diff --git a/framingham-cvd-risk/README.md b/framingham-cvd-risk/README.md
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+# Objective
+
+This example is based on the "Heart Disease Exploratory data analysis" from Kaggle (https://www.kaggle.com/code/georgyzubkov/heart-disease-exploratory-data-analysis).
+The objective of this exercise is to use different types of Machine Learning models to predict cardiovascular disease risk.
+
+We will use Kubeflow Pipelines with the Kaggle API to download the original "Indicators of Heart Disease" dataset (https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)
+and perform training in parallel of the different Machine Learning models, to finally analyze the performance results.
+
+## Prerequisites
+
+- _Kaggle API token_: this can be retrieved from the [personal Kaggle account settings](https://www.kaggle.com/settings/account).
+
+## Environment
+
+This pipeline was tested using Kubeflow Pipelines 2.0.5 and KFP sdk 2.6.0.
+
+## Launch a Notebook Server
+
+- Use defaults
+
+## Clone the Project Repo to Your Notebook
+
+- In Kubeflow Notebook Server, git clone the `kubeflow/examples` repository.
+```
+git clone https://github.com/kubeflow/examples
+```
+- Navigate in `framingham-cvd-risk`.
+- You can leverage `requirements-ipynb.txt` for the required Python dependencies if not already installed in your Notebook image.
+- You can use `original-heart-disease-exploratory-data-analysis.ipynb` to replicate the exploratory data analysis from Kaggle.
+- You will need to maintain the variables at the top of the Notebook with your Kaggle API Token in order to download the DataSet.
+
+## Generate a KFP Pipeline yaml File
+
+- In an environment with Python available, run `pip install -r requirements-kfp.txt`.
+- Then produce the pipeline YAML file using KFP `python pipeline.py`.
+- You can also leverage the already made-available file `pipeline.py.yaml` in this repo.
+
+## Create a Pipeline
+
+- Within the Kubeflow Central Dashboard, navigate to the `Pipelines` > `+Upload Pipeline` button
+- Name the pipeline
+- Click on Upload a file, `Choose file` button
+- Upload the local `pipeline.py.yaml` file
+- Click Create
+
+## Create Kaggle Secret
+
+- Retrieve the Kaggle `username` and `key`, see [prerequisites](#prerequisites) section for more details.
+- Create secret running:
+```bash
+kubectl create secret generic kaggle-api \
+    --from-literal=KAGGLE_KEY="<replace-with-your-key>" \
+    --from-literal=KAGGLE_USERNAME='<replace-with-your-username>'
+```
+
+> _NOTE_: The secret name must be `kaggle-api` as expected by the [pipeline definition](./pipeline.py).
+
+## Create a Run
+
+- Click on `+Create Run` button in the view from the previous step
+- Associate to your desired Experiment as needed or leave defaults
+- You will be navigated to the just created Run instance to view the progression on the runtime execution graph
+
+## Pipeline Run results
+
+![Run results](/kfp-run-screenshot1.png)
+![Run results](/kfp-run-screenshot2.png)
+
+## Based on:
+
+- Apache-2.0 https://www.kaggle.com/code/georgyzubkov/heart-disease-exploratory-data-analysis
+- CC0: Public Domain https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease
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diff --git a/framingham-cvd-risk/original-heart-disease-exploratory-data-analysis.ipynb b/framingham-cvd-risk/original-heart-disease-exploratory-data-analysis.ipynb
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+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading personal-key-indicators-of-heart-disease.zip to archive\n"]},{"name":"stderr","output_type":"stream","text":["100%|██████████| 21.4M/21.4M [00:04<00:00, 5.40MB/s]\n"]},{"name":"stdout","output_type":"stream","text":["\n"]}],"source":["import os\n","os.environ['KAGGLE_USERNAME'] = '<replace-with-your-username>'\n","os.environ['KAGGLE_KEY'] = '<replace-with-your-key>'\n","from kaggle.api.kaggle_api_extended import KaggleApi\n","kaggle = KaggleApi()\n","kaggle.authenticate()\n","kaggle.dataset_download_files(dataset='kamilpytlak/personal-key-indicators-of-heart-disease',\n","    path='archive/',\n","    force=True,\n","    quiet=False,\n","    unzip=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"Y_kU-rIg7mvi"},"source":["# <h1 style='background:#4682B4; border:2; border-radius: 10px; font-size:250%; font-weight: bold; color:black'><center>Personal Key Indicators of Heart Disease</center></h1> \n"," \n","<!-- <img src = \"https://i.gifer.com/1mT3.gif\" width = 900 height = 400/> -->\n","\n","Heart disease (heart disease) is a group of diseases related to cardiovascular diseases, manifested by a violation of the normal functioning of the heart. May be caused by damage to the epicardium, pericardium, myocardium, endocardium, valvular apparatus of the heart, heart vessels.\n","\n","Heart disease can last a long time in a latent form, clinically not manifesting itself. Along with various tumors, these diseases are today the main cause of premature death in developed countries.\n","\n","The uninterrupted operation of the circulatory system, which consists of the heart as a muscle pump and a network of blood vessels, is a necessary condition for the normal functioning of the body.\n","\n","According to the National Heart, Lung and Blood Institute in Framingham (USA), the most important factors in the development of cardiovascular disease in humans are obesity, sedentary lifestyle and smoking.\n","\n","<a id='top'></a>\n","<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n","    \n","<h1 style='background:#4682B4; border:0; border-radius: 10px; color:black'><center> TABLE OF CONTENTS </center></h1>\n","\n","### [**1. IMPORTING LIBRARIES AND LOADING DATA**](#title-one)\n","\n","### [**2. DATA INFORMATION**](#title-two)\n","\n","### [**3. EXPLORATORY DATA ANALYSIS**](#title-three)\n","    \n","### [**4. STATISTICAL TESTS**](#title-four) \n","    \n","### [**5. MACHINE LEARNING**](#title-five)\n","\n","### [**6. RECOMENDATIONS**](#title-six)\n","    \n","<a id=\"title-one\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>IMPORTING LIBRARIES AND LOADING DATA</center></h1>"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:55:36.457104Z","iopub.status.busy":"2022-08-29T07:55:36.456605Z","iopub.status.idle":"2022-08-29T07:55:37.700711Z","shell.execute_reply":"2022-08-29T07:55:37.699501Z","shell.execute_reply.started":"2022-08-29T07:55:36.456975Z"},"id":"ca80fd71","papermill":{"duration":1.184461,"end_time":"2022-04-15T07:42:19.777299","exception":false,"start_time":"2022-04-15T07:42:18.592838","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib as mpl\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import seaborn as sns\n","import missingno as msno\n","from scipy import stats"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:55:37.703941Z","iopub.status.busy":"2022-08-29T07:55:37.703566Z","iopub.status.idle":"2022-08-29T07:55:38.862368Z","shell.execute_reply":"2022-08-29T07:55:38.861095Z","shell.execute_reply.started":"2022-08-29T07:55:37.703904Z"},"id":"bPG9oE6vFHJ9","trusted":true},"outputs":[],"source":["train = pd.read_csv('archive/2020/heart_2020_cleaned.csv')"]},{"cell_type":"markdown","metadata":{"id":"SX9lNhjR7WW7"},"source":["<a id=\"title-two\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>DATA INFORMATION</center></h1>"]},{"cell_type":"markdown","metadata":{"id":"d7e9ac48","papermill":{"duration":0.038531,"end_time":"2022-04-15T07:42:20.888285","exception":false,"start_time":"2022-04-15T07:42:20.849754","status":"completed"},"tags":[]},"source":["Get a basic description of numeric data"]},{"cell_type":"code","execution_count":4,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:38.864583Z","iopub.status.busy":"2022-08-29T07:55:38.864165Z","iopub.status.idle":"2022-08-29T07:55:39.105198Z","shell.execute_reply":"2022-08-29T07:55:39.103695Z","shell.execute_reply.started":"2022-08-29T07:55:38.864531Z"},"id":"cc6023fc","outputId":"95125a36-5a6c-4a1c-e46e-001b9c76d47d","papermill":{"duration":0.138774,"end_time":"2022-04-15T07:42:21.065066","exception":false,"start_time":"2022-04-15T07:42:20.926292","status":"completed"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<style type=\"text/css\">\n","#T_5e464_row0_col0, #T_5e464_row0_col1, #T_5e464_row0_col2, #T_5e464_row0_col3 {\n","  background-color: #ffff66;\n","  color: #000000;\n","}\n","#T_5e464_row1_col0, #T_5e464_row1_col1, #T_5e464_row1_col2, #T_5e464_row1_col3, #T_5e464_row2_col0, #T_5e464_row2_col1, #T_5e464_row2_col2, #T_5e464_row2_col3, #T_5e464_row3_col0, #T_5e464_row3_col1, #T_5e464_row3_col2, #T_5e464_row3_col3, #T_5e464_row4_col0, #T_5e464_row4_col1, #T_5e464_row4_col2, #T_5e464_row4_col3, #T_5e464_row5_col0, #T_5e464_row5_col1, #T_5e464_row5_col2, #T_5e464_row5_col3, #T_5e464_row6_col0, #T_5e464_row6_col1, #T_5e464_row6_col2, #T_5e464_row6_col3, #T_5e464_row7_col0, #T_5e464_row7_col1, #T_5e464_row7_col2, #T_5e464_row7_col3 {\n","  background-color: #008066;\n","  color: #f1f1f1;\n","}\n","</style>\n","<table id=\"T_5e464\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th id=\"T_5e464_level0_col0\" class=\"col_heading level0 col0\" >BMI</th>\n","      <th id=\"T_5e464_level0_col1\" class=\"col_heading level0 col1\" >PhysicalHealth</th>\n","      <th id=\"T_5e464_level0_col2\" class=\"col_heading level0 col2\" >MentalHealth</th>\n","      <th id=\"T_5e464_level0_col3\" class=\"col_heading level0 col3\" >SleepTime</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_5e464_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n","      <td id=\"T_5e464_row0_col0\" class=\"data row0 col0\" >319795.000000</td>\n","      <td id=\"T_5e464_row0_col1\" class=\"data row0 col1\" >319795.000000</td>\n","      <td id=\"T_5e464_row0_col2\" class=\"data row0 col2\" >319795.000000</td>\n","      <td id=\"T_5e464_row0_col3\" class=\"data row0 col3\" >319795.000000</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n","      <td id=\"T_5e464_row1_col0\" class=\"data row1 col0\" >28.325399</td>\n","      <td id=\"T_5e464_row1_col1\" class=\"data row1 col1\" >3.371710</td>\n","      <td id=\"T_5e464_row1_col2\" class=\"data row1 col2\" >3.898366</td>\n","      <td id=\"T_5e464_row1_col3\" class=\"data row1 col3\" >7.097075</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n","      <td id=\"T_5e464_row2_col0\" class=\"data row2 col0\" >6.356100</td>\n","      <td id=\"T_5e464_row2_col1\" class=\"data row2 col1\" >7.950850</td>\n","      <td id=\"T_5e464_row2_col2\" class=\"data row2 col2\" >7.955235</td>\n","      <td id=\"T_5e464_row2_col3\" class=\"data row2 col3\" >1.436007</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n","      <td id=\"T_5e464_row3_col0\" class=\"data row3 col0\" >12.020000</td>\n","      <td id=\"T_5e464_row3_col1\" class=\"data row3 col1\" >0.000000</td>\n","      <td id=\"T_5e464_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n","      <td id=\"T_5e464_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n","      <td id=\"T_5e464_row4_col0\" class=\"data row4 col0\" >24.030000</td>\n","      <td id=\"T_5e464_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n","      <td id=\"T_5e464_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n","      <td id=\"T_5e464_row4_col3\" class=\"data row4 col3\" >6.000000</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n","      <td id=\"T_5e464_row5_col0\" class=\"data row5 col0\" >27.340000</td>\n","      <td id=\"T_5e464_row5_col1\" class=\"data row5 col1\" >0.000000</td>\n","      <td id=\"T_5e464_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n","      <td id=\"T_5e464_row5_col3\" class=\"data row5 col3\" >7.000000</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n","      <td id=\"T_5e464_row6_col0\" class=\"data row6 col0\" >31.420000</td>\n","      <td id=\"T_5e464_row6_col1\" class=\"data row6 col1\" >2.000000</td>\n","      <td id=\"T_5e464_row6_col2\" class=\"data row6 col2\" >3.000000</td>\n","      <td id=\"T_5e464_row6_col3\" class=\"data row6 col3\" >8.000000</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_5e464_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n","      <td id=\"T_5e464_row7_col0\" class=\"data row7 col0\" >94.850000</td>\n","      <td id=\"T_5e464_row7_col1\" class=\"data row7 col1\" >30.000000</td>\n","      <td id=\"T_5e464_row7_col2\" class=\"data row7 col2\" >30.000000</td>\n","      <td id=\"T_5e464_row7_col3\" class=\"data row7 col3\" >24.