy+8wPjx41m+fDnLly9nwoQJAEycOJEHHniAlStXMm7cOJ5//nk2bNhAbm4uAIsXL2bq1KnMnz+fqVOn0tzczOOPPx6/zia5ansLXh8YgHSzgVyrvzh9TZMLZe+K9G4aviciIkkvMAPT2bqagWnbtm2Af7hG4Jvzs5cHZmgKVajFHz9dzLIlEJQyGABfp/vqyesGA2SaDTjcPk42NCd0Mc1kKfgZbzpPodF56lqynqPbbruN22677ZzLR40axZYtW865fNasWcyaNavDZRaLhSVLlrBkyZIet1OgstH/ZYjNbMBoMJDb+i1Js9uLvcVDVroeS0V6K/3rFxGRlBLJGZjCEe4Y+8D6aWX1gL+mlM2WhtnoA5MJi9nSZv2evG5OS8NmM5FjM1HldFPnJimKvfbWgp/h0nkKjc5T13SOJFpONbQGpSz+yKfVbCTDYqLJ5aGmyaWglEgvpn/9IiKSMiI9A1M4Qi1O+elillW1/unFDT4fDkczrpZmDCYzbpO3zXY9et3RzCPbS0jzWgEoLa+PamHZnkqWgp/xpvMUGp2nrp19jkDBKYm8U43+IucZ5jPpePmZFppqPdTYWxieZzvXpiKS4hSUEhGRlBCNGZjCEW4ByMD6za7W4XvGtsvOtU13X7ekWclsraN+or45KR7Oo1lUM5XoPIVG56lrOj8SLWcP3wvIs6XxSa2TmqaWeDVLRBKACp2LiEjSi+YMTNHWHKwpFf1jZQYLnesBQEREYieQKRUYvgfQt3VW2Oqm6M08KyKJT5lSIiKS9M6egemFF15os2zfvn1s3LiRVatWsXbtWvLz8885A1NFRQWFhYVtZmCKNmcHhc6jJRCUqmxsocXtJc2s76ZERCT6auytQamzvoHJz0hrs0xEeqekC0odOnSIhx9+mIMHD2KxWLjmmmu49957yc/PZ//+/fzgBz+gtLSUvLw8Fi1axLx584Lbbt++nSeeeILKykpGjBjBihUrGDNmTBx7IyIikRDNGZiiLZaZUmlGMBvB7YXjdU4u6JsR/YOKiEivV+vwZ0Oln11TKsOfKVWjTCmRXi2pviJ1Op3cfvvtjBkzhtdff50///nP1NbWcv/991NXV8fChQu58cYbKSkpYdWqVTz00EMcOHAAgL179/Lggw+yZs0aSkpKmDlzJosWLcLhcMS5VyIi0psFg1IxuCMbDJDdOnTiWE14swuKiIh0VzAodXamVGZrppRqSon0akkVlDpx4gQXX3wxixcvJi0tjby8PG6++WZKSkrYtWsXubm5zJ8/H7PZzMSJE5kxYwabN28GYOvWrUyfPp2xY8disVhYsGABeXl57Ny5M869EhGR3qzZ7QFikykFkJ3mP9BHCkqJiEgM+Hy+DoNSfWOYKRX4AkhEEk9SBaVGjBjBU089hclkCr724osvctlll3HkyBFGjhzZZv3CwkIOHToE+GdS6my5iIhIPMRy+B5AptF/vA+rGmJzQBER6dWaXB5aPP6aiVZzBzWlopgp5fP5+MfJZjYfqOPNj6N/3/N6vZw6dYpTp07h9SoQJhKKpApKnc3n8/HYY4/x8ssvs2zZMux2Ozabrc06VquVpib/N8FdLQ+HwRD+T2C7VKU+nnubUD4biUDvYfL79PUmmT5/vVmsg1JZ/i+mKat1xuaAIiLSq51uzYRKMxna3OuCw/fs0cuUevbdaj6odQPwv0fro3acgKqqKp78236e/Nt+qqqqon48kVSQdIXOARobG7nvvvs4ePAgv/71r7nooouw2Ww0NLSNfjudTjIzMwGw2Ww4nc52y/Py8sI+ft++2d1qt82WHtJ6GRmhrRfOurHaZ0d9TOb+dCTU99FgNFBQ0PVnxWA0JNQ56qx/yfD+hLJuoI+p0h9o/3nr7nVKYu9MTanYRKXOBKWaY3I8ERHp3epah+7lpJswnPWNWKDQeZPLg9PlwWoxdbh9dzW1ePjNgTOBoQ9qYvNlTGZOfkyOI5Iqki4o9fHHH/Otb32LwYMHs23bNvLz/f/oR44cye7du9usW1paSlFREQBFRUUcOXKk3fIpU6aE3Ybq6gZ8YczaHbj2OhzNIW3X1BT6g0Ko60Z7nwaD/0G/oz4mY386Eu776PP6qApheIzP60uIc9TZexjtY8dqn2f3MdbHjvY+A583g8EfkArlOhVYV+LLGetMqdY7f53TTa3DRa7NEpsDi4hIr3S6NSjVx9o26JSZZiLNZKDF4+O0w8WgCAeljtY04fGB0QBeH5xocHG6qYW81mGDIpIYkmr4Xl1dHbfeeitXXnklv/jFL4IBKYDi4mKqqqrYtGkTLpeLPXv2sGPHDubMmQPA3Llz2bFjB3v27MHlcrFp0yaqq6spLi4Oux0+X/g/ge1Slfp47m1C+WwkAr2Hye/T15tk+vz1Zs2u2AalzEawtQamNAOfiIhEW20wU6ptPoTBYAh+MXI6CsXOP6r23+MKbEZyrf7H3gMnVE9RJNEkVabU73//e06cOMFf/vIXXnjhhTbL9u3bx8aNG1m1ahVr164lPz+f5cuXM2HCBAAmTpzIAw88wMqVK6moqKCwsJANGzaQm5sbh578/+zdeXxU1f3/8ddMZpKZLGQhCC5YlwStChJBNlFbNFJFCGXRtvnZYr+KxajVFtxAxSKIX+tSSkG+KMW2VFqwaFFkqbsoEBRBacHEyqIs2UhIJpkkM3N/f0xmICaQTDLJLHk/H+aR5J67fM4N3nPvZ845V0RExKvO3blJKYAkq4kal8HesmouPj258w4sIiJdji/h5O0p5W5UlmK3UlRV5+9NFUxfNXzw0i3WjDnGTLmzjh0HjnJlRvegH0tE2i6iklI333wzN9988wnL+/bty/Lly09YnpOTQ05OTkeEJiIi0ia1Lu8Nekwn9l1OtBgUAQVFR4FTO+/AIiLS5ZQfN6fUt5NSqQ3zSlV0QFLK11MqOc6M3Wbhi9I6PjtQEfTjiEj7RNTwPRERkWjT2W/fq69zEm/2PhTs6aRJX0VEpOsqP8GcUkCHDt/z95SKM5Nu9x77v6Uati4SbpSUEhERCaFjSanOG7+XZPVOKLbnSE2nHVNERLomX8Lp23NKwXFJqSD3lKpzefi63NvGdYs1kRjnTUpVOF1U17lPtqmIdDIlpUREREKos3tKASQ1PBccqqzDUefqvAOLiEiXU17jbWeSm+kp5Ru+Vx7kpNS+8ho8BsRbzdgtJmJjTCTGeh99Dx5VL2GRcKKklIiISAg5fUmpTmyRY2PA92zwlYYyiIhIByqvqQOgW1wzSamGnlLlQR6+55tPqndyLKaGnsinJHiPdehobVCPJSLto6SUiIhIiLg8Bm6PdyhdZ/aUAu/ErwBfljg698AiItKl+IbmNddTqqOG7+1rGJ5+RrdY/zJfUko9pUTCi5JSIiIiIeJ78x50flKqW6z3gJr0VUREOorL7aGq1tvWJduamVOqg4bvHar0Jp56Jlr9y3okWBrK1FNKJJwoKSUiIhIivvmkAMyd3lOqISlVoqSUiIh0DF+yyWzCP6fT8VLtsY3WC5aDDUP0eiQcS0qdkugbvqeeUiLhREkpERGREPElpWJjTP45LzpLcsPDQWGJg+rqagzD6NTji4hI9CttmCsqxW7F3Ew7l2L39l466nThcnualLeVL/F0yvFJKf/wPfWUEgknSkqJiIiESG299wY8ztL5zXFSLJiAEkcdT639lJqamk6PQUREoltZtXeS8+4Jsc2Wd7NZ8aWqyp3BeRusYRj+xNMp6iklEvaUlBIREQkRX0+pUCSlTK464i3e3lGVbmsLa4uIiASuzOHtKZUW33w7E2M2kWwP7rxSFTUuf/vqm0cKjiWoiqvqqA9irywRaR8lpURERELE2TDReVxMaJrj5IbJzsuC/CpuERERONZTKjW++Z5ScGwIX3mQ2qKDDZOcd0+IxXpc+5piiyHOYsYADmuyc5GwoaSUiIhIiPjeSJQQ2/Q12Z0hueGD6/JafWIsIiLB5/vQ40Q9pQBSG3pKHQlSTynf0L1Tu8U1Wm4ymeiZ5F2mpJRI+FBSSkREJESO1npvwJNsIUpKNXxwXV6rSc5FRCT4/HNKnaynVHxw38DnmzOqV5KtSVmvhqTUQc0rJRI2lJQSEREJkaM13kldk+IsLazZMXxJqaN1BnWaX0NERILMP6dUwol7Svl6UZU56oJyzEMNPaV6faunFMCp3byJKr2BTyR8KCklIiISIkdrfUmp0PSUsseA1QwGsPeIPjUWEZHgKm3FnFK+N/OVBCkp5esF9e3he3AsUaU38ImEDyWlREREQqTSGdqeUiYTpDQ8J+wqcoQkBhERiV5HGuaU6n6SOaV6BDkpdaynVNPhe+opJRJ+QnMXLNIFVFU7yMj8TsvrOao6IRoRCUfH95QqqwxNDGmxUOyEnYd1LRIRkeDxGAZHGnpKpcXHQk1Ns+ulJ3qTUsVVQUpKVR430blR3ahMPaVEwo+SUiIdxPB4uPfFd1tc76Hxl3RCNCISjo7vKVUWohjSbMBR2HlIPaVERCR4jta4cDe8RyM13sqR5nNS9EjwJoqC0VOqpt7tnzC9V5KNmqONk1K+nlKHK2vxGAZmk6ndxxSR9tHwPRERkRA56mx4+16I5pQCb08pgH3lTsqrg/PmIxEREd98Ut1sFqwxJ37s7N7QU6rMUYfL0763wfqG7iXExpBka9r/4pTEWMwmqHMblKnNEwkLSkqJiIiEyNEQzykFEBsD3WK9nxTvOHg0ZHGIiEh0KfMP3TvxfFIAqXYrMSbvSzd8w/3a6tgk503nkwKwxJhJb5jDSkP4RMKDklIiIiIhUtkwp5SVeoz2fTjcLt1t3tuBHQeUlBIRkeDwTXKedpI37wHEmE3+N/C1d14p33xSvZp5856PJjsXCS9KSomIiISIr6fUP7cW4naFbhhBd7u3p9SWvUdCFoOIiESXUn9S6uQ9pQB/Uqq980r5ej/1SjpxUkqTnYuEFyWlREREQqDW5aHW5QEgwXbim+fOcFpCDGYT/OdwFV+X12AYBtXV1f7v1dXVLe9EREQiUq3L8H9IEkwlVd6eSL6E08n0SIxrtE1b+Xo/nWj43vFlh9RTSiQsKCklIiISApUNk5ybTWAJcWtss5jo1ysegA27i6mpqWH+hu3UnOD13SIiEh2+qXTxamE1Nyz9POg9hw5UePd3WvKJE0Q+6cHuKXXS4XvesgPqKSUSFpSUEhERCQHfp9KJsTGYwuCV1MNO9yal1v/nMADWuJYfIkREJHJtP+Tg/a9rcRtwpMbF9Nd3tfvtd8f7xpeUOkmvJZ/0xCDNKXXUN6fUiY/ZO9UOwP4j+uBFJBwoKSUiIhICR2tD/+a94w05Ix4TUFhaw3ZNeC4iEvVW/bsMAzg1IYaE2Bi2f3OU1z4/FLT9+4fSdVJPKZfHoLjKN3zvxD2leqd4k1JfVziDmoQTkbZRUkpERCQEKn09peJiQhyJV1JcDN9J9P78u/f34Qnl6wBFRKRDHamu4+MDDgAu6RnLzy7tBcC63cVB2X91nZvyGu8w9dNbkZTq0dBTqqQdPaWKq7y9vizHvc2vOackxRFnMeP2GBys0BA+kVBTUkpERCQEKhqSUt1s4dFTCuCCFLCa4csyJ9sP1ykxJSISpTbsLsZjQJrNTLc4M1f3SQPgk/3llFW3bwgdHJuvqZvNQmIregT7Jjo/2I55nnzb9kyKw3ySYfFmk4kzG4bw7dMQPpGQU1JKREQkBHw9pZLCpKcUQFwM9Ev3Pjz8p7SOKf/4D89v/oYt+yqoa3hToIiIRL43/lMEwFnJ3mv+6SlxnN8zEY8B7xSWtnv/vknOT/YWvOP5kkQVThfl1fVtOuZXpd43xX4nzd7q4+09orfLioSaklIiIiIhcLTh7Xv2GAinDknnplj41WWnYTbBrqJq/vzJQaa9XsDEP27h3V0HcDgcGOEUsIiIBKTEUcfnBysBODPp2AcjV/dJB+DNIAzhC+TNewB2a4x/Hqi2JooKi73DETPSE1pcVz2lRMKHklIiIiIhUNkw0XnBgVLcrrZ9KtxRrslMYXRGAtOu/A4j+3Qn1RbDgaN1TH29kDv+9jHV1d4Hhurqav/PIiISGTbtKQMgI82G3XrscfD7md6k1CdfV+Coc7XrGL6hdK15857Pd9K8b4H19XgK1JcN250bQFJKb+ATCT0lpURERELA5fb2NooPg7fvGYZBTU1Nox5bCbFmrr+gBw+MOIs/XHcGZyd55+f4/AjM37gft95YJCISkTb+9wgAA09vnLz5Tlo8vVNsuDwG+XvL23WMYz2lTvwWvG87qyEptacs8ESRYRh8WeLtKdWapJTvDXzB7il1sLKOz4rr2Hygln8X6UMbkdZQUkpERCQEfnTJ6fwkqxffSQp9U+yqq+XFjbtxuRt/Mm4YBkeOlLHso91cnGpwUYp3+crPinjwtf9Q6/JgGAbV1dUa0iciEgFcHoNNe709pQaentikfNjZ3gnPN35V1q7jfBPg8D2As9LaPs9TcVUdR50uYkzHklsn851U7zqHKmtx1rsDPl5zShx1TF27l89L6vlvhYuH3/ya3UVVQdm3SDQL/Z2wiIhIF3RW93huG3IGsTEnfkNQZ7LGNv002+ms4fl3dmIyWzCZIDMZhvSyYjGbeKughF+v/oLiiirmb9hOTY2GQIiIhLsdByqoqnWTbLPQp3vThJEvKfXhV2Vt/rDB5fb4h8WdkdzypOM+x3pKBZ6U+rLU20uqd6qdOEvLj7jJdgtp8VaAoCSOPIbBQ2t2Ue500y3WRHe7mRqXh3tWfU5VbfuGQopEOyWlREREQsA7ZK46rCY5P55vSJ/F2jhZ1TvJzGNX9yYhNobPDlXxqzVfUWuK9W/j6zWlHlQiIuHnnQLvm/WGnZ1GjLnphyKXnJFMnMVMUVUdXzRMHB6oghIHTpeHpDgLZ7biTXg+vjmlDlQ4qQ3wja+BTHIOYDKZuPj0ZAC2fV0R0LGas/Y/RWzdV47NYuLyM2x8r7eN05KsFFfV8dIn37R7/yfi8XgoKiri8OHDeDx6S65EJiWlREREQqCmpobn3vws7CY593HV1fLCe//B7a5vsvyDz75gQLdqbGaD/RV1rP2vg/f+e4Samhrmb9hOdXU1ZWWl/H79sR5USlKJiISWYRi8VVACwFUNb9r7Nps1hqFnpQLwrza+hW/HN0cB6HtaEmZT63sDd4+3khRnwWMEPgF5QUNS6pxWJqUA+p/eDYBPG+JtK5fbw+KP9gJwY990usWZiY0x8dP+PQBYtvVrKmo6pq0vKSnhuX9t56lX8ykpKemQY4h0NCWlREREQsQa1/q5NjrL8ZOeW2Obj88aa6N7QhzfO9XEuamx1LnhoXVfcveru9lfE8Oug0f43frPMJnN/mRUdXW1hvmJiITQvw9VcriyFrvVzODvpJ5wvezzvMmU9buL2/RBwvYD3iRPv9O6BbSdyWTinO7e3lKfHWx9oshjGGze6528vd+prT9m1hnenlLbD1S06+Udq3ce5utyJ2nxVsacd+y8XvadJDJ7JOCoc/PnrV+3ef8tSUhOIzElrcP2L9LRlJQSERERP38PqVb04LJb4PGrTuOC9FisZhPbDlSy6Rsnv3j1v7xxIIaVBbX8dPnn3LzsE17beRB3TNNhfiIi0phvSFZRUVFQh2S9+YW3J83wc7pjs8accL0rzu2O3WrmQIWTnYcqAz7Ojoak1MWnJQe87dCzvUmd978sbfU2/z5USVl1PQmxMVzSu/XHzOyRSLw1hqpat//NfYGqdXl4vqGX1KTBZ2KzHnu8NptM/OKyswD42yffUOqoa9MxRKKdklIiIiLSyIl6SDW7boyJ/j3jeGFcBn2SPKTZTCTGem8vDGBfeS3/Perht+8f4JXdDma88QVv7zrI79d/qqF9IiLNKCkpYdGb21n05vagDcmqrnPzz88PAXB1Q0+oE7FZY7ji3O4AvL7zcEDHOXTUyeHKWmJMcEGvpIDj9B13y77yVr8V772GBNbQs9KwxrT+8dZiNtH3NG+MH7dxXqlXdhykqKqOUxJjGdfv1Cbll5+TxoW9knC6PCzdsr9NxxCJdl0uKVVaWsrtt9/OwIEDGTx4MLNnz8bl0hsRRES6OrUP7XNKopWLe8ZxVe9Y/u8HpzCmN1x/dhyPjOhNnxQz3SwePMD7e45y35pC1ux189t397Bk016Wbizg1yu38d7uQ5RWVOLxeJSkEpGwEKq2ISE5jYTk4A3JWrXjIBVOF2ek2PyJn5MZc1EvAF79/BCHjjpbfZx3Cr0JosweicTHnrg31olkpCdwarc4al0eNu8tb9U2739ZBsAVGYGfr6FnebdZtf0gngDbnPKaepZs3gfA/ww5s9m3/plMJqY09JZa8emBoLzpTyTadLmk1N133018fDzvv/8+K1eu5KOPPmLp0qWhDktEREJM7UPgfPNPeTzH5qFy1dXy4sbdeDwu7Bbo38NCv+4xXHW6mSvSazmnm7cnVbXLYPW/S1i4cS8LNh9ia7Gbqa8XcO3z2/j1P7bzyz9/hKO6utGxjk9UNde7qjXriIgEIhrahqpaF39pmNPoZ5f2xtLMW/e+7dIzUxjYO5l6t8GiD/e26ji1Lg9/yvf2Bhrbr1ebYjWZTFx+jjdptvY/RS2uv3nPEQpLHMSYYNhZgSelcvr2IjEuhq/KqgMaMmgYBrPXf0FZdT1npdkZfdGJ6zv4rFRGZKbj9hg8unY39e7gviWv3mNQ5zYCTqqJhAtLqAPoTHv37mXLli2899572O12evfuze23386TTz7JLbfcEurwREQkRNQ+tI2rvpYXP/qS+PgEXnivxD/szxob5y1vmJ/Kt7yb1aBfopkfDzyDeR8d5szuiRypNdixr4R6j0GVy0SN28T7e7xzmPz7r58z/uLT+G7PJEqOOlie/xWnpCbz37IaDh6txTAMTk+20TvFxtnpidhjDDYXHuT73z2DOIsZa4yJNz/fx9gB53BaahK9usXRzWbB1Iq3QfkSbjabDafT6f9ut9tbtb2IRL5QtA1uj8HLO0vZUVRHvNXEFyU1pKR5iG2mF05r1Ls9TPvnvylx1HFqtzhGXdizVduZTCbyLj+bm//6Ka/tPMyFvZKY0P+0k27z923fUNwwlG30hW1LSgFcd2FPVnx6gH99UUz2F+mM6NP8cENHnYvZG74AYEL/00i2WwM+VmKchQkXn8bSLftZuHEP/U9PbnE/Lo/Bs+98yTuFpVhjTDx23XdbHDZ471UZfLy/nIJiB79+ZSdPjLkA+0nm9ToZZ72bD/cc4e2CEj7eW0ZxtQuo5uV/H+WiUw9zxbnd+X5mOj2T4tq0f5HO1qWSUgUFBaSkpNCz57GL8bnnnsuBAwc4evQo3boF9oYIERGJDmof2s6XgDrZm/qO56qv5c8bd3NKrI2a8hpuG3oOSx1FuA0DS4yFo/XwZYWLA04L31TUMu+9rxptv7PsSKPf9xxxsueIk/e/Kvcv+/T9fY3W+fD1Av/PdquZnomxJMfHYng8eDweku1WeiTZSYu34vJAqaOWvaVVfFlSjclsps7tIcVmoc7l4ozUBBKsMcTHmulmj/MOT/G4sZhNxMXFYjGZiDGDx+3CHhdHjBlMHjdJdhs2q5k4Sww2qxmL2URNvRtHnRtHrYtyhxOzCRJsccRaYrDGmLCYTRjuehLtNqwWM1azN9HmS4qZTFBUZ1Be7sAwvL/7mGj0S3M/YhjeB+B6t4dqpxNrbCwxZjMxJjCbTZgxgQnMpob9ef/DbGrYe8PvJ0vSnayXWouf6Z9khZa2NY5bwwQcccORIw4MvPXumJhOUtcWdnzS4jYeE6B7Qiw9EvVg2lahaBt2F1Wx5JNi/+/5b+zFsm4f56Yn8N2eiXy3ZyK9utlIiI0hPjYGczP//7k8BhU19RQUO/jHjoPsO1JDvDWGJ8dcGNCcSxed2o2fXtqbP+Xv54k3C9m89wjfz0zn9GQb1hjvdcxtGBRX1fFuYQn//Nw7/9TNg89scxIN4MJeSfx0UG9e3LKfR97Yzc5DVQw8M5mkOAtuj4HLY/Df0mqWbf2ag0drOa1bHLcPP7vNx/vRJaezasdBviyp5ucvfcqNWadzVpqdhNgYbNYY3B6DOreHyloXhcUOXv/3Yb4s8fbk/dX3zuW8noktHqN7QiyzR32XX7+6k4/2HGHCknxy+vbiuz2T6GazYLPGEBdjxgDchoFhGHgM75sFK50uiqpqOVxZy+cHK9m6rxynq2lvq5p6D/n7ysnfV85Tb3/Jd3smcskZKfROtdEjMY7u8VYsZjNms/e6HWMyYTZ5LzEew8Dj8R7b+7OB26Dhu7f+dS6DOpeb0vIK6t0GCYmJxB7XNlka/k1YY0yNlnnbs7b/ezAMD1+VlFFeXk1KSirmduyrvTqq57XJBEcNE0eOVLfYXgRDnMVMr27h8wboLpWUcjgc2O32Rst8v1dXV7e6YTGbW765OJ7JBElJScRZzS1ul5SU1OqLeGvX7Yx9mkwQG2PG/a06Rmp9mhPI3zHYx+6MfZ7ob9gZx+6sfR5fx2ioz7fX895keH9vzXVKnT2OCUb70Nq24djfyITZ4yKGxnOTeHB7bxa/tV24LW/PviyxFsAFJlj63k7Am5CymCAtFtJOAVuCnRS7jVf+c4R6zJySGIfL5eLys5LZ+dV+EmJjMJljqKp1UWuKpareoN7lwYixYHgMDLMZt9uAGAuJcTEcqqyn3vD+v1RS7aKk+rjzXl4HB5u+eSnG4r1NspnNON2AycK+8trj1tDcIBLezCZY8pP+nN09/qTrHd92yDGheHY4v1ci91x2Kut2HqLaY6LGE4Oj3uCbCiffVDj51xeBT3x+ardY7h56KumWGkpLa/zLy8uP4K7xvimvtLSUmBhHkzh/fEECuE7hH/8uY+v+CrbuP/GE4IlxFsZ8N5Urz7BQWlrcpPz445WXHyEm5sQ3IhPPT+CLQ0l8driGf+w4yD92HGx2vTOT4/j15adRU1lGTTPlvmPWe2IpL4/FfIKhi7+99js89vbXlFbXs3DjnhPG5ZOeEMsdQ3oypLe1UV1PVseMJHhi5Jk8/cFBypxuXvrkQIvHaY4lxsxZSVaGnJlEZqKbnV+XYIuNpf8ZKeytsbJpfxVflDjZX+5kf/mhNh2jdYIzCX9g9rW8irTKlOHf4UeXnB7QNh317GAyutBECxs2bGDGjBls3rzZv2z37t2MGTOGrVu3kpQU+BsiREQk8ql9EBGRb1PbICLS8brU5yGZmZmUl5c3erXql19+Sa9evdSoiIh0YWofRETk29Q2iIh0vC6VlDrrrLMYMGAAc+bMoaqqiv3797NgwQImTJgQ6tBERCSE1D6IiMi3qW0QEel4XWr4HkBJSQm/+c1v2Lx5M2azmbFjxzJ16lRiYtr29gMREYkOah9EROTb1DaIiHSsLpeUEhERERERERGR0OtSw/dERERERERERCQ8KCklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUkFSWlrK7bffzsCBAxk8eDCzZ8/G5XI1u+67777L6NGj6d+/P9deey1vv/12J0fbNoHU8aWXXmLkyJFkZWUxcuRIli1b1snRBi6Q+vl88cUXXHzxxWzevLmTomyfQOq4ZcsWJk6cSFZWFldeeSWLFi3q5GjbJpA6vvjii4wYMYJLLrmE0aNHs27duk6Otn3KysrIzs4+6b+/SL3eRKO2XGO6ol27dnHzzTczaNAgLrvsMu69917KyspCHVZYcrvd3HTTTdx///2hDiUslZeXc++99zJ48GAuvfRSbr/9doqKikIdVtjZuXMnubm5DBw4kOHDh/PYY49RV1cX6rCkE3SV6200Xyuj/ToXrden5u7ht2/f7n/2GjFiBCtWrAhhhJ1LSakgufvuu4mPj+f9999n5cqVfPTRRyxdurTJenv27OHOO+/kl7/8JVu3buXOO+/k7rvv5vDhw50fdIBaW8d//etfPP300zzxxBN88sknzJ07l2effTbsH/hbWz+fmpoafv3rX+N0OjsvyHZqbR2//PJLJk+ezE9+8hM++eQTFi1axJIlS1i7dm3nBx2g1tbx3XffZdGiRTz//PN88skn3HHHHdx99918/fXXnR90G3z88cfceOON7Nu374TrRPL1JhoFeo3pipxOJ7fccgtZWVl88MEHvPbaa5SXl/Pggw+GOrSwNH/+fLZu3RrqMMLWnXfeSXV1NRs2bODtt98mJiaGhx56KNRhhRWPx8Ntt93GyJEj2bJlCytXruSDDz5g8eLFoQ5NOlhXut5G87Uymq9z0Xp9au4evqKigsmTJzN27Fjy8/OZPXs2jz/+ODt27AhhpJ1HSakg2Lt3L1u2bGHatGnY7XZ69+7N7bff3mzvoFWrVjFw4ECuvvpqLBYL1113HZdeeil/+9vfQhB56wVSx8OHD3PrrbfSv39/TCYTWVlZDB48mPz8/BBE3jqB1M/n0Ucf5eqrr+7EKNsnkDr+9a9/5aqrruKHP/whJpOJ888/n+XLlzNgwIAQRN56gdTxv//9L4Zh+L9iYmKwWq1YLJYQRB6YVatWMXXqVO65554W14vE6000ass1pis6cOAA559/Pnl5ecTGxpKamsqNN94Y1u1HqHz00UesX7+ea665JtShhKXPP/+c7du3M3fuXLp160ZiYiKzZs1i6tSpoQ4trFRUVFBcXIzH48EwDADMZjN2uz3EkUlH6yrX22i+Vkb7dS4ar08nuodfv349KSkp5ObmYrFYGDp0KKNHj+4y94lKSgVBQUEBKSkp9OzZ07/s3HPP5cCBAxw9erTRuoWFhfTp06fRsoyMDHbt2tUpsbZVIHXMzc1l8uTJ/t9LS0vJz8/noosu6rR4AxVI/QBeeeUV9u7dyx133NGZYbZLIHXcsWMHZ5xxBr/61a8YPHgw1157LVu2bKFHjx6dHXZAAqnjqFGjSE9P57rrruPCCy/kl7/8JXPnzqVXr16dHXbAhg8fzoYNG7juuutOul6kXm+iUaDXmK7qnHPO4fnnnycmJsa/bN26dVx44YUhjCr8lJaWMn36dJ566qmIvjnvSDt27CAjI4O///3vZGdnM3z4cJ544omwb8c6W2pqKpMmTeKJJ56gb9++XHnllZx11llMmjQp1KFJB+sK19tov1ZG+3UuGq9PJ7qHLygo6NL37EpKBYHD4WhyofP9Xl1d3eK6NputyXrhJpA6Hq+4uJhbb72Viy66iOuvv75DY2yPQOr35Zdf8swzz/DUU081asjDXSB1rKio4E9/+hNjxoxh48aN/OY3v+GJJ54I++F7gdSxvr6e888/nxUrVvDpp5/ym9/8hunTp7N79+5Oi7etevTo0aoeXZF6vYlGbb2GdmWGYfDMM8/w9ttvM3369FCHEzY8Hg/Tpk3j5ptv5vzzzw91OGGroqKC3bt3s2fPHlatWsUrr7zC4cOHue+++0IdWljxeDzYbDYeeughPv30U1577TW+/PJL5s2bF+rQpBNF4/W2K1wro/06F43XpxPdw3f1e3YlpYIgPj6empqaRst8vyckJDRabrfbm8xB5HQ6m6wXbgKpo8+nn37KhAkTOPvss1m4cGFYD4tqbf1qa2u55557ePDBBznttNM6Ncb2CuRvGBsby1VXXcX3vvc9LBYLl156KTk5ObzxxhudFm9bBFLHWbNmkZmZSb9+/YiNjWX8+PH079+fVatWdVq8HS1SrzfRqC3X0K6sqqqKu+66i9WrV/OXv/yF8847L9QhhY1FixYRGxvLTTfdFOpQwlpsbCwA06dPJzExkfT0dO6++27effddHA5HiKMLHxs2bGDdunX85Cc/ITY2lszMTPLy8njppZdCHZp0kmi93naFa2W0X+e60vWpq9+zKykVBJmZmZSXl1NSUuJf9uWXX9KrVy+SkpIardunTx8KCgoaLSssLCQzM7NTYm2rQOoIsHLlSiZNmsTPfvYznnrqKf9FM1y1tn6fffYZe/bsYfr06QwcOJCBAwcC8Itf/IKZM2d2dtgBCeRveO655zZ5s4Xb7faP5w5XgdTxwIEDTeposViwWq2dEmtniNTrTTQK9Brale3bt4/x48dTVVXFypUro+YBKVheffVVtmzZ4m+DXnvtNV577TV/eyReGRkZeDwe6uvr/cs8Hg9A2LdlnengwYNR3xbKiUXz9bYrXCuj/TrXla5PXf2eXUmpIDjrrLMYMGAAc+bMoaqqiv3797NgwQImTJjQZN0xY8awZcsW1qxZg8vlYs2aNWzZsoWcnJwQRN56gdRx3bp1zJw5k9///vf8/Oc/D0G0gWtt/QYOHMiOHTvYunWr/wvgueeeC/ukVCB/wx/96Ee8+eabvPrqqxiGQX5+PqtXr46qf6cjRozgL3/5Czt37sTj8bB27Vo2b97c4jxNkSRSrzfRKJB/m11ZRUUFP/vZz7jkkkt44YUXSEtLC3VIYWft2rV88skn/jbo+uuv5/rrr4/aN0u11bBhw+jduzcPPvggDoeDsrIynnnmGa6++moSExNDHV7YGD58OMXFxTz33HO43W7279/PwoULGT16dKhDkw4W7dfbrnCtjPbrXFe6PmVnZ1NSUsLSpUupr69n06ZNrF69mvHjx4c6tE6hpFSQzJs3D5fLxVVXXcUNN9zA5Zdfzu233w5AVlYW//znPwFvD5Q//OEPLFq0iEsvvZQFCxbw+9//nrPPPjuU4bdKa+s4f/583G43d911F1lZWf6vhx9+OJTht6i19Ytkra3j0KFDWbBgAX/6058YMGAADzzwAPfddx9XXXVVKMNvldbW8Y477iA3N5c777yTSy+9lP/7v//jD3/4A9/97ndDGX67Rcv1Jhqd7N+meP3jH//gwIEDvPHGGwwYMKBRGyISCKvVyp///GdiYmIYOXIkI0eOpFevXsyZMyfUoYWVjIwMFi1axFtvvcXgwYP56U9/yogRI1p8u6tEPl1vI1+0X+e60vUpNTWVJUuWsHbtWgYPHsyMGTOYMWMGQ4YMCXVoncJkREPfPhERERERERERiSjqKSUiIiIiIiIiIp1OSSkREREREREREel0SkqJiIiIiIiIiEinU1JKREREREREREQ6nZJSIiIiIiIiIiLS6ZSUEhERERERERGRTqeklIhImCkrKyM7O5vNmzcHvO0f//hHbrrppkbL3G43TzzxBMOGDSMrK4spU6ZQVFQUrHBFRCQMtKXtWLduHddffz39+/cnOzublStXdmCEIiIiTSkpJSISRj7++GNuvPFG9u3bF9B21dXVzJ07l7lz5zYpW7hwIRs3buTll1/m/fffx2azMWPGjGCFLCIiIdaWtmPTpk3cf//9TJs2jW3btjFr1iweffRRduzY0YGRioiINKaklIhImFi1ahVTp07lnnvuaVL24YcfMmHCBAYOHMioUaP45z//2ag8JyeH4uJifvzjHzfZdsWKFdx6662ceuqpJCYmMn36dN577z3279/fYXUREZHO0da2Y+nSpfz0pz/lyiuvxGQyMWTIEF5++WXOPPPMzgxfRES6OCWlRETCxPDhw9mwYQPXXXddo+W7du1iypQpTJ48mc2bNzNr1izmzJnD+++/71/nz3/+M0899RTdu3dvtG1lZSWHDh2iT58+/mXp6ekkJyeze/fujq2QiIh0uLa2HTt27CAlJYXJkyczePBgcnJy2LdvHykpKSGohYiIdFVKSomIhIkePXpgsViaLF++fDlXXXUV11xzDTExMVxyySXccMMNLFu2zL9Or169mt2nw+EAID4+vtFym83mLxMRkcjV1rajoqKCF154gSlTprBx40by8vK455572L59e2dXQUREurCmLZiIiISVb775hk2bNjFw4ED/Mrfb3aohFna7HYCamppGy51OJwkJCcENVEREwkZLbUdsbCzjx48nKysLgGuuuYahQ4eybt06Lr744pDELCIiXY+SUiIiYa5Xr1788Ic/5De/+Y1/WVFREYZhtLhtcnIyPXv2pLCw0D+Er7i4mPLy8kZD+kREJLq01Hace+651NXVNdrG7Xa3qm0REREJFg3fExEJcxMmTOC1117jgw8+wOPxsGfPHv7f//t/LFmypFXbjxs3joULF7J//36qqqqYM2cOgwYN0mS2IiJRrKW248c//jEvvfQSH374IR6Ph3Xr1rF582auv/76EEcuIiJdiXpKiYiEuYsvvpinn36ap59+ml/+8pfY7Xauv/56fvWrX7Vq+7y8PFwuF7m5uTgcDgYPHsyzzz7bsUGLiEhItdR2jB8/HrPZzOOPP87XX3/N6aefzjPPPMOFF14Y4shFRKQrMRnqoysiIiIiIiIiIp1Mw/dERERERERERKTTKSklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVERERERERERKTTKSklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVEgsQwjFCHICIiEUJthoiItEdntiNqs6QjKSklXdqIESO4//7727WPo0ePct9997F161b/sptuuombbrqpveGJiEgUWLNmDd///vfp27cvDz/8MIWFhfz4xz8OeD/3338/I0aM8P8eaBumtklEJDJ1VDvSGm09lkhrWUIdgEik+89//sMrr7zCuHHjQh2KiIiEoUcffZSzzjqLuXPn0rNnT1avXs22bdvavd/58+eTmJjY6vUfeeSRdh9TREQ6X0e1I63xxhtvdNqxpGtSUkpERESkA5WXl3PZZZcxePDgoO73ggsuCGj9jIyMoB5fREQ6R0e1IyLhQMP3pMurr6/nscce49JLL+XSSy/lvvvuo6yszF++YsUKxo0bR//+/enXrx85OTmsWbMGgM2bN/PTn/4UgJ/+9KeNhkUYhsHixYv53ve+R79+/bjxxhv57LPP/OW///3vyc7OZv78+QwePJirr76aI0eO4Ha7WbZsGaNHj6Zfv35873vf47e//S21tbWN4t64cSM/+clPGDBgAIMHD+bXv/41Bw8e9Jf/4x//oG/fvnz88ceMHz+evn37MnLkSN566y3++9//8rOf/YyLL76Y7OxsXn/9df92Ho+H3/3ud4wYMYKLLrqIESNG8PTTT1NfXx/cEy8iEgF27tzJz372MwYMGEBWVhaTJk1i+/bt/vK1a9cyZswY+vXrx9ixY9m2bRsXXHAB//jHP9i8eTPnnXceAH/4wx8477zzuP/++5k/fz4A5513Hr///e/bHNvxw/dGjhxJXl5ek3UmTpzI5MmTgabD98477zyWLVvG9OnTGTRoEFlZWdx1112UlJQ02scLL7zAVVddRb9+/fjRj37EW2+9xXnnncfmzZvbHLuISFcRzu1IS88dv//974N2LJETUVJKurw33niDzz//nLlz53LvvffyzjvvcPvttwOwbNkyHn74Ya666ioWLVrEk08+idVqZdq0aRw4cIALL7yQhx9+GICHH3640dCIjz/+mA0bNvDQQw/xxBNPcPjwYX7xi1/gcrn86xw4cIANGzbw9NNPc/fdd5OamsrDDz/MnDlzGDFiBAsXLiQ3N5e//OUv3H777f5JBl999VV+/vOf07NnT55++mkeeOABtm3bxo033khpaal//y6Xi1/96lf86Ec/YsGCBcTFxTF16lR+8Ytf8L3vfY/f/e539OjRg/vuu49Dhw4BsHjxYpYtW0ZeXh5Llizhxz/+Mc8//zzPPfdch/8tRETCSVVVFbfccgupqanMmzePZ555hpqaGv7nf/6HyspK3nzzTX75y1+SmZnJ/Pnzueaaa5gyZQoejweACy+8kL/97W8ATJgwgb/97W/ceeedTJgwAYC//e1vTJw4MSix5uTk8N5771FVVeVftm/fPnbs2EFOTs4Jt3vmmWfweDw8/fTT/jZwzpw5/vL58+fz29/+lmuvvZYFCxZw8cUXc8899wQlZhGRaBfu7UhLzx0TJ07skDZL5HgaviddXrdu3Xj++ef983KkpqaSl5fHBx98wP79+/n5z3/e6NPnM844g3HjxvHJJ59w/fXX+4dDZGRkNBoaERsby//93/+RkpICeBulGTNmUFhYyPnnnw94k0b33Xcfw4YNA7wTCa5cuZK7776bKVOmAHDZZZdxyimncO+99/Lee+9x+eWX8+STTzJs2DCeeeYZ//EuueQSrrvuOpYsWcK0adMAb6+nX/ziF/4G5OjRo/zqV7/iZz/7GTfffDMA6enpjB8/ns8//5xevXqxZcsWLrzwQsaPHw/AoEGDsNvtAc1bIiISDQoLCykrK+Omm25iwIABAJxzzjksX76cqqoq/vCHP3DRRRfx1FNPAXDFFVdgMpl49tlnAUhMTKR///4A9OrVq9HPgP/3YBgzZgzz5s1jw4YN/PCHPwRg9erVJCQkcNVVV51wuz59+vD444/7f9+xYwdr164FoLq6msWLF5Obm8vUqVMBGD58ODU1Nf6HJBERObFwbkda89xx5ZVXdkibJXI89ZSSLu/KK69slHAZMWIEVquVDz/8kPvvv59p06ZRWVnJZ599xurVq1m2bBlAi8PZMjIy/Akp8CazACorKxut16dPH//PW7ZsAWD06NGN1hk1ahQxMTFs3ryZr776iuLi4ibrnHnmmWRlZTUZTpGVleX/OT09HWjcqPhiPHr0KACDBw/mww8/5Cc/+Ql//OMf+fLLL/l//+//MXbs2JPWV0Qk2mRmZpKWlsaUKVN45JFHeOutt+jRowf33nsvKSkp7Ny5s0nCZ8yYMSGJ9YwzzmDAgAGNhmO//vrrjBw5EpvNdsLtvv2Q0atXL2pqagD49NNPcTqd/OAHP2i0zvXXXx+8wEVEolg4tyOtee4Q6QxKSkmX50vU+JjNZlJSUjh69Cj79u1j0qRJXHrppfz4xz9m8eLF/mSUbyjdicTHxzfZL+Dvjtvc8SsqKgDo0aNHo3UsFgupqalUVlZSXl7ebNy+Zd9OejXXw+lkDyi33HILDz/8ME6nkyeeeILrrruO0aNH89FHH51wGxGRaJSQkMCyZcu48sorWbNmDVOmTGHo0KE8/PDD/kR+Wlpao2169uwZilABGDt2LB999BFHjhzhP//5D19++eVJh+4B2O32Rr+bzWZ/++abX/HbdWyu/RERkabCuR1pzXOHSGfQ8D3p8nwNgo/b7ebIkSOkpqYyefJkrFYrf//737nggguwWCwUFhbyz3/+s0NiSU5OBqC4uNjfswq8vbJ8Mfl6Nn17Ilrfdqmpqe2KwWw2k5ubS25uLqWlpbz77rs899xz3HnnnXz44YfExsa2a/8iIpHknHPO4cknn8TtdrNjxw5effVVXnrpJU455RTMZnOTa7Hvg4NQ+MEPfsCsWbPYsGEDe/fu5dRTT2XQoEFt3p9vyEZZWRnnnHOOf/nxLwMREZGTC9d2pDXPHSKdQT2lpMv78MMPG00+vm7dOlwuF9/97nf56quvmDBhAv369cNi8eZw33vvPeBYj6eYmJigxeJ7eFi9enWj5a+//jput5sBAwZw9tln06NHjybr7N+/n08//ZRLLrmkXTH86Ec/4rHHHgOge/fujBs3jtzcXCorKxtNoCsiEu3Wrl3LkCFDKC4uJiYmhqysLGbOnEm3bt0oKysjKyuLdevWNeoB+/bbb7e4X1/P2WBLSkri+9//Pm+++SZr165l9OjR7TrW+eefT1JSEuvXr2+0fN26de0NVUSkSwjndqQ1zx3BOpbIyainlHR5JSUl3Hnnndx0003s2bOHp59+mssuu4xrr72W3/72tyxbtoxevXrRrVs3PvjgA1588UUA/5wbSUlJALzzzjskJyf7JzFvi4yMDH74wx8yf/58nE4ngwcP5j//+Q/z589n8ODBXH755ZjNZn71q1/xwAMPcM899zB27FiOHDnC/PnzSU5O9k9g3laXXnopS5YsIT09naysLA4fPswf//hHBg0a1KR7sYhINLvkkkvweDzk5eUxefJkEhISeOONN6isrOSaa67huuuuY9KkSdx+++38+Mc/Zt++ffzud79rcb/dunUD4LXXXuPiiy+md+/eQYt57Nix5OXl4Xa72z0vSWJiIrfccgvz5s3DbrczaNAgtmzZwksvvQToQUVEpCXh3I605rkjWMcSORndTUiXd8MNN5Cenk5eXh6/+93vGD16NPPnz8dkMrFgwQJ69uzJ/fffz913382nn37KwoULOeecc9i6dSvgncDw+uuvZ9myZf63E7XH7NmzueOOO3j99deZPHkyy5Yt46abbmLx4sX+B4Bx48Yxb9489u7dS15eHnPnziUrK4uVK1c2GRceqF/+8pf84he/4OWXX+aWW25h7ty5DB8+nHnz5rW7biIikeSUU07h+eefJykpienTp3Pbbbexc+dOfv/73zNkyBAGDhzICy+8QElJCXl5eSxfvpz77ruvxf1ec8019O3bl/vvv58XXnghqDFffvnlJCcnc8EFF5CZmdnu/d12223ccccdvPLKK9x2221s3brV39Z9e+5EERFpLNzbkdY8d3RkmyUCYDJamq1ZRERERFrl66+/5qqrruLxxx9n3LhxoQ6nXVwuF6+99hqDBw/m1FNP9S9ftmwZjz32GJs3b/Z/gi4iIsERTe2ISGto+J6IiIhIiBiGgdvtbnG9mJgYTCZTJ0R0jMViYfHixbz44otMmTKF1NRUdu3axe9+9zvGjh2rhJSISBgI53ZEpDWUlBIREREJkVWrVvHAAw+0uF6oPjF/7rnnePrpp5k5cyZHjx7ltNNOY9KkSdx2222dHouIiDQV7u2ISEs0fE9EREQkRI4cOcLXX3/d4npnnHGGXs8tIiJNqB2RSKeklIiIiIiIiIiIdDq9fU9ERERERERERDqdklIiIiIiIiIiItLplJQSEREREREREZFOp6SUiIiIiIiIiIh0OkuoA4hEpaWVaHr4Y0wm6N49SeelFXSuWk/nqvXaeq5820lwNHf+o/HfseoUGaKxThCd9Qq3OqltCL62ts/h8m8iGuicBp/OafCF+zntqPZBSak2MAzC8h9JqOm8tJ7OVevpXLWezlVonez8R+PfRnWKDNFYJ4jOekVjnTpbWVkZN954I4899hiDBw8GYN26dSxYsID9+/eTkpLCuHHjuP322zGbvQNGVq1axYIFCyguLuacc87hoYceIisrCwC3281vf/tbXn31VWpqahgyZAiPPvoop5xySkBxtfVvq38TwadzGnw6p8HX1c6phu+JiIiIiEhE+/jjj7nxxhvZt2+ff9nnn3/Ovffey913383WrVtZvHgx//jHP1i6dCkAmzdvZtasWcydO5f8/HzGjBnDlClTqKmpAWDhwoVs3LiRl19+mffffx+bzcaMGTNCUT0RkailpJSIiIiIiESsVatWMXXqVO65555Gy7/55ht+9KMf8f3vfx+z2cy5555LdnY2+fn5AKxYsYJRo0YxYMAArFYrkyZNIjU1lTVr1vjLb731Vk499VQSExOZPn067733Hvv37+/0OoqIRCslpUREREREJGINHz6cDRs2cN111zVaPnLkSB544AH/706nk3feeYcLL7wQgMLCQvr06dNom4yMDHbt2kVlZSWHDh1qVJ6enk5ycjK7d+/uwNqIiHQtmlNKREREREQiVo8ePVpcp6qqil/+8pfYbDYmTZoEgMPhwG63N1rPZrNRXV2Nw+EAID4+vkm5r6y1TKaAVvevH+h2cmI6p8Gncxp84X5OOyouJaVERERERCRq/fe//+Wuu+6ie/fu/OlPfyIxMREAu92O0+lstK7T6SQ1NdWfrPLNL3V8eUJCQkDHb+vbqvQWxODTOQ0+ndPg62rnVEkpERERERGJSu+++y6/+tWvuOGGG/j1r3+NxXLs8SczM5OCgoJG6xcWFnLFFVeQnJxMz549Gw3xKy4upry8vMmQv5YE+nr3cH8tfCTSOQ0+ndPgC/dz6osv2JSUEhERERGRqPPpp5+Sl5fHzJkzmTBhQpPyCRMmkJeXx7XXXsuAAQNYtmwZpaWlZGdnAzBu3DgWLlxI3759SU1NZc6cOQwaNIgzzzwzoDja+nr3rvZa+M6gcxp8OqfB19XOqZJSIiIiIiISdZ577jlcLhezZ89m9uzZ/uUDBgzg+eefZ+jQoTzyyCPMnDmTw4cPk5GRweLFi0lJSQEgLy8Pl8tFbm4uDoeDwYMH8+yzz4amMiIiUUpJKRERERERiQrHvxnvueeea3H9nJwccnJymi2zWq1MnTqVqVOnBi0+ERFpzBzqAEREREREREREpOtRUkpERERERERERDqdklIiIiIiIiIiItLpNKeUhBWPx0NJSQkA6enpmM3Km4qItJZhGJSVlQGQlpaGyWQKcUQiIiIiIiemJ34JKyUlJSx6czuL3tzuT06JiEjrlJWV8dxbO3jurR3+5JSIiEQewzAwutI74UWky1JPKQk7CclpoQ5BRCRixSelhDoEEREREZFWUU8pERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVERERERERERKTTWUIdgIiIiASXYRgcOVIGQFpaGiaTKcQRiYiIiIg0FZY9pdasWcMFF1xAVlaW/2vatGkAbN++nYkTJ5KVlcWIESNYsWJFo21XrVpFdnY2/fv3Z9y4cWzbts1f5na7eeKJJxg2bBhZWVlMmTKFoqKiTq2biIhIR6upquDFD7/kubd2UFZWFupwRERERESaFZZJqc8++4ycnBy2bdvm/3ryySepqKhg8uTJjB07lvz8fGbPns3jjz/Ojh07ANi8eTOzZs1i7ty55OfnM2bMGKZMmUJNTQ0ACxcuZOPGjbz88su8//772Gw2ZsyYEcqqioiIdAh7YjLxSSmhDkNERERE5ITCNil10UUXNVm+fv16UlJSyM3NxWKxMHToUEaPHs2yZcsAWLFiBaNGjWLAgAFYrVYmTZpEamoqa9as8ZffeuutnHrqqSQmJjJ9+nTee+899u/f36n1ExERERERERHp6sIuKeXxeNi5cyfvvPMO3//+97niiit46KGHqKiooKCggD59+jRaPyMjg127dgFQWFh4wvLKykoOHTrUqDw9PZ3k5GR2797d8RUTERERERERERG/sJvovKysjAsuuICRI0cyb948jhw5wn333ce0adPo0aMHdru90fo2m43q6moAHA7HCcsdDgcA8fHxTcp9Za2l+WIb852PYJyX4/dhMkXfuQ7muYp2Olet19ZzpXMrIiIiIiKhFHZJqfT0dP9wPAC73c60adO44YYbGDduHE6ns9H6TqeThIQE/7rNlaempvqTVb75pZrbvrW6d08KaP2uIhjnxe2uxm6PbdhfIunp0Xmu9W+o9XSuWk/nSkREREREIknYJaV27drFa6+9xq9//Wv/K6zr6uowm83069ePF198sdH6hYWFZGZmApCZmUlBQUGT8iuuuILk5GR69uzZaIhfcXEx5eXlTYb8taS0tBLDaGsNo4/J5H0YDsZ5KS2toqamzv9zTEx8C1tElmCeq2inc9V6bT1Xvu0kehmGwZEjZaSlpfnbVBERERGRcBF2c0qlpKSwbNkynn/+eVwuFwcOHODJJ5/khz/8ISNHjqSkpISlS5dSX1/Ppk2bWL16NePHjwdgwoQJrF69mk2bNlFfX8/SpUspLS0lOzsbgHHjxrFw4UL2799PVVUVc+bMYdCgQZx55pkBxWgY+vr2VzDPS7Sf52ium85V5J0riW6OygpmrNnNP7ftwdAfXERERETCTNj1lOrVqxeLFi3i6aefZuHChcTFxTFq1CimTZtGXFwcS5YsYfbs2cybN4+0tDRmzJjBkCFDABg6dCiPPPIIM2fO5PDhw2RkZLB48WJSUlIAyMvLw+VykZubi8PhYPDgwTz77LOhq6yIiEgHMQyDT8tM7KvvxmNv7+e1XWU8M74fiXHWUIcmIiIiIgKEYVIKYNCgQSxfvrzZsr59+56wDCAnJ4ecnJxmy6xWK1OnTmXq1KlBiVNERCRcfXnUwz6HGTAwA58edLBkYyF3jfhuqEMTEREREQHCcPieiIiItN+eSg8A51gqGdDD+xnUqp1lVNW6QhmWiIiIiIifklIiIiJRxuGCynowYdDLUs3pCSa6xZqpqnOz4tMDoQ5PRERERARQUkpERCTqHKr2vmmvexxYTQYmk4nvpnvnklrz+UFKSko08bmIiIiIhJySUiIiIlHmUI03KdXLfizx1N1UDRjsKa9l5vJ3KCsrC1F0IiIiIiJeSkqJiIhEEZfHoKTW+/PxSanYGEiL8zb7lZbkUIQmIiIiItKIklIiIiJR5GitBwMTsWZItDYu6xXv7UFVXBcTgshERERERBpTUkpERCSKlNd637rXLdbUpKyX3dvsl9bHUOfydGpcIiIiIiLfpqSUiIhIFKlwepNNSdamSalusRBrNvBgorDM2dmhiYhIKxmGoRdSiEiXoKSUiIhIFKmodQPeBNS3mUwmUhuWf1FS04lRiYiIiIg0paSUiIhIFKnwDd9rpqcUQFqc95P3XcVKSomIiIhIaCkpJSIiEiVq6t046r1Jp6Rm5pQCSI31lu8uqe60uEREREREmqOklIiISJTYX1EHeOeNiotpPimVEuf9frCynvLq+s4KTURERESkCSWlREREosTe8loAullPvE6sGRJivEP8dh6q7IywRERERESapaSUiIhIlDhw1JuUSrSe/I1NyRbvZOg7Dx3t8JhERCQwvjfv6e17ItIVKCklIiISJYodLgDsMSdfL8ni7SlVUOzo6JBERKQNnl63K9QhiIh0CiWlREREokRJwxxRdsvJ1+vWMHzvi6Kqjg5JRETawtT8vIAiItFGSSkREZEoUexoSErFnHzIh6+n1IGjtVQ6XR0el4iIiIhIc5SUEhERiQKGYRzrKdXC8D2rGU5J8M6GXlCi3lIiIiIiEhpKSomIiESBo04XtS5vD6mWklIA56TZACgo0rxSIiIiIhIaSkqJiIhEOMMw+OLrIgDiYkzEtKJ19yWlvihWTykRiQ5lZWVkZ2ezefNm/7Lt27czceJEsrKyGDFiBCtWrGi0zapVq8jOzqZ///6MGzeObdu2+cvcbjdPPPEEw4YNIysriylTplBUVNRp9RER6QqUlBIREYlwZWVl/OmjLwCwtTCflI8/KaWeUiISBT7++GNuvPFG9u3b519WUVHB5MmTGTt2LPn5+cyePZvHH3+cHTt2ALB582ZmzZrF3Llzyc/PZ8yYMUyZMoWamhoAFi5cyMaNG3n55Zd5//33sdlszJgxIyT1ExGJVkpKiYhIVNi5cye5ubkMHDiQ4cOH89hjj1FXVwe075PySOG2xANgt7T8xibDMEiPcQLw31IHLrenQ2MTEelIq1atYurUqdxzzz2Nlq9fv56UlBRyc3OxWCwMHTqU0aNHs2zZMgBWrFjBqFGjGDBgAFarlUmTJpGamsqaNWv85bfeeiunnnoqiYmJTJ8+nffee4/9+/d3eh1FRKKVklIiIhLxPB4Pt912GyNHjmTLli2sXLmSDz74gMWLF7f7k/JIUV3vm0+q5aRUbXUlb3y6F4vJoM5tsOdIZNVVROR4w4cPZ8OGDVx33XWNlhcUFNCnT59GyzIyMti1axcAhYWFJyyvrKzk0KFDjcrT09NJTk5m9+7dHVQTEZGuR0kpERGJeBUVFRQXF+PxeDAMb3LGbDZjt9vb/Ul5pKh2eXs72S2tWz8+KZnUhpULNK+UiESwHj16YLE0vfg5HA7sdnujZTabjerq6hbLHQ7v0Ob4+Pgm5b6y1jKZAv9q63b60jnVOY3sr3A/px2hlbeuIiIi4Ss1NZVJkybxxBNP8L//+7+43W6uuuoqJk2axNy5c5v9JHzlypWA95Py8ePHNyn3fZLeWs011MffXHQkk+lYT6n4huF7JhMYAKZjP397WYrNTHG1m4IiB9dd0PpjHf89GqhOkSMa6xVudQqXOILBbrdTWVnZaJnT6SQhIcFf7nQ6m5Snpqb6k1Xf7jV7/Pat1b17UkDr+z5c6d49iZiYVrxOVVot0L+FtEznNPi62jlVUkpERCKex+PBZrPx0EMPMWHCBPbu3csdd9zBvHnz2vVJeSBOdgPR8TcXtThd3oeY5AQLtlgwW6146j3Y4qzY7N6fv72sp9tEQVk9X5U7SU8PLMZovGFSnSJHNNYrGusUan369GHjxo2NlhUWFpKZmQlAZmYmBQUFTcqvuOIKkpOT6dmzZ6MhfsXFxZSXlzf5oKMlpaWVGK17B0UDw7+d2aykVDCYTN7/xwL/W8iJ6JwGX7ifU198waaklIiIRLwNGzawbt061q5dC3gfNPLy8pg9ezajR49u8yflgWjuBqKzbi5KS6uorvcO3zO73Dg9dZhdZjyuOpy19Thr6vG4mi5LiLECsPObCoqLj2JqRReJcL9hagvVKXJEY73CrU4d9dARCtnZ2Tz55JMsXbqU3NxcPv74Y1avXs2CBQsAmDBhAnl5eVx77bUMGDCAZcuWUVpaSnZ2NgDjxo1j4cKF9O3bl9TUVObMmcOgQYM488wzA4rDMGjT37at28mJ6ZwGn85p8HW1c6qklIiIRLyDBw/637TnY7FYsFqt7fqkPBAnu4Ho6JuLWpeHhpwUseZjx/T+cOznby9Ltpkxm+BITT3FVXX0SIxr9TGj8YZJdYoc0VivaKxTqKWmprJkyRJmz57NvHnzSEtLY8aMGQwZMgSAoUOH8sgjjzBz5kwOHz5MRkYGixcvJiUlBYC8vDxcLhe5ubk4HA4GDx7Ms88+G7oKiYhEISWlREQk4g0fPpynnnqK5557jltvvZUDBw6wcOFCRo8e3e5PyiNBhdMNgAkDqxnwtG47i9nEGd3i2FdRyxfFjoCSUiIi4ejbb8br27cvy5cvP+H6OTk55OTkNFtmtVqZOnUqU6dODWqMIiJyjJJSIiIS8TIyMli0aBHPPvsszz//PElJSYwZM4a8vDxiY2Pb9Ul5JKhwugCIiwGTyUQgnS3OSbN5k1JFVVx2dlrHBCgiIiIi0gwlpUREJCoMGzaMYcOGNVvWnk/KI0F5Q08p39C9QJybZuOdryooKA7sFeciIiIiIu3VhttXERERCSfH95QK1NlpNgC+KKoKZkgiIiIiIi1SUkpERCTClfuSUubAZ0k+tyEpte9IDTX17qDGJSIiIiJyMkpKiYiIRLiKmobhe23oKZVqt5AWb8UAvizRED4RERER6TxKSomIiES48lpfT6m2bd/nlERAQ/hEREREpHMpKSUiIhLhKmraPqcUQJ8eDUkpTXYuIiIiIp1ISSkREZEIV9Hw9r1A55QyDIMjR8o41e7drqDYQWlpKYYR+NxUIiIiIiKBUlJKREQkwvkmOg90Tqmaqgpe/PBLtnyxF4Avi6t4+pUPKCsrC3aIIiIiIiJNKCklIiIS4cr9PaUC39aemMwpqcmYTeCo92CydwtydCIiIiIizVNSSkREJILV1LlwujxA2+eUijGbOKNbHABH64MVmYiItJVhGBpKLSJdgpJSIiIiEeyrg8WAt0G3mNq+nzNTfEmpduxERERERCQASkqJiIhEMP8k5zFgakc+6axUb1Kq0hWMqEREREREWqaklIiISASraOMk58czDIN0q3fc3tE69ZQSERERkc5hCXUAIiIi0naVtd6eUrHmtieTaqoq2HqwDrBTWQ8ezWMiIiIiIp1APaVEREQiWGVdQ1KqHT2lALonJ2HGwI2JoirNdi4iIiIiHU9JKRERkQjm6yllbUdPKQCzyURCQ//pA5V17Q1LRERERKRFSkqJiIhEsCr/8L327yvB6v3+zVElpURERESk4ykpJSIiEsF8w/eswUhKWbxzSR04Wtv+nYmIiIiItEBJKRERkQjm7ykV0/635iX6hu+pp5SIiIiIdIKwTUq53W5uuukm7r//fv+y7du3M3HiRLKyshgxYgQrVqxotM2qVavIzs6mf//+jBs3jm3btjXa3xNPPMGwYcPIyspiypQpFBUVdVp9REREOkIwe0olWr09pTR8T0REREQ6Q9gmpebPn8/WrVv9v1dUVDB58mTGjh1Lfn4+s2fP5vHHH2fHjh0AbN68mVmzZjF37lzy8/MZM2YMU6ZMoaamBoCFCxeyceNGXn75Zd5//31sNhszZswISd1ERESCparWAwRpTqmGnlKHqupxeYz271BERERE5CTCMin10UcfsX79eq655hr/svXr15OSkkJubi4Wi4WhQ4cyevRoli1bBsCKFSsYNWoUAwYMwGq1MmnSJFJTU1mzZo2//NZbb+XUU08lMTGR6dOn895777F///6Q1FFERCQYKv0Tnbd/+J49BswYuDwGh446270/EREREZGTCbukVGlpKdOnT+epp57Cbrf7lxcUFNCnT59G62ZkZLBr1y4ACgsLT1heWVnJoUOHGpWnp6eTnJzM7t27A47RZNLXt7+CeV6i/TxHc910riLvXElkMwyDKt/wvZj2789kAnuMt4fU1+U17d+hiIiIiMhJWEIdwPE8Hg/Tpk3j5ptv5vzzz29U5nA4GiWpAGw2G9XV1S2WOxwOAOLj45uU+8oC0b17UsDbdAXBOC9udzV2e2zD/hJJT4/Oc61/Q62nc9V6Olddj9Plob5hmF2sGQjCiLuEGA8Ot5l9R5wMOav9+xMREREROZGwSkotWrSI2NhYbrrppiZldrudysrKRsucTicJCQn+cqfT2aQ8NTXVn6zyzS/V3PaBKC2txNBUG34mk/dhOBjnpbS0ipqaOv/PMTHxLWwRWYJ5rqKdzlXrtfVc+baTyFVRUw+ACYMYE0FJSsXHeOeo2q+eUiIiIiLSwcIqKfXqq69SVFTEwIEDAfxJpn/961/ce++9bNy4sdH6hYWFZGZmApCZmUlBQUGT8iuuuILk5GR69uzZaIhfcXEx5eXlTYb8tYZhoIfkZgTjvBy/fTSf52iuW7DpXLWezlXXU1nrAsBqMjCZTMHISWE3e/dyoEJzSomIiIhIxwqrOaXWrl3LJ598wtatW9m6dSvXX389119/PVu3biU7O5uSkhKWLl1KfX09mzZtYvXq1YwfPx6ACRMmsHr1ajZt2kR9fT1Lly6ltLSU7OxsAMaNG8fChQvZv38/VVVVzJkzh0GDBnHmmWeGssoiIiJtdtR5LCkVLL45pZSUEhEREZGOFlY9pU4mNTWVJUuWMHv2bObNm0daWhozZsxgyJAhAAwdOpRHHnmEmTNncvjwYTIyMli8eDEpKSkA5OXl4XK5yM3NxeFwMHjwYJ599tnQVUhERKSdOiIpFW/2Dt87UOHEMLw9sEREREREOkJYJ6Xmzp3b6Pe+ffuyfPnyE66fk5NDTk5Os2VWq5WpU6cyderUoMYoIiISKked3jmlrA2JpGCwNfSUqq53U15TT2p8bND2LSIirWMYBobG5ItIFxBWw/dERESk9Xw9pSxB7CkVY4L0eO9nVhrCJyIiIiIdSUkpERGRCNURw/cAeiZ6e0d9o6SUiIiIiHQgJaVEREQi1PFv3wumXklWQD2lRERERKRjKSklIiISoSpqgp+UMgyDFIsbUE8pEREREelYYT3RuYiIiJyYf6LzICalaqsrOXjACsSxt6QyaPsVEREREfk29ZQSERGJUP7he0F8+x5ASkI8AIeq6oO6XxERaZn3rXt6856IdA1KSomIiESoig6a6DzBagKgqKoOt0cPRiIinc0wjIbklIhIdFNSSkREJEJVNiSlLEFOStliwGwCtwFFVbVB3beIiIiIiI+SUhHE4/FQVFREUVERHk9wh2qIiEhkcXuMDnv7nslk8veW0hv4RERERKSjKCkVQUpKSlj05nYWvbmdkpKSUIcjIiIhVNWQkILgJ6UAEmO9twjflCspJSIiIiIdQ2/fizAJyWmhDkFERMLA0Yahe3aLGbMp+PtPsJoBN98cVVJKRERERDqGekqJiIhEoKMNPaUS42I6ZP+JsRq+JyIiIiIdS0kpERGRCHTUWQ9AUmwHJaWsGr4nIiIiIh1LSSkREZEI5HvzXlKH9ZTy3iIc0PA9EYlgO3fuJDc3l4EDBzJ8+HAee+wx6urqANi+fTsTJ04kKyuLESNGsGLFikbbrlq1iuzsbPr378+4cePYtm1bp8VtGAaGEfz5AkVEwo2SUiIiIhGowukbvtcxTXlCQ0+pUkcdNXUuSktLKS0t1UOSiEQMj8fDbbfdxsiRI9myZQsrV67kgw8+YPHixVRUVDB58mTGjh1Lfn4+s2fP5vHHH2fHjh0AbN68mVmzZjF37lzy8/MZM2YMU6ZMoaamJsS1EhGJLkpKiYiIRKCOHr4XG3MsMbVrfxHPvbWD597aQVlZWYccT0Qk2CoqKiguLsbj8fgT6mazGbvdzvr160lJSSE3NxeLxcLQoUMZPXo0y5YtA2DFihWMGjWKAQMGYLVamTRpEqmpqaxZsyaUVRIRiTpKSomIiESgo86OnegcID3ee5twsLKW+KQU4pNSOuxYIiLBlpqayqRJk3jiiSfo27cvV155JWeddRaTJk2ioKCAPn36NFo/IyODXbt2AVBYWHjSchERCQ5LqAMQERGRwPmSUkmxMVR2wP5rqipw1hiAha+KKjrgCCIiHcvj8WCz2XjooYeYMGECe/fu5Y477mDevHk4HA7sdnuj9W02G9XV1QAtlgfCZGpb/CZT27eVxnznUeczeHROgy/cz2lHxaWklIiISASq7ISeUkm2WA7XeSiqdpNiU+dqEYksGzZsYN26daxduxaAzMxM8vLymD17NqNHj6aysnFK3+l0kpCQAIDdbsfpdDYpT01NDTiO7t2TAlrfN9wwLS0Rm80W8PHkxAL9W0jLdE6Dr6udUyWlREREIpB/TqkOTEolWL3fixwuUmyxHXYcEZGOcPDgQf+b9nwsFgtWq5U+ffqwcePGRmWFhYVkZmYC3gRWQUFBk/Irrrgi4DhKSysJ5B0RhuHxbxcXVx/w8aQpk8n7oB/o30JOTOc0+ML9nPriCzZ97CkiIhKBjtYeG77XUeIt3n7axdXuDjuGiEhHGT58OMXFxTz33HO43W7279/PwoULGT16NNnZ2ZSUlLB06VLq6+vZtGkTq1evZvz48QBMmDCB1atXs2nTJurr61m6dCmlpaVkZ2cHHIdhBP7V1u30pXOqcxrZX+F+TjuCekqJiIhEoM6Y6NyXlCqqdmMYBqZwneRARKQZGRkZLFq0iGeffZbnn3+epKQkxowZQ15eHrGxsSxZsoTZs2czb9480tLSmDFjBkOGDAFg6NChPPLII8ycOZPDhw+TkZHB4sWLSUlJCW2lRESijJJSIiIiEciXlDKcHdfFO6HhLqHGZVDnhjjdNYhIhBk2bBjDhg1rtqxv374sX778hNvm5OSQk5PTUaGJiAgaviciIhJxnPVual3eOUdeyf+C2lpnC1u0TYzZhL2ht1RVvadDjiEiIiIiXZeSUiIiIhGmsmE+KROQlNStQ4+VYPUmpRx1SkqJiIiISHApKSUiIhJhqmq9E49bY7xvQulIibHeW4UqJaVEREREJMiUlBIREYkwvp5SVnPHTzzuT0rVd9DEVSIiIiLSZSkpJSIiEmGqGpJSsTEdn5RKsKqnlIiIiIh0DCWlREQkKpSXl3PvvfcyePBgLr30Um6//XaKiooA2L59OxMnTiQrK4sRI0awYsWKRtuuWrWK7Oxs+vfvz7hx49i2bVsoqtBqVZ3aU0oTnYuIiIhIx1BSSkREosKdd95JdXU1GzZs4O233yYmJoaHHnqIiooKJk+ezNixY8nPz2f27Nk8/vjj7NixA4DNmzcza9Ys5s6dS35+PmPGjGHKlCnU1NSEuEYn1pk9pZIahu856gw8hobwiYiIiEjwKCklIiIR7/PPP2f79u3MnTuXbt26kZiYyKxZs5g6dSrr168nJSWF3NxcLBYLQ4cOZfTo0SxbtgyAFStWMGrUKAYMGIDVamXSpEmkpqayZs2aENfqxCqPm+i8o9ktJqxmMIBqzSslIiIiIkFkCXUAIiIi7bVjxw4yMjL4+9//zksvvURNTQ2XX3459913HwUFBfTp06fR+hkZGaxcuRKAwsJCxo8f36R8165dAcXQ3FvwfMuC/YY8R11DTynf8D2T9xgGTb83VxbI+iaTiVMSLHxT6aKqztNhdQol1SlyRGO9wq1O4RKHiIh0DUpKiYhIxKuoqGD37t1cdNFFrFq1CqfTyb333st9991Heno6dru90fo2m43q6moAHA7HSctbq3v3pDaVtUV9w1NjfJwFmy0WW5wVm92Kp96D2dr4e3Nlgaxvt8dyRoqbbypd1GImLS2xQ+oUDlSnyBGN9YrGOomIiLRESSkREYl4sbGxAEyfPp24uDgSExO5++67ueGGGxg3bhxOp7PR+k6nk4SEBADsdnuz5ampqQHFUFpaybenXDKZvA+azZW1R0mFd74rk8eD01mHs7YeZ009HlcdZpe50ffmygJZH2JItXqPW1ZVR1lZFenp6UGvUyh11N8plKKxThCd9Qq3OvniERER6QxKSomISMTLyMjA4/FQX19PXFwcAB6P921x3/3ud/nrX//aaP3CwkIyMzMByMzMpKCgoEn5FVdcEVAMhsEJHyhPVtYWlU7fnFIN42yO2/+3vzdXFuj6PeO9twtVdUajsnB4gA4m1SlyRGO9orFOIiIiLdFE5yIiEvGGDRtG7969efDBB3E4HJSVlfHMM89w9dVXc/3111NSUsLSpUupr69n06ZNrF692j+P1IQJE1i9ejWbNm2ivr6epUuXUlpaSnZ2dohrdWK+t+91xkTnhmGQgLdnVmW9p+MPKCIiIiJdhpJSIiIS8axWK3/+85+JiYlh5MiRjBw5kl69ejFnzhxSU1NZsmQJa9euZfDgwcyYMYMZM2YwZMgQAIYOHcojjzzCzJkzGTRoEK+//jqLFy8mJSUltJU6icrab0103oFqqir4YIe3J1lVnQdDXTlEREREJEiCPnxv8+bNDB48ONi7FRGRKBaMtqNnz54888wzzZb17duX5cuXn3DbnJwccnJy2nX8znSsp5QJXB1/vOT4OHCCywMVDUMHRUSCQc8OIiJdW9B7St11111cffXV/OEPf+DAgQPB3r2IiEQhtR2Bqar1JoY6o6cUgNkE9oahggcq6zrlmCLSNej6LyLStQU9KfXBBx8wbdo0Pv/8c0aOHMnPf/5zXnvtNerqdBMrIiLNU9vRei6PQXW9b6LzzjtugtWbADuopJSIBJGu/yIiXVvQk1JWq5WRI0eycOFC3n33Xa6++mqWLFnC8OHDefTRR9m1a1ewDykiIhFObUfrOWqPjdezdlJPKYCEhgH/B4/qQVFEgkfXfxGRrq3DJjovLS1l9erVvPLKKxQWFjJ48GDi4uKYNGkSzz33XEcdVkREIpjajpb5JzmPAbOpE5NSDT2lNHxPRDqCrv/HGIaB3ikhIl1F0Cc6f/3113n11Vf58MMPOeeccxg3bhzPPfccaWlpAFx55ZXk5eXxi1/8ItiHFhGRCKW2o/W+KSoFwOTx4KxxdtpxEywaviciwafrv4hI1xb0pNSjjz7KqFGjWL58ORdddFGT8rPPPptJkyYF+7AiIhLB1Ha0nqPOAzS8ea8TJVi93w9V1nfqcUUkuun6LyLStQU9KfXBBx+wf/9+evbsCcCnn35KUlIS5557LgC9evXirrvuCvZhRUQkgqntaD3/JOcdNgC/eb6eUmU1LqrrXC2sLSLSOrr+i4h0bUG/pX3zzTcZO3Yse/bsAWDbtm1MnDiRd999N9iHEhGRKKG2o/WqfD2lOjkpFRtjIrbhmDu+PIChCU9EJAh0/W+ed14pXWdFJPoF/ZZ2/vz5LFiwwN/99uabb+Z3v/sdTz31VLAPJSIiUUJtR+s56nw9pTr/YSXe4j3mHzbspKysrNOPLyLRR9d/EZGuLehJqYMHD3L55Zc3WjZ8+HAOHDgQ7EOJiEiUUNvReseSUp1/7ISGpFSt2db5BxeRqKTrv4hI1xb0W9rTTz+d999/v9Gyjz76iNNOOy3YhxIRkSihtqP1HPXe4XuWzp3nHICEhpkoj9a6O//gIhKVdP1vnobviUhXEfSJzidPnkxeXh7XXHMNp59+OgcOHGDDhg088cQTrd7HRx99xNNPP82XX36J3W7nBz/4AdOmTcNms7F9+3Yee+wxCgsLSU1NZcqUKUycONG/7apVq1iwYAHFxcWcc845PPTQQ2RlZQHgdrv57W9/y6uvvkpNTQ1Dhgzh0Ucf5ZRTTgn2aRARkQAEo+3oKqpC2FMqvuGuoarW0/kHF5GopOu/iEjXFvRb2tGjR7N48WKsVis7d+7EZrOxZMkSRo4c2arty8rKuO222/jxj3/M1q1bWbVqFVu2bOH//u//qKioYPLkyYwdO5b8/Hxmz57N448/zo4dOwDYvHkzs2bNYu7cueTn5zNmzBimTJlCTU0NAAsXLmTjxo28/PLLvP/++9hsNmbMmBHsUyAiIgFqb9vRlVSHaKJzODZ8r1I9pUQkSHT9FxHp2oLeUwpg8ODBDB48uE3bpqWl8eGHH5KYmIhhGJSXl1NbW0taWhrr168nJSWF3NxcAIYOHcro0aNZtmwZ/fr1Y8WKFYwaNYoBAwYAMGnSJP72t7+xZs0axo8fz4oVK5g6dSqnnnoqANOnT2f48OHs37+f3r17B6fyIiLSJu1pO7oCwzAoKyuj3OEEQttTqrLOg0fDSkQkSHT9FxHpuoKelDp8+DALFy5kz549eDyNu/f/6U9/atU+EhMTAbjyyis5fPgwAwcOZNy4cTz77LP06dOn0boZGRmsXLkSgMLCQsaPH9+kfNeuXVRWVnLo0KFG26enp5OcnMzu3bsDSkqZQjCPx7ePazKFLo5v88URjHjCtY7BEsxzFe10rlqvrecqnM5tMNqOaFdWVsZzb+1g3xEDsITk7Xv2GDABHgPKql2kd+/0EEQkyuj6LyLStQU9KfXAAw9QUlLC97//faxWa7v2tX79eioqKpg6dSp33XUXPXv2xG63N1rHZrNRXV0NgMPhOGG5w+EAID4+vkm5r6y1undPCrQqQeF2V2O3xzbEkEh6esfH4fF4KC4uBqBHjx6YzSf+aD4Y5yUUdQyFUP0bikQ6V60XyecqmG1HNItPSsFtOgqANQRJRbMJ7BaodsGhqjr6tLyJiMhJ6fovItK1BT0p9dlnn7Fu3TrS0tLavS+bzYbNZmPatGlMnDiRm266icrKykbrOJ1OEhISALDb7Tidziblqamp/mSVb36p5rZvrdLSSkIxaqG0tIqamjr/zzEx8S1s0X5FRUU896/tAPzi6oubnRTeZPI+DAfjvISijp0pmOcq2ulctV5bz5Vvu3AQzLYj2jW8fA9LCIbvASRYTFS7DA5W1ocmABGJKrr+i4h0bUG/pU1KSiI2NrbN23/yySf84Ac/oK6uzr+srq4Oq9VKRkYGBQUFjdYvLCwkMzMTgMzMzBOWJycn07NnTwoLC/1lxcXFlJeXNxkS2BLDCN1XKGJISE4jITmtxbgiuY6d/TcMdQyR8qVz1fHnKly0t+3oKgzD8CelQjGnFEB8Q0eGw5V1J19RRKQVdP1vnsfjaTKcUUQkGgX9lvb222/ngQceYMeOHRw4cKDRV2ucd955OJ1OnnrqKerq6vjmm2944oknmDBhAiNHjqSkpISlS5dSX1/Ppk2bWL16tX8eqQkTJrB69Wo2bdpEfX09S5cupbS0lOzsbADGjRvHwoUL2b9/P1VVVcyZM4dBgwZx5plnBvs0iIhIANrbdnQVbgMMvOP2QpWUSrB4j3+oSj2lRKT9dP0XEenagj58b8aMGQBs2LABAJPJhGEYmEwm/vOf/7S4fUJCAs8//zxz5szhsssuIykpidGjR5OXl0dsbCxLlixh9uzZzJs3j7S0NGbMmMGQIUMA79v4HnnkEWbOnMnhw4fJyMhg8eLFpKSkAJCXl4fL5SI3NxeHw8HgwYN59tlng30KREQkQO1tO7qKevex7m2WEE1Un9AwmdUh9ZQSkSDQ9V9EpGsLelLqzTffbPc+MjIyWLJkSbNlffv2Zfny5SfcNicnh5ycnGbLrFYrU6dOZerUqe2OUUREgicYbUdXUOfxJqWs5tC9PTGh4c7hoJJSIhIEuv6LiHRtQe/8f/rpp3P66adTUVHBzp076dGjBzabjdNPPz3YhxIRkSihtqN16t3e76EaugcQ39BFq7TaRZ1L852ISPvo+i8i0rUF/ba2tLSUH/3oR9xwww3cd9997N+/n6uvvppt27YF+1AiIhIl1Ha0zvE9pUIlLsb75j8DOFRZG7pARCQq6PovItK1Bf22ds6cOfTp04f8/HwsFgvnnnsukydP5n//93+DfSgREYkSajtaxzenlMUcorF7eOd7SYyNAeBARU3I4hCR6KDrv4hI1xb0pNSmTZt44IEHsNvtmBomvLjlllsoLCwM9qFERCRKqO1oHV9SKpQ9pQCS4rwBfFPhDG0gIhLxdP0XEenagn5ba7VacTq9N6mG4b15djgcJCQkBPtQIiISJdR2tE5dwxROoU9K+XpKKSklIu2j67+ISNcW9NvaESNGMG3aNPbs2YPJZKK0tJRHH32UK6+8MtiHEhGRKKG2o3WO9ZQK3fA9gKRY7+2DklIi0l66/ouIdG1BT0r9+te/Jj4+nh/84AccPXqU4cOHU1NTw9SpU4N9KBERiRJqO1onHCY6B0hs6Cml4Xsi0l66/ouIdG2WYO8wISGBefPmUVZWxtdff02vXr045ZRTgn0YERGJImo7Widc5pTqFqeeUiISHB19/S8vL2fOnDm8++67eDweLr30UmbOnMkpp5zC9u3beeyxxygsLCQ1NZUpU6YwceJE/7arVq1iwYIFFBcXc8455/DQQw+RlZUVtNhERKQDklL5+fmNft+7dy979+4F4NJLLw324UREJAqo7WidujAZvufrKVXhdFFV6yIxLui3EyLSRXT09f/OO+8kOTmZDRs2YDabeeCBB3jooYf43//9XyZPnsxdd93FjTfeSH5+Pnl5eZx33nn069ePzZs3M2vWLBYvXky/fv1YtmwZU6ZM4e2338Zut7c7LhER8Qr6XeRNN93UZJnZbObUU0/lzTffDPbhREQkCqjtaJ36MJnoPDbGRLe4GI7WujlQ4aTPKYmhDUhEIlZHXv8///xztm/fzocffkhiovc6NWvWLIqLi1m/fj0pKSnk5uYCMHToUEaPHs2yZcvo168fK1asYNSoUQwYMACASZMm8be//Y01a9Ywfvz4dsUlIiLHBD0ptWvXrka/l5WV8Yc//IHTTz892IcSEZEoobajdcJl+B5ArySrklIi0m4def3fsWMHGRkZ/P3vf+ell16ipqaGyy+/nPvuu4+CggL69OnTaP2MjAxWrlwJQGFhYZPkU0ZGRpN4RUSkfTr8tjYtLY1p06bx4osvdvShREQkSqjtaJ5vonNLiJNShmGQFuf9+euKmtAGIyJRJZjX/4qKCnbv3s2ePXtYtWoVr7zyCocPH+a+++7D4XA0GYZns9morq4GaLE8ECZT4F9t3U5fOqc6p5H9Fe7ntCN0yiQQFRUV1NbWdsahREQkSqjtaMrfU6qj7gpaqbqygqIjTsDKV4crgN4hjUdEokuwrv+xsbEATJ8+nbi4OBITE7n77ru54YYbGDduHE5n45c1OJ1OEhISALDb7c2Wp6amBhxH9+5JAa3vdrv92/mGHUpwBPq3kJbpnAZfVzunQU9KPfDAA41+r6+v5+OPP2bYsGHBPpSIiEQJtR0t8xhG2MwpBdAtPg6qPRyqqgt1KCISwTry+p+RkYHH46G+vp64OG/3To/HeyH97ne/y1//+tdG6xcWFpKZmQlAZmYmBQUFTcqvuOKKgOMoLa3EMFq/vsfj9m/ndAawoZyQyeR90A/0byEnpnMafOF+Tn3xBVuH39bGxcVx0003MWvWrI4+lIiIRAm1HU05XR7/z6EevgeQYPH21jpUVR/iSEQkmgTz+j9s2DB69+7Ngw8+iMPhoKysjGeeeYarr76a66+/npKSEpYuXUp9fT2bNm1i9erV/nmkJkyYwOrVq9m0aRP19fUsXbqU0tJSsrOzA47DMAL/aut2+tI51TmN7K9wP6cdIeg9pR5//PFg71JERKKc2o6WVdd5k1ImDGJMQAfdGLRWgtWblDpcWYdhGJhCPKRQRCJTR17/rVYrf/7zn5k7dy4jR46ktraWESNGMH36dLp168aSJUuYPXs28+bNIy0tjRkzZjBkyBDA+za+Rx55hJkzZ3L48GEyMjJYvHgxKSkpHRaviEhXFPSk1Pz581u13h133BHsQ4uISIRS29Gy6oaxexYzmEymUOekiG+4g6h1G5RW15OeEBvagEQkInX09b9nz54888wzzZb17duX5cuXn3DbnJwccnJy2nRcERFpnaAnpQoKCli/fj3nn38+Z599NocOHeKTTz7hggsu8E8cqE9TRUTkeGo7WlZd751jxBImp8FsMhFvNVFdb3CgwqmklIi0ia7/IiJdW9CTUmazmQceeICf/vSn/mWvvvoqb7/9Ns8++2ywDyciIlFAbUfLfD2lwmGSc59Eq5nqejcHKpz0O61bqMMRkQik67+ISNcW9Fvbd999l9zc3EbLrr/+ej766KNgH0pERKKE2o6W+eaUCodJzn0SY729F76pqAlxJCISqXT9FxHp2oJ+a5uWlkZ+fn6jZe+//z69evUK9qFERCRKqO1omW/4njWMRrEkNHTbOlDhDHEkIhKpdP0XEenagj5877bbbmPy5MmMHDmS0047jf379/P222/z+9//PtiHEhGRKKG2o2XHJjoP9RTnxyTGKiklIu2j67+ISNcW9KTUxIkTOf300/nnP//Jv//9b3r37s3y5cs577zzgn0oERGJEmo7WuYIs4nOARIaum0pKSUibaXrv4hI1xb0pBTAsGHDGDZsGGVlZaSlpXXEIUREJMqo7Tg535xS4TTRuS8pdaiyFpfbgyUmjIITkYih639jhmEA4dMrVkSkIwX97rG+vp5nnnmGAQMGMGLECPbv38/48eMpKioK9qFERCRKqO1o2bHheyEO5HjOSkwYeAxvYkpEJFC6/jfPMIyG5JSISHQL+q3t/Pnz2bRpE7/73e+wWq10796dXr16MXv27GAfSkREooTajpaF40TnJhPEx3gfmjSET0TaQtd/EZGuLejD91avXs1LL71Ez549MZlMxMfH8/jjj5OdnR3sQ4mISJRQ29GysOwpBdjNHhxus5JSItImuv6LiHRtQb+1ra6u9o8F93U5tdlsmM1hdhctIiJhQ21Hy3xzSoXT2/cA7A09pb5RUkpE2kDX/+Zp+J6IdBVBv9r379+f+fPnA2AyeccY/PnPf6Zv377BPpSIiEQJtR0tC8fhe+DtKQUavicibaPrv4hI1xb04XsPPvggkyZNYtWqVTgcDq677jocDgd//OMfg30oERGJEmo7Wha2w/d8c0odVVJKRAKn67+ISNcW9KRUeno6r7/+Ou+88w7ffPMNvXr14nvf+x6JiYnBPpSIiEQJtR0t8yWlrGGWlNJE5yLSHrr+i4h0bUFPSl1//fX885//5Nprrw32rkVEJEqp7Tg5wzCorvMO37OE6fC9sup6quvcxMfGhDgiEYkkuv6LiHRtHfJ5a01NTUfsVkREopjajhOrdXlwN8x3G27D96xmSIz1BqUhfCLSFrr+i4h0XUHvKTV48GAmTpzIFVdcwSmnnNKo7I477gj24UREJAqo7Tg5R0MvKQi/nlIAPRNjqSpzcqDCSUZ6QqjDEZEIouu/iEjXFvSk1Ndff03v3r356quv+Oqrr/zLfW/TEBER+bZgth1ut5tJkyZx+umnM3fuXAC2b9/OY489RmFhIampqUyZMoWJEyf6t1m1ahULFiyguLiYc845h4ceeoisrKz2VyxIfEkpqxnCsTntlWTly4aklIhIIPTsICLStQUtKfU///M/vPDCC/z5z38GwOl0YrPZgrV7ERGJQh3RdsyfP5+tW7dy+umnA1BRUcHkyZO56667uPHGG8nPzycvL4/zzjuPfv36sXnzZmbNmsXixYvp168fy5YtY8qUKbz99tvY7fZ21zEYHHUuAKzm8HxI65UYC8A3SkqJSCvp2UFERCCIc0pt27at0e9XXHFFsHYtIiJRKthtx0cffcT69eu55ppr/MvWr19PSkoKubm5WCwWhg4dyujRo1m2bBkAK1asYNSoUQwYMACr1cqkSZNITU1lzZo17YolmBy1DT2lYsI0KZXkTUqpp5SItJaeHUREBDpoonPwvilIREQkEO1pO0pLS5k+fTpPPfVUox5OBQUF9OnTp9G6GRkZ7Nq1C4DCwsKTlocDX0+pcJvkHLx/s0S8yagDFZqsWETaRs8OIiJdU9DnlPLROHAREQlUW9sOj8fDtGnTuPnmmzn//PMblTkcjibD8Gw2G9XV1a0qb63mQvcta2+TeGxOKZN/f8Zx3zE1XXaysvas/221NZV8+MUBwM435U7AiLh7gGD9ncJJNNYJorNe4VanUMURadcNEREJjg5LSomIiHSWRYsWERsby0033dSkzG63U1lZ2WiZ0+kkISHBX+50OpuUp6amBhRD9+5JbSprDVNsKQC22BhstljMViueeo//uy3Ois3eeNnJytq/fq3/d1uclZSeaVBWQ43LQ0y8jbSE2HbVN1Ta+3cKR9FYJ4jOekVjnURERFoStKSUy+XilVde8f9eX1/f6HeAsWPHButwIiISBYLVdrz66qsUFRUxcOBAAH+S6V//+hf33nsvGzdubLR+YWEhmZmZAGRmZlJQUNCkPND5TUpLK/n26BOTyfug2VxZIA6XOQAwGwZOZx1mlxmP69h3Z209zpr6RstOVtbe9WMt+H931tZTX+vCbjFR4zLY8d9iLjq1W9srGwLB+juFk2isE0RnvcKtTr54OpqeHUREBIKYlEpPT2fevHn+31NTUxv9bjKZ1LCIiEgjwWo71q5d2+j3+++/H4C5c+dy5MgRnnzySZYuXUpubi4ff/wxq1evZsGCBQBMmDCBvLw8rr32WgYMGMCyZcsoLS0lOzs7oLoYBid8oDxZWWtU+SY6Nx/b3/HfMZouO1lZe9ZvoqEsMdZMjcvNN+VOLuwVWUkpn/b+ncJRNNYJorNe0Vink9Gzg4iIQBCTUm+99VawdiUiIl1EZ7QdqampLFmyhNmzZzNv3jzS0tKYMWMGQ4YMAWDo0KE88sgjzJw5k8OHD5ORkcHixYtJSUnp8Nhay1Hrneg8XN++B5BgNVGM3sAnIq2jZwcREQHNKSUiIlFo7ty5jX7v27cvy5cvP+H6OTk55OTkdHRYbfbtic7DUWKstxvXN0pKiYiIiEgrheHLpUVEROR4jjpvTylLGLfavqTU/vKaEEciIiIiIpEijG9vRUREBKDc0dD7yOUKbSAnkRznvaX4qrQ6xJGIiIiISKRQUkpERCTMVdd7gPDuKdWtoadUWXU9R531IY5GRERERCJBGN/eioiICEB1nTcpZQ3jVtsaYyI93jtV5Z4yDeETEWkrwzC61JsYRaRrC+PbWxEREQGorvdOdG4J44nODcOgV4JvCJ8jxNGIiIiISCQIy6TUrl27uPnmmxk0aBCXXXYZ9957L2VlZQBs376diRMnkpWVxYgRI1ixYkWjbVetWkV2djb9+/dn3LhxbNu2zV/mdrt54oknGDZsGFlZWUyZMoWioqJOrZuIiEigfMP3rOGbk6KmqoKjVd5k1K4DZSGORkREREQiQdglpZxOJ7fccgtZWVl88MEHvPbaa5SXl/Pggw9SUVHB5MmTGTt2LPn5+cyePZvHH3+cHTt2ALB582ZmzZrF3Llzyc/PZ8yYMUyZMoWaGu8wgoULF7Jx40Zefvll3n//fWw2GzNmzAhldUVERE6q3u2hzu0dxxHOc0oBpMbHAbC/vDbEkYiIiIhIJAi729sDBw5w/vnnk5eXR2xsLKmpqdx4443k5+ezfv16UlJSyM3NxWKxMHToUEaPHs2yZcsAWLFiBaNGjWLAgAFYrVYmTZpEamoqa9as8ZffeuutnHrqqSQmJjJ9+nTee+899u/fH8oqdwkej4eioiKKiorweDyhDkdEJGI46tz+n8N5TimApIauXPsq6kIciYhIZCuqqueN3aWhDkNEpMNZQh3At51zzjk8//zzjZatW7eOCy+8kIKCAvr06dOoLCMjg5UrVwJQWFjI+PHjm5Tv2rWLyspKDh061Gj79PR0kpOT2b17N7179251jKYQDZ84/rgmU+fE0Zpj+padLJ7S0hIWvbkdgF9cfTGnnHJKm48Xkevl/QABAABJREFUyVpzrsRL56r12nqudG4jg6POBUAMBiaTiXCe+9aXlDpcVUety0NcuHftEhEJUxv3VrL2iwqGnNuLM1PtoQ5HRKTDhF1S6niGYfDss8/y9ttv85e//IU//elP2O2NL8o2m43q6moAHA7HCcsdDu88F/Hx8U3KfWWt1b17UqBVCQq3uxq7PbYhhkTS0zs+jkCOebLz4nZXk96rV4v7CUUdQyFU/4Yikc5V6+lcRSdHbcMk56ZwTkd5xcVAXIyJWrfBV6UOzu+pf5MiIm1R6/Je8yud9YCSUiISvcI2KVVVVcUDDzzAzp07+ctf/sJ5552H3W6nsrKy0XpOp5OEhAQA7HY7TqezSXlqaqo/WeWbX6q57VurtLQyJK9pLS2toqamzv9zTEx8C1t0zjFNJu/D8MnOS2tjD0UdO1NrzpV46Vy1XlvPlW87CW++4XuRkJQymUyk2MwcdrjZXVSlpJSISBt5Ghp0lyf8r/0iIu0Rlkmpffv2ceutt3LaaaexcuVK0tLSAOjTpw8bN25stG5hYSGZmZkAZGZmUlBQ0KT8iiuuIDk5mZ49e1JYWOgfwldcXEx5eXmTIYEtMQxC8pB8/DE7K4ZAjnmy8tbuJxR1DIVorluw6Vy1ns5VdPIP34uApBRAakNS6ouiwHohi4iIl2EYuBumYFVSSkSiXdhN9lBRUcHPfvYzLrnkEl544QV/QgogOzubkpISli5dSn19PZs2bWL16tX+eaQmTJjA6tWr2bRpE/X19SxdupTS0lKys7MBGDduHAsXLmT//v1UVVUxZ84cBg0axJlnnhmSuoqIiLQkkobvAaTaYgDYXVRFaWkphjKlIiIBcXsM//yBSkqJSLQLu55S//jHPzhw4ABvvPEGa9eubVS2bds2lixZwuzZs5k3bx5paWnMmDGDIUOGADB06FAeeeQRZs6cyeHDh8nIyGDx4sWkpKQAkJeXh8vlIjc3F4fDweDBg3n22Wc7uYYiIiKtV9XQUypyklLez7u+KKri6Vc+4Fdjh9O9e/cQRyUiEjlq3ceu90pKiUi0C7uk1M0338zNN998wvK+ffuyfPnyE5bn5OSQk5PTbJnVamXq1KlMnTq13XGKiIh0hkjrKdUtzozVbKLG5cFj6xbqcEREIk6dy+P/2eWOjGu/iEhbhd3wPYksHo+HoqIiDh8+jMfjaXkDEREJiCPCekqZTSbOSo0DoKI+xMGIiESg2uOSUm7dX4tIlFNSStqlpKSE5/61nadezaekpCTU4YiIRJ1IevueT0Z37xtvy+tMIY5ERMTL7XZz0003cf/99/uXbd++nYkTJ5KVlcWIESNYsWJFo21WrVpFdnY2/fv3Z9y4cWzbtq1TYq1zH9dTSsP3RCTKKSkl7ZaQnEZiSlrLK4qISMCqIjAp1Sfdm5Q6oqSUiISJ+fPns3XrVv/vFRUVTJ48mbFjx5Kfn8/s2bN5/PHH2bFjBwCbN29m1qxZzJ07l/z8fMaMGcOUKVOoqanp8FgbDd9TUkpEopySUiIiImHMURtZw/cAzu/h6ynlfYuUiEgoffTRR6xfv55rrrnGv2z9+vWkpKSQm5uLxWJh6NChjB49mmXLlgGwYsUKRo0axYABA7BarUyaNInU1FTWrFnT4fHWak4pEelClJQSEREJY77hezERlJQ6MzkOu8WMyzCxr6I21OGISBdWWlrK9OnTeeqpp7Db7f7lBQUF9OnTp9G6GRkZ7Nq1C4DCwsKTlnek2kbD9zSnlIhEt7B7+56IiIgc459TKsRxBCLGbKJPup3thxzsKq5hYEaoIxKRrsjj8TBt2jRuvvlmzj///EZlDoejUZIKwGazUV1d3aryQJgCHMlcf1xSym0Evr005TuHOpfBo3MafOF+Tjsqrki6xxUREelyqhqG71kjpKeUYRgcOVLGd5Jg+yHYXRz4A5yISDAsWrSI2NhYbrrppiZldrudysrKRsucTicJCQn+cqfT2aQ8NTU14Di6d08KaP24AxXHfrbHkp4e2PZyYoH+LaRlOqfB19XOqZJSIiIiYcyXlIqU4Xs1VRW8+GERh2o8QCK7ijt+UmARkea8+uqrFBUVMXDgQAB/kulf//oX9957Lxs3bmy0fmFhIZmZmQBkZmZSUFDQpPyKK64IOI7S0kqMAC7hJUeOJfMrjtZQUlJ5krWlNUwm74N+oH8LOTGd0+AL93Pqiy/YlJQSEREJY77he5HSUwrAnphMr1gnVMHe8locdS4SYnXLISKda+3atY1+v//++wGYO3cuR44c4cknn2Tp0qXk5uby8ccfs3r1ahYsWADAhAkTyMvL49prr2XAgAEsW7aM0tJSsrOzA47DMAjoAbP2W2/fC8eH00gV6N9CWqZzGnxd7ZzqDlFERCRM1bs9/oeTSHr7HoAtBmxmD06Pmf8cqmLgmSmhDklExC81NZUlS5Ywe/Zs5s2bR1paGjNmzGDIkCEADB06lEceeYSZM2dy+PBhMjIyWLx4MSkpKR0eW53eviciXYiSUiIiImHKUev2/xxpSSmAZIsHZ52Zzw8eVVJKREJu7ty5jX7v27cvy5cvP+H6OTk55OTkdHRYTejteyLSlZhDHYCIiIg0r6rOO5+U3WIO2zexnEyKxZtU+/yg5kMREWmtum8N3xMRiWZKSomIiIQp3yTn8bGR2VwnW70PVp8fqsToSpMjiIi0Q91xQ/aUlBKRaBeZd7kiIiJdQFXD8L0Ea0yII2mbbhYPMSYoddRxuLI21OGIiESE43tKuZWUEpEop6SUiIhImHLURXZPqRgTnJ1mAzSET0SktercGr4nIl1HZN7lioiIdAGR3lMK4Px0OwD5XxVRWlqqYXwiIi1olJTS2/dEJMopKSUiIhKmIn1OKYDzesQD8E5hCc+9tYOysrIQRyQiEt5qXcfPKaW374lIdIvcu1wREZEoV9mQlLIY9URqB6Pze3h7SlXUmbAlJoc4GhGR8Fert++JSBeipJSIiEiYKqmoAuCrw0eorXWGOJrAGYZBvLuKBKsJtwHlTn3iLyLSEs0pJSJdiZJSIiIiYaq63vtgYouLDXEkbVNbXcmfP/ovNqMOgNIad4gjEhEJf3r7noh0JUpKiYiIhKnqOm8SxxrBrbU9MZn0eO9E7SXVSkqJiLREPaVEpCuJ4NtcERGR6OZo6CllMYU4kHZKjfM+VJXWaPieiEhL6o6f6Fxv3xORKKeklIiISJhy+HtKRfZDSWrD6MOjdR4qa9VbSkTkZBr3lFIyX0Sim5JSIiIiYco3p1QkD98DiIuBBIv35y9KakIbjIhImKvV8D0R6UIi/DZXREQkejmiYE4pn7Q47xjEXcXVIY5ERCS8HT/RuZJSIhLtouA2V0REJDpVR8mcUgCpvqSUekqJiJxUnVtzSolI16GklIiISBhye4yoGb4HkGrzJqV2F9dgGHrIEhE5kVqX5pQSka4jCm5zJVAej4eioiKKiorwqKETEQlL1XXHJgS3REFrnRxrwmyCo7VuvqlwhjocEZGwdfzwPbeG74lIlIuC21wJVElJCYve3M6iN7dTUlIS6nBERKQZjjoXAGYTxETB8L0Yk4mUOO9tx67DVSGORkQkPHkMg/rjElGaU0pEop2SUl1UQnIaCclpoQ5DREROoKrW21Mq1hwFGakGafYYAHYVKSklItKc43tJgZJSIhL9lJQSEREJQ1W13p5SlpgQBxJEqTbvbcdu9ZQSEWlWnftbSSlNdC4iUU5JKRERkTBU1TB8L1p7SmmycxGRptRTSkS6GiWlREREwpCjYfieNRomlGqQHGsixgTlNfUcrqwNdTgiImGn9ts9pZSUEpEop6SUiIhImDEMg8NlFQBYo6inVF31UWzUA7Bb80qJiDRR52qchHLpTdkiEuWUlBIREQkzZWVlvLnrGwDMhjvE0QRXSqz3u97AJyLSVK2r8TXfrZ5SIhLllJQSEREJRxYbEF09pQCSLN5P/fUGPhGRpmob5pSyNDylafieiEQ7JaVERETCUH3DG5csUdZSd2tISmn4nohIU76371kaPpDwGODRiyFEJIpF2a2uiIhIdKhv+HTcGmUtdZLFgwkorqqjxFEX6nBERMKKb06p43vJutxKSolI9IqyW10REZHocCwpFV3D9ywm6J0cB8DWwgOUlpZiqBeAiAgAGT0S6B5v5Yxkq3+ZhvCJSDRTUkpERCQM1TfMdWuJrpwUAOd2986X9df8PTz31g7KyspCHJGISHjomRTHqz/Pov+pdv8yvYFPRKKZklIiIiJhqC5Kh+8BZDQkpSo9scQnpYQ2GBGRMGMymTj+8wj1lBKRaBaFt7oiItIV7dq1i5tvvplBgwZx2WWXce+99/p74Gzfvp2JEyeSlZXFiBEjWLFiRaNtV61aRXZ2Nv3792fcuHFs27YtFFVoxDfReVQmpdK8PQDKnO4W1hQR6ZqOT0y5lZQSkSgWhbe6IiLS1TidTm655RaysrL44IMPeO211ygvL+fBBx+koqKCyZMnM3bsWPLz85k9ezaPP/44O3bsAGDz5s3MmjWLuXPnkp+fz5gxY5gyZQo1NTUhrZNvTilLlM0pBd6eUiagut6gxqVhKSIizfFd/tVTSkSimZJSElE8Hg9FRUUUFRXh0fh6EWlw4MABzj//fPLy8oiNjSU1NZUbb7yR/Px81q9fT0pKCrm5uVgsFoYOHcro0aNZtmwZACtWrGDUqFEMGDAAq9XKpEmTSE1NZc2aNSGrj2EY/jmloq2nlGEY1DkqOCPJAkBJtXpLiYg0x2TyZqX09j0RiWZRdqsr0a6kpIRFb25n0ZvbKSkpCXU4IhImzjnnHJ5//nliYmL8y9atW8eFF15IQUEBffr0abR+RkYGu3btAqCwsPCk5aFQ6zLwPYJEW1KqtrqSFz/8EqPOASgpJSJyIuopJSJdgSXUAYj4VDpd3L1mDy6XiyGnxZ1wvYTktE6MSkQijWEYPPvss7z99tv85S9/4U9/+hN2u73ROjabjerqagAcDsdJy1vL1MwoO9+y5spOptrlTdSYMLCYwXADJu9+DJp+D7SsPes3rWTg+4pPSqaHu5ava6GkxoPJFPg5Cqa2/p3CWTTWCaKzXuFWp3CJoyszDAMwjktKaXSAiEQvJaUkbPxjx0EKSp0AnJemf5oiEriqqioeeOABdu7cyV/+8hfOO+887HY7lZWVjdZzOp0kJCQAYLfbcTqdTcpTU1MDOnb37kltKmvOntJyAKxmE/b4WDz1BrY4Kza7FU+9B7O18fdAy9q/fq3/97bu61SPm21lHspq3HRLiSc9PbBz1BEC/TtFgmisE0RnvaKxTtI+ZpMJMNRTSkSimp78JSzUuz38bds3/t8Lj7hCGI2IRKJ9+/Zx6623ctppp7Fy5UrS0ry9Kvv06cPGjRsbrVtYWEhmZiYAmZmZFBQUNCm/4oorAjp+aWklxreeG0wm74Nmc2Un802xN4kWYzJw1tTjcdXhrK33/2z+/+zdeXhU5fn/8fdM9o0sBMIiiBKCVUACSABRFI1UkKUs2hZpsSoW0vpFG1QEqxWDUqtSRNCiiFZ+agFRURRtq4JAWCyCG0hQIRqWLCRkT2bm/P4YZmAgCZlsM5n5vK4rF8x5zjm5nzOTeebc8ywWs8u/7pY1dv/gQBocl2NbkLWaYHMAVTbYvj+PywPCznFVmk9Dnydv5ot1At+sl7fVyRGPeI5hGBjGacP3NKeUiPgwr56poqCggNTUVLZt2+bc1phlva1WKwsWLGDIkCEkJyczffp0jh071mL1kdr9+9tcckuqCA6wt74/FFkordI8IyJSP0VFRfz2t7+lX79+vPDCC86EFEBqaip5eXmsWLGC6upqMjMzWbduHRMmTABg4sSJrFu3jszMTKqrq1mxYgX5+fmkpqa6FYNh1PxTV1ltPyWV9qEaQeZT5+CM853+r7tljdn/7Io37FwmE8SF2N/zvzxS5vY1auqfhjxP3v7ji3Xy1Xp5W53EsxzD9xwjKdVTSkR8mdcmpT777DNuuukmDh065NzW2GW9ly5dyubNm1mzZg2bNm0iNDSUuXPneqR+4mrzdwUAjL84jjbBJiwG7Pyp1MNRiUhr8cYbb5CTk8N7771H//79SU5Odv7ExsayfPly3n//fVJSUpg7dy5z585l0KBBAAwePJgHH3yQhx56iIEDB/Luu++ybNkyYmJiPFaf0pNL7/naJOdnig+z33J9fkTv9yIiZzKfnODLqqSUiPgwrxy+t3btWhYtWsSsWbO46667nNtPX9YbcFnWu0+fPi7LegNMnTqV119/nfXr1zNhwgRWrVpFeno6HTt2BGDOnDkMHTqU7OxsunTp0vIVFaesPPsNSc/4MLYfCuBElYXsE5UejkpEWotbbrmFW265pdby3r1789prr9VaPnbsWMaOHdscoTVIaZW9p1Sgj0843D7UDNj48kgZFquNwAAfz8KJiLhBE52LiD/wyk9/Q4cO5cMPP2TkyJEu2xuzrHdxcTFHjhxxKY+Pjyc6Opp9+/Y1U02kPqqtNn4osPdmuyA2hDYh9pdldlGVJ8MSEfGYMmdPKd/+drxNMIQEmCi32PjqSPG5DxAR8SMmZ1LKt9sCEfFvXtlTql27djVuP9ey3XWVl5bae+KEh4efVe4oqy9PLZV7+u9tzPLZ7pznXPuevq2uc9V1noPHy7DaDCJDAmgXEUj0yTlGfiyqqvH31Td2b+Rtyz57M12r+mvotdK19V5lVafmlPJlJpOJ9hEBZJ+wsDO7kEs7R3s6JBERr+EYvqeJzkXEl3llUqo2jVnW25GscswvVdPx9eWpFUms1jLCwoJPxhDZ4OWz3TnPufa1WssIDbWXx8XVfq66zvNp9gkAftaxDfHxUbRrEwJUklNcRWxcJAFmU73O05poVZv607WqP10r3+FY6CHQx5NSAAnh9qTU1gN5/C6lKyZlS0VEgNOH7ykpJSK+q1UlpRqzrHd0dDQJCQkuQ/xyc3MpLCw8a8jfuXhqyd78/BLKy6uc/w8ICD/HEY0/z7n2zc8voaKiitDQYAoKaj9XXef533d5AHSLCbWXWS0EmKDKarDnQC5dYsPqdZ7WwNuWffZmulb119BrpWW/vVfJyZ5SwX6QlIoxlQEBfHGkhMPH8uiUUHNvaRGRhti7dy8LFizgq6++IigoiMsvv5z77ruPuLg4du/ezSOPPEJWVhaxsbFMnz6dSZMmOY9du3YtS5YsITc3lwsvvJAHHniA5OTkFotdq++JiD9oVR93G7us9/jx41m6dCnZ2dmUlJQwf/58Bg4cSNeuXd2KwxuW6G3J89Rn3/qcq7bzOCY57x4fgWHYuypHnbwT+z7/7GXCveF5aOy193QMreVH16r5r5V4p+Iq/1h9DyAiEEIDwIaJr4+VeTocEfEhFRUV3HbbbSQnJ/Ppp5/yzjvvUFhYyP3339/oVb1bgiY6FxF/0Ko+7jZ2We+0tDSGDRvG5MmTGTZsGJWVlSxcuNBzFRIAsnLtSanE+FPDKNucnFfqhwLdoIiI/ymutCelgn18onOw99hrF2Z/z999xL05HkVE6pKTk8NFF11EWloawcHBxMbGctNNN7Fjxw6XVb0DAwNdVvUGXFb1DgoKYurUqcTGxrJ+/foWi98xnNmqnlIi4sO8fvjemSvjNWZZ76CgINLT00lPT2/SGKXhSiotHCuxD8frHh9BWVElAG2CzYBVSSkR8Usllf7TUwqgXaiZ7BIrn+coKSUiTefCCy/k+eefd9m2YcMGLrnkklpX9V69ejVgnwLEMRrj9HLHqt8tQXNKiYg/8PqklPi2g8ftXaDbRgQTGRKIIwXV5uTwvYMFLddFWkTEW5zqKeXhQFqIo6fUt/nllFRaiAzRxxMRaVqGYbBw4UI++ugjXnnlFV5++eUGr+rtroaujutoAiw2QyvmNpJWdW56uqZNz9uvaXPFpU994lEHT/aEOj/WtdGPDLa/4g+fqDjrGBERX2YYhl/NKQUQHmgiKthEcZXBjkOFXN0j3tMhiYgPKSkpYfbs2Xz11Ve88sor9OzZs1GrervL3UVFysoCAAgKCgCqCQ0LbrUrTnsbLfDS9HRNm56/XVMlpcSjHD2lzo9zTUpFnLwTyy2pospiI9gf1kUXEQHKq23OoRr+0lMKoFNkIPsKqtn8XYGSUiLSZA4dOsTtt99Op06dWL16NXFxcUDjVvV2l7ur45aX27+0tVntE5wXnqggL6+4rkPkHLSqc9PTNW163n5Nm2vlbj/6uCve6JCzp1S4y/aQAAgJMGEAR4orPRCZiIhnnKioBuxziQR4afft5tApyv492ebvCzC88ZOYiLQ6RUVF/Pa3v6Vfv3688MILzoQUNH5Vb3c0dHXc01ff8/Qqv77w09DnQj+6prqmrvE1NfWUEo+qraeUyWQiITKIQ0VVHC6qoOsZw/tERHxVUYUFgOAAk9fOKdAc2oWZCQkwkVdaxb5jJVyU4F9d10Wk6b3xxhvk5OTw3nvv8f7777uU7dq1i+XLl5ORkcGiRYuIi4urdVXvo0ePkpiY6LKqd0swn2wELNZmuhMUEfECSkqJx9gMg0Mnk1Jdz+gpBdD+ZFIqR/NKiYgfcfSUCvGnblJAVdkJIo1qKgll03cFSkqJSKPdcsst3HLLLbWWN2ZV75ZgHzMA1ubqniAi4gU0fE885mhxJZUWG4FmE52iQ88q7xAZBEBOkZJSIuI/TpzWU8rfdAy31/mj/XkejkRExPNM6iklIn5ASalWpKDcwv7j1VT7SMPkWHnvvJhQAs1n33y1j7AnpbQCn4j4E39OSrULthBggv25pfxQ4P6y6yIivuTUnFK+8dlfRKQmSkq1Ist2HGXnkSr+e6iC4kqrp8NptO8L7EP3usWdPXQPIEE9pUTED51KSnk4EA8INkO/TpEA/OfbXA9HIyLiWUpKiYg/UFKqlSirsrLtxxIACips/PXTHA9H1HjfHrPXp0e7iBrLEyKDAcg54f7qezabjWPHjnHs2DFsNlvDgxQRaWGOOaX8sacUwBXd2gDw4T4lpUTEv5mwtwPVVn2WFRHfpaRUK/Hpd/lUWg1CTn5z/vnhUiqqW3dvKUdSKqldZI3ljp5S+aVVbtc1Ly+P5/6zm+f+s5u8vIbPTaLkloi0NMfqe/420bnD4K5tCAowcSCvjL1Hiz0djoiIxziagSolpUTEhykp1Uo4vjHuHhNEWKAJmwHfHC3xcFQNV2218V2+fb6QpPY1J6Wigs1EnBy/crgBvaUiouOIiI5reJA0XXJLRKS+/HlOKcMwsJQVMaSLfeW913f8gKFVp0TETwWcvFMrr1ZSSkR8l5JSrUCVxcbWH44D0LVNAG3D7E/bl4dPeDKsRvk+vwyLzSAyJICObUJq3MdkMtH55Kp8PxaWt2R4LpoiuSUiUl/+PHyvsqyYl7YcoDT/JwDe/7aAnKP6QkBE/JNjIaDWPjpCRKQuSkq1Aj8UlFFpsREZbCYmxEz8yaTUF4db77CGb3NPDd1zLHdbky6xYQBkezApJSLSkk74+fC9sMhoOseEExEIFsPEx98XeTokERGPcCalLOopJSK+S0mpVuBAfikA58eEYDKZaBtmH9L2Rc6JZh3WUFJp4ZXdufxUbGny3/PtMXudahu653BejD0p9WOhVuATEf9QVO6/PaUcTCboFmX/iPLG1/kawicifinAZH/vU08pEfFlSkq1Agfy7HMvnR9jH+YWF2rGbIK80iqOFrs/11J9PfnRAV7dk8/GHyvZ+GMl1iZcjnafc5LzmlfeczjPC4bviYi0JH/vKeVwQRszgWY4VFjJlpND2EVE/EmAyTF8Tz2lRMR3KSnVChzIO9VTCuxdebud/P/eZprsfPdPRaz76igAZhPklFjZkt00wwXLqqzsybHPh9W7Y5s693UM31NSSkT8QUW11TlMw597SgEEmU0kxtpXYX1l548ejkZEpOU5hu+Vq6eUiPgwJaVage/OSEoBdIm2///Q8eZJ1jyz6XsAUrtHc3Fb+03Bm980zTfV/ztcisVm0DU2jPPjwurc1zF8L+dEJRYthysiPi6/rAqwJ6SC1EKTFBdEgAl2Hipk79HWO4+iiEhDOFbf05xSIuLL9JHXy5VVWck5YR+i1zU62Ln9vDb2/x88XtbkvzOvtIrPf7L3ZLr50ngSYwMxm2BvbnmTrPi3/Ud7764rLmxb5yTnAO0igwkJNGO1GRxpxqGKIiLeIL/UPp9UTGjgOd8f/YG5spi2ZvuXL+otJSL+JtCknlIi4vuUlPJy35+c5LxtRDDRoYHO7Z1OJqWao6fUpgP5GMDFHaKIjwgiLNDM+W3sv3vN7sONOrfNMNjxk71OV3SPO+f+ZpOJzs0wr5TNZuPYsWMcO3YMm03fPomIdygotfeUigsLPMee/uPCSPt8hh/uy+UnDeUWET/i6ClVabFh04IPIuKjlJTyco5Jzru3DXfZ3rkZk1IbD+QDMKx7W+e27jH2G6T/fpvXqBVAckqsnKi00iY0kEs7R9frmC4nh/BlN+EKfHl5eTz3n90895/d5OXlNdl5RUQawzF8L0ZJKaeQqiLaBlmxGfCPTVmeDkdEpMU45pQCe2JKRMQXKSnl5X4osCelLjgrKWWf56mgrJrikys1NYWyKivbD9rnjroy8VRSKj7MTEJkEGXVVj7Oym/QuW2GwZ5c+w3XuN4dXRraujjmlTpY0LRDFSOi44iIPndvLRGRllJwcvherJJSLi6Ot38R80FWIYdPNN0XFCIi3sxknEpENeZLYRERb6aklJfLPjlUoWus64Tg4UEBxEc4eks1XbJm28HjVFkNOkWHuvTOMplMDL/QvlLeuydX5XPXD0UWiioNIoPN/HbgefU+LrGdPY5vc0sb9HtFRFoLR08pJaVcxYea6RARgMVm8OK2Q54OR0SkWRmGgWHYP387VmItr1ZPKRHxTUpKebmDJ4fndYk9e5U6x8p1B5twCN8npw3dO3OS3WsujMYEZB487vYqSEUVFj4/Zr/ZurFXW9qEBtX72IvaRwHw7bESjacXEZ+WX6qkVG16tbN/EfP2l0fVW0pE/EZYUAAAFRb1lBIR36SklBez2gznpK5n9pQ6fVtTJaUsNoNPHUmp04buOXSMCua6i9oB8NyWg26de+n2o1RaISbEzJiL3Bsy161tOCGBZkqrrBwprnbrWBGR1sSx+l5sqJJSZ2ofEUhyxwis6i0lIn7CMAxCAu1fEleop5SI+CglpbzY0eJKqqwGQQEmOkSFnlXeNdY+rO1QQdMkpfbkFFFUYSG6jknIpw3pRoAJPv2ugH/t+gmjHj2X/rs/j00HizEBKR2DCQpwb5nzQLOJxPgIAA4U6NtxEfFdBRq+V6fJfe1fjLz9xRF2f5dTrzZIRKQ1Cw2095Qq15xSIuKjlJTyYo65os6LDiOghknBz3f2lGqaOaU+3m/vJXX5hXG1TkLeNTaMm/p1BuDx/x5gypos/nOwnN1HyjlRcXZjWVRezYJ/7wfgZ22DiAsLaFBsFyVEApClpJSI+DAN36udYRh0Dq6kb0IIVgPmvLefgoICT4clItKsQgPtt2sVWn1PRHyUklJe7NBxewKmpvmk4NTwvezj5Y2ea6nKYmP91/YJzIf3aFfnvjOHXcj/DbuQQLOJ4+VWjpXZ+Cyngj+++z1fHzk115RhGDz27ywKyqrpEh1Mr/j6zyN1pqT29qTUdwWVDT6HiIg3K6uyOm86YhuYwPdl5SVFvLTlAJEVxzABRytMbMt2b35DEZHWxDCMU0kp9ZQSER+lpJQXc/SUqmk+KYDO0aEEmE1UWGwcK25csua/+/MoqrDQPjKYyy+se84nk8nEzQPO44Ppg1k48nwGdAimTYiZ3FIL017fzZbv7d9cv/PVUf79bS4BJrh7SMcae3vV10Unk1L788s1XENEfJKjl1RYkJmKkiL0Vne2sMho4qMj6R5t//jy9y05FJZrrkER8V0hzqSUekqJiG9SUsqLZRfWvvIeQGCAmc7R9rmmDjVysvM1u3MA+EWfjrUO3TtTVGggPdqG0SM2iDEXtaF/pwgqLTbuXvslt776OQ9v+BaA24ecT1J8zXWorx7tIogIDqC4ykZ+hRplEfE9jvmkYkIDePa9bVSUa7hybS6OMdMm2ExBuYWMD77Vyqwi4rNCg+y3a5pTSkR8lZJSXsyRaOoaU3tCx9GLqjFJqfe+OcrnP50gwGxibO8ODTpHcICJP199Htf/rD1WA/bknABg4qUd+e3Arg2OzSEowMzlF9h7cP1YrEZZRHzP6fNJhUZEeTga7xZgNjG4cwgBJvg4K5/FG7/3dEgiIk3OMAxCTi4QpDmlRMRXaSZVL1VRbeWnQvu35Be0Da91v67Oyc4blpTKOVHFgn8fBOB3KV1oFxnSoPMABAWYeHjkRfxmYBd2HCrk4oTIWlfxa4hhiW35YF8uPxZb6Ns+uMnOKyLiDXJLTpvkXCPSzinMWkKPwGL2Vsfwz50/YrZW8atL2xEXF4fJ1PDh4iIi3iRMPaVExMepp5SXOni8HAOIDg0kLrz2CcLPj7MnrA41YAW+E5U27vvwEKVVVnp3bMPvBp3f0HBdJMZH8Kt+nZs0IQUw5II4As1QXGVwolLfFomIb/mxyP5FRIdIJd3r6/zoIH7Xvz0AL+06xv+98QX5+fkYhuH8V0SktTIMg+AAzSklIr5NSSkv9X2+Pcl0QdvwOr/xPb+Bw/cqLQYfHaogv8zCBXHh/HXMz+o9l5SnRIYEcmmHCACyCtWNQER8y48n5xHs1EZJKXfc2Lsdtw5IAGDfCTMvfnaM/Px8nnrrUwoKCjwcnYhIwxmGwb7cEgAqLeopJSK+SUkpL/V9filQ99A9ODV8L6eogqp6jjW3GQZbcyopsxh0igriuZv6EN+IYXstacxFsQBkHbdQVGHxcDQiIk3HkZTqGKWkVH0ZhsHx4wVc29lMvwR7O/avL/N44+t8wiLbeDg6EZHGCzSfGr5nGIZ6gIqIz1FSykt9d7Kn1IVtI+rcLz4imIjgAGwGHKznEL6NPxRzuNRKgAlmX9mZ2PDWcwPUv1MEsaFmrAa8vfe4p8MREWkSVpvBTyeH73VSUqreKsuKeWnLAVZs+przw230TbBfu+d3HCXH/VHtIiJeJ8BkT0Jp+J6I+ColpbzU6cP36mIymejRzp642p9bes7z2gyDf32RB8DFbYO4MC60kZG2LJPJxCVt7XNsrf26gKPFlR6OSESk8XJLKqm2GgSaTbSLqH0eQTlbWGS0s1fU+cHldAmtxgD+V2DmSHGVZ4MTEWmkk4vvaaJzEfFZSkp5oSqLzTmM48JzJKUAerSLBODbY+dOSn2Slc/BoiqCzJAU1zpvfM6LCqBdmJlKq8HCj7/zdDgiIo3248nVVjtFhxLg5fP7eTOTCfp1CCM22MBimMj4OLveQ9tFRLxRwMm5ZSv0XiYiPkpJKS906Hg5VgMiQwKIjzj3MI4kZ0+pknPu++pnP9qPiQ0iOKB13viYTCb6dwjGbIJ/f5vL5u81ka2ItG7ZJ7+IOC+mdfVe9UZmk4nL4m0EmQz251ewaKO+vBCR1svxcb1CPaVExEcpKeWF9h4rBqB724g6V95z6NH+ZE+p3NI6Jz88WFDGrp9OYDZBYmxg0wTrIbGhAc5JzzM++JYTFVqNT0RaL0dPqS4xYR6OxDeEB0LvKPvw7td35fCfb3M9HJGISMOYOTmnlHpKiYiPUlLKC+3JOQFA7071Wzmoe9twzCYoLK8mr7T2+TPe+eooAP06RRAe1Pqf+il929E1Nozckioeem8fFptWIxGR1skxZLuzklJNpl2wlUm92gIwb8O3zmssItKaBJ78yK45pUTEV7X+zIQP+iLH3lOqTz2TUqFBAZwfa5976ttaJju32gze/dqelLque3QTROl5oYFm/nJ9T4IDTGz6roBHNuyjrEoNtoi0Po7VU8+L1vC9pvTbfglc2rkNpVVWZq7ZQ2GZJj4Xkdbl1PA99ZQSEd+kpJSXKam0cCDPnliqb08pwLkC37fHap5XKvPgcXJLqogODWTgeVGND9RL9OrYhnmjfoYJePfrY/zihe3ct+5rXvjsGN8WVFNcpQZcRLxbcYWF7/LsSakOwdUcP15AHSOxpZ4Mw6C46DjTLgklNMDgYGElaa9/RnmVxdOhiYjUm1mr74mIj1NSyst8efgEBvYVmOozybmDo1fV9oPHayxf9+URAK6/OIGgVjrBeW2G94jnqfG96BwdSkFZNf/5No83vi7gs6NVvHOgnAf+k+1M9LnDMAy+P17BvoJqvius5rCWFheRZrDH8b4fFcy/Mr9hxaavqays8HRYrV5lWTEvbTnA2zv3cXmHIIJMBt8WVJO+dg8Wq76wEJHWIUBzSomIj2vds137IHeH7jkMuSAOPjrArp9OUFJpITLk1FNbWFbNJ1n5AIy+JAEoa7J4vcXlF8Rx2dQBfPZjId/llXEwt5AtPxRyrMzG/3JK+fXLn/GLPh35ZXJnurUNxzAMqqwGZdX21ZmsoRXEhgURGhRARbWVT78r4OUd2Xxz9FTPs51vfcdtg61MTelK4BlLtttsNvLy8gCIj4/HbFa+V0Tq5/MfiwDolRBOeEgIABXlSko1hbDIaAxrBaYAE4Pa29h81Mz2H0uY/c43PDLqZ4QE6r1aRLxbgMnAbLJPxfFTUQWd2tjbifoshiQi0hooKeVlMk/2dHI3KdUlNowuMaFkF1aw41AhV/eId5a9+/VRLDaDi9pHktQ+kmPHfC8pBRAcaGZwtzgGd4vj2LFgQo1Kiqts5FcFsDW7hDW7D7Nm92HCgwKw2mxUWu3fPL33/Q/AD4B9nioDqDz5bVSQ2UR8mJkqm0F+uY3nthzkx8Jy/vzznphP+zCQl5fHc//ZDcAd11xK+/btXWJzJK1MJoiLi2juSyEircjun+xJqUsSwjlaWOzhaHxX2xDo26aSL0rC+Dgrnxmr9jBv5EV00jxeIuLFzBj06RDJ54dL+Hh/Hr/u39nTIYmINCl9RehFfiwsZ0/OCcwmuDqxrdvHD7kgDoAt3xc4t1VabKz87EcAxl/asWkCbUWigs3Mveo8nr2xD0MvjCPAbKKs2upMSAUHQNuwQOeQxgqLjUqLjfaRwfwupQsvTejOVV1DST0/lLuHdCTAZJ+76smPDmCcMelLRHQcEdFxNcbhSFo9++/d5OZqaXIRsauy2PjqiD0R1Ssh3MPR+L74IAv3DY4lPMjMnpwT/HLFTpZ8+j25xZXk5eWd9b4uIuJphmFw+fn2+WD/s1/vUyLie9RTyou89/UxAAZ2jSU+MsTt4wdfEMfru3LYeCCfu6uthAUFsO7LI+SWVNE+MphRFyc0dcitRv8uMfTvEkNJpYXC8mry8/PZ8M0RAs0mbh7QhXbt2lFaZaWwvBqL1eD8uDBMJhPHjtmfE5PJxDXdo4mNiebP6/fy+q4cIkICmX55t3rHUFvCqqE0ZFCk9duRXUiV1SAmNIAwS4kmOG9mlWXF7Pj2R/qHV/NlWSTHq+HFbdms2JZNTJCVYYntSO7Wju5tI0iICiE6zP4xqaCggLi4OA2XERGPyC4oxQR8ebiYoycq6BijLzFExHcoKeUlbIbBe98cBeD6i9ufY++aXdYlhk5tQsg5UcnyzEOM69OBZVsPAvDbgV0I1twZRIYEEhkSSHBVsMu8UCaTyVlWl5//rD0llRYW/CeL5ZmHqKi28n/DLmyy+E5PNEHdyaZzDRkUEe/32v9+AiAuqJqXPv2G8Gj9HTe3sMhoQq0VDIsNIc8SSG6ZlX0F1RyvDuDNbwp485tTvY2DzCbiwsxUVVaQ3DWe3ufFcX4kJLYNpUO7eJcklWEYFBTYj1UCS0SaUoDNwiUJEXx5tJRZb3/DI6N6ckF8y66m7eihpfc2EWlqfpeUys/P54EHHmD79u0EBAQwZswY7r33XgIDPXcpbDYbL366n+zCCsKDA1zmg3JHcKCZu69OJP2tr/jnzh9564sjHC+vJjE+gjG9OjRx1P5rYt9OVFpsLPzkO/7fZz/xzZFibu4di2EYjW6oHYmmiOg4SosKzplsaureVyL+rKXbh+/yS8n84TgmoFeHKMyV6ibVkkwmE/HmUiIDKukQU0WxKYKE2Gi+K6zmh8IqKm0mqm0GR0utQBD//a6I/35nn//LjEH32GD6d2vHxR2iaGOuIsxayrtfHsZsht8P70Pbtu4PwxcR7+MN9w6GYdAhIoCDIQF8m1fG71d9yQu/6st5MWEtFoOISHPxu6TUzJkzSUhIYNOmTeTl5TF9+nRWrFjBbbfd5rGY/pf1E8t2HgFM/LZvPGFBAQ0+15Xd40jt2Y4P9+VyvLyajm1CWDShF6GNOKecbfKA84iPCGbeB9+y66cT7PrpBFHBJtqHB9D220L6W0OJjwgm5uSKfjbDwGozsAEV1VYi6ng6IqLjiIzRzYxIS2vp9sExZHtAxxAigsyUVzbLr5FzcPScCi0+DiXVdKOSi89vj8VSQaURQllVJUVl1VzcpR0HjlfxRW4VlVbYf7ya/cdzXM4VYAogKtjMiY0/0vf8Ci5qH0H7wGpCg8zqPSXSSnnDvYNhGMSFmHl61PnMfPcHCsqqmbFqD0+Nu5ju7Vq2x5SISFPzq6TUwYMH2b59Oxs3biQsLIwuXbowY8YMHn/8cY8kpUqrLKz/+hhPf3IQq2GifbiZkUkxjTqnyWTikVEX8at+nTlSXEly5zYNmp9Kzm3Ez9pzaec2PLv5Bz7Ym0txlUFxlYXF247AtiPO/cwmsJ3WAWL1vl1EBAfQPjKEdpHBtI8KoX1kMG1CgygvKyHreDVhVSVUlVvY9MMJ4orsKwJWWWxUWqxUWmxUVNsoKCpmb341IYHwv5xSegaU0bFNiDMBqTmnROrPE+1Dj2gzbUMMgsuOURHasB6y0rTCIqOxWSoACDBBRKCJcDMEFB+nKM9GW2sl13dpR0llJUeLq+nULo6DJywcKqqmzGLDapgorLS59KoCg/BA6Nkukk5tgmkTEkC7mCjCgwOoKi8jwARms4m46DaEBwdgVJYRGWzmvIR4okKDXIaa14djGKHJBG3bRjbxFRLxL95y72AYBtXVVbzx+Y8M7hjC5/kWDp+o5JZXd/O7lK6MvDiBdpHBSnyLSKvkV0mp/fv3ExMTQ0LCqQm/u3fvTk5ODidOnKBNmzbN8nt/Kipn56FCiiutlFRaOFZcyQ8FZew7VkLVyVXg2oebGdI5FHMjGpPTkxCXdIind6fmqY+c0qFNKA9dfxFTe0fz908PUVBhIyQ4iJ+KLRSUVWOxGS4JKYfSKivfF5TxfUFZLWe2z0uSmZMD5NSyzymZOdlANgCxYUF0aBNCqNnG4ePFBJigV+c4oiMjMJnAbDKd/LHfCJkwqCgvByAyIhyz2YwJ+34mE87/g0FZWSkmTERGRmAymew/2PcBg9LSUgCiIiNPnhs4bR/7+ewnPf2x42Vv/zUGxcUlmE3QuXN7SksqnWU1ne/czr1Tfc5zrl3qdw73/74Nzn4BnTkZtskEQ0OCG3B2cfBE+9C/cyQjEttQVmxr8nNL0wuLjMawVtjnIAyCautxTCU2zjcq+VkXe8+qclsIVeZA2pgt7DxazfFKg0rDTJkFdh0uZdfh0pNny6vhN/x0xuMs++8NMhMZbMZsMmEY9jkoDezvAwb25JnZZCLADAEmExg2TlRUYwLOa/sDIYGBBGIlNNBMgNmEmZPvpyfbgdCQEKqqqjCbICo8jKhQ+/yKUSGBRAQHnGwL7Ps63ucMw36TXFxSgmEYREWd6qlhGGA7+Z/T4zQMg9LSEkxAVGQUppNthGHY20mbYWAzDE4U2yf8D4+IOHnsqbYlIiKCsrJSoiJDMYzAk/U++WPCXr+T205/Tz79PdPl7dNl+6kHte7Pme+/dR/juEZRkZH2i3fGm/fpj6JySiguLndeL047R03qjBH7SUpKSriwQxz9usQoWdFAnrp3qI0JE0EmG5e2NWOxmskts/HMpz/wzKc/EB4UQFRIAOHBAYQHBRAWHEBE8MnHwYGEBZoJCQogJNBMgMmE2Wz/u7b/HzDAevLv0Wqz9/Cvthr8VFTOoePl5JVW0bFNCBfEhdM5JoygADNBASYCTCYCA05+NnPzdVbX7nV9ZnKUOP5uHfvWeT4TREUVO//OTm0/9++p7XzuHlNXgLWVNCSGuo6q+5q7d4wJiM4rp+hE+dlvRHWcsMmvaxPyxEQKrq9HaJNfzokT5Wd93m9KIYFmLu3UhsAA7+i04FdJqdLSUsLCXMdeOx6XlZXVu2Exm8++KazLPW9/TU7R2eMyggMDOD82iKGdgiguOo65uprCwuMEBNT8Z1dYeBxr+Qnn/8/cLz+/gP+3+WsAfn35xbRtW/N8Q+c6jzv7OsqrbcEUFgZjruUb3fr+zvr+Pm85j0NV2Qk6BZXRKQjG9o6jbds4DMOgrNqgymJwoug4739zBLMJfjX4QoqqA8kvtVBQbqGgzEJ+uYWyahtlFRVkF1ZgDgjCYrHQNjwYAux/psGBJoJMJkICzYQEmrFWVZKVX0aVzURIUCDHq6C82ka1zSC7sOJkZCFgwObsUqC0xthdFZ17F8CRNKtdfj3Pcy7Hmug8vu/8bdn88+a+br036R7llKZoH2pqGxzXuKaygAAT1ooSjKpyTIFWDEuV81+ztQqqS8+5raFljdnfsFmxWW0YVnOL/+7mqjeWKrBUYFS5d66wkEBsFhtUl2K2VBEVaMVWXUXuTz8w+PwkbJYqqk3BlFZZOZpfQFBMJ6psVqotBhabgTkoBJvNRnV1NQQEYjXAagrEapiwGKf+QMstUNtH5WpOLzv5rzkIgIPHK4FzjQstOe3/xefYtzaH3dz/yLl3kSZylGW/7ENS+/r3mlPbcIqn7h3MZvvnPWuQ2WXOUsNsTxKHAsPOCyan1OBgsY2ik3MSllbbKK224XhnaGrFuWV8m1vbF6oi0prcfFlnbh98vlvHNFf74FdJqfDwcMpP9ghxcDyOiIio93ni4twbu/3B3Ve5tX/tLuTyy89VPqAJzuPu72yK87T072vpuGHE1fU5j4h4QlO0D3W1DTWVtW0bxYIe7n0YEBGRluOpe4e2baN46KaEc+8oIuIDvKO/Vgvp0aMHhYWFziFuAAcOHKBDhw4uXc9FRMS/qH0QEZEzqW0QEWl+fpWU6tatG/3792f+/PmUlJSQnZ3NkiVLmDhxoqdDExERD1L7ICIiZ1LbICLS/ExGbTMo+qi8vDwefvhhtm3bhtlsZty4caSnpxMQEODp0ERExIPUPoiIyJnUNoiINC+/S0qJiIiIiIiIiIjn+dXwPRERERERERER8Q5KSomIiIiIiIiISItTUkpERERERERERFqcklIiIiIiIiIiItLilJSSRtm7dy+33HILAwcO5PLLL+eee+6hoKDA02F5LavVypQpU7jvvvs8HYpXKyws5J577iElJYXLLruMGTNmcOzYMU+H5ZW++uorJk+ezIABAxg6dCiPPPIIVVVVng7L7+Xn5zNjxgwGDBhASkoKGRkZWCwWT4d1lrrewx988EF69epFcnKy8+f11193Hrt27VpSU1Pp27cv48ePZ9euXc4yq9XKggULGDJkCMnJyUyfPr3F/obXr1/PxRdf7BL3rFmzANi9ezeTJk0iOTmZ4cOHs2rVKpdjvbVOb7/9tkt9kpOT6dWrF7169QJa33NVUFBAamoq27Ztc25rzuemJf4ea6rThg0bGDt2LP369WP48OEsXrwYm83mLL/++uu59NJLXZ63AwcOeE2dpHm58xx+8sknjB49mr59+3L99dfz0UcftXC0rYM71/S2226jd+/eLn9/GzdubOGIW4+a3uPOpNepe+pzTf3mdWqINFB5eblx+eWXG3//+9+NyspKo6CgwLj99tuNO+64w9Ohea2FCxcaF110kXHvvfd6OhSvdvPNNxtpaWlGUVGRUVxcbPzhD38wpk2b5umwvI7VajUuv/xy46WXXjKsVqtx+PBhY8SIEcbixYs9HZrfu/nmm40//elPRllZmXHo0CFj1KhRxrJlyzwdlotzvYf/4he/MN54440aj83MzDSSk5ONnTt3GlVVVcaLL75opKSkGGVlZYZhGMbTTz9tjB492sjJyTGKi4uNmTNnGrfffnuL1Ouxxx4z7rvvvrO2FxYWGgMHDjReeeUVo7q62tiyZYuRnJxs7N692+vrdKYjR44Yl19+ufHmm28ahtG6nqudO3ca1157rZGUlGRkZmYahtH8z01z/z3WVKcvvvjC6NOnj/Hf//7XsFqtRlZWlnH11VcbL7zwgmEYhlFcXGz07NnT+PHHH2s8p6frJM2vvs/h999/b/Tu3dv48MMPjerqauPdd981+vTpYxw5csQDUXs3d/4uUlJSjG3btrVwhK1TTe9xZ9Lr1D31uaaG4T+vUyWlpMEOHDhg3HrrrYbFYnFu+/e//23069fPg1F5ry1bthgjR4407rzzTiWl6vDFF18YvXv3NoqLi53bjh8/bnz77bcejMo7FRQUGElJScaLL75oWCwW4/Dhw8b111/vvOkRz/jhhx+MpKQklw9i7777rnHVVVd5MKqz1fUeXllZaVxyySW1/t396U9/MubOneuy7ec//7mxevVqwzAM48orrzTefvttZ1lubq7Rs2dP49ChQ81QE1eTJ082XnnllbO2/+tf/zKuu+46l21//vOfjXvuuccwDO+u0+lsNpsxZcoUY86cOYZhGK3quXrjjTeMq666ynj33XddPog353PT3H+PtdXp/fffN+bPn++y7/z5843f//73hmEYxtatW42UlJRaz+vJOknzc+c5fPLJJ41bbrnFZdutt95q/P3vf2/2OFsTd67poUOHjIsuusjls6bUrLb3uDPpdVp/9b2m/vQ61fA9abALL7yQ559/noCAAOe2DRs2cMkll3gwKu+Un5/PnDlzeOKJJwgLC/N0OF5tz549JCYm8q9//YvU1FSGDh3KggULaNeunadD8zqxsbFMnTqVBQsW0Lt3b4YNG0a3bt2YOnWqp0Pza/v37ycmJoaEhATntu7du5OTk8OJEyc8GJmrut7D9+7di8ViYdGiRQwZMoQRI0bwj3/8wzn0KCsri6SkJJfzJSYmsnfvXoqLizly5IhLeXx8PNHR0ezbt69Z62Sz2fjqq6/4+OOPufrqq7nyyit54IEHKCoqYv/+/bXG7M11OtNbb71FVlaWcxh4a3quhg4dyocffsjIkSNdtjfnc9Pcf4+11WnEiBHMnj3b+biiooKPP/7Y+Rnpiy++ICwsjJtvvpmUlBTGjx/vHOri6TpJ83PnOazr9S+nuHNNv/jiCyIiIrjrrrsYNGgQN9xwA6tXr27pkFuF2t7jzqTXaf3V95r60+tUSSlpEoZh8NRTT/HRRx8xZ84cT4fjVWw2G7NmzeKWW27hoosu8nQ4Xq+oqIh9+/bxww8/sHbtWt58802OHj3Kvffe6+nQvI7NZiM0NJQHHniAzz//nHfeeYcDBw6waNEiT4fm10pLS89KPjsel5WVeSKkczrzPby4uJiBAwcyZcoUPvnkEx5//HH++c9/snz5cqDmOoaGhlJWVkZpaSkA4eHhZ5U7yppLQUEBF198MSNGjGD9+vW89tpr/PDDD8yaNavOmMF763Q6m83G0qVL+f3vf09kZCRAq3qu2rVrR2Bg4Fnbm/O5ae6/x9rqdLqSkhLS0tIIDQ11fmlgMpno3bs3jzzyCJs2bWLq1Kn88Y9/5PPPP/d4naT5ufMcnuvvQ+zcuaZVVVX07duXu+66i02bNnHfffeRkZHBe++912Lxthb1eY8DvU7dUd9r6k+vUyWlpNFKSkq48847WbduHa+88go9e/b0dEhe5bnnniM4OJgpU6Z4OpRWITg4GIA5c+YQGRlJfHw8M2fO5JNPPmnRm7/W4MMPP2TDhg38+te/Jjg4mB49epCWlsarr77q6dD8Wnh4OOXl5S7bHI8jIiI8EVKdanoPv/zyy3n55ZcZOHAgQUFB9OnTh9/+9resX78esH/Qr6iocDlPRUUFERERzg+lZ14DR3lzio+PZ+XKlUycOJGwsDA6derErFmz2LhxI4Zh1BozeG+dTrdt2zaOHTvGxIkTndta63N1urpiPFf5uerg6b/H7777jl/+8pdYLBZefvllZzLxtttuY9GiRXTr1o3g4GDGjBnDkCFD2LBhg9fXSRrPnefwXH8fYufONR03bhzPP/88F198MUFBQQwdOpRx48b55M1+S9HrtOn50+tUSSlplEOHDjFhwgRKSkpYvXq1ElI1eOutt9i+fTsDBgxgwIABvPPOO7zzzjsMGDDA06F5pcTERGw2G9XV1c5tjmEohmF4KiyvdPjw4bNW2gsMDCQoKMhDEQlAjx49KCwsJC8vz7ntwIEDdOjQgaioKA9Gdrba3sP//e9/89prr7nsW1VVRWhoKGCv4/79+13Ks7Ky6NGjB9HR0SQkJJCVleUsy83NpbCw8Kyu/U1t7969/O1vf3N5r6iqqsJsNtOnT59aYwbvrdPpNmzYQGpqqksPmtb6XJ0uKSmp2Z4bT/49fvLJJ0yaNIkrrriCF154gejoaGfZCy+8wNatW132r6qqIiQkxKvrJE3DnefwXH8fYufONV29evVZN/aOvz9pGL1Om54/vU6VlJIGKyoq4re//S39+vXjhRdeIC4uztMheaX333+f//3vf+zcuZOdO3dyww03cMMNN7Bz505Ph+aVhgwZQpcuXbj//vspLS2loKCAp556imuvvdb5DbPYDR06lNzcXJ599lmsVivZ2dksXbqU0aNHezo0v9atWzf69+/P/PnzKSkpITs7myVLlrj0bvEGdb2HG4bBo48+ytatWzEMg127dvHyyy9z0003ATBx4kTWrVtHZmYm1dXVrFixgvz8fFJTUwEYP348S5cuJTs7m5KSEubPn8/AgQPp2rVrs9YpJiaGlStX8vzzz2OxWMjJyeHxxx/nF7/4BSNGjCAvL48VK1ZQXV1NZmYm69atY8KECV5dp9N99tlnXHbZZS7bWutzdbrU1NRme2489ff4+eefk5aWxuzZs7n33nvPGqpx+PBh/vKXv5CdnY3FYmH16tXs2rWLX/ziF15bJ2k67jyHY8aMYfv27axfvx6LxcL69evZvn07Y8eO9UDk3suda1pSUsK8efP4+uuvsdlsfPzxx7zzzjvO901xn16nTc+vXqcem2JdWr3ly5cbSUlJxqWXXmr07dvX5Udqd++992r1vXM4cuSIMXPmTOPyyy83BgwYYNxzzz1GUVGRp8PySps3bzYmTZpk9O/f37jqqquMJ5980qisrPR0WH4vNzfX+OMf/2gMHDjQGDRokPHYY4+5rHLnDc71Hv7qq68a1113nXHppZca11xzzVkr2r355pvGiBEjjL59+xoTJ040Pv/8c2dZVVWV8fjjjxtXXHGF0a9fP2P69OlGXl5ei9Rr27Ztxk033WQkJycbgwYNMubNm2dUVFQYhmEYe/bscZZdc801xpo1a1pFnRz69u1rfPzxx2dtb43P1ZkrDjXnc9NSf4+n1+mOO+4wevbsedbf1q233moYhn3VxIyMDGPo0KHGpZdeakyYMMHlenhLnaT51PUc9u3b13jrrbec+27cuNEYM2aM0bdvX2PUqFE1vg9I/a+pzWYznnnmGePqq682+vTpY4waNcp47733PBl6q3Dm+7Zep41X1zX1p9epyTA0HkZERERERERERFqWhu+JiIiIiIiIiEiLU1JKRERERERERERanJJSIiIiIiIiIiLS4pSUEhERERERERGRFqeklIiIiIiIiIiItDglpUREREREREREpMUpKSUiIiIiIiIiIi1OSSkRERERERERER9TUFBAamoq27Ztq/cxGzZs4IYbbqBv376kpqayevXqZowQApv17CIiIiIiIiIi0qI+++wz7rvvPg4dOlTvYzIzM7nvvvtYuHAhV155Jdu2beP2228nKSmJPn36NEuc6iklIiIiIiIiIuIj1q5dS3p6OnfddddZZVu2bGHixIkMGDCAUaNG8fbbbzvLVqxYwW9+8xuGDRuGyWRi0KBBrFmzhq5duzZbrEpKiYiIiIiIiIj4iKFDh/Lhhx8ycuRIl+179+5l+vTpTJs2jW3btjFv3jzmz5/Ppk2bANizZw8xMTFMmzaNlJQUxo4dy6FDh4iJiWm2WJWUEhERERERERHxEe3atSMw8OzZml577TWuueYarrvuOgICAujXrx833ngjK1euBKCoqIgXXniB6dOns3nzZtLS0rjrrrvYvXt3s8WqOaVERERERERERHzcTz/9RGZmJgMGDHBus1qtzuF5wcHBTJgwgeTkZACuu+46Bg8ezIYNG7j00kubJSYlpUREREREREREfFyHDh34xS9+wcMPP+zcduzYMQzDAKB79+5UVVW5HGO1Wp3lzUHD90REREREREREfNzEiRN55513+PTTT7HZbPzwww/cfPPNLF++HIBf/epXvPrqq2zZsgWbzcaGDRvYtm0bN9xwQ7PFpJ5SIiIiIiIiIiI+7tJLL+XJJ5/kySef5P/+7/8ICwvjhhtu4O677wZgwoQJmM1mHn30UX788Uc6d+7MU089xSWXXNJsMZmM5uyHJSIiIiIiIiIiUgMN3xMRERERERERkRanpJSIiIiIiIiIiLQ4JaVERERERERERKTFKSklIiIiIiIiIiItTkkpERERERERERFpcUpKiYiIiIiIiIhIi1NSSkREREREREREWpySUiIiIiIiIiIi0uKUlBIRERERERERkRanpJSIiIiIiIiIiLQ4JaVERERERERERKTFKSklIiIiIiIiIiItTkkpERERERERERFpcUpKiYiIiIiIiIhIi1NSSkREREREREREWpySUiIiIiIiIiIi0uKUlBIRERFpQYZheDqERvOFOoiI+Au9Z4s3U1JKpIkcOXKEm2++md69ezN48GB69uzJtm3bPB2WiIh4kaysLH71q1812fm2bdvGiBEj6NWrF7feemuTnbc2J06c4N5772Xnzp3N/rtEROTc7rvvPoYPH+58PGXKFKZMmeJ8vGrVKhYsWOB8/MYbb9CzZ09+/PHHFo1TpDaBng5AxFe89NJL7Nq1i8cff5yDBw+ycOFCT4ckIiJe5r333mPXrl1Ndr4FCxZgs9n4xz/+Qdu2bZvsvLX55ptvePPNNxk/fnyz/y4REXHfgw8+6PJ46dKlDBw40Pn4qquu4vXXX6d9+/YtHZpIjZSUEmkihYWFtG/fnpEjR6qHlIiItIjCwkIuu+wyhgwZ4ulQRETECyQmJtZZHhcXR1xcXAtFI3JuGr4n0gSGDx/OG2+8QU5ODj179mTx4sVn7fPFF19w6623kpKSQr9+/fj973/P/v37XfY5duwYs2fPZtiwYfTp04eJEyfyn//8x2Ufx/knTJhA//79WbJkCTabjb///e8MHz6cXr16MXz4cJ588kmqq6ubtd4iIr5s3LhxTJ8+3WXbiBEjGDp0qMu2mTNncvPNN1NRUcETTzzBddddR69evejXrx+33HIL33zzDQBPP/20s33o2bMnTz/9NICzp1Nqaiq9evVixIgR/POf/3T5HVOmTCE9PZ0777yTfv36MW3aNHr27MlPP/3Em2++6Rwy/vTTT5OamsrixYtJSUnh2muv5fjx41itVlauXMno0aPp06cPV111FX/729+orKx0/o777ruPqVOnsmbNGueQwDFjxvDJJ58A9qGCv/nNbwD4zW9+4zI8RETEnxmGwcqVKxk1ahR9+vQhNTWVZcuWOedy2rx5M7/+9a/p378/KSkp/OlPf+Lw4cPO49944w0uvvhidu/ezU033UTv3r256qqrWLZsmcvvKSoqYvbs2aSkpHDZZZfx+OOPY7PZXPY5ffje8OHD+emnn1i7dq1zyF5Nw/eaKj6RhlBSSqQJLF68mGHDhtGuXTtef/11Jk6c6FKemZnJr371K2w2GxkZGTzyyCMcPnyYX/7ylxw4cACAvLw8Jk6cyPbt27nrrrt4+umn6dy5M2lpabz99tsu51u6dCkjRozgySef5JprrmHZsmWsXLmStLQ0li9fzq9+9Suef/55nn322Ra7BiIivuaqq65i+/btWK1WwD534A8//EBubi7ff/89AFarlS1btnD11Vdzzz33sHr1aqZNm8by5cu57777+Pbbb7nrrrswDINJkyY524fXX3+dSZMmAfDQQw+xaNEixowZw7PPPsvPf/5z5s+fzzPPPOMSz3vvvUdQUBDPPPMMU6ZM4fXXX6ddu3YMGzaM119/nUsuuQSAnJwcPvzwQ5588klmzpxJbGwsf/7zn5k/fz7Dhw9n6dKlTJ48mVdeeYUZM2a4TID75Zdf8sILL3DnnXfyzDPPEBgYyJ133klRURGXXHIJf/7znwH485//fNYQERERf/Xkk0+SkZHBsGHDWLp0KZMmTeKpp55iyZIlvPXWW/zud78jISGBJ598ktmzZ7Nr1y5uuukm8vPzneew2WzMnDmTkSNH8o9//IP+/fvzt7/9jU2bNjnLb7vtNj7++GPS09NZsGABu3btYv369bXGtXjxYpd2oqYhe00Vn0hDafieSBO4+OKLiYuLIzg4mL59+7p88wzwxBNP0KVLF55//nkCAgIAGDp0KKmpqTz99NMsXLiQF198kYKCAt577z26dOkCwLBhw5g6dSp//etfueGGGzCb7XnkPn36MG3aNOf5//rXv3LJJZcwYcIEAAYOHEhYWBiRkZEtUX0REZ901VVXsXTpUvbs2UNycjJbt26lS5cunDhxgu3bt3PBBRfw+eefU1RUxNVXX82WLVt44IEHGDlyJGB/Ly4tLeWxxx4jNzeXDh060KFDBwD69u0LwPfff8+//vUv7r77buf7+tChQzGZTDz33HP8+te/JjY2FgCz2cy8efMIDw93xhgcHExcXJzzfAAWi4V7773XOaQvKyuL1atXM3PmTGfPr8svv5z27dtzzz33sHHjRoYNGwZAcXExb7zxBl27dgUgPDycm2++mczMTEaMGOEcFpKYmHjOISIiIv7gxIkTvPjii0yZMoV77rkHsL/HFhQU8Nlnn/Hqq68yZMgQnnrqKecx/fr1Y+TIkSxfvpxZs2YB9t5WM2bMcH5h0b9/fz788EM+/vhjrrjiCjZu3MiePXt47rnnuOqqqwAYNGiQyyTnZ7r44otrbCccbDYbjz/+eJPEJ9JQ6ikl0szKysr44osvGDlypDMhBdCmTRuuvvpq5/xT27dvJzk52ZmQchgzZgy5ubl89913zm1JSUku+6SkpLBlyxZ+/etf8+KLL3LgwAFuvvlmxo0b13wVExHxcX369CE2NpYtW7YAsHXrVgYNGsSll17K9u3bAdi4cSPdunXjwgsv5IUXXmDkyJEcO3aMHTt28Prrr/PRRx8B1DqcOjMzE8MwGD58OBaLxfkzfPhwKisr+eyzz5z7nnfeeS4Jqbqc3k44Yh09erTLPqNGjSIgIMBlHsS4uDhnQgpwJtHKy8vr9XtFRPzN559/TnV1NampqS7b77vvPubMmUNubu5Z779du3YlOTn5rHlok5OTnf93JJPKysoA2LlzJ0FBQVx55ZXOfcLDw51fKjTE999/32TxiTSUklIizay4uBjDMIiPjz+rLD4+nuLiYsA+Rry2fcD+LcyZ2xxuu+02/vznP1NRUcGCBQsYOXIko0ePZuvWrU1ZFRERv2I2m7nyyiud76WZmZmkpKQwcOBAduzYAdiTUldffTUAmzZt4vrrr+eKK67gjjvu4M033yQ4OBjAZYjc6QoLCwF7guiSSy5x/ji+iT569Khz35raiNqcvm9RUREA7dq1c9knMDCQ2NhYZzsEEBYW5rKPyWQCOGvOEhERsXO8j9c0ebij7Fz3AQ6hoaEuj81ms7P9KCoqIiYmxjlywuHM93Z3NGV8Ig2l4XsizSwqKgqTyUReXt5ZZbm5ucTExAAQHR1d6z6Ac/hGTcxmM5MnT2by5Mnk5+fzySef8Oyzz/LHP/6RLVu2OG+KRETEPVdddRX33HMPX331FUePHmXgwIEcPXqUv/3tb+zcuZNvvvmG++67j0OHDpGWlsY111zDc8895+xttHLlyjrn22jTpg0AL730EhEREWeVd+rUqdF1iI6OBuztyXnnnefcXl1dzfHjx+tsX0REpG6O9/GCggIuvPBC5/bDhw+zb98+gFo/47vz/hsbG+tcuOL00ReOxFJDOO5DmiI+kYZSTymRZhYeHk6vXr1Yv369c7JcsPeg+vjjj+nfvz8Al112Gbt27SI7O9vl+Lfffpt27dpx/vnn1/o7fvnLX/LII48A0LZtW8aPH8/kyZMpLi6mpKSkGWolIuIfhg4dimEYLF26lG7dupGQkMAll1xCVFQUTzzxBFFRUfTv358vv/ySyspK7rjjDpfhb46ElOOb5DO/4b7ssssAOH78OL1793b+FBYWsnDhwkbdbDgMHDgQgHXr1rlsf/fdd7Farc52qD5OvxESERH7UO+goKCzVsx+6aWXWLhwIe3atTvr/Tc7O5vPP/+cfv361fv3DB48GIvFwr///W/ntqqqKjZv3lzncWe2O6e74IILmiw+kYZSTymRFvCnP/2JW2+9ldtuu42bb76Z6upq/vGPf1BVVcUf/vAHAG655RbefvttbrnlFv7whz8QGxvLm2++SWZmJvPnz6+zQbnssstYvnw58fHxJCcnc/ToUV588UUGDhxYY1diERGpnzZt2pCcnMyHH37ITTfdBNgTMwMGDOCjjz7ihhtuIDAwkEsuuYTAwEAef/xxfve731FVVcUbb7zBxx9/DOCcc8Pxjfo777zDpZdeSlJSEmPGjOGBBx7gp59+olevXnz//fc89dRTnHfeeXTr1q3RdUhMTOQXv/gFixcvpqKigpSUFL755hsWL15MSkqKWxPURkVFAfDxxx8THR3NRRdd1Oj4RERas7i4OH7zm9/w0ksvERwczKBBg/jiiy945ZVXuPvuu4mJiWH27NncddddjBs3juPHj7N48WKio6O55ZZb6v17Bg8ezNChQ5k7dy75+fl07tyZl19+mYKCAtq2bVvrcW3atOHrr79m+/bt9OnTx6XMbDZz9913N0l8Ig2lpJRICxg8eDAvvvgiixYt4u677yY4OJgBAwawYMECevToAdjHg7/66qs88cQTZGRkUF1dzUUXXcSSJUu45ppr6jz///3f/xEcHMyaNWt45plniIqKYvjw4fzpT39qieqJiPi0YcOGsWPHDlJSUpzbBg0axEcffeRcAen888/niSeeYPHixUyfPp3o6Gj69u3LP//5T6ZMmcLOnTvp2bMn1113HW+99Rb33XcfEydO5KGHHuLRRx/lueee47XXXuPIkSO0bduWkSNHMnPmzCbrmZSRkcH555/PmjVreOGFF2jfvj1TpkwhLS2tzi89ztSjRw9uuOEG57DEd955p0niExFpzWbNmkV8fDyvvvoqy5cv57zzzuP+++/n17/+NQARERE899xzpKWlERkZyRVXXMHdd9/t9nxQixcv5m9/+xuLFi2isrKSkSNHcuONN57VS+t0v/vd75g/fz633norL7744lnl48ePb7L4RBrCZGhmMhERERERERERaWGaU0pERERERERERFqcklIiIiIiIiIiItLilJQSEREREREREZEWp6SUiIiIiIiIiIi0OCWlRERERERERESkxSkpJSIiIiIiIiIiLU5JKRERERERERERaXFKSomIiIiIiIiISIsL9HQArVF+fjGGUf/9TSZo2zbK7eNaC9Wv9fP1Oqp+dR8nTUNtw9l8vY6qX+um+tV9nDQdtQ9NQ9elZrouNdN1qZ23tQ9KSjWAYdCgF3ZDj2stVL/Wz9frqPpJc1LbUDtfr6Pq17qpftLc1D40LV2Xmum61EzXpXbecm00fE9ERERERERERFqcklIiIiIiIiIiItLilJQSERERERGvV1BQQGpqKtu2bTur7NixYwwZMoQ33njDZfvatWtJTU2lb9++jB8/nl27djnLrFYrCxYsYMiQISQnJzN9+nSOHTvmLM/Pz2fGjBkMGDCAlJQUMjIysFgszVdBERE/pKSUiIiIiIh4tc8++4ybbrqJQ4cOnVVms9lIT0/n+PHjLtu3bdvGvHnzeOyxx9ixYwdjxoxh+vTplJeXA7B06VI2b97MmjVr2LRpE6GhocydO9d5/MyZMwkPD2fTpk2sXr2arVu3smLFimatp4iIv1FSSkREREREvNbatWtJT0/nrrvuqrH8mWeeoUOHDnTs2NFl+6pVqxg1ahT9+/cnKCiIqVOnEhsby/r1653lt99+Ox07diQyMpI5c+awceNGsrOzOXjwINu3b2fWrFmEhYXRpUsXZsyYwcqVK5u9viIi/kSr74mIiIiIiNcaOnQoo0ePJjAw8KzEVGZmJu+++y5r1qxh9OjRLmVZWVlMmDDBZVtiYiJ79+6luLiYI0eOkJSU5CyLj48nOjqaffv2ARATE0NCQoKzvHv37uTk5HDixAnatGlT7/hNpnrv6rK/u8f5Ol2Xmum61EzXpXYNvTbNdS2VlBIREREREa/Vrl27Grfn5+dz//33s2jRIiIiIs4qLy0tJSwszGVbaGgoZWVllJaWAhAeHn5WuaPszGMdj8vKytxKSrVtG1XvfZviOF+n61IzXZea6brUzluujZJSIiIiIiLSqhiGwT333MOUKVPo1atXjfuEhYVRUVHhsq2iooLY2Fhngskxv9Tp5RERERiGcVaZ43FNCbC65OcXYxj1399kst8sunucr9N1qZmuS810XWrX0GvjOK6pKSklIiIiIiKtyuHDh9m+fTu7d+/mmWeeAaCkpIS//OUvbNiwgeeee44ePXqwf/9+l+OysrK48soriY6OJiEhgaysLOcQvtzcXAoLC0lKSsJms1FYWEheXh7x8fEAHDhwgA4dOhAV5d5NmWHQoJvihh7n63RdaqbrUjNdl9p5y7XRROciIiIiItKqdOrUiS+++IKdO3c6fzp16sSDDz7Ic889B8DEiRNZt24dmZmZVFdXs2LFCvLz80lNTQVg/PjxLF26lOzsbEpKSpg/fz4DBw6ka9eudOvWjf79+zN//nxKSkrIzs5myZIlTJw40ZPVFhHxOeopJSIiIiIiPmfw4ME8+OCDPPTQQxw9epTExESWLVtGTEwMAGlpaVgsFiZPnkxpaSkpKSksXLjQefyiRYt4+OGHueaaazCbzYwbN44ZM2Z4pjIiIj5KSSkREREPOFZcyXv787nq/BhCgwI8HY6ISKvgWBmvJv/973/P2jZ27FjGjh1b4/5BQUGkp6eTnp5eY3l8fDyLFi1qWKAiIlIvSkqJiIh4wIvbsln1eQ73p/bgF306ejocERERaYUqKyvZuvVLCgtLXeYH6tOnLyEhIZ4LTKSelJRqIeMm3shPR/JqLY+PjeXl5StaLiAREfEo68lPjvmlVR6ORERERFqrPXs+58UNW4nteIFz29GD+5kGXHZZiucCE6knJaVayLG844yb9fday998/P9aMBoREfG08JND9sqqrR6ORERERFqzjhck0b7bxZ4OQ6RBtPqeiIiIB4QF2Zvg8iolpURERETEPykpJSIi4gHhweopJSIiIiL+TUkpERERDwg7OXyvXEkpEREREfFTSkqJiIh4gLOnlIbviYiIiIifUlJKRETEA8KdPaVsHo5ERERERMQzlJQSERHxgDD1lBIRERERP+fRpFRBQQGpqals27bNuW337t1MmjSJ5ORkhg8fzqpVq1yOWbt2LampqfTt25fx48eza9cuZ5nVamXBggUMGTKE5ORkpk+fzrFjx5zl+fn5zJgxgwEDBpCSkkJGRgYWi6X5KyoiInIGR0+psiq1QyIiIiLinzyWlPrss8+46aabOHTokHNbUVER06ZNY9y4cezYsYOMjAweffRR9uzZA8C2bduYN28ejz32GDt27GDMmDFMnz6d8vJyAJYuXcrmzZtZs2YNmzZtIjQ0lLlz5zrPP3PmTMLDw9m0aROrV69m69atrFixokXrLSIiAqcmOi/T8D0RERER8VMeSUqtXbuW9PR07rrrLpftH3zwATExMUyePJnAwEAGDx7M6NGjWblyJQCrVq1i1KhR9O/fn6CgIKZOnUpsbCzr1693lt9+++107NiRyMhI5syZw8aNG8nOzubgwYNs376dWbNmERYWRpcuXZgxY4bz3CIiIi0pIlir74mIiIiIf/NIUmro0KF8+OGHjBw50mX7/v37SUpKctmWmJjI3r17AcjKyqq1vLi4mCNHjriUx8fHEx0dzb59+9i/fz8xMTEkJCQ4y7t3705OTg4nTpxo6iqKiIjUyTGnVKXFhsVmeDgaEREREZGWF+iJX9quXbsat5eWlhIWFuayLTQ0lLKysnOWl5aWAhAeHn5WuaPszGMdj8vKymjTpk294zeZ6r2ry/4mExh13He4e15vcXr9fJGv1w98v46qX93HiWc45pQCqKi2EhnikSZZRERERMRjvOoTcFhYGMXFxS7bKioqiIiIcJZXVFScVR4bG+tMMDnmlzrzeMMwzipzPHacv77ato1ya3+HsLCQWsuCAgOIj2/Yeb1FQ69La+Hr9QPfr6PqJ94kKMBEoNmExWZQVqWklIiIiIj4H6/6BJyUlMTmzZtdtmVlZdGjRw8AevTowf79+88qv/LKK4mOjiYhIcFliF9ubi6FhYUkJSVhs9koLCwkLy+P+Ph4AA4cOECHDh2IinLvRi4/v7jOHk9ncvRGKC+vrPW4aouVvLzimgu9nMlkvxl297q0Fr5eP/D9Oqp+dR8nnmEymQgPDuBEhYUyzSslIiIiIn7IY6vv1SQ1NZW8vDxWrFhBdXU1mZmZrFu3jgkTJgAwceJE1q1bR2ZmJtXV1axYsYL8/HxSU1MBGD9+PEuXLiU7O5uSkhLmz5/PwIED6dq1K926daN///7Mnz+fkpISsrOzWbJkCRMnTnQ7TsNw/8dxXFOf11t+Wnv8/l4/f6ij6lf7ceI5ESd7R2mycxERERHxR17VUyo2Npbly5eTkZHBokWLiIuLY+7cuQwaNAiAwYMH8+CDD/LQQw9x9OhREhMTWbZsGTExMQCkpaVhsViYPHkypaWlpKSksHDhQuf5Fy1axMMPP8w111yD2Wxm3LhxzJgxwwM1FRERgfCTk52XVSkpJSIiIiL+x+NJqX379rk87t27N6+99lqt+48dO5axY8fWWBYUFER6ejrp6ek1lsfHx7No0aKGBysiItKE1FNKRERERPyZVw3fExER8SfqKSUiIiIi/kxJKREREQ+JCFZPKRERERHxX0pKiYiIeEj4yeF7ZdU2D0ciIiIiItLylJQSERHxkMgQx/A9i4cjERERERFpeUpKiYiIeEj4yeF7ZVXqKSUiIiIi/kdJKREREQ+JODnRueaUEhERERF/pKSUiIiIh5yaU0pJKRERERHxP0pKiYiIeIizp1SVklIiIiIi4n+UlBIREfEQ55xS6iklIiIiIn5ISSkREREPiQhRTykRERER8V9KSomIiHiIekqJiIiIiD9TUkpERMRDwrX6noiIiIj4MSWlREREPCQwwN4MW6yGhyMREREREWl5SkqJiIh4SKDZBIDVUFJKRERERPyPklIiIiIeEqSeUiIiIiLix5SUEhER8ZDAAHtPKYtNSSkRERER8T9KSomIiHhIkPlkTymbzcORiIh4v4KCAlJTU9m2bZtz24YNGxg7diz9+vVj+PDhLF68GNtp76lr164lNTWVvn37Mn78eHbt2uUss1qtLFiwgCFDhpCcnMz06dM5duyYszw/P58ZM2YwYMAAUlJSyMjIwGKxtExlRUT8hJJSIiIiHqKeUiIi9fPZZ59x0003cejQIee2L7/8knvuuYeZM2eyc+dOli1bxhtvvMGKFSsA2LZtG/PmzeOxxx5jx44djBkzhunTp1NeXg7A0qVL2bx5M2vWrGHTpk2EhoYyd+5c5/lnzpxJeHg4mzZtYvXq1WzdutV5bhERaRpKSomIiHiIMymlOaVERGq1du1a0tPTueuuu1y2//TTT/zyl7/k6quvxmw20717d1JTU9mxYwcAq1atYtSoUfTv35+goCCmTp1KbGws69evd5bffvvtdOzYkcjISObMmcPGjRvJzs7m4MGDbN++nVmzZhEWFkaXLl2YMWMGK1eubPH6i4j4skBPByAiIuKvHMP3DMBqMwg4uRqfiIicMnToUEaPHk1gYKBLYmrEiBGMGDHC+biiooKPP/6Y0aNHA5CVlcWECRNczpWYmMjevXspLi7myJEjJCUlOcvi4+OJjo5m3759AMTExJCQkOAs7969Ozk5OZw4cYI2bdrUO36Tm2/tjv3dPc7X6brU7PTrcvpiviaTf18rvV5q19Br01zXUkkpERERD3H0lAL7ED4lpUREztauXbtz7lNSUsL//d//ERoaytSpUwEoLS0lLCzMZb/Q0FDKysooLS0FIDw8/KxyR9mZxzoel5WVuZWUats2qt77NsVxvk7XxVV0dASQT1hYiHNbaGgQMTERxMfrWun1UjtvuTZKSomIiHhIUMCpUfQWm40QjaoXEXHbd999x5133knbtm15+eWXiYyMBOxJpIqKCpd9KyoqiI2NdSaYHPNLnV4eERGBYRhnlTkeR0REuBVffn6xSw+WczGZ7DeL7h7n63RdalZUZE+ilpdXOq9LRUU1hYWl5OUVezAyz9LrpXYNvTaO45qaklIiIiIeEnhazyjNKyUi4r5PPvmEu+++mxtvvJE//elPBAaeur3p0aMH+/fvd9k/KyuLK6+8kujoaBISEsjKynIO4cvNzaWwsJCkpCRsNhuFhYXk5eURHx8PwIEDB+jQoQNRUe7dlBkGDbopbuhxvk7XxZXjWpx5TXSd7HQdauct10ZfyYqIiFexWq1MmTKF++67z7lt9+7dTJo0ieTkZIYPH86qVatcjmmtS36fPlxPK/CJiLjn888/Jy0tjdmzZ3Pvvfe6JKQAJk6cyLp168jMzKS6upoVK1aQn59PamoqAOPHj2fp0qVkZ2dTUlLC/PnzGThwIF27dqVbt27079+f+fPnU1JSQnZ2NkuWLGHixImeqKqIiM9SUkpERLzK4sWL2blzp/NxUVER06ZNY9y4cezYsYOMjAweffRR9uzZA7TuJb9NJpMzMaWklIiIe5599lksFgsZGRkkJyc7f2677TYABg8ezIMPPshDDz3EwIEDeffdd1m2bBkxMTEApKWlMWzYMCZPnsywYcOorKxk4cKFzvMvWrQIi8XCNddcw4033sgVV1zBjBkzPFBTERHfpeF7IiLiNbZu3coHH3zAdddd59z2wQcfEBMTw+TJkwH7Tcbo0aNZuXIlffr0cVnyG2Dq1Km8/vrrrF+/ngkTJrBq1SrS09Pp2LEjAHPmzGHo0KFkZ2djs9nYvn07GzdudFny+/HHH3fe1DS3QLMJq83AYrO1yO8TEWnNHCvjgT0pdS5jx45l7NixNZYFBQWRnp5Oenp6jeXx8fEsWrSoYYGKiEi9KCklIiJeIT8/nzlz5rBkyRKXnkr79+93WbIb7Et6r169Gmj9S34Hmk1UAlab4XPLFvv6csyqX+um+tV9nIiISEtQUkpERDzOZrMxa9YsbrnlFi666CKXsrqW9D5XeWtY8js40ExplZWo6HCfXbrZW5Ycbi6qX+um+omIiHiOklIiIuJxzz33HMHBwUyZMuWssrCwMIqLXZc0dizZ7ShvzUt+m092S8jLLyEuwK1f6fV8fTlm1a91U/3qPk5ERKQlKCklIiIe99Zbb3Hs2DEGDBgA4Ewy/fvf/+aee+5h8+bNLvtnZWXRo0cPoPUv+R14cqLzaqvhkzfG4D1LDjcX1a91U/1EREQ8R6vviYiIx73//vv873//Y+fOnezcuZMbbriBG264gZ07d5KamkpeXh4rVqygurqazMxM1q1b55xHqrUv+R2o1fdERERExE+pp5SIiHi12NhYli9fTkZGBosWLSIuLo65c+cyaNAgwHXJ76NHj5KYmHjWkt8Wi4XJkydTWlpKSkrKWUt+P/zww1xzzTWYzWbGjRvXokt+BwY4klJafU9ERERE/IuSUiIi4nUee+wxl8e9e/fmtddeq3X/1rzkt7OnlFU9pURERETEv2j4noiIiAcFmu1NsYbviYiIiIi/UVJKRETEgzSnlIiIiIj4KyWlREREPOjUnFJKSomIiIiIf1FSSkRExINOzSmlic5FRERExL8oKSUiIuJBGr4nIiIiIv5KSSkREREP0kTnIiIiIuKvlJQSERHxoAD1lBIRERERP6WklIiIiAedmlNKSSkRERER8S9KSomIiHjQqdX3NNG5iIiIiPgXJaVEREQ8yNFTyqrheyIiIiLiZ5SUEhER8SCtviciIiIi/kpJKREREQ8KDNDqeyIiIiLin5SUEhER8SD1lBIRERERf6WklIiIiAdp9T0RERER8VdKSomIiHjQqZ5SWn1PRERERPyLklIiIiIeFBig1fdERERExD8pKSUiIuJBgWZNdC4iIiIi/skrk1JfffUVkydPZsCAAQwdOpRHHnmEqqoqAHbv3s2kSZNITk5m+PDhrFq1yuXYtWvXkpqaSt++fRk/fjy7du1yllmtVhYsWMCQIUNITk5m+vTpHDt2rEXrJiIicjrNKSUiIiIi/srrklI2m4077riDESNGsH37dlavXs2nn37KsmXLKCoqYtq0aYwbN44dO3aQkZHBo48+yp49ewDYtm0b8+bN47HHHmPHjh2MGTOG6dOnU15eDsDSpUvZvHkza9asYdOmTYSGhjJ37lxPVldERPyc5pQSEREREX8V6OkAzlRUVERubi42mw3DsH9rbDabCQsL44MPPiAmJobJkycDMHjwYEaPHs3KlSvp06cPq1atYtSoUfTv3x+AqVOn8vrrr7N+/XomTJjAqlWrSE9Pp2PHjgDMmTOHoUOHkp2dTZcuXTxTYWkVxk28kZ+O5NVaHh8by8vLV7RcQCLiMxxzSmn4noiIiIj4G69LSsXGxjJ16lQWLFjAX//6V6xWK9dccw1Tp07lscceIykpyWX/xMREVq9eDUBWVhYTJkw4q3zv3r0UFxdz5MgRl+Pj4+OJjo5m3759biWlTCb36uTY32QCo457DnfP6y1Or58vMpngWN5xfnHP32t9/t58/P9adf394Tk8/V9f09D6+er1aG00p5SIiIiI+CuvS0rZbDZCQ0N54IEHmDhxIgcPHuQPf/gDixYtorS0lLCwMJf9Q0NDKSsrA6izvLS0FIDw8PCzyh1l9dW2bZS71QIgLCyk1rKgwADi4xt2Xm/R0OvSWvj68we+/xyqfuKNAk4OpNecUiIiIiLib7wuKfXhhx+yYcMG3n//fQB69OhBWloaGRkZjB49muLiYpf9KyoqiIiIACAsLIyKioqzymNjY53JKsf8UjUdX1/5+cV19ng6k6M3Qnl5Za3HVVus5OUV11zo5Uwm+82wu9eltfD15w/84zlU/Wo/TjxLPaVERERExF95XVLq8OHDzpX2HAIDAwkKCiIpKYnNmze7lGVlZdGjRw/AnsDav3//WeVXXnkl0dHRJCQkkJWV5RzCl5ubS2Fh4VlDAs/FMOoehlfXcY0p93YNvS6tha8/f+Afz6HqJ95GE52LiIiIiL/yutX3hg4dSm5uLs8++yxWq5Xs7GyWLl3K6NGjSU1NJS8vjxUrVlBdXU1mZibr1q1zziM1ceJE1q1bR2ZmJtXV1axYsYL8/HxSU1MBGD9+PEuXLiU7O5uSkhLmz5/PwIED6dq1qyerLCIifswx0blVPaVERERExM94XU+pxMREnnvuORYuXMjzzz9PVFQUY8aMIS0tjeDgYJYvX05GRgaLFi0iLi6OuXPnMmjQIMC+Gt+DDz7IQw89xNGjR0lMTGTZsmXExMQAkJaWhsViYfLkyZSWlpKSksLChQs9V1kREfF7p3pKKSklIiIiIv7F65JSAEOGDGHIkCE1lvXu3ZvXXnut1mPHjh3L2LFjaywLCgoiPT2d9PT0JolTRESksTSnlIiIiIj4K68bviciIuJPnD2ltPqeiIiIiPgZJaVEREQ8yDGnlHpKiYjUraCggNTUVLZt2+bctnv3biZNmkRycjLDhw9n1apVLsesXbuW1NRU+vbty/jx49m1a5ezzGq1smDBAoYMGUJycjLTp0/n2LFjzvL8/HxmzJjBgAEDSElJISMjA4vF0vwVFRHxI0pKiYiIeJBW3xMRObfPPvuMm266iUOHDjm3FRUVMW3aNMaNG8eOHTvIyMjg0UcfZc+ePQBs27aNefPm8dhjj7Fjxw7GjBnD9OnTKS8vB2Dp0qVs3ryZNWvWsGnTJkJDQ5k7d67z/DNnziQ8PJxNmzaxevVqtm7dyooVK1q03iIivk5JKREREQ/SROciInVbu3Yt6enp3HXXXS7bP/jgA2JiYpg8eTKBgYEMHjyY0aNHs3LlSgBWrVrFqFGj6N+/P0FBQUydOpXY2FjWr1/vLL/99tvp2LEjkZGRzJkzh40bN5Kdnc3BgwfZvn07s2bNIiwsjC5dujBjxgznuUVEpGl45UTnIiIi/sI50bnmlBIRqdHQoUMZPXo0gYGBLomp/fv3k5SU5LJvYmIiq1evBiArK4sJEyacVb53716Ki4s5cuSIy/Hx8fFER0ezb98+AGJiYkhISHCWd+/enZycHE6cOEGbNm3qHb/JVP+6nr6/u8f5Ol2Xmp1+XQzDdbs/Xyu9XmrX0GvTXNdSSSkREREP0pxSIiJ1a9euXY3bS0tLCQsLc9kWGhpKWVnZOctLS0sBCA8PP6vcUXbmsY7HZWVlbiWl2raNqve+TXGcr9N1cRUdHQHkExYW4twWGhpETEwE8fG6Vnq91M5bro2SUiIiIh6k4XsiIg0TFhZGcXGxy7aKigoiIiKc5RUVFWeVx8bGOhNMjvmlzjzeMIyzyhyPHeevr/z8YpceLOdiMtlvFt09ztfputSsqMieRC0vr3Rel4qKagoLS8nLK67jSN+m10vtGnptHMc1NSWlREREPEgTnYuINExSUhKbN2922ZaVlUWPHj0A6NGjB/v37z+r/MorryQ6OpqEhASysrKcQ/hyc3MpLCwkKSkJm81GYWEheXl5xMfHA3DgwAE6dOhAVJR7N2WGQYNuiht6nK/TdXHluBZnXhNdJztdh9p5y7XRROciIiIe5ExKaU4pERG3pKamkpeXx4oVK6iuriYzM5N169Y555GaOHEi69atIzMzk+rqalasWEF+fj6pqakAjB8/nqVLl5KdnU1JSQnz589n4MCBdO3alW7dutG/f3/mz59PSUkJ2dnZLFmyhIkTJ3qyyiIiPkc9pURERDwoQMP3REQaJDY2luXLl5ORkcGiRYuIi4tj7ty5DBo0CIDBgwfz4IMP8tBDD3H06FESExNZtmwZMTExAKSlpWGxWJg8eTKlpaWkpKSwcOFC5/kXLVrEww8/zDXXXIPZbGbcuHHMmDHDAzUVEfFdSkqJiIh4UGDAydX3bAaGYWDSMjEiIrVyrIzn0Lt3b1577bVa9x87dixjx46tsSwoKIj09HTS09NrLI+Pj2fRokUND1ZERM5Jw/dEREQ8yDF8D0Aj+ERERETEnygpJSIi4kEuSSkN4RMRERERP6KklIiIiAednpTSCnwiIiIi4k+UlBIREfEgx5xSoBX4RERERMS/KCklIiLiQQGnzWuuFfhERERExJ8oKSUiIuJBJpPJOYRPSSkRERER8SdKSomIiHjYqaSU5pQSEREREf+hpJSIiIiHBZ4cw6c5pURERETEnygpJSIi4mGBZntzrOF7IiIiIuJPlJQSERHxMM0pJSIiIiL+SEkpERERD1NSSkRERET8kZJSIiIiHnZqTilNdC4iIiIi/kNJKREREQ9TTykRERER8UdKSomIiHhYgJJSIiIiIuKHlJQSERHxMMfqe1YlpURERETEjygpJSIi4mGO4XtKSomIiIiIP1FSSkRExMM0fE9ERERE/JGSUiIiIh6mnlIiIiIi4o+UlBIREa+wdetWJk2aRL9+/bj88suZN28eFRUVAOzevZtJkyaRnJzM8OHDWbVqlcuxa9euJTU1lb59+zJ+/Hh27drlLLNarSxYsIAhQ4aQnJzM9OnTOXbsmLM8Pz+fGTNmMGDAAFJSUsjIyMBisbRMpU9STykRERER8UdKSomIiMcVFBRwxx138Ktf/YqdO3eydu1atm/fzj/+8Q+KioqYNm0a48aNY8eOHWRkZPDoo4+yZ88eALZt28a8efN47LHH2LFjB2PGjGH69OmUl5cDsHTpUjZv3syaNWvYtGkToaGhzJ071/m7Z86cSXh4OJs2bWL16tVs3bqVFStWtGj9A9RTSkRERET8kJJSIiLicXFxcWzZsoXx48djMpkoLCyksrKSuLg4PvjgA2JiYpg8eTKBgYEMHjyY0aNHs3LlSgBWrVrFqFGj6N+/P0FBQUydOpXY2FjWr1/vLL/99tvp2LEjkZGRzJkzh40bN5Kdnc3BgwfZvn07s2bNIiwsjC5dujBjxgznuVuKhu+JiIiIiD8K9HQAIiIiAJGRkQAMGzaMo0ePMmDAAMaPH8/ChQtJSkpy2TcxMZHVq1cDkJWVxYQJE84q37t3L8XFxRw5csTl+Pj4eKKjo9m3bx8AMTExJCQkOMu7d+9OTk4OJ06coE2bNvWO32Ryr76O/U2mU0kpi2Fz+zze7PQ6+iLVr3VT/eo+TkREpCUoKSUiIl7lgw8+oKioiPT0dO68804SEhIICwtz2Sc0NJSysjIASktLay0vLS0FIDw8/KxyR9mZxzoel5WVuZWUats2qt77nnlcRFiw/XeHhxAf37DzeLOGXpvWQvVr3VQ/ERERz1FSSkREvEpoaCihoaHMmjWLSZMmMWXKFIqLi132qaioICIiArAnkRwTop9eHhsb60wwOeaXOvN4wzDOKnM8dpy/vvLzizHcGH1nMtlvFvPzi7FU2ydWLzxRTl5e8TmObD1Or6M716a1UP1aN9Wv7uNERERaguaUEhERj/vf//7Hz3/+c6qqqpzbqqqqCAoKIjExkf3797vsn5WVRY8ePQDo0aNHreXR0dEkJCSQlZXlLMvNzaWwsJCkpCR69OhBYWEheXl5zvIDBw7QoUMHoqLcuykzDPd/HMc5V9+zGg06jzf/NPTatJYf1a91/6h+tR8nIiLSEpSUEhERj+vZsycVFRU88cQTVFVV8dNPP7FgwQImTpzIiBEjyMvLY8WKFVRXV5OZmcm6deuc80hNnDiRdevWkZmZSXV1NStWrCA/P5/U1FQAxo8fz9KlS8nOzqakpIT58+czcOBAunbtSrdu3ejfvz/z58+npKSE7OxslixZwsSJE1u0/proXERERET8kYbviYiIx0VERPD8888zf/58Lr/8cqKiohg9ejRpaWkEBwezfPlyMjIyWLRoEXFxccydO5dBgwYBMHjwYB588EEeeughjh49SmJiIsuWLSMmJgaAtLQ0LBYLkydPprS0lJSUFBYuXOj83YsWLeLhhx/mmmuuwWw2M27cOGbMmNGi9Xf2lFJSSkRERET8iNtJqW3btpGSktIcsYiISCvVFG1DYmIiy5cvr7Gsd+/evPbaa7UeO3bsWMaOHVtjWVBQEOnp6aSnp9dYHh8fz6JFi9wPuAkFmu0dl9VTSkR8je4dRESkLm4P37vzzju59tpreeaZZ8jJyWmOmEREpJVR29A46iklIr5K7YOIiNTF7aTUp59+yqxZs/jyyy8ZMWIEv/vd73jnnXdcJqcVERH/orahcQJMmlNKRHyT2gcREamL20mpoKAgRowYwdKlS/nkk0+49tprWb58OUOHDuUvf/kLe/fubY44RUTEi6ltaJzAgJNJKS17JSI+Ru2DiIjUpcGr7+Xn57Nu3TrefPNNsrKySElJISQkhKlTp/Lss882ZYwiItJKqG1oGOfwPauSUiLim9Q+iIhITdye6Pzdd9/lrbfeYsuWLVx44YWMHz+eZ599lri4OACGDRtGWloav//975s8WBER8U5qGxon0KyeUiLim9Q+iIhIXdxOSv3lL39h1KhRvPbaa/Tq1eus8gsuuICpU6c2RWwiItJKqG1onED1lBIRH6X2QURE6uJ2UurTTz8lOzubhIQEAD7//HOioqLo3r07AB06dODOO+9s2ihFRMSrqW1oHMdE5xb1lBIRH6P2QURE6uL2nFL/+c9/GDduHD/88AMAu3btYtKkSXzyySdNHZuIiLQSahsaxznRuVbfExEf09ztw1dffcXkyZMZMGAAQ4cO5ZFHHnGu7Ld7924mTZpEcnIyw4cPZ9WqVS7Hrl27ltTUVPr27cv48ePZtWuXs8xqtbJgwQKGDBlCcnIy06dP59ixY00Ss4iInOJ2Umrx4sUsWbLE2f32lltu4e9//ztPPPFEkwcnIiKtg9qGxnH2lNLwPRHxMc3ZPthsNu644w5GjBjB9u3bWb16NZ9++inLli2jqKiIadOmMW7cOHbs2EFGRgaPPvooe/bsAWDbtm3MmzePxx57jB07djBmzBimT59OeXk5AEuXLmXz5s2sWbOGTZs2ERoayty5cxsds4iIuHI7KXX48GGuuOIKl21Dhw4lJyenyYISEZHWRW1D4zh7Smn4noj4mOZsH4qKisjNzcVms2GcfP80m82EhYXxwQcfEBMTw+TJkwkMDGTw4MGMHj2alStXArBq1SpGjRpF//79CQoKYurUqcTGxrJ+/Xpn+e23307Hjh2JjIxkzpw5bNy4kezs7EbHLSIip7idlOrcuTObNm1y2bZ161Y6derUZEEVFhZyzz33kJKSwmWXXcaMGTOc3WXVDVdExPu0RNvgy071lLJ5OBIRkabVnO1DbGwsU6dOZcGCBfTu3Zthw4bRrVs3pk6dyv79+0lKSnLZPzExkb179wKQlZVVa3lxcTFHjhxxKY+Pjyc6Opp9+/a5HafJ5P5PQ4/z9R9dl7qvS2Nfd772o+vQ9NemObg90fm0adNIS0vjuuuuo3PnzuTk5PDhhx+yYMGCJgvqj3/8I9HR0Xz44YeYzWZmz57NAw88wF//+lemTZvGnXfeyU033cSOHTtIS0ujZ8+e9OnTx9kNd9myZfTp04eVK1cyffp0PvroI8LCwly64UZFRfHAAw8wd+5c/vGPfzRZ7CIi/qgl2gZfFnBy9T31lBIRX9Oc7YPNZiM0NJQHHniAiRMncvDgQf7whz+waNEiSktLCQsLc9k/NDSUsrIygDrLS0tLAQgPDz+r3FHmjrZto9w+pjHH+TpdF1fR0RFAPmFhIc5toaFBxMREEB+va6XXS+285dq4nZQaPXo07du358033+Srr76iY8eOLF++nH79+jVJQF9++SW7d+9my5YtREZGAjBv3jxyc3NduuECLt1w+/Tp49INF2Dq1Km8/vrrrF+/ngkTJrBq1SrS09Pp2LEjAHPmzGHo0KFkZ2fTpUuXJolfRMQfNXfb4Os00bmI+KrmbB8+/PBDNmzYwPvvvw9Ajx49SEtLIyMjg9GjR1NcXOyyf0VFBREREQCEhYVRUVFxVnlsbKwzWeWYX6qm492Rn1+MO985mEz2m0V3j/N1ui41KyqyJ0rLyyud16WioprCwlLy8orrONK36fVSu4ZeG8dxTc3tpBRASkoKKSkpTR0LAHv27CExMZF//etfvPrqq5SXl3PFFVdw77331toNd/Xq1YC9G+6ECRPOKq9PN1x3klLudls7vXtcXU96c3WHa26n188X+frzB/71HPqihtavqa9Hc7YNvs45fE9JKRHxQc3VPhw+fNi50p5DYGAgQUFBJCUlsXnzZpeyrKwsevToAdgTWPv37z+r/MorryQ6OpqEhASXIX65ubkUFhaedS9SH4ZR92fIpj7O1+m6uHJcizOvia6Tna5D7bzl2ridlDp69ChLly7lhx9+wGZznfvi5ZdfbnRARUVF7Nu3j169erF27VoqKiq45557uPfee4mPj/eKbrgNzQ6e3qXyTEGBAa2+e6W3dP9rLr7+/IHvP4eqX/Np7rbB1wUG2Kd4VE8pEfE1zdk+DB06lCeeeIJnn32W22+/nZycHJYuXcro0aNJTU3l8ccfZ8WKFUyePJnPPvuMdevWsWTJEgAmTpxIWloa119/Pf3792flypXk5+eTmpoKwPjx41m6dCm9e/cmNjaW+fPnM3DgQLp27dqomEVExJXbSanZs2eTl5fH1VdfTVBQUJMHFBwcDNiH1oWEhBAZGcnMmTO58cYbGT9+fI3dbFu6G25DurnZf3dlrcdVW6yttnulr3eN9PXnD/zjOVT9aj+uKTR32+Dr1FNKRHxVc7YPiYmJPPfccyxcuJDnn3+eqKgoxowZQ1paGsHBwSxfvpyMjAwWLVpEXFwcc+fOZdCgQYB9GpAHH3yQhx56iKNHj5KYmMiyZcuIiYkBIC0tDYvFwuTJkyktLSUlJYWFCxc2afwiItKApNQXX3zBhg0biIuLa454SExMxGazUV1dTUiIvWeK41uVn/3sZ/y///f/XPb3RDfcxnTBbUy5t/OW7n/NxdefP/CP51D1ax7N3Tb4ukCz5pQSEd/U3O3DkCFDGDJkSI1lvXv35rXXXqv12LFjxzJ27Ngay4KCgkhPTyc9Pb1J4hQRkZqZ3T0gKirK2ZupOQwZMoQuXbpw//33U1paSkFBAU899RTXXnstN9xwA3l5eaxYsYLq6moyMzNZt26dcx6piRMnsm7dOjIzM6murmbFihU1dsPNzs6mpKRE3XBFRJpIc7cNvs6RlFJPKRHxNWofRESkLm73lJoxYwazZ8/m9ttvJz4+3qWsU6dOjQ4oKCiIf/7znzz22GOMGDGCyspKhg8fzpw5c2jTpo264YqIeKHmbht8XYCSUiLio9Q+iIhIXdxOSs2dOxewL8EKYDKZMAwDk8nEN9980yRBJSQk8NRTT9VYpm64IiLepyXaBl+m4Xsi4qvUPoiISF3cTkr95z//aY44RESkFVPb0DjqKSUivkrtg4iI1MXtOaU6d+5M586dKSoq4quvvqJdu3aEhobSuXPn5ohPRERaAbUNjROgnlIi4qPUPoiISF3cTkrl5+fzy1/+khtvvJF7772X7Oxsrr32Wnbt2tUc8YmISCugtqFxNHxPRHyV2gcREamL20mp+fPnk5SUxI4dOwgMDKR79+5MmzaNv/71r80Rn4iItAJqGxpHw/dExFepfRARkbq4nZTKzMxk9uzZhIWFYTLZP0TfdtttZGVlNXlwIiLSOqhtaBz1lBIRX6X2QURE6uJ2UiooKIiKigoADMP+4bm0tJSIiIimjUxERFoNtQ2Nc6qnlM3DkYiINC21DyIiUhe3k1LDhw9n1qxZ/PDDD5hMJvLz8/nLX/7CsGHDmiM+ERFpBdQ2NI56SomIr1L7ICIidXE7KfWnP/2J8PBwfv7zn3PixAmGDh1KeXk56enpzRGfiIi0AmobGkdJKRHxVWofRESkLoHuHhAREcGiRYsoKCjgxx9/pEOHDrRv3745YhMRkVZCbUPjOIbvWQ378BbHvCsiIq2d2gcREamL20mpHTt2uDw+ePAgBw8eBOCyyy5rmqhERKRVUdvQOIHmUx2XrTaDwAAlpUTEN6h9EBGRuridlJoyZcpZ28xmMx07duQ///lPkwQlIiKti9qGxnH0lAKw2AwCAzwYjIhIE1L7ICIidXE7KbV3716XxwUFBTzzzDN07ty5yYISEZHWRW1D45yZlBIR8RVqH0REpC5uT3R+pri4OGbNmsVLL73UFPGIiIgPUNvgnsDTklKa7FxEfJnaBxEROV2jk1IARUVFVFZWNsWpRETER6htqL/TclLqKSUiPk/tg4iIOLg9fG/27Nkuj6urq/nss88YMmRIkwUlIiKti9qGxjGZTASaTVhshnpKiYhPUfsgIiJ1cTspdaaQkBCmTJnCTTfd1BTxiIiID1Db4L6Ak0kp9ZQSEV+m9kFERE7ndlLq0UcfbY44RESkFVPb0HiBZhOVaE4pEfEtah9ERKQubielFi9eXK/9/vCHP7gdjIiItE5qGxrPMdm5klIi4kvUPoiISF3cTkrt37+fDz74gIsuuogLLriAI0eO8L///Y+LL76YiIgIwD43hoiI+A+1DY0XcDIppeF7IuJL1D6IiEhd3E5Kmc1mZs+ezW9+8xvntrfeeouPPvqIhQsXNmVsIiLSSqhtaDz1lBIRX6T2QURE6mJ294BPPvmEyZMnu2y74YYb2Lp1a5MFJSIirYvahsY71VPK5uFIRESajtoHERGpi9tJqbi4OHbs2OGybdOmTXTo0KHJghIRkdZFbUPjafieiPgitQ8iIlIXt4fv3XHHHUybNo0RI0bQqVMnsrOz+eijj3j66aebIz4REWkF1DY0nnP4nqGklIj4DrUPIiJSF7eTUpMmTaJz5868/fbbfP3113Tp0oXXXnuNnj17Nkd8IiLSCqhtaDxnTymrklIi4jvUPoiISF3cTkoBDBkyhCFDhlBQUEBcXFxTxyQiIq2Q2obGCTTbR9Srp5SI+Bq1DyIiUhu355Sqrq7mqaeeon///gwfPpzs7GwmTJjAsWPHmiM+ERFpBdQ2NJ56SomIL1L7ICIidXE7KbV48WIyMzP5+9//TlBQEG3btqVDhw5kZGQ0R3wiItIKqG1ovADTyTmlNNG5iPgQtQ8iIlIXt4fvrVu3jldffZWEhARMJhPh4eE8+uijpKamNkd8IiLSCqhtaLzAAE10LiK+R+2DiIjUxe2eUmVlZc6x4MbJD86hoaGYzW6fSkREfITahsbT8D0R8UVqH0REpC5utwZ9+/Zl8eLFAJhODjX45z//Se/evZs2MhERaTXUNjReoFk9pUTE96h9EBGRurg9fO/+++9n6tSprF27ltLSUkaOHElpaSkvvvhic8QnIiKtgNqGxgtUTykR8UFqH0REpC5uJ6Xi4+N59913+fjjj/npp5/o0KEDV111FZGRkc0Rn4iItAJqGxrPMdG5RT2lRMSHqH0QEZG6uJ2UuuGGG3j77be5/vrrmyMeERFphdQ2NMyhQ4fYv/8gACXFZfZt2dns4ahb54mLa8t553Vp8vhERBqruduHwsJC5s+fzyeffILNZuOyyy7joYceon379uzevZtHHnmErKwsYmNjmT59OpMmTXIeu3btWpYsWUJubi4XXnghDzzwAMnJyQBYrVb+9re/8dZbb1FeXs6gQYP4y1/+Qvv27ZulHiIi/srtpBRAeXm5vt0QEREXahvc8+OP2Qy5/DLKy+zJqPjRs4i4eBhPL17E/M/edutcoWHhbNm8Q4kpEfFKzdk+/PGPfyQ6OpoPP/wQs9nM7NmzeeCBB/jrX//KtGnTuPPOO7npppvYsWMHaWlp9OzZkz59+rBt2zbmzZvHsmXL6NOnDytXrmT69Ol89NFHhIWFsXTpUjZv3syaNWuIiorigQceYO7cufzjH/9olnqIiPgrt5NSKSkpTJo0iSuvvPKsbwr+8Ic/NFlgIiLSeqhtcF9+fj7lZWVMvOtR2p93AZ+fCOGnCrh83G+58Ne/rvd5jv34Paufmk1BQb6SUiLidZqzffjyyy/ZvXs3W7ZscSa95s2bR25uLh988AExMTFMnjwZgMGDBzN69GhWrlxJnz59WLVqFaNGjaJ///4ATJ06lddff53169czYcIEVq1aRXp6Oh07dgRgzpw5DB06lOzsbLp00XutiEhTcTsp9eOPP9KlSxe+//57vv/+e+d2x2oaIiLif9Q2NFz78y6gU/eL2Z+VDxWlRMa1p1PnaE+HJSLSJJqzfdizZw+JiYn861//4tVXX6W8vJwrrriCe++9l/3795OUlOSyf2JiIqtXrwYgKyuLCRMmnFW+d+9eiouLOXLkiMvx8fHxREdHs2/fPiWlRESaUL2TUrfeeisvvPAC//znPwGoqKggNDS02QITERHv15Rtw969e1mwYAFfffUVQUFBXH755dx3333ExcU167wg+fn5PPDAA2zfvp2AgADGjBnDvffeS2Bgg0a4N9jJxfewaZ5zEfEBLXHvUFRUxL59++jVqxdr166loqKCe+65h3vvvZf4+HjCwsJc9g8NDaXs5JDp0tLSWstLS0sBCA8PP6vcUeYOd/Nvjv31vY4rXZeanX5dTl8rxWTy72ul10vtGnptmuta1vsT965du1weX3nllWzfvr3JAxIRkdajqdqGiooKbrvtNm688Uaee+45SktLuffee7n//vtZsGBBs84LMnPmTBISEti0aRN5eXlMnz6dFStWcNtttzXJNaov88mW3tDqeyLiA1ri3iE4OBiwD60LCQkhMjKSmTNncuONNzJ+/HgqKipc9q+oqCAiIgKAsLCwGstjY2Odyary8vJaj3dH27ZRbh/TmON8na6Lq+joCCCfsLAQ57bQ0CBiYiKIj9e10uuldt5ybRr8NbA+NIuIyJka2jbk5ORw0UUXkZaWRkBAAMHBwdx0003cc889zToviM1mY/v27WzcuJGwsDC6dOnCjBkzePzxx1s8KWVSTykR8WHNce+QmJiIzWajurqakBD7DbnNZgPgZz/7Gf/v//0/l/2zsrLo0aMHAD169GD//v1nlV955ZVER0eTkJBAVlaWcwhfbm4uhYWFZw0JrI/8/GLcqb7JZL9ZdPc4X6frUrOiInvvvfLySud1qaioprCwlLy8Yg9G5ll6vdSuodfGcVxTMzf0QM0TIiIiZ2po23DhhRfy/PPPExAQ4Ny2YcMGLrnkklrnBdm7dy+Ay03DmeXnmhdk//79xMTEkJCQ4Czv3r07OTk5nDhxwq06OLrJu/NjP9D+j6OnlK0Rn5waEkNz/3hrXKqf6qf61X1cU2uOe4chQ4bQpUsX7r//fkpLSykoKOCpp57i2muv5YYbbiAvL48VK1ZQXV1NZmYm69atc84jNXHiRNatW0dmZibV1dWsWLGC/Px8UlNTARg/fjxLly4lOzubkpIS5s+fz8CBA+natavbcRqG+z8NPc7Xf3Rd6r4ujX3d+dqPrkPTX5vm0LITZoiIiJyDYRgsXLiQjz76iFdeeYWXX365WecFOfNYx+OysjLatGlT77jd/eYoJsY+BCQ0JJjw8BCCg+1NckBAAOHhIXUd6iI01D58JTbWe7vpe0v38Oai+rVuql/rFRQUxD//+U8ee+wxRowYQWVlJcOHD2fOnDm0adOG5cuXk5GRwaJFi4iLi2Pu3LkMGjQIsPe6ffDBB3nooYc4evQoiYmJLFu2jJiYGADS0tKwWCxMnjyZ0tJSUlJSWLhwoecqKyLio+qdlLJYLLz55pvOx9XV1S6PAcaNG9dEYYmISGvQ1G1DSUkJs2fP5quvvuKVV16hZ8+ehIWFUVzs2v28qeYFMQzjrDLHY3fnDXG3C3RhoT0pVlFZRVlZJVaLFYCqagtlZZX1Pk9FRRUAx497Xzd9k8m3u86rfq2b6lf3cY3VUvcOCQkJPPXUUzWW9e7dm9dee63WY8eOHcvYsWNrLAsKCiI9PZ309PRGxygiIrWrd1IqPj6eRYsWOR/Hxsa6PDaZTEpKiYj4maZsGw4dOsTtt99Op06dWL16NXFxcQAkJSWxefNml32bal4Qm81GYWEheXl5xMfHA3DgwAE6dOhAVJR7N2Xudmt27nvy31PD99z6tTWf08s0Z5dvb6D6tW6qX/PQvYOIiNRHvZNS//3vf5szDhERaYWaqm0oKirit7/9LYMGDSIjIwOz+dSUh6mpqTz++OOsWLGCyZMn89lnn7Fu3TqWLFkC2OcFSUtL4/rrr6d///6sXLmyxnlBevfuTWxs7FnzgvTv35/58+fz8MMPc/z4cZYsWcLEiRObpF7uMJ+cbqUxc0qJiHgL3TuIiEh9aE4pERHxuDfeeIOcnBzee+893n//fZeyXbt2Neu8IIsWLeLhhx/mmmuuwWw2M27cOGbMmNFSVXdy9JRSTkpERERE/IWSUiIi4nG33HILt9xyS63lzTkvyJlDTDzFpJ5SIiIiIk2isrKSrVu/pLCw1OULvz59+hISUv8FZaT5KSklIiLiBZpiTikRERERgT17PufFDVuJ7XiBc9vRg/uZBlx2WYrnApOzKCklIiLiBTSnlIiIiEjT6XhBEu27XezpMOQczOfeRURERJqbekqJiIiIiL/x2qSU1WplypQp3Hfffc5tu3fvZtKkSSQnJzN8+HBWrVrlcszatWtJTU2lb9++jB8/nl27drmcb8GCBQwZMoTk5GSmT5/OsWPHWqw+IiIidXEsOKieUiIiIiLiL7w2KbV48WJ27tzpfFxUVMS0adMYN24cO3bsICMjg0cffZQ9e/YAsG3bNubNm8djjz3Gjh07GDNmDNOnT6e8vByApUuXsnnzZtasWcOmTZsIDQ1l7ty5LVIXm2FgmAJa5HeJiEjrFODoKaWuUiIiIiLiJ7wyKbV161Y++OADrrvuOue2Dz74gJiYGCZPnkxgYCCDBw9m9OjRrFy5EoBVq1YxatQo+vfvT1BQEFOnTiU2Npb169c7y2+//XY6duxIZGQkc+bMYePGjWRnZzd7faa9tpujfadSVF7d7L9LRERaJ8fwPatyUiIiIiLiJ7wuKZWfn8+cOXN44oknCAsLc27fv38/SUlJLvsmJiayd+9eALKysmotLy4u5siRIy7l8fHxREdHs2/fPrdjNJnc+wkNCsAWEsWH+/Kostia7Lze9NPa469v/ZrydeFtP75QB9WvYceJd9BE5yIiIiLib7xq9T2bzcasWbO45ZZbuOiii1zKSktLXZJUAKGhoZSV/X/27jw8qvJsA/g9WzKTdSYJhKAgQhJwCSQEE0BEBePGEgxR7Bf9jK1gIda6RKQFhMoXllorojVa1FILFQtILRZB2mpBlBAwgKJgIhACIfs2M8ns5/tjFhgSIJNMZr1/15ULcrZ53zmTmXOeed7nbb/ieq1WCwAICwvrtN6+zhWxsZEubf/q/4zGuKUfoRkR+L6hHeOGxnbaRiaVIC7OteP6GlefF3+jUIRecl0gnD8g8M8h+0e+TGyLSjEoRURERETBwqeCUm+99RZCQkLw8MMPd1qnUCigVqudlul0OoSHhzvW63S6TutVKpUjWGWvL9XV/q5obFTDlXsGqQiIOr0XLYl34USdBqMGRHTaxmgyo6FB3cXevk8kst4Mu/q8+At7NklHh/6S/fPn8wcExzlk/y69H/mG8zWlvNwQIiIiIiIP8amg1EcffYS6ujqMGTMGABxBpn/961+YP38+9u7d67R9RUUFkpKSAABJSUkoLy/vtH7ixImIjo5GfHy80xC/+vp6tLS0dBry1x2CAJdvbENbKwEADVoDOoxmKGSdC5/7+81yT54Xf3KlvgVC34PhHLJ/5Kvsw/fMPIlEREREFCR8qqbUjh078PXXX+PAgQM4cOAApk6diqlTp+LAgQPIyspCQ0MD1q1bB6PRiH379mHbtm2YOXMmACA3Nxfbtm3Dvn37YDQasW7dOjQ2NiIrKwsAkJOTg+LiYlRVVUGj0WD58uXIyMjA4MGDPdI3ibEdseEyAMCZFt0VtiYiomBjL3TO4XtEREREFCx8KlPqclQqFd59910UFRVhzZo1iImJwaJFizB27FgAwLhx47BkyRIsXboUtbW1SExMxNq1a6FUKgEABQUFMJlMyMvLg1arRWZmJlavXu3RPlwdrUCj1ogzLR1I6uf6sEEiIgpcEjGH7xERERFRcPHpoNTKlSudfk9JScHGjRsvuX12djays7O7XCeTyVBYWIjCwkK3ttEVg5RyHK5uw9lWZkoREZEzEWffIyIiIqIg41PD9wJdv4gQiAB0GC1oN5i93RwiIvIh9kLnrClFRERERMGCQSkPkkrEiJJbk9Oa2g1ebg0REfkSsX34HmNSRERERBQkGJTysJgwa7Hzpnajl1tCRES+xJ4pZWFUioiIiIiCBINSHhYTFgKAQSkiInJmS5SCANaVIiIiIqLgwKCUh8WE2zOlOHyPiIjOE9srnYND+IiIiIgoODAo5WH24Xst7UZ+E05ERA72mlIAM6WIiIiIKDgwKOVhkaFSSMUimAWgtcPk7eYQEZGPuCAmxbpSRERERBQUGJTyMJFIdEGxcw7hIyIiK7FIBHtcijEpIiIiIgoGDEp5QbTCGpRq0zFTioiIzrPXlTJz+B4RERERBQEGpbwgKlQKgEEpIiJyJrZ9KnP4HhEREREFAwalvCBKzqAUERF1Zs+UYkyKiIiIiIIBg1JeYA9KqfUMShER0XkSR1CKUSkiIiIiCnwMSnmBPSilNZhhMlu83BoiIvIVjuF7DEoRERFRN1gEgRnW5NcYlPKCUKkYIRLrt+FtzJYiIiIbR6Fzfl9BREREV7Dz+zr8co8B22oi8K8fGrzdHKIeYVDKC0QiEaLknIGPiIiciTl8j4iIiLppy+FqmGyXDCcb26E18N6S/A+DUl7CYudERHQxWxItzAxKERER0WWodSYcqW4DAISKrSnWZ1p03mwSUY8wKOUljmLnDEoREZGNWMzZ94iIiOjKSiqbYRaAAWEiDAmz3lNWNXd4uVVErmNQykvsQalWBqWIiMjGMXyPUSkiIiK6jC9PNgEAblCJ0T/Uek95tlXHawjyOwxKeUlkqDUopWGhcyIisrElSrGmFBGRi8xmMx5++GEsWLDAsezw4cO4//77kZaWhkmTJmHTpk1O+2zduhVZWVlITU1FTk4OysrKnI63atUqjB8/HmlpaZg7dy7q6uo81h+iyxEEAV+dagYA3BArhkpmgVwqhsEsoFaj93LriFzDoJSXRIRKAAAagxkCbz582sGqFqgHjsGppnZvN4WIApzEPvsePxeIiFzy+uuv48CBA47fW1tbMWfOHMyYMQOlpaUoKirCihUrcOTIEQBASUkJli1bhpUrV6K0tBTTp0/H3Llz0dFhHf5UXFyMvXv3YsuWLdizZw/kcjkWLVrklb4RXayp3YgGrQEiAMOiRBCJgPjIUABAo9bo3cYRuYhBKS8JD7FmSpktAvQmzv3tqz74+iwe/+AI1IMnYNfxBtTzmwci6kOOmlL8WCAi6ravvvoKn376Ke68807Hsk8//RRKpRJ5eXmQSqUYN24cpk2bhg0bNgAANm3ahClTpiA9PR0ymQz5+flQqVTYvn27Y/3s2bORkJCAiIgILFy4ELt370ZVVZVX+kh0ocpm65flCdFyhNhmSVEqrLO7t3YwKEX+hUEpL5GIRVDIrE+/Rm/2cmuoKzqjGe/sOw0AEOvVAIAvTzYzs42I+gyH7xERuaaxsRELFy7Eyy+/DIVC4VheXl6O5ORkp20TExNx7NgxAEBFRcUl16vVatTU1Ditj4uLQ3R0NI4fP96HvSHqnlNN1oy+a1TnX/NKhTXpoUXHoBT5F6m3GxDMIkKl6DAaoDGwrpQv+vDIOTR3GHFVtBzGz95B85jHUKcx4GRTB4bGhnm7eUQUgOzD9xiUIiK6MovFgueeew6PPvooRowY4bROq9U6BakAQC6Xo729/YrrtVotACAsLKzTevs6V9je2l3e3tX9Ah2fl/NO28qKDIkNg0hk/b8yzJ4pZQLCrc9TMD9XF75eLrysCvbnBej531JfPW8MSnlRRIgE9WCxc18kCAI+KKsGAORnDsKfdqpx/YBIHK5uw8nGdgaliKhPOIbvMSZFRHRFb731FkJCQvDwww93WqdQKKBWq52W6XQ6hIeHO9brdLpO61UqlSNYZa8v1dX+roiNjXR5n97sF+j4vADVGgMA4IZBKkQLHQAaMUBlfW1qDWZIVTIoleGIiwve5yo6OhxAIxSKUMcyuZzPy4V85W+JQSkvinDMwMfhe76mqkWH6lYdZBIR7h7RH38CcE2MAoer23CmpQMWQXBM3U5E5C724XtmRqWIiK7oo48+Ql1dHcaMGQMAjiDTv/71L8yfPx979+512r6iogJJSUkAgKSkJJSXl3daP3HiRERHRyM+Pt5piF99fT1aWlo6DfnrjsZGNVxJgBWJrDeLru4X6Pi8nFdeaw24xoWI0Vpnzd4TTCYoZGJ0GC1oaregpUWLhgb15Q4T0Fpbrc9LR4fe8XrR6YxB/7wAPf9bsu/nbgxKedH5oBQzpXxNSaV1itWRA6OgCLHOlNgvIgShUjH0Jgtq1XokRMm92UQiCkBiDt8jIuq2HTt2OP2+YMECAMDKlSvR3NyMl156CevWrUNeXh4OHjyIbdu24Y033gAA5ObmoqCgAPfccw/S09OxYcMGNDY2IisrCwCQk5OD4uJipKSkQKVSYfny5cjIyMDgwYNdbqcgoEdBlJ7uF+iC/XkxmCyobrUGYK9RKXCq1rpcEIBouQwdRj00FnHQP0/2vl/8HAT783IhX3kuGJTyoohQa7BDY2CmlK/ZbwtKZV6jciwTi0QYpJSjoqEdp5s7GJQiIrc7X1PKyw0hIvJzKpUK7777LoqKirBmzRrExMRg0aJFGDt2LABg3LhxWLJkCZYuXYra2lokJiZi7dq1UCqVAICCggKYTCbk5eVBq9UiMzMTq1ev9l6HiGyqWjpgEYDwEAliw0Nw6oJ1SoUUNWo92s0SbzWPyGUMSnlRRMj5TKkIL7eFzjNZBByoagEAZAxWOq0bpFKgoqEdZ1p0yLzG820josAmts2Ja2FUiojIZStXrnT6PSUlBRs3brzk9tnZ2cjOzu5ynUwmQ2FhIQoLC93aRqLeqrQXOY8Jg+iiciLRCmuxcy2DUuRHxN5uQDCzZ0p1GC0QRHzj8BXHatXQ6M2IDJViRLzzmNmEKGuhvOZ2I4xmizeaR0QBzD58z+wLudRERETkc87ahu5drew8aiNabk16aLfwNp/8B1+tXhQqFUNin2kplLlSvuLoOWvhu1FXRTnOj114iBRhMgkEAI1aoxdaR0SBTMzhe0RERHQZNW16AOiylIi9ZrGOQSnyI3y1epFIJEKErYi2OTTKy60hu2N1GgDAiP5dBwr7RYQAAOq1eo+1iYiCg8T2qczZ94iIiKgrNWrrPcgA2wiOC4Xb7i0NghgGM68lyD8wKOVl9mi2hUEpn3HcHpSK7zooFWcLSjVoDB5rExEFB86+R0RERJdT02YdvjcgsnOmVKhUDKltpEeLgdcS5B8YlPIye10pc2jkFbYkT9CbLDjRoAUADL9UplS4LVOKQSkicjMO3yMiIqLLqbVlSsV3kSklEokc95dNOo82i6jHGJTyMmZK+ZaKBi3MAqBUyBAf2fmNHjg/fK9VZ4LBxGLnROQ+9jJ2zJQiIiKii7UbzGjVmQAAAy5xr2Kf4b1Zz2sJ8g8MSnnZ+ZpSzJTyBcdrrUXOR/SP6DTFqp1cJnF8A9GgZbYUEbmPY/ILBqWIiIjoIvYsqYhQiSO54WKOTCkGpchPMCjlZcyU8i32IufDL1FPyi4mzJot1dzOGfiIyH3sw/fMTMIkIiKii9SoL11Pyi7cdn/ZpGNQivwDg1JedmFNKYHfjHvdjw3tAIDkfuGX3S4mTAYAaO5gUIqI3IfD94iIiOhSatouPfOenf3+ksP3yF8wKOVl4bYxv5DI0MIAh9dVNlmDUtfEhF12O5XCFpRiphQRuRFn3yMiIqJLqbEXOb9EPSngfE2pJr1HmkTUawxKeZlELIJCZj0N9jcZ8o6WdqOjcOA1KsVlt1VdkCnFDDcichdHTSkO3yMiIqKL1LbZh+9dLlPqfKFz3qeQP2BQygfY3zjs6ZjkHadsWVIDIkMhl0kuu220QgYRAL3Jgg4j7x6JyD3sw/fMvIgkIiKii9iTGAZEXaamVIgEgACjBRyJQ36BQSkfYJ+Bj5lS3lXZbA1KDbnC0D0AkIpFiJTbvoXgED4ichMO3yMiIqJLsc++1z8y5JLbSMQihIgEp+2JfBmDUj7gfKaUzsstCW6VTR0AgGtiLj90z85RV6rD0GdtIqLgwuF7RERE1BVBEFCvsd539I+49PA9AJCLrRcSdRrep5DvY1DKB3D4nm+wD98brLpyphRwQV0pZkoRuVVTUxOysrJQUlLiWHb48GHcf//9SEtLw6RJk7Bp0yanfbZu3YqsrCykpqYiJycHZWVljnVmsxmrVq3C+PHjkZaWhrlz56Kurs6xvrGxEfPmzcOYMWOQmZmJoqIimEymvu9oFzj7HhEREXVFrTdBb7IGm+LCL50pBQChtqBUvYb3l+T7GJTyAfZpOzl8z7sqm62ZUkNczpTyzs0rUSA6ePAgZs2ahdOnTzuWtba2Ys6cOZgxYwZKS0tRVFSEFStW4MiRIwCAkpISLFu2DCtXrkRpaSmmT5+OuXPnoqPD+jddXFyMvXv3YsuWLdizZw/kcjkWLVrkOP5TTz2FsLAw7NmzB5s3b8ZXX32FdevWebTfdvbhe6wpRURERBeyZz1Fy6VXrH8rtw3fY6YU+QMGpXwAh+95n9FswdkW+/C97mVKRduCUq0sIEjkFlu3bkVhYSGefvppp+WffvoplEol8vLyIJVKMW7cOEybNg0bNmwAAGzatAlTpkxBeno6ZDIZ8vPzoVKpsH37dsf62bNnIyEhAREREVi4cCF2796NqqoqVFZWYv/+/XjuueegUCgwaNAgzJs3z3FsTztfU8orD09EREQ+yp711O8KQ/eACzKlmPRAfoBBKR9gL3Te1G6Ezmj2cmuC09kWHcwCoJCJ0T/i8umwdkqFNZioM1lgkV56Bgwi6p4JEyZg165duPfee52Wl5eXIzk52WlZYmIijh07BgCoqKi45Hq1Wo2amhqn9XFxcYiOjsbx48dRXl4OpVKJ+Ph4x/phw4ahuroabW1t7u7iFUlsn8ocvkdEREQXqldbs576deNexV5Tql7LTCnyfVJvN4CAUKkYMBsASQjqNAYMVnVv+Bi5j33mvWtUYRDZMhWuRCYRIzxEAq3BDLNC1ZfNIwoK/fr163K5VquFQuH8viiXy9He3n7F9VqtFgAQFhbWab193cX72n9vb29HVFRUt9vfzbeOzttfsJ8jU6qHqVKutqGv2dvja+1yF/bPv7F/l9+PiHxLnS1T6kpFzgHWlCL/wqCUDxCJRJDo1TCHxeJcm45BKS845eLMe3bRCqktKBXTF80iIliDRGq12mmZTqdDeHi4Y71Op+u0XqVSOQJM9vpSF+8vCEKndfbf7cfvrtjYSJe2Vyqtx5eHhiAszHqBKUismbNmAVAoQroVJJfLrd+YqlThiItzrQ2e4upz42/YP//G/hGRP7DPvOdSphRrSpEfYFDKR4j1bTCHxaKWM/B5RaVt5r3u1pOyU8plqG7Vw8SgFFGfSU5Oxt69e52WVVRUICkpCQCQlJSE8vLyTusnTpyI6OhoxMfHOw3xq6+vR0tLC5KTk2GxWNDS0oKGhgbExcUBAH788UcMGDAAkZGu3cg1Nqrhyqi7lhZrppZOb0B7u/W9X3/BEG5tu96ROXU5Op31grO5WYuGBvUVtvYskch6Q+zqc+Mv2D//xv5dfj8i8i32TKl+kd3IlLIVOm/TmaAzmq9YGJ3Im3yyptSxY8fw6KOPIiMjAzfffDPmz5+PpqYmAH07Lbg3SXTW2iXnWOzcKxyZUi5mqdmLnZvDOHyPqK9kZWWhoaEB69atg9FoxL59+7Bt2zbMnDkTAJCbm4tt27Zh3759MBqNWLduHRobG5GVlQUAyMnJQXFxMaqqqqDRaLB8+XJkZGRg8ODBGDJkCNLT07F8+XJoNBpUVVXhjTfeQG5ursvtFATXf6w7nj+GRHw+CNWTIXw9aUNf//hqu9g/9o/9u/x+RORb7FlP3al/KxUJCBE770fkq3wuKKXT6fDYY48hLS0NX3zxBT7++GO0tLTg17/+dZ9PC+5NYr312+0azpDgFadtNaWGuJopZSt2zuF7RH1HpVLh3XffxY4dO5CZmYlFixZh0aJFGDt2LABg3LhxWLJkCZYuXYqMjAz885//xNq1a6FUKgEABQUFuPXWW5GXl4dbb70Ver0eq1evdhx/zZo1MJlMmDx5Mh544AHccsstmDdvnhd6CqfMKM7AR0RERHauzL4nEgHKUOs1RR3rSpGP87nhe9XV1RgxYgQKCgogkUgQEhKCWbNmYf78+U7TggNwmhZ85MiRTtOCA0B+fj4++OADbN++HTNnzsSmTZtQWFiIhIQEAMDChQsxYcIEVFVVYdCgQV7rMwBI9NZMqRpmSnlcS7sRrToTALhcz0tpz5SSK2EyWyCV+Fycl8gvHT9+3On3lJQUbNy48ZLbZ2dnIzs7u8t1MpkMhYWFKCws7HJ9XFwc1qxZ0/PGutEFiVIwM12BiIiIABjNFjS1GwF0L1MKAKJDgLoOZkqR7/O5O+ihQ4fi7bffhkRyftzrzp07ccMNN/TptODeJrYHpZgp5XGnbPWkEqJCXR5vHR4isQ63EUtwtpUBRSLqHZFIBIktMGVmqhQREREBaNBaA0syicjxpfiV2DOlOAMf+Tqfy5S6kCAIWL16NT777DOsX78e7733Xp9OC95dPZ1aVyS69Dh9e6ZUrVoPAUK3itv6Cn+fUrmy5XyR8676cLnzJxKJoJRL0dhuxOmWDgyJdW34n6/w93N4Jezf5fcj3yIVi2E2W2BiUIqIiIgA1NkSF/qFd29mXgBQhtiH7zFTinybzwalNBoNfvWrX+Ho0aNYv349hg8f3qfTgruipzOSKBSXHv8batFBLAKMZgEIDUFclLxHj+FN/jpTS13HGQDAiIHRl51O/VLnLzYiFI3tRtTrzD47HXt3+es57C72j/yBRCwCzGBQioiIiACcH4LXnXpSdspQ+77MlCLf5pNBqdOnT2P27NkYOHAgNm/ejJgYaxHpvpwW3BU9mVoXADo69Jfcz2Q0ol9EKGrVenxX2QhxQpRLbfImf59S+fuzLQCAeIW0y+nUr3T+ImxTW3x3psXnpmPvLn8/h1fC/l1+P/ItUokIMAImcwC+WImIiMhldY4i592rJwVckCmlZqYU+TafqynV2tqKRx55BKNHj8Y777zjCEgBfTstuCt6MyXv5QyItIazq1v1Xp9C2FNTDvvCT2WTNXtusErRo/MXbRvXfaqx3et9CdZzyP717r2JfIvUVu3cZLF4uSVERETkC3qWKcWaUuQffC5T6sMPP0R1dTU++eQT7Nixw2ldWVkZ3n33XRQVFWHNmjWIiYm55LTgtbW1SExM7DQtuMlkQl5eHrRaLTIzM52mBfe2AVGhOFztnzPwzch9AGdrGi65Pk6lwnvvrvNcg7rJaLbgbIs1KDUkpmf1oOzFBiubO66wJRHRlZ0PSjFqSEREROcDS65kSkXbMqXqtQZYBP+qWUzBxeeCUo8++igeffTRS67vy2nBvW2ArY5UTZv/RbPrGpox47lXL7n+7y/90oOt6b6zLTqYBSBMJnHpTf5C0XLrn1FLhxEtHcZuz4hBRNQVBqWIiIjoQvZMqf4uZEpFhwAiWGfzbW43Ija8Z/c6RH3N54bvBTP78L0atf8FpfzVqSb7zHuKbs9kcTGZRAyx3lpLqtJ2PCKinpJKbEEp1pQiIiIiXJApFdn9wJJELEKMLRDFIXzkyxiU8iEJjkwp/xu+56/sQ+4GqxS9Oo6kvcnpeEREPSUVWz+amSlFREREgiCgrgeZUtbtrUEp+/5EvohBKR8SH8VMKU+zZzb1tJ6UnaSjyel4REQ9xeF7REREZKfWm6A3WSc/iXNxCJ69MDozpciXMSjlQ+zD99p0JmgNJi+3Jjicss28d00vg1JSR1CKmVJE1Dv2oJSZs+8REREFPXuWU7RcCrlM4tK+/ZgpRX6AQSkfEhEqRWSotWi2PxY79zeCIKCy2VZTqrfD9zqaAZyvUUVE1FPMlCIi6r5jx47h0UcfRUZGBm6++WbMnz8fTU3WLwsPHz6M+++/H2lpaZg0aRI2bdrktO/WrVuRlZWF1NRU5OTkoKyszLHObDZj1apVGD9+PNLS0jB37lzU1dV5tG9EwIUz77k2dA84P9yvniNxyIcxKOVjBnAIn8e0dBjRpjNBBPfVlDrTqoPJzOwGIuo5FjonIuoenU6Hxx57DGlpafjiiy/w8ccfo6WlBb/+9a/R2tqKOXPmYMaMGSgtLUVRURFWrFiBI0eOAABKSkqwbNkyrFy5EqWlpZg+fTrmzp2Ljg5r1ntxcTH27t2LLVu2YM+ePZDL5Vi0aJE3u0tBql5tzXLqyUzh9n3qmSlFPoxBKR9jH8J3rpXFzvuafajdgKhQl1NhLyY2qCGXimG2CDjDc0dEvSBhoXMiom6prq7GiBEjUFBQgJCQEKhUKsyaNQulpaX49NNPoVQqkZeXB6lUinHjxmHatGnYsGEDAGDTpk2YMmUK0tPTIZPJkJ+fD5VKhe3btzvWz549GwkJCYiIiMDChQuxe/duVFVVebPLFITqbJlSrhY5v3CfOtaUIh/GoJSPuUppzdg5y8BGn3MM3etlPSkAEF1wHNaVIqLe4PA9IqLuGTp0KN5++21IJOe/XNy5cyduuOEGlJeXIzk52Wn7xMREHDt2DABQUVFxyfVqtRo1NTVO6+Pi4hAdHY3jx4/3YY+IOrNnOfUoUyqSmVLk+6TebgA5uzpaDgA408LARl9zFDnv5dA9u2tUChyv09hm4It1yzGJKPgwKEVE5DpBELB69Wp89tlnWL9+Pd577z0oFM7XeHK5HO3t1i8ltVrtJddrtVoAQFhYWKf19nWuEIl6tr2r+wW6YH1e7FlO8VGhXfb9wudFEJyXx9tG4aj1JnQYzQgL6d3oEH9yuecl2F5DF+vp31JfPW8MSvmYq5kp5TGVTe7LlAKAIfZMqWYWOyeinjsflGJ9OiKi7tBoNPjVr36Fo0ePYv369Rg+fDgUCgXUarXTdjqdDuHh4QAAhUIBnU7Xab1KpXIEq+z1pbra3xWxsZEu79Ob/QJdsD0vDe1GAEDy1SrExXXue3R0OIBGKBTnh/fJ5TIoleEYcpUKkXIp1DoTDFIJBnexf6C63PPS1fMYjHzlb4lBKR9zlfJ8ppQgCBAFexi3D1U2Wy80hsS4KVPKdpxTHL5HRL3AQudERN13+vRpzJ49GwMHDsTmzZsRExMDAEhOTsbevXudtq2oqEBSUhIAICkpCeXl5Z3WT5w4EdHR0YiPj3ca4ldfX4+WlpZOQ/66o7FR7ZSpcSUikfVm0dX9Al2wPi9nbF94hwkWNDSoO61vbbVm73V06B3Pi05nREuLFg0NasRHhEKtM+H7yiYog6h4z5Wel2DW078l+37uFkQvS/8wMEoOEYAOowVNtqg4uZ/RbMHZFvvwPfdkSp2vKcVMKSLqOQ7fIyLqntbWVjzyyCMYPXo03nnnHUdACgCysrLQ0NCAdevWwWg0Yt++fdi2bRtmzpwJAMjNzcW2bduwb98+GI1GrFu3Do2NjcjKygIA5OTkoLi4GFVVVdBoNFi+fDkyMjIwePBgl9spCK7/9HS/QP8JtudFrTNBozcDsBYtv9Lz0tXrzj67+7k2vdf7463XS2//HgPxpzfvTe7GTCkfEyIVIz4yFDVqPc60dCA23PWCdnRlZ1t0MAtAmEzSo6KBXRlsq03VqjOhpd0IZZjMLcclouDCoBQRUfd8+OGHqK6uxieffIIdO3Y4rSsrK8O7776LoqIirFmzBjExMVi0aBHGjh0LABg3bhyWLFmCpUuXora2FomJiVi7di2USiUAoKCgACaTCXl5edBqtcjMzMTq1as93EMKdjVqaz2paLm0x/Wg7HWl7McKFt80mnGqXYpIoxmKXs60Tn2LQSkfdLVSjhq1HmdbdRh1VbS3mxOQTjnqSSncNkRSIZNggC2gWNncDmUYzx0RuU4qtiYxmxmUIiK6rEcffRSPPvroJdenpKRg48aNl1yfnZ2N7OzsLtfJZDIUFhaisLCw1+0k6qnaNluR88jQK2x5aQNs+9a2BU/N4n98U4M3vjUBkOObg2eRNbyfI4GAfA+H7/mgq2zFzjkDX9850WgNSg1xU5FzO3tdqUrWlSKiHmJNKSIiIgKAGrU1kDQgSt7jY9j3DZZMqa/PtKBo1w8AALnYAosA7D3RBJOZE8j4KmZK+aCro+3FzoMnmu1pJxqthe+Gxro5KKUKQ0lliyMTi4jIVRy+R0RERABQq+59ppR939ogCUr9eX8VLAIwpr8Y8WINPm+KhsZgxpFqNeK83Tgv0Ov1OHLkkNMykQiYNOkW7zSoCwxK+aCrmSnV5+yZUsPiXJ/W93Icxc6bee6IqGfsQSmzIICzsBIREQWvGtvwvQG9Gb4XdT4oZREEiAP4uuJ0cwe+PNkMEYBpQ6T4ugrIvEaJ/5Q34ptzbbjV/RPH+bwjRw7hjx/vQfw1SY5ltZXlUCrDkZR0oxdbdh6DUj5okG28a2VzB29I+oDJIjgymYbG9c3wPWZKEVFP2YNSgPX9SibhZwAREVEwsg+5sweWeqJfeAjEIsBoFtDUbkRcAE+ktelQNQBg/LUx6K84PzKmpLIFWoMZ9cbgnIgq/pokDB4xytvNuCTWlPJB16gUEAFo05nQ3GH0dnMCzpmWDhjNAuRSMRJ6MT67K/YaVWdbdRy3TEQ9IrkoKEVERETByV6cvDfD96QSsSMQFcjFzs0WATu+rwMA3J820LFcJBI5SrbUGAI3IOfPGJTyQXKZBAm2ulInG5lx424nGqxR82tjw9yevto/IgQKmRhmi8CaYETUIyKRyBGYYlCKiIgoOJktAmo1BgC9K3R+4f7n2gK3rtS359rQ0mFEZKgUmYOVTuvsJVvqjDLoTLy28jUMSvmoIY5Z3BiUcrcfG+1D99xbTwqw3kxeo7JG4k/y3BFRDzmKnXMGPiIioqBUr9HDbBEgFYt6PeQuwTb871wAZ0rt/rERADD+WhWkEucwR1y4DFFyKSwQ4WgzR7P4GgalfJR9GNjJJhbMdrcTDbYi526eec8usZ812FVRr+2T4xNR4OMMfERERMGtyjbp1cBoudPQ/p6wT6RVFcATadmDUhOHxXZaJxKJMNhWt/kYg1I+h0EpH3WtLSjFgtnuV9GgAdA3mVIAkGQLSv1Qr+mT4xNR4DsflOKFExERUTCqspUCGWQLKPXGIEdQKjAzpaqaO3CqqQMSsQjjhsR0uc1VtvI4x1p4beVrGJTyUdfasnhOsaaUW7UbzKi0ZZ+N6B/RJ48x3HbcH+oYlCKinmGmFBERUXA7a8tqulrZ+4mZ7Mc4G6CZUvsqmwEAowZGIVIu7XKbhKhQiCCgUWed+Ip8B4NSPuoaW6ZUjVqPdoPZy60JHOX1GggA4sJDENtH06HaM6Wq2/RQ60x98hhEFNikEtaUIiIiCmb2rKar3ZApZT9GTZseBlPgZQrttwWlMq9RXXIbmUQMpdTktD35BgalfJRSIYNKIQPAIXzudLzOWudpeB9lSQFAlFzmKCZY3sBsKSJynVRs/Xg2M1OKiIgoKNmzedwxfC8mTIYwmQQCgOoAK3Zutgg4WNUKALjpoln3LhZrD0qdbunjVpErGJTyYcNsGTflrE3kNsfr1ACA4fF9F5QCgOR+9iF8LHZORK7j8D0iIqLgJQiCIyjljuF7IpEIV9mOE2hD147XaaDWmxAeIsF1AyIvu22szAgAKD3dwi/+fAiDUj5suC2wcZyBDbfxRKYUACT3D7c9HgOKROQ6BqWIiIiCV2O7ER1GC8Qi6+x77hCoxc5LbVlPYwYpHddPlxIlMUMuAdp0Jt6n+RAGpXzY8HgGNtzJaLbgxwZ7UKpvZt6zswe9vq9V9+njEFFgknD2PSIioqBlL0g+IDIUMol7btkDtdi5vT7UlYbuAYBYBCQrrc9nCetK+QwGpXzYiP7W9MPyeg3TC92gokELk0VAZKgUA6Pc843DpdyYEAUAONHQDo2exc6JyDUyW6Fzg4nv/URERMGmyjF0r/f1pOyudmRKBU5QSmc049BZaz2pjMsUOb/QdSprCIR1pXwHg1I+bLBKAblUjA6jJaDePLzlyNk2AMCNCZEQiS6f2tlbseEhGBgthwDgaA2zpYjINQqZBACgM3H2VSIiomBzqslW5FzlvqDUYNuxTjUGziRah8+2wWAW0D8iBENiuvdcjVCKbPu2QmfkdZYvYFDKh0nEIiTZip3/4IND+CyCgE++r8VvPjmOxuHZOFjVitYOo7ebdUmHq61BqVFXRXnk8VISrJlu39gel4iou+xBqQ4jh+8REREFm4p6a8kR+72gOwyLsx6ruk0fMCM59p+2DsHLuEbV7aSD/goR4iNDYTQLOHyW92m+gEEpH5dsq010rNa3glJnWjrwv+vL8ML249h2tBZ61bX4+kwrNh8+h2+q2yAIvjfk5LAttXPUwGiPPF6KbQjft+eYKUVErpHLrB/P/AaPiIgo+NhnX0+Mc19QSqmQoX9ECAA46uz6u5LKFgBAxjXKbu8jEomQYas/ZQ9qkXcxKOXjro+3Ztt8e853orgVDVo8tvEwjtdpEBEqwaOZgxB98jMMjAqFRQD2VbZgX2WLTwWmatp0qNMYIBEBNyRcfqpQd7lxoD0o5ZtBOiLyXeczpRiUIiIiCiatHUbUaQwAzmc3uUuiLfOqIgCCUs3tBseEYBmDu1dPyu4mWxBrvy2oRd7FoJSPS73amtXzbY0aepP3h3E0ag345ZZv0Kg1IDEuHJvyx6DglmsRXnsY917fH+OGWN8Qvj2nxiEfSoe0p2Ym949w3Oz1teR+4QiVitGqM+FkU+CM3SaivqewZUpx+B4REVFwsQeMBkaFIiJU6tZjJ8ZZR+GU1/t/UKrUVqg8MS4cseEhLu17ky2IdbxOg5Z23y0/EywYlPJxg5RyxIaHwGgWcLTGu0Eek9mCBdu+Q53GgGtUCrz5wEjERYQ61otEItyYEOkITB2oasXZFp23muukzDZ0b+RAz9STAgCZRIxRtsdjFJ6IXCG3Bc9NFgFGMwNTREREwcI+tC6xX4Tbj53YL8zpMfzZ/h4M3bOLCw/BsLgwCAAOVLW4s1nUA+4NvZLbiUQipF0VjX/9UI+yM60YfbXSa235c2kVDp1tQ3iIBL+bcQOiFbIut7sxIRLN7QYcq9Pis4oG5IxM8HBLnQmCgH2nrOOFb3IxtbO3xg5RYf/pFpRUNuPB0Vd59LHJ2YzcB3C2puGS6+NUKrz37jrPNYjoMmRiESQiEcyCAJ3RApmE3yEREREFA3sWU2JcmNuPnXRBppQgCH0+I3lfEQQBJZXni5z3RMZgFX5saMdXp5pwx/B+7mweuYhBKT+QdrU1KHXojPcypcrrNXj7q9MAgOfvSMSQmMu/SY4bokKdxoCmdiP2nmyCNysqnW7uwNlWHaRiEW6yFbXzlMxrVABO4mBVC4xm3lh6U11DM2Y89+ol1//9pV96sDVElycSiaCQiaExmNFhNCNSzo9rIiKiYFDRh5lS18QoIBWLoDWYca5Nj4HRcrc/hidUtehQo9ZDJhFh9NU9m8Tq5qExeP/rs/jiRBMsggCxnwboAgHvkP2A/Q/tcHUrTF4YxmEyW/CbHT/AZBFw67BY3D2i/xX3kUrEuC0xFiIRcKqpA/q4ZA+0tGtf2bKkUq+ORliIZ+pJ2SX2C0dMmAwdRguOVPtOjS0i8n32IXwdPlBPkIiIiPqezmh2FO8e3t/9QSmZROyY0c+XJtJylX0UzMiBUT2uFzz66miEh0jQ1G7E9z42032wYVDKDwyNC3MENg5WtXr88dftr8LxOg2i5VIsyErqdppnbHgI0q6y1lTSDJuMpnZDXzbzkr482QQAGD/Es0P3AEAsEjlSSr88ySlHiaj77MXOdZyBj4iIKCgcrVHDaBYQFx6CQcq+yWJKsyU8fH3G8/eV7vJ5hbUkx/ghMT0+hkwixljb/eGeHxvd0i7qGQal/IBYJMLEYbEAzv8BekpFvRbv7LMO2yuclIg4F2c2SL0qGjFhMgiyMLz074q+aOJlafQmxxvu+Gt7/qbVG/Zz968f6iEI3hzISET+xP7NXweDUkREREHha1sCwuiro/us3pMjKOWFZAd3aG434GtbcfJJyXG9OtaEodb7w2AJSpktAmra9DirD0Gb3ncy8RmU8hO3JVr/4P77YyMsHgpsmCwCXtx53DFs764RrheAk4hFuHVYLCBY8K8fGvCv4/V90NJL+095A/QmC4bEKDA01v3FArvjlqExUMjEqG7V4dtzaq+0gYj8j9yWKdVh9J2LBiIiIuo7X9tmDB89qGd1kroj7SrrsU82tXttJEtv/LeiEWbBOrzxaqWiV8eacG0sJCLgh3otTjW1u6mFvqmquQObDp3DtqO1+LY9HNsqdN5ukgODUn7ipsFKhIdIUK8x4LsazwQ2/lJahe9rNYiSS7HgjsQeR+vjIkIQVlUCAPjtvyvQ7ME3v+3f1QIA7r0+3muzS8hlEke21KceDsoRkf+yZ0px+B4REVHgM5ot+MZWgzath8W7u0MZJsMw28x+h/xwCN9/yq0jhyYl9S5LCrA+F2NtQwA/sd03BqIqfQh2HKuHWm9CqFSM/jIDbhsc6u1mOTAo5SdCpGLH8LPt39X1+eOdaNRi7VeVAIBnbhuGuIjevWjDqkowLC4MzR1GvPSfH93RxCs616Zz1OC657orF2fvS3fZisN/eqwOBhYtJqJuOD98j+8ZREREge6bc23QmyxQKWS49goznffW6KuVAIDS0y19+jjuVtOmQ0mltU7v5F4O3bO793rrfdon39d5bESSJx2sN+O7duvraUT/CDw4eiDSIrS4KtKzE4BdDoNSfiQ7ZQAA4J9Ha6HRm/rscYxmC5Z+chxGs4Cbr41x/KH2hkgwY8ndwyERAbuO1+M/P/R9xtCWw+cAAOmDojEgyrvTnY4dokK/iBA0tRux41jfBxWJyP8pHMP3mClFREQU6D49Zr0/Gn+tqs9HeNxsS3b4T3kDTBb/CcRs/aYGFsF6f3eNmwJ3E4fFIjxEgnNtepT5YebY5Xx1qgnrjpkAiDCifwQmDFUhROJ7ISDfaxFdUsZgJYbEKNBuNOOfR/suvfC13Scdw/Z+5cJse1dyXXwkHskYBABYvqsc1a19N461tcOITWXVAICfjL6qzx6nu2QSsaMd6w+cCcgovC8yWwTUa/SoqNfCJFfCaGbGCfkPe6aU1mDmJAlEREQBzGi2OGrv3nNdfJ8/XuY1SqgUMjS1G7G/0j9mCDeZLfjomxoAQO6ogW47rlwmwZ222snrD5xx23G97Uh1G+Z/9B3MAjBAZsDNQ/s+2NlTDEr5EZFIhPtTrYGNv359Fvo+GAa2/btavP/1WQDAkruHIz7SvWNNfzb2GlwXH4FWnQnP/+O7PquVsvHrs2g3mpHULxy32Oo5edt9IxMQHiLBycZ2fFbu2VkUg4laZ8KHR87hF1u+weQ/fIl73yrBg38+iLrUfKzbfwYflFXjy5NNqFXreaNPPi1aLoVYBOhNFrTp+i47loiIiLzry5PNaNWZEBsegjGDlX3+eFKJ2BGI+eR7/xjFsf27OjRqDYgND8Ftie69v3tozCCIRcAXJ5rwQ53Grcf2hop6LZ768FvoTBZcpxIhJVwLsY8GpAAGpfzO1Bvi0S8iBNWtOqw/UOXWYx843YJlO38AAPzvTYMcxbndKUQqxm+nXw+lQoZjdRr86uPvYXJz9srp5g78xRblfjRzsM/8AUaESvGgLVvq95/92KdDMINRvUaPV/97AlP/WIIVu8qx71QztAYzJCJApZBBZNIDANp0Jhyt0eAf39bi79/U4od6Dcx+lLZMwUMqEaOfrZ5fjVrv5dYQERFRX9l6xFp25K4R/SARe+bexV5z9/PyBjRqfXsWvg6jGcV7TwEAHhpzNaRuHoI2WKXA5GRrkM5eV9lfnWnpwBNbvoFab0JKQhTmXC+Dh15SPcaglJ8JC5HglxOHAgD+VFLltqkrSyqb8dTWb2GyCJiUFIeCW4a45bhdGRAlx++yr0eoVIwvTjTh1/885rasL5NFwIs7jkNvsuCmwUq3FcBzl/yMQbhaKUedxoBX/3uCmTpuUNXcgaJPf0D22/ux/sAZtBvNGBobhiduuRYbHh6NL566BbsKxiHhQDEeHnMVsobHITEuDBIR0KA14L8VTfigrBrtA9PRbmDtHvItCVHWoNS5NgaliIiIAtGB0y3Ye7IJEhGQMzLBY497/YBIXD8gEjqTBX/80rcDMetKTqNBa8DAaDkeSHXf0L0L/TRzMCQi4POKRscMf/7mTEsHCjYdQaPWgMS4cKzOuQGhEh+PSAGQersBntbY2IjFixdj//79kEgkmD59Op5//nlIpf7zVNw5oh/+/m0NDpxuwVMffos//U8qVGEhPTqWIAjYdKgar3x+AiaLgPHXqvCbe4b3eXbRqKuisWra9XjuH0fxWXkD5m06gqIpI3pVkNwiCPi/ncdxuLoNYTIJFt+V7DNZUnZymQQL7kjCE5u/wd+/qcFglQIP3zTI280CAMzIfQBnay79BhynUuG9d9d5rkFX8O25Nmw4cBb/Ka+HPdEp9aooPJIxCDdfG9PlmGm5TIIhMWEYEhOGsUPMOF6nwdFzGmgNZmDobZi2tgQPpA7ErLSroAyTebhH5G2++PkwICoUOGudbYaIiDzPFz8bgpVer8dXX32LlhYtLvxed+TIVISG+s709q4wmi145XPrzOQ5owa6rXh3d4hEIjx161DM+eAw/v7NOeSMSsDw/hEee/zu2nuiCX8qsY4QenLitQiR9k1eTWK/cPxvxiD8qaQKq/5VjuviI5Dg5cmyXHGsVo1n/34UdRoDBinleG3mjYiS+8f9TNC9mz711FOIj4/Hnj170NDQgLlz52LdunV47LHHvN20bhOJRCiaMgKPbijD2VYdHtt4GKumXY/EfuEuHae8XoNXPj/hmAo0a3g/LL17eJ/9oV/s5qExeG1mCgo/Oooj1W34yXsHMWf8EMwcmeByG1o6jFi+qxyflTdAIgJ+c89wn30TybxGhaduHYrV/z2BNbtPol5jwC8mXguZh2dCMJgsaNAaYDBZYIGAs1oxJv/iZcgkIoRIxZ0Cen9/6ZcebV9XNHoT/lvRiE2HqnG0Ru1YPmFoDB65aRBSr47u9rEUMglSr4pGSkIUyuu1+PJoBdoQg7f3ncb6A2cw7cYBuOe6/rgxIdJniwKSe/ni50N8ZChEANR6MzR6EyJCg+5jm4hcJAgCvqtR44d6LZTRLbj9mmgA/BzrKV/8bAhWR44cwp92fgVVwrWOZbWV5ZgD4KabMr3XsB4yWwQs+eQ4fqjXIiJUgtnjBnu8DWlXR+P2pDh8Vt6AZ7Z+i3d+kur1Wcsv9NWpJiz85/cQYM0isw+x6yuPjb0Ge35sQkWDFj//2xH8ITcFVysVffqYvWUyW/C3Q9X4w56TMJgFXBsbhjdyUxAX4T+B2qC6uq2srMT+/fuxe/duKBQKDBo0CPPmzcNLL73kdx8sMWEheDUnBQWbj+B0cwceXv81pt4Qj6k3xOOGhChIuxg4KggCzrbqUHq6BbuO1zuCUSESEQpuuRY/GX2Vx2++0wcp8V7eaLzwyTF8e06N33/2I9aVnMa918djwtAYjIiPQHhI1y9TQRDwY2M7/nW8HpsPVaNVZ4JELMJv7h6O25J8a9jexfLGXA2N3oS3953G+1+fxX8rGvA/6VdjcnKcW95A9CYL6jV61KqtP3X2fzUGx+/NHUbnnUY9jI22GQtFsA4VDQ+RICJUiogQCToS0vDfigYMiJQjPioU0XJpn79e2g1mlNdr8F2tBl+ebMKB0y2OaWtlEhHuHNEfeelXIalfz7/VkYhFGBEfge/fW4fCVWuxrqQKx+o02HSoGpsOVSMhKhQTh8Vi5MAo3JgQhYSoUAapApCvfj6ESMSIiwhBvcaA0tMtuC0xlq8/IurSmZYO7Pi+Dtu/q0VVy/nsynV5qbhhQJQXW+a/fPWzIZBZBAFtHSY0aA1o1Boc/za2G3DmnBH1MTeiXR6LUKkYYTIJRAYZTrVZMKhNh9jwEI9/ydsTgiDgWJ01OaDsTCukYhFWTL2uxyNfeuvXWUk41diOk03teGRDGX5561DckdzPY4kKXalq7sCGg2ew9cg5WAQgfVA0CicN6/PHDZGK8WrOjXj8b4dxpkWH/3nvIH6aORjZKQO8dn4upU6tx2flDdh0qBqVzR0AgFuGxmDpPcP9JkPKLqiCUuXl5VAqlYiPPz/N5rBhw1BdXY22tjZERfnXB/aQ2DCsf3g0Xtz5A7440YS/f1ODv39TgxCJCFdFKxAWIoFCJobJIqBBa0CDxgDdBbWbxCLY6kdd69UI8CCVAmsfTMW2b2vw9leVqNMYsP7AGceUnDFhMlytVEAuFUMqEcEiAK0dRlS1dECjP18D6NqYMLx473CMiI/0Vldc8vjNQzAiPgLLd5Wjuk2P3332I3732Y9IiArF1UoFYsNDEBMmQ4hEDLFYBKlIBLEYMJkF6E0Wx4/OZIbeZEFTuxHN7QY0tRutw9G6IVQqRqhUDIlIhNa2VohCwmEWBAiwTkOvNZhRp7EVPhw2CYUffefYVy4Vo39kKKLkUkSGSh3/hkjFkEnEkIlF1n8lIkjEIggCIACOOlr2tGujxQKd0YIOoxk6owUagwl1agPqNNbg2cVVt4bEKHD3df1x38gExLjxw0EEAZOT+2FSUhz2V7Zg29Ea7P6xEefa9PigrBof2AJ2CpkYAyLlGBBl7Xt4iBRhIRKEhUgQIhFDBEAkAsQikeNfscia4SiC/TkAAMHxHAjo/Nxc+PvFywSnZdbfskZehWvCg+ot3a18+fPhpsFKfPJdHSoa2tFhtGBAVCikYhEkttdYa4cUEan34PMqA8qFaq+181IiIuTQaAJ3+KGv9a+7pQq7W9EwIiIUGo3+Cse98tG6067utqk723WnZqNIBISFhUKrvXzNNnc+p67UkmxpbUFH+6VrhwoAOkwCGnUCKtvMOKc9f40XKgGSlBJkDI1Fci++uAl23vhsMJktePvLkzhV2wCj0QgxrNcRYhGQEB+PEJkUEpH12koiFjmuO4Dz1x7A+esO6/fU1uX2be3XFIDzNYljGTpfr11u2YWvavu1yYXHQxfLjGYBGr0JbToTNHoTWnVGNGqt17Gmy05AEwK0qy/4PQJlh4z47aH9AKwz18ZFhCA2LASx4SEID5FALpNALhVDLpM4PWf25+j8c2i/Zrvwuuv882O56Dm4+DmxdHGNduF2WoMZtWo9jtVqcLbV+rkhl4qx5O7hGDsk5jJ97ltKhQyv5abgF5u/wcmmdiz55Dhe+k8FbhgQiauiFYgIlSIyVOIIUl3utWURbH0XBMfz5XhuLniOLlwmQIBZAJrbDTjXpseZlg6cuSCwnn3jAMyfnOixgGP/yFAU3z8Si7cfw6GzbfjDF6fw5t5TGBYXjsGqMMSEyaC036OJrF9wW6/3ra+fi183l/q76s7f1IWvo3aDGS0dRjS1G3GyqR3Vreefo2i5FHMnDMF9IxN8rnxNdwTVHYxWq4VC4Rx8sf/e3t7e7Q8Wsbj7FyiA/aInDKEy8SX3Cw8Lg7gHf2exESF4deaNOHK2DX//5hxKKlug0ZtRrzUAWudtpRIxVCESJPePwLghKkxOisNAZe/TM93RvxCxCDNTE5CdEo8vTzXjvxUNKKtqQ2O7EQazgBONXV2UiRATHoK0gVG4+/r+mJgY22WGWG/15fm7PTkO465V4eOjdfjXD/X4vkYDtd6M72t7PhWpSCRChC041D8iBP0jQtEvQoZ+kaHoFx6K+MgQ9IsIRb+IEETZsp1EIuCBhx7C3U8sh8kswGCyBofaDRZo9SZojGZUHD2Eodenoq5Nh+YO68yBTe3WN8a+Eh4qRVy4DEn9IpAyMAoThsbgmhjXA6iunUMRxg1VYdxQFXRGM0oqW3DoTCuO1mpQUa+F2SKgXmuw/o35kA++Oo5/F97p8nsTWbnj88HVzwapVILIyEho6s+i6RIZoQCgAHBTrBTfaeRo1ZnQqrt45s4IXDP9l9h60gScPNX9BhBRABEjXCZAd+Yo2r/bjeaKEpwz6lAaFob7dn2OgQOv6vaR+NlwnjfuHU7Ut+ODw7X2PZ1X1td17yB+TC6TAADCpEBUiAhRMiAqVIRIKdDW0oizbXqERihhsIhgFETQ6gwQy+TQCVKYBcAsALVqA2rVvnWddjGlXIobY8WYNkSCmI5KHDrU80LjJ05UoKW6FSLh/OusrfY0ToRrILM9n93x3CgBn58JwRc1FrQZgKM1Ghyt6fk9SW9EhUqRpBThjqslSFS24btvv3b5GL19XuYOF3BAFYovzllQpRFQ3aZHtQ9NPBMZKsWgCBHG9BdjTH8xFEI1jhzu/OXkiRMVaKttQl3I+eyptrrTkEiSexTX6AsiIYim/9q1axcWLVqEkpISx7Ljx49j+vTpOHDgACIj/SPDhoiI3IufD0REdDF+NhAR9T3fH3TrRklJSWhpaUFDw/kZxn788UcMGDCAHypEREGMnw9ERHQxfjYQEfW9oApKDRkyBOnp6Vi+fDk0Gg2qqqrwxhtvIDc319tNIyIiL+LnAxERXYyfDUREfS+ohu8BQENDA1588UWUlJRALBZjxowZKCwshETS/fG2REQUePj5QEREF+NnAxFR3wq6oBQREREREREREXlfUA3fIyIiIiIiIiIi38CgFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKOVmTU1NyMrKQklJySW3+e9//4tp06YhNTUV99xzDz777DMPtrB3utO/xx57DCkpKUhLS3P87N6924OtdN2xY8fw6KOPIiMjAzfffDPmz5+PpqamLrf1x/PnSv/88fwBwFdffYX7778fo0ePxs0334xly5ZBp9N1ua0/nkNX+uev5zAQNTY2Yt68eRgzZgwyMzNRVFQEk8nU5bb++LoEXOvj+++/j7vuugtpaWm46667sGHDBg+31nWu9M/uhx9+wKhRoy77WekrXOnf/v37cf/99yMtLQ233nor3nrrLQ+31nWu9O/Pf/4zJk2ahNGjR2PatGnYuXOnh1vbc4F+/RlsunM+g40r17LBxJXrw2BkNpvx8MMPY8GCBd5uik/Yvn07rr/+eqd7hOeee87bzQIEcpsDBw4Id9xxh5CcnCzs27evy21OnjwppKSkCLt27RKMRqPwz3/+Uxg5cqRQU1Pj4da6rjv9EwRByMzMFEpKSjzYst7p6OgQbr75ZuHVV18V9Hq90NTUJMyePVt4/PHHO23rj+fPlf4Jgv+dP0EQhMbGRiElJUXYsmWLYDabhdraWmHq1KnCq6++2mlbfzyHrvRPEPzzHAaqhx56SHj22WeF9vZ24fTp08KUKVOEtWvXdtrOH1+Xdt3t465du4QxY8YIZWVlgsViEb7++mthzJgxwo4dO7zQ6u7rbv/s2tvbhalTp17xs9JXdLd/FRUVwqhRo4QPP/xQsFgswvfffy9kZGQIn3zyiRda3X3d7d/nn38ujBs3Tvjxxx8FQRCEHTt2CCNGjBCqqqo83WSXBfr1Z7Dp7vV2MHH1WjZYuHp9GIxWr14tjBgxQnj++ee93RSfsHLlSmHBggXebkYnzJRyk61bt6KwsBBPP/30FbcbM2YM7rjjDkilUtx777246aab8MEHH3iopT3T3f5VVVWhtbUV119/vYda1nvV1dUYMWIECgoKEBISApVKhVmzZqG0tLTTtv54/lzpnz+ePwCIiYnBl19+iZycHIhEIrS0tECv1yMmJqbTtv54Dl3pn7+ew0BUWVmJ/fv347nnnoNCocCgQYMwb968LrOD/PF1CbjWx9raWsyePRupqakQiURIS0tDZmZml+9FvsKV/tn95je/wR133OHBVvacK/3761//ismTJ+O+++6DSCTCiBEjsHHjRqSnp3uh5d3jSv9OnDgBQRAcPxKJBDKZDFKp1Ast775Av/4MNt09n8HGlWvZYOLK9WEw+uqrr/Dpp5/izjvv9HZTfMY333yDG2+80dvN6IRBKTeZMGECdu3ahXvvvfey21VUVCA5OdlpWWJiIo4dO9aXzeu17vbvm2++QXh4OJ5++mmMHTsWU6dOxebNmz3Uyp4ZOnQo3n77bUgkEseynTt34oYbbui0rT+eP1f654/nzy4iIgIAcOutt2LatGno168fcnJyOm3nj+cQ6H7//PkcBpry8nIolUrEx8c7lg0bNgzV1dVoa2tz2tZfX5eu9DEvLw9z5sxx/N7Y2IjS0lKfvDiyc6V/APD3v/8dlZWVeOKJJzzZzB5zpX9HjhzB1VdfjWeeeQaZmZm45557sH//fvTr18/Tze42V/o3ZcoUxMXF4d5778UNN9yAX/7yl1i5ciUGDBjg6Wa7JNCvP4NNd89nsHHlWjbYdPf6MNg0NjZi4cKFePnll6FQKLzdHJ9gsVhw9OhRfP7557j99tsxceJELF68GK2trd5uGoNS7tKvX79ufZum1Wo7/WHI5XK0t7f3VdPcorv9MxgMSE1NxdNPP409e/ZgwYIFKCoqwieffOKBVvaeIAh45ZVX8Nlnn2HhwoWd1vvr+bO7Uv/8/fwBwKeffordu3dDLBbjySef7LTe38/hlfoXCOcwUHT1WrP/fvHrzV9fl6708UL19fWYPXs2brzxRkydOrVP29gbrvTvxx9/xCuvvIKXX37Z6cbJl7nSv9bWVrz33nuYPn069u7dixdffBGrVq3Cjh07PNZeV7nSP6PRiBEjRmDTpk04dOgQXnzxRSxcuBDHjx/3WHt7ItCvP4NNd89nMLvStWywutL1YTCxWCx47rnn8Oijj2LEiBHebo7PaGpqwvXXX4+77roL27dvx8aNG3Hq1CmfqCnFoJSHKRSKTsXndDodwsPDvdQi95oxYwbefvttXH/99ZDJZJgwYQJmzJjhFzfEGo0GTz75JLZt24b169dj+PDhnbbx5/PXnf758/mzk8vliI+Px3PPPYc9e/Z0iv778zkErty/QDiHgSIsLAwdHR1Oy+y/X/x689fXpSt9tDt06BByc3Nx7bXXori42KdvwLrbP71ej6effhq//vWvMXDgQI+2sTdcOX8hISGYPHkybrvtNkilUtx0003Izs726fcWV/q3bNkyJCUlYeTIkQgJCcHMmTORmpqKrVu3eqy9fclf32OILtSda9lgdaXrw2Dy1ltvISQkBA8//LC3m+JT4uLisGHDBuTm5kKhUGDgwIF47rnnsHv3bmg0Gq+2jUEpD0tOTkZ5ebnTsoqKCiQlJXmpRe61efPmTheoBoMBoaGhXmpR95w+fRozZ86ERqPB5s2bL/kh56/nr7v989fz9/XXX+Puu++GwWBwLDMYDJDJZJ2+GfbHc+hK//z1HAaipKQktLS0oKGhwbHsxx9/xIABAxAZGem0rT++LgHX+ghYX5/5+fl45JFH8PLLLyMkJMSTzXVZd/v3zTff4NSpU1i4cCHGjBmDMWPGAAB+/vOfY+nSpZ5udre5cv6GDRvm9B4EWGc1EgTBI23tCVf6V11d3al/UqkUMpnMI23ta/76HkNk191r2WDiyvVhMPnoo4+wf/9+x+fxxx9/jI8//tjx2Rysjh07ht/97ndOn9sGgwFisdjr12MMSnnY9OnTsX//fmzfvh0mkwnbt2/H/v37kZ2d7e2muYVGo8GyZcvw3XffwWKx4PPPP8fHH3+MWbNmebtpl9Ta2opHHnkEo0ePxjvvvHPZ4oD+eP5c6Z8/nj8AGD58OHQ6HV5++WUYDAacPXsWq1atQm5ubqc3WX88h670z1/PYSAaMmQI0tPTsXz5cmg0GlRVVeGNN95Abm5up2398XUJuNbHnTt3YunSpXjttdfw05/+1AutdV13+zdmzBgcOXIEBw4ccPwAwJtvvunTQSlXzt+DDz6If//73/joo48gCAJKS0uxbds2n36NutK/SZMmYf369Th69CgsFgt27NiBkpKSgKnt46/vMUSAa9eywcSV68NgsmPHDnz99deOz+OpU6di6tSpjs/mYKVUKrFhwwa8/fbbMJlMqK6uxksvvYT77rvP+68Xr837F8AunsI1NTVV+Oijjxy/7969W5g+fbqQmpoqTJkyRfj888+90cweu1z/LBaL8Ic//EG4/fbbhZEjRwpTpkzx+emi3333XSE5OVkYNWqUkJqa6vQjCP5//lzpnz+eP7vy8nLh0UcfFcaMGSPcfvvtwu9//3tBr9cLguD/51AQut8/fz6Hgai+vl74xS9+IWRkZAhjx44VVq5cKZhMJkEQAuN1KQjd7+PUqVOFESNGdHofWrx4sTebf0WunMML+ct07q707/PPPxdycnKEtLQ0YfLkycL777/vrWZ3W3f7ZzQahTVr1gi33367MHr0aOG+++4Tdu/e7c2muyzQrz+Djb+8h3jCla5lg9nlrg/J6vnnnxeef/55bzfDJ5SUlAizZs0S0tLShLFjxwrLli0TdDqdt5sliATBh/OuiYiIiIiIiIgoIHH4HhEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERERERETkcQxKERERERERERGRxzEoRUREREREREREHsegFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERERERETkcQxKERERERERERGRxzEoRUREREREREREHsegFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERH1MEARvN4GIiPoQ3+fdh89lcGFQishPlZSUYPjw4SgpKfF2U4iI6ALbt2/H7bffjpSUFLzwwguoqKjAT37ykz55rA8//BDDhw/HmTNn+uT4RETUmbve5xcsWIBJkyb1QQv9V3FxMd555x1vN4M8iEEpIiIiIjf6zW9+g/79++Ptt9/GT3/6U3zyyScoKyvzdrOIiMhN+D7fd1avXo2Ojg5vN4M8SOrtBhAREREFkpaWFtx8883IzMz0dlOIiKgP8H2eyH2YKUXkJkajEb/73e8wceJEjBw5Ej/72c/w97//3TGsYsGCBXjkkUewZMkSjBkzBvfddx9MJhOamprwm9/8BrfffjtuvPFGZGRkoKCgoNNQjI0bN+Kuu+7CyJEj8dBDD6G6urpTG6qrq/HMM88gIyMDo0aNwiOPPILvvvvOU08BEZHfO3r0KB555BGkp6cjLS0N+fn5OHz4sGP9jh07MH36dIwcORIzZsxAWVkZrr/+enz44YeOYdUA8Ic//AHDhw/HggUL8PrrrwMAhg8fjtdee82l9mzatAk5OTlITU3FyJEjkZ2dje3bt3fa7uuvv8aMGTOQkpKCadOmddpGrVZjxYoVuOOOO5CSkoKpU6di8+bNjvWLFy/G2LFjYTKZnPZ76aWXkJGRAYPBAAD44Ycf8Pjjj2P06NEYPXo0CgoKUFVV5VKfiIi8ydfe5wHggw8+wG233YaRI0d2ef1eWlqKn/3sZ7jppptw4403YtKkSXjttddgsVgc22zfvt3R7rFjx6KwsBB1dXVOx9m0aROmTJmCG2+8Ebfddhtee+01p/f9BQsW4Gc/+xn+9re/4Y477sDIkSPx4IMP4uTJk/jss88wbdo0jBo1Cvfffz++//57p2MfOHAADz30EEaNGoWMjAw8//zzaGpqcqz/8MMPcf311+Pw4cOYNWsWUlJScNttt2Ht2rWObezP7euvv+74PwU+BqWI3OSFF17An//8Zzz00EP4wx/+gLi4OCxevNhpmwMHDqCyshKvvfYaCgoKIJFI8Pjjj2Pv3r149tln8c4772DevHn48ssv8cILLzj2W79+PZYsWYJbbrkFb7zxBkaNGtXp2E1NTXjwwQdx9OhRLF68GC+//DIsFgvy8vLw448/euQ5ICLyZxqNBo899hhUKhXWrFmDV155BR0dHfjZz34GtVqNf//73/jlL3+JpKQkvP7667jzzjsxd+5cx03BDTfcgA8++AAAkJubiw8++AC/+MUvkJubC8B603H//fd3uz0bNmzACy+8gMmTJ+Ott97CSy+9BJlMhueee67TFxOLFy/G3XffjT/84Q9ITEzE008/jS+++AIAoNPp8D//8z/4xz/+gZ/+9Kd44403kJ6ejoULF+LNN98EAGRnZ6O5uRlfffWV45iCIGD79u24++67ERISgpMnT+LBBx9EY2MjVq5ciaKiIlRVVeEnP/kJGhsbe/7EExF5iK+9zwNATU0NXnvtNTz11FP4/e9/j9bWVvzv//6vI6Bz7Ngx5OfnQ6lU4pVXXkFxcTFGjx6N119/Hf/85z8BAAcPHkRhYSHuvPNOrF27Fr/61a+wb98+PPvss47Heeutt7B48WKMGzcOb775JvLy8rB27Vqnew4AOHToEP7yl79gwYIFWL58OSoqKjBnzhysWLECjz/+OFasWIFz586hsLDQsU9paSny8/Mhl8uxevVq/PrXv8b+/fvxv//7v9DpdI7tLBYLnnrqKdx777344x//iPT0dPzud7/Dnj17HM/fhc8tBQmBiHqtsrJSGD58uPDuu+86Lf/pT38qJCcnC1VVVcLzzz8vJCcnC6dOnXKsr6mpER5++GGhtLTUab9ly5YJN9xwgyAIgmCxWIRx48YJv/jFL5y2eeGFF4Tk5GRh3759giAIwu9//3shJSVFOHPmjGMbvV4vTJ48udO+RETUWVlZmZCcnCwcOHDAsayyslJYtWqVUF1dLdx3331CTk6O0z5vvPGGkJycLGzZssWxLDk5WVizZo3j9zVr1gjJyckut2fFihXCb3/7W6dl3377rZCcnCxs27ZNEARB2LJli5CcnCy89dZbTtvNmDFDmDVrliAIgrBhw4ZO/RIEQfj1r38tpKSkCM3NzYLFYhEmTZokLFiwwLG+tLTUab9nnnlGGDdunKBWqx3bNDc3C+np6cLKlStd7h8Rkaf52vu8/f6grKzMsayurk4YOXKk8PLLLwuCIAhbt24VHnvsMcFsNju2MZvNQnp6urB48WJBEAThrbfeElJTUwWdTufY5vPPPxdee+01wWKxCG1tbcKoUaOEF154wenx//a3vwnJycnCDz/84NSeiooKxzaLFy8WkpOThS+//NKx7J133hGSk5OF1tZWQRAEYdasWcLUqVMFk8nk2ObEiRPCddddJ6xfv14QhPOfV3/7298c2+j1eiElJUV48cUXHcsufm4p8DFTisgNSkpKIAgC7r77bqflU6dOdfpdLpdj8ODBjt/j4+Px3nvvYcyYMaiursZXX32F9evX4+uvv4bRaAQAnDhxAo2NjZg8ebLTse655x6n37/66itcd911iI+Ph8lkgslkglgsxsSJE/Hll1+6s7tERAEpKSkJMTExmDt3LpYsWYL//Oc/6NevH+bPnw+lUomjR492ei+ePn16n7VnwYIFeO6556BWq/HNN99g27Zt2LBhAwA4PiPsLv5MuOOOO3Do0CFotVrs378fV111FdLT0zu1Xa/X4/DhwxCJRJg+fTp27drlGKr38ccfY9CgQY799u3bh8zMTMjlcsfnTEREBMaMGcPPGSLyC772Pg8AAwcORGpqquP3fv36ITU11fG+OmPGDKxduxZGoxHl5eX417/+hddeew1ms9nxWXDTTTdBp9Nh2rRpeOWVV3Dw4EFMmDABTzzxBEQiEcrKytDR0YFJkyY53r9NJpNj5r+9e/c6Hj86OhrDhg1zag8ApzYqlUoAQFtbGzo6OnD48GHceuutEATBcexBgwZh2LBhTscGgLS0NMf/Q0JCEBMTg/b29t4/keS3WOicyA3s6bWxsbFOy+Pi4px+j42NhUgkclr2j3/8A7///e9x7tw5KJVKjBgxAnK53LG+tbUVABATE+O0n/0Dwq6lpQWVlZW44YYbumxjR0cHFAqFC70iIgou4eHh2LBhA4qLi7F9+3Zs3LgRCoUC06dPR0FBAYDO78Xx8fF91p7Tp0/jhRdewL59+yCVSjF06FBHjQ1BEJy2vfgzITY2FoIgQKPRoLW1tdPnEXD+M6qtrQ2A9cbnjTfewO7du3Hbbbdhx44d+J//+R/H9i0tLdi+fXuXNa0ufl6IiHyRr73PA53vFwDre/i5c+cAWIdgL1u2DB999BFMJhOuvvpqpKWlQSqVOj4L0tLS8Mc//hHr1q3DO++8gzfffBP9+vXD7Nmz8cgjj6ClpQUAMGfOnC7bcGHtqYiIiC63udR9RFtbGywWC9auXetUH8ouNDTU6fcL73MAQCwWd/pMo+DCoBSRG9g/rBobG5GQkOBYfqUaGwcOHMDzzz+Phx56CD/72c8wYMAAAMBvf/tbHDx4EACgUqm6PJb9w8UuMjISGRkZmD9/fpePFRIS0v0OEREFqaFDh+Kll16C2WzGkSNH8NFHH+H9999H//79IRaL0dDQ4LT9xe/F7mKxWDBnzhzIZDL87W9/w/XXXw+pVIqKigr84x//6LR9a2ur04V+Q0MDJBIJoqOjER0djcrKyk771NfXAzj/OXPNNdcgNTUVn3zyCWQyGZqbm50yBCIjIzF+/Hg8+uijnY4llfKSkoj8g6+8z9vZvxi4UH19vSM4VlRUhJ07d2L16tUYP348wsLCAADjxo1z2ueWW27BLbfcgo6ODuzbtw/vvfceli9fjtTUVERFRQEAfve732HIkCGdHq+rwFh3hYeHQyQSIT8/H1OmTOm0nl+K05Vw+B6RG6Snp0MikeDTTz91Wn7x7xcrKyuDxWLBk08+6QhImc1mR7quxWLBkCFDkJCQgB07djjt+9lnnzn9npGRgZMnT+Laa69FSkqK4+cf//gHNm3aBIlE0ttuEhEFtB07dmDs2LGor6+HRCJBWloali5diqioKDQ1NSEtLQ07d+50mu3o4vfirojFrl9uNTc34+TJk8jNzcXIkSMdQZ/du3cDgFMbADiKxNrX7dixA6NGjYJcLsdNN92Es2fPOr7ssPvHP/4BmUyGkSNHOpZNnz4du3fvxscff4zU1FSnm5eMjAxUVFTguuuuc3zG3HjjjVi3bh127drlch+JiDzNl97n7SorK52+ODh37hzKysqQmZkJwFrEPDMzE3fccYcjIPXtt9+iqanJ0c5Vq1YhNzcXgiBAoVDg9ttvx/PPP+843qhRoyCTyVBbW+t0nyCTyfDyyy93mvXbFREREbj++utx4sQJp2Pbi8WXlJS4dLzePJfkn/i1FpEbDBo0CDNnzsTvf/97GI1GjBgxArt27XJ8iF3qzdV+I/Diiy9i5syZaGtrw/r163Hs2DEAQHt7OyIiIlBYWIhnn30WixYtwt13341Dhw7h/fffdzpWfn4+PvroI+Tn5+OnP/0pVCoVtm/fjr/97W/41a9+1Ye9JyIKDKNHj4bFYkFBQQHmzJmD8PBwfPLJJ1Cr1bjzzjtx7733Ij8/H/PmzcNPfvITnD59Gq+++uoVj2v/hvrjjz/GqFGjMGjQoCvuExsbi6uuugobNmzAgAEDEBUVhS+++AJ//vOfAViHZF9o9erVMJvNSEhIwPvvv4+TJ0/iT3/6EwAgJycHf/3rX/HEE0/gySefxKBBg/Cf//wHW7ZswRNPPOFoHwBMmTIFK1aswD//+U8sXLjQ6THmzZuHBx98EI8//jh+8pOfIDQ0FB988AH+9a9/Yc2aNVfsExGRt/nS+7xdaGgo5s2bh6effhpmsxmvvvoqlEolHnnkEQDW+4VPPvkE77//PoYNG4Zjx46huLgYIpHI8Vkwbtw4/OlPf8KCBQswffp0GI1GvP3221AqlRg7diyUSiUee+wxvPrqq9BoNMjMzERtbS1effVViEQijBgxogfP5nnPPPMM5syZg2effRbTp0+H2WzGu+++i8OHD2Pu3LkuHSsqKgplZWUoLS3FmDFjOpU+ocDDMCSRmyxevBgPPvgg3n33XcybNw81NTWON2H7txoXy8zMxAsvvICysjLMnj0bK1aswMCBA/H6668DgONb7alTp+KVV17BoUOHMHfuXHz22Wd48cUXnY4VHx+PjRs34qqrrsLSpUvx85//HEeOHEFRURHy8/P7ruNERAGif//+ePvttxEZGYmFCxfi8ccfx9GjR/Haa69h7NixGDNmDN555x00NDSgoKAAGzdudHwTfTl33nknUlJSsGDBArzzzjvdbs8bb7yB+Ph4LFiwAE899RQOHTqE4uJiDB06FAcOHHDatqioCO+99x7mzZuH2tparF27FhkZGQCsQyf+8pe/YNKkSVizZg3mzp2LgwcPoqioCL/4xS+cjqNUKnHrrbdCLBbj3nvvdVo3YsQIbNiwASKRCPPnz8eTTz6J+vp6/OEPf8Cdd97Z7X4REXmLr73PA8Dw4cPxwAMPYOnSpZg/fz4GDx6Mv/71r47hewsWLMAdd9yB1atX4/HHH8emTZswd+5cPPDAAygrK4PZbMbEiRPxu9/9DuXl5XjiiSfwzDPPQKFQ4L333nMUJX/qqaewYMEC7Nq1C7Nnz8ZLL72E9PR0rF+/HpGRkS4/lxeaMGEC3nnnHdTU1ODJJ5/E/PnzIZFI8Kc//cmpQHp3/PznP8c333yD2bNnO+pqUWATCawqRtRrLS0t2L17N2655RZHbQ7Amkr74Ycfupy2SkRE/uHMmTOYPHkyVqxYgZycHG83h4iI3Izv80R9i8P3iNxAoVCgqKgI1113HR555BGEhYXh66+/xl/+8hf8/Oc/93bziIjIRwiCALPZfMXtJBIJhywQEfkhvs8TuYZBKSI3CA0Nxbp167B69WosWLAAHR0dGDx4MBYsWIC8vDxvN4+IiHzE1q1bu1Xnj9/IExH5J77PE7mGw/eIiIiIPKS5ublbsxxdffXVTsPBiYjIP/B9nsg1DEoREREREREREZHHcfY9IiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij2NQioiIiIiIiIiIPE7q7Qb4o8ZGNXy5PLxIBMTGRvp8O3sq0PsHBH4f2T/fYG8nuUdjoxqAf5x7d/OX17y7sd/sdyDiZ4P7ufqaCbbXGvsZOIKlr8Haz776fGBQqgcEAX7x4vOXdvZUoPcPCPw+sn8USC4818F67tnv4MJ+E3VPT18zwfJaYz8DT7D0lf10Dw7fIyIiIiIiIiIij2NQioiIiIiIiIiIPI5BKSIiIiIiIiIi8jgGpYiIyK80NTUhKysLJSUljmU7d+5EdnY2Ro8ejUmTJuH111+HxWJxrN+6dSuysrKQmpqKnJwclJWVOdaZzWasWrUK48ePR1paGubOnYu6ujqP9omIiIiIKBgxKEVERH7j4MGDmDVrFk6fPu1Y9u2332L+/Pl46qmncODAAaxduxYffvgh1q1bBwAoKSnBsmXLsHLlSpSWlmL69OmYO3cuOjo6AADFxcXYu3cvtmzZgj179kAul2PRokXe6B4RERERUVBhUIqIiPzC1q1bUVhYiKefftpp+dmzZ/Hggw/i9ttvh1gsxrBhw5CVlYXS0lIAwKZNmzBlyhSkp6dDJpMhPz8fKpUK27dvd6yfPXs2EhISEBERgYULF2L37t2oqqryeB+JiIiIiIIJg1JEROQXJkyYgF27duHee+91Wn7XXXfhV7/6leN3nU6Hzz//HDfccAMAoKKiAsnJyU77JCYm4tixY1Cr1aipqXFaHxcXh+joaBw/frwPe0NERERERFJvN4CIiKg7+vXrd8VtNBoNfvnLX0IulyM/Px8AoNVqoVAonLaTy+Vob2+HVqsFAISFhXVab1/XXSJR1/8PBvb+st/Bgf32bjv6WqD3j4iIfAuDUkREFBBOnDiBJ598ErGxsXjvvfcQEREBAFAoFNDpdE7b6nQ6qFQqR7DKXl/qwvXh4eEuPX5sbGSX/w8m7HdwYb+JiIiotxiUIiIiv/ff//4XzzzzDB544AE8++yzkErPf7wlJSWhvLzcafuKigpMnDgR0dHRiI+PdxriV19fj5aWlk5D/q6ksVENwHrD2tiohiD0slN+RCRiv9nvwBcs/bb3k4iIyBMYlCIiIr926NAhFBQUYOnSpcjNze20Pjc3FwUFBbjnnnuQnp6ODRs2oLGxEVlZWQCAnJwcFBcXIyUlBSqVCsuXL0dGRgYGDx7sUjsuvEkVBAT0TeulsN/Bhf0mIiKi3mJQiugyLBYLGhoaAFiLH4vFnBuAyNe8+eabMJlMKCoqQlFRkWN5eno63n77bYwbNw5LlizB0qVLUVtbi8TERKxduxZKpRIAUFBQAJPJhLy8PGi1WmRmZmL16tXe6YwHWCwW1NXVAgD694/n+xoRkY+xWCw4e/Ysmpo06N9/AN+niSigMShFdBkNDQ1469+HAQCPTx6F/v37e7lFRATAaWa8N99884rbZ2dnIzs7u8t1MpkMhYWFKCwsdFv7fFldXS3e3FUGAPh5VhoGDEjwcouIiOhCtbU1+NPub6HTGfHzrDQkJAz0dpOIiPoMg1JEVxAeHePtJhARuVWEKs7bTSAiosuIjOkPWYfe280gIupzzAUlIiIiIiIiIiKPY1CKiIiIiIiIiIg8jkEpIiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij+Pse0RERAHGYrGgrq4WANC/fzzEYn4HRURERES+h1epREREAaaurhZv7irDm7vKHMEpIiIiIiJfw0wpIiKiABShivN2E4iIiIiILouZUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREQ+r6mpCVlZWSgpKXEs27lzJ7KzszF69GhMmjQJr7/+OiwWi2P91q1bkZWVhdTUVOTk5KCsrMyxzmw2Y9WqVRg/fjzS0tIwd+5c1NXVOdY3NjZi3rx5GDNmDDIzM1FUVASTyeSZzhIRBQkGpYiIiAKUYLGgrq4WNTXnUFNzzulGjYjInxw8eBCzZs3C6dOnHcu+/fZbzJ8/H0899RQOHDiAtWvX4sMPP8S6desAACUlJVi2bBlWrlyJ0tJSTJ8+HXPnzkVHRwcAoLi4GHv37sWWLVuwZ88eyOVyLFq0yHH8p556CmFhYdizZw82b96Mr776ynFsIiJyDwaliIiIApSmtQnrv/oR6w9U4c1dZairq/V2k4iIXLZ161YUFhbi6aefdlp+9uxZPPjgg7j99tshFosxbNgwZGVlobS0FACwadMmTJkyBenp6ZDJZMjPz4dKpcL27dsd62fPno2EhARERERg4cKF2L17N6qqqlBZWYn9+/fjueeeg0KhwKBBgzBv3jxs2LDB4/0nIgpkUm83gIiIiPpOuDIGUTH9vd0MIqIemzBhAqZNmwapVOoUmLrrrrtw1113OX7X6XT4/PPPMW3aNABARUUFZs6c6XSsxMREHDt2DGq1GjU1NUhOTnasi4uLQ3R0NI4fPw4AUCqViI+Pd6wfNmwYqqur0dbWhqioqG63XyRyrb+O7UXW/7u6v7+w9ytQ+2cXLP0EgqevwdrPvuovg1JEREREROSz+vXrd8VtNBoNfvnLX0IulyM/Px8AoNVqoVAonLaTy+Vob2+HVqsFAISFhXVab1938b7239vb210KSsXGRnZ7WwDQ6yMA1EEuD0VMTATi4lzb39+4+vz4q2DpJxA8fWU/3YNBKSIiogB3vE6DH2sNuGmIDsPj5N5uDhGRW504cQJPPvkkYmNj8d577yEiIgKANYik0+mcttXpdFCpVI4Ak72+1IXrw8PDIQhCp3X238PDw11qX2OjGoLQ/e2bmzW2tujR1KRBaKjapcfzFyKR9WbX1efH3wRLP4Hg6Wuw9tP+u7sxKEVERBTADGZg74lmmAUBz+2sQv71cgiCAFGg55wTUVD473//i2eeeQYPPPAAnn32WUil529vkpKSUF5e7rR9RUUFJk6ciOjoaMTHx6OiosIxhK++vh4tLS1ITk6GxWJBS0sLGhoaEBcXBwD48ccfMWDAAERGunZTJghw6cbVsa3g+r7+KBj6CARPP4Hg6Sv76R4sdE5ERBTATmtFMAsCxBAgAPjgezXa29u93Swiol47dOgQCgoK8Ktf/QrPP/+8U0AKAHJzc7Ft2zbs27cPRqMR69atQ2NjI7KysgAAOTk5KC4uRlVVFTQaDZYvX46MjAwMHjwYQ4YMQXp6OpYvXw6NRoOqqiq88cYbyM3N9UZXiYgClk8GpY4ePYq8vDyMGTMGEyZMwP/93//BYDAAAA4fPoz7778faWlpmDRpEjZt2uS079atW5GVlYXU1FTk5OSgrKzMsc5sNmPVqlUYP3480tLSMHfuXNTV1Xm0b0RERJ5iEYBTWmtG1PAIA0QQ0C7IoDV5uWFERG7w5ptvwmQyoaioCGlpaY6fxx57DAAwbtw4LFmyBEuXLkVGRgb++c9/Yu3atVAqlQCAgoIC3HrrrcjLy8Ott94KvV6P1atXO46/Zs0amEwmTJ48GQ888ABuueUWzJs3zws9JSIKXD43fM9iseDxxx/HnDlz8Je//AV1dXWO6VsfeughzJkzB08++SRmzZqF0tJSFBQUYPjw4Rg5ciRKSkqwbNkyrF27FiNHjsSGDRswd+5cfPbZZ1AoFCguLsbevXuxZcsWREZGYvHixVi0aBH++Mc/ervbREREbtdslEBnFkEuFWOwwog6Ywga9UBthwj9o73dOiIi19lnxgOsQakryc7ORnZ2dpfrZDIZCgsLUVhY2OX6uLg4rFmzpmcNJSKibvG5TKnW1lbU19fDYrFAsA1cFIvFUCgU+PTTT6FUKpGXlwepVIpx48Zh2rRp2LBhAwBg06ZNmDJlCtLT0yGTyRzBrO3btzvWz549GwkJCYiIiMDChQuxe/duVFVVea2/REREfaXVZP2YHxAVCokIiJdbP1frdKwnRURERETe53NBKZVKhfz8fKxatQopKSm49dZbMWTIEOTn56O8vNxRiNAuMTERx44dAwCnQoUXr1er1aipqXFaHxcXh+joaKdvXLpDJPL9H39ppz/0z1vnnefQv6waTSEAAQAASURBVH/8pX8U2NS2oFRseAgAoL8tKNWoB4yWIKjMSUREREQ+zSeH78nlcixevBi5ubmorKzEE088gTVr1kCr1Tqmb7WTy+WOgq2XW6/VagEAYWFhndbb13VXX0yD2Bf8pZ095Yn+mc3tUChCbI8Xgbg4zz6nPIf+LdD7R76v1WgLSoXJAD0QIQNCxRboLWK06hmUIiIiIiLv8rmg1K5du7Bz507s2LEDgHUq14KCAhQVFWHatGlQq9VO2+t0OoSHhwMAFAoFdDpdp/UqlcoRrOro6Ljk/t3V2Kj26akfRSLrzbCvt7OnPNm/xkYNOjoMjv9LJGFX2MM9eA79m7/0z95OCkxmiwCt+XymVGuzdXmk1AK9QYxWvcWLrSMiIiIi8sGg1Llz5xwz7dlJpVLIZDIkJydj7969TusqKiqQlJQEwBrAKi8v77R+4sSJiI6ORnx8vNMQv/r6erS0tHQa8nclggCfvtG085d29pQn+nfh8b3xfPIc+rdA7x/5tlaDAAEiyMQCwkMkaLUtj5RY0ACghZlSRERERORlPldTasKECaivr8ebb74Js9mMqqoqFBcXY9q0acjKykJDQwPWrVsHo9GIffv2Ydu2bZg5cyYAIDc3F9u2bcO+fftgNBqxbt06NDY2IisrCwCQk5OD4uJiVFVVQaPRYPny5cjIyMDgwYO92WUiIiK3a9ZZM6GiZYDoggJikVLrcmZKEREREZG3+VymVGJiIt566y2sXr0ab7/9NiIjIzF9+nQUFBQgJCQE7777LoqKirBmzRrExMRg0aJFGDt2LABg3LhxWLJkCZYuXYra2lokJiZi7dq1UCqVAICCggKYTCbk5eVBq9UiMzMTq1ev9l5niYiI+og9EypK5pwRdT4oJThmuSUiIiIi8gafC0oBwPjx4zF+/Pgu16WkpGDjxo2X3Dc7OxvZ2dldrpPJZCgsLERhYaFb2klEROSrNAZrwClC5rw8QmqBCAIMFhGaOsxI8ELbiIiIiIgAHxy+R0RERL2nNVmDUgqJczaURASE276SOtWi93SziIiIiIgcGJQiIiIKMIIgoN1oD0p1Xh9pG9JX2WLovJKIiIiIyEMYlCIiIgowbXoLzLYEKUUXA/UjbMuq1QxKEREREZH3MChFREQUYOq1RgBAqNgCiajz+jBbUKpGY/Rgq4iIiIiInDEoRUREFGDqtCYAgFzc9ex64VLr8nNqBqWIiIiIyHsYlCIiIgow9e3WYJNCYulyvT1TqqHdBKO5622IiIiIiPoag1JEREQBxp4pdfHMe3ahYussfBYBONfGGfiIiIiIyDsYlCIiIgow9ppSiksM3xOJgAiZtdjUmZYOj7WLiIiIiOhCDEoREREFmHpHptSlh+ZFhNiDUjqPtImIiIiI6GIMShEREQUYx/C9S2RKAUC4LVPqbCszpYiIiIjIOxiUIiIiv9LU1ISsrCyUlJQ4lh0+fBj3338/0tLSMGnSJGzatMlpn61btyIrKwupqanIyclBWVmZY53ZbMaqVaswfvx4pKWlYe7cuairq/NYf9xNZzSjTW8GcIVMKRkzpYiIiIjIuxiUIiIiv3Hw4EHMmjULp0+fdixrbW3FnDlzMGPGDJSWlqKoqAgrVqzAkSNHAAAlJSVYtmwZVq5cidLSUkyfPh1z585FR4c1Q6i4uBh79+7Fli1bsGfPHsjlcixatMgr/XOHxnYDAGshc6no0tudH77HTCkiIiIi8g4GpYiIyC9s3boVhYWFePrpp52Wf/rpp1AqlcjLy4NUKsW4ceMwbdo0bNiwAQCwadMmTJkyBenp6ZDJZMjPz4dKpcL27dsd62fPno2EhARERERg4cKF2L17N6qqqjzex96yWCyoqKoBAIRKrAXNL8WeKVXdqoMgXHqYHxERERFRX5F6uwFERETdMWHCBEybNg1SqdQpMFVeXo7k5GSnbRMTE7F582YAQEVFBWbOnNlp/bFjx6BWq1FTU+O0f1xcHKKjo3H8+HEMGjSo2+27MAB0uWBQX6qvr8XGfT8AUEAmunygyV5vSmeyoKXDgJjw0B4/rr2/3uq3t7Df3m2HpwVLvwO9f0RE5FsYlCIiIr/Qr1+/LpdrtVooFAqnZXK5HO3t7Vdcr9VqAQBhYWGd1tvXdVdsbGSX//ckg0GNkCgV0KqDQiaCQh4CUUgIwsJCHf8XxGbrvwYtQkVS6AUxzjS3IPmaxF4/vrf67W3sd3AJ1n4TERH1BQaliIjIrykUCqjVaqdlOp0O4eHhjvU6na7TepVK5QhW2etLdbV/dzU2WtsQGxuJxkY1vDEirqlJA3WHdeY9GSzo0BkgtkjQ3q53/N9iOP+vQiaD3gBUVLdh5NXqKxz90kQi7/bbW9hv9jsQ2ftJRETkCQxKERGRX0tOTsbevXudllVUVCApKQkAkJSUhPLy8k7rJ06ciOjoaMTHx6OiosIxhK++vh4tLS2dhgReyYU3qYIAr9y0CgKgM1kfOERy5e0VEgEtEKFWY3RLe73Vb29jv4NLsPabiIioL7DQORER+bWsrCw0NDRg3bp1MBqN2LdvH7Zt2+aoI5Wbm4tt27Zh3759MBqNWLduHRobG5GVlQUAyMnJQXFxMaqqqqDRaLB8+XJkZGRg8ODB3uxWj+nM1rvl0G58witsgat6rakPW0RERERE1DVmShERkV9TqVR49913UVRUhDVr1iAmJgaLFi3C2LFjAQDjxo3DkiVLsHTpUtTW1iIxMRFr166FUqkEABQUFMBkMiEvLw9arRaZmZlYvXq19zrUS3qz9d/QbmRKhdmuAuq0xr5rEBERERHRJTAoRUREfuf48eNOv6ekpGDjxo2X3D47OxvZ2dldrpPJZCgsLERhYaFb2+gtjuF74iuPL1JIrNvUMVOKiIiIiLyAw/eIiIgCiN4+fK87NaVsX03VM1OKiIiIiLyAQSkiIqIAYbYI54fvuVBTSm2woN1g7ruGERERERF1gUEpIiKiANFmj0hBQEg3PuFlYusPAJxr0/VZu4iIiIiIusKgFBERUYBo0VmDUiEiASJR9/YJl1k3rGnT91WziIiIiIi6xKAUERFRgGjRWQuWd6fIuV2YLShVzUwpIiIiIvIwBqWIiIgCRKstUyrUhaBUuNSeKcWgFBERERF5FoNSREREAcIxfM+VoJQtU+och+8RERERkYcxKEVERBQg7IXOezJ8j5lSRERERORpDEoREREFCI3BAgCQdbPIOcBMKSIiIiLyHgaliIiIAoS6J5lStppSDVoDDCZLn7SLiIiIiKgrDEoREREFCI3BGpSSuRCUCpUAIRJrYKpWzWwpIvJdTU1NyMrKQklJiWPZ4cOHcf/99yMtLQ2TJk3Cpk2bnPbZunUrsrKykJqaipycHJSVlTnWmc1mrFq1CuPHj0daWhrmzp2Luro6x/rGxkbMmzcPY8aMQWZmJoqKimAymfq+o0REQYRBKSIiogChdgzf635QSiQSoX+4FABwjnWliMhHHTx4ELNmzcLp06cdy1pbWzFnzhzMmDEDpaWlKCoqwooVK3DkyBEAQElJCZYtW4aVK1eitLQU06dPx9y5c9HR0QEAKC4uxt69e7Flyxbs2bMHcrkcixYtchz/qaeeQlhYGPbs2YPNmzfjq6++wrp16zzabyKiQMegFBERUYCwD99zJVMKAPqHywAANawrRUQ+aOvWrSgsLMTTTz/ttPzTTz+FUqlEXl4epFIpxo0bh2nTpmHDhg0AgE2bNmHKlClIT0+HTCZDfn4+VCoVtm/f7lg/e/ZsJCQkICIiAgsXLsTu3btRVVWFyspK7N+/H8899xwUCgUGDRqEefPmOY5NRETuwaAUERFRgND2IFMKAPoxU4qIfNiECROwa9cu3HvvvU7Ly8vLkZyc7LQsMTERx44dAwBUVFRccr1arUZNTY3T+ri4OERHR+P48eMoLy+HUqlEfHy8Y/2wYcNQXV2NtrY2d3eRiChoSb3dACIiIuo9k0WA1mgNSrlS6Bw4nyl1jjWliMgH9evXr8vlWq0WCoXCaZlcLkd7e/sV12u1WgBAWFhYp/X2dRfva/+9vb0dUVFR3W6/yIUZUZ22F1n/7+r+/sLer0Dtn12w9BMInr4Gaz/7qr8MShEREQUAje588V2pixcN9ppSNcyUIiI/olAooFarnZbpdDqEh4c71ut0uk7rVSqVI8Bkry918f6CIHRaZ//dfvzuio2NdGl7vT4CQB3k8lDExEQgLs61/f2Nq8+PvwqWfgLB01f20z0YlCIiIgoArTojAEAmBsQuBqX62TOlWFOKiPxIcnIy9u7d67SsoqICSUlJAICkpCSUl5d3Wj9x4kRER0cjPj7eaYhffX09WlpakJycDIvFgpaWFjQ0NCAuLg4A8OOPP2LAgAGIjHTtBq2xUQ3BhQTW5mYNAECn06OpSYPQUPUV9vBPIpH1ZtfV58ffBEs/geDpa7D20/67u7GmFBERUQBos2VKhUhcz622Z0rVqvUwWwL46oqIAkpWVhYaGhqwbt06GI1G7Nu3D9u2bcPMmTMBALm5udi2bRv27dsHo9GIdevWobGxEVlZWQCAnJwcFBcXo6qqChqNBsuXL0dGRgYGDx6MIUOGID09HcuXL4dGo0FVVRXeeOMN5ObmutxOQXD9x7pjz/b1p5+ePj/+9hMs/QymvgZrP/sCM6WIiIgCgCMo1YOvm2IUUkjEIpgtAuo1egyIkru5dURE7qdSqfDuu++iqKgIa9asQUxMDBYtWoSxY8cCAMaNG4clS5Zg6dKlqK2tRWJiItauXQulUgkAKCgogMlkQl5eHrRaLTIzM7F69WrH8desWYMXX3wRkydPhlgsxowZMzBv3jwv9JSIKHAxKEVERBQA7MP3epIpJRGLEB8ZiupWHWraGJQiIt91/Phxp99TUlKwcePGS26fnZ2N7OzsLtfJZDIUFhaisLCwy/VxcXFYs2ZNzxtLRERXxOF7REREAeD88L2e7Z8QFQoAOKdmsXMiIiIi8gwGpYiIiPycxWJBdUMzgJ4N3wPgyI6qYbFzIiIiIvIQBqWIiIj8XF1dLb6qqAEAiCymHh0jIdKWKdXGTCkiIiIi8gwGpYiIiAKAILVmOvU0UyrBlil1jplSREREROQhDEoREREFAIPF+q+sx8P3rJlSNcyUIiIiIiIPYVCKiIgoAOjNAgBAJhZ6tP+FmVKC0LNjEBERERG5QurtBhAREVHvGczWf3uaKRVvqymlN1nQpDXAqGkCAPTvHw+xmN9hEREREZH7MShFREQUAIwWe6YUgB4kOoVIxYgLD0GD1oDvKqvx76+/BwD8PCsNAwYkuLGlRERERERWDEoREREFAKO9ppQIPQpKAUBCVCgatAbUaY2IUMW5rW1ERERERF1hPj4REZGfM5gtsCVKQdqLT/YBtrpS9VqTG1pFRERERHR5DEoRERH5uXZ7mhQESEU9P06CbQa+OgaliIiIiMgDGJQiIiLyc+0Ga1BKKgJEvQpKWTOl6rRGdzSLiIiIiOiyGJQiIiLyc1qjPSjVw2JSNgxKEREREZEnMShFRETk59rdFJQawOF7RERERORBnH2PiIjIz9mDUrIefNUkWCyoq6sFAIhtx9EaLDCaBcgkvRgLSERERER0BQxKERER+Tmt4cJMKdcCSZrWJqz/qgP9rzJA09yAyJAoqA0WaE0ClAxKEREREVEfYlCKiIjIz7UbzQB6FpQCgHBlDKJi+gMA+hkFqA16tBsFKEPd2UoiIiIiImesKUVEROTnzteU6v2x+odbv6/SGntXn4qIiIiI6EqYKUVEROTnHLPviXsXSBIsFkSIbXWlGJQiIiIioj7GoBQREZGfa7fVlJL1cvY9TWsTTrUAQATadEYAst42jYiIiIjoknxy+F5LSwvmz5+PzMxM3HTTTZg3bx7q6uoAAIcPH8b999+PtLQ0TJo0CZs2bXLad+vWrcjKykJqaipycnJQVlbmWGc2m7Fq1SqMHz8eaWlpmDt3ruO4RERE/krrxuF70ZEKAECHmUXOiYiIiKhv+WRQ6he/+AXa29uxa9cufPbZZ5BIJFi8eDFaW1sxZ84czJgxA6WlpSgqKsKKFStw5MgRAEBJSQmWLVuGlStXorS0FNOnT8fcuXPR0dEBACguLsbevXuxZcsW7NmzB3K5HIsWLfJmV4mIiHqtw3jh7Hu9o5DYjmnq9aGIiIiIiC7L54JS3377LQ4fPoyVK1ciKioKERERWLZsGQoLC/Hpp59CqVQiLy8PUqkU48aNw7Rp07BhwwYAwKZNmzBlyhSkp6dDJpMhPz8fKpUK27dvd6yfPXs2EhISEBERgYULF2L37t2oqqryZpeJiMgNjh49iry8PIwZMwYTJkzA//3f/8FgMADoXZatP3BXTSkACLMFpfQWEcwW1pUiIiIior7jc0GpI0eOIDExEX/729+QlZWFCRMmYNWqVejXrx/Ky8uRnJzstH1iYiKOHTsGAKioqLjkerVajZqaGqf1cXFxiI6OxvHjx11qo0jk+z/+0k5/6J+3zjvPoX//+Ev/AoXFYsHjjz+Ou+66C/v378fmzZvxxRdfYO3atb3OsvUH52tK9f5YMjEggTUY1W5iUIqIiIiI+o7PFTpvbW3F8ePHceONN2Lr1q3Q6XSYP38+nn/+ecTFxUGhUDhtL5fL0d7eDgDQarWXXK/VagEAYWFhndbb13VXbGykq93yCn9pZ095on9mczsUihDb40UgLs6zzynPoX8L9P75ktbWVtTX18NisUAQrIEUsVgMhULhlGULwCnLduTIkU5ZtgCQn5+PDz74ANu3b8fMmTO91idXtLtx+J5IBCgkFmjMEs7AR0RERER9yueCUiEh1gDAwoULERoaioiICDz11FN44IEHkJOTA51O57S9TqdDeHg4AEChUHS5XqVSOYJVF3/zfeH+3dXYqIbgw9fpIpH1ZtjX29lTnuxfY6MGHR0Gx/8lkrAr7OEePIf+zV/6Z29nIFCpVMjPz8eqVavw29/+FmazGZMnT0Z+fj5WrlzZZRbt5s2bAVizbC8OPl2YhdtdF2aeeToLrd1oBuCeoBQAKCQCNGZAaxS6lVV3YXZgMGG/vdsOTwuWfgd6/4iIyLf4XFAqMTERFosFRqMRoaGhAKzDMgDguuuuw1//+len7SsqKpCUlAQASEpKQnl5eaf1EydORHR0NOLj452G+NXX16OlpaXTzcqVCAJ8+kbTzl/a2VOe6N+Fx/fE41ksFjQ0NEAkAkymdohEcohEPjfK1m34GiV3sVgskMvlWLx4MXJzc1FZWYknnngCa9asuWwWLXD5LFtXXBjg82Swz2wR0GEbZhepkEEuD4EgNkMUcv7fsLBQKOQhTssut01EiAH1BsAAMWJiup8lGihBTlex38ElWPtNRETUF3wuKDV+/HgMGjQIv/71r7FixQro9Xq88soruOOOOzB16lSsWbMG69atQ15eHg4ePIht27bhjTfeAADk5uaioKAA99xzD9LT07FhwwY0NjYiKysLAJCTk4Pi4mKkpKRApVJh+fLlyMjIwODBg73ZZSKHhoYGvPXvwwiPjoFZr0H+hOvQr19/bzeLyOft2rULO3fuxI4dOwBYv6QoKChAUVERpk2bBrVa7bR9d7NsXdHYaH0MT2fJtemMjv+bDAboRBJYDAaILef/bW/Xo0PnvOxy24QIJgAStHaY0NSkQUiI+tINgP9kB7ob+81+B6JAyqIlIiLf53NBKZlMhr/85S9YuXIl7rrrLuj1ekyaNAkLFy5EVFQU3n33XRQVFWHNmjWIiYnBokWLMHbsWADWOiFLlizB0qVLUVtbi8TERKxduxZKpRIAUFBQAJPJhLy8PGi1WmRmZmL16tXe6yxRF8KjYxChjIVZH+LtphD5jXPnzjlm2rOTSqWQyWRITk7G3r17ndZ1N8vWFZ7OrLTT6K1D9yQiQOymYTcKia3QuVFwqS/Bmh3IfgeXYO03ERFRX/C5oBQAxMfH45VXXulyXUpKCjZu3HjJfbOzs5Gdnd3lOplMhsLCQhQWFrqlnURE5BsmTJiAl19+GW+++SZmz56N6upqFBcXY9q0acjKysJLL73U4yxbX6fRmwAAUjeO9FVIrMPmWeiciIiIiPqSTwaliIiIXJGYmIi33noLq1evxttvv43IyEhMnz4dBQUFCAkJ6VWWra+zZ0rJ3JUmBUAhtgajOkzWmlVERERERH2BQSkiIgoI48ePx/jx47tc15ssW1+nNVgzpWQS9x0zVCxADAEWiFDfbsJV7js0EREREZFD4E7rRUREFAT6IlNKJAIUtq+tajXGy29MRERERNRDDEoRERH5MXtNKZmbP9HDbEGpGgaliIiIiKiPMChFRETkx7QG92dKAUCYbQY+ZkoRERERUV9hUIqIiMiPOTKl3FhTCjifKcWgFBERERH1FQaliIiI/FjfDd+zZ0qZ3HtgIiIiIiIbBqWIiIj8mKbPhu9Z/2VNKSIiIiLqKwxKERER+TFtHxc6b9Wb0W4LfBER+ZqjR48iLy8PY8aMwYQJE/B///d/MBgMAIDDhw/j/vvvR1paGiZNmoRNmzY57bt161ZkZWUhNTUVOTk5KCsrc6wzm81YtWoVxo8fj7S0NMydOxd1dXUe7RsRUTBgUIqIiMiP9VWmlEwMhNiuEqpbdW49NhGRO1gsFjz++OO46667sH//fmzevBlffPEF1q5di9bWVsyZMwczZsxAaWkpioqKsGLFChw5cgQAUFJSgmXLlmHlypUoLS3F9OnTMXfuXHR0dAAAiouLsXfvXmzZsgV79uyBXC7HokWLvNldIqKAxKAUERGRH+urQucAEC6zBrrOMihFRD6otbUV9fX1sFgsEARrHTyxWAyFQoFPP/0USqUSeXl5kEqlGDduHKZNm4YNGzYAADZt2oQpU6YgPT0dMpkM+fn5UKlU2L59u2P97NmzkZCQgIiICCxcuBC7d+9GVVWV1/pLRBSIGJQiIiLyY9o+ypQCgIgQe1Cqw+3HJiLqLZVKhfz8fKxatQopKSm49dZbMWTIEOTn56O8vBzJyclO2ycmJuLYsWMAgIqKikuuV6vVqKmpcVofFxeH6OhoHD9+vO87RkQURKTebgARERH1nL2mVEgffM1kz5Ti8D0i8kUWiwVyuRyLFy9Gbm4uKisr8cQTT2DNmjXQarVQKBRO28vlcrS3twPAZddrtVoAQFhYWKf19nWuELn4nYFje5H1/67u7y/s/QrU/tkFSz+B4OlrsPazr/rr9qBUSUkJMjMz3X1YIiLyY/xs6BuCIDiG70nFIri7HDmH7xFRb/Xl+/+uXbuwc+dO7NixAwCQlJSEgoICFBUVYdq0aVCr1U7b63Q6hIeHAwAUCgV0Ol2n9SqVyhGssteX6mp/V8TGRrq0vV4fAaAOcnkoYmIiEBfn2v7+xtXnx18FSz+B4Okr++kebg9KPfnkk4iMjMR9992H++67DwMHDnT3QxARkZ/hZ0Pf0JksMFvLqEAmgduDUhHMlCKiXurL9/9z5845Ztqzk0qlkMlkSE5Oxt69e53WVVRUICkpCYA1gFVeXt5p/cSJExEdHY34+HinIX719fVoaWnpNOSvOxob1bCVvOqW5mYNAECn06OpSYPQUPUV9vBPIpH1ZtfV58ffBEs/geDpa7D20/67u7k92f+LL77Ac889h2+//RZ33XUXfvrTn+Ljjz/u9IFBRETBg58NfcM+dE8sAqR9kFJ94fA9IZCvuv6fvTuPj6q+9z/+mpnMZCYLWQiERVAxAVxAIsii1gWL2AriRdD2R23hXqUXUWtbXMFqqyjcarVIRUUttdBiUdGiVkHrioCgCEoFCQoJhOzrJJlklvP7YzIjgRASmMlkJu/n4zGPJGeZ8/lOkvM953O+i4iETTjP/xdccAElJSU8+eSTeL1e8vPzWbJkCRMnTmTcuHGUlpaybNky3G43GzduZM2aNVx99dUATJkyhTVr1rBx40bcbjfLli2jrKyMcePGATB58mSWLFlCfn4+TqeTBx98kJEjR9K/f/92x2kY7X/5dzy+faPpdbyfT7S9uko5u1JZu2o5wyHkLaWsVivjx49n/PjxlJeX8+abb/Lcc8/xu9/9jiuuuIJrr72WwYMHh/qwIiLSialuCA9ng79tlMNqxhSGjv4JVhMm/C2yyuvcdE+0hfwYIhLbwnn+z8rK4qmnnuKxxx7jmWeeITk5mSuvvJLZs2djs9l47rnnmD9/PosWLSI9PZ158+YxevRoAMaMGcO9997LfffdR1FREVlZWSxdupTU1FQAZs+ejcfjYdq0adTW1jJq1Cgee+yxEH0qIiISELaBzsvKynjttdd4/fXXyc3N5aKLLiI+Pp7p06czffp0/vd//zdchxYRkU5KdUNoORv9LaUSreGZTNdiMtE9IY7SOg8HqlxKSonIcQvX+f+8887jvPPOa3HdkCFDWLly5VH3nTRpEpMmTWpxndVqZc6cOcyZM+e44hIRkbYJeVLq9ddf59VXX+Xjjz9mwIABTJ48mSeffJL09HQALrroImbPnq0bDxGRLkR1Q3jUNrWUSghTUgqgV5KV0joPBVUuhvbpFrbjiEhs0vlfRERaE/Kk1G9/+1uuuOIKVq5cyVlnnXXE+lNPPZXp06eH+rAiItKJqW4Ij3C3lALITIrjy2I4UFV/7I1FRA6j87+IiLQm5Empjz76iPz8fDIzMwH4/PPPSU5O5rTTTgOgV69e3HLLLaE+rIiIdGKqG8LD2TTQucNqBnxhOUavJCugGfhE5Pjo/C8iIq0J+aPVd955h6uuuoq9e/cCsHXrVqZOncr7778f6kOJiEiUUN0QHoGBzhNtlrAdI7MpKXVASSkROQ46/4uISGtC3lJq8eLFPPHEE8HmuTNmzCArK4vf//73XHTRRaE+nIiIRAHVDeFR29R9L9xjSoFaSonI8dH5X0REWhPyq9iDBw/yve99r9myCy64gIKCglAfSkREooTqhvBwdsBA54GWUkU1DXi84ekiKCKxS+d/ERFpTcivYvv27cuHH37YbNmGDRvo06dPqA8lIiJRQnVDeATGlArnQOdpdgvxcWZ8BhTWNITtOCISm3T+FxGR1oS8+97MmTOZPXs2l112GX379qWgoIB169axcOHCUB9KRESihOqG8KhtbGopZTPTEKbedSaTiT7d7HxbXseBShcnpTrCcyARiUk6/4uISGtC/mh14sSJLF26FKvVyo4dO7Db7Tz33HOMHz8+1IcSEZEoobohPDqipRRA31Q7AAeqNa6UiLSPzv8iItKakLeUAhg1ahSjRo0Kx1uLiEiUUt0Qes7G8I8pBdCnW1NSqlJJKRFpP53/RUTkaEKelCoqKmLJkiXs3bsXn6/5gKjPP/98qA8nIiJRQHVDeARaSiVYLWE9TqCllGbgE5H20vlfRERaE/Kk1F133UVpaSmXXHIJVqs11G8vIiJRSHVDeATGlEq0dVBLqar6sB5HRGKPzv8iItKakCelvvjiC9566y3S09ND/dYiIhKlVDeER6CllCPc3fdS1FJKRI6Pzv8iItKakF/FJicnY7PZQv22IiISxVQ3hJ7H66PB4+8KE+6BzgNJqSqXJ5gIExFpC53/RUSkNSG/ir3xxhu566672L59OwUFBc1eIiLSNaluCL3AIOcQvoHODZ+P4uIinBUlpNj9jasPqLWUiLSDzv8iItKakHffmzdvHgDr1q0DwGQyYRgGJpOJr776KtSHExGRKKC6IfQO7bpnMZvCc4yqcpZvqCchoYBeiRlUuTwcqKxnUM+ksBxPRGKPzv8iItKakCel3nnnnVC/pYiIRDnVDaFX2xAY5DzkVXkzianpJCYm0rvByq4yF3kVGuxcRNpO538REWlNyNv79+3bl759+1JVVcWOHTvo0aMHdrudvn37hvpQIiISJVQ3hJ6z0d9SKine0iHH65PsnzVrf6W674lI2+n8LyIirQl5UqqsrIwf/ehHXHPNNdxxxx3k5+fz/e9/n61bt4b6UCIiEiVUN4Ses6mlVFJ8eFtKBfRuSkrlVaqllIi0nc7/IiLSmpAnpR588EEGDhzI5s2biYuL47TTTmPmzJn83//9X6gPJSIiUUJ1Q+jVNrWUSrR1TEup3sn+2bP2KyklIu2g87+IiLQm5EmpjRs3ctddd+FwODCZ/AOvXn/99eTm5ob6UCIiEiVUN4ReYKDzjmgpZfh8WBsqAShxNlLb4A77MUUkNuj8LyIirQl5UspqteJy+cebMAwDgNraWhITE0N9KBERiRKqG0KvtrGp+16YBzoH/yx8r2z5BlvTVcP2bw6E/ZgiEht0/hcRkdaEPCk1duxYbrvtNvbu3YvJZKKsrIzf/va3XHTRRaE+lIiIRAnVDaEXaCmV2EEDnSemppPi8Hfh27m/hMLCg/h8vg45tohEL53/RUSkNSFPSv36178mISGByy+/nOrqai644ALq6+uZM2dOqA8lIiJRQnVD6AUHOu+AllIB3Rz+Y725s4Qn122luLiow44tItFJ538REWlNyK9kExMTWbRoEeXl5ezfv59evXrRs2fPUB9GRESiiOqG0AsOdN5BLaUAUuz+ywa3NYGktIQOO66IRC+d/0VEpDUhT0pt3ry52c/79u1j3759AJx77rmhPpyIiEQB1Q2hF2wpFR8HGB1yzG52KwB1HlOHHE9Eop/O/yIi0pqQJ6Wuu+66I5aZzWZ69+7NO++8E+rDiYhIFOiIuqGyspIHH3yQ999/H5/Px7nnnst9991Hz5492bZtGw888AC5ubmkpaUxa9Yspk6dGtx39erVPPHEE5SUlDBgwADuuececnJyQhJXuARn37NZAE+HHLNbU0up2o45nIjEAN0biIhIa0KelNq5c2ezn8vLy/nTn/5E3759Q30oERGJEh1RN9x8882kpKSwbt06zGYzd911F/fccw//93//x8yZM7nlllu49tpr2bx5M7Nnz2bQoEEMHTqUTZs2cf/997N06VKGDh3KihUrmDVrFu+++y4OhyNk8YWaM9h9L46OSkoFuu+5vCY8vo5pnSUi0U33BiIi0pqQD3R+uPT0dG677Tb+8pe/hPtQIiISJUJdN3z55Zds27aNBQsW0K1bN5KSkrj//vuZM2cOa9euJTU1lWnTphEXF8eYMWOYOHEiK1asAGDVqlVcccUVDB8+HKvVyvTp00lLS+ONN94ISWzhUtus+17HiI8zE2fyJ6OcbiWlRKT9dG8gIiKHCntSCqCqqoqGhoaOOJSIiESJUNYN27dvJysri3/84x+MGzeOCy64gIULF9KjRw92797NwIEDm22flZUVfHqfm5vb6vrOxufzUVh4kBqXG4BEW8cNdG4ymUi0+ABwNiopJSLHR/cGIiISEPLHq3fddVezn91uN59++innnXdeqA8lIiJRItx1Q1VVFbt27eKss85i9erVuFwubr/9du644w4yMjKO6IZnt9upq6sDoLa2ttX1bWUytfx9qJWUFLFk7VZq3YmAiWR7HB53+I53uASLjyqPBafbwGQi+ILwlrszUrkjG0dH6yrlDnX5dG8gIiKtCXub//j4eK677jquvfbacB9KRESiRKjrBpvNBsDcuXOJj48nKSmJW2+9lWuuuYbJkyfjcrmabe9yuUhMTATA4XC0uD4tLa1dMXTvntzi96HW2FhDWq/eUFIDQLypnrpGJ3a7FY/dhslmwzB7m31NSIjHcZR17d0mJd7MwQao95lIT08iI6Njyt2ZqdxdS1ctd6jo3kBERA4V8qTUQw89FOq3FBGRKBfuuiErKwufz4fb7SY+Ph7wd3MDOP300/nb3/7WbPvc3Fyys7MByM7OZvfu3Uesv/DCC9sVQ1mZP0nUvXsyZWU1GGHq3VZe7qS6thEAEwZL3t5J8b7dJPXoi6+xEbPPcsTXuroG6l0tr2vvNjbDDViorPdSXu7EZqvBZAp/uTsjlVvljkWBcoaK7g1ERKQ1IU9KLV68uE3b3XTTTaE+tIiIdFLhrhvOO+88+vXrx913381DDz1EQ0MDjz76KN///veZMGECixYtYtmyZUybNo1PP/2UNWvW8MQTTwAwZcoUZs+ezQ9+8AOGDx/OihUrKCsrY9y4ce2K4dCbVMMgbDethgHuppnv4syQ0j0TZ2VZeA7WgsS4wJhSviPKGc5yd2Yqd9fSVct9vHRvICIirQl5Umr37t2sXbuWwYMHc+qpp1JYWMhnn33GGWecEewqYYr1zvgiItJMuOsGq9XKX//6VxYsWMD48eNpaGhg7NixzJ07l27duvHcc88xf/58Fi1aRHp6OvPmzWP06NEAjBkzhnvvvZf77ruPoqIisrKyWLp0KampqaEoeli4/XkhrBGoThOaBjqv84DL4+v4AEQkqujeQEREWhPypJTZbOauu+7ipz/9aXDZq6++yrvvvstjjz0W6sOJiEgU6Ii6ITMzk0cffbTFdUOGDGHlypVH3XfSpElMmjQpJHF0hENbSnU0mwmsZgO3z8SB6kZO6fgQRCSK6N5ARERaE/LL2ffff59p06Y1WzZhwgQ2bNgQ6kOJiEiUUN0QWm6v/2tcBBoXmEyQ3PRIK7+qseMDEJGoovO/iIi0JuRJqfT0dDZv3txs2YcffkivXr1CfSgREYkSqhtCK9BSyhqBllIASVb/8ZWUEpFj0flfRERaE/Luez//+c+ZOXMm48ePp0+fPuTn5/Puu+/y+OOPh/pQIiISJVQ3hFZgTKk4c2RGW04KtJSqVlJKRFoX7vN/ZWUlDz74IO+//z4+n49zzz2X++67j549e7Jt2zYeeOABcnNzSUtLY9asWUydOjW47+rVq3niiScoKSlhwIAB3HPPPeTk5ADg9Xp5+OGHefXVV6mvr2f06NH89re/pWfPniGJW0RE/EL+jHXq1Kk8+eSTWCwW/vOf/5CamsrKlSu54IILQn0oERGJEqobQivYUipCYwMnq6WUiLRRuM//N998M3V1daxbt453330Xi8XCPffcQ1VVFTNnzuSqq65i8+bNzJ8/n4ceeojt27cDsGnTJu6//34WLFjA5s2bufLKK5k1axb19fUALFmyhPXr1/PSSy/x4YcfYrfbmTdvXkhiFhGR74S8pRT4p+Y+77zzKC8vJz09PRyHEBGRKKO6IXSCY0pFqvte09XDwRo3bq8PW6QCEZGoEK7z/5dffsm2bdv4+OOPSUpKAuD++++npKSEtWvXkpqaGhzPasyYMUycOJEVK1YwdOhQVq1axRVXXMHw4cMBmD59Oi+88AJvvPEGV199NatWrWLOnDn07t0bgLlz53LBBReQn59Pv379QlYGEZGuLuRXkW63m0cffZThw4czduxY8vPzufrqqykuLg71oUREJEqobgitSM6+B2C3+I/tNSC/sj4yQYhIVAjn+X/79u1kZWXxj3/8g3HjxnHBBRewcOFCevTowe7duxk4cGCz7bOysti5cycAubm5R11fU1NDYWFhs/UZGRmkpKSwa9euE45bRES+E/LL2cWLF7Nx40b++Mc/YrVa6d69O7169WL+/Pnteh+v18t1113HnXfeGVy2bds2pk6dSk5ODmPHjmXVqlXN9lm9ejXjxo1j2LBhTJ48ma1btzZ7v4ULF3LeeeeRk5PDrFmzdDMkItJBQlU3iF9gTKlIdd8zmaCbzX/wvWV1kQlCRKJCOM//VVVV7Nq1i71797J69WpeeeUVioqKuOOOO6itrcXhcDTb3m63U1fnP2e1tr62thaAhISEI9YH1rWHydT+l3/H49s3ml7H+/lE26urlLMrlbWrljMcQt59b82aNfz9738nMzMTk8lEQkICDz30EOPGjWvX+yxevJgtW7bQt29fgGC/8FtuuYVrr72WzZs3M3v2bAYNGsTQoUOD/cKXLl3K0KFDWbFiBbNmzeLdd9/F4XA06xeenJzMPffcw7x583j66adD/RGIiMhhQlU3iN93A51HLoYUm5lyl5fc0louHdQjcoGISKcWzvO/zWYD/F3r4uPjSUpK4tZbb+Waa65h8uTJuFyuZtu7XC4SExMBcDgcLa5PS0sLJqsC40u1tH97dO+e3K7tGxqSgGLs9njS05PIyGjf/tGmvZ9PtOoq5YSuU1aVMzRCnpSqq6sL9hU3DH/3Arvdjtnc9ivnDRs2sHbtWi677LLgMvULFxGJXqGoG+Q7bm9T9z1TZGbfA0iJ9z8u213S/lYDItJ1hPP8n5WVhc/nw+12Ex8fD4DP58/an3766fztb39rtn1ubi7Z2dkAZGdns3v37iPWX3jhhaSkpJCZmdmsi19JSQmVlZVHdPlri7KyGox2nK4rKpwAuFwNlJc7iY+vafcxo4HJ5L/Zbe/nE226Sjmh65S1q5Yz8HOohfxuYNiwYSxevBgAU1P7rr/+9a8MGTKkTfuXlZUxd+5cHnnkkWZNatUvXEQkep1o3SDNBbvvRTCnl2b3/x6/VlJKRFoRzvP/eeedR79+/bj77rupra2lvLycRx99lO9///tMmDCB0tJSli1bhtvtZuPGjaxZs4arr74agClTprBmzRo2btyI2+1m2bJllJWVBVtwTZ48mSVLlpCfn4/T6eTBBx9k5MiR9O/fv91xGkb7X/4dj2/faHod7+cTba+uUs6uVNauWs5wCHlLqbvvvpvp06ezevVqamtr+eEPf0htbS1//vOfj7mvz+fjtttuY8aMGQwePLjZus7WL7wzO7TvZyzqyPIdeoxw9qM9/HiH99+NNfob7Rw6Mr4TqRvkSJEe6BwgNd5/8IIqF84GDxmRC0VEOrFwnv+tVit//etfWbBgAePHj6ehoYGxY8cyd+5cunXrxnPPPcf8+fNZtGgR6enpzJs3j9GjRwP+Xhf33nsv9913H0VFRWRlZbF06VJSU1MBmD17Nh6Ph2nTplFbW8uoUaN47LHHTjhmERFpLuRJqYyMDF5//XXee+89Dhw4QK9evbj44ouD07S25qmnnsJms3Hdddcdsc7hcFBT07zparT0C4+UaInzeHVE+bzeOhwOW9Pxwt+nP3A8hyOeGldNzI8joL/RruNE6gY5UqQHOgewWUxkJMRRWudhd0ktp/RNi1wwItJphfv8n5mZyaOPPtriuiFDhrBy5cqj7jtp0iQmTZrU4jqr1cqcOXOYM2dOSOIUEZGWhTwpNWHCBP75z3/ygx/8oN37vvrqqxQXFzNixAiAYJLp7bff5vbbb2f9+vXNto+WfuEdLdb7uHZk+crKnNTXNwa/t1gSjrFHaI4XZ28AoLw8/MeMBP2Ndg7h6hfekhOpG6S5Rq+PpoZSEW0pBTAgLZ7SOg9fFzvRkPUi0hKd/0VEpDVhuZw9vEVSW7355pt89tlnbNmyhS1btjBhwgQmTJjAli1bGDduXFT3C490389Ye3Vk+Tr69x44ln6H0f2KlvJ1pOOtG6S5ukAzKSAuwl1ET03zDyyscaVEpDU6/4uIyNGEvKXUqFGjmDp1KhdeeCE9e/Zstu6mm2467vdNS0tTv3ARkSgVrrqhK6pr9Cel4kxGxMctOzXN373562JnZAMRkU5L538REWlNyJNS+/fvp1+/fnz77bd8++23weWm47hyXrBgQbOf1S9cRCQ6hbJu6Opq3d8lpSLttHQ7ALtLanG5vRGORkQ6I53/RUSkNSFLSv3P//wPzz77LH/9618B/3hQdrs9VG8vIiJRSHVD6NV1oqRUZmIcaQ4rFfVu/nOwmv4JIX/WJSJRSud/ERFpi5CNKbV169ZmP1944YWhemsREYlSqhtCL5CUskZ4kHPwt3Q4s7d/sPzP8yojG4yIdCo6/4uISFuE7ZLW6OgRdEVEpNNT3XDiahs7T0spgLOaklJb8ysjG4iIdGo6/4uISEvClpRSP3ERETmc6oYT15m67wGc1asbAJ/nV0Q4EhHpzHT+FxGRlnSCxv8iIiLSVnVNA4rHdZL7uzN6+VtK5ZfXU1HXGOFoRERERCSahGxEUo/HwyuvvBL82e12N/sZ4KqrrgrV4UREJAqobgi94Ox75s7RUirZHsep6Ql8W17HtgPVXJSVEemQRKQT0PlfRETaImRJqYyMDBYtWhT8OS0trdnPJpNJFY+ISBejuiH06prGlLJ2ku57AOf0S+Hb8jo+3V+lpJSIADr/i4hI24QsKfXvf/87VG8lIiIxQnVD6AVbSkW4+57h81FcXATAOSd146VtB/m0abBz3yHrevbMxGzWaAEiXY3O/yIi0hYhS0qJiIhI+NV2kpZSzqpylm+oJyGhgB99bwgAu4trqap3U19VypPr/NPB/++4HHr16h3JUEVERESkk9KjSxERkSgSHOi8E4wplZiaTlJaBmmOOE7rkYgBfH6gCoCktAyS0tSVT0RERESOTkkpERGRKNJZWkoFBLrxDe3lAGBzXmVkAxIRERGRqKGklIiISBTpLGNKBTiryvnrx3uoqKkF4ONvSiMckYiIiIhECyWlREREokhdU1LK2gm67wUkpqYzqH9vTEB+VSPFte5IhyQiIiIiUUBJKRERkSjR6PHR6PUno+I6Sfe9ALvVQrrd33zrs4K6CEcjIiIiItFASSkREZEo4Wz0BL/vLN33DtU7yQLApwW1EY5ERERERKKBklIiIiJRwtngn3nPagZTZ0xKJfovK7YV1uE1OldLLhERERHpfJSUEhERiRLOBn9LKWsnrb3T4k2k2i3UewyK63yRDkdEREREOrlOelkrIiIih6sJJqU6YTMpwGQyMaZfEgD7a5SUEhEREZHWKSklIiISJWoDSSlLhANpxXlNSakDTi8+deETERERkVYoKSUiIhIlvhtTqnO2lAI4K9NBss1MgxdK6tVaSkRERESOTkkpERGRKFHTyceUMnw+ykuLGZrhb8qlLnwiIiIi0pq4SAcgIiIibRMc6NzSOVtKOavKWb6hnmqXAfRkf4268ImIiIjI0XXSZ60iIiJyOGejv/uerRPX3omp6fRJSyDOZODywq5SV6RDEhEREZFOqhNf1oqIiMihnJ189r0Aiwl62v0tpD7Oc0Y4GhERERHprJSUEhERiRLOKJh9L6B3gj8ptT7PqS58IiIiItIiJaVERESiRLS0lALoafcPyF5S52Hr/qpIhyMiIiIinZCSUiIiElO8Xi/XXXcdd955Z3DZtm3bmDp1Kjk5OYwdO5ZVq1Y122f16tWMGzeOYcOGMXnyZLZu3drRYbeJs6HzjykVYDFBv2R/k67XdhQFl/t8PgoLD1JYeBCfT7PziYiIiHRlUXBZKyIi0naLFy9my5YtwZ+rqqqYOXMmV111FZs3b2b+/Pk89NBDbN++HYBNmzZx//33s2DBAjZv3syVV17JrFmzqK+vj1QRjqqmk8++d7hTU/xJqXe+LqGuaZD24uIinly3lSfXbaW4uKi13UVEREQkxikpJSIiMWPDhg2sXbuWyy67LLhs7dq1pKamMm3aNOLi4hgzZgwTJ05kxYoVAKxatYorrriC4cOHY7VamT59OmlpabzxxhuRKsZRfdd9L8KBtFF3u4k+yVbq3T7W7SoOLk9KyyApLSOCkYmIiIhIZxAll7UiIiKtKysrY+7cuTzyyCM4HI7g8t27dzNw4MBm22ZlZbFz504AcnNzW13fWRiGgbOptVE0jCkFYDKZGJ+VAsCqzw9iaMBzERERETlEXKQDEBEROVE+n4/bbruNGTNmMHjw4GbramtrmyWpAOx2O3V1dW1a31YmU8vfh4rL7cPr8yd1rBbwhv4QJ6ylYo/L6sbfvihnV7GTLwtr6HnI4zCTKTyfVUcJxB7NZTgeKndk4wi3WC+fiIh0LkpKiYhI1Hvqqaew2Wxcd911R6xzOBzU1NQ0W+ZyuUhMTAyud7lcR6xPS0trVwzduye3+H2oFFX7Y7SYIDnBhqnBhslmIyEhHofd/71h9rb4tSO2AbAfts7hsHFK7xSuPLsPqz7dz0tfFjH34l44HP7t09OTyMgI/WfV0cLx+44GKreIiIicKCWlREQk6r366qsUFxczYsQIgGCS6e233+b2229n/fr1zbbPzc0lOzsbgOzsbHbv3n3E+gsvvLBdMZSV+RNf3bsnU1ZWQ6h7quWV+VtuJVjNuFxu6l2NmH0W6uoagt/7Glv+2hHbALhcjZgOWWaxWCkvd/JfZ/Zk1af7eWP7QSacbKO+vhGA8nInNlvNUcvc2ZlM4ft9d2Yqd2yXO1BOERGRjqAxpUREJOq9+eabfPbZZ2zZsoUtW7YwYcIEJkyYwJYtWxg3bhylpaUsW7YMt9vNxo0bWbNmDVdffTUAU6ZMYc2aNWzcuBG3282yZcsoKytj3Lhx7YrBMAjeqAa+D+WrxuUf5DzB1nmr7sPv0w2fj6KiIpK9NVyc1R0D+Pv2su/Wh+Fz6uhXrJRD5Va5Dy+niIhIR1BLKRERiWlpaWk899xzzJ8/n0WLFpGens68efMYPXo0AGPGjOHee+/lvvvuo6ioiKysLJYuXUpqampkAz9MtcsNgM3kI1oGDHdWlbN8Qz0JCQX81/AzeC+3jA/3ORl/io2U+M6bXBMRERGRjqGklIiIxJwFCxY0+3nIkCGsXLnyqNtPmjSJSZMmhTusE1JQ7G9hVO2so67OGuFo2i4xNZ3ExEQGpMdzcVZ33sst4z9lHsb0sUU6NBGJIV6vl+nTp9O3b99gHbBt2zYeeOABcnNzSUtLY9asWUydOjW4z+rVq3niiScoKSlhwIAB3HPPPeTk5ATf7+GHH+bVV1+lvr6e0aNH89vf/paePXtGpHwiIrFKjylFRESiQK3bB4DdGr3Pk64fczIAeTU+qhp8EY5GRGLJ4sWL2bJlS/DnqqoqZs6cyVVXXcXmzZuZP38+Dz30ENu3bwdg06ZN3H///SxYsIDNmzdz5ZVXMmvWLOrr6wFYsmQJ69ev56WXXuLDDz/Ebrczb968iJRNRCSWKSklIiISBWobvQDEmaOj615LBvVMYkw//6yHX5Z6IhyNiMSKDRs2sHbtWi677LLgsrVr15Kamsq0adOIi4tjzJgxTJw4kRUrVgCwatUqrrjiCoYPH47VamX69OmkpaXxxhtvBNffcMMN9O7dm6SkJObOncsHH3xAfn5+RMooIhKrlJQSERGJAoGWUnGm6E1KAfy/Id0B2O/0kVvminA0IhLtysrKmDt3Lo888ggOhyO4fPfu3QwcOLDZtllZWezcuRPwz7J6tPU1NTUUFhY2W5+RkUFKSgq7du1qd4wmU/tf/h2Pb99oeh3v5xNtr65Szq5U1q5aznCI3j4AIiIiXUhdU1LKGqYLgo5ySlo8Jyeb2Vfj4/ltZVxw5qmRDklEopTP5+O2225jxowZDB48uNm62traZkkqALvdTl1d3THX19bWApCQkHDE+sC69ujePbld2zc0JAHF2O3xpKcnkZHRvv2jTXs/n2jVVcoJXaesKmdoKCklIiISBWobm1pKRXH3vYCzMuLIq2lk68E6Ps2vZHi/1EiHJCJR6KmnnsJms3Hdddcdsc7hcFBTU9NsmcvlIjExMbje5XIdsT4tLS2YrAqML9XS/u1RVlZDeyZNrahwNh2vgfJyJ/HxNcfYIzqZTP6b3fZ+PtGmq5QTuk5Zu2o5Az+HmpJSIiIiUaA22FIq+q9+kmxmBqRa2FPp5U8f7uXZH5+NKVxtwkUkZr366qsUFxczYsQIgGCS6e233+b2229n/fr1zbbPzc0lOzsbgOzsbHbv3n3E+gsvvJCUlBQyMzObdfErKSmhsrLyiC5/bWEYtOvGNbit0f59o1FXKCN0nXJC1ymryhkaGlNKREQkCtQFBjqPgaQUwJnd47BZTHxxsJr135ZHOhwRiUJvvvkmn332GVu2bGHLli1MmDCBCRMmsGXLFsaNG0dpaSnLli3D7XazceNG1qxZw9VXXw3AlClTWLNmDRs3bsTtdrNs2TLKysoYN24cAJMnT2bJkiXk5+fjdDp58MEHGTlyJP37949kkUVEYo5aSomIiESB4EDnUfg4yfD5KC4uAqC4uAjDMHDEmZgwMJWXv6rguY35nH9qulpLiUjIpKWl8dxzzzF//nwWLVpEeno68+bNY/To0QCMGTOGe++9l/vuu4+ioiKysrJYunQpqampAMyePRuPx8O0adOora1l1KhRPPbYY5ErkIhIjFJSSkREJAoExpSKxu57zqpylm+op2ffRor2fk1Sj74kOBxckOlmzS5/a6nP9ldpbCkROSELFixo9vOQIUNYuXLlUbefNGkSkyZNanGd1Wplzpw5zJkzJ6QxiohIc1H4vFVEQsnn81FcXExxcTE+ny/S4YjIUQRm34vW7nuJqel0S+9JQko64E9UrfnsW3rHNwKwbFN+JMMTERERkQhQUkqkiystLeWpd7bx1DvbKC0tjXQ4ItICn2EEk1LWGKq5E1PTGdIrEbMJNu6r4D+FsTnDlIiIiIi0LIYubUXkeCWmpJPY1HpBRDqfukYvgfZR0dpS6mgS42BULysAf96UB/hbcBYWHqSw8KBacIqIiIjEMI0pJSIi0sk5GzwAmE1gibGxwJ1V5XidjUA67+eWsbe8DntjFU+u2wrA/47LoVev3pENUkRERETCQi2lREREOjlngxeIra57h+qZnkKfRDMGsGprAQBJaRkkpWVENjARERERCasYvbwVERGJHTVNLaWs5hhrJnWI7DQLAK/tKKLO7Y1wNCIiIiLSEZSUEhER6eQC3feslggHEkaZCWb6dbNR5/by9h4NeC4iXZPL7eXPn5VysMYT6VBERDqEklIiIiKdXKCllC2GW0qZTCauGJQCwLo9VRhGbA3oLiLSFruKnbz8VSWbDrgiHYqISIdQUkpERKSTq3E1JaViuKUUwEWnJBMfZ2ZvZSMVDUpKiUjXc3JaAgBVDT4avDoPikjsU1JKRESkk6vuAmNKGT4fdZWljOrrvyH7plJdV0Sk60lNsNKvmxWA0npfhKMREQk/JaVEREQ6ua7QUspZVc7yDXsweRsAyKv24vWplYCIdD2n93AAUFqvc6CIxD4lpURERDq5apcbiO0xpQASU9PJOqkXdrMPt2HigFOtBESk6zmjpx2AErWUEpEuQEkpERGRTq66C7SUCjCbTJxk95f32ypvhKMREel4ZzS1lKpwGTR4lJgSkdimpJSIiEgnF5x9zxLbLaUCTnL4W4YV1vkoqXVHOBoRkY7VKymOeIsJH5Bf3RjpcEREwkpJKRERkU4u2FKqi9TaCRaDdJt/LJX39tZEOBoRkY5lMplItPkfQlTWq8WoiMS2LnJ5KyIiEr0CSSlrF2kpBXBSoj8p9eE+JaVEpOtxxDUlpVxKSolIbOuUSamdO3cyY8YMRo4cyfnnn8/tt99OeXk5ANu2bWPq1Knk5OQwduxYVq1a1Wzf1atXM27cOIYNG8bkyZPZunVrcJ3X62XhwoWcd9555OTkMGvWLIqLizu0bCIiIu0V6L4X3wXGlAro5TAwA99WNPJtWV2kwxER6VD2OP9tmpJSIhLrOl1SyuVycf3115OTk8NHH33Ea6+9RmVlJXfffTdVVVXMnDmTq666is2bNzN//nweeughtm/fDsCmTZu4//77WbBgAZs3b+bKK69k1qxZ1NfXA7BkyRLWr1/PSy+9xIcffojdbmfevHmRLK6IiEirGjy+4EC31hiffe9QNjP0SvRfpqzdqQdIItK12IMtpTwRjkREJLw6XVKqoKCAwYMHM3v2bGw2G2lpaVx77bVs3ryZtWvXkpqayrRp04iLi2PMmDFMnDiRFStWALBq1SquuOIKhg8fjtVqZfr06aSlpfHGG28E199www307t2bpKQk5s6dywcffEB+fn4kiywiInJUNS7/QN9mE1g7Xa0dXv26+ZuGrd1VgmEYEY5GRKTjBLrvVamllIjEuLhIB3C4AQMG8MwzzzRb9tZbb3HmmWeye/duBg4c2GxdVlYWL774IgC5ublcffXVR6zfuXMnNTU1FBYWNts/IyODlJQUdu3aRb9+/doco6mTP6gOxNfZ4zwePp+P0tJSvN46zGY7JlN479AO/QxNpvB/pof/7jrymB11vEOPGYt/oxA95evs8YlfVdNT8kSrGVMX+6X1TTJjs5jIq6jn6+JaBmUmRTokEZEOYbdqTCkR6Ro6XVLqUIZh8Nhjj/Huu++yfPlynn/+eRwOR7Nt7HY7dXX+sSZqa2uPur62thaAhISEI9YH1rVV9+7J7S1KRERLnO1RVFTEnz/8CoBfTzqXzMzMsB7P663D4bAB0L17EhkZ4f1MA8dzOOKpcdWQnt5xx4SOKeOhYvFv9FCxXj7pGDVNSamkrjSgVBOr2cS5fRNZn+dk7a5iJaVEpMvQmFIi0lV02qSU0+nkrrvuYseOHSxfvpxBgwbhcDioqWk+C4/L5SIxMREAh8OBy+U6Yn1aWlowWRUYX6ql/duqrKyGztyLwGTy3wx39jiPR1mZkzh7Ena7jfJyJxZLwrF3OsHj1dc3Br/vqOPF2RsAYrKMENt/oxA95QvEKZ1bddMg50m2LtZ3r8mFJyf7k1I7S5j9vVMxd7HWYiLSNQVn32tQUkpEYlunTErl5eVxww030KdPH1588UXS09MBGDhwIOvXr2+2bW5uLtnZ2QBkZ2eze/fuI9ZfeOGFpKSkkJmZSW5ubrALX0lJCZWVlUd0CTwWw6BT32gGREuc7XFomTqifIe+f0ceL5bLePixY+1v9FCxXj7pGMGWUjYL4ItsMBEwvE8CCTYLhTUNvP/lt1x05imYzV0zQSciXUcgKVXT4MXrM7B0oYkuRKRr6XRXdVVVVfzsZz/jnHPO4dlnnw0mpADGjRtHaWkpy5Ytw+12s3HjRtasWRMcR2rKlCmsWbOGjRs34na7WbZsGWVlZYwbNw6AyZMns2TJEvLz83E6nTz44IOMHDmS/v37R6SsIiIix1LVNNB5chdtKRUfZ2ZUX38Lzic+/Ibi4qIIRyQiEn7xTUkpnwGV9e4IRyMiEj6drqXUyy+/TEFBAf/617948803m63bunUrzz33HPPnz2fRokWkp6czb948Ro8eDcCYMWO49957ue+++ygqKiIrK4ulS5eSmpoKwOzZs/F4PEybNo3a2lpGjRrFY4891sElFBERabtAS6lEmwXomlODX3hyMu9+W0Nhow2vT80PRST2mU0m4i3Q4IWKOjfdE22RDklEJCw6XVJqxowZzJgx46jrhwwZwsqVK4+6ftKkSUyaNKnFdVarlTlz5jBnzpwTjlNERKQj1Bw6plQXy0kZPh/FxUX0sRjYzODywpfF9fTtE+nIRETCL95iosFrUFbXSBbtGwNXRCRadM2+ACIiIlGiqtmYUl2Ls6qc5Rv28PePdtDL4R9P6/29NcfYS0QkNtibTvvldY2RDUREJIyUlBIREenEAt33kuO7ZpWdmJpOQko6fRP83fY+2ldDXaNmoxKR2GdvGleqok5jSolI7OqaV7giIiJRwOfzUVpTB4C3vgajC0/nmG6DJKuJeo/B21+XRDocEZGws1v8SamyWiWlRCR2KSklIiLSSRUXF5Ff4U9Krf/PXurq6iIcUeSYTHBqir8vyz+/KIxwNCIi4RffNPpvhbrviUgMU1JKRESkE3Mb/qq6W3JShCOJvFNTLJhNsK2gml3FzkiHIyISVvFmf0upwNiCIiKxSEkpERGRTsrjM3D7x/fGZu66XfcC7GaD4T39TQee+XA3Pp8vwhGJiIRPYH6Lape674lI7FJSSkREpJOqafhuQG+rKYKBdBLOqnKMav94Uu/vrebLb/dHOCIRkfCxNY0pVVWvllIiEruUlBIREemkqpuSUvFxZkxKSgHQq3s3+nSLx8DEC1+URzocEZGwiW9qKVWlllIiEsOUlBI5Bo/PIK/aw1cl9ZEORURasXPnTmbMmMHIkSM5//zzuf322ykv9ycttm3bxtSpU8nJyWHs2LGsWrWq2b6rV69m3LhxDBs2jMmTJ7N169ZIFOEIgaSUPU7V9aFG9E8FYN2ear4qqolsMCIiYWI7ZEyprjz7qojENl3lirTiP8V1vLK7jvUHGrh7XR5V9XpSJdIZuVwurr/+enJycvjoo4947bXXqKys5O6776aqqoqZM2dy1VVXsXnzZubPn89DDz3E9u3bAdi0aRP3338/CxYsYPPmzVx55ZXMmjWL+vrIJ6KDSSmrJcKRdC6ZyfH0TzZjAAvfzsXj1dhSIhJ7AmNKeX0GtY3e1jcWEYlSSkqJtGLdnqrgIMONXoMNeysiG5CItKigoIDBgwcze/ZsbDYbaWlpXHvttWzevJm1a9eSmprKtGnTiIuLY8yYMUycOJEVK1YAsGrVKq644gqGDx+O1Wpl+vTppKWl8cYbb0S4VFDtUkupozm7h5UEq5kdhTX86aO9kQ5HRCTk4sym78aVUhc+EYlRusoVacX2wjoAUuP9/yof7CmLZDgichQDBgzgmWeewWL5rkXRW2+9xZlnnsnu3bsZOHBgs+2zsrLYuXMnALm5ua2ubyuTieC4T4HvT/RV3ejPitut0VFdd+SwVwlWE7eOyQRg+Zb9rNy4C8Pwheyzb+sLOvZ4neWlcsf2SzqPbk0DS2mwcxGJVXGRDkCksyqsdlHodGMChvW08l5+Axv2luP2+rBaouMGUaQrMgyDxx57jHfffZfly5fz/PPP43A4mm1jt9upq/MnnWtra1td31bduye3+P2JaDT5zzXJDhsOw4bJZsMwe5t9TUiIx2FveV1HbuP/3DouDne9jQnDerHX6eVvnxXzh/WF9M1IYvKYwSH57NsjVL/vaKNyS2ewc+dOFi5cyI4dO7BarZx//vnceeedpKens23bNh544AFyc3NJS0tj1qxZTJ06Nbjv6tWreeKJJygpKWHAgAHcc8895OTkAOD1enn44Yd59dVXqa+vZ/To0fz2t7+lZ8+eHVq+JJuZ0jq1lBKR2KWklMhRfJpfBUCa3UxmooVUu4VKl5et+6sYeXJahKMTkZY4nU7uuusuduzYwfLlyxk0aBAOh4OamuaDYbtcLhITEwFwOBy4XK4j1qelte//vKzMf4zu3ZMpK6shFGPSFlf647JgUO9qxOyz4Gts/rWuruGo6zpyG//n1oipo+Kob6S83MmPTu/GR7tLyavxcddrezAaGvneGSdjNof/4YHJFNrfd7RQuWO73IFyRoPAeILXXHMNTz31FLW1tdxxxx3cfffdLFy4kJkzZ3LLLbcEu3PPnj2bQYMGMXTo0OB4gkuXLmXo0KGsWLGCWbNm8e677+JwOFiyZAnr16/npZdeIjk5mXvuuYd58+bx9NNPd2gZk5taSlWrpZSIxCg19xA5ii35lQBkJlowm0zk9PbfwG47UB3BqETkaPLy8rj66qtxOp28+OKLDBo0CICBAweye/fuZtvm5uaSnZ0NQHZ2dqvr28owCN6oBr4/0VdVcKDz6KiuO/o+3TDAhImRva1kWD00eOGut/axbuNWDh48yMGDB/F6fSH7fbT0CsTR1V4qd2y/okU4xxNctWoVN9xwA7179yYpKYm5c+fywQcfkJ+f36FlTLb5z/9qKSUisSo6rnJFImDbAX9LqZ4J/n+TU9PiAfimrH1dekQk/KqqqvjZz37GOeecw7PPPkt6enpw3bhx4ygtLWXZsmW43W42btzImjVruPrqqwGYMmUKa9asYePGjbjdbpYtW0ZZWRnjxo2LVHGCgrPvxWn2vcMZPh/FxUUUFxdhBs5JcZFiNWg0zNy/oZqlG/N4ct1WiouLIh2qiIRJuMYTrKmpobCwsNn6jIwMUlJS2LVrV7vjPO5xvUyHjCnl8kR8rDGN06ZyqqwqZzio+55ICxo9Pg5U+bvNpNn9Sal+Kf6k1LfltRGLS0Ra9vLLL1NQUMC//vUv3nzzzWbrtm7dynPPPcf8+fNZtGgR6enpzJs3j9GjRwMwZswY7r33Xu677z6KiorIyspi6dKlpKamRqAkzR06+56ekTfnrCpn+YZ6DHc9ST36EmeGkRk+1hebqPNa2FIKF2R2j3SYItJBQjmeYG2t/1ovISHhiPWBde3R3u6QDQ1JQDF2ezy2ODtQTSMmMjKio1tle0VLd9ET1VXKCV2nrCpnaCgpJdKCA1UufAY44szYm6biPTnVP5DvvvJ6PD6DOHOYUsUi0m4zZsxgxowZR10/ZMgQVq5cedT1kyZNYtKkSeEI7YQEW0pZLUpKtSAxNR1fY33w53gLjEip56OKBAqqGvjGrsscka4g1OMJBpJV9fX1R92/Pdo7DllFhbPpeA0kJloBKKqoo7S0prXdoo7J1LXGaYv1ckLXKWtXLWfg51DT1ZpIC/Iq/BchfbpZMTW1U+yRaMUeZ8bl8bG/sp5T0hNaewsRkRPicntp8PqvdOxxZmLrViR8kuIMBncz+E+ViW0lHqpcXnpFOigRCZu8vDxuuOEG+vTpw4svvhjsvj1w4EDWr1/fbNu2jCd44YUXkpKSQmZmZrMufiUlJVRWVh7R5a8t2jtWV3BbA5Jt/u57lfXumL35jbaxzI5XVykndJ2yqpyhoTGlRFqQX9mUlEq2BZeZTSZO7e5PRH2rcaVEJMwq6/1to8wmsFrUMrM9Tk0y6J5gxe2Dv2zeT2HhQXw+X6TDEpEQC+d4gpMnT2bJkiXk5+fjdDp58MEHGTlyJP379+/QMibZvhtTSkQkFqmllEgL8isOSUp5v2vafWr3BL4qcvJtWR2XtG9iLhGRdimv8yel4i0EW2xK25hMMPLkVP71VQlv5zXgefNzfnX5MHr16h3p0EQkhMI5nuDs2bPxeDxMmzaN2tpaRo0axWOPPdbBJYTk+KbZ9+rViVtEYpOSUiItyAu0lOpmo7DikKRUU5e9b8o02LmIhFdpbSMA9jglpI5H3xQ73a0eytxx5Hm6RTocEQmDcI4naLVamTNnDnPmzDnhOE9Et2BLKSWlRCQ2qfueSAu+ayllbbb81O7+wS2/Ufc9EQmzQFLKoa57x8VkMpGd6P8Mv632Ulanri8iEn2S4/1JKWeDF4+vCwxeIyJdjpJSIodxub0U1TQA0Lebrdm6k9P9s7Hsr6zH6Aqj2olIxJQ6/echtZQ6fuk2H+k2A58BL39VEelwRETaLclmJlALVKoLn4jEICWlRA6zv8rfXS8p3kK3pqdTAX262TEB9W4fZXW6MDgePp+P4uJiioqKNPCwSCuCLaXU0f6EZHfzn2fe3F1FeV1jhKMREWkfi9lEisPfcr9S154iEoOUlBI5TKDrXr9UxxGDC9vizPTqFg/AgaZxp6R9SktLefLtbTzy6mZKS0sjHY5Ip1XqDCSl1FLqRGTEQ7rdRKPX4G+fHoh0OCIi7ZaW4E9KKbEuIrFISSmRwwSSUv3THC2u75vqX54fhqSUYRjsrfLw+YFq9pQ3xmwXwcSUdJJS04+9oUgXpoHOQ8NkgjO6+5ubrfq8gNx9+yksPEhh4UG11hSRqJDelJSqUEspEYlBSkqJHCYw816/1JaTUv1S7QDsr3S1uP5EbD5Qy4aCBjbnVfL+3lo+2e8M+TFEJDp8131PSakT1SfRzCmpNuoavdz7r69YviWfJ9dtpbi4KNKhiYgcU5rDP8ZphcaUEpEYpKSUyGGC3feO0lLqpJTvBjsPtde/9g/Ea2uabev1rytDfgwR6fy8PoNyJaVCxmQycc2Z/taZe+vjsadkkJSWEeGoRETaJi3YUkrd90Qk9igpJXKYQLe8o3XfOyktkJQKbUupgioXnx6oBWBstv9m6dMDtRyo0thVIl1NZb0brwEm4LD5FuQ4ndc/ib7JVhp98J/CmkiHIyLSZt+NKaWWUiISe5SUEjlEvdtLSdPgwkfrvndSSqD7XmiTRa9+cRAD6JVopl+agz7JcRjAq18UhvQ4ItL5BQY5T7FbMJvUUioULGYT15zlby31+YFqXJ7YHLNPRGKPxpQSkVimpJTIIQJd91LsccHpdw93UlOyqsrlodoVuouDTfsqATglxT8gb3Z3//gBG/dWhOwYIhIdAuNJpTviIhxJbLn41GTS7CbcXoMvSj2RDkdEpE3Smq5JNaaUiMQiJaVEDhHoune08aQAEmwWuif6E0ah6sLX6PHxdYl/UPMMh7+vTq8k/wXIrmIntY26eRLpSkprGwBId6jvXiiZTSZyevjPrd9UefmqRN2jRaTzS0toGuhcY0qJSAxSUkrkEHkVrY8nFRDqLnxflzhxew26xVtIsvq76iTazGQmWfEZ8EVBdUiOIyLRIdCNOE0tpULC8PkoLi6iuLiIDIeJgT0SAXh8YzGNHl+EoxMRaZ3GlBKRWKaklMghgjPvHWU8qYBQD3b+5UH/oLsDM+yYDhk/5sye/uN8fkBJKZGuRN33QstZVc7yDXv46wdfUldXx6iTU7FbIL+6kT9vyot0eCIirQp036tt9CqRLiIxR0kpkUMca+a9gFC3lPryoD/pNCij+XHP7JkAwOcHqkJyHBGJDgeq/AnvzCQlpUIlMTWdhBT/QOd2q4VzMv03ecs+ySe3tDaSoYmItCrZHofF7H9oqXGlRCTWKCklcohA973WxpSC71pShSoptaNpevLBRySl/D9/ebAGt1dPxkS6ioKmpFRgbDkJvb4JMKxHHB6fwb2vfcmBggJ8Pp1nRaTzMZtM3w12rnGlRCTGKCkl0sTZ4An21T9m973UppZSVSfefa+yzh3sBjgww95sXb8UG93scTR4fOzRk3yRLsHrM4JJqUwlpcKmtrqChNoiLPj4uqyBu9fsoLi4KNJhiYi0SONKiUisUlJKpEmg1VN6gpWk+Na7zJzUlLQqcTbicntP6LiBVlInpzlIsjWfactkMnF6ZhIA/2naTkRiW7GzAY/PwGox0V1jSoVVenoqpyf7Wx3sqo2n0KmbPRHpnAItpSrVfU9EYoySUiJN8to4yDlAisNKclPi6kRbSwXGkzqrd3KL68/o5V/+n0LnCR1HRKLDgaaWk7272YNjiEj49LN7SLcZeA144pNiDMOIdEgiIkfonmgDvpudVUQkVigpJdIkMMj5scaTCgh24as4sXGlAjPvndm7W4vrT89sSkoVqaWUSFdwoMp/TumbYj/GlhIKJhMMTfNhNsHWg3W88Z/iSIckInKE3t3iAThYHZqZn0VEOgslpUSa5Fe0bea9gEAXvhNpKeUzjGD3vWO1lPqmtPaEuwqKSOcXmHlPSamOk2SFs7r7W78++t4eyjWQsIh0Mn2a6oSCEIxnKiLSmSgpJdIkr8Jfybel+55/u6aWUicwA19eRT01DR7i48xkZyS2uE3PJBvdE214DdhVrC58IrEu0H2vbxvPRRIaA1NN9Es2U+Xy8Pt3ciMdjohIM727+a871VJKRGKNklIiTfIq6oC2d9/rn5YAwN7yuuM+5o6mrnuDeyYRZ2n539FkMnFGYLDzIiWlRGKdWkpFRl11BZmeMsDg7a9LeW1HYaRDEhEJCrSUOljdoLHvRCSmKCklAlS73FS5PEDbW0qdluFPSuWW1B73xUFgkPMzj9J1L+D04GDnGldKJNYFklJ2j5Pi4iLdfHSgXt27cVZ3/wxXC9/OJbekNsIRiYj4ZSbHYzZBg8dHWZ1m4BOR2KGklAiwp9Tf2ikzOZ4Em6VN+5ySnoAJqHJ5KD/Oi4PvxpNqeZDzgDOUlBLpEsrrGqmsd2MCXv/kP/z1gy+pqzv+1pjSfoPTTJyRbsHl8TF71efsr9DnLyKRZ7WY6ZHUNNi5xpUSkRiipJQIsLvE3y0uu0fL4zq1xG61BLv67Slt/9N0l9vL101P4Y82yHnAmU0z8OVV1FPT1KJLRGJPYNy4Pt2spHbPICElPcIRdT111RWkNxTRzWaivN7Lz1/4nNzjOMeLiIRaH83AJyIxSEkpEQgmhwa2IykFMKB7Uxe+47hh2VXsxOszSE+w0is5vtVtUxOswQuRr4pC21rqs4JadpQ2kl/twetTNyGRSNrVNG7caWmtnxMkvFLT0phwVm+SrCaKaz38z98+588ffEXBwQJ8Pl+kwxORLkoz8IlILFJSSgSC44Zk9Uhq135ZTTPmfVPa/u4dga57Q3p3w2QyHXP7cHTh+8sn+dzzTj7bS9x8dKCBhz8qwKfxa0QiZlex/1w0QEmpiEuMj+P7J9sYmumgzu3lic0l/OSFXSx751Mlp0QkIgIz8BWopZSIxBAlpaTL8/qMYEun9nTfAzitKSm1p6z9LaW+bJp571iDnAec3tSF76sQzcD3xn+KWPzhtwD0TrRgBj7YV8NT6/eG5P1FpP12FfvPC6ela+a9ziDeYuJ3Y/tyw/AM4kxQ47WwZHs90174mr9t2E2jR4kpEek4vQMz8FU1RDgSEZHQUVJKolIoW/PkV9bT4PERH2du88x7Aacd0lKqPTEZhsEXBf6Z9441nlRAoKXUlwerT3g2rrpGL49/4E9IXXNWdy7ub2dkbxsAyz7JZ2957Azs6/P5KC4upri4WC0bpFNzNnjIr/Q//R6QrpZSnYXFbOLKwWlMOC2erIRG4kwGTq+ZP24s4sqln/DEe7lUuzQTloiE30mp/qRULF2niYgoKSVR5a3dlazJrWPZ1kpuf2sfeRX1J/yeuwNd9zISsZiP3Y3uUP3SHMTHmalze9lX3vZYDlS5KKxpIM5sOubMewFn9k4mzmyi2NkYvHE9Xs9vzqe0tpE+KXb+39DuAJyaamXkSUn4DGKqtVRpaSlPvbONp97ZRmlpaaTDETmqr5smXMhMjqdbfNtmAZXwMnw+iouLKC4uwmaGgUmNXNrbx5DuFlLjTZTWNvJ/b+7ih09u5KE3tvP13jwKCw8qAS4iYTG4ZzIWExTWNFCoLnwiEiOUlJKosWLLfhZtLMTp9rcS+rKonp/89VO+Lj6x7mw7i9o/816AP6nkb8G09UBVm/f7ZF8FAEP6dMNhbdvNp8NqYWgffwJrc15FOyP9TlW9m79/egCAWy48Favlu9PAz4ZlYALe/rqUnSEeUD2SElPSSdQsZtLJfVHg/58b3LN9Y9tJ+Dirylm+YQ9//eBL6ur8LROsZuhnqeLsuGJG9rKSZHbj8hi8/FUlP3vpG25/9UsOHDwY4chFJBYl2CwMbKojth2ojnA0zfl8Pg4eLOCgxtwTkXZSUkqiwpa8Sh57/xsATu9u5arTu3FGDwf1bh93vfYVtY2e43/v/EoAhvVNOa79zznJv9/W/W1PSm3O8x/z3P6p7TpWYPtP9lW2a79D/ePzAurcXrJ7JDI2O6PZulPS7Fx+ek8Ant2Yd9zHEJH2W/9NGQCnp5kpLi464W66EhqJqekktJDUTk5L5+xTe3NZX4Nzu3tJsnjxYOYrZzw3vZ7H+m/KIxCtiMS6wPVqex6GdoSiokKeXLeVJ9dtpaioMNLhiEgUUVJKOj2X28uD674GYHxWCsN62kh3WPjNJSfRM8lGXkU9j777zXG9d7XLzVdNs9m1N0EUkNOUlPosv7JNN5E+wwgmpUYeZ1Lq0/xKvL7237DWNXp54TN/K6npI/u1OOvfjFH9MQHv5Zaxp7T9A7iLSPtVu9xsbxpnbte3e5u1zJHOzWSCTAdckF7P0DQfdgsU1Li5dfWX/HL1l+SHoJu5iEjAsKbrzs/DnJRqreXT0dYlp2WQnJZx+FuJiLQqLtIBSHTz+XzBcXoyMjIwm0Of53xuUx75lS4yEm38z/CerN5eAEA3u4UHrjidmS9s49UvC5l4ViZnt7O106f5VRjAKekOeiYf38DCQ3p3w9I01lNBtYu+Ka0Plv51sZMql4cEq4Uze7VtkPOAM3slk2izUOXysKvYGRz8vK1Wbz9IlctDv1Q7lw7s0eI2p3ZP4JLsDP69u5Rln+Rz/w8Ht+sYItJ+G/dW4DWgX4qNnhl2aixqJRVtzCbon2hwWpqVOq+Jt/Ma+eibcjbureAH2Sn89/lZnJSWEOkwRSTKnd00lMOe0jqqXW662a1t2s/n8wVbMGVm9sJkMlFW20heZT0VtY3sLy6jzu0jKTkZk8lMdXUVG772P8g8b2AF3bp9d41dVV3F+l0F+ICzTynFkZCEt6GOXRUe4i0mviqpx57SSKrD2uIDUBGRQykpJSckMIg0wM8vPZuePXuG9P13lzh5fvN+AG67NItEW/MnNTknpTDprF68+mUhC9/J5fmfnENcOwYrD4ztNLJ/2nHHaLdaOCMzmS8OVvNZftUxk1Jrd5b4j3lyKnGW9iXx4ixmRp6cxru7S1m3q6RdSalGj4/lW/yf5c9G9mt1UPf/HtWff+8uZe3OYmaOOZl+ae2blfBoPF4fm/Jr2FHaSLLDoLTOTcupMZGu5aOmrl4j+yaCVy1rolmDs4LGunrOiXOzx5dBudfGml2VvP71Fi7JymBs/3jOynTQK7NXWB7kiEhs655o45R0B3vL63n1i0KuO7ffEdscmoACSOneg40781j28R6qPRYSk/I5UOOhttHbwhHKDvneP9vfzq1lhy0H8D/Mzf1PJVDZbM2mwgOw7gBJ8Rb6pTo4Oc1Bj3gv/VJs5AzoS58UO2UlRZhM0NCQhNWahMmk86FIV6WklJywcA0g7fUZPLhuN16fwcVZ3RmbnUFxcfER2930vVN5L7eU3SW1/GPrAf7f8JPa9P6GYbCpKSl1vF33AkaenMoXB6tZt6uEiWf1Oup2Hp/BG1/5yzDhzMzjOtYVZ/Tk3d2lvPGfImZ/79Q2J+Fe21FIaW0jPZNs/PCM1o89KDOJ809NZ/235fxlcz7zLht4XLEeanNeBff9axfFzsamJW42H/iG6851c8OYk9s986FIrKisc/Pv3f4Wp+f2TWRrnpJS0S4xNR1HYz09rRZ83Xrw2b4yiup8vLO7lHd2g93sY+LpZVwz8jROSVfrKRFpn+vO7cf9b33Nc5vymHhmL1ITvmst5TMMPv06jyc/2EWtOZGSWje13ly8BgSSTDQ0AP4WnnaTjzijEbvNRhxePB4PjoRE6murscYnEBdnoXeiBbvdERymoqHBRVGdD2+jC6/HTVJyN6qrKjHFJ+LBgscH5Q0GzgYvXxU5+arokEmJPigkzgx2k5c0h5W0uAb+94LTGDHwZLWqEumilJSSTuvFzwv48mANiTYLt43NOup2qQlWbvreqcxft5un1u/j+wN7tKkr3qf5VeRXurDHmRlxgkmpCWdm8uzGPDburaCw2kWvbvYWt9u0t4Ky2kbSHFbOP/X4knnnn5pOmsNKeZ2bjXvLuWBA92Pu42zw8NTH+wD/hYy1DS20Zozqx/pvy3l9RxHXjTiJk0/gxumvm/N5/INvMYBUu4VUGzg9UFrn5dmNeeSW1PLAFYOxt3EmQpFY8o/PD9Dg8TG4ZxJn9LCzVXMMxAyTCU5KdZDsiePM7lb+nd/IxwfduHxmVu2oYNWOLQzsHs+VQ/sy/vRMUh1t64YjIl3bFWdk8sJnB/i6pJZZq7Zz+ek9cTZ4+E9hDTsOVlPr9gEOwAf4r6262UwkWSE93sT3+to4s18PzPUVvLe/EWdlLSZrHIbbhcnqoM/JJ3Egdwcmqw/DXUttdS2Z3U7m4N6vMcXFY/M0cEZGXwx3Q9P2fTmQW/nd9rW1eKyNNFgSiE/rxYHiEhptKdT5zNS5odEHTiw4nT7ysXLja3n0TCrkggHduSirOyP6pWKLU8spka5CSSnplAqqXDzx0V4AZn/v1GMmma4c0ot/flnIFwdruO/NXTx+9ZBjtrx5Yau/n/wVZ2aSFH9i/wonpToY0S+FLflVrPmyiBvOO/mIbQzDCB7z8tN7trvrXkCcxczlp/fk758d4B9bC9qUlFq6YR/ldW76pzmYPLR3m45zdt8UxpySxoa9FSx4ezdPTB3a7idYPsPg8Q++DXYbnHRWL346pBsvfn4Au91Kj6QEHvv4IO/vKePGVdv5w1VnNXvaJxLrnA0e/rHVP07ez0b2w2Q6/plEpfNyVpXz3sF6TO56vt+7L4U1jeTVmin3xvN1WQMPv/sNj73/DWf0cHB2rwQuGtyXQZnJStSLSIssZhO3X3oat7z0JbmltSz+8Ntm6834SLGZOKl7N+KcxdjcNdi89XRL6Yvhruc/e2opbzA4uPdrkjP6HvN4SakZdOueSU1FKSarA8PdeovepNQMDHd9U8KqDymeimDCyumspd7jheRekJTO/nInJXVeip2NvLz9IC9vP4gjzsQ5fRIZmulgzMC+ZPdIOu7rZhHp/LpcUqqsrIx77rmHTz75BIvFwpVXXskdd9xBXFyX+yg6rbpGL3Ne3UGd28vQPt24+uxjJ1HMJhO/GT+I65Z/xua8Sp7duI+Z551y1O3zKur5YI+/b/w1OX1CEveVQ3qxJb+KF7cVMDWnzxFPvN/PLWPD3grizKY2lak1U4b1YdXnBWzYW8H7uWVclHX0xNSmvRXBGffmjD2tXU+ebr80ix/95VO25Fex6vMCrsk59oVLgMfr44F1u3l9RxEAt1x4Kted2y/YBdNkMnHxqd3I7tODX7+6gy8O1vA/Kz/n0f86i/4hGsMqUvxjORRRVuake/fwTAAgodfR9YNhGMxfu5sql4f+aQ4uyc6gpFjTaMeqxNR0fI31mE3Q2+6lbzcbKZl9+WzXN+TXmXH6rGwvqmd7UT1/3VaGxQR9utk4NdXGqWnxnNGvB31TEuidEk9yfJy6uYh0kM5w73DoGFGB2e6MkhLGJFdRZEqlrKaegT0SOSnJTHGdF0t9BRabgz4np3EgtwBTcnqzRNKhSaaOlpyWQZK7HpPVoE/fFE4yiin1VVDRaKKMZMo8Nuo9cazPc7I+z8mSzSXYLCZ6JMbRMyGOjIQ4//eJVgb1y6R3ip2eSfFt6gUgIp1Tl8vE3HrrrWRmZvLhhx9SWlrKrFmzWLZsGddff32kQ4tqgT7mJ6qq3s2dr33F7pJa0hOszL9iMOY2Xnif0j2B2y/N4ndvfc3SDXlYLWamj+x3xIW7y+1l7mtf4TNg9ClpDOieGJLYL83uwXPpeewtr2f+2q/5vyvPCB67oMrF7/+dC8B1555YVziA/mkOfjLiJJZ9ks/v/53L4MwkMltoTfZVUQ13vvYfvIa/i+GYU9rXZfCkVAf/e/4p/PH9b3jk3T1kJNoYe5RZ+w5VWO1i7us72V5QjcUE88YPZMKZLY+1NeykFJ750TB+8fIX5FXU899/28rcywZySfbxTymcX1HPx9+Ws2FvBTuLqimv82Axw8cHv2XYSZWMPiWNUSenkWwPzymwtLSUZR99hcvVGJYJACQ8Orp+eH1HEW9/XYLFBDeNSKeosIDS0pKQnU+l80uwWTg1wc1pqQ6q62op9zmocFuoagSn2yC/qpH8qkY+2OeEz78bZDjBaqZncjzpCVYcZp+/W7Tdwkk90uie6F+elmAjPcFKos2iBJbICegM9w5FRYU8uW4ryWkZwS50hqeB5Iy+jDm5Lwdyd1BbUU5+iX+ZEWX/8ilpGSQ31nOy1YavsZ5qHNTb0zlQVkO126DeY3Cg2s2BanfzHTc2PegE0hwWeiZaOSk9iV7J8STQQI9EK9l9e5KRbKd7glWtrUQ6qS6VlNq3bx+ffPIJH3zwAQ6Hg379+nHjjTfy+9//XkmpNqh3e/mmrI5vSmvZU1rHN2W17C6uoazOgwG8kruL3t3yOC0jgQEZiWQ1vfqm2o+ZWHK5vfx7dylPrd9LQXUDDquZ308686hjMx3NxLN6UVjdwNMb9vHER3vZklfJjFH9Gda3GyaTia9LnDz23jfsLHaS6rBy97jsE/hEmrPFmbn/h4OZ8bfPeS+3jJtf+oKrhvSmpLaRZZvyKK9zc1Kqnf8e1T8kx/vv0f1Zu6uEgioX//23rdw2NosLBqRjMZsodjbyzy8L+fOmPNxeg7P7dOOu7x9fWacN78ve8jpe/aKQO9Z8xdRhVUwb0bfFWQb3ltWxZkchL2wtoMHjIynewv0/HHzMLoandk/gz/8vh1+/soMdhTXc/s//cP6p6fz4nL6c0y/lmE+/AuMorP+2nI++KSev4shm5R4f7KtsYF9lIa9+WYjFBEP7dOO8U9M5f0A6WRmJIblxa/D4qHJ5sCalYljdeH1KMESDSNQPe4vKAIOze1h544MN/KupS0RSj7a3SJTYkRRn0M1m0L+xhjrqaTA14knuBYnp7C8qpdYN9T4zbizUuX3sLa9nb/nh57ryI97XajHRzWYhwWYmwWoi0WomwWoh0WomOd5Mt3gLqY44Tu6VQfeEeFIccdjjLMRZTNgsZuIsJrw+gwaPL/hq9PoocHkpKKrmYEkZXsMgIz0dW5wZm8WM1WLGajFhtZiJM0FVRRlWi4k+mZnEWy3EmU1KlElUiPS9g9HU8hogKbV7q13oAl3mop3JBKlWOKNfKr0aDvi7+5m8mLv1wp7ag4KDB6nHSr3bR50HXIYFAxPl9V7K673sLHUd9o4Hg9+lOuLonmAjyWqQZo+jR2oSibY4HFYzXlctDquZ3hnpJMVbSbBZcFj950KrxYTVfMh5zez/qkl6REKjSyWldu/eTWpqKpmZ3808dtppp1FQUEB1dTXdunULy3E3flvOv3cWgGFgdzgAE4Hb1MATccMAo+nnQ29h/cuN4Hr/wubLDMPAa4DX56Pe1YDPMEhKSqS+wY0JE2aTv3ub2eTvMnX4zxaTCVPTssBXt9dHvdtLvdtHibOBopoGyusOezpxGJfH4NvyOr4tr4Ovv2sObI/zP9HNSLSRlmANnszB31WvqKaB3SVOGv3TgtC7WzyPXHUm2T2SjuvzvuG8k0myx7H4g2/4JK+ST/IqAYgzm/A0JQji48wsmHg6vduZ9DqWwZnJ3DUum/97J5dN+yrZtK8yuC67RyKP/tdZIRsjxGG18OQ1Q7n5xS/YV1HPbf/8Dyb8ZXN5fMHtLjytO/dePvC4B4w0mUzc+f1s4swmXtp2kFWfF7Dq8wL6ptjpnWLHajZR2+hlf2V9s7+RnL7d+M3lgzgptW1d8bon2nj62rNZumEfz2/OZ/235az/thyH1cyp3RPJSLRhjzMTH2fG4zOod3upafCQV1FPSXBGP784s4lhff0Jp5MTfLyfW4zXB+f0T+ebGhMf7y3n27I6th6oZuuBav700V4SrBZ6p8TTu5udRJul2Y0VgNtr4Pb6cPv8Xxs9PurcXmobvDgbPTgbvNQ2enB7myehVu3ahcO6m6T4OJJscSTFW0hs+j4x3n9zduj/owmC/4eB/3+f0XRuMPzjdBn4v/p8BvUuF4YBY0/vzfcHqUXW8YpE/XD1GWlUOZ306JHJgT2VmG0OfI3Rf0MhJy4pLZ2ExnrMNujdN4UergPBvw8jzkGty0V1nZtGjwdTUjq2xFRKy8qod/to9IGbONyY8Rgm3F6DsnoPZcf80yo6wagPHnsTvgl+ZzWbiDObiLf6z7VmfFjNJv9yi/9roj0eW5wleCNos5iIswQSX6YWH3gFGhoaGNTV1X63ABMJCQn+E+wR+xjU1dUB+LcBapt+PrTlogmwO2yYTdYj3qelFo6HLgp86zvkWI7gNWHza0EMg/r6+u+2aW8C75D9T+6RyozR/dW16ThF6t4hwFlVzvINtcFWUSnH34g8ajXr7tczicTqRkxWS3DMKl+jE7fFQVLPvuzbt5fqejcuL3hsSdR7DFxecBsWDKCy3kNl/aFjN9a0cMQjZ/o+GhNgMfuvOy0mU9M1XdPPZrCYTMH1dlscPq8XyyH3Yv7rvUPuy/huucVsavrZvxz8pwL/daIpePzAcrPJf44LrKuvqwMMDj17mE3fnW9MmEhITGz2Xv5zYS0AiU3rDMOgrrY2eA4zmUz+dYe8c+AUFdjfbrficrlJSGj7A99Dj32s/Q7dNnDuNZlM7TreiTIBjgQb9XWNze7r21qGSDg0PqvZxLTRp9EntXPMANylklK1tbVNFwDfCfxcV1fX5orFbG5+oXEsj76XS3Ft4ATY0skvDIoOf0pw4pLi40ixm+mXEk//lHhOSrGSQgNb9pVgBi4amEmDJYH8qgbyqhrJq2wgv8qN22dQXuduNalli7PQK9nC5dmpXD4wlQRzPWUtXEFXVlbgra/G7bNRWWnDfJQnFJedbGPof53GyzvK2XzASU2DP0ljs5gY3ieRacMy6JXgpqyspNUyB44X+N5iOfbJ5fzeFpZMGsAr/ynnQHUjcRYT5/ZJ5NLTUolrrKas7Oj7Bo7nsVnwuGpbLSOADXjk8v688lU5//62imqXv5xJFjODMuyMz07heycn466tpKz2xMp4Q04qI3vZeOWrcr4sqqfK5aHK5Wy2TYo9jrN7JXBZVgrD+yZi8jopK2u+zbF+hz86I4kL+2Xx+s4KNuyvodrlI6+ivsXWTwFJ8XGkOyyc3SuR4X0TGZqZQILNfwFeVlaO3eOPYXByN84/JZ3rhiRT7HSz9WAtnxXU8kVRPY1eg6KaRopqGo96nGOJj7NwtDHz690+6t2NlBzl93Cicsu/5bLT25eU6mR1ZUSFon44dOiwttQTcXEWLK4qGmri8LlqMDweDE99i18basqOuk1r6zpyG1dVKb6GyMfRkZ9ZY00ZnroavD5Lh8VhivOQ4KknMcGB4XFjMlfTIy2Z4ppSTAmOpm38X2vrG2h0e7Cl9CKxewalRYX4LDY8Hh8eixW314fHZKXRCw0+8FlsuL1gmI6ewDAbPkz4sJgMTIaXOIsFk+HD6zMwx1lxuz0YJrP/oRlmDEz4TCag5ROOxwceX+BBigGHPpqrDvXA/85jb3LMbUJ5Em/Le9Wd0BE+3l/HRdkZnN6r7Q/6VDd8J1L3DhaLmcaacjyuGhzx8fgsPrz1VTRUl+Jx1WD2NOLzNGD2NLa4LBzrwnKcmlK8dVV4fJYTiiEurpFuvm70MCrITIr3r4tzN32Np0fvk9if9y019W7cHgNLt3QavOBye7AlJFPrrMUcn4DHZ+D2GZji4mlwezBMluC5zIfpmP8cXsBrgNvb9MN36QqoO/7ry7Zr7/mitXNQa+vaci5tz3aR3i8cOlMsLTNbvuW28We2uk3gTz5wDgtX/WAyutDgFevWrWPevHls2rQpuGzXrl1ceeWVbNmyheTk5AhGJyIikaL6QUREDqe6QUQk/LpUW97s7GwqKyspLf2ua9mePXvo1auXKhURkS5M9YOIiBxOdYOISPh1qaTUKaecwvDhw3nwwQdxOp3k5+fzxBNPMGXKlEiHJiIiEaT6QUREDqe6QUQk/LpU9z3wT9X+u9/9jk2bNmE2m7nqqquYM2cOFktoBp8WEZHopPpBREQOp7pBRCS8ulxSSkREREREREREIq9Ldd8TEREREREREZHOQUkpERERERERERHpcEpKiYiIiIiIiIhIh1NSSkREREREREREOpySUjFm586dzJgxg5EjR3L++edz++23U15eHumwQs7r9XLddddx5513RjqUkKqsrOT2229n1KhRnHvuudx4440UFxdHOqyQ2bFjB9OmTWPEiBFccMEFPPDAAzQ2NkY6rJAoLy9n3LhxbNq0Kbhs27ZtTJ06lZycHMaOHcuqVasiGKGEW1lZGTfeeCMjRoxg1KhRzJ8/H4/HE+mw2q21euRYf9OrV69m3LhxDBs2jMmTJ7N169bgOq/Xy8KFCznvvPPIyclh1qxZnfL81lL9Esvlbq3eieVyt1YfxXK5pXOLhXrkeK6Houl/qivVkRs2bGDq1Kmcc845nH/++dx///24XC4g9sraFer+N954gzPOOIOcnJzg67bbbgM6QVkNiRn19fXG+eefb/zxj380GhoajPLycuOGG24wfv7zn0c6tJB77LHHjMGDBxt33HFHpEMJqZ/85CfG7NmzjaqqKqOmpsa46aabjJkzZ0Y6rJDwer3G+eefb/zlL38xvF6vcfDgQWP8+PHG4sWLIx3aCduyZYvx/e9/3xg4cKCxceNGwzAMo7Ky0hg5cqSxfPlyw+12Gx9//LGRk5NjbNu2LcLRSrj85Cc/MX79618bdXV1Rl5ennHFFVcYS5cujXRY7dJaPXKsv+mNGzcaOTk5xpYtW4zGxkbjz3/+szFq1Cijrq7OMAzDePzxx42JEycaBQUFRk1NjXHrrbcaN9xwQySL26LD65dYL/fR6p1YLndr9VEsl1s6v2ivR47neiia/qe6Uh1ZVlZmDBkyxHjppZcMr9drFBUVGRMmTDD++Mc/xlxZDaNr1P0LFiww7rzzziOWd4ayKikVQ/bs2WP8z//8j+HxeILL3n77beOcc86JYFSh9/HHHxs//OEPjVtuuSWmklJffPGFMWTIEKOmpia4rKKiwvj6668jGFXolJeXGwMHDjT+/Oc/Gx6Pxzh48KDxgx/8wHj22WcjHdoJefnll42LL77YeP3115tdhP3jH/8wLrvssmbb/uY3vzFuv/32SIQpYbZ3715j4MCBRmFhYXDZ66+/blx88cURjKr9WqtHjvU3/etf/9qYN29es/WXX3658eKLLxqGYRgXXnih8c9//jO4rqSkxBg0aJCRl5cXruK0W0v1SyyXu7V6J5bL3Vp9FMvlls4t2uuR470eiqb/qa5WRwbqBp/PZ+zatcsYN26c8de//jXmytpV6v5p06YZy5cvP2J5Zyiruu/FkAEDBvDMM89gsViCy9566y3OPPPMCEYVWmVlZcydO5dHHnkEh8MR6XBCavv27WRlZfGPf/yDcePGccEFF7Bw4UJ69OgR6dBCIi0tjenTp7Nw4UKGDBnCRRddxCmnnML06dMjHdoJueCCC1i3bh0//OEPmy3fvXs3AwcObLYsKyuLnTt3dmR40kF2795NamoqmZmZwWWnnXYaBQUFVFdXRzCy9mmtHjnW33Rubu5R19fU1FBYWNhsfUZGBikpKezatSuMJWq7o9UvsVzu1uqdWC53a/VRLJdbOrdor0eO93oomv6nulodmZSUBMBFF13ExIkT6dGjB5MnT46psnaVut/n87Fjxw7ee+89LrnkEi688ELuueceqqqqOkVZlZSKUYZh8Oijj/Luu+8yd+7cSIcTEj6fj9tuu40ZM2YwePDgSIcTclVVVezatYu9e/eyevVqXnnlFYqKirjjjjsiHVpI+Hw+7HY799xzD59//jmvvfYae/bsYdGiRZEO7YT06NGDuLi4I5bX1tYekTi12+3U1dV1VGjSgVr6fQd+jtbf+eH1yLH+pltbX1tbC0BCQsIR6wPrIqm1+iWWy91avRPL5W6tPorlckvnFu31yPFeD0Xr/1RXqiPXrl3LBx98gNls5pZbbomZsnalur+8vJwzzjiD8ePH88Ybb7By5Ur27t3Lbbfd1inKqqRUDHI6ndxyyy2sWbOG5cuXM2jQoEiHFBJPPfUUNpuN6667LtKhhIXNZgNg7ty5JCUlkZGRwa233sr777/fKSqkE7Vu3Treeust/t//+3/YbDays7OZPXs2f//73yMdWlg4HI7gYJABLpeLxMTECEUk4ZSQkEB9fX2zZYGfo/F33lI9cqy/6dbWBy5mDv+MOsv/RGv1SyyXu7V6xzCMmC13a/VRLP++pXOLtXokIBb/p7paHWm328nMzOS2227jww8/jJmydqW6PyMjgxUrVjBlyhQcDgd9+vThtttu44MPPugU9b2SUjEmLy+Pq6++GqfTyYsvvhgzCSmAV199lU8++YQRI0YwYsQIXnvtNV577TVGjBgR6dBCIisrC5/Ph9vtDi7z+XyA/2lMtDt48OARM+3FxcVhtVojFFF4DRw4kN27dzdblpubS3Z2doQiknDKzs6msrKS0tLS4LI9e/bQq1cvkpOTIxhZ+x2tHjnW33R2dvZR16ekpJCZmUlubm5wXUlJCZWVlUc0CY+E1uqXWC53a/XO6aefHrPlbq0+iuXft3RusVSPHCrW/qe6Sh352Wefcfnllzc7VzY2NmK1WsnKyoqJsnalun/nzp08/PDDze4pGxsbMZvNDB06NPJlbfPoU9LpVVZWGhdffLFx5513Gl6vN9LhhN0dd9wRUwOdNzY2GuPGjTNuvvlmw+l0GmVlZcZPf/pTY/bs2ZEOLSR2795tnHXWWcaSJUsMj8dj5OXlGRMmTDAWLFgQ6dBC5tCBPcvLy40RI0YYf/7zn43GxkZjw4YNRk5OjrFhw4YIRynh8uMf/9j45S9/adTU1ARnTVq0aFGkw2qX1uqRY/1NB2Zr2bBhQ3B2lnPPPdeoqKgwDMMwHn30UWPChAlGXl5ecHaWn/zkJx1dxDY5tH6J5XK3Vu/Ecrlbq49iudzS+cVCPWIY7bseiqb/qa5URzqdTuOiiy4yHnzwQaOhocHYv3+/MWXKFOPee++NubIGxHLdf/DgQWPYsGHG008/bbjdbuPAgQPGNddcY9x9992doqxKSsWQ5557zhg4cKBx9tlnG8OGDWv2ikWxlpQyDMMoLCw0br31VuP88883RowYYdx+++1GVVVVpMMKmfXr1xtTp041hg8fblx88cXGH/7wB6OhoSHSYYXMoRdhhmEY27dvN6699lojJyfHuPTSS42XXnopgtFJuJWUlBg333yzMXLkSGP06NHGggULms3QEw2OVY8c62/6lVdeMcaPH28MGzbMmDJlivH5558H1zU2Nhq///3vje9973vGOeecY8yaNcsoLS3t0PK11eH1SyyXu7V6J5bL3Vp9FMvlls4tFuoRw2j/9VC0/E91tTpy9+7dxowZM4wRI0YYl1xyScyfJ2O97t+0aVOwPKNHjzbuv/9+w+VyGYYR+bKaDCMG+gWJiIiIiIiIiEhU0ZhSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEumiDMOIdAgiItKJqZ4QEQkdnVNFWqaklEgn8fjjjzNo0KCQvNfYsWO58847Adi/fz+DBg3i5ZdfBqC6upo77riDLVu2hORYIiISWwoLC/n5z3/OgQMHTvi9Dq+DRES6gjfeeINLLrmEIUOG8Jvf/Ibc3Fx+/OMft/t97rzzTsaOHRv8+dBr/La47rrruO6669p93Pa66aabWozr73//O4MGDTri9Zvf/CbsMUn0iIt0ACISeosXLyYpKanFdV999RWvvPIKkydP7uCoREQkGnz88ce899573HPPPZEORUQkKv32t7/llFNOYcGCBWRmZrJmzRq2bt16wu/b2jV+S+69994TPmZrvF4vDz74IOvWreO//uu/jlj/1VdfkZWVxfz585st7969e1jjkuiipJRIDDrjjDMiHYKIiIiISJdUWVnJ+eefz6hRo0L6vu29xs/Kygrp8Q+1c+dO7r//fr788kvsdvtRtxk6dCjDhg0LWxwS/dR9T6QdFi5cyNChQ6mpqWm2/OmnnyYnJ4eFCxcybtw4Fi9ezKhRo/j+979PRUVFu47x9ttvM378eIYMGcLUqVPZsGFDcN2mTZsYNGgQmzZtarbP4U1zj9a0d9OmTfz0pz8F4Kc//WmHNOcVEekswnkOf/zxx4+676pVq7jiiis466yzuPjii3n88cfxeDzBfe+8806mT5/OSy+9xPjx4znrrLO48soref/995sdY+/evdxyyy2cf/75DBs2jOuuu45PP/00uH78+PHMnj37iNimTp3KzJkzAf9T7aeffpoJEyYEbxR+9KMfBeual19+mbvuuguASy+9tFldcqxyAKxdu5Yrr7ySoUOH8l//9V/s3LmzTZ+fiEhnsWPHDn72s58xfPhwcnJymD59Otu2bQuuf/PNN4PnuauuuoqtW7dyxhln8PLLLwev1QH+9Kc/MWjQIO68804WL14MwKBBg3j88cePO7ZDr/Hbcs4//B5h0KBBrFixgrlz5zJy5EhycnK45ZZbKC0tbfYezz77LJdeeilDhw7lRz/6Ef/+97+PuAe544478Pl8vPDCCy22fPL5fHz99dcMHjz4uMsrXYOSUiLtMGXKFBoaGnjzzTebLX/llVe4/PLLSUhIoKCggHXr1vGHP/yBW2+9lbS0tHYd4+677+anP/0pjz/+OImJidxwww3k5uaGJP4zzzwz2If7N7/5Tdib9IqIdCbhPoe3tO9TTz3FPffcw5gxY3jyySeZNm0aS5cuPWI8jS+//JJnn32WW265hT/96U/ExcVxyy23UFVVBUBubi6TJ08mPz+fefPm8fDDD2MymfjZz37GJ598AsCkSZP44IMPcDqdwffNy8tj+/btTJo0CYCHH36YP/3pT1x77bU888wz/O53v6OiooJf/OIX1NXVcfHFFzNr1izA303kxhtvBGhTOf79739zyy23kJ2dzeLFi/nBD37Abbfd1ubPT0Qk0pxOJ9dffz1paWksWrSIRx99lPr6ev7nf/6Hmpoa3nnnHX7xi18Ez3OXXXYZs2bNwufzAf5r7RdeeAHw1zkvvPACN998M1OmTAHghRdeYOrUqSGJtS3n/JY8+uij+Hw+/vCHP3D77bfz3nvv8eCDDwbXL168mIcffpgf/OAHPPHEE5x99tn88pe/POJ9Fi5cyN///vejJp2+/fZb6uvr2bZtG+PHj+fMM89k/PjxvPLKK8dfaIlJ6r4n0g6nnXYaOTk5vPrqq8EKZfv27ezZs4ff/e53bNiwAY/Hwx133MF55513XMe49957ueKKKwAYM2YMl156KUuWLOGRRx454fiTkpKCzXizsrLC2qRXRKSzCfc5/PB9a2pqWLJkCddeey3z5s0D4IILLiA1NZV58+YxY8YMsrOzg9u+/PLL9O/fH4CEhAR+8pOfsHHjRsaPH8/ixYuxWq08//zzJCcnA3DxxRczYcIEfv/737Nq1SquvPJKFi1a1GxsjzVr1pCYmMill14KQHFxMb/85S+bPTm32+3cfPPN7Nq1i5ycnGAMp59+OieddFKby/GnP/2JM888M1hfXXjhhQAhqb9ERDpCbm4u5eXlXHfddQwfPhyAAQMGsHLlSpxOJ3/6058466yzmp3nTCYTjz32GOC/1g50VevVq1ez74GQdmNryzm/JQMHDuShhx4K/rx9+/bgw5q6ujqWLl3KtGnTmDNnDuA/39fX1weTbQHHagEVaClbUFDAnXfeSVxcHK+88gp33HEHjY2NXHPNNe0vtMQktZQSaaerr76aLVu2sH//foDgTcSIESOC2wwcOPC43ttisXDZZZcFf46Pj+fCCy/k448/PrGgRUQECO85/PB9t27dSn19PWPHjsXj8QRfgZmU1q9fH9w2PT09mAyC725g6uvrAfjkk0+45JJLggkpgLi4OK644gq++OILamtrOemkkxg+fDivv/56cJvXX3+d8ePHB8f7eOSRR5g+fTrl5eVs3bqVl19+mX/+858AuN3uFsvUlnK4XC527NhxxI3QD37wg3Z+giIikZOdnU16ejqzZs3i3nvv5d///jc9evTg9ttvJzU1tcXz3JVXXhmRWNtyzm/J4YmxXr16Beuazz//HJfLxeWXX95smwkTJrQ7vlGjRvH000+zbNkyLrnkEr73ve/xyCOPcN5557Fo0SIMw2j3e0psUlJKpJ1++MMf4nA4+Oc//0ljYyP/+te/jphtIiMj47jeOzU1FavV2mxZ9+7dqa6uPu54RUTkO+E8hx++b2VlJQAzZ87kzDPPDL4CLamKi4uD2zocjmbvYzKZAIJdQqqqqlqMKyMjA8Mwgt03rrrqKjZs2EBFRQVfffUVe/bsadaN44svvmDKlCmMGTOG6dOns2LFCsxm/+Xg0W4Q2lKOqqoqDMMgPT292b49e/Zs5dMSEelcEhMTWbFiBRdddBFvvPEGs2bNYsyYMfzmN78JXo8ffp7LzMyMRKjAsc/5LTm8vjGbzcHzf3l5OXBkGY+nXszIyOCiiy46IkF20UUXUVJScsQ4VtJ1qfueSDslJiZy+eWX869//YvTTz+d6upqrrrqqpC8d01NDYZhBG9GAEpLS4MVw+E3KQG1tbUkJiaGJAYRkVgWznP44bp16wb4x3E65ZRTjljfnov8lJSUFi/gS0pKAIJjX11++eXcf//9rFu3jn379tG7d29GjhwJfDdWyqBBg3jttdc47bTTMJvNvP/++7z11lsnVI7U1FTMZvMRMQYSWiIi0WLAgAH8/ve/x+v1sn37dl599VX+/ve/07Nnz053nmvtnH88Aq10y8vLGTBgQHB5IFnVHp988gkFBQVH1LENDQ1YLBZSUlKOO06JLWopJXIcpkyZwtdff81zzz3H6NGj6dOnT0jet7GxkY0bNwZ/rq2t5b333gtOJ5uUlATAwYMHg9tUVVWxZ8+eNh/DYrGEJFYRkWgVrnP44c4++2ysVitFRUUMGTIk+LJarTzyyCPBLoRtce655/Luu+82mznQ6/Xy+uuvM2TIEGw2GwDJyclccsklvPPOO7z55ptMnDgx2BLqm2++obKykp/+9KdkZ2cHl3/wwQfAdw88AsvbU474+HhycnJYu3ZtsxZX//73v4/jkxMRiYw333yT0aNHU1JSgsViIScnh/vuu49u3bpRXl5OTk4Ob731VrMHxO++++4x3/fw82qotHbOPx6DBw8mOTmZtWvXNlve2oOLo9mwYQN33nkn+/btCy7z+Xy89dZbnH322cF6S0QtpUSOw/DhwxkwYACffPIJDz/8cMje12q1cvfdd/OrX/2KpKQknn76aVwuV3D2o0GDBtG7d28WL15McnIyZrOZp59++ohmuK0JjEfy3nvvkZKSomlaRaTLCdc5/HBpaWlcf/31/PGPf8TpdDJq1CiKior44x//iMlkatf596abbuKDDz7gpz/9KTNnzsRms7F8+XLy8/N55plnmm171VVXMXv2bLxeb7OxTk499VSSkpJ48skniYuLIy4ujrfeeosXX3wR+G78qkDLqHXr1nHhhRdy2mmntakcv/rVr/jZz37GTTfdxLXXXsvevXtZsmTJCX2GIiId6ZxzzsHn8zF79mxmzpxJYmIi//rXv6ipqeGyyy7jhz/8IdOnT+fGG2/kxz/+MXl5efzxj3885vsGzquvvfYaZ599Nv369QtZzEc75x+PpKQkrr/+ehYtWoTD4WDkyJF88skn/P3vfwfal1z78Y9/zAsvvMD//u//cvPNN+NwOFixYgVff/01zz///AnFKbFFLaVEjtPFF19McnIy48aNC9l7pqSkcNttt/Hoo49yyy23YLFYWL58ebD5rMViYdGiRfTs2ZNf/epXPPDAA/zgBz9oNjj6sWRnZzNhwgRWrFgRnFVDRKSrCcc5vCW33nord955J+vWreOGG27g97//PcOHD2f58uXNBi0/luzsbP72t7+RkZHB3XffzW233YZhGDz//PNHzBT4ve99j5SUFM4444zg7H7gfyjxxBNPYBgGv/jFL7j99tspKChg+fLlJCYmsmXLFsA/OO15553HI488wsKFC9tcjhEjRrB06VKKioq46aabWLlyZbNpxkVEOruePXvyzDPPkJyczNy5c/n5z3/Ojh07ePzxxxk9ejQjRozg2WefpbS0lNmzZ7Ny5UruuOOOY77vZZddxpAhQ7jzzjt59tlnQxrz0c75x+vnP/85N910E6+88go///nP2bJlS/CeISEhoc3v07NnT/72t7+RnZ3NAw88wK233orL5WLZsmWcc845JxynxA6ToWHvRdrNMAwmTpzIqFGjuOeeeyIdjoiItIPO4SIiEir79+/n0ksv5aGHHmLy5MmRDueEeDweXnvtNUaNGkXv3r2Dy1esWMEDDzzApk2bgq2+REJF3fdE2sHpdLJs2TK++OIL9u7dyxNPPNHq9oZh4PV6j/m+ZrM5bH3NRUTET+dwERGJtLbWLRaLpdnkRx0hLi6OpUuX8pe//IVZs2aRlpbGzp07+eMf/8hVV12lhJSEhZJSIu1gt9tZuXIlPp+P+fPn079//1a3/+STT/jpT396zPe96aabuPnmm0MVpoiItEDncBERibTVq1dz1113HXO7SLW8evLJJ/nDH/7AfffdR3V1NX369GH69On8/Oc/7/BYpGtQ9z2RMHI6nXz77bfH3K5nz55kZmZ2QEQiItJWOoeLiEioVVRUtGn21ZNOOom0tLQOiEgkspSUEhERERERERGRDqcBEEREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHi4t0ANGorKyG9gwPbzJB9+7J7d6vM1EZOodoL0O0xw+xVYby8hrS05MjHU7M6Ip1Qzjoc2mZPpcj6TNp2Yl+LoH9JXSi4W802v+fFH/kRXsZFH/bjxFqSkodB8PguH7Rx7tfZ6IydA7RXoZojx9ipwwSOl25bggHfS4t0+dyJH0mLdPn0nlE0+8immJtieKPvGgvg+LveOq+JyIiIiIiIiIiHU5JKRERERERERER6XBKSomIiIiIiIiISIdTUkpERERERERERDqcklIiIiIiIiIiItLhlJQSEREREREREZEOp6SUiIiIiIiIiIh0OCWlRERERERERESkwykpJSIiIiIiIiIiHU5JKRERERERERER6XBKSomIiIiIiIiISIdTUkpERCQCPj9Qxcznt1BY7Yp0KCIiIiHj9XrZu/fb4Mvr9UY6JBHpxOIiHYCISKz4759fT1l55VHXd09P5bmnnum4gKRTe3tXCWv/U0TfZBs3XnBqpMMREREJifz8PJ58awupPXpTWXKQ/x0Pp5yiek5EWqaklIhIiJSVV3LFLx8+6vrXH53TgdFIZ3daRiIAXxRURzgSERGR0Ert0Zv0XidFOgwRiQLqviciIhIBQ3p3A2BHYQ0enxHhaEREREREOp6SUiIiIhFwavcEkuLjqHf7+Ka0NtLhiIiIiIh0OHXfExERiQCL2cTgHna27Hfy5pb/4DrJdtzvlZ7enZNO6hfC6EREREREwk9JKRERkQjYvz+f91/+C4kjr2bJP17jgTceO+73sjsS+Hj9ZiWmRERERCSqKCklIiISAWVlZdTu+4LEkVfTa9glXHvp6ON6n+L93/Lio3dRXl6mpJSIiIiIRBUlpUREpFPYuXMnCxcuZMeOHVitVs4//3zuvPNO0tPT2bZtGw888AC5ubmkpaUxa9Yspk6dGtx39erVPPHEE5SUlDBgwADuuececnJyAPB6vTz88MO8+uqr1NfXM3r0aH7729/Ss2dPwJ8cuueee/jkk0+wWCxceeWV3HHHHcTFhb+K9FQXA+A2WelzmqbLFhEREZGuRQOdi4hIxLlcLq6//npycnL46KOPeO2116isrOTuu++mqqqKmTNnctVVV7F582bmz5/PQw89xPbt2wHYtGkT999/PwsWLGDz5s1ceeWVzJo1i/r6egCWLFnC+vXreemll/jwww+x2+3MmzcveOxbb72VhIQEPvzwQ1588UU2bNjAsmXLOqTc3vpqABo8PnyGZuATERERka5FSSkREYm4goICBg8ezOzZs7HZbKSlpXHttdeyefNm1q5dS2pqKtOmTSMuLo4xY8YwceJEVqxYAcCqVau44oorGD58OFarlenTp5OWlsYbb7wRXH/DDTfQu3dvkpKSmDt3Lh988AH5+fns27ePTz75hNtuuw2Hw0G/fv248cYbg+8dbr76muD3jR5fhxxTRERERKSzUPc9ERGJuAEDBvDMM880W/bWW29x5plnsnv3bgYOHNhsXVZWFi+++CIAubm5XH311Ues37lzJzU1NRQWFjbbPyMjg5SUFHbt2gVAamoqmZmZwfWnnXYaBQUFVFdX061btzaXwWRq86bfbe/zEmcy8BgmXB4fdqulfW9ygjF0RoEyxEJZQkmfy5H0mbTsRD8XfZ4iItKRlJQSEZFOxTAMHnvsMd59912WL1/O888/j8PhaLaN3W6nrq4OgNra2qOur62tBSAhIeGI9YF1h+8b+Lmurq5dSanu3ZPbvC1AamoiAPEW8HgAi4WEhPh2vQeA3W4DIC0tkYyM9sXQmbX38+wq9LkcSZ9Jy/S5iIhINFBSSkREOg2n08ldd93Fjh07WL58OYMGDcLhcFBTU9NsO5fLRWKiP6njcDhwuVxHrE9LSwsmmALjSx2+v2EYR6wL/Bx4/7YqK6uhPcNCVVb6k2JWfICFSqeLVFv7e9W7XI0AVFTUUlpac4ytOz+TyX8z3d7PM9bpczmSPpOWnejnEthfJNS8Xi/5+XnBn/v164/FcmIthEUk+nXqMaXKy8sZN24cmzZtCi576623mDRpEueccw5jx45l8eLF+HzfjcOxevVqxo0bx7Bhw5g8eTJbt24NrvN6vSxcuJDzzjuPnJwcZs2aRXFxcYeWSUREWpaXl8fVV1+N0+nkxRdfZNCgQQAMHDiQ3bt3N9s2NzeX7OxsALKzs4+6PiUlhczMTHJzc4PrSkpKqKysZODAgWRnZ1NZWUlpaWlw/Z49e+jVqxfJye27KTOM9r8ArGb/Nw0hGFPqeGLojK9YKos+F30m0fi5iIRDfn4eT761hZWfHeDJt7YEE1Rer5e9e78Nvrxeb4QjFZGO1GmTUp9++inXXnsteXnfZdO//PJLbr/9dm699Va2bNnC0qVLefnll4OzJJ3oDEwiIhIZVVVV/OxnP+Occ87h2WefJT09Pbhu3LhxlJaWsmzZMtxuNxs3bmTNmjXBcaSmTJnCmjVr2LhxI263m2XLllFWVsa4ceMAmDx5MkuWLCE/Px+n08mDDz7IyJEj6d+/P6eccgrDhw/nwQcfxOl0kp+fzxNPPMGUKVM6rOy2pqSUy62BzkVETkRLD7S3bdvG1KlTycnJYezYsaxatarZPnqg3bFSe/QmvddJpPboHVx2tGSViHQNnTIptXr1aubMmcMvf/nLZssPHDjAj370Iy655BLMZjOnnXYa48aNY/PmzcCJzcAkIhJuX3+9k0lTp7T4+u+fXx/p8CLq5ZdfpqCggH/9618MHz6cnJyc4CstLY3nnnuON998k1GjRjFv3jzmzZvH6NGjARgzZgz33nsv9913HyNHjuT1119n6dKlpKamAjB79mwuuugipk2bxkUXXURDQwOPPfZY8NiLFi3C4/Fw6aWXcs011/C9732PG2+8scPKbjM1JaU8ejIsInK8WnqgXVVVxcyZM7nqqqvYvHkz8+fP56GHHmL79u2AHmh3Ji0lq0Ska+iUY0pdcMEFTJw4kbi4uGaJqfHjxzN+/Pjgzy6Xi/fee4+JEycCJzYDU79+/cJcKhHp6jw+uOKXD7e47vVH53RwNJ3LjBkzmDFjxlHXDxkyhJUrVx51/aRJk5g0aVKL66xWK3PmzGHOnJY/44yMDBYtWtS+gENILaVERE7M6tWrWbRoEbfddluze4e1a9eSmprKtGnTAP9DjIkTJ7JixQqGDh3a7IE2wPTp03nhhRd44403uPrqq1m1ahVz5syhd29/omTu3LlccMEF5Ofn695BRCREOmVSqkePHsfcxul08otf/AK73c706dOBE5+Bqa2Oa9rv49ivM1EZOodoL0O0xw/hLUNHfS6x8HuIJaEcU0pEpCs62gPt3bt3N3sgDf4H1i+++CLQcQ+0o6G+DeW1weHvYTJ992rP8uM5ZjR81i2J9vgh+sug+Nt+jFDrlEmpY/nmm2+45ZZb6N69O88//zxJSUnAic3A1B7HOyNJLMxkojJ0DtFehmiPH1ouQ5zVQkJC/FH3MZk46vo4q4WMjI79XNLTo//3EAu+676npJSIyPE42gPt1h5YH2t9KB9oR9N1Tyhira5OwuGwkZAQT73DRnp6EhkZye1eHqn4Iyna44foL4Pi73hRl5R6//33+dWvfsU111zDr3/9a+LivivC0WZguvDCC5vNwBR44nHoDEzt0d4pdmNhymKVoXOI9jJEe/zQehk8bi91dQ1H3dcwOOp6j9tLaWlNKEM9qkAZystrlJjqBGxNozu63BpTSkQklBwOBzU1zevWQx9Id9QD7Wi47gnlNVp5uZP6+kbq6hqor2+kvNxJt2417V4eqfgjIdrjh+gvg+Jv+zFCLaqSUp9//jmzZ8/mvvvua3FmpClTpjB79mx+8IMfMHz4cFasWNHiDExDhgwhLS2t2QxM7XG80+XGwjS7KkPnEO1liPb4ITxl6OjPJNp/B7HCpu57IiJhMXDgQNavX99sWW5uLtnZ2UDHPdCOpuueUMR6+P6B92zv8uM9drR81i2J9vgh+sug+Dtep5x972iefPJJPB4P8+fPbzYz0/XX+2etOtEZmERERDqa1fRdUsoXbVcRIiKd2Lhx4ygtLWXZsmW43W42btzImjVrguNITZkyhTVr1rBx40bcbjfLli1r8YF2fn4+TqfzuB9oi4jI0XX6llK7du0Kfv/kk08ec/sTmYFJRESkowVaShlAo8eH3WqJbEAiIjEiLS2N5557jvnz57No0SLS09OZN28eo0ePBpo/0C4qKiIrK+uIB9oej4dp06ZRW1vLqFGj9EBbRCTEOn1SSkREJJaZTWC1mHB7DVxKSomInJBDH2gDDBkyhJUrVx51ez3QFhGJrKjqviciIhKL7HH+RJTGlRIRERGRrkQtpURERCIsPs5MTYOSUiIiErsKnR7eOeAjuaKYk+0aQ1FE/NRSSkREJMKsFhMAbq+SUiIiEntqXB4WfFxCZSPkV7r4qNBgW5Er0mGJSCegpJSIiEiEfZeU0pNjERGJPX/84BsO1HhwWODkNAcAf9leidenek+kq1NSSkREJMKsZn91rJZSIiISaxo8PtbtLAFgRA8TF56WjtUM+dVu1nxZGOHoRCTSlJQSERGJMKulKSmlJ8YiIhJjPi10Uef20iPBQg872K0WBqf6Wwiv+rwgwtGJSKQpKSUiIhJhGlNKRERikc/nZd3XZQCcnerFaHr2cnISWEzwdUkt+dXuCEYoIpGmpJSIiEiEaUwpERGJReWlJeyo8Ndthft243TWABBvMTGslx2AD/NqIxafiESeklIiIiIRFuy+p5ZSIiISQ6q8VgxMJMVbyEzr1mzd/2fvzuOjqu/F/7/OLJmZrDNJIAmbLCGgAooom4h1SbmKgLIUf+V6i17UIrW33uIOFy3icvVbLdeCiuVyW6lYtNRSKWBbrYqCERERDSQIJATIMtlmz2y/PyYzEJNAJpnJzCTv5+ORB2Q+c855n5PknDnv8/m8P1cNTAHgo3I7fr88lBGit5KklBBCCBFjZ5JS8qFcCCFEz9Hg1QKQm6Zr1XZ5nh6DVkW13UtDU3dHJoSIF5KUEkIIIWJMakoJIYToiRp8gaRUThtJKZ1GxRWDTACcdnRrWEKIOKKJdQBCiJ7ljrsXYa6tb7e9X242L734UvcFJEQCkJ5SQgghehqf3x/qKZWTpqOhuvV7rhxi4oMjZk7b5fonRG8lSSkhRESZa+uZft9z7bbvePHBboxGiMSgVTX3lPJJTykhhBA9Q53djRcVGsWPKVlLQxvvmTwkEwCzC5xub/cGKISICzJ8TwghhIgx6SklhBCip6m0uADI0HhRKUqb78lN1zMoPdCb6kS9s9tiE0LED0lKCSGEEDEWrCnVJDWlhBBC9BA1tkD18nTNua9tY3P1AJxokKSUEL2RJKWEEEKIGEtq7inl8fplWmwhhBA9Qq3dDUCa+tzD8kb3DSSlTjY45RooRC8kSSkhhBAixoI9pfyA1ycfyIUQQiQ2v99PXXNSKvU8PaVGZiWhAmxNXqyebghOCBFXJCklhBBCxJhGdabWRpPUlRJCCJHgbB7w+Pyo8JOsOvd1TadRkRnoLEWVoxuCE0LEFUlKCSGEEDGmKMqZGfikrpQQQogE1xAoJ0WKykM7Nc5b6KsPvKnaIQ9mhOhtJCklhBBCxIHQDHwyfE8IIUSCCyalUlUdG4/X1xD4t9oJPqkrJUSvIkkpIYQQcaW2tpbCwkL27NkDwH/9138xduzYFl8XXngh//7v/x5a5oYbbuCSSy5p8Z4jR44A4PV6eeaZZ5g8eTJjx45l8eLFVFVVhZY1m83cc889XH755UyYMIFVq1bh8XR/UYtgXSnpKSWEECLRNTQFEksdTUqZdKBVKTT54Fi9O5qhCSHijCSlhBBCxI29e/cyf/58ysrKQq/94he/YN++faGv//mf/yE9PZ2HHnoIAKvVytGjR9m2bVuL9w0bNgyAtWvXsmvXLt566y0+/PBD9Ho9y5YtC63/Zz/7GcnJyXz44Ye8+eabfPLJJ2zYsKFb9xvO6iklNaWEEEIkuMYwe0qpFIW8DB0AB6qc0QpLCBGHJCklhBAiLmzZsoWlS5dy3333tfue2tpali5dyqOPPsrw4cMB+OqrrzAajfTv37/NZTZv3sydd95JXl4eqampPProo3zwwQeUl5dz/PhxPv30U+6//34MBgMDBw7knnvuYePGjVHZx3ORnlJCCCF6ArfPH5pFL6WDSSmAfhmBaucHqlzRCEsIEac0sQ5ACCGEAJgyZQozZsxAo9G0m5h67rnnGDVqFDNnzgy9duDAAQwGA//6r/9KSUkJ/fv359577+Waa67BYrFw+vRpCgoKQu/Pzs4mIyODQ4cOAWA0GsnJyQm1Dxs2jJMnT9LY2Eh6enqH4+9IIdc239/875mkVOd7SoUbQzwK7kNP2JdIkuPSmhyTtnX1uMjxFF1V2ZyR0qoUkpSOP2jpnx5ISn1jdtHk8ZGkkf4TQvQGkpQSQggRF/r06XPO9vLycv785z+zefPmFq8risLo0aP5z//8T/r168f27du59957ee2118jNzQUgOTm5xTJ6vR6bzQaAwWBo0Rb83m63h5WUyspK6/B7AYzGlEAsuiSSk3Xok5ovyWoVycm6Dq9Hr08CwGRKITs7vBjiWbjHs7eQ49KaHJO2yXERsXKqOSmVYdC2m+T0+bxUVJwAoKLiBD4fZCVr0anA5fVz4FQj4wYauyliIUQsSVJKCCFEQnjrrbdCRc7PtmjRohbfz5w5k7/85S/s2LGDH//4xwA4HI4W73E6naSkpOD3+1u1Bb9PSUkJKz6z2UI4EwbV1weSYk5XE3a7C1XzsnaHG7u940MXnM5A4Y66Ohs1NZaOBxCnFCVwMx3u8ezp5Li0JsekbV09LsHlheisk5ZAofIMvQbauZw1mqt4/biVAUMUyg4dwJg3mGxFoY8BTtigqKxeklJC9BKSlBJCCJEQdu7cyR133NHq9d/85jdcdNFFTJo0KfRaU1MTOp2OjIwMcnJyKC0tDQ3hq66upr6+noKCAnw+H/X19dTU1JCdnQ3AkSNHyM3NJS0tvJsyv5+wbgBD723+NzR8z9f5mlI96cY83OPZW8hxaU2OSdvkuIhYORnqKdV+UgogPSuHzNwB1FefCr3W16BwwuanqKyeH18Z7UiFEPFABuoKIYSIe3V1dRw5coQrrriiVdupU6d4/PHHKS8vx+Px8Oabb7Jv3z5uueUWAGbPns3atWspLy/HarXy5JNPMn78eAYNGsTgwYMZN24cTz75JFarlfLyctasWcPcuXO7excjUlNKCCGEiLVQTymDNuxl+wbKSnHwtAVbU8eLpAshEpf0lBJCCBH3TpwI1J04uyB50AMPPIBKpeKHP/whFouF/Px8XnnlFS644AIAlixZgsfjYcGCBdhsNiZMmMALL7wQWn716tX84he/4LrrrkOlUnHzzTdzzz33dMt+nU2rDjwnktn3hBBCJLJQTSm9lnAHladoFXJSVFTavOw70cCUoVmRD1AIEVckKSWEECLuBGfGCxo9enSr14KSkpJ45JFHeOSRR9ps12q1LF26lKVLl7bZnp2dzerVq7sWcAScSUpJTykhhBCJyeL00OAKPFzJMGjCTkoBjOqjp9Jm49Pj9ZKUEqIXkOF7QgghRBzQqALD9zw+SUoJIYRITGV1dgD0akhSd+5Wc3TzGL6isvpIhSWEiGOSlBJCCCHiwJmklAzfE0IIkZjK6gMz2KaGX04qZFRfHQClNTZq7U2RCEsIEcckKSWEEFHi9fk5Ue/A3uSNdSgiAWiaC517ZPieEEKIBFVR7wQgtQtFYjJ0aob3SQHgM+ktJUSPJzWlhBAiChxuL+8eqqHSEpgLOT87GVBiG5SIazJ8TwghRKKraAgkpVK0XfvMc8UgIyXVNj4tq+f7I/tGIjQhRJySnlJCCBFhPr+fbV9XUWlx0dz5hdIaO7qLvhfTuER806gCl2RJSgkhhEhUoaRUF7s+XDHICEhdKSF6A0lKCSFEhJVU26i1u9FpVMy+JI8pQ00A6C+bRYPDHePoRLzSyvA9IYQQCa6iuaZUV5NSYwdkoFYpnGxwUtHgaNHm9Xo5duxo6MvrlTIJQiQySUoJIUQEeX1+Pi9vAODS/ukYDVpG9k2lf4YeRZPEV6c7Mzmy6A2k0LkQQohE5vL4qLIGCpOndKHQOUBKkoaLctIAQp+rgsrLy3hpx2ds+ryCl3Z8Rnl5Wdc2JoSIKUlKCSFEBB0x27A2eUnWqrkoJxUARVEY3S/wwepItV2GZ4k2BZNSXn9gCKgQQgiRSE41D90zaBSSInCXOW5gBgB7TzS0ajP2ySMzdwDGPnld35AQIqbiOilVW1tLYWEhe/bsCb22f/9+5s2bx9ixY7n22mvZvHlzi2W2bNlCYWEhl156KbNnz2bfvn2hNq/XyzPPPMPkyZMZO3Ysixcvpqqqqtv2RwjR8x2ptgNwYW4qGvWZU2z/DD0+Wx0ur4/jtfZYhSfiWDApBVJXSgghROIJ1pPqm6JBUbo+uUswKfV5eX2X1yWEiF9xm5Tau3cv8+fPp6zsTHfMhoYG7rrrLm6++WaKiopYtWoVTz31FF9++SUAe/bsYeXKlTz99NMUFRUxc+ZMFi9ejMMRGIe8du1adu3axVtvvcWHH36IXq9n2bJlMdk/IUTP49UaQh/IhmUlt2hTKQpNpbuBQM0pIb5LfXZSSupKCSGESDAnmutJ5XShoJTP56Wi4gTHjh0lw1OHWqVwqtHVqq6UEKLniMuk1JYtW1i6dCn33Xdfi9d37tyJ0WhkwYIFaDQaJk2axIwZM9i4cSMAmzdvZvr06YwbNw6tVsvChQsxmUxs27Yt1H7nnXeSl5dHamoqjz76KB988AHl5eXdvo9CiJ7HmTkcP9AnJYkMQ+tiCk3fFgFwssGJxyt1g0RLiqKcVVdKklJCCCESS/DBXFeSUo3mKl7fXcqmzyv4v79/zjBj4PPU3vLWQ/iEED1DXCalpkyZwrvvvsuNN97Y4vWSkhIKCgpavJafn09xcTEApaWl7bZbLBZOnz7doj07O5uMjAwOHToUpT0RQvQmjqx8AIZlJ7fZ7ms4TUqSGq8fTltc3RmaSBBS7FwIIUSiOpOUUndpPelZOaF6URf30QEyhE+InqyLk3VGR58+fdp83WazYTAYWrym1+ux2+3nbbfZAsNlkpOTW7UH2zoq3CHSwfdHYGh1zMg+xIdE34dEjx/a3wdbk4em1ECxzQsy205KQaC21OFqGxX1TgYYW56vuuu49ISfQ0+lUSvgkeF7QgghEs+pxjM1peoi0LHJ5/OSowRmLd5zzIzH40GjicvbVyFEFyTUX7XBYMBiaTmdutPpJCUlJdTudDpbtZtMplCyKlhfqq3lOyorKy3c0Lu0XDyRfYgP8bwPGq2a5GTdOd8Tz/F31Hf34YuvK0GlJsOgJTez7XOKosDQvmkcrrZx0uJqcZw0WjXZ2d17XDIzE//n0NNoVCrAK8P3hBBCJJxgUio7WUMkxqE0mqsot9pQGEqN3cveQ0eZcPHwCKxZCBFPEiopVVBQwK5du1q8VlpayvDhgZPT8OHDKSkpadU+depUMjIyyMnJaTHEr7q6mvr6+lZD/s7HbLYQzmzdihK4gQ13uXgi+xAfEmEfPG4vdnvbQ9OCPXPiOf7zae9nsPPASQD6peva3X+/H7INgS7tNdYmaurtJCcFvve4vdTUWNpcLtKC+1Bba5HEVJyRmlJCCCESkcXpweryAtAnuWvD985myupL36YkKi1NfF3tZELE1iyEiBdxWVOqPYWFhdTU1LBhwwbcbje7d+9m69atzJkzB4C5c+eydetWdu/ejdvtZsOGDZjNZgoLCwGYPXs2a9eupby8HKvVypNPPsn48eMZNGhQWHH4/eF/dXa5ePqSfYiPr3jfh/P97cR7/J39Gew+VgfAAKP+nMfAoFWTmRwo2ln5nbpSsdgHEV+CSSm3FMIXQgiRQIK9pDL0GvSayN5i5qUHPlsdrJZ6nEL0RAnVU8pkMrF+/XpWrVrF6tWryczMZNmyZUycOBGASZMmsWLFCh577DEqKyvJz89n3bp1GI1GAJYsWYLH42HBggXYbDYmTJjACy+8ELsdEkL0CKcbnZTVOcDvo1/6uZNSAH1TddTa3VRZXQzJar/+lOh9tOpAUsorPaWEEEIkkFONgYRRv4zzfw4KV166ji8q4GCNC788UROix4n7pNR3Z8YbPXo0mzZtavf9s2bNYtasWW22abVali5dytKlSyMaoxCid9tXEajmqbVVkaQZfN73901LorgKqixNUY5MJBoZvieEECIRnW7uKZXbgYdz4cpJ06EANXYvJxud532/ECKxJNTwPSGEiEf7KxoBSGo82aH3900NFDivsTXhkyd+4izq0PA9+b0QQohIOHjwIAsWLODyyy9nypQpPPHEEzQ1BR4K7d+/n3nz5jF27FiuvfZaNm/e3GLZLVu2UFhYyKWXXsrs2bPZt29fLHYhIQR7SuWln3uym87QqlVkNq92b3kEpvUTQsQVSUoJIUQXfdHcUyrJ0rGklNGgQatW8Pj81Nnd0QxNJBitOnBZlp5SQgjRdT6fj7vvvptp06bx6aef8uabb/LRRx+xbt06GhoauOuuu7j55pspKipi1apVPPXUU3z55ZcA7Nmzh5UrV/L0009TVFTEzJkzWbx4cauZvEXAaUv0ekoBZDev9vPy+qisXwgRO5KUEkKILmh0ujlSYwcgyXKqQ8soikKf1CQAqqwyhE+ccWb4nhQ6F0KIrmpoaKC6uhqfzxeqRaRSqTAYDOzcuROj0ciCBQvQaDRMmjSJGTNmsHHjRgA2b97M9OnTGTduHFqtloULF2Iymdi2bVssdyluhXpKpUW+pxRAH0Pg+ri3vEHqSgnRw0hSSgghuuDASQsAg0wG1J6OPz0NDuGrtspMMuKMUFJKhu8JIUSXmUwmFi5cyDPPPMPo0aO5+uqrGTx4MAsXLqSkpISCgoIW78/Pz6e4uBiA0tLSc7aHQ1ES46srsQZrSuVl6EPriqQsHagVOG1xUe3wtnl8E+lYR/r4x8tXou+DxN+xbURa3Bc6F0KIeBYcundJv3T2hrFcdkqgp5TZJsP3xBma5tn3ZPieEEJ0nc/nQ6/Xs3z5cubOncvx48f5yU9+wurVq7HZbBgMhhbv1+v12O2B3s/naw9HVlZa53eim3UmVqfbS21zOYJRQ7KoPe3AYEgiOVmHXq9FpdN2+f9pKTpG9lU4WOngmI3Q+h2GJDIzU0NxJ9Kxbkuixw+Jvw8Sf/eTpJQQQnTBwdOBnlKjwkxKZSZrAaizS7FzcYZGJTWlhBAiUt5991127NjB9u3bARg+fDhLlixh1apVzJgxA4vF0uL9TqeTlJQUAAwGA06ns1W7yWQKOw6z2UK8X+oVJXAz25lYj9cGEnXJWjVum5PaWisORxN2uwun043K7+7y/x2OJgqMKRyshM+ON5Cpagq9XltrJSPD0un440FXjn+8SPR9kPg7vo1Ik6SUEEJ0kt/v55vKwAfai3PCO0Gn6zVoVIFi5w0OTzTCEwlIakoJIUTknDp1KjTTXpBGo0Gr1VJQUMCuXbtatJWWljJ8+HAgkMAqKSlp1T516tSw4/D7SZib3M7EWmkJlCLom5YEKFHb1wuzdWw5ZOHrGhdT+p55/eyYE+lYtyXR44fE3weJv/tJTSkhhOik8nonVpeXJLXCsOzksJZVFCXUW8psl2LnIiA0fE9qSgkhRJdNmTKF6upqXnrpJbxeL+Xl5axdu5YZM2ZQWFhITU0NGzZswO12s3v3brZu3cqcOXMAmDt3Llu3bmX37t243W42bNiA2WymsLAwxnsVf6qbJ23pkxqdIudBI7J0qBSotHlxeOQ6KURPIT2lhBCik75pHrpX0DcVjTr8HH9mShJV1iZqpa6UaHamp5R82BZCiK7Kz8/n5Zdf5oUXXuDVV18lLS2NmTNnsmTJEpKSkli/fj2rVq1i9erVZGZmsmzZMiZOnAjApEmTWLFiBY899hiVlZXk5+ezbt06jEZjbHcqDgV7SiUrTRw7dpSKihNEo8NvslZFQZ9Uiqus1Dihf+Q3IYSIAUlKCSFEJ33dPHTvojCH7gVlNfeUqu1AT6k77l6Euba+/XVlGln/8qudiiPe1NbWMn/+fJ544gkmTJgAwIoVK3jrrbfQarWh9z300EPMnz8fgC1btrBmzRqqq6sZOnQoy5cvZ+zYsQB4vV6ee+453n77bRwOBxMnTuTxxx+nb99A33+z2czy5cv59NNPUavVzJw5kwcffBCNpvsvkVqpKSWEEBE1efJkJk+e3Gbb6NGj2bRpU7vLzpo1i1mzZkUrtB4j2FPq+MkqNn3upOzQAYx5g8mOwrbGDshoTkrJdVKInkKSUkL0QL0pgRFLwZ5SF+amdmr5rLNm4DOe573m2nqm3/dcu+3vPL+0UzHEm7179/LQQw9RVlbW4vUDBw6wcuVKbrnlllbL7Nmzh5UrV7Ju3TrGjBnDxo0bWbx4Me+99x4Gg4G1a9eya9cu3nrrLdLS0li+fDnLli3jlVdeAeBnP/sZOTk5fPjhh9TU1LB48WI2bNjAokWLumWfzybD94QQQiSKwJDIMo5V1QGQkZZKZu4A6qtPRW2bYwdk8PrnFdQ4z/9eIURikKSUED1Qb0lgxJLX56e4ygrARbmd6yllau4pZXd7SdfoIxZbotqyZQurV6/m/vvv57777gu93tTUxOHDhxk1alSby23evJnp06czbtw4ABYuXMgbb7zBtm3bmDNnDps3b2bp0qXk5eUB8OijjzJlyhTKy8vx+Xx8+umnfPDBBxgMBgYOHMg999zDs88+G5uklAzfE0IIkSDKy8t4acdnlDblBF5wO6K+zUv7pwPQ6Aan2xv17Qkhok8KnQshRCccq7XjcPswaFVcYAqvyHlQklpFapIaALch/Cmme5opU6bw7rvvcuONN7Z4vbi4GI/Hw+rVq5k8eTLTpk3jlVdewddcsKK0tJSCgoIWy+Tn51NcXIzFYuH06dMt2rOzs8nIyODQoUOUlJRgNBrJyckJtQ8bNoyTJ0/S2NgYxb1tWzAp5ZbZ94QQQiQAY588XP7AZxm9KvoPVEzJSQxIC/SrON1cy0oIkdikp5QQQnTC181D90bmpKFuTiR0hjFZi7XJi8eQGanQElafPn3afN1isTB+/Hhuu+02fvnLX/LNN9+wZMkSVCoVixYtwmazYTAYWiyj1+ux2+3YbDYAkpOTW7UH2767bPB7u91Oenp6h+NXwvw1CL3/rOW62lMq3BjiUXAfesK+RJIcl9bkmLStq8dFjqcIh8/vx+4OPEjRdUNSCuDCbB0nLB5ONbpIl47mQiQ8SUoJIUQnBJNSF+Z0rp5UkNGg5US9U5JS53DllVdy5ZVXhr4fM2YMP/rRj9i2bRuLFi3CYDDgdLYsLuF0OjGZTKEEk8PhaNWekpKC3+9v1Rb8PiUlJaw4s7LCG8ZpNAbWr9clkZwcmEZbpQ1clv1+0OmTOpTw1OsDtclMphSyszs3lDQehXs8ews5Lq3JMWmbHBfRHZzNI+gU/CQp3ZOUuqiPjneP2jjd6GKEJKWESHiSlBJCiE74+nSgntTFnawnFWQyBOpKSVKqfX/729+oqanh1ltvDb3W1NSEXh/4JDp8+HBKSkpaLFNaWsrUqVPJyMggJyenxRC/6upq6uvrKSgowOfzUV9fT01NDdnZgXmCjhw5Qm5uLmlp4f1szWYL/jA+j9fXB3pqOV1N2O2BIQjes3pINVqd6DTnH2XvdAZmPaqrs1FTYwkj4vikKIGb6XCPZ08nx6U1OSZt6+pxCS4vREc4PIF/dYqv23rZXZgVeJBjtjXh9knXPiESndSUEkKIMDV5fByuDiSlLszpYlKqudi5W5JS7fL7/Tz11FN88skn+P1+9u3bx29/+1vmz58PwNy5c9m6dSu7d+/G7XazYcMGzGYzhYWFAMyePZu1a9dSXl6O1WrlySefZPz48QwaNIjBgwczbtw4nnzySaxWK+Xl5axZs4a5c+d2Is7wvwILnlmHSjkzms/jDb+uVGdiiMevnrQvclzkmCTicRGio4I9pXRK9xUdz0rWkKIBP2CWWfiESHjSU0oIIcJ0uNKC2+snXa9hgLFr/caNzT2lfLo0rC4PqTo5LX9XYWEhDz/8MI899hiVlZVkZ2dz7733MmvWLAAmTZrEihUrQu35+fmsW7cOo9EIwJIlS/B4PCxYsACbzcaECRN44YUXQutfvXo1v/jFL7juuutQqVTcfPPN3HPPPTHYU1AUBY1Kwe3zywx8Qggh4t7ZPaW6U7YebFaoccq1UohEJ3c/QggRpv0n6oFAPSmli33VdRoVBq0Kh9vH8Vo7F+d1vLB2T3bo0KEW3996660thu9916xZs0JJqu/SarUsXbqUpUuXttmenZ3N6tWrOx9shGnUgaSUV5JSQggh4pzDG7hW6VTd11MKIFuvcNzqp0Z6SgmR8GT4nhBChOnAiQag60P3goJ1pb412yOyPpHY1F2cgU8IIYToLrHsKQVQ5wJXJ4a7CyHihySlhBAiTPubk1IXdbHIeVBwCN/xOsd53il6A40kpYQQQiQIR6imVPcmhlI0kKxV4wNKa5u6ddtCiMiS4XtCdNIddy/CXFvfbntWppH1L7/afQGJbuF0ezlcGZjh7MKc1IisM8MQOBWXS1JKABpV4HmRJKWEEELEu1BPKZUXUEdtOz6fl4qKEwBUVJzA74fcdB3fmu18U+PipqhtWQgRbZKUEqKTzLX1TL/vuXbb33m+7fo1IrEdrrbh9fnJStaSk6aLyDrT9YGeUuX1kpQSZ4bvSU0pIYQQ8czv94d6Sumj3FOq0VzF68etDBiiUHboAMa8weSlG/nWbOfrGldUty2EiC4ZvieEEGH4+nRzL6nctC4XOQ/K0AeeD5TVOfDJXNy9ngzfE0IIkQisbh/BS1WSEv1C5+lZOWTmDiA9sw8Q6CkFcNjchEfqSgmRsCQpJYQQYQgmpSJVTwogTacBvw+Xx0e1Veoi9HaSlBJCCJEIapu7Sek0KtSReU4XFpNBS5IKXF4/xVXW7g9ACBERkpQSQogwhJJSEaonBaBSKaidgeLpUldKnElKyVNfIYQQ8SuYlEpJil4tqXNRFIWs5ln4Pi9viEkMQoiuk6SUEEJ0kMXp4VhtIGl0cV7kekoBaJz1AJRJXaleL1hTyuOVnlJCCCHiV6yTUgDZ+sA1c98JSUoJkagkKSWEEB30dfOsewMzDZiSkyK67lBSqlaSUr2dRi2FzoUQQsQ/cxwkpfo095TaV9Eg100hEpQkpYQQooMOngokpS4daIr4uoNJKZmBT0hNKSGEEIngTE+p2E3onpEEeo2C1eXlUHOJBSFEYpGklBBCdNDB5g87lwzIiPi61cGklNSU6vUkKSWEECIRBJNSyTHsKaVSFEZkBWbhKzpWG7M4hBCdF7u0thBCxJk77l6Euba+zTY/UH35naAxMHaQMeLbDvaUOtHgwOvzh+oKid5Howo8L5KklBBCiHhW6zwzfM8dwzguzNaxv9LJp0drmV6QFcNIhBCdIUkpIYRoZq6tZ/p9z7XZZnV5eP3zk6hVChf3y8DaYI/ottUuK1q1gtvr57TFSf8MQ0TXLxJHMCEptTGEEELEs1BNKZ2a+hjGcVF2oKfUnqO1+P1+QB7sCZFIZPieEEJ0QJW1CYDhfVLQayPfTV3BzwBjIBElQ/h6tzPD93wxjkQIIYRom9vrw9oUuE4lR+FzUTiGmZJIUivUWF2UyWcoIRKOJKWEEKIDqiwuAEblpkVtG4Oak1Jldc6obUPEP6kpJYQQIt7V2gMD9hRAp4ntLWWSWmFUXjoA+040xDQWIUT4JCklhBAdUN3cU+rivOglpQaagkmpyA4NFIlFo25OSnklKSWEECI+1doDn4t0alCU2A+XG9s8Cc3nkpQSIuFIUkoIIc7D5/dTYwt8+Ao+iYuGYFKqvF66nvdmaukpJYQQIs7V2gI9pfSxHbkXEkxKSU8pIRKPJKWEEOI86uxuPD4/isfFBZnRK0B+gUlqSokzw/ek0LkQQoh4ZT6rp1Q8GNMvHbVK4VSji1ONUgZBiEQis+8JIRLKHXcvwlxb3257VqaR9S+/GtFtBouca22VqKLYRX1gc02pkw1OPF4fGrU8N+iNNKrAz116SgkhhIhXtc09yOOlp1RykppR/TPYX17PvhMN5F2kj3VIQogOkqSUECKhmGvrmX7fc+22v/P80ohvM1jkPMlaGfF1n61PahJ6jQqnx0dFg5MLMpOjuj0Rn6TQuRBCiHgXLHQeLz2lACYMyQwlpW68KCfW4QghOighH8MfPHiQBQsWcPnllzNlyhSeeOIJmpoC2fr9+/czb948xo4dy7XXXsvmzZtbLLtlyxYKCwu59NJLmT17Nvv27YvFLgghEsjpYFLKcjKq21EURepKCUlKCSGEiHtnCp3Hvsh50PjBmYDUlRIi0SRcUsrn83H33Xczbdo0Pv30U958800++ugj1q1bR0NDA3fddRc333wzRUVFrFq1iqeeeoovv/wSgD179rBy5UqefvppioqKmDlzJosXL8bhkJs/IUTb7E1eGp0eAJIsp6K+vf4Zge7mJxukHkJvpT6rppTfL4kpIYQQ8cdsj69C5wBXDM5EAY7XOUJJMyFE/Eu4pFRDQwPV1dX4fL7Qh3WVSoXBYGDnzp0YjUYWLFiARqNh0qRJzJgxg40bNwKwefNmpk+fzrhx49BqtSxcuBCTycS2bdtiuUtCiDgWLJaZlaxF5Y3+B5z+GYGeUhWSlOq1NGc9dZZi50IIIeJRsKZUPA3fy0jWMiw7BYAvpLeUEAkj4ZJSJpOJhQsX8swzzzB69GiuvvpqBg8ezMKFCykpKaGgoKDF+/Pz8ykuLgagtLT0nO0dpSjhf3V2uXj6kn1ova5o/K5EYh9iFdv5th2Jn0E0960tpxsDQ/dy03Xn3YeuUhQYYAz0lKpocEZ1v8/eFxFfgsP3QIbwCSGEiE+1cdhTCmDsgAwAPpeklBAJI+EKnft8PvR6PcuXL2fu3LkcP36cn/zkJ6xevRqbzYbB0HK6dr1ej91uBzhve0dlZaV1KvbOLhdPZB/O0GjVJCfrztmenR2d43W+fYhlbOfbNnTtZxDNfWtr3ZXNTwIv6JOKRasOxd7WPpwvNkWh3fZg3BcOMgW2a21qsR/R2O/MzMT/e+6JVIqCSgGfX5JSQggh4o/H56fBEX+FzgEuG5DB5i9OSl0pIRJIwiWl3n33XXbs2MH27dsBGD58OEuWLGHVqlXMmDEDi8XS4v1Op5OUlEA3ToPBgNPpbNVuMpnCisFsthBOmQ9FCdzAhrtcPJF9aM3j9mK3u87ZXlNjabf9fO64axE1tfUtX1RAo1FjTEtj/Suvxiy2czl7241OD8fr7Hi8frJTkhhgCvQC6srPIJr79t11O9xezNZAUsqUpMbj9mI2W9r9PTpfbH4/7bYH405TAis9brZRXd2I0tydKZL7HfxbqK21SGIqTqlVCj6vX5JSQggh4k69vQk/oAC6OBp34/V6yfYHklEl1TbqbS6MKed+UCqEiL2IJ6X27NnDhAkTIr3akFOnToVm2gvSaDRotVoKCgrYtWtXi7bS0lKGDx8OBBJYJSUlrdqnTp0aVgx+P526oe7scvFE9iH8bXVWTW090+97rtXryck6Nq+6t8v7EM1j4Pf72VvewL6KxhavZ6ckoTFkRf1nEKl1B+s6ZSZrSU5St1h3NPbB74fcND0K4HD7qLW7yUxOCmv5cLcXKdE+9/c2GpWC2+uXmlJCiB5Prh+JJ1jkPF2niqtSAMePH2fTP/eRqsnB6oF/fHmE2ZMuinVYQojziHhu+6c//SnXX389v/71rzl5MvLTp0+ZMoXq6mpeeuklvF4v5eXlrF27lhkzZlBYWEhNTQ0bNmzA7Xaze/dutm7dypw5cwCYO3cuW7duZffu3bjdbjZs2IDZbKawsDDicQoRSY1ON8WVVnYdreXNvSeoHLuQ63/9MVN+9RFTfvURN768m7ve2M8v3zvCrqO1+IndJwQ/8OG3taGEVF66jvzsZJLUCjW2JipHzGHP8bqYxReOivpAUmpA84x43SFJo6Jvmq7F9hNBJM/9tbW1FBYWsmfPntBrO3bsYNasWVx22WVce+21vPjii/h8vlD7DTfcwCWXXMLYsWNDX0eOHAECT06feeYZJk+ezNixY1m8eDFVVVWhZc1mM/fccw+XX345EyZMYNWqVXg8ni7tQ1dpVIHLs/SUEkL0dNG+dxCR5fV6+ebbMgBS1D58Mb5O+XxeKipOcOzYUcrKysjIzqWfKTBKptjcfg9zIUT8iHhPqY8++oh//OMf/OlPf+Kll17iiiuuYPbs2Xz/+98nKanjT/zbk5+fz8svv8wLL7zAq6++SlpaGjNnzmTJkiUkJSWxfv16Vq1axerVq8nMzGTZsmVMnDgRgEmTJrFixQoee+wxKisryc/PZ926dRiNxi7HJUSkeXTpFJXVc7zWQV3zuP0QXToNzjM3zdXWJqqtTew70cDrn1eguux2vjzZyMW5aaHp5buLo8+FnKqyoQBXDctkRN9UAOxNXt4vNVPRAD/741f8avYorhgU3tDZ7uT3+zkRTEoZDed5d2T1z9BTaXFR0eBkdL/0bt12Z0Xq3L93714eeughysrKQq999dVXPPDAA7zwwgtcffXVHD16lDvvvJPk5GTuuOMOrFYrR48e5e9//zv9+/dvtc61a9eya9cu3nrrLdLS0li+fDnLli3jlVdeAeBnP/sZOTk5fPjhh9TU1LB48WI2bNjAokWLun5gOilY7FySUkKIni7a9w4issrLy9jy2REgA6vVitUA2TGMp9FcxevHrQwYolB57GsMmQPIyTBxuNrGIXP0Z00WQnRdxJNSWq2WadOmMW3aNGpra9m+fTvr16/nF7/4BdOnT2f+/PmMHDmyS9uYPHkykydPbrNt9OjRbNq0qd1lZ82axaxZs7q0fSGiaX9FAxv3VlB16b9R1dzbSCEw+1uflCRyTcl8/of/4dfPPYNBq8bvhzp7E8frHOw70cA/S83Ukcqe4/UUV1q5riCbrJTu+VBX0eCgYfD3ABg3MCOUkAJITlIzbWQfXt/xTxzGodz/9te8euul5PdJ6ZbYwlXncGN3e1GrFHLSu7ceQf8MPZ+faKCiwdGt2+2KSJz7t2zZwurVq7n//vu57777Qq9XVFRw6623cs011wAwbNgwCgsLKSoq4o477uCrr77CaDS2mZAC2Lx5M0uXLiUvLw+ARx99lClTplBeXo7P5+PTTz/lgw8+wGAwMHDgQO655x6effZZSUoJIUQ36I57BxFZqmQjuPzNpQ28sQ6H9KwcMnMH4LTU0OSHnOYe56V1TXi8PjTqOCp8JYRoJWqFzs1mM3/5y1945513KC0t5eqrr0an07Fw4UIWLlzIj3/842htWoiEVNHg4Ff/PMp7JTWBFxQV/TP0FPRNYaDRgE4TuKAmJ+v44+fvc9+Pf9TmevSKGqfPiGnqbTQ4Pbx9oJLvDc9iaFZy6D2HDxcza97cdmPJyjSy/uX2C6m359cfHsOv1pKXruOS/q17+KhVCtnf7iR75gPsO9HIf/zxAOt/ODb04SGelNcFeknlpelCCYLu0t8YGC6YSMP3grpy7p8yZQozZsxAo9G0SEoFb1aCnE4n77//PjNmzADgwIEDGAwG/vVf/5WSkhL69+/PvffeyzXXXIPFYuH06dMUFBSEls/OziYjI4NDhw4BYDQaycnJCbUPGzaMkydP0tjYSHp6bHqqBXs4Sk0pIURvIfcOicPlDVybkhTfed4ZG0aDhiQVNHn9HKqycnFeYvQ6F6K3inhS6p133uHtt9/m448/ZujQocyePZuXXnqJzMxMAK6++mqWLFkiFxYhmnl8fv53dxkbPi2jyetHrcBNF+fy8cbnuPHHj7SzDG0WQQ969u6bmHvvA7xfaqa83sk/DtfgH57FsOyUDi3/zvNLw96Pg6caefdQNfj9TBpsQtVO5UvF7+W5WRfz769/wbFaBz/741esu/USUnXxNRno0Vo7ABdkdu/QPYD+GYFtBgutJ4JInPv79Olz3u1YrVb+4z/+A71ez8KFCwFQFIXRo0fzn//5n/Tr14/t27dz77338tprr5GbmwtAcnJyi/Xo9XpsNhsQmJn1bMHv7XZ7WEmpcIu9ht7fxnIadXNPKW94H/jjqeBsZwX3oSfsSyTJcWlNjknbunpcuvt4yr1D4nE1d44KJKXi7w9QURQydXDaAftPNkpSSog4F/G7wMcff5zp06ezadMmRo0a1ap9yJAhoRsJIXq7KouLZe98EyoKfsUgI/95zTDys1OY9Wptl9at16r5/sg+fHiklsPVNt4vNZOSpCE3SkPRXvr4OACGmm/ISrngnO/NMGj51ezR3PH6F5TW2Hjgz1/zq9mj0Hawe3WDw80Rs52TDU5sTV7UikJmipbhfVKIRL8Sq8tDtTVQh2BwZvJ53h15/ZsLqydSUqo7zv3ffvstP/3pT8nKyuK3v/0tqamB4aHfHWY3c+ZM/vKXv7Bjx47QTYzD0XIopNPpJCUlBb/f36ot+H1KSnhDS7Oy0sJ6v9EYWL9el0Rycsu/S502MNujSqNu1fZden1geK7JlEJ2dngxxLNwj2dvIcelNTkmbUuU4yL3DonH2SIppY5pLO3J0iucdvjZX9HID8fFOhohxLlEpdB5eXl5aCjEF198QVpaGsOGDQMgNzeXn/70p5HerBAJ59PjdSx7p5g6h5uUJDUPXz+c74/sgxLBR5QqReGqYZm4fX6Omu387XA1t4zOjdj6g0prbOw+VodKgbQTnwL/ct5l+mXoeeGWi7nrjf0UldXzwJ+/5qmbLkSvbf/DzbFaO3XDvs/mL061Sj7VOdwcqbGjvXgex2vtXNCFZFKwl1Rumq65XkL3Cg7fq7K4aPL4SNLEfy2EaJ/7//nPf/Kf//mf/OAHP+DnP/85Gs2Zy9dvfvMbLrroIiZNmhR6rampCZ1OR0ZGBjk5OZSWloaG8FVXV1NfX09BQQE+n4/6+npqamrIzg6Uaj1y5Ai5ubmkpYV3Q2c2W/CHkRWtrw/01HK6mrDbW84QpDSvyO50t2r7LqczkECtq7NRU2MJI+L4pCiBm+lwj2dPJ8elNTkmbevqcQku313k3iHxuJo78cZ1Uqr5ec7+k414PB5OnCgPtQ0cOAi1Oj7jFqI3ividzt///nduvvlmjh07BsC+ffuYN28e//znPyO9KSES1vZvqvjpH7+izuFmeJ8UfvuvlzHtwr4RTUgFqRSFq4dlkpmsxeH28c8jXeuB1ZZNeysA+F5+NhpXY4eXG5mTxn/PvAidRsVH39Zy9x++5JjZ3up9R2psLHvnG+Zv+AxHn5H4CfQmumpoJjdd3JdpI/twcW4qWpWCOy2Pf/3d53xwxNzp/fm2JhDDkKzu7yUFYDJoMWhV+IFTjYnRWyqa5/4vvviCJUuW8PDDD/Pggw+2SEgBnDp1iscff5zy8nI8Hg9vvvkm+/bt45ZbbgFg9uzZrF27lvLycqxWK08++STjx49n0KBBDB48mHHjxvHkk09itVopLy9nzZo1zJ3bfs219vj94X8FFmy9LnUnC513JoZ4/OpJ+yLHRY5JIh6X7iT3DomnZU+p+JSpA7UCZlsTew8d5aUdn7Hp8wpe2vEZ5eVl51+BEKLbRLyn1IsvvsiaNWtC3W9vv/128vPzefbZZ7n66qsjvTkhEs5fDp7m8e2HAfj+iD4sn1Zwzt5BkaBVq7iuIJs/fnmaigYnSSOmRmzdZlsTf/2mEoAfjuvPf/0uvOUnDs7kf+aM5ud/OsjXpy38f7/dy5ShmVyYk4bD7WVveQMHTp1JdOlrv+Vfrp5Mn9SWMwoOMhkY0y+dt97bgzNjIA+8fZD/+pcR3HhRznc3eU61tiaqrE0o0KI4fHdSFIX+GQZKa2xUNDi71Ouru0Tz3P/SSy/h8XhYtWoVq1atCr0+btw4Xn31VR544AFUKhU//OEPsVgs5Ofn88orr3DBBRcAsGTJEjweDwsWLMBmszFhwgReeOGF0HpWr17NL37xC6677jpUKhU333wz99xzT5di7iqNFDoXQvQScu+QWLx+/3dqSsUntUphqElLSW0Th8wujH3yyMwdEOuwhBBtiHhS6tSpU1x11VUtXpsyZUqLmZSE6K3+frialTsCCam5l+Rx/3X57RYEjzSjQcv4QUY+OVaHYdws7E3eiAxNe2v/SZq8fkblpTGmX+cKSY4dkMHv/+0ynvpbCR8freP9UjPvl57p6aRW4Or8bO6YMIgHfrKaPtO/1+Z6UnUasorf5rI7VvLOwUoe334IjUrh+yP7djiWbyqtAAzONMRk6F5Q/wx9KCmVCCJ97g/OjAeBpNS5JCUl8cgjj/DII21PDKDValm6dClLl7ZdwD87O5vVq1d3Ks5o0agCHZnD7SklhBCJRu4dEovVdSYRpY3jpBTAiKwkSmqbKK5xIaXOhYhfEU9K9e/fnw8//LDFxeWTTz6hX79+kd6UEAnl4KlGVvz1ED4/zBqdywPX5UdluN65XJybSmm1jWqgqKyeq/OzurQ+p9vLm1+cAuCH4wZ0aX9y0/X8avZoSqtt/KOkmipLE2qVwoi+KVw1LIs+qR0r0K74fayYVoBGpfD2gdP8118PodeqmTrs/PvqU2kpqQnU+bkwN7YFYoN1pSrqEyMpJef+yNJ0cvieEEIkGrl+JJb65m5Seo0KVfxNvNfCyCwdfymxcsjcxBVd+8grhIiiiCel7rrrLpYsWcL3v/99+vfvz8mTJ3n33Xd55plnIr0pIRJGja2Jn7/9NS6PjylDM3n4+uHdnpCCwLCwSUNM/PmrSg5X27goN7XDyZ62bP+mijqHm9w0HdcMz45IjPl9UsjvE96sZ9+lKAoPXz8cp9vLjuJqHtr6Nc/fPIoJg03nXM6Wdylur58MvYZ+UZqlsKPOzMDnOM8744Oc+yNLo25OSnnj+ym0EEJ0lVw/EkuDM3BdMkS59EQkjGiudl7e6OYSY5xn0IToxSJe6HzGjBmsW7cOrVbLwYMH0ev1rF+/nmnTpkV6U0LEtcOHi5k1by4z583l5qf/gNnWhMZupvSNJ5k9fx533L3o/CuJgpw0HU2lewD4+Ggd/k5WNPX7/fz+80CB8/mX9Q/17IgXapXCYzeM5Hv5Wbi9fn7+9kH2nWho9/1mWxPWfoE5g8cNzIhJ0vBs/TMMAAkzfE/O/ZGllppSQoheItrXj/r6eh544AEmTJjAFVdcwT333ENVVRUA+/fvZ968eYwdO5Zrr72WzZs3t1h2y5YtFBYWcumllzJ79mz27dsXkZgSWUNzTymDNv5nBjbq1Qww6vEDteeeyFYIEUMR7ykFMGHCBCZMmBCNVQuRMDw+mH7fc3x5spFTx+tRqxRunjQK03VjAXjn+bbr23QHx94/kVwwkSprE6U1doZ3omfSnuN1HDXbSdaquXl0boeXK/7ma2aeY2azrEwj619+Nex4goLJwCC/okY3Yjou42B+/Psi1t82novzWlYW8Pv9/PK9I/jVSfRJSYpZgfOzBXtKnWxwdjpx2N3k3B85MnxPCNGbRPP6ce+995KRkcG7776LSqXi4YcfZvny5fz3f/83d911Fz/96U+ZP38+RUVFLFmyhBEjRjBmzBj27NnDypUrWbduHWPGjGHjxo0sXryY9957D4PBEJVYE0H92T2lEiDRc0m/dE7UOzG75HoqRLyKeFKqsrKStWvXcuzYMXy+lsMOfvvb30Z6c0LEtQaHm8/KA71zJg02YUrWxjiiAL+jgbED0ikqa6CorJ4hmQY06vCeeL3e3EtqxqgcUnUdP5W4vX6m3/dcu+1dTdYFk4EtXvP62F5czalG+Okfv2LljSOZPCQTCCSkXt1dxs5D1eD3MWmIKea9pADympNStiYvDQ5PjKM5Pzn3R5YkpYQQvUU0rx9fffUV+/fv5+OPPyY1NRWAlStXUl1dzc6dOzEajSxYsACASZMmMWPGDDZu3MiYMWPYvHkz06dPZ9y4QC/qhQsX8sYbb7Bt2zbmzJnTpbgSWainVFJiJKXG9M/gna+rMCdGx3MheqWIJ6UefvhhampquOaaa9Bq4+MGXIhY+ejbWrw+P/0ydIzs27U6SZE2Ki+dbyqtWF1evjxl4bIBGR1e9pjZzsdH61CA+WP7Ry/ICNGoVXx/ZB9+//ciGsnjP/74FVOGZjI6L5295fV8WlYPQMbR98mZvLDd9Rw+XMzMuXPRaNV43N7W7UdKmB6hmHUaFX1Tk6iyNiVEXSk590eWRobvCSF6iWheP7788kvy8/P5wx/+wOuvv47D4eCqq67iwQcfpKSkhIKCghbvz8/P58033wSgtLS0VfIpPz+f4uLiiMaYaOoTaPgeBHpKQWD4ni9Bep4L0dtEPCl14MABduzYQWZmZqRXLURC0V4wlpONLtSKwlVDs+Ki983ZNCqF8YOM/KPEzP6KRkb2TSU5qWNFKzftC/SSumpYFgNNidGFPUmtIuubLXzvx6vY9HkFH31by0ff1ja3Kfz4ysG8ufurc64j2AsrOVmH3d768eDXd98U0Zj7Z+ibk1Lx/3hPzv2RpVEFPuxLTykhRE8XzetHQ0MDhw4dYtSoUWzZsgWn08kDDzzAgw8+SHZ2dqtheHq9HrvdDoDNZjtnezji7CNgm4Ixni/WeC103lb8igJDs5NJ0SrY3H7MNjfq5tfj7WfS0eMfzxJ9HyT+jm8j0iKelEpLSyMpKSnSqxUioXi8PgxXzAbgkv5ppOujUr6ty4ZmJfPVKQtV1iY+K69n6rDzz5fb4HDzl4OVAPxwXPz3kjqbyufh59cMY/aYPHYWV3HK4qJvahI3XZzLIJOBN2Md4Hf0MxrYV9GYEEkpOfdHllqG7wkheoloXj+C63300UfR6XSkpqbys5/9jB/84AfMnj0bp7Pl9dXpdJKSEujZbjAY2mw3mc49k29bsrLSOrkH3e98sVo9geuSMVWPt0GLSqclOVmHXh/b/xsMgZn2dM2vOwxJZGam0rdPOhfnJvNpuY1ap4dBza9nZ8fnzySRflfak+j7IPF3v4jfKd9zzz08/PDD3HnnnWRnt5wivl+/fpHenBBx6eBpK6rULFKS1KFuw/FIURQmDjbx568qOVRl4+LcNLJSzv3BcOPeE7g8Pob3SQlryF88GZKVzN1XDo51GOcVLHZeUR//SSk590eWRt2clPJKUkoI0bNF8/qRn5+Pz+fD7Xaj0wWSFsG6VRdeeCG///3vW7y/tLSU4cOHAzB8+HBKSkpatU+dOjXsOMxmC/E+ckxRAjezbcXq9XopLy8DoLox0FNc5fNic7pR+d3Y7S6cMf6/wxGIy+UKvG6zOfjyy2Jqa630xQbA8RobfYxN1NZaSU+3dN/B7YBzHf9Ekej7IPF3fBuRFvGk1LJlywB49913gcBNr9/vR1EUvvnmm0hvToi40+Txsf9kIwCXD8wIu4B4d8tJ0zEkK5mjZjsffVvLjFE57b63xuri9b2BoXt3Tbog7oYk9jShpFQC1JSSc39kSU0pIURvEc3rx+TJkxk4cCCPPPIITz31FC6Xi+eff57rr7+em266idWrV7NhwwYWLFjA3r172bp1K2vWrAFg7ty5LFmyhBtuuIFx48axceNGzGYzhYWFYcfh95MwN7ltxVpWVsZLOz4jIzuXxiY/KAoGrZp4SusEYw7+22iu4vXjVgYMUTjy7RHQDuNUoxN/Rnz/POI5to5K9H2Q+LtfxJNSf//73yO9SiESylenLbg8PrwNp8nvMzDW4XTIxAuMnKh3UGVt4uvT1nbf99LHx3F6fIzKS+Pq/PMP9RNdcyYp5STe039y7o+sM7Pv+c7zTiGESGzRvH5otVp+97vf8fTTTzNt2jRcLhfXXnstjz76KOnp6axfv55Vq1axevVqMjMzWbZsGRMnTgQCs/GtWLGCxx57jMrKSvLz81m3bh1GozFq8cYzY588krPy8B8LPJyMt5pSbUnPyiEzdwC5Vac4bPfT5IX6pnj/RCVE7xPxpFT//oEaM19//TUnTpzge9/7HhaLhawsuYEVPZ/b6+OrU4HnRs4v3kH1L+NjHFHHpOo0jB9kZNfROj49XocppW+r9+w6WsvbB04DcO/UIdJLqhv0NwYKrFZaXOQo8d3jTs79kRWsKeX1B2YLUsnfmxCih4r29SMnJ4fnn3++zbbRo0ezadOmdpedNWsWs2bNikgcPYHDHXhQolH8oetUIlApYNJ4qXFrqI7/ighC9DoRv8sxm83ceuut/OAHP+DBBx+kvLyc66+/nn379kV6U0LEnZJqGy6PjzSdBvexz2MdTlguzEllkMmA1w+1BTdxvPbM7DJHzXZ+sf0QALde1p/LBhhjFGXvkpWsRadR4fODNym+ixbKuT+yNGd92JchfEKInkyuH4nD4fYCkKQk3nXJpA3EXu1IvNiF6OkinpR68sknKSgooKioCI1Gw7Bhw7jrrrv47//+70hvSoi44vP7OXAy0EtqdF5awg3mVRSFa/KzMBo0+HSp/GjjPl795Dgb9pSxaNMX1Nrd5GensGTK4FiH2msoikK/5iF8Hn18F5WXc39knZ2Ukhn4hBA9mVw/EkcoKaVKvOtSZnNSqsYpD3uEiDcRT0rt3r2bhx9+GIPBEBres2jRIkpLSyO9KSHiyvFaB40uDzq1ioK+KbEOp1OSNCqmX5RDUmMFtiYvL398nF9/dIxGp4fReWmsnTcGfQLUEOhJgnWlvLr4ncUR5NwfaYqi0DwBn3x4FkL0aHL9SBzBpJROlXj1DtPUPpLUCh4/fFvfFOtwhBBniXhNKa1Wi9PpxGAw4G/uKWKz2UhJScybdCE66kBzLakLc1PRxvmMe+eSnKQm65st3L7sV3xyrA6318dVQzO58aIcSUjFQP8E6Skl5/7I06hUeL0+6SklhOjR5PqROII1pRKxp5SiQF66nuN1Dr6qdhH+HIpCiGiJ+J3ztddey/3338+xY8dQFAWz2czjjz/O1VdfHelNCRE3Ki0uKi0uVApcnBvftX86QvH7uGVMHv898yKev2UUsy/pJwmpGOmXID2l5NwfeerQDHyJ9+FfCCE6Sq4ficPelLg1peDMZ6qDVa4YRyKEOFvEk1I///nPSU5O5l/+5V9obGxkypQpOBwOli5dGulNCRE3Djb3ksrPTiE5SZI3InL6pQeTUvGd7JRzf+RpmsfvebyJ+eFfCCE6Qq4fiSORa0oB9EvXAVBsduH2Jt4QRCF6qogP30tJSWH16tXU1tZy4sQJcnNz6du39fTyQvQUTreXo80z1V3UA3pJifiSlyA9peTcH3nBYudSU0oI0ZPJ9SNxJPLwPQBTspYkFbi8fr46ZWHsgPgujSBEbxHxpFRRUVGL748fP87x48cBuOKKKyK9OSFirrTGjs8PWclaslO0sQ5H9DDBnlI+bTJury9u65XJuT/yNKHhe/I0VwjRc8n1I3GECp0n6PA9RVHoa4ATNth9rFaSUkLEiYgnpW677bZWr6lUKvLy8vj73/8e6c0JEVN+v5/iSisAI3JSQ7PGCBEpaXoNaToNFpcHi8tDZnJSrENqk5z7I09qSgkhegO5fiQGv9+f8D2lAHINCidsfnYdrWPxlCGxDkcIQRSSUsXFxS2+r62t5de//jX9+/eP9KaEiLlqaxN1DjdqRSE/O7xZYg4fLmbWvLnttx8pYXpXA0xAclxay0vXYan2YHF5yUyOdTRtk3N/5GkkKSWE6AXk+pEY3D4IXo4SOSmV0/w56lCVlWqriz6putgGJISIfFLquzIzM7n//vuZNm0ad9xxR7Q3J0S3OlQV6CU1JMuAThPesCqPD6bf91y77V/ffVOXYktUclxa65eh53C1DavTE+tQOkzO/V0nSSkhRG8k14/45AqM3EOND3UCDwzQqxWGmbQcqWvik6N1zBydG+uQhOj1uqU4SUNDAy6XTL0pehafSsuRmkCB85E5qTGORvRkwSmMLa7ESUqBnPu7Si2FzoUQvZRcP+KPszkplaQkfp3Dy3IDn6s+PlYb40iEEBCFnlIPP/xwi+/dbjd79+5l8uTJkd6UEDHlzMrH7fOTrteQmyZdf0X0BIudx3NSSs79kadpLmrv8UpSSgjRc8n1IzG4mnNRPSEpNTZXz+ZvGtl9rA6P1xe63gohYiPqw/d0Oh233XYb8+fPj/amhOhW9uwRABT0SZEC5yKq8pp7SlnjOCn1XXLu7zoZvieE6I3k+hGfelJPqWGmJIwGLfUON/tPNjJuoDHWIQnRq0U8KfXUU09FepVCxJ1qq4um9IEADAuzwLkQ4Qr1lAp+IoxDcu6PvDNJqcS/ARBCiPbI9SMxuJp77faEpJRKUZg02MRfv6ni46N1kpQSIsYinpR68cUXO/S+n/zkJ5HetBDd5t1D1aAo5KQlka6PeodD0cvlZQSGh7q8Ppo8PpLCLKrfHeTcH3lq6SklhOgF5PqRGEI9pVSJn5QCuHJIZnNSqpZ7pw5p0eb1eikvLwt9P3DgINRqdXeHKESvEfG76ZKSEnbu3MnIkSMZMmQIp0+f5vPPP+eiiy4iJSXQo0SGOolEt/2bKkB6SYnukZKkQeV24NMasLg8ZGmSYh1SK5E899fW1jJ//nyeeOIJJkyYAMD+/ft54oknKC0txWQysXjxYubNmxdaZsuWLaxZs4bq6mqGDh3K8uXLGTt2LBD4cPncc8/x9ttv43A4mDhxIo8//jh9+/YFwGw2s3z5cj799FPUajUzZ87kwQcfRKOJbcJZI4XOhRC9gNw7JAZXi+F78fdwLFwTBptQKVBaY+NUo5O85l7pAOXlZby04zOMffKorz7Fj6fB4MFDzrE2IURXRPwTt0ql4uGHH+bf/u3fQq+9/fbbvPfee7zwwguR3pwQ3e5YrZ1vKq3g9zE0KznW4YheQu1qPJOUSom/pFSkzv179+7loYceoqzszBPKhoYG7rrrLn76058yf/58ioqKWLJkCSNGjGDMmDHs2bOHlStXsm7dOsaMGcPGjRtZvHgx7733HgaDgbVr17Jr1y7eeust0tLSWL58OcuWLeOVV14B4Gc/+xk5OTl8+OGH1NTUsHjxYjZs2MCiRYsidnw6Q2pKCSF6A7l3SAw9LSllNGi5pH8G+0408H6pmf/vsv4t2/vkkZk7IEbRCdG7RPyM8s9//pMFCxa0eO2mm27ik08+ifSmhIiJHc29pHT1ZRi00pVXdA+1qxGI32LnkTj3b9myhaVLl3Lfffe1eH3nzp0YjUYWLFiARqNh0qRJzJgxg40bNwKwefNmpk+fzrhx49BqtSxcuBCTycS2bdtC7XfeeSd5eXmkpqby6KOP8sEHH1BeXs7x48f59NNPuf/++zEYDAwcOJB77rkntO5YkqSUEKI3kHuHxNBTCp37fF4qKk5w7NhRLskMXGffK6mJcVRC9G4RT0plZmZSVFTU4rUPP/yQ3NzciG2jvr6eBx54gAkTJnDFFVdwzz33UFUVSBTs37+fefPmMXbsWK699lo2b97cYtktW7ZQWFjIpZdeyuzZs9m3b1/E4hI9n9/vZ0dx4HfNUHMoxtGI3iSYlIrXYueROPdPmTKFd999lxtvvLHF6yUlJRQUFLR4LT8/n+LiYgBKS0vbbbdYLJw+fbpFe3Z2NhkZGRw6dIiSkhKMRiM5OTmh9mHDhnHy5EkaGxs7HDuAooT/FViw7fUFp6j2eDuelOpMDPH41ZP2RY6LHJNEPC7dqTvuHUTX+P3+7/SUSlyN5ipe313Kps8rKCktAeCLEw2YbU0xjkyI3iviw/fuvvtu7rrrLqZNm0a/fv0oLy/nvffe43/+538ito17772XjIwM3n333VCX3+XLl/Pf//3fXRriIcT5fH3aQnm9E71Ghb7u21iHI3oRTTApFac9pSJx7u/Tp0+br9tstlbnaL1ej91uP2+7zWYDIDk5uVV7sO27ywa/t9vtpKendzj+rKy0Dr8XwGgM1ErR65JITta1ak+2uwHwK7TZHqTXB4ZzmkwpZGeHF0M8C/d49hZyXFqTY9K2RDku3XHvILrG6fETfD6S6EkpgPSsnNDQvGH1ao7UufngiJlbxuTFODIheqeIJ6XmzZtH//79+fOf/8zXX3/NwIED2bRpEyNGjIjI+r/66iv279/Pxx9/TGpqKgArV66kurq6xRAPoMUQjzFjxrQY4gGwcOFC3njjDbZt28acOXMiEp/o2bYXVwNwdX4WBz5yxzga0Zuo4zwpFc1zv8FgwGKxtHjN6XSGCuAaDAacTmerdpPJFEowORyONpf3+/2t2oLfB9ffUWazBX8YI+3q6wNJMaerCbvd1ard6w48lm5y+9psD3I6A0936+ps1NRY2n1folCUwM10uMezp5Pj0pock7Z19bgEl+8u0b53EF1X39xNSqNS0Cg9649tQr9kjtQ18F5JjSSlhIiRqEwtNHnyZCZPnkxtbS2ZmZkRXfeXX35Jfn4+f/jDH3j99ddxOBxcddVVPPjgg+0O8XjzzTeBwBCP7yafzh4CIsS5eHx+djYP3Zs2si8HYhyPCN/hw8XMmje3/fYjJUzvxnjCoXYFkg0Wlwe/3088zkQUrXN/QUEBu3btavFaaWkpw4cPB2D48OGUlJS0ap86dSoZGRnk5OS0GOJXXV1NfX09BQUF+Hw+6uvrqampITs7G4AjR46Qm5tLWlp4N2V+P2HdAIbe284ynakp1ZNuzMM9nr2FHJfW5Ji0LZGOSzTvHUTXNTgDvaMM2sQvcP5d4/sb+P3BBorK6rG6PKTqYjvzrhC9UcTPLG63m+eff55x48Zx7bXXUl5ezpw5c0I1n7qqoaGBQ4cOcezYMbZs2cKf/vQnKisrefDBB7s0xCMcvbXmQW/fh73l9dTa3WToNUwaYgr/lzcCujMP0ZVjfK51nv1vd/P4YPp9z7X75XGfv15TNPfhXMc12FPK7fXT1E6Noc78LURKNM/9hYWF1NTUsGHDBtxuN7t372br1q2hhwxz585l69at7N69G7fbzYYNGzCbzRQWFgIwe/Zs1q5dS3l5OVarlSeffJLx48czaNAgBg8ezLhx43jyySexWq2Ul5ezZs0a5s5tP3nZXc4kpRJ/qIQQQrQn2vcOousamntK9cQJfvqnaRmSlYzH5+ejb2tjHY4QvVLEU8Evvvgiu3fv5le/+hX33XcfWVlZ5ObmsmrVKn71q191ef1JSYHaGY8++ig6nY7U1FR+9rOf8YMf/IDZs2e3OYSjI0M8wtHZLs2JMrb/XHrzPvzzvUANqZsu6UdeTgYarfqcdV4U5dx1YLrSHs11A2i06k7XpjnfcQEwGBL3uAVjb2sfurL+8x3zJLUfg1aNw+3FrSiYvrOezvzMMjMj9/cczXO/yWRi/fr1rFq1itWrV5OZmcmyZcuYOHEiEBiqvWLFCh577DEqKyvJz89n3bp1GI1GAJYsWYLH42HBggXYbDYmTJjQYprx1atX84tf/ILrrrsOlUrFzTffzD333NOlmCNBZt8TQvQG0b53EF1XH+oppYYeWL3imuHZHDWX8V5JDf9yYd9YhyNErxPxpNTWrVt5/fXXycnJQVEUkpOTeeqpp0JPrLsqPz8fn8+H2+1GpwvclPmanyJfeOGF/P73v2/x/o4O8QhHuGP0FSXxax709n1we31s/+o0AFMHG6mpseBxe89Z58XvJ+LtwaRINNZ9No/be87aNHfctYia2vo220qOlDCtnXUHe+Y4HK52fwbR3LdIrNvhcGEw6Nrch66s/3zH3OP2kqoLJKWq6x2kqpVW7R2tJxT8W6ittUQsMRXpc/+hQy1ntxw9ejSbNm1q9/2zZs1i1qxZbbZptVqWLl3K0qVL22zPzs5m9erVnYozmkJJKa8/bodsCiFEV0X73kF0XYueUj0wKXVtfjbrd5ex62gttqb4rN0pRE8W8aSU3W4PjQX3N9+x6fV6VKrIjBScPHkyAwcO5JFHHuGpp57C5XLx/PPPc/3113PTTTexevVqNmzYwIIFC9i7dy9bt25lzZo1QGCIx5IlS7jhhhsYN24cGzdubDHEo6M6O0Y/kcb2t6e37sOeY/VYXB6yUpK4pF9GzI5Bd273XNuqqa1n+n3Ptdn27N03nXedifw7FM19ON8603Qaqq1N7RY7DzemSO5DtM/9vZFGHTh2fsDnB7XkpIQQPZBcP+JfvTOYlOpZPxOfz0tFxQn69fOTl6rhlNXDP0vNXJR8/mWFEJET8TPLpZdeyosvvggQeqr7u9/9jtGjR0dk/Vqtlt/97neo1WqmTZvGtGnTyM3N5cknnwwN8di+fTsTJkxg2bJl7Q7xGD9+PO+8806LIR5CtOdvhwOz7l07PBu1Su4MRWykNRffjMcZ+KJ97u+NNGeda2QInxCip5LrR/xrcDUP30vqWTWlGs1VvL67lDf2nUTfVA/AX7+RWmZCdLeI95R65JFHWLhwIVu2bMFms3HjjTdis9n43//934htIycnh+eff77Ntq4M8RCiLW6vj3+WmgG4riA7xtGI3ixNH/gwaI3DpFR3nPt7G7VKQVECPdo8Xh86Tc96Qi2EECDXj0TQkwudp2flkJk7gAJ3OUdP+Pn0eB11FxvOv6AQImIinpTKzs7mnXfe4f3336eiooLc3Fy+973vkZqaGulNCdEtPj1+Zujepf0zYh2O6MWC0xRbnOefJbC7ybk/OjQqBbfXLz2lhBA9llw/4t+ZQucqHDGOJVpStQrDM7WU1DbxcXl4M7MLIbom4kmpm266iT//+c/ccMMNkV61EDEhQ/dEvDh7+F68Fb6Wc390SFJKCNHTyfUj/p3dU6qnJqUArhqYTEltEx+W27nUGOtohOg9ojIWwOHoyacr0ZvI0D0RT4I9pTw+Py6PL8bRtCbn/sjTNhf6laSUEKInk+tH/HK6vTg9gWtQcg8cvne2yQOSUStwpK4Ji1uuu0J0l4j3lJowYQLz5s1j6tSp9O3bt0XbT37yk0hvToiokqF7Ip5oVArJWjV2txeLy4M+jj4cyrk/OjTNU+55vPLhWAjRM8n1I76Z7U0AqBTQ9vBpYDP0asZfYOKTY3WUW/1cEOuAhOglIp6UOnHiBAMHDuTo0aMcPXo09Ho8DTMRoqNk6J6IN6m6YFLKS584Krch5/7oCM7A5/bFX884IYSIBLl+xLdamxsAvap3/ExuuKgvnxyr47gVrvTLAyEhukPEklL//u//zm9+8xt+97vfAeB0OtHr9ZFavRDdTobuiXiUptdQZW3C4oyPGfjk3B9dwaSUDN8TQvQ0cv1IDLXNPaV08dM5O6quyc/GoDmM3ePndKMLXawDEqIXiFhNqX379rX4furUqZFatRAx8WmZDN0T8efsYufxQM790SXD94QQPZVcPxKD2R7oKdVbklJ6rZrJA5IBOFRti3E0QvQOUSl0DuCX7o4iwf3tkAzdE/EnmJSyxklS6rvk3B9ZGil0LoToJeT6EZ9qbYGeUvpekpQCuGZwCgBHzXbccv0VIuqilpTqDWOORc8lQ/dEvIq3nlLfJef+yDozfE9qSgkheja5fsSn2l7WUwqgIDOJVG3ggVCFdJYSIuqilpQSIpHJ0D0Rr1KbPxVanF55qtwLyPA9IYQQsRSsKaXv4TPvnU1RFAanBvb3mEWuv0JEW8QKnXs8Hv70pz+Fvne73S2+B7j55psjtTkhokqG7ol4ldrcU8rr9+Nw+0hOiu2jSzn3R5cUOhdC9FRy/UgMweF7vamnFMCgVDhYB2YXnLK4GRzrgITowSKWlMrOzmb16tWh700mU4vvFUWRC4tICDJ0T8QztUohJUmNrcmLxeWJeVJKzv3RpW2uKSU1LYQQPY1cPxJDsNB5T68p5fN5qag4AUBFxQl0Khhg1FNe7+S94zYmjY5xgEL0YBFLSv3jH/+I1KqEiKkiGbon4lyqToOtyYvV5SEnLbaTFcu5P7rODN+TmlJCiJ5Frh+JITh8r6f3lGo0V/H6cSsDhiiUHTqAMW8wBX2zKK938s8yOw/6/DJ6QogokZpSQnzH+6U1AHwvP0suPiIupQXrSsVpsXMROTJ8TwghRKw0eXxYXV6g5/eUAkjPyiEzdwDpmX0AuMBkIEkFtQ4vn5bVxTg6IXouSUoJcRavzx8aundNvgzdE/EpNAOf0xvjSES0SVJKCCFErJibe0lpVKDthXeNapXCwNTA/7d+VRnbYITowSI2fE+IRHPH3Ysw19a3eM2VmkftqHkoHhdrn3qQCS+/EpvghDiHNH1zUkp6SvV4GnXgLkBm3xNCCNHdzM1Fzo16NYrSO69DF6QqHGn088/SGhqdbtL12liHJESPI0kp0WuZa+uZft9zLV7bfawO8ykLw3JN1H1WG6PIhDi3UE8pSUr1eNJTSgghRKyEklI6NdA7P3MYk+CCDC3HG9zsKK5m3qX9Yh2SED1OL+yIKUTb/H4/x2sdAFyQmRzjaIRoX2pzUsrq8uD3S7KiJzuTlJJC50IIIbrXmZ5SvfeWUVEUrrkgBYCtX52OcTRC9Ey99wwjxHfU2d00ujyoFRho1Mc6HCHalapTowA+P9ibpK5UTxZKSsnwPSGEEN3MbHMDgeF7vdmUQcmoVQrfVFoprbHFOhwhehxJSgnR7FhzL6n+RgNatfxpiPilUhRSmmfgs0pSqkfTqGX4nhBCRILX6+W2227joYceCr22f/9+5s2bx9ixY7n22mvZvHlzi2W2bNlCYWEhl156KbNnz2bfvn3dHXZM1TT3lDL18qRUhk7NVUMzAektJUQ0yJ23EM2O1dkBGJxpiHEkQpzfmRn4emeNh95Cowpcpt2SlBJCiC558cUX+eyzz0LfNzQ0cNddd3HzzTdTVFTEqlWreOqpp/jyyy8B2LNnDytXruTpp5+mqKiImTNnsnjxYhwOR6x2odudXei8t5sxKheA7d9U4fHKkHohIkkKnQtB4MbebHOjAINMkpQS3e/w4WJmzZvbfvuREqaf9X2grpRLip33cNrmnlJenx+/34+iKDGOSAghEs8nn3zCzp07+f73vx96befOnRiNRhYsWADApEmTmDFjBhs3bmTMmDFs3ryZ6dOnM27cOAAWLlzIG2+8wbZt25gzZ05M9qO7me3BQucqevv0P5OHZJKZrKXW7mbX0Vquzs+OdUhC9BiSlBKCM72kctN1GLTyNEh0P4+PVrNBnu3ru29q8X1a8/A9SUr1bMGaUhBITAWH8wkhhOgYs9nMo48+ypo1a9iwYUPo9ZKSEgoKClq8Nz8/nzfffBOA0tLSVsmn/Px8iouLox5zvJCeUmdoVAo3XpTDa5+dYOtXlZKUEiKCJCklBHDMHJx1T3pJicSQFpqBT2pK9WTqs5JSbp8fjdwXCCFEh/l8Pu6//35uv/12Ro4c2aLNZrNhMLT83KfX67Hb7R1qD0cidHINxhj81+/3h5JSJkP8X3y+G3+k160oMHNUICn10dFa6uxNZKYkRXQbZ/+biBJ9HyT+jm8j0iQpJXo9h9tLpcUFwGBTcoyjEaJjQjWleklPqT//+c+sWLGixWtud2BWoK+++ooVK1bw1ltvodVqQ+0PPfQQ8+fPBwLFatesWUN1dTVDhw5l+fLljB07FggUv33uued4++23cTgcTJw4kccff5y+fft20961T6UoqBUFr98vxc6FECJML7/8MklJSdx2222t2gwGAxaLpcVrTqeTlJSUULvT6WzVbjKZwo4jKyst7GViJRhrg91NU/PMr0Pz0vn4eB3JyTr0ei0qnTbu/m8w6ADQRXj9DkMSmZmpZGenkZ2dxiUDjewvr+eDsgYWXTU0asc/kSX6Pkj83U+SUqLXK6tz4AeyUrSk6c/8SYRb40eI7pSqD/aU8uD39/xkxcyZM5k5c2bo+8rKSubMmcP9998PwIEDB1i5ciW33HJLq2WDxWrXrVvHmDFj2LhxI4sXL+a9997DYDCwdu1adu3axVtvvUVaWhrLly9n2bJlvPLKK922f+eiUSt4PX483p7/cxZCiEh6++23qaqq4vLLLwcIJZn+9re/8cADD7Br164W7y8tLWX48OEADB8+nJKSklbtU6dODTsOs9lCvF+qFSVwMxuM9ag50CMsTafB2mjH4WjCbnfhdLpR+d1x93+HI/CA2eWK7PodjiZqa62kpwcSmDeMyGZ/eT2v7znOrJHZEav1+N3jn4gSfR8k/o5vI9IkKSV6veO1zUP3vtNLKtwaP0J0p5QkNQrg84O9qXcN4fP7/dx///1873vfY9asWTQ1NXH48GFGjRrV5vvPV6x28+bNLF26lLy8PAAeffRRpkyZQnl5OQMHDuy2/WqPRqXgAjw+me1HCCHCsX379hbfP/TQQwA8/fTT1NXV8eyzz7JhwwYWLFjA3r172bp1K2vWrAFg7ty5LFmyhBtuuIFx48axceNGzGYzhYWFYcfh95MwN7nBWGusgaF7WSnahIg9GGOkY/X5vJw4cSK03uvy+/H8+99ypMbON5VWLsyJ7A16Iv2utCfR90Hi736qWAcgRCx5fX5ONgSemg0y6WMcjRAdp1IUUkLFzntXUurtt9+mtLQ0dHNRXFyMx+Nh9erVTJ48mWnTpvHKK6/ga07ilJaWtlnMtri4GIvFwunTp1u0Z2dnk5GRwaFDh7pvp84hWOxcekoJIUTkmEwm1q9fz/bt25kwYQLLli1j2bLJs/SLAAEAAElEQVRlTJw4EQjMxrdixQoee+wxxo8fzzvvvMO6deswGo2xDbybBOtJZUWwblIiajRX8fruUjZ9XsFLOz6jvvokVw3NBODd4uoYRydEzyA9pUSvdtriwu3zY9CqyO7lF12ReNJ0GqwuL9ZeUlcKAkVr165dy49//GNSU1MBsFgsjB8/nttuu41f/vKXfPPNNyxZsgSVSsWiRYvOWazWZrMBkJyc3Ko92NZR4fbgD73/PMtpm2fc60hNqUQtznm2RC80Gi1yXFqTY9K2rh6Xnnw8n3766Rbfjx49mk2bNrX7/lmzZjFr1qxohxWXzPbmpFSyfD5Oz8ohM3dA6PvCEX342+Ea3j1Uzb1Th0RsCJ8QvZUkpUSvVlYXGLo30GiQC4pIOGk6Dadw9Zpi5xCoD1VVVcXcuWfqvV155ZVceeWVoe/HjBnDj370I7Zt28aiRYvOWaw2mKxyOByt2oPFbjsq3DH2RmNg/XpdEsnJunbfl6RRA25UGnWb79PrAzcMJlMK2dmJV9yyPYlYqLM7yHFpTY5J2+S4iK44M3xPklJBPp+XiooT9M/th16jcNri4sApC2P6pcc6NCESmiSlRK9WHkxKmQzneacQ8Se1l83AB7Bjxw4KCwtb9Gz629/+Rk1NDbfeemvotaamJvT6wJDccxWrzcjIICcnp8UQv+rqaurr61sN+TufcAtL1tcHemI5XYHise1pHr2HzdH2+5zOwI1DXZ2NmhpLq/ZEk+iFRqNFjktrckza1tXjEq1CtiKxBHtKBUYS9J7PGefSaK7i9eNWBgxRMGLnNAbePVQtSSkhukhqSoley6PLoMHpQVFgQIbUkxKJJ625ppS1F9WU2rt3L1dccUWL1/x+P0899RSffPIJfr+fffv28dvf/pb58+cDgWK1W7duZffu3bjdbjZs2NCiWO3s2bNZu3Yt5eXlWK1WnnzyScaPH8+gQYPCii1YWDKcr8CC516vtjkr5e7A8L3OxBCPXz1pX+S4yDFJxOMihNSUaltwKN+QrMDDsb8dqsbbgeuzEKJ90lNK9FpO4wUA5KbpSNJIflYknrSzekqlxjiW7nLixAn69u3b4rXCwkIefvhhHnvsMSorK8nOzubee+8N1QE5u1htZWUl+fn5LYrVLlmyBI/Hw4IFC7DZbEyYMIEXXnihm/esfVp14Pzk9srse0IIIbpHTXNSymOtpaK+BpkAtqUcAyRrFWpsTXxR0cC4gcZYhyREwpKklOi1XKYhAAySoXsiQaXqA6dwq8tDeNWPEte+ffvafP3WW29tMXzvu85VrFar1bJ06VKWLl0akRgjLVjo3C2z7wkhhOgm1ZZALcbdR2toOHoAY95gsmMcUzxRKwoT+hl477iNdw9VS1JKiC6Q7iGiV3K4vbjS+wOBIudCJKKUJDWKAj4/+LS9JS3V+wR7SnkkKSWEEKIbeLw+Gl2BrlG5ef1Iz+wT44ji0+QBgXuIfxyu6dAMuUKItklSSvRKn5XVg0pDqk6N0SAdBkViUikKqUmBulIenRTZ7Kk0oZpSMnZCCCFE9NXa3QAogF5KXLRrVF89GXoNdQ43e8vrYx2OEAlLzjKiV9pzvA6AAUYDiqLEOBohOi84A59XklI91pmaUvIUVgghRPQFZ97TqZHPye3w+bxUnqrgijwdADuLq2IckRCJS5JSolf6tKwekFn3ROILFjv36iUp1VOdqSklPaWEEEJEX3DmPb06xoHEsUZzFa/vLsXlsAPwj8PVeOQ6LUSnSFJK9DrVVhdHzXbw+8lL18U6HCG6JE16SvV4weF7Uq9CCCFEd5CkVMekZ+VQMHgAOjVYm3zsaX7oLYQIT8ImpbxeL7fddhsPPfRQ6LX9+/czb948xo4dy7XXXsvmzZtbLLNlyxYKCwu59NJLmT17druzOIme7dPj9QBobVXotXK1FYktVR+sKZUW40hEtMjwPSGEEN2pRpJSHaZSFPonB67PW/Z+y7FjR/F6vTGOSojEkrBJqRdffJHPPvss9H1DQwN33XUXN998M0VFRaxatYqnnnqKL7/8EoA9e/awcuVKnn76aYqKipg5cyaLFy/G4XDEahdEjHzaXE9K11Ae40iE6DrpKdXzyfA9IYQQ3clsCxQ6l6RUxxg9gXuLj8vtrNn+GeXlZTGOSIjEkpDTjn3yySfs3LmT73//+6HXdu7cidFoZMGCBQBMmjSJGTNmsHHjRsaMGcPmzZuZPn0648aNA2DhwoW88cYbbNu2jTlz5sRkP3qDO+5ehLm2vt32rEwj619+tdvi8fv9oXpSuga5YIjEl6HXAuBXaWMciYgWrSrw/EiG7wkhhOgOweF7Oo0UOe8Io8qNTvHh8qlwpuTGOhwhEk7CJaXMZjOPPvooa9asYcOGDaHXS0pKKCgoaPHe/Px83nzzTQBKS0tbJZ/y8/MpLi4OO4ZwJ6EIvj+RJ6/o7D6Ya+uZft9z7ba/8/zSqB6XO+5aRE0wKaaAPzWb6ot/CD4PZfs/BH4WvY1HQXf+DkVjW2f/HvkT9P463vYhOUnN9QXZfP72FhTlhg4t0xPOSb2J9JQSQgjRnaSmVHgUBXJ0HsqcSZywxcGHQyESTEIlpXw+H/fffz+33347I0eObNFms9kwGAwtXtPr9djt9g61hyMrq3O1Wzq7XDwJdx80WjXJye0XE9do1WRnR++41FsszHv0f0Lff1Fez6nD1QzKTudAk+ucsSkKcdse7W2f7+dyrp/r+dYNYDAk7nELxt7WPsQqtouTdRy2nwz7bykzM/HPSb3B2TWl/H6/TM8thBAiqqSmVPhykgJJqVN2cMlDJCHCklBJqZdffpmkpCRuu+22Vm0GgwGLxdLiNafTSUpKSqjd6XS2ajeZTGHHYTZbwuohoSiBZE64y8WTzu6Dx+3Fbneds72mxtJue1edvX1FgfLaQBIyNzWJL/2cMzZ/HLYHkyLR3vbXBw8y+XvT2m0vOVLCtHaWP9e6g/fSDoer3d+jaO5bJNbtcLgwGHRt7kMsYwvnbyn491xba5HEVALQNPeU8gNenz/0vRBCCBFpfr+famvg84ZBklIdlqHxkZqkxtrk5YvTTkYMi3VEQiSOhEpKvf3221RVVXH55ZcDhJJMf/vb33jggQfYtWtXi/eXlpYyfPhwAIYPH05JSUmr9qlTp4Ydh9/fuWE7nV0unkRjH7rrmHh9fk7UBQrb98/Qd89GI6y7jpXHxzmHXT57902dWm8w/kT+O4jnfQg3pnjcB9GaRnUmCeX2+dHITYIQQogoaXB4aGqe7dWQUHeKsaUoMDQrmS9PWdh1ws78WAckRAJJqNn3tm/fzueff85nn33GZ599xk033cRNN93EZ599RmFhITU1NWzYsAG3283u3bvZunVrqI7U3Llz2bp1K7t378btdrNhwwbMZjOFhYUx3ivRXaqtTTR5feg0KrJSpCi0ECIxqBQllJhyeyWTKIQQInoqm3tJZehUqGS4eFiGZicD8PkpJw63N8bRCJE4ekz+22QysX79elatWsXq1avJzMxk2bJlTJw4EQjMxrdixQoee+wxKisryc/PZ926dRiNxtgGLrpNRX2gZ12/DL3UZBFCJBStWsHj8+OROhVCCCGiqNoSSEplGtSAJFbCkZ2SRIoGbB4/75XUcONFObEOSYiEkNBJqaeffrrF96NHj2bTpk3tvn/WrFnMmjUr2mGJOFXREEhKJerQPSFE76VRqQAfbp/0lBJCCBE9wZ5SkpQKn6IoXJCq8HW9n7cPnJaklBAdlFDD94ToLLfXF7rISlJKCJFotOrg8D3pKSWEECJ6qiyBmfcyZeq9TrkgDRTg8xMNHK8Nf5Z3IXqjhO4pJeLDHXcvwlxb32bb4SMlTO/ecNp0qjEwU1q6XkO6Xn7thRCJ5UxSSnpKCSGEiJ6q5oe4Wcka3JJTCVuyRmFsro7PTzv581enuXfq0FiHJETck7tz0WXm2vp2Z2r7upOztEVacOjeoMzkGEcihBDh06oCHZs9MnxPCCFEFFUFa0rp1VRKUqpTrhucwuennfzlYCWLrxyMRi2Dk4Q4F/kLEb3Cyeak1EBJSgkhEpAM3xNCCNEdqq2B4XtZBhm+1xk+n5dcnxmjXkWt3c1bu7/B65XaXEKciySlRI9nb/JSa3cDMNAkSSkhROIJPmWV4XtCCCGiqbLF7HsiXI3mKv7w6RFydIGHSP9bdJLy8rIYRyVEfJOklIg4r89PtbWJOrsbVLEfIRrsJZWVosWQJBdYIUTi0aqkp5QQQojosro82JoCvXqkp1TnpWflcMmQPADMXh2VVk+MIxIivsU+YyB6DIfby97yBg5X2/A21z3JuPUZPj/RwJi8tJiNpw7Wk5JZ94QQiUob7CklNaWEEEJEyenmz8wpSWoMWum70BUZBi0DMvScaHDyTqmFCaNiHZEQ8UvONiIiTjU62fzFKb6ptOL1+dFpVGjVCkqSgb3lDbzzdRUuT/c/4fcjSSkhROLTNNeU8khPKSGEEFFS2Rj4zNw3VRfjSHqG0f3SAPjHMRsWp/SWEqI9kpQSXeYwDWVbc9IpM1nL9Iv6ctvl/fnRFQOw/fM36DQqqqxNvPN1ZbffUHn1RmxNXlQK5KbJBVYIkZjOFDqXnlJCCCGi41Tzg9y+aUkxjqRn6J+hJ10LLq+fLV+einU4QsQtSUqJLvmsrJ664Tfg88OQTAOzRuXQL0OPoigoioL76F5uuqgveo0Ks83Nx8fqujU+V8ZAIJCQkulYhRCJSquS4XtCCCGiK9hTqo/0lIoIRVEYnhF4qPTGvgqpCylEO+QuXXTa8Vo7S98+CCo1gzMNXFuQ3WbiJzMliWsLsgE4VGXjqNnebTEGk1L9jTJ0TwiRuM70lJIPtEIIIaLjVIMDgL4yuiBiBqaCUR8YNfLuoepYhyNEXJJC54I77l6Euba+3fasTCP/+8qrLV6zN3m5/89fY2vyktR4kmsmTEClKO2uo3+Gnkv7p/NFRSOfHKtjgFEfKtwbLV6fH1f6wND2hRAiUSU1ny+bJCklhBAiSk43uADom5oEuGIbTA+hVhRuGJbG6wcbeO2zE9xwYV+Uc9wzCdEbSVJKYK6tZ/p9z7Xb/s7zS1u99tw/SjlqttMnNQnV3m1opk0873bG9k+ntMaG1eVlf0Ujlw8ydiXs8yqutODX6EhSK2SlyNh4IRLdtm3bWLp0KTrdmSe4119/Pc8++yz79+/niSeeoLS0FJPJxOLFi5k3b17ofVu2bGHNmjVUV1czdOhQli9fztixYwHwer0899xzvP322zgcDiZOnMjjjz9O3759u30f25OkaU5KeWT4nhBCiOg43djcUypVhySlIqdwaApvH7ZSUm3jgyO1XJ2fFeuQhIgrMnxPhO0fh6vZerASlQKrpl+I2t2x4XgatYqJF5gAOHDKgsPtjWaY7D4eqF/VL0N/zl5cQojEcODAAWbNmsW+fftCX88++ywNDQ3cdddd3HzzzRQVFbFq1SqeeuopvvzySwD27NnDypUrefrppykqKmLmzJksXrwYhyPw4Xvt2rXs2rWLt956iw8//BC9Xs+yZctiuautBIfvSU8pIYQQ0XK6QWbfizSfz0tj9SkKhyQDsO6TY/j98oBJiLNJUkqEpcriYtW7JQD8aPxAxg7ICGv5wZkGslOS8Pj8fHmyMRohhuxuLqo+wGiI6naEEN3jwIEDjBo1qtXrO3fuxGg0smDBAjQaDZMmTWLGjBls3LgRgM2bNzN9+nTGjRuHVqtl4cKFmEwmtm3bFmq/8847ycvLIzU1lUcffZQPPviA8vLybt2/czl7+J58mBVCCBFpbq+PGmsTILPvRVKjuYrXd5fidVpR4+NQlY2Pvq2NdVhCxBVJSokO8/n8PPbXQzQ6PVyYk8pdky4Iex2KonDZwEAi6+vTVrya6NR6sjg9HGhOeg2QIudCJDyfz8fBgwd5//33ueaaa5g6dSrLly+noaGBkpISCgoKWrw/Pz+f4uJiAEpLS9ttt1gsnD59ukV7dnY2GRkZHDp0KPo71kHB4Xt+f6BenhBCCBFJ1c0JKY0K6itPUFFxAp9cbyIiPSuHvP4DGZYRuJav++S4PGAS4ixSU0p02G8+OsqnZfXoNSpW3jiyzZn2OmKQUU9WihazzY29b+teD5FQVFaH1w8aRy1pukFR2YYQovvU1tZy0UUXMW3aNFavXk1dXR0PPvgg999/P3369MFgaNkjUq/XY7cHhhbbbLZ22202GwDJycmt2oNtHRXuKOHQ+zuwnFaloAB+oMnrR6OOTAzxKLgPPWFfIkmOS2tyTNrW1eMix7N3qrIGakhp/B7e2HeSskMHMOYNJjvGcfUkBRkKx63wTaWVj4/WceXQzFiHJERckKSU6JDDVVae3RHoNXDfNcO4IDP5PEu0T1EURuel836pGVvuGDxeX6cTXO35pHnonq7+OHBpRNcthOh+2dnZoeF4AAaDgfvvv58f/OAHzJ49G6fT2eL9TqeTlJSU0HvbajeZTKFkVbC+VFvLd1RWVlpY7zcaA+vX65JITj5//Y4kjQqXx4dKqyE5+czQCr0+8H+TKYXs7PBiiGfhHs/eQo5La3JM2ibHRYSjyhJISqUmacjMHUB99akYR9Tz6NQK3x+awtYSCy9/fIzJQ0wyE58QSFJKdIBPpeHRvxTT5PVxdX4Wt4zO7fI6h2Yls+d4HQ5S+UdJDd8fGblZrvx+/1lJqbKIrVcIETvFxcX85S9/4ec//3noA1xTUxMqlYoxY8bwf//3fy3eX1payvDhwwEYPnw4JSUlrdqnTp1KRkYGOTk5LYb4VVdXU19f32rI3/mYzRbC6Y1fXx/oieV0NWG3n3+WI61KwQU0Wp3olTMbcjoDQy7q6mzU1FjCijkeKUrgZjrc49nTyXFpTY5J27p6XILLi96lqnn4nl7uDqNqVkEafztm45tKK29+8g1X9DPg9XpRqRQaG9OprbUyYMAg1Op2ukQL0QNJTSlxXo0XTOForZ2+aTqWfX94RDL6apXChTmpALyx72SX13e2o7V2Ki0udBoVusYTEV23ECI2jEYjGzdu5NVXX8Xj8XDy5EmeffZZbrnlFqZNm0ZNTQ0bNmzA7Xaze/dutm7dypw5cwCYO3cuW7duZffu3bjdbjZs2IDZbKawsBCA2bNns3btWsrLy7FarTz55JOMHz+eQYPCG/rr94f/FViwY+sP1pU61wx8nYkhHr960r7IcZFjkojHRfQ+wZ5SBklKRVWGXs38sf0BWLPnFK/vPcH/+8NO/t8fP2D9R0dZu/0zysvlobroXeS0I87pWK0de84YAH75g0sxJSdF7MPKhTlpfF5Wx5cnG/n6tIWLciPzVO6To3UAjB2QQZnfG5F1CiFiKzc3l5dffplf/vKXrF27Fp1Ox/Tp07n//vvR6XSsX7+eVatWsXr1ajIzM1m2bBkTJ04EYNKkSaxYsYLHHnuMyspK8vPzWbduHUajEYAlS5bg8XhYsGABNpuNCRMm8MILL8RuZ9uhDc3AJ3eMQgghIiuUlFLLcLJo8fm8VFSc4OqcfmxSg9WrpV6bSXpmH1S6FLLyBuJwNMU6TCG6nSSlRLusLg8fHAlMWXrbFQOYMjw7okNDkpPUGMwlOPqM5A/7KnjshpERWe/u5qF7kwabkOcMQvQc48ePZ9OmTW22jR49ut02gFmzZjFr1qw227RaLUuXLmXp0qURiTNakppvFJo87feUEkIIITrjVGMgKZUsd4dR02iu4vXjVgYMUchyVVKhyWFveQOXqWX4kujd5PdftMnj9fHuoRpcHh9aaxX3TBkcle2knN4PwM5D1dTau/5kwOn28vmJegAmDZYZLYQQPUdHhu8JIYQQnXG6MTAhiCSlois9K4fM3AHkZyhoFT8NTg+nPfpYhyVETMlppwe44+5FmGvr223PyjSy/uVXO7w+v9/PB0dqqbE1odOoyCjZhlY9NwKRtpZkq+Si3DS+Pm3hzwdOs3BCeDVcvquorJ4mr5+cNB2DMw3nX0AIIRJEUvPwPbckpYQQQkSQy+PDbHcDkpTqLhrFz2BDEyV2HUebUunnd5x/ISF6KDnt9ADm2nqm3/dcu+3vPB/ekJT9Jxs5YrajKHB9QTb7PmrsaojnNPeSPH5x2sIfvzzFbVcMRK3q/Fj290trALh6WJZMsSqE6FGCSakmj9SUEkIIETnBXlJ6jUKSjKPpNgP1bircydjdUOHSMCrWAQkRI3LaES2UVNsoKmsAYPJgE/0yot+dtHBEH9L1Gk41uvj4aG2n1+Px+UM1sL43PCtS4QkhRFxI0jTXlJKeUkIIISLoVHNSKidNKw91u5FagUsHpAPwrT1JekKLXkuSUiKktNrGP0vNAFycmxqx2fDOR69VM+PiXADe3H+y0+vZX9FAvcNNhl7D2AHGCEUnhBDxIdRTSj60CiGEiKBgkfPcVG2MI+l9RvZNRa94afKrOHCiIdbhCBETkpQS+IEvTzbyXqkZP1DQJ4VJg03dGsOcS/IA+ORoHSfqOzem+v3mhNqUYVloujAEUAgh4tGZ4XuSlBJCCBEZXq+X4vJKAFJw4ffLEPHupFYpDEmyAvDZ8TrcPjn+oveRmlK9XJ29ibrhN3LqeD0Q6CE1cbCpRdfdw4eLmTl3LhqtGo/b22odh4+UML2LcQw0GZg42MTuY3X8cf8pfnr10LCW9/j87CyuAuC64dldjEYIIeKPNjT7nnxgFUIIERnl5WV8XFoJGPi2/CTDs5LJyot1VL1LrsbJcXcadjccaZQH66L3kaRUL1Vvd7PlwCl+W1SOMysflQITLjBxcW5qq7HkHh9Mv+85kpN12O2uVuv6+u6bOh3H4cPFzJoXmNnPaRoCI2bw2iclvLvmEVQ+T4dnDiwqq6PWHhi61929vIQQojskqaWmlBBChKu4uJhnnnmGgwcPotVqufLKK3nooYfIzMxk//79PPHEE5SWlmIymVi8eDHz5s0LLbtlyxbWrFlDdXU1Q4cOZfny5YwdOzaGexMdHrUBPGBKS451KL2SSoGhyU18ZdVT0uDHIT2iRS8jSakE5/P7aUrN5YuKBursblweHypFQatW0KpVJKlVWPpdzh/2VeD2+qmyuiiutLL/ZCPe5u6hWlsV0yeOpk+qrtvjDya8gvvyh32nsGBg2PxHuSg3rcMzB277OtBL6vsj+6JRy6hUIUTPI8P3hBAiPE6nk0WLFvGDH/yAl19+GZvNxoMPPsgjjzzCM888w1133cVPf/pT5s+fT1FREUuWLGHEiBGMGTOGPXv2sHLlStatW8eYMWPYuHEjixcv5r333sNgMMR61yLK5gn8a1B5AXVMY+mtcpI8nDBoqXe42XnEyoX5sY5IiO4jd+8JyuPz86cvT3HLb4qoGfUDisoaKK2xU17v5Hidg9IaO980J58sgybz7D+O8MI/v+X3eyv4/EQDXp+fkX1TefyGEWQfeCMmCanvUikKo/MCxdUPnLLg6+CYdqvLw/slNQDceFHfqMUnhBCxlKSRQudCCBGOkydPMnLkSJYsWUJSUhImkymUgNq5cydGo5EFCxag0WiYNGkSM2bMYOPGjQBs3ryZ6dOnM27cOLRaLQsXLsRkMrFt27YY71VkeXx+HM3VOQxqub7EikqBK4ZkAvDnEguONkqmCNFTSU+pBHSi3sGj7xTz9WkLAIqnicE5RrKStRi0avyA2+vD7fXR5PFz5MvdTJ5yNRqVQlZKEoMzk7likJEBxsBTnpeJn/okBX1T2FveQKPTw7HajhU8//NXp3F6fAzJSubibpoxUAghuluwp5TPH7iJkAkdhBDi3IYOHcqrr7YsA7Fjxw4uvvhiSkpKKCgoaNGWn5/Pm2++CUBpaSlz5sxp1V5cXBx2HEocn65rnYHkh1oBneLHE8exnkvwGMfzsT4XRYGCnDQ+PlxJo8vHH788xb9ePiDWYYWlJ/wMzv430XRH/NFatySlEsQddy/CXFuPKy2P2hEz8Wt0KB4naSc+5eTuP3H9r95sd9naP/2dp59Z3I3Rdp5WreLivFQ+P9HI3vIGDJz7N9/r8/PGvpMA3HpZ/1b1sIQQoqfQqs+c35o8PjRJMsRCCCE6yu/388ILL/Dee+/x2muv8dvf/rbVMDy9Xo/dbgfAZrOdsz0cWVnx+9DUrmgBSDdoURTQ6bQkJ+vQ67WoEuj/BkNg1Ec8xQ+EtYxapTC6j4bdpzy89lkFP76uAL028a718fz73hESf/eTpFSCMNfWM/aOJ9heXI3f56dvahLXFfQj9aoCnn1/U6zDi6hReekcPGWl3uGG7IJzvveDI2ZONjjJ0Gu48UIZuieE6LmC9QLdXj9NXh/JUvdDCCE6xGq18vDDD3Pw4EFee+01RowYgcFgwGKxtHif0+kkJSUFAIPBgNPpbNVuMoU/oY7ZbKGDVSm6XUnzyIs0XeCa4nK5sdtdOJ1uVP7E+b/D4Yq7+IEOL6PGDUA/vZc+yWqqrS5efa+EWy/rD4DX66W8vCz0cxs4cBBqdXx9DlCUQEIknn/fz0Xi7/g2Ik1qSiUItyGTnYeq8fr8DDTqmX5RX1J1PTOnqNOoGNM/8MveOHAyVpenzfd5vD5+/eFRAGZfkpeQTxKEECIcuua6Ui4pdi6EEB1SVlbGnDlzsFqtvPnmm4wYMQKAgoICSkpKWry3tLSU4cOHAzB8+PBztofD74/fr0pr4HN2WvN9RSLejMOZuBM9fgWFW0akA/B/n5bjdPvw+wO/x2u3f8breytYu/0zysrKYv6709ZXcF8S9Uvi79g2Ik2SUgmg3uGmduRM3F4/uWk6Ckf06fEzzI3KTSNNp8GnS2PtR8fafM9b+09xvM6ByaDl364Y2L0BCiFEDOg1geS7U5JSQghxXg0NDfzoRz/isssu4ze/+Q2ZmZmhtsLCQmpqatiwYQNut5vdu3ezdevWUB2puXPnsnXrVnbv3o3b7WbDhg2YzWYKCwtjtTtRcbp56r10fc982J2IvndBCjlpOqqtTbx94FTodWOfPDJzB2DskxfD6ISIvJ6d2egB/H4/v9h+CK8unXS9hsIR2ah7QXFbjVrFVUMDHxz+8MVJdhZXtWgvqbby64+OAnD3lRf02F5jQghxtlBPKZmVRwghzuuPf/wjJ0+e5K9//Svjxo1j7NixoS+TycT69evZvn07EyZMYNmyZSxbtoyJEycCMGnSJFasWMFjjz3G+PHjeeedd1i3bh1GozG2OxVhVbaWPaVE7GnVCgvHBx64/2Z3GbamtkeNCNFTJNzZp7i4mGeeeYaDBw+i1Wq58soreeihh8jMzGT//v088cQTlJaWYjKZWLx4MfPmzQstu2XLFtasWUN1dTVDhw5l+fLljB07NoZ7c35v7T/Fh9/Wgs/D9QW5vWqIWn+jnpRT+7DljeWx7YdweXxMvziHr09beGjrNzjcPi4fZGTWaHlaIIToHfTaQFJKekoJIcT53X777dx+++3tto8ePZpNm9qvzTpr1ixmzZoVjdDigt/v57Q12FNKS8fmvRbdYdboXH6/9wTl9U5+W3SCG/rHOiIhoieheko5nU4WLVrE2LFj+eijj/jLX/5CfX09jzzyCA0NDdx1113cfPPNFBUVsWrVKp566im+/PJLAPbs2cPKlSt5+umnKSoqYubMmSxevBiHI35Pv9VWFy8210xKL9tFVkpSjCPqfunHP+K6gmzcXj+/2HGYqat3cfvvv6DS4mKQycAzMy6UadGFEL2GXmpKCSGEiJAGpweHJ1AkJljoXMQHrVrFT6YOBWDjZycwO6S3lOi5EiopdfLkSUaOHMmSJUtISkrCZDIxf/58ioqK2LlzJ0ajkQULFqDRaJg0aRIzZsxg48aNAGzevJnp06czbtw4tFotCxcuxGQysW3bthjvVft++d4RbE1eLs5NI+X0l7EOJyYU/Dxx40juvWoIyVp16EZs+sU5rLv1EtL12hhHKIQQ3UcXrCnllqSUEEKIrqmoDzyc16vp8fVqE9E1+Vlc0i8dl8fHG183xjocIaImoYbvDR06lFdffbXFazt27ODiiy+mpKSEgoKCFm35+fm8+eabQGC2jGDhwrPbi4uLoxt0J330rZm/Ha5BrcAjhcO5/09RKnWfADRqFf82fiDzxvbDbGsiOUlNZnLv6zUmhBBnhu9JTSkhhBBdU9HgBCA1oe4Iezafz0tFxYnQ9z+ZcgF3/uEA7x+zcV1/hcxzLCtEokrYU5Df7+eFF17gvffe47XXXuO3v/0tBoOhxXv0ej12ux0Am812zvZwKGGOFgu+/1zL3XHXImpq6wHwqTRUX/KvoEtHX7GX+5espuRISfsLhxFDV5dXlOhNBXm+bScnqUlOMpz7zR1YTyz2IVK6+nOMtZ70M4jHfejo70dHzkkiPsnwPSGEEJFyoj6QlEqRgQdxo9FcxevHrQwYolBffYofT7uc6wuy+dvhGvab/QweGGcfPoWIgIRMSlmtVh5++GEOHjzIa6+9xogRIzAYDFgslhbvczqdpKSkAGAwGHA6na3aTSZT2NvPykrrVNznWq7eYmHeo/8DwJ6jZk5/W0uaXsNtP/wBWvWt/OLfCklO1rW5rKLQbhuARqsmO7v9bWu06nMuryhgMATag/92dPvni62rsZ9PW/sW3IeuxhbL9kSODdr+PYqH2DrS3tm/hWjH1pm/lczMzv9tidjQy/A9IYQQEXK8LvBwPlUrT6niSXpWDpm5A0Lf/2TqEP5ZWkO1E47XOUiPYWxCREPCJaXKysq488476devH2+++SaZmYFOjAUFBezatavFe0tLSxk+fDgAw4cPp6SkpFX71KlTw47BbLaE1UNCUQIJqXMt53F7sdtdONxe9h6rA+CKgRm4XW7cBHpk2O2uNpc9V1tw3TU1lnO2n2t5vx8cDhcGgw6Hw9VqH7oSW1djP5+z9y2YUAjuQ1dji0V7cB/iMbaOtAV75rT1exTr2Dra3tm/hWjHFs7fSvCcVFtrkcRUgtHJ8D0hhBARcrw2UFMqTXpKxbX+GQZmFKTzx+JGdh+r57pc6S0lepaEqmjX0NDAj370Iy677DJ+85vfhBJSAIWFhdTU1LBhwwbcbje7d+9m69atoTpSc+fOZevWrezevRu3282GDRswm80UFhaGHUcwoRHO1/mWC/r8RANun5/slCSGZiV36Xh1NOaOLn/2v92pM8e7rX2L5T5ESiLHDj3rZxCP+9CZc5JILGcP3/PLD1AIIUQn+f3+s3pKxTgYcV43j0hDrwaLy0Op1DwXPUxC9ZT64x//yMmTJ/nrX//K9u3bW7Tt27eP9evXs2rVKlavXk1mZibLli1j4sSJAEyaNIkVK1bw2GOPUVlZSX5+PuvWrcNoNMZgT9rW4HDzTaUVgAkXGFGk4IsQQoizBJNSPj+4fX6S1HKdEEIIEb5auxury4uCFDqPV2cXPa+tPMnFRthrhuJ6P3UOL4NjGp0QkZNQp6Dbb7+d22+/vd320aNHs2nTpnbbZ82axaxZs6IR2nnNvXUBJ0/XtNt++EgJ+vIG/H4YYNTTL0PfjdEJIYRIBBq1CrWi4PX7cbp9JMkU3kIIIToh2EuqT7IatUp63sajs4uelx06gCl3MH1TM6myNvH7g/WMvTDWEQoRGQmVlEpkVeY6pt/3XLvthx7+Md+aAxeH8YOM3RSVEEKIRKPXqrA1eXF5vMhlXAghRGcE60n1S9MCTbENRrQrWPS8vvoUigKTBpt4+6tK3j9u5+BpCxfnSm1Qkfjk02yc0F1yIwDDspLJSkmK6LoPHy5m1ry57bcfKWF6RLcohBAiWnSaQFJKZuATQgjRWcGkVF6qBnySlEoUfdN0DEqFMiv8v38c4Tf/3yVS8kUkPElKxQGzrYmkCy4FYOyAjIiv3+PjnL20vr77pohvUwghIq24uJhnnnmGgwcPotVqufLKK3nooYfIzMxkxYoVvPXWW2i1Z6q1PvTQQ8yfPx+ALVu2sGbNGqqrqxk6dCjLly9n7NixAHi9Xp577jnefvttHA4HEydO5PHHH6dv374x2c/z0WvPFDsXQgghOiM4fK9/moa6hhgHI8IyyqRQ6YADpxrZUVzNv1wYn59XhOgoKUYRB/adCFwJhmYlY0qW6S+EEOK7nE4nixYtYuzYsXz00Uf85S9/ob6+nkceeQSAAwcOsHLlSvbt2xf6Ciak9uzZw8qVK3n66acpKipi5syZLF68GIcj8JR47dq17Nq1i7feeosPP/wQvV7PsmXLYrav56PXqAFwSlJKCCFEJ5XVnT18TyQSg0Zh9sh0AP7ng29xuL0xjkiIrpGkVIzV2po4WuvA7/cxdkB6rMMRQoi4dPLkSUaOHMmSJUtISkrCZDIxf/58ioqKaGpq4vDhw4waNarNZTdv3sz06dMZN24cWq2WhQsXYjKZ2LZtW6j9zjvvJC8vj9TUVB599FE++OADysvLu3MXO0zXPAOfUz6ECiGE6ASn28uJ+kBSaoAkpRKOz+flspQG+iSrqbI28X97ymIdkhBdIsP3YuzzikYA3Mf2kTl5cGyDSUB33L0Ic219u+1SL0uInmHo0KG8+uqrLV7bsWMHF198McXFxXg8HlavXs3evXtJS0tjzpw5LFq0CJVKRWlpKXPmzGmxbH5+PsXFxVgsFk6fPk1BQUGoLTs7m4yMDA4dOsTAgQM7HGO4JR1C7w9zOUPz8D1HGz2lekJZieA+9IR9iSQ5Lq3JMWlbV4+LHM+e71itHZ8fMvQajHrpo5BoGs1VvHncyrC++VTb4bdFJ5g1Jo+8dJm9XSQmSUrFUK29iaPNM+459/8VfnhLjCNKPObaeqmXJUQv4/f7eeGFF3jvvfd47bXXqKmpYfz48dx222388pe/5JtvvmHJkiWoVCoWLVqEzWbDYDC0WIder8dut2Oz2QBITk5u1R5s66isrPBmwDEaUwLb0iWRnKzr+HKpeqARl9dPcrIOvT4wOYbJlEJ2ds+ZhSfc49lbyHFpTY5J2+S4iPaU1gSub/l9UqRIdoJKz8rhgqEDONJYTrXTz5PvlrB69ij5eYqEJEmpGNp3ItBLakimgX31J2McjRBCxD+r1crDDz/MwYMHee211xgxYgQjRozgyiuvDL1nzJgx/OhHP2Lbtm0sWrQIg8GA0+lssR6n04nJZAolq4L1pc5uT0lJCSs2s9mC39/x99fXB24KnK4m7HZXh5fTENiIxRFYzukMzJpUV2ejpsbS8QDilKIEbqbDPZ49nRyX1uSYtK2rxyW4vOi5/n/27jw8qvLs4/h3MpnsewJhFyEBBUECyC4oNqIiSwOoLdKiVSxEqVRALCi2lK1uiAgqSnlbqSggKpUquILIpiIgCiQgEPYsJGRPZua8f4SMxIAkYZKZyfw+F3Nd5DxnuZ+TzDlz7nmWlPRzSamY6t3nxL2YTCY6RZv47ARsOXSGVTtPMLxTE1eHJVJtaq/pImcKSjl4rpVUbcy4JyJS3xw5coRhw4aRl5fHypUradu2LQAfffQRy5cvr7BuSUkJAQFlzdjj4+NJSUmpUJ6amkp8fDzh4eHExsaSmprqKEtPTyc7O7tCl76qMIzqv8o2rN55CPYrG+g8v6TymFI1icEdX/WpLjovOieeeF6kfks9l5SKNBVy7NhR7Hb90j1VmJ+JuzuEAzDv84N8f9Lzv5wS76OklIuUz7jXMiqQ6GA/F0cjIuLecnJy+P3vf0/nzp157bXXiIqKcpQZhsHs2bPZvHkzhmGwY8cO/vWvfzlm3xs+fDhr1qxhy5YtlJaWsnTpUjIzM0lMTAQgKSmJRYsWkZaWRl5eHrNmzaJbt260aNHCJXW9lPKkVGGpXQ8SIiJSbftPl/XW2Hcim2UbdpOXp0SGJ7uldQh9WkVRbLUz6d09nM4txmazcejQj46XzabJUcR9qfueC5wpKOXAuVZSndVKSkTkkt5++22OHz/O//73Pz744IMKZTt27OCxxx7jySef5NSpU8TExPDQQw8xZMgQAHr27Mn06dMd5XFxcSxevJiIiAgAkpOTsVqtjBw5kvz8fLp37868efPquIZVF+Drg48J7AYUaAY+ERGphuyCUrKLyibKuKJ5U9JyT7o4IrlcPiYTM267itHLdnD4TCFjV+xias9I3tqwg4gGjclOP8EfB0DLlle6OlSRC1JSygV2HCtrJXVFpFpJiYhUxT333MM999xz0fK77rqLu+6666LlQ4YMcSSpfs5isTBx4kQmTpx42XHWBZPJRJDFTF6J7YJd+ERERC6mfJDzYF+wmNVppr4I8ffluaHtGPPmtxw5U8jjnxTSJTqWqEbNXB2ayCXpSlTHzhSUciDjXCup5molJSIi1Rd0rgtfgZJSIiJSDT+cKuuqF67vxesd29nTtOEkgWY4XQQbTtrJL7a6OiyRS1JSqo6VjyV1RWQgMWolJSIiNfBLg52LiIhcTPlA2FH+JhdHIs5gt9s4duwohw79yLFjR4lt0JDBHZvgb7KRb/PhvT2nyCvV+JPi3pSUqkNZBSWOsaS6qJWUiIjUkFpKiYhITZQnpSL9XRyIOMXZzNO8sSWV5d8ccwxaHxbgS5fALAJ97OQV2/j8hMHhnBJXhypyUUpK1aEdR8tmutCMeyIicjmC/MqGhMwvUbN8ERGpmjMFJRw/WwwoKVWfhJ0bOyosqoFjWaCPnevCC4kKslBkg+mfp7P7+FkXRilycRrovI6UBERxRDPuVdv+/XsZMmL4xcsPpDCwDuMREXEHwee3lApwcTAiIuIRvj+ZB0CTEF8sPnYXRyO1zd/H4Pb2sfx311Gyiu0kr9zF00Pa0+2KSFeHJlKBklJ1JKfJdQBcqVZS1WK1w8AJT1+0/PsHbq/DaERE3EPQ+WNKKSklIiJVUN51r3WUH1Dk2mCkTvj7+nB9IxOHCv3ZebqICau/Y/agdvRtHe3q0EQc1H2vDqSk51EYGQdoxj0REbl85w90bmj8UhERqYI955JScZH6gtyb+GBnVMsiujUJpMRmMPm971m397SrwxJxUFKqDiz+8ggAraKDiArSTUBERC5PiH9ZQ2er3aDE0AxKIiLyy6x2g2+Plc0CflW0BpTyJmczT7Ni2wGa+hURay7AZjeY9v5eXvt0NzabJkwR11NSqg7sT88Dw0ZCszBXhyIiIvWAr4+JEP+y1lJ5ViWlRETkl+07nUd+iY1Qf1+uiLC4OhypY2HRscQ0bk77gFwakoMBvPTNGeat241dTa7FxZSUqgPPJ11Dox9WqpWUiIg4TURA2UNFvk23chER+WXfpGUDkNAsHLNJX2Z4K5MJOkaZ6dgkFIDl35/lkXf2kFNY6ljHZrNx6NCPjpdaU0lt0yfZOnBFVBB+hRmuDkNEROqR8MCyLnxKSomIyKV8dS4p1UXj23o9kwm6XxFJQpSBrwm+OJjFb5ZuY+uhTADS0o7w0odfsfybY7z04VekpR1xccRS32n2PREREQ8UHniupZRVSSkREbk4q93g26Nl40k1Nudz7Nhp7HYXByUuF1VymquMEn70bU56gY0HV+3h5rYNSGrtS0SDxkQ1aubqEMVLKCklIiLigcIDym7heWopJSIiv2D38bMUlNrxxc6WA6dJ27+biMYtiXF1YOJyjaMj6dyqGV/sPcqPubBuXzqfp5q4MtSgW4wyl1I39ElWRETEA0WcaylVYDOBSbdzERG5sM9Sy4YRaRLiQ3Tj5oRFNXBxROJO/H19uDbK4M8dID7Kj2Kbwd5sePOb4+zNNiiyKjkltUufYkVERDxQsJ8Zs48JAxO+EY1cHY6IiLghwzDYcKBsrKAmQRrgXC7sbOZpvth9gA5hpbQuPUKw2Uaxzc6eMwYPfnCCN745RvF5ySkNhi7OpKSUiIiIBzKZTI4ufJaopi6ORkRE3NHBzAKOZhdh8YHYQFdHI+4sLDqW6MbNaRnpT8/wQm6IiybYF3KK7Tz76QGSXtvG27tOYLXZNRi6OJXGlBIREfFQkYEWsgpK8WvYytWhiIiIG/o0pazrXoeGAfj6lLg4GvEUJhPENwgm0ppFg8gI3knJ53ReCbPXp/DapoPc1MhGSFSsBkMXp1BLKREREQ/VMNQfAP+mV7s4EhERcTd2w+D9708B0LNZkIujEU/kYzKR2CqEt//QjdEdI/Az2ThdYOONg/DRMTvHsotcHaLUA0pKiYiIeKjYUD8A/Jpehd0wXByNiIi4kx1HcziaXUSwn5keTdV3T2rO39eHgfGh3NrCl24tIrBgJ9/mw9ofTrP1tJ3MQqurQxQPpqSUiIiIh4oO8sOMgTkghBP5mh1HxNvZDYOMvGKKSjXosMB7350EILFtAwJ89dgn1We32zh27CiHDv3IsWNH8QGubRpGr+AMmgeUYAKO5sOEdSdZ/s0xbHZ9QSbVpzGlREREPJSPj4lwi42sUl9Ss/UQKlKfHD2aRlZWZpXW3ZtlZd3hEvaesVJ87lLQONiHAfERjO3fHh+TZl3zNhn5JXy8v2w8qcHXNIKSqv0tiZzvbOZp3jicR7MrTRzZt5uIxi2JAXxNBlcFl9C1TQs+33eSrGKDpz85wPqUTCbdcCVtG4a6OnTxIEpKiYiIeLAoi52sUjiQo6SUSH1x9GgavXpfR1FhwS+u5xMUQdTNYwlu27tS2Yl8O0u/zaJtdCq/6hRfW6GKm3p9+1GKrXY6NA7jmsahHD6spJTUTFh02YDm2eknKpXFBPtxQ2MTURHhvPF9LjvTsrn73zu4uW0D/ti7Jc0j1W1ULk1JKREREQ8WaSlLRn2XYcVmNzD7qEWEiKfLysqkqLCA4RNm07DZlRdcJ7vUh69yAii2+2DCoHmglRYBpYT62jH7+fHd4dPs2LiO6Jv+VMfRi6tlFZSwaudxAAa18uPw4UMcO3YUu3p5Sy0wmUwMaB3KsB5tWbT5CO/tPMG6fel8vD+d/vExDOvUhM7NwjGpxaZchJJSIiIiHizGz4at8Cw5hLH9yBl6tIxydUgi4iQNm11Jk9btKi0/kJHPltQsbIZBRKAv/eNjiA72c5QHBfljlJbw+ba3Mfs8XIcRiztYsOFHiqx2Wkf6seXbPexr2LhC1ysRZyofd6qZCZIan+Xk/gwOE8OpQli/P4P1+zNoHOLLgHaNuCG+AVfHhqhLsVSgpJSIiIgH8zFBwQ8bCe08kLXfn1ZSSqQeMwyDr9Ny2HHsLADNIwLoHx+DnwaxlnO2HznDmj2nMAH3XBvBjsPWi3a9EnGG88edOnXoe8KjmjG4TQu+/XYHhwt9yTKFcyLPytJtR1m67SiRAT5cd0UUXVtE0rV5BM0iAtSKysspKSUiIuLh8vZ8SmjngXyakkF+iZVgP93eReqbUpudz1IzOZRVCEDHxqFcd0WEWhyIQ0ZeMU/+bx8Aw65tTNtoX3YcdnFQ4hXKx50qys2g5NwEfGFmK9dG+9OkdTO279zDyWIzZ00hnCmys25fBuv2lQ3EHxlgJi7Kj9YRFrrFN6N9kzCigvx+4WhS3+hTq4iIiIcrOb6XRkE+nCyws2RLGg/1vfAYNCLimc4WlbJ+XwZZBaX4mOD6VlG0aRji6rDEjeQVW5n47vecziuhaagvg67w0ThS4hb8zD40thTRNCSY5nHN2X/oCMG+Jo4U+7M/s5gzRTa2Hy9k+/FCln//PQBh/r40CDLRMNiX2GBf4po0IDLIj4hACxGBFsICfAn0NZFx6hjmc4n55s1bYDabXVlVqSGvS0plZmby+OOPs23bNsxmM4MHD+bRRx/F19frToWIiJzH0+8PI+L9eWFnIcu+PsrA9g1pFR3s6pBE5DIZBnx/Mpeth7Ox2g0CLT4ktm1AbKi/q0PzGp5wbzh5tog/v7OHlPR8Qvx8aGE7yXu77RpHStyO2ceEf/5psvPyaHdlPEHH92CKuRLfyCYcyzhDsc1EehGcLbZythgOnCkt23Bf7kX36WsCH2xE+KcRGuhHiMVETJCZ2GALMUFmrrmyKc2jgokOsji6CdpsNtLSjjj20bx5C3x9ldByFfe5mtaRhx9+mNjYWDZu3EhGRgZjx45l6dKl3Hfffa4OTUREXMjT7w+dGlro0yqQLw5mMeHt73hxREeaRWgqZhFPZLMbBF11PRvPBJKbfgaARqH+3BgfTYi/1318dyl3vjcUltpYvesEL395mIISG+H+PtwXb+dgbgONIyVurby7X3b6CXwsdlo2CSM4cx95RXn0vCKO1NT9+EY1IyCqMceOHye/1I45IITC4lJ8fC0UWMF2rpug1QAwc7oITheVVD7Y9iwAAnx9aBIeQLOIQEJNxaQcOU5MZDj23HQeutmgbVyrOqu/VORVd7XDhw+zbds2NmzYQGBgIM2bN2fcuHE89dRTbnFjERER16gv94fHfhXPH9/aSVp2Eb97fQcjuzbl5rYNNYioiJsrKLGRll3Iwcx8vk7L4eO9eTQY8ii5VvAzm+jSPIJ2jTRjVV1zh3uDYRhY7QZFpXZyi62kZRdy5Ewhu4+f5YuDWeQWWwEI8ymhZwN/NnzznVpHiccKi46lYZPm5GWexMfPRssmYYRk7sMnLJiWbVpwcPd28vLyaHZlPIf27SYk9goatYznx73fYbcEEtXkCtJ+TKXYJxBzSCRnzuZhM0xkl0CR1c7BzAIOZhaUH439pwwghi3vHKVByGmaRATiby8hKtBMVKCZ5g1jCA2wEOxvJtjPl2A/M4EWM75mE74+5S8fx89mH1ONr9M2u0GpzU6pzaDUbsdmNygptXH02DHshoGvj4nWLZsTFuCHr7l+TW7hVUmplJQUIiIiiI2NdSxr3bo1x48f5+zZs4SFhVVpPz4+Zc2pq8pkgqCgQPwtPhfdLjQk5BdnTvml8svZtqrl/hYf/Mw+2C5QB1fHVtVyk4kKdXCn2KpaXl4Hd4ytKmUm009/T7XxXric2KpaXtP3Qm3HFhQUhE8V70/l90o92/zEGfeH6t4bfH3NhIaGkpd+jKzLGJg8N/0YoaGhHDp0ELPZzKTO/izaaSctz87rXx3j9a+OYfGBCD8Tof4mLD5lM/aZTCZ8ALMPlP8pmEwmjOpU4gIudx8mE1h8fSm12lweizP3U9/OiwGYMGFw4f38fPeXOtqlwvl5cdnxwddixlpqKys3zi//5R1W2t+5BVU9N5XWOH9BFfZhGFBihyIr5FsN8kt/tr6PmUBbNk39S7i6YRB+1kKyj14yrHPHh3w/P/LOXRvMZnOV7w/nwpdzXPHskF1YymNrfiDtTAElVoNSm8EvDQvVKtKf3g1sZKZnEREUQoG/H6V5Z8jPPElpfg4+pSVV+n9B1klK8rKxYqn2tu7wf3eMH6jyNmZrCXkZx7EW5GByk3Pqrr+DQP9A/C2+BPtbMBWcwZyfSUBRJj72QGLsERSVnMLHL5AmkSEczUghPz+PmMbNyco5S48ObbEHRHDwdDY/pBdRYg4gv8SODR8KS+0cSM8/9846120wJf/Cb7xfer9T9tnKx2SCsn9ln7PK/28yOZZZ7QZWe1lCqmrDv6UDYPExEWAxEWAu+7+vjwmL2YS/nxmb1e64Bxn8dO05//8Ws4nkG9pwbdOqXcPK1db9wWQ449Oah3j33Xd57rnn+OyzzxzLjhw5QmJiIp9//jmNGjVyXXAiIuIyuj+IiMjP6d4gIlL76le7r0sICgqisLCwwrLyn4ODNSCsiIi30v1BRER+TvcGEZHa51VJqfj4eLKzs8nIyHAsO3DgAI0aNSI0NNSFkYmIiCvp/iAiIj+ne4OISO3zqqRUy5Yt6dKlC7NmzSIvL4+0tDQWLlzI8OHDXR2aiIi4kO4PIiLyc7o3iIjUPq8aUwogIyODv/3tb2zduhUfHx+GDh3KxIkTMZvNrg5NRERcSPcHERH5Od0bRERql9clpURERERERERExPW8qvueiIiIiIiIiIi4ByWlRERERERERESkzikpJSIiIiIiIiIidU5JKRERERERERERqXNKStVQVlYWiYmJbN261bHs888/Z+jQoSQkJDB48GDWr19fYZvVq1eTmJhIp06dSEpKYseOHY4ym83G3Llz6dWrFwkJCYwdO5bTp0+7VR0Mw+DFF1+kf//+dO7cmUGDBvHBBx94VB3Ot2nTJq6++mqOHj3qcXX48MMPuf322+nUqROJiYmsXLnSo+pgt9t57rnn6Nu3L126dOGOO+5g27ZtdV6HvXv3cs8999CtWzd69+7N5MmTycrKAmDnzp2MGDGChIQE+vfvz4oVKyps6y7v55rWwR3fz94kMzOTcePG0bVrV7p3787MmTOxWq2uDqvWrV27lnbt2pGQkOB4TZo0Cbi895ynutC101OuPbXpQudl+vTpXHPNNRX+dt58801HeX09L/XhPiWu46nXmPrwd79582ZGjBhB586d6d27NzNmzKCoqMij6mCz2Rg1ahRTpkxxLPOU2Gvr80Zd1SE7O5vJkyfTvXt3rrvuOsaNG+c4jifEX22GVNtXX31l/OpXvzLatGljbNmyxTAMw/juu++M9u3bG2+99ZZRWlpqbN++3UhISHCUb9myxUhISDC++uoro6SkxPjnP/9pdO/e3SgoKDAMwzBeeOEFY9CgQcbx48eN3Nxc4+GHHzbuv/9+t6rDP//5T6N///5GamqqYbfbjY8//tjo0KGDsXPnTo+pQ7nTp08bvXv3Ntq0aWOkpaU5lntCHTZv3mx06tTJ+Oyzzwy73W5s3rzZuOaaazzq97Bs2TLjtttuM06ePGnYbDbjn//8p9GpUyejqKiozupQWFho9O7d23j++eeN4uJiIysry7j//vuNBx54wMjOzja6detmvP7660Zpaanx5ZdfGgkJCY5z7C7v58upg7u9n73N3XffbTzyyCNGQUGBceTIEWPgwIHG4sWLXR1WrZszZ44xZcqUSssv9z3niS507fSUa09tutB5MQzD+PWvf228/fbbF9ymvp6X+nCfEtfx1GtMffi7z8zMNDp06GCsWrXKsNlsxqlTp4zbb7/deP755z2mDoZhGPPmzTOuuuoq49FHHzUMwzP+fsrV1ueNuqrD3XffbSQnJxs5OTlGbm6u8eCDDxpjxozxmPirS0mpanr77beNG264wXj//fcrXOSfeuopY9SoURXWfeKJJ4w//elPhmEYxiOPPGJMmzatQvktt9xirFy50jAMw+jbt6/x3nvvOcrS09ONtm3bGkeOHHGbOjz//PPGqlWrKpQPHTrU+Oc//+kxdTAMw7DZbMbvfvc7Y968eZWSUp5QhwceeMB49tlnK5Tv27fPOHPmjMfUYcaMGcYtt9xiHD9+3LBarcbSpUuNHj16OJJSdVGHAwcOGH/4wx8Mq9XqWPbRRx8ZnTt3Nt566y3j5ptvrhT/5MmTDcNwn/fz5dTBnd7P3ubQoUNGmzZtjJMnTzqWvf/++8YNN9zgwqjqxsiRI43XX3+90vLLfc95motdOz3l2lNbLnZeiouLjfbt2xv79++/4Hb19bzUh/uUuIYnX2Pqy999bm6uYRiGYbfbjX379hmJiYnGv//9b4+pw5dffmncdtttxvjx4x1JKU+J3TBq7/NGXdRh9+7dRocOHRx/Q4ZhGGfOnDH279/vEfHXhLrvVVOfPn1Yv349t912W4XlNpuNoKCgCst8fHw4ePAgAKmpqbRp06ZCeVxcHHv37iU3N5eTJ09WKI+JiSE8PJx9+/a5TR3Gjx9PUlKSo+zAgQOkpKTQvn17j6kDwMKFC4mOjmbYsGEV1vOUOuzatYuIiAjGjBlD9+7dGTJkCEeOHCEiIsJj6nDXXXdRVFTEDTfcQIcOHZg3bx7z58/H39+/zurQqlUrXn31Vcxms2PZhx9+SPv27UlJSbno+xXc5/18OXVwp/ezt0lJSSEiIoLY2FjHstatW3P8+HHOnj3rwshql91uZ8+ePXz22WfceOON9O3bl8cff5ycnJzLes95ootdOz3l2lNbLnZe9u7di9VqZf78+fTq1YsBAwbwyiuvYLfbgfp7XurDfUpcw5OvMfXl7z4kJASAfv36MWjQIBo0aEBSUpJH1CEzM5OpU6fyzDPPEBgY6FjuCbFD7X3eqKs67Nq1i7i4ON566y0SExPp06cPc+fOpUGDBh4Rf00oKVVNDRo0wNfXt9LyxMREvvjiCz788EOsVitff/01a9eupbi4GID8/PwKb2qAgIAACgoKyM/PB6j0EB8QEOAoc4c6nO/HH3/k/vvvZ/DgwVx33XUeU4dt27bx3nvv8be//a3Stp5Sh5ycHF577TXGjh3Lpk2bSE5OZsKECezcudNj6lBaWkq3bt343//+xzfffMN9993H+PHjSU9Pr/M6QNn4Ss899xyffvopU6dO/cX3K7jX+7mmdTifq9/P3uZCv5vyny/0+6kvsrKyaNeuHQMGDGDt2rUsX76cQ4cOMWnSpMt6z3mii107PfHa40wXOy+5ubl069aNUaNG8fnnn/PUU0/x73//myVLlgD1/7xA/bhPSd2pL9eY+vB3v27dOjZs2ICPjw/jx493+zrY7XYmTZrEPffcw1VXXVWhzN1jL1dbnzfqqg45OTns27ePQ4cOsXr1at555x1OnTrFo48+6hHx14SSUk7SuXNn/vGPf7BgwQJ69+7Na6+9RlJSEmFhYUDZA0f54HblioqKCA4OdvzhFBYWXrC8rlyqDuU++eQT7rzzTm6++WZmzpwJ4BF1yMrKYsqUKTz11FOOby/O5wl1APDz82PYsGEkJCTg6+vLzTffTM+ePfnwww89pg6TJ0+mb9++tGrVioCAAJKTkwkNDeWDDz6o8zrk5eUxfvx41qxZw+uvv07btm1/8f0K7vd+rkkdyrnz+7m+CgoKqnRuy3+uz+c3JiaGZcuWMXz4cAIDA2nSpAmTJk1iw4YNGIZR4/dcfeJp15660rt3b/71r3/RrVs3LBYLHTt25Pe//z1r164F6v95qQ/3KXEPnvR3U1/+7gMCAoiNjWXSpEls3LjR7evw8ssv4+fnx6hRoyqVuXvs5Wrr80Zd1cHPzw+AqVOnEhISQkxMDA8//DCff/65R8RfE0pKOUl2djbx8fGsWbOGrVu3snDhQk6cOME111wDQHx8PCkpKRW2SU1NJT4+nvDwcGJjY0lNTXWUpaenk52dXan5nSvrAPDiiy/yyCOP8PjjjzNlyhRMJhOAR9Rh48aNZGZm8oc//IGuXbsyePBgAAYPHswrr7ziEXWAsm4+JSUlFbax2WwYhuExdTh+/HilOvj6+mKxWOq0DkeOHGHYsGHk5eWxcuVK2rZtC0CbNm0u+n4F93o/17QO4P7v5/oqPj6e7OxsMjIyHMsOHDhAo0aNCA0NdWFktWvv3r08/fTTGIbhWFZSUoKPjw8dO3as8XuuPvGka09d+uijj1i+fHmFZSUlJQQEBAD1+7zUh/uUuA9P+bvx9L/7b775hltuuaXCZ92SkhIsFgtxcXFuXYd3332Xbdu20bVrV7p27cp///tf/vvf/9K1a1ePOf+19XmjruoQFxeH3W6ntLTUsay8u/rVV1/t9vHXiKsGs6oPzh848NtvvzU6depk/PDDD0Zpaanx/vvvGx07dnQMylk+Mv7mzZsdI+Ffd911jsGpn3vuOeP22283jhw54hgJ/+6773arOixZssTo0qWLsWfPngvuyxPqcL60tLRKA517Qh1WrlxpXHvttcamTZsMm81mfPDBB0b79u2N7777zmPqMHHiRCMxMdE4cuSIUVJSYixdutTo2rWrY+DnuqhDdna2ccMNNxhTpkwxbDZbhbKsrCyja9euxj//+U+jpKTE2Lx5s+P9axju836+nDq46/vZW/zmN78xJkyYYOTm5jpm35s/f76rw6pVJ06cMDp16mS88sorRmlpqXHs2DHjjjvuMP7yl79c9nvOk51/7fSUa09dOP+8rFu3zujYsaPx5ZdfGna73fjmm2+M7t27G++8845hGPX3vNSH+5S4nqddY+rD331eXp7Rr18/Y9asWUZxcbFx9OhRY/jw4cb06dM9pg7lHn30UcdA554Se21+3qiLOpSUlBiJiYnGQw89ZOTl5RmZmZnG7373OyM5Odkj4q8JJaUuw8+nK/7Pf/5j3HjjjUanTp2MpKQk48svv6yw/jvvvGMMGDDA6NSpkzF8+HDj22+/dZSVlJQYTz31lHH99dcbnTt3NsaOHWtkZGS4TR3sdrvRpUsXo127dkanTp0qvBYtWuQRdfi5CyWlPKUOb7/9tnH77bcbnTp1MgYOHGisW7fOo+qQl5dnzJgxw7j++uuNrl27GiNHjnRMZVpXdViyZInRpk0b49prr630N20YhrFr1y7jzjvvNBISEoybbrqp0kx17vB+rmkd3Pn97C3S09ONhx56yOjWrZvRo0cPY86cORVmGqqvtm7d6vib7NGjhzFjxgzHrJuX857zZD+/dnrCtacu/Py8vPHGG8bNN99sXHvttcZNN91UaVal+nhe6sN9SlzP064x9eXvPiUlxbjnnnuMrl27GjfeeKPx7LPPGsXFxR5VB8OomJTypNhr6/NGXdXh5MmTxsMPP2z07t3b6Nq1qzF58mQjJyfHY+KvLpNhnNeuTUREREREREREpA5oTCkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJ1AHDMOrlsUREREREpO7os77UN0pKiTjZ2rVrufHGG+nQoQNPPPEEqamp/OY3v6n2fqZMmUL//v2rtc2ljvXdd9/Rvn17jh49Wqmsd+/etG3bttIrPT292rGLiHiq/v37M2XKFFeHISIiUsnHH3/Mo48+6vh569attG3blq1bt7owKpHL4+vqAETqm7/+9a+0bNmSOXPmEBsby5o1a9ixY0edHPt///vfRY+1d+9eHnjgAaxWa6WyjIwMMjIyeOyxx+jUqVOFsoiIiFqIVEREREREqmPp0qUVfm7fvj1vvvkmcXFxrglIxAmUlBJxsuzsbHr37k337t1dHQoAJSUlvP766zz//PMEBARccJ3vv/8egMTERJo2bVqX4YmIiIiISA2EhIRU+kJZxNOo+57Iefbs2cPvf/97unTpQkJCAqNHj2bnzp2O8g8++IDBgwfTsWNHhg4dyo4dO2jXrh1vv/22o/kswIsvvkjbtm2ZMmUKCxYsAKBt27a88MILNY7NZrOxbNkyBg0aRMeOHbnhhht4+umnKS4uBuCFF1644LE2bNjAggUL+OMf/8jEiRMvuO+9e/cSFhamhJSICFBaWso//vEPevfuTadOnbj33ns5fPiwo3zTpk389re/pUuXLnTv3p1HHnmEEydOOMrffvtt2rZtW6mr9M+7Bn755ZfceeedJCQkcN111zFu3DgOHjxYYZuPPvqIpKQkOnToQO/evfn73/9OQUFBtet09OhRJk+eTJ8+fWjfvj09e/Zk8uTJnDlzpkK9n376afr27UvHjh35wx/+wDvvvFOpLl999RV333031157Ld26dePRRx8lKyur2jGJiNSV/v37M2vWLH7/+9/TuXNnnnjiCbKzs3niiSfo1asXHTp04I477mDz5s0Vtmvbti3Lli1j6tSpdOvWjYSEBMaPH09GRkaF9dauXUtSUhIJCQn07t2bJ554gpycHAC++eYb2rZty0cffVRhmwMHDtC2bVv+97//AZe+To8aNYpt27axbds2R5e9C3Xf2717N3/4wx/o3r07nTt35o9//CMpKSmO8vJtNm/ezL333su1115Lr169mDt37gV7VFzKihUrSEpKolOnTnTs2JEhQ4awdu3aCuvs2LGDkSNH0qlTJ2644Qb+7//+j9GjR1e4JxYXF/OPf/yDfv36cc011zBo0KBK+5H6SUkpkXPy8vK47777iIyMZP78+Tz33HMUFhbyhz/8gdzcXD7++GP+9Kc/ER8fz4IFC7j55psZO3Ysdrsd+Kn5LMDw4cN58803eeihhxg+fDgAb775JiNGjKhxfE888QSzZs2if//+LFq0iJEjR/L6668zbtw4DMNgxIgRFzxWhw4d+OSTTxg7dixms/mC+/7hhx8ICwvjwQcfdCTkJkyYwOnTp2scr4iIp1q7di0pKSnMmTOHJ554gt27dzNhwgQA3n33Xe69915iY2N59tlneeyxx9ixYwd33nknmZmZVT5GWloaY8eOpX379ixatIi///3vHDx4kDFjxjjuK2vWrCE5OZlWrVrx4osv8uCDD/Lee+85rvtVVVhYyO9+9zsOHDjA9OnTee2117j77rv573//y7PPPutY74knnuD//u//uPvuu3nxxReJiYnh8ccfr7Cv7du3M3r0aAICApg3bx5/+ctf2LZtG7/73e8oKiqqckwiInVt2bJlji9uhwwZwu9//3s+/vhjJkyYwIIFC2jUqBH33XdfpcTUc889h91u59lnn2Xy5Ml89tlnzJo1y1G+cOFCJkyYwLXXXsv8+fNJTk7mww8/ZNSoURQVFdG5c2euuOKKSgmWNWvWEBoaSv/+/at0nZ4+fTrt2rWjXbt2vPnmm7Rv375SHbds2cJvfvMb7HY7M2fO5O9//zsnTpzgrrvu4sCBAxXWnThxIl26dOGll15i0KBBLFmyhJUrV1b7nD7xxBPcdNNNvPzyyzz11FNYLBYmTZrE8ePHgbLk2+jRowF49tlneeihh3jllVf4+uuvHfsxDIPk5GSWL1/OPffcw6JFixzPI++88061YhLPo+57IuekpqaSlZXFqFGj6NKlCwCtWrVi+fLl5OXl8eKLL3LNNdfwzDPPANC3b19MJhPz5s0DKjafbdSoUYX/A5fVtDY1NZWVK1fy8MMPM3bsWKBsYPKGDRsyefJkNmzYQL9+/S54rNjY2Evu/4cffuDUqVPccccdjB49mgMHDjB//nxGjRrF6tWrCQoKqnHsIiKeJjY2loULF2KxWAA4fPgwL730Enl5eTz11FP06tWL5557zrF+586due2221iyZAmTJk2q0jF27dpFUVERDzzwgOM63bhxYz7++GMKCgoIDg7m6aef5vrrr+fpp592bNeyZUtGjx7N559/zg033FClYx06dIhGjRoxZ84cWrRoAUCPHj3YvXs327ZtA+DIkSOsXr2aRx99lHvuuQeA66+/noyMDL744gvHvp555hmuvPJKXn75ZccXHddeey0DBw5k1apVjBw5skoxiYjUtYYNGzJlyhR8fHx466232Lt3L2+99RbXXnstUPbZftSoUTz99NOsWrXKsV2bNm2YPXu24+ddu3bxwQcfAJCTk8OiRYsYMWIE06dPr7DNyJEjefvtt/ntb3/L4MGDee211ygsLCQwMBCA999/n1tuuQV/f39++OGHS16n4+LiCAkJAS7+XPHMM8/QvHlzXn31Vcc1uk+fPiQmJvLCCy84nlsARowYQXJyMgA9e/bko48+4rPPPuOuu+6q8jlNS0vj3nvvdewHoFmzZiQlJfHNN9/QpEkTXn75ZUJCQnj11VcddW/VqlWF43z55Zds3LiR5557jttuuw0ouwcVFhby9NNPc/vtt+Prq9RFfaWWUiLnxMfHExUVxdixY5k+fTqffPIJDRo0YPLkyURERLBnzx5uuummCtsMHjy4TmIrvxkNGjSowvKBAwdiNpsve8aN2bNn89Zbb/HAAw/QtWtX7rzzTubPn8+hQ4f07YSIeJ2OHTs6ElIAzZs3B8rG30tPT690LW7RogUJCQnVuhZfe+21+Pv7M3z4cGbPns2XX37JVVddxYQJEwgJCeHgwYOcPHmS/v37Y7VaHa/rrruOkJAQNm3aVOVjXX311fznP/+hWbNmpKWlsXHjRpYsWcLBgwcpLS0FyrpzGIbBLbfcUmHb22+/3fH/wsJCdu7cSb9+/TAMwxFT8+bNad26dbViEhGpa61bt8bHp+zxd/PmzTRo0ID27ds7rmU2m40bb7yR7777ztH1DiongBo1akRhYSEA3377LSUlJZXuC127dqVp06aO+8KQIUMoKCjg008/BcoSW0eOHGHIkCFA1a7Tl1JQUMDu3bu57bbbKvSOCAsL48Ybb6x0j0pISKhUr+p2D58yZQqTJk0iNzeX3bt3s2bNGpYtWwbgiHvLli3069fPkZAqP/b5w4Zs3rwZk8lEv379Ktzz+vfvT3p6eoXuh1L/KN0ock5wcDDLli1j0aJFrF27luXLlxMYGMjgwYMd2f+oqKgK21SlFZIzlN8YGzRoUGG5r68vkZGR5ObmXtb+f35TAujSpQuhoaHs3bv3svYtIuJpft46tPwhpvxDfkxMTKVtYmJiHJNGVEWzZs14/fXXeeWVV3jrrbdYunQpYWFh/Pa3v+VPf/oT2dnZQNmMrn/9618rbV/d7tX//Oc/efnllzlz5gwxMTG0b9+ewMBAx/2jfEyo6OjoSvUqd/bsWex2O4sXL2bx4sWVjuHv71+tmERE6tL517Ps7GzS09Mv2AUOID09nfDwcIAKyRQouyeUd6Eu/4x+sftC+TW2efPmdO7cmffff5/bbruNNWvW0LRpU7p27epY/1LX6UvJzc3FMIxLxlLu5xMgnV+vqjpy5AhPPPEEW7ZswdfXl1atWjnG2C3fV1ZWVqV7C1R8rsnOzsYwDDp37nzB45w+fZqrr766WrGJ51BSSuQ8rVq14qmnnsJms7Fr1y7effdd3njjDRo2bIiPj0+lQQ3LHxpqW/lNMT09nWbNmjmWl5aWcubMGSIjI2u877Nnz7Ju3To6depUYTpZwzAoLS29rH2LiNQnERERAJXuBVB2fS6/XppMJgDH2FDl8vPzK/zcsWNHFixYQElJCV9//TVvvvkmL730Em3btiU+Ph6AyZMn061bt0rHK78vVMWaNWuYM2cOjzzyCMOHD3d8wfKnP/2J3bt3Az99yZKZmUnjxo0d254/TlZwcDAmk4nRo0czcODASsf5+YObiIi7Cg0NpWXLlhW6R5/v/M/bv6T8WpyRkUHr1q0rlKWnpzta2kJZa6mZM2eSm5vL//73P4YNG+a4X1TlOl2VOplMpoveo8rvYc5it9sZM2YMFouFt956i3bt2uHr60tqairvvfeeY71GjRpdcMzFzMxMrrzySkfsQUFB/Otf/7rgsa644gqnxi7uRd33RM754IMP6NGjB+np6ZjNZhISEnjyyScJCwsjKyuLhIQEPvzwwwoPGeVNcH9J+Tfsl6P8gWTNmjUVlr///vvYbDbHGFg1OZbFYuGvf/0rr7zySoXlH3/8MUVFRXTv3r2GUYuI1C9+fn40aNCg0rU4LS2Nb7/91vENb/mYH+fPyHfw4MEKX2QsXbqU/v37U1JSgp+fHz179mTGjBmO7Vq1akV0dDRHjx6lQ4cOjlejRo145plnqtUq6+uvvyY0NJQxY8Y4HnTy8/P5+uuvHfe0Ll26YDabWbduXYVtz/85JCSEdu3acfDgwQoxlU8AcrldyUVE6kq3bt04ceIE0dHRFa5nmzdvrjAe06Vce+21+Pn5VbovfPXVVxw/frxCy59bb70VgOeff5709PQKw4BU5ToNv/xZPygoiGuuuYa1a9dis9kcy3Nzc/nss88czwvOcubMGX788UeGDx9Ox44dHWM+bdiwAfjpi5nrrruODRs2OGYMh7LxbM+f1bVbt24UFBRgGEaF30dKSgovvvhijWYFFM+hllIi53Tu3Bm73U5ycjJjxowhODiY//3vf+Tm5nLzzTdz2223MXr0aMaNG8dvfvMbjhw5wvPPP3/J/YaFhQHw3//+l2uvvbbCNyZVFRcXx69//WsWLFjgSBT98MMPLFiwgO7du3P99dfX+FiBgYHcd999LFy4kOjoaPr27cu+fft44YUXuOGGG+jVq1e14xURqY9MJhN//vOfeeyxx5gwYQJDhw7lzJkzLFiwgPDwcMcA4T169CAwMJA5c+bw8MMPk5+fz4IFCyp8S92jRw+efvppkpOTufvuuzGbzSxfvhw/Pz9uvPFGzGYzEyZM4IknnsBsNnPjjTdy9uxZFi5cyKlTpy7a5eRCOnbsyBtvvMGcOXO48cYbOX36NK+99hoZGRmOb/mbN2/OsGHDePbZZyktLeWqq65i/fr1ji9fyh+E/vznPzNmzBgeeeQRBg8ejM1mY8mSJezcudMxEYeIiLtLSkri9ddf55577uGPf/wjjRs35ssvv2Tx4sXcfffdFcYV/CURERGMGTOGBQsWYLFYuOmmmzh69CjPP/88cXFxJCUlOdYNDw/nxhtv5D//+Q8dOnSo0LKqKtdpKPusv2PHDjZv3ky7du0qxfPII4/whz/8gfvuu4+7776b0tJSXnnlFUpKSnjwwQcv44xVFh0dTdOmTVm2bBmNGjUiLCyML774gv/7v/8DcIy79cc//pG1a9dy3333ce+993L27Fmef/55TCaTo6VYv379uO666xg3bhzjxo2jdevW7Nq1ixdeeIE+ffpUGkJF6hclpUTOadiwIa+++irPP/88U6dOpbCwkPj4eF544QV69OgBwGuvvcZTTz1FcnIyV1xxBY8++ijTpk37xf3efPPNvPvuu0yZMoXhw4fz5JNP1ii+mTNncsUVV7Bq1Spee+01GjZsyKhRo0hOTnY8LNT0WA899BAxMTG88cYbLFu2jIiICO68807Gjx9fo1hFROqrpKQkgoODefnll0lOTiYkJITrr7+eP//5z47xMUJDQ5k/fz7PPPMMycnJNG3alAcffLDCxBFXXXUVL730Ei+++CJ//vOfsdlsXHPNNSxZsoRWrVoBZTMjBQcH8+qrr/Lmm28SFBRE586defrpp6v1Bcevf/1rjh49yqpVq/jPf/5DbGws/fr147e//S2PP/44qampxMXF8fjjjxMUFMSSJUvIy8ujZ8+ejB07lhdffNExzlafPn147bXXWLBgAePHj8disdC+fXv++c9/XtYssyIidSkoKIhly5bxzDPP8NRTT5Gbm0vTpk155JFHuPfee6u1r/LP0a+//jorVqwgIiKCW265hYcffrhSt+bBgwfz4YcfVposqarX6ZEjR/Ldd99x//33M3v2bBo2bFhhPz179uSf//wn8+fP589//jN+fn507dqVuXPnOrqFO9PChQuZOXMmU6ZMwc/Pj7i4OBYtWsSsWbP46quvGDVqFFdccQWvvfYa//jHPxg/fjzR0dE88MADLFq0iODgYKDsi49XXnmF559/npdffpnMzExiY2MZPXp0hZn9pH4yGdUdzUxEHI4ePcpNN93E7NmzK3wTIiIi4kmys7PZsGED119/fYWxBOfOncvbb7+trnkiIlIjmzdvxmKxVBjUPScnh969ezN58mR+97vfuTA6cQdqKSVShwzDqNDH+2LMZrOjOauIiMiF2Gy2S86UZDKZqjQ2SmBgIDNnzuTqq6/m97//PUFBQXzzzTf8+9//5o9//KOzQhYRETfn7OeVPXv2OFputW/fnjNnzrBkyRJCQ0O5/fbbnRGyeDglpUTq0OrVq3nssccuuZ5aXomIyKUkJiZy7NixX1ynadOmfPLJJ5fcl7+/P0uXLmXevHlMmTKFwsJCWrRowZQpUxg5cqSzQhYRETfn7OeVe++9l5KSEt544w1OnDhBUFAQ3bp1Y+7cuRorSgB13xOpU2fOnKkw08TFNGvWrEL3CRERkZ/bt28fJSUlv7iOn58fbdu2raOIRETE0+l5ReqaklIiIiIiIiIiIlLnfFwdgIiIiIiIiIiIeB8lpURERERExG1lZ2czefJkunfvznXXXce4ceM4ffo0ADt37mTEiBEkJCTQv39/VqxYUWHb1atXk5iYSKdOnUhKSmLHjh2OMpvNxty5c+nVqxcJCQmMHTvWsV+AzMxMxo0bR9euXenevTszZ87EarXWTaVFRLyEklIiIiIiIuK2HnroIQoKCli/fj2ffvopZrOZxx9/nJycHMaMGcPQoUPZvn07M2fOZPbs2ezatQuArVu3MmPGDObMmcP27dsZPHgwY8eOpbCwEIBFixaxadMmVq1axcaNGwkICGDatGmO4z788MMEBQWxceNGVq5cyebNm1m6dKkrToGISL2lMaVERERERMQtfffdd/z2t7/lyy+/JCQkBChrOZWens63337Lq6++yocffuhYf/r06RQVFTF37lwmTpxIYGAgM2bMcJTfeuut3HfffQwbNox+/foxceJEBg0aBEBGRgZ9+vRh/fr12O12br75ZjZs2EBsbCwAa9eu5amnnuLTTz+twzMgIlK/+bo6AE+UmZlLdVJ5JhNER4dWe7v6wtvrDzoH3l5/cM9zUB6TOIc7/W5/zh3//qpLdXAPqoN7qM06uNu9YdeuXcTFxfHWW2/xxhtvUFhYyPXXX8+jjz5KSkoKbdq0qbB+XFwcK1euBCA1NZVhw4ZVKt+7dy+5ubmcPHmywvYxMTGEh4ezb98+ACIiIhwJKYDWrVtz/Phxzp49S1hYWJXroGeHqvHWeoP31l319qx619b9QUmpGjAMavTHU9Pt6gtvrz/oHHh7/UHnoD7zhN+tJ8R4KaqDe1Ad3EN9qMOl5OTksG/fPq655hpWr15NUVERkydP5tFHHyUmJobAwMAK6wcEBFBQUABAfn7+Rcvz8/MBCAoKqlReXvbzbct/LigoqFZSSkRELk5JKRERERERcUt+fn4ATJ06FX9/f0JCQnj44Ye54447SEpKoqioqML6RUVFBAcHA2VJpAuVR0ZGOhJM5eNL/Xx7wzAqlZX/XL7/qqppywJ3arFWl7y13uC9dVe9vZuSUiIiIiIi4pbi4uKw2+2Ulpbi7+8PgN1uB+Dqq6/mP//5T4X1U1NTiY+PByA+Pp6UlJRK5X379iU8PJzY2FhSU1MdXfjS09PJzs6mTZs22O12srOzycjIICYmBoADBw7QqFEjQkOr9yCp7ntV4631Bu+tu+rtWfWure57mn1PRERERETcUq9evWjevDl/+ctfyM/PJysri+eee45f/epX3H777WRkZLB06VJKS0vZsmULa9ascYwjNXz4cNasWcOWLVsoLS1l6dKlZGZmkpiYCEBSUhKLFi0iLS2NvLw8Zs2aRbdu3WjRogUtW7akS5cuzJo1i7y8PNLS0li4cCHDhw+vdh3Ku1lW51XT7Tz95a319ua6q96e9aoNSkqJiIiIiIhbslgs/Pvf/8ZsNjNgwAAGDBhAo0aNmDVrFpGRkSxZsoQPPviA7t27M23aNKZNm0aPHj0A6NmzJ9OnT+fJJ5+kW7duvP/++yxevJiIiAgAkpOT6devHyNHjqRfv34UFxczb948x7Hnz5+P1Wrlpptu4o477uD6669n3LhxLjgLIiL1l8kwaivfVX9lZFS/CW5MTGi1t6svvL3+oHPg7fUH9zwH5TGJc7jT7/bn3PHvr7pUB/egOriH2qyD7g3Op2eHqvHWeoP31l319qx619b9QS2lRERERERERESkzikpJSIiIiIiIiIidU5JKRERERERERERqXNKSomIiIiIiIiISJ1TUkpEREREREREROqcklIiIiIiIiIiIlLnlJSqI4ZhOF4iIiIiIiI/l11Qyu9f38G/Nx9ydSgiInXC19UBeJOXNh7kgT6tXB2GiIh4iXsfuI/MrGwAfC1mrKW2CuXRUREseflVF0QmIiIXsudkLntO5rLi66PcGh/t6nBERGqdklJ1yWRydQQiIuJFMrOyGTjhaQCCgvwpKCiuUP7+cxNdEZaIiFyExVz2vFBcandxJCIidUPd90RERERERNyAv2/Z41mJTUkpEfEOSkqJiIiIiIi4AYu57PGs+GfdrUVE6islpeqQBjoXEREREZGL8TvXUqrYqpZSIuId3DoplZWVRWJiIlu3bnUs+/DDDxkyZAidO3emf//+LFiwALv9p4v26tWrSUxMpFOnTiQlJbFjxw5Hmc1mY+7cufTq1YuEhATGjh3L6dOn67ROIiIiIiIiF+JvVlJKRLyL2yalvv76a+68806OHDniWPbdd98xefJkHn74Yb766isWL17M22+/zdKlSwHYunUrM2bMYM6cOWzfvp3BgwczduxYCgsLAVi0aBGbNm1i1apVbNy4kYCAAKZNm+aK6omIiIiIiFTwU0spdd8TEe/glkmp1atXM3HiRCZMmFBh+bFjx7jrrru48cYb8fHxoXXr1iQmJrJ9+3YAVqxYwcCBA+nSpQsWi4XRo0cTGRnJ2rVrHeX3338/jRs3JiQkhKlTp7JhwwbS0tLqvI4iIiIiIiLnK28pVWozsGvYDxHxAm6ZlOrTpw/r16/ntttuq7B8wIABPPbYY46fi4qK+Oyzz2jfvj0AqamptGnTpsI2cXFx7N27l9zcXE6ePFmhPCYmhvDwcPbt21eLtREREREREbm08pZSACXqwiciXsDX1QFcSIMGDS65Tl5eHn/6058ICAhg9OjRAOTn5xMYGFhhvYCAAAoKCsjPzwcgKCioUnl5WVWZTNVa3bG+yfTTy5ucX39v5e3nwNvrD+55DtwpFhEREQE/80835xKbHX9fswujERGpfW6ZlLqUgwcPMn78eKKjo/nXv/5FSEgIAIGBgRQVFVVYt6ioiMjISEeyqnx8qfPLg4ODq3X86OjQasdsGAaBgf7ExITi4+OWDdRqXU3OW33j7efA2+sPOgciIiJycWYfEz4msBvnWkr5uzoiEZHa5XFJqc8//5w///nP3HHHHTzyyCP4+v5Uhfj4eFJSUiqsn5qaSt++fQkPDyc2NrZCF7/09HSys7Mrdfm7lMzMXKrTxdtkgqioEAoLi8nIyPW6pJTJVPYgXt3zVp94+znw9vqDe56D8phERETEPZhMJvzMPhRZ7ZqBT0S8gkclpb799luSk5N58sknGT58eKXy4cOHk5yczK233kqXLl1YtmwZmZmZJCYmApCUlMSiRYvo0KEDkZGRzJo1i27dutGiRYtqxWEY1Oihsnw7d3kgrWveXPdy3n4OvL3+oHMgIiIiv8zftywpVWrTBwYRqf88qsnOSy+9hNVqZebMmSQkJDhe9913HwA9e/Zk+vTpPPnkk3Tr1o3333+fxYsXExERAUBycjL9+vVj5MiR9OvXj+LiYubNm+e6ComISLVlZWWRmJjI1q1bHcs+/PBDhgwZQufOnenfvz8LFizAbv/pG+bVq1eTmJhIp06dSEpKYseOHY4ym83G3Llz6dWrFwkJCYwdO5bTp0/XaZ1ERETKlQ92XmxTSykRqf/cvqXU+TPjvfTSS5dcf8iQIQwZMuSCZRaLhYkTJzJx4kSnxSciInXn66+/ZsqUKRw5csSx7LvvvmPy5MnMmzePfv368eOPP3L//fcTFBTEvffey9atW5kxYwaLFy+mY8eOLFu2jLFjx/Lpp58SGBjIokWL2LRpE6tWrSI0NJTHH3+cadOm8corr7iwpiIi4q38zGVJKc2+JyLewO2TUiIiIlDW2mn+/PlMmjSJCRMmOJYfO3aMu+66ixtvvBGA1q1bk5iYyPbt27n33ntZsWIFAwcOpEuXLgCMHj2aN998k7Vr1zJs2DBWrFjBxIkTady4MQBTp06lT58+pKWl0bx587qvaDXc+8B9ZGZlX7R8/4EUBtZdOCIi4gTlSSmNKSUi3kBJKRER8Qh9+vRh0KBB+Pr6VkhKDRgwgAEDBjh+Lioq4rPPPmPQoEFA2YQXw4YNq7CvuLg49u7dS25uLidPnqww4UVMTAzh4eHs27fP7ZNSmVnZDJzw9EXLv3/g9jqMRkREnKG8+16Juu+JiBdQUkpERDxCgwYNLrlOXl4ef/rTnwgICGD06NEA5OfnExgYWGG9gIAACgoKyM/PByAoKKhSeXlZVZlM1Vq9TpXHZjJVHmjfneM+3/l18FSqg3tQHaq2b3Edf1913xMR76GklIiI1AsHDx5k/PjxREdH869//YuQkBAAAgMDKSoqqrBuUVERkZGRjmRVYWFhpfLg4OBqHT86OvQyoq8ZX4uZoCD/i5abTFQoDwysuK6vxUxMTN3HfTlccZ6dTXVwD6qDuCs/c1lmUC2lRMQbKCklIiIe7/PPP+fPf/4zd9xxB4888gi+vj/d3uLj40lJSamwfmpqKn379iU8PJzY2FhSU1MdXfjS09PJzs6u0KWvKjIzcyu1Qqpt1lIbBQXFFy03DCgoKMZkKktIFRYWV4jRWmojIyO3DiK9fCZT2QO4K86zs6gO7kF1qNq+xXX81FJKRLyIklIiIuLRvv32W5KTk3nyyScZPnx4pfLhw4eTnJzMrbfeSpcuXVi2bBmZmZkkJiYCkJSUxKJFi+jQoQORkZHMmjWLbt260aJFi2rFYRiVu8a5i/K4LhSfu8Z8Me58nqtKdXAPqoO4K8dA52opJSJeQEkpERHxaC+99BJWq5WZM2cyc+ZMx/IuXbrw6quv0rNnT6ZPn86TTz7JqVOniIuLY/HixURERACQnJyM1Wpl5MiR5Ofn0717d+bNm+eayoiIiNf7qaWUMo4iUv8pKSUiIh5n3759jv+/9NJLl1x/yJAhDBky5IJlFouFiRMnMnHiRKfFJyIiUlP+5S2l1H1PRLyAj6sDEBERERERkTLlLaVK1X1PRLyAklIiIiIiIiJuwtF9T0kpEfECSkqJiIiIiIi4CT913xMRL6KklIiIiIiIiJvwM5sAKFFSSkS8gJJSIiIiIiIibsL/XPe9YnXfExEvoKSUiIiIiIiIm9BA5yLiTZSUEhERERERcRM/jSlluDgSEZHap6SUiIiIiIiImyhPSmlMKRHxBkpKiYiIiIiIuIny7nsl6r4nIl5ASSkRERERERE34RjoXC2lRMQLKCklIiIiIiLiJhzd99RSSkS8gK+rAxARERFxpnsfuI/MrOyLlkdHRbDk5VfrLiARkWoobymlMaVExBsoKSUiIiL1SmZWNgMnPH3R8vefm1iH0YiIVI/GlBIRb6LueyIiIiIiIm7Cz2wC1FJKRLyDklJ1yDAMx0tERERERC5t7dq1tGvXjoSEBMdr0qRJAOzcuZMRI0aQkJBA//79WbFiRYVtV69eTWJiIp06dSIpKYkdO3Y4ymw2G3PnzqVXr14kJCQwduxYTp8+7SjPzMxk3LhxdO3ale7duzNz5kysVmut17d8TKlitZQSES+gpFQde2XTj64OQURERETEY+zevZshQ4awY8cOx+upp54iJyeHMWPGMHToULZv387MmTOZPXs2u3btAmDr1q3MmDGDOXPmsH37dgYPHszYsWMpLCwEYNGiRWzatIlVq1axceNGAgICmDZtmuO4Dz/8MEFBQWzcuJGVK1eyefNmli5dWuv19dOYUiLiRZSUqmsmk6sjEBERERHxGLt37+aaa66ptHzdunVEREQwcuRIfH196dmzJ4MGDWLZsmUArFixgoEDB9KlSxcsFgujR48mMjKStWvXOsrvv/9+GjduTEhICFOnTmXDhg2kpaVx+PBhtm3bxqRJkwgMDKR58+aMGzfOse/a5Bjo3KbeFSJS/2mgcxERERERcUt2u509e/YQGBjIq6++is1mo1+/fkycOJGUlBTatGlTYf24uDhWrlwJQGpqKsOGDatUvnfvXnJzczl58mSF7WNiYggPD2ffvn0AREREEBsb6yhv3bo1x48f5+zZs4SFhVW5DtX9Tro8KWWzG9gMA18f7/hSu/w8eeN3+N5ad9XbtXFUV23Fq6SUiIiIiIi4paysLNq1a8eAAQOYP38+Z86c4dFHH2XSpEk0aNCAwMDACusHBARQUFAAQH5+/kXL8/PzAQgKCqpUXl72823Lfy4oKKhWUio6OrTK6wIElfw0blVYRBBBft71yFbd81WfeGvdVW/v5l1XOBERERER8RgxMTEVuswFBgYyadIk7rjjDpKSkigqKqqwflFREcHBwY51L1QeGRnpSDCVjy/18+0Nw6hUVv5z+f6rKjMzl+rMc2Q7b+Xjp84SEWip1vE8lclU9pBe3fNVH3hr3VVvz6p3edzOpqSUiIiIiIi4pb179/Lf//6XRx55BNO5viMlJSX4+PjQsWNH/u///q/C+qmpqcTHxwMQHx9PSkpKpfK+ffsSHh5ObGwsqampji586enpZGdn06ZNG+x2O9nZ2WRkZBATEwPAgQMHaNSoEaGh1XsoMwyq9eBpNpnw9TFhtRsUl9oxAqp1OI9X3fNVn3hr3VVv76aBzkVERERExC1FRESwbNkyXn31VaxWK8ePH+epp57i17/+NQMGDCAjI4OlS5dSWlrKli1bWLNmjWMcqeHDh7NmzRq2bNlCaWkpS5cuJTMzk8TERACSkpJYtGgRaWlp5OXlMWvWLLp160aLFi1o2bIlXbp0YdasWeTl5ZGWlsbChQsZPnx4ndT7p8HONQOfiNRvailVhwzDADxsNDMRERERERdp1KgRL7/8Ms8++yyLFi3C39+fgQMHMmnSJPz9/VmyZAkzZ85k/vz5REVFMW3aNHr06AFAz549mT59Ok8++SSnTp0iLi6OxYsXExERAUBycjJWq5WRI0eSn59P9+7dmTdvnuPY8+fP529/+xs33XQTPj4+DB06lHHjxtVJvf0tZvJLbEpKiUi9p6SUiIiIiIi4rW7durF8+fILlnXo0OGiZQBDhgxhyJAhFyyzWCxMnDiRiRMnXrA8JiaG+fPnVz9gJ/Azn2spZVVSSkTqN3XfExERERERcSNmn7LeFTaNNyMi9ZySUiIiIiIiIm7EkZSyKyslIvWbklIiIiIiIiJupDwpZVdSSkTqOSWlRERERERE3MhP3feUlBKR+k1JKRERERERETfiq+57IuIllJQSERERERFxIz4mtZQSEe+gpJSIiIiIiIgb8TWXjynl4kBERGqZklIiIiIiIiJupLyllFXd90SknnPrpFRWVhaJiYls3brVsWznzp2MGDGChIQE+vfvz4oVKypss3r1ahITE+nUqRNJSUns2LHDUWaz2Zg7dy69evUiISGBsWPHcvr06Tqrj4iIiIiIyKX4aqBzEfESbpuU+vrrr7nzzjs5cuSIY1lOTg5jxoxh6NChbN++nZkzZzJ79mx27doFwNatW5kxYwZz5sxh+/btDB48mLFjx1JYWAjAokWL2LRpE6tWrWLjxo0EBAQwbdo0l9RPRESktpmCItl7Ko+M/BJXhyIiItXg41PefU9JKRGp39wyKbV69WomTpzIhAkTKixft24dERERjBw5El9fX3r27MmgQYNYtmwZACtWrGDgwIF06dIFi8XC6NGjiYyMZO3atY7y+++/n8aNGxMSEsLUqVPZsGEDaWlpdV5HERGR2rTz2FnC75jJxoNZvLv7JN+fOOvqkEREpIo0+56IeAu3TEr16dOH9evXc9ttt1VYnpKSQps2bSosi4uLY+/evQCkpqZetDw3N5eTJ09WKI+JiSE8PJx9+/bVUk1ERETq3uncYrYfyQYg1N8XuwHrvz/FsZwi1wYmIiJVYlb3PRHxEr6uDuBCGjRocMHl+fn5BAYGVlgWEBBAQUHBJcvz8/MBCAoKqlReXlZV58YdrPb6JlPFl7c4v/7eytvPgbfXH9zzHLhTLOI8dsPg8wOZGEDJwe3c+dskNhzIYn96PtuPZNPkmlhM+uWLiLg1s1pKiYiXcMuk1MUEBgaSm5tbYVlRURHBwcGO8qKiokrlkZGRjmRV+fhSF9q+qqKjQ6sbOoZhEBjoj4+PiZiYUK98IKjJeatvvP0ceHv9QedAat+x7CKyC634m33I2foWppHDuK5FBD9mFZCeV8LhM4W0jAq69I5ERMRlyrvv2dVSSkTqOY9KSrVp04ZNmzZVWJaamkp8fDwA8fHxpKSkVCrv27cv4eHhxMbGVujil56eTnZ2dqUuf5eSmZlLde4PJhNERYVQWFiMyeRDRkauVyWlTKayB/Hqnrf6xNvPgbfXH9zzHJTHJPXLvvSy1r9xDYI4VXyulbCfmU7NI9h+6Ay7j+cqKSUi4uZ8TGopJSLewS3HlLqYxMREMjIyWLp0KaWlpWzZsoU1a9YwbNgwAIYPH86aNWvYsmULpaWlLF26lMzMTBITEwFISkpi0aJFpKWlkZeXx6xZs+jWrRstWrSoVhyGUf1XTberLy9vr7/OgervrufAE2VlZZGYmMjWrVsdy3bu3MmIESNISEigf//+rFixosI2q1evJjExkU6dOpGUlMSOHTscZTabjblz59KrVy8SEhIYO3Ysp0+frrP6OFNRqY3DWWVd2ts2DKlQ1qFpOAAnc4vJK7bWeWwiIlJ1vubyMaVcHIiISC3zqKRUZGQkS5Ys4YMPPqB79+5MmzaNadOm0aNHDwB69uzJ9OnTefLJJ+nWrRvvv/8+ixcvJiIiAoDk5GT69evHyJEj6devH8XFxcybN891FRIRkWr5+uuvufPOOzly5IhjWU5ODmPGjGHo0KFs376dmTNnMnv2bHbt2gXA1q1bmTFjBnPmzGH79u0MHjyYsWPHOrpzL1q0iE2bNrFq1So2btxIQEAA06ZNc0n9LteBzALsBsQEW4gO9qtQFhpgoVGoPwAHMwtcEZ6IiFSRWkqJiLdw++57P58Zr0OHDixfvvyi6w8ZMoQhQ4ZcsMxisTBx4kQmTpzo1BhFRKT2rV69mvnz5zNp0iQmTJjgWL5u3ToiIiIYOXIkUPYFxaBBg1i2bBkdO3ZkxYoVDBw4kC5dugAwevRo3nzzTdauXcuwYcNYsWIFEydOpHHjxgBMnTqVPn36kJaWRvPmzeu+opfhaHZZou3K6AuPldg6JoiTucWkZuTTsUlYXYYmIiLV4KuBzkXES3hUSykREfFeffr0Yf369dx2220VlqekpFQaGzAuLo69e/cCVBhL8Oflubm5nDx5skJ5TEwM4eHhlb4UuZSfz7BaF6/z2ewGx3OKAWgeEVApNihLSpmAzPxSzhZZXRb35dS3uufFnX5f1amDO79UB/d41WYdxPXMPmWPaRroXETqO7dvKSUiIgLQoEGDCy7Pz893zLBaLiAggIKCgkuW5+efGwg8KKhSeXlZVbli0Hhfi5mgoLIuecfOFGK1GwRazDSLCcFkMmEy4SgHiAwLonF4AMdzikgvtOJrMRMT41mD3VflPJ9/Xi5W7sp614cJBlQH91Af6iAXZj7XdMCqllIiUs8pKSUiIh4tMDCQ3NzcCsuKiooIDg52lBcVFVUqj4yMdCSryseXutD2VeWKmRWtpTYKCspaRx04dRaAJmH+FBaWAGWD2RcUFGMyQWCgP4WFxTQO8+d4ThE/ns7FWmojIyP3ovt3JyZT1WewPP+8XKzcFfWuTh3clergHmqzDuX7FtdSSykR8RZKSomIiEdr06YNmzZtqrAsNTWV+Ph4AOLj40lJSalU3rdvX8LDw4mNja3QxS89PZ3s7OxKXf4uxdUzGh7LKUu8NftZ1z2oOPtjs/AAvk7L4VhOEdGYPO6h3Fnn2ZX1dvXfijOoDu6hPtRBLqy8pZTGlBKR+k5jSomIiEdLTEwkIyODpUuXUlpaypYtW1izZg3Dhg0DYPjw4axZs4YtW7ZQWlrK0qVLyczMJDExEYCkpCQWLVpEWloaeXl5zJo1i27dutGiRQtXVqtarDY76fllraMah1dOSp0vJsQPP7OJEptBaUhsXYQnIiLV5HuupZRNOSkRqefUUkpERDxaZGQkS5YsYebMmcyfP5+oqCimTZtGjx49gLLZ+KZPn86TTz7JqVOniIuLY/HixURERACQnJyM1Wpl5MiR5Ofn0717d+bNm+e6CtVARn4phgGBFh9C/My/uK6PyUTT8AB+zCqkONyzZhcUEfEWPudGnFdLKRGp75SUEhERj/PzmfE6dOjA8uXLL7r+kCFDGDJkyAXLLBYLEydOZOLEiU6NsS6dzisbP6lhiD+mKkyd1fhcUqoktElthyYiIjXga1ZSSkS8g7rviYiIeLj0vLKuew1D/aq0fqPQspnpSkIba2YnERE3ZPYpS0ppoHMRqe+UlBIREfFw5S2lGoT4V2n9yCALFrMJw+zHgfT82gxNRERqwKzueyLiJZSUEhER8WAFJTbyim0ANAiuWkspH5OJ2HOtpXYez6m12EREpGbKW0opKSUi9Z2SUiIiIh4s/VwrqchAC36+Vb+tlyelvj12tlbiEhGRmnMkpdR9T0TqOSWlREREPFhmQSkA0cGWam1XPq7UzmNqKSUi4m4cY0rZXRyIiEgtU1JKRETEg2Xllw1yHl3FrnvlGoT4gWHndF6Jo7WViIi4h/KklFUtpUSknlNSSkRExINlnWspFRVUvZZSFrMPvoVZAHx/MtfpcYmISM35akwpEfESSkrVIcMwAN1YRETEOew+vuQUWYHqt5QCsOSdApSUEhFxNz9139Ozg4jUb0pKiYiIeChrUDQAgRYfAi3mam/vl38agO9P5jk1LhERuTwa6FxEvIWvqwMQERHxVvc+cB+ZWdkXLY+OimDJy69etLw0KAaAqKDqt5KCn1pK/XAqF8MwMJlMNdqPiIg4l1nd90TESygpJSIi4iKZWdkMnPD0Rcvff27iL25fnpSq7sx75SwFGVjMJnKKrBzLKaJZRGCN9iM/uVSisUmjGF5a8FLdBSQiHslsUlJKRLyDklJ1xFDTWxERcTLrZbaUMhl24huE8P3JXL4/mauklBNcKtH44YJH6zAaEfFUjjGl9AghIvWcxpQSERHxUNbASAAiA2vWUgqgfaNQAPZosHMREbeh7nsi4i2UlBIREfFAZwpKsFuCAIgIrHnD53aNQgDNwCci4k7Kk1JW9bYQkXpOSak6ZhiGuvKJiMhl+zGrAIBQfzO+5prfztudaym191QeVn0jLyLiFnx9yq7rdl2XRaSeU1JKRETEAx3KLEtKRVxG1z2AKyKDCLKYKbLaHfsUERHXKv+uQd33RKS+U1JKRETEAx08l0C6nPGkoKyLyFWx6sInIuJOzOUtpdTDQkTqOSWlREREPNCP5S2lgi4vKQU/deH7/pSSUiIi7qC8pZS6VYtIfaeklIiIiAc6lOWc7nvwU1JqzwklpUTEPdlsNkaNGsWUKVMcy3bu3MmIESNISEigf//+rFixosI2q1evJjExkU6dOpGUlMSOHTsq7G/u3Ln06tWLhIQExo4dy+nTpx3lmZmZjBs3jq5du9K9e3dmzpyJ1Wqt/Yqe81NLqTo7pIiISygpJSIi4mHyiq2czisBLr/7Hvw0A19qRj4lVvtl709ExNkWLFjAV1995fg5JyeHMWPGMHToULZv387MmTOZPXs2u3btAmDr1q3MmDGDOXPmsH37dgYPHszYsWMpLCwEYNGiRWzatIlVq1axceNGAgICmDZtmmP/Dz/8MEFBQWzcuJGVK1eyefNmli5dWmf1NZvKZt/TmFIiUt8pKSUiIuJhDp8pe6jyKcnHz/fyb+VNwgIID/DFajdIzci/7P2JiDjT5s2bWbduHTfffLNj2bp164iIiGDkyJH4+vrSs2dPBg0axLJlywBYsWIFAwcOpEuXLlgsFkaPHk1kZCRr1651lN9///00btyYkJAQpk6dyoYNG0hLS+Pw4cNs27aNSZMmERgYSPPmzRk3bpxj33XB7KOklIh4B19XByAiIiLVk3YuKeVblO2U/ZlMJq6ODWXL4TP8cCrX0Z1P6pd7H7iPzKzsi5ZHR0Ww5OVX6y4gkSrIzMxk6tSpLFy4sEJLpZSUFNq0aVNh3bi4OFauXAlAamoqw4YNq1S+d+9ecnNzOXnyZIXtY2JiCA8PZ9++fQBEREQQGxvrKG/dujXHjx/n7NmzhIWFObualfiazyWlNNC5iNRzSkqJiIh4GGcnpaCsC9+Ww2f44WQeXOu03YobyczKZuCEpy9a/v5zE+swGpFLs9vtTJo0iXvuuYerrrqqQll+fj6BgYEVlgUEBFBQUHDJ8vz8shahQUFBlcrLy36+bfnPBQUF1U5KneuJV631fc7rvlfd7T1VeT29pb7n89a6q96ujaO6aiteJaVEREQ8zOEzZQ9dZicmpa6O1Qx8IuJeXn75Zfz8/Bg1alSlssDAQHJzK16vioqKCA4OdpQXFRVVKo+MjHQkmMrHl/r59oZhVCor/7l8/9URHV391qdHC7LL/mMyERPjXa1Xa3K+6gtvrbvq7d2UlBIREfEwadllD1q+hdlO2+fV57rsHczIp6jURoDF7LR9i4jUxLvvvsvp06fp2rUrgCPJ9NFHHzF58mQ2bdpUYf3U1FTi4+MBiI+PJyUlpVJ53759CQ8PJzY2ltTUVEcXvvT0dLKzs2nTpg12u53s7GwyMjKIiYkB4MCBAzRq1IjQ0Oo/RGZm5lKdXngm009jSpVabWRkeMeXBSZT2UN6dc9XfeCtdVe9Pave5XE7mwY6FxER8SCGYZzXfe+M0/bbMMSPqCALNgP2p2uwcxFxvQ8++IBvvvmGr776iq+++orbb7+d22+/na+++orExEQyMjJYunQppaWlbNmyhTVr1jjGkRo+fDhr1qxhy5YtlJaWsnTpUjIzM0lMTAQgKSmJRYsWkZaWRl5eHrNmzaJbt260aNGCli1b0qVLF2bNmkVeXh5paWksXLiQ4cOH16gehlH9l2Og8xps68mvmp6v+vDy1rqr3p71qg1qKSUiIuJBsgtLyS22AuBblOO0/ZpMJto1CuWLg1n8cDKXjk1qfyBfEZGaioyMZMmSJcycOZP58+cTFRXFtGnT6NGjBwA9e/Zk+vTpPPnkk5w6dYq4uDgWL15MREQEAMnJyVitVkaOHEl+fj7du3dn3rx5jv3Pnz+fv/3tb9x00034+PgwdOhQxo0bV2f10+x7IuItlJQSERHxIEfOtZKKDfXHZNicuu92seeSUhpXSkTc0Jw5cyr83KFDB5YvX37R9YcMGcKQIUMuWGaxWJg4cSITJ154gP+YmBjmz59f82Avk5JSIuIt1H1PRETEg6RllyWlmkcGXmLN6ru6UQgA35/Kc/q+RUSk6nzPJaXstdVfRkTETSgpJSIi4kHKW0pdUQtJqavOzcB3KLOA/BKr0/cvIiJV43Nu7nWrWkqJSD2n7nsiIiIepHyQ8+YRgWx28r5jgv1oGOLH6bwS7p38BEXH9l903eioCJa8/KqTIxAREQBfs1pKiYh3UFJKRETEg5S3lKqN7nsA7RqFcjo1kwx7MHdOePqi673/3IXHYRERkctnNmlMKRHxDuq+JyIi4iEMw3CMKdUiovaSUgAlIbG1sn8REbk0s2NMqbJrv4hIfeWRSak9e/YwcuRIunbtSp8+ffj73/9OSUkJADt37mTEiBEkJCTQv39/VqxYUWHb1atXk5iYSKdOnUhKSmLHjh2uqIKIiEi1ZeSXUFhqx8cETSMCauUY1zQuS0qVhjSqlf2LiMillSelAGzKSYlIPeZxSSm73c4DDzzAgAED2LZtGytXruSLL75g8eLF5OTkMGbMGIYOHcr27duZOXMms2fPZteuXQBs3bqVGTNmMGfOHLZv387gwYMZO3YshYWFLq6ViIjIpZV33WscFoDFXDu38HaNQvExgc0/TIOdi4i4SIWklLrwiUg95nFJqZycHNLT07Hb7Y6mrD4+PgQGBrJu3ToiIiIYOXIkvr6+9OzZk0GDBrFs2TIAVqxYwcCBA+nSpQsWi4XRo0cTGRnJ2rVrXVklERGRKqnt8aQAgv18aR0TDMDp3JJaO46IiFycr89Pj2ka7FxE6jOPS0pFRkYyevRo5s6dS4cOHejXrx8tW7Zk9OjRpKSk0KZNmwrrx8XFsXfvXgBSU1N/sbyqTKbqvy53e09/eWu9dQ5Uf3c/B+JZymfeu6IWk1IAHRqHAXA6r7hWjyMiIhd2Xk5KLaVEpF7zuNn37HY7AQEBPP744wwfPpzDhw/z4IMPMn/+fPLz8wkMrPhBPSAggIKCAoBLlldVdHRojeIOCvLDZPIhJiYUHx+Pywdetpqct/rG28+Bt9cfdA7k8pQPct68lgY5L3dN41De3nWiVltK3fvAfWRmZV+0PDoqgiUvv1prxxcRcWfnt5RSUkpE6jOPS0qtX7+eDz/8kA8++ACA+Ph4kpOTmTlzJoMGDSI3N7fC+kVFRQQHl3VDCAwMpKioqFJ5ZGRktWLIzMylOq1oTSaIjAymoKAEk8lERkauVyWlTKayB/Hqnrf6xNvPgbfXH9zzHJTHVF/s2bOHWbNmsW/fPgICArjllluYPHkyfn5+7Ny5k7///e+kpqYSGRnJ2LFjGTFihGPb1atXs3DhQtLT02nVqhWPP/44CQkJLqzNhdVF9z34qaVUel4JNrtRYWwTZ8nMymbghKcvWv7+cxOdfkwREU9x/mXX5i4fHEREaoHHJaVOnDjhmGmvnK+vLxaLhTZt2rBp06YKZampqcTHxwNlCayUlJRK5X379q1WDIbBZT1UXu72nspb630+bz8H3l5/0DmoLeWTYIwZM4Z///vfnD592jFu4N13382YMWMYP348d955J9u3byc5OZm2bdvSsWNHxyQYixcvpmPHjixbtoyxY8fy6aefVmpd60p2w+BYTtkXKy1qOSl1RVQgPqWF2CyBZOSXEBvqX6vHExGRikwmE2ZT2cx7drWUEpF6zOOa6/Tp04f09HReeuklbDYbaWlpLFq0iEGDBpGYmEhGRgZLly6ltLSULVu2sGbNGoYNGwbA8OHDWbNmDVu2bKG0tJSlS5eSmZlJYmKii2slIiKXwxsmwcjIK6HYasdsgka1nCQymUz4nT0GwImzGldKRMQVfM41l7IqKSUi9ZjHJaXi4uJ4+eWX+eSTT+jevTu/+93v6N+/PxMmTCAyMpIlS5bwwQcf0L17d6ZNm8a0adPo0aMHAD179mT69Ok8+eSTdOvWjffff5/FixcTERHh2kqJiMhlcYdJMGpbeSup2LAAfM21f/v2yz0OwMmzRZdYU0REaoP53IwkykmJSH3mcd33AHr16kWvXr0uWNahQweWL19+0W2HDBnCkCFDais0ERFxAXeYBKO2ZjMs3++xnLLxpJpFBFT7WOfP/vjz7qMX21d5S6lTucXYDQOfC6xY2zM4nr//8+vg7H3XFWfXoTZcKjZPqMOlqA5V27e4Xvl4fhroXETqM49MSomIiJzPHSbBqMmg8b4WM0FBF++K52sxExNTtt8zJWUtl1rHhjmWXWp7k4kK5YGBFdc9f/8/F1h6Bj+zDyU2O4V2Ew1Cq75tVVSn7uerynmu6b6d4VLHBtdNMODM81IfJklQHcTdKSklIt5ASSkREfF47jAJRk1mVrSW2igouPiYTdZSGxkZZQm1/SdyAIj2NzuWXWp7w4CCgmJMprKEVGFhcYUYz9//z9lKrcSG+pGWXcSBU2cJNoddNLaaqE7doXozWJaW2th7LJvjOUUUltpoGh7AldFBWM51e7zc2H/JL9WrvAXKL9Xh3jH3kZGVfdH9x0RFsOSVV50eW3n5pc6LO84kWl2qQ9X2La5X3n1Ps++JSH3m9KTU1q1b6d69u7N3KyIiHqy27w19+vThmWee4aWXXuL+++/n+PHjFSbBeOqpp1i6dCkjR47k66+/Zs2aNSxcuBAomwQjOTmZW2+9lS5durBs2bIaTYJRWzMrlu/zWHZZa65mEQHVPk75+hfa7pf21TQ8gLTsIo5lF9GxSVil8tp+TrpYvL903PS8YrKuGsqHe9Mdy1IzCthx7CwDrmpARKDlovuubef/HgyjrPVDkdVGqdXAarcTFmAhIyubgROevug+3n9uYq3GXtV914eZRFWHuqFng5rzUUspEfECTk9KjR8/ntDQUH7961/z61//miZNmjj7ECIi4mFq+95QPgnGvHnzePXVVwkNDWXw4MEkJyfj5+fHkiVLmDlzJvPnzycqKuqik2CcOnWKuLg4t5wEo3yg86bhAXV2zGYRgXA4mxNni7Ha7HUywPrlyC4oZdyKXRRHtMBsgviGIQT4+pCSns/ZIivvfXeKoR1iXRaf3TAoCG/JzHX7+fbYWdLOFFaaVcun832s3nWSBiF+NA7zp1GYP8F+atgunkvPBjXne+6Sa3f3zKOIyGVw+qecL774gk8++YR33nmHl156ieuuu46kpCRuvvlm/Pz8nH04ERHxAHVxb6jPk2Dkl1jJKigFziWK6khEoC/BfmbyS2yczC2u02NXV0GJjYdXf8ehrEJ8inNJ6t7G0SqqfaNQPtybTkZ+CZ/sz8TXVLfJNbthsP90Pt8czSE/biCrd52stI4JMAC7XxAZ+SVk5Jfww6k8ABqH+RPfIJgro4PqNG4RZ9CzQc2VTzChllIiUp85PSllsVgYMGAAAwYMICsriw8++IAlS5bwt7/9jYEDB3LnnXdy1VVXOfuwIiLixnRvuDzHz7WSCg/wJcS/7lrNmEwmmoYHsD89n6PZRW6blCqx2pn83h72nMwlPMAXv2/fIeKGqY7yID8ziW1jeHvnSdLzSwhpel2dxZZXbOWTlAxO5ZaNeeZTWsiIbq3pdkUk8Q2CiQy0YDH74GOCnCIrv/3jQ3QcPp5TucWcPFtMRn4JJ84Wc+JsMZsPncG/WXdyi6yEBqj1lHgGXf9rzjHQuXJSIlKP1donmszMTP773//y/vvvk5qaSr9+/fD392f06NGMHj2aP/7xj7V1aBERcVO6N9RM+XhSTeqw6165ZhFlSam07EJ6UL0ZCeuCzW4w/X972Xo4m0CLD88nXcNfPjtTab0Qf196t4rkk5RM8ht35nRuMQ1Df3mWvIu594H7yLzIYOT7D6Qw8Nz/M/NLWPv9aYqsdixmE12ahfPJ7D+x8evWbLzIvn88kMLQB6bQ6lyrqLxiKynp+aSk55NTZKW0WXeGvLqN0d2a89uuzfA999Aq4u50/a8+tZQSEW/g9KTU+++/z7vvvsuXX35Jq1atSEpK4qWXXiIqKgqAfv36kZycrBuPiIgX0b3h8hzNKR/kvO5bKjWPCMTHBNmFVs4UlBIZZHHq/g3DwGSqWWLFMAz+8XEqH+3PwNfHxFND2tO+ceUB2cu1ig5iz8k8TuXCS5sO8cQtbWt03MxfGIz8+wduL1snv4T3vz9NsdVOVJCFxLYxhAVY+LC09BcHMi/fvlyIvy8JzcLp1DSMH7MK+fzbfeQSzQsbf+Sj/ek8MaAtcQ2Ca1QPkbqg63/N+WqgcxHxAk5PSv31r39l4MCBLF++nGuuuaZS+ZVXXsno0aOdfVgREXFjujdcnmPZhUDdDnJezs/Xh2YRARw5U8SPmQVEBoVf1v4Mw2D7kWyy2tzG618dpbDUjr/Zh5gQP66MDiQ+JrjKA6q/9OVh3t51AhPw94FX0f2KX27JZTKZ6HFFBO9+d4r3vz/FvT1a1Eqir6DExod70ym22mkY4setVzfEz/fyxrEymUy0ig7i+13/4b7pLzDvs4P8cCqPUa9/w7g+Lbm7a7MaJ/dEapOu/zX3U/c9JaVEpP6qlYHO09LSiI0tm93m22+/JTQ0lNatWwPQqFEjxo8f7+zDioiIG9O94fL81FKq7pNSAFdGBZUlpbIK6Ny85kmpzPwSZq7bz8aDWRAVB6V2AIptdo7lFHEsp4iv03Lo2CQMu8/FP6IYhsGCjT/yr+1HAZjyqzhuatOgSjE0DPXHP/swxRFX8OaO4zxyY+sa1+eCTD58tD+D/BIb4QG+3OKEhFSF3WMw+JpG9GoZyaz1KWw8mMX8DT/yw6k8nhjQhgCL2WnHEnEGXf9rTt33RMQbOH36mY8//pihQ4dy6NAhAHbs2MGIESP4/PPPnX0oERHxELo3XJ7ygc6bhrtmoPErooIwmSCroJQz52YBrK7U9HxGvf4NGw9mYTGbCDq5k8HXxDKyS1OSOjaiW4sIQvzNFJba2Xo4m9MJ9/Dv7WmcLap4vNO5xTzyzh5HQupP/VqRdG31ppgPPrEDgPd2nySv2Fqj+lyMf4ebOZVbjMVs4uarGuDvxITU+WJC/HlmaHsevSkOs4+J9fvSGf/2d06vj8jl0vW/5spbStnVUkpE6jGnt5RasGABCxcudDTPveeee4iLi+Opp56iX79+zj6ciIh4AN0bas5mN35KSrmopZS/rw8tIgI5fKaQvafy6Hll9QY8//5kLg+u3E1usZUro4KYeftVTBz3PLGhg4Cy2fGig/3o0DiUlIx8vj16lrMEMn/Dj7z85WESmobTONyfM8U2vkjJwGo38PUx8div4hncoVH165NzhFbRQRzMLOCd3Se5u2uzau/jQjLzSwjoVDbMee8ro4gIdO74Wz9nMpkY3qkJV0YH8cg7e9hxNIeHVu1mwfAOBPtpdj5xD7r+15z5XI9ctZQSkfrM6V/fnThxguuvv77Csj59+nD8+HFnH0pERDyE7g01dzqv2JGEaRhSs9ninOHq2BAA9qfnYbXZq7zdj5kFjF9VlpDq2CSMxXddS3yDkAuu6+Njom3DEEYkNCbiwHriYoIpttrZcvgMq3ed5LN96VjtBh2bhPHvuzvXKCEFYAJ+26UpAMu/OYbVCQ98hmGw6ccsTD5mWkYFEhcTdNn7rKouzSN4+c5rCQ/w5bsTuTzyzh5KrFX/HYnUJl3/a+6nMaVcHIiISC1yelKqadOmbNxYcaLjzZs306RJ9ZrWi4hI/aF7Q80dyy5rJdUkPMDxgOIKzSICCPX3pcRmkJpRUKVtTp4t4sGVu8gpsnJ1bAjzh11DeBVaD/mYTASl/8B/fteZZaM689iv4hjT6wqmD2rHm6O78NpvOl32jHO3XB1LZKCFU7nFfLI//bL2BXAws4BTuSUYpcX0bBlZ54OOt20YwvxhHQj2M/N1Wg5zPkrBUJcfcQO6/tecxpQSEW/g9LbdY8aMITk5mZtvvpmmTZty/Phx1q9fz9y5c519KBER8RC6N9TcsRzXzbx3PpPJRLtGIWw9nM2OYzmEmn55QO0zBSU8uHI3p/NKaBkVyPyk6ncpM5lMtGkYQpuGIZhMEBMTSkZGLs7Itfj7+jCiUxNe2XyYZV8fI7Ftgxonkqw2O9sOZwNQ9N06QvomX36ANdCuUSizB13Nw29/x5o9p2jbMIQ7Ozd1SSwi5XT9rzlfHyWlRKT+c3pLqUGDBrF48WIsFgt79uwhICCAJUuWMGDAAGcfSkREPITuDTV3NLt8kHPXJqWgrAtfkMVMXrGN/EYdL7peXrGV8au+4/CZQmJD/XlhWAcigmp3fKWaGNapMX5mE9+fzGXX8bM13s/uE7nkldgI9jNT/N1HToyw+hbPnkzIjxsAePrj/dz2+7EMGTGcISOGs/9AiktjE++k63/NaaBzEfEGtTIKZvfu3enevXtt7FpERDyU7g01c8wxyLlrZt47n8XsQ9cW4Ww4kEVu024cyiygZXTFsZNyCkv509vfsfd0Hj6lBRgb/8UD67Mr7Wv/gRQG1jCOex+4j8ysyvus7r6jgvy45eqGvPfdKZZ/c4xrm4ZXO5b8EivfHitLaHVrEcFqW81mJ3SWzKxs7nh4JOv3ZXD4TCEl3UZx+7WN8fUx8f0Dt7s0NvFeuv7XTHn3PWeMeyci4q6cnpQ6deoUixYt4tChQ9jtFQfZ/Ne//uXsw4mIiAfQvaHmHEmpWmgptX//XoaMGH7hsoskduIbBLPvdD6ncmHCO9+xaERHGoWVxfbDqVwef38vh88UYiotZEjnVsT0nXbB/V9OgiQzK5uBE56+aHl19v2bzs1477tTfJKSwYmzRTQOq955/upIDla7QcMQP1rX4eDmv8RkMtG3dRSrdp4kp8jKjqM5XNciwtVhiZfS9b/mHC2llJQSkXrM6Umpxx57jIyMDG688UYsFvdrqi8iInVP94aaO5Hz00Dnzma1c9HkzsUSOz4mE4ltY3jji+85mg13LP2KG+JiyC4sZdvhM9gMaBDiB5teJ6bvX5wes7PFNQjmuhYRbD+SzVs7jvOnfq2qvG16Xgn70/MB6OGCwc1/SYDFTK8rI/lofwY7j52ldbR7JMzE++j6X3PmcwOt2NR9T0TqMacnpXbv3s2HH35IVFSUs3ctIiIeSveGmrH7WDhTWNYdrEk1W/DUpkCLmegf3qHxwIfYefws//vhtKPsV21iePSmeH7/0cVbMrmb33ZpyvYj2byz+wT397yCIL9fHsQdwDAMNh86A0BcTBCxof61HWa1XRkdRMuoQA5lFfLluVhF6pqu/zVndsy+5+JARERqkdOTUqGhofj5+Tl7tyIi4sF0b6gZm38oAKH+voQG1MowkDXmW5zDK3ddyxcHs/gxswAfE/RtHc0VUZ7XIqfXlVG0iAzkyJlC1nx3skoz1v2YVcip3GLMPia37hrX44pI0s4UcuJsMZYWnVwdjnghXf9rzqd89j21lBKReszps++NGzeOxx57jF27dnH8+PEKLxER8U66N9SMzT8MgMZh7tcKB8q68vVtHc3vuzVn1HXNPTIhBWX1+M25RNT/bU+jqNT2i+sbJjNbD5e1PLq2SSgh/u6VMDxfaIAvHZuU/R0FdBmiWbykzun6X3PlY0rZNKaUiNRjTv8UNW1a2YCm69evB8oG2zQMA5PJxA8//ODsw4mIiAfQvaFmypNStTGelFQ0+JpG/N+2NE7mFrPi2+OMuq75RdfNbd6dvGIbwX5mR8LHnXVsGsb3p/IoDo8lJT2ftg1DXB2SeBFd/2vO16SBzkWk/nN6Uurjjz929i5FRMTD6d5QM1YlpeqMn68PD/S+gr9+sJ+l29K4tV0sMcGVuxz9cCqXvMadAeh9ZSQWs9MbnTudn9mHTk3D2Ho4m2+O5hAXE+xogSFS23T9rzl13xMRb+D0T1JNmzaladOm5OTksGfPHho0aEBAQABNm156fAYREamfdG+oGUdLKTca5Lw+u/XqWNo2DOFskZW/f7gf42cPgtkFpUx573sw+dAqOsijuiu2iw3BXpBDXrGNAxn5rg5HvIiu/zWn7nsi4g2cnpTKzMzkrrvu4o477uDRRx8lLS2NX/3qV+zYscPZhxIREQ+he0PNOMaUUkupOmH2MfHXW9viZzax6ccsFmw85EhMZReUMvHdPRw/W4y5KIfeV0a6ONrq8TX7ULynrMXKzuNnKyXcRGqLrv81Zz7XoFFJKRGpz5yelJo1axZt2rRh+/bt+Pr60rp1a8aMGcM//vEPZx/KIxmGoQ+CIuJ1dG+oGWuAuu/VtdYxwfz5xtYA/Gt7GuNW7OL5zw8y8t9fs/P4WYIsZqL2rSHAYnZxpNVXvP8L/MwmsgutHMoqdHU44iV0/a+58pZSmqBAROozpyeltmzZwmOPPUZgYCCmc4Pz3XfffaSmpjr7UB5JSSkR8Ua6N1RfidWO4VuWjHLX2ffqq2HXNmHazfGYTfBVWg6vf3WU03klNIsI4LXfdsJSmOXqEGumtIh2jUIB2HMy18XBiLfQ9b/mfM6dL6vdxYGIiNQipw90brFYKCoqIjAw0JF8yc/PJzg42NmHEhERD6F7Q/XlFlsBCA/wJdjP6bdruYQhHRrTqWk4H+/P4MTZIq5tGsaN8TEe/7u4OjaEncfOcuJsMVkFJUQFVR7MXcSZdP2vOY0pJSLewOktpfr378+kSZM4dOgQJpOJzMxM/vrXv9KvXz9nH0pERDyE7g3VV56UUtc917kiKoh7e7Rg6s1tuL19I49PSAGE+PtyRVQgAD+czHNxNOINnHH937x5MyNGjKBz58707t2bGTNmUFRUBMDOnTsZMWIECQkJ9O/fnxUrVlTYdvXq1SQmJtKpUyeSkpIqjGVls9mYO3cuvXr1IiEhgbFjx3L69GlHeWZmJuPGjaNr1650796dmTNnYrVaL/OMVJ2vuu+JiBdwelLqkUceISgoiFtuuYWzZ8/Sp08fCgsLmThxorMPJSIiHkL3hurLU1JKakl5F76U9HxK1C9IatnlXv+zsrJ44IEH+M1vfsNXX33F6tWr2bZtG6+88go5OTmMGTOGoUOHsn37dmbOnMns2bPZtWsXAFu3bmXGjBnMmTOH7du3M3jwYMaOHUthYdmYaosWLWLTpk2sWrWKjRs3EhAQwLRp0xzHfvjhhwkKCmLjxo2sXLmSzZs3s3TpUqefo4sp776nllIiUp85/Su/4OBg5s+fT1ZWFkePHqVRo0Y0bNjQ2YcREREPontD9eUW2QBoEqaklDhXkzB/wgN8ySmykpKRT/tzSSqR2nC51/+oqCi+/PJLQkJCMAyD7OxsiouLiYqKYt26dURERDBy5EgAevbsyaBBg1i2bBkdO3ZkxYoVDBw4kC5dugAwevRo3nzzTdauXcuwYcNYsWIFEydOpHHjxgBMnTqVPn36kJaWht1uZ9u2bWzYsIHAwECaN2/OuHHjeOqpp7jvvvucf6IuwNF9Ty2lRKQec3pSavv27RV+Pnz4MIcPHwbguuuuc/bhRETEA+jeUH3l3fcaq6WUOJnJZKJdo1A2HzrD9ydzaRcb4hiAWsTZnHH9DwkJAaBfv36cOnWKrl27kpSUxLx582jTpk2FdePi4li5ciUAqampDBs2rFL53r17yc3N5eTJkxW2j4mJITw8nH379gEQERFBbGyso7x169YcP36cs2fPEhYWVqXYAar79ipf33yuT4vNblR7H56ovI7eUNef89a6q96ujaO6aitepyelRo0aVWmZj48PjRs35uOPP3b24URExAPo3lB9jjGl1FJKakGbBsFsP5JNdqGVE2eL1U1Uao0zr//r1q0jJyeHiRMnMn78eGJjYwkMDKywTkBAAAUFBUDZgOoXK8/PzwcgKCioUnl52c+3Lf+5oKCgWkmp6OiatUYMDSl7X1r8fImJ8Z4WjTU9X/WBt9Zd9fZuTk9K7d27t8LPWVlZvPjiizRt2tTZhxIREQ+he0P1GIbh9QOd79+/lyEjhldY5msxYy21sf9ACgNdFJerXei8VCiv4rnx8/WhdUwQ+07nsz8932v/zqT2OfP6HxAQQEBAAJMmTWLEiBGMGjWK3NzcCusUFRU5ZvYLDAx0DIh+fnlkZKQjwVQ+vtTPtzcMo1JZ+c/VnTkwMzOX6vTAM5nKHlZLikoByC8sISMj9xJbeb7yelf3fNUH3lp31duz6l0et7PV+jQyUVFRTJo0iQEDBnDvvffW9uHcnmEYjulwRUS8le4Nv6zEZlBqK7tXNA7zd3E0rmG1w8AJT1dYFhTkT0FBMd8/cLuLonK9C52X81Xn3LRpEMK+0/n8mFlA7ysjnRGeyCVV9/r/zTff8Je//IX33nsPPz8/AEpKSrBYLMTFxbFp06YK66emphIfHw9AfHw8KSkplcr79u1LeHg4sbGxpKamOrrwpaenk52dTZs2bbDb7WRnZ5ORkUFMTAwABw4coFGjRoSGVu+hzDCo0YOn+byBzr3p8aGm56s+8Na6q97ezemz711ITk4OxcXFdXEoERHxELo3XFxuUVkrKZ+SfAIsZhdHI/VVbKgfYQG+WO0GP2YWuDoc8SLVuf63bduWoqIinnnmGUpKSjh27Bhz585l+PDhDBgwgIyMDJYuXUppaSlbtmxhzZo1jnGkhg8fzpo1a9iyZQulpaUsXbqUzMxMEhMTAUhKSmLRokWkpaWRl5fHrFmz6NatGy1atKBly5Z06dKFWbNmkZeXR1paGgsXLmT48Iu3VHQ2x0Dnmn1PROoxp7eUeuyxxyr8XFpaytdff02vXr2cfSgREfEQujdUT3nXPXNx/e+uIa5jMpmIbxDM12k57E/Px8PGWxUPcbnX/+DgYF599VVmzZpF7969CQ0NZdCgQSQnJ+Pn58eSJUuYOXMm8+fPJyoqimnTptGjRw+gbDa+6dOn8+STT3Lq1Cni4uJYvHgxERERACQnJ2O1Whk5ciT5+fl0796defPmOY49f/58/va3v3HTTTfh4+PD0KFDGTdunFPOS1WUD3RuVVJKROqxWu++5+/vz6hRo7jzzjtr+1AiIuIhdG/4ZeVJKd/iHBdHIvVdeVLqxNliGvppwFWpfTW5/sfFxbFkyZILlnXo0IHly5dfdNshQ4YwZMiQC5ZZLBYmTpzIxIkTL1geExPD/Pnzqxyns6mllIh4A6cnpWbPnu3sXYqIiIfTvaF61FJK6kqovy+Nw/w5cbaYwgZXuTocqYd0/a853/IxpTTojIjUY05PSi1YsKBK6z344IM1PkZ2djazZs3i888/x263c9111/Hkk0/SsGFDdu7cyd///ndSU1OJjIxk7NixjBgxwrHt6tWrWbhwIenp6bRq1YrHH3+chISEGsdSVYZhYLfbNZCZiHilurg31Cd5jqTUWRdHIt6gTYNgTpwtpiDmagzDwGRSRz5xHl3/a04tpUTEGzg9KZWSksK6deu46qqruPLKKzl58iTffPMN7dq1c0yferkfdh566CHCw8NZv349Pj4+PPbYYzz++OP84x//YMyYMYwfP54777yT7du3k5ycTNu2benYsSNbt25lxowZLF68mI4dO7Js2TLGjh3Lp59+6pgWVkREnK8u7g31SW6RDVBSSurGldFBbPrxDNbACHafyKVjkzBXhyT1iK7/NaeklIh4A6cnpcqTRL/73e8cy959910+/fTTCgMH1tR3333Hzp07+fLLLwkJCQFgxowZpKens27dOiIiIhg5ciRQNrjhoEGDWLZsGR07dmTFihUMHDiQLl26ADB69GjefPNN1q5d65ilo7YYaiIlIl6stu8N9YlhGOeNKaWklNQ+i9mHllGBpGYUsH5fupJS4lS6/tecklIi4g18nL3Dzz//3JEUKnf77bezefNmp+x/165dxMXF8dZbb5GYmEifPn2YO3cuDRo0ICUlhTZt2lRYPy4ujr179wKQmpr6i+VVZTJV/3U529aHlzfXXedA9Xfnc1BXavveUJ8UWe2OmZY0ppTUlVYxZS1W1u9L1wOwOJWu/zVXnpSy2l0ciIhILXJ6S6moqCi2b9/umIoVYOPGjTRq1Mgp+8/JyWHfvn1cc801rF69mqKiIiZPnsyjjz5KTExMpW54AQEBFBQUAJCfn/+L5VUVHV392WmsViuBgRbARExMKBaLpdr78HQ1OW/1jbefA2+vP3jvOajtewN45niDF1I+nlSQxYzJsLkkBvE+zcIDMFmLyMyHHUdz6NoiwtUhST1RF9f/+koDnYuIN3B6UuqBBx5gzJgxDBgwgCZNmpCWlsann37KCy+84JT9+/n5ATB16lT8/f0JCQnh4Ycf5o477iApKYmioqIK6xcVFTn6qwcGBl6wPDIysloxZGbmVmvAcpMJwsMDKSgoASAjI9erklImU9mDeHXPW33i7efA2+sP7nkOymOqC7V9b4D6M97g2aKypFRogNNv0SIXZfYxEZiZSkHsNazbd1pJKXGaurj+11fqvici3sDpn3hHjBhB06ZNee+99/j+++9p3rw5y5cvp23btk7Zf1xcHHa7ndLSUvz9/QGw28vatF599dX85z//qbB+amoq8fHxAMTHx5OSklKpvG/fvtWKwTC4rIfKy93eU3lrvc/n7efA2+sP3nsOavve4KnjDV5IXnFZ66hQfzPVa8crcnkCM/dTEHsNn+zPYFL/OCxmp4/yIF6otq//9ZmSUiLiDWrla9hevXrRq1cvsrKyiIqKcvq+mzdvzl/+8hdmz55NcXExzz33HL/61a+4/fbbmT9/PkuXLmXkyJF8/fXXrFmzhoULFwIwfPhwkpOTufXWW+nSpQvLli0jMzOTxMREp8YoIiKV1ea94fzxBt944w0KCwu5/vrrefTRRy863uDKlSuBsi8nfp58qsl4g85SPsh5iL+vWyel9u/fy5ARwy9efiCFgXUYj1w+v7PHiAqykFVQyrbD2fRu5dz3qXiv2rz+12dKSomIN3B6Uqq0tJQFCxbw+uuvY7PZWLNmDQ8//DCLFi2iYcOGl71/i8XCv//9b+bMmcOAAQMoLi6mf//+TJ06lbCwMJYsWcLMmTOZP38+UVFRTJs2zdGHvWfPnkyfPp0nn3ySU6dOERcXx+LFi4mIiLjsuERE5OJq+97gDuMNOmvg+PKkVKi/L6edtN/zB9p3Vks9qx0GTnj6ouXfP3C7cw50zvl1cOb+6lJt/B6cyYTBr9o24K0dx1m37zR9WldOHjj79+AKqkPV9u0stX39r88cSSl3vGCIiDiJ05NSCxYsYMuWLTz//PNMmDCB6OhoGjVqxMyZM3n++eedcozY2Fiee+65C5Z16NCB5cuXX3TbIUOGMGTIEKfEISIiVVPb9wZ3GG+wJuNz+VrMBAX5V1iWX1LWfS8mPJDDFjMxMRff74W2P5/JRIXywED/XyyvapkrywMD/f+/vTuPj6o89D/+PTPZF0hCkE1ck0BdkAiCKIJFI5VFKUu5t9QWroo/QFu5RRSl17YWhGtvtUihVqWplaoFRUVRtBYVkSVQBJeiCcoiexISsmeW8/sjmSHDmkkmZ7bP+/WKZs76nCdhTs53nqXVx445S72ezZnq/Wznlk7+Ofizf2vWN6dexl99vv6+db8+KCpRSvskJcTaT7ltJEzawDVYw4png0gV4519j1AKQOQKeCi1cuVKvfDCC+rUqZMMw1BSUpIeffRRusgBQBRr63tDKIw32JJB7J0Ol6qr67yvTdPUsZqGllJxMuV0uFRcXNHs/U9kmlJ1dZ0MoyEIqamp8ymjZ/2Z9j3bsa1af+I1tObYX3z+ua65fuhp12dmpGnJn5457foz1fuZzu1pgXLiz6G5+7d2/dn2dTpc6p4Uo86p8TpYUafXCnbrhpyOJ11DqE3a4C+uoXnHDhSeDVrO7pl9j1AKQAQLeChVXV3t7StuNt4lExISZLMxWCYARKu2vjeEwniDgRjEvsbhlss0ZUhKjrN7j9tanmOE6wO4FNhrOFvXwzcfn9EmdRUOPwdDhvJ6dNRfN3+rd3Yc0ZDsjqfcLhImbeAarMGzQcsxphSAaBDwu0Hv3r21cOFCSZLRmO7/9a9/1eWXXx7oUwEAwkRb3xs84w3a7XYNHTpUQ4cOVefOnTV37lylp6dryZIlevvtt9W/f3/Nnj37tOMN9uvXT2+++WbQxhv0jCeVFGf3PowAVrupZ0MQ9dHXpaqqdwa5NAh3PBu0HKEUgGgQ8JZSDz74oCZOnKgVK1aoqqpKw4YNU1VVlf785z8H+lQAgDBhxb0hEsYbbDrIORAsPc5J0XnpidpztEYfFJVo2CWdgl0khDGeDVqOgc4BRIOA/9WbmZmpN998U++//7727dunzp076/rrr1dKSkqgTwUACBPcG5qn0hNKJRBKIXgMw9CNPTpqyYY9+udXxYRSaBXe/1suhpZSAKJAwP/qHTFihF5//XXdfPPNgT50RDBN09ufHgCiBfeG5qmo9bSUOvWMZ4BVbszJ1JINe7R+V0MXvuQ4glK0DO//LecZ6JzZ9wBEsjYZYbCmpqYtDgsACGPcG86uos4lie57CL6szGSdl56oepepj3aWBrs4CHO8/7cMY0oBiAYB/6u3f//+GjdunAYNGqRzzjnHZ93dd98d6NMBAMIA94bmYUwphArDMDQkO1P5m/bqvcJiDf3OOWffCTgF3v9bjlAKQDQI+F+93377rbp3765vvvlG33zzjXe5Z7YNAED04d5wdqZpeseUSmFMKYSAG3M6Kn/TXn38Tamq611KiqNbKfzH+3/LeUIpU5LbNGWjzgBEoID91Xv77bfr2Wef1V//+ldJUm1trRISEgJ1eABAGOLe0HzV9S65TcmQlMzDP0JAzjnJ6tY+QfvKa/XxN6W6sUfHYBcJYYT3/9bzDHQuSU6XqbgYQikAkSdgY0pt3brV5/WgQYMCdWgAQJji3tB8nq57KfF2Pg1HSDAMQzfkZEqS3vvqSJBLg3DD+3/r2ZvcC1xMlAQgQrXJQOeSmGHuBMy6BwDcG87EM8h5CuNJIYTckNPQOuqjr0tV63AFuTQIZ7z/+8/epKUU40oBiFRtFkrRTxwAcCLuDafHIOcIRd/plKIu7eJV63Tr411Hg10chDHe//3XNJRyEkoBiFBtFkoBAIDmq6htDKUY5BwhpGEWvobWUu99SRc+wEpNMilaSgGIWAH7y9fpdOrVV1/1vnY4HD6vJWnUqFGBOl3Y+feBCn3wzTFd0SUp2EUBAMtwb2i+SlpKIUTd2CNTS7d8Sxc++IX3/9YzDEN2myGX2ySUAhCxAvaXb2ZmphYsWOB9nZ6e7vPaMIyovvH8z8ov9O2xeu2vqNeDNwW7NABgDe4NzXe8+x4z7yG0XNo5VZ1S43Wook4bdh/V2C5pwS4SwgDv/4ER4wmlGJMLQIQKWCj1z3/+M1CHikjHahoeNtym5OamAiBKcG9oHrdpqrKegc4Rmhq68GXqhX/t03tfFmvs1RcGu0gIA7z/B4ZnBj5aSgGIVIwpZZErzm3n/b6ouDqIJQEAhJqqepdMs2H8kOQ4Wkoh9NyQkylJ+nBnieqcdOEDrOIZ7JyBzgFEKkIpqzSZcWTj7rLglQMAEHIqGwc5T4mPYYYqhKTLu7ZTx5Q4VdW79FFhcbCLA0QNTyhFSykAkYo+AhZxutze7zfuKdPt1wSxMACAkFLBIOcIcTbDkHvvNin9O/rp//1V7YvePWmbDhlpWvLUM0EoHRC5CKUARDr++rWIw3X8RkL3PQBAUwxyjrCw/3Mp/TtydL5U3xt+k/dh2ePNx2cEqWBA5LI3/jNjoHMAkYruexYwTVOOJi2lmgZUAABU1DHIOUJfXMUBJcbaVO9068Cx2mAXB4gKMbSUAhDhCKUs4mwSRDm4qQAAmqhoHFMqNYFQCqHLkKnz0xMlSbtLa4JcGiA60H0PQKQjlLKI0328pZTLbcqkCS4AoFElY0ohTJyfkSRJ2n20hr9lAAsw+x6ASEcoZRHnCV32+LQDACBJbrepqvqG7nuEUgh1XdsnKNZuqKrepeKq+mAXB4h4tJQCEOn469ciJ3bZc7hNxTCeLQBEvcp6l0xJdsNQYqzvZ0VffbVDt44be9p9v9pZqOFtXD6gqRibofM7JKvocKV2ldaoY0p8sIsERDS70RhK0TIRQIQilLKIs8lA5w2vTSk2SIUBAIQMz8x7KfF2GYbvbGZOtzR8+m9Pu+8Xd41o07IBp3Jxx4ZQandpja46Ly3YxQEiGi2lAEQ6uu9Z5MR+4E3HmAIARC8GOUe4uaBDsgxDOlrjUHmNI9jFASIas+8BiHSEUhY5cUwph4sbCwCAQc4RfhJi7erSrqHb3u6jzMIHtCVaSgGIdPwFbBHHCS2jmEEDACAd775HKIVg82cMswvSk7S/vE67SmvUq2s7awoIRCFm3wMQ6fgL2CIntpTixgIAkKSKuoaZ91Limf0CweXPGGbnZyTq411HdbiiTrUOlxJi+f0F2oK3+x4DnQOIUHTfs8iJIZTDxZhSAIDjY0q1Y0wphJGU+BilJ8bKlPRteW2wiwNELLrvAYh0hFIWOWn2PW4sABD1TMOuaoenpRShFMLLeemJkqS9jCsFtBm7QSgFILIRSlnEcdLse9xYACDaueJTJDV0z0iI4ZaM8NI9PUGStLesVm66FqEN7dixQ5MmTVK/fv107bXXaubMmSotLZUkbdu2TePGjVNubq6GDBmiZcuW+ey7YsUK5eXlqXfv3ho9erS2bt3qXedyuTR//nxdc801ys3N1ZQpU3T48GHv+pKSEk2dOlV9+/ZV//79NWfOHDmdTmsuuhEtpQBEOv4CtshJY0rRfQ8Aop4zvr2khkHOjcZPw4Fw0Sk1XnF2Q3VOt45U1ge7OIhQtbW1uuOOO5Sbm6uPPvpIb7zxhsrKyvTggw+qvLxckydP1qhRo1RQUKA5c+bo0Ucf1fbt2yVJGzdu1COPPKJ58+apoKBAt9xyi6ZMmaKamobWfYsXL9a6dev08ssva+3atUpISNDs2bO957733nuVlJSktWvXavny5Vq/fr3y8/Mtue49e/Zo27ZPVFlR3vD622+1ffsnfn19++1eS8oKAK1BXwGLOBtn37MbksukpRQAQHLFp0pikHOEJ5th6Ny0RH1dUk0XPrSZ/fv3q2fPnpo2bZrsdrvi4uI0fvx4zZw5U++8847S0tI0YcIESdKAAQM0cuRILV26VL169dKyZcs0fPhw9enTR5I0ceJEvfTSS1q1apXGjBmjZcuWacaMGerSpYsk6aGHHtLAgQO1d+9eud1ubdq0SR9++KESExPVvXt3TZ06VY899pjuuOOONr3mb7/dq2uuvUo11dXKHDlDyZdcrycXLtDcLa/7dZyExCR9vK5A557bvY1KCgCtRyhlEUdjS6kYmyGXyzyp5RQAIPq4PC2lGOQcYap7eoK+LqnWnrIaxQe7MIhIF110kZ555hmfZatXr9all16qwsJC5eTk+KzLysrS8uXLJUlFRUUaM2bMSet37NihiooKHTx40Gf/zMxMtW/fXl9++aUkKS0tTZ06dfKuv/jii7V//34dO3ZM7dq1a/Y1+NsQtrS0RDXV1Ro3/VHta9dT+2qla0f9RBf98IfNPsbhb7/R8sdnqbS0RN27h0co5amnaGw4HK3XznUHtxz+aqvy8lewBUzT9PYDj7EZqnOZcrjpvgcA0c7TUiqVQc4RprqnNQx2XlLlUKfY5CCXBpHONE098cQTWrNmjZ5//nk999xzSkxM9NkmISFB1dXVkqSqqqrTrq+qqpIkJSUlnbTes+7EfT2vq6ur/QqlOnRIbfa2kpSW1vBvqXtWD1U6OmjfgWPK6NRVWRdkNPsYCQlxkqT09GRlZvp3/mDzt74iSbReO9cd3fgr2AJNu+rZG0fxoqUUAMAZ3/BQQyiFcJUYa1fHlDgdqaxXbdr5wS4OIlhlZaVmzZqlzz//XM8//7x69OihxMREVVRU+GxXW1ur5OSGUCcxMVG1tbUnrU9PT/cGTJ7xpU7c3zTNk9Z5XnuO31wlJRXyZy6AsrKGUKy2rl5us2GG1rp6p6qr65p9jNrahnHejh6tUnFxxVm2Dg2G0fCQ7m99RYJovXauO7yu21PuQOOvYAs0DaViGmfQYEwpAICLUAoRoHtaoo5U1qsu7YJgFwURas+ePbrzzjvVtWtXLV++XBkZDS2GcnJytG7dOp9ti4qKlJ2dLUnKzs5WYWHhSesHDRqk9u3bq1OnTioqKvJ24Tty5IjKysqUk5Mjt9utsrIyFRcXKzMzU5K0c+dOde7cWamp/j2Umab8evD0bmvKOwlGa2a4DKeHXsn/+ook0XrtXHd0C9vZ91wul2677TY98MAD3mWtmRK2LTVtFeUJpei+BwDRrdbhkjuu4dN2BjpHOOueliBJqmt/Lh+6IeDKy8v1k5/8RFdeeaWeffZZbyAlSXl5eSouLlZ+fr4cDoc2bNiglStXeseRGjt2rFauXKkNGzbI4XAoPz9fJSUlysvLkySNHj1aixcv1t69e1VZWam5c+eqX79+Ou+883TBBReoT58+mjt3riorK7V3714tWrRIY8eOtfT6Gx8dxD8tAJEqbD+aXbhwoTZv3qxu3bpJkndK2J/+9KcaP368CgoKNG3aNPXo0UO9evXyTgn79NNPq1evXlq6dKmmTJmiNWvWnNRfPNCcTQIoe+OnHXTfA4DoduBYQzeMWLuh+Jiw/YwIUGZKnOLshuqVoH8frNDlXZs/1g5wNq+88or279+vt956S2+//bbPuq1bt2rJkiWaM2eOFixYoIyMDM2ePVtXX321pIbZ+B5++GH98pe/1KFDh5SVlaWnn35aaWlpkqRp06bJ6XRqwoQJqqqqUv/+/fXEE094j79gwQL9+te/1g033CCbzaZRo0Zp6tSpVl26pOMtpWhNASBShWUotX79er3zzju66aabvMtaOyVsW/J8amhIstl8lwEAotP+Yw3jnKTGx3gfOoBwZDMMdWufoG9Ka7Rx91FCKQTUpEmTNGnSpNOuv/zyy/Xiiy+edv2tt96qW2+99ZTrYmNjNWPGDM2YMeOU6zMzM7VgwQL/Chxgx1tK8ewAIDKF3UezJSUleuihh/R///d/Pi2cTjcl7I4dOyTJp7/4qdb7wzD8+/LMvGczjle40236fZxw/mpJvUXaV7TXQbRff6jWQSQKl+7dB8qPh1JAuOvWOAvfxt1Hg1wSILLYGm/WZFIAIlVY/SXsdrt13333adKkSerZs6fPujNN+dqc9f7wd8T5CrPhZmIzDMXG2iU5FJ8YF3bTs7YWU15SB9F+/RJ1YIVw6d69n1AKEaRb+4ZxpT7df0yVdU6l8HsNBITn8yNaSgGIVGH1F8NTTz2luLg43XbbbSeta82UsP7yd+rG4tKGaV0NQzJdDeNLlR+rCZvpWVvLMMJzystAivY6iPbrl0KzDjxliiTh1L3b04s7Izm2zc4BWKVdQozcxw5L7c7RmHtmKeHoNydt0yEjTUueeqZNzv9fd92hktKy065vy3MDbcnTUoopkgBEqrAKpV577TUdPnxYffv2lSRvyPSPf/xDM2fObPGUsP7yd+pGh6tJ973GG4vDZYbMg6lVmPKSOoj265eog7bk6d69aNEi5efne5efrnv38uXLJTXcC04Mn1rSvdvf7pB3XnOeVv3tj8ruP8W/HVugaffRcP39a3oNVp6vLY4ZKT+HE6/Bse/fim93jjoP+k9de1HGSfu++fiMNvv5lZSWafj03552fdNzW/271Bba8hrCuV4ikefnYYbrmwYAnEVYhVInzrjhGS9k3rx5Onr0qB577DHl5+drwoQJ2rJli1auXKlFixZJapgSdtq0abr55pvVp08fLV261GdK2LbkmX3PZhjeG0vTGfkAAK0TCt27/W11likptXK3UlISTruNYUhJSfEBW5+YGH/G9W157kCtT0yMb/Nzx8Taz9jFPibW3uJ6k07+Ofizf2vWB+LYnrKf6hqcB/6t+O8M1v6KulMe52z12hpn+pmc7tyR0FI0Eq4BZ+ZtKUUmBSBChVUodSbp6emtmhK2LTldTWbf84ZS3FkAIFBCoXt3S7pmOh0uVVfXnXa9aSog6z1hQk1NnU8Zz7R/oM4dqPUnXkNbnvuLzz/XNdcPPe36wp2FGtqCevN8MHXiz8GfsrVmfSCOXVNTd8rfJUlyHPhKhqSyaocOlVYpNcH3z0ynw9VmQxec7d9S03OHYndqf7XlNURi1+5wRkspAJEurEOpefPm+bxuzZSwbcnZdPa9Jt33AACBEQrdu0O5a6anXKFavuaw8hqcbp2xK9hjd41o0XEj/ufgqNU5qfE6VFGnfeW16pmQctr9g+HEc4fyv9nmioRrwJnRUgpApLMFuwDR4HgoZdBSCgDawNtvv61//etf2rx5szZv3qwRI0ZoxIgR2rx5s/Ly8lRcXKz8/Hw5HA5t2LBBK1eu9I4jNXbsWK1cuVIbNmyQw+FQfn6+Zd27gUjjmYVvX3ntWbYE0Bw2WkoBiHBh3VIqXHgCKMNo0n3PxZhSAGCFUO7eDUSabu0T9K9vy7WvvFamacpg1GygVQxaSgGIcIRSFvAEUD7d97izAECbCZfu3UCkOSclTrE2Q3VOt0qrHeqQHBfsIgFhzfOBtpuWUgAiFN33LOBo0n3P83kh3fcAAECksdkMdW7XMAseXfiA1js+0HlwywEAbYWWUhbwzL5no/seAACIcN3SErS3rFb7ymvVq2u7YBdHkvTVVzt067ix3tcxsXY5HS7v6w4ZaVry1DPBKBpwRrbGj7TdIpUCEJkIpSzgO/ue7zIAAIBI0q1dw2DnB4/VyeU2ZbcFf1ypE2dUTEqKV3V1nff1m4/PCEaxgLOipRSASEf3PQs43Z4xpQzvYIWe1lMAAACRJD0pVomxNjndpg5X1J19BwCnZWOgcwARjlDKAqeafc/hpvseAACIPIZhqGv7htZSjCsFtI7N21KKVApAZCKUsoB3TCk1HVOKGwsAAIhM3UIolHK43LJ3ylbRkSodqaxnFjOEFYOWUgAiHGNKWcDZZPY9TxNcxpQCAACRyhNKHamsV73TrbgY6z8HNU1Tnx+s1L++LVfqzdO1pqhEkpQSZ9fA7I46NzXW+8APhCrPB9qEqQAiFS2lLOA6Vfc9WkoBAIAIlRIfo3YJMTIlHThmfWspt2nq/aISrd91VHVOt9xVZeqUGq9Yu6HKepfe/vyg3i8q8f6NBoQqT3BKJgUgUhFKWaDpQOfHW0oxphQAAIhcwezC99HXpSoqrpYhacAF6Tq2fLZuuayTftSnm/qc216GIRUVV2v1jiO0XkdIo6UUgEhHKGUBT6somyF5GonzBxAAAIhkx0Mpa2fg++pIpb48XCVD0o09MnVZl1TJbPgwMMZu05Xd2+vWK7oq1mZoX3mt1hQWyxTd+BCabLSUAhDhGFPKAsfHlGKgcwAAEB26tIuXJJXVOFRd77LknJV1Tq37+qgk6cru7XVBRtIptzu/Q7LyenbU2/8+rF2lNUo5t99pj/lfd92hktKy067vkJGmJU8906pyA6fjiUvd4tkBQGQilLKA09Xw6ZxhGPKMp0n3PQAAEMkSYu3KTI5TcVW9ZV34Nuwuk9NtqlNqvHp3a3fGbbu1T9Cgizvo/aISVXbrp/W7SjXggoyTtispLdPw6b897XHefHxGq8sNnA4tpQBEOrrvWcCnpdQJywAAACKVleNKHSiv1TclDeNIXXthuvdh/kyyOybrO51SJMPQL97coYNBGJQdOBODMaUARDhCKQt4Qykx+x4AAIgeXds3dOHbX17bpp2PTEkFe8skST07pahDclyz9736gnTFVh5Wea1TD76xw9vCHQgFnnCVz7MBRCpCKQscbynVdPY97iwAACCydU6Nl92QqupdciWktdl56tt116GKetkNKffcM3fbO1GMzVB64Sqlxsfo0wPH9MyGPW1USsB/npZSJi2lAEQoQikLeAY1N5oOdM6YUgAAIMLF2G3qlNrQWqquffc2O09F40DlPTulKDnO/yFTY+qOaVZetiTpzxv3aNu+8oCWD2gpWkoBiHSEUhbwBFBNZ9+j+x4AAIgGXRvHlWqrUOqzA8dU366bbIZ0RVf/Wkk1ldejo4Zfco7cpvQ/q3aoss4ZwFICLWNjTCkAEY5QygJNu+8ZdN8DAABRxDPYeV27c+Vqg79//rZlnyTp4sxkJce3bmLpGUOy1LV9gvYfq9Nj/ywKRPGAVjnefS+45QCAtkIoZQFP972mLaVcbpO+4QAAIOJlpsQpzm7IjEnQFwcrAnrsg8dq9c+vjkiSLu+S2urjpcTH6Nc395DNkFZ9cVjv7Djc6mMCreHpvmeKcaUARCZCKQscbyl1PJRquhwAACBS2QxD3dISJUkff1Ma0GO/vO2AXKYUV77Xrxn3zuSKbu31X/3PkyTN+0eRXHEpATku0BKeUEpiXCkAkYlQygKe8MloMvte0+UAAACR7Ly0hi586wIYStU73Xrt04OSpOSD2wN2XEm6fcD5uqxLqirqnDp68U2M54OgsTf5QNvF7yGACEQoZYGmA50bTZcz2DkAAIgC5za2lPr3oUqVVNUH5JhrCot1tMahjilxSjj6dUCO6RFjM/Trm3sqMdam+vbnavv+YwE9PtBctibdLNpiTDYACDZCKQscH1PK8Om+52gMqwAAACJZUpxdsZWHJEnrdwWmtdTybfslSd+/vIsMBf5hvXt6omYMyZIkFewp156jNQE/B3A2DRMlNXxPKAUgErVuihI0i3dMKTV04bPbDLncJi2lAABA1Igv2yVHSid9uLNUIy7t3KpjFR2p0if7jsluSKN6ddYbASrjiUZe2kn/99wrqu50mf5ZWKxRl3dWWmKsX8f4r7vuUElp2WnXd8hI05KnnmllSRHJYgxDDtOkGymAiEQoZYHjY0o1vI7xhFJ82gEAAKJEwtGvVXluf63/plS1DpcSYu0tPpanldTgrEx1TIkPVBFPYhiG2u96X6lZfXSook6rdxzRqMs7Kz6m+Z0NSkrLNHz6b0+7/s3HZwSiqIhgNpsh8ewAIELRfc8Cx2ffa0ilYhv78DlcdN8DAADRIbbqiDqnxqvW6dbG3UdbfJyqeqfe+uKwJGls7y6BKt5pGaZbeT0ylRJn17Fap1bvOMzfcLCUvfEZgpE/AEQiQikLHA+lGl7bG7/h0w4AABAtDEmDszpIktYUlbT4OCs/O6Rqh0sXZCSqb/e0wBTuLBJj7bqpZ0fF2Q0dqqjX2/8+ononCQGs4Xl2YPY9AJGIUMoCTtfx2fekhu57EqEUAACILt/NzpQkrd1Z0qLWRm7T1N+37pMkjc/tJsMwzrJH4HRIjtOwS85RrN3QwYo6rfz8kCpqnZadH9HL3vjExkDnACIRY0pZ4PiYUo3d9+yNoRRNvwEAQBS5olt7dUiOU0lVvdbvOqpBF3fwa/91X5dqb1mtUuNjNPzSTgEr11df7dCt48aeet3OQg1v/L5jSrxGXtpJb/37sEqrHXpl+wEldciRaZqWBmSILt6WUoRSACIQoZQFPDcQz3TFdN8DAADRKMZmaGjPjvrbln1664tDzQ6lPDPYFfccJaWdJ/c3G/UfP/ydd33T4KglnG6ddjDyL+4a4fO6Q3KcRl3eWe99VazDlfWqz/6e7nhxm37U91wNvChDsXY6IiCwPGNK0X0PQCQilLJA04HOTdOk+x4AAIhaN3/nHP1tyz59uLNElXVOpcSf/c/RktIyDbhrrl7edlCGpJEjRio1/vve9ScGR20tJT5GIy/tpE/2H9O/dhVr+/5jmvn6F0qMtenSLu3UrV2CYhpbxpdddIP+8eUR1bncqneacrrdirHZlJoQo47JcXLGt7O07Ag/tJQCEMn4KMcCTpfvQOfHGscfYOYWAAAQbXqck6ILOySp3mXqnR2Hm73f5wcqJUnnZyQqtRlBVluz2QxdeW57nfPJc5rYr7sykmJV43Br854yvfbZQb287YBe3nZA1edcqm9Ka7S/vE7FVfUqq3GquKpe35RUa9OeMh3Onah7X/lMRUeqgn1JCFHellKEUgAiUPDv6FHAO6ZU42tPOEVLKQAAEG0Mw9Coyzvr8fe/1otb9+v7vbqcdTwmV2ySChtDm8u6pFpRzGazO6o07boLNWXgBSo8XKWvjlTqUEWdPH/lvfDiC+o1eLji7DbFxdgUYzPkcLlVVuPUvvJafXu0Wuu+KdW6nUfUbu/HSj6wVU1ro0NGmpY89UwwLg0h4vjse0EuCAC0AVpKWcAztIBngHOb4RnonDsLAACIPrdc1llJsfaG1kK7y866fWW3q+QyTZ2TEqfOqfFtX8AWsBmGenRK0cjLOuuOAefrzsav1P2bdUnnVGV1TNZ56Ynq2j5B52ck6Ypu7TTsknNUseJXOj89UbLZdez865Q84gHdfO9jGj79txo+/bcqKS0L9qWFjNLSUuXl5Wnjxo3eZdu2bdO4ceOUm5urIUOGaNmyZT77rFixQnl5eerdu7dGjx6trVu3ete5XC7Nnz9f11xzjXJzczVlyhQdPny89V5JSYmmTp2qvn37qn///pozZ46cTutnXKT7HoBIRihlgZk3ZOmBodlKiGmobk9LKQc3FgAAEIVS4mM08rKG2fOe3/ztGbc9cKxWVedcJknqe15axM1y5644orwemRp4UboMQyoqrtY/vyqWm0GtfWzZskXjx4/Xnj17vMvKy8s1efJkjRo1SgUFBZozZ44effRRbd++XZK0ceNGPfLII5o3b54KCgp0yy23aMqUKaqpqZEkLV68WOvWrdPLL7+stWvXKiEhQbNnz/Ye/95771VSUpLWrl2r5cuXa/369crPz7f0uqXjzw6EUgAiEaGUBYb27KifXN3d+9rTUooxpQAAQLT6jyu7yW4ztGH3UW3YVXra7X63Zqdks6tLu3h1a59gYQmtYxiGvtMpVXk5mbIZ0jelNdqw62iwixUyVqxYoRkzZmj69Ok+y9955x2lpaVpwoQJiomJ0YABAzRy5EgtXbpUkrRs2TINHz5cffr0UWxsrCZOnKj09HStWrXKu/7OO+9Uly5dlJKSooceekgffvih9u7dq927d2vTpk267777lJiYqO7du2vq1KneY1vpePc9QikAkYdQKggae/Gp1kkoBQAAotO5aYn6Qe+ukqQnPvj6lGNt/rOwWO8XlUhul665IN3qIlru/IwkfTc7U5L0+cFK7ThUGeQShYaBAwfq3Xff1bBhw3yWFxYWKicnx2dZVlaWduzYIUkqKio67fqKigodPHjQZ31mZqbat2+vL7/8UoWFhUpLS1OnTp286y+++GLt379fx44dC/QlnhHd9wBEsrALpXbs2KFJkyapX79+uvbaazVz5kyVljZ8utaaPuVWimm8sdQRSgEAgCh2x4Dz1D4hRjuLqxtaRDVRVFyl36z+SpKUsn+LMpLjglFEy13UIUl9u7eXJH2866gciRlBLlHwdezYUTExJ8/PVFVVpcTERJ9lCQkJqq6uPuv6qqqGgfOTkpJOWl9VVXXKfT2vPcdvLsPw/6thx4b/eWbfc7cwlGrJ+YP1FW7l5dq57mi77rYQVrPv1dbW6o477tAPfvADPfXUU6qqqtL999+vBx98UPPnz9fkyZP105/+VOPHj1dBQYGmTZumHj16qFevXt4+5U8//bR69eqlpUuXasqUKVqzZs1JN5xAM01TZpPmtp5PO+ocrjY9LwBEkx07dmj+/Pn6/PPPFRsbq2uvvVYPPPCAMjIytG3bNv3mN79RUVGR0tPTNWXKFI0bN86774oVK7Ro0SIdOXJEF110kX7xi18oNzc3iFcDRId2CbH6xdAc3ffaF1r2yX7ZbYZu63uu/n2oUvP+UaiKOqeu6NpOhzcWSBob7OJapne3djp4rE7fltfqaNb35HC5FWsPu8+S21xiYqIqKip8ltXW1io5Odm7vra29qT16enp3r//PeNLnbi/aZonrfO89hy/uTp08G/GyLS0huMnxMcpKSleCfENj2yG3aakpOYN9J+Q0BDipqcnKzMztGasPBt/6yuSROu1c93RLaxCqf3796tnz56aNm2a7Ha74uLiNH78eM2cOdOnT7kknz7lvXr18ulTLkkTJ07USy+9pFWrVmnMmDFtXvbH3/nS+72npRTd9wAgMML1QwsA0uCsTN0z6EIt+PAbvfivfXrxX/u86y7qkKT/G3WpfvxKdH2QZxiGrs/uoOWfHFBtcqb+vHGPJl9zQbCLFXJycnK0bt06n2VFRUXKzs6WJGVnZ6uwsPCk9YMGDVL79u3VqVMnny5+R44cUVlZmXJycuR2u1VWVqbi4mJlZjZ0qdy5c6c6d+6s1FT/HiRLSirkz3BQZWUNrbhq6+pVXV0nd+M4tHX1TlVX1zXrGLW19ZKko0erVFxccZatQ4NhNDyk+1tfkSBar53rDq/r9pQ70MLqI5eLLrpIzzzzjOx2u3fZ6tWrdemll7aqT7m/WtTMrUlbN09LqXqnO+jN72ieSB1w/dFdB5Gi6YcWcXFxSk9P9wZQrR0IF0Dbu+2q7nr8+5fqgoyGIDgx1qYfX9VdS37YW+0TY4NcuuBIjLXrmgvTJUlLNu7V1yVVQS5R6MnLy1NxcbHy8/PlcDi0YcMGrVy50vuB89ixY7Vy5Upt2LBBDodD+fn5KikpUV5eniRp9OjRWrx4sfbu3avKykrNnTtX/fr103nnnacLLrhAffr00dy5c1VZWam9e/dq0aJFGjvW/xZ7pun/V8OODf/zdN9ztfDhtSXnD9ZXuJWXa+e6o+2620JYtZRqyjRNPfHEE1qzZo2ef/55Pffccy3uU+4vf9NBl6vh072kpDi53aYS4ho+7TBi7WHXnLY1aJ5IHUT79UvUQVvxfGjR1Nk+tFi+fLmkhg8tTmwx29IPLQC03MCLOujaCzNk6vhMxdHuog5J+nhjgWozLtLja77WgjGXyaBuvNLT07VkyRLNmTNHCxYsUEZGhmbPnq2rr75aUkPPiYcffli//OUvdejQIWVlZenpp59WWlqaJGnatGlyOp2aMGGCqqqq1L9/fz3xxBPe4y9YsEC//vWvdcMNN8hms2nUqFGaOnWq5dfJQOcAIllYhlKVlZWaNWuWPv/8cz3//PPq0aNHq/qU+8vfZnZud0MoVV1dL7fbLbOxCe7RitqwaU7bGoYRns0TAyna6yDar18KzTrwlCnSBOtDi1B+TmzaUi9Ufv/81fQawlWk/Rxacg1n+/lZEbi0xc+hrYptGIZK3s9X8qiHtWH3UX1v8n1KKNslGVJMjF1pqala8qdnznoc/84Z0MMF3Jdffunz+vLLL9eLL7542u1vvfVW3XrrradcFxsbqxkzZmjGjBmnXJ+ZmakFCxa0vLAB4hlOjFAKQCQKu1Bqz549uvPOO9W1a1ctX75cGRkNM5K0pk+5v/xtunbitvbGm32dwx22f5S2RFs2+QsX0V4H0X79EnXQ1oL5oUVLAr6YWPsZB601DAV0fWJi/BnXt+W5A7U+MTE+ZMvWnH2lk38OoVK25qz3lP1U13C2/WNa0UK8Lf6tNL2G1tTb2a6rtWV3VhSrz4WZ2rL7qFy5YzT66vO9LWdW/O+9UdXqPlod777HHxAAIk9YhVLl5eX6yU9+oquvvlpz5syRzXZ8SKy8vDw99thjys/P14QJE7RlyxatXLlSixYtktTQp3zatGm6+eab1adPHy1dutSnT7mVvLPvMdA5AARMsD+0aEkrOKfDdcZBa01TAVnvCRNqaup8ynim/QN17kCtP/EaQqlszV3naYFy4s8hFMrW3PU1NXWn/F1qzv5Oh6vFLcQD+W/lVP8eWlNvZ7uuQJT9sk7J+mJ/ucprHCr4ulhXdGunxMR4OZ0tr9PTidRWtOGM7nsAIllYDXT+yiuvaP/+/XrrrbfUp08f5ebmer88fcrffvtt9e/fX7Nnzz5tn/J+/frpzTff9OlT3tZM05TZ+JeP3Tv7XnTNJAMAbcXzocWVV16pZ5991htISa0fCLe5QmWwyNOVren/wxHXEBpaew2hMLBqW/wc2rrscXab+p2XJkn617flqqlv/BsyjAayRcsRSgGIZGHVUmrSpEmaNGnSade3pk+5lWIMWkoBQCA1/dDi7bff9lm3devWVg2ECwChILtjsj47UKGSaoe27T+m77ZPCnaRYBG67wGIZGEVSkUKz2CFhFIAEBiR8qEFAJyOYRjqc16a3tlxRJ8frFS/i5z66qsdunXc2NPu0yEjTUueCuxA6LCezdtSKsgFAYA2QCgVBN7uew7uLAAAAGie89ISdE5KnA5X1qtgV6mcbmn49N+edvs3Hz/1rHIILzGNzw5uuu8BiECEUkFwvPseY0oBAACEs7O1VvpqZ6GGB+hchmGo73lpWvXFYX2275iMZP9mCUV4stF9D0AEI5QKguMDndNSCgAAIJydrbXSF3eNCOj5urVPUNd28dp/rE4JVwwL6LERmjxDfzDQOYBIFFaz70UKTxNcxpQCAACAv/o2zsQXl3W1jtU6g1sYtDnvQOeEUgAiEKFUEHg+7aClFAAAAPzVKTVe52ckybDZ9cm+8mAXB23M08uC7nsAIhHd94Kg6acdTpdbMXayQQAAENnONPYSs8T5r/9FGdpdWq2vjlSpd7f2apfAn/WRyhNKuU3JNE0Zjc8SABAJuHsFgefGIjW0lkohlAIAABHuTGMvMUuc/7q0T5Rj3xeK7XaJPtlXrkEXdwh2kdBG7E1CKJfbVIydUApA5CANCYKm9xHGlQIAAEBL1H6ySpL01ZEqVTC2VMRq+oG2ix58ACIMoVQQGIah+JiGqieUAgAAQEu4jnytbu0TZJpibKkI1iSTYrBzABGHUCpICKUAAADQWlee216S9OWRKlXU0VoqEhmG4e1pwWDnACINoVSQJDSGUrVOV5BLAgAAgHDVuV28uraPb2gt9e2xYBcHbcQ7Ax8tpQBEGEIpi5hNPtUwTfN4SykHLaUAAADQcp7WUl8dqaS1VIQilAIQqQilgsA0TcXb6b4HAACA1uvSLkFd28XLbUqf7KO1VCTyzMDnpvsegAhDKBUk8bF03wMAAEBgXNm9sbXU4UpV0loq4tgaW0o5aSkFIMIQSgUJA50DAAAgULq0S1AXWktFrJjGUMrNowOACEMoFSSe7nu1hFIAAAAIgD6emfhoLRVxbI3d95h9D0CkiQl2AaJR04HOaxnoHAAARLmvvtqhW8eNPf36nYUabmF5wlWX9g2tpQ4cq6O1VIRhoHMAkYpQKgjcbrfi7XZJUh1jSgEAgCjndEvDp//2tOu/uGuEhaUJb1ee215vfnFYXx6uVMe4lGAXBwHS2MmCUApAxKH7XpB8XVIliTGlAAAAEDhd2x8fW6qia99gFwcBYqf7HoAIRSgVJJ4muIwpBQAAgEC6snFsqepzLlVxZV2QS4NAoPsegEhFKBUknhk0aCkFAACAQOrSLl7ntk+QDJvKaxnwPBLENvbfq3cRSgGILIRSQWCaprcJLmNKAQAAIJAMw1Bej0yd88lfdHFmcrCLgwBIaJwkqc7BswOAyEIoFSSellLMvgcAAIBAi7HbFFPHDHyRIiG2ceZuelkAiDCEUkHimUGD7nsAAAAAziQhpmHm7hpaSgGIMIRSQXK8+x6hFAAAAIDT87SU4tkBQKQhlAoSe0MmddKnHaZpymSqVwAAAACNPC2lGPoDQKQhlAoSz6cdRyrrg1wSAAAAAKHs+JhSdN8DEFkIpYIkNa7h044Dx2rlctMyCgAAAMCpeWbfq3eZPDsAiCiEUkESb2+Ygc/pNnWksi7YxQEAAAAQouJjbGoc/YNxpQBEFEIpCzSMEeX7iYbNMNQ5NV6StK+8NgilAgAAABAODMNQfGNrqVpm4AMQQQilgqhrO0IpAAAAAGfnGVeqhpZSACIIoVSQuN1udWkXJ4lQCgAAAMCZHZ+Bj5ZSACIHoVQQ7T1aLUnaV1YT5JIAAAAACGWellKMKQUgkhBKBVFK4wx8+2kpBQAAAOAMPC2lahyEUgAiB6FUkJimqeS4hupv2n3P7XbL7eZGAwAAAOA4T0upWifd9wBEDkKpIEqKafh/abVD1fUNNxfTNL1fAAAAACA16b5HSykAEYRQKohiDCnebkiStu8v9y5/6qNvCKcAAAAAeHkHOmdMKQARhFAqiEzT1IUZCZKkpZv3SZKq6l06UFGn+iholkvoBgAAADSPp6WUp4cFAEQCQqkg69EhQXZD2rD7qP625VvdtvQTrf6qVKOe3axXtx8IdvEAAAAAhIAOSXGSpKM1DlXVO4NcGgAIjKgLpUpKSjR16lT17dtX/fv315w5c+R0Bu9NPdEuDb4oTZL0+Ptf68CxuoZyVjs09x9FevLDr1XvdMntdtOyCADaUKjdHwAAwRdK94a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       "text/plain": [
        "<Figure size 1200x2800 with 19 Axes>"
       ]
@@ -3200,7 +3295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -3329,7 +3424,7 @@
        "P-value      2.417229e-17    2.430304e-10    1.000000  "
       ]
      },
-     "execution_count": 243,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3398,14 +3493,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\ADMIN\\anaconda3\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
       "  vif = 1. / (1. - r_squared_i)\n"
      ]
     },
@@ -3553,7 +3648,7 @@
        "16      total_sqft        inf"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3587,6 +3682,1099 @@
     "<br>Addressing multicollinearity in this case may require further investigation, such as feature selection, dimensionality reduction techniques or applying regularization methods to mitigate multicollinearity effects and improve model performance. \n",
     "<br>Overall, understanding the VIF values can help refine the regression model and ensure the reliability of the coefficient estimates.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MODELLING."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Baseline model - simple linear model.\n",
+    "2. log transformation. \n",
+    "3. Multiple Linear Regression\n",
+    "4. Residual modelling.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Baseline model \n",
+    "\n",
+    " Baseline models provide a reference point for comparing the performance of more complex models. \n",
+    " Its purpose is to establish a benchmark against which the performance of more sophisticated models can be evaluated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FUNCTIONS TO BE USED."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#use only numeric columns.\n",
+    "def numeric_col(housing_data):\n",
+    "    '''returns a dataframe with only numeric values'''\n",
+    "    for column in housing_data.columns:\n",
+    "        if is_numeric_dtype(housing_data[column]) == False:\n",
+    "            housing_data = housing_data.drop(column, axis=1)\n",
+    "        else:\n",
+    "            continue\n",
+    "    return housing_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set a function for the predictor and target variale.\n",
+    "def X_Y(housing_data, target):\n",
+    "    '''Returns a series of target (y) values and a DataFrame of predictors (X)'''\n",
+    "    y = housing_data[target]  # target variable\n",
+    "    X = housing_data.drop(target, axis=1)  # predictor features\n",
+    "    return y, X\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#A higher train score indicates that the model fits the training data well.\n",
+    "#A high test score suggests that the model is able to make accurate predictions on data it hasn't seen before, which is the ultimate goal in machine learning.\n",
+    "\n",
+    "def get_metrics(X_train, X_test, y_train, y_test):\n",
+    "    ''' Parameters are X train, X test, y train, & y_test\n",
+    "        Performs multiple regression on the split test and returns metrics'''\n",
+    "\n",
+    "    # Initialize Linear Regression model\n",
+    "    lr = LinearRegression()\n",
+    "\n",
+    "    lr.fit(X_train, y_train)\n",
+    "\n",
+    "    train_score = lr.score(X_train, y_train)\n",
+    "    test_score = lr.score(X_test, y_test)\n",
+    "\n",
+    "    y_hat_train = lr.predict(X_train)\n",
+    "    y_hat_test = lr.predict(X_test)\n",
+    "\n",
+    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_hat_train))\n",
+    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_hat_test))\n",
+    "\n",
+    "    return train_score, test_score, train_rmse, test_rmse\n",
+    "#These scores provide insights into how well the model is performing both on the data it was trained on and on new data.\n",
+    "# They help assess the model's overall effectiveness and whether it is overfitting or underfitting.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test(housing_data, size=0.20):\n",
+    "    '''Takes in dataframe, and size of test for the split\n",
+    "        Returns the train_set and test_set'''\n",
+    "    train_set, test_set = train_test_split(housing_data, test_size=size, random_state=42)\n",
+    "    return train_set, test_set\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIMPLE LINEAR REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def simple_linear_regression(housing_data):\n",
+    "    '''Creates a simple linear regression model with prices as the target variable \n",
+    "    and the number of bedrooms as the predictor. Returns the model along with R-squared, \n",
+    "    Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both train and test sets.'''\n",
+    "    \n",
+    "    # Extracting features and target variable\n",
+    "    X = housing_data[['sqft_living']]  # Predictor feature\n",
+    "    y = housing_data['price']  # Target variable (prices)\n",
+    "    \n",
+    "    # Splitting the data into train and test sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    \n",
+    "    # Create a linear regression model\n",
+    "    model = LinearRegression()\n",
+    "    \n",
+    "    # Fit the model to the training data\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Calculate predictions for train and test sets\n",
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate R-squared for train and test sets\n",
+    "    r2_train = r2_score(y_train, y_train_pred)\n",
+    "    r2_test = r2_score(y_test, y_test_pred)\n",
+    "    \n",
+    "    # Calculate Mean Squared Error (MSE) for train and test sets\n",
+    "    mse_train = mean_squared_error(y_train, y_train_pred)\n",
+    "    mse_test = mean_squared_error(y_test, y_test_pred)\n",
+    "    \n",
+    "    # Calculate Root Mean Squared Error (RMSE) for train and test sets\n",
+    "    rmse_train = np.sqrt(mse_train)\n",
+    "    rmse_test = np.sqrt(mse_test)\n",
+    "    \n",
+    "    # Print coefficients, R-squared, MSE, and RMSE for train and test sets\n",
+    "    print(\"Training set:\")\n",
+    "    print(\"Intercept:\", model.intercept_)\n",
+    "    print(\"Coefficient:\", model.coef_[0])\n",
+    "    print(\"R-squared:\", r2_train)\n",
+    "    print(\"Mean Squared Error:\", mse_train)\n",
+    "    print(\"Root Mean Squared Error:\", rmse_train)\n",
+    "    \n",
+    "    print(\"\\nTest set:\")\n",
+    "    print(\"R-squared:\", r2_test)\n",
+    "    print(\"Mean Squared Error:\", mse_test)\n",
+    "    print(\"Root Mean Squared Error:\", rmse_test)\n",
+    "    \n",
+    "    return model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "# Assuming housing_data is your dataset\n",
+    "# model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test = simple_linear_regression(housing_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set:\n",
+      "Intercept: -44593.95245340909\n",
+      "Coefficient: 281.4088930446383\n",
+      "R-squared: 0.49587806055811734\n",
+      "Mean Squared Error: 68601152192.940285\n",
+      "Root Mean Squared Error: 261918.21661148407\n",
+      "\n",
+      "Test set:\n",
+      "R-squared: 0.48264829402430887\n",
+      "Mean Squared Error: 68845100756.10751\n",
+      "Root Mean Squared Error: 262383.4993975565\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(LinearRegression(),\n",
+       " 0.49587806055811734,\n",
+       " 68601152192.940285,\n",
+       " 261918.21661148407,\n",
+       " 0.48264829402430887,\n",
+       " 68845100756.10751,\n",
+       " 262383.4993975565)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simple_linear_regression(housing_data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.\n",
+    "\n",
+    "\n",
+    "Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.\n",
+    "\n",
+    "\n",
+    "R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.\n",
+    "\n",
+    "\n",
+    "Test Set:\n",
+    "\n",
+    "R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared: 0.48264829402430875\n",
+      "Mean Squared Error: 68845100756.10753\n",
+      "Root Mean Squared Error: 262383.4993975565\n",
+      "Intercept: 540631.1560929463\n",
+      "Coefficients: [259767.82181675]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#STANDARDIZE OUR MODEL TO GET RID OF THE NEGATIVE INTERCEPT.\n",
+    "\n",
+    "\n",
+    "# Assuming you have your data loaded into X and y\n",
+    "# X should be your independent variables and y should be your dependent variable\n",
+    "\n",
+    "# Split data into training and test sets\n",
+    "X = housing_data[['sqft_living']]  # Predictor feature\n",
+    "y = housing_data['price']  # Target variable (prices)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize the StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Fit and transform the scaler on the training data\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "\n",
+    "# Transform the test data using the scaler fitted on the training data\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Fit the linear regression model on the scaled training data\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Predict on the scaled test data\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "# Calculate R-squared and mean squared error on the test set\n",
+    "r_squared = r2_score(y_test, y_pred)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "# Print the metrics\n",
+    "print(\"R-squared:\", r_squared)\n",
+    "print(\"Mean Squared Error:\", mse)\n",
+    "print(\"Root Mean Squared Error:\", rmse)\n",
+    "\n",
+    "# Print the intercept and coefficients of the model\n",
+    "print(\"Intercept:\", model.intercept_)\n",
+    "print(\"Coefficients:\", model.coef_)\n",
+    "\n",
+    "#This code will scale your data using StandardScaler, which standardizes features by removing the mean and scaling to unit variance. \n",
+    "#It ensures that the intercept is not negative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared (0.48): This value indicates that approximately 48% of the variability in the dependent variable (the house prices) is explained by the independent (sqft living). In simpler terms, the model captures about 48% of the patterns in the data.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE) (68845100756.11): This is the average squared difference between the actual values and the predicted values from the model. It's a measure of the model's accuracy, where lower values indicate better performance. In this case, the average squared difference is quite large, indicating that there's still room for improvement in the model's predictive accuracy.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE and provides a measure of the typical deviation of the predicted values from the actual values. It's in the same units as the dependent variable. Here, the RMSE indicates that, on average, the predicted values differ from the actual values by approximately 262,383.50 units.\n",
+    "\n",
+    "\n",
+    "Intercept (540631.16): This is the estimated value of the dependent variable when all independent variables are set to zero. In this case, it suggests that when all other factors are zero, we would expect the dependent variable to be around 540,631.16.\n",
+    "\n",
+    "\n",
+    "Coefficient (259767.82): This represents the change in the house prices for a one-unit change in the sqft living, while holding other variables constant. In this case, for every one-unit increase in the sqft living, we'd expect the house prices to increase by approximately 259,767.82 units.\n",
+    "\n",
+    "\n",
+    "Overall, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MULTIPLE LINEAR REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 47548419769.890594\n",
+      "R-squared: 0.6426869041626193\n",
+      "          Feature    Coefficient\n",
+      "0           const       0.000000\n",
+      "1       bathrooms   24855.944322\n",
+      "2     sqft_living  117349.853313\n",
+      "3          floors   20057.401179\n",
+      "4      waterfront   63378.047594\n",
+      "5       condition   11809.217548\n",
+      "6           grade  154829.185004\n",
+      "7   sqft_basement   14434.666872\n",
+      "8        yr_built -108064.021718\n",
+      "9    yr_renovated    7255.426167\n",
+      "10  sqft_living15   25765.797630\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Define a function to keep only numeric columns\n",
+    "def only_numeric(housing_data):\n",
+    "    '''returns a DataFrame with only numeric values'''\n",
+    "    numeric_columns = [column for column in housing_data.columns if is_numeric_dtype(housing_data[column])]\n",
+    "    return housing_data[numeric_columns]\n",
+    "\n",
+    "# Sample features and target variable\n",
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "\n",
+    "    # Split the data into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Standardize the features\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train)\n",
+    "    X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "    # Build a basic linear regression model\n",
+    "    model = LinearRegression()\n",
+    "    model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "    # Make predictions on the test set\n",
+    "    y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "    # Evaluate the model\n",
+    "    mse = mean_squared_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "    # Display results\n",
+    "    print(\"Mean Squared Error:\", mse)\n",
+    "    print(\"R-squared:\", r2)\n",
+    "\n",
+    "    # Display coefficients\n",
+    "    coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
+    "    print(coefficients)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean Squared Error (MSE): The MSE is a measure of the average squared difference between the actual and predicted values. In this case, the MSE is approximately \n",
+    "4.75\n",
+    "×\n",
+    "1\n",
+    "0\n",
+    "10\n",
+    "4.75×10 \n",
+    "10\n",
+    "\n",
+    "\n",
+    " , which means, on average, the squared difference between the actual housing prices and the predicted prices is \n",
+    "4.75\n",
+    "×\n",
+    "1\n",
+    "0\n",
+    "10\n",
+    "4.75×10 \n",
+    "10\n",
+    " .\n",
+    "\n",
+    "\n",
+    "R-squared (\n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "\n",
+    "\n",
+    " ): The \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  score measures the proportion of the variance in the target variable (housing prices) that is explained by the independent variables (features) in the model. \n",
+    "  \n",
+    "  An \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  score of 0.643 means that approximately 64.3% of the variance in housing prices is explained by the features included in the model. In other words, the model accounts for 64.3% of the variability in housing prices.\n",
+    "\n",
+    "\n",
+    "Coefficients:\n",
+    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero. In this case, it's not meaningful as it's unlikely for all features to be zero.\n",
+    "\n",
+    "\n",
+    "bathrooms: For each additional bathroom, the predicted housing price increases by approximately $24,855.\n",
+    "\n",
+    "\n",
+    "sqft_living: For each additional square foot of living space, the predicted housing price increases by approximately $117,350.\n",
+    "\n",
+    "\n",
+    "floors: Houses with an additional floor have a predicted price increase of approximately $20,057.\n",
+    "\n",
+    "\n",
+    "waterfront: Properties with waterfront views have a predicted price increase of approximately $63,378 compared to those without.\n",
+    "\n",
+    "\n",
+    "condition: Better condition properties (on a scale from 1 to 5) tend to have a predicted price increase of approximately $11,809 for each unit increase in condition.\n",
+    "\n",
+    "grade: Higher grade properties (on a scale from 1 to 13) have a predicted price increase of approximately $154,829 for each unit increase in grade.\n",
+    "\n",
+    "\n",
+    "sqft_basement: For each additional square foot of basement space, the predicted price increases by approximately $14,435.\n",
+    "\n",
+    "\n",
+    "yr_built: Each additional year of age decreases the predicted price by approximately $108,064.\n",
+    "\n",
+    "\n",
+    "yr_renovated: For each year renovated, the predicted price increases by approximately $7,255.\n",
+    "\n",
+    "\n",
+    "sqft_living15: For each additional square foot of living space in the nearest 15 neighbors' homes, the predicted price increases by approximately $25,766.\n",
+    "\n",
+    "\n",
+    "These coefficients indicate the strength and direction of the relationship between each feature and the target variable, holding other features constant. For example, features like square footage of living space, grade, and whether the property has a waterfront view have a substantial positive impact on the predicted housing price, while the year the property was built has a negative impact.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.641\n",
+      "Model:                            OLS   Adj. R-squared:                  0.641\n",
+      "Method:                 Least Squares   F-statistic:                     3768.\n",
+      "Date:                Tue, 30 Apr 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:34:27   Log-Likelihood:            -2.9014e+05\n",
+      "No. Observations:               21142   AIC:                         5.803e+05\n",
+      "Df Residuals:                   21131   BIC:                         5.804e+05\n",
+      "Df Model:                          10                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "=================================================================================\n",
+      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "---------------------------------------------------------------------------------\n",
+      "const          6.604e+06   1.41e+05     46.979      0.000    6.33e+06    6.88e+06\n",
+      "bathrooms       3.47e+04   3547.740      9.781      0.000    2.77e+04    4.17e+04\n",
+      "sqft_living     129.1822      3.765     34.313      0.000     121.803     136.562\n",
+      "floors         3.576e+04   3886.909      9.201      0.000    2.81e+04    4.34e+04\n",
+      "waterfront     7.828e+05   1.88e+04     41.703      0.000    7.46e+05     8.2e+05\n",
+      "condition      1.788e+04   2568.771      6.961      0.000    1.28e+04    2.29e+04\n",
+      "grade          1.319e+05   2290.923     57.586      0.000    1.27e+05    1.36e+05\n",
+      "sqft_basement    27.2065      4.581      5.939      0.000      18.228      36.185\n",
+      "yr_built      -3728.7602     71.951    -51.823      0.000   -3869.790   -3587.730\n",
+      "yr_renovated     18.3132      4.407      4.156      0.000       9.676      26.950\n",
+      "sqft_living15    34.6185      3.669      9.435      0.000      27.427      41.810\n",
+      "==============================================================================\n",
+      "Omnibus:                    16539.863   Durbin-Watson:                   1.978\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1296266.479\n",
+      "Skew:                           3.184   Prob(JB):                         0.00\n",
+      "Kurtosis:                      40.828   Cond. No.                     3.35e+05\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, 3.35e+05. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fit the OLS model\n",
+    "model = sm.OLS(y, X).fit()\n",
+    "\n",
+    "# Get the summary\n",
+    "summary = model.summary()\n",
+    "\n",
+    "# Print the summary\n",
+    "print(summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared (R²): The coefficient of determination is 0.641, indicating that approximately 64.1% of the variance in the housing prices is explained by the independent variables included in the model.\n",
+    "\n",
+    "\n",
+    "Adjusted R-squared: The adjusted R-squared is also 0.641, which adjusts for the number of predictors in the model. It's useful when comparing models with different numbers of predictors.\n",
+    "\n",
+    "\n",
+    "F-statistic: The F-statistic is 3768, with a p-value close to zero, indicating that the overall model is statistically significant.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The standard errors, condition number, and other diagnostic information are also provided. \n",
+    "The condition number being large (3.35e+05) suggests that there may be strong multicollinearity or other numerical issues in the model. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
+      "  order = pd.unique(vector)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "inf_coefs = list(zip(coefficients[\"Feature\"], coefficients[\"Coefficient\"]))\n",
+    "inf_coefs.sort(key=lambda x: abs(x[1]), reverse=True)  # Sort coefficients by absolute value\n",
+    "\n",
+    "# Create a color palette with the specified color\n",
+    "color = \"#589aff\"\n",
+    "colors = [color if coef[1] > 0 else \"lightgray\" for coef in inf_coefs]\n",
+    "\n",
+    "# Create the bar plot\n",
+    "fig, ax = plt.subplots(figsize=(18, 8))\n",
+    "ax = sns.barplot(x=[x[0] for x in inf_coefs], y=[x[1] for x in inf_coefs], palette=colors)\n",
+    "plt.xticks(rotation=45)\n",
+    "ax.set_ylabel(\"Price Coefficients\", fontsize=15)\n",
+    "ax.set_xlabel(\"House Features\", fontsize=15)\n",
+    "ax.set_title(\"House Features and Sale Prices\", fontsize=20);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.\n",
+    "\n",
+    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# POLYNOMIAL REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Polynomial regression is a type of regression analysis where the relationship between the independent variable (or variables) and the dependent variable is modeled as an nth degree polynomial. Unlike simple linear regression, which assumes a linear relationship between the variables, polynomial regression can capture more complex relationships by introducing polynomial terms.\n",
+    "\n",
+    "In polynomial regression, the relationship between the independent variable \n",
+    "𝑥\n",
+    "x and the dependent variable \n",
+    "𝑦\n",
+    "y is modeled as:\n",
+    "\n",
+    "𝑦\n",
+    "\n",
+    "=\n",
+    "𝛽\n",
+    "0\n",
+    "+\n",
+    "𝛽\n",
+    "1\n",
+    "𝑥\n",
+    "+\n",
+    "𝛽\n",
+    "2\n",
+    "𝑥\n",
+    "2\n",
+    "+\n",
+    "𝛽\n",
+    "3\n",
+    "𝑥\n",
+    "3\n",
+    "+\n",
+    ".\n",
+    ".\n",
+    ".\n",
+    "+\n",
+    "𝛽\n",
+    "𝑛\n",
+    "𝑥\n",
+    "𝑛\n",
+    "+\n",
+    "𝜀\n",
+    "y=β \n",
+    "0\n",
+    "​\n",
+    " +β \n",
+    "1\n",
+    "​\n",
+    " x+β \n",
+    "2\n",
+    "​\n",
+    " x \n",
+    "2\n",
+    " +β \n",
+    "3\n",
+    "​\n",
+    " x \n",
+    "3\n",
+    " +...+β \n",
+    "n\n",
+    "​\n",
+    " x \n",
+    "n\n",
+    " +ε"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Model (Degree 2)- MSE: 35637716070.71762\n",
+      "Polynomial Model (Degree 2)- R-squared: 0.7321925161881991\n"
+     ]
+    }
+   ],
+   "source": [
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "    \n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Polynomial Regression\n",
+    "# Choose the degree of the polynomial\n",
+    "degree = 2\n",
+    "\n",
+    "# Create polynomial features\n",
+    "poly = PolynomialFeatures(degree)\n",
+    "X_train_poly = poly.fit_transform(X_train)\n",
+    "X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "# Build a polynomial regression model\n",
+    "poly_model = LinearRegression()\n",
+    "poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "# Evaluate the polynomial model\n",
+    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
+    "r2_poly = r2_score(y_test, y_pred_poly)\n",
+    "print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
+    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean Squared Error (MSE): This value represents the average squared difference between the actual house prices and the predicted prices by the polynomial model.\n",
+    " A lower MSE indicates better performance, and in this case, the MSE is lower than the previous model's MSE, suggesting that the polynomial model fits the data better.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "Generally,The \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  value for the first model is 0.641, indicating that approximately 64.1% of the variance in housing prices is explained by the independent variables included in the model. On the other hand, the \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  value for the polynomial model of degree 2 is 0.732, suggesting that approximately 73.2% of the variance in housing prices is explained by this polynomial model.\n",
+    "\n",
+    "Comparing the two \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  values, we can see that the polynomial model of degree 2 explains more variance in housing prices compared to the first model. This suggests that the polynomial model provides a better fit to the data and captures more of the variability in housing prices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.diagnostic import het_breuschpagan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TEST FOR HOMOSCEDASCITICY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breusch-Pagan test p-value: 3.975373048166964e-258\n"
+     ]
+    }
+   ],
+   "source": [
+    "poly = PolynomialFeatures(degree)\n",
+    "X_train_poly = poly.fit_transform(X_train)\n",
+    "X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "# Build a polynomial regression model\n",
+    "poly_model = LinearRegression()\n",
+    "poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred_poly\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Perform Breusch-Pagan test\n",
+    "lm, lm_p_value, fvalue, f_p_value = het_breuschpagan(residuals, X_test_poly)\n",
+    "print(\"Breusch-Pagan test p-value:\", lm_p_value)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.\n",
+    "\n",
+    "Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Log transformation.\n",
+    "Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. \n",
+    "Common transformations include taking the natural logarithm, square root, or reciprocal of the variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Define function to keep only numeric columns\n",
+    "def only_numeric(df):\n",
+    "    return df.select_dtypes(include=[np.number])\n",
+    "\n",
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "    \n",
+    "    # Log transform features and target variable\n",
+    "    X_log = np.log(X + 1)  # Adding 1 to avoid log(0)\n",
+    "    y_log = np.log(y)\n",
+    "    \n",
+    "    # Split the data into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Polynomial Regression\n",
+    "    # Choose the degree of the polynomial\n",
+    "    degree = 2\n",
+    "\n",
+    "    # Create polynomial features\n",
+    "    poly = PolynomialFeatures(degree)\n",
+    "    X_train_poly = poly.fit_transform(X_train)\n",
+    "    X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "    # Build a polynomial regression model\n",
+    "    poly_model = LinearRegression()\n",
+    "    poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "    # Make predictions on the test set\n",
+    "    y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "    # Reverse log transformation for evaluation\n",
+    "    y_pred = np.exp(y_pred_poly)\n",
+    "    y_test_original = np.exp(y_test)\n",
+    "\n",
+    "    # Evaluate the polynomial model\n",
+    "    mse_poly = mean_squared_error(y_test_original, y_pred)\n",
+    "    r2_poly = r2_score(y_test_original, y_pred)\n",
+    "    print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
+    "    print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
+    "    \n",
+    "    # Convert to DataFrame for summary\n",
+    "    #X_poly_df = pd.DataFrame(X_train_poly, columns=poly.get_feature_names(features))\n",
+    "\n",
+    "    # Fit the OLS model\n",
+    "    model = sm.OLS(y_train,X_train_poly)\n",
+    "    results = model.fit()\n",
+    "\n",
+    "    # Print the summary\n",
+    "    print(results.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.\n",
+    "\n",
+    "MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.\n",
+    "\n",
+    "Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example:\n",
+    "The coefficient for const (intercept) is 4987.94.\n",
+    "The coefficient for x1 (bathrooms) is 3457.38.\n",
+    "The coefficient for x2 (sqft_living) is -16.53.\n",
+    "The coefficient for x3 (floors) is -16.98.\n",
+    "The coefficient for x4 (waterfront) is -59.75.\n",
+    "The coefficient for x5 (condition) is -66.70.\n",
+    "The coefficient for x6 (grade) is 21.71.\n",
+    "The coefficient for x7 (sqft_basement) is 56.12.\n",
+    "The coefficient for x8 (yr_built) and other coefficients follow.\n",
+    "\n",
+    "P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.\n",
+    "\n",
+    "\n",
+    "F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.\n",
+    "\n",
+    "\n",
+    "Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# REGRESSION RESULTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
+    "\n",
+    "Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73\n",
+    "\n",
+    "The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assumptions\n",
+    "\n",
+    "Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.\n",
+    "\n",
+    "Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.\n",
+    "\n",
+    "Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.\n",
+    "\n",
+    "No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Limitations\n",
+    "Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:\n",
+    "\n",
+    "Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.\n",
+    "\n",
+    "\n",
+    "Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.\n",
+    "\n",
+    "Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.\n",
+    "\n",
+    "Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recommedations\n",
+    "To mitigate multicollinearity, strategies such as feature selection, principal component analysis (PCA), or regularization methods like ridge regression or Lasso regression can be employed. These techniques prioritize essential predictors and enhance the model's interpretability while stabilizing it against multicollinearity.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Before opting for polynomial regression, it's essential to validate the assumption of linearity between predictors and the target variable. If this assumption doesn't hold, alternative regression techniques such as generalized additive models (GAMs) or spline regression should be considered to better capture intricate relationships.\n",
+    "\n",
+    "\n",
+    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis"
+   ]
   }
  ],
  "metadata": {
@@ -3605,7 +4793,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

