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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## **Assignment #1: EDA & Dataset**\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "w84cR3AZIU0e"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Overview**\n",
+ "\n",
+ "In this first assignment, you will gain hands-on experience selecting and preparing a dataset, performing exploratory data analysis (EDA), adding it to HF's Datasets, and presenting your findings in a short video.\n",
+ "\n",
+ "Through these steps, you’ll begin building essential data science skills in Python.\n",
+ "\n",
+ "Think of your notebook as a cohesive story where EDA reveals the narrative of your data, which provides the resolution to the main question or goal."
+ ],
+ "metadata": {
+ "id": "n7afdXdxIbLA"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Objectives**\n",
+ "\n",
+ "1. Explore various data tools/hubs (e.g., Kaggle, UCI, or local data) to find a suitable dataset.\n",
+ "2. Prepare the selected dataset, focusing on cleaning, transformation, manipulation, and quality.\n",
+ "3. Build foundational EDA skills, including missing data, outlier handling, and clear data visualization.\n",
+ "4. Upload your work and your dataset to HuggingFace.\n",
+ "5. Communicate findings concisely in both a structured notebook, README file, and a short video presentation, demonstrating clarity in each step of the workflow."
+ ],
+ "metadata": {
+ "id": "lJAPMumvIUyW"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Submission Guidelines**\n",
+ "\n",
+ "1. Please note that this is an individual assignment.\n",
+ "2. Submit a text file with your info and the link to HF's dataset.\n",
+ "3. The dataset will include all of your work.\n",
+ " - **Python Notebook**: a well-structured notebook (e.g.`.ipynb` file) with clear comments or markdown explanations of each step.\n",
+ " - **Video Link**: the README file will include your short video presentation at the beginning of the file.\n",
+ "4. **Oral Report**: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting ±10 minutes.\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "MwRmaJBiIjMR"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Evaluation Criteria**\n",
+ "\n",
+ "- **Organization & Clarity (20%)**: Overall structure of your HF Dataset, code, notebook, clear communication, and a concise summary.\n",
+ "- **Data Handling (30%)**: Quality of data cleaning, appropriate handling of outliers, and more.\n",
+ "- **Visualizations (30%)**: well-presented relevant visuals.\n",
+ "- **Presentation (20%)**: Clearly communicated approach and findings in your 2–3 minute overview.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "hD9SZmagIjOV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Additional Tips**\n",
+ "\n",
+ "- The first thing you should do is download a copy of this notebook to your drive.\n",
+ "- Keep your dataset size manageable. If the dataset is large, you can sample a subset.\n",
+ "- A few clear visuals are more effective than many complicated ones.\n",
+ "- Ask for help or feedback early if you get stuck."
+ ],
+ "metadata": {
+ "id": "h3vpVHSxIUwI"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
"
+ ],
+ "metadata": {
+ "id": "7ql9QlVfaSEL"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 1: Select a Dataset**\n",
+ "\n",
+ "1. Choose a numeric tabular dataset, such as the . If you prefer, you may use other open-source datasets; [Hugginface](https://huggingface.co/datasets?task_categories=task_categories:tabular-classification&sort=trending), [Kaggle](https://www.kaggle.com/datasets?tags=13302-Classification&minUsabilityRating=8.00+or+higher), etc.\n",
+ "\n",
+ "\n",
+ " Examples for a good dataset:\n",
+ " - \"Determine a genre of a song\"\n",
+ " - \"Determine the type of flowers\"\n",
+ " - \"Determine the animal - cat or dog\"\n",
+ " - \"Determine the category of a product\"\n",
+ " - \"Determine if an email is spam or not spam\"\n",
+ " - \"Determine whether a tumor is malignant or benign\"\n",
+ " - \"Determine whether a transaction is fraudulent or not\"\n",
+ " - \"Determine whether a student is likely to pass a course\"\n",
+ "\n",
+ "2. Avoid choosing a \"basic\"/\"small\" dataset.\n",
+ " - 1K rows and more.\n",
+ " - 10 features and more.\n",
+ "\n",
+ "\n",
+ "3. Please submit your dataset [here](https://forms.gle/YYiRLXJnbwUfwuwc7), to share it with the class so everyone can see.\n",
+ "And make sure your chosen dataset is unique using this [link](https://docs.google.com/spreadsheets/d/1M8uojrzhSyVnOlSAJpzCKxrhWdzPR77k4x8Kxvr8VDk/edit?usp=sharing).\n",
+ "\n",
+ " *Note: Due to their popularity, the following are datasets you may not choose.*\n",
+ " > - Iris dataset\n",
+ " > - Wine dataset\n",
+ " > - Titanic dataset\n",
+ " > - Boston Housing dataset\n",
+ "\n",
+ "4. Choose a dataset with moslty numaric values. This way you would have enough information to work on, and you could drop columns that aren't numeric.\n",
+ "\n",
+ "5. Briefly describe your chosen dataset (source, size, features) and the question you want to answer.\n",
+ "\n",
+ "6. Clearly identify the target variable to predict (if exists).\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "a_vsO0Q1IOMT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "GFaHgQJyKwa5"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "\"\"\"\n",
+ "=====================================================\n",
+ "DATASET DESCRIPTION & RESEARCH QUESTION\n",
+ "=====================================================\n",
+ "\n",
+ "1. CHOSEN DATASET: Bank Customer Churn Prediction\n",
+ "\n",
+ "2. SOURCE:\n",
+ " Kaggle\n",
+ " (https://www.kaggle.com/datasets/shubhammeshram579/bank-customer-churn-prediction)\n",
+ "\n",
+ "3. SIZE:\n",
+ " - 10,000 rows (samples/customers)\n",
+ " - 14 original features (columns)\n",
+ "\n",
+ "4. KEY FEATURES (After dropping irrelevant columns like RowNumber, CustomerId, Surname):\n",
+ " - CreditScore (Numeric): Customer's credit score.\n",
+ " - Geography (Categorical): Location of the customer (e.g., France, Spain, Germany).\n",
+ " - Gender (Categorical): Male/Female.\n",
+ " - Age (Numeric): Customer's age.\n",
+ " - Tenure (Numeric): Number of years the customer has been with the bank.\n",
+ " - Balance (Numeric): Customer's account balance.\n",
+ " - NumOfProducts (Numeric): Number of bank products the customer uses.\n",
+ " - HasCrCard (Binary): 1 if the customer has a credit card, 0 otherwise.\n",
+ " - IsActiveMember (Binary): 1 if the customer is an active member, 0 otherwise.\n",
+ " - EstimatedSalary (Numeric): Customer's estimated salary.\n",
+ " - Exited (Binary): The target variable.\n",
+ "\n",
+ "5. TARGET VARIABLE TO PREDICT:\n",
+ " - 'Exited'\n",
+ " - This is a binary variable:\n",
+ " - 1 = The customer has churned (left the bank).\n",
+ " - 0 = The customer has stayed with the bank.\n",
+ "\n",
+ "6. QUESTION TO ANSWER (Research Question):\n",
+ " - \"Can we build a machine learning model that accurately predicts whether a customer\n",
+ " will churn (leave the bank) based on their demographic, financial, and\n",
+ " relational data?\"\n",
+ " - This is a supervised binary classification problem.\n",
+ "\"\"\"\n"
+ ],
+ "metadata": {
+ "id": "OI0MZzohKwfE"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "Lgum4x_tKwjL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "\"\"\"\n",
+ "=====================================================\n",
+ "TARGET VARIABLE IDENTIFICATION\n",
+ "=====================================================\n",
+ "\n",
+ "The target variable for this project (the variable we want to predict) is:\n",
+ "\n",
+ "'Exited'\n",
+ "\n",
+ "---\n",
+ "DETAILED DESCRIPTION\n",
+ "---\n",
+ "\n",
+ "1. **Variable Name:** `Exited`\n",
+ "\n",
+ "2. **Data Type:** Binary / Categorical (represented as an integer `int64` in the dataset).\n",
+ "\n",
+ "3. **Meaning (The \"Classes\"):**\n",
+ " * **1** = The customer **churned** (left the bank). This is often called the \"positive\" class in classification problems.\n",
+ " * **0** = The customer **stayed** (did not leave the bank). This is the \"negative\" class.\n",
+ "\n",
+ "4. **Problem Type:**\n",
+ " Because the target variable is categorical with two distinct outcomes (0 or 1), this is a **Supervised Binary Classification** problem. The goal of the model is to learn the patterns from the other features (Age, Balance, etc.) to correctly classify a customer as either a 0 or a 1.\n",
+ "\"\"\"\n"
+ ],
+ "metadata": {
+ "id": "SWIrnfSLKwnE"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "4t2QNyE6IPKS"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 2: Exploratory Data Analysis (EDA)**\n",
+ "\n",
+ "Use your EDA to tell the story of your data - highlight interesting patterns, anomalies, or relationships that lead you toward your classification goal. Ask interesting questions, and answer them.\n",
+ "\n",
+ "\n",
+ "1. **Data Cleaning** : Check for missing values, duplicate entries, scaling/normalize issues, parsing dates, fixing typos, or any inconsistencies. Document how you address them.\n",
+ "2. **Outlier Detection & Handling**: Identify outliers and decide whether to keep or remove them, providing a short justification.\n",
+ "3. **Descriptive Statistics**: Summarize the data (e.g., mean, median, correlations) to reveal patterns.\n",
+ "4. Read further on [A Comprehensive Guide to Mastering Exploratory Data Analysis](https://www.dasca.org/world-of-data-science/article/a-comprehensive-guide-to-mastering-exploratory-data-analysis).\n",
+ "5. **Visualizations**: Use a set of plots (e.g., histograms, scatter plots, box plots) to illustrate **key insights.** Label charts, axes, and legends clearly.\n",
+ "\n",
+ "Tip: not necessarily in this order."
+ ],
+ "metadata": {
+ "id": "6eLmNWJJIPS0"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ ""
+ ],
+ "metadata": {
+ "id": "e3JpM_9Wl84T"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "# --- LOAD THE DATA ---\n",
+ "\n",
+ "# Paste the path you copied in Step 1 inside the quotes below.\n",
+ "# It should look exactly like '/content/Bank Customer Churn Prediction.csv'\n",
+ "file_path = '/content/Churn_Modelling.csv'\n",
+ "\n",
+ "try:\n",
+ " # 'df' is the standard name for a DataFrame\n",
+ " df = pd.read_csv(file_path)\n",
+ "\n",
+ " print(\"--- Successfully loaded the data into 'df'! ---\")\n",
+ " print(\"This is the 'head' (first 5 rows) of your data:\")\n",
+ " print(df.head())\n",
+ "\n",
+ "except FileNotFoundError:\n",
+ " print(f\"--- ERROR: File not found at path: {file_path} ---\")\n",
+ " print(\"Are you sure you copied the correct path?\")\n",
+ " print(\"Please try Step 1 (Right-click -> Copy path) again.\")"
+ ],
+ "metadata": {
+ "id": "fUEbi2wOhssS",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "36341d77-f9be-4d9e-a805-4f4c4f786213"
+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--- Successfully loaded the data into 'df'! ---\n",
+ "This is the 'head' (first 5 rows) of your data:\n",
+ " RowNumber CustomerId Surname CreditScore Geography Gender Age \\\n",
+ "0 1 15634602 Hargrave 619 France Female 42 \n",
+ "1 2 15647311 Hill 608 Spain Female 41 \n",
+ "2 3 15619304 Onio 502 France Female 42 \n",
+ "3 4 15701354 Boni 699 France Female 39 \n",
+ "4 5 15737888 Mitchell 850 Spain Female 43 \n",
+ "\n",
+ " Tenure Balance NumOfProducts HasCrCard IsActiveMember \\\n",
+ "0 2 0.00 1 1 1 \n",
+ "1 1 83807.86 1 0 1 \n",
+ "2 8 159660.80 3 1 0 \n",
+ "3 1 0.00 2 0 0 \n",
+ "4 2 125510.82 1 1 1 \n",
+ "\n",
+ " EstimatedSalary Exited \n",
+ "0 101348.88 1 \n",
+ "1 112542.58 0 \n",
+ "2 113931.57 1 \n",
+ "3 93826.63 0 \n",
+ "4 79084.10 0 \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "w69eaRq6hr9K"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# ===================================================================\n",
+ "# SECTION A: DATA CLEANING & VALIDATION SCRIPT\n",
+ "# ===================================================================\n",
+ "# This script assumes 'df' is already loaded with the dataset\n",
+ "# (e.g., from 'Churn_Modelling.csv').\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "print(f\"--- Starting Data Cleaning ---\")\n",
+ "print(f\"Original shape (before cleaning): {df.shape}\\n\")\n",
+ "\n",
+ "# --- 1. Checking for Irrelevant Columns (Initial Drop) ---\n",
+ "# We drop columns that are identifiers or text not useful for modeling.\n",
+ "print(\"--- 1. Checking for Irrelevant Columns ---\")\n",
+ "irrelevant_cols = ['RowNumber', 'CustomerId', 'Surname']\n",
+ "\n",
+ "# Check which of these columns actually exist in df before dropping\n",
+ "cols_to_drop = [col for col in irrelevant_cols if col in df.columns]\n",
+ "\n",
+ "if cols_to_drop:\n",
+ " df = df.drop(columns=cols_to_drop)\n",
+ " print(f\"DOCUMENTATION: Dropped {cols_to_drop} as they are irrelevant identifiers.\")\n",
+ "else:\n",
+ " print(\"DOCUMENTATION: Irrelevant columns (e.g., RowNumber) already dropped or not found.\")\n",
+ "print(f\"Current data shape: {df.shape}\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 2. Checking for Missing Values (NaN) ---\n",
+ "print(\"--- 2. Checking for Missing Values (NaN) ---\")\n",
+ "missing_values_sum = df.isnull().sum()\n",
+ "total_missing = missing_values_sum.sum()\n",
+ "\n",
+ "if total_missing == 0:\n",
+ " print(\"DOCUMENTATION: No missing values (NaN) found in any column. No imputation needed.\\n\")\n",
+ "else:\n",
+ " print(f\"DOCUMENTATION: Found {total_missing} total missing values.\")\n",
+ " print(missing_values_sum[missing_values_sum > 0])\n",
+ " # ADDRESS: Since this dataset is clean, no action is taken.\n",
+ " # If values were missing, we would apply imputation (e.g., median for numeric, mode for categorical).\n",
+ " print(\"ACTION: (No action needed for this specific dataset)\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 3. Checking for Duplicate Entries ---\n",
+ "print(\"--- 3. Checking for Duplicate Entries ---\")\n",
+ "duplicate_count = df.duplicated().sum()\n",
+ "\n",
+ "print(f\"Total duplicate rows found: {duplicate_count}\")\n",
+ "\n",
+ "if duplicate_count > 0:\n",
+ " print(f\"DOCUMENTATION: Found and removed {duplicate_count} duplicate rows.\")\n",
+ " # ACTION: Remove duplicates, keeping the first occurrence\n",
+ " df = df.drop_duplicates(keep='first')\n",
+ " print(f\"New data shape after dropping duplicates: {df.shape}\\n\")\n",
+ "else:\n",
+ " print(\"DOCUMENTATION: No duplicate rows found. No action needed.\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 4. Checking for Typos & Inconsistencies (Categorical Columns) ---\n",
+ "print(\"--- 4. Checking for Typos & Inconsistencies (Categorical Columns) ---\")\n",
+ "# Identify categorical columns (dtype 'object')\n",
+ "categorical_cols = df.select_dtypes(include=['object']).columns\n",
+ "print(f\"Checking categorical columns: {list(categorical_cols)}\")\n",
+ "\n",
+ "if not categorical_cols.empty:\n",
+ " for col in categorical_cols:\n",
+ " unique_values = df[col].unique()\n",
+ " print(f\" - Unique values in '{col}': {unique_values}\")\n",
+ " print(\"\\nDOCUMENTATION: Checked unique values in 'Geography' and 'Gender'.\")\n",
+ " print(\" - All values appear consistent (e.g., 'France', 'Spain', 'Germany' and 'Male', 'Female').\")\n",
+ " print(\" - No typos or inconsistencies (like 'Franse' or 'male') were found. No fixing needed.\\n\")\n",
+ "else:\n",
+ " print(\"DOCUMENTATION: No categorical columns of type 'object' found to check for typos.\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 5. Checking for Date Columns (Parsing Dates) ---\n",
+ "print(\"--- 5. Checking for Date Columns (Parsing Dates) ---\")\n",
+ "# Check for columns that are likely dates (either datetime objects or strings)\n",
+ "date_cols = df.select_dtypes(include=['datetime64', 'datetime']).columns\n",
+ "\n",
+ "if len(date_cols) > 0:\n",
+ " print(f\"DOCUMENTATION: Found datetime columns: {list(date_cols)}. They are already parsed.\")\n",
+ "else:\n",
+ " print(\"DOCUMENTATION: No pre-formatted datetime columns (datetime64) found.\")\n",
+ " print(\" - No string columns appear to be dates that need parsing.\")\n",
+ " print(\" - The 'Tenure' column is already a numeric integer (representing years).\")\n",
+ " print(\" - No date parsing is required for this dataset.\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 6. Checking for Scaling / Normalization Issues ---\n",
+ "# We identify the issue here, but fix it *after* train-test split.\n",
+ "print(\"--- 6. Checking for Scaling / Normalization Issues ---\")\n",
+ "print(\"Numeric feature statistics (descriptive analysis):\")\n",
+ "# Select only numeric columns for 'describe'\n",
+ "numeric_df = df.select_dtypes(include=np.number)\n",
+ "# .T transposes the output for easier reading\n",
+ "print(numeric_df.describe().T)\n",
+ "\n",
+ "print(\"\\nDOCUMENTATION (Identification of Issue):\")\n",
+ "print(\" - ISSUE: The numeric features have vastly different scales and ranges.\")\n",
+ "print(\" - Example: 'Balance' (0 to ~250k) vs. 'Age' (18 to 92) vs. 'NumOfProducts' (1 to 4).\")\n",
+ "print(\" - HOW TO ADDRESS: This will be fixed during the 'Preprocessing' phase (Step C).\")\n",
+ "print(\" - ACTION: We will apply 'StandardScaler' *after* splitting data into training and testing sets to prevent data leakage.\\n\")\n",
+ "\n",
+ "\n",
+ "print(\"--- DATA CLEANING CHECK COMPLETE ---\")\n",
+ "print(f\"Final shape after cleaning: {df.shape}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VKqg_2YtROSd",
+ "outputId": "630384b5-7ccc-4d63-a7fd-d6e2b944096e"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--- Starting Data Cleaning ---\n",
+ "Original shape (before cleaning): (10000, 14)\n",
+ "\n",
+ "--- 1. Checking for Irrelevant Columns ---\n",
+ "DOCUMENTATION: Dropped ['RowNumber', 'CustomerId', 'Surname'] as they are irrelevant identifiers.\n",
+ "Current data shape: (10000, 11)\n",
+ "\n",
+ "--- 2. Checking for Missing Values (NaN) ---\n",
+ "DOCUMENTATION: No missing values (NaN) found in any column. No imputation needed.\n",
+ "\n",
+ "--- 3. Checking for Duplicate Entries ---\n",
+ "Total duplicate rows found: 0\n",
+ "DOCUMENTATION: No duplicate rows found. No action needed.\n",
+ "\n",
+ "--- 4. Checking for Typos & Inconsistencies (Categorical Columns) ---\n",
+ "Checking categorical columns: ['Geography', 'Gender']\n",
+ " - Unique values in 'Geography': ['France' 'Spain' 'Germany']\n",
+ " - Unique values in 'Gender': ['Female' 'Male']\n",
+ "\n",
+ "DOCUMENTATION: Checked unique values in 'Geography' and 'Gender'.\n",
+ " - All values appear consistent (e.g., 'France', 'Spain', 'Germany' and 'Male', 'Female').\n",
+ " - No typos or inconsistencies (like 'Franse' or 'male') were found. No fixing needed.\n",
+ "\n",
+ "--- 5. Checking for Date Columns (Parsing Dates) ---\n",
+ "DOCUMENTATION: No pre-formatted datetime columns (datetime64) found.\n",
+ " - No string columns appear to be dates that need parsing.\n",
+ " - The 'Tenure' column is already a numeric integer (representing years).\n",
+ " - No date parsing is required for this dataset.\n",
+ "\n",
+ "--- 6. Checking for Scaling / Normalization Issues ---\n",
+ "Numeric feature statistics (descriptive analysis):\n",
+ " count mean std min 25% \\\n",
+ "CreditScore 10000.0 650.528800 96.653299 350.00 584.00 \n",
+ "Age 10000.0 38.921800 10.487806 18.00 32.00 \n",
+ "Tenure 10000.0 5.012800 2.892174 0.00 3.00 \n",
+ "Balance 10000.0 76485.889288 62397.405202 0.00 0.00 \n",
+ "NumOfProducts 10000.0 1.530200 0.581654 1.00 1.00 \n",
+ "HasCrCard 10000.0 0.705500 0.455840 0.00 0.00 \n",
+ "IsActiveMember 10000.0 0.515100 0.499797 0.00 0.00 \n",
+ "EstimatedSalary 10000.0 100090.239881 57510.492818 11.58 51002.11 \n",
+ "Exited 10000.0 0.203700 0.402769 0.00 0.00 \n",
+ "\n",
+ " 50% 75% max \n",
+ "CreditScore 652.000 718.0000 850.00 \n",
+ "Age 37.000 44.0000 92.00 \n",
+ "Tenure 5.000 7.0000 10.00 \n",
+ "Balance 97198.540 127644.2400 250898.09 \n",
+ "NumOfProducts 1.000 2.0000 4.00 \n",
+ "HasCrCard 1.000 1.0000 1.00 \n",
+ "IsActiveMember 1.000 1.0000 1.00 \n",
+ "EstimatedSalary 100193.915 149388.2475 199992.48 \n",
+ "Exited 0.000 0.0000 1.00 \n",
+ "\n",
+ "DOCUMENTATION (Identification of Issue):\n",
+ " - ISSUE: The numeric features have vastly different scales and ranges.\n",
+ " - Example: 'Balance' (0 to ~250k) vs. 'Age' (18 to 92) vs. 'NumOfProducts' (1 to 4).\n",
+ " - HOW TO ADDRESS: This will be fixed during the 'Preprocessing' phase (Step C).\n",
+ " - ACTION: We will apply 'StandardScaler' *after* splitting data into training and testing sets to prevent data leakage.\n",
+ "\n",
+ "--- DATA CLEANING CHECK COMPLETE ---\n",
+ "Final shape after cleaning: (10000, 11)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# ===================================================================\n",
+ "# SECTION C: OUTLIER DETECTION & HANDLING\n",
+ "# ===================================================================\n",
+ "# This script assumes 'df' is the cleaned DataFrame from the previous step.\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "print(\"--- 1. Identifying Numeric Features for Outlier Analysis ---\")\n",
+ "# Select only numeric columns that are not binary (like 'Exited', 'Gender', etc.)\n",
+ "# We also exclude the one-hot encoded 'Geography' columns if they exist.\n",
+ "# Based on our 'df.describe()' from the cleaning step:\n",
+ "numeric_features = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n",
+ "print(f\"Analyzing outliers for: {numeric_features}\\n\")\n",
+ "\n",
+ "# --- 2. Visual Detection using Boxplots ---\n",
+ "print(\"--- 2. Visual Detection (Boxplots) ---\")\n",
+ "print(\"Generating boxplots for numeric features...\")\n",
+ "\n",
+ "plt.figure(figsize=(16, 10))\n",
+ "# Create a 2x3 grid of subplots\n",
+ "for i, col in enumerate(numeric_features):\n",
+ " plt.subplot(2, 3, i + 1) # 2 rows, 3 columns, plot number i+1\n",
+ " sns.boxplot(y=df[col], color='skyblue')\n",
+ " plt.title(f'Boxplot for {col}')\n",
+ " plt.ylabel(col)\n",
+ "\n",
+ "plt.tight_layout() # Adjust subplots to fit in figure area.\n",
+ "plt.show() #\n",
+ "\n",
+ "print(\"\\n--- Visual Analysis of Boxplots ---\")\n",
+ "print(\" - CreditScore: Shows outliers on the lower end (e.g., scores < 400).\")\n",
+ "print(\" - Age: Shows outliers on the higher end (e.g., ages > 75-80).\")\n",
+ "print(\" - NumOfProducts: Shows outliers (e.g., 4 products).\")\n",
+ "print(\" - Balance, Tenure, EstimatedSalary: Appear to have few or no significant outliers.\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 3. Statistical Detection (IQR Method) ---\n",
+ "# We calculate the number of outliers based on the IQR rule.\n",
+ "# IQR = Q3 (75th percentile) - Q1 (25th percentile)\n",
+ "# Lower bound = Q1 - 1.5 * IQR\n",
+ "# Upper bound = Q3 + 1.5 * IQR\n",
+ "# Anything outside these bounds is statistically an outlier.\n",
+ "\n",
+ "print(\"--- 3. Statistical Detection (IQR Method) ---\")\n",
+ "print(\"Calculating outlier counts...\")\n",
+ "\n",
+ "for col in numeric_features:\n",
+ " Q1 = df[col].quantile(0.25)\n",
+ " Q3 = df[col].quantile(0.75)\n",
+ " IQR = Q3 - Q1\n",
+ "\n",
+ " lower_bound = Q1 - 1.5 * IQR\n",
+ " upper_bound = Q3 + 1.5 * IQR\n",
+ "\n",
+ " # Count outliers\n",
+ " outlier_count = df[(df[col] < lower_bound) | (df[col] > upper_bound)].shape[0]\n",
+ "\n",
+ " print(f\" - {col}: Found {outlier_count} outliers (values < {lower_bound:.2f} or > {upper_bound:.2f})\")\n",
+ "\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 4. Decision & Justification (Documentation) ---\n",
+ "print(\"--- 4. Outlier Handling Decision & Justification ---\")\n",
+ "print(\" - PROBLEM TYPE: Customer Churn Prediction.\")\n",
+ "print(\" - OUTLIER ANALYSIS:\")\n",
+ "print(\" The outliers found (e.g., very high 'Age', very low 'CreditScore', 4 'NumOfProducts')\")\n",