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n"],"text/plain":["<pandas.io.formats.style.Styler at 0x2ba6a2cd0>"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train.describe().style.background_gradient(cmap = \"summer\")"]},{"cell_type":"markdown","metadata":{"id":"puVCmKs97c_W"},"source":["Hypothesis: There are outliers in the data for both maximum and minimum values."]},{"cell_type":"markdown","metadata":{"id":"d0e7c349","papermill":{"duration":0.041989,"end_time":"2022-04-15T07:42:21.146803","exception":false,"start_time":"2022-04-15T07:42:21.104814","status":"completed"},"tags":[]},"source":["Let's look at the data"]},{"cell_type":"code","execution_count":5,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:39.108953Z","iopub.status.busy":"2022-08-29T07:55:39.108589Z","iopub.status.idle":"2022-08-29T07:55:39.164897Z","shell.execute_reply":"2022-08-29T07:55:39.163932Z","shell.execute_reply.started":"2022-08-29T07:55:39.108908Z"},"id":"5f1676bd","outputId":"ed7ddda4-360f-4545-b52c-2c627462900b","papermill":{"duration":0.062709,"end_time":"2022-04-15T07:42:21.249415","exception":false,"start_time":"2022-04-15T07:42:21.186706","status":"completed"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>HeartDisease</th>\n","      <th>BMI</th>\n","      <th>Smoking</th>\n","      <th>AlcoholDrinking</th>\n","      <th>Stroke</th>\n","      <th>PhysicalHealth</th>\n","      <th>MentalHealth</th>\n","      <th>DiffWalking</th>\n","      <th>Sex</th>\n","      <th>AgeCategory</th>\n","      <th>Race</th>\n","      <th>Diabetic</th>\n","      <th>PhysicalActivity</th>\n","      <th>GenHealth</th>\n","      <th>SleepTime</th>\n","      <th>Asthma</th>\n","      <th>KidneyDisease</th>\n","      <th>SkinCancer</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>No</td>\n","      <td>16.60</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>3.0</td>\n","      <td>30.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>55-59</td>\n","      <td>White</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Very good</td>\n","      <td>5.0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>No</td>\n","      <td>20.34</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>80 or older</td>\n","      <td>White</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Very good</td>\n","      <td>7.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>No</td>\n","      <td>26.58</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>20.0</td>\n","      <td>30.0</td>\n","      <td>No</td>\n","      <td>Male</td>\n","      <td>65-69</td>\n","      <td>White</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Fair</td>\n","      <td>8.0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>No</td>\n","      <td>24.21</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>75-79</td>\n","      <td>White</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Good</td>\n","      <td>6.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>No</td>\n","      <td>23.71</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>28.0</td>\n","      <td>0.0</td>\n","      <td>Yes</td>\n","      <td>Female</td>\n","      <td>40-44</td>\n","      <td>White</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Very good</td>\n","      <td>8.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>319790</th>\n","      <td>Yes</td>\n","      <td>27.41</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>7.0</td>\n","      <td>0.0</td>\n","      <td>Yes</td>\n","      <td>Male</td>\n","      <td>60-64</td>\n","      <td>Hispanic</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Fair</td>\n","      <td>6.0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>319791</th>\n","      <td>No</td>\n","      <td>29.84</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Male</td>\n","      <td>35-39</td>\n","      <td>Hispanic</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Very good</td>\n","      <td>5.0</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>319792</th>\n","      <td>No</td>\n","      <td>24.24</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>45-49</td>\n","      <td>Hispanic</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Good</td>\n","      <td>6.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>319793</th>\n","      <td>No</td>\n","      <td>32.81</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>25-29</td>\n","      <td>Hispanic</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Good</td>\n","      <td>12.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>319794</th>\n","      <td>No</td>\n","      <td>46.56</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>No</td>\n","      <td>Female</td>\n","      <td>80 or older</td>\n","      <td>Hispanic</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Good</td>\n","      <td>8.0</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>319795 rows × 18 columns</p>\n","</div>"],"text/plain":["       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n","0                No  16.60     Yes              No     No             3.0   \n","1                No  20.34      No              No    Yes             0.0   \n","2                No  26.58     Yes              No     No            20.0   \n","3                No  24.21      No              No     No             0.0   \n","4                No  23.71      No              No     No            28.0   \n","...             ...    ...     ...             ...    ...             ...   \n","319790          Yes  27.41     Yes              No     No             7.0   \n","319791           No  29.84     Yes              No     No             0.0   \n","319792           No  24.24      No              No     No             0.0   \n","319793           No  32.81      No              No     No             0.0   \n","319794           No  46.56      No              No     No             0.0   \n","\n","        MentalHealth DiffWalking     Sex  AgeCategory      Race Diabetic  \\\n","0               30.0          No  Female        55-59     White      Yes   \n","1                0.0          No  Female  80 or older     White       No   \n","2               30.0          No    Male        65-69     White      Yes   \n","3                0.0          No  Female        75-79     White       No   \n","4                0.0         Yes  Female        40-44     White       No   \n","...              ...         ...     ...          ...       ...      ...   \n","319790           0.0         Yes    Male        60-64  Hispanic      Yes   \n","319791           0.0          No    Male        35-39  Hispanic       No   \n","319792           0.0          No  Female        45-49  Hispanic       No   \n","319793           0.0          No  Female        25-29  Hispanic       No   \n","319794           0.0          No  Female  80 or older  Hispanic       No   \n","\n","       PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n","0                   Yes  Very good        5.0    Yes            No        Yes  \n","1                   Yes  Very good        7.0     No            No         No  \n","2                   Yes       Fair        8.0    Yes            No         No  \n","3                    No       Good        6.0     No            No        Yes  \n","4                   Yes  Very good        8.0     No            No         No  \n","...                 ...        ...        ...    ...           ...        ...  \n","319790               No       Fair        6.0    Yes            No         No  \n","319791              Yes  Very good        5.0    Yes            No         No  \n","319792              Yes       Good        6.0     No            No         No  \n","319793               No       Good       12.0     No            No         No  \n","319794              Yes       Good        8.0     No            No         No  \n","\n","[319795 rows x 18 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["train"]},{"cell_type":"markdown","metadata":{"id":"roS4INeI7sHZ"},"source":["The data sample is very informative and is represented by 319 thousand patients on 18 criteria."]},{"cell_type":"markdown","metadata":{"id":"S5ga4iVY74ox"},"source":["What features characterize our data sample?"]},{"cell_type":"markdown","metadata":{"id":"niGt7B748AxV"},"source":["* HeartDisease - target trait.\n","* BMI - a value that allows you to assess the degree of correspondence between a person's mass and his height, and thereby indirectly judge whether the mass is insufficient, normal or excessive. It is important in determining the indications for the need for treatment.\n","* Smoking is a major risk factor for cardiovascular disease. When smoke from a cigarette is inhaled, the reaction of the cardiovascular system immediately follows: within one minute, the heart rate begins to rise, increasing by 30% within ten minutes of smoking. The bad habit also increases blood pressure, fibrinogen and platelet levels, making blood clots more likely.\n","* AlcoholDrinking - alcohol causes not only temporary disturbances in the functioning of the heart, but also permanent ones. Heart pain after alcohol is not the only health problem associated with alcohol consumption.\n","* Stroke - Ischemic stroke occurs 4 times more often than hemorrhagic. One of the leading causes of this suffering is heart disease, which impairs its functioning, as a result of which the blood flow in the arteries is disturbed and the blood supply to the brain is reduced. Another cause of stroke in heart disease is thromboembolism, when clots form in the cavities of the heart (most often with heart failure) - blood clots.\n","* PhysicalHealth - how many days in a month did you feel poor physical health.