From 079cbfaac08ab96be17ec425649c7d41d19e11da Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 1 May 2024 12:34:43 +0300
Subject: [PATCH 13/27] additional modelling changes

---
 student.ipynb | 1290 ++++++++++++++++++++++++++++++++-----------------
 1 file changed, 836 insertions(+), 454 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 41fdf478..ffb0e513 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +497,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +684,7 @@
        "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -850,104 +850,104 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>3102</th>\n",
-       "      <td>5151600195</td>\n",
-       "      <td>5/4/2015</td>\n",
-       "      <td>283200.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1830</td>\n",
-       "      <td>12540</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>1130</td>\n",
-       "      <td>700.0</td>\n",
-       "      <td>1958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>12540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18253</th>\n",
-       "      <td>2558160220</td>\n",
-       "      <td>12/10/2014</td>\n",
-       "      <td>385000.0</td>\n",
-       "      <td>4</td>\n",
+       "      <th>19757</th>\n",
+       "      <td>5676000004</td>\n",
+       "      <td>11/18/2014</td>\n",
+       "      <td>399000.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>2.50</td>\n",
-       "      <td>2030</td>\n",
-       "      <td>11375</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1430</td>\n",
+       "      <td>1250</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>NO</td>\n",
        "      <td>Average</td>\n",
        "      <td>7 Average</td>\n",
-       "      <td>1330</td>\n",
-       "      <td>700.0</td>\n",
-       "      <td>1969</td>\n",
+       "      <td>1430</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1500</td>\n",
-       "      <td>9160</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>1269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17466</th>\n",
-       "      <td>1525079056</td>\n",
-       "      <td>5/2/2014</td>\n",
-       "      <td>284000.0</td>\n",
+       "      <th>2821</th>\n",
+       "      <td>9828200147</td>\n",
+       "      <td>4/10/2015</td>\n",
+       "      <td>425000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1180</td>\n",
        "      <td>1800</td>\n",
-       "      <td>23103</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>Average</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1800</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1180</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1968</td>\n",
+       "      <td>1994</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1410</td>\n",
-       "      <td>18163</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>1948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16157</th>\n",
-       "      <td>8081900101</td>\n",
-       "      <td>5/28/2014</td>\n",
-       "      <td>960000.0</td>\n",
-       "      <td>4</td>\n",
+       "      <th>3151</th>\n",
+       "      <td>8944750850</td>\n",
+       "      <td>5/21/2014</td>\n",
+       "      <td>288400.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>2.25</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>4560</td>\n",
+       "      <td>1870</td>\n",
+       "      <td>3230</td>\n",
        "      <td>2.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Very Good</td>\n",
-       "      <td>9 Better</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>610.0</td>\n",
-       "      <td>1929</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1870</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1997</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2150</td>\n",
-       "      <td>5100</td>\n",
+       "      <td>1620</td>\n",
+       "      <td>3363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10199</th>\n",
+       "      <td>3298600340</td>\n",
+       "      <td>9/5/2014</td>\n",
+       "      <td>400000.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3320</td>\n",
+       "      <td>15600</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>1660</td>\n",
+       "      <td>1660.0</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2330</td>\n",
+       "      <td>15360</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3439</th>\n",
-       "      <td>6300500475</td>\n",
-       "      <td>9/2/2014</td>\n",
-       "      <td>412000.0</td>\n",
+       "      <th>18048</th>\n",
+       "      <td>7853220610</td>\n",
+       "      <td>7/29/2014</td>\n",
+       "      <td>457000.0</td>\n",
        "      <td>3</td>\n",
        "      <td>2.50</td>\n",
-       "      <td>1553</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NO</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>5694</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Average</td>\n",
        "      <td>8 Good</td>\n",
-       "      <td>1553</td>\n",
+       "      <td>2050</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1509</td>\n",
-       "      <td>2431</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2680</td>\n",
+       "      <td>7187</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -955,28 +955,28 @@
       ],
       "text/plain": [
        "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "3102   5151600195    5/4/2015  283200.0         4       1.75         1830   \n",
-       "18253  2558160220  12/10/2014  385000.0         4       2.50         2030   \n",
-       "17466  1525079056    5/2/2014  284000.0         3       1.75         1800   \n",
-       "16157  8081900101   5/28/2014  960000.0         4       2.25         2410   \n",
-       "3439   6300500475    9/2/2014  412000.0         3       2.50         1553   \n",
+       "19757  5676000004  11/18/2014  399000.0         3       2.50         1430   \n",
+       "2821   9828200147   4/10/2015  425000.0         3       2.00         1180   \n",
+       "3151   8944750850   5/21/2014  288400.0         3       2.25         1870   \n",
+       "10199  3298600340    9/5/2014  400000.0         6       3.00         3320   \n",
+       "18048  7853220610   7/29/2014  457000.0         3       2.50         2050   \n",
        "\n",
-       "       sqft_lot  floors waterfront  condition      grade  sqft_above  \\\n",
-       "3102      12540     1.0        NaN       Good     8 Good        1130   \n",
-       "18253     11375     1.0         NO    Average  7 Average        1330   \n",
-       "17466     23103     1.0        NaN    Average  7 Average        1800   \n",
-       "16157      4560     2.0         NO  Very Good   9 Better        1800   \n",
-       "3439       1991     3.0         NO    Average     8 Good        1553   \n",
+       "       sqft_lot  floors waterfront condition      grade  sqft_above  \\\n",
+       "19757      1250     3.0         NO   Average  7 Average        1430   \n",
+       "2821       1800     2.0        NaN   Average     8 Good        1180   \n",
+       "3151       3230     2.0         NO   Average  7 Average        1870   \n",
+       "10199     15600     1.0         NO      Good     8 Good        1660   \n",
+       "18048      5694     2.0        NaN   Average     8 Good        2050   \n",
        "\n",
        "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "3102          700.0      1958           NaN           2020       12540  \n",
-       "18253         700.0      1969           0.0           1500        9160  \n",
-       "17466           0.0      1968           0.0           1410       18163  \n",
-       "16157         610.0      1929           0.0           2150        5100  \n",
-       "3439            0.0      2014           0.0           1509        2431  "
+       "19757           0.0      2007           0.0           1360        1269  \n",
+       "2821            0.0      1994           0.0           1500        1948  \n",
+       "3151            0.0      1997           0.0           1620        3363  \n",
+       "10199        1660.0      1977           NaN           2330       15360  \n",
+       "18048           0.0      2004           NaN           2680        7187  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,7 +997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1050,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1177,7 +1177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1263,133 +1263,133 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>8401</th>\n",
-       "      <td>2324800110</td>\n",
-       "      <td>2014-06-12</td>\n",
-       "      <td>699000.0</td>\n",
+       "      <th>7979</th>\n",
+       "      <td>3886903615</td>\n",
+       "      <td>2015-04-16</td>\n",
+       "      <td>1290000.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>3280</td>\n",
-       "      <td>27441</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3430</td>\n",
+       "      <td>7200</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>7.0</td>\n",
-       "      <td>3280</td>\n",
+       "      <td>3430</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1996</td>\n",
+       "      <td>2014</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3200</td>\n",
-       "      <td>26960</td>\n",
+       "      <td>1530</td>\n",
+       "      <td>7800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>9510920070</td>\n",
-       "      <td>2014-07-10</td>\n",
-       "      <td>879000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>3360</td>\n",
-       "      <td>22111</td>\n",
+       "      <th>4578</th>\n",
+       "      <td>9478500590</td>\n",
+       "      <td>2015-04-08</td>\n",
+       "      <td>302500.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1690</td>\n",
+       "      <td>4476</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3360</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1690</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1994</td>\n",
+       "      <td>2008</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3150</td>\n",
-       "      <td>11374</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>4488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3958</th>\n",
-       "      <td>4113800300</td>\n",
-       "      <td>2015-04-14</td>\n",
-       "      <td>600000.0</td>\n",
+       "      <th>10023</th>\n",
+       "      <td>3423600060</td>\n",
+       "      <td>2014-12-02</td>\n",
+       "      <td>665000.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2420</td>\n",
-       "      <td>7744</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2280</td>\n",
+       "      <td>3680</td>\n",
+       "      <td>1.5</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2420</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1994</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1470</td>\n",
+       "      <td>810.0</td>\n",
+       "      <td>1926</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2820</td>\n",
-       "      <td>11129</td>\n",
+       "      <td>1850</td>\n",
+       "      <td>3680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7966</th>\n",
-       "      <td>3122069029</td>\n",
-       "      <td>2014-06-19</td>\n",
-       "      <td>120000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>990</td>\n",
-       "      <td>39964</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>11990</th>\n",
+       "      <td>7852150200</td>\n",
+       "      <td>2014-09-23</td>\n",
+       "      <td>389950.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>6396</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>990</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1700</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1945</td>\n",
+       "      <td>2003</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1560</td>\n",
-       "      <td>8990</td>\n",
+       "      <td>1700</td>\n",
+       "      <td>4444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6996</th>\n",
-       "      <td>2767601100</td>\n",
-       "      <td>2014-10-27</td>\n",
-       "      <td>513000.0</td>\n",
+       "      <th>20266</th>\n",
+       "      <td>3356402705</td>\n",
+       "      <td>2015-03-17</td>\n",
+       "      <td>216000.0</td>\n",
        "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1847</td>\n",
+       "      <td>8000</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>2090</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1480</td>\n",
-       "      <td>610.0</td>\n",
-       "      <td>1951</td>\n",
+       "      <td>1847</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1510</td>\n",
-       "      <td>5000</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>8000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "8401  2324800110 2014-06-12  699000.0         4        2.5         3280   \n",
-       "295   9510920070 2014-07-10  879000.0         4        2.5         3360   \n",
-       "3958  4113800300 2015-04-14  600000.0         4        2.5         2420   \n",
-       "7966  3122069029 2014-06-19  120000.0         2        1.0          990   \n",
-       "6996  2767601100 2014-10-27  513000.0         4        2.0         2090   \n",
+       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
+       "7979   3886903615 2015-04-16  1290000.0         4       2.50         3430   \n",
+       "4578   9478500590 2015-04-08   302500.0         3       2.50         1690   \n",
+       "10023  3423600060 2014-12-02   665000.0         4       1.75         2280   \n",
+       "11990  7852150200 2014-09-23   389950.0         3       2.50         1700   \n",
+       "20266  3356402705 2015-03-17   216000.0         4       2.50         1847   \n",
        "\n",
-       "      sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "8401     27441     2.0           0        3.0    7.0        3280   \n",
-       "295      22111     2.0           0        3.0    8.0        3360   \n",
-       "3958      7744     2.0           0        3.0    7.0        2420   \n",
-       "7966     39964     1.0           0        2.0    2.0         990   \n",
-       "6996      4000     1.0           0        3.0    5.0        1480   \n",
+       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "7979       7200     2.0           0        3.0    7.0        3430   \n",
+       "4578       4476     2.0           0        3.0    5.0        1690   \n",
+       "10023      3680     1.5           0        5.0    5.0        1470   \n",
+       "11990      6396     2.0           0        3.0    5.0        1700   \n",
+       "20266      8000     2.0           0        3.0    5.0        1847   \n",
        "\n",
-       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "8401            0.0      1996           0.0           3200       26960  \n",
-       "295             0.0      1994           0.0           3150       11374  \n",
-       "3958            0.0      1994           0.0           2820       11129  \n",
-       "7966            0.0      1945           0.0           1560        8990  \n",
-       "6996          610.0      1951           0.0           1510        5000  "
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "7979             0.0      2014           0.0           1530        7800  \n",
+       "4578             0.0      2008           0.0           2250        4488  \n",
+       "10023          810.0      1926           0.0           1850        3680  \n",
+       "11990            0.0      2003           0.0           1700        4444  \n",
+       "20266            0.0      2008           0.0           1767        8000  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1420,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1493,7 +1493,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1538,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1586,155 +1586,155 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>5606</th>\n",
-       "      <td>4024101434</td>\n",
-       "      <td>2014-08-08</td>\n",
-       "      <td>318000.0</td>\n",
+       "      <th>8540</th>\n",
+       "      <td>3034200366</td>\n",
+       "      <td>2014-12-03</td>\n",
+       "      <td>409000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1010</td>\n",
-       "      <td>7200</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>9065</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>1010</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1440</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1948</td>\n",
+       "      <td>1972</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1590</td>\n",
-       "      <td>7663</td>\n",
-       "      <td>76</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>8812</td>\n",
+       "      <td>52</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>9220.0</td>\n",
+       "      <td>11945.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18192</th>\n",
-       "      <td>9471200065</td>\n",
-       "      <td>2014-10-15</td>\n",
-       "      <td>1860000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>5570</td>\n",
-       "      <td>9600</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>19433</th>\n",
+       "      <td>2023059052</td>\n",
+       "      <td>2015-05-04</td>\n",
+       "      <td>450000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>92721</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3860</td>\n",
-       "      <td>1710.0</td>\n",
-       "      <td>1952</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>1946</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3170</td>\n",
-       "      <td>10400</td>\n",
-       "      <td>72</td>\n",
+       "      <td>1860</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>78</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>20740.0</td>\n",
+       "      <td>95421.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12922</th>\n",
-       "      <td>3222049044</td>\n",
-       "      <td>2014-06-12</td>\n",
-       "      <td>835000.0</td>\n",
+       "      <th>7003</th>\n",
+       "      <td>7751800080</td>\n",
+       "      <td>2015-01-27</td>\n",
+       "      <td>465000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2790</td>\n",
-       "      <td>12523</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1600</td>\n",
-       "      <td>1190.0</td>\n",
-       "      <td>1977</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1460</td>\n",
+       "      <td>9879</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1460</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2990</td>\n",
-       "      <td>11476</td>\n",
-       "      <td>47</td>\n",
+       "      <td>1956</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>18103.0</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>10050</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12799.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8840</th>\n",
-       "      <td>2817900180</td>\n",
-       "      <td>2015-04-11</td>\n",
-       "      <td>380000.0</td>\n",
+       "      <th>10151</th>\n",
+       "      <td>1180002075</td>\n",
+       "      <td>2014-08-25</td>\n",
+       "      <td>235000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>3.25</td>\n",
-       "      <td>2090</td>\n",
-       "      <td>51212</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>6000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1510</td>\n",
-       "      <td>580.0</td>\n",
-       "      <td>1989</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1210</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2690</td>\n",
-       "      <td>40820</td>\n",
-       "      <td>35</td>\n",
+       "      <td>1930</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>55392.0</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8420.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4657</th>\n",
-       "      <td>9547205225</td>\n",
-       "      <td>2014-08-14</td>\n",
-       "      <td>540000.0</td>\n",
+       "      <th>3506</th>\n",
+       "      <td>3449900090</td>\n",
+       "      <td>2015-04-10</td>\n",
+       "      <td>454200.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1630</td>\n",
-       "      <td>6120</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2630</td>\n",
+       "      <td>5379</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>980</td>\n",
-       "      <td>650.0</td>\n",
-       "      <td>1918</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2630</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1630</td>\n",
-       "      <td>4080</td>\n",
-       "      <td>106</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2630</td>\n",
+       "      <td>5379</td>\n",
+       "      <td>20</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>9380.0</td>\n",
+       "      <td>10639.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "5606   4024101434 2014-08-08   318000.0         3       1.00         1010   \n",
-       "18192  9471200065 2014-10-15  1860000.0         5       3.25         5570   \n",
-       "12922  3222049044 2014-06-12   835000.0         3       3.00         2790   \n",
-       "8840   2817900180 2015-04-11   380000.0         3       3.25         2090   \n",
-       "4657   9547205225 2014-08-14   540000.0         4       1.75         1630   \n",
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "8540   3034200366 2014-12-03  409000.0         3       1.75         1440   \n",
+       "19433  2023059052 2015-05-04  450000.0         3       1.00         1350   \n",
+       "7003   7751800080 2015-01-27  465000.0         3       1.50         1460   \n",
+       "10151  1180002075 2014-08-25  235000.0         3       1.00         1210   \n",
+       "3506   3449900090 2015-04-10  454200.0         4       2.50         2630   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "5606       7200     1.0           0        5.0    4.0        1010   \n",
-       "18192      9600     2.0           0        5.0    7.0        3860   \n",
-       "12922     12523     2.0           1        4.0    6.0        1600   \n",
-       "8840      51212     1.0           0        3.0    6.0        1510   \n",
-       "4657       6120     1.0           0        5.0    5.0         980   \n",
+       "8540       9065     1.0           0        4.0    6.0        1440   \n",
+       "19433     92721     1.0           0        2.0    4.0        1200   \n",
+       "7003       9879     1.0           0        3.0    5.0        1460   \n",
+       "10151      6000     1.0           0        3.0    5.0        1210   \n",
+       "3506       5379     2.0           0        3.0    6.0        2630   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "5606             0.0      1948           0.0           1590        7663   \n",
-       "18192         1710.0      1952           0.0           3170       10400   \n",
-       "12922         1190.0      1977           0.0           2990       11476   \n",
-       "8840           580.0      1989           0.0           2690       40820   \n",
-       "4657           650.0      1918           0.0           1630        4080   \n",
+       "8540             0.0      1972           0.0           1990        8812   \n",
+       "19433          150.0      1946           0.0           1860        8096   \n",
+       "7003             0.0      1956           0.0           1610       10050   \n",
+       "10151            0.0      1930           0.0           1210        6000   \n",
+       "3506             0.0      2004           0.0           2630        5379   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  \n",
-       "5606          76             0.0      9220.0  \n",
-       "18192         72             0.0     20740.0  \n",
-       "12922         47             0.0     18103.0  \n",
-       "8840          35             0.0     55392.0  \n",
-       "4657         106             0.0      9380.0  "
+       "8540          52             0.0     11945.0  \n",
+       "19433         78             0.0     95421.0  \n",
+       "7003          68             0.0     12799.0  \n",
+       "10151         94             0.0      8420.0  \n",
+       "3506          20             0.0     10639.0  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -1816,7 +1816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1892,7 +1892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1945,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1958,7 +1958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1967,7 +1967,7 @@
        "Text(0.5, 0, 'Number of Bedrooms')"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2005,7 +2005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2022,7 +2022,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2042,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -2091,124 +2091,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4614</th>\n",
-       "      <td>2114700115</td>\n",
-       "      <td>2015-04-07</td>\n",
-       "      <td>291700.0</td>\n",
+       "      <th>18795</th>\n",
+       "      <td>3904960690</td>\n",
+       "      <td>2015-04-17</td>\n",
+       "      <td>612000.0</td>\n",
        "      <td>3</td>\n",
        "      <td>2.50</td>\n",
-       "      <td>1970</td>\n",
-       "      <td>4120</td>\n",
-       "      <td>1.5</td>\n",
+       "      <td>2120</td>\n",
+       "      <td>7401</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1230</td>\n",
-       "      <td>740.0</td>\n",
-       "      <td>1927</td>\n",
+       "      <td>2120</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1470</td>\n",
-       "      <td>4080</td>\n",
-       "      <td>97</td>\n",
+       "      <td>1989</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8060.0</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>7972</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11641.0</td>\n",
+       "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1011</th>\n",
-       "      <td>865100055</td>\n",
-       "      <td>2014-06-12</td>\n",
-       "      <td>900000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2460</td>\n",
-       "      <td>44431</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>16001</th>\n",
+       "      <td>7560000050</td>\n",
+       "      <td>2015-04-23</td>\n",
+       "      <td>730000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>3502</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2460</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1957</td>\n",
+       "      <td>1970</td>\n",
+       "      <td>470.0</td>\n",
+       "      <td>2000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>44431</td>\n",
-       "      <td>67</td>\n",
+       "      <td>2440</td>\n",
+       "      <td>3417</td>\n",
+       "      <td>24</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>49351.0</td>\n",
+       "      <td>8382.0</td>\n",
        "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7116</th>\n",
-       "      <td>8563020380</td>\n",
-       "      <td>2014-05-20</td>\n",
-       "      <td>519900.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>9350</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>9811</th>\n",
+       "      <td>3598600049</td>\n",
+       "      <td>2015-04-24</td>\n",
+       "      <td>224000.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>840</td>\n",
+       "      <td>7203</td>\n",
+       "      <td>1.5</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1820</td>\n",
+       "      <td>840</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1967</td>\n",
+       "      <td>1949</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2260</td>\n",
-       "      <td>9299</td>\n",
-       "      <td>57</td>\n",
+       "      <td>1560</td>\n",
+       "      <td>8603</td>\n",
+       "      <td>75</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12990.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>8883.0</td>\n",
+       "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4142</th>\n",
-       "      <td>461002551</td>\n",
-       "      <td>2014-10-04</td>\n",
-       "      <td>330600.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>580</td>\n",
-       "      <td>1799</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>15108</th>\n",
+       "      <td>9332800020</td>\n",
+       "      <td>2014-07-15</td>\n",
+       "      <td>745000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2290</td>\n",
+       "      <td>10409</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>580</td>\n",
+       "      <td>2290</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1908</td>\n",
-       "      <td>2005.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>116</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2959.0</td>\n",
-       "      <td>300K-600K</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2040</td>\n",
+       "      <td>10409</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14989.0</td>\n",
+       "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20907</th>\n",
-       "      <td>7852070210</td>\n",
-       "      <td>2014-05-27</td>\n",
-       "      <td>1150000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>5940</td>\n",
-       "      <td>11533</td>\n",
+       "      <th>20642</th>\n",
+       "      <td>4305500030</td>\n",
+       "      <td>2015-05-01</td>\n",
+       "      <td>625000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3220</td>\n",
+       "      <td>6409</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>4950</td>\n",
-       "      <td>990.0</td>\n",
-       "      <td>2004</td>\n",
+       "      <td>3220</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4240</td>\n",
-       "      <td>12813</td>\n",
-       "      <td>20</td>\n",
+       "      <td>2008</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>23413.0</td>\n",
-       "      <td>1M-2M</td>\n",
+       "      <td>3330</td>\n",
+       "      <td>6231</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12849.0</td>\n",
+       "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2216,38 +2216,38 @@
        "</div>"
       ],
       "text/plain": [
-       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "4614   2114700115 2015-04-07   291700.0         3       2.50         1970   \n",
-       "1011    865100055 2014-06-12   900000.0         4       2.25         2460   \n",
-       "7116   8563020380 2014-05-20   519900.0         4       2.00         1820   \n",
-       "4142    461002551 2014-10-04   330600.0         1       1.00          580   \n",
-       "20907  7852070210 2014-05-27  1150000.0         4       3.00         5940   \n",
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "18795  3904960690 2015-04-17  612000.0         3       2.50         2120   \n",
+       "16001  7560000050 2015-04-23  730000.0         3       3.50         2440   \n",
+       "9811   3598600049 2015-04-24  224000.0         1       0.75          840   \n",
+       "15108  9332800020 2014-07-15  745000.0         4       2.25         2290   \n",
+       "20642  4305500030 2015-05-01  625000.0         3       2.50         3220   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "4614       4120     1.5           0        3.0  ...        1230   \n",
-       "1011      44431     1.0           0        4.0  ...        2460   \n",
-       "7116       9350     1.0           0        4.0  ...        1820   \n",
-       "4142       1799     1.0           0        3.0  ...         580   \n",
-       "20907     11533     2.0           0        3.0  ...        4950   \n",
+       "18795      7401     2.0           0        3.0  ...        2120   \n",
+       "16001      3502     2.0           0        3.0  ...        1970   \n",
+       "9811       7203     1.5           0        3.0  ...         840   \n",
+       "15108     10409     2.0           0        3.0  ...        2290   \n",
+       "20642      6409     2.0           0        3.0  ...        3220   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "4614           740.0      1927           0.0           1470        4080   \n",
-       "1011             0.0      1957           0.0           2830       44431   \n",
-       "7116             0.0      1967           0.0           2260        9299   \n",
-       "4142             0.0      1908        2005.0           1260        4000   \n",
-       "20907          990.0      2004           0.0           4240       12813   \n",
+       "18795            0.0      1989           0.0           2010        7972   \n",
+       "16001          470.0      2000           0.0           2440        3417   \n",
+       "9811             0.0      1949           0.0           1560        8603   \n",
+       "15108            0.0      1972           0.0           2040       10409   \n",
+       "20642            0.0      2008           0.0           3330        6231   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  price_range  \n",
-       "4614          97             0.0      8060.0    100K-300K  \n",
-       "1011          67             0.0     49351.0      600K-1M  \n",
-       "7116          57             0.0     12990.0    300K-600K  \n",
-       "4142         116            19.0      2959.0    300K-600K  \n",
-       "20907         20             0.0     23413.0        1M-2M  \n",
+       "18795         35             0.0     11641.0      600K-1M  \n",
+       "16001         24             0.0      8382.0      600K-1M  \n",
+       "9811          75             0.0      8883.0    100K-300K  \n",
+       "15108         52             0.0     14989.0      600K-1M  \n",
+       "20642         16             0.0     12849.0      600K-1M  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2270,12 +2270,21 @@
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "KeyboardInterrupt\n",
-      "\n"
+     "ename": "AttributeError",
+     "evalue": "Rectangle.set() got an unexpected keyword argument 'legend'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[26], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Create a bar plot\u001b[39;00m\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 6\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice_range\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtotal_sqft\u001b[39m\u001b[38;5;124m\"\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, errorbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice_range\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcrest\u001b[39m\u001b[38;5;124m\"\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotal Square Footage by Price Range.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrice Range\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2763\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2528\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2519\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l, b, w, h, c, e, lw, htch, lbl \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[0;32m   2520\u001b[0m     r \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle(\n\u001b[0;32m   2521\u001b[0m         xy\u001b[38;5;241m=\u001b[39m(l, b), width\u001b[38;5;241m=\u001b[39mw, height\u001b[38;5;241m=\u001b[39mh,\n\u001b[0;32m   2522\u001b[0m         facecolor\u001b[38;5;241m=\u001b[39mc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2526\u001b[0m         hatch\u001b[38;5;241m=\u001b[39mhtch,\n\u001b[0;32m   2527\u001b[0m         )\n\u001b[1;32m-> 2528\u001b[0m     r\u001b[38;5;241m.\u001b[39m_internal_update(kwargs)\n\u001b[0;32m   2529\u001b[0m     r\u001b[38;5;241m.\u001b[39mget_path()\u001b[38;5;241m.\u001b[39m_interpolation_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m   2530\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_props(\n\u001b[0;32m   1220\u001b[0m         kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{cls.__name__}\u001b[39;00m\u001b[38;5;124m.set() got an unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1221\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{prop_name!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
+      "\u001b[1;31mAttributeError\u001b[0m: Rectangle.set() got an unexpected keyword argument 'legend'"
      ]
     },
     {
@@ -2411,7 +2420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2421,7 +2430,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[51], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(nrows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m6\u001b[39m), sharey\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Bar plot for condition vs. price\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m], hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      6\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition vs. Price\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "Cell \u001b[1;32mIn[29], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(nrows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m6\u001b[39m), sharey\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Bar plot for condition vs. price\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m], hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      6\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition vs. Price\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
       "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2763\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
       "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
       "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
@@ -2491,7 +2500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -2557,7 +2566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -2844,7 +2853,7 @@
        "std          5.824659  4.156724e+04  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2903,7 +2912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2930,7 +2939,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2962,7 +2971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3167,7 +3176,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3197,7 +3206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -3207,7 +3216,13 @@
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
       "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
       "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
@@ -3295,7 +3310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -3424,7 +3439,7 @@
        "P-value      2.417229e-17    2.430304e-10    1.000000  "
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3493,7 +3508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -3648,7 +3663,7 @@
        "16      total_sqft        inf"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3719,7 +3734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3736,7 +3751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3750,7 +3765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3789,7 +3804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3809,7 +3824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3872,7 +3887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3904,7 +3919,7 @@
        " 262383.4993975565)"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3962,7 +3977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -4055,7 +4070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -4228,7 +4243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -4240,8 +4255,8 @@
       "Dep. Variable:                  price   R-squared:                       0.641\n",
       "Model:                            OLS   Adj. R-squared:                  0.641\n",
       "Method:                 Least Squares   F-statistic:                     3768.\n",
-      "Date:                Tue, 30 Apr 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        17:34:27   Log-Likelihood:            -2.9014e+05\n",
+      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        12:24:01   Log-Likelihood:            -2.9014e+05\n",
       "No. Observations:               21142   AIC:                         5.803e+05\n",
       "Df Residuals:                   21131   BIC:                         5.804e+05\n",
       "Df Model:                          10                                         \n",
@@ -4308,7 +4323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -4356,6 +4371,46 @@
     "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESIDUALS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4436,7 +4491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -4490,7 +4545,8 @@
     "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
     "r2_poly = r2_score(y_test, y_pred_poly)\n",
     "print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
-    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)"
+    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
+    "\n"
    ]
   },
   {
@@ -4526,9 +4582,49 @@
     "  values, we can see that the polynomial model of degree 2 explains more variance in housing prices compared to the first model. This suggests that the polynomial model provides a better fit to the data and captures more of the variability in housing prices."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESIDUALS"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred_poly\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred_poly, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4537,7 +4633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4546,7 +4642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -4597,11 +4693,264 @@
     "Common transformations include taking the natural logarithm, square root, or reciprocal of the variables."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Log transformation of the multiple linear regression."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (Log Transformed): 0.09804039958945562\n",
+      "R-squared (Log Transformed): 0.6469123715948973\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'OLSResults' object has no attribute 'coef_'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[57], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse_log)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR-squared (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, r2_log)\n\u001b[1;32m---> 29\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m\"\u001b[39m: X\u001b[38;5;241m.\u001b[39mcolumns, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoefficient\u001b[39m\u001b[38;5;124m\"\u001b[39m: model\u001b[38;5;241m.\u001b[39mcoef_})\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(coefficients)\n",
+      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\base\\wrapper.py:34\u001b[0m, in \u001b[0;36mResultsWrapper.__getattribute__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m     32\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(results, attr)\n\u001b[0;32m     35\u001b[0m data \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mdata\n\u001b[0;32m     36\u001b[0m how \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_attrs\u001b[38;5;241m.\u001b[39mget(attr)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'OLSResults' object has no attribute 'coef_'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Log transformation of features and target variable\n",
+    "X_log = np.log1p(X)\n",
+    "y_log = np.log1p(y)\n",
+    "\n",
+    "# Split the log-transformed data into training and testing sets\n",
+    "X_train_log, X_test_log, y_train_log, y_test_log = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Standardize the log-transformed features\n",
+    "scaler_log = StandardScaler()\n",
+    "X_train_scaled_log = scaler_log.fit_transform(X_train_log)\n",
+    "X_test_scaled_log = scaler_log.transform(X_test_log)\n",
+    "\n",
+    "# Build a linear regression model on the log-transformed data\n",
+    "model_log = LinearRegression()\n",
+    "model_log.fit(X_train_scaled_log, y_train_log)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_log = model_log.predict(X_test_scaled_log)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "mse_log = mean_squared_error(y_test_log, y_pred_log)\n",
+    "r2_log = r2_score(y_test_log, y_pred_log)\n",
+    "\n",
+    "# Display results\n",
+    "print(\"Mean Squared Error (Log Transformed):\", mse_log)\n",
+    "print(\"R-squared (Log Transformed):\", r2_log)\n",
+    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
+    "print(coefficients)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals_log = y_test_log - y_pred_log\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred_log, residuals_log, color='blue')\n",
+    "plt.title('Residuals Plot (Log Transformed)')\n",
+    "plt.xlabel('Predicted Values (Log Transformed)')\n",
+    "plt.ylabel('Residuals (Log Transformed)')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot house prices against coefficients\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.barh(X.columns, model_log.coef_)\n",
+    "plt.xlabel('Coefficient Value')\n",
+    "plt.ylabel('Features')\n",
+    "plt.title('House Prices vs. Coefficients of Variables')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "visualization of the positive and negative coefficient variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Log transformation of the polynomial model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Model (Degree 2)- MSE: 37774083176.83915\n",
+      "Polynomial Model (Degree 2)- R-squared: 0.7161383140038227\n",
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.679\n",
+      "Model:                            OLS   Adj. R-squared:                  0.678\n",
+      "Method:                 Least Squares   F-statistic:                     557.0\n",
+      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        12:27:46   Log-Likelihood:                -3540.2\n",
+      "No. Observations:               16913   AIC:                             7210.\n",
+      "Df Residuals:                   16848   BIC:                             7713.\n",
+      "Df Model:                          64                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       4987.9435    522.414      9.548      0.000    3963.957    6011.930\n",
+      "x1          3457.3790    362.110      9.548      0.000    2747.606    4167.152\n",
+      "x2           -16.5312      7.635     -2.165      0.030     -31.497      -1.566\n",
+      "x3           -16.9803      5.626     -3.018      0.003     -28.008      -5.953\n",
+      "x4           -59.7513      8.202     -7.285      0.000     -75.827     -43.675\n",
+      "x5           -66.7001     16.473     -4.049      0.000     -98.988     -34.412\n",
+      "x6            21.7086      8.506      2.552      0.011       5.036      38.381\n",
+      "x7            56.1227     10.699      5.246      0.000      35.152      77.093\n",
+      "x8             0.1415      0.453      0.313      0.755      -0.745       1.028\n",
+      "x9         -1530.3915    159.119     -9.618      0.000   -1842.282   -1218.501\n",
+      "x10           -7.2389      1.397     -5.181      0.000      -9.977      -4.500\n",
+      "x11           27.1030      5.183      5.229      0.000      16.943      37.263\n",
+      "x12         2396.4725    250.995      9.548      0.000    1904.495    2888.450\n",
+      "x13          -11.4586      5.292     -2.165      0.030     -21.832      -1.085\n",
+      "x14          -11.7699      3.900     -3.018      0.003     -19.414      -4.126\n",
+      "x15          -41.4165      5.685     -7.285      0.000     -52.560     -30.273\n",
+      "x16          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
+      "x17           15.0473      5.896      2.552      0.011       3.491      26.604\n",
+      "x18           38.9013      7.416      5.246      0.000      24.366      53.437\n",
+      "x19            0.0981      0.314      0.313      0.755      -0.517       0.713\n",
+      "x20        -1060.7866    110.293     -9.618      0.000   -1276.973    -844.601\n",
+      "x21           -5.0176      0.968     -5.181      0.000      -6.916      -3.119\n",
+      "x22           18.7864      3.593      5.229      0.000      11.744      25.829\n",
+      "x23            0.0476      0.079      0.603      0.546      -0.107       0.202\n",
+      "x24            0.2000      0.083      2.399      0.016       0.037       0.363\n",
+      "x25           -0.4148      0.109     -3.823      0.000      -0.628      -0.202\n",
+      "x26            0.1250      0.245      0.511      0.609      -0.355       0.605\n",
+      "x27            0.1981      0.122      1.628      0.104      -0.040       0.437\n",
+      "x28            0.2251      0.161      1.396      0.163      -0.091       0.541\n",
+      "x29           -0.0032      0.006     -0.531      0.595      -0.015       0.009\n",
+      "x30            3.1411      1.477      2.127      0.033       0.247       6.036\n",
+      "x31           -0.0103      0.012     -0.883      0.377      -0.033       0.013\n",
+      "x32           -0.1564      0.078     -2.000      0.046      -0.310      -0.003\n",
+      "x33            0.0859      0.039      2.183      0.029       0.009       0.163\n",
+      "x34           -0.0407      0.079     -0.516      0.606      -0.195       0.114\n",
+      "x35            0.3258      0.195      1.668      0.095      -0.057       0.709\n",
+      "x36           -0.2649      0.091     -2.905      0.004      -0.444      -0.086\n",
+      "x37            0.1059      0.111      0.957      0.339      -0.111       0.323\n",
+      "x38           -0.0112      0.005     -2.119      0.034      -0.022      -0.001\n",
+      "x39            3.2391      1.094      2.960      0.003       1.094       5.384\n",
+      "x40            0.0084      0.008      0.989      0.323      -0.008       0.025\n",
+      "x41           -0.0497      0.054     -0.924      0.355      -0.155       0.056\n",
+      "x42            0.6010      0.091      6.569      0.000       0.422       0.780\n",
+      "x43            0.1950      0.270      0.721      0.471      -0.335       0.725\n",
+      "x44            0.3964      0.139      2.845      0.004       0.123       0.670\n",
+      "x45           -0.0224      0.156     -0.143      0.886      -0.329       0.284\n",
+      "x46            0.0471      0.006      7.361      0.000       0.035       0.060\n",
+      "x47           11.9015      1.585      7.511      0.000       8.796      15.007\n",
+      "x48            0.0005      0.012      0.044      0.965      -0.023       0.024\n",
+      "x49           -0.3608      0.079     -4.586      0.000      -0.515      -0.207\n",
+      "x50          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
+      "x51           -0.2814      0.262     -1.074      0.283      -0.795       0.232\n",
+      "x52           -1.4097      0.386     -3.649      0.000      -2.167      -0.652\n",
+      "x53           -0.0189      0.015     -1.223      0.221      -0.049       0.011\n",
+      "x54           17.2366      4.274      4.033      0.000       8.859      25.614\n",
+      "x55           -0.0160      0.016     -1.020      0.308      -0.047       0.015\n",
+      "x56            0.1612      0.187      0.861      0.389      -0.206       0.528\n",
+      "x57           -0.0348      0.081     -0.430      0.667      -0.193       0.124\n",
+      "x58           -0.5518      0.172     -3.212      0.001      -0.888      -0.215\n",
+      "x59            0.0147      0.008      1.939      0.053      -0.000       0.030\n",
+      "x60           -4.5317      1.668     -2.716      0.007      -7.802      -1.261\n",
+      "x61           -0.0425      0.016     -2.603      0.009      -0.074      -0.010\n",
+      "x62            0.6652      0.082      8.073      0.000       0.504       0.827\n",
+      "x63            0.7108      0.132      5.394      0.000       0.452       0.969\n",
+      "x64           -0.0074      0.009     -0.830      0.406      -0.025       0.010\n",
+      "x65          -10.6811      2.084     -5.125      0.000     -14.766      -6.596\n",
+      "x66           -0.0058      0.016     -0.357      0.721      -0.038       0.026\n",
+      "x67           -0.4727      0.098     -4.817      0.000      -0.665      -0.280\n",
+      "x68           -0.0051      0.002     -2.881      0.004      -0.009      -0.002\n",
+      "x69           -0.0156      0.088     -0.178      0.859      -0.188       0.157\n",
+      "x70        -3.158e-05      0.001     -0.045      0.964      -0.001       0.001\n",
+      "x71           -0.0010      0.004     -0.221      0.825      -0.009       0.007\n",
+      "x72          150.7551     15.531      9.707      0.000     120.313     181.198\n",
+      "x73            0.7643      0.175      4.359      0.000       0.421       1.108\n",
+      "x74           -5.7615      1.013     -5.690      0.000      -7.746      -3.777\n",
+      "x75            0.6353      0.261      2.434      0.015       0.124       1.147\n",
+      "x76            0.0173      0.007      2.394      0.017       0.003       0.031\n",
+      "x77            0.3068      0.032      9.733      0.000       0.245       0.369\n",
+      "==============================================================================\n",
+      "Omnibus:                       86.286   Durbin-Watson:                   2.008\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              100.078\n",
+      "Skew:                          -0.119   Prob(JB):                     1.85e-22\n",
+      "Kurtosis:                       3.291   Cond. No.                     1.52e+16\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The smallest eigenvalue is 1.69e-24. This might indicate that there are\n",
+      "strong multicollinearity problems or that the design matrix is singular.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -4711,6 +5060,39 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test_original - y_pred\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 27458b4ebf06161ff8abf0d12425e6e6bc8ba234 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 1 May 2024 14:45:46 +0300
Subject: [PATCH 14/27] explanations

---
 student.ipynb | 33 +++++++++++++++++++++++----------
 1 file changed, 23 insertions(+), 10 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index ffb0e513..98875d24 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -4070,7 +4070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -4243,7 +4243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -4256,7 +4256,7 @@
       "Model:                            OLS   Adj. R-squared:                  0.641\n",
       "Method:                 Least Squares   F-statistic:                     3768.\n",
       "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        12:24:01   Log-Likelihood:            -2.9014e+05\n",
+      "Time:                        13:34:57   Log-Likelihood:            -2.9014e+05\n",
       "No. Observations:               21142   AIC:                         5.803e+05\n",
       "Df Residuals:                   21131   BIC:                         5.804e+05\n",
       "Df Model:                          10                                         \n",
@@ -4323,7 +4323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -4380,7 +4380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -4411,6 +4411,13 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot is useful in understanding the assumptions of linear regression and detecting violations. The ideal case is to see a random distribution of residuals around the y-axis, centered around zero. This suggests that the residuals are normally distributed, and there are no systematic patterns in the errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -4702,7 +4709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -4715,14 +4722,13 @@
     },
     {
      "ename": "AttributeError",
-     "evalue": "'OLSResults' object has no attribute 'coef_'",
+     "evalue": "'OLS' object has no attribute 'coef_'",
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[57], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse_log)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR-squared (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, r2_log)\n\u001b[1;32m---> 29\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m\"\u001b[39m: X\u001b[38;5;241m.\u001b[39mcolumns, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoefficient\u001b[39m\u001b[38;5;124m\"\u001b[39m: model\u001b[38;5;241m.\u001b[39mcoef_})\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(coefficients)\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\base\\wrapper.py:34\u001b[0m, in \u001b[0;36mResultsWrapper.__getattribute__\u001b[1;34m(self, attr)\u001b[0m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m     32\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(results, attr)\n\u001b[0;32m     35\u001b[0m data \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mdata\n\u001b[0;32m     36\u001b[0m how \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_attrs\u001b[38;5;241m.\u001b[39mget(attr)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'OLSResults' object has no attribute 'coef_'"
+      "Cell \u001b[1;32mIn[60], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse_log)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR-squared (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, r2_log)\n\u001b[1;32m---> 29\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m\"\u001b[39m: X\u001b[38;5;241m.\u001b[39mcolumns, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoefficient\u001b[39m\u001b[38;5;124m\"\u001b[39m: model\u001b[38;5;241m.\u001b[39mcoef_})\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(coefficients)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'OLS' object has no attribute 'coef_'"
      ]
     }
    ],
@@ -4792,6 +4798,13 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 56,

From 70f86106b20215b90811839218567f79c5a58e42 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 1 May 2024 16:47:23 +0300
Subject: [PATCH 15/27] FINAL COPY

---
 student.ipynb | 765 +++++++++++++++++++++++++-------------------------
 1 file changed, 384 insertions(+), 381 deletions(-)

diff --git a/student.ipynb b/student.ipynb
index 98875d24..ee2a22a0 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -40,19 +40,25 @@
     "\n",
     "\n",
     "1. \n",
-    "Market Analysis: Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
+    "Market Analysis:\n",
+    "\n",
+    " Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
     "\n",
     "\n",
     "\n",
     "\n",
     "2. \n",
-    "Property Valuation: By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
+    "Property Valuation: \n",
+    "\n",
+    "By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
     "\n",
     "\n",
     "\n",
     "\n",
     "3. \n",
-    "Targeted Marketing: Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
+    "Targeted Marketing: \n",
+    "\n",
+    "Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
     "\n",
     "\n",
     "\n",
@@ -106,8 +112,6 @@
     "\n",
     "\n",
     "Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price.\n",
-    "Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on \n",
-    "property prices.\n",
     "\n",
     "\n",
     "\n",
@@ -219,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +501,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +688,7 @@
        "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
       ]
      },
-     "execution_count": 5,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -746,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -805,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -850,104 +854,104 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>19757</th>\n",
-       "      <td>5676000004</td>\n",
-       "      <td>11/18/2014</td>\n",
-       "      <td>399000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1430</td>\n",
-       "      <td>1250</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1430</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2821</th>\n",
-       "      <td>9828200147</td>\n",
-       "      <td>4/10/2015</td>\n",
-       "      <td>425000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>1800</td>\n",
+       "      <th>20146</th>\n",
+       "      <td>7967000130</td>\n",
+       "      <td>4/1/2015</td>\n",
+       "      <td>370228.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>4000</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>NO</td>\n",
        "      <td>Average</td>\n",
        "      <td>8 Good</td>\n",
-       "      <td>1180</td>\n",
+       "      <td>2050</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1994</td>\n",
+       "      <td>2014</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1500</td>\n",
-       "      <td>1948</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>4000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3151</th>\n",
-       "      <td>8944750850</td>\n",
-       "      <td>5/21/2014</td>\n",
-       "      <td>288400.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>1870</td>\n",
-       "      <td>3230</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>13983</th>\n",
+       "      <td>7338000150</td>\n",
+       "      <td>1/29/2015</td>\n",
+       "      <td>160000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1870</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>1070</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1997</td>\n",
+       "      <td>1983</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1620</td>\n",
-       "      <td>3363</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>4200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10199</th>\n",
-       "      <td>3298600340</td>\n",
-       "      <td>9/5/2014</td>\n",
-       "      <td>400000.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>3320</td>\n",
-       "      <td>15600</td>\n",
+       "      <th>3605</th>\n",
+       "      <td>8658303585</td>\n",
+       "      <td>8/7/2014</td>\n",
+       "      <td>252500.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>900</td>\n",
+       "      <td>7500</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
        "      <td>Good</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>1660</td>\n",
-       "      <td>1660.0</td>\n",
-       "      <td>1977</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2330</td>\n",
-       "      <td>15360</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>10000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18048</th>\n",
-       "      <td>7853220610</td>\n",
-       "      <td>7/29/2014</td>\n",
-       "      <td>457000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>5694</td>\n",
+       "      <th>9229</th>\n",
+       "      <td>1787250210</td>\n",
+       "      <td>12/22/2014</td>\n",
+       "      <td>379000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>5225</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>NO</td>\n",
        "      <td>Average</td>\n",
        "      <td>8 Good</td>\n",
-       "      <td>2050</td>\n",
+       "      <td>2410</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2680</td>\n",
-       "      <td>7187</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>5378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11243</th>\n",
+       "      <td>1843200350</td>\n",
+       "      <td>7/22/2014</td>\n",
+       "      <td>150000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>1934</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1978</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>1898</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -955,28 +959,28 @@
       ],
       "text/plain": [
        "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "19757  5676000004  11/18/2014  399000.0         3       2.50         1430   \n",
-       "2821   9828200147   4/10/2015  425000.0         3       2.00         1180   \n",
-       "3151   8944750850   5/21/2014  288400.0         3       2.25         1870   \n",
-       "10199  3298600340    9/5/2014  400000.0         6       3.00         3320   \n",
-       "18048  7853220610   7/29/2014  457000.0         3       2.50         2050   \n",
+       "20146  7967000130    4/1/2015  370228.0         4       3.00         2050   \n",
+       "13983  7338000150   1/29/2015  160000.0         2       1.00         1070   \n",
+       "3605   8658303585    8/7/2014  252500.0         2       1.00          900   \n",
+       "9229   1787250210  12/22/2014  379000.0         4       2.75         2410   \n",
+       "11243  1843200350   7/22/2014  150000.0         2       1.50         1360   \n",
        "\n",
-       "       sqft_lot  floors waterfront condition      grade  sqft_above  \\\n",
-       "19757      1250     3.0         NO   Average  7 Average        1430   \n",
-       "2821       1800     2.0        NaN   Average     8 Good        1180   \n",
-       "3151       3230     2.0         NO   Average  7 Average        1870   \n",
-       "10199     15600     1.0         NO      Good     8 Good        1660   \n",
-       "18048      5694     2.0        NaN   Average     8 Good        2050   \n",
+       "       sqft_lot  floors waterfront condition          grade  sqft_above  \\\n",
+       "20146      4000     2.0         NO   Average         8 Good        2050   \n",
+       "13983      4200     1.0         NO      Good  6 Low Average        1070   \n",
+       "3605       7500     1.0         NO      Good  6 Low Average         900   \n",
+       "9229       5225     2.0         NO   Average         8 Good        2410   \n",
+       "11243      1934     2.0         NO      Good      7 Average        1360   \n",
        "\n",
        "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "19757           0.0      2007           0.0           1360        1269  \n",
-       "2821            0.0      1994           0.0           1500        1948  \n",
-       "3151            0.0      1997           0.0           1620        3363  \n",
-       "10199        1660.0      1977           NaN           2330       15360  \n",
-       "18048           0.0      2004           NaN           2680        7187  "
+       "20146           0.0      2014           0.0           2050        4000  \n",
+       "13983           0.0      1983           0.0           1150        4200  \n",
+       "3605            0.0      1961           0.0           1190       10000  \n",
+       "9229            0.0      2001           0.0           2300        5378  \n",
+       "11243           0.0      1978           0.0           1360        1898  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -997,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -1026,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1093,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1115,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -1158,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -1177,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1188,7 +1192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -1263,133 +1267,133 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>7979</th>\n",
-       "      <td>3886903615</td>\n",
-       "      <td>2015-04-16</td>\n",
-       "      <td>1290000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3430</td>\n",
-       "      <td>7200</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>4511</th>\n",
+       "      <td>723049333</td>\n",
+       "      <td>2015-04-05</td>\n",
+       "      <td>285000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1490</td>\n",
+       "      <td>10367</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>3430</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>1957</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1530</td>\n",
-       "      <td>7800</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>8254</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4578</th>\n",
-       "      <td>9478500590</td>\n",
-       "      <td>2015-04-08</td>\n",
-       "      <td>302500.0</td>\n",
+       "      <th>1181</th>\n",
+       "      <td>7625700935</td>\n",
+       "      <td>2014-06-05</td>\n",
+       "      <td>875000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>4476</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>3250</td>\n",
+       "      <td>6000</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2008</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2001</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2250</td>\n",
-       "      <td>4488</td>\n",
+       "      <td>1650</td>\n",
+       "      <td>6000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10023</th>\n",
-       "      <td>3423600060</td>\n",
-       "      <td>2014-12-02</td>\n",
-       "      <td>665000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>2280</td>\n",
-       "      <td>3680</td>\n",
-       "      <td>1.5</td>\n",
+       "      <th>1875</th>\n",
+       "      <td>1853081000</td>\n",
+       "      <td>2014-07-17</td>\n",
+       "      <td>820000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2830</td>\n",
+       "      <td>6137</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1470</td>\n",
-       "      <td>810.0</td>\n",
-       "      <td>1926</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2830</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2010</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1850</td>\n",
-       "      <td>3680</td>\n",
+       "      <td>3170</td>\n",
+       "      <td>6285</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11990</th>\n",
-       "      <td>7852150200</td>\n",
-       "      <td>2014-09-23</td>\n",
-       "      <td>389950.0</td>\n",
-       "      <td>3</td>\n",
+       "      <th>12273</th>\n",
+       "      <td>7016200030</td>\n",
+       "      <td>2015-03-20</td>\n",
+       "      <td>480000.0</td>\n",
+       "      <td>4</td>\n",
        "      <td>2.50</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>6396</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>7966</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2003</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>1970</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1700</td>\n",
-       "      <td>4444</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>7500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20266</th>\n",
-       "      <td>3356402705</td>\n",
-       "      <td>2015-03-17</td>\n",
-       "      <td>216000.0</td>\n",
+       "      <th>8546</th>\n",
+       "      <td>7981900110</td>\n",
+       "      <td>2014-10-03</td>\n",
+       "      <td>350000.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>1847</td>\n",
-       "      <td>8000</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>3175</td>\n",
+       "      <td>1.5</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1847</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2008</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>1966</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1767</td>\n",
-       "      <td>8000</td>\n",
+       "      <td>1260</td>\n",
+       "      <td>3175</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               id       date      price  bedrooms  bathrooms  sqft_living  \\\n",
-       "7979   3886903615 2015-04-16  1290000.0         4       2.50         3430   \n",
-       "4578   9478500590 2015-04-08   302500.0         3       2.50         1690   \n",
-       "10023  3423600060 2014-12-02   665000.0         4       1.75         2280   \n",
-       "11990  7852150200 2014-09-23   389950.0         3       2.50         1700   \n",
-       "20266  3356402705 2015-03-17   216000.0         4       2.50         1847   \n",
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "4511    723049333 2015-04-05  285000.0         3       1.50         1490   \n",
+       "1181   7625700935 2014-06-05  875000.0         3       3.50         3250   \n",
+       "1875   1853081000 2014-07-17  820000.0         5       2.75         2830   \n",
+       "12273  7016200030 2015-03-20  480000.0         4       2.50         2080   \n",
+       "8546   7981900110 2014-10-03  350000.0         4       2.75         2300   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "7979       7200     2.0           0        3.0    7.0        3430   \n",
-       "4578       4476     2.0           0        3.0    5.0        1690   \n",
-       "10023      3680     1.5           0        5.0    5.0        1470   \n",
-       "11990      6396     2.0           0        3.0    5.0        1700   \n",
-       "20266      8000     2.0           0        3.0    5.0        1847   \n",
+       "4511      10367     1.0           0        3.0    5.0        1010   \n",
+       "1181       6000     2.0           0        3.0    8.0        2500   \n",
+       "1875       6137     2.0           0        3.0    7.0        2830   \n",
+       "12273      7966     1.0           0        3.0    5.0        1200   \n",
+       "8546       3175     1.5           0        3.0    4.0        1340   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "7979             0.0      2014           0.0           1530        7800  \n",
-       "4578             0.0      2008           0.0           2250        4488  \n",
-       "10023          810.0      1926           0.0           1850        3680  \n",
-       "11990            0.0      2003           0.0           1700        4444  \n",
-       "20266            0.0      2008           0.0           1767        8000  "
+       "4511           480.0      1957           0.0           1000        8254  \n",
+       "1181           750.0      2001           0.0           1650        6000  \n",
+       "1875             0.0      2010           0.0           3170        6285  \n",
+       "12273          880.0      1970           0.0           1920        7500  \n",
+       "8546           960.0      1966           0.0           1260        3175  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1420,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -1493,7 +1497,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -1816,7 +1820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -1892,7 +1896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1945,7 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1958,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1967,7 +1971,7 @@
        "Text(0.5, 0, 'Number of Bedrooms')"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2005,7 +2009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -2022,7 +2026,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2042,7 +2046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
@@ -2091,124 +2095,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>18795</th>\n",
-       "      <td>3904960690</td>\n",
-       "      <td>2015-04-17</td>\n",
-       "      <td>612000.0</td>\n",
+       "      <th>1188</th>\n",
+       "      <td>2624049185</td>\n",
+       "      <td>2014-09-09</td>\n",
+       "      <td>405000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2120</td>\n",
-       "      <td>7401</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>5355</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2120</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1989</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1956</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>7972</td>\n",
-       "      <td>35</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>6225</td>\n",
+       "      <td>68</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11641.0</td>\n",
-       "      <td>600K-1M</td>\n",
+       "      <td>8875.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16001</th>\n",
-       "      <td>7560000050</td>\n",
-       "      <td>2015-04-23</td>\n",
-       "      <td>730000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>2440</td>\n",
-       "      <td>3502</td>\n",
+       "      <th>20611</th>\n",
+       "      <td>2895800390</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>359800.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>2752</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1970</td>\n",
-       "      <td>470.0</td>\n",
-       "      <td>2000</td>\n",
+       "      <td>2170</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2440</td>\n",
-       "      <td>3417</td>\n",
-       "      <td>24</td>\n",
+       "      <td>2014</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8382.0</td>\n",
-       "      <td>600K-1M</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>2752</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7092.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9811</th>\n",
-       "      <td>3598600049</td>\n",
-       "      <td>2015-04-24</td>\n",
-       "      <td>224000.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>840</td>\n",
-       "      <td>7203</td>\n",
-       "      <td>1.5</td>\n",
+       "      <th>2209</th>\n",
+       "      <td>3438500339</td>\n",
+       "      <td>2014-05-26</td>\n",
+       "      <td>276000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>840</td>\n",
+       "      <td>1140</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1949</td>\n",
+       "      <td>1960</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1560</td>\n",
-       "      <td>8603</td>\n",
-       "      <td>75</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>64</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8883.0</td>\n",
+       "      <td>7280.0</td>\n",
        "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15108</th>\n",
-       "      <td>9332800020</td>\n",
-       "      <td>2014-07-15</td>\n",
-       "      <td>745000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2290</td>\n",
-       "      <td>10409</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>18431</th>\n",
+       "      <td>4167960330</td>\n",
+       "      <td>2015-01-09</td>\n",
+       "      <td>270000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>7750</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2290</td>\n",
+       "      <td>1820</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1972</td>\n",
+       "      <td>1992</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2040</td>\n",
-       "      <td>10409</td>\n",
-       "      <td>52</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>8084</td>\n",
+       "      <td>32</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>14989.0</td>\n",
-       "      <td>600K-1M</td>\n",
+       "      <td>11390.0</td>\n",
+       "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20642</th>\n",
-       "      <td>4305500030</td>\n",
-       "      <td>2015-05-01</td>\n",
-       "      <td>625000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>3220</td>\n",
-       "      <td>6409</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>11609</th>\n",
+       "      <td>1972201550</td>\n",
+       "      <td>2014-07-16</td>\n",
+       "      <td>565000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>2452</td>\n",
+       "      <td>1.5</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>3220</td>\n",
+       "      <td>1540</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2008</td>\n",
+       "      <td>1906</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3330</td>\n",
-       "      <td>6231</td>\n",
-       "      <td>16</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>118</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12849.0</td>\n",
-       "      <td>600K-1M</td>\n",
+       "      <td>5532.0</td>\n",
+       "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2217,37 +2221,37 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "18795  3904960690 2015-04-17  612000.0         3       2.50         2120   \n",
-       "16001  7560000050 2015-04-23  730000.0         3       3.50         2440   \n",
-       "9811   3598600049 2015-04-24  224000.0         1       0.75          840   \n",
-       "15108  9332800020 2014-07-15  745000.0         4       2.25         2290   \n",
-       "20642  4305500030 2015-05-01  625000.0         3       2.50         3220   \n",
+       "1188   2624049185 2014-09-09  405000.0         3       1.75         1760   \n",
+       "20611  2895800390 2014-08-07  359800.0         5       2.50         2170   \n",
+       "2209   3438500339 2014-05-26  276000.0         3       1.00         1140   \n",
+       "18431  4167960330 2015-01-09  270000.0         3       2.00         1820   \n",
+       "11609  1972201550 2014-07-16  565000.0         4       1.00         1540   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "18795      7401     2.0           0        3.0  ...        2120   \n",
-       "16001      3502     2.0           0        3.0  ...        1970   \n",
-       "9811       7203     1.5           0        3.0  ...         840   \n",
-       "15108     10409     2.0           0        3.0  ...        2290   \n",
-       "20642      6409     2.0           0        3.0  ...        3220   \n",
+       "1188       5355     1.0           0        3.0  ...        1160   \n",
+       "20611      2752     2.0           0        3.0  ...        2170   \n",
+       "2209       5000     1.0           0        3.0  ...        1140   \n",
+       "18431      7750     1.0           0        3.0  ...        1820   \n",
+       "11609      2452     1.5           0        4.0  ...        1540   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "18795            0.0      1989           0.0           2010        7972   \n",
-       "16001          470.0      2000           0.0           2440        3417   \n",
-       "9811             0.0      1949           0.0           1560        8603   \n",
-       "15108            0.0      1972           0.0           2040       10409   \n",
-       "20642            0.0      2008           0.0           3330        6231   \n",
+       "1188           600.0      1956           0.0           1790        6225   \n",
+       "20611            0.0      2014           0.0           1800        2752   \n",
+       "2209             0.0      1960           0.0           1140        5000   \n",
+       "18431            0.0      1992           0.0           2080        8084   \n",
+       "11609            0.0      1906           0.0           1290        3360   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  price_range  \n",
-       "18795         35             0.0     11641.0      600K-1M  \n",
-       "16001         24             0.0      8382.0      600K-1M  \n",
-       "9811          75             0.0      8883.0    100K-300K  \n",
-       "15108         52             0.0     14989.0      600K-1M  \n",
-       "20642         16             0.0     12849.0      600K-1M  \n",
+       "1188          68             0.0      8875.0    300K-600K  \n",
+       "20611         10             0.0      7092.0    300K-600K  \n",
+       "2209          64             0.0      7280.0    100K-300K  \n",
+       "18431         32             0.0     11390.0    100K-300K  \n",
+       "11609        118             0.0      5532.0    300K-600K  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2258,7 +2262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -2269,27 +2273,9 @@
       "  grouped_vals = vals.groupby(grouper)\n"
      ]
     },
-    {
-     "ename": "AttributeError",
-     "evalue": "Rectangle.set() got an unexpected keyword argument 'legend'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[26], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Create a bar plot\u001b[39;00m\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 6\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice_range\u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtotal_sqft\u001b[39m\u001b[38;5;124m\"\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, errorbar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprice_range\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcrest\u001b[39m\u001b[38;5;124m\"\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotal Square Footage by Price Range.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrice Range\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2763\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2528\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2519\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l, b, w, h, c, e, lw, htch, lbl \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[0;32m   2520\u001b[0m     r \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle(\n\u001b[0;32m   2521\u001b[0m         xy\u001b[38;5;241m=\u001b[39m(l, b), width\u001b[38;5;241m=\u001b[39mw, height\u001b[38;5;241m=\u001b[39mh,\n\u001b[0;32m   2522\u001b[0m         facecolor\u001b[38;5;241m=\u001b[39mc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2526\u001b[0m         hatch\u001b[38;5;241m=\u001b[39mhtch,\n\u001b[0;32m   2527\u001b[0m         )\n\u001b[1;32m-> 2528\u001b[0m     r\u001b[38;5;241m.\u001b[39m_internal_update(kwargs)\n\u001b[0;32m   2529\u001b[0m     r\u001b[38;5;241m.\u001b[39mget_path()\u001b[38;5;241m.\u001b[39m_interpolation_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m   2530\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_props(\n\u001b[0;32m   1220\u001b[0m         kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{cls.__name__}\u001b[39;00m\u001b[38;5;124m.set() got an unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1221\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{prop_name!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Rectangle.set() got an unexpected keyword argument 'legend'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -2305,7 +2291,7 @@
     "\n",
     "# Create a bar plot\n",
     "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\", legend=False)\n",
+    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\")\n",
     "plt.title(\"Total Square Footage by Price Range.\")\n",
     "plt.xlabel(\"Price Range\")\n",
     "plt.ylabel(\"Total Square Footage\")\n",
@@ -2377,7 +2363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -2420,30 +2406,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Rectangle.set() got an unexpected keyword argument 'legend'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[29], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(nrows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, ncols\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m14\u001b[39m, \u001b[38;5;241m6\u001b[39m), sharey\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Bar plot for condition vs. price\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m sns\u001b[38;5;241m.\u001b[39mbarplot(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprice\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mhousing_data, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m], hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcondition\u001b[39m\u001b[38;5;124m\"\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m, legend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m      6\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition vs. Price\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      7\u001b[0m axes[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCondition\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[1;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[1;32m-> 2763\u001b[0m plotter\u001b[38;5;241m.\u001b[39mplot(ax, kwargs)\n\u001b[0;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1586\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[1;34m(self, ax, bar_kws)\u001b[0m\n\u001b[0;32m   1584\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;28mself\u001b[39m, ax, bar_kws):\n\u001b[0;32m   1585\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mannotate_axes(ax)\n\u001b[0;32m   1588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:1569\u001b[0m, in \u001b[0;36m_BarPlotter.draw_bars\u001b[1;34m(self, ax, kws)\u001b[0m\n\u001b[0;32m   1565\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hue_level \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names):\n\u001b[0;32m   1566\u001b[0m \n\u001b[0;32m   1567\u001b[0m     \u001b[38;5;66;03m# Draw the bars\u001b[39;00m\n\u001b[0;32m   1568\u001b[0m     offpos \u001b[38;5;241m=\u001b[39m barpos \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_offsets[j]\n\u001b[1;32m-> 1569\u001b[0m     barfunc(offpos, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstatistic[:, j], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnested_width,\n\u001b[0;32m   1570\u001b[0m             color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolors[j], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1571\u001b[0m             label\u001b[38;5;241m=\u001b[39mhue_level, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkws)\n\u001b[0;32m   1573\u001b[0m     \u001b[38;5;66;03m# Draw the confidence intervals\u001b[39;00m\n\u001b[0;32m   1574\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfint\u001b[38;5;241m.\u001b[39msize:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:2528\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m   2519\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l, b, w, h, c, e, lw, htch, lbl \u001b[38;5;129;01min\u001b[39;00m args:\n\u001b[0;32m   2520\u001b[0m     r \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle(\n\u001b[0;32m   2521\u001b[0m         xy\u001b[38;5;241m=\u001b[39m(l, b), width\u001b[38;5;241m=\u001b[39mw, height\u001b[38;5;241m=\u001b[39mh,\n\u001b[0;32m   2522\u001b[0m         facecolor\u001b[38;5;241m=\u001b[39mc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2526\u001b[0m         hatch\u001b[38;5;241m=\u001b[39mhtch,\n\u001b[0;32m   2527\u001b[0m         )\n\u001b[1;32m-> 2528\u001b[0m     r\u001b[38;5;241m.\u001b[39m_internal_update(kwargs)\n\u001b[0;32m   2529\u001b[0m     r\u001b[38;5;241m.\u001b[39mget_path()\u001b[38;5;241m.\u001b[39m_interpolation_steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m   2530\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1219\u001b[0m, in \u001b[0;36mArtist._internal_update\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m   1212\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_internal_update\u001b[39m(\u001b[38;5;28mself\u001b[39m, kwargs):\n\u001b[0;32m   1213\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1214\u001b[0m \u001b[38;5;124;03m    Update artist properties without prenormalizing them, but generating\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m \u001b[38;5;124;03m    errors as if calling `set`.\u001b[39;00m\n\u001b[0;32m   1216\u001b[0m \n\u001b[0;32m   1217\u001b[0m \u001b[38;5;124;03m    The lack of prenormalization is to maintain backcompatibility.\u001b[39;00m\n\u001b[0;32m   1218\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1219\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_props(\n\u001b[0;32m   1220\u001b[0m         kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{cls.__name__}\u001b[39;00m\u001b[38;5;124m.set() got an unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1221\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{prop_name!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\matplotlib\\artist.py:1193\u001b[0m, in \u001b[0;36mArtist._update_props\u001b[1;34m(self, props, errfmt)\u001b[0m\n\u001b[0;32m   1191\u001b[0m             func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   1192\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(func):\n\u001b[1;32m-> 1193\u001b[0m                 \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[0;32m   1194\u001b[0m                     errfmt\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m), prop_name\u001b[38;5;241m=\u001b[39mk))\n\u001b[0;32m   1195\u001b[0m             ret\u001b[38;5;241m.\u001b[39mappend(func(v))\n\u001b[0;32m   1196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret:\n",
-      "\u001b[1;31mAttributeError\u001b[0m: Rectangle.set() got an unexpected keyword argument 'legend'"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1400x600 with 2 Axes>"
       ]
@@ -2453,18 +2421,17 @@
     }
    ],
    "source": [
-    "# Create subplots with a shared y-axis\n",
     "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6), sharey=True)\n",
     "\n",
     "# Bar plot for condition vs. price\n",
-    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], hue=\"condition\", palette='viridis', legend=False)\n",
+    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], palette='viridis')\n",
     "axes[0].set_title('Condition vs. Price')\n",
     "axes[0].set_xlabel('Condition')\n",
     "axes[0].set_ylabel('Price')\n",
     "axes[0].grid(True)\n",
     "\n",
     "# Bar plot for grade vs. price\n",
-    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], hue=\"grade\", palette='viridis', legend=False)\n",
+    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], palette='viridis')\n",
     "axes[1].set_title('Grade vs. Price')\n",
     "axes[1].set_xlabel('Grade')\n",
     "axes[1].grid(True)\n",
@@ -2473,7 +2440,7 @@
     "plt.tight_layout()\n",
     "\n",
     "# Show plots\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -4199,7 +4166,7 @@
     "\n",
     "\n",
     "Coefficients:\n",
-    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero. In this case, it's not meaningful as it's unlikely for all features to be zero.\n",
+    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero.\n",
     "\n",
     "\n",
     "bathrooms: For each additional bathroom, the predicted housing price increases by approximately $24,855.\n",
@@ -4368,7 +4335,10 @@
    "source": [
     "The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.\n",
     "\n",
-    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n"
+    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
+    "\n",
+    "\n",
+    "Addressing negative correlations in house prices, such as with the year built, is crucial. Older properties often have lower prices due to depreciation and maintenance issues. However, strategic renovations, updates, and modernization efforts offer opportunities to increase their value. Homeowners and investors can enhance the appeal and value of older properties in the market by undertaking such initiatives."
    ]
   },
   {
@@ -4709,7 +4679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -4719,17 +4689,6 @@
       "Mean Squared Error (Log Transformed): 0.09804039958945562\n",
       "R-squared (Log Transformed): 0.6469123715948973\n"
      ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'OLS' object has no attribute 'coef_'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[60], line 29\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse_log)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mR-squared (Log Transformed):\u001b[39m\u001b[38;5;124m\"\u001b[39m, r2_log)\n\u001b[1;32m---> 29\u001b[0m coefficients \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature\u001b[39m\u001b[38;5;124m\"\u001b[39m: X\u001b[38;5;241m.\u001b[39mcolumns, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoefficient\u001b[39m\u001b[38;5;124m\"\u001b[39m: model\u001b[38;5;241m.\u001b[39mcoef_})\n\u001b[0;32m     30\u001b[0m \u001b[38;5;28mprint\u001b[39m(coefficients)\n",
-      "\u001b[1;31mAttributeError\u001b[0m: 'OLS' object has no attribute 'coef_'"
-     ]
     }
    ],
    "source": [
@@ -4761,13 +4720,12 @@
     "# Display results\n",
     "print(\"Mean Squared Error (Log Transformed):\", mse_log)\n",
     "print(\"R-squared (Log Transformed):\", r2_log)\n",
-    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
-    "print(coefficients)\n"
+    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model_log.coef_})\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -4789,7 +4747,7 @@
     "\n",
     "# Plot residuals vs predicted values\n",
     "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred_log, residuals_log, color='blue')\n",
+    "plt.scatter(y_pred_log, residuals_log, color='blue',)\n",
     "plt.title('Residuals Plot (Log Transformed)')\n",
     "plt.xlabel('Predicted Values (Log Transformed)')\n",
     "plt.ylabel('Residuals (Log Transformed)')\n",
@@ -5161,6 +5119,7 @@
    "metadata": {},
    "source": [
     "# Recommedations\n",
+    "# Statistical recommedations.\n",
     "To mitigate multicollinearity, strategies such as feature selection, principal component analysis (PCA), or regularization methods like ridge regression or Lasso regression can be employed. These techniques prioritize essential predictors and enhance the model's interpretability while stabilizing it against multicollinearity.\n",
     "\n",
     "\n",
@@ -5168,7 +5127,51 @@
     "Before opting for polynomial regression, it's essential to validate the assumption of linearity between predictors and the target variable. If this assumption doesn't hold, alternative regression techniques such as generalized additive models (GAMs) or spline regression should be considered to better capture intricate relationships.\n",
     "\n",
     "\n",
-    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis"
+    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis\n",
+    "# Recommedation to real estate clients:Home owners and investors.\n",
+    "\n",
+    "\n",
+    "1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.\n",
+    "\n",
+    "\n",
+    "2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.\n",
+    "\n",
+    "\n",
+    "3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.\n",
+    "\n",
+    "\n",
+    "4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.\n",
+    "\n",
+    "\n",
+    "5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.\n",
+    "\n",
+    "\n",
+    "6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CONCLUSION\n",
+    "\n",
+    "Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.\n",
+    "\n",
+    "\n",
+    "Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.\n",
+    "\n",
+    "\n",
+    "Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.\n",
+    "\n",
+    "\n",
+    "Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.\n"
    ]
   }
  ],

From a3a720702b318455c2268e864755bea306a2697b Mon Sep 17 00:00:00 2001
From: WairimuMundia <156019539+WairimuMundia@users.noreply.github.com>
Date: Wed, 1 May 2024 10:09:39 -0400
Subject: [PATCH 16/27] Add files via upload

---
 README ().md | 261 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 261 insertions(+)
 create mode 100644 README ().md

diff --git a/README ().md b/README ().md
new file mode 100644
index 00000000..b73faf72
--- /dev/null
+++ b/README ().md	
@@ -0,0 +1,261 @@
+# REAL ESTATE SALES PREDICTION MODEL
+
+## Project Overview
+
+In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.
+
+
+### Business Problem
+
+Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
+### The Data Understanding
+
+King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
+**Target Variable**
+
+price: Sale price of the house .
+
+**Unique identifier**
+
+id - Unique identifier for a house
+
+**Property Characteristics:**
+
+
+
+bathrooms: Number of bathrooms.
+
+sqft_living: Square footage of living space in the home.
+
+sqft_lot: Square footage of the lot.
+
+floors: Number of floors (levels) in the house.
+
+waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
+
+view: Quality of view from the house, categorized into various types.
+
+condition: Overall condition of the house, categorized based on maintenance.
+
+grade: Overall grade of the house, reflecting construction and design quality.
+
+Additional Features:
+sqft_above: Square footage of house apart from the basement.
+
+sqft_basement: Square footage of the basement.
+
+yr_built: Year when the house was built.
+
+yr_renovated: Year when the house was renovated.
+
+zipcode: ZIP Code of the property.
+
+lat: Latitude coordinate of the property.
+
+long: Longitude coordinate of the property.
+
+sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
+
+sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.bedrooms: Number of bedrooms.
+
+bathrooms: Number of bathrooms.
+
+sqft_living: Square footage of living space in the home.
+
+sqft_lot: Square footage of the lot.
+
+floors: Number of floors (levels) in the house.
+
+waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
+
+view: Quality of view from the house, categorized into various types.
+
+condition: Overall condition of the house, categorized based on maintenance.
+
+grade: Overall grade of the house, reflecting construction and design quality.
+
+**Additional Features:**
+
+sqft_above: Square footage of house apart from the basement.
+
+sqft_basement: Square footage of the basement.
+
+yr_built: Year when the house was built.
+
+yr_renovated: Year when the house was renovated.
+
+zipcode: ZIP Code of the property.
+
+lat: Latitude coordinate of the property.
+
+long: Longitude coordinate of the property.
+
+sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
+
+sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.
+### Key Points
+
+# **OBJECTIVES**
+
+
+**Main Objective:**
+
+The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, furnishing clients with valuable insights to facilitate informed investment decisions.
+
+Specific Goals:
+
+i). Identification of Key Influencing Factors on House Prices:
+
+Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price. Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on property prices.
+
+ii). Assessment of Model Performance:
+
+Employ metrics such as mean squared error, R-squared values, and residual analysis to evaluate the model's accuracy in predicting house prices effectively.
+
+iii). Provision of Actionable Recommendations:
+
+Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.
+
+# **TABLE OF CONTENT**
+1.   Data Preparation
+2.   Data cleaning
+1.   Exploratory data analysis
+2.   Statistical Analysis
+1.   Modelling
+2.   Regression Results
+1.   Conclusion
+2.   Recomendations
+
+
+#**Statistical Analysis**
+Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:
+
+Descriptive Statistics
+Correlation matrix
+Distribution Analysis
+Inferential Statistics using Hypothesis Testing and Analysis of Variance
+MultiColinierity
+# *Modelling**
+Baseline model - simple linear model.
+2. log transformation. 
+3. Multiple Linear Regression
+4. Residual modelling.
+
+# **REGRESSION RESULTS**
+
+**SIMPLE LINEAR REGRESSION** 
+Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.
+
+Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.
+
+R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.
+
+Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.
+
+Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.
+
+Test Set:
+
+R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.
+
+Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.
+
+Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data
+Overall
+, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance.
+# ** Multiple Linear Regresion 
+![alt text](image-1.png)
+# **RESIDUALS**
+![alt text](image-3.png)
+A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
+
+Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
+The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
+
+We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
+#**Log transformation**.
+Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
+
+Log transformation of the multiple linear regression.
+![alt text](image-4.png)
+
+![alt text](image-5.png)
+The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model.
+visualization of the positive and negative coefficient variables.
+R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
+
+MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.
+
+Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example: The coefficient for const (intercept) is 4987.94. The coefficient for x1 (bathrooms) is 3457.38. The coefficient for x2 (sqft_living) is -16.53. The coefficient for x3 (floors) is -16.98. The coefficient for x4 (waterfront) is -59.75. The coefficient for x5 (condition) is -66.70. The coefficient for x6 (grade) is 21.71. The coefficient for x7 (sqft_basement) is 56.12. The coefficient for x8 (yr_built) and other coefficients follow.
+
+P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.
+
+F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.
+
+Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.
+
+![alt text](image-6.png)
+#**REGRESSION Results**
+
+From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
+
+Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73
+
+The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
+# **Assumptions**
+Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
+
+Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
+
+Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
+
+No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
+#**Limitations**
+Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
+
+Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.
+
+Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.
+
+Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.
+
+Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance.
+
+**Limitations**
+1.   The dataset could have more property based characteristics
+2.   Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
+
+1. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
+1. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
+Overall the model was the best fit model for this predictions
+
+# **RECOMENDATIONS**
+
+1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.
+
+
+2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.
+
+
+3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.
+
+
+4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.
+
+
+5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.
+
+
+6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
+# ** Conclusion**
+
+Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
+
+
+Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
+
+
+Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
+
+
+Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.

From 3acdb8ec1835fd56cf64e2b0b43fda54ae27e820 Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 17:48:06 +0300
Subject: [PATCH 17/27] update README.md

---
 README.md | 320 +++++++++++++++++++++++++-----------------------------
 1 file changed, 148 insertions(+), 172 deletions(-)

diff --git a/README.md b/README.md
index 5dd0f84d..e770ce06 100644
--- a/README.md
+++ b/README.md
@@ -1,285 +1,261 @@
-# Phase 2 Project Description
-
-Another module down - you're almost half way there!
-
-![awesome](https://raw.githubusercontent.com/learn-co-curriculum/dsc-phase-2-project-v2-3/main/halfway-there.gif)
-
-All that remains in Phase 2 is to put your newfound data science skills to use with a large project!
-
-In this project description, we will cover:
-
-* Project Overview: the project goal, audience, and dataset
-* Deliverables: the specific items you are required to produce for this project
-* Grading: how your project will be scored
-* Getting Started: guidance for how to begin working
+# REAL ESTATE SALES PREDICTION MODEL
 
 ## Project Overview
 
-For this project, you will use multiple linear regression modeling to analyze house sales in a northwestern county.
+In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.
+
 
 ### Business Problem
 
-It is up to you to define a stakeholder and business problem appropriate to this dataset.
+Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
+### The Data Understanding
 
-If you are struggling to define a stakeholder, we recommend you complete a project for a real estate agency that helps homeowners buy and/or sell homes. A business problem you could focus on for this stakeholder is the need to provide advice to homeowners about how home renovations might increase the estimated value of their homes, and by what amount.
+King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
+**Target Variable**
 
-### The Data
+price: Sale price of the house .
 
-This project uses the King County House Sales dataset, which can be found in  `kc_house_data.csv` in the data folder in this assignment's GitHub repository. The description of the column names can be found in `column_names.md` in the same folder. As with most real world data sets, the column names are not perfectly described, so you'll have to do some research or use your best judgment if you have questions about what the data means.
+**Unique identifier**
 
-It is up to you to decide what data from this dataset to use and how to use it. If you are feeling overwhelmed or behind, we recommend you **ignore** some or all of the following features:
+id - Unique identifier for a house
 
-* `date`
-* `view`
-* `sqft_above`
-* `sqft_basement`
-* `yr_renovated`
-* `zipcode`
-* `lat`
-* `long`
-* `sqft_living15`
-* `sqft_lot15`
+**Property Characteristics:**
 
-### Key Points
 
-* **Your goal in regression modeling is to yield findings to support relevant recommendations. Those findings should include a metric describing overall model performance as well as at least two regression model coefficients.** As you explore the data and refine your stakeholder and business problem definitions, make sure you are also thinking about how a linear regression model adds value to your analysis. "The assignment was to use linear regression" is not an acceptable answer! You can also use additional statistical techniques other than linear regression, so long as you clearly explain why you are using each technique.
 
-* **You should demonstrate an iterative approach to modeling.** This means that you must build multiple models. Begin with a basic model, evaluate it, and then provide justification for and proceed to a new model. After you finish refining your models, you should provide 1-3 paragraphs in the notebook discussing your final model.
+bathrooms: Number of bathrooms.
 
-* **Data visualization and analysis are no longer explicit project requirements, but they are still very important.** In Phase 1, your project stopped earlier in the CRISP-DM process. Now you are going a step further, to modeling. Data visualization and analysis will help you build better models and tell a better story to your stakeholders.
+sqft_living: Square footage of living space in the home.
 
-## Deliverables
+sqft_lot: Square footage of the lot.
 
-There are three deliverables for this project:
+floors: Number of floors (levels) in the house.
 
-* A **non-technical presentation**
-* A **Jupyter Notebook**
-* A **GitHub repository**
+waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
 
-The deliverables requirements are almost the same as in the Phase 1 Project, and you can review those extended descriptions [here](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#deliverables). In general, everything is the same except the "Data Visualization" and "Data Analysis" requirements have been replaced by "Modeling" and "Regression Results" requirements.
+view: Quality of view from the house, categorized into various types.
 
-### Non-Technical Presentation
+condition: Overall condition of the house, categorized based on maintenance.
 
-Recall that the non-technical presentation is a slide deck presenting your analysis to ***business stakeholders***, and should be presented live as well as submitted in PDF form on Canvas.
+grade: Overall grade of the house, reflecting construction and design quality.
 
-We recommend that you follow this structure, although the slide titles should be specific to your project:
+Additional Features:
+sqft_above: Square footage of house apart from the basement.
 
-1. Beginning
-    - Overview
-    - Business and Data Understanding
-2. Middle
-    - **Modeling**
-    - **Regression Results**
-3. End
-    - Recommendations
-    - Next Steps
-    - Thank you
+sqft_basement: Square footage of the basement.
 
-Make sure that your discussion of modeling and regression results is geared towards a non-technical audience! Assume that their prior knowledge of regression modeling is minimal. You don't need to explain how linear regression works, but you should explain why linear regression is useful for the problem context. Make sure you translate any metrics or coefficients into their plain language implications.
+yr_built: Year when the house was built.
 
-The graded elements for the non-technical presentation are the same as in [Phase 1](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#deliverables).
+yr_renovated: Year when the house was renovated.
 
-### Jupyter Notebook
+zipcode: ZIP Code of the property.
 
-Recall that the Jupyter Notebook is a notebook that uses Python and Markdown to present your analysis to a ***data science audience***. You will submit the notebook in PDF format on Canvas as well as in `.ipynb` format in your GitHub repository.
+lat: Latitude coordinate of the property.
 
-The graded elements for the Jupyter Notebook are:
+long: Longitude coordinate of the property.
 
-* Business Understanding
-* Data Understanding
-* Data Preparation
-* **Modeling**
-* **Regression Results**
-* Code Quality
+sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
 
-### GitHub Repository
+sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.bedrooms: Number of bedrooms.
 
-Recall that the GitHub repository is the cloud-hosted directory containing all of your project files as well as their version history.
+bathrooms: Number of bathrooms.
 
-The requirements are the same as in [Phase 1](https://github.com/learn-co-curriculum/dsc-phase-1-project-v2-3#github-repository), except for the required sections in the `README.md`.
+sqft_living: Square footage of living space in the home.
 
-For this project, the `README.md` file should contain:
+sqft_lot: Square footage of the lot.
 
-* Overview
-* Business and Data Understanding
-  * Explain your stakeholder audience here
-* **Modeling**
-* **Regression Results**
-* Conclusion
+floors: Number of floors (levels) in the house.
 
-Just like in Phase 1, the `README.md` file should be the bridge between your non technical presentation and the Jupyter Notebook. It should not contain the code used to develop your analysis, but should provide a more in-depth explanation of your methodology and analysis than what is described in your presentation slides.
+waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
 
-## Grading
+view: Quality of view from the house, categorized into various types.
 
-***To pass this project, you must pass each project rubric objective.*** The project rubric objectives for Phase 2 are:
+condition: Overall condition of the house, categorized based on maintenance.
 
-1. Attention to Detail
-2. Statistical Communication
-3. Data Preparation Fundamentals
-4. Linear Modeling
+grade: Overall grade of the house, reflecting construction and design quality.
 
-### Attention to Detail
+**Additional Features:**
 
-Just like in Phase 1, this rubric objective is based on your completion of checklist items. ***In Phase 2, you need to complete 70% (7 out of 10) or more of the checklist elements in order to pass the Attention to Detail objective.***
+sqft_above: Square footage of house apart from the basement.
 
-**NOTE THAT THE PASSING BAR IS HIGHER IN PHASE 2 THAN IT WAS IN PHASE 1!**
+sqft_basement: Square footage of the basement.
 
-The standard will increase with each Phase, until you will be required to complete all elements to pass Phase 5 (Capstone).
+yr_built: Year when the house was built.
 
-#### Exceeds Objective
+yr_renovated: Year when the house was renovated.
 
-80% or more of the project checklist items are complete
+zipcode: ZIP Code of the property.
 
-#### Meets Objective (Passing Bar)
+lat: Latitude coordinate of the property.
 
-70% of the project checklist items are complete
+long: Longitude coordinate of the property.
 
-#### Approaching Objective
+sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
+
+sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.
+### Key Points
 
-60% of the project checklist items are complete
+# **OBJECTIVES**
 
-#### Does Not Meet Objective
 
-50% or fewer of the project checklist items are complete
+**Main Objective:**
 
-### Statistical Communication
+The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, furnishing clients with valuable insights to facilitate informed investment decisions.
 
-Recall that communication is one of the key data science "soft skills". In Phase 2, we are specifically focused on Statistical Communication. We define Statistical Communication as:
+Specific Goals:
 
-> Communicating **results of statistical analyses** to diverse audiences via writing and live presentation
+i). Identification of Key Influencing Factors on House Prices:
 
-Note that this is the same as in Phase 1, except we are replacing "basic data analysis" with "statistical analyses".
+Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price. Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on property prices.
 
-High-quality Statistical Communication includes rationale, results, limitations, and recommendations:
+ii). Assessment of Model Performance:
 
-* **Rationale:** Explaining why you are using statistical analyses rather than basic data analysis
-  * For example, why are you using regression coefficients rather than just a graph?
-  * What about the problem or data is suitable for this form of analysis?
-  * For a data science audience, this includes your reasoning for the changes you applied while iterating between models.
-* **Results:** Describing the overall model metrics and feature coefficients
-  * You need at least one overall model metric (e.g. r-squared or RMSE) and at least two feature coefficients.
-  * For a business audience, make sure you connect any metrics to real-world implications. You do not need to get into the details of how linear regression works.
-  * For a data science audience, you don't need to explain what a metric is, but make sure you explain why you chose that particular one.
-* **Limitations:** Identifying the limitations and/or uncertainty present in your analysis
-  * This could include p-values/alpha values, confidence intervals, assumptions of linear regression, missing data, etc.
-  * In general, this should be more in-depth for a data science audience and more surface-level for a business audience.
-* **Recommendations:** Interpreting the model results and limitations in the context of the business problem
-  * What should stakeholders _do_ with this information?
+Employ metrics such as mean squared error, R-squared values, and residual analysis to evaluate the model's accuracy in predicting house prices effectively.
 
-#### Exceeds Objective
+iii). Provision of Actionable Recommendations:
 
-Communicates the rationale, results, limitations, and specific recommendations of statistical analyses
+Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.
 
-> See above for extended explanations of these terms.
+# **TABLE OF CONTENT**
+1.   Data Preparation
+2.   Data cleaning
+1.   Exploratory data analysis
+2.   Statistical Analysis
+1.   Modelling
+2.   Regression Results
+1.   Conclusion
+2.   Recomendations
 
-#### Meets Objective (Passing Bar)
 
-Successfully communicates the results of statistical analyses without any major errors
+#**Statistical Analysis**
+Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:
 
-> The minimum requirement is to communicate the _results_, meaning at least one overall model metric (e.g. r-squared or RMSE) as well as at least two feature coefficients. See the Approaching Objective section for an explanation of what a "major error" means.
+Descriptive Statistics
+Correlation matrix
+Distribution Analysis
+Inferential Statistics using Hypothesis Testing and Analysis of Variance
+MultiColinierity
+# *Modelling**
+Baseline model - simple linear model.
+2. log transformation. 
+3. Multiple Linear Regression
+4. Residual modelling.
 
-#### Approaching Objective
+# **REGRESSION RESULTS**
 
-Communicates the results of statistical analyses with at least one major error
+**SIMPLE LINEAR REGRESSION** 
+Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.
 
-> A major error means that some aspect of your explanation is fundamentally incorrect. For example, if a feature coefficient is negative and you say that an increase in that feature results in an increase of the target, that would be a major error. Another example would be if you say that the feature with the highest coefficient is the "most statistically significant" while ignoring the p-value. One more example would be reporting a coefficient that is not statistically significant, rather than saying "no statistically significant linear relationship was found"
+Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.
 
-> "**If a coefficient's t-statistic is not significant, don't interpret it at all.** You can't be sure that the value of the corresponding parameter in the underlying regression model isn't really zero." _DeVeaux, Velleman, and Bock (2012), Stats: Data and Models, 3rd edition, pg. 801_. Check out [this website](https://web.ma.utexas.edu/users/mks/statmistakes/TOC.html) for extensive additional examples of mistakes using statistics.
+R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.
 
-> The easiest way to avoid making a major error is to have someone double-check your work. Reach out to peers on Slack and ask them to confirm whether your interpretation makes sense!
+Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.
 
-#### Does Not Meet Objective
+Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.
 
-Does not communicate the results of statistical analyses
+Test Set:
 
-> It is not sufficient to just display the entire results summary. You need to pull out at least one overall model metric (e.g. r-squared, RMSE) and at least two feature coefficients, and explain what those numbers mean.
+R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.
 
-### Data Preparation Fundamentals
+Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.
 
-We define this objective as:
+Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data
+Overall
+, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance.
+# ** Multiple Linear Regresion 
+![alt text](image-1.png)
+# **RESIDUALS**
+![alt text](image-3.png)
+A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
 
-> Applying appropriate **preprocessing** and feature engineering steps to tabular data in preparation for statistical modeling
+Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
+The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
 
-The two most important components of preprocessing for the Phase 2 project are:
+We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
+#**Log transformation**.
+Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
 
-* **Handling Missing Values:** Missing values may be present in the features you want to use, either encoded as `NaN` or as some other value such as `"?"`. Before you can build a linear regression model, make sure you identify and address any missing values using techniques such as dropping or replacing data.
-* **Handling Non-Numeric Data:** A linear regression model needs all of the features to be numeric, not categorical. For this project, ***be sure to pick at least one non-numeric feature and try including it in a model.*** You can identify that a feature is currently non-numeric if the type is `object` when you run `.info()` on your dataframe. Once you have identified the non-numeric features, address them using techniques such as ordinal or one-hot (dummy) encoding.
+Log transformation of the multiple linear regression.
+![alt text](image-4.png)
 
-There is no single correct way to handle either of these situations! Use your best judgement to decide what to do, and be sure to explain your rationale in the Markdown of your notebook.
+![alt text](image-5.png)
+The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model.
+visualization of the positive and negative coefficient variables.
+R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
 
-Feature engineering is encouraged but not required for this project.
+MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.
 
-#### Exceeds Objective
+Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example: The coefficient for const (intercept) is 4987.94. The coefficient for x1 (bathrooms) is 3457.38. The coefficient for x2 (sqft_living) is -16.53. The coefficient for x3 (floors) is -16.98. The coefficient for x4 (waterfront) is -59.75. The coefficient for x5 (condition) is -66.70. The coefficient for x6 (grade) is 21.71. The coefficient for x7 (sqft_basement) is 56.12. The coefficient for x8 (yr_built) and other coefficients follow.
 
-Goes above and beyond with data preparation, such as feature engineering or merging in outside datasets
+P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.
 
-> One example of feature engineering could be using the `date` feature to create a new feature called `season`, which represents whether the home was sold in Spring, Summer, Fall, or Winter.
+F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.
 
-> One example of merging in outside datasets could be finding data based on ZIP Code, such as household income or walkability, and joining that data with the provided CSV.
+Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.
 
-#### Meets Objective (Passing Bar)
+![alt text](image-6.png)
+#**REGRESSION Results**
 
-Successfully prepares data for modeling, including converting at least one non-numeric feature into ordinal or binary data and handling missing data as needed
+From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
 
-> As a reminder, you can identify the non-numeric features by calling `.info()` on the dataframe and looking for type `object`.
+Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73
 
-> Your final model does not necessarily need to include any features that were originally non-numeric, but you need to demonstrate your ability to handle this type of data.
+The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
+# **Assumptions**
+Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
 
-#### Approaching Objective
+Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
 
-Prepares some data successfully, but is unable to utilize non-numeric data
+Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
 
-> If you simply subset the dataframe to only columns with type `int64` or `float64`, your model will run, but you will not pass this objective.
+No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
+#**Limitations**
+Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
 
-#### Does Not Meet Objective
+Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.
 
-Does not prepare data for modeling
+Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.
 
-### Linear Modeling
+Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.
 
-According to [Kaggle's 2020 State of Data Science and Machine Learning Survey](https://www.kaggle.com/kaggle-survey-2020), linear and logistic regression are the most popular machine learning algorithms, used by 83.7% of data scientists. They are small, fast models compared to some of the models you will learn later, but have limitations in the kinds of relationships they are able to learn.
+Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance.
 
-In this project you are required to use linear regression as the primary statistical analysis, although you are free to use additional statistical techniques as appropriate.
+**Limitations**
+1.   The dataset could have more property based characteristics
+2.   Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
 
-#### Exceeds Objective
+1. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
+1. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
+Overall the model was the best fit model for this predictions
 
-Goes above and beyond in the modeling process, such as recursive feature selection
+# **RECOMENDATIONS**
 
-#### Meets Objective (Passing Bar)
+1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.
 
-Successfully builds a baseline model as well as at least one iterated model, and correctly extracts insights from a final model without any major errors
 
-> We are looking for you to (1) create a baseline model, (2) iterate on that model, making adjustments that are supported by regression theory or by descriptive analysis of the data, and (3) select a final model and report on its metrics and coefficients
+2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.
 
-> Ideally you would include written justifications for each model iteration, but at minimum the iterations must be _justifiable_
 
-> For an explanation of "major errors", see the description below
+3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.
 
-#### Approaching Objective
 
-Builds multiple models with at least one major error
+4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.
 
-> The number one major error to avoid is including the target as one of your features. For example, if the target is `price` you should NOT make a "price per square foot" feature, because that feature would not be available if you didn't already know the price.
 
-> Other examples of major errors include: using a target other than `price`, attempting only simple linear regression (not multiple linear regression), dropping multiple one-hot encoded columns without explaining the resulting baseline, or using a unique identifier (`id` in this dataset) as a feature.
+5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.
 
-#### Does Not Meet Objective
 
-Does not build multiple linear regression models
+6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
+# ** Conclusion**
 
-## Getting Started
+Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
 
-Please start by reviewing the contents of this project description. If you have any questions, please ask your instructor ASAP.
 
-Next, you will need to complete the [***Project Proposal***](#project_proposal) which must be reviewed by your instructor before you can continue with the project.
+Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
 
-Here are some suggestions for creating your GitHub repository:
 
-1. Fork the [Phase 2 Project Repository](https://github.com/learn-co-curriculum/dsc-phase-2-project-v2-3), clone it locally, and work in the `student.ipynb` file. Make sure to also add and commit a PDF of your presentation to your repository with a file name of `presentation.pdf`.
-2. Or, create a new repository from scratch by going to [github.com/new](https://github.com/new) and copying the data files from the Phase 2 Project Repository into your new repository.
-   - Recall that you can refer to the [Phase 1 Project Template](https://github.com/learn-co-curriculum/dsc-project-template) as an example structure
-   - This option will result in the most professional-looking portfolio repository, but can be more complicated to use. So if you are getting stuck with this option, try forking the project repository instead
+Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
 
-## Summary
 
-This is your first modeling project! Take what you have learned in Phase 2 to create a project with a more sophisticated analysis than you completed in Phase 1. You will build on these skills as we move into the predictive machine learning mindset in Phase 3. You've got this!
+Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.

From dc62812a6541e9e6a8df003b30d170271f4bb068 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 1 May 2024 17:53:16 +0300
Subject: [PATCH 18/27] readme changes

---
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 student.ipynb                     |  902 +++--
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final Project Submission\n",
+    "\n",
+    "Please fill out:\n",
+    "* Student name:\n",
+    "1. Winfred Kinya Bundi.\n",
+    "2. Carol Mundia.\n",
+    "3. Paul Muniu.\n",
+    "4. Dennis Mwenda.\n",
+    "* Student pace: Full time Hybrid\n",
+    "* Scheduled project review date/time:2/05/2024 \n",
+    "* Instructor name: Mwikali Maryanne.\n",
+    "* Blog post URL:git@github.com:winnycodegurl/dsc-phase-2-projectgroup4.git\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## INTRODUCTION\n",
+    "\n",
+    "In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BUSINESS UNDERSTANDING\n",
+    "\n",
+    "\n",
+    "\n",
+    "The provided dataset encompasses details on homes sold, encompassing their attributes and sale prices. This dataset holds significant potential for real estate agencies across various avenues:\n",
+    "\n",
+    "\n",
+    "\n",
+    "1. \n",
+    "Market Analysis:\n",
+    "\n",
+    " Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "2. \n",
+    "Property Valuation: \n",
+    "\n",
+    "By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "3. \n",
+    "Targeted Marketing: \n",
+    "\n",
+    "Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PROBLEM STATEMENT.\n",
+    "\n",
+    "\n",
+    "\n",
+    "In King County, people involved in real estate have trouble figuring out what affects property values and trends in the market.\n",
+    "This study wants to help by looking at things like what features a property has, where it's located, what buyers prefer,\n",
+    "and how things change over time. By understanding these things better, people in real estate can make smarter choices about\n",
+    "buying and selling property and how they position themselves in the market.\n",
+    "The main goal is to give them practical advice that helps them do well in King County's real estate market, \n",
+    "which is always changing.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OBJECTIVES.\n",
+    "\n",
+    "Main OBJECTIVE\n",
+    "\n",
+    "\n",
+    "The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients \n",
+    "on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, \n",
+    "furnishing clients with valuable insights to facilitate informed investment decisions.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Specific Goals:\n",
+    "\n",
+    "\n",
+    "i). Identification of Key Influencing Factors on House Prices:\n",
+    "\n",
+    "\n",
+    "\n",
+    "Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price.\n",
+    "\n",
+    "\n",
+    "\n",
+    "ii). Assessment of Model Performance:\n",
+    "\n",
+    "\n",
+    "Employ metrics such as mean squared error, R-squared values, and residual analysis to \n",
+    "evaluate the model's accuracy in predicting house prices effectively.\n",
+    "\n",
+    "    \n",
+    "\n",
+    "iii). Provision of Actionable Recommendations:\n",
+    "\n",
+    "\n",
+    "\n",
+    "Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. \n",
+    "Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Understanding.\n",
+    "\n",
+    "\n",
+    "King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments.\n",
+    "\n",
+    "### Whereby the dataset contains:\n",
+    "\n",
+    "### Target Variable\n",
+    "\n",
+    "price: Sale price of the house .\n",
+    "\n",
+    "### Unique identifier\n",
+    "\n",
+    "id - Unique identifier for a house\n",
+    "\n",
+    "### Property Characteristics:\n",
+    "\n",
+    "bedrooms: Number of bedrooms.\n",
+    "\n",
+    "bathrooms: Number of bathrooms.\n",
+    "\n",
+    "sqft_living: Square footage of living space in the home.\n",
+    "\n",
+    "sqft_lot: Square footage of the lot.\n",
+    "\n",
+    "floors: Number of floors (levels) in the house.\n",
+    "\n",
+    "waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).\n",
+    "\n",
+    "view: Quality of view from the house, categorized into various types.\n",
+    "\n",
+    "condition: Overall condition of the house, categorized based on maintenance.\n",
+    "\n",
+    "grade: Overall grade of the house, reflecting construction and design quality.\n",
+    "\n",
+    "### Additional Features:\n",
+    "\n",
+    "sqft_above: Square footage of house apart from the basement.\n",
+    "\n",
+    "sqft_basement: Square footage of the basement.\n",
+    "\n",
+    "yr_built: Year when the house was built.\n",
+    "\n",
+    "yr_renovated: Year when the house was renovated.\n",
+    "\n",
+    "zipcode: ZIP Code of the property.\n",
+    "\n",
+    "lat: Latitude coordinate of the property.\n",
+    "\n",
+    "long: Longitude coordinate of the property.\n",
+    "\n",
+    "sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.\n",
+    "\n",
+    "sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.\n",
+    "\n",
+    "### TABLE OF CONTENTS\n",
+    "1.Data Preparation\n",
+    "\n",
+    "2.Data cleaning\n",
+    "\n",
+    "3.Exploratory data analysis\n",
+    "\n",
+    "4.Statistical Analysis\n",
+    "\n",
+    "5.Modelling\n",
+    "\n",
+    "6.Regression Results\n",
+    "\n",
+    "7.Conclusion\n",
+    "\n",
+    "8.Reccomendations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. DATA PREPARATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing necessary libraries for data analysis and visualization\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt  # for data visualization.\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns  # for enhanced data visualization.\n",
+    "from pandas.api.types import is_numeric_dtype  # Used to check if a data type is numeric.\n",
+    "\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor  # For calculating Variance Inflation Factor (VIF).\n",
+    "from statsmodels.graphics.regressionplots import plot_partregress_grid  # For partial regression plots.\n",
+    "from sklearn.model_selection import train_test_split  # Used to split data into training and testing sets.\n",
+    "from sklearn.preprocessing import PolynomialFeatures  # Generate polynomial features.\n",
+    "from sklearn.linear_model import LinearRegression  # Linear Regression model.\n",
+    "from sklearn.preprocessing import StandardScaler  # Standardizing/Scaling features.\n",
+    "from sklearn.feature_selection import RFE  # Recursive Feature Elimination for feature selection.\n",
+    "from sklearn.metrics import mean_squared_error, r2_score  # Evaluation metrics for model performance.\n",
+    "import statsmodels.api as sm\n",
+    "from scipy.stats import kstest\n",
+    "\n",
+    "# Statsmodels is used to create statistical models.\n",
+    "from scipy import stats  # Scientific computing library for statistical tests.\n",
+    "from scipy.stats import f_oneway  # One-way ANOVA statistical test.\n",
+    "from scipy.stats import ttest_ind  # Independent sample t-test for comparing means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *loading the King County House Sales dataset*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "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"
+     ]
+    },
+    {
+     "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": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset to inspect the initial state of the data\n",
+    "file_path = 'data/kc_house_data.csv'\n",
+    "housing_data = pd.read_csv(file_path)\n",
+    "\n",
+    "# Display basic information and the first few rows of the dataset\n",
+    "housing_data.info()\n",
+    "housing_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *loading the column.md dataset*\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\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>Column_name</th>\n",
+       "      <th>Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>* `id`</td>\n",
+       "      <td>Unique identifier for a house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>* `date`</td>\n",
+       "      <td>Date house was sold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>* `price`</td>\n",
+       "      <td>Sale price (prediction target)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>* `bedrooms`</td>\n",
+       "      <td>Number of bedrooms</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>* `bathrooms`</td>\n",
+       "      <td>Number of bathrooms</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>* `sqft_living`</td>\n",
+       "      <td>Square footage of living space in the home</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>* `sqft_lot`</td>\n",
+       "      <td>Square footage of the lot</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>* `floors`</td>\n",
+       "      <td>Number of floors (levels) in house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>* `waterfront`</td>\n",
+       "      <td>Whether the house is on a waterfront</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>* `view`</td>\n",
+       "      <td>Quality of view from house</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>* `condition`</td>\n",
+       "      <td>How good the overall condition of the house is...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>* `grade`</td>\n",
+       "      <td>Overall grade of the house. Related to the con...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>* `sqft_above`</td>\n",
+       "      <td>Square footage of house apart from basement</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>* `sqft_basement`</td>\n",
+       "      <td>Square footage of the basement</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>* `yr_built`</td>\n",
+       "      <td>Year when house was built</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>* `yr_renovated`</td>\n",
+       "      <td>Year when house was renovated</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>* `zipcode`</td>\n",
+       "      <td>ZIP Code used by the United States Postal Service</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>* `lat`</td>\n",
+       "      <td>Latitude coordinate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>* `long`</td>\n",
+       "      <td>Longitude coordinate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>* `sqft_living15`</td>\n",
+       "      <td>The square footage of interior housing living ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>* `sqft_lot15`</td>\n",
+       "      <td>The square footage of the land lots of the nea...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Column_name                                        Description\n",
+       "0              * `id`                      Unique identifier for a house\n",
+       "1            * `date`                                Date house was sold\n",
+       "2           * `price`                     Sale price (prediction target)\n",
+       "3        * `bedrooms`                                 Number of bedrooms\n",
+       "4       * `bathrooms`                                Number of bathrooms\n",
+       "5     * `sqft_living`         Square footage of living space in the home\n",
+       "6        * `sqft_lot`                          Square footage of the lot\n",
+       "7          * `floors`                 Number of floors (levels) in house\n",
+       "8      * `waterfront`               Whether the house is on a waterfront\n",
+       "9            * `view`                         Quality of view from house\n",
+       "10      * `condition`  How good the overall condition of the house is...\n",
+       "11          * `grade`  Overall grade of the house. Related to the con...\n",
+       "12     * `sqft_above`        Square footage of house apart from basement\n",
+       "13  * `sqft_basement`                     Square footage of the basement\n",
+       "14       * `yr_built`                          Year when house was built\n",
+       "15   * `yr_renovated`                      Year when house was renovated\n",
+       "16        * `zipcode`  ZIP Code used by the United States Postal Service\n",
+       "17            * `lat`                                Latitude coordinate\n",
+       "18           * `long`                               Longitude coordinate\n",
+       "19  * `sqft_living15`  The square footage of interior housing living ...\n",
+       "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_names_file = \"data/column_names.md\"\n",
+    "\n",
+    "with open(column_names_file, \"r\") as file:\n",
+    "    markdown_content = file.readlines()\n",
+    "\n",
+    "column_names = []\n",
+    "description = []\n",
+    "for line in markdown_content:\n",
+    "    parts = line.split('-', 1)\n",
+    "    if len(parts) == 2:  # Check if split produces two parts\n",
+    "        column_names.append(parts[0].strip())\n",
+    "        description.append(parts[1].strip())\n",
+    "\n",
+    "# Create DataFrame\n",
+    "data = {\n",
+    "    \"Column_name\": column_names,\n",
+    "    \"Description\": description\n",
+    "}\n",
+    "column_name_df = pd.DataFrame(data)\n",
+    "\n",
+    "column_name_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 3 columns with missing values.\n",
+      "waterfront      2376\n",
+      "view              63\n",
+      "yr_renovated    3842\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating function to check counts of missing values\n",
+    "def has_missing_values(df):\n",
+    "    missing_values = df.isnull().sum()\n",
+    "    num_missing_values = missing_values[missing_values > 0].count()\n",
+    "    if num_missing_values == 0:\n",
+    "        print(\"There are no missing values in the DataFrame.\")\n",
+    "    else:\n",
+    "        print(f\"There are {num_missing_values} columns with missing values.\")\n",
+    "        print(missing_values[missing_values > 0])\n",
+    "        \n",
+    "has_missing_values(housing_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are no duplicate rows in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#creating a function to check for duplicates.\n",
+    "def has_duplicates(df):\n",
+    "    num_duplicates = df.duplicated().sum()\n",
+    "    if num_duplicates == 0:\n",
+    "        print(\"There are no duplicate rows in the DataFrame.\")\n",
+    "    else:\n",
+    "        print(f\"There are {num_duplicates} duplicate rows in the DataFrame.\")\n",
+    "\n",
+    "has_duplicates(housing_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset contains 21,597 entries and 21 features. Here’s a brief overview of the data:\n",
+    "\n",
+    "### Columns and their Data Types:\n",
+    "#### Numerical:\n",
+    "id, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15\n",
+    "\n",
+    "#### Categorical:\n",
+    "date (format object, should be datetime), waterfront, view, condition, grade, sqft_basement (format object, should be numeric)\n",
+    "\n",
+    "#### Missing Values:\n",
+    "waterfront: 2,376 missing values\n",
+    "view: 63 missing values\n",
+    "yr_renovated: 3,842 missing values\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. DATA CLEANING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a)Dropping columns:\n",
+    "We're dropping some columns during data cleaning to streamline our analysis and focus on the most relevant features. By removing unnecessary or redundant columns, we aim to simplify the dataset and improve the efficiency of subsequent analytical processes. This helps in reducing noise and enhancing the clarity of our findings."
+   ]
+  },
+  {
+   "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>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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20146</th>\n",
+       "      <td>7967000130</td>\n",
+       "      <td>4/1/2015</td>\n",
+       "      <td>370228.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>4000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2050</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13983</th>\n",
+       "      <td>7338000150</td>\n",
+       "      <td>1/29/2015</td>\n",
+       "      <td>160000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>4200</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>1070</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>4200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3605</th>\n",
+       "      <td>8658303585</td>\n",
+       "      <td>8/7/2014</td>\n",
+       "      <td>252500.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>900</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>6 Low Average</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1961</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>10000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9229</th>\n",
+       "      <td>1787250210</td>\n",
+       "      <td>12/22/2014</td>\n",
+       "      <td>379000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>5225</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>8 Good</td>\n",
+       "      <td>2410</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>5378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11243</th>\n",
+       "      <td>1843200350</td>\n",
+       "      <td>7/22/2014</td>\n",
+       "      <td>150000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>1934</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1978</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1360</td>\n",
+       "      <td>1898</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "20146  7967000130    4/1/2015  370228.0         4       3.00         2050   \n",
+       "13983  7338000150   1/29/2015  160000.0         2       1.00         1070   \n",
+       "3605   8658303585    8/7/2014  252500.0         2       1.00          900   \n",
+       "9229   1787250210  12/22/2014  379000.0         4       2.75         2410   \n",
+       "11243  1843200350   7/22/2014  150000.0         2       1.50         1360   \n",
+       "\n",
+       "       sqft_lot  floors waterfront condition          grade  sqft_above  \\\n",
+       "20146      4000     2.0         NO   Average         8 Good        2050   \n",
+       "13983      4200     1.0         NO      Good  6 Low Average        1070   \n",
+       "3605       7500     1.0         NO      Good  6 Low Average         900   \n",
+       "9229       5225     2.0         NO   Average         8 Good        2410   \n",
+       "11243      1934     2.0         NO      Good      7 Average        1360   \n",
+       "\n",
+       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "20146           0.0      2014           0.0           2050        4000  \n",
+       "13983           0.0      1983           0.0           1150        4200  \n",
+       "3605            0.0      1961           0.0           1190       10000  \n",
+       "9229            0.0      2001           0.0           2300        5378  \n",
+       "11243           0.0      1978           0.0           1360        1898  "
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data = housing_data.drop(['long','lat','view', 'zipcode'], axis=1)\n",
+    "housing_data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b)Checking for placeholders\n",
+    "\n",
+    "Placeholders are values used to denote missing, unknown, or invalid data within a dataset. Common examples include \"N/A\", \"-\", \"UNKNOWN\", \"NULL\", #, etc. It's important to identify and handle placeholders properly during data preprocessing to ensure accurate analysis and modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Column 'sqft_basement': Found 454 occurrences of potential placeholder .\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define a list of common placeholder values\n",
+    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
+    "\n",
+    "# Check for potential placeholders in the DataFrame\n",
+    "found_placeholder = False\n",
+    "for column in housing_data.columns:\n",
+    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
+    "    if placeholder_count > 0:\n",
+    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
+    "        found_placeholder = True\n",
+    "\n",
+    "if not found_placeholder:\n",
+    "    print(\"No potential placeholders found in the DataFrame.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Convert the common placeholders to lowercase for case-insensitive matching\n",
+    "common_placeholders_lower = [placeholder.lower() for placeholder in common_placeholders]\n",
+    "\n",
+    "# Replace any of the common placeholders with NaN\n",
+    "housing_data['sqft_basement'] = housing_data['sqft_basement'].replace(common_placeholders_lower, pd.NA)\n",
+    "\n",
+    "# Drop rows with NaN in the sqft_basement column\n",
+    "housing_data.dropna(subset=['sqft_basement'], inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Counter-check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No potential placeholders found in the DataFrame.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# confirm no more placeholders\n",
+    "# Define a list of common placeholder values\n",
+    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
+    "\n",
+    "# Check for potential placeholders in the DataFrame\n",
+    "found_placeholder = False\n",
+    "for column in housing_data.columns:\n",
+    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
+    "    if placeholder_count > 0:\n",
+    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
+    "        found_placeholder = True\n",
+    "\n",
+    "if not found_placeholder:\n",
+    "    print(\"No potential placeholders found in the DataFrame.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "### b) Handling Missing Values:\n",
+    "waterfront: \n",
+    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
+    "<br> yr_renovated: \n",
+    "A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For categorical data, impute missing values with mode or specific marker\n",
+    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
+    "\n",
+    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
+    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c) Convert Data\n",
+    "Convert date from object to datetime format.\n",
+    "sqft_basement: Convert from object to numeric, handling any non-numeric entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
+    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
+    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change waterfront to integer\n",
+    "housing_data['waterfront'] = housing_data['waterfront'].apply(lambda x: 0 if x == 'NO' else 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['7 Average' '6 Low Average' '8 Good' '11 Excellent' '9 Better' '5 Fair'\n",
+      " '10 Very Good' '12 Luxury' '4 Low' '3 Poor' '13 Mansion']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# checking \"grade\" column\n",
+    "unique_grade = housing_data.grade.unique()\n",
+    "print(unique_grade)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Average' 'Very Good' 'Good' 'Poor' 'Fair']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# checking \"condition\"column\n",
+    "unique_condition = housing_data.condition.unique()\n",
+    "print(unique_condition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert grade and condition into representative numbers for easier Exploratory analysis.\n",
+    "housing_data['condition'] = housing_data['condition'].map({'Poor': 1,'Fair': 2,'Average': 3,'Good': 4,'Very Good': 5}).astype(float)\n",
+    "housing_data['grade'] = housing_data['grade'].map({'3 Poor': 1,'4 Low': 2,'5 Fair': 3,'6 Low Average': 4,'7 Average': 5,'8 Good': 6,'9 Better': 7,'10 Very Good': 8,'11 Excellent': 9,'12 Luxury': 10,'13 Mansion': 11}).astype(float) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21143 entries, 0 to 21596\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column         Non-Null Count  Dtype         \n",
+      "---  ------         --------------  -----         \n",
+      " 0   id             21143 non-null  int64         \n",
+      " 1   date           21143 non-null  datetime64[ns]\n",
+      " 2   price          21143 non-null  float64       \n",
+      " 3   bedrooms       21143 non-null  int64         \n",
+      " 4   bathrooms      21143 non-null  float64       \n",
+      " 5   sqft_living    21143 non-null  int64         \n",
+      " 6   sqft_lot       21143 non-null  int64         \n",
+      " 7   floors         21143 non-null  float64       \n",
+      " 8   waterfront     21143 non-null  int64         \n",
+      " 9   condition      21143 non-null  float64       \n",
+      " 10  grade          21143 non-null  float64       \n",
+      " 11  sqft_above     21143 non-null  int64         \n",
+      " 12  sqft_basement  21143 non-null  float64       \n",
+      " 13  yr_built       21143 non-null  int64         \n",
+      " 14  yr_renovated   21143 non-null  float64       \n",
+      " 15  sqft_living15  21143 non-null  int64         \n",
+      " 16  sqft_lot15     21143 non-null  int64         \n",
+      "dtypes: datetime64[ns](1), float64(7), int64(9)\n",
+      "memory usage: 2.9 MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4511</th>\n",
+       "      <td>723049333</td>\n",
+       "      <td>2015-04-05</td>\n",
+       "      <td>285000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1490</td>\n",
+       "      <td>10367</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>8254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1181</th>\n",
+       "      <td>7625700935</td>\n",
+       "      <td>2014-06-05</td>\n",
+       "      <td>875000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>3250</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1650</td>\n",
+       "      <td>6000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1875</th>\n",
+       "      <td>1853081000</td>\n",
+       "      <td>2014-07-17</td>\n",
+       "      <td>820000.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2830</td>\n",
+       "      <td>6137</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>2830</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3170</td>\n",
+       "      <td>6285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12273</th>\n",
+       "      <td>7016200030</td>\n",
+       "      <td>2015-03-20</td>\n",
+       "      <td>480000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>7966</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>1970</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>7500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8546</th>\n",
+       "      <td>7981900110</td>\n",
+       "      <td>2014-10-03</td>\n",
+       "      <td>350000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.75</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>3175</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1340</td>\n",
+       "      <td>960.0</td>\n",
+       "      <td>1966</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1260</td>\n",
+       "      <td>3175</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "4511    723049333 2015-04-05  285000.0         3       1.50         1490   \n",
+       "1181   7625700935 2014-06-05  875000.0         3       3.50         3250   \n",
+       "1875   1853081000 2014-07-17  820000.0         5       2.75         2830   \n",
+       "12273  7016200030 2015-03-20  480000.0         4       2.50         2080   \n",
+       "8546   7981900110 2014-10-03  350000.0         4       2.75         2300   \n",
+       "\n",
+       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "4511      10367     1.0           0        3.0    5.0        1010   \n",
+       "1181       6000     2.0           0        3.0    8.0        2500   \n",
+       "1875       6137     2.0           0        3.0    7.0        2830   \n",
+       "12273      7966     1.0           0        3.0    5.0        1200   \n",
+       "8546       3175     1.5           0        3.0    4.0        1340   \n",
+       "\n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
+       "4511           480.0      1957           0.0           1000        8254  \n",
+       "1181           750.0      2001           0.0           1650        6000  \n",
+       "1875             0.0      2010           0.0           3170        6285  \n",
+       "12273          880.0      1970           0.0           1920        7500  \n",
+       "8546           960.0      1966           0.0           1260        3175  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Check transformations\n",
+    "housing_data.info()\n",
+    "housing_data.sample(5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Engineering:\n",
+    "\n",
+    "Create additional features that might be informative for our modeling:\n",
+    "\n",
+    "**House Age**: Calculate the age of the house from the 'yr_built' column to the current year.\n",
+    "\n",
+    "**Renovation Age**: If a house has been renovated ('yr_renovated' > 0), calculate the years since the renovation.\n",
+    "\n",
+    "**Total Square Footage**: Sum up 'sqft_living', 'sqft_lot', 'sqft_above', and 'sqft_basement' for a total square footage feature.\n",
+    "\n",
+    "These new features could reveal deeper insights into the housing prices and help improve the performance of our statistical models.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "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>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8010.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>73</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>12382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>91</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11540.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>59</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8920.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11440.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   house_age  renovation_age  total_sqft\n",
+       "0         69             0.0      8010.0\n",
+       "1         73            33.0     12382.0\n",
+       "2         91             0.0     11540.0\n",
+       "3         59             0.0      8920.0\n",
+       "4         37             0.0     11440.0"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Current year for age calculations\n",
+    "from datetime import datetime\n",
+    "\n",
+    "current_year = datetime.now().year\n",
+    "\n",
+    "# Feature Engineering\n",
+    "housing_data['house_age'] = current_year - housing_data['yr_built']\n",
+    "housing_data['renovation_age'] = housing_data.apply(\n",
+    "    lambda x: 0 if x['yr_renovated'] == 0 else current_year - x['yr_renovated'], axis=1\n",
+    ")\n",
+    "housing_data['total_sqft'] = housing_data['sqft_living'] + housing_data['sqft_lot'] + \\\n",
+    "                                     housing_data['sqft_above'] + housing_data['sqft_basement']\n",
+    "\n",
+    "# Display the new features\n",
+    "housing_data[['house_age', 'renovation_age', 'total_sqft']].head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The new features have been successfully added:\n",
+    "\n",
+    "**House Age**: Represents the age of the house since it was built.\n",
+    "\n",
+    "**Renovation Age**: If renovated, this indicates the number of years since the last renovation; otherwise, it is 0.\n",
+    "\n",
+    "**Total Square Footage**: Sum of the living area, lot size, above-ground level area, and basement area\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### *Sample data check.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8540</th>\n",
+       "      <td>3034200366</td>\n",
+       "      <td>2014-12-03</td>\n",
+       "      <td>409000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>9065</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1440</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1990</td>\n",
+       "      <td>8812</td>\n",
+       "      <td>52</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11945.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19433</th>\n",
+       "      <td>2023059052</td>\n",
+       "      <td>2015-05-04</td>\n",
+       "      <td>450000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>92721</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>1946</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1860</td>\n",
+       "      <td>8096</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95421.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7003</th>\n",
+       "      <td>7751800080</td>\n",
+       "      <td>2015-01-27</td>\n",
+       "      <td>465000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>1460</td>\n",
+       "      <td>9879</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1460</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1956</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1610</td>\n",
+       "      <td>10050</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12799.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10151</th>\n",
+       "      <td>1180002075</td>\n",
+       "      <td>2014-08-25</td>\n",
+       "      <td>235000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1210</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8420.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3506</th>\n",
+       "      <td>3449900090</td>\n",
+       "      <td>2015-04-10</td>\n",
+       "      <td>454200.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2630</td>\n",
+       "      <td>5379</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2630</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2630</td>\n",
+       "      <td>5379</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10639.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
+       "8540   3034200366 2014-12-03  409000.0         3       1.75         1440   \n",
+       "19433  2023059052 2015-05-04  450000.0         3       1.00         1350   \n",
+       "7003   7751800080 2015-01-27  465000.0         3       1.50         1460   \n",
+       "10151  1180002075 2014-08-25  235000.0         3       1.00         1210   \n",
+       "3506   3449900090 2015-04-10  454200.0         4       2.50         2630   \n",
+       "\n",
+       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
+       "8540       9065     1.0           0        4.0    6.0        1440   \n",
+       "19433     92721     1.0           0        2.0    4.0        1200   \n",
+       "7003       9879     1.0           0        3.0    5.0        1460   \n",
+       "10151      6000     1.0           0        3.0    5.0        1210   \n",
+       "3506       5379     2.0           0        3.0    6.0        2630   \n",
+       "\n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "8540             0.0      1972           0.0           1990        8812   \n",
+       "19433          150.0      1946           0.0           1860        8096   \n",
+       "7003             0.0      1956           0.0           1610       10050   \n",
+       "10151            0.0      1930           0.0           1210        6000   \n",
+       "3506             0.0      2004           0.0           2630        5379   \n",
+       "\n",
+       "       house_age  renovation_age  total_sqft  \n",
+       "8540          52             0.0     11945.0  \n",
+       "19433         78             0.0     95421.0  \n",
+       "7003          68             0.0     12799.0  \n",
+       "10151         94             0.0      8420.0  \n",
+       "3506          20             0.0     10639.0  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 21143 entries, 0 to 21596\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column          Non-Null Count  Dtype         \n",
+      "---  ------          --------------  -----         \n",
+      " 0   id              21143 non-null  int64         \n",
+      " 1   date            21143 non-null  datetime64[ns]\n",
+      " 2   price           21143 non-null  float64       \n",
+      " 3   bedrooms        21143 non-null  int64         \n",
+      " 4   bathrooms       21143 non-null  float64       \n",
+      " 5   sqft_living     21143 non-null  int64         \n",
+      " 6   sqft_lot        21143 non-null  int64         \n",
+      " 7   floors          21143 non-null  float64       \n",
+      " 8   waterfront      21143 non-null  int64         \n",
+      " 9   condition       21143 non-null  float64       \n",
+      " 10  grade           21143 non-null  float64       \n",
+      " 11  sqft_above      21143 non-null  int64         \n",
+      " 12  sqft_basement   21143 non-null  float64       \n",
+      " 13  yr_built        21143 non-null  int64         \n",
+      " 14  yr_renovated    21143 non-null  float64       \n",
+      " 15  sqft_living15   21143 non-null  int64         \n",
+      " 16  sqft_lot15      21143 non-null  int64         \n",
+      " 17  house_age       21143 non-null  int64         \n",
+      " 18  renovation_age  21143 non-null  float64       \n",
+      " 19  total_sqft      21143 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(9), int64(10)\n",
+      "memory usage: 3.4 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "housing_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. EXPLORATORY DATA ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next is EDA; Exploratory Data Analysis is a crucial step in data analysis. This process will involve examining and understanding the structure, patterns, and relationships within the dataset. It will aid us uncover insights, detect anomalies, and inform subsequent analysis and modeling decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a.) Univariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Distribution of House Prices.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the style for seaborn\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
+    "\n",
+    "# Histogram\n",
+    "sns.histplot(housing_data['price'], bins=\"auto\", kde=False, color='#003399', ax=axes[0])\n",
+    "axes[0].set_title('Histogram of House Prices')\n",
+    "axes[0].set_xlabel('Price')\n",
+    "axes[0].set_ylabel('Frequency')\n",
+    "\n",
+    "# Density Plot\n",
+    "sns.histplot(housing_data['price'], bins=\"auto\", kde=True, color='#339933', ax=axes[1])\n",
+    "axes[1].set_title('Density Plot of House Prices')\n",
+    "axes[1].set_xlabel('Price')\n",
+    "axes[1].set_ylabel('Density')\n",
+    "\n",
+    "# A common title\n",
+    "plt.suptitle('Distribution Analysis of House Prices.', fontsize=16)\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The histogram depicts the distribution of house prices, with most bars are clustered towards the left, suggesting that a significant number of houses are priced lower. The density plot illustrates a curve representing the density of house prices. Similar to the histogram, the curve peaks sharply on the left and gradually tapers off, indicating a right-skewed distribution. \n",
+    "<br>In summary, the majority of house prices are concentrated at the lower end, creating a skewed distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Distribution of Bedrooms, Bathrooms and Floors.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
+    "\n",
+    "# Common title for all subplots\n",
+    "fig.suptitle('Distribution of Bedrooms, Bathrooms, and Floors.', fontsize=16)\n",
+    "\n",
+    "# Box plot for bedrooms\n",
+    "sns.boxplot(x=housing_data['bedrooms'], ax=axes[0])\n",
+    "axes[0].set_title('Bedrooms')\n",
+    "axes[0].set_xlabel('Number of Bedrooms')\n",
+    "\n",
+    "# Box plot for bathrooms\n",
+    "sns.boxplot(x=housing_data['bathrooms'], ax=axes[1])\n",
+    "axes[1].set_title('Bathrooms')\n",
+    "axes[1].set_xlabel('Number of Bathrooms')\n",
+    "\n",
+    "# Box plot for floors\n",
+    "sns.boxplot(x=housing_data['floors'], ax=axes[2])\n",
+    "axes[2].set_title('Floors')\n",
+    "axes[2].set_xlabel('Number of Floors')\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After analyzing the distribution of bedrooms, bathrooms and floors, an outlier was detected in the bedroom column. To ensure the accuracy of our analysis, the outlier value was identified and subsequently removed from the dataset. The box plot below displays the distribution of bedrooms after excluding the outlier value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify the outlier value\n",
+    "outlier_value = housing_data['bedrooms'].max()  \n",
+    "\n",
+    "# Filter the DataFrame to exclude the outlier\n",
+    "housing_data = housing_data[housing_data['bedrooms'] != outlier_value]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Number of Bedrooms')"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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e+4OwePFiQqHQMfeFQiEWL15cwRMd37Zt21i3bt1Rc4dCIdatW8e2bdsqfKZAniGIiooq80G/oqvoRHxztqysLGrWrBngNOXz1Vdf8Ytf/OLIicq7tKVVwXXOz8/n2muvBSr313BVNWfOnDIf9OfMmVOB05TPf/7nf9KrV68y91c2DzzwQJnP0D7wwAMVOE35ZGZmMn369GNGQWxsLJmZmQFMVbYmTZrQtm1bPv7441Jzx8TE0Lp1a5o0aVLhMwUSBCtXrizzKcvKerjg2xISEkhMTAx6jOOqase2v62qrHNVsmrVqjK/Byvb4QLguIcDKtvhAoAzzzyTBg0aHPOwwbnnnlvpDhcAxMXFcc011xzzsEGvXr0q3eECOPIgOmLECCZMmHDUvpEjR1a6wwVw5IeGIUOGcMsttxxzexA/VAT2WwbfdYdTGe+IpOqoKn4PVsWZv+uFg/Pnz6/gScpvxIgRRz0gRUVFcf/99wc00fH95Cc/ITk5udS2evXq0aNHj4AmOr5GjRrRv3//yFpHRUXRv39/GjZsGMg8/tqhpCqlefPmZZ6ujKrir/BVxV+VnDlzZqnTVWHmG2+8kXPOOQeAunXr0r9//8BmCTQIvl31lbnypeqoKn4PPvvss2Weroz69OlT5unKqEWLFtStWxc48kDVokWLgCc6vvr165ORkQFARkYG9evXD3ii40tISGDYsGHUr1+foUOHkpCQENgsgf/p4qpwByRVZytWrGDDhg1cfvnlQY9SblXxfqMqrnNV+XPF3zR27Ngqt86dOnUK9E8Wl/CQgSRJMggkSZJBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZKA2KAHqGoSEhJo0aIFeXl5JCQkBD2OKpGSr42SjyWpKjEITlBUVBRPPfUUGzZsICoqKuhxVIlERUUxY8aMyMeSVJUYBCchKirKO3wdk18XkqoqX0MgSZIMAkmSZBBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkoDYoAdQxYoqDhEOFQY9xnFFFRcFPYIknVYMgtNM0qbXgx5BklQJecjgNJCQkBD0CJKkSs5nCE4DCQkJvPXWW0GPccJCoRAbN240aCSpAhgEp4GoqCgSExODHuOEhUIh4uPjiYqKCnoUSar2PGQgSZIMAkmSZBBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJg0CSJGEQSJIkDAJJkoRBIEmSMAgkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAkmShEEgSZIwCCRJEgaBJEnCIJAkSRgEkiQJiC3PmcLhMAChUOiUDlNVlKyD63Fquc4Vw3WuGK5zxXCdSytZh5LH8bJEhctxroKCAjZt2vT9J5MkSRUuNTWVuLi4Ms9TriAoLi6mqKiI6OhooqKifrABJUnSqRMOhykuLiY2Npbo6LJfJVCuIJAkSdWbLyqUJEkGgSRJMggkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEJyQP//5z9x66620a9eOK664gpEjR7J3796gx6q2QqEQN910E6NHjw56lGrrq6++YuTIkbRv3562bdty9913s3v37qDHqna2bNnCjTfeSJs2bUhPT2fcuHEUFBQEPVa1sXfvXrp3786HH34Y2fbpp59y/fXXk5aWRteuXXn99dcDnLBqMAjKKT8/n9tvv520tDTef/99Fi9ezFdffcWDDz4Y9GjV1syZM/noo4+CHqNau/feezl8+DDLli1j5cqVxMTEMGbMmKDHqlaKi4sZNGgQPXv2ZO3atWRlZfH+++/z7LPPBj1atbB+/Xr69evHtm3bItv279/PnXfeyc9+9jPWrVvH+PHjefzxx9m4cWOAk1Z+BkE5ffHFF1x88cUMHjyYuLg4ateuTb9+/Vi3bl3Qo1VLH3zwAX/4wx/o0aNH0KNUW5s3b+bTTz9lwoQJnHXWWSQlJfHoo48yfPjwoEerVvbv309OTg7FxcWRN5iJjo4mMTEx4MmqvjfeeIPhw4czdOjQUtv/8Ic/UKtWLW688UZiY2Pp2LEjvXr14qWXXgpo0qrBICinpk2bMmfOHGJiYiLb3n77bS699NIAp6qe9uzZw0MPPcSUKVO80zyFNm7cSLNmzXjttdfo3r076enpTJw4keTk5KBHq1Zq167NgAEDmDhxIqmpqWRkZHDBBRcwYMCAoEer8tLT01m2bBlXX311qe2ff/45F110UaltzZo1489//nNFjlflGAQnIRwOM3XqVFauXMlDDz0U9DjVSnFxMSNGjODWW2/l4osvDnqcam3//v189tln/P3vf+eNN97g97//Pbt27WLUqFFBj1atFBcXk5CQwJgxY9iwYQOLFy/mb3/7G9OnTw96tCovOTmZ2NjYo7YfOnToqB8mEhISOHz4cEWNViUZBCcoLy+P++67j0WLFvHiiy+SkpIS9EjVyjPPPENcXBw33XRT0KNUeyVvhfrQQw+RlJRE3bp1+bd/+zeys7M5dOhQwNNVH8uWLePtt9+mf//+xMXF0bx5cwYPHsz8+fODHq3aSkxMJD8/v9S2/Px8atasGdBEVcPRaaXvtG3bNu644w7OO+88srKyqFOnTtAjVTv/9V//xe7du2nTpg1A5Jt6+fLlvsDwB9asWTOKi4spLCwkPj4eOPLTLIBvgvrD2blz51G/URAbG0uNGjUCmqj6u+iii/jjH/9Yattf//pXmjdvHtBEVYPPEJTT/v37ueWWW2jVqhX/8R//YQycIkuXLuXjjz/mo48+4qOPPiIzM5PMzExj4BTo1KkTjRs35sEHH+TQoUPs3buXqVOn0q1bN5KSkoIer9pIT08nJyeH2bNnEwqF2L59O08//TS9evUKerRqq3v37uTm5jJv3jwKCwtZs2YNixYtok+fPkGPVqkZBOW0YMECvvjiC9566y1at25NWlpa5J9UFdWoUYMXXniBmJgYevbsSc+ePWnQoAGPPfZY0KNVK82aNeOZZ57hnXfeoX379tx888107dr1qFfG64dTu3ZtnnvuOZYuXUr79u15+OGHefjhh+nQoUPQo1VqUWGfG5Qk6bTnMwSSJMkgkCRJBoEkScIgkCRJGASSJAmDQJIkYRBIkiQMAomUlBTuvPPOo/5c74IFC+jatespuc2uXbuyYMGCU3Ld5bFkyRI6duxI69atWblyZal9O3bsICUlhcsvv5y0tDQuu+wyOnTowPDhwzlw4MBJ3+bo0aMZPXr09x1d0iliEEhAdnY2c+bMCXqMCvP6669zzTXXsH79erp06XLM8yxevJhPPvmETz/9lMWLF/O///u/jB8/voInlVRRDAIJuOmmm5g2bRoff/zxMfeX/NS8Y8eOyLYZM2ZE3pVxwYIF9O/fn4kTJ9KuXTs6dOjACy+8wGuvvUaXLl1o3bo1Y8eOLXWdW7Zs4brrrqNdu3YMHDiQv//975F927Zt46677qJ9+/Z06dKFqVOnRt4gZ8GCBVx33XXcdttttGnThkWLFh017759+xgzZgzp6em0b9+eQYMGRa6/b9++rFmzhldeeYVu3bqVa33q1q1L79692bx5c2Rbbm4uw4cP54orriA9PZ2xY8eSl5cX2b9ixQquueYaLr/8cgYNGsS+fftKrd1tt91Gnz59aNeuHevWrStzZoDPPvuMO+64g3bt2nHllVfy7//+7xw8ePCk1v/tt9/mmmuuoXXr1lx11VXMmjWrXOsgVWcGgcSRN0Pp168fw4YN46uvvjqp61i/fj3169dnzZo13HfffTz++ON8+OGHLFmyhHnz5pGVlcW6desi51++fDmPP/447733Ho0aNWLQoEEUFRVx+PBhBgwYQPPmzXn33Xd5+eWXWb16NTNmzIhcdsuWLfTq1YvVq1fTvXv3o2a577772LZtG2+88QbZ2dk0bdqUAQMGkJeXR1ZWFm3atGHQoEEsX768XJ9bTk4Oy5Yto0ePHsCRd0W8++67iY6O5u2332bRokXs3r078qC7detWhgwZwqBBg/joo4+4/vrree+990pd5wcffMDw4cNZuXIlaWlpZc68b98+br75Zpo1a8a7777L7373O/7nf/6HkSNHnvD65+fnM2LECMaOHcv69euZMmUKzz77LBs3biz/f7ZUDRkE0v8zatQo6tSpw+jRo0/q7X/POOMMbrnlFqKjo0lPTycUCjFw4EASExNJTU2lXr16/OMf/4ic/7bbbiMlJYX4+HhGjx7Njh072LhxI6tWraKgoIBhw4YRHx/Pueeey5AhQ3jppZcil61RowY//elPiYuLIyEhodQc27dvZ+3atYwZM4bk5GQSEhIYPnw4RUVFZGdnl/vz6d27N23atCEtLY309HS2bdtGZmYmAJs3b2bLli386le/Iikpidq1azNq1CjefPNN9u3bx5IlS2jRogW9e/cmNjaWbt26HXVoonHjxnTs2JGaNWuyc+fOMmdesWIFNWrUYPjw4SQkJJCcnMyYMWN45513yMnJOeH1T0hIICsriw8++IALL7yQ9evX07JlyxP7D5eqmdigB5Aqi7i4OJ566imuvfZannvuOWrXrn1Cl69VqxZRUVEAREcfae2zzjorsj86Opri4uLI6UaNGkU+TkxMpFatWuzatYt//OMf7N27l7Zt20b2h8NhCgsL2bNnDwDJycmR2/i23Nxc4MgDbomYmBjOPffcUkFyPAsXLozMmJeXx/Tp0/n5z3/OkiVL2LFjB6FQiIyMjFKXiYuLY/v27ezatYvzzjuv1L4mTZqUOmxQr169cs8cDoc577zziImJiewvma3kcyrv+ickJDB//nxmzZrF/fffT15eHj179uThhx/m7LPPLvf6SNWNQSB9Q5MmTXj00UcZOXIk1113XWR7yQNRYWFhZNs3H9yAyINRee3evTvyccnT4g0bNqSoqIgmTZqwdOnSUvv37NlDnTp1jntbDRs2BI68DqF58+YAhEIhvvjiC5KTk09oxhJJSUncd999PP/886xfv54GDRqQkJDAhx9+GFmbgoICtm/fzvnnn0+DBg1YtWpVqev48ssviY+Pj5z+5udwvJlr1KjBF198QSgUitzetm3bgCNxtHXr1nKvf15eHrt372bKlCkA/Pd//zfDhg1j9uzZjBo16iRWR6oePGQgfcvVV19Nnz59ePXVVyPbzjnnHM4++2zefPNNwuEwW7ZsKfWAfTKee+45tm7dyj//+U/Gjx/PJZdcQosWLejSpQuHDh1izpw5FBQUcODAAUaNGsXQoUPL9aBXr149MjIyGDduHDk5OeTn5zN58mRCodB3/kbB8Xz99dc8//zzJCQkkJqaSsuWLTn//POZMGEChw4dIj8/n8cee4wBAwYQCoXo3bs3f/nLX3jttdcoKiri/fffZ9myZSc9c8kzEZMnTyY/P5+cnBzGjx9Phw4dIjFRXocOHeKOO+5g0aJFhMNh6tWrR3R09Ak/IyRVNwaBdAwPPvggl1xySeR0XFwcjz76KG+99RatWrViwoQJ/PznP/9et9GtWzfuuusurrzySvbv38+sWbOIjo4mKSmJefPm8eGHH3LllVfSrVs3oqOjefrpp8t93U888QSNGzfm2muvpVOnTnz22Wc8//zz1KpVq9zXkZmZSVpaGmlpaXTq1InVq1cze/ZsGjduTGxsLM888wy5ubn06NEj8hqDuXPnEh8fT+PGjZk9ezYvvfQSrVu3ZtasWcd88WN5Zz7zzDOZO3cuf/nLX8jIyCAzM5OGDRsybdq0cn8+JerXr8/06dN59tlnadWqFZmZmXTo0IEBAwac8HVJ1UlU+GRePSVJkqoVnyGQJEkGgSRJMggkSRIGgSRJwiCQJEkYBJIkCYNAkiRhEEiSJAwCSZKEQSBJkjAIJEkSBoEkSQL+D7X5xamrxROAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Box plot for bedrooms\n",
+    "sns.boxplot(x=housing_data['bedrooms'])\n",
+    "plt.title('Bedrooms')\n",
+    "plt.xlabel('Number of Bedrooms')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### b.) Bivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Total Square Footage of houses by Price Range.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price_range\n",
+       "300K-600K    10560\n",
+       "600K-1M       4691\n",
+       "100K-300K     4433\n",
+       "1M-2M         1234\n",
+       "2M-5M          187\n",
+       "70K-100K        30\n",
+       "5M-8M            7\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the labels with ranges\n",
+    "labels = [\"70K-100K\", \"100K-300K\", \"300K-600K\", \"600K-1M\", \"1M-2M\", \"2M-5M\", \"5M-8M\"]\n",
+    "\n",
+    "# Cut the data into the specified ranges and assign labels\n",
+    "housing_data.loc[:,\"price_range\"] = pd.cut(housing_data.price,\n",
+    "                                            bins=[70000, 100000, 300000, 600000, 1000000, 2000000, 5000000, 8000000],\n",
+    "                                            labels=labels)\n",
+    "\n",
+    "# Count the occurrences of each category\n",
+    "housing_data['price_range'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "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>condition</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>price_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1188</th>\n",
+       "      <td>2624049185</td>\n",
+       "      <td>2014-09-09</td>\n",
+       "      <td>405000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>5355</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1956</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1790</td>\n",
+       "      <td>6225</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8875.0</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20611</th>\n",
+       "      <td>2895800390</td>\n",
+       "      <td>2014-08-07</td>\n",
+       "      <td>359800.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>2752</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2170</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>2752</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7092.0</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2209</th>\n",
+       "      <td>3438500339</td>\n",
+       "      <td>2014-05-26</td>\n",
+       "      <td>276000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1140</td>\n",
+       "      <td>5000</td>\n",
+       "      <td>64</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7280.0</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18431</th>\n",
+       "      <td>4167960330</td>\n",
+       "      <td>2015-01-09</td>\n",
+       "      <td>270000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>7750</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1820</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>8084</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11390.0</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11609</th>\n",
+       "      <td>1972201550</td>\n",
+       "      <td>2014-07-16</td>\n",
+       "      <td>565000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>2452</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1906</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3360</td>\n",
+       "      <td>118</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5532.0</td>\n",
+       "      <td>300K-600K</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",
+       "1188   2624049185 2014-09-09  405000.0         3       1.75         1760   \n",
+       "20611  2895800390 2014-08-07  359800.0         5       2.50         2170   \n",
+       "2209   3438500339 2014-05-26  276000.0         3       1.00         1140   \n",
+       "18431  4167960330 2015-01-09  270000.0         3       2.00         1820   \n",
+       "11609  1972201550 2014-07-16  565000.0         4       1.00         1540   \n",
+       "\n",
+       "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
+       "1188       5355     1.0           0        3.0  ...        1160   \n",
+       "20611      2752     2.0           0        3.0  ...        2170   \n",
+       "2209       5000     1.0           0        3.0  ...        1140   \n",
+       "18431      7750     1.0           0        3.0  ...        1820   \n",
+       "11609      2452     1.5           0        4.0  ...        1540   \n",
+       "\n",
+       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "1188           600.0      1956           0.0           1790        6225   \n",
+       "20611            0.0      2014           0.0           1800        2752   \n",
+       "2209             0.0      1960           0.0           1140        5000   \n",
+       "18431            0.0      1992           0.0           2080        8084   \n",
+       "11609            0.0      1906           0.0           1290        3360   \n",
+       "\n",
+       "       house_age  renovation_age  total_sqft  price_range  \n",
+       "1188          68             0.0      8875.0    300K-600K  \n",
+       "20611         10             0.0      7092.0    300K-600K  \n",
+       "2209          64             0.0      7280.0    100K-300K  \n",
+       "18431         32             0.0     11390.0    100K-300K  \n",
+       "11609        118             0.0      5532.0    300K-600K  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  grouped_vals = vals.groupby(grouper)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Set the style of seaborn\n",
+    "sns.set_style(\"darkgrid\")\n",
+    "\n",
+    "# Create a bar plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\")\n",
+    "plt.title(\"Total Square Footage by Price Range.\")\n",
+    "plt.xlabel(\"Price Range\")\n",
+    "plt.ylabel(\"Total Square Footage\")\n",
+    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
+    "plt.show();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "The bar plot illustrates the relationship between price range and total square footage. Each bar represents the total square footage of houses within different price range categories. \n",
+    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. \n",
+    "<br>This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Relationship between bedrooms, bathrooms, and house price**\n",
+    "\n",
+    "Created a scatter plot to visualize the relationship.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot for the relationship between bedrooms, bathrooms, and house price\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(housing_data['bedrooms'], housing_data['bathrooms'], c=housing_data['price'], cmap='winter', alpha=0.5)\n",
+    "plt.colorbar(label='House Price')\n",
+    "plt.xlabel('Number of Bedrooms')\n",
+    "plt.ylabel('Number of Bathrooms')\n",
+    "plt.title('Relationship between Bedrooms, Bathrooms, and House Price')\n",
+    "plt.grid(True)\n",
+    "plt.show();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both the real estate companies(sellers) and buyers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
+    "A house with a good balance of bedrooms and bathrooms tends to attract a wider range of potential buyers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#House age and house price**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Line plot for house age vs. average house price\n",
+    "average_price_by_age = housing_data.groupby('house_age')['price'].mean()\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(average_price_by_age.index, average_price_by_age.values, marker='o', linestyle='-')\n",
+    "plt.xlabel('House Age (Years)')\n",
+    "plt.ylabel('Average House Price')\n",
+    "plt.title('Average House Price by House Age.')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The scatter plot depicts the relationship between house age and prices; and reveals a lack of a clear linear trend. Instead, prices exhibit significant variation across different house ages. While the graph provides insights into how house prices fluctuate based on their age, no discernible pattern emerges. \n",
+    "In summary, the graph shows how house prices fluctuate based on their age, but no specific pattern emerges. This suggests that other factors beyond house age play a more influential role in determining house prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**#Prices of houses in relation to their respective Condition and Grade**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6), sharey=True)\n",
+    "\n",
+    "# Bar plot for condition vs. price\n",
+    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], palette='viridis')\n",
+    "axes[0].set_title('Condition vs. Price')\n",
+    "axes[0].set_xlabel('Condition')\n",
+    "axes[0].set_ylabel('Price')\n",
+    "axes[0].grid(True)\n",
+    "\n",
+    "# Bar plot for grade vs. price\n",
+    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], palette='viridis')\n",
+    "axes[1].set_title('Grade vs. Price')\n",
+    "axes[1].set_xlabel('Grade')\n",
+    "axes[1].grid(True)\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Show plots\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "The visualization presents comparisons between house prices and their condition and grade ratings. On the left, the condition of houses, rated from 1 to 5, shows relatively consistent prices across different condition levels, with no significant price increase observed for better conditions. Conversely, on the right, the grade of houses, ranging from 1 to 11, demonstrates a clear positive correlation with prices, indicating that higher-grade properties command higher prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c.) Multivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **#Correlation matrix**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Exclude the 'id', date and 'price_range' column from the correlation matrix\n",
+    "correlation_matrix = housing_data.drop(columns=['id', 'price_range', 'date']).corr()\n",
+    "\n",
+    "# Set up the matplotlib figure\n",
+    "plt.figure(figsize=(20, 20))\n",
+    "\n",
+    "# Create a heatmap using seaborn\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
+    "\n",
+    "# Add a title\n",
+    "plt.title('Correlation Matrix')\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Price has a moderate positive correlation with sqft living (0.70), grade (0.67), sqft above (0.61). This means that as the values of these features increase, the price of the house also tends to increase."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4.STATISTICAL ANALYSIS.\n",
+    "\n",
+    "Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:\n",
+    "\n",
+    "\n",
+    "1. Descriptive Statistics\n",
+    "2. Correlation matrix\n",
+    "3. Distribution Analysis\n",
+    "4. Inferential Statistics using Hypothesis Testing and Analysis of Variance\n",
+    "5. MultiColinierity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **a.) Descriptive Statistics**\n",
+    "\n",
+    "Initial insights into the central tendency, dispersion, and shape of the data distribution.\n",
+    "<br>Understanding the characteristics of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>21142.000000</td>\n",
+       "      <td>2.114200e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.581107e+09</td>\n",
+       "      <td>2014-10-29 08:57:27.772206848</td>\n",
+       "      <td>5.405060e+05</td>\n",
+       "      <td>3.371157</td>\n",
+       "      <td>2.116096</td>\n",
+       "      <td>2080.942531</td>\n",
+       "      <td>1.508757e+04</td>\n",
+       "      <td>1.493615</td>\n",
+       "      <td>0.006716</td>\n",
+       "      <td>3.409848</td>\n",
+       "      <td>5.658310</td>\n",
+       "      <td>1789.104437</td>\n",
+       "      <td>291.838095</td>\n",
+       "      <td>1971.024359</td>\n",
+       "      <td>68.259720</td>\n",
+       "      <td>1987.302478</td>\n",
+       "      <td>12739.322202</td>\n",
+       "      <td>52.975641</td>\n",
+       "      <td>0.955681</td>\n",
+       "      <td>1.924945e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000102e+06</td>\n",
+       "      <td>2014-05-02 00:00:00</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>0.000000</td>\n",
+       "      <td>1.000000</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>399.000000</td>\n",
+       "      <td>651.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.013000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.123049e+09</td>\n",
+       "      <td>2014-07-22 00:00:00</td>\n",
+       "      <td>3.220000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.750000</td>\n",
+       "      <td>1430.000000</td>\n",
+       "      <td>5.043000e+03</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1200.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1952.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1490.000000</td>\n",
+       "      <td>5100.000000</td>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.820000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.904940e+09</td>\n",
+       "      <td>2014-10-16 00:00:00</td>\n",
+       "      <td>4.500000e+05</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.250000</td>\n",
+       "      <td>1910.000000</td>\n",
+       "      <td>7.620000e+03</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1560.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1975.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1840.000000</td>\n",
+       "      <td>7626.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.159350e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.309100e+09</td>\n",
+       "      <td>2015-02-18 00:00:00</td>\n",
+       "      <td>6.450000e+05</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>2550.000000</td>\n",
+       "      <td>1.069575e+04</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2210.000000</td>\n",
+       "      <td>560.000000</td>\n",
+       "      <td>1997.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2360.000000</td>\n",
+       "      <td>10087.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.546000e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>9.900000e+09</td>\n",
+       "      <td>2015-05-27 00:00:00</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>1.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>9410.000000</td>\n",
+       "      <td>4820.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>2015.000000</td>\n",
+       "      <td>6210.000000</td>\n",
+       "      <td>871200.000000</td>\n",
+       "      <td>124.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>1.653959e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.876357e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.680831e+05</td>\n",
+       "      <td>0.902213</td>\n",
+       "      <td>0.768545</td>\n",
+       "      <td>918.563816</td>\n",
+       "      <td>4.121013e+04</td>\n",
+       "      <td>0.539252</td>\n",
+       "      <td>0.081680</td>\n",
+       "      <td>0.650422</td>\n",
+       "      <td>1.174272</td>\n",
+       "      <td>828.413341</td>\n",
+       "      <td>442.504364</td>\n",
+       "      <td>29.322166</td>\n",
+       "      <td>362.774103</td>\n",
+       "      <td>685.671655</td>\n",
+       "      <td>27169.859971</td>\n",
+       "      <td>29.322166</td>\n",
+       "      <td>5.824659</td>\n",
+       "      <td>4.156724e+04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 id                           date         price  \\\n",
+       "count  2.114200e+04                          21142  2.114200e+04   \n",
+       "mean   4.581107e+09  2014-10-29 08:57:27.772206848  5.405060e+05   \n",
+       "min    1.000102e+06            2014-05-02 00:00:00  7.800000e+04   \n",
+       "25%    2.123049e+09            2014-07-22 00:00:00  3.220000e+05   \n",
+       "50%    3.904940e+09            2014-10-16 00:00:00  4.500000e+05   \n",
+       "75%    7.309100e+09            2015-02-18 00:00:00  6.450000e+05   \n",
+       "max    9.900000e+09            2015-05-27 00:00:00  7.700000e+06   \n",
+       "std    2.876357e+09                            NaN  3.680831e+05   \n",
+       "\n",
+       "           bedrooms     bathrooms   sqft_living      sqft_lot        floors  \\\n",
+       "count  21142.000000  21142.000000  21142.000000  2.114200e+04  21142.000000   \n",
+       "mean       3.371157      2.116096   2080.942531  1.508757e+04      1.493615   \n",
+       "min        1.000000      0.500000    370.000000  5.200000e+02      1.000000   \n",
+       "25%        3.000000      1.750000   1430.000000  5.043000e+03      1.000000   \n",
+       "50%        3.000000      2.250000   1910.000000  7.620000e+03      1.500000   \n",
+       "75%        4.000000      2.500000   2550.000000  1.069575e+04      2.000000   \n",
+       "max       11.000000      8.000000  13540.000000  1.651359e+06      3.500000   \n",
+       "std        0.902213      0.768545    918.563816  4.121013e+04      0.539252   \n",
+       "\n",
+       "         waterfront     condition         grade    sqft_above  sqft_basement  \\\n",
+       "count  21142.000000  21142.000000  21142.000000  21142.000000   21142.000000   \n",
+       "mean       0.006716      3.409848      5.658310   1789.104437     291.838095   \n",
+       "min        0.000000      1.000000      1.000000    370.000000       0.000000   \n",
+       "25%        0.000000      3.000000      5.000000   1200.000000       0.000000   \n",
+       "50%        0.000000      3.000000      5.000000   1560.000000       0.000000   \n",
+       "75%        0.000000      4.000000      6.000000   2210.000000     560.000000   \n",
+       "max        1.000000      5.000000     11.000000   9410.000000    4820.000000   \n",
+       "std        0.081680      0.650422      1.174272    828.413341     442.504364   \n",
+       "\n",
+       "           yr_built  yr_renovated  sqft_living15     sqft_lot15     house_age  \\\n",
+       "count  21142.000000  21142.000000   21142.000000   21142.000000  21142.000000   \n",
+       "mean    1971.024359     68.259720    1987.302478   12739.322202     52.975641   \n",
+       "min     1900.000000      0.000000     399.000000     651.000000      9.000000   \n",
+       "25%     1952.000000      0.000000    1490.000000    5100.000000     27.000000   \n",
+       "50%     1975.000000      0.000000    1840.000000    7626.000000     49.000000   \n",
+       "75%     1997.000000      0.000000    2360.000000   10087.000000     72.000000   \n",
+       "max     2015.000000   2015.000000    6210.000000  871200.000000    124.000000   \n",
+       "std       29.322166    362.774103     685.671655   27169.859971     29.322166   \n",
+       "\n",
+       "       renovation_age    total_sqft  \n",
+       "count    21142.000000  2.114200e+04  \n",
+       "mean         0.955681  1.924945e+04  \n",
+       "min          0.000000  2.013000e+03  \n",
+       "25%          0.000000  8.820000e+03  \n",
+       "50%          0.000000  1.159350e+04  \n",
+       "75%          0.000000  1.546000e+04  \n",
+       "max         90.000000  1.653959e+06  \n",
+       "std          5.824659  4.156724e+04  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "housing_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Price Distribution:**\n",
+    "\n",
+    "The price of houses in the dataset varies widely, ranging from $78,000 to $7,700,000, with an average price of $540,506. The standard deviation of $368,083.1 indicates a significant dispersion around the mean, suggesting a diverse range of housing prices.\n",
+    "\n",
+    "**Bedrooms and Bathrooms:** \n",
+    "\n",
+    "The average number of bedrooms is approximately 3.37, while the average number of bathrooms is about 2.12. The standard deviations for both variables are relatively small, indicating less variability compared to other features.\n",
+    "\n",
+    "**Square Footage:**\n",
+    "\n",
+    " The average square footage of living space is around 2,080, with a standard deviation of 918.56. Similarly, the average lot size is approximately 15,087 square feet, with a larger standard deviation of 41,210.13, suggesting more variability in lot sizes compared to living space.\n",
+    "\n",
+    "**Floors:**\n",
+    "\n",
+    "On average, houses have 1.49 floors, with a standard deviation of 0.54. This indicates some variability in the number of floors, although most houses seem to have either one or two floors.\n",
+    "\n",
+    "**Waterfront Property:**\n",
+    "\n",
+    " Only a small percentage (0.7%) of the houses are waterfront properties, based on the average value. This feature is likely represented as a binary variable (0 for no waterfront, 1 for waterfront), with most houses being non-waterfront properties.\n",
+    "\n",
+    "**Condition and Grade:**\n",
+    "\n",
+    "The average condition of houses is approximately 3.41, with a standard deviation of 0.65, suggesting some variability in the condition ratings. Similarly, the average grade is around 5.66, with a standard deviation of 1.17, indicating variations in the overall quality of houses.\n",
+    "\n",
+    "**Year Built and Year Renovated:**\n",
+    "\n",
+    "The houses in the dataset span a wide range of construction years, from 1900 to 2015, with an average year of construction around 1971. The standard deviation of 29.32 indicates some variability in the construction years. Additionally, the average year of renovation is approximately 68.26, with a standard deviation of 443.5, suggesting that most houses have not been renovated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **b.) Correlation Analysis with House Prices**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Correlation analysis was performed to examine the relationship between various features and the target variable, 'price'. \n",
+    "<BR>Here are the correlation coefficients between each feature and the price:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "price             1.000000\n",
+       "bedrooms          0.316573\n",
+       "bathrooms         0.525899\n",
+       "sqft_living       0.702340\n",
+       "sqft_lot          0.087940\n",
+       "floors            0.256372\n",
+       "waterfront        0.265970\n",
+       "condition         0.035264\n",
+       "grade             0.667751\n",
+       "sqft_above        0.605167\n",
+       "sqft_basement     0.325003\n",
+       "yr_built          0.054471\n",
+       "yr_renovated      0.116721\n",
+       "sqft_living15     0.586441\n",
+       "sqft_lot15        0.083196\n",
+       "house_age        -0.054471\n",
+       "renovation_age    0.082356\n",
+       "total_sqft        0.118225\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "drop_var = ['id', 'price_range', 'date']\n",
+    "\n",
+    "correlation = housing_data.drop(drop_var, axis=1).corrwith(housing_data['price'])\n",
+    "correlation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These correlation coefficients indicate the strength and direction of the linear relationship between each feature and the price of the houses.\n",
+    "<br> Features with higher positive correlation coefficients, such as 'sqft_living', 'grade', 'bathrooms', and 'sqft_above', have a stronger positive linear relationship with the price, indicating that as these feature values increase, the price tends to increase as well. Conversely, features with low or negative correlation coefficients, such as 'condition', 'yr_built', 'sqft_lot', and 'house_age', have weaker or negative linear relationships with the price.\n",
+    "<br>However, it's imperative to underscore that correlation does not imply causation. There could be an underlying third factor driving changes in both features, underscoring the need for thorough investigation beyond correlation analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **c.) Distribution Analysis**\n",
+    "\n",
+    "Distribution analysis involves understanding the distribution of data, such as whether it follows a normal distribution and skewed distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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>condition</th>\n",
+       "      <th>...</th>\n",
+       "      <th>sqft_above</th>\n",
+       "      <th>sqft_basement</th>\n",
+       "      <th>yr_built</th>\n",
+       "      <th>yr_renovated</th>\n",
+       "      <th>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "      <th>price_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7129300520</td>\n",
+       "      <td>2014-10-13</td>\n",
+       "      <td>12.309987</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>7.074117</td>\n",
+       "      <td>8.639588</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.074117</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1955</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.201171</td>\n",
+       "      <td>8.639588</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.988571</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6414100192</td>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>13.195616</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>1.178655</td>\n",
+       "      <td>7.852050</td>\n",
+       "      <td>8.887791</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.682943</td>\n",
+       "      <td>5.993961</td>\n",
+       "      <td>1951</td>\n",
+       "      <td>7.596894</td>\n",
+       "      <td>7.433075</td>\n",
+       "      <td>8.941153</td>\n",
+       "      <td>73</td>\n",
+       "      <td>3.526361</td>\n",
+       "      <td>9.424080</td>\n",
+       "      <td>300K-600K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5631500400</td>\n",
+       "      <td>2015-02-25</td>\n",
+       "      <td>12.100718</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>6.647688</td>\n",
+       "      <td>9.210440</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.647688</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1933</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.908755</td>\n",
+       "      <td>8.995041</td>\n",
+       "      <td>91</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.353661</td>\n",
+       "      <td>100K-300K</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2487200875</td>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>13.311331</td>\n",
+       "      <td>1.609438</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>7.581210</td>\n",
+       "      <td>8.517393</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.791759</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.957497</td>\n",
+       "      <td>6.814543</td>\n",
+       "      <td>1965</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.215975</td>\n",
+       "      <td>8.517393</td>\n",
+       "      <td>59</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.096163</td>\n",
+       "      <td>600K-1M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1954400510</td>\n",
+       "      <td>2015-02-18</td>\n",
+       "      <td>13.142168</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>1.098612</td>\n",
+       "      <td>7.427144</td>\n",
+       "      <td>8.997271</td>\n",
+       "      <td>0.693147</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.386294</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.427144</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.496097</td>\n",
+       "      <td>8.923191</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>9.344959</td>\n",
+       "      <td>300K-600K</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 2014-10-13  12.309987  1.386294   0.693147     7.074117   \n",
+       "1  6414100192 2014-12-09  13.195616  1.386294   1.178655     7.852050   \n",
+       "2  5631500400 2015-02-25  12.100718  1.098612   0.693147     6.647688   \n",
+       "3  2487200875 2014-12-09  13.311331  1.609438   1.386294     7.581210   \n",
+       "4  1954400510 2015-02-18  13.142168  1.386294   1.098612     7.427144   \n",
+       "\n",
+       "   sqft_lot    floors  waterfront  condition  ...  sqft_above  sqft_basement  \\\n",
+       "0  8.639588  0.693147         0.0   1.386294  ...    7.074117       0.000000   \n",
+       "1  8.887791  1.098612         0.0   1.386294  ...    7.682943       5.993961   \n",
+       "2  9.210440  0.693147         0.0   1.386294  ...    6.647688       0.000000   \n",
+       "3  8.517393  0.693147         0.0   1.791759  ...    6.957497       6.814543   \n",
+       "4  8.997271  0.693147         0.0   1.386294  ...    7.427144       0.000000   \n",
+       "\n",
+       "   yr_built  yr_renovated  sqft_living15  sqft_lot15  house_age  \\\n",
+       "0      1955      0.000000       7.201171    8.639588         69   \n",
+       "1      1951      7.596894       7.433075    8.941153         73   \n",
+       "2      1933      0.000000       7.908755    8.995041         91   \n",
+       "3      1965      0.000000       7.215975    8.517393         59   \n",
+       "4      1987      0.000000       7.496097    8.923191         37   \n",
+       "\n",
+       "   renovation_age  total_sqft  price_range  \n",
+       "0        0.000000    8.988571    100K-300K  \n",
+       "1        3.526361    9.424080    300K-600K  \n",
+       "2        0.000000    9.353661    100K-300K  \n",
+       "3        0.000000    9.096163      600K-1M  \n",
+       "4        0.000000    9.344959    300K-600K  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import skew\n",
+    "\n",
+    "# Select numerical variables only\n",
+    "numerical_data = housing_data.select_dtypes(include=['number'])\n",
+    "\n",
+    "# Compute skewness for each numerical variable\n",
+    "skewness = numerical_data.apply(lambda x: skew(x.dropna()))\n",
+    "\n",
+    "# Select variables with skewness above a certain threshold (e.g., 0.5)\n",
+    "skewed_variables = skewness[abs(skewness) > 0.5].index\n",
+    "\n",
+    "# Log transformation for skewed variables\n",
+    "df_log = housing_data.copy()  # Create a copy of the original DataFrame to preserve the original data\n",
+    "df_log[skewed_variables] = housing_data[skewed_variables].apply(lambda x: np.log1p(x))\n",
+    "\n",
+    "# Check the distributions before and after transformation if needed\n",
+    "# For example, you can use histograms or density plots to visualize the distributions\n",
+    "\n",
+    "# Print the first few rows of the transformed data to verify\n",
+    "df_log.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x2800 with 19 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Selecting numerical columns\n",
+    "numerical_columns = housing_data.select_dtypes(include=[np.number]).columns\n",
+    "\n",
+    "# Calculate the number of rows and columns for subplots\n",
+    "num_cols = len(numerical_columns)\n",
+    "num_rows = (num_cols + 2) // 3  # Calculate the number of rows needed, rounding up\n",
+    "\n",
+    "# Plot histograms for numerical variables\n",
+    "plt.figure(figsize=(12, num_rows * 4))  # Adjust the height based on the number of rows\n",
+    "for i, col in enumerate(numerical_columns, 1):\n",
+    "    plt.subplot(num_rows, 3, i)\n",
+    "    sns.histplot(housing_data[col], kde=True, edgecolor='black')\n",
+    "    plt.title(col)\n",
+    "    plt.xlabel('')\n",
+    "    plt.ylabel('Frequency')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Conclusion**\n",
+    "\n",
+    "In conclusion, the log transformation has effectively addressed skewness in the distribution of numerical variables within the dataset. Prior to transformation, variables such as house price, bedrooms, bathrooms, and various square footage measurements exhibited skewed distributions with long tails. However, after applying the log transformation, these distributions appear to be more symmetric and closer to a normal distribution. This transformation has enhanced the suitability of the data for statistical analysis by reducing skewness and improving the interpretability of the variables. It's important to acknowledge that while the log transformation has provided valuable improvements, it alters the scale and interpretation of the variables, necessitating careful consideration in subsequent analyses. Overall, the transformed variables are now better suited for further statistical modeling and analysis in the context of predicting property values and understanding real estate market trends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **d.) Inferential Statistics.**\n",
+    "\n",
+    "*We used one-way ANOVA approach*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1. \u001b[1mbedrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "2. \u001b[1mbathrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "3. \u001b[1msqft_living\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "4. \u001b[1msqft_lot\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
+      "5. \u001b[1mfloors\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "6. \u001b[1mwaterfront\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "7. \u001b[1mcondition\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "8. \u001b[1mgrade\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "9. \u001b[1msqft_above\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "10. \u001b[1msqft_basement\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "11. \u001b[1myr_built\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "12. \u001b[1myr_renovated\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "13. \u001b[1msqft_living15\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "14. \u001b[1msqft_lot15\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
+      "15. \u001b[1mhouse_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "16. \u001b[1mrenovation_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
+      "17. \u001b[1mtotal_sqft\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n"
+     ]
+    },
+    {
+     "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>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>condition</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>sqft_living15</th>\n",
+       "      <th>sqft_lot15</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>renovation_age</th>\n",
+       "      <th>total_sqft</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F-statistic</th>\n",
+       "      <td>1.841471e+00</td>\n",
+       "      <td>3.728667</td>\n",
+       "      <td>7.702589</td>\n",
+       "      <td>0.732713</td>\n",
+       "      <td>1.662592e+00</td>\n",
+       "      <td>1.787176e+00</td>\n",
+       "      <td>1.051111</td>\n",
+       "      <td>7.29306</td>\n",
+       "      <td>5.098114</td>\n",
+       "      <td>1.750817e+00</td>\n",
+       "      <td>1.236748e+00</td>\n",
+       "      <td>1.046716</td>\n",
+       "      <td>5.209978</td>\n",
+       "      <td>0.709841</td>\n",
+       "      <td>1.236748e+00</td>\n",
+       "      <td>1.171737e+00</td>\n",
+       "      <td>0.775778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P-value</th>\n",
+       "      <td>1.765957e-139</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.808184e-95</td>\n",
+       "      <td>1.258742e-125</td>\n",
+       "      <td>0.026289</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.406487e-116</td>\n",
+       "      <td>2.417229e-17</td>\n",
+       "      <td>0.037943</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.417229e-17</td>\n",
+       "      <td>2.430304e-10</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  bedrooms  bathrooms  sqft_living  sqft_lot        floors  \\\n",
+       "F-statistic   1.841471e+00   3.728667     7.702589  0.732713  1.662592e+00   \n",
+       "P-value      1.765957e-139   0.000000     0.000000  1.000000  1.808184e-95   \n",
+       "\n",
+       "                waterfront  condition    grade  sqft_above  sqft_basement  \\\n",
+       "F-statistic   1.787176e+00   1.051111  7.29306    5.098114   1.750817e+00   \n",
+       "P-value      1.258742e-125   0.026289  0.00000    0.000000  1.406487e-116   \n",
+       "\n",
+       "                 yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
+       "F-statistic  1.236748e+00      1.046716       5.209978    0.709841   \n",
+       "P-value      2.417229e-17      0.037943       0.000000    1.000000   \n",
+       "\n",
+       "                house_age  renovation_age  total_sqft  \n",
+       "F-statistic  1.236748e+00    1.171737e+00    0.775778  \n",
+       "P-value      2.417229e-17    2.430304e-10    1.000000  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "from scipy.stats import f_oneway\n",
+    "\n",
+    "# List of features of interest\n",
+    "features_of_interest = ['bedrooms', 'bathrooms', 'sqft_living',\n",
+    "                        'sqft_lot', 'floors', 'waterfront', 'condition', \n",
+    "                        'grade', 'sqft_above', 'sqft_basement', 'yr_built', \n",
+    "                        'yr_renovated', 'sqft_living15', 'sqft_lot15', \n",
+    "                        'house_age', 'renovation_age', 'total_sqft']\n",
+    "\n",
+    "# Create an empty DataFrame to store ANOVA results\n",
+    "anova_results = pd.DataFrame(index=['F-statistic', 'P-value'])\n",
+    "\n",
+    "# Perform ANOVA for each feature\n",
+    "significant_features = []\n",
+    "\n",
+    "for i, column in enumerate(features_of_interest, 1):\n",
+    "    groups = [housing_data[column][housing_data['price'] == category]\n",
+    "              for category in housing_data['price'].unique()]\n",
+    "\n",
+    "    # Perform ANOVA\n",
+    "    f_statistic, p_value = f_oneway(*groups)\n",
+    "\n",
+    "    # Store results in the DataFrame\n",
+    "    anova_results[column] = [f_statistic, p_value]\n",
+    "\n",
+    "    # Print interpretation\n",
+    "    if p_value < 0.05:\n",
+    "        significant_features.append(column)\n",
+    "        print(f\"{i}. \\033[1m{column}\\033[0m: Reject the null hypothesis. There is a statistically significant relationship.\")\n",
+    "    else:\n",
+    "        print(f\"{i}. \\033[1m{column}\\033[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\")\n",
+    "\n",
+    "# Display ANOVA results\n",
+    "anova_results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Conclusion**\n",
+    "\n",
+    "The features listed under \"Reject the Null Hypothesis\" have a statistically significant relationship with housing prices.\n",
+    "\n",
+    "These features are important predictors of housing prices in the given dataset.\n",
+    "\n",
+    "On the other hand, features listed under \"Fail to Reject the Null Hypothesis\" do not show a statistically significant relationship with housing prices based on the ANOVA test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **e.) Multicollinearity.**\n",
+    "\n",
+    "**Assessing Multicollinearity with Variance Inflation Factor (VIF)**\n",
+    "<br>In this analysis, we utilize the Variance Inflation Factor (VIF) to investigate multicollinearity among predictor variables in our regression model. Multicollinearity occurs when predictor variables are highly correlated with each other, which can lead to unreliable coefficient estimates. By computing the VIF for each predictor variable, we identify potential multicollinearity issues among property characteristics. \n",
+    "<br>High VIF values indicate a strong correlation between a predictor variable and the other variables in the model. Hence, it's crucial to examine the VIF values to ensure the reliability of our regression analysis and to address any multicollinearity detected.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  vif = 1. / (1. - r_squared_i)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1mVariance Inflation Factor (VIF):\u001b[0m\n"
+     ]
+    },
+    {
+     "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>Feature</th>\n",
+       "      <th>VIF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>bedrooms</td>\n",
+       "      <td>1.688154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>bathrooms</td>\n",
+       "      <td>3.366371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sqft_living</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sqft_lot</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>floors</td>\n",
+       "      <td>1.934470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>waterfront</td>\n",
+       "      <td>1.028593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>condition</td>\n",
+       "      <td>1.218261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>grade</td>\n",
+       "      <td>3.235515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>sqft_above</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>sqft_basement</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>yr_built</td>\n",
+       "      <td>94.556823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>yr_renovated</td>\n",
+       "      <td>4.226966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>sqft_living15</td>\n",
+       "      <td>2.763013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>sqft_lot15</td>\n",
+       "      <td>2.130997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>house_age</td>\n",
+       "      <td>7.743224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>renovation_age</td>\n",
+       "      <td>4.109794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>total_sqft</td>\n",
+       "      <td>inf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Feature        VIF\n",
+       "0         bedrooms   1.688154\n",
+       "1        bathrooms   3.366371\n",
+       "2      sqft_living        inf\n",
+       "3         sqft_lot        inf\n",
+       "4           floors   1.934470\n",
+       "5       waterfront   1.028593\n",
+       "6        condition   1.218261\n",
+       "7            grade   3.235515\n",
+       "8       sqft_above        inf\n",
+       "9    sqft_basement        inf\n",
+       "10        yr_built  94.556823\n",
+       "11    yr_renovated   4.226966\n",
+       "12   sqft_living15   2.763013\n",
+       "13      sqft_lot15   2.130997\n",
+       "14       house_age   7.743224\n",
+       "15  renovation_age   4.109794\n",
+       "16      total_sqft        inf"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "# Compute Variance Inflation Factor (VIF) to detect multicollinearity\n",
+    "\n",
+    "X = housing_data[['bedrooms', 'bathrooms', 'sqft_living','sqft_lot', 'floors', 'waterfront', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15', 'house_age', 'renovation_age', 'total_sqft']]\n",
+    "# Calculate the correlation matrix\n",
+    "correlation_matrix = X.corr()\n",
+    "\n",
+    "# Calculate VIF for each feature\n",
+    "vif_data = pd.DataFrame()\n",
+    "vif_data[\"Feature\"] = X.columns\n",
+    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
+    "\n",
+    "# Print VIF for each feature\n",
+    "print(\"\\n\\033[1mVariance Inflation Factor (VIF):\\033[0m\")\n",
+    "vif_data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Variance Inflation Factor (VIF) Analysis**\n",
+    "<br>The Variance Inflation Factor (VIF) was calculated to assess multicollinearity among the features in the dataset. A VIF value greater than 5 is typically considered indicative of multicollinearity. The results revealed that most features exhibited low levels of multicollinearity, with VIF values below 5. \n",
+    "<br>However, 'yr_built' displayed a remarkably high VIF of 94.56, indicating strong multicollinearity with other features. Additionally, 'sqft_living', 'sqft_lot', 'sqft_above', 'sqft_basement', and 'total_sqft' exhibited infinite VIF values, suggesting perfect multicollinearity. \n",
+    "<br>Addressing multicollinearity in this case may require further investigation, such as feature selection, dimensionality reduction techniques or applying regularization methods to mitigate multicollinearity effects and improve model performance. \n",
+    "<br>Overall, understanding the VIF values can help refine the regression model and ensure the reliability of the coefficient estimates.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MODELLING."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Baseline model - simple linear model.\n",
+    "2. log transformation. \n",
+    "3. Multiple Linear Regression\n",
+    "4. Residual modelling.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Baseline model \n",
+    "\n",
+    " Baseline models provide a reference point for comparing the performance of more complex models. \n",
+    " Its purpose is to establish a benchmark against which the performance of more sophisticated models can be evaluated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FUNCTIONS TO BE USED."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#use only numeric columns.\n",
+    "def numeric_col(housing_data):\n",
+    "    '''returns a dataframe with only numeric values'''\n",
+    "    for column in housing_data.columns:\n",
+    "        if is_numeric_dtype(housing_data[column]) == False:\n",
+    "            housing_data = housing_data.drop(column, axis=1)\n",
+    "        else:\n",
+    "            continue\n",
+    "    return housing_data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Set a function for the predictor and target variale.\n",
+    "def X_Y(housing_data, target):\n",
+    "    '''Returns a series of target (y) values and a DataFrame of predictors (X)'''\n",
+    "    y = housing_data[target]  # target variable\n",
+    "    X = housing_data.drop(target, axis=1)  # predictor features\n",
+    "    return y, X\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#A higher train score indicates that the model fits the training data well.\n",
+    "#A high test score suggests that the model is able to make accurate predictions on data it hasn't seen before, which is the ultimate goal in machine learning.\n",
+    "\n",
+    "def get_metrics(X_train, X_test, y_train, y_test):\n",
+    "    ''' Parameters are X train, X test, y train, & y_test\n",
+    "        Performs multiple regression on the split test and returns metrics'''\n",
+    "\n",
+    "    # Initialize Linear Regression model\n",
+    "    lr = LinearRegression()\n",
+    "\n",
+    "    lr.fit(X_train, y_train)\n",
+    "\n",
+    "    train_score = lr.score(X_train, y_train)\n",
+    "    test_score = lr.score(X_test, y_test)\n",
+    "\n",
+    "    y_hat_train = lr.predict(X_train)\n",
+    "    y_hat_test = lr.predict(X_test)\n",
+    "\n",
+    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_hat_train))\n",
+    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_hat_test))\n",
+    "\n",
+    "    return train_score, test_score, train_rmse, test_rmse\n",
+    "#These scores provide insights into how well the model is performing both on the data it was trained on and on new data.\n",
+    "# They help assess the model's overall effectiveness and whether it is overfitting or underfitting.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_test(housing_data, size=0.20):\n",
+    "    '''Takes in dataframe, and size of test for the split\n",
+    "        Returns the train_set and test_set'''\n",
+    "    train_set, test_set = train_test_split(housing_data, test_size=size, random_state=42)\n",
+    "    return train_set, test_set\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIMPLE LINEAR REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def simple_linear_regression(housing_data):\n",
+    "    '''Creates a simple linear regression model with prices as the target variable \n",
+    "    and the number of bedrooms as the predictor. Returns the model along with R-squared, \n",
+    "    Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both train and test sets.'''\n",
+    "    \n",
+    "    # Extracting features and target variable\n",
+    "    X = housing_data[['sqft_living']]  # Predictor feature\n",
+    "    y = housing_data['price']  # Target variable (prices)\n",
+    "    \n",
+    "    # Splitting the data into train and test sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    \n",
+    "    # Create a linear regression model\n",
+    "    model = LinearRegression()\n",
+    "    \n",
+    "    # Fit the model to the training data\n",
+    "    model.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Calculate predictions for train and test sets\n",
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "    \n",
+    "    # Calculate R-squared for train and test sets\n",
+    "    r2_train = r2_score(y_train, y_train_pred)\n",
+    "    r2_test = r2_score(y_test, y_test_pred)\n",
+    "    \n",
+    "    # Calculate Mean Squared Error (MSE) for train and test sets\n",
+    "    mse_train = mean_squared_error(y_train, y_train_pred)\n",
+    "    mse_test = mean_squared_error(y_test, y_test_pred)\n",
+    "    \n",
+    "    # Calculate Root Mean Squared Error (RMSE) for train and test sets\n",
+    "    rmse_train = np.sqrt(mse_train)\n",
+    "    rmse_test = np.sqrt(mse_test)\n",
+    "    \n",
+    "    # Print coefficients, R-squared, MSE, and RMSE for train and test sets\n",
+    "    print(\"Training set:\")\n",
+    "    print(\"Intercept:\", model.intercept_)\n",
+    "    print(\"Coefficient:\", model.coef_[0])\n",
+    "    print(\"R-squared:\", r2_train)\n",
+    "    print(\"Mean Squared Error:\", mse_train)\n",
+    "    print(\"Root Mean Squared Error:\", rmse_train)\n",
+    "    \n",
+    "    print(\"\\nTest set:\")\n",
+    "    print(\"R-squared:\", r2_test)\n",
+    "    print(\"Mean Squared Error:\", mse_test)\n",
+    "    print(\"Root Mean Squared Error:\", rmse_test)\n",
+    "    \n",
+    "    return model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "# Assuming housing_data is your dataset\n",
+    "# model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test = simple_linear_regression(housing_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set:\n",
+      "Intercept: -44593.95245340909\n",
+      "Coefficient: 281.4088930446383\n",
+      "R-squared: 0.49587806055811734\n",
+      "Mean Squared Error: 68601152192.940285\n",
+      "Root Mean Squared Error: 261918.21661148407\n",
+      "\n",
+      "Test set:\n",
+      "R-squared: 0.48264829402430887\n",
+      "Mean Squared Error: 68845100756.10751\n",
+      "Root Mean Squared Error: 262383.4993975565\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(LinearRegression(),\n",
+       " 0.49587806055811734,\n",
+       " 68601152192.940285,\n",
+       " 261918.21661148407,\n",
+       " 0.48264829402430887,\n",
+       " 68845100756.10751,\n",
+       " 262383.4993975565)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simple_linear_regression(housing_data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.\n",
+    "\n",
+    "\n",
+    "Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.\n",
+    "\n",
+    "\n",
+    "R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.\n",
+    "\n",
+    "\n",
+    "Test Set:\n",
+    "\n",
+    "R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R-squared: 0.48264829402430875\n",
+      "Mean Squared Error: 68845100756.10753\n",
+      "Root Mean Squared Error: 262383.4993975565\n",
+      "Intercept: 540631.1560929463\n",
+      "Coefficients: [259767.82181675]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#STANDARDIZE OUR MODEL TO GET RID OF THE NEGATIVE INTERCEPT.\n",
+    "\n",
+    "\n",
+    "# Assuming you have your data loaded into X and y\n",
+    "# X should be your independent variables and y should be your dependent variable\n",
+    "\n",
+    "# Split data into training and test sets\n",
+    "X = housing_data[['sqft_living']]  # Predictor feature\n",
+    "y = housing_data['price']  # Target variable (prices)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize the StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# Fit and transform the scaler on the training data\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "\n",
+    "# Transform the test data using the scaler fitted on the training data\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Fit the linear regression model on the scaled training data\n",
+    "model = LinearRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# Predict on the scaled test data\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "# Calculate R-squared and mean squared error on the test set\n",
+    "r_squared = r2_score(y_test, y_pred)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "\n",
+    "# Print the metrics\n",
+    "print(\"R-squared:\", r_squared)\n",
+    "print(\"Mean Squared Error:\", mse)\n",
+    "print(\"Root Mean Squared Error:\", rmse)\n",
+    "\n",
+    "# Print the intercept and coefficients of the model\n",
+    "print(\"Intercept:\", model.intercept_)\n",
+    "print(\"Coefficients:\", model.coef_)\n",
+    "\n",
+    "#This code will scale your data using StandardScaler, which standardizes features by removing the mean and scaling to unit variance. \n",
+    "#It ensures that the intercept is not negative"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared (0.48): This value indicates that approximately 48% of the variability in the dependent variable (the house prices) is explained by the independent (sqft living). In simpler terms, the model captures about 48% of the patterns in the data.\n",
+    "\n",
+    "\n",
+    "Mean Squared Error (MSE) (68845100756.11): This is the average squared difference between the actual values and the predicted values from the model. It's a measure of the model's accuracy, where lower values indicate better performance. In this case, the average squared difference is quite large, indicating that there's still room for improvement in the model's predictive accuracy.\n",
+    "\n",
+    "\n",
+    "Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE and provides a measure of the typical deviation of the predicted values from the actual values. It's in the same units as the dependent variable. Here, the RMSE indicates that, on average, the predicted values differ from the actual values by approximately 262,383.50 units.\n",
+    "\n",
+    "\n",
+    "Intercept (540631.16): This is the estimated value of the dependent variable when all independent variables are set to zero. In this case, it suggests that when all other factors are zero, we would expect the dependent variable to be around 540,631.16.\n",
+    "\n",
+    "\n",
+    "Coefficient (259767.82): This represents the change in the house prices for a one-unit change in the sqft living, while holding other variables constant. In this case, for every one-unit increase in the sqft living, we'd expect the house prices to increase by approximately 259,767.82 units.\n",
+    "\n",
+    "\n",
+    "Overall, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MULTIPLE LINEAR REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 47548419769.890594\n",
+      "R-squared: 0.6426869041626193\n",
+      "          Feature    Coefficient\n",
+      "0           const       0.000000\n",
+      "1       bathrooms   24855.944322\n",
+      "2     sqft_living  117349.853313\n",
+      "3          floors   20057.401179\n",
+      "4      waterfront   63378.047594\n",
+      "5       condition   11809.217548\n",
+      "6           grade  154829.185004\n",
+      "7   sqft_basement   14434.666872\n",
+      "8        yr_built -108064.021718\n",
+      "9    yr_renovated    7255.426167\n",
+      "10  sqft_living15   25765.797630\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Define a function to keep only numeric columns\n",
+    "def only_numeric(housing_data):\n",
+    "    '''returns a DataFrame with only numeric values'''\n",
+    "    numeric_columns = [column for column in housing_data.columns if is_numeric_dtype(housing_data[column])]\n",
+    "    return housing_data[numeric_columns]\n",
+    "\n",
+    "# Sample features and target variable\n",
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "\n",
+    "    # Split the data into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Standardize the features\n",
+    "    scaler = StandardScaler()\n",
+    "    X_train_scaled = scaler.fit_transform(X_train)\n",
+    "    X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "    # Build a basic linear regression model\n",
+    "    model = LinearRegression()\n",
+    "    model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "    # Make predictions on the test set\n",
+    "    y_pred = model.predict(X_test_scaled)\n",
+    "\n",
+    "    # Evaluate the model\n",
+    "    mse = mean_squared_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "    # Display results\n",
+    "    print(\"Mean Squared Error:\", mse)\n",
+    "    print(\"R-squared:\", r2)\n",
+    "\n",
+    "    # Display coefficients\n",
+    "    coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
+    "    print(coefficients)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean Squared Error (MSE): The MSE is a measure of the average squared difference between the actual and predicted values. In this case, the MSE is approximately \n",
+    "4.75\n",
+    "×\n",
+    "1\n",
+    "0\n",
+    "10\n",
+    "4.75×10 \n",
+    "10\n",
+    "\n",
+    "\n",
+    " , which means, on average, the squared difference between the actual housing prices and the predicted prices is \n",
+    "4.75\n",
+    "×\n",
+    "1\n",
+    "0\n",
+    "10\n",
+    "4.75×10 \n",
+    "10\n",
+    " .\n",
+    "\n",
+    "\n",
+    "R-squared (\n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "\n",
+    "\n",
+    " ): The \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  score measures the proportion of the variance in the target variable (housing prices) that is explained by the independent variables (features) in the model. \n",
+    "  \n",
+    "  An \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  score of 0.643 means that approximately 64.3% of the variance in housing prices is explained by the features included in the model. In other words, the model accounts for 64.3% of the variability in housing prices.\n",
+    "\n",
+    "\n",
+    "Coefficients:\n",
+    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero.\n",
+    "\n",
+    "\n",
+    "bathrooms: For each additional bathroom, the predicted housing price increases by approximately $24,855.\n",
+    "\n",
+    "\n",
+    "sqft_living: For each additional square foot of living space, the predicted housing price increases by approximately $117,350.\n",
+    "\n",
+    "\n",
+    "floors: Houses with an additional floor have a predicted price increase of approximately $20,057.\n",
+    "\n",
+    "\n",
+    "waterfront: Properties with waterfront views have a predicted price increase of approximately $63,378 compared to those without.\n",
+    "\n",
+    "\n",
+    "condition: Better condition properties (on a scale from 1 to 5) tend to have a predicted price increase of approximately $11,809 for each unit increase in condition.\n",
+    "\n",
+    "grade: Higher grade properties (on a scale from 1 to 13) have a predicted price increase of approximately $154,829 for each unit increase in grade.\n",
+    "\n",
+    "\n",
+    "sqft_basement: For each additional square foot of basement space, the predicted price increases by approximately $14,435.\n",
+    "\n",
+    "\n",
+    "yr_built: Each additional year of age decreases the predicted price by approximately $108,064.\n",
+    "\n",
+    "\n",
+    "yr_renovated: For each year renovated, the predicted price increases by approximately $7,255.\n",
+    "\n",
+    "\n",
+    "sqft_living15: For each additional square foot of living space in the nearest 15 neighbors' homes, the predicted price increases by approximately $25,766.\n",
+    "\n",
+    "\n",
+    "These coefficients indicate the strength and direction of the relationship between each feature and the target variable, holding other features constant. For example, features like square footage of living space, grade, and whether the property has a waterfront view have a substantial positive impact on the predicted housing price, while the year the property was built has a negative impact.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.641\n",
+      "Model:                            OLS   Adj. R-squared:                  0.641\n",
+      "Method:                 Least Squares   F-statistic:                     3768.\n",
+      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        13:34:57   Log-Likelihood:            -2.9014e+05\n",
+      "No. Observations:               21142   AIC:                         5.803e+05\n",
+      "Df Residuals:                   21131   BIC:                         5.804e+05\n",
+      "Df Model:                          10                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "=================================================================================\n",
+      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "---------------------------------------------------------------------------------\n",
+      "const          6.604e+06   1.41e+05     46.979      0.000    6.33e+06    6.88e+06\n",
+      "bathrooms       3.47e+04   3547.740      9.781      0.000    2.77e+04    4.17e+04\n",
+      "sqft_living     129.1822      3.765     34.313      0.000     121.803     136.562\n",
+      "floors         3.576e+04   3886.909      9.201      0.000    2.81e+04    4.34e+04\n",
+      "waterfront     7.828e+05   1.88e+04     41.703      0.000    7.46e+05     8.2e+05\n",
+      "condition      1.788e+04   2568.771      6.961      0.000    1.28e+04    2.29e+04\n",
+      "grade          1.319e+05   2290.923     57.586      0.000    1.27e+05    1.36e+05\n",
+      "sqft_basement    27.2065      4.581      5.939      0.000      18.228      36.185\n",
+      "yr_built      -3728.7602     71.951    -51.823      0.000   -3869.790   -3587.730\n",
+      "yr_renovated     18.3132      4.407      4.156      0.000       9.676      26.950\n",
+      "sqft_living15    34.6185      3.669      9.435      0.000      27.427      41.810\n",
+      "==============================================================================\n",
+      "Omnibus:                    16539.863   Durbin-Watson:                   1.978\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1296266.479\n",
+      "Skew:                           3.184   Prob(JB):                         0.00\n",
+      "Kurtosis:                      40.828   Cond. No.                     3.35e+05\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, 3.35e+05. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fit the OLS model\n",
+    "model = sm.OLS(y, X).fit()\n",
+    "\n",
+    "# Get the summary\n",
+    "summary = model.summary()\n",
+    "\n",
+    "# Print the summary\n",
+    "print(summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared (R²): The coefficient of determination is 0.641, indicating that approximately 64.1% of the variance in the housing prices is explained by the independent variables included in the model.\n",
+    "\n",
+    "\n",
+    "Adjusted R-squared: The adjusted R-squared is also 0.641, which adjusts for the number of predictors in the model. It's useful when comparing models with different numbers of predictors.\n",
+    "\n",
+    "\n",
+    "F-statistic: The F-statistic is 3768, with a p-value close to zero, indicating that the overall model is statistically significant.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The standard errors, condition number, and other diagnostic information are also provided. \n",
+    "The condition number being large (3.35e+05) suggests that there may be strong multicollinearity or other numerical issues in the model. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
+      "  order = pd.unique(vector)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "inf_coefs = list(zip(coefficients[\"Feature\"], coefficients[\"Coefficient\"]))\n",
+    "inf_coefs.sort(key=lambda x: abs(x[1]), reverse=True)  # Sort coefficients by absolute value\n",
+    "\n",
+    "# Create a color palette with the specified color\n",
+    "color = \"#589aff\"\n",
+    "colors = [color if coef[1] > 0 else \"lightgray\" for coef in inf_coefs]\n",
+    "\n",
+    "# Create the bar plot\n",
+    "fig, ax = plt.subplots(figsize=(18, 8))\n",
+    "ax = sns.barplot(x=[x[0] for x in inf_coefs], y=[x[1] for x in inf_coefs], palette=colors)\n",
+    "plt.xticks(rotation=45)\n",
+    "ax.set_ylabel(\"Price Coefficients\", fontsize=15)\n",
+    "ax.set_xlabel(\"House Features\", fontsize=15)\n",
+    "ax.set_title(\"House Features and Sale Prices\", fontsize=20);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.\n",
+    "\n",
+    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
+    "\n",
+    "\n",
+    "Addressing negative correlations in house prices, such as with the year built, is crucial. Older properties often have lower prices due to depreciation and maintenance issues. However, strategic renovations, updates, and modernization efforts offer opportunities to increase their value. Homeowners and investors can enhance the appeal and value of older properties in the market by undertaking such initiatives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESIDUALS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot is useful in understanding the assumptions of linear regression and detecting violations. The ideal case is to see a random distribution of residuals around the y-axis, centered around zero. This suggests that the residuals are normally distributed, and there are no systematic patterns in the errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# POLYNOMIAL REGRESSION."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Polynomial regression is a type of regression analysis where the relationship between the independent variable (or variables) and the dependent variable is modeled as an nth degree polynomial. Unlike simple linear regression, which assumes a linear relationship between the variables, polynomial regression can capture more complex relationships by introducing polynomial terms.\n",
+    "\n",
+    "In polynomial regression, the relationship between the independent variable \n",
+    "𝑥\n",
+    "x and the dependent variable \n",
+    "𝑦\n",
+    "y is modeled as:\n",
+    "\n",
+    "𝑦\n",
+    "\n",
+    "=\n",
+    "𝛽\n",
+    "0\n",
+    "+\n",
+    "𝛽\n",
+    "1\n",
+    "𝑥\n",
+    "+\n",
+    "𝛽\n",
+    "2\n",
+    "𝑥\n",
+    "2\n",
+    "+\n",
+    "𝛽\n",
+    "3\n",
+    "𝑥\n",
+    "3\n",
+    "+\n",
+    ".\n",
+    ".\n",
+    ".\n",
+    "+\n",
+    "𝛽\n",
+    "𝑛\n",
+    "𝑥\n",
+    "𝑛\n",
+    "+\n",
+    "𝜀\n",
+    "y=β \n",
+    "0\n",
+    "​\n",
+    " +β \n",
+    "1\n",
+    "​\n",
+    " x+β \n",
+    "2\n",
+    "​\n",
+    " x \n",
+    "2\n",
+    " +β \n",
+    "3\n",
+    "​\n",
+    " x \n",
+    "3\n",
+    " +...+β \n",
+    "n\n",
+    "​\n",
+    " x \n",
+    "n\n",
+    " +ε"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Model (Degree 2)- MSE: 35637716070.71762\n",
+      "Polynomial Model (Degree 2)- R-squared: 0.7321925161881991\n"
+     ]
+    }
+   ],
+   "source": [
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "    \n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Polynomial Regression\n",
+    "# Choose the degree of the polynomial\n",
+    "degree = 2\n",
+    "\n",
+    "# Create polynomial features\n",
+    "poly = PolynomialFeatures(degree)\n",
+    "X_train_poly = poly.fit_transform(X_train)\n",
+    "X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "# Build a polynomial regression model\n",
+    "poly_model = LinearRegression()\n",
+    "poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "# Evaluate the polynomial model\n",
+    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
+    "r2_poly = r2_score(y_test, y_pred_poly)\n",
+    "print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
+    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean Squared Error (MSE): This value represents the average squared difference between the actual house prices and the predicted prices by the polynomial model.\n",
+    " A lower MSE indicates better performance, and in this case, the MSE is lower than the previous model's MSE, suggesting that the polynomial model fits the data better.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "Generally,The \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  value for the first model is 0.641, indicating that approximately 64.1% of the variance in housing prices is explained by the independent variables included in the model. On the other hand, the \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  value for the polynomial model of degree 2 is 0.732, suggesting that approximately 73.2% of the variance in housing prices is explained by this polynomial model.\n",
+    "\n",
+    "Comparing the two \n",
+    "𝑅\n",
+    "2\n",
+    "R \n",
+    "2\n",
+    "  values, we can see that the polynomial model of degree 2 explains more variance in housing prices compared to the first model. This suggests that the polynomial model provides a better fit to the data and captures more of the variability in housing prices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RESIDUALS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred_poly\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred_poly, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.stats.diagnostic import het_breuschpagan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TEST FOR HOMOSCEDASCITICY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breusch-Pagan test p-value: 3.975373048166964e-258\n"
+     ]
+    }
+   ],
+   "source": [
+    "poly = PolynomialFeatures(degree)\n",
+    "X_train_poly = poly.fit_transform(X_train)\n",
+    "X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "# Build a polynomial regression model\n",
+    "poly_model = LinearRegression()\n",
+    "poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test - y_pred_poly\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Perform Breusch-Pagan test\n",
+    "lm, lm_p_value, fvalue, f_p_value = het_breuschpagan(residuals, X_test_poly)\n",
+    "print(\"Breusch-Pagan test p-value:\", lm_p_value)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.\n",
+    "\n",
+    "Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Log transformation.\n",
+    "Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. \n",
+    "Common transformations include taking the natural logarithm, square root, or reciprocal of the variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Log transformation of the multiple linear regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error (Log Transformed): 0.09804039958945562\n",
+      "R-squared (Log Transformed): 0.6469123715948973\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Log transformation of features and target variable\n",
+    "X_log = np.log1p(X)\n",
+    "y_log = np.log1p(y)\n",
+    "\n",
+    "# Split the log-transformed data into training and testing sets\n",
+    "X_train_log, X_test_log, y_train_log, y_test_log = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Standardize the log-transformed features\n",
+    "scaler_log = StandardScaler()\n",
+    "X_train_scaled_log = scaler_log.fit_transform(X_train_log)\n",
+    "X_test_scaled_log = scaler_log.transform(X_test_log)\n",
+    "\n",
+    "# Build a linear regression model on the log-transformed data\n",
+    "model_log = LinearRegression()\n",
+    "model_log.fit(X_train_scaled_log, y_train_log)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_log = model_log.predict(X_test_scaled_log)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "mse_log = mean_squared_error(y_test_log, y_pred_log)\n",
+    "r2_log = r2_score(y_test_log, y_pred_log)\n",
+    "\n",
+    "# Display results\n",
+    "print(\"Mean Squared Error (Log Transformed):\", mse_log)\n",
+    "print(\"R-squared (Log Transformed):\", r2_log)\n",
+    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model_log.coef_})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals_log = y_test_log - y_pred_log\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred_log, residuals_log, color='blue',)\n",
+    "plt.title('Residuals Plot (Log Transformed)')\n",
+    "plt.xlabel('Predicted Values (Log Transformed)')\n",
+    "plt.ylabel('Residuals (Log Transformed)')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot house prices against coefficients\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.barh(X.columns, model_log.coef_)\n",
+    "plt.xlabel('Coefficient Value')\n",
+    "plt.ylabel('Features')\n",
+    "plt.title('House Prices vs. Coefficients of Variables')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "visualization of the positive and negative coefficient variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Log transformation of the polynomial model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Polynomial Model (Degree 2)- MSE: 37774083176.83915\n",
+      "Polynomial Model (Degree 2)- R-squared: 0.7161383140038227\n",
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  price   R-squared:                       0.679\n",
+      "Model:                            OLS   Adj. R-squared:                  0.678\n",
+      "Method:                 Least Squares   F-statistic:                     557.0\n",
+      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        12:27:46   Log-Likelihood:                -3540.2\n",
+      "No. Observations:               16913   AIC:                             7210.\n",
+      "Df Residuals:                   16848   BIC:                             7713.\n",
+      "Df Model:                          64                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       4987.9435    522.414      9.548      0.000    3963.957    6011.930\n",
+      "x1          3457.3790    362.110      9.548      0.000    2747.606    4167.152\n",
+      "x2           -16.5312      7.635     -2.165      0.030     -31.497      -1.566\n",
+      "x3           -16.9803      5.626     -3.018      0.003     -28.008      -5.953\n",
+      "x4           -59.7513      8.202     -7.285      0.000     -75.827     -43.675\n",
+      "x5           -66.7001     16.473     -4.049      0.000     -98.988     -34.412\n",
+      "x6            21.7086      8.506      2.552      0.011       5.036      38.381\n",
+      "x7            56.1227     10.699      5.246      0.000      35.152      77.093\n",
+      "x8             0.1415      0.453      0.313      0.755      -0.745       1.028\n",
+      "x9         -1530.3915    159.119     -9.618      0.000   -1842.282   -1218.501\n",
+      "x10           -7.2389      1.397     -5.181      0.000      -9.977      -4.500\n",
+      "x11           27.1030      5.183      5.229      0.000      16.943      37.263\n",
+      "x12         2396.4725    250.995      9.548      0.000    1904.495    2888.450\n",
+      "x13          -11.4586      5.292     -2.165      0.030     -21.832      -1.085\n",
+      "x14          -11.7699      3.900     -3.018      0.003     -19.414      -4.126\n",
+      "x15          -41.4165      5.685     -7.285      0.000     -52.560     -30.273\n",
+      "x16          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
+      "x17           15.0473      5.896      2.552      0.011       3.491      26.604\n",
+      "x18           38.9013      7.416      5.246      0.000      24.366      53.437\n",
+      "x19            0.0981      0.314      0.313      0.755      -0.517       0.713\n",
+      "x20        -1060.7866    110.293     -9.618      0.000   -1276.973    -844.601\n",
+      "x21           -5.0176      0.968     -5.181      0.000      -6.916      -3.119\n",
+      "x22           18.7864      3.593      5.229      0.000      11.744      25.829\n",
+      "x23            0.0476      0.079      0.603      0.546      -0.107       0.202\n",
+      "x24            0.2000      0.083      2.399      0.016       0.037       0.363\n",
+      "x25           -0.4148      0.109     -3.823      0.000      -0.628      -0.202\n",
+      "x26            0.1250      0.245      0.511      0.609      -0.355       0.605\n",
+      "x27            0.1981      0.122      1.628      0.104      -0.040       0.437\n",
+      "x28            0.2251      0.161      1.396      0.163      -0.091       0.541\n",
+      "x29           -0.0032      0.006     -0.531      0.595      -0.015       0.009\n",
+      "x30            3.1411      1.477      2.127      0.033       0.247       6.036\n",
+      "x31           -0.0103      0.012     -0.883      0.377      -0.033       0.013\n",
+      "x32           -0.1564      0.078     -2.000      0.046      -0.310      -0.003\n",
+      "x33            0.0859      0.039      2.183      0.029       0.009       0.163\n",
+      "x34           -0.0407      0.079     -0.516      0.606      -0.195       0.114\n",
+      "x35            0.3258      0.195      1.668      0.095      -0.057       0.709\n",
+      "x36           -0.2649      0.091     -2.905      0.004      -0.444      -0.086\n",
+      "x37            0.1059      0.111      0.957      0.339      -0.111       0.323\n",
+      "x38           -0.0112      0.005     -2.119      0.034      -0.022      -0.001\n",
+      "x39            3.2391      1.094      2.960      0.003       1.094       5.384\n",
+      "x40            0.0084      0.008      0.989      0.323      -0.008       0.025\n",
+      "x41           -0.0497      0.054     -0.924      0.355      -0.155       0.056\n",
+      "x42            0.6010      0.091      6.569      0.000       0.422       0.780\n",
+      "x43            0.1950      0.270      0.721      0.471      -0.335       0.725\n",
+      "x44            0.3964      0.139      2.845      0.004       0.123       0.670\n",
+      "x45           -0.0224      0.156     -0.143      0.886      -0.329       0.284\n",
+      "x46            0.0471      0.006      7.361      0.000       0.035       0.060\n",
+      "x47           11.9015      1.585      7.511      0.000       8.796      15.007\n",
+      "x48            0.0005      0.012      0.044      0.965      -0.023       0.024\n",
+      "x49           -0.3608      0.079     -4.586      0.000      -0.515      -0.207\n",
+      "x50          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
+      "x51           -0.2814      0.262     -1.074      0.283      -0.795       0.232\n",
+      "x52           -1.4097      0.386     -3.649      0.000      -2.167      -0.652\n",
+      "x53           -0.0189      0.015     -1.223      0.221      -0.049       0.011\n",
+      "x54           17.2366      4.274      4.033      0.000       8.859      25.614\n",
+      "x55           -0.0160      0.016     -1.020      0.308      -0.047       0.015\n",
+      "x56            0.1612      0.187      0.861      0.389      -0.206       0.528\n",
+      "x57           -0.0348      0.081     -0.430      0.667      -0.193       0.124\n",
+      "x58           -0.5518      0.172     -3.212      0.001      -0.888      -0.215\n",
+      "x59            0.0147      0.008      1.939      0.053      -0.000       0.030\n",
+      "x60           -4.5317      1.668     -2.716      0.007      -7.802      -1.261\n",
+      "x61           -0.0425      0.016     -2.603      0.009      -0.074      -0.010\n",
+      "x62            0.6652      0.082      8.073      0.000       0.504       0.827\n",
+      "x63            0.7108      0.132      5.394      0.000       0.452       0.969\n",
+      "x64           -0.0074      0.009     -0.830      0.406      -0.025       0.010\n",
+      "x65          -10.6811      2.084     -5.125      0.000     -14.766      -6.596\n",
+      "x66           -0.0058      0.016     -0.357      0.721      -0.038       0.026\n",
+      "x67           -0.4727      0.098     -4.817      0.000      -0.665      -0.280\n",
+      "x68           -0.0051      0.002     -2.881      0.004      -0.009      -0.002\n",
+      "x69           -0.0156      0.088     -0.178      0.859      -0.188       0.157\n",
+      "x70        -3.158e-05      0.001     -0.045      0.964      -0.001       0.001\n",
+      "x71           -0.0010      0.004     -0.221      0.825      -0.009       0.007\n",
+      "x72          150.7551     15.531      9.707      0.000     120.313     181.198\n",
+      "x73            0.7643      0.175      4.359      0.000       0.421       1.108\n",
+      "x74           -5.7615      1.013     -5.690      0.000      -7.746      -3.777\n",
+      "x75            0.6353      0.261      2.434      0.015       0.124       1.147\n",
+      "x76            0.0173      0.007      2.394      0.017       0.003       0.031\n",
+      "x77            0.3068      0.032      9.733      0.000       0.245       0.369\n",
+      "==============================================================================\n",
+      "Omnibus:                       86.286   Durbin-Watson:                   2.008\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              100.078\n",
+      "Skew:                          -0.119   Prob(JB):                     1.85e-22\n",
+      "Kurtosis:                       3.291   Cond. No.                     1.52e+16\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The smallest eigenvalue is 1.69e-24. This might indicate that there are\n",
+      "strong multicollinearity problems or that the design matrix is singular.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Define function to keep only numeric columns\n",
+    "def only_numeric(df):\n",
+    "    return df.select_dtypes(include=[np.number])\n",
+    "\n",
+    "features = ['bathrooms', 'sqft_living',\n",
+    "            'floors', 'waterfront', 'condition', 'grade',\n",
+    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
+    "target = 'price'\n",
+    "\n",
+    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
+    "\n",
+    "# Keep only numeric columns\n",
+    "housing_data_numeric = only_numeric(housing_data)\n",
+    "\n",
+    "# Check if all features exist in the DataFrame\n",
+    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
+    "if missing_features:\n",
+    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
+    "else:\n",
+    "    # Extract features and target variable\n",
+    "    X = sm.add_constant(housing_data_numeric[features])\n",
+    "    y = housing_data_numeric[target]\n",
+    "    \n",
+    "    # Log transform features and target variable\n",
+    "    X_log = np.log(X + 1)  # Adding 1 to avoid log(0)\n",
+    "    y_log = np.log(y)\n",
+    "    \n",
+    "    # Split the data into training and testing sets\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Polynomial Regression\n",
+    "    # Choose the degree of the polynomial\n",
+    "    degree = 2\n",
+    "\n",
+    "    # Create polynomial features\n",
+    "    poly = PolynomialFeatures(degree)\n",
+    "    X_train_poly = poly.fit_transform(X_train)\n",
+    "    X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "    # Build a polynomial regression model\n",
+    "    poly_model = LinearRegression()\n",
+    "    poly_model.fit(X_train_poly, y_train)\n",
+    "\n",
+    "    # Make predictions on the test set\n",
+    "    y_pred_poly = poly_model.predict(X_test_poly)\n",
+    "\n",
+    "    # Reverse log transformation for evaluation\n",
+    "    y_pred = np.exp(y_pred_poly)\n",
+    "    y_test_original = np.exp(y_test)\n",
+    "\n",
+    "    # Evaluate the polynomial model\n",
+    "    mse_poly = mean_squared_error(y_test_original, y_pred)\n",
+    "    r2_poly = r2_score(y_test_original, y_pred)\n",
+    "    print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
+    "    print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
+    "    \n",
+    "    # Convert to DataFrame for summary\n",
+    "    #X_poly_df = pd.DataFrame(X_train_poly, columns=poly.get_feature_names(features))\n",
+    "\n",
+    "    # Fit the OLS model\n",
+    "    model = sm.OLS(y_train,X_train_poly)\n",
+    "    results = model.fit()\n",
+    "\n",
+    "    # Print the summary\n",
+    "    print(results.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.\n",
+    "\n",
+    "MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.\n",
+    "\n",
+    "Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example:\n",
+    "The coefficient for const (intercept) is 4987.94.\n",
+    "The coefficient for x1 (bathrooms) is 3457.38.\n",
+    "The coefficient for x2 (sqft_living) is -16.53.\n",
+    "The coefficient for x3 (floors) is -16.98.\n",
+    "The coefficient for x4 (waterfront) is -59.75.\n",
+    "The coefficient for x5 (condition) is -66.70.\n",
+    "The coefficient for x6 (grade) is 21.71.\n",
+    "The coefficient for x7 (sqft_basement) is 56.12.\n",
+    "The coefficient for x8 (yr_built) and other coefficients follow.\n",
+    "\n",
+    "P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.\n",
+    "\n",
+    "\n",
+    "F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.\n",
+    "\n",
+    "\n",
+    "Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Calculate residuals\n",
+    "residuals = y_test_original - y_pred\n",
+    "\n",
+    "# Plot residuals vs predicted values\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(y_pred, residuals, color='blue')\n",
+    "plt.title('Residuals Plot')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y=0, color='red', linestyle='--')\n",
+    "plt.grid(True)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# REGRESSION RESULTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
+    "\n",
+    "Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73\n",
+    "\n",
+    "The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assumptions\n",
+    "\n",
+    "Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.\n",
+    "\n",
+    "Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.\n",
+    "\n",
+    "Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.\n",
+    "\n",
+    "No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Limitations\n",
+    "Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:\n",
+    "\n",
+    "Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.\n",
+    "\n",
+    "\n",
+    "Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.\n",
+    "\n",
+    "Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.\n",
+    "\n",
+    "Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recommedations\n",
+    "# Statistical recommedations.\n",
+    "To mitigate multicollinearity, strategies such as feature selection, principal component analysis (PCA), or regularization methods like ridge regression or Lasso regression can be employed. These techniques prioritize essential predictors and enhance the model's interpretability while stabilizing it against multicollinearity.\n",
+    "\n",
+    "\n",
+    "\n",
+    "Before opting for polynomial regression, it's essential to validate the assumption of linearity between predictors and the target variable. If this assumption doesn't hold, alternative regression techniques such as generalized additive models (GAMs) or spline regression should be considered to better capture intricate relationships.\n",
+    "\n",
+    "\n",
+    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis\n",
+    "# Recommedation to real estate clients:Home owners and investors.\n",
+    "\n",
+    "\n",
+    "1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.\n",
+    "\n",
+    "\n",
+    "2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.\n",
+    "\n",
+    "\n",
+    "3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.\n",
+    "\n",
+    "\n",
+    "4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.\n",
+    "\n",
+    "\n",
+    "5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.\n",
+    "\n",
+    "\n",
+    "6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CONCLUSION\n",
+    "\n",
+    "Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.\n",
+    "\n",
+    "\n",
+    "Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.\n",
+    "\n",
+    "\n",
+    "Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.\n",
+    "\n",
+    "\n",
+    "Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
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+}
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HcmV?d00001

diff --git a/student.ipynb b/student.ipynb
index ee2a22a0..db69c240 100644
--- a/student.ipynb
+++ b/student.ipynb
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -501,7 +501,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -525,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -688,7 +688,7 @@
        "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
       ]
      },
-     "execution_count": 72,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -854,104 +854,104 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>20146</th>\n",
-       "      <td>7967000130</td>\n",
-       "      <td>4/1/2015</td>\n",
-       "      <td>370228.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13983</th>\n",
-       "      <td>7338000150</td>\n",
-       "      <td>1/29/2015</td>\n",
-       "      <td>160000.0</td>\n",
+       "      <th>1039</th>\n",
+       "      <td>16000435</td>\n",
+       "      <td>3/16/2015</td>\n",
+       "      <td>218500.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>4200</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>8961</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
        "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1983</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1390</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>1949</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>4200</td>\n",
+       "      <td>1502</td>\n",
+       "      <td>6798</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3605</th>\n",
-       "      <td>8658303585</td>\n",
-       "      <td>8/7/2014</td>\n",
-       "      <td>252500.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>900</td>\n",
-       "      <td>7500</td>\n",
+       "      <th>8661</th>\n",
+       "      <td>766800090</td>\n",
+       "      <td>5/25/2014</td>\n",
+       "      <td>195000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>1570</td>\n",
+       "      <td>8459</td>\n",
        "      <td>1.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>900</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>7 Average</td>\n",
+       "      <td>1570</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1961</td>\n",
+       "      <td>1991</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1190</td>\n",
-       "      <td>10000</td>\n",
+       "      <td>1650</td>\n",
+       "      <td>8844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9229</th>\n",
-       "      <td>1787250210</td>\n",
-       "      <td>12/22/2014</td>\n",
-       "      <td>379000.0</td>\n",
+       "      <th>12936</th>\n",
+       "      <td>4137000540</td>\n",
+       "      <td>1/22/2015</td>\n",
+       "      <td>329950.0</td>\n",
        "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>5225</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2140</td>\n",
+       "      <td>8874</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>Average</td>\n",
        "      <td>8 Good</td>\n",
-       "      <td>2410</td>\n",
+       "      <td>2140</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2001</td>\n",
+       "      <td>1986</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>5378</td>\n",
+       "      <td>2140</td>\n",
+       "      <td>8789</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11243</th>\n",
-       "      <td>1843200350</td>\n",
-       "      <td>7/22/2014</td>\n",
-       "      <td>150000.0</td>\n",
-       "      <td>2</td>\n",
+       "      <th>6875</th>\n",
+       "      <td>11501310</td>\n",
+       "      <td>11/21/2014</td>\n",
+       "      <td>715000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.25</td>\n",
+       "      <td>3060</td>\n",
+       "      <td>9055</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NO</td>\n",
+       "      <td>Average</td>\n",
+       "      <td>10 Very Good</td>\n",
+       "      <td>2460</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>1994</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2990</td>\n",
+       "      <td>9598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7864</th>\n",
+       "      <td>3445400120</td>\n",
+       "      <td>7/25/2014</td>\n",
+       "      <td>267500.0</td>\n",
+       "      <td>3</td>\n",
        "      <td>1.50</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1934</td>\n",
+       "      <td>1390</td>\n",
+       "      <td>2153</td>\n",
        "      <td>2.0</td>\n",
        "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
+       "      <td>Average</td>\n",
        "      <td>7 Average</td>\n",
-       "      <td>1360</td>\n",
+       "      <td>1390</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1978</td>\n",
+       "      <td>2001</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1898</td>\n",
+       "      <td>1100</td>\n",
+       "      <td>2617</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -959,28 +959,28 @@
       ],
       "text/plain": [
        "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "20146  7967000130    4/1/2015  370228.0         4       3.00         2050   \n",
-       "13983  7338000150   1/29/2015  160000.0         2       1.00         1070   \n",
-       "3605   8658303585    8/7/2014  252500.0         2       1.00          900   \n",
-       "9229   1787250210  12/22/2014  379000.0         4       2.75         2410   \n",
-       "11243  1843200350   7/22/2014  150000.0         2       1.50         1360   \n",
+       "1039     16000435   3/16/2015  218500.0         2       1.00         1600   \n",
+       "8661    766800090   5/25/2014  195000.0         3       1.75         1570   \n",
+       "12936  4137000540   1/22/2015  329950.0         4       2.25         2140   \n",
+       "6875     11501310  11/21/2014  715000.0         3       3.25         3060   \n",
+       "7864   3445400120   7/25/2014  267500.0         3       1.50         1390   \n",
        "\n",
-       "       sqft_lot  floors waterfront condition          grade  sqft_above  \\\n",
-       "20146      4000     2.0         NO   Average         8 Good        2050   \n",
-       "13983      4200     1.0         NO      Good  6 Low Average        1070   \n",
-       "3605       7500     1.0         NO      Good  6 Low Average         900   \n",
-       "9229       5225     2.0         NO   Average         8 Good        2410   \n",
-       "11243      1934     2.0         NO      Good      7 Average        1360   \n",
+       "       sqft_lot  floors waterfront condition         grade  sqft_above  \\\n",
+       "1039       8961     1.0         NO      Good     7 Average        1390   \n",
+       "8661       8459     1.0         NO   Average     7 Average        1570   \n",
+       "12936      8874     2.0        NaN   Average        8 Good        2140   \n",
+       "6875       9055     2.0         NO   Average  10 Very Good        2460   \n",
+       "7864       2153     2.0         NO   Average     7 Average        1390   \n",
        "\n",
        "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "20146           0.0      2014           0.0           2050        4000  \n",
-       "13983           0.0      1983           0.0           1150        4200  \n",
-       "3605            0.0      1961           0.0           1190       10000  \n",
-       "9229            0.0      2001           0.0           2300        5378  \n",
-       "11243           0.0      1978           0.0           1360        1898  "
+       "1039          210.0      1949           0.0           1502        6798  \n",
+       "8661            0.0      1991           0.0           1650        8844  \n",
+       "12936           0.0      1986           0.0           2140        8789  \n",
+       "6875          600.0      1994           0.0           2990        9598  \n",
+       "7864            0.0      2001           0.0           1100        2617  "
       ]
      },
-     "execution_count": 75,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1054,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -1097,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1119,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1132,7 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1142,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -1162,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
@@ -1181,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1192,7 +1192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -1267,104 +1267,104 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>4511</th>\n",
-       "      <td>723049333</td>\n",
-       "      <td>2015-04-05</td>\n",
-       "      <td>285000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1490</td>\n",
-       "      <td>10367</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>7860</th>\n",
+       "      <td>1438000110</td>\n",
+       "      <td>2014-06-03</td>\n",
+       "      <td>580135.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>3150</td>\n",
+       "      <td>5886</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1010</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1957</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3150</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>8254</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>5886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1181</th>\n",
-       "      <td>7625700935</td>\n",
-       "      <td>2014-06-05</td>\n",
-       "      <td>875000.0</td>\n",
+       "      <th>650</th>\n",
+       "      <td>4338800370</td>\n",
+       "      <td>2014-11-17</td>\n",
+       "      <td>220000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>3250</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>6020</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>750.0</td>\n",
-       "      <td>2001</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1650</td>\n",
-       "      <td>6000</td>\n",
+       "      <td>1944</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>8640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1875</th>\n",
-       "      <td>1853081000</td>\n",
-       "      <td>2014-07-17</td>\n",
-       "      <td>820000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>6137</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>14316</th>\n",
+       "      <td>8731980880</td>\n",
+       "      <td>2014-12-22</td>\n",
+       "      <td>340000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2180</td>\n",
+       "      <td>8000</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>7.0</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
+       "      <td>1630</td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>1975</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3170</td>\n",
-       "      <td>6285</td>\n",
+       "      <td>2310</td>\n",
+       "      <td>8000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12273</th>\n",
-       "      <td>7016200030</td>\n",
-       "      <td>2015-03-20</td>\n",
-       "      <td>480000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>7966</td>\n",
+       "      <th>1562</th>\n",
+       "      <td>7522500070</td>\n",
+       "      <td>2015-02-04</td>\n",
+       "      <td>610000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>5750</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>5.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>1970</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>760.0</td>\n",
+       "      <td>1947</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>7500</td>\n",
+       "      <td>1320</td>\n",
+       "      <td>5625</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8546</th>\n",
-       "      <td>7981900110</td>\n",
-       "      <td>2014-10-03</td>\n",
-       "      <td>350000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>3175</td>\n",
+       "      <th>12233</th>\n",
+       "      <td>2767601805</td>\n",
+       "      <td>2015-02-13</td>\n",
+       "      <td>600000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1370</td>\n",
+       "      <td>5000</td>\n",
        "      <td>1.5</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>960.0</td>\n",
-       "      <td>1966</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1370</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>3175</td>\n",
+       "      <td>1905</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1500</td>\n",
+       "      <td>5000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1372,28 +1372,28 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "4511    723049333 2015-04-05  285000.0         3       1.50         1490   \n",
-       "1181   7625700935 2014-06-05  875000.0         3       3.50         3250   \n",
-       "1875   1853081000 2014-07-17  820000.0         5       2.75         2830   \n",
-       "12273  7016200030 2015-03-20  480000.0         4       2.50         2080   \n",
-       "8546   7981900110 2014-10-03  350000.0         4       2.75         2300   \n",
+       "7860   1438000110 2014-06-03  580135.0         4       2.50         3150   \n",
+       "650    4338800370 2014-11-17  220000.0         3       1.00         1000   \n",
+       "14316  8731980880 2014-12-22  340000.0         4       2.25         2180   \n",
+       "1562   7522500070 2015-02-04  610000.0         3       1.00         1800   \n",
+       "12233  2767601805 2015-02-13  600000.0         2       1.00         1370   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "4511      10367     1.0           0        3.0    5.0        1010   \n",
-       "1181       6000     2.0           0        3.0    8.0        2500   \n",
-       "1875       6137     2.0           0        3.0    7.0        2830   \n",
-       "12273      7966     1.0           0        3.0    5.0        1200   \n",
-       "8546       3175     1.5           0        3.0    4.0        1340   \n",
+       "7860       5886     2.0           0        3.0    6.0        3150   \n",
+       "650        6020     1.0           0        3.0    4.0        1000   \n",
+       "14316      8000     1.0           0        4.0    7.0        1630   \n",
+       "1562       5750     1.0           0        3.0    5.0        1040   \n",
+       "12233      5000     1.5           0        3.0    5.0        1370   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "4511           480.0      1957           0.0           1000        8254  \n",
-       "1181           750.0      2001           0.0           1650        6000  \n",
-       "1875             0.0      2010           0.0           3170        6285  \n",
-       "12273          880.0      1970           0.0           1920        7500  \n",
-       "8546           960.0      1966           0.0           1260        3175  "
+       "7860             0.0      2014           0.0           2650        5886  \n",
+       "650              0.0      1944           0.0           1300        8640  \n",
+       "14316          550.0      1975           0.0           2310        8000  \n",
+       "1562           760.0      1947           0.0           1320        5625  \n",
+       "12233            0.0      1905           0.0           1500        5000  "
       ]
      },
-     "execution_count": 84,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1424,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +1497,7 @@
        "4         37             0.0     11440.0"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1542,7 +1542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -1590,119 +1590,119 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>8540</th>\n",
-       "      <td>3034200366</td>\n",
-       "      <td>2014-12-03</td>\n",
-       "      <td>409000.0</td>\n",
+       "      <th>13945</th>\n",
+       "      <td>8651540040</td>\n",
+       "      <td>2014-07-18</td>\n",
+       "      <td>549000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>9065</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>1920</td>\n",
+       "      <td>10961</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>1440</td>\n",
+       "      <td>1920</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1972</td>\n",
+       "      <td>1981</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>8812</td>\n",
-       "      <td>52</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>10706</td>\n",
+       "      <td>43</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11945.0</td>\n",
+       "      <td>14801.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19433</th>\n",
-       "      <td>2023059052</td>\n",
-       "      <td>2015-05-04</td>\n",
-       "      <td>450000.0</td>\n",
+       "      <th>279</th>\n",
+       "      <td>9412900055</td>\n",
+       "      <td>2015-05-05</td>\n",
+       "      <td>405000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1350</td>\n",
-       "      <td>92721</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2390</td>\n",
+       "      <td>6000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>1946</td>\n",
+       "      <td>1240</td>\n",
+       "      <td>1150.0</td>\n",
+       "      <td>1908</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1860</td>\n",
-       "      <td>8096</td>\n",
-       "      <td>78</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>116</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>95421.0</td>\n",
+       "      <td>10780.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7003</th>\n",
-       "      <td>7751800080</td>\n",
-       "      <td>2015-01-27</td>\n",
-       "      <td>465000.0</td>\n",
+       "      <th>14809</th>\n",
+       "      <td>7312200120</td>\n",
+       "      <td>2014-07-23</td>\n",
+       "      <td>450000.0</td>\n",
        "      <td>3</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>9879</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>10013</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1460</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1760</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1956</td>\n",
+       "      <td>1983</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1610</td>\n",
-       "      <td>10050</td>\n",
-       "      <td>68</td>\n",
+       "      <td>1810</td>\n",
+       "      <td>9768</td>\n",
+       "      <td>41</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12799.0</td>\n",
+       "      <td>13533.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10151</th>\n",
-       "      <td>1180002075</td>\n",
-       "      <td>2014-08-25</td>\n",
-       "      <td>235000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1210</td>\n",
+       "      <th>18432</th>\n",
+       "      <td>2484700155</td>\n",
+       "      <td>2014-10-14</td>\n",
+       "      <td>705000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2060</td>\n",
        "      <td>6000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1210</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1370</td>\n",
+       "      <td>690.0</td>\n",
+       "      <td>1954</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1930</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>6600</td>\n",
+       "      <td>70</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>94</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8420.0</td>\n",
+       "      <td>10120.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3506</th>\n",
-       "      <td>3449900090</td>\n",
-       "      <td>2015-04-10</td>\n",
-       "      <td>454200.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>12094</th>\n",
+       "      <td>3822200036</td>\n",
+       "      <td>2014-06-24</td>\n",
+       "      <td>257500.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>9265</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2630</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1180</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2004</td>\n",
+       "      <td>1940</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>20</td>\n",
+       "      <td>460</td>\n",
+       "      <td>18000</td>\n",
+       "      <td>84</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>10639.0</td>\n",
+       "      <td>11625.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1710,35 +1710,35 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "8540   3034200366 2014-12-03  409000.0         3       1.75         1440   \n",
-       "19433  2023059052 2015-05-04  450000.0         3       1.00         1350   \n",
-       "7003   7751800080 2015-01-27  465000.0         3       1.50         1460   \n",
-       "10151  1180002075 2014-08-25  235000.0         3       1.00         1210   \n",
-       "3506   3449900090 2015-04-10  454200.0         4       2.50         2630   \n",
+       "13945  8651540040 2014-07-18  549000.0         3       2.25         1920   \n",
+       "279    9412900055 2015-05-05  405000.0         3       1.75         2390   \n",
+       "14809  7312200120 2014-07-23  450000.0         3       2.25         1760   \n",
+       "18432  2484700155 2014-10-14  705000.0         4       2.00         2060   \n",
+       "12094  3822200036 2014-06-24  257500.0         2       2.00         1180   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "8540       9065     1.0           0        4.0    6.0        1440   \n",
-       "19433     92721     1.0           0        2.0    4.0        1200   \n",
-       "7003       9879     1.0           0        3.0    5.0        1460   \n",
-       "10151      6000     1.0           0        3.0    5.0        1210   \n",
-       "3506       5379     2.0           0        3.0    6.0        2630   \n",
+       "13945     10961     2.0           0        3.0    6.0        1920   \n",
+       "279        6000     1.0           0        3.0    4.0        1240   \n",
+       "14809     10013     2.0           0        4.0    6.0        1760   \n",
+       "18432      6000     1.0           0        4.0    6.0        1370   \n",
+       "12094      9265     1.0           0        3.0    5.0        1180   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "8540             0.0      1972           0.0           1990        8812   \n",
-       "19433          150.0      1946           0.0           1860        8096   \n",
-       "7003             0.0      1956           0.0           1610       10050   \n",
-       "10151            0.0      1930           0.0           1210        6000   \n",
-       "3506             0.0      2004           0.0           2630        5379   \n",
+       "13945            0.0      1981           0.0           2000       10706   \n",
+       "279           1150.0      1908           0.0           2020        6000   \n",
+       "14809            0.0      1983           0.0           1810        9768   \n",
+       "18432          690.0      1954           0.0           2060        6600   \n",
+       "12094            0.0      1940           0.0            460       18000   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  \n",
-       "8540          52             0.0     11945.0  \n",
-       "19433         78             0.0     95421.0  \n",
-       "7003          68             0.0     12799.0  \n",
-       "10151         94             0.0      8420.0  \n",
-       "3506          20             0.0     10639.0  "
+       "13945         43             0.0     14801.0  \n",
+       "279          116             0.0     10780.0  \n",
+       "14809         41             0.0     13533.0  \n",
+       "18432         70             0.0     10120.0  \n",
+       "12094         84             0.0     11625.0  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1749,7 +1749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -1820,7 +1820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -1828,13 +1828,7 @@
      "output_type": "stream",
      "text": [
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
       "  with pd.option_context('mode.use_inf_as_na', True):\n"
      ]
@@ -1896,7 +1890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -1949,7 +1943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1962,7 +1956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -1971,7 +1965,7 @@
        "Text(0.5, 0, 'Number of Bedrooms')"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2009,7 +2003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
@@ -2026,7 +2020,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2046,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -2095,123 +2089,123 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1188</th>\n",
-       "      <td>2624049185</td>\n",
-       "      <td>2014-09-09</td>\n",
-       "      <td>405000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1760</td>\n",
-       "      <td>5355</td>\n",
+       "      <th>7780</th>\n",
+       "      <td>1025079086</td>\n",
+       "      <td>2014-08-20</td>\n",
+       "      <td>365000.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>960</td>\n",
+       "      <td>223898</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1160</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>1956</td>\n",
+       "      <td>960</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1985</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1790</td>\n",
-       "      <td>6225</td>\n",
-       "      <td>68</td>\n",
+       "      <td>1830</td>\n",
+       "      <td>230868</td>\n",
+       "      <td>39</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8875.0</td>\n",
+       "      <td>225818.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20611</th>\n",
-       "      <td>2895800390</td>\n",
-       "      <td>2014-08-07</td>\n",
-       "      <td>359800.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>13342</th>\n",
+       "      <td>3558900450</td>\n",
+       "      <td>2014-07-11</td>\n",
+       "      <td>530000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>2130</td>\n",
+       "      <td>8640</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
+       "      <td>1430</td>\n",
+       "      <td>700.0</td>\n",
+       "      <td>1969</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>10</td>\n",
+       "      <td>2120</td>\n",
+       "      <td>8826</td>\n",
+       "      <td>55</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7092.0</td>\n",
+       "      <td>12900.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2209</th>\n",
-       "      <td>3438500339</td>\n",
-       "      <td>2014-05-26</td>\n",
-       "      <td>276000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>14479</th>\n",
+       "      <td>1041440360</td>\n",
+       "      <td>2015-01-05</td>\n",
+       "      <td>299999.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>4828</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0</td>\n",
        "      <td>3.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1140</td>\n",
+       "      <td>1981</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1960</td>\n",
+       "      <td>2013</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>64</td>\n",
+       "      <td>1981</td>\n",
+       "      <td>3783</td>\n",
+       "      <td>11</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7280.0</td>\n",
+       "      <td>8790.0</td>\n",
        "      <td>100K-300K</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18431</th>\n",
-       "      <td>4167960330</td>\n",
-       "      <td>2015-01-09</td>\n",
-       "      <td>270000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>7750</td>\n",
+       "      <th>3861</th>\n",
+       "      <td>7752000090</td>\n",
+       "      <td>2015-01-13</td>\n",
+       "      <td>635000.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>10050</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1820</td>\n",
+       "      <td>2400</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1992</td>\n",
+       "      <td>1957</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>8084</td>\n",
-       "      <td>32</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>10050</td>\n",
+       "      <td>67</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11390.0</td>\n",
-       "      <td>100K-300K</td>\n",
+       "      <td>14850.0</td>\n",
+       "      <td>600K-1M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11609</th>\n",
-       "      <td>1972201550</td>\n",
-       "      <td>2014-07-16</td>\n",
-       "      <td>565000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>2452</td>\n",
-       "      <td>1.5</td>\n",
+       "      <th>17604</th>\n",
+       "      <td>3876313170</td>\n",
+       "      <td>2014-12-09</td>\n",
+       "      <td>436000.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.25</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>8000</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0</td>\n",
        "      <td>4.0</td>\n",
        "      <td>...</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1906</td>\n",
+       "      <td>1350</td>\n",
+       "      <td>420.0</td>\n",
+       "      <td>1976</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1290</td>\n",
-       "      <td>3360</td>\n",
-       "      <td>118</td>\n",
+       "      <td>1850</td>\n",
+       "      <td>7875</td>\n",
+       "      <td>48</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5532.0</td>\n",
+       "      <td>11540.0</td>\n",
        "      <td>300K-600K</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2221,37 +2215,37 @@
       ],
       "text/plain": [
        "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "1188   2624049185 2014-09-09  405000.0         3       1.75         1760   \n",
-       "20611  2895800390 2014-08-07  359800.0         5       2.50         2170   \n",
-       "2209   3438500339 2014-05-26  276000.0         3       1.00         1140   \n",
-       "18431  4167960330 2015-01-09  270000.0         3       2.00         1820   \n",
-       "11609  1972201550 2014-07-16  565000.0         4       1.00         1540   \n",
+       "7780   1025079086 2014-08-20  365000.0         2       1.00          960   \n",
+       "13342  3558900450 2014-07-11  530000.0         4       2.25         2130   \n",
+       "14479  1041440360 2015-01-05  299999.0         4       2.50         1981   \n",
+       "3861   7752000090 2015-01-13  635000.0         4       1.75         2400   \n",
+       "17604  3876313170 2014-12-09  436000.0         3       2.25         1770   \n",
        "\n",
        "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "1188       5355     1.0           0        3.0  ...        1160   \n",
-       "20611      2752     2.0           0        3.0  ...        2170   \n",
-       "2209       5000     1.0           0        3.0  ...        1140   \n",
-       "18431      7750     1.0           0        3.0  ...        1820   \n",
-       "11609      2452     1.5           0        4.0  ...        1540   \n",
+       "7780     223898     1.0           0        3.0  ...         960   \n",
+       "13342      8640     1.0           0        3.0  ...        1430   \n",
+       "14479      4828     2.0           0        3.0  ...        1981   \n",
+       "3861      10050     1.0           0        5.0  ...        2400   \n",
+       "17604      8000     1.0           0        4.0  ...        1350   \n",
        "\n",
        "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "1188           600.0      1956           0.0           1790        6225   \n",
-       "20611            0.0      2014           0.0           1800        2752   \n",
-       "2209             0.0      1960           0.0           1140        5000   \n",
-       "18431            0.0      1992           0.0           2080        8084   \n",
-       "11609            0.0      1906           0.0           1290        3360   \n",
+       "7780             0.0      1985           0.0           1830      230868   \n",
+       "13342          700.0      1969           0.0           2120        8826   \n",
+       "14479            0.0      2013           0.0           1981        3783   \n",
+       "3861             0.0      1957           0.0           1680       10050   \n",
+       "17604          420.0      1976           0.0           1850        7875   \n",
        "\n",
        "       house_age  renovation_age  total_sqft  price_range  \n",
-       "1188          68             0.0      8875.0    300K-600K  \n",
-       "20611         10             0.0      7092.0    300K-600K  \n",
-       "2209          64             0.0      7280.0    100K-300K  \n",
-       "18431         32             0.0     11390.0    100K-300K  \n",
-       "11609        118             0.0      5532.0    300K-600K  \n",
+       "7780          39             0.0    225818.0    300K-600K  \n",
+       "13342         55             0.0     12900.0    300K-600K  \n",
+       "14479         11             0.0      8790.0    100K-300K  \n",
+       "3861          67             0.0     14850.0      600K-1M  \n",
+       "17604         48             0.0     11540.0    300K-600K  \n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2262,7 +2256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
@@ -2320,7 +2314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
@@ -2363,7 +2357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -2406,12 +2400,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1400x600 with 2 Axes>"
       ]
@@ -2467,7 +2461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -2533,7 +2527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -2820,7 +2814,7 @@
        "std          5.824659  4.156724e+04  "
       ]
      },
-     "execution_count": 31,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2879,7 +2873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -2906,7 +2900,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2938,7 +2932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -3143,7 +3137,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3173,7 +3167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -3183,13 +3177,7 @@
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
       "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
       "  with pd.option_context('mode.use_inf_as_na', True):\n",
       "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
@@ -3277,7 +3265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -3406,7 +3394,7 @@
        "P-value      2.417229e-17    2.430304e-10    1.000000  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3475,7 +3463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -3630,7 +3618,7 @@
        "16      total_sqft        inf"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3701,7 +3689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3718,7 +3706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3732,7 +3720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3771,7 +3759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3791,7 +3779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3854,7 +3842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
@@ -3886,7 +3874,7 @@
        " 262383.4993975565)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3944,7 +3932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [
     {
@@ -4037,7 +4025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -4210,7 +4198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
@@ -4223,7 +4211,7 @@
       "Model:                            OLS   Adj. R-squared:                  0.641\n",
       "Method:                 Least Squares   F-statistic:                     3768.\n",
       "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        13:34:57   Log-Likelihood:            -2.9014e+05\n",
+      "Time:                        17:52:01   Log-Likelihood:            -2.9014e+05\n",
       "No. Observations:               21142   AIC:                         5.803e+05\n",
       "Df Residuals:                   21131   BIC:                         5.804e+05\n",
       "Df Model:                          10                                         \n",
@@ -4290,7 +4278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
@@ -4350,7 +4338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -4468,7 +4456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
@@ -4568,7 +4556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
@@ -4601,7 +4589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4610,7 +4598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4619,7 +4607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [
     {
@@ -4679,7 +4667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
@@ -4725,7 +4713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
@@ -4765,7 +4753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
@@ -4807,7 +4795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
@@ -4822,7 +4810,7 @@
       "Model:                            OLS   Adj. R-squared:                  0.678\n",
       "Method:                 Least Squares   F-statistic:                     557.0\n",
       "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        12:27:46   Log-Likelihood:                -3540.2\n",
+      "Time:                        17:52:07   Log-Likelihood:                -3540.2\n",
       "No. Observations:               16913   AIC:                             7210.\n",
       "Df Residuals:                   16848   BIC:                             7713.\n",
       "Df Model:                          64                                         \n",
@@ -5033,7 +5021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {

From d51ef166359b72da16fa4c9dc00838fa355f658d Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 19:15:15 +0300
Subject: [PATCH 19/27] update README.md

---
 README.md | 180 +++++++++++++-----------------------------------------
 1 file changed, 42 insertions(+), 138 deletions(-)

diff --git a/README.md b/README.md
index e770ce06..5b0cd08c 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@ In the fast-paced world of real estate, it's crucial for agencies to provide cli
 ### Business Problem
 
 Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
+
 ### The Data Understanding
 
 King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
@@ -20,79 +21,8 @@ price: Sale price of the house .
 id - Unique identifier for a house
 
 **Property Characteristics:**
+![property-characteristics](images/property-characteristics.png)
 
-
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-Additional Features:
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.bedrooms: Number of bedrooms.
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-**Additional Features:**
-
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.
 ### Key Points
 
 # **OBJECTIVES**
@@ -119,12 +49,12 @@ Offer practical recommendations to real estate agencies aimed at enhancing profi
 # **TABLE OF CONTENT**
 1.   Data Preparation
 2.   Data cleaning
-1.   Exploratory data analysis
-2.   Statistical Analysis
-1.   Modelling
-2.   Regression Results
-1.   Conclusion
-2.   Recomendations
+3.   Exploratory data analysis
+4.   Statistical Analysis
+5.   Modelling
+6.   Regression Results
+7.   Conclusion
+8.   Recomendations
 
 
 #**Statistical Analysis**
@@ -136,7 +66,7 @@ Distribution Analysis
 Inferential Statistics using Hypothesis Testing and Analysis of Variance
 MultiColinierity
 # *Modelling**
-Baseline model - simple linear model.
+1. Baseline model - simple linear model.
 2. log transformation. 
 3. Multiple Linear Regression
 4. Residual modelling.
@@ -144,89 +74,62 @@ Baseline model - simple linear model.
 # **REGRESSION RESULTS**
 
 **SIMPLE LINEAR REGRESSION** 
-Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.
-
-Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.
-
-R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.
-
-Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.
-
-Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.
-
-Test Set:
+R-squared (0.48): Indicates that approximately 48% of the variability in house prices is explained by the square footage of living space. It measures how well the model captures patterns in the data.
+Mean Squared Error (MSE) (68845100756.11): Represents the average squared difference between actual and predicted house prices. Lower values indicate better accuracy, but here, the MSE is quite large, suggesting room for improvement.
+Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE, providing a measure of typical deviation between predicted and actual house prices. The RMSE is approximately 262,383.50 units.
+Intercept (540631.16): Estimated house price when all independent variables are zero. It's around 540,631.16 units, suggesting a baseline value.
+Coefficient (259767.82): Represents the change in house prices for a one-unit increase in square footage of living space, with other variables held constant. For every one-unit increase in square footage, house prices are expected to increase by approximately 259,767.82 units.
 