+ "print(\" are NOT data entry errors. A 90-year-old customer is a real customer.\")\n",
+ "print(\" A customer with a credit score of 380 is a real (if risky) customer.\")\n",
+ "print(\" - JUSTIFICATION:\")\n",
+ "print(\" In a churn problem, these extreme (but valid) values are often highly informative.\")\n",
+ "print(\" For example, extremely old customers might be more loyal (less likely to churn),\")\n",
+ "print(\" or customers with 4 products might be 'all-in' and also less likely to churn.\")\n",
+ "print(\" Removing these data points would mean removing valuable, real-world information\")\n",
+ "print(\" and would bias the model towards the 'average' customer, failing to learn\")\n",
+ "print(\" from these important minority cases.\")\n",
+ "print(\"\\n - DECISION: **KEEP ALL OUTLIERS**.\")\n",
+ "print(\" - ACTION: No rows will be removed based on outlier detection.\")\n",
+ "\n",
+ "\n",
+ "# --- (Optional) Code to Remove Outliers (FOR DEMONSTRATION ONLY - DO NOT RUN) ---\n",
+ "#\n",
+ "# print(\"\\n--- OPTIONAL: How to remove outliers (NOT RECOMMENDED for this problem) ---\")\n",
+ "#\n",
+ "# # Example: Removing outliers from 'Age' only\n",
+ "# Q1_age = df['Age'].quantile(0.25)\n",
+ "# Q3_age = df['Age'].quantile(0.75)\n",
+ "# IQR_age = Q3_age - Q1_age\n",
+ "# lower_bound_age = Q1_age - 1.5 * IQR_age\n",
+ "# upper_bound_age = Q3_age + 1.5 * IQR_age\n",
+ "#\n",
+ "# df_no_age_outliers = df[(df['Age'] >= lower_bound_age) & (df['Age'] <= upper_bound_age)]\n",
+ "# print(f\"Original shape: {df.shape}\")\n",
+ "# print(f\"Shape after removing 'Age' outliers: {df_no_age_outliers.shape}\")\n",
+ "#\n",
+ "# print(\"--- End of Outlier Detection ---\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "pkpRr4OQR0Er",
+ "outputId": "e14c98aa-a55d-4256-8429-57efd4aed4da"
+ },
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--- 1. Identifying Numeric Features for Outlier Analysis ---\n",
+ "Analyzing outliers for: ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n",
+ "\n",
+ "--- 2. Visual Detection (Boxplots) ---\n",
+ "Generating boxplots for numeric features...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
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+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "--- Visual Analysis of Boxplots ---\n",
+ " - CreditScore: Shows outliers on the lower end (e.g., scores < 400).\n",
+ " - Age: Shows outliers on the higher end (e.g., ages > 75-80).\n",
+ " - NumOfProducts: Shows outliers (e.g., 4 products).\n",
+ " - Balance, Tenure, EstimatedSalary: Appear to have few or no significant outliers.\n",
+ "\n",
+ "--- 3. Statistical Detection (IQR Method) ---\n",
+ "Calculating outlier counts...\n",
+ " - CreditScore: Found 15 outliers (values < 383.00 or > 919.00)\n",
+ " - Age: Found 359 outliers (values < 14.00 or > 62.00)\n",
+ " - Tenure: Found 0 outliers (values < -3.00 or > 13.00)\n",
+ " - Balance: Found 0 outliers (values < -191466.36 or > 319110.60)\n",
+ " - NumOfProducts: Found 60 outliers (values < -0.50 or > 3.50)\n",
+ " - EstimatedSalary: Found 0 outliers (values < -96577.10 or > 296967.45)\n",
+ "\n",
+ "\n",
+ "--- 4. Outlier Handling Decision & Justification ---\n",
+ " - PROBLEM TYPE: Customer Churn Prediction.\n",
+ " - OUTLIER ANALYSIS:\n",
+ " The outliers found (e.g., very high 'Age', very low 'CreditScore', 4 'NumOfProducts')\n",
+ " are NOT data entry errors. A 90-year-old customer is a real customer.\n",
+ " A customer with a credit score of 380 is a real (if risky) customer.\n",
+ " - JUSTIFICATION:\n",
+ " In a churn problem, these extreme (but valid) values are often highly informative.\n",
+ " For example, extremely old customers might be more loyal (less likely to churn),\n",
+ " or customers with 4 products might be 'all-in' and also less likely to churn.\n",
+ " Removing these data points would mean removing valuable, real-world information\n",
+ " and would bias the model towards the 'average' customer, failing to learn\n",
+ " from these important minority cases.\n",
+ "\n",
+ " - DECISION: **KEEP ALL OUTLIERS**.\n",
+ " - ACTION: No rows will be removed based on outlier detection.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# ===================================================================\n",
+ "# SECTION D: DESCRIPTIVE STATISTICS\n",
+ "# ===================================================================\n",
+ "# This script assumes 'df' is the cleaned DataFrame from the previous step.\n",
+ "\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "print(\"--- 1. Descriptive Statistics for NUMERIC Features ---\")\n",
+ "# .describe() calculates mean, median (50%), std, min, max, and quartiles.\n",
+ "# We select only numeric types for this.\n",
+ "numeric_df = df.select_dtypes(include=np.number)\n",
+ "# .T (Transpose) swaps rows and columns to make the table easier to read.\n",
+ "print(numeric_df.describe().T)\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "\n",
+ "print(\"--- 2. Descriptive Statistics for CATEGORICAL Features ---\")\n",
+ "# .describe(include='object') calculates count, number of unique values,\n",
+ "# the 'top' (most frequent) value, and its frequency ('freq').\n",
+ "categorical_df = df.select_dtypes(include='object')\n",
+ "if not categorical_df.empty:\n",
+ " print(categorical_df.describe().T)\n",
+ "else:\n",
+ " print(\"No categorical (object) columns found.\")\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "\n",
+ "print(\"--- 3. Target Variable Distribution (Pattern Recognition) ---\")\n",
+ "# This is the MOST important pattern: Class Imbalance.\n",
+ "# We check the percentage of customers who stayed (0) vs. churned (1).\n",
+ "print(\"Target Variable (Exited) Distribution:\")\n",
+ "# normalize=True gives percentages instead of raw counts\n",
+ "exit_distribution = df['Exited'].value_counts(normalize=True) * 100\n",
+ "print(exit_distribution)\n",
+ "print(\"\\nPATTERN FOUND: The dataset is imbalanced. Approximately 80% of customers stayed (0)\")\n",
+ "print(\"and only 20% churned (1). This must be handled during modeling.\\n\")\n",
+ "\n",
+ "# Visualizing the imbalance\n",
+ "print(\"Visualizing Target Variable Imbalance:\")\n",
+ "sns.countplot(x='Exited', data=df, palette='pastel')\n",
+ "plt.title('Class Distribution (0: Stayed vs 1: Exited)')\n",
+ "plt.xlabel('Exited')\n",
+ "plt.ylabel('Count')\n",
+ "plt.show() #\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "\n",
+ "print(\"--- 4. Correlation Analysis (Pattern Recognition) ---\")\n",
+ "# We calculate the correlation matrix for all numeric features.\n",
+ "# This reveals linear relationships between features.\n",
+ "corr_matrix = numeric_df.corr()\n",
+ "\n",
+ "# --- 4a. Correlation with Target Variable ---\n",
+ "# This is the most useful part: what features are most correlated with 'Exited'?\n",
+ "print(\"Correlation with Target Variable (Exited):\")\n",
+ "# Sort the 'Exited' column from the matrix to see the strongest correlations\n",
+ "target_corr = corr_matrix['Exited'].sort_values(ascending=False)\n",
+ "print(target_corr)\n",
+ "print(\"\\nPATTERN FOUND (Examples):\")\n",
+ "print(\" - 'Age' has a positive correlation (older customers are slightly more likely to exit).\")\n",
+ "print(\" - 'Balance' has a positive correlation (customers with higher balance are slightly more likely to exit).\")\n",
+ "print(\" - 'IsActiveMember' has a negative correlation (active members are less likely to exit).\")\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "\n",
+ "# --- 4b. Correlation Heatmap ---\n",
+ "# A heatmap is the best way to visualize the correlation matrix.\n",
+ "# 'coolwarm' cmap: red = positive correlation, blue = negative correlation.\n",
+ "print(\"Generating Full Correlation Heatmap...\")\n",
+ "plt.figure(figsize=(12, 9)) # Set the size of the figure\n",
+ "sns.heatmap(corr_matrix,\n",
+ " annot=True, # Show the correlation numbers on the map\n",
+ " fmt='.2f', # Format numbers to 2 decimal places\n",
+ " cmap='coolwarm', # Color map (red-blue)\n",
+ " linewidths=0.5) # Add lines between cells\n",
+ "plt.title('Correlation Heatmap of Numeric Features')\n",
+ "plt.show() #\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "print(\"--- Descriptive Statistics Complete ---\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "hdSvCxJaSiGQ",
+ "outputId": "1ddde651-6a94-43be-8436-7d2840dd384c"
+ },
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--- 1. Descriptive Statistics for NUMERIC Features ---\n",
+ " count mean std min 25% \\\n",
+ "CreditScore 10000.0 650.528800 96.653299 350.00 584.00 \n",
+ "Age 10000.0 38.921800 10.487806 18.00 32.00 \n",
+ "Tenure 10000.0 5.012800 2.892174 0.00 3.00 \n",
+ "Balance 10000.0 76485.889288 62397.405202 0.00 0.00 \n",
+ "NumOfProducts 10000.0 1.530200 0.581654 1.00 1.00 \n",
+ "HasCrCard 10000.0 0.705500 0.455840 0.00 0.00 \n",
+ "IsActiveMember 10000.0 0.515100 0.499797 0.00 0.00 \n",
+ "EstimatedSalary 10000.0 100090.239881 57510.492818 11.58 51002.11 \n",
+ "Exited 10000.0 0.203700 0.402769 0.00 0.00 \n",
+ "\n",
+ " 50% 75% max \n",
+ "CreditScore 652.000 718.0000 850.00 \n",
+ "Age 37.000 44.0000 92.00 \n",
+ "Tenure 5.000 7.0000 10.00 \n",
+ "Balance 97198.540 127644.2400 250898.09 \n",
+ "NumOfProducts 1.000 2.0000 4.00 \n",
+ "HasCrCard 1.000 1.0000 1.00 \n",
+ "IsActiveMember 1.000 1.0000 1.00 \n",
+ "EstimatedSalary 100193.915 149388.2475 199992.48 \n",
+ "Exited 0.000 0.0000 1.00 \n",
+ "\n",
+ "\n",
+ "--- 2. Descriptive Statistics for CATEGORICAL Features ---\n",
+ " count unique top freq\n",
+ "Geography 10000 3 France 5014\n",
+ "Gender 10000 2 Male 5457\n",
+ "\n",
+ "\n",
+ "--- 3. Target Variable Distribution (Pattern Recognition) ---\n",
+ "Target Variable (Exited) Distribution:\n",
+ "Exited\n",
+ "0 79.63\n",
+ "1 20.37\n",
+ "Name: proportion, dtype: float64\n",
+ "\n",
+ "PATTERN FOUND: The dataset is imbalanced. Approximately 80% of customers stayed (0)\n",
+ "and only 20% churned (1). This must be handled during modeling.\n",
+ "\n",
+ "Visualizing Target Variable Imbalance:\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/tmp/ipython-input-1219189886.py:43: FutureWarning: \n",
+ "\n",
+ "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+ "\n",
+ " sns.countplot(x='Exited', data=df, palette='pastel')\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "\n",
+ "--- 4. Correlation Analysis (Pattern Recognition) ---\n",
+ "Correlation with Target Variable (Exited):\n",
+ "Exited 1.000000\n",
+ "Age 0.285323\n",
+ "Balance 0.118533\n",
+ "EstimatedSalary 0.012097\n",
+ "HasCrCard -0.007138\n",
+ "Tenure -0.014001\n",
+ "CreditScore -0.027094\n",
+ "NumOfProducts -0.047820\n",
+ "IsActiveMember -0.156128\n",
+ "Name: Exited, dtype: float64\n",
+ "\n",
+ "PATTERN FOUND (Examples):\n",
+ " - 'Age' has a positive correlation (older customers are slightly more likely to exit).\n",
+ " - 'Balance' has a positive correlation (customers with higher balance are slightly more likely to exit).\n",
+ " - 'IsActiveMember' has a negative correlation (active members are less likely to exit).\n",
+ "\n",
+ "\n",
+ "Generating Full Correlation Heatmap...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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kcjkuXryISZMmVTiyX5mkUqnG7WIF9yv/FwqFAnXr1sWCBQs0vl/UGYyMjESHDh3g7++PBQsWwM3NDUZGRtizZw++++675yqX8vKmaXvJ/B47dgw9e/ZE69atsXTpUjg7O8PQ0BBr1qzBL7/88syf/7KYPHky1q9fj7lz56J3795l3i+vbZQ3u+B5yhUoLtv/0l4qao9VQaFQQBAE/PXXXxrzV5TewsJCdOrUCcnJyZg0aRL8/f1hamqK6OhoDBs27Jnq7fN+DzKZrMzFIIVCAQcHB9UMg9KKLqgJgoDffvsNp0+fxp9//ol9+/bh3XffxbfffovTp09XuE4HERGpY4eeiOg5vf7661i+fDlOnTqFkJCQCsO6u7tDoVDg9u3bqpFPAIiLi0Nqairc3d3/czq8vLwAKC8yPOtIdZHDhw8jKSkJ27dvR+vWrVXbo6KiyoR91osRRXmJiIgo897NmzdhZ2dXbY+18vLywuXLl9GhQ4cK8/Pnn38iNzcXO3fuVBsJLjm1ucizlsvz2rZtG+RyOfbt26c2WrlmzRqN4YtGnku6desWTExMKly8zt3dHVeuXIFCoVDrmBXdbvEidbMkLy8vvP3221i2bJnGZ6tbW1sjNTW1zPbKmCFQOh3Af2sv/7ZfhUKBGzdulHuh4EX2LYoiatWqBV9f33LDXb16Fbdu3cK6devUFlfUdPtJefW2aGZC6e/ieb4HLy8v/P3332jRosUzXQxp1qwZmjVrhlmzZuGXX37BW2+9hU2bNmHkyJHP/JlERK863kNPRPScPvvsM5iammLkyJGIi4sr835kZCQWLVoEAOjWrRsA5aroJRWNFJdesfx5BAcHw8vLC/Pnz0dGRkaZ9xMSEsqNWzTaV3JkOC8vD0uXLi0T1tTU9Jmm7Do7OyMoKAjr1q1T6xRcu3YN+/fvV5VFdRgwYACio6OxYsWKMu9lZ2cjMzMTgOZySUtL09iZNjU11dgRfVFSqRSCIKiNjN67d09tJf6STp06pXZv/cOHD/HHH3/gtddeK3fUGlDWzdjYWGzevFm1raCgAIsXL4aZmRnatGnz4pl5asqUKcjPz8c333xT5j0vLy+kpaXhypUrqm0xMTFlngDxol6kvVSkd+/ekEgk+Oqrr8qMhL/oTJO+fftCKpVixowZZfYliiKSkpIAaK63oiiqfodKKrqoVrruuru7QyqVlnnkoabfhPIMGDAAhYWFmDlzZpn3CgoKVJ+ZkpJSJj9FF0NKP0aSiIgqxhF6IqLn5OXlhV9++QVvvvkmAgICMGTIENSpUwd5eXk4efKk6tFfAFC/fn0MHToUy5cvV01zP3v2LNatW4fevXujXbt2/zkdEokEK1euRNeuXREYGIjhw4fD1dUV0dHR+Oeff2BhYYE///xTY9zmzZvD2toaQ4cOxdixYyEIAtavX6+xAxIcHIzNmzdj/PjxaNy4MczMzNCjRw+N+503bx66du2KkJAQjBgxQvXYOktLS0yfPv0/5/VFvfPOO9iyZQv+7//+D//88w9atGiBwsJC3Lx5E1u2bMG+ffvQqFEjvPbaazAyMkKPHj0watQoZGRkYMWKFXBwcEBMTIzaPoODg/Hjjz/i66+/hre3NxwcHFSL7L2I7t27Y8GCBejSpQsGDx6M+Ph4LFmyBN7e3mqd3iJ16tRB586d1R5bBygfpVaR999/H8uWLcOwYcNw4cIFeHh44LfffsOJEyewcOHCZ1748VkUjdKXfMZ9kYEDB2LSpEno06cPxo4di6ysLPz444/w9fXVuAjgf/Ui7aUi3t7emDx5MmbOnIlWrVqhb9++kMlkOHfuHFxcXDBnzpz/nGYvLy98/fXXCA0Nxb1799C7d2+Ym5sjKioKO3bswPvvv4+JEyfC398fXl5emDhxIqKjo2FhYYFt27aVuZcegGoBxbFjx6Jz586QSqUYOHAgLC0t0b9/fyxevBiCIMDLywu7du1SW1/i37Rp0wajRo3CnDlzEBYWhtdeew2Ghoa4ffs2tm7dikWLFuGNN97AunXrsHTpUvTp0wdeXl5IT0/HihUrYGFhUa0X/oiIdFLVL6xPRKQfbt26Jb733nuih4eHaGRkJJqbm4stWrQQFy9erPbYt/z8fHHGjBlirVq1RENDQ9HNzU0MDQ1VCyOK5T8aTNOjvkq6dOmS2LdvX9HW1laUyWSiu7u7OGDAAPHgwYOqMJoeW3fixAmxWbNmorGxseji4iJ+9tln4r59+8o8uiojI0McPHiwaGVlpfZILU2PsxJFUfz777/FFi1aiMbGxqKFhYXYo0cP8caNG2phih7XlZCQoLZdUzo1GTp0qGhqalru+yj12DpRVD6Sbe7cuWJgYKAok8lEa2trMTg4WJwxY4aYlpamCrdz506xXr16olwuFz08PMS5c+eKq1evLpOu2NhYsXv37qK5ubkIQPXouaI8lH4kV3l51pSXVatWiT4+PqJMJhP9/f3FNWvWlPsYt9GjR4sbNmxQhW/QoEGZR4+VJy4uThw+fLhoZ2cnGhkZiXXr1i3zfYrif39sXUm3b98WpVKpxrq8f/9+sU6dOqKRkZHo5+cnbtiwocL8llRUD+fNm6e2vbx28yztpbzvquR7pa1evVps0KCBqm61adNGPHDgQDml9O+fU9K2bdvEli1biqampqKpqano7+8vjh49WoyIiFCFuXHjhtixY0fRzMxMtLOzE9977z3x8uXLZdpoQUGB+NFHH4n29vaiIAhqeUlISBD79esnmpiYiNbW1uKoUaPEa9euaXxsXUXtb/ny5WJwcLBobGwsmpubi3Xr1hU/++wz1eMLL168KA4aNEisWbOmKJPJRAcHB/H1119Xe/wiERE9G0EUK3nlISIiIqoSgiBg9OjR+OGHH6o7KURERFQNeA89ERERERERkQ5ih56IiIiIiIhIB7FDT0RERERERKSD2KEnIiLSUaIo8v55IiIiLTh69Ch69OgBFxcXCIJQ7uNjSzp8+DAaNmwImUwGb29vrF27VuvpZIeeiIiIiIiIqITMzEzUr18fS5YseabwUVFR6N69O9q1a4ewsDCMGzcOI0eOxL59+7SaTq5yT0RERERERFQOQRCwY8cO9O7du9wwkyZNwu7du3Ht2jXVtoEDByI1NRV79+7VWto4Qk9ERERERER6Lzc3F0+ePFF75ebmVsq+T506hY4dO6pt69y5M06dOlUp+y+PgVb3TkRERERERK+s3YZ+1Z0ElXOTB2HGjBlq26ZNm4bp06e/8L5jY2Ph6Oiots3R0RFPnjxBdnY2jI2NX/gzNGGHnsr1MjU+XdY9PwKzNxdWdzJ03hdvSjFnC8uxMoQOkGLub4rqToZemPSGBPO3syxf1MS+Ev5OVpIv3pRiwR+8m7IyjO8l8LhTCUIHSDFjQ351J0MvTHvbsLqToPNCQ0Mxfvx4tW0ymayaUlM52KEnIiIiIiIivSeTybTWgXdyckJcXJzatri4OFhYWGhtdB5gh56IiIiIiIi0RDAUqjsJVSIkJAR79uxR23bgwAGEhIRo9XO5KB4RERERERFRCRkZGQgLC0NYWBgA5WPpwsLC8ODBAwDK6ftDhgxRhf+///s/3L17F5999hlu3ryJpUuXYsuWLfjkk0+0mk526ImIiIiIiIhKOH/+PBo0aIAGDRoAAMaPH48GDRrgyy+/BADExMSoOvcAUKtWLezevRsHDhxA/fr18e2332LlypXo3LmzVtPJKfdERERERESkFRID3Zxy37ZtW4hi+YuMrl27VmOcS5cuaTFVZXGEnoiIiIiIiEgHcYSeiIiIiIiItEIw5BiyNrF0iYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEgrdHVRPF3BEXoiIiIiIiIiHcQOPREREREREZEO4pR7IiIiIiIi0grBkFPutYkj9EREREREREQ6iCP0REREREREpBVcFE+7OEJPREREREREpIPYoSciIiIiIiLSQZxyT0RERERERFrBRfG0iyP0RERERERERDqIHXoiIiIiIiIiHcQp90RERERERKQVXOVeuzhCT0RERERERKSD2KEnIiIiIiIi0kGcck9ERERERERaIUg55V6bOEJPREREREREpIM4Qk9ERERERERaIeEIvVZxhJ6IiIiIiIhIB7FDT0RERERERKSDOOWeiIiIiIiItEKQcMq9NnGEnoiIiIiIiEgHsUP/DARBwO+//w4AuHfvHgRBQFhYWLWmiYiIiIiIiF5tOjvlPjY2FrNmzcLu3bsRHR0NBwcHBAUFYdy4cejQoYPWPtfNzQ0xMTGws7MDABw+fBjt2rVDSkoKrKysVOESEhLw5ZdfYvfu3YiLi4O1tTXq16+PL7/8Ei1atNBa+nSRTctG8JwwApYN60Du4oDz/T5E3M6DFcdp3QS1538Os9o+yHkYgztzfsSjn3eohXH/YDA8x4+AzMkeT67cxPVxM5F27qo2s/JSaV1HQJCnAJkh8CgR2HtBgZSMiuMEewto6i/ATA7EpQL7LyoQk1z8vlQCdAwSEFBTgIEEuBsL7LugQGauVrNSbRp6C2jqpyyP+FRg/yX18ijNvwbQuo4ElqZAcjpw+IoCkbHqYVoFlvhekpTl92/fiz64cXojrh1bjeyMRFg7+SPk9cmwd6tXbvioq3tx8e/vkZEaDQtbdzTqPAFufm1U79+7vh83z25GUvR15Ganodfo7bB1CaiKrFSr66c24spRZTnaOPmjec/JcKigHO9e3YvzB75HRoqyHJt0mYCa/sXlGHVtP8LPbEbi03Ls+9GrUY5FtPE7GeQpINBdgJM1IDMU8O32QuTmazUb1e7ayY24fGQVstMTYevsjxa9psChZvn1MvLKXpzftwjpKdGwtHNH064TUTOguF6e378YkZf3ICM1FhIDQ9i7BqJxl3FwrFm/KrJT7f7LceLfjldSCdAhSEBtNwFSCXA3TrnfLD09fgNA23oSNPSRQG4IPEwQsftsIZLTK47T2FeC5rUlMDMGYlNE/HVOgcdJour915tKUMtJAnNjIK9Aud+/LxUi6YmWM6PjBCnHkLVJJ0v33r17CA4OxqFDhzBv3jxcvXoVe/fuRbt27TB69GiNcfLzK+doKpVK4eTkBAODiq+F9OvXD5cuXcK6detw69Yt7Ny5E23btkVSUlKlpEOTvLw8re1bm6SmJnhyJQLXxs54pvDGHjXQeOcyJB0+g+ONeiFq8TrUXfY17Dq1VIVx7t8VAfNCcfvrJTjepA/Sr9xE092rYGRvo61svFSa+Qto5CPgr/MKrP1bgfxCYGAbCSr6PQ1wE9AhSMDx6yJW71cgPlXEwDYSmMiKw3RqIMDbRcCOkwps+EcBM2Ogb0ud/Bn5VwFuAjrUf1oeBxSISxXxZmv18ijJ1Rbo1UyCy1HK8rv9WES/FhLYWRSHKfpe9l5QYN1BBfILgDdbV/y96IO7V/bg7J65CGo/Gj1Hb4ONkx/2rX0P2Rmafw/j7l/C4S0T4duoH3qN3o6aAR1wcONHSIm7pQpTkJcNR/eGaNR5QlVlo9pFXtmD07vnomGH0egzZhtsnf3w1+qKy/HQponwa9QPfT7aDo/aHXBgw0dIjlUvRyePhmjS9dUpxyLa+p00NADuxog4eUMsf0d65E7YHpz6838I7jga/T7eDhtnP+xeNbLcehl77yIO/jIBfo3fQL+Pd8AjsCP2/TxGrV5a2nugRe+p6D9+J3p9sBHm1q7Ys3IEsjMquKKqJ/7LceJZjlcdgwR4OwvYcUqBjYcVMJcD/Vro78GnRW0JmvpLsPtMIVbuLUBeAfB2e4MKyzHQXcBrwRIcuVKIZXsKEJcCvN1eqlaOj5NE/HGqEEv+LMCGQwUQBOCdDgYQeIs4VSOdbMkffvghBEHA2bNn0a9fP/j6+iIwMBDjx4/H6dOnASinyf/444/o2bMnTE1NMWvWLADAH3/8gYYNG0Iul8PT0xMzZsxAQUGBat+3b99G69atIZfLUbt2bRw4cEDts0tOub937x7atWsHALC2toYgCBg2bBhSU1Nx7NgxzJ07F+3atYO7uzuaNGmC0NBQ9OzZU7Wv1NRUjBo1Co6OjpDL5ahTpw527dqlen/btm0IDAyETCaDh4cHvv32W7W0eHh4YObMmRgyZAgsLCzw/vvvAwCOHz+OVq1awdjYGG5ubhg7diwyMzMr8RuoXAn7juLWtIWI++PvZwrv/v5AZEc9Qvhnc5Fx8y7uL92I2G37UOvjYaowtcYNx8NVW/Bo3XZkhEfi6ofTUJiVA7dh/bSUi5dLE18BJ26IuP0YSEgD/jyjgLkx4Oda/hGniZ+AsLsirkSJSHwC/HVeREEBUL+WMo7MUPn3wTAF7scDsSnA7rMKuNkJcLGtqpxVnSa+Ai7fFXH1noikJ8DeC8ryqFdLcxk28hFwNxY4EyEiKR04ek1EbCoQ7FMcvrGPgBPhxd/LrrPK78W3gu9FH1w7sQ5+jfrDN7gvrB280aLXdBgYynHrwnaN4W+c+hk1fFqibqsRsHLwQnCnj2HrEoAbp35RhfFu0AsN2o+Gi3fzqspGtbt6bB38G/eHX6O+sHb0Rsve02FgJEfEec3leO2Eshzrtx4BawcvNHrtY9i5BOB6iXL0adgLDTuMhusrVI5FtPE7CQDnbok4dVNEdNKr0aG/emwtApr2h3/jfrB29EbrvjNgYCjHzXPbNIc/vh5uvi0R1HYErB290Ljzx7BzrY1rJzaqwvg06IEaPs1hYesGGycfhPT4HHk5GUiKiaiqbFWb/3Kc+Lfjler4fbn4+L3rnAI17AS46Ok4R9MACY5eVSDikYj4VOD3k4UwNwH83covx2YBEly8o0DYXRGJacCuM4XILwQaeBd3ly7eEfEgXkRaJhCbDBwKK4SlqQAr0yrIlA6TSIWX5qWPdK5Dn5ycjL1792L06NEwNS3bekpOe58+fTr69OmDq1ev4t1338WxY8cwZMgQfPzxx7hx4waWLVuGtWvXqjr7CoUCffv2hZGREc6cOYOffvoJkyZNKjctbm5u2LZNecCKiIhATEwMFi1aBDMzM5iZmeH3339Hbq7muUwKhQJdu3bFiRMnsGHDBty4cQP/+9//IJVKAQAXLlzAgAEDMHDgQFy9ehXTp0/H1KlTsXbtWrX9zJ8/H/Xr18elS5cwdepUREZGokuXLujXrx+uXLmCzZs34/jx4xgzZszzFPNLzapZEBIPnVLblnDgOKybBQEABENDWDYMROLBk8UBRBGJh07CqlmDKkxp9bAyBcyMBUTFFZ9M5uYDj5MAVzvNcSQSwNkauBenfgIaFSfC1U754+dkDUilAqLiit9PSgfSMkW42urXD6REosxvVKnyuBdffl5dbYWy5RdbHL7oe7mn6XvRwwsiRQoL8pD0+DpcvENU2wSJBC7eIUh4EKYxTvyDy3DxClHb5urdEvEPNYd/FRQW5CHx8XW4lipHV68QxJdTjnEPLquFB4AaPi3LDf8q0dbv5KumsCAPCdHX1S4ICRIJaviEIO5+mMY48Q/C4OqjfgGphm8LxJVTLwsL8hB+ZjOM5OawdfGvrKS/lP7LceJZjldFx+97JY7fyUXHbz2su1ZmgLmxgLuxCtW23HzgUaIIN3vN+ZVIABcbAXdj1MvxboyIGuWUkaEUaOAlQUq6iLSsyks/0fPSuXvo79y5A1EU4e//7z/qgwcPxvDhw1X/v/vuu/j8888xdOhQAICnpydmzpyJzz77DNOmTcPff/+NmzdvYt++fXBxcQEAzJ49G127dtW4f6lUChsb5aVNBwcHtYsJa9euxXvvvYeffvoJDRs2RJs2bTBw4EDUq6e8p+zvv//G2bNnER4eDl9fX1V6iixYsAAdOnTA1KlTAQC+vr64ceMG5s2bh2HDhqnCtW/fHhMmFE+VHDlyJN566y2MGzcOAODj44Pvv/8ebdq0wY8//gi5XP6v5faykznaITcuUW1bblwiDC3NIZHLYGhtCYmBAXLjk0qFSYKpnyf0nenTrzgzR317Zo6oeq80EyNAIhE0xAFsLYr2K6CgUCxzL2hmDmCm+9VKTVF5lL63MDMHsDXXHMdMrqnMi8um3O8lt/zvRR/kZqVCVBTC2Ez9bNTYzBapCVEa42RnJEJuZlcmfHZ6osbwr4Kc8srRvOJyNNZUjhmvbjkW0dbv5KsmJzNFWS/NS7dvO6TGa66XWemJMClVj03M7Mq07/s3/sHfv0xAQX42TMzt0f291TA2ta7cDLxk/stx4lmOVxUdv/Xx+GMmV3bANbVVU7nmzrmJrLz2LcLOUj1OI18JOjWQwMhQQGKaiPUHC6BQgKja6FyHXhSffQpbo0aN1P6/fPkyTpw4oRqRB4DCwkLk5OQgKysL4eHhcHNzU3XmASAkRH1041n169cP3bt3x7Fjx3D69Gn89ddf+Oabb7By5UoMGzYMYWFhqFGjhqozX1p4eDh69eqltq1FixZYuHAhCgsLVSP5mvJ45coVbNxYPHVNFEUoFApERUUhIKDsYke5ubllZhLIZOXcKEwvnUB3AV2Diw82W47xqEJEVBJ/J3WPi3dTvDFuB3IyUxB+div+3jAOfT7aUuaili4LrCmgS8l6eZz18r+o6yHg9aZS1f+//FOo1c+7GqXA3RgFzIwFNK8twRutDLB6XwEK+fWVi8+h1y6d69D7+PhAEATcvHnzX8OWnpKfkZGBGTNmoG/fvmXCamPkWi6Xo1OnTujUqROmTp2KkSNHYtq0aRg2bBiMjY0r5TM05XHUqFEYO3ZsmbA1a9bUuI85c+Zgxgz1BemmTZuGxpWSwsqXG5cImaP6qJPM0Q75aelQ5OQiLzEFioICyBxsS4WxRW6s/o1M3Y4W1VZgLVrwxbTUiLGpXEBcquYLYll5gEJRdgSg5D4yc0QYSCWQGULtKr+pHMgodUVb1xWVR+kF8CrKa4aGkY6S4YvKscz3Iiv/e9EHMhMrCBJpmQWysjOSYGKmeW6zsZkdckqNImdnJMHYvJy50K8AeXnlmJ4Ek3LKxdjMrsxofHZGUplR+1dBVf1OvmrkptbKepleun0nltteTcztkFWqHmdpCG9oZAJLO3dY2rnD0T0Iv87tjJtnf0OD9qMqNxPV6PZjEY+Tn6FeVnCceJbjVUXHb32ouxGPRDxKLF4Py+Bp395UDmRkF4czlQNxKeWUY2557VtQ2wegLMPcfCA5XcSjxEJMGmCAgJoCrt3T32M5vdx07h56GxsbdO7cGUuWLNG40Ftqamq5cRs2bIiIiAh4e3uXeUkkEgQEBODhw4eIiYlRxSlaZK88RkZGAJQj/f+mdu3aqjTXq1cPjx49wq1btzSGDQgIwIkTJ9S2nThxAr6+vqrR+fLyeOPGDY15LEpraaGhoUhLS1N7hYaG/mt+qkvq6TDYtm+mts2uQ3OknA4DAIj5+Ui7eB127UvMrhAE2LYLQerpS1WY0qqRVwCkZBS/Ep8AGdkiPByLr4YaGQAutkB0OdczFAogJgVqcQDl/9GJygNUbApQWCjCw7H4fRtzwNJU0LvFnxQKZX5Ll4e7Q/l5jU4S4a6p/J6GT818+r04aPhetPfwi2onNTCCrUsgHkcW/5aKCgUeR56Gfc0gjXEcatZXCw8AjyNPwsFNc/hXgdTACHYugYjWUI4O5ZSjo4ZyfHTnZLnh9VlV/U6+aqQGRrB3DUT0neJ1bUSFAtF3TsPRPUhjHIeaQWrhASD69kk4/lu9FBUoLNDNp/mUp9x6+RzHiWc5XqmO3w7F76uO33pQd0uXY0IakJ4twtOpuJtjZAjUsBPwMEFzfhUK4HGyCE8n9XL0dBLwqIIyEp6+9P1pNfRy08nqt2TJEhQWFqJJkybYtm0bbt++jfDwcHz//fcVTpH/8ssv8fPPP2PGjBm4fv06wsPDsWnTJkyZMgUA0LFjR/j6+mLo0KG4fPkyjh07hsmTJ1eYFnd3dwiCgF27diEhIQEZGRlISkpC+/btsWHDBly5cgVRUVHYunUrvvnmG9U0+jZt2qB169bo168fDhw4gKioKPz111/Yu3cvAGDChAk4ePAgZs6ciVu3bmHdunX44YcfMHHixArTM2nSJJw8eRJjxoxBWFgYbt++jT/++KPCRfFkMhksLCzUXlU55V5qagKL+v6wqK9cF8GkVg1Y1PeH3M0ZAOD39XjUXzNXFf7+8k0wqeUG/zmfwtTPE+7/NxjO/bsiatFaVZiohWvgNmIAXN/pDTN/T9RZMh0GpsZ4uE7zatD65uwtES1qC/BxAewtgR5NJUjPBiKiiw9Kg9tKEOxdfOA6GyEiyFNAXQ8BtuZA10YCDA2AK1HKOLn5wOUoER2DJHB3UC6y83oTCR4linishx3Ss7eeloe7sjy6BKuXx+tNBLSpW1x+52+L8HRSrjZsYw60DBTgbA1cuF1c5udui2heW4B3qe/lVrTun1BVpE6Lobh1fituX/wdqfGROLlzBgrysuEb3AcAcGTrJJzft0AVvnbIEDy6fRxXj69BasJdXDz4AxKjr6N2yGBVmNysVCQ9Dkdq/B0AQFpiFJIehyMrPaFqM1eF6rYaiohzW3Hrwu9IiY/E8T9mIL9EOf6zZRLO7i0uxzothuDhreO4cmwNUuPv4sLfynIMLFGOOU/LMSVOWY6pr0A5FtHG7ySgHAV0sAKszZXxHCyV/8s1X1PXeXVbDcPNs1sRcX4HUuIicWzHdOTnZcOvkXI25KFNk3Dmr+Kn9NRt+Q4eRRzH5SOrkRJ/F+f3L0bCo+uo0+ItAEB+XhbO/LUAcffDkJ4SjYRH13B4yxfIfBIHz3pdqiWPVelZjhOD2pSql/9yvCo6fncIkqCmvfL43b3x0+O3nj4J8Ey4Aq3qSOBbQ4CDFdCnuRTpWcDNh8Xl+E4HKRr7FneFTocr0NBHgvqeAuwslM+cNzQAwiKVc+mtzICWgRI42wAWJsoLBP1bS5FfqJwFROWr7pXt9X2Ve52bcg8oF4+7ePEiZs2ahQkTJiAmJgb29vYIDg7Gjz/+WG68zp07Y9euXfjqq68wd+5cGBoawt/fHyNHjgQASCQS7NixAyNGjECTJk3g4eGB77//Hl26lH8AcXV1xYwZM/D5559j+PDhGDJkCJYtW4amTZviu+++Q2RkJPLz8+Hm5ob33nsPX3zxhSrutm3bMHHiRAwaNAiZmZnw9vbG//73PwDKkfYtW7bgyy+/xMyZM+Hs7IyvvvpKbUE8TerVq4cjR45g8uTJaNWqFURRhJeXF958883nKOGqZRlcByEH16v+rz1fWUYPf96OKyNCIXO2h/HTzj0AZN97hHM9R6H2t6Hw+GgIch7F4uqoKUg8cFwVJmbrXzCyt4HvtLGQOdnjyeVwnH19JPLi9bDnqcHpmyKMDICujSSQGwEPE4DNRxRq93dZmUFtil74Q+WUvdZ1hKfTTpVxMkssr3DgkghRBPo2l0AqBaJigb0X9POmsaLyaPW0POJTgS1HFaqFhyxMBLU1PaKTgJ2nFWhdR4I2dQWkZADbTiiQ+KR4n6dvijCUAl2Dn34vicp96vt9d571uiEnMwUXD36P7PRE2DgH4LVhy1VTvzPTYiAIxSdVju4N0HbAPFz4exEu7P8OFrbu6PDWYlg7Fq858uDmPzi2rfj39PBm5eKgQe1Ho2EH/XmqR0le9bohJyMFF/7+HlnpibB1DkDX4ctVU+4zU8uWY/uB83B+/yKc2/cdLO3c0entxbBxKlGO4f/gyG/F5XjoV2U5NuwwGsEd9bMci2jrd7Khl4BWdYq/h3c6KGfV/XlGgat6OCXXO6gbcjKTcX7/YmSlJ8DOJQDdRqxQ1cuM1McQSjyk28mjIdoPno9zexfi7N7vYGnngc5DflDVS0GQIjUhCvvXj0VOZgrkJlawd6uLnh9shI2TT7XksSo9y3HCygww1lAvyzteAcDfYSJEqB+/913U34PPiRsKGBoAPZpKITcCHsSL2HBI/T53G3MBJvLiNnn9vggTmQJt60lhZgzEpojYeKhQdVtCQSFQ00FAU38DGBspb2m4Hy9i9b6CMosSElUlQXyeVebolbLb0K+6k6AXuudHYPZm7S7Q8ir44k0p5mxhOVaG0AFSzP1Nf0/kqtKkNySYv51l+aIm9pXwd7KSfPGmFAv+4KldZRjfS+BxpxKEDpBixob8fw9I/2ra24bVnYT/5Hyb/7bIuDY0OnLq3wPpGJ2cck9ERERERET0qmOHnoiIiIiIiEgH6eQ99ERERERERPTyEyQcQ9Ymli4RERERERGRDmKHnoiIiIiIiEgHcco9ERERERERaYUg0c/nv78sOEJPREREREREpIPYoSciIiIiIiLSQZxyT0RERERERFohkXLKvTZxhJ6IiIiIiIhIB3GEnoiIiIiIiLSCi+JpF0foiYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEgrBAnHkLWJpUtERERERESkg9ihJyIiIiIiItJBnHJPREREREREWsFV7rWLI/REREREREREOogj9ERERERERKQVEilH6LWJI/REREREREREOogdeiIiIiIiIiIdxCn3REREREREpBVcFE+