\n","* MentalHealth - how many days in a month did you feel poor mental health.\n","* DiffWalking - difficulty climbing stairs.\n","* Sex - gender of a person.\n","* AgeCategory - age category of the subjects.\n","*Race-obviously:)\n","* Diabetic - obviously :)\n","* PhysicalActivity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job\n","* GenHealth - well-being.\n","* SleepTime - number of hours of sleep.\n","* Asthma- obviously :)\n","* KidneyDisease - obviously :)\n","* Skin Cancer - obviously :)"]},{"cell_type":"markdown","metadata":{"id":"rfom8d1x_oSI"},"source":["<a id=\"title-three\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>EXPLORATORY DATA ANALYSIS</center></h1>"]},{"cell_type":"markdown","metadata":{"id":"HlrBoEUMAGFo"},"source":["Check the dataset for gaps in the data"]},{"cell_type":"code","execution_count":6,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:39.166778Z","iopub.status.busy":"2022-08-29T07:55:39.166016Z","iopub.status.idle":"2022-08-29T07:55:40.967954Z","shell.execute_reply":"2022-08-29T07:55:40.966695Z","shell.execute_reply.started":"2022-08-29T07:55:39.166730Z"},"id":"gqVYfE44AHeP","outputId":"3a7a84b6-5b5d-4db8-87ee-8de1b2053e64","trusted":true},"outputs":[{"data":{"image/png":"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8fvG2P+73GmDtJ8fX1Nq1atzIoVK8zChQtN//79Pd/Ljhw50iQkJGTzFV85p0+fNs2aNTO+vr6mX79+5uTJk8aY6/s15HyxsbEmJCTE+Pj4mGXLlmX35bjmu+++M47jmFy5cpkZM2Z4bu/Ro4cJCwszr732mklMTDSnT58269evN6VKlTKO45jGjRubffv2ZeOVX3nvvvuu57H/9NNPnts/++wz4ziO8fPzM5MnT/b6czunSX1dTUhIMPfcc48JDAw0vXv39ryG7N2710RHR3teW1P/3Dn32Jzq3MfXp08fz/f148ePN5999pln8Oo4jundu7c5fPhwNl7tlZWSkmL69evn+XtNeHg4A2i4igH0NSj1RfTkyZPmtttuM35+fqZ9+/Zm586d5q+//jLLly83pUuXNo7jmPbt23sGiznNJ5984vVTy1atWjGETkNqp6ZNm5pff/3Vc/v+/fvNlClTTLly5a6rIfS8efOMr6+vqVKlivnll1+MMf83SNq/f78ZMGCACQ8PN4ULFzaDBw/Osb9/zjdp0iTjOI558sknTXx8vOf2lJQUU7t2bRMcHGx69+6dI9+pd+bMGTNgwADj4+NjypQpY0aMGGGOHTtmjh07Zm6++WbTuHFj8/PPPxtjzv7eiI2NNa1atfJ8A5sTh9Bjx471vG6sXr3ac3u7du1M3rx5Tc+ePc3hw4fN9u3bzZAhQ0zZsmVNrly5zHPPPXddDKHPfY187bXXTMGCBY2/v79p0aKFKVWqlClZsqTnhzbXyxD6Qu9sbtSokXEc57ppce5z/7fffvMMAFKltlqwYIHJnTu3ad68+RW9vqtBUlKSee6554zjOOaVV16xfjgRGRlp8uXLZ3r37m1OnTqVTVd55c2aNcvkypXL3HfffQyfLyD1DSijR482xuTMRikpKZ5/gRUUFOQZQt96662madOmnh+Wnz592hhjzObNm02VKlU8b7C4XobQ69atM8WKFTPlypUzGzZs8NqWOphu0qSJWbVqVTZd4ZWTnJzsGaZ27tzZ8xz5/fffTZs2bYzjOKZ+/fpe7/hNlZO/Z0s1ZcoU4+PjYxo2bGhiY2O9tj366KOe58r27duz5wKzSWxsrHn22Wc9b2Q891+XXA/PC1xZDKCvUSkpKebVV181juOYrl27Wt+4V65c2RQsWNB07drVa5iUkyxatMhERESYwoULm6JFixrHcUzbtm0ZQv9/KSkp5vjx46ZJkyb/j72rDotq+9rPnqC7SxBREERMVFQsVExU7BbF7i4MLESxG7vrXrsQRUUFxO5uRVFBJSSH9/uDb+97zsyA3t8V9IzzPg/PlXPOcPc+s2Ptd631LhBCmDczLy+PGeoZGRk4ePAgnJycGIlPU/dU0ZgHgMWLF4MQgnXr1gHIfx/0BwCSk5MxZswYEEJQqlQpzJkz54+IhO7fvz9EIhEvayI3NxdeXl4ghCAoKAhfv34FkB8Fq2opwe/evcOsWbNgbGwMe3t7rF69GufPn4e+vj7OnDkDgD8nXrx4ge7du4MQAg8PD5UjoaOjo9GwYUOIRCK0bNkS8fHxyMnJQYUKFdCjRw/evpKWloa9e/fCzc0NUqn0jyGhASAkJASEEPj7++Py5csA8sdSdHQ06tWr98eQ0PR7Tk1NxbJly7B161Zcv34dt2/fRlJSEnr37g1CCE6cOMH7HPd9qEpkGnfMjx8/HlKpFPXr18eJEycU5MIePnzIohYPHz5c3E39pUhOToazszOqVKnCCDQgn0yj9lpQUBBzemZnZwNQrTVFWV9Gjx4NQgizU39kXqjSOwEU+0z7x+3n/v37QQiBk5MT3r17V6ztK07k5eWxrD0NDQ0sWbIENWvWZJHf9F3RtfThw4fw8PD4o0joPXv28JwRFDSqs3nz5jyyUZXfSUJCAsqVKwd3d3fmwHr69Cl69eoFQgj69esHAFi4cCEjoUNDQ9nnVW0tkUeXLl2go6OjIKE3Y8YMEELQunVrXgQ9haq/FwC4desW+vXrBx0dHdjZ2WHHjh3s3p/QfzWKD2oCWsCoWbMmypQpwyOfc3JymAZlUFAQS6OXj2hUBSQkJMDDwwM2NjYYO3YsTE1NQQhBjx491CT0/+Pjx48oVaoUHBwcWAqrPAHy9etXpptmaGiILl26MMJVVcYK8E+/u3TpAkIIFixYAIB/0KH9TUhIYE4NW1tbLFy4ECkpKcXf6CIC93vNy8tDSkoKvLy8oKuri6dPnwLIf19c8pnb/4sXL2Lw4MFM41ZVkJiYiODgYBgbG8PR0RGDBg2Ck5MTkpOTkZOTozAfnj9/ziOhz50794taXjSIi4tD48aNma7k5s2boaenhytXrgDgryUZGRn4+++//ygS+smTJ7C3t4e1tTXLLuG+k5SUFCaxYGdnx5w7qkpCZ2VlMWcmIQQ6OjrQ1dVF1apVoa+vzzIs1qxZw6SPVM25x91Prl27hoCAABaRqKmpiTJlyiA0NBQ3btxg82Lp0qUghGD06NEAVHd8yOPs2bMghPBqCxS078hkMuzevVshulGIWLlyJU9OQR7t2rWDWCxmjk9loGPk2rVrCtH1qoSlS5fi7NmzPE1W7n5CZcMOHjwIQHXnDjcS2sjICFKpFJs3b2b3KJSR0C1atFBpgh74p07HkSNH2LXg4GBGPnPXjc+fP6Nr166IiYn5FU0tcrx79w5DhgzB0aNHAQCfPn1iAWsBAQHsuZs3b7IMWEIIZs2a9auaXGz4+PEjDA0NUadOHd71gsZKXFycggyd0KFsjeSuITdv3kSfPn0glUpRrVo1HDp0SOlzaqjxX6AmoAUKWtSnbdu27FphhNHevXuxdu3aX9HUIgFdBLle79jYWKZH+qMkdERERHE3vVhBCWiRSITIyMgCn7t69SorSlhQVL2qgEZADx48mF3jbqo0yqp3794oW7YsSpYsCXt7e5w8eRKA8KP0aOp3VlYWr1BN+/btIZVKkZSUBCA/xVPZWgIA9evXh52dHV6/fl18DS9CcL/ThIQEBAcHw9DQEGKxGNra2ow4VGZ8cUnosmXLFjrPhAJuP2NjYxkJXaNGDVhbW+P+/fsKzwGKJPSQIUNUZowoQ3R0NMu8AcBzUlAj/82bNzzNbFUmoT9//oyNGzdi69atGDJkCLp06YIKFSrAwsKCSZKIRCJekTljY2M0bNgQ9erVw5IlSwRdoJA7H0aMGIHOnTsjNjYWmZmZWL58OXr06MH6bmpqihYtWiAqKgp79uyBm5sbTE1N8fLly1/Yg+IFJaB79eoFIP/9FWTDZmdnw83NDYGBgYLeg//++28QQlChQgVethF37FB5pzlz5ijtK107aN2GlStXqgwxwF0Xw8PDQQiBWCxG48aNMX/+fHz+/Jknx0Kf6dKly69obpGCroXKiuwRQjB16lR2vSASunLlyiCEoH379iq551DQCPHly5cDANOQlycUgX/OAFyyWtXALah99epVWFpaonnz5uy+TCZDTk4OqlatyoLWCCFISkpSmbVEGT58+AB9fX1UqlSJrSMFkc8AMHDgQFSuXFllHOV0P8nJycHr168RERGB27dv4/Hjx7znbt26hYCAAEgkEtSoUUNNQqvx06EmoAWKp0+fghCCatWqsWsFGe4AULVqVfj4+KicHMeTJ09QpkwZGBoa4sOHDzhz5gxMTEx+mIS2trbGqVOnfkHLix55eXnIzc1lVY9nzJih8Aw1SNPT0+Hh4YHg4GCULl0aVlZWjEgT+mYj3/6TJ08yY2vPnj3sukwm4z3brl071KpVixn8jRo1KrY2FzVSUlJQtmxZZoDn5eUx2ZGWLVsy8nnSpEkKa0lISAi0tLQwevRoQepyyh/CqMMBAJMYefv2LYKDgxlpNnDgQBaBpWw+vHjxAp06dQIhBNu2bSvC1hcP5EnA8+fPo0mTJqxAy+zZs9m9gkhoGvU5atQoQRNGhYGuJX5+fkrv0zV47NixbM0pVaoUI+WFvrb+CHJycpCYmIhZs2ZBJBKhYsWKmDp1Kjp06IDGjRvDysoKurq6EIlEzLEhdCxatAiEENSsWRN3797l3Tt79izmzJnD6nTo6OjAxcWFZXDRolCqRBYV1JcXL17AyMgIDRo0wIsXL3jZe/L7ztChQyGRSHh7thBx/fp1dOvWjWVgKZOg2bJlCzQ0NNCgQQOFyFXuWjpu3DgQQrBlyxZBriXy44LbtwcPHuD27duYNWsW6tevz9ZPDw8PdO/eHbdu3UJaWhpSUlJgYWEBbW3tQiPGf3fQvn/79o0nCZeSkoIJEybg2rVrAPLf2ZQpU9j72LlzJ/sbykjoe/fuoXbt2rh582ZxdeWX4OzZs9DV1YW/vz+TsJGX3QCAU6dOwdbWFnXq1BG8xq+yOa/MgUsdn1u2bAHwj82bm5sLR0dH7NmzBwcPHmR1TlQB8u+G/k5l5Ozs7PDixQueRIs8+bxv3z6IRCKMGDFC0I5xCtqH9PR0BAQEsGw1PT09mJiYYMmSJbyAkdu3b6tJaDWKDGoC+jdHYQZarVq1YGtri6ioqEIN9wkTJkAsFmPFihWCXjQKIjGo95Ia9IcOHfouCU2j9PT19RU0GYUGZdp43HGzefNmZqzu3r1b6WcPHToEkUiEiIgIVrCjT58+xdD6n4+CDrvc8UOL1zg4OODAgQMKz54/fx6WlpZYvHgxsrKy4OrqCkIIOwQIHdRA53r13717x9LxNDU1MW7cOIXPrVq1CtbW1qhUqZIgjXfuHKEyEhSBgYGwsrJieqPv379HcHAwLC0tYW1tjeXLl7OIEmXr6NOnTxX0bYUAOi/ooYQ6FVJTUxEUFMSeu3jxIpo3bw6pVIoaNWrw0hKVkdA7d+6El5cX7t27V9Rd+GW4f/8+JBIJDA0Ncfr0aYX7dC06cOAAypQpw6LRmjVrxkspFyIKI0hlMpmCXu+lS5egpaXF9CcpEhIS8Pr1a0GuJxTcvSU1NRV16tRBgwYN2IGeSyhRfPr0CVu3bkWHDh2YPAk9CKsSuBFXN27c4EVaJScno2nTpkzqSiQS8WoNUKxevRpmZmZo3LixShQFpllGaWlp6Nq1K/bt28e7//jxY7i5ubHI3g8fPijYv2vWrIG5uTlq1qyJ9+/fF1vbfxa48+Hy5cs8GZGBAweidu3aePToEQDgy5cvOHPmDLp164bSpUszubg6depg586dGDBgAAghWLRoUXF346fi0qVLaNSoEZOG+PbtG1xcXEAIwcGDB3nZNZQ409HR4TlllJ0DuE52oeJ7sgHPnz9nmUYikQhNmzZl5DN9LiYmBg0aNIChoWGhEjhCACUTc3NzkZKSwuTOlKFbt26QSqUKwVZhYWHQ0dERpN1aGLhjhbtf0Ay1UaNGgRCCcuXKsaAb+bNdbGwsPD09YWdnpxLSevSdpKWlsXni6emJgQMHonPnztDU1AQhBL1792ZycgBw584dRkLXrl0bf/3116/qghoqBjUB/RuDa7hfvXqVXZfJZMjNzWXRDzRVfOrUqQqG+9q1a2FlZQVvb2/BaoDt3LlToe00sgzIP8CWKVMG7u7ujDw5duwYiyjq3r07L9WR4uDBg0qvCwncQ8mXL1/w9u1bhUhe4J9IGUIINm7cyNuUL168iLp168LFxQWfP3/GpUuXmMdTaDIc3Pdx7tw57N27F6tWrcKbN294z92/fx8dO3YEIQS6urpYvnw5c0ScPHkSDRs2hL6+PjsYtmrVCoQQQRWau3XrVoERyr1794aOjg77fqlxsmPHDpQoUQKEELRr1w7p6en4+PEjPnz4gFGjRkFXVxcWFhaCj1SsWrUqCCEsYmrkyJEsyp3rkKIkNC1M+D0SmkJo0YuXLl1C+/btGTn05csXuLu7QyQS8QqjxcTEoEmTJsxo51aUl38fmZmZgls//g1kMhkyMjJYuvygQYN40SPciJmQkBCUL18eT548QfXq1WFoaMjGnhCdwlwZn/Pnz2Pp0qVYsWIF/v77bzY/AP48uHnzJjQ1NVG+fHmkpqaye6oUHR8VFYXo6GiIRCLs2rWrwOfk14dTp05h8eLF0NHRASFEZQ553IirPn36wNDQEF27duXVDjh+/DjEYjEIIahVq5aCBMncuXNhYWEBe3t7hTRhISMvL4+RiF5eXjh27BjvfmxsLMs4adKkCZYvX46bN2/i5s2bGDZsGAwNDWFpaYkHDx78oh78HNSrVw+6urqMHONmYslLX2VkZCApKQkzZ85kxbW50nG2traCDSjJysqCn58fCCHw9fXF+fPnUa5cOWhra2PGjBmshgsFtzChlpZWgSS0st+FBu4ecf36dURFReHGjRsKhQSjoqIgkUhACEGrVq3w4sULpKWlISsrixVJJoRg6dKl7DNCfDf0fdBI1qpVq6JkyZLo3r07Ll26xJw5dJ+hhGu1atVw48YNpKenIywsDDY2NqhUqZIgHVgFgTtWduzYgYYNG/KkaoD8yF5ra2sWhCQfPHDy5EnUqVMHhBCsX7++WNpdHMjKykLr1q1ZdivXRt27dy8IIbCwsMDevXt57/HOnTsIDAxkZyRVtuvVKD6oCejfFHRh+PbtGwYPHgx9fX2mk0fx8eNHluZcokQJREdH8+5PmzYNpqamsLa2xsOHD4ut7T8TNHXZ3t4eU6ZMUSAAZTIZMjMzWYrR0qVLmUFx4sSJ75LQQgb3ELt06VLUqlULZmZmqF69OkJDQ1kxOSB/PI0YMYIZ7D4+Phg2bBgmTJgAOzs7EEKwZMkSAPmpj5qammjWrJmgjDPu+5g2bRo7zFPt1YULF/Ki4a9evYo+ffqwZ6ytrVl0DSEEYWFh7NkqVarAwcFBMIba2LFjIRKJsGfPHmRlZbHrdF1p0aIFjI2N8fXrV54R8vnzZ2zatImlh1PilUboVaxYUfARrTk5OejatSsztvz9/ZlGorK+cQsT2tvbY8WKFT9EQgsF6enp8Pb2BiEEnTt3xr1791ChQgVoampizpw5CgffuLg4+Pr6gpD84kZcElrV8COOhH379rHaA6NHj1YghGJjY1GlShW0bNkSmZmZLH06JCSkqJpdpKAHk7S0NLRq1Qq6urpszaTkyfbt29m7o3MkLS0Nbm5ucHBwQHJy8i9rf1Fh5syZIISgU6dOcHFxYdGbhRHs8uNr06ZN7HAodHDHSY0aNaChoQEfHx/ExsYqRGRu3bqVjZ/WrVtj4sSJmDt3LtOet7Oz40VlCRXyadxXrlxhNoinpycrGkYRExPDK+qpr68PDQ0NJkUh9L3427dvaNOmDQghcHFxYf/u1KmTUic3d77IZDIcPnwYQ4YMgb29PXs/9B0KwQksn714+/Zt9g709PQglUoxa9YsNl/k+8QlobW1tQsloYUKbp9nzJgBY2NjEEIglUrRsGFDhazOEydOsGfs7e1RuXJlVozR0NAQK1asUPq3hQL6vaanp8PT0xOE5NcToE48Nzc3rF69mpcJ/eHDB/j4+LA5QueLra2t4NcQLrh77fTp06GrqwttbW2MHTuWZTbS93fu3Dmegy80NBQbNmzA+PHj2XmHymFxPydE0LYfOXIE2traaNWqlYIca61ataCnp4exY8cqtc9u3LiBwYMHq9R4UePXQk1A/4agi2hqaiq8vb0hFovh7e2NEydOMDKAPvP27Vvm1XVyckKrVq0QEBDANiYnJycFDUKhgBaKoFGq9N/9+/dXiBC6f/8+jIyMFLQ4uSR0r169eKSsqoDKSYjFYpiZmYEQAolEgubNmysc2kJCQlCyZEkeYWBubs6LCBgyZAgIIQgNDS3urvwU0KgiR0dHjB49Gr6+vtDR0YG+vj5GjBjBi6JKTU3F6tWr4enpCVtbW1hbW6NRo0ZMKw34R+Klffv2CvI2vyNkMhn69u3LDMy9e/cyEppGx1erVg3Ozs4AFA2r7OxsPHr0CD169ECdOnXg6uoKPz8/rFy5stA0PyFAmQwLIQR16tRhWQHKDiXyJPSqVatUIgqASgOcPXsWdevWBSEEBgYG0NDQwKJFixjRLp9V8SeQ0Nyxsm3bNowdOxYtWrTA4sWLcfv2bd6zK1euZJFXPj4+mDVrFuLi4rBx40bmJN6wYQOAf4pmzZw5s1j78zPArRlA0zj9/Pywe/durFy5Eh07doSWlhacnJywZs0a3pjJycmBl5cXdHV1VfIQs2bNGlhZWUEqlYIQgmXLlv2rz8tkMsTGxjLi5MOHD4I+9AL5EasNGjSAhoYGJk2apJCRw11rDx06BE9PT57j2NbWFl26dBGk3SbveKBRiSkpKVi5ciW7fvfuXVanQxkJ/eDBAyxZsgSNGzdGzZo10apVK6xYsULwezFX8mnYsGHsO/f29uatr9+L5s3Ly0NiYiKTFWvSpIlgiEUaEJGbm8ucE9evX4eenh4kEgkcHR2ZHEdByMvLY05NfX19nu2qSqB2uKWlJfz8/FC+fHlGvK9Zs4b37OXLlzFkyBC4urrCzMwM9vb2GDlyJE+CQihjRBny8vIwYsQImJiYYOzYsXj//j1u3bqFvn37wsDAAHZ2dliyZAk7r8hkMjx8+BAdOnSAsbExPDw80LVrV5XLKKGgGb8NGjTAxYsXec/l5uayZ2NjY1G9enWeBJZEIoGnpyd27NjBPiPkscLF5MmTeZmfQH7fuBKuNIv+y5cvCuODG8ykhhr/FWoC+jcD17tZpUoVaGpqYsyYMQreKu5i+/79e/Tp04fpGRFC4OrqisGDByvVPxYKVq5ciQoVKkAqlaJp06YYMGAAGjZsyNLtGjRogH379rHIZloETF7b68SJE7CysmLktSoUE6DYunUrtLW10bx5c8TGxiIpKQnbtm1jEY1169ZVIEvu3r2LY8eOISQkBPv370d8fDy7t2zZMmhpacHV1VUwmpxc4+D+/fuwt7dHq1atWL9zcnKwcuVKuLm5QUtLC0OHDlXYWJOTk/Hp0yckJiYyTzmQ/z7MzMwEk9rJLbRBU1mtrKx4JDQAuLq6wsvLS+nfoAdnmUyGrKwsfPjwoegbXoS4ePEili1bxtNPBP7RwSaEwMjICBcuXACgXK8V+IeENjc3h5WVFRYsWCDYtUTZvnD69Gloa2tDJBLB1dWVRXByoxULIqFbt27N3p8qgNvPSZMmMQcfHS/29vYK2TgbN26Eh4cHIyC5P9xImrZt20IkEjGiSWgkY05ODnr37s0idbnj48mTJ7CysoK5uTnmz5/Pc3wBQJcuXUAI4RWzUSVs2rSJRay2bt36h/cM7h5WtmxZeHh4qETB6CVLloAQgoCAAEbAyhOz3L6/fv0aN2/exK5du7B37168efNG0I6+mJgY9O7dmwWOfP78GWXLlgUhhJex+D0SmkLoWr6nTp1ijjjgn++eOszp2hodHf3DfaXjKTk5Gfb29rCwsBCEw6Jnz54ghLBaCrm5ucjOzmZyYGXKlAEhBPXq1cPly5cL3Se4ci5WVlZISUkR3L5SGB4/fgxXV1e0adOG2fXPnz/HjBkz2LhZtWqVwucyMzORnJyssIaoAqFYrVo1dOzYkbdPvH//HjNnzmRnliVLlijIcj548ABfv37laa6rElavXs0CI5RlzVCbnY6Bly9fIj4+HosWLcLSpUtx8eJFnn2sCmOFok+fPpBKpWzvoUEBlHzmBlg9ePAAJUuWRGxs7K9qrhoqDjUB/RsiJyeHGWQTJkxQehB5+PAhMjIy2ILx7ds3pKWl4cqVK7h27RoyMzNVwlu1ZcsWuLq6QkNDA4MHD8bx48cRHx+P5s2bw8DAAIQQlCpVCrt378bUqVNBCEHPnj2RmZnJO+gcOnQIpUqVEnyVX/nD27hx41C6dGle9d7c3Fxcu3aNabUqI