-R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.
-
-Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.
-
-Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data
-Overall
-, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance.
 # ** Multiple Linear Regresion 
-![alt text](image-1.png)
+![Multiple-Linear-Regression](images/Multiple-Linear-Regression.png)
+
 # **RESIDUALS**
-![alt text](image-3.png)
+![Residuals](images/Residuals.png)
 A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
 
 Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
 The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
 
 We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
-#**Log transformation**.
-Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
-
-Log transformation of the multiple linear regression.
-![alt text](image-4.png)
-
-![alt text](image-5.png)
-The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model.
-visualization of the positive and negative coefficient variables.
-R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
-
-MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.
 
-Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example: The coefficient for const (intercept) is 4987.94. The coefficient for x1 (bathrooms) is 3457.38. The coefficient for x2 (sqft_living) is -16.53. The coefficient for x3 (floors) is -16.98. The coefficient for x4 (waterfront) is -59.75. The coefficient for x5 (condition) is -66.70. The coefficient for x6 (grade) is 21.71. The coefficient for x7 (sqft_basement) is 56.12. The coefficient for x8 (yr_built) and other coefficients follow.
+# **Log transformation**.
+Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
 
-P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.
+*Log transformation of the multiple linear regression.*
 
-F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.
+![Log-transformation](images/Log-transformation.png)
 
-Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.
+Residual Analysis: The model's residuals are randomly distributed, indicating no systematic bias. Log transformation helps visualize residuals and patterns. A horizontal red line suggests linearity and normality but does not conclusively determine model goodness-of-fit.
+R-squared (71.61%): Indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
+Mean Squared Error (MSE): Approximately 37,774,083,176.84, representing the average squared difference between predicted and actual house prices.
+Coefficients: Represent the estimated effect of each predictor variable on the target variable. For example, the coefficient for bathrooms is 3457.38, sqft_living is -16.53, floors is -16.98, waterfront is -59.75, condition is -66.70, and so on.
+P-values: Indicate the statistical significance of the coefficients. Some coefficients have p-values < 0.05, indicating statistical significance, while others do not.
+F-statistic: Tests the overall significance of the model. A low p-value (< 0.05) suggests the model as a whole is statistically significant.
 
-![alt text](image-6.png)
-#**REGRESSION Results**
+# **REGRESSION Results**
 
 From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
 
 Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73
 
 The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
-# **Assumptions**
-Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
-
-Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
 
-Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
 
-No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
-#**Limitations**
-Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
+# **Assumptions**
+1. Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
 
-Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.
+2. Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
 
-Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.
+3. Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
 
-Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.
+4. No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
 
-Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance.
 
 **Limitations**
-1.   The dataset could have more property based characteristics
-2.   Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
-
-1. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
-1. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
+1. The dataset could have more property based characteristics
+2. Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
+3. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
+4. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
 Overall the model was the best fit model for this predictions
 
 # **RECOMENDATIONS**
@@ -247,15 +150,16 @@ Overall the model was the best fit model for this predictions
 
 
 6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
+
 # ** Conclusion**
 
-Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
+1. Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
 
 
-Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
+2. Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
 
 
-Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
+3. Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
 
 
-Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.
+4. Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.

From e21432fda5aed9e5987b387b715351e9ce791b05 Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 19:17:12 +0300
Subject: [PATCH 20/27] add images

---
 .vscode/extensions.json               |   3 +
 README ().md                          | 261 --------------------------
 images/Log-transformation.png         | Bin 0 -> 60865 bytes
 images/Multiple-Linear-Regression.png | Bin 0 -> 51532 bytes
 images/Polynomial-Regression.png      | Bin 0 -> 28625 bytes
 images/Residuals.png                  | Bin 0 -> 31283 bytes
 images/Simple linear Regression.png   | Bin 0 -> 51532 bytes
 images/property-characteristics.png   | Bin 0 -> 114287 bytes
 8 files changed, 3 insertions(+), 261 deletions(-)
 create mode 100644 .vscode/extensions.json
 delete mode 100644 README ().md
 create mode 100644 images/Log-transformation.png
 create mode 100644 images/Multiple-Linear-Regression.png
 create mode 100644 images/Polynomial-Regression.png
 create mode 100644 images/Residuals.png
 create mode 100644 images/Simple linear Regression.png
 create mode 100644 images/property-characteristics.png

diff --git a/.vscode/extensions.json b/.vscode/extensions.json
new file mode 100644
index 00000000..7e257db9
--- /dev/null
+++ b/.vscode/extensions.json
@@ -0,0 +1,3 @@
+{
+    "recommendations": []
+}
\ No newline at end of file
diff --git a/README ().md b/README ().md
deleted file mode 100644
index b73faf72..00000000
--- a/README ().md	
+++ /dev/null
@@ -1,261 +0,0 @@
-# REAL ESTATE SALES PREDICTION MODEL
-
-## Project Overview
-
-In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.
-
-
-### Business Problem
-
-Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
-### The Data Understanding
-
-King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
-**Target Variable**
-
-price: Sale price of the house .
-
-**Unique identifier**
-
-id - Unique identifier for a house
-
-**Property Characteristics:**
-
-
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-Additional Features:
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.bedrooms: Number of bedrooms.
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-**Additional Features:**
-
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.
-### Key Points
-
-# **OBJECTIVES**
-
-
-**Main Objective:**
-
-The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, furnishing clients with valuable insights to facilitate informed investment decisions.
-
-Specific Goals:
-
-i). Identification of Key Influencing Factors on House Prices:
-
-Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price. Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on property prices.
-
-ii). Assessment of Model Performance:
-
-Employ metrics such as mean squared error, R-squared values, and residual analysis to evaluate the model's accuracy in predicting house prices effectively.
-
-iii). Provision of Actionable Recommendations:
-
-Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.
-
-# **TABLE OF CONTENT**
-1.   Data Preparation
-2.   Data cleaning
-1.   Exploratory data analysis
-2.   Statistical Analysis
-1.   Modelling
-2.   Regression Results
-1.   Conclusion
-2.   Recomendations
-
-
-#**Statistical Analysis**
-Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:
-
-Descriptive Statistics
-Correlation matrix
-Distribution Analysis
-Inferential Statistics using Hypothesis Testing and Analysis of Variance
-MultiColinierity
-# *Modelling**
-Baseline model - simple linear model.
-2. log transformation. 
-3. Multiple Linear Regression
-4. Residual modelling.
-
-# **REGRESSION RESULTS**
-
-**SIMPLE LINEAR REGRESSION** 
-Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.
-
-Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.
-
-R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.
-
-Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.
-
-Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.
-
-Test Set:
-
-R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.
-
-Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.
-
-Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data
-Overall
-, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance.
-# ** Multiple Linear Regresion 
-![alt text](image-1.png)
-# **RESIDUALS**
-![alt text](image-3.png)
-A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
-
-Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
-The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
-
-We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
-#**Log transformation**.
-Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
-
-Log transformation of the multiple linear regression.
-![alt text](image-4.png)
-
-![alt text](image-5.png)
-The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model.
-visualization of the positive and negative coefficient variables.
-R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
-
-MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.
-
-Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example: The coefficient for const (intercept) is 4987.94. The coefficient for x1 (bathrooms) is 3457.38. The coefficient for x2 (sqft_living) is -16.53. The coefficient for x3 (floors) is -16.98. The coefficient for x4 (waterfront) is -59.75. The coefficient for x5 (condition) is -66.70. The coefficient for x6 (grade) is 21.71. The coefficient for x7 (sqft_basement) is 56.12. The coefficient for x8 (yr_built) and other coefficients follow.
-
-P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.
-
-F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.
-
-Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.
-
-![alt text](image-6.png)
-#**REGRESSION Results**
-
-From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
-
-Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73
-
-The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
-# **Assumptions**
-Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
-
-Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
-
-Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
-
-No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
-#**Limitations**
-Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
-
-Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.
-
-Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.
-
-Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.
-
-Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance.
-
-**Limitations**
-1.   The dataset could have more property based characteristics
-2.   Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
-
-1. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
-1. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
-Overall the model was the best fit model for this predictions
-
-# **RECOMENDATIONS**
-
-1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.
-
-
-2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.
-
-
-3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.
-
-
-4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.
-
-
-5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.
-
-
-6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
-# ** Conclusion**
-
-Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
-
-
-Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
-
-
-Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
-
-
-Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.
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From 2d069253be1ecf002696b07c7a75a9ccade28fcc Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 19:57:02 +0300
Subject: [PATCH 21/27] update image

---
 README.md        |   7 ++++---
 images/intro.png | Bin 0 -> 386229 bytes
 2 files changed, 4 insertions(+), 3 deletions(-)
 create mode 100644 images/intro.png

diff --git a/README.md b/README.md
index 5b0cd08c..308e26cd 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
 # REAL ESTATE SALES PREDICTION MODEL
+![intro](images/intro.png)
 
 ## Project Overview
 
@@ -65,7 +66,7 @@ Correlation matrix
 Distribution Analysis
 Inferential Statistics using Hypothesis Testing and Analysis of Variance
 MultiColinierity
-# *Modelling**
+# **Modelling**
 1. Baseline model - simple linear model.
 2. log transformation. 
 3. Multiple Linear Regression
@@ -80,7 +81,7 @@ Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE,
 Intercept (540631.16): Estimated house price when all independent variables are zero. It's around 540,631.16 units, suggesting a baseline value.
 Coefficient (259767.82): Represents the change in house prices for a one-unit increase in square footage of living space, with other variables held constant. For every one-unit increase in square footage, house prices are expected to increase by approximately 259,767.82 units.
 
-# ** Multiple Linear Regresion 
+# **Multiple Linear Regresion**
 ![Multiple-Linear-Regression](images/Multiple-Linear-Regression.png)
 
 # **RESIDUALS**
@@ -151,7 +152,7 @@ Overall the model was the best fit model for this predictions
 
 6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
 
-# ** Conclusion**
+# **Conclusion**
 
 1. Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
 
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From 82e50e71f45429507ccd495466310014c55e12a3 Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 20:05:18 +0300
Subject: [PATCH 22/27] update_changes

---
 README.md | 70 +++++++------------------------------------------------
 1 file changed, 8 insertions(+), 62 deletions(-)

diff --git a/README.md b/README.md
index 308e26cd..35d7bfed 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,6 @@
 
 In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.
 
-
 ### Business Problem
 
 Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
@@ -13,60 +12,30 @@ Real estate experts in King County need help understanding what factors influenc
 ### The Data Understanding
 
 King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
-**Target Variable**
 
+**Target Variable**
 price: Sale price of the house .
 
-**Unique identifier**
-
-id - Unique identifier for a house
-
 **Property Characteristics:**
 ![property-characteristics](images/property-characteristics.png)
 
-### Key Points
-
-# **OBJECTIVES**
-
 
 **Main Objective:**
 
 The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, furnishing clients with valuable insights to facilitate informed investment decisions.
 
-Specific Goals:
-
-i). Identification of Key Influencing Factors on House Prices:
-
-Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price. Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on property prices.
-
-ii). Assessment of Model Performance:
-
-Employ metrics such as mean squared error, R-squared values, and residual analysis to evaluate the model's accuracy in predicting house prices effectively.
-
-iii). Provision of Actionable Recommendations:
-
-Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.
-
-# **TABLE OF CONTENT**
-1.   Data Preparation
-2.   Data cleaning
-3.   Exploratory data analysis
-4.   Statistical Analysis
-5.   Modelling
-6.   Regression Results
-7.   Conclusion
-8.   Recomendations
-
 
 #**Statistical Analysis**
 Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:
 
-Descriptive Statistics
-Correlation matrix
-Distribution Analysis
-Inferential Statistics using Hypothesis Testing and Analysis of Variance
-MultiColinierity
+1. Descriptive Statistics
+2. Correlation matrix
+3. Distribution Analysis
+4. Inferential Statistics using Hypothesis Testing and Analysis of Variance
+5. MultiColinierity
+
 # **Modelling**
+
 1. Baseline model - simple linear model.
 2. log transformation. 
 3. Multiple Linear Regression
@@ -87,10 +56,6 @@ Coefficient (259767.82): Represents the change in house prices for a one-unit in
 # **RESIDUALS**
 ![Residuals](images/Residuals.png)
 A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
-
-Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
-The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
-
 We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
 
 # **Log transformation**.
@@ -100,12 +65,6 @@ Transforming the dependent variable or one or more independent variables can som
 
 ![Log-transformation](images/Log-transformation.png)
 
-Residual Analysis: The model's residuals are randomly distributed, indicating no systematic bias. Log transformation helps visualize residuals and patterns. A horizontal red line suggests linearity and normality but does not conclusively determine model goodness-of-fit.
-R-squared (71.61%): Indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
-Mean Squared Error (MSE): Approximately 37,774,083,176.84, representing the average squared difference between predicted and actual house prices.
-Coefficients: Represent the estimated effect of each predictor variable on the target variable. For example, the coefficient for bathrooms is 3457.38, sqft_living is -16.53, floors is -16.98, waterfront is -59.75, condition is -66.70, and so on.
-P-values: Indicate the statistical significance of the coefficients. Some coefficients have p-values < 0.05, indicating statistical significance, while others do not.
-F-statistic: Tests the overall significance of the model. A low p-value (< 0.05) suggests the model as a whole is statistically significant.
 
 # **REGRESSION Results**
 
@@ -116,16 +75,6 @@ Polynomial Regression is the preferred model beacuse from the evaluation it has
 The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
 
 
-# **Assumptions**
-1. Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
-
-2. Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
-
-3. Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
-
-4. No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
-
-
 **Limitations**
 1. The dataset could have more property based characteristics
 2. Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
@@ -156,11 +105,8 @@ Overall the model was the best fit model for this predictions
 
 1. Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
 
-
 2. Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
 
-
 3. Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
 
-
 4. Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.

From c8f02dfe28fb2ae1d52dc07c4087aaf5ebff3165 Mon Sep 17 00:00:00 2001
From: Paul Muniu <muniupauljr@gmail.com>
Date: Wed, 1 May 2024 20:21:15 +0300
Subject: [PATCH 23/27] delete unwanted files

---
 Final Copy -Project Phase 2.ipynb | 5199 -----------------------------
 Project Group4 Notebook.pdf       |  Bin 161155 -> 0 bytes
 2 files changed, 5199 deletions(-)
 delete mode 100644 Final Copy -Project Phase 2.ipynb
 delete mode 100644 Project Group4 Notebook.pdf