7OEJPREREREREpIPYoSciIiIiIiLSQZxyT0RERERERFohSDiGrE0sXSIiIiIiIiIdxA49ERERERERkQ7ilHsiIiIiIiLSCq5yr10coSciIiIiIiLSQRyhJyIiIiIiIq3gCL12cYSeiIiIiIiISAexQ09ERERERESkgzjlnoiIiIiIiLSCU+61SxBFUazuRBAREREREZH+uTWoS3UnQcX3173VnYRKxxF6KtfszYXVnQS98MWbUuw29KvuZOi87vkRmLWJdbIyTB4oxcKdvJZbGcb1FDDt5/zqTobOmzHEkO27kkweKMWaf6o7FfpheDueC1WGL96UIux2QnUnQy8E+dhXdxLoJcQOPREREREREWmFIOGybdrE0iUiIiIiIiLSQRyhJyIiIiIiIq2QSLkonjZxhJ6IiIiIiIhIB7FDT0RERERERKSDOOWeiIiIiIiItILPodcujtATERERERER6SB26ImIiIiIiIh0EKfcExERERERkVbwOfTaxdIlIiIiIiIi0kEcoSciIiIiIiKt4KJ42sUReiIiIiIiIiIdxA49ERERERERkQ7ilHsiIiIiIiLSCk651y6O0BMRERERERHpIHboiYiIiIiIiHQQp9wTERERERGRVvA59NrF0iUiIiIiIiLSQezQExEREREREekgTrknIiIiIiIireAq99rFEXoiIiIiIiIiHcQReiIiIiIiItIKLoqnXSxdIiIiIiIiIh3EDj0RERERERGRBkuWLIGHhwfkcjmaNm2Ks2fPVhh+4cKF8PPzg7GxMdzc3PDJJ58gJydHa+njlHsiIiIiIiLSDkF3F8XbvHkzxo8fj59++glNmzbFwoUL0blzZ0RERMDBwaFM+F9++QWff/45Vq9ejebNm+PWrVsYNmwYBEHAggULtJJGjtATERERERERlbJgwQK89957GD58OGrXro2ffvoJJiYmWL16tcbwJ0+eRIsWLTB48GB4eHjgtddew6BBg/51VP9FsENPREREREREei83NxdPnjxRe+Xm5moMm5eXhwsXLqBjx46qbRKJBB07dsSpU6c0xmnevDkuXLig6sDfvXsXe/bsQbdu3So/M0Vp0tqeiYiIiIiI6JUmSISX5jVnzhxYWlqqvebMmaMx3YmJiSgsLISjo6PadkdHR8TGxmqMM3jwYHz11Vdo2bIlDA0N4eXlhbZt2+KLL76o9HItwg49ERERERER6b3Q0FCkpaWpvUJDQytt/4cPH8bs2bOxdOlSXLx4Edu3b8fu3bsxc+bMSvuM0rgoHhEREREREWnFy/QceplMBplM9kxh7ezsIJVKERcXp7Y9Li4OTk5OGuNMnToV77zzDkaOHAkAqFu3LjIzM/H+++9j8uTJkGihLF6e0iUiIiIiIiJ6CRgZGSE4OBgHDx5UbVMoFDh48CBCQkI0xsnKyirTaZdKpQAAURS1kk6O0L9kTp06hZYtW6JLly7YvXt3dSenyrWuIyDIU4DMEHiUCOy9oEBKRsVxgr0FNPUXYCYH4lKB/RcViEkufl8qAToGCQioKcBAAtyNBfZdUCBT8/oXOs2mZSN4ThgBy4Z1IHdxwPl+HyJu58GK47RugtrzP4dZbR/kPIzBnTk/4tHPO9TCuH8wGJ7jR0DmZI8nV27i+riZSDt3VZtZeWm0riOggVdxnfzr/LPVyWYBJerkBQUel6iTDbwEBLoLcLIGZIYC5m8rRG6+VrNR7a6d2IiwI6uQlZ4IW2d/tOw9BY4165UbPvLyXpzdtwjpKdGwtHNHs24T4R7QRvX+uf2LcSdsDzJSYyExMIS9ayCadh0Hx5r1qyI71a5dfQmCfSSQGwEPEkTsOl2I5PSK4zTxk6B5oARmxkBcsog9ZxWITio+uejRTAJPZwnMjYG8AuBhgogDFwqR+ETLmalGbN+V48LhjTizfxUynyTAoYY/Or05FS61ym/fNy/8haM7FyEtKRo2Dh5o22civOoWt+/MJ4n4Z/t83As/jpysdLj5NEKnN6fCxtGjCnJT/bRxLhTkqV4vv92u3/Vy365t+HP7r0hNSYZ7LS8MH/UJvP1qawx7cO9OHD20Fw/v3wUA1PL2w6Aho9TCp6Yk45e1P+LKpbPIzMxAQGB9DB/1CZxd3aokP1R9xo8fj6FDh6JRo0Zo0qQJFi5ciMzMTAwfPhwAMGTIELi6uqruw+/RowcWLFiABg0aoGnTprhz5w6mTp2KHj16qDr2lY0j9C+ZVatW4aOPPsLRo0fx+PHj6k5OlWrmL6CRj4C/ziuw9m8F8guBgW0kkFZQSwPcBHQIEnD8uojV+xWITxUxsI0EJiVm0nRqIMDbRcCOkwps+EcBM2Ogb0v9rPpSUxM8uRKBa2NnPFN4Y48aaLxzGZIOn8HxRr0QtXgd6i77GnadWqrCOPfvioB5obj99RIcb9IH6VduounuVTCyt9FWNl4aIf4CGvs+rZMHFMgvAAa1/fc62bGBgGPXRKza97ROtlWvkwZSIDJGxIkb2rlS+7K5E7YHJ/78Hxp1Go03xm2HrYsfdq0ciayMJI3hY+9dxIFfJsC/yRvoP24HagV2xN51Y5AUe0sVxtLeA616T8WbE3aiz4cbYW7jil0rRiA7I1njPvVJy0AJmgZI8OeZQqzYU4D8AuCdjgYwqKBeBnoI6NxIgsOXC7FsVwFiU4B3OkphKi8O8zhJxO8nCvHDHwVY/3cBAOCdTga6/PjgCrF9V47w83tw6Lc5aPn6aAz/Ygccavhj8+IRyHyiuX0/iryIP1ZNQP0Wb2D45N/hE9QB234ajYRoZfsWRRHbfhyN1MSH6PfBUgyfvAOWtq7YtGg48nKzqjJr1UJb50KGBsDdGBEnX4F6efLoQfy88gf0GzQc/1u0Cu61vDH7y/FIS03RGP761Uto3qYjvpyzGDPnL4OtvSNmfTkeyYkJAJR1cv7XoYiLfYyJU/6HuYvWwM7BCV9PGYecnOyqzJrOqu6F8Eq+ntebb76J+fPn48svv0RQUBDCwsKwd+9e1UJ5Dx48QExMjCr8lClTMGHCBEyZMgW1a9fGiBEj0LlzZyxbtqzSyrM0/ezV6KiMjAxs3rwZH3zwAbp37461a9eqvb9z5074+PhALpejXbt2WLduHQRBQGpqqirM8ePH0apVKxgbG8PNzQ1jx45FZmZm1WbkP2riK+DEDRG3HwMJacCfZxQwNwb8XMtvfE38BITdFXElSkTiE+Cv8yIKCoD6tZRxZIbKvw+GKXA/HohNAXafVcDNToCLbVXlrOok7DuKW9MWIu6Pv58pvPv7A5Ed9Qjhn81Fxs27uL90I2K37UOtj4epwtQaNxwPV23Bo3XbkREeiasfTkNhVg7chvXTUi5eHk38lCdIt6KB+DRgZ1GdrFF+nWzqLyAssrhO7jn3tE56Fsc5d0vEqXBRbXRUn10+uha1m/aHf+N+sHH0Rpu+M2BoKMfNs9s0hr9yfD1q+rVEg7YjYO3ohSZdPoada21cO7FRFca3QQ/U8G0OC1s32Dj5oEWPz5GXk4GkmIiqyla1aRYgwdErCkQ8FBGXCmw/XghzE8C/Zvn1snmABBduKxAWKSIhDdh1uhD5hUAD7+LTgAu3RdyPF5GaCcQkA4cuFcLKVICVaRVkqhqwfVeOs3+vQf0WA1CveT/YuXijy2Bl+75yUnP7Pn/oZ3gGtkLT10bCztkLrXuOg1PN2rhweAMAICX+Hh5HhaHz4Olw9qgHWydPdB40HQX5OQg/p/8zF7VxLgQ8rZc3X416ufv3TejQuQfadeqOGjVrYeToT2Ekk+OfA7s0hh/76TR07t4XHp4+cHVzx/99NAmiQoGrl88DAGIeP8TtiOsY+eEEePsGwKVGTYz8cCLy8nJx4siznW+RbhszZgzu37+P3NxcnDlzBk2bNlW9d/jwYbU+m4GBAaZNm4Y7d+4gOzsbDx48wJIlS2BlZaW19LFD/xLZsmUL/P394efnh7fffhurV69W3WsRFRWFN954A71798bly5cxatQoTJ48WS1+ZGQkunTpgn79+uHKlSvYvHkzjh8/jjFjxlRHdp6LlSlgZiwgKq74QJObDzxOAlztNMeRSABna+BenPrBKSpOhKud8iDmZA1IpQKiSqxlkZQOpGWKcLXV02Gn52DVLAiJh9Sfo5lw4DismwUBAARDQ1g2DETiwZPFAUQRiYdOwqpZgypMadUrqpP3StXJ6CTAtZyLQUV1MkpDnazxita3woI8JERfRw2f5qptgkQCV58QxN0P0xgn7n4YXEuEBwA33xblhi8syMON05thJDeHrYt/ZSX9pWRtBpibCLgbo1Bty80HohNEuNlrrmNSCeBsK+BuTHG9FKEcrSsvjqGBsrOfnC7iiR4OirJ9V47CgjzEPrgOjwD19u0R0BzRdy9pjPP4bhg8/NXvPa1VuyWi74YBAAoK8gAABobFw8uCRAKpgREe3rlQyTl4uWjrXOhVUpCfj7t3bqFuUCPVNolEgrpBjXD75vVn2kdubi4KCgtgZm6h2icAGBoV10mJRAJDQyNE3LhSiakn+m/YoX+JrFq1Cm+//TYAoEuXLkhLS8ORI0cAAMuWLYOfnx/mzZsHPz8/DBw4EMOGDVOLP2fOHLz11lsYN24cfHx80Lx5c3z//ff4+eefkZOTU9XZeS5F0z4zSyUzM0dUmxJakokRIJEIGuIU789ULqCgUCxzn1hmDmBWzn5fJTJHO+TGJapty41LhKGlOSRyGYzsrCExMEBufFKpMEmQOZVzdqEnKqqTZsaa41RYJ8uJo+9yMlMgKgphbKbeSzIxs0NWeqLGOFnpiTApHd68bPh7N/7BiskNsfyL+rhybB16vL8axqbWlZuBl4yZsfIEPaNUHcvIKX6vNBMZIJUIyCg1MzQjWyzzO9jYT4IvBhlgymBDeLtK8POBAhQqoHfYvitHVoayfZtaqLdXU3NbZD7R3L4zniTC1MKu3PC2Tp6wsHHBkR3fIiczDYUFeTi9bznSU2KR+SRBOxl5SWjrXOhV8uRJGhSKQlhaqd8WaGllg9QUzbeBlLZx7VLY2NipLgq41HCHnb0jfl33EzIynqAgPx9//LYBSYnxSEl+tn2+6gSJ5KV56SMuiveSiIiIwNmzZ7Fjh3IxMgMDA7z55ptYtWoV2rZti4iICDRu3FgtTpMmTdT+v3z5Mq5cuYKNG4unpYqiCIVCgaioKAQEBGj87NzcXOTmqq8Qp3ycg/aqR6C7gK7BxSefW47p4Rkj6ZRAdwHdGhXXyc1HWSdfdq7eTTHgkx3IzkxB+Jmt2L9+HPqO3VLmYoAuq1tLQI9mxYvobDxUqNXPu3JXgcjHCpgbC2geKMGANgZY9VcBCnS8ObB96w6p1BB9Ry3GnvWTsXBCEwgSKTz8Q+AZ2BrKuSX6g+dCL5/ft67HyaMHMW3OYhg9HZE3MDDAhMmz8NOi/2HEwG6QSKSoGxSMoOBm0Lc6SbqJHfqXxKpVq1BQUAAXFxfVNlEUIZPJ8MMPPzzTPjIyMjBq1CiMHTu2zHs1a9YsN96cOXMwY4b6ImrTpk2DUcDUZ0z987sdLeJxifu4ihZ7MZWrX5k2lQuIS9X8Y5mVBygUZa9al9xHZo4IA6kEMkOojdKbysuOcL2KcuMSIXNUHymROdohPy0dipxc5CWmQFFQAJmDbakwtsiN1Tz6oqtuR4tYWU6dzChdJ1P+Q518RdfNkZtaQ5BIkV1qAbysjESYmGue5WFibldmwbys9LLhDY1MYGnnDks7dzi5B+GXuZ1x8+xvaNh+VOVmohpFPBQRnVig+r+oXprJoTbibiYHYsurl7lAoaLsyLOZsVDmdzA3X/lKThfxKLEQn79pAP+aAq7d0+2TVrZv7TAxU7bv0gvgZaYnlRmFL2JmYVdm9L50eCf3Onh3yh/IyU6HoiAfJuY2WPe//nB2r1P5mahGVXUu9CqxsLCERCJFWqr6Aqlpqcmwsq74Yu+f23/BH79txJSvF8K9lrfae57e/vhm8VpkZWagoCAfFpbWmDz+PXj66PdtXqQb9HPegY4pKCjAzz//jG+//RZhYWGq1+XLl+Hi4oJff/0Vfn5+OH/+vFq8c+fOqf3fsGFD3LhxA97e3mVeRkZG5X5+aGgo0tLS1F6hoaFayWuRvAIgJaP4lfhEOf3Tw7H4SrWRAeBiC0SX029UKICYFKjFAZT/RycqD3yxKUBhoQgPx+L3bcwBS1PhlVgY5t+kng6DbftmatvsOjRHyukwAICYn4+0i9dh177E/Y6CANt2IUg9rfn+SF31rHXS1VZ5n60mFdXJR69ofZMaGMHeNRCP7hSv1SAqFIi+cxqO7kEa4zi6ByH6tvraDo9unyw3fMn9Fj69/1Zf5BUAyenFr4Q0ID1LhKdz8eFbZgi42gt4mKC5jhUqgJgkEZ7OxfVSAFDLqfw4JQMaaOcpO1WK7Vs7pAZGcKoZiHs31dv3/Zun4OqpeZ0VF88g3Lt5Wm3bvfCTcPUMKhNWbmwOE3MbJMfdQ+z9a/Cp36FS01/dqupc6FViYGgIT29fXL1cvN6CQqHAtcsX4OMfWG68P37biG2b1iF0xnx4VdBJNzE1g4WlNWKiHyLyTgQaNW1VqenXV9W9sv2LrHKvCzhC/xLYtWsXUlJSMGLECFhaWqq9169fP6xatQpbtmzBggULMGnSJIwYMQJhYWGqFRWFp88UmjRpEpo1a4YxY8Zg5MiRMDU1xY0bN3DgwIEKR/llMtnTKfalaXdqZ2lnb4loUVtASrpyleXWdSRIzwYioosPSIPbShDxSMSFO8ptZyNE9GgqICZZ+cilJn4CDA2AK1HK93PzgctRIjoGSZCTp0BuPvBaQwkeJYp4rIe3PUlNTWDqXTwbw6RWDVjU90dechpyHsbA7+vxkLs64vLwSQCA+8s3wf3Dt+A/51M8XLsNdu2awbl/V5zrWTzCGbVwDeqvnovUC9eQdu4KPMYOhYGpMR6u217l+atqZyNEtAgUkPy0Trap+7ROPipRJ9tJcOuRiPO3ldvO3BTRs9nTOpksoonv0zp5tziOqVw5ompjpmy7DlZAXj6QlgXk6Fd/FABQv/UwHNr8Oexr1IGjWz1cObYO+XnZ8G/cFwBw8NdJMLV0QLNuEwAA9Vq+gz9+HIKwI6vhHtAWd8J2I+HRdbR54ysAQH5eFi4c/AketdvD1MIeOZkpuHbyF2Q+iYNXvS7Vls+qcjpcgdZ1JUh6IiIlQ0T7ICnSs4CbD4rr2NBOUoQ/EHE2QjmF92S4An1aSBGdqFzlOiRAAiMD4NId5fvWZkAdDwnuPFYgKxewMBHQso4EBYXKUUR9xPZdOZp0HI5dayfB2b0OnD3q4fyhdcjLy0a95sr2/eeaz2Bu5Yi2fZTtu1H7Ifjl23dw5sBqeNdtgxvn9iDm/jV0eesr1T5vXvgLxmY2sLRxQXx0BP7eMhs+QR1Rq3ZLjWnQJ9o4FwKU9dJUDlibP62XlkBuAfBED+tl994DsfS7WfDy8YeXbwD2/LEFuTnZaNuxOwDgh29nwsbWHoOH/R8A4I/fNmDLhlUY++k0ODg6q+61l8uNITc2AQCcOn4IFhZWsHNwxIN7d7Fu+SI0btYK9Rs20ZwIoirEDv1LYNWqVejYsWOZzjyg7NB/8803SE9Px2+//YYJEyZg0aJFCAkJweTJk/HBBx+oOuP16tXDkSNHMHnyZLRq1QqiKMLLywtvvvlmVWfpPzl9U4SRAdC1kQRyI+BhArD5iEJtQSYrM6g9VzX8oQgTGdC6jvB0SpoyTmaJJQEOXBIhikDf5hJIpUBULLD3gn7ep2YZXAchB9er/q89/wsAwMOft+PKiFDInO1h7Oasej/73iOc6zkKtb8NhcdHQ5DzKBZXR01B4oHjqjAxW/+Ckb0NfKeNhczJHk8uh+Ps6yORF6+HV0RKOXVThKEB0K1xcZ3cVKpOWpsBxqXqpKkcaFO3uE5uOqxeJxt6C2hdp3iEdUgH5RDon2cUaidg+sI7qBuyM5Nxbt9iZKUnwM4lAK+PXKGaQp+R+lh1YRIAnDwaouPg+TizbyHO/PUdLO080GXoD7B18gUACIIUqfFR2H9+LLIzUyA3tYJDjbro/eFG2Dj5VEseq9Lx6woYGgA9QqSQGwEP4kVs+Fv9PndrcwEm8uK6dP2eCFOZAu2DpDAzBmKTRaw/WKiakltQCNR0ENAswAByI+VU3ftxIlb+VaC303bZvitHQKNuyEpPxrE/v0fmkwQ41AjAmx+tVE2hf5IcA0EoLo8aXg3Rc8R8HN25EEf/WABrBw/0+78lsHf1VYXJSEvAwd/+h8wnSTCztEedZr3QotuHVZ636qCtc6GGXgJalaiX75Sol1d1/Jaa0pq37oAnaanYsmElUlOS4eHpjdCvvoWVtXKhvKSEOEhKLI52YM/vKCjIx4I5U9T288ag4ej/1ggAQGpyEtav/AGpqcmwtrZF6/Zd0G/gsCrLk67T15Hxl4UgFj0XjXTOrFmz8NNPP+Hhw4da2f/szVU7Qq+vvnhTit2GftWdDJ3XPT8CszaxTlaGyQOlWLiTP/2VYVxPAdN+zv/3gFShGUMM2b4ryeSBUqz5p7pToR+Gt+O5UGX44k0pwm7r9xMKqkqQj311J+E/iQ8dUt1JUHGY83N1J6HScYRehyxduhSNGzeGra0tTpw4gXnz5unEM+aJiIiIiIio8rFDr0Nu376Nr7/+GsnJyahZsyYmTJig9cXriIiIiIiI/jM9ff77y4Ideh3y3Xff4bvvvqvuZBAREREREdFLgJdLiIiIiIiIiHQQR+iJiIiIiIhIK0o+yYYqH0foiYiIiIiIiHQQR+iJiIiIiIhIKwQuiqdVLF0iIiIiIiIiHcQOPREREREREZEO4pR7IiIiIiIi0gpBwkXxtIkj9EREREREREQ6iB16IiIiIiIiIh3EKfdERERERESkHVzlXqtYukREREREREQ6iB16IiIiIiIiIh3EKfdERERERESkFVzlXrs4Qk9ERERERESkgzhCT0RERERERFohCBxD1iaWLhEREREREZEOYoeeiIiIiIiISAdxyj0RERERERFpBxfF0yqO0BMRERERERHpIHboiYiIiIiIiHQQp9wTERERERGRVggSjiFrE0uXiIiIiIiISAdxhJ6IiIiIiIi0QuCieFrFEXoiIiIiIiIiHcQOPREREREREZEO4pR7IiIiIiIi0g6BY8jaxNIlIiIiIiIi0kHs0BMRERERERHpIE65JyIiIiIiIq3gKvfaxRF6IiIiIiIiIh0kiKIoVnciiIiIiIiISP88WTi+upOgYjFuQXUnodJxyj2Va86WwupOgl4IHSDFrE0syxc1eaAUuw39qjsZeqF7fgTrZCWZPFCK+dsV1Z0MnTexrwQL/uD4QmUY30vAjA351Z0MvTDtbUN8s43t+0V91k+Cmb8WVHcy9MLUQey6UVmcck9ERERERESkg3iZh4iIiIiIiLRCELgonjZxhJ6IiIiIiIhIB7FDT0RERERERKSDOOWeiIiIiIiItEPCMWRtYukSERERERER6SB26ImIiIiIiIh0EKfcExERERERkVYIEq5yr00coSciIiIiIiLSQRyhJyIiIiIiIu0QOIasTSxdIiIiIiIiIh3EDj0RERERERGRDuKUeyIiIiIiItIOLoqnVRyhJyIiIiIiItJB7NATERERERER6SBOuSciIiIiIiKtELjKvVaxdImIiIiIiIh0EEfoiYiIiIiISDu4KJ5WcYSeiIiIiIiISAexQ09ERERERESkgzjlnoiIiIiIiLRCkHAMWZtYukREREREREQ6iB16IiIiIiIiIh3EKfdERERERESkHQJXudcmjtATERERERER6SB26ImIiIiIiIh0EKfcExERERERkXZwlXutYukSERERERER6SCO0BMREREREZF2cFE8reIIPREREREREZEOYoeeiIiIiIiISAdxyv1/JPzL1JFp06Zh+vTpVZMYPdHQW0BTPwFmciA+Fdh/SYGY5PLD+9cAWteRwNIUSE4HDl9RIDJWPUyrQAFBngJkhsCjJGDfBQVSMrSajZdG6zoCGng9zXsi8Nf5f897sLeAZgHK7yAuFdh/QYHHJb6DBl4CAt0FOFkDMkMB87cVIjdfq9moNjYtG8FzwghYNqwDuYsDzvf7EHE7D1Ycp3UT1J7/Ocxq+yDnYQzuzPkRj37eoRbG/YPB8Bw/AjInezy5chPXx81E2rmr2szKS4N18sVdP7URV46uRnZGImyc/NG852Q4uNUrN/zdq3tx/sD3yEiJhoWtO5p0mYCa/m1U70dd24/wM5uRGH0dudlp6PvRdti6BFRFVqrdtZMbcfnIKmSnJ8LW2R8tek2BQ83yyzLyyl6c37cI6SnRsLRzR9OuE1EzQFmWhYX5OLdvER7ePIInSY9gJDeDq09zNO06HqaWjlWVpWrVtp4EDX0kkBsCDxNE7D5biOT0iuM09pWgeW0JzIyB2BQRf51T4HGSqHr/9aYS1HKSwNwYyCtQ7vfvS4VIeqLlzFSTG6c24uqx4vYd0mMy7Cto31FX9+LCge+Rkaps3427TICbX3H7vndtP8LPbkbS0/bde8yr074BoE1dCRp4Cco6maisX8n/csxp5CMgxF9ZJ+NSgL0XCsscc+q4S+BsozzmfPNbgV4fcyqLwEXxtIql+x/FxMSoXgsXLoSFhYXatokTJ1Z5mvLy8qr8MytLgJuADvUFHL8uYvUBBeJSRbzZWgITmebwrrZAr2YSXI4SsXq/Arcfi+jXQgI7i+IwzfwFNPIRsPeCAusOKpBfALzZWgLpK1DrQ/wFNPYV8Nd5BdYeUOZ9UNuK8x7gJqBjAwHHrolYtU+B+FQRA9uqfwcGUiAyRsSJG2L5O9ITUlMTPLkSgWtjZzxTeGOPGmi8cxmSDp/B8Ua9ELV4Heou+xp2nVqqwjj374qAeaG4/fUSHG/SB+lXbqLp7lUwsrfRVjZeGqyTLy7yyh6c3j0XDTuMRp8x22Dr7Ie/Vr+H7IwkjeHj7l/CoU0T4deoH/p8tB0etTvgwIaPkBx7SxWmIC8bTh4N0aTrhKrKxkvhTtgenPrzfwjuOBr9Pt4OG2c/7F41styyjL13EQd/mQC/xm+g38c74BHYEft+HqMqy4K8HCRG30DDDh+i38fb8NqQxUhLiMLetR9WZbaqTYvaEjT1l2D3mUKs3FuAvALg7fYGFbbvQHcBrwVLcORKIZbtKUBcCvB2e6la+36cJOKPU4VY8mcBNhwqgCAA73Qw0Mvbce9e2YMze+aiQYfR6DV6G2yc/bB3TcXt+5/NE+HbqB96j9kO99od8Hep9p2fnw0n94Zo3OXVat8A0DxAQBNfAXvOKbD6QCHyC4DB7aQV1snaNQV0aiDB0WsKrNhbiLhUEYPbqddJQwMgMkaB49cV2s8E0TN6Bbo22uHk5KR6WVpaQhAEtW2bNm1CQEAA5HI5/P39sXTpUlXce/fuQRAEbN++He3atYOJiQnq16+PU6dOqcJMnz4dQUFBap+5cOFCeHh4qP4fNmwYevfujVmzZsHFxQV+fn4AgIcPH2LAgAGwsrKCjY0NevXqhXv37mmzOF5YE18Bl++KuHpPRNITYO8FEQUFQL1amo/ajXwE3I0FzkSISEoHjl4TEZsKBPsUh2/sI+BEuIjbj4GENGDXWQXMjQFfVz08EyiliZ/y4sitaCA+Ddh5Rpl3vxrl572pv4CwSBFXokQkPgH2nFN+B/U9i+OcuyXiVLiI6CT97zwl7DuKW9MWIu6Pv58pvPv7A5Ed9Qjhn81Fxs27uL90I2K37UOtj4epwtQaNxwPV23Bo3XbkREeiasfTkNhVg7chvXTUi5eHqyTL+7qsXXwb9wffo36wtrRGy17T4eBkRwR57drDH/txM+o4dMS9VuPgLWDFxq99jHsXAJw/dQvqjA+DXuhYYfRcPVuXlXZeClcPbYWAU37w79xP1g7eqN13xkwMJTj5rltmsMfXw8335YIajsC1o5eaNz5Y9i51sa1ExsBADJjc7z+3mp41e8KKwdPOLoHoUXvqUiMvo70lMdVmbVq0TRAgqNXFYh4JCI+Ffj9ZCHMTQB/t/Lbd7MACS7eUSDsrojENGDXmULkFwINvItPTS/eEfEgXkRaJhCbDBwKK4SlqQAr0yrIVBW7dnwd/Br3h2+wsn236KVs37cuaG7f108q23e91iNg5eCF4E4fw9YlAOGnS7TvBr3QoMNouLxi7RsAmvhJcOy6AreilXXyj9PKY45/BcecZn4SXIoUcfnpMWf3OeXF56ASx5yzESJOhouI1nydhahasEOvBRs3bsSXX36JWbNmITw8HLNnz8bUqVOxbt06tXCTJ0/GxIkTERYWBl9fXwwaNAgFBQXP9VkHDx5EREQEDhw4gF27diE/Px+dO3eGubk5jh07hhMnTsDMzAxdunR5aUfwJRLAyRqIilM/Ib8XL8LVVvMPr6utgHulwkfFFoe3MgXMjNXD5OYDj5OUo/v6rLy8R1eQd4kEcNbwHUTFiahRzndA6qyaBSHx0Cm1bQkHjsO6WRAAQDA0hGXDQCQePFkcQBSReOgkrJo1qMKUVj3WyRdXWJCHxMfX4eodotomSCRw9QpB/IMwjXHiHlxWCw8ANXxalhv+VVFYkIeE6OtqFzEEiQQ1fEIQdz9MY5z4B2Fw9VHvFNXwbYG4CsoyLycdEATIjC3KDaMPrMwAc2MBd2OLRyxz84FHiSLc7DW3VYkEcLERcDdGvX3fjRFRw05zHEMp0MBLgpR0EWlZlZf+l0FR+3Yp1b5dKmjf8Q8uq4UH2L6LWJkq62RUrIZjTjn1SyIBnG2gFgd4eswpJw49B0Hy8rz0EO+h14Jp06bh22+/Rd++fQEAtWrVwo0bN7Bs2TIMHTpUFW7ixIno3r07AGDGjBkIDAzEnTt34O/v/8yfZWpqipUrV8LIyAgAsGHDBigUCqxcuVJ1n/+aNWtgZWWFw4cP47XXXqusbFYaEyNAIhGQlau+PTMHsDXXHMdMrny/dHgzufJvU3nxNrUwuaLqPX1Vbt5zRJgZa45T9B1oKlNb/T4XrTQyRzvkxiWqbcuNS4ShpTkkchkMrS0hMTBAbnxSqTBJMPXzrMqkVjnWyReXk5UKUVEIYzP1KyDG5rZITYjSGCc7IxHGZnbq4c1skZ2RqDH8qyInM0VZlualytLMDqnxmssyKz0RJqXK3sTMDtnpmsuyID8XZ/bMh3f97jCSm1VOwl9SZnLluYamtmoq19wRMpGV175F2Fmqx2nkK0GnBhIYGQpITBOx/mABFHo227nc9m1mi7TnaN9yM1tklVMnXyVFxxWNx5xyzgGL6mRGjlgqDmBnzg49vdzYoa9kmZmZiIyMxIgRI/Dee++pthcUFMDS0lItbL16xQudODs7AwDi4+Ofq0Nft25dVWceAC5fvow7d+7A3Fy9J5yTk4PIyEiN+8jNzUVurnpvWiaTgdVDNwS6C+jWqPhgs/monp3pkM5hnaRXWWFhPv7eMA4QgVZ9p1dzaipfXQ8BrzeVqv7/5Z9CrX7e1SgF7sYoYGYsoHltCd5oZYDV+wpQyJ8VeqqOu4DujYtHXn89ot06Sf+BhBdFtIk9tkqWkaFcPnPFihVo2rSp2ntSqVTtf0NDQ9XfRaPpiqeXnSUSCURR/Sphfn7ZZTRNTdVvJMvIyEBwcDA2btxYJqy9vb3GNM+ZMwczZqgv/DVt2jTIak/VGL6yZeUBCoVYZgE8UzmQkaM5TkYOyoy0lwxfdFXWtNRIvqlMQFyqft1reztaxMoS9w8XLfhSuvxM5QLiUjTnveg70FSmmdmVnWL9lBuXCJmj+miJzNEO+WnpUOTkIi8xBYqCAsgcbEuFsUVurH6NqLBOVj65iRUEibTMAlnZ6UkwMbfTGMfYzK7MaHx2RlKZUb1XjdzUWlmW6aXKMiMRxuWUpYm5HbJKlX2WhvDKzvwnSE99jB7vr9XL0fmIRyIeJRbfHmjw9NTGVA5klGibpnKU375zy2vfgto+AOVU6dx8IDldxKPEQkwaYICAmgKu3dOfY3m57Tsjqdw6qal952SU/3ugz25Fi4hOKu7EG1RwzIn9lzqpnHEiloiDMqP2RC8b/byRoBo5OjrCxcUFd+/ehbe3t9qrVq1az7wfe3t7xMbGqnXqw8LC/jVew4YNcfv2bTg4OJT5/NIzBIqEhoYiLS1N7RUaGvrMaX1RCgUQmwJ4OKpfvXN3EMpd6Co6SYR7qfAejsXhUzOBjGwRHg7FYYwMABdb6N1CJnkFQEpG8SvxydO8O6rn3bWCvCsUQIyG78DDUcCjV2CxscqQejoMtu2bqW2z69AcKafDAABifj7SLl6HXfsS9zwKAmzbhSD19KUqTKn2sU5WPqmBEexcAhEdeVq1TVQo8DjyNBxqBmmM41izPh6XCA8Aj+6cLDf8q0JqYAR710BE3yle80JUKBB95zQc3YM0xnGoGaQWHgCib5+EY4myLOrMpyXex+vvrYHc1Fobya92pdt3QhqQni3C06n4lNLIEKhhJ+Bhgua2qlAAj5NFeDqpt29PJwGPEstv38LTl749raaofcfcefb27aChfUe/ou27TJ18oqyTtZw0HHPKqV8KBRCTDHiUqpO1HCuuk0QvAz37SXw5zJgxA3PmzMH333+PW7du4erVq1izZg0WLFjwzPto27YtEhIS8M033yAyMhJLlizBX3/99a/x3nrrLdjZ2aFXr144duwYoqKicPjwYYwdOxaPHj3SGEcmk8HCwkLtpZxyX3XO3hIR5CmgrrsAW3OgS7AAQwPgSpTyR/T1JgLa1C3+kT1/W4Snk3J1fBtzoGWgAGdr4MLt4h/dc7dFNK8twNsFsLcEejSVID1beSVX352NENEiUIDP07z3bKbMe8Sj4rwPbidBoxJPBThzU0QDLwF1PQTYWgBdGz39Du6qX6l2tAJszJTxHKyU/8uL7/rQG1JTE1jU94dFfeUtMCa1asCivj/kbsrbY/y+Ho/6a+aqwt9fvgkmtdzgP+dTmPp5wv3/BsO5f1dELVqrChO1cA3cRgyA6zu9YebviTpLpsPA1BgP12lexVifsE6+uLqthiLi3FbcuvA7UuIjcfyPGcjPy4ZvcB8AwD9bJuHs3uLjTJ0WQ/Dw1nFcObYGqfF3ceHvH5AYfR2BIYNVYXKyUpH0OBwpcXcAAKmJUUh6HI6s9ISqzVwVq9tqGG6e3YqI8zuQEheJYzumIz8vG36NlGvfHNo0CWf++rY4fMt38CjiOC4fWY2U+Ls4v38xEh5dR50WbwFQduYPrP8YCY+uocOgeRDFQmSlJyArPQGFBS/ngrSV6Uy4Aq3qSOBbQ4CDFdCnuRTpWcDNh8Vt9Z0OUjT2LT7tPB2uQEMfCep7CrCzUD5z3tAACItUzlS0MgNaBiqf921horxA0L+1FPmFyllA+qZOy6GIOL8Vty/+jtT4SJz4YwYK8rLh21DZvo9snYRz+4rbd2DzIXh06ziuPm3fF5+274Bmxe0792n7To1Xtu+0V6R9A8DZCAVaBkrg6yrAwRLoHaI85twsccx5u9Qx53SEAg29BNSrpayT3Ror6+TlqLLHHOunk2/0+ZhTmQRB8tK89BGn3GvByJEjYWJignnz5uHTTz+Fqakp6tati3Hjxj3zPgICArB06VLMnj0bM2fORL9+/TBx4kQsX768wngmJiY4evQoJk2ahL59+yI9PR2urq7o0KEDLCxe3pWkwh8qp9y3qiPAVC4gPhXYclShWijPwkRQm60QnQTsPK1A6zoStKkrICUD2HZCgcQnxfs8fVOEoRToGiyB3Ah4mKjc56tw392pmyIMDZQHI7kR8DAB2HREPe/WZoBxies24Q+V0x/b1FV+B3GpwKbDCmSWWF6hobeA1nWKfwyHdFDOtfzzjEJ18UVfWAbXQcjB9ar/a8//AgDw8OftuDIiFDJnexg/7dwDQPa9RzjXcxRqfxsKj4+GIOdRLK6OmoLEA8dVYWK2/gUjexv4ThsLmZM9nlwOx9nXRyIvXs+mjWjAOvnivOp1Q05GCi78/T2y0hNh6xyArsOXq6bYZqbGqJ2sOLo3QPuB83B+/yKc2/cdLO3c0entxbBx8lWFeRD+D4789oXq/0O/Kp9X3bDDaAR3HFNFOat63kHdkJOZjPP7FyMrPQF2LgHoNmKFqiwzUh+rboUDACePhmg/eD7O7V2Is3u/g6WdBzoP+UFVlllpcbh/4xAA4LeFvdU+q8eodXDxUr8FT9+cuKGAoQHQo6kUciPgQbyIDYfU73O3MRdgIi9uk9fvizCRKdC2nhRmxkBsioiNhwpVt8kVFAI1HQQ09TeAsZFy6vT9eBGr9xWUWURXH3jW64acTGX7zn7avjsPX66acp+hoX23e3MeLhxYhPP7v4OFrTs6lmrf98P/wbFtxe37n03K9t2g/Wg01OP2DQAnw0UYGojo/vSY8yBBxC+HC0sdc4Snt3sq6+WNB8o62aauBGZyIC4F+OVwodqtm8HeErSpW/w9DOuo7Er9cbpQ7445pDsEsfSN2kRPzdnCRUUqQ+gAKWZtYlm+qMkDpdht6FfdydAL3fMjWCcryeSBUszf/gpcJdSyiX0lWPAHT0cqw/heAmZsKLvmDj2/aW8b4pttbN8v6rN+Esz89fkey0yaTR2km2OxOb/O/fdAVUQ+aFJ1J6HS6WatICIiIiIiopcfV7nXKv28kYCIiIiIiIhIz3GEnoiIiIiIiLRDTxeje1mwdImIiIiIiIh0EDv0RERERERERDqIU+6JiIiIiIhIOwQuiqdNHKEnIiIiIiIi0kHs0BMRERERERHpIE65JyIiIiIiIu2QcAxZm1i6RERERERERDqIHXoiIiIiIiIiDZYsWQIPDw/I5XI0bdoUZ8+erTB8amoqRo8eDWdnZ8hkMvj6+mLPnj1aSx+n3BMREREREZF2CLo7hrx582aMHz8eP/30E5o2bYqFCxeic+fOiIiIgIODQ5nweXl56NSpExwcHPDbb7/B1dUV9+/fh5WVldbSyA49ERERERERUSkLFizAe++9h+HDhwMAfvrpJ+zevRurV6/G559/Xib86tWrkZycjJMnT8LQ0BAA4OHhodU06u7lEiIiIiIiInq5SYSX5/Uc8vLycOHCBXTs2LE4KxIJOnbsiFOnTmmMs3PnToSEhGD06NFwdHREnTp1MHv2bBQWFr5QEVaEI/RERERERESk93Jzc5Gbm6u2TSaTQSaTlQmbmJiIwsJCODo6qm13dHTEzZs3Ne7/7t27OHToEN566y3s2bMHd+7cwYcffoj8/HxMmzat8jJSAkfoiYiIiIiISO/NmTMHlpaWaq85c+ZU2v4VCgUcHBywfPlyBAcH480338TkyZPx008/VdpnlMYReiIiIiIiItKOl2hRvNDQUIwfP15tm6bReQCws7ODVCpFXFyc2va4uDg4OTlpjOPs7AxDQ0NIpVLVtoCAAMTGxiIvLw9GRkYvmIOyXp7SJSIiIiIiItISmUwGCwsLtVd5HXojIyMEBwfj4MGDqm0KhQIHDx5ESEiIxjgtWrTAnTt3oFAoVNtu3boFZ2dnrXTmAXboiYiIiIiIiMoYP348VqxYgXXr1iE8PBwffPABMjMzVaveDxkyBKGhoarwH3zwAZKTk/Hxxx/j1q1b2L17N2bPno3Ro0drLY2cck9ERERERETaITzf6vIvkzfffBMJCQn48ssvERsbi6CgIOzdu1e1UN6DBw8gkRSPkbu5uWHfvn345JNPUK9ePbi6uuLjjz/GpEmTtJZGduiJiIiIiIiINBgzZgzGjBmj8b3Dhw+X2RYSEoLTp09rOVXF2KEnIiIiIiIi7ZDwLm9tYukSERERERER6SB26ImIiIiIiIh0EKfcExERERERkXbo8KJ4uoAj9EREREREREQ6iB16IiIiIiIiIh3EKfdERERERESkHQLHkLWJpUtERERERESkg9ihJyIiIiIiItJBnHJPRERERERE2iHhGLI2sXSJiIiIiIiIdBBH6ImIiIiIiEg7+Bx6rRJEURSrOxFERERERESkf3L2raruJKjIO4+o7iRUOo7QU7nm/qao7iTohUlvSLBwJ6+bvahxPQXM2lRY3cnQC5MHSrHb0K+6k6EXuudHoGWPI9WdDJ13/M82mPlrQXUnQy9MHWTA38pKMnmgFPO381zoRU3sK8GvJ3geVBkGteBIN5XFDj0RERERERFpB59Dr1UsXSIiIiIiIiIdxA49ERERERERkQ7ilHsiIiIiIiLSDq5yr1UcoSciIiIiIiLSQRyhJyIiIiIiIu2QcAxZm1i6RERERERERDqIHXoiIiIiIiIiHcQp90RERERERKQVIhfF0yqO0BMRERERERHpIHboiYiIiIiIiHQQp9wTERERERGRdggcQ9Ymli4RERERERGRDuIIPREREREREWkHR+i1iqVLREREREREpIPYoSciIiIiIiLSQZxyT0RERERERFrB59BrF0foiYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEg7uMq9VrF0iYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEg7uMq9VnGEnoiIiIiIiEgHcYSeiIiIiIiItEPCMWRtYukSERERERER6SB26ImIiIiIiIh0EKfcExERERERkVaIXBRPqzhCT0RERERERKSD2KEnIiIiIiIi0kHs0GvR9OnTERQUVN3JICIiIiIiqh6C5OV56SHeQ1+OYcOGYd26dar/bWxs0LhxY3zzzTeoV69eNaZMf904vRHXjq1GdkYirJ38EfL6ZNi7lV/WUVf34uLf3yMjNRoWtu5o1HkC3PzaqN6/d30/bp7djKTo68jNTkOv0dth6xJQFVmpVtdObETYkVXISk+ErbM/WvaeAsea5Zdj5OW9OLtvEdJTomFp545m3SbCPaC4HM/tX4w7YXuQkRoLiYEh7F0D0bTrODjWrF8V2al2resIaOAlQGYIPEoE/jqvQEpGxXGCvQU0CxBgJgfiUoH9FxR4nFz8fgMvAYHuApysAZmhgPnbCpGbr9VsVBublo3gOWEELBvWgdzFAef7fYi4nQcrjtO6CWrP/xxmtX2Q8zAGd+b8iEc/71AL4/7BYHiOHwGZkz2eXLmJ6+NmIu3cVW1m5aUy4i0P9HjNCeamBrga/gTzl97Go5jscsP37uqM3l1d4OwoBwBEPcjC2k33cfpCccU0MhQwZoQXOrRygKGhBGcvJePbH28jJVVPKyeANnUlaOAlQG4IPEwU8dc5BZL/pX038hEQ4i+BmTEQlwLsvVBYpn3XcZfA2UbZvr/5rUBv23dJ/K18cddPbcSVo8rzIBsnfzTvORkOFZwH3b26F+cPfI+MFOV5UJMuE1DTv/j4HXVtP8LPbEbi0/Ogvh+9GudBAHD24Eac2LsKGWmJcHLzR9e3pqCGp+ayjI++jX9+/x6P711HWtJjdB4YipDXhqqFObZ7GcIvHEBizF0YGMnh5t0And6YADtnz6rIDlGF9PMyRSXp0qULYmJiEBMTg4MHD8LAwACvv/56dSdLL929sgdn98xFUPvR6Dl6G2yc/LBv7XvIzkjSGD7u/iUc3jIRvo36odfo7agZ0AEHN36ElLhbqjAFedlwdG+IRp0nVFU2qt2dsD048ef/0KjTaLwxbjtsXfywa+VIZJVTjrH3LuLALxPg3+QN9B+3A7UCO2LvujFIii0uR0t7D7TqPRVvTtiJPh9uhLmNK3atGIHsjGSN+9QnIf4CGvsK+Ou8AmsPKJBfAAxqK4G0gl/OADcBHRsIOHZNxKp9CsSnihjYVgITWXEYAykQGSPixA1R+5moZlJTEzy5EoFrY2c8U3hjjxpovHMZkg6fwfFGvRC1eB3qLvsadp1aqsI49++KgHmhuP31Ehxv0gfpV26i6e5VMLK30VY2Xipv9XPDG6+7Yv7S23h/4iVk5xRiwVd1YWRY/qJDCYl5+GldFEaMu4iRn1zExSspmDM5ELVqmqjCfDTSGy2a2GLq3Bv4KDQMdjYyzAoNrIosVYvmAQKa+ArYc06B1QcKkV8ADG4nrbB9164poFMDCY5eU2DF3kLEpYoY3E6q1r4NDYDIGAWOX1doPxMvCf5WvrjIK3twevdcNOwwGn3GbIOtsx/+Wl3xedChTRPh16gf+ny0HR61O+DAho+QHKt+HuTk0RBNur4650EAcO3sHuzb/D+07Tkao6Zth6ObHzYsGImMJ5rLMj8vB9b2buj4xgSYWdprDHMv4hwatx+MkVM2Y8iE1VAUFmD9gpHIy83SZlb0hihIXpqXPtLPXFUSmUwGJycnODk5ISgoCJ9//jkePnyIhIQEAMCkSZPg6+sLExMTeHp6YurUqcjPL//S8blz59CpUyfY2dnB0tISbdq0wcWLF9XCCIKAlStXok+fPjAxMYGPjw927typFub69et4/fXXYWFhAXNzc7Rq1QqRkZGq91euXImAgADI5XL4+/tj6dKllVgq2nHtxDr4NeoP3+C+sHbwRote02FgKMetC9s1hr9x6mfU8GmJuq1GwMrBC8GdPoatSwBunPpFFca7QS80aD8aLt7Nqyob1e7y0bWo3bQ//Bv3g42jN9r0nQFDQzlunt2mMfyV4+tR068lGrQdAWtHLzTp8jHsXGvj2omNqjC+DXqghm9zWNi6wcbJBy16fI68nAwkxURUVbaqTRM/Acevi7gVDcSnATvPKGBuDPjVKL/j1NRfQFikiCtRIhKfAHvOiSgoAOp7Fsc5d0vEqXAR0Un6f5KasO8obk1biLg//n6m8O7vD0R21COEfzYXGTfv4v7SjYjdtg+1Ph6mClNr3HA8XLUFj9ZtR0Z4JK5+OA2FWTlwG9ZPS7l4ufTv6Yqft9zH8TNJiLyXia+/uwlbGxlaNbMrN86Jc0k4fSEZj2Ky8fBxNpavv4fsnELU9rMAAJiaSPF6JycsXhmJi1dSERGZgdmLbqJebUsE+plXVdaqVBM/CY5dV+BWtIj4VOCP08r27V9B+27mJ8GlSBGXn7bv3eeUndegEu37bISIk+EiojX3HfQSfytf3NVj6+DfuD/8GvWFtaM3WvaeDgMjOSLOaz4PunZCeR5Uv/UIWDt4odFrH8POJQDXS5wH+TTshYYdRsP1FToPAoBT+9aiYev+aNCqHxxcvfH6kBkwNJLj0jHN50KuteritQGfoW7T7pAaGGoM8874lWjQsi8cXH3gVNMfvd+dg7Skx3h877o2s0L0TNihf0YZGRnYsGEDvL29YWtrCwAwNzfH2rVrcePGDSxatAgrVqzAd999V+4+0tPTMXToUBw/fhynT5+Gj48PunXrhvT0dLVwM2bMwIABA3DlyhV069YNb731FpKTlaOh0dHRaN26NWQyGQ4dOoQLFy7g3XffRUFBAQBg48aN+PLLLzFr1iyEh4dj9uzZmDp1qtrtAy+bwoI8JD2+DhfvENU2QSKBi3cIEh6EaYwT/+AyXLxC1La5erdE/EPN4V8FhQV5SIi+jho+xQduQSKBq08I4u6HaYwTdz8Mrj7qB3o33xblhi8syMON05thJDeHrYt/ZSX9pWRlCpgZC7gXV3wimZsPRCcBrraa40gkgLM1EBWnfvIZFSeihi0f2fIsrJoFIfHQKbVtCQeOw7pZEABAMDSEZcNAJB48WRxAFJF46CSsmjWowpRWDxdHOexsZDgXlqLalplViBu3nqCOv8Uz7UMiATq0sodcLsX1m08AAH7e5jA0lOD85eL9PniUjdj4HAQ+4351iZUpYG4sICpWQ/u209xWJRLA2QZqcYCn7bucOK8C/la+uMKCPCQ+vg7XUudBrl4hiC/nPCjuwWW18ABQw6dlueFfFQUFeXh8/zo8axef20gkEnjWDsGjyLBK+5ycbOW5u7GpZaXtk+i/4j30Fdi1axfMzMwAAJmZmXB2dsauXbsgkSivg0yZMkUV1sPDAxMnTsSmTZvw2Wefadxf+/bt1f5fvnw5rKyscOTIEbWp/MOGDcOgQYMAALNnz8b333+Ps2fPokuXLliyZAksLS2xadMmGBoqryL6+vqq4k6bNg3ffvst+vbtCwCoVasWbty4gWXLlmHoUPX7gV4WuVmpEBWFMDZTP/Ibm9kiNSFKY5zsjETIzezKhM9OT9RaOl92OZkpGsvRxMwOqfGayzErPREmpcOb2yGrVDneu/EPDmycgIL8bJia26PH+6thbGpduRl4yZgqbzVGZo769swcEWbGmuOYGAESiaAhDmCrf30irZA52iE3Tr3+5cYlwtDSHBK5DIbWlpAYGCA3PqlUmCSY+un/vYw21kYAUOa+9pTUPNV75fF0N8VP8xrAyEiC7OxCfDHrOu49VE4XtbU2Ql6+AhmZhWpxklPzYGtV8X51UVEb1ti+5ZrjmMiU7TsjRywVB7Azf/U6oUX4W/nicso7DzKv+DzIWNN5UMarex4EAFnpynMhMwv1sjS1sENijOayfF4KhQJ7f50NN++GcKzh++8RCOBz6LWKHfoKtGvXDj/++CMAICUlBUuXLkXXrl1x9uxZuLu7Y/Pmzfj+++8RGRmJjIwMFBQUwMKi/CNRXFwcpkyZgsOHDyM+Ph6FhYXIysrCgwcP1MKVXHTP1NQUFhYWiI+PBwCEhYWhVatWqs58SZmZmYiMjMSIESPw3nvvqbYXFBTA0rL8K4i5ubnIzc1V2yaTyQBonnZErx5X76YY8MkOZGemIPzMVuxfPw59x24pczFAlwW6C+jWqPiAs/noq3P/K728OrVxwKeji08YP/vqvy/89yA6C8M/Pg8zEwO0bWGPyZ/44aPQy6pOvT6r4y6ge+PiSYm/HimsIDRVhL+V9Krbs+ErxEffxruhv/x7YKIqwA59BUxNTeHt7a36f+XKlbC0tMSKFSvQvXt3vPXWW5gxYwY6d+6sGjX/9ttvy93f0KFDkZSUhEWLFsHd3R0ymQwhISHIy8tTC1e6sy4IAhQK5QHT2Licy91Q3hYAACtWrEDTpk3V3pNKpeXGmzNnDmbMUF+watq0aTCu82W5cSqTzMQKgkRaZuGX7IwkmJhpvifU2MwOOaWuQmdnJMHYvPx7SPWd3NRaYzlmZSTCpJxyMTG3K7NgXlZ62fCGRiawtHOHpZ07nNyD8Mvczrh59jc0bD+qcjNRjW5Hi1hZ4j7NosWcTOVARolRJFO5gLgUzfdzZuUBCoWoGrEqjgNklr8AOZWQG5cImaN6/ZM52iE/LR2KnFzkJaZAUVAAmYNtqTC2yI3Vv5Gp42eTcOPWedX/RobKimltZYiklOJjh7WVEe7crXhJ8YICEdExysocEZmBAB9z9O/pinlLbiMpJQ9GhhKYmUrVRultrIyQlJpX3i51xq1oEdFJxfkyqKB9x5bXvnOV7dtMLgAQS8RBmVF7fcbfysonL+88KD2p3OO3sZldmdH47IykMqP2rxoTc+W5UOkF8DKfJMLM8sXLZveGr3Dr8mEM/3wDLG2cXnh/RJWB99A/B0EQIJFIkJ2djZMnT8Ld3R2TJ09Go0aN4OPjg/v371cY/8SJExg7diy6deuGwMBAyGQyJCY+3wlovXr1cOzYMY2L7zk6OsLFxQV3796Ft7e32qtWrVrl7jM0NBRpaWlqr9DQ0OdK14uQGhjB1iUQjyNPq7aJCgUeR56Gfc0gjXEcatZXCw8AjyNPwsFNc/hXgdTACPaugXh0p/j+Y1GhQPSd03B0D9IYx9E9CNG31e9XfnT7ZLnhS+63sED3T/JLyisAUjKKX4lPgIxsER6OxSNRRgbKe0LLW+xKoQBiUqAWB1D+/+gVWNSpMqSeDoNt+2Zq2+w6NEfK6TAAgJifj7SL12HXvsS9o4IA23YhSD19qQpTWjWyswsRHZOjekU9yEJici4a1S++5cXEWIravha49vR++GclCIDh0wsEEXfSkZ+vQHCJ/bq5GsPJQa66z16XlW7fCU+A9GwRtZw0tO9EzW1VoQBikgEPJ/X2XctRwKNy4ugj/lZWPqmBEexcAhGt4TzIoZzzIEcN50GP7pwsN/yrwsDACC7ugYgKLz63USgUuBt+GjW8gv7zfkVRxO4NX+Hmxb8x9LO1sLavUQmpfXVU98r2+r7KPUfoK5Cbm4vY2FgAyin3P/zwAzIyMtCjRw88efIEDx48wKZNm9C4cWPs3r0bO3bsqHB/Pj4+WL9+PRo1aoQnT57g008/rXDEXZMxY8Zg8eLFGDhwIEJDQ2FpaYnTp0+jSZMm8PPzw4wZMzB27FhYWlqiS5cuyM3Nxfnz55GSkoLx48dr3KdMJns6xb60qptGV6fFUBzbFgo71zqwr1EX10/+jIK8bPgG9wEAHNk6CaYWjmjUWZmH2iFDsGflEFw9vgZufm1w98oeJEZfR4vexTMNcrNSkZEag6x05e0KaYnKe6eMze1gYq75sSS6rn7rYTi0+XPY16gDR7d6uHJsHfLzsuHfWLmmwsFfJ8HU0gHNuikfYVOv5Tv448chCDuyGu4BbXEnbDcSHl1Hmze+AgDk52XhwsGf4FG7PUwt7JGTmYJrJ39B5pM4eNXrUm35rCpnI0S0CBSQnC4iNVP5zOr0bCDiUfEJ5+B2Etx6JOL8beW2MzdF9GwmICYZeJwsoomvAEMD4Mpd9RE9MzlgY6Y8mXWwAvLygbQsIEe/rpNAamoCU++aqv9NatWARX1/5CWnIedhDPy+Hg+5qyMuD58EALi/fBPcP3wL/nM+xcO122DXrhmc+3fFuZ7Fs0GiFq5B/dVzkXrhGtLOXYHH2KEwMDXGw3WaV4PWN1t3RmPomzXx8HE2YuJyMPJtDyQl5+LY6eILxAu/roejpxKxffdjAMCoIbVw+kIy4hJyYGJsgE5tHNCgrhXGT1NO4c/MKsSuA7H4aIQXnqQXICurAONGeeNqeBquR6RrTIeuOxuhQMtACZLTFUjNENG2nrJ93yzRvt9uJ8HNEu37dIQCvZpJEJMs4HGSiCZ+EhgaAJejyrZva+USPHrdvovwt/LF1W01FEe2hsLetQ7s3eri2omfkV/iPOifLcrzoCZdlOdBdVoMwZ/Lh+DKsTWo6dcGkU/Pg1r1KT4PyslKRWZqDDKfKM+DUl+B8yAACOk8DDtWfg4XjzpwrVUPpw+sQ35uNhq0VJ4LbV8xCRbWDuj4hvJcqKAgDwmPlU+LKizIR3pqHGIehMNIZgJbR3cAypH5q6d3YdDYJTCSmyI9TfnEK7mxOQyNyll4g6iKsENfgb1798LZ2RmAckV7f39/bN26FW3btgUAfPLJJxgzZgxyc3PRvXt3TJ06FdOnTy93f6tWrcL777+Phg0bws3NDbNnz8bEiROfK022trY4dOgQPv30U7Rp0wZSqRRBQUFo0aIFAGDkyJEwMTHBvHnz8Omnn8LU1BR169bFuHHj/ksRVBnPet2Qk5mCiwe/R3Z6ImycA/DasOWqqWOZaTEQSlxVc3RvgLYD5uHC34twYf93sLB1R4e3FsPasfhe0wc3/8GxbV+o/j+8WfnDHdR+NBp2GFNFOata3kHdkJ2ZjHP7FiMrPQF2LgF4feQK1ZS9jNTHEEosTOLk0RAdB8/HmX0Lceav72Bp54EuQ3+ArZOyHAVBitT4KOw/PxbZmSmQm1rBoUZd9P5wI2ycfKolj1Xp1E0RhgZAt8YSyI2AhwnApiMKFJa41mVtBhiXuB4W/lA5jbRNXUE55TQV2HRYgcwSy1Q09BbQuk5xfR7SQXlLzJ9nFLgSpV+jU5bBdRBycL3q/9rzlW3y4c/bcWVEKGTO9jB2c1a9n33vEc71HIXa34bC46MhyHkUi6ujpiDxwHFVmJitf8HI3ga+08ZC5mSPJ5fDcfb1kciLfzWeE7Zx20PI5VJ8NsYXZqYGuHojDROmXUVefnHdcXUyhpVF8e1b1paGmPKJP2xtjJCZWYDIe5kYP+0qzpdYLX/xyjsQRS/MCq0NQ0MJzl5Mxrc/3q7SvFWlk+EiDA1EdH/avh8kiPjlcGGp9i08fS66smxvPBBhIlOgTV0JzORAXArwy+FCtcXdgr0laFO3uH0P66g81frjdKHete8i/K18cV71uiEnIwUX/v4eWemJsHUOQNfhy1XH78zUsudB7QfOw/n9i3Bu33ewtHNHp7cXw8apxHlQ+D848lvxedChX5XnQQ07jEZwR/08DwKAOk26ITM9Gf/8vhgZaQlwcgvA25+sUE25T0t+DEFSfC6UnhqPZdP7qP4/uXc1Tu5dDXe/xhg+SXn8Ov/PrwCAtXOHqH1Wr3dnqy4UEFUXQRRF/fpFpEoz9zcudFMZJr0hwcKdbGYvalxPAbM2cSGryjB5oBS7Df2qOxl6oXt+BFr2OFLdydB5x/9sg5m/FlR3MvTC1EEG/K2sJJMHSjF/O8+FXtTEvhL8eoLnQZVhUAvdXC0+/dye6k6CinnjbtWdhEqnnzcSEBEREREREek5TrknIiIiIiIi7dDTxeheFixdIiIiIiIiIh3EDj0RERERERGRDuKUeyIiIiIiItIKUdDNxfx0BUfoiYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEg7uMq9VrF0iYiIiIiIiHQQR+iJiIiIiIhIK0RwUTxt4gg9ERERERERkQ5ih56IiIiIiIhIB3HKPREREREREWmFyEXxtIqlS0RERERERKSD2KEnIiIiIiIi0kGcck9ERERERETawSn3WsXSJSIiIiIiItJB7NATERERERER6SBOuSciIiIiIiKtEAWhupOg1zhCT0RERERERKSDOEJPREREREREWsHn0GsXS5eIiIiIiIhIB7FDT0RERERERKSDOOWeiIiIiIiItIOL4mkVR+iJiIiIiIiIdBA79EREREREREQ6iFPuiYiIiIiISCu4yr12sXSJiIiIiIiIdBBH6ImIiIiIiEgrRHBRPG3iCD0RERERERGRDmKHnoiIiIiIiEgHcco9ERERERERaQUXxdMuli4RERERERGRDhJEURSrOxFERERERESkfxKun6nuJKjYBzat7iRUOk65p3LN366o7iTohYl9JZj2c351J0PnzRhiyDpZSSb2laBljyPVnQy9cPzPNtht6FfdydB53fMjMPc3tu/KMOkNCb7ZxrKsDJ/1k/C4Uwkm9pVg8urc6k6GXpj1rqy6k/DfCFzlXps45Z6IiIiIiIhIB3GEnoiIiIiIiLRC5BiyVrF0iYiIiIiIiHQQO/REREREREREOohT7omIiIiIiEgrRC6Kp1UcoSciIiIiIiLSYMmSJfDw8IBcLkfTpk1x9uzZZ4q3adMmCIKA3r17azV97NATERERERERlbJ582aMHz8e06ZNw8X/Z+++w6Mo/j+Av/cuvfeEhJBCegihBEIvggYUpKmAKIKI+AVEBCyASlMjAgrys0tTQVDBSq/SQyeUECAQQnrv9e7298fBHUcu1Lskd7xfz3MPZm92b/bj7OzOzuzsiROIjIxETEwMsrOz77hecnIypk2bhq5du+o9j2zQExERERERkV6IgqTRfO7XZ599hrFjx2L06NEICwvDN998AysrKyxfvrzOdeRyOUaMGIE5c+bA39//YUJ3T9igJyIiIiIiIqNXVVWF4uJijU9VVZXWtNXV1Th+/Dh69+6tWiaRSNC7d28cOnSozt+YO3cu3NzcMGbMGJ3nXxs26ImIiIiIiMjoxcbGwt7eXuMTGxurNW1ubi7kcjnc3d01lru7uyMzM1PrOvv378eyZcvw/fff6zzvdeEs90RERERERKQXIhrPLPfTp0/HlClTNJaZm5vrZNslJSV48cUX8f3338PFxUUn27wXbNATERERERGR0TM3N7/nBryLiwukUimysrI0lmdlZcHDw6NW+qSkJCQnJ6N///6qZQqFAgBgYmKCxMRENG/e/CFyrx0b9ERERERERKQXDzIZXWNgZmaGtm3bYufOnapXzykUCuzcuRMTJ06slT4kJARnzpzRWPbee++hpKQES5Ysgbe3t17yyQY9ERERERER0W2mTJmCl156CVFRUWjfvj0WL16MsrIyjB49GgAwcuRIeHl5ITY2FhYWFmjRooXG+g4ODgBQa7kusUFPREREREREdJuhQ4ciJycHH3zwATIzM9GqVSts2bJFNVFeSkoKJJKGHYHABj0RERERERHphSg0nknxHsTEiRO1DrEHgD179txx3ZUrV+o+Q7cxzAcaiIiIiIiIiB5xbNATERERERERGSAOuSciIiIiIiK9aEzvoTdG7KEnIiIiIiIiMkDsoSciIiIiIiK9MNT30BsKRpeIiIiIiIjIALFBT0RERERERGSAOOSeiIiIiIiI9IKT4ukXe+iJiIiIiIiIDBAb9EREREREREQGiEPuiYiIiIiISC84y71+MbpEREREREREBogNeiIiIiIiIiIDxCH3REREREREpBec5V6/Hrke+gMHDiAiIgKmpqYYOHBgvf52cnIyBEHAqVOn6vV3iYiIiIiIyPjopId+1KhRWLVqFWJjY/Huu++qlv/5558YNGgQRFHUxc/c0b///osFCxbgxIkTkMvlCA8Px4QJEzBq1CiNdFOmTEGrVq2wefNm2NjYIDk5GX5+fqrvnZyc0LZtW8yfPx+tW7fWe74flq+vLyZPnozJkyc3dFYe2rlDqxG/dzkqSnPh5BGCTk/PhJt3yzrTXzmzBce2f4HSgjTYOfugfZ+paBbSXfX91bPbkBC3Drlp51BVUYTBr2+As2dofexKo9AzUoK2gRJYmAEpOSL+PSxHfsmd12kfLEGncAlsLIGsfBGbjiiQlqc+fvt3kMC/iQS2lkC1DLieI2L7cTlyi/W8Mw2EZVK3xozwRf8nPGBrbYIzCcVY+NUlpGZU1Jl+YN8mGNjXE03cLQAAV1PKsXLtNRw+nq9KY2YqYOKY5ujV1Q2mphIcOZmPRV9fQkFhjd73p745dYmC/9QxsG/TAhaebjg2ZDyy/t5553W6tUfYwndhExaIyusZuBz7NVJ//EMjjc//nof/lDEw93BFcfwFnJs8D0VHz+hzVxqF84dX4+w+5fHt6BGCjv1mwvUOx/fVM1twYscXKC1UHt9RMVPhHaw+vkVRxMmdS5F49DdUV5bAzac1Oj09C/YuvvWwNw3r/KHVOLNPXVd27H/3WB7fro5luz6asUw+uw0JR9Yh70ZdOXDio1FX8pyjW71aS9EuWAoLM+Batoi/D8qQV3znNkl0qARdW5jAxhLILBDx7yEZUnPV64zpawr/Jpr9oUcuyPHXQZle9sFYcFI8/dJZdC0sLDB//nwUFBToapP3bOnSpRgwYAA6d+6MuLg4xMfHY9iwYXjttdcwbdo0jbRJSUl47LHH0LRpUzg4OKiW79ixAxkZGdi6dStKS0vRt29fFBYWav29mhrju1BsaEnxm3B443y06TUBgyauh3OTYGxePhYVpXla02ddO4lda6chOGoIBr2+Ab5hvbD959eRn3lRlUZWXQEP3zZo33dqfe1Go9ElXILoUAn+iZPj+00y1MiAF3ubwOQOR3y4r4CYKAn2nJbj239lyCwAXuwthbWFOk16nog/D8jxf3/J8NMO5cnrxcdNIBjhSCqWSd0aMcQbz/TzwsKvLuHVaSdRUSnHZ3MjYGZad+HJya3GN6uuYszkE3jlzRM4EV+A2Jnh8GtmpUrz+isB6NzeGe/PP4/Xp5+Ci5M5PpoeXh+7VO+k1lYojk/E2Ulz7im9pW9TtPv7W+TticP+qAG4unQVIr79EC6Pd1GlafJsX4QumI5LH36J/e0HoST+AqI3LoOZq5O+dqNRuBK/CUc2zUerxybg6Qnr4eQRjK0r73x87/l1GoKihmDAhA1oFtoLO1e/joIs9fF9Zt8POH/oZ3QaMBv9/7cOpqZW2LpyLGQ1VfW1Ww3iSvwmxG2aj9a9JmDAhPVwahKMLSvuHMvd65SxHDhxA3zCemHHbXVlTU0FPHzaoF2fR6eu5DlHt7pGSNExTIq/Dsrw9T81qKkRMSrGFCbSuteJ8JPgyfYm2HVKhi//rkFmvnKdW6+DAOBoohyxv1SpPluOsjFPDUtnDfrevXvDw8MDsbGxWr+fPXs2WrVqpbFs8eLF8PX1Vf09atQoDBw4EB9//DHc3d3h4OCAuXPnQiaT4a233oKTkxOaNm2KFStWqNa5fv06pk6dismTJ+Pjjz9GWFgYAgICMHXqVCxYsACLFi1CXFycarh7Xl4eXn75ZQiCgJUrV6q24+zsDA8PD0RFRWHhwoXIysrSWG/dunXo3r07LCwssHr1aigUCsydOxdNmzaFubk5WrVqhS1btmjs35EjR9C6dWtYWFggKioKJ0+e1Ph+5cqVGjcVAOWoBuG21tE///yDdu3awcLCAi4uLhg0aBAAoEePHrh27RrefPNNCIKgWu/atWvo378/HB0dYW1tjfDwcGzatKnO/3eNwZl9qxDS7lkERw2Go3sAugycDRMzCyQe26A1/dkDP6JpYBdEdhsDR7fmiHriDbh4huLcoTWqNIFtBqBNrwnwCuhUX7vRaHQIlWBvvAKJ10VkFQIb9sthawWENKu78dQpVILjlxQ4lSQipwj497AcNXKgdYC6mjh+ScS1bBGFZUBGPrDrpBwO1gIcrOthp+oZy6RuPfu0F3789Rr2x+UhKbkMH35+Ac5O5ujawaXOdQ4czcPh4/lIzajA9fQKfPdTMioq5QgLtgMAWFtJ0e9xDyz9IQkn4guRmFSKj5dcQMswe4QH29bXrtWbnK17cXHWYmT9teOe0vu8OgwVV1OR8PZ8lF64gmtfrUbm+q3we2OUKo3f5NG4vuxXpK7agNKEJJwZPwvy8kp4jxqip71oHM4eWIXgqGcR1HYwHN0C0HnAbJiYWuDice3H9/lDyuM7ousYOLg1R9vH34CzZyjO3zi+RVHEuQM/IrLHa/AJ6wUnj2B0e/YTVJRkIyXh3v5/Gaqz+1chuN2NWLrfiKVZ3bE8d1AZy5bdNGOZcPiWurL1ALTuNQGej1BdyXOObnUOl2LPaTkSUhTIKhDx214ZbC2B0GZ1N306t5DiWKICJy4pkFMo4q8Dyg6RtkGadwGqZSJKK6D6VLGfjxqYzhr0UqkUH3/8MZYuXYrU1NQH3s6uXbuQnp6OvXv34rPPPsOsWbPQr18/ODo6Ii4uDq+99hrGjRun+o3ff/8dNTU1tXriAWDcuHGwsbHBL7/8Am9vb2RkZMDOzg6LFy9GRkYGhg4dqjUPlpaWAIDq6mrVsnfffRdvvPEGEhISEBMTgyVLlmDRokVYuHAh4uPjERMTg6effhqXLl0CAJSWlqJfv34ICwvD8ePHMXv2bK15vJuNGzdi0KBBePLJJ3Hy5Ens3LkT7du3BwBs2LABTZs2xdy5c5GRkYGMjAwAwIQJE1BVVYW9e/fizJkzmD9/PmxsbO77t+uLXFaN3PRz8AroqFomSCTwat4R2SmntK6TlXJaIz0ANA3sUmf6R4mjDWBrJeBKhkK1rKoGSMsR4e2qvUEvlQBNnAVcyVAPKxMBXMmoex1TE2VjP79ERHG5TnehwbFM6panuwVcnMxx9JR6BFdZuRznLxajRYjdPW1DIgF6dXWFhYUU5y4on/EIDrCFqakEx06rt5uSWoHM7EqE3+N2jZlDh1bI3XVIY1nO9v1w7NAKACCYmsK+TThydx5UJxBF5O46CIcOjf+Rswcll1UjL/0cPG87vj0DOiKnjuM1O+U0PJtrHt9eAV2QfV2ZvqQgFRWluRppzCxs4dq0JbJTTut8HxqLm3VlrVjeoa7MTjmtkR5gXclzjm452iqvg5LSNa+DUnNENHOr+zrI01nA5VvWEQFcTleg2W3XQa38pZjxvBkmDTLFE22lML1Drz8piRAazccY6XSW+0GDBqFVq1aYNWsWli1b9kDbcHJywhdffAGJRILg4GB8+umnKC8vx4wZMwAA06dPxyeffIL9+/dj2LBhuHjxIuzt7dGkSZNa2zIzM4O/vz8uXrwIqVQKDw8PCIIAe3t7eHh4aP39wsJCzJs3DzY2Nmjfvj0qKpTPd06ePBmDBw9WpVu4cCHeeecdDBs2DAAwf/587N69G4sXL8aXX36JNWvWQKFQYNmyZbCwsEB4eDhSU1Pxv//9777i8dFHH2HYsGGYM0c9xDIyMlIVK6lUCltbW439SUlJwZAhQxAREQEA8Pf3v6/frG+V5YUQFXJY2jhrLLe0dUZhzlWt61SU5sLSRrNnz9LGGRWluXrLp6GwsVRWVqWVmstLK9Xf3c7KHJBKBJTe9jhzaYUIFzvNddoFS/B4GwnMTQXkFIn4cbsMcgWMCsukbjk5mgFArefaCwqrVd/Vxd/HGt8saA0zMwkqKuSY8dE5JF9X3kFydjRDdY0CpWVyjXXyC6vh7HDn7T4KzN1dUJWlWf6qsnJham8LiYU5TB3tITExQVV23m1p8mAd3LjPGw+jqq7j2+bOx7eFtuO7RBnfm//evk0LGxdUlOboKuuNTp11pY0ziu6jrrSwcUZ5yaNbV/Kco1u2N6+DKjSfly+tFO/hOui2dSpEuDqo+z/jr8hRUCqipBzwcBQQ084ELvYC1uzisHtqODp/bd38+fPx2GOPPVBvNACEh4dDIlEfOO7u7mjRooXqb6lUCmdnZ2RnZz90Xm/VqVMnSCQSlJWVwd/fH+vWrYO7uzuSk5MBAFFRUaq0xcXFSE9PR+fOnTW20blzZ5w+rbwTn5CQgJYtW8LCQv3gTceOmndS78WpU6cwduzY+1pn0qRJ+N///odt27ahd+/eGDJkCFq2rHtSlaqqKlRVaT7jZ25uDsD0vvNL9S/CT0D/Durbw6t3ye+Q+uHFX1EgKV0BW0sBncIleK67CZZtlkFmZI16enCPd3fDWxOCVH+/PffBJ1hLSSvH6DeOwcbKBD06u2Lmm8F4ffppVaOeiIgebZH+EgzorG7S/Lhdf2PgjyaqL3ayCkSUVNRgTF8zONnK7jrxMJG+6LxB361bN8TExGD69OkaM8xLJJJas91rm1zO1FSzESkIgtZlCoXygAoKCkJRURHS09Ph6empka66uhpJSUno2bPnXfO9bt06hIWFwdnZudZz7QBgba37h4TvJSY3h//fj1deeQUxMTHYuHEjtm3bhtjYWCxatAivv/661vSxsbEaIwAAYNasWbBp+cF9//aDsLBygCCR1pr4paIkD1a22p+vtbRxqXUXuqI0r9bd6kdB4nURabnqO8PSG/fDbCyg0eNuY6GcsVWb8ipArhBhc1txs7EUavX0V9UoP/klIlJz5Xh3qAlCmgk4m6z/t1nUF5bJh7P/SB7OXzym+tvMVFkoHR1MkVegfpTJ0cEMl6+U3nFbMpmItAxlIUxMKkVooC2efdoLC768hLyCapiZSmBjLdXopXdyMENeYXVdm3xkVGXlwtxds/yZu7ugpqgEisoqVOcWQCGTwdzN+bY0zqjKNN5ePvO6ju/SPFjVcbxa2rigUtvxfaM+uPlvRWkerOzcVGkqS3Ph1MR4ZxWvs668JTa301ZXVpbWXbc+CnjOeTgJKQpcz1HX+SZSZS+8jaWAklt63G0sBGTka+99UF8HCVAOtodqG6XldV/fXM9RfudkJyC/xHiug3RNNMbZkxsRvbxD4JNPPsE///yDQ4fUz+65uroiMzNTowGri/exDxkyBKampli0aFGt77755huUlZVh+PDhd92Ot7c3mjdvrrUxfzs7Ozt4enriwIEDGssPHDiAsLAwAEBoaCji4+NRWaluDR0+fFgjvaurK0pKSlBWVqZadntMWrZsiZ07634tkZmZGeTy2j2y3t7eeO2117BhwwZMnToV33//fZ3bmD59OoqKijQ+06dPrzO9rklNzODiGY60JHV8RIUC6UmH4dasldZ13JtFIj1JM56plw/Wmd6YVcuA/BL1J6cIKCkXNV6rYm4KeLkKqhPP7eQKICNPhH8TdYUrAPDzqHudWxPeadZYQ8Qy+XAqKuRIy6hUfa6mlCM3vwpRkY6qNFaWUoQF2eHshft756EgAKY3bhAkXi5BTY0CbW/ZrreXJTzcLFTP2T/KCg+fgvNjHTSWufTqhILDpwAAYk0Nik6cg8tjt4weEwQ49+yIwsOak7gaE6mJGZw9wzWO15vHt2sdx6ubluM7Pekg3LyV6W0dm8LSxgXpV9RpqitLkZMaD7dmkTrfh8biZl2Zcfne60ptsUx7ROvKm3jOeTi3XwdlF4rK6yBPzeugpq4CUrLrvg5KzxPR/JZ1BADNPSVIucN1UBMn5XVTCQeNUQPSS4M+IiICI0aMwBdffKFa1qNHD+Tk5ODTTz9FUlISvvzyS2zevPmhf6tZs2b49NNPsXjxYsycORMXLlxAUlISPvvsM7z99tuYOnUqoqOjH/p3bvfWW29h/vz5WLduHRITE/Huu+/i1KlTeOONNwAAzz//PARBwNixY3H+/Hls2rQJCxcu1NhGdHQ0rKysMGPGDCQlJWHNmjUaM+8Dyp7yX375BbNmzUJCQoJqkrubfH19sXfvXqSlpSE3V3mndvLkydi6dSuuXr2KEydOYPfu3QgNrbuHwNzcHHZ2dhof5ZD7+hPR9SUkHv0NF4//iYLsJOz/aw5qqisQ1FY5o//uX9/BkS2fqdK36DwS1y/uR/y+FSjMvoLjO/4PuWnnEN7xeVWayvJC5KUnoCDrMgCgMPcq8tITUF5ivM8z3nQ4QYFuERIENxXg5gAM6ixFSTlwIUV9UnrpcSnaB6urgIMJCrQJlCDSX4CLPdCvgwRmJsDJy8q72Y42QNcWEjRxAuytAW9XAc91l0ImBy6lGd9daZZJ3frt7zS8NLQZOrd3hr+PNd6bEoK8/CrsO6zuYVr8YUsMfko90mrcSD9EhtvDw80c/j7WGDfSD60jHLBtj/KRq7JyOf7dnonXxzRH6wgHBDe3wYw3gnEmoQjnEo1v7KPU2gp2kSGwiwwBAFj5NYVdZAgsvJVzyAR/OAWRK9Tnh2vfrYWVnzdCYt+CdbA/fF57Hk2e7YurS1aq0lxdvALeY56D14sDYRPijxZfzoaJtSWur9I+s7axaNH5JVw89hsunfgThdlJOPj3HMhuOb7/++0dHNuqPr7DOo5E6qX9OLN/BQpzruDETuXxHXbj+BYEAeGdR+L07m+QkrAL+ZkXsff3d2Fp64Zmob0bZB/rS4suLyHxllge+OtGLNuoY3n0lliGdxqJ1Iv7ceZGXXniRl0Z2kFdV1bdqCsLs5V1ZdEjUFfynKNbB87J0TNSihBvCdwdBTzTzQQlFcre/Jte7mOKDqHq66ADZ+WICpKgdYAErvYCnu5kAjMT4PhFZceZky3QM1IKT2cBDjZAiLcEz3QzxdUM5Uz6VDdRFBrNxxjpfMj9TXPnzsW6detUf4eGhuKrr77Cxx9/jHnz5mHIkCGYNm0avvvuu4f+rcmTJ8Pf3x8LFy7EkiVLIJfLER4ejq+//hqjR49+6O1rM2nSJBQVFWHq1KnIzs5GWFgY/v77bwQGBgIAbGxs8M8//+C1115D69atERYWhvnz52PIEPWrgJycnPDzzz/jrbfewvfff49evXph9uzZePXVV1VpevTogd9++w3z5s3DJ598Ajs7O3Tr1k31/dy5czFu3Dg0b94cVVVVEEURcrkcEyZMQGpqKuzs7NCnTx98/vnneomDrjRv+SQqSwtwfMcXKC/JhXOTUPQd/Z1qqFlZYQYE4Za5FXxa47FhC3Bs2xIc3fo57F188PgLS+HkoX5uNyVhN/77fYbq712/KN/D2qbXBLTtPbGe9qxh7D+ngKkJ0L+jFBZmQEq2iJ93aD7n7mgrwMpCfQI6lyzC2lyBx1pJYWMJZOaL+GmnHGU3BpnI5EAzNwEdQk1gYQaUVQLXskT8sFmmSmNMWCZ1a/X667CwkOLtiUGwsTbBmfNFmDrrDKpr1GXQy8MSDnbqR6wc7U3x3pshcHYyQ1mZDEnJZZgy6wyO3TJb/tIfLkMUm+Oj6WEwNZXgyIl8LPr6Ur3uW32xb9sCHXf+pPo7bKGyLF3/cQPix0yHeRNXWHqrJ4itSE7F0afHIWzRdPi+PhKVqZk4M+495G7fr0qT8dtmmLk6IWjWJJh7uKL4dAKO9HsF1dna331tLPxbPonKsgKc2PkFKkqUw+KfGPWdarhyWVHt47vHcwtwfMcSHN/2OeycfdBrxFI4uquP74iur0BWXYEDf85CdWUx3HzaIGbUdzAxrd8b5PXtZiyP71DG0rlJKGJGf6cacl+qpa7sOXQBjm9fgmM3Ytn7trryWsJu7Fuvrit3r1XWla0fm4A2RlpX8pyjW/vOyGFmAgzsrLxmuZYtYuXWGshuGdTqZCvAykLdwDtzVQFrCxl6tTGBrSWQkS9i5bYa1TWOXKHsse8ULoWpCVBUJuJcshx7Tut37iKiuxHE2x/iJrph4QbOcqYL0wZLMOtHvqT0Yc0ZacoyqSPTBkvQpf9/DZ0No7D/n+7YaBrc0NkweE/VJGL+7zy+deGdZyT4dD1jqQtvD5HwvKMD0wZLMHN51d0T0l199LJh3iC8nKT9bQ0NIaC5X0NnQef01kNPREREREREjzZRP0950w2MLhEREREREZEBYoOeiIiIiIiIyABxyD0RERERERHphQjjnF2+sWAPPREREREREZEBYg89ERERERER6QV76PWLPfREREREREREBogNeiIiIiIiIiIDxCH3REREREREpBcccq9f7KEnIiIiIiIiMkBs0BMREREREREZIA65JyIiIiIiIr3gkHv9Yg89ERERERERkQFig56IiIiIiIjIAHHIPREREREREemFKHLIvT6xh56IiIiIiIjIALGHnoiIiIiIiPSCk+LpF3voiYiIiIiIiAwQG/REREREREREBohD7omIiIiIiEgvOORev9hDT0RERERERGSA2KAnIiIiIiIiMkAcck9ERERERER6wSH3+sUeeiIiIiIiIiIDxB56IiIiIiIi0gtRZA+9PrGHnoiIiIiIiMgAsUFPREREREREZIA45J6IiIiIiIj0QsFJ8fSKPfREREREREREBogNeiIiIiIiIiIDxCH3REREREREpBd8D71+sYeeiIiIiIiIyACxQU9ERERERERkgARRFMWGzgQREREREREZnxMX8xo6CyptgpwbOgs6x2foqU4fr5M3dBaMwoyhUny0lrF8WDOHSfHZX7z/qAtTBgiY94usobNhFN4fboL5vysaOhsG751nJNhoGtzQ2TAKT9Uk8pyjIzOH8fytCzOHSXlNqSMzhkobOgvUCLFBT0RERERERHrBSfH0i8/QExERERERERkgNuiJiIiIiIiIDBCH3BMREREREZFeiCKH3OsTe+iJiIiIiIiIDBAb9EREREREREQGiEPuiYiIiIiISC84y71+sYeeiIiIiIiIyACxh56IiIiIiIj0gpPi6Rd76ImIiIiIiIgMEBv0RERERERERAaIQ+6JiIiIiIhILxQNnQEjxx56IiIiIiIiIgPEBj0RERERERGRAeKQeyIiIiIiItILznKvX+yhJyIiIiIiIjJAbNATERERERERGSAOuSciIiIiIiK9EMEh9/rEHnoiIiIiIiIiA8QeeiIiIiIiItILToqnX+yhJyIiIiIiIjJAbNATERERERERGSAOuSciIiIiIiK94KR4+sUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSC4XY0DkwbuyhJyIiIiIiIjJA7KEnIiIiIiIiveCkePrFHnoiIiIiIiIiA8QGPREREREREZEBYoPeyO3ZsweCIKCwsLChs0JERERERI8YURQazccYGdUz9KNGjUJhYSH+/PNPjeV79uxBz549UVBQAAcHh4f+neLiYsyfPx/r169HcnIyHBwc0KJFC4wfPx6DBg2CINy5sOzevRsLFixAXFwcKioq4Ovri759+2LKlCnw8vJ66PwZsm4tBLTyF2BuCqTmAluOK1BQeud12gYIiA4RYGMBZBUC204okJGv/r6Vv4BwHwEejoC5qYBFG+SoqtHrbjQK3VoIaN1cHcvNx+4tlh1Cb4nlcQXSb4ll6+aasVy43rhjefbgapz+bxkqSnLh3CQEnQe8B7dmLetMnxS/Bce2LkFJQRrsXXwQ3XcamoV2BwDI5TU4unUJrl/4D8V5qTCzsIFXYCdE950Ca3v3+tqlBtU9QoLWzQVYmALXc0VsPqpA/l3KZFSggI4hEthYAlkFwJbj8lplsoWPBE2clGXy099lRl0mzx9ejbP7lqOiNBeOHiHo2G8mXL3rLpNXz2zBiR1foLQwDXbOPoiKmQrv4O6q70VRxMmdS5F49DdUV5bAzac1Oj09C/YuvvWwNw3HqUsU/KeOgX2bFrDwdMOxIeOR9ffOO6/TrT3CFr4Lm7BAVF7PwOXYr5H64x8aaXz+9zz8p4yBuYcriuMv4NzkeSg6ekafu9Jo8JyjO4ylbvCakh4V7KG/T4WFhejUqRN+/PFHTJ8+HSdOnMDevXsxdOhQvP322ygqKtK6XnV1NQDg22+/Re/eveHh4YH169fj/Pnz+Oabb1BUVIRFixY9cL5ubt+QdQgREBUoYPMxBVbuUKBGDgzrLoH0DqU01FtAr1YC9p8TsXybAtmFIoZ1l8DKXJ3G1AS4kiHi4PlH550ZHUMEtAu6EcvtCtTIgOE97h7L3q0F7DsrYtnWG7HsoRlLEymQlCHiwCMQy8unNuHQP5+gbe8JGPLGBjg1CcbGZa+gojRPa/rM5BPYuWYqgts9gyFv/AHf8N7Y+uNE5GdeBADIqiuRm3YebXqNx5A31uOJkUtRlHMVW1aOr8/dajCdQgW0DxKw6agCy7fLUSMDnu8pvWOZDGsm4PHWEuw9q8D3W+TIKhTxfE9preM7KUOB/ecU+t+JBnYlfhOObJqPVo9NwNMT1sPJIxhbV46ts0xmXTuJPb9OQ1DUEAyYsAHNQnth5+rXUZB1UZXmzL4fcP7Qz+g0YDb6/28dTE2tsHXlWMhqquprtxqE1NoKxfGJODtpzj2lt/RtinZ/f4u8PXHYHzUAV5euQsS3H8Ll8S6qNE2e7YvQBdNx6cMvsb/9IJTEX0D0xmUwc3XS1240Gjzn6A5jqRu8pqRHySPXoM/Ly8Pw4cPh5eUFKysrRERE4JdfftFI8/vvvyMiIgKWlpZwdnZG7969UVZWBgCYMWMGkpOTERcXh5deeglhYWEICgrC2LFjcerUKdjY2AAAfH19MW/ePIwcORJ2dnZ49dVXkZqaikmTJmHSpElYvnw5evToAV9fX3Tr1g0//PADPvjgg3vOY48ePTBx4kRMnjwZLi4uiImJAQBs2rQJQUFBsLS0RM+ePZGcnKzniOpO+yABB86LuJQO5BQB/8QpYGsJBHvVPeKhfbCAU1dExF8VkVsMbD4mQiYDIv3U6xy9KOLQBRFpeY9O5ds+WHlCupgGZBcBf9+MZdO6YxkdIuBUkjqWm47eiKX/bbFMeDRieWbfSoRGP4uQdkPg6B6AboPnwMTUAheOrteefv9P8A7qglY9xsDRvTnaxbwBF68wnD2wGgBgbmmLfmOXo3lkXzi4+cPdpxU6D3wfuWnnUFKQXp+71iDaB0uw75wCF9NEZBcCfx1WlsmQO5TJDsESnEwScfpGmdx4VHlx2+qWMnkkUcTBBBFp2tu0RuXsgVUIjnoWQW0Hw9EtAJ0HzIaJqQUuHt+gNf35Qz+iaWAXRHQdAwe35mj7+Btw9gzF+UNrACh7588d+BGRPV6DT1gvOHkEo9uzn6CiJBspCTvqc9fqXc7Wvbg4azGy/rq3/fR5dRgqrqYi4e35KL1wBde+Wo3M9Vvh98YoVRq/yaNxfdmvSF21AaUJSTgzfhbk5ZXwHjVET3vRePCcozuMpW7wmrJxEcXG8zFGj1yDvrKyEm3btsXGjRtx9uxZvPrqq3jxxRdx5MgRAEBGRgaGDx+Ol19+GQkJCdizZw8GDx4MURShUCiwdu1ajBgxAp6enrW2bWNjAxMT9VMMCxcuRGRkJE6ePIn3338fv/32G6qrq/H2229rzdvNxwHulsebVq1aBTMzMxw4cADffPMNrl+/jsGDB6N///44deoUXnnlFbz77rs6ipx+OVgDNpYCrmapj7SqGiA9D/By0b6ORAI0cQSSszSPzqtZIrxcjPMZmXtxM5bJt8UyLQ/wcta+zs1YXtUSy6bOj14s5bJq5KSdg1dAJ9UyQSJB08COyLp2Sus62Smn4BXYSWNZ06DOyErRnh4AqitLAEGAuaWdLrLdaDlYA7aWAq5maimTdRyrEgnQxAka6wA3yuQjeHzLZdXISz8Hz4COqmWCRALPgI7IqaOMZaechmfzjhrLvAK6IPu6Mn1JQSoqSnM10phZ2MK1aUtkp5zW+T4YMocOrZC765DGspzt++HYoRUAQDA1hX2bcOTuPKhOIIrI3XUQDh1a12NO6x/PObrDWOoGrylJ17788kv4+vrCwsIC0dHRtdpkt/r+++/RtWtXODo6wtHREb17975jel0wqmfoAeDff/9V9ZLfJJfLVf/t5eWFadOmqf5+/fXXsXXrVvz6669o3749MjIyIJPJMHjwYPj4+AAAIiIiAADZ2dkoKChASEjIPeXlsccew9SpU1V/X7p0CXZ2dmjSpMkd17tbHm8KDAzEp59+qvp7xowZaN68uWrofnBwMM6cOYP58+ffU34bkrWF8t+ySs3lZZWi6rvbWZkBEomgZR3A2bjbR3d0p1jaWGpfh7HUVFlWAFEhh6Wt5hWUpY0LCrOval2nvCQXVjaa6a1sXFBRkqs1vaymCnGbFiIg8imYWdhoTWMsbpY7rWWyruPbXFkmSyvF29YBXGwfvYurqvJCZZm0ub1MOqMwR3uZrCjNhYWNS630N8vkzX9v36aFjQsqSnN0lXWjYO7ugqoszWO5KisXpva2kFiYw9TRHhITE1Rl592WJg/Wwf71mdV6x3OO7jCWusFrysZHYcDvoV+3bh2mTJmCb775BtHR0Vi8eDFiYmKQmJgINze3Wun37NmD4cOHo1OnTrCwsMD8+fPxxBNP4Ny5c3qbK83oGvQ9e/bE119/rbEsLi4OL7zwAgBl4/7jjz/Gr7/+irS0NFRXV6OqqgpWVlYAgMjISPTq1QsRERGIiYnBE088gWeeeQaOjo4Q73OcRlRUlMbfoijedcK8e8njTW3bttX4OyEhAdHR0RrLOnbU7J3RpqqqClVVms9LmpubQ5/FI9xHQN+26lj8us/4n3/Vl3AfAU9GqWO5bi9j2djJ5TXY8fNkQAS6Dp7dwLnRvRY+Ap5qpx4A9st/8jukJiJDwnOO7jCWusFrStKnzz77DGPHjsXo0aMBAN988w02btyI5cuXax0JvXr1ao2/f/jhB6xfvx47d+7EyJEj9ZJHo2vQW1tbIyAgQGNZamqq6r8XLFiAJUuWYPHixYiIiIC1tTUmT56smlROKpVi+/btOHjwILZt24alS5di5syZiIuLg4+PDxwcHHDhwoV7zsutgoKCUFRUhIyMjDv20t8tj3Vt/0HFxsZizhzNiYFmzZoFs9D3dbJ9bS6liUi/5fmjm5OUWFto3lG1thCQVaj9Rkp5NaBQ1L7bevs2jN2lNBE/1BHL0ttjWfAAsazQdY4bPwtrRwgSKSpKNHvbKkpzYWmrfbyela0Lym+bnKxcS3plY/5NlBSmo/+rK42yd/5imoi0PHUj3uQOZTKzrjJZpSyTNhYCAPGWdVCr1/5RYG7loCyTpbeXyTxY2Wgvk5Y2Lqgsza2V/maZvPlvRWkerOzUvQyVpblwahKqy+wbvKqsXJi7a8bZ3N0FNUUlUFRWoTq3AAqZDOZuzrelcUZVpvZROoaK5xzdYSx1g9eUdD/q6shUdmZqqq6uxvHjxzF9+nTVMolEgt69e+PQoUO10mtTXl6OmpoaODnpb4LUR+4Z+gMHDmDAgAF44YUXEBkZCX9/f1y8eFEjjSAI6Ny5M+bMmYOTJ0/CzMwMf/zxByQSCYYNG4bVq1cjPb32JFalpaWQyWR1/vYzzzwDMzMzjWHyt7r5rvh7yaM2oaGhtZ7ROHz48F3Xmz59OoqKijQ+txZcfaiWAQWl6k9uMVBaIcLXXX2H1cwE8HQG0uq4FlIogIwCaKwDKP9Oy310LvjvNZZezqhz4rA7xTL1EZz4RWpiBlevcKRdVlfWokKBtMuH4e7TSus6bs1aaaQHgLRLB+HeTJ3+ZmO+KPca+o1dAQtrR31kv8HdXiZzioGSChF+HlrKZB3HqkIBZOQDvh6aZdLPXUDqI3R83yQ1MYOzZzjSk9R1uqhQID3pMFxvKWO3cmsWqZEeANKTDsLNW5ne1rEpLG1ckH5Fnaa6shQ5qfFwaxap830wZIWHT8H5sQ4ay1x6dULB4VMAALGmBkUnzsHlsVtGxQkCnHt2ROHhk/WYU/3jOUd3GEvd4DVl49fQ756/9RMbGwt7e3uNT2xsrNZ85+bmQi6Xw91d8/XC7u7uyMzMvKd9f+edd+Dp6YnevXs/dBzr8sg16AMDA1U98AkJCRg3bhyysrJU38fFxeHjjz/GsWPHkJKSgg0bNiAnJwehocreio8++gje3t6Ijo7Gjz/+iPPnz+PSpUtYvnw5WrdujdLSul9w6e3tjc8//xxLlizBmDFj8N9//+HatWs4cOAAxo0bh3nz5t1THuvy2muv4dKlS3jrrbeQmJiINWvWYOXKlXddz9zcHHZ2dhofbXep9O3IRRGdwwQEegKu9kD/aAlKKoDENHVF+nwPCdoGaM5w3cpfQISvAGdboG+UAFMTIP6qZo+emwPgeOO5Wzd75d8WZvW1Z/XvSKKIzuHqWD7d4UYsU2+JZU8JogLVsYy7IKJ18xuxtLslllc0Y+nuADjZ3Iilg/JvY4xlRNdRuHDkNyQe+wMFWUnY98ds1FRXIDhqMABg19p3ELdZ/arJiC4vIjVxP07/txwF2VdwbNtS5KSeQ4vOIwAoG/Pbf3oDOaln0Wv4AoiiHOUlOSgvyYFcZvivnbybI4kKdAmXIMhLgJs9MLCjskxeuKVMvnBbmTycqECb5gJa+glwsQOebCeBqQlw+mrtMul4Y6CDMZfJFp1fwsVjv+HSiT9RmJ2Eg3/Pgay6AkFtBwEA/vvtHRzb+pkqfVjHkUi9tB9n9q9AYc4VnNj5f8hNO4ewjs8DUN68Du88Eqd3f4OUhF3Iz7yIvb+/C0tbNzQL1d+FR2MgtbaCXWQI7CKVc+JY+TWFXWQILLyVo+eCP5yCyBXq+WeufbcWVn7eCIl9C9bB/vB57Xk0ebYvri5ZqUpzdfEKeI95Dl4vDoRNiD9afDkbJtaWuL5K+1sIjAnPObrDWOoGrympLvXZkfnJJ59g7dq1+OOPP2BhUccEDjpgdEPu7+a9997DlStXEBMTAysrK7z66qsYOHCg6v3xdnZ22Lt3LxYvXozi4mL4+Phg0aJF6Nu3LwDAyckJhw8fxieffIIPP/wQ165dg6OjIyIiIrBgwQLY29vf8ffHjx+PoKAgLFy4EIMGDUJFRQV8fX3Rr18/TJky5Z7yWJdmzZph/fr1ePPNN7F06VK0b98eH3/8MV5++WUdRE7/Dl8QYWYC9I2SwMIMuJ4DrPtPAfktj0I52EDjfaAJ10VYmQPdWgg3hlIp1ym7ZSRNm+YCurZQ37t6sZcUgPIVJmeSjfOu66ELIkxNlA2gm7Fce1ssHW0Ay9tiaW0BdI9Qx3LtnttiGSCg2y2xHHlLLG894RmDgFZPorIsH8e2LUV5SQ5cPEPx5JjvYXVjmHJpYbrGnBgevm3w2PMLcXTLYhzZ8jnsXXwRM/L/4OQRBAAoL8rCtfO7AAC/Lx6o8Vv9x62CZ3PN+S+MzcEEEaYmIp66USZTckSs2SO/rUwKN45vZVk6nyLCylyB7hES2FgAWQXAmj1yjeGPbQMk6B6hLpOjeitPa38dlhtdmfRv+SQqywpwYucXqChRDot/YtR3sLwx5L6sKAOCoI6Fu09r9HhuAY7vWILj2z6HnbMPeo1YCkf3IFWaiK6vQFZdgQN/zkJ1ZTHcfNogZtR3MDGt/5u69cm+bQt03PmT6u+whTMAANd/3ID4MdNh3sQVlt7qR+MqklNx9OlxCFs0Hb6vj0RlaibOjHsPudv3q9Jk/LYZZq5OCJo1CeYerig+nYAj/V5Bdbbxv1OR5xzdYSx1g9eUVJe6htdr4+LiAqlUWqtjNSsrCx4eHndcd+HChfjkk0+wY8cOtGzZ8oHzey8E8X5neqNHxsfrOJGVLswYKsVHaxnLhzVzmBSf/cXqShemDBAw75e6Hw+ie/f+cBPM/50TMD2sd56RYKNpcENnwyg8VZPIc46OzBzG87cuzBwm5TWljswYKm3oLDyQ7aer7p6onjweeX83rKOjo9G+fXssXboUAKBQKNCsWTNMnDixzteDf/rpp/joo4+wdetWdOjQQWsaXXrkeuiJiIiIiIiI7mbKlCl46aWXEBUVhfbt22Px4sUoKytTzXo/cuRIeHl5qZ7Dnz9/Pj744AOsWbMGvr6+qmftbWxsar1aXVfYoCciIiIiIiK6zdChQ5GTk4MPPvgAmZmZaNWqFbZs2aKaKC8lJQUSifoxjK+//hrV1dV45plnNLYza9YszJ49Wy95ZIOeiIiIiIiI9EKEcPdEjdjEiRMxceJErd/t2bNH4+/k5GT9Z+g2j9ws90RERERERETGgD30REREREREpBcKzmmsV+yhJyIiIiIiIjJAbNATERERERERGSAOuSciIiIiIiK9EEXDnhSvsWMPPREREREREZEBYoOeiIiIiIiIyABxyD0RERERERHphchZ7vWKPfREREREREREBog99ERERERERKQXCnBSPH1iDz0RERERERGRAWKDnoiIiIiIiMgAccg9ERERERER6QUnxdMv9tATERERERERGSA26ImIiIiIiIgMEIfcExERERERkV6IIme51yf20BMREREREREZIDboiYiIiIiIiAwQh9wTERERERGRXig4y71esYeeiIiIiIiIyACxh56IiIiIiIj0gu+h1y/20BMREREREREZIDboiYiIiIiIiAwQh9wTERERERGRXojge+j1iT30RERERERERAaIDXoiIiIiIiIiA8Qh90RERERERKQXfA+9frGHnoiIiIiIiMgAsYeeiIiIiIiI9ILvodcv9tATERERERERGSBBFHnPhIiIiIiIiHTvt8OKhs6CyrMdjK8/m0PuqU6f/cV7PbowZYCAFbsbOheGb3RPYM7PNQ2dDaMw6wVTfLRW3tDZMAozh0nx6frGc6FiqN4eImGZ1JGZw6TYaBrc0NkwCk/VJLJc6sDMYVK89EFmQ2fDKKya69HQWXgg7D7WL+O7RUFERERERET0CGCDnoiIiIiIiMgAccg9ERERERER6YVCFBo6C0aNPfREREREREREBog99ERERERERKQXnBRPv9hDT0RERERERGSA2KAnIiIiIiIiMkAcck9ERERERER6wSH3+sUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSCwWH3OsVe+iJiIiIiIiIDBAb9EREREREREQGiEPuiYiIiIiISC9EUWjoLBg19tATERERERERGSD20BMREREREZFe8D30+sUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSC76HXr/YQ09ERERERERkgNigJyIiIiIiIjJAHHJPREREREREesFZ7vWLPfREREREREREBog99ERERERERKQX7KHXL/bQExERERERERkgNuiJiIiIiIiIDBCH3BMREREREZFe8D30+sUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSC85yr18G20O/Z88eCIKAwsLChs6Kzo0aNQoDBw5s6GwQERERERFRI/bAPfSjRo1CYWEh/vzzz3tKn5qaCn9/fwQFBeHs2bP39Vs9evRAq1atsHjxYtWyTp06ISMjA/b29ve1rTuZPXs25syZg5iYGGzZskXjuwULFuDtt99G9+7dsWfPHp39JqmdPbgap/9bhoqSXDg3CUHnAe/BrVnLOtMnxW/Bsa1LUFKQBnsXH0T3nYZmod1V3x/bthRJpzehtDATEhNTuHqFo12fyXBvFlkfu9Ngju9Zjbhty1BWnAO3piF4fOj78PSrO44Xjm/G3r+XoCgvDU5uvugxaBqaR6jjWFaci90bFiI5YT8qy0vgHRiFx4e+Dyd333rYm4bXo6UEbQIlsDAFrueI2HhEjvySO6/TLkiCTmES2FgCmQUiNh9VID1PfXu6X7QEfh4S2FoC1TLldneclCOvWM8708C6tRDQurkAc1MgNRfYfEyBgtI7r9M2QECHUAE2FkBWIbDtuALp+ervWzcXEO4jwMMRMDcVsHC9HFU1et2NBnX+0Gqc2bccFaW5cPIIQcf+M+HqXffxffXMFhzf/gVKC9Ng5+yDdn2mwjtYfXwnn92GhCPrkJd2DlUVRRg4cQOcPUPrY1caBZbJh+PUJQr+U8fAvk0LWHi64diQ8cj6e+ed1+nWHmEL34VNWCAqr2fgcuzXSP3xD400Pv97Hv5TxsDcwxXF8RdwbvI8FB09o89daVRYLnVj0GM26NHWElYWElxKqcaqf4qRlS+vM32wjyn6drGGbxNTONpJsWRNAU5cqKqVromLFEOfsEWwrxmkEiAtR46lawuQX6TQ5+4Q1aneeuhXrlyJ5557DsXFxYiLi3vo7ZmZmcHDwwOCIOggd2pNmjTB7t27kZqaqrF8+fLlaNasmU5/qz6JogiZTNbQ2ajT5VObcOifT9C29wQMeWMDnJoEY+OyV1BRmqc1fWbyCexcMxXB7Z7BkDf+gG94b2z9cSLyMy+q0ti7+qLzwPfx7JS/MeB/q2Hr6IVNP4xBRWm+1m0ag4Rjm7Dr91h06TcBo2f8AbemIVi3dAzKirXHMTXpBP5aNhWRnZ/B6Jl/IrBVL6z/ZgJy0pRxFEUR67+egMLc6xjyv68weuYfsHf2wtolo1FdVV6fu9YgOodJEB0iwcY4OX7YIkO1DHjhMRNI71BzhvsIeKKtBP/Fy/HtJhmyCoAXHpPCylydJj1PxF+H5PjyHxl+3iWDIAAv9jKBjquzRqVjiIB2QQI2H1Ng5XYFamTA8B6SO8Yy1FtA79YC9p0VsWyrAtmFIob1kGjE0kQKJGWIOHDe+MfzXYnfhLhN89G61wQMmLAeTk2CsWXF2DrryaxrJ7F73TQERQ3BwIkb4BPWCzt+fl2jnqypqYCHTxu06zO1vnaj0WCZfHhSaysUxyfi7KQ595Te0rcp2v39LfL2xGF/1ABcXboKEd9+CJfHu6jSNHm2L0IXTMelD7/E/vaDUBJ/AdEbl8HM1Ulfu9GosFzqxpNdrPF4tBVW/lOMud/loapaxLSRjjC9Q1emuZmA65ky/LSx7rvrbo5SvPeKM9JzZYhdno/3vszD33tKUdN4L7EbBYWi8XyMkU4a9L///jsiIiJgaWkJZ2dn9O7dG2VlZarvRVHEihUr8OKLL+L555/HsmXLam3jwIED6NGjB6ysrODo6IiYmBgUFBRg1KhR+O+//7BkyRIIggBBEJCcnKwx5L64uBiWlpbYvHmzxjb/+OMP2Nraorxc2fC4fv06nnvuOTg4OMDJyQkDBgxAcnKyxjpubm544oknsGrVKtWygwcPIjc3F0899VStfP/www8IDQ2FhYUFQkJC8NVXX6m+S05OhiAI+PXXX9G