diff --git a/Final Copy -Project Phase 2.ipynb b/Final Copy -Project Phase 2.ipynb
deleted file mode 100644
index ee2a22a0..00000000
--- a/Final Copy -Project Phase 2.ipynb	
+++ /dev/null
@@ -1,5199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Final Project Submission\n",
-    "\n",
-    "Please fill out:\n",
-    "* Student name:\n",
-    "1. Winfred Kinya Bundi.\n",
-    "2. Carol Mundia.\n",
-    "3. Paul Muniu.\n",
-    "4. Dennis Mwenda.\n",
-    "* Student pace: Full time Hybrid\n",
-    "* Scheduled project review date/time:2/05/2024 \n",
-    "* Instructor name: Mwikali Maryanne.\n",
-    "* Blog post URL:git@github.com:winnycodegurl/dsc-phase-2-projectgroup4.git\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## INTRODUCTION\n",
-    "\n",
-    "In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## BUSINESS UNDERSTANDING\n",
-    "\n",
-    "\n",
-    "\n",
-    "The provided dataset encompasses details on homes sold, encompassing their attributes and sale prices. This dataset holds significant potential for real estate agencies across various avenues:\n",
-    "\n",
-    "\n",
-    "\n",
-    "1. \n",
-    "Market Analysis:\n",
-    "\n",
-    " Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "2. \n",
-    "Property Valuation: \n",
-    "\n",
-    "By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "3. \n",
-    "Targeted Marketing: \n",
-    "\n",
-    "Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## PROBLEM STATEMENT.\n",
-    "\n",
-    "\n",
-    "\n",
-    "In King County, people involved in real estate have trouble figuring out what affects property values and trends in the market.\n",
-    "This study wants to help by looking at things like what features a property has, where it's located, what buyers prefer,\n",
-    "and how things change over time. By understanding these things better, people in real estate can make smarter choices about\n",
-    "buying and selling property and how they position themselves in the market.\n",
-    "The main goal is to give them practical advice that helps them do well in King County's real estate market, \n",
-    "which is always changing.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## OBJECTIVES.\n",
-    "\n",
-    "Main OBJECTIVE\n",
-    "\n",
-    "\n",
-    "The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients \n",
-    "on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, \n",
-    "furnishing clients with valuable insights to facilitate informed investment decisions.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Specific Goals:\n",
-    "\n",
-    "\n",
-    "i). Identification of Key Influencing Factors on House Prices:\n",
-    "\n",
-    "\n",
-    "\n",
-    "Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price.\n",
-    "\n",
-    "\n",
-    "\n",
-    "ii). Assessment of Model Performance:\n",
-    "\n",
-    "\n",
-    "Employ metrics such as mean squared error, R-squared values, and residual analysis to \n",
-    "evaluate the model's accuracy in predicting house prices effectively.\n",
-    "\n",
-    "    \n",
-    "\n",
-    "iii). Provision of Actionable Recommendations:\n",
-    "\n",
-    "\n",
-    "\n",
-    "Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. \n",
-    "Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Understanding.\n",
-    "\n",
-    "\n",
-    "King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments.\n",
-    "\n",
-    "### Whereby the dataset contains:\n",
-    "\n",
-    "### Target Variable\n",
-    "\n",
-    "price: Sale price of the house .\n",
-    "\n",
-    "### Unique identifier\n",
-    "\n",
-    "id - Unique identifier for a house\n",
-    "\n",
-    "### Property Characteristics:\n",
-    "\n",
-    "bedrooms: Number of bedrooms.\n",
-    "\n",
-    "bathrooms: Number of bathrooms.\n",
-    "\n",
-    "sqft_living: Square footage of living space in the home.\n",
-    "\n",
-    "sqft_lot: Square footage of the lot.\n",
-    "\n",
-    "floors: Number of floors (levels) in the house.\n",
-    "\n",
-    "waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).\n",
-    "\n",
-    "view: Quality of view from the house, categorized into various types.\n",
-    "\n",
-    "condition: Overall condition of the house, categorized based on maintenance.\n",
-    "\n",
-    "grade: Overall grade of the house, reflecting construction and design quality.\n",
-    "\n",
-    "### Additional Features:\n",
-    "\n",
-    "sqft_above: Square footage of house apart from the basement.\n",
-    "\n",
-    "sqft_basement: Square footage of the basement.\n",
-    "\n",
-    "yr_built: Year when the house was built.\n",
-    "\n",
-    "yr_renovated: Year when the house was renovated.\n",
-    "\n",
-    "zipcode: ZIP Code of the property.\n",
-    "\n",
-    "lat: Latitude coordinate of the property.\n",
-    "\n",
-    "long: Longitude coordinate of the property.\n",
-    "\n",
-    "sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.\n",
-    "\n",
-    "sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.\n",
-    "\n",
-    "### TABLE OF CONTENTS\n",
-    "1.Data Preparation\n",
-    "\n",
-    "2.Data cleaning\n",
-    "\n",
-    "3.Exploratory data analysis\n",
-    "\n",
-    "4.Statistical Analysis\n",
-    "\n",
-    "5.Modelling\n",
-    "\n",
-    "6.Regression Results\n",
-    "\n",
-    "7.Conclusion\n",
-    "\n",
-    "8.Reccomendations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. DATA PREPARATION"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing necessary libraries for data analysis and visualization\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt  # for data visualization.\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns  # for enhanced data visualization.\n",
-    "from pandas.api.types import is_numeric_dtype  # Used to check if a data type is numeric.\n",
-    "\n",
-    "from statsmodels.stats.outliers_influence import variance_inflation_factor  # For calculating Variance Inflation Factor (VIF).\n",
-    "from statsmodels.graphics.regressionplots import plot_partregress_grid  # For partial regression plots.\n",
-    "from sklearn.model_selection import train_test_split  # Used to split data into training and testing sets.\n",
-    "from sklearn.preprocessing import PolynomialFeatures  # Generate polynomial features.\n",
-    "from sklearn.linear_model import LinearRegression  # Linear Regression model.\n",
-    "from sklearn.preprocessing import StandardScaler  # Standardizing/Scaling features.\n",
-    "from sklearn.feature_selection import RFE  # Recursive Feature Elimination for feature selection.\n",
-    "from sklearn.metrics import mean_squared_error, r2_score  # Evaluation metrics for model performance.\n",
-    "import statsmodels.api as sm\n",
-    "from scipy.stats import kstest\n",
-    "\n",
-    "# Statsmodels is used to create statistical models.\n",
-    "from scipy import stats  # Scientific computing library for statistical tests.\n",
-    "from scipy.stats import f_oneway  # One-way ANOVA statistical test.\n",
-    "from scipy.stats import ttest_ind  # Independent sample t-test for comparing means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *loading the King County House Sales dataset*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "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"
-     ]
-    },
-    {
-     "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": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the dataset to inspect the initial state of the data\n",
-    "file_path = 'data/kc_house_data.csv'\n",
-    "housing_data = pd.read_csv(file_path)\n",
-    "\n",
-    "# Display basic information and the first few rows of the dataset\n",
-    "housing_data.info()\n",
-    "housing_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *loading the column.md dataset*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "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>Column_name</th>\n",
-       "      <th>Description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>* `id`</td>\n",
-       "      <td>Unique identifier for a house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>* `date`</td>\n",
-       "      <td>Date house was sold</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>* `price`</td>\n",
-       "      <td>Sale price (prediction target)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>* `bedrooms`</td>\n",
-       "      <td>Number of bedrooms</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>* `bathrooms`</td>\n",
-       "      <td>Number of bathrooms</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>* `sqft_living`</td>\n",
-       "      <td>Square footage of living space in the home</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>* `sqft_lot`</td>\n",
-       "      <td>Square footage of the lot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>* `floors`</td>\n",
-       "      <td>Number of floors (levels) in house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>* `waterfront`</td>\n",
-       "      <td>Whether the house is on a waterfront</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>* `view`</td>\n",
-       "      <td>Quality of view from house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>* `condition`</td>\n",
-       "      <td>How good the overall condition of the house is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>* `grade`</td>\n",
-       "      <td>Overall grade of the house. Related to the con...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>* `sqft_above`</td>\n",
-       "      <td>Square footage of house apart from basement</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>* `sqft_basement`</td>\n",
-       "      <td>Square footage of the basement</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>* `yr_built`</td>\n",
-       "      <td>Year when house was built</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>* `yr_renovated`</td>\n",
-       "      <td>Year when house was renovated</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>* `zipcode`</td>\n",
-       "      <td>ZIP Code used by the United States Postal Service</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>* `lat`</td>\n",
-       "      <td>Latitude coordinate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>* `long`</td>\n",
-       "      <td>Longitude coordinate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>* `sqft_living15`</td>\n",
-       "      <td>The square footage of interior housing living ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>* `sqft_lot15`</td>\n",
-       "      <td>The square footage of the land lots of the nea...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Column_name                                        Description\n",
-       "0              * `id`                      Unique identifier for a house\n",
-       "1            * `date`                                Date house was sold\n",
-       "2           * `price`                     Sale price (prediction target)\n",
-       "3        * `bedrooms`                                 Number of bedrooms\n",
-       "4       * `bathrooms`                                Number of bathrooms\n",
-       "5     * `sqft_living`         Square footage of living space in the home\n",
-       "6        * `sqft_lot`                          Square footage of the lot\n",
-       "7          * `floors`                 Number of floors (levels) in house\n",
-       "8      * `waterfront`               Whether the house is on a waterfront\n",
-       "9            * `view`                         Quality of view from house\n",
-       "10      * `condition`  How good the overall condition of the house is...\n",
-       "11          * `grade`  Overall grade of the house. Related to the con...\n",
-       "12     * `sqft_above`        Square footage of house apart from basement\n",
-       "13  * `sqft_basement`                     Square footage of the basement\n",
-       "14       * `yr_built`                          Year when house was built\n",
-       "15   * `yr_renovated`                      Year when house was renovated\n",
-       "16        * `zipcode`  ZIP Code used by the United States Postal Service\n",
-       "17            * `lat`                                Latitude coordinate\n",
-       "18           * `long`                               Longitude coordinate\n",
-       "19  * `sqft_living15`  The square footage of interior housing living ...\n",
-       "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "column_names_file = \"data/column_names.md\"\n",
-    "\n",
-    "with open(column_names_file, \"r\") as file:\n",
-    "    markdown_content = file.readlines()\n",
-    "\n",
-    "column_names = []\n",
-    "description = []\n",
-    "for line in markdown_content:\n",
-    "    parts = line.split('-', 1)\n",
-    "    if len(parts) == 2:  # Check if split produces two parts\n",
-    "        column_names.append(parts[0].strip())\n",
-    "        description.append(parts[1].strip())\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = {\n",
-    "    \"Column_name\": column_names,\n",
-    "    \"Description\": description\n",
-    "}\n",
-    "column_name_df = pd.DataFrame(data)\n",
-    "\n",
-    "column_name_df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3 columns with missing values.\n",
-      "waterfront      2376\n",
-      "view              63\n",
-      "yr_renovated    3842\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating function to check counts of missing values\n",
-    "def has_missing_values(df):\n",
-    "    missing_values = df.isnull().sum()\n",
-    "    num_missing_values = missing_values[missing_values > 0].count()\n",
-    "    if num_missing_values == 0:\n",
-    "        print(\"There are no missing values in the DataFrame.\")\n",
-    "    else:\n",
-    "        print(f\"There are {num_missing_values} columns with missing values.\")\n",
-    "        print(missing_values[missing_values > 0])\n",
-    "        \n",
-    "has_missing_values(housing_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are no duplicate rows in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#creating a function to check for duplicates.\n",
-    "def has_duplicates(df):\n",
-    "    num_duplicates = df.duplicated().sum()\n",
-    "    if num_duplicates == 0:\n",
-    "        print(\"There are no duplicate rows in the DataFrame.\")\n",
-    "    else:\n",
-    "        print(f\"There are {num_duplicates} duplicate rows in the DataFrame.\")\n",
-    "\n",
-    "has_duplicates(housing_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The dataset contains 21,597 entries and 21 features. Here’s a brief overview of the data:\n",
-    "\n",
-    "### Columns and their Data Types:\n",
-    "#### Numerical:\n",
-    "id, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15\n",
-    "\n",
-    "#### Categorical:\n",
-    "date (format object, should be datetime), waterfront, view, condition, grade, sqft_basement (format object, should be numeric)\n",
-    "\n",
-    "#### Missing Values:\n",
-    "waterfront: 2,376 missing values\n",
-    "view: 63 missing values\n",
-    "yr_renovated: 3,842 missing values\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. DATA CLEANING"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a)Dropping columns:\n",
-    "We're dropping some columns during data cleaning to streamline our analysis and focus on the most relevant features. By removing unnecessary or redundant columns, we aim to simplify the dataset and improve the efficiency of subsequent analytical processes. This helps in reducing noise and enhancing the clarity of our findings."
-   ]
-  },
-  {
-   "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",
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-       "\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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20146</th>\n",
-       "      <td>7967000130</td>\n",
-       "      <td>4/1/2015</td>\n",
-       "      <td>370228.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13983</th>\n",
-       "      <td>7338000150</td>\n",
-       "      <td>1/29/2015</td>\n",
-       "      <td>160000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>4200</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1983</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>4200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3605</th>\n",
-       "      <td>8658303585</td>\n",
-       "      <td>8/7/2014</td>\n",
-       "      <td>252500.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>900</td>\n",
-       "      <td>7500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>900</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1961</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1190</td>\n",
-       "      <td>10000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9229</th>\n",
-       "      <td>1787250210</td>\n",
-       "      <td>12/22/2014</td>\n",
-       "      <td>379000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>5225</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>5378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11243</th>\n",
-       "      <td>1843200350</td>\n",
-       "      <td>7/22/2014</td>\n",
-       "      <td>150000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1934</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1978</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1898</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "20146  7967000130    4/1/2015  370228.0         4       3.00         2050   \n",
-       "13983  7338000150   1/29/2015  160000.0         2       1.00         1070   \n",
-       "3605   8658303585    8/7/2014  252500.0         2       1.00          900   \n",
-       "9229   1787250210  12/22/2014  379000.0         4       2.75         2410   \n",
-       "11243  1843200350   7/22/2014  150000.0         2       1.50         1360   \n",
-       "\n",
-       "       sqft_lot  floors waterfront condition          grade  sqft_above  \\\n",
-       "20146      4000     2.0         NO   Average         8 Good        2050   \n",
-       "13983      4200     1.0         NO      Good  6 Low Average        1070   \n",
-       "3605       7500     1.0         NO      Good  6 Low Average         900   \n",
-       "9229       5225     2.0         NO   Average         8 Good        2410   \n",
-       "11243      1934     2.0         NO      Good      7 Average        1360   \n",
-       "\n",
-       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "20146           0.0      2014           0.0           2050        4000  \n",
-       "13983           0.0      1983           0.0           1150        4200  \n",
-       "3605            0.0      1961           0.0           1190       10000  \n",
-       "9229            0.0      2001           0.0           2300        5378  \n",
-       "11243           0.0      1978           0.0           1360        1898  "
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data = housing_data.drop(['long','lat','view', 'zipcode'], axis=1)\n",
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b)Checking for placeholders\n",
-    "\n",
-    "Placeholders are values used to denote missing, unknown, or invalid data within a dataset. Common examples include \"N/A\", \"-\", \"UNKNOWN\", \"NULL\", #, etc. It's important to identify and handle placeholders properly during data preprocessing to ensure accurate analysis and modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column 'sqft_basement': Found 454 occurrences of potential placeholder .\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define a list of common placeholder values\n",
-    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
-    "\n",
-    "# Check for potential placeholders in the DataFrame\n",
-    "found_placeholder = False\n",
-    "for column in housing_data.columns:\n",
-    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
-    "    if placeholder_count > 0:\n",
-    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
-    "        found_placeholder = True\n",
-    "\n",
-    "if not found_placeholder:\n",
-    "    print(\"No potential placeholders found in the DataFrame.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Convert the common placeholders to lowercase for case-insensitive matching\n",
-    "common_placeholders_lower = [placeholder.lower() for placeholder in common_placeholders]\n",
-    "\n",
-    "# Replace any of the common placeholders with NaN\n",
-    "housing_data['sqft_basement'] = housing_data['sqft_basement'].replace(common_placeholders_lower, pd.NA)\n",
-    "\n",
-    "# Drop rows with NaN in the sqft_basement column\n",
-    "housing_data.dropna(subset=['sqft_basement'], inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Counter-check"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No potential placeholders found in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# confirm no more placeholders\n",
-    "# Define a list of common placeholder values\n",
-    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
-    "\n",
-    "# Check for potential placeholders in the DataFrame\n",
-    "found_placeholder = False\n",
-    "for column in housing_data.columns:\n",
-    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
-    "    if placeholder_count > 0:\n",
-    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
-    "        found_placeholder = True\n",
-    "\n",
-    "if not found_placeholder:\n",
-    "    print(\"No potential placeholders found in the DataFrame.\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "### b) Handling Missing Values:\n",
-    "waterfront: \n",
-    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
-    "<br> yr_renovated: \n",
-    "A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# For categorical data, impute missing values with mode or specific marker\n",
-    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
-    "\n",
-    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
-    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### c) Convert Data\n",
-    "Convert date from object to datetime format.\n",
-    "sqft_basement: Convert from object to numeric, handling any non-numeric entries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
-    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
-    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change waterfront to integer\n",
-    "housing_data['waterfront'] = housing_data['waterfront'].apply(lambda x: 0 if x == 'NO' else 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['7 Average' '6 Low Average' '8 Good' '11 Excellent' '9 Better' '5 Fair'\n",
-      " '10 Very Good' '12 Luxury' '4 Low' '3 Poor' '13 Mansion']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# checking \"grade\" column\n",
-    "unique_grade = housing_data.grade.unique()\n",
-    "print(unique_grade)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Average' 'Very Good' 'Good' 'Poor' 'Fair']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# checking \"condition\"column\n",
-    "unique_condition = housing_data.condition.unique()\n",
-    "print(unique_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert grade and condition into representative numbers for easier Exploratory analysis.\n",
-    "housing_data['condition'] = housing_data['condition'].map({'Poor': 1,'Fair': 2,'Average': 3,'Good': 4,'Very Good': 5}).astype(float)\n",
-    "housing_data['grade'] = housing_data['grade'].map({'3 Poor': 1,'4 Low': 2,'5 Fair': 3,'6 Low Average': 4,'7 Average': 5,'8 Good': 6,'9 Better': 7,'10 Very Good': 8,'11 Excellent': 9,'12 Luxury': 10,'13 Mansion': 11}).astype(float) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 21143 entries, 0 to 21596\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column         Non-Null Count  Dtype         \n",
-      "---  ------         --------------  -----         \n",
-      " 0   id             21143 non-null  int64         \n",
-      " 1   date           21143 non-null  datetime64[ns]\n",
-      " 2   price          21143 non-null  float64       \n",
-      " 3   bedrooms       21143 non-null  int64         \n",
-      " 4   bathrooms      21143 non-null  float64       \n",
-      " 5   sqft_living    21143 non-null  int64         \n",
-      " 6   sqft_lot       21143 non-null  int64         \n",
-      " 7   floors         21143 non-null  float64       \n",
-      " 8   waterfront     21143 non-null  int64         \n",
-      " 9   condition      21143 non-null  float64       \n",
-      " 10  grade          21143 non-null  float64       \n",
-      " 11  sqft_above     21143 non-null  int64         \n",
-      " 12  sqft_basement  21143 non-null  float64       \n",
-      " 13  yr_built       21143 non-null  int64         \n",
-      " 14  yr_renovated   21143 non-null  float64       \n",
-      " 15  sqft_living15  21143 non-null  int64         \n",
-      " 16  sqft_lot15     21143 non-null  int64         \n",
-      "dtypes: datetime64[ns](1), float64(7), int64(9)\n",
-      "memory usage: 2.9 MB\n"
-     ]
-    },
-    {
-     "data": {
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-       "      <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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4511</th>\n",
-       "      <td>723049333</td>\n",
-       "      <td>2015-04-05</td>\n",
-       "      <td>285000.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1010</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>8254</td>\n",
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-       "    <tr>\n",
-       "      <th>1181</th>\n",
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-       "      <td>2014-06-05</td>\n",
-       "      <td>875000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>3250</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>750.0</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1650</td>\n",
-       "      <td>6000</td>\n",
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-       "      <td>5</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>6137</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3170</td>\n",
-       "      <td>6285</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12273</th>\n",
-       "      <td>7016200030</td>\n",
-       "      <td>2015-03-20</td>\n",
-       "      <td>480000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>7966</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>1970</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>7500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8546</th>\n",
-       "      <td>7981900110</td>\n",
-       "      <td>2014-10-03</td>\n",
-       "      <td>350000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>3175</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>960.0</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>3175</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "4511    723049333 2015-04-05  285000.0         3       1.50         1490   \n",
-       "1181   7625700935 2014-06-05  875000.0         3       3.50         3250   \n",
-       "1875   1853081000 2014-07-17  820000.0         5       2.75         2830   \n",
-       "12273  7016200030 2015-03-20  480000.0         4       2.50         2080   \n",
-       "8546   7981900110 2014-10-03  350000.0         4       2.75         2300   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "4511      10367     1.0           0        3.0    5.0        1010   \n",
-       "1181       6000     2.0           0        3.0    8.0        2500   \n",
-       "1875       6137     2.0           0        3.0    7.0        2830   \n",
-       "12273      7966     1.0           0        3.0    5.0        1200   \n",
-       "8546       3175     1.5           0        3.0    4.0        1340   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "4511           480.0      1957           0.0           1000        8254  \n",
-       "1181           750.0      2001           0.0           1650        6000  \n",
-       "1875             0.0      2010           0.0           3170        6285  \n",
-       "12273          880.0      1970           0.0           1920        7500  \n",
-       "8546           960.0      1966           0.0           1260        3175  "
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Check transformations\n",
-    "housing_data.info()\n",
-    "housing_data.sample(5)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Feature Engineering:\n",
-    "\n",
-    "Create additional features that might be informative for our modeling:\n",
-    "\n",
-    "**House Age**: Calculate the age of the house from the 'yr_built' column to the current year.\n",
-    "\n",
-    "**Renovation Age**: If a house has been renovated ('yr_renovated' > 0), calculate the years since the renovation.\n",
-    "\n",
-    "**Total Square Footage**: Sum up 'sqft_living', 'sqft_lot', 'sqft_above', and 'sqft_basement' for a total square footage feature.\n",
-    "\n",
-    "These new features could reveal deeper insights into the housing prices and help improve the performance of our statistical models.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>69</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8010.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>73</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>12382.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>91</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11540.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>59</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8920.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>37</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11440.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   house_age  renovation_age  total_sqft\n",
-       "0         69             0.0      8010.0\n",
-       "1         73            33.0     12382.0\n",
-       "2         91             0.0     11540.0\n",
-       "3         59             0.0      8920.0\n",
-       "4         37             0.0     11440.0"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Current year for age calculations\n",
-    "from datetime import datetime\n",
-    "\n",
-    "current_year = datetime.now().year\n",
-    "\n",
-    "# Feature Engineering\n",
-    "housing_data['house_age'] = current_year - housing_data['yr_built']\n",
-    "housing_data['renovation_age'] = housing_data.apply(\n",
-    "    lambda x: 0 if x['yr_renovated'] == 0 else current_year - x['yr_renovated'], axis=1\n",
-    ")\n",
-    "housing_data['total_sqft'] = housing_data['sqft_living'] + housing_data['sqft_lot'] + \\\n",
-    "                                     housing_data['sqft_above'] + housing_data['sqft_basement']\n",
-    "\n",
-    "# Display the new features\n",
-    "housing_data[['house_age', 'renovation_age', 'total_sqft']].head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The new features have been successfully added:\n",
-    "\n",
-    "**House Age**: Represents the age of the house since it was built.\n",
-    "\n",
-    "**Renovation Age**: If renovated, this indicates the number of years since the last renovation; otherwise, it is 0.\n",
-    "\n",
-    "**Total Square Footage**: Sum of the living area, lot size, above-ground level area, and basement area\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### *Sample data check.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8540</th>\n",
-       "      <td>3034200366</td>\n",
-       "      <td>2014-12-03</td>\n",
-       "      <td>409000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>9065</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1972</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>8812</td>\n",
-       "      <td>52</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11945.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19433</th>\n",
-       "      <td>2023059052</td>\n",
-       "      <td>2015-05-04</td>\n",
-       "      <td>450000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1350</td>\n",
-       "      <td>92721</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>1946</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1860</td>\n",
-       "      <td>8096</td>\n",
-       "      <td>78</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>95421.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7003</th>\n",
-       "      <td>7751800080</td>\n",
-       "      <td>2015-01-27</td>\n",
-       "      <td>465000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>9879</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1956</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1610</td>\n",
-       "      <td>10050</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12799.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10151</th>\n",
-       "      <td>1180002075</td>\n",
-       "      <td>2014-08-25</td>\n",
-       "      <td>235000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>94</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8420.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3506</th>\n",
-       "      <td>3449900090</td>\n",
-       "      <td>2015-04-10</td>\n",
-       "      <td>454200.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>20</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10639.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "8540   3034200366 2014-12-03  409000.0         3       1.75         1440   \n",
-       "19433  2023059052 2015-05-04  450000.0         3       1.00         1350   \n",
-       "7003   7751800080 2015-01-27  465000.0         3       1.50         1460   \n",
-       "10151  1180002075 2014-08-25  235000.0         3       1.00         1210   \n",
-       "3506   3449900090 2015-04-10  454200.0         4       2.50         2630   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "8540       9065     1.0           0        4.0    6.0        1440   \n",
-       "19433     92721     1.0           0        2.0    4.0        1200   \n",
-       "7003       9879     1.0           0        3.0    5.0        1460   \n",
-       "10151      6000     1.0           0        3.0    5.0        1210   \n",
-       "3506       5379     2.0           0        3.0    6.0        2630   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "8540             0.0      1972           0.0           1990        8812   \n",
-       "19433          150.0      1946           0.0           1860        8096   \n",
-       "7003             0.0      1956           0.0           1610       10050   \n",
-       "10151            0.0      1930           0.0           1210        6000   \n",
-       "3506             0.0      2004           0.0           2630        5379   \n",
-       "\n",
-       "       house_age  renovation_age  total_sqft  \n",
-       "8540          52             0.0     11945.0  \n",
-       "19433         78             0.0     95421.0  \n",
-       "7003          68             0.0     12799.0  \n",
-       "10151         94             0.0      8420.0  \n",
-       "3506          20             0.0     10639.0  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 21143 entries, 0 to 21596\n",
-      "Data columns (total 20 columns):\n",
-      " #   Column          Non-Null Count  Dtype         \n",
-      "---  ------          --------------  -----         \n",
-      " 0   id              21143 non-null  int64         \n",
-      " 1   date            21143 non-null  datetime64[ns]\n",
-      " 2   price           21143 non-null  float64       \n",
-      " 3   bedrooms        21143 non-null  int64         \n",
-      " 4   bathrooms       21143 non-null  float64       \n",
-      " 5   sqft_living     21143 non-null  int64         \n",
-      " 6   sqft_lot        21143 non-null  int64         \n",
-      " 7   floors          21143 non-null  float64       \n",
-      " 8   waterfront      21143 non-null  int64         \n",
-      " 9   condition       21143 non-null  float64       \n",
-      " 10  grade           21143 non-null  float64       \n",
-      " 11  sqft_above      21143 non-null  int64         \n",
-      " 12  sqft_basement   21143 non-null  float64       \n",
-      " 13  yr_built        21143 non-null  int64         \n",
-      " 14  yr_renovated    21143 non-null  float64       \n",
-      " 15  sqft_living15   21143 non-null  int64         \n",
-      " 16  sqft_lot15      21143 non-null  int64         \n",
-      " 17  house_age       21143 non-null  int64         \n",
-      " 18  renovation_age  21143 non-null  float64       \n",
-      " 19  total_sqft      21143 non-null  float64       \n",
-      "dtypes: datetime64[ns](1), float64(9), int64(10)\n",
-      "memory usage: 3.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "housing_data.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. EXPLORATORY DATA ANALYSIS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next is EDA; Exploratory Data Analysis is a crucial step in data analysis. This process will involve examining and understanding the structure, patterns, and relationships within the dataset. It will aid us uncover insights, detect anomalies, and inform subsequent analysis and modeling decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a.) Univariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Distribution of House Prices.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set the style for seaborn\n",
-    "sns.set_style(\"whitegrid\")\n",
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
-    "\n",
-    "# Histogram\n",
-    "sns.histplot(housing_data['price'], bins=\"auto\", kde=False, color='#003399', ax=axes[0])\n",
-    "axes[0].set_title('Histogram of House Prices')\n",
-    "axes[0].set_xlabel('Price')\n",
-    "axes[0].set_ylabel('Frequency')\n",
-    "\n",
-    "# Density Plot\n",
-    "sns.histplot(housing_data['price'], bins=\"auto\", kde=True, color='#339933', ax=axes[1])\n",
-    "axes[1].set_title('Density Plot of House Prices')\n",
-    "axes[1].set_xlabel('Price')\n",
-    "axes[1].set_ylabel('Density')\n",
-    "\n",
-    "# A common title\n",
-    "plt.suptitle('Distribution Analysis of House Prices.', fontsize=16)\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The histogram depicts the distribution of house prices, with most bars are clustered towards the left, suggesting that a significant number of houses are priced lower. The density plot illustrates a curve representing the density of house prices. Similar to the histogram, the curve peaks sharply on the left and gradually tapers off, indicating a right-skewed distribution. \n",
-    "<br>In summary, the majority of house prices are concentrated at the lower end, creating a skewed distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Distribution of Bedrooms, Bathrooms and Floors.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8qUePHqWKiorSAw88UJ7+l7/8pbzfvf322+XpX3zxRWm//fYrVVRUlA4++ODy9G+fn3bdddfyMVZZWVk66KCDyvOOPvrocttMnDixtOmmm5YqKiqq7ff1OV/W17hx40prrrlmqUePHqU333yz2rynnnqqtMYaa5QqKipK//vf/8rTq85lq622WunWW28tT58yZUppp512KlVUVJROP/308vTnn3++1L59+1KPHj1K//3vf8vT33nnnfI5oqKioto+MitV23XXXXet82esOtZ22mmn8rQPP/ywtNZaa5Xat29fGj58eLXlb7zxxlL79u1LXbp0KX3wwQfl6fU9Z9a2Dz711FPlz1F17JdKX+/PVWU99dRTdf6MAADwbYb6BACgwSyyyCJJMtvh2z766KO0bNmy2l1IzZo1ywEHHJDjjz8+2267bb3ed4899ij/v3nz2Xdxe/TokV133bX8um3btjnttNPSqlWr3HrrrZk0aVK93n9OTJ06Ndddd12aN2+ec845p1pbLLHEEjnnnHPSvHnzXHPNNTMNn9iyZcv89re/rXZn44477pjWrVtnwoQJGT9+fK3v/d577+W+++7LQgstlLPOOqtaOT/96U9z6qmnJkkuv/zyhvio2W233crDorZv3z6dOnXK1ltvnX/+85/Zeuutc9RRR1Vbfvjw4fnss8+y0047Zauttqo2b8cdd0yfPn0yduzY3HfffUmSZ555Ji+//HJWX331HHjggeVlW7VqlZNOOqnWOxOrhuSs0rx589x9990ZN25cunfvnoMPPrjaHTwbbrhh9t1330yfPj1XXnllkrnbls2aNctxxx2XVq1aJUlat26dzTffPEmyzDLLZPDgweVll1xyyay99tpJkrfffjtJ8sUXX2Ty5Mlp27ZttTtVW7ZsmSOOOCInnXRSNtxww1l+/vrYeOONq23Hzp07Z8CAARkxYkT23Xff7LbbbtWWb9WqVTbccMMcfvjhadnym8FmWrRoUb5b8t13361XHf7v//4v3bt3L79efPHFy+eLV155Zabll1lmmWy55Zbl182bN8+oUaPy0ksvZcUVV8yJJ55Y7S7MTp06lffHb+//f/nLX5Ikv/vd76rdxbvQQgtl2LBhWXjhhXPvvfeWt8u3HXXUUeVjrFmzZuV9unXr1jnmmGPKbbPgggtmgw02SJJq5TT0+fLbxo8fn379+uWggw7Kz372s2rzunXrllVXXTVJzdtpo402yjbbbFN+vcACC5S366uvvlqefv3116dUKuXAAw/MaqutVp6+wgor5De/+c0c1XvkyJHV9sXv/vvvf/9b6/rXX399Jk+enF/84hfZfvvtq80bMGBAfvnLX+bLL7/Mtddem2Tuzpk17YMffvhhkq/PD1XHfvL1/nzKKafktNNOy3LLLVfPVgEAgK8Z6hMAgAbz1VdfJclMQ519V/fu3fPYY49l2223zYABA8rPlVpiiSWqBXJ10aZNm/z0pz+t1zr9+/efadqSSy6Zjh075plnnsmLL76YHj161KvM+nrhhRcyZcqUdOjQocbhQFdcccV07Ngxzz//fEaPHp111123PO+nP/3pTEMKtm7dOosttljGjRs32+dDPfXUU0mS9ddfPwsvvPBM83v27JmllloqH3zwQd55551ahyuti/XWW69aaDFt2rSMHz8+L730Uu64445MmTIlZ599dtq0aZMk5ee6ffszf1vv3r3z8MMPZ8SIEdlqq63Kn6d3794zLbvAAgukd+/e5WctfldFRcVM06qeM7fFFlvUuM6WW26Ziy66qLzc3GzLFVdccabha6ter7rqqtUCs+SbcL3quWtLLLFEVlpppbzxxhv55S9/mW233Ta9e/dO+/bt87Of/WymMGdubLLJJmnbtm359dSpU/PBBx/kv//9by6//PJMnDgxJ5xwQjl833XXXWc6nidPnpwxY8aUn3H33efHzc63Q9oqP/7xj5N8HYJ+V23bd5NNNpmpfZNk8803z3HHHZfnn38+06ZNy8cff5x33303iy22WNZZZ52Zll944YWzwQYb5K677srIkSOrnY9at26dNdZYo9ryVdt3ueWWK2/PKlWvp06dWp7WkOfL71pjjTVmev5qZWVlxo4dmxdeeCGfffZZkm/O7d9W07ZYeumlk6TaOajq+KwpgN5www3TqlWrGsuvzRJLLJH11ltvlvN/9KMf1bp+VZ1qO8Zvvvnm8r4yN+fMmvbBLl26pFWrVrn77rvz+eefZ4sttsgGG2yQZZdddpbnPQAAqCvBHwAADebTTz9NMvsvXX/3u9/l4IMPzgsvvJALL7wwF154YRZddNFsuOGGGTBgQL1Ct5q+hJ2dWd1JURUgVN2N8X2qeo/a7upYfvnl8/zzz89Un++GBVWqQozKysq5fu/lllsuH330UT788MO5Dv4OOOCA9OzZc6bpH3/8cQYPHpz7778/Z555Zk444YQkyf/+978kycEHH1xruR988EGSbz7PMsssU+NytT3jr6Z9dXbtU/Xssqo7K+dmW9b0/lXB+XfD3W/P+7bzzjsvhx56aF5//fUMGzYsw4YNy9JLL52+fftmp512mil4mlPHHHNMjW05duzY7L333rn22muzwgorZO+99y7PGz9+fK699tqMGDEib731VsaPH59SqVT+HKVSqV51qGnfb9GiRZJkxowZM82rbfvOar9o165dFl988Xz00UeZMGFCnbdv8vXded+28MILz3QXcn23b0OeL2syY8aM3HPPPbnzzjvz2muv5b333pvpRxw1baea2rZqW3z7HFTVfssuu+xMy7dq1SrLLrtsxo4dW686r7zyyhk2bFi91vm22W3T727PuTln1tROP/7xj8vnvH//+9/597//neTrsL9fv37ZeeedyyEqAADUl+APAIAG8/LLLydJ2rdvX+tyyy67bIYPH56nn346999/fx5//PG88sorufXWW3Prrbdm3333zZFHHlmn96zL0J7ftcACC9Q4verL7ZruAvqumkKG+qh6r9rujqxa5ttDEc5unfqYk/duSEsssUSOP/74bLfddhk+fHiOO+64NG/evNy2ffv2zUILLTTL9VdZZZUks2+P2rZnTevObttUza8aom9utmVd9rXZad++fe6666488cQTefDBB/Pvf/87b731Vq6//vrccMMNOf744zNw4MC5fp9ZWWGFFXLkkUfmkEMOyQ033FAO/kaMGJEDDjggkyZNyo9//OOstdZaWXnllbPGGmtkueWWy4ABA+r9XvU93mvbvrX59vZq7O3bkOfL75o0aVJ23333jB49Ou3atUuHDh2y3nrrpaKiImuvvXZOPvnk8t1u31XX89DslqsKC39IdT3G63Pure86W265ZXr37p0HH3wwjzzySJ588smMGTMmY8aMyV/+8pdcddVV6dSpU90+EAAAfIvgDwCABvPwww8nSa1DsH3b2muvXX5m2ccff5x//OMfOffcc/PnP/85gwYNmuUdXHNrVnf0vffee0m+ufOvKmSo6Q66qiHw5lTV3Ry1PeOs6i6Y2p5RN6+993dVPUNs6tSpmTBhQpZccsksvfTSeeutt7LbbrvVaV+qupOoavt9V33v4Jxd+1S1TdXwpU2hPVu0aJFevXqlV69eSZL3338/V199da688soMGzYs//d//1ftWWINrSqErbpbs1Qq5dhjj82kSZNy4oknZpdddqm2/H/+85/vrS6zM7vt9eWXX2bChAlp0aJFFl100fKwlY19vHwf58srrrgio0ePznrrrZcLL7xwpqD9888/n+t6L7PMMnnzzTfz3nvvlfeTKqVSaaa7JH8ISy+9dN588828++67M9Up+WZbf9/H+EILLZRtttmm/KzEl156Keecc07+9a9/5fzzz8+f//znOpcFAABV6v/zaAAAqMFdd92VN954I8suu2z69Okzy+XeeOON9O/fP/vss0+16UsssUT222+/tG/fPpWVlRk3blyShru77dsee+yxmaa99957efHFF7Pwwgtn9dVXT/L1kH/J11+yf9czzzxTY9l1rW+HDh3Stm3b/Oc//6lxmLt33nknL730UvkunIa09tprp1mzZvn3v/+diRMnzjT/ySefzIQJE7L88svnJz/5SYO+93e9+eabSb5+VuOiiy6a5OtnmiUpPwfuu84+++xst912ufHGG5N88yzABx98cKY7MWfMmFHj9q5N1fv/85//rHH+XXfdlSTlIRYbc1uOHDkyW2yxRXmY1Co/+clPcvTRR2eRRRbJpEmTanz+XUN66623knwTmo8fPz5jx47NIossMlPolyT/+te/ksx+WNrvQ9X2feCBBzJ9+vSZ5v/zn/9MZWVlunXrlubNm+cnP/lJlltuuXzyySflZ7592xdffFH+PFVlN5T6ni/r69lnn02SDBw4cKbQb9y4cXn99deTzN12qgrv77vvvpnmjRo1Kl9++eUclz2nqrbTPffcU+P87x7jDX3OvPLKK9O3b9/ccsst1aavueaaGTp0aJJvhjIGAID6EvwBADBXSqVS7r777hx//PFJkuOOO67W4SF/+tOfZvz48fnXv/41U7Dy4osv5vXXX8+CCy6YlVZaKck3w3JOmjSpwUKCW265pdqX0J9//nmGDh2aGTNmZODAgeX6r7baakm+/nL6pZdeKi//v//9b5bPl6pad3ZBS9u2bfN///d/qayszBFHHJEJEyaU502YMCGHH354Kisrs/3226dt27Zz9kFnYYUVVsjGG2+ciRMnZujQoZk0aVJ53tixY3PcccclSXbdddcGfd/v+uyzz3Lqqacm+XrYu6phEXfccce0a9cuf/vb33LnnXdWW+ehhx7KlVdemZdeeqkconXs2DHdunXLm2++mTPPPLO8n1RWVmbYsGF5++2361WvLbbYIksvvXRGjhyZSy65pNrQkI8++mguv/zytGjRIjvvvHOSxt2Wq666at55553ccsstefrpp6vNe/jhh/P5559n+eW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-      "text/plain": [
-       "<Figure size 1800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
-    "\n",
-    "# Common title for all subplots\n",
-    "fig.suptitle('Distribution of Bedrooms, Bathrooms, and Floors.', fontsize=16)\n",
-    "\n",
-    "# Box plot for bedrooms\n",
-    "sns.boxplot(x=housing_data['bedrooms'], ax=axes[0])\n",
-    "axes[0].set_title('Bedrooms')\n",
-    "axes[0].set_xlabel('Number of Bedrooms')\n",
-    "\n",
-    "# Box plot for bathrooms\n",
-    "sns.boxplot(x=housing_data['bathrooms'], ax=axes[1])\n",
-    "axes[1].set_title('Bathrooms')\n",
-    "axes[1].set_xlabel('Number of Bathrooms')\n",
-    "\n",
-    "# Box plot for floors\n",
-    "sns.boxplot(x=housing_data['floors'], ax=axes[2])\n",
-    "axes[2].set_title('Floors')\n",
-    "axes[2].set_xlabel('Number of Floors')\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After analyzing the distribution of bedrooms, bathrooms and floors, an outlier was detected in the bedroom column. To ensure the accuracy of our analysis, the outlier value was identified and subsequently removed from the dataset. The box plot below displays the distribution of bedrooms after excluding the outlier value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Identify the outlier value\n",
-    "outlier_value = housing_data['bedrooms'].max()  \n",
-    "\n",
-    "# Filter the DataFrame to exclude the outlier\n",
-    "housing_data = housing_data[housing_data['bedrooms'] != outlier_value]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Number of Bedrooms')"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Box plot for bedrooms\n",
-    "sns.boxplot(x=housing_data['bedrooms'])\n",
-    "plt.title('Bedrooms')\n",
-    "plt.xlabel('Number of Bedrooms')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b.) Bivariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Total Square Footage of houses by Price Range.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "price_range\n",
-       "300K-600K    10560\n",
-       "600K-1M       4691\n",
-       "100K-300K     4433\n",
-       "1M-2M         1234\n",
-       "2M-5M          187\n",
-       "70K-100K        30\n",
-       "5M-8M            7\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Define the labels with ranges\n",
-    "labels = [\"70K-100K\", \"100K-300K\", \"300K-600K\", \"600K-1M\", \"1M-2M\", \"2M-5M\", \"5M-8M\"]\n",
-    "\n",
-    "# Cut the data into the specified ranges and assign labels\n",
-    "housing_data.loc[:,\"price_range\"] = pd.cut(housing_data.price,\n",
-    "                                            bins=[70000, 100000, 300000, 600000, 1000000, 2000000, 5000000, 8000000],\n",
-    "                                            labels=labels)\n",
-    "\n",
-    "# Count the occurrences of each category\n",
-    "housing_data['price_range'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "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>condition</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "      <th>price_range</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1188</th>\n",
-       "      <td>2624049185</td>\n",
-       "      <td>2014-09-09</td>\n",
-       "      <td>405000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1760</td>\n",
-       "      <td>5355</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1160</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>1956</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1790</td>\n",
-       "      <td>6225</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8875.0</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20611</th>\n",
-       "      <td>2895800390</td>\n",
-       "      <td>2014-08-07</td>\n",
-       "      <td>359800.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7092.0</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2209</th>\n",
-       "      <td>3438500339</td>\n",
-       "      <td>2014-05-26</td>\n",
-       "      <td>276000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>64</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7280.0</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18431</th>\n",
-       "      <td>4167960330</td>\n",
-       "      <td>2015-01-09</td>\n",
-       "      <td>270000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>7750</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>8084</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11390.0</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11609</th>\n",
-       "      <td>1972201550</td>\n",
-       "      <td>2014-07-16</td>\n",
-       "      <td>565000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>2452</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1906</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1290</td>\n",
-       "      <td>3360</td>\n",
-       "      <td>118</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5532.0</td>\n",
-       "      <td>300K-600K</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",
-       "1188   2624049185 2014-09-09  405000.0         3       1.75         1760   \n",
-       "20611  2895800390 2014-08-07  359800.0         5       2.50         2170   \n",
-       "2209   3438500339 2014-05-26  276000.0         3       1.00         1140   \n",
-       "18431  4167960330 2015-01-09  270000.0         3       2.00         1820   \n",
-       "11609  1972201550 2014-07-16  565000.0         4       1.00         1540   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "1188       5355     1.0           0        3.0  ...        1160   \n",
-       "20611      2752     2.0           0        3.0  ...        2170   \n",
-       "2209       5000     1.0           0        3.0  ...        1140   \n",
-       "18431      7750     1.0           0        3.0  ...        1820   \n",
-       "11609      2452     1.5           0        4.0  ...        1540   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "1188           600.0      1956           0.0           1790        6225   \n",
-       "20611            0.0      2014           0.0           1800        2752   \n",
-       "2209             0.0      1960           0.0           1140        5000   \n",
-       "18431            0.0      1992           0.0           2080        8084   \n",
-       "11609            0.0      1906           0.0           1290        3360   \n",
-       "\n",
-       "       house_age  renovation_age  total_sqft  price_range  \n",
-       "1188          68             0.0      8875.0    300K-600K  \n",
-       "20611         10             0.0      7092.0    300K-600K  \n",
-       "2209          64             0.0      7280.0    100K-300K  \n",
-       "18431         32             0.0     11390.0    100K-300K  \n",
-       "11609        118             0.0      5532.0    300K-600K  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  grouped_vals = vals.groupby(grouper)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Set the style of seaborn\n",
-    "sns.set_style(\"darkgrid\")\n",
-    "\n",
-    "# Create a bar plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\")\n",
-    "plt.title(\"Total Square Footage by Price Range.\")\n",
-    "plt.xlabel(\"Price Range\")\n",
-    "plt.ylabel(\"Total Square Footage\")\n",
-    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The bar plot illustrates the relationship between price range and total square footage. Each bar represents the total square footage of houses within different price range categories. \n",
-    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. \n",
-    "<br>This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Relationship between bedrooms, bathrooms, and house price**\n",
-    "\n",
-    "Created a scatter plot to visualize the relationship.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot for the relationship between bedrooms, bathrooms, and house price\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(housing_data['bedrooms'], housing_data['bathrooms'], c=housing_data['price'], cmap='winter', alpha=0.5)\n",
-    "plt.colorbar(label='House Price')\n",
-    "plt.xlabel('Number of Bedrooms')\n",
-    "plt.ylabel('Number of Bathrooms')\n",
-    "plt.title('Relationship between Bedrooms, Bathrooms, and House Price')\n",
-    "plt.grid(True)\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both the real estate companies(sellers) and buyers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
-    "A house with a good balance of bedrooms and bathrooms tends to attract a wider range of potential buyers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#House age and house price**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Line plot for house age vs. average house price\n",
-    "average_price_by_age = housing_data.groupby('house_age')['price'].mean()\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(average_price_by_age.index, average_price_by_age.values, marker='o', linestyle='-')\n",
-    "plt.xlabel('House Age (Years)')\n",
-    "plt.ylabel('Average House Price')\n",
-    "plt.title('Average House Price by House Age.')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The scatter plot depicts the relationship between house age and prices; and reveals a lack of a clear linear trend. Instead, prices exhibit significant variation across different house ages. While the graph provides insights into how house prices fluctuate based on their age, no discernible pattern emerges. \n",
-    "In summary, the graph shows how house prices fluctuate based on their age, but no specific pattern emerges. This suggests that other factors beyond house age play a more influential role in determining house prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Prices of houses in relation to their respective Condition and Grade**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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tttty8MEH54EHHmi2DEOS7LDDDtlhhx3y1ltv5c9//nMuv/zyHHXUURkyZEg++9nPLvZr1dfXN33p2Ke19NJL55VXXlno/ieeeCI9evTIMssskyQZO3ZsunfvvtB+n/vc51qkDgCAtrDlllvmvvvuy9tvv50ePXokWXhu1bNnz489zimnnJJ77rkn5557bjbeeON069YtSZqtt9qrV69MmzYtc+fObbbUwoJz2vlzraOPPjobbrjhQq/zUeHkyiuvnEGDBuU3v/lNOnfunGnTpmXHHXdsery+vj4HHnhgDjzwwEycODH/+7//m4suuihHHHFE06eqFlfPnj1bdP45derUhe6///77s8Yaa2TppZfOKqus0my5iAUt7vJtAIvrwz8b0A7ccsstOfLII3P44Ycv9NiDDz6Y3XbbLUOGDMn222+f22+/vemxq666Kv/1X/+VzTbbLKVSKRtttFFuuummj/yoLlAce++9d6ZPn55zzjlnocemTJmSMWPGNE0W11133dTX1+eOO+5ott9jjz2WiRMnZr311lvs191oo43yyiuv5Jlnnmm6b+rUqXnyySc/8nkf9TGslqzvw+y0006ZNGlSLrjggpRKpWZf1HbYYYfle9/7XpJ5E9WvfvWrOeiggzJ37tyPvIKitQ0ZMiTjx4/PP/7xj6b7Zs+ene9///u54YYbssEGGySZt8bx2muv3fRv+vTpOffccz9VkA4A0Nb233//zJ07N8cdd1xmz5690OMzZ87M+PHjP/Y4jz/+eIYOHZqtttqqKbD929/+lqlTpzYtw/ClL30pDQ0N+f3vf9/0vNmzZ+cvf/lL0+0vfOEL6d27d1555ZVmc62+ffvm7LPPzt///vePrGPHHXfMAw88kDvvvDODBg1qWq935syZ2WabbXLllVcmmfeH9pEjR2b77bfPa6+99rH9taYhQ4bkT3/6U7Pz/49//CP77bdfnnnmmWy44YZ59dVX07t372bn5KGHHsqYMWOaBeAALaFdX2m7ySabZMSIEamrq2sW3D733HM58MAD89Of/jRbbrllnnrqqRx00EHp1atXvvzlL+fpp5/O0KFDs99+++Wpp55K37598/3vf3+h9YCAYho0aFAOPfTQnHvuuXnhhReyyy67pFevXnn++edz5ZVX5p133slll12WUqmUnj17Zr/99suFF16Yzp07Z8stt8wrr7yS8847LwMGDFiib6jdaaedcvXVV+d73/teDj/88PTo0SMXX3zxh65DNt8yyyyTJ554Io8++miGDBnS7LGWrO/DDBgwIGuuuWZ+8YtfZOutt87SSy/d9NhGG22UE088MWeccUY23XTTvPnmm03fLjx/PdmXX345U6dO/VRr9y6pXXfdNddcc00OPPDAHHrooVl22WVz3XXXZebMmdlzzz2z0korZccdd8wPf/jDTJgwIWuttVZefPHFnHPOOVlhhRUW+UUeAABFtdpqq+Xss8/OMccck5133jlf//rXM3DgwDQ0NOSJJ57IjTfemMmTJ2efffb5yOOss846+c1vfpNf/vKXWXXVVfPcc8/l4osvTqlUynvvvZdkXmi7ySab5Ac/+EGmTJmS/v375+qrr87UqVOblgfo1KlTDj/88Jxwwgnp1KlTtthii7z55pu56KKLMmnSpA9dImC+7bffPqeddlruuuuuHH/88U33d+nSJWuuuWbT3HfgwIF58cUXc8stt2SbbbZp2u/vf/976uvrM2DAgE96SpfYQQcdlD322CP77rtv9tprr8yePTvnnXde1lxzzWy66aZpaGjItddem1GjRuWAAw5Iv3798uCDD+byyy/Pt7/97XTu3LnNagVqQ7sObRdcc2dBv/rVr7Lllls2Ldq+3nrr5etf/3quu+66fPnLX86MGTNyxRVX5IILLsjaa6+d++67L4cffniuvfbapm+BBIrtwAMPzBe/+MVcd911Oe200zJ9+vT07ds3m266aQ444IBmH4///ve/nz59+uTaa6/Nr3/96/Ts2TPbbrttDjvssGZrxH6c+vr6jB07NqeeempOOeWUlEqlfP3rX8+KK66YKVOmfOjzDjjggFx00UXZd999c/fddy/0eEvV91F22mmnPPvss80+mpYk3/jGNzJnzpz86le/yi9+8Yt06dIlX/rSl3LUUUc1TTwvuuii3HLLLc2uem1tPXr0yLXXXpszzzwzp5xyShoaGrLuuuvmmmuuafpUxGmnnZZLL700v/rVr/Laa6+ld+/e2W677XLYYYe50gEAaHe22mqr3H777fnlL3+ZG2+8MRMmTEilUsmKK66Y7bbbLt/4xjc+9g/To0ePzpw5c3Luuedm9uzZWWGFFXLggQfmX//6V+67776mJREuvPDCnHXWWTn//PMza9asbLfddvn617+eP/zhD03H2n333dO9e/eMGTMm119/fbp165b11lsvZ5111scu4dWzZ89sttlmuf/++7Pddts1e+zHP/5xzj333Fx55ZV544030rt37+y222459NBDm/b53ve+l/79++eaa65Z8hP5CX3xi1/MNddck7PPPjuHH354unfvns022yxHHnlk6uvrU19fn+uuuy5nn312fvrTn+att95q+i6Ivffeu83qBGpHqfJR32DTjgwcODBXX311hg4dmn333TcPP/xws7Uu586dm5VWWim33XZbBg8enP/6r/9qdnXufvvtlwEDBuToo4+uRvkAAAAAAEna+ZW2H6Zv377ZZZdd8uMf/7jpvtdff73p2zJXXXXVhdYJmjt37kd+AzsAAAAAQFto119E9mF222233Hnnnfnzn/+cxsbGvPTSS/n2t7/dtNj5N7/5zfzyl7/Mgw8+mMbGxtxzzz0ZN25cdthhhypXDgAAAADUug55pe26666bn/3sZ/nZz36WQw89NF27ds0OO+yQ//7v/06SfO1rX0u5XM5pp52WV155Jf37988555zzsYupAwAAAAC0tg6zpi0AAAAAQEfQIZdHAAAAAABor4S2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIHUVbuAT2rKlLdSq1+hViolvXsvXdPnAOOA9xkLJMYB8xgH75+DImjL96HW3vta6levHZNeO65a6levHZNe2+51P067DW0rlXT4wfNxnAMS44D3GQskxgHzGAfFUI33odbe+1rqV68dk147rlrqV68dk16rz/IIAAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKpK7aBQAAAAAAtJWJEyfkqqsuT319XUaOHJV+/fpXu6SFCG0BAAAAgJoxduwVefrpJ5MkDQ2NGT36hOoWtAiWRwAAAAAAasaECeMXuV0kQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUSFVD27lz52bPPffM6NGjq1kGAAAAAEBhVDW0vfDCC/PYY49VswQAAAAAgEKpWmj70EMP5d57781XvvKVapUAAAAAAFA4ddV40SlTpuT444/PRRddlKuuuuoTHaNUatma2pP5vdfyOcA44H3GAolxwDzGQbF6b8taau29r6V+9dox6bXjqqV+9dox1UqvH+yvGvO2j9PmoW1jY2OOOuqojBo1KmusscYnPk7v3ku3YFXtk3NAYhzwPmOBxDhgHuOgGKrxPtTae19L/eq1Y9Jrx1VL/eq1Y+rovZbL5WbbffoUr982D20vvfTS1NfXZ8899/xUx5ky5a1UKi1UVDtTKs37j6eWzwHGAe8zFkiMA+YxDt4/B0XQlu9Drb33tdSvXjsmvXZctdSvXjumWum1sbGx2fbkyW+12Wsv7ny1zUPb2267La+//nqGDBmSJJk5c2aS5Pe///0SfSlZpZIOPXgWh3NAYhzwPmOBxDhgHuOgGKrxPtTae19L/eq1Y9Jrx1VL/eq1Y+rovX6wtyL22uah7W9/+9tmt0ePHp0kOf3009u6FAAAAACAwil//C4AAAAAALSVNr/S9oNcYQsAAAAA8D5X2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgddUuAAAAAABgUcrlUsrlUoses1Ra8Hil1NW1znWtjY2VNDZWPtFzhbYAAAAAQOGUy6X06tU15XKnFj9u89fo3qLHn6+xcW6mTXvvEwW3QlsAAAAAoHDmXWXbKU/968S8/d5LLXbcWbMnN9v+yzN7tdix5+vRdZWsO+CklMsloS0AAAAA0LG8/d5LefPdf7bY8RorXTL/q74aK3Na9NgtxReRAQAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECqEto+9NBD2X333bPeeutl2LBhOfnkkzNz5sxqlAIAAAAAUChtHtpOnTo1+++/f775zW/mscceyy233JJHHnkkl112WVuXAgAAAABQOHVt/YLLLrtsHnzwwfTo0SOVSiXTp0/PrFmzsuyyy7Z1KQAAAAAAhdPmoW2S9OjRI0my2WabZdKkSRkyZEh23XXXapQCAAAAAFAoVQlt57v33nszY8aMHHnkkTnkkEMyZsyYxX5uqdSKhRXc/N5r+RxgHPA+Y4HEOGAe46BYvbdlLbX23tdSv3rtmPTacdVSv3rtmGqp17a04Plc3HNbqlQqldYpZ/E9/fTT2X333fPII4/kM5/5TLXLAQAAAAAK4i/P7JU33/1nix3vsjO75M3p877qa5mejdnv6Jktduz5lum2eoatPfYTP7/Nr7T961//muOOOy6333576uvrkySzZ89O586d07Vr18U+zpQpb6X6cXN1lEpJ795L1/Q5wDjgfcYCiXHAPMbB++egCNryfai1976W+tVrx6TXjquW+tVrx1S0Xjt1KqdXr+7VLuNTmTbtncyd29h0e3Hnq20e2g4cODAzZ87M2WefnSOOOCJvvPFGzjjjjOy2225NIe7iqFRSiMFTTc4BiXHA+4wFEuOAeYyDYqjG+1Br730t9avXjkmvHVct9avXjqmWem0Ln+Rcllu+jI/WvXv3jBkzJs8//3yGDRuWPffcMxtvvHGOO+64ti4FAAAAAKBwqvJFZAMGDMiVV15ZjZcGAAAAACi0Nr/SFgAAAACADye0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAAGrGsstXmrZ7L7BdJHXVLgAAAAAAoK0M33527qvUJ0m22H52latZNKEtAAAAAFAzll2ukt1Gzap2GR/J8ggAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABfKpQtupU6e2VB0AAAAAAOQThLYNDQ0555xzsv7662f48OEZP358vva1r+X1119vjfoAAKBq/v73v+fee+/N7NmzM2XKlGqXAwBAjVji0PaCCy7Iww8/nPPOOy+dO3dO796907dv35xyyimtUR8AALS5KVOm5Bvf+Ea+/vWv55hjjsn48eOz1VZb5Yknnqh2aQAA1IAlDm3vuOOOnH/++dlkk01SKpXSrVu3nHbaaXn44Ydboz4AAGhzp556alZfffU8+uijqaury6qrrpr99tsvZ555ZrVLAwCgBixxaPvuu+9m2WWXTZJUKpUkSZcuXVIu+04zAAA6hocffjjHHntsunbtmlKplCTZZ5998q9//avKlQEAUAuWOGkdNGhQLrzwwiRpmsBec801WXvttVu2MgAAqJLOnTtn5syZSd6/UOGdd95J9+7dq1kWAAA1YolD2+OPPz533HFHNt1007zzzjvZbrvtcvXVV2f06NGtUR8AALS54cOH56ijjspLL72UUqmUKVOm5KSTTspmm21W7dIAAKgBdUv6hBVXXDF33XVX/vjHP2bChAnp27dvNt988/To0aM16gMAgDZ3xBFH5Nhjj822226bJNlkk02y2Wab5cc//nGVKwMAoBYscWg7e/bsXHLJJdltt93y1a9+NWPHjs2YMWNyyCGHWNcWAIAOoXv37jn//PMzderUvPLKK/nsZz+bz372s9UuCwCAGrHEKetpp52WBx54IJ06dUqSrLnmmvnzn/+cs846q8WLAwCAapg4cWK+8Y1v5NVXX80666yTq666Kt/85jfzxhtvVLs0AABqwBKHtvfee2+uuOKKfO5zn0uSDBkyJJdcckluv/32Fi8OAACq4aSTTsoXvvCFrLzyykmSfffdNwMGDMjJJ59c5coAAKgFS7w8wqxZs9KtW7dm9/Xo0SMNDQ0tVhQAAFTTE088kb/85S/p3LlzkmTZZZfND37wg2y66aZVrgwAgFqwxFfaDhkyJKeddlpmz56dZF6Ie+aZZ2a99dZr8eIAAKAa6urqMnXq1Gb3zZgxI126dKlSRQAA1JIlvtL2+OOPzz777JP11lsvvXr1yrRp0/L5z38+l1xySWvUBwAAbW7bbbfNIYccksMOOyz9+vXLq6++mvPPPz/bbLNNtUsDAKAGLHFou+KKK+buu+/O448/nsmTJ6dv375ZZ511Ule3xIcCAIBCOuqoo3LSSSdl//33z+zZs1NfX5+dd945hx12WLVLAwCgBix20vraa6+lb9++mThxYpJkhRVWyAorrJAkef3115Ok6cvJAACgPevatWtOP/30nHzyyZkxY0Z69+6dUqlU7bIAAKgRix3abrfddvnrX/+a4cOHLzRhrVQqKZVK+b//+78WLxAAANrKnXfemR122CG33nrrh+6z8847t1k9AABtZeLECbnqqstTX1+XkSNHpV+//tUuqaYtdmh71113JUluv/32dO/evdUKAgCAarnkkkuyww475Pzzz1/k46VSSWgLAHRIY8dekaeffjJJ0tDQmNGjT6huQTVusUPbfv36JUkOOOCA3H777enRo0erFQUAANVw5513JknOPPPMDB48OJ06dapyRQAAbWPChPGL3KY6yp/kSe+9915L1wEAAIVx8MEHZ/bs2dUuAwCAGrXYV9rON3To0Oy+++7ZdNNNs/zyyzd77Hvf+16LFQYAANWy4oor5plnnsmGG25Y7VIAAKhBSxzavvLKK1lxxRXz4osv5sUXX2y637fpAgDQUXzmM5/JqFGjssIKK2T55ZdvNte9+uqrq1gZAAC1YIlC2wsvvDA9evTIJptskpEjR7ZWTQAAUFWDBw/O4MGDM3v27MyYMSO9evVKXd0SX+8AAACfyGKvaXvmmWfmF7/4RTp37pzzzz8/l112WWvWBQAAVfOd73wnL7/8cq666qpcf/31GTt2bKZMmZL99tuv2qUBAFADFju0vfPOOzN27Nicf/75Of/883PHHXe0Zl0AAFA1J598cv7zn//k4osvzt13351zzz03zzzzTM4666xqlwYAQA1Y7M94vfXWW1lttdWSJOuvv34mTZrUakUBAEA13Xffffntb3+b3r17J0m+8IUvZI011shOO+2U4447rsrVAQDQ0S32lbbl8vu7Ws8LAICObKmllkqnTp2a3de9e/d07dq1ShUBAFBLFju0rVQqrVkHAAAUxgEHHJBDDjkkzz33XN5777289NJLOfbYY7Pddttl4sSJTf8AAKA1LPYlsw0NDbn11lubbs+ZM6fZ7STZeeedW6gsAAConp/85CdJ5s1vS6VSswsYrrzyylQqlZRKpfzf//1ftUoEAKADW+zQtk+fPjn//PObbvfq1avZ7VKpJLQFAKBD+MMf/lDtEgAAqGGLHdred999rVkHAAAURv/+/atdAgAANWyx17QFAAAAAKD1CW0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKJC6ahcAAAAAACy+crmUcrnUoscslRY8Xil1da1zrWdjYyWNjZVWOXZHIrQFAAAAgHaiXC6lV6+uKZc7tfhxm79G9xY9/nyNjXMzbdp7gtuPIbQFAAAAgHZi3lW2nfKbf16Qqe9OaLHjvjN7WrPt654c3WLHnm/Zbv3z1dW/n3K5JLT9GEJbAAAAAGhnpr47IW+882KLHW9upaHZdksemyXni8gAAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFUpXQ9rnnnsuoUaOy4YYbZtiwYTn66KMzderUapQCAAAAAFAobR7azpw5M/vss08GDx6cP//5z7nzzjszffr0HHfccW1dCgAAAABA4bR5aDtx4sSsscYaOfjgg1NfX59evXpljz32yKOPPtrWpQAAAAAAFE5dW7/gF77whYwZM6bZfffcc0/WXHPNJTpOqdSSVbUv83uv5XOAccD7jAUS44B5jINi9d6WtdTae19L/eq1Y9Jrx1VL/eqVT6uWzueCvS5u320e2i6oUqnk3HPPzf/+7//m2muvXaLn9u69dCtV1X44ByTGAe8zFkiMA+YxDoqhGu9Drb33tdSvXjsmvXZctdSvXjuOHn065b3pjUmSpZfr1Kqv1atX91Y9fpF80l6rFtq+/fbbOfbYY/Pss8/m2muvzcCBA5fo+VOmvJVKpZWKK7hSad4Pilo+BxgHvM9YIDEOmMc4eP8cFEFbvg+19t7XUr967Zj02nHVUr96rZ5OncqtEnqutV33/O2ud5Ika361dUPVadPeydy5jR+7X2v12pY+2OvizlerEtq+/PLL2XffffO5z30uN954Y5ZddtklPkalkkL8h1JNzgGJccD7jAUS44B5jINiqMb7UGvvfS31q9eOSa8dVy31q9eOo0efumy012fa7PU68rn8oE/Sa5t/EdmMGTOy1157Zb311ssVV1zxiQJbAAAAAICOqs2vtL355pszceLE/OY3v8lvf/vbZo898cQTbV0OAAAAAEChtHloO2rUqIwaNaqtXxYAAAAAoF1o8+URAAAAAAD4cEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKJC6ahcAAAAAAEU0ceKEXHXV5amvr8vIkaPSr1//apdEjRDaAgAAAMAijB17RZ5++skkSUNDY0aPPqG6BVEzLI8AAAAAAIswYcL4RW5DaxPaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgdRVuwAAAAAA+DTK5VLK5VKLH7dUWvCYpdTVtc71j42NlTQ2Vlrl2LRPQlsAAAAA2q1yuZSevbqmU7lTqxx7we1evbq3+GskydzGuZk+7T3BLU2EtgAAAAC0W+VyKZ3KnTL2b1fntXcnteixZ8ya0Wz7jEd+2qLHT5K+3T6bvdb6r5TLJaEtTYS2AAAAALR7r707Ka+89UqLHnNuZW6z7ZY+PnwYX0QGAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAALAI9b2Xatpeqs9SH7EntCyhLQAAAAAsQt+t+6X753uk++d75LNb9at2OdSQumoXAAAAAABFtFTvpbLyN1epdhnUIFfaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgdRVuwAAAAAA2o+JEyfkqqsuT319XUaOHJV+/fpXuyTocIS2AAAAACy2sWOvyNNPP5kkaWhozOjRJ1S3IOiALI8AAAAAwGKbMGH8IreBliO0BQAAAAAoEMsjAAAAAHRA5XIp5XKpxY9bKi14zFLq6lr+msDGxkoaGystflxoL4S2AAAAAB1MuVxKz17d0qnc8oHqgkFwuVxKr17dW/w15jY2Zvq0dwW31CyhLQAAAEAHUy6X0qlczukPXZ+X33y9RY895b03m20fdM8FLXr8lZZZPqO/tEfK5ZLQlpoltAUAAADooF5+8/X8a9rEFj3m3GXqkzffS5I0fKa+xY8PVPmLyKZOnZqtt94648aNq2YZAAAAACym8pdWSvovk/RfJuWNVqp2OdAhVe1K28cffzyjR4/Oyy+/XK0SAAAAAFhCpZ5d02m7NapdBnRoVbnS9pZbbsmRRx6Zww8/vBovDwAAAABQWFW50naTTTbJiBEjUldX94mD21Lp4/fpqOb3XsvnAOOA9xkLJMYB8xgHxeq9LWuptfe+lvrVa8ek146r1vptC7V2Lmup31rtdXH7rkpou9xyy33qY/TuvXQLVNK+OQckxgHvMxZIjAPmMQ6KoRrvQ62997XUr147Jr12XLXWb2vp1at7tUtoU7XUr14/XtXWtP20pkx5K5VKtauojlJp3i+AWj4HGAe8z1ggMQ6Yxzh4/xwUQVu+D7X23tdSv3rtmPTacRWp306dyu0+GJs27Z3Mndv4sft1hF6T2uq3lntd3Plquw1tK5VU/QdgtTkHJMYB7zMWSIwD5jEOiqEa70Otvfe11K9eOya9dly11m9rqrXzWEv96vWjVeWLyAAAAAAAWDShLQAAAABAgVR9eYR//OMf1S4BAAAAAKAwXGkLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAokLpqFwAAAADQ3k2cOCFXXXV56uvrMnLkqPTr17/aJQHtmNAWAAAA4FMaO/aKPP30k0mShobGjB59QnULAto1yyMAAAAAfEoTJoxf5DbAJ+FKWwAAAKBmlMullMulFj9uqbTgMUupq2v56+QaGytpbKy0+HGB4hHaAgAAADWhXC6lZ69u6VRu+UB1wSC4XC6lV6/uLf4acxsbM33au4JbqAFCWwAAAKAmlMuldCqXc/zvbsyLU99o0WNPq19wu5RvXX9xix7/88sul1O23i3lckloCzVAaAsAAADUlBenvpHnJr/aoscsr/G5dJs1K0kyY41+mdTCxwdqi9AWAAAA4FNqXLpb3t5kzWqXAXQQLb+ICwAAAAAAn5jQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACqat2AQAAfHoTJ07IVVddnvr6uowcOSr9+vWvdkkAAMAnJLQFAOgAxo69Ik8//WSSpKGhMaNHn1DdggAAgE/M8ggAAB3AhAnjF7kNAAC0P660BQBqWrlcSrlcqnYZn1qptGAPpdTVdYy/zTc2VtLYWKl2GQAA0KaEtgBAzSqXS+nVq2vK5U7VLuVTWzB4ntdX9ypW03IaG+dm2rT3BLcAANQUoS0AULPmXWXbKb/55wWZ+u6Eapfzqbwze1qz7eueHF3FalrGst3656urfz/lckloCwBATRHaAgA1b+q7E/LGOy9Wu4xPpWvvSt7+f7lttz6Vdt8PAADUso6x2BkAQI1ba7vuWW7Vzllu1c5Z86sdY2kEAACoVa60BQDoAHr0qctGe32m2mUAAAAtQGgL0I5NnDghV111eerr6zJy5Kj069e/2iUBAEASc1WAT0NoC9SkeV8+VPr4HQvu6quvyNNPP5kkmTu3kuOPP7G6BbWQxsaKLx0CAGhDrTE/bqu5qrkj0BEJbYGaUy6X0rNnt3Tq1P6X9X711fe/7X7ixFfSq1fHWMdy7tzGTJ/+rsn3YnIVCwDwabTW/Lit5qrmjkBHJLQFak65XEqnTuUcd+tN+ffkydUu51OZvsDVENPKpXxjzKVVrKZlfKFPn5y689dSLpdMvBfT2LHvX8XS0NCY0aNPqG5BAEC70lrz47aYq5o7Ah2V0BaoWf+ePDnPvfZqtcv4VMor9U/3mTOTJNNW6p9X23k/ba2jLJMxceIrTdsTJrySurr2fxV54qOOANDWWnp+bK4K8MkJbQHascZu3fLWoLWrXUa7VC6X0rNXt3Qqt/+Ac8HguVwudZxlMhobM32ajzoCQHtlrgrwyQltAahJ5XIpncrlnP7Q9Xn5zderXc6n8na390PNt7snB91zQRWraRkrLbN8Rn9pDx91BAAAapLQFoCa9vKbr+df0yZWu4xPpTLks8nseR89fG/95dt9PwAAALVOaAsA7VypZ9d02m6NapcBAABAC2n/C/kBAAAAAHQgrrRtZyZOnJCrrro89fV1GTlyVPr161/tkgAAAACAFiS0bWfGjr0iTz/9ZJKkoaExo0efUN2CAAAAAIAWZXmEdmbChPGL3AYAAAAAOgahLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgvogMAAAAFqFcLqVcLrXa8Tt1ar3rqBobK2lsrLTa8QFoXUJbAAAA+IByuZSePbu1arDaq1f3Vjv23LmNmT79XcEtQDsltAUAAIAPKJdL6dSpnB/+4pa89PrkapezRFZZvk9O/tYuKZdLQluAdkpoC+3QxIkTctVVl6e+vi4jR45Kv379q10SAAB0SC+9Pjn/mPBatcsAoMYIbaEdGjv2ijz99JNJkoaGxowefUJ1CwIAAACgxbTe4jxAq5kwYfwitwEAAABo/4S2AAAAAAAFUjPLI5TLpZTLpWqX8amVSgv2UEpdXcfI3RsbKxbIBwAAAIDUSGhbLpfSs2e3dOrU/gPOBYPncrmUXr26V7GaljN3bmOmT3+31YNb4X2xCe8BAAAAaii07dSpnLNOvT7jX36j2uV8KlOnvNVs+9ADLqxiNS1jxZWWy5HH7ZFyudSqgZ3wvvjaKrwHAKBYJk6ckKuuujz19XUZOXJU+vXrX+2SAKCqaiK0nW/8y2/khecnVruMT6UhDe9vNzS0+37a0vzw/sdX3JL/vDq52uV8KpNnvNVs+7s/ubyK1bSMlfv1yQnf3aXVw3sAAD651vrk2tVXX5Gnn34ySTJ3biXHH39ii79G4pNdALQfNRXaQpL859XJ+ef416pdxqfSuWFu5k+VGxrmtvt+AAAovtb85Nqrr05o2p448ZVW+ySZT3YB0F4IbQEAAPhYrfnJtTfnlJttt8YnyXyyC4D2RGgL7VClvntKDbOatgEA6HiKus5rq3xyrceKqXtvZpJkRo8VM8MnyQCocUJbaIca+gxI3RvPN20DANDxjB37/jqvDQ2NGT36hOoW1Jrqu6Wh/7rVrgIACkNoC+2RSS0AtEut9SVO87XGWqPzFeULnIp69WlrmDBh/CK3AYCOT2gLAABtYN6XOHVNp06dWu01WuvLm5Jk7ty5mT79vaoHt0W8+rS1wvhSacFjllJX1/KhfFHCeACgOaFtO1NK11Qyu2kbAID2Yd6XOHXKmUdfkZdfeLXa5SyRlVbtl6PP/G4hvsCpaFefzgvju7XKVc4LBsHlcqlVQvm5cxszffq7VX9fAYDmhLbtTOesnDn5T9M2AADty8svvJoX/q/6YSMtY14YX87pF9yclydMbtFjvz2ztMB2OQeNvqxFj79S/z4Z/f1dCxHGAwDNCW3bmXK6ZqmsUe0yAACABbw8YXL+9eJrLXrMSmnFpPOsJMl7pRVa/PgAQHEJbQEAgBZnnddPr1TXLZ0+s061ywAAqkBoCwAAtCjrvAIAfDpCWwAAoEXNX+f1rFOvz/iX32jRY0+d8laz7UMPuLBFj7/iSsvlyOP2sM4rAFBVQlsAAKBVjH/5jbzw/MQWPWZDGt7fbmho8eMDABRBy39eCQAAoJWU0nWR2wAAHYnQFgAAaDc6Z+WU85mU85l0zsrVLgcAoFVYHgEAAGg3yumapbJGtcsAAGhVrrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAAqkKqHtlClTctBBB2XIkCEZOnRoTjnllDQ0NFSjFAAAAACAQqlKaHvYYYelW7du+dOf/pQbb7wxDz30UK666qpqlAIAAAAAUChtHtr+5z//ySOPPJKjjjoqXbt2zYorrpiDDjoo1113XVuXAgAAAABQOG0e2j7//PPp2bNnPvvZzzbdt+qqq2bixIl5880327ocAAAAAIBCqWvrF3znnXfStWvXZvfNv/3uu+9mmWWWWazjlMtJpbJkr73qgH5ZqkvnJXsSrW6FFfo0bZfb4M8Iq63UN13qjYOiWbFv76btthgHSfL/9e2brp2NhaJZpXfbjoUBvT6XLp2Mg6JZYZnlmrbbYhws32OV1JWXav0XYoks261f0/bijoNSqZWK+QQ+bL464IsrpUvX+rYv6FNY4fN9m7aX5L/J9jj//qRz0wGr9E2XpdpZr/0+2e/c9jqf/qTzzYGfa3/9rtznk/XaHufHn3TuuMZy/dK1rn31unKvT/jzqR3Odz/pXHDFHiukvty+fscmyWe7Ld+0vST9tsc57CeZ3yXJMt0GplO5SytU1Hq6d1m5aXvBXhd3vlqqVJY0+vx0fve73+UHP/hBxo0b13TfP/7xj+y444557LHHsvTSS7dlOQAAAAAAhdLmyyOsttpqmT59eiZPntx03wsvvJC+ffsKbAEAAACAmtfmoe0qq6yS9ddfP6eeemrefvvtjB8/PhdddFF22223ti4FAAAAAKBw2nx5hCSZPHlyfvzjH2fcuHEpl8vZeeedc+SRR6ZTp05tXQoAAAAAQKFUJbQFAAAAAGDR2nx5BAAAAAAAPpzQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoW3BTZ06NVtvvXXGjRv3ofvcf//9GTFiRAYNGpSvfvWr+d///d82rJDW9Nxzz2XUqFHZcMMNM2zYsBx99NGZOnXqIvc1Djq2hx56KLvvvnvWW2+9DBs2LCeffHJmzpy5yH2NhY5t7ty52XPPPTN69OgP3ccY6NjuvvvufPGLX8zgwYOb/h111FGL3NdY6JhqYX5Ya3OgWvs9Xyu/y2rp5/X06dNz9NFHZ+jQodlggw1y0EEH5fXXX1/kvu2919tvv73Zezp48OCstdZaWWuttRa5f3vv99lnn83IkSMzZMiQbLLJJvnJT36S2bNnL3Lf9t7rCy+8kO9+97sZMmRINt9881x88cVpbGxc5L7ttddFzSGeeuqp7L777hk8eHCGDx+eX//61x95jMsvvzybbrppBg0alD333DP//ve/W7vsT+zD5kxPPPFE1l577Y99ftV7rVBYjz32WGWrrbaqrL766pWHH354kfu8+OKLlbXXXrvyu9/9rjJnzpzKXXfdVVlnnXUqr732WhtXS0t77733KsOGDaucd955lVmzZlWmTp1a2XfffSv777//QvsaBx3blClTKmuvvXblpptuqsydO7cyadKkyg477FA577zzFtrXWOj4zj333Moaa6xROeaYYxb5uDHQ8Z1++umV0aNHf+x+xkLHVAvzw1qbA9Xi7/la+V1WSz+vv/3tb1cOPvjgyowZMypvvfVW5Xvf+15lv/32W2i/jtDrB7322muVYcOGVW699daFHmvv/c6dO7cybNiwytixYytz586tvPrqq5VtttmmcuGFFy60b3vv9e23365svvnmleOPP77yzjvvVF555ZXKDjvsULngggsW2re99rqoOcT06dMrG264YeXaa6+tzJkzp/Lggw9WBg8eXHnqqacWeYybb7658uUvf7nyz3/+szJz5szKaaedVtl+++0rjY2NbdnKYllUv42NjZVf//rXlUGDBlVWX331j3x+EXp1pW1B3XLLLTnyyCNz+OGHf+x+Q4YMyVZbbZW6urpst9122WCDDXL99de3UaW0lokTJ2aNNdbIwQcfnPr6+vTq1St77LFHHn300YX2NQ46tmWXXTYPPvhgdt1115RKpUyfPj2zZs3Ksssuu9C+xkLH9tBDD+Xee+/NV77ylQ/dxxjo+J555pkPvZpnQcZCx1Mr88NamwPV2u/5WvpdVis/r//2t7/lqaeeyumnn55lllkmPXr0yMknn5wjjzxyoX3be68fVKlUctRRR2XzzTfPTjvttNDj7b3fGTNm5I033khjY2MqlUqSpFwup2vXrgvt2957ffzxxzNlypSccMIJ6datW/r3758DDzwwv/zlL5t6n6899vphc4h77703PXv2zMiRI1NXV5cvfelLGTFiRK677rpFHueGG27It771ray22mpZaqmlcsQRR2TixIkf+emfaviwfo877rj8+te/ziGHHPKxxyhCr0Lbgtpkk03yu9/9Ltttt91H7vevf/0rq6++erP7BgwYkOeee641y6MNfOELX8iYMWPSqVOnpvvuueeerLnmmgvtaxx0fD169EiSbLbZZhkxYkSWW2657LrrrgvtZyx0XFOmTMnxxx+fs88+e5ET5fmMgY6tsbExzz77bP74xz9miy22yKabbpof/vCHmTFjxkL7GgsdT63MD2txDlQrv+dr6XdZLf28fvrppzNgwIDccMMN2XrrrbPJJpvkjDPOyHLLLbfQvu291w+67bbb8q9//etDl/po7/326tUr3/nOd3LGGWdk7bXXzmabbZZVVlkl3/nOdxbat7332tjYmM6dO6dz585N95VKpUyePDlvvvlms33bY68fNod4/vnnl6iXD/beuXPnrLLKKoXr/cP6PfTQQ3P99dfni1/84sceowi9Cm0LarnllktdXd3H7vfOO+8sNOHp0qVL3n333dYqjSqoVCo555xz8r//+785/vjjF3rcOKgd9957bx544IGUy+VF/nXQWOiYGhsbc9RRR2XUqFFZY401PnJfY6Bjmzp1ar74xS9mm222yd13351f/epXeemllxa5RqKx0PHU4vyw1uZAHfn3fK39Lquln9czZszIP/7xj7z00ku55ZZbcuutt2bSpEk55phjFtq3vfe6oMbGxlx88cU54IADmv7w8kHtvd/GxsZ06dIlP/zhD/Pkk0/mzjvvzAsvvJDzzz9/oX3be6/rrbdeunTpkrPPPjvvvfdeJkyYkCuuuCJJFlpjvD32+mFziCXtpb30/mH99u3bd7GPUYRehbbtXNeuXRf6ATJz5sx07969ShXR0t5+++0ccsghueOOO3Lttddm4MCBC+1jHNSOLl265LOf/WyOOuqo/OlPf1roag1joWO69NJLU19fnz333PNj9zUGOrY+ffrkuuuuy2677ZauXbvmc5/7XI466qg88MADefvtt5vtayzUro7y3tfiHKgj/56vtd9ltfTzur6+Pkly/PHHp0ePHunTp08OO+yw3H///XnnnXea7dvee13QuHHj8vrrr2e33Xb70H3ae7+/+93vcs899+Rb3/pW6uvrs9pqq+Xggw/OL3/5y4X2be+9LrPMMrn88svz1FNPZfPNN89hhx2WnXfeuemxBbX3Xhe0pL10pN4/ThF6Fdq2c6uvvnqef/75Zvf961//ymqrrValimhJL7/8cr72ta/l7bffzo033rjI/1lJjIOO7q9//Wu23XbbZt/SOnv27HTu3Hmhv/wZCx3TbbfdlkceeSRDhgzJkCFDcuedd+bOO+/MkCFDFtrXGOjYnnvuuZx11lnN1labPXt2yuVy0/80z2cs1K6O8N7X0hyoVn7P19rvslr6eT1gwIA0NjZmzpw5Tfc1NjYmyUJrgbb3Xhd0zz33ZOutt063bt0+dJ/23u+rr77a7GdTktTV1TVbQmC+9t7r7Nmz09DQkKuvvjrjxo3Lr3/965TL5QwYMKBD/Sz+oCXtZbXVVmu2/5w5c/LSSy8ttMRCR1CEXoW27dyOO+6YRx55JHfffXcaGhpy991355FHHlnkIui0LzNmzMhee+2V9dZbL1dcccUiv4xiPuOgYxs4cGBmzpyZs88+O7Nnz86ECRNyxhlnZLfddlto0m8sdEy//e1v89e//jWPPfZYHnvsseywww7ZYYcd8thjjy20rzHQsfXs2TPXXXddxowZk4aGhkycODE//elPs8suu/h5QJP2/t7X2hyoVn7P19rvslr6eb3xxhtnxRVXzHHHHZd33nknU6dOzTnnnJOtttpqoWUD2nuvC3r88cezwQYbfOQ+7b3fTTbZJG+88UYuueSSzJ07N+PHj8/FF1+cESNGLLRve+81Sb773e/mxhtvTKVSyd/+9rdccskl2WuvvRbaryP0Ot/WW2+dyZMn56qrrsqcOXPy8MMP54477sjXvva1Re7/ta99Lddee22ee+65zJo1K2effXb69OmzyD/AtXeF6LVC4a2++uqVhx9+uOn2oEGDKrfddlvT7QceeKCy4447VgYNGlTZfvvtK3/84x+rUSYt7Morr6ysvvrqlXXXXbcyaNCgZv8qFeOg1jz//POVUaNGVYYMGVLZYostKj/72c8qs2bNqlQqxkItOuaYYyrHHHNM021joLaMGzeusscee1QGDx5c2WijjSonn3xyZebMmZVKxVioJR15fliLc6Ba/D1fC7/Launn9WuvvVY57LDDKsOGDasMGTKkcvTRR1dmzJhRqVQ6Xq/zDRo0aJG1d7R+//KXv1R23333yvrrr1/ZfPPNO/TPp0ceeaSyyy67VAYNGlTZcsstK1dffXXTYx2p1w/OIZ5++ummn1Vbbrll5aabbmp67NFHH60MGjSoMmHChEqlUqk0NjZWrrjiisrw4cMrgwYNquy5556Vf//7323ew5L4YL+VSqXy8MMPV1ZfffVm9xWx11Kl8oHPKwAAAAAAUDWWRwAAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2ADXgpZdeqnYJAAAAwGIS2gK0khdffDHHHHNMNt100wwePDhbbbVVzjrrrLzzzjut8nrDhw/PzTffnCTZZ599cskllyRJ7rvvvnz3u99t2m/BxwAA4ONMnDgxJ554YoYPH55BgwZlww03zHe/+9385S9/abHXuPnmmzN8+PAWOx5Aeye0BWgFf/3rX7PLLrukf//+ufXWW/PEE0/k8ssvz1NPPZW99947c+fObdXXHzNmTA444IAkyfTp01OpVBb5GAAAfJR//vOf2XHHHTN79uxcfvnlefzxx3Pvvfdmxx13zMEHH5z777+/2iUCdEhCW4BWcMIJJ2TnnXfOIYcckmWXXTZJ8vnPfz7nnHNOevfunfHjx2fChAk57LDD8qUvfSnDhg3LEUcckddffz1JMm7cuAwfPjwXX3xxvvzlL2fDDTfM97///bz99ttJkkqlkksuuSSbbLJJhgwZkjPOOKNZELznnnvmggsuyLhx43LiiSdm4sSJGTx4cCZNmtT0WJI0Njbmsssuy1ZbbZX1118/u+22W/70pz81HWf48OG59NJLs/POO2fw4MHZeeed8/DDD7fVaQQAoMpOOOGEDBs2LKeddlpWXXXVdOrUKT179sxOO+2UE088MXPmzMnNN9+cXXfdNXvvvXeGDBmSO+64I5MmTcphhx2W4cOHZ911182WW26ZG2+8sem4L7zwQvbcc88MHjw4I0aMyN///vdmr/vss89mzz33zAYbbJCvfOUrueqqq5pdiADQ0QltAVrYyy+/nOeffz477LDDQo/16dMnF110Ufr375+99947nTp1yr333pvf/OY3SZIDDjggDQ0NSZIJEyZk0qRJ+d3vfpdf//rXeeKJJ/KLX/wiSXLTTTdl7NixufTSS/Pggw+mc+fOee211xZ6vaFDh+akk07K5z73uTzxxBP57Gc/2+zx//mf/8l1112X8847L+PGjcvee++dgw46KE8//XTTPjfddFPOO++8PPjgg1ljjTXyox/9qKVOFQAABfbaa6/liSeeyDe+8Y1FPr7LLrtkq622SjIvZB0xYkQefPDBbL311vnBD36Qzp0756677spf//rXfPvb387JJ5+cd955J3PmzMn++++f1VZbLQ8//HB+9rOf5fe//33TcSdNmpS99tor2267bR588MFcdNFF+cUvfpHrr7++TfoGKAKhLUALmzp1apJ5Ae2HeeyxxzJ+/PicdNJJWXrppbPMMsvkpJNOynPPPZe//e1vTfsdfPDB6dKlS1ZeeeUMHTo0L774YpLktttuy9e//vWsueaaqa+vz6GHHppevXotca033XRT9ttvv6y55pqpq6vLdtttl+HDhze7CmK33XbLyiuvnK5du2bEiBG+1AwAoEbMvyigb9++Tfc99NBDGTJkSIYMGZLBgwdnm222SZJ07tw5O+20U+rr69OlS5f85Cc/yYknnpjOnTtn4sSJ6d69e2bOnJkZM2bkiSeeyKuvvpqjjz46Sy21VFZbbbWMGjWq6TVuv/32rLrqqhk5cmQ6d+6cAQMG5Lvf/W6uu+66tj0BAFVUV+0CADqa5ZZbLknyxhtvZJVVVlno8cmTJ2fKlCnp1atXevTo0XR/jx490rNnz0yYMKEp8J1/rGTeRHj+R8Jef/319OvXr+mxTp065XOf+9wS1zp58uSsuOKKze5bYYUV8txzzzXdXjB8rqur87E0AIAaMX8uOmnSpHz+859PknzpS1/KY489lmTel4ddeOGFTfuWy+9fFzZ+/PiceeaZeemll7LKKqtk5ZVXTjJvea5JkyalV69e6dKlS9P+K620UtP2hAkT8uyzz2bIkCFN9zU2NqZTp06t1ClA8bjSFqCF9e/fP6uvvnruvvvuhR6bMmVKtthii0yYMCHTpk1rWqM2Sd56661MmzatWVD7Yfr27Zvx48c33a5UKk3r4S5prQseJ5k3wV5++eWX+FgAAHQs/fv3z9prr51f//rXH7tvqVRq2p6//MFOO+2UcePG5YYbbshee+3V9Hi/fv0yderUvPPOO033LbjUV9++fTN06NA89thjTf/+8Ic/5JZbbmmhzgCKT2gL0Ap++MMf5qabbsqFF16YadOmpVKp5P/+7/9ywAEHZM0118zee++dAQMG5MQTT8xbb72Vt956Kz/60Y+y0korZb311vvY4+++++654YYb8sQTT2TOnDm5+OKL88Ybbyxy36WWWirvvfde01q5HzzOZZddlmeffTZz587Nb37zm9x3333ZZZddPvU5AACg/Tv11FPzpz/9KT/84Q/z4osvplKp5O23386tt96aCy64YJF/7J8zZ05mzpyZLl26pFQqZeLEifnpT3/a9NjgwYPz+c9/Pj/5yU/y3nvv5T//+U+uvPLKpuePGDEiTz75ZG6//fY0NDTk9ddfzwEHHJDTTz+9zfoGqDahLUAr2HDDDXPttdfm73//e7bffvust956OeSQQ7LRRhtlzJgx6dy5cy699NI0NDRkm222yRZbbJE5c+bk5z//eerqPn7lmh122CGHHHJIDj/88Gy44YYZP358Bg4cuMh9N9hgg/Tu3TsbbLBB/vGPfzR7bNSoURk5cmQOP/zwDBkyJJdeeml+9rOfZcMNN2yR8wAAQPu2+uqr584770yXLl1ywAEHZP31189mm22WG264Ifvss0+uvvrqhZ7TrVu3nHrqqfmf//mfDB48OP/1X/+VYcOGpU+fPvnnP/+ZTp065bLLLsvrr7+ejTfeOPvss0+23HLLpuf3798/Y8aMyfXXX5+NN944O+20U77whS8IbYGaUqpYnBAAAAAAoDBcaQsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQP5/+1lQDJ6FW80AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6), sharey=True)\n",
-    "\n",
-    "# Bar plot for condition vs. price\n",
-    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], palette='viridis')\n",
-    "axes[0].set_title('Condition vs. Price')\n",
-    "axes[0].set_xlabel('Condition')\n",
-    "axes[0].set_ylabel('Price')\n",
-    "axes[0].grid(True)\n",
-    "\n",
-    "# Bar plot for grade vs. price\n",
-    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], palette='viridis')\n",
-    "axes[1].set_title('Grade vs. Price')\n",
-    "axes[1].set_xlabel('Grade')\n",
-    "axes[1].grid(True)\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The visualization presents comparisons between house prices and their condition and grade ratings. On the left, the condition of houses, rated from 1 to 5, shows relatively consistent prices across different condition levels, with no significant price increase observed for better conditions. Conversely, on the right, the grade of houses, ranging from 1 to 11, demonstrates a clear positive correlation with prices, indicating that higher-grade properties command higher prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### c.) Multivariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **#Correlation matrix**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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rVxqYMuNMmjRJX3zxhVq3bq2iRYvq6tWrWrFihapXr65ixYrZH8d7MvDoMW2RvEaNGlqzZo1DH/Jr164pODhYu3fvNjCZcd5++229/PLL9t3eIYWEhKhnz54OyxDNjl6ofy4xMVE3b97UU089xevz/0VHRytHjhz04kYK1atX1+7dux02fEpISFDNmjX1/fffG5jMOM2bN9fkyZNVvHhx+9iFCxcUEhKiLVu2GJjMPRw8eFChoaH2WdRm3BgZqfur1RhmXYVRtWpV7d+/Xx4eHvL391d4eLiSk5NVo0YN000IeuWVV/TTTz/Jx8cn1eNmXA3ZsGFDXbx4MdXNByVzb0BYt25dbdy40WFvnfj4eDVp0kRff/21/vjjD7344os6ePCggSkzVrVq1bR3716HyXQPnk9sNpuqVKlimr2Y2rZtqwEDBjisVDp27JhGjx6tFStWGJgMwL9l2qpF48aN1bt3b/Xt21c+Pj66cuWKpk2bpkaNGhkdzTAdO3ZUu3btVKJEiRQ9Yc06W6tv377q2LGjAgICUrwm48ePNyiVseiF+n+YmZS6pKQkTZw4UStXrlRCQoK8vLzUvHlzDR8+3KEgajbbt2/XihUrdPnyZeXNm1dBQUFq1qyZ0bEM88QTTyg2NtbeG1aSYmNjTb3h68WLF1O0EcmfP7+uXbtmUCLjnT9/XmvXrtX69esVHx+v5s2ba8mSJSpRooTR0Qz15ZdfasmSJbp27Rq9piX16tXL4fuYmBh99dVXCg4ONiiR8UqVKqUVK1bof//7n/0GwsaNG03XzkqSFi1apFdffVWtWrVS+/btjY7jFpYvX65XXnlF/fr1U5MmTYyO41bu3LmjW7duOVyP3L59W7GxsfbvzTYZJmvWrDp69Kief/55+9jx48ft1/XR0dGmun47ffq0Kleu7DBWsmRJnT9/3phAANKNaYvk77zzjt577z1169ZNiYmJ8vLyUosWLdS3b1+joxlmxIgRqlixoqpUqULLlf9v3LhxypMnj2k/dKbmjz/+0KBBgzRnzhzT90L9u/0LLBaLKYvks2bN0oEDBzRlyhSHHu1TpkzRwIEDjY5niNDQUL333ntq27at6tWrp4iICI0aNUoJCQmmLeLUrVtXAwYM0PDhw1WwYEFdvHhRY8eOVd26dY2OZpiyZcvqgw8+0MCBA+Xl5aX4+HiNHTs2xQcxM2nSpImqVaumAQMGqEGDBqa+0Xbfw72ms2XLpqioKI0fP960vaZT2yemQYMG6t+/vzp37mxAIuO9++676tSpk9atW6c7d+7ojTfe0I8//qhPPvnE6GgZLkuWLJowYYI6d+6s4OBgziO6t4Hp+PHj9c4776hRo0ZsivyAxo0b66233lL//v3te6ZMmzZNDRs2VGxsrMaOHasqVaoYHTNDderUSSEhIXrllVfk5+eny5cva+XKleratasiIyP15ptvKjAw0OiYGaZ48eJatGiRXn/9dfvY7NmzVbZsWQNTAUgPpm23cl9SUpJu3rypPHnymO6O8MPov51ShQoVtHfvXorkD6AX6j8TFhampk2bGh0jQ9SvX1+LFi1y2NAoIiJC7du317fffmtgMuM0b95cQ4YMUbVq1exj+/fv1+jRo7Vx40YDkxnn999/V69evRQeHm5//61Vq5YmTpyYYuWOWfz666/q1q2brly5Yt8srGjRopo3b96ftgl43F28eNFUm6OlBb2m0yY5OVkBAQGmbd8k3WsluX79el2+fFkFChRQs2bN5Ovra3Qsw+zfv1+lS5dWjhw5jI7iNtauXasXX3xRefLkMTqK2/jjjz80btw4hYaGKj4+XpkzZ1ZQUJAGDBigY8eOaeHChRo1apTy5ctndNQMtWHDBn311Ve6cuWKfH191bZtWzVs2FAnT57U/v371aFDB9NMtDt06JC6d++urFmzqkCBAoqMjJSXl5cWLlxo6o3FgceB6Yrk9wtVf9UmwYwzPyWpXbt2Gjt2rMNmE2b33//+V/PnzzfdRRDSX6VKlXT48GGjY2QIf39/fffddw59yJOSklSjRo00bRT1OKpSpYpDMVi6txFhlSpVTPN78WcuXryo6Oho+fn5KW/evEbHMdzdu3d1+PBhXbt2TQUKFFClSpVMP8Pvs88+S9GqqFu3bqad3ECv6ZQefm9JSkrS5s2bdfz4ca1atcqgVMYaO3ashg0blmJ84MCB+vDDDw1IBDxa7t69q99//53JdEjVjRs3tGvXLkVHR6tQoUKqU6eOMmfObHQsAP+S6dqtzJkzR02bNv3TNglmbY8g3dtErWPHjmrcuLFy5szpcMysM4RbtmypLl26qHXr1sqZM6fDBZLZfk9GjRqlUaNGafDgwX/6GLP2aU8LM92PfPbZZ7V8+XK9+uqr9rHly5erZMmSBqYyVoECBRQeHu6wwU94eLgpZ/QdOnRIlStXTlHUOn/+vL2Xo7+/vwHJ3ENSUpKefvppFSxYUNK9mwinT59WgwYNDE5mjM8++0yLFi1SSEiIvX3TJ598Ig8PD4WEhBgdzxD0mk6pQ4cODt97eHioePHiGjlypEGJjBEVFaXvvvtOkrRy5coUS/9v376tbdu2GRHNLfzZjfpMmTIpd+7cppwB2r17d82ePTvF+KuvvqqlS5cakMg97N+/X1FRUfbr96SkJJ06dSrVG09mcOPGDS1ZskRRUVGyWq2S7r0mp0+f1vr16w1OZ4zk5GS1atVKiYmJWrVqlb7++ms1btzY6FgA/iXTFcnDwsIkSR9++KEqVqxomiVBaXHw4EEVLVpUp06dchg3853z+z22lyxZ4jBuxpsp9y8SbTabqX8n/ikzvWZ9+/ZVly5dtH79ehUqVEgRERE6c+aMFixYYHQ0w7z22mt666231LZtW/trsmLFir+86fS4euONN3T48OEURa37LBaLTpw4kcGp3MNXX32lMWPG6I8//nAYz5Mnj2mL5MuXL9esWbP03HPP2ccqVaqkXr16mbZITq/plE6ePGl0BLeQK1cuLV26VDExMUpMTEwxKcjb29u0E18kadCgQYqMjJSHh4e9pZXVapWHh4eSk5NVrFgxzZ0797Fv8XTp0iX7quo9e/akaKUYGxub4vOgmYwdO1bLly+3t9tMTk5WXFycXnzxRYOTGWfw4ME6f/68cufOrdjYWPn6+mrPnj2m3Qh35cqVGjdunH788UdNnDhRGzdulMVi0a+//qoePXoYHQ/Av2C6div3BQQE6OuvvzbVLsxAerh+/XqKDTvx98zUbkW611s5LCxM169fV8GCBRUYGCg/Pz+jYxlq9erVWr16ta5fvy4/Pz8FBwcz4wQOGjRooPbt2ytbtmwKDw/Xa6+9pokTJ+qFF17QG2+8YXQ8Q9xfdfBgyxmr1Sp/f38dOnTIwGTGioqK0vr16xUZGUmv6f8vNjZWu3fvVlRUlAoWLKhatWqZeul7165dTX1zOjVTp05VZGSkRowYoWzZsunOnTsaP368fH191bFjR02dOlURERGaM2eO0VFdymq1ql+/foqJibGv7nqQt7e3WrRoYZq9dB72wgsvaObMmYqPj9f69ev1/vvv64MPPtCdO3c0evRoo+MZonLlytq4caOioqI0b948zZgxQ+vWrVNYWJjmz59vdLwM99///lcDBw5UtWrVVLVqVc2fP1958+ZVhw4d9PXXXxsdD8C/YLqZ5PcVKlRIR44ccVj6Dmn79u0p+n42a9bM6FiGOnr0qFatWmV/TVq1amW6Hc0fVKdOHdWuXVtBQUGqXbu26XvlInVFihRRnTp1dOnSJeXLl8+0mw7eN2bMGPXr10+tWrUyOorbaNGiRar7g9SrV087d+7M+EBu4LffftNrr72my5cv66uvvlKZMmX0/vvvq1OnTqYtkhcuXFjbtm1To0aN7GPbtm1T4cKFDUxlvPz585v2dyI1R44c0euvv67MmTOrQIECunz5sry8vPTJJ5+Ydq+d1Arkd+/e1enTpx1WZpjJ2rVrtXHjRvskqaxZs2rIkCFq0qSJunfvrgEDBphitrCHh4emTp0qSRo2bJjGjh1rcCL3Eh8frwoVKui3337TsWPHZLFY1LNnT7388stGRzOMp6en8ufPryxZsthXGQQGBpp2f4MrV67ohRde0OHDh+Xp6alKlSpJkm7dumVwMgD/lmmL5Dly5FDnzp1VsGBB5cuXz6EVwv0WG2YTGhqq9957T23btlW9evUUERGhUaNGKSEhQcHBwUbHM8SePXvUo0cP1atXT88++6wiIiLUuXNnTZ48WfXr1zc6niHWrVunNWvWaOTIkbLZbGrZsqWCgoJM2ccRqfvtt9/05ptv6tSpU8qRI4du3LihIkWKaOHChSpQoIDR8QwRGhqqIUOGGB3DcBEREfbep2fOnEnRbiY2NlYJCQlGRHMLefLkUVJSknx8fHTu3DlJkq+vr6Kjow1OZpwePXqob9++2rx5s71V0Y4dO/50bxkz2L17t8aOHavLly+n2O/CrK2Kxo8fr86dO+vNN9+UdK813LRp0zR69Gh9+umnxoYzyO7duzVq1CiHvsrSvWLXkSNHDExmnDt37ujWrVsOK4lv376t2NhY+/dmao8n3WstcvXqVYWGhury5cvKly+fmjZtaurr+gIFCig6Olp58+bV1atXlZSUpMyZMzv8npiNn5+fjh49qrJlyyouLk4xMTHy9PQ07TVbjhw5dOHCBW3ZssU+6XL//v1sQA88BkxbJK9YsaIqVqyoxMRE3bx5U7ly5ZKnp2lfDknS/PnzNWPGDFWrVs0+Vrt2bY0ePdq0RfJp06bpgw8+UJMmTexjmzZt0qxZs0xbJC9evLjefvtt9e/fX3v27NG6devUsmVLlSlTxrQ3mNLCTJ2tPvjgAxUpUkSLFy9WtmzZdPv2bY0aNUrjx4+3z1wym9atW+u9995Ty5YtU9yYNVOLhKefftreBzY1uXPn1uTJkzM4lfsoX768RowYoeHDh6tIkSJatmyZMmfOnGIzbTOpX7++PvnkE61Zs0bHjh2Tn5+fPv/8c5UvX97oaIYZPXq0GjZsyGquB5w5c8Zh/xiLxaIePXqoevXqBqYy1sSJE9WwYUP95z//0alTp9S0aVPNnDlTQUFBRkczTOPGjfXWW2+pf//+8vX1VWRkpKZNm6aGDRsqNjZWY8eONd1q0SNHjqhTp04qVqyYChYsqCNHjmjevHlasGBBijYsZlG7dm116tRJn332mfz9/TVkyBB5e3urSJEiRkczTLt27dShQwdt2LBBTZs21WuvvSZPT0/TbrTeuXNn+2r7JUuW6NChQ+rWrZvpNosGHkem7UkeGxur0aNHa/PmzUpMTFSWLFnUokULDR48WF5eXkbHM0SVKlUUHh7uULyxWq2qUqWKqXopP8jf318HDhxI0QvVzK/Jgw4ePKjQ0FBt377dXtAxo59++knPP/98ivFvvvlGtWrVkiT17NkzxcZIj6uaNWtq8+bNevLJJ+1jt2/f1ksvvaSDBw8amMw4pUqVsn99/xx7fxNcs878nDVrFpsbPeTatWv2pe8RERF68803lZCQoPHjx5u+9Zkk3bhxQ7ly5TI6huEqV66sgwcPsvn8Azp06KC+ffs6FPV+/vlnjRw5UmvWrDEwmXGef/55HTp0SJcuXdLw4cO1ZMkSnTlzRv369VNoaKjR8Qzxxx9/aNy4cQoNDVV8fLwyZ86soKAgDRgwQMeOHdPChQs1atQo5cuXz+ioGaZjx46qX7++OnbsaB/77LPPtHnzZtNe1yclJemzzz5T27ZtdefOHQ0bNky3b9/WiBEjTNuqSLp3Ti1VqpQsFosWLVqkuLg4denSRTly5DA6miEuXrwoT09P+fj4KCYmRpGRkSpbtqz9eGr9/gG4P9MWyd99912dP39evXv3lo+Pjy5evKjp06erUqVKpl0S37RpU40YMcKhT/uBAwc0ZswYhYWFGZjMOA0aNND06dMdClzHjx9Xv379tGXLFgOTGef8+fNau3at1q9fr/j4eDVv3lzBwcEqUaKE0dEMk9qmnLGxsXrxxRf1ww8/GJTKODVq1NC2bduULVs2+1hsbKwaNWqkvXv3Gpgs4+3evVu1a9fW5cuX//QxZtvQ9P6HhvDw8D99jFlnJm3YsEH169eXt7e3pHv9g5OSkky9yXhsbKwmTJig0NBQ+6SGV155RX379jXtpIa3335bL7/8surVq2d0FMPdv/kcERGhnTt3KigoSAULFtS1a9e0atUqNWzYUKNGjTI2pEHq1q2rHTt26O7du6pTp4727dsn6d759a/Ov2Zw9+5d/f7778qTJ4/p2qs8LCAgQHv37nVYUZ2UlKRq1aqZenPkvxMSEqJ58+YZHcOtpPZ5yMx4PYBHk2n7i+zcuVObN29Wnjx5JEnFihVTqVKl9N///te0RfLXXntNb731ltq2bWvv+7lixYoUPWPNJDg4WN27d1e3bt1UsGBBRUREaP78+WrXrp3R0QzTpEkTVatWTQMGDFCDBg1MW6S4cOGCAgMDlZycLJvNptKlS6d4zP1NXMwmICBAo0aN0nvvvaesWbMqLi5Oo0aNMuVGyW+//bbCw8PVuXNnbd261eg4buGNN97Q4cOH1aFDh1SPm3l2/XvvvaeGDRvav/f09DR9K7gPPvhAv/zyi2bNmmWf1DB16lRNnjxZ7777rtHxDNGxY0e1a9dOJUqU0H/+8x+HY2Zre3bgwAH716VLl9axY8d07NgxSffaw/36669GRTPcs88+q6lTp+qtt95Snjx5tHv3bmXOnNl+E86sfv75Z507dy5FG7wWLVoYE8hgWbJk0ZUrV1SoUCH72JUrV0w7Ozitvv/+e6MjuB2Tzr38U7wewKPJtJ+8vL29UyxTzZYtm6lnawUHB+uJJ57Q6tWrtX37dvn5+Wns2LFq3Lix0dEM88Ybb+iPP/7Q3Llzdf36dfn5+enVV19V586djY5mmK1btzpcSJtV4cKFtXLlSt26dUshISGaP3++w3Fvb2+VLFnSoHTGeuedd9S5c2dVrVpVOXPm1O+//64SJUpo7ty5RkfLcJkyZdK4ceMUGRn5p+12evbsmcGpjHV/Vs3JkycNTuJ+ypUrp40bN+q///2v0VHcxq5du7R+/Xrlzp1b0r1JDc8++6yCgoJMWyQfMWKEKlasqCpVqpi+5cqDfcj/zrx58xQSEuLCNO7lnXfeUe/evdWmTRv17t1bPXr0kNVq1cCBA42OZphJkyZp/vz5yps3r8MNSIvFYtoi+csvv6xevXppwIAB9glBkydP1ssvv2x0NDxizL4q42G8HsCjybTtVpYuXaqtW7dqyJAhKly4sKKiovTxxx/r6aefVvv27e2PM9OGag+Kjo5Wjhw5TD+DDf/n/ofLv+qrbbZiX/369bV9+3YNHDhQH374odFx3Mrdu3cVHh6umJgY+fn5qVy5cqYs5mzatEkrV67U/v37U+1LaLFYTDfzMzIy8m8fY9b33tatW+vYsWPy8vLSU0895fABa8eOHQYmM06DBg20atUqh1mNt27dUuPGje3tI8ymYsWKOnjwoDJlymR0lEeK2Ze+X7t2TXFxcSpatKjRUQxTp04dvffee6pdu7bRUdzGH3/8oZEjR2rDhg1KSkqSt7e3WrdurYEDBypz5sxGx3NbZj+fpIbXxBGvB/BoMm0FdOzYsZLuLa2zWCwOy2EWLlxoyg3VEhMT9dFHH2nlypVKSEiQl5eXmjdvruHDh5u2pYbNZtPixYu1YsUKXb58WXnz5lVQUJC6detmurvD4eHhCgkJcVja/CCzvR7SvZtJJ06c0LZt23TlypVUl9WZqdj3cPGzcOHCKly4sCQpKipKkrleD+lee6ImTZqoZcuWTs14fJzVq1cvxfni/nvufWZ6733Qq6++muq4Gc+v988nLVq0UL9+/TRo0CD5+fnp2rVrmjhxojp16mRsQAOVLl1aFy9eVLFixYyO8kgx27ygZs2aKSgoSM2bN1euXLlMtRnln4mLi7NvqI57vL29NWHCBI0ePVo3b95McYMWAAAzMW2R3Kwzsv7K7NmzdeDAAU2ZMsVhud2UKVNMuzRz8eLFWrRokUJCQuyvySeffCIPDw9TLdmVZG8nQqHv/9SrV08tW7aUxWJJsYGaGW+0PVj8vF+MePB7s70eD7p586ZiY2P15JNPGh3FcPfff9etW6dDhw7pnXfe0dNPP60rV67oo48+UoUKFYwNaKDp06enWpzIlCmTvvzyS9WtW1ddu3aVh4eHAeky1v3zyf1zSfPmzR3OJ7t27TLd+/B91atXV8eOHdW4cWPlzJnT4ZjZVnQ5w2yFv//9739au3atPv74Y9WrV09BQUGqWbOm0bEMVadOHYWGhqp58+ZGRzHc2rVr//YxZm1BAwAwL9O2W0FK9evX16JFixz6TUdERKh9+/b69ttvDUxmnCZNmujjjz/Wc889Zx87fvy4evXqZbobLVxMpy4qKkqNGzdWWFhYqsf9/PwyOJFxqlSponXr1qlBgwbavn17qrP2zPR6PKhevXpasWKF8ubNa3QUt1G7dm2tX7/eoY3G7du31bhxY+3du9fAZMaZM2eOvvzyS73++usqVKiQLl++rIULF6pmzZoqVqyYvvjiCzVp0kS9evUyOqrLXb58+W8fY9bzyV9temu29k3OMOvS97Nnz2r16tUKCwvTE088oVatWpn2Zkrv3r21fft2FSlSRE899ZTDMbP97Tw8ueNhFovFdJ91nGHW88lf4TVxxOsBPJpMO5McKd28eVM+Pj4OYz4+PkpISDAokfGuXbumUqVKOYyVKlVKv//+uzGBDDRt2rS/PG7WTY/y58+vL774wrTFmgd5eXnp008/1RNPPKE1a9akWiQ36wfzgIAABQcHq1atWimWvJv1NYmLi5PVanUYu3PnjpKSkgxKZLwtW7Zo7ty5euaZZ+xjVatW1dtvv60RI0aoTp066tChgymK5H91Tr17965Onz5t2vMuK7rgjOLFi6tnz54qWbKkpk6dqgULFpj2fadkyZKm3VT9YTt37kzzY8PCwtS0aVMXpnn0MM8wJV4TR0WKFDE6AoB/gCI57J599lktX77coSfq8uXLTX0xWbhwYW3btk2NGjWyj23bts3eZ9lMuJj+c2mdfTR+/HgXJzHW8OHDtXLlSlmtVu3fvz/FcbMtdX/QpUuXVKhQIZ07d07nzp2zj5v5NXnppZfUo0cP9e7dWz4+Prp48aKmTp1qqnPHwy5cuJDiQ9X93xtJKliwoG7dumVAMuN8/fXXeu+99xQVFeXwAdzT01NHjhwxMJmxzp49q2XLlunq1asaM2aMNmzY8Kc97WFe3333ndauXatt27apSJEi6tq1q6lbjZj15sC/NWLECFO+N9+8eVMXL17Uc889p7t37zrs0fXBBx8YmMwYiYmJiomJSTHB4f5+Q2ZYeeDMyurVq1e7NgwAl6BIDru+ffuqS5cuWr9+vQoVKqSIiAidOXNGCxYsMDqaYXr06KG+fftq8+bN9tdkx44dfzur2uzMdjHt7e2tVatWqU6dOipatKiuXr2qLVu2yN/f31QbZd3fpDI4OJiZjg/h9UhpxIgReu+999StWzclJibK29tb//3vfzVo0CCjoxmmVKlSmjt3rkMhZ+HChSpRooQk6ZtvvjHd7OmPPvpIDRs21H/+8x+dOnVKTZs21cyZMxUUFGR0NMPs3btXvXr1Ut26dbVv3z4lJCRo5syZunPnjmn7tKeF2WY51q5dW3FxcXr55Ze1ePFilS1b1uhIhhk1apRGjRqlwYMH/+ljHveJDP+G2f524uLiNGLECG3YsEGZM2fW6tWr1blzZy1atMi+YXLDhg0NTpmxNm3apJEjR+r27dv2sYf3G8qdO7dR8TIMK6uBxx89yeHg3LlzCg0N1fXr11WwYEEFBgaa7gP5w/bv3681a9bo+vXr8vPzU1BQkMqXL290LLdWsWJF/fDDD0bHyDBdu3ZVmzZtHFYc7Nu3TwsWLDD1TSY4YuZn6hITE/X7778rV65cypQpk9FxDHX8+HG98cYb8vT0lI+Pj65cuSKr1arZs2crMTFRr732mqZOnfq3vWQfJ88//7wOHTqkS5cuafjw4VqyZInOnDmjfv36KTQ01Oh4hmjdurV69+6t2rVry9/fX+Hh4Tpy5Ij69u1ripl8qRkzZoz69ev3l5sjv//++xoyZEgGpjLWV199pZdffllZsmQxOorhRo4cqffee0+DBg360xVcFMn/nNl6K48cOVLXrl3TwIED1aZNG+3bt0/jxo3TxYsXTXtd//LLL6thw4Zq2bKlPD0d51mavVYA4PFCkRx2rVq10uLFi//yA4bZdO/eXRMnTuQ1cZLZLqYrVqyoQ4cOycPDwz6WnJys6tWr6+DBgwYmg7t4cObnrl27tGHDBrVq1UqdO3c29czPn3/+WefOnUsxS83Ms3BiY2O1a9cuXblyRX5+fqpXr56yZMmi33//XcnJycqTJ4/RETNU3bp1tWPHDt29e1d16tTRvn37JMleHDajKlWqKDw8XBaLRVWrVrW/z1SpUkXff/+9wemMUbVqVe3bty9F8cbsjh49qlWrVuny5cvKmzevWrVqpSpVqhgdyzDXr19PsWEn/p7Zrutr1aql0NBQ5ciRw36OTUhIUK1atUx7XV+xYkWFh4dzjn3AxYsXHVrBJSUl6fTp0+rUqZOxwQD8K5zlYHft2jWjI7idH374waH/HJAaPz8/bdq0SYGBgfax1atX25dkApMmTdLkyZPtMz99fHw0b9489e3b17RF8kmTJmn+/PnKmzevw4cusy9VffLJJ9WsWbMU4zlz5sz4MG7g2Wef1dSpU/XWW28pT5482r17tzJnzixvb2+joxnG19dXhw8fVuXKle1jR44cSbH5upm0bt1ao0ePVqtWrZQ3b16H2cL3++WazZ49e9SjRw/Vq1dPzz77rCIiItS5c2dNnjxZ9evXNzqeIerUqaPatWsrKChItWvXdpjcANxntVrtn//uF0AfHDOjMmXK6MyZMypVqpTRUdzC3LlzNXnyZPt7zf3WM6VLl6ZIDjziKJLD7qWXXlLHjh3VqFEj5cuXz+EDhlkLFk2bNlXv3r3VrFmzFB+6/P39DUwGd9KvXz/16dNHn3/+uX0DwnPnzmnRokVGR4ObuHDhgmrVqiXp/zbrLFeunG7evGlkLEOtX79ec+bMUe3atY2OAjf2zjvvqHfv3mrTpo169+6tHj16yGq1auDAgUZHM0y3bt3UvXt3/e9//1NSUpLmz5+vJUuWqH///kZHM8z999svv/xS0r3z7MP9cs1m2rRp+uCDD9SkSRP72KZNmzRr1izTFsnXrVunNWvWaOTIkbLZbGrZsqWCgoL09NNPGx0NbqRatWoaPXq0RowYYb9mmzJliqpWrWpwMuNUqlRJnTp1UuPGjVOsxjDjhrhffPGFpk2bJi8vL+3cuVP9+/fXmDFjTH2zGnhc0G4Fdn/W49RisZi2x+Wf3S0384eutDDbskxJOn36tLZs2aLo6GjlzZtXAQEBpl7SDEfNmzfXyJEjVblyZfvS3SNHjmjIkCGm7avs7++vgwcP/ml/WCA1165dU1xcnIoWLWofCwsLM9Vm0ZK0e/duff7557p8+bIKFCiQYl8Ms3n33XdVrVo1+fv7pzinmLVfrr+/vw4cOOAwW9pqtapKlSqmu0Z7mNVq1Z49e7Ru3Tp9/fXXKlOmjBYvXmx0LLdltr2GoqOj1b17dx0/flzJycnKnDmzihQpojlz5ih//vxGxzNEhw4dUh23WCym/Nu5/zdx9epV9ejRQ6tXr1ZMTIyCgoK0c+dOo+MB+BeYSQ47TugpnTx50ugIjySz3XvbuXOnhg0bpn379mnWrFmaM2eO5s2bp6FDh6pNmzZGx4MbYOZnSnXq1FFoaKiaN29udBQ8QvLly5dibMSIEaYrkteuXdu+CiM2NtbUbQCke22KPvjgA2XPnl2tWrVSq1atTFvMui9nzpw6ffq0w4SPkydPKm/evAamcg8eHh7KnDmzsmbNKi8vLyUlJRkdyTA//fSTnn/++RTj33zzjX0F3AsvvJDRsQyVJ08erVixQkeOHLHfiCxfvryeeOIJo6MZZsmSJUZHcCv58uVTbGys8ufPr0uXLslmsyl37tymXiEKPC6YSY40bXxl5tYiycnJun79upKTkx3GzdrjMi0X0z179tSMGTMyOpphgoODFRwcrKCgINWsWVMTJkxQ7ty51a9fP23bts3oeHATzPx01Lt3b23fvl1FihRJsXTXjLOS8M+ZbZbj2bNnNWnSJM2cOVPbtm1Tv379lC1bNs2aNcuhT7nZJCUladeuXVqzZo327t0rf39/tW7dWvXr1zflTYR58+Zp2bJl6tatmwoWLKiIiAjNnz9f7dq10xtvvGF0PEOcP39ea9eu1fr16xUfH6/mzZsrODhYJUqUMDqaYVJb/RkbG6sXX3zRVOfVhx09elRly5bVrVu3NHfuXOXOnVuvvfaaqTeu3L59u1asWGHfCDgoKCjVPVTMYNiwYYqMjNSUKVPUu3dvlStXTt7e3tq4caM2btxodDwA/wJFcthnmDy4PDVHjhy6ffu2rFarcubMqe+++86oeIbasmWLBg8erPj4eHtvS7P3uORiOqWAgAAdOHBAx48fV/v27e27v5utcIM/t2nTJoe+sPetWLFCbdu2NSCR8f7qRpoZ+1vinzNbi6+uXbsqX758ev/999WkSRO1atVK2bJl09q1a7Vy5Uqj47mFH3/8UaNHj9bx48eVI0cOtWrVSj169FD27NmNjpZhbDabZsyYodWrV+v69evy8/NTcHCwOnfubNoNK0uXLq1q1aopKChIDRo0MOXNE+nePimBgYFKTk62f655WKVKlfT5558bkM54s2fP1ieffKJDhw5p4MCBOnr0qDw8PFS9enUNHTrU6HiGCA0N1Xvvvae2bdvab7p9+eWXGjRokIKDg42Ol+FiY2P18ccfq1evXoqOjlafPn0UGxur8ePHm27lBfC4oUgOuwULFuj06dMaNmyYsmfPrjt37mjChAnKkSOHBgwYYHQ8QzRo0EAtW7bUyy+/rEyZMjkcM1OPSy6m/1qdOnX01VdfadmyZfrxxx/1ySef6OTJk+rRowdtjEwsPj5eN27ckCQFBgZq48aNDq2Ibt++rVdeeYUbKbrX/zNHjhymnqGFf85sRfKaNWtq165dioqKUqNGjXTgwAFly5ZNlStXNtXr8LDffvtNYWFhWrdunc6ePavatWurVatW8vX11ZQpUxQbG6ulS5caHRMGunjxogoVKmR0DLdw4sQJ3bp1SyEhIZo/f77DMW9vb5UsWVJZsmQxKJ2xAgMD9fHHH6tYsWLy9/fXihUrlDdvXjVv3lx79+41Op4hmjdvriFDhqhatWr2sf3792v06NGmnDmdlpXVAB5NfBqF3YIFC7Rz505lzpxZkpQ1a1YNHTpUtWrVMm2R/ObNm+rRo4fRMQxXuHBhrVy58m8vps2qdevWatGihW7duqVp06bp6NGjev3119WlSxejo8FAsbGxCgwMVEJCgqR7myM/vCKlfv36Bqc0TlJSkiZOnKiVK1cqISFBXl5eat68uYYPH27a2X1AWty9e1c2m0179+5VmTJl9OSTTyomJkbe3t5GRzNM165dtX//fhUrVkytWrXSf//7X+XOndt+vH///qZbtZOcnKwtW7bo/PnzslqtDsfMtlpn3rx5CgkJ0bp16/70MWZ7TXr16qXt27erUaNGqlq1qtFx3Mq1a9dUqlQpfffdd8qePbt91XV8fLzByYwTGRmpgIAAh7GqVavq6tWrBiUyVufOnVNdWd2nTx8mvwCPOIrksLNarYqOjnaYIX3p0iVTb1JSrlw5nTx50mHTI7PiYvrP9erVS1WrVpW3t7cqVKigK1euaPTo0WrYsKHR0WCgvHnzavv27YqPj1ezZs0UFhbmcNzb2ztFL24zmTVrlg4cOKApU6bYl+5OnjxZU6ZM0cCBA42OB7itGjVqqFevXjp58qS6du2qixcvauDAgapTp47R0QxTsGBBLVu2TOXLl0/1uJ+fn1atWpXBqYw1cuRIbdiwQaVKlXJYpZPaasDHXXh4uEJCQnTgwIFUj5vxNYmOjtaJEye0bds2XblyRaktLjfr/kv58+dXeHi41q5dq+rVq0uSwsLCTL0KoUCBAgoPD3f4DBgeHm6q35GHV1aXLl06xWMqVapkQDIA6Yl2K7AbP368du/erddff10+Pj66ePGiPvnkEzVv3ly9e/c2Ol6Gut8r98KFC9q3b5+aNGminDlzOjzGbDNOKlasqC+++ELt2rVL0TbiPjNdKAHOsFqtqfaAvXv3rmlbjNSvX1+LFi1y+NAZERGh9u3b69tvvzUwGR41Ztv/IS4uTgsXLpS3t7dCQkJ08uRJrVq1Sv3791fWrFmNjgc38cILL2jOnDkqV66c0VHghgYMGKANGzakeoPA7PsvbdmyRQMHDlTmzJm1bNkyRUVFKSQkRNOnTzftzciVK1fqww8/VNu2bVWoUCFFRERoxYoVGjx4sFq3bm10vAzzcJuiB9uQmr1NEfC4oEgOu7t372rmzJlav369oqKi5OPjo+DgYL3xxhumm2HRoUOHvzxusVi0ePHiDErjHriYBv65iIgIzZw5U1FRUfZl70lJSTp37pz2799vcDpj+Pv767vvvnO4SZCUlKQaNWooPDzcwGRwJ2np+9mzZ8+/3AjWLMx80w0pVa9eXXv27DH1itD71q5d+7ePadGihctzuJuoqCg1btw4xUq3+8y0/9LD/vjjD0n3Cp+xsbG6c+eO8uXLZ3AqY61evTrFRsCNGzc2OpYhHtzfgH11gMcLRXLgL/z222/KmzdvivFffvlFzzzzjAGJjMXFNPDPdOjQQTabTbly5VJ0dLSee+45rV27Vp06dTLdqpT7Xn31VTVu3FivvvqqfWzJkiXavHmzaTcBRkqpbcoZGxurF1980VSzxx/ETTekxbhx45Q3b16FhIQYHcVw9erV+8vjFotFO3bsyKA07uXEiROpto0ws7+6Ue/v75+BSeCu2FcHeHxRJIeDvXv3aunSpYqKitLcuXO1cOFCDRgwwLR3RlP7cJ6cnCx/f/8U42bBxTTgvIoVK+rrr79WZGSkpkyZorlz5+qbb77R3LlzTVsQ/v7779WlSxeVKlXKvnT3zJkzWrBgAT0dTe7hvp+prWCqVKmSaf92uOmGtGjXrp0OHz6sLFmyOGxiKsm0BeG0CAsLU9OmTY2OkWEGDx6cpseNHz/exUncR2p7UXl4eMjHx8d0fzujRo3SqFGj/vL3xEy/G/dNnTpVO3fuVP/+/R321alZsyb76gCPOHNWPpGq0NBQjR8/XsHBwTp48KAkaefOnbJYLKY62V+4cEFdu3aVzWZTfHy8XnrpJYfjCQkJpp4xndY2M2a8YAL+TJYsWexLMU+fPi1JqlWrlt59912DkxmnSpUqGjp0qH766Sd5enqqbt26atOmDQVyqHDhwlq5cqVD388H3e/7aVZHjx51uOk2bNgw1apVS3PnzqVIDrvg4GAFBwcbHeORM2LECFMVyb29vbVq1SrVqVNHRYsW1dWrV7Vlyxb5+/ubtr3IyZMnHb6PiYnRzJkzTfn5j/mUqQsNDXXYV6d48eIqXry42rdvb6q6CfA4okgOu3nz5mnWrFmqUKGCvvjiC+XNm1dz585Vx44dTXWyL1y4sIYOHaobN25o1KhRKT5went7m3qpHRfTgPOefvpp7d69W7Vr15bVatXFixfl5eWlu3fvGh3NMNOmTdOaNWu0aNEiFSlSRDt27ND777+vmzdv6vXXXzc6HgzWq1cvbd++XY0aNVLVqlWNjuNWuOmGtGjZsqX965iYmBSzyZE6sxUFL168qI8//liNGjWyj7Vs2VILFixgwsv/lzt3br3zzjtq1KiRunTpYnScDPXee+9Jkl555ZU/3R/EjG7evCkfHx+HMR8fHyUkJBiUCEB6oUgOu6tXr9rf/O4vbS5cuLDu3LljZCxD1K1bV5JUsGBBPpw/hItpwHndunVT7969FRYWprZt2+qVV17RE088kWKlipmsWrVKn3/+uX0WzksvvaRnnnlGr732GkVyKDo6WidOnNC2bdt05cqVVAtXvr6+BiQzHjfdkBZ3797V9OnTtXTpUiUnJys0NFR9+/bVnDlzUt1vB/ek1t7pcXb48OEUq3UCAgLUt29fYwK5qZs3b9o38zSjzp07p7o/SJ8+fUy5P8izzz6r5cuXO+yrs3z5clOvcgMeFxTJYXd/Jl/9+vXtY/v27VPhwoUNTGWsChUq6KuvvkqxOdbp06c1e/Zsg9MZg4tpwHkLFixQ9+7dlSlTJvXo0UNFihRRbGysWrRoYXQ0w8TGxqY6C8eMN2aRUr169dSyZUtZLJYUm+7d71N+4sQJg9IZKyQkhJtu+FvTp0/X/v37NXXqVPXr10958uRRgQIFNHbsWE2dOtXoeHATfn5+2rRpkwIDA+1jq1evVrFixQxMZayH+28nJSXp0KFDqlGjhkGJjPHw/iCp7Ull1hZ5ffv2VZcuXbR+/foU++oAeLRRJIddv3791KNHD7300ktKSEjQqFGjFBoaqkmTJhkdzTBDhgzRt99+q1y5cikpKUlZs2bVL7/8YurCFhfTgPOaNGminTt3aubMmSpVqpQaNGighg0bysvLy+hohilTpozmzZunHj162McWLlyY6oZZMJ+PP/5YAwcOVOPGjRUWFmZ0HLdSr149bd26VXny5OGmG/5UaGioli1bpvz588tisShr1qwaP368GjRoYHQ0uJF+/fqpT58++vzzz+Xj46OLFy/q3LlzWrRokdHR3Ia3t7c6dOigtm3bGh0lQ7E/yJ/bvHmz1q1bp9DQUF2/fl0NGjTQlClTNHXqVNPeOAAeFxab2Rqv4S+dPHlSK1as0OXLl1WgQAEFBQWpfPnyRscyTEBAgJYtW6aYmBgtW7ZMH3/8sRYuXKiff/5ZU6ZMMTqeIXbs2KE+ffqofPnyKS6my5Yta3Q8wK3Fxsbqm2++0a5du7R9+3YVLFhQoaGhRscyxLFjx9SlSxdlyZJFBQoU0NWrV3X37l198sknFMphd+LEiVRnr5md1WrVzz//rKioKPn5+fH+ixSqVaumb7/9VpkyZZK/v7/Cw8OVmJio2rVr67vvvjM6ntuqVKlSirYSj7vTp09ry5Ytio6OVt68eRUQEKAqVaoYHQtu5OLFi/b2eGYVFRVlP3eOHDnS3q/9vtu3b2vSpEmmbD8DPE6YSQ67uLg4LV26VOvXr1diYqKyZs2qJ598UqVKlTLtbEer1apixYopZ86c9mXd7du318KFCw1OZpyXXnpJq1evtl9M165dWwMHDuQDOvA3YmNjtX//foWHh+vnn39WcnKyqfvClilTRlu3btWuXbt07do1+fj4qE6dOsqePbvR0eBGFi9enKbHmWlPjAsXLqhbt266dOmScubMqRs3bqhMmTKaMWMGG2jDrkKFCpoxY4b69etn77O9ZMkSlStXzuBk7s1s88d27typYcOGad++fZo1a5bmzJmjefPmaejQoWrTpo3R8Qzz2Wef2SeO5c2bV0FBQerWrZvpetbf9+STT2ratGmptiBdv369wekyRq5cubR06VLFxMQoMTFR06ZNczju7e2tnj17GpQOQHqhSA67CRMm6JdfftHs2bPtM4SnTp2qyZMn69133zU6niEKFChgv3MeHR2tO3fuyMPDQ3FxcUZHMwwX04Dz2rRpoxMnTqhEiRIKCAjQsGHDVLVqVXl7exsdzVA5cuSgRQT+kre3t1atWqU6deqoaNGiunr1qrZs2SJ/f3/TFoTHjBmjatWqadCgQcqcObNiY2M1btw4jR49WjNmzDA6HtzEkCFD1KlTJ61Zs0ZxcXF6+eWXFRcXZ+o2Gj/99JOef/75FOPffPONatWqJUl64YUXMjqWoWbPnq2+ffvKarVq6dKlmjFjhnLnzq1+/fqZ9rr+s88+06JFixQSEqKCBQsqIiJCn3zyiTw8PBQSEmJ0PEMMHjxY58+fV+7cuRUXFycfHx/t2bNH7du3NzpahvHy8tKqVaskSV27dqX/OPCYot0K7GrWrKn169crd+7c9rGrV68qKChIe/bsMTCZcebNm6clS5Zo1apVmjRpkq5evSpvb2/Fx8dryZIlRsczRHBwsIKDgxUUFKSaNWtqwoQJ9ovpbdu2GR0PcEsdOnTQsWPHVL58eb3wwguqWbMmLSSANOjatavatGmjRo0a2cf27dunBQsWmPYDakBAgL799luHVX7x8fGqU6eODhw4YGAyuJM7d+7IYrHo66+/trdRrFOnjp588kmjoxkmtVYqsbGxevHFF03bIiEgIEAHDhzQ8ePH1b59e4WHh8vT01MVK1Y07WvSpEkTffzxx3ruuefsY8ePH1evXr20Y8cOA5MZp3Llytq4caOioqI0b948zZgxQ+vWrVNYWFiKXuUA8ChjJjnssmTJoieeeMJhLGvWrPYlVWYUEhKiQoUKKVu2bOrbt6/mzp2r2NhYDR8+3OhohomIiFCbNm10/PhxxcfHq0aNGvL09NT169eNjga4rSVLlujOnTvav3+/vv32W/Xr10+3b99WjRo1NHHiRKPjAW7r8OHDKT6ABwQEqG/fvsYEcgN+fn6KiIhQiRIl7GNXr15Vzpw5jQsFt9O0aVOtX79eTZo0MTqKoS5cuKDAwEAlJyfLZrOleoPazBvtZcmSRdHR0dq5c6cqV64sT09PnTx5Urly5TI6mmGuXbuWYm+UUqVK6ffffzcmkBvw9PRU/vz5lSVLFp06dUqSFBgYqA8//NDgZACQviiSQ5GRkZKkFi1aqF+/fho0aJD8/Px07do1TZw4UZ06dTI2oIHi4uK0Z88eDRo0SImJicqSJYvatm2r/PnzGx3NMFxMA/9M1qxZ9cILL+iJJ56Qh4eHNm7cyKxP4G/4+flp06ZNCgwMtI+tXr1axYoVMzCVMdauXSvpXkHvjTfeUNeuXe3XawsXLlT9+vWNDQi3Ex8fb+qZ45JUuHBhrVy5Urdu3VJISEiKm27e3t4qWbKkQemM17p1a7Vo0UK3bt3StGnTdPToUb3++uvq0qWL0dEMU7hwYW3bts1hBdO2bdtUuHBhA1MZy8/PT0ePHlXZsmUVFxenmJgYeXp6KiEhwehoAJCuaLcClSpVShaLxWGjmvubkthsNlksFvumlWYzfPhwnT59Wr1793bo0x4QEGDaPu3Tp0/Xl19+ab+YzpMnj/1i2qx9+oC/s3jxYn3zzTcKDw+Xj4+P6tevr/r166tChQpGRwPc2o4dO9SnTx+VL1/e/j587tw5LVq0yHQbRterV+8vj1ssFtO2AkBKgwcP1nfffadatWql6N9vts3l6tevr+3bt2vgwIHMfE3FgQMH5O3trQoVKujKlSs6cuSIGjZsaHQsw2zfvl19+/ZVgwYNVKhQIV24cEE7d+7UtGnTVLduXaPjGWLVqlUaN26cNmzYoE8//VTfffedfXb5nDlzjI4HAOmGIjl0+fLlv32Mn59fBiRxP/RpTx0X04BzWrdurQYNGqh+/foOLRIA/L3Tp09ry5Ytio6OVt68eRUQEKAqVaoYHcuthYWFqWnTpkbHgIE6dOiQ6rjFYtHixYszOI2xKlasqC+++ELt2rXTxo0bldrHX19fXwOSwV0dOHBAq1evVnR0tPz8/NS6dWuVL1/e6FiG+vnnn+2T6xYtWqS4uDh16dJFOXLkMDoaAKQbiuTAX2jQoIFWrVrl8OZ/69YtNW7cWPv27TMwGQAAj7+dO3dq2LBh2rdvn2bNmqU5c+bIYrFo6NChatOmjdHx3FZqGxQCD5s3b54pVgEOGDBAGzZssK+UfZDZV80ipWvXrmnmzJmKiIjQ3bt3HX5vzHaD6b6FCxeqRYsWDhPHAOBxRJEcSMX9Pu1r1qzRoUOHUvRpr1Chgik+VAAAYKTg4GAFBwcrKChINWvW1IQJE5Q7d27169dP27ZtMzqe26pYsaJ++OEHo2PAzZnpZkpUVJQaN26ssLCwVI+bddUsUurcubNu3rypF198UZkyZXI4ZrZWRfe1adNGJ06cUJ06dRQcHKwXX3wx1ZtOAPCoo0gOpII+7QAAGC8gIEAHDhzQ8ePH1b59e4WHh8vT05Mi8N8wU/ET/5zZ/o5OnDih0qVLGx0Dbq5ixYr65ptvlD17dqOjuJWzZ89q9erVCg0NlYeHh1q2bKnWrVurYMGCRkcDgHTjaXQAwB2x8RUAAMbLkiWLoqOjtXPnTlWuXFmenp46efKkcuXKZXQ04JFntpmgaW2VMX78eBcngTvz8fGRh4eH0THcTvHixfXOO+9owIAB2rVrl8aNG6e5c+fq+PHjRkcDgHRDkRxIBUsuAQAwXuvWrdWiRQvdunVL06ZN09GjR/X666+rS5cuRkcD8Ijx9vbWqlWrVKdOHRUtWlRXr17Vli1b5O/vr3z58hkdDwa7326zefPmGjx4sLp3755iU0qzb/C6f/9+rVu37v+1d2+hTd99HMc/sdlCPTBPrbbZxVZkVGi7HmyrbnPDSVsoYhtpRFRwtqiUigf0wgNSDzh64dzUzRNdwUXGSnF4wFq0FQSDdTg2KqYIIraWKDZWaz2gNt3FIDx96p7JQ+zvn+T9uvP3z8X7Qnrxze//jZqampSSkqKVK1eaTgKAsGLdCgAAACyrtbVVDodDmZmZ8vv9amtrU0FBgeksS2PdCt5ErP0/KS8vl9vtVmFhYejM6/WqtrZWtbW1BstgBazb/Gd79uzRqVOn1NfXp7lz56qsrEypqammswAg7BiSAwAAAFEk1nZN4/8Ta0PyrKwsXb16ddAqjf7+fs2YMUNXrlwxWAYr6Orq+tfPxOrbxosXL1ZZWZmKiorkcDhM5wDAW8O6FQAAACBC/Pnnn/r444+HnF+8eFGzZs2SJH3yySfDnYUIFGt3pZxOpxobG1VcXBw6O378uFJSUgxWwSpidQD+Jjwej4LBoK5du6Y7d+4oMTFR2dnZ7G4HEHW4SQ4AAABEiNfd/u3r69Nnn33G7XGE7NixQ2vXrtXo0aP/8TO7du3Spk2bhrHKrObmZq1evVoZGRlKSkpSZ2enbt26pbq6OqWlpZnOAyyru7tbK1asUHt7u8aOHauenh598MEH+vHHHzV58mTTeQAQNgzJAQAAAAu7ffu2iouL1d/fH9qN+9+ys7N17NgxA3Wwory8PHm9XtntvDj8n27cuKGmpiYFAgElJCQoPz9f06ZNM50FWNr69es1MDCg7du3a9SoUXr8+LGqq6v16tUrfffdd6bzACBsGJIDAAAAFufz+dTb26vly5fryJEjg545HA599NFHio+PN1QHq6mpqdGTJ0/kcrmUkJAw6IuV5ORkg2XmtLS0aMuWLfJ6vfrhhx908OBB2Ww2bd68WW6323QeYFmffvqpzp49O+jNlMePH+vLL79knz+AqMLVAgAAAMDiVq1apfPnz6uwsFB5eXmmc2BxdXV1kqT6+npJks1mC72F4PP5TKYZc+DAAa1Zs0bBYFAej0f79+/X+PHjtXbtWobkwP8QDAaHvMFks9n0zjvvGCoCgLeDITkAAABgcYFAQD6fT+fOnZPf73/tjy7G6g1hDDVv3jxNnz5dubm5r13PE4s6Ojrkdrt1/fp1PXv2TDNnzpTdbld3d7fpNMDS8vPzVV1drW3btmnkyJF68uSJqqur+cIWQNRhSA4AAABY3OzZs1VaWiqbzabZs2cPehbrN4Qx1OjRo1VTU6MxY8bI5XLJ5XJp0qRJprOMio+PVyAQUEtLi3JycmS329Xe3q5x48aZTgMsbcOGDfrqq6+Ul5ensWPH6uHDh5oyZYoOHTpkOg0Awoqd5AAAAEAEuHfvnoqKinT69OnXPnc6ncNcBCt7+fKlLly4oF9//VWXLl1Sbm6u5s+frzlz5ujdd981nTfs9u3bp/r6evX29mrv3r2aMGGCKioqtGzZMi1fvtx0HmBpr1690m+//aYHDx7I6XQqPT1dcXFxprMAIKwYkgMAAAARwufzaerUqaYzEGH++OMPbd++XdevX9d7770nl8ulyspKjRkzxnTasGptbZXD4VBmZqb8fr/a2tpUUFBgOguwvBcvXujBgwcKBoODzlnzBSCaMCQHAAAAIsTGjRvf6HNff/31Wy6B1d2/f1+nT5/WiRMndPPmTX3++edyuVxKTk7Wt99+q76+Pnk8HtOZACyusbFRW7duVV9fX+iMNV8AohE7yQEAAIAI4XA41NDQoC+++EIffvih7t69q6amJuXm5ioxMdF0HiyivLxcly9fVkpKilwul+bNm6fx48eHnq9bt04LFiwwWAggUuzbt0+LFi1SaWmp7HZGSACiF3/hAAAAgAjR2dmp3bt3q7CwMHRWWlqq2tpabo8j5P3339fPP/+sjIyM1z53Op1qaGgY5ioAkcjv96uqqooBOYCox7oVAAAAIEJkZWXp6tWrGjFiROisv79fM2bM0JUrVwyWAQCi0eLFi7VlyxalpqaaTgGAt4qvAgEAAIAI4XQ61djYqOLi4tDZ8ePHlZKSYrAKABCtsrOztXTpUhUVFWnixImDnlVVVRmqAoDw4yY5AAAAECGam5u1evVqZWRkKCkpSZ2dnbp165bq6uqUlpZmOg8AEGWWLFny2nObzaajR48Ocw0AvD0MyQEAAIAIcuPGDTU1NSkQCCghIUH5+fmaNm2a6SwAAAAgYo34948AAAAAsIKWlhYtXbpUq1atUmJiog4dOqTy8nLV19ebTgMARKmbN29q586dqqqqUk9Pjzwej+kkAAg7huQAAABAhDhw4IDWrFmjYDAoj8ej/fv369ixYzpy5IjpNABAFLp06ZLKysrU09Mjr9er58+f6/vvv9fhw4dNpwFAWDEkBwAAACJER0eH3G632tvb9ezZM82cOVNpaWnq7u42nQYAiELffPON9uzZo927dysuLk5JSUk6fPiwfvnlF9NpABBWDMkBAACACBEfH69AIKCWlhbl5OTIbrervb1d48aNM50GAIhCt2/f1qxZsyT9/WOdkpSenq5Hjx6ZzAKAsLObDgAAAADwZubPn6+SkhL19vZq7969unbtmioqKrRs2TLTaQCAKJScnKzff/9dOTk5obO2tjYlJSUZrAKA8LMNDAwMmI4AAAAA8GZaW1vlcDiUmZkpv9+vtrY2FRQUmM4CAEShM2fOqLq6WgsXLtTRo0dVWVmpn376SevWrVNJSYnpPAAIG4bkAAAAAAAAGGLHjh3KysrSyZMn1dXVpcmTJ8vtdquwsNB0GgCEFUNyAAAAAAAADJGXlyev1yu7nW29AKIbQ3IAAAAAAAAMUVNTo6dPn6q0tFQJCQmhH++U/t5XDgDRgiE5AAAAAAAAhkhNTR30b5vNpoGBAdlsNvl8PkNVABB+DMkBAAAAAAAwRFdX1z8+czqdw1gCAG8XQ3IAAAAAAAAAQMwaYToAAAAAAAAAAABTGJIDAAAAAAAAAGIWQ3IAAAAAAAAAQMxiSA4AAAAAAAAAiFkMyQEAAAAAAAAAMYshOQAAAAAAAAAgZjEkBwAAAAAAAADELIbkAAAAAAAAAICY9RfrwAZ7Iy79FQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Exclude the 'id', date and 'price_range' column from the correlation matrix\n",
-    "correlation_matrix = housing_data.drop(columns=['id', 'price_range', 'date']).corr()\n",
-    "\n",
-    "# Set up the matplotlib figure\n",
-    "plt.figure(figsize=(20, 20))\n",
-    "\n",
-    "# Create a heatmap using seaborn\n",
-    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Correlation Matrix')\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Price has a moderate positive correlation with sqft living (0.70), grade (0.67), sqft above (0.61). This means that as the values of these features increase, the price of the house also tends to increase."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4.STATISTICAL ANALYSIS.\n",
-    "\n",
-    "Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:\n",
-    "\n",
-    "\n",
-    "1. Descriptive Statistics\n",
-    "2. Correlation matrix\n",
-    "3. Distribution Analysis\n",
-    "4. Inferential Statistics using Hypothesis Testing and Analysis of Variance\n",
-    "5. MultiColinierity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **a.) Descriptive Statistics**\n",
-    "\n",
-    "Initial insights into the central tendency, dispersion, and shape of the data distribution.\n",
-    "<br>Understanding the characteristics of the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.581107e+09</td>\n",
-       "      <td>2014-10-29 08:57:27.772206848</td>\n",
-       "      <td>5.405060e+05</td>\n",
-       "      <td>3.371157</td>\n",
-       "      <td>2.116096</td>\n",
-       "      <td>2080.942531</td>\n",
-       "      <td>1.508757e+04</td>\n",
-       "      <td>1.493615</td>\n",
-       "      <td>0.006716</td>\n",
-       "      <td>3.409848</td>\n",
-       "      <td>5.658310</td>\n",
-       "      <td>1789.104437</td>\n",
-       "      <td>291.838095</td>\n",
-       "      <td>1971.024359</td>\n",
-       "      <td>68.259720</td>\n",
-       "      <td>1987.302478</td>\n",
-       "      <td>12739.322202</td>\n",
-       "      <td>52.975641</td>\n",
-       "      <td>0.955681</td>\n",
-       "      <td>1.924945e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000102e+06</td>\n",
-       "      <td>2014-05-02 00:00:00</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>0.000000</td>\n",
-       "      <td>1.000000</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>399.000000</td>\n",
-       "      <td>651.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.013000e+03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.123049e+09</td>\n",
-       "      <td>2014-07-22 00:00:00</td>\n",
-       "      <td>3.220000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.750000</td>\n",
-       "      <td>1430.000000</td>\n",
-       "      <td>5.043000e+03</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1200.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1952.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1490.000000</td>\n",
-       "      <td>5100.000000</td>\n",
-       "      <td>27.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>8.820000e+03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.904940e+09</td>\n",
-       "      <td>2014-10-16 00:00:00</td>\n",
-       "      <td>4.500000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.250000</td>\n",
-       "      <td>1910.000000</td>\n",
-       "      <td>7.620000e+03</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1560.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1975.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1840.000000</td>\n",
-       "      <td>7626.000000</td>\n",
-       "      <td>49.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.159350e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>7.309100e+09</td>\n",
-       "      <td>2015-02-18 00:00:00</td>\n",
-       "      <td>6.450000e+05</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>2550.000000</td>\n",
-       "      <td>1.069575e+04</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2210.000000</td>\n",
-       "      <td>560.000000</td>\n",
-       "      <td>1997.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2360.000000</td>\n",
-       "      <td>10087.000000</td>\n",
-       "      <td>72.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.546000e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9.900000e+09</td>\n",
-       "      <td>2015-05-27 00:00:00</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>1.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>11.000000</td>\n",
-       "      <td>9410.000000</td>\n",
-       "      <td>4820.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>6210.000000</td>\n",
-       "      <td>871200.000000</td>\n",
-       "      <td>124.000000</td>\n",
-       "      <td>90.000000</td>\n",
-       "      <td>1.653959e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.876357e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.680831e+05</td>\n",
-       "      <td>0.902213</td>\n",
-       "      <td>0.768545</td>\n",
-       "      <td>918.563816</td>\n",
-       "      <td>4.121013e+04</td>\n",
-       "      <td>0.539252</td>\n",
-       "      <td>0.081680</td>\n",
-       "      <td>0.650422</td>\n",
-       "      <td>1.174272</td>\n",
-       "      <td>828.413341</td>\n",
-       "      <td>442.504364</td>\n",
-       "      <td>29.322166</td>\n",
-       "      <td>362.774103</td>\n",
-       "      <td>685.671655</td>\n",
-       "      <td>27169.859971</td>\n",
-       "      <td>29.322166</td>\n",
-       "      <td>5.824659</td>\n",
-       "      <td>4.156724e+04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 id                           date         price  \\\n",
-       "count  2.114200e+04                          21142  2.114200e+04   \n",
-       "mean   4.581107e+09  2014-10-29 08:57:27.772206848  5.405060e+05   \n",
-       "min    1.000102e+06            2014-05-02 00:00:00  7.800000e+04   \n",
-       "25%    2.123049e+09            2014-07-22 00:00:00  3.220000e+05   \n",
-       "50%    3.904940e+09            2014-10-16 00:00:00  4.500000e+05   \n",
-       "75%    7.309100e+09            2015-02-18 00:00:00  6.450000e+05   \n",
-       "max    9.900000e+09            2015-05-27 00:00:00  7.700000e+06   \n",
-       "std    2.876357e+09                            NaN  3.680831e+05   \n",
-       "\n",
-       "           bedrooms     bathrooms   sqft_living      sqft_lot        floors  \\\n",
-       "count  21142.000000  21142.000000  21142.000000  2.114200e+04  21142.000000   \n",
-       "mean       3.371157      2.116096   2080.942531  1.508757e+04      1.493615   \n",
-       "min        1.000000      0.500000    370.000000  5.200000e+02      1.000000   \n",
-       "25%        3.000000      1.750000   1430.000000  5.043000e+03      1.000000   \n",
-       "50%        3.000000      2.250000   1910.000000  7.620000e+03      1.500000   \n",
-       "75%        4.000000      2.500000   2550.000000  1.069575e+04      2.000000   \n",
-       "max       11.000000      8.000000  13540.000000  1.651359e+06      3.500000   \n",
-       "std        0.902213      0.768545    918.563816  4.121013e+04      0.539252   \n",
-       "\n",
-       "         waterfront     condition         grade    sqft_above  sqft_basement  \\\n",
-       "count  21142.000000  21142.000000  21142.000000  21142.000000   21142.000000   \n",
-       "mean       0.006716      3.409848      5.658310   1789.104437     291.838095   \n",
-       "min        0.000000      1.000000      1.000000    370.000000       0.000000   \n",
-       "25%        0.000000      3.000000      5.000000   1200.000000       0.000000   \n",
-       "50%        0.000000      3.000000      5.000000   1560.000000       0.000000   \n",
-       "75%        0.000000      4.000000      6.000000   2210.000000     560.000000   \n",
-       "max        1.000000      5.000000     11.000000   9410.000000    4820.000000   \n",
-       "std        0.081680      0.650422      1.174272    828.413341     442.504364   \n",
-       "\n",
-       "           yr_built  yr_renovated  sqft_living15     sqft_lot15     house_age  \\\n",
-       "count  21142.000000  21142.000000   21142.000000   21142.000000  21142.000000   \n",
-       "mean    1971.024359     68.259720    1987.302478   12739.322202     52.975641   \n",
-       "min     1900.000000      0.000000     399.000000     651.000000      9.000000   \n",
-       "25%     1952.000000      0.000000    1490.000000    5100.000000     27.000000   \n",
-       "50%     1975.000000      0.000000    1840.000000    7626.000000     49.000000   \n",
-       "75%     1997.000000      0.000000    2360.000000   10087.000000     72.000000   \n",
-       "max     2015.000000   2015.000000    6210.000000  871200.000000    124.000000   \n",
-       "std       29.322166    362.774103     685.671655   27169.859971     29.322166   \n",
-       "\n",
-       "       renovation_age    total_sqft  \n",
-       "count    21142.000000  2.114200e+04  \n",
-       "mean         0.955681  1.924945e+04  \n",
-       "min          0.000000  2.013000e+03  \n",
-       "25%          0.000000  8.820000e+03  \n",
-       "50%          0.000000  1.159350e+04  \n",
-       "75%          0.000000  1.546000e+04  \n",
-       "max         90.000000  1.653959e+06  \n",
-       "std          5.824659  4.156724e+04  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Price Distribution:**\n",
-    "\n",
-    "The price of houses in the dataset varies widely, ranging from $78,000 to $7,700,000, with an average price of $540,506. The standard deviation of $368,083.1 indicates a significant dispersion around the mean, suggesting a diverse range of housing prices.\n",
-    "\n",
-    "**Bedrooms and Bathrooms:** \n",
-    "\n",
-    "The average number of bedrooms is approximately 3.37, while the average number of bathrooms is about 2.12. The standard deviations for both variables are relatively small, indicating less variability compared to other features.\n",
-    "\n",
-    "**Square Footage:**\n",
-    "\n",
-    " The average square footage of living space is around 2,080, with a standard deviation of 918.56. Similarly, the average lot size is approximately 15,087 square feet, with a larger standard deviation of 41,210.13, suggesting more variability in lot sizes compared to living space.\n",
-    "\n",
-    "**Floors:**\n",
-    "\n",
-    "On average, houses have 1.49 floors, with a standard deviation of 0.54. This indicates some variability in the number of floors, although most houses seem to have either one or two floors.\n",
-    "\n",
-    "**Waterfront Property:**\n",
-    "\n",
-    " Only a small percentage (0.7%) of the houses are waterfront properties, based on the average value. This feature is likely represented as a binary variable (0 for no waterfront, 1 for waterfront), with most houses being non-waterfront properties.\n",
-    "\n",
-    "**Condition and Grade:**\n",
-    "\n",
-    "The average condition of houses is approximately 3.41, with a standard deviation of 0.65, suggesting some variability in the condition ratings. Similarly, the average grade is around 5.66, with a standard deviation of 1.17, indicating variations in the overall quality of houses.\n",
-    "\n",
-    "**Year Built and Year Renovated:**\n",
-    "\n",
-    "The houses in the dataset span a wide range of construction years, from 1900 to 2015, with an average year of construction around 1971. The standard deviation of 29.32 indicates some variability in the construction years. Additionally, the average year of renovation is approximately 68.26, with a standard deviation of 443.5, suggesting that most houses have not been renovated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **b.) Correlation Analysis with House Prices**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Correlation analysis was performed to examine the relationship between various features and the target variable, 'price'. \n",
-    "<BR>Here are the correlation coefficients between each feature and the price:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "price             1.000000\n",
-       "bedrooms          0.316573\n",
-       "bathrooms         0.525899\n",
-       "sqft_living       0.702340\n",
-       "sqft_lot          0.087940\n",
-       "floors            0.256372\n",
-       "waterfront        0.265970\n",
-       "condition         0.035264\n",
-       "grade             0.667751\n",
-       "sqft_above        0.605167\n",
-       "sqft_basement     0.325003\n",
-       "yr_built          0.054471\n",
-       "yr_renovated      0.116721\n",
-       "sqft_living15     0.586441\n",
-       "sqft_lot15        0.083196\n",
-       "house_age        -0.054471\n",
-       "renovation_age    0.082356\n",
-       "total_sqft        0.118225\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drop_var = ['id', 'price_range', 'date']\n",
-    "\n",
-    "correlation = housing_data.drop(drop_var, axis=1).corrwith(housing_data['price'])\n",
-    "correlation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These correlation coefficients indicate the strength and direction of the linear relationship between each feature and the price of the houses.\n",
-    "<br> Features with higher positive correlation coefficients, such as 'sqft_living', 'grade', 'bathrooms', and 'sqft_above', have a stronger positive linear relationship with the price, indicating that as these feature values increase, the price tends to increase as well. Conversely, features with low or negative correlation coefficients, such as 'condition', 'yr_built', 'sqft_lot', and 'house_age', have weaker or negative linear relationships with the price.\n",
-    "<br>However, it's imperative to underscore that correlation does not imply causation. There could be an underlying third factor driving changes in both features, underscoring the need for thorough investigation beyond correlation analysis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **c.) Distribution Analysis**\n",
-    "\n",
-    "Distribution analysis involves understanding the distribution of data, such as whether it follows a normal distribution and skewed distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "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>condition</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "      <th>price_range</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7129300520</td>\n",
-       "      <td>2014-10-13</td>\n",
-       "      <td>12.309987</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>7.074117</td>\n",
-       "      <td>8.639588</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.074117</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.201171</td>\n",
-       "      <td>8.639588</td>\n",
-       "      <td>69</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>8.988571</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6414100192</td>\n",
-       "      <td>2014-12-09</td>\n",
-       "      <td>13.195616</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>1.178655</td>\n",
-       "      <td>7.852050</td>\n",
-       "      <td>8.887791</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.682943</td>\n",
-       "      <td>5.993961</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>7.596894</td>\n",
-       "      <td>7.433075</td>\n",
-       "      <td>8.941153</td>\n",
-       "      <td>73</td>\n",
-       "      <td>3.526361</td>\n",
-       "      <td>9.424080</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5631500400</td>\n",
-       "      <td>2015-02-25</td>\n",
-       "      <td>12.100718</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>6.647688</td>\n",
-       "      <td>9.210440</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.647688</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.908755</td>\n",
-       "      <td>8.995041</td>\n",
-       "      <td>91</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.353661</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2487200875</td>\n",
-       "      <td>2014-12-09</td>\n",
-       "      <td>13.311331</td>\n",
-       "      <td>1.609438</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>7.581210</td>\n",
-       "      <td>8.517393</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.791759</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.957497</td>\n",
-       "      <td>6.814543</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.215975</td>\n",
-       "      <td>8.517393</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.096163</td>\n",
-       "      <td>600K-1M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1954400510</td>\n",
-       "      <td>2015-02-18</td>\n",
-       "      <td>13.142168</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>7.427144</td>\n",
-       "      <td>8.997271</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.427144</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.496097</td>\n",
-       "      <td>8.923191</td>\n",
-       "      <td>37</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.344959</td>\n",
-       "      <td>300K-600K</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 2014-10-13  12.309987  1.386294   0.693147     7.074117   \n",
-       "1  6414100192 2014-12-09  13.195616  1.386294   1.178655     7.852050   \n",
-       "2  5631500400 2015-02-25  12.100718  1.098612   0.693147     6.647688   \n",
-       "3  2487200875 2014-12-09  13.311331  1.609438   1.386294     7.581210   \n",
-       "4  1954400510 2015-02-18  13.142168  1.386294   1.098612     7.427144   \n",
-       "\n",
-       "   sqft_lot    floors  waterfront  condition  ...  sqft_above  sqft_basement  \\\n",
-       "0  8.639588  0.693147         0.0   1.386294  ...    7.074117       0.000000   \n",
-       "1  8.887791  1.098612         0.0   1.386294  ...    7.682943       5.993961   \n",
-       "2  9.210440  0.693147         0.0   1.386294  ...    6.647688       0.000000   \n",
-       "3  8.517393  0.693147         0.0   1.791759  ...    6.957497       6.814543   \n",
-       "4  8.997271  0.693147         0.0   1.386294  ...    7.427144       0.000000   \n",
-       "\n",
-       "   yr_built  yr_renovated  sqft_living15  sqft_lot15  house_age  \\\n",
-       "0      1955      0.000000       7.201171    8.639588         69   \n",
-       "1      1951      7.596894       7.433075    8.941153         73   \n",
-       "2      1933      0.000000       7.908755    8.995041         91   \n",
-       "3      1965      0.000000       7.215975    8.517393         59   \n",
-       "4      1987      0.000000       7.496097    8.923191         37   \n",
-       "\n",
-       "   renovation_age  total_sqft  price_range  \n",
-       "0        0.000000    8.988571    100K-300K  \n",
-       "1        3.526361    9.424080    300K-600K  \n",
-       "2        0.000000    9.353661    100K-300K  \n",
-       "3        0.000000    9.096163      600K-1M  \n",
-       "4        0.000000    9.344959    300K-600K  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.stats import skew\n",
-    "\n",
-    "# Select numerical variables only\n",
-    "numerical_data = housing_data.select_dtypes(include=['number'])\n",
-    "\n",
-    "# Compute skewness for each numerical variable\n",
-    "skewness = numerical_data.apply(lambda x: skew(x.dropna()))\n",
-    "\n",
-    "# Select variables with skewness above a certain threshold (e.g., 0.5)\n",
-    "skewed_variables = skewness[abs(skewness) > 0.5].index\n",
-    "\n",
-    "# Log transformation for skewed variables\n",
-    "df_log = housing_data.copy()  # Create a copy of the original DataFrame to preserve the original data\n",
-    "df_log[skewed_variables] = housing_data[skewed_variables].apply(lambda x: np.log1p(x))\n",
-    "\n",
-    "# Check the distributions before and after transformation if needed\n",
-    "# For example, you can use histograms or density plots to visualize the distributions\n",
-    "\n",
-    "# Print the first few rows of the transformed data to verify\n",
-    "df_log.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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A0tJS5syZ0275oUOHaGhooLy8vM32BQUF5OTkcPjwYQByc3MZMGBAcPmFF17IiRMnqK+vp0+fPiG332AIva/xEGhforezu9S/5Jbq/YPk6WO02qeglIiIiIiIJKx+/fp1+Lrdbsdms7V5zWq10tTU1OVyu90OQEZGRrvlgWWf3jbwe1NTU1hBqUQcDtmRZGlnd6l/yS3V+we9o48dUVBKRERERESSjs1mo6Ghoc1rTqeTzMzM4HKn09lueV5eXjDA5HA4Otze5/O1Wxb4PbD/UCVLnZhEb2d3qX/JLdX7B8nTR9WUEhERERERaTVy5Eh2797d5rXS0lKKiooAKCoq4siRI+2WT5kyhZycHAYMGEBpaWlwCF9lZSW1tbWMHDkSr9dLbW0tVVVVFBQUAPDBBx8wcOBAsrPDeyhLljoxydLO7lL/kluq9w96Rx87Yox3A0RERERERMJVXFxMVVUVmzZtwuVysWfPHnbs2BGsIzV37lx27NjBnj17cLlcbNq0ierqaoqLiwGYPXs269evp6ysjMbGRlavXs24ceMYPnw4559/PmPHjmX16tU0NjZSVlbGE088wdy5c+PZZRGRlKNMKRERERERSTp5eXls3LiRVatWsXbtWvLz81m+fDkTJkwA/LPxPfDAA6xcuZKKigoKCwvZsGEDubm5ACxevBi32838+fOx2+2MHz+exx9/PLj/tWvX8n//7//l85//PEajkRtvvJHvfOc7ceipiEjqMvh8vTFBrGeqqsIb62kwQEFBdtjbJRP1Mfmlev9AfTzXuhIZoX6uesPnsKd0jkKj8xQanaeunX2OQPeGSEv0z16q/xtR/5JbqvcPkqeP0Xp20PA9ERERERERERGJOQWlREREREREREQk5hSUEhERERERERGRmFNQSkREREREREREYk5BKRERERERERERiTkFpURERBKYz+ejqakJTZYrIiLJzOv1curUKU6dOoXX6413c0QkQZjj3QCRaJo8ZQLlJ092ud7AQYN4/dU9MWiRiEh4HA4H617azx3FnyEjIyPezREREemWqqoqnvzbfgC+/fnP0L9//zi3SEQSgYJSktLKT55k6dOvdLneI7dOjUFrRES6x5JujXcTREREeiwzJz/eTRCRBKPheyIiIiIiIiIiEnMKSomIiIiIiIiISMwpKCUiIiIiIiIiIjGnoJSIiIiIiIiIiMScglIiIiIiIiIiIhJzCkqJiIiIiIiIiEjMJXRQqqamhuLiYvbu3Rt87cUXX2TWrFlceeWVTJs2jXXr1uH1eoPLt2/fTnFxMVdccQWzZ89m3759wWUej4eHH36YSZMmMWbMGBYtWsSpU6di2icREREREREREQFzvBtwLm+//Tb33nsvH3/8cfC19957j6VLl/L4448zdepUjh49yre+9S0yMjL4xje+wd69e3nwwQfZsGEDo0ePZvPmzSxatIiXX34Zm83G+vXr2b17N8899xzZ2dmsWLGC5cuX8/Of/zyOPZWAyVMmUH7yZJfrDRw0iNdf3RODFomIiIiIiIhItCRkUGr79u2sXbuWu+++m//+7/8Ovn78+HG++tWv8rnPfQ6ACy+8kOLiYkpKSvjGN77B1q1bmT59OmPHjgVgwYIFPPvss+zcuZM5c+awdetWlixZwqBBgwBYtmwZkydPpqysjGHDhsW+o9JG+cmTLH36lS7Xe+TWqTFojYiIiIiIiIhEU0IO35s8eTIvvfQSX/7yl9u8fv3113PfffcFf3c6nfzv//4vl112GQClpaWMHDmyzTaFhYUcOnSIhoYGysvL2ywvKCggJyeHw4cPR7E3IiISSR0N7d6/fz/z5s1jzJgxTJs2ja1bt7bZRkO7RUREREQST0IGpfr164fZ3HkSV2NjI4sXL8ZqtbJgwQIA7HY7NputzXpWq5WmpibsdjsAGRkZ7ZYHloXKYAj/p7vbJdNPT/sYjfcg0vtM9fcx1funPna8bjJ5++23ufnmm9sM7a6rq2PhwoXceOONlJSUsGrVKh566CEOHDgAEBzavWbNGkpKSpg5cyaLFi3C4XAAtBna/dprr2G1Wlm+fHlc+hfg8/loamrC5/PFtR0iIiIiItGUkMP3uvLhhx9y55130rdvX371q1+RlZUFgM1mw+l0tlnX6XSSl5cXDFYFHkLOXp6ZmRnW8fv2ze5Wu7u7XU9devkoTpw4EdK6gwcP5v333u32sXrSR4PRQEZGekjrFRSEdpxo7DNe72OsJHv/Qv289/SznuiS/X3syLmGdu/atYvc3Fzmz58PwMSJE5kxYwabN29m9OjRSTm02+Fw8NNd73D7tSOxWm1dbyAiIiIikoSSLij1yiuv8D//8z/cdNNNfO9732uTUVVUVMSRI0farF9aWsqUKVPIyclhwIABbYb4VVZWUltb227IX1eqqxsI58trg8H/gBjudpFy/JPjIdVqAn+9pqqqhrCPEYk++rw+mpqaQ1ov1DZGcp/xfh+jLVX619nn3WAAmy0dh6OZh7/evc96ogvnfQysmywmT57MjBkzMJvNbYJSR44c6XDo9rZt2wD/fWDOnDntlocytDucoFSomWdnZ7N1to7BYODJl9/nP6ddetZrITcnqYVyjkTnKVQ6T13TORIRkXhJqqDUO++8w+LFi1m5ciVz585tt3zu3LksXryYL33pS4wdO5bNmzdTXV1NcXExALNnz2b9+vWMGjWKvLw8Vq9ezbhx4xg+fHhY7fD56NZDe3e3i7WetDFWfYzGMULdZ7K8j92Vyv0L9OvTf6aiVHwf+/Xr1+HrnQ3d7mp5JId2hxvg62z9piYTVlsaxswM+vbNxmZLo6AgG5vNhsPhwGazYegFT4/JFDSNJ52n0Og8dU3nSEREYi2pglI/+9nPcLvdrFq1ilWrVgVfHzt2LE899RQTJ07kgQceYOXKlVRUVFBYWMiGDRvIzc0FYPHixbjdbubPn4/dbmf8+PE8/vjj8emMiIhEhM1mo6Ghbdbb2UOzYzW0O9Qsw1Cy2ZqamnA6WjAYPVRXN+BwtLRm9jXw0137+e4XPtMukJZKUiVzM9p0nkKj89S1s88RKDglIiKxk/BBqbNnxvvZz37W5fqzZs1i1qxZHS6zWCwsWbKEJUuWRKx9IiISXyNHjmT37t1tXistLaWoqAiI3dDucLPTOlv/7Nc/ndlnSbemZCZcR3pLP3tK5yk0Ok9d0/kREZFYS8jZ90REREJVXFxMVVUVmzZtwuVysWfPHnbs2BGsIzV37lx27NjBnj17cLlcbNq0qcOh3WVlZTQ2NnZ7aLeIiIiIiIQn4TOlREREOpOXl8fGjRtZtWoVa9euJT8/n+XLlzNhwgQADe0WEREREUlQCkqJiEjSOXtoN8CoUaPYsmXLOdfX0G4RERERkcSj4XsiIiIiIiIiIhJzCkqJiIiIiIiIiEjMKSglIiKSoHw+Hw6HQzNiiYiIiEhKUlBKREQkQblbmvnFq//C43bFuykiIiIiIhGnoJSIiEgCs6RZ490EEREREZGoUFBKREQkgZ1sgr0nWzhd34hP4/hEREREJIUoKCUiIpKgfD7YXwPHGrx8/y+HcDod8W6SiIiIiEjEKCglIiKSoE63gMPj/3uD1xTfxoiIiIiIRJiCUiIiIgnqRNOZv9c2e+PXEBERERGRKFBQSkREJAH5fL42Qam6Zp9qSomIiIhISjHHuwEiIiLSnt3t/wl8e+TyQkWjK65tEhERERGJJGVKiYiIJKBmjz8rymqG7DT/ax/WOOPYIhERERGRyFJQSnodn8+Hx6shMCKS2FytJaQsBsix+P/+gYJSIiIiIpJCNHxPegWny8P7FY18UGWnzuEGIC/DwsUDsrhkQFacWyci0l4gKGU2Qk4aYIejp5s5PzuuzRIRERERiRgFpSTFGTh4soG3ympp8bTNjqppcvHG0dOUVtoxpGXEqX0iIh1znxWUymrNlKpodHF+tiV+jRIRERERiSAFpSRl1TpcZH35e7zx0WkA8jMsjBqczeA+VgA+qnHwVlktpxpbyPry93C4PNgspng2WUQkyNU6zNhiAGvrpanG4QYUlBIRERGR1KCaUpKSjtU08Y3f7MMy/DOYDAYmnZ/HV0YPZGS/LLLSzWSlm7l8UDYzLxtAmsmAeeBIfvzyB/FutohI0NnD9wJBqVqHG69PNfFEREREJDUoU0pSzttltSz90/vUO9146iuZM3kU+ZlpHa6bn5lG8UX9eP79U/zh3XJuuGwAnxmSE+MWS28zecoEyk+eDGndgYMG8fqre6LcIklEgeF7FiOkG8EA+ACnW0EpEREREUkNCkpJSnn+YAU/2PVv3F4flw/K5vWnF5N//Y5OtxmcY6X5X6+QfslUHv5bKb++5UqMBkOMWiy9UfnJkyx9+pWQ1n3k1qlRbo0kqsDwPbMBDAawmsHhBoeCUiIiIiKSIjR8T1KC1+fjidePsvKFw7i9Pq4bWcD6eaPxOepD2t6xZwuZaSaOVNp542hNlFsrItI111mZUgA2kz9Y7nApKCUiIiIiqUFBKUl6jc1ulvzhIL/cWwbAgnHDWHXDJVjDKFrua27kK6MHAbD5rU+i0k4RkXCcPfsegNXcGpQKLBARERERSXIKSklS+6DKzjd+8w6vfVhDutnI//3yRSy+9oJuDb+7ecxgTEYDb5XVcaiiIQqtFREJ3aczpaytA+6VKSUiIiIiqUI1pSQpub0+nikpY8Obx3B5fPTPSuPRWZdx6cDsbu2vscnO5LEXkfn575BWNJF59zyMY/cz7dYbMnQIr/7vGz1tvohIl86uKQWQjj9KZW/xxKtJIiIiIiIRpaCUJB3z4Iv5xm/28a+KRgAmj8hn2RdGUnCOGfZC4fN6Wfr0K3x82sGLhyrJu/KLLP7WN9tlXD1622d70HIRkdC5P50p1Toi2elRppSIiIiIpAYFpSQp+Hw+yuubefuTOrJnLuNfFY1kpZv43ucuZPqlAzBEaLa8ITlW0s1GHC4vJ+uaGZJrjch+RUTC5WpXU8r/p2bfExEREZFUoaCUJDSHy8ORSjuHTzVS63AD4PO4uWnscL4xfhgFWekRPZ7JaOCCvhkcqmiktMquoJSIxIXP5ztTU6o15h7MlFJQSkRERERShIJSknB8Ph/H65wcOmXnWE0TrWVVMBsNFPbLZM+Pbmfp0n9G7fgXtgalPj7twOfztcnCamxs5MLC80Laz8BBg3j91T3RaqaIpDDnWTPsmdsN3/PX1RMRERERSXYKSkmnTtQ5OVrTRLPLQ47NwkX9s8hKj9LHxpzO++UNvHuygXqnO/hyQWYaFw/I5MK+maSZjbzZWB2d47cakJ2OxWjA6fZSbXdRkHWmVpW3tfZUKB65dWq0migiKa7prGLmpta4uNHtxEA6PgzUNLnokxWnxomIJJCDBw+yevVqDh8+jNVq5Ytf/CJLly4lLS2N/fv384Mf/IDS0lLy8vJYtGgR8+bNC267fft2nnjiCSorKxkxYgQrVqxgzJgxAHg8Hn74wx/yxz/+EYfDwYQJE/j+979P//7949VVEZGUpKCUdOjvR6p4+h9lvF/e0OZ1A/CZIX2YeflArruoHzaLqcfHOtXQzO/eOUHO1x5n99HTAKSZDBQWZHLxgCz69qCAeXeYjAYG51g5dtpBWa2jTVBKRCQW7C3+TCmLEQLJmgYDpBmh2Qt1ZwXuRUR6K6/Xy7e//W0WLlzIM888w6lTp1iwYAF5eXl87WtfY+HChdx5553cfPPNlJSUsHjxYi666CJGjx7N3r17efDBB9mwYQOjR49m8+bNLFq0iJdffhmbzcb69evZvXs3zz33HNnZ2axYsYLly5fz85//PN7dFhFJKQpKSRt2l4fPfOeHpF04DvDXb/LUnoAWB4bMfEx9+vHO8XreOV7Pj17+gC9fOoCbxwzm/L4ZYR/rUEUDv3n7OLsOV+Lx+jBas+iTbubyQdmM7J+JxWSMdPdCNjTXH5T6pNbJmKE5cWuHiPRO9hZ/0Mn8qTkcAkGpBgWlRESoq6ujsrISr9eLz+cf1mw0GrHZbOzatYvc3Fzmz58PwMSJE5kxYwabN29m9OjRbN26lenTpzN27FgAFixYwLPPPsvOnTuZM2cOW7duZcmSJQwaNAiAZcuWMXnyZMrKyhg2bFh8OiwikoIUlJKgFreXgq8sJ23opRgM8JnBfbh8UDY2y4jgOo3Nbkqr7Ox599/Ycwaw9Z0TbH3nBJMuyOPbnyvikrx0DJx7Jjyny8Nf/13JHw6Us/9EffD1MUNzeO2p7/PNlT/CGKGZ9HpiaK4NOE1FYzMtbi9p5vgFyESkd/H5fNQ0NgH+TKmzBX6vb1ZQSkQkLy+PBQsW8PDDD/PII4/g8Xj4/Oc/z4IFC1izZg0jR45ss35hYSHbtm0DoLS0lDlz5rRbfujQIRoaGigvL2+zfUFBATk5ORw+fDjsoFQC/Ne2U2dn5MbiOIG/x+q8xKp/8aL+Jb9k6WO02qeglADg9fn467+rsA69lDSTgS9e0p8B2e1ntstKN3PFkBx23X83m/+6j2f/eZzXP6zhjaOneePoPxjcJ51rL+zL6MF9GJxjxWo2Ud/s4qPqJv75SR27j9bQ2OyvlWIyGrhuZAH/MXYolw7MpnD52wkRkALoYzXTx2qm3ummvKGZ4Xm2eDdJRHoJh8PBH94+Cpj4dDzcYvQBBuqbPR1tKiLSq3i9XqxWKytWrGDu3LkcO3aMO+64g7Vr12K327HZ2v7/zWq10tTkD/p3ttxutwOQkZHRbnlgWTj69s0Oe5t4iHY7PZ4mbLa01mNlUVAQ2/OSLO9Dd6l/ya839LEjCkoJAG+X1XG8zonX5WT6qPNDqKPkY/x5eYw/L4+y0w62vnOCHQcrOFHfzLP7TvDsvhPn3HJwjpUbRw3khssG0C+rfeArUQzMTqfe6aZCQSkRiTGfKQ3wYOlg+B5o+J6ICMBLL73Eiy++yAsvvABAUVERixcvZtWqVcyYMYOGhra1UZ1OJ5mZmQDYbDacTme75Xl5ecFglcPhOOf24aiubsCXwJOmGgz+h+Fot7O6uhGHoyX4d5Mp/PIf3RGr/sWL+pf8kqWPgXZGmoJSQkVDM+8c9w+lq3lhHQVT1oa1/bA8G9+bdiErbrycnW+Xseej0xw+1UhlYwvNbi+Z6SaG5tq4dEAWE8/PZ/SQPgmTEdWZAdnp/LvSTnl9c7ybIiK9jMvr/x9J+0wp/58aviciAidPnqSlpaXNa2azGYvFwsiRI9m9e3ebZaWlpRQVFQH+ANaRI0faLZ8yZQo5OTkMGDCA0tLS4BC+yspKamtr2w0JDIXPR0I/aAZEu51n7zse5yRZ3ofuUv+SX2/oY0cUlOrlvF4fr31QA8DIfpl8fPj1bu8rI83MZ4sKmFpYEHxt8pQJfHjyJO8Cf+li+0Z7Y7ePHQ0D+/izuCobW/B4fZiMiR9IE5HU4PJPvtdBTSn//1QanBq+JyIyefJkfvSjH/Gzn/2Mb33rW5w4cYL169czY8YMiouLefTRR9m0aRPz58/n7bffZseOHTzxxBMAzJ07l8WLF/OlL32JsWPHsnnzZqqrqykuLgZg9uzZrF+/nlGjRpGXl8fq1asZN24cw4cPj2eXRURSjoJSvdzBigZOO1ykm42MPy+Xv0Z4/+UnT7L06VdCWnfFnCsjfPSeybGaSTcbaXZ7qba30L+DGlsiItHgbg1KfTpTKk2ZUiIiQYWFhTz55JM8/vjjPPXUU2RnZzNz5kwWL15MWloaGzduZNWqVaxdu5b8/HyWL1/OhAkTAP9sfA888AArV66koqKCwsJCNmzYQG5uLgCLFy/G7XYzf/587HY748eP5/HHH49fZ0VEUpSCUr2Yy+PlnU/8w/auHp6L1WKKc4sSi8FgYEB2Oh+fdlDe0KyglIjETDAo9akEzUCNqQYFpUREAJg0aRKTJk3qcNmoUaPYsmXLObedNWsWs2bN6nCZxWJhyZIlLFmyJCLtFBGRjiX0PPc1NTUUFxezd+/e4Gv79+9n3rx5jBkzhmnTprF169Y222zfvp3i4mKuuOIKZs+ezb59+4LLPB4PDz/8MJMmTWLMmDEsWrSIU6dOxaw/iea9kw043V76pJu5qF/4RRt7gwHZ/oLvpxpUV0pEYsfTWlDg06OGgzWlNHxPRERERFJAwgal3n77bW6++WY+/vjj4Gt1dXUsXLiQG2+8kZKSElatWsVDDz3EgQMHANi7dy8PPvgga9asoaSkhJkzZ7Jo0aLgzBnr169n9+7dPPfcc7z22mtYrVaWL18el/7Fm8vj5d2T/hlJxg7Lwah6SR3ql+nPjqqyu+LcEhHpTVrrnLcLSqW11pTS8D0RERERSQUJGZTavn07S5Ys4b//+7/bvL5r1y5yc3OZP38+ZrOZiRMnMmPGDDZv3gzA1q1bmT59OmPHjsVisbBgwQLy8vLYuXNncPm3vvUtBg0aRFZWFsuWLePVV1+lrKws5n2MtyOVdprdXrLTzYwoiM10rMmob6YF8A+VaQ6MpxERiTJPa1DKdM5MKQWlRERERCT5JWRQavLkybz00kt8+ctfbvP6kSNH2k3DWlhYyKFDhwDaTNv66eUNDQ2Ul5e3WV5QUEBOTg6HDx8Oq30GQ/g/3d0uEj+f5vP5gllSlw/KxtjRSt04D+d6LRq62/dwWS0mstL9tbaqm1qCx450OxPlJxnbHM57fnYf49nXcEXzfZTEFMyU+tTrgaBUi8eH06UhfCIiIiKS3BKy0Hm/fv06fN1ut2Oz2dq8ZrVaaWpq6nK53W4HICMjo93ywLJQ9e2bHdb6Pd2upwxGAxkZZ4p0H62yU+90k242csV5+aR9anqns9ftbJ8FBe378+k+fvrYXenJsc+1bqjHP9d6A/vYKK1spK7Znylls4W2v3DamUji9TmNlFDec5stPa7vTzify+62M9nfx97uXMP3zAYwAD782VKaoEJEREREkllCBqXOxWaz0dDQ0OY1p9NJZmZmcLnT6Wy3PC8vLxisCtSX6mj7UFVXN9BagzYkBoP/ATHc7SLF5/XR1HSmUPf+j08DUNQvE3eLC3dL2/XPXrezfVZVnXkvztXHTx+7K905dlfrhnr8c62Xa/U/9J2s9Qc/HY7mkN7HcNqZCOL9OY2Uzt5zg8EfkHI4muP6/oTzuQy3neG8j4F1JfGca/iewQBpJmj2+INSmhVURERERJJZUgWlRo4cye7du9u8VlpaSlFREQBFRUUcOXKk3fIpU6aQk5PDgAED2gzxq6yspLa2tt2Qv674fHTrob2720WSvcXNx6f9gbmL+2f1aF8d9SVWfYzleSzI9M/AV9XYEvax4/1+d0cifE6jJdCvT/+Z6JL1eiPdd65MKYA0o4Fmj486pyZgEBEREZHklpA1pc6luLiYqqoqNm3ahMvlYs+ePezYsYM5c+YAMHfuXHbs2MGePXtwuVxs2rSJ6upqiouLAZg9ezbr16+nrKyMxsZGVq9ezbhx4xg+fHg8uxVT/z5lxwcMyE4nL8MS7+YkhYIsf1CqzunGYLHGuTUi0ht4WyOKHQalWkfsqdi5iIiIiCS7pMqUysvLY+PGjaxatYq1a9eSn5/P8uXLmTBhAgATJ07kgQceYOXKlVRUVFBYWMiGDRvIzc0FYPHixbjdbubPn4/dbmf8+PE8/vjj8etQjPl8Pv5d6a+fdXH/8IYs9mY2iwmbxYjD5cXSd1i8myMivYCns0wpk7+qVL0ypUREREQkySV8UOrTM+ONGjWKLVu2nHP9WbNmMWvWrA6XWSwWlixZwpIlSyLaxmRRZW+h3unGZDRwft+MrjeQoLwMC466ZiwFvSerTkTip9Phe8qUEhEREZEUkVTD96RnjrRmSZ2fZyPNpLc+HPk2/xA+BaVEJBaChc47WJbWGqmqdSgoJSIiIiLJTZGJXsLr8/FhtX/2uMJ+GroXrkD9LUtfBaVEJPo6z5Tyv9jYrKCUiIiIiCQ3BaV6iZP1zThcXtLNRobmqFh3uIJBKWVKiSSsgwcPMn/+fK666iomT57MD37wA1pa/LNm7t+/n3nz5jFmzBimTZvG1q1b22y7fft2iouLueKKK5g9ezb79u2LRxeCOgtKWVrv3Bq+JyIiIiLJTkGpXuKj1iyp8/NtGDt6ypFO5dn8QSlzdl+cLk+cWyMin+b1evn2t7/N9ddfzz/+8Q+2bdvG66+/zoYNG6irq2PhwoXceOONlJSUsGrVKh566CEOHDgAwN69e3nwwQdZs2YNJSUlzJw5k0WLFuFwOOLWn64LnStTSkRERESSn4JSvYKBj2r8D1cX5KvAeXekmY1ktVYXPu3QjFciiaauro7Kykq8Xi8+nz+iYzQasdls7Nq1i9zcXObPn4/ZbGbixInMmDGDzZs3A7B161amT5/O2LFjsVgsLFiwgLy8PHbu3BmXvnh9PlpjUpg6Ckq13rkbFJQSERERkSSnoFQvYBpYSJPLg8VkYLCG7nVbYAjf6SYFpUQSTV5eHgsWLODhhx9m1KhRTJ06lfPPP58FCxZw5MgRRo4c2Wb9wsJCDh06BEBpaWmny0NlMIT+09n67sDYPdpnShkMYGl9saHZHdYxk+0n3HPaW390nnSeonGOREREYsUc7wZI9KVdcBUA5+XZMEVo6F5jk53CovPavGYwGvCd9TAF0GhvjMjxEkGuzUJZrZNaZUqJJByv14vVamXFihXMnTuXY8eOcccdd7B27Vrsdjs2m63N+larlaYm/7DmrpaHqm/f7Iis7zGeuY5m2tKC122z0QcmEwZjGtCCvcVDQUF4x0w24Z7T3krnKTQ6T13TORIRkVhTUCrF+Xw+LCOuBuD8CA7d83m9LH36lTavZWSk09TU3Oa1FXOujNgx4y23ta5UnaZhF0k4L730Ei+++CIvvPACAEVFRSxevJhVq1YxY8YMGhoa2qzvdDrJzPTPRGqz2XA6ne2W5+XlhdWG6uoGfL6u1zMY/A9+51q/vKo++PdmZ0swa8HV0ozBZMZnCVyLXFRW1mNIwbSGrs6R+Ok8hUbnqWtnnyNQcEpERGJHQakUd+hUI6bsfpiNBoblauheT+TY/P9cap3KlBJJNCdPngzOtBdgNpuxWCyMHDmS3bt3t1lWWlpKUVER4A9gHTlypN3yKVOmhNUGn4+wHnjPtX5La5VzI+2H0fh8YPGXt8Pt9eFwebEFXkhB4Z7T3krnKTQ6T13T+RERkVhTTakU9/KRKgCG5Voxm/R290QgU6qx2YPb441za0TkbJMnT6ayspKf/exneDweysrKWL9+PTNmzKC4uJiqqio2bdqEy+Viz5497Nixgzlz5gAwd+5cduzYwZ49e3C5XGzatInq6mqKi4vj0peW1uvLuUZbmw1nCqA3OJW5KSIiIiLJS5lSKS4QlIrk0L3eymo24nU2YrRmUe90k5+ZFu8miUirwsJCnnzySR5//HGeeuopsrOzmTlzJosXLyYtLY2NGzeyatUq1q5dS35+PsuXL2fChAkATJw4kQceeICVK1dSUVFBYWEhGzZsIDc3Ny59cQUypc4RlDIYDGSlm6lzumlodtM/Oz2GrRMRERERiRwFpVLYsZomPqpx4PO4GZ5n63oD6ZTBYMBVc5z0wRdRq6CUSMKZNGkSkyZN6nDZqFGj2LJlyzm3nTVrFrNmzYpW08Li6iJTCiAr3eQPSilTSkRERESSmMZzpbDXP6wBwH3yEGlmvdWR4Dp9HEAz8CUYn89Htb0Fp8sT76aI9FigppSps6BUmr+OVEOzglIiIiIikryUKZXCXvuwGgDXsX3Al+PbmBThrjkB+Ge9ksTw71ONPPr3Ut457p+xLOvLS3C4PCld/FlSW1c1pQCy0/23bwWlRERERCSZKX0mRdU7XbzzSR0Aro/2xbk1qcNV8wkAdQ49CCaCf1U08I3fvhMMSAFYhn+G7QfKadTDuiQp11mz751LVro/6KrPuYiIiIgkMwWlUtSbR0/j8cEFfTPwNlTGuzkpw336JAD1ehCMuyp7C0v+cJBmt5erhuWw41vj+O2tY/HUnsTe4uHVD2rwaW5rSUIh1ZRqHb5Xr5pSIiIiIpLEFJRKUYGhe9eOyI9zS1KLu64CgGa3l2a3N86t6d1+8sqHnGps4YL8DB6ddRkD+1gpLMik8S8/xmQwcLzOyaFT9ng3UyRsLV3MvgdnMqU0fE9EREREkpmCUinI7fXxxtHTAFw7om+cW5NafC4nNov/n41mvYqfgyfreeFfpzAA//fLF5GVfqY8nreunKuG5wDwz7I6PF5lS0lycYVQ6DxQU0rD90REREQkmSkolYL2H6+jodlNjtXMqMF94t2clBN4GNQQvvj5yatHAZh+2QAuHpDdbvllA7PJsJhocnkorVK2lCQXDd8TERERkd5CQakU9NoHNQBcMyIfU2dPNdItfaytQSk9DMbFwfIG9n1Sh9lo4D+vOb/DdUxGA6MG+4NV+4/Xq7aUJBWXN/The8qUEhEREZFkpqBUCjpTT0pD96IhEJTS8L342PLP4wB84eJ+DMhOP+d6F/fPwmIyUOd0U17fHKvmifSY3en/vIYyfK+h2ROLJomIiIiIRIWCUinmWE0TH592YDIamHB+Xrybk5LODN9zxbklvU9lYzMvHfbPJvnVK4d0um6a2ciIvhkAHNEQPkkigZpSnd2gA8P3Gpy6DomIiIhI8lJQKsW8/qF/6N6VQ3PaFH+WyMlWplTc7Hz/FB6vj9GD+3BJB7WkPq2oIBOAD6ubcKvguSSJkGpKBWffU6aUiIiIiCQvBaVSTHDo3oUauhctgeF7jc0evAp0xIzP5+P5gxUAzLx8QEjbDOyTTmaaCZfHx8enHdFsnkjEhFJT6uzZ97yqmSYiIiIiSUpBqRRS73Txzid1AFw7Ij/OrUldGRYTJoMBH9DYomypWPlXRSNHa5pINxv5/Mh+IW1jMBgobM2WOlrdFM3miURMcPheJ0GpzNbhez7ArmwpERGRDnm9Xk6dOkVFRQVerzfezRGRDigolULePHoajw8u6JvB0FxbvJuTsgwGQ3AIn2bgi52d7/uzpD5b2Desoann5fv/LXxS68CjzDZJAoFMqc4KnaebjaSb/bfwBs3AJyIi0qGqqip+9tf9/OiPJVRVVcW7OSLSARUdSiG9Zda9xiY7hUXnhbauvTEqbehjNVHrcOlhMEa8Ph9/+7f/PxJfvKR/WNv2z0rDZjHicHkpb2gmO8sajSaKRExLCJlSPp+PrDQTzW6vrkMiIiKdyMzJx2ZLi3czROQcFJRKEW6vjzeOngZgyoWpPXTP5/Wy9OlXQlp3xZwro9KG4Ax8ypSKiXdP1FNlbyEzzcS44eHNKmkwGBiWa+PflXaO1TRRNCgnSq0UiYxQhu85nQ5aXP6Z9xoVlBIRERGRJKXheyli//E6Gprd5FjNXD6oT7ybk/L6WC2AZuCLlb8f8WdJTR6RT5o5/MtWYAjfx6cd+FQUWhJccPa9LtYLDN9TcFxEREREkpUypVLEax/UAHDNiHxMnX29LhERrCmlDIUemTxlAuUnT3a5Xt7XfwIZ+UwLscD5pw3JsWI0QEOzhzqHCyVwSyILZfY9gLTWolMaviciIiIiyUpBqRTRW+pJJYo+Zw3f8/l8GAwKBHZH+cmTXQ7FrLa38PsD5aSbjUw6P7yhewEWk5H+WemUNzTzyWkHI/JUV0oSV2D4XmeFzgEsrSto+J6IiIiIJCsN30sBx2qa+Pi0A7PRwIRuPrRLeLLT/dOxuzw+mt2aXjaaymqdAFw9PBerxdTt/QzO8QeiymqaItIukWgJpaYUQFrrChq+JyIiIiLJSkGpFPD6h/6he1cOzSErXclvsWA2GcloDZBo6Ex0fVLrAGDi+T0r4D8kJx2AMtWVkgQX7vA9ZUqJiIiISLJSUCoFBIbuTb5QQ/diKVhXSlkKUdPi9lLe0AzApAt6lgXYLysds9GAw+WhpskVieaJREXIw/da7+C6BomIiIhIslJQKsnVO12880kdANeO6FkmiYSnj4JSUXe8zonPB57akwzNtfVoXyajgYF9/NlSJ+qckWieSFR0lSnl8/lwOByYfB4AapuaY9U0EREREZGIUlAqyb159DQeH1zQN6PHD+0SnrOLnUt0lLUO3XOVHYjI/oa01pU6rqCUJDCXx1+n7lw3aHdLM7949V/BoFS9o4WmJtVKExEREZHko6BUktOse/ETGL6nei7R4fP5+KS1yLnr4/0R2Weg2Hl5fTMYu180XSSaQil0bkmzBofv2Vs02YKIiIiIJCcFpZKY2+vjjaOnAZhyoYbuxVpW6wx8jc2eOLckNZ1ucmFv8WAyGHCfOBSRffbNsGC1GHF5fZj6XRCRfYpEWmD4Xqg1pRpbdA0SERERkeSkoFQS23+8joZmNzlWM5cP6hPv5vQ6gZkOG1vcms0tCspas6QG56SDJzKFyQ0GA0PzMgCwDLksIvsUibRQMqWgbVBK1yARERERSUbmeDegOw4ePMjq1as5fPgwVquVL37xiyxdupS0tDT279/PD37wA0pLS8nLy2PRokXMmzcvuO327dt54oknqKysZMSIEaxYsYIxY8ZEvc2XXj6K458c73K9gYMG8fqre0La52sf1ABwzYh8TF09vUjEZVpMGACvDxwuLxlpGg4WSYF6UkNzbUSmopTfsDwbpacaMQ+5NIJ7FYmcUINSaa1BKafbR2NTE5mZmVFumYiIiIhIZCVdUMrr9fLtb3+bhQsX8swzz3Dq1CkWLFhAXl4eX/va11i4cCF33nknN998MyUlJSxevJiLLrqI0aNHs3fvXh588EE2bNjA6NGj2bx5M4sWLeLll1/GZotukfATJ06w9OlXulzvkVunhrxP1ZOKL6PRQEaaCXuLh4Zmt4JSEeT2eKlo8M8oNizXGtF9D2vNlDIPKKTZ7SXdrIRRSRw+n6/L2fcCzv7oNrZ4GBDFdomIiIiIREPSPY3V1dVRWVmJ1+sNDlcwGo3YbDZ27dpFbm4u8+fPx2w2M3HiRGbMmMHmzZsB2Lp1K9OnT2fs2LFYLJZgMGvnzp3x7FK3HKtp4uPTDsxGAxPOz4t3c3qt4BA+FTuPqIqGFrw+yEwz0cca2dh5boYFm8WIwZzGwfL6iO5bpKc83jPD8Lq6QRsNYG4NXNlVV0pEREREklDSZUrl5eWxYMECHn74YR555BE8Hg+f//znWbBgAWvWrGHkyJFt1i8sLGTbtm0AlJaWMmfOnHbLDx0Kr4iyIcyRcoH1DQYIpexHKPt//ah/6N6Vw3KCs8DFU7h9TEYd9TE73URFw7mLnYf7WYmns/sXbyfq/fWkBvVJx2Aw0Nhkp7DovJC2bbQ3nnOZweCvKzWoj5UPq5vY90kdY4flRqLJURXOexLO+5gI77W05T47KBXC+2MxgtsDjZqBT0RERESSUPyjGWHyer1YrVZWrFjB3LlzOXbsGHfccQdr167Fbre3G4ZntVppamoC6HJ5qPr2ze5W22229C7XMRgNFBR0vf89H9cC8KXRg7tc32A0kJHR9bEDQl23o/U66mOsjh2rfZ7dx7ysdKhqwun1tds+1Pcy0XT3890d5/pslje0AHBBv2wyMtLxeb2s3BparbXvfemyLt/L8woy+bC6iQPljXF5j8L5N9ndz1Es30eJnEA9KQg9KOXwKFNKRERERJJT0gWlXnrpJV588UVeeOEFAIqKili8eDGrVq1ixowZNDQ0tFnf6XQGi7/abDacTme75Xl54Q1/q65uCCsbKJCN4HA0d7mdz+ujqqqh03XqnS5KWjOlxgzI7HJ9n9dHU1NzyO0Ndd2z1zMY/MGajvoY7WPHap8dvY/prU+NtfbmdtuH8l4mEoPBH8gI9/PdEx19Nls8XipaM6X62kzB5ZF4zwOf0wKb/9L39rHTnCivIy3GdaXC+TcZ7uconPcxsK4kDrf3TMZTKIlswRn4zpGtKSKS6mpra1m9ejWvvPIKXq+Xq6++mpUrV9K/f/8eTYDk8Xj44Q9/yB//+EccDgcTJkzg+9//Pv37949XV0VEUlLS1ZQ6efIkLS0tbV4zm81YLBZGjhzJkSNH2iwrLS2lqKgI8AewOlseKp8v/J/AdpHY/xsfnsbjgwv6ZjAkxxbSsaMt3D4mo476mNVa3PxcD4Td+azE8yfWbe5IRX0zPvxDI7PTIxs3Dxwzx2rG21RHs9vLwZMNcTnP4bY7Wu+jJJZAppSB0IZXBoNSGr4nIr3Ud7/7XZqamnjppZd4+eWXMZlMrFixgrq6OhYuXMiNN95ISUkJq1at4qGHHuLAAf+cvoEJkNasWUNJSQkzZ85k0aJFOBz+2X/Xr1/P7t27ee6553jttdewWq0sX748nl0VEUlJSReUmjx5MpWVlfzsZz/D4/FQVlbG+vXrmTFjBsXFxVRVVbFp0yZcLhd79uxhx44dwTpSc+fOZceOHezZsweXy8WmTZuorq6muLg4zr0Kj2bdSxyBQucNKnQeMWfqSUV21r2zGQwG3Cf9teTe/qQ2ascRCZc7xJn3AtJa7+IavicivdF7773H/v37WbNmDX369CErK4sHH3yQJUuW9HgCpK1bt/Ktb32LQYMGkZWVxbJly3j11VcpKyuLZ5dFRFJO0g3fKyws5Mknn+Txxx/nqaeeIjs7m5kzZ7J48WLS0tLYuHEjq1atYu3ateTn57N8+XImTJgAwMSJE3nggQdYuXIlFRUVFBYWsmHDBnJzc+PbqTC4vT7eOHoagCkX5se5NZKV7s+Ucnl8NLu9pMd4GFgqOlHnH9Y2OCd6QSkA94l/kXbheN4uq+ObE8693uQpEyg/eTKkfQ4cNIjXXw2t9pVIR4JBqRDXD2RKNSgoJSK90IEDBygsLOR3v/sdv/3tb3E4HFx77bXcc889HDlypNsTIDU0NFBeXt5m+4KCAnJycjh8+DDDhg0Lq52JPrFIrCa7OXv//slnonu8Tx8nVseMtUSarCgaUr1/kDx9jFb7Ih6U2rt3L+PHj4/0btuYNGkSkyZN6nDZqFGj2LJlyzm3nTVrFrNmzYpW06Ju//E6Gprd5NosXD6oT7yb0+tZTEasZiNOt5fGZjfp5rR4NympNbu9VNv9w3MH9wm9mH13uE74M6UOnKjH5fFiMXUcBig/eZKlT78S0j4fuXVqxNqXamJxb0gFLo9/GF6oN31LMFNKw/dEJDFF8/pfV1fH4cOHufzyy9m+fTtOp5OlS5dyzz33UFBQ0O0JkOx2OwAZGRntlgeWhSNZ6jdGu50eTxM2W1rrsbJiMtmMx9OE1eo/Zn5+bI4ZL8nyOeuuVO8f9I4+diTiQak777yT7OxsvvKVr/CVr3yFwYMHR/oQvdprH/gLnF9zQR6mUMd3SFRlpZtxultobHbTN1NBqZ4or3fiw1/zKTPC9aQ+zXv6OLk2C7UOF++XN/CZITlRPV5vp3tDaMIdvnemppQypUQkMUXz+p+W5v9/17Jly0hPTycrK4u77rqLm266idmzZ3c4wVEoEyAFglWB+lIdbR+OWE4i0x2xmuymuroRh6Ml+HeTKaOLLSJzTKezBas1jZqa2Bwz1uIxWVEspXr/IHn6GK1JkiI+1uj111/n7rvv5r333uP666/nG9/4Bn/+85/bFSeX7gnUk5qselIJIzCET7Nf9dyJev/QvUFRHroXcOVQfyDq7bK6mByvN9O9ITTu1kypcIfv6fojIokqmtf/wsJCvF4vLpcr+Jq3dRbTSy65pNsTIOXk5DBgwABKS0uDyyorK6mtrW03JDAU8Z7IJpEmu4nXOUmW90H96539S6Y+RkPEg1IWi4Xrr7+e9evX88orr3DdddexceNGJk+ezPe//30OHToU6UP2Gsdqmvj4tAOz0cCE8/Pi3RxpFSh23qhi5z12os7/jWW0h+4FjB2WC0BJWW1Mjteb6d4QmkCmlIbviUiqiOb1f9KkSQwbNoz7778fu91OTU0Njz32GNdddx033HBDjyZAmj17NuvXr6esrIzGxkZWr17NuHHjGD58eETOi4iI+EWtKnN1dTU7duzgD3/4A6WlpYwfP5709HQWLFjAz372s2gdNqW9/qF/6N6VQ3OCgRCJv0CmlAoN94zT5aGmyf9NZzRn3jvbuPNyAX+tNqdL718s6N7QOZfHH5QKd/Y9Dd8TkUQXjeu/xWLhmWeewWQycf3113P99dczcOBAVq9eTV5eHhs3buSFF15g/PjxLF++/JwTII0bN47nn3++zQRIixcvZurUqcyfP5+pU6fS3NzM448/HqGzISIiARGPbDz//PP88Y9/5I033mDEiBHMnj2bn/3sZ+Tn+2eKmzp1KosXL+Y///M/I33olBcYunfthRq6l0iy0pQpFQknW4fu5dosZKSZYnLM8/Js9M9K41RjC/uP1zNeGYhRo3tDaNzebg7fU1BKRBJUtK//AwYM4LHHHutwWU8mQLJYLCxZsoQlS5Z0q10iIhKaiAelvv/97zN9+nS2bNnC5Zdf3m75BRdcwIIFCyJ92JRX73Txzif+ujeTR+THuTVytmwN34uIWA/dAzAYDIw7L48/H6xg77HTCkpFke4NoQk3U+rs4Xs+nw9Dos8lLCK9jq7/IiLSmYgHpV5//XXKysoYMGAAAO+88w7Z2dlceOGFAAwcOJA777wz0odNeW8ePY3HBxf0zWBorq3rDSRmAsP3HC4vbq8Ps2ZF7JZAkfPBMSpyHjDuvFz+fLCCf3xcG9Pj9ja6N4SmuzWlXF4fzW4vVktssgxFREKl67+IiHQm4jWl/va3v3HjjTfy0UcfAbBv3z7mzZvHK6+8EulD9SrBoXuadS/hpJuNwUCUXdlS3dLU4qHWEagnFbtMKYCrh/uzow6faqTarpngokX3htAEglKh3pzNZwWvlK0pIolI138REelMxINS69at44knngim595222385Cc/4Uc/+lGkD9VruL0+3jh6GoApF2roXqIxGAzBbClNy949J+v9Q/fyMywxz/QoyEzjkgFZALxxtCamx+5NdG8IjcvTWlMqxEwpg+FMtlS9glIikoB0/RcRkc5EPCh18uRJrr322javTZ48mRMnTkT6UL3GwZP1NDS7ybGauXxQn3g3RzoQmA2xQQ+F3XKiLj5D9wKuucAf7A3McBlpLo+XFrc3KvtOFro3hCbc4XtwZga+BqeuPyKSeHT9FxGRzkQ8KDVkyBBee+21Nq+9+eabDB48ONKH6jXe/MifJTXuvDxMqleUkLJU7LxHTtTHvsj52QKTB+w9djqYqRIJ//yklu9ue5fP/nQ3k3/yOnM2lrDz/YqI7T+Z6N4QGrcnvNn34KwZ+JSpKSIJSNd/ERHpTMQLnS9cuJDFixfzhS98gSFDhnDixAleeuklHn744UgfqtcIBKUmfmpmsMlTJlB+8mSX2zfaG6PSLjkjOzB8T9Oyh83e7Kbe6cYADOwTn0ypSwZmk59hoabJxb5P6hh3Xs9m4XN7ffz45Q/Y+k7bb4E/Pu3ggb8cJvO6xXh9Poy9aKa0WNwbamtrWb16Na+88gper5err76alStX0r9/f/bv388PfvADSktLycvLY9GiRcybNy+47fbt23niiSeorKxkxIgRrFixgjFjxkSsbaEK1pQK46NxZvieKwotEhHpGT0biIhIZyIelJoxYwb9+/fnD3/4AwcPHmTQoEFs3LiRK6+8MtKH6hVqm1z8q7wBgAmfCkqVnzzJ0qe7LhK5Yo7OfbRlpSlTqrsCs+71zUwj3Rzx5M2QGA0GJo/I50/vVfD3I1U9Cko1u73c/+d/8eoH/skJvjJ6IP/flUPpYzWz471yfv7mMSicwD+O1bb7N53KYnFv+O53v0tOTg4vvfQSRqOR++67jxUrVvDII4+wcOFC7rzzTm6++WZKSkpYvHgxF110EaNHj2bv3r08+OCDbNiwgdGjR7N582YWLVrEyy+/jM0W29lOXZ7uB6UanAqKi0ji0bOBiIh0JuJBKYDx48czfvz4aOy619l77DQ+oKhfJv2y4jO0Sbp2ptC5glLhOlHXOnQvJ76f7+KL+vGn9yr427+rWPK5CzGbuhcge+ivR3j1g2rSzUZWTb+YqYUFwWULxg9ncI6VZc8f4t2TDQzJsTIsL7ZBj3iK5r3hvffeY//+/bzxxhtkZfkL1z/44INUVlaya9cucnNzmT9/PgATJ05kxowZbN68mdGjR7N161amT5/O2LFjAViwYAHPPvssO3fuZM6cOVFp77kEa0qFsc2Z4Xu6/ohIYtKzgYiInEvEg1IVFRWsX7+ejz76CK+3bW2WX/3qV5E+XMp785h/6N6EHg4nkugK1pRq8fS6YVk9daaeVHyG7gVcNTyPPJuF0w4XJWW1TDw//Jku0y//As8frMBogB/OupQJHezjCxf353urH8P6mS+x59hphuRYMfaCWnHRvjccOHCAwsJCfve73/Hb3/4Wh8PBtddeyz333MORI0cYOXJkm/ULCwvZtm0bAKWlpe2CT4WFhRw6dKjH7QpXuLPvAVgMPsBAvVPD90Qk8ejZQEREOhPxoNR9991HVVUVn/vc57BYLJHefcprbLJTWHRe8PecW36KMTOXJ/7PYtaeeL/tuqoVlTAy0kwYDODzQVOLJxikks4ZswtobPZgAAbEqch5gNlo4PMjC9i2/yQvHqoMOyh1vM6JbdJ/AHDnlBEdBqQCHG9vJ3fsdGodbv51qpHLBmb3qO3JINr3hrq6Og4fPszll1/O9u3bcTqdLF26lHvuuYeCgoJ2w/CsVitNTU0A2O32TpeHKtRYdGC9jtb3dFFTqqNtTD43YKG2qTmsWfsSWWfnSM7QeQqNzlPXonmO9GwgIiKdifiT87vvvsuLL75Ifn74WQYCPq83WCeq2t7C7w+UYzYa+K9VT7SbeU+1ohKH0WAgM81EY7MHu4JSITMPvgSAfllppHVzuFwkffGS/mzbf5K/Ha7kfz47gj7W0P7z3OB087d/V2EwmvjSJf35j7FDOt+gxcGVw3J44+hp3j3RwKUDsjCk+NNStO8NaWlpACxbtoz09HSysrK46667uOmmm5g9ezZOp7PN+k6nk8zMTABsNluHy/PywstQ7ds3vOBiR+ub0/2fOYvZhM3W9jpiNvrAZMJibvu5zEj3QSO4MFBQkFoBznDPaW+l8xQanaeuReMc6dlAREQ6E/En5+zs7ODDgfRMWe2ZWjufDkhJ4slON9PY7KGh2c2AbNX/CoV58KUADM6J79C9gNGD+1DUL5MjlXb+9F4FX7tqaJfbuDxedh2upNntxX3qQ+6/85qQAkwX9cvkrY9raWh2U1brZHiK15aK9r2hsLAQr9eLy+UiPd3/7y8wTOSSSy7hN7/5TZv1S0tLKSoqAqCoqIgjR460Wz5lypSw2lBd3YDP1/V6BoP/wa+j9Rsa/dd9r8eDw9G2cLmrpRmDyYzb1Hb4Cx4XkEZVvZOqqoaw2pyoOjtHcobOU2h0nrp29jmCyAan9GwgIiKdiXhqwne+8x3uu+8+Dhw4wIkTJ9r8SHg+qXUAMDQntR9WU0WwrpSKDYfE5/NhGeLPlBoU56F7AQaDgZuuGAzA1n3Hg0OpzsXn8/HqBzXUNLmwWYw0vvgTrBZTSMcym4yM7O8vyP1+eWoEEjoT7XvDpEmTGDZsGPfffz92u52amhoee+wxrrvuOm644QaqqqrYtGkTLpeLPXv2sGPHjmAdqblz57Jjxw727NmDy+Vi06ZNVFdXU1xcHFYbfL7Qf861vruL4XsdPVCbW9dtbPaE1YZE/wn3nPbWH50nnadonKNI0rOBiIh0JuKZUsuXLwfgpZdeAvwPeT6fD4PBwL/+9a9IHy5lub0+TjU0AzAkNzGySKRzWWmBGfg0LXsoPj7twJjVF6MBBiZQZtkXL+nPuteOcqK+mecPVnS67v4T9XxY3YTBAJ8fWcAz9pqwjnXpgCzeO9lAWa2TphYPGWmhBbSSUbTvDRaLhWeeeYY1a9Zw/fXX09zczLRp01i2bBl9+vRh48aNrFq1irVr15Kfn8/y5cuZMGEC4J+N74EHHmDlypVUVFRQWFjIhg0byM3N7XG7wuXy+J8Gw/nGKM3o36axRdceEUk8ejYQEZHORDwo9be//S3Su+yVKhub8fjAZjGSY1V9omSgTKnw/OPjWgAGZKdjToB6UgFWi4kF44fzk1c+5P+9fhTSOs5U/KDKTsnHdQBMOj+PQd2YPTDHZqF/VhqnGlv4qKaJS1O44Hks7g0DBgzgscce63DZqFGj2LJlyzm3nTVrFrNmzYpW00Lmbh1yGE6JsUCmVIOuPSKSgPRsICIinYn4k+CQIUMYMmQIdXV1HDx4kH79+mG1WhkypIvCv9LGyXp/ltSgPtaUL4CcKrLS/VkuDcqUCsk/jp0GYEiC1JM6281jBnNeno2aJhcZ13wd36fGMhyraeJ/S6sBf7bTJQOyun2sC/pmAPBhdXgzvSUb3RtCE8yUCuOyb2nNlLK3ePBGetyNiEgP6fovIiKdiXhQqrq6mq9+9avcdNNN3HPPPZSVlXHdddexb9++SB8qpZW3BqUGJkitHena2ZlSnw5iSFser4+3y/xZRokYlLKYjCz9fCFGA6RfNJndR0/T4vbi8nj5Z1kdLx2uwuuDC/JtTLwgr0eB4/Pz/UGp8vpmHK7UDWjq3hCaYE2pMLaxtK7s9UGThvCJSILR9V9ERDoT8aDU6tWrGTlyJCUlJZjNZi688EIWLlzII488EulDpSyv10dFaz2pQQlUa0c6FwhKub0+mt3eLtbu3Q6daqSh2Y2vuYmCrMSckWfceXks+8JIAP5V0civ3/qEp0s+4e1P6vABF/XPZFpRAcYeZjL2sZrpm2nBBxyrcfS84QlK94bQuDz+a0c4mVImw5n1NXxYRBKNrv8iItKZiAel9uzZw3333YfNZgtmD9x+++2UlpZG+lApq8regtvrI91kJC/DEu/mSIjMRgO21pQFDeHrXEnr0D3XiX/1OKgTTTMvH0jjiz8hx2rG0zojUZ90M58r7Mu1I/IxhhM56MR5ef66VcfrnBHZXyLSvSE0gUypcD9ZgWypeqeCUiKSWHT9FxGRzkS8grbFYsHpdGKz2YJDmOx2O5mZmZE+VMoqbzgzdE/1pJJLdroZh6tF2QpdCBQ5d3/yHvCVuLalK66jbzH3ikE0ON2YjAYy00wR/3c5JMfGPz+p53idMzgjUarRvSE07m7UlAJ/UKrZo2LnIpJ4dP0XEZHORDxTatq0adx999189NFHGAwGqqur+f73v8/UqVMjfaiUdVL1pJJWYAifHgzPzenysP+4v56U6/jBOLcmNEaDgRybhax0c1QCRv2z0rAYDTS7vVQ3uSK+/0Sge0NoArPvdScoBRq+JyKJR9d/ERHpTMSDUt/73vfIyMjgi1/8IvX19UyePBmHw8GSJUsifagUZaC83j+EZ5CCUkkn+6xi59KxAyfqafH46JeVhrf2ZLybkxCMRkMwCH28NjWH8OneEBpXN4fvpbVGsTR8T0QSja7/IiLSmYgP38vMzGTt2rXU1NTwySefMHDgQPr37x/pw6QsS8FwWjw+LEYDfTMTswC0nFtWugmARtWUOqfA0L1xw3P5d3ybklCG5Fopq3VyvM7JZ4b0iXdzIk73htC0tM7A6PN6AFPI2wUypZSlKSKJRtd/ERHpTMSDUiUlJW1+P3bsGMeOHQPg6quvjvThUk76sMsBGJCdntAFoKVj2Rq+16V/tBY5v3p4Hr+Oc1sSyeA+VgAqGprx+nwp9+9f94bQBAuda/ieiKQIXf9FRKQzEQ9K3XLLLe1eMxqNDBo0iL/97W+RPlzKSR96KaB6UskqK8rD9yZPmUD5ya6HvA0cNIjXX90TlTb0RJW9hX9VNAIw/vy8OLcmseRlWLCYDLg8PmqaXBSkWKak7g2hcXt6VlNKw/dEJNHo+i8iIp2JeFDq0KFDbX6vqanh//2//8eQIUMifaiUlD5oJODPlJLkExi+1+LxYUjLiPj+y0+eZOnTr3S53iO3Jmbx0DeP1gBwyYCslAu69JTRYKB/VjrH65ycamhOufOje0NogplS+MLazmI0AD5lSolIwtH1X0REOhPxQueflp+fz913383TTz8d7UMlvaYWD+Y+/QAoyEqtB9LewmIyYjX7/1kZswvi3JrE80ZrUOqaC/Lj3JLENCDb/+++oqE5zi2JPt0bOhYodN7dTKkG1bMTkQSn67+IiJwt4plSHamrq6O5OfUfsnrqVKP/HOXZLKSZoh4vlCjJSjfjdLdgzOob76bExTmHGBpN5N76BIb0DH689Bs8euoDGu2NsW9gAuvfmiFZ0dAS55bEhu4N7XkCQakwt0trrYne4HRFtkEiIlGg67+IiAREPCh13333tfnd5XLx9ttvM2nSpEgfKuWcan0Q7Z+tLKlklp1uosoOxux+8W5KXJxriOHJOid/fv8UVrOR2x/5BQaDgRVzroxDCxNX/yx/UKqh2U1Ti4eMtNBnX0t0ujeExuXpWaFzZUqJSKLR9V9ERDoT9Uyp9PR0brnlFm6++eZoHyrpBTKlAg+mkpwCxc6N2b0zU+pcPq51ADA014ohxWaWi5R0s5E8m4XTDheVjc2clx/5umSJQveGjrm7mSllaR3vp5k/RSTR6fovIiJni3hQ6qGHHor0LnsFr89Hld2fKdVP9aSS2pmgVO/MlDqXstNOAIbn2eLcksRWkJXGaYeLKntLSgWldG8IjaeHNaVU6FxEEo2u/yIi0pmIB6XWrVsX0np33HFHpA+d1OqdblweH16Xk7wMS7ybIz2Q3ToDnwqdn9HQ7Oa0w4UBGJqroFRnCjLTOFJpp8qeWrWBdG8ITaDQeXeH79lbPLi9PszhRrVERKJE138REelMxINSR44cYdeuXVx88cVccMEFlJeX889//pNLL72UzMxMAA3d6UAgS8pV+RFGw8g4t0Z6IpgplaWgVEDZaf/Qvf7Z6aSbVcS/MwWZ/qB0tT21ip3r3tA1n8/Xg+F7Z/7e2Owm16YvN0QkMej6LyIinYl4UMpoNHLffffx9a9/PfjaH//4R15++WUef/zxiByjtraW1atX88orr+D1ern66qtZuXIl/fv3Z//+/fzgBz+gtLSUvLw8Fi1axLx584Lbbt++nSeeeILKykpGjBjBihUrGDNmTETa1ROBB9CWU0fj3BLpqexAUMqWnXLFqrvrWI0/KDU8zxrnliS+/Ez/8F17i4emltQpWh2Le0OyCwzdg/AzpYwGAzaLEYfLq6CUiCQUXf9FRKQzEU9ZeOWVV5g/f36b12644QbefPPNiB3ju9/9Lk1NTbz00ku8/PLLmEwmVqxYQV1dHQsXLuTGG2+kpKSEVatW8dBDD3HgwAEA9u7dy4MPPsiaNWsoKSlh5syZLFq0CIfDEbG2dVd161CdlooP49wS6ak0s5E0k/+J8mS9M86tiT+ny8Px1vNwfgrVSIqWNJORHKs/sJlK2VKxuDckO/dZQanu3JyzWgPg9U7VlRKRxKHrv4iIdCbiQan8/HxKSkravPbaa68xcODAiOz/vffeY//+/axZs4Y+ffqQlZXFgw8+yJIlS9i1axe5ubnMnz8fs9nMxIkTmTFjBps3bwZg69atTJ8+nbFjx2KxWFiwYAF5eXns3LkzIm3rLp/PF3z4dFUqUyoVBLKlFJSCj2oc+HyQn2FR9kaIClqzpapSKCgV7XtDKnB5zgpKdWMkS5/WYGaDglIikkB0/RcRkc5EfPjet7/9bRYuXMj111/P4MGDKSsr4+WXX+anP/1pRPZ/4MABCgsL+d3vfsdvf/tbHA4H1157Lffccw9Hjhxh5Mi29ZgKCwvZtm0bAKWlpcyZM6fd8kOHDkWkbd3V1OLB6fZiAFxVH8e1LRIZWelmqptcnKxvjndT4u5odRMAI/oqSypUBVlpfFDdlFKZUtG+N6QCt9cb/Ht3qqsEJlmoc6ZWkXwRSW66/ouISGciHpSaN28eQ4YM4U9/+hPvv/8+w4YNY8uWLVx00UUR2X9dXR2HDx/m8ssvZ/v27TidTpYuXco999xDQUEBNlvbmb2sVitNTf6HYrvd3unyUIVb6yOwvsEAPl/75VVN/geIXJuFY+7kfAjtqo+pIJw+np0pFa/anT35nEbK2UP3LkiAoNSn+xiN9yYS+8xvnYGzpvXaEM4+w+lbLD+b0b43pIJAppQBX7fem8B1R8P3RCSR6PovIiKdiXhQCmDSpElMmjSJmpoa8vPzI7rvtDT/sJZly5aRnp5OVlYWd911FzfddBOzZ8/G6Ww7XMrpdAZn9rDZbB0uz8vLC6sNfftmd6vtNlt6h683VNgBGJDjLwKdkdHxeh0Jdd1Y7bOjPiZzfzpyrvfxbHnZ6VDeQI3TQ0FB9z4vHTEYDSG102A0dPu43f18n33sQBs/PFGHz+fP/BncN6vD9ePxntts6SGfo1DPeWDdSOxziMkEVFLvdGOwpHXrvezp+xgN0bw3pILgzHvdDBb2UVBKRBKUrv8iInIuEQ9KuVwu1q1bx69//Ws8Hg87duzgrrvuYv369fTv37/H+y8sLMTr9eJyuUhP9z/UeVuHPFxyySX85je/abN+aWkpRUVFABQVFXHkyJF2y6dMmRJWG6qrG8LKBgp84+1wNHe43claf6ZWTuvQi6am0Id8hbputPdpMPgf9DvqYzL2pyNdvY9ns7ZWazta2UhVVUNIxw+Fz+sLqT8+ry/s4xoM/kBGuJ/vjo4daOPhE/UAnJ9nO2e7Y/met/mchniOQj3ngXUjsk+fj3SzkWa3F2PO4LDey3Dex8C6sRDte0MqcHn897LuJrAFhu8pKCUiiUTXfxER6UzEC52vW7eOPXv28JOf/ASLxULfvn0ZOHAgq1atisj+J02axLBhw7j//vux2+3U1NTw2GOPcd1113HDDTdQVVXFpk2bcLlc7Nmzhx07dgTrSM2dO5cdO3awZ88eXC4XmzZtorq6muLi4rDa4POF/xPYriOBmff6thY3TkZd9TEVhNPHwDCa47WObn1eOvschdPe7n5OI9HGRBu6B+3fw0if80jt02AwBIfwmfoOi+r7GCvRvjekgp5mSmVbA5lSqiklIolD138REelMxDOlduzYwW9/+1sGDBiAwWAgIyODhx56KOzAz7lYLBaeeeYZ1qxZw/XXX09zczPTpk1j2bJl9OnTh40bN7Jq1SrWrl1Lfn4+y5cvZ8KECQBMnDiRBx54gJUrV1JRUUFhYSEbNmwgNzc3Im3rjha3l4Zm/7fafTM0M1mqCMyCVed00+B0Bx8We5PeOOteY5OdwqLzul7P3tjlOvkZFk7WN2PKGxqJpsVdtO8NqaAnQSmfz4fV4AH81x0RkUSh67+IiHQm4k/KTU1NwbHivtav4a1WK0Zj5JKyBgwYwGOPPdbhslGjRrFly5Zzbjtr1ixmzZoVsbb0VGB2raw0E1aLKc6tkUixmIx4m+owZuRwvM7BxdbEq+8Tbf+u9NdKu7AgMbKkYsHn9bL06Ve6XG/FnCu7XCc/w585aeqbGkGpWNwbkp27B8P33K5mdh/6GEijLoyhriIi0abrv4iIdCbid4MrrriCdevWAf4hKADPPPMMo0aNivShUkJ1kz8olcxD96Rj3vpTAByvc3axZuqpc7ioaGjGABT1y4x3c5JSXmD4Xv6wOLckMnRv6FpPh+/ZWicCqXO4gg9+IiLxpuu/iIh0JuKZUvfffz8LFixg+/bt2O12vvzlL2O32/nlL38Z6UOlhKpgPaneMbypN/HUn8I8sIjjtb0vKBXIkhqaayUzrfcNXYyEQFDKmJlHrcOV9EMgdW/omsvTGpTq5vZprRuWNzTjcDjIyOg9WYoikrh0/RcRkc5E/GmxoKCA559/nv/93//l+PHjDBw4kM9+9rNkZXU8HXxvFxi+p0yp1BPIlPqkzhHnlsSYwci/T/mDUsqS6r40k5HMNBP2Fg/HaprIHZIT7yb1iO4NXXO3ziRr6GamlKU1KOXyRqhBIiIRoOu/iIh0JuJBqRtuuIE//elPfOlLX4r0rlOO1+ej1tGaKZWhoFSqCQ7f62WZUpbzr6TJ5cFmMXJ+vjI1eiLXZsHe4uGjmiY+k+RBKd0buhbMlOpmUCqttSyhxwfNbi/61yciiUDXfxER6UxUKgw6HL0sM6Sb6p1uvD4wGw1kpavIearprTWl0i/7PAAX9c/C1N2nawEg1+b/3uBYTWpcU3Vv6FywplQ3tzcbzhRJD8zqKiKSCHT9FxGRc4l4ptT48eOZN28eU6ZMoX///m2W3XHHHZE+XFI73eTPksq1WYKFHyV1eBoqASivd+L2eDGbUn+WmY+qm7AMvRyAiwcoLb+nclrrSH1U0xTnlvSc7g1dc7XOvtfdWK7B4B/C1+KFeqcngi0TEek+Xf9FRKQzEQ9KffLJJwwbNoyjR49y9OjR4OsKurR3unXoXqCgsaQWn72WdLORZreX8oZmhuba4t2kqNv89icAnJdnIztdBc57KlDc/Njp5P+GWfeGrgUypXpyRgJBKWVKiUii0PVfREQ6E7Gnxm9+85v84he/4JlnngHA6XRitVojtfuUVNuaKZWX5LNqybn4GJpr5YOqJo6ddqR8UKra3sLO9ysAGDU4O86tSQ25Vv8l+nitA5fHiyUJs+10bwhdcPheD57T0oxgR0EpEYk/Xf9FRCQUEXvC2bdvX5vfp0yZEqldp6xAplSuMqVS1vA8f6nhj1Mg06Urv9t3nBaPD3dFKQOz0+PdnJSQkWbC1+LA44NPkrRgvu4NoXP3cPge+INSAPXNGr4nIvGl67+IiIQial+7+3y+aO06JXh9PuocypRKdcPz/NlRH6dATaDO1DlcPLvvBADOd55XSn6EGAwGPLUngdSoKwW6N3QmOPse3T9Hlta7ujKlRCTR6PovIiIdiVpQSg+lnWtwuvH4wKSZ91LaeYGgVIpnSv3mn8ext3go6peJ6+jb8W5OSgkEpY6lSFBK94ZzC9aU6kmmVOvtpEGFzkUkwUT7+u/xeLjlllu49957g6/t37+fefPmMWbMGKZNm8bWrVvbbLN9+3aKi4u54oormD17dpvsLo/Hw8MPP8ykSZMYM2YMixYt4tSpU1Htg4hIb5R8BUpSRHDons2MUQ9pKWt4LwhKnW5q4dl/Hgfg9onnQQ+yPKQ9byBTKoU/Q+LX09n34Ozhe8qUEpHeZd26dbz11lvB3+vq6li4cCE33ngjJSUlrFq1ioceeogDBw4AsHfvXh588EHWrFlDSUkJM2fOZNGiRTgc/vvt+vXr2b17N8899xyvvfYaVquV5cuXx6VvIiKpLGKFzt1uN3/4wx+Cv7tcrja/A9x4442ROlzSq9XQvV7hvNaaUuUNzThdHqyW1MuK2/Dmx9hbPFzcP4vPFvaNd3NSTrJnSuneEDpXoNB5D/YRCErVORWUEpH4iuX1/80332TXrl184QtfCL62a9cucnNzmT9/PgATJ05kxowZbN68mdGjR7N161amT5/O2LFjAViwYAHPPvssO3fuZM6cOWzdupUlS5YwaNAgAJYtW8bkyZMpKytj2LBhEWm3iIhEMChVUFDA2rVrg7/n5eW1+d1gMOjB4yynAzPvqch5SsuxmeljNVPvdFNW66CoX1a8mxRRH1U38fv9/lpS/zV1hLL+osBT6z+/H9U04fP5km74m+4NoYtEofPAaPBah4JSIhJfsbr+V1dXs2zZMp544gk2bdoUfP3IkSOMHDmyzbqFhYVs27YNgNLSUubMmdNu+aFDh2hoaKC8vLzN9gUFBeTk5HD48GEFpUREIihiQam///3vkdpVr3Bm+J6CUqnMYDAwPM/Geycb+Ph09IJSTS0e3j1ZT43dhdfnwzpmBpWNzfTLit4seD6fj0f/XorHB9eOyOeq4blRO1Zv5q2rwAA0NnuoaXLRNzMt3k0Ki+4NoQsWOo/A8L1ANq6ISLzE4vrv9Xq5++67ue2227j44ovbLLPb7dhstjavWa1Wmpqaulxut9sByMjIaLc8sCwcif59UqB90W7n2fs3GGJzXs4+TqyOGWuxev/iJdX7B8nTx2i1L2JBKQmd1+ejtsn/LbaG76W+QFDqWE10agIdrW7ildLq4NAfANv4m5j3y7e4c+oIvjJqYFSya3YdquQfH9eSZjLwP5+7MOL7l1YeF4NyrJyoc/JRTVPSBaUkdJEYvhfIlDqtTCkR6QWefPJJ0tLSuOWWW9ots9lsNDQ0tHnN6XSSmZkZXO50Otstz8vLCwarAvWlOto+HH37Zoe9TTxEu50eTxM2W1rrsbIoKIj+efF4mrBa/cfMz4/NMeMlWT5n3ZXq/YPe0ceOKCgVB43NHjw+HyYDZFv1FqS68/P937IdjUJNoI9qmvjbv6vwAQWZaVwyIAuvz8cre9/C3v9CHnrpCEdONbJkWiGmnqRffMrpphZ+/L8fAPCNCcMZmmvrYgvpifPzbZyoc3Kspomxw3Lj3RyJkkgUOg8EpeqdbjxeX0T/3YuIJJo//vGPnDp1iquuugogGGT661//ytKlS9m9e3eb9UtLSykqKgKgqKiII0eOtFs+ZcoUcnJyGDBgAKWlpcEhfJWVldTW1rYbEhiK6uoGfAk8D4zB4H8YjnY7q6sbcThagn83mTK62CIyx3Q6W7Ba06ipic0xYy1W71+8pHr/IHn6GGhnpCkiEgeBoXs5Notq8PQCI/r6v1H7sCr8dO/O1Dlc/P1INT6gsCCDqYV9g5+nP2//Pv/n16+x7rWjbNt/EnuLh5VfuqjDz9vkKRMoP+kvpm0wGvB5O74SDhw0iNdf3YPP52P1S0eoaXJxYUEGt1ylugrRdn5+Bm8cPc0xzcCX0twRHL7nA+qcLvIzlFknIqnrhRdeaPP7vffeC8CaNWs4ffo0jz76KJs2bWL+/Pm8/fbb7NixgyeeeAKAuXPnsnjxYr70pS8xduxYNm/eTHV1NcXFxQDMnj2b9evXM2rUKPLy8li9ejXjxo1j+PDhYbfT5yOhHzQDot3Os/cdq3Ny9nGS5X3oLvUv+fWGPnZEQak4CBY519C9XuHCAv83Mh/VNOH2+jBHIHPB6/Pxygc1eLw+BvVJbxOQAsDn4+vjhjE4x8rynYf4y79OYbOYuPe6wnZD+cpPnmTp068AkJGRTlNTc4fHfOTWqQBsf7ec/y2txmw08P0vXUyauSeDjSQU5+Wf+QxJ6nJ5/ZlSPblCGA3+wFSL13+vUVBKRHqrvLw8Nm7cyKpVq1i7di35+fksX76cCRMmAP7Z+B544AFWrlxJRUUFhYWFbNiwgdzcXAAWL16M2+1m/vz52O12xo8fz+OPPx6/DomIpCgFpeIgUIA2VzPv9QqDc6xYzUacbi+f1DqCw/l64t+n7FQ0NGMxGtoHpM5y3UX98Pp8LH/+EL8/cBKrxchdU0d0u8bUvyoa+OHfSwFYdM35XNQ/tWYTTFTn5fmHR34UpbpkkhgiUegcIN1soKXFF/wCRESkt1izZk2b30eNGsWWLVvOuf6sWbOYNWtWh8ssFgtLlixhyZIlEW2jiIi0pRSHOAgEpZQp1TsYDQYu6OsPREViCJ/b4+Wfn9QBMHZ4DtnpnceWv3Bxf5Zf769/8Ju3j/PkG8e6dVxDZh5L/nAQl8fH1Av7csvVQ7u1HwlfIChVXu+kxe2Nc2skWiJRUwrA2lpXqqappYctEhERERGJLgWlYszn81EXrCmlRLXe4sICf12pD6p7Pvzq/YpG7C0eMtNMXDogtEJzMy8fyN3T/DPk/WLPxzz9j7Kwjul0ecj+8t2camzhgr4ZPPDFi6Iyo590rG9mGjaLEa8PTtQ7u96gl/N4PNxyyy3B2iIA+/fvZ968eYwZM4Zp06axdevWNtts376d4uJirrjiCmbPns2+ffti3WzcEZh9DyDd5P+3GfgCREREREQkUSkoFWNOt5eW1iEafbrIcJHUEQhK9ThTymji3RP+6Y2vHJoT1sxaN40Zwh3XXgDAuteO8rt9x0ParqnFw58PnsLUdxh9M9P4yezLNWtkjBkMhuAMh2Uqdt6ldevW8dZbbwV/r6urY+HChdx4442UlJSwatUqHnroIQ4cOADA3r17efDBB1mzZg0lJSXMnDmTRYsWtZsKPNoilSkVCErVaPieiIiIiCQ4BaViLJAllZVuwmzS6e8tAsXOP6jqWaaU5bwxNLk82CxGivplhr39reOG8Y0J/lljHv37Bzzx+lH/3J7ncKqhmT+8W85phwuv/TQ/mzeaQX2s3W6/dN/w1iF8ZbUKSnXmzTffZNeuXXzhC18IvrZr1y5yc3OZP38+ZrOZiRMnMmPGDDZv3gzA1q1bmT59OmPHjsVisbBgwQLy8vLYuXNnTNt+pqZUz6ZdSW8dvqdMKRERERFJdIqKxFid0w1AjlX1pHqTwtZMqY9PN+F0ebq9n/TLPg/ARf2zwsqSOtt/TjqPW8cNA+CXe8vIvvEBTtY58Z01/2hDs5s3jtbwp/cqsLd4yLGaafjTKs7v2/Mi7dI9gUypT2o1fO9cqqurWbZsGT/60Y+w2WzB148cOcLIkSPbrFtYWMihQ4cAKC0t7XR5qAyG0H86Wj84fM9w7lhxKK+nm89kSoXTpkT7Cfec9tYfnSedp2icIxERkVjRGJwYq1U9qV6pIDONvplpVNtb+HelndGD+4S9j4+qm7AMvRwDcPGA7s96ZzAYuOPaCygsyOShl47QNOBC/vz+KTLTTORmpNHU7Ob0WRkWI/pmcO2IfB6vq+j2MaXnhuX6M9Q0fK9jXq+Xu+++m9tuu42LL764zTK73d4mSAVgtVppamoKaXmo+vYNrcbbudb3tT4JpqdbsFrBYm775YXZ6AOTqcvX+2AC3DS6PBQUhNemRBPuOe2tdJ5Co/PUNZ0jERGJNUVGYqzOoUyp3shgMHDJgCxe/7CG98sbuhWUeu7AScA/jKurGfdC8cVL+nPVsBym/dcaMi+fhr3Fg73lTMBjUJ90rhjSJ5ihI/E1TMP3OvXkk0+SlpbGLbfc0m6ZzWajoaGhzWtOp5PMzMzgcqfT2W55Xl5eWG2orm7AF8LIO4PB/+D36fUdzf77g6fFhdPgw21qO9Oiq6UZg8nc5esGTxoAp+qdVFW17XeyONc5krZ0nkKj89S1s88RKDglIiKxo6BUjAWG7+UqU6rXuXRgdjAoFS6Hy8OfD5YDcEkPsqQ+rSArnaZXN/KfC27hVGMLXqMRn9tDfoaFTBXiTyjDWoODJ+uduDxeLKpJ18Yf//hHTp06xVVXXQUQDDL99a9/ZenSpezevbvN+qWlpRQVFQFQVFTEkSNH2i2fMmVKWG3w+QjrgffT6wcKnRsM595PKK8HakqdbnIl/QN4uOe0t9J5Co3OU9d0fkREJNb0VBNDXp+Pemfr8D1lSvU6lw70f+v4r4rwg1K7Dp2isdmDp66CobmRLzRuNhkZnGNl5IBshuXZFJBKQAWZaVjNRrw+OFGnulKf9sILL/DPf/6Tt956i7feeosbbriBG264gbfeeovi4mKqqqrYtGkTLpeLPXv2sGPHDubMmQPA3Llz2bFjB3v27MHlcrFp0yaqq6spLi6OaR9cgZpSPdyPtbWmVL3THQx0iYiIiIgkIgWlYqix2YPXByYDZAa+ypZe49LWDKdjNQ4aW4fphMLn87H1Hf/Qveb3/45BFUh7JYPBEBzCp2Ln4cnLy2Pjxo288MILjB8/nuXLl7N8+XImTJgAwMSJE3nggQdYuXIl48aN4/nnn2fDhg3k5ubGtJ3u1gBSN+cwCEozgrl1J9X2lp42S0REREQkapQOEUN1rcWj+1gtGBVY6HXyMtIY1Cedk/XNHKpo5KrhuSFtd7C8gcOnGkkzGag99CpwT1TbKYlraK6NI5V21ZUKwZo1a9r8PmrUKLZs2XLO9WfNmsWsWbOi3axOuTxnZt/rCYPBQL7NzCm7i7Kqegb2iXx2pYiIiIhIJChTKoYC9aQ0817vddlAf4Hzd47XhbzNtndOAFB8UT98zY1RaZckB83Al9pc3tZMqQjsq2+G/z5T3eTqYk0RERERkfhRUCqGAplSqifVe109PAeAko9rQ1q/1uHipcOVAMy9YnC0miVJIlDsXJlSqSlSmVJwJihVZVdQSkREREQSl4JSMaRMKbl6uH+K+QMn6nG4PF2uv+O9clo8Pi7un8VlAzU9c293pqaUglKpKFI1pQDyM/xfflSpppSIiIiIJDAFpWJImVIyNNfKwOx03F5fl0P4vD4fz+33Fzife8UgFTgXhrZmSp2ocwYDGJIaPF4frYlSEbkxFyhTSkRERESSgIJSsWKy0Njiz4xRplTvZTAYuLq1wHnJsdpO133zo9Mcr3OSlW7i+ov7R79xkvD6ZaWRbjbi8cHJ+uZ4N0ciyO31Bf8eifhzfiAo1aRMKRERERFJXApKxYixzwAA0kwGrGad9t5s3Hn+IXyvH63B5/Odc73Nb30CwIzLBmK1mGLSNklsRoOBoYFi5xrCl1JcZ2W+9XT4ns/nI9vk358ypUREREQkkSk6EiOm3IEA5NosGobVy11zQT5pJgNHq5s4fKrj2fQOVTRQ8nEtJgP8f2OHxLiFksiCxc41A19KcXvOBKh7emN2tzTz8sFjAFQrKCUiIiIiCUxBqRgx5gwCoI9VQ/d6u2yrmamFBQA8//6pDtd5+h/+LKnrLurHoD7WmLVNEp9m4EtNLq8/s8lkNERk+F6WNR2AxhZPSJMqiIiIiIjEg4JSMWLK8WdK5dhU5Fxg+qX+4Zwv/utUu4LV756o56//rgTg61cPi3nbJLENDc7A54xzSySSXK2ZUuZITL0HmA1gat1VZaPqSomIiIhIYkraoJTH4+GWW27h3nvvDb62f/9+5s2bx5gxY5g2bRpbt25ts8327dspLi7miiuuYPbs2ezbty9m7TUGglLKlBJg/Pl59M1M47TDxe/eORF83evz8aOXPwBgxmUDGNk/K15NlAQ1TDWlUlKgppQlQkEpgwFsraXoKhtVFF9EREREElPSBqXWrVvHW2+9Ffy9rq6OhQsXcuONN1JSUsKqVat46KGHOHDgAAB79+7lwQcfZM2aNZSUlDBz5kwWLVqEwxGbBzvjWTWlRMxGA/856TwAntx9jIoG/0Pj2leOcrC8gQyLie9ce0E8mygJKjB873ids82MbZLcXN7IZkoBWFuDUlXKlBIRERGRBJWUaTtvvvkmu3bt4gtf+ELwtV27dpGbm8v8+fMBmDhxIjNmzGDz5s2MHj2arVu3Mn36dMaOHQvAggULePbZZ9m5cydz5syJantrHS6M1mxANaXkjJmjBvKn9yp492Q9Czbvo6hfJm9+dBqAe64rpCAzrdv7bmyyU1h0Xmjr2jsuti6JqX92OmkmAy0eH+X1Toa2BqkkuQWG8VpMkQtK2cxAM5xSppSIiIiIJKiki5BUV1ezbNkynnjiCTZt2hR8/ciRI4wcObLNuoWFhWzbtg2A0tLSdsGnwsJCDh06FHYbwi1CG5glKzPNhMWUtMlpnQqcE4MBfCmavNGdPnb2WTEZDKz80kju+v17lNU6qbL7sxnumjqC6ZcN6FFbfV4vS59+JaR1V8y5sk1bO+tfsk8ceXYfz/4z0Z3dTpPBwNBcGx9WN/FJrYNhebYO1w2lb8nS/94g0jWl4MzwvUAmpoiIiIhIokmqoJTX6+Xuu+/mtttu4+KLL26zzG63Y7O1fTizWq00NTWFtDwcfftmh7V+9bE6APIy08jISO9y/VDWCXfdWO3TZmv/WjL3pyMd9bEjBqOBgoLOPysFBdm89L0Ctr5VRr3TTfGlAxg54NzbGIyGqJ+jc/UvlP4E1kv099xmS0+a/nTUzgsHZPNhdROn3b5z9iHc65TEV2D2vUh+cZHReoc/Wa+glIiIiIgkpqQKSj355JOkpaVxyy23tFtms9loaGho85rT6SQzMzO43Ol0tluel5cXdjuqqxvCygZ6v6wGgOw0E01NXT8chLJOuOtGe58Gg/9B3+FobndukrE/HQlklXTUx474vD6qqhq6XhH4UlHf4N8728bn9UXtHHX2HgaOHUp/wmljOO2MxD7P7mNDYyM5ufld7qvR3hjX/nR03gdk+GvT/euT2nbLDAZ/QCqU61RgXYm/aGRKWU0+wEB5vWZqFBEREZHElFRBqT/+8Y+cOnWKq666CiAYZPrrX//K0qVL2b17d5v1S0tLKSoqAqCoqIgjR460Wz5lypSw2+HzhTdE7ViNf/heji2pTndYAucjVYfuQff6mEznI5T+JVN/OnJ2H0Md4hgY3hgvjU12LixsWx8s7dJpZE65jad//2ee+NaPARg4aBCvv7onuE641ymJL3drUCqSNaXSfC1AuoJSIiIiIpKwkipK8sILL7T5/d577wVgzZo1nD59mkcffZRNmzYxf/583n77bXbs2METTzwBwNy5c1m8eDFf+tKXGDt2LJs3b6a6upri4uKotzsYlLJq5j1pb/KUCZSfPBnSuipK3vt0FDw7Xudk5/un6HfR1Sz+qn/ZI7dOjUfzJEJcrYXOI1tTyh/oqm/20NTiISPNFLF9i4iIiIhEQlIFpTqTl5fHxo0bWbVqFWvXriU/P5/ly5czYcIEwD8b3wMPPMDKlSupqKigsLCQDRs2kJubG/W21TT5C1jn2hSUkvbKT54Muyi59G6BWTwbmt14fT6Mqlie9FzeyGdKWYz+H5fXX+z8gr4ZEdu3iIiIiEgkJHVQas2aNW1+HzVqFFu2bDnn+rNmzWLWrFnRblY7/zV1BEtX/B/6TLwv5scWkdSTmWbCaACvDxqbPcEglSSvM5lSkZ2h1WbyB6XKG5wKSomIiIhIwons/36lQ9MvG0Dzey/GuxkikiKMBkMwEFXvdMW5NRIJ0agpBWAz+/dXrhn4RERERCQBKSglIpKE+rTWqKt3uuPcEokElzfyNaUAMlqT6MobFJQSERERkcSjoJSISBIKZErVKSiVElxRzpQ6XmPHp+kYRURERCTBKCglIpKEcoLD9xSUSgXu1kLnka4pFciU+mdZDQ6HI6L7FhERERHpKQWlRESSkGpKpZZAoXNLhIfvBTKlHIpdioiIiEgCUlBKRCQJnV1TyqthWUkvUOjcHOHhe4FMqSa3T58TEREREUk4mkdcRCQJZaWbMBrA6wN7iyfezZEeChQ6txgNRDKpKd3oC35OappcZGVGcOciIpLUvF4vVVVVGAyQn68bhIjEh4JSIiJJyGgwkJ1ups7pVl2pFBAodI7PQyQTmrzuFtIMJpw+AxUNLQzvF7l9i4hIcquqquLJv+0HYFnfLEymjDi3SER6Iw3fExFJUsEZ+ByqK5XsAjWlDnxciccT2fczw+IfEljR2BLR/YqISPLLzMknMyc/3s0QkV5MQSkRkSSVYztTV0qSW2D2PYs58gnMGSb/n6cUlBIRERGRBKPheyISksYmO4VF53W9nr0xBq0ROHsGPgWlkl0gUyrCk+8BYGu90ytTSkREREQSjYJSIhISn9fL0qdf6XK9FXOujEFrBCAnMHxPQamkF6gpFY305cAMfKcaFJQSERERkcSi4XsiIkmqj9U/fK/B6QKikGIjMRMMSkXhbQwM31OmlIiIiIgkGgWlRESSVFa6CYMBPD4wZObFuznSA25vYPhe5KNSgeF7qiklIiIiIolGQSkRkSRlNBjok+6POJhyBsa5NdITUc2Uag1K1TndOFyeyB9ARERERKSbFJQSEUligWLnxpwBcW6J9EQ0C51bjP4fgIr65sgfQERERESkmxSUEhFJYoGglElBqaTm8kYvUwogw+zfcXmDMzoHEBERERHpBgWlRESSWE5rsXNlSiU3dxQzpQAyLK1BKWVKiYiIiEgCUVBKRCSJBTOl+igolcyCNaWitP9AptTJBgWlRERERCRxKCglIpLEcmz+TClDRm58GyI90hyjTKmKeg3fExEREZHEoaCUiEgS62M1c/XwHBz/2BrvpkgPNLv9QSlT1GtKKVNKRETia19FC//1/Ec0ON3xboqIJAAFpUREktwVQ3Jo+dfL8W6G9EAwKBWlVKlgUEo1pUREJI7sLi+Ha1yU1jgp+fh0vJsjIglAQSkREZE4a3Z7gChmSgWG7zU04/X5onMQERGRLnxQ6yZwFzpSaY9rW0QkMSgoJSIiEmctUR6+ZzP761W5vT6OV9XhU2BKRERizOP18WHtmSF7pVUKSomIglIiIiJx5fX5aGmdfc8Upbuy0WCgb4a/KP66vx/E4XBE50AiIjF26NAhbrvtNsaNG8c111zD0qVLqampAWD//v3MmzePMWPGMG3aNLZubVt/cfv27RQXF3PFFVcwe/Zs9u3bF1zm8Xh4+OGHmTRpEmPGjGHRokWcOnUqpn1LNf+qdOBwn/lSREEpEQEFpUREROIqkCUF0cuUAhiQneY/HpboHUREJIacTie33347Y8aM4fXXX+fPf/4ztbW13H///dTV1bFw4UJuvPFGSkpKWLVqFQ899BAHDhwAYO/evTz44IOsWbOGkpISZs6cyaJFi4JB+/Xr17N7926ee+45XnvtNaxWK8uXL49nd5NeRaMLgNx0/yPoJ7VOmlo88WySiCQABaVERETiyBmjoFT/LH9QqtGloXsikhpOnDjBxRdfzOLFi0lLSyMvL4+bb76ZkpISdu3aRW5uLvPnz8dsNjNx4kRmzJjB5s2bAdi6dSvTp09n7NixWCwWFixYQF5eHjt37gwu/9a3vsWgQYPIyspi2bJlvPrqq5SVlcWzy0mtusk/dC/XaiTfZgbgA2VLifR6CkqJiIjEUWDmPbPRgMEQvahUX6v/lm9v1hTcIpIaRowYwVNPPYXJZAq+9uKLL3LZZZdx5MgRRo4c2Wb9wsJCDh06BEBpaek5lzc0NFBeXt5meUFBATk5ORw+fDiKPUpt1Q5/plSG2cD5uemAhvCJCJjj3QAREZHeLBCUSjdH93uifpn+YXt2ZUqJSAry+Xw8/vjjvPzyy/z617/mV7/6FTabrc06VquVpqYmAOx2+zmX2+3+QElGRka75YFl4Yji9w09EmjXp/+MlkCmVIbZwHl56fzzpJ3SKntUj2swtO1for4XPRGr9y9eUr1/kDx9jFb7FJQSERGJo2a3v55GejTH7gGDshWUEpHU1NjYyH333cfBgwf59a9/zUUXXYTNZqOhoaHNek6nk8zMTABsNhtOp7Pd8ry8vGCw6tOTQpy9fTj69s0Oe5tY8HiasNnSgr9Hu521Lf4vYXIyLRQNyob3a6hxuikoiN5xPZ4mrFZ/H/Pzs6J6rHhL1M9ZpKR6/6B39LEjCkqJiEhKOHToEA8//DAHDx7EYrFwzTXXcO+995Kfn8/+/fv5wQ9+QGlpKXl5eSxatIh58+YFt92+fTtPPPEElZWVjBgxghUrVjBmzJiYtDuQKWUxGfBFMV40sLXQud3lwxfNA4mIxNDHH3/Mt771LQYPHsy2bdvIz88HYOTIkezevbvNuqWlpRQVFQFQVFTEkSNH2i2fMmUKOTk5DBgwoM0Qv8rKSmpra9sN+QtFdXVDVK/v3VVd3YjD0RLMfoh2OyvqWwAweT1YPP6sqZOnHVRVNXS2WY9UVzfidLZgtaZRU9OIyZTR9UZJxmDwBzMS9XPWU6neP0iePgbaGWmqKSUiIkkvmjMwRVsgKNXkbMHjdkXtOANah++5fVDrUF0pEUl+dXV13HrrrVx55ZX84he/CAakAIqLi6mqqmLTpk24XC727NnDjh07mDNnDgBz585lx44d7NmzB5fLxaZNm6iurqa4uBiA2bNns379esrKymhsbGT16tWMGzeO4cOHh91Ony9xfwLti3Y7XR4ftc4zw/dyrf7ciJqmlpj2M1V/1L/k/0mWPkaDglIiIpL0ojkDU7QFZt8zRXn4XprZSOtkR5yob47qsUREYuH3v/89J06c4C9/+Qtjx45lzJgxwZ+8vDw2btzICy+8wPjx41m+fDnLly9nwoQJAEycOJEHHniAlStXMm7cOJ5//nk2bNhAbm4uAIsXL2bq1KnMnz+fqVOn0tzczOOPPx6/zia5ansLXh8YgHSzgVyrvzh9TZMLZe+K9G4aviciIkkvMAPT2bqagWnbtm2Af7hG4Jvzs5cHZmgKVajFHz9dzLIlEJQyGABfp/vqyesGA2SaDTjcPk42NCd0Mc1kKfgZbzpPodF56lqynqPbbruN22677ZzLR40axZYtW865fNasWcyaNavDZRaLhSVLlrBkyZIet1OgstH/ZYjNbMBoMJDb+i1Js9uLvcVDVroeS0V6K/3rFxGRlBLJGZjCEe4Y+8D6aWX1gL+mlM2WhtnoA5MJi9nSZv2evG5OS8NmM5FjM1HldFPnJimKvfbWgp/h0nkKjc5T13SOJFpONbQGpSz+yKfVbCTDYqLJ5aGmyaWglEgvpn/9IiKSMiI9A1M4Qi1O+elillW1/unFDT4fDkczrpZmDCYzbpO3zXY9et3RzCPbS0jzWgEoLa+PamHZnkqWgp/xpvMUGp2nrp19jkDBKYm8U43+IucZ5jPpePmZFppqPdTYWxieZzvXpiKS4hSUEhGRlBCNGZjCEW4ByMD6za7W4XvGtsvOtU13X7ekWclsraN+or45KR7Oo1lUM5XoPIVG56lrOj8SLWcP3wvIs6XxSa2TmqaWeDVLRBKACp2LiEjSi+YMTNHWHKwpFf1jZQYLnesBQEREYieQKRUYvgfQt3VW2Oqm6M08KyKJT5lSIiKS9M6egemFF15os2zfvn1s3LiRVatWsXbtWvLz8885A1NFRQWFhYVtZmCKNmcHhc6jJRCUqmxsocXtJc2s76ZERCT6auytQamzvoHJz0hrs0xEeqekC0odOnSIhx9+mIMHD2KxWLjmmmu49957yc/PZ//+/fzgBz+gtLSUvLw8Fi1axLx584Lbbt++nSeeeILKykpGjBjBihUrGDNmTBx7IyIikRDNGZiiLZaZUmlGMBvB7YXjdU4u6JsR/YOKiEivV+vwZ0Oln11TKsOfKVWjTCmRXi2pviJ1Op3cfvvtjBkzhtdff50///nP1NbWcv/991NXV8fChQu58cYbKSkpYdWqVTz00EMcOHAAgL179/Lggw+yZs0aSkpKmDlzJosWLcLhcMS5VyIi0psFg1IxuCMbDJDdOnTiWE14swuKiIh0VzAodXamVGZrppRqSon0akkVlDpx4gQXX3wxixcvJi0tjby8PG6++WZKSkrYtWsXubm5zJ8/H7PZzMSJE5kxYwabN28GYOvWrUyfPp2xY8disVhYsGABeXl57Ny5M869EhGR3qzZ7QFikykFkJ3mP9BHCkqJiEgM+Hy+DoNSfWOYKRX4AkhEEk9SBaVGjBjBU089hclkCr724osvctlll3HkyBFGjhzZZv3CwkIOHToE+GdS6my5iIhIPMRy+B5AptF/vA+rGmJzQBER6dWaXB5aPP6aiVZzBzWlopgp5fP5+MfJZjYfqOPNj6N/3/N6vZw6dYpTp07h9SoQJhKKpApKnc3n8/HYY4/x8ssvs2zZMux2Ozabrc06VquVpib/N8FdLQ+HwRD+T2C7VKU+nnubUD4biUDvYfL79PUmmT5/vVmsg1JZ/i+mKat1xuaAIiLSq51uzYRKMxna3OuCw/fs0cuUevbdaj6odQPwv0fro3acgKqqKp78236e/Nt+qqqqon48kVSQdIXOARobG7nvvvs4ePAgv/71r7nooouw2Ww0NLSNfjudTjIzMwGw2Ww4nc52y/Py8sI+ft++2d1qt82WHtJ6GRmhrRfOurHaZ0d9TOb+dCTU99FgNFBQ0PVnxWA0JNQ56qx/yfD+hLJuoI+p0h9o/3nr7nVKYu9MTanYRKXOBKWaY3I8ERHp3epah+7lpJswnPWNWKDQeZPLg9PlwWoxdbh9dzW1ePjNgTOBoQ9qYvNlTGZOfkyOI5Iqki4o9fHHH/Otb32LwYMHs23bNvLz/f/oR44cye7du9usW1paSlFREQBFRUUcOXKk3fIpU6aE3Ybq6gZ8YczaHbj2OhzNIW3X1BT6g0Ko60Z7nwaD/0G/oz4mY386Eu776PP6qApheIzP60uIc9TZexjtY8dqn2f3MdbHjvY+A583g8EfkArlOhVYV+LLGetMqdY7f53TTa3DRa7NEpsDi4hIr3S6NSjVx9o26JSZZiLNZKDF4+O0w8WgCAeljtY04fGB0QBeH5xocHG6qYW81mGDIpIYkmr4Xl1dHbfeeitXXnklv/jFL4IBKYDi4mKqqqrYtGkTLpeLPXv2sGPHDubMmQPA3Llz2bFjB3v27MHlcrFp0yaqq6spLi4Oux0+X/g/ge1Slfp47m1C+WwkAr2Hye/T15tk+vz1Zs2u2AalzEawtQamNAOfiIhEW20wU6ptPoTBYAh+MXI6CsXOP6r23+MKbEZyrf7H3gMnVE9RJNEkVabU73//e06cOMFf/vIXXnjhhTbL9u3bx8aNG1m1ahVr164lPz+f5cuXM2HCBAAmTpzIAw88wMqVK6moqKCwsJANGzaQm5sbh578/+zdeXxU1f3/8ddMZpKZLGQhCC5YlwStChJBNlFbNFJFCGXRtvnZYr+KxajVFtxAxSKIX+tSSkG+KMW2VFqwaFFkqbsoEBRBacHEyqIs2UhIJpkkM3N/f0xmICaQTDLJLHk/H+aR5J67fM4N3nPvZ845V0RExKvO3blJKYAkq4kal8HesmouPj258w4sIiJdji/h5O0p5W5UlmK3UlRV5+9NFUxfNXzw0i3WjDnGTLmzjh0HjnJlRvegH0tE2i6iklI333wzN9988wnL+/bty/Lly09YnpOTQ05OTkeEJiIi0ia1Lu8Nekwn9l1OtBgUAQVFR4FTO+/AIiLS5ZQfN6fUt5NSqQ3zSlV0QFLK11MqOc6M3Wbhi9I6PjtQEfTjiEj7RNTwPRERkWjT2W/fq69zEm/2PhTs6aRJX0VEpOsqP8GcUkCHDt/z95SKM5Nu9x77v6Uati4SbpSUEhERCaFjSanOG7+XZPVOKLbnSE2nHVNERLomX8Lp23NKwXFJqSD3lKpzefi63NvGdYs1kRjnTUpVOF1U17lPtqmIdDIlpUREREKos3tKASQ1PBccqqzDUefqvAOLiEiXU17jbWeSm+kp5Ru+Vx7kpNS+8ho8BsRbzdgtJmJjTCTGeh99Dx5VL2GRcKKklIiISAg5fUmpTmyRY2PA92zwlYYyiIhIByqvqQOgW1wzSamGnlLlQR6+55tPqndyLKaGnsinJHiPdehobVCPJSLto6SUiIhIiLg8Bm6PdyhdZ/aUAu/ErwBfljg698AiItKl+IbmNddTqqOG7+1rGJ5+RrdY/zJfUko9pUTCi5JSIiIiIeJ78x50flKqW6z3gJr0VUREOorL7aGq1tvWJduamVOqg4bvHar0Jp56Jlr9y3okWBrK1FNKJJwoKSUiIhIivvmkAMyd3lOqISlVoqSUiIh0DF+yyWzCP6fT8VLtsY3WC5aDDUP0eiQcS0qdkugbvqeeUiLhREkpERGREPElpWJjTP45LzpLcsPDQWGJg+rqagzD6NTji4hI9CttmCsqxW7F3Ew7l2L39l466nThcnualLeVL/F0yvFJKf/wPfWUEgknSkqJiIiESG299wY8ztL5zXFSLJiAEkcdT639lJqamk6PQUREoltZtXeS8+4Jsc2Wd7NZ8aWqyp3BeRusYRj+xNMp6iklEvaUlBIREQkRX0+pUCSlTK464i3e3lGVbmsLa4uIiASuzOHtKZUW33w7E2M2kWwP7rxSFTUuf/vqm0cKjiWoiqvqqA9irywRaR8lpURERELE2TDReVxMaJrj5IbJzsuC/CpuERERONZTKjW++Z5ScGwIX3mQ2qKDDZOcd0+IxXpc+5piiyHOYsYADmuyc5GwoaSUiIhIiPjeSJQQ2/Q12Z0hueGD6/JafWIsIiLB5/vQ40Q9pQBSG3pKHQlSTynf0L1Tu8U1Wm4ymeiZ5F2mpJRI+FBSSkREJESO1npvwJNsIUpKNXxwXV6rSc5FRCT4/HNKnaynVHxw38DnmzOqV5KtSVmvhqTUQc0rJRI2lJQSEREJkaM13kldk+IsLazZMXxJqaN1BnWaX0NERILMP6dUwol7Svl6UZU56oJyzEMNPaV6faunFMCp3byJKr2BTyR8KCklIiISIkdrfUmp0PSUsseA1QwGsPeIPjUWEZHgKm3FnFK+N/OVBCkp5esF9e3he3AsUaU38ImEDyWlREREQqTSGdqeUiYTpDQ8J+wqcoQkBhERiV5HGuaU6n6SOaV6BDkpdaynVNPhe+opJRJ+QnMXLNIFVFU7yMj8TsvrOao6IRoRCUfH95QqqwxNDGmxUOyEnYd1LRIRkeDxGAZHGnpKpcXHQk1Ns+ulJ3qTUsVVQUpKVR430blR3ahMPaVEwo+SUiIdxPB4uPfFd1tc76Hxl3RCNCISjo7vKVUWohjSbMBR2HlIPaVERCR4jta4cDe8RyM13sqR5nNS9EjwJoqC0VOqpt7tnzC9V5KNmqONk1K+nlKHK2vxGAZmk6ndxxSR9tHwPRERkRA56mx4+16I5pQCb08pgH3lTsqrg/PmIxEREd98Ut1sFqwxJ37s7N7QU6rMUYfL0763wfqG7iXExpBka9r/4pTEWMwmqHMblKnNEwkLSkqJiIiEyNEQzykFEBsD3WK9nxTvOHg0ZHGIiEh0KfMP3TvxfFIAqXYrMSbvSzd8w/3a6tgk503nkwKwxJhJb5jDSkP4RMKDklIiIiIhUtkwp5SVeoz2fTjcLt1t3tuBHQeUlBIRkeDwTXKedpI37wHEmE3+N/C1d14p33xSvZp5856PJjsXCS9KSomIiISIr6fUP7cW4naFbhhBd7u3p9SWvUdCFoOIiESXUn9S6uQ9pQB/Uqq980r5ej/1SjpxUkqTnYuEFyWlREREQqDW5aHW5QEgwXbim+fOcFpCDGYT/OdwFV+X12AYBtXV1f7v1dXVLe9EREQiUq3L8H9IEkwlVd6eSL6E08n0SIxrtE1b+Xo/nWj43vFlh9RTSiQsKCklIiISApUNk5ybTWAJcWtss5jo1ysegA27i6mpqWH+hu3UnOD13SIiEh2+qXTxamE1Nyz9POg9hw5UePd3WvKJE0Q+6cHuKXXS4XvesgPqKSUSFpSUEhERCQHfp9KJsTGYwuCV1MNO9yal1v/nMADWuJYfIkREJHJtP+Tg/a9rcRtwpMbF9Nd3tfvtd8f7xpeUOkmvJZ/0xCDNKXXUN6fUiY/ZO9UOwP4j+uBFJBwoKSUiIhICR2tD/+a94w05Ix4TUFhaw3ZNeC4iEvVW/bsMAzg1IYaE2Bi2f3OU1z4/FLT9+4fSdVJPKZfHoLjKN3zvxD2leqd4k1JfVziDmoQTkbZRUkpERCQEKn09peJiQhyJV1JcDN9J9P78u/f34Qnl6wBFRKRDHamu4+MDDgAu6RnLzy7tBcC63cVB2X91nZvyGu8w9dNbkZTq0dBTqqQdPaWKq7y9vizHvc2vOackxRFnMeP2GBys0BA+kVBTUkpERCQEKhqSUt1s4dFTCuCCFLCa4csyJ9sP1ykxJSISpTbsLsZjQJrNTLc4M1f3SQPgk/3llFW3bwgdHJuvqZvNQmIregT7Jjo/2I55nnzb9kyKw3ySYfFmk4kzG4bw7dMQPpGQU1JKREQkBHw9pZLCpKcUQFwM9Ev3Pjz8p7SOKf/4D89v/oYt+yqoa3hToIiIRL43/lMEwFnJ3mv+6SlxnN8zEY8B7xSWtnv/vknOT/YWvOP5kkQVThfl1fVtOuZXpd43xX4nzd7q4+09orfLioSaklIiIiIhcLTh7Xv2GAinDknnplj41WWnYTbBrqJq/vzJQaa9XsDEP27h3V0HcDgcGOEUsIiIBKTEUcfnBysBODPp2AcjV/dJB+DNIAzhC+TNewB2a4x/Hqi2JooKi73DETPSE1pcVz2lRMKHklIiIiIhUNkw0XnBgVLcrrZ9KtxRrslMYXRGAtOu/A4j+3Qn1RbDgaN1TH29kDv+9jHV1d4Hhurqav/PIiISGTbtKQMgI82G3XrscfD7md6k1CdfV+Coc7XrGL6hdK15857Pd9K8b4H19XgK1JcN250bQFJKb+ATCT0lpURERELA5fb2NooPg7fvGYZBTU1Nox5bCbFmrr+gBw+MOIs/XHcGZyd55+f4/AjM37gft95YJCISkTb+9wgAA09vnLz5Tlo8vVNsuDwG+XvL23WMYz2lTvwWvG87qyEptacs8ESRYRh8WeLtKdWapJTvDXzB7il1sLKOz4rr2Hygln8X6UMbkdZQUkpERCQEfnTJ6fwkqxffSQp9U+yqq+XFjbtxuRt/Mm4YBkeOlLHso91cnGpwUYp3+crPinjwtf9Q6/JgGAbV1dUa0iciEgFcHoNNe709pQaentikfNjZ3gnPN35V1q7jfBPg8D2As9LaPs9TcVUdR50uYkzHklsn851U7zqHKmtx1rsDPl5zShx1TF27l89L6vlvhYuH3/ya3UVVQdm3SDQL/Z2wiIhIF3RW93huG3IGsTEnfkNQZ7LGNv002+ms4fl3dmIyWzCZIDMZhvSyYjGbeKughF+v/oLiiirmb9hOTY2GQIiIhLsdByqoqnWTbLPQp3vThJEvKfXhV2Vt/rDB5fb4h8WdkdzypOM+x3pKBZ6U+rLU20uqd6qdOEvLj7jJdgtp8VaAoCSOPIbBQ2t2Ue500y3WRHe7mRqXh3tWfU5VbfuGQopEOyWlREREQsA7ZK46rCY5P55vSJ/F2jhZ1TvJzGNX9yYhNobPDlXxqzVfUWuK9W/j6zWlHlQiIuHnnQLvm/WGnZ1GjLnphyKXnJFMnMVMUVUdXzRMHB6oghIHTpeHpDgLZ7biTXg+vjmlDlQ4qQ3wja+BTHIOYDKZuPj0ZAC2fV0R0LGas/Y/RWzdV47NYuLyM2x8r7eN05KsFFfV8dIn37R7/yfi8XgoKiri8OHDeDx6S65EJiWlREREQqCmpobn3vws7CY593HV1fLCe//B7a5vsvyDz75gQLdqbGaD/RV1rP2vg/f+e4Samhrmb9hOdXU1ZWWl/H79sR5USlKJiISWYRi8VVACwFUNb9r7Nps1hqFnpQLwrza+hW/HN0cB6HtaEmZT63sDd4+3khRnwWMEPgF5QUNS6pxWJqUA+p/eDYBPG+JtK5fbw+KP9gJwY990usWZiY0x8dP+PQBYtvVrKmo6pq0vKSnhuX9t56lX8ykpKemQY4h0NCWlREREQsQa1/q5NjrL8ZOeW2Obj88aa6N7QhzfO9XEuamx1LnhoXVfcveru9lfE8Oug0f43frPMJnN/mRUdXW1hvmJiITQvw9VcriyFrvVzODvpJ5wvezzvMmU9buL2/RBwvYD3iRPv9O6BbSdyWTinO7e3lKfHWx9oshjGGze6528vd+prT9m1hnenlLbD1S06+Udq3ce5utyJ2nxVsacd+y8XvadJDJ7JOCoc/PnrV+3ef8tSUhOIzElrcP2L9LRlJQSERERP38PqVb04LJb4PGrTuOC9FisZhPbDlSy6Rsnv3j1v7xxIIaVBbX8dPnn3LzsE17beRB3TNNhfiIi0phvSFZRUVFQh2S9+YW3J83wc7pjs8accL0rzu2O3WrmQIWTnYcqAz7Ojoak1MWnJQe87dCzvUmd978sbfU2/z5USVl1PQmxMVzSu/XHzOyRSLw1hqpat//NfYGqdXl4vqGX1KTBZ2KzHnu8NptM/OKyswD42yffUOqoa9MxRKKdklIiIiLSyIl6SDW7boyJ/j3jeGFcBn2SPKTZTCTGem8vDGBfeS3/Perht+8f4JXdDma88QVv7zrI79d/qqF9IiLNKCkpYdGb21n05vagDcmqrnPzz88PAXB1Q0+oE7FZY7ji3O4AvL7zcEDHOXTUyeHKWmJMcEGvpIDj9B13y77yVr8V772GBNbQs9KwxrT+8dZiNtH3NG+MH7dxXqlXdhykqKqOUxJjGdfv1Cbll5+TxoW9knC6PCzdsr9NxxCJdl0uKVVaWsrtt9/OwIEDGTx4MLNnz8bl0hsRRES6OrUP7XNKopWLe8ZxVe9Y/u8HpzCmN1x/dhyPjOhNnxQz3SwePMD7e45y35pC1ux189t397Bk016Wbizg1yu38d7uQ5RWVOLxeJSkEpGwEKq2ISE5jYTk4A3JWrXjIBVOF2ek2PyJn5MZc1EvAF79/BCHjjpbfZx3Cr0JosweicTHnrg31olkpCdwarc4al0eNu8tb9U2739ZBsAVGYGfr6FnebdZtf0gngDbnPKaepZs3gfA/ww5s9m3/plMJqY09JZa8emBoLzpTyTadLmk1N133018fDzvv/8+K1eu5KOPPmLp0qWhDktEREJM7UPgfPNPeTzH5qFy1dXy4sbdeDwu7Bbo38NCv+4xXHW6mSvSazmnm7cnVbXLYPW/S1i4cS8LNh9ia7Gbqa8XcO3z2/j1P7bzyz9/hKO6utGxjk9UNde7qjXriIgEIhrahqpaF39pmNPoZ5f2xtLMW/e+7dIzUxjYO5l6t8GiD/e26ji1Lg9/yvf2Bhrbr1ebYjWZTFx+jjdptvY/RS2uv3nPEQpLHMSYYNhZgSelcvr2IjEuhq/KqgMaMmgYBrPXf0FZdT1npdkZfdGJ6zv4rFRGZKbj9hg8unY39e7gviWv3mNQ5zYCTqqJhAtLqAPoTHv37mXLli2899572O12evfuze23386TTz7JLbfcEurwREQkRNQ+tI2rvpYXP/qS+PgEXnivxD/szxob5y1vmJ/Kt7yb1aBfopkfDzyDeR8d5szuiRypNdixr4R6j0GVy0SN28T7e7xzmPz7r58z/uLT+G7PJEqOOlie/xWnpCbz37IaDh6txTAMTk+20TvFxtnpidhjDDYXHuT73z2DOIsZa4yJNz/fx9gB53BaahK9usXRzWbB1Iq3QfkSbjabDafT6f9ut9tbtb2IRL5QtA1uj8HLO0vZUVRHvNXEFyU1pKR5iG2mF05r1Ls9TPvnvylx1HFqtzhGXdizVduZTCbyLj+bm//6Ka/tPMyFvZKY0P+0k27z923fUNwwlG30hW1LSgFcd2FPVnx6gH99UUz2F+mM6NP8cENHnYvZG74AYEL/00i2WwM+VmKchQkXn8bSLftZuHEP/U9PbnE/Lo/Bs+98yTuFpVhjTDx23XdbHDZ471UZfLy/nIJiB79+ZSdPjLkA+0nm9ToZZ72bD/cc4e2CEj7eW0ZxtQuo5uV/H+WiUw9zxbnd+X5mOj2T4tq0f5HO1qWSUgUFBaSkpNCz57GL8bnnnsuBAwc4evQo3boF9oYIERGJDmof2s6XgDrZm/qO56qv5c8bd3NKrI2a8hpuG3oOSx1FuA0DS4yFo/XwZYWLA04L31TUMu+9rxptv7PsSKPf9xxxsueIk/e/Kvcv+/T9fY3W+fD1Av/PdquZnomxJMfHYng8eDweku1WeiTZSYu34vJAqaOWvaVVfFlSjclsps7tIcVmoc7l4ozUBBKsMcTHmulmj/MOT/G4sZhNxMXFYjGZiDGDx+3CHhdHjBlMHjdJdhs2q5k4Sww2qxmL2URNvRtHnRtHrYtyhxOzCRJsccRaYrDGmLCYTRjuehLtNqwWM1azN9HmS4qZTFBUZ1Be7sAwvL/7mGj0S3M/YhjeB+B6t4dqpxNrbCwxZjMxJjCbTZgxgQnMpob9ef/DbGrYe8PvJ0vSnayXWouf6Z9khZa2NY5bwwQcccORIw4MvPXumJhOUtcWdnzS4jYeE6B7Qiw9EvVg2lahaBt2F1Wx5JNi/+/5b+zFsm4f56Yn8N2eiXy3ZyK9utlIiI0hPjYGczP//7k8BhU19RQUO/jHjoPsO1JDvDWGJ8dcGNCcSxed2o2fXtqbP+Xv54k3C9m89wjfz0zn9GQb1hjvdcxtGBRX1fFuYQn//Nw7/9TNg89scxIN4MJeSfx0UG9e3LKfR97Yzc5DVQw8M5mkOAtuj4HLY/Df0mqWbf2ag0drOa1bHLcPP7vNx/vRJaezasdBviyp5ucvfcqNWadzVpqdhNgYbNYY3B6DOreHyloXhcUOXv/3Yb4s8fbk/dX3zuW8noktHqN7QiyzR32XX7+6k4/2HGHCknxy+vbiuz2T6GazYLPGEBdjxgDchoFhGHgM75sFK50uiqpqOVxZy+cHK9m6rxynq2lvq5p6D/n7ysnfV85Tb3/Jd3smcskZKfROtdEjMY7u8VYsZjNms/e6HWMyYTZ5LzEew8Dj8R7b+7OB26Dhu7f+dS6DOpeb0vIK6t0GCYmJxB7XNlka/k1YY0yNlnnbs7b/ezAMD1+VlFFeXk1KSirmduyrvTqq57XJBEcNE0eOVLfYXgRDnMVMr27h8wboLpWUcjgc2O32Rst8v1dXV7e6YTGbW765OJ7JBElJScRZzS1ul5SU1OqLeGvX7Yx9mkwQG2PG/a06Rmp9mhPI3zHYx+6MfZ7ob9gZx+6sfR5fx2ioz7fX895keH9vzXVKnT2OCUb70Nq24djfyITZ4yKGxnOTeHB7bxa/tV24LW/PviyxFsAFJlj63k7Am5CymCAtFtJOAVuCnRS7jVf+c4R6zJySGIfL5eLys5LZ+dV+EmJjMJljqKp1UWuKpareoN7lwYixYHgMDLMZt9uAGAuJcTEcqqyn3vD+v1RS7aKk+rjzXl4HB5u+eSnG4r1NspnNON2AycK+8trj1tDcIBLezCZY8pP+nN09/qTrHd92yDGheHY4v1ci91x2Kut2HqLaY6LGE4Oj3uCbCiffVDj51xeBT3x+ardY7h56KumWGkpLa/zLy8uP4K7xvimvtLSUmBhHkzh/fEECuE7hH/8uY+v+CrbuP/GE4IlxFsZ8N5Urz7BQWlrcpPz445WXHyEm5sQ3IhPPT+CLQ0l8driGf+w4yD92HGx2vTOT4/j15adRU1lGTTPlvmPWe2IpL4/FfIKhi7+99js89vbXlFbXs3DjnhPG5ZOeEMsdQ3oypLe1UV1PVseMJHhi5Jk8/cFBypxuXvrkQIvHaY4lxsxZSVaGnJlEZqKbnV+XYIuNpf8ZKeytsbJpfxVflDjZX+5kf/mhNh2jdYIzCX9g9rW8irTKlOHf4UeXnB7QNh317GAyutBECxs2bGDGjBls3rzZv2z37t2MGTOGrVu3kpQU+BsiREQk8ql9EBGRb1PbICLS8brU5yGZmZmUl5c3erXql19+Sa9evdSoiIh0YWofRETk29Q2iIh0vC6VlDrrrLMYMGAAc+bMoaqqiv3797NgwQImTJgQ6tBERCSE1D6IiMi3qW0QEel4XWr4HkBJSQm/+c1v2Lx5M2azmbFjxzJ16lRiYtr29gMREYkOah9EROTb1DaIiHSsLpeUEhERERERERGR0OtSw/dERERERERERCQ8KCklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUkFSWlrK7bffzsCBAxk8eDCzZ8/G5XI1u+67777L6NGj6d+/P9deey1vv/12J0fbNoHU8aWXXmLkyJFkZWUxcuRIli1b1snRBi6Q+vl88cUXXHzxxWzevLmTomyfQOq4ZcsWJk6cSFZWFldeeSWLFi3q5GjbJpA6vvjii4wYMYJLLrmE0aNHs27duk6Otn3KysrIzs4+6b+/SL3eRKO2XGO6ol27dnHzzTczaNAgLrvsMu69917KyspCHVZYcrvd3HTTTdx///2hDiUslZeXc++99zJ48GAuvfRSbr/9doqKikIdVtjZuXMnubm5DBw4kOHDh/PYY49RV1cX6rCkE3SV6200Xyuj/ToXrden5u7ht2/f7n/2GjFiBCtWrAhhhJ1LSakgufvuu4mPj+f9999n5cqVfPTRRyxdurTJenv27OHOO+/kl7/8JVu3buXOO+/k7rvv5vDhw50fdIBaW8d//etfPP300zzxxBN88sknzJ07l2effTbsH/hbWz+fmpoafv3rX+N0OjsvyHZqbR2//PJLJk+ezE9+8hM++eQTFi1axJIlS1i7dm3nBx2g1tbx3XffZdGiRTz//PN88skn3HHHHdx99918/fXXnR90G3z88cfceOON7Nu374TrRPL1JhoFeo3pipxOJ7fccgtZWVl88MEHvPbaa5SXl/Pggw+GOrSwNH/+fLZu3RrqMMLWnXfeSXV1NRs2bODtt98mJiaGhx56KNRhhRWPx8Ntt93GyJEj2bJlCytXruSDDz5g8eLFoQ5NOlhXut5G87Uymq9z0Xp9au4evqKigsmTJzN27Fjy8/OZPXs2jz/+ODt27AhhpJ1HSakg2Lt3L1u2bGHatGnY7XZ69+7N7bff3mzvoFWrVjFw4ECuvvpqLBYL1113HZdeeil/+9vfQhB56wVSx8OHD3PrrbfSv39/TCYTWVlZDB48mPz8/BBE3jqB1M/n0Ucf5eqrr+7EKNsnkDr+9a9/5aqrruKHP/whJpOJ888/n+XLlzNgwIAQRN56gdTxv//9L4Zh+L9iYmKwWq1YLJYQRB6YVatWMXXqVO65554W14vE6000ass1pis6cOAA559/Pnl5ecTGxpKamsqNN94Y1u1HqHz00UesX7+ea665JtShhKXPP/+c7du3M3fuXLp160ZiYiKzZs1i6tSpoQ4trFRUVFBcXIzH48EwDADMZjN2uz3EkUlH6yrX22i+Vkb7dS4ar08nuodfv349KSkp5ObmYrFYGDp0KKNHj+4y94lKSgVBQUEBKSkp9OzZ07/s3HPP5cCBAxw9erTRuoWFhfTp06fRsoyMDHbt2tUpsbZVIHXMzc1l8uTJ/t9LS0vJz8/noosu6rR4AxVI/QBeeeUV9u7dyx133NGZYbZLIHXcsWMHZ5xxBr/61a8YPHgw1157LVu2bKFHjx6dHXZAAqnjqFGjSE9P57rrruPCCy/kl7/8JXPnzqVXr16dHXbAhg8fzoYNG7juuutOul6kXm+iUaDXmK7qnHPO4fnnnycmJsa/bN26dVx44YUhjCr8lJaWMn36dJ566qmIvjnvSDt27CAjI4O///3vZGdnM3z4cJ544omwb8c6W2pqKpMmTeKJJ56gb9++XHnllZx11llMmjQp1KFJB+sK19tov1ZG+3UuGq9PJ7qHLygo6NL37EpKBYHD4WhyofP9Xl1d3eK6NputyXrhJpA6Hq+4uJhbb72Viy66iOuvv75DY2yPQOr35Zdf8swzz/DUU081asjDXSB1rKio4E9/+hNjxoxh48aN/OY3v+GJJ54I++F7gdSxvr6e888/nxUrVvDpp5/ym9/8hunTp7N79+5Oi7etevTo0aoeXZF6vYlGbb2GdmWGYfDMM8/w9ttvM3369FCHEzY8Hg/Tpk3j5ptv5vzzzw91OGGroqKC3bt3s2fPHlatWsUrr7zC4cOHue+++0IdWljxeDzYbDYeeughPv30U1577TW+/PJL5s2bF+rQpBNF4/W2K1wro/06F43XpxPdw3f1e3YlpYIgPj6empqaRst8vyckJDRabrfbm8xB5HQ6m6wXbgKpo8+nn37KhAkTOPvss1m4cGFYD4tqbf1qa2u55557ePDBBznttNM6Ncb2CuRvGBsby1VXXcX3vvc9LBYLl156KTk5ObzxxhudFm9bBFLHWbNmkZmZSb9+/YiNjWX8+PH079+fVatWdVq8HS1SrzfRqC3X0K6sqqqKu+66i9WrV/OXv/yF8847L9QhhY1FixYRGxvLTTfdFOpQwlpsbCwA06dPJzExkfT0dO6++27effddHA5HiKMLHxs2bGDdunX85Cc/ITY2lszMTPLy8njppZdCHZp0kmi93naFa2W0X+e60vWpq9+zKykVBJmZmZSXl1NSUuJf9uWXX9KrVy+SkpIardunTx8KCgoaLSssLCQzM7NTYm2rQOoIsHLlSiZNmsTPfvYznnrqKf9FM1y1tn6fffYZe/bsYfr06QwcOJCBAwcC8Itf/IKZM2d2dtgBCeRveO655zZ5s4Xb7faP5w5XgdTxwIEDTeposViwWq2dEmtniNTrTTQK9Brale3bt4/x48dTVVXFypUro+YBKVheffVVtmzZ4m+DXnvtNV577TV/eyReGRkZeDwe6uvr/cs8Hg9A2LdlnengwYNR3xbKiUXz9bYrXCuj/TrXla5PXf2eXUmpIDjrrLMYMGAAc+bMoaqqiv3797NgwQImTJjQZN0xY8awZcsW1qxZg8vlYs2aNWzZsoWcnJwQRN56gdRx3bp1zJw5k9///vf8/Oc/D0G0gWtt/QYOHMiOHTvYunWr/wvgueeeC/ukVCB/wx/96Ee8+eabvPrqqxiGQX5+PqtXr46qf6cjRozgL3/5Czt37sTj8bB27Vo2b97c4jxNkSRSrzfRKJB/m11ZRUUFP/vZz7jkkkt44YUXSEtLC3VIYWft2rV88skn/jbo+uuv5/rrr4/aN0u11bBhw+jduzcPPvggDoeDsrIynnnmGa6++moSExNDHV7YGD58OMXFxTz33HO43W7279/PwoULGT16dKhDkw4W7dfbrnCtjPbrXFe6PmVnZ1NSUsLSpUupr69n06ZNrF69mvHjx4c6tE6hpFSQzJs3D5fLxVVXXcUNN9zA5Zdfzu233w5AVlYW//znPwFvD5Q//OEPLFq0iEsvvZQFCxbw+9//nrPPPjuU4bdKa+s4f/583G43d911F1lZWf6vhx9+OJTht6i19Ytkra3j0KFDWbBgAX/6058YMGAADzzwAPfddx9XXXVVKMNvldbW8Y477iA3N5c777yTSy+9lP/7v//jD3/4A9/97ndDGX67Rcv1Jhqd7N+meP3jH//gwIEDvPHGGwwYMKBRGyISCKvVyp///GdiYmIYOXIkI0eOpFevXsyZMyfUoYWVjIwMFi1axFtvvcXgwYP56U9/yogRI1p8u6tEPl1vI1+0X+e60vUpNTWVJUuWsHbtWgYPHsyMGTOYMWMGQ4YMCXVoncJkREPfPhERERERERERiSjqKSUiIiIiIiIiIp1OSSkREREREREREel0SkqJiIiIiIiIiEinU1JKREREREREREQ6nZJSIiIiIiIiIiLS6ZSUEhERERERERGRTqeklIhImCkrKyM7O5vNmzcHvO0f//hHbrrppkbL3G43TzzxBMOGDSMrK4spU6ZQVFQUrHBFRCQMtKXtWLduHddffz39+/cnOzublStXdmCEIiIiTSkpJSISRj7++GNuvPFG9u3bF9B21dXVzJ07l7lz5zYpW7hwIRs3buTll1/m/fffx2azMWPGjGCFLCIiIdaWtmPTpk3cf//9TJs2jW3btjFr1iweffRRduzY0YGRioiINKaklIhImFi1ahVTp07lnnvuaVL24YcfMmHCBAYOHMioUaP45z//2ag8JyeH4uJifvzjHzfZdsWKFdx6662ceuqpJCYmMn36dN577z3279/fYXUREZHO0da2Y+nSpfz0pz/lyiuvxGQyMWTIEF5++WXOPPPMzgxfRES6OCWlRETCxPDhw9mwYQPXXXddo+W7du1iypQpTJ48mc2bNzNr1izmzJnD+++/71/nz3/+M0899RTdu3dvtG1lZSWHDh2iT58+/mXp6ekkJyeze/fujq2QiIh0uLa2HTt27CAlJYXJkyczePBgcnJy2LdvHykpKSGohYiIdFVKSomIhIkePXpgsViaLF++fDlXXXUV11xzDTExMVxyySXccMMNLFu2zL9Or169mt2nw+EAID4+vtFym83mLxMRkcjV1rajoqKCF154gSlTprBx40by8vK455572L59e2dXQUREurCmLZiIiISVb775hk2bNjFw4ED/Mrfb3aohFna7HYCamppGy51OJwkJCcENVEREwkZLbUdsbCzjx48nKysLgGuuuYahQ4eybt06Lr744pDELCIiXY+SUiIiYa5Xr1788Ic/5De/+Y1/WVFREYZhtLhtcnIyPXv2pLCw0D+Er7i4mPLy8kZD+kREJLq01Hace+651NXVNdrG7Xa3qm0REREJFg3fExEJcxMmTOC1117jgw8+wOPxsGfPHv7f//t/LFmypFXbjxs3joULF7J//36qqqqYM2cOgwYN0mS2IiJRrKW248c//jEvvfQSH374IR6Ph3Xr1rF582auv/76EEcuIiJdiXpKiYiEuYsvvpinn36ap59+ml/+8pfY7Xauv/56fvWrX7Vq+7y8PFwuF7m5uTgcDgYPHsyzzz7bsUGLiEhItdR2jB8/HrPZzOOPP87XX3/N6aefzjPPPMOFF14Y4shFRKQrMRnqoysiIiIiIiIiIp1Mw/dERERERERERKTTKSklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVERERERERERKTTKSklIiIiIiIiIiKdTkkpERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVEgsQwjFCHICIiEUJthoiItEdntiNqs6QjKSklXdqIESO4//7727WPo0ePct9997F161b/sptuuombbrqpveGJiEgUWLNmDd///vfp27cvDz/8MIWFhfz4xz8OeD/3338/I0aM8P8eaBumtklEJDJ1VDvSGm09lkhrWUIdgEik+89//sMrr7zCuHHjQh2KiIiEoUcffZSzzjqLuXPn0rNnT1avXs22bdvavd/58+eTmJjY6vUfeeSRdh9TREQ6X0e1I63xxhtvdNqxpGtSUkpERESkA5WXl3PZZZcxePDgoO73ggsuCGj9jIyMoB5fREQ6R0e1IyLhQMP3pMurr6/nscce49JLL+XSSy/lvvvuo6yszF++YsUKxo0bR//+/enXrx85OTmsWbMGgM2bN/PTn/4UgJ/+9KeNhkUYhsHixYv53ve+R79+/bjxxhv57LPP/OW///3vyc7OZv78+QwePJirr76aI0eO4Ha7WbZsGaNHj6Zfv35873vf47e//S21tbWN4t64cSM/+clPGDBgAIMHD+bXv/41Bw8e9Jf/4x//oG/fvnz88ceMHz+evn37MnLkSN566y3++9//8rOf/YyLL76Y7OxsXn/9df92Ho+H3/3ud4wYMYKLLrqIESNG8PTTT1NfXx/cEy8iEgF27tzJz372MwYMGEBWVhaTJk1i+/bt/vK1a9cyZswY+vXrx9ixY9m2bRsXXHAB//jHP9i8eTPnnXceAH/4wx8477zzuP/++5k/fz4A5513Hr///e/bHNvxw/dGjhxJXl5ek3UmTpzI5MmTgabD98477zyWLVvG9OnTGTRoEFlZWdx1112UlJQ02scLL7zAVVddRb9+/fjRj37EW2+9xXnnncfmzZvbHLuISFcRzu1IS88dv//974N2LJETUVJKurw33niDzz//nLlz53LvvffyzjvvcPvttwOwbNkyHn74Ya666ioWLVrEk08+idVqZdq0aRw4cIALL7yQhx9+GICHH3640dCIjz/+mA0bNvDQQw/xxBNPcPjwYX7xi1/gcrn86xw4cIANGzbw9NNPc/fdd5OamsrDDz/MnDlzGDFiBAsXLiQ3N5e//OUv3H777f5JBl999VV+/vOf07NnT55++mkeeOABtm3bxo033khpaal//y6Xi1/96lf86Ec/YsGCBcTFxTF16lR+8Ytf8L3vfY/f/e539OjRg/vuu49Dhw4BsHjxYpYtW0ZeXh5Llizhxz/+Mc8//zzPPfdch/8tRETCSVVVFbfccgupqanMmzePZ555hpqaGv7nf/6HyspK3nzzTX75y1+SmZnJ/Pnzueaaa5gyZQoejweACy+8kL/97W8ATJgwgb/97W/ceeedTJgwAYC//e1vTJw4MSix5uTk8N5771FVVeVftm/fPnbs2EFOTs4Jt3vmmWfweDw8/fTT/jZwzpw5/vL58+fz29/+lmuvvZYFCxZw8cUXc8899wQlZhGRaBfu7UhLzx0TJ07skDZL5HgaviddXrdu3Xj++ef983KkpqaSl5fHBx98wP79+/n5z3/e6NPnM844g3HjxvHJJ59w/fXX+4dDZGRkNBoaERsby//93/+RkpICeBulGTNmUFhYyPnnnw94k0b33Xcfw4YNA7wTCa5cuZK7776bKVOmAHDZZZdxyimncO+99/Lee+9x+eWX8+STTzJs2DCeeeYZ//EuueQSrrvuOpYsWcK0adMAb6+nX/ziF/4G5OjRo/zqV7/iZz/7GTfffDMA6enpjB8/ns8//5xevXqxZcsWLrzwQsaPHw/AoEGDsNvtAc1bIiISDQoLCykrK+Omm25iwIABAJxzzjksX76cqqoq/vCHP3DRRRfx1FNPAXDFFVdgMpl49tlnAUhMTKR///4A9OrVq9HPgP/3YBgzZgzz5s1jw4YN/PCHPwRg9erVJCQkcNVVV51wuz59+vD444/7f9+xYwdr164FoLq6msWLF5Obm8vUqVMBGD58ODU1Nf6HJBERObFwbkda89xx5ZVXdkibJXI89ZSSLu/KK69slHAZMWIEVquVDz/8kPvvv59p06ZRWVnJZ599xurVq1m2bBlAi8PZMjIy/Akp8CazACorKxut16dPH//PW7ZsAWD06NGN1hk1ahQxMTFs3ryZr776iuLi4ibrnHnmmWRlZTUZTpGVleX/OT09HWjcqPhiPHr0KACDBw/mww8/5Cc/+Ql//OMf+fLLL/l//+//MXbs2JPWV0Qk2mRmZpKWlsaUKVN45JFHeOutt+jRowf33nsvKSkp7Ny5s0nCZ8yYMSGJ9YwzzmDAgAGNhmO//vrrjBw5EpvNdsLtvv2Q0atXL2pqagD49NNPcTqd/OAHP2i0zvXXXx+8wEVEolg4tyOtee4Q6QxKSkmX50vU+JjNZlJSUjh69Cj79u1j0qRJXHrppfz4xz9m8eLF/mSUbyjdicTHxzfZL+Dvjtvc8SsqKgDo0aNHo3UsFgupqalUVlZSXl7ebNy+Zd9OejXXw+lkDyi33HILDz/8ME6nkyeeeILrrruO0aNH89FHH51wGxGRaJSQkMCyZcu48sorWbNmDVOmTGHo0KE8/PDD/kR+Wlpao2169uwZilABGDt2LB999BFHjhzhP//5D19++eVJh+4B2O32Rr+bzWZ/++abX/HbdWyu/RERkabCuR1pzXOHSGfQ8D3p8nwNgo/b7ebIkSOkpqYyefJkrFYrf//737nggguwWCwUFhbyz3/+s0NiSU5OBqC4uNjfswq8vbJ8Mfl6Nn17Ilrfdqmpqe2KwWw2k5ubS25uLqWlpbz77rs899xz3HnnnXz44YfExsa2a/8iIpHknHPO4cknn8TtdrNjxw5effVVXnrpJU455RTMZnOTa7Hvg4NQ+MEPfsCsWbPYsGEDe/fu5dRTT2XQoEFt3p9vyEZZWRnnnHOOf/nxLwMREZGTC9d2pDXPHSKdQT2lpMv78MMPG00+vm7dOlwuF9/97nf56quvmDBhAv369cNi8eZw33vvPeBYj6eYmJigxeJ7eFi9enWj5a+//jput5sBAwZw9tln06NHjybr7N+/n08//ZRLLrmkXTH86Ec/4rHHHgOge/fujBs3jtzcXCorKxtNoCsiEu3Wrl3LkCFDKC4uJiYmhqysLGbOnEm3bt0oKysjKyuLdevWNeoB+/bbb7e4X1/P2WBLSkri+9//Pm+++SZr165l9OjR7TrW+eefT1JSEuvXr2+0fN26de0NVUSkSwjndqQ1zx3BOpbIyainlHR5JSUl3Hnnndx0003s2bOHp59+mssuu4xrr72W3/72tyxbtoxevXrRrVs3PvjgA1588UUA/5wbSUlJALzzzjskJyf7JzFvi4yMDH74wx8yf/58nE4ngwcP5j//+Q/z589n8ODBXH755ZjNZn71q1/xwAMPcM899zB27FiOHDnC/PnzSU5O9k9g3laXXnopS5YsIT09naysLA4fPswf//hHBg0a1KR7sYhINLvkkkvweDzk5eUxefJkEhISeOONN6isrOSaa67huuuuY9KkSdx+++38+Mc/Zt++ffzud79rcb/dunUD4LXXXuPiiy+md+/eQYt57Nix5OXl4Xa72z0vSWJiIrfccgvz5s3DbrczaNAgtmzZwksvvQToQUVEpCXh3I605rkjWMcSORndTUiXd8MNN5Cenk5eXh6/+93vGD16NPPnz8dkMrFgwQJ69uzJ/fffz913382nn37KwoULOeecc9i6dSvgncDw+uuvZ9myZf63E7XH7NmzueOOO3j99deZPHkyy5Yt46abbmLx4sX+B4Bx48Yxb9489u7dS15eHnPnziUrK4uVK1c2GRceqF/+8pf84he/4OWXX+aWW25h7ty5DB8+nHnz5rW7biIikeSUU07h+eefJykpienTp3Pbbbexc+dOfv/73zNkyBAGDhzICy+8QElJCXl5eSxfvpz77ruvxf1ec8019O3bl/vvv58XXnghqDFffvnlJCcnc8EFF5CZmdnu/d12223ccccdvPLKK9x2221s3brV39Z9e+5EERFpLNzbkdY8d3RkmyUCYDJamq1ZRERERFrl66+/5qqrruLxxx9n3LhxoQ6nXVwuF6+99hqDBw/m1FNP9S9ftmwZjz32GJs3b/Z/gi4iIsERTe2ISGto+J6IiIhIiBiGgdvtbnG9mJgYTCZTJ0R0jMViYfHixbz44otMmTKF1NRUdu3axe9+9zvGjh2rhJSISBgI53ZEpDWUlBIREREJkVWrVvHAAw+0uF6oPjF/7rnnePrpp5k5cyZHjx7ltNNOY9KkSdx2222dHouIiDQV7u2ISEs0fE9EREQkRI4cOcLXX3/d4npnnHGGXs8tIiJNqB2RSKeklIiIiIiIiIiIdDq9fU9ERERERERERDqdklIiIiIiIiIiItLplJQSEREREREREZFOp6SUiIiIiIiIiIh0OkuoA4hEpaWVaHr4Y0wm6N49SeelFXSuWk/nqvXaeq5820lwNHf+o/HfseoUGaKxThCd9Qq3OqltCL62ts/h8m8iGuicBp/OafCF+zntqPZBSak2MAzC8h9JqOm8tJ7OVevpXLWezlVonez8R+PfRnWKDNFYJ4jOekVjnTpbWVkZN954I4899hiDBw8GYN26dSxYsID9+/eTkpLCuHHjuP322zGbvQNGVq1axYIFCyguLuacc87hoYceIisrCwC3281vf/tbXn31VWpqahgyZAiPPvoop5xySkBxtfVvq38TwadzGnw6p8HX1c6phu+JiIiIiEhE+/jjj7nxxhvZt2+ff9nnn3/Ovffey913383WrVtZvHgx//jHP1i6dCkAmzdvZtasWcydO5f8/HzGjBnDlClTqKmpAWDhwoVs3LiRl19+mffffx+bzcaMGTNCUT0RkailpJSIiIiIiESsVatWMXXqVO65555Gy7/55ht+9KMf8f3vfx+z2cy5555LdnY2+fn5AKxYsYJRo0YxYMAArFYrkyZNIjU1lTVr1vjLb731Vk499VQSExOZPn067733Hvv37+/0OoqIRCslpUREREREJGINHz6cDRs2cN111zVaPnLkSB544AH/706nk3feeYcLL7wQgMLCQvr06dNom4yMDHbt2kVlZSWHDh1qVJ6enk5ycjK7d+/uwNqIiHQtmlNKREREREQiVo8ePVpcp6qqil/+8pfYbDYmTZoEgMPhwG63N1rPZrNRXV2Nw+EAID4+vkm5r6y1TKaAVvevH+h2cmI6p8Gncxp84X5OOyouJaVERERERCRq/fe//+Wuu+6ie/fu/OlPfyIxMREAu92O0+lstK7T6SQ1NdWfrPLNL3V8eUJCQkDHb+vbqvQWxODTOQ0+ndPg62rnVEkpERERERGJSu+++y6/+tWvuOGGG/j1r3+NxXLs8SczM5OCgoJG6xcWFnLFFVeQnJxMz549Gw3xKy4upry8vMmQv5YE+nr3cH8tfCTSOQ0+ndPgC/dz6osv2JSUEhERERGRqPPpp5+Sl5fHzJkzmTBhQpPyCRMmkJeXx7XXXsuAAQNYtmwZpaWlZGdnAzBu3DgWLlxI3759SU1NZc6cOQwaNIgzzzwzoDja+nr3rvZa+M6gcxp8OqfB19XOqZJSIiIiIiISdZ577jlcLhezZ89m9uzZ/uUDBgzg+eefZ+jQoTzyyCPMnDmTw4cPk5GRweLFi0lJSQEgLy8Pl8tFbm4uDoeDwYMH8+yzz4amMiIiUUpJKRERERERiQrHvxnvueeea3H9nJwccnJymi2zWq1MnTqVqVOnBi0+ERFpzBzqAEREREREREREpOtRUkpERERERERERDqdklIiIiIiIiIiItLpNKeUhBWPx0NJSQkA6enpmM3Km4qItJZhGJSVlQGQlpaGyWQKcUQiIiIiIiemJ34JKyUlJSx6czuL3tzuT06JiEjrlJWV8dxbO3jurR3+5JSIiEQewzAwutI74UWky1JPKQk7CclpoQ5BRCRixSelhDoEEREREZFWUU8pERERERERERHpdEpKiYiIiIiIiIhIp1NSSkREREREREREOp2SUiIiIiIiIiIi0umUlBIRERERERERkU6npJSIiIiIiIiIiHQ6JaVERERERERERKTTWUIdgIiIiASXYRgcOVIGQFpaGiaTKcQRiYiIiIg0FZY9pdasWcMFF1xAVlaW/2vatGkAbN++nYkTJ5KVlcWIESNYsWJFo21XrVpFdnY2/fv3Z9y4cWzbts1f5na7eeKJJxg2bBhZWVlMmTKFoqKiTq2biIhIR6upquDFD7/kubd2UFZWFupwRERERESaFZZJqc8++4ycnBy2bdvm/3ryySepqKhg8uTJjB07lvz8fGbPns3jjz/Ojh07ANi8eTOzZs1i7ty55OfnM2bMGKZMmUJNTQ0ACxcuZOPGjbz88su8//772Gw2ZsyYEcqqioiIdAh7YjLxSSmhDkNERERE5ITCNil10UUXNVm+fv16UlJSyM3NxWKxMHToUEaPHs2yZcsAWLFiBaNGjWLAgAFYrVYmTZpEamoqa9as8ZffeuutnHrqqSQmJjJ9+nTee+899u/f36n1ExERERERERHp6sIuKeXxeNi5cyfvvPMO3//+97niiit46KGHqKiooKCggD59+jRaPyMjg127dgFQWFh4wvLKykoOHTrUqDw9PZ3k5GR2797d8RUTERERERERERG/sJvovKysjAsuuICRI0cyb948jhw5wn333ce0adPo0aMHdru90fo2m43q6moAHA7HCcsdDgcA8fHxTcp9Za2l+WIb852PYJyX4/dhMkXfuQ7muYp2Olet19ZzpXMrIiIiIiKhFHZJqfT0dP9wPAC73c60adO44YYbGDduHE6ns9H6TqeThIQE/7rNlaempvqTVb75pZrbvrW6d08KaP2uIhjnxe2uxm6PbdhfIunp0Xmu9W+o9XSuWk/nSkREREREIknYJaV27drFa6+9xq9//Wv/K6zr6uowm83069ePF198sdH6hYWFZGZmApCZmUlBQUGT8iuuuILk5GR69uzZaIhfcXEx5eXlTYb8taS0tBLDaGsNo4/J5H0YDsZ5KS2toqamzv9zTEx8C1tElmCeq2inc9V6bT1Xvu0kehmGwZEjZaSlpfnbVBERERGRcBF2c0qlpKSwbNkynn/+eVwuFwcOHODJJ5/khz/8ISNHjqSkpISlS5dSX1/Ppk2bWL16NePHjwdgwoQJrF69mk2bNlFfX8/SpUspLS0lOzsbgHHjxrFw4UL2799PVVUVc+bMYdCgQZx55pkBxWgY+vr2VzDPS7Sf52ium85V5J0riW6OygpmrNnNP7ftwdAfXERERETCTNj1lOrVqxeLFi3i6aefZuHChcTFxTFq1CimTZtGXFwcS5YsYfbs2cybN4+0tDRmzJjBkCFDABg6dCiPPPIIM2fO5PDhw2RkZLB48WJSUlIAyMvLw+VykZubi8PhYPDgwTz77LOhq6yIiEgHMQyDT8tM7KvvxmNv7+e1XWU8M74fiXHWUIcmIiIiIgKEYVIKYNCgQSxfvrzZsr59+56wDCAnJ4ecnJxmy6xWK1OnTmXq1KlBiVNERCRcfXnUwz6HGTAwA58edLBkYyF3jfhuqEMTEREREQHCcPieiIiItN+eSg8A51gqGdDD+xnUqp1lVNW6QhmWiIiIiIifklIiIiJRxuGCynowYdDLUs3pCSa6xZqpqnOz4tMDoQ5PRERERARQUkpERCTqHKr2vmmvexxYTQYmk4nvpnvnklrz+UFKSko08bmIiIiIhJySUiIiIlHmUI03KdXLfizx1N1UDRjsKa9l5vJ3KCsrC1F0IiIiIiJeSkqJiIhEEZfHoKTW+/PxSanYGEiL8zb7lZbkUIQmIiIiItKIklIiIiJR5GitBwMTsWZItDYu6xXv7UFVXBcTgshERERERBpTUkpERCSKlNd637rXLdbUpKyX3dvsl9bHUOfydGpcIiIiIiLfpqSUiIhIFKlwepNNSdamSalusRBrNvBgorDM2dmhiYhIKxmGoRdSiEiXoKSUiIhIFKmodQPeBNS3mUwmUhuWf1FS04lRiYiIiIg0paSUiIhIFKnwDd9rpqcUQFqc95P3XcVKSomIiIhIaCkpJSIiEiVq6t046r1Jp6Rm5pQCSI31lu8uqe60uEREREREmqOklIiISJTYX1EHeOeNiotpPimVEuf9frCynvLq+s4KTURERESkCSWlREREosTe8loAullPvE6sGRJivEP8dh6q7IywRERERESapaSUiIhIlDhw1JuUSrSe/I1NyRbvZOg7Dx3t8JhERCQwvjfv6e17ItIVKCklIiISJYodLgDsMSdfL8ni7SlVUOzo6JBERKQNnl63K9QhiIh0CiWlREREokRJwxxRdsvJ1+vWMHzvi6Kqjg5JRETawtT8vIAiItFGSSkREZEoUexoSErFnHzIh6+n1IGjtVQ6XR0el4iIiIhIc5SUEhERiQKGYRzrKdXC8D2rGU5J8M6GXlCi3lIiIiIiEhpKSomIiESBo04XtS5vD6mWklIA56TZACgo0rxSIiIiIhIaSkqJiIhEOMMw+OLrIgDiYkzEtKJ19yWlvihWTykRiQ5lZWVkZ2ezefNm/7Lt27czceJEsrKyGDFiBCtWrGi0zapVq8jOzqZ///6MGzeObdu2+cvcbjdPPPEEw4YNIysriylTplBUVNRp9RER6QqUlBIREYlwZWVl/OmjLwCwtTCflI8/KaWeUiISBT7++GNuvPFG9u3b519WUVHB5MmTGTt2LPn5+cyePZvHH3+cHTt2ALB582ZmzZrF3Llzyc/PZ8yYMUyZMoWamhoAFi5cyMaNG3n55Zd5//33sdlszJgxIyT1ExGJVkpKiYhIVNi5cye5ubkMHDiQ4cOH89hjj1FXVwe075PySOG2xANgt7T8xibDMEiPcQLw31IHLrenQ2MTEelIq1atYurUqdxzzz2Nlq9fv56UlBRyc3OxWCwMHTqU0aNHs2zZMgBWrFjBqFGjGDBgAFarlUmTJpGamsqaNWv85bfeeiunnnoqiYmJTJ8+nffee4/9+/d3eh1FRKKVklIiIhLxPB4Pt912GyNHjmTLli2sXLmSDz74gMWLF7f7k/JIUV3vm0+q5aRUbXUlb3y6F4vJoM5tsOdIZNVVROR4w4cPZ8OGDVx33XWNlhcUFNCnT59GyzIyMti1axcAhYWFJyyvrKzk0KFDjcrT09NJTk5m9+7dHVQTEZGuR0kpERGJeBUVFRQXF+PxeDAMb3LGbDZjt9vb/Ul5pKh2eXs72S2tWz8+KZnUhpULNK+UiESwHj16YLE0vfg5HA7sdnujZTabjerq6hbLHQ7v0Ob4+Pgm5b6y1jKZAv9q63b60jnVOY3sr3A/px2hlbeuIiIi4Ss1NZVJkybxxBNP8L//+7+43W6uuuoqJk2axNy5c5v9JHzlypWA95Py8ePHNyn3fZLeWs011MffXHQkk+lYT6n4huF7JhMYAKZjP397WYrNTHG1m4IiB9dd0PpjHf89GqhOkSMa6xVudQqXOILBbrdTWVnZaJnT6SQhIcFf7nQ6m5Snpqb6k1Xf7jV7/Pat1b17UkDr+z5c6d49iZiYVrxOVVot0L+FtEznNPi62jlVUkpERCKex+PBZrPx0EMPMWHCBPbu3csdd9zBvHnz2vVJeSBOdgPR8TcXtThd3oeY5AQLtlgwW6146j3Y4qzY7N6fv72sp9tEQVk9X5U7SU8PLMZovGFSnSJHNNYrGusUan369GHjxo2NlhUWFpKZmQlAZmYmBQUFTcqvuOIKkpOT6dmzZ6MhfsXFxZSXlzf5oKMlpaWVGK17B0UDw7+d2aykVDCYTN7/xwL/W8iJ6JwGX7ifU198waaklIiIRLwNGzawbt061q5dC3gfNPLy8pg9ezajR49u8yflgWjuBqKzbi5KS6uorvcO3zO73Dg9dZhdZjyuOpy19Thr6vG4mi5LiLECsPObCoqLj2JqRReJcL9hagvVKXJEY73CrU4d9dARCtnZ2Tz55JMsXbqU3NxcPv74Y1avXs2CBQsAmDBhAnl5eVx77bUMGDCAZcuWUVpaSnZ2NgDjxo1j4cKF9O3bl9TUVObMmcOgQYM488wzA4rDMGjT37at28mJ6ZwGn85p8HW1c6qklIiIRLyDBw/637TnY7FYsFqt7fqkPBAnu4Ho6JuLWpeHhpwUseZjx/T+cOznby9Ltpkxm+BITT3FVXX0SIxr9TGj8YZJdYoc0VivaKxTqKWmprJkyRJmz57NvHnzSEtLY8aMGQwZMgSAoUOH8sgjjzBz5kwOHz5MRkYGixcvJiUlBYC8vDxcLhe5ubk4HA4GDx7Ms88+G7oKiYhEISWlREQk4g0fPpynnnqK5557jltvvZUDBw6wcOFCRo8e3e5PyiNBhdMNgAkDqxnwtG47i9nEGd3i2FdRyxfFjoCSUiIi4ejbb8br27cvy5cvP+H6OTk55OTkNFtmtVqZOnUqU6dODWqMIiJyjJJSIiIS8TIyMli0aBHPPvsszz//PElJSYwZM4a8vDxiY2Pb9Ul5JKhwugCIiwGTyUQgnS3OSbN5k1JFVVx2dlrHBCgiIiIi0gwlpUREJCoMGzaMYcOGNVvWnk/KI0F5Q08p39C9QJybZuOdryooKA7sFeciIiIiIu3VhttXERERCSfH95QK1NlpNgC+KKoKZkgiIiIiIi1SUkpERCTClfuSUubAZ0k+tyEpte9IDTX17qDGJSIiIiJyMkpKiYiIRLiKmobhe23oKZVqt5AWb8UAvizRED4RERER6TxKSomIiES48lpfT6m2bd/nlERAQ/hEREREpHMpKSUiIhLhKmraPqcUQJ8eDUkpTXYuIiIiIp1ISSkREZEIV9Hw9r1A55QyDIMjR8o41e7drqDYQWlpKYYR+NxUIiIiIiKBUlJKREQkwvkmOg90Tqmaqgpe/PBLtnyxF4Avi6t4+pUPKCsrC3aIIiIiIiJNKCklIiIS4cr9PaUC39aemMwpqcmYTeCo92CydwtydCIiIiIizVNSSkREJILV1LlwujxA2+eUijGbOKNbHABH64MVmYiItJVhGBpKLSJdgpJSIiIiEeyrg8WAt0G3mNq+nzNTfEmpduxERERERCQASkqJiIhEMP8k5zFgakc+6axUb1Kq0hWMqEREREREWqaklIiISASraOMk58czDIN0q3fc3tE69ZQSERERkc5hCXUAIiIi0naVtd6eUrHmtieTaqoq2HqwDrBTWQ8ezWMiIiIiIp1APaVEREQiWGVdQ1KqHT2lALonJ2HGwI2JoirNdi4iIiIiHU9JKRERkQjm6yllbUdPKQCzyURCQ//pA5V17Q1LRERERKRFSkqJiIhEsCr/8L327yvB6v3+zVElpURERESk4ykpJSIiEsF8w/eswUhKWbxzSR04Wtv+nYmIiIiItEBJKRERkQjm7ykV0/635iX6hu+pp5SIiIiIdIKwTUq53W5uuukm7r//fv+y7du3M3HiRLKyshgxYgQrVqxotM2qVavIzs6mf//+jBs3jm3btjXa3xNPPMGwYcPIyspiypQpFBUVdVp9REREOkIwe0olWr09pTR8T0REREQ6Q9gmpebPn8/WrVv9v1dUVDB58mTGjh1Lfn4+s2fP5vHHH2fHjh0AbN68mVmzZjF37lzy8/MZM2YMU6ZMoaamBoCFCxeyceNGXn75Zd5//31sNhszZswISd1ERESCparWAwRpTqmGnlKHqupxeYz271BERERE5CTCMin10UcfsX79eq655hr/svXr15OSkkJubi4Wi4WhQ4cyevRoli1bBsCKFSsYNWoUAwYMwGq1MmnSJFJTU1mzZo2//NZbb+XUU08lMTGR6dOn895777F///6Q1FFERCQYKv0Tnbd/+J49BswYuDwGh446270/EREREZGTCbukVGlpKdOnT+epp57Cbrf7lxcUFNCnT59G62ZkZLBr1y4ACgsLT1heWVnJoUOHGpWnp6eTnJzM7t27A47RZNLXt7+CeV6i/TxHc910riLvXElkMwyDKt/wvZj2789kAnuMt4fU1+U17d+hiIiIiMhJWEIdwPE8Hg/Tpk3j5ptv5vzzz29U5nA4GiWpAGw2G9XV1S2WOxwOAOLj45uU+8oC0b17UsDbdAXBOC9udzV2e2zD/hJJT4/Oc61/Q62nc9V6Olddj9Plob5hmF2sGQjCiLuEGA8Ot5l9R5wMOav9+xMREREROZGwSkotWrSI2NhYbrrppiZldrudysrKRsucTicJCQn+cqfT2aQ8NTXVn6zyzS/V3PaBKC2txNBUG34mk/dhOBjnpbS0ipqaOv/PMTHxLWwRWYJ5rqKdzlXrtfVc+baTyFVRUw+ACYMYE0FJSsXHeOeo2q+eUiIiIiLSwcIqKfXqq69SVFTEwIEDAfxJpn/961/ce++9bNy4sdH6hYWFZGZmApCZmUlBQUGT8iuuuILk5GR69uzZaIhfcXEx5eXlTYb8tYZhoIfkZgTjvBy/fTSf52iuW7DpXLWezlXXU1nrAsBqMjCZTMHISWE3e/dyoEJzSomIiIhIxwqrOaXWrl3LJ598wtatW9m6dSvXX389119/PVu3biU7O5uSkhKWLl1KfX09mzZtYvXq1YwfPx6ACRMmsHr1ajZt2kR9fT1Lly6ltLSU7OxsAMaNG8fChQvZv38/VVVVzJkzh0GDBnHmmWeGssoiIiJtdtR5LCkVLL45pZSUEhEREZGOFlY9pU4mNTWVJUuWMHv2bObNm0daWhozZsxgyJAhAAwdOpRHHnmEmTNncvjwYTIyMli8eDEpKSkA5OXl4XK5yM3NxeFwMHjwYJ599tnQVUhERKSdOiIpFW/2Dt87UOHEMLw9sEREREREOkJYJ6Xmzp3b6Pe+ffuyfPnyE66fk5NDTk5Os2VWq5WpU6cyderUoMYoIiISKked3jmlrA2JpGCwNfSUqq53U15TT2p8bND2LSIirWMYBobG5ItIFxBWw/dERESk9Xw9pSxB7CkVY4L0eO9nVhrCJyIiIiIdSUkpERGRCNURw/cAeiZ6e0d9o6SUiIiIiHQgJaVEREQi1PFv3wumXklWQD2lRERERKRjKSklIiISoSpqgp+UMgyDFIsbUE8pEREREelYYT3RuYiIiJyYf6LzICalaqsrOXjACsSxt6QyaPsVEREREfk29ZQSERGJUP7he0F8+x5ASkI8AIeq6oO6XxERaZn3rXt6856IdA1KSomIiESoig6a6DzBagKgqKoOt0cPRiIinc0wjIbklIhIdFNSSkREJEJVNiSlLEFOStliwGwCtwFFVbVB3beIiIiIiI+SUhHE4/FQVFREUVERHk9wh2qIiEhkcXuMDnv7nslk8veW0hv4RERERKSjKCkVQUpKSlj05nYWvbmdkpKSUIcjIiIhVNWQkILgJ6UAEmO9twjflCspJSIiIiIdQ2/fizAJyWmhDkFERMLA0Yahe3aLGbMp+PtPsJoBN98cVVJKRERERDqGekqJiIhEoKMNPaUS42I6ZP+JsRq+JyIiIiIdS0kpERGRCHTUWQ9AUmwHJaWsGr4nIiIiIh1LSSkREZEI5HvzXlKH9ZTy3iIc0PA9EYlgO3fuJDc3l4EDBzJ8+HAee+wx6urqANi+fTsTJ04kKyuLESNGsGLFikbbrlq1iuzsbPr378+4cePYtm1bp8VtGAaGEfz5AkVEwo2SUiIiIhGowukbvtcxTXlCQ0+pUkcdNXUuSktLKS0t1UOSiEQMj8fDbbfdxsiRI9myZQsrV67kgw8+YPHixVRUVDB58mTGjh1Lfn4+s2fP5vHHH2fHjh0AbN68mVmzZjF37lzy8/MZM2YMU6ZMoaamJsS1EhGJLkpKiYiIRKCOHr4XG3MsMbVrfxHPvbWD597aQVlZWYccT0Qk2CoqKiguLsbj8fgT6mazGbvdzvr160lJSSE3NxeLxcLQoUMZPXo0y5YtA2DFihWMGjWKAQMGYLVamTRpEqmpqaxZsyaUVRIRiTpKSomIiESgo86OnegcID3ee5twsLKW+KQU4pNSOuxYIiLBlpqayqRJk3jiiSfo27cvV155JWeddRaTJk2ioKCAPn36NFo/IyODXbt2AVBYWHjSchERCQ5LqAMQERGRwPmSUkmxMVR2wP5rqipw1hiAha+KKjrgCCIiHcvj8WCz2XjooYeYMGECe/fu5Y477mDevHk4HA7sdnuj9W02G9XV1QAtlgfCZGpb/CZT27eVxnznUeczeHROgy/cz2lHxaWklIiISASq7ISeUkm2WA7XeSiqdpNiU+dqEYksGzZsYN26daxduxaAzMxM8vLymD17NqNHj6aysnFK3+l0kpCQAIDdbsfpdDYpT01NDTiO7t2TAlrfN9wwLS0Rm80W8PHkxAL9W0jLdE6Dr6udUyWlREREIpB/TqkOTEolWL3fixwuUmyxHXYcEZGOcPDgQf+b9nwsFgtWq5U+ffqwcePGRmWFhYVkZmYC3gRWQUFBk/Irrrgi4DhKSysJ5B0RhuHxbxcXVx/w8aQpk8n7oB/o30JOTOc0+ML9nPriCzZ97CkiIhKBjtYeG77XUeIt3n7axdXuDjuGiEhHGT58OMXFxTz33HO43W7279/PwoULGT16NNnZ2ZSUlLB06VLq6+vZtGkTq1evZvz48QBMmDCB1atXs2nTJurr61m6dCmlpaVkZ2cHHIdhBP7V1u30pXOqcxrZX+F+TjuCekqJiIhEoM6Y6NyXlCqqdmMYBqZwneRARKQZGRkZLFq0iGeffZbnn3+epKQkxowZQ15eHrGxsSxZsoTZs2czb9480tLSmDFjBkOGDAFg6NChPPLII8ycOZPDhw+TkZHB4sWLSUlJCW2lRESijJJSIiIiEciXlDKcHdfFO6HhLqHGZVDnhjjdNYhIhBk2bBjDhg1rtqxv374sX778hNvm5OSQk5PTUaGJiAgaviciIhJxnPVual3eOUdeyf+C2lpnC1u0TYzZhL2ht1RVvadDjiEiIiIiXZeSUiIiIhGmsmE+KROQlNStQ4+VYPUmpRx1SkqJiIiISHApKSUiIhJhqmq9E49bY7xvQulIibHeW4UqJaVEREREJMiUlBIREYkwvp5SVnPHTzzuT0rVd9DEVSIiIiLSZSkpJSIiEmGqGpJSsTEdn5RKsKqnlIiIiIh0DCWlREQkKpSXl3PvvfcyePBgLr30Um6//XaKiooA2L59OxMnTiQrK4sRI0awYsWKRtuuWrWK7Oxs+vfvz7hx49i2bVsoqtBqVZ3aU0oTnYuIiIhIx1BSSkREosKdd95JdXU1GzZs4O233yYmJoaHHnqIiooKJk+ezNixY8nPz2f27Nk8/vjj7NixA4DNmzcza9Ys5s6dS35+PmPGjGHKlCnU1NSEuEYn1pk9pZIahu856gw8hobwiYiIiEjwKCklIiIR7/PPP2f79u3MnTuXbt26kZiYyKxZs5g6dSrr168nJSWF3NxcLBYLQ4cOZfTo0SxbtgyAFStWMGrUKAYMGIDVamXSpEmkpqayZs2aENfqxCqPm+i8o9ktJqxmMIBqzSslIiIiIkFkCXUAIiIi7bVjxw4yMjL4+9//zksvvURNTQ2XX3459913HwUFBfTp06fR+hkZGaxcuRKAwsJCxo8f36R8165dAcXQ3FvwfMuC/YY8R11DTynf8D2T9xgGTb83VxbI+iaTiVMSLHxT6aKqztNhdQol1SlyRGO9wq1O4RKHiIh0DUpKiYhIxKuoqGD37t1cdNFFrFq1CqfTyb333st9991Heno6dru90fo2m43q6moAHA7HSctbq3v3pDaVtUV9w1NjfJwFmy0WW5wVm92Kp96D2dr4e3Nlgaxvt8dyRoqbbypd1GImLS2xQ+oUDlSnyBGN9YrGOomIiLRESSkREYl4sbGxAEyfPp24uDgSExO5++67ueGGGxg3bhxOp7PR+k6nk4SEBADsdnuz5ampqQHFUFpaybenXDKZvA+azZW1R0mFd74rk8eD01mHs7YeZ009HlcdZpe50ffmygJZH2JItXqPW1ZVR1lZFenp6UGvUyh11N8plKKxThCd9Qq3OvniERER6QxKSomISMTLyMjA4/FQX19PXFwcAB6P921x3/3ud/nrX//aaP3CwkIyMzMByMzMpKCgoEn5FVdcEVAMhsEJHyhPVtYWlU7fnFIN42yO2/+3vzdXFuj6PeO9twtVdUajsnB4gA4m1SlyRGO9orFOIiIiLdFE5yIiEvGGDRtG7969efDBB3E4HJSVlfHMM89w9dVXc/3111NSUsLSpUupr69n06ZNrF692j+P1IQJE1i9ejWbNm2ivr6epUuXUlpaSnZ2dohrdWK+t+91xkTnhmGQgLdnVmW9p+MPKCIiIiJdhpJSIiIS8axWK3/+85+JiYlh5MiRjBw5kl69ejFnzhxSU1NZsmQJa9euZfDgwcyYMYMZM2YwZMgQAIYOHcojjzzCzJkzGTRoEK+//jqLFy8mJSUltJU6icrab0103oFqqir4YIe3J1lVnQdDXTlEREREJEiCPnxv8+bNDB48ONi7FRGRKBaMtqNnz54888wzzZb17duX5cuXn3DbnJwccnJy2nX8znSsp5QJXB1/vOT4OHCCywMVDUMHRUSCQc8OIiJdW9B7St11111cffXV/OEPf+DAgQPB3r2IiEQhtR2Bqar1JoY6o6cUgNkE9oahggcq6zrlmCLSNej6LyLStQU9KfXBBx8wbdo0Pv/8c0aOHMnPf/5zXnvtNerqdBMrIiLNU9vRei6PQXW9b6LzzjtugtWbADuopJSIBJGu/yIiXVvQk1JWq5WRI0eycOFC3n33Xa6++mqWLFnC8OHDefTRR9m1a1ewDykiIhFObUfrOWqPjdezdlJPKYCEhgH/B4/qQVFEgkfXfxGRrq3DJjovLS1l9erVvPLKKxQWFjJ48GDi4uKYNGkSzz33XEcdVkREIpjajpb5JzmPAbOpE5NSDT2lNHxPRDqCrv/HGIaB3ikhIl1F0Cc6f/3113n11Vf58MMPOeeccxg3bhzPPfccaWlpAFx55ZXk5eXxi1/8ItiHFhGRCKW2o/W+KSoFwOTx4KxxdtpxEywaviciwafrv4hI1xb0pNSjjz7KqFGjWL58ORdddFGT8rPPPptJkyYF+7AiIhLB1Ha0nqPOAzS8ea8TJVi93w9V1nfqcUUkuun6LyLStQU9KfXBBx+wf/9+evbsCcCnn35KUlIS5557LgC9evXirrvuCvZhRUQkgqntaD3/JOcdNgC/eb6eUmU1LqrrXC2sLSLSOrr+i4h0bUG/pX3zzTcZO3Yse/bsAWDbtm1MnDiRd999N9iHEhGRKKG2o/WqfD2lOjkpFRtjIrbhmDu+PIChCU9EJAh0/W+ed14pXWdFJPoF/ZZ2/vz5LFiwwN/99uabb+Z3v/sdTz31VLAPJSIiUUJtR+s56nw9pTr/YSXe4j3mHzbspKysrNOPLyLRR9d/EZGuLehJqYMHD3L55Zc3WjZ8+HAOHDgQ7EOJiEiUUNvReseSUp1/7ISGpFSt2db5BxeRqKTrv4hI1xb0W9rTTz+d999/v9Gyjz76iNNOOy3YhxIRkSihtqP1HPXe4XuWzp3nHICEhpkoj9a6O//gIhKVdP1vnobviUhXEfSJzidPnkxeXh7XXHMNp59+OgcOHGDDhg088cQTrd7HRx99xNNPP82XX36J3W7nBz/4AdOmTcNms7F9+3Yee+wxCgsLSU1NZcqUKUycONG/7apVq1iwYAHFxcWcc845PPTQQ2RlZQHgdrv57W9/y6uvvkpNTQ1Dhgzh0Ucf5ZRTTgn2aRARkQAEo+3oKqpC2FMqvuGuoarW0/kHF5GopOu/iEjXFvRb2tGjR7N48WKsVis7d+7EZrOxZMkSRo4c2arty8rKuO222/jxj3/M1q1bWbVqFVu2bOH//u//qKioYPLkyYwdO5b8/Hxmz57N448/zo4dOwDYvHkzs2bNYu7cueTn5zNmzBimTJlCTU0NAAsXLmTjxo28/PLLvP/++9hsNmbMmBHsUyAiIgFqb9vRlVSHaKJzODZ8r1I9pUQkSHT9FxHp2oLeUwpg8ODBDB48uE3bpqWl8eGHH5KYmIhhGJSXl1NbW0taWhrr168nJSWF3NxcAIYOHcro0aNZtmwZ/fr1Y8WKFYwaNYoBAwYAMGnSJP72t7+xZs0axo8fz4oVK5g6dSqnnnoqANOnT2f48OHs37+f3r17B6fyIiLSJu1pO7oCwzAoKyuj3OEEQttTqrLOg0fDSkQkSHT9FxHpuoKelDp8+DALFy5kz549eDyNu/f/6U9/atU+EhMTAbjyyis5fPgwAwcOZNy4cTz77LP06dOn0boZGRmsXLkSgMLCQsaPH9+kfNeuXVRWVnLo0KFG26enp5OcnMzu3bsDSkqZQjCPx7ePazKFLo5v88URjHjCtY7BEsxzFe10rlqvrecqnM5tMNqOaFdWVsZzb+1g3xEDsITk7Xv2GDABHgPKql2kd+/0EEQkyuj6LyLStQU9KfXAAw9QUlLC97//faxWa7v2tX79eioqKpg6dSp33XUXPXv2xG63N1rHZrNRXV0NgMPhOGG5w+EAID4+vkm5r6y1undPCrQqQeF2V2O3xzbEkEh6esfH4fF4KC4uBqBHjx6YzSf+aD4Y5yUUdQyFUP0bikQ6V60XyecqmG1HNItPSsFtOgqANQRJRbMJ7BaodsGhqjr6tLyJiMhJ6fovItK1BT0p9dlnn7Fu3TrS0tLavS+bzYbNZmPatGlMnDiRm266icrKykbrOJ1OEhISALDb7Tidziblqamp/mSVb36p5rZvrdLSSkIxaqG0tIqamjr/zzEx8S1s0X5FRUU896/tAPzi6oubnRTeZPI+DAfjvISijp0pmOcq2ulctV5bz5Vvu3AQzLYj2jW8fA9LCIbvASRYTFS7DA5W1ocmABGJKrr+i4h0bUG/pU1KSiI2NrbN23/yySf84Ac/oK6uzr+srq4Oq9VKRkYGBQUFjdYvLCwkMzMTgMzMzBOWJycn07NnTwoLC/1lxcXFlJeXNxkS2BLDCN1XKGJISE4jITmtxbgiuY6d/TcMdQyR8qVz1fHnKly0t+3oKgzD8CelQjGnFEB8Q0eGw5V1J19RRKQVdP1vnsfjaTKcUUQkGgX9lvb222/ngQceYMeOHRw4cKDRV2ucd955OJ1OnnrqKerq6vjmm2944oknmDBhAiNHjqSkpISlS5dSX1/Ppk2bWL16tX8eqQkTJrB69Wo2bdpEfX09S5cupbS0lOzsbADGjRvHwoUL2b9/P1VVVcyZM4dBgwZx5plnBvs0iIhIANrbdnQVbgMMvOP2QpWUSrB4j3+oSj2lRKT9dP0XEenagj58b8aMGQBs2LABAJPJhGEYmEwm/vOf/7S4fUJCAs8//zxz5szhsssuIykpidGjR5OXl0dsbCxLlixh9uzZzJs3j7S0NGbMmMGQIUMA79v4HnnkEWbOnMnhw4fJyMhg8eLFpKSkAJCXl4fL5SI3NxeHw8HgwYN59tlng30KREQkQO1tO7qKevex7m2WEE1Un9AwmdUh9ZQSkSDQ9V9EpGsLelLqzTffbPc+MjIyWLJkSbNlffv2Zfny5SfcNicnh5ycnGbLrFYrU6dOZerUqe2OUUREgicYbUdXUOfxJqWs5tC9PTGh4c7hoJJSIhIEuv6LiHRtQe/8f/rpp3P66adTUVHBzp076dGjBzabjdNPPz3YhxIRkSihtqN16t3e76EaugcQ39BFq7TaRZ1L852ISPvo+i8i0rUF/ba2tLSUH/3oR9xwww3cd9997N+/n6uvvppt27YF+1AiIhIl1Ha0zvE9pUIlLsb75j8DOFRZG7pARCQq6PovItK1Bf22ds6cOfTp04f8/HwsFgvnnnsukydP5n//93+DfSgREYkSajtaxzenlMUcorF7eOd7SYyNAeBARU3I4hCR6KDrv4hI1xb0pNSmTZt44IEHsNvtmBomvLjlllsoLCwM9qFERCRKqO1oHV9SKpQ9pQCS4rwBfFPhDG0gIhLxdP0XEenagn5ba7VacTq9N6mG4b15djgcJCQkBPtQIiISJdR2tE5dwxROoU9K+XpKKSklIu2j67+ISNcW9NvaESNGMG3aNPbs2YPJZKK0tJRHH32UK6+8MtiHEhGRKKG2o3WO9ZQK3fA9gKRY7+2DklIi0l66/ouIdG1BT0r9+te/Jj4+nh/84AccPXqU4cOHU1NTw9SpU4N9KBERiRJqO1onHCY6B0hs6Cml4Xsi0l66/ouIdG2WYO8wISGBefPmUVZWxtdff02vXr045ZRTgn0YERGJImo7Widc5pTqFqeeUiISHB19/S8vL2fOnDm8++67eDweLr30UmbOnMkpp5zC9u3beeyxxygsLCQ1NZUpU6YwceJE/7arVq1iwYIFFBcXc8455/DQQw+RlZUVtNhERKQDklL5+fmNft+7dy979+4F4NJLLw324UREJAqo7WidujAZvufrKVXhdFFV6yIxLui3EyLSRXT09f/OO+8kOTmZDRs2YDabeeCBB3jooYf43//9XyZPnsxdd93FjTfeSH5+Pnl5eZx33nn069ePzZs3M2vWLBYvXky/fv1YtmwZU6ZM4e2338Zut7c7LhER8Qr6XeRNN93UZJnZbObUU0/lzTffDPbhREQkCqjtaJ36MJnoPDbGRLe4GI7WujlQ4aTPKYmhDUhEIlZHXv8///xztm/fzocffkhiovc6NWvWLIqLi1m/fj0pKSnk5uYCMHToUEaPHs2yZcvo168fK1asYNSoUQwYMACASZMm8be//Y01a9Ywfvz4dsUlIiLHBD0ptWvXrka/l5WV8Yc//IHTTz892IcSEZEoobajdcJl+B5ArySrklIi0m4def3fsWMHGRkZ/P3vf+ell16ipqaGyy+/nPvuu4+CggL69OnTaP2MjAxWrlwJQGFhYZPkU0ZGRpN4RUSkfTr8tjYtLY1p06bx4osvdvShREQkSqjtaJ5vonNLiJNShmGQFuf9+euKmtAGIyJRJZjX/4qKCnbv3s2ePXtYtWoVr7zyCocPH+a+++7D4XA0GYZns9morq4GaLE8ECZT4F9t3U5fOqc6p5H9Fe7ntCN0yiQQFRUV1NbWdsahREQkSqjtaMrfU6qj7gpaqbqygqIjTsDKV4crgN4hjUdEokuwrv+xsbEATJ8+nbi4OBITE7n77ru54YYbGDduHE5n45c1OJ1OEhISALDb7c2Wp6amBhxH9+5JAa3vdrv92/mGHUpwBPq3kJbpnAZfVzunQU9KPfDAA41+r6+v5+OPP2bYsGHBPpSIiEQJtR0t8xhG2MwpBdAtPg6qPRyqqgt1KCISwTry+p+RkYHH46G+vp64OG/3To/HeyH97ne/y1//+tdG6xcWFpKZmQlAZmYmBQUFTcqvuOKKgOMoLa3EMFq/vsfj9m/ndAawoZyQyeR90A/0byEnpnMafOF+Tn3xBVuH39bGxcVx0003MWvWrI4+lIiIRAm1HU05XR7/z6EevgeQYPH21jpUVR/iSEQkmgTz+j9s2DB69+7Ngw8+iMPhoKysjGeeeYarr76a66+/npKSEpYuXUp9fT2bNm1i9erV/nmkJkyYwOrVq9m0aRP19fUsXbqU0tJSsrOzA47DMAL/aut2+tI51TmN7K9wP6cdIeg9pR5//PFg71JERKKc2o6WVdd5k1ImDGJMQAfdGLRWgtWblDpcWYdhGJhCPKRQRCJTR17/rVYrf/7zn5k7dy4jR46ktraWESNGMH36dLp168aSJUuYPXs28+bNIy0tjRkzZjBkyBDA+za+Rx55hJkzZ3L48GEyMjJYvHgxKSkpHRaviEhXFPSk1Pz581u13h133BHsQ4uISIRS29Gy6oaxexYzmEymUOekiG+4g6h1G5RW15OeEBvagEQkInX09b9nz54888wzzZb17duX5cuXn3DbnJwccnJy2nRcERFpnaAnpQoKCli/fj3nn38+Z599NocOHeKTTz7hggsu8E8cqE9TRUTkeGo7WlZd751jxBImp8FsMhFvNVFdb3CgwqmklIi0ia7/IiJdW9CTUmazmQceeICf/vSn/mWvvvoqb7/9Ns8++2ywDyciIlFAbUfLfD2lwmGSc59Eq5nqejcHKpz0O61bqMMRkQik67+ISNcW9Fvbd999l9zc3EbLrr/+ej766KNgH0pERKKE2o6W+eaUCodJzn0SY729F76pqAlxJCISqXT9FxHp2oJ+a5uWlkZ+fn6jZe+//z69evUK9qFERCRKqO1omW/4njWMRrEkNHTbOlDhDHEkIhKpdP0XEenagj5877bbbmPy5MmMHDmS0047jf379/P222/z+9//PtiHEhGRKKG2o2XHJjoP9RTnxyTGKiklIu2j67+ISNcW9KTUxIkTOf300/nnP//Jv//9b3r37s3y5cs577zzgn0oERGJEmo7WuYIs4nOARIaum0pKSUibaXrv4hI1xb0pBTAsGHDGDZsGGVlZaSlpXXEIUREJMqo7Tg535xS4TTRuS8pdaiyFpfbgyUmjIITkYih639jhmEA4dMrVkSkIwX97rG+vp5nnnmGAQMGMGLECPbv38/48eMpKioK9qFERCRKqO1o2bHheyEO5HjOSkwYeAxvYkpEJFC6/jfPMIyG5JSISHQL+q3t/Pnz2bRpE7/73e+wWq10796dXr16MXv27GAfSkREooTajpaF40TnJhPEx3gfmjSET0TaQtd/EZGuLejD91avXs1LL71Ez549MZlMxMfH8/jjj5OdnR3sQ4mISJRQ29GysOwpBdjNHhxus5JSItImuv6LiHRtQb+1ra6u9o8F93U5tdlsmM1hdhctIiJhQ21Hy3xzSoXT2/cA7A09pb5RUkpE2kDX/+Zp+J6IdBVBv9r379+f+fPnA2AyeccY/PnPf6Zv377BPpSIiEQJtR0tC8fhe+DtKQUavicibaPrv4hI1xb04XsPPvggkyZNYtWqVTgcDq677jocDgd//OMfg30oERGJEmo7Wha2w/d8c0odVVJKRAKn67+ISNcW9KRUeno6r7/+Ou+88w7ffPMNvXr14nvf+x6JiYnBPpSIiEQJtR0t8yWlrGGWlNJE5yLSHrr+i4h0bUFPSl1//fX885//5Nprrw32rkVEJEqp7Tg5wzCorvMO37OE6fC9sup6quvcxMfGhDgiEYkkuv6LiHRtHfJ5a01NTUfsVkREopjajhOrdXlwN8x3G27D96xmSIz1BqUhfCLSFrr+i4h0XUHvKTV48GAmTpzIFVdcwSmnnNKo7I477gj24UREJAqo7Tg5R0MvKQi/nlIAPRNjqSpzcqDCSUZ6QqjDEZEIouu/iEjXFvSk1Ndff03v3r356quv+Oqrr/zLfW/TEBER+bZgth1ut5tJkyZx+umnM3fuXAC2b9/OY489RmFhIampqUyZMoWJEyf6t1m1ahULFiyguLiYc845h4ceeoisrKz2VyxIfEkpqxnCsTntlWTly4aklIhIIPTsICLStQUtKfU///M/vPDCC/z5z38GwOl0YrPZgrV7ERGJQh3RdsyfP5+tW7dy+umnA1BRUcHkyZO56667uPHGG8nPzycvL4/zzjuPfv36sXnzZmbNmsXixYvp168fy5YtY8qUKbz99tvY7fZ21zEYHHUuAKzm8HxI65UYC8A3SkqJSCvp2UFERCCIc0pt27at0e9XXHFFsHYtIiJRKthtx0cffcT69eu55ppr/MvWr19PSkoKubm5WCwWhg4dyujRo1m2bBkAK1asYNSoUQwYMACr1cqkSZNITU1lzZo17YolmBy1DT2lYsI0KZXkTUqpp5SItJaeHUREBDpoonPwvilIREQkEO1pO0pLS5k+fTpPPfVUox5OBQUF9OnTp9G6GRkZ7Nq1C4DCwsKTlocDX0+pcJvkHLx/s0S8yagDFZqsWETaRs8OIiJdU9DnlPLROHAREQlUW9sOj8fDtGnTuPnmmzn//PMblTkcjibD8Gw2G9XV1a0qb63mQvcta2+TeGxOKZN/f8Zx3zE1XXaysvas/221NZV8+MUBwM435U7AiLh7gGD9ncJJNNYJorNe4VanUMURadcNEREJjg5LSomIiHSWRYsWERsby0033dSkzG63U1lZ2WiZ0+kkISHBX+50OpuUp6amBhRD9+5JbSprDVNsKQC22BhstljMViueeo//uy3Ois3eeNnJytq/fq3/d1uclZSeaVBWQ43LQ0y8jbSE2HbVN1Ta+3cKR9FYJ4jOekVjnURERFoStKSUy+XilVde8f9eX1/f6HeAsWPHButwIiISBYLVdrz66qsUFRUxcOBAAH+S6V//+hf33nsvGzdubLR+YWEhmZmZAGRmZlJQUNCkPND5TUpLK/n26BOTyfug2VxZIA6XOQAwGwZOZx1mlxmP69h3Z209zpr6RstOVtbe9WMt+H931tZTX+vCbjFR4zLY8d9iLjq1W9srGwLB+juFk2isE0RnvcKtTr54OpqeHUREBIKYlEpPT2fevHn+31NTUxv9bjKZ1LCIiEgjwWo71q5d2+j3+++/H4C5c+dy5MgRnnzySZYuXUpubi4ff/wxq1evZsGCBQBMmDCBvLw8rr32WgYMGMCyZcsoLS0lOzs7oLoYBid8oDxZWWtU+SY6Nx/b3/HfMZouO1lZe9ZvoqEsMdZMjcvNN+VOLuwVWUkpn/b+ncJRNNYJorNe0Vink9Gzg4iIQBCTUm+99VawdiUiIl1EZ7QdqampLFmyhNmzZzNv3jzS0tKYMWMGQ4YMAWDo0KE88sgjzJw5k8OHD5ORkcHixYtJSUnp8Nhay1Hrneg8XN++B5BgNVGM3sAnIq2jZwcREQHNKSUiIlFo7ty5jX7v27cvy5cvP+H6OTk55OTkdHRYbfbtic7DUWKstxvXN0pKiYiIiEgrheHLpUVEROR4jjpvTylLGLfavqTU/vKaEEciIiIiIpEijG9vRUREBKDc0dD7yOUKbSAnkRznvaX4qrQ6xJGIiIiISKRQUkpERCTMVdd7gPDuKdWtoadUWXU9R531IY5GRERERCJBGN/eioiICEB1nTcpZQ3jVtsaYyI93jtV5Z4yDeETEWkrwzC61JsYRaRrC+PbWxEREQGorvdOdG4J44nODcOgV4JvCJ8jxNGIiIiISCQIy6TUrl27uPnmmxk0aBCXXXYZ9957L2VlZQBs376diRMnkpWVxYgRI1ixYkWjbVetWkV2djb9+/dn3LhxbNu2zV/mdrt54oknGDZsGFlZWUyZMoWioqJOrZuIiEigfMP3rOGbk6KmqoKjVd5k1K4DZSGORkREREQiQdglpZxOJ7fccgtZWVl88MEHvPbaa5SXl/Pggw9SUVHB5MmTGTt2LPn5+cyePZvHH3+cHTt2ALB582ZmzZrF3Llzyc/PZ8yYMUyZMoWaGu8wgoULF7Jx40Zefvll3n//fWw2GzNmzAhldUVERE6q3u2hzu0dxxHOc0oBpMbHAbC/vDbEkYiIiIhIJAi729sDBw5w/vnnk5eXR2xsLKmpqdx4443k5+ezfv16UlJSyM3NxWKxMHToUEaPHs2yZcsAWLFiBaNGjWLAgAFYrVYmTZpEamoqa9as8ZffeuutnHrqqSQmJjJ9+nTee+899u/fH8oqdwkej4eioiKKiorweDyhDkdEJGI46tz+n8N5TimApIauXPsq6kIciYhIZCuqqueN3aWhDkNEpMNZQh3At51zzjk8//zzjZatW7eOCy+8kIKCAvr06dOoLCMjg5UrVwJQWFjI+PHjm5Tv2rWLyspKDh061Gj79PR0kpOT2b17N7179251jKYQDZ84/rgmU+fE0Zpj+padLJ7S0hIWvbkdgF9cfTGnnHJKm48Xkevl/QABAABJREFUyVpzrsRL56r12nqudG4jg6POBUAMBiaTiXCe+9aXlDpcVUety0NcuHftEhEJUxv3VrL2iwqGnNuLM1PtoQ5HRKTDhF1S6niGYfDss8/y9ttv85e//IU//elP2O2NL8o2m43q6moAHA7HCcsdDu88F/Hx8U3KfWWt1b17UqBVCQq3uxq7PbYhhkTS0zs+jkCOebLz4nZXk96rV4v7CUUdQyFU/4Yikc5V6+lcRSdHbcMk56ZwTkd5xcVAXIyJWrfBV6UOzu+pf5MiIm1R6/Je8yud9YCSUiISvcI2KVVVVcUDDzzAzp07+ctf/sJ5552H3W6nsrKy0XpOp5OEhAQA7HY7TqezSXlqaqo/WeWbX6q57VurtLQyJK9pLS2toqamzv9zTEx8C1t0zjFNJu/D8MnOS2tjD0UdO1NrzpV46Vy1XlvPlW87CW++4XuRkJQymUyk2MwcdrjZXVSlpJSISBt5Ghp0lyf8r/0iIu0Rlkmpffv2ceutt3LaaaexcuVK0tLSAOjTpw8bN25stG5hYSGZmZkAZGZmUlBQ0KT8iiuuIDk5mZ49e1JYWOgfwldcXEx5eXmTIYEtMQxC8pB8/DE7K4ZAjnmy8tbuJxR1DIVorluw6Vy1ns5VdPIP34uApBRAakNS6ouiwHohi4iIl2EYuBumYFVSSkSiXdhN9lBRUcHPfvYzLrnkEl544QV/QgogOzubkpISli5dSn19PZs2bWL16tX+eaQmTJjA6tWr2bRpE/X19SxdupTS0lKys7MBGDduHAsXLmT//v1UVVUxZ84cBg0axJlnnhmSuoqIiLQkkobvAaTaYgDYXVRFaWkphjKlIiIBcXsM//yBSkqJSLQLu55S//jHPzhw4ABvvPEGa9eubVS2bds2lixZwuzZs5k3bx5paWnMmDGDIUOGADB06FAeeeQRZs6cyeHDh8nIyGDx4sWkpKQAkJeXh8vlIjc3F4fDweDBg3n22Wc7uYYiIiKtV9XQUypyklLez7u+KKri6Vc+4Fdjh9O9e/cQRyUiEjlq3ceu90pKiUi0C7uk1M0338zNN998wvK+ffuyfPnyE5bn5OSQk5PTbJnVamXq1KlMnTq13XGKiIh0hkjrKdUtzozVbKLG5cFj6xbqcEREIk6dy+P/2eWOjGu/iEhbhd3wPYksHo+HoqIiDh8+jMfjaXkDEREJiCPCekqZTSbOSo0DoKI+xMGIiESg2uOSUm7dX4tIlFNSStqlpKSE5/61nadezaekpCTU4YiIRJ1IevueT0Z37xtvy+tMIY5ERMTL7XZz0003cf/99/uXbd++nYkTJ5KVlcWIESNYsWJFo21WrVpFdnY2/fv3Z9y4cWzbtq1TYq1zH9dTSsP3RCTKKSkl7ZaQnEZiSlrLK4qISMCqIjAp1Sfdm5Q6oqSUiISJ+fPns3XrVv/vFRUVTJ48mbFjx5Kfn8/s2bN5/PHH2bFjBwCbN29m1qxZzJ07l/z8fMaMGcOUKVOoqanp8FgbDd9TUkpEopySUiIiImHMURtZw/cAzu/h6ynlfYuUiEgoffTRR6xfv55rrrnGv2z9+vWkpKSQm5uLxWJh6NChjB49mmXLlgGwYsUKRo0axYABA7BarUyaNInU1FTWrFnT4fHWak4pEelClJQSEREJY77hezERlJQ6MzkOu8WMyzCxr6I21OGISBdWWlrK9OnTeeqpp7Db7f7lBQUF9OnTp9G6GRkZ7Nq1C4DCwsKTlnek2kbD9zSnlIhEt7B7+56IiIgc459TKsRxBCLGbKJPup3thxzsKq5hYEaoIxKRrsjj8TBt2jRuvvlmzj///EZlDoejUZIKwGazUV1d3aryQJgCHMlcf1xSym0Evr005TuHOpfBo3MafOF+Tjsqrki6xxUREelyqhqG71kjpKeUYRgcOVLGd5Jg+yHYXRz4A5yISDAsWrSI2NhYbrrppiZldrudysrKRsucTicJCQn+cqfT2aQ8NTU14Di6d08KaP24AxXHfrbHkp4e2PZyYoH+LaRlOqfB19XOqZJSIiIiYcyXlIqU4Xs1VRW8+GERh2o8QCK7ijt+UmARkea8+uqrFBUVMXDgQAB/kulf//oX9957Lxs3bmy0fmFhIZmZmQBkZmZSUFDQpPyKK64IOI7S0kqMAC7hJUeOJfMrjtZQUlJ5krWlNUwm74N+oH8LOTGd0+AL93Pqiy/YlJQSEREJY77he5HSUwrAnphMr1gnVMHe8locdS4SYnXLISKda+3atY1+v//++wGYO3cuR44c4cknn2Tp0qXk5uby8ccfs3r1ahYsWADAhAkTyMvL49prr2XAgAEsW7aM0tJSsrOzA47DMAjoAbP2W2/fC8eH00gV6N9CWqZzGnxd7ZzqDlFERCRM1bs9/oeTSHr7HoAtBmxmD06Pmf8cqmLgmSmhDklExC81NZUlS5Ywe/Zs5s2bR1paGjNmzGDIkCEADB06lEceeYSZM2dy+PBhMjIyWLx4MSkpKR0eW53eviciXYiSUiIiImHKUev2/xxpSSmAZIsHZ52Zzw8eVVJKREJu7ty5jX7v27cvy5cvP+H6OTk55OTkdHRYTejteyLSlZhDHYCIiIg0r6rOO5+U3WIO2zexnEyKxZtU+/yg5kMREWmtum8N3xMRiWZKSomIiIQp3yTn8bGR2VwnW70PVp8fqsToSpMjiIi0Q91xQ/aUlBKRaBeZd7kiIiJdQFXD8L0Ea0yII2mbbhYPMSYoddRxuLI21OGIiESE43tKuZWUEpEop6SUiIhImHLURXZPqRgTnJ1mAzSET0SktercGr4nIl1HZN7lioiIdAGR3lMK4Px0OwD5XxVRWlqqYXwiIi1olJTS2/dEJMopKSUiIhKmIn1OKYDzesQD8E5hCc+9tYOysrIQRyQiEt5qXcfPKaW374lIdIvcu1wREZEoV9mQlLIY9URqB6Pze3h7SlXUmbAlJoc4GhGR8Fert++JSBeipJSIiEiYKqmoAuCrw0eorXWGOJrAGYZBvLuKBKsJtwHlTn3iLyLSEs0pJSJdiZJSIiIiYaq63vtgYouLDXEkbVNbXcmfP/ovNqMOgNIad4gjEhEJf3r7noh0JUpKiYiIhKnqOm8SxxrBrbU9MZn0eO9E7SXVSkqJiLREPaVEpCuJ4NtcERGR6OZo6CllMYU4kHZKjfM+VJXWaPieiEhL6o6f6Fxv3xORKKeklIiISJhy+HtKRfZDSWrD6MOjdR4qa9VbSkTkZBr3lFIyX0Sim5JSIiIiYco3p1QkD98DiIuBBIv35y9KakIbjIhImKvV8D0R6UIi/DZXREQkejmiYE4pn7Q47xjEXcXVIY5ERCS8HT/RuZJSIhLtouA2V0REJDpVR8mcUgCpvqSUekqJiJxUnVtzSolI16GklIiISBhye4yoGb4HkGrzJqV2F9dgGHrIEhE5kVqX5pQSka4jCm5zJVAej4eioiKKiorwqKETEQlL1XXHJgS3REFrnRxrwmyCo7VuvqlwhjocEZGwdfzwPbeG74lIlIuC21wJVElJCYve3M6iN7dTUlIS6nBERKQZjjoXAGYTxETB8L0Yk4mUOO9tx67DVSGORkQkPHkMg/rjElGaU0pEop2SUl1UQnIaCclpoQ5DREROoKrW21Mq1hwFGakGafYYAHYVKSklItKc43tJgZJSIhL9lJQSEREJQ1W13p5SlpgQBxJEqTbvbcdu9ZQSEWlWnftbSSlNdC4iUU5JKRERkTBU1TB8L1p7SmmycxGRptRTSkS6GiWlREREwpCjYfieNRomlGqQHGsixgTlNfUcrqwNdTgiImGn9ts9pZSUEpEop6SUiIhImDEMg8NlFQBYo6inVF31UWzUA7Bb80qJiDRR52qchHLpTdkiEuWUlBIREQkzZWVlvLnrGwDMhjvE0QRXSqz3u97AJyLSVK2r8TXfrZ5SIhLllJQSEREJRxYbEF09pQCSLN5P/fUGPhGRpmob5pSyNDylafieiEQ7JaVERETCUH3DG5csUdZSd2tISmn4nohIU76371kaPpDwGODRiyFEJIpF2a2uiIhIdKhv+HTcGmUtdZLFgwkorqqjxFEX6nBERMKKb06p43vJutxKSolI9IqyW10REZHocCwpFV3D9ywm6J0cB8DWwgOUlpZiqBeAiAgAGT0S6B5v5Yxkq3+ZhvCJSDRTUkpERCQM1TfMdWuJrpwUAOd2986X9df8PTz31g7KyspCHJGISHjomRTHqz/Pov+pdv8yvYFPRKKZklIiIiJhqC5Kh+8BZDQkpSo9scQnpYQ2GBGRMGMymTj+8wj1lBKRaBaFt7oiItIV7dq1i5tvvplBgwZx2WWXce+99/p74Gzfvp2JEyeSlZXFiBEjWLFiRaNtV61aRXZ2Nv3792fcuHFs27YtFFVoxDfReVQmpdK8PQDKnO4W1hQR6ZqOT0y5lZQSkSgWhbe6IiLS1TidTm655RaysrL44IMPeO211ygvL+fBBx+koqKCyZMnM3bsWPLz85k9ezaPP/44O3bsAGDz5s3MmjWLuXPnkp+fz5gxY5gyZQo1NTUhrZNvTilLlM0pBd6eUiagut6gxqVhKSIizfFd/tVTSkSimZJSElE8Hg9FRUUUFRXh0fh6EWlw4MABzj//fPLy8oiNjSU1NZUbb7yR/Px81q9fT0pKCrm5uVgsFoYOHcro0aNZtmwZACtWrGDUqFEMGDAAq9XKpEmTSE1NZc2aNSGrj2EY/jmloq2nlGEY1DkqOCPJAkBJtXpLiYg0x2TyZqX09j0RiWZRdqsr0a6kpIRFb25n0ZvbKSkpCXU4IhImzjnnHJ5//nliYmL8y9atW8eFF15IQUEBffr0abR+RkYGu3btAqCwsPCk5aFQ6zLwPYJEW1KqtrqSFz/8EqPOASgpJSJyIuopJSJdgSXUAYj4VDpd3L1mDy6XiyGnxZ1wvYTktE6MSkQijWEYPPvss7z99tv85S9/4U9/+hN2u73ROjabjerqagAcDsdJy1vL1MwoO9+y5spOptrlTdSYMLCYwXADJu9+DJp+D7SsPes3rWTg+4pPSqaHu5ava6GkxoPJFPg5Cqa2/p3CWTTWCaKzXuFWp3CJoyszDAMwjktKaXSAiEQvJaUkbPxjx0EKSp0AnJemf5oiEriqqioeeOABdu7cyV/+8hfOO+887HY7lZWVjdZzOp0kJCQAYLfbcTqdTcpTU1MDOnb37kltKmvOntJyAKxmE/b4WDz1BrY4Kza7FU+9B7O18fdAy9q/fq3/97bu61SPm21lHspq3HRLiSc9PbBz1BEC/TtFgmisE0RnvaKxTtI+ZpMJMNRTSkSimp78JSzUuz38bds3/t8Lj7hCGI2IRKJ9+/Zx6623ctppp7Fy5UrS0ry9Kvv06cPGjRsbrVtYWEhmZiYAmZmZFBQUNCm/4oorAjp+aWklxreeG0wm74Nmc2Un802xN4kWYzJw1tTjcdXhrK33/2z+/+zdeXhU5fn/8fdM9o0sBMIiiBKCVUACSABRFI1UkKUs2hZpsSoW0vpFG1QEqxWDUqtSRNCiiFZ+agFRURRtq4JAWCyCG0hQIRqWLCRkT2bm/P4YZmAgCZlsM5n5vK4rF8x5zjm5nzOTeebc8ywWs8u/7pY1dv/gQBocl2NbkLWaYHMAVTbYvj+PywPCznFVmk9Dnydv5ot1At+sl7fVyRGPeI5hGBjGacP3NKeUiPgwr56poqCggNTUVLZt2+bc1phlva1WKwsWLGDIkCEkJyczffp0jh071mL1kdr9+9tcckuqCA6wt74/FFkordI8IyJSP0VFRfz2t7+lX79+vPDCC86EFEBqaip5eXmsWLGC6upqMjMzWbduHRMmTABg4sSJrFu3jszMTKqrq1mxYgX5+fmkpqa6FYNh1PxTV1ltPyWV9qEaQeZT5+CM853+r7tljdn/7Io37FwmE8SF2N/zvzxS5vY1auqfhjxP3v7ji3Xy1Xp5W53EsxzD9xwjKdVTSkR8mdcmpT777DNuuukmDh065NzW2GW9ly5dyubNm1mzZg2bNm0iNDSUuXPneqR+4mrzdwUAjL84jjbBJiwG7Pyp1MNRiUhr8cYbb5CTk8N7771H//79SU5Odv7ExsayfPly3n//fVJSUpg7dy5z585l0KBBAAwePJgHH3yQhx56iIEDB/Luu++ybNkyYmJiPFaf0pNL7/naJOdnig+z33J9fkTv9yIiZzKfnODLqqSUiPgwrxy+t3btWhYtWsSsWbO46667nNtPX9YbcFnWu0+fPi7LegNMnTqV119/nfXr1zNhwgRWrVpFeno6HTt2BGDOnDkMHTqU7OxsunTp0vIVFaesPPsNSc/4MLYfCuBElYXsE5UejkpEWotbbrmFW265pdby3r1789prr9VaPnbsWMaOHdscoTVIaZW9p1Sgj0843D7UDNj48kgZFquNwAAfz8KJiLhBE52LiD/wyk9/Q4cO5cMPP2TkyJEu2xuzrHdxcTFHjhxxKY+Pjyc6Opp9+/Y1U02kPqqtNn4osPdmuyA2hDYh9pdldlGVJ8MSEfGYMmdPKd/+drxNMIQEmCi32PjqSPG5DxAR8SMmZ1LKt9sCEfFvXtlTql27djVuP9ey3XWVl5bae+KEh4efVe4oqy9PLZV7+u9tzPLZ7pznXPuevq2uc9V1noPHy7DaDCJDAmgXEUj0yTlGfiyqqvH31Td2b+Rtyz57M12r+mvotdK19V5lVafmlPJlJpOJ9hEBZJ+wsDO7kEs7R3s6JBERr+EYvqeJzkXEl3llUqo2jVnW25GscswvVdPx9eWpFUms1jLCwoJPxhDZ4OWz3TnPufa1WssIDbWXx8XVfq66zvNp9gkAftaxDfHxUbRrEwJUklNcRWxcJAFmU73O05poVZv607WqP10r3+FY6CHQx5NSAAnh9qTU1gN5/C6lKyZlS0VEgNOH7ykpJSK+q1UlpRqzrHd0dDQJCQkuQ/xyc3MpLCw8a8jfuXhqyd78/BLKy6uc/w8ICD/HEY0/z7n2zc8voaKiitDQYAoKaj9XXef533d5AHSLCbWXWS0EmKDKarDnQC5dYsPqdZ7WwNuWffZmulb119BrpWW/vVfJyZ5SwX6QlIoxlQEBfHGkhMPH8uiUUHNvaRGRhti7dy8LFizgq6++IigoiMsvv5z77ruPuLg4du/ezSOPPEJWVhaxsbFMnz6dSZMmOY9du3YtS5YsITc3lwsvvJAHHniA5OTkFotdq++JiD9oVR93G7us9/jx41m6dCnZ2dmUlJQwf/58Bg4cSNeuXd2KwxuW6G3J89Rn3/qcq7bzOCY57x4fgWHYuypHnbwT+z7/7GXCveF5aOy193QMreVH16r5r5V4p+Iq/1h9DyAiEEIDwIaJr4+VeTocEfEhFRUV3HbbbSQnJ/Ppp5/yzjvvUFhYyP3339/oVb1bgiY6FxF/0Ko+7jZ2We+0tDSGDRvG5MmTGTZsGJWVlSxcuNBzFRIAsnLtSanE+FPDKNucnFfqhwLdoIiI/ymutCelgn18onOw99hrF2Z/z999xL05HkVE6pKTk8NFF11EWloawcHBxMbGctNNN7Fjxw6XVb0DAwNdVvUGXFb1DgoKYurUqcTGxrJ+/foWi98xnNmqnlIi4sO8fvjemSvjNWZZ76CgINLT00lPT2/SGKXhSiotHCuxD8frHh9BWVElAG2CzYBVSSkR8Usllf7TUwqgXaiZ7BIrn+coKSUiTefCCy/k+eefd9m2YcMGLrnkklpX9V69ejVgnwLEMRrj9HLHqt8tQXNKiYg/8PqklPi2g8ftXaDbRgQTGRKIIwXV5uTwvYMFLddFWkTEW5zqKeXhQFqIo6fUt/nllFRaiAzRxxMRaVqGYbBw4UI++ugjXnnlFV5++eUGr+rtroaujutoAiw2QyvmNpJWdW56uqZNz9uvaXPFpU994lEHT/aEOj/WtdGPDLa/4g+fqDjrGBERX2YYhl/NKQUQHmgiKthEcZXBjkOFXN0j3tMhiYgPKSkpYfbs2Xz11Ve88sor9OzZs1GrervL3UVFysoCAAgKCgCqCQ0LbrUrTnsbLfDS9HRNm56/XVMlpcSjHD2lzo9zTUpFnLwTyy2pospiI9gf1kUXEQHKq23OoRr+0lMKoFNkIPsKqtn8XYGSUiLSZA4dOsTtt99Op06dWL16NXFxcUDjVvV2l7ur45aX27+0tVntE5wXnqggL6+4rkPkHLSqc9PTNW163n5Nm2vlbj/6uCve6JCzp1S4y/aQAAgJMGEAR4orPRCZiIhnnKioBuxziQR4afft5tApyv492ebvCzC88ZOYiLQ6RUVF/Pa3v6Vfv3688MILzoQUNH5Vb3c0dHXc01ff8/Qqv77w09DnQj+6prqmrvE1NfWUEo+qraeUyWQiITKIQ0VVHC6qoOsZw/tERHxVUYUFgOAAk9fOKdAc2oWZCQkwkVdaxb5jJVyU4F9d10Wk6b3xxhvk5OTw3nvv8f7777uU7dq1i+XLl5ORkcGiRYuIi4urdVXvo0ePkpiY6LKqd0swn2wELNZmuhMUEfECSkqJx9gMg0Mnk1Jdz+gpBdD+ZFIqR/NKiYgfcfSUCvGnblJAVdkJIo1qKgll03cFSkqJSKPdcsst3HLLLbWWN2ZV75ZgHzMA1ubqniAi4gU0fE885mhxJZUWG4FmE52iQ88q7xAZBEBOkZJSIuI/TpzWU8rfdAy31/mj/XkejkRExPNM6iklIn5ASalWpKDcwv7j1VT7SMPkWHnvvJhQAs1n33y1j7AnpbQCn4j4E39OSrULthBggv25pfxQ4P6y6yIivuTUnFK+8dlfRKQmSkq1Ist2HGXnkSr+e6iC4kqrp8NptO8L7EP3usWdPXQPIEE9pUTED51KSnk4EA8INkO/TpEA/OfbXA9HIyLiWUpKiYg/UFKqlSirsrLtxxIACips/PXTHA9H1HjfHrPXp0e7iBrLEyKDAcg54f7qezabjWPHjnHs2DFsNlvDgxQRaWGOOaX8sacUwBXd2gDw4T4lpUTEv5mwtwPVVn2WFRHfpaRUK/Hpd/lUWg1CTn5z/vnhUiqqW3dvKUdSKqldZI3ljp5S+aVVbtc1Ly+P5/6zm+f+s5u8vIbPTaLkloi0NMfqe/420bnD4K5tCAowcSCvjL1Hiz0djoiIxziagSolpUTEhykp1Uo4vjHuHhNEWKAJmwHfHC3xcFQNV2218V2+fb6QpPY1J6Wigs1EnBy/crgBvaUiouOIiI5reJA0XXJLRKS+/HlOKcMwsJQVMaSLfeW913f8gKFVp0TETwWcvFMrr1ZSSkR8l5JSrUCVxcbWH44D0LVNAG3D7E/bl4dPeDKsRvk+vwyLzSAyJICObUJq3MdkMtH55Kp8PxaWt2R4LpoiuSUiUl/+PHyvsqyYl7YcoDT/JwDe/7aAnKP6QkBE/JNjIaDWPjpCRKQuSkq1Aj8UlFFpsREZbCYmxEz8yaTUF4db77CGb3NPDd1zLHdbky6xYQBkezApJSLSkk74+fC9sMhoOseEExEIFsPEx98XeTokERGPcCalLOopJSK+S0mpVuBAfikA58eEYDKZaBtmH9L2Rc6JZh3WUFJp4ZXdufxUbGny3/PtMXudahu653BejD0p9WOhVuATEf9QVO6/PaUcTCboFmX/iPLG1/kawicifinAZH/vU08pEfFlSkq1Agfy7HMvnR9jH+YWF2rGbIK80iqOFrs/11J9PfnRAV7dk8/GHyvZ+GMl1iZcjnafc5LzmlfeczjPC4bviYi0JH/vKeVwQRszgWY4VFjJlpND2EVE/EmAyTF8Tz2lRMR3KSnVChzIO9VTCuxdebud/P/eZprsfPdPRaz76igAZhPklFjZkt00wwXLqqzsybHPh9W7Y5s693UM31NSSkT8QUW11TlMw597SgEEmU0kxtpXYX1l548ejkZEpOU5hu+Vq6eUiPgwJaVage/OSEoBdIm2///Q8eZJ1jyz6XsAUrtHc3Fb+03Bm980zTfV/ztcisVm0DU2jPPjwurc1zF8L+dEJRYthysiPi6/rAqwJ6SC1EKTFBdEgAl2Hipk79HWO4+iiEhDOFbf05xSIuLL9JHXy5VVWck5YR+i1zU62Ln9vDb2/x88XtbkvzOvtIrPf7L3ZLr50ngSYwMxm2BvbnmTrPi3/Ud7764rLmxb5yTnAO0igwkJNGO1GRxpxqGKIiLeIL/UPp9UTGjgOd8f/YG5spi2ZvuXL+otJSL+JtCknlIi4vuUlPJy35+c5LxtRDDRoYHO7Z1OJqWao6fUpgP5GMDFHaKIjwgiLNDM+W3sv3vN7sONOrfNMNjxk71OV3SPO+f+ZpOJzs0wr5TNZuPYsWMcO3YMm03fPomIdygotfeUigsLPMee/uPCSPt8hh/uy+UnDeUWET/i6ClVabFh04IPIuKjlJTyco5Jzru3DXfZ3rkZk1IbD+QDMKx7W+e27jH2G6T/fpvXqBVAckqsnKi00iY0kEs7R9frmC4nh/BlN+EKfHl5eTz3n90895/d5OXlNdl5RUQawzF8L0ZJKaeQqiLaBlmxGfCPTVmeDkdEpMU45pQCe2JKRMQXKSnl5X4osCelLjgrKWWf56mgrJrikys1NYWyKivbD9rnjroy8VRSKj7MTEJkEGXVVj7Oym/QuW2GwZ5c+w3XuN4dXRraujjmlTpY0LRDFSOi44iIPndvLRGRllJwcvherJJSLi6Ot38R80FWIYdPNN0XFCIi3sxknEpENeZLYRERb6aklJfLPjlUoWus64Tg4UEBxEc4eks1XbJm28HjVFkNOkWHuvTOMplMDL/QvlLeuydX5XPXD0UWiioNIoPN/HbgefU+LrGdPY5vc0sb9HtFRFoLR08pJaVcxYea6RARgMVm8OK2Q54OR0SkWRmGgWHYP387VmItr1ZPKRHxTUpKebmDJ4fndYk9e5U6x8p1B5twCN8npw3dO3OS3WsujMYEZB487vYqSEUVFj4/Zr/ZurFXW9qEBtX72IvaRwHw7bESjacXEZ+WX6qkVG16tbN/EfP2l0fVW0pE/EZYUAAAFRb1lBIR36SklBez2gznpK5n9pQ6fVtTJaUsNoNPHUmp04buOXSMCua6i9oB8NyWg26de+n2o1RaISbEzJiL3Bsy161tOCGBZkqrrBwprnbrWBGR1sSx+l5sqJJSZ2ofEUhyxwis6i0lIn7CMAxCAu1fEleop5SI+CglpbzY0eJKqqwGQQEmOkSFnlXeNdY+rO1QQdMkpfbkFFFUYSG6jknIpw3pRoAJPv2ugH/t+gmjHj2X/rs/j00HizEBKR2DCQpwb5nzQLOJxPgIAA4U6NtxEfFdBRq+V6fJfe1fjLz9xRF2f5dTrzZIRKQ1Cw2095Qq15xSIuKjlJTyYo65os6LDiOghknBz3f2lGqaOaU+3m/vJXX5hXG1TkLeNTaMm/p1BuDx/x5gypos/nOwnN1HyjlRcXZjWVRezYJ/7wfgZ22DiAsLaFBsFyVEApClpJSI+DAN36udYRh0Dq6kb0IIVgPmvLefgoICT4clItKsQgPtt2sVWn1PRHyUklJe7NBxewKmpvmk4NTwvezj5Y2ea6nKYmP91/YJzIf3aFfnvjOHXcj/DbuQQLOJ4+VWjpXZ+Cyngj+++z1fHzk115RhGDz27ywKyqrpEh1Mr/j6zyN1pqT29qTUdwWVDT6HiIg3K6uyOm86YhuYwPdl5SVFvLTlAJEVxzABRytMbMt2b35DEZHWxDCMU0kp9ZQSER+lpJQXc/SUqmk+KYDO0aEEmE1UWGwcK25csua/+/MoqrDQPjKYyy+se84nk8nEzQPO44Ppg1k48nwGdAimTYiZ3FIL017fzZbv7d9cv/PVUf79bS4BJrh7SMcae3vV10Unk1L788s1XENEfJKjl1RYkJmKkiL0Vne2sMho4qMj6R5t//jy9y05FJZrrkER8V0hzqSUekqJiG9SUsqLZRfWvvIeQGCAmc7R9rmmDjVysvM1u3MA+EWfjrUO3TtTVGggPdqG0SM2iDEXtaF/pwgqLTbuXvslt776OQ9v+BaA24ecT1J8zXWorx7tIogIDqC4ykZ+hRplEfE9jvmkYkIDePa9bVSUa7hybS6OMdMm2ExBuYWMD77Vyqwi4rNCg+y3a5pTSkR8lZJSXsyRaOoaU3tCx9GLqjFJqfe+OcrnP50gwGxibO8ODTpHcICJP199Htf/rD1WA/bknABg4qUd+e3Arg2OzSEowMzlF9h7cP1YrEZZRHzP6fNJhUZEeTga7xZgNjG4cwgBJvg4K5/FG7/3dEgiIk3OMAxCTi4QpDmlRMRXaSZVL1VRbeWnQvu35Be0Da91v67Oyc4blpTKOVHFgn8fBOB3KV1oFxnSoPMABAWYeHjkRfxmYBd2HCrk4oTIWlfxa4hhiW35YF8uPxZb6Ns+uMnOKyLiDXJLTpvkXCPSzinMWkKPwGL2Vsfwz50/YrZW8atL2xEXF4fJ1PDh4iIi3iRMPaVExMepp5SXOni8HAOIDg0kLrz2CcLPj7MnrA41YAW+E5U27vvwEKVVVnp3bMPvBp3f0HBdJMZH8Kt+nZs0IQUw5II4As1QXGVwolLfFomIb/mxyP5FRIdIJd3r6/zoIH7Xvz0AL+06xv+98QX5+fkYhuH8V0SktTIMg+AAzSklIr5NSSkv9X2+Pcl0QdvwOr/xPb+Bw/cqLQYfHaogv8zCBXHh/HXMz+o9l5SnRIYEcmmHCACyCtWNQER8y48n5xHs1EZJKXfc2Lsdtw5IAGDfCTMvfnaM/Px8nnrrUwoKCjwcnYhIwxmGwb7cEgAqLeopJSK+SUkpL/V9filQ99A9ODV8L6eogqp6jjW3GQZbcyopsxh0igriuZv6EN+IYXstacxFsQBkHbdQVGHxcDQiIk3HkZTqGKWkVH0ZhsHx4wVc29lMvwR7O/avL/N44+t8wiLbeDg6EZHGCzSfGr5nGIZ6gIqIz1FSykt9d7Kn1IVtI+rcLz4imIjgAGwGHKznEL6NPxRzuNRKgAlmX9mZ2PDWcwPUv1MEsaFmrAa8vfe4p8MREWkSVpvBTyeH73VSUqreKsuKeWnLAVZs+przw230TbBfu+d3HCXH/VHtIiJeJ8BkT0Jp+J6I+ColpbzU6cP36mIymejRzp642p9bes7z2gyDf32RB8DFbYO4MC60kZG2LJPJxCVt7XNsrf26gKPFlR6OSESk8XJLKqm2GgSaTbSLqH0eQTlbWGS0s1fU+cHldAmtxgD+V2DmSHGVZ4MTEWmkk4vvaaJzEfFZSkp5oSqLzTmM48JzJKUAerSLBODbY+dOSn2Slc/BoiqCzJAU1zpvfM6LCqBdmJlKq8HCj7/zdDgiIo3248nVVjtFhxLg5fP7eTOTCfp1CCM22MBimMj4OLveQ9tFRLxRwMm5ZSv0XiYiPkpJKS906Hg5VgMiQwKIjzj3MI4kZ0+pknPu++pnP9qPiQ0iOKB13viYTCb6dwjGbIJ/f5vL5u81ka2ItG7ZJ7+IOC+mdfVe9UZmk4nL4m0EmQz251ewaKO+vBCR1svxcb1CPaVExEcpKeWF9h4rBqB724g6V95z6NH+ZE+p3NI6Jz88WFDGrp9OYDZBYmxg0wTrIbGhAc5JzzM++JYTFVqNT0RaL0dPqS4xYR6OxDeEB0LvKPvw7td35fCfb3M9HJGISMOYOTmnlHpKiYiPUlLKC+3JOQFA7071Wzmoe9twzCYoLK8mr7T2+TPe+eooAP06RRAe1Pqf+il929E1Nozckioeem8fFptWIxGR1skxZLuzklJNpl2wlUm92gIwb8O3zmssItKaBJ78yK45pUTEV7X+zIQP+iLH3lOqTz2TUqFBAZwfa5976ttaJju32gze/dqelLque3QTROl5oYFm/nJ9T4IDTGz6roBHNuyjrEoNtoi0Po7VU8+L1vC9pvTbfglc2rkNpVVWZq7ZQ2GZJj4Xkdbl1PA99ZQSEd+kpJSXKam0cCDPnliqb08pwLkC37fHap5XKvPgcXJLqogODWTgeVGND9RL9OrYhnmjfoYJePfrY/zihe3ct+5rXvjsGN8WVFNcpQZcRLxbcYWF7/LsSakOwdUcP15AHSOxpZ4Mw6C46DjTLgklNMDgYGElaa9/RnmVxdOhiYjUm1mr74mIj1NSyst8efgEBvYVmOozybmDo1fV9oPHayxf9+URAK6/OIGgVjrBeW2G94jnqfG96BwdSkFZNf/5No83vi7gs6NVvHOgnAf+k+1M9LnDMAy+P17BvoJqvius5rCWFheRZrDH8b4fFcy/Mr9hxaavqays8HRYrV5lWTEvbTnA2zv3cXmHIIJMBt8WVJO+dg8Wq76wEJHWIUBzSomIj2vds137IHeH7jkMuSAOPjrArp9OUFJpITLk1FNbWFbNJ1n5AIy+JAEoa7J4vcXlF8Rx2dQBfPZjId/llXEwt5AtPxRyrMzG/3JK+fXLn/GLPh35ZXJnurUNxzAMqqwGZdX21ZmsoRXEhgURGhRARbWVT78r4OUd2Xxz9FTPs51vfcdtg61MTelK4BlLtttsNvLy8gCIj4/HbFa+V0Tq5/MfiwDolRBOeEgIABXlSko1hbDIaAxrBaYAE4Pa29h81Mz2H0uY/c43PDLqZ4QE6r1aRLxbgMnAbLJPxfFTUQWd2tjbifoshiQi0hooKeVlMk/2dHI3KdUlNowuMaFkF1aw41AhV/eId5a9+/VRLDaDi9pHktQ+kmPHfC8pBRAcaGZwtzgGd4vj2LFgQo1Kiqts5FcFsDW7hDW7D7Nm92HCgwKw2mxUWu3fPL33/Q/AD4B9nioDqDz5bVSQ2UR8mJkqm0F+uY3nthzkx8Jy/vzznphP+zCQl5fHc//ZDcAd11xK+/btXWJzJK1MJoiLi2juSyEircjun+xJqUsSwjlaWOzhaHxX2xDo26aSL0rC+Dgrnxmr9jBv5EV00jxeIuLFzBj06RDJ54dL+Hh/Hr/u39nTIYmINCl9RehFfiwsZ0/OCcwmuDqxrdvHD7kgDoAt3xc4t1VabKz87EcAxl/asWkCbUWigs3Mveo8nr2xD0MvjCPAbKKs2upMSAUHQNuwQOeQxgqLjUqLjfaRwfwupQsvTejOVV1DST0/lLuHdCTAZJ+76smPDmCcMelLRHQcEdFxNcbhSFo9++/d5OZqaXIRsauy2PjqiD0R1Ssh3MPR+L74IAv3DY4lPMjMnpwT/HLFTpZ8+j25xZXk5eWd9b4uIuJphmFw+fn2+WD/s1/vUyLie9RTyou89/UxAAZ2jSU+MsTt4wdfEMfru3LYeCCfu6uthAUFsO7LI+SWVNE+MphRFyc0dcitRv8uMfTvEkNJpYXC8mry8/PZ8M0RAs0mbh7QhXbt2lFaZaWwvBqL1eD8uDBMJhPHjtmfE5PJxDXdo4mNiebP6/fy+q4cIkICmX55t3rHUFvCqqE0ZFCk9duRXUiV1SAmNIAwS4kmOG9mlWXF7Pj2R/qHV/NlWSTHq+HFbdms2JZNTJCVYYntSO7Wju5tI0iICiE6zP4xqaCggLi4OA2XERGPyC4oxQR8ebiYoycq6BijLzFExHcoKeUlbIbBe98cBeD6i9ufY++aXdYlhk5tQsg5UcnyzEOM69OBZVsPAvDbgV0I1twZRIYEEhkSSHBVsMu8UCaTyVlWl5//rD0llRYW/CeL5ZmHqKi28n/DLmyy+E5PNEHdyaZzDRkUEe/32v9+AiAuqJqXPv2G8Gj9HTe3sMhoQq0VDIsNIc8SSG6ZlX0F1RyvDuDNbwp485tTvY2DzCbiwsxUVVaQ3DWe3ufFcX4kJLYNpUO7eJcklWEYFBTYj1UCS0SaUoDNwiUJEXx5tJRZb3/DI6N6ckF8y66m7eihpfc2EWlqfpeUys/P54EHHmD79u0EBAQwZswY7r33XgIDPXcpbDYbL366n+zCCsKDA1zmg3JHcKCZu69OJP2tr/jnzh9564sjHC+vJjE+gjG9OjRx1P5rYt9OVFpsLPzkO/7fZz/xzZFibu4di2EYjW6oHYmmiOg4SosKzplsaureVyL+rKXbh+/yS8n84TgmoFeHKMyV6ibVkkwmE/HmUiIDKukQU0WxKYKE2Gi+K6zmh8IqKm0mqm0GR0utQBD//a6I/35nn//LjEH32GD6d2vHxR2iaGOuIsxayrtfHsZsht8P70Pbtu4PwxcR7+MN9w6GYdAhIoCDIQF8m1fG71d9yQu/6st5MWEtFoOISHPxu6TUzJkzSUhIYNOmTeTl5TF9+nRWrFjBbbfd5rGY/pf1E8t2HgFM/LZvPGFBAQ0+15Xd40jt2Y4P9+VyvLyajm1CWDShF6GNOKecbfKA84iPCGbeB9+y66cT7PrpBFHBJtqHB9D220L6W0OJjwgm5uSKfjbDwGozsAEV1VYi6ng6IqLjiIzRzYxIS2vp9sExZHtAxxAigsyUVzbLr5FzcPScCi0+DiXVdKOSi89vj8VSQaURQllVJUVl1VzcpR0HjlfxRW4VlVbYf7ya/cdzXM4VYAogKtjMiY0/0vf8Ci5qH0H7wGpCg8zqPSXSSnnDvYNhGMSFmHl61PnMfPcHCsqqmbFqD0+Nu5ju7Vq2x5SISFPzq6TUwYMH2b59Oxs3biQsLIwuXbowY8YMHn/8cY8kpUqrLKz/+hhPf3IQq2GifbiZkUkxjTqnyWTikVEX8at+nTlSXEly5zYNmp9Kzm3Ez9pzaec2PLv5Bz7Ym0txlUFxlYXF247AtiPO/cwmsJ3WAWL1vl1EBAfQPjKEdpHBtI8KoX1kMG1CgygvKyHreDVhVSVUlVvY9MMJ4orsKwJWWWxUWqxUWmxUVNsoKCpmb341IYHwv5xSegaU0bFNiDMBqTmnROrPE+1Dj2gzbUMMgsuOURHasB6y0rTCIqOxWSoACDBBRKCJcDMEFB+nKM9GW2sl13dpR0llJUeLq+nULo6DJywcKqqmzGLDapgorLS59KoCg/BA6Nkukk5tgmkTEkC7mCjCgwOoKi8jwARms4m46DaEBwdgVJYRGWzmvIR4okKDXIaa14djGKHJBG3bRjbxFRLxL95y72AYBtXVVbzx+Y8M7hjC5/kWDp+o5JZXd/O7lK6MvDiBdpHBSnyLSKvkV0mp/fv3ExMTQ0LCqQm/u3fvTk5ODidOnKBNmzbN8nt/Kipn56FCiiutlFRaOFZcyQ8FZew7VkLVyVXg2oebGdI5FHMjGpPTkxCXdIind6fmqY+c0qFNKA9dfxFTe0fz908PUVBhIyQ4iJ+KLRSUVWOxGS4JKYfSKivfF5TxfUFZLWe2z0uSmZMD5NSyzymZOdlANgCxYUF0aBNCqNnG4ePFBJigV+c4oiMjMJnAbDKd/LHfCJkwqCgvByAyIhyz2YwJ+34mE87/g0FZWSkmTERGRmAymew/2PcBg9LSUgCiIiNPnhs4bR/7+ewnPf2x42Vv/zUGxcUlmE3QuXN7SksqnWU1ne/czr1Tfc5zrl3qdw73/74Nzn4BnTkZtskEQ0OCG3B2cfBE+9C/cyQjEttQVmxr8nNL0wuLjMawVtjnIAyCautxTCU2zjcq+VkXe8+qclsIVeZA2pgt7DxazfFKg0rDTJkFdh0uZdfh0pNny6vhN/x0xuMs++8NMhMZbMZsMmEY9jkoDezvAwb25JnZZCLADAEmExg2TlRUYwLOa/sDIYGBBGIlNNBMgNmEmZPvpyfbgdCQEKqqqjCbICo8jKhQ+/yKUSGBRAQHnGwL7Ps63ucMw36TXFxSgmEYREWd6qlhGGA7+Z/T4zQMg9LSEkxAVGQUppNthGHY20mbYWAzDE4U2yf8D4+IOHnsqbYlIiKCsrJSoiJDMYzAk/U++WPCXr+T205/Tz79PdPl7dNl+6kHte7Pme+/dR/juEZRkZH2i3fGm/fpj6JySiguLndeL047R03qjBH7SUpKSriwQxz9usQoWdFAnrp3qI0JE0EmG5e2NWOxmskts/HMpz/wzKc/EB4UQFRIAOHBAYQHBRAWHEBE8MnHwYGEBZoJCQogJNBMgMmE2Wz/u7b/HzDAevLv0Wqz9/Cvthr8VFTOoePl5JVW0bFNCBfEhdM5JoygADNBASYCTCYCA05+NnPzdVbX7nV9ZnKUOP5uHfvWeT4TREUVO//OTm0/9++p7XzuHlNXgLWVNCSGuo6q+5q7d4wJiM4rp+hE+dlvRHWcsMmvaxPyxEQKrq9HaJNfzokT5Wd93m9KIYFmLu3UhsAA7+i04FdJqdLSUsLCXMdeOx6XlZXVu2Exm8++KazLPW9/TU7R2eMyggMDOD82iKGdgiguOo65uprCwuMEBNT8Z1dYeBxr+Qnn/8/cLz+/gP+3+WsAfn35xbRtW/N8Q+c6jzv7OsqrbcEUFgZjruUb3fr+zvr+Pm85j0NV2Qk6BZXRKQjG9o6jbds4DMOgrNqgymJwoug4739zBLMJfjX4QoqqA8kvtVBQbqGgzEJ+uYWyahtlFRVkF1ZgDgjCYrHQNjwYAux/psGBJoJMJkICzYQEmrFWVZKVX0aVzURIUCDHq6C82ka1zSC7sOJkZCFgwObsUqC0xthdFZ17F8CRNKtdfj3Pcy7Hmug8vu/8bdn88+a+br036R7llKZoH2pqGxzXuKaygAAT1ooSjKpyTIFWDEuV81+ztQqqS8+5raFljdnfsFmxWW0YVnOL/+7mqjeWKrBUYFS5d66wkEBsFhtUl2K2VBEVaMVWXUXuTz8w+PwkbJYqqk3BlFZZOZpfQFBMJ6psVqotBhabgTkoBJvNRnV1NQQEYjXAagrEapiwGKf+QMstUNtH5WpOLzv5rzkIgIPHK4FzjQstOe3/xefYtzaH3dz/yLl3kSZylGW/7ENS+/r3mlPbcIqn7h3MZvvnPWuQ2WXOUsNsTxKHAsPOCyan1OBgsY2ik3MSllbbKK224XhnaGrFuWV8m1vbF6oi0prcfFlnbh98vlvHNFf74FdJqfDwcMpP9ghxcDyOiIio93ni4twbu/3B3Ve5tX/tLuTyy89VPqAJzuPu72yK87T072vpuGHE1fU5j4h4QlO0D3W1DTWVtW0bxYIe7n0YEBGRluOpe4e2baN46KaEc+8oIuIDvKO/Vgvp0aMHhYWFziFuAAcOHKBDhw4uXc9FRMS/qH0QEZEzqW0QEWl+fpWU6tatG/3792f+/PmUlJSQnZ3NkiVLmDhxoqdDExERD1L7ICIiZ1LbICLS/ExGbTMo+qi8vDwefvhhtm3bhtlsZty4caSnpxMQEODp0ERExIPUPoiIyJnUNoiINC+/S0qJiIiIiIiIiIjn+dXwPRERERERERER8Q5KSomIiIiIiIiISItTUkpERERERERERFqcklIiIiIiIiIiItLilJSSRtm7dy+33HILAwcO5PLLL+eee+6hoKDA02F5LavVypQpU7jvvvs8HYpXKyws5J577iElJYXLLruMGTNmcOzYMU+H5ZW++uorJk+ezIABAxg6dCiPPPIIVVVVng7L7+Xn5zNjxgwGDBhASkoKGRkZWCwWT4d1lrrewx988EF69epFcnKy8+f11193Hrt27VpSU1Pp27cv48ePZ9euXc4yq9XKggULGDJkCMnJyUyfPr3F/obXr1/PxRdf7BL3rFmzANi9ezeTJk0iOTmZ4cOHs2rVKpdjvbVOb7/9tkt9kpOT6dWrF7169QJa33NVUFBAamoq27Ztc25rzuemJf4ea6rThg0bGDt2LP369WP48OEsXrwYm83mLL/++uu59NJLXZ63AwcOeE2dpHm58xx+8sknjB49mr59+3L99dfz0UcftXC0rYM71/S2226jd+/eLn9/GzdubOGIW4+a3uPOpNepe+pzTf3mdWqINFB5eblx+eWXG3//+9+NyspKo6CgwLj99tuNO+64w9Ohea2FCxcaF110kXHvvfd6OhSvdvPNNxtpaWlGUVGRUVxcbPzhD38wpk2b5umwvI7VajUuv/xy46WXXjKsVqtx+PBhY8SIEcbixYs9HZrfu/nmm40//elPRllZmXHo0CFj1KhRxrJlyzwdlotzvYf/4he/MN54440aj83MzDSSk5ONnTt3GlVVVcaLL75opKSkGGVlZYZhGMbTTz9tjB492sjJyTGKi4uNmTNnGrfffnuL1Ouxxx4z7rvvvrO2FxYWGgMHDjReeeUVo7q62tiyZYuRnJxs7N692+vrdKYjR44Yl19+ufHmm28ahtG6nqudO3ca1157rZGUlGRkZmYahtH8z01z/z3WVKcvvvjC6NOnj/Hf//7XsFqtRlZWlnH11VcbL7zwgmEYhlFcXGz07NnT+PHHH2s8p6frJM2vvs/h999/b/Tu3dv48MMPjerqauPdd981+vTpYxw5csQDUXs3d/4uUlJSjG3btrVwhK1TTe9xZ9Lr1D31uaaG4T+vUyWlpMEOHDhg3HrrrYbFYnFu+/e//23069fPg1F5ry1bthgjR4407rzzTiWl6vDFF18YvXv3NoqLi53bjh8/bnz77bcejMo7FRQUGElJScaLL75oWCwW4/Dhw8b111/vvOkRz/jhhx+MpKQklw9i7777rnHVVVd5MKqz1fUeXllZaVxyySW1/t396U9/MubOneuy7ec//7mxevVqwzAM48orrzTefvttZ1lubq7Rs2dP49ChQ81QE1eTJ082XnnllbO2/+tf/zKuu+46l21//vOfjXvuuccwDO+u0+lsNpsxZcoUY86cOYZhGK3quXrjjTeMq666ynj33XddPog353PT3H+PtdXp/fffN+bPn++y7/z5843f//73hmEYxtatW42UlJRaz+vJOknzc+c5fPLJJ41bbrnFZdutt95q/P3vf2/2OFsTd67poUOHjIsuusjls6bUrLb3uDPpdVp/9b2m/vQ61fA9abALL7yQ559/noCAAOe2DRs2cMkll3gwKu+Un5/PnDlzeOKJJwgLC/N0OF5tz549JCYm8q9//YvU1FSGDh3KggULaNeunadD8zqxsbFMnTqVBQsW0Lt3b4YNG0a3bt2YOnWqp0Pza/v37ycmJoaEhATntu7du5OTk8OJEyc8GJmrut7D9+7di8ViYdGiRQwZMoQRI0bwj3/8wzn0KCsri6SkJJfzJSYmsnfvXoqLizly5IhLeXx8PNHR0ezbt69Z62Sz2fjqq6/4+OOPufrqq7nyyit54IEHKCoqYv/+/bXG7M11OtNbb71FVlaWcxh4a3quhg4dyocffsjIkSNdtjfnc9Pcf4+11WnEiBHMnj3b+biiooKPP/7Y+Rnpiy++ICwsjJtvvpmUlBTGjx/vHOri6TpJ83PnOazr9S+nuHNNv/jiCyIiIrjrrrsYNGgQN9xwA6tXr27pkFuF2t7jzqTXaf3V95r60+tUSSlpEoZh8NRTT/HRRx8xZ84cT4fjVWw2G7NmzeKWW27hoosu8nQ4Xq+oqIh9+/bxww8/sHbtWt58802OHj3Kvffe6+nQvI7NZiM0NJQHHniAzz//nHfeeYcDBw6waNEiT4fm10pLS89KPjsel5WVeSKkczrzPby4uJiBAwcyZcoUPvnkEx5//HH++c9/snz5cqDmOoaGhlJWVkZpaSkA4eHhZ5U7yppLQUEBF198MSNGjGD9+vW89tpr/PDDD8yaNavOmMF763Q6m83G0qVL+f3vf09kZCRAq3qu2rVrR2Bg4Fnbm/O5ae6/x9rqdLqSkhLS0tIIDQ11fmlgMpno3bs3jzzyCJs2bWLq1Kn88Y9/5PPPP/d4naT5ufMcnuvvQ+zcuaZVVVX07duXu+66i02bNnHfffeRkZHBe++912Lxthb1eY8DvU7dUd9r6k+vUyWlpNFKSkq48847WbduHa+88go9e/b0dEhe5bnnniM4OJgpU6Z4OpRWITg4GIA5c+YQGRlJfHw8M2fO5JNPPmnRm7/W4MMPP2TDhg38+te/Jjg4mB49epCWlsarr77q6dD8Wnh4OOXl5S7bHI8jIiI8EVKdanoPv/zyy3n55ZcZOHAgQUFB9OnTh9/+9resX78esH/Qr6iocDlPRUUFERERzg+lZ14DR3lzio+PZ+XKlUycOJGwsDA6derErFmz2LhxI4Zh1BozeG+dTrdt2zaOHTvGxIkTndta63N1urpiPFf5uerg6b/H7777jl/+8pdYLBZefvllZzLxtttuY9GiRXTr1o3g4GDGjBnDkCFD2LBhg9fXSRrPnefwXH8fYufONR03bhzPP/88F198MUFBQQwdOpRx48b55M1+S9HrtOn50+tUSSlplEOHDjFhwgRKSkpYvXq1ElI1eOutt9i+fTsDBgxgwIABvPPOO7zzzjsMGDDA06F5pcTERGw2G9XV1c5tjmEohmF4KiyvdPjw4bNW2gsMDCQoKMhDEQlAjx49KCwsJC8vz7ntwIEDdOjQgaioKA9Gdrba3sP//e9/89prr7nsW1VVRWhoKGCv4/79+13Ks7Ky6NGjB9HR0SQkJJCVleUsy83NpbCw8Kyu/U1t7969/O1vf3N5r6iqqsJsNtOnT59aYwbvrdPpNmzYQGpqqksPmtb6XJ0uKSmp2Z4bT/49fvLJJ0yaNIkrrriCF154gejoaGfZCy+8wNatW132r6qqIiQkxKvrJE3DnefwXH8fYufONV29evVZN/aOvz9pGL1Om54/vU6VlJIGKyoq4re//S39+vXjhRdeIC4uztMheaX333+f//3vf+zcuZOdO3dyww03cMMNN7Bz505Ph+aVhgwZQpcuXbj//vspLS2loKCAp556imuvvdb5DbPYDR06lNzcXJ599lmsVivZ2dksXbqU0aNHezo0v9atWzf69+/P/PnzKSkpITs7myVLlrj0bvEGdb2HG4bBo48+ytatWzEMg127dvHyyy9z0003ATBx4kTWrVtHZmYm1dXVrFixgvz8fFJTUwEYP348S5cuJTs7m5KSEubPn8/AgQPp2rVrs9YpJiaGlStX8vzzz2OxWMjJyeHxxx/nF7/4BSNGjCAvL48VK1ZQXV1NZmYm69atY8KECV5dp9N99tlnXHbZZS7bWutzdbrU1NRme2489ff4+eefk5aWxuzZs7n33nvPGqpx+PBh/vKXv5CdnY3FYmH16tXs2rWLX/ziF15bJ2k67jyHY8aMYfv27axfvx6LxcL69evZvn07Y8eO9UDk3suda1pSUsK8efP4+uuvsdlsfPzxx7zzzjvO901xn16nTc+vXqcem2JdWr3ly5cbSUlJxqWXXmr07dvX5Udqd++992r1vXM4cuSIMXPmTOPyyy83BgwYYNxzzz1GUVGRp8PySps3bzYmTZpk9O/f37jqqquMJ5980qisrPR0WH4vNzfX+OMf/2gMHDjQGDRokPHYY4+5rHLnDc71Hv7qq68a1113nXHppZca11xzzVkr2r355pvGiBEjjL59+xoTJ040Pv/8c2dZVVWV8fjjjxtXXHGF0a9fP2P69OlGXl5ei9Rr27Ztxk033WQkJycbgwYNMubNm2dUVFQYhmEYe/bscZZdc801xpo1a1pFnRz69u1rfPzxx2dtb43P1ZkrDjXnc9NSf4+n1+mOO+4wevbsedbf1q233moYhn3VxIyMDGPo0KHGpZdeakyYMMHlenhLnaT51PUc9u3b13jrrbec+27cuNEYM2aM0bdvX2PUqFE1vg9I/a+pzWYznnnmGePqq682+vTpY4waNcp47733PBl6q3Dm+7Zep41X1zX1p9epyTA0HkZERERERERERFqWhu+JiIiIiIiIiEiLU1JKRERERERERERanJJSIiIiIiIiIiLS4pSUEhERERERERGRFqeklIiIiIiIiIiItDglpUREREREREREpMUpKSUiIiIiIiIiIi1OSSkRERERERERER9TUFBAamoq27Ztq/cxGzZs4IYbbqBv376kpqayevXqZowQApv17CIiIiIiIiIi0qI+++wz7rvvPg4dOlTvYzIzM7nvvvtYuHAhV155Jdu2beP2228nKSmJPn36NEuc6iklIiIiIiIiIuIj1q5dS3p6OnfddddZZVu2bGHixIkMGDCAUaNG8fbbbzvLVqxYwW9+8xuGDRuGyWRi0KBBrFmzhq5duzZbrEpKiYiIiIiIiIj4iKFDh/Lhhx8ycuRIl+179+5l+vTpTJs2jW3btjFv3jzmz5/Ppk2bANizZw8xMTFMmzaNlJQUxo4dy6FDh4iJiWm2WJWUEhERERERERHxEe3atSMw8OzZml577TWuueYarrvuOgICAujXrx833ngjK1euBKCoqIgXXniB6dOns3nzZtLS0rjrrrvYvXt3s8WqOaVERERERERERHzcTz/9RGZmJgMGDHBus1qtzuF5wcHBTJgwgeTkZACuu+46Bg8ezIYNG7j00kubJSYlpUREREREREREfFyHDh34xS9+wcMPP+zcduzYMQzDAKB79+5UVVW5HGO1Wp3lzUHD90REREREREREfNzEiRN55513+PTTT7HZbPzwww/cfPPNLF++HIBf/epXvPrqq2zZsgWbzcaGDRvYtm0bN9xwQ7PFpJ5SIiIiIiIiIiI+7tJLL+XJJ5/kySef5P/+7/8ICwvjhhtu4O677wZgwoQJmM1mHn30UX788Uc6d+7MU089xSWXXNJsMZmM5uyHJSIiIiIiIiIiUgMN3xMRERERERERkRanpJSIiIiIiIiIiLQ4JaVERERERERERKTFKSklIiIiIiIiIiItTkkpERERERERERFpcUpKiYiIiIiIiIhIi1NSSkREREREREREWpySUiIiIiIiIiIi0uKUlBIRERERERERkRanpJSIiIiIiIiIiLQ4JaVERERERERERKTFKSklIiIiIiIiIiItTkkpERERERERERFpcUpKiYiIiIiIiIhIi1NSSkREREREREREWpySUiIiIiIiIiIi0uKUlBIRERFpQYZheDqERvOFOoiI+Au9Z4s3U1JKpIkcOXKEm2++md69ezN48GB69uzJtm3bPB2WiIh4kaysLH71q1812fm2bdvGiBEj6NWrF7feemuTnbc2J06c4N5772Xnzp3N/rtEROTc7rvvPoYPH+58PGXKFKZMmeJ8vGrVKhYsWOB8/MYbb9CzZ09+/PHHFo1TpDaBng5AxFe89NJL7Nq1i8cff5yDBw+ycOFCT4ckIiJe5r333mPXrl1Ndr4FCxZgs9n4xz/+Qdu2bZvsvLX55ptvePPNNxk/fnyz/y4REXHfgw8+6PJ46dKlDBw40Pn4qquu4vXXX6d9+/YtHZpIjZSUEmkihYWFtG/fnpEjR6qHlIiItIjCwkIuu+wyhgwZ4ulQRETECyQmJtZZHhcXR1xcXAtFI3JuGr4n0gSGDx/OG2+8QU5ODj179mTx4sVn7fPFF19w6623kpKSQr9+/fj973/P/v37XfY5duwYs2fPZtiwYfTp04eJEyfyn//8x2Ufx/knTJhA//79WbJkCTabjb///e8MHz6cXr16MXz4cJ588kmqq6ubtd4iIr5s3LhxTJ8+3WXbiBEjGDp0qMu2mTNncvPNN1NRUcETTzzBddddR69evejXrx+33HIL33zzDQBPP/20s33o2bMnTz/9NICzp1Nqaiq9evVixIgR/POf/3T5HVOmTCE9PZ0777yTfv36MW3aNHr27MlPP/3Em2++6Rwy/vTTT5OamsrixYtJSUnh2muv5fjx41itVlauXMno0aPp06cPV111FX/729+orKx0/o777ruPqVOnsmbNGueQwDFjxvDJJ58A9qGCv/nNbwD4zW9+4zI8RETEnxmGwcqVKxk1ahR9+vQhNTWVZcuWOedy2rx5M7/+9a/p378/KSkp/OlPf+Lw4cPO49944w0uvvhidu/ezU033UTv3r256qqrWLZsmcvvKSoqYvbs2aSkpHDZZZfx+OOPY7PZXPY5ffje8OHD+emnn1i7dq1zyF5Nw/eaKj6RhlBSSqQJLF68mGHDhtGuXTtef/11Jk6c6FKemZnJr371K2w2GxkZGTzyyCMcPnyYX/7ylxw4cACAvLw8Jk6cyPbt27nrrrt4+umn6dy5M2lpabz99tsu51u6dCkjRozgySef5JprrmHZsmWsXLmStLQ0li9fzq9+9Suef/55nn322Ra7BiIivuaqq65i+/btWK1WwD534A8//EBubi7ff/89AFarlS1btnD11Vdzzz33sHr1aqZNm8by5cu57777+Pbbb7nrrrswDINJkyY524fXX3+dSZMmAfDQQw+xaNEixowZw7PPPsvPf/5z5s+fzzPPPOMSz3vvvUdQUBDPPPMMU6ZM4fXXX6ddu3YMGzaM119/nUsuuQSAnJwcPvzwQ5588klmzpxJbGwsf/7zn5k/fz7Dhw9n6dKlTJ48mVdeeYUZM2a4TID75Zdf8sILL3DnnXfyzDPPEBgYyJ133klRURGXXHIJf/7znwH485//fNYQERERf/Xkk0+SkZHBsGHDWLp0KZMmTeKpp55iyZIlvPXWW/zud78jISGBJ598ktmzZ7Nr1y5uuukm8vPzneew2WzMnDmTkSNH8o9//IP+/fvzt7/9jU2bNjnLb7vtNj7++GPS09NZsGABu3btYv369bXGtXjxYpd2oqYhe00Vn0hDafieSBO4+OKLiYuLIzg4mL59+7p88wzwxBNP0KVLF55//nkCAgIAGDp0KKmpqTz99NMsXLiQF198kYKCAt577z26dOkCwLBhw5g6dSp//etfueGGGzCb7XnkPn36MG3aNOf5//rXv3LJJZcwYcIEAAYOHEhYWBiRkZEtUX0REZ901VVXsXTpUvbs2UNycjJbt26lS5cunDhxgu3bt3PBBRfw+eefU1RUxNVXX82WLVt44IEHGDlyJGB/Ly4tLeWxxx4jNzeXDh060KFDBwD69u0LwPfff8+//vUv7r77buf7+tChQzGZTDz33HP8+te/JjY2FgCz2cy8efMIDw93xhgcHExcXJzzfAAWi4V7773XOaQvKyuL1atXM3PmTGfPr8svv5z27dtzzz33sHHjRoYNGwZAcXExb7zxBl27dgUgPDycm2++mczMTEaMGOEcFpKYmHjOISIiIv7gxIkTvPjii0yZMoV77rkHsL/HFhQU8Nlnn/Hqq68yZMgQnnrqKecx/fr1Y+TIkSxfvpxZs2YB9t5WM2bMcH5h0b9/fz788EM+/vhjrrjiCjZu3MiePXt47rnnuOqqqwAYNGiQyyTnZ7r44otrbCccbDYbjz/+eJPEJ9JQ6ikl0szKysr44osvGDlypDMhBdCmTRuuvvpq5/xT27dvJzk52ZmQchgzZgy5ubl89913zm1JSUku+6SkpLBlyxZ+/etf8+KLL3LgwAFuvvlmxo0b13wVExHxcX369CE2NpYtW7YAsHXrVgYNGsSll17K9u3bAdi4cSPdunXjwgsv5IUXXmDkyJEcO3aMHTt28Prrr/PRRx8B1DqcOjMzE8MwGD58OBaLxfkzfPhwKisr+eyzz5z7nnfeeS4Jqbqc3k44Yh09erTLPqNGjSIgIMBlHsS4uDhnQgpwJtHKy8vr9XtFRPzN559/TnV1NampqS7b77vvPubMmUNubu5Z779du3YlOTn5rHlok5OTnf93JJPKysoA2LlzJ0FBQVx55ZXOfcLDw51fKjTE999/32TxiTSUklIizay4uBjDMIiPjz+rLD4+nuLiYsA+Rry2fcD+LcyZ2xxuu+02/vznP1NRUcGCBQsYOXIko0ePZuvWrU1ZFRERv2I2m7nyyiud76WZmZmkpKQwcOBAduzYAdiTUldffTUAmzZt4vrrr+eKK67gjjvu4M033yQ4OBjAZYjc6QoLCwF7guiSSy5x/ji+iT569Khz35raiNqcvm9RUREA7dq1c9knMDCQ2NhYZzsEEBYW5rKPyWQCOGvOEhERsXO8j9c0ebij7Fz3AQ6hoaEuj81ms7P9KCoqIiYmxjlywuHM93Z3NGV8Ig2l4XsizSwqKgqTyUReXt5ZZbm5ucTExAAQHR1d6z6Ac/hGTcxmM5MnT2by5Mnk5+fzySef8Oyzz/LHP/6RLVu2OG+KRETEPVdddRX33HMPX331FUePHmXgwIEcPXqUv/3tb+zcuZNvvvmG++67j0OHDpGWlsY111zDc8895+xttHLlyjrn22jTpg0AL730EhEREWeVd+rUqdF1iI6OBuztyXnnnefcXl1dzfHjx+tsX0REpG6O9/GCggIuvPBC5/bDhw+zb98+gFo/47vz/hsbG+tcuOL00ReOxFJDOO5DmiI+kYZSTymRZhYeHk6vXr1Yv369c7JcsPeg+vjjj+nfvz8Al112Gbt27SI7O9vl+Lfffpt27dpx/vnn1/o7fvnLX/LII48A0LZtW8aPH8/kyZMpLi6mpKSkGWolIuIfhg4dimEYLF26lG7dupGQkMAll1xCVFQUTzzxBFFRUfTv358vv/ySyspK7rjjDpfhb46ElOOb5DO/4b7ssssAOH78OL1793b+FBYWsnDhwkbdbDgMHDgQgHXr1rlsf/fdd7Farc52qD5OvxESERH7UO+goKCzVsx+6aWXWLhwIe3atTvr/Tc7O5vPP/+cfv361fv3DB48GIvFwr///W/ntqqqKjZv3lzncWe2O6e74IILmiw+kYZSTymRFvCnP/2JW2+9ldtuu42bb76Z6upq/vGPf1BVVcUf/vAHAG655RbefvttbrnlFv7whz8QGxvLm2++SWZmJvPnz6+zQbnssstYvnw58fHxJCcnc/ToUV588UUGDhxYY1diERGpnzZt2pCcnMyHH37ITTfdBNgTMwMGDOCjjz7ihhtuIDAwkEsuuYTAwEAef/xxfve731FVVcUbb7zBxx9/DOCcc8Pxjfo777zDpZdeSlJSEmPGjOGBBx7gp59+olevXnz//fc89dRTnHfeeXTr1q3RdUhMTOQXv/gFixcvpqKigpSUFL755hsWL15MSkqKWxPURkVFAfDxxx8THR3NRRdd1Oj4RERas7i4OH7zm9/w0ksvERwczKBBg/jiiy945ZVXuPvuu4mJiWH27NncddddjBs3juPHj7N48WKio6O55ZZb6v17Bg8ezNChQ5k7dy75+fl07tyZl19+mYKCAtq2bVvrcW3atOHrr79m+/bt9OnTx6XMbDZz9913N0l8Ig2lpJRICxg8eDAvvvgiixYt4u677yY4OJgBAwawYMECevToAdjHg7/66qs88cQTZGRkUF1dzUUXXcSSJUu45ppr6jz///3f/xEcHMyaNWt45plniIqKYvjw4fzpT39qieqJiPi0YcOGsWPHDlJSUpzbBg0axEcffeRcAen888/niSeeYPHixUyfPp3o6Gj69u3LP//5T6ZMmcLOnTvp2bMn1113HW+99Rb33XcfEydO5KGHHuLRRx/lueee47XXXuPIkSO0bduWkSNHMnPmzCbrmZSRkcH555/PmjVreOGFF2jfvj1TpkwhLS2tzi89ztSjRw9uuOEG57DEd955p0niExFpzWbNmkV8fDyvvvoqy5cv57zzzuP+++/n17/+NQARERE899xzpKWlERkZyRVXXMHdd9/t9nxQixcv5m9/+xuLFi2isrKSkSNHcuONN57VS+t0v/vd75g/fz633norL7744lnl48ePb7L4RBrCZGhmMhERERERERERaWGaU0pERERERERERFqcklIiIiIiIiIiItLilJQSEREREREREZEWp6SUiIiIiIiIiIi0OCWlRERERERERESkxSkpJSIiIiIiIiIiLU5JKRERERERERERaXFKSomIiIiIiIiISIsL9HQArVF+fjGGUf/9TSZo2zbK7eNaC9Wv9fP1Oqp+dR8nTUNtw9l8vY6qX+um+tV9nDQdtQ9NQ9elZrouNdN1qZ23tQ9KSjWAYdCgF3ZDj2stVL/Wz9frqPpJc1LbUDtfr6Pq17qpftLc1D40LV2Xmum61EzXpXbecm00fE9ERERERERERFqcklIiIiIiIiIiItLilJQSERERERGvV1BQQGpqKtu2bTur7NixYwwZMoQ33njDZfvatWtJTU2lb9++jB8/nl27djnLrFYrCxYsYMiQISQnJzN9+nSOHTvmLM/Pz2fGjBkMGDCAlJQUMjIysFgszVdBERE/pKSUiIiIiIh4tc8++4ybbrqJQ4cOnVVms9lIT0/n+PHjLtu3bdvGvHnzeOyxx9ixYwdjxoxh+vTplJeXA7B06VI2b97MmjVr2LRpE6GhocydO9d5/MyZMwkPD2fTpk2sXr2arVu3smLFimatp4iIv1FSSkREREREvNbatWtJT0/nrrvuqrH8mWeeoUOHDnTs2NFl+6pVqxg1ahT9+/cnKCiIqVOnEhsby/r1653lt99+Ox07diQyMpI5c+awceNGsrOzOXjwINu3b2fWrFmEhYXRpUsXZsyYwcqVK5u9viIi/kSr74mIiIiIiNcaOnQoo0ePJjAw8KzEVGZmJu+++y5r1qxh9OjRLmVZWVlMmDDBZVtiYiJ79+6luLiYI0eOkJSU5CyLj48nOjqaffv2ARATE0NCQoKzvHv37uTk5HDixAnatGlT7/hNpnrv6rK/u8f5Ol2Xmum61EzXpXYNvTbNdS2VlBIREREREa/Vrl27Grfn5+dz//33s2jRIiIiIs4qLy0tJSwszGVbaGgoZWVllJaWAhAeHn5WuaPszGMdj8vKytxKSrVtG1XvfZviOF+n61IzXZea6brUzluujZJSIiIiIiLSqhiGwT333MOUKVPo1atXjfuEhYVRUVHhsq2iooLY2Fhngskxv9Tp5RERERiGcVaZ43FNCbC65OcXYxj1399kst8sunucr9N1qZmuS810XWrX0GvjOK6pKSklIiIiIiKtyuHDh9m+fTu7d+/mmWeeAaCkpIS//OUvbNiwgeeee44ePXqwf/9+l+OysrK48soriY6OJiEhgaysLOcQvtzcXAoLC0lKSsJms1FYWEheXh7x8fEAHDhwgA4dOhAV5d5NmWHQoJvihh7n63RdaqbrUjNdl9p5y7XRROciIiIiItKqdOrUiS+++IKdO3c6fzp16sSDDz7Ic889B8DEiRNZt24dmZmZVFdXs2LFCvLz80lNTQVg/PjxLF26lOzsbEpKSpg/fz4DBw6ka9eudOvWjf79+zN//nxKSkrIzs5myZIlTJw40ZPVFhHxOeopJSIiIiIiPmfw4ME8+OCDPPTQQxw9epTExESWLVtGTEwMAGlpaVgsFiZPnkxpaSkpKSksXLjQefyiRYt4+OGHueaaazCbzYwbN44ZM2Z4pjIiIj5KSSkREREPOFZcyXv787nq/BhCgwI8HY6ISKvgWBmvJv/973/P2jZ27FjGjh1b4/5BQUGkp6eTnp5eY3l8fDyLFi1qWKAiIlIvSkqJiIh4wIvbsln1eQ73p/bgF306ejocERERaYUqKyvZuvVLCgtLXeYH6tOnLyEhIZ4LTKSelJRqIeMm3shPR/JqLY+PjeXl5StaLiAREfEo68lPjvmlVR6ORERERFqrPXs+58UNW4nteIFz29GD+5kGXHZZiucCE6knJaVayLG844yb9fday998/P9aMBoREfG08JND9sqqrR6ORERERFqzjhck0b7bxZ4OQ6RBtPqeiIiIB4QF2Zvg8iolpURERETEPykpJSIi4gHhweopJSIiIiL+TUkpERERDwg7OXyvXEkpEREREfFTSkqJiIh4gLOnlIbviYiIiIifUlJKRETEA8KdPaVsHo5ERERERMQzlJQSERHxgDD1lBIRERERP+fRpFRBQQGpqals27bNuW337t1MmjSJ5ORkhg8fzqpVq1yOWbt2LampqfTt25fx48eza9cuZ5nVamXBggUMGTKE5ORkpk+fzrFjx5zl+fn5zJgxgwEDBpCSkkJGRgYWi6X5KyoiInIGR0+psiq1QyIiIiLinzyWlPrss8+46aabOHTokHNbUVER06ZNY9y4cezYsYOMjAweffRR9uzZA8C2bduYN28ejz32GDt27GDMmDFMnz6d8vJyAJYuXcrmzZtZs2YNmzZtIjQ0lLlz5zrPP3PmTMLDw9m0aROrV69m69atrFixokXrLSIiAqcmOi/T8D0RERER8VMeSUqtXbuW9PR07rrrLpftH3zwATExMUyePJnAwEAGDx7M6NGjWblyJQCrVq1i1KhR9O/fn6CgIKZOnUpsbCzr1693lt9+++107NiRyMhI5syZw8aNG8nOzubgwYNs376dWbNmERYWRpcuXZgxY4bz3CIiIi0pIlir74mIiIiIf/NIUmro0KF8+OGHjBw50mX7/v37SUpKctmWmJjI3r17AcjKyqq1vLi4mCNHjriUx8fHEx0dzb59+9i/fz8xMTEkJCQ4y7t3705OTg4nTpxo6iqKiIjUyTGnVKXFhsVmeDgaEREREZGWF+iJX9quXbsat5eWlhIWFuayLTQ0lLKysnOWl5aWAhAeHn5WuaPszGMdj8vKymjTpk294zeZ6r2ry/4mExh13He4e15vcXr9fJGv1w98v46qX93HiWc45pQCqKi2EhnikSZZRERERMRjvOoTcFhYGMXFxS7bKioqiIiIcJZXVFScVR4bG+tMMDnmlzrzeMMwzipzPHacv77ato1ya3+HsLCQWsuCAgOIj2/Yeb1FQ69La+Hr9QPfr6PqJ94kKMBEoNmExWZQVqWklIiIiIj4H6/6BJyUlMTmzZtdtmVlZdGjRw8AevTowf79+88qv/LKK4mOjiYhIcFliF9ubi6FhYUkJSVhs9koLCwkLy+P+Ph4AA4cOECHDh2IinLvRi4/v7jOHk9ncvRGKC+vrPW4aouVvLzimgu9nMlkvxl297q0Fr5eP/D9Oqp+dR8nnmEymQgPDuBEhYUyzSslIiIiIn7IY6vv1SQ1NZW8vDxWrFhBdXU1mZmZrFu3jgkTJgAwceJE1q1bR2ZmJtXV1axYsYL8/HxSU1MBGD9+PEuXLiU7O5uSkhLmz5/PwIED6dq1K926daN///7Mnz+fkpISsrOzWbJkCRMnTnQ7TsNw/8dxXFOf11t+Wnv8/l4/f6ij6lf7ceI5ESd7R2mycxERERHxR17VUyo2Npbly5eTkZHBokWLiIuLY+7cuQwaNAiAwYMH8+CDD/LQQw9x9OhREhMTWbZsGTExMQCkpaVhsViYPHkypaWlpKSksHDhQuf5Fy1axMMPP8w111yD2Wxm3LhxzJgxwwM1FRERgfCTk52XVSkpJSIiIiL+x+NJqX379rk87t27N6+99lqt+48dO5axY8fWWBYUFER6ejrp6ek1lsfHx7No0aKGBysiItKE1FNKRERERPyZVw3fExER8SfqKSUiIiIi/kxJKREREQ+JCFZPKRERERHxX0pKiYiIeEj4yeF7ZdU2D0ciIiIiItLylJQSERHxkMgQx/A9i4cjERERERFpeUpKiYiIeEj4yeF7ZVXqKSUiIiIi/kdJKREREQ+JODnRueaUEhERERF/pKSUiIiIh5yaU0pJKRERERHxP0pKiYiIeIizp1SVklIiIiIi4n+UlBIREfEQ55xS6iklIiIiIn5ISSkREREPiQhRTykRERER8V9KSomIiHiIekqJiIiIiD9TUkpERMRDwrX6noiIiIj4MSWlREREPCQwwN4MW6yGhyMREREREWl5SkqJiIh4SKDZBIDVUFJKRERERPyPklIiIiIeEqSeUiIiIiLix5SUEhER8ZDAAHtPKYtNSSkRERER8T9KSomIiHhIkPlkTymbzcORiIh4v4KCAlJTU9m2bZtz24YNGxg7diz9+vVj+PDhLF68GNtp76lr164lNTWVvn37Mn78eHbt2uUss1qtLFiwgCFDhpCcnMz06dM5duyYszw/P58ZM2YwYMAAUlJSyMjIwGKxtExlRUT8hJJSIiIiHqKeUiIi9fPZZ59x0003cejQIee2L7/8knvuuYeZM2eyc+dOli1bxhtvvMGKFSsA2LZtG/PmzeOxxx5jx44djBkzhunTp1NeXg7A0qVL2bx5M2vWrGHTpk2EhoYyd+5c5/lnzpxJeHg4mzZtYvXq1WzdutV5bhERaRpKSomIiHiIMymlOaVERGq1du1a0tPTueuuu1y2//TTT/zyl7/k6quvxmw20717d1JTU9mxYwcAq1atYtSoUfTv35+goCCmTp1KbGws69evd5bffvvtdOzYkcjISObMmcPGjRvJzs7m4MGDbN++nVmzZhEWFkaXLl2YMWMGK1eubPH6i4j4skBPByAiIuKvHMP3DMBqMwg4uRqfiIicMnToUEaPHk1gYKBLYmrEiBGMGDHC+biiooKPP/6Y0aNHA5CVlcWECRNczpWYmMjevXspLi7myJEjJCUlOcvi4+OJjo5m3759AMTExJCQkOAs7969Ozk5OZw4cYI2bdrUO36Tm2/tjv3dPc7X6brU7PTrcvpiviaTf18rvV5q19Br01zXUkkpERERD3H0lAL7ED4lpUREztauXbtz7lNSUsL//d//ERoaytSpUwEoLS0lLCzMZb/Q0FDKysooLS0FIDw8/KxyR9mZxzoel5WVuZWUats2qt77NsVxvk7XxVV0dASQT1hYiHNbaGgQMTERxMfrWun1UjtvuTZKSomIiHhIUMCpUfQWm40QjaoXEXHbd999x5133knbtm15+eWXiYyMBOxJpIqKCpd9KyoqiI2NdSaYHPNLnV4eERGBYRhnlTkeR0REuBVffn6xSw+WczGZ7DeL7h7n63RdalZUZE+ilpdXOq9LRUU1hYWl5OUVezAyz9LrpXYNvTaO45qaklIiIiIeEnhazyjNKyUi4r5PPvmEu+++mxtvvJE//elPBAaeur3p0aMH+/fvd9k/KyuLK6+8kujoaBISEsjKynIO4cvNzaWwsJCkpCRsNhuFhYXk5eURHx8PwIEDB+jQoQNRUe7dlBkGDbopbuhxvk7XxZXjWpx5TXSd7HQdauct10ZfyYqIiFexWq1MmTKF++67z7lt9+7dTJo0ieTkZIYPH86qVatcjmmtS36fPlxPK/CJiLjn888/Jy0tjdmzZ3Pvvfe6JKQAJk6cyLp168jMzKS6upoVK1aQn59PamoqAOPHj2fp0qVkZ2dTUlLC/PnzGThwIF27dqVbt27079+f+fPnU1JSQnZ2NkuWLGHixImeqKqIiM9SUkpERLzK4sWL2blzp/NxUVER06ZNY9y4cezYsYOMjAweffRR9uzZA7TuJb9NJpMzMaWklIiIe5599lksFgsZGRkkJyc7f2677TYABg8ezIMPPshDDz3EwIEDeffdd1m2bBkxMTEApKWlMWzYMCZPnsywYcOorKxk4cKFzvMvWrQIi8XCNddcw4033sgVV1zBjBkzPFBTERHfpeF7IiLiNbZu3coHH3zAdddd59z2wQcfEBMTw+TJkwH7Tcbo0aNZuXIlffr0cVnyG2Dq1Km8/vrrrF+/ngkTJrBq1SrS09Pp2LEjAHPmzGHo0KFkZ2djs9nYvn07GzdudFny+/HHH3fe1DS3QLMJq83AYrO1yO8TEWnNHCvjgT0pdS5jx45l7NixNZYFBQWRnp5Oenp6jeXx8fEsWrSoYYGKiEi9KCklIiJeIT8/nzlz5rBkyRKXnkr79+93WbIb7Et6r169Gmj9S34Hmk1UAlab4XPLFvv6csyqX+um+tV9nIiISEtQUkpERDzOZrMxa9YsbrnlFi666CKXsrqW9D5XeWtY8js40ExplZWo6HCfXbrZW5Ycbi6qX+um+omIiHiOklIiIuJxzz33HMHBwUyZMuWssrCwMIqLXZc0dizZ7ShvzUt+m092S8jLLyEuwK1f6fV8fTlm1a91U/3qPk5ERKQlKCklIiIe99Zbb3Hs2DEGDBgA4Ewy/fvf/+aee+5h8+bNLvtnZWXRo0cPoPUv+R14cqLzaqvhkzfG4D1LDjcX1a91U/1EREQ8R6vviYiIx73//vv873//Y+fOnezcuZMbbriBG264gZ07d5KamkpeXh4rVqygurqazMxM1q1b55xHqrUv+R2o1fdERERExE+pp5SIiHi12NhYli9fTkZGBosWLSIuLo65c+cyaNAgwHXJ76NHj5KYmHjWkt8Wi4XJkydTWlpKSkrKWUt+P/zww1xzzTWYzWbGjRvXokt+BwY4klJafU9ERERE/IuSUiIi4nUee+wxl8e9e/fmtddeq3X/1rzkt7OnlFU9pURERETEv2j4noiIiAcFmu1NsYbviYiIiIi/UVJKRETEgzSnlIiIiIj4KyWlREREPOjUnFJKSomIiIiIf1FSSkRExINOzSmlic5FRERExL8oKSUiIuJBGr4nIiIiIv5KSSkREREP0kTnIiIiIuKvlJQSERHxoAD1lBIRERERP6WklIiIiAedmlNKSSkRERER8S9KSomIiHjQqdX3NNG5iIiIiPgXJaVEREQ8yNFTyqrheyIiIiLiZ5SUEhER8SCtviciIiIi/kpJKREREQ8KDNDqeyIiIiLin5SUEhER8SD1lBIRERERf6WklIiIiAdp9T0RERER8VdKSomIiHjQqZ5SWn1PRERERPyLklIiIiIeFBig1fdERERExD8pKSUiIuJBgWZNdC4iIiIi/skrk1JfffUVkydPZsCAAQwdOpRHHnmEqqoqAHbv3s2kSZNITk5m+PDhrFq1yuXYtWvXkpqaSt++fRk/fjy7du1yllmtVhYsWMCQIUNITk5m+vTpHDt2rEXrJiIicjrNKSUiIiIi/srrklI2m4077riDESNGsH37dlavXs2nn37KsmXLKCoqYtq0aYwbN44dO3aQkZHBo48+yp49ewDYtm0b8+bN47HHHmPHjh2MGTOG6dOnU15eDsDSpUvZvHkza9asYdOmTYSGhjJ37lxPVldERPyc5pQSEREREX8V6OkAzlRUVERubi42mw3DsH9rbDabCQsL44MPPiAmJobJkycDMHjwYEaPHs3KlSvp06cPq1atYtSoUfTv3x+AqVOn8vrrr7N+/XomTJjAqlWrSE9Pp2PHjgDMmTOHoUOHkp2dTZcuXTxTYWkVxk28kZ+O5NVaHh8by8vLV7RcQCLiMxxzSmn4noiIiIj4G69LSsXGxjJ16lQWLFjAX//6V6xWK9dccw1Tp07lscceIykpyWX/xMREVq9eDUBWVhYTJkw4q3zv3r0UFxdz5MgRl+Pj4+OJjo5m3759biWlTCb36uTY32QCo457DnfP6y1Or58vMpngWN5xfnHP32t9/t58/P9adf394Tk8/V9f09D6+er1aG00p5SIiIiI+CuvS0rZbDZCQ0N54IEHmDhxIgcPHuQPf/gDixYtorS0lLCwMJf9Q0NDKSsrA6izvLS0FIDw8PCzyh1l9dW2bZS71QIgLCyk1rKgwADi4xt2Xm/R0OvSWvj68we+/xyqfuKNAk4OpNecUiIiIiLib7wuKfXhhx+yYcMG3n//fQB69OhBWloaGRkZjB49muLiYpf9KyoqiIiIACAsLIyKioqzymNjY53JKsf8UjUdX1/5+cV19ng6k6M3Qnl5Za3HVVus5OUV11zo5Uwm+82wu9eltfD15w/84zlU/Wo/TjxLPaVERERExF95XVLq8OHDzpX2HAIDAwkKCiIpKYnNmze7lGVlZdGjRw/AnsDav3//WeVXXnkl0dHRJCQkkJWV5RzCl5ubS2Fh4VlDAs/FMOoehlfXcY0p93YNvS6tha8/f+Afz6HqJ95GE52LiIiIiL/yutX3hg4dSm5uLs8++yxWq5Xs7GyWLl3K6NGjSU1NJS8vjxUrVlBdXU1mZibr1q1zziM1ceJE1q1bR2ZmJtXV1axYsYL8/HxSU1MBGD9+PEuXLiU7O5uSkhLmz5/PwIED6dq1qyerLCIifswx0blVPaVERERExM94XU+pxMREnnvuORYuXMjzzz9PVFQUY8aMIS0tjeDgYJYvX05GRgaLFi0iLi6OuXPnMmjQIMC+Gt+DDz7IQw89xNGjR0lMTGTZsmXExMQAkJaWhsViYfLkyZSWlpKSksLChQs9V1kREfF7p3pKKSklIiIiIv7F65JSAEOGDGHIkCE1lvXu3ZvXXnut1mPHjh3L2LFjaywLCgoiPT2d9PT0JolTRESksTSnlIiIiIj4K68bviciIuJPnD2ltPqeiIiIiPgZJaVEREQ8yDGnlHpKiYjUraCggNTUVLZt2+bctnv3biZNmkRycjLDhw9n1apVLsesXbuW1NRU+vbty/jx49m1a5ezzGq1smDBAoYMGUJycjLTp0/n2LFjzvL8/HxmzJjBgAEDSElJISMjA4vF0vwVFRHxI0pKiYiIeJBW3xMRObfPPvuMm266iUOHDjm3FRUVMW3aNMaNG8eOHTvIyMjg0UcfZc+ePQBs27aNefPm8dhjj7Fjxw7GjBnD9OnTKS8vB2Dp0qVs3ryZNWvWsGnTJkJDQ5k7d67z/DNnziQ8PJxNmzaxevVqtm7dyooVK1q03iIivk5JKREREQ/SROciInVbu3Yt6enp3HXXXS7bP/jgA2JiYpg8eTKBgYEMHjyY0aNHs3LlSgBWrVrFqFGj6N+/P0FBQUydOpXY2FjWr1/vLL/99tvp2LEjkZGRzJkzh40bN5Kdnc3BgwfZvn07s2bNIiwsjC5dujBjxgznuUVEpGl45UTnIiIi/sI50bnmlBIRqdHQoUMZPXo0gYGBLomp/fv3k5SU5LJvYmIiq1evBiArK4sJEyacVb53716Ki4s5cuSIy/Hx8fFER0ezb98+AGJiYkhISHCWd+/enZycHE6cOEGbNm3qHb/JVP+6nr6/u8f5Ol2Xmp1+XQzDdbs/Xyu9XmrX0GvTXNdSSSkREREP0pxSIiJ1a9euXY3bS0tLCQsLc9kWGhpKWVnZOctLS0sBCA8PP6vcUXbmsY7HZWVlbiWl2raNqve+TXGcr9N1cRUdHQHkExYW4twWGhpETEwE8fG6Vnq91M5bro2SUiIiIh6k4XsiIg0TFhZGcXGxy7aKigoiIiKc5RUVFWeVx8bGOhNMjvmlzjzeMIyzyhyPHeevr/z8YpceLOdiMtlvFt09ztfputSsqMieRC0vr3Rel4qKagoLS8nLK67jSN+m10vtGnptHMc1NSWlREREPEgTnYuINExSUhKbN2922ZaVlUWPHj0A6NGjB/v37z+r/MorryQ6OpqEhASysrKcQ/hyc3MpLCwkKSkJm81GYWEheXl5xMfHA3DgwAE6dOhAVJR7N2WGQYNuiht6nK/TdXHluBZnXhNdJztdh9p5y7XRROciIiIe5ExKaU4pERG3pKamkpeXx4oVK6iuriYzM5N169Y555GaOHEi69atIzMzk+rqalasWEF+fj6pqakAjB8/nqVLl5KdnU1JSQnz589n4MCBdO3alW7dutG/f3/mz59PSUkJ2dnZLFmyhIkTJ3qyyiIiPkc9pURERDwoQMP3REQaJDY2luXLl5ORkcGiRYuIi4tj7ty5DBo0CIDBgwfz4IMP8tBDD3H06FESExNZtmwZMTExAKSlpWGxWJg8eTKlpaWkpKSwcOFC5/kXLVrEww8/zDXXXIPZbGbcuHHMmDHDAzUVEfFdSkqJiIh4UGDAydX3bAaGYWDSMjEiIrVyrIzn0Lt3b1577bVa9x87dixjx46tsSwoKIj09HTS09NrLI+Pj2fRokUND1ZERM5Jw/dEREQ8yDF8D0Aj+ERERETEnygpJSIi4kEuSSkN4RMRERERP6KklIiIiAednpTSCnwiIiIi4k+UlBIREfEgx5xSoBX4RERERMS/KCklIiLiQQGnzWuuFfhERERExJ8oKSUiIuJBJpPJOYRPSSkRERER8SdKSomIiHjYqaSU5pQSEREREf+hpJSIiIiHBZ4cw6c5pURERETEnygpJSIi4mGBZntzrOF7IiIiIuJPlJQSERHxMM0pJSIiIiL+SEkpERERD1NSSkRERET8kZJSIiIiHnZqTilNdC4iIiIi/kNJKREREQ9TTykRERER8UdKSomIiHhYgJJSIiIiIuKHlJQSERHxMMfqe1YlpURERETEjygpJSIi4mGO4XtKSomIiIiIP1FSSkRExMM0fE9ERERE/JGSUiIiIh6mnlIiIiIi4o+UlBIREa+wdetWJk2aRL9+/bj88suZN28eFRUVAOzevZtJkyaRnJzM8OHDWbVqlcuxa9euJTU1lb59+zJ+/Hh27drlLLNarSxYsIAhQ4aQnJzM9OnTOXbsmLM8Pz+fGTNmMGDAAFJSUsjIyMBisbRMpU9STykRERER8UdKSomIiMcVFBRwxx138Ktf/YqdO3eydu1atm/fzj/+8Q+KioqYNm0a48aNY8eOHWRkZPDoo4+yZ88eALZt28a8efN47LHH2LFjB2PGjGH69OmUl5cDsHTpUjZv3syaNWvYtGkToaGhzJ071/m7Z86cSXh4OJs2bWL16tVs3bqVFStWtGj9A9RTSkRERET8kJJSIiLicXFxcWzZsoXx48djMpkoLCyksrKSuLg4PvjgA2JiYpg8eTKBgYEMHjyY0aNHs3LlSgBWrVrFqFGj6N+/P0FBQUydOpXY2FjWr1/vLL/99tvp2LEjkZGRzJkzh40bN5Kdnc3BgwfZvn07s2bNIiwsjC5dujBjxgznuVuKhu+JiIiIiD8K9HQAIiIiAJGRkQAMGzaMo0ePMmDAAMaPH8/ChQtJSkpy2TcxMZHVq1cDkJWVxYQJE84q37t3L8XFxRw5csTl+Pj4eKKjo9m3bx8AMTExJCQkOMu7d+9OTk4OJ06coE2bNvWO32Ryr76O/U2mU0kpi2Fz+zze7PQ6+iLVr3VT/eo+TkREpCUoKSUiIl7lgw8+oKioiPT0dO68804SEhIICwtz2Sc0NJSysjIASktLay0vLS0FIDw8/KxyR9mZxzoel5WVuZWUats2qt77nnlcRFiw/XeHhxAf37DzeLOGXpvWQvVr3VQ/ERERz1FSSkREvEpoaCihoaHMmjWLSZMmMWXKFIqLi132qaioICIiArAnkRwTop9eHhsb60wwOeaXOvN4wzDOKnM8dpy/vvLzizHcGH1nMtlvFvPzi7FU2ydWLzxRTl5e8TmObD1Or6M716a1UP1aN9Wv7uNERERaguaUEhERj/vf//7Hz3/+c6qqqpzbqqqqCAoKIjExkf3797vsn5WVRY8ePQDo0aNHreXR0dEkJCSQlZXlLMvNzaWwsJCkpCR69OhBYWEheXl5zvIDBw7QoUMHoqLcuykzDPd/HMc5V9+zGg06jzf/NPTatJYf1a91/6h+tR8nIiLSEpSUEhERj+vZsycVFRU88cQTVFVV8dNPP7FgwQImTpzIiBEjyMvLY8WKFVRXV5OZmcm6deuc80hNnDiRdevWkZmZSXV1NStWrCA/P5/U1FQAxo8fz9KlS8nOzqakpIT58+czcOBAunbtSrdu3ejfvz/z58+npKSE7OxslixZwsSJE1u0/proXERERET8kYbviYiIx0VERPD8888zf/58Lr/8cqKiohg9ejRpaWkEBwezfPlyMjIyWLRoEXFxccydO5dBgwYBMHjwYB588EEeeughjh49SmJiIsuWLSMmJgaAtLQ0LBYLkydPprS0lJSUFBYuXOj83YsWLeLhhx/mmmuuwWw2M27cOGbMmNGi9Xf2lFJSSkRERET8iNtJqW3btpGSktIcsYiISCvVFG1DYmIiy5cvr7Gsd+/evPbaa7UeO3bsWMaOHVtjWVBQEOnp6aSnp9dYHh8fz6JFi9wPuAkFmu0dl9VTSkR8je4dRESkLm4P37vzzju59tpreeaZZ8jJyWmOmEREpJVR29A46iklIr5K7YOIiNTF7aTUp59+yqxZs/jyyy8ZMWIEv/vd73jnnXdcJqcVERH/orahcQJMmlNKRHyT2gcREamL20mpoKAgRowYwdKlS/nkk0+49tprWb58OUOHDuUvf/kLe/fubY44RUTEi6ltaJzAgJNJKS17JSI+Ru2DiIjUpcGr7+Xn57Nu3TrefPNNsrKySElJISQkhKlTp/Lss882ZYwiItJKqG1oGOfwPauSUiLim9Q+iIhITdye6Pzdd9/lrbfeYsuWLVx44YWMHz+eZ599lri4OACGDRtGWloav//975s8WBER8U5qGxon0KyeUiLim9Q+iIhIXdxOSv3lL39h1KhRvPbaa/Tq1eus8gsuuICpU6c2RWwiItJKqG1onED1lBIRH6X2QURE6uJ2UurTTz8lOzubhIQEAD7//HOioqLo3r07AB06dODOO+9s2ihFRMSrqW1oHMdE5xb1lBIRH6P2QURE6uL2nFL/+c9/GDduHD/88AMAu3btYtKkSXzyySdNHZuIiLQSahsaxznRuVbfExEf09ztw1dffcXkyZMZMGAAQ4cO5ZFHHnGu7Ld7924mTZpEcnIyw4cPZ9WqVS7Hrl27ltTUVPr27cv48ePZtWuXs8xqtbJgwQKGDBlCcnIy06dP59ixY00Ss4iInOJ2Umrx4sUsWbLE2f32lltu4e9//ztPPPFEkwcnIiKtg9qGxnH2lNLwPRHxMc3ZPthsNu644w5GjBjB9u3bWb16NZ9++inLli2jqKiIadOmMW7cOHbs2EFGRgaPPvooe/bsAWDbtm3MmzePxx57jB07djBmzBimT59OeXk5AEuXLmXz5s2sWbOGTZs2ERoayty5cxsds4iIuHI7KXX48GGuuOIKl21Dhw4lJyenyYISEZHWRW1D4zh7Smn4noj4mOZsH4qKisjNzcVms2GcfP80m82EhYXxwQcfEBMTw+TJkwkMDGTw4MGMHj2alStXArBq1SpGjRpF//79CQoKYurUqcTGxrJ+/Xpn+e23307Hjh2JjIxkzpw5bNy4kezs7EbHLSIip7idlOrcuTObNm1y2bZ161Y6derUZEEVFhZyzz33kJKSwmWXXcaMGTOc3WXVDVdExPu0RNvgy071lLJ5OBIRkabVnO1DbGwsU6dOZcGCBfTu3Zthw4bRrVs3pk6dyv79+0lKSnLZPzExkb179wKQlZVVa3lxcTFHjhxxKY+Pjyc6Opp9+/a5HafJ5P5PQ4/z9R9dl7qvS2Nfd772o+vQ9NemObg90fm0adNIS0vjuuuuo3PnzuTk5PDhhx+yYMGCJgvqj3/8I9HR0Xz44YeYzWZmz57NAw88wF//+lemTZvGnXfeyU033cSOHTtIS0ujZ8+e9OnTx9kNd9myZfTp04eVK1cyffp0PvroI8LCwly64UZFRfHAAw8wd+5c/vGPfzRZ7CIi/qgl2gZfFnBy9T31lBIRX9Oc7YPNZiM0NJQHHniAiRMncvDgQf7whz+waNEiSktLCQsLc9k/NDSUsrIygDrLS0tLAQgPDz+r3FHmjrZto9w+pjHH+TpdF1fR0RFAPmFhIc5toaFBxMREEB+va6XXS+285dq4nZQaPXo07du358033+Srr76iY8eOLF++nH79+jVJQF9++SW7d+9my5YtREZGAjBv3jxyc3NduuECLt1w+/Tp49INF2Dq1Km8/vrrrF+/ngkTJrBq1SrS09Pp2LEjAHPmzGHo0KFkZ2fTpUuXJolfRMQfNXfb4Os00bmI+KrmbB8+/PBDNmzYwPvvvw9Ajx49SEtLIyMjg9GjR1NcXOyyf0VFBREREQCEhYVRUVFxVnlsbKwzWeWYX6qm492Rn1+MO985mEz2m0V3j/N1ui41KyqyJ0rLyyud16WioprCwlLy8orrONK36fVSu4ZeG8dxTc3tpBRASkoKKSkpTR0LAHv27CExMZF//etfvPrqq5SXl3PFFVdw77331toNd/Xq1YC9G+6ECRPOKq9PN1x3klLudls7vXtcXU96c3WHa26n188X+frzB/71HPqihtavqa9Hc7YNvs45fE9JKRHxQc3VPhw+fNi50p5DYGAgQUFBJCUlsXnzZpeyrKwsevToAdgTWPv37z+r/MorryQ6OpqEhASXIX65ubkUFhaedS9SH4ZR92fIpj7O1+m6uHJcizOvia6Tna5D7bzl2ridlDp69ChLly7lhx9+wGZznfvi5ZdfbnRARUVF7Nu3j169erF27VoqKiq45557uPfee4mPj/eKbrgNzQ6e3qXyTEGBAa2+e6W3dP9rLr7+/IHvP4eqX/Np7rbB1wUG2Kd4VE8pEfE1zdk+DB06lCeeeIJnn32W22+/nZycHJYuXcro0aNJTU3l8ccfZ8WKFUyePJnPPvuMdevWsWTJEgAmTpxIWloa119/Pf3792flypXk5+eTmpoKwPjx41m6dCm9e/cmNjaW+fPnM3DgQLp27dqomEVExJXbSanZs2eTl5fH1VdfTVBQUJMHFBwcDNiH1oWEhBAZGcnMmTO58cYbGT9+fI3dbFu6G25DurnZf3dlrcdVW6yttnulr3eN9PXnD/zjOVT9aj+uKTR32+Dr1FNKRHxVc7YPiYmJPPfccyxcuJDnn3+eqKgoxowZQ1paGsHBwSxfvpyMjAwWLVpEXFwcc+fOZdCgQYB9GpAHH3yQhx56iKNHj5KYmMiyZcuIiYkBIC0tDYvFwuTJkyktLSUlJYWFCxc2afwiItKApNQXX3zBhg0biIuLa454SExMxGazUV1dTUiIvWeK41uVn/3sZ/y///f/XPb3RDfcxnTBbUy5t/OW7n/NxdefP/CP51D1ax7N3Tb4ukCz5pQSEd/U3O3DkCFDGDJkSI1lvXv35rXXXqv12LFjxzJ27Ngay4KCgkhPTyc9Pb1J4hQRkZqZ3T0gKirK2ZupOQwZMoQuXbpw//33U1paSkFBAU899RTXXnstN9xwA3l5eaxYsYLq6moyMzNZt26dcx6piRMnsm7dOjIzM6murmbFihU1dsPNzs6mpKRE3XBFRJpIc7cNvs6RlFJPKRHxNWofRESkLm73lJoxYwazZ8/m9ttvJz4+3qWsU6dOjQ4oKCiIf/7znzz22GOMGDGCyspKhg8fzpw5c2jTpo264YqIeKHmbht8XYCSUiLio9Q+iIhIXdxOSs2dOxewL8EKYDKZMAwDk8nEN9980yRBJSQk8NRTT9VYpm64IiLepyXaBl+m4Xsi4qvUPoiISF3cTkr95z//aY44RESkFVPb0DjqKSUivkrtg4iI1MXtOaU6d+5M586dKSoq4quvvqJdu3aEhobSuXPn5ohPRERaAbUNjROgnlIi4qPUPoiISF3cTkrl5+fzy1/+khtvvJF7772X7Oxsrr32Wnbt2tUc8YmISCugtqFxNHxPRHyV2gcREamL20mp+fPnk5SUxI4dOwgMDKR79+5MmzaNv/71r80Rn4iItAJqGxpHw/dExFepfRARkbq4nZTKzMxk9uzZhIWFYTLZP0TfdtttZGVlNXlwIiLSOqhtaBz1lBIRX6X2QURE6uJ2UiooKIiKigoADMP+4bm0tJSIiIimjUxERFoNtQ2Nc6qnlM3DkYiINC21DyIiUhe3k1LDhw9n1qxZ/PDDD5hMJvLz8/nLX/7CsGHDmiM+ERFpBdQ2NI56SomIr1L7ICIidXE7KfWnP/2J8PBwfv7zn3PixAmGDh1KeXk56enpzRGfiIi0AmobGkdJKRHxVWofRESkLoHuHhAREcGiRYsoKCjgxx9/pEOHDrRv3745YhMRkVZCbUPjOIbvWQ378BbHvCsiIq2d2gcREamL20mpHTt2uDw+ePAgBw8eBOCyyy5rmqhERKRVUdvQOIHmUx2XrTaDwAAlpUTEN6h9EBGRuridlJoyZcpZ28xmMx07duQ///lPkwQlIiKti9qGxnH0lAKw2AwCAzwYjIhIE1L7ICIidXE7KbV3716XxwUFBTzzzDN07ty5yYISEZHWRW1D45yZlBIR8RVqH0REpC5uT3R+pri4OGbNmsVLL73UFPGIiIgPUNvgnsDTklKa7FxEfJnaBxEROV2jk1IARUVFVFZWNsWpRETER6htqL/TclLqKSUiPk/tg4iIOLg9fG/27Nkuj6urq/nss88YMmRIkwUlIiKti9qGxjGZTASaTVhshnpKiYhPUfsgIiJ1cTspdaaQkBCmTJnCTTfd1BTxiIiID1Db4L6Ak0kp9ZQSEV+m9kFERE7ndlLq0UcfbY44RESkFVPb0HiBZhOVaE4pEfEtah9ERKQubielFi9eXK/9/vCHP7gdjIiItE5qGxrPMdm5klIi4kvUPoiISF3cTkrt37+fDz74gIsuuogLLriAI0eO8L///Y+LL76YiIgIwD43hoiI+A+1DY0XcDIppeF7IuJL1D6IiEhd3E5Kmc1mZs+ezW9+8xvntrfeeouPPvqIhQsXNmVsIiLSSqhtaDz1lBIRX6T2QURE6mJ294BPPvmEyZMnu2y74YYb2Lp1a5MFJSIirYvahsY71VPK5uFIRESajtoHERGpi9tJqbi4OHbs2OGybdOmTXTo0KHJghIRkdZFbUPjafieiPgitQ8iIlIXt4fv3XHHHUybNo0RI0bQqVMnsrOz+eijj3j66aebIz4REWkF1DY0nnP4nqGklIj4DrUPIiJSF7eTUpMmTaJz5868/fbbfP3113Tp0oXXXnuNnj17Nkd8IiLSCqhtaDxnTymrklIi4jvUPoiISF3cTkoBDBkyhCFDhlBQUEBcXFxTxyQiIq2Q2obGCTTbR9Srp5SI+Bq1DyIiUhu355Sqrq7mqaeeon///gwfPpzs7GwmTJjAsWPHmiM+ERFpBdQ2NJ56SomIL1L7ICIidXE7KbV48WIyMzP5+9//TlBQEG3btqVDhw5kZGQ0R3wiItIKqG1ovADTyTmlNNG5iPgQtQ8iIlIXt4fvrVu3jldffZWEhARMJhPh4eE8+uijpKamNkd8IiLSCqhtaLzAAE10LiK+R+2DiIjUxe2eUmVlZc6x4MbJD86hoaGYzW6fSkREfITahsbT8D0R8UVqH0REpC5utwZ9+/Zl8eLFAJhODjX45z//Se/evZs2MhERaTXUNjReoFk9pUTE96h9EBGRurg9fO/+++9n6tSprF27ltLSUkaOHElpaSkvvvhic8QnIiKtgNqGxgtUTykR8UFqH0REpC5uJ6Xi4+N59913+fjjj/npp5/o0KEDV111FZGRkc0Rn4iItAJqGxrPMdG5RT2lRMSHqH0QEZG6uJ2UuuGGG3j77be5/vrrmyMeERFphdQ2NMyhQ4fYv/8gACXFZfZt2dns4ahb54mLa8t553Vp8vhERBqruduHwsJC5s+fzyeffILNZuOyyy7joYceon379uzevZtHHnmErKwsYmNjmT59OpMmTXIeu3btWpYsWUJubi4XXnghDzzwAMnJyQBYrVb+9re/8dZbb1FeXs6gQYP4y1/+Qvv27ZulHiIi/srtpBRAeXm5vt0QEREXahvc8+OP2Qy5/DLKy+zJqPjRs4i4eBhPL17E/M/edutcoWHhbNm8Q4kpEfFKzdk+/PGPfyQ6OpoPP/wQs9nM7NmzeeCBB/jrX//KtGnTuPPOO7npppvYsWMHaWlp9OzZkz59+rBt2zbmzZvHsmXL6NOnDytXrmT69Ol89NFHhIWFsXTpUjZv3syaNWuIiorigQceYO7cufzjH/9olnqIiPgrt5NSKSkpTJo0iSuvvPKsbwr+8Ic/NFlgIiLSeqhtcF9+fj7lZWVMvOtR2p93AZ+fCOGnCrh83G+58Ne/rvd5jv34Paufmk1BQb6SUiLidZqzffjyyy/ZvXs3W7ZscSa95s2bR25uLh988AExMTFMnjwZgMGDBzN69GhWrlxJnz59WLVqFaNGjaJ///4ATJ06lddff53169czYcIEVq1aRXp6Oh07dgRgzpw5DB06lOzsbLp00XutiEhTcTsp9eOPP9KlSxe+//57vv/+e+d2x2oaIiLif9Q2NFz78y6gU/eL2Z+VDxWlRMa1p1PnaE+HJSLSJJqzfdizZw+JiYn861//4tVXX6W8vJwrrriCe++9l/3795OUlOSyf2JiIqtXrwYgKyuLCRMmnFW+d+9eiouLOXLkiMvx8fHxREdHs2/fPiWlRESaUL2TUrfeeisvvPAC//znPwGoqKggNDS02QITERHv15Rtw969e1mwYAFfffUVQUFBXH755dx3333ExcU167wg+fn5PPDAA2zfvp2AgADGjBnDvffeS2Bgg0a4N9jJxfewaZ5zEfEBLXHvUFRUxL59++jVqxdr166loqKCe+65h3vvvZf4+HjCwsJc9g8NDaXs5JDp0tLSWstLS0sBCA8PP6vcUeYOd/Nvjv31vY4rXZeanX5dTl8rxWTy72ul10vtGnptmuta1vsT965du1weX3nllWzfvr3JAxIRkdajqdqGiooKbrvtNm688Uaee+45SktLuffee7n//vtZsGBBs84LMnPmTBISEti0aRN5eXlMnz6dFStWcNtttzXJNaov88mW3tDqeyLiA1ri3iE4OBiwD60LCQkhMjKSmTNncuONNzJ+/HgqKipc9q+oqCAiIgKAsLCwGstjY2Odyary8vJaj3dH27ZRbh/TmON8na6Lq+joCCCfsLAQ57bQ0CBiYiKIj9e10uuldt5ybRr8NbA+NIuIyJka2jbk5ORw0UUXkZaWRkBAAMHBwdx0003cc889zToviM1mY/v27WzcuJGwsDC6dOnCjBkzePzxx1s8KWVSTykR8WHNce+QmJiIzWajurqakBD7DbnNZgPgZz/7Gf/v//0/l/2zsrLo0aMHAD169GD//v1nlV955ZVER0eTkJBAVlaWcwhfbm4uhYWFZw0JrI/8/GLcqb7JZL9ZdPc4X6frUrOiInvvvfLySud1qaioprCwlLy8Yg9G5ll6vdSuodfGcVxTMzf0QM0TIiIiZ2po23DhhRfy/PPPExAQ4Ny2YcMGLrnkklrnBdm7dy+Ay03DmeXnmhdk//79xMTEkJCQ4Czv3r07OTk5nDhxwq06OLrJu/NjP9D+j6OnlK0Rn5waEkNz/3hrXKqf6qf61X1cU2uOe4chQ4bQpUsX7r//fkpLSykoKOCpp57i2muv5YYbbiAvL48VK1ZQXV1NZmYm69atc84jNXHiRNatW0dmZibV1dWsWLGC/Px8UlNTARg/fjxLly4lOzubkpIS5s+fz8CBA+natavbcRqG+z8NPc7Xf3Rd6r4ujX3d+dqPrkPTX5vm0LITZoiIiJyDYRgsXLiQjz76iFdeeYWXX365WecFOfNYx+OysjLatGlT77jd/eYoJsY+BCQ0JJjw8BCCg+1NckBAAOHhIXUd6iI01D58JTbWe7vpe0v38Oai+rVuql/rFRQUxD//+U8ee+wxRowYQWVlJcOHD2fOnDm0adOG5cuXk5GRwaJFi4iLi2Pu3LkMGjQIsPe6ffDBB3nooYc4evQoiYmJLFu2jJiYGADS0tKwWCxMnjyZ0tJSUlJSWLhwoecqKyLio+qdlLJYLLz55pvOx9XV1S6PAcaNG9dEYYmISGvQ1G1DSUkJs2fP5quvvuKVV16hZ8+ehIWFUVzs2v28qeYFMQzjrDLHY3fnDXG3C3RhoT0pVlFZRVlZJVaLFYCqagtlZZX1Pk9FRRUAx497Xzd9k8m3u86rfq2b6lf3cY3VUvcOCQkJPPXUUzWW9e7dm9dee63WY8eOHcvYsWNrLAsKCiI9PZ309PRGxygiIrWrd1IqPj6eRYsWOR/Hxsa6PDaZTEpKiYj4maZsGw4dOsTtt99Op06dWL16NXFxcQAkJSWxefNml32bal4Qm81GYWEheXl5xMfHA3DgwAE6dOhAVJR7N2Xudmt27nvy31PD99z6tTWf08s0Z5dvb6D6tW6qX/PQvYOIiNRHvZNS//3vf5szDhERaYWaqm0oKirit7/9LYMGDSIjIwOz+dSUh6mpqTz++OOsWLGCyZMn89lnn7Fu3TqWLFkC2OcFSUtL4/rrr6d///6sXLmyxnlBevfuTWxs7FnzgvTv35/58+fz8MMPc/z4cZYsWcLEiRObpF7uMJ+cbqUxc0qJiHgL3TuIiEh9aE4pERHxuDfeeIOcnBzee+893n//fZeyXbt2Neu8IIsWLeLhhx/mmmuuwWw2M27cOGbMmNFSVXdy9JRSTkpERERE/IWSUiIi4nG33HILt9xyS63lzTkvyJlDTDzFpJ5SIiIiIk2isrKSrVu/pLCw1OULvz59+hISUv8FZaT5KSklIiLiBZpiTikRERERgT17PufFDVuJ7XiBc9vRg/uZBlx2WYrnApOzKCklIiLiBTSnlIiIiEjT6XhBEu27XezpMOQczOfeRURERJqbekqJiIiIiL/x2qSU1WplypQp3Hfffc5tu3fvZtKkSSQnJzN8+HBWrVrlcszatWtJTU2lb9++jB8/nl27drmcb8GCBQwZMoTk5GSmT5/OsWPHWqw+IiIidXEsOKieUiIiIiLiL7w2KbV48WJ27tzpfFxUVMS0adMYN24cO3bsICMjg0cffZQ9e/YAsG3bNubNm8djjz3Gjh07GDNmDNOnT6e8vByApUuXsnnzZtasWcOmTZsIDQ1l7ty5LVIXm2FgmAJa5HeJiEjrFODoKaWuUiIiIiLiJ7wyKbV161Y++OADrrvuOue2Dz74gJiYGCZPnkxgYCCDBw9m9OjRrFy5EoBVq1YxatQo+vfvT1BQEFOnTiU2Npb169c7y2+//XY6duxIZGQkc+bMYePGjWRnZzd7faa9tpujfadSVF7d7L9LRERaJ8fwPatyUiIiIiLiJ7wuKZWfn8+cOXN44oknCAsLc27fv38/SUlJLvsmJiayd+9eALKysmotLy4u5siRIy7l8fHxREdHs2/fPrdjNJnc+wkNCsAWEsWH+/Kostia7Lze9NPa469v/ZrydeFtP75QB9WvYceJd9BE5yIiIiLib7xq9T2bzcasWbO45ZZbuOiii1zKSktLXZJUAKGhoZSV/X/27jw8qvJsA/g9WzKTdSYJhKAgQhJwCSQEE0BEBePGEgxR7Bf9jK1gIda6RKQFhMoXllorojVa1FILFQtILRZB2mpBlBAwgKJgIhACIfs2M8ns5/tjFhgSIJNMZr1/15ULcrZ53zmTmXOeed7nbb/ieq1WCwAICwvrtN6+zhWxsZEubf/q/4zGuKUfoRkR+L6hHeOGxnbaRiaVIC7OteP6GlefF3+jUIRecl0gnD8g8M8h+0e+TGyLSjEoRURERETBwqeCUm+99RZCQkLw8MMPd1qnUCigVqudlul0OoSHhzvW63S6TutVKpUjWGWvL9XV/q5obFTDlXsGqQiIOr0XLYl34USdBqMGRHTaxmgyo6FB3cXevk8kst4Mu/q8+At7NklHh/6S/fPn8wcExzlk/y69H/mG8zWlvNwQIiIiIiIP8amg1EcffYS6ujqMGTMGABxBpn/961+YP38+9u7d67R9RUUFkpKSAABJSUkoLy/vtH7ixImIjo5GfHy80xC/+vp6tLS0dBry1x2CAJdvbENbKwEADVoDOoxmKGSdC5/7+81yT54Xf3KlvgVC34PhHLJ/5Kvsw/fMPIlEREREFCR8qqbUjh078PXXX+PAgQM4cOAApk6diqlTp+LAgQPIyspCQ0MD1q1bB6PRiH379mHbtm2YOXMmACA3Nxfbtm3Dvn37YDQasW7dOjQ2NiIrKwsAkJOTg+LiYlRVVUGj0WD58uXIyMjA4MGDPdI3ibEdseEyAMCZFt0VtiYiomBjL3TO4XtEREREFCx8KlPqclQqFd59910UFRVhzZo1iImJwaJFizB27FgAwLhx47BkyRIsXboUtbW1SExMxNq1a6FUKgEABQUFMJlMyMvLg1arRWZmJlavXu3RPlwdrUCj1ogzLR1I6uf6sEEiIgpcEjGH7xERERFRcPHpoNTKlSudfk9JScHGjRsvuX12djays7O7XCeTyVBYWIjCwkK3ttEVg5RyHK5uw9lWZkoREZEzEWffIyIiIqIg41PD9wJdv4gQiAB0GC1oN5i93RwiIvIh9kLnrClFRERERMGCQSkPkkrEiJJbk9Oa2g1ebg0REfkSsX34HmNSRERERBQkGJTysJgwa7Hzpnajl1tCRES+xJ4pZWFUioiIiIiCBINSHhYTFgKAQSkiInJmS5SCANaVIiIiIqLgwKCUh8WE2zOlOHyPiIjOE9srnYND+IiIiIgoODAo5WH24Xst7UZ+E05ERA72mlIAM6WIiIiIKDgwKOVhkaFSSMUimAWgtcPk7eYQEZGPuCAmxbpSRERERBQUGJTyMJFIdEGxcw7hIyIiK7FIBHtcijEpIiIiIgoGDEp5QbTCGpRq0zFTioiIzrPXlTJz+B4RERERBQEGpbwgKlQKgEEpIiJyJrZ9KnP4HhEREREFAwalvCBKzqAUERF1Zs+UYkyKiIiIiIIBg1JeYA9KqfUMShER0XkSR1CKUSkiIiIiCnwMSnmBPSilNZhhMlu83BoiIvIVjuF7DEoRERFRN1gEgRnW5NcYlPKCUKkYIRLrt+FtzJYiIiIbR6Fzfl9BREREV7Dz+zr8co8B22oi8K8fGrzdHKIeYVDKC0QiEaLknIGPiIiciTl8j4iIiLppy+FqmGyXDCcb26E18N6S/A+DUl7CYudERHQxWxItzAxKERER0WWodSYcqW4DAISKrSnWZ1p03mwSUY8wKOUljmLnDEoREZGNWMzZ94iIiOjKSiqbYRaAAWEiDAmz3lNWNXd4uVVErmNQykvsQalWBqWIiMjGMXyPUSkiIiK6jC9PNgEAblCJ0T/Uek95tlXHawjyOwxKeUlkqDUopWGhcyIisrElSrGmFBGRi8xmMx5++GEsWLDAsezw4cO4//77kZaWhkmTJmHTpk1O+2zduhVZWVlITU1FTk4OysrKnI63atUqjB8/HmlpaZg7dy7q6uo81h+iyxEEAV+dagYA3BArhkpmgVwqhsEsoFaj93LriFzDoJSXRIRKAAAagxkCbz582sGqFqgHjsGppnZvN4WIApzEPvsePxeIiFzy+uuv48CBA47fW1tbMWfOHMyYMQOlpaUoKirCihUrcOTIEQBASUkJli1bhpUrV6K0tBTTp0/H3Llz0dFhHf5UXFyMvXv3YsuWLdizZw/kcjkWLVrklb4RXayp3YgGrQEiAMOiRBCJgPjIUABAo9bo3cYRuYhBKS8JD7FmSpktAvQmzv3tqz74+iwe/+AI1IMnYNfxBtTzmwci6kOOmlL8WCAi6ravvvoKn376Ke68807Hsk8//RRKpRJ5eXmQSqUYN24cpk2bhg0bNgAANm3ahClTpiA9PR0ymQz5+flQqVTYvn27Y/3s2bORkJCAiIgILFy4ELt370ZVVZVX+kh0ocpm65flCdFyhNhmSVEqrLO7t3YwKEX+hUEpL5GIRVDIrE+/Rm/2cmuoKzqjGe/sOw0AEOvVAIAvTzYzs42I+gyH7xERuaaxsRELFy7Eyy+/DIVC4VheXl6O5ORkp20TExNx7NgxAEBFRcUl16vVatTU1Ditj4uLQ3R0NI4fP96HvSHqnlNN1oy+a1TnX/NKhTXpoUXHoBT5F6m3GxDMIkKl6DAaoDGwrpQv+vDIOTR3GHFVtBzGz95B85jHUKcx4GRTB4bGhnm7eUQUgOzD9xiUIiK6MovFgueeew6PPvooRowY4bROq9U6BakAQC6Xo729/YrrtVotACAsLKzTevs6V9je2l3e3tX9Ah2fl/NO28qKDIkNg0hk/b8yzJ4pZQLCrc9TMD9XF75eLrysCvbnBej531JfPW8MSnlRRIgE9WCxc18kCAI+KKsGAORnDsKfdqpx/YBIHK5uw8nGdgaliKhPOIbvMSZFRHRFb731FkJCQvDwww93WqdQKKBWq52W6XQ6hIeHO9brdLpO61UqlSNYZa8v1dX+roiNjXR5n97sF+j4vADVGgMA4IZBKkQLHQAaMUBlfW1qDWZIVTIoleGIiwve5yo6OhxAIxSKUMcyuZzPy4V85W+JQSkvinDMwMfhe76mqkWH6lYdZBIR7h7RH38CcE2MAoer23CmpQMWQXBM3U5E5C724XtmRqWIiK7oo48+Ql1dHcaMGQMAjiDTv/71L8yfPx979+512r6iogJJSUkAgKSkJJSXl3daP3HiRERHRyM+Pt5piF99fT1aWlo6DfnrjsZGNVxJgBWJrDeLru4X6Pi8nFdeaw24xoWI0Vpnzd4TTCYoZGJ0GC1oaregpUWLhgb15Q4T0Fpbrc9LR4fe8XrR6YxB/7wAPf9bsu/nbgxKedH5oBQzpXxNSaV1itWRA6OgCLHOlNgvIgShUjH0Jgtq1XokRMm92UQiCkBiDt8jIuq2HTt2OP2+YMECAMDKlSvR3NyMl156CevWrUNeXh4OHjyIbdu24Y033gAA5ObmoqCgAPfccw/S09OxYcMGNDY2IisrCwCQk5OD4uJipKSkQKVSYfny5cjIyMDgwYNdbqcgoEdBlJ7uF+iC/XkxmCyobrUGYK9RKXCq1rpcEIBouQwdRj00FnHQP0/2vl/8HAT783IhX3kuGJTyoohQa7BDY2CmlK/ZbwtKZV6jciwTi0QYpJSjoqEdp5s7GJQiIrc7X1PKyw0hIvJzKpUK7777LoqKirBmzRrExMRg0aJFGDt2LABg3LhxWLJkCZYuXYra2lokJiZi7dq1UCqVAICCggKYTCbk5eVBq9UiMzMTq1ev9l6HiGyqWjpgEYDwEAliw0Nw6oJ1SoUUNWo92s0SbzWPyGUMSnlRRMj5TKkIL7eFzjNZBByoagEAZAxWOq0bpFKgoqEdZ1p0yLzG820josAmts2Ja2FUiojIZStXrnT6PSUlBRs3brzk9tnZ2cjOzu5ynUwmQ2FhIQoLC93aRqLeqrQXOY8Jg+iiciLRCmuxcy2DUuRHxN5uQDCzZ0p1GC0QRHzj8BXHatXQ6M2IDJViRLzzmNmEKGuhvOZ2I4xmizeaR0QBzD58z+wLudRERETkc87ahu5drew8aiNabk16aLfwNp/8B1+tXhQqFUNin2kplLlSvuLoOWvhu1FXRTnOj114iBRhMgkEAI1aoxdaR0SBTMzhe0RERHQZNW16AOiylIi9ZrGOQSnyI3y1epFIJEKErYi2OTTKy60hu2N1GgDAiP5dBwr7RYQAAOq1eo+1iYiCg8T2qczZ94iIiKgrNWrrPcgA2wiOC4Xb7i0NghgGM68lyD8wKOVl9mi2hUEpn3HcHpSK7zooFWcLSjVoDB5rExEFB86+R0RERJdT02YdvjcgsnOmVKhUDKltpEeLgdcS5B8YlPIye10pc2jkFbYkT9CbLDjRoAUADL9UplS4LVOKQSkicjMO3yMiIqLLqbVlSsV3kSklEokc95dNOo82i6jHGJTyMmZK+ZaKBi3MAqBUyBAf2fmNHjg/fK9VZ4LBxGLnROQ+9jJ2zJQiIiKii7UbzGjVmQAAAy5xr2Kf4b1Zz2sJ8g8MSnnZ+ZpSzJTyBcdrrUXOR/SP6DTFqp1cJnF8A9GgZbYUEbmPY/ILBqWIiIjoIvYsqYhQiSO54WKOTCkGpchPMCjlZcyU8i32IufDL1FPyi4mzJot1dzOGfiIyH3sw/fMTMIkIiKii9SoL11Pyi7cdn/ZpGNQivwDg1JedmFNKYHfjHvdjw3tAIDkfuGX3S4mTAYAaO5gUIqI3IfD94iIiOhSatouPfOenf3+ksP3yF8wKOVl4bYxv5DI0MIAh9dVNlmDUtfEhF12O5XCFpRiphQRuRFn3yMiIqJLqbEXOb9EPSngfE2pJr1HmkTUawxKeZlELIJCZj0N9jcZ8o6WdqOjcOA1KsVlt1VdkCnFDDcichdHTSkO3yMiIqKL1LbZh+9dLlPqfKFz3qeQP2BQygfY3zjs6ZjkHadsWVIDIkMhl0kuu220QgYRAL3Jgg4j7x6JyD3sw/fMvIgkIiKii9iTGAZEXaamVIgEgACjBRyJQ36BQSkfYJ+Bj5lS3lXZbA1KDbnC0D0AkIpFiJTbvoXgED4ichMO3yMiIqJLsc++1z8y5JLbSMQihIgEp+2JfBmDUj7gfKaUzsstCW6VTR0AgGtiLj90z85RV6rD0GdtIqLgwuF7RERE1BVBEFCvsd539I+49PA9AJCLrRcSdRrep5DvY1DKB3D4nm+wD98brLpyphRwQV0pZkoRuVVTUxOysrJQUlLiWHb48GHcf//9SEtLw6RJk7Bp0yanfbZu3YqsrCykpqYiJycHZWVljnVmsxmrVq3C+PHjkZaWhrlz56Kurs6xvrGxEfPmzcOYMWOQmZmJoqIimEymvu9oFzj7HhEREXVFrTdBb7IGm+LCL50pBQChtqBUvYb3l+T7GJTyAfZpOzl8z7sqm62ZUkNczpTyzs0rUSA6ePAgZs2ahdOnTzuWtba2Ys6cOZgxYwZKS0tRVFSEFStW4MiRIwCAkpISLFu2DCtXrkRpaSmmT5+OuXPnoqPD+jddXFyMvXv3YsuWLdizZw/kcjkWLVrkOP5TTz2FsLAw7NmzB5s3b8ZXX32FdevWebTfdvbhe6wpRURERBeyZz1Fy6VXrH8rtw3fY6YU+QMGpXwAh+95n9FswdkW+/C97mVKRduCUq0sIEjkFlu3bkVhYSGefvppp+WffvoplEol8vLyIJVKMW7cOEybNg0bNmwAAGzatAlTpkxBeno6ZDIZ8vPzoVKpsH37dsf62bNnIyEhAREREVi4cCF2796NqqoqVFZWYv/+/XjuueegUCgwaNAgzJs3z3FsTztfU8orD09EREQ+yp711O8KQ/eACzKlmPRAfoBBKR9gL3Te1G6Ezmj2cmuC09kWHcwCoJCJ0T/i8umwdkqFNZioM1lgkV56Bgwi6p4JEyZg165duPfee52Wl5eXIzk52WlZYmIijh07BgCoqKi45Hq1Wo2amhqn9XFxcYiOjsbx48dRXl4OpVKJ+Ph4x/phw4ahuroabW1t7u7iFUlsn8ocvkdEREQXqldbs576deNexV5Tql7LTCnyfVJvN4CAUKkYMBsASQjqNAYMVnVv+Bi5j33mvWtUYRDZMhWuRCYRIzxEAq3BDLNC1ZfNIwoK/fr163K5VquFQuH8viiXy9He3n7F9VqtFgAQFhbWab193cX72n9vb29HVFRUt9vfzbeOzttfsJ8jU6qHqVKutqGv2dvja+1yF/bPv7F/l9+PiHxLnS1T6kpFzgHWlCL/wqCUDxCJRJDo1TCHxeJcm45BKS845eLMe3bRCqktKBXTF80iIliDRGq12mmZTqdDeHi4Y71Op+u0XqVSOQJM9vpSF+8vCEKndfbf7cfvrtjYSJe2Vyqtx5eHhiAszHqBKUismbNmAVAoQroVJJfLrd+YqlThiItzrQ2e4upz42/YP//G/hGRP7DPvOdSphRrSpEfYFDKR4j1bTCHxaKWM/B5RaVt5r3u1pOyU8plqG7Vw8SgFFGfSU5Oxt69e52WVVRUICkpCQCQlJSE8vLyTusnTpyI6OhoxMfHOw3xq6+vR0tLC5KTk2GxWNDS0oKGhgbExcUBAH788UcMGDAAkZGu3cg1Nqrhyqi7lhZrppZOb0B7u/W9X3/BEG5tu96ROXU5Op31grO5WYuGBvUVtvYskch6Q+zqc+Mv2D//xv5dfj8i8i32TKl+kd3IlLIVOm/TmaAzmq9YGJ3Im3yyptSxY8fw6KOPIiMjAzfffDPmz5+PpqYmAH07Lbg3SXTW2iXnWOzcKxyZUi5mqdmLnZvDOHyPqK9kZWWhoaEB69atg9FoxL59+7Bt2zbMnDkTAJCbm4tt27Zh3759MBqNWLduHRobG5GVlQUAyMnJQXFxMaqqqqDRaLB8+XJkZGRg8ODBGDJkCNLT07F8+XJoNBpUVVXhjTfeQG5ursvtFATXf6w7nj+GRHw+CNWTIXw9aUNf//hqu9g/9o/9u/x+RORb7FlP3al/KxUJCBE770fkq3wuKKXT6fDYY48hLS0NX3zxBT7++GO0tLTg17/+dZ9PC+5NYr312+0azpDgFadtNaWGuJopZSt2zuF7RH1HpVLh3XffxY4dO5CZmYlFixZh0aJFGDt2LABg3LhxWLJkCZYuXYqMjAz885//xNq1a6FUKgEABQUFuPXWW5GXl4dbb70Ver0eq1evdhx/zZo1MJlMmDx5Mh544AHccsstmDdvnhd6CqfMKM7AR0RERHauzL4nEgHKUOs1RR3rSpGP87nhe9XV1RgxYgQKCgogkUgQEhKCWbNmYf78+U7TggNwmhZ85MiRTtOCA0B+fj4++OADbN++HTNnzsSmTZtQWFiIhIQEAMDChQsxYcIEVFVVYdCgQV7rMwBI9NZMqRpmSnlcS7sRrToTALhcz0tpz5SSK2EyWyCV+Fycl8gvHT9+3On3lJQUbNy48ZLbZ2dnIzs7u8t1MpkMhYWFKCws7HJ9XFwc1qxZ0/PGutEFiVIwM12BiIiIABjNFjS1GwF0L1MKAKJDgLoOZkqR7/O5O+ihQ4fi7bffhkRyftzrzp07ccMNN/TptODeJrYHpZgp5XGnbPWkEqJCXR5vHR4isQ63EUtwtpUBRSLqHZFIBIktMGVmqhQREREBaNBaA0syicjxpfiV2DOlOAMf+Tqfy5S6kCAIWL16NT777DOsX78e7733Xp9OC95dPZ1aVyS69Dh9e6ZUrVoPAUK3itv6Cn+fUrmy5XyR8676cLnzJxKJoJRL0dhuxOmWDgyJdW34n6/w93N4Jezf5fcj3yIVi2E2W2BiUIqIiIgA1NkSF/qFd29mXgBQhtiH7zFTinybzwalNBoNfvWrX+Ho0aNYv349hg8f3qfTgruipzOSKBSXHv8batFBLAKMZgEIDUFclLxHj+FN/jpTS13HGQDAiIHRl51O/VLnLzYiFI3tRtTrzD47HXt3+es57C72j/yBRCwCzGBQioiIiACcH4LXnXpSdspQ+77MlCLf5pNBqdOnT2P27NkYOHAgNm/ejJgYaxHpvpwW3BU9mVoXADo69Jfcz2Q0ol9EKGrVenxX2QhxQpRLbfImf59S+fuzLQCAeIW0y+nUr3T+ImxTW3x3psXnpmPvLn8/h1fC/l1+P/ItUokIMAImcwC+WImIiMhldY4i592rJwVckCmlZqYU+TafqynV2tqKRx55BKNHj8Y777zjCEgBfTstuCt6MyXv5QyItIazq1v1Xp9C2FNTDvvCT2WTNXtusErRo/MXbRvXfaqx3et9CdZzyP717r2JfIvUVu3cZLF4uSVERETkC3qWKcWaUuQffC5T6sMPP0R1dTU++eQT7Nixw2ldWVkZ3n33XRQVFWHNmjWIiYm55LTgtbW1SExM7DQtuMlkQl5eHrRaLTIzM52mBfe2AVGhOFztnzPwzch9AGdrGi65Pk6lwnvvrvNcg7rJaLbgbIs1KDUkpmf1oOzFBiubO66wJRHRlZ0PSjFqSEREROcDS65kSkXbMqXqtQZYBP+qWUzBxeeCUo8++igeffTRS67vy2nBvW2ArY5UTZv/RbPrGpox47lXL7n+7y/90oOt6b6zLTqYBSBMJnHpTf5C0XLrn1FLhxEtHcZuz4hBRNQVBqWIiIjoQvZMqf4uZEpFhwAiWGfzbW43Ija8Z/c6RH3N54bvBTP78L0atf8FpfzVqSb7zHuKbs9kcTGZRAyx3lpLqtJ2PCKinpJKbEEp1pQiIiIiXJApFdn9wJJELEKMLRDFIXzkyxiU8iEJjkwp/xu+56/sQ+4GqxS9Oo6kvcnpeEREPSUVWz+amSlFREREgiCgrgeZUtbtrUEp+/5EvohBKR8SH8VMKU+zZzb1tJ6UnaSjyel4REQ9xeF7REREZKfWm6A3WSc/iXNxCJ69MDozpciXMSjlQ+zD99p0JmgNJi+3Jjicss28d00vg1JSR1CKmVJE1Dv2oJSZs+8REREFPXuWU7RcCrlM4tK+/ZgpRX6AQSkfEhEqRWSotWi2PxY79zeCIKCy2VZTqrfD9zqaAZyvUUVE1FPMlCIi6r5jx47h0UcfRUZGBm6++WbMnz8fTU3WLwsPHz6M+++/H2lpaZg0aRI2bdrktO/WrVuRlZWF1NRU5OTkoKyszLHObDZj1apVGD9+PNLS0jB37lzU1dV5tG9EwIUz77k2dA84P9yvniNxyIcxKOVjBnAIn8e0dBjRpjNBBPfVlDrTqoPJzOwGIuo5FjonIuoenU6Hxx57DGlpafjiiy/w8ccfo6WlBb/+9a/R2tqKOXPmYMaMGSgtLUVRURFWrFiBI0eOAABKSkqwbNkyrFy5EqWlpZg+fTrmzp2Ljg5r1ntxcTH27t2LLVu2YM+ePZDL5Vi0aJE3u0tBql5tzXLqyUzh9n3qmSlFPoxBKR9jH8J3rpXFzvuafajdgKhQl1NhLyY2qCGXimG2CDjDc0dEvSBhoXMiom6prq7GiBEjUFBQgJCQEKhUKsyaNQulpaX49NNPoVQqkZeXB6lUinHjxmHatGnYsGEDAGDTpk2YMmUK0tPTIZPJkJ+fD5VKhe3btzvWz549GwkJCYiIiMDChQuxe/duVFVVebPLFITqbJlSrhY5v3CfOtaUIh/GoJSPuUppzdg5y8BGn3MM3etlPSkAEF1wHNaVIqLe4PA9IqLuGTp0KN5++21IJOe/XNy5cyduuOEGlJeXIzk52Wn7xMREHDt2DABQUVFxyfVqtRo1NTVO6+Pi4hAdHY3jx4/3YY+IOrNnOfUoUyqSmVLk+6TebgA5uzpaDgA408LARl9zFDnv5dA9u2tUChyv09hm4It1yzGJKPgwKEVE5DpBELB69Wp89tlnWL9+Pd577z0oFM7XeHK5HO3t1i8ltVrtJddrtVoAQFhYWKf19nWuEIl6tr2r+wW6YH1e7FlO8VGhXfb9wudFEJyXx9tG4aj1JnQYzQgL6d3oEH9yuecl2F5DF+vp31JfPW8MSvmYq5kp5TGVTe7LlAKAIfZMqWYWOyeinjsflGJ9OiKi7tBoNPjVr36Fo0ePYv369Rg+fDgUCgXUarXTdjqdDuHh4QAAhUIBnU7Xab1KpXIEq+z1pbra3xWxsZEu79Ob/QJdsD0vDe1GAEDy1SrExXXue3R0OIBGKBTnh/fJ5TIoleEYcpUKkXIp1DoTDFIJBnexf6C63PPS1fMYjHzlb4lBKR9zlfJ8ppQgCBAFexi3D1U2Wy80hsS4KVPKdpxTHL5HRL3AQudERN13+vRpzJ49GwMHDsTmzZsRExMDAEhOTsbevXudtq2oqEBSUhIAICkpCeXl5Z3WT5w4EdHR0YiPj3ca4ldfX4+WlpZOQ/66o7FR7ZSpcSUikfVm0dX9Al2wPi9nbF94hwkWNDSoO61vbbVm73V06B3Pi05nREuLFg0NasRHhEKtM+H7yiYog6h4z5Wel2DW078l+37uFkQvS/8wMEoOEYAOowVNtqg4uZ/RbMHZFvvwPfdkSp2vKcVMKSLqOQ7fIyLqntbWVjzyyCMYPXo03nnnHUdACgCysrLQ0NCAdevWwWg0Yt++fdi2bRtmzpwJAMjNzcW2bduwb98+GI1GrFu3Do2NjcjKygIA5OTkoLi4GFVVVdBoNFi+fDkyMjIwePBgl9spCK7/9HS/QP8JtudFrTNBozcDsBYtv9Lz0tXrzj67+7k2vdf7463XS2//HgPxpzfvTe7GTCkfEyIVIz4yFDVqPc60dCA23PWCdnRlZ1t0MAtAmEzSo6KBXRlsq03VqjOhpd0IZZjMLcclouDCoBQRUfd8+OGHqK6uxieffIIdO3Y4rSsrK8O7776LoqIirFmzBjExMVi0aBHGjh0LABg3bhyWLFmCpUuXora2FomJiVi7di2USiUAoKCgACaTCXl5edBqtcjMzMTq1as93EMKdjVqaz2paLm0x/Wg7HWl7McKFt80mnGqXYpIoxmKXs60Tn2LQSkfdLVSjhq1HmdbdRh1VbS3mxOQTjnqSSncNkRSIZNggC2gWNncDmUYzx0RuU4qtiYxmxmUIiK6rEcffRSPPvroJdenpKRg48aNl1yfnZ2N7OzsLtfJZDIUFhaisLCw1+0k6qnaNluR88jQK2x5aQNs+9a2BU/N4n98U4M3vjUBkOObg2eRNbyfI4GAfA+H7/mgq2zFzjkDX9850WgNSg1xU5FzO3tdqUrWlSKiHmJNKSIiIgKAGrU1kDQgSt7jY9j3DZZMqa/PtKBo1w8AALnYAosA7D3RBJOZE8j4KmZK+aCro+3FzoMnmu1pJxqthe+Gxro5KKUKQ0lliyMTi4jIVRy+R0RERABQq+59ppR939ogCUr9eX8VLAIwpr8Y8WINPm+KhsZgxpFqNeK83Tgv0Ov1OHLkkNMykQiYNOkW7zSoCwxK+aCrmSnV5+yZUsPiXJ/W93Icxc6bee6IqGfsQSmzIICzsBIREQWvGtvwvQG9Gb4XdT4oZREEiAP4uuJ0cwe+PNkMEYBpQ6T4ugrIvEaJ/5Q34ptzbbjV/RPH+bwjRw7hjx/vQfw1SY5ltZXlUCrDkZR0oxdbdh6DUj5okG28a2VzB29I+oDJIjgymYbG9c3wPWZKEVFP2YNSgPX9SibhZwAREVEwsg+5sweWeqJfeAjEIsBoFtDUbkRcAE+ktelQNQBg/LUx6K84PzKmpLIFWoMZ9cbgnIgq/pokDB4xytvNuCTWlPJB16gUEAFo05nQ3GH0dnMCzpmWDhjNAuRSMRJ6MT67K/YaVWdbdRy3TEQ9IrkoKEVERETByV6cvDfD96QSsSMQFcjFzs0WATu+rwMA3J820LFcJBI5SrbUGAI3IOfPGJTyQXKZBAm2ulInG5lx424nGqxR82tjw9yevto/IgQKmRhmi8CaYETUIyKRyBGYYlCKiIgoOJktAmo1BgC9K3R+4f7n2gK3rtS359rQ0mFEZKgUmYOVTuvsJVvqjDLoTLy28jUMSvmoIY5Z3BiUcrcfG+1D99xbTwqw3kxeo7JG4k/y3BFRDzmKnXMGPiIioqBUr9HDbBEgFYt6PeQuwTb871wAZ0rt/rERADD+WhWkEucwR1y4DFFyKSwQ4WgzR7P4GgalfJR9GNjJJhbMdrcTDbYi526eec8usZ812FVRr+2T4xNR4OMMfERERMGtyjbp1cBoudPQ/p6wT6RVFcATadmDUhOHxXZaJxKJMNhWt/kYg1I+h0EpH3WtLSjFgtnuV9GgAdA3mVIAkGQLSv1Qr+mT4xNR4DsflOKFExERUTCqspUCGWQLKPXGIEdQKjAzpaqaO3CqqQMSsQjjhsR0uc1VtvI4x1p4beVrGJTyUdfasnhOsaaUW7UbzKi0ZZ+N6B/RJ48x3HbcH+oYlCKinmGmFBERUXA7a8tqulrZ+4mZ7Mc4G6CZUvsqmwEAowZGIVIu7XKbhKhQiCCgUWed+Ip8B4NSPuoaW6ZUjVqPdoPZy60JHOX1GggA4sJDENtH06HaM6Wq2/RQ60x98hhEFNikEtaUIiIiCmb2rKar3ZApZT9GTZseBlPgZQrttwWlMq9RXXIbmUQMpdTktD35BgalfJRSIYNKIQPAIXzudLzOWudpeB9lSQFAlFzmKCZY3sBsKSJynVRs/Xg2M1OKiIgoKNmzedwxfC8mTIYwmQQCgOoAK3Zutgg4WNUKALjpoln3LhZrD0qdbunjVpErGJTyYcNsGTflrE3kNsfr1ACA4fF9F5QCgOR+9iF8LHZORK7j8D0iIqLgJQiCIyjljuF7IpEIV9mOE2hD147XaaDWmxAeIsF1AyIvu22szAgAKD3dwi/+fAiDUj5suC2wcZyBDbfxRKYUACT3D7c9HgOKROQ6BqWIiIiCV2O7ER1GC8Qi6+x77hCoxc5LbVlPYwYpHddPlxIlMUMuAdp0Jt6n+RAGpXzY8HgGNtzJaLbgxwZ7UKpvZt6zswe9vq9V9+njEFFgknD2PSIioqBlL0g+IDIUMol7btkDtdi5vT7UlYbuAYBYBCQrrc9nCetK+QwGpXzYiP7W9MPyeg3TC92gokELk0VAZKgUA6Pc843DpdyYEAUAONHQDo2exc6JyDUyW6Fzg4nv/URERMGmyjF0r/f1pOyudmRKBU5QSmc049BZaz2pjMsUOb/QdSprCIR1pXwHg1I+bLBKAblUjA6jJaDePLzlyNk2AMCNCZEQiS6f2tlbseEhGBgthwDgaA2zpYjINQqZBACgM3H2VSIiomBzqslW5FzlvqDUYNuxTjUGziRah8+2wWAW0D8iBENiuvdcjVCKbPu2QmfkdZYvYFDKh0nEIiTZip3/4IND+CyCgE++r8VvPjmOxuHZOFjVitYOo7ebdUmHq61BqVFXRXnk8VISrJlu39gel4iou+xBqQ4jh+8REREFm4p6a8kR+72gOwyLsx6ruk0fMCM59p+2DsHLuEbV7aSD/goR4iNDYTQLOHyW92m+gEEpH5dsq010rNa3glJnWjrwv+vL8ML249h2tBZ61bX4+kwrNh8+h2+q2yAIvjfk5LAttXPUwGiPPF6KbQjft+eYKUVErpHLrB/P/AaPiIgo+NhnX0+Mc19QSqmQoX9ECAA46uz6u5LKFgBAxjXKbu8jEomQYas/ZQ9qkXcxKOXjro+3Ztt8e853orgVDVo8tvEwjtdpEBEqwaOZgxB98jMMjAqFRQD2VbZgX2WLTwWmatp0qNMYIBEBNyRcfqpQd7lxoD0o5ZtBOiLyXeczpRiUIiIiCiatHUbUaQwAzmc3uUuiLfOqIgCCUs3tBseEYBmDu1dPyu4mWxBrvy2oRd7FoJSPS73amtXzbY0aepP3h3E0ag345ZZv0Kg1IDEuHJvyx6DglmsRXnsY917fH+OGWN8Qvj2nxiEfSoe0p2Ym949w3Oz1teR+4QiVitGqM+FkU+CM3SaivqewZUpx+B4REVFwsQeMBkaFIiJU6tZjJ8ZZR+GU1/t/UKrUVqg8MS4cseEhLu17ky2IdbxOg5Z23y0/EywYlPJxg5RyxIaHwGgWcLTGu0Eek9mCBdu+Q53GgGtUCrz5wEjERYQ61otEItyYEOkITB2oasXZFp23muukzDZ0b+RAz9STAgCZRIxRtsdjFJ6IXCG3Bc9NFgFGMwNTREREwcI+tC6xX4Tbj53YL8zpMfzZ/h4M3bOLCw/BsLgwCAAOVLW4s1nUA+4NvZLbiUQipF0VjX/9UI+yM60YfbXSa235c2kVDp1tQ3iIBL+bcQOiFbIut7sxIRLN7QYcq9Pis4oG5IxM8HBLnQmCgH2nrOOFb3IxtbO3xg5RYf/pFpRUNuPB0Vd59LHJ2YzcB3C2puGS6+NUKrz37jrPNYjoMmRiESQiEcyCAJ3RApmE3yEREREFA3sWU2JcmNuPnXRBppQgCH0+I3lfEQQBJZXni5z3RMZgFX5saMdXp5pwx/B+7mweuYhBKT+QdrU1KHXojPcypcrrNXj7q9MAgOfvSMSQmMu/SY4bokKdxoCmdiP2nmyCNysqnW7uwNlWHaRiEW6yFbXzlMxrVABO4mBVC4xm3lh6U11DM2Y89+ol1//9pV96sDVElycSiaCQiaExmNFhNCNSzo9rIiKiYFDRh5lS18QoIBWLoDWYca5Nj4HRcrc/hidUtehQo9ZDJhFh9NU9m8Tq5qExeP/rs/jiRBMsggCxnwboAgHvkP2A/Q/tcHUrTF4YxmEyW/CbHT/AZBFw67BY3D2i/xX3kUrEuC0xFiIRcKqpA/q4ZA+0tGtf2bKkUq+ORliIZ+pJ2SX2C0dMmAwdRguOVPtOjS0i8n32IXwdPlBPkIiIiPqezmh2FO8e3t/9QSmZROyY0c+XJtJylX0UzMiBUT2uFzz66miEh0jQ1G7E9z42032wYVDKDwyNC3MENg5WtXr88dftr8LxOg2i5VIsyErqdppnbHgI0q6y1lTSDJuMpnZDXzbzkr482QQAGD/Es0P3AEAsEjlSSr88ySlHiaj77MXOdZyBj4iIKCgcrVHDaBYQFx6CQcq+yWJKsyU8fH3G8/eV7vJ5hbUkx/ghMT0+hkwixljb/eGeHxvd0i7qGQal/IBYJMLEYbEAzv8BekpFvRbv7LMO2yuclIg4F2c2SL0qGjFhMgiyMLz074q+aOJlafQmxxvu+Gt7/qbVG/Zz968f6iEI3hzISET+xP7NXweDUkREREHha1sCwuiro/us3pMjKOWFZAd3aG434GtbcfJJyXG9OtaEodb7w2AJSpktAmra9DirD0Gb3ncy8RmU8hO3JVr/4P77YyMsHgpsmCwCXtx53DFs764RrheAk4hFuHVYLCBY8K8fGvCv4/V90NJL+095A/QmC4bEKDA01v3FArvjlqExUMjEqG7V4dtzaq+0gYj8j9yWKdVh9J2LBiIiIuo7X9tmDB89qGd1kroj7SrrsU82tXttJEtv/LeiEWbBOrzxaqWiV8eacG0sJCLgh3otTjW1u6mFvqmquQObDp3DtqO1+LY9HNsqdN5ukgODUn7ipsFKhIdIUK8x4LsazwQ2/lJahe9rNYiSS7HgjsQeR+vjIkIQVlUCAPjtvyvQ7ME3v+3f1QIA7r0+3muzS8hlEke21KceDsoRkf+yZ0px+B4REVHgM5ot+MZWgzath8W7u0MZJsMw28x+h/xwCN9/yq0jhyYl9S5LCrA+F2NtQwA/sd03BqIqfQh2HKuHWm9CqFSM/jIDbhsc6u1mOTAo5SdCpGLH8LPt39X1+eOdaNRi7VeVAIBnbhuGuIjevWjDqkowLC4MzR1GvPSfH93RxCs616Zz1OC657orF2fvS3fZisN/eqwOBhYtJqJuOD98j+8ZREREge6bc23QmyxQKWS49goznffW6KuVAIDS0y19+jjuVtOmQ0mltU7v5F4O3bO793rrfdon39d5bESSJx2sN+O7duvraUT/CDw4eiDSIrS4KtKzE4BdDoNSfiQ7ZQAA4J9Ha6HRm/rscYxmC5Z+chxGs4Cbr41x/KH2hkgwY8ndwyERAbuO1+M/P/R9xtCWw+cAAOmDojEgyrvTnY4dokK/iBA0tRux41jfBxWJyP8pHMP3mClFREQU6D49Zr0/Gn+tqs9HeNxsS3b4T3kDTBb/CcRs/aYGFsF6f3eNmwJ3E4fFIjxEgnNtepT5YebY5Xx1qgnrjpkAiDCifwQmDFUhROJ7ISDfaxFdUsZgJYbEKNBuNOOfR/suvfC13Scdw/Z+5cJse1dyXXwkHskYBABYvqsc1a19N461tcOITWXVAICfjL6qzx6nu2QSsaMd6w+cCcgovC8yWwTUa/SoqNfCJFfCaGbGCfkPe6aU1mDmJAlEREQBzGi2OGrv3nNdfJ8/XuY1SqgUMjS1G7G/0j9mCDeZLfjomxoAQO6ogW47rlwmwZ222snrD5xx23G97Uh1G+Z/9B3MAjBAZsDNQ/s+2NlTDEr5EZFIhPtTrYGNv359Fvo+GAa2/btavP/1WQDAkruHIz7SvWNNfzb2GlwXH4FWnQnP/+O7PquVsvHrs2g3mpHULxy32Oo5edt9IxMQHiLBycZ2fFbu2VkUg4laZ8KHR87hF1u+weQ/fIl73yrBg38+iLrUfKzbfwYflFXjy5NNqFXreaNPPi1aLoVYBOhNFrTp+i47loiIiLzry5PNaNWZEBsegjGDlX3+eFKJ2BGI+eR7/xjFsf27OjRqDYgND8Ftie69v3tozCCIRcAXJ5rwQ53Grcf2hop6LZ768FvoTBZcpxIhJVwLsY8GpAAGpfzO1Bvi0S8iBNWtOqw/UOXWYx843YJlO38AAPzvTYMcxbndKUQqxm+nXw+lQoZjdRr86uPvYXJz9srp5g78xRblfjRzsM/8AUaESvGgLVvq95/92KdDMINRvUaPV/97AlP/WIIVu8qx71QztAYzJCJApZBBZNIDANp0Jhyt0eAf39bi79/U4od6Dcx+lLZMwUMqEaOfrZ5fjVrv5dYQERFRX9l6xFp25K4R/SARe+bexV5z9/PyBjRqfXsWvg6jGcV7TwEAHhpzNaRuHoI2WKXA5GRrkM5eV9lfnWnpwBNbvoFab0JKQhTmXC+Dh15SPcaglJ8JC5HglxOHAgD+VFLltqkrSyqb8dTWb2GyCJiUFIeCW4a45bhdGRAlx++yr0eoVIwvTjTh1/885rasL5NFwIs7jkNvsuCmwUq3FcBzl/yMQbhaKUedxoBX/3uCmTpuUNXcgaJPf0D22/ux/sAZtBvNGBobhiduuRYbHh6NL566BbsKxiHhQDEeHnMVsobHITEuDBIR0KA14L8VTfigrBrtA9PRbmDtHvItCVHWoNS5NgaliIiIAtGB0y3Ye7IJEhGQMzLBY497/YBIXD8gEjqTBX/80rcDMetKTqNBa8DAaDkeSHXf0L0L/TRzMCQi4POKRscMf/7mTEsHCjYdQaPWgMS4cKzOuQGhEh+PSAGQersBntbY2IjFixdj//79kEgkmD59Op5//nlIpf7zVNw5oh/+/m0NDpxuwVMffos//U8qVGEhPTqWIAjYdKgar3x+AiaLgPHXqvCbe4b3eXbRqKuisWra9XjuH0fxWXkD5m06gqIpI3pVkNwiCPi/ncdxuLoNYTIJFt+V7DNZUnZymQQL7kjCE5u/wd+/qcFglQIP3zTI280CAMzIfQBnay79BhynUuG9d9d5rkFX8O25Nmw4cBb/Ka+HPdEp9aooPJIxCDdfG9PlmGm5TIIhMWEYEhOGsUPMOF6nwdFzGmgNZmDobZi2tgQPpA7ErLSroAyTebhH5G2++PkwICoUOGudbYaIiDzPFz8bgpVer8dXX32LlhYtLvxed+TIVISG+s709q4wmi145XPrzOQ5owa6rXh3d4hEIjx161DM+eAw/v7NOeSMSsDw/hEee/zu2nuiCX8qsY4QenLitQiR9k1eTWK/cPxvxiD8qaQKq/5VjuviI5Dg5cmyXHGsVo1n/34UdRoDBinleG3mjYiS+8f9TNC9mz711FOIj4/Hnj170NDQgLlz52LdunV47LHHvN20bhOJRCiaMgKPbijD2VYdHtt4GKumXY/EfuEuHae8XoNXPj/hmAo0a3g/LL17eJ/9oV/s5qExeG1mCgo/Oooj1W34yXsHMWf8EMwcmeByG1o6jFi+qxyflTdAIgJ+c89wn30TybxGhaduHYrV/z2BNbtPol5jwC8mXguZh2dCMJgsaNAaYDBZYIGAs1oxJv/iZcgkIoRIxZ0Cen9/6ZcebV9XNHoT/lvRiE2HqnG0Ru1YPmFoDB65aRBSr47u9rEUMglSr4pGSkIUyuu1+PJoBdoQg7f3ncb6A2cw7cYBuOe6/rgxIdJniwKSe/ni50N8ZChEANR6MzR6EyJCg+5jm4hcJAgCvqtR44d6LZTRLbj9mmgA/BzrKV/8bAhWR44cwp92fgVVwrWOZbWV5ZgD4KabMr3XsB4yWwQs+eQ4fqjXIiJUgtnjBnu8DWlXR+P2pDh8Vt6AZ7Z+i3d+kur1Wcsv9NWpJiz85/cQYM0isw+x6yuPjb0Ge35sQkWDFj//2xH8ITcFVysVffqYvWUyW/C3Q9X4w56TMJgFXBsbhjdyUxAX4T+B2qC6uq2srMT+/fuxe/duKBQKDBo0CPPmzcNLL73kdx8sMWEheDUnBQWbj+B0cwceXv81pt4Qj6k3xOOGhChIuxg4KggCzrbqUHq6BbuO1zuCUSESEQpuuRY/GX2Vx2++0wcp8V7eaLzwyTF8e06N33/2I9aVnMa918djwtAYjIiPQHhI1y9TQRDwY2M7/nW8HpsPVaNVZ4JELMJv7h6O25J8a9jexfLGXA2N3oS3953G+1+fxX8rGvA/6VdjcnKcW95A9CYL6jV61KqtP3X2fzUGx+/NHUbnnUY9jI22GQtFsA4VDQ+RICJUiogQCToS0vDfigYMiJQjPioU0XJpn79e2g1mlNdr8F2tBl+ebMKB0y2OaWtlEhHuHNEfeelXIalfz7/VkYhFGBEfge/fW4fCVWuxrqQKx+o02HSoGpsOVSMhKhQTh8Vi5MAo3JgQhYSoUAapApCvfj6ESMSIiwhBvcaA0tMtuC0xlq8/IurSmZYO7Pi+Dtu/q0VVy/nsynV5qbhhQJQXW+a/fPWzIZBZBAFtHSY0aA1o1Boc/za2G3DmnBH1MTeiXR6LUKkYYTIJRAYZTrVZMKhNh9jwEI9/ydsTgiDgWJ01OaDsTCukYhFWTL2uxyNfeuvXWUk41diOk03teGRDGX5561DckdzPY4kKXalq7sCGg2ew9cg5WAQgfVA0CicN6/PHDZGK8WrOjXj8b4dxpkWH/3nvIH6aORjZKQO8dn4upU6tx2flDdh0qBqVzR0AgFuGxmDpPcP9JkPKLqiCUuXl5VAqlYiPPz/N5rBhw1BdXY22tjZERfnXB/aQ2DCsf3g0Xtz5A7440YS/f1ODv39TgxCJCFdFKxAWIoFCJobJIqBBa0CDxgDdBbWbxCLY6kdd69UI8CCVAmsfTMW2b2vw9leVqNMYsP7AGceUnDFhMlytVEAuFUMqEcEiAK0dRlS1dECjP18D6NqYMLx473CMiI/0Vldc8vjNQzAiPgLLd5Wjuk2P3332I3732Y9IiArF1UoFYsNDEBMmQ4hEDLFYBKlIBLEYMJkF6E0Wx4/OZIbeZEFTuxHN7QY0tRutw9G6IVQqRqhUDIlIhNa2VohCwmEWBAiwTkOvNZhRp7EVPhw2CYUffefYVy4Vo39kKKLkUkSGSh3/hkjFkEnEkIlF1n8lIkjEIggCIACOOlr2tGujxQKd0YIOoxk6owUagwl1agPqNNbg2cVVt4bEKHD3df1x38gExLjxw0EEAZOT+2FSUhz2V7Zg29Ea7P6xEefa9PigrBof2AJ2CpkYAyLlGBBl7Xt4iBRhIRKEhUgQIhFDBEAkAsQikeNfscia4SiC/TkAAMHxHAjo/Nxc+PvFywSnZdbfskZehWvCg+ot3a18+fPhpsFKfPJdHSoa2tFhtGBAVCikYhEkttdYa4cUEan34PMqA8qFaq+181IiIuTQaAJ3+KGv9a+7pQq7W9EwIiIUGo3+Cse98tG6067utqk723WnZqNIBISFhUKrvXzNNnc+p67UkmxpbUFH+6VrhwoAOkwCGnUCKtvMOKc9f40XKgGSlBJkDI1Fci++uAl23vhsMJktePvLkzhV2wCj0QgxrNcRYhGQEB+PEJkUEpH12koiFjmuO4Dz1x7A+esO6/fU1uX2be3XFIDzNYljGTpfr11u2YWvavu1yYXHQxfLjGYBGr0JbToTNHoTWnVGNGqt17Gmy05AEwK0qy/4PQJlh4z47aH9AKwz18ZFhCA2LASx4SEID5FALpNALhVDLpM4PWf25+j8c2i/Zrvwuuv882O56Dm4+DmxdHGNduF2WoMZtWo9jtVqcLbV+rkhl4qx5O7hGDsk5jJ97ltKhQyv5abgF5u/wcmmdiz55Dhe+k8FbhgQiauiFYgIlSIyVOIIUl3utWURbH0XBMfz5XhuLniOLlwmQIBZAJrbDTjXpseZlg6cuSCwnn3jAMyfnOixgGP/yFAU3z8Si7cfw6GzbfjDF6fw5t5TGBYXjsGqMMSEyaC036OJrF9wW6/3ra+fi183l/q76s7f1IWvo3aDGS0dRjS1G3GyqR3Vreefo2i5FHMnDMF9IxN8rnxNdwTVHYxWq4VC4Rx8sf/e3t7e7Q8Wsbj7FyiA/aInDKEy8SX3Cw8Lg7gHf2exESF4deaNOHK2DX//5hxKKlug0ZtRrzUAWudtpRIxVCESJPePwLghKkxOisNAZe/TM93RvxCxCDNTE5CdEo8vTzXjvxUNKKtqQ2O7EQazgBONXV2UiRATHoK0gVG4+/r+mJgY22WGWG/15fm7PTkO465V4eOjdfjXD/X4vkYDtd6M72t7PhWpSCRChC041D8iBP0jQtEvQoZ+kaHoFx6K+MgQ9IsIRb+IEETZsp1EIuCBhx7C3U8sh8kswGCyBofaDRZo9SZojGZUHD2Eodenoq5Nh+YO68yBTe3WN8a+Eh4qRVy4DEn9IpAyMAoThsbgmhjXA6iunUMRxg1VYdxQFXRGM0oqW3DoTCuO1mpQUa+F2SKgXmuw/o35kA++Oo5/F97p8nsTWbnj88HVzwapVILIyEho6s+i6RIZoQCgAHBTrBTfaeRo1ZnQqrt45s4IXDP9l9h60gScPNX9BhBRABEjXCZAd+Yo2r/bjeaKEpwz6lAaFob7dn2OgQOv6vaR+NlwnjfuHU7Ut+ODw7X2PZ1X1td17yB+TC6TAADCpEBUiAhRMiAqVIRIKdDW0oizbXqERihhsIhgFETQ6gwQy+TQCVKYBcAsALVqA2rVvnWddjGlXIobY8WYNkSCmI5KHDrU80LjJ05UoKW6FSLh/OusrfY0ToRrILM9n93x3CgBn58JwRc1FrQZgKM1Ghyt6fk9SW9EhUqRpBThjqslSFS24btvv3b5GL19XuYOF3BAFYovzllQpRFQ3aZHtQ9NPBMZKsWgCBHG9BdjTH8xFEI1jhzu/OXkiRMVaKttQl3I+eyptrrTkEiSexTX6AsiIYim/9q1axcWLVqEkpISx7Ljx49j+vTpOHDgACIj/SPDhoiI3IufD0REdDF+NhAR9T3fH3TrRklJSWhpaUFDw/kZxn788UcMGDCAHypEREGMnw9ERHQxfjYQEfW9oApKDRkyBOnp6Vi+fDk0Gg2qqqrwxhtvIDc319tNIyIiL+LnAxERXYyfDUREfS+ohu8BQENDA1588UWUlJRALBZjxowZKCwshETS/fG2REQUePj5QEREF+NnAxFR3wq6oBQREREREREREXlfUA3fIyIiIiIiIiIi38CgFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKOVmTU1NyMrKQklJySW3+e9//4tp06YhNTUV99xzDz777DMPtrB3utO/xx57DCkpKUhLS3P87N6924OtdN2xY8fw6KOPIiMjAzfffDPmz5+PpqamLrf1x/PnSv/88fwBwFdffYX7778fo0ePxs0334xly5ZBp9N1ua0/nkNX+uev5zAQNTY2Yt68eRgzZgwyMzNRVFQEk8nU5bb++LoEXOvj+++/j7vuugtpaWm46667sGHDBg+31nWu9M/uhx9+wKhRoy77WekrXOnf/v37cf/99yMtLQ233nor3nrrLQ+31nWu9O/Pf/4zJk2ahNGjR2PatGnYuXOnh1vbc4F+/RlsunM+g40r17LBxJXrw2BkNpvx8MMPY8GCBd5uik/Yvn07rr/+eqd7hOeee87bzQIEcpsDBw4Id9xxh5CcnCzs27evy21OnjwppKSkCLt27RKMRqPwz3/+Uxg5cqRQU1Pj4da6rjv9EwRByMzMFEpKSjzYst7p6OgQbr75ZuHVV18V9Hq90NTUJMyePVt4/PHHO23rj+fPlf4Jgv+dP0EQhMbGRiElJUXYsmWLYDabhdraWmHq1KnCq6++2mlbfzyHrvRPEPzzHAaqhx56SHj22WeF9vZ24fTp08KUKVOEtWvXdtrOH1+Xdt3t465du4QxY8YIZWVlgsViEb7++mthzJgxwo4dO7zQ6u7rbv/s2tvbhalTp17xs9JXdLd/FRUVwqhRo4QPP/xQsFgswvfffy9kZGQIn3zyiRda3X3d7d/nn38ujBs3Tvjxxx8FQRCEHTt2CCNGjBCqqqo83WSXBfr1Z7Dp7vV2MHH1WjZYuHp9GIxWr14tjBgxQnj++ee93RSfsHLlSmHBggXebkYnzJRyk61bt6KwsBBPP/30FbcbM2YM7rjjDkilUtx777246aab8MEHH3iopT3T3f5VVVWhtbUV119/vYda1nvV1dUYMWIECgoKEBISApVKhVmzZqG0tLTTtv54/lzpnz+ePwCIiYnBl19+iZycHIhEIrS0tECv1yMmJqbTtv54Dl3pn7+ew0BUWVmJ/fv347nnnoNCocCgQYMwb968LrOD/PF1CbjWx9raWsyePRupqakQiURIS0tDZmZml+9FvsKV/tn95je/wR133OHBVvacK/3761//ismTJ+O+++6DSCTCiBEjsHHjRqSnp3uh5d3jSv9OnDgBQRAcPxKJBDKZDFKp1Ast775Av/4MNt09n8HGlWvZYOLK9WEw+uqrr/Dpp5/izjvv9HZTfMY333yDG2+80dvN6IRBKTeZMGECdu3ahXvvvfey21VUVCA5OdlpWWJiIo4dO9aXzeu17vbvm2++QXh4OJ5++mmMHTsWU6dOxebNmz3Uyp4ZOnQo3n77bUgkEseynTt34oYbbui0rT+eP1f654/nzy4iIgIAcOutt2LatGno168fcnJyOm3nj+cQ6H7//PkcBpry8nIolUrEx8c7lg0bNgzV1dVoa2tz2tZfX5eu9DEvLw9z5sxx/N7Y2IjS0lKfvDiyc6V/APD3v/8dlZWVeOKJJzzZzB5zpX9HjhzB1VdfjWeeeQaZmZm45557sH//fvTr18/Tze42V/o3ZcoUxMXF4d5778UNN9yAX/7yl1i5ciUGDBjg6Wa7JNCvP4NNd89nsHHlWjbYdPf6MNg0NjZi4cKFePnll6FQKLzdHJ9gsVhw9OhRfP7557j99tsxceJELF68GK2trd5uGoNS7tKvX79ufZum1Wo7/WHI5XK0t7f3VdPcorv9MxgMSE1NxdNPP409e/ZgwYIFKCoqwieffOKBVvaeIAh45ZVX8Nlnn2HhwoWd1vvr+bO7Uv/8/fwBwKeffordu3dDLBbjySef7LTe38/hlfoXCOcwUHT1WrP/fvHrzV9fl6708UL19fWYPXs2brzxRkydOrVP29gbrvTvxx9/xCuvvIKXX37Z6cbJl7nSv9bWVrz33nuYPn069u7dixdffBGrVq3Cjh07PNZeV7nSP6PRiBEjRmDTpk04dOgQXnzxRSxcuBDHjx/3WHt7ItCvP4NNd89nMLvStWywutL1YTCxWCx47rnn8Oijj2LEiBHebo7PaGpqwvXXX4+77roL27dvx8aNG3Hq1CmfqCnFoJSHKRSKTsXndDodwsPDvdQi95oxYwbefvttXH/99ZDJZJgwYQJmzJjhFzfEGo0GTz75JLZt24b169dj+PDhnbbx5/PXnf758/mzk8vliI+Px3PPPYc9e/Z0iv778zkErty/QDiHgSIsLAwdHR1Oy+y/X/x689fXpSt9tDt06BByc3Nx7bXXori42KdvwLrbP71ej6effhq//vWvMXDgQI+2sTdcOX8hISGYPHkybrvtNkilUtx0003Izs726fcWV/q3bNkyJCUlYeTIkQgJCcHMmTORmpqKrVu3eqy9fclf32OILtSda9lgdaXrw2Dy1ltvISQkBA8//LC3m+JT4uLisGHDBuTm5kKhUGDgwIF47rnnsHv3bmg0Gq+2jUEpD0tOTkZ5ebnTsoqKCiQlJXmpRe61efPmTheoBoMBoaGhXmpR95w+fRozZ86ERqPB5s2bL/kh56/nr7v989fz9/XXX+Puu++GwWBwLDMYDJDJZJ2+GfbHc+hK//z1HAaipKQktLS0oKGhwbHsxx9/xIABAxAZGem0rT++LgHX+ghYX5/5+fl45JFH8PLLLyMkJMSTzXVZd/v3zTff4NSpU1i4cCHGjBmDMWPGAAB+/vOfY+nSpZ5udre5cv6GDRvm9B4EWGc1EgTBI23tCVf6V11d3al/UqkUMpnMI23ta/76HkNk191r2WDiyvVhMPnoo4+wf/9+x+fxxx9/jI8//tjx2Rysjh07ht/97ndOn9sGgwFisdjr12MMSnnY9OnTsX//fmzfvh0mkwnbt2/H/v37kZ2d7e2muYVGo8GyZcvw3XffwWKx4PPPP8fHH3+MWbNmebtpl9Ta2opHHnkEo0ePxjvvvHPZ4oD+eP5c6Z8/nj8AGD58OHQ6HV5++WUYDAacPXsWq1atQm5ubqc3WX88h670z1/PYSAaMmQI0tPTsXz5cmg0GlRVVeGNN95Abm5up2398XUJuNbHnTt3YunSpXjttdfw05/+1AutdV13+zdmzBgcOXIEBw4ccPwAwJtvvunTQSlXzt+DDz6If//73/joo48gCAJKS0uxbds2n36NutK/SZMmYf369Th69CgsFgt27NiBkpKSgKnt46/vMUSAa9eywcSV68NgsmPHDnz99deOz+OpU6di6tSpjs/mYKVUKrFhwwa8/fbbMJlMqK6uxksvvYT77rvP+68Xr837F8AunsI1NTVV+Oijjxy/7969W5g+fbqQmpoqTJkyRfj888+90cweu1z/LBaL8Ic//EG4/fbbhZEjRwpTpkzx+emi3333XSE5OVkYNWqUkJqa6vQjCP5//lzpnz+eP7vy8nLh0UcfFcaMGSPcfvvtwu9//3tBr9cLguD/51AQut8/fz6Hgai+vl74xS9+IWRkZAhjx44VVq5cKZhMJkEQAuN1KQjd7+PUqVOFESNGdHofWrx4sTebf0WunMML+ct07q707/PPPxdycnKEtLQ0YfLkycL777/vrWZ3W3f7ZzQahTVr1gi33367MHr0aOG+++4Tdu/e7c2muyzQrz+Djb+8h3jCla5lg9nlrg/J6vnnnxeef/55bzfDJ5SUlAizZs0S0tLShLFjxwrLli0TdDqdt5sliATBh/OuiYiIiIiIiIgoIHH4HhEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERERERETkcQxKERERERERERGRxzEoRUREREREREREHsegFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERERERETkcQxKERERERERERGRxzEoRUREREREREREHsegFBEREREREREReRyDUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREREREQex6AUERERERERERF5HINSRERERH1MEARvN4GIiPoQ3+fdh89lcGFQishPlZSUYPjw4SgpKfF2U4iI6ALbt2/H7bffjpSUFLzwwguoqKjAT37ykz55rA8//BDDhw/HmTNn+uT4RETUmbve5xcsWIBJkyb1QQv9V3FxMd555x1vN4M8iEEpIiIiIjf6zW9+g/79++Ptt9/GT3/6U3zyyScoKyvzdrOIiMhN+D7fd1avXo2Ojg5vN4M8SOrtBhAREREFkpaWFtx8883IzMz0dlOIiKgP8H2eyH2YKUXkJkajEb/73e8wceJEjBw5Ej/72c/w97//3TGsYsGCBXjkkUewZMkSjBkzBvfddx9MJhOamprwm9/8BrfffjtuvPFGZGRkoKCgoNNQjI0bN+Kuu+7CyJEj8dBDD6G6urpTG6qrq/HMM88gIyMDo0aNwiOPPILvvvvOU08BEZHfO3r0KB555BGkp6cjLS0N+fn5OHz4sGP9jh07MH36dIwcORIzZsxAWVkZrr/+enz44YeOYdUA8Ic//AHDhw/HggUL8PrrrwMAhg8fjtdee82l9mzatAk5OTlITU3FyJEjkZ2dje3bt3fa7uuvv8aMGTOQkpKCadOmddpGrVZjxYoVuOOOO5CSkoKpU6di8+bNjvWLFy/G2LFjYTKZnPZ76aWXkJGRAYPBAAD44Ycf8Pjjj2P06NEYPXo0CgoKUFVV5VKfiIi8ydfe5wHggw8+wG233YaRI0d2ef1eWlqKn/3sZ7jppptw4403YtKkSXjttddgsVgc22zfvt3R7rFjx6KwsBB1dXVOx9m0aROmTJmCG2+8Ebfddhtee+01p/f9BQsW4Gc/+xn+9re/4Y477sDIkSPx4IMP4uTJk/jss88wbdo0jBo1Cvfffz++//57p2MfOHAADz30EEaNGoWMjAw8//zzaGpqcqz/8MMPcf311+Pw4cOYNWsWUlJScNttt2Ht2rWObezP7euvv+74PwU+BqWI3OSFF17An//8Zzz00EP4wx/+gLi4OCxevNhpmwMHDqCyshKvvfYaCgoKIJFI8Pjjj2Pv3r149tln8c4772DevHn48ssv8cILLzj2W79+PZYsWYJbbrkFb7zxBkaNGtXp2E1NTXjwwQdx9OhRLF68GC+//DIsFgvy8vLw448/euQ5ICLyZxqNBo899hhUKhXWrFmDV155BR0dHfjZz34GtVqNf//73/jlL3+JpKQkvP7667jzzjsxd+5cx03BDTfcgA8++AAAkJubiw8++AC/+MUvkJubC8B603H//fd3uz0bNmzACy+8gMmTJ+Ott97CSy+9BJlMhueee67TFxOLFy/G3XffjT/84Q9ITEzE008/jS+++AIAoNPp8D//8z/4xz/+gZ/+9Kd44403kJ6ejoULF+LNN98EAGRnZ6O5uRlfffWV45iCIGD79u24++67ERISgpMnT+LBBx9EY2MjVq5ciaKiIlRVVeEnP/kJGhsbe/7EExF5iK+9zwNATU0NXnvtNTz11FP4/e9/j9bWVvzv//6vI6Bz7Ngx5OfnQ6lU4pVXXkFxcTFGjx6N119/Hf/85z8BAAcPHkRhYSHuvPNOrF27Fr/61a+wb98+PPvss47Heeutt7B48WKMGzcOb775JvLy8rB27Vqnew4AOHToEP7yl79gwYIFWL58OSoqKjBnzhysWLECjz/+OFasWIFz586hsLDQsU9paSny8/Mhl8uxevVq/PrXv8b+/fvxv//7v9DpdI7tLBYLnnrqKdx777344x//iPT0dPzud7/Dnj17HM/fhc8tBQmBiHqtsrJSGD58uPDuu+86Lf/pT38qJCcnC1VVVcLzzz8vJCcnC6dOnXKsr6mpER5++GGhtLTUab9ly5YJN9xwgyAIgmCxWIRx48YJv/jFL5y2eeGFF4Tk5GRh3759giAIwu9//3shJSVFOHPmjGMbvV4vTJ48udO+RETUWVlZmZCcnCwcOHDAsayyslJYtWqVUF1dLdx3331CTk6O0z5vvPGGkJycLGzZssWxLDk5WVizZo3j9zVr1gjJyckut2fFihXCb3/7W6dl3377rZCcnCxs27ZNEARB2LJli5CcnCy89dZbTtvNmDFDmDVrliAIgrBhw4ZO/RIEQfj1r38tpKSkCM3NzYLFYhEmTZokLFiwwLG+tLTUab9nnnlGGDdunKBWqx3bNDc3C+np6cLKlStd7h8Rkaf52vu8/f6grKzMsayurk4YOXKk8PLLLwuCIAhbt24VHnvsMcFsNju2MZvNQnp6urB48WJBEAThrbfeElJTUwWdTufY5vPPPxdee+01wWKxCG1tbcKoUaOEF154wenx//a3vwnJycnCDz/84NSeiooKxzaLFy8WkpOThS+//NKx7J133hGSk5OF1tZWQRAEYdasWcLUqVMFk8nk2ObEiRPCddddJ6xfv14QhPOfV3/7298c2+j1eiElJUV48cUXHcsufm4p8DFTisgNSkpKIAgC7r77bqflU6dOdfpdLpdj8ODBjt/j4+Px3nvvYcyYMaiursZXX32F9evX4+uvv4bRaAQAnDhxAo2NjZg8ebLTse655x6n37/66itcd911iI+Ph8lkgslkglgsxsSJE/Hll1+6s7tERAEpKSkJMTExmDt3LpYsWYL//Oc/6NevH+bPnw+lUomjR492ei+ePn16n7VnwYIFeO6556BWq/HNN99g27Zt2LBhAwA4PiPsLv5MuOOOO3Do0CFotVrs378fV111FdLT0zu1Xa/X4/DhwxCJRJg+fTp27drlGKr38ccfY9CgQY799u3bh8zMTMjlcsfnTEREBMaMGcPPGSLyC772Pg8AAwcORGpqquP3fv36ITU11fG+OmPGDKxduxZGoxHl5eX417/+hddeew1ms9nxWXDTTTdBp9Nh2rRpeOWVV3Dw4EFMmDABTzzxBEQiEcrKytDR0YFJkyY53r9NJpNj5r+9e/c6Hj86OhrDhg1zag8ApzYqlUoAQFtbGzo6OnD48GHceuutEATBcexBgwZh2LBhTscGgLS0NMf/Q0JCEBMTg/b29t4/keS3WOicyA3s6bWxsbFOy+Pi4px+j42NhUgkclr2j3/8A7///e9x7tw5KJVKjBgxAnK53LG+tbUVABATE+O0n/0Dwq6lpQWVlZW44YYbumxjR0cHFAqFC70iIgou4eHh2LBhA4qLi7F9+3Zs3LgRCoUC06dPR0FBAYDO78Xx8fF91p7Tp0/jhRdewL59+yCVSjF06FBHjQ1BEJy2vfgzITY2FoIgQKPRoLW1tdPnEXD+M6qtrQ2A9cbnjTfewO7du3Hbbbdhx44d+J//+R/H9i0tLdi+fXuXNa0ufl6IiHyRr73PA53vFwDre/i5c+cAWIdgL1u2DB999BFMJhOuvvpqpKWlQSqVOj4L0tLS8Mc//hHr1q3DO++8gzfffBP9+vXD7Nmz8cgjj6ClpQUAMGfOnC7bcGHtqYiIiC63udR9RFtbGywWC9auXetUH8ouNDTU6fcL73MAQCwWd/pMo+DCoBSRG9g/rBobG5GQkOBYfqUaGwcOHMDzzz+Phx56CD/72c8wYMAAAMBvf/tbHDx4EACgUqm6PJb9w8UuMjISGRkZmD9/fpePFRIS0v0OEREFqaFDh+Kll16C2WzGkSNH8NFHH+H9999H//79IRaL0dDQ4LT9xe/F7mKxWDBnzhzIZDL87W9/w/XXXw+pVIqKigr84x//6LR9a2ur04V+Q0MDJBIJoqOjER0djcrKyk771NfXAzj/OXPNNdcgNTUVn3zyCWQyGZqbm50yBCIjIzF+/Hg8+uijnY4llfKSkoj8g6+8z9vZvxi4UH19vSM4VlRUhJ07d2L16tUYP348wsLCAADjxo1z2ueWW27BLbfcgo6ODuzbtw/vvfceli9fjtTUVERFRQEAfve732HIkCGdHq+rwFh3hYeHQyQSIT8/H1OmTOm0nl+K05Vw+B6RG6Snp0MikeDTTz91Wn7x7xcrKyuDxWLBk08+6QhImc1mR7quxWLBkCFDkJCQgB07djjt+9lnnzn9npGRgZMnT+Laa69FSkqK4+cf//gHNm3aBIlE0ttuEhEFtB07dmDs2LGor6+HRCJBWloali5diqioKDQ1NSEtLQ07d+50mu3o4vfirojFrl9uNTc34+TJk8jNzcXIkSMdQZ/du3cDgFMbADiKxNrX7dixA6NGjYJcLsdNN92Es2fPOr7ssPvHP/4BmUyGkSNHOpZNnz4du3fvxscff4zU1FSnm5eMjAxUVFTguuuuc3zG3HjjjVi3bh127drlch+JiDzNl97n7SorK52+ODh37hzKysqQmZkJwFrEPDMzE3fccYcjIPXtt9+iqanJ0c5Vq1YhNzcXgiBAoVDg9ttvx/PPP+843qhRoyCTyVBbW+t0nyCTyfDyyy93mvXbFREREbj++utx4sQJp2Pbi8WXlJS4dLzePJfkn/i1FpEbDBo0CDNnzsTvf/97GI1GjBgxArt27XJ8iF3qzdV+I/Diiy9i5syZaGtrw/r163Hs2DEAQHt7OyIiIlBYWIhnn30WixYtwt13341Dhw7h/fffdzpWfn4+PvroI+Tn5+OnP/0pVCoVtm/fjr/97W/41a9+1Ye9JyIKDKNHj4bFYkFBQQHmzJmD8PBwfPLJJ1Cr1bjzzjtx7733Ij8/H/PmzcNPfvITnD59Gq+++uoVj2v/hvrjjz/GqFGjMGjQoCvuExsbi6uuugobNmzAgAEDEBUVhS+++AJ//vOfAViHZF9o9erVMJvNSEhIwPvvv4+TJ0/iT3/6EwAgJycHf/3rX/HEE0/gySefxKBBg/Cf//wHW7ZswRNPPOFoHwBMmTIFK1aswD//+U8sXLjQ6THmzZuHBx98EI8//jh+8pOfIDQ0FB988AH+9a9/Yc2aNVfsExGRt/nS+7xdaGgo5s2bh6effhpmsxmvvvoqlEolHnnkEQDW+4VPPvkE77//PoYNG4Zjx46huLgYIpHI8Vkwbtw4/OlPf8KCBQswffp0GI1GvP3221AqlRg7diyUSiUee+wxvPrqq9BoNMjMzERtbS1effVViEQijBgxogfP5nnPPPMM5syZg2effRbTp0+H2WzGu+++i8OHD2Pu3LkuHSsqKgplZWUoLS3FmDFjOpU+ocDDMCSRmyxevBgPPvgg3n33XcybNw81NTWON2H7txoXy8zMxAsvvICysjLMnj0bK1aswMCBA/H6668DgONb7alTp+KVV17BoUOHMHfuXHz22Wd48cUXnY4VHx+PjRs34qqrrsLSpUvx85//HEeOHEFRURHy8/P7ruNERAGif//+ePvttxEZGYmFCxfi8ccfx9GjR/Haa69h7NixGDNmDN555x00NDSgoKAAGzdudHwTfTl33nknUlJSsGDBArzzzjvdbs8bb7yB+Ph4LFiwAE899RQOHTqE4uJiDB06FAcOHHDatqioCO+99x7mzZuH2tparF27FhkZGQCsQyf+8pe/YNKkSVizZg3mzp2LgwcPoqioCL/4xS+cjqNUKnHrrbdCLBbj3nvvdVo3YsQIbNiwASKRCPPnz8eTTz6J+vp6/OEPf8Cdd97Z7X4REXmLr73PA8Dw4cPxwAMPYOnSpZg/fz4GDx6Mv/71r47hewsWLMAdd9yB1atX4/HHH8emTZswd+5cPPDAAygrK4PZbMbEiRPxu9/9DuXl5XjiiSfwzDPPQKFQ4L333nMUJX/qqaewYMEC7Nq1C7Nnz8ZLL72E9PR0rF+/HpGRkS4/lxeaMGEC3nnnHdTU1ODJJ5/E/PnzIZFI8Kc//cmpQHp3/PznP8c333yD2bNnO+pqUWATCawqRtRrLS0t2L17N2655RZHbQ7Amkr74Ycfupy2SkRE/uHMmTOYPHkyVqxYgZycHG83h4iI3Izv80R9i8P3iNxAoVCgqKgI1113HR555BGEhYXh66+/xl/+8hf8/Oc/93bziIjIRwiCALPZfMXtJBIJhywQEfkhvs8TuYZBKSI3CA0Nxbp167B69WosWLAAHR0dGDx4MBYsWIC8vDxvN4+IiHzE1q1bu1Xnj9/IExH5J77PE7mGw/eIiIiIPKS5ublbsxxdffXVTsPBiYjIP/B9nsg1DEoREREREREREZHHcfY9IiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij2NQioiIiIiIiIiIPE7q7Qb4o8ZGNXy5PLxIBMTGRvp8O3sq0PsHBH4f2T/fYG8nuUdjoxqAf5x7d/OX17y7sd/sdyDiZ4P7ufqaCbbXGvsZOIKlr8Haz776fGBQqgcEAX7x4vOXdvZUoPcPCPw+sn8USC4818F67tnv4MJ+E3VPT18zwfJaYz8DT7D0lf10Dw7fIyIiIiIiIiIij2NQioiIiIiIiIiIPI5BKSIiIiIiIiIi8jgGpYiIyK80NTUhKysLJSUljmU7d+5EdnY2Ro8ejUmTJuH111+HxWJxrN+6dSuysrKQmpqKnJwclJWVOdaZzWasWrUK48ePR1paGubOnYu6ujqP9omIiIiIKBgxKEVERH7j4MGDmDVrFk6fPu1Y9u2332L+/Pl46qmncODAAaxduxYffvgh1q1bBwAoKSnBsmXLsHLlSpSWlmL69OmYO3cuOjo6AADFxcXYu3cvtmzZgj179kAul2PRokXe6B4RERERUVBhUIqIiPzC1q1bUVhYiKefftpp+dmzZ/Hggw/i9ttvh1gsxrBhw5CVlYXS0lIAwKZNmzBlyhSkp6dDJpMhPz8fKpUK27dvd6yfPXs2EhISEBERgYULF2L37t2oqqryeB+JiIiIiIIJg1JEROQXJkyYgF27duHee+91Wn7XXXfhV7/6leN3nU6Hzz//HDfccAMAoKKiAsnJyU77JCYm4tixY1Cr1aipqXFaHxcXh+joaBw/frwPe0NERERERFJvN4CIiKg7+vXrd8VtNBoNfvnLX0IulyM/Px8AoNVqoVAonLaTy+Vob2+HVqsFAISFhXVab1/XXSJR1/8PBvb+st/Bgf32bjv6WqD3j4iIfAuDUkREFBBOnDiBJ598ErGxsXjvvfcQEREBAFAoFNDpdE7b6nQ6qFQqR7DKXl/qwvXh4eEuPX5sbGSX/w8m7HdwYb+JiIiotxiUIiIiv/ff//4XzzzzDB544AE8++yzkErPf7wlJSWhvLzcafuKigpMnDgR0dHRiI+PdxriV19fj5aWlk5D/q6ksVENwHrD2tiohiD0slN+RCRiv9nvwBcs/bb3k4iIyBMYlCIiIr926NAhFBQUYOnSpcjNze20Pjc3FwUFBbjnnnuQnp6ODRs2oLGxEVlZWQCAnJwcFBcXIyUlBSqVCsuXL0dGRgYGDx7sUjsuvEkVBAT0TeulsN/Bhf0mIiKi3mJQiugyLBYLGhoaAFiLH4vFnBuAyNe8+eabMJlMKCoqQlFRkWN5eno63n77bYwbNw5LlizB0qVLUVtbi8TERKxduxZKpRIAUFBQAJPJhLy8PGi1WmRmZmL16tXe6YwHWCwW1NXVAgD694/n+xoRkY+xWCw4e/Ysmpo06N9/AN+niSigMShFdBkNDQ1469+HAQCPTx6F/v37e7lFRATAaWa8N99884rbZ2dnIzs7u8t1MpkMhYWFKCwsdFv7fFldXS3e3FUGAPh5VhoGDEjwcouIiOhCtbU1+NPub6HTGfHzrDQkJAz0dpOIiPoMg1JEVxAeHePtJhARuVWEKs7bTSAiosuIjOkPWYfe280gIupzzAUlIiIiIiIiIiKPY1CKiIiIiIiIiIg8jkEpIiIiIiIiIiLyOAaliIiIiIiIiIjI4xiUIiIiIiIiIiIij+Pse0RERAHGYrGgrq4WANC/fzzEYn4HRURERES+h1epREREAaaurhZv7irDm7vKHMEpIiIiIiJfw0wpIiKiABShivN2E4iIiIiILouZUkRERERERERE5HEMShERERERERERkccxKEVERERERERERB7HoBQREREREREREXkcg1JERERERERERORxDEoREREREREREZHHMShFREREREQ+r6mpCVlZWSgpKXEs27lzJ7KzszF69GhMmjQJr7/+OiwWi2P91q1bkZWVhdTUVOTk5KCsrMyxzmw2Y9WqVRg/fjzS0tIwd+5c1NXVOdY3NjZi3rx5GDNmDDIzM1FUVASTyeSZzhIRBQkGpYiIiAKUYLGgrq4WNTXnUFNzzulGjYjInxw8eBCzZs3C6dOnHcu+/fZbzJ8/H0899RQOHDiAtWvX4sMPP8S6desAACUlJVi2bBlWrlyJ0tJSTJ8+HXPnzkVHRwcAoLi4GHv37sWWLVuwZ88eyOVyLFq0yHH8p556CmFhYdizZw82b96Mr776ynFsIiJyDwaliIiIApSmtQnrv/oR6w9U4c1dZairq/V2k4iIXLZ161YUFhbi6aefdlp+9uxZPPjgg7j99tshFosxbNgwZGVlobS0FACwadMmTJkyBenp6ZDJZMjPz4dKpcL27dsd62fPno2EhARERERg4cKF2L17N6qqqlBZWYn9+/fjueeeg0KhwKBBgzBv3jxs2LDB4/0nIgpkUm83gIiIiPpOuDIGUTH9vd0MIqIemzBhAqZNmwapVOoUmLrrrrtw1113OX7X6XT4/PPPMW3aNABARUUFZs6c6XSsxMREHDt2DGq1GjU1NUhOTnasi4uLQ3R0NI4fPw4AUCqViI+Pd6wfNmwYqqur0dbWhqioqG63XyRyrb+O7UXW/7u6v7+w9ytQ+2cXLP0EgqevwdrPvuovg1JEREREROSz+vXrd8VtNBoNfvnLX0IulyM/Px8AoNVqoVAonLaTy+Vob2+HVqsFAISFhXVab1938b7239vb210KSsXGRnZ7WwDQ6yMA1EEuD0VMTATi4lzb39+4+vz4q2DpJxA8fWU/3YNBKSIiogB3vE6DH2sNuGmIDsPj5N5uDhGRW504cQJPPvkkYmNj8d577yEiIgKANYik0+mcttXpdFCpVI4Ak72+1IXrw8PDIQhCp3X238PDw11qX2OjGoLQ/e2bmzW2tujR1KRBaKjapcfzFyKR9WbX1efH3wRLP4Hg6Wuw9tP+u7sxKEVERBTADGZg74lmmAUBz+2sQv71cgiCAFGg55wTUVD473//i2eeeQYPPPAAnn32WUil529vkpKSUF5e7rR9RUUFJk6ciOjoaMTHx6OiosIxhK++vh4tLS1ITk6GxWJBS0sLGhoaEBcXBwD48ccfMWDAAERGunZTJghw6cbVsa3g+r7+KBj6CARPP4Hg6Sv76R4sdE5ERBTATmtFMAsCxBAgAPjgezXa29u93Swiol47dOgQCgoK8Ktf/QrPP/+8U0AKAHJzc7Ft2zbs27cPRqMR69atQ2NjI7KysgAAOTk5KC4uRlVVFTQaDZYvX46MjAwMHjwYQ4YMQXp6OpYvXw6NRoOqqiq88cYbyM3N9UZXiYgClk8GpY4ePYq8vDyMGTMGEyZMwP/93//BYDAAAA4fPoz7778faWlpmDRpEjZt2uS079atW5GVlYXU1FTk5OSgrKzMsc5sNmPVqlUYP3480tLSMHfuXNTV1Xm0b0RERJ5iEYBTWmtG1PAIA0QQ0C7IoDV5uWFERG7w5ptvwmQyoaioCGlpaY6fxx57DAAwbtw4LFmyBEuXLkVGRgb++c9/Yu3atVAqlQCAgoIC3HrrrcjLy8Ott94KvV6P1atXO46/Zs0amEwmTJ48GQ888ABuueUWzJs3zws9JSIKXD43fM9iseDxxx/HnDlz8Je//AV1dXWO6VsfeughzJkzB08++SRmzZqF0tJSFBQUYPjw4Rg5ciRKSkqwbNkyrF27FiNHjsSGDRswd+5cfPbZZ1AoFCguLsbevXuxZcsWREZGYvHixVi0aBH++Mc/ervbREREbtdslEBnFkEuFWOwwog6Ywga9UBthwj9o73dOiIi19lnxgOsQakryc7ORnZ2dpfrZDIZCgsLUVhY2OX6uLg4rFmzpmcNJSKibvG5TKnW1lbU19fDYrFAsA1cFIvFUCgU+PTTT6FUKpGXlwepVIpx48Zh2rRp2LBhAwBg06ZNmDJlCtLT0yGTyRzBrO3btzvWz549GwkJCYiIiMDChQuxe/duVFVVea2/REREfaXVZP2YHxAVCokIiJdbP1frdKwnRURERETe53NBKZVKhfz8fKxatQopKSm49dZbMWTIEOTn56O8vNxRiNAuMTERx44dAwCnQoUXr1er1aipqXFaHxcXh+joaKdvXLpDJPL9H39ppz/0z1vnnefQv6waTSEAAQAASURBVH/8pX8U2NS2oFRseAgAoL8tKNWoB4yWIKjMSUREREQ+zSeH78nlcixevBi5ubmorKzEE088gTVr1kCr1Tqmb7WTy+WOgq2XW6/VagEAYWFhndbb13VXX0yD2Bf8pZ095Yn+mc3tUChCbI8Xgbg4zz6nPIf+LdD7R76v1WgLSoXJAD0QIQNCxRboLWK06hmUIiIiIiLv8rmg1K5du7Bz507s2LEDgHUq14KCAhQVFWHatGlQq9VO2+t0OoSHhwMAFAoFdDpdp/UqlcoRrOro6Ljk/t3V2Kj26akfRSLrzbCvt7OnPNm/xkYNOjoMjv9LJGFX2MM9eA79m7/0z95OCkxmiwCt+XymVGuzdXmk1AK9QYxWvcWLrSMiIiIi8sGg1Llz5xwz7dlJpVLIZDIkJydj7969TusqKiqQlJQEwBrAKi8v77R+4sSJiI6ORnx8vNMQv/r6erS0tHQa8nclggCfvtG085d29pQn+nfh8b3xfPIc+rdA7x/5tlaDAAEiyMQCwkMkaLUtj5RY0ACghZlSRERERORlPldTasKECaivr8ebb74Js9mMqqoqFBcXY9q0acjKykJDQwPWrVsHo9GIffv2Ydu2bZg5cyYAIDc3F9u2bcO+fftgNBqxbt06NDY2IisrCwCQk5OD4uJiVFVVQaPRYPny5cjIyMDgwYO92WUiIiK3a9ZZM6GiZYDoggJikVLrcmZKEREREZG3+VymVGJiIt566y2sXr0ab7/9NiIjIzF9+nQUFBQgJCQE7777LoqKirBmzRrExMRg0aJFGDt2LABg3LhxWLJkCZYuXYra2lokJiZi7dq1UCqVAICCggKYTCbk5eVBq9UiMzMTq1ev9l5niYiI+og9EypK5pwRdT4oJThmuSUiIiIi8gafC0oBwPjx4zF+/Pgu16WkpGDjxo2X3Dc7OxvZ2dldrpPJZCgsLERhYaFb2klEROSrNAZrwClC5rw8QmqBCAIMFhGaOsxI8ELbiIiIiIgAHxy+R0RERL2nNVmDUgqJczaURASE276SOtWi93SziIiIiIgcGJQiIiIKMIIgoN1oD0p1Xh9pG9JX2WLovJKIiIiIyEMYlCIiIgowbXoLzLYEKUUXA/UjbMuq1QxKEREREZH3MChFREQUYOq1RgBAqNgCiajz+jBbUKpGY/Rgq4iIiIiInDEoRUREFGDqtCYAgFzc9ex64VLr8nNqBqWIiIiIyHsYlCIiIgow9e3WYJNCYulyvT1TqqHdBKO5622IiIiIiPoag1JEREQBxp4pdfHMe3ahYussfBYBONfGGfiIiIiIyDsYlCIiIgow9ppSiksM3xOJgAiZtdjUmZYOj7WLiIiIiOhCDEoREREFmHpHptSlh+ZFhNiDUjqPtImIiIiI6GIMShEREQUYx/C9S2RKAUC4LVPqbCszpYiIiIjIOxiUIiIiv9LU1ISsrCyUlJQ4lh0+fBj3338/0tLSMGnSJGzatMlpn61btyIrKwupqanIyclBWVmZY53ZbMaqVaswfvx4pKWlYe7cuairq/NYf9xNZzSjTW8GcIVMKRkzpYiIiIjIuxiUIiIiv3Hw4EHMmjULp0+fdixrbW3FnDlzMGPGDJSWlqKoqAgrVqzAkSNHAAAlJSVYtmwZVq5cidLSUkyfPh1z585FR4c1Q6i4uBh79+7Fli1bsGfPHsjlcixatMgr/XOHxnYDAGshc6no0tudH77HTCkiIiIi8g4GpYiIyC9s3boVhYWFePrpp52Wf/rpp1AqlcjLy4NUKsW4ceMwbdo0bNiwAQCwadMmTJkyBenp6ZDJZMjPz4dKpcL27dsd62fPno2EhARERERg4cKF2L17N6qqqjzex96yWCyoqKoBAIRKrAXNL8WeKVXdqoMgXHqYHxERERFRX5F6uwFERETdMWHCBEybNg1SqdQpMFVeXo7k5GSnbRMTE7F582YAQEVFBWbOnNlp/bFjx6BWq1FTU+O0f1xcHKKjo3H8+HEMGjSo2+27MAB0uWBQX6qvr8XGfT8AUEAmunygyV5vSmeyoKXDgJjw0B4/rr2/3uq3t7Df3m2HpwVLvwO9f0RE5FsYlCIiIr/Qr1+/LpdrtVooFAqnZXK5HO3t7Vdcr9VqAQBhYWGd1tvXdVdsbGSX//ckg0GNkCgV0KqDQiaCQh4CUUgIwsJCHf8XxGbrvwYtQkVS6AUxzjS3IPmaxF4/vrf67W3sd3AJ1n4TERH1BQaliIjIrykUCqjVaqdlOp0O4eHhjvU6na7TepVK5QhW2etLdbV/dzU2WtsQGxuJxkY1vDEirqlJA3WHdeY9GSzo0BkgtkjQ3q53/N9iOP+vQiaD3gBUVLdh5NXqKxz90kQi7/bbW9hv9jsQ2ftJRETkCQxKERGRX0tOTsbevXudllVUVCApKQkAkJSUhPLy8k7rJ06ciOjoaMTHx6OiosIxhK++vh4tLS2dhgReyYU3qYIAr9y0CgKgM1kfOERy5e0VEgEtEKFWY3RLe73Vb29jv4NLsPabiIioL7DQORER+bWsrCw0NDRg3bp1MBqN2LdvH7Zt2+aoI5Wbm4tt27Zh3759MBqNWLduHRobG5GVlQUAyMnJQXFxMaqqqqDRaLB8+XJkZGRg8ODB3uxWj+nM1rvl0G58witsgat6rakPW0RERERE1DVmShERkV9TqVR49913UVRUhDVr1iAmJgaLFi3C2LFjAQDjxo3DkiVLsHTpUtTW1iIxMRFr166FUqkEABQUFMBkMiEvLw9arRaZmZlYvXq19zrUS3qz9d/QbmRKhdmuAuq0xr5rEBERERHRJTAoRUREfuf48eNOv6ekpGDjxo2X3D47OxvZ2dldrpPJZCgsLERhYaFb2+gtjuF74iuPL1JIrNvUMVOKiIiIiLyAw/eIiIgCiN4+fK87NaVsX03VM1OKiIiIiLyAQSkiIqIAYbYI54fvuVBTSm2woN1g7ruGERERERF1gUEpIiKiANFmj0hBQEg3PuFlYusPAJxr0/VZu4iIiIiIusKgFBERUYBo0VmDUiEiASJR9/YJl1k3rGnT91WziIiIiIi6xKAUERFRgGjRWQuWd6fIuV2YLShVzUwpIiIiIvIwBqWIiIgCRKstUyrUhaBUuNSeKcWgFBERERF5FoNSREREAcIxfM+VoJQtU+och+8RERERkYcxKEVERBQg7IXOezJ8j5lSRERERORpDEoREREFCI3BAgCQdbPIOcBMKSIiIiLyHgaliIiIAoS6J5lStppSDVoDDCZLn7SLiIiIiKgrDEoREREFCI3BGpSSuRCUCpUAIRJrYKpWzWwpIvJdTU1NyMrKQklJiWPZ4cOHcf/99yMtLQ2TJk3Cpk2bnPbZunUrsrKykJqaipycHJSVlTnWmc1mrFq1CuPHj0daWhrmzp2Luro6x/rGxkbMmzcPY8aMQWZmJoqKimAymfq+o0REQYRBKSIiogChdgzf635QSiQSoX+4FABwjnWliMhHHTx4ELNmzcLp06cdy1pbWzFnzhzMmDEDpaWlKCoqwooVK3DkyBEAQElJCZYtW4aVK1eitLQU06dPx9y5c9HR0QEAKC4uxt69e7Flyxbs2bMHcrkcixYtchz/qaeeQlhYGPbs2YPNmzfjq6++wrp16zzabyKiQMegFBERUYCwD99zJVMKAPqHywAANawrRUQ+aOvWrSgsLMTTTz/ttPzTTz+FUqlEXl4epFIpxo0bh2nTpmHDhg0AgE2bNmHKlClIT0+HTCZDfn4+VCoVtm/f7lg/e/ZsJCQkICIiAgsXLsTu3btRVVWFyspK7N+/H8899xwUCgUGDRqEefPmOY5NRETuwaAUERFRgND2IFMKAPoxU4qIfNiECROwa9cu3HvvvU7Ly8vLkZyc7LQsMTERx44dAwBUVFRccr1arUZNTY3T+ri4OERHR+P48eMoLy+HUqlEfHy8Y/2wYcNQXV2NtrY2d3eRiChoSb3dACIiIuo9k0WA1mgNSrlS6Bw4nyl1jjWliMgH9evXr8vlWq0WCoXCaZlcLkd7e/sV12u1WgBAWFhYp/X2dRfva/+9vb0dUVFR3W6/yIUZUZ22F1n/7+r+/sLer0Dtn12w9BMInr4Gaz/7qr8MShEREQUAje588V2pixcN9ppSNcyUIiI/olAooFarnZbpdDqEh4c71ut0uk7rVSqVI8Bkry918f6CIHRaZ//dfvzuio2NdGl7vT4CQB3k8lDExEQgLs61/f2Nq8+PvwqWfgLB01f20z0YlCIiIgoArTojAEAmBsQuBqX62TOlWFOKiPxIcnIy9u7d67SsoqICSUlJAICkpCSUl5d3Wj9x4kRER0cjPj7eaYhffX09WlpakJycDIvFgpaWFjQ0NCAuLg4A8OOPP2LAgAGIjHTtBq2xUQ3BhQTW5mYNAECn06OpSYPQUPUV9vBPIpH1ZtfV58ffBEs/geDpa7D20/67u7GmFBERUQBos2VKhUhcz622Z0rVqvUwWwL46oqIAkpWVhYaGhqwbt06GI1G7Nu3D9u2bcPMmTMBALm5udi2bRv27dsHo9GIdevWobGxEVlZWQCAnJwcFBcXo6qqChqNBsuXL0dGRgYGDx6MIUOGID09HcuXL4dGo0FVVRXeeOMN5ObmutxOQXD9x7pjz/b1p5+ePj/+9hMs/QymvgZrP/sCM6WIiIgCgCMo1YOvm2IUUkjEIpgtAuo1egyIkru5dURE7qdSqfDuu++iqKgIa9asQUxMDBYtWoSxY8cCAMaNG4clS5Zg6dKlqK2tRWJiItauXQulUgkAKCgogMlkQl5eHrRaLTIzM7F69WrH8desWYMXX3wRkydPhlgsxowZMzBv3jwv9JSIKHAxKEVERBQA7MP3epIpJRGLEB8ZiupWHWraGJQiIt91/Phxp99TUlKwcePGS26fnZ2N7OzsLtfJZDIUFhaisLCwy/VxcXFYs2ZNzxtLRERXxOF7REREAeD88L2e7Z8QFQoAOKdmsXMiIiIi8gwGpYiIiPycxWJBdUMzgJ4N3wPgyI6qYbFzIiIiIvIQBqWIiIj8XF1dLb6qqAEAiCymHh0jIdKWKdXGTCkiIiIi8gwGpYiIiAKAILVmOvU0UyrBlil1jplSREREROQhDEoREREFAIPF+q+sx8P3rJlSNcyUIiIiIiIPYVCKiIgoAOjNAgBAJhZ6tP+FmVKC0LNjEBERERG5QurtBhAREVHvGczWf3uaKRVvqymlN1nQpDXAqGkCAPTvHw+xmN9hEREREZH7MShFREQUAIwWe6YUgB4kOoVIxYgLD0GD1oDvKqvx76+/BwD8PCsNAwYkuLGlRERERERWDEoREREFAKO9ppQIPQpKAUBCVCgatAbUaY2IUMW5rW1ERERERF1hPj4REZGfM5gtsCVKQdqLT/YBtrpS9VqTG1pFRERERHR5DEoRERH5uXZ7mhQESEU9P06CbQa+OgaliIiIiMgDGJQiIiLyc+0Ga1BKKgJEvQpKWTOl6rRGdzSLiIiIiOiyGJQiIiLyc1qjPSjVw2JSNgxKEREREZEnMShFRETk59rdFJQawOF7RERERORBnH2PiIjIz9mDUrIefNUkWCyoq6sFAIhtx9EaLDCaBcgkvRgLSERERER0BQxKERER+Tmt4cJMKdcCSZrWJqz/qgP9rzJA09yAyJAoqA0WaE0ClAxKEREREVEfYlCKiIjIz7UbzQB6FpQCgHBlDKJi+gMA+hkFqA16tBsFKEPd2UoiIiIiImesKUVEROTnzteU6v2x+odbv6/SGntXn4qIiIiI6EqYKUVEROTnHLPviXsXSBIsFkSIbXWlGJQiIiIioj7GoBQREZGfa7fVlJL1cvY9TWsTTrUAQATadEYAst42jYiIiIjoknxy+F5LSwvmz5+PzMxM3HTTTZg3bx7q6uoAAIcPH8b999+PtLQ0TJo0CZs2bXLad+vWrcjKykJqaipycnJQVlbmWGc2m7Fq1SqMHz8eaWlpmDt3ruO4RERE/krrxuF70ZEKAECHmUXOiYiIiKhv+WRQ6he/+AXa29uxa9cufPbZZ5BIJFi8eDFaW1sxZ84czJgxA6WlpSgqKsKKFStw5MgRAEBJSQmWLVuGlStXorS0FNOnT8fcuXPR0dEBACguLsbevXuxZcsW7NmzB3K5HIsWLfJmV4mIiHqtw3jh7Hu9o5DYjmnq9aGIiIiIiC7L54JS3377LQ4fPoyVK1ciKioKERERWLZsGQoLC/Hpp59CqVQiLy8PUqkU48aNw7Rp07BhwwYAwKZNmzBlyhSkp6dDJpMhPz8fKpUK27dvd6yfPXs2EhISEBERgYULF2L37t2oqqryZpeJiMgNjh49iry8PIwZMwYTJkzA//3f/8FgMADoXZatP3BXTSkACLMFpfQWEcwW1pUiIiIior7jc0GpI0eOIDExEX/729+QlZWFCRMmYNWqVejXrx/Ky8uRnJzstH1iYiKOHTsGAKioqLjkerVajZqaGqf1cXFxiI6OxvHjx11qo0jk+z/+0k5/6J+3zjvPoX//+Ev/AoXFYsHjjz+Ou+66C/v378fmzZvxxRdfYO3atb3OsvUH52tK9f5YMjEggTUY1W5iUIqIiIiI+o7PFTpvbW3F8ePHceONN2Lr1q3Q6XSYP38+nn/+ecTFxUGhUDhtL5fL0d7eDgDQarWXXK/VagEAYWFhndbb13VXbGykq93yCn9pZ095on9mczsUihDb40UgLs6zzynPoX8L9P75ktbWVtTX18NisUAQrIEUsVgMhULhlGULwCnLduTIkU5ZtgCQn5+PDz74ANu3b8fMmTO91idXtLtx+J5IBCgkFmjMEs7AR0RERER9yueCUiEh1gDAwoULERoaioiICDz11FN44IEHkJOTA51O57S9TqdDeHg4AEChUHS5XqVSOYJVF3/zfeH+3dXYqIbgw9fpIpH1ZtjX29lTnuxfY6MGHR0Gx/8lkrAr7OEePIf+zV/6Z29nIFCpVMjPz8eqVavw29/+FmazGZMnT0Z+fj5WrlzZZRbt5s2bAVizbC8OPl2YhdtdF2aeeToLrd1oBuCeoBQAKCQCNGZAaxS6lVV3YXZgMGG/vdsOTwuWfgd6/4iIyLf4XFAqMTERFosFRqMRoaGhAKzDMgDguuuuw1//+len7SsqKpCUlAQASEpKQnl5eaf1EydORHR0NOLj452G+NXX16OlpaXTzcqVCAJ8+kbTzl/a2VOe6N+Fx/fE41ksFjQ0NEAkAkymdohEcohEPjfK1m34GiV3sVgskMvlWLx4MXJzc1FZWYknnngCa9asuWwWLXD5LFtXXBjg82Swz2wR0GEbZhepkEEuD4EgNkMUcv7fsLBQKOQhTssut01EiAH1BsAAMWJiup8lGihBTlex38ElWPtNRETUF3wuKDV+/HgMGjQIv/71r7FixQro9Xq88soruOOOOzB16lSsWbMG69atQ15eHg4ePIht27bhjTfeAADk5uaioKAA99xzD9LT07FhwwY0NjYiKysLAJCTk4Pi4mKkpKRApVJh+fLlyMjIwODBg73ZZSKHhoYGvPXvwwiPjoFZr0H+hOvQr19/bzeLyOft2rULO3fuxI4dOwBYv6QoKChAUVERpk2bBrVa7bR9d7NsXdHYaH0MT2fJtemMjv+bDAboRBJYDAaILef/bW/Xo0PnvOxy24QIJgAStHaY0NSkQUiI+tINgP9kB7ob+81+B6JAyqIlIiLf53NBKZlMhr/85S9YuXIl7rrrLuj1ekyaNAkLFy5EVFQU3n33XRQVFWHNmjWIiYnBokWLMHbsWADWOiFLlizB0qVLUVtbi8TERKxduxZKpRIAUFBQAJPJhLy8PGi1WmRmZmL16tXe6yxRF8KjYxChjIVZH+LtphD5jXPnzjlm2rOTSqWQyWRITk7G3r17ndZ1N8vWFZ7OrLTT6K1D9yQiQOymYTcKia3QuVFwqS/Bmh3IfgeXYO03ERFRX/C5oBQAxMfH45VXXulyXUpKCjZu3HjJfbOzs5Gdnd3lOplMhsLCQhQWFrqlnURE5BsmTJiAl19+GW+++SZmz56N6upqFBcXY9q0acjKysJLL73U4yxbX6fRmwAAUjeO9FVIrMPmWeiciIiIiPqSTwaliIiIXJGYmIi33noLq1evxttvv43IyEhMnz4dBQUFCAkJ6VWWra+zZ0rJ3JUmBUAhtgajOkzWmlVERERERH2BQSkiIgoI48ePx/jx47tc15ssW1+nNVgzpWQS9x0zVCxADAEWiFDfbsJV7js0EREREZFD4E7rRUREFAT6IlNKJAIUtq+tajXGy29MRERERNRDDEoRERH5MXtNKZmbP9HDbEGpGgaliIiIiKiPMChFRETkx7QG92dKAUCYbQY+ZkoRERERUV9hUIqIiMiPOTKl3FhTCjifKcWgFBERERH1FQaliIiI/FjfDd+zZ0qZ3HtgIiIiIiIbBqWIiIj8mKbPhu9Z/2VNKSIiIiLqKwxKERER+TFtHxc6b9Wb0W4LfBER+ZqjR48iLy8PY8aMwYQJE/B///d/MBgMAIDDhw/j/vvvR1paGiZNmoRNmzY57bt161ZkZWUhNTUVOTk5KCsrc6wzm81YtWoVxo8fj7S0NMydOxd1dXUe7RsRUTBgUIqIiMiP9VWmlEwMhNiuEqpbdW49NhGRO1gsFjz++OO46667sH//fmzevBlffPEF1q5di9bWVsyZMwczZsxAaWkpioqKsGLFChw5cgQAUFJSgmXLlmHlypUoLS3F9OnTMXfuXHR0dAAAiouLsXfvXmzZsgV79uyBXC7HokWLvNldIqKAxKAUERGRH+urQucAEC6zBrrOMihFRD6otbUV9fX1sFgsEARrHTyxWAyFQoFPP/0USqUSeXl5kEqlGDduHKZNm4YNGzYAADZt2oQpU6YgPT0dMpkM+fn5UKlU2L59u2P97NmzkZCQgIiICCxcuBC7d+9GVVWV1/pLRBSIGJQiIiLyY9o+ypQCgIgQe1Cqw+3HJiLqLZVKhfz8fKxatQopKSm49dZbMWTIEOTn56O8vBzJyclO2ycmJuLYsWMAgIqKikuuV6vVqKmpcVofFxeH6OhoHD9+vO87RkQURKTebgARERH1nL2mVEgffM1kz5Ti8D0i8kUWiwVyuRyLFy9Gbm4uKisr8cQTT2DNmjXQarVQKBRO28vlcrS3twPAZddrtVoAQFhYWKf19nWuELn4nYFje5H1/67u7y/s/QrU/tkFSz+B4OlrsPazr/rr9qBUSUkJMjMz3X1YIiLyY/xs6BuCIDiG70nFIri7HDmH7xFRb/Xl+/+uXbuwc+dO7NixAwCQlJSEgoICFBUVYdq0aVCr1U7b63Q6hIeHAwAUCgV0Ol2n9SqVyhGssteX6mp/V8TGRrq0vV4fAaAOcnkoYmIiEBfn2v7+xtXnx18FSz+B4Okr++kebg9KPfnkk4iMjMR9992H++67DwMHDnT3QxARkZ/hZ0Pf0JksMFvLqEAmgduDUhHMlCKiXurL9/9z5845Ztqzk0qlkMlkSE5Oxt69e53WVVRUICkpCYA1gFVeXt5p/cSJExEdHY34+HinIX719fVoaWnpNOSvOxob1bCVvOqW5mYNAECn06OpSYPQUPUV9vBPIpH1ZtfV58ffBEs/geDpa7D20/67u7k92f+LL77Ac889h2+//RZ33XUXfvrTn+Ljjz/u9IFBRETBg58NfcM+dE8sAqR9kFJ94fA9IZCvuv6fvTuPj6q+9z/+mpnMZCYLWQiERVAxAVxAIsii1gWL2AriRdD2R23hXqUXUWtbXMFqqyjcarVIRUUttdBiUdGiVkHrioCgCEoFCQoJhOzrJJlklvP7YzIjgRASmMlkJu/n4zGPJGeZ8/lOkvM953O+i4iETTjP/xdccAElJSU8+eSTeL1e8vPzWbJkCRMnTmTcuHGUlpaybNky3G43GzduZM2aNVx99dUATJkyhTVr1rBx40bcbjfLli2jrKyMcePGATB58mSWLFlCfn4+TqeTBx98kJEjR9K/f/92x2kY7X/5dzy+faPpdbyfT7S9uko5u1JZu2o5wyHkLaWsVivjx49n/PjxlJeX8+abb/Lcc8/xu9/9jiuuuIJrr72WwYMHh/qwIiLSialuCA9ng79tlMNqxhSGjv4JVhMm/C2yyuvcdE+0hfwYIhLbwnn+z8rK4qmnnuKxxx7jmWeeITk5mSuvvJLZs2djs9l47rnnmD9/PosWLSI9PZ158+YxevRoAMaMGcO9997LfffdR1FREVlZWSxdupTU1FQAZs+ejcfjYdq0adTW1jJq1Cgee+yxEH0qIiISELaBzsvKynjttdd4/fXXyc3N5aKLLiI+Pp7p06czffp0/vd//zdchxYRkU5KdUNoORv9LaUSreGZTNdiMtE9IY7SOg8HqlxKSonIcQvX+f+8887jvPPOa3HdkCFDWLly5VH3nTRpEpMmTWpxndVqZc6cOcyZM+e44hIRkbYJeVLq9ddf59VXX+Xjjz9mwIABTJ48mSeffJL09HQALrroImbPnq0bDxGRLkR1Q3jUNrWUSghTUgqgV5KV0joPBVUuhvbpFrbjiEhs0vlfRERaE/Kk1G9/+1uuuOIKVq5cyVlnnXXE+lNPPZXp06eH+rAiItKJqW4Ij3C3lALITIrjy2I4UFV/7I1FRA6j87+IiLQm5Empjz76iPz8fDIzMwH4/PPPSU5O5rTTTgOgV69e3HLLLaE+rIiIdGKqG8LD2TTQucNqBnxhOUavJCugGfhE5Pjo/C8iIq0J+aPVd955h6uuuoq9e/cCsHXrVqZOncr7778f6kOJiEiUUN0QHoGBzhNtlrAdI7MpKXVASSkROQ46/4uISGtC3lJq8eLFPPHEE8HmuTNmzCArK4vf//73XHTRRaE+nIiIRAHVDeFR29R9L9xjSoFaSonI8dH5X0REWhPyq9iDBw/yve99r9myCy64gIKCglAfSkREooTqhvBwdsBA54GWUkU1DXi84ekiKCKxS+d/ERFpTcivYvv27cuHH37YbNmGDRvo06dPqA8lIiJRQnVDeATGlArnQOdpdgvxcWZ8BhTWNITtOCISm3T+FxGR1oS8+97MmTOZPXs2l112GX379qWgoIB169axcOHCUB9KRESihOqG8KhtbGopZTPTEKbedSaTiT7d7HxbXseBShcnpTrCcyARiUk6/4uISGtC/mh14sSJLF26FKvVyo4dO7Db7Tz33HOMHz8+1IcSEZEoobohPDqipRRA31Q7AAeqNa6UiLSPzv8iItKakLeUAhg1ahSjRo0Kx1uLiEiUUt0Qes7G8I8pBdCnW1NSqlJJKRFpP53/RUTkaEKelCoqKmLJkiXs3bsXn6/5gKjPP/98qA8nIiJRQHVDeARaSiVYLWE9TqCllGbgE5H20vlfRERaE/Kk1F133UVpaSmXXHIJVqs11G8vIiJRSHVDeATGlEq0dVBLqar6sB5HRGKPzv8iItKakCelvvjiC9566y3S09ND/dYiIhKlVDeER6CllCPc3fdS1FJKRI6Pzv8iItKakF/FJicnY7PZQv22IiISxVQ3hJ7H66PB4+8KE+6BzgNJqSqXJ5gIExFpC53/RUSkNSG/ir3xxhu566672L59OwUFBc1eIiLSNaluCL3AIOcQvoHODZ+P4uIinBUlpNj9jasPqLWUiLSDzv8iItKakHffmzdvHgDr1q0DwGQyYRgGJpOJr776KtSHExGRKKC6IfQO7bpnMZvCc4yqcpZvqCchoYBeiRlUuTwcqKxnUM+ksBxPRGKPzv8iItKakCel3nnnnVC/pYiIRDnVDaFX2xAY5DzkVXkzianpJCYm0rvByq4yF3kVGuxcRNpO538REWlNyNv79+3bl759+1JVVcWOHTvo0aMHdrudvn37hvpQIiISJVQ3hJ6z0d9SKine0iHH65PsnzVrf6W674lI2+n8LyIirQl5UqqsrIwf/ehHXHPNNdxxxx3k5+fz/e9/n61bt4b6UCIiEiVUN4Ses6mlVFJ8eFtKBfRuSkrlVaqllIi0nc7/IiLSmpAnpR588EEGDhzI5s2biYuL47TTTmPmzJn83//9X6gPJSIiUUJ1Q+jVNrWUSrR1TEup3sn+2bP2KyklIu2g87+IiLQm5EmpjRs3ctddd+FwODCZ/AOvXn/99eTm5ob6UCIiEiVUN4ReYKDzjmgpZfh8WBsqAShxNlLb4A77MUUkNuj8LyIirQl5UspqteJy+cebMAwDgNraWhITE0N9KBERiRKqG0KvtrGp+16YBzoH/yx8r2z5BlvTVcP2bw6E/ZgiEht0/hcRkdaEPCk1duxYbrvtNvbu3YvJZKKsrIzf/va3XHTRRaE+lIiIRAnVDaEXaCmV2EEDnSemppPi8Hfh27m/hMLCg/h8vg45tohEL53/RUSkNSFPSv36178mISGByy+/nOrqai644ALq6+uZM2dOqA8lIiJRQnVD6AUHOu+AllIB3Rz+Y725s4Qn122luLiow44tItFJ538REWlNyK9kExMTWbRoEeXl5ezfv59evXrRs2fPUB9GRESiiOqG0AsOdN5BLaUAUuz+ywa3NYGktIQOO66IRC+d/0VEpDUhT0pt3ry52c/79u1j3759AJx77rmhPpyIiEQB1Q2hF2wpFR8HGB1yzG52KwB1HlOHHE9Eop/O/yIi0pqQJ6Wuu+66I5aZzWZ69+7NO++8E+rDiYhIFOiIuqGyspIHH3yQ999/H5/Px7nnnst9991Hz5492bZtGw888AC5ubmkpaUxa9Yspk6dGtx39erVPPHEE5SUlDBgwADuuececnJyQhJXuARn37NZAE+HHLNbU0up2o45nIjEAN0biIhIa0KelNq5c2ezn8vLy/nTn/5E3759Q30oERGJEh1RN9x8882kpKSwbt06zGYzd911F/fccw//93//x8yZM7nlllu49tpr2bx5M7Nnz2bQoEEMHTqUTZs2cf/997N06VKGDh3KihUrmDVrFu+++y4OhyNk8YWaM9h9L46OSkoFuu+5vCY8vo5pnSUi0U33BiIi0pqQD3R+uPT0dG677Tb+8pe/hPtQIiISJUJdN3z55Zds27aNBQsW0K1bN5KSkrj//vuZM2cOa9euJTU1lWnTphEXF8eYMWOYOHEiK1asAGDVqlVcccUVDB8+HKvVyvTp00lLS+ONN94ISWzhUtus+17HiI8zE2fyJ6OcbiWlRKT9dG8gIiKHCntSCqCqqoqGhoaOOJSIiESJUNYN27dvJysri3/84x+MGzeOCy64gIULF9KjRw92797NwIEDm22flZUVfHqfm5vb6vrOxufzUVh4kBqXG4BEW8cNdG4ymUi0+ABwNiopJSLHR/cGIiISEPLHq3fddVezn91uN59++innnXdeqA8lIiJRItx1Q1VVFbt27eKss85i9erVuFwubr/9du644w4yMjKO6IZnt9upq6sDoLa2ttX1bWUytfx9qJWUFLFk7VZq3YmAiWR7HB53+I53uASLjyqPBafbwGQi+ILwlrszUrkjG0dH6yrlDnX5dG8gIiKtCXub//j4eK677jquvfbacB9KRESiRKjrBpvNBsDcuXOJj48nKSmJW2+9lWuuuYbJkyfjcrmabe9yuUhMTATA4XC0uD4tLa1dMXTvntzi96HW2FhDWq/eUFIDQLypnrpGJ3a7FY/dhslmwzB7m31NSIjHcZR17d0mJd7MwQao95lIT08iI6Njyt2ZqdxdS1ctd6jo3kBERA4V8qTUQw89FOq3FBGRKBfuuiErKwufz4fb7SY+Ph7wd3MDOP300/nb3/7WbPvc3Fyys7MByM7OZvfu3Uesv/DCC9sVQ1mZP0nUvXsyZWU1GGHq3VZe7qS6thEAEwZL3t5J8b7dJPXoi6+xEbPPcsTXuroG6l0tr2vvNjbDDViorPdSXu7EZqvBZAp/uTsjlVvljkWBcoaK7g1ERKQ1IU9KLV68uE3b3XTTTaE+tIiIdFLhrhvOO+88+vXrx913381DDz1EQ0MDjz76KN///veZMGECixYtYtmyZUybNo1PP/2UNWvW8MQTTwAwZcoUZs+ezQ9+8AOGDx/OihUrKCsrY9y4ce2K4dCbVMMgbDethgHuppnv4syQ0j0TZ2VZeA7WgsS4wJhSviPKGc5yd2Yqd9fSVct9vHRvICIirQl5Umr37t2sXbuWwYMHc+qpp1JYWMhnn33GGWecEewqYYr1zvgiItJMuOsGq9XKX//6VxYsWMD48eNpaGhg7NixzJ07l27duvHcc88xf/58Fi1aRHp6OvPmzWP06NEAjBkzhnvvvZf77ruPoqIisrKyWLp0KampqaEoeli4/XkhrBGoThOaBjqv84DL4+v4AEQkqujeQEREWhPypJTZbOauu+7ipz/9aXDZq6++yrvvvstjjz0W6sOJiEgU6Ii6ITMzk0cffbTFdUOGDGHlypVH3XfSpElMmjQpJHF0hENbSnU0mwmsZgO3z8SB6kZO6fgQRCSK6N5ARERaE/LL2ffff59p06Y1WzZhwgQ2bNgQ6kOJiEiUUN0QWm6v/2tcBBoXmEyQ3PRIK7+qseMDEJGoovO/iIi0JuRJqfT0dDZv3txs2YcffkivXr1CfSgREYkSqhtCK9BSyhqBllIASVb/8ZWUEpFj0flfRERaE/Luez//+c+ZOXMm48ePp0+fPuTn5/Puu+/y+OOPh/pQIiISJVQ3hFZgTKk4c2RGW04KtJSqVlJKRFoX7vN/ZWUlDz74IO+//z4+n49zzz2X++67j549e7Jt2zYeeOABcnNzSUtLY9asWUydOjW47+rVq3niiScoKSlhwIAB3HPPPeTk5ADg9Xp5+OGHefXVV6mvr2f06NH89re/pWfPniGJW0RE/EL+jHXq1Kk8+eSTWCwW/vOf/5CamsrKlSu54IILQn0oERGJEqobQivYUipCYwMnq6WUiLRRuM//N998M3V1daxbt453330Xi8XCPffcQ1VVFTNnzuSqq65i8+bNzJ8/n4ceeojt27cDsGnTJu6//34WLFjA5s2bufLKK5k1axb19fUALFmyhPXr1/PSSy/x4YcfYrfbmTdvXkhiFhGR74S8pRT4p+Y+77zzKC8vJz09PRyHEBGRKKO6IXSCY0pFqvte09XDwRo3bq8PW6QCEZGoEK7z/5dffsm2bdv4+OOPSUpKAuD++++npKSEtWvXkpqaGhzPasyYMUycOJEVK1YwdOhQVq1axRVXXMHw4cMBmD59Oi+88AJvvPEGV199NatWrWLOnDn07t0bgLlz53LBBReQn59Pv379QlYGEZGuLuRXkW63m0cffZThw4czduxY8vPzufrqqykuLg71oUREJEqobgitSM6+B2C3+I/tNSC/sj4yQYhIVAjn+X/79u1kZWXxj3/8g3HjxnHBBRewcOFCevTowe7duxk4cGCz7bOysti5cycAubm5R11fU1NDYWFhs/UZGRmkpKSwa9euE45bRES+E/LL2cWLF7Nx40b++Mc/YrVa6d69O7169WL+/Pnteh+v18t1113HnXfeGVy2bds2pk6dSk5ODmPHjmXVqlXN9lm9ejXjxo1j2LBhTJ48ma1btzZ7v4ULF3LeeeeRk5PDrFmzdDMkItJBQlU3iF9gTKlIdd8zmaCbzX/wvWV1kQlCRKJCOM//VVVV7Nq1i71797J69WpeeeUVioqKuOOOO6itrcXhcDTb3m63U1fnP2e1tr62thaAhISEI9YH1rWHydT+l3/H49s3ml7H+/lE26urlLMrlbWrljMcQt59b82aNfz9738nMzMTk8lEQkICDz30EOPGjWvX+yxevJgtW7bQt29fgGC/8FtuuYVrr72WzZs3M3v2bAYNGsTQoUOD/cKXLl3K0KFDWbFiBbNmzeLdd9/F4XA06xeenJzMPffcw7x583j66adD/RGIiMhhQlU3iN93A51HLoYUm5lyl5fc0louHdQjcoGISKcWzvO/zWYD/F3r4uPjSUpK4tZbb+Waa65h8uTJuFyuZtu7XC4SExMBcDgcLa5PS0sLJqsC40u1tH97dO+e3K7tGxqSgGLs9njS05PIyGjf/tGmvZ9PtOoq5YSuU1aVMzRCnpSqq6sL9hU3DH/3Arvdjtnc9ivnDRs2sHbtWi677LLgMvULFxGJXqGoG+Q7bm9T9z1TZGbfA0iJ9z8u213S/lYDItJ1hPP8n5WVhc/nw+12Ex8fD4DP58/an3766fztb39rtn1ubi7Z2dkAZGdns3v37iPWX3jhhaSkpJCZmdmsi19JSQmVlZVHdPlri7KyGox2nK4rKpwAuFwNlJc7iY+vafcxo4HJ5L/Zbe/nE226Sjmh65S1q5Yz8HOohfxuYNiwYSxevBgAU1P7rr/+9a8MGTKkTfuXlZUxd+5cHnnkkWZNatUvXEQkep1o3SDNBbvvRTCnl2b3/x6/VlJKRFoRzvP/eeedR79+/bj77rupra2lvLycRx99lO9///tMmDCB0tJSli1bhtvtZuPGjaxZs4arr74agClTprBmzRo2btyI2+1m2bJllJWVBVtwTZ48mSVLlpCfn4/T6eTBBx9k5MiR9O/fv91xGkb7X/4dj2/faHod7+cTba+uUs6uVNauWs5wCHlLqbvvvpvp06ezevVqamtr+eEPf0htbS1//vOfj7mvz+fjtttuY8aMGQwePLjZus7WL7wzO7TvZyzqyPIdeoxw9qM9/HiH99+NNfob7Rw6Mr4TqRvkSJEe6BwgNd5/8IIqF84GDxmRC0VEOrFwnv+tVit//etfWbBgAePHj6ehoYGxY8cyd+5cunXrxnPPPcf8+fNZtGgR6enpzJs3j9GjRwP+Xhf33nsv9913H0VFRWRlZbF06VJSU1MBmD17Nh6Ph2nTplFbW8uoUaN47LHHTjhmERFpLuRJqYyMDF5//XXee+89Dhw4QK9evbj44ouD07S25qmnnsJms3Hdddcdsc7hcFBT07zparT0C4+UaInzeHVE+bzeOhwOW9Pxwt+nP3A8hyOeGldNzI8joL/RruNE6gY5UqQHOgewWUxkJMRRWudhd0ktp/RNi1wwItJphfv8n5mZyaOPPtriuiFDhrBy5cqj7jtp0iQmTZrU4jqr1cqcOXOYM2dOSOIUEZGWhTwpNWHCBP75z3/ygx/8oN37vvrqqxQXFzNixAiAYJLp7bff5vbbb2f9+vXNto+WfuEdLdb7uHZk+crKnNTXNwa/t1gSjrFHaI4XZ28AoLw8/MeMBP2Ndg7h6hfekhOpG6S5Rq+PpoZSEW0pBTAgLZ7SOg9fFzvRkPUi0hKd/0VEpDVhuZw9vEVSW7355pt89tlnbNmyhS1btjBhwgQmTJjAli1bGDduXFT3C490389Ye3Vk+Tr69x44ln6H0f2KlvJ1pOOtG6S5ukAzKSAuwl1ET03zDyyscaVEpDU6/4uIyNGEvKXUqFGjmDp1KhdeeCE9e/Zstu6mm2467vdNS0tTv3ARkSgVrrqhK6pr9Cel4kxGxMctOzXN373562JnZAMRkU5L538REWlNyJNS+/fvp1+/fnz77bd8++23weWm47hyXrBgQbOf1S9cRCQ6hbJu6Opq3d8lpSLttHQ7ALtLanG5vRGORkQ6I53/RUSkNSFLSv3P//wPzz77LH/9618B/3hQdrs9VG8vIiJRSHVD6NV1oqRUZmIcaQ4rFfVu/nOwmv4JIX/WJSJRSud/ERFpi5CNKbV169ZmP1944YWhemsREYlSqhtCL5CUskZ4kHPwt3Q4s7d/sPzP8yojG4yIdCo6/4uISFuE7ZLW6OgRdEVEpNNT3XDiahs7T0spgLOaklJb8ysjG4iIdGo6/4uISEvClpRSP3ERETmc6oYT15m67wGc1asbAJ/nV0Q4EhHpzHT+FxGRlnSCxv8iIiLSVnVNA4rHdZL7uzN6+VtK5ZfXU1HXGOFoRERERCSahGxEUo/HwyuvvBL82e12N/sZ4KqrrgrV4UREJAqobgi94Ox75s7RUirZHsep6Ql8W17HtgPVXJSVEemQRKQT0PlfRETaImRJqYyMDBYtWhT8OS0trdnPJpNJFY+ISBejuiH06prGlLJ2ku57AOf0S+Hb8jo+3V+lpJSIADr/i4hI24QsKfXvf/87VG8lIiIxQnVD6AVbSkW4+57h81FcXATAOSd146VtB/m0abBz3yHrevbMxGzWaAEiXY3O/yIi0hYhS0qJiIhI+NV2kpZSzqpylm+oJyGhgB99bwgAu4trqap3U19VypPr/NPB/++4HHr16h3JUEVERESkk9KjSxERkSgSHOi8E4wplZiaTlJaBmmOOE7rkYgBfH6gCoCktAyS0tSVT0RERESOTkkpERGRKNJZWkoFBLrxDe3lAGBzXmVkAxIRERGRqKGklIiISBTpLGNKBTiryvnrx3uoqKkF4ONvSiMckYiIiIhECyWlREREokhdU1LK2gm67wUkpqYzqH9vTEB+VSPFte5IhyQiIiIiUUBJKRERkSjR6PHR6PUno+I6Sfe9ALvVQrrd33zrs4K6CEcjIiIiItFASSkREZEo4Wz0BL/vLN33DtU7yQLApwW1EY5ERERERKKBklIiIiJRwtngn3nPagZTZ0xKJfovK7YV1uE1OldLLhERERHpfJSUEhERiRLOBn9LKWsnrb3T4k2k2i3UewyK63yRDkdEREREOrlOelkrIiIih6sJJqU6YTMpwGQyMaZfEgD7a5SUEhEREZHWKSklIiISJWoDSSlLhANpxXlNSakDTi8+deETERERkVYoKSUiIhIlvhtTqnO2lAI4K9NBss1MgxdK6tVaSkRERESOTkkpERGRKFHTyceUMnw+ykuLGZrhb8qlLnwiIiIi0pq4SAcgIiIibRMc6NzSOVtKOavKWb6hnmqXAfRkf4268ImIiIjI0XXSZ60iIiJyOGejv/uerRPX3omp6fRJSyDOZODywq5SV6RDEhEREZFOqhNf1oqIiMihnJ189r0Aiwl62v0tpD7Oc0Y4GhERERHprJSUEhERiRLOKJh9L6B3gj8ptT7PqS58IiIiItIiJaVERESiRLS0lALoafcPyF5S52Hr/qpIhyMiIiIinZCSUiIiElO8Xi/XXXcdd955Z3DZtm3bmDp1Kjk5OYwdO5ZVq1Y122f16tWMGzeOYcOGMXnyZLZu3drRYbeJs6HzjykVYDFBv2R/k67XdhQFl/t8PgoLD1JYeBCfT7PziYiIiHRlUXBZKyIi0naLFy9my5YtwZ+rqqqYOXMmV111FZs3b2b+/Pk89NBDbN++HYBNmzZx//33s2DBAjZv3syVV17JrFmzqK+vj1QRjqqmk8++d7hTU/xJqXe+LqGuaZD24uIinly3lSfXbaW4uKi13UVEREQkxikpJSIiMWPDhg2sXbuWyy67LLhs7dq1pKamMm3aNOLi4hgzZgwTJ05kxYoVAKxatYorrriC4cOHY7VamT59OmlpabzxxhuRKsZRfdd9L8KBtFF3u4k+yVbq3T7W7SoOLk9KyyApLSOCkYmIiIhIZxAll7UiIiKtKysrY+7cuTzyyCM4HI7g8t27dzNw4MBm22ZlZbFz504AcnNzW13fWRiGgbOptVE0jCkFYDKZGJ+VAsCqzw9iaMBzERERETlEXKQDEBEROVE+n4/bbruNGTNmMHjw4GbramtrmyWpAOx2O3V1dW1a31YmU8vfh4rL7cPr8yd1rBbwhv4QJ6ylYo/L6sbfvihnV7GTLwtr6HnI4zCTKTyfVUcJxB7NZTgeKndk4wi3WC+fiIh0LkpKiYhI1Hvqqaew2Wxcd911R6xzOBzU1NQ0W+ZyuUhMTAyud7lcR6xPS0trVwzduye3+H2oFFX7Y7SYIDnBhqnBhslmIyEhHofd/71h9rb4tSO2AbAfts7hsHFK7xSuPLsPqz7dz0tfFjH34l44HP7t09OTyMgI/WfV0cLx+44GKreIiIicKCWlREQk6r366qsUFxczYsQIgGCS6e233+b2229n/fr1zbbPzc0lOzsbgOzsbHbv3n3E+gsvvLBdMZSV+RNf3bsnU1ZWQ6h7quWV+VtuJVjNuFxu6l2NmH0W6uoagt/7Glv+2hHbALhcjZgOWWaxWCkvd/JfZ/Zk1af7eWP7QSacbKO+vhGA8nInNlvNUcvc2ZlM4ft9d2Yqd2yXO1BOERGRjqAxpUREJOq9+eabfPbZZ2zZsoUtW7YwYcIEJkyYwJYtWxg3bhylpaUsW7YMt9vNxo0bWbNmDVdffTUAU6ZMYc2aNWzcuBG3282yZcsoKytj3Lhx7YrBMAjeqAa+D+WrxuUf5DzB1nmr7sPv0w2fj6KiIpK9NVyc1R0D+Pv2su/Wh+Fz6uhXrJRD5Va5Dy+niIhIR1BLKRERiWlpaWk899xzzJ8/n0WLFpGens68efMYPXo0AGPGjOHee+/lvvvuo6ioiKysLJYuXUpqampkAz9MtcsNgM3kI1oGDHdWlbN8Qz0JCQX81/AzeC+3jA/3ORl/io2U+M6bXBMRERGRjqGklIiIxJwFCxY0+3nIkCGsXLnyqNtPmjSJSZMmhTusE1JQ7G9hVO2so67OGuFo2i4xNZ3ExEQGpMdzcVZ33sst4z9lHsb0sUU6NBGJIV6vl+nTp9O3b99gHbBt2zYeeOABcnNzSUtLY9asWUydOjW4z+rVq3niiScoKSlhwIAB3HPPPeTk5ATf7+GHH+bVV1+lvr6e0aNH89vf/paePXtGpHwiIrFKjylFRESiQK3bB4DdGr3Pk64fczIAeTU+qhp8EY5GRGLJ4sWL2bJlS/DnqqoqZs6cyVVXXcXmzZuZP38+Dz30ENu3bwdg06ZN3H///SxYsIDNmzdz5ZVXMmvWLOrr6wFYsmQJ69ev56WXXuLDDz/Ebrczb968iJRNRCSWKSklIiISBWobvQDEmaOj615LBvVMYkw//6yHX5Z6IhyNiMSKDRs2sHbtWi677LLgsrVr15Kamsq0adOIi4tjzJgxTJw4kRUrVgCwatUqrrjiCoYPH47VamX69OmkpaXxxhtvBNffcMMN9O7dm6SkJObOncsHH3xAfn5+RMooIhKrlJQSERGJAoGWUnGm6E1KAfy/Id0B2O/0kVvminA0IhLtysrKmDt3Lo888ggOhyO4fPfu3QwcOLDZtllZWezcuRPwz7J6tPU1NTUUFhY2W5+RkUFKSgq7du1qd4wmU/tf/h2Pb99oeh3v5xNtr65Szq5U1q5aznCI3j4AIiIiXUhdU1LKGqYLgo5ySlo8Jyeb2Vfj4/ltZVxw5qmRDklEopTP5+O2225jxowZDB48uNm62traZkkqALvdTl1d3THX19bWApCQkHDE+sC69ujePbld2zc0JAHF2O3xpKcnkZHRvv2jTXs/n2jVVcoJXaesKmdoKCklIiISBWobm1pKRXH3vYCzMuLIq2lk68E6Ps2vZHi/1EiHJCJR6KmnnsJms3Hdddcdsc7hcFBTU9NsmcvlIjExMbje5XIdsT4tLS2YrAqML9XS/u1RVlZDeyZNrahwNh2vgfJyJ/HxNcfYIzqZTP6b3fZ+PtGmq5QTuk5Zu2o5Az+HmpJSIiIiUaA22FIq+q9+kmxmBqRa2FPp5U8f7uXZH5+NKVxtwkUkZr366qsUFxczYsQIgGCS6e233+b2229n/fr1zbbPzc0lOzsbgOzsbHbv3n3E+gsvvJCUlBQyMzObdfErKSmhsrLyiC5/bWEYtOvGNbit0f59o1FXKCN0nXJC1ymryhkaGlNKREQkCtQFBjqPgaQUwJnd47BZTHxxsJr135ZHOhwRiUJvvvkmn332GVu2bGHLli1MmDCBCRMmsGXLFsaNG0dpaSnLli3D7XazceNG1qxZw9VXXw3AlClTWLNmDRs3bsTtdrNs2TLKysoYN24cAJMnT2bJkiXk5+fjdDp58MEHGTlyJP37949kkUVEYo5aSomIiESB4EDnUfg4yfD5KC4uAqC4uAjDMHDEmZgwMJWXv6rguY35nH9qulpLiUjIpKWl8dxzzzF//nwWLVpEeno68+bNY/To0QCMGTOGe++9l/vuu4+ioiKysrJYunQpqampAMyePRuPx8O0adOora1l1KhRPPbYY5ErkIhIjFJSSkREJAoExpSKxu57zqpylm+op2ffRor2fk1Sj74kOBxckOlmzS5/a6nP9ldpbCkROSELFixo9vOQIUNYuXLlUbefNGkSkyZNanGd1Wplzpw5zJkzJ6QxiohIc1H4vFVEQsnn81FcXExxcTE+ny/S4YjIUQRm34vW7nuJqel0S+9JQko64E9UrfnsW3rHNwKwbFN+JMMTERERkQhQUkqkiystLeWpd7bx1DvbKC0tjXQ4ItICn2EEk1LWGKq5E1PTGdIrEbMJNu6r4D+FsTnDlIiIiIi0LIYubUXkeCWmpJPY1HpBRDqfukYvgfZR0dpS6mgS42BULysAf96UB/hbcBYWHqSw8KBacIqIiIjEMI0pJSIi0sk5GzwAmE1gibGxwJ1V5XidjUA67+eWsbe8DntjFU+u2wrA/47LoVev3pENUkRERETCQi2lREREOjlngxeIra57h+qZnkKfRDMGsGprAQBJaRkkpWVENjARERERCasYvbwVERGJHTVNLaWs5hhrJnWI7DQLAK/tKKLO7Y1wNCIiIiLSEZSUEhER6eQC3feslggHEkaZCWb6dbNR5/by9h4NeC4iXZPL7eXPn5VysMYT6VBERDqEklIiIiKdXKCllC2GW0qZTCauGJQCwLo9VRhGbA3oLiLSFruKnbz8VSWbDrgiHYqISIdQUkpERKSTq3E1JaViuKUUwEWnJBMfZ2ZvZSMVDUpKiUjXc3JaAgBVDT4avDoPikjsU1JKRESkk6vuAmNKGT4fdZWljOrrvyH7plJdV0Sk60lNsNKvmxWA0npfhKMREQk/JaVEREQ6ua7QUspZVc7yDXsweRsAyKv24vWplYCIdD2n93AAUFqvc6CIxD4lpURERDq5apcbiO0xpQASU9PJOqkXdrMPt2HigFOtBESk6zmjpx2AErWUEpEuQEkpERGRTq66C7SUCjCbTJxk95f32ypvhKMREel4ZzS1lKpwGTR4lJgSkdimpJSIiEgnF5x9zxLbLaUCTnL4W4YV1vkoqXVHOBoRkY7VKymOeIsJH5Bf3RjpcEREwkpJKRERkU4u2FKqi9TaCRaDdJt/LJX39tZEOBoRkY5lMplItPkfQlTWq8WoiMS2LnJ5KyIiEr0CSSlrF2kpBXBSoj8p9eE+JaVEpOtxxDUlpVxKSolIbOuUSamdO3cyY8YMRo4cyfnnn8/tt99OeXk5ANu2bWPq1Knk5OQwduxYVq1a1Wzf1atXM27cOIYNG8bkyZPZunVrcJ3X62XhwoWcd9555OTkMGvWLIqLizu0bCIiIu0V6L4X3wXGlAro5TAwA99WNPJtWV2kwxER6VD2OP9tmpJSIhLrOl1SyuVycf3115OTk8NHH33Ea6+9RmVlJXfffTdVVVXMnDmTq666is2bNzN//nweeughtm/fDsCmTZu4//77WbBgAZs3b+bKK69k1qxZ1NfXA7BkyRLWr1/PSy+9xIcffojdbmfevHmRLK6IiEirGjy+4EC31hiffe9QNjP0SvRfpqzdqQdIItK12IMtpTwRjkREJLw6XVKqoKCAwYMHM3v2bGw2G2lpaVx77bVs3ryZtWvXkpqayrRp04iLi2PMmDFMnDiRFStWALBq1SquuOIKhg8fjtVqZfr06aSlpfHGG28E199www307t2bpKQk5s6dywcffEB+fn4kiywiInJUNS7/QN9mE1g7Xa0dXv26+ZuGrd1VgmEYEY5GRKTjBLrvVamllIjEuLhIB3C4AQMG8MwzzzRb9tZbb3HmmWeye/duBg4c2GxdVlYWL774IgC5ublcffXVR6zfuXMnNTU1FBYWNts/IyODlJQUdu3aRb9+/doco6mTP6gOxNfZ4zwePp+P0tJSvN46zGY7JlN479AO/QxNpvB/pof/7jrymB11vEOPGYt/oxA95evs8YlfVdNT8kSrGVMX+6X1TTJjs5jIq6jn6+JaBmUmRTokEZEOYbdqTCkR6Ro6XVLqUIZh8Nhjj/Huu++yfPlynn/+eRwOR7Nt7HY7dXX+sSZqa2uPur62thaAhISEI9YH1rVV9+7J7S1KRERLnO1RVFTEnz/8CoBfTzqXzMzMsB7P663D4bAB0L17EhkZ4f1MA8dzOOKpcdWQnt5xx4SOKeOhYvFv9FCxXj7pGDVNSamkrjSgVBOr2cS5fRNZn+dk7a5iJaVEpMvQmFIi0lV02qSU0+nkrrvuYseOHSxfvpxBgwbhcDioqWk+C4/L5SIxMREAh8OBy+U6Yn1aWlowWRUYX6ql/duqrKyGztyLwGTy3wx39jiPR1mZkzh7Ena7jfJyJxZLwrF3OsHj1dc3Br/vqOPF2RsAYrKMENt/oxA95QvEKZ1bddMg50m2LtZ3r8mFJyf7k1I7S5j9vVMxd7HWYiLSNQVn32tQUkpEYlunTErl5eVxww030KdPH1588UXS09MBGDhwIOvXr2+2bW5uLtnZ2QBkZ2eze/fuI9ZfeOGFpKSkkJmZSW5ubrALX0lJCZWVlUd0CTwWw6BT32gGREuc7XFomTqifIe+f0ceL5bLePixY+1v9FCxXj7pGMGWUjYL4ItsMBEwvE8CCTYLhTUNvP/lt1x05imYzV0zQSciXUcgKVXT4MXrM7B0oYkuRKRr6XRXdVVVVfzsZz/jnHPO4dlnnw0mpADGjRtHaWkpy5Ytw+12s3HjRtasWRMcR2rKlCmsWbOGjRs34na7WbZsGWVlZYwbNw6AyZMns2TJEvLz83E6nTz44IOMHDmS/v37R6SsIiIix1LVNNB5chdtKRUfZ2ZUX38Lzic+/Ibi4qIIRyQiEn7xTUkpnwGV9e4IRyMiEj6drqXUyy+/TEFBAf/617948803m63bunUrzz33HPPnz2fRokWkp6czb948Ro8eDcCYMWO49957ue+++ygqKiIrK4ulS5eSmpoKwOzZs/F4PEybNo3a2lpGjRrFY4891sElFBERabtAS6lEmwXomlODX3hyMu9+W0Nhow2vT80PRST2mU0m4i3Q4IWKOjfdE22RDklEJCw6XVJqxowZzJgx46jrhwwZwsqVK4+6ftKkSUyaNKnFdVarlTlz5jBnzpwTjlNERKQj1Bw6plQXy0kZPh/FxUX0sRjYzODywpfF9fTtE+nIRETCL95iosFrUFbXSBbtGwNXRCRadM2+ACIiIlGiqtmYUl2Ls6qc5Rv28PePdtDL4R9P6/29NcfYS0QkNtibTvvldY2RDUREJIyUlBIREenEAt33kuO7ZpWdmJpOQko6fRP83fY+2ldDXaNmoxKR2GdvGleqok5jSolI7OqaV7giIiJRwOfzUVpTB4C3vgajC0/nmG6DJKuJeo/B21+XRDocEZGws1v8SamyWiWlRCR2KSklIiLSSRUXF5Ff4U9Krf/PXurq6iIcUeSYTHBqir8vyz+/KIxwNCIi4RffNPpvhbrviUgMU1JKRESkE3Mb/qq6W3JShCOJvFNTLJhNsK2gml3FzkiHIyISVvFmf0upwNiCIiKxSEkpERGRTsrjM3D7x/fGZu66XfcC7GaD4T39TQee+XA3Pp8vwhGJiIRPYH6Lape674lI7FJSSkREpJOqafhuQG+rKYKBdBLOqnKMav94Uu/vrebLb/dHOCIRkfCxNY0pVVWvllIiEruUlBIREemkqpuSUvFxZkxKSgHQq3s3+nSLx8DEC1+URzocEZGwiW9qKVWlllIiEsOUlBI5Bo/PIK/aw1cl9ZEORURasXPnTmbMmMHIkSM5//zzuf322ykv9ycttm3bxtSpU8nJyWHs2LGsWrWq2b6rV69m3LhxDBs2jMmTJ7N169ZIFOEIgaSUPU7V9aFG9E8FYN2ear4qqolsMCIiYWI7ZEyprjz7qojENl3lirTiP8V1vLK7jvUHGrh7XR5V9XpSJdIZuVwurr/+enJycvjoo4947bXXqKys5O6776aqqoqZM2dy1VVXsXnzZubPn89DDz3E9u3bAdi0aRP3338/CxYsYPPmzVx55ZXMmjWL+vrIJ6KDSSmrJcKRdC6ZyfH0TzZjAAvfzsXj1dhSIhJ7AmNKeX0GtY3e1jcWEYlSSkqJtGLdnqrgIMONXoMNeysiG5CItKigoIDBgwcze/ZsbDYbaWlpXHvttWzevJm1a9eSmprKtGnTiIuLY8yYMUycOJEVK1YAsGrVKq644gqGDx+O1Wpl+vTppKWl8cYbb0S4VFDtUkupozm7h5UEq5kdhTX86aO9kQ5HRCTk4sym78aVUhc+EYlRusoVacX2wjoAUuP9/yof7CmLZDgichQDBgzgmWeewWL5rkXRW2+9xZlnnsnu3bsZOHBgs+2zsrLYuXMnALm5ua2ubyuTieC4T4HvT/RV3ejPitut0VFdd+SwVwlWE7eOyQRg+Zb9rNy4C8Pwheyzb+sLOvZ4neWlcsf2SzqPbk0DS2mwcxGJVXGRDkCksyqsdlHodGMChvW08l5+Axv2luP2+rBaouMGUaQrMgyDxx57jHfffZfly5fz/PPP43A4mm1jt9upq/MnnWtra1td31bduye3+P2JaDT5zzXJDhsOw4bJZsMwe5t9TUiIx2FveV1HbuP/3DouDne9jQnDerHX6eVvnxXzh/WF9M1IYvKYwSH57NsjVL/vaKNyS2ewc+dOFi5cyI4dO7BarZx//vnceeedpKens23bNh544AFyc3NJS0tj1qxZTJ06Nbjv6tWreeKJJygpKWHAgAHcc8895OTkAOD1enn44Yd59dVXqa+vZ/To0fz2t7+lZ8+eHVq+JJuZ0jq1lBKR2KWklMhRfJpfBUCa3UxmooVUu4VKl5et+6sYeXJahKMTkZY4nU7uuusuduzYwfLlyxk0aBAOh4OamuaDYbtcLhITEwFwOBy4XK4j1qelte//vKzMf4zu3ZMpK6shFGPSFlf647JgUO9qxOyz4Gts/rWuruGo6zpyG//n1oipo+Kob6S83MmPTu/GR7tLyavxcddrezAaGvneGSdjNof/4YHJFNrfd7RQuWO73IFyRoPAeILXXHMNTz31FLW1tdxxxx3cfffdLFy4kJkzZ3LLLbcEu3PPnj2bQYMGMXTo0OB4gkuXLmXo0KGsWLGCWbNm8e677+JwOFiyZAnr16/npZdeIjk5mXvuuYd58+bx9NNPd2gZk5taSlWrpZSIxCg19xA5ii35lQBkJlowm0zk9PbfwG47UB3BqETkaPLy8rj66qtxOp28+OKLDBo0CICBAweye/fuZtvm5uaSnZ0NQHZ2dqvr28owCN6oBr4/0VdVcKDz6KiuO/o+3TDAhImRva1kWD00eOGut/axbuNWDh48yMGDB/F6fSH7fbT0CsTR1V4qd2y/okU4xxNctWoVN9xwA7179yYpKYm5c+fywQcfkJ+f36FlTLb5z/9qKSUisSo6rnJFImDbAX9LqZ4J/n+TU9PiAfimrH1dekQk/KqqqvjZz37GOeecw7PPPkt6enpw3bhx4ygtLWXZsmW43W42btzImjVruPrqqwGYMmUKa9asYePGjbjdbpYtW0ZZWRnjxo2LVHGCgrPvxWn2vcMZPh/FxUUUFxdhBs5JcZFiNWg0zNy/oZqlG/N4ct1WiouLIh2qiIRJuMYTrKmpobCwsNn6jIwMUlJS2LVrV7vjPO5xvUyHjCnl8kR8rDGN06ZyqqwqZzio+55ICxo9Pg5U+bvNpNn9Sal+Kf6k1LfltRGLS0Ra9vLLL1NQUMC//vUv3nzzzWbrtm7dynPPPcf8+fNZtGgR6enpzJs3j9GjRwMwZswY7r33Xu677z6KiorIyspi6dKlpKamRqAkzR06+56ekTfnrCpn+YZ6DHc9ST36EmeGkRk+1hebqPNa2FIKF2R2j3SYItJBQjmeYG2t/1ovISHhiPWBde3R3u6QDQ1JQDF2ezy2ODtQTSMmMjKio1tle0VLd9ET1VXKCV2nrCpnaCgpJdKCA1UufAY44szYm6biPTnVP5DvvvJ6PD6DOHOYUsUi0m4zZsxgxowZR10/ZMgQVq5cedT1kyZNYtKkSeEI7YQEW0pZLUpKtSAxNR1fY33w53gLjEip56OKBAqqGvjGrsscka4g1OMJBpJV9fX1R92/Pdo7DllFhbPpeA0kJloBKKqoo7S0prXdoo7J1LXGaYv1ckLXKWtXLWfg51DT1ZpIC/Iq/BchfbpZMTW1U+yRaMUeZ8bl8bG/sp5T0hNaewsRkRPicntp8PqvdOxxZmLrViR8kuIMBncz+E+ViW0lHqpcXnpFOigRCZu8vDxuuOEG+vTpw4svvhjsvj1w4EDWr1/fbNu2jCd44YUXkpKSQmZmZrMufiUlJVRWVh7R5a8t2jtWV3BbA5Jt/u57lfXumL35jbaxzI5XVykndJ2yqpyhoTGlRFqQX9mUlEq2BZeZTSZO7e5PRH2rcaVEJMwq6/1to8wmsFrUMrM9Tk0y6J5gxe2Dv2zeT2HhQXw+X6TDEpEQC+d4gpMnT2bJkiXk5+fjdDp58MEHGTlyJP379+/QMibZvhtTSkQkFqmllEgL8isOSUp5v2vafWr3BL4qcvJtWR2XtG9iLhGRdimv8yel4i0EW2xK25hMMPLkVP71VQlv5zXgefNzfnX5MHr16h3p0EQkhMI5nuDs2bPxeDxMmzaN2tpaRo0axWOPPdbBJYTk+KbZ9+rViVtEYpOSUiItyAu0lOpmo7DikKRUU5e9b8o02LmIhFdpbSMA9jglpI5H3xQ73a0eytxx5Hm6RTocEQmDcI4naLVamTNnDnPmzDnhOE9Et2BLKSWlRCQ2qfueSAu+ayllbbb81O7+wS2/Ufc9EQmzQFLKoa57x8VkMpGd6P8Mv632Ulanri8iEn2S4/1JKWeDF4+vCwxeIyJdjpJSIodxub0U1TQA0Lebrdm6k9P9s7Hsr6zH6Aqj2olIxJQ6/echtZQ6fuk2H+k2A58BL39VEelwRETaLclmJlALVKoLn4jEICWlRA6zv8rfXS8p3kK3pqdTAX262TEB9W4fZXW6MDgePp+P4uJiioqKNPCwSCuCLaXU0f6EZHfzn2fe3F1FeV1jhKMREWkfi9lEisPfcr9S154iEoOUlBI5TKDrXr9UxxGDC9vizPTqFg/AgaZxp6R9SktLefLtbTzy6mZKS0sjHY5Ip1XqDCSl1FLqRGTEQ7rdRKPX4G+fHoh0OCIi7ZaW4E9KKbEuIrFISSmRwwSSUv3THC2u75vqX54fhqSUYRjsrfLw+YFq9pQ3xmwXwcSUdJJS04+9oUgXpoHOQ8NkgjO6+5ubrfq8gNx9+yksPEhh4UG11hSRqJDelJSqUEspEYlBSkqJHCYw816/1JaTUv1S7QDsr3S1uP5EbD5Qy4aCBjbnVfL+3lo+2e8M+TFEJDp8131PSakT1SfRzCmpNuoavdz7r69YviWfJ9dtpbi4KNKhiYgcU5rDP8ZphcaUEpEYpKSUyGGC3feO0lLqpJTvBjsPtde/9g/Ea2uabev1rytDfgwR6fy8PoNyJaVCxmQycc2Z/taZe+vjsadkkJSWEeGoRETaJi3YUkrd90Qk9igpJXKYQLe8o3XfOyktkJQKbUupgioXnx6oBWBstv9m6dMDtRyo0thVIl1NZb0brwEm4LD5FuQ4ndc/ib7JVhp98J/CmkiHIyLSZt+NKaWWUiISe5SUEjlEvdtLSdPgwkfrvndSSqD7XmiTRa9+cRAD6JVopl+agz7JcRjAq18UhvQ4ItL5BQY5T7FbMJvUUioULGYT15zlby31+YFqXJ7YHLNPRGKPxpQSkVimpJTIIQJd91LsccHpdw93UlOyqsrlodoVuouDTfsqATglxT8gb3Z3//gBG/dWhOwYIhIdAuNJpTviIhxJbLn41GTS7CbcXoMvSj2RDkdEpE3Smq5JNaaUiMQiJaVEDhHoune08aQAEmwWuif6E0ah6sLX6PHxdYl/UPMMh7+vTq8k/wXIrmIntY26eRLpSkprGwBId6jvXiiZTSZyevjPrd9UefmqRN2jRaTzS0toGuhcY0qJSAxSUkrkEHkVrY8nFRDqLnxflzhxew26xVtIsvq76iTazGQmWfEZ8EVBdUiOIyLRIdCNOE0tpULC8PkoLi6iuLiIDIeJgT0SAXh8YzGNHl+EoxMRaZ3GlBKRWKaklMghgjPvHWU8qYBQD3b+5UH/oLsDM+yYDhk/5sye/uN8fkBJKZGuRN33QstZVc7yDXv46wdfUldXx6iTU7FbIL+6kT9vyot0eCIirQp036tt9CqRLiIxR0kpkUMca+a9gFC3lPryoD/pNCij+XHP7JkAwOcHqkJyHBGJDgeq/AnvzCQlpUIlMTWdhBT/QOd2q4VzMv03ecs+ySe3tDaSoYmItCrZHofF7H9oqXGlRCTWKCklcohA973WxpSC71pShSoptaNpevLBRySl/D9/ebAGt1dPxkS6ioKmpFRgbDkJvb4JMKxHHB6fwb2vfcmBggJ8Pp1nRaTzMZtM3w12rnGlRCTGKCkl0sTZ4An21T9m973UppZSVSfefa+yzh3sBjgww95sXb8UG93scTR4fOzRk3yRLsHrM4JJqUwlpcKmtrqChNoiLPj4uqyBu9fsoLi4KNJhiYi0SONKiUisUlJKpEmg1VN6gpWk+Na7zJzUlLQqcTbicntP6LiBVlInpzlIsjWfactkMnF6ZhIA/2naTkRiW7GzAY/PwGox0V1jSoVVenoqpyf7Wx3sqo2n0KmbPRHpnAItpSrVfU9EYoySUiJN8to4yDlAisNKclPi6kRbSwXGkzqrd3KL68/o5V/+n0LnCR1HRKLDgaaWk7272YNjiEj49LN7SLcZeA144pNiDMOIdEgiIkfonmgDvpudVUQkVigpJdIkMMj5scaTCgh24as4sXGlAjPvndm7W4vrT89sSkoVqaWUSFdwoMp/TumbYj/GlhIKJhMMTfNhNsHWg3W88Z/iSIckInKE3t3iAThYHZqZn0VEOgslpUSa5Fe0bea9gEAXvhNpKeUzjGD3vWO1lPqmtPaEuwqKSOcXmHlPSamOk2SFs7r7W78++t4eyjWQsIh0Mn2a6oSCEIxnKiLSmSgpJdIkr8Jfybel+55/u6aWUicwA19eRT01DR7i48xkZyS2uE3PJBvdE214DdhVrC58IrEu0H2vbxvPRRIaA1NN9Es2U+Xy8Pt3ciMdjohIM727+a871VJKRGKNklIiTfIq6oC2d9/rn5YAwN7yuuM+5o6mrnuDeyYRZ2n539FkMnFGYLDzIiWlRGKdWkpFRl11BZmeMsDg7a9LeW1HYaRDEhEJCrSUOljdoLHvRCSmKCklAlS73FS5PEDbW0qdluFPSuWW1B73xUFgkPMzj9J1L+D04GDnGldKJNYFklJ2j5Pi4iLdfHSgXt27cVZ3/wxXC9/OJbekNsIRiYj4ZSbHYzZBg8dHWZ1m4BOR2KGklAiwp9Tf2ikzOZ4Em6VN+5ySnoAJqHJ5KD/Oi4PvxpNqeZDzgDOUlBLpEsrrGqmsd2MCXv/kP/z1gy+pqzv+1pjSfoPTTJyRbsHl8TF71efsr9DnLyKRZ7WY6ZHUNNi5xpUSkRiipJQIsLvE3y0uu0fL4zq1xG61BLv67Slt/9N0l9vL101P4Y82yHnAmU0z8OVV1FPT1KJLRGJPYNy4Pt2spHbPICElPcIRdT111RWkNxTRzWaivN7Lz1/4nNzjOMeLiIRaH83AJyIxSEkpEQgmhwa2IykFMKB7Uxe+47hh2VXsxOszSE+w0is5vtVtUxOswQuRr4pC21rqs4JadpQ2kl/twetTNyGRSNrVNG7caWmtnxMkvFLT0phwVm+SrCaKaz38z98+588ffEXBwQJ8Pl+kwxORLkoz8IlILFJSSgSC44Zk9Uhq135ZTTPmfVPa/u4dga57Q3p3w2QyHXP7cHTh+8sn+dzzTj7bS9x8dKCBhz8qwKfxa0QiZlex/1w0QEmpiEuMj+P7J9sYmumgzu3lic0l/OSFXSx751Mlp0QkIgIz8BWopZSIxBAlpaTL8/qMYEun9nTfAzitKSm1p6z9LaW+bJp571iDnAec3tSF76sQzcD3xn+KWPzhtwD0TrRgBj7YV8NT6/eG5P1FpP12FfvPC6ela+a9ziDeYuJ3Y/tyw/AM4kxQ47WwZHs90174mr9t2E2jR4kpEek4vQMz8FU1RDgSEZHQUVJKolIoW/PkV9bT4PERH2du88x7Aacd0lKqPTEZhsEXBf6Z9441nlRAoKXUlwerT3g2rrpGL49/4E9IXXNWdy7ub2dkbxsAyz7JZ2957Azs6/P5KC4upri4WC0bpFNzNnjIr/Q//R6QrpZSnYXFbOLKwWlMOC2erIRG4kwGTq+ZP24s4sqln/DEe7lUuzQTloiE30mp/qRULF2niYgoKSVR5a3dlazJrWPZ1kpuf2sfeRX1J/yeuwNd9zISsZiP3Y3uUP3SHMTHmalze9lX3vZYDlS5KKxpIM5sOubMewFn9k4mzmyi2NkYvHE9Xs9vzqe0tpE+KXb+39DuAJyaamXkSUn4DGKqtVRpaSlPvbONp97ZRmlpaaTDETmqr5smXMhMjqdbfNtmAZXwMnw+iouLKC4uwmaGgUmNXNrbx5DuFlLjTZTWNvJ/b+7ih09u5KE3tvP13jwKCw8qAS4iYTG4ZzIWExTWNFCoLnwiEiOUlJKosWLLfhZtLMTp9rcS+rKonp/89VO+Lj6x7mw7i9o/816AP6nkb8G09UBVm/f7ZF8FAEP6dMNhbdvNp8NqYWgffwJrc15FOyP9TlW9m79/egCAWy48Favlu9PAz4ZlYALe/rqUnSEeUD2SElPSSdQsZtLJfVHg/58b3LN9Y9tJ+Dirylm+YQ9//eBL6ur8LROsZuhnqeLsuGJG9rKSZHbj8hi8/FUlP3vpG25/9UsOHDwY4chFJBYl2CwMbKojth2ojnA0zfl8Pg4eLOCgxtwTkXZSUkqiwpa8Sh57/xsATu9u5arTu3FGDwf1bh93vfYVtY2e43/v/EoAhvVNOa79zznJv9/W/W1PSm3O8x/z3P6p7TpWYPtP9lW2a79D/ePzAurcXrJ7JDI2O6PZulPS7Fx+ek8Ant2Yd9zHEJH2W/9NGQCnp5kpLi464W66EhqJqekktJDUTk5L5+xTe3NZX4Nzu3tJsnjxYOYrZzw3vZ7H+m/KIxCtiMS6wPVqex6GdoSiokKeXLeVJ9dtpaioMNLhiEgUUVJKOj2X28uD674GYHxWCsN62kh3WPjNJSfRM8lGXkU9j777zXG9d7XLzVdNs9m1N0EUkNOUlPosv7JNN5E+wwgmpUYeZ1Lq0/xKvL7237DWNXp54TN/K6npI/u1OOvfjFH9MQHv5Zaxp7T9A7iLSPtVu9xsbxpnbte3e5u1zJHOzWSCTAdckF7P0DQfdgsU1Li5dfWX/HL1l+SHoJu5iEjAsKbrzs/DnJRqreXT0dYlp2WQnJZx+FuJiLQqLtIBSHTz+XzBcXoyMjIwm0Of53xuUx75lS4yEm38z/CerN5eAEA3u4UHrjidmS9s49UvC5l4ViZnt7O106f5VRjAKekOeiYf38DCQ3p3w9I01lNBtYu+Ka0Plv51sZMql4cEq4Uze7VtkPOAM3slk2izUOXysKvYGRz8vK1Wbz9IlctDv1Q7lw7s0eI2p3ZP4JLsDP69u5Rln+Rz/w8Ht+sYItJ+G/dW4DWgX4qNnhl2aixqJRVtzCbon2hwWpqVOq+Jt/Ma+eibcjbureAH2Sn89/lZnJSWEOkwRSTKnd00lMOe0jqqXW662a1t2s/n8wVbMGVm9sJkMlFW20heZT0VtY3sLy6jzu0jKTkZk8lMdXUVG772P8g8b2AF3bp9d41dVV3F+l0F+ICzTynFkZCEt6GOXRUe4i0mviqpx57SSKrD2uIDUBGRQykpJSckMIg0wM8vPZuePXuG9P13lzh5fvN+AG67NItEW/MnNTknpTDprF68+mUhC9/J5fmfnENcOwYrD4ztNLJ/2nHHaLdaOCMzmS8OVvNZftUxk1Jrd5b4j3lyKnGW9iXx4ixmRp6cxru7S1m3q6RdSalGj4/lW/yf5c9G9mt1UPf/HtWff+8uZe3OYmaOOZl+ae2blfBoPF4fm/Jr2FHaSLLDoLTOTcupMZGu5aOmrl4j+yaCVy1rolmDs4LGunrOiXOzx5dBudfGml2VvP71Fi7JymBs/3jOynTQK7NXWB7kiEhs655o45R0B3vL63n1i0KuO7ffEdscmoACSOneg40781j28R6qPRYSk/I5UOOhttHbwhHKDvneP9vfzq1lhy0H8D/Mzf1PJVDZbM2mwgOw7gBJ8Rb6pTo4Oc1Bj3gv/VJs5AzoS58UO2UlRZhM0NCQhNWahMmk86FIV6WklJywcA0g7fUZPLhuN16fwcVZ3RmbnUFxcfER2930vVN5L7eU3SW1/GPrAf7f8JPa9P6GYbCpKSl1vF33AkaenMoXB6tZt6uEiWf1Oup2Hp/BG1/5yzDhzMzjOtYVZ/Tk3d2lvPGfImZ/79Q2J+Fe21FIaW0jPZNs/PCM1o89KDOJ809NZ/235fxlcz7zLht4XLEeanNeBff9axfFzsamJW42H/iG6851c8OYk9s986FIrKisc/Pv3f4Wp+f2TWRrnpJS0S4xNR1HYz09rRZ83Xrw2b4yiup8vLO7lHd2g93sY+LpZVwz8jROSVfrKRFpn+vO7cf9b33Nc5vymHhmL1ITvmst5TMMPv06jyc/2EWtOZGSWje13ly8BgSSTDQ0AP4WnnaTjzijEbvNRhxePB4PjoRE6murscYnEBdnoXeiBbvdERymoqHBRVGdD2+jC6/HTVJyN6qrKjHFJ+LBgscH5Q0GzgYvXxU5+arokEmJPigkzgx2k5c0h5W0uAb+94LTGDHwZLWqEumilJSSTuvFzwv48mANiTYLt43NOup2qQlWbvreqcxft5un1u/j+wN7tKkr3qf5VeRXurDHmRlxgkmpCWdm8uzGPDburaCw2kWvbvYWt9u0t4Ky2kbSHFbOP/X4knnnn5pOmsNKeZ2bjXvLuWBA92Pu42zw8NTH+wD/hYy1DS20Zozqx/pvy3l9RxHXjTiJk0/gxumvm/N5/INvMYBUu4VUGzg9UFrn5dmNeeSW1PLAFYOxt3EmQpFY8o/PD9Dg8TG4ZxJn9LCzVXMMxAyTCU5KdZDsiePM7lb+nd/IxwfduHxmVu2oYNWOLQzsHs+VQ/sy/vRMUh1t64YjIl3bFWdk8sJnB/i6pJZZq7Zz+ek9cTZ4+E9hDTsOVlPr9gEOwAf4r6262UwkWSE93sT3+to4s18PzPUVvLe/EWdlLSZrHIbbhcnqoM/JJ3Egdwcmqw/DXUttdS2Z3U7m4N6vMcXFY/M0cEZGXwx3Q9P2fTmQW/nd9rW1eKyNNFgSiE/rxYHiEhptKdT5zNS5odEHTiw4nT7ysXLja3n0TCrkggHduSirOyP6pWKLU8spka5CSSnplAqqXDzx0V4AZn/v1GMmma4c0ot/flnIFwdruO/NXTx+9ZBjtrx5Yau/n/wVZ2aSFH9i/wonpToY0S+FLflVrPmyiBvOO/mIbQzDCB7z8tN7trvrXkCcxczlp/fk758d4B9bC9qUlFq6YR/ldW76pzmYPLR3m45zdt8UxpySxoa9FSx4ezdPTB3a7idYPsPg8Q++DXYbnHRWL346pBsvfn4Au91Kj6QEHvv4IO/vKePGVdv5w1VnNXvaJxLrnA0e/rHVP07ez0b2w2Q6/plEpfNyVpXz3sF6TO56vt+7L4U1jeTVmin3xvN1WQMPv/sNj73/DWf0cHB2rwQuGtyXQZnJStSLSIssZhO3X3oat7z0JbmltSz+8Ntm6834SLGZOKl7N+KcxdjcNdi89XRL6Yvhruc/e2opbzA4uPdrkjP6HvN4SakZdOueSU1FKSarA8PdeovepNQMDHd9U8KqDymeimDCyumspd7jheRekJTO/nInJXVeip2NvLz9IC9vP4gjzsQ5fRIZmulgzMC+ZPdIOu7rZhHp/LpcUqqsrIx77rmHTz75BIvFwpVXXskdd9xBXFyX+yg6rbpGL3Ne3UGd28vQPt24+uxjJ1HMJhO/GT+I65Z/xua8Sp7duI+Z551y1O3zKur5YI+/b/w1OX1CEveVQ3qxJb+KF7cVMDWnzxFPvN/PLWPD3grizKY2lak1U4b1YdXnBWzYW8H7uWVclHX0xNSmvRXBGffmjD2tXU+ebr80ix/95VO25Fex6vMCrsk59oVLgMfr44F1u3l9RxEAt1x4Kted2y/YBdNkMnHxqd3I7tODX7+6gy8O1vA/Kz/n0f86i/4hGsMqUvxjORRRVuake/fwTAAgodfR9YNhGMxfu5sql4f+aQ4uyc6gpFjTaMeqxNR0fI31mE3Q2+6lbzcbKZl9+WzXN+TXmXH6rGwvqmd7UT1/3VaGxQR9utk4NdXGqWnxnNGvB31TEuidEk9yfJy6uYh0kM5w73DoGFGB2e6MkhLGJFdRZEqlrKaegT0SOSnJTHGdF0t9BRabgz4np3EgtwBTcnqzRNKhSaaOlpyWQZK7HpPVoE/fFE4yiin1VVDRaKKMZMo8Nuo9cazPc7I+z8mSzSXYLCZ6JMbRMyGOjIQ4//eJVgb1y6R3ip2eSfFt6gUgIp1Tl8vE3HrrrWRmZvLhhx9SWlrKrFmzWLZsGddff32kQ4tqgT7mJ6qq3s2dr33F7pJa0hOszL9iMOY2Xnif0j2B2y/N4ndvfc3SDXlYLWamj+x3xIW7y+1l7mtf4TNg9ClpDOieGJLYL83uwXPpeewtr2f+2q/5vyvPCB67oMrF7/+dC8B1555YVziA/mkOfjLiJJZ9ks/v/53L4MwkMltoTfZVUQ13vvYfvIa/i+GYU9rXZfCkVAf/e/4p/PH9b3jk3T1kJNoYe5RZ+w5VWO1i7us72V5QjcUE88YPZMKZLY+1NeykFJ750TB+8fIX5FXU899/28rcywZySfbxTymcX1HPx9+Ws2FvBTuLqimv82Axw8cHv2XYSZWMPiWNUSenkWwPzymwtLSUZR99hcvVGJYJACQ8Orp+eH1HEW9/XYLFBDeNSKeosIDS0pKQnU+l80uwWTg1wc1pqQ6q62op9zmocFuoagSn2yC/qpH8qkY+2OeEz78bZDjBaqZncjzpCVYcZp+/W7Tdwkk90uie6F+elmAjPcFKos2iBJbICegM9w5FRYU8uW4ryWkZwS50hqeB5Iy+jDm5Lwdyd1BbUU5+iX+ZEWX/8ilpGSQ31nOy1YavsZ5qHNTb0zlQVkO126DeY3Cg2s2BanfzHTc2PegE0hwWeiZaOSk9iV7J8STQQI9EK9l9e5KRbKd7glWtrUQ6qS6VlNq3bx+ffPIJH3zwAQ6Hg379+nHjjTfy+9//XkmpNqh3e/mmrI5vSmvZU1rHN2W17C6uoazOgwG8kruL3t3yOC0jgQEZiWQ1vfqm2o+ZWHK5vfx7dylPrd9LQXUDDquZ308686hjMx3NxLN6UVjdwNMb9vHER3vZklfJjFH9Gda3GyaTia9LnDz23jfsLHaS6rBy97jsE/hEmrPFmbn/h4OZ8bfPeS+3jJtf+oKrhvSmpLaRZZvyKK9zc1Kqnf8e1T8kx/vv0f1Zu6uEgioX//23rdw2NosLBqRjMZsodjbyzy8L+fOmPNxeg7P7dOOu7x9fWacN78ve8jpe/aKQO9Z8xdRhVUwb0bfFWQb3ltWxZkchL2wtoMHjIynewv0/HHzMLoandk/gz/8vh1+/soMdhTXc/s//cP6p6fz4nL6c0y/lmE+/AuMorP+2nI++KSev4shm5R4f7KtsYF9lIa9+WYjFBEP7dOO8U9M5f0A6WRmJIblxa/D4qHJ5sCalYljdeH1KMESDSNQPe4vKAIOze1h544MN/KupS0RSj7a3SJTYkRRn0M1m0L+xhjrqaTA14knuBYnp7C8qpdYN9T4zbizUuX3sLa9nb/nh57ryI97XajHRzWYhwWYmwWoi0WomwWoh0WomOd5Mt3gLqY44Tu6VQfeEeFIccdjjLMRZTNgsZuIsJrw+gwaPL/hq9PoocHkpKKrmYEkZXsMgIz0dW5wZm8WM1WLGajFhtZiJM0FVRRlWi4k+mZnEWy3EmU1KlElUiPS9g9HU8hogKbV7q13oAl3mop3JBKlWOKNfKr0aDvi7+5m8mLv1wp7ag4KDB6nHSr3bR50HXIYFAxPl9V7K673sLHUd9o4Hg9+lOuLonmAjyWqQZo+jR2oSibY4HFYzXlctDquZ3hnpJMVbSbBZcFj950KrxYTVfMh5zez/qkl6REKjSyWldu/eTWpqKpmZ3808dtppp1FQUEB1dTXdunULy3E3flvOv3cWgGFgdzgAE4Hb1MATccMAo+nnQ29h/cuN4Hr/wubLDMPAa4DX56Pe1YDPMEhKSqS+wY0JE2aTv3ub2eTvMnX4zxaTCVPTssBXt9dHvdtLvdtHibOBopoGyusOezpxGJfH4NvyOr4tr4Ovv2sObI/zP9HNSLSRlmANnszB31WvqKaB3SVOGv3TgtC7WzyPXHUm2T2SjuvzvuG8k0myx7H4g2/4JK+ST/IqAYgzm/A0JQji48wsmHg6vduZ9DqWwZnJ3DUum/97J5dN+yrZtK8yuC67RyKP/tdZIRsjxGG18OQ1Q7n5xS/YV1HPbf/8Dyb8ZXN5fMHtLjytO/dePvC4B4w0mUzc+f1s4swmXtp2kFWfF7Dq8wL6ptjpnWLHajZR2+hlf2V9s7+RnL7d+M3lgzgptW1d8bon2nj62rNZumEfz2/OZ/235az/thyH1cyp3RPJSLRhjzMTH2fG4zOod3upafCQV1FPSXBGP784s4lhff0Jp5MTfLyfW4zXB+f0T+ebGhMf7y3n27I6th6oZuuBav700V4SrBZ6p8TTu5udRJul2Y0VgNtr4Pb6cPv8Xxs9PurcXmobvDgbPTgbvNQ2enB7myehVu3ahcO6m6T4OJJscSTFW0hs+j4x3n9zduj/owmC/4eB/3+f0XRuMPzjdBn4v/p8BvUuF4YBY0/vzfcHqUXW8YpE/XD1GWlUOZ306JHJgT2VmG0OfI3Rf0MhJy4pLZ2ExnrMNujdN4UergPBvw8jzkGty0V1nZtGjwdTUjq2xFRKy8qod/to9IGbONyY8Rgm3F6DsnoPZcf80yo6wagPHnsTvgl+ZzWbiDObiLf6z7VmfFjNJv9yi/9roj0eW5wleCNos5iIswQSX6YWH3gFGhoaGNTV1X63ABMJCQn+E+wR+xjU1dUB+LcBapt+PrTlogmwO2yYTdYj3qelFo6HLgp86zvkWI7gNWHza0EMg/r6+u+2aW8C75D9T+6RyozR/dW16ThF6t4hwFlVzvINtcFWUSnH34g8ajXr7tczicTqRkxWS3DMKl+jE7fFQVLPvuzbt5fqejcuL3hsSdR7DFxecBsWDKCy3kNl/aFjN9a0cMQjZ/o+GhNgMfuvOy0mU9M1XdPPZrCYTMH1dlscPq8XyyH3Yv7rvUPuy/huucVsavrZvxz8pwL/daIpePzAcrPJf44LrKuvqwMMDj17mE3fnW9MmEhITGz2Xv5zYS0AiU3rDMOgrrY2eA4zmUz+dYe8c+AUFdjfbrficrlJSGj7A99Dj32s/Q7dNnDuNZlM7TreiTIBjgQb9XWNze7r21qGSDg0PqvZxLTRp9EntXPMANylklK1tbVNFwDfCfxcV1fX5orFbG5+oXEsj76XS3Ft4ATY0skvDIoOf0pw4pLi40ixm+mXEk//lHhOSrGSQgNb9pVgBi4amEmDJYH8qgbyqhrJq2wgv8qN22dQXuduNalli7PQK9nC5dmpXD4wlQRzPWUtXEFXVlbgra/G7bNRWWnDfJQnFJedbGPof53GyzvK2XzASU2DP0ljs5gY3ieRacMy6JXgpqyspNUyB44X+N5iOfbJ5fzeFpZMGsAr/ynnQHUjcRYT5/ZJ5NLTUolrrKas7Oj7Bo7nsVnwuGpbLSOADXjk8v688lU5//62imqXv5xJFjODMuyMz07heycn466tpKz2xMp4Q04qI3vZeOWrcr4sqqfK5aHK5Wy2TYo9jrN7JXBZVgrD+yZi8jopK2u+zbF+hz86I4kL+2Xx+s4KNuyvodrlI6+ivsXWTwFJ8XGkOyyc3SuR4X0TGZqZQILNfwFeVlaO3eOPYXByN84/JZ3rhiRT7HSz9WAtnxXU8kVRPY1eg6KaRopqGo96nGOJj7NwtDHz690+6t2NlBzl93Cicsu/5bLT25eU6mR1ZUSFon44dOiwttQTcXEWLK4qGmri8LlqMDweDE99i18basqOuk1r6zpyG1dVKb6GyMfRkZ9ZY00ZnroavD5Lh8VhivOQ4KknMcGB4XFjMlfTIy2Z4ppSTAmOpm38X2vrG2h0e7Cl9CKxewalRYX4LDY8Hh8eixW314fHZKXRCw0+8FlsuL1gmI6ewDAbPkz4sJgMTIaXOIsFk+HD6zMwx1lxuz0YJrP/oRlmDEz4TCag5ROOxwceX+BBigGHPpqrDvXA/85jb3LMbUJ5Em/Le9Wd0BE+3l/HRdkZnN6r7Q/6VDd8J1L3DhaLmcaacjyuGhzx8fgsPrz1VTRUl+Jx1WD2NOLzNGD2NLa4LBzrwnKcmlK8dVV4fJYTiiEurpFuvm70MCrITIr3r4tzN32Np0fvk9if9y019W7cHgNLt3QavOBye7AlJFPrrMUcn4DHZ+D2GZji4mlwezBMluC5zIfpmP8cXsBrgNvb9MN36QqoO/7ry7Zr7/mitXNQa+vaci5tz3aR3i8cOlMsLTNbvuW28We2uk3gTz5wDgtX/WAyutDgFevWrWPevHls2rQpuGzXrl1ceeWVbNmyheTk5AhGJyIikaL6QUREDqe6QUQk/LpUW97s7GwqKyspLf2ua9mePXvo1auXKhURkS5M9YOIiBxOdYOISPh1qaTUKaecwvDhw3nwwQdxOp3k5+fzxBNPMGXKlEiHJiIiEaT6QUREDqe6QUQk/LpU9z3wT9X+u9/9jk2bNmE2m7nqqquYM2cOFktoBp8WEZHopPpBREQOp7pBRCS8ulxSSkREREREREREIq9Ldd8TEREREREREZHOQUkpERERERERERHpcEpKiYiIiIiIiIhIh1NSSkREREREREREOpySUjFm586dzJgxg5EjR3L++edz++23U15eHumwQs7r9XLddddx5513RjqUkKqsrOT2229n1KhRnHvuudx4440UFxdHOqyQ2bFjB9OmTWPEiBFccMEFPPDAAzQ2NkY6rJAoLy9n3LhxbNq0Kbhs27ZtTJ06lZycHMaOHcuqVasiGKGEW1lZGTfeeCMjRoxg1KhRzJ8/H4/HE+mw2q21euRYf9OrV69m3LhxDBs2jMmTJ7N169bgOq/Xy8KFCznvvPPIyclh1qxZnfL81lL9Esvlbq3eieVyt1YfxXK5pXOLhXrkeK6Houl/qivVkRs2bGDq1Kmcc845nH/++dx///24XC4g9sraFer+N954gzPOOIOcnJzg67bbbgM6QVkNiRn19fXG+eefb/zxj380GhoajPLycuOGG24wfv7zn0c6tJB77LHHjMGDBxt33HFHpEMJqZ/85CfG7NmzjaqqKqOmpsa46aabjJkzZ0Y6rJDwer3G+eefb/zlL38xvF6vcfDgQWP8+PHG4sWLIx3aCduyZYvx/e9/3xg4cKCxceNGwzAMo7Ky0hg5cqSxfPlyw+12Gx9//LGRk5NjbNu2LcLRSrj85Cc/MX79618bdXV1Rl5ennHFFVcYS5cujXRY7dJaPXKsv+mNGzcaOTk5xpYtW4zGxkbjz3/+szFq1Cijrq7OMAzDePzxx42JEycaBQUFRk1NjXHrrbcaN9xwQySL26LD65dYL/fR6p1YLndr9VEsl1s6v2ivR47neiia/qe6Uh1ZVlZmDBkyxHjppZcMr9drFBUVGRMmTDD++Mc/xlxZDaNr1P0LFiww7rzzziOWd4ayKikVQ/bs2WP8z//8j+HxeILL3n77beOcc86JYFSh9/HHHxs//OEPjVtuuSWmklJffPGFMWTIEKOmpia4rKKiwvj6668jGFXolJeXGwMHDjT+/Oc/Gx6Pxzh48KDxgx/8wHj22WcjHdoJefnll42LL77YeP3115tdhP3jH/8wLrvssmbb/uY3vzFuv/32SIQpYbZ3715j4MCBRmFhYXDZ66+/blx88cURjKr9WqtHjvU3/etf/9qYN29es/WXX3658eKLLxqGYRgXXnih8c9//jO4rqSkxBg0aJCRl5cXruK0W0v1SyyXu7V6J5bL3Vp9FMvlls4t2uuR470eiqb/qa5WRwbqBp/PZ+zatcsYN26c8de//jXmytpV6v5p06YZy5cvP2J5Zyiruu/FkAEDBvDMM89gsViCy9566y3OPPPMCEYVWmVlZcydO5dHHnkEh8MR6XBCavv27WRlZfGPf/yDcePGccEFF7Bw4UJ69OgR6dBCIi0tjenTp7Nw4UKGDBnCRRddxCmnnML06dMjHdoJueCCC1i3bh0//OEPmy3fvXs3AwcObLYsKyuLnTt3dmR40kF2795NamoqmZmZwWWnnXYaBQUFVFdXRzCy9mmtHjnW33Rubu5R19fU1FBYWNhsfUZGBikpKezatSuMJWq7o9UvsVzu1uqdWC53a/VRLJdbOrdor0eO93oomv6nulodmZSUBMBFF13ExIkT6dGjB5MnT46psnaVut/n87Fjxw7ee+89LrnkEi688ELuueceqqqqOkVZlZSKUYZh8Oijj/Luu+8yd+7cSIcTEj6fj9tuu40ZM2YwePDgSIcTclVVVezatYu9e/eyevVqXnnlFYqKirjjjjsiHVpI+Hw+7HY799xzD59//jmvvfYae/bsYdGiRZEO7YT06NGDuLi4I5bX1tYekTi12+3U1dV1VGjSgVr6fQd+jtbf+eH1yLH+pltbX1tbC0BCQsIR6wPrIqm1+iWWy91avRPL5W6tPorlckvnFu31yPFeD0Xr/1RXqiPXrl3LBx98gNls5pZbbomZsnalur+8vJwzzjiD8ePH88Ybb7By5Ur27t3Lbbfd1inKqqRUDHI6ndxyyy2sWbOG5cuXM2jQoEiHFBJPPfUUNpuN6667LtKhhIXNZgNg7ty5JCUlkZGRwa233sr777/fKSqkE7Vu3Treeust/t//+3/YbDays7OZPXs2f//73yMdWlg4HI7gYJABLpeLxMTECEUk4ZSQkEB9fX2zZYGfo/F33lI9cqy/6dbWBy5mDv+MOsv/RGv1SyyXu7V6xzCMmC13a/VRLP++pXOLtXokIBb/p7paHWm328nMzOS2227jww8/jJmydqW6PyMjgxUrVjBlyhQcDgd9+vThtttu44MPPugU9b2SUjEmLy+Pq6++GqfTyYsvvhgzCSmAV199lU8++YQRI0YwYsQIXnvtNV577TVGjBgR6dBCIisrC5/Ph9vtDi7z+XyA/2lMtDt48OARM+3FxcVhtVojFFF4DRw4kN27dzdblpubS3Z2doQiknDKzs6msrKS0tLS4LI9e/bQq1cvkpOTIxhZ+x2tHjnW33R2dvZR16ekpJCZmUlubm5wXUlJCZWVlUc0CY+E1uqXWC53a/XO6aefHrPlbq0+iuXft3RusVSPHCrW/qe6Sh352Wefcfnllzc7VzY2NmK1WsnKyoqJsnalun/nzp08/PDDze4pGxsbMZvNDB06NPJlbfPoU9LpVVZWGhdffLFx5513Gl6vN9LhhN0dd9wRUwOdNzY2GuPGjTNuvvlmw+l0GmVlZcZPf/pTY/bs2ZEOLSR2795tnHXWWcaSJUsMj8dj5OXlGRMmTDAWLFgQ6dBC5tCBPcvLy40RI0YYf/7zn43GxkZjw4YNRk5OjrFhw4YIRynh8uMf/9j45S9/adTU1ARnTVq0aFGkw2qX1uqRY/1NB2Zr2bBhQ3B2lnPPPdeoqKgwDMMwHn30UWPChAlGXl5ecHaWn/zkJx1dxDY5tH6J5XK3Vu/Ecrlbq49iudzS+cVCPWIY7bseiqb/qa5URzqdTuOiiy4yHnzwQaOhocHYv3+/MWXKFOPee++NubIGxHLdf/DgQWPYsGHG008/bbjdbuPAgQPGNddcY9x9992doqxKSsWQ5557zhg4cKBx9tlnG8OGDWv2ikWxlpQyDMMoLCw0br31VuP88883RowYYdx+++1GVVVVpMMKmfXr1xtTp041hg8fblx88cXGH/7wB6OhoSHSYYXMoRdhhmEY27dvN6699lojJyfHuPTSS42XXnopgtFJuJWUlBg333yzMXLkSGP06NHGggULms3QEw2OVY8c62/6lVdeMcaPH28MGzbMmDJlivH5558H1zU2Nhq///3vje9973vGOeecY8yaNcsoLS3t0PK11eH1SyyXu7V6J5bL3Vp9FMvlls4tFuoRw2j/9VC0/E91tTpy9+7dxowZM4wRI0YYl1xyScyfJ2O97t+0aVOwPKNHjzbuv/9+w+VyGYYR+bKaDCMG+gWJiIiIiIiIiEhU0ZhSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEhERERERERGRDqeklIiIiIiIiIiIdDglpUREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHU1JKREREREREREQ6nJJSIiIiIiIiIiLS4ZSUEumiDMOIdAgiItKJqZ4QEQkdnVNFWqaklEgn8fjjjzNo0KCQvNfYsWO58847Adi/fz+DBg3i5ZdfBqC6upo77riDLVu2hORYIiISWwoLC/n5z3/OgQMHTvi9Dq+DRES6gjfeeINLLrmEIUOG8Jvf/Ibc3Fx+/OMft/t97rzzTsaOHRv8+dBr/La47rrruO6669p93Pa66aabWozr73//O4MGDTri9Zvf/CbsMUn0iIt0ACISeosXLyYpKanFdV999RWvvPIKkydP7uCoREQkGnz88ce899573HPPPZEORUQkKv32t7/llFNOYcGCBWRmZrJmzRq2bt16wu/b2jV+S+69994TPmZrvF4vDz74IOvWreO//uu/jlj/1VdfkZWVxfz585st7969e1jjkuiipJRIDDrjjDMiHYKIiIiISJdUWVnJ+eefz6hRo0L6vu29xs/Kygrp8Q+1c+dO7r//fr788kvsdvtRtxk6dCjDhg0LWxwS/dR9T6QdFi5cyNChQ6mpqWm2/OmnnyYnJ4eFCxcybtw4Fi9ezKhRo/j+979PRUVFu47x9ttvM378eIYMGcLUqVPZsGFDcN2mTZsYNGgQmzZtarbP4U1zj9a0d9OmTfz0pz8F4Kc//WmHNOcVEekswnkOf/zxx4+676pVq7jiiis466yzuPjii3n88cfxeDzBfe+8806mT5/OSy+9xPjx4znrrLO48soref/995sdY+/evdxyyy2cf/75DBs2jOuuu45PP/00uH78+PHMnj37iNimTp3KzJkzAf9T7aeffpoJEyYEbxR+9KMfBeual19+mbvuuguASy+9tFldcqxyAKxdu5Yrr7ySoUOH8l//9V/s3LmzTZ+fiEhnsWPHDn72s58xfPhwcnJymD59Otu2bQuuf/PNN4PnuauuuoqtW7dyxhln8PLLLwev1QH+9Kc/MWjQIO68804WL14MwKBBg3j88cePO7ZDr/Hbcs4//B5h0KBBrFixgrlz5zJy5EhycnK45ZZbKC0tbfYezz77LJdeeilDhw7lRz/6Ef/+97+PuAe544478Pl8vPDCCy22fPL5fHz99dcMHjz4uMsrXYOSUiLtMGXKFBoaGnjzzTebLX/llVe4/PLLSUhIoKCggHXr1vGHP/yBW2+9lbS0tHYd4+677+anP/0pjz/+OImJidxwww3k5uaGJP4zzzwz2If7N7/5Tdib9IqIdCbhPoe3tO9TTz3FPffcw5gxY3jyySeZNm0aS5cuPWI8jS+//JJnn32WW265hT/96U/ExcVxyy23UFVVBUBubi6TJ08mPz+fefPm8fDDD2MymfjZz37GJ598AsCkSZP44IMPcDqdwffNy8tj+/btTJo0CYCHH36YP/3pT1x77bU888wz/O53v6OiooJf/OIX1NXVcfHFFzNr1izA303kxhtvBGhTOf79739zyy23kJ2dzeLFi/nBD37Abbfd1ubPT0Qk0pxOJ9dffz1paWksWrSIRx99lPr6ev7nf/6Hmpoa3nnnHX7xi18Ez3OXXXYZs2bNwufzAf5r7RdeeAHw1zkvvPACN998M1OmTAHghRdeYOrUqSGJtS3n/JY8+uij+Hw+/vCHP3D77bfz3nvv8eCDDwbXL168mIcffpgf/OAHPPHEE5x99tn88pe/POJ9Fi5cyN///vejJp2+/fZb6uvr2bZtG+PHj+fMM89k/PjxvPLKK8dfaIlJ6r4n0g6nnXYaOTk5vPrqq8EKZfv27ezZs4ff/e53bNiwAY/Hwx133MF55513XMe49957ueKKKwAYM2YMl156KUuWLOGRRx454fiTkpKCzXizsrLC2qRXRKSzCfc5/PB9a2pqWLJkCddeey3z5s0D4IILLiA1NZV58+YxY8YMsrOzg9u+/PLL9O/fH4CEhAR+8pOfsHHjRsaPH8/ixYuxWq08//zzJCcnA3DxxRczYcIEfv/737Nq1SquvPJKFi1a1GxsjzVr1pCYmMill14KQHFxMb/85S+bPTm32+3cfPPN7Nq1i5ycnGAMp59+OieddFKby/GnP/2JM888M1hfXXjhhQAhqb9ERDpCbm4u5eXlXHfddQwfPhyAAQMGsHLlSpxOJ3/6058466yzmp3nTCYTjz32GOC/1g50VevVq1ez74GQdmNryzm/JQMHDuShhx4K/rx9+/bgw5q6ujqWLl3KtGnTmDNnDuA/39fX1weTbQHHagEVaClbUFDAnXfeSVxcHK+88gp33HEHjY2NXHPNNe0vtMQktZQSaaerr76aLVu2sH//foDgTcSIESOC2wwcOPC43ttisXDZZZcFf46Pj+fCCy/k448/PrGgRUQECO85/PB9t27dSn19PWPHjsXj8QRfgZmU1q9fH9w2PT09mAyC725g6uvrAfjkk0+45JJLggkpgLi4OK644gq++OILamtrOemkkxg+fDivv/56cJvXX3+d8ePHB8f7eOSRR5g+fTrl5eVs3bqVl19+mX/+858AuN3uFsvUlnK4XC527NhxxI3QD37wg3Z+giIikZOdnU16ejqzZs3i3nvv5d///jc9evTg9ttvJzU1tcXz3JVXXhmRWNtyzm/J4YmxXr16Beuazz//HJfLxeWXX95smwkTJrQ7vlGjRvH000+zbNkyLrnkEr73ve/xyCOPcN5557Fo0SIMw2j3e0psUlJKpJ1++MMf4nA4+Oc//0ljYyP/+te/jphtIiMj47jeOzU1FavV2mxZ9+7dqa6uPu54RUTkO+E8hx++b2VlJQAzZ87kzDPPDL4CLamKi4uD2zocjmbvYzKZAIJdQqqqqlqMKyMjA8Mwgt03rrrqKjZs2EBFRQVfffUVe/bsadaN44svvmDKlCmMGTOG6dOns2LFCsxm/+Xg0W4Q2lKOqqoqDMMgPT292b49e/Zs5dMSEelcEhMTWbFiBRdddBFvvPEGs2bNYsyYMfzmN78JXo8ffp7LzMyMRKjAsc/5LTm8vjGbzcHzf3l5OXBkGY+nXszIyOCiiy46IkF20UUXUVJScsQ4VtJ1qfueSDslJiZy+eWX869//YvTTz+d6upqrrrqqpC8d01NDYZhBG9GAEpLS4MVw+E3KQG1tbUkJiaGJAYRkVgWznP44bp16wb4x3E65ZRTjljfnov8lJSUFi/gS0pKAIJjX11++eXcf//9rFu3jn379tG7d29GjhwJfDdWyqBBg3jttdc47bTTMJvNvP/++7z11lsnVI7U1FTMZvMRMQYSWiIi0WLAgAH8/ve/x+v1sn37dl599VX+/ve/07Nnz053nmvtnH88Aq10y8vLGTBgQHB5IFnVHp988gkFBQVH1LENDQ1YLBZSUlKOO06JLWopJXIcpkyZwtdff81zzz3H6NGj6dOnT0jet7GxkY0bNwZ/rq2t5b333gtOJ5uUlATAwYMHg9tUVVWxZ8+eNh/DYrGEJFYRkWgVrnP44c4++2ysVitFRUUMGTIk+LJarTzyyCPBLoRtce655/Luu+82mznQ6/Xy+uuvM2TIEGw2GwDJyclccsklvPPOO7z55ptMnDgx2BLqm2++obKykp/+9KdkZ2cHl3/wwQfAdw88AsvbU474+HhycnJYu3ZtsxZX//73v4/jkxMRiYw333yT0aNHU1JSgsViIScnh/vuu49u3bpRXl5OTk4Ob731VrMHxO++++4x3/fw82qotHbOPx6DBw8mOTmZtWvXNlve2oOLo9mwYQN33nkn+/btCy7z+Xy89dZbnH322cF6S0QtpUSOw/DhwxkwYACffPIJDz/8cMje12q1cvfdd/OrX/2KpKQknn76aVwuV3D2o0GDBtG7d28WL15McnIyZrOZp59++ohmuK0JjEfy3nvvkZKSomlaRaTLCdc5/HBpaWlcf/31/PGPf8TpdDJq1CiKior44x//iMlkatf596abbuKDDz7gpz/9KTNnzsRms7F8+XLy8/N55plnmm171VVXMXv2bLxeb7OxTk499VSSkpJ48skniYuLIy4ujrfeeosXX3wR+G78qkDLqHXr1nHhhRdy2mmntakcv/rVr/jZz37GTTfdxLXXXsvevXtZsmTJCX2GIiId6ZxzzsHn8zF79mxmzpxJYmIi//rXv6ipqeGyyy7jhz/8IdOnT+fGG2/kxz/+MXl5efzxj3885vsGzquvvfYaZ599Nv369QtZzEc75x+PpKQkrr/+ehYtWoTD4WDkyJF88skn/P3vfwfal1z78Y9/zAsvvMD//u//cvPNN+NwOFixYgVff/01zz///AnFKbFFLaVEjtPFF19McnIy48aNC9l7pqSkcNttt/Hoo49yyy23YLFYWL58ebD5rMViYdGiRfTs2ZNf/epXPPDAA/zgBz9oNjj6sWRnZzNhwgRWrFgRnFVDRKSrCcc5vCW33nord955J+vWreOGG27g97//PcOHD2f58uXNBi0/luzsbP72t7+RkZHB3XffzW233YZhGDz//PNHzBT4ve99j5SUFM4444zg7H7gfyjxxBNPYBgGv/jFL7j99tspKChg+fLlJCYmsmXLFsA/OO15553HI488wsKFC9tcjhEjRrB06VKKioq46aabWLlyZbNpxkVEOruePXvyzDPPkJyczNy5c/n5z3/Ojh07ePzxxxk9ejQjRozg2WefpbS0lNmzZ7Ny5UruuOOOY77vZZddxpAhQ7jzzjt59tlnQxrz0c75x+vnP/85N910E6+88go///nP2bJlS/CeISEhoc3v07NnT/72t7+RnZ3NAw88wK233orL5WLZsmWcc845JxynxA6ToWHvRdrNMAwmTpzIqFGjuOeeeyIdjoiItIPO4SIiEir79+/n0ksv5aGHHmLy5MmRDueEeDweXnvtNUaNGkXv3r2Dy1esWMEDDzzApk2bgq2+REJF3fdE2sHpdLJs2TK++OIL9u7dyxNPPNHq9oZh4PV6j/m+ZrM5bH3NRUTET+dwERGJtLbWLRaLpdnkRx0hLi6OpUuX8pe//IVZs2aRlpbGzp07+eMf/8hVV12lhJSEhZJSIu1gt9tZuXIlPp+P+fPn079//1a3/+STT/jpT396zPe96aabuPnmm0MVpoiItEDncBERibTVq1dz1113HXO7SLW8evLJJ/nDH/7AfffdR3V1NX369GH69On8/Oc/7/BYpGtQ9z2RMHI6nXz77bfH3K5nz55kZmZ2QEQiItJWOoeLiEioVVRUtGn21ZNOOom0tLQOiEgkspSUEhERERERERGRDqcBEEREREREREREpMMpKSUiIiIiIiIiIh1OSSkREREREREREelwSkqJiIiIiIiIiEiHi4t0ANGorKyG9gwPbzJB9+7J7d6vM1EZOodoL0O0xw+xVYby8hrS05MjHU7M6Ip1Qzjoc2mZPpcj6TNp2Yl+LoH9JXSi4W802v+fFH/kRXsZFH/bjxFqSkodB8PguH7Rx7tfZ6IydA7RXoZojx9ipwwSOl25bggHfS4t0+dyJH0mLdPn0nlE0+8immJtieKPvGgvg+LveOq+JyIiIiIiIiIiHU5JKRERERERERER6XBKSomIiIiIiIiISIdTUkpERERERERERDqcklIiIiIiIiIiItLhlJQSEREREREREZEOp6SUiIiIiIiIiIh0OCWlRERERERERESkwykpJSIiIiIiIiIiHU5JKRERERERERER6XBKSomIiIiIiIiISIdTUkpERCQCPj9Qxcznt1BY7Yp0KCIiIiHj9XrZu/fb4Mvr9UY6JBHpxOIiHYCISKz4759fT1l55VHXd09P5bmnnum4gKRTe3tXCWv/U0TfZBs3XnBqpMMREREJifz8PJ58awupPXpTWXKQ/x0Pp5yiek5EWqaklIhIiJSVV3LFLx8+6vrXH53TgdFIZ3daRiIAXxRURzgSERGR0Ert0Zv0XidFOgwRiQLqviciIhIBQ3p3A2BHYQ0enxHhaEREREREOp6SUiIiIhFwavcEkuLjqHf7+Ka0NtLhiIiIiIh0OHXfExERiQCL2cTgHna27Hfy5pb/4DrJdtzvlZ7enZNO6hfC6EREREREwk9JKRERkQjYvz+f91/+C4kjr2bJP17jgTceO+73sjsS+Hj9ZiWmRERERCSqKCklIiISAWVlZdTu+4LEkVfTa9glXHvp6ON6n+L93/Lio3dRXl6mpJSIiIiIRBUlpUREpFPYuXMnCxcuZMeOHVitVs4//3zuvPNO0tPT2bZtGw888AC5ubmkpaUxa9Yspk6dGtx39erVPPHEE5SUlDBgwADuuececnJyAPB6vTz88MO8+uqr1NfXM3r0aH7729/Ss2dPwJ8cuueee/jkk0+wWCxceeWV3HHHHcTFhb+K9FQXA+A2WelzmqbLFhEREZGuRQOdi4hIxLlcLq6//npycnL46KOPeO2116isrOTuu++mqqqKmTNnctVVV7F582bmz5/PQw89xPbt2wHYtGkT999/PwsWLGDz5s1ceeWVzJo1i/r6egCWLFnC+vXreemll/jwww+x2+3MmzcveOxbb72VhIQEPvzwQ1588UU2bNjAsmXLOqTc3vpqABo8PnyGZuATERERka5FSSkREYm4goICBg8ezOzZs7HZbKSlpXHttdeyefNm1q5dS2pqKtOmTSMuLo4xY8YwceJEVqxYAcCqVau44oorGD58OFarlenTp5OWlsYbb7wRXH/DDTfQu3dvkpKSmDt3Lh988AH5+fns27ePTz75hNtuuw2Hw0G/fv248cYbg+8dbr76muD3jR5fhxxTRERERKSzUPc9ERGJuAEDBvDMM880W/bWW29x5plnsnv3bgYOHNhsXVZWFi+++CIAubm5XH311Ues37lzJzU1NRQWFjbbPyMjg5SUFHbt2gVAamoqmZmZwfWnnXYaBQUFVFdX061btzaXwWRq86bfbe/zEmcy8BgmXB4fdqulfW9ygjF0RoEyxEJZQkmfy5H0mbTsRD8XfZ4iItKRlJQSEZFOxTAMHnvsMd59912WL1/O888/j8PhaLaN3W6nrq4OgNra2qOur62tBSAhIeGI9YF1h+8b+Lmurq5dSanu3ZPbvC1AamoiAPEW8HgAi4WEhPh2vQeA3W4DIC0tkYyM9sXQmbX38+wq9LkcSZ9Jy/S5iIhINFBSSkREOg2n08ldd93Fjh07WL58OYMGDcLhcFBTU9NsO5fLRWKiP6njcDhwuVxHrE9LSwsmmALjSx2+v2EYR6wL/Bx4/7YqK6uhPcNCVVb6k2JWfICFSqeLVFv7e9W7XI0AVFTUUlpac4ytOz+TyX8z3d7PM9bpczmSPpOWnejnEthfJNS8Xi/5+XnBn/v164/FcmIthEUk+nXqMaXKy8sZN24cmzZtCi576623mDRpEueccw5jx45l8eLF+HzfjcOxevVqxo0bx7Bhw5g8eTJbt24NrvN6vSxcuJDzzjuPnJwcZs2aRXFxcYeWSUREWpaXl8fVV1+N0+nkxRdfZNCgQQAMHDiQ3bt3N9s2NzeX7OxsALKzs4+6PiUlhczMTHJzc4PrSkpKqKysZODAgWRnZ1NZWUlpaWlw/Z49e+jVqxfJye27KTOM9r8ArGb/Nw0hGFPqeGLojK9YKos+F30m0fi5iIRDfn4eT761hZWfHeDJt7YEE1Rer5e9e78Nvrxeb4QjFZGO1GmTUp9++inXXnsteXnfZdO//PJLbr/9dm699Va2bNnC0qVLefnll4OzJJ3oDEwiIhIZVVVV/OxnP+Occ87h2WefJT09Pbhu3LhxlJaWsmzZMtxuNxs3bmTNmjXBcaSmTJnCmjVr2LhxI263m2XLllFWVsa4ceMAmDx5MkuWLCE/Px+n08mDDz7IyJEj6d+/P6eccgrDhw/nwQcfxOl0kp+fzxNPPMGUKVM6rOy2pqSUy62BzkVETkRLD7S3bdvG1KlTycnJYezYsaxatarZPnqg3bFSe/QmvddJpPboHVx2tGSViHQNnTIptXr1aubMmcMvf/nLZssPHDjAj370Iy655BLMZjOnnXYa48aNY/PmzcCJzcAkIhJuX3+9k0lTp7T4+u+fXx/p8CLq5ZdfpqCggH/9618MHz6cnJyc4CstLY3nnnuON998k1GjRjFv3jzmzZvH6NGjARgzZgz33nsv9913HyNHjuT1119n6dKlpKamAjB79mwuuugipk2bxkUXXURDQwOPPfZY8NiLFi3C4/Fw6aWXcs011/C9732PG2+8scPKbjM1JaU8ejIsInK8WnqgXVVVxcyZM7nqqqvYvHkz8+fP56GHHmL79u2AHmh3Ji0lq0Ska+iUY0pdcMEFTJw4kbi4uGaJqfHjxzN+/Pjgzy6Xi/fee4+JEycCJzYDU79+/cJcKhHp6jw+uOKXD7e47vVH53RwNJ3LjBkzmDFjxlHXDxkyhJUrVx51/aRJk5g0aVKL66xWK3PmzGHOnJY/44yMDBYtWtS+gENILaVERE7M6tWrWbRoEbfddluze4e1a9eSmprKtGnTAP9DjIkTJ7JixQqGDh3a7IE2wPTp03nhhRd44403uPrqq1m1ahVz5syhd29/omTu3LlccMEF5Ofn695BRCREOmVSqkePHsfcxul08otf/AK73c706dOBE5+Bqa2Oa9rv49ivM1EZOodoL0O0xw/hLUNHfS6x8HuIJaEcU0pEpCs62gPt3bt3N3sgDf4H1i+++CLQcQ+0o6G+DeW1weHvYTJ992rP8uM5ZjR81i2J9vgh+sug+Nt+jFDrlEmpY/nmm2+45ZZb6N69O88//zxJSUnAic3A1B7HOyNJLMxkojJ0DtFehmiPH1ouQ5zVQkJC/FH3MZk46vo4q4WMjI79XNLTo//3EAu+676npJSIyPE42gPt1h5YH2t9KB9oR9N1Tyhira5OwuGwkZAQT73DRnp6EhkZye1eHqn4Iyna44foL4Pi73hRl5R6//33+dWvfsU111zDr3/9a+LivivC0WZguvDCC5vNwBR44nHoDEzt0d4pdmNhymKVoXOI9jJEe/zQehk8bi91dQ1H3dcwOOp6j9tLaWlNKEM9qkAZystrlJjqBGxNozu63BpTSkQklBwOBzU1zevWQx9Id9QD7Wi47gnlNVp5uZP6+kbq6hqor2+kvNxJt2417V4eqfgjIdrjh+gvg+Jv+zFCLaqSUp9//jmzZ8/mvvvua3FmpClTpjB79mx+8IMfMHz4cFasWNHiDExDhgwhLS2t2QxM7XG80+XGwjS7KkPnEO1liPb4ITxl6OjPJNp/B7HCpu57IiJhMXDgQNavX99sWW5uLtnZ2UDHPdCOpuueUMR6+P6B92zv8uM9drR81i2J9vgh+sug+Dtep5x972iefPJJPB4P8+fPbzYz0/XX+2etOtEZmERERDqa1fRdUsoXbVcRIiKd2Lhx4ygtLWXZsmW43W42btzImjVrguNITZkyhTVr1rBx40bcbjfLli1r8YF2fn4+TqfzuB9oi4jI0XX6llK7du0Kfv/kk08ec/sTmYFJRESkowVaShlAo8eH3WqJbEAiIjEiLS2N5557jvnz57No0SLS09OZN28eo0ePBpo/0C4qKiIrK+uIB9oej4dp06ZRW1vLqFGj9EBbRCTEOn1SSkREJJaZTWC1mHB7DVxKSomInJBDH2gDDBkyhJUrVx51ez3QFhGJrKjqviciIhKL7HH+RJTGlRIRERGRrkQtpURERCIsPs5MTYOSUiIiErsKnR7eOeAjuaKYk+0aQ1FE/NRSSkREJMKsFhMAbq+SUiIiEntqXB4WfFxCZSPkV7r4qNBgW5Er0mGJSCegpJSIiEiEfZeU0pNjERGJPX/84BsO1HhwWODkNAcAf9leidenek+kq1NSSkREJMKsZn91rJZSIiISaxo8PtbtLAFgRA8TF56WjtUM+dVu1nxZGOHoRCTSlJQSERGJMKulKSmlJ8YiIhJjPi10Uef20iPBQg872K0WBqf6Wwiv+rwgwtGJSKQpKSUiIhJhGlNKRERikc/nZd3XZQCcnerFaHr2cnISWEzwdUkt+dXuCEYoIpGmpJSIiEiEaUwpERGJReWlJeyo8Ndthft243TWABBvMTGslx2AD/NqIxafiESeklIiIiIRFuy+p5ZSIiISQ6q8VgxMJMVbyEzr1mzd/2fvzuOjqu/F/7/OLJmZrDNJIAmbLCGgAooom4h1SbmKgLIUf+V6i17UIrW33uIOFy3icvVbLdeCiuVyW6lYtNRSKWBbrYqCERERDSQIJATIMtlmz2y/PyYzEJNAJpnJzCTv5+ORB2Q+c855n5PknDnv8/m8P1cNTAHgo3I7fr88lBGit5KklBBCCBFjZ5JS8qFcCCFEz9Hg1QKQm6Zr1XZ5nh6DVkW13UtDU3dHJoSIF5KUEkIIIWJMakoJIYToiRp8gaRUThtJKZ1GxRWDTACcdnRrWEKIOKKJdQBCiJ7ljrsXYa6tb7e9X242L734UvcFJEQCkJ5SQgghehqf3x/qKZWTpqOhuvV7rhxi4oMjZk7b5fonRG8lSSkhRESZa+uZft9z7bbvePHBboxGiMSgVTX3lPJJTykhhBA9Q53djRcVGsWPKVlLQxvvmTwkEwCzC5xub/cGKISICzJ8TwghhIgx6SklhBCip6m0uADI0HhRKUqb78lN1zMoPdCb6kS9s9tiE0LED0lKCSGEEDEWrCnVJDWlhBBC9BA1tkD18nTNua9tY3P1AJxokKSUEL2RJKWEEEKIGEtq7inl8fplWmwhhBA9Qq3dDUCa+tzD8kb3DSSlTjY45RooRC8kSSkhhBAixoI9pfyA1ycfyIUQQiQ2v99PXXNSKvU8PaVGZiWhAmxNXqyebghOCBFXJCklhBBCxJhGdabWRpPUlRJCCJHgbB7w+Pyo8JOsOvd1TadRkRnoLEWVoxuCE0LEFUlKCSGEEDGmKMqZGfikrpQQQogE1xAoJ0WKykM7Nc5b6KsPvKnaIQ9mhOhtJCklhBBCxIHQDHwyfE8IIUSCCyalUlUdG4/X1xD4t9oJPqkrJUSvIkkpIYQQcaW2tpbCwkL27NkDwH/9138xduzYFl8XXngh//7v/x5a5oYbbuCSSy5p8Z4jR44A4PV6eeaZZ5g8eTJjx45l8eLFVFVVhZY1m83cc889XH755UyYMIFVq1bh8XR/UYtgXSnpKSWEECLRNTQFEksdTUqZdKBVKTT54Fi9O5qhCSHijCSlhBBCxI29e/cyf/58ysrKQq/94he/YN++faGv//mf/yE9PZ2HHnoIAKvVytGjR9m2bVuL9w0bNgyAtWvXsmvXLt566y0+/PBD9Ho9y5YtC63/Zz/7GcnJyXz44Ye8+eabfPLJJ2zYsKFb9xvO6iklNaWEEEIkuMYwe0qpFIW8DB0AB6qc0QpLCBGHJCklhBAiLmzZsoWlS5dy3333tfue2tpali5dyqOPPsrw4cMB+OqrrzAajfTv37/NZTZv3sydd95JXl4eqampPProo3zwwQeUl5dz/PhxPv30U+6//34MBgMDBw7knnvuYePGjVHZx3ORnlJCCCF6ArfPH5pFL6WDSSmAfhmBaucHqlzRCEsIEac0sQ5ACCGEAJgyZQozZsxAo9G0m5h67rnnGDVqFDNnzgy9duDAAQwGA//6r/9KSUkJ/fv359577+Waa67BYrFw+vRpCgoKQu/Pzs4mIyODQ4cOAWA0GsnJyQm1Dxs2jJMnT9LY2Eh6enqH4+9IIdc239/875mkVOd7SoUbQzwK7kNP2JdIkuPSmhyTtnX1uMjxFF1V2ZyR0qoUkpSOP2jpnx5ISn1jdtHk8ZGkkf4TQvQGkpQSQggRF/r06XPO9vLycv785z+zefPmFq8risLo0aP5z//8T/r168f27du59957ee2118jNzQUgOTm5xTJ6vR6bzQaAwWBo0Rb83m63h5WUyspK6/B7AYzGlEAsuiSSk3Xok5ovyWoVycm6Dq9Hr08CwGRKITs7vBjiWbjHs7eQ49KaHJO2yXERsXKqOSmVYdC2m+T0+bxUVJwAoKLiBD4fZCVr0anA5fVz4FQj4wYauyliIUQsSVJKCCFEQnjrrbdCRc7PtmjRohbfz5w5k7/85S/s2LGDH//4xwA4HI4W73E6naSkpOD3+1u1Bb9PSUkJKz6z2UI4EwbV1weSYk5XE3a7C1XzsnaHG7u940MXnM5A4Y66Ohs1NZaOBxCnFCVwMx3u8ezp5Li0JsekbV09LsHlheisk5ZAofIMvQbauZw1mqt4/biVAUMUyg4dwJg3mGxFoY8BTtigqKxeklJC9BKSlBJCCJEQdu7cyR133NHq9d/85jdcdNFFTJo0KfRaU1MTOp2OjIwMcnJyKC0tDQ3hq66upr6+noKCAnw+H/X19dTU1JCdnQ3AkSNHyM3NJS0tvJsyv5+wbgBD723+NzR8z9f5mlI96cY83OPZW8hxaU2OSdvkuIhYORnqKdV+UgogPSuHzNwB1FefCr3W16BwwuanqKyeH18Z7UiFEPFABuoKIYSIe3V1dRw5coQrrriiVdupU6d4/PHHKS8vx+Px8Oabb7Jv3z5uueUWAGbPns3atWspLy/HarXy5JNPMn78eAYNGsTgwYMZN24cTz75JFarlfLyctasWcPcuXO7excjUlNKCCGEiLVQTymDNuxl+wbKSnHwtAVbU8eLpAshEpf0lBJCCBH3TpwI1J04uyB50AMPPIBKpeKHP/whFouF/Px8XnnlFS644AIAlixZgsfjYcGCBdhsNiZMmMALL7wQWn716tX84he/4LrrrkOlUnHzzTdzzz33dMt+nU2rDjwnktn3hBBCJLJQTSm9lnAHladoFXJSVFTavOw70cCUoVmRD1AIEVckKSWEECLuBGfGCxo9enSr14KSkpJ45JFHeOSRR9ps12q1LF26lKVLl7bZnp2dzerVq7sWcAScSUpJTykhhBCJyeL00OAKPFzJMGjCTkoBjOqjp9Jm49Pj9ZKUEqIXkOF7QgghRBzQqALD9zw+SUoJIYRITGV1dgD0akhSd+5Wc3TzGL6isvpIhSWEiGOSlBJCCCHiwJmklAzfE0IIkZjK6gMz2KaGX04qZFRfHQClNTZq7U2RCEsIEcckKSWEEFHi9fk5Ue/A3uSNdSgiAWiaC517ZPieEEKIBFVR7wQgtQtFYjJ0aob3SQHgM+ktJUSPJzWlhBAiChxuL+8eqqHSEpgLOT87GVBiG5SIazJ8TwghRKKraAgkpVK0XfvMc8UgIyXVNj4tq+f7I/tGIjQhRJySnlJCCBFhPr+fbV9XUWlx0dz5hdIaO7qLvhfTuER806gCl2RJSgkhhEhUoaRUF7s+XDHICEhdKSF6A0lKCSFEhJVU26i1u9FpVMy+JI8pQ00A6C+bRYPDHePoRLzSyvA9IYQQCa6iuaZUV5NSYwdkoFYpnGxwUtHgaNHm9Xo5duxo6MvrlTIJQiQySUoJIUQEeX1+Pi9vAODS/ukYDVpG9k2lf4YeRZPEV6c7Mzmy6A2k0LkQQohE5vL4qLIGCpOndKHQOUBKkoaLctIAQp+rgsrLy3hpx2ds+ryCl3Z8Rnl5Wdc2JoSIKUlKCSFEBB0x27A2eUnWqrkoJxUARVEY3S/wwepItV2GZ4k2BZNSXn9gCKgQQgiRSE41D90zaBSSInCXOW5gBgB7TzS0ajP2ySMzdwDGPnld35AQIqbiOilVW1tLYWEhe/bsCb22f/9+5s2bx9ixY7n22mvZvHlzi2W2bNlCYWEhl156KbNnz2bfvn2hNq/XyzPPPMPkyZMZO3Ysixcvpqqqqtv2RwjR8x2ptgNwYW4qGvWZU2z/DD0+Wx0ur4/jtfZYhSfiWDApBVJXSgghROIJ1pPqm6JBUbo+uUswKfV5eX2X1yWEiF9xm5Tau3cv8+fPp6zsTHfMhoYG7rrrLm6++WaKiopYtWoVTz31FF9++SUAe/bsYeXKlTz99NMUFRUxc+ZMFi9ejMMRGIe8du1adu3axVtvvcWHH36IXq9n2bJlMdk/IUTP49UaQh/IhmUlt2hTKQpNpbuBQM0pIb5LfXZSSupKCSGESDAnmutJ5XShoJTP56Wi4gTHjh0lw1OHWqVwqtHVqq6UEKLniMuk1JYtW1i6dCn33Xdfi9d37tyJ0WhkwYIFaDQaJk2axIwZM9i4cSMAmzdvZvr06YwbNw6tVsvChQsxmUxs27Yt1H7nnXeSl5dHamoqjz76KB988AHl5eXdvo9CiJ7HmTkcP9AnJYkMQ+tiCk3fFgFwssGJxyt1g0RLiqKcVVdKklJCCCESS/DBXFeSUo3mKl7fXcqmzyv4v79/zjBj4PPU3vLWQ/iEED1DXCalpkyZwrvvvsuNN97Y4vWSkhIKCgpavJafn09xcTEApaWl7bZbLBZOnz7doj07O5uMjAwOHToUpT0RQvQmjqx8AIZlJ7fZ7ms4TUqSGq8fTltc3RmaSBBS7FwIIUSiOpOUUndpPelZOaF6URf30QEyhE+InqyLk3VGR58+fdp83WazYTAYWrym1+ux2+3nbbfZAsNlkpOTW7UH2zoq3CHSwfdHYGh1zMg+xIdE34dEjx/a3wdbk4em1ECxzQsy205KQaC21OFqGxX1TgYYW56vuuu49ISfQ0+lUSvgkeF7QgghEs+pxjM1peoi0LHJ5/OSowRmLd5zzIzH40GjicvbVyFEFyTUX7XBYMBiaTmdutPpJCUlJdTudDpbtZtMplCyKlhfqq3lOyorKy3c0Lu0XDyRfYgP8bwPGq2a5GTdOd8Tz/F31Hf34YuvK0GlJsOgJTez7XOKosDQvmkcrrZx0uJqcZw0WjXZ2d17XDIzE//n0NNoVCrAK8P3hBBCJJxgUio7WUMkxqE0mqsot9pQGEqN3cveQ0eZcPHwCKxZCBFPEiopVVBQwK5du1q8VlpayvDhgZPT8OHDKSkpadU+depUMjIyyMnJaTHEr7q6mvr6+lZD/s7HbLYQzmzdihK4gQ13uXgi+xAfEmEfPG4vdnvbQ9OCPXPiOf7zae9nsPPASQD6peva3X+/H7INgS7tNdYmaurtJCcFvve4vdTUWNpcLtKC+1Bba5HEVJyRmlJCCCESkcXpweryAtAnuWvD985myupL36YkKi1NfF3tZELE1iyEiBdxWVOqPYWFhdTU1LBhwwbcbje7d+9m69atzJkzB4C5c+eydetWdu/ejdvtZsOGDZjNZgoLCwGYPXs2a9eupby8HKvVypNPPsn48eMZNGhQWHH4/eF/dXa5ePqSfYiPr3jfh/P97cR7/J39Gew+VgfAAKP+nMfAoFWTmRwo2ln5nbpSsdgHEV+CSSm3FMIXQgiRQIK9pDL0GvSayN5i5qUHPlsdrJZ6nEL0RAnVU8pkMrF+/XpWrVrF6tWryczMZNmyZUycOBGASZMmsWLFCh577DEqKyvJz89n3bp1GI1GAJYsWYLH42HBggXYbDYmTJjACy+8ELsdEkL0CKcbnZTVOcDvo1/6uZNSAH1TddTa3VRZXQzJar/+lOh9tOpAUsorPaWEEEIkkFONgYRRv4zzfw4KV166ji8q4GCNC788UROix4n7pNR3Z8YbPXo0mzZtavf9s2bNYtasWW22abVali5dytKlSyMaoxCid9tXEajmqbVVkaQZfN73901LorgKqixNUY5MJBoZvieEECIRnW7uKZXbgYdz4cpJ06EANXYvJxud532/ECKxJNTwPSGEiEf7KxoBSGo82aH3900NFDivsTXhkyd+4izq0PA9+b0QQohIOHjwIAsWLODyyy9nypQpPPHEEzQ1BR4K7d+/n3nz5jF27FiuvfZaNm/e3GLZLVu2UFhYyKWXXsrs2bPZt29fLHYhIQR7SuWln3uym87QqlVkNq92b3kEpvUTQsQVSUoJIUQXfdHcUyrJ0rGklNGgQatW8Pj81Nnd0QxNJBitOnBZlp5SQgjRdT6fj7vvvptp06bx6aef8uabb/LRRx+xbt06GhoauOuuu7j55pspKipi1apVPPXUU3z55ZcA7Nmzh5UrV/L0009TVFTEzJkzWbx4cauZvEXAaUv0ekoBZDev9vPy+qisXwgRO5KUEkKILmh0ujlSYwcgyXKqQ8soikKf1CQAqqwyhE+ccWb4nhQ6F0KIrmpoaKC6uhqfzxeqRaRSqTAYDOzcuROj0ciCBQvQaDRMmjSJGTNmsHHjRgA2b97M9OnTGTduHFqtloULF2Iymdi2bVssdyluhXpKpUW+pxRAH0Pg+ri3vEHqSgnRw0hSSgghuuDASQsAg0wG1J6OPz0NDuGrtspMMuKMUFJKhu8JIUSXmUwmFi5cyDPPPMPo0aO5+uqrGTx4MAsXLqSkpISCgoIW78/Pz6e4uBiA0tLSc7aHQ1ES46srsQZrSuVl6EPriqQsHagVOG1xUe3wtnl8E+lYR/r4x8tXou+DxN+xbURa3Bc6F0KIeBYcundJv3T2hrFcdkqgp5TZJsP3xBma5tn3ZPieEEJ0nc/nQ6/Xs3z5cubOncvx48f5yU9+wurVq7HZbBgMhhbv1+v12O2B3s/naw9HVlZa53eim3UmVqfbS21zOYJRQ7KoPe3AYEgiOVmHXq9FpdN2+f9pKTpG9lU4WOngmI3Q+h2GJDIzU0NxJ9Kxbkuixw+Jvw8Sf/eTpJQQQnTBwdOBnlKjwkxKZSZrAaizS7FzcYZGJTWlhBAiUt5991127NjB9u3bARg+fDhLlixh1apVzJgxA4vF0uL9TqeTlJQUAAwGA06ns1W7yWQKOw6z2UK8X+oVJXAz25lYj9cGEnXJWjVum5PaWisORxN2uwun043K7+7y/x2OJgqMKRyshM+ON5Cpagq9XltrJSPD0un440FXjn+8SPR9kPg7vo1Ik6SUEEJ0kt/v55vKwAfai3PCO0Gn6zVoVIFi5w0OTzTCEwlIakoJIUTknDp1KjTTXpBGo0Gr1VJQUMCuXbtatJWWljJ8+HAgkMAqKSlp1T516tSw4/D7SZib3M7EWmkJlCLom5YEKFHb1wuzdWw5ZOHrGhdT+p55/eyYE+lYtyXR44fE3weJv/tJTSkhhOik8nonVpeXJLXCsOzksJZVFCXUW8psl2LnIiA0fE9qSgkhRJdNmTKF6upqXnrpJbxeL+Xl5axdu5YZM2ZQWFhITU0NGzZswO12s3v3brZu3cqcOXMAmDt3Llu3bmX37t243W42bNiA2WymsLAwxnsVf6qbJ23pkxqdIudBI7J0qBSotHlxeOQ6KURPIT2lhBCik75pHrpX0DcVjTr8HH9mShJV1iZqpa6UaHamp5R82BZCiK7Kz8/n5Zdf5oUXXuDVV18lLS2NmTNnsmTJEpKSkli/fj2rVq1i9erVZGZmsmzZMiZOnAjApEmTWLFiBY899hiVlZXk5+ezbt06jEZjbHcqDgV7SiUrTRw7dpSKihNEo8NvslZFQZ9Uiqus1Dihf+Q3IYSIAUlKCSFEJ33dPHTvojCH7gVlNfeUqu1AT6k77l6Euba+/XVlGln/8qudiiPe1NbWMn/+fJ544gkmTJgAwIoVK3jrrbfQarWh9z300EPMnz8fgC1btrBmzRqqq6sZOnQoy5cvZ+zYsQB4vV6ee+453n77bRwOBxMnTuTxxx+nb99A33+z2czy5cv59NNPUavVzJw5kwcffBCNpvsvkVqpKSWEEBE1efJkJk+e3Gbb6NGj2bRpU7vLzpo1i1mzZkUrtB4j2FPq+MkqNn3upOzQAYx5g8mOwrbGDshoTkrJdVKInkKSUkL0QL0pgRFLwZ5SF+amdmr5rLNm4DOe573m2nqm3/dcu+3vPL+0UzHEm7179/LQQw9RVlbW4vUDBw6wcuVKbrnlllbL7Nmzh5UrV7Ju3TrGjBnDxo0bWbx4Me+99x4Gg4G1a9eya9cu3nrrLdLS0li+fDnLli3jlVdeAeBnP/sZOTk5fPjhh9TU1LB48WI2bNjAokWLumWfzybD94QQQiSKwJDIMo5V1QGQkZZKZu4A6qtPRW2bYwdk8PrnFdQ4z/9eIURikKSUED1Qb0lgxJLX56e4ygrARbmd6yllau4pZXd7SdfoIxZbotqyZQurV6/m/vvv57777gu93tTUxOHDhxk1alSby23evJnp06czbtw4ABYuXMgbb7zBtm3bmDNnDps3b2bp0qXk5eUB8OijjzJlyhTKy8vx+Xx8+umnfPDBBxgMBgYOHMg999zDs88+G5uklAzfE0IIkSDKy8t4acdnlDblBF5wO6K+zUv7pwPQ6Aan2xv17Qkhok8KnQshRCccq7XjcPswaFVcYAqvyHlQklpFapIaALch/Cmme5opU6bw7rvvcuONN7Z4vbi4GI/Hw+rVq5k8eTLTpk3jlVdewddcsKK0tJSCgoIWy+Tn51NcXIzFYuH06dMt2rOzs8nIyODQoUOUlJRgNBrJyckJtQ8bNoyTJ0/S2NgYxb1tWzAp5ZbZ94QQQiQAY588XP7AZxm9KvoPVEzJSQxIC/SrON1cy0oIkdikp5QQQnTC181D90bmpKFuTiR0hjFZi7XJi8eQGanQElafPn3afN1isTB+/Hhuu+02fvnLX/LNN9+wZMkSVCoVixYtwmazYTAYWiyj1+ux2+3YbDYAkpOTW7UH2767bPB7u91Oenp6h+NXwvw1CL3/rOW62lMq3BjiUXAfesK+RJIcl9bkmLStq8dFjqcIh8/vx+4OPEjRdUNSCuDCbB0nLB5ONbpIl47mQiQ8SUoJIUQnBJNSF+Z0rp5UkNGg5US9U5JS53DllVdy5ZVXhr4fM2YMP/rRj9i2bRuLFi3CYDDgdLYsLuF0OjGZTKEEk8PhaNWekpKC3+9v1Rb8PiUlJaw4s7LCG8ZpNAbWr9clkZwcmEZbpQ1clv1+0OmTOpTw1OsDtclMphSyszs3lDQehXs8ews5Lq3JMWmbHBfRHZzNI+gU/CQp3ZOUuqiPjneP2jjd6GKEJKWESHiSlBJCiE74+nSgntTFnawnFWQyBOpKSVKqfX/729+oqanh1ltvDb3W1NSEXh/4JDp8+HBKSkpaLFNaWsrUqVPJyMggJyenxRC/6upq6uvrKSgowOfzUV9fT01NDdnZgXmCjhw5Qm5uLmlp4f1szWYL/jA+j9fXB3pqOV1N2O2BIQjes3pINVqd6DTnH2XvdAZmPaqrs1FTYwkj4vikKIGb6XCPZ08nx6U1OSZt6+pxCS4vREc4PIF/dYqv23rZXZgVeJBjtjXh9knXPiESndSUEkKIMDV5fByuDiSlLszpYlKqudi5W5JS7fL7/Tz11FN88skn+P1+9u3bx29/+1vmz58PwNy5c9m6dSu7d+/G7XazYcMGzGYzhYWFAMyePZu1a9dSXl6O1WrlySefZPz48QwaNIjBgwczbtw4nnzySaxWK+Xl5axZs4a5c+d2Is7wvwILnlmHSjkzms/jDb+uVGdiiMevnrQvclzkmCTicRGio4I9pXRK9xUdz0rWkKIBP2CWWfiESHjSU0oIIcJ0uNKC2+snXa9hgLFr/caNzT2lfLo0rC4PqTo5LX9XYWEhDz/8MI899hiVlZVkZ2dz7733MmvWLAAmTZrEihUrQu35+fmsW7cOo9EIwJIlS/B4PCxYsACbzcaECRN44YUXQutfvXo1v/jFL7juuutQqVTcfPPN3HPPPTHYU1AUBY1Kwe3zywx8Qggh4t7ZPaW6U7YebFaoccq1UohEJ3c/QggRpv0n6oFAPSmli33VdRoVBq0Kh9vH8Vo7F+d1vLB2T3bo0KEW3996660thu9916xZs0JJqu/SarUsXbqUpUuXttmenZ3N6tWrOx9shGnUgaSUV5JSQggh4pzDG7hW6VTd11MKIFuvcNzqp0Z6SgmR8GT4nhBChOnAiQag60P3goJ1pb412yOyPpHY1F2cgU8IIYToLrHsKQVQ5wJXJ4a7CyHihySlhBAiTPubk1IXdbHIeVBwCN/xOsd53il6A40kpYQQQiQIR6imVPcmhlI0kKxV4wNKa5u6ddtCiMiS4XtCdNIddy/CXFvfbntWppH1L7/afQGJbuF0ezlcGZjh7MKc1IisM8MQOBWXS1JKABpV4HmRJKWEEELEu1BPKZUXUEdtOz6fl4qKEwBUVJzA74fcdB3fmu18U+PipqhtWQgRbZKUEqKTzLX1TL/vuXbb33m+7fo1IrEdrrbh9fnJStaSk6aLyDrT9YGeUuX1kpQSZ4bvSU0pIYQQ8czv94d6Sumj3FOq0VzF68etDBiiUHboAMa8weSlG/nWbOfrGldUty2EiC4ZvieEEGH4+nRzL6nctC4XOQ/K0AeeD5TVOfDJXNy9ngzfE0IIkQisbh/BS1WSEv1C5+lZOWTmDiA9sw8Q6CkFcNjchEfqSgmRsCQpJYQQYQgmpSJVTwogTacBvw+Xx0e1Veoi9HaSlBJCCJEIapu7Sek0KtSReU4XFpNBS5IKXF4/xVXW7g9ACBERkpQSQogwhJJSEaonBaBSKaidgeLpUldKnElKyVNfIYQQ8SuYlEpJil4tqXNRFIWs5ln4Pi9viEkMQoiuk6SUEEJ0kMXp4VhtIGl0cV7kekoBaJz1AJRJXaleL1hTyuOVnlJCCCHiV6yTUgDZ+sA1c98JSUoJkagkKSWEEB30dfOsewMzDZiSkyK67lBSqlaSUr2dRi2FzoUQQsQ/cxwkpfo095TaV9Eg100hEpQkpYQQooMOngokpS4daIr4uoNJKZmBT0hNKSGEEIngTE+p2E3onpEEeo2C1eXlUHOJBSFEYpGklBBCdNDB5g87lwzIiPi61cGklNSU6vUkKSWEECIRBJNSyTHsKaVSFEZkBWbhKzpWG7M4hBCdF7u0thBCxJk77l6Euba+zTY/UH35naAxMHaQMeLbDvaUOtHgwOvzh+oKid5Howo8L5KklBBCiHhW6zwzfM8dwzguzNaxv9LJp0drmV6QFcNIhBCdIUkpIYRoZq6tZ/p9z7XZZnV5eP3zk6hVChf3y8DaYI/ottUuK1q1gtvr57TFSf8MQ0TXLxJHMCEptTGEEELEs1BNKZ2a+hjGcVF2oKfUnqO1+P1+QB7sCZFIZPieEEJ0QJW1CYDhfVLQayPfTV3BzwBjIBElQ/h6tzPD93wxjkQIIYRom9vrw9oUuE4lR+FzUTiGmZJIUivUWF2UyWcoIRKOJKWEEKIDqiwuAEblpkVtG4Oak1Jldc6obUPEP6kpJYQQIt7V2gMD9hRAp4ntLWWSWmFUXjoA+040xDQWIUT4JCklhBAdUN3cU+rivOglpQaagkmpyA4NFIlFo25OSnklKSWEECI+1doDn4t0alCU2A+XG9s8Cc3nkpQSIuFIUkoIIc7D5/dTYwt8+Ao+iYuGYFKqvF66nvdmaukpJYQQIs7V2gI9pfSxHbkXEkxKSU8pIRKPJKWEEOI86uxuPD4/isfFBZnRK0B+gUlqSokzw/ek0LkQQoh4ZT6rp1Q8GNMvHbVK4VSji1ONUgZBiEQis+8JIRLKHXcvwlxb3257VqaR9S+/GtFtBouca22VqKLYRX1gc02pkw1OPF4fGrU8N+iNNKrAz116SgkhhIhXtc09yOOlp1RykppR/TPYX17PvhMN5F2kj3VIQogOkqSUECKhmGvrmX7fc+22v/P80ohvM1jkPMlaGfF1n61PahJ6jQqnx0dFg5MLMpOjuj0Rn6TQuRBCiHgXLHQeLz2lACYMyQwlpW68KCfW4QghOighH8MfPHiQBQsWcPnllzNlyhSeeOIJmpoC2fr9+/czb948xo4dy7XXXsvmzZtbLLtlyxYKCwu59NJLmT17Nvv27YvFLgghEsjpYFLKcjKq21EURepKCUlKCSGEiHtnCp3Hvsh50PjBmYDUlRIi0SRcUsrn83H33Xczbdo0Pv30U958800++ugj1q1bR0NDA3fddRc333wzRUVFrFq1iqeeeoovv/wSgD179rBy5UqefvppioqKmDlzJosXL8bhkJs/IUTb7E1eGp0eAJIsp6K+vf4Zge7mJxukHkJvpT6rppTfL4kpIYQQ8cdsj69C5wBXDM5EAY7XOUJJMyFE/Eu4pFRDQwPV1dX4fL7Qh3WVSoXBYGDnzp0YjUYWLFiARqNh0qRJzJgxg40bNwKwefNmpk+fzrhx49BqtSxcuBCTycS2bdtiuUtCiDgWLJaZlaxF5Y3+B5z+GYGeUhWSlOq1NGc9dZZi50IIIeJRsKZUPA3fy0jWMiw7BYAvpLeUEAkj4ZJSJpOJhQsX8swzzzB69GiuvvpqBg8ezMKFCykpKaGgoKDF+/Pz8ykuLgagtLT0nO0dpSjhf3V2uXj6kn1ova5o/K5EYh9iFdv5th2Jn0E0960tpxsDQ/dy03Xn3YeuUhQYYAz0lKpocEZ1v8/eFxFfgsP3QIbwCSGEiE+1cdhTCmDsgAwAPpeklBAJI+EKnft8PvR6PcuXL2fu3LkcP36cn/zkJ6xevRqbzYbB0HK6dr1ej91uBzhve0dlZaV1KvbOLhdPZB/O0GjVJCfrztmenR2d43W+fYhlbOfbNnTtZxDNfWtr3ZXNTwIv6JOKRasOxd7WPpwvNkWh3fZg3BcOMgW2a21qsR/R2O/MzMT/e+6JVIqCSgGfX5JSQggh4o/H56fBEX+FzgEuG5DB5i9OSl0pIRJIwiWl3n33XXbs2MH27dsBGD58OEuWLGHVqlXMmDEDi8XS4v1Op5OUlEA3ToPBgNPpbNVuMpnCisFsthBOmQ9FCdzAhrtcPJF9aM3j9mK3u87ZXlNjabf9fO64axE1tfUtX1RAo1FjTEtj/Suvxiy2czl7241OD8fr7Hi8frJTkhhgCvQC6srPIJr79t11O9xezNZAUsqUpMbj9mI2W9r9PTpfbH4/7bYH405TAis9brZRXd2I0tydKZL7HfxbqK21SGIqTqlVCj6vX5JSQggh4k69vQk/oAC6OBp34/V6yfYHklEl1TbqbS6MKed+UCqEiL2IJ6X27NnDhAkTIr3akFOnToVm2gvSaDRotVoKCgrYtWtXi7bS0lKGDx8OBBJYJSUlrdqnTp0aVgx+P526oe7scvFE9iH8bXVWTW090+97rtXryck6Nq+6t8v7EM1j4Pf72VvewL6KxhavZ6ckoTFkRf1nEKl1B+s6ZSZrSU5St1h3NPbB74fcND0K4HD7qLW7yUxOCmv5cLcXKdE+9/c2GpWC2+uXmlJCiB5Prh+JJ1jkPF2niqtSAMePH2fTP/eRqsnB6oF/fHmE2ZMuinVYQojziHhu+6c//SnXX389v/71rzl5MvLTp0+ZMoXq6mpeeuklvF4v5eXlrF27lhkzZlBYWEhNTQ0bNmzA7Xaze/dutm7dypw5cwCYO3cuW7duZffu3bjdbjZs2IDZbKawsDDicQoRSY1ON8WVVnYdreXNvSeoHLuQ63/9MVN+9RFTfvURN768m7ve2M8v3zvCrqO1+IndJwQ/8OG3taGEVF66jvzsZJLUCjW2JipHzGHP8bqYxReOivpAUmpA84x43SFJo6Jvmq7F9hNBJM/9tbW1FBYWsmfPntBrO3bsYNasWVx22WVce+21vPjii/h8vlD7DTfcwCWXXMLYsWNDX0eOHAECT06feeYZJk+ezNixY1m8eDFVVVWhZc1mM/fccw+XX345EyZMYNWqVXg8ni7tQ1dpVIHLs/SUEkL0dNG+dxCR5fV6+ebbMgBS1D58Mb5O+XxeKipOcOzYUcrKysjIzqWfKTBKptjcfg9zIUT8iHhPqY8++oh//OMf/OlPf+Kll17iiiuuYPbs2Xz/+98nKanjT/zbk5+fz8svv8wLL7zAq6++SlpaGjNnzmTJkiUkJSWxfv16Vq1axerVq8nMzGTZsmVMnDgRgEmTJrFixQoee+wxKisryc/PZ926dRiNxi7HJUSkeXTpFJXVc7zWQV3zuP0QXToNzjM3zdXWJqqtTew70cDrn1eguux2vjzZyMW5aaHp5buLo8+FnKqyoQBXDctkRN9UAOxNXt4vNVPRAD/741f8avYorhgU3tDZ7uT3+zkRTEoZDed5d2T1z9BTaXFR0eBkdL/0bt12Z0Xq3L93714eeughysrKQq999dVXPPDAA7zwwgtcffXVHD16lDvvvJPk5GTuuOMOrFYrR48e5e9//zv9+/dvtc61a9eya9cu3nrrLdLS0li+fDnLli3jlVdeAeBnP/sZOTk5fPjhh9TU1LB48WI2bNjAokWLun5gOilY7FySUkKIni7a9w4issrLy9jy2REgA6vVitUA2TGMp9FcxevHrQwYolB57GsMmQPIyTBxuNrGIXP0Z00WQnRdxJNSWq2WadOmMW3aNGpra9m+fTvr16/nF7/4BdOnT2f+/PmMHDmyS9uYPHkykydPbrNt9OjRbNq0qd1lZ82axaxZs7q0fSGiaX9FAxv3VlB16b9R1dzbSCEw+1uflCRyTcl8/of/4dfPPYNBq8bvhzp7E8frHOw70cA/S83Ukcqe4/UUV1q5riCbrJTu+VBX0eCgYfD3ABg3MCOUkAJITlIzbWQfXt/xTxzGodz/9te8euul5PdJ6ZbYwlXncGN3e1GrFHLSu7ceQf8MPZ+faKCiwdGt2+2KSJz7t2zZwurVq7n//vu57777Qq9XVFRw6623cs011wAwbNgwCgsLKSoq4o477uCrr77CaDS2mZAC2Lx5M0uXLiUvLw+ARx99lClTplBeXo7P5+PTTz/lgw8+wGAwMHDgQO655x6effZZSUoJIUQ36I57BxFZqmQjuPzNpQ28sQ6H9KwcMnMH4LTU0OSHnOYe56V1TXi8PjTqOCp8JYRoJWqFzs1mM3/5y1945513KC0t5eqrr0an07Fw4UIWLlzIj3/842htWoiEVNHg4Ff/PMp7JTWBFxQV/TP0FPRNYaDRgE4TuKAmJ+v44+fvc9+Pf9TmevSKGqfPiGnqbTQ4Pbx9oJLvDc9iaFZy6D2HDxcza97cdmPJyjSy/uX2C6m359cfHsOv1pKXruOS/q17+KhVCtnf7iR75gPsO9HIf/zxAOt/ODb04SGelNcFeknlpelCCYLu0t8YGC6YSMP3grpy7p8yZQozZsxAo9G0SEoFb1aCnE4n77//PjNmzADgwIEDGAwG/vVf/5WSkhL69+/PvffeyzXXXIPFYuH06dMUFBSEls/OziYjI4NDhw4BYDQaycnJCbUPGzaMkydP0tjYSHp6bHqqBXs4Sk0pIURvIfcOicPlDVybkhTfed4ZG0aDhiQVNHn9HKqycnFeYvQ6F6K3inhS6p133uHtt9/m448/ZujQocyePZuXXnqJzMxMAK6++mqWLFkiFxYhmnl8fv53dxkbPi2jyetHrcBNF+fy8cbnuPHHj7SzDG0WQQ969u6bmHvvA7xfaqa83sk/DtfgH57FsOyUDi3/zvNLw96Pg6caefdQNfj9TBpsQtVO5UvF7+W5WRfz769/wbFaBz/741esu/USUnXxNRno0Vo7ABdkdu/QPYD+GYFtBgutJ4JInPv79Olz3u1YrVb+4z/+A71ez8KFCwFQFIXRo0fzn//5n/Tr14/t27dz77338tprr5GbmwtAcnJyi/Xo9XpsNhsQmJn1bMHv7XZ7WEmpcIu9ht7fxnIadXNPKW94H/jjqeBsZwX3oSfsSyTJcWlNjknbunpcuvt4yr1D4nE1d44KJKXi7w9QURQydXDaAftPNkpSSog4F/G7wMcff5zp06ezadMmRo0a1ap9yJAhoRsJIXq7KouLZe98EyoKfsUgI/95zTDys1OY9Wptl9at16r5/sg+fHiklsPVNt4vNZOSpCE3SkPRXvr4OACGmm/ISrngnO/NMGj51ezR3PH6F5TW2Hjgz1/zq9mj0Hawe3WDw80Rs52TDU5sTV7UikJmipbhfVKIRL8Sq8tDtTVQh2BwZvJ53h15/ZsLqydSUqo7zv3ffvstP/3pT8nKyuK3v/0tqamB4aHfHWY3c+ZM/vKXv7Bjx47QTYzD0XIopNPpJCUlBb/f36ot+H1KSnhDS7Oy0sJ6v9EYWL9el0Rycsu/S502MNujSqNu1fZden1geK7JlEJ2dngxxLNwj2dvIcelNTkmbUuU4yL3DonH2SIppY5pLO3J0iucdvjZX9HID8fFOhohxLlEpdB5eXl5aCjEF198QVpaGsOGDQMgNzeXn/70p5HerBAJ59PjdSx7p5g6h5uUJDUPXz+c74/sgxLBR5QqReGqYZm4fX6Omu387XA1t4zOjdj6g0prbOw+VodKgbQTnwL/ct5l+mXoeeGWi7nrjf0UldXzwJ+/5qmbLkSvbf/DzbFaO3XDvs/mL061Sj7VOdwcqbGjvXgex2vtXNCFZFKwl1Rumq65XkL3Cg7fq7K4aPL4SNLEfy2EaJ/7//nPf/Kf//mf/OAHP+DnP/85Gs2Zy9dvfvMbLrroIiZNmhR6rampCZ1OR0ZGBjk5OZSWloaG8FVXV1NfX09BQQE+n4/6+npqamrIzg6Uaj1y5Ai5ubmkpYV3Q2c2W/CHkRWtrw/01HK6mrDbW84QpDSvyO50t2r7LqczkECtq7NRU2MJI+L4pCiBm+lwj2dPJ8elNTkmbevqcQku313k3iHxuJo78cZ1Uqr5ec7+k414PB5OnCgPtQ0cOAi1Oj7jFqI3ividzt///nduvvlmjh07BsC+ffuYN28e//znPyO9KSES1vZvqvjpH7+izuFmeJ8UfvuvlzHtwr4RTUgFqRSFq4dlkpmsxeH28c8jXeuB1ZZNeysA+F5+NhpXY4eXG5mTxn/PvAidRsVH39Zy9x++5JjZ3up9R2psLHvnG+Zv+AxHn5H4CfQmumpoJjdd3JdpI/twcW4qWpWCOy2Pf/3d53xwxNzp/fm2JhDDkKzu7yUFYDJoMWhV+IFTjYnRWyqa5/4vvviCJUuW8PDDD/Pggw+2SEgBnDp1iscff5zy8nI8Hg9vvvkm+/bt45ZbbgFg9uzZrF27lvLycqxWK08++STjx49n0KBBDB48mHHjxvHkk09itVopLy9nzZo1zJ3bfs219vj94X8FFmy9LnUnC513JoZ4/OpJ+yLHRY5JIh6X7iT3DomnZU+p+JSpA7UCZlsTew8d5aUdn7Hp8wpe2vEZ5eVl51+BEKLbRLyn1IsvvsiaNWtC3W9vv/128vPzefbZZ7n66qsjvTkhEs5fDp7m8e2HAfj+iD4sn1Zwzt5BkaBVq7iuIJs/fnmaigYnSSOmRmzdZlsTf/2mEoAfjuvPf/0uvOUnDs7kf+aM5ud/OsjXpy38f7/dy5ShmVyYk4bD7WVveQMHTp1JdOlrv+Vfrp5Mn9SWMwoOMhkY0y+dt97bgzNjIA+8fZD/+pcR3HhRznc3eU61tiaqrE0o0KI4fHdSFIX+GQZKa2xUNDi71Ouru0Tz3P/SSy/h8XhYtWoVq1atCr0+btw4Xn31VR544AFUKhU//OEPsVgs5Ofn88orr3DBBRcAsGTJEjweDwsWLMBmszFhwgReeOGF0HpWr17NL37xC6677jpUKhU333wz99xzT5di7iqNFDoXQvQScu+QWLx+/3dqSsUntUphqElLSW0Th8wujH3yyMwdEOuwhBBtiHhS6tSpU1x11VUtXpsyZUqLmZSE6K3+frialTsCCam5l+Rx/3X57RYEjzSjQcv4QUY+OVaHYdws7E3eiAxNe2v/SZq8fkblpTGmX+cKSY4dkMHv/+0ynvpbCR8freP9UjPvl57p6aRW4Or8bO6YMIgHfrKaPtO/1+Z6UnUasorf5rI7VvLOwUoe334IjUrh+yP7djiWbyqtAAzONMRk6F5Q/wx9KCmVCCJ97g/OjAeBpNS5JCUl8cgjj/DII21PDKDValm6dClLl7ZdwD87O5vVq1d3Ks5o0agCHZnD7SklhBCJRu4dEovVdSYRpY3jpBTAiKwkSmqbKK5xIaXOhYhfEU9K9e/fnw8//LDFxeWTTz6hX79+kd6UEAnl4KlGVvz1ED4/zBqdywPX5UdluN65XJybSmm1jWqgqKyeq/OzurQ+p9vLm1+cAuCH4wZ0aX9y0/X8avZoSqtt/KOkmipLE2qVwoi+KVw1LIs+qR0r0K74fayYVoBGpfD2gdP8118PodeqmTrs/PvqU2kpqQnU+bkwN7YFYoN1pSrqEyMpJef+yNJ0cvieEEIkGrl+JJb65m5Seo0KVfxNvNfCyCwdfymxcsjcxBVd+8grhIiiiCel7rrrLpYsWcL3v/99+vfvz8mTJ3n33Xd55plnIr0pIRJGja2Jn7/9NS6PjylDM3n4+uHdnpCCwLCwSUNM/PmrSg5X27goN7XDyZ62bP+mijqHm9w0HdcMz45IjPl9UsjvE96sZ9+lKAoPXz8cp9vLjuJqHtr6Nc/fPIoJg03nXM6Wdylur58MvYZ+UZqlsKPOzMDnOM8744Oc+yNLo25OSnnj+ym0EEJ0lVw/EkuDM3BdMkS59EQkjGiudl7e6OYSY5xn0IToxSJe6HzGjBmsW7cOrVbLwYMH0ev1rF+/nmnTpkV6U0LEtcOHi5k1by4z583l5qf/gNnWhMZupvSNJ5k9fx533L3o/CuJgpw0HU2lewD4+Ggd/k5WNPX7/fz+80CB8/mX9Q/17IgXapXCYzeM5Hv5Wbi9fn7+9kH2nWho9/1mWxPWfoE5g8cNzIhJ0vBs/TMMAAkzfE/O/ZGllppSQoheItrXj/r6eh544AEmTJjAFVdcwT333ENVVRUA+/fvZ968eYwdO5Zrr72WzZs3t1h2y5YtFBYWcumllzJ79mz27dsXkZgSWUNzTymDNv5nBjbq1Qww6vEDteeeyFYIEUMR7ykFMGHCBCZMmBCNVQuRMDw+mH7fc3x5spFTx+tRqxRunjQK03VjAXjn+bbr23QHx94/kVwwkSprE6U1doZ3omfSnuN1HDXbSdaquXl0boeXK/7ma2aeY2azrEwj619+Nex4goLJwCC/okY3Yjou42B+/Psi1t82novzWlYW8Pv9/PK9I/jVSfRJSYpZgfOzBXtKnWxwdjpx2N3k3B85MnxPCNGbRPP6ce+995KRkcG7776LSqXi4YcfZvny5fz3f/83d911Fz/96U+ZP38+RUVFLFmyhBEjRjBmzBj27NnDypUrWbduHWPGjGHjxo0sXryY9957D4PBEJVYE0H92T2lEiDRc0m/dE7UOzG75HoqRLyKeFKqsrKStWvXcuzYMXy+lsMOfvvb30Z6c0LEtQaHm8/KA71zJg02YUrWxjiiAL+jgbED0ikqa6CorJ4hmQY06vCeeL3e3EtqxqgcUnUdP5W4vX6m3/dcu+1dTdYFk4EtXvP62F5czalG+Okfv2LljSOZPCQTCCSkXt1dxs5D1eD3MWmIKea9pADympNStiYvDQ5PjKM5Pzn3R5YkpYQQvUU0rx9fffUV+/fv5+OPPyY1NRWAlStXUl1dzc6dOzEajSxYsACASZMmMWPGDDZu3MiYMWPYvHkz06dPZ9y4QC/qhQsX8sYbb7Bt2zbmzJnTpbgSWainVFJiJKXG9M/gna+rMCdGx3MheqWIJ6UefvhhampquOaaa9Bq4+MGXIhY+ejbWrw+P/0ydIzs27U6SZE2Ki+dbyqtWF1evjxl4bIBGR1e9pjZzsdH61CA+WP7Ry/ICNGoVXx/ZB9+//ciGsnjP/74FVOGZjI6L5295fV8WlYPQMbR98mZvLDd9Rw+XMzMuXPRaNV43N7W7UdKmB6hmHUaFX1Tk6iyNiVEXSk590eWRobvCSF6iWheP7788kvy8/P5wx/+wOuvv47D4eCqq67iwQcfpKSkhIKCghbvz8/P58033wSgtLS0VfIpPz+f4uLiiMaYaOoTaPgeBHpKQWD4ni9Bep4L0dtEPCl14MABduzYQWZmZqRXLURC0V4wlpONLtSKwlVDs+Ki983ZNCqF8YOM/KPEzP6KRkb2TSU5qWNFKzftC/SSumpYFgNNidGFPUmtIuubLXzvx6vY9HkFH31by0ff1ja3Kfz4ysG8ufurc64j2AsrOVmH3d768eDXd98U0Zj7Z+ibk1Lx/3hPzv2RpVEFPuxLTykhRE8XzetHQ0MDhw4dYtSoUWzZsgWn08kDDzzAgw8+SHZ2dqtheHq9HrvdDoDNZjtnezji7CNgm4Ixni/WeC103lb8igJDs5NJ0SrY3H7MNjfq5tfj7WfS0eMfzxJ9HyT+jm8j0iKelEpLSyMpKSnSqxUioXi8PgxXzAbgkv5ppOujUr6ty4ZmJfPVKQtV1iY+K69n6rDzz5fb4HDzl4OVAPxwXPz3kjqbyufh59cMY/aYPHYWV3HK4qJvahI3XZzLIJOBN2Md4Hf0MxrYV9GYEEkpOfdHllqG7wkheoloXj+C63300UfR6XSkpqbys5/9jB/84AfMnj0bp7Pl9dXpdJKSEujZbjAY2mw3mc49k29bsrLSOrkH3e98sVo9geuSMVWPt0GLSqclOVmHXh/b/xsMgZn2dM2vOwxJZGam0rdPOhfnJvNpuY1ap4dBza9nZ8fnzySRflfak+j7IPF3v4jfKd9zzz08/PDD3HnnnWRnt5wivl+/fpHenBBx6eBpK6rULFKS1KFuw/FIURQmDjbx568qOVRl4+LcNLJSzv3BcOPeE7g8Pob3SQlryF88GZKVzN1XDo51GOcVLHZeUR//SSk590eWRt2clPJKUkoI0bNF8/qRn5+Pz+fD7Xaj0wWSFsG6VRdeeCG///3vW7y/tLSU4cOHAzB8+HBKSkpatU+dOjXsOMxmC/E+ckxRAjezbcXq9XopLy8DoLox0FNc5fNic7pR+d3Y7S6cMf6/wxGIy+UKvG6zOfjyy2Jqa630xQbA8RobfYxN1NZaSU+3dN/B7YBzHf9Ekej7IPF3fBuRFvGk1LJlywB49913gcBNr9/vR1EUvvnmm0hvToi40+Txsf9kIwCXD8wIu4B4d8tJ0zEkK5mjZjsffVvLjFE57b63xuri9b2BoXt3Tbog7oYk9jShpFQC1JSSc39kSU0pIURvEc3rx+TJkxk4cCCPPPIITz31FC6Xi+eff57rr7+em266idWrV7NhwwYWLFjA3r172bp1K2vWrAFg7ty5LFmyhBtuuIFx48axceNGzGYzhYWFYcfh95MwN7ltxVpWVsZLOz4jIzuXxiY/KAoGrZp4SusEYw7+22iu4vXjVgYMUTjy7RHQDuNUoxN/Rnz/POI5to5K9H2Q+LtfxJNSf//73yO9SiESylenLbg8PrwNp8nvMzDW4XTIxAuMnKh3UGVt4uvT1nbf99LHx3F6fIzKS+Pq/PMP9RNdcyYp5STe039y7o+sM7Pv+c7zTiGESGzRvH5otVp+97vf8fTTTzNt2jRcLhfXXnstjz76KOnp6axfv55Vq1axevVqMjMzWbZsGRMnTgQCs/GtWLGCxx57jMrKSvLz81m3bh1GozFq8cYzY588krPy8B8LPJyMt5pSbUnPyiEzdwC5Vac4bPfT5IX6pnj/RCVE7xPxpFT//oEaM19//TUnTpzge9/7HhaLhawsuYEVPZ/b6+OrU4HnRs4v3kH1L+NjHFHHpOo0jB9kZNfROj49XocppW+r9+w6WsvbB04DcO/UIdJLqhv0NwYKrFZaXOQo8d3jTs79kRWsKeX1B2YLUsnfmxCih4r29SMnJ4fnn3++zbbRo0ezadOmdpedNWsWs2bNikgcPYHDHXhQolH8oetUIlApYNJ4qXFrqI7/ighC9DoRv8sxm83ceuut/OAHP+DBBx+kvLyc66+/nn379kV6U0LEnZJqGy6PjzSdBvexz2MdTlguzEllkMmA1w+1BTdxvPbM7DJHzXZ+sf0QALde1p/LBhhjFGXvkpWsRadR4fODNym+ixbKuT+yNGd92JchfEKInkyuH4nD4fYCkKQk3nXJpA3EXu1IvNiF6OkinpR68sknKSgooKioCI1Gw7Bhw7jrrrv47//+70hvSoi44vP7OXAy0EtqdF5awg3mVRSFa/KzMBo0+HSp/GjjPl795Dgb9pSxaNMX1Nrd5GensGTK4FiH2msoikK/5iF8Hn18F5WXc39knZ2Ukhn4hBA9mVw/EkcoKaVKvOtSZnNSqsYpD3uEiDcRT0rt3r2bhx9+GIPBEBres2jRIkpLSyO9KSHiyvFaB40uDzq1ioK+KbEOp1OSNCqmX5RDUmMFtiYvL398nF9/dIxGp4fReWmsnTcGfQLUEOhJgnWlvLr4ncUR5NwfaYqi0DwBn3x4FkL0aHL9SBzBpJROlXj1DtPUPpLUCh4/fFvfFOtwhBBniXhNKa1Wi9PpxGAw4G/uKWKz2UhJScybdCE66kBzLakLc1PRxvmMe+eSnKQm65st3L7sV3xyrA6318dVQzO58aIcSUjFQP8E6Skl5/7I06hUeL0+6SklhOjR5PqROII1pRKxp5SiQF66nuN1Dr6qdhH+HIpCiGiJ+J3ztddey/3338+xY8dQFAWz2czjjz/O1VdfHelNCRE3Ki0uKi0uVApcnBvftX86QvH7uGVMHv898yKev2UUsy/pJwmpGOmXID2l5NwfeerQDHyJ9+FfCCE6Sq4ficPelLg1peDMZ6qDVa4YRyKEOFvEk1I///nPSU5O5l/+5V9obGxkypQpOBwOli5dGulNCRE3Djb3ksrPTiE5SZI3InL6pQeTUvGd7JRzf+RpmsfvebyJ+eFfCCE6Qq4fiSORa0oB9EvXAVBsduH2Jt4QRCF6qogP30tJSWH16tXU1tZy4sQJcnNz6du39fTyQvQUTreXo80z1V3UA3pJifiSlyA9peTcH3nBYudSU0oI0ZPJ9SNxJPLwPQBTspYkFbi8fr46ZWHsgPgujSBEbxHxpFRRUVGL748fP87x48cBuOKKKyK9OSFirrTGjs8PWclaslO0sQ5H9DDBnlI+bTJury9u65XJuT/yNKHhe/I0VwjRc8n1I3GECp0n6PA9RVHoa4ATNth9rFaSUkLEiYgnpW677bZWr6lUKvLy8vj73/8e6c0JEVN+v5/iSisAI3JSQ7PGCBEpaXoNaToNFpcHi8tDZnJSrENqk5z7I09qSgkhegO5fiQGv9+f8D2lAHINCidsfnYdrWPxlCGxDkcIQRSSUsXFxS2+r62t5de//jX9+/eP9KaEiLlqaxN1DjdqRSE/O7xZYg4fLmbWvLnttx8pYXpXA0xAclxay0vXYan2YHF5yUyOdTRtk3N/5GkkKSWE6AXk+pEY3D4IXo4SOSmV0/w56lCVlWqriz6putgGJISIfFLquzIzM7n//vuZNm0ad9xxR7Q3J0S3OlQV6CU1JMuAThPesCqPD6bf91y77V/ffVOXYktUclxa65eh53C1DavTE+tQOkzO/V0nSSkhRG8k14/45AqM3EOND3UCDwzQqxWGmbQcqWvik6N1zBydG+uQhOj1uqU4SUNDAy6XTL0pehafSsuRmkCB85E5qTGORvRkwSmMLa7ESUqBnPu7Si2FzoUQvZRcP+KPszkplaQkfp3Dy3IDn6s+PlYb40iEEBCFnlIPP/xwi+/dbjd79+5l8uTJkd6UEDHlzMrH7fOTrteQmyZdf0X0BIudx3NSSs79kadpLmrv8UpSSgjRc8n1IzG4mnNRPSEpNTZXz+ZvGtl9rA6P1xe63gohYiPqw/d0Oh233XYb8+fPj/amhOhW9uwRABT0SZEC5yKq8pp7SlnjOCn1XXLu7zoZvieE6I3k+hGfelJPqWGmJIwGLfUON/tPNjJuoDHWIQnRq0U8KfXUU09FepVCxJ1qq4um9IEADAuzwLkQ4Qr1lAp+IoxDcu6PvDNJqcS/ARBCiPbI9SMxuJp77faEpJRKUZg02MRfv6ni46N1kpQSIsYinpR68cUXO/S+n/zkJ5HetBDd5t1D1aAo5KQlka6PeodD0cvlZQSGh7q8Ppo8PpLCLKrfHeTcH3lq6SklhOgF5PqRGEI9pVSJn5QCuHJIZnNSqpZ7pw5p0eb1eikvLwt9P3DgINRqdXeHKESvEfG76ZKSEnbu3MnIkSMZMmQIp0+f5vPPP+eiiy4iJSXQo0SGOolEt/2bKkB6SYnukZKkQeV24NMasLg8ZGmSYh1SK5E899fW1jJ//nyeeOIJJkyYAMD+/ft54oknKC0txWQysXjxYubNmxdaZsuWLaxZs4bq6mqGDh3K8uXLGTt2LBD4cPncc8/x9ttv43A4mDhxIo8//jh9+/YFwGw2s3z5cj799FPUajUzZ87kwQcfRKOJbcJZI4XOhRC9gNw7JAZXi+F78fdwLFwTBptQKVBaY+NUo5O85l7pAOXlZby04zOMffKorz7Fj6fB4MFDzrE2IURXRPwTt0ql4uGHH+bf/u3fQq+9/fbbvPfee7zwwguR3pwQ3e5YrZ1vKq3g9zE0KznW4YheQu1qPJOUSom/pFSkzv179+7loYceoqzszBPKhoYG7rrrLn76058yf/58ioqKWLJkCSNGjGDMmDHs2bOHlStXsm7dOsaMGcPGjRtZvHgx7733HgaDgbVr17Jr1y7eeust0tLSWL58OcuWLeOVV14B4Gc/+xk5OTl8+OGH1NTUsHjxYjZs2MCiRYsidnw6Q2pKCSF6A7l3SAw9LSllNGi5pH8G+0408H6pmf/vsv4t2/vkkZk7IEbRCdG7RPyM8s9//pMFCxa0eO2mm27ik08+ifSmhIiJHc29pHT1ZRi00pVXdA+1qxGI32LnkTj3b9myhaVLl3Lfffe1eH3nzp0YjUYWLFiARqNh0qRJzJgxg40bNwKwefNmpk+fzrhx49BqtSxcuBCTycS2bdtC7XfeeSd5eXmkpqby6KOP8sEHH1BeXs7x48f59NNPuf/++zEYDAwcOJB77rkntO5YkqSUEKI3kHuHxNBTCp37fF4qKk5w7NhRLskMXGffK6mJcVRC9G4RT0plZmZSVFTU4rUPP/yQ3NzciG2jvr6eBx54gAkTJnDFFVdwzz33UFUVSBTs37+fefPmMXbsWK699lo2b97cYtktW7ZQWFjIpZdeyuzZs9m3b1/E4hI9n9/vZ0dx4HfNUHMoxtGI3iSYlIrXYueROPdPmTKFd999lxtvvLHF6yUlJRQUFLR4LT8/n+LiYgBKS0vbbbdYLJw+fbpFe3Z2NhkZGRw6dIiSkhKMRiM5OTmh9mHDhnHy5EkaGxs7HDuAooT/FViw7fUFp6j2eDuelOpMDPH41ZP2RY6LHJNEPC7dqTvuHUTX+P3+7/SUSlyN5ipe313Kps8rKCktAeCLEw2YbU0xjkyI3iviw/fuvvtu7rrrLqZNm0a/fv0oLy/nvffe43/+538ito17772XjIwM3n333VCX3+XLl/Pf//3fXRriIcT5fH3aQnm9E71Ghb7u21iHI3oRTTApFac9pSJx7u/Tp0+br9tstlbnaL1ej91uP2+7zWYDIDk5uVV7sO27ywa/t9vtpKendzj+rKy0Dr8XwGgM1ErR65JITta1ak+2uwHwK7TZHqTXB4ZzmkwpZGeHF0M8C/d49hZyXFqTY9K2RDku3XHvILrG6fETfD6S6EkpgPSsnNDQvGH1ao7UufngiJlbxuTFODIheqeIJ6XmzZtH//79+fOf/8zXX3/NwIED2bRpEyNGjIjI+r/66iv279/Pxx9/TGpqKgArV66kurq6xRAPoMUQjzFjxrQY4gGwcOFC3njjDbZt28acOXMiEp/o2bYXVwNwdX4WBz5yxzga0Zuo4zwpFc1zv8FgwGKxtHjN6XSGCuAaDAacTmerdpPJFEowORyONpf3+/2t2oLfB9ffUWazBX8YI+3q6wNJMaerCbvd1ard6w48lm5y+9psD3I6A0936+ps1NRY2n1folCUwM10uMezp5Pj0pock7Z19bgEl+8u0b53EF1X39xNSqNS0Cg9649tQr9kjtQ18F5JjSSlhIiRqEwtNHnyZCZPnkxtbS2ZmZkRXfeXX35Jfn4+f/jDH3j99ddxOBxcddVVPPjgg+0O8XjzzTeBwBCP7yafzh4CIsS5eHx+djYP3Zs2si8HYhyPCN/hw8XMmje3/fYjJUzvxnjCoXYFkg0Wlwe/3088zkQUrXN/QUEBu3btavFaaWkpw4cPB2D48OGUlJS0ap86dSoZGRnk5OS0GOJXXV1NfX09BQUF+Hw+6uvrqampITs7G4AjR46Qm5tLWlp4N2V+P2HdAIbe284ynakp1ZNuzMM9nr2FHJfW5Ji0LZGOSzTvHUTXNTgDvaMM2sQvcP5d4/sb+P3BBorK6rG6PKTqYjvzrhC9UcTPLG63m+eff55x48Zx7bXXUl5ezpw5c0I1n7qqoaGBQ4cOcezYMbZs2cKf/vQnKisrefDBB7s0xCMcvbXmQW/fh73l9dTa3WToNUwaYgr/lzcCujMP0ZVjfK51nv1vd/P4YPp9z7X75XGfv15TNPfhXMc12FPK7fXT1E6Noc78LURKNM/9hYWF1NTUsGHDBtxuN7t372br1q2hhwxz585l69at7N69G7fbzYYNGzCbzRQWFgIwe/Zs1q5dS3l5OVarlSeffJLx48czaNAgBg8ezLhx43jyySexWq2Ul5ezZs0a5s5tP3nZXc4kpRJ/qIQQQrQn2vcOousamntK9cQJfvqnaRmSlYzH5+ejb2tjHY4QvVLEU8Evvvgiu3fv5le/+hX33XcfWVlZ5ObmsmrVKn71q191ef1JSYHaGY8++ig6nY7U1FR+9rOf8YMf/IDZs2e3OYSjI0M8wtHZLs2JMrb/XHrzPvzzvUANqZsu6UdeTgYarfqcdV4U5dx1YLrSHs11A2i06k7XpjnfcQEwGBL3uAVjb2sfurL+8x3zJLUfg1aNw+3FrSiYvrOezvzMMjMj9/cczXO/yWRi/fr1rFq1itWrV5OZmcmyZcuYOHEiEBiqvWLFCh577DEqKyvJz89n3bp1GI1GAJYsWYLH42HBggXYbDYmTJjQYprx1atX84tf/ILrrrsOlUrFzTffzD333NOlmCNBZt8TQvQG0b53EF1XH+oppYYeWL3imuHZHDWX8V5JDf9yYd9YhyNErxPxpNTWrVt5/fXXycnJQVEUkpOTeeqpp0JPrLsqPz8fn8+H2+1GpwvclPmanyJfeOGF/P73v2/x/o4O8QhHuGP0FSXxax709n1we31s/+o0AFMHG6mpseBxe89Z58XvJ+LtwaRINNZ9No/be87aNHfctYia2vo220qOlDCtnXUHe+Y4HK52fwbR3LdIrNvhcGEw6Nrch66s/3zH3OP2kqoLJKWq6x2kqpVW7R2tJxT8W6ittUQsMRXpc/+hQy1ntxw9ejSbNm1q9/2zZs1i1qxZbbZptVqWLl3K0qVL22zPzs5m9erVnYozmkJJKa8/bodsCiFEV0X73kF0XYueUj0wKXVtfjbrd5ex62gttqb4rN0pRE8W8aSU3W4PjQX3N9+x6fV6VKrIjBScPHkyAwcO5JFHHuGpp57C5XLx/PPPc/3113PTTTexevVqNmzYwIIFC9i7dy9bt25lzZo1QGCIx5IlS7jhhhsYN24cGzdubDHEo6M6O0Y/kcb2t6e37sOeY/VYXB6yUpK4pF9GzI5Bd273XNuqqa1n+n3Ptdn27N03nXedifw7FM19ON8603Qaqq1N7RY7DzemSO5DtM/9vZFGHTh2fsDnB7XkpIQQPZBcP+JfvTOYlOpZPxOfz0tFxQn69fOTl6rhlNXDP0vNXJR8/mWFEJET8TPLpZdeyosvvggQeqr7u9/9jtGjR0dk/Vqtlt/97neo1WqmTZvGtGnTyM3N5cknnwwN8di+fTsTJkxg2bJl7Q7xGD9+PO+8806LIR5CtOdvhwOz7l07PBu1Su4MRWykNRffjMcZ+KJ97u+NNGeda2QInxCip5LrR/xrcDUP30vqWTWlGs1VvL67lDf2nUTfVA/AX7+RWmZCdLeI95R65JFHWLhwIVu2bMFms3HjjTdis9n43//934htIycnh+eff77Ntq4M8RCiLW6vj3+WmgG4riA7xtGI3ixNH/gwaI3DpFR3nPt7G7VKQVECPdo8Xh86Tc96Qi2EECDXj0TQkwudp2flkJk7gAJ3OUdP+Pn0eB11FxvOv6AQImIinpTKzs7mnXfe4f3336eiooLc3Fy+973vkZqaGulNCdEtPj1+Zujepf0zYh2O6MWC0xRbnOefJbC7ybk/OjQqBbfXLz2lhBA9llw/4t+ZQucqHDGOJVpStQrDM7WU1DbxcXl4M7MLIbom4kmpm266iT//+c/ccMMNkV61EDEhQ/dEvDh7+F68Fb6Wc390SFJKCNHTyfUj/p3dU6qnJqUArhqYTEltEx+W27nUGOtohOg9ojIWwOHoyacr0ZvI0D0RT4I9pTw+Py6PL8bRtCbn/sjTNhf6laSUEKInk+tH/HK6vTg9gWtQcg8cvne2yQOSUStwpK4Ji1uuu0J0l4j3lJowYQLz5s1j6tSp9O3bt0XbT37yk0hvToiokqF7Ip5oVArJWjV2txeLy4M+jj4cyrk/OjTNU+55vPLhWAjRM8n1I76Z7U0AqBTQ9vBpYDP0asZfYOKTY3WUW/1cEOuAhOglIp6UOnHiBAMHDuTo0aMcPXo09Ho8DTMRoqNk6J6IN6m6YFLKS584Krch5/7oCM7A5/bFX884IYSIBLl+xLdamxsAvap3/ExuuKgvnxyr47gVrvTLAyEhukPEklL//u//zm9+8xt+97vfAeB0OtHr9ZFavRDdTobuiXiUptdQZW3C4oyPGfjk3B9dwaSUDN8TQvQ0cv1IDLXNPaV08dM5O6quyc/GoDmM3ePndKMLXawDEqIXiFhNqX379rX4furUqZFatRAx8WmZDN0T8efsYufxQM790SXD94QQPZVcPxKD2R7oKdVbklJ6rZrJA5IBOFRti3E0QvQOUSl0DuCX7o4iwf3tkAzdE/EnmJSyxklS6rvk3B9ZGil0LoToJeT6EZ9qbYGeUvpekpQCuGZwCgBHzXbccv0VIuqilpTqDWOORc8lQ/dEvIq3nlLfJef+yDozfE9qSgkheja5fsSn2l7WUwqgIDOJVG3ggVCFdJYSIuqilpQSIpHJ0D0Rr1KbPxVanF55qtwLyPA9IYQQsRSsKaXv4TPvnU1RFAanBvb3mEWuv0JEW8QKnXs8Hv70pz+Fvne73S2+B7j55psjtTkhokqG7ol4ldrcU8rr9+Nw+0hOiu2jSzn3R5cUOhdC9FRy/UgMweF7vamnFMCgVDhYB2YXnLK4GRzrgITowSKWlMrOzmb16tWh700mU4vvFUWRC4tICDJ0T8QztUohJUmNrcmLxeWJeVJKzv3RpW2uKSU1LYQQPY1cPxJDsNB5T68p5fN5qag4AUBFxQl0Khhg1FNe7+S94zYmjY5xgEL0YBFLSv3jH/+I1KqEiKkiGbon4lyqToOtyYvV5SEnLbaTFcu5P7rODN+TmlJCiJ5Frh+JITh8r6f3lGo0V/H6cSsDhiiUHTqAMW8wBX2zKK938s8yOw/6/DJ6QogokZpSQnzH+6U1AHwvP0suPiIupQXrSsVpsXMROTJ8TwghRKw0eXxYXV6g5/eUAkjPyiEzdwDpmX0AuMBkIEkFtQ4vn5bVxTg6IXouSUoJcRavzx8aundNvgzdE/EpNAOf0xvjSES0SVJKCCFErJibe0lpVKDthXeNapXCwNTA/7d+VRnbYITowSI2fE+IRHPH3Ysw19a3eM2VmkftqHkoHhdrn3qQCS+/EpvghDiHNH1zUkp6SvV4GnXgLkBm3xNCCNHdzM1Fzo16NYrSO69DF6QqHGn088/SGhqdbtL12liHJESPI0kp0WuZa+uZft9zLV7bfawO8ykLw3JN1H1WG6PIhDi3UE8pSUr1eNJTSgghRKyEklI6NdA7P3MYk+CCDC3HG9zsKK5m3qX9Yh2SED1OL+yIKUTb/H4/x2sdAFyQmRzjaIRoX2pzUsrq8uD3S7KiJzuTlJJC50IIIbrXmZ5SvfeWUVEUrrkgBYCtX52OcTRC9Ey99wwjxHfU2d00ujyoFRho1Mc6HCHalapTowA+P9ibpK5UTxZKSsnwPSGEEN3MbHMDgeF7vdmUQcmoVQrfVFoprbHFOhwhehxJSgnR7FhzL6n+RgNatfxpiPilUhRSmmfgs0pSqkfTqGX4nhBCRILX6+W2227joYceCr22f/9+5s2bx9ixY7n22mvZvHlzi2W2bNlCYWEhl156KbNnz2bfvn3dHXZM1TT3lDL18qRUhk7NVUMzAektJUQ0yJ23EM2O1dkBGJxpiHEkQpzfmRn4emeNh95Cowpcpt2SlBJCiC558cUX+eyzz0LfNzQ0cNddd3HzzTdTVFTEqlWreOqpp/jyyy8B2LNnDytXruTpp5+mqKiImTNnsnjxYhwOR6x2odudXei8t5sxKheA7d9U4fHKkHohIkkKnQtB4MbebHOjAINMkpQS3e/w4WJmzZvbfvuREqaf9X2grpRLip33cNrmnlJenx+/34+iKDGOSAghEs8nn3zCzp07+f73vx96befOnRiNRhYsWADApEmTmDFjBhs3bmTMmDFs3ryZ6dOnM27cOAAWLlzIG2+8wbZt25gzZ05M9qO7me3BQucqevv0P5OHZJKZrKXW7mbX0Vquzs+OdUhC9BiSlBKCM72kctN1GLTyNEh0P4+PVrNBnu3ru29q8X1a8/A9SUr1bMGaUhBITAWH8wkhhOgYs9nMo48+ypo1a9iwYUPo9ZKSEgoKClq8Nz8/nzfffBOA0tLSVsmn/Px8iouLox5zvJCeUmdoVAo3XpTDa5+dYOtXlZKUEiKCJCklBHDMHJx1T3pJicSQFpqBT2pK9WTqs5JSbp8fjdwXCCFEh/l8Pu6//35uv/12Ro4c2aLNZrNhMLT83KfX67Hb7R1qD0cidHINxhj81+/3h5JSJkP8X3y+G3+k160oMHNUICn10dFa6uxNZKYkRXQbZ/+biBJ9HyT+jm8j0iQpJXo9h9tLpcUFwGBTcoyjEaJjQjWleklPqT//+c+sWLGixWtud2BWoK+++ooVK1bw1ltvodVqQ+0PPfQQ8+fPBwLFatesWUN1dTVDhw5l+fLljB07FggUv33uued4++23cTgcTJw4kccff5y+fft20961T6UoqBUFr98vxc6FECJML7/8MklJSdx2222t2gwGAxaLpcVrTqeTlJSUULvT6WzVbjKZwo4jKyst7GViJRhrg91NU/PMr0Pz0vn4eB3JyTr0ei0qnTbu/m8w6ADQRXj9DkMSmZmpZGenkZ2dxiUDjewvr+eDsgYWXTU0asc/kSX6Pkj83U+SUqLXK6tz4AeyUrSk6c/8SYRb40eI7pSqD/aU8uD39/xkxcyZM5k5c2bo+8rKSubMmcP9998PwIEDB1i5ciW33HJLq2WDxWrXrVvHmDFj2LhxI4sXL+a9997DYDCwdu1adu3axVtvvUVaWhrLly9n2bJlvPLKK922f+eiUSt4PX483p7/cxZCiEh6++23qaqq4vLLLwcIJZn+9re/8cADD7Br164W7y8tLWX48OEADB8+nJKSklbtU6dODTsOs9lCvF+qFSVwMxuM9ag50CMsTafB2mjH4WjCbnfhdLpR+d1x93+HI/CA2eWK7PodjiZqa62kpwcSmDeMyGZ/eT2v7znOrJHZEav1+N3jn4gSfR8k/o5vI9IkKSV6veO1zUP3vtNLKtwaP0J0p5QkNQrg84O9qXcN4fP7/dx///1873vfY9asWTQ1NXH48GFGjRrV5vvPV6x28+bNLF26lLy8PAAeffRRpkyZQnl5OQMHDuy2/WqPRqXgAjw+me1HCCHCsX379hbfP/TQQwA8/fTT1NXV8eyzz7JhwwYWLFjA3r172bp1K2vWrAFg7ty5LFmyhBtuuIFx48axceNGzGYzhYWFYcfh95MwN7nBWGusgaF7WSnahIg9GGOkY/X5vJw4cSK03uvy+/H8+99ypMbON5VWLsyJ7A16Iv2utCfR90Hi736qWAcgRCx5fX5ONgSemg0y6WMcjRAdp1IUUkLFzntXUurtt9+mtLQ0dHNRXFyMx+Nh9erVTJ48mWnTpvHKK6/ga07ilJaWtlnMtri4GIvFwunTp1u0Z2dnk5GRwaFDh7pvp84hWOxcekoJIUTkmEwm1q9fz/bt25kwYQLLli1j2bLJs/SLAAEAAElEQVRlTJw4EQjMxrdixQoee+wxxo8fzzvvvMO6deswGo2xDbybBOtJZUWwblIiajRX8fruUjZ9XsFLOz6jvvokVw3NBODd4uoYRydEzyA9pUSvdtriwu3zY9CqyO7lF12ReNJ0GqwuL9ZeUlcKAkVr165dy49//GNSU1MBsFgsjB8/nttuu41f/vKXfPPNNyxZsgSVSsWiRYvOWazWZrMBkJyc3Ko92NZR4fbgD73/PMtpm2fc60hNqUQtznm2RC80Gi1yXFqTY9K2rh6Xnnw8n3766Rbfjx49mk2bNrX7/lmzZjFr1qxohxWXzPbmpFSyfD5Oz8ohM3dA6PvCEX342+Ea3j1Uzb1Th0RsCJ8QvZUkpUSvVlYXGLo30GiQC4pIOGk6Dadw9Zpi5xCoD1VVVcXcuWfqvV155ZVceeWVoe/HjBnDj370I7Zt28aiRYvOWaw2mKxyOByt2oPFbjsq3DH2RmNg/XpdEsnJunbfl6RRA25UGnWb79PrAzcMJlMK2dmJV9yyPYlYqLM7yHFpTY5J2+S4iK44M3xPklJBPp+XiooT9M/th16jcNri4sApC2P6pcc6NCESmiSlRK9WHkxKmQzneacQ8Se1l83AB7Bjxw4KCwtb9Gz629/+Rk1NDbfeemvotaamJvT6wJDccxWrzcjIICcnp8UQv+rqaurr61sN+TufcAtL1tcHemI5XYHise1pHr2HzdH2+5zOwI1DXZ2NmhpLq/ZEk+iFRqNFjktrckza1tXjEq1CtiKxBHtKBUYS9J7PGefSaK7i9eNWBgxRMGLnNAbePVQtSSkhukhqSoley6PLoMHpQVFgQIbUkxKJJ625ppS1F9WU2rt3L1dccUWL1/x+P0899RSffPIJfr+fffv28dvf/pb58+cDgWK1W7duZffu3bjdbjZs2NCiWO3s2bNZu3Yt5eXlWK1WnnzyScaPH8+gQYPCii1YWDKcr8CC516vtjkr5e7A8L3OxBCPXz1pX+S4yDFJxOMihNSUaltwKN+QrMDDsb8dqsbbgeuzEKJ90lNK9FpO4wUA5KbpSNJIflYknrSzekqlxjiW7nLixAn69u3b4rXCwkIefvhhHnvsMSorK8nOzubee+8N1QE5u1htZWUl+fn5LYrVLlmyBI/Hw4IFC7DZbEyYMIEXXnihm/esfVp14Pzk9srse0IIIbpHTXNSymOtpaK+BpkAtqUcAyRrFWpsTXxR0cC4gcZYhyREwpKklOi1XKYhAAySoXsiQaXqA6dwq8tDeNWPEte+ffvafP3WW29tMXzvu85VrFar1bJ06VKWLl0akRgjLVjo3C2z7wkhhOgm1ZZALcbdR2toOHoAY95gsmMcUzxRKwoT+hl477iNdw9VS1JKiC6Q7iGiV3K4vbjS+wOBIudCJKKUJDWKAj4/+LS9JS3V+wR7SnkkKSWEEKIbeLw+Gl2BrlG5ef1Iz+wT44ji0+QBgXuIfxyu6dAMuUKItklSSvRKn5XVg0pDqk6N0SAdBkViUikKqUmBulIenRTZ7Kk0oZpSMnZCCCFE9NXa3QAogF5KXLRrVF89GXoNdQ43e8vrYx2OEAlLzjKiV9pzvA6AAUYDiqLEOBohOi84A59XklI91pmaUvIUVgghRPQFZ97TqZHPye3w+bxUnqrgijwdADuLq2IckRCJS5JSolf6tKwekFn3ROILFjv36iUp1VOdqSklPaWEEEJEX3DmPb06xoHEsUZzFa/vLsXlsAPwj8PVeOQ6LUSnSFJK9DrVVhdHzXbw+8lL18U6HCG6JE16SvV4weF7Uq9CCCFEd5CkVMekZ+VQMHgAOjVYm3zsaX7oLYQIT8ImpbxeL7fddhsPPfRQ6LX9+/czb948xo4dy7XXXsvmzZtbLLNlyxYKCwu59NJLmT17druzOIme7dPj9QBobVXotXK1FYktVR+sKZUW40hEtMjwPSGEEN2pRpJSHaZSFPonB67PW/Z+y7FjR/F6vTGOSojEkrBJqRdffJHPPvss9H1DQwN33XUXN998M0VFRaxatYqnnnqKL7/8EoA9e/awcuVKnn76aYqKipg5cyaLFy/G4XDEahdEjHzaXE9K11Ae40iE6DrpKdXzyfA9IYQQ3clsCxQ6l6RUxxg9gXuLj8vtrNn+GeXlZTGOSIjEkpDTjn3yySfs3LmT73//+6HXdu7cidFoZMGCBQBMmjSJGTNmsHHjRsaMGcPmzZuZPn0648aNA2DhwoW88cYbbNu2jTlz5sRkP3qDO+5ehLm2vt32rEwj619+tdvi8fv9oXpSuga5YIjEl6HXAuBXaWMciYgWrSrw/EiG7wkhhOgOweF7Oo0UOe8Io8qNTvHh8qlwpuTGOhwhEk7CJaXMZjOPPvooa9asYcOGDaHXS0pKKCgoaPHe/Px83nzzTQBKS0tbJZ/y8/MpLi4OO4ZwJ6EIvj+RJ6/o7D6Ya+uZft9z7ba/8/zSqB6XO+5aRE0wKaaAPzWb6ot/CD4PZfs/BH4WvY1HQXf+DkVjW2f/HvkT9P463vYhOUnN9QXZfP72FhTlhg4t0xPOSb2J9JQSQgjRnaSmVHgUBXJ0HsqcSZywxcGHQyESTEIlpXw+H/fffz+33347I0eObNFms9kwGAwtXtPr9djt9g61hyMrq3O1Wzq7XDwJdx80WjXJye0XE9do1WRnR++41FsszHv0f0Lff1Fez6nD1QzKTudAk+ucsSkKcdse7W2f7+dyrp/r+dYNYDAk7nELxt7WPsQqtouTdRy2nwz7bykzM/HPSb3B2TWl/H6/TM8thBAiqqSmVPhykgJJqVN2cMlDJCHCklBJqZdffpmkpCRuu+22Vm0GgwGLxdLiNafTSUpKSqjd6XS2ajeZTGHHYTZbwuohoSiBZE64y8WTzu6Dx+3Fbneds72mxtJue1edvX1FgfLaQBIyNzWJL/2cMzZ/HLYHkyLR3vbXBw8y+XvT2m0vOVLCtHaWP9e6g/fSDoer3d+jaO5bJNbtcLgwGHRt7kMsYwvnbyn491xba5HEVALQNPeU8gNenz/0vRBCCBFpfr+famvg84ZBklIdlqHxkZqkxtrk5YvTTkYMi3VEQiSOhEpKvf3221RVVXH55ZcDhJJMf/vb33jggQfYtWtXi/eXlpYyfPhwAIYPH05JSUmr9qlTp4Ydh9/fuWE7nV0unkRjH7rrmHh9fk7UBQrb98/Qd89GI6y7jpXHxzmHXT57902dWm8w/kT+O4jnfQg3pnjcB9GaRnUmCeX2+dHITYIQQogoaXB4aGqe7dWQUHeKsaUoMDQrmS9PWdh1ws78WAckRAJJqNn3tm/fzueff85nn33GZ599xk033cRNN93EZ599RmFhITU1NWzYsAG3283u3bvZunVrqI7U3Llz2bp1K7t378btdrNhwwbMZjOFhYUx3ivRXaqtTTR5feg0KrJSpCi0ECIxqBQllJhyeyWTKIQQInoqm3tJZehUqGS4eFiGZicD8PkpJw63N8bRCJE4ekz+22QysX79elatWsXq1avJzMxk2bJlTJw4EQjMxrdixQoee+wxKisryc/PZ926dRiNxtgGLrpNRX2gZ12/DL3UZBFCJBStWsHj8+OROhVCCCGiqNoSSEplGtSAJFbCkZ2SRIoGbB4/75XUcONFObEOSYiEkNBJqaeffrrF96NHj2bTpk3tvn/WrFnMmjUr2mGJOFXREEhKJerQPSFE76VRqQAfbp/0lBJCCBE9wZ5SkpQKn6IoXJCq8HW9n7cPnJaklBAdlFDD94ToLLfXF7rISlJKCJFotOrg8D3pKSWEECJ6qiyBmfcyZeq9TrkgDRTg8xMNHK8Nf5Z3IXqjhO4pJeLDHXcvwlxb32bb4SMlTO/ecNp0qjEwU1q6XkO6Xn7thRCJ5UxSSnpKCSGEiJ6q5oe4Wcka3JJTCVuyRmFsro7PTzv581enuXfq0FiHJETck7tz0WXm2vp2Z2r7upOztEVacOjeoMzkGEcihBDh06oCHZs9MnxPCCFEFFUFa0rp1VRKUqpTrhucwuennfzlYCWLrxyMRi2Dk4Q4F/kLEb3Cyeak1EBJSgkhEpAM3xNCCNEdqq2B4XtZBhm+1xk+n5dcnxmjXkWt3c1bu7/B65XaXEKciySlRI9nb/JSa3cDMNAkSSkhROIJPmWV4XtCCCGiqbLF7HsiXI3mKv7w6RFydIGHSP9bdJLy8rIYRyVEfJOklIg4r89PtbWJOrsbVLEfIRrsJZWVosWQJBdYIUTi0aqkp5QQQojosro82JoCvXqkp1TnpWflcMmQPADMXh2VVk+MIxIivsU+YyB6DIfby97yBg5X2/A21z3JuPUZPj/RwJi8tJiNpw7Wk5JZ94QQiUob7CklNaWEEEJEyenmz8wpSWoMWum70BUZBi0DMvScaHDyTqmFCaNiHZEQ8UvONiIiTjU62fzFKb6ptOL1+dFpVGjVCkqSgb3lDbzzdRUuT/c/4fcjSSkhROLTNNeU8khPKSGEEFFS2Rj4zNw3VRfjSHqG0f3SAPjHMRsWp/SWEqI9kpQSXeYwDWVbc9IpM1nL9Iv6ctvl/fnRFQOw/fM36DQqqqxNvPN1ZbffUHn1RmxNXlQK5KbJBVYIkZjOFDqXnlJCCCGi41Tzg9y+aUkxjqRn6J+hJ10LLq+fLV+einU4QsQtSUqJLvmsrJ664Tfg88OQTAOzRuXQL0OPoigoioL76F5uuqgveo0Ks83Nx8fqujU+V8ZAIJCQkulYhRCJSquS4XtCCCGiK9hTqo/0lIoIRVEYnhF4qPTGvgqpCylEO+QuXXTa8Vo7S98+CCo1gzMNXFuQ3WbiJzMliWsLsgE4VGXjqNnebTEGk1L9jTJ0TwiRuM70lJIPtEIIIaLjVIMDgL4yuiBiBqaCUR8YNfLuoepYhyNEXJJC54I77l6Euba+3fasTCP/+8qrLV6zN3m5/89fY2vyktR4kmsmTEClKO2uo3+Gnkv7p/NFRSOfHKtjgFEfKtwbLV6fH1f6wND2hRAiUSU1ny+bJCklhBAiSk43uADom5oEuGIbTA+hVhRuGJbG6wcbeO2zE9xwYV+Uc9wzCdEbSVJKYK6tZ/p9z7Xb/s7zS1u99tw/SjlqttMnNQnV3m1opk0873bG9k+ntMaG1eVlf0Ujlw8ydiXs8yqutODX6EhSK2SlyNh4IRLdtm3bWLp0KTrdmSe4119/Pc8++yz79+/niSeeoLS0FJPJxOLFi5k3b17ofVu2bGHNmjVUV1czdOhQli9fztixYwHwer0899xzvP322zgcDiZOnMjjjz9O3759u30f25OkaU5KeWT4nhBCiOg43djcUypVhySlIqdwaApvH7ZSUm3jgyO1XJ2fFeuQhIgrMnxPhO0fh6vZerASlQKrpl+I2t2x4XgatYqJF5gAOHDKgsPtjWaY7D4eqF/VL0N/zl5cQojEcODAAWbNmsW+fftCX88++ywNDQ3cdddd3HzzzRQVFbFq1SqeeuopvvzySwD27NnDypUrefrppykqKmLmzJksXrwYhyPw4Xvt2rXs2rWLt956iw8//BC9Xs+yZctiuautBIfvSU8pIYQQ0XK6QWbfizSfz0tj9SkKhyQDsO6TY/j98oBJiLNJUkqEpcriYtW7JQD8aPxAxg7ICGv5wZkGslOS8Pj8fHmyMRohhuxuLqo+wGiI6naEEN3jwIEDjBo1qtXrO3fuxGg0smDBAjQaDZMmTWLGjBls3LgRgM2bNzN9+nTGjRuHVqtl4cKFmEwmtm3bFmq/8847ycvLIzU1lUcffZQPPviA8vLybt2/czl7+J58mBVCCBFpbq+PGmsTILPvRVKjuYrXd5fidVpR4+NQlY2Pvq2NdVhCxBVJSokO8/n8PPbXQzQ6PVyYk8pdky4Iex2KonDZwEAi6+vTVrya6NR6sjg9HGhOeg2QIudCJDyfz8fBgwd5//33ueaaa5g6dSrLly+noaGBkpISCgoKWrw/Pz+f4uJiAEpLS9ttt1gsnD59ukV7dnY2GRkZHDp0KPo71kHB4Xt+f6BenhBCCBFJ1c0JKY0K6itPUFFxAp9cbyIiPSuHvP4DGZYRuJav++S4PGAS4ixSU0p02G8+OsqnZfXoNSpW3jiyzZn2OmKQUU9WihazzY29b+teD5FQVFaH1w8aRy1pukFR2YYQovvU1tZy0UUXMW3aNFavXk1dXR0PPvgg999/P3369MFgaNkjUq/XY7cHhhbbbLZ22202GwDJycmt2oNtHRXuKOHQ+zuwnFaloAB+oMnrR6OOTAzxKLgPPWFfIkmOS2tyTNrW1eMix7N3qrIGakhp/B7e2HeSskMHMOYNJjvGcfUkBRkKx63wTaWVj4/WceXQzFiHJERckKSU6JDDVVae3RHoNXDfNcO4IDP5PEu0T1EURuel836pGVvuGDxeX6cTXO35pHnonq7+OHBpRNcthOh+2dnZoeF4AAaDgfvvv58f/OAHzJ49G6fT2eL9TqeTlJSU0HvbajeZTKFkVbC+VFvLd1RWVlpY7zcaA+vX65JITj5//Y4kjQqXx4dKqyE5+czQCr0+8H+TKYXs7PBiiGfhHs/eQo5La3JM2ibHRYSjyhJISqUmacjMHUB99akYR9Tz6NQK3x+awtYSCy9/fIzJQ0wyE58QSFJKdIBPpeHRvxTT5PVxdX4Wt4zO7fI6h2Yls+d4HQ5S+UdJDd8fGblZrvx+/1lJqbKIrVcIETvFxcX85S9/4ec//3noA1xTUxMqlYoxY8bwf//3fy3eX1payvDhwwEYPnw4JSUlrdqnTp1KRkYGOTk5LYb4VVdXU19f32rI3/mYzRbC6Y1fXx/oieV0NWG3n3+WI61KwQU0Wp3olTMbcjoDQy7q6mzU1FjCijkeKUrgZjrc49nTyXFpTY5J27p6XILLi96lqnn4nl7uDqNqVkEafztm45tKK29+8g1X9DPg9XpRqRQaG9OprbUyYMAg1Op2ukQL0QNJTSlxXo0XTOForZ2+aTqWfX94RDL6apXChTmpALyx72SX13e2o7V2Ki0udBoVusYTEV23ECI2jEYjGzdu5NVXX8Xj8XDy5EmeffZZbrnlFqZNm0ZNTQ0bNmzA7Xaze/dutm7dypw5cwCYO3cuW7duZffu3bjdbjZs2IDZbKawsBCA2bNns3btWsrLy7FarTz55JOMHz+eQYPCG/rr94f/FViwY+sP1pU61wx8nYkhHr960r7IcZFjkojHRfQ+wZ5SBklKRVWGXs38sf0BWLPnFK/vPcH/+8NO/t8fP2D9R0dZu/0zysvlobroXeS0I87pWK0de84YAH75g0sxJSdF7MPKhTlpfF5Wx5cnG/n6tIWLciPzVO6To3UAjB2QQZnfG5F1CiFiKzc3l5dffplf/vKXrF27Fp1Ox/Tp07n//vvR6XSsX7+eVatWsXr1ajIzM1m2bBkTJ04EYNKkSaxYsYLHHnuMyspK8vPzWbduHUajEYAlS5bg8XhYsGABNpuNCRMm8MILL8RuZ9uhDc3AJ3eMQgghIiuUlFLLcLJo8fm8VFSc4OqcfmxSg9WrpV6bSXpmH1S6FLLyBuJwNMU6TCG6nSSlRLusLg8fHAlMWXrbFQOYMjw7okNDkpPUGMwlOPqM5A/7KnjshpERWe/u5qF7kwabkOcMQvQc48ePZ9OmTW22jR49ut02gFmzZjFr1qw227RaLUuXLmXp0qURiTNakppvFJo87feUEkIIITrjVGMgKZUsd4dR02iu4vXjVgYMUchyVVKhyWFveQOXqWX4kujd5PdftMnj9fHuoRpcHh9aaxX3TBkcle2knN4PwM5D1dTau/5kwOn28vmJegAmDZYZLYQQPUdHhu8JIYQQnXG6MTAhiCSlois9K4fM3AHkZyhoFT8NTg+nPfpYhyVETMlppwe44+5FmGvr223PyjSy/uVXO7w+v9/PB0dqqbE1odOoyCjZhlY9NwKRtpZkq+Si3DS+Pm3hzwdOs3BCeDVcvquorJ4mr5+cNB2DMw3nX0AIIRJEUvPwPbckpYQQQkSQy+PDbHcDkpTqLhrFz2BDEyV2HUebUunnd5x/ISF6KDnt9ADm2nqm3/dcu+3vPB/ekJT9Jxs5YrajKHB9QTb7PmrsaojnNPeSPH5x2sIfvzzFbVcMRK3q/Fj290trALh6WJZMsSqE6FGCSakmj9SUEkIIETnBXlJ6jUKSjKPpNgP1bircydjdUOHSMCrWAQkRI3LaES2UVNsoKmsAYPJgE/0yot+dtHBEH9L1Gk41uvj4aG2n1+Px+UM1sL43PCtS4QkhRFxI0jTXlJKeUkIIISLoVHNSKidNKw91u5FagUsHpAPwrT1JekKLXkuSUiKktNrGP0vNAFycmxqx2fDOR69VM+PiXADe3H+y0+vZX9FAvcNNhl7D2AHGCEUnhBDxIdRTSj60CiGEiKBgkfPcVG2MI+l9RvZNRa94afKrOHCiIdbhCBETkpQS+IEvTzbyXqkZP1DQJ4VJg03dGsOcS/IA+ORoHSfqOzem+v3mhNqUYVloujAEUAgh4tGZ4XuSlBJCCBEZXq+X4vJKAFJw4ffLEPHupFYpDEmyAvDZ8TrcPjn+oveRmlK9XJ29ibrhN3LqeD0Q6CE1cbCpRdfdw4eLmTl3LhqtGo/b22odh4+UML2LcQw0GZg42MTuY3X8cf8pfnr10LCW9/j87CyuAuC64dldjEYIIeKPNjT7nnxgFUIIERnl5WV8XFoJGPi2/CTDs5LJyot1VL1LrsbJcXcadjccaZQH66L3kaRUL1Vvd7PlwCl+W1SOMysflQITLjBxcW5qq7HkHh9Mv+85kpN12O2uVuv6+u6bOh3H4cPFzJoXmNnPaRoCI2bw2iclvLvmEVQ+T4dnDiwqq6PWHhi61929vIQQojskqaWmlBBChKu4uJhnnnmGgwcPotVqufLKK3nooYfIzMxk//79PPHEE5SWlmIymVi8eDHz5s0LLbtlyxbWrFlDdXU1Q4cOZfny5YwdOzaGexMdHrUBPGBKS451KL2SSoGhyU18ZdVT0uDHIT2iRS8jSakE5/P7aUrN5YuKBursblweHypFQatW0KpVJKlVWPpdzh/2VeD2+qmyuiiutLL/ZCPe5u6hWlsV0yeOpk+qrtvjDya8gvvyh32nsGBg2PxHuSg3rcMzB277OtBL6vsj+6JRy6hUIUTPI8P3hBAiPE6nk0WLFvGDH/yAl19+GZvNxoMPPsgjjzzCM888w1133cVPf/pT5s+fT1FREUuWLGHEiBGMGTOGPXv2sHLlStatW8eYMWPYuHEjixcv5r333sNgMMR61yLK5gn8a1B5AXVMY+mtcpI8nDBoqXe42XnEyoX5sY5IiO4jd+8JyuPz86cvT3HLb4qoGfUDisoaKK2xU17v5Hidg9IaO980J58sgybz7D+O8MI/v+X3eyv4/EQDXp+fkX1TefyGEWQfeCMmCanvUikKo/MCxdUPnLLg6+CYdqvLw/slNQDceFHfqMUnhBCxlKSRQudCCBGOkydPMnLkSJYsWUJSUhImkymUgNq5cydGo5EFCxag0WiYNGkSM2bMYOPGjQBs3ryZ6dOnM27cOLRaLQsXLsRkMrFt27YY71VkeXx+HM3VOQxqub7EikqBK4ZkAvDnEguONkqmCNFTSU+pBHSi3sGj7xTz9WkLAIqnicE5RrKStRi0avyA2+vD7fXR5PFz5MvdTJ5yNRqVQlZKEoMzk7likJEBxsBTnpeJn/okBX1T2FveQKPTw7HajhU8//NXp3F6fAzJSubibpoxUAghuluwp5TPH7iJkAkdhBDi3IYOHcqrr7YsA7Fjxw4uvvhiSkpKKCgoaNGWn5/Pm2++CUBpaSlz5sxp1V5cXBx2HEocn65rnYHkh1oBneLHE8exnkvwGMfzsT4XRYGCnDQ+PlxJo8vHH788xb9ePiDWYYWlJ/wMzv430XRH/NFatySlEsQddy/CXFuPKy2P2hEz8Wt0KB4naSc+5eTuP3H9r95sd9naP/2dp59Z3I3Rdp5WreLivFQ+P9HI3vIGDJz7N9/r8/PGvpMA3HpZ/1b1sIQQoqfQqs+c35o8PjRJMsRCCCE6yu/388ILL/Dee+/x2muv8dvf/rbVMDy9Xo/dbgfAZrOdsz0cWVnx+9DUrmgBSDdoURTQ6bQkJ+vQ67WoEuj/BkNg1Ec8xQ+EtYxapTC6j4bdpzy89lkFP76uAL028a718fz73hESf/eTpFSCMNfWM/aOJ9heXI3f56dvahLXFfQj9aoCnn1/U6zDi6hReekcPGWl3uGG7IJzvveDI2ZONjjJ0Gu48UIZuieE6LmC9QLdXj9NXh/JUvdDCCE6xGq18vDDD3Pw4EFee+01RowYgcFgwGKxtHif0+kkJSUFAIPBgNPpbNVuMoU/oY7ZbKGDVSm6XUnzyIs0XeCa4nK5sdtdOJ1uVP7E+b/D4Yq7+IEOL6PGDUA/vZc+yWqqrS5efa+EWy/rD4DX66W8vCz0cxs4cBBqdXx9DlCUQEIknn/fz0Xi7/g2Ik1qSiUItyGTnYeq8fr8DDTqmX5RX1J1PTOnqNOoGNM/8MveOHAyVpenzfd5vD5+/eFRAGZfkpeQTxKEECIcuua6Ui4pdi6EEB1SVlbGnDlzsFqtvPnmm4wYMQKAgoICSkpKWry3tLSU4cOHAzB8+PBztofD74/fr0pr4HN2WvN9RSLejMOZuBM9fgWFW0akA/B/n5bjdPvw+wO/x2u3f8breytYu/0zysrKYv6709ZXcF8S9Uvi79g2Ik2SUgmg3uGmduRM3F4/uWk6Ckf06fEzzI3KTSNNp8GnS2PtR8fafM9b+09xvM6ByaDl364Y2L0BCiFEDOg1geS7U5JSQghxXg0NDfzoRz/isssu4ze/+Q2ZmZmhtsLCQmpqatiwYQNut5vdu3ezdevWUB2puXPnsnXrVnbv3o3b7WbDhg2YzWYKCwtjtTtRcbp56r10fc982J2IvndBCjlpOqqtTbx94FTodWOfPDJzB2DskxfD6ISIvJ6d2egB/H4/v9h+CK8unXS9hsIR2ah7QXFbjVrFVUMDHxz+8MVJdhZXtWgvqbby64+OAnD3lRf02F5jQghxtlBPKZmVRwghzuuPf/wjJ0+e5K9//Svjxo1j7NixoS+TycT69evZvn07EyZMYNmyZSxbtoyJEycCMGnSJFasWMFjjz3G+PHjeeedd1i3bh1GozG2OxVhVbaWPaVE7GnVCgvHBx64/2Z3GbamtkeNCNFTJNzZp7i4mGeeeYaDBw+i1Wq58soreeihh8jMzGT//v088cQTlJaWYjKZWLx4MfPmzQstu2XLFtasWUN1dTVDhw5l+fLljB07NoZ7c35v7T/Fh9/Wgs/D9QW5vWqIWn+jnpRT+7DljeWx7YdweXxMvziHr09beGjrNzjcPi4fZGTWaHlaIIToHfTaQFJKekoJIcT53X777dx+++3tto8ePZpNm9qvzTpr1ixmzZoVjdDigt/v57Q12FNKS8fmvRbdYdboXH6/9wTl9U5+W3SCG/rHOiIhoieheko5nU4WLVrE2LFj+eijj/jLX/5CfX09jzzyCA0NDdx1113cfPPNFBUVsWrVKp566im+/PJLAPbs2cPKlSt5+umnKSoqYubMmSxevBiHI35Pv9VWFy8210xKL9tFVkpSjCPqfunHP+K6gmzcXj+/2HGYqat3cfvvv6DS4mKQycAzMy6UadGFEL2GXmpKCSGEiJAGpweHJ1AkJljoXMQHrVrFT6YOBWDjZycwO6S3lOi5EiopdfLkSUaOHMmSJUtISkrCZDIxf/58ioqK2LlzJ0ajkQULFqDRaJg0aRIzZsxg48aNAGzevJnp06czbtw4tFotCxcuxGQysW3bthjvVft++d4RbE1eLs5NI+X0l7EOJyYU/Dxx40juvWoIyVp16EZs+sU5rLv1EtL12hhHKIQQ3UcXrCnllqSUEEKIrqmoDzyc16vp8fVqE9E1+Vlc0i8dl8fHG183xjocIaImoYbvDR06lFdffbXFazt27ODiiy+mpKSEgoKCFm35+fm8+eabQGC2jGDhwrPbi4uLoxt0J330rZm/Ha5BrcAjhcO5/09RKnWfADRqFf82fiDzxvbDbGsiOUlNZnLv6zUmhBBnhu9JTSkhhBBdU9HgBCA1oe4Iezafz0tFxYnQ9z+ZcgF3/uEA7x+zcV1/hcxzLCtEokrYU5Df7+eFF17gvffe47XXXuO3v/0tBoOhxXv0ej12ux0Am812zvZwKGGOFgu+/1zL3XHXImpq6wHwqTRUX/KvoEtHX7GX+5espuRISfsLhxFDV5dXlOhNBXm+bScnqUlOMpz7zR1YTyz2IVK6+nOMtZ70M4jHfejo70dHzkkiPsnwPSGEEJFyoj6QlEqRgQdxo9FcxevHrQwYolBffYofT7uc6wuy+dvhGvab/QweGGcfPoWIgIRMSlmtVh5++GEOHjzIa6+9xogRIzAYDFgslhbvczqdpKSkAGAwGHA6na3aTSZT2NvPykrrVNznWq7eYmHeo/8DwJ6jZk5/W0uaXsNtP/wBWvWt/OLfCklO1rW5rKLQbhuARqsmO7v9bWu06nMuryhgMATag/92dPvni62rsZ9PW/sW3IeuxhbL9kSODdr+PYqH2DrS3tm/hWjH1pm/lczMzv9tidjQy/A9IYQQEXK8LvBwPlUrT6niSXpWDpm5A0Lf/2TqEP5ZWkO1E47XOUiPYWxCREPCJaXKysq488476devH2+++SaZmYFOjAUFBezatavFe0tLSxk+fDgAw4cPp6SkpFX71KlTw47BbLaE1UNCUQIJqXMt53F7sdtdONxe9h6rA+CKgRm4XW7cBHpk2O2uNpc9V1tw3TU1lnO2n2t5vx8cDhcGgw6Hw9VqH7oSW1djP5+z9y2YUAjuQ1dji0V7cB/iMbaOtAV75rT1exTr2Dra3tm/hWjHFs7fSvCcVFtrkcRUgtHJ8D0hhBARcrw2UFMqTXpKxbX+GQZmFKTzx+JGdh+r57pc6S0lepaEqmjX0NDAj370Iy677DJ+85vfhBJSAIWFhdTU1LBhwwbcbje7d+9m69atoTpSc+fOZevWrezevRu3282GDRswm80UFhaGHUcwoRHO1/mWC/r8RANun5/slCSGZiV36Xh1NOaOLn/2v92pM8e7rX2L5T5ESiLHDj3rZxCP+9CZc5JILGcP3/PLD1AIIUQn+f3+s3pKxTgYcV43j0hDrwaLy0Op1DwXPUxC9ZT64x//yMmTJ/nrX//K9u3bW7Tt27eP9evXs2rVKlavXk1mZibLli1j4sSJAEyaNIkVK1bw2GOPUVlZSX5+PuvWrcNoNMZgT9rW4HDzTaUVgAkXGFGk4IsQQoizBJNSPj+4fX6S1HKdEEIIEb5auxury4uCFDqPV2cXPa+tPMnFRthrhuJ6P3UOL4NjGp0QkZNQp6Dbb7+d22+/vd320aNHs2nTpnbbZ82axaxZs6IR2nnNvXUBJ0/XtNt++EgJ+vIG/H4YYNTTL0PfjdEJIYRIBBq1CrWi4PX7cbp9JMkU3kIIIToh2EuqT7IatUp63sajs4uelx06gCl3MH1TM6myNvH7g/WMvTDWEQoRGQmVlEpkVeY6pt/3XLvthx7+Md+aAxeH8YOM3RSVEEKIRKPXqrA1eXF5vMhlXAghRGcE60n1S9MCTbENRrQrWPS8vvoUigKTBpt4+6tK3j9u5+BpCxfnSm1Qkfjk02yc0F1yIwDDspLJSkmK6LoPHy5m1ry57bcfKWF6RLcohBAiWnSaQFJKZuATQgjRWcGkVF6qBnySlEoUfdN0DEqFMiv8v38c4Tf/3yVS8kUkPElKxQGzrYmkCy4FYOyAjIiv3+PjnL20vr77pohvUwghIq24uJhnnnmGgwcPotVqufLKK3nooYfIzMxkxYoVvPXWW2i1Z6q1PvTQQ8yfPx+ALVu2sGbNGqqrqxk6dCjLly9n7NixAHi9Xp577jnefvttHA4HEydO5PHHH6dv374x2c/z0WvPFDsXQgghOiM4fK9/moa6hhgHI8IyyqRQ6YADpxrZUVzNv1wYn59XhOgoKUYRB/adCFwJhmYlY0qW6S+EEOK7nE4nixYtYuzYsXz00Uf85S9/ob6+nkceeQSAAwcOsHLlSvbt2xf6Ciak9uzZw8qVK3n66acpKipi5syZLF68GIcj8JR47dq17Nq1i7feeosPP/wQvV7PsmXLYrav56PXqAFwSlJKCCFEJ5XVnT18TyQSg0Zh9sh0AP7ng29xuL0xjkiIrpGkVIzV2po4WuvA7/cxdkB6rMMRQoi4dPLkSUaOHMmSJUtISkrCZDIxf/58ioqKaGpq4vDhw4waNarNZTdv3sz06dMZN24cWq2WhQsXYjKZ2LZtW6j9zjvvJC8vj9TUVB599FE++OADysvLu3MXO0zXPAOfUz6ECiGE6ASn28uJ+kBSaoAkpRKOz+flspQG+iSrqbI28X97ymIdkhBdIsP3YuzzikYA3Mf2kTl5cGyDSUB33L0Ic219u+1SL0uInmHo0KG8+uqrLV7bsWMHF198McXFxXg8HlavXs3evXtJS0tjzpw5LFq0CJVKRWlpKXPmzGmxbH5+PsXFxVgsFk6fPk1BQUGoLTs7m4yMDA4dOsTAgQM7HGO4JR1C7w9zOUPz8D1HGz2lekJZieA+9IR9iSQ5Lq3JMWlbV4+LHM+e71itHZ8fMvQajHrpo5BoGs1VvHncyrC++VTb4bdFJ5g1Jo+8dJm9XSQmSUrFUK29iaPNM+459/8VfnhLjCNKPObaeqmXJUQv4/f7eeGFF3jvvfd47bXXqKmpYfz48dx222388pe/5JtvvmHJkiWoVCoWLVqEzWbDYDC0WIder8dut2Oz2QBITk5u1R5s66isrPBmwDEaUwLb0iWRnKzr+HKpeqARl9dPcrIOvT4wOYbJlEJ2ds+ZhSfc49lbyHFpTY5J2+S4iPaU1gSub/l9UqRIdoJKz8rhgqEDONJYTrXTz5PvlrB69ij5eYqEJEmpGNp3ItBLakimgX31J2McjRBCxD+r1crDDz/MwYMHee211xgxYgQjRozgyiuvDL1nzJgx/OhHP2Lbtm0sWrQIg8GA0+lssR6n04nJZAolq4L1pc5uT0lJCSs2s9mC39/x99fXB24KnK4m7HZXh5fTENiIxRFYzukMzJpUV2ejpsbS8QDilKIEbqbDPZ49nRyX1uSYtK2rxyW4vOi5/n/27jw8qvLs4/h3MpnsewJhFyEBBUECyC4oNqIiSwOoLdKiVSxEqVRALCi2lK1uiAgqSnlbqSggKpUquILIpiIgCiQgEPYsJGRPZua8f4SMxIAkYZKZyfw+F3Nd5DxnuZ+TzDlz7nmWlPRzSamY6t3nxL2YTCY6RZv47ARsOXSGVTtPMLxTE1eHJVJtaq/pImcKSjl4rpVUbcy4JyJS3xw5coRhw4aRl5fHypUradu2LQAfffQRy5cvr7BuSUkJAQFlzdjj4+NJSUmpUJ6amkp8fDzh4eHExsaSmprqKEtPTyc7O7tCl76qMIzqv8o2rN55CPYrG+g8v6TymFI1icEdX/WpLjovOieeeF6kfks9l5SKNBVy7NhR7Hb90j1VmJ+JuzuEAzDv84N8f9Lzv5wS76OklIuUz7jXMiqQ6GA/F0cjIuLecnJy+P3vf0/nzp157bXXiIqKcpQZhsHs2bPZvHkzhmGwY8cO/vWvfzlm3xs+fDhr1qxhy5YtlJaWsnTpUjIzM0lMTAQgKSmJRYsWkZaWRl5eHrNmzaJbt260aNHCJXW9lPKkVGGpXQ8SIiJSbftPl/XW2Hcim2UbdpOXp0SGJ7uldQh9WkVRbLUz6d09nM4txmazcejQj46XzabJUcR9qfueC5wpKOXAuVZSndVKSkTkkt5++22OHz/O//73Pz744IMKZTt27OCxxx7jySef5NSpU8TExPDQQw8xZMgQAHr27Mn06dMd5XFxcSxevJiIiAgAkpOTsVqtjBw5kvz8fLp37868efPquIZVF+Drg48J7AYUaAY+ERGphuyCUrKLyibKuKJ5U9JyT7o4IrlcPiYTM267itHLdnD4TCFjV+xias9I3tqwg4gGjclOP8EfB0DLlle6OlSRC1JSygV2HCtrJXVFpFpJiYhUxT333MM999xz0fK77rqLu+6666LlQ4YMcSSpfs5isTBx4kQmTpx42XHWBZPJRJDFTF6J7YJd+ERERC6mfJDzYF+wmNVppr4I8ffluaHtGPPmtxw5U8jjnxTSJTqWqEbNXB2ayCXpSlTHzhSUciDjXCup5molJSIi1Rd0rgtfgZJSIiJSDT+cKuuqF67vxesd29nTtOEkgWY4XQQbTtrJL7a6OiyRS1JSqo6VjyV1RWQgMWolJSIiNfBLg52LiIhcTPlA2FH+JhdHIs5gt9s4duwohw79yLFjR4lt0JDBHZvgb7KRb/PhvT2nyCvV+JPi3pSUqkNZBSWOsaS6qJWUiIjUkFpKiYhITZQnpSL9XRyIOMXZzNO8sSWV5d8ccwxaHxbgS5fALAJ97OQV2/j8hMHhnBJXhypyUUpK1aEdR8tmutCMeyIicjmC/MqGhMwvUbN8ERGpmjMFJRw/WwwoKVWfhJ0bOyosqoFjWaCPnevCC4kKslBkg+mfp7P7+FkXRilycRrovI6UBERxRDPuVdv+/XsZMmL4xcsPpDCwDuMREXEHwee3lApwcTAiIuIRvj+ZB0CTEF8sPnYXRyO1zd/H4Pb2sfx311Gyiu0kr9zF00Pa0+2KSFeHJlKBklJ1JKfJdQBcqVZS1WK1w8AJT1+0/PsHbq/DaERE3EPQ+WNKKSklIiJVUN51r3WUH1Dk2mCkTvj7+nB9IxOHCv3ZebqICau/Y/agdvRtHe3q0EQc1H2vDqSk51EYGQdoxj0REbl85w90bmj8UhERqYI955JScZH6gtyb+GBnVMsiujUJpMRmMPm971m397SrwxJxUFKqDiz+8ggAraKDiArSTUBERC5PiH9ZQ2er3aDE0AxKIiLyy6x2g2+Plc0CflW0BpTyJmczT7Ni2wGa+hURay7AZjeY9v5eXvt0NzabJkwR11NSqg7sT88Dw0ZCszBXhyIiIvWAr4+JEP+y1lJ5ViWlRETkl+07nUd+iY1Qf1+uiLC4OhypY2HRscQ0bk77gFwakoMBvPTNGeat241dTa7FxZSUqgPPJ11Dox9WqpWUiIg4TURA2UNFvk23chER+WXfpGUDkNAsHLNJX2Z4K5MJOkaZ6dgkFIDl35/lkXf2kFNY6ljHZrNx6NCPjpdaU0lt0yfZOnBFVBB+hRmuDkNEROqR8MCyLnxKSomIyKV8dS4p1UXj23o9kwm6XxFJQpSBrwm+OJjFb5ZuY+uhTADS0o7w0odfsfybY7z04VekpR1xccRS32n2PREREQ8UHniupZRVSSkREbk4q93g26Nl40k1Nudz7Nhp7HYXByUuF1VymquMEn70bU56gY0HV+3h5rYNSGrtS0SDxkQ1aubqEMVLKCklIiLigcIDym7heWopJSIiv2D38bMUlNrxxc6WA6dJ27+biMYtiXF1YOJyjaMj6dyqGV/sPcqPubBuXzqfp5q4MtSgW4wyl1I39ElWRETEA0WcaylVYDOBSbdzERG5sM9Sy4YRaRLiQ3Tj5oRFNXBxROJO/H19uDbK4M8dID7Kj2Kbwd5sePOb4+zNNiiyKjkltUufYkVERDxQsJ8Zs48JAxO+EY1cHY6IiLghwzDYcKBsrKAmQRrgXC7sbOZpvth9gA5hpbQuPUKw2Uaxzc6eMwYPfnCCN745RvF5ySkNhi7OpKSUiIiIBzKZTI4ufJaopi6ORkRE3NHBzAKOZhdh8YHYQFdHI+4sLDqW6MbNaRnpT8/wQm6IiybYF3KK7Tz76QGSXtvG27tOYLXZNRi6OJXGlBIREfFQkYEWsgpK8WvYytWhiIiIG/o0pazrXoeGAfj6lLg4GvEUJhPENwgm0ppFg8gI3knJ53ReCbPXp/DapoPc1MhGSFSsBkMXp1BLKREREQ/VMNQfAP+mV7s4EhERcTd2w+D9708B0LNZkIujEU/kYzKR2CqEt//QjdEdI/Az2ThdYOONg/DRMTvHsotcHaLUA0pKiYiIeKjYUD8A/Jpehd0wXByNiIi4kx1HcziaXUSwn5keTdV3T2rO39eHgfGh3NrCl24tIrBgJ9/mw9ofTrP1tJ3MQqurQxQPpqSUiIiIh4oO8sOMgTkghBP5mh1HxNvZDYOMvGKKSjXosMB7350EILFtAwJ89dgn1We32zh27CiHDv3IsWNH8QGubRpGr+AMmgeUYAKO5sOEdSdZ/s0xbHZ9QSbVpzGlREREPJSPj4lwi42sUl9Ss/UQKlKfHD2aRlZWZpXW3ZtlZd3hEvaesVJ87lLQONiHAfERjO3fHh+TZl3zNhn5JXy8v2w8qcHXNIKSqv0tiZzvbOZp3jicR7MrTRzZt5uIxi2JAXxNBlcFl9C1TQs+33eSrGKDpz85wPqUTCbdcCVtG4a6OnTxIEpKiYiIeLAoi52sUjiQo6SUSH1x9GgavXpfR1FhwS+u5xMUQdTNYwlu27tS2Yl8O0u/zaJtdCq/6hRfW6GKm3p9+1GKrXY6NA7jmsahHD6spJTUTFh02YDm2eknKpXFBPtxQ2MTURHhvPF9LjvTsrn73zu4uW0D/ti7Jc0j1W1ULk1JKREREQ8WaSlLRn2XYcVmNzD7qEWEiKfLysqkqLCA4RNm07DZlRdcJ7vUh69yAii2+2DCoHmglRYBpYT62jH7+fHd4dPs2LiO6Jv+VMfRi6tlFZSwaudxAAa18uPw4UMcO3YUu3p5Sy0wmUwMaB3KsB5tWbT5CO/tPMG6fel8vD+d/vExDOvUhM7NwjGpxaZchJJSIiIiHizGz4at8Cw5hLH9yBl6tIxydUgi4iQNm11Jk9btKi0/kJHPltQsbIZBRKAv/eNjiA72c5QHBfljlJbw+ba3Mfs8XIcRiztYsOFHiqx2Wkf6seXbPexr2LhC1ysRZyofd6qZCZIan+Xk/gwOE8OpQli/P4P1+zNoHOLLgHaNuCG+AVfHhqhLsVSgpJSIiIgH8zFBwQ8bCe08kLXfn1ZSSqQeMwyDr9Ny2HHsLADNIwLoHx+DnwaxlnO2HznDmj2nMAH3XBvBjsPWi3a9EnGG88edOnXoe8KjmjG4TQu+/XYHhwt9yTKFcyLPytJtR1m67SiRAT5cd0UUXVtE0rV5BM0iAtSKysspKSUiIuLh8vZ8SmjngXyakkF+iZVgP93eReqbUpudz1IzOZRVCEDHxqFcd0WEWhyIQ0ZeMU/+bx8Aw65tTNtoX3YcdnFQ4hXKx50qys2g5NwEfGFmK9dG+9OkdTO279zDyWIzZ00hnCmys25fBuv2lQ3EHxlgJi7Kj9YRFrrFN6N9kzCigvx+4WhS3+hTq4iIiIcrOb6XRkE+nCyws2RLGg/1vfAYNCLimc4WlbJ+XwZZBaX4mOD6VlG0aRji6rDEjeQVW5n47vecziuhaagvg67w0ThS4hb8zD40thTRNCSY5nHN2X/oCMG+Jo4U+7M/s5gzRTa2Hy9k+/FCln//PQBh/r40CDLRMNiX2GBf4po0IDLIj4hACxGBFsICfAn0NZFx6hjmc4n55s1bYDabXVlVqSGvS0plZmby+OOPs23bNsxmM4MHD+bRRx/F19frToWIiJzH0+8PI+L9eWFnIcu+PsrA9g1pFR3s6pBE5DIZBnx/Mpeth7Ox2g0CLT4ktm1AbKi/q0PzGp5wbzh5tog/v7OHlPR8Qvx8aGE7yXu77RpHStyO2ceEf/5psvPyaHdlPEHH92CKuRLfyCYcyzhDsc1EehGcLbZythgOnCkt23Bf7kX36WsCH2xE+KcRGuhHiMVETJCZ2GALMUFmrrmyKc2jgokOsji6CdpsNtLSjjj20bx5C3x9ldByFfe5mtaRhx9+mNjYWDZu3EhGRgZjx45l6dKl3Hfffa4OTUREXMjT7w+dGlro0yqQLw5mMeHt73hxREeaRWgqZhFPZLMbBF11PRvPBJKbfgaARqH+3BgfTYi/1318dyl3vjcUltpYvesEL395mIISG+H+PtwXb+dgbgONIyVurby7X3b6CXwsdlo2CSM4cx95RXn0vCKO1NT9+EY1IyCqMceOHye/1I45IITC4lJ8fC0UWMF2rpug1QAwc7oITheVVD7Y9iwAAnx9aBIeQLOIQEJNxaQcOU5MZDj23HQeutmgbVyrOqu/VORVd7XDhw+zbds2NmzYQGBgIM2bN2fcuHE89dRTbnFjERER16gv94fHfhXPH9/aSVp2Eb97fQcjuzbl5rYNNYioiJsrKLGRll3Iwcx8vk7L4eO9eTQY8ii5VvAzm+jSPIJ2jTRjVV1zh3uDYRhY7QZFpXZyi62kZRdy5Ewhu4+f5YuDWeQWWwEI8ymhZwN/NnzznVpHiccKi46lYZPm5GWexMfPRssmYYRk7sMnLJiWbVpwcPd28vLyaHZlPIf27SYk9goatYznx73fYbcEEtXkCtJ+TKXYJxBzSCRnzuZhM0xkl0CR1c7BzAIOZhaUH439pwwghi3vHKVByGmaRATiby8hKtBMVKCZ5g1jCA2wEOxvJtjPl2A/M4EWM75mE74+5S8fx89mH1ONr9M2u0GpzU6pzaDUbsdmNygptXH02DHshoGvj4nWLZsTFuCHr7l+TW7hVUmplJQUIiIiiI2NdSxr3bo1x48f5+zZs4SFhVVpPz4+Zc2pq8pkgqCgQPwtPhfdLjQk5BdnTvml8svZtqrl/hYf/Mw+2C5QB1fHVtVyk4kKdXCn2KpaXl4Hd4ytKmUm009/T7XxXric2KpaXtP3Qm3HFhQUhE8V70/l90o92/zEGfeH6t4bfH3NhIaGkpd+jKzLGJg8N/0YoaGhHDp0ELPZzKTO/izaaSctz87rXx3j9a+OYfGBCD8Tof4mLD5lM/aZTCZ8ALMPlP8pmEwmjOpU4gIudx8mE1h8fSm12lweizP3U9/OiwGYMGFw4f38fPeXOtqlwvl5cdnxwddixlpqKys3zi//5R1W2t+5BVU9N5XWOH9BFfZhGFBihyIr5FsN8kt/tr6PmUBbNk39S7i6YRB+1kKyj14yrHPHh3w/P/LOXRvMZnOV7w/nwpdzXPHskF1YymNrfiDtTAElVoNSm8EvDQvVKtKf3g1sZKZnEREUQoG/H6V5Z8jPPElpfg4+pSVV+n9B1klK8rKxYqn2tu7wf3eMH6jyNmZrCXkZx7EW5GByk3Pqrr+DQP9A/C2+BPtbMBWcwZyfSUBRJj72QGLsERSVnMLHL5AmkSEczUghPz+PmMbNyco5S48ObbEHRHDwdDY/pBdRYg4gv8SODR8KS+0cSM8/9846120wJf/Cb7xfer9T9tnKx2SCsn9ln7PK/28yOZZZ7QZWe1lCqmrDv6UDYPExEWAxEWAu+7+vjwmL2YS/nxmb1e64Bxn8dO05//8Ws4nkG9pwbdOqXcPK1db9wWQ449Oah3j33Xd57rnn+OyzzxzLjhw5QmJiIp9//jmNGjVyXXAiIuIyuj+IiMjP6d4gIlL76le7r0sICgqisLCwwrLyn4ODNSCsiIi30v1BRER+TvcGEZHa51VJqfj4eLKzs8nIyHAsO3DgAI0aNSI0NNSFkYmIiCvp/iAiIj+ne4OISO3zqqRUy5Yt6dKlC7NmzSIvL4+0tDQWLlzI8OHDXR2aiIi4kO4PIiLyc7o3iIjUPq8aUwogIyODv/3tb2zduhUfHx+GDh3KxIkTMZvNrg5NRERcSPcHERH5Od0bRERql9clpURERERERERExPW8qvueiIiIiIiIiIi4ByWlRERERERERESkzikpJSIiIiIiIiIidU5JKRERERERERERqXNKStVQVlYWiYmJbN261bHs888/Z+jQoSQkJDB48GDWr19fYZvVq1eTmJhIp06dSEpKYseOHY4ym83G3Llz6dWrFwkJCYwdO5bTp0+7VR0Mw+DFF1+kf//+dO7cmUGDBvHBBx94VB3Ot2nTJq6++mqOHj3qcXX48MMPuf322+nUqROJiYmsXLnSo+pgt9t57rnn6Nu3L126dOGOO+5g27ZtdV6HvXv3cs8999CtWzd69+7N5MmTycrKAmDnzp2MGDGChIQE+vfvz4oVKyps6y7v55rWwR3fz94kMzOTcePG0bVrV7p3787MmTOxWq2uDqvWrV27lnbt2pGQkOB4TZo0Cbi895ynutC101OuPbXpQudl+vTpXHPNNRX+dt58801HeX09L/XhPiWu46nXmPrwd79582ZGjBhB586d6d27NzNmzKCoqMij6mCz2Rg1ahRTpkxxLPOU2Gvr80Zd1SE7O5vJkyfTvXt3rrvuOsaNG+c4jifEX22GVNtXX31l/OpXvzLatGljbNmyxTAMw/juu++M9u3bG2+99ZZRWlpqbN++3UhISHCUb9myxUhISDC++uoro6SkxPjnP/9pdO/e3SgoKDAMwzBeeOEFY9CgQcbx48eN3Nxc4+GHHzbuv/9+t6rDP//5T6N///5GamqqYbfbjY8//tjo0KGDsXPnTo+pQ7nTp08bvXv3Ntq0aWOkpaU5lntCHTZv3mx06tTJ+Oyzzwy73W5s3rzZuOaaazzq97Bs2TLjtttuM06ePGnYbDbjn//8p9GpUyejqKiozupQWFho9O7d23j++eeN4uJiIysry7j//vuNBx54wMjOzja6detmvP7660Zpaanx5ZdfGgkJCY5z7C7v58upg7u9n73N3XffbTzyyCNGQUGBceTIEWPgwIHG4sWLXR1WrZszZ44xZcqUSssv9z3niS507fSUa09tutB5MQzD+PWvf228/fbbF9ymvp6X+nCfEtfx1GtMffi7z8zMNDp06GCsWrXKsNlsxqlTp4zbb7/deP755z2mDoZhGPPmzTOuuuoq49FHHzUMwzP+fsrV1ueNuqrD3XffbSQnJxs5OTlGbm6u8eCDDxpjxozxmPirS0mpanr77beNG264wXj//fcrXOSfeuopY9SoURXWfeKJJ4w//elPhmEYxiOPPGJMmzatQvktt9xirFy50jAMw+jbt6/x3nvvOcrS09ONtm3bGkeOHHGbOjz//PPGqlWrKpQPHTrU+Oc//+kxdTAMw7DZbMbvfvc7Y968eZWSUp5QhwceeMB49tlnK5Tv27fPOHPmjMfUYcaMGcYtt9xiHD9+3LBarcbSpUuNHj16OJJSdVGHAwcOGH/4wx8Mq9XqWPbRRx8ZnTt3Nt566y3j5ptvrhT/5MmTDcNwn/fz5dTBnd7P3ubQoUNGmzZtjJMnTzqWvf/++8YNN9zgwqjqxsiRI43XX3+90vLLfc95motdOz3l2lNbLnZeiouLjfbt2xv79++/4Hb19bzUh/uUuIYnX2Pqy999bm6uYRiGYbfbjX379hmJiYnGv//9b4+pw5dffmncdtttxvjx4x1JKU+J3TBq7/NGXdRh9+7dRocOHRx/Q4ZhGGfOnDH279/vEfHXhLrvVVOfPn1Yv349t912W4XlNpuNoKCgCst8fHw4ePAgAKmpqbRp06ZCeVxcHHv37iU3N5eTJ09WKI+JiSE8PJx9+/a5TR3Gjx9PUlKSo+zAgQOkpKTQvn17j6kDwMKFC4mOjmbYsGEV1vOUOuzatYuIiAjGjBlD9+7dGTJkCEeOHCEiIsJj6nDXXXdRVFTEDTfcQIcOHZg3bx7z58/H39+/zurQqlUrXn31Vcxms2PZhx9+SPv27UlJSbno+xXc5/18OXVwp/ezt0lJSSEiIoLY2FjHstatW3P8+HHOnj3rwshql91uZ8+ePXz22WfceOON9O3bl8cff5ycnJzLes95ootdOz3l2lNbLnZe9u7di9VqZf78+fTq1YsBAwbwyiuvYLfbgfp7XurDfUpcw5OvMfXl7z4kJASAfv36MWjQIBo0aEBSUpJH1CEzM5OpU6fyzDPPEBgY6FjuCbFD7X3eqKs67Nq1i7i4ON566y0SExPp06cPc+fOpUGDBh4Rf00oKVVNDRo0wNfXt9LyxMREvvjiCz788EOsVitff/01a9eupbi4GID8/PwKb2qAgIAACgoKyM/PB6j0EB8QEOAoc4c6nO/HH3/k/vvvZ/DgwVx33XUeU4dt27bx3nvv8be//a3Stp5Sh5ycHF577TXGjh3Lpk2bSE5OZsKECezcudNj6lBaWkq3bt343//+xzfffMN9993H+PHjSU9Pr/M6QNn4Ss899xyffvopU6dO/cX3K7jX+7mmdTifq9/P3uZCv5vyny/0+6kvsrKyaNeuHQMGDGDt2rUsX76cQ4cOMWnSpMt6z3mii107PfHa40wXOy+5ubl069aNUaNG8fnnn/PUU0/x73//myVLlgD1/7xA/bhPSd2pL9eY+vB3v27dOjZs2ICPjw/jx493+zrY7XYmTZrEPffcw1VXXVWhzN1jL1dbnzfqqg45OTns27ePQ4cOsXr1at555x1OnTrFo48+6hHx14SSUk7SuXNn/vGPf7BgwQJ69+7Na6+9RlJSEmFhYUDZA0f54HblioqKCA4OdvzhFBYWXrC8rlyqDuU++eQT7rzzTm6++WZmzpwJ4BF1yMrKYsqUKTz11FOOby/O5wl1APDz82PYsGEkJCTg6+vLzTffTM+ePfnwww89pg6TJ0+mb9++tGrVioCAAJKTkwkNDeWDDz6o8zrk5eUxfvx41qxZw+uvv07btm1/8f0K7vd+rkkdyrnz+7m+CgoKqnRuy3+uz+c3JiaGZcuWMXz4cAIDA2nSpAmTJk1iw4YNGIZR4/dcfeJp15660rt3b/71r3/RrVs3LBYLHTt25Pe//z1r164F6v95qQ/3KXEPnvR3U1/+7gMCAoiNjWXSpEls3LjR7evw8ssv4+fnx6hRoyqVuXvs5Wrr80Zd1cHPzw+AqVOnEhISQkxMDA8//DCff/65R8RfE0pKOUl2djbx8fGsWbOGrVu3snDhQk6cOME111wDQHx8PCkpKRW2SU1NJT4+nvDwcGJjY0lNTXWUpaenk52dXan5nSvrAPDiiy/yyCOP8PjjjzNlyhRMJhOAR9Rh48aNZGZm8oc//IGuXbsyePBgAAYPHswrr7ziEXWAsm4+JSUlFbax2WwYhuExdTh+/HilOvj6+mKxWOq0DkeOHGHYsGHk5eWxcuVK2rZtC0CbNm0u+n4F93o/17QO4P7v5/oqPj6e7OxsMjIyHMsOHDhAo0aNCA0NdWFktWvv3r08/fTTGIbhWFZSUoKPjw8dO3as8XuuPvGka09d+uijj1i+fHmFZSUlJQQEBAD1+7zUh/uUuA9P+bvx9L/7b775hltuuaXCZ92SkhIsFgtxcXFuXYd3332Xbdu20bVrV7p27cp///tf/vvf/9K1a1ePOf+19XmjruoQFxeH3W6ntLTUsay8u/rVV1/t9vHXiKsGs6oPzh848NtvvzU6depk/PDDD0Zpaanx/vvvGx07dnQMylk+Mv7mzZsdI+Ffd911jsGpn3vuOeP22283jhw54hgJ/+6773arOixZssTo0qWLsWfPngvuyxPqcL60tLRKA517Qh1WrlxpXHvttcamTZsMm81mfPDBB0b79u2N7777zmPqMHHiRCMxMdE4cuSIUVJSYixdutTo2rWrY+DnuqhDdna2ccMNNxhTpkwxbDZbhbKsrCyja9euxj//+U+jpKTE2Lx5s+P9axju836+nDq46/vZW/zmN78xJkyYYOTm5jpm35s/f76rw6pVJ06cMDp16mS88sorRmlpqXHs2DHjjjvuMP7yl79c9nvOk51/7fSUa09dOP+8rFu3zujYsaPx5ZdfGna73fjmm2+M7t27G++8845hGPX3vNSH+5S4nqddY+rD331eXp7Rr18/Y9asWUZxcbFx9OhRY/jw4cb06dM9pg7lHn30UcdA554Se21+3qiLOpSUlBiJiYnGQw89ZOTl5RmZmZnG7373OyM5Odkj4q8JJaUuw8+nK/7Pf/5j3HjjjUanTp2MpKQk48svv6yw/jvvvGMMGDDA6NSpkzF8+HDj22+/dZSVlJQYTz31lHH99dcbnTt3NsaOHWtkZGS4TR3sdrvRpUsXo127dkanTp0qvBYtWuQRdfi5CyWlPKUOb7/9tnH77bcbnTp1MgYOHGisW7fOo+qQl5dnzJgxw7j++uuNrl27GiNHjnRMZVpXdViyZInRpk0b49prr630N20YhrFr1y7jzjvvNBISEoybbrqp0kx17vB+rmkd3Pn97C3S09ONhx56yOjWrZvRo0cPY86cORVmGqqvtm7d6vib7NGjhzFjxgzHrJuX857zZD+/dnrCtacu/Py8vPHGG8bNN99sXHvttcZNN91UaVal+nhe6sN9SlzP064x9eXvPiUlxbjnnnuMrl27GjfeeKPx7LPPGsXFxR5VB8OomJTypNhr6/NGXdXh5MmTxsMPP2z07t3b6Nq1qzF58mQjJyfHY+KvLpNhnNeuTUREREREREREpA5oTCkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJiIiIiIiIiEidU1JKRERERERERETqnJJSIiIiIiIiIiJS55SUEhERERERERGROqeklIiIiIiIiIiI1DklpUREREREREREpM4pKSUiIiIiIiIiInVOSSkREREREREREalzSkqJ1AHDMOrlsUREREREpO7os77UN0pKiTjZ2rVrufHGG+nQoQNPPPEEqamp/OY3v6n2fqZMmUL//v2rtc2ljvXdd9/Rvn17jh49Wqmsd+/etG3bttIrPT292rGLiHiq/v37M2XKFFeHISIiUsnHH3/Mo48+6vh569attG3blq1bt7owKpHL4+vqAETqm7/+9a+0bNmSOXPmEBsby5o1a9ixY0edHPt///vfRY+1d+9eHnjgAaxWa6WyjIwMMjIyeOyxx+jUqVOFsoiIiFqIVEREREREqmPp0qUVfm7fvj1vvvkmcXFxrglIxAmUlBJxsuzsbHr37k337t1dHQoAJSUlvP766zz//PMEBARccJ3vv/8egMTERJo2bVqX4YmIiIiISA2EhIRU+kJZxNOo+57Iefbs2cPvf/97unTpQkJCAqNHj2bnzp2O8g8++IDBgwfTsWNHhg4dyo4dO2jXrh1vv/22o/kswIsvvkjbtm2ZMmUKCxYsAKBt27a88MILNY7NZrOxbNkyBg0aRMeOHbnhhht4+umnKS4uBuCFF1644LE2bNjAggUL+OMf/8jEiRMvuO+9e/cSFhamhJSICFBaWso//vEPevfuTadOnbj33ns5fPiwo3zTpk389re/pUuXLnTv3p1HHnmEEydOOMrffvtt2rZtW6mr9M+7Bn755ZfceeedJCQkcN111zFu3DgOHjxYYZuPPvqIpKQkOnToQO/evfn73/9OQUFBtet09OhRJk+eTJ8+fWjfvj09e/Zk8uTJnDlzpkK9n376afr27UvHjh35wx/+wDvvvFOpLl999RV333031157Ld26dePRRx8lKyur2jGJiNSV/v37M2vWLH7/+9/TuXNnnnjiCbKzs3niiSfo1asXHTp04I477mDz5s0Vtmvbti3Lli1j6tSpdOvWjYSEBMaPH09GRkaF9dauXUtSUhIJCQn07t2bJ554gpycHAC++eYb2rZty0cffVRhmwMHDtC2bVv+97//AZe+To8aNYpt27axbds2R5e9C3Xf2717N3/4wx/o3r07nTt35o9//CMpKSmO8vJtNm/ezL333su1115Lr169mDt37gV7VFzKihUrSEpKolOnTnTs2JEhQ4awdu3aCuvs2LGDkSNH0qlTJ2644Qb+7//+j9GjR1e4JxYXF/OPf/yDfv36cc011zBo0KBK+5H6SUkpkXPy8vK47777iIyMZP78+Tz33HMUFhbyhz/8gdzcXD7++GP+9Kc/ER8fz4IFC7j55psZO3Ysdrsd+Kn5LMDw4cN58803eeihhxg+fDgAb775JiNGjKhxfE888QSzZs2if//+LFq0iJEjR/L6668zbtw4DMNgxIgRFzxWhw4d+OSTTxg7dixms/mC+/7hhx8ICwvjwQcfdCTkJkyYwOnTp2scr4iIp1q7di0pKSnMmTOHJ554gt27dzNhwgQA3n33Xe69915iY2N59tlneeyxx9ixYwd33nknmZmZVT5GWloaY8eOpX379ixatIi///3vHDx4kDFjxjjuK2vWrCE5OZlWrVrx4osv8uCDD/Lee+85rvtVVVhYyO9+9zsOHDjA9OnTee2117j77rv573//y7PPPutY74knnuD//u//uPvuu3nxxReJiYnh8ccfr7Cv7du3M3r0aAICApg3bx5/+ctf2LZtG7/73e8oKiqqckwiInVt2bJlji9uhwwZwu9//3s+/vhjJkyYwIIFC2jUqBH33XdfpcTUc889h91u59lnn2Xy5Ml89tlnzJo1y1G+cOFCJkyYwLXXXsv8+fNJTk7mww8/ZNSoURQVFdG5c2euuOKKSgmWNWvWEBoaSv/+/at0nZ4+fTrt2rWjXbt2vPnmm7Rv375SHbds2cJvfvMb7HY7M2fO5O9//zsnTpzgrrvu4sCBAxXWnThxIl26dOGll15i0KBBLFmyhJUrV1b7nD7xxBPcdNNNvPzyyzz11FNYLBYmTZrE8ePHgbLk2+jRowF49tlneeihh3jllVf4+uuvHfsxDIPk5GSWL1/OPffcw6JFixzPI++88061YhLPo+57IuekpqaSlZXFqFGj6NKlCwCtWrVi+fLl5OXl8eKLL3LNNdfwzDPPANC3b19MJhPz5s0DKjafbdSoUYX/A5fVtDY1NZWVK1fy8MMPM3bsWKBsYPKGDRsyefJkNmzYQL9+/S54rNjY2Evu/4cffuDUqVPccccdjB49mgMHDjB//nxGjRrF6tWrCQoKqnHsIiKeJjY2loULF2KxWAA4fPgwL730Enl5eTz11FP06tWL5557zrF+586due2221iyZAmTJk2q0jF27dpFUVERDzzwgOM63bhxYz7++GMKCgoIDg7m6aef5vrrr+fpp592bNeyZUtGjx7N559/zg033FClYx06dIhGjRoxZ84cWrRoAUCPHj3YvXs327ZtA+DIkSOsXr2aRx99lHvuuQeA66+/noyMDL744gvHvp555hmuvPJKXn75ZccXHddeey0DBw5k1apVjBw5skoxiYjUtYYNGzJlyhR8fHx466232Lt3L2+99RbXXnstUPbZftSoUTz99NOsWrXKsV2bNm2YPXu24+ddu3bxwQcfAJCTk8OiRYsYMWIE06dPr7DNyJEjefvtt/ntb3/L4MGDee211ygsLCQwMBCA999/n1tuuQV/f39++OGHS16n4+LiCAkJAS7+XPHMM8/QvHlzXn31Vcc1uk+fPiQmJvLCCy84nlsARowYQXJyMgA9e/bko48+4rPPPuOuu+6q8jlNS0vj3nvvdewHoFmzZiQlJfHNN9/QpEkTXn75ZUJCQnj11VcddW/VqlWF43z55Zds3LiR5557jttuuw0ouwcVFhby9NNPc/vtt+Prq9RFfaWWUiLnxMfHExUVxdixY5k+fTqffPIJDRo0YPLkyURERLBnzx5uuummCtsMHjy4TmIrvxkNGjSowvKBAwdiNpsve8aN2bNn89Zbb/HAAw/QtWtX7rzzTubPn8+hQ4f07YSIeJ2OHTs6ElIAzZs3B8rG30tPT690LW7RogUJCQnVuhZfe+21+Pv7M3z4cGbPns2XX37JVVddxYQJEwgJCeHgwYOcPHmS/v37Y7VaHa/rrruOkJAQNm3aVOVjXX311fznP/+hWbNmpKWlsXHjRpYsWcLBgwcpLS0FyrpzGIbBLbfcUmHb22+/3fH/wsJCdu7cSb9+/TAMwxFT8+bNad26dbViEhGpa61bt8bHp+zxd/PmzTRo0ID27ds7rmU2m40bb7yR7777ztH1DiongBo1akRhYSEA3377LSUlJZXuC127dqVp06aO+8KQIUMoKCjg008/BcoSW0eOHGHIkCFA1a7Tl1JQUMDu3bu57bbbKvSOCAsL48Ybb6x0j0pISKhUr+p2D58yZQqTJk0iNzeX3bt3s2bNGpYtWwbgiHvLli3069fPkZAqP/b5w4Zs3rwZk8lEv379Ktzz+vfvT3p6eoXuh1L/KN0ock5wcDDLli1j0aJFrF27luXLlxMYGMjgwYMd2f+oqKgK21SlFZIzlN8YGzRoUGG5r68vkZGR5ObmXtb+f35TAujSpQuhoaHs3bv3svYtIuJpft46tPwhpvxDfkxMTKVtYmJiHJNGVEWzZs14/fXXeeWVV3jrrbdYunQpYWFh/Pa3v+VPf/oT2dnZQNmMrn/9618rbV/d7tX//Oc/efnllzlz5gwxMTG0b9+ewMBAx/2jfEyo6OjoSvUqd/bsWex2O4sXL2bx4sWVjuHv71+tmERE6tL517Ps7GzS09Mv2AUOID09nfDwcIAKyRQouyeUd6Eu/4x+sftC+TW2efPmdO7cmffff5/bbruNNWvW0LRpU7p27epY/1LX6UvJzc3FMIxLxlLu5xMgnV+vqjpy5AhPPPEEW7ZswdfXl1atWjnG2C3fV1ZWVqV7C1R8rsnOzsYwDDp37nzB45w+fZqrr766WrGJ51BSSuQ8rVq14qmnnsJms7Fr1y7effdd3njjDRo2bIiPj0+lQQ3LHxpqW/lNMT09nWbNmjmWl5aWcubMGSIjI2u877Nnz7Ju3To6depUYTpZwzAoLS29rH2LiNQnERERAJXuBVB2fS6/XppMJgDH2FDl8vPzK/zcsWNHFixYQElJCV9//TVvvvkmL730Em3btiU+Ph6AyZMn061bt0rHK78vVMWaNWuYM2cOjzzyCMOHD3d8wfKnP/2J3bt3Az99yZKZmUnjxo0d254/TlZwcDAmk4nRo0czcODASsf5+YObiIi7Cg0NpWXLlhW6R5/v/M/bv6T8WpyRkUHr1q0rlKWnpzta2kJZa6mZM2eSm5vL//73P4YNG+a4X1TlOl2VOplMpoveo8rvYc5it9sZM2YMFouFt956i3bt2uHr60tqairvvfeeY71GjRpdcMzFzMxMrrzySkfsQUFB/Otf/7rgsa644gqnxi7uRd33RM754IMP6NGjB+np6ZjNZhISEnjyyScJCwsjKyuLhIQEPvzwwwoPGeVNcH9J+Tfsl6P8gWTNmjUVlr///vvYbDbHGFg1OZbFYuGvf/0rr7zySoXlH3/8MUVFRXTv3r2GUYuI1C9+fn40aNCg0rU4LS2Nb7/91vENb/mYH+fPyHfw4MEKX2QsXbqU/v37U1JSgp+fHz179mTGjBmO7Vq1akV0dDRHjx6lQ4cOjlejRo145plnqtUq6+uvvyY0NJQxY8Y4HnTy8/P5+uuvHfe0Ll26YDabWbduXYVtz/85JCSEdu3acfDgwQoxlU8AcrldyUVE6kq3bt04ceIE0dHRFa5nmzdvrjAe06Vce+21+Pn5VbovfPXVVxw/frxCy59bb70VgOeff5709PQKw4BU5ToNv/xZPygoiGuuuYa1a9dis9kcy3Nzc/nss88czwvOcubMGX788UeGDx9Ox44dHWM+bdiwAfjpi5nrrruODRs2OGYMh7LxbM+f1bVbt24UFBRgGEaF30dKSgovvvhijWYFFM+hllIi53Tu3Bm73U5ycjJjxowhODiY//3vf+Tm5nLzzTdz2223MXr0aMaNG8dvfvMbjhw5wvPPP3/J/YaFhQHw3//+l2uvvbbCNyZVFRcXx69//WsWLFjgSBT98MMPLFiwgO7du3P99dfX+FiBgYHcd999LFy4kOjoaPr27cu+fft44YUXuOGGG+jVq1e14xURqY9MJhN//vOfeeyxx5gwYQJDhw7lzJkzLFiwgPDwcMcA4T169CAwMJA5c+bw8MMPk5+fz4IFCyp8S92jRw+efvppkpOTufvuuzGbzSxfvhw/Pz9uvPFGzGYzEyZM4IknnsBsNnPjjTdy9uxZFi5cyKlTpy7a5eRCOnbsyBtvvMGcOXO48cYbOX36NK+99hoZGRmOb/mbN2/OsGHDePbZZyktLeWqq65i/fr1ji9fyh+E/vznPzNmzBgeeeQRBg8ejM1mY8mSJezcudMxEYeIiLtLSkri9ddf55577uGPf/wjjRs35ssvv2Tx4sXcfffdFcYV/CURERGMGTOGBQsWYLFYuOmmmzh69CjPP/88cXFxJCUlOdYNDw/nxhtv5D//+Q8dOnSo0LKqKtdpKPusv2PHDjZv3ky7du0qxfPII4/whz/8gfvuu4+7776b0tJSXnnlFUpKSnjwwQcv44xVFh0dTdOmTVm2bBmNGjUiLCyML774gv/7v/8DcIy79cc//pG1a9dy3333ce+993L27Fmef/55TCaTo6VYv379uO666xg3bhzjxo2jdevW7Nq1ixdeeIE+ffpUGkJF6hclpUTOadiwIa+++irPP/88U6dOpbCwkPj4eF544QV69OgBwGuvvcZTTz1FcnIyV1xxBY8++ijTpk37xf3efPPNvPvuu0yZMoXhw4fz5JNP1ii+mTNncsUVV7Bq1Spee+01GjZsyKhRo0hOTnY8LNT0WA899BAxMTG88cYbLFu2jIiICO68807Gjx9fo1hFROqrpKQkgoODefnll0lOTiYkJITrr7+eP//5z47xMUJDQ5k/fz7PPPMMycnJNG3alAcffLDCxBFXXXUVL730Ei+++CJ//vOfsdlsXHPNNSxZsoRWrVoBZTMjBQcH8+qrr/Lmm28SFBRE586defrpp6v1Bcevf/1rjh49yqpVq/jPf/5DbGws/fr147e//S2PP/44qampxMXF8fjjjxMUFMSSJUvIy8ujZ8+ejB07lhdffNExzlafPn147bXXWLBgAePHj8disdC+fXv++c9/XtYssyIidSkoKIhly5bxzDPP8NRTT5Gbm0vTpk155JFHuPfee6u1r/LP0a+//jorVqwgIiKCW265hYcffrhSt+bBgwfz4YcfVposqarX6ZEjR/Ldd99x//33M3v2bBo2bFhhPz179uSf//wn8+fP589//jN+fn507dqVuXPnOrqFO9PChQuZOXMmU6ZMwc/Pj7i4OBYtWsSsWbP46quvGDVqFFdccQWvvfYa//jHPxg/fjzR0dE88MADLFq0iODgYKDsi49XXnmF559/npdffpnMzExiY2MZPXp0hZn9pH4yGdUdzUxEHI4ePcpNN93E7NmzK3wTIiIi4kmys7PZsGED119/fYWxBOfOncvbb7+trnkiIlIjmzdvxmKxVBjUPScnh969ezN58mR+97vfuTA6cQdqKSVShwzDqNDH+2LMZrOjOauIiMiF2Gy2S86UZDKZqjQ2SmBgIDNnzuTqq6/m97//PUFBQXzzzTf8+9//5o9//KOzQhYRETfn7OeVPXv2OFputW/fnjNnzrBkyRJCQ0O5/fbbnRGyeDglpUTq0OrVq3nssccuuZ5aXomIyKUkJiZy7NixX1ynadOmfPLJJ5fcl7+/P0uXLmXevHlMmTKFwsJCWrRowZQpUxg5cqSzQhYRETfn7OeVe++9l5KSEt544w1OnDhBUFAQ3bp1Y+7cuRorSgB13xOpU2fOnKkw08TFNGvWrEL3CRERkZ/bt28fJSUlv7iOn58fbdu2raOIRETE0+l5ReqaklIiIiIiIiIiIlLnfFwdgIiIiIiIiIiIeB8lpURERERExG1lZ2czefJkunfvznXXXce4ceM4ffo0ADt37mTEiBEkJCTQv39/VqxYUWHb1atXk5iYSKdOnUhKSmLHjh2OMpvNxty5c+nVqxcJCQmMHTvWsV+AzMxMxo0bR9euXenevTszZ87EarXWTaVFRLyEklIiIiIiIuK2HnroIQoKCli/fj2ffvopZrOZxx9/nJycHMaMGcPQoUPZvn07M2fOZPbs2ezatQuArVu3MmPGDObMmcP27dsZPHgwY8eOpbCwEIBFixaxadMmVq1axcaNGwkICGDatGmO4z788MMEBQWxceNGVq5cyebNm1m6dKkrToGISL2lMaVERERERMQtfffdd/z2t7/lyy+/JCQkBChrOZWens63337Lq6++yocffuhYf/r06RQVFTF37lwmTpxIYGAgM2bMcJTfeuut3HfffQwbNox+/foxceJEBg0aBEBGRgZ9+vRh/fr12O12br75ZjZs2EBsbCwAa9eu5amnnuLTTz+twzMgIlK/+bo6AE+UmZlLdVJ5JhNER4dWe7v6wtvrDzoH3l5/cM9zUB6TOIc7/W5/zh3//qpLdXAPqoN7qM06uNu9YdeuXcTFxfHWW2/xxhtvUFhYyPXXX8+jjz5KSkoKbdq0qbB+XFwcK1euBCA1NZVhw4ZVKt+7dy+5ubmcPHmywvYxMTGEh4ezb98+ACIiIhwJKYDWrVtz/Phxzp49S1hYWJXroGeHqvHWeoP31l319qx619b9QUmpGjAMavTHU9Pt6gtvrz/oHHh7/UHnoD7zhN+tJ8R4KaqDe1Ad3EN9qMOl5OTksG/fPq655hpWr15NUVERkydP5tFHHyUmJobAwMAK6wcEBFBQUABAfn7+Rcvz8/MBCAoKqlReXvbzbct/LigoqFZSSkRELk5JKRERERERcUt+fn4ATJ06FX9/f0JCQnj44Ye54447SEpKoqioqML6RUVFBAcHA2VJpAuVR0ZGOhJM5eNL/Xx7wzAqlZX/XL7/qqppywJ3arFWl7y13uC9dVe9vZuSUiIiIiIi4pbi4uKw2+2Ulpbi7+8PgN1uB+Dqq6/mP//5T4X1U1NTiY+PByA+Pp6UlJRK5X379iU8PJzY2FhSU1MdXfjS09PJzs6mTZs22O12srOzycjIICYmBoADBw7QqFEjQkOr9yCp7ntV4631Bu+tu+rtWfWure57mn1PRERERETcUq9evWjevDl/+ctfyM/PJysri+eee45f/epX3H777WRkZLB06VJKS0vZsmULa9ascYwjNXz4cNasWcOWLVsoLS1l6dKlZGZmkpiYCEBSUhKLFi0iLS2NvLw8Zs2aRbdu3WjRogUtW7akS5cuzJo1i7y8PNLS0li4cCHDhw+vdh3Ku1lW51XT7Tz95a319ua6q96e9aoNSkqJiIiIiIhbslgs/Pvf/8ZsNjNgwAAGDBhAo0aNmDVrFpGRkSxZsoQPPviA7t27M23aNKZNm0aPHj0A6NmzJ9OnT+fJJ5+kW7duvP/++yxevJiIiAgAkpOT6devHyNHjqRfv34UFxczb948x7Hnz5+P1Wrlpptu4o477uD6669n3LhxLjgLIiL1l8kwaivfVX9lZFS/CW5MTGi1t6svvL3+oHPg7fUH9zwH5TGJc7jT7/bn3PHvr7pUB/egOriH2qyD7g3Op2eHqvHWeoP31l319qx619b9QS2lRERERERERESkzikpJSIiIiIiIiIidU5JKRERERERERERqXNKSomIiIiIiIiISJ1TUkpEREREREREROqcklIiIiIiIiIiIlLnlJSqI4ZhOF4iIiIiIiI/l11Qyu9f38G/Nx9ydSgiInXC19UBeJOXNh7kgT6tXB2GiIh4iXsfuI/MrGwAfC1mrKW2CuXRUREseflVF0QmIiIXsudkLntO5rLi66PcGh/t6nBERGqdklJ1yWRydQQiIuJFMrOyGTjhaQCCgvwpKCiuUP7+cxNdEZaIiFyExVz2vFBcandxJCIidUPd90RERERERNyAv2/Z41mJTUkpEfEOSkqJiIiIiIi4AYu57PGs+GfdrUVE6islpeqQBjoXEREREZGL8TvXUqrYqpZSIuId3DoplZWVRWJiIlu3bnUs+/DDDxkyZAidO3emf//+LFiwALv9p4v26tWrSUxMpFOnTiQlJbFjxw5Hmc1mY+7cufTq1YuEhATGjh3L6dOn67ROIiIiIiIiF+JvVlJKRLyL2yalvv76a+68806OHDniWPbdd98xefJkHn74Yb766isWL17M22+/zdKlSwHYunUrM2bMYM6cOWzfvp3BgwczduxYCgsLAVi0aBGbNm1i1apVbNy4kYCAAKZNm+aK6omIiIiIiFTwU0spdd8TEe/glkmp1atXM3HiRCZMmFBh+bFjx7jrrru48cYb8fHxoXXr1iQmJrJ9+3YAVqxYwcCBA+nSpQsWi4XRo0cTGRnJ2rVrHeX3338/jRs3JiQkhKlTp7JhwwbS0tLqvI4iIiIiIiLnK28pVWozsGvYDxHxAm6ZlOrTpw/r16/ntttuq7B8wIABPPbYY46fi4qK+Oyzz2jfvj0AqamptGnTpsI2cXFx7N27l9zcXE6ePFmhPCYmhvDwcPbt21eLtREREREREbm08pZSACXqwiciXsDX1QFcSIMGDS65Tl5eHn/6058ICAhg9OjRAOTn5xMYGFhhvYCAAAoKCsjPzwcgKCioUnl5WVWZTNVa3bG+yfTTy5ucX39v5e3nwNvrD+55DtwpFhEREQE/80835xKbHX9fswujERGpfW6ZlLqUgwcPMn78eKKjo/nXv/5FSEgIAIGBgRQVFVVYt6ioiMjISEeyqnx8qfPLg4ODq3X86OjQasdsGAaBgf7ExITi4+OWDdRqXU3OW33j7efA2+sPOgciIiJycWYfEz4msBvnWkr5uzoiEZHa5XFJqc8//5w///nP3HHHHTzyyCP4+v5Uhfj4eFJSUiqsn5qaSt++fQkPDyc2NrZCF7/09HSys7Mrdfm7lMzMXKrTxdtkgqioEAoLi8nIyPW6pJTJVPYgXt3zVp94+znw9vqDe56D8phERETEPZhMJvzMPhRZ7ZqBT0S8gkclpb799luSk5N58sknGT58eKXy4cOHk5yczK233kqXLl1YtmwZmZmZJCYmApCUlMSiRYvo0KEDkZGRzJo1i27dutGiRYtqxWEY1Oihsnw7d3kgrWveXPdy3n4OvL3+oHMgIiIiv8zftywpVWrTBwYRqf88qsnOSy+9hNVqZebMmSQkJDhe9913HwA9e/Zk+vTpPPnkk3Tr1o3333+fxYsXExERAUBycjL9+vVj5MiR9OvXj+LiYubNm+e6ComISLVlZWWRmJjI1q1bHcs+/PBDhgwZQufOnenfvz8LFizAbv/pG+bVq1eTmJhIp06dSEpKYseOHY4ym83G3Llz6dWrFwkJCYwdO5bTp0/XaZ1ERETKlQ92XmxTSykRqf/cvqXU+TPjvfTSS5dcf8iQIQwZMuSCZRaLhYkTJzJx4kSnxSciInXn66+/ZsqUKRw5csSx7LvvvmPy5MnMmzePfv368eOPP3L//fcTFBTEvffey9atW5kxYwaLFy+mY8eOLFu2jLFjx/Lpp58SGBjIokWL2LRpE6tWrSI0NJTHH3+cadOm8corr7iwpiIi4q38zGVJKc2+JyLewO2TUiIiIlDW2mn+/PlMmjSJCRMmOJYfO3aMu+66ixtvvBGA1q1bk5iYyPbt27n33ntZsWIFAwcOpEuXLgCMHj2aN998k7Vr1zJs2DBWrFjBxIkTady4MQBTp06lT58+pKWl0bx587qvaDXc+8B9ZGZlX7R8/4EUBtZdOCIi4gTlSSmNKSUi3kBJKRER8Qh9+vRh0KBB+Pr6VkhKDRgwgAEDBjh+Lioq4rPPPmPQoEFA2YQXw4YNq7CvuLg49u7dS25uLidPnqww4UVMTAzh4eHs27fP7ZNSmVnZDJzw9EXLv3/g9jqMRkREnKG8+16Juu+JiBdQUkpERDxCgwYNLrlOXl4ef/rTnwgICGD06NEA5OfnExgYWGG9gIAACgoKyM/PByAoKKhSeXlZVZlM1Vq9TpXHZjJVHmjfneM+3/l18FSqg3tQHaq2b3Edf1913xMR76GklIiI1AsHDx5k/PjxREdH869//YuQkBAAAgMDKSoqqrBuUVERkZGRjmRVYWFhpfLg4OBqHT86OvQyoq8ZX4uZoCD/i5abTFQoDwysuK6vxUxMTN3HfTlccZ6dTXVwD6qDuCs/c1lmUC2lRMQbKCklIiIe7/PPP+fPf/4zd9xxB4888gi+vj/d3uLj40lJSamwfmpqKn379iU8PJzY2FhSU1MdXfjS09PJzs6u0KWvKjIzcyu1Qqpt1lIbBQXFFy03DCgoKMZkKktIFRYWV4jRWmojIyO3DiK9fCZT2QO4K86zs6gO7kF1qNq+xXX81FJKRLyIklIiIuLRvv32W5KTk3nyyScZPnx4pfLhw4eTnJzMrbfeSpcuXVi2bBmZmZkkJiYCkJSUxKJFi+jQoQORkZHMmjWLbt260aJFi2rFYRiVu8a5i/K4LhSfu8Z8Me58nqtKdXAPqoO4K8dA52opJSJeQEkpERHxaC+99BJWq5WZM2cyc+ZMx/IuXbrw6quv0rNnT6ZPn86TTz7JqVOniIuLY/HixURERACQnJyM1Wpl5MiR5Ofn0717d+bNm+eayoiIiNf7qaWUMo4iUv8pKSUiIh5n3759jv+/9NJLl1x/yJAhDBky5IJlFouFiRMnMnHiRKfFJyIiUlP+5S2l1H1PRLyAj6sDEBERERERkTLlLaVK1X1PRLyAklIiIiIiIiJuwtF9T0kpEfECSkqJiIiIiIi4CT913xMRL6KklIiIiIiIiJvwM5sAKFFSSkS8gJJSIiIiIiIibsL/XPe9YnXfExEvoKSUiIiIiIiIm9BA5yLiTZSUEhERERERcRM/jSlluDgSEZHap6SUiIiIiIiImyhPSmlMKRHxBkpKiYiIiIiIuIny7nsl6r4nIl5ASSkRERERERE34RjoXC2lRMQLKCklIiIiIiLiJhzd99RSSkS8gK+rAxARERFxpnsfuI/MrOyLlkdHRbDk5VfrLiARkWoobymlMaVExBsoKSUiIiL1SmZWNgMnPH3R8vefm1iH0YiIVI/GlBIRb6LueyIiIiIiIm7Cz2wC1FJKRLyDklJ1yDAMx0tERERERC5t7dq1tGvXjoSEBMdr0qRJAOzcuZMRI0aQkJBA//79WbFiRYVtV69eTWJiIp06dSIpKYkdO3Y4ymw2G3PnzqVXr14kJCQwduxYTp8+7SjPzMxk3LhxdO3ale7duzNz5kysVmut17d8TKlitZQSES+gpFQde2XTj64OQURERETEY+zevZshQ4awY8cOx+upp54iJyeHMWPGMHToULZv387MmTOZPXs2u3btAmDr1q3MmDGDOXPmsH37dgYPHszYsWMpLCwEYNGiRWzatIlVq1axceNGAgICmDZtmuO4Dz/8MEFBQWzcuJGVK1eyefNmli5dWuv19dOYUiLiRZSUqmsmk6sjEBERERHxGLt37+aaa66ptHzdunVEREQwcuRIfH196dmzJ4MGDWLZsmUArFixgoEDB9KlSxcsFgujR48mMjKStWvXOsrvv/9+GjduTEhICFOnTmXDhg2kpaVx+PBhtm3bxqRJkwgMDKR58+aMGzfOse/a5Bjo3KbeFSJS/2mgcxERERERcUt2u509e/YQGBjIq6++is1mo1+/fkycOJGUlBTatGlTYf24uDhWrlwJQGpqKsOGDatUvnfvXnJzczl58mSF7WNiYggPD2ffvn0AREREEBsb6yhv3bo1x48f5+zZs4SFhVW5DtX9Tro8KWWzG9gMA18f7/hSu/w8eeN3+N5ad9XbtXFUV23Fq6SUiIiIiIi4paysLNq1a8eAAQOYP38+Z86c4dFHH2XSpEk0aNCAwMDACusHBARQUFAAQH5+/kXL8/PzAQgKCqpUXl72823Lfy4oKKhWUio6OrTK6wIElfw0blVYRBBBft71yFbd81WfeGvdVW/v5l1XOBERERER8RgxMTEVuswFBgYyadIk7rjjDpKSkigqKqqwflFREcHBwY51L1QeGRnpSDCVjy/18+0Nw6hUVv5z+f6rKjMzl+rMc2Q7b+Xjp84SEWip1vE8lclU9pBe3fNVH3hr3VVvz6p3edzOpqSUiIiIiIi4pb179/Lf//6XRx55BNO5viMlJSX4+PjQsWNH/u///q/C+qmpqcTHxwMQHx9PSkpKpfK+ffsSHh5ObGwsqampji586enpZGdn06ZNG+x2O9nZ2WRkZBATEwPAgQMHaNSoEaGh1XsoMwyq9eBpNpnw9TFhtRsUl9oxAqp1OI9X3fNVn3hr3VVv76aBzkVERERExC1FRESwbNkyXn31VaxWK8ePH+epp57i17/+NQMGDCAjI4OlS5dSWlrKli1bWLNmjWMcqeHDh7NmzRq2bNlCaWkpS5cuJTMzk8TERACSkpJYtGgRaWlp5OXlMWvWLLp160aLFi1o2bIlXbp0YdasWeTl5ZGWlsbChQsZPnx4ndT7p8HONQOfiNRvailVhwzDADxsNDMRERERERdp1KgRL7/8Ms8++yyLFi3C39+fgQMHMmnSJPz9/VmyZAkzZ85k/vz5REVFMW3aNHr06AFAz549mT59Ok8++SSnTp0iLi6OxYsXExERAUBycjJWq5WRI0eSn59P9+7dmTdvnuPY8+fP529/+xs33XQTPj4+DB06lHHjxtVJvf0tZvJLbEpKiUi9p6SUiIiIiIi4rW7durF8+fILlnXo0OGiZQBDhgxhyJAhFyyzWCxMnDiRiRMnXrA8JiaG+fPnVz9gJ/Azn2spZVVSSkTqN3XfExERERERcSNmn7LeFTaNNyMi9ZySUiIiIiIiIm7EkZSyKyslIvWbklIiIiIiIiJupDwpZVdSSkTqOSWlRERERERE3MhP3feUlBKR+k1JKRERERERETfiq+57IuIllJQSERERERFxIz4mtZQSEe+gpJSIiIiIiIgb8TWXjynl4kBERGqZklIiIiIiIiJupLyllFXd90SknnPrpFRWVhaJiYls3brVsWznzp2MGDGChIQE+vfvz4oVKypss3r1ahITE+nUqRNJSUns2LHDUWaz2Zg7dy69evUiISGBsWPHcvr06Tqrj4iIiIiIyKX4aqBzEfESbpuU+vrrr7nzzjs5cuSIY1lOTg5jxoxh6NChbN++nZkzZzJ79mx27doFwNatW5kxYwZz5sxh+/btDB48mLFjx1JYWAjAokWL2LRpE6tWrWLjxo0EBAQwbdo0l9RPRESktpmCItl7Ko+M/BJXhyIiItXg41PefU9JKRGp39wyKbV69WomTpzIhAkTKixft24dERERjBw5El9fX3r27MmgQYNYtmwZACtWrGDgwIF06dIFi8XC6NGjiYyMZO3atY7y+++/n8aNGxMSEsLUqVPZsGEDaWlpdV5HERGR2rTz2FnC75jJxoNZvLv7JN+fOOvqkEREpIo0+56IeAu3TEr16dOH9evXc9ttt1VYnpKSQps2bSosi4uLY+/evQCkpqZetDw3N5eTJ09WKI+JiSE8PJx9+/bVUk1ERETq3uncYrYfyQYg1N8XuwHrvz/FsZwi1wYmIiJVYlb3PRHxEr6uDuBCGjRocMHl+fn5BAYGVlgWEBBAQUHBJcvz8/MBCAoKqlReXlZV58YdrPb6JlPFl7c4v/7eytvPgbfXH9zzHLhTLOI8dsPg8wOZGEDJwe3c+dskNhzIYn96PtuPZNPkmlhM+uWLiLg1s1pKiYiXcMuk1MUEBgaSm5tbYVlRURHBwcGO8qKiokrlkZGRjmRV+fhSF9q+qqKjQ6sbOoZhEBjoj4+PiZiYUK98IKjJeatvvP0ceHv9QedAat+x7CKyC634m33I2foWppHDuK5FBD9mFZCeV8LhM4W0jAq69I5ERMRlyrvv2dVSSkTqOY9KSrVp04ZNmzZVWJaamkp8fDwA8fHxpKSkVCrv27cv4eHhxMbGVujil56eTnZ2dqUuf5eSmZlLde4PJhNERYVQWFiMyeRDRkauVyWlTKayB/Hqnrf6xNvPgbfXH9zzHJTHJPXLvvSy1r9xDYI4VXyulbCfmU7NI9h+6Ay7j+cqKSUi4uZ8TGopJSLewS3HlLqYxMREMjIyWLp0KaWlpWzZsoU1a9YwbNgwAIYPH86aNWvYsmULpaWlLF26lMzMTBITEwFISkpi0aJFpKWlkZeXx6xZs+jWrRstWrSoVhyGUf1XTberLy9vr7/OgervrufAE2VlZZGYmMjWrVsdy3bu3MmIESNISEigf//+rFixosI2q1evJjExkU6dOpGUlMSOHTscZTabjblz59KrVy8SEhIYO3Ysp0+frrP6OFNRqY3DWWVd2ts2DKlQ1qFpOAAnc4vJK7bWeWwiIlJ1vubyMaVcHIiISC3zqKRUZGQkS5Ys4YMPPqB79+5MmzaNadOm0aNHDwB69uzJ9OnTefLJJ+nWrRvvv/8+ixcvJiIiAoDk5GT69evHyJEj6devH8XFxcybN891FRIRkWr5+uuvufPOOzly5IhjWU5ODmPGjGHo0KFs376dmTNnMnv2bHbt2gXA1q1bmTFjBnPmzGH79u0MHjyYsWPHOrpzL1q0iE2bNrFq1So2btxIQEAA06ZNc0n9LteBzALsBsQEW4gO9qtQFhpgoVGoPwAHMwtcEZ6IiFSRWkqJiLdw++57P58Zr0OHDixfvvyi6w8ZMoQhQ4ZcsMxisTBx4kQmTpzo1BhFRKT2rV69mvnz5zNp0iQmTJjgWL5u3ToiIiIYOXIkUPYFxaBBg1i2bBkdO3ZkxYoVDBw4kC5dugAwevRo3nzzTdauXcuwYcNYsWIFEydOpHHjxgBMnTqVPn36kJaWRvPmzeu+opfhaHZZou3K6AuPldg6JoiTucWkZuTTsUlYXYYmIiLV4KuBzkXES3hUSykREfFeffr0Yf369dx2220VlqekpFQaGzAuLo69e/cCVBhL8Oflubm5nDx5skJ5TEwM4eHhlb4UuZSfz7BaF6/z2ewGx3OKAWgeEVApNihLSpmAzPxSzhZZXRb35dS3uufFnX5f1amDO79UB/d41WYdxPXMPmWPaRroXETqO7dvKSUiIgLQoEGDCy7Pz893zLBaLiAggIKCgkuW5+efGwg8KKhSeXlZVbli0Hhfi5mgoLIuecfOFGK1GwRazDSLCcFkMmEy4SgHiAwLonF4AMdzikgvtOJrMRMT41mD3VflPJ9/Xi5W7sp614cJBlQH91Af6iAXZj7XdMCqllIiUs8pKSUiIh4tMDCQ3NzcCsuKiooIDg52lBcVFVUqj4yMdCSryseXutD2VeWKmRWtpTYKCspaRx04dRaAJmH+FBaWAGWD2RcUFGMyQWCgP4WFxTQO8+d4ThE/ns7FWmojIyP3ovt3JyZT1WewPP+8XKzcFfWuTh3clergHmqzDuX7FtdSSykR8RZKSomIiEdr06YNmzZtqrAsNTWV+Ph4AOLj40lJSalU3rdvX8LDw4mNja3QxS89PZ3s7OxKXf4uxdUzGh7LKUu8NftZ1z2oOPtjs/AAvk7L4VhOEdGYPO6h3Fnn2ZX1dvXfijOoDu6hPtRBLqy8pZTGlBKR+k5jSomIiEdLTEwkIyODpUuXUlpaypYtW1izZg3Dhg0DYPjw4axZs4YtW7ZQWlrK0qVLyczMJDExEYCkpCQWLVpEWloaeXl5zJo1i27dutGiRQtXVqtarDY76fllraMah1dOSp0vJsQPP7OJEptBaUhsXYQnIiLV5HuupZRNOSkRqefUUkpERDxaZGQkS5YsYebMmcyfP5+oqCimTZtGjx49gLLZ+KZPn86TTz7JqVOniIuLY/HixURERACQnJyM1Wpl5MiR5Ofn0717d+bNm+e6CtVARn4phgGBFh9C/My/uK6PyUTT8AB+zCqkONyzZhcUEfEWPudGnFdLKRGp75SUEhERj/PzmfE6dOjA8uXLL7r+kCFDGDJkyAXLLBYLEydOZOLEiU6NsS6dzisbP6lhiD+mKkyd1fhcUqoktElthyYiIjXga1ZSSkS8g7rviYiIeLj0vLKuew1D/aq0fqPQspnpSkIba2YnERE3ZPYpS0ppoHMRqe+UlBIREfFw5S2lGoT4V2n9yCALFrMJw+zHgfT82gxNRERqwKzueyLiJZSUEhER8WAFJTbyim0ANAiuWkspH5OJ2HOtpXYez6m12EREpGbKW0opKSUi9Z2SUiIiIh4s/VwrqchAC36+Vb+tlyelvj12tlbiEhGRmnMkpdR9T0TqOSWlREREPFhmQSkA0cGWam1XPq7UzmNqKSUi4m4cY0rZXRyIiEgtU1JKRETEg2Xllw1yHl3FrnvlGoT4gWHndF6Jo7WViIi4h/KklFUtpUSknlNSSkRExINlnWspFRVUvZZSFrMPvoVZAHx/MtfpcYmISM35akwpEfESSkrVIcMwAN1YRETEOew+vuQUWYHqt5QCsOSdApSUEhFxNz9139Ozg4jUb0pKiYiIeChrUDQAgRYfAi3mam/vl38agO9P5jk1LhERuTwa6FxEvIWvqwMQERHxVvc+cB+ZWdkXLY+OimDJy69etLw0KAaAqKDqt5KCn1pK/XAqF8MwMJlMNdqPiIg4l1nd90TESygpJSIi4iKZWdkMnPD0Rcvff27iL25fnpSq7sx75SwFGVjMJnKKrBzLKaJZRGCN9iM/uVSisUmjGF5a8FLdBSQiHslsUlJKRLyDklJ1xFDTWxERcTLrZbaUMhl24huE8P3JXL4/mauklBNcKtH44YJH6zAaEfFUjjGl9AghIvWcxpQSERHxUNbASAAiA2vWUgqgfaNQAPZosHMREbeh7nsi4i2UlBIREfFAZwpKsFuCAIgIrHnD53aNQgDNwCci4k7Kk1JW9bYQkXpOSak6ZhiGuvKJiMhl+zGrAIBQfzO+5prfztudaym191QeVn0jLyLiFnx9yq7rdl2XRaSeU1JKRETEAx3KLEtKRVxG1z2AKyKDCLKYKbLaHfsUERHXKv+uQd33RKS+U1JKRETEAx08l0C6nPGkoKyLyFWx6sInIuJOzOUtpdTDQkTqOSWlREREPNCP5S2lgi4vKQU/deH7/pSSUiIi7qC8pZS6VYtIfaeklIiIiAc6lOWc7nvwU1JqzwklpUTEPdlsNkaNGsWUKVMcy3bu3MmIESNISEigf//+rFixosI2q1evJjExkU6dOpGUlMSOHTsq7G/u3Ln06tWLhIQExo4dy+nTpx3lmZmZjBs3jq5du9K9e3dmzpyJ1Wqt/Yqe81NLqTo7pIiISygpJSIi4mHyiq2czisBLr/7Hvw0A19qRj4lVvtl709ExNkWLFjAV1995fg5JyeHMWPGMHToULZv387MmTOZPXs2u3btAmDr1q3MmDGDOXPmsH37dgYPHszYsWMpLCwEYNGiRWzatIlVq1axceNGAgICmDZtmmP/Dz/8MEFBQWzcuJGVK1eyefNmli5dWmf1NZvKZt/TmFIiUt8pKSUiIuJhDp8pe6jyKcnHz/fyb+VNwgIID/DFajdIzci/7P2JiDjT5s2bWbduHTfffLNj2bp164iIiGDkyJH4+vrSs2dPBg0axLJlywBYsWIFAwcOpEuXLlgsFkaPHk1kZCRr1651lN9///00btyYkJAQpk6dyoYNG0hLS+Pw4cNs27aNSZMmERgYSPPmzRk3bpxj33XB7KOklIh4B19XByAiIiLVk3YuKeVblO2U/ZlMJq6ODWXL4TP8cCrX0Z1P6pd7H7iPzKzsi5ZHR0Ww5OVX6y4gkSrIzMxk6tSpLFy4sEJLpZSUFNq0aVNh3bi4OFauXAlAamoqw4YNq1S+d+9ecnNzOXnyZIXtY2JiCA8PZ9++fQBEREQQGxvrKG/dujXHjx/n7NmzhIWFObualfiazyWlNNC5iNRzSkqJiIh4GGcnpaCsC9+Ww2f44WQeXOu03YobyczKZuCEpy9a/v5zE+swGpFLs9vtTJo0iXvuuYerrrqqQll+fj6BgYEVlgUEBFBQUHDJ8vz8shahQUFBlcrLy36+bfnPBQUF1U5KneuJV631fc7rvlfd7T1VeT29pb7n89a6q96ujaO6aiteJaVEREQ8zOEzZQ9dZicmpa6O1Qx8IuJeXn75Zfz8/Bg1alSlssDAQHJzK16vioqKCA4OdpQXFRVVKo+MjHQkmMrHl/r59oZhVCor/7l8/9URHV391qdHC7LL/mMyERPjXa1Xa3K+6gtvrbvq7d2UlBIREfEwadllD1q+hdlO2+fV57rsHczIp6jURoDF7LR9i4jUxLvvvsvp06fp2rUrgCPJ9NFHHzF58mQ2bdpUYf3U1FTi4+MBiI+PJyUlpVJ53759CQ8PJzY2ltTUVEcXvvT0dLKzs2nTpg12u53s7GwyMjKIiYkB4MCBAzRq1IjQ0Oo/RGZm5lKdXngm009jSpVabWRkeMeXBSZT2UN6dc9XfeCtdVe9Pave5XE7mwY6FxER8SCGYZzXfe+M0/bbMMSPqCALNgP2p2uwcxFxvQ8++IBvvvmGr776iq+++orbb7+d22+/na+++orExEQyMjJYunQppaWlbNmyhTVr1jjGkRo+fDhr1qxhy5YtlJaWsnTpUjIzM0lMTAQgKSmJRYsWkZaWRl5eHrNmzaJbt260aNGCli1b0qVLF2bNmkVeXh5paWksXLiQ4cOH16gehlH9l2Og8xps68mvmp6v+vDy1rqr3p71qg1qKSUiIuJBsgtLyS22AuBblOO0/ZpMJto1CuWLg1n8cDKXjk1qfyBfEZGaioyMZMmSJcycOZP58+cTFRXFtGnT6NGjBwA9e/Zk+vTpPPnkk5w6dYq4uDgWL15MREQEAMnJyVitVkaOHEl+fj7du3dn3rx5jv3Pnz+fv/3tb9x00034+PgwdOhQxo0bV2f10+x7IuItlJQSERHxIEfOtZKKDfXHZNicuu92seeSUhpXSkTc0Jw5cyr83KFDB5YvX37R9YcMGcKQIUMuWGaxWJg4cSITJ154gP+YmBjmz59f82Avk5JSIuIt1H1PRETEg6RllyWlmkcGXmLN6ru6UQgA35/Kc/q+RUSk6nzPJaXstdVfRkTETSgpJSIi4kHKW0pdUQtJqavOzcB3KLOA/BKr0/cvIiJV43Nu7nWrWkqJSD2n7nsiIiIepHyQ8+YRgWx28r5jgv1oGOLH6bwS7p38BEXH9l903eioCJa8/KqTIxAREQBfs1pKiYh3UFJKRETEg5S3lKqN7nsA7RqFcjo1kwx7MHdOePqi673/3IXHYRERkctnNmlMKRHxDuq+JyIi4iEMw3CMKdUiovaSUgAlIbG1sn8REbk0s2NMqbJrv4hIfeWRSak9e/YwcuRIunbtSp8+ffj73/9OSUkJADt37mTEiBEkJCTQv39/VqxYUWHb1atXk5iYSKdOnUhKSmLHjh2uqIKIiEi1ZeSXUFhqx8cETSMCauUY1zQuS0qVhjSqlf2LiMillSelAGzKSYlIPeZxSSm73c4DDzzAgAED2LZtGytXruSLL75g8eLF5OTkMGbMGIYOHcr27duZOXMms2fPZteuXQBs3bqVGTNmMGfOHLZv387gwYMZO3YshYWFLq6ViIjIpZV33WscFoDFXDu38HaNQvExgc0/TIOdi4i4SIWklLrwiUg95nFJqZycHNLT07Hb7Y6mrD4+PgQGBrJu3ToiIiIYOXIkvr6+9OzZk0GDBrFs2TIAVqxYwcCBA+nSpQsWi4XRo0cTGRnJ2rVrXVklERGRKqnt8aQAgv18aR0TDMDp3JJaO46IiFycr89Pj2ka7FxE6jOPS0pFRkYyevRo5s6dS4cOHejXrx8tW7Zk9OjRpKSk0KZNmwrrx8XFsXfvXgBSU1N/sbyqTKbqvy53e09/eWu9dQ5Uf3c/B+JZymfeu6IWk1IAHRqHAXA6r7hWjyMiIhd2Xk5KLaVEpF7zuNn37HY7AQEBPP744wwfPpzDhw/z4IMPMn/+fPLz8wkMrPhBPSAggIKCAoBLlldVdHRojeIOCvLDZPIhJiYUHx+Pywdetpqct/rG28+Bt9cfdA7k8pQPct68lgY5L3dN41De3nWiVltK3fvAfWRmZV+0PDoqgiUvv1prxxcRcWfnt5RSUkpE6jOPS0qtX7+eDz/8kA8++ACA+Ph4kpOTmTlzJoMGDSI3N7fC+kVFRQQHl3VDCAwMpKioqFJ5ZGRktWLIzMylOq1oTSaIjAymoKAEk8lERkauVyWlTKayB/Hqnrf6xNvPgbfXH9zzHJTHVF/s2bOHWbNmsW/fPgICArjllluYPHkyfn5+7Ny5k7///e+kpqYSGRnJ2LFjGTFihGPb1atXs3DhQtLT02nVqhWPP/44CQkJLqzNhdVF9z34qaVUel4JNrtRYWwTZ8nMymbghKcvWv7+cxOdfkwREU9x/mXX5i4fHEREaoHHJaVOnDjhmGmvnK+vLxaLhTZt2rBp06YKZampqcTHxwNlCayUlJRK5X379q1WDIbBZT1UXu72nspb630+bz8H3l5/0DmoLeWTYIwZM4Z///vfnD592jFu4N13382YMWMYP348d955J9u3byc5OZm2bdvSsWNHxyQYixcvpmPHjixbtoyxY8fy6aefVmpd60p2w+BYTtkXKy1qOSl1RVQgPqWF2CyBZOSXEBvqX6vHExGRikwmE2ZT2cx7drWUEpF6zOOa6/Tp04f09HReeuklbDYbaWlpLFq0iEGDBpGYmEhGRgZLly6ltLSULVu2sGbNGoYNGwbA8OHDWbNmDVu2bKG0tJSlS5eSmZlJYmKii2slIiKXwxsmwcjIK6HYasdsgka1nCQymUz4nT0GwImzGldKRMQVfM41l7IqKSUi9ZjHJaXi4uJ4+eWX+eSTT+jevTu/+93v6N+/PxMmTCAyMpIlS5bwwQcf0L17d6ZNm8a0adPo0aMHAD179mT69Ok8+eSTdOvWjffff5/FixcTERHh2kqJiMhlcYdJMGpbeSup2LAAfM21f/v2yz0OwMmzRZdYU0REaoP53IwkykmJSH3mcd33AHr16kWvXr0uWNahQweWL19+0W2HDBnCkCFDais0ERFxAXeYBKO2ZjMs3++xnLLxpJpFBFT7WOfP/vjz7qMX21d5S6lTucXYDQOfC6xY2zM4nr//8+vg7H3XFWfXoTZcKjZPqMOlqA5V27e4Xvl4fhroXETqM49MSomIiJzPHSbBqMmg8b4WM0FBF++K52sxExNTtt8zJWUtl1rHhjmWXWp7k4kK5YGBFdc9f/8/F1h6Bj+zDyU2O4V2Ew1Cq75tVVSn7uerynmu6b6d4VLHBtdNMODM81IfJklQHcTdKSklIt5ASSkREfF47jAJRk1mVrSW2igouPiYTdZSGxkZZQm1/SdyAIj2NzuWXWp7w4CCgmJMprKEVGFhcYUYz9//z9lKrcSG+pGWXcSBU2cJNoddNLaaqE7doXozWJaW2th7LJvjOUUUltpoGh7AldFBWM51e7zc2H/JL9WrvAXKL9Xh3jH3kZGVfdH9x0RFsOSVV50eW3n5pc6LO84kWl2qQ9X2La5X3n1Ps++JSH3m9KTU1q1b6d69u7N3KyIiHqy27w19+vThmWee4aWXXuL+++/n+PHjFSbBeOqpp1i6dCkjR47k66+/Zs2aNSxcuBAomwQjOTmZW2+9lS5durBs2bIaTYJRWzMrlu/zWHZZa65mEQHVPk75+hfa7pf21TQ8gLTsIo5lF9GxSVil8tp+TrpYvL903PS8YrKuGsqHe9Mdy1IzCthx7CwDrmpARKDlovuubef/HgyjrPVDkdVGqdXAarcTFmAhIyubgROevug+3n9uYq3GXtV914eZRFWHuqFng5rzUUspEfECTk9KjR8/ntDQUH7961/z61//miZNmjj7ECIi4mFq+95QPgnGvHnzePXVVwkNDWXw4MEkJyfj5+fHkiVLmDlzJvPnzycqKuqik2CcOnWKuLg4t5wEo3yg86bhAXV2zGYRgXA4mxNni7Ha7HUywPrlyC4oZdyKXRRHtMBsgviGIQT4+pCSns/ZIivvfXeKoR1iXRaf3TAoCG/JzHX7+fbYWdLOFFaaVcun832s3nWSBiF+NA7zp1GYP8F+atgunkvPBjXne+6Sa3f3zKOIyGVw+qecL774gk8++YR33nmHl156ieuuu46kpCRuvvlm/Pz8nH04ERHxAHVxb6jPk2Dkl1jJKigFziWK6khEoC/BfmbyS2yczC2u02NXV0GJjYdXf8ehrEJ8inNJ6t7G0SqqfaNQPtybTkZ+CZ/sz8TXVLfJNbthsP90Pt8czSE/biCrd52stI4JMAC7XxAZ+SVk5Jfww6k8ABqH+RPfIJgro4PqNG4RZ9CzQc2VTzChllIiUp85PSllsVgYMGAAAwYMICsriw8++IAlS5bwt7/9jYEDB3LnnXdy1VVXOfuwIiLixnRvuDzHz7WSCg/wJcS/7lrNmEwmmoYHsD89n6PZRW6blCqx2pn83h72nMwlPMAXv2/fIeKGqY7yID8ziW1jeHvnSdLzSwhpel2dxZZXbOWTlAxO5ZaNeeZTWsiIbq3pdkUk8Q2CiQy0YDH74GOCnCIrv/3jQ3QcPp5TucWcPFtMRn4JJ84Wc+JsMZsPncG/WXdyi6yEBqj1lHgGXf9rzjHQuXJSIlKP1donmszMTP773//y/vvvk5qaSr9+/fD392f06NGMHj2aP/7xj7V1aBERcVO6N9RM+XhSTeqw6165ZhFlSam07EJ6UL0ZCeuCzW4w/X972Xo4m0CLD88nXcNfPjtTab0Qf196t4rkk5RM8ht35nRuMQ1Df3mWvIu594H7yLzIYOT7D6Qw8Nz/M/NLWPv9aYqsdixmE12ahfPJ7D+x8evWbLzIvn88kMLQB6bQ6lyrqLxiKynp+aSk55NTZKW0WXeGvLqN0d2a89uuzfA999Aq4u50/a8+tZQSEW/g9KTU+++/z7vvvsuXX35Jq1atSEpK4qWXXiIqKgqAfv36kZycrBuPiIgX0b3h8hzNKR/kvO5bKjWPCMTHBNmFVs4UlBIZZHHq/g3DwGSqWWLFMAz+8XEqH+3PwNfHxFND2tO+ceUB2cu1ig5iz8k8TuXCS5sO8cQtbWt03MxfGIz8+wduL1snv4T3vz9NsdVOVJCFxLYxhAVY+LC09BcHMi/fvlyIvy8JzcLp1DSMH7MK+fzbfeQSzQsbf+Sj/ek8MaAtcQ2Ca1QPkbqg63/N+WqgcxHxAk5PSv31r39l4MCBLF++nGuuuaZS+ZVXXsno0aOdfVgREXFjujdcnmPZhUDdDnJezs/Xh2YRARw5U8SPmQVEBoVf1v4Mw2D7kWyy2tzG618dpbDUjr/Zh5gQP66MDiQ+JrjKA6q/9OVh3t51AhPw94FX0f2KX27JZTKZ6HFFBO9+d4r3vz/FvT1a1Eqir6DExod70ym22mkY4setVzfEz/fyxrEymUy0ig7i+13/4b7pLzDvs4P8cCqPUa9/w7g+Lbm7a7MaJ/dEapOu/zX3U/c9JaVEpP6qlYHO09LSiI0tm93m22+/JTQ0lNatWwPQqFEjxo8f7+zDioiIG9O94fL81FKq7pNSAFdGBZUlpbIK6Ny85kmpzPwSZq7bz8aDWRAVB6V2AIptdo7lFHEsp4iv03Lo2CQMu8/FP6IYhsGCjT/yr+1HAZjyqzhuatOgSjE0DPXHP/swxRFX8OaO4zxyY+sa1+eCTD58tD+D/BIb4QG+3OKEhFSF3WMw+JpG9GoZyaz1KWw8mMX8DT/yw6k8nhjQhgCL2WnHEnEGXf9rTt33RMQbOH36mY8//pihQ4dy6NAhAHbs2MGIESP4/PPPnX0oERHxELo3XJ7ygc6bhrtmoPErooIwmSCroJQz52YBrK7U9HxGvf4NGw9mYTGbCDq5k8HXxDKyS1OSOjaiW4sIQvzNFJba2Xo4m9MJ9/Dv7WmcLap4vNO5xTzyzh5HQupP/VqRdG31ppgPPrEDgPd2nySv2Fqj+lyMf4ebOZVbjMVs4uarGuDvxITU+WJC/HlmaHsevSkOs4+J9fvSGf/2d06vj8jl0vW/5spbStnVUkpE6jGnt5RasGABCxcudDTPveeee4iLi+Opp56iX79+zj6ciIh4AN0bas5mN35KSrmopZS/rw8tIgI5fKaQvafy6Hll9QY8//5kLg+u3E1usZUro4KYeftVTBz3PLGhg4Cy2fGig/3o0DiUlIx8vj16lrMEMn/Dj7z85WESmobTONyfM8U2vkjJwGo38PUx8div4hncoVH165NzhFbRQRzMLOCd3Se5u2uzau/jQjLzSwjoVDbMee8ro4gIdO74Wz9nMpkY3qkJV0YH8cg7e9hxNIeHVu1mwfAOBPtpdj5xD7r+15z5XI9ctZQSkfrM6V/fnThxguuvv77Csj59+nD8+HFnH0pERDyE7g01dzqv2JGEaRhSs9ninOHq2BAA9qfnYbXZq7zdj5kFjF9VlpDq2CSMxXddS3yDkAuu6+Njom3DEEYkNCbiwHriYoIpttrZcvgMq3ed5LN96VjtBh2bhPHvuzvXKCEFYAJ+26UpAMu/OYbVCQ98hmGw6ccsTD5mWkYFEhcTdNn7rKouzSN4+c5rCQ/w5bsTuTzyzh5KrFX/HYnUJl3/a+6nMaVcHIiISC1yelKqadOmbNxYcaLjzZs306RJ9ZrWi4hI/aF7Q80dyy5rJdUkPMDxgOIKzSICCPX3pcRmkJpRUKVtTp4t4sGVu8gpsnJ1bAjzh11DeBVaD/mYTASl/8B/fteZZaM689iv4hjT6wqmD2rHm6O78NpvOl32jHO3XB1LZKCFU7nFfLI//bL2BXAws4BTuSUYpcX0bBlZ54OOt20YwvxhHQj2M/N1Wg5zPkrBUJcfcQO6/tecxpQSEW/g9LbdY8aMITk5mZtvvpmmTZty/Phx1q9fz9y5c519KBER8RC6N9TcsRzXzbx3PpPJRLtGIWw9nM2OYzmEmn55QO0zBSU8uHI3p/NKaBkVyPyk6ncpM5lMtGkYQpuGIZhMEBMTSkZGLs7Itfj7+jCiUxNe2XyYZV8fI7Ftgxonkqw2O9sOZwNQ9N06QvomX36ANdCuUSizB13Nw29/x5o9p2jbMIQ7Ozd1SSwi5XT9rzlfHyWlRKT+c3pLqUGDBrF48WIsFgt79uwhICCAJUuWMGDAAGcfSkREPITuDTV3NLt8kHPXJqWgrAtfkMVMXrGN/EYdL7peXrGV8au+4/CZQmJD/XlhWAcigmp3fKWaGNapMX5mE9+fzGXX8bM13s/uE7nkldgI9jNT/N1HToyw+hbPnkzIjxsAePrj/dz2+7EMGTGcISOGs/9AiktjE++k63/NaaBzEfEGtTIKZvfu3enevXtt7FpERDyU7g01c8wxyLlrZt47n8XsQ9cW4Ww4kEVu024cyiygZXTFsZNyCkv509vfsfd0Hj6lBRgb/8UD67Mr7Wv/gRQG1jCOex+4j8ysyvus7r6jgvy45eqGvPfdKZZ/c4xrm4ZXO5b8EivfHitLaHVrEcFqW81mJ3SWzKxs7nh4JOv3ZXD4TCEl3UZx+7WN8fUx8f0Dt7s0NvFeuv7XTHn3PWeMeyci4q6cnpQ6deoUixYt4tChQ9jtFQfZ/Ne//uXsw4mIiAfQvaHmHEmpWmgptX//XoaMGH7hsoskduIbBLPvdD6ncmHCO9+xaERHGoWVxfbDqVwef38vh88UYiotZEjnVsT0nXbB/V9OgiQzK5uBE56+aHl19v2bzs1477tTfJKSwYmzRTQOq955/upIDla7QcMQP1rX4eDmv8RkMtG3dRSrdp4kp8jKjqM5XNciwtVhiZfS9b/mHC2llJQSkXrM6Umpxx57jIyMDG688UYsFvdrqi8iInVP94aaO5Hz00Dnzma1c9HkzsUSOz4mE4ltY3jji+85mg13LP2KG+JiyC4sZdvhM9gMaBDiB5teJ6bvX5wes7PFNQjmuhYRbD+SzVs7jvOnfq2qvG16Xgn70/MB6OGCwc1/SYDFTK8rI/lofwY7j52ldbR7JMzE++j6X3PmcwOt2NR9T0TqMacnpXbv3s2HH35IVFSUs3ctIiIeSveGmrH7WDhTWNYdrEk1W/DUpkCLmegf3qHxwIfYefws//vhtKPsV21iePSmeH7/0cVbMrmb33ZpyvYj2byz+wT397yCIL9fHsQdwDAMNh86A0BcTBCxof61HWa1XRkdRMuoQA5lFfLluVhF6pqu/zVndsy+5+JARERqkdOTUqGhofj5+Tl7tyIi4sF0b6gZm38oAKH+voQG1MowkDXmW5zDK3ddyxcHs/gxswAfE/RtHc0VUZ7XIqfXlVG0iAzkyJlC1nx3skoz1v2YVcip3GLMPia37hrX44pI0s4UcuJsMZYWnVwdjnghXf9rzqd89j21lBKReszps++NGzeOxx57jF27dnH8+PEKLxER8U66N9SMzT8MgMZh7tcKB8q68vVtHc3vuzVn1HXNPTIhBWX1+M25RNT/bU+jqNT2i+sbJjNbD5e1PLq2SSgh/u6VMDxfaIAvHZuU/R0FdBmiWbykzun6X3PlY0rZNKaUiNRjTv8UNW1a2YCm69evB8oG2zQMA5PJxA8//ODsw4mIiAfQvaFmypNStTGelFQ0+JpG/N+2NE7mFrPi2+OMuq75RdfNbd6dvGIbwX5mR8LHnXVsGsb3p/IoDo8lJT2ftg1DXB2SeBFd/2vO16SBzkWk/nN6Uurjjz929i5FRMTD6d5QM1YlpeqMn68PD/S+gr9+sJ+l29K4tV0sMcGVuxz9cCqXvMadAeh9ZSQWs9MbnTudn9mHTk3D2Ho4m2+O5hAXE+xogSFS23T9rzl13xMRb+D0T1JNmzaladOm5OTksGfPHho0aEBAQABNm156fAYREamfdG+oGUdLKTca5Lw+u/XqWNo2DOFskZW/f7gf42cPgtkFpUx573sw+dAqOsijuiu2iw3BXpBDXrGNAxn5rg5HvIiu/zWn7nsi4g2cnpTKzMzkrrvu4o477uDRRx8lLS2NX/3qV+zYscPZhxIREQ+he0PNOMaUUkupOmH2MfHXW9viZzax6ccsFmw85EhMZReUMvHdPRw/W4y5KIfeV0a6ONrq8TX7ULynrMXKzuNnKyXcRGqLrv81Zz7XoFFJKRGpz5yelJo1axZt2rRh+/bt+Pr60rp1a8aMGcM//vEPZx/KIxmGoQ+CIuJ1dG+oGWuAuu/VtdYxwfz5xtYA/Gt7GuNW7OL5zw8y8t9fs/P4WYIsZqL2rSHAYnZxpNVXvP8L/MwmsgutHMoqdHU44iV0/a+58pZSmqBAROozpyeltmzZwmOPPUZgYCCmc4Pz3XfffaSmpjr7UB5JSSkR8Ua6N1RfidWO4VuWjHLX2ffqq2HXNmHazfGYTfBVWg6vf3WU03klNIsI4LXfdsJSmOXqEGumtIh2jUIB2HMy18XBiLfQ9b/mfM6dL6vdxYGIiNQipw90brFYKCoqIjAw0JF8yc/PJzg42NmHEhERD6F7Q/XlFlsBCA/wJdjP6bdruYQhHRrTqWk4H+/P4MTZIq5tGsaN8TEe/7u4OjaEncfOcuJsMVkFJUQFVR7MXcSZdP2vOY0pJSLewOktpfr378+kSZM4dOgQJpOJzMxM/vrXv9KvXz9nH0pERDyE7g3VV56UUtc917kiKoh7e7Rg6s1tuL19I49PSAGE+PtyRVQgAD+czHNxNOINnHH937x5MyNGjKBz58707t2bGTNmUFRUBMDOnTsZMWIECQkJ9O/fnxUrVlTYdvXq1SQmJtKpUyeSkpIqjGVls9mYO3cuvXr1IiEhgbFjx3L69GlHeWZmJuPGjaNr1650796dmTNnYrVaL/OMVJ2vuu+JiBdwelLqkUceISgoiFtuuYWzZ8/Sp08fCgsLmThxorMPJSIiHkL3hurLU1JKakl5F76U9HxK1C9IatnlXv+zsrJ44IEH+M1vfsNXX33F6tWr2bZtG6+88go5OTmMGTOGoUOHsn37dmbOnMns2bPZtWsXAFu3bmXGjBnMmTOH7du3M3jwYMaOHUthYdmYaosWLWLTpk2sWrWKjRs3EhAQwLRp0xzHfvjhhwkKCmLjxo2sXLmSzZs3s3TpUqefo4sp776nllIiUp85/Su/4OBg5s+fT1ZWFkePHqVRo0Y0bNjQ2YcREREPontD9eUW2QBoEqaklDhXkzB/wgN8ySmykpKRT/tzSSqR2nC51/+oqCi+/PJLQkJCMAyD7OxsiouLiYqKYt26dURERDBy5EgAevbsyaBBg1i2bBkdO3ZkxYoVDBw4kC5dugAwevRo3nzzTdauXcuwYcNYsWIFEydOpHHjxgBMnTqVPn36kJaWht1uZ9u2bWzYsIHAwECaN2/OuHHjeOqpp7jvvvucf6IuwNF9Ty2lRKQec3pSavv27RV+Pnz4MIcPHwbguuuuc/bhRETEA+jeUH3l3fcaq6WUOJnJZKJdo1A2HzrD9ydzaRcb4hiAWsTZnHH9DwkJAaBfv36cOnWKrl27kpSUxLx582jTpk2FdePi4li5ciUAqampDBs2rFL53r17yc3N5eTJkxW2j4mJITw8nH379gEQERFBbGyso7x169YcP36cs2fPEhYWVqXYAar79ipf33yuT4vNblR7H56ovI7eUNef89a6q96ujaO6aitepyelRo0aVWmZj48PjRs35uOPP3b24URExAPo3lB9jjGl1FJKakGbBsFsP5JNdqGVE2eL1U1Uao0zr//r1q0jJyeHiRMnMn78eGJjYwkMDKywTkBAAAUFBUDZgOoXK8/PzwcgKCioUnl52c+3Lf+5oKCgWkmp6OiatUYMDSl7X1r8fImJ8Z4WjTU9X/WBt9Zd9fZuTk9K7d27t8LPWVlZvPjiizRt2tTZhxIREQ+he0P1GIbh9QOd79+/lyEjhldY5msxYy21sf9ACgNdFJerXei8VCiv4rnx8/WhdUwQ+07nsz8932v/zqT2OfP6HxAQQEBAAJMmTWLEiBGMGjWK3NzcCusUFRU5ZvYLDAx0DIh+fnlkZKQjwVQ+vtTPtzcMo1JZ+c/VnTkwMzOX6vTAM5nKHlZLikoByC8sISMj9xJbeb7yelf3fNUH3lp31duz6l0et7PV+jQyUVFRTJo0iQEDBnDvvffW9uHcnmEYjulwRUS8le4Nv6zEZlBqK7tXNA7zd3E0rmG1w8AJT1dYFhTkT0FBMd8/cLuLonK9C52X81Xn3LRpEMK+0/n8mFlA7ysjnRGeyCVV9/r/zTff8Je//IX33nsPPz8/AEpKSrBYLMTFxbFp06YK66emphIfHw9AfHw8KSkplcr79u1LeHg4sbGxpKamOrrwpaenk52dTZs2bbDb7WRnZ5ORkUFMTAwABw4coFGjRoSGVu+hzDCo0YOn+byBzr3p8aGm56s+8Na6q97ezemz711ITk4OxcXFdXEoERHxELo3XFxuUVkrKZ+SfAIsZhdHI/VVbKgfYQG+WO0GP2YWuDoc8SLVuf63bduWoqIinnnmGUpKSjh27Bhz585l+PDhDBgwgIyMDJYuXUppaSlbtmxhzZo1jnGkhg8fzpo1a9iyZQulpaUsXbqUzMxMEhMTAUhKSmLRokWkpaWRl5fHrFmz6NatGy1atKBly5Z06dKFWbNmkZeXR1paGgsXLmT48Iu3VHQ2x0Dnmn1PROoxp7eUeuyxxyr8XFpaytdff02vXr2cfSgREfEQujdUT3nXPXNx/e+uIa5jMpmIbxDM12k57E/Px8PGWxUPcbnX/+DgYF599VVmzZpF7969CQ0NZdCgQSQnJ+Pn58eSJUuYOXMm8+fPJyoqimnTptGjRw+gbDa+6dOn8+STT3Lq1Cni4uJYvHgxERERACQnJ2O1Whk5ciT5+fl0796defPmOY49f/58/va3v3HTTTfh4+PD0KFDGTdunFPOS1WUD3RuVVJKROqxWu++5+/vz6hRo7jzzjtr+1AiIuIhdG/4ZeVJKd/iHBdHIvVdeVLqxNliGvppwFWpfTW5/sfFxbFkyZILlnXo0IHly5dfdNshQ4YwZMiQC5ZZLBYmTpzIxIkTL1geExPD/Pnzqxyns6mllIh4A6cnpWbPnu3sXYqIiIfTvaF61FJK6kqovy+Nw/w5cbaYwgZXuTocqYd0/a853/IxpTTojIjUY05PSi1YsKBK6z344IM1PkZ2djazZs3i888/x263c9111/Hkk0/SsGFDdu7cyd///ndSU1OJjIxk7NixjBgxwrHt6tWrWbhwIenp6bRq1YrHH3+chISEGsdSVYZhYLfbNZCZiHilurg31Cd5jqTUWRdHIt6gTYNgTpwtpiDmagzDwGRSRz5xHl3/a04tpUTEGzg9KZWSksK6deu46qqruPLKKzl58iTffPMN7dq1c0yferkfdh566CHCw8NZv349Pj4+PPbYYzz++OP84x//YMyYMYwfP54777yT7du3k5ycTNu2benYsSNbt25lxowZLF68mI4dO7Js2TLGjh3Lp59+6pgWVkREnK8u7g31SW6RDVBSSurGldFBbPrxDNbACHafyKVjkzBXhyT1iK7/NaeklIh4A6cnpcqTRL/73e8cy959910+/fTTCgMH1tR3333Hzp07+fLLLwkJCQFgxowZpKens27dOiIiIhg5ciRQNrjhoEGDWLZsGR07dmTFihUMHDiQLl26ADB69GjefPNN1q5d65ilo7YYaiIlIl6stu8N9YlhGOeNKaWklNQ+i9mHllGBpGYUsH5fupJS4lS6/tecklIi4g18nL3Dzz//3JEUKnf77bezefNmp+x/165dxMXF8dZbb5GYmEifPn2YO3cuDRo0ICUlhTZt2lRYPy4ujr179wKQmpr6i+VVZTJV/3U529aHlzfXXedA9Xfnc1BXavveUJ8UWe2OmZY0ppTUlVYxZS1W1u9L1wOwOJWu/zVXnpSy2l0ciIhILXJ6S6moqCi2b9/umIoVYOPGjTRq1Mgp+8/JyWHfvn1cc801rF69mqKiIiZPnsyjjz5KTExMpW54AQEBFBQUAJCfn/+L5VUVHV392WmsViuBgRbARExMKBaLpdr78HQ1OW/1jbefA2+vP3jvOajtewN45niDF1I+nlSQxYzJsLkkBvE+zcIDMFmLyMyHHUdz6NoiwtUhST1RF9f/+koDnYuIN3B6UuqBBx5gzJgxDBgwgCZNmpCWlsann37KCy+84JT9+/n5ATB16lT8/f0JCQnh4Ycf5o477iApKYmioqIK6xcVFTn6qwcGBl6wPDIysloxZGbmVmvAcpMJwsMDKSgoASAjI9erklImU9mDeHXPW33i7efA2+sP7nkOymOqC7V9b4D6M97g2aKypFRogNNv0SIXZfYxEZiZSkHsNazbd1pJKXGaurj+11fqvici3sDpn3hHjBhB06ZNee+99/j+++9p3rw5y5cvp23btk7Zf1xcHHa7ndLSUvz9/QGw28vatF599dX85z//qbB+amoq8fHxAMTHx5OSklKpvG/fvtWKwTC4rIfKy93eU3lrvc/n7efA2+sP3nsOavve4KnjDV5IXnFZ66hQfzPVa8crcnkCM/dTEHsNn+zPYFL/OCxmp4/yIF6otq//9ZmSUiLiDWrla9hevXrRq1cvsrKyiIqKcvq+mzdvzl/+8hdmz55NcXExzz33HL/61a+4/fbbmT9/PkuXLmXkyJF8/fXXrFmzhoULFwIwfPhwkpOTufXWW+nSpQvLli0jMzOTxMREp8YoIiKV1ea94fzxBt944w0KCwu5/vrrefTRRy863uDKlSuBsi8nfp58qsl4g85SPsh5iL+vWyel9u/fy5ARwy9efiCFgXUYj1w+v7PHiAqykFVQyrbD2fRu5dz3qXiv2rz+12dKSomIN3B6Uqq0tJQFCxbw+uuvY7PZWLNmDQ8//DCLFi2iYcOGl71/i8XCv//9b+bMmcOAAQMoLi6mf//+TJ06lbCwMJYsWcLMmTOZP38+UVFRTJs2zdGHvWfPnkyfPp0nn3ySU6dOERcXx+LFi4mIiLjsuERE5OJq+97gDuMNOmvg+PKkVKi/L6edtN/zB9p3Vks9qx0GTnj6ouXfP3C7cw50zvl1cOb+6lJt/B6cyYTBr9o24K0dx1m37zR9WldOHjj79+AKqkPV9u0stX39r88cSSl3vGCIiDiJ05NSCxYsYMuWLTz//PNMmDCB6OhoGjVqxMyZM3n++eedcozY2Fiee+65C5Z16NCB5cuXX3TbIUOGMGTIEKfEISIiVVPb9wZ3GG+wJuNz+VrMBAX5V1iWX1LWfS8mPJDDFjMxMRff74W2P5/JRIXywED/XyyvapkrywMD/f+/vTuPj6o89D/+PTPZF0hCkE1ck0BdkAiCKIJFI5VFKUu5t9QWroo/QFu5RRSl17YWhGtvtUihVqWplaoFRUVRtBYVkSVQBJeiCcoiexISsmeW8/sjmSHDmkkmZ7bP+/WKZs76nCdhTs53nqXVx445S72ezZnq/Wznlk7+Ofizf2vWN6dexl99vv6+db8+KCpRSvskJcTaT7ltJEzawDVYw4png0gV4519j1AKQOQKeCi1cuVKvfDCC+rUqZMMw1BSUpIeffRRusgBQBRr63tDKIw32JJB7J0Ol6qr67yvTdPUsZqGllJxMuV0uFRcXNHs/U9kmlJ1dZ0MoyEIqamp8ymjZ/2Z9j3bsa1af+I1tObYX3z+ua65fuhp12dmpGnJn5457foz1fuZzu1pgXLiz6G5+7d2/dn2dTpc6p4Uo86p8TpYUafXCnbrhpyOJ11DqE3a4C+uoXnHDhSeDVrO7pl9j1AKQAQLeChVXV3t7StuNt4lExISZLMxWCYARKu2vjeEwniDgRjEvsbhlss0ZUhKjrN7j9tanmOE6wO4FNhrOFvXwzcfn9EmdRUOPwdDhvJ6dNRfN3+rd3Yc0ZDsjqfcLhImbeAarMGzQcsxphSAaBDwu0Hv3r21cOFCSZLRmO7/9a9/1eWXXx7oUwEAwkRb3xs84w3a7XYNHTpUQ4cOVefOnTV37lylp6dryZIlevvtt9W/f3/Nnj37tOMN9uvXT2+++WbQxhv0jCeVFGf3PowAVrupZ0MQ9dHXpaqqdwa5NAh3PBu0HKEUgGgQ8JZSDz74oCZOnKgVK1aoqqpKw4YNU1VVlf785z8H+lQAgDBhxb0hEsYbbDrIORAsPc5J0XnpidpztEYfFJVo2CWdgl0khDGeDVqOgc4BRIOA/9WbmZmpN998U++//7727dunzp076/rrr1dKSkqgTwUACBPcG5qn0hNKJRBKIXgMw9CNPTpqyYY9+udXxYRSaBXe/1suhpZSAKJAwP/qHTFihF5//XXdfPPNgT50RDBN09ufHgCiBfeG5qmo9bSUOvWMZ4BVbszJ1JINe7R+V0MXvuQ4glK0DO//LecZ6JzZ9wBEsjYZYbCmpqYtDgsACGPcG86uos4lie57CL6szGSdl56oepepj3aWBrs4CHO8/7cMY0oBiAYB/6u3f//+GjdunAYNGqRzzjnHZ93dd98d6NMBAMIA94bmYUwphArDMDQkO1P5m/bqvcJiDf3OOWffCTgF3v9bjlAKQDQI+F+93377rbp3765vvvlG33zzjXe5Z7YNAED04d5wdqZpeseUSmFMKYSAG3M6Kn/TXn38Tamq611KiqNbKfzH+3/LeUIpU5LbNGWjzgBEoID91Xv77bfr2Wef1V//+ldJUm1trRISEgJ1eABAGOLe0HzV9S65TcmQlMzDP0JAzjnJ6tY+QfvKa/XxN6W6sUfHYBcJYYT3/9bzDHQuSU6XqbgYQikAkSdgY0pt3brV5/WgQYMCdWgAQJji3tB8nq57KfF2Pg1HSDAMQzfkZEqS3vvqSJBLg3DD+3/r2ZvcC1xMlAQgQrXJQOeSmGHuBMy6BwDcG87EM8h5CuNJIYTckNPQOuqjr0tV63AFuTQIZ7z/+8/epKUU40oBiFRtFkrRTxwAcCLuDafHIOcIRd/plKIu7eJV63Tr411Hg10chDHe//3XNJRyEkoBiFBtFkoBAIDmq6htDKUY5BwhpGEWvobWUu99SRc+wEpNMilaSgGIWAH7y9fpdOrVV1/1vnY4HD6vJWnUqFGBOl3Y+feBCn3wzTFd0SUp2EUBAMtwb2i+SlpKIUTd2CNTS7d8Sxc++IX3/9YzDEN2myGX2ySUAhCxAvaXb2ZmphYsWOB9nZ6e7vPaMIyovvH8z8ov9O2xeu2vqNeDNwW7NABgDe4NzXe8+x4z7yG0XNo5VZ1S43Wook4bdh/V2C5pwS4SwgDv/4ER4wmlGJMLQIQKWCj1z3/+M1CHikjHahoeNtym5OamAiBKcG9oHrdpqrKegc4Rmhq68GXqhX/t03tfFmvs1RcGu0gIA7z/B4ZnBj5aSgGIVIwpZZErzm3n/b6ouDqIJQEAhJqqepdMs2H8kOQ4Wkoh9NyQkylJ+nBnieqcdOEDrOIZ7JyBzgFEKkIpqzSZcWTj7rLglQMAEHIqGwc5T4mPYYYqhKTLu7ZTx5Q4VdW79FFhcbCLA0QNTyhFSykAkYo+AhZxutze7zfuKdPt1wSxMACAkFLBIOcIcTbDkHvvNin9O/rp//1V7YvePWmbDhlpWvLUM0EoHRC5CKUARDr++rWIw3X8RkL3PQBAUwxyjrCw/3Mp/TtydL5U3xt+k/dh2ePNx2cEqWBA5LI3/jNjoHMAkYruexYwTVOOJi2lmgZUAABU1DHIOUJfXMUBJcbaVO9068Cx2mAXB4gKMbSUAhDhCKUs4mwSRDm4qQAAmqhoHFMqNYFQCqHLkKnz0xMlSbtLa4JcGiA60H0PQKQjlLKI0328pZTLbcqkCS4AoFElY0ohTJyfkSRJ2n20hr9lAAsw+x6ASEcoZRHnCV32+LQDACBJbrepqvqG7nuEUgh1XdsnKNZuqKrepeKq+mAXB4h4tJQCEOn469ciJ3bZc7hNxTCeLQBEvcp6l0xJdsNQYqzvZ0VffbVDt44be9p9v9pZqOFtXD6gqRibofM7JKvocKV2ldaoY0p8sIsERDS70RhK0TIRQIQilLKIs8lA5w2vTSk2SIUBAIQMz8x7KfF2GYbvbGZOtzR8+m9Pu+8Xd41o07IBp3Jxx4ZQandpja46Ly3YxQEiGi2lAEQ6uu9Z5MR+4E3HmAIARC8GOUe4uaBDsgxDOlrjUHmNI9jFASIas+8BiHSEUhY5cUwph4sbCwCAQc4RfhJi7erSrqHb3u6jzMIHtCVaSgGIdPwFbBHHCS2jmEEDACAd775HKIVg82cMswvSk7S/vE67SmvUq2s7awoIRCFm3wMQ6fgL2CIntpTixgIAkKSKuoaZ91Limf0CweXPGGbnZyTq411HdbiiTrUOlxJi+f0F2oK3+x4DnQOIUHTfs8iJIZTDxZhSAIDjY0q1Y0wphJGU+BilJ8bKlPRteW2wiwNELLrvAYh0hFIWOWn2PW4sABD1TMOuaoenpRShFMLLeemJkqS9jCsFtBm7QSgFILIRSlnEcdLse9xYACDaueJTJDV0z0iI4ZaM8NI9PUGStLesVm66FqEN7dixQ5MmTVK/fv107bXXaubMmSotLZUkbdu2TePGjVNubq6GDBmiZcuW+ey7YsUK5eXlqXfv3ho9erS2bt3qXedyuTR//nxdc801ys3N1ZQpU3T48GHv+pKSEk2dOlV9+/ZV//79NWfOHDmdTmsuuhEtpQBEOv4CtshJY0rRfQ8Aop4zvr2khkHOjcZPw4Fw0Sk1XnF2Q3VOt45U1ge7OIhQtbW1uuOOO5Sbm6uPPvpIb7zxhsrKyvTggw+qvLxckydP1qhRo1RQUKA5c+bo0Ucf1fbt2yVJGzdu1COPPKJ58+apoKBAt9xyi6ZMmaKamobWfYsXL9a6dev08ssva+3atUpISNDs2bO957733nuVlJSktWvXavny5Vq/fr3y8/Mtue49e/Zo27ZPVFlR3vD622+1ffsnfn19++1eS8oKAK1BXwGLOBtn37MbksukpRQAQHLFp0pikHOEJ5th6Ny0RH1dUk0XPrSZ/fv3q2fPnpo2bZrsdrvi4uI0fvx4zZw5U++8847S0tI0YcIESdKAAQM0cuRILV26VL169dKyZcs0fPhw9enTR5I0ceJEvfTSS1q1apXGjBmjZcuWacaMGerSpYsk6aGHHtLAgQO1d+9eud1ubdq0SR9++KESExPVvXt3TZ06VY899pjuuOOONr3mb7/dq2uuvUo11dXKHDlDyZdcrycXLtDcLa/7dZyExCR9vK5A557bvY1KCgCtRyhlEUdjS6kYmyGXyzyp5RQAIPq4PC2lGOQcYap7eoK+LqnWnrIaxQe7MIhIF110kZ555hmfZatXr9all16qwsJC5eTk+KzLysrS8uXLJUlFRUUaM2bMSet37NihiooKHTx40Gf/zMxMtW/fXl9++aUkKS0tTZ06dfKuv/jii7V//34dO3ZM7dq1a/Y1+NsQtrS0RDXV1Ro3/VHta9dT+2qla0f9RBf98IfNPsbhb7/R8sdnqbS0RN27h0co5amnaGw4HK3XznUHtxz+aqvy8lewBUzT9PYDj7EZqnOZcrjpvgcA0c7TUiqVQc4RprqnNQx2XlLlUKfY5CCXBpHONE098cQTWrNmjZ5//nk999xzSkxM9NkmISFB1dXVkqSqqqrTrq+qqpIkJSUlnbTes+7EfT2vq6ur/QqlOnRIbfa2kpSW1vBvqXtWD1U6OmjfgWPK6NRVWRdkNPsYCQlxkqT09GRlZvp3/mDzt74iSbReO9cd3fgr2AJNu+rZG0fxoqUUAMAZ3/BQQyiFcJUYa1fHlDgdqaxXbdr5wS4OIlhlZaVmzZqlzz//XM8//7x69OihxMREVVRU+GxXW1ur5OSGUCcxMVG1tbUnrU9PT/cGTJ7xpU7c3zTNk9Z5XnuO31wlJRXyZy6AsrKGUKy2rl5us2GG1rp6p6qr65p9jNrahnHejh6tUnFxxVm2Dg2G0fCQ7m99RYJovXauO7yu21PuQOOvYAs0DaViGmfQYEwpAICLUAoRoHtaoo5U1qsu7YJgFwURas+ePbrzzjvVtWtXLV++XBkZDS2GcnJytG7dOp9ti4qKlJ2dLUnKzs5WYWHhSesHDRqk9u3bq1OnTioqKvJ24Tty5IjKysqUk5Mjt9utsrIyFRcXKzMzU5K0c+dOde7cWamp/j2Umab8evD0bmvKOwlGa2a4DKeHXsn/+ook0XrtXHd0C9vZ91wul2677TY98MAD3mWtmRK2LTVtFeUJpei+BwDRrdbhkjuu4dN2BjpHOOueliBJqmt/Lh+6IeDKy8v1k5/8RFdeeaWeffZZbyAlSXl5eSouLlZ+fr4cDoc2bNiglStXeseRGjt2rFauXKkNGzbI4XAoPz9fJSUlysvLkySNHj1aixcv1t69e1VZWam5c+eqX79+Ou+883TBBReoT58+mjt3riorK7V3714tWrRIY8eOtfT6Gx8dxD8tAJEqbD+aXbhwoTZv3qxu3bpJkndK2J/+9KcaP368CgoKNG3aNPXo0UO9evXyTgn79NNPq1evXlq6dKmmTJmiNWvWnNRfPNCcTQIoe+OnHXTfA4DoduBYQzeMWLuh+Jiw/YwIUGZKnOLshuqVoH8frNDlXZs/1g5wNq+88or279+vt956S2+//bbPuq1bt2rJkiWaM2eOFixYoIyMDM2ePVtXX321pIbZ+B5++GH98pe/1KFDh5SVlaWnn35aaWlpkqRp06bJ6XRqwoQJqqqqUv/+/fXEE094j79gwQL9+te/1g033CCbzaZRo0Zp6tSpVl26pOMtpWhNASBShWUotX79er3zzju66aabvMtaOyVsW/J8amhIstl8lwEAotP+Yw3jnKTGx3gfOoBwZDMMdWufoG9Ka7Rx91FCKQTUpEmTNGnSpNOuv/zyy/Xiiy+edv2tt96qW2+99ZTrYmNjNWPGDM2YMeOU6zMzM7VgwQL/Chxgx1tK8ewAIDKF3UezJSUleuihh/R///d/Pi2cTjcl7I4dOyTJp7/4qdb7wzD8+/LMvGczjle40236fZxw/mpJvUXaV7TXQbRff6jWQSQKl+7dB8qPh1JAuOvWOAvfxt1Hg1wSILLYGm/WZFIAIlVY/SXsdrt13333adKkSerZs6fPujNN+dqc9f7wd8T5CrPhZmIzDMXG2iU5FJ8YF3bTs7YWU15SB9F+/RJ1YIVw6d69n1AKEaRb+4ZxpT7df0yVdU6l8HsNBITn8yNaSgGIVGH1F8NTTz2luLg43XbbbSeta82UsP7yd+rG4tKGaV0NQzJdDeNLlR+rCZvpWVvLMMJzystAivY6iPbrl0KzDjxliiTh1L3b04s7Izm2zc4BWKVdQozcxw5L7c7RmHtmKeHoNydt0yEjTUueeqZNzv9fd92hktKy065vy3MDbcnTUoopkgBEqrAKpV577TUdPnxYffv2lSRvyPSPf/xDM2fObPGUsP7yd+pGh6tJ973GG4vDZYbMg6lVmPKSOoj265eog7bk6d69aNEi5efne5efrnv38uXLJTXcC04Mn1rSvdvf7pB3XnOeVv3tj8ruP8W/HVugaffRcP39a3oNVp6vLY4ZKT+HE6/Bse/fim93jjoP+k9de1HGSfu++fiMNvv5lZSWafj03552fdNzW/271Bba8hrCuV4ikefnYYbrmwYAnEVYhVInzrjhGS9k3rx5Onr0qB577DHl5+drwoQJ2rJli1auXKlFixZJapgSdtq0abr55pvVp08fLV261GdK2LbkmX3PZhjeG0vTGfkAAK0TCt27/W11likptXK3UlISTruNYUhJSfEBW5+YGH/G9W157kCtT0yMb/Nzx8Taz9jFPibW3uJ6k07+Ofizf2vWB+LYnrKf6hqcB/6t+O8M1v6KulMe52z12hpn+pmc7tyR0FI0Eq4BZ+ZtKUUmBSBChVUodSbp6emtmhK2LTldTWbf84ZS3FkAIFBCoXt3S7pmOh0uVVfXnXa9aSog6z1hQk1NnU8Zz7R/oM4dqPUnXkNbnvuLzz/XNdcPPe36wp2FGtqCevN8MHXiz8GfsrVmfSCOXVNTd8rfJUlyHPhKhqSyaocOlVYpNcH3z0ynw9VmQxec7d9S03OHYndqf7XlNURi1+5wRkspAJEurEOpefPm+bxuzZSwbcnZdPa9Jt33AACBEQrdu0O5a6anXKFavuaw8hqcbp2xK9hjd41o0XEj/ufgqNU5qfE6VFGnfeW16pmQctr9g+HEc4fyv9nmioRrwJnRUgpApLMFuwDR4HgoZdBSCgDawNtvv61//etf2rx5szZv3qwRI0ZoxIgR2rx5s/Ly8lRcXKz8/Hw5HA5t2LBBK1eu9I4jNXbsWK1cuVIbNmyQw+FQfn6+Zd27gUjjmYVvX3ntWbYE0Bw2WkoBiHBh3VIqXHgCKMNo0n3PxZhSAGCFUO7eDUSabu0T9K9vy7WvvFamacpg1GygVQxaSgGIcIRSFvAEUD7d97izAECbCZfu3UCkOSclTrE2Q3VOt0qrHeqQHBfsIgFhzfOBtpuWUgAiFN33LOBo0n3P83kh3fcAAECksdkMdW7XMAseXfiA1js+0HlwywEAbYWWUhbwzL5no/seAACIcN3SErS3rFb7ymvVq2u7YBdHkvTVVzt067ix3tcxsXY5HS7v6w4ZaVry1DPBKBpwRrbGj7TdIpUCEJkIpSzgO/ue7zIAAIBI0q1dw2DnB4/VyeU2ZbcFf1ypE2dUTEqKV3V1nff1m4/PCEaxgLOipRSASEf3PQs43Z4xpQzvYIWe1lMAAACRJD0pVomxNjndpg5X1J19BwCnZWOgcwARjlDKAqeafc/hpvseAACIPIZhqGv7htZSjCsFtI7N21KKVApAZCKUsoB3TCk1HVOKGwsAAIhM3UIolHK43LJ3ylbRkSodqaxnFjOEFYOWUgAiHGNKWcDZZPY9TxNcxpQCAACRyhNKHamsV73TrbgY6z8HNU1Tnx+s1L++LVfqzdO1pqhEkpQSZ9fA7I46NzXW+8APhCrPB9qEqQAiFS2lLOA6Vfc9WkoBAIAIlRIfo3YJMTIlHThmfWspt2nq/aISrd91VHVOt9xVZeqUGq9Yu6HKepfe/vyg3i8q8f6NBoQqT3BKJgUgUhFKWaDpQOfHW0oxphQAAIhcwezC99HXpSoqrpYhacAF6Tq2fLZuuayTftSnm/qc216GIRUVV2v1jiO0XkdIo6UUgEhHKGUBT6somyF5GonzBxAAAIhkx0Mpa2fg++pIpb48XCVD0o09MnVZl1TJbPgwMMZu05Xd2+vWK7oq1mZoX3mt1hQWyxTd+BCabLSUAhDhGFPKAsfHlGKgcwAAEB26tIuXJJXVOFRd77LknJV1Tq37+qgk6cru7XVBRtIptzu/Q7LyenbU2/8+rF2lNUo5t99pj/lfd92hktKy067vkJGmJU8906pyA6fjiUvd4tkBQGQilLKA09Xw6ZxhGPKMp0n3PQAAEMkSYu3KTI5TcVW9ZV34Nuwuk9NtqlNqvHp3a3fGbbu1T9Cgizvo/aISVXbrp/W7SjXggoyTtispLdPw6b897XHefHxGq8sNnA4tpQBEOrrvWcCnpdQJywAAACKVleNKHSiv1TclDeNIXXthuvdh/kyyOybrO51SJMPQL97coYNBGJQdOBODMaUARDhCKQt4Qykx+x4AAIgeXds3dOHbX17bpp2PTEkFe8skST07pahDclyz9736gnTFVh5Wea1TD76xw9vCHQgFnnCVz7MBRCpCKQscbynVdPY97iwAACCydU6Nl92QqupdciWktdl56tt116GKetkNKffcM3fbO1GMzVB64Sqlxsfo0wPH9MyGPW1USsB/npZSJi2lAEQoQikLeAY1N5oOdM6YUgAAIMLF2G3qlNrQWqquffc2O09F40DlPTulKDnO/yFTY+qOaVZetiTpzxv3aNu+8oCWD2gpWkoBiHSEUhbwBFBNZ9+j+x4AAIgGXRvHlWqrUOqzA8dU366bbIZ0RVf/Wkk1ldejo4Zfco7cpvQ/q3aoss4ZwFICLWNjTCkAEY5QygJNu+8ZdN8DAABRxDPYeV27c+Vqg79//rZlnyTp4sxkJce3bmLpGUOy1LV9gvYfq9Nj/ywKRPGAVjnefS+45QCAtkIoZQFP972mLaVcbpO+4QAAIOJlpsQpzm7IjEnQFwcrAnrsg8dq9c+vjkiSLu+S2urjpcTH6Nc395DNkFZ9cVjv7Djc6mMCreHpvmeKcaUARCZCKQscbyl1PJRquhwAACBS2QxD3dISJUkff1Ma0GO/vO2AXKYUV77Xrxn3zuSKbu31X/3PkyTN+0eRXHEpATku0BKeUEpiXCkAkYlQygKe8MloMvte0+UAAACR7Ly0hi586wIYStU73Xrt04OSpOSD2wN2XEm6fcD5uqxLqirqnDp68U2M54OgsTf5QNvF7yGACEQoZYGmA50bTZcz2DkAAIgC5za2lPr3oUqVVNUH5JhrCot1tMahjilxSjj6dUCO6RFjM/Trm3sqMdam+vbnavv+YwE9PtBctibdLNpiTDYACDZCKQscH1PK8Om+52gMqwAAACJZUpxdsZWHJEnrdwWmtdTybfslSd+/vIsMBf5hvXt6omYMyZIkFewp156jNQE/B3A2DRMlNXxPKAUgErVuihI0i3dMKTV04bPbDLncJi2lAABA1Igv2yVHSid9uLNUIy7t3KpjFR2p0if7jsluSKN6ddYbASrjiUZe2kn/99wrqu50mf5ZWKxRl3dWWmKsX8f4r7vuUElp2WnXd8hI05KnnmllSRHJYgxDDtOkGymAiEQoZYHjY0o1vI7xhFJ82gEAAKJEwtGvVXluf63/plS1DpcSYu0tPpanldTgrEx1TIkPVBFPYhiG2u96X6lZfXSook6rdxzRqMs7Kz6m+Z0NSkrLNHz6b0+7/s3HZwSiqIhgNpsh8ewAIELRfc8Cx2ffa0ilYhv78DlcdN8DAADRIbbqiDqnxqvW6dbG3UdbfJyqeqfe+uKwJGls7y6BKt5pGaZbeT0ylRJn17Fap1bvOMzfcLCUvfEZgpE/AEQiQikLHA+lGl7bG7/h0w4AABAtDEmDszpIktYUlbT4OCs/O6Rqh0sXZCSqb/e0wBTuLBJj7bqpZ0fF2Q0dqqjX2/8+ononCQGs4Xl2YPY9AJGIUMoCTtfx2fekhu57EqEUAACILt/NzpQkrd1Z0qLWRm7T1N+37pMkjc/tJsMwzrJH4HRIjtOwS85RrN3QwYo6rfz8kCpqnZadH9HL3vjExkDnACIRY0pZ4PiYUo3d9+yNoRRNvwEAQBS5olt7dUiOU0lVvdbvOqpBF3fwa/91X5dqb1mtUuNjNPzSTgEr11df7dCt48aeet3OQg1v/L5jSrxGXtpJb/37sEqrHXpl+wEldciRaZqWBmSILt6WUoRSACIQoZQFPDcQz3TFdN8DAADRKMZmaGjPjvrbln1664tDzQ6lPDPYFfccJaWdJ/c3G/UfP/ydd33T4KglnG6ddjDyL+4a4fO6Q3KcRl3eWe99VazDlfWqz/6e7nhxm37U91wNvChDsXY6IiCwPGNK0X0PQCQilLJA04HOTdOk+x4AAIhaN3/nHP1tyz59uLNElXVOpcSf/c/RktIyDbhrrl7edlCGpJEjRio1/vve9ScGR20tJT5GIy/tpE/2H9O/dhVr+/5jmvn6F0qMtenSLu3UrV2CYhpbxpdddIP+8eUR1bncqneacrrdirHZlJoQo47JcXLGt7O07Ag/tJQCEMn4KMcCTpfvQOfHGscfYOYWAAAQbXqck6ILOySp3mXqnR2Hm73f5wcqJUnnZyQqtRlBVluz2QxdeW57nfPJc5rYr7sykmJV43Br854yvfbZQb287YBe3nZA1edcqm9Ka7S/vE7FVfUqq3GquKpe35RUa9OeMh3Onah7X/lMRUeqgn1JCFHellKEUgAiUPDv6FHAO6ZU42tPOEVLKQAAEG0Mw9Coyzvr8fe/1otb9+v7vbqcdTwmV2ySChtDm8u6pFpRzGazO6o07boLNWXgBSo8XKWvjlTqUEWdPH/lvfDiC+o1eLji7DbFxdgUYzPkcLlVVuPUvvJafXu0Wuu+KdW6nUfUbu/HSj6wVU1ro0NGmpY89UwwLg0h4vjse0EuCAC0AVpKWcAztIBngHOb4RnonDsLAACIPrdc1llJsfaG1kK7y866fWW3q+QyTZ2TEqfOqfFtX8AWsBmGenRK0cjLOuuOAefrzsav1P2bdUnnVGV1TNZ56Ynq2j5B52ck6Ypu7TTsknNUseJXOj89UbLZdez865Q84gHdfO9jGj79txo+/bcqKS0L9qWFjNLSUuXl5Wnjxo3eZdu2bdO4ceOUm5urIUOGaNmyZT77rFixQnl5eerdu7dGjx6trVu3ete5XC7Nnz9f11xzjXJzczVlyhQdPny89V5JSYmmTp2qvn37qn///pozZ46cTutnXKT7HoBIRihlgZk3ZOmBodlKiGmobk9LKQc3FgAAEIVS4mM08rKG2fOe3/ztGbc9cKxWVedcJknqe15axM1y5644orwemRp4UboMQyoqrtY/vyqWm0GtfWzZskXjx4/Xnj17vMvKy8s1efJkjRo1SgUFBZozZ44effRRbd++XZK0ceNGPfLII5o3b54KCgp0yy23aMqUKaqpqZEkLV68WOvWrdPLL7+stWvXKiEhQbNnz/Ye/95771VSUpLWrl2r5cuXa/369crPz7f0uqXjzw6EUgAiEaGUBYb27KifXN3d+9rTUooxpQAAQLT6jyu7yW4ztGH3UW3YVXra7X63Zqdks6tLu3h1a59gYQmtYxiGvtMpVXk5mbIZ0jelNdqw62iwixUyVqxYoRkzZmj69Ok+y9955x2lpaVpwoQJiomJ0YABAzRy5EgtXbpUkrRs2TINHz5cffr0UWxsrCZOnKj09HStWrXKu/7OO+9Uly5dlJKSooceekgffvih9u7dq927d2vTpk267777lJiYqO7du2vq1KneY1vpePc9QikAkYdQKggae/Gp1kkoBQAAotO5aYn6Qe+ukqQnPvj6lGNt/rOwWO8XlUhul665IN3qIlru/IwkfTc7U5L0+cFK7ThUGeQShYaBAwfq3Xff1bBhw3yWFxYWKicnx2dZVlaWduzYIUkqKio67fqKigodPHjQZ31mZqbat2+vL7/8UoWFhUpLS1OnTp286y+++GLt379fx44dC/QlnhHd9wBEsrALpXbs2KFJkyapX79+uvbaazVz5kyVljZ8utaaPuVWimm8sdQRSgEAgCh2x4Dz1D4hRjuLqxtaRDVRVFyl36z+SpKUsn+LMpLjglFEy13UIUl9u7eXJH2866gciRlBLlHwdezYUTExJ8/PVFVVpcTERJ9lCQkJqq6uPuv6qqqGgfOTkpJOWl9VVXXKfT2vPcdvLsPw/6thx4b/eWbfc7cwlGrJ+YP1FW7l5dq57mi77rYQVrPv1dbW6o477tAPfvADPfXUU6qqqtL999+vBx98UPPnz9fkyZP105/+VOPHj1dBQYGmTZumHj16qFevXt4+5U8//bR69eqlpUuXasqUKVqzZs1JN5xAM01TZpPmtp5PO+ocrjY9LwBEkx07dmj+/Pn6/PPPFRsbq2uvvVYPPPCAMjIytG3bNv3mN79RUVGR0tPTNWXKFI0bN86774oVK7Ro0SIdOXJEF110kX7xi18oNzc3iFcDRId2CbH6xdAc3ffaF1r2yX7ZbYZu63uu/n2oUvP+UaiKOqeu6NpOhzcWSBob7OJapne3djp4rE7fltfqaNb35HC5FWsPu8+S21xiYqIqKip8ltXW1io5Odm7vra29qT16enp3r//PeNLnbi/aZonrfO89hy/uTp08G/GyLS0huMnxMcpKSleCfENj2yG3aakpOYN9J+Q0BDipqcnKzMztGasPBt/6yuSROu1c93RLaxCqf3796tnz56aNm2a7Ha74uLiNH78eM2cOdOnT7kknz7lvXr18ulTLkkTJ07USy+9pFWrVmnMmDFtXvbH3/nS+72npRTd9wAgMML1QwsA0uCsTN0z6EIt+PAbvfivfXrxX/u86y7qkKT/G3WpfvxKdH2QZxiGrs/uoOWfHFBtcqb+vHGPJl9zQbCLFXJycnK0bt06n2VFRUXKzs6WJGVnZ6uwsPCk9YMGDVL79u3VqVMnny5+R44cUVlZmXJycuR2u1VWVqbi4mJlZjZ0qdy5c6c6d+6s1FT/HiRLSirkz3BQZWUNrbhq6+pVXV0nd+M4tHX1TlVX1zXrGLW19ZKko0erVFxccZatQ4NhNDyk+1tfkSBar53rDq/r9pQ70MLqI5eLLrpIzzzzjOx2u3fZ6tWrdemll7aqT7m/WtTMrUlbN09LqXqnO+jN72ieSB1w/dFdB5Gi6YcWcXFxSk9P9wZQrR0IF0Dbu+2q7nr8+5fqgoyGIDgx1qYfX9VdS37YW+0TY4NcuuBIjLXrmgvTJUlLNu7V1yVVQS5R6MnLy1NxcbHy8/PlcDi0YcMGrVy50vuB89ixY7Vy5Upt2LBBDodD+fn5KikpUV5eniRp9OjRWrx4sfbu3avKykrNnTtX/fr103nnnacLLrhAffr00dy5c1VZWam9e/dq0aJFGjvW/xZ7pun/V8OODf/zdN9ztfDhtSXnD9ZXuJWXa+e6o+2620JYtZRqyjRNPfHEE1qzZo2ef/55Pffccy3uU+4vf9NBl6vh072kpDi53aYS4ho+7TBi7WHXnLY1aJ5IHUT79UvUQVvxfGjR1Nk+tFi+fLmkhg8tTmwx29IPLQC03MCLOujaCzNk6vhMxdHuog5J+nhjgWozLtLja77WgjGXyaBuvNLT07VkyRLNmTNHCxYsUEZGhmbPnq2rr75aUkPPiYcffli//OUvdejQIWVlZenpp59WWlqaJGnatGlyOp2aMGGCqqqq1L9/fz3xxBPe4y9YsEC//vWvdcMNN8hms2nUqFGaOnWq5dfJQOcAIllYhlKVlZWaNWuWPv/8cz3//PPq0aNHq/qU+8vfZnZud0MoVV1dL7fbLbOxCe7RitqwaU7bGoYRns0TAyna6yDar18KzTrwlCnSBOtDi1B+TmzaUi9Ufv/81fQawlWk/Rxacg1n+/lZEbi0xc+hrYptGIZK3s9X8qiHtWH3UX1v8n1KKNslGVJMjF1pqala8qdnznoc/84Z0MMF3Jdffunz+vLLL9eLL7542u1vvfVW3XrrradcFxsbqxkzZmjGjBmnXJ+ZmakFCxa0vLAB4hlOjFAKQCQKu1Bqz549uvPOO9W1a1ctX75cGRkNM5K0pk+5v/xtunbitvbGm32dwx22f5S2RFs2+QsX0V4H0X79EnXQ1oL5oUVLAr6YWPsZB601DAV0fWJi/BnXt+W5A7U+MTE+ZMvWnH2lk38OoVK25qz3lP1U13C2/WNa0UK8Lf6tNL2G1tTb2a6rtWV3VhSrz4WZ2rL7qFy5YzT66vO9LWdW/O+9UdXqPlod777HHxAAIk9YhVLl5eX6yU9+oquvvlpz5syRzXZ8SKy8vDw99thjys/P14QJE7RlyxatXLlSixYtktTQp3zatGm6+eab1adPHy1dutSnT7mVvLPvMdA5AARMsD+0aEkrOKfDdcZBa01TAVnvCRNqaup8ynim/QN17kCtP/EaQqlszV3naYFy4s8hFMrW3PU1NXWn/F1qzv5Oh6vFLcQD+W/lVP8eWlNvZ7uuQJT9sk7J+mJ/ucprHCr4ulhXdGunxMR4OZ0tr9PTidRWtOGM7nsAIllYDXT+yiuvaP/+/XrrrbfUp08f5ebmer88fcrffvtt9e/fX7Nnzz5tn/J+/frpzTff9OlT3tZM05TZ+JeP3Tv7XnTNJAMAbcXzocWVV16pZ5991htISa0fCLe5QmWwyNOVren/wxHXEBpaew2hMLBqW/wc2rrscXab+p2XJkn617flqqlv/BsyjAayRcsRSgGIZGHVUmrSpEmaNGnSade3pk+5lWIMWkoBQCA1/dDi7bff9lm3devWVg2ECwChILtjsj47UKGSaoe27T+m77ZPCnaRYBG67wGIZGEVSkUKz2CFhFIAEBiR8qEFAJyOYRjqc16a3tlxRJ8frFS/i5z66qsdunXc2NPu0yEjTUueCuxA6LCezdtSKsgFAYA2QCgVBN7uew7uLAAAAGie89ISdE5KnA5X1qtgV6mcbmn49N+edvs3Hz/1rHIILzGNzw5uuu8BiECEUkFwvPseY0oBAACEs7O1VvpqZ6GGB+hchmGo73lpWvXFYX2275iMZP9mCUV4stF9D0AEI5QKguMDndNSCgAAIJydrbXSF3eNCOj5urVPUNd28dp/rE4JVwwL6LERmjxDfzDQOYBIFFaz70UKTxNcxpQCAACAv/o2zsQXl3W1jtU6g1sYtDnvQOeEUgAiEKFUEHg+7aClFAAAAPzVKTVe52ckybDZ9cm+8mAXB23M08uC7nsAIhHd94Kg6acdTpdbMXayQQAAENnONPYSs8T5r/9FGdpdWq2vjlSpd7f2apfAn/WRyhNKuU3JNE0Zjc8SABAJuHsFgefGIjW0lkohlAIAABHuTGMvMUuc/7q0T5Rj3xeK7XaJPtlXrkEXdwh2kdBG7E1CKJfbVIydUApA5CANCYKm9xHGlQIAAEBL1H6ySpL01ZEqVTC2VMRq+oG2ix58ACIMoVQQGIah+JiGqieUAgAAQEu4jnytbu0TZJpibKkI1iSTYrBzABGHUCpICKUAAADQWlee216S9OWRKlXU0VoqEhmG4e1pwWDnACINoVSQJDSGUrVOV5BLAgAAgHDVuV28uraPb2gt9e2xYBcHbcQ7Ax8tpQBEGEIpi5hNPtUwTfN4SykHLaUAAADQcp7WUl8dqaS1VIQilAIQqQilgsA0TcXb6b4HAACA1uvSLkFd28XLbUqf7KO1VCTyzMDnpvsegAhDKBUk8bF03wMAAEBgXNm9sbXU4UpV0loq4tgaW0o5aSkFIMIQSgUJA50DAAAgULq0S1AXWktFrJjGUMrNowOACEMoFSSe7nu1hFIAAAAIgD6emfhoLRVxbI3d95h9D0CkiQl2AaJR04HOaxnoHAAARLmvvtqhW8eNPf36nYUabmF5wlWX9g2tpQ4cq6O1VIRhoHMAkYpQKgjcbrfi7XZJUh1jSgEAgCjndEvDp//2tOu/uGuEhaUJb1ee215vfnFYXx6uVMe4lGAXBwHS2MmCUApAxKH7XpB8XVIliTGlAAAAEDhd2x8fW6qia99gFwcBYqf7HoAIRSgVJJ4muIwpBQAAgEC6snFsqepzLlVxZV2QS4NAoPsegEhFKBUknhk0aCkFAACAQOrSLl7ntk+QDJvKaxnwPBLENvbfq3cRSgGILIRSQWCaprcJLmNKAQAAIJAMw1Bej0yd88lfdHFmcrCLgwBIaJwkqc7BswOAyEIoFSSellLMvgcAAIBAi7HbFFPHDHyRIiG2ceZuelkAiDCEUkHimUGD7nsAAAAAziQhpmHm7hpaSgGIMIRSQXK8+x6hFAAAAIDT87SU4tkBQKQhlAoSe0MmddKnHaZpymSqVwAAAACNPC2lGPoDQKQhlAoSz6cdRyrrg1wSAAAAAKHs+JhSdN8DEFkIpYIkNa7h044Dx2rlctMyCgAAAMCpeWbfq3eZPDsAiCiEUkESb2+Ygc/pNnWksi7YxQEAAAAQouJjbGoc/YNxpQBEFEIpCzSMEeX7iYbNMNQ5NV6StK+8NgilAgAAABAODMNQfGNrqVpm4AMQQQilgqhrO0IpAAAAAGfnGVeqhpZSACIIoVSQuN1udWkXJ4lQCgAAAMCZHZ+Bj5ZSACIHoVQQ7T1aLUnaV1YT5JIAAAAACGWellKMKQUgkhBKBVFK4wx8+2kpBQAAAOAMPC2lahyEUgAiB6FUkJimqeS4hupv2n3P7XbL7eZGAwAAAOA4T0upWifd9wBEDkKpIEqKafh/abVD1fUNNxfTNL1fAAAAACA16b5HSykAEYRQKohiDCnebkiStu8v9y5/6qNvCKcAAAAAeHkHOmdMKQARhFAqiEzT1IUZCZKkpZv3SZKq6l06UFGn+iholkvoBgAAADSPp6WUp4cFAEQCQqkg69EhQXZD2rD7qP625VvdtvQTrf6qVKOe3axXtx8IdvEAAAAAhIAOSXGSpKM1DlXVO4NcGgAIjKgLpUpKSjR16lT17dtX/fv315w5c+R0Bu9NPdEuDb4oTZL0+Ptf68CxuoZyVjs09x9FevLDr1XvdMntdtOyCADaUKjdHwAAwRdK94a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-      "text/plain": [
-       "<Figure size 1200x2800 with 19 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Selecting numerical columns\n",
-    "numerical_columns = housing_data.select_dtypes(include=[np.number]).columns\n",
-    "\n",
-    "# Calculate the number of rows and columns for subplots\n",
-    "num_cols = len(numerical_columns)\n",
-    "num_rows = (num_cols + 2) // 3  # Calculate the number of rows needed, rounding up\n",
-    "\n",
-    "# Plot histograms for numerical variables\n",
-    "plt.figure(figsize=(12, num_rows * 4))  # Adjust the height based on the number of rows\n",
-    "for i, col in enumerate(numerical_columns, 1):\n",
-    "    plt.subplot(num_rows, 3, i)\n",
-    "    sns.histplot(housing_data[col], kde=True, edgecolor='black')\n",
-    "    plt.title(col)\n",
-    "    plt.xlabel('')\n",
-    "    plt.ylabel('Frequency')\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Conclusion**\n",
-    "\n",
-    "In conclusion, the log transformation has effectively addressed skewness in the distribution of numerical variables within the dataset. Prior to transformation, variables such as house price, bedrooms, bathrooms, and various square footage measurements exhibited skewed distributions with long tails. However, after applying the log transformation, these distributions appear to be more symmetric and closer to a normal distribution. This transformation has enhanced the suitability of the data for statistical analysis by reducing skewness and improving the interpretability of the variables. It's important to acknowledge that while the log transformation has provided valuable improvements, it alters the scale and interpretation of the variables, necessitating careful consideration in subsequent analyses. Overall, the transformed variables are now better suited for further statistical modeling and analysis in the context of predicting property values and understanding real estate market trends."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **d.) Inferential Statistics.**\n",
-    "\n",
-    "*We used one-way ANOVA approach*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1. \u001b[1mbedrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "2. \u001b[1mbathrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "3. \u001b[1msqft_living\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "4. \u001b[1msqft_lot\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
-      "5. \u001b[1mfloors\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "6. \u001b[1mwaterfront\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "7. \u001b[1mcondition\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "8. \u001b[1mgrade\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "9. \u001b[1msqft_above\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "10. \u001b[1msqft_basement\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "11. \u001b[1myr_built\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "12. \u001b[1myr_renovated\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "13. \u001b[1msqft_living15\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "14. \u001b[1msqft_lot15\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
-      "15. \u001b[1mhouse_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "16. \u001b[1mrenovation_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "17. \u001b[1mtotal_sqft\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n"
-     ]
-    },
-    {
-     "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>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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F-statistic</th>\n",
-       "      <td>1.841471e+00</td>\n",
-       "      <td>3.728667</td>\n",
-       "      <td>7.702589</td>\n",
-       "      <td>0.732713</td>\n",
-       "      <td>1.662592e+00</td>\n",
-       "      <td>1.787176e+00</td>\n",
-       "      <td>1.051111</td>\n",
-       "      <td>7.29306</td>\n",
-       "      <td>5.098114</td>\n",
-       "      <td>1.750817e+00</td>\n",
-       "      <td>1.236748e+00</td>\n",
-       "      <td>1.046716</td>\n",
-       "      <td>5.209978</td>\n",
-       "      <td>0.709841</td>\n",
-       "      <td>1.236748e+00</td>\n",
-       "      <td>1.171737e+00</td>\n",
-       "      <td>0.775778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>P-value</th>\n",
-       "      <td>1.765957e-139</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.808184e-95</td>\n",
-       "      <td>1.258742e-125</td>\n",
-       "      <td>0.026289</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.406487e-116</td>\n",
-       "      <td>2.417229e-17</td>\n",
-       "      <td>0.037943</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.417229e-17</td>\n",
-       "      <td>2.430304e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  bedrooms  bathrooms  sqft_living  sqft_lot        floors  \\\n",
-       "F-statistic   1.841471e+00   3.728667     7.702589  0.732713  1.662592e+00   \n",
-       "P-value      1.765957e-139   0.000000     0.000000  1.000000  1.808184e-95   \n",
-       "\n",
-       "                waterfront  condition    grade  sqft_above  sqft_basement  \\\n",
-       "F-statistic   1.787176e+00   1.051111  7.29306    5.098114   1.750817e+00   \n",
-       "P-value      1.258742e-125   0.026289  0.00000    0.000000  1.406487e-116   \n",
-       "\n",
-       "                 yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "F-statistic  1.236748e+00      1.046716       5.209978    0.709841   \n",
-       "P-value      2.417229e-17      0.037943       0.000000    1.000000   \n",
-       "\n",
-       "                house_age  renovation_age  total_sqft  \n",
-       "F-statistic  1.236748e+00    1.171737e+00    0.775778  \n",
-       "P-value      2.417229e-17    2.430304e-10    1.000000  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "from scipy.stats import f_oneway\n",
-    "\n",
-    "# List of features of interest\n",
-    "features_of_interest = ['bedrooms', 'bathrooms', 'sqft_living',\n",
-    "                        'sqft_lot', 'floors', 'waterfront', 'condition', \n",
-    "                        'grade', 'sqft_above', 'sqft_basement', 'yr_built', \n",
-    "                        'yr_renovated', 'sqft_living15', 'sqft_lot15', \n",
-    "                        'house_age', 'renovation_age', 'total_sqft']\n",
-    "\n",
-    "# Create an empty DataFrame to store ANOVA results\n",
-    "anova_results = pd.DataFrame(index=['F-statistic', 'P-value'])\n",
-    "\n",
-    "# Perform ANOVA for each feature\n",
-    "significant_features = []\n",
-    "\n",
-    "for i, column in enumerate(features_of_interest, 1):\n",
-    "    groups = [housing_data[column][housing_data['price'] == category]\n",
-    "              for category in housing_data['price'].unique()]\n",
-    "\n",
-    "    # Perform ANOVA\n",
-    "    f_statistic, p_value = f_oneway(*groups)\n",
-    "\n",
-    "    # Store results in the DataFrame\n",
-    "    anova_results[column] = [f_statistic, p_value]\n",
-    "\n",
-    "    # Print interpretation\n",
-    "    if p_value < 0.05:\n",
-    "        significant_features.append(column)\n",
-    "        print(f\"{i}. \\033[1m{column}\\033[0m: Reject the null hypothesis. There is a statistically significant relationship.\")\n",
-    "    else:\n",
-    "        print(f\"{i}. \\033[1m{column}\\033[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\")\n",
-    "\n",
-    "# Display ANOVA results\n",
-    "anova_results\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Conclusion**\n",
-    "\n",
-    "The features listed under \"Reject the Null Hypothesis\" have a statistically significant relationship with housing prices.\n",
-    "\n",
-    "These features are important predictors of housing prices in the given dataset.\n",
-    "\n",
-    "On the other hand, features listed under \"Fail to Reject the Null Hypothesis\" do not show a statistically significant relationship with housing prices based on the ANOVA test."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **e.) Multicollinearity.**\n",
-    "\n",
-    "**Assessing Multicollinearity with Variance Inflation Factor (VIF)**\n",
-    "<br>In this analysis, we utilize the Variance Inflation Factor (VIF) to investigate multicollinearity among predictor variables in our regression model. Multicollinearity occurs when predictor variables are highly correlated with each other, which can lead to unreliable coefficient estimates. By computing the VIF for each predictor variable, we identify potential multicollinearity issues among property characteristics. \n",
-    "<br>High VIF values indicate a strong correlation between a predictor variable and the other variables in the model. Hence, it's crucial to examine the VIF values to ensure the reliability of our regression analysis and to address any multicollinearity detected.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
-      "  vif = 1. / (1. - r_squared_i)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1mVariance Inflation Factor (VIF):\u001b[0m\n"
-     ]
-    },
-    {
-     "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>Feature</th>\n",
-       "      <th>VIF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>bedrooms</td>\n",
-       "      <td>1.688154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>bathrooms</td>\n",
-       "      <td>3.366371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>sqft_living</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sqft_lot</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>floors</td>\n",
-       "      <td>1.934470</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>waterfront</td>\n",
-       "      <td>1.028593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>condition</td>\n",
-       "      <td>1.218261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>grade</td>\n",
-       "      <td>3.235515</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>sqft_above</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>sqft_basement</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>yr_built</td>\n",
-       "      <td>94.556823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>yr_renovated</td>\n",
-       "      <td>4.226966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>sqft_living15</td>\n",
-       "      <td>2.763013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>sqft_lot15</td>\n",
-       "      <td>2.130997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>house_age</td>\n",
-       "      <td>7.743224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>renovation_age</td>\n",
-       "      <td>4.109794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>total_sqft</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Feature        VIF\n",
-       "0         bedrooms   1.688154\n",
-       "1        bathrooms   3.366371\n",
-       "2      sqft_living        inf\n",
-       "3         sqft_lot        inf\n",
-       "4           floors   1.934470\n",
-       "5       waterfront   1.028593\n",
-       "6        condition   1.218261\n",
-       "7            grade   3.235515\n",
-       "8       sqft_above        inf\n",
-       "9    sqft_basement        inf\n",
-       "10        yr_built  94.556823\n",
-       "11    yr_renovated   4.226966\n",
-       "12   sqft_living15   2.763013\n",
-       "13      sqft_lot15   2.130997\n",
-       "14       house_age   7.743224\n",
-       "15  renovation_age   4.109794\n",
-       "16      total_sqft        inf"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
-    "\n",
-    "# Compute Variance Inflation Factor (VIF) to detect multicollinearity\n",
-    "\n",
-    "X = housing_data[['bedrooms', 'bathrooms', 'sqft_living','sqft_lot', 'floors', 'waterfront', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15', 'house_age', 'renovation_age', 'total_sqft']]\n",
-    "# Calculate the correlation matrix\n",
-    "correlation_matrix = X.corr()\n",
-    "\n",
-    "# Calculate VIF for each feature\n",
-    "vif_data = pd.DataFrame()\n",
-    "vif_data[\"Feature\"] = X.columns\n",
-    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
-    "\n",
-    "# Print VIF for each feature\n",
-    "print(\"\\n\\033[1mVariance Inflation Factor (VIF):\\033[0m\")\n",
-    "vif_data\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Variance Inflation Factor (VIF) Analysis**\n",
-    "<br>The Variance Inflation Factor (VIF) was calculated to assess multicollinearity among the features in the dataset. A VIF value greater than 5 is typically considered indicative of multicollinearity. The results revealed that most features exhibited low levels of multicollinearity, with VIF values below 5. \n",
-    "<br>However, 'yr_built' displayed a remarkably high VIF of 94.56, indicating strong multicollinearity with other features. Additionally, 'sqft_living', 'sqft_lot', 'sqft_above', 'sqft_basement', and 'total_sqft' exhibited infinite VIF values, suggesting perfect multicollinearity. \n",
-    "<br>Addressing multicollinearity in this case may require further investigation, such as feature selection, dimensionality reduction techniques or applying regularization methods to mitigate multicollinearity effects and improve model performance. \n",
-    "<br>Overall, understanding the VIF values can help refine the regression model and ensure the reliability of the coefficient estimates.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MODELLING."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. Baseline model - simple linear model.\n",
-    "2. log transformation. \n",
-    "3. Multiple Linear Regression\n",
-    "4. Residual modelling.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Baseline model \n",
-    "\n",
-    " Baseline models provide a reference point for comparing the performance of more complex models. \n",
-    " Its purpose is to establish a benchmark against which the performance of more sophisticated models can be evaluated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "FUNCTIONS TO BE USED."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#use only numeric columns.\n",
-    "def numeric_col(housing_data):\n",
-    "    '''returns a dataframe with only numeric values'''\n",
-    "    for column in housing_data.columns:\n",
-    "        if is_numeric_dtype(housing_data[column]) == False:\n",
-    "            housing_data = housing_data.drop(column, axis=1)\n",
-    "        else:\n",
-    "            continue\n",
-    "    return housing_data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set a function for the predictor and target variale.\n",
-    "def X_Y(housing_data, target):\n",
-    "    '''Returns a series of target (y) values and a DataFrame of predictors (X)'''\n",
-    "    y = housing_data[target]  # target variable\n",
-    "    X = housing_data.drop(target, axis=1)  # predictor features\n",
-    "    return y, X\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#A higher train score indicates that the model fits the training data well.\n",
-    "#A high test score suggests that the model is able to make accurate predictions on data it hasn't seen before, which is the ultimate goal in machine learning.\n",
-    "\n",
-    "def get_metrics(X_train, X_test, y_train, y_test):\n",
-    "    ''' Parameters are X train, X test, y train, & y_test\n",
-    "        Performs multiple regression on the split test and returns metrics'''\n",
-    "\n",
-    "    # Initialize Linear Regression model\n",
-    "    lr = LinearRegression()\n",
-    "\n",
-    "    lr.fit(X_train, y_train)\n",
-    "\n",
-    "    train_score = lr.score(X_train, y_train)\n",
-    "    test_score = lr.score(X_test, y_test)\n",
-    "\n",
-    "    y_hat_train = lr.predict(X_train)\n",
-    "    y_hat_test = lr.predict(X_test)\n",
-    "\n",
-    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_hat_train))\n",
-    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_hat_test))\n",
-    "\n",
-    "    return train_score, test_score, train_rmse, test_rmse\n",
-    "#These scores provide insights into how well the model is performing both on the data it was trained on and on new data.\n",
-    "# They help assess the model's overall effectiveness and whether it is overfitting or underfitting.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_test(housing_data, size=0.20):\n",
-    "    '''Takes in dataframe, and size of test for the split\n",
-    "        Returns the train_set and test_set'''\n",
-    "    train_set, test_set = train_test_split(housing_data, test_size=size, random_state=42)\n",
-    "    return train_set, test_set\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SIMPLE LINEAR REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def simple_linear_regression(housing_data):\n",
-    "    '''Creates a simple linear regression model with prices as the target variable \n",
-    "    and the number of bedrooms as the predictor. Returns the model along with R-squared, \n",
-    "    Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both train and test sets.'''\n",
-    "    \n",
-    "    # Extracting features and target variable\n",
-    "    X = housing_data[['sqft_living']]  # Predictor feature\n",
-    "    y = housing_data['price']  # Target variable (prices)\n",
-    "    \n",
-    "    # Splitting the data into train and test sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "    \n",
-    "    # Create a linear regression model\n",
-    "    model = LinearRegression()\n",
-    "    \n",
-    "    # Fit the model to the training data\n",
-    "    model.fit(X_train, y_train)\n",
-    "    \n",
-    "    # Calculate predictions for train and test sets\n",
-    "    y_train_pred = model.predict(X_train)\n",
-    "    y_test_pred = model.predict(X_test)\n",
-    "    \n",
-    "    # Calculate R-squared for train and test sets\n",
-    "    r2_train = r2_score(y_train, y_train_pred)\n",
-    "    r2_test = r2_score(y_test, y_test_pred)\n",
-    "    \n",
-    "    # Calculate Mean Squared Error (MSE) for train and test sets\n",
-    "    mse_train = mean_squared_error(y_train, y_train_pred)\n",
-    "    mse_test = mean_squared_error(y_test, y_test_pred)\n",
-    "    \n",
-    "    # Calculate Root Mean Squared Error (RMSE) for train and test sets\n",
-    "    rmse_train = np.sqrt(mse_train)\n",
-    "    rmse_test = np.sqrt(mse_test)\n",
-    "    \n",
-    "    # Print coefficients, R-squared, MSE, and RMSE for train and test sets\n",
-    "    print(\"Training set:\")\n",
-    "    print(\"Intercept:\", model.intercept_)\n",
-    "    print(\"Coefficient:\", model.coef_[0])\n",
-    "    print(\"R-squared:\", r2_train)\n",
-    "    print(\"Mean Squared Error:\", mse_train)\n",
-    "    print(\"Root Mean Squared Error:\", rmse_train)\n",
-    "    \n",
-    "    print(\"\\nTest set:\")\n",
-    "    print(\"R-squared:\", r2_test)\n",
-    "    print(\"Mean Squared Error:\", mse_test)\n",
-    "    print(\"Root Mean Squared Error:\", rmse_test)\n",
-    "    \n",
-    "    return model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test\n",
-    "\n",
-    "\n",
-    "# Example usage:\n",
-    "# Assuming housing_data is your dataset\n",
-    "# model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test = simple_linear_regression(housing_data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set:\n",
-      "Intercept: -44593.95245340909\n",
-      "Coefficient: 281.4088930446383\n",
-      "R-squared: 0.49587806055811734\n",
-      "Mean Squared Error: 68601152192.940285\n",
-      "Root Mean Squared Error: 261918.21661148407\n",
-      "\n",
-      "Test set:\n",
-      "R-squared: 0.48264829402430887\n",
-      "Mean Squared Error: 68845100756.10751\n",
-      "Root Mean Squared Error: 262383.4993975565\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(LinearRegression(),\n",
-       " 0.49587806055811734,\n",
-       " 68601152192.940285,\n",
-       " 261918.21661148407,\n",
-       " 0.48264829402430887,\n",
-       " 68845100756.10751,\n",
-       " 262383.4993975565)"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simple_linear_regression(housing_data)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.\n",
-    "\n",
-    "\n",
-    "Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.\n",
-    "\n",
-    "\n",
-    "R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.\n",
-    "\n",
-    "\n",
-    "Test Set:\n",
-    "\n",
-    "R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R-squared: 0.48264829402430875\n",
-      "Mean Squared Error: 68845100756.10753\n",
-      "Root Mean Squared Error: 262383.4993975565\n",
-      "Intercept: 540631.1560929463\n",
-      "Coefficients: [259767.82181675]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#STANDARDIZE OUR MODEL TO GET RID OF THE NEGATIVE INTERCEPT.\n",
-    "\n",
-    "\n",
-    "# Assuming you have your data loaded into X and y\n",
-    "# X should be your independent variables and y should be your dependent variable\n",
-    "\n",
-    "# Split data into training and test sets\n",
-    "X = housing_data[['sqft_living']]  # Predictor feature\n",
-    "y = housing_data['price']  # Target variable (prices)\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Initialize the StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "# Fit and transform the scaler on the training data\n",
-    "X_train_scaled = scaler.fit_transform(X_train)\n",
-    "\n",
-    "# Transform the test data using the scaler fitted on the training data\n",
-    "X_test_scaled = scaler.transform(X_test)\n",
-    "\n",
-    "# Fit the linear regression model on the scaled training data\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train_scaled, y_train)\n",
-    "\n",
-    "# Predict on the scaled test data\n",
-    "y_pred = model.predict(X_test_scaled)\n",
-    "\n",
-    "# Calculate R-squared and mean squared error on the test set\n",
-    "r_squared = r2_score(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "\n",
-    "# Print the metrics\n",
-    "print(\"R-squared:\", r_squared)\n",
-    "print(\"Mean Squared Error:\", mse)\n",
-    "print(\"Root Mean Squared Error:\", rmse)\n",
-    "\n",
-    "# Print the intercept and coefficients of the model\n",
-    "print(\"Intercept:\", model.intercept_)\n",
-    "print(\"Coefficients:\", model.coef_)\n",
-    "\n",
-    "#This code will scale your data using StandardScaler, which standardizes features by removing the mean and scaling to unit variance. \n",
-    "#It ensures that the intercept is not negative"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared (0.48): This value indicates that approximately 48% of the variability in the dependent variable (the house prices) is explained by the independent (sqft living). In simpler terms, the model captures about 48% of the patterns in the data.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE) (68845100756.11): This is the average squared difference between the actual values and the predicted values from the model. It's a measure of the model's accuracy, where lower values indicate better performance. In this case, the average squared difference is quite large, indicating that there's still room for improvement in the model's predictive accuracy.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE and provides a measure of the typical deviation of the predicted values from the actual values. It's in the same units as the dependent variable. Here, the RMSE indicates that, on average, the predicted values differ from the actual values by approximately 262,383.50 units.\n",
-    "\n",
-    "\n",
-    "Intercept (540631.16): This is the estimated value of the dependent variable when all independent variables are set to zero. In this case, it suggests that when all other factors are zero, we would expect the dependent variable to be around 540,631.16.\n",
-    "\n",
-    "\n",
-    "Coefficient (259767.82): This represents the change in the house prices for a one-unit change in the sqft living, while holding other variables constant. In this case, for every one-unit increase in the sqft living, we'd expect the house prices to increase by approximately 259,767.82 units.\n",
-    "\n",
-    "\n",
-    "Overall, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MULTIPLE LINEAR REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error: 47548419769.890594\n",
-      "R-squared: 0.6426869041626193\n",
-      "          Feature    Coefficient\n",
-      "0           const       0.000000\n",
-      "1       bathrooms   24855.944322\n",
-      "2     sqft_living  117349.853313\n",
-      "3          floors   20057.401179\n",
-      "4      waterfront   63378.047594\n",
-      "5       condition   11809.217548\n",
-      "6           grade  154829.185004\n",
-      "7   sqft_basement   14434.666872\n",
-      "8        yr_built -108064.021718\n",
-      "9    yr_renovated    7255.426167\n",
-      "10  sqft_living15   25765.797630\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "# Define a function to keep only numeric columns\n",
-    "def only_numeric(housing_data):\n",
-    "    '''returns a DataFrame with only numeric values'''\n",
-    "    numeric_columns = [column for column in housing_data.columns if is_numeric_dtype(housing_data[column])]\n",
-    "    return housing_data[numeric_columns]\n",
-    "\n",
-    "# Sample features and target variable\n",
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "\n",
-    "    # Split the data into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Standardize the features\n",
-    "    scaler = StandardScaler()\n",
-    "    X_train_scaled = scaler.fit_transform(X_train)\n",
-    "    X_test_scaled = scaler.transform(X_test)\n",
-    "\n",
-    "    # Build a basic linear regression model\n",
-    "    model = LinearRegression()\n",
-    "    model.fit(X_train_scaled, y_train)\n",
-    "\n",
-    "    # Make predictions on the test set\n",
-    "    y_pred = model.predict(X_test_scaled)\n",
-    "\n",
-    "    # Evaluate the model\n",
-    "    mse = mean_squared_error(y_test, y_pred)\n",
-    "    r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "    # Display results\n",
-    "    print(\"Mean Squared Error:\", mse)\n",
-    "    print(\"R-squared:\", r2)\n",
-    "\n",
-    "    # Display coefficients\n",
-    "    coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
-    "    print(coefficients)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mean Squared Error (MSE): The MSE is a measure of the average squared difference between the actual and predicted values. In this case, the MSE is approximately \n",
-    "4.75\n",
-    "×\n",
-    "1\n",
-    "0\n",
-    "10\n",
-    "4.75×10 \n",
-    "10\n",
-    "\n",
-    "\n",
-    " , which means, on average, the squared difference between the actual housing prices and the predicted prices is \n",
-    "4.75\n",
-    "×\n",
-    "1\n",
-    "0\n",
-    "10\n",
-    "4.75×10 \n",
-    "10\n",
-    " .\n",
-    "\n",
-    "\n",
-    "R-squared (\n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "\n",
-    "\n",
-    " ): The \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  score measures the proportion of the variance in the target variable (housing prices) that is explained by the independent variables (features) in the model. \n",
-    "  \n",
-    "  An \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  score of 0.643 means that approximately 64.3% of the variance in housing prices is explained by the features included in the model. In other words, the model accounts for 64.3% of the variability in housing prices.\n",
-    "\n",
-    "\n",
-    "Coefficients:\n",
-    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero.\n",
-    "\n",
-    "\n",
-    "bathrooms: For each additional bathroom, the predicted housing price increases by approximately $24,855.\n",
-    "\n",
-    "\n",
-    "sqft_living: For each additional square foot of living space, the predicted housing price increases by approximately $117,350.\n",
-    "\n",
-    "\n",
-    "floors: Houses with an additional floor have a predicted price increase of approximately $20,057.\n",
-    "\n",
-    "\n",
-    "waterfront: Properties with waterfront views have a predicted price increase of approximately $63,378 compared to those without.\n",
-    "\n",
-    "\n",
-    "condition: Better condition properties (on a scale from 1 to 5) tend to have a predicted price increase of approximately $11,809 for each unit increase in condition.\n",
-    "\n",
-    "grade: Higher grade properties (on a scale from 1 to 13) have a predicted price increase of approximately $154,829 for each unit increase in grade.\n",
-    "\n",
-    "\n",
-    "sqft_basement: For each additional square foot of basement space, the predicted price increases by approximately $14,435.\n",
-    "\n",
-    "\n",
-    "yr_built: Each additional year of age decreases the predicted price by approximately $108,064.\n",
-    "\n",
-    "\n",
-    "yr_renovated: For each year renovated, the predicted price increases by approximately $7,255.\n",
-    "\n",
-    "\n",
-    "sqft_living15: For each additional square foot of living space in the nearest 15 neighbors' homes, the predicted price increases by approximately $25,766.\n",
-    "\n",
-    "\n",
-    "These coefficients indicate the strength and direction of the relationship between each feature and the target variable, holding other features constant. For example, features like square footage of living space, grade, and whether the property has a waterfront view have a substantial positive impact on the predicted housing price, while the year the property was built has a negative impact.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  price   R-squared:                       0.641\n",
-      "Model:                            OLS   Adj. R-squared:                  0.641\n",
-      "Method:                 Least Squares   F-statistic:                     3768.\n",
-      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        13:34:57   Log-Likelihood:            -2.9014e+05\n",
-      "No. Observations:               21142   AIC:                         5.803e+05\n",
-      "Df Residuals:                   21131   BIC:                         5.804e+05\n",
-      "Df Model:                          10                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "=================================================================================\n",
-      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "---------------------------------------------------------------------------------\n",
-      "const          6.604e+06   1.41e+05     46.979      0.000    6.33e+06    6.88e+06\n",
-      "bathrooms       3.47e+04   3547.740      9.781      0.000    2.77e+04    4.17e+04\n",
-      "sqft_living     129.1822      3.765     34.313      0.000     121.803     136.562\n",
-      "floors         3.576e+04   3886.909      9.201      0.000    2.81e+04    4.34e+04\n",
-      "waterfront     7.828e+05   1.88e+04     41.703      0.000    7.46e+05     8.2e+05\n",
-      "condition      1.788e+04   2568.771      6.961      0.000    1.28e+04    2.29e+04\n",
-      "grade          1.319e+05   2290.923     57.586      0.000    1.27e+05    1.36e+05\n",
-      "sqft_basement    27.2065      4.581      5.939      0.000      18.228      36.185\n",
-      "yr_built      -3728.7602     71.951    -51.823      0.000   -3869.790   -3587.730\n",
-      "yr_renovated     18.3132      4.407      4.156      0.000       9.676      26.950\n",
-      "sqft_living15    34.6185      3.669      9.435      0.000      27.427      41.810\n",
-      "==============================================================================\n",
-      "Omnibus:                    16539.863   Durbin-Watson:                   1.978\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1296266.479\n",
-      "Skew:                           3.184   Prob(JB):                         0.00\n",
-      "Kurtosis:                      40.828   Cond. No.                     3.35e+05\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, 3.35e+05. This might indicate that there are\n",
-      "strong multicollinearity or other numerical problems.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit the OLS model\n",
-    "model = sm.OLS(y, X).fit()\n",
-    "\n",
-    "# Get the summary\n",
-    "summary = model.summary()\n",
-    "\n",
-    "# Print the summary\n",
-    "print(summary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared (R²): The coefficient of determination is 0.641, indicating that approximately 64.1% of the variance in the housing prices is explained by the independent variables included in the model.\n",
-    "\n",
-    "\n",
-    "Adjusted R-squared: The adjusted R-squared is also 0.641, which adjusts for the number of predictors in the model. It's useful when comparing models with different numbers of predictors.\n",
-    "\n",
-    "\n",
-    "F-statistic: The F-statistic is 3768, with a p-value close to zero, indicating that the overall model is statistically significant.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The standard errors, condition number, and other diagnostic information are also provided. \n",
-    "The condition number being large (3.35e+05) suggests that there may be strong multicollinearity or other numerical issues in the model. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  order = pd.unique(vector)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "inf_coefs = list(zip(coefficients[\"Feature\"], coefficients[\"Coefficient\"]))\n",
-    "inf_coefs.sort(key=lambda x: abs(x[1]), reverse=True)  # Sort coefficients by absolute value\n",
-    "\n",
-    "# Create a color palette with the specified color\n",
-    "color = \"#589aff\"\n",
-    "colors = [color if coef[1] > 0 else \"lightgray\" for coef in inf_coefs]\n",
-    "\n",
-    "# Create the bar plot\n",
-    "fig, ax = plt.subplots(figsize=(18, 8))\n",
-    "ax = sns.barplot(x=[x[0] for x in inf_coefs], y=[x[1] for x in inf_coefs], palette=colors)\n",
-    "plt.xticks(rotation=45)\n",
-    "ax.set_ylabel(\"Price Coefficients\", fontsize=15)\n",
-    "ax.set_xlabel(\"House Features\", fontsize=15)\n",
-    "ax.set_title(\"House Features and Sale Prices\", fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.\n",
-    "\n",
-    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
-    "\n",
-    "\n",
-    "Addressing negative correlations in house prices, such as with the year built, is crucial. Older properties often have lower prices due to depreciation and maintenance issues. However, strategic renovations, updates, and modernization efforts offer opportunities to increase their value. Homeowners and investors can enhance the appeal and value of older properties in the market by undertaking such initiatives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RESIDUALS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWEZOlh1Op0YPXo0Zs+eHeumEBEREVEMGDpYfeqpp/DJJ5/EuhlEREREFCOGDVZ37dqFd955B4MGDYp1U4iIiIgoRmyxboCSyspKzJkzB6tWrcJzzz0X0nMIgr5tMjp5fxNtv82G/WQe7CtzYD+ZA/vJHKLZT1pew3DBqsvlwsyZM3H33Xeja9euIT9PVlYrHVtlHom632bDfjIP9pU5sJ/Mgf1kDkbrJ8MFq8888wySk5MxevTosJ6nsvI0RFGnRpmAIEgfrkTbb7NhP5kH+8oc2E/mwH4yh2j2k/xaahguWN2yZQuOHj2Knj17AgBqa2sBAO+9956myVaiiIQ8IBJ1v82G/WQe7CtzYD+ZA/vJHIzWT4YLVt966y2v23LZqsceeywWzSEiIiKiGDJsNQAiIiIiIsONrDbHEVUiIiKixMWRVSIiIiIyLAarRERERGRYDFaJiIiIyLAYrBIRERGRYTFYJSIiIiLDYrBKRERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiAyLwSoRERERGRaDVSIiIiIyLAarRERERGRYDFaJiIiIyLAYrBIRERGRYTFYJSIiIiLDYrBKRERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiAyLwSoRERERGRaDVSIiIiIyLAarRERERGRYDFaJiIiIyLAYrBIRERGRYTFYJSIiIiLDYrBKRERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwDBms7tq1C7///e/Ro0cP9O3bFwsXLkRtbW2sm0VEREREUWa4YPX48eOYOHEibrvtNnzyyScoLS3Fnj17sHbt2lg3jYiIiIiizBbrBjTXunVrfPzxx2jZsiVEUcTJkydRV1eH1q1bx7ppRERERBRlhgtWAaBly5YAgP79++PIkSPo2bMnhg8fruk5BCESLTMueX8Tbb/Nhv1kHuwrc2A/mQP7yRyi2U9aXkMQRVGMXFPCU1tbi1OnTmHGjBlISUnBunXrYt0kIiIiIooiQwerss8//xy///3vsWfPHpx99tmqHlNZeRrG3zP9CAKQldUq4fbbbNhP5sG+Mgf2kzmwn8whmv0kv5YahksD+Ne//oWHHnoIW7duRXJyMgCgvr4eSUlJSEtLU/08ooiEPCASdb/Nhv1kHuwrc2A/mQP7yRyM1k+GqwbQpUsX1NbWYunSpaivr8fBgwfxl7/8BSNGjHAHr0RERESUGAwXrKanp2PdunX47rvv0LdvX4wePRpXXXUVHnrooVg3jYiIiIiizHBpAABw0UUXYcOGDbFuBhERERHFmOFGVomIiIiIZIYcWSUiIjIqpxMoK7PiyBEB7dqJyMtzwmqNdauI4heDVSIiIpXsdhuKi1NQXt50YTInx4WSkjoUFDhi2DKi+MU0ACIiIhXsdhvGjk1Febn30juHDgkYOzYVdjvHf4gigcEqERFREE4nUFyc0lh70jtYFUXpdnFxCpzO6LeNKN4xWCUiIgqirMzaeOlfeUFzURRQXm5BWRmTV4n0xmCViIgoiCNHlIPUULcjIvUYrBIREQXRrp26tSfVbkdE6jFYJSIiCiIvz4mcHBcEQTkYFQQROTku5OUxaZVIbwxWiYiIgrBagZKSOgDwCVjl2yUlday3ShQBDFaJiIhUKChwYP36WmRnewer2dki1q+vZZ1VoghhUTgiIiKVCgocyM93cAUroihisEpERKSB1Qr07cvcVKJoYRoAERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiAyLwSoRERERGRaDVSIiIiIyLAarRERERGRYDFaJiIiIyLAYrBIRERGRYTFYJSIiIiLDYrBKRERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiAyLwSoRERERGRaDVSIiIiIyLAarRERERGRYDFaJiIiIyLAYrBIRERGRYTFYJSIiIiLDYrBKRERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWCEFq1988QUA4Ndff8WSJUuwfv16OBwOXRtGRERERGTT+oDVq1dj3bp12LdvH0pKSvDFF1/AYrHg8OHDmDNnTiTaSEREREQJSnOwarfb8eKLL6K+vh5vv/02XnnlFbRt2xZDhw5lsEpERERkMk4nUFZmxdGjAjp3Bi6+GLAYKFFUc7B69OhRdO3aFbt27UKrVq3QtWtXAEBNTY3ujSMiIiKiyLHbbSguTkF5eVN0mpOTjpKSOhQUGCPFU3Pc3K5dO+zduxevvfYa+vTpA0Aabe3QoYNujfr6669x991348orr0Tfvn0xa9YsHD9+XLfnJyIiIkp0drsNY8emorxc8Lr/0CEBY8emwm7XPKYZEZqD1WnTpmHcuHH48MMPMXnyZOzatQsPPvggioqKdGlQbW0txo0bh+7du+Of//wn7HY7Tp48iYceekiX5yciIiJKdE4nUFycAlEEAO9gVRSl28XFKXA6o9+25jSHzL/73e9w7bXXAgBSUlJwzjnn4P3338c555yjS4PKy8vRtWtXTJ06FVarFcnJyRg5ciRmzZqly/MTERERJbqyMqvXpf/mRFFAebmAsjIr+vaNbcSqOljdu3dvwL///PPP6NWrV9gN6tixI9atW+d139tvv41LL71U0/MIQvBt4om8v4m232bDfjIP9pU5sJ/Mgf1kPEePquuMo0eFiPSbludUHayOHj06yIsK+Oqrr9S/sgqiKOKJJ57A9u3b8cILL2h6bFZWK13bYhaJut9mw34yD/aVObCfzIH9ZBydO6vdLg1t2kS2LcEIoihlKxhNVVUVHnzwQXz55ZdYvXo1unTpounxlZWnYcw9iwxBkE4CibbfZsN+Mg/2lTmwn8yB/WQ8TifQo0c6Dh0S3DmqngRBRE6OiH37qmG16v/68mdCjZCmedXU1ODUqVNwuVwAgIaGBnz77bcYOHBgKE/n45dffsH48eORk5ODTZs2oXXr1pqfQxSRkAdEou632bCfzIN9ZQ7sJ3NgPxmHxQKUlNRh7NhUCILoFbAKgtRJCxfWwWKJfZ9pDlZfffVVLFy4EHV1dV73Z2Vl6RKsnjp1CnfeeSfy8vKwaNEiWIxUlZaIiIgoThQUOLB+fW1jndWmYDUnR8TChcaps6o5WF2zZg3+9Kc/IT09HXv37sWdd96JJUuWoG/fvro0aPPmzSgvL8e2bdvw1ltvef3t008/1eU1iIiIiEgKWPPzHR4rWKXh4ourDbWCleac1W7duuHTTz/FwYMHMWPGDGzcuBHl5eW466678M4770SqnZpVVCRWXowgAG3atEq4/TYb9pN5sK/Mgf1kDuwnc4hmP8mvpYbmuDkrKwsNDQ3Izs7Gjz/+CADIyclBZWWl1qciIiIiIgpIc7D629/+FnPnzkVtbS0uvPBCvPzyyygtLUVGRkYEmkdEREREiUxzzuqDDz6I4uJiVFdXY+bMmZg0aRJqa2vx5z//ORLtIyIiIqIEpjlYPeecc7B27Vr3/5eVlaGhoQFpaWm6N46IiIiIEpvmYPW1117z+7dhw4aF0RQiIiIiIm+ag9UVK1Z43T516hRqamqQm5vLYJWIiIiIdKU5WP3ggw+8bouiiGeffRYnT57Uq01ERERERABCqAbQnCAIGDt2LLZs2aJHe4iIiIiI3HRZn+DHH3+EIAjBNyQiIiIi0kBzGsDo0aO9AtOGhgZ88803GDp0qK4NIyIiIiLSHKz27t3b67bFYsFdd92FG264QbdGEREREREBIQSr99xzTyTaQURERETkQ3Ww+uCDDwbdhqtYEREREZGeNE+wOnHiBLZu3YrTp08jIyMDdXV1sNvtqK+vj0T7iIiIiCiBqR5ZlUdNJ02ahBUrVmDAgAHuv/3zn//EmjVr9G8dERERESU0zSOru3fvxnXXXed1X58+ffDll1/q1igiIiKiUDmdwM6dVmzebMPOnVY4nbFuEYVDc7B67rnnYtu2bV73bd68GRdccIFujSIiIiIKhd1uQ25uOgoLW2DSpDQUFrZAbm467HbNc8rJIDT3XFFREe677z68+OKLyM7OxoEDB/Dtt98yDYCIiIhiym63YezYVIii9/2HDgkYOzYV69fXoqDAEZvGUcg0j6wOGDAAW7duxVVXXYX09HT0798fW7du9am/SkRERBQtTidQXJzSGKh6r6opitLt4uIUpgSYUEhj4h07dmS9VSIiIjKMsjIrysv9j8GJooDycgFlZVb07cuI1UxUB6tDhgzB66+/juuvv95ruVVP77//vm4NIyIiIlLryBHl2CTU7cg4VAerEyZMACCtYOUvWCUiIiKKhXbtxOAbadiOjEPTyCoADB8+3Ov+H374AS1btkS7du30bRkRERGRSnl5TuTkuHDokODOUfUkCCKys0Xk5TEFwGw0T7D617/+hWHDhgEANm7ciMGDB2PAgAF477339G4bERERkSpWK1BSUgdACkw9ybdLSupgtUa9aRQmzcHq0qVLce2110IURTzzzDN47LHH8NRTT+HJJ5+MRPuIiIiIVCkocGD9+lpkZ3sHq9nZIstWmZjmagD79+/HCy+8gP3796OiogI33XQTkpOTUVRUFIn2EREREalWUOBAfr4DZWVWHDkioF076dI/R1TNS3OwarVaUV1djR07dqBbt25ITk7GwYMH0bJly0i0j4iIiEgTqxUsTxVHNAerN9xwA0aNGoWDBw+iuLgY33//PaZOnYqCgoJItI+IiIiIEpjmYPXhhx/Gli1bkJqaiptuugk//fQTbr31Vtxxxx2RaB8RERERJbCQ0gCGDx+OU6dO4YsvvsAll1yCP/7xj7AyGYSIiIiIdKa5GkB1dTXuv/9+9O7dG6NGjcJPP/2EgQMHYv/+/ZFoHxERERElMM3B6uLFi3HmzBls27YNSUlJ6NChA6677josWrQoEu0jIiIdOZ3Azp1WbN5sw86dVjg5B4WIDE5zGsD27dvx+uuv4+yzz4YgCEhKSsLs2bPRr1+/SLSPiIh0YrfbUFycgvLypnGKnBwXSkrqWH+SiAxL88iqy+VCcnIyAEAURZ/7iIjIeOx2G8aOTUV5ufcylIcOCRg7NhV2u+axCyKiqNAcrObl5WHBggWoqamBIEgnvSeeeAJXXnml7o0jIqLwOZ1AcXEKpPEF72BVXkO9uDiFKQFEZEiag9UHH3wQP/zwA3r16oXTp0+je/fu2Lt3Lx544IFItI+IiMJUVmZtvPQvKP5dFAWUl1tQVsaqLszpJTKekEpXvfLKK/j3v/+NgwcPon379ujcuTOWLVuGhx9+OBJtJCKiMBw5ohykhrpdvGJOL5ExqR5Z/eqrrzBgwAD06dMHt956Ky644ALk5+ejRYsWGDlyJLZu3RrJdhIRGZqRR+TatRN13S4eMaeXyLhUB6uLFi1C586dsXr1arRs2RJr1qxBWVkZbrvtNrRq1QqlpaWRbCcRkWHZ7Tbk5qajsLAFJk1KQ2FhC+TmphsmwMnLcyInxwVBUA5GBUFETo4LeXkGirCjiDm9RMam+kz61Vdf4d1330Xr1q3RtWtXjBo1Cq+++ipGjRqFP/3pT7BYNKe/EhGFzemUcjKPHBHQrp2IvDwnormgnjwiJzaLA+URufXra2N+CdlqBUpK6jB2bCoEQXQHYADcAWxJSV1U3zcjacrpVSbl9AooK7Oib9/wItZYf16JzEh1hOlyudC6dWsAQPv27XH48GFMmjQJ06dPZ6BKRDER6xFNM43IFRQ4sH59LbKzvaPq7GzRHVAbOZUhkqKV0xvrzyuRWak+QuQyVbKkpCSMHj1a9wYREalhhBHNaI7I6aGgwIH8fIfiyF4iTy6KRk6vET6vRGYV8pBoUlISkpKS9GwLEZEqRhnRNOMse6sV6NvXieHDHejbtylQTeTJRZHO6TXK55XIrFSfgRwOB1577TX37YaGBq/bADBs2DCdmkVE5J9RRjTjYZZ9sEBKEEQUF6cgP98Rt7mVkc7pNcrnlcisVAerbdq0wYoVK9y3MzMzvW4LgsBglYiiwigjmvKI3KFDgleAIxMEEdnZoqFn2TOQksg5vVIqRFNfZmeLYadCGOXzSmRWqoPVDz74IJLtICJSzSgjmvEwy56BVJNAOb3hMMrnlcisOI2fiEzHSHVD1cyyNzIGUt48c3rz8pwoKwu/OoKRPq9EZhTfWfNEFJeMNqIZqRG5aIiHVIZI0LM6gtE+r0Rmw5FVIjIlo41oKs2yNwM5kALgM/InBVbAkCENKCtLnLqrkaiOYLTPK5GZCKLYvOpbfKioOO1Tzy6eCQLQpk2rhNtvs2E/6S9SKwIlWl8pjSRaLCJcrqaAzYh1V/XuJ6cTyM1NbwxU/Y8079tXHdLnzCgrWEW7HYl2PJlVNPtJfi01mAZARKYmj2hSeDxTGbZts2Ht2iS4XN7bJEIB+0hXRzDC5zWRF4Agc2IaABERAZACqbw8p8dl7sQrYB/v1RESfQEIMicGq0REccDpBHbuDH/metPIonIwJo0sWlBWZpKkXI3iuToCV9Iis+JPKCIik1O6rJuR4cKECQ0oKqrXlIsY7yOLwcRzdQQuAEFmxZFVIiIT83dZ9+RJCxYvTsEll6RrurQbzyOLagSrjgCYt8xUov8QIfNisEpE5Idel9YjJdBlXdmJEwLGjFGfi8gC9vFbZirRf4iQeTENgIhIgd1uw8MPAwcOtHDfZ7QZ08Eu60oEACKKi1OQn+8IOiLIAvYSMy/04E88pzhQfOPIKhFRM3a7DWPGpOLAAe/7jTZjWv3lWm2TouJ1ZFErsy704E88pzhQfDPGGZeIyCC8L617E0UBgqB+lDLStF6u1ZKLGI8ji9T0Q0SakNf0ecjOFg111YDIE4NVIiIPZpoxLV/W9bfaUnNag1sjFLAn/fGHCJkNg1UiIg9mmjHtmV8qrZyt3CbmIlJz/CFCZsKcVSIiD2abMS1f1s3M9D97H2AuIhGZF4NVIiIPZizdVFDgwH/+U41Zs+qQkeHy+luiTYoiovjDNAAiIg/epZvgNdHKyKOUViswY0Y9iorqmYtIpuF0wpSfV7O226wYrBIRNVNQ4MCGDbV4+OE0r/JVZpgxzVxEMgulZYKNVstYiVnbbWaCKCoVaDG/iorTiqVn4pUgAG3atEq4/TYb9lPooj2SIQhAZmYr2O1ncPgwR0+MiseUOTTvJ3mZ4Oarr8lXL4yaumLWdqsVzeNJfi1V2zJYjQ88YZsD+yk0sRjJYF+ZA/vJHDz7yeEAcnPT/ZZck6tX7NtXbagfh06nOduthVGDVUNPsDp+/DgGDhyI3bt3x7opRBQj8kiGZwFzwHirScUDpxPYudOKzZtt2LnTCmeMsgmM0g6KjKZaxsql1qRaxupXXIsWs7Y7Hhj2LL9v3z7Mnj0bv/zyS6ybQkQx4r2alPcXhNFWkzK7WIxeK6V2bNvGfMB4Z6Zaxp7M2u54YMiR1dLSUsyYMQNFRUWxbgoRxRBHMqIjFqPXdrsNubnpKCxsgUmT0lBY2AKXXJKOMWM4ih7vzFbLWGbWdscDQx75V199NYYMGQKbzRZywCok2A8beX8Tbb/Nhv2kzdGj6t6oo0cF3d/TROkrNaPXDz+cgptu0m/02nuSSpMTJ+TXV9+OROkns/Pspz59pFrGhw4JEEXl3M+cHBF9+jgN1a9mbbcW0TyetLyGIYPVtm3bhv0cWVnqknbjTaLut9mwn9Tp3Fntdmlo0yYybYj3vvrwQ6C83P/fRVHAwYMCvviiFQYMCP/1nE7g4YfhZ/KG/28vuR1ffdUK117r+/d476d4IffTypXAiBFQqGUMAAJWrBDQrp3x+tSs7dbKaMeTIYNVPVRWJtbMUEGQPlyJtt9mw37S5uKLgZyc9KAjGRdfXI2KCn1fO1H66ttvbQDSgm43YoQLy5eHnze6c6cVBw60CPnx335bg8sua2pDovST2TXvp379gA0bbJgzRzk/uV8/h+7HtB78tTs724XRoxtQUeHCli3mLXMXzeNJfi014jZYFUV/v9zjW6Lut9kYoZ+MvgKL3L4hQxrwzDPJEATRK2CV6xouXFgHiyVy76cR+iqSzjlH3c6dPClgzJjUsOtIHj4c3vXFc84RFfsj3vspXnj20+DBDtx4o0PxPGTkvmze7v37LXj++ST85S8p7m3MPinQaMdT3AarROSf0VdgUWqfFKw2bWOG1aTMIC8vcB5eEwFA+NUXQp18ItewzMtjHat4YtYV1+R22+02LFmS7BPYyZMCzb5IgFEYshoAEUWO0euW+mufywUAIiZMqEdp6Rns21fNLwEdWK1ASUld463AgaQe1Rfk4FgeGVd4FZ92yNuWlNQZavSfEluwyYmA9HfWCQ6f4YPVb775Br179451M4jigtFProHaB0gz/u12m+FSFsyuoMCB9etrkZGhbtQznDqSnsFx84BVvp2Z6X1/drYYsREqLkBAoWJpvehhGgBRAmk6uSqTTq4CysqsMbk0Z/T2xbOCAgfOOkvEiBHBJz+FW0dSDo6lVI+mL3o5tSM/XzmPUW9GT4chY+MiAdHDYJUogRj95Brr9nlOOmvfXkRBQURexue1jDLBrW/f4HUk9cobLShwBAxKI/1jxF+tV+YaklpcJCB6GKwSmZjWgMfoJ9dotU/tMp/nnQcsXGjD4MH6Bi1GHdGTL9GPHZvqt/qCnnmjsZpcw2V8SQ/BJidyUqB+DJ+zSkTKlJarzM1NDzhBKtjkFqluqStmJ9dotE/LMp8HDwJjxug76czoE9zkS/TZ2dHLG4025hqah5FzitXkX3NSoD4YrBKZUKgBj9FPrpFun7/3zf8yn9J/9Zp0ZqQJboGCgIICB/btq0Zp6RmsWVNj6OoLTqe0CpeWYCbW6SakTig/yKMtEX7cGYEgikYq+6qfiorEWs1EEIA2bVol3H6bjR795HQCubnpjQGX/0tP+/ZV+w3qjHoZWhaJ9gV734IpLT0T9iXrnTutKCwMPoFJj9cKxOj9r1ao+2GUfkgkWs993jnFvukoRgsEjZiDHopoxhLya6lhnJ8nRKSKHjPmg01uibVItC/Y+xaMHqNsRhjRi5eJReHsR6LlGpotkDJjTrFZFzcwCwarRCajV8ATy8ktar44w2mf0muEGwDqMeks1hPczBgEKAl3P6I9kSyW9BpFj2bAyxJ21BxzVolMJpoBj96TG6KRg+bvNfbvD+10p+eks1hPcIuXiUV67Eci5BrqNZkv2rmjRrgCQcbCkVUik4nWJUy98xqjcfk50GssWZKMzEwXTp5Uft+alvj0HGWT/qvXKFusR/TiJQjQaz+Mng4TDr1G0WORNmKEKxDx+JkwM46sEplMNGb0611eKRqz4NW8hiBIM/zVLvN53nnAhg36fhnHckQv1kGAXtS2r02b4NvJ6SbDhzvQt2/8BCV6jD7HqnpFLK9AmKECQSJisEpkQpEMeCLxBRWNy89qXuP4cQtmzapXfN82bKjFf/7TVK7ptdfO4McfEZHgMValoWKdhqCXYPshmzYt9nVrY0WP0edYpY3EqsSe0WsgJzIGq0QmFamAJxJfUNG4/Kz2sR07uvy+b1arFAi1ayfiyBEBH32EiNU8jcWIntHr7KoVaD88HT6cuEGGHqPosUwbifYVCCPVQCZfiXcEE8WRSMzoj8QXVChfnJFcStbf+6acp5tu2PqjoeTWyUGAtJ9NfZidLRp2P5XI+zFnTgoOHfL/w8osFQ70pkdueyyXP7Zao5tTzAoExsZglShG5BP0oUMCKisFZGU1fXnE8ks1El9QWr84Q5ncFe6Xs9nqj4YzAS6SQUA0J6cUFDhw9tkibrnFf4H/RA0y9JjMF43JnME+x9EqsRcvkw/jFdMAiGLAM4l/ypQ0PPxwKqZMMUYyfyTyGj0v2zbNum8iisCVVzqxZYsNjz+eHPWlZINdAhRFYPr0FHz0kTHWJg8nt04uR7Zli7TNzTfrl4YQi8kpFRUMMvwJ91J6rJY/jkWOaLxMPoxXXG41TnC5VXMQBGDHjlYYMUJUDIwkIgQhtssJyl8iABRHZEJt2/z5yXj66WT47rfY7L7mt5tePxJLyapdflPNc+nF3whlOMvtRnKZ1Vgtj/nxx1YMG8alUwMJd7Rbj89N8+8oPZaN1pPcnmCjyNFqT6wYdblVBqtxgsGqObhcQM+erXDggHIw1kRETk5sT4x6BzbBvpy0CBZ4BAr0lO7fvNmGSZPSVL22UvCl96XvQO99ZqYY0rr2kQwmYxl4yMfUwYNiQgcZkRbuZ7z5d5TaH4jR/JERqR/pZmLUYJU5q0RRVFZmxYEDQPBgLTp5doG+gPTOaww2gUGLUJaSDRQAarm013zSzrZt0V08Yfz4BlXP4/keRXqZ1VhOTrFagSefBEaMQNwvnRpLeueOGjFHNF4mH8YjBqtEUaT1xBvJE3Wg4K15kHrzzeHPpNZzX7TmjQULAJ99tjbgRJLm5OBr+fJkLFmSrNukLDVB5auvqjtte75HaoPJdeuS0LatqPmHSawDj+HDpcUb5sxhkGEWRs0RjedVzcyMwSolBKMsn6f1xBupE3Wg4G3MmFRkZoo4cULfvEY99iWU2cdqAsC5c1OwYEEdxo1Lhb98WSVr1ybpOlqpJqiUKke4cPy4+hnaaoPEhx9Odf+/lj43QuBRUODAjTcyyDCLaC0bHYpoVSAg9VgNgOKekZbPy8tz4rzzAhcyl4jIzo7MSkJqil+fOKH/7Fy1qw75E+olXbWLHGRliZg1q97vdkpOnozN4gkjRkipAGpnaIcSJGrpc6OsjBWvS6fGo3hZoIKig8EqxTWjlEbxLBc0fjwaA8VAAYSA2lpg2zb92xcseJPu138FF7WrDnm8qtctNeV25Pd582Ybdu6UykxpuUR94YUuVdsCIjIz1W27Y4f6cldqg8obb3RqKkkU/IeCUjkx9X3OwINCEe1Vqsi8WA0gTrAagK9Iz1BWm1qglBsqBzqel9qV2gfoPwNVy8x3JeHOzlV6P5pfepcmygCzZtWjY0eXqku6/nJwR41qwOLFKUHbNWtWHdavT0JlpZrf8CKGDXPgtdeSVGyr/pK61vI5WtJb/M10VkNtn0eyNJY/PPeZQ6B+MkqaFhm3GgCD1TjBE7avSJZGUfulHKxc0P3312PduiScPBm9kj9aaooqWbOmBsOHhxd4eH457d9vwfPPJ+HQodADnGDvc0aGiJMn/QeATX/3frwyEWvW1GL+fHmZz+CBn5YfHpEsn6P8QyE4LX0e7cCD5z5zYD+Zg1GDVU6wIkOIxBdcpGYob91qa5yI46283HsGuJqJPc89l9SY96gsEiV/gk1sCObbby3YudMatI+U+hSAYpWBoqL6kPtfzfssCFLqRfPSRoA0giv/U5evKuDYMcEruA5Gy4SrSJbPaT7T+dgxwWtSlT9acl45OYWI9MZglWIu1EuHwQJctV+wx44JcDqhKjjautWGCRNSoRzUCBDFpoBEzcxuLUtF6hXQB1ozvCl3UXllLUDAsmUpWLYscB8p9Wl6ughBEFFV1XRfVpYLf/lLHYYOdQQNcPztv5r3+fhxAbNm1eGFF5Ka5S9L/y+NbKv300/a0/3V/PCQ97G+HlixohaCIH0+9Ryh9AwmnU5g9epkQ87IJiKScYIVxVSoE6DUzPCXRxADT2SSyvWoqQ5gt0sjqi5XoMCmaQa4njUl7XYbLrtMv4oG/iY2ZGTIt5u/Z77vob8+8ten1dWCV6AKAJWVFowbl4r585MDtjdQf6t9nzt2dGHBgrrGfQnv+pb6iVi+/LW3+T6OGNEC06alIjkZEZvZzolRRGQGDFajTGm2cqJyOoE5cwKXUFKaiaw2wLVagcJCebWfwMFXsOBYvtSs1qFDguqR3ays4LO07XbfiT/hVjQoKHBg375qlJaewZo1NZg1qw5paYBSNQC1FQICXZIP5Omnk7F1q/8fJoH6e/9+daex77+3YO5cuQ9D+yEhl2C6++4GZGWFFrAqfS70qloRyvmFM7KJyOg4wSqKIjlT1ozJ648/nqxqlrbnBCgtM/wBbWvRB5rMpHVS0sKFtRg3rkHVzO4FC+owfrzSLO1Al+SDt1kLfxOU1BbIl/sonMlbbdq48O9/e++H2v4WReDw4UA5uMHfy+Ck3Fc5gCsuTsHatYFHhJs/PifHt6/0qlqh9vziL53CrDOyzXjuS0TsJ3Mw6gQrjqxGiVHqfRqF3W7D4sXqvug9L5uqLfD+8cdWFfVElR+rVMRd6yX9zEwRZWVWDBnS4J7Y48nzEuvQocojW41bhtxmtQKPhmqbpBZO6kNFhe9+qO3v0aMbgtSuDT8lIyfHe6QxP1/rD0wBf/xjg08AqHYfA/Wx2vNLoHQKFtQnIqNisBoFalYMCqfYutlovaTuedlUbTA0dmwq3nortG9bpdfQugLQvHkpKCxsgWeeSQEgQGj2lM0DH89L8kVFdY1bqQ+wwgkStQb1SuT3J9zlNJvvh5Z81OCrT2nfv7ZtgYkT67Bp0xmsXFmL+nq4L6+HsiLXs88mwW63eV2u37FD3efU33uh9vyydSt/MBOROfHsFAVqZivrXZ7IyIK9H02ky6aeM5HVBkMnTwp45hktl2ibKL2G+nJP0mMrK723cbmkv02c2IBbb03GxRdXw9LsLZBHtkIJPMMJEsObCCYiM1OE0wmvAE5t6kVzzfdD25rz6lefCta2jAwXJk5swKJFKXj+eRfuvTdV8fK6XFVBbbrEyZMCxoxJRWamGHBBCCXN3wv5sv2OHerOLw88ELi8l5qyWnoxa8oBEcUGR1ajIFL1Ps1Ky342n4msfjRLGs20WNTP/A60frnnrGn/z+d5v+/ldEGQLsNec03gMllaAk891lwPbzRUwIkTFowY0QKXXZaORx5JxqhRDY0jyVqeV3k/tKw5r/V9a/76nk6dErB4cTIefBAYM8Z3NLK8XAo6XS4phzU9XfUrAwBOnGj++Qj0OfV9bzwv5y9fru4qhTRBL/RUA72oqeQRS5wES2Q8DFajQNvoUPxTu5+zZtX7TDyTg8bA+YkSURQay0z5qxnaRA5eRo1qwJYtyl9S8qzpnBzl123TRh5d8x8QHDxowSOPIOCXYK9eTpVBdmilhZp/Gffqpf1ytpLKSgueeSYFixenICND1BDASROXlPZDS2mlvDwnsrP9lyqTA9t165Tyg31HG0URWLbM32IBUj9PmJCKV16xoboafl9XoSV+n8+X9JyPPNL03vjLT9WD5w/JUIO2QI8zeu6+0QNpM/L8PHz4IRj8U0hYDSAKtK73HQozzbQM9n4A0vvxr38pvx92uw1jxsir7oRegsjztVu3dkEU4XVp1rNgffP2l5VZceiQgMpKAVlZUnsPHRIwZUqa6jb4qwShdkZ9q1YuTJ7cgKKietWfG38zxgsLG/D003LahB5BkPQhHDasAR9+aPNapav5e5+d7cKiRYErYmzdasMDD6R4le9q/v7Z7Tbcf3+K4uX15kuVOp3AunVJqlZvCk6PSgOBZWW5sGRJHfLzHZoqXADSvmdliaioCD42IVd1CLVySaDHBWt7qOdBvc59wZbsZRkv7SJZAYcig9UAEhgLb3sL9n4IArBokfL7Iddmbdw6xBZ4B0stW7pw/Ljgc2nWs2C95+hAWZm01OiIEQ5MnNiAESOk2dPKs/n98zeapDZN4vRpCxYvTlE98hNoVGvVqmSkp+t5ZpLSHvbsseGLL7xrubZvr+117HYb5s71DlSzsqQC/56B6tixqQqX1yUZGd4T2qxWoG1bvfY3tPxcLSorpc/K8uXJmibDycfXY4/VqU6nCGehjkCPC9b2aKYiNMdJsPoz+ig6mQuD1Shh4W1vWt8POVgcPz61cU32cIID78dWVQmK98uefjoZF18c/NKg1tnh/r4EtaaDqDn5B/syFkWgujrc99WbHHzs3StNHExOBpYskZb2bN7+MWNS8fjjyaoXgDh+XMD48dI+19cDM2cGWohARFpaU6kp+bP0zTdmOv1J+/Xss0maHiUfT0OHOlT9YAZCC9rUBHtq2x6L3H09yodREwb/pDemAURZpGbBmikNwJOa90PpUlL0ec/29ndpMNQUBaWFD4JXHmgS7BJqOMX6w7VmTQ1uvlnd5WvPlIBgxfIBES1bSv3S9IPDv9LSMzhxQlD4LKmbya+PyKcMAMDvf1+P2293+BxPwS7Lqv2ceH5eAX0/X82fOxg9zn2bN9swaVLwFJ41a2owfHhiDSyEItTPEcWeUdMAOA4fZXJ5olCZpeSLUjsB5bYHej+888hiyX+5n0GDHNi7V9qvtm1FZGSIOHlSWzDiOZokp0lIQa+6QCpY+TP9Rqu0B1vt2omqy5XJo8Tr19ciM1MM8hh1Qaps2zYb1q71N7oXnYA1KUlEQ0OgCX/6tOG665w+nwOnU1qsori4zivX2vMcEmrlErWPy8hw4dSpwLn74VS2CBUnweqLFXBIbwxWTcRut2HOnJTGy+ASeSRqyBDj/NpXGr3JzJRqYHpOfgmWaB/aOvMiWrcWkZwsnQjVjkyGQg4Qr7gi3SufMhTffGPBzp3WsH98ND/5yz8avv1Wbfv8BW0iLBa5Xqy20d68PCe2bFF7qhEASD8Ciovrgm6txUsvyW1QmokfThDir2SZ73vZ0GDx+1qpqSJqa/X5vJ5zjvdrBBpR9fy8hRq0qX3chAkNWLIk2WeSXaxz94PVUY5lIG1GDP5Jb2ZK2kpo8uVlf/l+RklW95djeOKE7wSmYLmWoa6s1K+fEzffHGz5Tf00XwBAG6l9y5dLK15dfHE6Fi9ODnkSmefJ37MMz7Jl8vMFqhHrP1AFEKAMmPLziSJw++1SKbBjx9Tvh5wfGN776t0WQERVVaDPUuhVJQQBuPFGh88iD1peSxCA1q2lqhSBSm9lZalb+MBzxTQtE1201LX1pPZxRUX1IeWqR7rmabQnwcZ7LddQP0dE/jBn1QScTuCSS9Ibgz3lYKJ1axFHj1pw4oT//Y50CkHwHENfgXItH3ooBevWaV2FKpr5h+HS79Jv8/fRXxmeaOVM+mujllHyVatqUFKSoil317/IfC5ycqSqBFlZIg4eFPDGGzZs26ZtEpSnWbPqsGSJ9JlXGnmcMKG+cQnfwOTcymDHpNLxJ392/LXB34RQLY8LNVdd6WqMnjl20Si1lCjlnEL9HFFsGTVnlcGqDiIdBH70kRW33BI8Wf2994ArrlDeb71OkIH2NZxJFs0T7bdutWHChNTG0bxYil1wp540uudZQzTYxKTY7o+615cnRCl94cWW6C6FdeKEgLlz9Zv8t2ZNDZKT4fdYzcwUNU1cCXWii951VuWAXu05UkvNU72/XCN5Pk+0Wq5Kn4dzz3Vh4cL4CszjiVGDVWNcOzaxaPxK3rlT3Znyww+BK65QbqPSJCV5ychZs+pVFZZX2teMDBduvNGBfv2c+P770L+wPXMt7XYbxo1LhXECRKO0w78pU5pW+wo+mSm0/TnrLBd+/TVQAKz2uYNvY7GIqKwUMHSoVOJM+twZpx/++McGnDollc/S84Terp2Ivn2dyM93uAOmtm2lEemKCgEul5SnfviwutzKUCe6FBQ4vNqgNmhTelxlpW9AH+gcGazskTyxMT/fEZH81nAnwfoT6/2KBc/Pw9GjAjp3TsPFF1crpMwQBcZgNQz+gkA5j3TWrHp07OgK+9d5OF+GgScpSbcXL07B888nKa4iJI8yvPmmFc8+63tJ/uRJCzZuTMbGjaG3EWjKtZTbG3vmuuBQWpqE4mLpB8ebb+p9WEuTq3791d83jP4jtS4XMH58KiyWWq8vvEOHBMyalaqpCkAkrFqVjIyMQGkN2t6T5kGmHDDZ7TZMm5bqM1lRFOW0isCTlNROYNm/37dvlYI2NaOOno+z222KAb1n1Yfm55xgP7aCVb4wqnjdr2Dkz4M0igZUVIT3nUaJib9vQqSm6PHixSnuIvIXX5yuWPRcDbUnrmuv9b1P7SQlpckWW7facNll0gSdZ59NgbqVeuSJOmp5J9qHOqkqcozSjkCaCpbPn5+suXB8YJ6Tq/y/vv6khQpmzUpBfX3TF96IEQ5MmlQfgdfT3japskX4E7b8TeDxNzFKLouWkRF8klJenhPZ2f4nbUlEPP98UtBzk+eEvUCLY8hCLQwfr2WP4nW/iKKBwWqIggdVzb9gpKUxL7lE3dKYnvr2dTaWfvI/k7t1a5disKr+xOf95TFvXjLGjUsNoSST/HrqA9aCAmnUzOmET7WD2NEy890Ynn46CU8/rXVCmpEJqKiw4Iormo4Zu92GF18MFoxHY9gm/AoQMjnIzM93uGeIf/SRFXPmBA70RBF45JFarFpVg9LSM9i3r9pnlNJqBUaPbgjSXgGHDgVenSmUpTNDXRUqXssexet+EUUD0wBCFOqv3xMnpBSBDRvUJ9JbrcDSpf6KxEsntqVL62C1+q7AouXEJ1+GGj48Fbt22RD6F7L68kaAgLVrk7F2bTKyslzo3Dl+Ln9F23vvhdNnSmI9EUtSWSkdM8OGOfDaa8FOWUb7ovd9DzMzRYwf750itG2brXFSnNofhwJOnhTwyCOpivVSPXXsqK7clb9zWqi5lqGOJMZrzdN43S+iaODIaoiUcrzU8X/5K5CCAgc2bFCuTxgo8M3LcyIjQ92XlWzXriToE6QEqukJn9eorLQ0vjaFRu/AMvaBqkRqx2uvyZ9L/+0SBGDy5HqMGBHrVAFZ87aKOHFCQNeuLgwf7kDfvlKgqjRqqVawesXhjuhFe4Q02jVPoyVe94soGhishsButzXWQgx1FEf55B5MQYED//pXNUpLz2DNGunS37/+5XvpD2gqOr1liw033hirEiH6FmCnRKbuMyOKwJo1ySGsBKX1WA792AeafqyGtkpbs5YEyP8Ewi/QHu4IaSivW1DgwJQp9V6LG0iP8a58YTYFBQ5NiyIQkYRpABrp8eUiCyWVQE1ZFaUSU+oKshvjsi9R6AQAInbt0vZDsFUrEadPq1/IIrzZzE2zvgHoUqM12EzyUaMasHix/APbO41IFKW/+xPuCOnYsamal1e1221YtSrZ5312uaRKDLm5LtMGdqGWBSNKZBxZ1UjP2eqRSKT3NxGi6aQf6DUZqJL5iaKAykoLWrYMPCmxZUsXVq2qwaxZdY2lsNQdj9nZItatq0VOTrBZ9oG99ZZV95nfzZ9PnsG/eLG/ah7SfYsXp/id2R/uCKnWkUQ15fa0plEZjTzoIKeCMFAlCowjqxqp/3LxP0oZiUR6pxN4/31g+vRAJ3lppSPWuKNE0KePE+++a4O/SYkrVtQhP9+B3Nz0IFcd5KVOG5Cf73CPglksaKyzHNoViZdeSsbAgTWaHxeIZy69vzrQ3gF2U7v91T4NZ4QU0D6SmKj1SInIP46saqR2NLRlS/+jEIC+ifR2uw09eqTjhhuC134URQEpKSL6929Afn4D7rqrHvn5/i8BEpmVFKgGpu5KiYBZs+oxf740OWbLFht27rQiP18aNczJCe3X3+nT0opUakYtX3nlTONEycD1UhcvTobdblMxOuk7yhoo9zXcXEstI4msR0pEzXFkVSO15Uf27KnGihXJWLs2CSdPNv0myM4WdV2K1f/oiX91dQL+8Q/OuqdE4RusyeWWiovrVD3DyZOCT2kpuWTUvn3VWL48Gc88k4RTp7T9/i8rs2LBgrrG5YW9R2g9f9hed50TeXlOvPVWoFN2036ddZYYUi5soFHLaOVa6l2PVM2qW0RkbAxWNVJ7SSw5GZgxox5FRfURO1HqOdmLKP74PybkoEzt6Nzatb4/7uTL5lOm1CtOBlLju+8EbNwo55N68/xhu3WrLUigKpH3a+fO8E4y/t4XeYRUDgC3bLHpfl7Tsx6p0mRT+UeGWSdoESUipgGEQMslsUgm0uu/NCmTWSmxzJ+fgvR0McBleMBiUa4LLIrSsqurVyeH/IPx9deTFOqrSksWL1ggBVROJ/DAA2qXO25qdzgCjVpqXXZVK73qkYay6hYRGZMgivE53aai4nTEJxLF+vLSpk02TJniu2oVEanV/CThfaVEaWRPr9dtmuzof/Rw375qlJVZUVjYQtOzv/rqGYwbl4oTJ7QtG+z5uv5KSjWlHfleVdKzVqi/UdEFC+qQlSW6z7t9+jjRrl0rr3O+04nGtA3l/fdM19q71xgpArH+Pok0QQDatGkVle9mCl00+0l+LTX40zIMamqeRordbsPDD6fE5LWJzEF5RNSbXLLKd5uMDBEDBgjYtCky7QoUCMuX86XaqNqeOydHRO/eoZyXAo9ahrrsaqiUcmQrKwXMnesbwK5cCfTr1/RYtRUFrrgiHZWV4acIhBtoMl2BKDCOrJqQv9ENIvJkzEUuMjNdGDHCgWef1RqIBiOd8DZsqEVmpqh5NLZ1axcef9x/cLRzp7oR3tLSMxH5Ee//vCf18/r1NRgyRGr75s02TJqk5qqT8qQ2LSPE4Qaa0RytjiWOrJqDUUdWmbNqMpxURaSWnrnc/hcX0JrrfeKEBc8/r/WilrrXyMwUkZ/vCKmsU7DgKtolpeQlozdvtuGjj6yYMyfwQgHjx6di61bpfVW/4Ir68l1Kws2LDTZaraUtRPGMwarJqJ9Upf1LlIiUyMda8+NJut2jh/ZIorZWPvWGFlT52+bECQvKyqwhrY7XfMJoc2qf85tvLNi50xpWgNV8Etctt7TAoUOBz3sul4Bx41IxdmwK6uuB7Gz/9WsDkVIELO7lcP3RI9AMdj5X2xaieMdg1WTUjlq0aMFAlUg//pcq/emncE+j+h6rR44IQZdI9RRouVRPlZWCR2UEJdLfli9PCatCgL/RSnUEvP56MkaObIFff5UmsPm+B+re72DnWj0CTS6AQKQOg1WTUTu6ceaMniWtiEjie8Xi+HFB8X519D9G27UTA5Z/8np1laWg7HYbxo9Phcvlbwvf1wilRJSeaU7V1dLjMzK829amjT6LDugRaGpZAMEzLSLckWsis2E1AJNRUzA7I0NsXHaViLzpMelKaYQ1nNFRzzSD0NvWvFi+XA9amvyj/LxqVtRTH0CGXyEg2Cx+baR+SUmRSnkdOSKgslJAZqaIefNSUFkZuKxVsJFmPVbaUrsAQmWl/xXU4mHyFVEwDFZNJvAKWtJ/x49vwOLFLGtF5CvUYFAOJP09Xo8R0vACVVEERo1qwJYtNrRtK92urwdWrKiFIADHjglo00Z0/79cYgmQRuz8lV1SF0AGuhSuvHyrEv0vdws4fFjA7t1WvPBCUrP9kEfDlVchDBZc67HSlpoVEQsLGzB+vO+S2vLIdbxUCyAKhMGqCfkbMTnvPGDBglrceKOj8cSsrSA4EZmFd5CVni4Fp/5+pMqjcP36eQdOasou6RFAqn2OUCaGqaG2Xq2akWaZ2qW3gwW9/s7n2dnSKmZz50avti2RURmyzmplZSUefvhh7NmzB1arFUOHDsUDDzwAm019bJ0Itdw8C1G3by+ioKAFTpyQ9ttut2HMmNTGLRmwEoXj1lvrsXGj3nVRQ5eZ6cL48Q04dQp46aVknD4d+BhXqtmptr6n2vqqgaitvSqvPBU8zUn+m9pzm3KKhSCIyMqSgkJ5FFRr0BdKnVWlRQQA3/vUrl62adMZWK0w9OpXrLNqDkats2rIYHX06NFo164dFi5ciIqKCkyePBnDhg3DuHHjVD9Hoh0QSh8wu92GadNSUF3N/FWiUGVluTB/fh1mz05BVZURrlZIB/jUqfVYtSpZ9WQkz6VUAXXLkXpu6y+ADLRSWLDlW5XIQTQAxdHK9etrAQBz5qQ0lrMKRF0ecLgLGWhZwUpLcKt2cYOMDBdOnjR2PiuDVXNgsKrSzz//jEGDBmHHjh1o164dAODNN9/EkiVLsH37dtXPU/HzESjumtUKpKY23a6u9v8kFguQlhbatmfOwG9PCwLQokVo29bUQGlKrvsDVuNqeqqaGrgcLtx3nzQjV2y26ssZtHTfSkEtrPB/sj6DdA3btoD8BZGMOtjg/4SpZdsapEFsLGCRhHokoUGXbWuRChesmre1oQHJqPe7bR1S4GzMtNGyrRUOpKDO77b1SIYDSZq3tcCJVNT63bYBSWhAsuZtBbiQhhpdtnXAhnrIl7JFtMAZXbZ1woo6NB33LeD/WA68rXfw44IFtUjzs60nsXHbpmM5DWcg+JmYJUJATcBtRVgEwCUCIixe26aiBhb4nbaPF0ul9hcWtlC1bd++TtjtNkwZA1jh9DmPyO9H83OEfCyvXi2lJnlp0aIpyb6uDnB4//2tt2yYPz8Fhw5b3OeInBwXHp33K24aJH0uXS5g5cpkLH8i2d0G7+O+rvFYVg5WPbdd+9QpDBvs/3OJ1FS4o8/6eqDB/znCa9uGBml7j/2aPDm1sScF93EvCCJsYgPWr/7V670qK7Pi1tukvg18jvD+XDYgCQ4hSRodv7FWeo/9SU4GkqRzBJxOoNb/cY+kJGl7rdu6XBBqa/wHQc22RU2AvrDZgJTG414Upe9PPbbVEhvEaRwhb9rmgvZN/RRgWwBAetNxj9paBCxT0WxbweVCmwva+d/ek2gw7777rnjllVd63ff111+LnTt3Fk+dOqX+iVq1EkWp67z+1d0wSDx27Ff3P1eLForbiYBYf9XVXts6s7L8b9utu9e2jg7n+922oUtXr20bunT1u62jw/le29Z36+53W7FNG7GiwmPbq672u20VWnjdZcdN/p8X8Lr5vxgRcNsWqHLf/CvuDLhtGxx133wKUwJuewF+dN9cjBkBt70EX7hvzsO8gNv2xB73zRlYHHDb/tjuvjkFTwXc9ibY3TfvxF8DbjsC/+u+OQL/G3DbO/FX982bYA+47RQ85b7ZH9sDbjsDi903e2JPwG3nYZ775iX4IuC2izHDffMC/Bhw26cwxX2zDY4G3PavuNN9swWqAm77vxjhdVegbe24yeuuKvg/R2xHf6+7jqKN3233oKfXXT/iAr/bfoFLvO76Apf43fZHXOB11x709LvtUbQRp0+vFVevPiMCorgd/f1uW4UW4jPPnHGfT3757e8Cvm+eN4OdI479dMj9vDUjbw+47f88/rP42mvV4uHDv4pn7h4XcFvPc8SKlPsDbut5jvhq5EMBtz3xznZ3e6vmLQy47cnX3nBve/qxxwNu63mOuAsbAm6r9RwhCC7x3HOd4vHnA297+rHH3e09+dobAbetmrfQve2JdwKfT6pnznZve/yj3YG3nXqve9vKff8OuO2Zu8e5t634an/AbWtG3t70/fnToYDb1g4Z5vVdG2jbeI4jnFlZoiiK7lgiUBzhatHC63nrbhgU8H3z3LZ2yDApTlPJcBOsqqurkZbmfdlDvn3mzBmcddZZYT1/crJN9bBzUpLVe1vB/+WkJFuzbS3+t7VZLd7bWv1fyrJaBO9tbYGvpWVleWyb5H/blBRAqJc+QUSUOJYtS0GbNuq27dw5rWnb86zA5+oel5IMBLiQIJ3TGkdZXClJAZ/rjjvSgLaNo0JpgfOGp90DnMyQBr6SlgoIcNEBgHRKP+88oHPnwG3IyEgH5PNweuBKK2ef3aJp25apAbf1JAZJV9CafCKKAg4eFPDf/7ZAZoDtWrZMRUu5vWcHzo9NT09BurxtRnrAbVu0SEELedvMINumJTdtW9Uy4LZpaclIk7cVA4zsAkhNTUKqvG1a4JSRlBQbUlTGBvEcR1ga2+eOJQLEEQKaXcZPDhxSem2boi38NFwawLvvvovi4mLs3r3bfd8333yDoUOH4pNPPkGrVuo+IJW/JF4aQFZWK1TWeqcBBBq+f+3dszF+fCoAgWkATANgGkAI26pPA/DdNrw0AP/bBru0Lx3Lose2vs8rQMo1/eenTVe05Ut8ZWVWjLzVN6hpfo7YtPG0/7JNjWkAdrsN8x8Cjh5qam92exceeaSu6XJ4kJQB97mv8jTE1DTY30zGmDGpsIn1SPI6n4jwDPlqkQpRsGDDhloUDDqj/tJ+iGkAW7faMO1e74EYpXPEyhU1GDrUNy3iofln4b+HpOPICgfaZ9TgxEnlAMXzuF+76jQKbwrw3RXFNAB3PzX/yDENQHnbGKUBZJ3fvqmfIpwGkHW+ujQAw42sdurUCSdPnkRFRQXaNP6k/+GHH9C+fXvVgSoAiC1a+B819Ly/ReBffCFvmxZkBmeo26b6SbYXAKSnQ6zxOBH427bRzTc78K9/1WP16mSvL+dgtGxbjxSPgEK/bRuQ7A6AYrWtA0nuLwQ9t3XChjMqD00t27pg9Qoo9NpWhCUi2wJChLaFIbb1DDAlnge694iKlm09A2L/pIL5tUiDIEijcO6/NE5kemhRLSwWR9P5JCUVTiewfU8yzgQ5TuuQioMnRYgt/P/4tL+uXI1g/xERd0xu6V0/VG5Dcor0r/mupKdDrHHB4ZAmXoki0IAUNARopzQJqRaDBzsgIhlICnLcy21I0rCtLUn6B6B1B2vjj3Nl8jmidQcBYgvvL/zfDQduuLkOZWUO9yQul8uKW24JXimgbbYVotrvLotV/feclm0Fi7RtYz8pfjc331bN80KI0LYwxrbRjiMA96EoX7sPFkd4PW9KkNig+bYaLhcYbpr4hRdeiNzcXDz66KOoqqrCf//7X6xatQojRoyIddPi0vz59Zg8uR5QGF2RGGrgnShutW4tYupUefTd/3HnvXxqOJUJpEoArVt7v1Z2tqhYaN5utyE3Nx3Llqn7QRmoZmqgVbHkwLm4OEXzkqJNCxgEfl8WLqzFvn3VirPlI7WsaV6eE9nZLvjrW0EQkZPj8jsabbUCffs6MXy4A337OnHVVdKiBP6W0w32fERmYriRVQBYsWIFFixYgAEDBsBisWDYsGGYMmVKrJsVt+bPr0durgsPPJCCysqm3y9t2rjw6KN1yMgQ8Yc/yL+uYl22hyieiBgyxIG77mrAVVdJ5Y5yc12NZZmUj7WsLBEVFfqNMyxcKNUYDVR2ybsma2BqVm4KtiqW1pWvZGoXH2jbVlQsLRVKzVS1tm2zNV41Vy7xBahbRECm16IERGZgyGC1TZs2WLFiRaybkVCGDnVg8GCH31qBU6fW4+mnk9E874uIQmOxiFi7Virr9Ne/JuHNN2248EIX7r67Afn5TcdimzYiXC5g1y4rBEFKH3viCf2WU87OFgMGhIFGQZtTGySpDSq1rp6ldgUspe38BeShLGvavO5qZaWguGSqLCNDxNKl2gPiQKtfGa3OKlE4DDfBSi+JVng4GoV8589PxurVyXC5GKwShU46QMeNa0BFhYCtW21ex5TFImLy5HrMmyelBCiN9ml/vdAL9mtZwUrtKKTa51RTrN/z3OdwBF8BS2mf5ZWz1CySEGykUqm/LBaxcY6K8qIKOTnaFk5oTsuiBLHCRQHMwaiLAhhyZJWMad68ejz4YD3++tck/PSTBeef78LFF7vw/PM2vP66cZaiJDIyiwVwuQSsWycfM97fCC4XGq9iSCkBai+/+6ccIImiusvEakc3i4rqMGtWvaogKS9PyrcMFlRqzbcM9dK4XmkJ/kZnA//ADy3lgSiRMFglTZKTgYkTvcu2XHONE5dcYmtcr5ujrkT+iQpVYJofM9JM/VWrknHOOaKqy+96URqhU3tpvV8/9aN5kcy3DOXSuB5pCVrSJbQ+dyCRzLONJTOMFlP0GK4aAJmP1QosXVrXWAqx+RebqHAfUSJy1+ZRsa004njkSPCZ7aERIAjeM+7l2f6FhS0waVIaCgtbIDc3HZWVQkRmnctBZXa2umoEWp97375qlJaewZo1NSgtPeN39j8QXq6rTG0lglCe2x95JNczKAea8mztdnOOR/n7LJp1fyh8zFmNE0bIB1L6hd+6tVRT78QJz99FWr60iSiSSkvP4MQJQbHmqRygTplSj1WrpNQE78v20t/Xrav1KWSvVrgjaHqc++ScVa25rp42b7Zh0iQ1NW61P3egNuuRZxsNavvJO5XC97MY7g8ZCow5qxT3CgocXrOY5S8ewPvL6J13rFi9WinHlZUGiKLt0CEBJSX+a54KgojS0iQ8+2wt5s5NaTaKJ/3/3LkpsFgQUhAh1w+NJT3SEkIZGQ0n5SFS5b9iKVj9XUEQUVycgvx8hyECcIoepgGQrpoXrrZafe+bP78e69bVIivLO3kvJ0fE/ffXoXNnB5g6QBQdlZVCwMvXUtBjQVaWiAUL6qCU2mP2y85A+GkJ8qQxf+kSgAiLRb+Uh0iV/4qlYKkU8mexrIyRaqIx75mFTC1QXdcHHpCW0/7rX5PwwQdWbN8uf0zNc9IlMjr5MnFWlrofhocOCZgzR67vGnzUy4wTZPxdHVLTbjWjs2vX1iIrK/ACDGrpkWdrNPEYgJM+GKxSzAS6/CdXHZg4sQF2uw3335/SWG2AiIILnBfuefk5M1NdMLNjh7VZ7nmzV/S47HzihGDaGerhpCVEs0h/pMp/xVI8BuCkD06wihNGmGAVSfJ63Tt3WiGKwN/+loTjx1kqiygUrVu78PjjUvAUbHKRXLS+uho4dSp45tjEiXVYuzY5ahNkjHjui9aosjwZCYDiSK6RJiOp6Sc9JrpReIw6wYo5q2QKVqtUx/HBB+vx0EP1ePxxqVSW//wwIlIuHSfi+HEBX39tgdPZdPka8Hc8Cfj1V3WBKgBs2pTkd4IM4F0uK14p5e5HQiTLf8VCoM9iuPV3ydw4shonjDi6EGlKpbJatRJx+rR8i6OuZHbhVsgI/PjsbBcWLZJGWJvSbZSCUnXtaNnShaqq4EGtmmVUA/EcuWzfXkRBQQucOJE4577mzJAfrOU7Kl4XOjADo46sMliNE4kYrALKJ+lt28JdS53IKEKtSaw2yBUhCNLl4vx8B3r0kC7BhhogFxQ0wG5PCrrdmjU1GD48tKBDKZA57zxg4cIaDB7MQMaotH5HmSEAj0cMVqMs0YK2RA1W/ZFPdNu22fDqqzZUVgYLXFnjlYxJEKSAUlqmVTmntHluqJYlP+U8wBUrajFiRIsQWykiM1PEunW1uOWW4M9RVFSHfv2cmgMQ/wXjpTaY8dJ3ouB3lDkwWI2yRDsgeCLwz/MXeps2InbtsuKZZ5JRVeW7Eg8DVgpPJH/0KH1Gfe/LyXGhRw+nqhFOTzfd1IA339T2GM82bNggjc4GWlWp+fuj5dKu2VZsIm/8jjIHBqtRlmgHBE8E2jS/xFRZKWDmTH/5ejKOvlIs+fv8SbVSFy6sc5cq2rnTGsYoqTbNA86tW20YNy618a+Bjxcts9Z37rSisDD4PoWbD0uRwe8oczBqsMo6q5SQlGopDh7swPLlyVi7NgknT/oLWhmwUqz4+9wJqKwUkJ0tuj/Tffs6kZnpaqxNHLl81zZtROzZU41kj9WTpUUG1L2mliU0zVownrmXROFjsErUyGoFZsyoR1FRPcrKrDh0SAoCsrKky4uVlYLC2uhEkaYu+PMM0qxWYOnSOowZk6r68U3UbiugokLA3r3ea89rDRbVrmFvxoLxnNVOpA8Gq0TNBFrBRl4i9q23rNi0KUnFxC2icKkL/poHaQUFDmzYUIvp01Nw8mTkfmA1D0737w/tmAgW5IazYlMsRje9J4M1OXRIwNixqZwMRqQBv2mJNJAD2YUL6/HFF9UoLT2D8eProFx8HQHup9Al3vsp5XYq77cgiMjJcSkGafn5DqSlwe9j9eAZJDudwPPPJ4X0et9+a8HOnVa/CwYELhgv/VepYLzdbkNubjoKC1tg0qQ0FBa2QG5uOuz2yI3VOJ3S4geJvjgCkV4YrBKFSA5cFy2qx4YNtcjJ8f4CbdPGhcmT6wOutJWa6opGU+OM2dMwtP+AkQIc5dFEUQRGjWrAli02n2BPSmexKD42XEpBcmivJ70Xy5alBA0k/a3YdN55UjWC5iOV8uhm89QdeXQzUgFrWZm18dK/8vsgpT5YUFbG5FUiNZgGQKSDggIH8vMdipcae/VyNeatNX1xZWa6MH58A+69tx5XXhloXXYyl+hOwGvRQkRyMrB4cYr7Ps+cyEhNNvK39KX21/MN2oNdJm9+rDWtYOXwuuQebHRT7cSuUJh1MhiRUTFYJdKJv1xXzy/Xo0cFdO6chosvroal8bpGSUkdxo5NbRwl45eXuanpP736WER1tYDqau97PYM9/SYbeQfh2dmi4iSh0F5PeyDpeawJAhS3aRrdVKZ2YlcozDgZjMjImAZAFAXyl+vw4Q5ce633l6u/S5uJmJtpZGlpot90jtj0leDxz6MlHjmRvXpJk5L8t1ud7GwRs2bVYc2aGpSWnsG+fdWKo57yJCh/rycIItq0ceG+++o89sGXHpfJYzm6qeZ98JdnTES+OLJKZADNL23u32/BunVJOH5cXb1L8+dxGl9NjfRf3xFw4/2okEcN9+61ukfutdRPzcmRll+tqNA2e16eBKV0pUAO3BYvrkN9vbr9CCeQjOXoppr3QWkyGBEp48gqkUF4jr7OmFHvnvlMxpGZKaJ9e+/gJidHxLp1tcjJccFogeuRI4J75F4q1q9OSUkd+vWTPot9+2or8+TvSkF2tujOQ41GIKn36KbTKa2itXmz70Q2JfL70Pzz4vk+EJE6HFklMijftABlM2bUY9myZLhc+o3CjhhRj2PHBPzjH6GsFW9Wwd4bASdOCNi06QysVvhMpLNY0FiE3zjkYK+gwIHaWmDKlLSgj5kwoSHsQCrQhEMgvJqpauk5uqlncX8uNUqkHUdWiQxK7cjQ/ffXY+3aWgQqiZSUJGLYsAafUR5f0nOuXFmHjRtrkZHB0lrNVVQI7hFwz1HHggIHZs1SeX1blxFYbbVX1f74yc/XZ8TP80pB89HZwDVT9btMrmaUN5hQy1/Jjzt0yPtxhw9HtmwWUTxisEpkUFq+0IcOdSjWek1Lc+H+++vwyy9VWLu2Fp9+Wo1Zs5QXMRAEEYLQ9JxWqzTKFv8CTZzyFejSdFFRPbKzg6cDWJqdeTMzXbj1VrWBLmCxiH5r+PoL9rReFg902VvrJXElegSSal9n3z5pAY9gE8SaC7W4PxcFINKXIIrxeVGiouJ0Ql1uEQSgTZtWCbffZhNKP2m5BKl2WUm1z+l0Apdcko4TJ5SL0puf3AnqJx7t21cdcMRPHlEDEKAUmfS6ffo40bu3E1df7URenlNFzV3pcevW1WLoUIfmy9P+2iYHsHKQGOh5Aei63n3zz2yvXk7s3at+adRInvt27rSisLBF0O1KS894lb8K9XHxjN9R5hDNfpJfS9W2DFbjA08E5hBqP0VibXMtgW1TLma4Aau/vNDYVDTIyXGhoMCBtWuTVWwtKq6SpMRut2HOnJTG1ZzUt6WwsAGrVkltUQpYMzNdWLrU9weFls9GsADXe01774DW+zPrP9gNVSi5oZE8923ebMOkScHzfNesqcHw4Q53X9jtNqxfH/wzJT8uEfA7yhyMGqwyaYbIBPwtOGC052wiYvz4etx4oxOiKOV57t9vwZIlyQCUJ7sUFDTg9dcjO6FLDrgmTpTalpfnRFmZVVWwWlDgQGamCKdTuQh9823POkvEiBHBR9dk5eUCVq1KxpQp9SgtTVJc8ayoqN7ntbX2Y6DJT8EvXyuPROuxIpR3kNxEzg2dObMeHTu6dPuxpoaWqgVKgbZez0+xEYlBAgoNg1Ui8ksOXiTqRz7Hj2/AokW+OZhdu/ouPZuVJeKWWxpw3nlixINVpZWXgs1Ml0d97fYk2O1Jqi95V1RoHSkWIIoiSkuTsGdPtaZL4YC2L1Z/AW6wVZ8CfQbCWRFKTZDsb0nZSFJbtaCyUsD48b6Btr8rBnpUO6DI0rMCBIWPE6yIyK+m4EVb4HXTTconc8/JLhMm1CMry4WKCgueeSYFDz+cCovF/wz38GbQi2jd2oU9e3wn1gSayKYk2CxwWahLj5aXW7B3r9XvTHoldrsNubnpKCxsgUmT0lBY2AK5uemaZ5zrsZpTKM8R/HOmbSa+XtRMclywoA5z5yoH2tLtyFU7oMgItQIERQ6DVSLyS2vgoabQutUKnDgh4Nlnk1BZ6f38LnelLOUv+MzMUJcOFXD8uBQEKikocODZZ2vRurXSc4c2mzsvz4msrNBKf2l53/X8YtXjsnQoz6H1cxbNGfXBqhZkZYmaAm0uCmBsrORgTAxWicgvLYGH2hGjQF8G8u3mpZ2ys6XJTUuX+hsBVddOf0GR3W7D3LkpqKxUd0pUs3a91QrccktoAYna913vL9Zg5a201nZVK5QAV00f6CVQ+Su1gfbYsfWaymZRbAQb5Y/m546acCybiPwKns/ZRCkfVImavEiXC1i4sBZt24o++Zfr19f65L22aSOqyhFVCor8TexRQw5U/OWL5uerrTQgk8pjqQ34gr2XWvNIg636JL9Heq93r+Vz1pweqQtq+MvzVRtoFxQ4EqZMlZmp/TxF63NHEo6sEpFfgfM5pVG2CRO0jRipPcm3bSsq5mwqjXL93/9Vh7QOfOBR3uDkWeD+8kXlIExLvq2WgC8SX6yBLntv2FCLDRv0L+SvNW/YU6xn1GtdbIGMTUsFCIoejqwSUUBy8NJ8NDMnR91IanN6fBkojXKFsg588FFeZcFmgcv5ouvX16KkpM6jTm1gEyY0aHo/I/XFGqi8FYCAfwuVv8+Z0WfUBxuNBjiZykzUVoCI9ecu0XBRgDjBgsvmYOZ+0qvmoNMJ5Ob6X6lJ/jIItlKUEq3lZtQWfW/ePgB49tlazJ0rB1aB92P58mSv0kv+aF3RKJLvZax4fs7277dg8eJkCIL/FbeGDHEY4phiqaPAzHTuU7vSWzwy6qIADFbjhJlOBImM/SSJ5JeBlqBa7bKYnuQAJDNTVL2kZl6eEz16SEFlsMA2lAA9nr9YgwWBRjqmWETePyP1kxqJ+uODwWqUmeWA0IvZTgSJiv3UxAhfBsFHJoHsbBdWrKhFRYV3AKJ1Kc5IBpVGeC8jKVAQyGPKHMzYT4n444PBapSZ6YDQgxlPBImI/eTNCF8GgYNIARs21GDwYN+AT+2orOel/UgGlUZ4L2OBx5Q5sJ/MgcFqlCXaAcETgTmwn4xJKYg891wXVqywoF8/5b4KNV80UYPKSOExZQ7sJ3MwarDKagBElPCUZr/36eNEu3atUFGh/JhQZ4H7q9dJRETKGKwSEcE3iBRUlCb1V25J7QIJscQRXiIyCwarRERhCFaT1IjifUIWEcUXBqtERGEy06V9f8vLei5kwICViIyEy60SESWIQMvLyjm3xcUpcJoj7iaiBMFglYgoQTQtL6uckCuKAsrLLSgrM3AOAxElHAarREQJ4sgRFbPGNGxHRBQNDFaJiBJEu3bqCieq3Y6IKBoYrBIRJYi8PCdyclwA/AWjInJyXMjLY9IqERkHg1UiogRhtQKFhQ2Nt5oHrNLtwsIGQ5fdIqLEw2CViChBOJ1AaWlS463meanS7dLSJFYDICJDYbBKRJQgglUDAFgNgIiMh8EqEVGCYDUAIjIjBqtERAmC1QCIyIwYrBIRJQi5GoAgKAejgsBqAERkPAxWiYgShNUKlJTUAYBPwCrfLimpYzUAIjIUBqtERAmkoMCB9etrkZ3tHaxmZ4tYv74WBQWOGLWMiEiZLdYNICKi6CoocCA/34GyMiuOHBHQrp2IvDwnR1SJyJAYrBIRJSCrFejbl7mpRGR8DFaJiIhINacTHJWnqGKwSkRERKrY7TYUF6c0Li4hyclxoaSkjvnOFDGcYEVERERB2e02jB2bivJy70UjDh0SMHZsKux2jn9RZDBYJSIiooCcTqC4OAWiCDRfrlcUpdvFxSlwMg2aIoDBKhEREQVUVmZtvPSvvBSvKAooL7egrIzJq6Q/wwarNTU1GDlyJDZv3hzrphARESW0I0eUg9RQtyPSwpDB6nfffYc//vGP+L//+79YN4WIiCjhtWunvERvqNsRaWG4YHXXrl248847UVhYiJycnFg3h4iIKOHl5TmRk+PyWaZXJggicnJcyMtj0irpL+pT92pra3HkyBHFv7Vt2xZdu3bF9u3bkZKSgr/+9a8hv46QYFci5P1NtP02G/aTebCvzIH9FB02G7BoUR3GjEmFIIjuSVUA3AHsokV1sPmJKthP5hDNftLyGlEPVj/77DPccccdin97+umnccMNN+jyOllZrXR5HrNJ1P02G/aTebCvzIH9FHl33QWcdRZw333AgQNN9593noAnngCGD08L+hzsJ3MwWj9FPVjt3bs3vvnmm4i/TmXl6cYSG4lBEKQPV6Ltt9mwn8yDfWUO7Kfo6tcP+OQT5RWsKir8P479ZA7R7Cf5tdSI2wq+ooiEPCASdb/Nhv1kHuwrc2A/RY/FAlx1lXduqtr3nv1kDkbrJ8NNsCIiIiIikjFYJSIiIiLDMnQawAcffBDrJhARERFRDHFklYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiAyLwSoRERERGZahl1sNhyDEugXRJe9vou232bCfzIN9ZQ7sJ3NgP5lDNPtJy2sIoiiKkWsKEREREVHomAZARERERIbFYJWIiIiIDIvBKhEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVg1UQqKysxZcoU9OzZE71798aiRYvgcDgUt/3HP/6BIUOGoFu3bsjPz8f27duj3NrEpaWfxo0bh8svvxzdu3d3/9uxY0eUW5zYjh8/joEDB2L37t1+t+HxZAxq+orHVOx8/fXXuPvuu3HllVeib9++mDVrFo4fP664LY+p2NHST4Y5nkQyjVGjRon333+/eObMGfGXX34RBw8eLD777LM+2/3444/i5ZdfLr777rtiQ0OD+MYbb4i//e1vxcOHD8eg1YlHbT+Joij27t1b3L17d5RbSLJPPvlEvOGGG8TOnTuLZWVlitvweDIGNX0lijymYqWmpkbs27ev+OSTT4p1dXXi8ePHxfHjx4sTJ0702ZbHVOxo6SdRNM7xxJFVk/j555+xZ88ezJw5E2lpaejQoQOmTJmCF1980Wfb0tJS9OzZEzfccANsNhtuuukm9OrVC6+88koMWp5YtPTTf//7X5w6dQqXXHJJDFpKpaWlmDFjBoqKioJux+MpttT2FY+p2CkvL0fXrl0xdepUJCcnIzMzEyNHjsTevXt9tuUxFTta+slIxxODVZP47rvvkJGRgXbt2rnv+81vfoPy8nL8+uuvXtt+//336Ny5s9d9F110Eb7++uuotDWRaemnf//730hPT0dRURHy8vJQUFCATZs2RbvJCevqq6/Gu+++i5tuuingdjyeYk9tX/GYip2OHTti3bp1sFqt7vvefvttXHrppT7b8piKHS39ZKTjyRaTVyXNqqurkZaW5nWffPvMmTM466yzAm6bmpqKM2fORL6hCU5LP9XX16Nbt24oKipCp06dsHv3bkybNg3p6enIz8+ParsTUdu2bVVtx+Mp9tT2FY8pYxBFEU888QS2b9+OF154wefvPKaMIVg/Gel4YrBqEi1atEBNTY3XffLt9PR0r/vT0tJQW1vrdV9tba3PdqQ/Lf00bNgwDBs2zH376quvxrBhw7Bt2zZ+sRoIjyfz4DEVe1VVVXjwwQfx5Zdf4oUXXkCXLl18tuExFXtq+slIxxPTAEyiU6dOOHnyJCoqKtz3/fDDD2jfvj1atWrltW3nzp3x3Xffed33/fffo1OnTlFpayLT0k+bNm3Ctm3bvO6rr69HSkpKVNpK6vB4Mg8eU7H1yy+/4JZbbkFVVRU2bdqkGAABPKZiTW0/Gel4YrBqEhdeeCFyc3Px6KOPoqqqCv/973+xatUqjBgxwmfboUOHYs+ePXjzzTfhcDjw5ptvYs+ePbj55ptj0PLEoqWfqqqqsHDhQvznP/+By+XChx9+CLvdjpEjR8ag5eQPjyfz4DEVO6dOncKdd96JHj16YP369WjdurXfbXlMxY6WfjLU8RTrcgSk3rFjx8Rp06aJV155pZiXlyc+9thjosPhEEVRFLt16yZu2bLFve2OHTvEoUOHit26dRMHDx4sfvjhh7FqdsJR208ul0t8+umnxeuuu0787W9/Kw4ePFjctm1bLJuesJqXQ+LxZFyB+orHVOxs2LBB7Ny5s3jFFVeI3bp18/onijymjEJLPxnpeBJEURSjHyITEREREQXHNAAiIiIiMiwGq0RERERkWAxWiYiIiMiwGKwSERERkWExWCUiIiIiw2KwSkRERESGxWCViIiIiII6fvw4Bg4ciN27d6t+zNtvv42CggJ069YNAwcOxKZNmzS/LoNVIqIY+emnn2LdBCIiVfbt24eRI0fil19+Uf2YsrIyzJ49GzNnzsSnn36KhQsXYv78+fj88881vTaDVSKKW9dffz0uv/xydO/eHd27d0e3bt1w9dVX4y9/+QtcLpdurzN69GisXLkSADB37lzMnTs36GM++OADjB07NuTX3Lx5M66//nqf+2tqanDllVfib3/7m+Lj7r//fkyZMiWk5yaixFRaWooZM2agqKjI528ff/wxRowYgZ49e2Lw4MHYunWr+2/PPfcc7rjjDvTv3x+CICAvLw+vvvoqzj//fE2vbwt7D4iIDGz+/PkYPny4+/Y333yDu+66C2lpabj33nt1f70FCxao2u7kyZOIxAKCaWlpuOWWW/D3v/8dd9xxh9ffjh8/jrfffhvr1q3T/XWJKH5dffXVGDJkCGw2m1fA+vXXX2Py5MlYsmQJBgwYgM8++wxTpkxBZmYmrrnmGnz++efo3bs3JkyYgM8++wzt27fHtGnT0LlzZ02vz5FVIkooXbp0Qa9evfCf//wHgDQqOnv2bFx33XW49tprUVVVhV9++QWTJk1C7969cd1112H58uWor693P8ff//53DBgwAN27d8cDDzyAmpoa999mz56N2bNnu2//z//8DwYOHIju3btj+PDh2LVrF3bv3o158+ahvLwc3bt3x5EjR1BfX48nn3wSAwYMwJVXXonx48fj559/dj/PDz/8gNGjR6N79+4YMmSIu/1Kbr/9dnz//ff4v//7P6/7N23ahP/3//4f8vLy8MEHH+DWW29Fnz59cMUVV2DUqFGKaQm7d+9Gly5dvO5rvo9vvPEGhgwZgtzcXAwfPhz//Oc/3X/bu3cvhg8fjp49e2LgwIFYtGgRHA6H37YTkfG0bdsWNpvv+ObGjRsxYMAADBo0CFarFT169MAf/vAHvPjiiwCAU6dOYf369Zg8eTJ27tyJqVOnoqioCJ999pmm12ewSkQJo6GhAbt370ZZWRn69u3rvv/jjz/Gxo0bsXXrVlgsFtx1113o1KkTduzYgZdeegkff/yx+zL/rl27sGDBApSUlGDv3r244oor8O9//1vx9TZv3oxVq1Zh8eLF2LdvH2677TZMnjwZXbp0wfz585GTk4NPP/0U7dq1w/Lly/Hhhx/iueeew0cffYQrrrgCY8aMQV1dHRoaGjBx4kR06tQJZWVlWLZsGd577z2/+9mhQwf0798f//u//+u+z+Vy4ZVXXsEdd9yBw4cP47777sOECROwa9cufPjhhxBFEU8//bTm9/Qf//gH5s2bh7lz52LPnj2YNm0apk2bhu+++w4AMGvWLIwePRqffPIJ/vrXv+Ktt97C+++/r/l1iMh4Dh48iHfffRc9e/Z0/3v++edx6NAhAEBycjJuueUWdO/eHTabDYMGDUKfPn3w9ttva3odBqtEFNfmz5/vPon26dMHCxcuxN13341Ro0a5t+nXrx/atWuHs846Cx9++CHq6+sxffp0pKSkIDs7G/fdd597pGDr1q3uE67NZsPtt9+OSy65RPG1S0tLMXLkSHTv3h0WiwW///3vsWHDBqSmpnptJ4oiNm7ciOnTp6NDhw5ISUnB1KlT0dDQgA8//BCffvopDh06hFmzZiElJQWdOnXC3XffHXC/77jjDmzbtg1VVVUAgI8++ghVVVUYMmQIWrdujTfeeAPXX389qqqqcPjwYWRmZuLIkSOa398XXngBt912G3r16gWr1YrrrrsO119/PTZu3AgASElJwbZt27B9+3ZkZGTgH//4B373u99pfh0iMp727dujsLAQn3zyifvf22+/jbVr1wIAfvOb33hdlQIAp9OpOQWKOatEFNfmzZvnlbOq5JxzznH//8GDB3H8+HH06tXLfZ8oimhoaEBlZSWOHDmCSy+91OvxHTp0UHzeY8eOIScnx+u+Hj16+Gx3/PhxnDlzBvfddx8slqYxhIaGBhw8eBD19fXIzMz0CnKDTVC46qqrkJ2dDbvdjltvvRUvvfQSRo4cidTUVIiiCLvdjo0bN0IQBHTu3BlVVVWKl/mCOXjwIPbs2YOXX37ZfZ/T6UReXh4AKQ1i5cqVmD9/Po4dO4ZrrrkGjzzyCNq3b6/5tYjIWEaMGIG7774bgwYNwlVXXYVffvkFEyZMwHXXXYcHH3wQt912GxYuXIhrrrkGeXl5ePfdd7F7925Mnz5d0+swWCWihCcIgvv/27dvj/PPPx9vvfWW+76qqipUVlaidevWaN++Pf773/96Pf7w4cPo1KmTz/NmZ2e7L4fJli9fjqFDh3rdl5mZiZSUFGzYsAHdunVz379//360a9cOX331FY4fP47q6mqkp6e7XzOYP/7xj/j73/+O/v37Y+fOnZg/fz4AYNu2bXjhhRfw8ssv44ILLgAALFy4EN9++63Pc1itVgBAfX09kpOTAQAnTpxAZmam+/0aNmwYJkyY4H5MeXk5UlNTUVdXh++//x6PPPIIbDYbfvzxRxQXF+PRRx/FihUrgrafiIztiiuuwLJly7Bs2TLcd999SEtLQ0FBgTsYveWWW2CxWPDnP/8ZBw4cwLnnnovly5f7/OAPhmkAREQerrvuOlRXV2PdunWor6/Hr7/+igceeABFRUUQBAG33HIL3nvvPWzfvh0OhwOlpaV+JwsMHz4cr7zyCj7//HO4XC68+uqrePHFF93BaU1NDRwOBywWC0aMGIGlS5fi8OHDcLlcKC0tRUFBAX7++Wd0794d/+///T+UlJSgpqYGP//8MzZs2BB0XwoLC/Hzzz/jiSeewA033OAezTx9+jQsFot7lHXHjh147bXX0NDQ4PMc559/Pmw2G9544w0AUn5vWVmZ++9/+MMf8Le//c1dN/Hf//43hg8fDrvdDkEQMH36dGzYsAEOh8M9SUMOdInIfL755hv07t3bffvaa6/F5s2bsW/fPvzzn//E7Nmz3T9sAek89Prrr+PTTz+F3W7HwIEDNb8mR1aJiDy0bNkSzz33HB577DGsW7cOLpcLvXv3xurVqwEAubm5WLx4MR577DEUFRUhLy/Pa7KWpyFDhuDXX3/FzJkzcezYMVx00UV49tln0bp1a/Tq1QtZWVno1asXNm7ciAceeAArV67E7bffjpMnT6JDhw5YsWKFOx927dq1mDt3Lq666iq0adMGAwYMwDvvvBNwX1q0aIHhw4fjf/7nf/DSSy+57y8sLMS+ffswePBgWK1WdOzYEXfeeSdefPFFn/yyc845Bw899BBWrVqFhQsXIi8vD8OHD3dXQLjxxhtx5swZPPTQQygvL0dGRgbuuusujB49GoIgYPXq1fjLX/6CZ555BlarFf369cOMGTNC7h8iSjyCGIlCf0REREREOmAaABEREREZFoNVIiIiIjIsBqtEREREZFgMVomIiIjIsBisEhEREZFhMVglIiIiIsNisEpEREREhsVglYiIiIgMi8EqERERERkWg1UiIiIiMiwGq0RERERkWP8frtqmz+XHfi8AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This plot is useful in understanding the assumptions of linear regression and detecting violations. The ideal case is to see a random distribution of residuals around the y-axis, centered around zero. This suggests that the residuals are normally distributed, and there are no systematic patterns in the errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# POLYNOMIAL REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Polynomial regression is a type of regression analysis where the relationship between the independent variable (or variables) and the dependent variable is modeled as an nth degree polynomial. Unlike simple linear regression, which assumes a linear relationship between the variables, polynomial regression can capture more complex relationships by introducing polynomial terms.\n",
-    "\n",
-    "In polynomial regression, the relationship between the independent variable \n",
-    "𝑥\n",
-    "x and the dependent variable \n",
-    "𝑦\n",
-    "y is modeled as:\n",
-    "\n",
-    "𝑦\n",
-    "\n",
-    "=\n",
-    "𝛽\n",
-    "0\n",
-    "+\n",
-    "𝛽\n",
-    "1\n",
-    "𝑥\n",
-    "+\n",
-    "𝛽\n",
-    "2\n",
-    "𝑥\n",
-    "2\n",
-    "+\n",
-    "𝛽\n",
-    "3\n",
-    "𝑥\n",
-    "3\n",
-    "+\n",
-    ".\n",
-    ".\n",
-    ".\n",
-    "+\n",
-    "𝛽\n",
-    "𝑛\n",
-    "𝑥\n",
-    "𝑛\n",
-    "+\n",
-    "𝜀\n",
-    "y=β \n",
-    "0\n",
-    "​\n",
-    " +β \n",
-    "1\n",
-    "​\n",
-    " x+β \n",
-    "2\n",
-    "​\n",
-    " x \n",
-    "2\n",
-    " +β \n",
-    "3\n",
-    "​\n",
-    " x \n",
-    "3\n",
-    " +...+β \n",
-    "n\n",
-    "​\n",
-    " x \n",
-    "n\n",
-    " +ε"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Model (Degree 2)- MSE: 35637716070.71762\n",
-      "Polynomial Model (Degree 2)- R-squared: 0.7321925161881991\n"
-     ]
-    }
-   ],
-   "source": [
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "    \n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Polynomial Regression\n",
-    "# Choose the degree of the polynomial\n",
-    "degree = 2\n",
-    "\n",
-    "# Create polynomial features\n",
-    "poly = PolynomialFeatures(degree)\n",
-    "X_train_poly = poly.fit_transform(X_train)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "# Build a polynomial regression model\n",
-    "poly_model = LinearRegression()\n",
-    "poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "# Evaluate the polynomial model\n",
-    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
-    "r2_poly = r2_score(y_test, y_pred_poly)\n",
-    "print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
-    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mean Squared Error (MSE): This value represents the average squared difference between the actual house prices and the predicted prices by the polynomial model.\n",
-    " A lower MSE indicates better performance, and in this case, the MSE is lower than the previous model's MSE, suggesting that the polynomial model fits the data better.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " \n",
-    "Generally,The \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  value for the first model is 0.641, indicating that approximately 64.1% of the variance in housing prices is explained by the independent variables included in the model. On the other hand, the \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  value for the polynomial model of degree 2 is 0.732, suggesting that approximately 73.2% of the variance in housing prices is explained by this polynomial model.\n",
-    "\n",
-    "Comparing the two \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  values, we can see that the polynomial model of degree 2 explains more variance in housing prices compared to the first model. This suggests that the polynomial model provides a better fit to the data and captures more of the variability in housing prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RESIDUALS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred_poly\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred_poly, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from statsmodels.stats.diagnostic import het_breuschpagan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#TEST FOR HOMOSCEDASCITICY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Breusch-Pagan test p-value: 3.975373048166964e-258\n"
-     ]
-    }
-   ],
-   "source": [
-    "poly = PolynomialFeatures(degree)\n",
-    "X_train_poly = poly.fit_transform(X_train)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "# Build a polynomial regression model\n",
-    "poly_model = LinearRegression()\n",
-    "poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred_poly\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Perform Breusch-Pagan test\n",
-    "lm, lm_p_value, fvalue, f_p_value = het_breuschpagan(residuals, X_test_poly)\n",
-    "print(\"Breusch-Pagan test p-value:\", lm_p_value)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.\n",
-    "\n",
-    "Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Log transformation.\n",
-    "Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. \n",
-    "Common transformations include taking the natural logarithm, square root, or reciprocal of the variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Log transformation of the multiple linear regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error (Log Transformed): 0.09804039958945562\n",
-      "R-squared (Log Transformed): 0.6469123715948973\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Log transformation of features and target variable\n",
-    "X_log = np.log1p(X)\n",
-    "y_log = np.log1p(y)\n",
-    "\n",
-    "# Split the log-transformed data into training and testing sets\n",
-    "X_train_log, X_test_log, y_train_log, y_test_log = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Standardize the log-transformed features\n",
-    "scaler_log = StandardScaler()\n",
-    "X_train_scaled_log = scaler_log.fit_transform(X_train_log)\n",
-    "X_test_scaled_log = scaler_log.transform(X_test_log)\n",
-    "\n",
-    "# Build a linear regression model on the log-transformed data\n",
-    "model_log = LinearRegression()\n",
-    "model_log.fit(X_train_scaled_log, y_train_log)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_log = model_log.predict(X_test_scaled_log)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "mse_log = mean_squared_error(y_test_log, y_pred_log)\n",
-    "r2_log = r2_score(y_test_log, y_pred_log)\n",
-    "\n",
-    "# Display results\n",
-    "print(\"Mean Squared Error (Log Transformed):\", mse_log)\n",
-    "print(\"R-squared (Log Transformed):\", r2_log)\n",
-    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model_log.coef_})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals_log = y_test_log - y_pred_log\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred_log, residuals_log, color='blue',)\n",
-    "plt.title('Residuals Plot (Log Transformed)')\n",
-    "plt.xlabel('Predicted Values (Log Transformed)')\n",
-    "plt.ylabel('Residuals (Log Transformed)')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plot house prices against coefficients\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.barh(X.columns, model_log.coef_)\n",
-    "plt.xlabel('Coefficient Value')\n",
-    "plt.ylabel('Features')\n",
-    "plt.title('House Prices vs. Coefficients of Variables')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "visualization of the positive and negative coefficient variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Log transformation of the polynomial model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Model (Degree 2)- MSE: 37774083176.83915\n",
-      "Polynomial Model (Degree 2)- R-squared: 0.7161383140038227\n",
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  price   R-squared:                       0.679\n",
-      "Model:                            OLS   Adj. R-squared:                  0.678\n",
-      "Method:                 Least Squares   F-statistic:                     557.0\n",
-      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        12:27:46   Log-Likelihood:                -3540.2\n",
-      "No. Observations:               16913   AIC:                             7210.\n",
-      "Df Residuals:                   16848   BIC:                             7713.\n",
-      "Df Model:                          64                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "const       4987.9435    522.414      9.548      0.000    3963.957    6011.930\n",
-      "x1          3457.3790    362.110      9.548      0.000    2747.606    4167.152\n",
-      "x2           -16.5312      7.635     -2.165      0.030     -31.497      -1.566\n",
-      "x3           -16.9803      5.626     -3.018      0.003     -28.008      -5.953\n",
-      "x4           -59.7513      8.202     -7.285      0.000     -75.827     -43.675\n",
-      "x5           -66.7001     16.473     -4.049      0.000     -98.988     -34.412\n",
-      "x6            21.7086      8.506      2.552      0.011       5.036      38.381\n",
-      "x7            56.1227     10.699      5.246      0.000      35.152      77.093\n",
-      "x8             0.1415      0.453      0.313      0.755      -0.745       1.028\n",
-      "x9         -1530.3915    159.119     -9.618      0.000   -1842.282   -1218.501\n",
-      "x10           -7.2389      1.397     -5.181      0.000      -9.977      -4.500\n",
-      "x11           27.1030      5.183      5.229      0.000      16.943      37.263\n",
-      "x12         2396.4725    250.995      9.548      0.000    1904.495    2888.450\n",
-      "x13          -11.4586      5.292     -2.165      0.030     -21.832      -1.085\n",
-      "x14          -11.7699      3.900     -3.018      0.003     -19.414      -4.126\n",
-      "x15          -41.4165      5.685     -7.285      0.000     -52.560     -30.273\n",
-      "x16          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
-      "x17           15.0473      5.896      2.552      0.011       3.491      26.604\n",
-      "x18           38.9013      7.416      5.246      0.000      24.366      53.437\n",
-      "x19            0.0981      0.314      0.313      0.755      -0.517       0.713\n",
-      "x20        -1060.7866    110.293     -9.618      0.000   -1276.973    -844.601\n",
-      "x21           -5.0176      0.968     -5.181      0.000      -6.916      -3.119\n",
-      "x22           18.7864      3.593      5.229      0.000      11.744      25.829\n",
-      "x23            0.0476      0.079      0.603      0.546      -0.107       0.202\n",
-      "x24            0.2000      0.083      2.399      0.016       0.037       0.363\n",
-      "x25           -0.4148      0.109     -3.823      0.000      -0.628      -0.202\n",
-      "x26            0.1250      0.245      0.511      0.609      -0.355       0.605\n",
-      "x27            0.1981      0.122      1.628      0.104      -0.040       0.437\n",
-      "x28            0.2251      0.161      1.396      0.163      -0.091       0.541\n",
-      "x29           -0.0032      0.006     -0.531      0.595      -0.015       0.009\n",
-      "x30            3.1411      1.477      2.127      0.033       0.247       6.036\n",
-      "x31           -0.0103      0.012     -0.883      0.377      -0.033       0.013\n",
-      "x32           -0.1564      0.078     -2.000      0.046      -0.310      -0.003\n",
-      "x33            0.0859      0.039      2.183      0.029       0.009       0.163\n",
-      "x34           -0.0407      0.079     -0.516      0.606      -0.195       0.114\n",
-      "x35            0.3258      0.195      1.668      0.095      -0.057       0.709\n",
-      "x36           -0.2649      0.091     -2.905      0.004      -0.444      -0.086\n",
-      "x37            0.1059      0.111      0.957      0.339      -0.111       0.323\n",
-      "x38           -0.0112      0.005     -2.119      0.034      -0.022      -0.001\n",
-      "x39            3.2391      1.094      2.960      0.003       1.094       5.384\n",
-      "x40            0.0084      0.008      0.989      0.323      -0.008       0.025\n",
-      "x41           -0.0497      0.054     -0.924      0.355      -0.155       0.056\n",
-      "x42            0.6010      0.091      6.569      0.000       0.422       0.780\n",
-      "x43            0.1950      0.270      0.721      0.471      -0.335       0.725\n",
-      "x44            0.3964      0.139      2.845      0.004       0.123       0.670\n",
-      "x45           -0.0224      0.156     -0.143      0.886      -0.329       0.284\n",
-      "x46            0.0471      0.006      7.361      0.000       0.035       0.060\n",
-      "x47           11.9015      1.585      7.511      0.000       8.796      15.007\n",
-      "x48            0.0005      0.012      0.044      0.965      -0.023       0.024\n",
-      "x49           -0.3608      0.079     -4.586      0.000      -0.515      -0.207\n",
-      "x50          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
-      "x51           -0.2814      0.262     -1.074      0.283      -0.795       0.232\n",
-      "x52           -1.4097      0.386     -3.649      0.000      -2.167      -0.652\n",
-      "x53           -0.0189      0.015     -1.223      0.221      -0.049       0.011\n",
-      "x54           17.2366      4.274      4.033      0.000       8.859      25.614\n",
-      "x55           -0.0160      0.016     -1.020      0.308      -0.047       0.015\n",
-      "x56            0.1612      0.187      0.861      0.389      -0.206       0.528\n",
-      "x57           -0.0348      0.081     -0.430      0.667      -0.193       0.124\n",
-      "x58           -0.5518      0.172     -3.212      0.001      -0.888      -0.215\n",
-      "x59            0.0147      0.008      1.939      0.053      -0.000       0.030\n",
-      "x60           -4.5317      1.668     -2.716      0.007      -7.802      -1.261\n",
-      "x61           -0.0425      0.016     -2.603      0.009      -0.074      -0.010\n",
-      "x62            0.6652      0.082      8.073      0.000       0.504       0.827\n",
-      "x63            0.7108      0.132      5.394      0.000       0.452       0.969\n",
-      "x64           -0.0074      0.009     -0.830      0.406      -0.025       0.010\n",
-      "x65          -10.6811      2.084     -5.125      0.000     -14.766      -6.596\n",
-      "x66           -0.0058      0.016     -0.357      0.721      -0.038       0.026\n",
-      "x67           -0.4727      0.098     -4.817      0.000      -0.665      -0.280\n",
-      "x68           -0.0051      0.002     -2.881      0.004      -0.009      -0.002\n",
-      "x69           -0.0156      0.088     -0.178      0.859      -0.188       0.157\n",
-      "x70        -3.158e-05      0.001     -0.045      0.964      -0.001       0.001\n",
-      "x71           -0.0010      0.004     -0.221      0.825      -0.009       0.007\n",
-      "x72          150.7551     15.531      9.707      0.000     120.313     181.198\n",
-      "x73            0.7643      0.175      4.359      0.000       0.421       1.108\n",
-      "x74           -5.7615      1.013     -5.690      0.000      -7.746      -3.777\n",
-      "x75            0.6353      0.261      2.434      0.015       0.124       1.147\n",
-      "x76            0.0173      0.007      2.394      0.017       0.003       0.031\n",
-      "x77            0.3068      0.032      9.733      0.000       0.245       0.369\n",
-      "==============================================================================\n",
-      "Omnibus:                       86.286   Durbin-Watson:                   2.008\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              100.078\n",
-      "Skew:                          -0.119   Prob(JB):                     1.85e-22\n",
-      "Kurtosis:                       3.291   Cond. No.                     1.52e+16\n",
-      "==============================================================================\n",
-      "\n",
-      "Notes:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "[2] The smallest eigenvalue is 1.69e-24. This might indicate that there are\n",
-      "strong multicollinearity problems or that the design matrix is singular.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "import statsmodels.api as sm\n",
-    "\n",
-    "# Define function to keep only numeric columns\n",
-    "def only_numeric(df):\n",
-    "    return df.select_dtypes(include=[np.number])\n",
-    "\n",
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "    \n",
-    "    # Log transform features and target variable\n",
-    "    X_log = np.log(X + 1)  # Adding 1 to avoid log(0)\n",
-    "    y_log = np.log(y)\n",
-    "    \n",
-    "    # Split the data into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Polynomial Regression\n",
-    "    # Choose the degree of the polynomial\n",
-    "    degree = 2\n",
-    "\n",
-    "    # Create polynomial features\n",
-    "    poly = PolynomialFeatures(degree)\n",
-    "    X_train_poly = poly.fit_transform(X_train)\n",
-    "    X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "    # Build a polynomial regression model\n",
-    "    poly_model = LinearRegression()\n",
-    "    poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "    # Make predictions on the test set\n",
-    "    y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "    # Reverse log transformation for evaluation\n",
-    "    y_pred = np.exp(y_pred_poly)\n",
-    "    y_test_original = np.exp(y_test)\n",
-    "\n",
-    "    # Evaluate the polynomial model\n",
-    "    mse_poly = mean_squared_error(y_test_original, y_pred)\n",
-    "    r2_poly = r2_score(y_test_original, y_pred)\n",
-    "    print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
-    "    print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
-    "    \n",
-    "    # Convert to DataFrame for summary\n",
-    "    #X_poly_df = pd.DataFrame(X_train_poly, columns=poly.get_feature_names(features))\n",
-    "\n",
-    "    # Fit the OLS model\n",
-    "    model = sm.OLS(y_train,X_train_poly)\n",
-    "    results = model.fit()\n",
-    "\n",
-    "    # Print the summary\n",
-    "    print(results.summary())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.\n",
-    "\n",
-    "MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.\n",
-    "\n",
-    "Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example:\n",
-    "The coefficient for const (intercept) is 4987.94.\n",
-    "The coefficient for x1 (bathrooms) is 3457.38.\n",
-    "The coefficient for x2 (sqft_living) is -16.53.\n",
-    "The coefficient for x3 (floors) is -16.98.\n",
-    "The coefficient for x4 (waterfront) is -59.75.\n",
-    "The coefficient for x5 (condition) is -66.70.\n",
-    "The coefficient for x6 (grade) is 21.71.\n",
-    "The coefficient for x7 (sqft_basement) is 56.12.\n",
-    "The coefficient for x8 (yr_built) and other coefficients follow.\n",
-    "\n",
-    "P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.\n",
-    "\n",
-    "\n",
-    "F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.\n",
-    "\n",
-    "\n",
-    "Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test_original - y_pred\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# REGRESSION RESULTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
-    "\n",
-    "Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73\n",
-    "\n",
-    "The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Assumptions\n",
-    "\n",
-    "Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.\n",
-    "\n",
-    "Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.\n",
-    "\n",
-    "Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.\n",
-    "\n",
-    "No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Limitations\n",
-    "Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:\n",
-    "\n",
-    "Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.\n",
-    "\n",
-    "\n",
-    "Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.\n",
-    "\n",
-    "Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.\n",
-    "\n",
-    "Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Recommedations\n",
-    "# Statistical recommedations.\n",
-    "To mitigate multicollinearity, strategies such as feature selection, principal component analysis (PCA), or regularization methods like ridge regression or Lasso regression can be employed. These techniques prioritize essential predictors and enhance the model's interpretability while stabilizing it against multicollinearity.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Before opting for polynomial regression, it's essential to validate the assumption of linearity between predictors and the target variable. If this assumption doesn't hold, alternative regression techniques such as generalized additive models (GAMs) or spline regression should be considered to better capture intricate relationships.\n",
-    "\n",
-    "\n",
-    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis\n",
-    "# Recommedation to real estate clients:Home owners and investors.\n",
-    "\n",
-    "\n",
-    "1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.\n",
-    "\n",
-    "\n",
-    "2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.\n",
-    "\n",
-    "\n",
-    "3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.\n",
-    "\n",
-    "\n",
-    "4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.\n",
-    "\n",
-    "\n",
-    "5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.\n",
-    "\n",
-    "\n",
-    "6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CONCLUSION\n",
-    "\n",
-    "Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.\n",
-    "\n",
-    "\n",
-    "Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.\n",
-    "\n",
-    "\n",
-    "Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.\n",
-    "\n",
-    "\n",
-    "Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.\n"
-   ]
-  }
- ],
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-  "kernelspec": {
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-   "language": "python",
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From 0a440037de0b20dcd0b9a308c1a7d88dff9cfac8 Mon Sep 17 00:00:00 2001
From: WairimuMundia <156019539+WairimuMundia@users.noreply.github.com>
Date: Wed, 1 May 2024 15:49:44 -0400
Subject: [PATCH 24/27] Add files via upload