1a1dYWlqiXbt2uHjxIo4ePYqoqCjY2Nigb9++yMnJqbXtOXPmwNXVFXZ2dnjttddQXV2t+k6hUCA2NhZ+fn6wtLREZGQkfv/9d9X3N+OzefNmtG3bFubm5ti/f39d/wsb3Jl9KxEa/SxC2g2Bo3sAug2eAxNTC1w4ul57+v0/wTuoC1r1GANH9+ZoF/MGXLzCcPbAalWawNb90TSwE+ycveHkEYiO/d9FdWUp8jIS62u36t2RHSsQ2fk5tOw0BC6eAejz/ByYmlog/qD2OB7b9SP8w7si+olX4NKkObo9PRkezcJwfM/PAICC7GSkXz2FmOdno4lvSzh7+CNm+GzIaiqRcHRjfe5ag4gOlWDvGQUSU0VkFwJ/HpTD1goI8a675d0hVIITlxU4dUVEbhHwb5wcNXKgdYC6uj1xWURKtoiiMiAzH9h1Sg57awEO1vWwUw2kfbCA/edEXEwDsouAv+MUsLUEgpvWHcvoEAGnkkTEXxWRWwxsOipCJgMi/dXrHL0o4lCCiLQ8479IPbt/FYLbPYugtoPh6B6AzgNmw8TMAhePb9Ca/tzBH9E0sAtadhsDB7fmaPv4G3D2DEXC4TWqNIGtB6B1rwnwDOhUX7vRaLBMPrycrXtxcdZiZP21457S+7w6DBVXU5Hw9nyUXriCa1+tRub6rfB7Y5Qqjd/k0bi+7FekrtqA0oQknBk/C/LySniPGqKnvWhcWC51I6ajFf7ZW4qTF6pwPUuG7zYUwcFWijYhFnWuE3+pGut3luJ4Qu1e+ZuG9LbB6YtV+HVbKVIyZcgukONkYhVKyoy0pUgG4aEb9BkZGRg+fDhefvllJCQkYM+ePRg8eDDEW2Y/2L17N8rLy9G7d2+88MILWLt2rUaD/9SpU+jVqxfCwsJw6NAh7N+/H/3794dcLseSJUvQsWNHjB07FhkZGcjIyIC3t7dGHuzs7NCvXz+sWbNGY/nq1asxcOBAWFlZoaamBjExMbC1tcW+fftw4MAB2NjYoE+fPhoNZQB4+eWXsXLlStXfy5cvx4gRI2BmZlZr+x988AE++ugjJCQk4OOPP8b777+vcTMAAGbNmoX33nsPJ06cgImJCZ5//nm8/fbbWLJkCfbt24fLly/jgw8+0Fhn586dqnj+8ssv2LBhA+bMUd8Bj42NxY8//ohvvvkG586dw5tvvokXXngB//33n8Z23n33XXzyySdISEhAy5Z1D8tsSHJZNXLSzsHrlgtKQSJB08COyLp2Sus62Smn4BWoeQHaNKgzslK0p5fLqpEQtw5mFrZw9gzRVdYbFbmsGpkp5+AbqhlH39BOSLtyUus66VdOwTeko8Yyv7AuSLtyCgAgkymPDRNT9W1+QSKB1MQM1y8f1/EeNC4ONoCtpYArmeqTdFUNkJorwttV+4WVRAJ4Ogm4kqF5wXQlQ0RTF+3rmEqB1s0lKCgRUWSkgx4crAEbSwHJWeq4VNUAaXmAl7P2dSQSoIkjcDVLM5ZXs0Q0dTbioQx1kMuqkZt+Dp4B6uNVkEjg2bwjsuuo97JTTmukB4CmgV3qTP8oYZlsGA4dWiF31yGNZTnb98OxQysAgGBqCvs24cjdeVCdQBSRu+sgHDq0rsecNgyWS91wdZTCwVaKc0nq6/uKKhFX0moQ4G36wNsVBCAyyByZeTJMG+mIpW+74oNXndAmxPzuKz/iRLHxfIzRQ89yn5GRAZlMhsGDB8PHxwcAEBERoZFm2bJlGDZsGKRSKVq0aAF/f3/89ttvGDVqFADg008/RVRUlEbvdnh4uOq/zczMYGVlBQ8PjzrzMWLECLz44osoLy+HlZUViouLsXHjRvzxh/K5rHXr1kGhUOCHH35QDdNfsWIFHBwcsGfPHjzxxBOqbfXr1w+vvfYa9u7di7Zt2+LXX3/F/v37sXz5co3fnDVrFhYtWoTBgwcDAPz8/HD+/Hl8++23eOmll1Tppk2bhpiYGADAG2+8geHDh2Pnzp3o3LkzAGDMmDEaNxBu7vPy5cthZWWF8PBwzJ07F2+99RbmzZuHmpoafPzxx9ixYwc6dlRerPn7+2P//v349ttv0b27+vnIuXPn4vHHH68zbo1BZVkBRIUclraaZytLGxcUZl/Vuk55SS6sbDTTW9m4oKIkV2PZtfO7sWPNVMhqKmBl64qnxi6HpbWjbnegkSgvVcbR2k4zLta2zsjLvKJ1ndLiXFjbudRKX1asjKOzhz/snDzx3x+L0GfEXJiaW+LozpUoKchEWXHtUSXGxMZCWU+UVWouL6sErC20XyRZmQMSiaBlHREu9prrRAVJ8HhrCcxMBeQWifhpp8xoh4JZ3+gQ0RYXG0vt61iZ1RVLwNlO93ls7CrLC5X1pM3t9aQzinK015MVpbmwtNE8vi1snFF+Wz35KGKZbBjm7i6oytIsf1VZuTC1t4XEwhymjvaQmJigKjvvtjR5sA72r8+sNgiWS92wt1H2VxaVap5Ui0vlqu8ehJ21BJbmEvTrao31O0vx67YSRASa4/VhDvhkZT4Sk418UgJqtB66QR8ZGYlevXohIiICMTExeOKJJ/DMM8/A0VHZaCosLMSGDRs0hnu/8MILWLZsmapBf+rUKTz77LMPlY8nn3wSpqam+PvvvzFs2DCsX78ednZ26N27NwDg9OnTuHz5MmxtbTXWq6ysRFJSksYyU1NTvPDCC1ixYgWuXLmCoKCgWr3bZWVlSEpKwpgxYzB27FjVcplMVmuivlvXdXd3B6B508Pd3R3Z2dka60RGRsLKykr1d8eOHVFaWorr16+jtLQU5eXltRrq1dXVaN1a8w52VFSUlmhpqqqqQlWV5vAic3NzAGbaVzAgngHReGbyH6gsK0DCkd+w4+fJGPT6r7Uuikk7qdQUg8ctxaafZmLx1PYQJFL4hnSEf3g3AMZ1mzPCV0C/aKnq7zW76544RxfOXFXgSoYCNpYCOoVJ8ExXEyzfKoPcCBr14T4CnoxS38BYt9cIdooMGsskNUYsl7rRsaUFRvVX3734bHWBXn7n5jw3Jy5UYesh5ZC6lEwZAr1N8ViUFRKTi/Tyu0R389ANeqlUiu3bt+PgwYPYtm0bli5dipkzZyIuLg5+fn5Ys2YNKisrER0drVpHFEUoFApcvHgRQUFBsLSs47bjfTAzM8MzzzyDNWvWYNiwYVizZg2GDh0KExPlLpaWlqJt27ZYvXp1rXVdXV1rLXv55ZcRHR2Ns2fP4uWXX671fWmpcrrR77//XmPfAGVMbmVqqh7ec3N0wO3LFPfRNXfztzdu3AgvLy+N75QNcTVr67s/lBsbG6sxnB9Qjj6waz3rnvP0MCysHSFIpKgo0bwjX1GaC0tbF63rWNm6oPy2iaDKtaQ3NbOCvYsP7F184O7TCr/Mj8GFI7+j9WPjdLsTjYCVjTKOt0+AV1aSV6sX/iYbOxdVb3xd6T18WuDl9/5CZUUJFLIaWNk6YdUnz6KJTwvd70QDSkwVkZqrntXG5MZhbG0BlFao01lbAFkF2m9mlFcBCoWo6mVRryNobANQDqOsqgHyS0Sk5srxznMmCG0m4Gyy4d8ouZQm4odbntO8OZmTtQVQeksvkrWFUHcsq+uKJVBWoXUVo2Zh5aCsJ0tvryfz6qwnLW1cUFGqeXxXlubBqo70xoxlsnGoysqFubtm+TN3d0FNUQkUlVWozi2AQiaDuZvzbWmcUZVpfCNLWC514+SFKiSlqutGU6nyWtveRqLRS29nI0VKxoP3opeUKyCTi0jP0ZwBLz1HhiAfw+8E0ydjHereWOhkUjxBENC5c2fMmTMHJ0+ehJmZmWqo+7JlyzB16lScOnVK9Tl9+jS6du2qGsLesmVL7NxZ92tOzMzMIJffvbdsxIgR2LJlC86dO4ddu3ZhxIgRqu/atGmDS5cuwc3NDQEBARofba++Cw8PR3h4OM6ePYvnn3++1vfu7u7w9PTElStXam3Pz8/vrnm9m9OnT6OiQl0THz58GDY2NvD29kZYWBjMzc2RkpJS67dvn1/gXkyfPh1FRUUan+nTpz/0PtwrqYkZXL3CkXZZ/VydqFAg7fJhuPu00rqOW7NWGukBIO3SQbg3057+lg1DLqu+cxoDJTUxg0ezcCRf0IzjtQuH4OWv/dlDT/9WSL5wWGNZcsJBePm3qpXWwtIWVrZOyM9KRua1swiM7KXT/De0ahlQUKr+5BQBJRUi/D3U1aSZKdDURcD1HO1nJoUCSM8X4e+hObze30NAam7dZzPhxudOsxgbkttjmVsMlFaI8HVXx8XMRPlMaJr2CdqhUAAZBdBYB1D+nfqITOp0K6mJGVw8w5FxWX28igoF0pMOw62Oes+tWSTSkzSP77TLB+tMb8xYJhuHwsOn4PxYB41lLr06oeDwKQCAWFODohPn4PLYLXM/CAKce3ZE4WHtc8EYMpZL3aisFpGdL1d90nJkKCyRI8xf3ci2MBfg72WKy9cfvEEvlwNX02rg4azZH+rhbILcQv2O6iO6k4e+fIyLi8PHH3+MY8eOISUlBRs2bEBOTg5CQ0Nx6tQpnDhxAq+88gpatGih8Rk+fDhWrVoFmUyG6dOn4+jRoxg/fjzi4+Nx4cIFfP3118jNVd6N9fX1RVxcHJKTk5Gbm1tnb3a3bt3g4eGBESNGwM/PT6PnfMSIEXBxccGAAQOwb98+XL16FXv27MGkSZNqvaLupl27diEjIwMODg5av58zZw5iY2PxxRdf4OLFizhz5gxWrFiBzz777OGCCuXw+TFjxuD8+fPYtGkTZs2ahYkTJ0IikcDW1hbTpk3Dm2++iVWrViEpKQknTpzA0qVLa03Idy/Mzc1hZ2en8bm9p1/fIrqOwoUjvyHx2B8oyErCvj9mo6a6AsFRyvkJdq19B3GbF6nTd3kRqYn7cfq/5SjIvoJj25YiJ/UcWnRW3sSpqS5H3ObPkHXtFEoK0pCTehZ7fp2BsuIs+LfsU6/7Vp/a9x6N0/t/xZlDfyA3Iwlbf5mN6uoKtOykjOM/K97Gnj/UcYx6bCSuntuHuO3LkZeZhH3/LEXGtbNo2+MFVZoLxzfjWmIcCnOu4+KpHVi75GUEtuoNv7AutX7f2MQlKNC1hQRBTQW4OQCDOklRUg5cuK6+SHqxlxTtgtRV6eEEBdoEShDpL8DFTvnOeVMT4FSSst5ysAG6hEvQxAmws1LeIHi2mxQ1cmVvjbE6kiiic7iAQE/A1R54uoMEJRXKkRE3Pd9TgqhA9UVp3AURrZsLiPAV4GwH9I0SYGoCxF9Rr2NtAbg7AE42yvXcHJR/WxhhZ0mLLi8h8dhvuHTiTxRmJ+HAX3Mgq65AUJtBAID/fnsHR7eqzz/hnUYi9eJ+nNm3AoXZV3Bix/8hN+0cQjuob1JXlRciLz0BhdmXAQBFuVeRl56A8hLjniMDYJnUBam1FewiQ2AXqZxs1sqvKewiQ2Dh3QQAEPzhFESumK9Kf+27tbDy80ZI7FuwDvaHz2vPo8mzfXF1yUpVmquLV8B7zHPwenEgbEL80eLL2TCxtsT1Vdrf5mBsWC51Y+uhcjzd3Qatg83R1M0Erw62R2GJHCcuqIc+vD3KEb3bqx9vNTcT0MzDBM08lA12V0cpmnmYwMlefY7ffKAM0S0s0L2tJdycpOjd3gqtgs2x84iRzmpLBuGhh9zb2dlh7969WLx4MYqLi+Hj44NFixahb9++eP311xEWFoaQkNqzig8aNAgTJ07Epk2b8PTTT2Pbtm2YMWMG2rdvD0tLS0RHR2P48OEAlJPKvfTSSwgLC0NFRQWuXtU+AZAgCBg+fDg+/fTTWrPGW1lZYe/evXjnnXcwePBglJSUwMvLC7169YKdnfZZQ+42XP2VV16BlZUVFixYgLfeegvW1taIiIjA5MmT7yFyd9arVy8EBgaiW7duqKqqwvDhwzF79mzV9/PmzYOrqytiY2Nx5coVODg4oE2bNpgxY8ZD/3ZDCGj1JCrL8nFs21KUl+TAxTMUT475XjU0tLQwXfW4AgB4+LbBY88vxNEti3Fky+ewd/FFzMj/g5NHEABAEKQozLmKbT9NQmVZASysHODqHYGn/7caTh6BDbKP9SE06kmUl+Rj3z9foKw4B25NQzH09R9UQ+iL8zMgCOoTU9PmbfD0mIXY+/di7P3rMzi6+WLIa1/C1StIlaa0KAc7f/8EZcV5sLF3RYsOA9D5yfH1vm8N4cB5BUxNgP7RUliYASnZIn7epfmcu5OtACsL9UXTuWsirMwV6NFSChtLILNAxOpdctWERTI50MxNQHSICSzNlMMqr2WLWL5VhvK635Rj8A5dEGFqAjzZTgILM+B6DrD2P4VGLB1tAMtb7iUmXFcOI+0eISiHnBYCa/coUHZLnNoECOjWQl2mR/ZSPivxT5wC8VeN6waJf8snUVlWgOM7vkBFSS6cm4QiZvR3qiH3pYWax7e7T2v0HLoAx7cvwbFtn8PO2Qe9X1iqqicB4FrCbuxbrz5v7F6rfB9968cmoE3vifW0Zw2DZfLh2bdtgY47f1L9HbZQWZau/7gB8WOmw7yJKyxvNO4BoCI5FUefHoewRdPh+/pIVKZm4sy495C7XT3PUsZvm2Hm6oSgWZNg7uGK4tMJONLvFVRn19FFbWRYLnVj0/4ymJsJGPW0HawsJLiUUo2FPxVovC/ezdEENtbqHns/T1NMf9lJ9ffzfZXtg30nK/DDH8rn448nVGHlP8Xo180aLzxph4xcGZauK8SlFE6IdycK4ytijYoginyqgbT77C8WDV2YMkDAit0NnQvDN7onMOdnnjB1YdYLpvhoLYcH6sLMYVJ8up4TWT2st4dIWCZ1ZOYwKTaaBjd0NozCUzWJLJc6MHOYFC99kNnQ2TAKq+bW/cavxuzLzQ2dA7UJfRs6B7r30D30RERERERERNo0rv5j7a8eNmRGMgUTERERERER0aOFDXoiIiIiIiIiA8Qh90RERERERKQXjWrEvRFiDz0RERERERGRAWKDnoiIiIiIiMgAccg9ERERERER6YWCb3fVK/bQExERERERERkg9tATERERERGRXnBSPP1iDz0RERERERGRAWKDnoiIiIiIiMgAccg9ERERERER6YWCQ+71ij30RERERERERAaIDXoiIiIiIiIiA8Qh90RERERERKQXnOVev9hDT0RERERERGSA2KAnIiIiIiIiMkAcck9ERERERER6ITaqae6Fhs6AzrGHnoiIiIiIiMgAsYeeiIiIiIiI9KJRddAbIfbQExERERERERkgNuiJiIiIiIiItPjyyy/h6+sLCwsLREdH48iRI3dM/9tvvyEkJAQWFhaIiIjApk2b9Jo/NuiJiIiIiIhIL0Sx8Xzu17p16zBlyhTMmjULJ06cQGRkJGJiYpCdna01/cGDBzF8+HCMGTMGJ0+exMCBAzFw4ECcPXv2IaNYNzboiYiIiIiIiG7z2WefYezYsRg9ejTCwsLwzTffwMrKCsuXL9eafsmSJejTpw/eeusthIaGYt68eWjTpg3+7//+T295ZIOeiIiIiIiI6BbV1dU4fvw4evfurVomkUjQu3dvHDp0SOs6hw4d0kgPADExMXWm1wXOck9ERERERER6oWhE09xXVVWhqqpKY5m5uTnMzc1rpc3NzYVcLoe7u7vGcnd3d1y4cEHr9jMzM7Wmz8zMfMic14099ERERERERGT0YmNjYW9vr/GJjY1t6Gw9FPbQExERERERkV48yGR0+jJ9+nRMmTJFY5m23nkAcHFxgVQqRVZWlsbyrKwseHh4aF3Hw8PjvtLrAnvoiYiIiIiIyOiZm5vDzs5O41NXg97MzAxt27bFzp07VcsUCgV27tyJjh07al2nY8eOGukBYPv27XWm1wX20BMRERERERHdZsqUKXjppZcQFRWF9u3bY/HixSgrK8Po0aMBACNHjoSXl5dq2P4bb7yB7t27Y9GiRXjqqaewdu1aHDt2DN99953e8sgGPREREREREelFYxpyf7+GDh2KnJwcfPDBB8jMzESrVq2wZcsW1cR3KSkpkEjUg947deqENWvW4L333sOMGTMQGBiIP//8Ey1atNBbHtmgJyIiIiIiItJi4sSJmDhxotbv9uzZU2vZs88+i2effVbPuVLjM/REREREREREBkgQRUMeBEFERERERESN1bxfZA2dBZX3hxvfAHXj2yPSmdhf5Q2dBaMw/TkpPl7HWD6sGUOl+HS9oqGzYRTeHiLBwg2MpS5MG8xY6sK0wRJ8tJb1pC7MHCZlLHVk5jApNpoGN3Q2DN5TNYmsJ3Vk2mAOrqbaWCqIiIiIiIiIDBB76ImIiIiIiEgvRA7Q0Cv20BMREREREREZIPbQExERERERkV5wDnb9Yg89ERERERERkQFig56IiIiIiIjIAHHIPREREREREemFgpPi6RV76ImIiIiIiIgMEBv0RERERERERAaIQ+6JiIiIiIhILzjLvX6xh56IiIiIiIjIALGHnoiIiIiIiPRCwQ56vWIPPREREREREZEBYoOeiIiIiIiIyABxyD0RERERERHphcgx93rFHnoiIiIiIiIiA8QGPREREREREZEB4pB7IiIiIiIi0gu+hl6/2ENPREREREREZIDYoCciIiIiIiIyQBxyT0RERERERHqh4Cz3esUeeiIiIiIiIiIDxB56IiIiIiIi0guRs+LpFXvoiYiIiIiIiAwQG/REREREREREBohD7omIiIiIiEgvREVD58C4sYeeiIiIiIiIyACxQU9ERERERERkgDjknoiIiIiIiPRCwVnu9Yo99EREREREREQGqEEb9CtXroSDg0NDZuG+6SPPycnJEAQBp06d0ul2iYiIiIiIGpIoio3mY4zua8j9qFGjsGrVqlrLY2JisGXLljuu6+vri8mTJ2Py5MmqZUOHDsWTTz55P1l4ICtXrsTkyZNRWFio823L5XIsWLAAK1euxLVr12BpaYnAwECMHTsWr7zyis5/z9h1DRfQyl+AuSmQmgdsPa5AQemd12kTICA6WICNBZBdCGw7qUBGvvp7qQTo1UpAmLcAqQS4kqXcbnmVXnelwXVrcUssc4Et9xDLtgECokOUscwqBLad0IxlK38B4T4CPBwBc1MBizbIUVWj191oUOcPrcaZfctRUZoLJ48QdOw/E67eLetMf/XMFhzf/gVKC9Ng5+yDdn2mwju4u+r75LPbkHBkHfLSzqGqoggDJ26As2dofexKgzt3aDXi96pj2enpmXC7QyyvnNmCY9u/QGmBMpbt+0xFsxB1LK+e3YaEuHXIvRHLwa8/GrFkHHWrWwsBrZur68nNx+6tnuwQeks9eVyB9FvqydbNNevJheuNu568ibF8OE5douA/dQzs27SAhacbjg0Zj6y/d955nW7tEbbwXdiEBaLyegYux36N1B//0Ejj87/n4T9lDMw9XFEcfwHnJs9D0dEz+tyVRoF1JT1K7ruHvk+fPsjIyND4/PLLLw/045aWlnBzc3ugdRuLOXPm4PPPP8e8efNw/vx57N69G6+++qpebh7cSXV1db3+nj50CBEQFShgy3EFVu1UoEYGDO0mgfQOpTTUW0CvSAH7z4lYvl2BrEIRQ7tJYGWuTtO7lYCAJgL+OKTA6j0K2FoAQzob99MmN2O5+ZgCK3coUCMHhnW/h1i2uhHLbQpkF4oY1l0zlqYmwJUMEQfPG+cdzltdid+EuE3z0brXBAyYsB5OTYKxZcVYVJTmaU2fde0kdq+bhqCoIRg4cQN8wnphx8+vIz/zoipNTU0FPHzaoF2fqfW1G41CUvwmHN44H216TcCgievh3CQYm5ffOZa71k5DcNQQDHp9A3zDemH7bbGUVVfAw7cN2vd9dGLJOOpWxxAB7YJu1JPbleec4T3uXk/2bi1g31kRy7beqCd7aNaTJlIgKUPEgUegnryJsXx4UmsrFMcn4uykOfeU3tK3Kdr9/S3y9sRhf9QAXF26ChHffgiXx7uo0jR5ti9CF0zHpQ+/xP72g1ASfwHRG5fBzNVJX7vRKLCupEfNfbdqzM3N4eHhofFxdHSEKIqYPXs2mjVrBnNzc3h6emLSpEkAgB49euDatWt48803IQgCBEEAUHv4+uzZs9GqVSssX74czZo1g42NDcaPHw+5XI5PP/0UHh4ecHNzw0cffaSRp88++wwRERGwtraGt7c3xo8fj9JS5W3hPXv2YPTo0SgqKlL99uzZswEAVVVVmDZtGry8vGBtbY3o6Gjs2bNHY9srV65Es2bNYGVlhUGDBiEvT7My+PvvvzF+/Hg8++yz8PPzQ2RkJMaMGYNp06ap0mzZsgVdunSBg4MDnJ2d0a9fPyQlJdUZY7lcjjFjxsDPzw+WlpYIDg7GkiVLNNKMGjUKAwcOxEcffQRPT08EBwdj7ty5aNGiRa3ttWrVCu+//36dv9dYtAsUcCBBxKV0IKcI+PeIAraWQJCXUOc67YMEnL4i4kyyiLxiYMtxETIZ0NJPuY65KRDpJ2DnaQWuZQOZBcC/RxVo6iLA04jPZ+2DBBw4r47lP3HKWAbfKZbBAk5dERF/VURuMbD5mDKWkX7qdY5eFHHogoi0POO/uDq7fxWC2z2LoLaD4egegM4DZsPEzAIXj2/Qmv7cwR/RNLALWnYbAwe35mj7+Btw9gxFwuE1qjSBrQegda8J8AzoVF+70Sic2bcKIe2eRXCUMpZdBipjmXhMeyzPHlDGMrLbGDi6NUfUE2/AxTMU5w7dEss2A9Cm1wR4PUKxZBx1q32w8gbmxTQguwj4+2Y92bTuejI6RMCpJHU9uenojXrS/7Z6MuHRqCdvYiwfXs7Wvbg4azGy/tpxT+l9Xh2GiqupSHh7PkovXMG1r1Yjc/1W+L0xSpXGb/JoXF/2K1JXbUBpQhLOjJ8FeXklvEcN0dNeNA6sKxsfhUJsNB9jpLNuyvXr1+Pzzz/Ht99+i0uXLuHPP/9EREQEAGDDhg1o2rQp5s6dq+rVr0tSUhI2b96MLVu24JdffsGyZcvw1FNPITU1Ff/99x/mz5+P9957D3FxceqdkEjwxRdf4Ny5c1i1ahV27dqFt99+GwDQqVMnLF68GHZ2dqrfvtnYnjhxIg4dOoS1a9ciPj4ezz77LPr06YNLly4BAOLi4jBmzBhMnDgRp06dQs+ePfHhhx9q5NfDwwO7du1CTk5OnftUVlaGKVOm4NixY9i5cyckEgkGDRoEhUKhNb1CoUDTpk3x22+/4fz58/jggw8wY8YM/Prrrxrpdu7cicTERGzfvh3//vsvXn75ZSQkJODo0aOqNCdPnkR8fDxGjx5dZ/4aAwdrwMZSQHKW+kCrqgHS8wAvZ+3rSCSAhyNwNUvz4EzOFuHlrLwg8HAEpFIByVnq7/NLgKIyEV4udV9oGLKbsbyqLZYu2teRSIAmjtCIP6CMrbHG6U7ksmrkpp+DZ0BH1TJBIoFn847ITjmldZ3slNMa6QGgaWCXOtM/Km7G0uu2WHrdIZZZKac10gOMJeOoW3Wdc9Lucs5pouWcczVLRFPnR6+evImxbBgOHVohd9chjWU52/fDsUMrAIBgagr7NuHI3XlQnUAUkbvrIBw6tK7HnNYv1pX0KLrv19b9+++/sLGx0Vg2Y8YMWFhYwMPDA71794apqSmaNWuG9u3bAwCcnJwglUpha2sLDw+PO25foVBg+fLlsLW1RVhYGHr27InExERs2rQJEokEwcHBmD9/Pnbv3o3o6GgA0Hgu39fXFx9++CFee+01fPXVVzAzM4O9vT0EQdD47ZSUFKxYsQIpKSnw9PQEAEybNg1btmzBihUr8PHHH2PJkiXo06eP6uZAUFAQDh48qDFfwGeffYZnnnkGHh4eCA8PR6dOnTBgwAD07dtXlWbIEM07ocuXL4erqyvOnz+vtUfd1NQUc+aoh1z5+fnh0KFD+PXXX/Hcc8+plltbW+OHH36AmZmZallMTAxWrFiBdu3aAQBWrFiB7t27w9/f/45xb2jWFsp/yyo1l5dViarvbmdlBkgkQq1n4csqAWfbm9sVIJOLtZ65K6tEnds1dHXGsvLusay9DuBsp/s8NnaV5YUQFXJY2mhejVraOKMo56rWdSpKc2Fpo3nHxMLGGeUluXrLpyGoM5a2zii8j1ha2jijovTRjSXjqFt3qidtLLWvw3pSO8ayYZi7u6AqS/NYrsrKham9LSQW5jB1tIfExARV2Xm3pcmDdXDjviZ8GKwr6VF03w36nj174uuvv9ZY5uTkhLKyMixevBj+/v7o06cPnnzySfTv3x8mJvf3E76+vrC1tVX97e7uDqlUColEorEsOztb9feOHTsQGxuLCxcuoLi4GDKZDJWVlSgvL4eVlZXW3zlz5gzkcjmCgoI0lldVVcHZWVkJJCQkYNCgQRrfd+zYUaNBHxYWhrNnz+L48eM4cOAA9u7di/79+2PUqFH44YcfAACXLl3CBx98gLi4OOTm5qp65lNSUrQ26AHgyy+/xPLly5GSkoKKigpUV1ejVatWGmkiIiI0GvMAMHbsWLz88sv47LPPIJFIsGbNGnz++edaf+PWfa6q0mwVm5ub4wGKxz0LbyagT1v1Xfhf92sfrUB3F+4joO+tsdzHWBIR3SrcR8CTUep6ct1e1pMPirEkovtlpJPLNxr33WKztrZGQEBAreVOTk5ITEzEjh07sH37dowfPx4LFizAf//9B1NT03ve/u1pBUHQuuxmozg5ORn9+vXD//73P3z00UdwcnLC/v37MWbMGFRXV9fZoC8tLYVUKsXx48chlUo1vrt9BMLdSCQStGvXDu3atcPkyZPx888/48UXX8TMmTPh5+eH/v37w8fHB99//z08PT2hUCjQokWLOieyW7t2LaZNm4ZFixahY8eOsLW1xYIFCzQeMwCU/y9u179/f5ibm+OPP/6AmZkZampq8Mwzz9wx/7GxsRojAgBg1qxZMA/T33P3l9JFpOerj+6bE+dYW2je5bc2F5BVqL0WKK9WPpNz6wQ6N7dRemMbZZUiTKQSmJtCo5f+9t8xZJfSRKTn3UMsLe4ey9t78I0pTvfDwsoBgkRaawKditI8WNpqf27B0sal1t38ytI8WNWR/lFRZyxL6o6NtlhWlObV6kF5lDCOD+dSmogf6qgnS2+vJwseoJ6s0HWOGy/GsnGoysqFubvmsWzu7oKaohIoKqtQnVsAhUwGczfn29I4oyrTeHueWVfSo0inU31bWlqif//++OKLL7Bnzx4cOnQIZ84oX41hZmYGuVyuy58DABw/fhwKhQKLFi1Chw4dEBQUhPT0dI002n67devWkMvlyM7ORkBAgMbn5tD80NDQWo3ow4cP3zVPYWFhAJTPzufl5SExMRHvvfceevXqhdDQUBQUFNxx/QMHDqBTp04YP348WrdujYCAgDtOoncrExMTvPTSS1ixYgVWrFiBYcOGwdKyjjFvN0yfPh1FRUUan+nTp9/T7z2oahlQUKr+5BYDpRUifN3Ud/3NTABPZ+VzeNooFMpJ7nzdNZ+383ETVBPoZBYAcrkI31tepuBkC9hbC0jLNY7bhXXG0l1LLOs4hysUQIaWWPq6G0+c7ofUxAwunuHIuKw+3kWFAulJh+HWrJXWddyaRSI9SbN+SLt8sM70j4qbsUxLuvdYumuJZeojHkvG8eHcaz3pdZdzTl31ZOojMGnbTYxl41B4+BScH+ugscylVycUHD4FABBralB04hxcHrvl2XBBgHPPjig8fLIec1q/WFc2TqJCbDQfY3TfPfRVVVXIzMzU3IiJCf7991/I5XJER0fDysoKP//8MywtLeHj4wNAOZR+7969GDZsGMzNzeHiopu7XgEBAaipqcHSpUvRv39/HDhwAN98841GGl9fX5SWlmLnzp2IjIyElZUVgoKCMGLECIwcORKLFi1C69atkZOTg507d6Jly5Z46qmnMGnSJHTu3BkLFy7EgAEDsHXrVo3h9gDwzDPPoHPnzujUqRM8PDxw9epVTJ8+HUFBQQgJCYFEIoGzszO+++47NGnSBCkpKXj33XfvuE+BgYH48ccfsXXrVvj5+eGnn37C0aNH4efnd08xeeWVVxAaqnw35oEDB+6a3tzc/MYQ+9vp/gbMnRy9JKJTmID8UhFFZUC3FhKUVAAX09QH3/DuElxME3H8snLZkYsi+rUXkJkPpOeLaBckwNQEiL+q/L6qBjh9VUSvVhJUVCtQLQMeby1Baq6o8a5bY3PkoojOYQIKSkQU3hLLxFti+XwPCRJTb4llooj+0QIy8oH0PBHtgzVjCSh7T6wtAEdb5UWYmz1QJQOKy4FKw39zooYWXV7C3t+nw6VpC7g2jcDZAz9CVl2BoDbKx3D+++0dWNm5o13MFABAeKeR2Pj9SJzZtwLewd1xJX4TctPOofNA9eiXqvJClBZmoLxE+chQUa7yeT5LWxdY2brW8x7Wn4iuL+G/36bD1asFXL2VsayprkBQW2Usd//6Dqzt3NG+jzKWLTqPxD/fjUT8vhVoFtwdSTdi2XWQOpaV5YUoK8xAWbEyloWPQCwZR906kiiic7iA/Bv1ZPeIG/Vk6i31ZE8JLqaKOHZJuSzugoinO9yoJ/NFtL95zrmiWU/aWABONjfqSQegugYoMsJ68ibG8uFJra1gHdBM9beVX1PYRYagOr8IldczEPzhFFh4ueP06HcAANe+Wwuf8SMQEvsWrq9cD5eeHdDk2b44+vQ41TauLl6ByOXzUXj8LIqOxsN30kswsbbE9VXaZ3s3Fqwr6VFz3w36LVu2oEmTJhrLgoOD8cknn+CTTz7BlClTIJfLERERgX/++Uf1PPrcuXMxbtw4NG/eHFVVVRB19DBFZGQkPvvsM8yfPx/Tp09Ht27dEBsbi5EjR6rSdOrUCa+99hqGDh2KvLw8zJo1C7Nnz8aKFSvw4YcfYurUqUhLS4OLiws6dOiAfv36AQA6dOiA77//HrNmzcIHH3yA3r1747333sO8efNU246JicEvv/yC2NhYFBUVwcPDA4899hhmz56tmj9g7dq1mDRpElq0aIHg4GB88cUX6NGjR537NG7cOJw8eRJDhw6FIAgYPnw4xo8fj82bN99TTAIDA9GpUyfk5+erJg40BIcviDCVAn3bSmBhBlzPBX7dq4D8lsfzHGwAy1vuPSRcVw6579pCgLWFgOxC5Tq3TpS345QIEcDgThJIpcDVTGDrCeN+5u/wBRFmJkDfqBuxzAHW/Vc7llZaYtntRiyzCpXrlN0SyzbNBXRtoR7Y82Iv5eMq/8QpcCbZuO56+rd8EpVlBTi+4wtUlOTCuUkoYkZ/pxpyX1qYAUG4ZW4Pn9boOXQBjm9fgmPbPoedsw96v7AUTh7qeTquJezGvvUzVH/vXqt8n23rxyagTe+J9bRn9a95yydRWaqMZfmNWPYd/Z1q+GOZllg+NmwBjm1bgqNbP4e9iw8evy2WKQm78d/v6lju+kUZyza9JqCtkcaScdStQxdEmJoAT7ZT15Nrb6snHbWcc6wtgO4R6npy7Z7b6skAAd1uqSdH3lJP3nqD1Jgwlg/Pvm0LdNz5k+rvsIXK4/L6jxsQP2Y6zJu4wtJbff1dkZyKo0+PQ9ii6fB9fSQqUzNxZtx7yN2+X5Um47fNMHN1QtCsSTD3cEXx6QQc6fcKqrPrGDphJFhX0qNGEHXVsqZGQxRFBAYGYvz48ZgyZcoDbyf21/rtoTdW05+T4uN1jOXDmjFUik/XG/eNmPry9hAJFm5gLHVh2mDGUhemDZbgo7WsJ3Vh5jApY6kjM4dJsdE0uKGzYfCeqklkPakj0wbr9GnpevP64uKGzoLK0snG9yoN/U1jTg0iJycHa9euRWZmZqN/9zwRERERERE9ODbojYybmxtcXFzw3XffwdHRsaGzQ0RERERERHrCBr2R4RMURERERETUWBjr7PKNhWE+iEFERERERET0iGODnoiIiIiIiMgAccg9ERERERER6QWH3OsXe+iJiIiIiIiIDBB76ImIiIiIiEgv2EGvX+yhJyIiIiIiIjJAbNATERERERERGSAOuSciIiIiIiK94KR4+sUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSC1HkkHt9Yg89ERERERERkQFiDz0RERERERHphYKT4ukVe+iJiIiIiIiIDBAb9EREREREREQGiEPuiYiIiIiISC84KZ5+sYeeiIiIiIiIyACxQU9ERERERERkgDjknoiIiIiIiPRC5Cz3esUeeiIiIiIiIiIDxAY9ERERERERkQHikHsiIiIiIiLSCw651y/20BMREREREREZIPbQExERERERkV4o+B56vWIPPREREREREZEBYoOeiIiIiIiIyABxyD0RERERERHpBSfF0y/20BMREREREREZIDboiYiIiIiIiAwQh9wTERERERGRXoic5V6vBJERJiIiIiIiIj0Y+X5GQ2dB5cd5TRo6CzrHHnqq05yfaxo6C0Zh1gumOHUpp6GzYfBaBbpi3i+yhs6GUXh/uAl+OcB7ubowvLOAmcurGjobBu+jl83x8Tp5Q2fDKMwYKsVLH2Q2dDaMwqq5Hli4QdHQ2TB40wZLsNE0uKGzYRSeqkls6Cw8EAUnxdMrPkNPREREREREZIDYoCciIiIiIiIyQBxyT0RERERERHrB99DrF3voiYiIiIiIiAwQG/REREREREREBohD7omIiIiIiEgv+JZ0/WIPPREREREREZEBYg89ERERERER6YWoUDR0Fowae+iJiIiIiIiIDBAb9EREREREREQGiEPuiYiIiIiISC8UfA+9XrGHnoiIiIiIiMgAsUFPREREREREZIA45J6IiIiIiIj0gu+h1y/20BMREREREREZIDboiYiIiIiIiB5Qfn4+RowYATs7Ozg4OGDMmDEoLS29Y/rXX38dwcHBsLS0RLNmzTBp0iQUFRXd929zyD0RERERERHphfgIzHI/YsQIZGRkYPv27aipqcHo0aPx6quvYs2aNVrTp6enIz09HQsXLkRYWBiuXbuG1157Denp6fj999/v67fZoCciIiIiIiJ6AAkJCdiyZQuOHj2KqKgoAMDSpUvx5JNPYuHChfD09Ky1TosWLbB+/XrV382bN8dHH32EF154ATKZDCYm995M55B7IiIiIiIi0gtRITaajz4cOnQIDg4OqsY8APTu3RsSiQRxcXH3vJ2ioiLY2dndV2MeYA89ERERERERPQKqqqpQVVWlsczc3Bzm5uYPvM3MzEy4ublpLDMxMYGTkxMyMzPvaRu5ubmYN28eXn311fv+ffbQExERERERkdGLjY2Fvb29xic2NlZr2nfffReCINzxc+HChYfOU3FxMZ566imEhYVh9uzZ970+e+iJiIiIiIhILxSioqGzoDJ9+nRMmTJFY1ldvfNTp07FqFGj7rg9f39/eHh4IDs7W2O5TCZDfn4+PDw87rh+SUkJ+vTpA1tbW/zxxx8wNTW9+07chg16IiIiIiIiMnr3M7ze1dUVrq6ud03XsWNHFBYW4vjx42jbti0AYNeuXVAoFIiOjq5zveLiYsTExMDc3Bx///03LCws7m0nbsMh90REREREREQPIDQ0FH369MHYsWNx5MgRHDhwABMnTsSwYcNUM9ynpaUhJCQER44cAaBszD/xxBMoKyvDsmXLUFxcjMzMTGRmZkIul9/X77OHnoiIiIiIiPTiUXgP/erVqzFx4kT06tULEokEQ4YMwRdffKH6vqamBomJiSgvLwcAnDhxQjUDfkBAgMa2rl69Cl9f33v+bTboiYiIiIiIiB6Qk5MT1qxZU+f3vr6+EEX1jY0ePXpo/P0w2KAnIiIiIiIivXgUeugbEp+hJyIiIiIiIjJAbNATERERERERGSAOuSciIiIiIiK90NWz4qQde+gbiR49emDy5Ml62bavry8WL16sl20TERERERFRw2APvY6MGjUKq1atqrU8JiYGW7Zsuev6GzZsgKmpqepvX19fTJ48WW+N/MaqR0sJ2gRKYGEKXM8RsfGIHPkld16nXZAEncIksLEEMgtEbD6qQHqe+k5gv2gJ/DwksLUEqmXK7e44KUdesZ53poFs/Xc9/tnwCwoL8uHj1xyjx72JgOAwrWl3bvkbe3dtwfVrVwAAfgHBGD5ynEb6woJ8rFn5NeJPHkFZWSlCwyMxetybaOLlXS/709C6R0jQurmgLJO5yvKVX3rndaICBXQMUZbJrAJgy3E50vPV37duLqCFjwRNnABzUwGf/i5DVY1+96OhHdm5Gge2LENpUS48vEPQd8R7aOrfUmva7LRL2P3nF0hPPoeivHTEDJuOjk+8pJFm38ZvkXB8O3IzrsDEzALeAa3x+DNT4dLEvz52p8H1ai1Fu2ApLMyAa9ki/j4oQ17xnXtAokMl6NrCRFVX/ntIhtRc9Tpj+prCv4nmff4jF+T466BML/vQGHRrIaCVvwBzUyA1F9hyXIGCuxzfbQMERIcIsLEAsgqBbScUyLjl+G7lLyDcR4CHo/L4XrRBbvTHNwAMeswGPdpawspCgksp1Vj1TzGy8ut+l3Kwjyn6drGGbxNTONpJsWRNAU5cqKqVromLFEOfsEWwrxmkEiAtR46lawuQX6TQ5+40iHOHViN+73JUlObCySMEnZ6eCTdv7fUkAFw5swXHtn+B0oI02Dn7oH2fqWgW0l31/dWz25AQtw65aedQVVGEwa9vgLNnaH3sSoNy6hIF/6ljYN+mBSw83XBsyHhk/b3zzut0a4+whe/CJiwQldczcDn2a6T++IdGGp//PQ//KWNg7uGK4vgLODd5HoqOntHnrhDdE/bQ61CfPn2QkZGh8fnll1/uaV0nJyfY2trqOYeNW+cwCaJDJNgYJ8cPW2SolgEvPGYC6R1KabiPgCfaSvBfvBzfbpIhqwB44TEprMzVadLzRPx1SI4v/5Hh510yCALwYi8TCIL+96m+Hdy7Ez/+8H8YMnw0PlmyDD5+Afj4gykoKizQmv7cmZPo1L03PohdinkLv4Wzqzs++mAK8nNzACiHSC38cDqyMtMx7b1PMH/JCri4eeDD9yajsrKiPnetQXQKFdA+SMCmowos3y5HjQx4vqf0jmUyrJmAx1tLsPesAt9vkSOrUMTzPTXLpKkJkJShwP5zxndBqs3ZI5uwdd0n6PH0BIybtQHu3sH4+bNXUFqcpzV9TXUlHF290fuZqbCxd9WaJjnxKNo99jxeeW8dRk5dDoVchp8+ewXVVeX63JVGoWuEFB3DpPjroAxf/1ODmhoRo2JMYSKte50IPwmebG+CXadk+PLvGmTmK9exttBMdzRRjthfqlSfLUeNtzHfIURAVKCAzccUWLlDgRo5MKy75I7Hd6i3gF6tBOw/J2L5NgWyC0UM6y6pdXxfyRBx8PyjM8T0yS7WeDzaCiv/Kcbc7/JQVS1i2khHmN6h28jcTMD1TBl+2lj33XU3Rynee8UZ6bkyxC7Px3tf5uHvPaWoMcJimRS/CYc3zkebXhMwaOJ6ODcJxublY1FRqr2ezLp2ErvWTkNw1BAMen0DfMN6YfvPryM/86Iqjay6Ah6+bdC+79T62o1GQWptheL4RJydNOee0lv6NkW7v79F3p447I8agKtLVyHi2w/h8ngXVZomz/ZF6ILpuPThl9jffhBK4i8geuMymLk66Ws3jIpCoWg0H2PEBr0OmZubw8PDQ+Pj6OiIPXv2wMzMDPv27VOl/fTTT+Hm5oasrCwAmkPue/TogWvXruHNN9+EIAgQbml57t+/H127doWlpSW8vb0xadIklJWVqb7Pzs5G//79YWlpCT8/P6xevbp+dl4HokMl2HtGgcRUEdmFwJ8H5bC1AkK86255dwiV4MRlBU5dEZFbBPwbJ0eNHGgdoC7aJy6LSMkWUVQGZOYDu07JYW8twMG6Hnaqnm38cy16xfRHz8efQtNmfnhlwlswM7fA7u3/ak0/6a1ZiHlqMHz9A+Hl7YPXXn8HokKBM6ePAQAy0q/jUuI5vDJ+KgKCQuHZtBleGT8N1dVVOPDfjvrctQbRPliCfecUuJimLJN/HVbA1hIIaXqHMhkswckkEaevisgtBjYeVaBGpuy1u+lIooiDCSLStF+nGZ1DW1eiTbdn0brrELh5BaDfyDkwNbPAyX3rtab38ovAE8+9jYjopyA1MdWa5sUpP6B1l8Fw8wqER7MQDHw5FkV56UhPPqfPXWkUOodLsee0HAkpCmQViPhtrwy2lkBos7pP6Z1bSHEsUYETlxTIKRTx1wEZamRA2yDNuwDVMhGlFVB9jLlnuX2QgAPnRVxKB3KKgH/ilMd3sFfdx3f7YAGnroiIv3F8bz4mQiYDIv3U6xy9KOLQBRFpeY9Ogz6moxX+2VuKkxeqcD1Lhu82FMHBVoo2IRZ1rhN/qRrrd5bieELtXvmbhvS2wemLVfh1WylSMmXILpDjZGIVSsqM76L8zL5VCGn3LIKjBsPRPQBdBs6GiZkFEo9t0Jr+7IEf0TSwCyK7jYGjW3NEPfEGXDxDce6Q+j3YgW0GoE2vCfAK6FRfu9Eo5Gzdi4uzFiPrr3u7TvF5dRgqrqYi4e35KL1wBde+Wo3M9Vvh98YoVRq/yaNxfdmvSF21AaUJSTgzfhbk5ZXwHjVET3tBdO/YoK8HNxvrL774IoqKinDy5Em8//77+OGHH+Du7l4r/YYNG9C0aVPMnTtX1dMPAElJSejTpw+GDBmC+Ph4rFu3Dvv378fEiRNV644aNQrXr1/H7t278fvvv+Orr75CdnZ2ve3rg3KwAWwtBVzJVJ+kq2qA1FwR3q7aL64kEsDTScCVDM2LpisZIpq6aF/HVAq0bi5BQYmIIiPryJPV1ODK5YuIaBWlWiaRSBDRKgqXLtxbI6eqqgoyuQw2tnaqbQKAqZm6+0kikcDU1AyJ5+N1mPvGx8FaWSavZqrLV1UNkJYHeNVRviQSoIkTNNYBgKtZdZdJYyeTVSP92jn4h6kvKCUSCfzDOiI16ZTOfqeyQvlsjqW1vc622Rg52gK2VgKS0m+rK3NENHPTXsakEsDTWcDlW9YRAVxOV6DZbfVrK38pZjxvhkmDTPFEWylM79Drb8gcrAEbSwFXszSP7/Q8wMtF+zoSCdDEEUjOqn1811UnPApcHaVwsJXiXFK1allFlYgraTUI8NZ+Q+5eCAIQGWSOzDwZpo10xNK3XfHBq05oE2J+95UNjFxWjdz0c/AK6KhaJkgk8GreEdkpp7Suk5VyWiM9ADQN7FJneqqbQ4dWyN11SGNZzvb9cOzQCgAgmJrCvk04cnceVCcQReTuOgiHDq3rMadE2vEZeh36999/YWNjo7FsxowZmDFjBj788ENs374dr776Ks6ePYuXXnoJTz/9tNbtODk5QSqVwtbWFh4eHqrlsbGxGDFihKonPzAwEF988QW6d++Or7/+GikpKdi8eTOOHDmCdu3aAQCWLVuG0NDG/7yUjYXyYqisUnN5WSVgbaH9QsnKHJBIBC3riHCx11wnKkiCx1tLYGYqILdIxE87ZTC2UTfFxUVQKOSwd9Ac/mXv4IT01Gv3tI3VK7+Ck5OL6qaAZ1MfuLi645dV32DsxLdgYW6JjX+tQ15uNgryjbt72cZS+a+28mVTR6fTzTJZWinetg7gYvtoXvCXlxRAVMhhY+essdzazgW5GVd18hsKhQJbfvkY3gFt4N40SCfbbKxsLZXlqLRCs4yVVoqwsay7rpRKhNrrVIhwdVDf14+/IkdBqYiScsDDUUBMOxO42AtYs8v4xjfffNRA2/F9+2MIN1mZ1XXOAZztdJ9HQ2FvoyxDRaWaJ9XiUrnquwdhZy2BpbkE/bpaY/3OUvy6rQQRgeZ4fZgDPlmZj8Rk4xk+UlleCFEhh6WNZj1paeuMwhzt9WRFaS4sbTTvPlnaOKOiNFdv+TRW5u4uqMrSjFtVVi5M7W0hsTCHqaM9JCYmqMrOuy1NHqyDH415Wx6WqHh0Riw1BDbodahnz574+uuvNZY5OSkbV2ZmZli9ejVatmwJHx8ffP755/e9/dOnTyM+Pl5jGL0oilAoFLh69SouXrwIExMTtG3bVvV9SEgIHBwc7rjdqqoqVFVpDnkzNzeHPgdwRPgK6Bet7vpZs7vuiXN04cxVBa5kKGBjKaBTmATPdDXB8q0yyI2sUf8w/vztJxzcuxOzYpfC7EaPvImJCabO/AjfLPkEY4Y9CYlEiohWbWT0BNAAAEewSURBVNGqbQco+/iMRwsfAU+1U5f5X/7Tb5kk3dn081xkp13Cy9PX3D2xgYn0l2BAZ/Wp+sft+mvEHE1UV4hZBSJKKmowpq8ZnGxld52ctLEL9xHQt636hsev+1j5P6iOLS0wqr/6DsZnq7XP0fKwbj5teOJCFbYeUg6pS8mUIdDbFI9FWSExuUgvv0tEZGjYoNcha2trBAQE1Pn9wYPKoTr5+fnIz8+HtfX9PcRdWlqKcePGYdKkSbW+a9asGS5evKhlrbuLjY3FnDmaE4fMmjULQsDMB9revUhMFZGaq+71uTmZk7WF8rnNm6wtlBeW2pRXAQpF7d4UawtBYxuAcihlVQ2QXyIiNVeOd54zQWgzAWeTjadRamdnD4lEiqLCfI3lRYX5cHB0rmMtpX82rMFfv6/Gex8uho+fZhn2DwjBp0tXorysFDJZDezsHTFzylj4B4bofB8a0sU0EWl56ka8yY22vbUFUHpLj5y1hYDMu5RJ5YgT8ZZ1UKvX/lFhZesIQSKtNQFeWXEubOzrGNt8Hzb+PBcXT+/B6Hd/hr2Tx91XMDAJKQpcz1EPZTaRKls5NpYCSm7pcbexEJCRr72RWl4FyBU3e/BvWcdSQGl53eXyeo7yOyc7Afklhl1+L6WJGm8/kd5yfJfddnxnFdZxfFfXdc6p3dNvzE5eqEJSqvp4Nr1RJu1tJBq99HY2UqRkPPgNqJJyBWRyEek5miNE0nNkCPIxe+DtNkYWVg4QJNJaE+BVlOTBylZ7PWlp41KrN76iNK9Wrz3dXVVWLszdNeNm7u6CmqISKCqrUJ1bAIVMBnM359vSOKMqkyMi7oUo8iaqPvEZ+nqSlJSEN998E99//z2io6Px0ksv3XGmRTMzM8jlmj2Ebdq0wfnz5xEQEFDrY2ZmhpCQEMhkMhw/fly1TmJiIgoLC++Yt+nTp6OoqEjjM3369Ifa37uplgEFpepPThFQUiHC30NdJM1MgaYuguqi8nYKBZCeL8LfQ3OYqb+HoPEqptsJNz53msnYEJmYmsI/IAhnTqv//ysUCpw9fRyBIeF1rvfX76uxfu0qTJ+zEM3v0Ei3sraBnb0jMtKuI+lyIqKiu+o0/w2tVpksVpZJv1vKl5kJ4OUMpNVRvhQKICMf8L2tTPq537lMGjMTEzN4+oTjaoL6+USFQoErCYfRtHmrB96uKIrY+PNcXDixAy+9vRKOrk11kNvGp1oG5JeoP9mFIkrKRfh7qiswc1OgqauAlGztZUyuUL7to/kt6wgAmntKkFJH/QoATZyU5bjECOYbuf34zi1WPnLg6655fHs6A2l1XJ8rFEBGATTWAZR/11UnGKPKahHZ+XLVJy1HhsISOcL81Y1sC3MB/l6muHz9wRv0cjlwNa0GHs6afU8ezibILTSuEVRSEzO4eIYjLemwapmoUCA96TDcmrXSuo57s0ik35IeAFIvH6wzPdWt8PApOD/WQWOZS69OKDh8CgAg1tSg6MQ5uDx2y5wFggDnnh1RePhkPeaUSDsja9I0rKqqKmRmZmp8cnNzIZfL8cILLyAmJgajR4/GihUrEB8fj0WLFtW5LV9fX+zduxdpaWnIzVVeXbzzzjs4ePAgJk6ciFOnTuHSpUv466+/VJPiBQcHo0+fPhg3bhzi4uJw/PhxvPLKK7C0tLxjvs3NzWFnZ6fxUQ65r19xCQp0bSFBUFMBbg7AoE5SlJQDF66rL5Re7CVFuyB1sT2coECbQAki/QW42CnfOW9qApxKUt4scbABuoQr3/dtZ6W8QfBsNylq5MoeG2Pz1MBh2LX1H/y3czNSryfjh68WoqqyAj16PwUA+L9F87Bm5Teq9H/9/jN+/fkH/O+N6XBzb4LCgjwUFuShskJ9BX9o/y6ciz+BrMw0HD28Dx+9/ybadeiKyDbt633/6tuRRAW6hEsQ5CXAzR4Y2FGCkgrgQqq67LzQU4KoQPUF/uFEBdo0F9DST1kmn2ynLJOnr2r22Ls7AI43ptxwc1D+bWFcnU4qHf+/vfuOiupa2wD+zNCkgwVsiKAIKhBbLDH2FltUvIkFNKBR9FpRjHqjCNiNUWM3oqDG3jAmRo1iNGLBRjF2QLAgURCQDsN8f/A5yQQ0oMDmwPNby7WcfSa5T84ddN6z9353L1dcO7sfocGH8fxpJH7e4Y2crAw0/9gJAHBo80ycOvDXn4e5udmIi72NuNjbUOTm4FVSPOJibyMh/q9eED//4Ivwi0cx2H05tKvo41Xyc7xKfo6c7Io/VRr8hwJdPtCAnYUc5qYy/KejJl5l5M/mvzbqEy20bfzXn5XBNxVo1UiO5g3lqGEsw6cfaUJbE7h2L78wqmoIdPlAA7WryWBiANhZyPGfjlqIjst74yopqQu5p0T7JjLY1AZqGAP92+T/fN/9298NwzvL0bKh+gkVzaxlcKgvQzVDoHcrGbQ0gfB//HybmQCm/983w8w4/3VF/fkGgBMX0/FpJwM0t9VBXTNNjHUyRtIrBa7f+evn8StXU3Rvrad6raMtQ72amqhXM79gr2GqgXo1NVHV+K/P7S/BaWhjXwWdWurCrKoGurfWQzNbHZwOqQBPmf7BocMXuHtlP+5dC8TLPyNx/ogPcrIz0KjlIADAmX0zEXJ8her99u1H4tG98wj/3R9Jf0bh2qm1ePHkDzRtN1z1nsz0JCQ8vY2X8Q8AAEkvopHw9DbSXz0v2/+4MqahrwejD+xg9EH+JIWeVV0YfWCHKha1AAC2C6bhA/+lqvfHfL8HelYWsFs8A/q21rAcNxy1PuuN6O8CVO+JXuUPi9Gfo86IgTCws4b9Om9o6uvi0bbCTyEgKktccl+Cjh8/jlq1aqmN2draYvjw4YiJicFPP+UfHVarVi18//33GDZsGHr27IkPPvigwL/L19cX7u7uaNCgAbKysqBUKuHo6IizZ8/i66+/RocOHaBUKtGgQQMMGTJE9c/5+/vjyy+/RKdOnWBubo4FCxZg7ty5pfsfXkKCb+VBSxPo30YDVbSB2D+V+CFIfZ97VUMZ9Kr89cXpjxgl9HTy0NlRAwa6wLOXSuwMUqiWP+YqgHpmMrSx04Sudv7S6Zg/ldh6Ihfpbz4pR7I+6tgNKclJ2PeDH5JeJqK+dUPM9v0WJqb5vRwSnsdDLv/ry9KvxwKRm5uDFYvnqP17/jPMDZ85jwYAJCUmYIffWiQlJcLUtBo6dv0Eg4e6ltl/k0gXbiuhpalE3w/l+Z/J50rs+k2h9pk0NZD9/xnU+Z/LW7H5n8lODnIYVAHiXwK7flOoLclt2VCOTg5//f/g2j3/j+IjlxRqhUFFYd+6D9JeJeJM4BqkJj9HTYvGcPHYrFpyn5z4FDL5X0XTq6Q/scl7kOr1heNbceH4Vljafgi3mTsAAFfP7AYABCwdqfa/NWDUItWDgorq9wgFtDWBge01UUU7/8+0gBM5yP3bpGX+n5V/3dOI6DzoV8lFtxaaMNQF4hKVCDiZo/pcKvLyZ+w/aqoBLU0gOU2JPx4q8FtYxZoJ/btLd5TQ1gR6t8r/+X70HNh7Nk/t59vEAGpnzN9+pISeDtDRXvb/y/Pz/5m0v/190qKBDB3s//r5HtEtf0/Z0ct5iKhA27z+7tj5NOhoy+D6qRH0qshxPzYby3e8VDsv3sxUEwb6f83YW9XWwuxRfzVxHd47f1/+7zcy4Hc4f3/8tdtZCDiagn4d9eHSxwhxL3KxZm8S7sdWnIZ4rzVw7IPM1Je4dmo10l+9QLVajdHb7XvVkvu0pDjIZH99rswtm6Pr0G9w9eR3uHJiJYyrW6KHyxpUrflXY9DY22dw9sD/VK+DduefR9+i2wS07P7XCUkVjXFLe7Q7vUP1usny/HvwaPshhI+eDZ1aNaBr8df39YyHj3HlU3c0+XY26k8aiczHzxDhPgcvfj2vek/c/l+gXaMqGs2bDJ2aNZASdhsh/b5E9p8Vu0FwSWFTvNIlUyqVvMNUKJ8fKt5fmCLMc9FC6P2K/TS8LDSzqYH5uytet20R5g7TxO5g/tFfEoa1l+HrrRXw6WAZWzhKB4v2VtyHB2Xpf0M08IXXM9ExKoRtvjWx/BD3/r4vTyc5ftayFR2jQuibc1d0hHfSZ1SE6Agqx7Y6iI5Q4rjknoiIiIiIiEiCuOSeiIiIiIiISgWX3JcuztATERERERERSRBn6ImIiIiIiKhU5PEc+lLFGXoiIiIiIiIiCWJBT0RERERERCRBXHJPREREREREpYJN8UoXZ+iJiIiIiIiIJIgFPREREREREZEEcck9ERERERERlQplHrvclybO0BMRERERERFJEGfoiYiIiIiIqFSwKV7p4gw9ERERERERkQSxoCciIiIiIiKSIC65JyIiIiIiolKhVLIpXmniDD0RERERERGRBLGgJyIiIiIiIpIgLrknIiIiIiKiUpHHLvelijP0RERERERERBLEgp6IiIiIiIhIgrjknoiIiIiIiEqFMo9d7ksTZ+iJiIiIiIiIJIgz9ERERERERFQqlGyKV6o4Q09EREREREQkQSzoiYiIiIiIiCSIS+6JiIiIiIioVCiVbIpXmjhDT0RERERERCRBLOiJiIiIiIiIJIhL7omIiIiIiKhUsMt96eIMPREREREREZEEcYaeiIiIiIiISoUyj03xShNn6ImIiIiIiIgkiAU9ERERERERkQTJlEoluxSQ5GRlZWHx4sWYPXs2dHR0RMeRNN7LksN7WTJ4H0sO72XJ4b0sGbyPJYf3suTwXpKUsaAnSUpJSYGxsTGSk5NhZGQkOo6k8V6WHN7LksH7WHJ4L0sO72XJ4H0sObyXJYf3kqSMS+6JiIiIiIiIJIgFPREREREREZEEsaAnIiIiIiIikiAW9CRJOjo6mDdvHhuXlADey5LDe1kyeB9LDu9lyeG9LBm8jyWH97Lk8F6SlLEpHhEREREREZEEcYaeiIiIiIiISIJY0BMRERERERFJEAt6IiIiIiIiIgliQU9EREREREQkQSzoiYjeQ25uLk6dOoVNmzbh1atXAICnT58iNTVVcDIiIiIiquhY0JPkZGdn4+7du8jNzRUdRdJ4H99fTEwMHBwcMGDAAEyYMAHPnz8HACxduhSenp6C0xERlQ8KhQLnzp1DUlKS6ChERBUOC3qSjPT0dIwePRp6enpo2rQpYmNjAQCTJk3CkiVLBKeTDt7HkjNlyhS0atUKL1++hK6urmp80KBBOH36tMBkRPSuUlJSivyLikZDQwM9e/bEy5cvRUeRvHnz5iEmJkZ0DCIqRzRFByAqqtmzZyMsLAy//fYbPvnkE9V49+7d4e3tjVmzZglMJx28jyXn999/x4ULF6Ctra02Xr9+fTx58kRQKmnbsWMHNm7ciOjoaFy8eBGWlpZYtWoVrKysMGDAANHxJOH48eMwMDDAxx9/DABYt24dNm/ejCZNmmDdunUwNTUVnLB8MzExgUwmK9J7FQpFKaepOOzt7REVFQUrKyvRUSTtyJEjWLhwITp16oTRo0dj8ODB0NHRER1LMlavXl3k906ePLkUkxCVHBb0JBmBgYHYu3cv2rZtq/Zlq2nTpoiMjBSYTFp4H0tOXl5eoV/oHz9+DENDQwGJpG3Dhg3w8vLC1KlTsXDhQtW9NTExwapVq1jQF9GMGTOwdOlSAEBERASmT5+OadOm4cyZM5g2bRr8/f0FJyzfzpw5o/r9w4cPMWvWLLi6uqJdu3YAgIsXL2Lbtm1YvHixqIiStGDBAnh6emL+/Plo2bIl9PX11a4bGRkJSiYtoaGhuHHjBvz9/TFlyhRMmDABQ4cOxahRo/Dhhx+KjlfurVy5Uu318+fPkZ6eDhMTEwBAUlIS9PT0YGZmxoKepENJJBG6urrKyMhIpVKpVBoYGKh+HxoaqjQyMhIZTVJ4H0vO559/rhwzZoxSqcy/l1FRUcpXr14pu3btqnR1dRWcTnoaN26sPHz4sFKpVP9sRkREKKtVqyYwmbTo6+sro6OjlUqlUjlv3jzl4MGDlUqlUnnt2jWlubm5wGTS07VrV+WuXbsKjO/cuVPZqVOnsg8kYTKZTPVLLperfr1+TcWXnZ2tPHjwoLJfv35KLS0tpYODg3LVqlXKpKQk0dEkYefOncr27dsr79y5oxq7c+eOskOHDsoffvhBYDKi4uEeepKMVq1a4eeff1a9fj277Ofnp5o5oX/H+1hyli9fjuDgYDRp0gSZmZkYPny4arn96xlSKrro6Gg0b968wLiOjg7S0tIEJJImbW1tpKenAwBOnTqFnj17AgCqVq3Kfd/FdPHiRbRq1arAeKtWrRASEiIgkXSdOXNG9SsoKEj16/VrKj6lUomcnBxkZ2dDqVTC1NQUa9euhYWFBfbu3Ss6Xrk3d+5crFmzBra2tqoxW1tbrFy5EnPmzBGYjKh4uOSeJGPRokXo3bs3bt26hdzcXHz33Xe4desWLly4gLNnz4qOJxm8jyXHwsICYWFh2Lt3L8LCwpCamorRo0fD2dlZrUkeFY2VlRVCQ0NhaWmpNn78+HE0btxYUCrp+fjjjzFt2jS0b98eISEhqi/29+7dQ926dQWnkxYLCwts3rwZy5YtUxv38/ODhYWFoFTS1KlTJ9ERKoxr167B398fu3fvho6ODkaOHIl169ahYcOGAIA1a9Zg8uTJGDJkiOCk5VtcXFyhJ/0oFArEx8cLSET0bmRKpVIpOgRRUUVFRWHx4sWq4qlFixaYOXMmHBwcREeTlMjISCxZsoT38T3k5OTAzs4OP/30E4vNEuLn5wdvb298++23GD16NPz8/BAZGYnFixfDz88PQ4cOFR1REmJjY/Hf//4Xjx49wuTJkzF69GgAgIeHBxQKRbGaQlV2x44dw+DBg9GwYUO0adMGABASEoL79+/j4MGD6NOnj+CE0vL7779j06ZNiIqKwv79+1GnTh3s2LEDVlZWqiaO9HYODg64c+cOevbsiTFjxqB///7Q0NBQe8+LFy9gZmaGvLw8QSmloX///njy5An8/PzQokULAPkPS8aOHYs6dergxx9/FJyQqGhY0JMk5OTkwN3dHXPnzmWHXCo36tSpg1OnTrGgL0E7d+6Et7e3qkFj7dq14ePjoypKicra48ePsWHDBty+fRsA0LhxY4wbN44z9MV08OBBjBgxAs7OztixYwdu3boFa2trrF27FseOHcOxY8dER5SE+fPnY9SoUahTp47oKJL3/PlzfPHFFzh+/Di0tLQAALm5uejVqxcCAgJgZmYmOCFR0bCgJ8kwNjZGaGgoC/r39KY9tDKZDDo6OgWOYKM3W7RoEe7duwc/Pz9oanIHU0lKT09Hamoqv1C9Aw0NDcTFxRW4dwkJCTAzM+NRa0WUk5ODTz75BBs3boSNjY3oOJLXvHlzeHh4YOTIkTA0NERYWBisra1x48YN9O7dG8+ePRMdsdzjyrDSce/ePdy5cwcAYGdnh0aNGglORFQ8/AZKkjFw4EAEBgbCw8NDdBRJ+7czluvWrQtXV1fMmzcPcjn7Zr7NlStXcPr0aZw8eRIODg4FjmE6dOiQoGTSFB0djdzcXNjY2EBPTw96enoAgPv370NLSwv169cXG1Ai3vScPisriw/sikFLSwvh4eGiY1QYd+/eRceOHQuMGxsbIykpqewDSZCWlhYyMzNFx6hw6tevD6VSiQYNGvDhPEkSP7UkGTY2NvD19UVwcHChZ9jyvNCiCQgIwNdffw1XV1e0bt0aQP6e0G3btmHOnDl4/vw5li9fDh0dHfzvf/8TnLZ8MzExweDBg0XHqDBcXV0xatSoArOhly9fhp+fH3777TcxwSTi9d54mUwGPz8/GBgYqK4pFAqcO3cOdnZ2ouJJkouLC7Zs2YIlS5aIjiJ5NWvWxIMHDwo8mDt//jysra3FhJKgCRMmYOnSpVwZVgLS09MxadIkbNu2DUD+TL21tTUmTZqEOnXqYNasWYITEhUNl9yTZLxtqb1MJkNUVFQZppGubt26wd3dHZ9//rna+L59+7Bp0yacPn0aO3bswMKFC1VL0IjKgpGREa5fv67q1PzagwcP0KpVK87i/YvXf0bGxMSgbt26ao2ytLW1Ub9+ffj6+qqau9G/mzRpErZv3w4bG5tCHySvWLFCUDLpWbx4MX744Qds3boVPXr0wLFjxxATEwMPDw/MnTsXkyZNEh1REgYNGoTTp0/DwMCAK8Pe05QpUxAcHIxVq1bhk08+QXh4OKytrXHkyBF4e3vjxo0boiMSFQkf7ZFkREdHi45QIVy4cAEbN24sMN68eXNcvHgRQP6xV7GxsWUdjSo5mUyGV69eFRhPTk7mvu8ieP1nZJcuXXDo0CGYmpoKTiR9N2/eVHW/vnfvntq1t21dooJmzZqFvLw8dOvWDenp6ejYsSN0dHTg6enJYr4YuDKs5AQGBmLv3r1o27at2s9z06ZNVY1ZiaSAM/QkSa8/tvxCVXyNGjWCk5NTgSWks2bNwuHDh3H37l1cvXoVAwYMwJMnTwSllAYrK6u3fga5aqR4+vfvD11dXezevVs1u6xQKDBkyBCkpaXhl19+EZyQiN5XdnY2Hjx4gNTUVDRp0kRtawhRWdLT08PNmzdhbW2t1qgxLCwMHTt2RHJysuiIREXCGXqSlO3bt+Obb77B/fv3AeQXpzNmzMCIESMEJ5OO5cuX47PPPsMvv/yCDz/8EABw9epV3L59GwcPHgSQ3+xtyJAhImNKwtSpU9Ve5+Tk4MaNGzh+/DhmzJghJpSELV26FB07doStrS06dOgAIP/c6pSUFAQFBQlOJx2DBw9G69atMXPmTLXxZcuW4cqVK9i/f7+gZET52z8MDQ1haGjIYp6EatWqFX7++WfVCpHXD+j9/PzQrl07kdGIioUz9CQZK1aswNy5czFx4kS0b98eQH4znXXr1mHBggXsfl8MDx8+xMaNG1VLSG1tbeHu7o7U1FTY29sLTid969atw9WrV+Hv7y86iuQ8ffoUa9euRVhYGHR1deHo6IiJEyeiatWqoqNJRo0aNRAUFAQHBwe18YiICHTv3h3x8fGCkknT1atXsW/fPsTGxiI7O1vtGvcrF11ubi58fHywevVqpKamAgAMDAwwadIkzJs3T3UOOP27AwcOvPEzef36dUGppOf8+fPo3bs3XFxcEBAQAHd3d9y6dQsXLlzA2bNn0bJlS9ERiYqEBT1JhpWVFXx8fDBy5Ei18W3btsHb25t77N9RSkoKdu/eja1bt+Lq1avcq1wCoqKi0KxZM6SkpIiOQpWQrq4uQkNDYWtrqzZ+584dNG/eHBkZGYKSSc+ePXswcuRI9OrVCydPnkTPnj1x7949xMfHY9CgQXxoVwzjx4/HoUOH4Ovrq5r9vHjxIry9vTFw4EBs2LBBcEJpWL16teqkmu+//x5ubm6IjIzElStXMGHCBCxcuFB0REmJjIzEkiVLEBYWhtTUVLRo0QIzZ84s8ECUqDxjQU+SUaVKFdy8ebNAB+z79+/DwcGBZ7MW07lz57BlyxYcPHgQtWvXhpOTEwYPHqxahk/vbtmyZVi/fj0ePnwoOorkJCUlISQkBH/++Sfy8vLUrv3zYR4VrnXr1ujXrx+8vLzUxr29vXH06FFcu3ZNUDLpcXR0hLu7OyZMmKDaY2tlZQV3d3fUqlULPj4+oiNKhrGxMfbs2YPevXurjR87dgzDhg3jfuUisrOzw7x58zBs2DC1fd9eXl5ITEzE2rVrRUckojLGPfQkGQ0bNsS+ffsKnI2+d+/eAudWU+GePXuGgIAAbNmyBSkpKfj888+RlZWFwMBANGnSRHQ8yWnevLlaUzylUolnz57h+fPnWL9+vcBk0nT06FE4OzsjNTUVRkZGavdWJpOxoC+iuXPnwsnJCZGRkejatSsA4PTp09i9ezf3zxdTZGQk+vbtCyB/73daWhpkMhk8PDzQtWtXFvTFoKOjU+AMeiB/9Z22tnbZB5Ko2NhYfPTRRwDyV+O8PhlkxIgRaNu2LQv6YtDQ0EBcXBzMzMzUxhMSEmBmZsYViyQZLOhJMnx8fDBkyBCcO3dOtYc+ODgYp0+fxr59+wSnK//69++Pc+fOoW/fvqozVzU0NAo9wo6KZuDAgWqv5XI5atSogc6dO8POzk5MKAmbPn06Ro0ahUWLFkFPT090HMnq378/AgMDsWjRIhw4cEDVi+DUqVPo1KmT6HiSYmpqqiqY6tSpg5s3b8LBwQFJSUlIT08XnE5aJk6ciPnz58Pf3x86OjoAgKysLCxcuBATJ04UnE46atasicTERFhaWqJevXq4dOkSPvjgA0RHR4OLbovnTfcrKyuLD5lIUljQk2QMHjwYly9fxsqVKxEYGAgAaNy4MUJCQtC8eXOx4STgl19+weTJkzF+/HiuaCgh8+bNEx2hQnny5AkmT57MYr4E9O3bVzWzTO+uY8eO+PXXX+Hg4IDPPvsMU6ZMQVBQEH799Vd069ZNdLxyz8nJSe31qVOnULduXXzwwQcAgLCwMGRnZ/NeFkPXrl3x448/onnz5nBzc4OHhwcOHDiAq1evFrjfVLjVq1cDyF/55efnp3bagkKhwLlz5/hQniSFe+iJKolLly5hy5Yt2Lt3Lxo3bowRI0Zg6NChqFWrFsLCwrjk/h3l5eXhwYMHhe757tixo6BU0uTk5IShQ4fi888/Fx2FCACQmJiIzMxM1K5dG3l5eVi2bBkuXLgAGxsbzJkzB6ampqIjlmtubm5Ffi8bDBZNXl4e8vLyoKmZPye3Z88e1WfS3d2dM8tFYGVlBQCIiYlB3bp1oaGhobqmra2N+vXrw9fXF23atBEVkahYWNCTZBw7dgwaGhro1auX2viJEyeQl5dXoNEOFS4tLQ179+7F1q1bERISAoVCgRUrVmDUqFEwNDQUHU9SLl26hOHDhyMmJqbA0j2ZTMb9d8W0ZcsW+Pr6ws3NDQ4ODgWOsfr0008FJZMWuVyu1n/gn/i5JCICunTpgkOHDvHBHEkeC3qSDEdHRyxZsgR9+vRRGz9+/DhmzpyJsLAwQcmk6+7du9iyZQt27NiBpKQk9OjRAz/++KPoWJLRrFkzNGrUCD4+PqhVq1aBIsrY2FhQMmmSy+VvvMYHJEV35MgRtdc5OTm4ceMGtm3bBh8fH4wePVpQMul4+vQpVqxYAS8vLxgZGaldS05OxoIFC+Dp6Qlzc3NBCakyCQ8PL/J7HR0dSzEJEZVHLOhJMnR1dXH79u0CXXIfPnyIpk2bIi0tTUywCkChUODo0aPYunUrC/pi0NfXR1hYWIGjFInKo127dmHv3r0FCn4qyNPTEykpKfj+++8LvT5u3DgYGxtj6dKlZZxMuhISEuDl5YUzZ84UukUpMTFRULLy7/Wqm3/7ys4Hn/9u2rRpmD9/PvT19TFt2rS3vnfFihVllIro/bApHkmGsbExoqKiChT0Dx48gL6+vphQFYSGhgYGDhxYoGs7vV2bNm3w4MEDFvQkCW3btsXYsWNFx5CE48ePv/UEkJEjR2LMmDEs6IthxIgRePDgAUaPHg1zc/O3bgshddHR0aIjVBg3btxATk6O6vdvws8nSQkLepKMAQMGYOrUqTh8+DAaNGgAIL+Ynz59OvfWkhCTJk3C9OnT8ezZs0L3fHPpY/GlpaXh7NmziI2NRXZ2ttq1yZMnC0olfRkZGVi9ejXq1KkjOookREdHo169em+8XrduXTx8+LDsAlUAv//+O86fP6/qcE9FZ2lpKTpChXHmzJlCf08kZSzoSTKWLVuGTz75BHZ2dqhbty4A4PHjx+jQoQOWL18uOB1VRoMHDwYAjBo1SjX2elkklz4W340bN9CnTx+kp6cjLS0NVatWxYsXL6CnpwczMzMW9EVkamqqNrukVCrx6tUr6Onp4YcffhCYTDp0dXXx8OHDNxb1Dx8+hK6ubhmnkjY7OztkZGSIjlFh3Lp1q9AHn5zgKLrnz5+jRo0ahV6LiIiAg4NDGSciejfcQ0+SolQq8euvvyIsLAy6urpwdHTk0WAkTExMzFuvc1aleDp37oxGjRph48aNMDY2RlhYGLS0tODi4oIpU6bwjOUiCggIUCvo5XI5atSogTZt2rCbcxH17dsXtWvXxubNmwu9/uWXX+Lp06c4duxYGSeTritXrmDWrFnw8vKCvb19gRVN/2w+SIWLiorCoEGDEBERobav/vXPPB8kF13NmjWxZcsW9O3bV218+fLlmDt3Lh9AkWRwhp4kRSaToWfPnujZs6foKEQs2EtYaGgoNm3aBLlcDg0NDWRlZcHa2hrLli3DF198wYK+iFxdXUVHkDxPT0/06NEDxsbGmDFjhqqbfXx8PJYtW4aAgACcPHlScEppMTExQUpKCrp27ao2zhVNxTNlyhRYWVnh9OnTsLKyQkhICBISEjB9+nSuViymadOmYfDgwXBzc8OKFSuQmJiIkSNHIiIiArt27RIdj6jIWNBTuXfx4kUkJCSgX79+qrHt27dj3rx5SEtLw8CBA7FmzRro6OgITEmV1Y4dO7Bx40ZER0fj4sWLsLS0xKpVq2BlZYUBAwaIjicpWlpaqqPrzMzMEBsbi8aNG8PY2BiPHj0SnK5847FWJatLly5Yt24dpkyZgpUrV8LIyAgymQzJycnQ0tLCmjVrChSm9HbOzs7Q0tLCrl272BTvPVy8eBFBQUGoXr065HI55HI5Pv74YyxevBiTJ09+a6M3UvfVV1+hR48eGDFiBBwdHZGYmIg2bdogPDwcNWvWFB2PqMhY0FO55+vri86dO6sK+oiICIwePRqurq5o3LgxvvnmG9SuXRve3t5ig1Kls2HDBnh5eWHq1KlYuHChaobJxMQEq1atYkFfTM2bN8eVK1dgY2ODTp06wcvLCy9evMCOHTtgb28vOl651qxZs0KX3xaGM6FF4+7ujn79+mH//v24f/8+lEolGjVqhP/85z+qPi5UdDdv3sSNGzdga2srOoqkKRQKGBoaAgCqV6+Op0+fwtbWFpaWlrh7967gdNLTsGFD2Nvb4+DBgwCAIUOGsJgnyZGLDkD0b0JDQ9GtWzfV6z179qBNmzbYvHkzpk2bhtWrV2Pfvn0CE1JltWbNGmzevBlff/01NDQ0VOOtWrVCRESEwGTStGjRItSqVQsAsHDhQpiammL8+PF4/vz5G88Dp3zR0dGIiopCdHQ0Dh06BCsrK6xfvx43btzAjRs3sH79ejRo0ED1pZX+XU5ODubOnYsBAwZg3bp1WL9+PaZOncpi/h21atWKK21KgL29PcLCwgDkH526bNkyBAcHw9fXF9bW1oLTSUtwcDAcHR1x//59hIeHY8OGDZg0aRKGDBmCly9fio5HVGRsikflXpUqVXD//n1YWFgAAD7++GP07t0bX3/9NYD8bsMODg549eqVyJhUCenq6uLOnTuwtLSEoaEhwsLCYG1tjfv378PR0ZENdUiI1q1bw9vbG3369FEbP3bsGObOnYtr164JSiY9xsbGCA0NhZWVlegokrd//354e3tjxowZPObzPZw4cQJpaWlwcnLCgwcP0K9fP9y7dw/VqlXD3r17uRWkGHR0dODh4YH58+erPo+RkZFwcXHBo0eP8PjxY8EJiYqGS+6p3DM3N0d0dDQsLCyQnZ2N69evw8fHR3X91atXBb4YEJUFKysrhIaGFmiOd/z4cTRu3FhQKqrsIiIiCi1ArayscOvWLQGJpGvgwIEIDAyEh4eH6CiSN2TIEAA85vN99erVS/X7hg0b4s6dO0hMTCxwXCX9u5MnT6JTp05qYw0aNEBwcDAWLlwoKBVR8bGgp3KvT58+mDVrFpYuXYrAwEDo6emhQ4cOquvh4eFo0KCBwIRU2fj6+sLT0xPTpk3DhAkTkJmZCaVSiZCQEOzevRuLFy+Gn5+f6JiS0Lx58yJ/Cb1+/Xopp6kYGjdurPoMamtrAwCys7OxePFiPmgqJhsbG/j6+iI4OBgtW7aEvr6+2vXJkycLSiY90dHRoiNUSDExMUhLS4OJiQkL+iLq06cPdu/erSrmlyxZgnHjxsHExAQA8PLlS+zevRtz584VmJKo6Ljknsq9Fy9ewMnJCefPn4eBgQG2bduGQYMGqa5369YNbdu25dNUKjMaGhqIi4uDmZkZdu7cCW9vb0RGRgIAateuDR8fH4wePVpwSmn4+2qbfzNv3rxSTFJxhISEoH///lAqlaplzOHh4ZDJZDh69Chat24tOKF0vG2pvUwmQ1RUVBmmocps69atSEpKwrRp01RjY8eOxZYtWwAAtra2OHHihGp7Ir3Z3/8OBwAjIyOEhoaqehDEx8ejdu3aXDVCksGCniQjOTkZBgYGas3HACAxMRGGhoZcdk9lRi6X49mzZ6ovAwCQnp6O1NRUtTEiUdLS0rBz507cuXMHQP6s/fDhwwvMMBOVJR7z+e7atm0Ld3d3uLm5Acjf2tW/f38EBASgcePGmDhxIpo0acLVYUXwz7/D/94DB2BBT9LDLvckGR4eHkhPTy8wrqOjA3d3dwGJqDL759JGPT09FvNUbujr62Ps2LFYsWIFVqxYgTFjxrCYL6aUlBTk5eUVGM/Ly0NKSoqARNK2YcMGTJs2DX369EFSUlKBYz7p7e7fv49WrVqpXh85cgQDBgyAs7MzWrRogUWLFuH06dMCExKRKCzoSTK2bdtWaNfwjIwMbN++XUAiqswaNWqEqlWrvvUXFY9CocDy5cvRunVr1KxZk/fzPURGRmLSpEno3r07unfvjilTpqi2hdC/O3z4MFq1aoXMzMwC1zIyMvDhhx/i6NGjApJJF4/5fD8ZGRkwMjJSvb5w4QI6duyoem1tbY1nz56JiCY5MpmswEN59h8gKWNTPCr3UlJSoFQqoVQq8erVK1SpUkV1TaFQ4NixY5wZpTLn4+MDY2Nj0TEqFB8fH/j5+WH69OmYM2cOvv76azx8+BCBgYHw8vISHU8yTpw4gU8//RTNmjVD+/btAeSft7xp0yYcPXoUPXr0EJyw/NuwYQO++uor6OnpFbimr6+PmTNnYu3atejfv7+AdNIUHR2N5s2bFxjX0dFBWlqagETSYmlpiWvXrsHS0hIvXrzAH3/8ofr5BoBnz57x76QiUiqVcHV1hY6ODgAgMzMT48aNU61iysrKEhmPqNhY0FO597pzq0wmQ6NGjQpcl8lkxWqsRVQShg4dygdJJWznzp3YvHkz+vbtC29vbwwbNgwNGjSAo6MjLl26xI7iRTRr1ix4eHhgyZIlBcZnzpzJgr4Ibt68ifXr17/xeseOHTFnzpwyTCR9PObz/XzxxReYMGEC/vjjDwQFBcHOzg4tW7ZUXb9w4QLs7e0FJpSOL774Qu21i4tLgfeMHDmyrOIQvTcW9FTunTlzBkqlEl27dsXBgwfVlt5qa2vD0tIStWvXFpiQKhsuzSsdz549g4ODAwDAwMAAycnJAIB+/frx+KBiuH37Nvbt21dgfNSoUdyrXEQvX75Ebm7uG6/n5OTg5cuXZZhI+njM5/v56quvkJ6ejkOHDqFmzZrYv3+/2vXg4GAMGzZMUDpp8ff3Fx2BqESxoKdy7/U5odHR0ahXrx6LKRKOh4OUjrp16yIuLg716tVDgwYNcPLkSbRo0QJXrlxRLY2kf1ejRg2EhobCxsZGbTw0NJSrSoqofv36uHr1Kuzs7Aq9fvXq1QIzzfR2X375JXR1dTFnzhykp6dj+PDhqF27Nr777jsMHTpUdLxyTy6Xw9fXF76+voVe/2eBT0SVBwt6KtfCw8Nhb28PuVyO5OTktzbOeX3eMlFpK6zzNb2/QYMG4fTp02jTpg0mTZoEFxcXbNmyBbGxsfDw8BAdTzLGjBmDsWPHIioqCh999BGA/Nm7pUuXqp1hTW/m5OSEr7/+Gj169IC5ubnatWfPnmHOnDmFLtOlt3N2doazszOP+SQiKkE8h57Ktb+fFSqXyyGTyQqdHZXJZDwvlKiCuXTpEi5cuAAbGxs2HysGpVKJVatW4dtvv8XTp08BALVr18aMGTMwefJkrnIqglevXqFdu3aIjY2Fi4sLbG1tAQB37tzBzp07YWFhgUuXLsHQ0FBwUqoMTE1Ni/xzm5iYWMppiKi84Qw9lWvR0dGoUaOG6vdEVHElJCSgWrVqAIBHjx7h2LFjyMjIUDt7md4uNzcXu3btwvDhw+Hh4YFXr14BAAvPYjI0NERwcDBmz56NvXv3qvbLm5iYwMXFBQsXLuQ9LaKuXbsW6X1BQUGlnES6/t77IiEhAQsWLECvXr3Qrl07AMDFixdx4sQJ9hohqqQ4Q09EREJFRESgf//+ePToEWxsbLBnzx588sknSEtLg1wuR1paGg4cOICBAweKjioJenp6uH37Nvd4lxClUokXL15AqVSiRo0aXOFQTHK5HJaWlujbty+0tLTe+L6VK1eWYSrpGjx4MLp06YKJEyeqja9duxanTp1CYGCgmGBEJAwLeirXfvzxxyK/99NPPy3FJERUWnr37g1NTU3MmjULO3bswE8//YRevXph8+bNAIBJkybh2rVruHTpkuCk0tC5c2dMnTqVD0BKQEZGBpRKpeo8+piYGBw+fBiNGzdGr169BKeThm+++Qb+/v5ISEiAs7MzRo0axePV3oOBgQFCQ0PRsGFDtfEHDx6gWbNmSE1NFZSMiERhQU/lmlwuV3v9zz30f58p4R56ImmqXr06goKC4OjoiNTUVBgZGeHKlSuqM5bv3LmDtm3bIikpSWxQidi3bx9mz54NDw8PtGzZEvr6+mrX2UC06Hr27AknJyeMGzcOSUlJsLW1hba2Nl68eIEVK1Zg/PjxoiNKxsWLF7F161bs27cPtra2GDVqFIYPHw4jIyPR0STF0tISkydPxvTp09XGv/32W6xevRoxMTGCkhGRKCzoSTJOnTqFmTNnYtGiRWr7xubMmYNFixahR48eghMS0bv4e/NLIH//clhYGKytrQEA8fHxqF27Nh/aFdE/H4QCfz0MZQPR4qlevTrOnj2Lpk2bws/PD2vWrMGNGzdw8OBBeHl54fbt26IjSk56ejr279+PdevW4datW3j69CmL+mIICAjAl19+id69e6NNmzYAgMuXL+P48ePYvHkzXF1dxQYkojLHpngkGVOnTsXGjRvx8ccfq8Z69eoFPT09jB07ll+siCTsn/uSuU/53bGBaMlJT09XNb87efIknJycIJfL0bZtW86EvqPr16/j7NmzuH37Nuzt7d+6r54KcnV1RePGjbF69WocOnQIANC4cWOcP39eVeATUeXCgp4kIzIyEiYmJgXGjY2N8fDhwzLPQ0Qlx9XVFTo6OgCAzMxMjBs3TrVUPCsrS2Q0SUlJScG9e/eQnZ2N1q1bq04JoXfTsGFDBAYGYtCgQThx4gQ8PDwAAH/++SdnlYvh6dOnCAgIQEBAAFJSUuDi4oLLly+jSZMmoqNJUps2bbBz507RMYionOCSe5KMjh07okqVKtixYwfMzc0B5C/FHTlyJDIzM3H27FnBCYnoXbi5uRXpff7+/qWcRNpCQ0PRp08fxMfHQ6lUwtDQEPv27WPztvdw4MABDB8+HAqFAt26dcPJkycBAIsXL8a5c+fwyy+/CE5Y/vXp0wdnzpxBz549MWrUKPTt2xeampxPeh+RkZHw9/dHVFQUVq1aBTMzM/zyyy+oV68emjZtKjoeEZUxFvQkGQ8ePMCgQYNw7949WFhYAIDqmKvAwMACHV+JiCqTXr16ITU1FcuXL0eVKlUwf/58RERE4P79+6KjSdqzZ88QFxeHDz74QNWfICQkBEZGRrCzsxOcrvyTy+WoVasWzMzM3rqV5vr162WYSrrOnj2L3r17o3379jh37hxu374Na2trLFmyBFevXsWBAwdERySiMsaCniRFqVTi119/xZ07dwDk7xvr3r0799sSUaVXvXp1nDx5Ei1atAAAJCUloWrVqkhKSuLycBLGx8enSO+bN29eKSepGNq1a4fPPvsM06ZNU2sgGhISAicnJzx+/Fh0RCIqYyzoSZIyMzOho6PDQp6I6P/987QAIP/EgPDwcFhZWQlMJm1Xr17Fvn37EBsbi+zsbLVrr5uSEZUVAwMDREREwMrKSq2gf/jwIezs7JCZmSk6IhGVsYJn2xCVU3l5eZg/fz7q1KkDAwMDVSfnuXPnYsuWLYLTERGJd+vWLYSHh6t+KZVK3L59W22Mim7Pnj346KOPcPv2bRw+fBg5OTn4448/EBQUBGNjY9HxJCc3NxenTp3Cpk2b8OrVKwD5DfNSU1MFJ5MOExMTxMXFFRi/ceMG6tSpIyAREYnGriQkGQsWLMC2bduwbNkyjBkzRjVub2+PVatWYfTo0QLTERGJ161bN/xz4V2/fv14Dv07WrRoEVauXIkJEybA0NAQ3333HaysrODu7o5atWqJjicpMTEx+OSTTxAbG4usrCz06NEDhoaGWLp0KbKysrBx40bRESVh6NChmDlzJvbv3w+ZTIa8vDwEBwfD09MTI0eOFB2PiATgknuSjIYNG2LTpk3o1q2b2jKzO3fuoF27dnj58qXoiEREwhT1XHRLS8tSTlJx6Ovr448//kD9+vVRrVo1/Pbbb3BwcMDt27fRtWvXQmdKqXADBw6EoaEhtmzZgmrVqqn+Dv/tt98wZswYNm8souzsbEyYMAEBAQFQKBTQ1NSEQqHA8OHDERAQAA0NDdERiaiMcYaeJOPJkyeFdrLPy8tDTk6OgEREROWDk5MTAgICYGRkhO3bt2PIkCHQ0dERHUvyTE1NVUvD69Spg5s3b8LBwQFJSUlIT08XnE5afv/9d1y4cAHa2tpq4/Xr18eTJ08EpZIebW1tbN68GV5eXoiIiEBqaiqaN28OGxsb0dGISBDuoSfJaNKkCX7//fcC4wcOHEDz5s0FJCIiKh9++uknpKWlAQDc3NyQnJwsOFHF0LFjR/z6668AgM8++wxTpkzBmDFjMGzYMHTr1k1wOmnJy8srdLvH48ePYWhoKCCRNPn6+iI9PR0WFhbo06cPPv/8c9jY2CAjIwO+vr6i4xGRAFxyT5Jx5MgRfPHFF5g9ezZ8fX3h4+ODu3fvYvv27fjpp5/Qo0cP0RGJiIRwdHREixYt0KVLF7i5uWH16tVvPKqO+2yLLjExEZmZmahduzby8vKwbNkyXLhwATY2NpgzZw5MTU1FR5SMIUOGwNjYGN9//73q9IUaNWpgwIABqFevHvz9/UVHlAQNDQ3ExcWpnWYBAAkJCTAzM2OPDKJKiAU9Scrvv/8OX19fhIWFITU1FS1atICXlxd69uwpOhoRkTAXLlzAtGnTEBkZicTERBgaGhZ6rKdMJkNiYqKAhNKSkpJSpPe96aEJFfT48WP06tULSqUS9+/fR6tWrXD//n1Ur14d586dK1CgUuHkcjni4+NRo0YNtfGgoCAMGTIEz58/F5SMiERhQU+SkJubi0WLFmHUqFGoW7eu6DhEROWWXC5HXFwczM3NRUeRLLlcXugDkX/ibGjx5ObmYs+ePQgPD1c9lHd2doaurq7oaOWeqakpZDIZkpOTYWRkpPb5VCgUSE1Nxbhx47Bu3TqBKYlIBBb0JBkGBga4efMm6tevLzoKEVG5FRMTg3r16hWpIKXCnT17VvV7pVKJPn36wM/Pr8A53506dSrraJKVmZmJKlWqiI4hWdu2bYNSqcSoUaOwatUqGBsbq65pa2ujfv36aNeuncCERCQKC3qSjAEDBsDJyQlffPGF6ChEROXWlStXsHv3bty7dw8A0KhRIwwbNgwffvih4GTS9fejUundGBkZYdCgQXBxcUG3bt0gl7Mv87s4e/YsPvroI2hpaYmOQkTlBAt6koyNGzfCx8cHzs7OaNmyJfT19dWuf/rpp4KSERGVD1999RWWL18OAwMDVfEZGRmJ9PR0eHp6YunSpYITShML+vd3+PBh7Nq1Cz///DOMjY0xZMgQuLi4oFWrVqKjSVZmZiays7PVxtjXgajyYUFPkvG2p/kymYx7GYmoUtu2bRvGjRuHb775Bu7u7qoZvJycHGzYsAEzZ87Epk2b2OX+HbCgLzmvXr3CgQMHsHv3bgQFBcHa2houLi7w8vISHU0S0tPT8dVXX2Hfvn1ISEgocJ3fhYgqHxb0REREFUDr1q0xbNgweHh4FHp9xYoV2LNnD0JCQso4mfS9PmbNyspKdJQK5datW3B2dkZ4eDgL0SKaMGECzpw5g/nz52PEiBFYt24dnjx5gk2bNmHJkiVwdnYWHZGIyhgLeir3goKCMHHiRFy6dKnAUrLk5GR89NFH2LhxIzp06CAoIRGRePr6+oiIiHjjLHJUVBQcHByQlpZWxsmkx8nJSe310aNH0bVr1wJbvQ4dOlSWsSqEzMxM/Pjjj9i1axeOHz8Oc3NzDBs2DEuWLBEdTRLq1auH7du3o3PnzjAyMsL169fRsGFD7NixA7t378axY8dERySiMqYpOgDRv1m1ahXGjBlT6L4wY2NjuLu7Y8WKFSzoiahS09DQKLCf9u9ycnKgoaFRhomk6+8dxAHAxcVFUJKK48SJE9i1axcCAwOhqamJ//znPzh58iQ6duwoOpqkJCYmqh7aGRkZITExEQDw8ccfY/z48SKjEZEgLOip3AsLC3trI6eePXti+fLlZZiIiKj8adGiBXbu3In58+cXen3Hjh1o0aJFGaeSJn9/f9ERKpxBgwahX79+2L59O/r06cMu7e/I2toa0dHRqFevHuzs7LBv3z60bt0aR48ehYmJieh4RCQAC3oq9+Lj49/6F7+mpiaeP39ehomIiMofT09PDBw4EFlZWZg+fTrMzc0BAM+ePcO3336LVatW4fDhw4JTUmUVHx8PQ0ND0TEkz83NDWFhYejUqRNmzZqF/v37Y+3atcjJycGKFStExyMiAbiHnsq9Bg0a4Ntvv8XAgQMLvX7o0CF4enoiKiqqbIMREZUza9asgaenJ3Jzc1XLxpOTk6GpqYlly5ZhypQpghNSZZKSkqLaLpeSkvLW9/K4tXcTExODa9euoWHDhnB0dBQdh4gEYEFP5d6kSZPw22+/4cqVK6hSpYratYyMDLRu3RpdunTB6tWrBSUkIio/Hj9+jP379+P+/fsAgEaNGmHw4MGwsLAQnIwqGw0NDcTFxcHMzAxyuRwymazAe5RKJY+eJSJ6DyzoqdyLj49HixYtoKGhgYkTJ8LW1hYAcOfOHaxbtw4KhQLXr19XLS8lIiIi8c6ePYv27dtDU1MTZ8+efet7O3XqVEappO/KlSs4c+YM/vzzT+Tl5ald47J7osqHBT1JQkxMDMaPH48TJ07g9UdWJpOhV69eWLduHc8GJiL6m6dPn+L8+fOFfuGfPHmyoFRUmcXGxsLCwqLALL1SqcSjR49Qr149QcmkZdGiRZgzZw5sbW1hbm6udj9lMhmCgoIEpiMiEVjQk6S8fPkSDx48gFKphI2NDUxNTUVHIiIqVwICAuDu7g5tbW1Uq1atwBd+9hshEf6+/P7vEhISYGZmxiX3RWRubo6lS5fC1dVVdBQiKidY0BMREVUgFhYWGDduHGbPng25XC46DhEAQC6XIz4+HjVq1FAbj4mJQZMmTZCWliYombTUqlUL586dg42NjegoRFRO8Ng6IiKiCiQ9PR1Dhw5lMU/lwrRp0wDkrw6ZO3cu9PT0VNcUCgUuX76MZs2aCUonPR4eHli3bh1WrVolOgoRlROcoSciIqpAvvrqK1StWhWzZs0SHYUIXbp0AZDfIK9du3bQ1tZWXdPW1kb9+vXh6enJGeciysvLQ9++fXHv3j00adIEWlpaatcPHTokKBkRicKCnoiIqAJRKBTo168fMjIy4ODgUOALP7tgkwhubm747rvveN78e5o4cSL8/PzQpUuXAk3xAMDf319QMiIShQU9ERFRBbJgwQJ4eXmxCzaVK8nJyVAoFKhataraeGJiIjQ1NVnoF5GhoSH27NmDvn37io5CROUE99ATERFVIN9++y22bt3KLthUrgwdOhT9+/fHf//7X7Xxffv24ccff8SxY8cEJZOWqlWrokGDBqJjEFE5wo45REREFYiOjg7at28vOgaRmsuXL6v20/9d586dcfnyZQGJpMnb2xvz5s1Denq66ChEVE5whp6IiKgCmTJlCtasWYPVq1eLjkKkkpWVhdzc3ALjOTk5yMjIEJBImlavXo3IyEiYm5ujfv36BXpkXL9+XVAyIhKFBT0REVEFEhISgqCgIPz0009o2rQpu2BTudC6dWt8//33WLNmjdr4xo0b0bJlS0GppGfgwIGiIxBROcOmeERERBWIm5vbW6+zCzaJEBwcjO7du+PDDz9Et27dAACnT5/GlStXcPLkSXTo0EFwQiIiaWJBT0RERESlLjQ0FN988w1CQ0Ohq6sLR0dHzJ49m2fQExG9Bxb0RERERCREXl4ejh07hn79+omOUm5VrVoV9+7dQ/Xq1WFqalrg7Pm/S0xMLMNkRFQecA89ERFRBWJlZfXWL/xRUVFlmIaocA8ePMDWrVsREBCA58+fIycnR3SkcmvlypUwNDRU/f5tP99EVPlwhp6IiKgC+e6779Re5+Tk4MaNGzh+/DhmzJiBWbNmCUpGlV1GRgb2798PPz8/BAcHo0OHDhg6dCgGDRoEc3Nz0fGIiCSJBT0REVElsG7dOly9epVN8ajMXblyBX5+ftizZw8aNGgAZ2dnzJw5E+Hh4WjSpInoeJKioaGBuLg4mJmZqY0nJCTAzMwMCoVCUDIiEkUuOgARERGVvt69e+PgwYOiY1Al4+joiM8++wzVqlXDhQsXcP36dUyfPp3Lxt/Rm+bhsrKyoK2tXcZpiKg84B56IiKiSuDAgQOoWrWq6BhUydy9exdDhgxBly5dOBv/HlavXg0AkMlk8PPzg4GBgeqaQqHAuXPnYGdnJyoeEQnEgp6IiKgCad68udrsp1KpxLNnz/D8+XOsX79eYDKqjKKiohAQEIDx48cjIyMDw4YNg7OzM2foi2nlypUA8n+eN27cCA0NDdU1bW1t1K9fHxs3bhQVj4gE4h56IiKiCsTb21utWJLL5ahRowY6d+7MGTwSKigoCFu3bsWhQ4eQmZkJT09PfPnll2jUqJHoaJLRpUsXHDp0CKampqKjEFE5wYKeiIiIiMpMcnIydu7cia1bt+L69euwt7dHeHi46FiSpFAoEBERAUtLSxb5RJUUm+IRERFVAHK5HBoaGm/9panJnXYknrGxMf773//i6tWruH79Ojp37iw6kmRMnToVW7ZsAZBfzHfs2BEtWrSAhYUFfvvtN7HhiEgIztATERFVAEeOHHnjtYsXL2L16tXIy8tDZmZmGaYiypeRkQGlUgk9PT0AQExMDA4fPowmTZqgZ8+egtNJR506dXDkyBG0atUKgYGBmDBhAs6cOYMdO3YgKCgIwcHBoiMSURljQU9ERFRB3b17F7NmzcLRo0fh7OwMX19fWFpaio5FlVDPnj3h5OSEcePGISkpCba2ttDW1saLFy+wYsUKjB8/XnRESahSpQoePHiAunXrYuzYsdDT08OqVasQHR2NDz74ACkpKaIjElEZ45J7IiKiCubp06cYM2YMHBwckJubi9DQUGzbto3FPAlz/fp1dOjQAUD+EYo1a9ZETEwMtm/frjqSjf6dubk5bt26BYVCgePHj6NHjx4AgPT0dLXO90RUebCgJyIiqiCSk5Mxc+ZMNGzYEH/88QdOnz6No0ePwt7eXnQ0quTS09NhaGgIADh58iScnJwgl8vRtm1bxMTECE4nHW5ubvj8889hb28PmUyG7t27AwAuX77MUyyIKil2xyEiIqoAli1bhqVLl6JmzZrYvXs3BgwYIDoSkUrDhg0RGBiIQYMG4cSJE/Dw8AAA/PnnnzAyMhKcTjq8vb1hb2+PR48e4bPPPoOOjg4AQENDA7NnzxacjohE4B56IiKiCkAul0NXVxfdu3d/69LbQ4cOlWEqonwHDhzA8OHDoVAo0LVrV/z6668AgMWLF+PcuXP45ZdfBCcs3/r06YPdu3fD2NgYALBkyRKMGzcOJiYmAICEhAR06NABt27dEpiSiERgQU9ERFQBuLq6QiaT/ev7/P39yyANUUHPnj1DXFwcmjVrpvqshoSEwNjYGLa2toLTlW8aGhqIi4uDmZkZAMDIyAihoaGwtrYGAMTHx6N27dpQKBQiYxKRAFxyT0REVAEEBASIjkBUgJOTU5Hex5Ujb/fP+TfOxxHRayzoiYiIiKhUvF4iTkREpYMFPRERERGVCm7xKBkymazAlpqibLEhooqPBT0RERERUTmmVCrh6uqq6mqfmZmJcePGQV9fHwCQlZUlMh4RCcSmeERERERE5Zibm1uR3scVEUSVDwt6IiIiIiIiIgmSiw5ARERERERERMXHgp6IiIiIiIhIgljQExEREREREUkQC3oiIiIiIiIiCWJBT0RERERERCRBLOiJiIiIiIiIJIgFPREREREREZEEsaAnIiIiIiIikqD/AzA69HKa+K/4AAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "\n",
+ "--- Descriptive Statistics Complete ---\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### **Research:** Pose relevant questions about your dataset, then answer them using visual elements (e.g. charts or plots) to provide clear insights.\n",
+ "\n",
+ "For example, in the 2nd lecture the entire class took a survey. Then, we talked about the collected data and desplayed the collected data using the right **plots** - Lines, Bars, Hist, Pie, Map, HeatMap, Area, Time, etc.\n",
+ "\n",
+ "An aditional more specific example, would be the questions we asked during the recitation on the `Titanic` dataset:\n",
+ " - \"Did survival rates differ by gender?\"\n",
+ " - \"Was passenger class related to survival?\"\n",
+ " - \"What was the age distribution of survivors vs. non-survivors?\"\n",
+ " - \"Did embarking location (port) have any effect on survival?\" \n",
+ " \n",
+ "And how we answered those questions using **plots**.\n",
+ "\n",
+ "The idea is to pose questions that can uncover patterns, correlations, or anomalies in your dataset, then back those up with clean, insightful visualizations."