---
 Group 4 Power Point Presentation.pdf | Bin 0 -> 9797548 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Group 4 Power Point Presentation.pdf

diff --git a/Group 4 Power Point Presentation.pdf b/Group 4 Power Point Presentation.pdf
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From 9b147e55c48b9b7ddb2517709972db9f7b6f34b6 Mon Sep 17 00:00:00 2001
From: Winfred kinya <winfred.kinya@student.moringaschool.com>
Date: Wed, 1 May 2024 23:09:16 +0300
Subject: [PATCH 25/27] changes

---
 Final Copy -Project Phase 2.ipynb | 5199 -----------------------------
 Project Group4 Notebook.pdf       |  Bin 161155 -> 0 bytes
 README ().md                      |  261 --
 README.md                         |   10 +-
 4 files changed, 9 insertions(+), 5461 deletions(-)
 delete mode 100644 Final Copy -Project Phase 2.ipynb
 delete mode 100644 Project Group4 Notebook.pdf
 delete mode 100644 README ().md

diff --git a/Final Copy -Project Phase 2.ipynb b/Final Copy -Project Phase 2.ipynb
deleted file mode 100644
index ee2a22a0..00000000
--- a/Final Copy -Project Phase 2.ipynb	
+++ /dev/null
@@ -1,5199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Final Project Submission\n",
-    "\n",
-    "Please fill out:\n",
-    "* Student name:\n",
-    "1. Winfred Kinya Bundi.\n",
-    "2. Carol Mundia.\n",
-    "3. Paul Muniu.\n",
-    "4. Dennis Mwenda.\n",
-    "* Student pace: Full time Hybrid\n",
-    "* Scheduled project review date/time:2/05/2024 \n",
-    "* Instructor name: Mwikali Maryanne.\n",
-    "* Blog post URL:git@github.com:winnycodegurl/dsc-phase-2-projectgroup4.git\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## INTRODUCTION\n",
-    "\n",
-    "In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## BUSINESS UNDERSTANDING\n",
-    "\n",
-    "\n",
-    "\n",
-    "The provided dataset encompasses details on homes sold, encompassing their attributes and sale prices. This dataset holds significant potential for real estate agencies across various avenues:\n",
-    "\n",
-    "\n",
-    "\n",
-    "1. \n",
-    "Market Analysis:\n",
-    "\n",
-    " Leveraging the dataset, agencies can discern market trends, including the demand for different property types, burgeoning neighborhoods witnessing property value escalations, and the impact obetter f featurenws or property renovations on sale prices. By employing market segmentation techniques, such as demographic or psychographic segmentation, agencies can further refine their analysis to understand the preferences and behaviors of distinct customer segments within the real estate market\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "2. \n",
-    "Property Valuation: \n",
-    "\n",
-    "By comprehending the correlation between house features and sale prices, agencies can proficiently gauge property values for both sellers and buyers, ensuring equitable and competitive pricing strategies\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "3. \n",
-    "Targeted Marketing: \n",
-    "\n",
-    "Through discerning buyer preferences from the dataset, agencies can tailor marketing endeavors to resonate with potential buyers seeking specific property types or neighborhoods, thus enhancing the efficacy of their outreach efforts. Market segmentation insights can inform the development of targeted marketing campaigns tailored to the unique needs and preferences of different customer segments, thereby maximizing the impact of marketing investments.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## PROBLEM STATEMENT.\n",
-    "\n",
-    "\n",
-    "\n",
-    "In King County, people involved in real estate have trouble figuring out what affects property values and trends in the market.\n",
-    "This study wants to help by looking at things like what features a property has, where it's located, what buyers prefer,\n",
-    "and how things change over time. By understanding these things better, people in real estate can make smarter choices about\n",
-    "buying and selling property and how they position themselves in the market.\n",
-    "The main goal is to give them practical advice that helps them do well in King County's real estate market, \n",
-    "which is always changing.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## OBJECTIVES.\n",
-    "\n",
-    "Main OBJECTIVE\n",
-    "\n",
-    "\n",
-    "The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients \n",
-    "on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, \n",
-    "furnishing clients with valuable insights to facilitate informed investment decisions.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Specific Goals:\n",
-    "\n",
-    "\n",
-    "i). Identification of Key Influencing Factors on House Prices:\n",
-    "\n",
-    "\n",
-    "\n",
-    "Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price.\n",
-    "\n",
-    "\n",
-    "\n",
-    "ii). Assessment of Model Performance:\n",
-    "\n",
-    "\n",
-    "Employ metrics such as mean squared error, R-squared values, and residual analysis to \n",
-    "evaluate the model's accuracy in predicting house prices effectively.\n",
-    "\n",
-    "    \n",
-    "\n",
-    "iii). Provision of Actionable Recommendations:\n",
-    "\n",
-    "\n",
-    "\n",
-    "Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. \n",
-    "Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Understanding.\n",
-    "\n",
-    "\n",
-    "King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments.\n",
-    "\n",
-    "### Whereby the dataset contains:\n",
-    "\n",
-    "### Target Variable\n",
-    "\n",
-    "price: Sale price of the house .\n",
-    "\n",
-    "### Unique identifier\n",
-    "\n",
-    "id - Unique identifier for a house\n",
-    "\n",
-    "### Property Characteristics:\n",
-    "\n",
-    "bedrooms: Number of bedrooms.\n",
-    "\n",
-    "bathrooms: Number of bathrooms.\n",
-    "\n",
-    "sqft_living: Square footage of living space in the home.\n",
-    "\n",
-    "sqft_lot: Square footage of the lot.\n",
-    "\n",
-    "floors: Number of floors (levels) in the house.\n",
-    "\n",
-    "waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).\n",
-    "\n",
-    "view: Quality of view from the house, categorized into various types.\n",
-    "\n",
-    "condition: Overall condition of the house, categorized based on maintenance.\n",
-    "\n",
-    "grade: Overall grade of the house, reflecting construction and design quality.\n",
-    "\n",
-    "### Additional Features:\n",
-    "\n",
-    "sqft_above: Square footage of house apart from the basement.\n",
-    "\n",
-    "sqft_basement: Square footage of the basement.\n",
-    "\n",
-    "yr_built: Year when the house was built.\n",
-    "\n",
-    "yr_renovated: Year when the house was renovated.\n",
-    "\n",
-    "zipcode: ZIP Code of the property.\n",
-    "\n",
-    "lat: Latitude coordinate of the property.\n",
-    "\n",
-    "long: Longitude coordinate of the property.\n",
-    "\n",
-    "sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.\n",
-    "\n",
-    "sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.\n",
-    "\n",
-    "### TABLE OF CONTENTS\n",
-    "1.Data Preparation\n",
-    "\n",
-    "2.Data cleaning\n",
-    "\n",
-    "3.Exploratory data analysis\n",
-    "\n",
-    "4.Statistical Analysis\n",
-    "\n",
-    "5.Modelling\n",
-    "\n",
-    "6.Regression Results\n",
-    "\n",
-    "7.Conclusion\n",
-    "\n",
-    "8.Reccomendations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. DATA PREPARATION"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing necessary libraries for data analysis and visualization\n",
-    "\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt  # for data visualization.\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns  # for enhanced data visualization.\n",
-    "from pandas.api.types import is_numeric_dtype  # Used to check if a data type is numeric.\n",
-    "\n",
-    "from statsmodels.stats.outliers_influence import variance_inflation_factor  # For calculating Variance Inflation Factor (VIF).\n",
-    "from statsmodels.graphics.regressionplots import plot_partregress_grid  # For partial regression plots.\n",
-    "from sklearn.model_selection import train_test_split  # Used to split data into training and testing sets.\n",
-    "from sklearn.preprocessing import PolynomialFeatures  # Generate polynomial features.\n",
-    "from sklearn.linear_model import LinearRegression  # Linear Regression model.\n",
-    "from sklearn.preprocessing import StandardScaler  # Standardizing/Scaling features.\n",
-    "from sklearn.feature_selection import RFE  # Recursive Feature Elimination for feature selection.\n",
-    "from sklearn.metrics import mean_squared_error, r2_score  # Evaluation metrics for model performance.\n",
-    "import statsmodels.api as sm\n",
-    "from scipy.stats import kstest\n",
-    "\n",
-    "# Statsmodels is used to create statistical models.\n",
-    "from scipy import stats  # Scientific computing library for statistical tests.\n",
-    "from scipy.stats import f_oneway  # One-way ANOVA statistical test.\n",
-    "from scipy.stats import ttest_ind  # Independent sample t-test for comparing means."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *loading the King County House Sales dataset*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "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"
-     ]
-    },
-    {
-     "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": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the dataset to inspect the initial state of the data\n",
-    "file_path = 'data/kc_house_data.csv'\n",
-    "housing_data = pd.read_csv(file_path)\n",
-    "\n",
-    "# Display basic information and the first few rows of the dataset\n",
-    "housing_data.info()\n",
-    "housing_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *loading the column.md dataset*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "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>Column_name</th>\n",
-       "      <th>Description</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>* `id`</td>\n",
-       "      <td>Unique identifier for a house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>* `date`</td>\n",
-       "      <td>Date house was sold</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>* `price`</td>\n",
-       "      <td>Sale price (prediction target)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>* `bedrooms`</td>\n",
-       "      <td>Number of bedrooms</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>* `bathrooms`</td>\n",
-       "      <td>Number of bathrooms</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>* `sqft_living`</td>\n",
-       "      <td>Square footage of living space in the home</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>* `sqft_lot`</td>\n",
-       "      <td>Square footage of the lot</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>* `floors`</td>\n",
-       "      <td>Number of floors (levels) in house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>* `waterfront`</td>\n",
-       "      <td>Whether the house is on a waterfront</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>* `view`</td>\n",
-       "      <td>Quality of view from house</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>* `condition`</td>\n",
-       "      <td>How good the overall condition of the house is...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>* `grade`</td>\n",
-       "      <td>Overall grade of the house. Related to the con...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>* `sqft_above`</td>\n",
-       "      <td>Square footage of house apart from basement</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>* `sqft_basement`</td>\n",
-       "      <td>Square footage of the basement</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>* `yr_built`</td>\n",
-       "      <td>Year when house was built</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>* `yr_renovated`</td>\n",
-       "      <td>Year when house was renovated</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>* `zipcode`</td>\n",
-       "      <td>ZIP Code used by the United States Postal Service</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>* `lat`</td>\n",
-       "      <td>Latitude coordinate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>* `long`</td>\n",
-       "      <td>Longitude coordinate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>* `sqft_living15`</td>\n",
-       "      <td>The square footage of interior housing living ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>* `sqft_lot15`</td>\n",
-       "      <td>The square footage of the land lots of the nea...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          Column_name                                        Description\n",
-       "0              * `id`                      Unique identifier for a house\n",
-       "1            * `date`                                Date house was sold\n",
-       "2           * `price`                     Sale price (prediction target)\n",
-       "3        * `bedrooms`                                 Number of bedrooms\n",
-       "4       * `bathrooms`                                Number of bathrooms\n",
-       "5     * `sqft_living`         Square footage of living space in the home\n",
-       "6        * `sqft_lot`                          Square footage of the lot\n",
-       "7          * `floors`                 Number of floors (levels) in house\n",
-       "8      * `waterfront`               Whether the house is on a waterfront\n",
-       "9            * `view`                         Quality of view from house\n",
-       "10      * `condition`  How good the overall condition of the house is...\n",
-       "11          * `grade`  Overall grade of the house. Related to the con...\n",
-       "12     * `sqft_above`        Square footage of house apart from basement\n",
-       "13  * `sqft_basement`                     Square footage of the basement\n",
-       "14       * `yr_built`                          Year when house was built\n",
-       "15   * `yr_renovated`                      Year when house was renovated\n",
-       "16        * `zipcode`  ZIP Code used by the United States Postal Service\n",
-       "17            * `lat`                                Latitude coordinate\n",
-       "18           * `long`                               Longitude coordinate\n",
-       "19  * `sqft_living15`  The square footage of interior housing living ...\n",
-       "20     * `sqft_lot15`  The square footage of the land lots of the nea..."
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "column_names_file = \"data/column_names.md\"\n",
-    "\n",
-    "with open(column_names_file, \"r\") as file:\n",
-    "    markdown_content = file.readlines()\n",
-    "\n",
-    "column_names = []\n",
-    "description = []\n",
-    "for line in markdown_content:\n",
-    "    parts = line.split('-', 1)\n",
-    "    if len(parts) == 2:  # Check if split produces two parts\n",
-    "        column_names.append(parts[0].strip())\n",
-    "        description.append(parts[1].strip())\n",
-    "\n",
-    "# Create DataFrame\n",
-    "data = {\n",
-    "    \"Column_name\": column_names,\n",
-    "    \"Description\": description\n",
-    "}\n",
-    "column_name_df = pd.DataFrame(data)\n",
-    "\n",
-    "column_name_df\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 3 columns with missing values.\n",
-      "waterfront      2376\n",
-      "view              63\n",
-      "yr_renovated    3842\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Creating function to check counts of missing values\n",
-    "def has_missing_values(df):\n",
-    "    missing_values = df.isnull().sum()\n",
-    "    num_missing_values = missing_values[missing_values > 0].count()\n",
-    "    if num_missing_values == 0:\n",
-    "        print(\"There are no missing values in the DataFrame.\")\n",
-    "    else:\n",
-    "        print(f\"There are {num_missing_values} columns with missing values.\")\n",
-    "        print(missing_values[missing_values > 0])\n",
-    "        \n",
-    "has_missing_values(housing_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are no duplicate rows in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "#creating a function to check for duplicates.\n",
-    "def has_duplicates(df):\n",
-    "    num_duplicates = df.duplicated().sum()\n",
-    "    if num_duplicates == 0:\n",
-    "        print(\"There are no duplicate rows in the DataFrame.\")\n",
-    "    else:\n",
-    "        print(f\"There are {num_duplicates} duplicate rows in the DataFrame.\")\n",
-    "\n",
-    "has_duplicates(housing_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The dataset contains 21,597 entries and 21 features. Here’s a brief overview of the data:\n",
-    "\n",
-    "### Columns and their Data Types:\n",
-    "#### Numerical:\n",
-    "id, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, sqft_above, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15\n",
-    "\n",
-    "#### Categorical:\n",
-    "date (format object, should be datetime), waterfront, view, condition, grade, sqft_basement (format object, should be numeric)\n",
-    "\n",
-    "#### Missing Values:\n",
-    "waterfront: 2,376 missing values\n",
-    "view: 63 missing values\n",
-    "yr_renovated: 3,842 missing values\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. DATA CLEANING"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a)Dropping columns:\n",
-    "We're dropping some columns during data cleaning to streamline our analysis and focus on the most relevant features. By removing unnecessary or redundant columns, we aim to simplify the dataset and improve the efficiency of subsequent analytical processes. This helps in reducing noise and enhancing the clarity of our findings."
-   ]
-  },
-  {
-   "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",
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-       "\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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20146</th>\n",
-       "      <td>7967000130</td>\n",
-       "      <td>4/1/2015</td>\n",
-       "      <td>370228.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2050</td>\n",
-       "      <td>4000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13983</th>\n",
-       "      <td>7338000150</td>\n",
-       "      <td>1/29/2015</td>\n",
-       "      <td>160000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>4200</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>1070</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1983</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1150</td>\n",
-       "      <td>4200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3605</th>\n",
-       "      <td>8658303585</td>\n",
-       "      <td>8/7/2014</td>\n",
-       "      <td>252500.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>900</td>\n",
-       "      <td>7500</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>6 Low Average</td>\n",
-       "      <td>900</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1961</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1190</td>\n",
-       "      <td>10000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9229</th>\n",
-       "      <td>1787250210</td>\n",
-       "      <td>12/22/2014</td>\n",
-       "      <td>379000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>5225</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Average</td>\n",
-       "      <td>8 Good</td>\n",
-       "      <td>2410</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>5378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11243</th>\n",
-       "      <td>1843200350</td>\n",
-       "      <td>7/22/2014</td>\n",
-       "      <td>150000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1934</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>Good</td>\n",
-       "      <td>7 Average</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1978</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>1898</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id        date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "20146  7967000130    4/1/2015  370228.0         4       3.00         2050   \n",
-       "13983  7338000150   1/29/2015  160000.0         2       1.00         1070   \n",
-       "3605   8658303585    8/7/2014  252500.0         2       1.00          900   \n",
-       "9229   1787250210  12/22/2014  379000.0         4       2.75         2410   \n",
-       "11243  1843200350   7/22/2014  150000.0         2       1.50         1360   \n",
-       "\n",
-       "       sqft_lot  floors waterfront condition          grade  sqft_above  \\\n",
-       "20146      4000     2.0         NO   Average         8 Good        2050   \n",
-       "13983      4200     1.0         NO      Good  6 Low Average        1070   \n",
-       "3605       7500     1.0         NO      Good  6 Low Average         900   \n",
-       "9229       5225     2.0         NO   Average         8 Good        2410   \n",
-       "11243      1934     2.0         NO      Good      7 Average        1360   \n",
-       "\n",
-       "      sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "20146           0.0      2014           0.0           2050        4000  \n",
-       "13983           0.0      1983           0.0           1150        4200  \n",
-       "3605            0.0      1961           0.0           1190       10000  \n",
-       "9229            0.0      2001           0.0           2300        5378  \n",
-       "11243           0.0      1978           0.0           1360        1898  "
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data = housing_data.drop(['long','lat','view', 'zipcode'], axis=1)\n",
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b)Checking for placeholders\n",
-    "\n",
-    "Placeholders are values used to denote missing, unknown, or invalid data within a dataset. Common examples include \"N/A\", \"-\", \"UNKNOWN\", \"NULL\", #, etc. It's important to identify and handle placeholders properly during data preprocessing to ensure accurate analysis and modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column 'sqft_basement': Found 454 occurrences of potential placeholder .\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Define a list of common placeholder values\n",
-    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
-    "\n",
-    "# Check for potential placeholders in the DataFrame\n",
-    "found_placeholder = False\n",
-    "for column in housing_data.columns:\n",
-    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
-    "    if placeholder_count > 0:\n",
-    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
-    "        found_placeholder = True\n",
-    "\n",
-    "if not found_placeholder:\n",
-    "    print(\"No potential placeholders found in the DataFrame.\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# Convert the common placeholders to lowercase for case-insensitive matching\n",
-    "common_placeholders_lower = [placeholder.lower() for placeholder in common_placeholders]\n",
-    "\n",
-    "# Replace any of the common placeholders with NaN\n",
-    "housing_data['sqft_basement'] = housing_data['sqft_basement'].replace(common_placeholders_lower, pd.NA)\n",
-    "\n",
-    "# Drop rows with NaN in the sqft_basement column\n",
-    "housing_data.dropna(subset=['sqft_basement'], inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Counter-check"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No potential placeholders found in the DataFrame.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# confirm no more placeholders\n",
-    "# Define a list of common placeholder values\n",
-    "common_placeholders = [\"\", \"na\", \"n/a\", \"nan\", \"none\", \"null\", \"-\", \"--\", \"?\", \"??\", \"unknown\", \"missing\", \"void\", \"empty\", \"#\", \"#####\"]\n",
-    "\n",
-    "# Check for potential placeholders in the DataFrame\n",
-    "found_placeholder = False\n",
-    "for column in housing_data.columns:\n",
-    "    placeholder_count = housing_data[column].isin(common_placeholders).sum()\n",
-    "    if placeholder_count > 0:\n",
-    "        print(f\"Column '{column}': Found {placeholder_count} occurrences of potential placeholder .\")\n",
-    "        found_placeholder = True\n",
-    "\n",
-    "if not found_placeholder:\n",
-    "    print(\"No potential placeholders found in the DataFrame.\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "### b) Handling Missing Values:\n",
-    "waterfront: \n",
-    "Since these are categorical, we can replace missing values with the mode or create a separate category for missing values.\n",
-    "<br> yr_renovated: \n",
-    "A significant number of missing values suggest that these houses might not have been renovated. Impute with 0 or a specific marker value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# For categorical data, impute missing values with mode or specific marker\n",
-    "waterfront_mode = housing_data['waterfront'].mode()[0]\n",
-    "\n",
-    "housing_data['waterfront'].fillna(waterfront_mode, inplace=True)\n",
-    "housing_data['yr_renovated'].fillna(0, inplace=True)  # Assuming no renovation if NaN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### c) Convert Data\n",
-    "Convert date from object to datetime format.\n",
-    "sqft_basement: Convert from object to numeric, handling any non-numeric entries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "housing_data['date'] = pd.to_datetime(housing_data['date'])\n",
-    "housing_data['sqft_basement'] = pd.to_numeric(housing_data['sqft_basement'], errors='coerce')  # Convert to numeric, coerce errors\n",
-    "housing_data['sqft_basement'].fillna(0, inplace=True)  # Assuming no basement if NaN or non-numeric"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change waterfront to integer\n",
-    "housing_data['waterfront'] = housing_data['waterfront'].apply(lambda x: 0 if x == 'NO' else 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['7 Average' '6 Low Average' '8 Good' '11 Excellent' '9 Better' '5 Fair'\n",
-      " '10 Very Good' '12 Luxury' '4 Low' '3 Poor' '13 Mansion']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# checking \"grade\" column\n",
-    "unique_grade = housing_data.grade.unique()\n",
-    "print(unique_grade)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Average' 'Very Good' 'Good' 'Poor' 'Fair']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# checking \"condition\"column\n",
-    "unique_condition = housing_data.condition.unique()\n",
-    "print(unique_condition)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert grade and condition into representative numbers for easier Exploratory analysis.\n",
-    "housing_data['condition'] = housing_data['condition'].map({'Poor': 1,'Fair': 2,'Average': 3,'Good': 4,'Very Good': 5}).astype(float)\n",
-    "housing_data['grade'] = housing_data['grade'].map({'3 Poor': 1,'4 Low': 2,'5 Fair': 3,'6 Low Average': 4,'7 Average': 5,'8 Good': 6,'9 Better': 7,'10 Very Good': 8,'11 Excellent': 9,'12 Luxury': 10,'13 Mansion': 11}).astype(float) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 21143 entries, 0 to 21596\n",
-      "Data columns (total 17 columns):\n",
-      " #   Column         Non-Null Count  Dtype         \n",
-      "---  ------         --------------  -----         \n",
-      " 0   id             21143 non-null  int64         \n",
-      " 1   date           21143 non-null  datetime64[ns]\n",
-      " 2   price          21143 non-null  float64       \n",
-      " 3   bedrooms       21143 non-null  int64         \n",
-      " 4   bathrooms      21143 non-null  float64       \n",
-      " 5   sqft_living    21143 non-null  int64         \n",
-      " 6   sqft_lot       21143 non-null  int64         \n",
-      " 7   floors         21143 non-null  float64       \n",
-      " 8   waterfront     21143 non-null  int64         \n",
-      " 9   condition      21143 non-null  float64       \n",
-      " 10  grade          21143 non-null  float64       \n",
-      " 11  sqft_above     21143 non-null  int64         \n",
-      " 12  sqft_basement  21143 non-null  float64       \n",
-      " 13  yr_built       21143 non-null  int64         \n",
-      " 14  yr_renovated   21143 non-null  float64       \n",
-      " 15  sqft_living15  21143 non-null  int64         \n",
-      " 16  sqft_lot15     21143 non-null  int64         \n",
-      "dtypes: datetime64[ns](1), float64(7), int64(9)\n",
-      "memory usage: 2.9 MB\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "      <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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4511</th>\n",
-       "      <td>723049333</td>\n",
-       "      <td>2015-04-05</td>\n",
-       "      <td>285000.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1010</td>\n",
-       "      <td>480.0</td>\n",
-       "      <td>1957</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>8254</td>\n",
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-       "    <tr>\n",
-       "      <th>1181</th>\n",
-       "      <td>7625700935</td>\n",
-       "      <td>2014-06-05</td>\n",
-       "      <td>875000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.50</td>\n",
-       "      <td>3250</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2500</td>\n",
-       "      <td>750.0</td>\n",
-       "      <td>2001</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1650</td>\n",
-       "      <td>6000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1875</th>\n",
-       "      <td>1853081000</td>\n",
-       "      <td>2014-07-17</td>\n",
-       "      <td>820000.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>6137</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>2830</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3170</td>\n",
-       "      <td>6285</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12273</th>\n",
-       "      <td>7016200030</td>\n",
-       "      <td>2015-03-20</td>\n",
-       "      <td>480000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>7966</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>880.0</td>\n",
-       "      <td>1970</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1920</td>\n",
-       "      <td>7500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8546</th>\n",
-       "      <td>7981900110</td>\n",
-       "      <td>2014-10-03</td>\n",
-       "      <td>350000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.75</td>\n",
-       "      <td>2300</td>\n",
-       "      <td>3175</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>960.0</td>\n",
-       "      <td>1966</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1260</td>\n",
-       "      <td>3175</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "4511    723049333 2015-04-05  285000.0         3       1.50         1490   \n",
-       "1181   7625700935 2014-06-05  875000.0         3       3.50         3250   \n",
-       "1875   1853081000 2014-07-17  820000.0         5       2.75         2830   \n",
-       "12273  7016200030 2015-03-20  480000.0         4       2.50         2080   \n",
-       "8546   7981900110 2014-10-03  350000.0         4       2.75         2300   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "4511      10367     1.0           0        3.0    5.0        1010   \n",
-       "1181       6000     2.0           0        3.0    8.0        2500   \n",
-       "1875       6137     2.0           0        3.0    7.0        2830   \n",
-       "12273      7966     1.0           0        3.0    5.0        1200   \n",
-       "8546       3175     1.5           0        3.0    4.0        1340   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \n",
-       "4511           480.0      1957           0.0           1000        8254  \n",
-       "1181           750.0      2001           0.0           1650        6000  \n",
-       "1875             0.0      2010           0.0           3170        6285  \n",
-       "12273          880.0      1970           0.0           1920        7500  \n",
-       "8546           960.0      1966           0.0           1260        3175  "
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Check transformations\n",
-    "housing_data.info()\n",
-    "housing_data.sample(5)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Feature Engineering:\n",
-    "\n",
-    "Create additional features that might be informative for our modeling:\n",
-    "\n",
-    "**House Age**: Calculate the age of the house from the 'yr_built' column to the current year.\n",
-    "\n",
-    "**Renovation Age**: If a house has been renovated ('yr_renovated' > 0), calculate the years since the renovation.\n",
-    "\n",
-    "**Total Square Footage**: Sum up 'sqft_living', 'sqft_lot', 'sqft_above', and 'sqft_basement' for a total square footage feature.\n",
-    "\n",
-    "These new features could reveal deeper insights into the housing prices and help improve the performance of our statistical models.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "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>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>69</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8010.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>73</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>12382.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>91</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11540.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>59</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8920.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>37</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11440.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   house_age  renovation_age  total_sqft\n",
-       "0         69             0.0      8010.0\n",
-       "1         73            33.0     12382.0\n",
-       "2         91             0.0     11540.0\n",
-       "3         59             0.0      8920.0\n",
-       "4         37             0.0     11440.0"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Current year for age calculations\n",
-    "from datetime import datetime\n",
-    "\n",
-    "current_year = datetime.now().year\n",
-    "\n",
-    "# Feature Engineering\n",
-    "housing_data['house_age'] = current_year - housing_data['yr_built']\n",
-    "housing_data['renovation_age'] = housing_data.apply(\n",
-    "    lambda x: 0 if x['yr_renovated'] == 0 else current_year - x['yr_renovated'], axis=1\n",
-    ")\n",
-    "housing_data['total_sqft'] = housing_data['sqft_living'] + housing_data['sqft_lot'] + \\\n",
-    "                                     housing_data['sqft_above'] + housing_data['sqft_basement']\n",
-    "\n",
-    "# Display the new features\n",
-    "housing_data[['house_age', 'renovation_age', 'total_sqft']].head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The new features have been successfully added:\n",
-    "\n",
-    "**House Age**: Represents the age of the house since it was built.\n",
-    "\n",
-    "**Renovation Age**: If renovated, this indicates the number of years since the last renovation; otherwise, it is 0.\n",
-    "\n",
-    "**Total Square Footage**: Sum of the living area, lot size, above-ground level area, and basement area\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### *Sample data check.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8540</th>\n",
-       "      <td>3034200366</td>\n",
-       "      <td>2014-12-03</td>\n",
-       "      <td>409000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>9065</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1440</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1972</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1990</td>\n",
-       "      <td>8812</td>\n",
-       "      <td>52</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11945.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19433</th>\n",
-       "      <td>2023059052</td>\n",
-       "      <td>2015-05-04</td>\n",
-       "      <td>450000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1350</td>\n",
-       "      <td>92721</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1200</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>1946</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1860</td>\n",
-       "      <td>8096</td>\n",
-       "      <td>78</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>95421.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7003</th>\n",
-       "      <td>7751800080</td>\n",
-       "      <td>2015-01-27</td>\n",
-       "      <td>465000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>9879</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1460</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1956</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1610</td>\n",
-       "      <td>10050</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12799.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10151</th>\n",
-       "      <td>1180002075</td>\n",
-       "      <td>2014-08-25</td>\n",
-       "      <td>235000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1930</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1210</td>\n",
-       "      <td>6000</td>\n",
-       "      <td>94</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8420.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3506</th>\n",
-       "      <td>3449900090</td>\n",
-       "      <td>2015-04-10</td>\n",
-       "      <td>454200.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2630</td>\n",
-       "      <td>5379</td>\n",
-       "      <td>20</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10639.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               id       date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "8540   3034200366 2014-12-03  409000.0         3       1.75         1440   \n",
-       "19433  2023059052 2015-05-04  450000.0         3       1.00         1350   \n",
-       "7003   7751800080 2015-01-27  465000.0         3       1.50         1460   \n",
-       "10151  1180002075 2014-08-25  235000.0         3       1.00         1210   \n",
-       "3506   3449900090 2015-04-10  454200.0         4       2.50         2630   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  grade  sqft_above  \\\n",
-       "8540       9065     1.0           0        4.0    6.0        1440   \n",
-       "19433     92721     1.0           0        2.0    4.0        1200   \n",
-       "7003       9879     1.0           0        3.0    5.0        1460   \n",
-       "10151      6000     1.0           0        3.0    5.0        1210   \n",
-       "3506       5379     2.0           0        3.0    6.0        2630   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "8540             0.0      1972           0.0           1990        8812   \n",
-       "19433          150.0      1946           0.0           1860        8096   \n",
-       "7003             0.0      1956           0.0           1610       10050   \n",
-       "10151            0.0      1930           0.0           1210        6000   \n",
-       "3506             0.0      2004           0.0           2630        5379   \n",
-       "\n",
-       "       house_age  renovation_age  total_sqft  \n",
-       "8540          52             0.0     11945.0  \n",
-       "19433         78             0.0     95421.0  \n",
-       "7003          68             0.0     12799.0  \n",
-       "10151         94             0.0      8420.0  \n",
-       "3506          20             0.0     10639.0  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Index: 21143 entries, 0 to 21596\n",
-      "Data columns (total 20 columns):\n",
-      " #   Column          Non-Null Count  Dtype         \n",
-      "---  ------          --------------  -----         \n",
-      " 0   id              21143 non-null  int64         \n",
-      " 1   date            21143 non-null  datetime64[ns]\n",
-      " 2   price           21143 non-null  float64       \n",
-      " 3   bedrooms        21143 non-null  int64         \n",
-      " 4   bathrooms       21143 non-null  float64       \n",
-      " 5   sqft_living     21143 non-null  int64         \n",
-      " 6   sqft_lot        21143 non-null  int64         \n",
-      " 7   floors          21143 non-null  float64       \n",
-      " 8   waterfront      21143 non-null  int64         \n",
-      " 9   condition       21143 non-null  float64       \n",
-      " 10  grade           21143 non-null  float64       \n",
-      " 11  sqft_above      21143 non-null  int64         \n",
-      " 12  sqft_basement   21143 non-null  float64       \n",
-      " 13  yr_built        21143 non-null  int64         \n",
-      " 14  yr_renovated    21143 non-null  float64       \n",
-      " 15  sqft_living15   21143 non-null  int64         \n",
-      " 16  sqft_lot15      21143 non-null  int64         \n",
-      " 17  house_age       21143 non-null  int64         \n",
-      " 18  renovation_age  21143 non-null  float64       \n",
-      " 19  total_sqft      21143 non-null  float64       \n",
-      "dtypes: datetime64[ns](1), float64(9), int64(10)\n",
-      "memory usage: 3.4 MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "housing_data.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. EXPLORATORY DATA ANALYSIS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next is EDA; Exploratory Data Analysis is a crucial step in data analysis. This process will involve examining and understanding the structure, patterns, and relationships within the dataset. It will aid us uncover insights, detect anomalies, and inform subsequent analysis and modeling decisions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### a.) Univariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Distribution of House Prices.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set the style for seaborn\n",
-    "sns.set_style(\"whitegrid\")\n",
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))\n",
-    "\n",
-    "# Histogram\n",
-    "sns.histplot(housing_data['price'], bins=\"auto\", kde=False, color='#003399', ax=axes[0])\n",
-    "axes[0].set_title('Histogram of House Prices')\n",
-    "axes[0].set_xlabel('Price')\n",
-    "axes[0].set_ylabel('Frequency')\n",
-    "\n",
-    "# Density Plot\n",
-    "sns.histplot(housing_data['price'], bins=\"auto\", kde=True, color='#339933', ax=axes[1])\n",
-    "axes[1].set_title('Density Plot of House Prices')\n",
-    "axes[1].set_xlabel('Price')\n",
-    "axes[1].set_ylabel('Density')\n",
-    "\n",
-    "# A common title\n",
-    "plt.suptitle('Distribution Analysis of House Prices.', fontsize=16)\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The histogram depicts the distribution of house prices, with most bars are clustered towards the left, suggesting that a significant number of houses are priced lower. The density plot illustrates a curve representing the density of house prices. Similar to the histogram, the curve peaks sharply on the left and gradually tapers off, indicating a right-skewed distribution. \n",
-    "<br>In summary, the majority of house prices are concentrated at the lower end, creating a skewed distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Distribution of Bedrooms, Bathrooms and Floors.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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8qUePHqWKiorSAw88UJ7+l7/8pbzfvf322+XpX3zxRWm//fYrVVRUlA4++ODy9G+fn3bdddfyMVZZWVk66KCDyvOOPvrocttMnDixtOmmm5YqKiqq7ff1OV/W17hx40prrrlmqUePHqU333yz2rynnnqqtMYaa5QqKipK//vf/8rTq85lq622WunWW28tT58yZUppp512KlVUVJROP/308vTnn3++1L59+1KPHj1K//3vf8vT33nnnfI5oqKioto+MitV23XXXXet82esOtZ22mmn8rQPP/ywtNZaa5Xat29fGj58eLXlb7zxxlL79u1LXbp0KX3wwQfl6fU9Z9a2Dz711FPlz1F17JdKX+/PVWU99dRTdf6MAADwbYb6BACgwSyyyCJJMtvh2z766KO0bNmy2l1IzZo1ywEHHJDjjz8+2267bb3ed4899ij/v3nz2Xdxe/TokV133bX8um3btjnttNPSqlWr3HrrrZk0aVK93n9OTJ06Ndddd12aN2+ec845p1pbLLHEEjnnnHPSvHnzXHPNNTMNn9iyZcv89re/rXZn44477pjWrVtnwoQJGT9+fK3v/d577+W+++7LQgstlLPOOqtaOT/96U9z6qmnJkkuv/zyhvio2W233crDorZv3z6dOnXK1ltvnX/+85/Zeuutc9RRR1Vbfvjw4fnss8+y0047Zauttqo2b8cdd0yfPn0yduzY3HfffUmSZ555Ji+//HJWX331HHjggeVlW7VqlZNOOqnWOxOrhuSs0rx589x9990ZN25cunfvnoMPPrjaHTwbbrhh9t1330yfPj1XXnllkrnbls2aNctxxx2XVq1aJUlat26dzTffPEmyzDLLZPDgweVll1xyyay99tpJkrfffjtJ8sUXX2Ty5Mlp27ZttTtVW7ZsmSOOOCInnXRSNtxww1l+/vrYeOONq23Hzp07Z8CAARkxYkT23Xff7LbbbtWWb9WqVTbccMMcfvjhadnym8FmWrRoUb5b8t13361XHf7v//4v3bt3L79efPHFy+eLV155Zabll1lmmWy55Zbl182bN8+oUaPy0ksvZcUVV8yJJ55Y7S7MTp06lffHb+//f/nLX5Ikv/vd76rdxbvQQgtl2LBhWXjhhXPvvfeWt8u3HXXUUeVjrFmzZuV9unXr1jnmmGPKbbPgggtmgw02SJJq5TT0+fLbxo8fn379+uWggw7Kz372s2rzunXrllVXXTVJzdtpo402yjbbbFN+vcACC5S366uvvlqefv3116dUKuXAAw/MaqutVp6+wgor5De/+c0c1XvkyJHV9sXv/vvvf/9b6/rXX399Jk+enF/84hfZfvvtq80bMGBAfvnLX+bLL7/Mtddem2Tuzpk17YMffvhhkq/PD1XHfvL1/nzKKafktNNOy3LLLVfPVgEAgK8Z6hMAgAbz1VdfJclMQ519V/fu3fPYY49l2223zYABA8rPlVpiiSWqBXJ10aZNm/z0pz+t1zr9+/efadqSSy6Zjh075plnnsmLL76YHj161KvM+nrhhRcyZcqUdOjQocbhQFdcccV07Ngxzz//fEaPHp111123PO+nP/3pTEMKtm7dOosttljGjRs32+dDPfXUU0mS9ddfPwsvvPBM83v27JmllloqH3zwQd55551ahyuti/XWW69aaDFt2rSMHz8+L730Uu64445MmTIlZ599dtq0aZMk5ee6ffszf1vv3r3z8MMPZ8SIEdlqq63Kn6d3794zLbvAAgukd+/e5WctfldFRcVM06qeM7fFFlvUuM6WW26Ziy66qLzc3GzLFVdccabha6ter7rqqtUCs+SbcL3quWtLLLFEVlpppbzxxhv55S9/mW233Ta9e/dO+/bt87Of/WymMGdubLLJJmnbtm359dSpU/PBBx/kv//9by6//PJMnDgxJ5xwQjl833XXXWc6nidPnpwxY8aUn3H33efHzc63Q9oqP/7xj5N8HYJ+V23bd5NNNpmpfZNk8803z3HHHZfnn38+06ZNy8cff5x33303iy22WNZZZ52Zll944YWzwQYb5K677srIkSOrnY9at26dNdZYo9ryVdt3ueWWK2/PKlWvp06dWp7WkOfL71pjjTVmev5qZWVlxo4dmxdeeCGfffZZkm/O7d9W07ZYeumlk6TaOajq+KwpgN5www3TqlWrGsuvzRJLLJH11ltvlvN/9KMf1bp+VZ1qO8Zvvvnm8r4yN+fMmvbBLl26pFWrVrn77rvz+eefZ4sttsgGG2yQZZdddpbnPQAAqCvBHwAADebTTz9NMvsvXX/3u9/l4IMPzgsvvJALL7wwF154YRZddNFsuOGGGTBgQL1Ct5q+hJ2dWd1JURUgVN2N8X2qeo/a7upYfvnl8/zzz89Un++GBVWqQozKysq5fu/lllsuH330UT788MO5Dv4OOOCA9OzZc6bpH3/8cQYPHpz7778/Z555Zk444YQkyf/+978kycEHH1xruR988EGSbz7PMsssU+NytT3jr6Z9dXbtU/Xssqo7K+dmW9b0/lXB+XfD3W/P+7bzzjsvhx56aF5//fUMGzYsw4YNy9JLL52+fftmp512mil4mlPHHHNMjW05duzY7L333rn22muzwgorZO+99y7PGz9+fK699tqMGDEib731VsaPH59SqVT+HKVSqV51qGnfb9GiRZJkxowZM82rbfvOar9o165dFl988Xz00UeZMGFCnbdv8vXded+28MILz3QXcn23b0OeL2syY8aM3HPPPbnzzjvz2muv5b333pvpRxw1baea2rZqW3z7HFTVfssuu+xMy7dq1SrLLrtsxo4dW686r7zyyhk2bFi91vm22W3T727PuTln1tROP/7xj8vnvH//+9/597//neTrsL9fv37ZeeedyyEqAADUl+APAIAG8/LLLydJ2rdvX+tyyy67bIYPH56nn346999/fx5//PG88sorufXWW3Prrbdm3333zZFHHlmn96zL0J7ftcACC9Q4verL7ZruAvqumkKG+qh6r9rujqxa5ttDEc5unfqYk/duSEsssUSOP/74bLfddhk+fHiOO+64NG/evNy2ffv2zUILLTTL9VdZZZUks2+P2rZnTevObttUza8aom9utmVd9rXZad++fe6666488cQTefDBB/Pvf/87b731Vq6//vrccMMNOf744zNw4MC5fp9ZWWGFFXLkkUfmkEMOyQ033FAO/kaMGJEDDjggkyZNyo9//OOstdZaWXnllbPGGmtkueWWy4ABA+r9XvU93mvbvrX59vZq7O3bkOfL75o0aVJ23333jB49Ou3atUuHDh2y3nrrpaKiImuvvXZOPvnk8t1u31XX89DslqsKC39IdT3G63Pure86W265ZXr37p0HH3wwjzzySJ588smMGTMmY8aMyV/+8pdcddVV6dSpU90+EAAAfIvgDwCABvPwww8nSa1DsH3b2muvXX5m2ccff5x//OMfOffcc/PnP/85gwYNmuUdXHNrVnf0vffee0m+ufOvKmSo6Q66qiHw5lTV3Ry1PeOs6i6Y2p5RN6+993dVPUNs6tSpmTBhQpZccsksvfTSeeutt7LbbrvVaV+qupOoavt9V33v4Jxd+1S1TdXwpU2hPVu0aJFevXqlV69eSZL3338/V199da688soMGzYs//d//1ftWWINrSqErbpbs1Qq5dhjj82kSZNy4oknZpdddqm2/H/+85/vrS6zM7vt9eWXX2bChAlp0aJFFl100fKwlY19vHwf58srrrgio0ePznrrrZcLL7xwpqD9888/n+t6L7PMMnnzzTfz3nvvlfeTKqVSaaa7JH8ISy+9dN588828++67M9Up+WZbf9/H+EILLZRtttmm/KzEl156Keecc07+9a9/5fzzz8+f//znOpcFAABV6v/zaAAAqMFdd92VN954I8suu2z69Okzy+XeeOON9O/fP/vss0+16UsssUT222+/tG/fPpWVlRk3blyShru77dsee+yxmaa99957efHFF7Pwwgtn9dVXT/L1kH/J11+yf9czzzxTY9l1rW+HDh3Stm3b/Oc//6lxmLt33nknL730UvkunIa09tprp1mzZvn3v/+diRMnzjT/ySefzIQJE7L88svnJz/5SYO+93e9+eabSb5+VuOiiy6a5OtnmiUpPwfuu84+++xst912ufHGG5N88yzABx98cKY7MWfMmFHj9q5N1fv/85//rHH+XXfdlSTlIRYbc1uOHDkyW2yxRXmY1Co/+clPcvTRR2eRRRbJpEmTanz+XUN66623knwTmo8fPz5jx47NIossMlPolyT/+te/ksx+WNrvQ9X2feCBBzJ9+vSZ5v/zn/9MZWVlunXrlubNm+cnP/lJlltuuXzyySflZ7592xdffFH+PFVlN5T6ni/r69lnn02SDBw4cKbQb9y4cXn99deTzN12qgrv77vvvpnmjRo1Kl9++eUclz2nqrbTPffcU+P87x7jDX3OvPLKK9O3b9/ccsst1aavueaaGTp0aJJvhjIGAID6EvwBADBXSqVS7r777hx//PFJkuOOO67W4SF/+tOfZvz48fnXv/41U7Dy4osv5vXXX8+CCy6YlVZaKck3w3JOmjSpwUKCW265pdqX0J9//nmGDh2aGTNmZODAgeX6r7baakm+/nL6pZdeKi//v//9b5bPl6pad3ZBS9u2bfN///d/qayszBFHHJEJEyaU502YMCGHH354Kisrs/3226dt27Zz9kFnYYUVVsjGG2+ciRMnZujQoZk0aVJ53tixY3PcccclSXbdddcGfd/v+uyzz3Lqqacm+XrYu6phEXfccce0a9cuf/vb33LnnXdWW+ehhx7KlVdemZdeeqkconXs2DHdunXLm2++mTPPPLO8n1RWVmbYsGF5++2361WvLbbYIksvvXRGjhyZSy65pNrQkI8++mguv/zytGjRIjvvvHOSxt2Wq666at55553ccsstefrpp6vNe/jhh/P5559n+eW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-      "text/plain": [
-       "<Figure size 1800x600 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Create subplots\n",
-    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18, 6))\n",
-    "\n",
-    "# Common title for all subplots\n",
-    "fig.suptitle('Distribution of Bedrooms, Bathrooms, and Floors.', fontsize=16)\n",
-    "\n",
-    "# Box plot for bedrooms\n",
-    "sns.boxplot(x=housing_data['bedrooms'], ax=axes[0])\n",
-    "axes[0].set_title('Bedrooms')\n",
-    "axes[0].set_xlabel('Number of Bedrooms')\n",
-    "\n",
-    "# Box plot for bathrooms\n",
-    "sns.boxplot(x=housing_data['bathrooms'], ax=axes[1])\n",
-    "axes[1].set_title('Bathrooms')\n",
-    "axes[1].set_xlabel('Number of Bathrooms')\n",
-    "\n",
-    "# Box plot for floors\n",
-    "sns.boxplot(x=housing_data['floors'], ax=axes[2])\n",
-    "axes[2].set_title('Floors')\n",
-    "axes[2].set_xlabel('Number of Floors')\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After analyzing the distribution of bedrooms, bathrooms and floors, an outlier was detected in the bedroom column. To ensure the accuracy of our analysis, the outlier value was identified and subsequently removed from the dataset. The box plot below displays the distribution of bedrooms after excluding the outlier value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Identify the outlier value\n",
-    "outlier_value = housing_data['bedrooms'].max()  \n",
-    "\n",
-    "# Filter the DataFrame to exclude the outlier\n",
-    "housing_data = housing_data[housing_data['bedrooms'] != outlier_value]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Number of Bedrooms')"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Box plot for bedrooms\n",
-    "sns.boxplot(x=housing_data['bedrooms'])\n",
-    "plt.title('Bedrooms')\n",
-    "plt.xlabel('Number of Bedrooms')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### b.) Bivariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Total Square Footage of houses by Price Range.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "price_range\n",
-       "300K-600K    10560\n",
-       "600K-1M       4691\n",
-       "100K-300K     4433\n",
-       "1M-2M         1234\n",
-       "2M-5M          187\n",
-       "70K-100K        30\n",
-       "5M-8M            7\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Define the labels with ranges\n",
-    "labels = [\"70K-100K\", \"100K-300K\", \"300K-600K\", \"600K-1M\", \"1M-2M\", \"2M-5M\", \"5M-8M\"]\n",
-    "\n",
-    "# Cut the data into the specified ranges and assign labels\n",
-    "housing_data.loc[:,\"price_range\"] = pd.cut(housing_data.price,\n",
-    "                                            bins=[70000, 100000, 300000, 600000, 1000000, 2000000, 5000000, 8000000],\n",
-    "                                            labels=labels)\n",
-    "\n",
-    "# Count the occurrences of each category\n",
-    "housing_data['price_range'].value_counts()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "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>condition</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "      <th>price_range</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1188</th>\n",
-       "      <td>2624049185</td>\n",
-       "      <td>2014-09-09</td>\n",
-       "      <td>405000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.75</td>\n",
-       "      <td>1760</td>\n",
-       "      <td>5355</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1160</td>\n",
-       "      <td>600.0</td>\n",
-       "      <td>1956</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1790</td>\n",
-       "      <td>6225</td>\n",
-       "      <td>68</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8875.0</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20611</th>\n",
-       "      <td>2895800390</td>\n",
-       "      <td>2014-08-07</td>\n",
-       "      <td>359800.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>2752</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7092.0</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2209</th>\n",
-       "      <td>3438500339</td>\n",
-       "      <td>2014-05-26</td>\n",
-       "      <td>276000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1140</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>64</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7280.0</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18431</th>\n",
-       "      <td>4167960330</td>\n",
-       "      <td>2015-01-09</td>\n",
-       "      <td>270000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>7750</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1820</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1992</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2080</td>\n",
-       "      <td>8084</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11390.0</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11609</th>\n",
-       "      <td>1972201550</td>\n",
-       "      <td>2014-07-16</td>\n",
-       "      <td>565000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>2452</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1540</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1906</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1290</td>\n",
-       "      <td>3360</td>\n",
-       "      <td>118</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5532.0</td>\n",
-       "      <td>300K-600K</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",
-       "1188   2624049185 2014-09-09  405000.0         3       1.75         1760   \n",
-       "20611  2895800390 2014-08-07  359800.0         5       2.50         2170   \n",
-       "2209   3438500339 2014-05-26  276000.0         3       1.00         1140   \n",
-       "18431  4167960330 2015-01-09  270000.0         3       2.00         1820   \n",
-       "11609  1972201550 2014-07-16  565000.0         4       1.00         1540   \n",
-       "\n",
-       "       sqft_lot  floors  waterfront  condition  ...  sqft_above  \\\n",
-       "1188       5355     1.0           0        3.0  ...        1160   \n",
-       "20611      2752     2.0           0        3.0  ...        2170   \n",
-       "2209       5000     1.0           0        3.0  ...        1140   \n",
-       "18431      7750     1.0           0        3.0  ...        1820   \n",
-       "11609      2452     1.5           0        4.0  ...        1540   \n",
-       "\n",
-       "       sqft_basement  yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "1188           600.0      1956           0.0           1790        6225   \n",
-       "20611            0.0      2014           0.0           1800        2752   \n",
-       "2209             0.0      1960           0.0           1140        5000   \n",
-       "18431            0.0      1992           0.0           2080        8084   \n",
-       "11609            0.0      1906           0.0           1290        3360   \n",
-       "\n",
-       "       house_age  renovation_age  total_sqft  price_range  \n",
-       "1188          68             0.0      8875.0    300K-600K  \n",
-       "20611         10             0.0      7092.0    300K-600K  \n",
-       "2209          64             0.0      7280.0    100K-300K  \n",
-       "18431         32             0.0     11390.0    100K-300K  \n",
-       "11609        118             0.0      5532.0    300K-600K  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
-      "  grouped_vals = vals.groupby(grouper)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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SEhKs+qxbt04TJ07UoEGD1K5dO8svuP369dPvv//+wJrd3NxUpEgRTZky5Z7tD3oeWXq4u7vf93xIkqenp9zd3RUbG6vU1FSr4HXnF/0779U/pee47wSU6Oho5c+f37JudHS05d85c+aUwWC47/Ok/vnL/z/dCVf/PL5KlSpZXrdt21Zr1qzRkSNH9P3331tNcPEgfn5+//ocSLdHe6XbQeXOiJEknTp1SjExMZZgPWTIEFWvXv2u9R8UvO/Fy8tLo0aN0nvvvaeQkBBNnTpV0u0/bvTu3VsNGzbUnDlzLCNZS5cu1Y4dOzK0j3z58lmdxzuio6P1wgsvSLr9/e3i4qIvv/zyntt4/vnnM7RPAM82Zi8EgHRwcXFR2bJltWHDBqWlpVmW37hxQz///LPlYbJ/v/RQuj0xwenTp9WuXTuVL1/e8ov/9u3bJf3vL+sPU6RIEd28eVNffvnlPdc5ffq08uTJo9y5c6tSpUpydna+axa1fz7Y2dXVVZJ0+fJly7KDBw9a9Tlw4IDc3NzUo0cPS/BISEjQgQMHHlp79erVdfnyZXl5ealcuXKWr127dmn+/Pl3vVcZVa1aNR06dMgym98da9euVZ48efT888+revXqSktLu+u9uBN475y3v18Wmd7jrlKliuzs7LR582ardf/+2tXVVaVLl9apU6es3oPixYtrxowZ2rNnzwOP8dChQ1ajcUeOHNHFixdVs2ZNq/ehSJEimjx5smJjY5/4BA533sMffvjBavknn3yisWPHqmjRovLy8tKFCxes3oN8+fJp6tSp+vPPPzO8z8aNG+ull17S+vXrLe/hH3/8oVu3bqlnz55Wlw7eCVwZGVWuVq2atm/frlu3blmWHTt2zGpGyurVqysxMVFms9nquMLCwjRz5sy7Lr8FkL0x0gUA6TRo0CB17dpV3bp101tvvaWUlBTNnTtXycnJ6tOnj6Tbf/2Wbt/v4evrqwoVKqhgwYJaunSp8uXLp1y5cumXX37RokWLJP3vHpKHcXd3V3BwsEaPHq0333xTr7/+ugoVKqQbN25oy5YtWrNmjaZMmSKDwSBXV1f17t1bU6dOlbOzs+rUqaMdO3bcFTzq1aunCRMmaOTIkerevbsiIiI0Y8YMy+VYklS+fHn997//1cSJE9WgQQNduXJFCxYsUFRU1ENHKNq0aaMlS5aoS5cu6tWrl/Lnz69ff/1V8+bN01tvvXXXdN8Z1aVLF61du1ZdunRRnz595OnpqW+++Ua7d+/W+PHjZTQaVbduXdWoUUOjR4/WlStXVLp0ae3du1fz5s3Ta6+9ZrmfK1euXIqKitK2bdtUqlSpdB13oUKF1LZtW3388cdKSUlRyZIltWXLFss9RHeC3MCBA9WjRw8NGjRILVu2VFpamj7//HPL6OWD3Bkp7dWrl2JjYzV16lT5+fmpZcuWVv3atm2rqVOnqk6dOlajbk9CyZIl9eqrr2rKlClKSkpSmTJl9Msvv2jLli2aNm2a7OzsNGDAAI0aNUp2dnZq0KCBrl+/rlmzZikyMvK+l58+zLBhw9SyZUuNGzdOa9asUZkyZWRvb6/Jkyfr3XffVXJyslavXq2ff/5ZkjI0PX2vXr20YcMGdevWTe+++66uX7+u6dOny2AwWEaI69Wrp2rVqikwMFCBgYHy9fXVkSNH9Omnn+rFF1+0hPWIiAhFRESodOnS6XrAOoBnEyNdAJBOtWrV0hdffKHk5GQNHDhQI0eOlI+Pj5YvX67ixYtLuj2y0aVLF/3www/q1q2bkpOTNWvWLPn4+Oj9999X//799dtvv2n27NkqWrSoZRKC9HjjjTe0cOFCeXp66uOPP1aXLl00dOhQRUVFadGiRWrevLmlb/fu3TVq1Cht2bJFAQEBOnHihHr16mW1vRdeeEEfffSRLl26pB49emjRokUaO3as8ubNa+nz2muvqXfv3tq4caO6d++u0NBQVa1aVWPGjFFcXJzCw8PvW6+Li4uWLl2qKlWqaPLkyerevbs2b96sQYMGaejQoek+7vvJkyeP/vvf/6ps2bIKCQlRv379dPnyZc2aNUtt27aVdPsSyjvT+n/55Zfq0aOHNm3apAEDBigkJMSyrTZt2qhgwYLq3bu3vvnmm3Qf98iRI/XGG2/o888/V2BgoCIiIixBysXFRZL04osvasGCBYqIiFDfvn01ZMgQ2dnZ6Ysvvnjow58bNGigatWqafDgwRozZoyqV6+uRYsW3XUv2J2JKNq0afOv39dHMXnyZL399ttavHixevbsqV9++UXTpk2zTDLSvn17TZ06VQcPHlSvXr30wQcf6LnnntPixYvvmgglvYoWLapOnTrpr7/+0pIlS/T8889r6tSpioyMVEBAgOVZcYsXL5bBYMjQ/7Xnn39eCxYs0K1bt9S3b1998skn6t69u/LkyWP5o4TRaNTcuXPl7++vOXPmqGvXrlq2bJk6d+6sTz75xLKtFStWqEOHDg+9dw3As81gfpS7uAEAWc7q1as1dOhQbd269bHcy5PdxcXFafv27XrppZes7g376KOPtHr16odeOvg4zZs3T/Pnz9eOHTsYTXkMdu3aJQcHB6sJSa5du6Y6depoyJAhevvttzOxOgBZEZcXAgDwCJydnRUSEqJSpUrpnXfekYuLiw4ePGh5KPWTsGbNGv3111/66quv1KNHDwLXY3L06FGFhoZq4MCBKlOmjGJjY/X555/Lzc3NakQZANKL0AUAwCPIkSOHFi5cqGnTpun999/XzZs3VbhwYb3//vsPfPbW43T8+HEtW7ZMr7zyirp37/5E9pkd3Lkn7L///a8uX74sFxcXVa9eXR999NFdj2IAgPTg8kIAAAAAsCEm0gAAAAAAGyJ0AQAAAIANEboAAAAAwIYIXQAAAABgQ4QuAAAAALAhpox/BNHRN8ScjwAAAED2ZTBIXl5u6epL6HoEZrMIXQAAAADShcsLAQAAAMCGCF0AAAAAYEOELgAAAACwIe7pAgA8ViaTSWlpqZldBh4TOzt7GY38jRYA/g1CFwDgsTCbzbp+PUY3b8Zndil4zJydXZUrV24ZDIbMLgUAsiRCFwDgsbgTuFxdPeXomINf0J8BZrNZycm3FB8fK0lyd/fK5IoAIGsidAEA/jWTKc0SuFxdc2V2OXiMHB1zSJLi42Pl5ubJpYYA8Aj45AQA/GtpaWmS/vcLOp4td84r9+oBwKMhdAEAHhsuKXw2cV4B4N8hdAEAAACADRG6AADPpLfeel2bN2/M7DIAAGAiDQDAs2nJkuWZXQIAAJIIXQCALOLy5Utq376l+vcP0pdffqFbt5L04ot1NXBgsJYtW6o//jiiGzdu6OLFC5owYYrGjRutd9/toWbNWujmzZuaPTtUP/74g1JTU1S2bHkFBQ1Vvnz5lZiYoM8+m6Ffftmu5ORkValSVf36BSl37odPj75hwzqtWrVcuXLl0rFjRzVw4PuqXLmKQkM/1rFjRxUTE63cub31zjvvqnnzVpKkF1+sqv79g7Rq1XJFRUWpWLFiCgoaJl/fYpKkffv2aNas6bp48aJ8fX1VrlxF/fnnH5oxY64k6YcfvtfixV8oIuKyChYspF69+qh69Zq2e+MBAP8alxcCALKUn3/+UYsWLdNXX63S+fPn9fHHH0mSDhzYp4CA97Rq1XqVLVveap2PP/5Ix479qQULFmvt2s3KndtLo0cPkySNHz9GFy6c14IFi7V8+bdycXHVsGGDZTab01XPiRPH1LhxU61du1n16tXXxIlj5eBgr8WLl2vz5u1q27a9PvlkkhITEy3r/PDDZs2YMVdr1mxQjhxOmjlzuqTbwTI4eKBat26nDRu2qlev97R27WrLert2/aIpUyZowIAh2rDhR3Xt2lPDhw/RqVMn/9V7CgCwLUIXACBL6dt3oDw9PeXl5a1u3Xrpp5+2KiUlRQUKFFSVKtXk4uIie/v/XciRkpKirVs3q3v3APn45JOjo6Pee2+gBgwYrNjYGP3881b17x8kT8/ccnFxUb9+g3Ts2FGdOHE8XfU4ODioSZNmcnR0VI4cTgoOHqFBg96Xvb29IiMj5OKSU7du3dL169ct67Rr10FeXt5ydXXVyy830vnz5yRJmzdvVPHifmrVqo3s7e1VoUIltWz5mmW9VauWq3XrdqpYsbLs7OxUp85LqlPnJX377arH9O4CAGyBywsBAFnKc88VtvzbxyefkpOTdf36NXl757ln/+vXryk5OVn58uWzLHNzc1PJkqX1559/SJJ69OhstY6dnb0uX76okiVLPbSe3Lm9rB4YfOnSRc2cOV3nz59ToUKFVahQIUmS2WyyWucOe3t7S9uVK5HKly+/1fYLFCioP/88KkmKiLisQ4cO6JtvVlra09LSVKVKtYfWCQDIPIQuAECWEhV1RYULF5F0O+A4OTnJ3d1D0tl79vf0zC1HR0dFRkZY1ouNjdGSJYv0xhsdJUlLl66Ul5e3ZZ3Tp0+pQIGC6arn78+wSk1N1ZAh/dWjR2+1adNeBoNBx48f0/ffp28WxXz58mvnzh1WyyIiIiz/zpMnr5o08VenTp2t2nPk4KHUAPA04/JCAECWMnv2DCUkxCsq6qoWLJijV1/1t7qc8J+MRqOaNPHXggVzFRV1Vbdu3dLcubN19OjvypMnr2rXflHTp0/VtWtxSk1N1aJFC9S9+9uKj7+R4dpSUlKUlJQkJycnGQwGRUREaPbsUEvbwzRp0kxhYSe0ceN6paWl6ejRP7R27RpLe8uWr2nlymU6duz2yNfx43+qW7e39MMP32e4VgDAk8NIFwAgS3nuuULq1KmDkpKS1LjxqwoI6KslSxY+cJ333huguXNnqXv3d5SUlKRKlapo3LjbE3CMGDFGn332qbp06aiEhHi98IKvpk6dYTXylV7Ozs4aNmy05s//TNOmTZGnp6datHhNp0+f0qlT4Spc+PkHrp83r4/GjZukmTOn6+OPP1Lx4iVUo0YtxcXFSpIaNHhFN2/e1PjxHyoyMlK5cuXS66+/qXbtOmS4VgDILoxGg4xGw8M7/j+TySyTKX2TKaWXwZze6ZlgERV1Q7xrAPA/KSnJio6+LC+v/HJwcLTJPu5MGb9ixVrlz1/AJvvIbJGREbp2LU5+fiUtyz799BPFxERr9OhxmVbXkzi/AGALRqNBHh4usrNL/wV+aWkmxcUlPjR4GQySt7dburbJSBcAAE+Ja9fiFBjYTTNmzFPJkqUUHh6mzZs3qm/fgZldGgBkSUajQXZ2Rg2fvFSnz0c+tP8LhXwUMrijjEbDYx3tyrTQtWHDBgUFBVnd/PvKK69o8uTJOnz4sMaNG6fw8HB5enoqICBA7du3t/Rbs2aNZs2apatXr6po0aIaOXKkKlWqJOn2LE5TpkzRt99+q5s3b6pmzZr68MMPlTdvXklSdHS0Ro4cqb1798rOzk4tW7ZUcHDwA+8HAABkP8eOHVXfvr3u2+7jk19Llix/rPv08yupfv2C9MEHwxQdHaXcub3UqVNnNWr06mPdDwBkN6fPR+r4yYuZtv9MSxq///67WrVqpQkTJlgtv3btmnr06KG+ffuqQ4cO2rdvn3r37q0SJUqofPny2rNnj8aOHat58+apfPnyWrp0qQICAvTTTz/J2dlZs2fP1s6dO7Vq1Sq5ublp5MiRGjFihObOnStJ6t+/v3x8fLRjxw5FRUUpICBACxcuVLdu3TLjbQAApFP+/AX0yy/7n9j+SpUqoy1bdjy842PWokVrtWjR+onvFwBgO5k2e+Hvv/+usmXL3rV88+bN8vDwUMeOHWVvb69atWqpRYsWWrp0qSRpxYoV8vf3V5UqVeTg4KDOnTvL09NTGzZssLR3795d+fPnl6urq4YPH67t27fr/PnzOnv2rPbu3avBgwfL2dlZhQoVUmBgoGXbAAAAAPC4ZcpIl8lk0tGjR+Xs7Kz58+crLS1N9erVU1BQkMLCwuTn52fVv1ixYlq58vaDIMPDw9W2bdu72o8fP64bN24oIiLCan1vb2+5u7vrxIkTkiQPDw/5+PhY2n19fXXp0iVdv35duXLlSlf9hvRPfgIA2QKfi9mDwcC5BpB9POzzLiOfh5kSumJiYlS6dGk1adJEoaGhio2NVXBwsAYPHqw8efLI2dnZqr+Tk5MSExMlSQkJCfdtT0hIkCS5uLjc1X6n7Z/r3nmdmJiY7tDl5ZW+WUoAILtISkpSTIxRdnYG2dvzCMhnjclkkNFolKdnTjk5OWV2OQBgc56eOR/r9jIldHl7e1td0ufs7KzBgwfr9ddfV5s2bZSUlGTVPykpSTlz5rT0vVe7p6enJUDdvHnznuubzea72u68vrP99IiOZsp4APi7lJRkmUwmpaWZlZpqyuxy8JilpZllMpkUG5sgB4eHP+QZAJ4WdnbGRwpQsbEJSkt78M8zgyH9gzGZErqOHz+u9evXa9CgQTL8/7hccnKyjEajypcvr0WLFln1Dw8PV/HixSVJxYsXV1hY2F3tdevWlbu7u3x8fBQeHm65xPDq1auKi4uTn5+fTCaT4uLiFBUVJW/v2w+9PHnypPLlyyc3t/SPXpnNInQBwN/wmZg98PMPQHbyOD/vMuUaEA8PDy1dulTz589XamqqLl26pMmTJ+u1115TkyZNFBUVpYULFyolJUW7d+/WunXrLPdxtWvXTuvWrdPu3buVkpKihQsXKjo6Wo0aNZIktWnTRrNnz9b58+cVHx+v8ePHq3r16ipcuLCKFCmiKlWqaPz48YqPj9f58+c1a9YstWvXLjPeBgDIFozG25ccPqkvo5GbjgAAT5dMGenKly+f5syZo48//lizZ89Wjhw55O/vr8GDBytHjhz6/PPPFRISotDQUOXOnVsjRoxQzZo1JUm1atXS6NGj9cEHHygyMlLFihXTvHnz5OHhIUnq3bu3UlNT1bFjRyUkJKhGjRqaNm2aZd+hoaEaM2aMGjZsKKPRqNatWyswMDAT3gUAePYZjQZ5errIaHxyf+O7fRlc4mN9qCUAAP+GwWzmQoGMiorini4A+LuUlGRFR1+Wl1d+OTg4Wpbb29++ln79b1sUHR9j8zq8XHOrecVGio1NSNe9ZZs3b9TkyeOtlqWkpMhgMOinn3bp6NE/NG3aZJ05c0oeHp5655131bx5a0nSggVzdOjQAc2YMdeyblxcnAYP7qe0tDRNnjxNXl7e9913WlqaRo58X76+xdS1a0/L8tjYGE2aFKJDhw7Izs5OjRs3U+/e/WRvf/vvpLas6X7ud34B4Gl35+fQm30/TtfDkUv6FtRXoQPT9XPEYJC8vZ/ie7oAANlLdHyMIq9HZXYZd2ncuKkaN25qeX316hV16/a2AgP76vr16xo8uJ+6du2pVq3a6PDhQxo6NEhFixZT6dJ3P2cyIuKyBg7so4IFn9OYMRPvmi3Xum+EJk0ap717d8vXt5hV26hRQ5UnT159880mRUdH6f33B2r58q/05ptv27QmAIDtMK8vAACSzGazxo4dpdq1X1STJs20bduPypXLXW3bvi57e3tVqVJNjRu/qtWrV9y17smT4erV611VrFhZEyd+/MBwc+7cWXXt2lFlypRTuXLlrdouXDivQ4cOKDCwr5ycnFSw4HPq3LmbVq1aLkk2qwkAYFuELgAAJH3//QadPn1K7703QJJ0+vRJ+fr6WvUpUuQFhYdbz6D7228H1bt3d9Wu/aKGDBkuOzu7B+7H29tbX3/9rbp27Sk7O+sLTk6fPqlcudzl7Z3nb/ssqsjICN24ccNmNQEAbIvQBQDI9kwmkxYuXKC3335XLi63n+eSmJgoJyfr0SEnJyfdvJloeX327Bm9//5AlS5dRjt37lBU1NWH7svFJadcXV3v2XZ7n9YPH77z+ubNRJvVBACwLUIXACDbO3hwv6Kjo9S8eSvLMicnZ926lWTVLykpSS4uLpbXycm3NHHix5o0aZoKFCiooUODlJycbGl/663X1ajRS2rU6CW99dbrD63jfvuUboe1x1ETAODJI3QBALK9n3/+UXXr1re676loUV+dPn3Kqt+ZM6dVtOj/Lu8rXryEKlasLHt7e40dO1EREZetZkNcsmS5tmzZoS1bdmjJkuUPraNoUV9du3ZNMTHRf9vnKeXN6yNXV9fHUhMA4MkjdAEAbM7LNbd8cnnb/MvLNfcj1ff777+pYsXKVsvq1Wug6OhoLV/+lVJTU3Xw4H5t3rxJ/v6t7rkNb+88+vDD8dq8eaOWL//qkeooVKiwypevqOnTpyoxMUGXLl3UwoXz5e/fMtNqAgD8e0wZDwCwGZPJLJPJpOYVGz3BfZoy/GDkS5cuWk1eIUnu7h6aNm2mpk+fovnz58jDw0P9+wepcuWq991O5cpV1b17gGbOnK6iRYupatXqGa5/3LiP9PHHk9S+fUsZDEa9+qq/Onfulqk1AQD+HR6O/Ah4ODIAWHvQw3ONRoOMRsMTq+V20OND+nHi4cgAsioejgwAyBYIQQCA7I57ugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANsSU8QAAm+I5XQCA7I7QBQCwGaPRIA9PF9kZn9yFFWkmk+JiEwleAICnBqELAGAzRqNBdkajFu7YqIhrMTbfXz733Or8UlMZjYYMh67Y2Fj16tVFwcEjVLlyVUnS0aN/aNq0yTpz5pQ8PDz1zjvvqnnz1pZ1Nm5cr4UL5ys6OkrPP/+CBgwYrLJly0uS+vTpoUqVqqhr156W/sePH9Pgwf1Us2ZtBQePkL393T+GDxzYp88+m6GzZ8/IyclJDRo0VGBgX+XI4ZRpNQEA/h0+WQEANhdxLUbnY65kdhn3deTIbwoJ+UAXL16wLLt+/boGD+6nrl17qlWrNjp8+JCGDg1S0aLFVLp0WR08uF+ffDJZU6ZMV+nSZbVq1dd6//2BWrlyvZycnO7ax759ezR8+BC9/vp/1K1br3vWERsbq8GD+yso6H29+qq/YmJiNHBgby1Zskhdu/bMlJoAAP8eE2kAALK1jRvX68MPR6hHj0Cr5du2/ahcudzVtu3rsre3V5Uq1dS48atavXqFJGn9+m/VsGFjlS9fUfb29urQoaPc3T20devmu/axZcsmDR06SH37DnxguPH09NT69ZvVrFkLGQwGXb8ep+TkZHl4eGRaTQCAf4/QBQDI1qpXr6mvv/5GDRs2tlp++vRJ+fr6Wi0rUuQFhYeHpav9juXL/6sxY0YqOHiEmjdv9dB6XFxySpLatPHX22+/IS8vbzVr1jJTawIA/DuELgBAtubl5X3P+5gSExPl5ORstczJyUk3byamq126PYq2bNkSFS/up/Xrv1Vqamq661q2bLW++WajjEajRowIfipqAgA8GkIXAAD34OTkrFu3kqyWJSUlycXFJV3tkuTs7Kw5c77Q+PFTFR7+l2bMmGZpi4iIUKNGL1m+Jk8eb7WtHDmc5O2dRwEB72nPnl91/fp1m9cEALANQhcAAPdQtKivTp8+ZbXszJnTKlrUN13tklS/fkPlyZNX+fLl0+jRIVq9erm++26tJClfvnzasmWH5Wvw4GH6/ffDevPNtkpJSbFsIyUlRQ4ODnJ2drZ5TQAA2yB0AQBwD/XqNVB0dLSWL/9KqampOnhwvzZv3iR//9v3QPn7t9TmzZt08OB+paamavnyrxQTE6O6dRvcc3vVq9dUly7dNXXqRB09+sc9+/j6FldSUpI+++xTpaSkKCLismbMmCZ//1ZycHDIlJoAAP8eU8YDAGwun3vuLLcfd3cPTZs2U9OnT9H8+XPk4eGh/v2DLM/wqlq1ugYNCtaUKRN09eoVFSlSVFOmhCpXLvf7brNz5276888/NGxYkBYsWCxv7zxW7S4uLpo69VOFhk5VixaN5erqqsaNm6pz526ZVhMA4N8zmM3mjD09EoqKuiHeNQD4n5SUZEVHX5aXV345ODhalhuNBnl4usjO+OQurEgzmRQXm5jhhyPj/u53fgHgaWdvb5SnZ0692fdjHT958aH9S/oW1FehAxUbm6DUVNMD+xoMkre3W/rqSFcvAAAegclkVlxsooxGwxPdJ4ELAPA0IXQBAGyKEAQAyO6YSAMAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYENMGQ8AsCmj0cBzugAA2RqhCwBgM0ajQR6eLrIzPrkLK9JMJsXFJhK8AABPDUIXAMBmjEaD7IxGTdu4Vhdiom2+v+dye6l/05YyGg3pDl0HDuzTZ5/N0NmzZ+Tk5KQGDRoqMLCvcuRw0tGjf2jatMk6c+aUPDw89c4776p589aWdTduXK+FC+crOjpKzz//ggYMGKyyZctLkvr06aFKlaqoa9eelv7Hjx/T4MH9VLNmbQUHj5C9/d0/hq9fv6bp06dq166dMplMqlSpsgYNGipvb29JypSaAAD/Dp+sAACbuxATrdNXIjO7jLvExsZq8OD+Cgp6X6++6q+YmBgNHNhbS5YsUvv2/9Hgwf3UtWtPtWrVRocPH9LQoUEqWrSYSpcuq4MH9+uTTyZrypTpKl26rFat+lrvvz9QK1eul5OT01372rdvj4YPH6LXX/+PunXrdd+ahg8fIje3XPr6629kZ2dUSMgHmjRpnCZNmqbr169nSk0AgH+HiTQAANmWp6en1q/frGbNWshgMOj69TglJyfLw8ND27b9qFy53NW27euyt7dXlSrV1Ljxq1q9eoUkaf36b9WwYWOVL19R9vb26tCho9zdPbR16+a79rNlyyYNHTpIffsOfGC4OX78mI4e/UPDh4+Wm5ubXFxyKjh4hHr1ek+SMqUmAMC/R+gCAGRrLi45JUlt2vjr7bffkJeXt5o1a6nTp0/K19fXqm+RIi8oPDxMkh7afsfy5f/VmDEjFRw8Qs2bt3pgLceOHVWRIi9o7dpv1KFDa7Vq1UQzZkyzXFqYGTUBAP49QhcAAJKWLVutb77ZKKPRqBEjgpWYmCgnJ2erPk5OTrp5M1GSHtou3b6/atmyJSpe3E/r13+r1NTUB9Zw/fo1nTwZpgsXzumLL5bqiy++0tWrVzRu3Oh07dMWNQEA/j1CFwAAknLkcJK3dx4FBLynPXt+lZOTs27dSrLqk5SUJBcXF0l6aLskOTs7a86cLzR+/FSFh/+lGTOmWdoiIiLUqNFLlq/Jk8fLwcFRktS37yC5uORU7txe6tEjULt27bQEKlvWBACwDUIXACDb+v33w3rzzbZKSUmxLEtJSZGDg4OKFHlBp0+fsup/5sxpFS16+/K9okV9H9guSfXrN1SePHmVL18+jR4dotWrl+u779ZKkvLly6ctW3ZYvgYPHqYXXnhBZrNZqan/qyctzfT//zI/dJ//tiYAgG0QugAA2Zavb3ElJSXps88+VUpKiiIiLmvGjGny92+lBg0aKjo6WsuXf6XU1FQdPLhfmzdvkr//7Xug/P1bavPmTTp4cL9SU1O1fPlXiomJUd26De65r+rVa6pLl+6aOnWijh794559qlWrqQIFCmrChDFKTExUbGys5s2bpZdeqi8Xl5yqV6/BE68JAPDvMWU8AMDmnsvt9VTux8XFRVOnfqrQ0Klq0aKxXF1d1bhxU3Xu3E2Ojo6aNm2mpk+fovnz58jDw0P9+wepcuWqkqSqVatr0KBgTZkyQVevXlGRIkU1ZUqocuVyv+/+Onfupj///EPDhgVpwYLF8vbOY9Vub2+vGTPm6tNPP9Z//vOabt1K1osv1lW/fkGSJHd3jydeEwDg3zOYzeb0PT0SFlFRN8S7BgD/k5KSrOjoy/Lyym+5L0m6/XBkD08X2Rmf3IUVaSaT4mIT0/1wZDzc/c4vADzt7O2N8vTMqTf7fqzjJy8+tH9J34L6KnSgYmMTlJpqemBfg0Hy9nZLXx3p6gUAwCMwmcyKi02U0Wh4ovskcAEAniaELgCATRGCAADZHRNpAAAAAIANEboAAAAAwIYIXQAAAABgQ4QuAAAAALAhQhcAAAAA2BChCwAAAABsiCnjAQA2ZTQaeE4XACBbI3QBAGzGaDTIw8NFdnZP7sKKtDST4uISCV4AgKcGoQsAYDNGo0F2dkaNXbFGZ69G2Xx/z+fx1sj2r8loNKQ7dF2/fk3Tp0/Vrl07ZTKZVKlSZQ0aNFTe3t46evQPTZs2WWfOnJKHh6feeeddNW/e2rLuxo3rtXDhfEVHR+n551/QgAGDVbZseUlSnz49VKlSFXXt2tPS//jxYxo8uJ9q1qyt4OARsre//4/h2NhY9erVRcHBI1S5clVJ0sGD+9W3by/lyZNXq1atl9FoHWaDgwdo584dCg39zLIOACDzEboAADZ39mqUwi5HZHYZ9zR8+BC5ueXS119/Izs7o0JCPtCkSeM0YsQYDR7cT1279lSrVm10+PAhDR0apKJFi6l06bI6eHC/PvlksqZMma7Spctq1aqv9f77A7Vy5Xo5OTndtZ99+/Zo+PAhev31/6hbt14PrOnIkd8UEvKBLl68cM/2lJRk7du3RzVq1LIsi4mJ1u+/H/l3bwYAwCaYSAMAkG0dP35MR4/+oeHDR8vNzU0uLjkVHDxCvXq9p23bflSuXO5q2/Z12dvbq0qVamrc+FWtXr1CkrR+/bdq2LCxypevKHt7e3Xo0FHu7h7aunXzXfvZsmWThg4dpL59Bz40cG3cuF4ffjhCPXoE3rdPo0ZNtXHj+rvWq1//5Ud4FwAAtkboAgBkW8eOHVWRIi9o7dpv1KFDa7Vq1UQzZkyTt7e3Tp8+KV9fX6v+RYq8oPDwMEl6aPsdy5f/V2PGjFRw8Ag1b97qoTVVr15TX3/9jRo2bHzfPs2atdCOHT8rPj7esmzDhnXp2j4A4MkjdAEAsq3r16/p5MkwXbhwTl98sVRffPGVrl69onHjRisxMVFOTs5W/Z2cnHTzZqIkPbRduj36tGzZEhUv7qf1679VamrqQ2vy8vJ+4L1eklS8uJ8KF37eMqp25MhvMhqNKl26bLqOGwDwZBG6AADZloODoySpb99BcnHJqdy5vdSjR6B27dops9msW7eSrPonJSXJxcVFkuTk5PzAdklydnbWnDlfaPz4qQoP/0szZkyztEVERKhRo5csX5Mnj89Q7c2atdSmTbcvMfzuu7WMcgHAU4yJNAAA2dYLL7wgs9ms1NQU5ciRQ9LtKeel26NJa9astOp/5sxpFS16+5LCokV9dfr0qbvaa9WqY3ldv35D5cmTV5I0enSIgoL6qnhxP/n7t1S+fPm0ZcuOR669ceNXNWvWdIWFndCOHdsUEND3kbcFALAtQhcAwOaez+P9VO6nWrWaKlCgoCZMGKNhwz7QrVu3NG/eLL30Un01avSq5s+fo+XLv1KbNq/ryJHftHnzJk2cOFWS5O/fUsOGDdbLLzdS+fIVtXr1csXExKhu3Qb33Ff16jXVpUt3TZ06UUWKFFWZMv/uUkB3dw/Vrv2ixo4dpcqVq8rDw+NfbQ8AYDuELgCAzZhMZqWlmTSy/WtPbJ9paaZ0P6PL3t5eM2bM1aeffqz//Oc13bqVrBdfrKt+/YLk5uamadNmavr0KZo/f448PDzUv3+Q5flXVatW16BBwZoyZYKuXr2iIkWKasqUUOXK5X7f/XXu3E1//vmHhg0L0oIFi+XtnedfHWuzZi0VHDyAUS4AeMoZzGZz+n4ywSIq6oZ41wDgf1JSkhUdfVleXvkt90ndYTQaZDQanlgtJpM53aEL6fOg8wsATzN7e6M8PXPqzb4f6/jJiw/tX9K3oL4KHajY2ASlppoe2NdgkLy93dJXR7p6AQDwiAhBAIDsjtkLAQAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2xJTxAACb4jldAIDsjtAFALAZo9EgDw8X2dk9uQsr0tJMiotLJHgBAJ4amRq60tLS1LlzZxUsWFATJ06UJB0+fFjjxo1TeHi4PD09FRAQoPbt21vWWbNmjWbNmqWrV6+qaNGiGjlypCpVqmTZ3pQpU/Ttt9/q5s2bqlmzpj788EPlzZtXkhQdHa2RI0dq7969srOzU8uWLRUcHCx7e7InANiC0WiQnZ1RQxeu1KmIKJvvr2g+b03o3E5GoyHDoSs2Nla9enVRcPAIVa5cVQcP7lffvr2UJ09erVq1XkajdXAMDh6gnTt3KDT0M1WuXPWe2zx4cL/mzJmps2dPy9nZRfXqNVCvXu/JyclJGzas0/jxH6pMmXKaM+eLu9Z9553/6OTJMK1YsVb58xfI0LEAAJ4umZo2ZsyYof3796tgwYKSpGvXrqlHjx7q27evOnTooH379ql3794qUaKEypcvrz179mjs2LGaN2+eypcvr6VLlyogIEA//fSTnJ2dNXv2bO3cuVOrVq2Sm5ubRo4cqREjRmju3LmSpP79+8vHx0c7duxQVFSUAgICtHDhQnXr1i0z3wYAeOadiojS8QuXM7uM+zpy5DeFhHygixcv3NWWkpKsffv2qEaNWpZlMTHR+v33Iw/c5tWrVxQcPFDvvTdA/v4tFRV1VcOHD9Hs2aEaMGCIJMnV1VUnThzTuXNnVLhwEcu6x4//qYiIS4/n4AAAmS7TJtLYtWuXNm/erMaNG1uWbd68WR4eHurYsaPs7e1Vq1YttWjRQkuXLpUkrVixQv7+/qpSpYocHBzUuXNneXp6asOGDZb27t27K3/+/HJ1ddXw4cO1fft2nT9/XmfPntXevXs1ePBgOTs7q1ChQgoMDLRsGwCQPW3cuF4ffjhCPXoE3rO9UaOm2rhx/V3r1K//8gO3e+nSRb34Yl21bPma7Ozs5OOTT02aNNNvvx2y9HF1dVOtWnW0ceN3VuuuX79WDRs2/ucmAQBZVKaErujoaA0fPlxTp06Vs7OzZXlYWJj8/Pys+hYrVkzHjx+XJIWHh9+3/caNG4qIiLBq9/b2lru7u06cOKGwsDB5eHjIx8fH0u7r66tLly7p+vXrGarfYOCLL7744uufX1lV9eo19fXX39w35DRr1kI7dvys+Ph4y7ING9apefNWD9xuhQqVNHr0OMtrk8mkbdt+VIkSJa36NW3aQt9/v0Emk0mSdOvWLf388w9q2rT5ox2QDWX29xhffPHFV0a/bP2Zl15P/PJCk8mkwYMHq0uXLipZ0voHT0JCglUIkyQnJyclJiY+tD0hIUGS5OLiclf7nbZ/rnvndWJionLlypXuY/Dyckt3XwDIDpKSkhQTY5SdnUH29v/7e96TnEDj7zKyXx+fvHeta29vtGyjVKmSev75Ivr55y1q3bqtDh/+TXZ2dipfvrxV/wdJTU3RxInjdPnyJY0dO0H29kbLjI51676kyZNDdOjQPtWoUUs//PCTypQpa/kjYXq2b2smk0FGo1Genjnl5OSUqbUAwJPg6ZnzsW7viYeuOXPmyNHRUZ06dbqrzdnZWTdu3LBalpSUpJw5c1rak5KS7mr39PS0BKibN2/ec32z2XxX253Xd7afXtHRN2RmUiwAsEhJSZbJZFJamlmpqabMLkdpaaZHruPOumlpt9dPTTWpadMW+u67dWre/DWtXfuN/P1bWraflmbSgQMHFBTU17KNTp266O2335UkRUVFafTooUpISNCsWfPl6eml1FTT3yb6sFPjxk21fv06ValSQ+vWfas2bV637P/fHMvjkpZmlslkUmxsghwcUjK1FgDICDs74yMFqNjYBMvn8P0YDOkfjHnioevbb7/VlStXVLXq7Zme7oSoH374QUOGDNHOnTut+oeHh6t48eKSpOLFiyssLOyu9rp168rd3V0+Pj5WlyBevXpVcXFx8vPzk8lkUlxcnKKiouTt7S1JOnnypPLlyyc3t4yNXJnNInQBwN8865+JjRu/qlmzpiss7IR27NimgIC+Vu0VKlTSli077lrv2LGjev/9gapSpbqGDBl+31GiZs1aqkePdxQeHqYzZ06pTp2XdPXqFZscy7/Bzz8A2cnj/Lx74tcrbNq0SQcPHtT+/fu1f/9+NW/eXM2bN9f+/fvVqFEjRUVFaeHChUpJSdHu3bu1bt06tW3bVpLUrl07rVu3Trt371ZKSooWLlyo6OhoNWrUSJLUpk0bzZ49W+fPn1d8fLzGjx+v6tWrq3DhwipSpIiqVKmi8ePHKz4+XufPn9esWbPUrl27J/0WAACyGHd3D9Wu/aLGjh2lypWrysPD46HrXLx4QQMG9FaLFq9p1KixD7wsz9e3mJ5//gWNGTNCjRs341EmAPCMeao+1T09PfX5558rJCREoaGhyp07t0aMGKGaNWtKkmrVqqXRo0frgw8+UGRkpIoVK6Z58+ZZfvj17t1bqamp6tixoxISElSjRg1NmzbNsv3Q0FCNGTNGDRs2lNFoVOvWrRUYeO/ZqgAAj0/RfN5Zfj/NmrVUcPCAu0a57mfFiv8qPj5eX3+9VF9//b+Zcn188mvJkuV39ff3b6FPPpmsMWMmPraaAQBPB4PZzIUCGRUVxT1dAPB3KSnJio6+LC+v/HJwcLQsNxoN8vBweaITaqSlmRQXl5jhhyPj/u53fgHgaWdvf/uerjf7fqzjJy8+tH9J34L6KnSgYmMTHno/rcEgeXs/pfd0AQCyD5PJrLi4RMtMfU9qnwQuAMDThNAFALApQhAAILvL3Ad/AAAAAMAzjtAFAAAAADZE6AIAAAAAGyJ0AQAAAIANEboAAAAAwIYIXQAAAABgQ0wZDwCwKaPRwHO6AADZGqELAGAzRqNBHh4usrN7chdWpKWZFBeXSPACADw1CF0AAJsxGg2yszMqeObXOnXpqs33V7RAHn3Uu4OMRkO6Q1dY2F+aOXOaTpw4LgcHB1WrVkPvvTdQp06Fq2/fXsqTJ69WrVovo9E6OAYHD9DOnTsUGvqZKleues9tL1myUPPmzZajo6NlWbt2b6hnz97asGGdxo//UGXKlNOcOV/cte477/xHJ0+GacWKtcqfv0AG3gUAwNOG0AUAsLlTl67q2JlLmV3GXW7dSlJQUF+1bPmaJk+ersTEBI0bN1rjx3+oN97oKElKSUnWvn17VKNGLct6MTHR+v33Iw/d/vHjf+qdd7rq3Xd73LPd1dVVJ04c07lzZ1S4cBGr9SIinr73CwDwaJhIAwCQbUVGRqhYMT917txNDg4Ocnf3UKtWbXT48EFLn0aNmmrjxvVW623cuF7167/80O0fO/anSpYsfd92V1c31apVRxs3fme1fP36tWrYsHEGjwYA8LQidAEAsq3ChYto6tRQ2dnZWZb99NNWlShRyvK6WbMW2rHjZ8XHx1uWbdiwTs2bt3rgtmNjYxQZGaF169aoVatX1b59S82aNV23bt2y6te0aQt9//0GmUwmSdKtW7f0888/qGnT5v/+AAEATwVCFwAAksxms+bOnaWdO3eoX78gy/Lixf1UuPDz2rp1syTpyJHfZDQaVbp02QduLzo6WhUqVFKzZi20YsVaTZ48Xbt3/6oZM6ZZ9atd+0UlJydr//69kqRt235U6dJl5e2d5/EeIAAg0xC6AADZXkJCvEaMGKLNmzdq5sx58vUtZtXerFlLbdp0+xLD775be9co1+HDh9So0UuWry+//FzFihXXzJnz9NJL9eXo6KgiRV5Q587d9eOPm63Wtbe3V5Mm/7uE8bvv1srf/8GjaACArIWJNAAA2drFixcUFNRXPj75NH/+Ynl4eNzVp3HjVzVr1nSFhZ3Qjh3bFBDQ16q9QoVK2rJlh9WyQ4cO6I8/jqhTpy6WZSkpycqRw+mu7Tdr1lI9eryj8PAwnTlzSnXqvKSrV688ngMEAGQ6RroAANnW9evX1bdvL5UrV0EffzzjnoFLktzdPVS79osaO3aUKleuet9+f+fs7KwFC+Zo8+ZNMplMOnXqpL74Yr5atnztrr6+vsX0/PMvaMyYEWrcuJns7fmbKAA8S/hUBwDYXNECT+b+pIzuZ8OGtYqMjNCPP27RTz/9YNX20UefWL1u1qylgoMH3DXKdT8lS5bWhx+O1xdfzNfkySFydXVTixat9fbb796zv79/C33yyWSNGTMxQ8cAAHj6Gcxmc/qeHgmLqKgb4l0DgP9JSUlWdPRleXnll4PD/x4EbDQa5OHhIju7J3dhRVqaSXFxiel+ODIe7n7nFwCedvb2Rnl65tSbfT/W8ZMXH9q/pG9BfRU6ULGxCUpNNT2wr8EgeXu7pa+OdPUCAOARmExmxcUlymg0PNF9ErgAAE8TQhcAwKYIQQCA7I6JNAAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANsSU8QAAmzIaDTynCwCQrRG6AAA2YzQa5OHhIju7J3dhRVqaSXFxiQQvAMBTg9AFALAZo9EgOzujhk9eqtPnI22+vxcK+ShkcEcZjYZ0h66tWzdrzJiRcnR0tCyrW7e+/P1bqW/fXsqTJ69WrVovo9E6OAYHD9DOnTsUGvqZKleues9t79u3R7Nnf6oLF87Lzc1NzZu3UufO3WQwGLRhwzqNH/+hypQppzlzvrhr3Xfe+Y9OngzTihVrlT9/gQy8CwCApw2hCwBgc6fPR+r4yYuZXcY9HTv2p5o0aaZhw0ZbLT94cL8kKSUlWfv27VGNGrUsbTEx0fr99yMP3O61a3EaOnSQRo0ap7p16+vMmdPq06eH8ucvoFdf9Zckubq66sSJYzp37owKFy5iWff48T8VEXHpMR0hACCzMZEGACBbO378T5UoUeq+7Y0aNdXGjeutlm3cuF7167/8wO1GREQoKSlJZrNJZvPtUTeDwSAnJydLH1dXN9WqVUcbN35nte769WvVsGHjjB4KAOApRegCAGRbJpNJJ04c165dv6ht2+Z67bVm+uijEF2/ft3Sp1mzFtqx42fFx8dblm3YsE7Nm7d64Lb9/EqoQYNXNHz4ENWvX1NvvdVeDRs2Uv36Da36NW3aQt9/v0Emk0mSdOvWLf388w9q2rT5YztOAEDmInQBALKtuLhY+fmVUP36DbV06UrNnv25Llw4p7FjR1r6FC/up8KFn9fWrZslSUeO/Caj0ajSpcs+cNvJyclyd3fX2LETtXXrTs2evUA//LBZ69d/Y9Wvdu0XlZycrP3790qStm37UaVLl5W3d57He7AAgExD6AIAZFu5c3tp5sx5at68lZycnJQvXz4FBvbV7t2/KjEx0dKvWbOW2rTp9iWG33239q5RrsOHD6lRo5csX19++blWr16hS5cuqUGDV2Rvb69y5Sqoffs3tHr1Sqt17e3t1aTJ/y5h/O67tfL3f/AoGgAgayF0AQCyrfDwMM2e/anlnitJSk5OkdFolIODg2VZ48av6vjxYwoLO6EdO7apSRN/q+1UqFBJW7bssHy9/fa7ioyMUEpKslU/e3t7q+3e0axZS23f/pPCw8N05swp1anz0mM+UgBAZmL2QgCAzb1QyOep3E+uXLm0evVy5cqVSx06dFRUVJRmzZqupk2bW4Ujd3cP1a79osaOHaXKlavKw8PjoduuU+clrVmzQhs3rterr/orPDxMK1d+rbfe6nxXX1/fYnr++Rc0ZswINW7cTPb2/HgGgGcJn+oAAJsxmcxKSzMpZHDHJ7bPtDRTup/RlTevjyZNmqY5c2Zq0aLP5ejoqFdeaayAgL46evR3q77NmrVUcPAABQT0Tde2q1WroVGjxurLL7/QJ59MVu7cufXGGx3Vpk37e/b392+hTz6ZrDFjJqZr+wCArMNg/vs1FUiXqKgb4l0DgP9JSUlWdPRleXnll4ODo1Wb0WiQ0Wh4YrWYTOZ0hy6kz4POLwA8zeztjfL0zKk3+36crudFlvQtqK9CByo2NkGpqaYH9jUYJG9vt/TVka5eAAA8IkIQACC7YyINAAAAALAhQhcAAAAA2BChCwAAAABsiNAFAHhsmJvp2cR5BYB/J8OhKzk5WVu2bNHChQt18+ZNHT9+3BZ1AQCyEDs7O0lScvKtTK4EtnDnvNrZMf8WADyKDH16njt3Tu+++65SUlJ0/fp11atXT23bttWMGTPUoEEDW9UIAHjKGY12cnZ2VXx8rCTJ0TGHDIYnN008bMNsNis5+Zbi42Pl7Owqo5ELZADgUWQodIWEhKhNmzYKCAhQ9erV9cILL2jcuHEKDQ0ldAFANpcrV25JsgQvPDucnV0t5xcAkHEZCl2//fabPv30UxkMBstfMFu1aqWQkBCbFAcAyDoMBoPc3b3k5uaptLTUzC4Hj4mdnT0jXADwL2UodLm5uSkqKkoFChSwLLt69arc3d0fe2EAgKzJaDTKaHTM7DIAAHhqZOhPVy1atFCfPn20c+dOmUwmHTlyREFBQfL397dVfQAAAACQpWVopCswMFBJSUnq06ePbt68qU6dOqldu3bq06ePreoDAAAAgCwtQ6HLwcFBwcHBCg4OVkxMjDw9PZmdCgAAAAAeIEOha8aMGfdc7ujoKE9PT9WuXVsFCxZ8LIUBAAAAwLMgQ6Hrr7/+0ubNm1WuXDkVKlRIly5d0m+//aZy5copLS1NISEhmj17tmrVqmWregEAAAAgS8lQ6LK3t9eoUaP05ptvWpatWrVKe/bs0aRJk7RhwwZ9/PHHWrFixWMvFAAAAACyogzNXvjrr7+qQ4cOVstat26tbdu2SZKaNm2qU6dOPb7qAAAAACCLy1DocnFx0R9//GG17M8//5Sj4+3nsURHR8vZ2fnxVQcAAAAAWVyGLi/s3LmzevTooTfeeEMFCxbUxYsXtWLFCnXt2lWXLl1Sr169eGYXAAAAAPxNhkLX22+/LS8vL61atUqbN29WgQIF9MEHH6hx48Y6fvy42rRpo06dOtmqVgAAAADIcjIUuiTJ39//nqNZJUuWVMmSJR9LUQAAAADwrMhQ6IqNjdXixYsVGRkpk8kkSUpJSdFff/2ltWvX2qRAAAAA4HEyGg0yGg0ZWsdkMstkMtuoIjzrMhS6hg4dqjNnzih37tyKj49XgQIF9Msvv6hjx462qg8AAAB4bIxGgzw8XGRnl6H55JSWZlJcXCLBC48kQ6Fr37592rBhgyIjIzV37lzNmDFD3377rdavX2+r+gAAAIDHxmg0yM7OqOCZX+vUpavpWqdogTz6qHcHGY0GQhceSYYfjuzj4yNnZ2edOHFC0u17vCZNmmST4gAAAABbOHXpqo6duZTZZSCbyNC4asGCBfXHH38oV65cSkhIUExMjBITE5WUlGSr+gAAAAAgS8vQSNebb76pTp066bvvvlPz5s31zjvvyN7eXtWqVbNVfQAAAACQpWUodLVr105+fn7y9vbW4MGD9cUXXyghIUFdu3a1VX0AAAAAkKVl6PLCcePGqXz58nJ0dJSDg4N69OihAQMGaNy4cbaqDwAAAACytIeOdEVGRmrXrl2SpBUrVqhs2bJW7Tdu3NCWLVtsUx0AAAAAZHEPDV2enp5asmSJYmJilJycrNDQUKv2HDlyqE+fPjYrEAAAAACysoeGLkdHR61cuVKS1LVrVy1YsMDmRQEAAADAsyJDE2ksWLBACQkJ2rZtmy5evKi8efOqQYMGypUrl63qAwAAAIAsLUOh6+zZs+rcubNSUlJUoEABXbp0SR999JEWLVqk4sWL26pGAAAAAMiyMjR74YQJE/Tqq69q+/btWr58ubZv365WrVpp4sSJtqoPAAAAALK0DIWuw4cPa8CAATIab69mNBrVr18/HT582CbFAQAAAEBWl6HQZWdnp/j4eKtl8fHxcnZ2fqxFAQAAAMCzIkOhq0GDBho0aJBOnTql5ORknTx5UoMHD1aDBg0yvONdu3apffv2qly5surUqaOxY8cqKSlJ0u0Rtfbt26tSpUp6+eWXtWLFCqt116xZo0aNGqlixYpq06aNDh06ZGlLS0vTRx99pNq1a6tSpUoKCAjQlStXLO3R0dEKDAxU1apVVaNGDYWEhCg1NTXD9QMAAABAemQodA0aNEipqalq1qyZKlSoIH9/fzk6OiooKChDO42JiVHPnj31n//8R/v379eaNWu0d+9ezZ07V9euXVOPHj3UunVr7du3TyEhIZowYYKOHDkiSdqzZ4/Gjh2riRMnat++fWrZsqUCAgJ08+ZNSdLs2bO1c+dOrVq1Sjt27JCTk5NGjBhh2Xf//v3l4uKiHTt2aOXKldq1a5cWLlyYofoBAAAAIL0yNHuhh4eHFi9erPPnzys6OloFCxZUnjx5MrzT3Llz69dff5Wrq6vMZrPi4uJ069Yt5c6dW5s3b5aHh4c6duwoSapVq5ZatGihpUuXqnz58lqxYoX8/f1VpUoVSVLnzp319ddfa8OGDWrbtq1WrFihoKAg5c+fX5I0fPhwvfjiizp//rxMJpP27t2r7du3y9nZWYUKFVJgYKAmT56sbt26Zfg4AAAAAOBhMhS6JOmPP/7QypUrdfHiReXJk0dt2rRR1apVM7xjV1dXSVK9evUUGRmpqlWrqk2bNpo2bZr8/Pys+hYrVszygObw8HC1bdv2rvbjx4/rxo0bioiIsFrf29tb7u7uOnHihKTbwdHHx8fS7uvrq0uXLun69evpft6YwZDhwwUAAE85o9EgQwZ/yJvNZplMZhtVhKcRvwdmHw871xn5XshQ6Prll18UGBiol19+WSVKlNC5c+fUpUsXffLJJ3rllVcysimLzZs369q1awoKClLfvn3l4+Nz18QcTk5OSkxMlCQlJCTctz0hIUGS5OLiclf7nbZ/rnvndWJiYrpDl5eXWzqPDgAAZBVpJpPsjBm68+KR1kHW5emZM7NLwBPyuM91hkJXaGioPvroIzVt2tSybOPGjZo1a9Yjhy4nJyc5OTlp8ODBat++vTp16qQbN25Y9UlKSlLOnLcP3NnZ2TLhxt/bPT09LQHqzv1d/1zfbDbf1Xbn9Z3tp0d09A2Z+aMWAADPDDs7ozw9c2rowpU6FRGVrnWK5vPWhM7tFBuboLQ0k40rxONy51w/Cs511vOo5zs959pgSP9gTIZC1+nTp9WkSROrZU2aNNHw4cMzshkdPHhQw4YN09q1a+Xo6ChJSk5OloODg4oVK6adO3da9Q8PD1fx4sUlScWLF1dYWNhd7XXr1pW7u7t8fHwUHh5uucTw6tWriouLk5+fn0wmk+Li4hQVFSVvb29J0smTJ5UvXz65uaV/9MpsFqELAIBn0KmIKB2/cDnD6/F7QfbBuc4+Hue5ztB4uIeHh/766y+rZcePH8/wZBolSpRQUlKSpk6dquTkZF28eFEfffSR2rVrpyZNmigqKkoLFy5USkqKdu/erXXr1lnu42rXrp3WrVun3bt3KyUlRQsXLlR0dLQaNWokSWrTpo1mz56t8+fPKz4+XuPHj1f16tVVuHBhFSlSRFWqVNH48eMVHx+v8+fPa9asWWrXrl2G6gcAAACA9MrQSFf79u0VEBCgnj176rnnntO5c+c0b948vfnmmxnaac6cOTV//nyNHz9ederUkZubm1q0aKHevXvL0dFRn3/+uUJCQhQaGqrcuXNrxIgRqlmzpqTbsxmOHj1aH3zwgSIjI1WsWDHNmzdPHh4ekqTevXsrNTVVHTt2VEJCgmrUqKFp06ZZ9h0aGqoxY8aoYcOGMhqNat26tQIDAzNUPwAAAACkV4ZCV/fu3XXr1i3NmTNHUVFRKliwoN566y116dIlwzsuVqyYPv/883u2lStXTsuWLbvvuq1atVKrVq3u2ebg4KCgoKD7PjvM29tboaGhGa4XAAAAAB5FhkKXwWDQe++9p/fee89W9QAAAADAMyVd93T9fbZCSTp16pRNigEAAACAZ026QldERITV6zfeeMMmxQAAAADAsyZdoeufT2c3M1cmAAAAAKTLIz1C/Z8hDAAAAABwb48UugAAAAAA6ZOu2QuTk5M1Y8YMy+ukpCSr15LUp0+fx1sZAAAAADwD0hW6KlWqpD179lheV6hQweo1lxsCAAAAwL2lK3QtXrzY1nUAAAAAwDOJe7oAAAAAwIYIXQAAAABgQ4QuAAAAALAhQhcAAAAA2FCGQ1dMTIwWLlyokJAQxcfH66effrJFXQAAAADwTMhQ6Dp69KheffVVbdq0SStXrlRsbKz69eunVatW2ao+AAAAAMjSMhS6JkyYoPfff1/Lli2Tvb29ChUqpJkzZ2rBggW2qg8AAAAAsrQMha6//vpLrVq1kvS/ByK/9NJLioyMfPyVAQAAAMAzIEOhK3fu3Dp16pTVslOnTsnb2/uxFgUAAAAAz4oMha4333xTPXv21PLly5WamqoNGzaoX79+6tChg63qAwAAAIAszT4jnd9++23Z2dlp0aJFMplMCg0N1euvv67OnTvbqDwAAAAAyNoyFLrmz5+vN998Ux07drRVPQAAAADwTMnQ5YVz585Vjhw5bFULAAAAADxzMhS6XnrpJc2bN09XrlyxVT0AAAAA8EzJ0OWFBw4c0Hfffafp06ff1Xbs2LHHVhQAAAAAPCsyFLomTZpkqzoAAAAA4JmUodBVvXr1ey6PiYl5LMUAAAAAwLMmQ6HryJEjmjRpkiIjI2UymSRJKSkpiomJ0R9//GGTAgEAAAAgK8vQRBpjxoxRnjx59OKLL+qFF17QW2+9JTs7Ow0aNMhW9QEAAABAlpah0BUWFqYJEyaoY8eOSktLU5cuXfTJJ59o3bp1tqoPAAAAALK0DIWuXLlyycnJSYUKFVJYWJgkqWLFirp48aJNigMAAACArC5Doato0aL673//qxw5csjFxUXHjh3TyZMnZTAYbFUfAAAAAGRpGZpIo1+/fgoICFCdOnXUtWtXvf7667Kzs9N//vMfW9UHAAAAAFlahkJX5cqVtX37djk4OKhDhw4qVaqUbty4oTp16tiqPgAAAADI0jIUui5dumT12tvbW97e3rp06ZIKFCjwWAsDAAAAgGdBhkLXyy+/fN/7t44dO/ZYCgIAAACAZ0mGQtfWrVutXsfExGj+/Plq2LDhYy0KAAAAAJ4VGQpdBQsWvOv1uHHj9Nprr6lly5aPtTAAAAAAeBZkKHTdz/Xr1x/HZgAAeOoYjQYZjel/NIrJZJbJZLZhRQCArCZDoWvGjBlWr1NSUrRjxw5VrFjxcdYEAMBTwWg0yMPDRXZ26X+sZVqaSXFxiQQvAIBFhkLXnj17rF7b2dmpUqVK6tmz52MtCgCAp4HRaJCdnVFjV6zR2atRD+3/fB5vjWz/moxGA6ELAGCRodC1ePFiW9UBAMBT6+zVKIVdjsjsMgAAWVSGQtc333yTrn6tW7d+hFIAAAAA4NmTodC1YsUKHTp0SB4eHipUqJAiIyMVGRmpvHnzys7OTpJkMBgIXQAAAADw/zIUukqUKKHy5csrKCjIErLmzJmjy5cv64MPPrBFfQAAAACQpaV/OiZJ69at04ABAyyBS5K6du2q77///rEXBgAAAADPggyFLicnJ506dcpq2R9//CF3d/fHWhQAAAAAPCsydHlhx44d1bVrV7Vv314FChTQ+fPntXz5cg0bNsxW9QEAAABAlpah0NWrVy95eXlp7dq12rx5swoVKqRJkyapXr16tqoPAAAAALK0DIUuSWrfvr3at29vi1oAAAAA4JmT7nu69u/frx9//FGSdPPmTQUHB6tFixaaMWOGzYoDAAAAgKwuXaFr27Zt6tKli06cOCFJ+uijj7Rnzx516NBBP/zwgxYvXmzTIgEAAAAgq0pX6Jo3b57Gjh2rgIAApaamau3atRoyZIjeeustTZw4UcuXL7d1nQAAAACQJaUrdJ04cULNmzeXJB0/flw3b95UrVq1JEnFixfXhQsXbFchAAAAAGRh6QpdaWlpsre/PefG4cOHVbhwYXl6ekq6fX+X0Zihx30BAAAAQLaRrrRUpEgRHTlyRJL0008/WUa5pNsTbBQpUsQmxQEAAABAVpeuKeM7duyowMBAlSxZUrt379bq1aslSV999ZVmz56tXr162bRIAAAAAMiq0hW62rZtK0dHRx08eFDdunWTn5+fpNsTbLRp00YdO3a0aZEAAAAAkFWl++HILVq0UIsWLayW/fTTT4+9IAAAAAB4ljADBgAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbStdEGjNmzHhonz59+vzrYgAAAADgWZOu0LVnz54HthsMhsdSDAAAAAA8a9IVuhYvXmzrOgAAAADgmZTu53TdsXv3bkVGRspsNkuSUlJSdOLECY0YMeKxFwcAAAAAWV2GQte4ceO0bNky5cyZU5KUlpamhIQEvfTSSzYpDgAAAACyugyFro0bN2rJkiW6efOm1q5dq/Hjx+ujjz5SYmKireoDAAAAgCwtQ6Hr5s2bqlixoq5evaqjR4/KYDCoT58+atasma3qAwAAAIAsLUPP6cqXL5+io6OVJ08eRUREKCUlRU5OToqPj7dVfQAAAACQpWVopKtevXrq3LmzFi1apGrVqmnYsGHKkSOHihQpYqPyAAAAACBry9BI18CBA9WqVSs5ODho1KhRio2NVXh4uMaNG2er+gAAAAAgS8vQSNcPP/ygbt26SZLc3Nw0f/58SdLXX3+t0qVLP/7qAAAAACCLe2jounnzpmJjYyVJw4YNU8WKFS3P6JKkGzduaOLEierQoYPtqgQAAACALOqhoSs+Pl7+/v5KSkqSJL388suWNrPZLIPBoFdeecV2FQIAAABAFvbQ0JUnTx798MMPunnzplq0aKH169dbtefIkUPe3t42KxAAAAAAsrJ0TaTh5eWl5557TgcOHFDBggXl5OSk6OhoGY3GRwpcx48fV5cuXVS9enXVqVNHQ4YMUUxMjCTp8OHDat++vSpVqqSXX35ZK1assFp3zZo1atSokSpWrKg2bdro0KFDlra0tDR99NFHql27tipVqqSAgABduXLF0h4dHa3AwEBVrVpVNWrUUEhIiFJTUzNcPwAAAACkV4ZmL0xMTFRgYKDq1Kmj119/XS+//LLeffddXb9+Pd3bSEpKUrdu3VSpUiX98ssvWr9+veLi4jRs2DBdu3ZNPXr0UOvWrbVv3z6FhIRowoQJOnLkiCRpz549Gjt2rCZOnKh9+/apZcuWCggI0M2bNyVJs2fP1s6dO7Vq1Srt2LFDTk5OGjFihGXf/fv3l4uLi3bs2KGVK1dq165dWrhwYUbeAgAAAADIkAyFrqlTpyoxMVHfffedDh8+rG+//VYmk0mTJ09O9zYuXbqkkiVLqnfv3nJ0dJSnp6c6dOigffv2afPmzfLw8FDHjh1lb2+vWrVqqUWLFlq6dKkkacWKFfL391eVKlXk4OCgzp07y9PTUxs2bLC0d+/eXfnz55erq6uGDx+u7du36/z58zp79qz27t2rwYMHy9nZWYUKFVJgYKBl2wAAAABgCxkKXT/99JOmTp0qX19f5ciRQ35+fpo8ebJ++OGHdG+jaNGimj9/vuzs7CzLvv/+e5UpU0ZhYWHy8/Oz6l+sWDEdP35ckhQeHn7f9hs3bigiIsKq3dvbW+7u7jpx4oTCwsLk4eEhHx8fS7uvr68uXbqUoZE6AAAAAMiIDD2n6+bNm3Jzc7NalitXLplMpkfaudls1rRp0/TTTz9pyZIl+vLLL+Xs7GzVx8nJSYmJiZKkhISE+7YnJCRIklxcXO5qv9P2z3XvvE5MTFSuXLnSXbfBkO6uAIBsip8V2QvnO/vgXGcfDzvXGfleyFDoqlChgqZPn66goCAZDAaZzWZNnz5d5cqVy8hmJN2ein7o0KE6evSolixZohIlSsjZ2Vk3btyw6peUlKScOXNKuh2S7kxd//d2T09PS4C6c3/XP9c3m813td15fWf76eXl5fbwTgCAbMvTM2M/V5C1cb6zD8519vG4z3W6QteBAwdUpUoVDRo0SG+//bbWrl2rggUL6uLFizIYDPriiy8ytNNz586pe/fuKlCggFauXKncuXNLkvz8/LRz506rvuHh4SpevLgkqXjx4goLC7urvW7dunJ3d5ePj4/VJYhXr15VXFyc/Pz8ZDKZFBcXp6ioKMuMiydPnlS+fPnuGr17mOjoG/rb86EBAM8oOzvjI/3gjY1NUFrao10FgszxqOda4nxnNZzr7MWWn+MGQ/oHY9IVurp3766DBw+qRIkS+v7777V161ZFR0erYMGCqlevnlxdXdO1M0m6du2a3nnnHdWsWVMhISEyGv93W1mjRo00efJkLVy4UB07dtSBAwe0bt06zZo1S5LUrl079e7dW02bNlWVKlW0dOlSRUdHq1GjRpKkNm3aaPbs2SpXrpw8PT01fvx4Va9eXYULF5YkValSRePHj9eYMWMUGxurWbNmqV27dumu/Q6zWYQuAMAD8XMie+F8Zx+c6+zjcZ7rdIUu89/26OHhobZt2z7yDlevXq1Lly5p48aN2rRpk1XboUOH9PnnnyskJEShoaHKnTu3RowYoZo1a0qSatWqpdGjR+uDDz5QZGSkihUrpnnz5snDw0OS1Lt3b6Wmpqpjx45KSEhQjRo1NG3aNMv2Q0NDNWbMGDVs2FBGo1GtW7dWYGDgIx8LAAAAADxMukKX4THeMdilSxd16dLlvu3lypXTsmXL7tveqlUrtWrV6p5tDg4OCgoKUlBQ0D3bvb29FRoamrGCAQAAAOBfSFfounnzpho2bPjAPlu3bn0sBQEAAADAsyRdocvBwUF9+vSxdS0AAAAA8MxJV+iyt7fXa6+9ZutaAAAAAOCZY3x4F+uJNAAAAAAA6Zeu0NWyZUtb1wEAAAAAz6R0ha4PP/zQ1nUAAAAAwDMpXaELAAAAAPBoCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABuyz+wCACCrMRoNMhoNGVrHZDLLZDLbqCIAAPA0I3QBQAYYjQZ5eLrIzpixCwXSTCbFxSYSvAAAyIYIXQCQAUajQXZGo6ZtXKsLMdHpWue53F7q37SljEYDoQsAgGyI0AUAj+BCTLROX4nM7DIAAEAWwEQaAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbInQBAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbInQBAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbInQBAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbInQBAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYkH1mFwA8K4xGg4xGQ4bWMZnMMpnMNqoIAAAATwNCF/AYGI0GeXi6yM6YscHjNJNJcbGJBC8AAIBnGKELeAyMRoPsjEYt3LFREddi0rVOPvfc6vxSUxmNBkIXAADAM4zQBTxGEddidD7mSmaXAQAAgKcIE2kAAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbInQBAAAAgA0RugAAAADAhghdAAAAAGBDhC4AAAAAsCFCFwAAAADYEKELAAAAAGyI0AUAAAAANkToAgAAAAAbss/sAp51RqNBRqMh3f1NJrNMJrMNKwIAAADwJBG6bMhoNMjT00VGY/oHFE0mk2JjEwleAAAAwDOC0GVDt0e5jFr/2xZFx8c8tL+Xa241r9hIRqOB0AUAAAA8IwhdT0B0fIwir0dldhkAAAAAMgETaQAAAACADWV66IqJiVGjRo20Z88ey7LDhw+rffv2qlSpkl5++WWtWLHCap01a9aoUaNGqlixotq0aaNDhw5Z2tLS0vTRRx+pdu3aqlSpkgICAnTlyhVLe3R0tAIDA1W1alXVqFFDISEhSk1Ntf2BAgAAAMiWMjV0HThwQB06dNC5c+csy65du6YePXqodevW2rdvn0JCQjRhwgQdOXJEkrRnzx6NHTtWEydO1L59+9SyZUsFBATo5s2bkqTZs2dr586dWrVqlXbs2CEnJyeNGDHCsv3+/fvLxcVFO3bs0MqVK7Vr1y4tXLjwiR43AAAAgOwj00LXmjVrFBQUpAEDBlgt37x5szw8PNSxY0fZ29urVq1aatGihZYuXSpJWrFihfz9/VWlShU5ODioc+fO8vT01IYNGyzt3bt3V/78+eXq6qrhw4dr+/btOn/+vM6ePau9e/dq8ODBcnZ2VqFChRQYGGjZNgAAAAA8bpk2kcaLL76oFi1ayN7e3ip4hYWFyc/Pz6pvsWLFtHLlSklSeHi42rZte1f78ePHdePGDUVERFit7+3tLXd3d504cUKS5OHhIR8fH0u7r6+vLl26pOvXrytXrlzpqt2Q/sduPbInsQ88PTjf2QfnOvvgXGcvnO/sg3OdfTzsXGfkeyHTQleePHnuuTwhIUHOzs5Wy5ycnJSYmPjQ9oSEBEmSi4vLXe132v657p3XiYmJ6Q5dXl5u6er3qDw9c9p0+3i6cL6zD8519sG5zl4439kH5zr7eNzn+qmbMt7Z2Vk3btywWpaUlKScOXNa2pOSku5q9/T0tASoO/d3/XN9s9l8V9ud13e2nx7R0TdkTsdjtOzsjI90wmJjE5SWZsrwesg8j3quJc53VsO5zl74HM8++L+dfXCusxdbfo4bDOkfjHnqQpefn5927txptSw8PFzFixeXJBUvXlxhYWF3tdetW1fu7u7y8fFReHi45RLDq1evKi4uTn5+fjKZTIqLi1NUVJS8vb0lSSdPnlS+fPnk5pb+0SuzWekKXf+GrbePpwvnO/vgXGcfnOvshfOdfXCus4/Hea4zfcr4f2rUqJGioqK0cOFCpaSkaPfu3Vq3bp3lPq527dpp3bp12r17t1JSUrRw4UJFR0erUaNGkqQ2bdpo9uzZOn/+vOLj4zV+/HhVr15dhQsXVpEiRVSlShWNHz9e8fHxOn/+vGbNmqV27dpl5iEDAAAAeIY9dSNdnp6e+vzzzxUSEqLQ0FDlzp1bI0aMUM2aNSVJtWrV0ujRo/XBBx8oMjJSxYoV07x58+Th4SFJ6t27t1JTU9WxY0clJCSoRo0amjZtmmX7oaGhGjNmjBo2bCij0ajWrVsrMDAwE44UAAAAQHbwVISuOzML3lGuXDktW7bsvv1btWqlVq1a3bPNwcFBQUFBCgoKume7t7e3QkNDH71YAAAAAMiAp+7yQgAAAAB4lhC6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANgQoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG6AAAAAMCGCF0AAAAAYEOELgAAAACwIUIXAAAAANhQtgxd0dHRCgwMVNWqVVWjRg2FhIQoNTU1s8sCAAAA8AzKlqGrf//+cnFx0Y4dO7Ry5Urt2rVLCxcuzOyyAAAAADyDsl3oOnv2rPbu3avBgwfL2dlZhQoVUmBgoJYuXZrZpQEAAAB4BtlndgFPWlhYmDw8POTj42NZ5uvrq0uXLun69evKlSvXQ7dhNEpmc/r36ZMrjxzsHv5W587pabUPZD2FcueVo336/lv55Mpt+TfnO+spmtdHOewd0tW3YG7OdVZXPH8+OTk8/HwX8vay/JtznTWVKpRPzo7p+79dxIfznZWVej6/nHOk81zn87b8m3OdNZX0LShnJ8eH9nu+YB7Lvx92rg2G9O/fYDZnJD5kfd9++60++eQT/fzzz5Zl586dU6NGjbRt2zbly5cv84oDAAAA8MzJdlndxcVFN2/etFp253XOnDkzoyQAAAAAz7BsF7qKFy+uuLg4RUVFWZadPHlS+fLlk5ubWyZWBgAAAOBZlO1CV5EiRVSlShWNHz9e8fHxOn/+vGbNmqV27dpldmkAAAAAnkHZ7p4uSYqKitKYMWO0Z88eGY1GtW7dWkFBQbKzs8vs0gAAAAA8Y7Jl6AIAAACAJyXbXV4IAAAAAE8SoQsAAAAAbIjQBQAAAAA2ROgCAAAAABsidAEAAACADRG68Fjt2rUrs0sAgKfaX3/9pZSUlMwuA3hmmUymzC4Bj+hZPneELjw248eP1/z58zO7DDwGFy9ezOwSkEFjx47VL7/8ktll4CFGjRqlN954Q7t371Zqampml4MHCA8Pz+wS8Aj69++vUaNG8f8rC3rWzx2hC4/NtWvX5OvrK0lKS0vL5GrwqIKCgvT1119ndhnIgLCwMH333XcqWbJkZpeCB0hOTtaVK1eUmJiosWPH6tdff32m/6qblQUHB6t58+ZcvZHFnD59Wps2bdLKlSs1ceJE/n9lIdnh3BG68K/duUwmKipKXl5ekiQ7O7vMLAn/wunTp1W+fHlJEs9OzxoSExNlNps5X08xs9ksR0dHBQUFqUWLFsqTJ48GDhyoHTt2cN6eQsnJyZKkfv366eeff87cYpBuL7zwgvr166dq1app1apVGjx4cGaXhHTKDueO0IV/JS0tTQ4ODpKk+Ph45ciRQ5Kshoafxb9WPIvMZrPi4+MVGRlpOY8GgyGTq0J6ODg4yMfHRzlz5pR0+/8c/++eHiaTSQaDQWazWW5ubjKZTAoODlbHjh3Vv39/bd++neD1lLjz/6Z79+567bXXVLduXfXp00dbt27N5MrwIH//o1Px4sWVJ08effHFF9q7d68GDRqUydXhQbLTuSN04ZHNnTtXAwcOtPxF0M3NTZGRkTpx4oTOnDmjmzdvKjk5WUajkcsNs4A7vxQaDAa5uLhIsg7P/FL4dPnpp58UGRkpSYqMjJSzs7OlzWg0ymjk4/1pcOczULr9f8zHx0cVKlTQmDFjNGDAALVu3ZoRr6fInXPl7e2ts2fPql69egoKClK/fv30448/ZnJ1uB+DwWD5I+Err7yiy5cva8+ePZo3b55++eUXDRkyJJMrxP1kp3PHT2U8kpSUFDk6Oio6Olpjx45VUlKSEhIStGnTJnXq1Emvv/66GjVqpEaNGql169YaP378M3tjZFa3cOFCfffdd5Juj5jkyZNHkpSUlGQ1WsKo19Pjxx9/VEhIiFavXq34+HiZTCYdOXJEffv21aBBgzRhwgStXr1a27Zt0w8//KCEhITMLjlb6tOnj5o3b67169fr999/tyx/4403VKRIER04cECjR49W06ZN1aNHD+4fykQxMTFWr/PmzasuXbpo2bJlatasmd5991317duXSw2fMhMmTNCnn36q2NhY3bp1y7I8MDBQ586dk5+fn+bOnasdO3aod+/emVgp/ik7njv7zC4AWY/JZJKDg4PefPNNubu7a/ny5Ro2bJjOnTun8ePHq2jRokpOTtapU6d05swZxcTEqHXr1rK359vtaRMbG6tNmzbJw8NDLi4uKl68uMLCwjRixAjFxsYqX7588vLykru7u3Lnzq3KlSurWbNmmV12tvfyyy/rzz//1NatW+Xk5CRJyp07t6pVq6aDBw/q4sWL2r59u86fP6+cOXNq9erVlksP8WT89ddf2rdvn65du6bt27crLCxMderUUcuWLeXn56dChQpp7ty5mjNnjsaNGycnJyfly5cvs8vOloKCgrR79269//778vX1ValSpSRJFSpUUL58+XTx4kUNHDhQaWlp6tWrl+bOnau6detmctVYuXKlFi1aJEk6d+6cjEajAgICVKRIEZUuXVpTp07Vpk2b1KxZM82YMUNDhgxRZGSkfHx8MrlyrF69OlueO4OZ6xmQAbNmzdLly5dVsGBBVa1aVVWrVtU333yj5cuX6/jx41qzZo2ef/75zC4TGXD+/HlNmDBBBoNBlStX1sKFCzVy5EglJiYqKSlJv//+uyIiIhQbG6uJEyfKz88vs0vOts6ePasbN26obNmykqQFCxbo+++/182bN1WhQgWNGzdOJpNJRqNRCQkJunHjhuzs7Cyjl3hykpOTtWfPHn344Yd6/vnn1b9/f4WEhMjBwUEFChRQt27dFBwcrMGDB6tWrVqZXW62dfToUb333nu6dOmSKlSoIE9PT3l5eal3794qUKCA5s2bpy1btmj58uWSpBkzZqhp06aWmXqROUwmk06fPq2vv/5aW7ZsUb169eTs7KyVK1eqdevWatKkiW7cuKHly5fro48+Uq5cuXTr1i3L/crIXMePH9fKlSu1devWbHXuCF1It969eysiIkL169fXkSNHdO3aNdWvX1+BgYFat26dZsyYoapVq2rMmDGys7NTamoqo1tPqe+++043btxQsWLFVLVqVUVHR2vEiBEKDw+Xq6ur1qxZc9c6SUlJllEVPHl9+/bVpUuXdP78eZUtW1bt2rVT06ZN9dVXX2nGjBkqVaqUJk6caAlYd8IXnqyYmBjlzp1b0u3LsH/99VcNGDBAnTp10oABA3T48GF9/PHHunbtmo4fP6733nvvmbl0JitKTk7W9u3btXTpUiUkJGjAgAGaNWuWUlJSVLBgQXXu3FmfffaZevbsaZnVFZlr7Nixql27tho2bKgTJ05o2bJl+uWXXzRnzhzFxcVZQnKxYsV07tw5/fe//1WRIkUyu2zo9tU1OXPmlKOjo06dOqUvv/xSO3fuzDbnjt+IkS579uzR5cuXtWLFCtnZ2SkmJkabNm3SokWLlJqaqr59+yo1NVXffPONgoKCNGnSJMushni6BAQE6MKFC8qfP79CQ0NVt25dderUSZMmTdKIESN06NAhbd68WY0bN5Z0+xdHBweHZ+KvTFnV2LFjFRERoTlz5ujmzZuaPXu2vvrqK50/f149evSQwWDQypUrtW7dOvn7+8vHx4fAlQkWLFigffv2afjw4SpUqJAcHBz04osv6pNPPlH//v117do1ffDBB1q0aJF+/fVXHTx4UE2aNMnssrOlbdu2SZJcXV31yiuvyGAwaMaMGdq4caMWL16s/fv3a9myZerZs6diYmLk6+tL6HoKBAQE6PLlyxo5cqQkqUSJEnrzzTcVHx+vXr16KTQ0VMHBwWrfvr2++uor3bp1i8/Cp8SoUaN08eJFJSUlqV+/fqpevbr+85//KDExMfucOzOQDlu2bDG3aNHCatn169fNX3/9tbl169bm7777zpyammr+8ssvzT169DBHRkZmUqV4kGXLlpnbtWtneb1//35zQECAuXv37ubffvvNHBcXZw4ICDD36dPHvH79+kysFHckJSWZ+/TpY961a5fVsunTp5vfeust8/Lly81ms9n82WefmRs3bmxesGCBOTU1NbPKzdaWLFliLlGihLlPnz7mixcvWpanpaWZf/75Z3PlypXNw4cPtyw3mUyZUWa216dPH3O7du3MrVq1Mjdt2tS8YsUKs9l8++ecv7+/eciQIZa+u3btMs+YMcMcFhaWWeXi//Xo0cPcsWNHy+uYmBhzamqq2WQyma9evWoeNGiQ+ZVXXjEfOHDAbDbf/py8efNmZpWLv3nvvffMr7/+uvnIkSPmxo0bm+fMmWNpu3z5crY5d89YhIStlCtXTnFxcVq2bJllmZubmxo3bqwqVapo586dsrOz0+uvv67Jkycrb968mVgt7ufKlSuWofq0tDRVqVJFvXr1kpOTkxYtWiSDwWCZRINZ754OOXLkUGJiojZt2mS1rEePHipRooS+/fZbpaSkqGfPnurQoYMaN27Mw8mfMPP/X6UfHx+vTp066fDhwxoxYoQuX74s6fY05HdGvFauXKlx48ZJYkbQzPDBBx/o6tWrWrFihT799FPVq1fPMur14osvqn///jp27Jjl+UA1a9ZUQECAihUrlpllZ3urV6/W3r17NXXqVEnSl19+qaFDh6pjx47q27evnJycNGzYMFWtWlW9evXSkSNHlCNHDi6Jfwr8+eefiouL09KlS1WuXDlVq1ZN+/btU58+fTRt2jTlypVLwcHB2eLcEbpwX0eOHNGZM2d04cIF+fj46LXXXtP333+vjRs3Wvp4eHiofPnyOnbsmOXhyLly5crEqvEgL7zwgs6dO6ewsDDLL+bly5dXly5d9Oeff+rHH39UgQIFNHnyZA0ZMoQZ7zKR+W+32zZo0EBnz57Vzp07LcudnJwUFBSkv/76S+vWrZMkvfvuu3ruuecypd7s7E542rNnjypVqqS1a9cqPDzcKnjZ2dmpTp06+vzzz/Xmm29mZrnZVnJysq5evarg4GBJUqFChVSmTBkdOnRI169fl5OTk+rWrau+ffvqwIEDGjp0qCQ9e5c4ZUH58+dX5cqVdeLECS1ZskTz589XmzZt1KxZMyUmJqpbt25yc3NTt27d1KJFC7m7u2d2yfh/iYmJ+uuvv/Tbb79p8uTJWrdunV5++WV5eXnpwIEDGj58uHLnzq2uXbs+8+eOe7pwT71799b58+eVmpoqR0dHdevWTW+99ZamTJmilStX6tatW2rdurUk6erVq8qbNy/3cD2lYmJi5OrqKkdHR1WoUEGLFy/Wt99+q7feekv58uWT2WxWpUqVVK1aNW3btk0tWrRQ/vz5M7vsbO3OLKHPPfecqlatqg4dOuiXX37R/PnzZW9vrxo1aki6HbyqVq3KyPJTICkpSe+8847q1asnSVqzZo1ee+01DR8+XCEhIcqfP7/s7OxUu3btTK40+0pOTtbRo0d14MABVapUSdLtKzZ8fHyUK1cumUwmOTo66pVXXlGOHDmeqRv4s7r/a+9OA6K6soUNv1XFoFAqg4ii0CqOGLyJQhslIeKUaItjjLPGKU6IMqnRXDF2cJ4RwSnYjoCgRtvhkqRVNKItJiYaFdSoKCqIIIMgFFX1/fDjtHTS3fa9rQWynj9Sp04Vi+ycqr3O3nvtjh07cv78eYKDg/Hw8GDjxo20atUKeFbaf+7cuaSnp+Pq6sqsWbOwsLAwccSinIeHB02bNiU4OJhHjx6xbds25fpLTEwkIiKCzMxMmjVr9tq3nSRd4lcWLVrEo0eP2LdvH1evXuWHH34gODiY//7v/8bf35/o6GgiIyOJjY2lSZMmHDt2jC1btkihhUpo1qxZ3L9/n0ePHtGtWzcGDx5MQEAAn376KRqNhgEDBigl/i0tLalTp45MTTOx56uEpqSk8M0339CrVy/Wrl3L5MmTiYqKIjU1FR8fH06cOMGFCxeUO/LCdGrUqIGXlxcAJSUl2Nvbs3//fj788EP8/f1Zt25dld9jpqrTarV8+umnFUaR7927h16vB56NaO3atYuaNWvSv39/U4Up/k55JVY/Pz9u3rzJtWvXsLW1xWg0olKp+N3vfoe5uTllZWUAr3Wnvaopb6ONGzdy7tw5EhMTK8zGeOutt1Cr1crmyK9720nSJX4lOzubwYMHo9FoaNOmDW3atKFOnTrKdLPPPvuMixcvcvjwYZydnRk/fjxNmzY1ddji7wQHB5Oens6iRYs4fvw4ly9fZuLEiaxdu5awsDAWLVpEeno6DRs2xMrKigMHDrBjxw5Th12t/aMqoVu3bkWv1xMZGcnatWs5ePAge/fuxczMjE2bNuHs7Gzq0Ks1vV6PRqNRtsiwtLSkrKwMOzs74uLiGD16tNKxF6bl7e1NzZo1lceFhYVKJ3Dr1q2sW7eObdu2mSo88RvUajWlpaVYWFgwa9Ys8vPzK9zAOHLkCCUlJdStW9eEUYrfolKpMBgMaLVafHx8OH/+PJs2bWL27Nmo1Wr+8pe/oFKpsLW1NXWor4QkXUKh1+vR6/U8fPiQmzdvAn9bV+Lr60tRURHLly+nWbNmuLu74+7ubspwxT+Rl5dHdnY2K1aswNnZGVdXV65evcr27duZOnUqGzZsYNmyZRw7doyUlBQaNWrEtm3bZONjEysoKKCsrEwZbbSzs8PX1xczMzN27dqFi4sLwcHBFBYW8uTJE6ytrdFqtSaOunqJiIigZ8+eNGnSBJVKpSRcANHR0XTs2JFWrVphZmZGWVkZdevW5eDBg7IuqBLQ6/UVEi6A9PR03njjDQ4cOEBERATbtm3Dzc3NRBEKgEePHmFvb6881uv1ygjIjRs3aN26NTt37mTHjh24u7tz8uRJNm/eXOE1wjR+q+3KPx9PnTpF7dq1+frrr+nevTtvvvkmycnJbN68GRsbGxNF/GrJt4BQaDQaLCws+Oijj9iyZQsnTpxQFogbjUa6du2Ko6Mj9+7dM3Gk4p8xGo0UFRVx6dIlfvnlF+V4q1atGD16NO7u7qxZs4amTZsyefJkNm/ezLx585T58cJ03N3dycvL+80qoR4eHiQlJQHPpkk5OjpKwvWKJSYmKpt1ln82lncoZs6cycGDB2nevLlyfvnIl1QpfPVOnz5NYWGh8vj5mxnbt2/n0qVLyvF169axZMkSoqOjJeEysZkzZxIQEKDc+IW/XWOBgYFs374da2trOnbsSI8ePfD09CQmJoY2bdqYKmTx//2ztgsICGDfvn0MGzaMGTNm0Lt3bzw8PNi9e3e1uuYk6RIkJCSQnJysPO7evTujRo0iNDSUpKQkVCoVKpWKunXrYmNjw9OnT00YrfhXVCoVDRo0oFevXmzdupX09HTluRYtWtC1a1d++OEHsrOzlfNlHZfpfPvttxw6dIiUlBQcHR3p168fiYmJFUrEl1cJvXz5MsXFxSaMtnrT6/XY2Nig1+srrAsKDg7mwoULxMbGotFoMBgMFV4nSdertWTJEsaOHcuBAweUbS/KE+CQkBDi4uKUm0zNmjXDzs6OrVu38sYbb5gsZgGZmZmcOXOGv/71r6xcuZLbt28rz02bNo1r166xevVqzM3Nadq0KQEBAQwaNEhZlyxM51+13fXr11m4cCFarRYvLy8CAgIYOnRotWs7SbqqMaPRSGFhIRs2bCAmJoaUlBTg2XqEoUOH0r17d2bMmEFcXBw//fQTO3fu5Oeff1aqzojKZffu3Wzbto19+/ZhMBj44IMPsLCwYNOmTdy5c0c5r2vXrtja2lJQUGDCaAXA5MmTWbFiBbt27VJGkPv06YODgwN79uxh//79yrnlVUJlmpppNWjQAHNz8wqJVffu3Tl06JCymF/ayLTKp6ItXbqUuLg45UZhaGgo33//vbIeEqBTp07ExMRUGKEUpuHo6EhISAgdOnTgr3/9K7Nnz+bOnTvcv38fOzs7EhISsLCwUApmiMrjRdrO0tISnU5n6lBNSmV8/nadqFYMBgNFRUX06tWLWrVq4e7uzocffoiHhwcAubm5HD58mA0bNuDi4kJZWRnz5s2rVkPBVcWkSZN4/PgxNWvWJDs7m+3bt2NjY8ORI0fYs2cPWq2WKVOm0KpVK3bu3El0dDSxsbEyB96EVq1aRXJyMnFxccCztQoAVlZWWFhYEBkZyalTp7C1ta1QJVSuv1frp59+ok2bNmg0GiIjI/nxxx+Jior6zXPLysqUzrx49corpRUWFrJ69WqKiorYu3cvAQEBDBs2jHPnzuHt7Y2ZmRk6nU62OakkDAaDMqPmypUrxMbGMmzYMGbOnEmNGjVYsmSJMiIi7Va5SNv9e+TboRpTq9U8ffoUV1dXBg4cyO7du4mJicFoNOLp6YmtrS3Dhw/ngw8+oGbNmuj1emrVqmXqsMXfWb16NdnZ2cTHx5Obm8uoUaM4cuQIOp2Ofv36YWdnR0xMDAMGDKBjx47cvHmT8PBwSbhMLDMzk759+wKwbt06jh49ilqt5v79+4SEhChVQg8dOoSLi4tUCTWBXbt2ER8fz9ixY+nduzcWFhbcuHGD0NBQGjduTO3atWnSpAlOTk48fvxY1kWaWHmlNHhWlMbLy4uePXsyYcIE1Go1o0aNwszMDL1eX+07f5XJ8yPDrVu3Jicnhz179rB371569uzJ7NmzWbp0Kc7OztJulYy03b9Hkq5q7vr169SrV4/evXvTqFEjli9fTmxsLCqVShnxsrW1lekyldidO3cYPnw4ABs2bCAjI4MTJ05w8+ZNNm/ezMGDB1m1ahUpKSnUrl0bGxsb2UzXhAwGAwaDgXv37tGkSRNu3brFwYMH2bBhAyqViosXLxIcHIyDgwM+Pj5SJdSEevTowdmzZ4mPj8fS0pKCggKKi4spKioiPj6e7OxszMzMyMnJoW7dunz11VfY2dmZOuxqJyMjg4YNGwLPOoFarZYhQ4YQEhJCbGwsy5YtIyQkBJVKxYgRI6hRo4aJIxYAQUFBSvGuxo0bK2XDQ0JCCAsLIz8/n7179zJw4EBlj8Ln93gSphMYGIilpaW03b9Jkq5q7u2331bWaL355psEBgaycuVK9uzZQ1lZGW+//bYkXFVAeUevqKiIPXv24OrqSllZGePHj2f58uV8/vnnShItTEutVqNWq+nTpw/h4eFkZmYyatQoGjduTGlpKS4uLuzdu5fr16/j4+Nj6nCrpcLCQoqLi3FwcOCPf/wj8+fPJyYmhszMTAYPHsy0adMoKipCp9ORn5/PnTt3cHFxkYTLBPz9/UlMTCQwMJDGjRvTo0cP4Nmmq126dOHkyZP069eP0tJS5s6di7m5OaNHjzZx1OLEiRMcOnQIeDZCeePGDWbMmEGLFi1wdnZGpVIRHx/P+PHjiY+PZ9SoUSaOWJRLSUnh8OHDgLTdv0t609VY+TQMCwsLDAYDRqORdu3aERQUxJUrVzh8+LBUKqwCVqxYwXvvvQfAggULcHV1pbS0FDMzM9zd3WVhfyXl7e1N165d2b9/v1LpydzcHJVKhZWVlUzFMJGZM2fi5+dHnz59WLlyJVlZWYSFheHg4MCdO3fIz8/HaDRiZWVFnTp1cHZ2plOnTnIX1wQyMzPJysoC4NKlS8TExDB69GiSk5MpKiqiffv2REZGUlRUxMCBA1m2bBleXl4mjlro9XpcXV0JCwvDzMwMGxsbfH19mTt3Lp999hn79+9n1KhRHDp0iNu3b6PVaklISJBrrBJYs2YNOp2OkJAQzMzMsLe3l7b7N8hIVzVx584dnJ2dlcfPb1j36NGjCju5v/XWW3zxxRfY29vLNIwqoHzx+POL+Murd5mZmVGnTp0Ki12F6RmNRurVq8fAgQPJy8tj165dtG3bFhcXFy5fvsy5c+cICgoydZjVTnBwMOnp6SxYsIDr168TGxtLamoqI0eOZMGCBahUKtLS0khMTKR79+5yM8OESktLcXR05I9//CMLFy7k0qVLbN26lS1btrB+/XqKioqYNWsW9erVIzExkb59++Lr62vqsKu9hQsXkpGRwePHj3nnnXeYN28e8+bNY9euXcTFxfH111+zaNEiWrVqRWpqKrdu3ap2ZcUrq4kTJ1JQUMD06dNp27YtT548Yf369ezYsUPa7gVJ9cJq4NNPP8XS0pL58+cDFROuoKAgdDoda9euNWGE4kU9ffq0QiL8fFvq9XrlrnyXLl1IT09n9+7d7Ny5k2bNmpkq5GovIiKCnj170qRJE1QqVYU227lzJ7Vr1+bKlSvs3LkTV1dXrKysmDt3Lq1btzZx5NVLTk4OQUFBLF68GEdHRwBSU1PZsmULubm5TJs2jaZNmxIaGsqtW7f45JNPeP/9900cdfW0atUqLCwsGDNmDFZWVty4cYOAgABq1arFzp07ycnJITo6muPHj3Pt2jV69erF8uXLJUk2MX9/f3JzcxkyZAg3b95kx44dhIWFcfHiRaKiooiKiqJz587k5OSQmJjI8ePHmTVrFk2aNDF16NXelClTyM3NZffu3cqxwsJCdu/ezYoVK4iIiKBr167Sdv+CJF3VwJAhQxg4cCCDBg2qcHzmzJlcvXqVhIQEmcpUBXz66ad4eHgwcOBAoGLCFRYWhrOzM97e3ixevBidToe1tTVTp06lZcuWpgy7WktMTGTBggUkJSX9qsMXFBTErVu3SEhIAJ5NldJqtRiNRrRarSnCrdby8/Pp06cPkyZNYsiQIcrxtLQ0VqxYgYODA1988QWPHj1ixYoV+Pn54eTkZMKIq685c+Zw4MABpk+fzujRo5WqktOnT8fCwoKEhARUKhXXrl3j2rVrtGzZEldXV1OHXa2dOHGCNWvWsHfvXgDy8vKYOHEio0aN4v3332ft2rVs2LCBtWvX0qNHD6XgkGzBYHrr1q1j/fr1XL58GYC4uDguX75MTk4Obdq0wdLSksWLF7Nq1Sp69uwpbfdPyG2f15hOp6O0tJTc3Fxq165d4bnjx4+TlpamJFyy2WDllp+fT3JyMm3atFGOlSdcs2bN4rvvvmPEiBE0btyYqKgoNm3axMqVKyXhMjG9Xo+NjQ16vZ7n728FBwdz8eJF5a5hWVkZjo6OWFtbS8JlIlqtlq5du/Ljjz8qe6YBtGjRggkTJrB//35+/vln7O3t+eKLLyThMoHya0iv1/Pee++xYsUKNm7cSGlpKa6urqxZs4aysjIGDBiA0WikefPm9OrVSxKuSiA3NxcrKyvg2dTQOnXqAJCVlYVGo+GTTz5h4sSJ+Pv7880336BWq6XTXgkYDAYaNWqEt7c3qampbNq0icjISOzs7KhXrx5r167l4cOHzJkzh4CAAI4dOyZt90/If5XXmLm5eYViGfC3zTs7d+6Ml5eXknDJBVK55eXlkZeXpyRa5f70pz+RnJzMt99+i1qtprS0FAsLC6VCnjC9Bg0aYG5uXmFksnv37ixatEiuPxNLSEggOzubtm3b0rx5c4YOHYq/v7+ywWfjxo2BZ+tc27Vrp0ztlWvLNMrXrqalpREWFkavXr0ICgpCpVIxYcIEXF1dWb16NePGjWP48OHs2rVLWfMqTMvV1ZWsrCzu3btH/fr1gWdJdPlnn7W1NT4+PjRq1EimpFUiarUab29vTp48yeLFi7G0tGTz5s3KjYy2bduyefNm1qxZg5+fHy4uLiaOuHKTb/rXUEREBDVq1GDcuHGo1Wqsra3Jy8ujrKyMkpIS5UOufEqhdPgqP41Gg62trbI5dXlHfcCAAQwbNkzpvJcn18J0fvrpJ9q0aYNGo+HWrVtKovV8wly+FkgSLtOZPHkymZmZ2Nvbc+zYMTQaDXPmzGHJkiUEBgZSUlJCt27dePfdd9m9ezd379791YwB8eqVlJQwZMgQ3NzccHNzw2AwEBISgtFo5JNPPqFp06ZER0cr15skXJWDu7s769atw8nJCYPBgE6n4+nTp8qG4tu2bWPx4sWcPHkSe3t7E0crnmdnZ8fUqVMZPnw47dq1w9HREZ1Oh7m5OQ4ODlhZWdGgQQP8/PxMHWqlJ9/2r5mcnBwePnzIpUuXsLKy4sMPPyQjI4OlS5eyfPlyAOrXr0+dOnWoWbMmXbp0oX///vLFVAnt378fV1dX3N3dKSoqUqZjwN8S5fIk7PljwnR27dpFfHw8Y8eOpXfv3spak9DQUBo3bkzt2rVp0qQJTk5OPH78WOlwiFdr3759PHjwgH379gFw+fJlEhISmDZtGmvWrCE8PJyIiAjmzp1Lo0aNyMrKIjw8HAcHBxNHLqytrRkwYADw7KaFr68vKpWK2bNn8/TpU/z9/ZURSlG5tGjRAng2ZS03N5cHDx7QrFkz9uzZw/r164mLi5OEq5Jq2rQp4eHhGAyGClPgb9y4gVqtliUqL0h6aa8ZOzs7JkyYQExMDAkJCTx48AArKyvCwsIoLS2loKCAe/fuceXKFQoKCmjTpo0kXJXQzz//TEREBF5eXlhbW1NcXMyVK1eYNm0aBoOB+vXr4+rqio2NDZaWlnTs2JGmTZuaOuxqr0ePHpw9e5b4+HgsLS0pKCiguLiYoqIi4uPjyc7OxszMjJycHOrWrctXX30lG+qaQEFBgdIx1+v1uLm5odVqUavVhIaGsmbNGpYsWcL9+/cpKSmhXr16FbbVEKZVPktDo9FgNBrp3bs3paWlLF68mLFjx8o1VcmZmZlhbm5OkyZNWLVqFYcOHWLr1q288cYbpg5N/BMeHh6UlJSwdu1aHjx4gKWlJYcPHyY6OlrWIr8gqV74mkhISODhw4f813/9F82aNcPKyorIyEiOHTvGkydPOH78+K9eI1ObKrejR48SHR3Nm2++iZOTE5s2bSIgIICrV69SVFTE7du3SU9PR6VSER0dLUmXCRUWFlJcXIyDgwP5+fnMnz+fvLw8MjMzef/995k2bRpFRUXodDry8/O5c+cOLi4usmHkK1ZWVoZOp+PSpUtMmzaNrVu3VhhtTEtLIzw8nN///veMHDnShJGKF1XehVGpVBQWFkrnr4rIysrC29sba2trdu7cKaP+VYROp+Pbb79l3759NG/enH79+smWNP8GSbpeA8+vTSgoKECj0TB9+nTefPNNVq9ezb59+/Dz82P48OGAJFuV2ffff09BQQEdO3bEwsKCkydPEh4eTmFhIS4uLkRFRSnnGgwGjEYjhYWFFaYeildr5syZZGVlkZqayqBBg+jTpw8NGzbk888/58iRI3z00UfMmTNHRpRNLDQ0lF9++QW9Xs+MGTOIiYnBYDAwZcoUWrRooRRcmD9/PpmZmURGRpo6ZPGCyttOimZUHWVlZSxbtoyPPvpIqkuKakN63lVc+RTC59cm7N27l9mzZxMWFsaMGTNQqVT8+c9/pqSkhLFjx0rCVUlNnTqVO3fuUFxcjFqtZsCAAYwYMYJZs2Yxf/58MjIyuHLlirJprl6vx9zcXBIuEwoODiY9PZ0FCxZw/fp1YmNjSU1NZeTIkSxYsACVSkVaWhqJiYl0795dqt6ZiL+/P1lZWUyYMIF69erh7u5Ofn4+R44cYdOmTYwbN065025ra4tarcZgMEh7VRHliZYkXFWHmZkZISEh0h8R1Yr8317FFRUVKeVVn1+boFKpWLZsGcuWLWP06NE8ffqUpKQkPvzwQ6nAVQmV73WxZ88eLCwsiI6O5tixY2RkZBAcHExoaChLlixh3759lJWV4e7uLhtam1hOTg6PHj0iPDwcR0dHWrVqRfPmzdmyZQt/+tOfqF27NnPnziU0NJSNGzcCf6taKF6dn3/+mfT0dOLi4rCwsGDv3r0kJCRQVFTEgwcPcHV1JTg4GC8vL9RqNQkJCWzbtk0SLiFeMkm4RHUj3ypV1KRJk/j6669xcXHhu+++4+rVq0qJXBcXFwYNGkTDhg05ffo09erVY8yYMaxYsUISrkrIaDTy4MED+vbti6WlJSqVirFjx9K/f3/S09PZsmULHh4eTJw4kaSkJA4dOkRpaampw672zMzMuHnzJseOHVOOtWzZkvHjx6NWq4mLi0Or1TJnzhxatmyJu7u7CaOtvvR6PXq9HgsLC7Zs2cLq1atxcHDA3t6ey5cv8/jxY6ZMmUJGRgYGg4EdO3bI+hIhhBD/cXKboQoKCQnh+PHjdOjQgSFDhtCpUyeioqLw8/OjWbNmGI1GWrRogb29Pd999x0jR46UBfuVmEqlQqvVcvLkSfr06aOUgR8wYABFRUXExMTQr18/unXrhkajoXnz5rIfVyWg1Wrp2rUrP/74I56ensq6hBYtWjBhwgQ+/vhjhg4dSps2bfjiiy9k5MREbG1tuXv3LjExMTx48IDo6Gilrdzd3Vm+fDkLFizggw8+kDYSQgjx0sg3TBUzefJksrKymDJlCqmpqdSsWZMPPviAoqIiNmzYQFpamjKv3dbWFicnJ/R6vYmjFr/l+dEqLy8vcnJy+OabbyguLlaOjxgxAo1Gw1dffQWAj4+PJNAmlJCQwIYNG0hOTiYnJ4ehQ4fy448/Ehsby61bt5Tz3nrrLdq1a0eNGjUApDNvQs7OzkyZMoW4uDjOnDlDrVq10Ol0ADRq1AhbW1tZvyWEEOKlk5GuKmTcuHE8efKEmJgYjhw5QlJSEvBsnYjBYODIkSMEBgby7rvvAihrE8qnHYrKY+HChdy/fx8LCwu8vb3p27cvFy5cYPPmzZiZmdGlSxesra2BZyMnDRs2NHHE4vkqoceOHUOj0TBnzhyWLFlCYGAgJSUldOvWjXfffZfdu3dz9+5dmc5bSQwYMIDbt28THx/P+fPn6dmzJ/Cs8JAkXEIIIV4FSbqqiIMHD1JaWkpMTAzwrCOek5PDuXPn8PT0pGfPnjRr1oyzZ89y5swZGjZsyI4dO5Qd4EXl4e/vz7179xg/fjwnTpwgPj6epKQkli9fjk6n48svv+Ty5ct4enqSnp5OUlISU6ZMMXXY1dq+fft+VSU0ISGBadOmsWbNGsLDw4mIiGDu3Lk0atSIrKwswsPDcXBwMHHkAsDe3p6AgABUKhWBgYFER0fj4OCgbEJuY2Nj6hCFEEK85mSfririyZMnyshHaWkphYWFjB8/nsGDBzN48GATRyde1I0bN/j000/58ssv0Wq1GAwGjh8/zsaNG6lfvz6rV68mISGBkydPcvv2bRwcHJgxYwZubm6mDr1a27ZtGxcuXGDlypXo9Xo0Gg3p6els376d8+fPs2bNGuzt7bl//z4lJSXUq1ePunXrmjps8XdKSko4f/48Z86cwcXFhd///ve4uLiYOiwhhBDVgIx0VRHlCZfRaMTCwgI7Ozt8fX3ZsGEDnTp1wtnZWTaGrALy8/PJyMhQ1pSo1Wq8vb3RaDRs2rSJqKgoJk2aRP/+/SkqKsLMzExZFyRMx9XVlfXr13P16lWlsl15ldAHDx5w/PhxRo4cKZt8VnKWlpZ06tSJTp06mToUIYQQ1YxMZK9ink+qfHx8aNGiBUlJSZSUlEjCVYmVlJQAzwosNGrUiIiICMoHmc3MzHjnnXfw9vbm9OnTlJaWolar0Wq1knCZ0PLly5k1axbz5s1DrVbzzjvvEBUVRVpaGkCFKqGnT582cbRCCCGEqMwk6arCGjduzBtvvEFkZCQXL140dTjiH1i1ahVbtmwhPz8fgP79+5OWlsbmzZsxGAwAaDQavL29yc3NJS8vz5ThCmDq1KmkpKTQqlUrTpw4gbm5Ob6+vsqI5NWrVytUCW3QoIHSlkIIIYQQf0+mF1ZR5VMJ/fz8SE9PlwX7ldjDhw85cOAA5ubmTJgwgd69e5ORkcGpU6coLCwkICAAgHPnzlGjRg1q1qxp4oirt0OHDpGVlcWePXsASExMJCYmBo1GQ1ZWFm5ubgQHB+Pl5YVarVaqhEoFPCGEEEL8I5J0VVEqlUopdbx06VJThyN+Q3lirNfree+991ixYgUlJSX4+fnxySefEBsby9dff80777xD+/btOX/+PBs2bECr1Zo69Grtl19+wdHREYDIyEh++uknvLy8KCgo4OjRozg6OjJp0iSOHj0qVUKFEEII8UKkeqEQL1FZWRmDBg0iLCyMmzdvEhQUhJ+fH35+fuh0OqUj7+TkhKurK87OzqYOudpLTk6muLiYLl26cPbsWezt7WnWrBnwbOuG6Ohodu/ejbm5uYxuCSGEEOKFyEiXEC9RSUkJQ4YMwc3NDTc3NwwGAyEhIQCMHz8eOzs7hg0bZuIoxfM6duwIPEuYO3ToAIBOp8Pc3BwbGxtq1KiB0WiUhEsIIYQQL0ySLiFeImtrawYMGAA868T7+vqiUqmYPXs2JSUlTJ06VSoUVlIajQaouEfe3bt3qVWrFnq93pShCSGEEKKKkaRLiJfM3NwceNaJNxqN9O7dm9LSUhYvXsyYMWMk6aqkVCoV2dnZjBkzBicnJ+rUqcPJkyf58ssvlSRMCCGEEOJFyJouIV6h8stNpVJRWFgoRTMqubKyMk6cOEFycjKOjo507dqVpk2bmjosIYQQQlQxknQJ8YqVVzUs/1cIIYQQQrzeJOkSQgghhBBCiJdIym8JIYQQQgghxEskSZcQQgghhBBCvESSdAkhhBBCCCHESyRJlxBCCCGEEEK8RJJ0CSGEEEIIIcRLJEmXEEIIIYQQQrxEZqYOQAghhPhXunTpwsOHDzEze/a1ZTQa0Wq1+Pr6EhISglr963uI9+7d4w9/+AOHDh3CycnpPxbL2bNnGTVqFFZWVsoxg8FArVq18PHx4bPPPsPS0vI/9vuEEEJUfZJ0CSGEqBI+//xzBgwYoDxOTU3l448/pmbNmvj7+//qfCcnJ3744YeXFs/z720wGLhw4QKTJ0/G3t6eGTNmvLTfK4QQouqR6YVCCCGqpJYtW+Lp6cnly5cBGDlyJLNnz8bHx4fOnTuTmppKy5YtuXv3LgB37txh0qRJtG/fno4dOzJ//nxKS0sBSE9PZ9KkSXTo0AEfHx9WrVqlPPci1Go17dq1o0OHDly5cgV4Nhq3ceNGfH198fDwwNPTk6CgIJ4+fQrA7NmzmTdvHpMmTeKtt96ia9eubNu2TXnP3NxcAgICaN++PV27dmX79u24ubkpf8//NWYhhBCvjiRdQgghqhydTsfZs2c5c+YMXl5eyvHTp08TExPDgQMHsLa2Vo6XlZUxbtw4HBwcSEpK4s9//jMXLlwgPDycoqIiPv74Y5o3b05SUhK7du3i9OnThIeHv3A8er2e5ORkTp8+jbe3NwBHjhxh27ZthIeHk5KSQkxMDKdOneLgwYPK6/bu3cvIkSM5d+4cEyZMYPHixWRmZgIQHBxMQUEB3377LXv27OHYsWPo9XqA/0jMQgghXh2ZXiiEEKJK+Pzzz1m4cKHyuH79+owZM4YRI0Yox7y9vXF0dAQgPz9fOf7999+TkZHBnDlzqFmzJtbW1qxbtw6DwcDx48cpLS0lMDAQlUpFgwYNmD59Ov7+/gQFBf3DeDw8PAAoLS1Fp9Ph6enJnDlzlCmQ3t7etGvXjvr165OTk0Nubi42NjZKUgXQoUMHJWkcOHAgoaGhpKenA3Dq1CmOHDmCjY0NAHPmzOEPf/gDwP86ZiGEEKYhSZcQQogqITQ0tMKart9Sr1693zz+8OFDbG1tqVmzpnKsUaNGAPzP//wPOTk5eHp6Ks8ZjUZ0Oh2PHj3C3t7+N98zJSUFgPv37zNz5kxKS0vp0aNHhfdYtWoVx44dw87OjtatW6PT6TAajco5Dg4Oys/m5ubAs/Vh9+/frxAjgLOzs/JzRkbG/ypmIYQQpiFJlxBCiNeGSqX6zeP169cnNzeX4uJiJfFKSUnh0qVL1K9fHxcXF44ePaqcX1hYyKNHj7Czs/uXv7NBgwZERkYycOBApkyZQnR0NBqNhuXLl3Pv3j3+8pe/oNVqAfD19X2hv6O82mJGRgZNmjRRfn7+7/m/xCyEEOLVkjVdQgghXntt27alcePGLFmyhOLiYrKzs1m0aBE5OTn4+Pjw5MkTNm/eTGlpKfn5+cyaNYuAgIB/mMT9Pa1Wy+rVq/n++++JjIwEniVBlpaWaDQaSkpK+PLLL0lLS0On0/3L96tXrx4+Pj4sW7aMvLw88vLyWLp0qfL8fyJmIYQQr44kXUIIIV575ubmREVFkZmZSefOnenbty+enp74+/uj1WrZunUrZ8+exdvbm27duqFWq5Xk6UW1bt2awMBA1q9fzw8//MCMGTN4+vQpnTp1okuXLly4cIG+ffuSlpb2Qu8XFhaGSqWic+fO9O/fHzc3N+Vv+U/FLIQQ4tVQGZ+fXC6EEEKISuG7776jffv21KhRA3i2L1m/fv24cOGCbL4shBBVjIx0CSGEEJXQkiVLiIyMpKysjMLCQiIjI+nUqZMkXEIIUQVJ0iWEEEJUQitWrODChQu8/fbbdOnSBY1GU2FdlxBCiKpDphcKIYQQQgghxEskI11CCCGEEEII8RJJ0iWEEEIIIYQQL5EkXUIIIYQQQgjxEknSJYQQQgghhBAvkSRdQgghhBBCCPESSdIlhBBCCCGEEC+RJF1CCCGEEEII8RJJ0iWEEEIIIYQQL5EkXUIIIYQQQgjxEv0/71BheM6DafAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# Set the style of seaborn\n",
-    "sns.set_style(\"darkgrid\")\n",
-    "\n",
-    "# Create a bar plot\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "sns.barplot(x=\"price_range\", y=\"total_sqft\", data=housing_data, errorbar=None, hue=\"price_range\", palette=\"crest\")\n",
-    "plt.title(\"Total Square Footage by Price Range.\")\n",
-    "plt.xlabel(\"Price Range\")\n",
-    "plt.ylabel(\"Total Square Footage\")\n",
-    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The bar plot illustrates the relationship between price range and total square footage. Each bar represents the total square footage of houses within different price range categories. \n",
-    "From the graph, it is evident that there is a positive association between house size and price. Specifically, larger houses, as indicated by higher total square footage, tend to command higher prices. \n",
-    "<br>This suggests that there is a tendency for bigger houses to have a higher price, indicating a positive correlation between the size of the property and its price.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Relationship between bedrooms, bathrooms, and house price**\n",
-    "\n",
-    "Created a scatter plot to visualize the relationship.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# plot for the relationship between bedrooms, bathrooms, and house price\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.scatter(housing_data['bedrooms'], housing_data['bathrooms'], c=housing_data['price'], cmap='winter', alpha=0.5)\n",
-    "plt.colorbar(label='House Price')\n",
-    "plt.xlabel('Number of Bedrooms')\n",
-    "plt.ylabel('Number of Bathrooms')\n",
-    "plt.title('Relationship between Bedrooms, Bathrooms, and House Price')\n",
-    "plt.grid(True)\n",
-    "plt.show();\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The scatter plot reveals a clear relationship between the number of bedrooms, bathrooms, and house prices. It indicates that houses with more bedrooms and bathrooms tend to command higher prices, reflecting buyer preferences for space and convenience. However, there's a diminishing return on the value added by additional bedrooms beyond a certain point. Understanding this relationship is crucial for both the real estate companies(sellers) and buyers in the real estate market, allowing them to make informed decisions based on their needs and market dynamics.\n",
-    "A house with a good balance of bedrooms and bathrooms tends to attract a wider range of potential buyers."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#House age and house price**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Line plot for house age vs. average house price\n",
-    "average_price_by_age = housing_data.groupby('house_age')['price'].mean()\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.plot(average_price_by_age.index, average_price_by_age.values, marker='o', linestyle='-')\n",
-    "plt.xlabel('House Age (Years)')\n",
-    "plt.ylabel('Average House Price')\n",
-    "plt.title('Average House Price by House Age.')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The scatter plot depicts the relationship between house age and prices; and reveals a lack of a clear linear trend. Instead, prices exhibit significant variation across different house ages. While the graph provides insights into how house prices fluctuate based on their age, no discernible pattern emerges. \n",
-    "In summary, the graph shows how house prices fluctuate based on their age, but no specific pattern emerges. This suggests that other factors beyond house age play a more influential role in determining house prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**#Prices of houses in relation to their respective Condition and Grade**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6), sharey=True)\n",
-    "\n",
-    "# Bar plot for condition vs. price\n",
-    "sns.barplot(x='condition', y='price', data=housing_data, ax=axes[0], palette='viridis')\n",
-    "axes[0].set_title('Condition vs. Price')\n",
-    "axes[0].set_xlabel('Condition')\n",
-    "axes[0].set_ylabel('Price')\n",
-    "axes[0].grid(True)\n",
-    "\n",
-    "# Bar plot for grade vs. price\n",
-    "sns.barplot(x='grade', y='price', data=housing_data, ax=axes[1], palette='viridis')\n",
-    "axes[1].set_title('Grade vs. Price')\n",
-    "axes[1].set_xlabel('Grade')\n",
-    "axes[1].grid(True)\n",
-    "\n",
-    "# Adjust layout\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Show plots\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "The visualization presents comparisons between house prices and their condition and grade ratings. On the left, the condition of houses, rated from 1 to 5, shows relatively consistent prices across different condition levels, with no significant price increase observed for better conditions. Conversely, on the right, the grade of houses, ranging from 1 to 11, demonstrates a clear positive correlation with prices, indicating that higher-grade properties command higher prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### c.) Multivariate Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **#Correlation matrix**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Exclude the 'id', date and 'price_range' column from the correlation matrix\n",
-    "correlation_matrix = housing_data.drop(columns=['id', 'price_range', 'date']).corr()\n",
-    "\n",
-    "# Set up the matplotlib figure\n",
-    "plt.figure(figsize=(20, 20))\n",
-    "\n",
-    "# Create a heatmap using seaborn\n",
-    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
-    "\n",
-    "# Add a title\n",
-    "plt.title('Correlation Matrix')\n",
-    "\n",
-    "# Show the plot\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Price has a moderate positive correlation with sqft living (0.70), grade (0.67), sqft above (0.61). This means that as the values of these features increase, the price of the house also tends to increase."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4.STATISTICAL ANALYSIS.\n",
-    "\n",
-    "Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:\n",
-    "\n",
-    "\n",
-    "1. Descriptive Statistics\n",
-    "2. Correlation matrix\n",
-    "3. Distribution Analysis\n",
-    "4. Inferential Statistics using Hypothesis Testing and Analysis of Variance\n",
-    "5. MultiColinierity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **a.) Descriptive Statistics**\n",
-    "\n",
-    "Initial insights into the central tendency, dispersion, and shape of the data distribution.\n",
-    "<br>Understanding the characteristics of the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>21142.000000</td>\n",
-       "      <td>2.114200e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.581107e+09</td>\n",
-       "      <td>2014-10-29 08:57:27.772206848</td>\n",
-       "      <td>5.405060e+05</td>\n",
-       "      <td>3.371157</td>\n",
-       "      <td>2.116096</td>\n",
-       "      <td>2080.942531</td>\n",
-       "      <td>1.508757e+04</td>\n",
-       "      <td>1.493615</td>\n",
-       "      <td>0.006716</td>\n",
-       "      <td>3.409848</td>\n",
-       "      <td>5.658310</td>\n",
-       "      <td>1789.104437</td>\n",
-       "      <td>291.838095</td>\n",
-       "      <td>1971.024359</td>\n",
-       "      <td>68.259720</td>\n",
-       "      <td>1987.302478</td>\n",
-       "      <td>12739.322202</td>\n",
-       "      <td>52.975641</td>\n",
-       "      <td>0.955681</td>\n",
-       "      <td>1.924945e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000102e+06</td>\n",
-       "      <td>2014-05-02 00:00:00</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>0.000000</td>\n",
-       "      <td>1.000000</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>399.000000</td>\n",
-       "      <td>651.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2.013000e+03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.123049e+09</td>\n",
-       "      <td>2014-07-22 00:00:00</td>\n",
-       "      <td>3.220000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.750000</td>\n",
-       "      <td>1430.000000</td>\n",
-       "      <td>5.043000e+03</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1200.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1952.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1490.000000</td>\n",
-       "      <td>5100.000000</td>\n",
-       "      <td>27.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>8.820000e+03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.904940e+09</td>\n",
-       "      <td>2014-10-16 00:00:00</td>\n",
-       "      <td>4.500000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.250000</td>\n",
-       "      <td>1910.000000</td>\n",
-       "      <td>7.620000e+03</td>\n",
-       "      <td>1.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>1560.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1975.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1840.000000</td>\n",
-       "      <td>7626.000000</td>\n",
-       "      <td>49.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.159350e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>7.309100e+09</td>\n",
-       "      <td>2015-02-18 00:00:00</td>\n",
-       "      <td>6.450000e+05</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>2550.000000</td>\n",
-       "      <td>1.069575e+04</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>2210.000000</td>\n",
-       "      <td>560.000000</td>\n",
-       "      <td>1997.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2360.000000</td>\n",
-       "      <td>10087.000000</td>\n",
-       "      <td>72.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.546000e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9.900000e+09</td>\n",
-       "      <td>2015-05-27 00:00:00</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>1.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>11.000000</td>\n",
-       "      <td>9410.000000</td>\n",
-       "      <td>4820.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>6210.000000</td>\n",
-       "      <td>871200.000000</td>\n",
-       "      <td>124.000000</td>\n",
-       "      <td>90.000000</td>\n",
-       "      <td>1.653959e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.876357e+09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.680831e+05</td>\n",
-       "      <td>0.902213</td>\n",
-       "      <td>0.768545</td>\n",
-       "      <td>918.563816</td>\n",
-       "      <td>4.121013e+04</td>\n",
-       "      <td>0.539252</td>\n",
-       "      <td>0.081680</td>\n",
-       "      <td>0.650422</td>\n",
-       "      <td>1.174272</td>\n",
-       "      <td>828.413341</td>\n",
-       "      <td>442.504364</td>\n",
-       "      <td>29.322166</td>\n",
-       "      <td>362.774103</td>\n",
-       "      <td>685.671655</td>\n",
-       "      <td>27169.859971</td>\n",
-       "      <td>29.322166</td>\n",
-       "      <td>5.824659</td>\n",
-       "      <td>4.156724e+04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 id                           date         price  \\\n",
-       "count  2.114200e+04                          21142  2.114200e+04   \n",
-       "mean   4.581107e+09  2014-10-29 08:57:27.772206848  5.405060e+05   \n",
-       "min    1.000102e+06            2014-05-02 00:00:00  7.800000e+04   \n",
-       "25%    2.123049e+09            2014-07-22 00:00:00  3.220000e+05   \n",
-       "50%    3.904940e+09            2014-10-16 00:00:00  4.500000e+05   \n",
-       "75%    7.309100e+09            2015-02-18 00:00:00  6.450000e+05   \n",
-       "max    9.900000e+09            2015-05-27 00:00:00  7.700000e+06   \n",
-       "std    2.876357e+09                            NaN  3.680831e+05   \n",
-       "\n",
-       "           bedrooms     bathrooms   sqft_living      sqft_lot        floors  \\\n",
-       "count  21142.000000  21142.000000  21142.000000  2.114200e+04  21142.000000   \n",
-       "mean       3.371157      2.116096   2080.942531  1.508757e+04      1.493615   \n",
-       "min        1.000000      0.500000    370.000000  5.200000e+02      1.000000   \n",
-       "25%        3.000000      1.750000   1430.000000  5.043000e+03      1.000000   \n",
-       "50%        3.000000      2.250000   1910.000000  7.620000e+03      1.500000   \n",
-       "75%        4.000000      2.500000   2550.000000  1.069575e+04      2.000000   \n",
-       "max       11.000000      8.000000  13540.000000  1.651359e+06      3.500000   \n",
-       "std        0.902213      0.768545    918.563816  4.121013e+04      0.539252   \n",
-       "\n",
-       "         waterfront     condition         grade    sqft_above  sqft_basement  \\\n",
-       "count  21142.000000  21142.000000  21142.000000  21142.000000   21142.000000   \n",
-       "mean       0.006716      3.409848      5.658310   1789.104437     291.838095   \n",
-       "min        0.000000      1.000000      1.000000    370.000000       0.000000   \n",
-       "25%        0.000000      3.000000      5.000000   1200.000000       0.000000   \n",
-       "50%        0.000000      3.000000      5.000000   1560.000000       0.000000   \n",
-       "75%        0.000000      4.000000      6.000000   2210.000000     560.000000   \n",
-       "max        1.000000      5.000000     11.000000   9410.000000    4820.000000   \n",
-       "std        0.081680      0.650422      1.174272    828.413341     442.504364   \n",
-       "\n",
-       "           yr_built  yr_renovated  sqft_living15     sqft_lot15     house_age  \\\n",
-       "count  21142.000000  21142.000000   21142.000000   21142.000000  21142.000000   \n",
-       "mean    1971.024359     68.259720    1987.302478   12739.322202     52.975641   \n",
-       "min     1900.000000      0.000000     399.000000     651.000000      9.000000   \n",
-       "25%     1952.000000      0.000000    1490.000000    5100.000000     27.000000   \n",
-       "50%     1975.000000      0.000000    1840.000000    7626.000000     49.000000   \n",
-       "75%     1997.000000      0.000000    2360.000000   10087.000000     72.000000   \n",
-       "max     2015.000000   2015.000000    6210.000000  871200.000000    124.000000   \n",
-       "std       29.322166    362.774103     685.671655   27169.859971     29.322166   \n",
-       "\n",
-       "       renovation_age    total_sqft  \n",
-       "count    21142.000000  2.114200e+04  \n",
-       "mean         0.955681  1.924945e+04  \n",
-       "min          0.000000  2.013000e+03  \n",
-       "25%          0.000000  8.820000e+03  \n",
-       "50%          0.000000  1.159350e+04  \n",
-       "75%          0.000000  1.546000e+04  \n",
-       "max         90.000000  1.653959e+06  \n",
-       "std          5.824659  4.156724e+04  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "housing_data.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Price Distribution:**\n",
-    "\n",
-    "The price of houses in the dataset varies widely, ranging from $78,000 to $7,700,000, with an average price of $540,506. The standard deviation of $368,083.1 indicates a significant dispersion around the mean, suggesting a diverse range of housing prices.\n",
-    "\n",
-    "**Bedrooms and Bathrooms:** \n",
-    "\n",
-    "The average number of bedrooms is approximately 3.37, while the average number of bathrooms is about 2.12. The standard deviations for both variables are relatively small, indicating less variability compared to other features.\n",
-    "\n",
-    "**Square Footage:**\n",
-    "\n",
-    " The average square footage of living space is around 2,080, with a standard deviation of 918.56. Similarly, the average lot size is approximately 15,087 square feet, with a larger standard deviation of 41,210.13, suggesting more variability in lot sizes compared to living space.\n",
-    "\n",
-    "**Floors:**\n",
-    "\n",
-    "On average, houses have 1.49 floors, with a standard deviation of 0.54. This indicates some variability in the number of floors, although most houses seem to have either one or two floors.\n",
-    "\n",
-    "**Waterfront Property:**\n",
-    "\n",
-    " Only a small percentage (0.7%) of the houses are waterfront properties, based on the average value. This feature is likely represented as a binary variable (0 for no waterfront, 1 for waterfront), with most houses being non-waterfront properties.\n",
-    "\n",
-    "**Condition and Grade:**\n",
-    "\n",
-    "The average condition of houses is approximately 3.41, with a standard deviation of 0.65, suggesting some variability in the condition ratings. Similarly, the average grade is around 5.66, with a standard deviation of 1.17, indicating variations in the overall quality of houses.\n",
-    "\n",
-    "**Year Built and Year Renovated:**\n",
-    "\n",
-    "The houses in the dataset span a wide range of construction years, from 1900 to 2015, with an average year of construction around 1971. The standard deviation of 29.32 indicates some variability in the construction years. Additionally, the average year of renovation is approximately 68.26, with a standard deviation of 443.5, suggesting that most houses have not been renovated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **b.) Correlation Analysis with House Prices**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Correlation analysis was performed to examine the relationship between various features and the target variable, 'price'. \n",
-    "<BR>Here are the correlation coefficients between each feature and the price:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "price             1.000000\n",
-       "bedrooms          0.316573\n",
-       "bathrooms         0.525899\n",
-       "sqft_living       0.702340\n",
-       "sqft_lot          0.087940\n",
-       "floors            0.256372\n",
-       "waterfront        0.265970\n",
-       "condition         0.035264\n",
-       "grade             0.667751\n",
-       "sqft_above        0.605167\n",
-       "sqft_basement     0.325003\n",
-       "yr_built          0.054471\n",
-       "yr_renovated      0.116721\n",
-       "sqft_living15     0.586441\n",
-       "sqft_lot15        0.083196\n",
-       "house_age        -0.054471\n",
-       "renovation_age    0.082356\n",
-       "total_sqft        0.118225\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drop_var = ['id', 'price_range', 'date']\n",
-    "\n",
-    "correlation = housing_data.drop(drop_var, axis=1).corrwith(housing_data['price'])\n",
-    "correlation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These correlation coefficients indicate the strength and direction of the linear relationship between each feature and the price of the houses.\n",
-    "<br> Features with higher positive correlation coefficients, such as 'sqft_living', 'grade', 'bathrooms', and 'sqft_above', have a stronger positive linear relationship with the price, indicating that as these feature values increase, the price tends to increase as well. Conversely, features with low or negative correlation coefficients, such as 'condition', 'yr_built', 'sqft_lot', and 'house_age', have weaker or negative linear relationships with the price.\n",
-    "<br>However, it's imperative to underscore that correlation does not imply causation. There could be an underlying third factor driving changes in both features, underscoring the need for thorough investigation beyond correlation analysis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **c.) Distribution Analysis**\n",
-    "\n",
-    "Distribution analysis involves understanding the distribution of data, such as whether it follows a normal distribution and skewed distribution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "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>condition</th>\n",
-       "      <th>...</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "      <th>price_range</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7129300520</td>\n",
-       "      <td>2014-10-13</td>\n",
-       "      <td>12.309987</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>7.074117</td>\n",
-       "      <td>8.639588</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.074117</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.201171</td>\n",
-       "      <td>8.639588</td>\n",
-       "      <td>69</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>8.988571</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6414100192</td>\n",
-       "      <td>2014-12-09</td>\n",
-       "      <td>13.195616</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>1.178655</td>\n",
-       "      <td>7.852050</td>\n",
-       "      <td>8.887791</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.682943</td>\n",
-       "      <td>5.993961</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>7.596894</td>\n",
-       "      <td>7.433075</td>\n",
-       "      <td>8.941153</td>\n",
-       "      <td>73</td>\n",
-       "      <td>3.526361</td>\n",
-       "      <td>9.424080</td>\n",
-       "      <td>300K-600K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5631500400</td>\n",
-       "      <td>2015-02-25</td>\n",
-       "      <td>12.100718</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>6.647688</td>\n",
-       "      <td>9.210440</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.647688</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.908755</td>\n",
-       "      <td>8.995041</td>\n",
-       "      <td>91</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.353661</td>\n",
-       "      <td>100K-300K</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2487200875</td>\n",
-       "      <td>2014-12-09</td>\n",
-       "      <td>13.311331</td>\n",
-       "      <td>1.609438</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>7.581210</td>\n",
-       "      <td>8.517393</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.791759</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.957497</td>\n",
-       "      <td>6.814543</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.215975</td>\n",
-       "      <td>8.517393</td>\n",
-       "      <td>59</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.096163</td>\n",
-       "      <td>600K-1M</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1954400510</td>\n",
-       "      <td>2015-02-18</td>\n",
-       "      <td>13.142168</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>1.098612</td>\n",
-       "      <td>7.427144</td>\n",
-       "      <td>8.997271</td>\n",
-       "      <td>0.693147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.386294</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.427144</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.496097</td>\n",
-       "      <td>8.923191</td>\n",
-       "      <td>37</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9.344959</td>\n",
-       "      <td>300K-600K</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 2014-10-13  12.309987  1.386294   0.693147     7.074117   \n",
-       "1  6414100192 2014-12-09  13.195616  1.386294   1.178655     7.852050   \n",
-       "2  5631500400 2015-02-25  12.100718  1.098612   0.693147     6.647688   \n",
-       "3  2487200875 2014-12-09  13.311331  1.609438   1.386294     7.581210   \n",
-       "4  1954400510 2015-02-18  13.142168  1.386294   1.098612     7.427144   \n",
-       "\n",
-       "   sqft_lot    floors  waterfront  condition  ...  sqft_above  sqft_basement  \\\n",
-       "0  8.639588  0.693147         0.0   1.386294  ...    7.074117       0.000000   \n",
-       "1  8.887791  1.098612         0.0   1.386294  ...    7.682943       5.993961   \n",
-       "2  9.210440  0.693147         0.0   1.386294  ...    6.647688       0.000000   \n",
-       "3  8.517393  0.693147         0.0   1.791759  ...    6.957497       6.814543   \n",
-       "4  8.997271  0.693147         0.0   1.386294  ...    7.427144       0.000000   \n",
-       "\n",
-       "   yr_built  yr_renovated  sqft_living15  sqft_lot15  house_age  \\\n",
-       "0      1955      0.000000       7.201171    8.639588         69   \n",
-       "1      1951      7.596894       7.433075    8.941153         73   \n",
-       "2      1933      0.000000       7.908755    8.995041         91   \n",
-       "3      1965      0.000000       7.215975    8.517393         59   \n",
-       "4      1987      0.000000       7.496097    8.923191         37   \n",
-       "\n",
-       "   renovation_age  total_sqft  price_range  \n",
-       "0        0.000000    8.988571    100K-300K  \n",
-       "1        3.526361    9.424080    300K-600K  \n",
-       "2        0.000000    9.353661    100K-300K  \n",
-       "3        0.000000    9.096163      600K-1M  \n",
-       "4        0.000000    9.344959    300K-600K  \n",
-       "\n",
-       "[5 rows x 21 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scipy.stats import skew\n",
-    "\n",
-    "# Select numerical variables only\n",
-    "numerical_data = housing_data.select_dtypes(include=['number'])\n",
-    "\n",
-    "# Compute skewness for each numerical variable\n",
-    "skewness = numerical_data.apply(lambda x: skew(x.dropna()))\n",
-    "\n",
-    "# Select variables with skewness above a certain threshold (e.g., 0.5)\n",
-    "skewed_variables = skewness[abs(skewness) > 0.5].index\n",
-    "\n",
-    "# Log transformation for skewed variables\n",
-    "df_log = housing_data.copy()  # Create a copy of the original DataFrame to preserve the original data\n",
-    "df_log[skewed_variables] = housing_data[skewed_variables].apply(lambda x: np.log1p(x))\n",
-    "\n",
-    "# Check the distributions before and after transformation if needed\n",
-    "# For example, you can use histograms or density plots to visualize the distributions\n",
-    "\n",
-    "# Print the first few rows of the transformed data to verify\n",
-    "df_log.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n",
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
-      "  with pd.option_context('mode.use_inf_as_na', True):\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x2800 with 19 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Selecting numerical columns\n",
-    "numerical_columns = housing_data.select_dtypes(include=[np.number]).columns\n",
-    "\n",
-    "# Calculate the number of rows and columns for subplots\n",
-    "num_cols = len(numerical_columns)\n",
-    "num_rows = (num_cols + 2) // 3  # Calculate the number of rows needed, rounding up\n",
-    "\n",
-    "# Plot histograms for numerical variables\n",
-    "plt.figure(figsize=(12, num_rows * 4))  # Adjust the height based on the number of rows\n",
-    "for i, col in enumerate(numerical_columns, 1):\n",
-    "    plt.subplot(num_rows, 3, i)\n",
-    "    sns.histplot(housing_data[col], kde=True, edgecolor='black')\n",
-    "    plt.title(col)\n",
-    "    plt.xlabel('')\n",
-    "    plt.ylabel('Frequency')\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Conclusion**\n",
-    "\n",
-    "In conclusion, the log transformation has effectively addressed skewness in the distribution of numerical variables within the dataset. Prior to transformation, variables such as house price, bedrooms, bathrooms, and various square footage measurements exhibited skewed distributions with long tails. However, after applying the log transformation, these distributions appear to be more symmetric and closer to a normal distribution. This transformation has enhanced the suitability of the data for statistical analysis by reducing skewness and improving the interpretability of the variables. It's important to acknowledge that while the log transformation has provided valuable improvements, it alters the scale and interpretation of the variables, necessitating careful consideration in subsequent analyses. Overall, the transformed variables are now better suited for further statistical modeling and analysis in the context of predicting property values and understanding real estate market trends."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **d.) Inferential Statistics.**\n",
-    "\n",
-    "*We used one-way ANOVA approach*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1. \u001b[1mbedrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "2. \u001b[1mbathrooms\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "3. \u001b[1msqft_living\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "4. \u001b[1msqft_lot\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
-      "5. \u001b[1mfloors\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "6. \u001b[1mwaterfront\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "7. \u001b[1mcondition\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "8. \u001b[1mgrade\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "9. \u001b[1msqft_above\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "10. \u001b[1msqft_basement\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "11. \u001b[1myr_built\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "12. \u001b[1myr_renovated\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "13. \u001b[1msqft_living15\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "14. \u001b[1msqft_lot15\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n",
-      "15. \u001b[1mhouse_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "16. \u001b[1mrenovation_age\u001b[0m: Reject the null hypothesis. There is a statistically significant relationship.\n",
-      "17. \u001b[1mtotal_sqft\u001b[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\n"
-     ]
-    },
-    {
-     "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>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>condition</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>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
-       "      <th>house_age</th>\n",
-       "      <th>renovation_age</th>\n",
-       "      <th>total_sqft</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>F-statistic</th>\n",
-       "      <td>1.841471e+00</td>\n",
-       "      <td>3.728667</td>\n",
-       "      <td>7.702589</td>\n",
-       "      <td>0.732713</td>\n",
-       "      <td>1.662592e+00</td>\n",
-       "      <td>1.787176e+00</td>\n",
-       "      <td>1.051111</td>\n",
-       "      <td>7.29306</td>\n",
-       "      <td>5.098114</td>\n",
-       "      <td>1.750817e+00</td>\n",
-       "      <td>1.236748e+00</td>\n",
-       "      <td>1.046716</td>\n",
-       "      <td>5.209978</td>\n",
-       "      <td>0.709841</td>\n",
-       "      <td>1.236748e+00</td>\n",
-       "      <td>1.171737e+00</td>\n",
-       "      <td>0.775778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>P-value</th>\n",
-       "      <td>1.765957e-139</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.808184e-95</td>\n",
-       "      <td>1.258742e-125</td>\n",
-       "      <td>0.026289</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.406487e-116</td>\n",
-       "      <td>2.417229e-17</td>\n",
-       "      <td>0.037943</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.417229e-17</td>\n",
-       "      <td>2.430304e-10</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  bedrooms  bathrooms  sqft_living  sqft_lot        floors  \\\n",
-       "F-statistic   1.841471e+00   3.728667     7.702589  0.732713  1.662592e+00   \n",
-       "P-value      1.765957e-139   0.000000     0.000000  1.000000  1.808184e-95   \n",
-       "\n",
-       "                waterfront  condition    grade  sqft_above  sqft_basement  \\\n",
-       "F-statistic   1.787176e+00   1.051111  7.29306    5.098114   1.750817e+00   \n",
-       "P-value      1.258742e-125   0.026289  0.00000    0.000000  1.406487e-116   \n",
-       "\n",
-       "                 yr_built  yr_renovated  sqft_living15  sqft_lot15  \\\n",
-       "F-statistic  1.236748e+00      1.046716       5.209978    0.709841   \n",
-       "P-value      2.417229e-17      0.037943       0.000000    1.000000   \n",
-       "\n",
-       "                house_age  renovation_age  total_sqft  \n",
-       "F-statistic  1.236748e+00    1.171737e+00    0.775778  \n",
-       "P-value      2.417229e-17    2.430304e-10    1.000000  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\n",
-    "from scipy.stats import f_oneway\n",
-    "\n",
-    "# List of features of interest\n",
-    "features_of_interest = ['bedrooms', 'bathrooms', 'sqft_living',\n",
-    "                        'sqft_lot', 'floors', 'waterfront', 'condition', \n",
-    "                        'grade', 'sqft_above', 'sqft_basement', 'yr_built', \n",
-    "                        'yr_renovated', 'sqft_living15', 'sqft_lot15', \n",
-    "                        'house_age', 'renovation_age', 'total_sqft']\n",
-    "\n",
-    "# Create an empty DataFrame to store ANOVA results\n",
-    "anova_results = pd.DataFrame(index=['F-statistic', 'P-value'])\n",
-    "\n",
-    "# Perform ANOVA for each feature\n",
-    "significant_features = []\n",
-    "\n",
-    "for i, column in enumerate(features_of_interest, 1):\n",
-    "    groups = [housing_data[column][housing_data['price'] == category]\n",
-    "              for category in housing_data['price'].unique()]\n",
-    "\n",
-    "    # Perform ANOVA\n",
-    "    f_statistic, p_value = f_oneway(*groups)\n",
-    "\n",
-    "    # Store results in the DataFrame\n",
-    "    anova_results[column] = [f_statistic, p_value]\n",
-    "\n",
-    "    # Print interpretation\n",
-    "    if p_value < 0.05:\n",
-    "        significant_features.append(column)\n",
-    "        print(f\"{i}. \\033[1m{column}\\033[0m: Reject the null hypothesis. There is a statistically significant relationship.\")\n",
-    "    else:\n",
-    "        print(f\"{i}. \\033[1m{column}\\033[0m: Fail to reject the null hypothesis. There is no statistically significant relationship.\")\n",
-    "\n",
-    "# Display ANOVA results\n",
-    "anova_results\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Conclusion**\n",
-    "\n",
-    "The features listed under \"Reject the Null Hypothesis\" have a statistically significant relationship with housing prices.\n",
-    "\n",
-    "These features are important predictors of housing prices in the given dataset.\n",
-    "\n",
-    "On the other hand, features listed under \"Fail to Reject the Null Hypothesis\" do not show a statistically significant relationship with housing prices based on the ANOVA test."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### **e.) Multicollinearity.**\n",
-    "\n",
-    "**Assessing Multicollinearity with Variance Inflation Factor (VIF)**\n",
-    "<br>In this analysis, we utilize the Variance Inflation Factor (VIF) to investigate multicollinearity among predictor variables in our regression model. Multicollinearity occurs when predictor variables are highly correlated with each other, which can lead to unreliable coefficient estimates. By computing the VIF for each predictor variable, we identify potential multicollinearity issues among property characteristics. \n",
-    "<br>High VIF values indicate a strong correlation between a predictor variable and the other variables in the model. Hence, it's crucial to examine the VIF values to ensure the reliability of our regression analysis and to address any multicollinearity detected.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:198: RuntimeWarning: divide by zero encountered in scalar divide\n",
-      "  vif = 1. / (1. - r_squared_i)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1mVariance Inflation Factor (VIF):\u001b[0m\n"
-     ]
-    },
-    {
-     "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>Feature</th>\n",
-       "      <th>VIF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>bedrooms</td>\n",
-       "      <td>1.688154</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>bathrooms</td>\n",
-       "      <td>3.366371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>sqft_living</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sqft_lot</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>floors</td>\n",
-       "      <td>1.934470</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>waterfront</td>\n",
-       "      <td>1.028593</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>condition</td>\n",
-       "      <td>1.218261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>grade</td>\n",
-       "      <td>3.235515</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>sqft_above</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>sqft_basement</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>yr_built</td>\n",
-       "      <td>94.556823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>yr_renovated</td>\n",
-       "      <td>4.226966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>sqft_living15</td>\n",
-       "      <td>2.763013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>sqft_lot15</td>\n",
-       "      <td>2.130997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>house_age</td>\n",
-       "      <td>7.743224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>renovation_age</td>\n",
-       "      <td>4.109794</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>total_sqft</td>\n",
-       "      <td>inf</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Feature        VIF\n",
-       "0         bedrooms   1.688154\n",
-       "1        bathrooms   3.366371\n",
-       "2      sqft_living        inf\n",
-       "3         sqft_lot        inf\n",
-       "4           floors   1.934470\n",
-       "5       waterfront   1.028593\n",
-       "6        condition   1.218261\n",
-       "7            grade   3.235515\n",
-       "8       sqft_above        inf\n",
-       "9    sqft_basement        inf\n",
-       "10        yr_built  94.556823\n",
-       "11    yr_renovated   4.226966\n",
-       "12   sqft_living15   2.763013\n",
-       "13      sqft_lot15   2.130997\n",
-       "14       house_age   7.743224\n",
-       "15  renovation_age   4.109794\n",
-       "16      total_sqft        inf"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
-    "\n",
-    "# Compute Variance Inflation Factor (VIF) to detect multicollinearity\n",
-    "\n",
-    "X = housing_data[['bedrooms', 'bathrooms', 'sqft_living','sqft_lot', 'floors', 'waterfront', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15', 'house_age', 'renovation_age', 'total_sqft']]\n",
-    "# Calculate the correlation matrix\n",
-    "correlation_matrix = X.corr()\n",
-    "\n",
-    "# Calculate VIF for each feature\n",
-    "vif_data = pd.DataFrame()\n",
-    "vif_data[\"Feature\"] = X.columns\n",
-    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(len(X.columns))]\n",
-    "\n",
-    "# Print VIF for each feature\n",
-    "print(\"\\n\\033[1mVariance Inflation Factor (VIF):\\033[0m\")\n",
-    "vif_data\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Variance Inflation Factor (VIF) Analysis**\n",
-    "<br>The Variance Inflation Factor (VIF) was calculated to assess multicollinearity among the features in the dataset. A VIF value greater than 5 is typically considered indicative of multicollinearity. The results revealed that most features exhibited low levels of multicollinearity, with VIF values below 5. \n",
-    "<br>However, 'yr_built' displayed a remarkably high VIF of 94.56, indicating strong multicollinearity with other features. Additionally, 'sqft_living', 'sqft_lot', 'sqft_above', 'sqft_basement', and 'total_sqft' exhibited infinite VIF values, suggesting perfect multicollinearity. \n",
-    "<br>Addressing multicollinearity in this case may require further investigation, such as feature selection, dimensionality reduction techniques or applying regularization methods to mitigate multicollinearity effects and improve model performance. \n",
-    "<br>Overall, understanding the VIF values can help refine the regression model and ensure the reliability of the coefficient estimates.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MODELLING."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. Baseline model - simple linear model.\n",
-    "2. log transformation. \n",
-    "3. Multiple Linear Regression\n",
-    "4. Residual modelling.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Baseline model \n",
-    "\n",
-    " Baseline models provide a reference point for comparing the performance of more complex models. \n",
-    " Its purpose is to establish a benchmark against which the performance of more sophisticated models can be evaluated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "FUNCTIONS TO BE USED."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#use only numeric columns.\n",
-    "def numeric_col(housing_data):\n",
-    "    '''returns a dataframe with only numeric values'''\n",
-    "    for column in housing_data.columns:\n",
-    "        if is_numeric_dtype(housing_data[column]) == False:\n",
-    "            housing_data = housing_data.drop(column, axis=1)\n",
-    "        else:\n",
-    "            continue\n",
-    "    return housing_data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Set a function for the predictor and target variale.\n",
-    "def X_Y(housing_data, target):\n",
-    "    '''Returns a series of target (y) values and a DataFrame of predictors (X)'''\n",
-    "    y = housing_data[target]  # target variable\n",
-    "    X = housing_data.drop(target, axis=1)  # predictor features\n",
-    "    return y, X\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "#A higher train score indicates that the model fits the training data well.\n",
-    "#A high test score suggests that the model is able to make accurate predictions on data it hasn't seen before, which is the ultimate goal in machine learning.\n",
-    "\n",
-    "def get_metrics(X_train, X_test, y_train, y_test):\n",
-    "    ''' Parameters are X train, X test, y train, & y_test\n",
-    "        Performs multiple regression on the split test and returns metrics'''\n",
-    "\n",
-    "    # Initialize Linear Regression model\n",
-    "    lr = LinearRegression()\n",
-    "\n",
-    "    lr.fit(X_train, y_train)\n",
-    "\n",
-    "    train_score = lr.score(X_train, y_train)\n",
-    "    test_score = lr.score(X_test, y_test)\n",
-    "\n",
-    "    y_hat_train = lr.predict(X_train)\n",
-    "    y_hat_test = lr.predict(X_test)\n",
-    "\n",
-    "    train_rmse = np.sqrt(mean_squared_error(y_train, y_hat_train))\n",
-    "    test_rmse = np.sqrt(mean_squared_error(y_test, y_hat_test))\n",
-    "\n",
-    "    return train_score, test_score, train_rmse, test_rmse\n",
-    "#These scores provide insights into how well the model is performing both on the data it was trained on and on new data.\n",
-    "# They help assess the model's overall effectiveness and whether it is overfitting or underfitting.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_test(housing_data, size=0.20):\n",
-    "    '''Takes in dataframe, and size of test for the split\n",
-    "        Returns the train_set and test_set'''\n",
-    "    train_set, test_set = train_test_split(housing_data, test_size=size, random_state=42)\n",
-    "    return train_set, test_set\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SIMPLE LINEAR REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def simple_linear_regression(housing_data):\n",
-    "    '''Creates a simple linear regression model with prices as the target variable \n",
-    "    and the number of bedrooms as the predictor. Returns the model along with R-squared, \n",
-    "    Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for both train and test sets.'''\n",
-    "    \n",
-    "    # Extracting features and target variable\n",
-    "    X = housing_data[['sqft_living']]  # Predictor feature\n",
-    "    y = housing_data['price']  # Target variable (prices)\n",
-    "    \n",
-    "    # Splitting the data into train and test sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "    \n",
-    "    # Create a linear regression model\n",
-    "    model = LinearRegression()\n",
-    "    \n",
-    "    # Fit the model to the training data\n",
-    "    model.fit(X_train, y_train)\n",
-    "    \n",
-    "    # Calculate predictions for train and test sets\n",
-    "    y_train_pred = model.predict(X_train)\n",
-    "    y_test_pred = model.predict(X_test)\n",
-    "    \n",
-    "    # Calculate R-squared for train and test sets\n",
-    "    r2_train = r2_score(y_train, y_train_pred)\n",
-    "    r2_test = r2_score(y_test, y_test_pred)\n",
-    "    \n",
-    "    # Calculate Mean Squared Error (MSE) for train and test sets\n",
-    "    mse_train = mean_squared_error(y_train, y_train_pred)\n",
-    "    mse_test = mean_squared_error(y_test, y_test_pred)\n",
-    "    \n",
-    "    # Calculate Root Mean Squared Error (RMSE) for train and test sets\n",
-    "    rmse_train = np.sqrt(mse_train)\n",
-    "    rmse_test = np.sqrt(mse_test)\n",
-    "    \n",
-    "    # Print coefficients, R-squared, MSE, and RMSE for train and test sets\n",
-    "    print(\"Training set:\")\n",
-    "    print(\"Intercept:\", model.intercept_)\n",
-    "    print(\"Coefficient:\", model.coef_[0])\n",
-    "    print(\"R-squared:\", r2_train)\n",
-    "    print(\"Mean Squared Error:\", mse_train)\n",
-    "    print(\"Root Mean Squared Error:\", rmse_train)\n",
-    "    \n",
-    "    print(\"\\nTest set:\")\n",
-    "    print(\"R-squared:\", r2_test)\n",
-    "    print(\"Mean Squared Error:\", mse_test)\n",
-    "    print(\"Root Mean Squared Error:\", rmse_test)\n",
-    "    \n",
-    "    return model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test\n",
-    "\n",
-    "\n",
-    "# Example usage:\n",
-    "# Assuming housing_data is your dataset\n",
-    "# model, r2_train, mse_train, rmse_train, r2_test, mse_test, rmse_test = simple_linear_regression(housing_data)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set:\n",
-      "Intercept: -44593.95245340909\n",
-      "Coefficient: 281.4088930446383\n",
-      "R-squared: 0.49587806055811734\n",
-      "Mean Squared Error: 68601152192.940285\n",
-      "Root Mean Squared Error: 261918.21661148407\n",
-      "\n",
-      "Test set:\n",
-      "R-squared: 0.48264829402430887\n",
-      "Mean Squared Error: 68845100756.10751\n",
-      "Root Mean Squared Error: 262383.4993975565\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(LinearRegression(),\n",
-       " 0.49587806055811734,\n",
-       " 68601152192.940285,\n",
-       " 261918.21661148407,\n",
-       " 0.48264829402430887,\n",
-       " 68845100756.10751,\n",
-       " 262383.4993975565)"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simple_linear_regression(housing_data)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.\n",
-    "\n",
-    "\n",
-    "Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.\n",
-    "\n",
-    "\n",
-    "R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.\n",
-    "\n",
-    "\n",
-    "Test Set:\n",
-    "\n",
-    "R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R-squared: 0.48264829402430875\n",
-      "Mean Squared Error: 68845100756.10753\n",
-      "Root Mean Squared Error: 262383.4993975565\n",
-      "Intercept: 540631.1560929463\n",
-      "Coefficients: [259767.82181675]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#STANDARDIZE OUR MODEL TO GET RID OF THE NEGATIVE INTERCEPT.\n",
-    "\n",
-    "\n",
-    "# Assuming you have your data loaded into X and y\n",
-    "# X should be your independent variables and y should be your dependent variable\n",
-    "\n",
-    "# Split data into training and test sets\n",
-    "X = housing_data[['sqft_living']]  # Predictor feature\n",
-    "y = housing_data['price']  # Target variable (prices)\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Initialize the StandardScaler\n",
-    "scaler = StandardScaler()\n",
-    "\n",
-    "# Fit and transform the scaler on the training data\n",
-    "X_train_scaled = scaler.fit_transform(X_train)\n",
-    "\n",
-    "# Transform the test data using the scaler fitted on the training data\n",
-    "X_test_scaled = scaler.transform(X_test)\n",
-    "\n",
-    "# Fit the linear regression model on the scaled training data\n",
-    "model = LinearRegression()\n",
-    "model.fit(X_train_scaled, y_train)\n",
-    "\n",
-    "# Predict on the scaled test data\n",
-    "y_pred = model.predict(X_test_scaled)\n",
-    "\n",
-    "# Calculate R-squared and mean squared error on the test set\n",
-    "r_squared = r2_score(y_test, y_pred)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "rmse = np.sqrt(mse)\n",
-    "\n",
-    "# Print the metrics\n",
-    "print(\"R-squared:\", r_squared)\n",
-    "print(\"Mean Squared Error:\", mse)\n",
-    "print(\"Root Mean Squared Error:\", rmse)\n",
-    "\n",
-    "# Print the intercept and coefficients of the model\n",
-    "print(\"Intercept:\", model.intercept_)\n",
-    "print(\"Coefficients:\", model.coef_)\n",
-    "\n",
-    "#This code will scale your data using StandardScaler, which standardizes features by removing the mean and scaling to unit variance. \n",
-    "#It ensures that the intercept is not negative"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared (0.48): This value indicates that approximately 48% of the variability in the dependent variable (the house prices) is explained by the independent (sqft living). In simpler terms, the model captures about 48% of the patterns in the data.\n",
-    "\n",
-    "\n",
-    "Mean Squared Error (MSE) (68845100756.11): This is the average squared difference between the actual values and the predicted values from the model. It's a measure of the model's accuracy, where lower values indicate better performance. In this case, the average squared difference is quite large, indicating that there's still room for improvement in the model's predictive accuracy.\n",
-    "\n",
-    "\n",
-    "Root Mean Squared Error (RMSE) (262383.50): This is the square root of the MSE and provides a measure of the typical deviation of the predicted values from the actual values. It's in the same units as the dependent variable. Here, the RMSE indicates that, on average, the predicted values differ from the actual values by approximately 262,383.50 units.\n",
-    "\n",
-    "\n",
-    "Intercept (540631.16): This is the estimated value of the dependent variable when all independent variables are set to zero. In this case, it suggests that when all other factors are zero, we would expect the dependent variable to be around 540,631.16.\n",
-    "\n",
-    "\n",
-    "Coefficient (259767.82): This represents the change in the house prices for a one-unit change in the sqft living, while holding other variables constant. In this case, for every one-unit increase in the sqft living, we'd expect the house prices to increase by approximately 259,767.82 units.\n",
-    "\n",
-    "\n",
-    "Overall, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# MULTIPLE LINEAR REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error: 47548419769.890594\n",
-      "R-squared: 0.6426869041626193\n",
-      "          Feature    Coefficient\n",
-      "0           const       0.000000\n",
-      "1       bathrooms   24855.944322\n",
-      "2     sqft_living  117349.853313\n",
-      "3          floors   20057.401179\n",
-      "4      waterfront   63378.047594\n",
-      "5       condition   11809.217548\n",
-      "6           grade  154829.185004\n",
-      "7   sqft_basement   14434.666872\n",
-      "8        yr_built -108064.021718\n",
-      "9    yr_renovated    7255.426167\n",
-      "10  sqft_living15   25765.797630\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "# Define a function to keep only numeric columns\n",
-    "def only_numeric(housing_data):\n",
-    "    '''returns a DataFrame with only numeric values'''\n",
-    "    numeric_columns = [column for column in housing_data.columns if is_numeric_dtype(housing_data[column])]\n",
-    "    return housing_data[numeric_columns]\n",
-    "\n",
-    "# Sample features and target variable\n",
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "\n",
-    "    # Split the data into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Standardize the features\n",
-    "    scaler = StandardScaler()\n",
-    "    X_train_scaled = scaler.fit_transform(X_train)\n",
-    "    X_test_scaled = scaler.transform(X_test)\n",
-    "\n",
-    "    # Build a basic linear regression model\n",
-    "    model = LinearRegression()\n",
-    "    model.fit(X_train_scaled, y_train)\n",
-    "\n",
-    "    # Make predictions on the test set\n",
-    "    y_pred = model.predict(X_test_scaled)\n",
-    "\n",
-    "    # Evaluate the model\n",
-    "    mse = mean_squared_error(y_test, y_pred)\n",
-    "    r2 = r2_score(y_test, y_pred)\n",
-    "\n",
-    "    # Display results\n",
-    "    print(\"Mean Squared Error:\", mse)\n",
-    "    print(\"R-squared:\", r2)\n",
-    "\n",
-    "    # Display coefficients\n",
-    "    coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model.coef_})\n",
-    "    print(coefficients)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mean Squared Error (MSE): The MSE is a measure of the average squared difference between the actual and predicted values. In this case, the MSE is approximately \n",
-    "4.75\n",
-    "×\n",
-    "1\n",
-    "0\n",
-    "10\n",
-    "4.75×10 \n",
-    "10\n",
-    "\n",
-    "\n",
-    " , which means, on average, the squared difference between the actual housing prices and the predicted prices is \n",
-    "4.75\n",
-    "×\n",
-    "1\n",
-    "0\n",
-    "10\n",
-    "4.75×10 \n",
-    "10\n",
-    " .\n",
-    "\n",
-    "\n",
-    "R-squared (\n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "\n",
-    "\n",
-    " ): The \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  score measures the proportion of the variance in the target variable (housing prices) that is explained by the independent variables (features) in the model. \n",
-    "  \n",
-    "  An \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  score of 0.643 means that approximately 64.3% of the variance in housing prices is explained by the features included in the model. In other words, the model accounts for 64.3% of the variability in housing prices.\n",
-    "\n",
-    "\n",
-    "Coefficients:\n",
-    "Intercept (const): The intercept represents the estimated housing price when all independent variables are zero.\n",
-    "\n",
-    "\n",
-    "bathrooms: For each additional bathroom, the predicted housing price increases by approximately $24,855.\n",
-    "\n",
-    "\n",
-    "sqft_living: For each additional square foot of living space, the predicted housing price increases by approximately $117,350.\n",
-    "\n",
-    "\n",
-    "floors: Houses with an additional floor have a predicted price increase of approximately $20,057.\n",
-    "\n",
-    "\n",
-    "waterfront: Properties with waterfront views have a predicted price increase of approximately $63,378 compared to those without.\n",
-    "\n",
-    "\n",
-    "condition: Better condition properties (on a scale from 1 to 5) tend to have a predicted price increase of approximately $11,809 for each unit increase in condition.\n",
-    "\n",
-    "grade: Higher grade properties (on a scale from 1 to 13) have a predicted price increase of approximately $154,829 for each unit increase in grade.\n",
-    "\n",
-    "\n",
-    "sqft_basement: For each additional square foot of basement space, the predicted price increases by approximately $14,435.\n",
-    "\n",
-    "\n",
-    "yr_built: Each additional year of age decreases the predicted price by approximately $108,064.\n",
-    "\n",
-    "\n",
-    "yr_renovated: For each year renovated, the predicted price increases by approximately $7,255.\n",
-    "\n",
-    "\n",
-    "sqft_living15: For each additional square foot of living space in the nearest 15 neighbors' homes, the predicted price increases by approximately $25,766.\n",
-    "\n",
-    "\n",
-    "These coefficients indicate the strength and direction of the relationship between each feature and the target variable, holding other features constant. For example, features like square footage of living space, grade, and whether the property has a waterfront view have a substantial positive impact on the predicted housing price, while the year the property was built has a negative impact.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  price   R-squared:                       0.641\n",
-      "Model:                            OLS   Adj. R-squared:                  0.641\n",
-      "Method:                 Least Squares   F-statistic:                     3768.\n",
-      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        13:34:57   Log-Likelihood:            -2.9014e+05\n",
-      "No. Observations:               21142   AIC:                         5.803e+05\n",
-      "Df Residuals:                   21131   BIC:                         5.804e+05\n",
-      "Df Model:                          10                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "=================================================================================\n",
-      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "---------------------------------------------------------------------------------\n",
-      "const          6.604e+06   1.41e+05     46.979      0.000    6.33e+06    6.88e+06\n",
-      "bathrooms       3.47e+04   3547.740      9.781      0.000    2.77e+04    4.17e+04\n",
-      "sqft_living     129.1822      3.765     34.313      0.000     121.803     136.562\n",
-      "floors         3.576e+04   3886.909      9.201      0.000    2.81e+04    4.34e+04\n",
-      "waterfront     7.828e+05   1.88e+04     41.703      0.000    7.46e+05     8.2e+05\n",
-      "condition      1.788e+04   2568.771      6.961      0.000    1.28e+04    2.29e+04\n",
-      "grade          1.319e+05   2290.923     57.586      0.000    1.27e+05    1.36e+05\n",
-      "sqft_basement    27.2065      4.581      5.939      0.000      18.228      36.185\n",
-      "yr_built      -3728.7602     71.951    -51.823      0.000   -3869.790   -3587.730\n",
-      "yr_renovated     18.3132      4.407      4.156      0.000       9.676      26.950\n",
-      "sqft_living15    34.6185      3.669      9.435      0.000      27.427      41.810\n",
-      "==============================================================================\n",
-      "Omnibus:                    16539.863   Durbin-Watson:                   1.978\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1296266.479\n",
-      "Skew:                           3.184   Prob(JB):                         0.00\n",
-      "Kurtosis:                      40.828   Cond. No.                     3.35e+05\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, 3.35e+05. This might indicate that there are\n",
-      "strong multicollinearity or other numerical problems.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit the OLS model\n",
-    "model = sm.OLS(y, X).fit()\n",
-    "\n",
-    "# Get the summary\n",
-    "summary = model.summary()\n",
-    "\n",
-    "# Print the summary\n",
-    "print(summary)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared (R²): The coefficient of determination is 0.641, indicating that approximately 64.1% of the variance in the housing prices is explained by the independent variables included in the model.\n",
-    "\n",
-    "\n",
-    "Adjusted R-squared: The adjusted R-squared is also 0.641, which adjusts for the number of predictors in the model. It's useful when comparing models with different numbers of predictors.\n",
-    "\n",
-    "\n",
-    "F-statistic: The F-statistic is 3768, with a p-value close to zero, indicating that the overall model is statistically significant.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The standard errors, condition number, and other diagnostic information are also provided. \n",
-    "The condition number being large (3.35e+05) suggests that there may be strong multicollinearity or other numerical issues in the model. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\USER\\.anaconda\\A\\New folder\\Lib\\site-packages\\seaborn\\_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  order = pd.unique(vector)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "inf_coefs = list(zip(coefficients[\"Feature\"], coefficients[\"Coefficient\"]))\n",
-    "inf_coefs.sort(key=lambda x: abs(x[1]), reverse=True)  # Sort coefficients by absolute value\n",
-    "\n",
-    "# Create a color palette with the specified color\n",
-    "color = \"#589aff\"\n",
-    "colors = [color if coef[1] > 0 else \"lightgray\" for coef in inf_coefs]\n",
-    "\n",
-    "# Create the bar plot\n",
-    "fig, ax = plt.subplots(figsize=(18, 8))\n",
-    "ax = sns.barplot(x=[x[0] for x in inf_coefs], y=[x[1] for x in inf_coefs], palette=colors)\n",
-    "plt.xticks(rotation=45)\n",
-    "ax.set_ylabel(\"Price Coefficients\", fontsize=15)\n",
-    "ax.set_xlabel(\"House Features\", fontsize=15)\n",
-    "ax.set_title(\"House Features and Sale Prices\", fontsize=20);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.\n",
-    "\n",
-    "We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
-    "\n",
-    "\n",
-    "Addressing negative correlations in house prices, such as with the year built, is crucial. Older properties often have lower prices due to depreciation and maintenance issues. However, strategic renovations, updates, and modernization efforts offer opportunities to increase their value. Homeowners and investors can enhance the appeal and value of older properties in the market by undertaking such initiatives."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RESIDUALS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This plot is useful in understanding the assumptions of linear regression and detecting violations. The ideal case is to see a random distribution of residuals around the y-axis, centered around zero. This suggests that the residuals are normally distributed, and there are no systematic patterns in the errors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# POLYNOMIAL REGRESSION."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Polynomial regression is a type of regression analysis where the relationship between the independent variable (or variables) and the dependent variable is modeled as an nth degree polynomial. Unlike simple linear regression, which assumes a linear relationship between the variables, polynomial regression can capture more complex relationships by introducing polynomial terms.\n",
-    "\n",
-    "In polynomial regression, the relationship between the independent variable \n",
-    "𝑥\n",
-    "x and the dependent variable \n",
-    "𝑦\n",
-    "y is modeled as:\n",
-    "\n",
-    "𝑦\n",
-    "\n",
-    "=\n",
-    "𝛽\n",
-    "0\n",
-    "+\n",
-    "𝛽\n",
-    "1\n",
-    "𝑥\n",
-    "+\n",
-    "𝛽\n",
-    "2\n",
-    "𝑥\n",
-    "2\n",
-    "+\n",
-    "𝛽\n",
-    "3\n",
-    "𝑥\n",
-    "3\n",
-    "+\n",
-    ".\n",
-    ".\n",
-    ".\n",
-    "+\n",
-    "𝛽\n",
-    "𝑛\n",
-    "𝑥\n",
-    "𝑛\n",
-    "+\n",
-    "𝜀\n",
-    "y=β \n",
-    "0\n",
-    "​\n",
-    " +β \n",
-    "1\n",
-    "​\n",
-    " x+β \n",
-    "2\n",
-    "​\n",
-    " x \n",
-    "2\n",
-    " +β \n",
-    "3\n",
-    "​\n",
-    " x \n",
-    "3\n",
-    " +...+β \n",
-    "n\n",
-    "​\n",
-    " x \n",
-    "n\n",
-    " +ε"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Model (Degree 2)- MSE: 35637716070.71762\n",
-      "Polynomial Model (Degree 2)- R-squared: 0.7321925161881991\n"
-     ]
-    }
-   ],
-   "source": [
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "    \n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Polynomial Regression\n",
-    "# Choose the degree of the polynomial\n",
-    "degree = 2\n",
-    "\n",
-    "# Create polynomial features\n",
-    "poly = PolynomialFeatures(degree)\n",
-    "X_train_poly = poly.fit_transform(X_train)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "# Build a polynomial regression model\n",
-    "poly_model = LinearRegression()\n",
-    "poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "# Evaluate the polynomial model\n",
-    "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
-    "r2_poly = r2_score(y_test, y_pred_poly)\n",
-    "print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
-    "print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Mean Squared Error (MSE): This value represents the average squared difference between the actual house prices and the predicted prices by the polynomial model.\n",
-    " A lower MSE indicates better performance, and in this case, the MSE is lower than the previous model's MSE, suggesting that the polynomial model fits the data better.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " \n",
-    "Generally,The \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  value for the first model is 0.641, indicating that approximately 64.1% of the variance in housing prices is explained by the independent variables included in the model. On the other hand, the \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  value for the polynomial model of degree 2 is 0.732, suggesting that approximately 73.2% of the variance in housing prices is explained by this polynomial model.\n",
-    "\n",
-    "Comparing the two \n",
-    "𝑅\n",
-    "2\n",
-    "R \n",
-    "2\n",
-    "  values, we can see that the polynomial model of degree 2 explains more variance in housing prices compared to the first model. This suggests that the polynomial model provides a better fit to the data and captures more of the variability in housing prices."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# RESIDUALS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred_poly\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred_poly, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from statsmodels.stats.diagnostic import het_breuschpagan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#TEST FOR HOMOSCEDASCITICY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Breusch-Pagan test p-value: 3.975373048166964e-258\n"
-     ]
-    }
-   ],
-   "source": [
-    "poly = PolynomialFeatures(degree)\n",
-    "X_train_poly = poly.fit_transform(X_train)\n",
-    "X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "# Build a polynomial regression model\n",
-    "poly_model = LinearRegression()\n",
-    "poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test - y_pred_poly\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Perform Breusch-Pagan test\n",
-    "lm, lm_p_value, fvalue, f_p_value = het_breuschpagan(residuals, X_test_poly)\n",
-    "print(\"Breusch-Pagan test p-value:\", lm_p_value)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.\n",
-    "\n",
-    "Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Log transformation.\n",
-    "Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. \n",
-    "Common transformations include taking the natural logarithm, square root, or reciprocal of the variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Log transformation of the multiple linear regression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error (Log Transformed): 0.09804039958945562\n",
-      "R-squared (Log Transformed): 0.6469123715948973\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# Log transformation of features and target variable\n",
-    "X_log = np.log1p(X)\n",
-    "y_log = np.log1p(y)\n",
-    "\n",
-    "# Split the log-transformed data into training and testing sets\n",
-    "X_train_log, X_test_log, y_train_log, y_test_log = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
-    "\n",
-    "# Standardize the log-transformed features\n",
-    "scaler_log = StandardScaler()\n",
-    "X_train_scaled_log = scaler_log.fit_transform(X_train_log)\n",
-    "X_test_scaled_log = scaler_log.transform(X_test_log)\n",
-    "\n",
-    "# Build a linear regression model on the log-transformed data\n",
-    "model_log = LinearRegression()\n",
-    "model_log.fit(X_train_scaled_log, y_train_log)\n",
-    "\n",
-    "# Make predictions on the test set\n",
-    "y_pred_log = model_log.predict(X_test_scaled_log)\n",
-    "\n",
-    "# Evaluate the model\n",
-    "mse_log = mean_squared_error(y_test_log, y_pred_log)\n",
-    "r2_log = r2_score(y_test_log, y_pred_log)\n",
-    "\n",
-    "# Display results\n",
-    "print(\"Mean Squared Error (Log Transformed):\", mse_log)\n",
-    "print(\"R-squared (Log Transformed):\", r2_log)\n",
-    "coefficients = pd.DataFrame({\"Feature\": X.columns, \"Coefficient\": model_log.coef_})\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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LFi3CFVdcIdnfcOnevTtOnz7tl2kPAPv27UNSUhJuuukmAI0eSCWys7Nx9uxZzRXKevToAQD429/+5vf6G2+8AbfbjZycHLRu3RpXX301/u///s+vTSgGJisnTpxAZWUlfvOb3+C6667zjsHBgwcBBD/UKDFt2jRMnjwZgGA85uXleRPf5IzT7t2747333vPzdrrdbrzxxhu48cYbI6IT3KNHD3z77be44YYbvPdTly5d8OKLL3rHPjc3F6dOncJnn33m/dyFCxfwz3/+U/KYpaWlIYWoEEQiQx5bgiDwhz/8AUOGDMHSpUtRVFSEjIwMjBkzBmvXrkVVVRV69uzpjcnlOA6dOnVC8+bN0bp1ayxduhRVVVW4+uqr8fnnn+ODDz7A+PHjJc8zY8YMPProo5g8eTIeeOAB/PDDD0E6nNdffz2ysrKwfv16NG/eHCaTCVu3bvXbjv7FL34Bi8WCFStWYNSoUXA6nSgsLMT7778PAH5Z9SLdunWDx+PBpEmTMG7cOKSlpWH//v24fPlyRLRhhw0bhldeeQWTJ0/G1KlT0a5dO/z973/Hnj17MHnyZK9X/IorrsCnn37qldcSvay+9O7dG1u3bsV///vfoJjPf/7zn5JFHm677TZce+21KCgowPr16+FwONCzZ098+eWXWL9+PXr27Ik+ffqA4zhMnToVs2bNwsKFC3HXXXfhq6++woYNGwCwGd5a+fnPf45mzZph8+bNsFgssFgseOutt7xSaLW1tczHys3NxcKFC/Hss8+ib9++uHTpEtavX4/27dujU6dOkp+ZPHkyDh48iN/85jcYN24crFYrXn75ZZw8eTJiFb0mTpyIBx98EOPHj8evf/1rJCcn49VXX8U777yDdevWAQDuvfde/OlPf8LkyZMxffp0NGvWDJs2bZI09Hmex6effmqY6m4EYRTIsCUIAh06dMDIkSOxc+dOvPzyy/jtb3+LadOmoWXLlnjllVewfft2XHnllejVq5e3QhkgVHRavXo11q5di4qKCmRlZWHy5Mmy2/q33HILtm3bhtWrV2Py5Mlo27Ytnn76aUyYMMHbxmw2Y926dXj66acxY8YMZGZm4tFHH8WJEye88lo/+9nPsGrVKqxfvx6PPfYYrrzyStx888146aWXMHLkSHz88cd+CTiAEMu7fft2rF27FnPnzkVtbS2uu+46PP/8814ZJz1JSUnBSy+9hFWrVmHdunWoqqpChw4d/DLnAeDhhx/G559/jrFjx+KZZ56R1Kq95ZZbYLfb8cEHHwQZtv/4xz/wj3/8I+gzzzzzDK699losW7YMP/vZz7Bnzx7s2LEDV111FUaOHIlJkyZ5jdbBgwejpqYGO3bswJ49e3Dddddh7ty5mDt3LlJTU3UeGcGzunHjRixfvhyPP/440tLScMMNN+Dll1/G2LFj8fHHHzOXQn7wwQdRX1+PXbt24ZVXXoHNZkOvXr0we/ZsSW1mQEgkfOWVV7B69Wr84Q9/AMdxuOmmm/CnP/0Jt9xyi55f1UunTp3w5z//GWvWrMGcOXPA8zw6duyIDRs2YMCAAQCEpMj/9//+H55++mksW7YMHMfh/vvvR7t27YJCQ/7973+jsrIyJAk4gkhkOF7r3hZBEAQRdXbu3Ildu3bhrbfeUsz4D4Xi4mJ07twZHTp08L72/vvvY/z48Xj99ddlPZ9E7Pj973+PixcvYuPGjbHuCkEYCoqxJQiCiAMeeughuN1ub0yznuzbtw9jx47F3/72N3z88cfYvXs3FixYgB49epBRa0DOnDmDt99+O0iijSAI8tgSBEHEDZ988gmefPJJFBcX65rgVFFRgVWrVuHgwYO4cOECMjMz8atf/QpTp071KlEQxmHGjBm4/vrrZWPZCaIpQ4YtQRAEQRAEkRBQKAJBEARBEASREJBhSxAEQRAEQSQEZNgSBEEQBEEQCQEZtgRBEARBEERCQIYtQRAEQRAEkRBQ5bEGyssvw4j6EBwH2O3NDds/o0PjFx40fuFB4xceNH7hQeMXHjR+4aH3+InHU4MM2wZ4Hoa+cI3eP6ND4xceNH7hQeMXHjR+4UHjFx40fuER7fGjUASCIAiCIAgiISDDliAIgiAIgkgIyLAlCIIgCIIgEgIybAmCIAiCIIiEgAxbgiAIgiAIIiEgw5YgCIIgCIJICMiwJQiCIAiCIBICMmwJgiAIgiCIhIAMW4IgCIIgCCIhIMOWIAiCIAiCSAjIsCUIgiAIgiASAjJsCYIgCIIgiISADFuCIAiCIAgiIbDEugMEQRAEQRgXtxs4fNiMc+c4tGrFIzfXDbM51r0iCGnIsCUIgiAIQpLiYgvmzUvGmTONG7zZ2R4sXVqH/HxXDHtGENJQKAJBEARBEEEUF1swerQNZ85wfq+XlnIYPdqG4mLyjRHGgwxbgiAIgiD8cLuBefOSwfMA4G/Y8rzw97x5yXC7o983glCCDFuCIAiCIPw4fNjcEH7ASb7P8xzOnDHh8GEKtiWMBRm2BEEQBEH4ce6ctEEbajuCiBZk2BIEQRAE4UerVryu7QgiWpBhSxAEQRCEH7m5bmRne8Bx0oYrx/HIzvYgN5eCbAljQYYtQRAEQRB+mM3A0qV1ABBk3Ip/L11aR3q2hOEgw5YgCIIgiCDy813YscOBrCx/wzYri8eOHQ7SsSUMCYnQEQRBEAQhSX6+C3l5Lqo8RsQNZNgSBEEQBCGL2Qz07k2xtER8QKEIBEEQBEEQREJAHluCIAiCIIgI4XaDQjmiCBm2BEEQBEEQEaC42IJ585IbqrgJZGd7sHRpHSXfRQgKRSAIgiAMi9sNlJSYUVhoQUmJGW4K9STihOJiC0aPtuHMGf/qbKWlHEaPtqG4mHyLkYBGlSAIgjAkxcUWzJ8PnDqV6n2N1dtF279ELHG7gXnzksHzAOBv2PI8B47jMW9eMvLyXHRd6gx5bAmCIAjDUVxswahRNpw65f86i7eruNiCnJw0FBSkYsKEFBQUpCInJ408ZETUOHzY3BB+wEm+z/Mczpwx4fBhsmr1hgxbgiAIwlD4e7v84XnBUJg3L1kyLIG2fwkjcO6ctEEbajuCHTJsCYIgCEMRqrdLbfsXkDeICUJPWrWSeCoLox3BDhm2BEEQhKEoLQ3N20Xbv4RRyM11IzvbA46TNlw5jkd2tge5ufSUpTdk2BIEQRCGQUgYS2ZqG+jtou1fwiiYzcDSpXUAEGTcin8vXVpHiWMRgAxbgiAIwhCI8bHl5cqGp5y3i7Z/CSORn+/Cjh0OZGX5X29ZWTx27HCQjm2EoCh6giAIIuYoxcf6ouTtErd/S0s5b0xt4Gezsnja/iWiRn6+C3l5LpKeiyJk2BIEQRAxpzE+Vhm7ncfy5dI6tuL27+jRNnAc72fc0vYvESvMZqB3b3qYihYUikAQBEHEHNa418WLlYsz0PYvQTRtyGNLEARBxBzWuNdAg1UK2v4liKYLGbYEQRAJTjyUl9U7Ppa2fwmiaUKhCARBEAlMvJSXJXkkgiD0gAxbgiCIBCXeysvKxcdmZ1N8LEEQbBhrViMIgogzjLrNr1ZeluN4zJuXjLw8lyH6K+IbH/vTTxw6dkzBDTdUw0RuGIIgGCDDliAIIkSKiy2YNy/ZT6YqO9uDpUuVM/ejgZp8llBelsPhw2bDxaKK8bEcB2RmAmVlaDDQCYIglKFnYIIgiBAw+jY/lZclCKIpQoYtQRCERtS2+QHhfXcMHaFUXpYgiKYIGbYEQRAaadzml/Z2Ctv8Jhw+HLvgVVE+K1BhQITjeGRne6i8LEEQCQUZtgRBhITbDZSUmFFYaEFJiTmm3sloY7RtfqnfguSzCIJoilDyGEEQmjFy0lQ0MNI2v9pvsWOHo+H9RiM7K4tvMr8VQRBNC0N7bC9cuIC77roLR44ckW3zwQcfYPDgwbj55puRl5eH9957L4o9JIjEQ80Ta/SkqWhglG1+lt8iP9+F48erUVRUg82ba1FUVIPjx6vJqCUIIiExrGF7/PhxPPDAA/jf//4n2+aHH37AlClT8Pjjj+Pjjz/GlClTMG3aNJw7dy6KPSWIxCGwStXQoalo3x5eYzUekqaigRG2+bX8FqJ81rBhLvTubQydXYIgiEhgSMO2qKgIs2bNwvTp01Xb3XLLLbjzzjthsVgwaNAgdO/eHa+++mqUekoQiYOc9+/0aWDUKMH7Fw9JU9FCrkpWVlZ0qmTRb0EQBBGMIfcMb7vtNgwePBgWi0XRuP3222/RsWNHv9euvfZafPXVV5rPyRlUylHsl1H7Z3Ro/NhQ9v4J4zd/fjLmzatjOt5PP3FNYswHD3Zh0CCXbOWxSF5/P/3EdtB4/i3o/g0PGr/woPELD73Hj/U4hjRsW7ZsydSuuroaKSkpfq/ZbDbU1NRoPqfd3lzzZ6KJ0ftndGj8lHn/feDMGfn3eZ7D6dMcHI4U+UY+dOyYgsxMffqmBbcbOHQIKC0FsrKAPn0QlW33e+9Vfj8S11/AM71Cu9j8FnpC92940PiFB41feER7/Axp2LKSkpICh8Ph95rD4UBaWprmY5WXXzZkyUaOEy4Ko/YvmrjdkPWMyUHjx8Y331gAqButNlstsrOTUVrKeeM4fRGSpnjccEM1ysoi0FEFiostmDs3WB1g2bLYZf9H8vq74QYgOzvNkL+FXtD9Gx40fuFB4xceeo+feDw14tqw7dixI7744gu/17799lt06dJF87F43ti1yI3ev0gTrrxUUx8/Na66im1wWrcWZKJGj7aB43g/g0pMmlqypA4mU3THW4wPDjxnaSmHUaNsUYl5VSIS15/JBEP+FpGA7t/woPELDxq/8Ij2+BkyeYyVIUOG4OjRo3jzzTfhcrnw5ptv4ujRo7hXbV+QiCtIXiryaJGvinXSVCBNWanBaL8FQRBErIk7i6Br16546qmnMGTIEFxzzTXYsGEDVq5ciblz56JNmzZ4/vnn8fOf/zzW3SR0Qs1o4Tge8+YlIy/PRRJGYSDKV0l7/4T/+spX5ee7kJcnnzQVTRrVAaQR1AE4HD5sRu/eiWfdGum3IAiCiDWGN2y//vprv78//fRTv7/79OmDPn36RLNLRBRp6kZLNJGrUtW2LbB4sQP33OPv/RO1UWON0crbxoJQf4tQ4tYJgiCMjOENW6JpQ0ZLdAn0/rVuzSM/PxUVFS7DxpgZqbxtPNHUyyITBJGYkGFLGBoyWqKPr/eP46IjlxUOYnywkjpAVhYf8fK28YRSst3o0bFPtiMIggiVuE4eIxIfLUlNRNPECOVt44mmnGxHEETiQ4YtYWjIaCFYMLI6gNsNlJSYUVhoQUmJOeYGI5XiJQgikaFQBMLwyCU1ZWXxFA9IeDGiOkBxsQXz5wOnTqV6X4t1HCvFrRMEkciQYUvEBUY0WgjjYRSlBsC4cawUt04QRCJDhi0RNxjJaCEIJfzjWP2Jtf4yJdsRBJHIUIwtQRCEzhg5jjUe49aNFqdMEIRxIcOWIAhCZ4wex2rkZLtACguBbt3SUFCQigkTUlBQkIqcnDQqpU0QhCQ0MxAEkbDEqrJWPMSxhhK3Hu3xLC62YNQoBIVMxDpOmSAI40KGLUEQCUksK2vFSxyrlrj1aI+n2w3MnSuvtxvLOGWCIIwLhSIQBJFwiIoEvvJwQKOnL9Lb2P5xrP7vGTWOVYlYjGdjnLI0TUFvl2KLCUI7ZNgSBBE3sCz0RqmslZ/vws6dDrRp4/+6EeNYlYjVeBo9TjnSFBdbkJNDscUEoRW6QwgiTolV/GisYN0KZ/P0cTh82KxZPk7rmOfnuzByJFBcXIOzZ+Pzd4rkePoSOLYtW4YWp5wI94VRNZAJIh4gw5Yg4pBYxo+GQ6hGh5aFPlKevlDHXIxjldK0jQei4TmVGtusLA8yMjyorDRJjp1UnHK83he+qHnIKbaYIJShUASCiDNiHT8aKqFurWrdCmf19LG2E/sej2OuB5FWeJAb27NnOVRUcOD5YL1dIDhOOVF+o1hpIFM8L5EokGFLEHGEUeJHtRKO0aF1oWf1jLK2i9cx1wtR4SHYuBTgOB7Z2Z6QFB7UvZOA3Q6kpweem/N7LZF+o1jEFlM8L5FIkGFLEHGEkStayRGu0aF1oS8rY2vP2i4ex1xPIlmpjGVsy8uBigoOopdWpLKy8aEokX6jaGsgJ4qnmyBEyLAliDgiHjPFwzU6tC70ehsG8TjmehOpSmXaxkz+oai0NHF+o0h6yANJJE83QYjQoxhBxBHxUNEqkHANQ63FDvQujqB1zH0T5Fq35pGfz/RxwxNKpTI12K9TpYciDuXlbNeYke4LOUQP+ejRNnAc73cNq3nIA5Mze/VSvsajpXhBENGEPLYEEUdE05ujF+Ea41q3wvXeOtcy5oGxikOHpqJ9eyTMdq6o8DBsmAu9e4cvo6U2toHhB3LY7Xzc3RdKhOIhl4qT7dYtDYWF8ueh3QgiESHDliDiCNFoE7YOgzPFed54Fa30MMa1LvR6bp2zGsr790vHKp4+DYwaRbGKUrCMLQtZWXzE4oBjRX6+C8ePV6OoqAabN9eiqKgGx49Xyxq1cnGyI0bIP1jF4w4QQajB8Xy8qivqS1nZZUPqTHIckJnZ3LD9MzqJOH7FxRaMGmVr+Mt3IRO+4M6d+om36zV+4sILQHJrldXY1KqDq6dYv5JGal6eCzk5aQ2GhXz4w/Hj1XFlXEULubFdsqQOCxem4PRpXjGsRBzXRNCx1YrbDZVrTxiDjz8OvvbEz6qF7TTV6zYR149oovf4icdTbUeGrYBRL1y6scIj0cZPfRHTdyHSc/wSweiQM5RLSswoKEhV/XxRUU1UYhXl+in1OmCMSl1SfbNYgIMHm2PECOHiY3koSoTKY1oI99rT66EzEUm09SPaxMqwpb0xgogj4jnZIxLJR9FGjDENxEixinIPEAUF9SgqSvJ7PSPDAwCoqIj9w4bc2A4bJuxCzJ2b7LfVLoYfBPZT7jiJSrjXnhi2I1wz6uNLEEaHDFuCiCOMZECFQqIaHUaJVZQrPXzmDIcNG6xB7QV9WH+kyhTHmvx8FwYOjO+Hokihx7WXCA+dBCFChi1BxBFGMaAIf/SSGAtnG11Jk1T4m5d53R+h4hePefOSkZfnippxIxeKIJKoD0Xhon7tgUkRgsaXSBRIFYEgdCIatdbjUe4rUhiptr1ydr/wX7Ws/HDLmqoVwpB/PZhoV+qikq6hE8nKcAQRj5BhS8QlRjJqAGFh7tYtWENS74WZFjEBIxpCchJjmZnAuHH1yMjgZa9TJbmmUaNsWLnSqnqtRyL8JFoxwUrfXUmHlRCQu/ays3ns3g3DhJQQRDQgVYQGjJr1SFmZwWjJro/G+EVTfsv3nNFQGAh1/MRt5dJSoSqU3d64FR+u0e0fR2q8LG7xux84YMbu3UkoL1f+jdSULgJDCOR+Z9bseC1EWsWBReWjbVsOx45dhoncMKpIVR5r1YrWj1Ch9Tc8SO4rxhj1wqUbyx+tRk2kx8/tBjp3TmtIwpE2Slq04PHFF/rrQEZD1iiU8ZMyukV8jbJQ+h9tubNQ0XKdajVIlWSulA1kdqI1jqzffe/eGtx6a+KH1+gNrR/hQeMXHrEybOkZmIgblJJjxKSJefOSoxqW8OGH5gapJPm4xgsXTPjwQ/2tA73Lm+qB3LayyJkzQsb9U09ZQwolUIsjjXZsqBRar1Ot2/1y17pvmIqGoyGwgp1cSEskwn/iXeWDIAjjQYYtETcY0agpKWE7F2u7eEY5K1+EA88DGzZYJWMqR49WLj0bD4aQ1us0FAULuWs9P9+FOXOczMdp0YJHRoZ62eFIxTSTygc7RssrIAijQimnRNxgRKOGdXvFyNtYeoU0qBWPaETe4FOTmQrFEIp2JSqt16maXJPWc02f7sRLLyWhtFQ+PCY9XTBexe19pfGR08bVQ++WRSatbVuuSah8KJEIVfsIIlqQx5aIG4zo3WFNrDGqPqSenjg9HijUvO5qcmeC0eaBxyMYtLFQT9B6nSopXYRyLrMZWLasDhwnrZzBccDq1XXo00cwYJVCWiId/sOi8vHcczBEmE2sUFKNUNvhIIimCBm2RNxgRA3X3r3dDWVJ5Q2tjAyPIQ1bvRdMPR8o5IxkZSNQUA+orDRh+PBUdO6chlGj5L/fvn2WiGzthnKdysk1yV1Xate63PGkwgyUiEb4j1Jfd+50YNiwkA8d9xgxr4AgjA496hFxg2jUjB5tA8fxfluXsdJwNZuBVavqGuS+Ais7CX1atcp4urJqC6YYEjBoEPs2p2jQ6ZGVr1b+U6q2fSCN5WKlDAIe48bZ4PGoy2hpJdTr1LesaWkph4MHzdi1KwmB1xXrta5HmdRohf/I9dXSxFcotfAe4cGCw+HDZkM+PBNELCCPLRFX6OWJ0rtPO3fKe5yMGAMXCU+caNAJlbbkPdhSmfgirF73/HwXjh+vxu7dNUhPFz3mUuVi5ZPYfI1aQN+t3VCvU7NZMMiXLk3Grl1Wye+g5VoPVzkjmuE/RlT5iDVGzCsgCKPTxJ+HiXhED09UU+iTEpFaMNW8qdnZPAoK6rFxoxVAeF53MT60slKf53OW5DUt5Oe7MGiQC19+2RzffFOLq65SvybkErXEB4E5c5yYPt0ZteuKJblLLLxB6I8R8woIwuiQYUvEJaJ3x0gYsU9yRHLBDNxSl6o8lpPjCTJ+s7J4zaEAenuqxK3d7duT0LIlH/YDitkM9OsHdOniUlXGUJZLE4zul19OwvTp7HJe4cIaVgEIUlTx8FAXT9CDBUFohwxbgmiCRHrBVDPy9fJwR8pTNX++zfv/0ZJVMmo8pZwXXnwQAcSKZ/pKUbndwJEjZtTUAKmpZvTs2fSMZSPmFRCE0aEYW4JogrDILEV6wdQjppJF/ks+3peNaMkq6RkeoreYvxjTXFRUg82ba1FUVIPjx6sBICJSVKJM29ChqXjoIWDo0MjLtBkVI+YVEISR4XjeyNLx0cOotaCpVnV4GGX8ol0kgBU14XejjJ8SYlwqgCCPFs8DGRl8Q9ljFqSS0Bo92MePV2v63bSMX0mJGQUFqarHLCqqUfTYRkvM3+0WPbXSSXqhjpl/nHGwh7KpGnOhzCHxcP8aGRq/8NB7/MTjqbYjw1bAqBcu3VjhYYTxM3rVIKUF0wjjx4LSGN99twu//GUaysvDlyFTMyoD0TJ+oqGoFh6iZChG0yjUyxD3JVLGcjQx0kNsvNy/RoXGLzxiZdg2vX0dgogikSxHqhfxlPQmh1LMbkmJGeXl+kRdqYUBBBo1vXqxj2u48ZSs2sR6KD4AkVHWMGqcMStGfYh1u4GPPjKGsU0QkYZibAkiQlDVoOgiF7Orp3KCUrKaVPnebt3SUFjIfvxw4imjUSXMl0goa8SzbqtRS98WFgLdukW3rDRBxBIybAkiQpSURNfQIKRhNazs9tDLNSsZNSNGQJMRIZeopebxi7ZRGIkS1/Gq22rUh9jiYgtGjIDhjG2CiCRk2BJEBCgutmDMGJt6QxjT+5RIsBpgzz4bmkpEJIyaUBQjMjPZjD3WdmpEQlkjEsZyNIi2t5wFtxuYO9d4xjZBRBoybAlCZ0TvXWUlm8FqNO9TosFqgA0ZEloYgLpRA5w+zW7UhCLV5XYDX3zBNp1zITxHyfVJbykq8bcSjLHA+0JQuTCibqsRQyjY4pVpx4hIPGgfgiB0RLl6lD96VA0yUga2kVErMiAaYKEUjtDTqAkl+UjqM0qcP6/NuFLrU7yVk44ERgyhMKKxTRDRgAxbgtARNS9JI+EXQTBqBrZRYTXAtKpE6GXUhKKgIfcZPfqrpU96KWuID4YC0mWF9VR20Asjlr41orFNENGAQhEIQkdYvR/p6eFVDTJqBrbR0aPaWSDqcaFAmzbKcaFuNzBzpnI85Jw5ydi9uzEUQMvugNAPbfGpsUiIMmKsKgtGqOQXSON1Kf2+UeOVCSJcyLAliBCRijtk9X5s3epARgYfUslTo2ZgNxV8f/eDB8348EMz8vNdknGhrEbNmjXWhupo8gZdWZkJEyc2yjWtWWNVNAJD6YcvkTIylWKIY7F9HmpMc+BnjFb61mwGli0zlrFNENGA3DoEEQJyYQCLF9epbkmmp/N4/HEbSktDCyGIdxF7FrTEDkczzlgtntVk4uHxNP6dnc1j3ToOffu6ZMMF3G5g69YkTf0oLeWwfLmVuX1gLLEabjdw8CDbIGoxMtXCZ8LZPg/lOtArptn3M0aKN87Pd2H3bmDKFF4xtpwgEgkybAlCI0pxh2PH2jBxohMbN1olq0fxPFBREWwIaKlEluhJIVqMjWjGGbPEs4pe2/HjnRg40I1evdxo1ao5ysrkP3P4sBmVldo2z4Tris0IXLLEgTFj6pmNK63JaL5GppJxqXTfjBplw/jxTtx1lxtZWR6cPastVjVUA1WtPwMHupm/g+/9a6QHymHDgN69q6nyGNFk4HieKiADMGwtaKpVHR56jx9rLfvFi+uwYIH/QpuV5YHDIRq28p89frxacdEpKTGjoCBVta9FRTVhL7B6jJ8WT5q/4RBcUtbX8NfSNlzUfndffH9Hi0V9/AoLLZgwIUWXfvrDIztb/XryRW5MpQi8XpWMy7w8F/P4ZWR4UFHBgeMQ9GAIBP+u8n0W2u/cGXwdaPk9Wb8D6/0bTYy4fsSTkosRxy+e0Hv8xOOpQR5bgtAAaxiA3S4scL4TuMcDDB8ub5CyhhAYMQNbDi2eNLXYYd+MeIC9rR6LJrvaRePv+OGHZpjNQE0NkJpqRs+e0gt45LLSOTzyiJP5+2uVqgMaYzTVPJmzZzuZx0/Uf05P5/12N6S2z5X7LHi1Z84Mvg60/J6s3yERQoAiDSm5ENGAkscIQgOs2/v791uCMvBZ9UPVzmHEDGwptCo3aElWinb2fChhHaNH2zB0aCoeeggYOlRI+JJSq1BTVQiHDh086o0aUBtTX3wToliSGbXEEAsPJoDNBuzZU4NXXgH27pUuK6zeZw4VFSasWeMfj6zl9+R5DjwPbNnC9h3iNQQo0pCSCxEtyLAlCA2wqx4kBU3ULVuyfZalnVIG9uzZTjid0Ky2oCdalBvEDPP169kNB61xxqFkvvsSilc1sPKc3AKu9KAibKeHbvBq6TfrmM6YUednZLI8ZIQSQ1xaaoLZDPz615CVZistZevz1q1Jfr+59t+Tw8WL2mOOAwn3OoxXSMmFiCb0iEQQGlALA/AlcCucNcaItV1gBvaJEya89FISli9P9raJ1TYfa8jGmjVWvPxyEvO2MCAY/ibG5q1a8Zq3P31jADMzeXCcYPTZ7R6Ul6vHZDYaouwhEnKV0Vq04HHhAheUiOhv7OoTjsJq7PXp429ksms3e3Dxovp944vasYXfQ53KSpM3REB8mEpP9zQ8fGjxsEr/toD6mDflbfimoORCGAfy2BKEBvxr2SsRvBVeVsa2gLK2E/vTu7cbViuwYoU1yIMVq20+VmNn+XJr0NakGjwvPGBkZHgg783k0aKFYIhq2f4sLrYgJycNBQWpmDAhBSNGpGL48FRMnJiC8nKT99gKvYNg9GgPkcjPd+H48WoUFdVg8+ZaFBXV4IsvqrFzZ7BnPjubx6RJTnCcfuEo6oUmpAX9WQ3icePq/frHgtqx7XZtHmnx9x0xIrXBi8yuLuGPtjFv6tvwia7kQhiLxL6bCCIC5Oe7MH68E1u2JKu29Z2oI1XiUkvSVbTibrV9B22L2T/+YcZPP3Gor1du5/EA8+crbX8KiUVXXMGjd2839u/XXp42mPAWcKnStEraqDk5niAvb6gapeJD2+jRNkmpOkDacOve3e2j3yv1vQQP+9SpTnTqFNxfKVg9zoEGvxInTpiwYoU1Ir+v0pgb7f6MhSoBlfclogkZtgQRAgMHurFli3o734k6UmoGRtzmUw/ZUPZsKvHcc+oPFACHykoOlZXKbSoqOIwYkeqVYlNXBBAM4ubNPXj22TqcOGHCypVWn/fY0LqASxm8gLLRGwpyIRFKhtuxY2Z4PMpj5vEI7Xz7u3+/xSepjM2IDiQ3V9C9FXYq5PrAo3VrHi+9lKSonpCWxqO6Wss1KfRzzhwnpk+XV58w0v0Zq3CIcOa+eJIHI4wBhSIQUcPIiRNa+xbKtm2k1AyMuM3H8l2NRGkpp1jS1h8Oly+bYLfzeOWVYMNM8ZMy2/nhEKi+Ee6iLxUSIaVIIKL1+hP7u3RpHXbudCA7O/QStL5lY6VDCoTXfvOb+oZKf/LqCdXVJths2nYaOA54+WXlpEej3J+xDIcIde4LDA0Sy0kneugGER5k2BJRwcgTVCh9C3WijkQ9eaNu88l9V7udN2CyjHajYs0aK7M8loBQeS7aUmyhPFBqMZbDuf60GtFS5Oe7sHOnAxkZwcfPyOCxc6eDWfbM4dAa7y3ETG/fniQ7vpG8P1l/W7cbmDkztqoEWue+ph6XTIQOVR5rwKiVRRKh8kk0K0QFojZ+4fYt1K09PbfX9u2zYNw4m8J2sLYKVL59a92aR35+Kioqwq88tn+/BXv2WHySsIS+hWJUqsFxwvazUqW38NHad/lKWJEiGlvPYhUvtW3mUCpysc5/bjfw4YdmHDpkxunTHLKzefTp4/Ya5azV+vQgcHwjNT4sv604fk8+WYdnn1UP4dGjWqEaLHMfa4XHSFd5S4T1N5bEqvIYGbYNGPXCjfcbK9YTlNL46dW3WMaAsZVAFTLoFy50Mh0vcLFs2xZYsqQW99wTuiGkVvZUT8NTfCiZONGJDRu0x7+yo90oV7um9LyWovlAKZ4LUC+Dq+U7ssx/LAaemnGpJ0rlnwG2MsFqsP62HAdkZDRHZqaHSUt4+vQ6XH+9J+axrNEsG65EvK+/sSZWhi2FIhARJdoVorSgV9/0jnFkRUsJ1I0brapbd3Jbf6dPA6NGhb71p1721J/MTA8GD1Y3wuXIyuKxbZsDRUXa4l+1Ix6XfcZWuqb0DNeJtiA+6zaz3iFJrNvV6kUwQiX4s1Ljq2cIktbf9tAhMBfIWLMm2RChYkaJSybiEwpSISKKkSeo/fvZLn+9+6aXV4693r2Q9a0kKaS8WApPyqFKEqn3UzjfjBl16NPH7U2sysmxMHnYOI6H3c5j8eI6XHWVUFChpIR1bPQhuICCMuI15XQCL7yQhPffN+Pdd4OvxzNnOIwaZcO4cfXIy3MxXyuxyMRXU2jw9zI2Ihqheht4gTJacooPmZm8Ju1of5Qeijl8+KEZJhO843H0aDWOHQvv3tfy2952mxulpaxH9t+BCPV30QOj5g0Q8QEZtkREMeoE5XYDe/awXf569k3PmEet9e7PnOGwfLkVffu6gxbUSBpCrP3s2NHjd2w5TVVfRO/bqFH1+Ne/TNi9OykghjfyzJnjbKiepk3u66mnrNi0yaoqlQUAW7dasXWrlTl+++BBNmtJ74c2OVmyULRc3W7gyBEzamqA1FQzevYM/5qVMr67d3ejR4/IhCmMHm3z85aKv9+wYaEbilqdBVlZrEeOvcauSKSkEYmmAYUiEBEl1GpGkebwYTOTAZSZGV7ffLOWV6606prlG4rBvWZNsuQ2YyQ96y1bhvZwI7d960tGBo/0dB7Llydjy5bkqBq14rU7fboTR49WY8kSB373OyeuuEK+Ipr4mbffNmPDBmtDUQN2Aq+VwKz4ffuErf41a1i0fkMrBhKKZJ/WsB8xZGHo0FQ89BAwdKg+16zUbonVGkqYAtu4CSV7G9Ejo1+rs6BPHyjOwUrEKlQsUtKI0cLI0pZNAfLYEhEl1GpGkYZ1URw+vD6s5J1A76xUwpHoGZkzJxkOB7yeCBZxeuUiCPIEbjNGyrNeXGzB3LlqRhaPjAxp70ugh61lS0Eyq6yMw4kTJixfbpU4njocx6NZMx6XL4vjpj0JDBCu3f375X5n/99a/MyiRXWYMMEW0nl9vWgeD7BggdR52fqv1eMVzm6DFiOUNWRB6zWr1n+pMAUl3VsBuQRC6aRIPbygWr2ZotbvqFFSux9sCZCBv180EmZDKRZiBGJRBIOKWPhDHlsi4kRCuzVcWBfFgQNDe9SWS2pR8liVlZkwcSJ74oZyQowygUkmap51ADCZeJSXsxti4hgIVaGUqajgZGOexe3te+91wWQSjNqWLYVKUgKhGaVr1woFAux27Z4s8doFIPM7y3+mtJRrCD8Ibdtb9KKNGSN3XvWYZEDbA2W4mqKs91vLljxzYpSW3SCW/vtq6o4ezZq8KMSv+yMai5FJmA3Fm6mkKc2C7+8XTU1yPXSOo0kstHeNrBEfK0juqwGjynkkktxILJ4q5cZPXf5Hm/arL2oyYmz9ZpcBkvYMsyNK5qhLhwmJWSx90joGajJY4X5HXwK9J7W1wA03NENNDdtvtWSJA2PG1AMAunVLUyjnyiMzk8dzz5nQrFkNevZ0Y/9+C6ZMsTWUbg2X0DSAtXqP9JDFc7uBa69thupqyPSZR7NmwIsv1mLECHaZJxYZrbw8l+b+a9O99f8dMjI8DVXslNm8uTasWFstOrbi/Bc4B6vFFweOTSgScvHsTdSy/sZC2jKWGvEskNwXkfDEShZLri9KXg+OCz1EQi2ekAUtkkx5eS6sW+fA9Ol1yM+vR4sW2gI3xW3G/HwXtm1zwCQ7K7D3SesYKFVwkvd+a8du92Dx4saFv7jYgl690piNWkDwKprNQuUxtTKtZWUmtGkjXPf79wvfQzDu9ED7eMyYUafZ48UaH7t9e5Ji5auaGuXzVFcDZ89qi5tl2Q0KRdYvN9cNu531PhK8tunpHuzZU4Nt2xxMnwo3KTUUb2bgHKwUXxzo/Q1FQq4peROjLW0ZbUm/eIIMW6LJEqkQCb0yzVkmQnHhGDEiFWvWJKO4OAnJycCcOXWYMaOO6Ty+C6zdzitm6bNOzqGOwfz5Nr8FcN8+C7NWLwsXLnAYO1bYEgzVYG7VikdxsYU5vre0VJvmsDqhG0R9+mh/oGT9LefPt8kaLS+8kNSw2Mo/BPA8h08+Yeuc7zUrGnh79tRgxow6TJ9eh3XrBE+tlv77tjObgWefrUNjrLQaHCorTTCZBMMxWgmzejgLWOfBUBIAm1JJ3GhLWxpZIz7WJNaVRRAaUdPeDAW9pcvkJkK5JJuzZzmsWGHFtm0OzZI5ek3OeoxBaSmHMWNs0LPAgm/yDs9Do6EpjJe4fctKSQnw8ss2xjAKYdxuusmNf//bDI4DgpN9tBOOPJKW31JO+/SHH9h8KDyPkGSeAhP41qxp3JYPNTFyyBAXJk3yrV6nzqFDZuTmug2ZMKsEyzyoZW4IRd4tUkQrFCLa0pZG1oiPNeSxJZo8eodIqCdiaZvYpCZCtYWD54WM+cWLtSWZ6DU5sySj+SNfwUlvRE+GchhB0KcACNnlx46xhlkI3r4NG4A330xSadvIY4858c47tdi5M9iLJoSJsJy3kXCNKS2/pdwWKGv4RYcOHs2JUWqewfJyLmQP6sKFTmzf7miQcFNn9epk5OQIDz1yyVrjxjmRkcEbbotYbR7UMjcYxZsYzVCIaEtbGlUj3giQYUsQOsOStcxi3CpNhOoxrMLCYbfzzOEWbjfg8QBXXqmuw6o2OfuOAetWrrbXowvHAZMmOZGf72JSeQjjTLj77sZiAmIM5fjxwra4uu5t8FiHE1ojervy810ND1Fsxq2v0eJ2Ax98YFH5LA+O4/G739VrChFiiTMM5QHPlyFDXPjPf6obYm7Vv/+ZM5w3oU38/caNc8Ju96CszIQtW6S1pI2OFsPNCN7EaIdCRFt716ga8UaADFuCiADK8josSgHKEyHrgnDggJkpyUT0bAwfnoqLF6UNZqXJWUqQPD/fhdmznQzfNT7YuNGK4mKLJsmzUL57YKxnbq4b+/axSpsJ19aSJQ5s3lyLPXtqsG6dA04nJIXilYTkfb1dW7daAXAKiYXy3+PwYTODd5zDvffWw9qw65+f7/IWvZg8WVCiOHo0ODGK1TOo5QFPCqsVWLGiDhzHIq0n7JjMnp2MoiILPvrIjK1bk4Kum3iLNdViuMXamxirxKpoSlvGexGLSKLpjqqoqMDevXvx0UcfobS0FGazGVlZWejTpw8GDRqE9PT0CHWTIOIPqbi10lIOEyemqH42PZ3H6tXykkysC8KePUlYtMgpW+oUkI/VlerTqlXBfVKSHerQQWNpLd0Qv4x+iXxiXOAf/sCWlBfquTMz/X+IRvUFdlq25GG1AlOm2CR/l/x8l+LvBkDymmj02qp/N/F7sD6E+WpGB/fNhk2brEEyZVo8g8OGhRdPLxotc+cmM3jtOZSX+97rwWMWy5K1ocJaNCHWJXFZyy2vWGFFnz7BJcbDIRJ5G0rnisciFpGGScfW7XZjw4YN+H//7/+hS5cu6Nq1K1q3bg23242ffvoJx48fx3//+1+MHDkSEyZMgMUS3hNoeXk55s+fj6NHj8JsNmPIkCF44oknJI87ZswYHDlyxO+9tWvXom/fvprOaVSd2ETSsY0FRhs/Vn3Mv/61Bv36yU/6bjfQpUsaUwlZUfNT7jhserM87HYeS5bU+VVGU9NRHDzY5eNtVCI0TdZYHPfBB53YtSu0imcsZGV5sGxZo/E5apT2BLpZs+qwapVV9neZONGJjRvl309P51FRIa/Py9KfPXtq0KePm/maV9NTltLmPHjQzKR7u3t3Dfr21ceIYj2nFpTu0XCI1PzHkpDFojHMYniFkvxVWGjBhAnqDgQROW1no60fchhVKzhWOrZMFuhvfvMb9OjRA/v378dVV10l2ebs2bN46aWXMHLkSPzlL3/R1tsApk2bhlatWuHQoUMoKyvDY489hhdffBFjxowJavv5559jx44d6NGjR1jnJIhowFoG9/HHbV7jRgqzGRg+3NWwRayMklerpETZs9GIvwcqO1vQg12wQGm7j8e+fWpTDI/0dB4XLwrt9U4YE1QFdD0k3nxTjBeNTIhFaSmHUaNs2L7dgQUL1MoRS7NzZ5Li77JpU7BR6/u+coEBtu99/jznjdtOTvagrk7eULbbBbUJrdn0HONPwNqOhbIy/X/3eMtcV9oBEtHDmxhqeVqtIQ5yah7xAsvv0ZRg2t/64x//iMcff1zWqAWA1q1bY/bs2Vi+fHlYHfrxxx9x9OhRzJ49GykpKWjXrh0mTpyIP//5z0FtT548iYsXL6Jz585hnZMgooGWRJyzZ9Xj70SdTjXkJvniYkuDnJZ2RCkutQQ2lpjQgQOFwhCBcWl6EAllhUuXwiu+oY5w7GnTkhnVF4K5cEH5d1Eu6avPdztxwuSN266rU+5PebkJPXqkYc0aq6Zs+vPn2Y1svYhEXGiiZq6HUxI3nOQvraosTb2gQaLB5LFt164d8wG1tJXiv//9L9LT09GqVSvva9dccw3OnDmDS5cu4YorrvC+/tlnnyEtLQ3Tp0/HZ599hszMTPz2t7/FiBEjNJ9Xzyd6PRH7ZdT+GR2jjF9xsQVz5/p7HgSNS+n2ondq/vxkDBokHX/Xq5d6HFt2No9evdxB3581rlYO0bOnB7t2WXHwoAdLltQhPZ3H6NE2VFayGMWAnOeU4/iG7XT22FSTiccVVwje40hJjbHDoaoq1n0IDY7jkZHBY8UKq6brq7SUYy56ceCAGbfd5kbr1mwnaN2a994D4W7bivddOCWzRZTuUT0wwvxnsQC33abNWmTx3CvNjRaLIM03alSwlrAcYtztkSNmr/fTCOMXz+g9fqzHYTJsR44cCU7liH/605/YzqhCdXU1UlL8Y2PEv2tqavwMW6fTiZtvvhnTp0/HddddhyNHjmDKlClIS0tDXl6epvPa7epxG7HE6P0zOrEcv8JCYNSo4C1xtcmW5zmcPs3hyy+bo18/6TbPPw8MHx78unC7cli3jkOrVv7f3e0G5s/XY4tev9m+tNSEMWNSsHs3sGOH9Hdi7YP43adN47BwIXsfPB6uwaAmwoHnOTidnObrS8vDxJYtybj77mTcey/Qti1w+rT09cxxwvv5+akwm4V78fHHgVOnGtu0bQusXQsMG8be1+efB0aMCO8eUrpH9cDtBg4dEirfZWU1R58+METcJQvvvw+cOSP/Psvc+NvfAldcEfx7q1FTk4rMTP/XaP0Nj2iPH5Nh27NnTwDAqVOn8M4772D48OG4+uqrcfbsWfz1r3/FwIEDdetQamoqamtr/V4T/05L86/2M3ToUAwdOtT792233YahQ4di//79mg3b8nJjBodznHBRGLV/RifW4+d2A1OmpKmUE1Xm5ZeduHjRJelZunTJgoyM5CDPZEaGB6tW1aFvXxfKyvw/U1JixqlTeia/hB9vyvOC92rqVB7Hj1fjhRcsmDEj+HuxIMbg3X23C+vWpTXILLH2j0fz5oJebHW1UdQQ5cY38HU9lSBC+01NJqEkc1VVZM8vXiu9e1djyRIxwQ5Bn+F5Hjk5LpSVObB/v9Au0At4+jSPESOAnTvZ4yv79gV27gzehRG+f3A/pL6TeJ1K3aPh4HYLShpbtiShstI/NlUpbt9IfPONBYB68tc339SiSxf579O3L/Dxx4KH/uBBM1avVo9ZT02tQVlZo8eW1t/Q0Xv8xOOpwWTYTp48GQDw0EMPYevWrejWrZv3vV/96leYP39+iN0M5rrrrkNlZSXKysqQ2fDY9N1336F169Zo3tz/C+3evTvIO+t0OpGcrD3horG8pjExev+MTqzG76OPWJOz5Nmxw4odO6xBSRPy4QRCRrvcdz57Vm+vpH6SWqdPc/joIzPuuceFu+5yYdasZOzdmwSHg90w7dLFDZ4HundnU43wh8Ply8bz2kpvp6r9HdYZNbUePNiJv/0tiaGAhD4EXisTJ8qVvuWwd28S3n9fLE8svBZ4LI7jMXduMgYOZJfduuceFwYO9Jd1Ki/nMHasDcGJkMKNOHt2Ha65xuMXAqHnvFRcbMHMmdIPhGJSYjwkSF11FdugXHWVfDiXiMkE3HqrGz17urFrV5KqBFnPnm5JmTtaf0Mn2uOnadb/8ssv8ctf/tLvteuvvx4//PCDbh1q3749cnJy8PTTT6OqqgonT57Exo0bJeNmq6qqsGTJEvznP/+Bx+PB+++/j+LiYjzwwAO69YcgwiG8bGf/mcA3aUIpBk38Wy4RgjVRZerUuoZqS9Hl3DkOxcUW9OiRhl27rBqMWoG33hI8c4FJJ5FGSFTRf/Z+8MF6XHmlctWuSJyXBbG60ccfiz6S6I75uXOC8kJRkbKkXGUl12DssSWlsRJYhnbIEMHIDo7cE3YN/vznJFit0KV0dyDig64g0xZMPCVIRaKqFhU0aDpoMmyvueYavPjii36vbd68GZ06ddKzT1i3bh1cLhcGDBiA+++/H3369MHEiRMBAF27dsW+ffsAAI8++igeeeQRTJ48GV27dsXKlSvx7LPP4pZbbtG1PwQRKuzZzlLtpKSYhIXpww/ZKi6VlJiDKkuxLho33eSJmgfOlxMnTJLZ0Gz4hh1Ez8iaPr0OW7Y4Gv7S08jksWuX1WdLWf06iRbi9fPII/UMlcUiQ6tWPEN5aai810i4slvFxRZs3GiVvW/Ecrt6VxtTftBtJFQDPtpEygiNZmUwInYwFWgQ+eSTTzBhwgSkpqaidevWOHPmDDweD3bs2IHrr78+kv2MOEYVYI4XgWijEuvxEwsgKG1/pacLHdMSTzpjRh1TvFh6uicozs63shQgLZ4uJ+AfLo1KENIxiGL2ulDZyXghAXLMmlWH7dv9YxqV8b0Y5b5n5LRy9UC8lpxOaBLD14a86kVWlhCP/frr2sT4lQinUIKWYifp6YIhdeut+nhuWYtgiGzeXIthw2JrxLEWeQhFx1aPcwOxXz/iHUMXaBDp1q0b3n77bbz//vs4d+4cWrdujTvuuCMo9pUgCAHR8zB6dLDsjGhErlhRh/nzk6HFkGGdJAKz/H2FyLdtc+CJJ5L9athnZfGKhRdCJT3dg3Hj6tGxo0cmBlE4V1UVj6oqoyRtsbNypdZKZCzjalyjFgCee86Bfv2EymLaCDfJTXg4Ej12+mjACrJb4ZR4VSvj2oigvjF8eKouRhoAhhK//sRaN1fKYLXbPRg+XChHKxqakSpPSwUNEhvNK0h6ejo6duyIn//858jLy4PT6YxEvwgiYVDb/rLbec1bub17qwmQi3GX0uEMM2cmY/78ZL/kKrtdqCZmt/MhFwYIZPr0OhQV1eDLL6sxa5YT99zjwuzZTqSmSvc7XrVbBWLfd5tNPt5WDDFp3doj20aAzegRt7O1iuGzFe7wbRtMRgbvLVAinp89BES63b331uPwYf+wHS2EEsbAUmxAjeJiS8ODMQvaY1P1Rq7wQnm5CVu3WlFQkIqcnDTvmATGMVMMLKGGJsO2vLwcDz74IO6//3488cQTOHnyJO688058+umnkeofQSQEShV4tC2IwsLUu7dbNgat0aCVj7+tqDAFeXkuXBAyuvfvDz/+TzSiZs4UHnxff92ClSut6NYtDcuXJytIacXeOAwNI/Sbh8PR+P+++MbDDhnikmzj05rtbA0fV4qHjAzC9Ssa1r7nVzJuOY5HWpp80PimTYJRNWFCCgoKUtGlSxpDSehGQvGChpvQJRqJvrsuCmcDENsEKdZYYD0MfqLposmwffrpp9GxY0ccO3YMFosF11xzDcaNGxd2GV2CHbcbQclARHwg53nQuiCKC5OcJ1iM2VVH2pu7Z4+WxSTYQyjG0fbo4UKXLmleY2H58mTNW6aEFoSxbdaMD7omMjKEuM7ly5OxdasV2rym0vhu5cpdi5HE94EwP9+FnTsdyMiQ91bzPGCVjRgJHovychPGjLHhqaeUw0zEObm0lGtQEdE2BqEmdLEaiSIZGbwmrd5IwJboF18KDoTx0PQ4dPjwYbzzzjtISUnxViIbM2YMdu7cGZHOEf5EKpCeiC25ue6gJC85xo2r9/7WbrewWM2bV4fycg52u2DQeDzA8OGhFWDgea7hWB5cuMBSWpZD4EKens7D6eSwd6+0rigRSYRSvFarB3Pm1KF9ew8OHjRj1y5lOSytNGvm8TNsA6/F8nIOzz2nXU9cC4EPhGI85po1Vmzd6p/Il5XF45FH6rF8uVKfpK/NDRus6NrV4+PpbkRqTpYLA1JDaygDa0zvFVcAjz1Wh2nTnDHfxtfyHcUSt4cPmykeltCEJsM2KSkJDocDKSkpEMUUqqurgyqCEfojJ8bvmwxExm18YjYLBqvyoivQrp0Hbjewf3/wgpqV5cHIkfVo396jwTCVZsSIemzdamWssy68P316HZKSgBUrrJRBHGMuXOCwfLkVaWl8RCqoPfSQMNeUlJixf78Fe/ZY/OK1W7TwICPD06Cpqu/DjKiIIBUnajYDs2Y5MX26Myjh6PXXQ9nWFvr+5JPJuOeexuINYnWv5cu1Jg3Ko3XnhtVI3LgR+NWvnIa4J0MJ16BdHkIrmu70O+64A7Nnz8a8efPAcRzKy8uxdOlS3H777ZHqHwFhEp07V3rLSayaM29eMvLy2KvmEMZi+nQntm1LUjUE5s+3YfVqq6QIe2kpF2AcS3mO2DxJAwe60bOno8F4ZltYrrvOg6VL9VVTMAahjWFsETzpkSoLXFoKdOkiX9ntwgUTwtfzlVJPEEIKFi9WjhOVynoPRwmgrMzk9RwWFwuldIWETym0XStKhroSrN+nTRtNh40oYqKfnPyhFPPnJ8NmAzluCGY0zXozZ85EamoqBg4ciEuXLuG2225DbW0tZs2aFan+EQAOHQKTGL/RRbcJecxmYNUquQQY/78bjVrtJVWF+FvlBBsxazo/34VPPqnGmjWqhwUAlJdzuqkpEEqwVhoLXU5L7b2//c3KXK6YLaFMOtktOGZWePBbsCBZc2JRbq47rEp6YkW80aNtGryI6kYtoJ7QJZVbwVJopU0bD/r0YexqFGBN9POlvJwSyQhtaDJs09LSsG7dOnz44Yf461//ig8++ACbN28mHdsIU1rK1i7cqjlEbBETYLKzpRbzwL/ZdVDtdg82bqzF0KHOBl1b+YIAvvqggLAQTZkCpkpldrsB9jpVCaWP2h8gIkuki1eEX8GrsS0HIVJN2Rg3BaxEWVlCotPKldJGkNYKXqIg/803hx6r2bIlrylZiwWWilfFxRbk5KT5KTbk5KRh/35LTErEhpvALCYaBs9zclAiGaENzY9AH3/8MU6fPg2e53HixAnv60OHDtWzX4QPWVls7WItuk2Ej5gAs317EubPt+lwRCGR5/vvTdi7Vz2ByFcfVMRsBpYsqWuoVOa/teq7gMplpOuH2rYuS4hArI1SKWIZ2hBusQR1qqo43H+/E++8Y8GFC43n8RXk797djWPH/GNiAaGSl3T/OPA8WwiWdIKXFoTiDTyPMI4RzPTpdZgzRzmhiyW3YseO4JChrCw+IknFeiUw+xZeOHDAjL/8xYpLl9RKAVMiGcGGJsN24cKF2L17N6666iqvKgIAcBxHhm0E6dMHinFJocZoEcbEbAZOntQ3NvL550WJJyU4VFQELx6FhWgQgA/+vFipLCOD98odCZqakTCU5D3Ns2Y58X//Z8a//mWO0LkjSSz7G51z//Wv/klWzZrx+N3v6jFzZqNhF2iwlJSoZf2rGztyhiH7w4RQ4nnp0jqUlek7Vn37KhcbUJLz8s2tOH68OiLVuQKRG8szZziMGmXD9u0OSeUIOcQ46N693fjlLz2YOFG9LDLtShIsaDJs33zzTbz66qvo0qVLpPpDSGA2A8uW1WHUKPmyrLEU3SbYYa2PvnWrvvJMDgf7guC7eBQXWzBqFCQeqITr7t5767FgQTjeMD3gUFRkxnff0Q0QL1RVcVi5Mhk7diRh1ao6ScOMNY5Vrp2yzqvUZ4K91xkZPMaOrUdeniuE0sHSsDoi1OS8Ar2YkfRksozluHE2ANqMWxFW/WPalSRY0GTYNm/eHB07doxUXwgFxLikaG05EfrDso0nLiDqsG4ha99qFhcPJTUOMet+0yb95I7CQTBqyZvTiJxH0liKDhUVgrcvI4NHRYW/dF2nTmyG2scfm1FQEByOwKrz6stjjznRvDm8OrgVFSYsX56Mbdv0edDU4ohgNeyj4cVkGUuPh8OYMbaQikCoqSXQriShBU2G7WOPPYa5c+di9OjRuOKKK/zey87O1rVjRDC+cUmR3HIi9IdVh1jLYiwYA0rSQlqNWiGWUFw81PtiHAPJWH2JBmoGqtx7Rhsn4QEpUL6utJRDaSmbMblzpxUHDliCHvBDMfhefTXJLw5YREpezx/fG1v+nkxPb/QAK1FcbGkI/1EnGl5MLWMZivSkqJYwejTtShLho+lxtq6uDm+++SYKCgowYMAADBgwAHfccQcGDBgQqf4RAciVZSWMi1qsHCC8f/CgmTnLe/x4J/7zn2oUFdVg8+ZazJlTJ7GdpyXWNXjxoHg245Kkb6RKjJG6TrVde+IDou/9o93g4xr0d6XOr3wvZWXxmDTJGZTpn53NY+jQeqSnCzJjogc4JydN9l4XH4KFWHUleHAcj+7dI+/FZB/L0KUn5coysyhHEIQvHM+z1yO59dZbMWXKFNx2220wBeiztDGSCnQIlJVdNkRllkA4DsjMbG7Y/hkdI4xfSYkZBQWhlbiVo6ioJiimTozfPXDAjC1bWJLFGmnWjMe6df6LRyT6TehBKEoGWkMQjBWywIK4XX38eDXMZuF+yMlJ01QMIFT27KlBnz7uoBj68nIOY8fagh5qRS9koMEm9lkIN2Pr85w5dZg1yyn5nl7zn9Z+bd5ci2HDQjNEWfIQooUR1o94Ru/xE4+nhqZQBLfbjV//+tchd4ogmiKheT6lDQu1cqK5uW5MmsQuE5aS4sGkSf7Z6SKNcW8mmtQNRSjXU7SMVB4mE+DxRPOcDWcOSKZS2t7Wm/PnhWP7VjwTjUEtFSNDiQtevtyKTp08QQby4cNm/PQTh44dgRtuCNYK1oI4lqNGsc0t4YRHSFWNIwgtaLrUhw0bhj/96U+R6gsRJuEKZxORIbRJXog99Ec91qxxYVTfxszM9OC//62W1dIU1TgAqepRZOkmNtIqGCzcfbdoYMXmGvF9kJTb3pYm9P5K3eNq96JUxchQw398ixf4FnQYPz4F/fsD3brJhz6wkp/vwvbtDphMbJULCSJWaLrS//3vf+OFF17A2rVrceWVV/pp2b777ru6d45gRy/hbMIfPbbFQqmPLuDf1mQSsraVfk8tC+Py5XWwqoga5Oe7sHs3MGUK76fGQTQ1xAct9WvgwAFxWYnN9XL+PAe3G9771Dfpdv9+Xyk9qURLbSEYSjsoBw6wTRS+92yoD8Gip7qigmNKUlVCac4TpLwcGDNG9NxSkhdhPDTF2BYVFcm+V1BQoEuHYoVRY2hYYlT8M+7V47iaEuHE+Oj5sCD+RoCUJiwbLL8na1ysUlye/zmBjIzmeP31Ghw8KMTuatHETSziL+40NsR+nJTuU6n7WigsonWvXrgfpeStBP1nG1jGwTdePpy44I0ba7F0abJsHGxgDLIUrHNeU3GkUIxteMQqxlaTYfvYY49hxYoVaNasWVidMyJGvXDVLgy1oH6WySyRCfXGisTDQvilPdV/T/WFkYfdzmPJkjqvp0npunjjDQvmz0/BqVMhdzmOkZJPi1fDNp77zoJ0qWe5+zTQK1layjFVvgpE6gGRPdFKkNcLvJdDfQhessTBVIZbKvHU97xaEt2MkuQVKciwDY9YGbaaVthPP/0UVrW9SyKqhBLHRSjDKs+lNYY5P9+F48cbJbr27KlBVpZHIn5VHrXfU0zyAOTjYsvLTZg4MQUFBamqskOjRtmaqFELiL99s2Y8Zs+uw4kTVWje3IP4jS+O136zoO0+DZRNZK18FYjLhaDjs8e5S2/by8cFy/WRR1aWB3Y723eQClcKZc4j6UnCqGgybPPz8zF16lS88cYbOHr0KI4dO+b9R8QG1phK0iRlJ5IPC76LQZ8+boXkLGWUfk8tCTNinfd9+/yNW/+FrmlTVcVhxQorZsyw4fJlNoPFeMRjn8NDy32am+tGVpb2h5bVq/01ad1u4NAhtnkhLQ2yhRoCH4LnzFGaJzg4HMAPP7At53oluhGEUdGUPPbyyy8DAN5//32/1zmOw5dffqlbpwh2WJMNEqXGdrjbXyyfj+bDglypZDXUfs+8PBeuvJJHSYkZHg/wpz+JFZWkhfAD67yHIjuU6OzdG15WeewRf/tED0vwh+U+FRVAhLhYbeMjJmZNnOhEUVES831TXd0oTSY3L/mGDHTq5MHMmcmSVdAqKzksX25FRoYHlZXay9KSg4RIJDTN1F999VWk+kGESFOqsR1uwgLr56P5sOB2C6Vx582rQ3k5h4wMHgsXJuPCBfnkEZOJV6xKFEosb2Cdd1rAAkmk8Uik76LO+fMcCgstqg/C+fku7NzpkDUe5RDuUx4bNmgP0yst5Zjnpbw8F+bOTYaU4S3q4opoLUvb1BwkRGKjKXkMAM6ePYu//e1vOH36NK666irk5+fj6quvjlT/ooZRg8O1qCIAkJzMEkEVIdRkLnH8XnyxFqNGsX1eLQFLr4Q8uQWtoKAeGzdaJePdBHhwnPR3lhsnNhqTWQ4fpqpj8jQtj2cssNk8PuoboY61WCyi8fMsD8JuN/Dhh2aUlJjx3/9yKCmx+JTa1ZcHH3Ti1VeTmOalQ4fMGD6cTfHk5Zf9Pcdt2niwZIn8947WnBdvUPJYeMSFKsJnn32G3/72t+jQoQPatm2L//3vf/juu++wY8cO5OTkhNXhWGPUC5f1wkhk+ZVwlB9Euaqrr/Zo+nykHxbUDPXx453YssUq67WV6nMo5TilKCqqQW6uW5djEYQ2hOt/61YHnnhC9JyGcv35Tpbh3b9ut1Dda82a5BD6oUx6uhA6oDYv7d9vwYwZyaisVDewN2+uxb33unwqj6Xghhsuq1YeawoOEq2QYRsecaGKsGLFCjz++ON47bXXsGbNGuzZswfTp0/HypUrQ+4ooQ+ByQZFRTU4frw6ISaicBMbDh2C5s/LJWBlZfFhT/BqGcg8D2zdKm/UyvVZSza2EufOcTCbgYKC+rCOQ0SDxFtthw514auvTKioCP1abqwdFL6qidkM9O0bmVAuwVBVnpdWrbJi9GhbgwGsTqtWvF+Sar9+YPKyRnLOI4hooinG9uuvv8bOnTv9XnvooYewbt06XTtFhEai1tgON7GhtDS08/hWLNJTq1E9MYuDx8N2LN8+6xUX+/XXJqxcaQ0pZpCINonkTeeRlsZj794k9aYSn/UNO1B/KGxM3ALUk0pDrx4o31+OY9OpXbVKKSypET3yKSI15xFENNFk2KakpKC0tBTt2rXzvlZaWoorr7xS944RhEi4iQ1ZWaGfJxIPC3omZvn2uWXLcL13Quxo45YrxZIS0aW6OvSwA9aHQRHxPmQJ4xL1oUePtgUlZsmFPSjDMW/NshnS+pWzTVQHCdF00BSKMGjQIEyZMgWHDh3C999/jw8++ABTp07FoEGDItU/gvB6S+S0XjmOR3a2R9ZT0acPwvq83pw4EX4iSmCfi4stmDxZvepQIyzi72TUEtEk3HhubZ9t1Yr3xpUGSu2JEl6+xUvErfr09MB7hUNamvCaVj1q4XPhh5NkZPDYts2BjAwehYUWlJSYNReQIYhEQZPH9vHHH8eFCxcwceJE1NfXIzk5GcOHD8eUKVMi1T+CUPSWqMnYiJ8XNSpD+byeFBdbsHy5FeF5Q/377J+IxorUucmQJeIRbdetuGXfvbsbPXqkyca6cxyPefOSkZfn8s4NHg8aEtr879+aGuH/09N5TVJh1dWaui7L6NH1WLAg2Ou8bFkdfvtbfc4h0hRK6RLxDZMqwpw5c7B8+XJ89NFH6NWrF5xOJy5evIjMzExwXGIshkbNeqSszEZCUX7wHb+//S22yhF6qRbY7R6sWCH0Wa9jEkRTwDfD/8oreSb5rKKiGvTu7ca+fRaMG2fzkw8LPHbr1jzWr3fg3DkO8+cnN+hNy92X4oQezn0rxCXX1HCyCiu7d3Po21ef9SOR1XekoPU3PAwt99W1a1ccPXoUPXv2xCeffBJ+7wyIUS9curH80eotCBy/WHobSkrC1YblkZnJ45//rIa1Ia/r4EEzRowgvVmCYEE0wgBoks+yWtFQlUzdCBUN4aee8k3AjNRDp3J8L8fxaNuWw7Fj6nJfaoSqJR7P0PobHrEybJlCEa655hr069cPDocDAwYMkGzz7rvvaushQYRAuIkNsUyMCCdpTFw8li+v8xq1xcWCtiVBEGyhPevWOXDpEqcpdKdlSx5TprDHr587x8HtBoqKRHWHSO6kKB+b5zmcPCk8VJtMCKsUuZJEoVTYBkHECibDdtOmTfjoo48wb948TJ48OdJ9IoiEJJxylFlZvN92X2hxtf6IW6e1tZAViScIYyMatGzX7k8/cVi6VNpAC0SMxeV5aCpP3aoVzyDpF13GjLE16AILaA0fUPs+UhJqBBErmAzbli1bYsiQIbh48SIKCgoi3SeCSEi0aWHysNt5LFlS59Wm9K0wJuc9kTpOI/4SRTwPDB1aj02bSK+WiEe0P9WVl3OMBmdjgmZZGesDX6OO7Ouva8rLjjiBCW2i6gNr+ADrbtOhQ2ZKJiNijqZHymHDhuHFF18EAHz77be47777MH78eJw7dy4SfSOIhEJUdwCUZYE4ThBvX7GiDiNGuNC7t/9CwV5hTDjOpElOZGcHSxQBHLZsiXQMIBFfxFsgIdt1K8rj2e3s3+/uu13Iz3dp2GnhMHJkPczm8HZnBHiff+EeB1CqwHbwoFlVIoz1+6xenYycnDQ/mTSCiDaaDNslS5agqKgIALBo0SJkZ2fjyiuvxKJFiyLRN4JIOOTKVvqiVsJy/362RSM9XTjOwoVOLF5cB6mFUsjwJqOW4NGihVDhQKsWa3TxvYbZPamA8FB51VXsRurbb1vgdAo7LenpbNUfOnQQ2qlpb7Ocv/H7qR1DzghWjjsWS/aOGJGKCRNSUFCQKmuUavk+UhrABBFNNBm2R48exf/7f/8PFy9exCeffII//OEPWLJkCT7++ONI9Y8gEo78fBeOH69GUVENNm+uxe7dNdizR/j/oqIaHD9eLWvUut3A7t1sC8bWrQ6vJNiCBWKSGRmxRCCCsfLHP9Zh506pAgTGITubx/jxTo2f4jBzphP5+S5oUafkec5734wbV8/0GdGz6bs7E47XdezYeondlkbE3R2pXZlQfkc5o5R1twnw9wZTkYjo4nYLiYJNvUiHpkeq6upqpKen48CBA2jXrh1atWoFp9OZMFq2hDrxLM5tpL4rqTOIk5NUPw8fNuPCBbbnUd/PGCmRhTAawvz95JPJGDWqviF221jllJs392DHDgdMJuDDD7XftOvXW9GpkwcujWpUO3daceCABYsW1aFZMw+qqqR3OMREM9/qheLujKD7GtpYCvH0dSgv5/C//3EoLExCeXnjveybVDpvntNvfvN4wKTT64uSwoGW70PJZNGnqWkMK6HJsL3uuuuwceNGHDx4EP3790dVVRWee+45/OIXv4hU/wgDEesbJxzDtLjYgrlzo993tT4Hvl9ezklWEBL7qUUy7Px5oW04MmNE0+HCBQ4rVxpNPk4wsB95pB6PPWbzM+q0GN8OB4cxY2wYOpTN8+rLmTMcxo2T17D1rV4I+D+U5uW5kJfnwuHDZpSWcigv52C38/jmGxOee059rHfutGLnTuH/s7M9ePbZOtjtvOR8Eviw7HajIVnVpEk9Rckozc8Xvs/y5VasWaPef5p7ooOcSo7WJMFEQZNhu2jRIjz11FNo1qwZJk+ejP/85z84cuQI1q1bF6n+EQYh1jdOOEZ1YaEgrh7tvqv1Wep9qW1L335qSUoR24afyEI0DYxphNxyCxqUOwL7p72/e/cmQXuMrlhCVxrRawqIVQCl73ffh1gtSWwipaUcxo4V5oFhw9Tnq8ZS4ilBpcRZkDNKzWagb1831qxRPwbNPZGHNIaDYao81hQwamURI1Q+USvbKm7DHT9eHZEbJ5yKNx4PcMstzXHqlLR3J1J9V+vzxIlObNxolZiMlPt59Gg1undPQ2mpcqnO7OzG7yT+fmwyY1rhYTIBPI8IHJtoytjtHuTkuPH220nqjTWhV5hFYyXAt99Wv9+LipL8jF6TSQgXkO6LPvMVxwEHDzbHlCkezeFIYgU1KZxO4Je/TJMtGRzpNSFaGGH9VYO1oqXS7xkpDF15TMTtduOtt97CDz/8AI/HP0uUCjckLrEU5w73afTwYTNOnQr+bCT7ztLnTZukjFr1fh45Ym7wwtggvfg1bov6blEuXVqH0aNtIXlulOEwZEg9Xn/dEoFjE00TwWBcurQOEyawV/xiR69rlENZmTB3KN3vAO9TWreRxiVU6j7Wb74aNgzo3bsaH30keItbtODxwAMpCjrYwsNq9+7Sxxd3mvzDQnx6zgXPQUbASDkWcoTSR9Zwj6YUFqLJsF24cCHeeOMNdOrUCRZL40cpeSyxieWNE65RHYu+s/Q51KfX0aNtWL1ayF6fOTM5SHi9RQseK1cGh2fIJ37Iea/Yt2sHDnRhyBCXZH8IQjuCwThnTjKMGh7hy86dSSreULnvIBi9JpOvkcuG1vnKN/62pMSs8gDKweMBjh0LnlNZKh4GVkk0ArHOD2Eh1D6yhns0pbAQTYbte++9hz/96U+48cYbI9UfwoDE8sYJ1zBl7dO33+qnGhDJJ+PKysZ42//8pxolJWaUlJjBccLCdeut8k/4YuKH6BE4ccKEFSusEKqQBRq7AKtRkZnJ4+hRc4NRa6xseiJ+uXQpPpQ83nwznFAJwYhcssSBli15vP++Gbt2qVcC1DrXut3wemy//pptXAPnMfWKh0K1xKNHq2E1UDHDWOeHsBBOH9UqWkopdiQ6mgxbj8eDzp07R6ovhEGJ5Y0TrlGdm+tG27bA6dPK2+QrV1pRWwssXKhVI5O9L/ogGI9i+EXfvm707cs+7oGZ0506eYK8uHY7L7vNGAyPRx+1obo6PowQgpBHuG85LjBeXGlXQ5+HuJYteVitwK5dYnKbvAKD1rm2sBCYMiVNc4xt4DymLhsoqD6sXm1Fnz5upm30SIcHxENiVbh9VAo1M2pYSKTRdKXn5+djx44dkeoLYVCUxLkjfeOoVbwRS2XKTfRmM7B2rfiXssG5YYMV+/aFXy2Hpc8mU+jlMsWKQYcPhz/ggcUiiopq0KuXlgcUjoxaQlc4jkdmpsa9+bBpLHQgXRVQW1UvrWRm8pgyRa2ICg+e1zbXFhdbMGIENOnoys2prDtRq1cnK1Yx8+1bTk4aCgrUK5+Filr5cT3n0lDRo49yFS3VqlgmKppWpC+++AKrV69G165dMWDAAL9/RGITqxtHD6N62DBgzhwnlBciIbv3ySfDr5ajXKVHWJzGj3eC48IrX6pXyIPoxR02zIXcXDfeeYdKYRKxQrg/nn66DtnZHoRTtUsbHGbPdmLhQieOH6/GnDl1PmV0pTL/tZTzlX+IFY1IlwsND4jKc9SMGU7mudbtBubOVQodkO4PID2nat2JUiqtK269BxrcepfjjYfEKr36KOWkUKpimchounruu+8+3HfffZHqC2FwAmM0o5VZKpf4pCVJQazhrkZZmUkXhQT5ZC3h/19/PQkTJzpRWJjUIN2lnUiEPKxZY4XDQTGyRLQI9HwK/z9nTjL69XM16M5GB3GO2L/fghUrrBIJUlr1b8W24uf8v6uvEblnD9tSfPIk+7lDqTioNKeqhaQFIreNHs3wgHhIrNKzj0oVLZsSmgzbgoICydddWusUEnFLrG6ccI1qLROXXk/v+fkueDzAmDGiZFHjcUtLOWzYYA2pnnukYprdbmDr1ugZEgQhEGwwVlaasHevtSFmMDq9aNWKV0mQUi7UII9wHLvd06D7KuBrRL72GttSLJT0ZSOUeWzdOodszL5vLCdrKIaoWrN9exJatuS9Yxwt+ch4SKyKhz7GG5oM2//973/YsGEDzp0759Wxra+vx/fff4/Dhw9HpIMEIRKOUZ2b625YWNQ9GHo9vbvdwIIF0nFzor5lZaXWxSdyMc1r1lhRWUnxskQskI8vjDyNhgNLglSoLFlSh6ws6XK4PXu6sX+/+kNlz57s818o89jbb1sUk1HFnajZs5P9jHQ15s9v1CNuDPFQRg8HQzwkVsVDH+MNTavY3Llzcfr0aTRv3hwulwsdO3bEf//7XzzyyCOR6h9B6ILZDDz7bB2U4t2Eil3yiWhaUUsKkI7dU8Zuj0xMc3GxBcuXG0ijh2giaL8H9EWYC5YtEwwHdmNKu9GYlcV7Y9l79/bfbRozpr7BiJGfmziOx5gx9cznEz2BWtizx6KaY5Cf78KSJXWajusL68O8Xg6GeEisioc+xhOaPLaff/453n//fZw5cwbPPfcc5s2bh759+2LLli1UeYwwPEOGuDBpklOyCpCYFa3nk3EkEhKWLKlDXp4LJSXaQjKUZHXE7VeCaGpwnFDuVjQctBlTgdvxymVwlR6YrVahH8LcJHVc4X0t+rBmM1BQUI8NG5Jl+xZIeTlbjoG0cgQrviEd0dl6j1V+iBbioY/xgibDNiUlBVdeeSUsFgu++eYbAEDfvn3xxBNPRKRzBKE3Cxc60bWrB0884V8SMjtb/2o5kUhI+OEHE3Jy/DUps7I8GDmyHh06eCQnQ7WKNqEkmRBEorBxoxVdu3pgt/MoLeV8YmGVDUHpimHyCWJqBkpOjgdpaXyQfJ5ofGvV2Ha7gaIiMbxB39hcrYlkwYifCX28tBIPiVXx0Md4QJNhe/XVV+ODDz7A7bffDo/Hg5MnT8JqtVLyGBFXDBniwj33RP7JWJz8BVWE8Ly3HMcjI4OXDBcoLeWwfHmjx9XXaGWpaOMMvyYFQcQlYqz7uHE2eDxS1ffk8K8Y1qoVj/JyDgsWhKbcIl+qVkiey8nRrusb6gMra/a9XFyoFtLT/fMMWrfmMXJkPZxOofQveSyJUNBk2I4bNw5Tp05FcXExHnjgATz44IMwm82kY0vEHdF4Mm7cCpQOfRBQXxBEL0bjoqesqXnmjGC0btvmwIIF6rI6zz/vUO0DQSQunITnlY2WLXkMG9ZotIbywKymxBCq/JXWUCitIQDykobs7NjhgMkEb4nvl15Kkn1IJwhWNBm2ubm5ePvtt2G32zFx4kS0b98eVVVVGDp0aIS6RxDxS3GxBRs3KgXFyZXp9H8vK4vHI4/U+034ynDgeT4o3CLoTA2yOi4XcMUVPC5dirV+rb7VnAgidEJLcArlgVnNsxqq/JWWUKjAEADWUre+caGHDpmxejXbHCUa0bfeKhy3uFhaO9h3Z4mMW4IVTYZtfn4+9u3bB4tF+NigQYMi0imCiCekFgFAyQsj91rj675bnLm5brz+utZKPByzHM/48bYwjVq9DFIyaglWQimWoC8mEy97j8kZhlKvR6o6VmMcrElVC9g3ZEItJj8Q0ZjPzXVj164k1bhbKSM6WgUbiKaB5rp1tbW1aNasWST6QhBxh9wi8Mgj9WElZAVucUayMo42LV2+IWkm8DPkbSWiSWj6z3peox4PMHasDSaTvzdRbk4oKKhHUVGS5FzBgtY5wGwWZMxGjUqR1EcVS3sPHOj2Gt4sMflynlPWuNvAuONIeayJpgvH8+x1XX7/+9/jo48+Qt++fXHVVVf5vRfvcl9lZZejVuFGCxwHZGY2N2z/jE4kx89/EQheNMJZRIuKavwmcbcbyMlJ05yFfMUVngZvLFvYgzo8xo514uqredjtwnbirl0WvPoqaeASRiYyD17ilvrx49UShqFcMlqwCoCYRKVUeUo8h7b+AQcPNseUKR5VD6w4x8glu7L2Q8qwt9s9GD5cCFsIDGsoLLRgwoQU1e+yeXOt38N+NIjm+ssa/hFP6D1+4vHU0OSxPXXqFNq1a4fvv/8e33//vc/JyFNDGBe3W8iwPXtWvwlDbfsstNKb8gkcoZSzBIBLl0xoLEqhrrnJ0ENs2ybE0dntHjz7bB1oA4cwPpFZo3y9ibm5boZyvNJb7RwnJIeyVJ7SagANGwb07l2Njz4KDpfy1cP2ePQpdatVj5XVEx3JXatYozX8g1CGybBdtGgRFi1ahJdeeinS/SEIXSkutmD+fODUqVTva3pMGOylN+UMyGBvqZqGo5iFPHduMkpLlRZqdaM1UGYnFMrLTRgzxobevWniJZo2585xIZfj5XkOFy5wmDOnDi+/nKQoFxaqARSY1CZ1HD1L3WpJolPTxI1EwQYjEU74h94kiteYKQhw3759ke4HQehOcbEFo0bZcOqU/+vihFFcrDnE3IuWRA7RYA14FYELnVL5RNHrfPiwGXWq1SylJIOAzEwPNm6sRVFRDXbs0E/iq6TEAuVSxQSR2Hz9tQkHD4ZnAXTo4MHx49UoKqrB5s3CfXr8eLWfUTt6tC1IWkvrfCZ3nGiXuhURd6OA4LkykgUbjID6zp/wvlqZYz0oLrYgJycNBQWpmDAhBQUFqcjJSQtrnYwVTIathjBcgjAE/hOGP3pMGKyT+5w5TpXyk4JBOGtWnd8i5ovvhLN1qxUXLmhPSuN5DmVlJm+9+ltvFbwk0ka3FnwNdApJIhIRpYc24fU1a5KxZk14ZalbteK9ns5hw1zo3Tu47HW4BpCaZq7Sd+U4HtnZnoh4TsXdqMC5UulhPxFo9PLLe/PPnBHKHEcSvR6ajAJTbz0eDz7++GNFA7d79+66dYogwiXSmbas22fTpzsxdaoTv/xlmkyZTuHv1aut6NTJgyFD/Cdw+YpEoSF6mkON2ZWHjFoiMWnWjEdVlVI4kdRrcu2lXzeZgO7d5echveazUEOoouE51RqbmwhESupNC4kot8Zk2NbV1eGRRx6RfZ/jOHz55Ze6dYogwiXSE4aStE3gInD4sFmxUAIgyGeNGWPDzp2N3gll70po+Hqa8/NdmDjRiU2brCFXXiKI+EXe0LTbeSxZUocTJ0xYuVJO8UNOn1oqWVOuvfC6xwMcOyZvlO7fz+YxU5vPWOe7wBh81tLA4ZAo8Z1aMELiXCLKrTHdLSkpKfj0008j3ReC0I1oTBhyJSUDFwEtxrPvk3Gotd6lEb6nr6C8WBmNIo2IpkGgsSm90wIAK1bUweMRdlK0P1SG9hAqN08UF1uwdWsS0zHU5jPW+W77dgfMZuhiZLIYrE1VFcAIiXNG8BrrDdOqSXJeRLwhThhyMaR6xYvl57v8Ej52767B88874HQKyV5utxbj2T+eSln5QCvCsRYsEOLwIuENJghjo36dizGdADBmjE2iEAk7M2bUYfPmWixZwpaoKTVPiPepGqzzGcu8mJXlAcfpY9SyJCQlWnynFoyQOGcEr7HeUPIYkZD4Txj+7+k9YYgJH1YrMHWqDcOHN07iXbqk4fx5DtnZHrCqBhw6JBjErCVx2Wk0nNWSFgiiacHDbvfg6NFq5OW5MHNmeIlgANCnj5AENmZMfcgP2az3Kc+zzWdqhhTPAw4H/OawUDPj5QzWM2c4jBolGKzhJMWJSjGFhRavEyEeCTdxLtxxiJYTKJowGbbbtm2LdD8IQnfy813YudOBNm38X49Epq3cJF5ebsK4cTZkZrIHsa5enYwuXdLw/fd6hSH4c+4cF1fbSgQReTiUl5uwbp0Vq1ZZUVERzkOfvyEQjleOddfmjjtcyMjgmYwaOUMqPV34u6IifM+puvoCMHNmMkpKQlMFSCRpKiB45y9Q6k0OPcbBCF5jvdFUUjeRMWrJWiqpGx4cB2RkNEdxcY2ulcd8UStF6Y8WBYLIlAEtKqoBABQUpKq0JIimhlgFLJz7jvdLAhUJJY50y5YkzJ9vYz5z4PGU1g/f2NfMTB5TptgaDOnwy/qWlJiZ5pecHBeOH1c3wnzL6SqVMgegq9PCyOuv3uMQiTjnWJXUJcO2ASNeuICxb6x4IBrjxzqJC0jXjFf/TPgGru/iBAjGuFzSAkE0TcK710wmHlu3OoJk+0S0Zv7v3m3BxIkpzOcPNGpY5z/WOayoqIYpM76w0IIJE1j6zTbe4nnVnAhaDXApfH+j1q155OenoqLCWOtvpMZBb2WKWBm28em3JwgDoW1bX5ADMpmgUWIrvAVXaktJXx1bgkgEQg8/AKBo1ALaSs0C2hN2AnVHLYwrPOscJm5xqxk8WhJm1bDbG8M6Ii1NJeW1bNsWWLLEgnvuMY46Q6TGQev1aVQiE8RHEE0I7dmiHDweDr161TO3DxepuGIx1s5uN5ArgiDikIwMHnPmOHHPPS5dk5pC8XJprVbldgPnz7PNMTt2WJniOHNz3UhP10cce/jwxsIAkZSmksuTOH0a3mQ3o5CIEl16oumXuuOOOySlv5KSktCiRQv0798fo0ePhslE9jLRdMjNdcNu96gWYQjkn//UcvtpnaB4ZGbyWLy4zquDKOVhyc93YcAAF9q3b0bSXwQRIhUVJixfnoxt25K8f4uEE6dYVhb6/ciSeCbloZTGf1dHTCiTi+M0m4Fx4+qxfHn46hJ5eY3Hj5Q0lbI6g7AFbqTqW4ko0aUnmlbi+++/HzzPY/To0Vi0aBHGjh0Ls9mMW2+9FYMGDUJRURE2bNgQqb4ShCExm4Fnn62Dck35YGprI2NEcpyQALN8eR1GjPCvOS/FJ5+YG+JsY2XUNs3Jl4h3gu/3igqOWVWAxbMbjmEyf36yopdRzkMZfD8GhyqpSXEBwPTpTmRksMscBiIlMxUpaSo1WTWtXvBIk4gSXXqiybB96623sGXLFjz00EPo06cPHnzwQWzevBn//Oc/8cgjj2Dr1q0oLCyMVF8JwrAMGeLCpElOxtbaDGCtiGEHeXkupi3RAwdiNVn7jgMZt0Q8IV6vUlJW6kZgcbEF3br5yzR16xa8va9mwChRXi7oxUotySxyXPJ/N34vJWPPbAZWrapr0BHX2n9pmalISVPF29Z+Ikp06Ykmw/bHH39E+/bt/V5r164dvv/+ewBA27ZtcenSJd06RxDxxMKFTmzf7oDdrhRbJrcgqsGyMAiG4qJFwoSnpG8oeot277bgL39hK9cpd77QEcaANcGFIIyDth0OXyOwuNiCUaNsQaECpaVcUCynrwGj/V4Tjj9tGoIeavUs0KJk7OXnuzB7tlPzeTIz5bXGwy1oIEU8bu1HYhwSBU1LSqdOnbBlyxZMnjzZ+9rOnTtx7bXXAgAOHjyINoFq+CFQXl6O+fPn4+jRozCbzRgyZAieeOIJWCRWwA8++AArV67EyZMnkZWVhTlz5qB///5h94EgQmHIEBfuuceFw4fNOHDAjN27kwJib0NZSHy9mkqfF94bP94mmXQibolOnOhEUVESQ1ydGhzC97RycDXd+ZdoYpSWcpg7V4w7lfKM8pg1yz+WMy9PMA63bk1CZaW2+YPnOZw8KRiyt97aaN3q6XlUM/Y6dNCSRMbDbufxz39Ww2qVb5Wf70Jenks3aSrRMy4nfyjKZxlta1/vcUgUNBm28+bNw9ixY/Haa68hKysLpaWl8Hg82LRpEz755BNMmjQJa9euDbtT06ZNQ6tWrXDo0CGUlZXhsccew4svvogxY8b4tfvhhx8wZcoUrF69Gv369cPbb7+NadOm4e2330arVq3C7gdBhEPXrh7cdZcDHCdkHX/9tQlr1oSWTNGsGY/mzdkSQoSJWS4ujseGDQorhmaMsTVHEPHA+fOcX2JZMBwuXODw4Ydm9Onjlknu0r7rE2jIsnoe7XYPLlwIz9hj93IKuQErVtQpGrUiekpTiZ7x0aNtDaWFfQseCP816tZ+okh06Ykmw7Zz585466238N5776G0tBRt2rTBHXfcgZSUFFRWVuL999+H3W4Pq0M//vgjjh49ioMHDyIlJQXt2rXDxIkTsWLFiiDDtqioCLfccgvuvPNOAMCgQYNQWFiIV199FVOnTg2rHwQRClILkd3uwYgR9WjbNlTvJoeqKg4vvliDkhIzo3Est+iRIUoYjaagoyzc+0ePsllGJSVmXLzI+VSWCo9A47J7d1HJRVngf/HiOowdK2Xsscdx5ua6kZXlka1qJpKVxWPZstCrXIWLuLUvzN+N/WzbFli82GEoHVtCGc3RbRzHwWw2g+M41NfXo76+HikpKUhPT9elQ//973+Rnp7u53G95pprcObMGVy6dAlXXHGF9/Vvv/0WHTt29Pv8tddei6+++kr7iatrpEUDzWbA5lPSsLpa/hgmE5CSItvW4xEmtp9+4tCyFYcet1sbJ4UamfNzAFIDZg65toDweJnqU0Gmtla5EkBaWmhtHY7gwK1Q26amNj4W19VBcW9aS9uUFMDcYGA6nYBTQTc2JUX4/cS29QptbTZ4fziftgcOWDDlMQ48apDaMIE7YEN5uRlbtiTDgnqkoaphiQue4OuQDHfDLWlBPazwT0arPF2LVN6EVLj82prhQjLqgo4n4oQVLiRpbmuCGzY4ZNvWIwn1sGpuy8GDFNTq0tYFC5wQDX0eqajRpa0bZtSh8b5Phfx9r6WtByY4kBJS2xTUgJMJ/eDBoRapIbW1oRYmyN/3NUgLqW0yHDBD/r5vbMsxtE2FeM9YUQcL5O97LW1rkQK+IdUkCU4kQf6+19LWARs8MAe05XH0PR6pAfe1b1vxvrfU1WHZH5KQwlfDd64Q73uO49GqRR0W/eEyli1LRvmFYKNRaGtGy5Yczp5y4/C7DvTo4cbbb1vw1FPJqC1vvAp873sL6pHM1+GP8x0YOMAF68ZqPPVUMkrPmrxtr8oyCxJmeXVAtfx9j6Qk7H87FQ4HYIJHYo4QrtHp05yYPJ2HOaXBVevxCOuRwnG9bl21thYLkNxw3/O8sH7KkH+XpXFr/yzQJqMaeXkNlccCb1UttoGOdoRiWy22QTTsCA5Aikn4DrxEW612hMcDMFQeA6+BH374ge/Xrx/fu3dv/r777uN79+7N9+rVi//mm2+0HEaRvXv38rfffrvfaz/++CPfsWNHvrS01O/1Rx99lH/uuef8XluzZg3/6KOPaj9x8+Y8L/zMfv/q7rybP3/+kvefJzVVsh0P8M5bb/Nr67bbZdsexS18drabf+GFGv78+Uu8q93Vsm35zp35srLG49Zf30m2ravd1X59cN7cVbat2273b3vrbbJtPampfm3r7rxbvr+AX1vH4KHKbX8o9batfeAhxbZlX57wtq353RjFtuXHP+PLyi7xPM/zNZOmKra9cOiI97jVs59UbFvx9nvetlULlyi2vR3vef+ciPWKbQeh2Pvno3hBse0I/NX75wj8VbHto3jB++cgFCu2nYj13j9vx3uKbWdhuffPW3BUse1CLGy8nPG5YtvlmOX982f4XrHtekz0/pmJnxTbvoBHvX+mokqx7V8xwu8lpbbFGOT3UhXk54j3cLvfSz8hU7btUdzi99L3+Jls28/R2e+lz9FZtu33+JnfS0dxi2zbn5Dp99J7uF22bRVS/V4qxiDFcfP9868Yodg2FVXeP1/Ao4ptM/GT98/1mKjY9mf43vvncsxSbNsZn3v/XIiFim1vwVHvn7OwXLFt5OaIV3nAwwPa5oiR9n2Kbf81bjV/9qww/5XveUOx7dH7l/Ec5+EBj+ocUT37Se+8euHQEeW2k6Z625Yf/0yxbc3vxnjbln15QrFt7QMPNa5dP5QqtnUMHuq3zim1jZQd4by5q19bJTui/vpOfm3j0o5o3pzJnNPksX3mmWcwcOBAzJ49GyaTCR6PBytWrMAf//hH7NixQ8uhZElNTUVtwNOX+Hear/UOICUlBQ6H/xOgw+EIahcOVquFqTYxACQlmf3bShSz8KW01IRRo1KwezcwzKTc1m73Oa5ZPkbLbOL8+2CR3ycycQFtk+Tbcgio0WxVvnT82iYztBV/M5tyhr7d3qzxiS1FORCrRYtmQMO4pai0zchIazxuqvJWf3q6T9u08AXICYIg9Ee7NnXLlsCOrSagQL7NTTdZgVbNUVgI/GV8Kl5TON5bbyVBKj5XitTUZKSK82qG8hqemmJtbFvVTLFtSooVKWJbXsG7DMBmS4LNu74oJ9cmJ1uQzGgbRMqOSLIEtFWwIyxmk3/bBLMj/M7D8zzP2rhXr1744IMPYPWJ7HY4HLjtttvw8ccfM59UiR9++AG/+tWvUFJSgszMTADAm2++iWeffRYffPCBX9s1a9bgiy++wPbt272vjRkzBl26dMG0adM0nbf8f+cgORRhbiG43UDv3mkoPes/yYjbjIKQMo/jh87DbAo+P8cB9swrUF7rhrd7FIrA1jYlBZzZBLu9OcpLy8FHMBRh3z4LpkxNCWoqtc0oh1ooglxbCkWgUIT4DkVgaZsIoQgC941w4rXd4gM859OWhwUuTXNE0a5Kb/KW2w0cO2bG2bMcFi9OxpkLNrhDuO9f33MJt+bI3xuwWlH8VgpGjbKB492w+RxXvOY2b3YgPZ3HsAevVJ0jXt1VI3wHLeEFEQpFCGzL1dYI60f55eAll0IRGpGxIzgOweMXhh3BeTywX60uDKDJY2s2m1FVVYUWLVp4X6uqqkJKSvCCHirt27dHTk4Onn76aSxevBgVFRXYuHEjRowYEdR2yJAheOGFF/Dmm2/i7rvvxttvv42jR49i7ty5ms/Lp6bK/sZ+60Oqijc4oO1HJWZ8dzZVvjnP4fRpDh/9q7l0ZiMHIDUVfI3PhZEif7ygPthUfptQ2ybbZJtpbuvb3pos/NO5LZ9kBW9RSbUVj5tkFf5paNuinblhQZXHhSTv4qGGlrZuWFDDeCtraeuB2c/40KstD1NE2gJchNrCEG1rVa6vUNv6Gs96tvU19vVs60SyzwOKfm3rYfUaYZFs+/92pyEjwwOPB7h40V8KMPi+5yGX3HVVlhm33G4DbxZsg8OHzThXyeH8ZQ4nL/iPp5b7vvR8EniFdc7tBubOFYo78BLH5TgeTyxOxbx5dX7fW26OOF1pAp/qavy6AMCZ2NdaLW3BaW+blga+1iNtH4RhG0SkrRbbIBp2BIfg8QvHjmDcgNAkZNm/f3/MnDkTJ06cgNPpxHfffYfZs2frrhu7bt06uFwuDBgwAPfffz/69OmDiRMnAgC6du2Kffv2ARCSyjZs2IAtW7age/fu2LhxI55//nn8/Oc/17U/4RBvFU2I0AinQpAaHMerFH0gCMLY+M8LFRUcLl5kmfOljVqgUZGguNjiV4xl/nz2hwQp1OS5WMvPCooL4Z+PILSiyWM7c+ZMTJkyBYMGDQLXsB18++23Y+bMmbp2KjMzE+vWrZN879NPP/X7u0+fPujTp4+u59eTeKxoQmhHSQcxPITr4tln6zB/frKqZA7b8eghiiCii3QxhlDIyuIFRYJ8F4qLLbpJggE80tN5eDyCV1ZOxovVCWO383FZ9IBgw7tLYMDCEJoM2/T0dLz00ks4efIkysvL0aZNG7Rs2TJSfUsI4rWiCaEdOR3EcJk40QmTSQhHCt8oJaOWIIyBtntxxow69Onj9hoQviEB0seSe4j17uEHta2s5DB8eCqysz1e4zkQVieMaIBLFz3w9zoT8YWUXrvSNRNtQqqp2a5dO9x8881o2bIlLly4gAcffFDvfiUMvrW+A7ep6eZOPPLzXTh+vBpFRTW46y5BuzJcXnklCaNH21BRwbIQkuefIKJH9O63jh096N270Su2Zo0VpaXyIQFKRm2zZsr9FstvFxcH+77Uwq6EhGgPcnPd3of9rCz/tllZPHbscBjCCCK0Ie4SBDpvlK6ZaBNusXjU19fjX//6lx59SVjo5m5aiCUO//xnB7Zvd8BmCyc+VijBKe+V8UUqOp8giMgRvR0QX09pcbEFy5eHUhpbCGWqqjLBbvcgNdUDKc+u6F2dNy85KGldq7PG92F/8+ZaFBXV4Pjxalr34hC3W7gmpNYjpWsm2oRt2BJs0M3dNLnnHhcyMoDwjU32BVTNG9MIiwwIQRCxxNcDCjQaF9rgEXhfl5dzqKlRTwI7fDh4O1Grs0Z82B82zOXndSbiC9bEQalrJprE3mfchBBvbqLpcPiwuWG7MBoICSnJyYDV6sEFiTKbUu2DY+0IgtCG1qRM1vY8eN7fA9poXGhB6lzhKfbk57say88aMIGI0J94UXkiw5YgIkj4N7jWBZNDeTnnE/6g9nmpbG2CILQRyn3Ddm+3aMEjL6/RAxpto0EpWYycNU2LeFF5YjJs77jjDq+8VyDuWAdTEISBCe8GFz6blsY3FJ9hX9AcDrGtlhAGD6qqKDqJICLJffc58dprrPGxHC5c4HD4sNlrQEbLaCDFHiKQeFF5YjJsJ0+eLGvYEgQhj9pEoIzQXqmiotpntVBVRfc4kUgYU7O5bVvthqmYaZ6b6w5zTmGDFHsIKZT02o10zXA8ry7t7Ha7YWbsqZa2RqKsTKIWtAHgOCAzs7lh+2d0jDB+/iLqxlto/TGmMUAQ2jDedSx6sx56qB4rV2pN/hIQtUIBRHROMZImaSwxwvphRFh1bPUeP/F4ajDtOz788MP46KOPVNsdPHgQDz/8MMshCaLJkJ/v8hZZMD7GMgYIIjSMdR0L3i2gRw8XVq60gj1J07+dqBUKADt2OHDFFfr2ExDKdx89Soo9hDxGV3li8tiePHkSv//971FRUYH8/Hx07doVrVq1gsfjwU8//YTjx4/jwIEDuPLKK/HMM8/g6quvjkbfdcWoT2T0xBgeRhi/+PLYEomH8byXTY2MDCGZs6JCj6dbwfP7ySfVcDqB9u2b6T63LFniQMuWfJNXOjDC+hHPxMpjy2TYirz//vv4y1/+gqNHj6K2thYAkJKSgttuuw3Dhw9Hv379Qu5wrDHqhUs3VnjEevzcbiAnJ62hSku0jQup0pkEQUSajAwPRo+ux7XXenDihMmnmIJ+9+KcOXWYNcuJp56yYsMG/Y8vkp3tweLFdbDb+SYn6xXr9SPeiZVhq0nuq1+/fujXrx94nkdFRQVMJhPS09ND7SNBJAxuNyT1HEPTnNQTMmoJItps3+5Anz5uuN1A585pDa/qey8uX25Fp04eLFzoBABs3GiNiPF15gyHMWNs8O1/ODG4cnMlQehFSCsux3Fo0aIFGbVxitsNlJSYUVhoQUmJOebl7+Kd4mILcnLSUFCQigkTUlBQkIqcnDQUF1tiLFRNRi1BxILz54V778MPzQ3hB5G5F8XypQsXOnHyZBUefNAJqSpj4RHcdzHWV1RrYEVpriQIvYiLdBZCP2hi0RcxflYINWhEnPhPnKBbjCDij/AMQ1Fr9oUXkvTojAz+5UutVmDdujrs3OlAdrZ//zMzPRg/vg4zZtSFfC5fRJkn0bBmQW2upDWI0AtadZsQNLHoi1izXSpxQ5z4X345CVlZHq/GXzCxC9zq168+ZucmiNjCB/wLfE9afF7dG8ojK8uD3Fw3iostUZlTA3eFfDPWt2ypxXvvAZ9/Xo0lS5zo00e/7Tme9zeslWCZK7UYyQShBBm2TQSaWPSnMX5WeptRnPhHjhQMyGDjNtJGrfTxOY5HixYevP8+PcgQTRUOzZqJBixbmEBGBo9Jk5wNf0ndW8Jry5YJXtG5c0PTqtXK+fNc0LwtlrodNsyFfv3gjWEtL+egd6gCS7gV61zJYiQThBqaDdvPP/8cAHDp0iWsWLECO3bsgMtlDO0yQh6aWPSHNX62QwcPduxwICsrcDGJtFKCuIj5vNJgXFOGL9HUCa60Jxp8Uvckj+RkYN48J3budCAjI/gGysjgsXOnA/n5Lhw+bEZpKWtsrZzXmI35821M4WRuN7BggWhs6zfvsJT4ZZ0rY5uTQCQKmgzbTZs24dFHHwUALF26FO+99x6Kiorw7LPPRqRzhH7QxKI/rDXbW7XivduDc+bUIdyFTBv+v2dGBo/Zs50RTWghiPgg8PpXetDkUFoqPPjn57vwn/9UY8+eGsyYUYfp0+uwe3cN/vOfRoF6LfPoXXe5gmJig4u5KM8XSuFkYrLwihVWReeGVjiOR3a2EHahhpa5kiDCRdNeZHFxMf785z/D6XTirbfewquvvoqWLVtiyJAhmDt3bqT6SOgATSz6o1azXSyh6Tvxv/yymEwitagGeov0FtbnceECh4sXdTwkQUQU8R4wRpGJAwfM6N1bkKfq08ctG7OqZR6dOLEeubkOPwms7t3dOHLEjNGjbaislDK4g8PJOI7HvHnJyMtzeUMPiostmD8fOHUqVcO3VEfc+Vm6tI5JqiuUuZIgQkWTx/ann35Cp06dcPz4cTRv3hydOnWC3W73FmsgjIs4scglMWl5+iYEzGZ467YHjqvUxK8WDqK2eIUPB44D9uyJZKY2QehJ4INeuIR3jC1brIpeUVFCUZD7Uk80E+dc35jY3r3dsFoFr21lJbuHNTCcrLjYglGjbDh1SvPXDOqnyeT/PbKyeOzY4WDWsdU6VxJEOGjy2LZq1QrHjh3D3r170atXLwCCF7ddu3YR6RyhH+LEMnq0raFueeNkSRNL6OTnu7BjhwPz5iX7qU1kZfFBAuZGCPPgeQ5lZRzsdg8uXJD2nhCE8QiOFw/9OOExa1awV1S4/xv9RI2GoNz5hPcXL66TLVYQ6nyxf78Fublun2RhVoL7LK4NW7c6wq48pmWuJIhw0FRS96233sKcOXNgs9nwl7/8BefOncO4cePw/PPPx3U5XaDplNSVmoTDqSJjdKJVEtG3mk7Lljx4Higr818ESkrMKCjQd0swVMaOdWLbNrmwCIKIZyIftvDgg0706+fGiRMmrFhhlVSbUcNm8yAlBQ3x7gK+c3Go84Xd7sHWrQ4MH67ls8LkmJHBy/ZHK3IVxuKp8hiV1A2PWJXU1WTYAkBdnbCdkJycjKqqKtTU1OCqq64KrZcGwqgXbiRurHiaWMIl2hOT0oNDXp4LOTlpinFmLVrwKC+PvApfixYeXLhAan9EIhLteNxQzyfvId2xw6E6XygxbVodnntOi9yYIGU2b55Tl7UhURwoZNiGh6EN22PHjqkeqHv37mw9MyhGvXDpxgoPLeMXrsEvFsAI9N74LlYAMHq0DQAkw0G2bXPgiSeSI2jc+g4CeWoJwngIiVSffFKN/fstkvOFGr161eOjj7TE0vPIzuZx/Hh12E4OlnkwXoxbWn/Dw9CGbadOnVROxuHLL79k750BMeqFSzdWeLCOX7geBrcbyMlJa4gdk8/6PX5cWKyUzjV/vhVbtugv7i7EVnv/0v34BEHox5w5dZg1yyk5N6mRluZBdbX2h+Oiohr07h16ArGWeTAedgmj6RhJRGJl2DIlj3311Vdhd4ggjIq/h6ERURuSxcPQqHggjZCxzHl1MPPyXLKT4MCBbmzZEu63CsZu51FWRuEHhBFRS7ZqeixfbgUATJ/u9M4XpaUc5s9Pbqggpn84UygJa74G3fnzHPM8GI4BbTQSJfQiUdBcU7O2thYXL16Ex+MBANTX1+Obb77BXXfdpXvnCCLSqJUaltKGlEJrAQxR3kcKUZpNzusRGjyGDq3H9u2heYIbvb1keBCRgK6rYDgsX56Ml15KwrJljQaSzSaGM0mr2wwf7sLWrdaQzqhVxzwUbzJgDIUYvdDDMULoi6arcc+ePejVqxf69++PAQMGYMCAARg4cCCeeuqpSPWPICKKXqWG9SyAIUqzcRygLnHEvhC99lpoix2gLb6PICILj4wMD1q0kNflTiQCq4qJslmBJbpFbdm779ZuRIWiYy4adL7SXawkSiEgNccIILzvThzndFygyWO7efNmTJs2DWlpaTh27BgeffRRrFixAr17945U/wgiouhValjvyjri4jV7trjtKAfroqJHxTEybolYw4PjgFWrBHUe0XOZ2NemoOE7d24yrrySx/nzQvjS0aPVOHYsOJzp0CFtgZ2h6JgrGXQC0r9JolUY0xKClkihF0ZHk8f2/PnzePTRR9GrVy/873//wy9+8Qs8/fTTeO211yLVP4KIKHp5WiNRWSc/34UlS+rYPxA11CoqEYRW2K6n7OzGildynsvow3r+0PvJ8xxKS00YPjwVEyakoKAgFT16pKGigvNWKxPnFqHqGTtaq4gBoVRRTMxCQHo5Rgh90WTY2u121NfXIysrC99//z0AIDs7G+Xl5RHpHEFEGj1LDattEYYSZxX7RVsKmqQJOSJ1vfKYM6cOx49Xe+8jtxv46isTamrU+hPpe0jL/aBffwJDFERYH9anT69DUVGN35iyEoqhFs48aFT0DEEj9ENTKMJNN92EBQsWYP78+Wjfvj3+8pe/wGazIT09PULdI4jIonepYTXFA62ohTgIddwBnleLg2XdrmVtJ7SZPr0OLVrwmD/fxvAZgpCC7Zrr1MnjV8Z25sxkvypZUjRvDtTV8XA69X8YS072oK5Oi29I6INQHCX8/sglt7KGRc2Z4wx5XmI11JYscaBlSz5h5a/0DkEj9EGTx/b3v/89Lly4gOrqasyePRurVq3C/PnzMW3atAh1jyAij96eVlHxIHCLMBTUQhw4Drj7bpeKRqAWb4G2Bbe6msOYMfXIzvZoPA+RmGj1XrJ9huMak3CKiy0YNcqGigq1Bzkely9zcDojI3FXVxeacXrrrfoZOVLJrZEIiwqEdadrzJh6XeZBoxKNsSa0o7mkri8ulwv19fVISUnRs08xwagFEKhAQ3gkisC2nE5iQUE9Nm5UrlVvt3vgdgOVlUryYXxQnXg2BM9PZSWHrVvFSkcUqkCwoD3p669/rcFjj9lkdVzjgWbNeFRV6dv3zZtrMWxY4wO42w2sWWPF1q1JqKwMveiM0nwoqiIA0lUUEyHsIFoFfhIVQ1ceE9m7d6/se0OHDmU9jCExquFIhm14JNL4iQtNaSmH8nIOGRk8Fi6UF2vXSv/+9XjvPS1lOIFg4yTRM9SJWBIJozB68LDZeDgc+nuQfSuGSRlZGRkejB1bj+nT2cIPWA21RDfoEsUxEiviwrC94447/P6+ePEiamtrkZOTg5deekl7Lw2EUQ2fRDLMYkGijV+oguishF+IgSpIEZEkXh6c9OinEGqkfD/yyM5uLFHrXywgNC+q1mMkskGXaOtHtDF0SV2Rv//9735/8zyPbdu2obKyUlPnCIIIhnXrL5ITrLClKGZuh7Iwx4PRQcQfTe2BSfi+6oVRODzyiOCF1aOKYijHUKqiSBCxICy3D8dxGD16NF5//XW9+kMQuuJ2A++/DxQWWlBSYjZsBZjiYgtyctJQUNCoU5mTk+aV8lEXRCeISGEEV1UkYmoDv5ee3zO8vmZl8Rg3rp6pbYcOQnl7Paoo6lWJkSBiSdj7md9//z04jhZawngUF1vQrVsa+vcHxo8PNhaNglxpSl+dSnVBdD0RjIgrr+Tx/PO1uPJKwBjGDREbEnN+D1y27HZjXOP5+fX45JNq5OWxxaiK0luhFAtwu4GSErP3wb+0lAoOEPGPphV+5MiRfkZsfX09vv76awwZMkT3jhFEOMht24vGolEydt1uYOZM9a2/efOiX4Hs4kUOixYlN5TipYWMSAxSUz2YPLkeU6c6/UrSdu/uRo8eaQqa0dHho4/M2L/fgrw8lyaNVFZt2a+/NqGkxIzycg4LFvjH69vtHqZjUMEBwshoSh5bv369398mkwnXXHMN7rzzTpjjPFrcqMHhFLyuHbcbyMlJa/CAyi8IYsJFpPrAklCxcqUVy5cnqx5vyRKHDkUQhGIOHo9WrVEyagkjoHQtsqlzJCXx2LTJgSFDpB9q5SSstPcnHISksR07HADALKnldgPXXtsM1dWQ6ZfUGPm3bUwelT5GNOZOI0Hrb3jERfLY5MmTQ+4QQUSLxm17aYQ4MQ6HD5sjkvTAKoHjdsNH+1UZu51XqUDGhofNIeMDywJJxC9G+y2V+sMpvB/4mvQx6uuBMWNsGD/eiYED3UEPnGKxFuH+lT5GRoYHt9/uxjvvWFBVpfxtQkP4nvPmJeP48WrJ/mRl8UHzyRtvWBqMWq3naqQxeRQIHGsqOEDEC0we29///veqB3rmmWd06VCsMOoTGT0xaqew0IIJE9SLhgSKmuuBFqmckhIzCgpSmY5bVFSDigpOxptkNOOEMAZKSgI80tJ41NRwOiQkhn/9paV5cNddbhw8aMaFC5GRspNCTnPVd8clM1PwoJ4/37j7sn+/UP1MIJTvzjZmoj6tGAtbUmIGxwkqBLfe2miUu91Aly5pKC/Xb+zsdo/f8RJJn5YVWn/DIy48thUVFTh06BD69++Pdu3a4dy5c/i///s/3H333SF3lCD0hjX+S+84Ma1SOawJGBkZHq9nSc2bFAwZvYQ0ycnA2rUOPPGEWOQjVHy9fNqPc//9TqxdK3gBd++2YOLE6FWylIu5V5KwcruBuXPF8KHI3lviHLF/v/8u0OrV/obm4cNmXY1aAFiypA5ZWXxC6tMSiQ2TYSt6YydMmIB169ZhwIAB3vf+8Y9/YPPmzZHpHUGEgFjHnDXpQi+0hkCwGtZjx9Z7F5T8fBfy8lwoKTFjzBibSplcqLxHJDbK18WFCxzsdh7PPFOH8eNtYSZMhf7Z++9v1ETNyoquW4xV39UXofqf9tLToYxRq1Y8UyKs06n50KpkZfGkT0vEJZruziNHjqB///5+r/Xq1QtffPGFrp0iiHAwm4U4MKAxBEAkknFiWuV2RAM8sI+N8MjI8GD6dP9Vy2wW/gk14MlwTUzUDDyeoY06O3YkNRi1YR+qAe0HKitrvIZZ7gm9pee0arOGJnUlFQMs/104Toip797drbgLBAi7RJmZ+o2JeG69H/wJIlpoMmzbtGmD/fv3+71WWFiIn/3sZ7p2iiDCRUwCCfQA2e08xo1zIiOD171Yg9YQCCUDXFzwVq2SNsBJRzLRUa845R8CEBrFxUk6F/3Qfhxfo4zloVTqPT2KLbDeU/qFMEn/hr4P38eOsRVM4DghNEH+e7O+TgliRPyjybCdPn06nnjiCTz00EOYOXMmHnjgATz99NNMyWUEEW3y81345JNqvPceMH68E3a7B2VlJmzZkhyRYg1q3iYpT4icAZ6dzWPnTmmtXbdbSGTRB8qIiG/0uA5i+5A0ZYrN7z6UuyeysoR7YufO4Pf0+A6sBmturhtZWUpGpFb8+56VxXtjflmN7fPnOSxdWtdQdEKbERvYl/R0mhOI+EaTji0AnDhxAm+++SZ++ukntG7dGoMHD0a7du0i1b+oYdSsR8rKDA+OAw4ebI4RI3gmpYJwkdPBVDsXq+6tlJSYNGzJPJmZgrFPxDOhJ24ZAfHemD3biQ4dPN7rHwCOHDGjpiYVqak16NnTXwVgzRorli+3ikfxOaJwPJsNcDjYxiQtzYMVK+q8sfdq3sqnnrJiwwapc4eK0Oc5c5yYPt3pPT+rcoqoniA1P7RsCdx3Xx327k3ye71FCw8uXBD7Htl5MV6h9Tc8YqWKoNmwTVSMeuHSjRUeHg9wyy3NceqUdPJGJATHWXVsQzmulJSYlN6keK1wXLCBzfPw6ngapdoSoQdGU8DQ0h//tuL9MniwS3L+YynCcuWVPOrqONTWahsTtXtV7dz+38nbI9XzSs1F4rnUEmEDPyM+JLduzSM/PxUVFZfhcjW+3rIlj8mTbQ0ldKkQgxy0/oaHoQ3bwYMH429/+xvuuOMOv5K6vrz77rvae2kgjHrh0o0VHh9+aMbQoeweD71g9cBqOR7bYtq4MAOCLJFvBndWlgfLlvkv2tqqLemLxeKBy6X+nQh10tI8qK5ODO+76DXcudOB3/42JWj+Y9eAVjOupatvAYLHMi/PFXQfHz7Mdm673YMHH6zHxo2CZ5f13gqci0LdBRLaSK8fWj3BTZVorb96rxdGwdA6tuPGjQMgVB6TM2wJwohoVSrQCyUdzFBQkxITWbLEgTFjBHkwqfhhpzO4+hhLtaVI4XIlhiFmBHbscOB3v0tBbS1glAeF5GQPfvMbF5o357F6tXrpaBFfGa6RI4Pf37+fNTaeJQlP+tyzZiUHPRhmZ3uYd12WLKnDiBEu5OR4NN1bgXOR3P0pVX2MlVjNi0Qwkdrha8owzQ6DBw8GAAwbNszv9e+++w7NmjVDq1at9O8ZQehArIo16A3rAtOyJe81aqW0L8vLTRgzxoZJk5xYuLBRRky7Pi5hHHikpQGPPWbTvO0eaerqTNi2LQlDhtRr/izPczh9msOhQ0CXLo2vFxdbmEtRhwrPcw3xp/43UGkpx3xuMcEtP9+Fu+92oXPnZrh0Sf33kZqLxPtTL69eosyL8Q6LRjEZt9rR5C755JNPMHToUADArl27cM8992DAgAF45513ItE3ggib3Fw32raVkgcSiBfNRi0LkVIFNJENG6zYt8//udZsBi5d4hoyq41lIBFyCFvt1dUcKiqM6v3msG9fEtLSeAV9WnlKSxv/X7y2o4e0dqzJpPxdTCber5rbsWNmJqPWbpefi8RdoGHDXOjdO7yt6lAUXAh9UatUCQjv6y1L2RTQNBOuWrUK/fr1A8/z2LJlC/74xz9i/fr1WLt2baT6RxBhYTYD4uUZzWINeqNlIWoMW5BbSAVv7BNP+E+aovegokKLUau/YD6hhWg+gITzW3OorgZ4Xv4hU46srMb/V7+2owEHj4drMEikv4vHA4wd2yhjxrrjMnw4W/WzcIlVERuiEbVrWWvhEKIRTYbtiRMn8Pjjj+PEiRMoKyvDoEGD0K9fP5w6dSpS/SOIsBk2DJLal756kUZHy0LEuoiWlzdOmixeXnnIu5v46CEpxqFZM55ZJ5XjeLRp40GfPo2vGSnmc+xYJ0yyK6i/x411xyUvL3pzkZJecLzMi/FMtOOc3W4habCw0IKSEnNCe4I1qdObzWZUV1fj4MGDuPnmm2G1WnH69Gk0a9YsUv0jCF3Iz3dh4ED9YtRiAWsSiZa4OHHSZE1OC8Y4hgYRSfT5nauqTGj0/KqrFQgPayneV40U89m2LQ+PR/47CB43DocPm1FezsFkkm8vymtFe+tf79hdgp1oxjk3tQQ1TYbtnXfeiUceeQSnT5/GvHnz8O2332LSpEnIz8+PVP8IQjf0ViqIBYELUWYmD44TKg+VlJiRm+tGbq4bdrsH5eXqhqo4aRrJE0YYi2HDnCgstKo31IT69TZhgjNo0RVDcuR1l/XR8k1N9aC2Vlnbec0atjF54w0Ltm9XSjiL7dZ/IsyL8YjatazXw05TTFDTVKDB7Xbj9ddfh81mw6BBg/DDDz/gvffew29+8xuY4/wRz6g6saRjGx6JPH5KT+EeDzBmjK3hVWkDIDu7UYCdXReUaDrwyMjg4XIBly9HPzHNZOKxbZsDo0b569jK6y7LFUQQXr/nnnq88QabMTpnTh1WrFDTn2UzopU8teL7W7c6MGSI/sZFIs9/0SDS4xeORjELLIVMIlmII1Y6tppmK7PZjGHDhqF37974/PPPcfXVV+Phhx+Oe6OWIOINcUIM1MYUn8JNJmDSJKfMpwUvrxizW1JiRmkpB7vdA/0TwWg1jV84VFRwuHw5Nt58j0e4lgsL/V+Xiw3NzuYxaZIT2dnBr+/c6cCYMWwGgt3uwfTpTuzY4UDr1krXrygHJtdGeE/JqAWE78kad0wkFpGOc26qCWqaPLbV1dVYsGAB3njjDdhsNhQWFuJ3v/sdXnjhBXTo0CGS/Yw4Rn2ipSfu8EjE8dPyFP7GGxY88USyX1iC3e7B8OEupKfzeOmlJD8Beu1JQnJeK76hnK+WYxFEIDzateNw7NjloEQtuWpNSq8rV+8Trv3t2xu9p4cOmTF8eCgVzrSV001P92D1av3jHRNx/osm8V55rLDQggkTUlTbbd5ci2HDjL9jEBGP7fLly1FTU4P9+/cjKSkJ7dq1Q//+/bFs2bKQO0oQhDa0PIUPGeLC559XY/fuGuTn16NZMx7l5SZs3WrF8uXJDbXiQ6NRnSFwxuK9/SCjlghGywrH4eRJSHqU5HRdlV5furSuQadZug8DB7r8QgLOn2e7fgM9rna7aOiyfb6yUvBOS1ULJBIfPTWKfWmqhTg0Gbbvvfce/vjHP+LnP/85OI5DUlISnnzySXz22WeR6h9BEAFolYnZv9/SoKmZhKqqwM8G/y08FXswZowT0gaAsMW6bZtDUkaNINTRds3oldyYn+/CxIlyITrAW29Z/IzLli3Z+rljhwNFRTXYvLkWRUU1WLKkTmPPSJCf0J+mWohDk2Hr8XhgtYoB9XzQawRBRB4tT+HFxRaMGqWt6ALPcygrM+Gee1x44QUH2rTxfz8rS4hZHDLEhfx8Fz75pBpFRTXYuLEWzZp5GlqRp5aQpn9/N2w2bYatXh4ltxsoKpJTKPA3LouLLZg82SbTtuETDYbBrbe6/TxuoTzsJWq8IxE7mmohDk2GbW5uLhYvXoza2lpwwn4OnnvuOfTo0SMinSMIIhjWp/Du3d0+5Ue1G5qil4xT+ai4jZaZyTfolJJRS8jz3nsWOBxScd3BcByPdu0QskcpUJT+ww/ZwnjWrLFi9GibYqiOkmGgdo8qQdJ7hJ40xUIcmpLHysvL8dhjj+E///kP3G43bDYb2rdvj82bN6NVq1aR7GfEMWpwPQX/h0eijh+LTExGBh+WhJcoeRQsdyScY9YsJ3r1cuP8eQ4nTpiwcmUSPJ7oy0IR8Y50opV4Le/ezaFvX+n7VynpRkoOLz3dg8pK9WtUaKccI2u3e/Dss3WyMl3ysmTKFBXV6KYrm6jzX7RIpPGLVIKaErFKHtNk2AJCCMJnn32G06dPo3Xr1rjpppsSQu7LqBduIt1YsSCRx0+tmgxrRmwgoqoCz6PBYxWuQgJBqCGqaDReP9nZHixbVoff/jZF8v5Vuv4B+IjSB6oV6HeNqlVvkuqjXB8ioSmayPNfNKDxC4+4MGwrKyuRnp7u95rT6cSzzz6L+fPna+6kkTDqhUs3Vngk+vgpPYWzSxU1InrJZs92YvnyZJXWRFPCZuPhcET+wSU93YNx4+oxfboTFov0/etfTSnYy5uezjfElWurTsZxfMNn2XYeWIT0fe/REydMWL7cGmTE6yXIH9y/xJ7/Ig2NX3gYWu7ryy+/xIABA9CrVy888MADuHjxIgDg66+/RkFBAfbt2xdebwmCCAk5mRiWxBcB6birDh08Mu2JporDEZ3zXLzIYcUKK/bvl5a+cruFBC8pjWSe58DzaDBM5Yxw+UpiPA+MHVvP3FfROFVSM/C9R2fNckoqiSRyvCNBRBsmw3bZsmXo2LEjNm3ahGbNmmHz5s04fPgwfv3rX+OKK65AUVFRpPtJEAQjojeLRaM2I4PHnDl1Xpmi48erkZ/vSjhdQ0IPohNmomYsquk4h95PDnPmODF9ulNT4pdWNYP8fBeOH6/2kwcT7zuCIMKHSQ36yy+/xP/93/+hRYsW6NSpEx555BHs2bMHjzzyCKZNmwZTYEkYgiBigpI3yxff7V6peL7ycrFcqPJxCHXatQOaNXPjyy/jPxchWgjGIofDh824917/9yKpGtChg8crkTR6tA0cxzMnfmnpl+jFJQhCf5gsUo/HgxYtWgAAWrdujbNnz2LChAmYMWMGGbUEYSDUvVkC27c7MGuWtFHrdgMLFoQuE0b4c/IkEtCojY5HX8pYZN9N0N7H8+c5uN3yEklK0C4HQRgDJquUCxCyTEpKwsiRIyPSIYIgQofVa1RWJt+O1Tgmmi5CydjIk5nJ4/334dWhdbvZdJxbtPB4/98f5X7Pn29DTk4aiost3pCB3btrkJ7ukf1solZvIoh4JSR3a1JSEpKS5Kq3EAQRKoGC8lrLa7J6jU6ckL/1Q9/qlSu/ayRC7Q8fxmcTCR4mE++tNjd6tHx52nDgOB4ZGR5MnmxD//7A+PEpKChIRU5OGvbvt6hWU1q5sk6h3LPy71haymH0aBuKiy0wm4G+fd1YvboOHNe0qjcRRLzCFGPrcrmwd+9e79/19fV+fwPA0KFDdewWQcQngfJb3bu7cewYmyi2mi4tC7m5bmRleVT0Z3m8/HKSbHxtqFuq2dk8HnrICbdbkGXp3duNV1+14K9/NVrJ7VC0TMX2TV2rl4PHA3zyidkbI7pjh96/L9+gbBA8zqLRuWOHAzt2OBrul8Z2WVm83/2Sl+fy3o+ZmTzGjLE1FF5QODvPgeN4zJuXjLw8F8zmxtAEtfMRBBF7mHRs77jjDuWDcBzeffdd3ToVC4yqU0c6euERzfGTMkxNJh4ej7/ovNRCqKbLqUUKaOVKK5MGrVyFI7cbuO66tIbyuMrcf78Td9zhljXanU6gbdtmYDMGm7rRGD9s3lyLYcNccLuBnJw0lJZymqprKaN8HfgWMgDYqymVlJg1V+ILvEdiUb0pHGj9CA8av/CIlY4tk8f273//e9gdIohExt8wbcQTIAfr63ESDVU1Xc5A75EarBq0SiEHrEbKX/+ahIED3bIZ3lar4LktKWGZasiojRdathQu9FAVBMLBVzGhd2/5ay+QUEJsDhww+x0/ntUM4s0oJ4hQIUkDgggTZYmtYEMV8NfoVEvW0qqTyRpKINfu8GEzqqvZjQAlcXoAePhhdsH7xx+vU0zUIYyB7wNcKAoCyugvrwWEFmKzZYsVxcVM/h9DU1xsQU5OGgoKUjFhQmO8ciJ8N4IIhAxbgggTrSoCgYYq6wLN2o4la1wpi1ubwaBudGsxeL7+2oSBA13efkYXShBj/f6BqhqigsCePcoKAnqi1VAV48+19I3j1B/cjE5hITBqlM0vNhjwT5IjiESCDFuCkIFVoSBUFQHxc+F6WAMRt4eB0LK4Q/FsKY1Bbq7bK7+kxoEDSdi1ywqOA1JTI2Ec8ZA3YJUS7hKf++9nVziQukbMZqBPn0YFgUgZt6HKa5nNwMiR9dDyG2vdLdGLcNVRfI/z+OOQDXMC4t9wJ4hADGfY1tTU4Pe//z169uyJnJwczJkzB9XV1bLtFy5ciC5duqBr167ef6+++moUe0wkIlq27kJVERA/F66HVQq57WGWmvRq/ZFCaQzMZmDECG1Z4zwPVFdzGDq0Hps31+Kvf62BzaaHodR0DVc1Onf2YPt2B0wm+XFmuRbFay87O1IPJqHLa7HGnwcSyWpngegZNnD4sBmnTgF6hTkRRDxgOMN2yZIlKC0txVtvvYW3334bpaWlWLlypWz7zz77DEuWLMGnn37q/ffAAw9EscdEoiEmgrFu3Wk1BAONg3A9rHKEWpNe7I/g5VH+TqxGd16eVjkkYez37ROE8quqODgcGg+heGzfksEEABw6ZMaQIS5s3eqAlFdby7Xoe+1Nn16nWx+zs9UfzJQQk960Eq2qYlrnHjX0DnMiiHjAUIZtbW0t/va3v2Hq1KlIT0+H3W7HrFmzUFhYiNra2qD2TqcT33zzDbp06RKD3hKJiJpCARC8dadkmLIaB+F4WJUQs7iHDXOhd2/2LOi8PBcyMtQWc0FvlMXQCcULLGimcpg3Lxlz56rLl2mHFnNf3n3XguJiC4YMcWHnzmCPq9ZrUbz25sxxhvDbB8Jjzpw6pgczXwK39F0ab6NoVhULZe5RQ+8wJ4KIB6IeNe5wOHDu3DnJ92pra1FfX4+OHTt6X7vmmmvgcDjwww8/4IYbbvBr/9VXX8HlcmHdunU4fvw4mjdvjuHDh2PMmDEwmQxlsxNxQmMimDSBUkMicgLuJpO/5JeSoHt+vstPUD6WkjyHD5tRUaF2D3GYM4dNnD4cWagXXzRagYfERZSV870WS0s5lJdzsNt5ZGTwcLuhek0GSkstWlSHceNsCNaoVdcubtcOWLzYgXvu0WaVSulKC4ltbES7qlioc48SublutG0LnD4tfc+JmsBUDphIJKJu2P7rX//Cb37zG8n3Hn/8cQBAamqjiHZKSgoASMbZXr58GT169MDIkSOxevVqfPnll5g0aRJMJhPGjBmjqV+cQZ03Yr+M2j+jo3X8fvqJreFPP3FBxxw82IVBg1ySlcdKSzmUlQnVj1q04OHxSBsHFgtw222xX2RYx+GaazzMYzt4sOAJnDs3OWirNbrEcyEIX8+a3t9BMJyOHBEMJ4sFqKzksHRpcDW8ZcvkH2iKiy0Nv7F/oRKlSnjBxi4wfrwTeXlu5OenorLSpSrw7mtMnzhhwvLl1qDPqFUd8yU7O7pVxcKZe+SwWIC1a4ERIxD0QCka7suW1cFCwgiS0PobHnqPH/NxeAPxxRdf8B07duSrqqq8r12+fJnv2LEj/+WXXzIdY9u2bXxBQUGkukgkOO+9x/NC6pLyv/feYz/ma6/xfMuW/p9v25bn9+xpbONyCcd85RXhvy6Xvt9LK5EYBxHxu/7pTzxvMrGdh/41/mvWLLLHf+UV4Xfas4fnOS74fY4T/vlevyJyn5H7N3OmcC8E3htPPaXtXtizJ/g4of5r0YLn33kn+vdgJO85qfFp1076NySIeMdQz2k///nPkZSUhG+//Ra//OUvAQDfffcdkpKS0L59+6D277zzDsrKyvDggw96X3M6nbDZbJrPXV5uzJJ5HAfY7c0N2z+jo3X8brgByM6WLxEqxNzxuOGGapSVqR9v0SIrNmywItBb9f/bu/fwJsr0b+DfSXqkiK0FoRVwdQVPKFSOgqAUEFmKSPGAYJEzKqKLykEpFAQ8oMKqi0KBiiiKgmWXLcK6iiAiRUBA3p9HlvVES4UCaukhbTLvH+mkSTOTzGQmyTT9fq7L65I0nXnydNrceeZ+7vuXX0QMH+5clUpMBNaujUZxsfpVsWC78kogKSkBZ84olcByrjz7mwelbkcdOjj/u/9+aX6gcB7yJKKsDAjmXDVpUo6SEjumTk2o/R2on+/p/D146CERvXqdc915sNuh+D1yBEHE+vUiPv/8HPbtq1tpXbs2Gjk5db8LrVsDCxdWKKYiFBREYdw473bU/okez5dWMF94oRIdO9bgzBkNhzKA0X97nN/j/Pt3441/YP9++d9FtcdqjPj+q4/R8ycdzx9TBbbx8fEYNGgQnn/+ebz44osAgOeffx4ZGRmywaooinj66adx8cUXo0ePHjh06BDWrl2Lxx9/XPO5pc+xZmX28Zmd2vmzWJRzQaU3vgULqmCx+D/e5s1RbkFbfc7jrlghbYryPFhxsYBx4+J0bRzTyj0IbdFCVDVf9ee1/u3gN97wDtjdb+/m5Dhrp776aoxX+2HjSAMMR+DsGTzpF7zXIOVbdu9ux549/vM9jx8XsGdPXb6nv+9ROsbnnzuPUVAQJZs+cPw4MHas/O+C3Q7Mnq3U9c+3xETRIzVByn8fPNh/2kMwGPm3pz5RdB6/Z0+71+PkH99/9Qn1/AmiaK4fV1lZGZ599lls374d1dXV6NevH+bMmePKux08eDCGDBmC++67DwCwfv16vPbaaygpKUHz5s0xduxYjBo1SvN5T50y5ycyQQCaNz/PtOMzu0DnT27jSf2gzBe7HejQIQGlpYFvYpQCjQMHzgV984rc61Vj06ZyV2Aj5Va6B7JKq2L1gxSbDZg7NxZ5eUZvFDM6sNR6bsicP5xjUuIc66pVlbj11hrk50fhvvvi/X7X8uUVyMx0/hyzs2ORm6v957d8eQWGDq1B584JtbnXypuc6v8u7N5txbBhTbyer8Z775XDYkHYN2rWp/dvjzu+f+jD+dPH6PmTjuf3eWYLbMPFrBcuf7H00TN/SrfR1dDzhlvfe++Vo3fv4G0ok2pnBrLqJQU20u1gJ9/HqB+kSPNcXCxgzpxYlJaquZWtFBzWfzy8QeRtt9mwf39MbZF8J61VIUJJCqCSkkRV16/0wUbPB7lNm8oBQNP5JGoDcHeh/MAIBPZ3RM/fHnd8/9CH86dPuAJbU6UiEJmJVIczEEYWPB8/Pg5LlgQn39ZX7Uw1WrZ0ln969FEppcL/MaSyRatWRSMlRcTcufVXikX4D0iVglrgsceqsGpVdO1t5vAGkIMG2bFxI1BQUI4TJ5xVMR58MA4nTiDsY5NbUZYaAaxcWYnUVIfPfE/3MlGFhVbNQa37Mf75T3VvRfV/r7TWX/VVwsuoYNJdoKuvev72EDV2LPZKFARGFjw/ezawrkNq1NXO1BZkuReu371bqnmr7Rhz5sRhwgTvLkuBSk0VkZdXiV697Dh7Vvt4gkEq4SQ1ybBagRMnzDE2J/lGAHPnxuLJJ9V3w9P6Qa7+MQJtJOC/8Yfn40pNJoxsY+t+TCO7iBGROgxsiYJAesOt/8YamMC6DqkRyMpy/aBk926993Prj0H9mBYsqPRqF2ym9qBz5sThT3+CK4gpLjbD2HxvpnOuqFuQnCzKdsNr1UrE9Ok22GzOn73drv2DXP0A01+AqtQBTE076hkzqny2lA5GABqMLmJEpA4/MhIFgXunLWcau76AJpCuQ2oEsrJcv3uavuLbSt8sPa6cS5ucLGLs2GrEuO1XstuBkyfDHTx6jvn4cWDcuDg88IAN69dHh29YALR80CopEZCZ6dkNT6p0sXhxXYvjxEQHJkyoRlKSw2d5uORkEQsWVLnSD9xv8/vqTCddX0odwJS6/vnq8ifxF4AKgujqxqYlLSHQLmLBSIcgamwY2BIFQM0bkNIbrvyKmbrg1+jVSGmlzFcu5UUXCXjxxXKcPCn/Wnv1smPJEkOH5TUG77E527x265bgCl4Creygnb/yYd51XwH4KP0WSgLUBrfShx4p37OgIArPPSfXzcuC55+P9Xvcrl3tuP125SBT6feldWv/LXUDbUcdjDa2gPrfU/fnGVkNgagxY2BLpJGWNyC5N9zSUqF2w1Tdm9oFF4g4fdr/m6GRubtA3UqZs6KBd0UBUQQmTgROnXLWtrXbgX/+M8ojcOjZ047zzhPxxx/Gr5TedlsNNm+OUtxRK90ufuABG155xTvo0iInpxIvvxxT+3Pw1f7VuwZqXJwDlZW+AupwryJr4WwE4H7bX/0mQ+WvbdsWhc2bo3DrreoD1FatRGRkNMGZM/5ryway4SqQAFQNrTnDnpVJ6kjXdyjrWUcCrnw3biz3Vcus5TxYbkQfo+dPqTSWUn1WJfX/8Hbtake3br67DgWrRJFyqS7fK5PJyQ4MH+4MRN5/34qVK2NlnxcIQRCRlOQe7CsHFoIgQhBQ2+AhkADSea64ONSrwevtvPMcWLrUWcRf+vk1by5iwoS42g1rkUDEpEnVGDSoxhUQGFW+rnlzB44cUX8NB/vvn9rXVb/MmD92O9C5s7rfZwAB1fBVozG+f7AOsHmwjm2YmfXC5S+WPkbOn/RmpfQGBDjfgL74IrDgUwqaAUCu61AwVm38vyZ1kpMdOppRyDdxSEwUfeRsGsn9wvB/rpQUB7KyqnHppQ60bCnCZgPuusuYmsXBpT3XWwoIbDZorherREuQGOy/f1oCUK2/02p/n4MVXDvP1bjeP4xaeKj7vsY1f0YLV2AbKUsMREHnvzSWgOJiC5YuDSyXUsoxrL8LXalEkRECLfdVn3NlVao/q09KinPXfSAlxLRzrtY6KdXG9W53vHhxrKss1IgRxgR8ZiTdCj92zLi3ipISAXa7c7U0Pz/KVVkhHNRUVVDatOaP2t/nYKVDNDasREES5tgSqaT2jWXx4hhccYUjoEA00E0wgTLqzdL5xiG9gWtbGZwwoRqDB9d4vF61Bfv1EdG0qVgbQCuRex3ym8P0jCNUObjOjXhQOJ/3OKTKALm50fC/aU6dY8cstXcJ9N8qNiKXUk9VBTXH9vf7HGgNX/IUrI2A1PAwsCVSScsbSyAlgiSh7Dpk7JulMyhwpiWoD34GD67xer2heRMXUFZmRECp5xhaX6eeIFioDWqVzqlc19a5UU5vAC6iaVPnB7/6AtkkZWQuZTA/UPr7fVZTmcS9yxvJ48o3SZiKQKSS+qYLzgL3hYXm34brv3OTdpmZ1UhOdvh9nlLR/WCNy7y0vNEKaNpU77wEHhjrVVYmfyytt4qD0VRBCkAzM50ftEK1iz6Y6RCNCVe+ScLAlkgl9zcgNRrCyoDnazLmD/7KlTEyK7ba3rB9vdlHFu3XyMiRUmOMhjYvgtt/3qSOZ/4+EEZiLmU48usjTaDd6yjyMLAl0iAjowYzZthUPbehrAwovalqJ8JiUcrD9Py3mjds48YVWQYNCvW8hHb+/X0g9LfhUQqQP/vMaooNamplZNTgwIFz2LSp3GcLYJLHlW+SsNxXLbOW82C5EX2CMX92O3Dddc4SQUbXnQwnux1YujTGLQdS/Wqi701JnhYsqMSECdWq58Z9g1Dz5iKmTo3DiRPy+Yh1QrchS7vANmHVv66kefnkEyuWLlVTQ9hfWTOlcYlISBBRXu5vzo3hXtZK7vc3Pz9KVemxxESHR23hxtjFqzG+f7COrXmw3BdRA2G1AosWVUEQImtlwGoFHnvMhry8SqSmer6upCQHpk+vwsaN5Zg8ucorhzYlxVnUX40WLURNc+Oe+9injx2LFqlJnVDfOlY/redxBofOa0Xpe72vK1EE7rmnGv/8ZxR27XKuRpaUCLjhBjtSUvzl3boHrb6CWnnnzjk3ngUz/UHtrWK1d0LcO8MBnvm3Zik3RsbjyjdxxbaWWT+R8ROjPsGcv0ju7S6tBv76q4D27eNx5ZV/wGKpe7y4WEBpqYDk5Lod24WFwSs0X19BQRSmT4/V0RTCOBaLCIcj0JVMuZVl75XTpCTnBwml0mRJSQ6cOSNAEKCwqupvlVis/V7550id4GJjPbuzOV+7r+MqjcPze5QK6Mv9/trtwFVXJfho3qH8WgVBRGKid5e5SPm9rY/vH/pw/vQJ14oty30R+aFUKzPUNWdDSVoldf4hAU6dAv71L+VA3moFuna1+wl0RFgszufplZFRg8pK4IEHAm2OoJSqoD2F4a23KjBlSlzthjmtAa788xMTRaxeXYGTJwUcO2bBc8/F+HxjkFYn67q1ScdxoKYGtWXNfI1N8Hl8URRw+rSA994rh8UC1/VeWipgwoQ4aJm3KVNs2LQpOuCasVu3Rnm8xnoj9TkOURRqv9e76YbWcmNEZE4MbIl88LcqG8qas+Hk2aqyjntAkJTkb+VSgMMB7NtnTIF0fZunfK30aQtuz54V8NxzVbXtU0UD8lCddWMtFmDo0BpcdVWC3/xlqZFCfDywcmU5Tp1yBp4OBzB8uHHtfk+eFDB0qOeHuVWrKnH//XGoVpGJ0qOHHTk5NmRn2wL6QChVRHDSM8/yjSj01J8mInNgYEukQE0w1xhWd+x2YPZs5fJKUkCQna2uFJpRZdD8FbbXKjVVxLBh1V6rif60bCmiVy+7bPcqPZvYtm6Nwp49Vj+d0dzOVNtZyWoFMjOd12V+vrF/4pW6hj34oE3VBrZHHnFWFAn0A6G/7lJ6gl12piKKDAxsiWT4q5XZmFZ3du2CqlaVaruNGVUGTSrvM358XO3mKu1BTbNmDowY4UwpkVYNpdXE4mIBc+bEKqYY1O8IVT81RUohAOq33VUX7ObmRiMhQftcuX9wUD/Xvsck5dgqdQ37299iEBvrQFWVct5r06YievdWFzB65ngDV14JVwqEOoF/oGgI9aeJSBkDWyIZ7Dtep7hY3fOSk5272p0rlsq3+rW02/VHqnXrvVKqzh9/CFi5MtrjVrj7amJcHGRTDJSqX9RfibzySgfmzInHL7/UPSc1VURFhYizZ/2vNJ87p31znHswq35V2/c4RNE9OJf/oJeQAFRVAd5BpfMbX3pJXaUQ+fSfBCxcWKXpQ5H3hx11wW5DqT9NRPLCv6WYyITYd7xOSora54l48klfpbicczV3rrFdodzL+7zySgWaNfPfzlfir1NVIB2hpFJSGzdG4ZdfBDz1lLN279//XoEFCyqRnV2FiROrVewS1l7ntn65LKM6uGVk1NSmRCg3RTh92oIZM2xo1cp7rvLy1KXt+GuVW1oqqOoutWqV/M8sKYmdqYgiHVdsiWQ0hr7jStUe6uvdGz5X/dxvyTvbofpeGay/0q12HL6e575SeuiQFbm53rfMlfhbfddS/UJutdEpzqssWEKCiHPnVA/T36sAIF8/WQrOH3001quaQFycA5WVxq1vXHqpAwcPntO1McxX+s/cubF48skqTJzonX7ivoqekVGDwYO9f2Zbt0bJpq405PrTROSJgS2RDH+3cOvnVzY0WmrwSg0pxo3zHxBs26YuKpBWun2Nwz2YPHrUgry8aJw+7X+8gwbVaAps649JjprNTkqbDSWOegvJ6oNa/7fQLRbg/vttiivI33xjcStzVXcsZ06s/xq3BQXq3ipathSDtjFM+gCSnCzKpp/ULxkmNw6l1BUt5caIyNzYoKGWWQsws0C0PnrmTwpUAMgGcw21KoJnAOb7dbnPn686thkZNbDbgQ4dElQ1Tdi0qRxnzgiK4xBFIClJ9FMRwNlUoP7PQcs46o8p0Hxpux211QK01rJV015X7jmeAarSNVlQEIXZs2M9mhEoH9/XOJw1iJ25tsof9PS0kVbbKnf58gpkZtaoXumXo+d7Q3lMvfj+oQ/nTx82aCAymUhc3dFT7cHfLfnCQquqYDI52YGuXe3o1k2+PqszcBJ9FOGXCBBFEbNnx+KnnwT8/LMFf/qTA2PHVuPZZ6tqGwd4H9/rKAasvvsvQ6V49gCf4/9n528FWesYnCvOYtBu42tN/9FTQ9ro+tOR3IWQqKHh5jEiHyKt73hdAKa8CaioyFKbK+tNCggyM2vQq5fnipTajXTDh9dg3z7f43A+ri7gKi62YN68OKxeHYM5c+LQtm1THDxowZQpNv/f7SMokzaB5edHYfduq88Nb1u3Bn+N4Pbbfb8e95+drw8wekyebNO0kU4LKf2noW3u8rfhTW0aBxEZg79xRH6YobuYUbc5g1ntQe2K26BBNUGtJuFwAMuWxeCWW2qQmCi62s3KUVp917ICV1AQhdzcaGNfhIy2bdXN765dVnzySaAryL7dcosd8+YF1jXMH191ic26uYv1ronMhyu2RCZXUBCFzp0TMGxYE9x3XzyGDWuCzp0TAloJCla1B7vd+V9iogPypb48V9yCW03CGWBs2xYlE9SKAERMmmRTXH3XsgLn2eI1gJHWzklKiv+VSrUfrpYsiVXVBSyQMUhBrPQzLCkRXCvERlAqr5aaqn9VWMsKvFp674AQkfG4YktkYka39Q1GtQf5Elfym5ukFTej2+F6U05xEATnLv/58+XTD7SswGnLrVWeEwB+Vyp79rSraoARyOa1e+6pxtmzQu3Ks++fndzPOyXFgaysalx6qUP3Kq57Lrez81g8rrzyHCw6lmGClQPLetdE5sMVWyKT8hdkAcqNBZT4KtgfyO1epdXN+urnYfpuHCBCadXXCL5W0bSuwGkJWOoHZu5zoqYRhNUKDBtWLY2k/sigPi+5TlKSiMREEYsXx9aWSBN8jtPXavbixbGuOwodOiRg8+bA103cc7lvugm6buMHMwe2MdS7JmpoGNgSmVSwbnMG0k1Lju8NSs7KBomJDrz3nvwtf6VxJCU5/62nU5YackGp1hU4tQHLggWV+OmnMp+bEP1tVLTbgU2bpFxeuflWy/nB4bbbqnHmjOBVfcL58xQxeXKVxxj8/7zrlJZaMGFCHObP115P2EjB+HDorqFueCOKZExFIDKpYN7m1NJNS4n/2/ACzp51rgAqHXfQoBo0ayZi924rBMG5Stezp7NDVP0ya+6Skhw4e1bQtetfLijVugKnJrWjdWsBEydWw2LxvwnR10bFwEuKeUpNdbY+njtXysOVT7n417+iMW+ezaOcm9bzL1sWg7Q0B269NTxVRNQ2fVDqOudPQ9zwRhTpGNgSmVSwb3PqrfagN/CWy3tcv74u79E98G7Rwtm04dSpuiB84cIYLFsWA+/OXL4bDvjKI9aag6wmsPnb35zP01ugXG+e5iOPVKF3b7ur9bHWgE/7+Z3PnzkzFoMHh6cqQChyYCOx3jVRQ8bAlsikzN7WV0/grXZTnK/AOyfHWdf11VdjPNrVWizAzTfX4N//jgKgbRUtkBU4X4HNokVVyMyMx6lTii9DtcDzNEWkpoqYPr1u9TWQgC/Q85eWWgJeEdVL7ZiPHdO3Em7EHRAiMgZzbIl0CEYJIYnRG73q0zv2QPMLjcx7zMmx4aefyrBgQSXGj7e5cllfe60S06fbcP752vOIA8lBDkUjD3/z7Uv96ySQDyV6zh+uqgA9etiRkqJcgs5JxBtvROv+3fXVvISIQocrtkQBCkUbzWDd5jRi7IHmFxqd9xgTA0yeXO36t9xrS0x0YNKkakybZlMVcASyAhfsRh7u8+2dfiFPEESsXOkdjAdyN8DXz9ufcFUFsFqBrKxqLF7sq66vgOLiwPNsichcuGJLpJHdDjz/fAzGjQtNG02jVwOVyh8VFQkYNy5OU5mmQFY31a7ebdumfclL6bX99puA556L0dT61owrcNJ8JyerCxTvvLMagwd7/wwCvRug9PNWXhENf1WASy91+H8SjFlVDuYdnIZwfiIzEERR75aGyHDq1B+6N3cEgyAAzZufZ9rxmZ3R81dQEIXZs2NRXKz8mVBa7Tpw4JwpgiF3djvQuXOCjyL/gMUiIje3ErfeWqN6/rS0/N2924phw5qoGK2IvDzltIH65+za1Y5u3ZRfWzh+LsH6/bXZgI4dE1Ba6r92ra/V6s2bozBzZixKS7Wt3LvP/bZtVvzjH9Ey4xAhCNDVMcyI+VN7vW3aVK5rxTYUd3ACOf/LL1vQpw/fPwLB9199jJ4/6Xh+n8fA1smsFy5/sfQxcv48Nzz5X93R+0YZDFqDyiFDagy//tQE19IYEhOdK789e3oGynJv4snJDo8ATYnan4uWYF1JMH9/CwqiMG5cnHQmt6/IpykkJTnwwgt1QZbSHD77bJXm8lyBBsj+GDF/0vXmL+1Czwcepb8N0uq33nbA+s4vIC+vQnblnnzj+68+4QpsmYpApILv4vTyzNhGU8uY9BSu98X9NrhvAs6etWD48Cbo3DnBld6hlG7gXL30T80cFBREoXPnBAwb1sTVTevKKxPw/PMxXnOi9/av8beP5V/fmTPOVJOCgijFOTx9WsDEidpTaW69tQb/7/8Fd/NcoEKxCTOYTSDMfn4is2FgS6SCvy5gcszYRlP9mALraqZWRkYNJk2q9v/EWlLu8ubNUaq7XynxNwdKQd/ZsxYsXhyLq67yDLLrB8DuQbg/gX6/FMw4qb0mnc+bPTsWs2cbHwjJ5SSbJefTqG57coLVIdC48wPHjwfv/ERmw6oIRCpoWek0sr6sEbfD3Um74f2nATgFc9V50KAa5Oaqa7kqdcOaNSsWp04F9nlczc9Fzcq8tPI5ZYoNr7wS47MW75AhygGT2lq+cgLvQuasAOCL3m5ckmClJwQqWLVmQ9EEwsznJzIbrtgSqaB+pdO4Npp6VwPlqE8DcKr/uo1cgdNaF1UUBQ1BbWC3nNWtzDu/9uqrMQGveuq9fRyKIEXPOebPj8GECXFeOc9FRcZXDdEiGJUugt0h0OznJzIbBrZEKqgNwoy4tQko3w43opxYRkYNVq2qhMWi/FrkmisYHWj7yn3Uq345LLU/F/XBnACHQ3nV29/tZ723r0MRpAR6js2bo2pbHcsRIIqRlfMZaKOS0J0fuOii8JZcIwolBrZEKvgOwkQAImbMqMIXX+jfMBOMzSD1V1oHD65Bbm6la+zu5FY3gxVoK9dFVZac7D+IOHw4sI1MRgeMSoGy3tvHgXYBc6ZjOJCSEpxAzG4HZs6MhfO6VXqNwc05DbVgb04z+/mJzIaBLZFKSkFYaqqzNNZjj6nrauWP0ZtRlFZaLRYgL68Sqam+VzftdgRls5FEakCxcWM5EhOV259KAdezz/p/E4+JCeyWsxQw+m7Bqp5SoKz39rH/D1repOctWlSFRYuCEwgVFlpVlVwDIivnM5ib0/ScPzVVxMaNMEV1CqJQ4eYxIg2CtQHFnZGbQdRsUDpw4JzP17NrFwxtgSvHagX69LFjyZK6lrFKLXozMmpgsehvM6y0MU9qG+ss8a00x87mA0qbzPxtVAukpW19Su2WU1NFdOtWgx07rDh7tu7nVn9+gtGqWUuwGmk5n6H426D1/Ndfb0fLlufh1KnQjIHIDBjYEmkkbUAJFqM2g/hLaRAEEdnZsRg0qMbn6ykuVjUcQ1bglIK1+gGX3iDCX5eo1asr8eijsThzRj7oFEWgSRPg3Dn5rwO+Vz3dA2jn8eSDeH+vR2keAOCzz6zYvdsKUXRer/VXroMRiKm9dpOTIzPnM9h/G7SeX4icRXEi1RjYEpmMEat5gP+SUGpXWlNS1I3bqBU4tQFXoEGE2jJbgwbVYOnSGOTmRnusfCYmijhzRsC5c/LHT0wUPTp8+XqdRqya1p8HuaD9nXfky2wZHYj5LyfnnPRnn2XOJxEFBwNbIpMxajXPqJSG3r1hSKCtRbBWvrSsYlutwGOP2TBtms0VZDdvLmLqVLk2tq6jID7eWaNXDaNWTaW0im3brFixwrsigRS0r1xZieRkMWi3ytWkckyZYtPcspeISC1uHiMyISM2oxiV0mC1ImibjUItkI157rVPrVaguNhXnVvtO/711lZ13xy4YoV8RQJRdJbZmjQpztC6yHKka7f+psTmzR1YtaoSOTk2Q89HROSOK7ZEJqV3Nc+olAZpLMHYbBRqelexzdblSSmtQp4Ah8PzETVdzgIR7o1URNR4MbAlMjE9t+SNSmmQmDFY0dpyWO8qtpm6PKlp/+uPXPqFUcK9kYqIGiemIhBFMKPrawajJWmgAumEprdLVLi7TLlT1/7XP611kYmIzIyBLVGEkxogBNKJy6yUOqEVFQkYNy4O2dmx2L3b6tU0Qm+XJjN1eTI63SGSGiYQUePFwJaoETDTSqtevm/BOzdO5ebGKK7g6l3FDneXKcmxY8b++Y60hglE1Dgxx5aokdGal2o2/urzulPaHKU3Xzjc+cZ2O/DGG9Fw1oX1v9JqsYi1G8dCU66NiChcGNgSNSL+Om41BFpumfvaHKV3c1M4N0cVFlpry475lpFRjfHjq1FaKmDiRN+tihvShxsiIiVMRSBqJJTyUqVVTaPrmQaL1lvmkbg5Sm1wn5HhTD259VZzpE8QEQVbw3gnIyJdtHbcMjN/9XmVRNLmKLXB/cmTAux25+pyuNMniIhCgSu2RI1AIB23zMpXZQJfImlzlL+yY5I5c+I8NtCFYhOh3Q7s3m1Ffn6UbGUKIqJgYmBL1AiYrWOWXkqVCeSEsrZsqPgO7j3/HcpUk0BqCxMRGYmBLVEjYKaOWUZxr887eXIVADHstWVDSTm49041AZypKEasniqtyEZKDjcRNWwMbIkaATN1zDKSdGt9wQIb8vIa3+YoKbhfsKDS5/OMSjVRWpHdvDnKZw43YFxgTUTkCz9CEzUC0q3r8ePjIAiRWfIpXJujwl0X2GoFWrRQt9KuJ9VEWpEV652quFjAhAlx8FVP1xlYCygstIatRBoRNQ4MbIkaCenWtbOObV0QkpIiNqg6tr6EurasWeoCBzvVxF9Vjfp5vUoaSg43ETVcDGyJGhGWfDKOrxVMqdvZkCGhCW79lUDT213Mf7c3dQFrQ8rhJqKGiTm2RI1MKEo+RTr/K5ihzSn1VSXBiFQT9SutkZXDTUQNDwNbIiKNzFgXWKlKghEb6LSstDamyhREZD5MRSAi0sisdYGDlWqiNtXhySerMHdu5OZwE5H5MbAlItLIzHWBg7GBTm1VjYyMGgwezBxuIgofpiIQEWkUqXWBfVGb6sAcbiIKJ67YEhFp1BjqAsthVQ0iMjsGtkREGkgNGWw2YPp0G954IxrFxY0npzTUtYKJiLRgYEtEpJJcQ4aUFAdmzKjCpZc6uIJJRBRmzLElIlJBasjgvuMfAE6cEPDcczGIiQFzSomIwoyBLRGRH2ZryEBERPIY2BIR+WHGhgxEROSNObZEDYS0aYm70UPPrA0ZiIjIk2lXbCsqKnDXXXchPz/f5/MOHz6MO+64A2lpaUhPT8eGDRtCNEKi0CkoiELnzgkYNqwJ7rsvHsOGNUHnzgkoKOBn01Awc0MGIiKqY8rA9vvvv8eoUaNw6NAhn8/77bffMGnSJNx2223Yt28fFi1ahKeffhpffvllaAZKFAJKm5aKiwWMHx/H4DYEGmNDBiKihsh0ge2ePXtw7733YtiwYUhNTfX53A8++ACJiYkYNWoUoqKicP3112PIkCFYt25diEZLFFzctGQOUkMGAF7BbSQ3ZCAiamhCvtRTWVmJkpIS2a+1aNECV1xxBT7++GPExsbitdde83ms77//Hu3bt/d47LLLLsPGjRs1j0swaWqcNC6zjs/sGvr87d1r9aiZWp9z05KAvXutQSma39Dnz0hDhtQgL68Ss2fHeqyep6YqN2Tg/OnD+dOH86cP508fo+dP7XFCHtgePnwYo0ePlv3asmXL0L9/f9XHOnfuHOLj4z0ei4uLQ3l5ueZxJSefp/l7Qsns4zO7hjp/ai/l8vImaN48eONoqPNntDFjgKwsYNcuoLgYSEkBeve2wGqN9/l9nD99OH/6cP704fzpE+r5C3lg2717d3z77beGHCs+Ph5//PGHx2OVlZVISEjQfKzS0j9qb/eaiyA4Lwqzjs/sGvr8NWliBdBExfPKcepUcFZsG/L8BUuHDs7/AODMGeXncf704fzpw/nTh/Onj9HzJx3Pnwa966R9+/bYvXu3x2NHjx5Fu3btNB9LFGHqC9fs4zO7hjp/3bs7Ny0VFwuunFp3giAiJUVE9+72oL6+hjp/ZsH504fzpw/nTx/Onz6hnj/TbR7TYsCAATh16hTWrFmD6upqFBYW4l//+heGDx8e7qERGYKbloiIiNRrcIHt4MGDsXz5cgBAUlIS8vLysG3bNnTv3h3Z2dnIzs5Gjx49wjxKIuNkZNRg9epKpKR4BrYpKSJWr66U3bRERETUGAmiyAV2ADh1ypw5NIIANG9+nmnHZ3aRNH/h6DwWSfMXDpw/fTh/+nD+9OH86WP0/EnH86dB59gSNSZWK4JS0ouIiChSNLhUBCIiIiIiOQxsiYiIiCgiMLAlIiIioojAHFsiogYoHJsJiYjMjoEtEVEDU1AQhezsWBQV1d10S011YOHCKpZ/I6JGjakIREQNSEFBFMaPj0NRkWcnuuJiAePHx6GggOsVRNR4MbAlImog7HYgOzu2tiakZ2ArtVzOzo6FnVXhiKiRYmBLRNRAFBZaa9MPBNmvi6KAoiILCguZbEtEjRMDWyKiBqKkRD6gDfR5RESRhslYREQNRMuW6vpSqn1esLBiAxGFCwNbIqIGokcPO1JTHSguFlw5te4EQURKijOQDBdWbCCicGIqAhFRA2G1AgsXVgFwBrHupH8vXFgVttVRVmwgonBjYEtE1IBkZNRg9epKpKR4BrYpKSJWr64M26ooKzYQkRnw4zMRUQOTkVGDQYNqTJXHWlexQZ6zYoOAwkIrevVidEtEwcHAloioAbJaYaoAkRUbiMgMmIpARES6NZSKDUQU2RjYEhGRblLFhvqb2iSCICI11RHWig1EFPkY2BIRkW5mr9hARI0DA1siIjKEWSs2EFHjwc1jRERkGDNWbCCixoOBLRERGcpsFRuIqPFgKgIRERERRQQGtkREREQUERjYEhEREVFEYGBLRERERBGBgS0RERERRQQGtkREREQUERjYEhEREVFEYGBLRERERBGBgS0RERERRQQGtkREREQUERjYEhEREVFEYGBLRERERBGBgS0RERERRYSocA/ALAQh3COQJ43LrOMzO86fPpw/fTh/+nD+9OH86cP508fo+VN7HEEURdGYUxIRERERhQ9TEYiIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwDaPTp09jwIAB2Lt3r8fjBw8exDXXXOPzex0OB9LS0tCpUyekpaW5/isvLw/mkE1Fbv7+/e9/Y+jQobjuuuuQnp6Ov//973A4HIrHWLlyJfr06YNOnTohKysLx44dC8XQTUHv/DX2a1Bu/tatW4ebb74ZaWlpuPnmm/Hmm2/6PAavv8Dnj9ef/PsHAPz666/o2bMn8vPzfR6D11/g88frz3v+cnJy0KFDB4/5eOeddxSPEbTrT6Sw2L9/v9i/f3+xffv2YmFhoSiKouhwOMQNGzaInTp1Etu3b+/z+7/99lvx6quvFquqqkIxXNORm78jR46I1157rbh9+3bRbreLR48eFfv27SuuXr1a9hj5+fli7969xe+++06srKwUn376aXHw4MGiw+EI5UsJCyPmrzFfg3Lz99FHH4ldu3YVjxw5IoqiKB4+fFi85pprxD179sgeg9efvvnj9ec5fxK73S5mZWWJV1xxhfjee+8pHoPXn7754/XnPX/Dhg0T8/PzVR0jmNcfV2zDYNOmTXjssccwbdo0j8efeOIJbNiwAQ899JDfYxw5cgSXX345YmJigjVM01Kav+PHj2PEiBHo27cvLBYL/vznP2PAgAHYt2+f7HHeffddjBw5Eu3atUNsbCweffRRFBUVyX6CjyRGzV9jvQaV5i89PR3bt29Hhw4dUFNTgzNnzkAQBDRr1kz2OLz+9M0fr79psl9ftmwZWrVqhZSUFJ/H4fWnb/54/XnOn81mw3fffYcOHTqoOk4wrz8GtmFwww034D//+Q/+8pe/eDz+8MMP45133sFVV13l9xhHjhxBVVUVhg8fjh49emDUqFH44osvgjVkU1Gav4EDB+Lxxx93/buyshI7duzA1VdfLXuco0ePon379q5/R0dH409/+hO++eab4AzcJIyav8Z6DSrNHwA0bdoUx44dw7XXXotJkybh7rvvVvx95vWnb/54/XnPX2FhIbZs2YKcnBy/x+H1p2/+eP15zt8333yDmpoavPTSS+jZsycGDhyI3NxcxVS2YF5/DGzDoEWLFoiKivJ6vFWrVqqPERcXh2uvvRavvPIKduzYgfT0dIwfPx4///yzkUM1JaX5c1dWVoYpU6YgLi4OY8aMkX3OuXPnEB8f7/FYXFxcxOdIGTV/jfUa9Dd/bdq0weHDh7Fx40Zs2bIFubm5ss/j9SdP7fzx+vNUWlqKJ554As8//zwSEhL8HofXnyet88frz9Mff/yBbt26ISsrCzt37sRzzz2HN954A3l5ebLHCeb1x8C2gZo1axaeeuoptGzZEnFxcRg/fjxSU1Oxc+fOcA8t7I4dO4YRI0agpqYGa9euRdOmTWWfFx8fj8rKSo/HKisrVf1Ri2Rq54/XoLzo6GhER0fjmmuuwejRo1FQUCD7PF5/8tTOH6+/OqIoYsaMGcjKylJ9K5jXX51A5o/Xn6devXph7dq16NatG6Kjo3Httdfi3nvvxfvvvy/7/GBefwxsG6ilS5fiq6++8njMZrMhNjY2TCMyh507d+KOO+5A7969sXr1apx//vmKz23Xrh2+//5717+rq6vxww8/eNweaWy0zB+vQU9r1qzBX//6V4/HbDab4hzy+vOkdf54/dUpLi7G559/jmXLlqFLly7o0qULioqKMH/+fEyePFn2e3j91Qlk/nj9efrwww+xfv16j8dsNhvi4uJknx/M64+BbQP13XffYdGiRTh58iRsNhv+/ve/o6ysDAMGDAj30MLm0KFDmDJlCh5//HHMnDnT7+324cOH480338Q333yDqqoqvPDCC2jevDm6dOkSohGbi9b54zXoqUuXLvjwww/x/vvvw+Fw4MCBA1i7di3uvvtu2efz+vOkdf54/dVJTU3FkSNHsH//ftd/qampyMnJwYoVK2S/h9dfnUDmj9efJ1EU8fTTT2PPnj0QRREHDx7E2rVrcdddd8k+P5jXHwPbBmL//v1IS0tDUVERAODpp59G27ZtMXToUHTv3h2ff/45XnvtNSQmJoZ3oGG0fPly1NTUYNGiRR519CZMmADAew5vv/12jBkzBlOmTEGPHj3w1VdfYcWKFYiOjg7nywgbrfPHa9BThw4d8NJLL2H58uXo0qUL5s2bh9mzZ7s2WfD6803r/PH604bXnz68/nwbMGAAHn/8ccybNw9paWmYPn06pk6diqFDhwII7fUniKIo6j4KEREREVGYccWWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJSIiIqKIwMCWiIiIiCICA1siIiIiiggMbImIiIgoIjCwJaIG7Ycffgj3EIiIyCQY2BKRT+np6bjmmmtcncg6deqEG264Ac8++ywcDodh58nKysLLL78MAJg7dy7mzp3r93u2b9+O8ePHB3zO/Px8pKenez1eUVGBbt26Ye3atbLf9+ijj+KBBx4I6Nih8Pvvv2P48OH4/fffsXfvXlx++eVBP2dRUZFHx7orrrjC47qROtiFSnl5OcaPH4+OHTti1KhRIT23Gu7Xh/vPi4j08d0MnogIwPz585GZmen697fffosxY8YgPj4eDz30kOHne/LJJ1U97+zZswhG88T4+HgMHz4cGzZswOjRoz2+dvr0afz73//GqlWrDD+vURYsWIA777wTzZo1C9k5U1NTcfDgQde/09PT8eCDD3pcN6H09ddf49NPP8XevXtN3+a0WbNmGDFiBBYuXIjFixeHezhEDRpXbIlIs8svvxxdu3bFV199BcC52jpr1iz07dsXN910E8rKyvDTTz/hvvvuQ/fu3dG3b18sXboUNpvNdYwNGzagX79+SEtLw8yZM1FRUeH62qxZszBr1izXv19//XUMGDAAaWlpyMzMxJ49e7B3717k5OS4VgpLSkpgs9nw4osvol+/fujWrRsmTpyIH3/80XWc//73v8jKykJaWhqGDBniGr+ckSNH4ujRozh06JDH4xs3bsQll1yCHj16YPv27RgxYgSuv/56dOzYEffcc49saoTcqmn917hlyxYMGTIEnTt3RmZmJj799FPX1/bt24fMzEx06dIFAwYMwKJFi1BTUyM77u+++w47d+7EsGHDFF+bu8rKSixevBg33ngjunbtiqysLHz55Zeur//yyy8YP348rrvuOtxyyy1Ys2ZNwCvAs2bNwkMPPYRBgwahR48e+Omnn/DFF19g9OjRuOGGG3DNNdcgMzPTNed79+5Feno6Xn31VfTu3RvdunXD1KlTUVZWBgAoKSnBhAkT0K1bN/Tp0wcPPvggfv31V3z44YcYO3YsAKBv377YsGEDHA4HcnNz0b9/f3Tu3Bm33347du3a5Rpbeno65s6di169euG2227Dnj17kJ6ejlWrVqFXr17o3LkzlixZgo8++ggDBw5EWloapk6d6rqm9V57Q4cOxY4dO/Ddd98FNLdE5MTAlog0qa6uxt69e1FYWIhevXq5Hv/ss8+wfv16bN68GRaLBWPGjEG7du3wySef4K233sJnn33mSjXYs2cPnnzySSxcuBD79u1Dx44dceTIEdnz5efn45VXXsHixYtx4MAB3H333bj//vtx+eWXY/78+a6VwpYtW2Lp0qXYsWMH1qxZg127dqFjx44YN24cqqqqUF1djcmTJ6Ndu3YoLCzEkiVL8OGHHyq+zjZt2uDGG2/Eu+++63rM4XDgnXfewejRo3HixAk8/PDDmDRpEvbs2YMdO3ZAFEUsW7ZM85zu3LkTOTk5mDt3Lj7//HNMnToVU6dOxffffw8AmDFjBrKysrB//3689tpr2LZtGz766CPZY7399tvo378/YmJiVJ173rx5+PTTT7F27Vrs3r0b/fv3x5gxY1BUVAS73Y7JkyfjwgsvxKefforVq1fjH//4h+bX527Xrl148cUX8cEHH+DCCy/E/fffj4EDB+KTTz7B3r170bZtW49Vy+PHj6OkpAT/+c9/sGHDBhw8eBBvvfUWAGDJkiVo1aoVdu/ejffffx/l5eWu4HXlypUAgIMHD+KOO+7AsmXLsG7dOrz44ovYu3cvxo0bhwceeMAjiP/yyy+xdetWrF27FhaLBcePH8fJkyexY8cOLFmyBCtWrMC6devw7rvvYvPmzdi7dy/ef/99ANB97cXExKBfv35Yv369rvklauwY2BKRX/Pnz0eXLl3QpUsXXH/99ViwYAHGjh2Le+65x/WcPn36oGXLlmjWrBl27NgBm82GRx55BLGxsUhJScHDDz+MdevWAQA2b96Mm2++Gddffz2ioqIwcuRIXHXVVbLn3rRpE+666y6kpaXBYrHgjjvuQF5eHuLi4jyeJ4oi1q9fj0ceeQRt2rRBbGwspkyZgurqauzYsQMHDx5EcXExZsyYgdjYWLRr1861qqdk9OjR2Lp1q2uFcNeuXSgrK8OQIUNwwQUXYMuWLUhPT0dZWRlOnDiBpKQklJSUaJ7fN998E3fffTe6du0Kq9WKvn37Ij093RXkxMbGYuvWrfj444+RmJiInTt3YuDAgbLHKiwsRFpamqrzVlVVoaCgAI8++iguvvhixMTE4N5778Wll16KgoICHDp0CD/88APmzJmDJk2a4KKLLsK0adM0vz53nTp1Qvv27dGsWTNER0fjnXfewciRI2Gz2XD8+HEkJiZ6zeGUKVMQFxeHiy++GN27d8f//vc/17wcOHAAW7Zswblz57Bq1SpkZ2fLnve9997DpEmTcPXVVyMqKgp/+ctfkJ6ejo0bN7qeM3DgQDRr1swjhWPy5MmIjo7GDTfcAAC4++67cf7556NNmzZo164dfvnlF8Ouveuuuw579uzRNb9EjR1zbInIr5ycHL+5khdeeKHr/48fP47Tp0+ja9eursdEUUR1dTVKS0tRUlKCq6++2uP727RpI3vckydPIjU11eOx6667zut5p0+fRnl5OR5++GFYLHWf2aurq3H8+HHYbDYkJSV5BMRt27b1+Zp69uyJlJQUFBQUYMSIEXjrrbdw1113IS4uDqIooqCgAOvXr4cgCGjfvj3KysoQFaX9z+rx48fx+eef4+2333Y9Zrfb0aNHDwDOVIyXX34Z8+fPx8mTJ9G7d2/MmzcPrVq18jpWcXExWrZsqeq8v/32G6qrq9G6dWuPx1u3bo1ffvkFF110EZKSktCkSROPr+nhfp1YrVbs3bsXEydORHl5OS677DJERUV55U23aNHC9f/R0dGur2dnZ2PFihVYvXo1Zs2ahSuuuALZ2dno0qWL13lPnTrldY21bt0a33zzjezYJElJSa6xAvAIei0WC0RRNOzaa9myJU6cOOH1OBGpx8CWiAwhCILr/1u1aoW2bdti27ZtrsfKyspQWlqKCy64AK1atcLPP//s8f0nTpxAu3btvI6bkpKC4uJij8eWLl2KW2+91eOxpKQkxMbGIi8vD506dXI9fuzYMbRs2RJff/01Tp8+jXPnziEhIcF1Tn9GjRqFDRs24MYbb8Tu3bsxf/58AMDWrVvx5ptv4u2338bFF18MwLlpSy5HUgqKbDabK0XgzJkzrqCpVatWuO222zBp0iTX9xQVFSEuLg5VVVU4evQo5s2bh6ioKPzvf/9DdnY2nnrqKbz00kte5xIEQfWGuubNmyM2NhY///wz/vznP7se/+mnn5Ceno7U1FScPn0aFRUViI+Pd41LD/fr5PDhw1iwYAHWr1+PDh06AADy8vJcK7L+fPXVV7jrrrswdepUnD59GsuWLcODDz6IwsJCr+dedNFFXtfczz//7BHMuo/N12P1GXXt2e12j8CYiLTjbxARGa5v376uW8M2mw2///47Zs6ciWnTpkEQBAwfPhwffvghPv74Y9TU1GDTpk04fPiw7LEyMzPxzjvv4Msvv4TD4cB7772HdevWuYKJiooK1NTUwGKx4Pbbb8cLL7yAEydOwOFwYNOmTcjIyMCPP/6ItLQ0XHLJJVi4cCEqKirw448/Ii8vz+9rGTZsGH788Uf87W9/Q//+/V2rpH/88QcsFotr9faTTz7BP/7xD1RXV3sdo23btoiKisKWLVsAOPOR3YOvO++8E2vXrnXlex45cgSZmZkoKCiAIAh45JFHkJeXh5qaGrRo0QJRUVGuoLi+iy66SDYd4sSJEx7//frrr7BYLBg+fDiWLFmCH3/8ETabDa+//jqOHj2KwYMHo2PHjrjsssvwzDPPoKKiAiUlJbLBdKDc5xAADh06hLVr13psMvRl+fLlWLBgAcrKytCsWTPEx8crzssdd9yB3Nxc/N///R/sdju2bt2K7du3q95k54tR196vv/7qdXeCiLThii0RGa5p06ZYs2YNnnnmGaxatQoOhwPdu3fHq6++CgDo3LkzFi9ejGeeeQbTpk1Djx49PDaiuRsyZAh+//13TJ8+HSdPnsRll12GlStX4oILLkDXrl2RnJyMrl27Yv369Zg5cyZefvlljBw5EmfPnkWbNm3w0ksvufJ3c3NzMXfuXPTs2RPNmzdHv3798MEHH/h8LU2aNEFmZiZef/1116YlwBnwHjhwAIMHD4bVasWll16Ke++9F+vWrfMKzC688EI88cQTeOWVV7BgwQL06NEDmZmZrkoQt9xyC8rLy/HEE0+gqKgIiYmJGDNmDLKysiAIAl599VU8++yzWLFiBaxWK/r06YPHHntMdry9evXCgQMHcOedd3o8fuONN3r8u3nz5ti9ezdmzJiBl19+GWPGjMHZs2dx+eWXY/Xq1bjkkksAAC+99BJycnJw/fXXo1WrVkhPT8fXX3/tc87U6tWrF0aOHIlRo0bB4XCgdevWyMrKwgsvvIBTp075/f4nn3wS8+fPR79+/WCz2dChQwe8+OKLss8dO3YsHA4Hpk2bhpMnT+Liiy/GkiVL0K1bN0NeixHX3oEDB1y5vEQUGEEMRhFIIiIKi2+++Qb33nsvdu7c6bXBTqvKykocPHgQ3bp1c6VTbN++HTk5OR6lski/iooK3HTTTXjzzTdlU3KISB2mIhARRZArrrgCvXv3Rn5+vu5jRUdH469//SveffddOBwOlJaWIi8vD3379jVgpOQuPz8fN910E4NaIp24YktEFGHOnDmDcePGYc2aNTj//PN1HWv//v1YvHgx/vvf/yI2NhYDBw7E9OnTPSolkD6//fYbxowZg9dee830XdKIzI6BLRERERFFBKYiEBEREVFEYGBLRERERBGBgS0RERERRQQGtkREREQUERjYEhEREVFEYGBLRERERBGBgS0RERERRQQGtkREREQUERjYEhEREVFE+P89VWlmYLtbjwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals_log = y_test_log - y_pred_log\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred_log, residuals_log, color='blue',)\n",
-    "plt.title('Residuals Plot (Log Transformed)')\n",
-    "plt.xlabel('Predicted Values (Log Transformed)')\n",
-    "plt.ylabel('Residuals (Log Transformed)')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Plot house prices against coefficients\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.barh(X.columns, model_log.coef_)\n",
-    "plt.xlabel('Coefficient Value')\n",
-    "plt.ylabel('Features')\n",
-    "plt.title('House Prices vs. Coefficients of Variables')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "visualization of the positive and negative coefficient variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Log transformation of the polynomial model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Polynomial Model (Degree 2)- MSE: 37774083176.83915\n",
-      "Polynomial Model (Degree 2)- R-squared: 0.7161383140038227\n",
-      "                            OLS Regression Results                            \n",
-      "==============================================================================\n",
-      "Dep. Variable:                  price   R-squared:                       0.679\n",
-      "Model:                            OLS   Adj. R-squared:                  0.678\n",
-      "Method:                 Least Squares   F-statistic:                     557.0\n",
-      "Date:                Wed, 01 May 2024   Prob (F-statistic):               0.00\n",
-      "Time:                        12:27:46   Log-Likelihood:                -3540.2\n",
-      "No. Observations:               16913   AIC:                             7210.\n",
-      "Df Residuals:                   16848   BIC:                             7713.\n",
-      "Df Model:                          64                                         \n",
-      "Covariance Type:            nonrobust                                         \n",
-      "==============================================================================\n",
-      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
-      "------------------------------------------------------------------------------\n",
-      "const       4987.9435    522.414      9.548      0.000    3963.957    6011.930\n",
-      "x1          3457.3790    362.110      9.548      0.000    2747.606    4167.152\n",
-      "x2           -16.5312      7.635     -2.165      0.030     -31.497      -1.566\n",
-      "x3           -16.9803      5.626     -3.018      0.003     -28.008      -5.953\n",
-      "x4           -59.7513      8.202     -7.285      0.000     -75.827     -43.675\n",
-      "x5           -66.7001     16.473     -4.049      0.000     -98.988     -34.412\n",
-      "x6            21.7086      8.506      2.552      0.011       5.036      38.381\n",
-      "x7            56.1227     10.699      5.246      0.000      35.152      77.093\n",
-      "x8             0.1415      0.453      0.313      0.755      -0.745       1.028\n",
-      "x9         -1530.3915    159.119     -9.618      0.000   -1842.282   -1218.501\n",
-      "x10           -7.2389      1.397     -5.181      0.000      -9.977      -4.500\n",
-      "x11           27.1030      5.183      5.229      0.000      16.943      37.263\n",
-      "x12         2396.4725    250.995      9.548      0.000    1904.495    2888.450\n",
-      "x13          -11.4586      5.292     -2.165      0.030     -21.832      -1.085\n",
-      "x14          -11.7699      3.900     -3.018      0.003     -19.414      -4.126\n",
-      "x15          -41.4165      5.685     -7.285      0.000     -52.560     -30.273\n",
-      "x16          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
-      "x17           15.0473      5.896      2.552      0.011       3.491      26.604\n",
-      "x18           38.9013      7.416      5.246      0.000      24.366      53.437\n",
-      "x19            0.0981      0.314      0.313      0.755      -0.517       0.713\n",
-      "x20        -1060.7866    110.293     -9.618      0.000   -1276.973    -844.601\n",
-      "x21           -5.0176      0.968     -5.181      0.000      -6.916      -3.119\n",
-      "x22           18.7864      3.593      5.229      0.000      11.744      25.829\n",
-      "x23            0.0476      0.079      0.603      0.546      -0.107       0.202\n",
-      "x24            0.2000      0.083      2.399      0.016       0.037       0.363\n",
-      "x25           -0.4148      0.109     -3.823      0.000      -0.628      -0.202\n",
-      "x26            0.1250      0.245      0.511      0.609      -0.355       0.605\n",
-      "x27            0.1981      0.122      1.628      0.104      -0.040       0.437\n",
-      "x28            0.2251      0.161      1.396      0.163      -0.091       0.541\n",
-      "x29           -0.0032      0.006     -0.531      0.595      -0.015       0.009\n",
-      "x30            3.1411      1.477      2.127      0.033       0.247       6.036\n",
-      "x31           -0.0103      0.012     -0.883      0.377      -0.033       0.013\n",
-      "x32           -0.1564      0.078     -2.000      0.046      -0.310      -0.003\n",
-      "x33            0.0859      0.039      2.183      0.029       0.009       0.163\n",
-      "x34           -0.0407      0.079     -0.516      0.606      -0.195       0.114\n",
-      "x35            0.3258      0.195      1.668      0.095      -0.057       0.709\n",
-      "x36           -0.2649      0.091     -2.905      0.004      -0.444      -0.086\n",
-      "x37            0.1059      0.111      0.957      0.339      -0.111       0.323\n",
-      "x38           -0.0112      0.005     -2.119      0.034      -0.022      -0.001\n",
-      "x39            3.2391      1.094      2.960      0.003       1.094       5.384\n",
-      "x40            0.0084      0.008      0.989      0.323      -0.008       0.025\n",
-      "x41           -0.0497      0.054     -0.924      0.355      -0.155       0.056\n",
-      "x42            0.6010      0.091      6.569      0.000       0.422       0.780\n",
-      "x43            0.1950      0.270      0.721      0.471      -0.335       0.725\n",
-      "x44            0.3964      0.139      2.845      0.004       0.123       0.670\n",
-      "x45           -0.0224      0.156     -0.143      0.886      -0.329       0.284\n",
-      "x46            0.0471      0.006      7.361      0.000       0.035       0.060\n",
-      "x47           11.9015      1.585      7.511      0.000       8.796      15.007\n",
-      "x48            0.0005      0.012      0.044      0.965      -0.023       0.024\n",
-      "x49           -0.3608      0.079     -4.586      0.000      -0.515      -0.207\n",
-      "x50          -46.2330     11.418     -4.049      0.000     -68.614     -23.852\n",
-      "x51           -0.2814      0.262     -1.074      0.283      -0.795       0.232\n",
-      "x52           -1.4097      0.386     -3.649      0.000      -2.167      -0.652\n",
-      "x53           -0.0189      0.015     -1.223      0.221      -0.049       0.011\n",
-      "x54           17.2366      4.274      4.033      0.000       8.859      25.614\n",
-      "x55           -0.0160      0.016     -1.020      0.308      -0.047       0.015\n",
-      "x56            0.1612      0.187      0.861      0.389      -0.206       0.528\n",
-      "x57           -0.0348      0.081     -0.430      0.667      -0.193       0.124\n",
-      "x58           -0.5518      0.172     -3.212      0.001      -0.888      -0.215\n",
-      "x59            0.0147      0.008      1.939      0.053      -0.000       0.030\n",
-      "x60           -4.5317      1.668     -2.716      0.007      -7.802      -1.261\n",
-      "x61           -0.0425      0.016     -2.603      0.009      -0.074      -0.010\n",
-      "x62            0.6652      0.082      8.073      0.000       0.504       0.827\n",
-      "x63            0.7108      0.132      5.394      0.000       0.452       0.969\n",
-      "x64           -0.0074      0.009     -0.830      0.406      -0.025       0.010\n",
-      "x65          -10.6811      2.084     -5.125      0.000     -14.766      -6.596\n",
-      "x66           -0.0058      0.016     -0.357      0.721      -0.038       0.026\n",
-      "x67           -0.4727      0.098     -4.817      0.000      -0.665      -0.280\n",
-      "x68           -0.0051      0.002     -2.881      0.004      -0.009      -0.002\n",
-      "x69           -0.0156      0.088     -0.178      0.859      -0.188       0.157\n",
-      "x70        -3.158e-05      0.001     -0.045      0.964      -0.001       0.001\n",
-      "x71           -0.0010      0.004     -0.221      0.825      -0.009       0.007\n",
-      "x72          150.7551     15.531      9.707      0.000     120.313     181.198\n",
-      "x73            0.7643      0.175      4.359      0.000       0.421       1.108\n",
-      "x74           -5.7615      1.013     -5.690      0.000      -7.746      -3.777\n",
-      "x75            0.6353      0.261      2.434      0.015       0.124       1.147\n",
-      "x76            0.0173      0.007      2.394      0.017       0.003       0.031\n",
-      "x77            0.3068      0.032      9.733      0.000       0.245       0.369\n",
-      "==============================================================================\n",
-      "Omnibus:                       86.286   Durbin-Watson:                   2.008\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              100.078\n",
-      "Skew:                          -0.119   Prob(JB):                     1.85e-22\n",
-      "Kurtosis:                       3.291   Cond. No.                     1.52e+16\n",
-      "==============================================================================\n",
-      "\n",
-      "Notes:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "[2] The smallest eigenvalue is 1.69e-24. This might indicate that there are\n",
-      "strong multicollinearity problems or that the design matrix is singular.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "import statsmodels.api as sm\n",
-    "\n",
-    "# Define function to keep only numeric columns\n",
-    "def only_numeric(df):\n",
-    "    return df.select_dtypes(include=[np.number])\n",
-    "\n",
-    "features = ['bathrooms', 'sqft_living',\n",
-    "            'floors', 'waterfront', 'condition', 'grade',\n",
-    "            'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15']\n",
-    "target = 'price'\n",
-    "\n",
-    "# Load your housing data (assuming it's already loaded into 'housing_data' DataFrame)\n",
-    "\n",
-    "# Keep only numeric columns\n",
-    "housing_data_numeric = only_numeric(housing_data)\n",
-    "\n",
-    "# Check if all features exist in the DataFrame\n",
-    "missing_features = [feature for feature in features if feature not in housing_data_numeric.columns]\n",
-    "if missing_features:\n",
-    "    print(\"The following features are not present in the dataset:\", missing_features)\n",
-    "else:\n",
-    "    # Extract features and target variable\n",
-    "    X = sm.add_constant(housing_data_numeric[features])\n",
-    "    y = housing_data_numeric[target]\n",
-    "    \n",
-    "    # Log transform features and target variable\n",
-    "    X_log = np.log(X + 1)  # Adding 1 to avoid log(0)\n",
-    "    y_log = np.log(y)\n",
-    "    \n",
-    "    # Split the data into training and testing sets\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X_log, y_log, test_size=0.2, random_state=42)\n",
-    "\n",
-    "    # Polynomial Regression\n",
-    "    # Choose the degree of the polynomial\n",
-    "    degree = 2\n",
-    "\n",
-    "    # Create polynomial features\n",
-    "    poly = PolynomialFeatures(degree)\n",
-    "    X_train_poly = poly.fit_transform(X_train)\n",
-    "    X_test_poly = poly.transform(X_test)\n",
-    "\n",
-    "    # Build a polynomial regression model\n",
-    "    poly_model = LinearRegression()\n",
-    "    poly_model.fit(X_train_poly, y_train)\n",
-    "\n",
-    "    # Make predictions on the test set\n",
-    "    y_pred_poly = poly_model.predict(X_test_poly)\n",
-    "\n",
-    "    # Reverse log transformation for evaluation\n",
-    "    y_pred = np.exp(y_pred_poly)\n",
-    "    y_test_original = np.exp(y_test)\n",
-    "\n",
-    "    # Evaluate the polynomial model\n",
-    "    mse_poly = mean_squared_error(y_test_original, y_pred)\n",
-    "    r2_poly = r2_score(y_test_original, y_pred)\n",
-    "    print(\"Polynomial Model (Degree {})- MSE:\".format(degree), mse_poly)\n",
-    "    print(\"Polynomial Model (Degree {})- R-squared:\".format(degree), r2_poly)\n",
-    "    \n",
-    "    # Convert to DataFrame for summary\n",
-    "    #X_poly_df = pd.DataFrame(X_train_poly, columns=poly.get_feature_names(features))\n",
-    "\n",
-    "    # Fit the OLS model\n",
-    "    model = sm.OLS(y_train,X_train_poly)\n",
-    "    results = model.fit()\n",
-    "\n",
-    "    # Print the summary\n",
-    "    print(results.summary())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.\n",
-    "\n",
-    "MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.\n",
-    "\n",
-    "Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example:\n",
-    "The coefficient for const (intercept) is 4987.94.\n",
-    "The coefficient for x1 (bathrooms) is 3457.38.\n",
-    "The coefficient for x2 (sqft_living) is -16.53.\n",
-    "The coefficient for x3 (floors) is -16.98.\n",
-    "The coefficient for x4 (waterfront) is -59.75.\n",
-    "The coefficient for x5 (condition) is -66.70.\n",
-    "The coefficient for x6 (grade) is 21.71.\n",
-    "The coefficient for x7 (sqft_basement) is 56.12.\n",
-    "The coefficient for x8 (yr_built) and other coefficients follow.\n",
-    "\n",
-    "P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.\n",
-    "\n",
-    "\n",
-    "F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.\n",
-    "\n",
-    "\n",
-    "Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Calculate residuals\n",
-    "residuals = y_test_original - y_pred\n",
-    "\n",
-    "# Plot residuals vs predicted values\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.scatter(y_pred, residuals, color='blue')\n",
-    "plt.title('Residuals Plot')\n",
-    "plt.xlabel('Predicted Values')\n",
-    "plt.ylabel('Residuals')\n",
-    "plt.axhline(y=0, color='red', linestyle='--')\n",
-    "plt.grid(True)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# REGRESSION RESULTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.\n",
-    "\n",
-    "Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73\n",
-    "\n",
-    "The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Assumptions\n",
-    "\n",
-    "Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.\n",
-    "\n",
-    "Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.\n",
-    "\n",
-    "Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.\n",
-    "\n",
-    "No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Limitations\n",
-    "Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:\n",
-    "\n",
-    "Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.\n",
-    "\n",
-    "\n",
-    "Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.\n",
-    "\n",
-    "Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.\n",
-    "\n",
-    "Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Recommedations\n",
-    "# Statistical recommedations.\n",
-    "To mitigate multicollinearity, strategies such as feature selection, principal component analysis (PCA), or regularization methods like ridge regression or Lasso regression can be employed. These techniques prioritize essential predictors and enhance the model's interpretability while stabilizing it against multicollinearity.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Before opting for polynomial regression, it's essential to validate the assumption of linearity between predictors and the target variable. If this assumption doesn't hold, alternative regression techniques such as generalized additive models (GAMs) or spline regression should be considered to better capture intricate relationships.\n",
-    "\n",
-    "\n",
-    "Preventing heteroscedasticity, or addressing it if it's present, is crucial for ensuring the reliability of linear regression analysis\n",
-    "# Recommedation to real estate clients:Home owners and investors.\n",
-    "\n",
-    "\n",
-    "1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.\n",
-    "\n",
-    "\n",
-    "2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.\n",
-    "\n",
-    "\n",
-    "3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.\n",
-    "\n",
-    "\n",
-    "4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.\n",
-    "\n",
-    "\n",
-    "5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.\n",
-    "\n",
-    "\n",
-    "6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# CONCLUSION\n",
-    "\n",
-    "Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.\n",
-    "\n",
-    "\n",
-    "Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.\n",
-    "\n",
-    "\n",
-    "Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.\n",
-    "\n",
-    "\n",
-    "Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.\n"
-   ]
-  }
- ],
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-  "kernelspec": {
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-   "language": "python",
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-# REAL ESTATE SALES PREDICTION MODEL
-
-## Project Overview
-
-In the fast-paced world of real estate, it's crucial for agencies to provide clients with precise information. Clients, whether they're looking to become homeowners or investors, rely on real estate companies for guidance on important decisions such as pricing, market trends, and property evaluations. To meet this need, real estate agencies can benefit from a sophisticated regression-based tool. This tool uses various property variables like the number of bedrooms, year built, floor count, living area, condition, location, and amenities to accurately predict property prices. By employing regression analysis, agencies can offer clients more precise pricing estimates, leading to better-informed decisions. Ultimately, this tool aims to improve client satisfaction, streamline decision-making processes, and drive success for real estate agencies.
-
-
-### Business Problem
-
-Real estate experts in King County need help understanding what factors influence property values and market trends. This study aims to analyze property features, locations, buyer preferences, and market changes over time. By gaining insights from this analysis, real estate professionals can make informed decisions about buying, selling, and positioning themselves in the dynamic King County market. The goal is to provide practical advice to help them succeed in this ever-changing real estate landscape.
-### The Data Understanding
-
-King County, Washington, situated in the northwest of the United States, is known for its vibrant housing market centered around Seattle. The county has experienced significant growth due to its strong economy and cultural importance, attracting a large number of residents and creating high demand for housing in both urban and suburban areas. Seattle, with its impressive skyline, is especially sought after by tech professionals and city lovers. King County's real estate market is competitive, offering a range of neighborhoods to suit different preferences, from historic areas to modern suburban developments
-**Target Variable**
-
-price: Sale price of the house .
-
-**Unique identifier**
-
-id - Unique identifier for a house
-
-**Property Characteristics:**
-
-
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-Additional Features:
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.bedrooms: Number of bedrooms.
-
-bathrooms: Number of bathrooms.
-
-sqft_living: Square footage of living space in the home.
-
-sqft_lot: Square footage of the lot.
-
-floors: Number of floors (levels) in the house.
-
-waterfront: Indicates whether the house is on a waterfront (categorical: YES/NO).
-
-view: Quality of view from the house, categorized into various types.
-
-condition: Overall condition of the house, categorized based on maintenance.
-
-grade: Overall grade of the house, reflecting construction and design quality.
-
-**Additional Features:**
-
-sqft_above: Square footage of house apart from the basement.
-
-sqft_basement: Square footage of the basement.
-
-yr_built: Year when the house was built.
-
-yr_renovated: Year when the house was renovated.
-
-zipcode: ZIP Code of the property.
-
-lat: Latitude coordinate of the property.
-
-long: Longitude coordinate of the property.
-
-sqft_living15: Square footage of interior housing living space for the nearest 15 neighbors.
-
-sqft_lot15: Square footage of the land lots of the nearest 15 neighbors.
-### Key Points
-
-# **OBJECTIVES**
-
-
-**Main Objective:**
-
-The primary aim of this project is to develop a predictive regression model to support real estate agencies in advising clients on house prices. This model is intended to anticipate potential changes in property value based on property characteristics, furnishing clients with valuable insights to facilitate informed investment decisions.
-
-Specific Goals:
-
-i). Identification of Key Influencing Factors on House Prices:
-
-Analyze various property features, including bedrooms, bathrooms, and square footage, to determine their impact on sale price. Investigate location-related attributes such as zip codes and geographic coordinates to further comprehend their effect on property prices.
-
-ii). Assessment of Model Performance:
-
-Employ metrics such as mean squared error, R-squared values, and residual analysis to evaluate the model's accuracy in predicting house prices effectively.
-
-iii). Provision of Actionable Recommendations:
-
-Offer practical recommendations to real estate agencies aimed at enhancing profitability and market presence. Utilize insights gleaned from the model to optimize marketing strategies and enhance overall decision-making processes.
-
-# **TABLE OF CONTENT**
-1.   Data Preparation
-2.   Data cleaning
-1.   Exploratory data analysis
-2.   Statistical Analysis
-1.   Modelling
-2.   Regression Results
-1.   Conclusion
-2.   Recomendations
-
-
-#**Statistical Analysis**
-Statistical analysis plays a crucial role in understanding relationships within datasets, identifying patterns, and gaining insights. In this regression modeling project aimed at predicting property values, several key steps in statistical analysis are essential:
-
-Descriptive Statistics
-Correlation matrix
-Distribution Analysis
-Inferential Statistics using Hypothesis Testing and Analysis of Variance
-MultiColinierity
-# *Modelling**
-Baseline model - simple linear model.
-2. log transformation. 
-3. Multiple Linear Regression
-4. Residual modelling.
-
-# **REGRESSION RESULTS**
-
-**SIMPLE LINEAR REGRESSION** 
-Intercept: This is like the starting point or baseline of our predictions. For example, if all other factors were zero, we'd expect the value to be around -44593.95. It's where our line would intersect the y-axis on a graph.
-
-Coefficient: This tells us how much the predicted value changes for each unit increase in the independent variable. In this case, for every one-unit increase in the independent variable, we'd expect the dependent variable to increase by about 281.41.
-
-R-squared: This is a measure of how well our model explains the variation in the data. An R-squared value of 0.49 means that about 49% of the variability in the dependent variable is explained by our model. So, it's doing a decent job, but there's still room for improvement.
-
-Mean Squared Error (MSE): This tells us, on average, how much our predictions differ from the actual values in the training data. Here, the average squared difference is around 68601152192.94, which is quite large but it's relative to the scale of the dependent variable.
-
-Root Mean Squared Error (RMSE): This is just the square root of the MSE. It gives us an idea of the average amount our predictions are off by. Here, it's around 261918.22, which is the typical difference between our predicted values and the actual values in the training data.
-
-Test Set:
-
-R-squared: Similar to the training set, this tells us how well our model explains the variation in the test data. An R-squared value of 0.48 means that about 48% of the variability in the dependent variable is explained by our model when evaluated on the test data.
-
-Mean Squared Error (MSE): This is the average squared difference between our predictions and the actual values in the test data. It's around 68845100756.11, similar to the MSE in the training set.
-
-Root Mean Squared Error (RMSE): Again, this is just the square root of the MSE for the test data. It's around 262383.50, which is the typical difference between our predicted values and the actual values in the test data
-Overall
-, the model suggests that there is a positive relationship between the independent and dependent variables. However, given the moderate R-squared value and the relatively high MSE and RMSE, it's clear that there may be other factors influencing the dependent variable that are not captured by the model. Further investigation or refinement of the model may be needed to improve its predictive performance.
-# ** Multiple Linear Regresion 
-![alt text](image-1.png)
-# **RESIDUALS**
-![alt text](image-3.png)
-A p-value of 3.975373048166964e-258, which is extremely close to zero, indicates strong evidence against the null hypothesis of homoscedasticity. In other words, there is a significant presence of heteroscedasticity in your data.
-
-Heteroscedasticity refers to the situation where the variability of a variable is unequal across the range of values of a second variable that predicts it. In the context of regression analysis, it means that the variance of the errors is not constant across observations.
-The model identifies the main factors that affect house prices, giving useful advice to real estate companies when they help customers. It's not perfect, but it does an okay job with a 63.1% score. We still need to check if it can predict prices well. However, the model does its job by helping with decisions and making marketing better by using details about houses and the market.
-
-We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
-#**Log transformation**.
-Transforming the dependent variable or one or more independent variables can sometimes stabilize the variance. Common transformations include taking the natural logarithm, square root, or reciprocal of the variables.
-
-Log transformation of the multiple linear regression.
-![alt text](image-4.png)
-
-![alt text](image-5.png)
-The model's residuals are randomly distributed, indicating no systematic bias. The log transformation visualizes residuals and patterns. A horizontal red line suggests linearity and normality are satisfied, but does not provide a definitive answer on model goodness fit and additional technique's could be used to assess the model.
-visualization of the positive and negative coefficient variables.
-R-squared: The R-squared value indicates that approximately 71.61% of the variance in the target variable (price) is explained by the model.
-
-MSE (Mean Squared Error): The MSE of approximately 37,774,083,176.84 represents the average squared difference between the predicted and actual house prices.
-
-Coefficients: The coefficients represent the estimated effect of each predictor variable on the target variable. For example: The coefficient for const (intercept) is 4987.94. The coefficient for x1 (bathrooms) is 3457.38. The coefficient for x2 (sqft_living) is -16.53. The coefficient for x3 (floors) is -16.98. The coefficient for x4 (waterfront) is -59.75. The coefficient for x5 (condition) is -66.70. The coefficient for x6 (grade) is 21.71. The coefficient for x7 (sqft_basement) is 56.12. The coefficient for x8 (yr_built) and other coefficients follow.
-
-P-values: P-values indicate the statistical significance of the coefficients. A small p-value (typically < 0.05) suggests that the corresponding predictor variable is statistically significant in predicting the target variable. In this summary, some coefficients have p-values less than 0.05, indicating statistical significance, while others do not.
-
-F-statistic: The F-statistic tests the overall significance of the model. A low p-value (typically < 0.05) suggests that at least one of the predictors has a non-zero coefficient, indicating that the model as a whole is statistically significant.
-
-Overall, the summary provides valuable insights into the relationships between the predictor variables and the target variable, as well as the overall goodness-of-fit of the model.
-
-![alt text](image-6.png)
-#**REGRESSION Results**
-
-From all the models observed,We can see the positive correlation between the house prices and all other features except the year built which indicates a negative correlation.
-
-Polynomial Regression is the preferred model beacuse from the evaluation it has the highest R-squared value of 0.73
-
-The features below impact price such that an increase will cause an increase in the price of the property. 'bedrooms','bathrooms', 'sqft_living','floors', 'waterfront','view''condition', 'grade', 'sqft_above','sqft_basement', 'yr_renovated', 'sqft_living15','renovated', 'basement'
-# **Assumptions**
-Linearity: The relationship between the independent variables (predictors) and the dependent variable (response) is linear. This means that a change in the independent variable corresponds to a proportional change in the dependent variable.
-
-Independence of errors: The errors (residuals) from the regression model should be independent of each other. In other words, the residual for one observation should not predict the residual for another observation.
-
-Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
-
-No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
-#**Limitations**
-Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
-
-Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.
-
-Multicollinearity Concerns: The presence of correlated predictors, such as square footage and number of bedrooms, can introduce multicollinearity issues. This makes it difficult to discern the individual impact of each feature accurately, potentially affecting the model's reliability.
-
-Assumption Violations: Polynomial regression relies on the assumption of linearity between predictors and the target variable. However, in reality, this assumption may not always hold true, leading to biased estimates and less dependable predictions.Also heteroscedasticity was present.
-
-Overfitting Risk: Polynomial regression models, especially those with high degrees, are prone to overfitting. This occurs when the model fits the training data too closely, capturing noise rather than underlying patterns. As a result, the model may struggle to generalize well to unseen data, impacting its predictive performance.
-
-**Limitations**
-1.   The dataset could have more property based characteristics
-2.   Multicollinearity:The presence of correlated predictors (e.g., square footage and number of bedrooms) can lead to multicollinearity issues, making it challenging to interpret the individual effects of each feature accurately
-
-1. Assumption Violations:Polynomial regression assumes linearity between predictors and the target variable, which may not hold true in all cases. Violations of this assumption can lead to biased estimates and unreliable predictions.
-1. Overfitting: Polynomial regression models, particularly those with high degrees, are susceptible to overfitting, where the model fits the training data too closely and may not generalize well to unseen data.
-Overall the model was the best fit model for this predictions
-
-# **RECOMENDATIONS**
-
-1.Invest in Larger Properties: Investors seeking maximum returns should focus on larger houses, as there's a positive correlation between total square footage and price. Such properties have the potential for higher profits upon resale or rental.
-
-
-2.Upgrade Existing Properties: Homeowners can increase their property's value by investing in upgrades that increase square footage, such as adding extra rooms or expanding living spaces.
-
-
-3.Optimize Bedroom and Bathroom Ratios: It's essential to find the right balance between bedrooms and bathrooms to maximize property value. Consulting with real estate professionals can help determine the optimal ratio based on market trends and buyer preferences.
-
-
-4.Focus on Quality Over Quantity: Prioritize quality improvements that enhance functionality and aesthetics, such as renovating bathrooms with modern fixtures or upgrading kitchen appliances, to add perceived value to the property.
-
-
-5.Highlight Features in Listings: Emphasize the number of bedrooms and bathrooms in property listings to attract buyers who prioritize space and convenience. Highlight unique features that add versatility to the property.
-
-
-6.Differentiate Marketing Strategies: Tailor marketing strategies based on property condition and grade ratings. Highlight the benefits of higher-grade properties to attract premium buyers, while emphasizing renovation potential for properties with lower condition ratings.
-# ** Conclusion**
-
-Property Size Matters: There is a clear positive correlation between the size of a property, indicated by total square footage, and its price. Investing in larger properties can potentially yield higher returns for investors and increase market value for homeowners.
-
-
-Strategic Upgrades Add Value: Upgrading existing properties with strategic renovations and expansions, particularly those that increase square footage, can enhance their market value. Quality improvements that improve functionality and aesthetics are key to maximizing property value.
-
-
-Balance is Key: While adding extra bedrooms and bathrooms can increase a property's price, there's a point of diminishing returns. It's important to strike a balance between quantity and quality, optimizing the bedroom-to-bathroom ratio to align with market trends and buyer preferences.
-
-
-Marketing Differentiation is Essential: Tailoring marketing strategies based on property condition and grade ratings is crucial. Highlighting the unique features and benefits of higher-grade properties can attract premium buyers, while emphasizing renovation potential for properties with lower ratings can appeal to savvy investors.
diff --git a/README.md b/README.md
index e770ce06..6a55675b 100644
--- a/README.md
+++ b/README.md
@@ -135,10 +135,18 @@ Correlation matrix
 Distribution Analysis
 Inferential Statistics using Hypothesis Testing and Analysis of Variance
 MultiColinierity
+
+
 # *Modelling**
 Baseline model - simple linear model.
+
+
+
 2. log transformation. 
+
+
 3. Multiple Linear Regression
+
 4. Residual modelling.
 
 # **REGRESSION RESULTS**
@@ -210,7 +218,7 @@ Independence of errors: The errors (residuals) from the regression model should
 Normality of errors: The errors are normally distributed. This implies that the residuals should follow a normal distribution with a mean of zero.
 
 No perfect multicollinearity: There should be no perfect linear relationship between the independent variables. In other words, no independent variable should be a perfect linear combination of other independent variables.
-#**Limitations**
+# **Limitations**
 Despite its effectiveness in predicting property prices, the model has several limitations that need to be addressed:
 
 Limited Property Characteristics: The dataset may lack comprehensive property-based characteristics, potentially limiting the model's ability to capture the full range of factors influencing housing prices.

From 685dbfab2bdc6b7526c071cba7e6047165b540ec Mon Sep 17 00:00:00 2001
From: winnycodegurl <162214319+winnycodegurl@users.noreply.github.com>
Date: Wed, 1 May 2024 23:21:36 +0300
Subject: [PATCH 26/27] Add files via upload

powerpoints

From 0b5631dd39504802ed5f4b4c1432677ed5a49144 Mon Sep 17 00:00:00 2001
From: WairimuMundia <156019539+WairimuMundia@users.noreply.github.com>
Date: Wed, 1 May 2024 16:32:00 -0400
Subject: [PATCH 27/27] Add files via upload