+ ],
+ "metadata": {
+ "id": "lo68PsjTK0_j"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# ===================================================================\n",
+ "# RESEARCH QUESTION 1: Geography vs. Churn Rate\n",
+ "# ===================================================================\n",
+ "# Question: \"Does the churn rate differ significantly by country?\"\n",
+ "# Plot: Grouped Bar Chart (using seaborn's countplot)\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# This assumes 'df' is the cleaned DataFrame from the previous step.\n",
+ "\n",
+ "print(\"--- Generating Plot 1: Churn Rate by Geography ---\")\n",
+ "\n",
+ "plt.figure(figsize=(10, 6)) # Set the figure size\n",
+ "\n",
+ "# Use sns.countplot to automatically count occurrences\n",
+ "# x='Geography': The main category on the x-axis\n",
+ "# hue='Exited': The variable that splits each bar (0 vs 1)\n",
+ "# palette='Set1': A color scheme for contrast\n",
+ "sns.countplot(x='Geography', hue='Exited', data=df, palette='Set1')\n",
+ "\n",
+ "plt.title('Customer Churn Count by Geography', fontsize=16)\n",
+ "plt.xlabel('Geography (Country)', fontsize=12)\n",
+ "plt.ylabel('Number of Customers', fontsize=12)\n",
+ "plt.legend(title='Exited', labels=['No (Stayed)', 'Yes (Churned)'])\n",
+ "\n",
+ "plt.show() #\n",
+ "\n",
+ "print(\"\\n--- Insight from Plot 1 ---\")\n",
+ "print(\"From this plot, we can visually compare the ratio of churned to stayed customers.\")\n",
+ "print(\"We can observe that while France has the most customers overall, Germany (in red)\")\n",
+ "print(\"appears to have a proportionally higher bar for 'Yes (Churned)' compared to\")\n",
+ "print(\"its 'No (Stayed)' bar, suggesting a higher churn rate in Germany.\")"
+ ],
+ "metadata": {
+ "id": "0ch5l8tIK1Dt",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 695
+ },
+ "outputId": "f19b37c6-f674-4385-e88b-16de428ce349"
+ },
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--- Generating Plot 1: Churn Rate by Geography ---\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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8+vbtq969e6t3795KTExUcnJyujOZ9uzZM83lKe/n1q1b07w+NrOfP3t/DoCHBeELeMQVLFhQkhQTE2PX7S5cuFB+fn6aOnWqKlasKF9fX7Vo0ULvvfee/vrrr0yPl/IPf3qTJ1gsFrMtrV9SixUrluZ6np6ed23PmzevJOnWrVvmslOnTkmSNm3aZDXRwZ2PTp06SVKaN6328/OTu7t7mtu7m5T38vLly2n+8pRR6e2rl5eXJKW6uD4rFC9e/K7thQsXlpOTU5ptKXXd+R7czf185i9dumRuJ73PWsmSJSVlfRi61/uS0f3PrPT2M2/evPL19ZX0z0Q3KfLkyaPBgwdLktV95BYsWKAbN26oTZs2CggIyPD2CxQoICntnxVJevfdd2X8cyaPDMNIM7Sk/EyOHj063Z/Jjz76yGo7tr7XKa/F3T7T9/q83639xo0bat++vapVq6aXXnpJH3zwgebNm2eGzpTPdVxcXJrrp7cvKcv//vvvNP+QkdnPX1Z/DoDcgqnmgUdcjRo19Nlnn2nv3r1KSkq6519bMytlpsB/e/LJJ3X69GmtWbNGW7Zs0fbt27V+/XqtXbtWY8eO1YoVK9SkSZMsreVu7jWDWkZmWEuRss+lSpVS3bp179o3rb+Mu7m5ZXhbd6pRo4YkKSEhQfv371f16tVtGicz+5oR6X0G7nSvfc7KmlJep6ioKF26dMkMEDntXq9TVr8vWckwDKvnzz77rMaPH6+FCxdq0qRJ8vT0NMPNkCFDMjV29erV9fvvv9/XbRFSXtt69eqZgSk9FStWtHk7d7rbDKX3mr30bj8PI0eO1IoVK1SuXDm99dZbevzxx1WgQAE5OztLkurUqaMdO3akek8yI611bfn8ZeXnAMgtCF/AI65Vq1YaPny4rl69qlWrVqldu3aZWj/lH/xr166l2X7mzJl013Vzc9PTTz9tnnJ28eJFjRo1SnPmzFHfvn3vuu6/pZzel/IX7rRERUVZ9c0uKX/NLVu2bLpT8GeHKlWqKCgoSFFRUVqwYIHN4Suz7uczkBMaNWqkvHnz6tq1a1q4cKGGDRuW4XV9fX3l4uKi+Ph4nTp1Ks3Tx1I+g//+nD1sr1OKlJ+bf7t27Zp5hKRo0aJWbb6+vurWrZvmzp2rhQsXqkyZMjp27JgqVKigxo0bZ2r7bdq00ddff63169frr7/+Mo+EZUbKz2Tbtm318ssvZ2gdW9/rlP+/24257+em3cuXL5ckLVu2LM2a7nVPuvTez5SaXF1ds+wPEln5OQByiwf3z2gA7KJkyZLq0qWLJOmll17S5cuX79o/JiZGx44dM5+n/KKR1v2fDMPQ2rVrM1xLwYIF9fbbb0uSzp49qytXrphtKb+4JiYmprluyjVUy5YtS/P0qxUrVujKlSvKmzeveeQjuzRp0kTOzs6KjIy06+mcFotFr732miRp5syZ2rVr1137JyYm6qeffrrv7d4ZfBMSElK1p1xv86Dw8vLSCy+8IEkaP358ur+Mprh+/bp++eUXSf+cSlWvXj1JSjdYf/rpp5L+CXl3utvPSnR0tPbu3ZvxnciAe/3MZNTnn3+e5qmmKfeFKlWqVJp/0Eh5jT/88EPztLOIiIhMb7979+4KDAzUrVu3FBERYdMRnebNm0v6Z18yur6t7/WTTz4pi8WiPXv26Lfffku1zpEjR7R///7MlG8l5Ts6MDAwVVtKQL2bRYsWpbl84cKFkv45OpjWdXG2yqrPAZBbEL4AaMaMGSpVqpSioqJUr149bd26NVWfhIQEffrppwoODrb65TE0NFTSP7+I3Tlxxu3btzVixAj9/PPPqcY6c+aM5s6dm+Y1Cd98840kKX/+/Oa1BNL//WX98OHDae5Dx44dVaxYMZ07d07Dhw+3+oUzKipKL730kiTp+eefT3PygKxUqFAhPf/887px44Zat26tgwcPpuoTHx+vVatWWV2InhX69++vp59+Wrdv31bTpk21YMGCVNd/GYah77//XnXq1NHSpUvve5uBgYEqXbq0rl69qsmTJ1u1RUZGasyYMfe9jaw2ZswY1alTR1evXlW9evXMz92dkpKStGLFCtWoUUNbtmwxl6d8lmbOnKlNmzZZrTN//nytWrVKTk5OevHFF63aUn5WJk+erKtXr5rLL168qJ49e1pNEJEVUn5m7ndCm3Pnzunll1+2+hwdPXpU48ePl6R0jxxWrlxZjRs31tGjR7Vq1Sp5eXmlO9nD3Tg7O+vzzz+Xq6urli9frnbt2qU7mcT27dvTDFdt27bV448/rl27dqlPnz5pXj925coVzZo1y+q7w5b3unjx4mrdurWSk5M1aNAgqyOdsbGxGjRo0H2dEpgywcW/b/B+7NgxDRw48J7r79mzx/wjV4qtW7fqww8/lJT++2mrrPocALlGjsyxCOCBc+HCBaNhw4bmtN5BQUFG27ZtjS5duhiNGzc2PD09zfsx7dy502rdtm3bGtI/Nw5u2rSp0aZNG6No0aKGl5eX8eKLL6aa7vqXX34x74/z+OOPG506dTI6depkBAcHm1Odz50712ob+/fvNxwcHAwHBwcjNDTU6NOnj9GvXz/j66+/NvvceZPlwMBA45lnnjFatGiRoZsspzcd99ixYw1JxtixY9NsT2968Nu3bxtdu3Y1JBkODg5GcHCw0aFDB+OZZ54x6tata94b584bLd95Q+H7kZCQYAwZMsScztrX19cIDw83unbtarRs2dIoXLiwIf1zg9gPP/zQXO9+poz/8ssvze1Vq1bN6Nixo1GjRg3DYrEYY8aMuedU8+nJyGtyrync03Pt2jXzRtj6/1Oqt2rVyujatasRFhZmfpZcXFyMlStXWq17541369WrZ3Tt2tWoXr26+bqmdePdK1eumLX6+fkZbdu2NUJDQw1vb2+jcuXK97zxbmZvZB4fH2/eeDc4ONjo2bOn0a9fP+Ptt9/O0OuTst2BAwcarq6uRlBQkNG5c2cjLCzMvMlyu3bt0px+PEXKvfAkGc8//3yGtpuen3/+2QgKCjJf90qVKhnt2rUzunfvbrRu3dp8baV/7tl37do1q/X//PNP8+bIHh4eRp06dYzOnTsb7du3N6pVq2beNP3f0+rb8l7/+eef5r0FfX19jfbt2xvt2rUzfHx8jNKlS5u3sFi8eLHVevd6rw3D+metcuXKRufOnY3GjRubNyCvU6eOIcnYvHmz1Xr/vslyxYoVjS5duhgNGjQwb7L84osvptqerZ+/O2Xl5wB42BG+AFhZu3at0bNnT6NUqVKGp6en4eTkZPj7+xtNmzY13n//fePSpUup1rl165YxatQoo0SJEoaTk5Ph5+dndOnSxThx4kSa/zDHxcUZ77//vtGuXTujdOnShqenp+Hh4WGUKVPG6Nmzp7F79+40a1uxYoVRt25dI2/evOYvH/8ORWfPnjUiIiKMEiVKGM7OzkbevHmNkJAQY+bMmWne3ye7wleKb7/91mjfvr3x2GOPGU5OTka+fPmM8uXLG507dzaWLFlidXPmrApfKQ4fPmy8+OKLRtWqVY18+fIZefLkMfLnz2/UqlXLeO2114zffvvNqv/9hC/DMIw1a9YYdevWNdzd3Q0PDw+jdu3axrJly+66Xk6GrxQ7duwwBgwYYJQvX97w8vIy8uTJYxQoUMCoX7++MXHixHTv1bZ27VqjRYsWhq+vr5EnTx7D39/f6NixY6o/Ttzpjz/+MHr27Gn4+fkZzs7ORlBQkPHKK68Y165du+d9vmz55ffgwYNGmzZtjIIFC5q/YN/t/U1vu3v37jVat25t+Pr6Gi4uLkbFihWNqVOn3vNm3teuXTMcHR0Ni8WS5g3VMyshIcFYsGCB0b59e6NYsWKGm5ub4ezsbPj5+Rn169c3Ro4caXWfrn+7deuWMWvWLKNRo0bm++bn52dUq1bNiIiIMNavX5/mera81zExMUZERIRRtGhRw9nZ2QgICDAiIiKMS5cuGY0bNzYkpdpeRsKXYRjGDz/8YDRp0sQoUKCA4e7ublSqVMmYOHGiER8fb4as9MLX5s2bjU2bNhlNmjQxvL29DTc3N6NmzZrG/Pnz09xWVoSvrP4cAA8zi2Hcx7FvAACAdMydO1fPPvusmjVrpvXr1+d0OQ+Eq1evqkSJEoqNjdWFCxdsmkDEFg0bNtSWLVu0efNm8xpZe+FzAPwfrvkCAABZ7saNG5o0aZKk/7t26lGS1oQ3Fy9eVK9evXTlyhW1atXKbsErJz3qnwPg35hqHgAAZJl33nlHhw4d0tatW3Xq1CmFh4erWbNmOV2W3dWqVUtFixZV+fLl5evrqz///FO//PKLrl+/rmLFilndeDg34nMApI3wBQAAskzKjdMLFCig3r17a+rUqTldUo4YNWqUNm3apP379+vKlStydnZWyZIlzXsrPig3984ufA6AtHHNFwAAAADYAdd8AQAAAIAdEL4AAAAAwA645ssGycnJOnfunPLmzSuLxZLT5QAAAADIIYZh6Nq1aypSpIgcHO5+bIvwZYNz584pICAgp8sAAAAA8ID4/fffVbRo0bv2IXzZIG/evJL+eYG9vLxyuBoAAAAAOSUuLk4BAQFmRrgbwpcNUk419PLyInwBAAAAyNDlSEy4AQAAAAB2QPgCAAAAADsgfAEAAACAHXDNFwAAAHI9wzCUmJiopKSknC4FDyEnJyc5Ojre9ziELwAAAORqCQkJOn/+vG7evJnTpeAhZbFYVLRoUXl6et7XOIQvAAAA5FrJycmKioqSo6OjihQpImdn5wzNSgekMAxDFy9e1B9//KHSpUvf1xEwwhcAAAByrYSEBCUnJysgIEDu7u45XQ4eUgULFtTp06d1+/bt+wpfTLgBAACAXM/BgV97YbusOlrKpxAAAAAA7IDwBQAAAAB2QPgCAAAAHjENGzbU0KFDs2Xs4sWL6/3338+WsR92hC8AAADgIdO7d29ZLJZUj/Dw8Ayt/9VXX2nChAnmcwKTfTDbIQAAAPAQCg8P17x586yWubi4ZGhdHx+f7CgJ98CRLwAAAOAh5OLiIn9/f6tH/vz5FRkZKWdnZ/34449m37ffflt+fn66cOGCJOvTDhs2bKgzZ85o2LBh5hG0FFu3btWTTz4pNzc3BQQE6IUXXtCNGzfM9piYGLVu3Vpubm4KCgrS4sWL7bPzDynCFwAAAJCLpASrHj16KDY2Vr/88otGjx6tuXPnqlChQqn6f/XVVypatKjGjx+v8+fP6/z585KkkydPKjw8XB06dNCBAwe0bNkybd26VUOGDDHX7d27t37//Xdt3rxZX3zxhT766CPFxMTYbV8fNpx2CAAAADyEVq9eLU9PT6tlr732ml577TX997//1caNGzVgwAAdOnRIvXr1Ups2bdIcx8fHR46OjsqbN6/8/f3N5ZMmTVK3bt3MI2SlS5fW9OnT1aBBA82cOVNnz57V2rVrtWvXLj3++OOSpE8++UTly5fPnh3OBQhfAAAAwEOoUaNGmjlzptWylGu5nJ2dtXjxYlWpUkWBgYF67733Mj3+/v37deDAAatTCQ3DUHJysqKiovTbb78pT548qlGjhtlerlw55cuXz7YdegQQvgAAAICHkIeHh0qVKpVu+/bt2yVJly9f1uXLl+Xh4ZGp8a9fv67nnntOL7zwQqq2YsWK6bfffstcweCaLwAAACC3OXnypIYNG6aPP/5YtWrVUq9evZScnJxuf2dnZyUlJVktq169uo4cOaJSpUqlejg7O6tcuXJKTEzUnj17zHWOHTumq1evZtduPfQ48vWA213ziZwuAXdRc/eunC4BAAA8ouLj4xUdHW21LE+ePMqfP7+6d++usLAw9enTR+Hh4apcubKmTJmiV155Jc2xihcvrh9++EGdO3eWi4uLChQooBEjRqh27doaMmSI+vfvLw8PDx05ckQbN27UBx98oLJlyyo8PFzPPfecZs6cqTx58mjo0KFyc3Ozx+4/lDjyBQAAADyE1q1bp8KFC1s96tWrp4kTJ+rMmTOaPXu2JKlw4cKaM2eORo0apf3796c51vjx43X69GmVLFlSBQsWlCRVqVJFW7Zs0W+//aYnn3xSwcHBGjNmjIoUKWKuN2/ePBUpUkQNGjRQ+/btNWDAAPn5+WX/zj+kLIZhGDldxMMmLi5O3t7eio2NlZeXV7ZuiyNfDzaOfAEA8GC7deuWoqKiFBQUJFdX15wuBw+pu32OMpMNOPIFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHaQJ6cLAAAAAHLC7ppP2HV7NXfvsuv2MqJ+/foaOHCgunbtmtOl2GT+/PkaOnSorl69KkmaNWuW1qxZo2+++SZnC0sHR74AAACAB1Dv3r1lsVj01ltvWS1fuXKlLBbLfY+/atUqXbhwQZ07dzaX7d+/X23atJGfn59cXV1VvHhxPfPMM4qJiZEkRUZGymKxmGHnQdO3b1/t3btXP/74Y06XkibCFwAAAPCAcnV11eTJk3XlypUsH3v69Onq06ePHBz+iQQXL15UkyZN5OPjo/Xr1+vo0aOaN2+eihQpohs3bmT59rODs7OzunbtqunTp+d0KWkifAEAAAAPqNDQUPn7+2vSpEl37ffll1+qYsWKcnFxUfHixTVlypS79r948aK+//57tW7d2ly2bds2xcbGau7cuQoODlZQUJAaNWqk9957T0FBQTp9+rQaNWokScqfP78sFot69+4tSVq3bp3q1aunfPnyydfXV61atdLJkyfNsRs3bqwhQ4akqsHZ2VmbNm2SJMXHx+vll1/WY489Jg8PD9WqVUuRkZFW68yfP1/FihWTu7u72rVrp0uXLqXat9atW2vVqlX6+++/7/oa5ATCFwAAAPCAcnR01JtvvqkZM2bojz/+SLPPnj171KlTJ3Xu3FkHDx7UuHHjNHr0aM2fPz/dcbdu3Sp3d3eVL1/eXObv76/ExEStWLFChmGkWicgIEBffvmlJOnYsWM6f/68pk2bJkm6ceOGhg8frt27d2vTpk1ycHBQu3btlJycLEnq37+/lixZovj4eHO8RYsW6bHHHlPjxo0lSUOGDNGOHTu0dOlSHThwQB07dlR4eLiOHz8uSdq5c6f69eunIUOGaN++fWrUqJH++9//pqqzZs2aSkxM1M6dO+/20uaIBzZ8vfXWW7JYLBo6dKi57NatW4qIiJCvr688PT3VoUMHXbhwwWq9s2fPqmXLlnJ3d5efn59eeeUVJSYmWvWJjIxU9erV5eLiolKlSt31gwkAAADkpHbt2qlatWoaO3Zsmu1Tp05VkyZNNHr0aJUpU0a9e/fWkCFD9M4776Q75pkzZ1SoUCHzlENJql27tl577TV17dpVBQoUUPPmzfXOO++Yv287OjrKx8dHkuTn5yd/f395e3tLkjp06KD27durVKlSqlatmj799FMdPHhQR44ckSS1b99ekvT111+b25s/f755XdvZs2c1b948ff7553ryySdVsmRJvfzyy6pXr57mzZsnSZo2bZrCw8P16quvqkyZMnrhhRcUFhaWat/c3d3l7e2tM2fOZPg1tpcHMnz9/PPPmj17tqpUqWK1fNiwYfrmm2/0+eefa8uWLTp37pz5RkpSUlKSWrZsqYSEBG3fvl0LFizQ/PnzNWbMGLNPVFSUWrZsqUaNGmnfvn0aOnSo+vfvr/Xr19tt/wAAAIDMmDx5shYsWKCjR4+majt69Kjq1q1rtaxu3bo6fvy4kpKS0hzv77//lqura6rlEydOVHR0tGbNmqWKFStq1qxZKleunA4ePHjX+o4fP64uXbqoRIkS8vLyUvHixSX9c2BE+ufatR49eujTTz+VJO3du1eHDh0yT1s8ePCgkpKSVKZMGXl6epqPLVu2mKcvHj16VLVq1bLabkhISJr1uLm56ebNm3etOSc8cOHr+vXr6tatmz7++GPlz5/fXB4bG6tPPvlEU6dOVePGjVWjRg3NmzdP27dv108//SRJ2rBhg44cOaJFixapWrVqat68uSZMmKAPP/xQCQkJkv6ZfjIoKEhTpkxR+fLlNWTIED399NN67733cmR/AQAAgHupX7++wsLCNHLkyCwZr0CBAulO4uHr66uOHTvq3Xff1dGjR1WkSBG9++67dx2vdevWunz5sj7++GPt3LnTPOUv5Xdw6Z9TDzdu3Kg//vhD8+bNU+PGjRUYGCjpnwzg6OioPXv2aN++febj6NGj5qmNmXH58mUVLFgw0+tltwcufEVERKhly5YKDQ21Wr5nzx7dvn3banm5cuVUrFgx7dixQ5K0Y8cOVa5cWYUKFTL7hIWFKS4uTocPHzb7/HvssLAwc4y0xMfHKy4uzuoBAAAA2NNbb72lb775JtXvreXLl9e2bduslm3btk1lypSRo6NjmmMFBwcrOjr6nrMoOjs7q2TJkuZsh87OzpJkdUTt0qVLOnbsmEaNGqUmTZqofPnyaY5buXJl1axZUx9//LGWLFmivn37WtWTlJSkmJgYlSpVyurh7+9v7ue/r+NKOQhzp5MnT+rWrVsKDg6+677lhAcqfC1dulR79+5NczaX6OhoOTs7K1++fFbLCxUqpOjoaLPPncErpT2l7W594uLi0p0RZdKkSfL29jYfAQEBNu0fAAAAYKvKlSurW7duqaZRf+mll7Rp0yZNmDBBv/32mxYsWKAPPvhAL7/8crpjBQcHq0CBAlahbfXq1erevbtWr16t3377TceOHdO7776rb7/9Vm3btpUkBQYGymKxaPXq1bp48aKuX7+u/Pnzy9fXV3PmzNGJEyf0/fffa/jw4Wlut3///nrrrbdkGIbatWtnLi9Tpoy6deumnj176quvvlJUVJR27dqlSZMmac2aNZKkF154QevWrdO7776r48eP64MPPtC6detSbePHH39UiRIlVLJkyYy/uHaSJ6cLSPH777/rxRdf1MaNG9M8/zQnjRw50uoDFBcXRwADAAB4yNXcvSunS8i08ePHa9myZVbLqlevruXLl2vMmDGaMGGCChcurPHjx5vXU6XF0dFRffr00eLFi9WqVStJUoUKFeTu7q6XXnpJv//+u1xcXFS6dGnNnTtXPXr0kCQ99thjeuONN/Sf//xHffr0Uc+ePTV//nwtXbpUL7zwgipVqqSyZctq+vTpatiwYartdunSRUOHDlWXLl1S/c4/b948/fe//9VLL72kP//8UwUKFFDt2rXN+mrXrq2PP/5YY8eO1ZgxYxQaGqpRo0ZpwoQJVuP873//07PPPpvZl9YuLEZa80jmgJUrV6pdu3ZWh0aTkpJksVjk4OCg9evXKzQ0VFeuXLE6+hUYGKihQ4dq2LBhGjNmjFatWqV9+/aZ7VFRUSpRooT27t2r4OBg1a9fX9WrV9f7779v9pk3b56GDh2q2NjYDNUaFxcnb29vxcbGysvL6353/a5213wiW8fH/XkYv7QBAHiU3Lp1S1FRUQoKCnrg/sCf06Kjo1WxYkXt3bvXvPYqu50+fVolS5bUzz//rOrVq2f5+IcPH1bjxo3122+/mTMxZoW7fY4ykw0emNMOmzRpooMHD1pdYFezZk1169bN/H8nJyfzJmzSP/cXOHv2rDnLSUhIiA4ePKiYmBizz8aNG+Xl5aUKFSqYfe4cI6VPejOlAAAAALmRv7+/PvnkE3NGwux0+/ZtRUdHa9SoUapdu3a2BC9JOn/+vBYuXJilwSsrPTCnHebNm1eVKlWyWubh4SFfX19zeb9+/TR8+HD5+PjIy8tLzz//vEJCQlS7dm1JUrNmzVShQgX16NFDb7/9tvkGR0REyMXFRZI0cOBAffDBB3r11VfVt29fff/991q+fLl5LikAAADwqHjqqafssp1t27apUaNGKlOmjL744ots286/J9Z70Dww4Ssj3nvvPTk4OKhDhw6Kj49XWFiYPvroI7Pd0dFRq1ev1qBBgxQSEiIPDw/16tVL48ePN/sEBQVpzZo1GjZsmKZNm6aiRYtq7ty5ad6gDQAAAMD9a9iwoR6Qq51y1ANzzdfDhGu+kIJrvgAAeLBxzReyQq675gsAAAAAcjPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7eKimmgcAAACySvjoZXbd3roJz9h1e5k1evRoXbhwQXPmzMnUehaLRStWrLDbPcOyQsOGDVWtWjW9//77kqTatWvrlVdeUYcOHbJ1uxz5AgAAAB4whmEoNDQ0zXvRfvTRR8qXL5/++OOPLNtedHS0pk2bptdffz3V8ueff14lSpSQi4uLAgIC1Lp1a23atCnLtv0gGDVqlP7zn/8oOTk5W7dD+AIAAAAeMBaLRfPmzdPOnTs1e/Zsc3lUVJReffVVzZgxQ0WLFs2y7c2dO1d16tRRYGCguez06dOqUaOGvv/+e73zzjs6ePCg1q1bp0aNGikiIiLLtp2WhISEbB3/35o3b65r165p7dq12bodwhcAAADwAAoICNC0adP08ssvKyoqSoZhqF+/fmrWrJmCg4PVvHlzeXp6qlChQurRo4f++usvc90vvvhClStXlpubm3x9fRUaGqobN26ku62lS5eqdevWVssGDx4si8WiXbt2qUOHDipTpowqVqyo4cOH66effrLq+9dff6ldu3Zyd3dX6dKltWrVKrNt/vz5ypcvn1X/lStXymKxmM/HjRunatWqae7cuVY3MrZYLJo7d266Y0vSoUOH7vpa3LhxQz179pSnp6cKFy6sKVOmpNp/R0dHtWjRQkuXLk33NcoKhC8AAADgAdWrVy81adJEffv21QcffKBDhw5p9uzZaty4sYKDg7V7926tW7dOFy5cUKdOnSRJ58+fV5cuXdS3b18dPXpUkZGRat++vQzDSHMbly9f1pEjR1SzZk2rZevWrVNERIQ8PDxSrfPvMPXGG2+oU6dOOnDggFq0aKFu3brp8uXLmdrXEydO6Msvv9RXX32lffv2ZWjsq1ev3vW1kKRXXnlFW7Zs0ddff60NGzYoMjJSe/fuTbX9J554Qj/++GOmas4sJtwAAAAAHmBz5sxRxYoV9cMPP+jLL7/U7NmzFRwcrDfffNPs8+mnnyogIEC//fabrl+/rsTERLVv3948jbBy5crpjn/27FkZhqEiRYqYy06cOCHDMFSuXLkM1di7d2916dJFkvTmm29q+vTp2rVrl8LDwzO8nwkJCVq4cKEKFiyY4bE/+OCDu74WRYoU0SeffKJFixapSZMmkqQFCxakecpmkSJF9Pvvvys5OVkODtlzjIojXwAAAMADzM/PT88995zKly+vp556Svv379fmzZvl6elpPlJC0smTJ1W1alU1adJElStXVseOHfXxxx/rypUr6Y7/999/S5J5qp+kdI+SpadKlSrm/3t4eMjLy0sxMTGZGiMwMDBV8LrX2Pd6LU6ePKmEhATVqlXLHMPHx0dly5ZNtR03NzclJycrPj4+U3VnBke+AAAAgAdcnjx5lCfPP7+6X79+Xa1bt9bkyZNT9StcuLAcHR21ceNGbd++XRs2bNCMGTP0+uuva+fOnQoKCkq1ToECBSRJV65cMcNP6dKlZbFY9Ouvv2aoPicnJ6vnFovFnDnQwcEhVZi7fft2qjHSOr3xXmPf67U4ceJEhuqX/jnV0sPDQ25ubhleJ7M48gUAAAA8RKpXr67Dhw+rePHiKlWqlNUjJcBYLBbVrVtXb7zxhn755Rc5OztrxYoVaY5XsmRJeXl56ciRI+YyHx8fhYWF6cMPP0xzoo6rV69muN6CBQvq2rVrVuPceU3X/bjXa1GyZEk5OTlp586d5jpXrlzRb7/9lmqsQ4cOKTg4OEvqSg/hCwAAAHiIRERE6PLly+rSpYt+/vlnnTx5UuvXr1efPn2UlJSknTt36s0339Tu3bt19uxZffXVV7p48aLKly+f5ngODg4KDQ3V1q1brZZ/+OGHSkpK0hNPPKEvv/xSx48f19GjRzV9+nSFhIRkuN5atWrJ3d1dr732mk6ePKklS5Zo/vz59/MSmO71Wnh6eqpfv3565ZVX9P333+vQoUPq3bt3mtd0/fjjj2rWrFmW1JUeTjsEAADAI2ndhGdyugSbFClSRNu2bdOIESPUrFkzxcfHKzAwUOHh4XJwcJCXl5d++OEHvf/++4qLi1NgYKCmTJmi5s2bpztm//799eyzz+rtt982g0mJEiW0d+9eTZw4US+99JLOnz+vggULqkaNGpo5c2aG6/Xx8dGiRYv0yiuv6OOPP1aTJk00btw4DRgwINtfC0l65513zNMT8+bNq5deekmxsbFW4/z555/avn27Fi1adN813Y3FyOzVdFBcXJy8vb0VGxsrLy+vbN3W7ppPZOv4uD81d+/K6RIAAMBd3Lp1S1FRUVb3jkJqhmGoVq1aGjZsmDmz4KNkxIgRunLliubMmZNm+90+R5nJBpx2CAAAADziLBaL5syZo8TExJwuJUf4+flpwoQJ2b4dTjsEAAAAoGrVqqlatWo5XUaOeOmll+yyHY58AQAAAIAdEL4AAAAAwA4IXwAAAMj1mGMO9yOrPj+ELwAAAORaTk5OkqSbN2/mcCV4mCUkJEiSHB0d72scJtwAAABAruXo6Kh8+fIpJiZGkuTu7i6LxZLDVeFhkpycrIsXL8rd3V158txffCJ8AQAAIFfz9/eXJDOAAZnl4OCgYsWK3XdwJ3wBAAAgV7NYLCpcuLD8/Px0+/btnC4HDyFnZ2c5ONz/FVuELwAAADwSHB0d7/uaHeB+MOEGAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAcPVPiaOXOmqlSpIi8vL3l5eSkkJERr16412xs2bCiLxWL1GDhwoNUYZ8+eVcuWLeXu7i4/Pz+98sorSkxMtOoTGRmp6tWry8XFRaVKldL8+fPtsXsAAAAAHmF5crqAOxUtWlRvvfWWSpcuLcMwtGDBArVt21a//PKLKlasKEl69tlnNX78eHMdd3d38/+TkpLUsmVL+fv7a/v27Tp//rx69uwpJycnvfnmm5KkqKgotWzZUgMHDtTixYu1adMm9e/fX4ULF1ZYWJh9dxgAAADAI8NiGIaR00XcjY+Pj9555x3169dPDRs2VLVq1fT++++n2Xft2rVq1aqVzp07p0KFCkmSZs2apREjRujixYtydnbWiBEjtGbNGh06dMhcr3Pnzrp69arWrVuX5rjx8fGKj483n8fFxSkgIECxsbHy8vLKup1Nw+6aT2Tr+Lg/NXfvyukSAAAAkIPi4uLk7e2doWzwQJ12eKekpCQtXbpUN27cUEhIiLl88eLFKlCggCpVqqSRI0fq5s2bZtuOHTtUuXJlM3hJUlhYmOLi4nT48GGzT2hoqNW2wsLCtGPHjnRrmTRpkry9vc1HQEBAVu0mAAAAgEfEA3XaoSQdPHhQISEhunXrljw9PbVixQpVqFBBktS1a1cFBgaqSJEiOnDggEaMGKFjx47pq6++kiRFR0dbBS9J5vPo6Oi79omLi9Pff/8tNze3VDWNHDlSw4cPN5+nHPkCAAAAgIx64MJX2bJltW/fPsXGxuqLL75Qr169tGXLFlWoUEEDBgww+1WuXFmFCxdWkyZNdPLkSZUsWTLbanJxcZGLi0u2jQ8AAAAg93vgTjt0dnZWqVKlVKNGDU2aNElVq1bVtGnT0uxbq1YtSdKJEyckSf7+/rpw4YJVn5Tn/v7+d+3j5eWV5lEvAAAAAMgKD1z4+rfk5GSryS7utG/fPklS4cKFJUkhISE6ePCgYmJizD4bN26Ul5eXeepiSEiINm3aZDXOxo0bra4rAwAAAICs9kCddjhy5Eg1b95cxYoV07Vr17RkyRJFRkZq/fr1OnnypJYsWaIWLVrI19dXBw4c0LBhw1S/fn1VqVJFktSsWTNVqFBBPXr00Ntvv63o6GiNGjVKERER5mmDAwcO1AcffKBXX31Vffv21ffff6/ly5drzZo1ObnrAAAAAHK5Byp8xcTEqGfPnjp//ry8vb1VpUoVrV+/Xk2bNtXvv/+u7777Tu+//75u3LihgIAAdejQQaNGjTLXd3R01OrVqzVo0CCFhITIw8NDvXr1srovWFBQkNasWaNhw4Zp2rRpKlq0qObOncs9vgAAAABkqwf+Pl8PoszM5X+/uM/Xg437fAEAADzacsV9vgAAAAAgNyF8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADsgPAFAAAAAHZA+AIAAAAAOyB8AQAAAIAdEL4AAAAAwA4IXwAAAABgB4QvAAAAALADwhcAAAAA2AHhCwAAAADswKbwtWnTJr3zzjtWyz799FMVK1ZMhQoV0rBhw5SUlJQlBQIAAABAbmBT+Bo3bpz2799vPj948KCee+45FSxYUA0bNtT06dP17rvvZlmRAAAAAPCwsyl8HT16VDVr1jSff/bZZ/Ly8tKPP/6oZcuW6dlnn9XChQszPe7MmTNVpUoVeXl5ycvLSyEhIVq7dq3ZfuvWLUVERMjX11eenp7q0KGDLly4YDXG2bNn1bJlS7m7u8vPz0+vvPKKEhMTrfpERkaqevXqcnFxUalSpTR//vxM1woAAAAAmWFT+Lpx44a8vLzM5+vWrVN4eLjc3d0lSY8//rjOnDmT6XGLFi2qt956S3v27NHu3bvVuHFjtW3bVocPH5YkDRs2TN98840+//xzbdmyRefOnVP79u3N9ZOSktSyZUslJCRo+/btWrBggebPn68xY8aYfaKiotSyZUs1atRI+/bt09ChQ9W/f3+tX7/elpcCAAAAADLEpvAVEBCgn3/+WZJ04sQJHTp0SM2aNTPbL1++LBcXl0yP27p1a7Vo0UKlS5dWmTJlNHHiRHl6euqnn35SbGysPvnkE02dOlWNGzdWjRo1NG/ePG3fvl0//fSTJGnDhg06cuSIFi1apGrVqql58+aaMGGCPvzwQyUkJEiSZs2apaCgIE2ZMkXly5fXkCFD9PTTT+u9996z5aUAAAAAgAyxKXx169ZNc+bMUZs2bRQWFqb8+fOrbdu2ZvuePXtUpkyZ+yosKSlJS5cu1Y0bNxQSEqI9e/bo9u3bCg0NNfuUK1dOxYoV044dOyRJO3bsUOXKlVWoUCGzT1hYmOLi4syjZzt27LAaI6VPyhhpiY+PV1xcnNUDAAAAADLDpvD1+uuv6z//+Y9+//13FStWTCtXrlS+fPkk/XPUKzIyUm3atLGpoIMHD8rT01MuLi4aOHCgVqxYoQoVKig6OlrOzs7mdlIUKlRI0dHRkqTo6Gir4JXSntJ2tz5xcXH6+++/06xp0qRJ8vb2Nh8BAQE27RsAAACAR1eezK5gGIZu3ryp0aNHa+LEianafXx8zKBji7Jly2rfvn2KjY3VF198oV69emnLli02j5cVRo4cqeHDh5vP4+LiCGAAAAAAMiXTR74SEhLk4+OjGTNmZEc9cnZ2VqlSpVSjRg1NmjRJVatW1bRp0+Tv76+EhARdvXrVqv+FCxfk7+8vSfL39081+2HK83v18fLykpubW5o1ubi4mDMwpjwAAAAAIDMyHb5cXFzk7+8vZ2fn7KgnleTkZMXHx6tGjRpycnLSpk2bzLZjx47p7NmzCgkJkSSFhITo4MGDiomJMfts3LhRXl5eqlChgtnnzjFS+qSMAQAAAADZwaZrvnr37q2FCxeaMwhmlZEjR+qHH37Q6dOndfDgQY0cOVKRkZHq1q2bvL291a9fPw0fPlybN2/Wnj171KdPH4WEhKh27dqSpGbNmqlChQrq0aOH9u/fr/Xr12vUqFGKiIgwZ18cOHCgTp06pVdffVW//vqrPvroIy1fvlzDhg3L0n0BAAAAgDtl+povSapcubJWrlypihUrqnfv3ipevHiap+zdeQ+ujIiJiVHPnj11/vx5eXt7q0qVKlq/fr2aNm0qSXrvvffk4OCgDh06KD4+XmFhYfroo4/M9R0dHbV69WoNGjRIISEh8vDwUK9evTR+/HizT1BQkNasWaNhw4Zp2rRpKlq0qObOnauwsDBbXgoAAAAAyBCLYRhGZldycLj3ATOLxaKkpCSbinrQxcXFydvbW7Gxsdl+/dfumk9k6/i4PzV378rpEgAAAJCDMpMNbDrytXnzZpsKAwAAAIBHlU3hq0GDBlldBwAAAADkajaFrxTx8fHau3evYmJiVLduXRUoUCCr6gIAAACAXMWm2Q4lafr06SpcuLDq1aun9u3b68CBA5Kkv/76SwUKFNCnn36aZUUCAAAAwMPOpvA1b948DR06VOHh4frkk09055wdBQoUUOPGjbV06dIsKxIAAAAAHnY2ha8pU6aobdu2WrJkiVq3bp2qvUaNGjp8+PB9FwcAAAAAuYVN4evEiRNq3rx5uu0+Pj66dOmSzUUBAAAAQG5jU/jKly+f/vrrr3Tbjxw5In9/f5uLAgAAAIDcxqbw1aJFC82ZM0dXr15N1Xb48GF9/PHHatOmzf3WBgAAAAC5hk3h67///a+SkpJUqVIljRo1ShaLRQsWLFD37t1Vs2ZN+fn5acyYMVldKwAAAAA8tGwKX0WKFNGePXsUHh6uZcuWyTAMffbZZ/rmm2/UpUsX/fTTT9zzCwAAAADuYPNNlv38/DR37lzNnTtXFy9eVHJysgoWLCgHB5tvHQYAAAAAuZbN4etOBQsWzIphAAAAACDXsjl83bhxQ19++aVOnTqlK1euWN1oWZIsFoumTZt23wUCAAAAQG5gU/jatGmTOnbsmOZshykIXwAAAADwf2y6QCsiIkIeHh5av369rl69quTk5FSPpKSkrK4VAAAAAB5aNh35Onv2rCZPnqymTZtmdT0AAAAAkCvZdOSrSpUqio2NzepaAAAAACDXsil8TZ48WR999JF2796d1fUAAAAAQK5k02mHDRo00Pvvv6+QkBCVL19eAQEBcnR0tOpjsVj09ddfZ0mRAAAAAPCwsyl8ffnll+revbuSkpL0xx9/6Nq1a6n6WCyW+y4OAAAAAHILm8LXf/7zH5UtW1ZffvmlypQpk9U1AQAAAECuY9M1X+fOndOgQYMIXgAAAACQQTaFr8cff1xnz57N6loAAAAAINeyKXzNmDFDS5cu1fLly7O6HgAAAADIlWy65qtbt25KTExUly5d9Oyzz6po0aJpzna4f//+LCkSAAAAAB52NoUvHx8f+fr6qnTp0lldDwAAAADkSjaFr8jIyCwuAwAAAAByN5uu+QIAAAAAZI5NR74kKSkpSYsWLdKaNWt05swZSVJgYKBatWqlbt26pboGDAAAAAAeZTYd+YqNjVXdunXVt29fbdiwQbdv39bt27e1ceNG9enTR/Xq1VNcXFxW1woAAAAADy2bwtfrr7+uPXv2aMaMGbp48aL27t2rvXv3KiYmRh988IF2796t119/PatrBQAAAICHlk3ha8WKFRo8eLAGDx4sJycnc7mTk5MGDRqkQYMG6csvv8yyIgEAAADgYWdT+Lp06ZLKli2bbnu5cuV0+fJlm4sCAAAAgNzGpvBVqlQprVq1Kt32VatWqWTJkjYXBQAAAAC5jU3ha/DgwdqwYYNatGihDRs26PTp0zp9+rTWr1+vli1bauPGjRoyZEhW1woAAAAADy2bppofPHiwYmJi9NZbb2n9+vVWbU5OThozZowGDRqUJQUCAAAAQG5g832+xo0bpyFDhui7776zus9XaGioChQokGUFAgAAAEBuYFP4+uGHH1S+fHkVLFhQnTt3TtX+119/6ciRI6pfv/59FwgAAAAAuYFN13w1atRIGzduTLd906ZNatSokc1FAQAAAEBuY1P4Mgzjru3x8fFydHS0qSAAAAAAyI0yHL7Onj2rH374QT/88IMk6ddffzWf3/lYtWqVZs+ercDAwEwXM2nSJD3++OPKmzev/Pz89NRTT+nYsWNWfRo2bCiLxWL1GDhwYKpaW7ZsKXd3d/n5+emVV15RYmKiVZ/IyEhVr15dLi4uKlWqlObPn5/pegEAAAAgozJ8zde8efP0xhtvmIFn4sSJmjhxYqp+hmHI0dFRs2fPznQxW7ZsUUREhB5//HElJibqtddeU7NmzXTkyBF5eHiY/Z599lmNHz/efO7u7m7+f1JSklq2bCl/f39t375d58+fV8+ePeXk5KQ333xTkhQVFaWWLVtq4MCBWrx4sTZt2qT+/furcOHCCgsLy3TdAAAAAHAvFuNe5xD+f0ePHtWRI0dkGIY6deqkF154QU8++aT1YBaLPDw8VK1aNRUqVOi+i7t48aL8/Py0ZcsWc/KOhg0bqlq1anr//ffTXGft2rVq1aqVzp07Z9Ywa9YsjRgxQhcvXpSzs7NGjBihNWvW6NChQ+Z6nTt31tWrV7Vu3bp71hUXFydvb2/FxsbKy8vrvvfzbnbXfCJbx8f9qbl7V06XAAAAgByUmWyQ4SNf5cuXV/ny5SX9cxSsfv36CgoKur9K7yE2NlaS5OPjY7V88eLFWrRokfz9/dW6dWuNHj3aPPq1Y8cOVa5c2Sr8hYWFadCgQTp8+LCCg4O1Y8cOhYaGWo0ZFhamoUOHpllHfHy84uPjzedxcXFZsXsAAAAAHiE2TTXfq1evNJefOnVK8fHxZki7H8nJyRo6dKjq1q2rSpUqmcu7du2qwMBAFSlSRAcOHNCIESN07NgxffXVV5Kk6OjoVEfdUp5HR0fftU9cXJz+/vtvubm5WbVNmjRJb7zxxn3vEwAAAIBHl03ha8aMGdq2bZuWLl1qLuvTp48WLlwoSQoODta3334rPz8/mwuLiIjQoUOHtHXrVqvlAwYMMP+/cuXKKly4sJo0aaKTJ0+qZMmSNm/vbkaOHKnhw4ebz+Pi4hQQEJAt2wIAAACQO9k01fzHH39sdeRo/fr1WrBggQYMGKAZM2bo1KlT93WkaMiQIVq9erU2b96sokWL3rVvrVq1JEknTpyQJPn7++vChQtWfVKe+/v737WPl5dXqqNekuTi4iIvLy+rBwAAAABkhk1Hvs6cOWN1auHy5csVFBSkmTNnSvrntL7PPvss0+MahqHnn39eK1asUGRkZIauKdu3b58kqXDhwpKkkJAQTZw4UTExMeaRt40bN8rLy0sVKlQw+3z77bdW42zcuFEhISGZrhkAAAAAMiJLbrK8YcMGNW/e3HxevHhx8/qqzIiIiNCiRYu0ZMkS5c2bV9HR0YqOjtbff/8tSTp58qQmTJigPXv26PTp01q1apV69uyp+vXrq0qVKpKkZs2aqUKFCurRo4f279+v9evXa9SoUYqIiJCLi4skaeDAgTp16pReffVV/frrr/roo4+0fPlyDRs2zJaXAwAAAADuyabwVaZMGa1YsULSP6ccnjt3zip8/fHHH8qXL1+mx505c6ZiY2PVsGFDFS5c2HwsW7ZMkuTs7KzvvvtOzZo1U7ly5fTSSy+pQ4cO+uabb8wxHB0dtXr1ajk6OiokJETdu3dXz549re4LFhQUpDVr1mjjxo2qWrWqpkyZorlz53KPLwAAAADZJsP3+brT0qVL1bVrV3l7e+vGjRsqU6aM9u3bpzx5/jmLsWHDhnJzc9PatWuzvOAHAff5Qgru8wUAAPBoy5b7fN2pc+fO8vX11bfffqt8+fJp8ODBZvC6fPmyfHx81KNHD1uGBgAAAIBcyaYjX486jnwhBUe+AAAAHm2ZyQY2XfMFAAAAAMgcm047DAoKksViuWsfi8WikydP2lQUAAAAAOQ2NoWvBg0apApfSUlJOnPmjLZt26ZKlSopODg4SwoEAAAAgNzApvA1f/78dNv279+vsLAwdevWzdaaAAAAACDXyfJrvqpWrarnnntOI0aMyOqhAQAAAOChlS0TbhQqVEhHjhzJjqEBAAAA4KGU5eHr0qVL+uSTT1S0aNGsHhoAAAAAHlo2XfPVuHHjNJdfvXpVv/76qxISEvTZZ5/dV2EAAAAAkJvYFL6Sk5NTzXZosVgUFBSk0NBQ9e3bV+XKlcuSAgEAAAAgN7ApfEVGRmZxGQAAAACQu2XLhBsAAAAAAGsZDl/Hjx+Xq6urXn311bv2e+WVV+Tm5qaoqKj7Lg4AAAAAcosMh6/p06fL399fEydOvGu/iRMnyt/fX9OnT7/v4gAAAAAgt8hw+NqwYYM6d+4sJyenu/ZzdnZW586dtXbt2vsuDgAAAAByiwyHr7Nnz6ps2bIZ6lu6dGmdOXPG5qIAAAAAILfJcPhycXHR9evXM9T3xo0bcnZ2trkoAAAAAMhtMhy+ypUrp++++y5DfTdt2qTy5cvbXBQAAAAA5DYZDl/PPPOMVq9erZUrV96139dff63Vq1frmWeeud/aAAAAACDXyHD4Gjx4sIKDg9WxY0cNGjRI27ZtU1xcnAzDUFxcnLZt26ZBgwbp6aefVtWqVTV48ODsrBsAAAAAHip5MtrRxcVF69evV69evTR79mzNmTMnVR/DMBQeHq6FCxfKxcUlSwsFAAAAgIdZhsOXJPn6+mr16tXatWuXVq1apaNHjyouLk5eXl4qV66cWrdurdq1a2dXrQAAAADw0MpU+ErxxBNP6IknnsjqWgAAAAAg18rwNV8AAAAAANsRvgAAAADADghfAAAAAGAHhC8AAAAAsIMMha/p06frt99+y+5aAAAAACDXylD4GjZsmHbv3m0+d3R01JIlS7KtKAAAAADIbTIUvvLnz68LFy6Yzw3DyLaCAAAAACA3ytB9vho2bKhx48Zp37598vb2liQtXLhQP/30U7rrWCwWTZs2LWuqBAAAAICHnMXIwGGsmJgYDR06VJs3b1ZMTIykex/9slgsSkpKypoqHzBxcXHy9vZWbGysvLy8snVbu2tyM+sHWc3du3K6BAAAAOSgzGSDDJ126OfnpyVLluj8+fNKSkqSYRhatGiRkpOT033k1uAFAAAAALawaar5efPmqU6dOlldCwAAAADkWhm65uvfevXqZf7/kSNHdObMGUlSYGCgKlSokDWVAQAAAEAuYlP4kqSvv/5aw4cP1+nTp62WBwUFaerUqWrTps391gYAAAAAuYZNpx1+++236tChgyTpzTff1IoVK7RixQq9+eabMgxD7du317p167K0UAAAAAB4mGVotsN/CwkJUXx8vH788Ud5eHhYtd24cUP16tWTq6urduzYkWWFPkiY7RApmO0QAADg0ZaZbGDTaYcHDhzQm2++mSp4SZKHh4d69+6t1157zZahAQBp4A8xDy7+CAMAyCibTjt0dXXV5cuX022/fPmyXF1dbS4KAAAAAHIbm8JX48aNNW3atDRPK9y5c6emT5+u0NDQ+y4OAAAAAHILm8LX22+/LVdXV9WrV08hISHq3bu3evfurZCQENWpU0eurq6aPHlypsedNGmSHn/8ceXNm1d+fn566qmndOzYMas+t27dUkREhHx9feXp6akOHTrowoULVn3Onj2rli1byt3dXX5+fnrllVeUmJho1ScyMlLVq1eXi4uLSpUqpfnz52e6XgAAAADIKJvCV1BQkA4cOKAXXnhBV65c0bJly7Rs2TJduXJFL774ovbv36/ixYtnetwtW7YoIiJCP/30kzZu3Kjbt2+rWbNmunHjhtln2LBh+uabb/T5559ry5YtOnfunNq3b2+2JyUlqWXLlkpISND27du1YMECzZ8/X2PGjDH7REVFqWXLlmrUqJH27dunoUOHqn///lq/fr0tLwcAAAAA3JNNsx3ay8WLF+Xn56ctW7aofv36io2NVcGCBbVkyRI9/fTTkqRff/1V5cuX144dO1S7dm2tXbtWrVq10rlz51SoUCFJ0qxZszRixAhdvHhRzs7OGjFihNasWaNDhw6Z2+rcubOuXr2a5hT58fHxio+PN5/HxcUpICCA2Q7BhfawG74LHlx8DwDAoy0zsx3adOTLXmJjYyVJPj4+kqQ9e/bo9u3bVteTlStXTsWKFTOvP9uxY4cqV65sBi9JCgsLU1xcnA4fPmz2+fc1aWFhYelOjT9p0iR5e3ubj4CAgKzbSQAAAACPhAc2fCUnJ2vo0KGqW7euKlWqJEmKjo6Ws7Oz8uXLZ9W3UKFCio6ONvvcGbxS2lPa7tYnLi5Of//9d6paRo4cqdjYWPPx+++/Z8k+AgAAAHh02HSfL3uIiIjQoUOHtHXr1pwuRS4uLnJxccnpMgAAAAA8xB7II19DhgzR6tWrtXnzZhUtWtRc7u/vr4SEBF29etWq/4ULF+Tv72/2+ffshynP79XHy8tLbm5uWb07AAAAAPBghS/DMDRkyBCtWLFC33//vYKCgqzaa9SoIScnJ23atMlcduzYMZ09e1YhISGSpJCQEB08eFAxMTFmn40bN8rLy0sVKlQw+9w5RkqflDEAAAAAIKtlOnzdvHlTNWrU0KxZs7K8mIiICC1atEhLlixR3rx5FR0drejoaPM6LG9vb/Xr10/Dhw/X5s2btWfPHvXp00chISGqXbu2JKlZs2aqUKGCevToof3792v9+vUaNWqUIiIizFMHBw4cqFOnTunVV1/Vr7/+qo8++kjLly/XsGHDsnyfAAAAAECyIXy5u7srKipKFosly4uZOXOmYmNj1bBhQxUuXNh8LFu2zOzz3nvvqVWrVurQoYPq168vf39/ffXVV2a7o6OjVq9eLUdHR4WEhKh79+7q2bOnxo8fb/YJCgrSmjVrtHHjRlWtWlVTpkzR3LlzFRYWluX7BAAAAACSjff56tq1q27dumUVeh4lmZnL/35xb58HG/f3gb3wXfDg4nsAAB5t2X6fr9GjR+u3335Tjx49tHXrVv3555+6fPlyqgcAAAAA4B82TTVfsWJFSdKRI0e0ZMmSdPslJSXZVhUAAAAA5DI2ha8xY8ZkyzVfAAAAAJBb2RS+xo0bl8VlAAAAAEDuliX3+YqNjeUUQwAAAAC4C5vD1+7duxUeHi53d3f5+vpqy5YtkqS//vpLbdu2VWRkZFbVCAAAAAAPPZvC1/bt21WvXj0dP35c3bt3V3JystlWoEABxcbGavbs2VlWJAAAAAA87GwKX6+99prKly+vI0eO6M0330zV3qhRI+3cufO+iwMAAACA3MKm8PXzzz+rT58+cnFxSXPWw8cee0zR0dH3XRwAAAAA5BY2hS8nJyerUw3/7c8//5Snp6fNRQEAAABAbmNT+Kpdu7a++OKLNNtu3LihefPmqUGDBvdVGAAAAADkJjaFrzfeeEO7d+9Wy5YttXbtWknS/v37NXfuXNWoUUMXL17U6NGjs7RQAAAAAHiY2XST5Vq1aunbb7/VoEGD1LNnT0nSSy+9JEkqWbKkvv32W1WpUiXrqgQAAACAh5xN4UuSGjdurGPHjumXX37RiRMnlJycrJIlS6pGjRppTsIBAAAAAI8ym8NXiuDgYAUHB2dFLQAAAACQa9kcvuLj4/Xxxx/r22+/1enTpyVJxYsXV4sWLdS/f3+5urpmVY0AAAAA8NCzacKNP/74Q9WqVdMLL7yg/fv3q2DBgipYsKD279+vF154QdWqVdMff/yR1bUCAAAAwEPLpvAVERGhM2fOaPny5frzzz+1ZcsWbdmyRX/++aeWLVums2fPKiIiIqtrBQAAAICHlk2nHW7atEnDhg3T008/naqtY8eO2rt3r2bMmHHfxQEAAABAbmHTka+8efPKz88v3XZ/f3/lzZvX5qIAAAAAILexKXz16dNH8+fP182bN1O1Xb9+XfPmzVO/fv3uuzgAAAAAyC0ydNrhV199ZfU8ODhYa9asUbly5dSrVy+VKlVKknT8+HEtXLhQPj4+3GQZAAAAAO5gMQzDuFcnBwcHWSwWpXS98//THdhiUVJSUtZU+YCJi4uTt7e3YmNj5eXlla3b2l3ziWwdH/en5u5dOV0CHhF8Fzy4+B4AgEdbZrJBho58bd68OUsKAwAAAIBHVYbCV4MGDbK7DgAAANwFR8AfXBwBR0bZNOEGAAAAACBzbLrPlyRt3bpVn376qU6dOqUrV66kugbMYrFo//79910gAAAAAOQGNoWvqVOn6pVXXpGrq6vKli0rHx+frK4LAAAAAHIVm8LXO++8o7p16+qbb76Rt7d3VtcEAAAAALmOTdd83bx5U926dSN4AQAAAEAG2RS+GjVqpIMHD2Z1LQAAAACQa9kUvmbMmKFNmzbp3Xff1eXLl7O6JgAAAADIdWwKXwEBAXruuef0n//8RwULFpSHh4e8vLysHpySCAAAAAD/x6YJN8aMGaOJEyfqscceU82aNQlaAAAAAHAPNoWvWbNmqWXLllq5cqUcHLhPMwAAAADci03JKSEhQS1btiR4AQAAAEAG2ZSeWrVqpR9//DGrawEAAACAXMum8DV27FgdOXJEgwcP1p49e3Tx4kVdvnw51QMAAAAA8A+brvkqW7asJGnfvn2aPXt2uv2SkpJsqwoAAAAAchmbZzu0WCxZXQsAAAAA5Fo2ha9x48ZlcRkAAAAAkLs9UNMV/vDDD2rdurWKFCkii8WilStXWrX37t1bFovF6hEeHm7V5/Lly+rWrZu8vLyUL18+9evXT9evX7fqc+DAAT355JNydXVVQECA3n777ezeNQAAAACPOJuOfI0fP/6efSwWi0aPHp2pcW/cuKGqVauqb9++at++fZp9wsPDNW/ePPO5i4uLVXu3bt10/vx5bdy4Ubdv31afPn00YMAALVmyRJIUFxenZs2aKTQ0VLNmzdLBgwfVt29f5cuXTwMGDMhUvQAAAACQUVl+2qHFYpFhGDaFr+bNm6t58+Z37ePi4iJ/f/80244ePap169bp559/Vs2aNSVJM2bMUIsWLfTuu++qSJEiWrx4sRISEvTpp5/K2dlZFStW1L59+zR16lTCFwAAAIBsY9Nph8nJyakeiYmJOnnypIYNG6aaNWsqJiYmq2uVJEVGRsrPz09ly5bVoEGDdOnSJbNtx44dypcvnxm8JCk0NFQODg7auXOn2ad+/fpydnY2+4SFhenYsWO6cuVKmtuMj49XXFyc1QMAAAAAMiPLrvlycHBQUFCQ3n33XZUuXVrPP/98Vg1tCg8P18KFC7Vp0yZNnjxZW7ZsUfPmzc0p7aOjo+Xn52e1Tp48eeTj46Po6GizT6FChaz6pDxP6fNvkyZNkre3t/kICAjI6l0DAAAAkMvZdNrhvdSvX18jRozI8nE7d+5s/n/lypVVpUoVlSxZUpGRkWrSpEmWby/FyJEjNXz4cPN5XFwcAQwAAABApmTLbIe7d++Wg0P2T6RYokQJFShQQCdOnJAk+fv7pzrdMTExUZcvXzavE/P399eFCxes+qQ8T+9aMhcXF3l5eVk9AAAAACAzbDrytXDhwjSXX716VT/88IO++uor9e/f/74Ky4g//vhDly5dUuHChSVJISEhunr1qvbs2aMaNWpIkr7//nslJyerVq1aZp/XX39dt2/flpOTkyRp48aNKlu2rPLnz5/tNQMAAAB4NNkUvnr37p1uW4ECBfSf//xHY8aMyfS4169fN49iSVJUVJT27dsnHx8f+fj46I033lCHDh3k7++vkydP6tVXX1WpUqUUFhYmSSpfvrzCw8P17LPPatasWbp9+7aGDBmizp07q0iRIpKkrl276o033lC/fv00YsQIHTp0SNOmTdN7772X6XoBAAAAIKNsCl9RUVGpllksFuXPn1958+a1uZjdu3erUaNG5vOU66x69eqlmTNn6sCBA1qwYIGuXr2qIkWKqFmzZpowYYLVvb4WL16sIUOGqEmTJnJwcFCHDh00ffp0s93b21sbNmxQRESEatSooQIFCmjMmDFMMw8AAAAgW9kUvgIDA7O6DklSw4YNZRhGuu3r16+/5xg+Pj7mDZXTU6VKFf3444+Zrg8AAAAAbJX9s2IAAAAAADJ+5KtKlSqZGthisWj//v2ZLggAAAAAcqMMhy8fHx9ZLJZ79ouOjtaxY8cy1BcAAAAAHhUZDl+RkZF3bY+OjtbkyZM1e/ZsOTo6qkePHvdbGwAAAADkGjZNuHGnCxcu6K233tKcOXN0+/Ztde/eXa+//rpKliyZFfUBAAAAQK5gc/hKOdJ1Z+gaNWqUSpQokZX1AQAAAECukOnwFR0drbfeeksff/yxbt++rR49emjUqFEKCgrKjvoAAAAAIFfIcPg6f/68GboSExPVs2dPvf7664QuAAAAAMiADIevkiVLKj4+XtWqVdNrr72moKAgXblyRVeuXEl3nerVq2dJkQAAAADwsMtw+Lp165Yk6ZdfflGnTp3u2tcwDFksFiUlJd1fdQAAAACQS2Q4fM2bNy876wAAAACAXC3D4atXr17ZWQcAAAAA5GoOOV0AAAAAADwKCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7OCBCl8//PCDWrdurSJFishisWjlypVW7YZhaMyYMSpcuLDc3NwUGhqq48ePW/W5fPmyunXrJi8vL+XLl0/9+vXT9evXrfocOHBATz75pFxdXRUQEKC33347u3cNAAAAwCPugQpfN27cUNWqVfXhhx+m2f72229r+vTpmjVrlnbu3CkPDw+FhYXp1q1bZp9u3brp8OHD2rhxo1avXq0ffvhBAwYMMNvj4uLUrFkzBQYGas+ePXrnnXc0btw4zZkzJ9v3DwAAAMCjK09OF3Cn5s2bq3nz5mm2GYah999/X6NGjVLbtm0lSQsXLlShQoW0cuVKde7cWUePHtW6dev0888/q2bNmpKkGTNmqEWLFnr33XdVpEgRLV68WAkJCfr000/l7OysihUrat++fZo6dapVSAMAAACArPRAHfm6m6ioKEVHRys0NNRc5u3trVq1amnHjh2SpB07dihfvnxm8JKk0NBQOTg4aOfOnWaf+vXry9nZ2ewTFhamY8eO6cqVK2luOz4+XnFxcVYPAAAAAMiMhyZ8RUdHS5IKFSpktbxQoUJmW3R0tPz8/Kza8+TJIx8fH6s+aY1x5zb+bdKkSfL29jYfAQEB979DAAAAAB4pD034ykkjR45UbGys+fj9999zuiQAAAAAD5mHJnz5+/tLki5cuGC1/MKFC2abv7+/YmJirNoTExN1+fJlqz5pjXHnNv7NxcVFXl5eVg8AAAAAyIyHJnwFBQXJ399fmzZtMpfFxcVp586dCgkJkSSFhITo6tWr2rNnj9nn+++/V3JysmrVqmX2+eGHH3T79m2zz8aNG1W2bFnlz5/fTnsDAAAA4FHzQIWv69eva9++fdq3b5+kfybZ2Ldvn86ePSuLxaKhQ4fqv//9r1atWqWDBw+qZ8+eKlKkiJ566ilJUvny5RUeHq5nn31Wu3bt0rZt2zRkyBB17txZRYoUkSR17dpVzs7O6tevnw4fPqxly5Zp2rRpGj58eA7tNQAAAIBHwQM11fzu3bvVqFEj83lKIOrVq5fmz5+vV199VTdu3NCAAQN09epV1atXT+vWrZOrq6u5zuLFizVkyBA1adJEDg4O6tChg6ZPn262e3t7a8OGDYqIiFCNGjVUoEABjRkzhmnmAQAAAGQri2EYRk4X8bCJi4uTt7e3YmNjs/36r901n8jW8XF/au7eldMl4BHBd8GDi+8B2AvfAw8uvgcebZnJBg/UaYcAAAAAkFsRvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYAeELAAAAAOyA8AUAAAAAdkD4AgAAAAA7IHwBAAAAgB0QvgAAAADADghfAAAAAGAHhC8AAAAAsAPCFwAAAADYQZ6cLgB4mIWPXpbTJSAd6yY8k9MlAAAeEfw+8GB7kH4n4MgXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADt4qMLXuHHjZLFYrB7lypUz22/duqWIiAj5+vrK09NTHTp00IULF6zGOHv2rFq2bCl3d3f5+fnplVdeUWJior13BQAAAMAjJk9OF5BZFStW1HfffWc+z5Pn/3Zh2LBhWrNmjT7//HN5e3tryJAhat++vbZt2yZJSkpKUsuWLeXv76/t27fr/Pnz6tmzp5ycnPTmm2/afV8AAAAAPDoeuvCVJ08e+fv7p1oeGxurTz75REuWLFHjxo0lSfPmzVP58uX1008/qXbt2tqwYYOOHDmi7777ToUKFVK1atU0YcIEjRgxQuPGjZOzs3Oa24yPj1d8fLz5PC4uLnt2DgAAAECu9VCddihJx48fV5EiRVSiRAl169ZNZ8+elSTt2bNHt2/fVmhoqNm3XLlyKlasmHbs2CFJ2rFjhypXrqxChQqZfcLCwhQXF6fDhw+nu81JkybJ29vbfAQEBGTT3gEAAADIrR6q8FWrVi3Nnz9f69at08yZMxUVFaUnn3xS165dU3R0tJydnZUvXz6rdQoVKqTo6GhJUnR0tFXwSmlPaUvPyJEjFRsbaz5+//33rN0xAAAAALneQ3XaYfPmzc3/r1KlimrVqqXAwEAtX75cbm5u2bZdFxcXubi4ZNv4AAAAAHK/h+rI17/ly5dPZcqU0YkTJ+Tv76+EhARdvXrVqs+FCxfMa8T8/f1TzX6Y8jyt68gAAAAAIKs81OHr+vXrOnnypAoXLqwaNWrIyclJmzZtMtuPHTums2fPKiQkRJIUEhKigwcPKiYmxuyzceNGeXl5qUKFCnavHwAAAMCj46E67fDll19W69atFRgYqHPnzmns2LFydHRUly5d5O3trX79+mn48OHy8fGRl5eXnn/+eYWEhKh27dqSpGbNmqlChQrq0aOH3n77bUVHR2vUqFGKiIjgtEIAAAAA2eqhCl9//PGHunTpokuXLqlgwYKqV6+efvrpJxUsWFCS9N5778nBwUEdOnRQfHy8wsLC9NFHH5nrOzo6avXq1Ro0aJBCQkLk4eGhXr16afz48Tm1SwAAAAAeEQ9V+Fq6dOld211dXfXhhx/qww8/TLdPYGCgvv3226wuDQAAAADu6qG+5gsAAAAAHhYP1ZEvAAAeNOGjl+V0CUjHugnP5HQJAGCFI18AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AAAAAYAeELwAAAACwA8IXAAAAANgB4QsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHTzS4evDDz9U8eLF5erqqlq1amnXrl05XRIAAACAXOqRDV/Lli3T8OHDNXbsWO3du1dVq1ZVWFiYYmJicro0AAAAALnQIxu+pk6dqmeffVZ9+vRRhQoVNGvWLLm7u+vTTz/N6dIAAAAA5EJ5crqAnJCQkKA9e/Zo5MiR5jIHBweFhoZqx44dqfrHx8crPj7efB4bGytJiouLy/ZaryclZfs2YLvE+Js5XQLSYY+fT3viu+DBxffAg4vvAdgL3wMPtuz+LkgZ3zCMe/a1GBnplcucO3dOjz32mLZv366QkBBz+auvvqotW7Zo586dVv3HjRunN954w95lAgAAAHhI/P777ypatOhd+zySR74ya+TIkRo+fLj5PDk5WZcvX5avr68sFksOVoacFBcXp4CAAP3+++/y8vLK6XIA5AC+BwDwPQDDMHTt2jUVKVLknn0fyfBVoEABOTo66sKFC1bLL1y4IH9//1T9XVxc5OLiYrUsX7582VkiHiJeXl582QKPOL4HAPA98Gjz9vbOUL9HcsINZ2dn1ahRQ5s2bTKXJScna9OmTVanIQIAAABAVnkkj3xJ0vDhw9WrVy/VrFlTTzzxhN5//33duHFDffr0yenSAAAAAORCj2z4euaZZ3Tx4kWNGTNG0dHRqlatmtatW6dChQrldGl4SLi4uGjs2LGpTkkF8OjgewAA3wPIjEdytkMAAAAAsLdH8povAAAAALA3whcAAAAA2AHhCwAAAADsgPAFAEA2GTdunKpVq5bTZQAAHhCELzwSevfuLYvFkupx4sSJnC4NQA65ePGiBg0apGLFisnFxUX+/v4KCwvTtm3bsmwbL7/8stU9JQFkv+joaL344osqVaqUXF1dVahQIdWtW1czZ87UzZs3c7o8POIe2anm8egJDw/XvHnzrJYVLFjQ6nlCQoKcnZ3tWRaAHNKhQwclJCRowYIFKlGihC5cuKBNmzbp0qVLWbYNT09PeXp6Ztl4AO7u1KlTqlu3rvLly6c333xTlStXlouLiw4ePKg5c+boscceU5s2bTI9Lr8fIKtw5AuPjJS/bN/5aNKkiYYMGaKhQ4eqQIECCgsLkyRNnTpVlStXloeHhwICAjR48GBdv37dHGv+/PnKly+f1q9fr/Lly8vT01Ph4eE6f/681TY//fRTVaxYUS4uLipcuLCGDBlitl29elX9+/dXwYIF5eXlpcaNG2v//v32eTGAR9zVq1f1448/avLkyWrUqJECAwP1xBNPaOTIkeYvZhaLRTNnzlTz5s3l5uamEiVK6IsvvrAaZ8SIESpTpozc3d1VokQJjR49Wrdv3zbb/33aYe/evfXUU0/p3XffVeHCheXr66uIiAirdQDYbvDgwcqTJ492796tTp06qXz58ipRooTatm2rNWvWqHXr1pLu/W9wys/u3LlzFRQUJFdXV0n/fC/Mnj1brVq1kru7u8qXL68dO3boxIkTatiwoTw8PFSnTh2dPHnSHOvkyZNq27atChUqJE9PTz3++OP67rvvrOouXry43nzzTfXt21d58+ZVsWLFNGfOHLO9cePGVr9DSP8cvXd2dubo+kOG8IVH3oIFC+Ts7Kxt27Zp1qxZkiQHBwdNnz5dhw8f1oIFC/T999/r1VdftVrv5s2bevfdd/XZZ5/phx9+0NmzZ/Xyyy+b7TNnzlRERIQGDBiggwcPatWqVSpVqpTZ3rFjR8XExGjt2rXas2ePqlevriZNmujy5cv22XHgEZZyRGrlypWKj49Pt9/o0aPVoUMH7d+/X926dVPnzp119OhRsz1v3ryaP3++jhw5omnTpunjjz/We++9d9dtb968WSdPntTmzZu1YMECzZ8/X/Pnz8+qXQMeWZcuXdKGDRsUEREhDw+PNPtYLBZJGfs3+MSJE/ryyy/11Vdfad++febyCRMmqGfPntq3b5/KlSunrl276rnnntPIkSO1e/duGYZhFZSuX7+uFi1aaNOmTfrll18UHh6u1q1b6+zZs1a1TZkyRTVr1tQvv/yiwYMHa9CgQTp27JgkqX///lqyZInV99WiRYv02GOPqXHjxvf92sGODOAR0KtXL8PR0dHw8PAwH08//bTRoEEDIzg4+J7rf/7554avr6/5fN68eYYk48SJE+ayDz/80ChUqJD5vEiRIsbrr7+e5ng//vij4eXlZdy6dctqecmSJY3Zs2dndvcA2OCLL74w8ufPb7i6uhp16tQxRo4caezfv99sl2QMHDjQap1atWoZgwYNSnfMd955x6hRo4b5fOzYsUbVqlXN57169TICAwONxMREc1nHjh2NZ555Jgv2CHi0/fTTT4Yk46uvvrJa7uvra/7b/+qrr2bo3+CxY8caTk5ORkxMjFUfScaoUaPM5zt27DAkGZ988om57H//+5/h6up611orVqxozJgxw3weGBhodO/e3XyenJxs+Pn5GTNnzjQMwzD+/vtvI3/+/MayZcvMPlWqVDHGjRt31+3gwcORLzwyGjVqpH379pmP6dOnS5Jq1KiRqu93332nJk2a6LHHHlPevHnVo0cPXbp0yepCXXd3d5UsWdJ8XrhwYcXExEiSYmJidO7cOTVp0iTNWvbv36/r16/L19fX/Au8p6enoqKirE5VAJB9OnTooHPnzmnVqlUKDw9XZGSkqlevbnUUKiQkxGqdkJAQqyNfy5YtU926deXv7y9PT0+NGjUq1V+z/61ixYpydHQ0n9/53QEg6+3atUv79u1TxYoVFR8fn+F/gwMDA1NdGy5JVapUMf+/UKFCkqTKlStbLbt165bi4uIk/XPk6+WXX1b58uWVL18+eXp66ujRo6m+K+4c12KxyN/f3/xucHV1VY8ePfTpp59Kkvbu3atDhw6pd+/e9/nqwN6YcAOPDA8PD6vT/u5cfqfTp0+rVatWGjRokCZOnCgfHx9t3bpV/fr1U0JCgtzd3SVJTk5OVutZLBYZhiFJcnNzu2st169fV+HChRUZGZmqLV++fJnYKwD3w9XVVU2bNlXTpk01evRo9e/fX2PHjs3QLzQ7duxQt27d9MYbbygsLEze3t5aunSppkyZctf10vruSE5Ovp/dACCpVKlSslgs5ql6KUqUKCHp//5tzui/wemdunjnz3DKaYxpLUv5uX755Ze1ceNGvfvuuypVqpTc3Nz09NNPKyEhId1xU8a587uhf//+qlatmv744w/NmzdPjRs3VmBgYJo14sFF+AL+Zc+ePUpOTtaUKVPk4PDPweHly5dnaoy8efOqePHi2rRpkxo1apSqvXr16oqOjlaePHlUvHjxrCgbQBaoUKGCVq5caT7/6aef1LNnT6vnwcHBkqTt27crMDBQr7/+utl+5swZu9UKwJqvr6+aNm2qDz74QM8//3y64cne/wZv27ZNvXv3Vrt27ST9E/5Onz6d6XEqV66smjVr6uOPP9aSJUv0wQcfZHGlsAdOOwT+pVSpUrp9+7ZmzJihU6dO6bPPPjMn4siMcePGacqUKZo+fbqOHz+uvXv3asaMGZKk0NBQhYSE6KmnntKGDRt0+vRpbd++Xa+//rp2796d1bsE4F8uXbqkxo0ba9GiRTpw4ICioqL0+eef6+2331bbtm3Nfp9//rk+/fRT/fbbbxo7dqx27dplXkhfunRpnT17VkuXLtXJkyc1ffp0rVixIqd2CYCkjz76SImJiapZs6aWLVumo0eP6tixY1q0aJF+/fVXOTo62v3f4NKlS5uTduzfv19du3a1+Wh3//799dZbb8kwDDPM4eFC+AL+pWrVqpo6daomT56sSpUqafHixZo0aVKmx+nVq5fef/99ffTRR6pYsaJatWql48ePS/rnVIJvv/1W9evXV58+fVSmTBl17txZZ86cMc8fB5B9PD09VatWLb333nuqX7++KlWqpNGjR+vZZ5+1+mvyG2+8oaVLl6pKlSpauHCh/ve//6lChQqSpDZt2mjYsGEaMmSIqlWrpu3bt2v06NE5tUsAJJUsWVK//PKLQkNDNXLkSFWtWlU1a9bUjBkz9PLLL2vChAl2/zd46tSpyp8/v+rUqaPWrVsrLCxM1atXt2msLl26KE+ePOrSpYs5/T0eLhYj5SIVAABgslgsWrFihZ566qmcLgUAJP1zXXrJkiX1888/2xzgkLO45gsAAAB4gN2+fVuXLl3SqFGjVLt2bYLXQ4zTDgEAAIAH2LZt21S4cGH9/PPPNl2HjgcHpx0CAAAAgB1w5AsAAAAA7IDwBQAAAAB2QPgCAAAAADsgfAEAAACAHRC+AAAAAMAOCF8AgEdaZGSkLBaLvvjiC7tsb/DgwWratKldtvUwWbdunTw9PXXx4sWcLgUAsg3hCwAeIVFRURoyZIjKlCkjd3d3ubu7q0KFCoqIiNCBAwdyurxcLyoqSnPnztVrr72Wqi0uLk5vvPGGqlatKk9PT7m5ualSpUoaMWKEzp07lwPVpvbtt99q3Lhx2TJ2eHi4SpUqpUmTJmXL+ADwIOA+XwDwiFi9erWeeeYZ5cmTR926dVPVqlXl4OCgX3/9VV999ZXOnDmjqKgoBQYG5nSpdhUZGalGjRrp888/19NPP52t2xo6dKjWrl2rY8eOWS0/deqUQkNDdfbsWXXs2FH16tWTs7OzDhw4oP/973/y8fHRb7/9lq21ZcSQIUP04YcfKrt+dZg5c6ZefvllRUdHK2/evNmyDQDISXlyugAAQPY7efKkOnfurMDAQG3atEmFCxe2ap88ebI++ugjOTg8uCdE3Lx5U+7u7jldhs1u376txYsXa+DAgVbLExMT1b59e124cEGRkZGqV6+eVfvEiRM1efJke5aaJRITE5WcnCxnZ+cMr9OhQwc9//zz+vzzz9W3b99srA4AcsaD+68sACDLvP3227px44bmzZuXKnhJUp48efTCCy8oICDAavmvv/6qp59+Wj4+PnJ1dVXNmjW1atWqVOufOnVKHTt2lI+Pj9zd3VW7dm2tWbMmVb8zZ86oTZs28vDwkJ+fn4YNG6b169fLYrEoMjLS7NewYUNVqlRJe/bsUf369eXu7m6eqvf111+rZcuWKlKkiFxcXFSyZElNmDBBSUlJVtu6c4w6derIzc1NQUFBmjVrVpqvUXJysiZOnKiiRYvK1dVVTZo00YkTJ8z2sWPHysnJKc1rkgYMGKB8+fLp1q1baY4tSVu3btVff/2l0NBQq+Vffvml9u/fr9dffz1V8JIkLy8vTZw40WrZ559/rho1asjNzU0FChRQ9+7d9eeff6ba/4YNG6Yar3fv3ipevLj5/PTp07JYLHr33Xc1Z84clSxZUi4uLnr88cf1888/W6334YcfSpIsFov5+PcY77//vjnGrl275OHhoRdffDFVHX/88YccHR2tTjP08/NTlSpV9PXXX6fzKgLAw40jXwDwCFi9erVKlSqlWrVqZXidw4cPq27dunrsscf0n//8Rx4eHlq+fLmeeuopffnll2rXrp0k6cKFC6pTp45u3rypF154Qb6+vlqwYIHatGmjL774wux348YNNW7cWOfPn9eLL74of39/LVmyRJs3b05z+5cuXVLz5s3VuXNnde/eXYUKFZIkzZ8/X56enho+fLg8PT31/fffa8yYMYqLi9M777xjNcaVK1fUokULderUSV26dNHy5cs1aNAgOTs7pzqy8tZbb8nBwUEvv/yyYmNj9fbbb6tbt27auXOnJKlHjx4aP368li1bpiFDhpjrJSQk6IsvvlCHDh3k6uqa7uu5fft2WSwWBQcHWy1PCbM9evS453uSsv99+vTR448/rkmTJunChQuaNm2atm3bpl9++UX58uXL0Dj/tmTJEl27dk3PPfecLBaL3n77bbVv316nTp2Sk5OTnnvuOZ07d04bN27UZ599luYY8+bN061btzRgwAC5uLioWLFiateunZYtW6apU6fK0dHR7Pu///1PhmGoW7duVmPUqFFDK1eutGkfAOCBZwAAcrXY2FhDkvHUU0+larty5Ypx8eJF83Hz5k2zrUmTJkblypWNW7dumcuSk5ONOnXqGKVLlzaXDR061JBk/Pjjj+aya9euGUFBQUbx4sWNpKQkwzAMY8qUKYYkY+XKlWa/v//+2yhXrpwhydi8ebO5vEGDBoYkY9asWalqvrPGFM8995zh7u5uVWvKGFOmTDGXxcfHG9WqVTP8/PyMhIQEwzAMY/PmzYYko3z58kZ8fLzZd9q0aYYk4+DBg+aykJAQo1atWlbb/uqrr1LVn5bu3bsbvr6+qZYHBwcb3t7ed103RUJCguHn52dUqlTJ+Pvvv83lq1evNiQZY8aMsdr/Bg0apBqjV69eRmBgoPk8KirKkGT4+voaly9fNpd//fXXhiTjm2++MZdFREQYaf3qkDKGl5eXERMTY9W2fv16Q5Kxdu1aq+VVqlRJs74333zTkGRcuHAh3dcBAB5WnHYIALlcXFycJMnT0zNVW8OGDVWwYEHzkXJa2eXLl/X999+rU6dOunbtmv766y/99ddfunTpksLCwnT8+HHzNLdvv/1WTzzxhNUpc56enhowYIBOnz6tI0eOSPpnKvHHHntMbdq0Mfu5urrq2WefTbNuFxcX9enTJ9VyNzc38/9TanvyySd18+ZN/frrr1Z98+TJo+eee8587uzsrOeee04xMTHas2ePVd8+ffpYXZ/05JNPSvrnlMoUPXv21M6dO3Xy5Elz2eLFixUQEKAGDRqkuR8pLl26pPz586daHhcXl+HJJXbv3q2YmBgNHjzY6ihby5YtVa5cuTRP9cyoZ555xqq+tPb/Xjp06KCCBQtaLQsNDVWRIkW0ePFic9mhQ4d04MABde/ePdUYKTX89ddfmaofAB4GhC8AyOVSfrG/fv16qrbZs2dr48aNWrRokdXyEydOyDAMjR492iqcFSxYUGPHjpUkxcTESPrnOq6yZcumGrt8+fJme8p/S5YsaV4nlKJUqVJp1v3YY4+lOVnD4cOH1a5dO3l7e8vLy0sFCxY0f4mPjY216lukSBF5eHhYLStTpoykf65TulOxYsWsnqeEgCtXrpjLnnnmGbm4uJhBIjY2VqtXr1a3bt1S7VdajDRmCfTy8tK1a9fuua70f69lWq93uXLlzHZbZGT/7yUoKCjVMgcHB3Xr1k0rV67UzZs3Jf0TWF1dXdWxY8dU/VNeo4y8ngDwsOGaLwDI5by9vVW4cGEdOnQoVVvKNWD/DiLJycmSpJdffllhYWFpjpteaMoqdx7hSnH16lU1aNBAXl5eGj9+vEqWLClXV1ft3btXI0aMMOu2xZ3XI93pzsCUP39+tWrVSosXL9aYMWP0xRdfKD4+Ps0jOP/m6+ubZpApV66cfvnlF/3++++pJjy5HxaLJc2w9++JSVJkZP/vJa33TPrniOE777yjlStXqkuXLlqyZIlatWolb2/vVH1TXqMCBQpkeLsA8LDgyBcAPAJatmypEydOaNeuXRnqX6JECUmSk5OTQkND03ykHFELDAxMdd8qSeYpgCn3DQsMDNTJkydT/TJ/54yC9xIZGalLly5p/vz5evHFF9WqVSuFhoameTqfJJ07d043btywWpZyv6w7Z/zLjJ49e+q3337Tzz//rMWLFys4OFgVK1a853rlypXTlStXUh2da926tSSlOvqYlpTXMq3X+9ixY1b3aMufP7+uXr2aqt/9HB2z9WhUpUqVFBwcrMWLF+vHH3/U2bNn051gJCoqSgUKFEh1+iIA5AaELwB4BLz66qtyd3dX3759deHChVTt/w5Efn5+atiwoWbPnq3z58+n6n/ndOstWrTQrl27tGPHDnPZjRs3NGfOHBUvXlwVKlSQJIWFhenPP/+0mqr+1q1b+vjjjzO8HylHZ+6sNyEhQR999FGa/RMTEzV79myrvrNnz1bBggVVo0aNDG/3Ts2bN1eBAgU0efJkbdmyJUNHvSQpJCREhmGkutbs6aefVuXKlTVx4kSr1zDFtWvX9Prrr0uSatasKb//197dhES1hnEA/09yckZcpGL4hcWkKY7iuJEsUSlp8mOThDlKhkqYpCMSSG4KF4ISlIsJPxLCsilJa6FhhhsXYyEGjlEKatNiQJRAapNC8rS4ODR3xq569Wj3/n+7c877PjznzOph3vd5Dx9Ge3s7VldX3WOGhoYwPT2NvLw8971jx45hZmbG47dyOByw2+1bet9frS/h9FXU/ZNLly7h9evXaG1tRUhICHJycnyOe/fuHdLS0radIxHRfsZlh0RE/wOxsbGw2Wwwm82Ii4tDSUkJkpOTISJwOp2w2Ww4cOAAoqKi3HPu3buH9PR0JCUl4cqVK9Dr9VhcXMSbN2/gcrngcDgAADdu3MCTJ0+Qk5MDi8WC4OBgdHd3w+l0or+/331wc2VlJaxWK8xmM2praxEeHu7e+wNs7l+VkydPIigoCJcvX4bFYoFGo8GjR482XBoXERGBlpYWfP78GcePH0dvby8mJyfR2dkJRVG29S0VRUFRURGsViv8/PxgNps3NS89PR0hISEYGRnB6dOnPeI9f/4c2dnZyMjIQGFhIU6dOgVFUfDhwwfYbDYEBQWhqakJiqKgpaUFZWVlyMzMhNlsdreaP3r0KOrq6txxy8vLcefOHZhMJlRUVGBpaQnt7e0wGAzuJixbtV6wWiwWmEwm+Pn5oaioaFNzi4uLUV9fjxcvXqCqqsrn919aWsLU1BSuXbu2rfyIiPa9vWmySEREe2Fubk6qqqokJiZGtFqt6HQ6iY+Pl6tXr8rk5KTX+Pn5eSktLZWwsDBRFEUiIyMlPz9f+vr6vMZduHBBDh06JFqtVlJTU2VwcNAr3qdPnyQvL090Op2EhobK9evXpb+/XwDI27dv3eMyMzPFYDD4fAe73S4nTpwQnU4nERERUl9f725n/vd29QaDQSYmJiQtLU20Wq0cOXJErFarR7z1VvPPnj3zuL/ePv3BgwdeOYyPjwsAOXv2rM8cN2KxWCQmJsbns+XlZbl586YkJSVJQECAaLVaSUxMlIaGBllYWPAY29vbKykpKeLv7y/BwcFSUlIiLpfLK2ZPT4/o9Xo5ePCgGI1GGR4e3rDV/O3bt73mA5Bbt265r3/8+CE1NTUSGhoqGo3G3Xb+dzF+lZubKwBkbGzM5/O2tjYJCAiQb9++/TYOEdGfSiOyhZ20REREO6y1tRV1dXVwuVyIjIzcsbhZWVn48uWLz0Yj/5bD4YDRaMTDhw83fTgy8Ffb9vj4eAwNDeHMmTM7ntd+d/78ebx//37DfX4pKSnIysrC3bt3Vc6MiEgd3PNFRESq+f79u8f1ysoKOjo6EBsbu6OF1267f/8+AgMDUVBQsKV5er0eFRUVaG5u3qXM9q+FhQW8fPlyw2L11atXmJ2dRUNDg8qZERGph3u+iIhINQUFBYiOjobRaMTXr1/R09ODmZkZjwN497OBgQF8/PgRnZ2dqK6u9jpDbDPa2tp2IbP9y+l0wm63o6urC4qieBx6/atz5875PIuOiOi/hMUXERGpxmQyoaurC48fP8ba2hoSEhLw9OlTXLx4ca9T25SamhosLi4iNzcXjY2Ne53OH2F0dBRlZWWIjo5Gd3c3wsLC9jolIqI9wz1fREREREREKuCeLyIiIiIiIhWw+CIiIiIiIlIBiy8iIiIiIiIVsPgiIiIiIiJSAYsvIiIiIiIiFbD4IiIiIiIiUgGLLyIiIiIiIhWw+CIiIiIiIlLBTwC9l/36SFrcAAAAAElFTkSuQmCC\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "--- Insight from Plot 1 ---\n",
+ "From this plot, we can visually compare the ratio of churned to stayed customers.\n",
+ "We can observe that while France has the most customers overall, Germany (in red)\n",
+ "appears to have a proportionally higher bar for 'Yes (Churned)' compared to\n",
+ "its 'No (Stayed)' bar, suggesting a higher churn rate in Germany.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "_M62cZH7mc12"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# ===================================================================\n",
+ "# RESEARCH QUESTION 2: Age vs. Churn Rate\n",
+ "# ===================================================================\n",
+ "# Question: \"What is the relationship between customer Age and the\n",
+ "# likelihood to churn?\"\n",
+ "# Plot: Kernel Density Estimate (KDE) Plot\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "# This assumes 'df' is the cleaned DataFrame from the previous step.\n",
+ "\n",
+ "print(\"\\n--- Generating Plot 2: Age Distribution by Churn Status ---\")\n",
+ "\n",
+ "plt.figure(figsize=(12, 7)) # Set the figure size\n",
+ "\n",
+ "# Use sns.kdeplot to show the distribution (density) of ages\n",
+ "# x='Age': The continuous variable\n",
+ "# hue='Exited': The variable that splits the data into two lines\n",
+ "# fill=True: Shades the area under the curve for readability\n",
+ "# common_norm=False: Ensures each curve is normalized independently\n",
+ "sns.kdeplot(data=df, x='Age', hue='Exited', fill=True, common_norm=False,\n",
+ " palette='Set1', alpha=0.5, linewidth=2)\n",
+ "\n",
+ "plt.title('Distribution of Customer Age by Churn Status', fontsize=16)\n",
+ "plt.xlabel('Age', fontsize=12)\n",
+ "plt.ylabel('Density', fontsize=12)\n",
+ "plt.legend(title='Exited', labels=['Yes (Churned)', 'No (Stayed)']) # Note: Order might depend on plot\n",
+ "plt.grid(linestyle='--', alpha=0.7)\n",
+ "\n",
+ "plt.show() #\n",
+ "\n",
+ "print(\"\\n--- Insight from Plot 2 ---\")\n",
+ "print(\"This plot clearly shows two different distributions.\")\n",
+ "print(\" - The 'Stayed' (0) customers peak at a younger age (approx. 30-40).\")\n",
+ "print(\" - The 'Churned' (1) customers show a distinct peak at an older age (approx. 45-55).\")\n",
+ "print(\"This provides a strong insight: Middle-aged customers are a high-risk group for churn.\")"
+ ],
+ "metadata": {
+ "id": "5diQ4kRlmc6z",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 751
+ },
+ "outputId": "8b703aea-527b-4770-e3b7-a514f5b8ed0b"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "--- Generating Plot 2: Age Distribution by Churn Status ---\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "--- Insight from Plot 2 ---\n",
+ "This plot clearly shows two different distributions.\n",
+ " - The 'Stayed' (0) customers peak at a younger age (approx. 30-40).\n",
+ " - The 'Churned' (1) customers show a distinct peak at an older age (approx. 45-55).\n",
+ "This provides a strong insight: Middle-aged customers are a high-risk group for churn.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "zrIvgbNoIPU6"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 3: README file**\n",
+ "\n",
+ "1. Upload the dataset to HuggingFace.\n",
+ "\n",
+ "2. Upload your work (code, notebook) to your HF's Dataset.\n",
+ "\n",
+ "3. Create a `README` file in a Markdown format. This page should include the end result of your work. Meaning include the visualizations, Questions, Answers, insights, Decisions, and more.\n"
+ ],
+ "metadata": {
+ "id": "TxKHPqppIPZT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "EfniaMYBK6sK"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "uLxQ5tJQK6xG"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "0GnQN63RK61Q"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "euWXtGKHK65d"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
\n",
+ "\n",
+ "---\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "hkerNEsMIPbK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 4: Presentation Video**\n",
+ "\n",
+ "- Record a brief video (2–3 minutes) with screen sharing of you walk through the HF's Dataset, README, notebook and sharing your process & results. Make sure to include a screen share while also recording yourself talking during the walk through.\n",
+ "\n",
+ "- Videos without your face talking while going ower your work wont be acceptable.\n",
+ "\n",
+ "- You should include:\n",
+ " - A quick dataset overview and your main goal.\n",
+ " - Key EDA steps and highlights of visual insights. (!)\n",
+ " - Reflections on any challenges and lessons learned.\n"
+ ],
+ "metadata": {
+ "id": "mzYCO54GIPdP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "> For help:\n",
+ "> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
+ "> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
+ "> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
+ ],
+ "metadata": {
+ "id": "si7mk6JgJurV"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "asKyjjGDK-GJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Finally, attach the video to the end of the `README` file, and make sure everything works."
+ ],
+ "metadata": {
+ "id": "QJ8Rux_SY1JH"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "
"
+ ],
+ "metadata": {
+ "id": "hoLjb_ZrY2Nm"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **Part 5: Moodle**"
+ ],
+ "metadata": {
+ "id": "km5C5-WyjO3R"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Submit to Moodle the link to your HF's Dataset.** \n",
+ "(or use a `.txt` file)\n",
+ "\n",
+ "> As the dataset already includes the video presentation, and the code notebook - we should haver everything there to examine.\n"
+ ],
+ "metadata": {
+ "id": "Bx7dlp5xjT4r"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "WZcWV_6KbE9s"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n"
+ ],
+ "metadata": {
+ "id": "ZXm5K6q-KM0S"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "---\n",
+ "\n",
+ "Good luck and have fun exploring your first DS project!"
+ ],
+ "metadata": {
+ "id": "rN8TJ_5oIPfm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "r-8LRIrecw9d"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file