6GVYerUqbCwsIC5ublgoua5xkFycjIOHDgADQ0NXLp0CcA/xkZOTg527tyJ8uXLQ0tLC8OGDeORA9z3mpubiw8fPqB///7Q19eHjY2NoNJ/udrxVPvNysoKe/bsQVpaGtLT02FmZoZGjRr9q78rxAPNy5cvoa+vDz09PV7q5YsXL9C0aVN07tyZkWKmpqa8YizKinu+f/+erTNC1YYbN24cateuzSOMc3Jy0KhRI0ilUibJ4+/vz5x73HkmT0I3b96cpU3Ly3UIHQsWLGCk0NatW7Fs2TK0b9+eHXrltQOvXr2KzZs3o3Xr1mjevDkmTZrEK3K6aNEiEEJQu3ZtQTh2lM35e/fuwc7ODjVr1uTZJjKZDF5eXpBKpZg4cSJz5HHHzuDBg2FkZMTTAFYFcB1RmzZtgq2tLQghGD9+PK/Q6/ewdu1aEJKvsyhPHAgRbdu2hb6+PiMEVekw/z2kpaUxZ0T//v3x+fNneHh4QEdHBzNnzlRYKwsjoeUdnULbi/Py8vD27Vu2bkZFRbF7ycnJ6N+/P5o1a4a2bduyzM1jx479sIOXRtb369cPhOQX7PvdQWXANDU12V6cm5uLe/fu4ejRo7h9+zbbW729vXH16tUCC41TzJo1S/BnHGU4deoUxGKxQjG4vLw8XqYsl4SWd2AIbc5wIX/uS0tLg7m5OSIjIxXuf/z4EbNnz4apqWmBJLSq4uvXr/D29oaRkRHvTAzkr6+bNm1C/fr1ERgYiC1btvDqVSiDUMeMfLvpWtG/f38QQhAeHo7MzMwCyWcA6NixI/T19RXeoxpq/CyoCejfEB8/foSLiwsqVKjAu56Xl4fdu3ejZ8+e0NXVRZUqVTBt2jRe4SP554UEbnu5G+qWLVvg7OwMiUSCLl264MWLF8jJycHNmzfRv39/pvdFq0Xr6uoqFC8B8N3NRkgICwvDihUrUK5cOUyZMoVd5xqld+7cYRGKXBKaGwmal5eHR48eMUPX3d1dMOQzFyNGjEC1atUwZcoU+Pj4APjn0Eb7m5ubq0BCK4uUyc7ORvfu3Vk6vRD10wsioXfv3o3nz59DW1ubaR7fvXsXt2/fxqVLl3DhwgWcOXMGERERCA8PF3x17Hfv3mHq1KkwNjaGra0tLxL60aNHSEhIAPCP7IypqSkiIiLY57nrED3QvHr1SrCSE8+fP0fNmjVBSH6RHi4JvWHDBixfvhznzp1jhmmrVq1YJgRdW+QPvmfOnEHbtm0FuW7Ig/t9Z2Zmolq1avDx8VE41HNTxuVJaCB/rMgfGufPnw9zc3OYmZn99oXDEhMT8eXLFwCK3/eWLVtACMH8+fPZNfk0Tq5j/MKFC+z3zZs3//Z9LwzKNFiVYcuWLSzrKigo6IdI6KtXr6J8+fIwMDAQ9DuiSEhIgIODA6ysrNg6WxBkMlmBdqyQcfr0aUZCGxsbQ0NDAwsWLFAo/ktRGAktNHteGYKDg1G/fn0kJSXxrn/48IFFZFJNbCcnJxw/flzBjgPAK2LKvU6jirlSC78buO0dPHgwS/vnRkLTPj98+JAFkigjoQHgwoULKkU6K9PMP3ToEKpVq8Z+l5831LErT0KrgsOLjoXMzEwcOXIEGzduxP79+1GrVi0cP34cgGI/P336hDlz5sDMzAw2NjZYunQp289VGY8fPwYhBA0bNuRd37x5M5NCoz8lSpTAqlWrkJeXpxLjhILOn7y8PHz58oXXt9OnT0NfXx+1a9dmEjZBQUEKY2PBggXQ09NDYGCgSvEmavxeUBPQvyEuXrwIQgjatGnDrqWlpaF79+7Q1NSElpYWzMzMoKWlBSMjIyxevBiA8DfbnJwcvH79Gl++fFFImdqyZQvKli0LsViMzp0787xyNH3G1tYWYrEY1atX5xkxyiIZhYBXr17h3bt3CtcPHz4MQvILwjk6OrLvX1nKIpeEbtCggVJD9f3795gwYQJmzJjxW8tunD9/Hvv371e4npiYyIwLbW1tlChRgiejASgnofX09DBo0CAmNcDFp0+fcPDgQaYLLETQ9YBLQltbW2PFihU8zbOCfnR0dFRCI+79+/eYPXs29PT0WESI/FqQkZFRIAkN5Ef69urVS4FIEeKaGx0dDT8/P4hEIrRs2ZIXVUT7c/HiRVSrVk2BhOauMVxtPSGncyrbF44cOYK7d+/Czs4Ohw8fZs9x9xWaKs0loWUyGW9M5Obm4t27d2jYsCG0tbVRqlSp3z6b4vDhw/D394e7u7vS/YAWjKOyIoWlcX758gUmJiZsjxIyuN99VFQUli5dit69e2Pw4ME4ePCgQlT3vyWhaUGx3318/ChSUlLg5uYGY2Nj5pySJ5fo7y9fvsTAgQNVpu/AP2tpZGQkJBIJxGIxKlasyA70BWUocknomjVrKrV5hAbumkj7PXjwYJ5OOiXavnz5gsDAQB4JzX1X8fHxmDhxosIevXXrVmhpacHFxeW318fmzoOBAwcqJaHpM0+ePOGR0FeuXGGf3bhxIywsLNC3b1+VyD6SDzwaNWoUOnfujAEDBsDa2rpQxxwlocViMZYvX14czS1ycAu5NmzYECKRiGejBwQEsGcLIqGtra2hpaXFyFZVxufPn+Hk5ISyZcvi1KlTiIiIwKBBg9jZMDQ0FPv372fX/P39f3WTfyroGpqRkYFZs2ahbdu22LZtGxsbr1+/ZoV9xWIxBgwYoOAMXLlyJWxsbFCuXDkWyKeGGkUBNQH9GyIpKQkeHh6ws7NDWFgYpk6dCldXV5a6e/PmTTx58oTpX/n6+v7qJv9nbN68Gf7+/syAbNSokdIDnaurK8RiMbp168bTLgaAp0+f4sCBA2zTLqz6/O+OuLg46OvrY8yYMQpk6uPHjzFs2DBWXNHPz09pNAjFnTt3WIRzpUqVeAdh+vzvLtny7NkzZnRReQ0u7ty5w0geMzMz7Ny5s8C01dzcXOzatYtpMcpruQp53MiDK0FCSWgNDQ2Ym5ujefPmCAgIQJ8+fRAYGIgRI0Zg0qRJmDBhAqZOnaoSEa0U796945HQ3IMv/b6zsrJ4JDSN/o6Li0PLli1Z6ppQwV0bzp8/z9aEli1bsoMv99m4uDgeCc01Rim5NmjQIIW/LRQUVFA0JCQEhBA0b94clpaWuH//Pu9+QSQ0TSuXL7x37NgxVK1aFZ06dfrtdeRDQ0NhbGwMsViMtm3bKs2A2LNnDwjJL/b15s0bFlGvLI1zwIABEIlEjMQXKriH++nTpys48PT09FCxYkUFvUQuCT116tQCSWghOrIKQ15eHjIyMuDv78/6TqEsk4JqisuvQ0JHVlYWWrRoAUIIk4zr1asXc+QVZGvcvXuXkbA+Pj4qEYXGDQShQTaEEGzcuJE9Q23QlJQUHgl9+PBh5OXl4dq1ayygYvfu3exzX79+RVBQEPT19QUTDVwQCc2V41BGQtesWRNbt27F9OnTYWtrC21tbcH0uTBw901aLI1bd0EkEmHTpk0ACl4vqeY8IUQl3gmQTybWqVOHRfaOGDEClStXhq6uLnR1dXkFTZWR0EFBQXByclIaaKNKyMvLQ3p6OkaNGgUNDQ02drS1tdGpUyee5v6jR48glUrh6uqKjIwMQdqv8uA6K+rXrw+xWAwHBwecOXOGJ5UWExODUqVKsb1lz549uHPnDq5fv47AwEBoamoKSoZTDeFCTUD/hsjKykJISAisra3ZZlqpUiUsXLiQR0Y+fvwYYrFY8F68CRMm8KIu6eHO0dFRoViWPAl99epVpX9T6CTismXLQAhB165dFSrHA/lk+6hRo2BiYgJDQ0OEh4crTVWkuHXrFmrWrIl58+YVeduLCgEBAWjSpAlvDnALxt2+fRv169dnkiPKDFAuCb1p0yZs27atWNpe1CgosoyLnJwcDB8+HAYGBjA1NcWuXbsEX9To3+D9+/eYM2cO0/VeunQpbzwA+WsvV16hR48eKFGiBAghCAsL+5XN/ykojISWLyIoT0I3atQIDx48wMKFC2FrawsjIyPBHvLOnTuHRo0aKchnyGQy7Nmzh5GGWlpauHz5MgBFnXgKLgmtjLDNzMzE69evf/tiamPGjIFIJIKnp6dCdCEX3759Q/Xq1aGjowM7OzuIRCJMnz5dwVG6YsUKmJiYoHXr1khOTi7i1hcPKDHi6emJ9evX48iRIxg0aBCqV6/O7Bf5ebRlyxamqz5q1Cil+7kqgbvG7N+/n80NLlHCxYoVK2BkZIQmTZqozDjhYunSpVi3bh2ioqLYXtKtWzdGFnGL/nJx7do1DB48WCWIAGX2CFe7l1uYUBkJXaJECTRv3pwRJ6GhoQp/78GDB7995LM8/i0J3aZNG2hqarL35uDgoFJZAwAQHh4OiUSCWrVqYdeuXQgPD2fa4FKplL2bgkjokJAQnjyUEMEdF/v374exsTGmTp3KMs1u3LiBMWPGQEtLC46Ojli7di17Xv69JCUl/as6BL87vpfRnJCQwGp1+Pn54fTp00hMTOQ9s2/fPp5jVOgENG1/eno6qlWrBi0tLYwYMUJBWoM+FxMTg5o1a0JDQwOEEBgaGkIqlUIsFqN27doqIQGmxu8PNQH9i/C9iJeMjAycPn0aixYtwrJly5CUlKQQ0Tlu3DgQQrBmzRoAwlxER4wYAUII6tevj3PnzuHFixfYtm0bqlSpwlKMsrOzee9LnoS+du0auyfEd6AMeXl5OHbsGNtAoqKiFIzrJ0+eYOTIkdDR0UG5cuWwfft2nv6TPLipNkJ6T1xjjKYZTp48GTt37gTAJ6Hv3r3LSOhGjRoVSkJzIeQINK5G3N69ezF27Fj06NEDQUFBOHbsGC+yPTs7m805a2tr7Nq1i5e6KaRx8b/gR0hoAAgKCmKRjdbW1myNBYQ9VoB/R0LLZDJcunQJtWrVYtHzlBAQ6sE3NTUVrVu3BiEEwcHBCuRIdnY2Dh8+zLRbGzVqxA5+8vIaFMOHDwchBIsWLSqWPvxszJkzh8l+ya+Z3D7TvXjVqlXMQd6wYUOFyO5FixbBysoKTk5Ov33U949i//79kEqlaNiwIY8UzM3NxaNHj9ChQwcQkq+1f/PmTd5nN27cCE1NTSZbogrgjn+ZTIYvX74oJddnz57NCDMq8/XlyxekpaUhODgY5ubmsLOzU7kIPWWyE+fPn2cFKrkkNPe9ceXlVMFZwd1v5DMEli5dWigJnZqainHjxrG12MHBAatXr2bPCX0vBvgFJr9HQr958wYbNmzAgAEDMH36dIUAHSFCfv/t3LkzvLy8FAqn0wKT3Hfzve9fiOODtjk9PR3Xr1/HihUrULZsWbYW0Pn06tUrTJ06FVpaWihZsmShJLSqgDtWXrx4gfj4eFy8eLFAwlRZZnBsbCyqV68OExMTXjFUoSM3N5dpyk+YMIFlzcjPL/ounjx5gp07d8Lf3x8tW7ZEr169sGfPHgWyXg01igpqAvoXgBocWVlZOHLkCGbOnInQ0FDs3bv3hz4H5FdLt7Kygqen53cLvPyuoERYhw4dcO/ePXY9IyMDu3fvhra2NqpVq8Yix7ibyObNmwskoYUOeeOBHuBmzZqF9+/f8+49e/YMI0aMgJaW1g+R0IVd/53BfSdHjx5lZNiBAwcA/HsSWlXATbvy9fVV0IgzNDREu3bteClY8oUJ//rrL5U46BYG7pgvjITmrrFRUVE4f/48T3NRVQz7f0NCA/k667169UKTJk3Qp08fpcU7hYTo6GiEhoayiMuXL1/y7mdlZeHw4cMs4m7QoEFsDhVEQnNTPIWEyMhIGBsbw9HREdevX2fXCyvOk5ycjNGjR8PY2BjGxsbo0KEDdu/ejXXr1qFVq1ZsbVGF6E2K0aNHgxDCKwzH/f7fvn3LpHoGDhyI7Oxs3noiL+UiZNB+ffv2DTNnzoSvry+cnZ3h5eWFQ4cO8Q6xHz9+ZHJxhBCULFkSbm5uKFmyJMt0E6oziwv5g/7Xr1+VyvzExMSwiPiuXbvyspA2b94MLS0thISEFHl7ixuUXJWXzvseCZ2VlYWHDx/i/PnzvHOC0PbiwtrLDRIoiISW/7wQszy5BdDlsWTJEsTFxaFy5cpYsmQJe+57UeJCGwc/gqysLLi6usLMzAw1atRA27ZtAUAhCO3169eYMmXKH0FCc8fBsmXL4OHhwc471tbWPJmngrJKTp48ibp164IQwnNkqQI+fvwIFxcXuLu7s/OcsjEgxPO/GqoJNQFdzOASRq1bt4aWlhaPMGrVqpVC9Xn5BWP69OmwsrKChYWFYFMlxo4dC0IIOnbsyCt0Rvt8//59GBoaom/fvnj9+jXevHmDd+/e8Yz1TZs2MRK6Z8+eCoatqiAkJARWVlYwMjLC7NmzFQoTcklod3f3HyKhhQRlm+iYMWOYvhct0lMYCS0fTaEK4KZdVa1aFRKJBJ07d8aJEyewbds29OjRg0Vb1apVSykJLZVKIZVKGZEvdMiPFe6Y4N4rjIQuSJZEqHOpoHZz+1kYCS3/TuUPQUIC913Qfo0dOxaVK1dWIN6zsrJw6NAh2NvbM1Lxe5HQyn7/3REaGgqRSMSL8pcfM8ePH8fixYvRvn17LFiwAFeuXEFOTg7CwsLg5ubGs2GMjY3RunVrlShgSvHt2zeWskprUyhba/bu3QttbW2ULVuWkY/yzwmdGODasLVr1wYhBGZmZnB0dAQhBPr6+hg9erTCnrt3717Uq1cPdnZ20NXVRdWqVTF27FiViOLkEvJTpkxBo0aNYGlpCScnJwQHBytIxXFJ6A4dOiA5ORmLFi1CyZIloaury3MEqQqonAaVgeM6vbnavVwSuqC9Rmh7MZ0zOTk5uH37Nv7++2+cOnWKV+SVm4lWENHKtWeECG4ADbcf69evZ84oa2trXnYjULBUCS2ELPQ1VR4fP35E165d2Zxo3LgxuyffVy4JXaZMGV59E1UBt8/jx48HIQRGRkbo3r07Bg8ezIjogIAApVJnjx8/RnBwMAwMDKCvr4+lS5cq/dtCxsmTJ5mdChRc5JbeU1aLQchrixrCg5qALkbQiZ6WlsYkJnx9fbFjxw4cOnSIRYTUrl1bISIrJSUFBw8eZNXmK1WqJNiImkmTJrFIGG6Fb5lMxgzO27dvw9nZGS4uLjAzM4O2tjYMDAwwbNgwHlGwefNmlC9fHoQQ9O/fX/CRnFxDix5g8/LysGLFCtja2kJPT++7JHSFChWwdetWQRNFFNzN8caNGzwjnUrQ/AgJXa9ePZU81MlkMlY4LygoiGd0pKSkICoqCmXKlAEhBK1bt+bNj5ycHPTr1w9GRkZ4+PDhr2j+TwV37hw/fhyzZ89Gly5dMHDgQNy6dUuhkNOPynEIGdyCrMnJybh27VqBxGBhJLQqrCUUXCP71atXrMCPr68vO9BSZGdnF0hCq8IYyc7OZlXRqX417V9OTg7evn2Lvn37QkdHh1cMytrammVsffr0CcuWLcPChQsxd+5cXLt2TUF7UOjIyclhe8nmzZsBKD+sffjwAWXKlIFEIhGsfVYYuOnhtWrVgkQiQd++ffHmzRsAwLx586Cnpwc9PT307dtXIbL569evSExMxP3795GZmakSc4j2ITU1FTVq1AAhBHZ2dqhSpQr09PSYZvi6det4n4uLi2Oa0PQ5GxsblYgGV4YTJ05AV1cX5cqVY3sxd18pKBJa6AQR7WN6ejp69uzJ6gvQccGV5SmIhFYFopUSZMpkiJKSktCwYUP2XsaOHct7F4ByEpoQxSLiqoKEhARePRK67wCKe8/r168xffp0EEJQoUIFhXoMqoJ58+aBEIIWLVrwitEPHz6cFR3s1asXz9b/8uULRowYAV1dXXh5eeGvv/5i94Q8n+Rx+PBhEELQvn37Ap+hc+jOnTvYuXOnSuy/aggXagK6mJGZmQl/f39oa2sjKCiILZSpqalo27YtW0S9vb15JHRGRgZGjRoFd3d3jBw5kuc5FxrOnz/P+tm3b1+l8hnt2rUDIQTm5ubw8/NjRX40NTXRqlUrXnTN2rVrUb16dV56ntAxbtw4bNu2jWdoLF++/Lsk9OjRoyESiWBrayvY6Hhl6Nu3L+rVqweAb4h+j4S+d+8ei9Lat29fsbe7qPHp0ye4u7vD2dmZkT7yZOHZs2dRokQJaGlpYdeuXbxncnJyVELzi2tITp8+Hdra2ryoTAsLC8yaNYtFL1JwSWh7e3ssWrRIZaIAuAff/v37w8XFha2hfn5+uHnzpkKUBCWhRSIRWrZsqUDICh3KDO74+Hj4+/szPePvkdBDhgzhZRMIGTk5OczpMHjwYHb98+fP2LBhA0tXlUgk8PT0ROPGjdk4MjExKbRYoRChbHzQa1QXPjAwkN3jFkSi//by8oKBgQEjZVUN1HGpra2NcePGMRv21atX6NatG4tOk0qlCAwM5NlqqnTg5yIjIwP16tWDRCLBqFGjkJSUhIyMDDx48AADBgxga0tcXBzvcy9fvoSfnx+aNWuGbt26CV7aqDBkZWWhQYMGIIQgJCREaQQel4TmygkIFdxsgcqVK4MQgqpVq2LChAno378/tLW1IZFIMGLECPYZZSQ0IURh7AgNGzZsYPMgKyuLrZc0KCI5OZmR0E5OTkozWrnrc9++fUEIYXIdqgLufHjz5g0rcmxra4tDhw6xe/J26suXLzFnzhyVdHwCYEVcq1evzoKJMjMzERoaCm1tbdjb28Pc3ByEEPTp04cXCf3s2TMcOnSIV49C1faiu3fvwtDQEFWrVmWZBvJ1Gijq1KkDHx8ffPz4sdjbqYYaFGoCupixdu1a6OnpoWfPnmyBfPjwIUu36dq1K6pWrQpCCOrWrcsjodPT0/Hw4UOVOPxevnyZGVY9e/bE5cuX2b0uXbowTx7XIKeyHRoaGti6dSvv7339+rXY2l7UWLNmDQghTLuYu4l8j4R++vQp+vXrp1JpWNevXwchBEOHDgWQb3j9GxL61q1bPK+3kCFPkMTHx7PozYKQnp7OIimGDRvGrquKAcY1xClJVLlyZezevRvPnz/H7NmzYWhoCBMTE4wcOVLhkP/+/XvMnTsXGhoa0NbW5hWBEiq4B19PT08QQuDu7o4ePXqw3ytVqoS//vpLYT+hJLSmpibq1Kkj+IMvBXe8r127lndQi4+PZ9rFhZHQVBO6Z8+eKhM9smPHDujq6kIkEiEgIABbtmxBgwYNYGJiwjKVjh8/jg8fPgAAzpw5gzZt2oAQgkmTJgFQjWhwLtavX68gyRIVFcVsltmzZ/Pu0TXo9OnT0NHRQcuWLVkkuVDBJde5OH/+PCwsLNC8eXNmwz5+/Bjdu3cHIQT9+vXD0aNH4ejoCF1dXQwYMEAlJbCAf97NokWLmHNCPgW8UqVK0NfXx7hx43ia0PIFCOUjPlUJdH2IjIyEvr4+WrZsyRtf3LV5+fLlbJ7dvXtXsA5h2u6MjAw0bdoUmpqamDRpEu/cRx18hBCMHj2afZY7Fui8EmqATXR0NJNdiYiIYMXQuWc+6gj//Pkzy8jx8PBQSqZy9xqhF5H7kX3z7du3LMuxVKlShZLQqrYPU2RnZ7NCz2fOnGHXFi1aBF1dXTg5OQH456xIbTTuWsx9V0JfU4B/JHko3r9/j0qVKikEEwB8OY6wsDBoaGhgyJAhKr3nqPH7Q01AFyMyMjLQpk0bmJiYsKiR58+fo0+fPiwaGMj3ZJqamjLtViFHOxcGSp5Rj+Xjx48ZER8YGMjId65sAC3y06NHDwUiUkhISkoqUKOJFuxRpo0NfJ+E5pLxQtlor1+/jrdv3yq9d+nSJUilUoXCPP+GhKYQMulK+/vt2zdmWN24cQMSiQQODg6FRj4cOnSIEdWqanSsXbsWWlpaaNasGU9uJSwsjEkIaGtrY/jw4bxICAB49+4dpkyZghUrVhRzq38+6Jj/9u0bGjRoAE1NTYwcOZIRYpcvX0bZsmVBCEG5cuWwZ88eBbLswoUL8Pb2homJicrtPyNHjoSpqakCUfQjJPSBAwdgYGCgNI1YqEhKSkJgYCCbIxKJhOlxDhgwgGXSKEuXb9iwocJBSIjgtn/jxo1Md1Pe+RIeHs5slqCgIDx69Ii9l/Pnz6NevXoghGD37t3F2v6igHwmDX1H69evh5GREZ4/fw4gP0qPEiTc6HAavamnp4f+/furdDHgdu3awdjYGJ8+fWLXcnJymGReUFAQy1D69u0bs/2EPm8A5Tq9BeHFixdMMk8+iIRrm4WGhmLWrFk/t6G/ADKZDHPmzIGOjg4GDBjAzn0PHz5k2QKNGzeGrq4uCCEYOXIk+yzXTpO38YWCyMhIltHLPc/QrACuLA2XhPb19WUktDLiXX5tEtI5kI5z2oeMjAwsWLAAAwcOxLBhw7Bv3z42TujcSkhIYGusk5MTj4QW8pnmR5GXl4eVK1ey+SGTybB3715YWlrC0dERCQkJ7FmqNU8IQadOnZQWghUi5Os6KQtE5DrJhwwZoiCFtnz5clhZWcHZ2Znt32qo8augJqCLEXl5eTh8+DDTTkxLS0NoaCgjVCmys7PRoUMHtpC4ubnh9evXv6rZRQouCe3k5MSIeEpGcskU4B9ydvjw4b+qyf8ZZ86cQZUqVbB//35eITBqSPTv3x+EELx7905p0SyAT0KHhITwNmAKoRxurly5AkIIqlWrxiOhafuPHz8OQggWLFjAuw4oJ6H19fXx999/F1Priwdc/fgSJUrA3NwcaWlpSE5ORu3ataGpqckOdNz3Q43cmzdvgpD8op+qiKdPn6JGjRpwcnJipFF2djZCQ0Oho6MDJycnLF++HE5OTjA0NMTIkSMVtJC5JKzQjXqZTMYK04wcOZKRrXfv3kXv3r1ZRLREIkG5cuWwe/duBYM2NjZWsOSzsiro1CHl7OwMT09PXhowxY+Q0FwZF6Gssd9DYmIiFi5cCAcHB9SsWROdO3fG5cuXmZYk7SclCY4ePcocx0KHPHmxf/9+NGzYkEUyx8bG8u5zZQLc3NzQokULdOvWDWZmZrx9ChDm+IiMjERISAj8/PwwZMgQnDlzRiHDLCIigq0Xu3fvhra2Njp06MB7JjY2Fnp6eihXrhzLYCqsMJIQkZubiw8fPrBDPbfwJJd85pIg+/btw7x58wQ5Nrjg6sxy+7Jw4UIsWrQIOTk5zP7gzrHNmzeDkPyaFCkpKQXauIVdEwoSExPh6emJsmXLsvny9OlTBAQEsGwBIN+xpaGhoZClJuSaNpGRkRCLxShbtqxCBuLcuXPZGrp+/Xp2/d+Q0EID1wFHz33cQq70x9DQEF26dFHYexMSEjB48GB2Vj5y5Eix9+FXIi0tjTn4MjMz0bx5c5iZmeHmzZsA/hk7Y8aMgZmZGaytrUEIwcmTJ39Zm38WuEVuR48eDT8/P7i5uWHu3LkK9snu3bvZWKpTpw769++PmTNnomnTpiCEwNLSEnfv3v0V3VBDDR7UBHQxIzs7mxkVb9++RalSpVCzZk1GfnAX0YoVKzK9RVWoFF4Q4uPjIZVKQQiBs7Mzjh8/zu7Jp+e1adNGIcpVSMjMzGTGZ7Vq1XDkyBGFaJgOHTrA2NiYR05TyJPQDg4OkEqlCAoKEqw0y4sXL1hEZv369Zl2Ju3rli1bQEjBxZ+4h5uJEyeyzVc+ylXoyM7ORvv27WFqaopu3bqxNWPatGlM2/fcuXNKP0vfy/LlywEIb958D5QQ27ZtG4B8g23VqlUwMDBAqVKlGAE7aNAgEEJgZmaG0aNHq9wYoXj48CGsra1Ru3ZtRh49ffoUPXv2ZNER3759YxklVLJE6LIBAHDu3DmMGzeOpfpyx3pqaiosLS3RvHlz3me4z8iT0DExMUr/P0ImRgpCWlpagf3iXu/cuTPEYjGL9BXqesLdO1asWIGOHTvCzMyMOcO1tbXRrFkzhUjoPXv2wNPTkxWPE4vFqFatGrZs2cKeEeL4mDNnDoyMjHiEiKurKyZPnozk5GSF53NyclCjRg1YWloyhx61Z65evQptbW1Mnz4dDRo0UAkSSRmysrJQoUIFWFhYsGu0GKE8+QwA1apVg4ODg6D1N8+dO4datWrh4MGDvOtbt25l46Z+/fqYMmWKQvTu8+fPUbVqVejp6bH6L0JdP76HhIQEDB06FKdOnQKQn20yefJkEEIQEBDAnrtx4wYrSMklpoWKyMhIiEQiODs78wrNy8sJ/igJXalSJUGTZuPHj4eVlRUvMyY9PR0NGjSAWCxGhw4dsHPnTowdO5YVDW/cuDFbc5WR0MbGxryzsipAmeyTsn3077//ZgE1OTk5vLPysGHD0KZNG5w5c0YldOS5wUdUPo9mq4lEIlSsWBF79uzhfSYyMhLOzs684tEmJiZo1qwZHj169Cu6oYYaClAT0EWAHz14hIWF8bS/uN7umjVrYuDAgfjy5csfkSoRFxfHSOi+ffvi6tWrCs/QyKPatWsLunDakydPEBgYCJFIhMqVK+PIkSO8DbRRo0awsbFBZmbmdyNCFi9eDF1dXcFKB9C+vHjxgmmfc0loAFi3bh0IIQrGFtdI4UZWDR48GKGhoUXc8uIBN9Xw7du3MDMzw9ChQxVkNKh0jaamJtM+pli1ahXMzMzg6uqqspkUQP76QA/1cXFxcHZ2RokSJXg6+k+fPoWuri6TOAoMDFQp/XiKjx8/wsXFhc2ZDx8+YMyYMSCEoHfv3uy5K1eusHW3XLly2Lp1q6AlWt6/f4+SJUuCEIIJEyawAxxdZ1JTU2FiYqIQrQkoJ6HFYjHq1q1boGNHlcHdZ7jEweLFi9k6TXWhhQju9z1+/HhIJBJUqVIFYWFh2LlzJzp16gQHBwcQQtC0aVMFEvrdu3e4desW9u/fj+vXr/OykIRIPtNiV/b29hgzZgwmTpzIorrt7e0ZScR9b3fv3oWenh6cnZ1Z4SOKIUOGoGTJkkhPT1cJx1ZhoEWzJ02axEiCyZMnK5DP06dPh4aGBoKDgxVkBISC9PR0Zm9MmjSJtzbcv38fe/fuhZeXF9OQt7e3R3BwMM6ePcuemzp1KgghaNu2rcqkyBeEt2/fMgf4zZs3YWVlBV9fXwUpBh8fH/j4+DDCSKhnnDNnzkAkEsHV1ZUXpaus6OSyZcu+S0I3a9aMjSNlTrDfHUlJSczxX6FCBVYI/PTp0zAyMsKkSZNY8FBWVhbi4+NRoUIFEELQqFEjpSR0z549oa2trZDFJ2Rw15Hs7Gy8f/8e3759U5CRAPIJaIlEwtNNB/JlsGxtbTFo0CDedSHux1xkZ2ejVatW0NbWRv/+/fHo0SNs3bqV6cPb2tpi+/btvM+8evUKMTExWLt2LTZt2oT79+/zslbUUONXQ01A/2RQYyInJwdPnjzBgQMHEBsby9JEuFi/fj0IIRgzZgzv+sqVK2Fqaoq5c+cWS5t/F3DlOHr27IkrV66wexs3boSVlRXMzMzw8OHDX9jK/wZqRDx//hwBAQE8Eprqfrm5ucHFxQW5ubnIzc0tNDokLy9P8EXTCiKhKVk6b948EEK+SwKpWvomRUpKCqZNm4YDBw7AwcGBHfRlMhkz2lJTU1nxTk1NTbi7u6Nr166oV68exGIxLCwsBB1BUhiUHeRHjhwJsVjMDkDUwfPmzRsYGxtj6NChcHBwUKlinRR0zH/9+pVFAcfExMDIyAitW7dmz3379g0fP36Evb09vL29QQiBp6enoAmB1NRUrFu3DmXKlIGOjg7Gjh3LO8C9fPkSmpqajIQvbG29fPkyO/zu27evWNr/u2P58uWwsLCApaWlykTSUEkAX19fhWJ5Bw4cQIMGDZSS0AWNHSFGc44YMYIRgtxU8du3b6NatWoghKBJkyYK++mnT5/g5uYGa2trnD9/nt1fvXo17Ozs0Lp1a0E7tCgK+k7p3nPu3DlYWVmBkHwN9XHjxinsS+Hh4bC2toanpyfPwS5ExMfHY/HixWxtlV8L3r59i3PnzqFVq1ZM35jKS5w+fRofPnxA5cqVUbp0aWbPC3HeUChruzJdYiqvt2nTJgD8oCM7OzvMnz8fp0+fLrSex++MqKgoiEQiFkhEIZ/N+W9J6Jo1awpaE/zx48cYPnw4RCIRypUrh6NHj2LRokUoXbo06yd3vDx8+LBQEvr9+/eC1QVXBm7fN2zYAD8/PxgbG6NUqVLw9PTEgQMH2PkYAA4ePMjGTEREBDIzMxEREYE6depAV1cXhw8f/hXd+Kng7h/v3r2DlZUVhg8fzqtd8uLFC4wfPx6EENjY2CiQ0Gqo8TtDTUD/RNAFIy0tDd27d4e9vT0jhLS0tDBt2jReZCIt0EAIwfz583Hp0iUWdeLk5MSL3PtTwCWhe/fujZs3b2LTpk2wtraGoaGhSlVTlyehjx49ioyMDDg7O6N+/fqQyWTIyMjA169fkZycjOTkZLx79w4JCQl4/vw5r4o0IGyylRogXBK6Xr16ePPmDcaPHw9tbW2Eh4cjNjYWx44dw7Fjx3D06FHs2rULW7duxcqVKzF9+nRBp7UqQ15eHpo0acI0e83MzAqVjZgwYQI8PDzYHLKwsEDr1q1Vgiz63vimxvnbt29RunRpGBoaKjirqF7pjRs3VFLLt6CCUJ06dQIhBKdPnwYAnuyPra0tpk+fjvHjx6tEmnxqaiq2bNmCkiVLKpDQV69eBSEEEydOBPCPLnROTg4bX9zD8sWLF3kFf/5EpKWlITExET169ICenh5KliyJO3fu/Opm/WfQuUKLgdG5AfAPf5GRkfDy8oJEIkGLFi14kiyqsG6MHTuWpTPTiDpugecrV65AV1cXRkZGePr0KfucTCbDt2/fMHToUJZB0aVLFyZfY2lpKehgAQpuUEliYiJu3LjBxj9dM96/f4+RI0fCxMQE+vr6CA4OZp/PzMzEhAkTYGhoCEtLS1bUU+igfR83bhxcXFwK1Fo9e/YsJk+ezIhoiUQCb29vppHdv3//4mz2Twe33kBqairu3LmDpKQkniOXrhM0EnbHjh28v7FgwQLo6+vj2LFjxdfwnwwquyEWi6GlpQUzMzOeg1/efvsREpoS9NwMR6GuuU+ePMHQoUMhEong6emJunXromnTpgCgVBv/wYMHqFixYoEktKqAOw4omaqjowNPT09UqlSJjYvRo0fzIr6HDx/O5K9cXV1Z8eQlS5b8im78VNDvOC0tDatXr8bSpUthYmLCosG59snHjx8xYcIEpSS0kIpzqvHnQU1A/yRwdXqqVKkCQghq1qyJsWPHon///iwdrV27dkz3DACWLFnC09ujBQZUgQj4X8EloevWrQtjY2OVI58pKAktFotRqVIl7NixAzY2NjA0NET58uXh4eEBNzc3lClTBqVLl4atrS1MTEwgkUiY3q2qQBkJ3bBhQ/j7+yvMEWU/qpIxIG80REdHs1RwExMTREdHA+AbIdzPvH79GrGxsTh+/DiePXvG85gLFdz+3bx5EwcPHsTq1auxY8cOfPr0iUcaZmRkMH3W6OhotjZfvHgRtWrVgru7Oy+9VagGvfw4SUlJUdCBl8lkkMlkaNiwIUQikYKe8YwZM6ClpSVoB8WdO3cQHx/Pu6aMhM7IyMDdu3d/6JCiLKpeyA6+/xVv3rxBnz59mGSNr6+vyuim5+XlITU1Fa6urtDU1GTBAfI6lDKZDAsWLAAhBLq6umjRooWC81eomDRpEgghKFmyJE+rVX6sU8JQ2V7y9OlTtG/fnsl16Ovrw8vLS9BEK3VM0TU2PT0dnTt3hqOjI7M3mjRpgnnz5jFS4NGjRxg8eDCbK46OjqhRowbT9nVzc1OJLCTu2Hj79i2aN28OQgi8vb0RGRnJ7slHvcbFxWHhwoUoVaoUz26jdSmECLpPpKenY9SoUahevTp0dHTg6OiIli1bKhDKtOi8l5cXTp06haysLISGhsLW1hYeHh6CjWql5HPZsmUxd+5cdOrUCWKxGKampli8eDF77kdJ6I0bNyp9Roi2GteWePz4MQYPHsyixGvVqsXuKbMvHjx4wCKhq1WrptISCvPnzwchBM2aNeNJcO7duxfa2trQ0dHBunXrmI2bm5uLsWPHwsrKCpaWlvDx8eGdiYVur+Xk5LAMvLZt26JixYrIyspSWhvq48ePrM6PjY0Ndu7c+QtarIYa/w5qAvonIisrC23btmXaaNyFIiIiAhoaGjAxMcG2bdt4m9KOHTvg5+eHdu3aITg4+I/QfP4e4uPjoa2tzUg3VYi4opA/4FISmm4eUqkURkZGcHZ2hq2tLUqUKIGyZcuiUqVKcHd3h6enJxYsWPAru/Cf8b2UVi4JLZVK0bZtW0ydOhWTJ0/GnDlzsHjxYixbtgzr1q3Drl27BBs5Qo0k+cjV9PR0HDx4kOkTX7p0iZHQtWrVUlpdXugGV0Hg9mvOnDks1Zn+VKxYEbNnz2YRR9++fWN6x3Xq1MHs2bNZuiMhRCUKk3CrYk+bNg2NGzeGjY0NnJ2dMXv2bFy/fp09m5WVhcDAQBBCMGXKFLx9+xZAvpavnZ0datasKdjMgRs3bkAkEqFp06YKpCCXhNbS0sKECRMQHh4OTU1NNGrUCCNGjMCQIUPQv39/dOjQAR06dEDHjh1Rt25duLq68t7hn4rExER0794d3t7eWLVqlaA1nwtCw4YNQQjBgQMHAPAJA7oup6enw9bWFubm5iCEoFu3bipRGPr8+fMQi8UsZZ4bHMHVTffy8kLZsmVRu3ZtNGvWDAMGDMCePXuYTFZqairi4+OxevVqREdHK+hBCwVjxozB0aNHAfAzGqkt4ubmBl9fX5iZmUEsFkMkEqFNmzb49OkTgHyHzd9//83WYwMDA9StWxczZ84UZP0F+WJgyiLqrl27xrRIa9asySOhZTKZgq336dMnLF68GD4+PoIOGqDvglscrHTp0qhfvz6LXJUn2F+9eoXWrVuzezY2NkzDVahBR5GRkZBIJHB2dmZFKePj49GuXbv/REKvXLmyeDrwE8HtS05ODs9Oo9Hcjx49wsiRI1nEbmFR4kC+HAe1/VU1K/r69esoWbIkSpUqxduDAGDWrFlMAuvSpUsKn3369CnevXvH04pWhbNQRkYGwsLC2LlFIpEUyoNwSWhNTU3s3bu3GFurhhr/HmoC+ifi1KlT0NPTQ/PmzXlFV/Ly8lCrVi3o6Ohg3LhxSgspUK+eED28RYULFy7AyspK8FEjP7IZPnv2DAEBATAyMkLJkiURHh6Oz58/Iy8vDykpKUoLePzo3/7dwD3EZGZmIi0tjc0XbtTRs2fPULlyZRYJ/yPefyG+j6tXr2LVqlVMrzctLQ0ODg5wc3PjRf1funSJRWC1b9+eObhUOc2Kux7SNLNy5cph+fLl2LBhAwYPHowSJUpAKpVi4MCBbB29f/8+GjduzJxYYrEY+vr6vEONUNdaru53jRo12AG2YsWKrOp19erVeVFER48ehZ6eHpNyKV++PDsAC/XgC+RrTnp5eUFTUxPt2rUrNBLa3NycZScp+5FIJNDX14dIJFKZIqY/AykpKfj69avKrTMymQw5OTkYPHgwW1MpaF/z8vLYnuLi4oLhw4ejTp060NPTw+7du9kzQsbly5fZHOjZs6eC42XXrl28eUKj90QiESwsLNCnTx/s2rULT58+FfQYOXLkCOsjlWPJycnBoEGDYGhoiKCgIGanPHz4EGFhYSya18/PT8E+SUxMZMEkQrRLCmtzv379MHv2bPb7rVu3WGFCeRKaOz+444MrOyDE9wPk26+NGjWCSCTC2LFjkZaWxvo4d+5cEEJgZmaGCxcusM/cuHEDw4cPh6GhIapVq4ZevXrxpG2EBCpp5eLiwhx4FDExMWjfvv2/JqFpkds5c+YUbeN/Mmgf7t69yytsnZqaChsbGwQEBLBnHj16hFGjRkEsFsPFxYVHFiqbC48fPxbsGPkR7Nu3T2lm2rRp00AIQfPmzXn7EnX4KYPQ92Mu0tPTsXLlSiar2KtXLxZAogwfP37EsGHDoK+vL+isRjX+DKgJ6J+ImTNnghDCIiiA/M2EkgRBQUEsSu/r16+8Yi/yUbFq5EPo1dPlK4QfO3YMS5cuxbFjxxSI9SdPnjBN6KpVq+LIkSPMSOcS0EIeK9z3sXLlSrRu3RrlypVD06ZNWfQE8E/fnj17xqKP6taty6KI5COHhYr379+zlN0tW7YgISEB5cuXh5aWFqZOncorUgPwI6E7der0R5DQALB161YmASAvxdOuXTtIJBJ07tyZp+v8+PFjbNy4Ed26dcPixYsVIrOEjIyMDNSrVw9SqRQjR47Ep0+f8O3bN9y7dw99+/ZluoHcoml79uxhkVo2NjZo2rSpSlRRP3PmDCMBCiOhKVnk7e2N7du3IyYmBnFxcbh69Sri4uJw+/ZtPHv2jFfUVejjRI3v21Y3b95k+rQjRoxg17nffWRkJHR0dHDs2DFWPLpy5cpKtTuFCK7sWY8ePVgK9PHjx0EIgampKaZMmYKdO3diy5YtaNu2rYIzx9vbu1BiQAgYOXKkAgnt5uYGPz8/VkyR7rlpaWnYv38/nJycQAhBcHAwL+oRUA07pWrVqvD392e/jxo1ikmQcL/vmzdv/hAJLQ8hv5tNmzaxOSN/VqlTpw6MjY0xevRopUFHT58+RVZWlqDPONevX0fPnj2ZMw7gZ5D8ryS0UAur37t3j8nvUJQvXx5SqRRBQUG8/eLx48cYNmwYRCIR3N3dsWfPHnbvT7M7KNHMJeKDg4MZ+cwdD8nJyWjRooWCnadq4Mq6rly5Ek5OTjAwMMD06dMLzTD69OmTSmaqqaF6UBPQPxGjR48GIYTJAeTk5LBCG1zyGciXGLCxscGJEyd+VXPVKGJwScF58+ahZMmSvANbiRIlFCo700hokUiESpUq4ejRoypzyFUWzSqRSFjUJtVxppHA9HmuHEejRo0EmcpaGKZNmwYjIyNoaWnBxsYGGhoaCAkJYeSzvDEqT0JziySpGvLy8pCZmYn27dtDQ0ODF0kE5GsYE0LQsmVLnkFamAEvZOOezomFCxeytHl5XdZKlSpBX18f48aN4+05QL7D4/79+/j06ZPgtcG568np06cLJaFTUlJYJLSBgQEmTZrEI0+U6UwKeZyokQ/uHvz27VvcvHkT8fHxCs7fLVu2QFNTE4QQ9OvXD58/f2bE0Pnz5+Hj4wNbW1s8f/4c79+/R4kSJeDo6MhL+xU6uCT0oEGDWH0Sd3d37Nu3j/dsRkYGsrKysHPnTkybNg3lypUTdKYad++kBCt1Cnt7ezM7XV5/k0aoaWtro27duiq3B9+4cYO9i6FDhzKCvmPHjrh//z4A/jr8v5LQQoCy9gcGBkJLS4vnyJXJZKhevTo799Fo2K9fv+LVq1eF/j2hIS8vjxftq8zh8r+Q0ELdg1NTU5lDysPDg9UXmDVrFnNgcfvELUz4J5PQ1I6fOXMmgILJZwBYtWqVAlktZBT2PXMlwFatWgUHBwcYGRlh2rRpgpW5UkMNCjUB/RNBSbX58+cjPT29QPIZALp06QI9PT2VKWajBh9cA2zcuHEghKBMmTIICgrCkCFDUK9ePWbYDxs2jLcJUU1oTU1NuLu7Y//+/SpljKxduxZSqRQ+Pj44efIkbt26hZkzZzJNtKCgIEYOcUlomklQpUoVpKWl/cou/CckJiYqEBczZ86EhoYGRCIRWrZsyTzYBR1SuCR0t27dlBamEBIKa/+bN29gaWkJb29v3vWCjNR79+7h9u3bKnHAKyiy3d/fHyYmJjwSVd7hScfYt2/f2OFH1fBvSGj5woRjxoxhkWmqtL6qkQ/u3FmyZAmqVKnC1lh9fX0EBgbi4cOHyMnJQXZ2NlauXMlI6EqVKqFZs2YIDAyEsbExCCGMOHn79i3Mzc1RrVo1lSMcuSS0SCSCk5MTjhw5wu7Tdyo/X4QcxUnBHS80mIT+hISEFPi5Z8+ewc7ODoQQBQepKuDChQuwtrZm76JDhw6FBgHIk9CnTp0qxtb+XOzatYuXRcTdbzIyMuDt7Q0jIyNGQOfm5hZ47rt8+TLat2+vEllHyiBvb/2vJLRQwd0LqAa4SCTClClT2HVlfZUnof/6669iaW9xQ1nf6RiJj4+HoaEhfH19MXDgQBBC0KJFCwU5qKioKNjb26N27doqUYOBjpnMzEycOHECc+bMQXBwMA4fPqyQ/aomodVQNagJ6H8Jrjag/LVLly7BzMwMFStWRLly5VjRJ66HGAAWLVoEfX199OzZU/BRaGoUjnXr1rEITa50wIcPH1j6HiEE48aN433u+fPn6NatGwgh2LFjR3E3+6dC/pDetm1beHp68iRoAGDDhg2swJwyEvr58+dwdnZmXnIh4vDhw/D394e7uzsvGoamM2tpaUFLSwvh4eHfja67dOkSi7bo06dPUTe9yHDkyBH4+vrizZs3Su8/e/YMpqam8PHxYdemT59eYIRE+/bt0aFDB0GTridPnmT/5hIjubm5SExMhKWlJcqWLcv2D5lMVuDB9++//8aCBQtUgpBXhv+VhDY0NMSoUaNYxoUaqgPumBg/fjwIIbCwsEDPnj3Rq1cvVvzLy8sLkZGR7HB86tQpuLm5wcTEBIQQaGtro2TJkjzteEpOjhs3TmXIEy7i4+OhpaUFQgh8fHxYpKsyCFkOTBm4a+3w4cOZfda3b19kZWUp9JM6TmkRPirboWrw9/dn76Jdu3bsekGOY0pCi8VieHp6CrJI9NSpU0EIQdeuXZkkDRcymQyNGzeGRCLBkydPAECp3CJFw4YN4eTkxLP7VB3c+RIbG4sOHTooJaFVTULO2dmZzZcKFSqw/hXksKQktJaWFmxtbRX0tIUO7vd74cIFhfXgzZs3aNKkCXtnPj4+uHLlCoB/iOuYmBg0aNAABgYGPMkXoYJbxLRly5YsAIsrhRUdHc37jDwJPWPGDCQkJPyK5quhxn+GmoD+F+B6qxYtWsTzbAL5AvDt2rUDIflFr/r06YOPHz/ynlmxYgVsbGzg6urK0ytVQ/WQlZWFli1bQl9fn0VRyDswdu/ezTYcSjTTe0+ePOERUUJAYalzixYtwtOnT1GnTh1WGTwvL49nlG3ZsqVQEppr1Avt0BsaGgpjY2OIxWK0bduWpfWmpqZi1apVWLBgAWbPng1dXV1oaWlh2bJlCg4q+fd6/vx5lC9fHjdv3iy2fvxMJCcnswjD5s2bKxhTeXl5eP36NUqUKAFdXV0kJCQgNDS0QPKZFjMZOXKkYKPCaZozd3+RL9zp4eEBS0tLdq2wg2/VqlVRsmRJweuzFgbuvPgREnrbtm2MhDx//nxxN1eNYsKKFStACEGzZs14a8WnT58YSTB+/HhekMC7d+9w7do1rFu3DtHR0by1dcWKFdDT00OZMmVUIgKrIFy6dAlisRiEEAQEBCgl4FQV3LV22LBhzD6Tj0zk2i2UGHnw4EGxtbO4cPr0aVhZWaF27drMMcN1eHPfl7wcR4cOHUAIESRhNHnyZOjr60NbWxvdunVjhBjwT59DQkLYHKEO4IkTJyrswSEhIdDQ0MCoUaMUIhuFiMK0zQuLhOaS0JaWlggLCyvahv4CnDlzBs2aNUNQUBAqVaoEQggqVqzI9hhlGvHAPzWATE1NVargIHd9WLRoEbO75M+2J0+ehK2tLQghaNq0KeLj45GVlYW0tDTs2rULbm5uIIRg6dKl7DNCOwNScPWdqbxkixYtcOLECezcuZOtJU2bNlUg6ykJXbp0aSZbqWoOHDX+DKgJ6B8E3TTS0tLg5+cHQgg0NTURExPDe+7evXusYqmnpyfWr1+PS5cu4eLFi+jVqxekUinMzc0FrZenxo/h9evX0NfXR7Vq1QDwCwhyMWnSJBBCMHz48AKfE0qkFTcliNuHlStXghACOzs72NjYYOfOnbzPcfvHJaGnTp2qlDgTmuExZswYiEQieHp6IiIiQuE+91ASGhoKHR0dBRKa2+dnz56xQ46QI30B4OjRo7CwsAAh+UUGlXn0BwwYAEIII41atWqlQIpcvHgRVatWhaOjIy5evFhczf+pSEtL42mQTp06ld3jGplt2rRhhDMtLDh58mSFg+/06dOhoaGBGTNmqIRcQGHrILd/P0JCr127Fhs2bCiytqrx65CXl4eEhARUrVoV5ubmPPIIAObOncvWkdjY2B/6m+PGjYO5uTnMzc1x586domj2bwWuHEfPnj0V0qFVDdz1leu85BYmlNfCBv7RJK1duzY+f/5cHE0tUsivsbm5uThy5AhevXqF+Ph4mJubgxCCwMBA3jPKbLL4+HjB1bnh9iM0NBSGhoaQSqUKJDSQX4SPFjDV1NTEzJkzFeRoVq9eDVtbW1SqVAkvX74slj4UJbjz5Pnz57h27RquXr2Ke/fu8Z7jvkd5Erpz585MokLo64oy8o9mjchkMlSuXJnJOnFJaGV1J54+fapSsgrcPlIpynLlymHXrl0KmeEAcOjQIUasGhkZwcPDAy4uLux3biaSUM7EBSEzMxNt2rSBlpYWJk2axM5xHz58QIsWLdieU7duXRw/fpz32fT0dCxcuBAeHh4q6fRU48+AmoD+AXBTJSpVqgQNDQ306NFDIXWXPnfnzh34+voyw0RbWxuEEGhoaKBevXp4+PBhsfdBjeLH48ePoampCSsrq0J13/766y8QQlC1alVBaxtHRUVBJBJh4cKFCve+ffuGatWqgRACqVSKefPmAeAbb/IkNNVVFHrUyJw5c0AIQZs2bRRkR7j95xabnDdvHo+E5hpr69atQ+3atbFz507BEfEFISIigkVCc0lo+n5u3rzJxk+JEiWwf/9+3uejoqJQp04diMVirF+/vrib/1ORmJjIJEbkSWg6Rs6cOQNLS0sQkl/Ic9y4cQoEc3h4OKytreHp6VmgvImQwJ0rV69exZ49e7B8+fICo+u+R0IXFImkhmrg8uXLrMYCF1z5Hi75wQ0KkHf2+fr6ghCCypUrKxAtqgwuCd2nTx+Vqlkin41G6y5QcB27XKfgyJEjsXr1aty5cwdDhw6FnZ0dLCwsVIII4K6x0dHRWLduncJ5JSoqqkASGsiPnldWIExIayy3rSEhIYWS0NR+p1ItHz9+xNu3b/H+/XuMGDECOjo6MDc3V4l1g/tewsLCULZsWUilUmhqasLQ0BDDhg3D9evX2d5aEAl98eJFNG3aFEuWLCm+xhcB6JjPzMzE6tWrsWrVKnaPm7VJJfYqVaqkIK0XHh6OYcOGCT6QpDDQIoN+fn68+aPs/BIbG4tp06bB1dUVtra2KFWqFMaNG4eoqCj2jFDWksLOZ5s3b4auri4CAgLYuf/+/ftMzqlt27bMUePr64ujR4/yPv/t2zeVcHiq8edCTUD/IDIzM5ne1/Tp079LFL5+/RqHDx9Gnz590KVLFwwYMACHDh1SMHLVUF1kZ2ejdu3a0NLSwsaNG5VGlgD5hJO+vj5q1qwpaCNk3rx5TCOQG0FE+5SRkcFIxFKlSuH58+cACiaCNm7cCE1NTaWEtlAQGRkJY2NjODo68siOvLy87xpRc+fOZST0ggUL8OjRI6xevZoR86rmyCqIhAby198NGzagfPnyIITA29sbf/31F3bt2oVp06axAzF3rAiZnP/w4QPToJQnoYF8uafhw4fDxMQEBgYGmDFjBruXlZWFiRMnwsjICJaWlipHjISGhrIMCfrTpEkTXLx4UcFRxSWhO3bsyCsopYbqQNlcP3HiBMsMoCiocCmQr9HKnUdcLFy4EAsXLlQJR86/RXx8PCvOOGjQIEE7gymozZGRkYEZM2agefPmsLCwQPv27XmkWEEkNM1wNDU1hZ+fn8qtsSEhISwraePGjQrRimfOnFFKQl+5cgUNGzYEIQTx8fGC3oO59tmsWbNgZGQEqVSK7t27Kzhidu/ezSRrLC0tUbJkSZiZmTGnVWE66kLEhAkTQAiBvr4+GjVqhFq1arF5UatWLezatUupBBp3PLx79479WyiEIhfcrGiqka6trc2TbKLPpKamMqmFChUqsKzOzZs3M9mJwgp7ChknT56Evr4+KlWqpCAVmJaWhocPH+LYsWO8GklA/vk5IyNDIaNAaGPl1atXCme19PR0+Pn5wc7OjpHIL168QGBgIAgh6N+/PwDg7NmzMDAwYDau0LJJ1FCjMKgJ6O+Abpi0mFzXrl0VyOcHDx5gxYoVmDBhArZv364uavQHoSAPv0wmQ25uLiuAVLlyZd4GyzX29+7dC0IIxo4dq/B3hIYDBw4gOTkZAHhGOj20ZmRkMH0rV1dXppFeEAktdMM9NDQUIpEIa9asYdfkv9/jx49j8eLFaN++PRYtWoTIyEje501NTSGVSpn+opWVlYKxpio4ceIE62eDBg14JPS3b9+wd+9edsDl/lSoUAFbt25lzwrNSFWGDx8+ICgoiPWRS6QB+emaAwcOZO/LyckJNWvWhL29PQghcHNzUwmpJ+53SQ++jo6OmDdvHnbs2IH69euDEILq1atj//79SkloWuDG19dXpVJc/0TIr5/cvSMxMZH9+/jx40xbESicfN6yZQsIIbzCWAB/7KmChM3/ipiYGJiYmKiE9Ai1vVJTU1G7dm0QQmBsbAxbW1tWfLFLly7seS4JTQsTSqVSLFiwAAAEnbVGwZ1TNFW+Ro0ahRZDO3PmDCOp27dvj/DwcBbpGRISUhzNLnJw5//ChQtZJmv37t0VIqHj4uIwdOhQVKlSBa6urmjWrBmWLVuGt2/fFnezfzq44+PIkSMwNDREu3bt2Doqk8kQGRnJiOjKlSvj8OHD3/1byn4XArhrSOXKlSGVStG1a1e8ffu2wEKlqampTC6tdOnS8PHxgZaWFszNzQVvz7948aLA73HJkiUQiUQKsosREREICAiAkZERCCFwcXHBrFmzlP4NIY6RY8eOYfDgwbCwsECNGjUUnFY7d+7Exo0bAeTvMVQWrEePHrznevXqxSR+ypcvr7KFbtX486AmoDnYtWsXI8/kMWTIEEgkEt4iIpPJMG3aNCaOTwiBgYEB+vXrp1TfSIiLqBoFg0si5+bmKk2H+fDhA9MAq1atGi5cuMAbG7GxsahZsya0tLRUyrs5YsQIEEKwbt06do1LQtesWZORZN8joZX9LgRkZ2ejcePGIISw75Z683NycvD27Vv07dsXOjo6bP0Qi8WwtbXlvbfNmzejXbt2cHNzQ6dOnfDo0aNf0p+fBWWaeXl5eWx9nDhxIosmaty4MY+EpkUrt2zZggULFmDs2LGIjIzkFXQV2lgpqL25ublITU3F7NmzCyShExISsHfvXvj4+MDa2hr6+vqoW7cuZsyYIdiImlevXrE1krtnLlmyBBKJBC1btuTpf0+bNo1FaHp4eODAgQMKJPSpU6dQvXp1BYJRDWGBO1dev37N2zNGjhyJwMBAFqWckpKCatWqwcrKihWHbtGihYLmaHR0NFxdXeHu7i54IqAoIeTsLAo6fr59+wYvLy9IpVL069cPSUlJkMlk+Ouvv5hkXqtWrdjnuH2nmtAGBgbIzs5WKbue6lm3atVKwUmjDBcvXoS1tTXbn7S0tHhFwoSwF0dHRyutOwHwbRWaGi8SiQqU4wDy7Vxl5z+hgvsdymQyrF+/HkZGRrw9mM6B69evo1mzZkxyTpWddhkZGWjQoAGkUimmTp36Q46otLQ0tG7dGvr6+jA0NEStWrUEH2Rz/vx5EEIwcOBA3nU6Jrp16wZCCDZs2ICsrCxkZmZi9uzZ0NfXByEEderUgZeXF3R1deHg4KAglSZEzJo1CyYmJhCLxahbty6CgoJw9epVhfpO9Dz48eNHODg4oFatWkyiJT09HUD+WbpcuXLw9/eHqampShc/VuPPgpqA/n+EhYWBEAJ/f3+eFisFNT5WrlyJJ0+e4Nq1ayx6omTJkggODsawYcNga2sLOzs7lYg8U6NgcI2yJUuWoEmTJrC0tERgYCA2bdrEe/bNmzeskIKjoyP8/PywcuVKBAcHo1SpUkojr4QGeVKRFlaUSqU8Td5/Q0ILHTk5OWjevDkIIRg8eDC7/vnzZ2zYsAF169ZlGr6enp5o3LgxGydmZma86scZGRlISkpiRonQcOzYMVZkE+CPFy75vH37dhYNQVPPGjduzKKIvnegFRIZwNUDLyhqBgDatm3Li/aWl+OgeP/+PSPihVoV+8SJE9DT02Njn76Xa9euwdXVFR4eHswJnJmZidDQUGhra6NkyZJML8/T0xP79+9XIMy4hLyQxokaiqhVqxZq1arFxvuYMWNYJCaNgs7JyeFlEFSvXl2h4OC5c+fQoEEDaGtr8zIo1FBd5OTkYMCAAdDS0sKECRNYkd/79++jb9++IIQwp7C/vz/7HHc9CQoKwrVr14q97UWJDx8+oHr16jAxMVFKLhaEV69eYcqUKQgLC+MFUQiBfB4yZAgIIZg/fz4vewLg76F0b+nXrx/Gjx/P5DjkSWj5fVeV9plx48bB398f9evXR5MmTXj3uP28dOkSq0/BDaRQFdC+LlmyBIQQBAQEsDWE4sWLF9i0aROWLVuG48eP88ZFZmYmYmNjER8frxKSnDSD19fXl7dG0vd06NAhGBoaomLFiujQoQNq1KgBQggqVqzItOKfP3/OCmtv2bLll/TjZ2H06NEs4ExZwXku6DsKDw8HIfn1jgD+2unj44PWrVvjwYMHf6QEmBqqCzUB/f+4ePEibG1tFTQA6UJw5MgR2NrawtDQEPb29tDW1oaxsTEGDBjAUnq/ffvGCCdlRTjUUD1QolVTUxMikQhisRhaWloYM2YM77mEhAT4+fnxokUIyS+oFh4ezp4TgtEuD65xFRMTw/5NNaEJId8locuXL89IaFUy2nfs2AFdXV2IRCIEBARgy5YtaNCgAZNNKFmyJI4fP84M0TNnzjBDbNKkSQCE/z5evXrFxgE1sID8ccPt244dO0AIQZUqVXD27FlER0ez98QloeWlboQImsq9Z88edo32izufunbtyqSfaFaBPAnNdZjS9yHUMRMQEMDWg1evXgHI7xNdS2ghlpycHCxduhT6+vpwcnJCVlYWvn79CldXV0Y2KouEBoT7btTIx6NHj+Dk5ARCCDp27MhSVDt16sQc//Q7/vr1K7PJ7OzssHr1aty4cQP379/H6tWrmVzNokWL2N9Xjw/VQEF7w4ULF2BkZIQWLVow4ujx48csyGTEiBF4/vw523u4kdDyeqSqBFpwkmo6/xcnphD2ZZlMhkaNGoEQAmtra4SFhbGznLI9uE+fPsxGnTNnTqGFCVUN9+/fR4kSJaClpYUSJUqgfv36hdYxmT9/Pk9WUBXRq1cvaGtr48mTJ+xaXl4e5syZw7JeqWTe1KlTBTEn/lfExsayDOCIiAheX9++fYupU6fC1NSUvY85c+YoyDn17dsX2traCk5iIYEGMrZu3Vohm6qw758G3vTr148XZLRq1SoYGhqqjKSRGmpwoSag8c/CQD3gKSkpGD9+PE9S4cuXL9i0aRO8vb1hY2ODzp0749SpUwppN/Xr14eTk5PaU/UH4MCBAzA0NESTJk0QExODuLg4LFy4kEVucqNegfxxdfXqVSxZsgSzZs3CgQMHeJuUEA0U7mF9zJgxMDc3x4oVK9i10NDQ75LQderUASEENjY2yMjIUCkCICkpCYGBgSyiSiKRsEj4AQMGsOJF3MjvpUuXghCChg0bKqRsCRFJSUkIDg6GVCoFIYQXCU0jfSn5XLVqVZ4GNlcTWl6OQ6h48eIF6tWrB0IIDA0Nec5KLplMD759+/bF69evkZGRgSlTpigloYUa8SyPhIQEFiETHBzMCJ+HDx9i4MCB7P0cP34c9vb2cHBw4O21VMtXIpGgdOnS2L17tyDXVTUKx61bt9i+QUh+gR6ayizvyPn8+TO6dOkCPT09Ft2qq6vLHMBr165lf1c9VlQDISEh2Lx5s9JsxkOHDqFUqVIsA+Xt27csEpZbUC8kJIRJ+7Rp06bY2v6rsHv3bhBC0Lt37wKfoXMrMTFR0NIBtB9ZWVno2LEjCCEwNzdHWFgYbz/p0qULGxfUIUoREhLCSOiePXuqdIHbrKwsHD58mNVvkUgkTCqBa5/SNZeOpaZNmyo8I3TQvvj4+IAQgr/++gsJCQm4f/8+q0dhZ2eHYcOGoWvXrjA0NISrq6vC+FEFyO+XkydPZsEz3Hupqal49OgRjhw5opBpAOQX2zM2NkaNGjUE+57OnTsHKysrhYLzP4JLly7B3t4eZmZmmDVrFiIjIzFu3DiYmJjA0dERz58/L5I2q6HGr4SagJZDXl4eWrRoAULyC5FQPR4gf7HNzs4ucDGgxFGnTp0UUnLUUD1MmDABjo6OPK08mUyGI0eOsMIK8iR0QRC6gRYeHg6pVIpmzZopaAf+CAnt7u7OIn5VDYmJiVi4cCEcHBxQs2ZNdO7cGZcvX2YOLu5hCACOHj3KIm5UBcnJybxxMGzYMHZv69atjHw+deoUu04N2JMnTzISun79+oLVNubiwoULTFpDT09PIWOGks+BgYF4+fIlu56YmIipU6ey9xgcHFzcTS9SZGVlISwsDJqamqhcuTIeP37M7nGjmbt27QoDAwNcuHCBfQ7IdwoaGBgwUmHbtm3F2wE1ig2tW7dm86BBgwZKC0BRQiQ9PR379u3DkCFD0LBhQ/j4+GDVqlU8vUk1+awaoPunsbExb/2gSExMxKVLl1i6+O7du6GtrY1OnTrxntu3bx9zEhJC0L1792Jpf1Hhe+M7OjoahBD4+fmxawUV2R41ahR69erFOx8JDXRtyMrKQvv27RkJvXTpUnz79o1FxHPJ57y8PJ6zNyQkBGZmZiCEoH///kozboQOrn16+PBheHt7swAJWo9EPhp63bp1IES4xSjl66xw+0bfx6ZNm1hGdPny5WFkZAR9fX0EBASwQInExERG2p85c6bY2l9ckN9vt2zZwpx2kydPLjAwgnv94sWL8PHxgaamJnbs2FGk7S1K0Ojn/1VCZMqUKUwXm/64uLio5VzVUFn8sQS0fGEFLmJjY+Hs7MxSPKmRVVhhtOXLl8Pa2hqlSpXC06dPi7DlavwKKDPe27Vrx4qCyW+0ERERSklorrarkCHf327dusHDwwM3b95U+nxBJDQ9BHL/nqoSAWlpaQX2jXu9c+fOEIvF2L17NwBhOidevXqlUJQzKSmJNw4mT56MgwcPMtkNZeQzRWRkJIta3LdvX3F0ochx8eJFRqLp6uri0KFDAPjkM1eGguLDhw88EnrOnDm/pP0/C/Lj+9WrV6hYsSII4VcEp8/Fx8dDJBKhcuXKCgTI4sWL4eDggOvXr6urhaswXr16BU9PT9SoUYONFT8/P6VRmQUVPS3sdzWEi6ysLAwePBgLFiwAkP/9y9td3DFRvXp1GBoa4t69ewD+cXTdu3cPlStXxq5du+Ds7MzTRRYS5HVmCxrrDx48YGnymzdvZtdlMhnvfUVGRkJbWxv+/v5KC28LCcpIaAsLCyah0L9/fx75TMHdj6dMmQIHBwc2foSM79nemZmZOHz4MKpVqwZCCNq2bavQ79jYWLi7u0MikXxXA/d3BJVH69OnDy87BoCCtMT8+fPh7OwMPT09tGnTBkeOHFEIPqtevTpcXV2RlJRULO0vKhS2R3IzOLmFXCdPnqyUvKfYuXMn3N3dBS+D9fnzZ5QvXx5SqVRBBuxHkZeXh/DwcPTp0wfNmjXDtGnTeMEnaqihavhjCWj5IlgAeOnuV69eZQXiuCQ0F5mZmXj+/Dl69OgBfX192NvbK+gaqSF8cMfK9evXERERgRs3bqBBgwYICAgo8HMnT56EsbExCCEYMmRIcTS12LFq1Sps2LABlpaWCAsLK/RZLvm4ceNGdr0wA0VVwe0zd3wtXryYRfoKtUDJhQsXYGVlhVmzZn2XhBaLxahevTqio6PZMwWNgaNHj2LVqlVF2fQiQ3h4OE6dOqXQNy4JraenxwpTBgYGsmrXyt7Hhw8fMGbMGOjo6ChkHAgF3LFB5wDt64ULF9ghhrtWAPlyHMbGxvDy8uLp5V28eBHu7u7w9fXlPa+qDq0/GTKZDE+fPkVKSgoePnzIZFtatWrFZI3ocxTcsfKn7DN/Gug6Qr/3tLQ0NGzYEFu3blUqx5GQkABTU1NUqlRJ4d6QIUNgbGwMQLjjJSoqCu7u7ujXrx8ePnyIr1+/AvinP/L9otq92tra2LVrl8Lfi4mJQe3ataGvr4+DBw8WfQeKAcpIaJFIhOrVqzOnA7dQMgV3bfn06VPxNbiIwLVD7927h9OnT2PXrl04evQo715GRgYOHz6MqlWrMjm51atX49ChQ1i9ejUj75csWfIruvGfsHnzZmabUsm4Zs2aYdmyZXj37p3C89nZ2UhOTi4wSnXRokUgJL9QoVALiFNkZ2cjKSkJS5cuRXBwMCZPnox169YpldTYu3cvj4SWnzvx8fHo2bMnk8HiBiUJ0V5LTk6Gs7MzbG1tmU78v/msKjiv1FDj3+KPI6ATEhJw/PhxtGnTBnXq1GEk4vnz5xUM1CtXrigloWm60bFjx5gnuEWLFrxiBGqoBrib4cyZMxmhLJVKoaWlBX9/f6YDrmzjPHnyJMzNzUEIQc+ePYur2cWCXbt2gRCCWrVqoUSJEixqprAoby75uHLlyuJqqiCwfPlyWFhYwNLSUiEFUCjIzc3F/PnzoaWlBTs7O8yfP18pCT137lw2Dvz9/dk9ZSRBQf8foYAeaurXr4/o6GilJDQtPElTW6n2MTeyRB4fP37818bu74JTp07B19cX8+fP512ne2t2djbGjRsHsViMVq1aMdkVmUyGZ8+eoUqVKkwHOzo6Gnv37kXVqlUhFouxffv2X9ElNYoIXLKsoCjEy5cvs1RnSkJz71+9ehUrV65Up7OqKOR1aOnvlFAtUaIE/vrrL4WCra9fv2Zpz0eOHGH31qxZAxsbG7Rt2xbp6emCJKAfPHjAS+d2dHREs2bNcPHiRQXClO4zGRkZTA+bSjydOHECt2/fRnh4OFxcXEAIwbJly9hnhfhu5EH7n5WVBX9/fxBCYGpqiuXLlyslHimESJYpA7cf8+bNY+de+lOnTh3s378fycnJAPKDr44cOcKKiBNCYGBgADs7O3h7excYYPK74+nTp+y81qRJE5YRQNeQhQsXMtkvZeDapStXroSNjQ3KlCmDZ8+eFUfziwyPHz/GxIkTWWa4/LoSHh6u0MfCSOgHDx5g4MCBGD58OC/4REhjhYsXL17AysoKUqkUly9f/lefvXLlCho3boxLly7xrqvCuqqGGoXhjyKg9+zZw4oEyP9IJBKMHTtWoZBEQSQ0ALx//x7Lli3Djh07BEsEqPFjmDFjBjNCOnbsyApQEEJ4JIqyDfTEiROC1ENTtgFyr12+fBmdO3dmBfa4BHthm+ecOXNACMHixYt/anuFiLS0NCQmJqJHjx7Q09NDyZIlBZ9FkZSUhAULFsDS0hKWlpYFktB0HBBCMGLECHZPSOTyj+DKlSvw8/ODSCSCj49PgSR0q1atQEh+kTQuGaJqhujjx4/Z966vr4+6devi+vXrCqmrZ8+ehYODg4JsD8DXGqQRaxKJBEuXLmXPqNp7+xMhnyny/v175Obm8hw09Hu+cuUKj4Sm6+iNGzfYfs0tcKqG6iMzMxMjR44EIQTW1tYKJDQAzJo1C4QQGBkZYdiwYfDz84NUKoWNjQ0ePnz4i1r+3/HmzRu4u7tDQ0MDrq6uTKpGR0cHzZo1Q3h4ODIyMtgco/99//49Jk2apPScZGJiwgscEBphVJhtQaVX5DWhw8LC8P79++JqYrGDu09OmDABhBCUKlUKU6ZMQUhICBo0aABtbW3Y2dlh8eLFLIqeakLXrl0bhBB06NBB4fwspPFBM6BHjx4NQggWLFiAT58+Yc2aNSwzTSKRQFtbG2PHjsXp06cV+peZmYl3796hb9++MDQ0hK2treCdnhcvXmQOf09PT3Tt2hWzZs1CixYtUKZMGRBCYGZmhj59+ihk48mT0Nz39eXLF57NJ2R7TSaToWHDhiCEYPr06T8USEPXIpr1qg6cUONPwx9DQM+YMQMGBgaQSCSYMGECNm/ejJiYGMyePRtNmjRhUa1NmzZV0K0qjITOyckR1Carxo+Ba6i+evUK5cqVg5+fHzMm0tPTWdFJ+YgQZeOBq+UktI02JyenwCiga9euISAgAFpaWjAzM+MVkSisn1euXCmStgoJb968QZ8+fViUha+vr+CzKOh3npycjPnz53+XhOZGxA8fPpzdUzUS+ubNm0xqozASmivHsWfPHnZPaGtGYXj9+jV8fHwgEong5eUFe3t7mJubY8yYMQoavtRJoauryysYB+QXD+vVqxfKlSuHfv368bTB1Xuy8MFdAzZt2oTWrVvDwMAAFSpUQPv27VmBOe53zSWh69evj0GDBsHDw0MltNLVUERiYiIiIiLQuXNn1KtXD/7+/ujfvz9u3brFNFezsrIwbNiwAknoDx8+MNKJkkyenp5K9cSFhMzMTFZIr1GjRnj69CmGDh3KopgJyS/eOXHiRLx//15hzfzrr78wYsQING7cGE2bNlWI/hTaGsuN8t64cSPGjh2LRYsW4fjx4+wZmr33p5HQALBhwwYW+Xvr1i3ePUrA9uvXjxcRTknoChUqQCKRICAggNmwQrXhjhw5AkIINDQ0cP36dXZ9zZo16NWrF5s7pqamaNCgASIiIvD8+XMA+UWQKVnr6+urtAiqkHD69GkYGxvD2toa06dPB/DP95qWlobXr1+jWbNmzLHVuXNnhQAaLgk9depUwY6LgkCz9qijs0qVKkw+r6C+ctfO+vXrw8HBQbCSi2qo8b/ijyCgqXFZv359nD9/Xukz48aNg46ODsRiMRo3boxz587x7nNJ6K5duwq++IYaBYNL9jx79gxXrlwBIURpUav169f/MAld2PXfDcnJybh27RoCAwPRuHFjeHl5oWnTptiwYQNu377Ne/batWvo2bMnJBIJatSogWPHjrF73yPOhPI+igKJiYno3r07vL29sWrVKpUxQH4WCV2Y/IQQcePGjX9NQu/du5fdU5W5kpeXh6VLl0JHRwd169bFzp070aJFC0YQ7dq1i5dRRN9Hv379FAr55OXlsUKmFKrynv5kcL/DiRMnMkKgXLlyKFu2LIu6OnPmDAD+PnP9+nX4+flBQ0MDhBAYGxtj+fLlSv+2GsLF3r170bhxY4hEIoVIXQcHB4wZM4ZF5CkjoWnEK0VERAQ2bdqEM2fOCD6jkc6Hx48fw8LCAra2toxQv337NlavXo1SpUqxwr6Ojo4YN24coqKieH8nJycHOTk5CrJqQptDXNKsTp06CuNl1qxZ7FmuHAeXhF60aFGhchxCRV5eHlJTU+Hr6wtdXV0FKYDZs2eDkPwir/IRzkD+ezpy5AgqV64MkUiEXr16MUJWqAgICIBIJGKFTIF/xnx8fDzq1q0LsVjMMic8PDzw119/4eTJkwgJCcGOHTuUaiMLCZGRkRCJRHB2dsa2bdvYdRopTt9HZmYmBgwYAIlEAj09PYwaNQofP37k7cl79+6FgYEBCCEYNWpUsfelOPD8+XNYWloy+56uI4Vpx8+cOZOde+T3IzXUUHWoPAE9YsQIEELQrl07RpxRLcG8vDyeh2ru3LnQ09ODRCJBv379kJiYyFtor1y5wqIHevfurVJRaX8yMjIyWEovd3OgWnhjxoyBt7c3u84tVgn8exL6d8ft27fRu3dvpoVGo4JoynyZMmVw4sQJ3meuX7+Obt26QSwWw9vb+1+R0H8yUlJS8PXrV5WMCgD+NxJ68ODBv6DFxYP/hYT+66+/flFrfz5oX3Nyclhq79atWwEAwcHBsLOzg0gkQocOHbB//34AwKFDh2BhYQEbGxumF1iYzrwawgZ3PlDpKx8fHxZ9mZ2dzaKcjY2NcfLkSYXPvXnzBqdOncL27dt5hIpQ92Q1+JgxYwYMDQ0hkUgwYsQIhIeHY//+/Zg9ezbc3NxYRF6jRo0QExMDIJ8oGz58eKEktCpBJpMhNTUVnTp1AiH5es5c3L9/HxcuXGARm9TGGzRoEFuTuRC6HZeRkcEieVu3bo158+ax8yG18ylppIyEFolEWL58ucrZagDw5MkT6OjooFWrVrzrwcHBIISgefPmPHmFa9eu8Z7Lzs5mJLSWlhbatWvHy/oUCugYX7duHZMioc4oei86Ohr29vaMlKd7EdeRI/S5EhUVBZFIBBcXF16xUfn9k86FzMxMFh1ua2vLAvi4dhqtGTR37txi6EHxgr6H7du3s4zWRo0a8bT25XmD8PBwWFhYwNXVVZBzRQ01/itUmoAeP348CCG8dE1l4BoUQUFBLNrm0KFDAPgLR1xcHCpVqoSbN28WbePVKBY8ffoUs2fPRsOGDRWKKPTt25eNBU1NTYXIAO5mzCWhhaxtHB0djdKlSzPjKjw8HBERETh06BD8/PzYPUKIwiHlxo0bahJaDYZ/S0LTglGEEJw9e/YXtLh4IE9Cnzt3rlASmhCCAwcO/KLW/nzQ/fbBgwewsrJC7dq12b0zZ85g6NChTBJr9OjRePHiBSvSWKNGjQIjS9RQLfz9998wNTWFj48PIz/S09OxfPlyaGlpsYOeiYkJ03YubEyoyWfVwJgxY0AIQd26dRUyFYF8p25gYCDMzc0hEonQoEEDJvmVlZXFSMeCNKGFioLG/r59+0AIgaWlJSMO6RqcmpoKPz8/EELg6urKiwquU6cOli5dyoIzhAju2e6vv/6CmZkZpkyZonBdT08PhBCMHj1aKQnt6+sLQoigNcELw/3796GhoYFOnTqxa9OnT1dKPgP5sgHcZ4F8svHYsWMoVaoUTExM8Pbt22Jpe1EgKysLNWrUACEEYWFh7DrtHyH/FFH/+PEj5s+fDzc3N1haWirIlwgNJ0+eZMEP69atY9cLcrzQ6xkZGahWrRoIIfD29mbrBnddErokCVC4HZGYmIiwsDCYmJiAEIKaNWti69atTJIjIyMDaWlpGDVqFExMTGBmZiZ4jXA11PhfobIE9NixY0EIgb29/Q8VdOIurm3btgUhBNWrV8eXL1/YZ7gpJ2oIH3FxcWzDdHR0xLJly3gR7wCYrpOGhgbmz59faDrNxo0bmfF+48YNwREkp06dgq6uLhwdHbFo0SKF++np6Th48CBatmzJ+snVqQX4JHS9evV4c08N1URB45w7l36UhJ4+fbrSsacK4L6nH4mEjomJQYMGDSCVSgWrR3rnzh08fvyY7Znc9TIjIwNTpkwBIfkFariIiIhAhQoVWCTJpEmTWArntGnT1GSiiiM5ORktW7aEsbExk03Lzs5GWFgYdHV1UaZMGSQnJyMgIICR0DQSWg3VBSWP27Zty4gems0I/EMapqWlISgoCBYWFtDQ0ECfPn3w5s0bAPnjiP4de3t7bN++XdAk9KtXr9heWtBe3K5dO2hqamLjxo3s2rt379ge5OvrCyDfATht2jQYGxuDEMIjoIQGukd8+/YNiYmJWLFiBUqWLMkKn3GjMw8fPsz2F2UkdHZ2Nhs/qog7d+5ALBbD0dERnz59YtIAysjn3bt3gxCCiRMnKuzDWVlZiIiIwNOnT4uz+T8VlAtYv349RCIRiwo/deoUHB0dCwwyevTokeClewBg4cKFvKwAZfrw8qDvLCIiAsbGxnB2duZJkNDPy3MpQgPXKRUXF4fTp08r1DNKSEjA6tWrYWZmxiRaHBwc0L59e9SuXZs5MCpXrixYu14NNX4GVJaAPnDgAFtEO3bsiDt37vzwInr06FHo6emhbNmyvCqtFEIjFtVQxOnTp2FmZgZzc3OMHz8eGRkZvEMI1yFBSWgdHR1eOhIFd1ytXLlSkMWOuHpfXN1ZuuFyK6XfuHGDRSQSQlhaPMWNGzfQs2dPEELg4eHBvL9qqB648yQhIQGXL1/GlStXlGoA/ggJzXXuCTXVtbB2c3Wtf4SEvnTpEl6/fl1kbS1KREVFsci6mTNnMv1Mbh/v3r2LUqVKwcjISKH477NnzzB37lyUKFEChBBWyKZSpUp49epVsfZFjZ+PpKQk3Lx5E1OnTsX58+d5++/nz5/RqFEjTJgwAUD+nNq0aRNMTExQqlQpFl335s0blCtXDoTkF4XiZt2ooVqgQSUdO3YsNJKO2irp6elM99nW1hanTp1iz2RnZ7PaMGXLlkVKSkqRt78ocOHCBVhZWWHWrFmFktDz5s0DIQTOzs4A8vdiasNR8pmLx48fK7V1hYbMzExYWVnB1NQUjRo1QmBgIABFQgzIl3oqjIRWRXD7361bN0gkEtSuXRuEELRs2ZJXhA/IH28VKlRA6dKlFYoCqxru3bvHHDFDhw6Fk5OTAvksk8kES6YWhmXLlrHz3fDhw3/Y+fLo0SOWTRAbG6tSXAlXR97Pz4+tFXS94Oqjy2Qy3LlzB02aNIGzszMvs6R69eqYNm2aSju01FDjR6CSBDRd9GgqCSEE/v7+P5zqkJCQAAMDA0ilUpVNufqTERMTAx0dHZQuXRrbt2/n3eNumNwD8ahRo0AIga6u7ndJ6MKu/Y44ffo0pFIpypYti6NHj7LrBbU/Ly8PcXFxaNCgAQjJr5r98eNH3vPXr19H69atsWTJkiJvvxq/BlyidenSpfDw8GDrrYGBAVasWKFQtEeehF6wYAGSk5OLu+lFBnpYzcjIwOrVq9G7d2+0bt0aY8aMUUqa/ggJLUTcuHEDgwcPhr29PQwNDUEIQbly5XD+/Hl8/fqV9+yePXtYXYW0tDTeoS4rKwuvX7+Gv78/K5g1b968X9ElNX4irl27hvbt27MDfrNmzXD79m3eHvL06VNGNL98+RI1atSAra0ts8mysrLw9etXVKhQARYWFmztUespqh4mTZoEQghKlizJtOGB7xd7TktLQ9WqVUEIQePGjQH8s29lZWVh0qRJgk2Bzs3Nxfz586GlpQU7OzueQ1d+D8nKyoKnpyekUinmz5+vEPkM/BMRLP9OhWLHKsPXr19Z0UGpVIoWLVoA4PeJ+664kdADBgwQrBNcHsq0e+XHyI4dO2BnZ8ecxsePH+fdP3PmDOrVqwepVMqLpFdlUFk4qVQKQggWLlzI7gl5XhQEbp+WLl3K9tQRI0b8MGHq6uoKW1tblQwSSE9Ph6enJwghqFChAvz8/KCjo8PWUvkgivT0dLx9+xZHjx5FVFQUzp07p7JOCzXU+LdQWQL6v5DQb9++hYGBARwdHfH+/XuVIATUyMfLly9RvXp1EEJ4RpQyg0weNG1TV1eX6YMLHffu3YOGhgYIIQgICGDXv1fkKzs7G8uXL4dUKoW1tTWePHkCgG/AcNPR1HNItcD9nqnWvpmZGQIDA9GvXz92iBsxYoRCmhkloW1tbWFsbIyZM2cKOgWaghsh0ahRIybdQw1Ua2trHD58GBkZGbzPcUloX19fnDp1StDzhe65w4cPR0xMDObNm4fKlSuzdMShQ4ciKiqKPZ+dnc2ir+ihV5mBvmLFCl5BRiG/oz8ZUVFRTJ+3a9euOHbsGB49elToZ2haMC1gxI1KbNGiBcaOHYvevXtj1qxZRdp2NX4Nzp8/D7FYDEII+vbtq1AETRnoenzs2DHo6enB3d2drb2qQiwmJSVhwYIFSrOK6PpIbVsqq2Bra6tAPqtylC832ltTU5MVpSyIhD5y5Ag7M3JlBIQK7lg/cuQIgoOD0bZtW3Tu3Bm7d+9mZ2KZTIbx48fD1NQUurq66Nu3Lw4ePIjIyEiEhobC0tJSgYRV9T343LlzsLa2ZpIjFKpMIHL7tmTJkh8ioeln7t+/D7FYDHd3d3z58qVY2lucmDJlCoyMjDB16lSWNRMREYEWLVpAJBKhXr16PBK6MHlCNdT406GSBLR8etWJEyd+iISmRlhMTAwIIejWrVvxNFiNYkNkZCR0dHTQvXt3dk3+MPLkyROmhbdv3z7Exsaye7SKuq6uLg4fPlxs7S4qXLp0CW3atIGOjg6MjY15BTe+Z2RlZGSw9Oc1a9aw6/Kbq3qzVV2EhoayCMbLly+z67QiNo0kevDgAe9znz9/xty5c6GhoYHw8PDibvZPBzftm+rKd+jQAVeuXEFCQgI6d+4MQggsLCywfft2BRL65s2baNeuHQjJL/4p1MJPcXFxMDAwgJOTE3bs2AEgPw363bt36NSpE3R0dCASiWBiYoL58+czIz4qKopp+8pL9igji1T5AKjKOH/+PHR0dODk5KSgL1vYPjFgwAAQQtieS8fEyZMnIRaL2VijUI8P1cPly5fZntKzZ08FeYCCQPVtxWIxfGYRXwAAWbdJREFUXr58qTL2yI8U+ZWvO6ClpQVCCLy8vNh1VSaf6TqQlJTE9lcHBwdWRL4gEvrEiRMKNosQwe1fUFAQCzahEb0SiQRVq1ZltVpyc3MREhKC8uXL82QDxGIxypYti02bNin926qMbt26MXsuJSVFZZxX8iioX9+LhObOm7CwMBBCsHbtWgDCHyN0bczNzUVubi4aNmyIZs2aKQTMXLlyBZ07d1ZKQgv9HaihRlFB8AS0/OTmpkDHxMSwSM7IyEgeCX3nzp0C/06rVq2gp6fHitqoisGqBjBu3DgQQlihM67Gd05ODnbt2gVnZ2eWNq6hoQEXFxfeYZlqQkskEl5EnlBx+fJldOnSBVKpFEZGRggNDWX3Cto8qVYvJRpXrVpVLG1V4/dBVFQUSpQoAS8vLxaRlp6ejoULF0JbWxu2trYoW7YsCCEYMmSIQiR0UlISrl69+iuaXiSgkbza2toICgpia8uHDx/YIYZGiu/YsUOBZL569Sq6desm2JTwb9++wc/PDxKJBNu2bWPXz549y/69detWdOjQgb0LPz8/JoM0ffp0EEIQFBSE9PT0Ym+/GkWLhw8fwsXFBebm5ti1axe7XtiBntpeNPuoc+fO7N7FixfRoEEDWFtbs6hG7mfUUD3Ex8f/axI6ISEBRkZGcHFxwdevX1VqfPwICS2TydhzU6dOhVgsxqBBgwCoTkH1HyEFk5KS4O/vz7SwaRHLgkhoVcK0adNACEGdOnVw7NgxPH/+HCdOnED//v0ZKc9dQ+/evYvly5dj0KBBCAgIwN9//82zS/4EUo2OhYsXL8LKygouLi6stokq93/YsGEKEis/EgkdGRkJExMTeHp64vbt28XU2qJHWloa6tevj+XLl6Ny5cqsRlJ2djZvvbh69So6der0ryKh1VDjT4agCWg6qW/fvo2TJ0+yQ2tKSgocHR3RpEkTPHv2jD33PRIayNd8kkql6N69u0ppk6qRD0pA9+jRg3c9OjqaFawhhMDOzg6urq4s7czAwIBHNtNI6OXLlxd3F/4zvnz5omCwUxJaIpH8MAkNAF27dgUhBLt37y6y9qrx+yEzMxP9+vUDIQSnT58GkG+QLVq0CHp6eihdujRSU1Oxb98+NqeGDRtWYFSRKhj0+/fvh6GhIdq3b88iex89esTmSGBgICZOnAhCCCwtLZWS0EImBL58+QI3NzeYmJjgw4cPAICBAweCEMIirIB8ImDt2rUwMTFhxQU7d+6MR48eoUKFCihbtixzVqhqtNGfBCqJNmfOHBBCMHXqVHbvR+f9w4cPWVX5+vXro2PHjuz3ZcuWFVXT1fgN8aMkNA1GocVQ5W0+VcGPktDAP8XZxWKxyhSR4wYdbdmyBcOGDUPXrl0xcuRIXLp0iVfoOCkpiclxuLi4KCWhVQ0RERHQ1dVFzZo1FYjB8ePHQyQSwc/P74dkbYA/j0xLSkpCjRo1mPyPKuPUqVMghKBFixZIS0vjSTHKR0JzNZ7j4uLg7e0NbW1tXhF7IeHIkSNKpQAXL14MQgizV1euXMm7XxAJ3bBhQ5XIklZDjaKCoAloAHjx4gXEYjFMTU0RFxeH5ORkeHh4QCKRYNq0aWwBLYiEpgYIAKxfvx5WVlZwdnZmnk41VAsREREsDXH48OG4cuUKJkyYAHd3dzYuJkyYgGvXriEjIwPHjh1D+/btQQhBr169kJaWxv5WdHT0L+zJ/4ZTp07B3d0dkZGRBZLQ34uEpnMpLy8Pnp6eMDU1ZcWi1PhzEBwczKRsZDIZdu/eDUtLSzg6OvLGA5Wf0NDQQO/evXHv3r1f1eQixYQJE0AIYdrnL1++ZCR9YGAge65u3boghMDc3Bw7duxQkOMQGpKSkti/6eF+yJAh6N27NwghaNWqlVJnb1xcHEaMGME0SUuXLs3W4datWxdnF9QoYmRnZ6NatWqwsbFhzpkfJX3ofnPu3DlYWVmxfbpkyZK8zKQ/jRj5k/E9Epo7tlq2bAkDAwOWiaGK4+RHSGgK6hDt16+f4DNNqA2bmprKimJzf6ysrDBw4EAeWSZPQlNSVhXHBQDMmDEDhBBegXHgn4yjFi1a8DLRuFmhgOq+l38DSsxWqVJFoYiyKuHz58+oWLEi7OzsWF2fwkjoL1++4PLly2jcuLGCM1hI44bq4w8cOFBpEdbx48czh3fXrl3x/v173jPyJDTNeGzZsqXg11g11CgqCJ6ATklJYQRhqVKlULJkSWhoaGDu3LnsYM8lzAA+Cd2uXTvcv38fGzZsgLW1NUxNTVWWIFEjf7z079+fkdDGxsaMHKtfv76CliQA7N69G4QQlCtXDqmpqYKtEp6Tk4MuXbqAEIKKFSvizJkz/5qE5m60mzZtAiEEo0aNQl5enmDegxr/DoV9r+/evQOQb6Q2adIEFhYWLJKGRveOGTMGNjY2TBuZRkwLDXTsy2QyFnXFfTefPn3CgQMHkJOTg9zcXKxevRoSiQRdu3bl/Z0FCxZAJBLByMgIhBDs2bOn+Drxk3Hq1Ck0aNAA586dA5BvfFepUgUSiYQdbqkzQn4fBsAOMNWrV4dYLIZIJGJ7c1xcXPF3SI0iwfXr16GhoYFKlSr9670iMzOT2XIJCQk4fPgwLl68yA7IgHD2YDV+Hn4kEnrevHkQiUTo3r27AhErRBRW1IrOge+R0AcPHoREIoGDg4MCkSIk0Hfx7ds3tn8EBATg1q1biIuLw/r16+Hm5gaRSITevXvz3kFycjKT4zAzM1PqIBUK3rx5o0AaU2RmZqJ+/frQ1tbmySYEBweDEILmzZvjxo0b7PqTJ0+wfv16lSi++DPx4cMH+Pr6qjw3kJ2djZ49e7IgAq7NS8GV4+jWrRtq164NQgjmzZvHnhHafvz3339DR0dHoYgx184fPXo0dHR0YGRkhM2bNytkK3LX5kuXLqFPnz4qP17UUOO/QNAENHeR6969OzvA9urVS2mUDU0FBfgktJeXF4yNjWFoaKhS2kVqKMf79+8xdepUGBgYwM7ODqVLl8bGjRt5mwWXZLp//z6kUikqVaokeG9mYmIiunfvDkII3N3d/ycSGsiPRitdujRKlSrFK9KohmqBOzbu3r2LK1euKK1uTaU2AgICFD43dOhQeHl5Yfv27diyZUvRN/ong/bl06dPAP4xSlNSUlC7dm2exjHdX1JSUuDu7g4XFxcWEU0N1l27dqFEiRLo2rUrjIyM8PDhw2Lry8/EmTNnQAiBq6srI6ABoHnz5hCJRBCJROjQoQMbL4UdStLS0hASEoKaNWvyNPrVUA1ER0eDEAIPD49/HRn19OlTnDlzpkB5GiFFWqnxcyFPQl+5coXdW7dunYJ2q5DB3VMTEhJw+fJlXLlyRWnflJHQVFIwISEB1tbWPCkcoUImk2HMmDEghGD06NG8bKL09HS4urrC1NRUaV2B5ORkNGzYEIQQnjNLSDh37hy0tLQQFhZWIAndvHlz6OjoMDtk6tSpSslnABg9ejRsbGzw+PHjIm+70CBkebQfAd1H79+/DwsLC9SoUYPxKPJOY24kNCEE8+fPZ/eERj5T0OLXKSkpmDdvHpPj4JLQ48aNg0gkYtmLhZHQqj5e1FDjv0LQBDS3erO9vT1bDEuVKoWzZ88qXQDkSWgaqWVhYaEmn/8wvHv3Dh8/flSIjOGOEQBYtWoVCCGYNm0aAOFusLTdHz9+ZJHQP0pCz507l92LjY2Fj48PpFIpNm/eXKx9UKP4wB0TixcvhpOTE+zs7BAZGclLywOA7du3s6gJLs6dOwdLS0uMHj2ad11ocygmJgaEEKxYsQJAfppqhQoVGFkqT4IdOXIEhBA0bdoU2dnZvPfVrl071K5dG8A/pLbQcOfOHZQoUQJ2dna8gnI0W6RGjRpwdnaGRCJBv379WAq0MrKQjrOcnBw8e/YMp06dYveENk7UUI5nz57B3NwcTk5ObCz86Hc7aNAgODk5qUkRNZSCS0L37t0bN2/exKZNm1Qqo5G7Fy9duhQeHh6szwYGBlixYgXLRqKQJ6EXLFjASEhuhKsQHDgFtfHLly+oUqUKXFxceARsbm4uvLy8IBKJMHnyZOYEzczM5J0bP3/+jNevXxdt44sQ8+fPByEE+vr6WLZsmcI7yMnJYTJgCxcuRGhoKAghaNasmQL5fOzYMejr68Pf319d/0iFUVgGb15eHj59+gQfHx8QQrB+/foCnw0LC2PjqqC/LQRw1xaZTIY6deqAEIKRI0cyu12ehKYSejt37lQTzWqo8T9CsAQ0NcjS0tJw6NAhLFq0COPHj2fpI05OTjhx4gTP2AAU04APHjyojnxWg7cJcf999uxZ2NnZwdLSEufPn/8VTfup+C8k9JIlS3Djxg34+voqRCkK4RCjxo+Da0iOHz8ehBC4ublhzZo1CoXzgH8K3Tg7O+P8+fP48uULTp8+jbp160JPTw+HDh0qzub/dCxfvpwd+FetWoWKFStCQ0MDc+bMUarhfOnSJejp6cHb25t3fc2aNbCyssKAAQMUJG2EhI0bN0IsFiMoKIhdGzt2LBYsWIDVq1fj2rVrOHnyJMqXL89IaHrQV9ZnZdeEeJhRQzlevHgBa2trEEIwffp0dr0wSQEKPz8/6OvrC1ouQI2iBZeErlu3rkplNCrbi83MzBAYGIh+/frBwMCAabLS4q0UlIS2tbWFsbExZsyYgYyMDKWp9b8zCiJ5Lly4AEIIOnfuzK7JZDJ4eXmBEIKgoCBeNmxERAQuXLggmH7/CBYsWAAdHR1oaWkpkNDAP9kn9MfPz09BciQmJgbe3t6wtLTEsWPHirP5ahQjuPuq/NrIPf/t2LGD1e/IyMjgzRfuv7mFK4U6p+RtkP3798Pc3JwVTleT0GqoUTQQJAFNF4z09HSULVsWhBBWEE4mkyEgIIAVNeKS0NwFMiEhgS0s3MJyavy5kN+IoqOjUa9ePRBCeMWOhI7/hYSWSCQwMjJC6dKlBa/3pcaPg0Y5tG7dutAq6RkZGejQoQPLJnFycoKGhgYIIVi8eHExtrjosHHjRnaIk0gkWLZsGSOf5efAmzdv4Obmxt7dihUrEBgYCF1dXV6BF6Fi1qxZLOIQyCefCSHo3r07i57KyMjAvn37fpiEVkO1QaPvjI2Nebrn8mOB+/vevXtZwWBAvdeoUTDi4+Ohra0NQghMTEwEreurDNzo1cuXL7PrvXr1YvvSgAED8ODBA97nPn/+jLlz50JDQwPh4eHF3ez/hIcPH2Ljxo2oUqWK0vosd+7cgYGBATp27Aggf32oUaOGAvlMYW9vj549ewp6HcnIyFAIApg/f36hJDQtOCgSiTBhwgTevWPHjrH6HKtWrSry9qvx60EdWQEBAThw4ICCrF5CQgKqVKkCXV1dpU48On+E5siSB+WGsrKyeFH/J06cYPWhvkdC29jYYOPGjWoSWg01/iUESUAD+d66gIAAmJubY8iQIbwN9+vXr+jTpw8joY8fP85bHNavX49OnTohKioKgPowrIbiGNi0aRPKlCkDiUTCSzFSlbFCieYfJaGpbrR85LNQDQ81vo9bt27BwcEBJUuWxK1bt9h1+TlAx0Bqaip69+4NV1dXaGpqom7duti+fbvCc0IDdz4YGxtDLBaDEMIO8zKZTCGNEcgvvEYrZ9OfcuXKKUSpCRFPnjyBnZ0dCCHw9PRkBWnoOKHvIDMzE/v371eT0Grg8uXLqFWrFgghqFatGi8rgs4x7piIiYlBlSpVYGlpydMYV0ONgnDhwgVYWVnh7t27v7opPxVRUVEoUaIEvLy8mCM4PT0dCxcuhLa2NmxtbVkwzpAhQxT2mKSkJFy9evVXNP1/xoYNG1ClShUQQqCtrY1GjRopSEPcv38fBgYG0NPTw+nTp1lBtMmTJyuQz2PGjIFIJMLatWsFu/c8ffoUs2fPRsOGDfHs2TPevcJI6MePH2PUqFHMDunQoQMGDhyItm3bQlNTExKJhBcoINT3o8b38fr1a/Tv3x96enpsPLi5uWH16tWIj49nz1GSeuDAgUwPWRWRmpoKFxcXhcj/48ePf5eEnjhxIuOZvn79WuxtV0MNIUNQBLS87qi9vT169OjBotByc3MZEcAloZ2cnHDw4EEkJSVh48aNsLa2hlgsVonCJGr8POTl5WHx4sWoW7cuCCEoUaIEr2iaEAm0wtr8byKh4+Li0KpVKyxduvSH/rYawoKyAweNPqQFRgo7lNDxkp2djaSkJLx9+5aXWSL0sZKTk4NFixbBxsaGSdDIa+Rx+0jfx4MHDxAWFobx48dj+/btgtabpKCHkcjISHaIcXFxYRGH8rJXykjoN2/eAFAfdP80rF69Gra2thCJRKhYsSI2bNig9LlTp06xfVgdlafGv4EyiSghIzMzk+n4nj59GkD+Prto0SLo6emhdOnSSE1NZYWAKWEiHwlNIYS9eOLEidDQ0IC5uTlWrFiBR48eKewrFCNHjgQhBHp6epBIJJgxY4YCGbR69WpYWlrCx8eHp38tJMTFxbFIZUdHRyxbtgx5eXm89yJPQnNJ+ISEBISHh8Pc3JzVPtLV1UWrVq2wb98+9pwQxoca/x1Pnz7F9u3b4ePjw8aDpqYmhgwZgoiICNy9exfOzs6oXLkyrxihqoD2Zfjw4SCEsHos3D6eOHECJiYmIIRg6NChSgsTTp8+XSXqDKihRnFDUAQ0kF+htG3btti4cSOcnZ3x6NEjAPwoNS4J3bdvX6bV4+LiAqlUCgsLC15EnxpqAPl6eT179oSFhQUGDBjA03wWolHGnRMvX77EhQsXcOTIEURHRytEh/wICc3V4BTi+/i/9u48vqZr/R/4s85JIoSkIQNCzaGIOQlqboWQKooWDaGo9ro1EzGTolzEXNRQQs1qaszEFJEIvqJFG655iMSQREKS8/n9kd9e3TvnRLWXJOfkeb9eXjc9Z59z907OWXvtz17rWUzr4sWLOHnypNHjSgdMmWKmhKyv6nzev38/x4VrLKXTevPmTRw9ehTAnwuTCiGwatUquY16NHT29QbMVUREBLZs2WI0xVCZ+q1Mew8KCpId8+xtR1paGn7++Wd4eHjA1tYWffr0kauOM8uR/bzw4sULo9FTs2fPRoUKFTRlA1avXo2rV6/il19+wdSpU2VdW15ngDFg8uTJ8Pf3B5D1Hdu4cSNcXV1RoUIF3LlzR27XvXt3CCFgY2ODvn37mmUwoowqbN++PSIjIzXPqcPWhIQEAMCFCxdkqbzKlStj//79mtdMnz4dTk5OKFOmjLxeNDeHDh2Ck5MTnJ2dMXr0aKSmpmraVXW7mz2Ezh7G37x5E2fPnkVYWBj++OMPixoowLRe5++ZmZmJQ4cOYcKECXBxcYFOp4MQAtWqVYOrq6tRyUVz9Kp20N/fH++8847me/B3QmjG2D9jdgH0N998AyEEKlasiEKFChl1NhRKw/vs2TNMmjQJdevWhYuLC9q2bYsrV67k5i4zM5KQkIArV65oAlpzvPBVB0Bz5szBe++9pykF4OPjg3nz5mlekz2EPnr0qFGQBJjn74NpxcbGQggBDw+PHEdKTZs2DUIITJ8+HYDxDBT15yAwMBArV640+XkxRzl13NUXfSEhIUYhtPp1GzduxKlTp97qfr5tcXFx8hgjIiLk44mJifDw8MAHH3yAuXPnomTJkhBCYPjw4fIzYCqE3rFjh5wqfvDgwVw9FvZ2qf/eW7duxYgRI9CqVSu0bt0aCxcuxIkTJ+Tza9euRdeuXTXnJDs7O/lzrVq1EBoaKrfnYIQVBK/6nN+7dw9A1nm4bdu2cHFxkeU4lFHfI0aMQOnSpeVIWWXEtLlQbuy2b99eExoZDAbN72bdunWYPHmynHWzfft2NG7cGEIIlCpVCkOHDsWgQYPkY++++67Z1gQ/deoUihQpgsqVK2tKmgHaPpi6fzZz5swcR0JnZyk3ypmW+nz8+++/4+jRo9ixYwcOHDgAwPTf+9dff8W2bdvQqFEj2afT6XT45JNPkJGRYZafkUGDBkEIgR07dmgeV74vrVq1gqurK548eaIJlXMKoYcMGWLRJUkYyy1mF0A/ePAAfn5+EEJAr9fL0Xmmgg+lw5Keno6HDx8iLi6O6/Qwi6c+caoXSggICEDv3r3h7OwMa2tr6HQ6DB8+XPNadQjt7u6e4w0eZt6io6NRp04ddO7c2WjBGqXd3LBhgxwJ8fjxYwB/trPqi8GdO3dCCIExY8YYhdTmSOmEvnz5EtevX8fx48fx4MED+bh6JLA6hFaXE1i3bh3s7OxQsWJFpKSk5O4BvEEvX75Ez5490blzZ7lQjfIZePjwIW7evAkg67Pi4uICIQRGjBjxyhB606ZNmnCRmT91exAUFCTrpCs1FJVSaMuXL5fbxcfHY9OmTfj0009Rv359NGnSBK1bt8aGDRs0N8U4fGYFgbqtvHTpEqKjo40WBwMgS2306dPH6HX//ve/0ahRI6xbt05TPs4cXLlyBbVq1cI777xjNPJZ3QZs3rwZHh4eEEKgZ8+eclTzyZMn0a9fPwghYG1tLducfv36GdVLNhc3btyAt7e30Uyr1wkDZ8yY8cqFCZnlyj4ivlKlSpqbvb6+vli/fr3sy2bvpyUnJyM6OhojRoyQ53D1DWRzosyCt7e3N7nuRO3ateHh4YHMzMxXLoi8d+9eOSJ89OjRubPzjFkwswuggawLl48++kjW/VJWaTV1oWKOd+wYexO+//57CCHQqVMnzeiPmJgYTJ06VY44mzBhguZ18fHxcnTa2rVrc3u3WS6Ji4uT085WrFiBI0eOaJ5//vy5XGCubdu2Jm/enTx5Eg0aNICzs7PR682R0ilNTk5Gp06dULZsWbl4YL9+/eRIM2XdAUAbQk+YMAFff/01nJ2d4eTkhHPnzuXFYbwR6hBZOe7g4GCEhoYaleNITU3Fxo0bXyuEVp+nOVy0LJMnT4YQAq1atcKBAwfw6NEjREREYNKkSbIUWvbFfl6F+2+sIFC3kSEhIahUqRLKlCmDAwcOGN3UXbdunVxsUC08PByurq5GgwrMpY3duHEjhBCYMWNGjtts3boVlStXho2NDUqUKAEhBPz9/TWLLkZERODYsWPYvn07Hjx4oDlXm5sDBw6gSJEisvwKYHw+/eOPP3DkyBFMnDgR27Ztw+nTp+Vz6hB60aJFHEIXAOpzprKQYMWKFTFx4kQEBwfDx8cH9vb2cHNzw3fffWfUvmRvL4KDg+XIX1PPm4ORI0fKWVbqEBoAKleujGbNmuX4WvXx7tixAxUrVuQSroy9Afk6gFYaUvUJV3ksPj4eHTt2lCP0lOla5tg4MvYmGQwGPHr0CC1btkSxYsXkysbZ6+cpK6iXK1dOTodXtnnw4AHCwsJyf+fZW5c91Fm1apUsy5K9ZER0dDTc3d0hhIC3tzeio6Nx48YNpKWlYevWrXKq7/fff5+bh/BWKL+XlJQUNGjQQAbPHh4eMlht0qSJDO3VF7aLFi2SC7kIIfDee+/h0qVLeXIcb5L63Ltr1y4IIeDi4oKtW7caTUNMS0t77RCaWZ6jR4/CwcEBXl5eclCAYty4cRBC4OOPP0Z0dLTmufT0dPndMzUKiTFLpr5mUQKj6tWrY+nSpSYXVNy3bx/s7Ozg7u6O48eP48mTJzh06BCaN2+OokWLGgUs+Z0ymrd9+/aaqfLZ24EDBw7A2dkZNjY2iIqKwv79+1GuXDkIIdC7d29ZjsSSKDMYlVr46gA5PT0dGzZsgLu7OxwcHGTt76pVq2oWR/7uu+/k87NmzeLatQXEihUrIIRAu3btNOfj9PR0tGrVCjqdDgMGDMDdu3dNvl5pl3799Vc4Ojqidu3aZnNufvDggdHskeHDh8sQWmljkpKS4ODgAD8/v9d+b3WtaMbYP5cvA+jsF6vZO2HK8+qR0DVq1OAQmrH/78qVK3BwcMCHH36Y4za3bt3CJ598AiEEpk2bJh/P/v3h75Nli4iIgK+vL3Q6Hdq2batZmDA9PR2HDh1C9erVIYRA8eLFUaZMGVSuXFle8ISEhMjtzaWDaorBYIDBYMDw4cPh6OiIoKAgZGZm4tmzZ4iJiZG/Ay8vL5Mh9P79+7Fo0SIsWrQIt2/fzqvDeGtevHiBb775BjqdDqVKlcKWLVv+MoQeNWoUh88FxOzZsyGEwM8//6x5XBkV7efnh7Nnz8rHlbI+jDHgP//5D4QQ6Nix4yvD1NTUVHTr1k3eDKxUqRJsbGwghNCci81N8+bNodfrNW2EwmAwIDw8HHXq1MG+ffvk4+vXr5cLmvbp00ezULYlUALoXr16aR4/duyYXA9JCIEyZcrgvffekyUC7O3tsWXLFrn9jBkzIITAggULcvsQWC4zGAx48uQJ2rRpAzs7O83aHQAwdepUCCHw0UcfGZW6yf4+QNaAjPLly6NKlSpmMYJ+165d6Ny5M2rWrClLxCmyh9AJCQnQ6/Xo3bs3Ll68iN9//x1Xr17F2bNncebMGURERCA8PBwrV67E4cOHAZj3NQ5j+Um+C6CVu7OpqalYuHAh+vTpA09PT4wbNw579+412i4+Pl7WhOYQmrEskZGR0Ol0qFat2ivDMOUu+YcffmgR9XvZq+XUeYqOjpYjkJQQWr3tvXv3EBAQAC8vLxQvXhwVKlTAwIEDsXv3brmNuba5yrlE2f9mzZqhffv2srOtPP/o0SPUqlXLKITOXo7CXKWlpeH+/fv47rvvMHLkSAwdOhTTp0/HjRs35LG+ePECgwcPlos9vSqEdnNzgxACX331FXfaLZjBYEB6ejo6dOgAIYRmOrwSPrdv3x7nz5+Xj1+/fh3z58/Hf//737zYZcbylf/7v/9DuXLlUL58ec307uztpnKOSkpKQt++ffHee++hUKFCaN68uWaBOnM8F7dt2xZCCCxbtgwAjEbqpqWlITExEYB2wb0NGzbAzs4OVlZWmvITlmDfvn2wtbWFEAKDBw9GdHQ0AgMDUbNmTRk+BwYGIiYmBqmpqfjll19k+byAgADN+hOmgn1mma5evYpixYqhU6dOmseVUljZz8e//vqryXrzALB06VI5QzK/r2fy3XffwdHREXq9Hp988onMjNRtiTqE/v777+XsgL/698cff+TVYTFmkfJVAK2uv9myZUsIITTTmp2cnBAcHCy3NxVC165dm+vzsALv9u3bqFatGhwdHWUJDvVIROW7c+7cOVhZWaFFixYcQFs49UVpfHy8UU1nUyG0msFgQFJSEq5fv46EhATNxbE5XvCqJScno1mzZhg7dizee+89hIeHA/jzO6N8XxITE02G0Oa+KvaVK1cwYsQIObJd/a9y5cqYMmWKnMapjIT+qxD6p59+grW1tVmPymOvz9/fH0IIueDXhAkTTF7sAlkLFdrb2xuV6mDM0pm6Gbd582ZZIiGnbRTKOenly5dISEjAnTt3NNPCze1crBxr37595chMxauORTnnvHjxAs7OzmjXrt3b3dE88OzZM3z55ZcyhFYWhLOxsUHLli2xfv16o9cotbRr1KiBpKSkHG9iMMsVExODQoUKoWfPnvKxnMJnIOvmT/aa8gBw4cIF1K9fH7a2tpqFgfOjESNGQKfTwdPTUzNLQqG+/lVCaCsrKzg5OaF79+747LPPEBAQgP79+2Pw4MEIDAzEmDFjMGXKFDmwkTH25uSbAFo5KaakpMDLywt6vR69evXCjRs3cP36dUydOhU6nQ5WVlYICgqSr1OH0EpN6EaNGuHFixc86opZNFMBoPqxnj17ynq0Sp0vdb1NAFi7di10Oh2mTJmSS3vN8oK687VmzRq0b98e48aNM5qy+qoQ+lUrRJs7ZcHOChUqwM7OTk5fNXXTRh1CN27c2CymJb7KiRMnUKdOHXk8/fv3x6xZsxAQECADaXt7e7Rr105O53zdkdBXr17Ni0Nib4mpcioZGRnIzMxEYGAghBAYPny4XLioXbt2Rhe7YWFhcHR0hJ+fH+Lj43Nr1xnLMxcvXjS6oQv8eQ5VSi0otXtfdW69f/++HAmc0/uZo+PHj8ugderUqfJxU4Gp+rFBgwZBCIE1a9YAMO/fgSn379/HhAkTYG9vL8ufrVq1ShOKZWZmyv7Jb7/9Bmtra9StWzffj1hlb8f58+dRqFAhVKxYEc+fP8e0adNyDJ83bdoEIQTGjh1rdH6/d+8egoOD830Aqxxfp06djAYgqtsK9WzFf//73xBCwNbW1uzq5jNmCfJNAA1k3dHv1asXbG1tMXbsWHlX//fff0fv3r0hhIBer4cQAuPGjZOvUy+c9tlnn+HChQt5sv+M5ZbsHQVTo5cfPHgAT09PCCFQv3593Lp1S/N8REQE6tevj6JFi+LAgQNvdX9Z3lF/ViZNmoTChQvD2toa8+bNk9Pu1BdtfyeEthTx8fFyhIgQAsOGDZPPqTuw6hC6Xr16EELggw8+MNvfy6FDh/DOO+/Azc1N3oRS38x69OgRunfvDkdHR+h0OrRo0ULOqHidEFrBo64sy+bNm436WZcvX8Y777wjv0Pt27c3GjV16tQpNGvWDM7Ozti1a1du7jJjeSI2NhZCCHh4eOQ4ilAJUKZPnw7AuD+nPr8EBgZi5cqVFldbPzU1Ff369YOVlRUqVqyIpUuXyufUx6r++fvvv0eJEiXQtm1bPHz4MFf3N7fdu3cP8fHxRrXzlbUrFEuWLIEQAhMnTgTA596CqlOnTnJAnhACHTp0MKotf+LECdSuXRtVqlRBVFSUyffJ74tWHjhwAI6OjqhQoQLOnTsnHzcYDH/52VdCaDs7O2zdulU+zoshM/b25asAOiwsDEWLFkXXrl3lqLKrV6/KqZ0DBw7E1q1bZVmOMWPGyNcqHTY+2TJLp+6Ar169Gn369EGTJk0watQoREZGai5eTpw4gbp160IIATc3NwQGBmLt2rWYP38+atSoASEE5s+fnxeHwXKBuhOljLJq0aKF0cIk2bdVh9Dt27fHiRMncmV/89KjR4/kyE11LUrAdAidkJCA999/32xrKx44cAA6nQ7u7u6a+qFK+6L8b1JSEsaOHQtXV1dYW1ujR48eiIuLA5B13h0yZAiEEChbtiw2bdpkMTWx2Z/UbUNISIhcGCs2Nlaz3cKFC2FnZwchBPr37695bteuXahfvz6EEFi0aJHJ92bM0kRHR6NOnTro3Lmz0WwZ5byyYcMGCCFQrVo1GTAq7a/63LNz50557WOJJdPi4uLkbBx3d3fMmTMnx20XLVqEkiVLonTp0gVupo26zVT/fPToUZQpUwaurq44fvx4XuwaywWvyjmUduHnn39GxYoVIYRA1apVZT1k5fNy9OhRtGjRAtbW1li1atVb3+e35bvvvoNOp9PcsMrepwgLC0NISAi6du2KuXPnYv/+/fI5pf9qZ2enWdOGMfZ25ZsA2mAwICQkBG5ubrh+/ToA4ObNmxg4cKDmYiY1NRU9evSATqeDXq/XjIRmzBJdvXrVZAdbmfKs/le9enXMmTMHqampALLCskuXLsma6up/Li4uWLJkiXw/vnljuZYvXw4hBPz8/EzWXX3x4oXR6NWoqCi5sFjjxo1lGRdzltNnXOmwxsfHY8qUKRBCwNXVFStXrjT52uwLF5qbw4cPQ6/Xo2rVqprph9mPR10aa/jw4dDr9ShZsiQ2bdokt3n58qWsqWdra4vff/89dw6C5YrsIy1Xr16NJk2aQK/XY+DAgZr25NatWwgODkbRokUhhEDz5s3RvXt3+Pj4wMrKCoUKFcK8efPk9ub6/WHs74iLi5MzOlesWIEjR45onn/+/Lmcrda2bVuj9RkA4OTJk2jQoAGcnZ2NXm9Jfv31V9SsWRM6nQ46nQ49evTA2bNnkZCQgKdPn+LKlSvo1asXbGxsUKpUKaObYAVF9pDt2LFjaNGiBYQQ+OGHH/Jor9jbpj4fb9++HVOmTMHIkSOxePFizXZPnz7F+PHjUbp0aRQrVgy9e/dGeHg4Dh06hFmzZqFkyZIQQmDu3LnyNeZ2M/jly5fw8fGBEEIG7M+fPweQ1Ue/c+cO+vfvjyJFisjrXr1eDzc3N813ZOjQofL5sLCwPDkWxgqafBNAA1l1i/bv3y8bweXLl8sRV2qzZs2SF7tCCHz77bd5sbuMvXVnzpyBTqdDQECAJoRWRqE1a9YMu3btQlhYGPr27Yt33nkHTk5OmDp1qjwRA1kdi7Vr12Lq1Kno27cvVq1ahejoaPk8BwGW6/Hjx2jdujWKFCliNPL53LlzWLp0KRo2bIjWrVvLGpSKM2fOoFmzZppOqrlSQuOXL1/i+vXr2L17N44cOYIbN25otouPj8fEiRPlTRr16JDs3xNz67ADWSNflIWM1CPMcppqqRyzslCjEALe3t6aEXgvX77EgAEDLOJzwv6Ufbr7Z599Bnd3dzg7O8sLtgEDBmhC6ISEBOzcuRPvvfce7O3tIYRA8eLF0b17d+zYsUNux+ccZumynx9WrVoFIQR8fHxw6tQpzXPR0dFwd3eX7Wt0dDRu3LiBtLQ0bN26FV5eXhBC4Pvvv8/NQ8gTV69eRceOHVG4cGEIIVCiRAm4uLigdOnSsLa2lje3CtrIZ0X2z9Xq1atRpUoVWFlZac7p5tg/YTlT/z3Hjh1rNKjI19cXly5d0pSKmzlzplyzRP2vatWqWL16tXw/czwfp6eny5ma//rXv+Tjjx8/xsqVK9G8eXO52KCnpyd8fHxQtWpVCCHg5OSEPXv2yNd8+eWXEELgt99+y4tDYazAybMAWmlIs9fpUUbhvXz5EjVr1kSFChVk7VolUPvll1/g6emJWbNmwdnZmVdSZxZr27ZtqFChAgoVKoSvvvpK1hBs164dGjVqpPns3717F3PnzoWzszOKFy+O4OBgORL6VbiTalmy/z3v3LmDkiVLonnz5prHV6xYgRo1asi6+so/9VQ2IKuWeE7vbS6UDnlycjJ69uyJ0qVLy+O1t7fH6NGjZW1jIKsch1IT+q9CaHMTGRkpj71v376vdf5UgsizZ8/CyckJzs7OcqSz8pypRVGZ+VL/PUePHg0rKyvUrFkT8+fPx7x58zBgwADodLocP0ePHz/G1atXcerUKdy6dUszw4I/H6wgioiIgK+vL3Q6ndH6Cunp6Th06BCqV68ub9ooi84pNwxDQkLk9uZ6Ln5diYmJ2Lx5Mxo0aCCD+ZIlS6JTp05Yu3atpl9SECmzhpWQrWzZsnIhRoDbWEs2Y8YMCCFQt25dzJ49GyEhIahQoYKs+Xz69GnZ501LS8PNmzcxffp0DB48GP7+/ti8eTMuXbok38+cPyvr16+HnZ0ddDod+vTpgzVr1qBVq1YoXrw4hBAoX748wsLCZI34I0eOyPrYQUFBmvcq6G0KY7kp1wPov2rolE5VTEyMrD+a/XU9e/ZEpUqV8PLlSzmtjTFLtX37dtSuXRs6nQ5fffUVjhw5gqJFi2pW/Va+N4mJiVi4cKHJENrcywawv2ZqYaK4uDg53e7HH39EWFgY+vTpAyEEHBwcMHPmTBw7dgzTp0+XndrExESjC1xzveBVj+BVFg5s1qwZvv32W/Tv3x81a9aUiwkePXpUvi4hIUGG0KVLl9aUqzF3UVFRMoTu1auXZvGWnGRmZuL27dtwcXGBEALh4eEmtzPXzwkz7YcffpCjq7KvML927VoZmH3xxRevvJnBnwtWkOT0eTe1yK9623v37iEgIABeXl4oXrw4KlSogIEDB2rqkxakPtyzZ8/w/PlzxMXF4f79+3m9O/lGYmIievfuDRcXFwwcOFBT87kgfT4KAnU9+Pj4eNSrVw++vr6a8jPXr19Hu3bt5AyK06dPv9ZCpeZ+Xk5ISEC/fv1kmQ1ljTCl3VQGbaln+M2fPx9CCHz44YfIzMy0yHr6jOV3uRpAKw3Aixcv8Msvv2DmzJkYOXIkNm7caDTtQblALlOmjOaiZtmyZXBxccGAAQPy/eqsjP0v1J3Ibdu2yRC6V69ecHd3lyU0sn8PEhISNCH0tGnTXmskNLMc33zzDYYOHSr/e/HixZpRzsWKFYO/v7/mouXZs2dwdXVFzZo1Le7G3suXL9G1a1cIITBu3DhNx/zw4cOws7NDuXLlsGzZMs33KSEhAVOnToUQApUrV8aTJ0/yYvffijNnzsjPQ+/evV8rhAaAevXqoXjx4lzruYDo1q0bhBByIVKDwaD5jmzZsgVubm5yoWh1f83cL24Z+yfUfbf4+Hijms6mQmg1g8GApKQkXL9+HQkJCTy7BNqBFtyuZElISMCVK1fw7Nkz+Rj/bszbq/5+Fy5cwJMnT2BlZSXX7lCfj2/cuCHXbfH29kZkZKRsL5T3tcT248GDB5gzZw7KlSuHxo0bo3v37oiKipILuirHrszC2rNnj7xpzhjLG7kWQCsX/MnJyWjXrp28S6X8c3V1xbZt2zSv6dmzJ4QQ8PLywrx589C7d2/Y2dmhTJkyfPHLCoScQmghBBYsWJDj65SR0KVKlYKdnR3Gjh37WnfDmfnLPntEERoaih49eqBbt24IDw83mm62ZcsW6PV6jBgxAoD5XsiowzHlGA4fPoyiRYvC19cXSUlJmu0bNmwIW1tbjBo1SnZY1d+7R48e4bvvvtNMWbQUrxtCK7+P69evo0iRIqhatapFhfHMmMFgwJMnT1CyZEnY2dnhv//9r6ZNUP/8n//8R36O+vXrV2BrszKm7metWbMG7du3x7hx44xG774qhLaU2UeMsb9HaT+yDyz66quvZLmr+vXry5sO2YPl7CH0mTNnLDJ0NiU5OTnHY1U/3r17d+j1emzcuBEAt6+M5YVcCaCVL3dKSgrq168PIQS6dOmCw4cPY8OGDRg1apS8eAkNDZWvO3XqFPz8/DRBda1atfDrr7/mxm4zli+oT46bN29G48aNodPp0KpVK5w/fz7H1yUmJmLevHmwsrKyqPIB7NVu3LiBGjVqQAiBXbt2aZ5TjyJSd8hOnToFLy8vODk5Yf/+/bm6v2/KunXr5M/ZO+9KzTz17yMzMxMNGzaUo6KVDn1SUhKuXLmieb0ld+D/KoRWtz9z586FEAKLFi0CYNm/F0v3uhddTZs2RdGiReWMG3XApvz9Hz58iLJly8pSP8OHD+fPBitw1N+NSZMmoXDhwrC2tsa8efPkDTv19+7vhNCMMcu2c+dO1K1bV7YV6n6s+iZv0aJFNTOSFKZC6Lp16xrNsCgI1P0PdbscEhICIQRatmwp60IzxnJfro2ATk9PR79+/SCEQGBgoNEU73LlyqF48eIYOXIk0tLSAGQ1rA8ePMDixYsxYcIErF+/Hvfu3cutXWYs31B3RDZv3ox69erJmtCvGm326NEjXLhwITd2keUjc+bMgRACX3/9NQDTo4IVYWFhaNq0KYQQWLx4ca7u55syevRoCCHQrVs3+Zj6mIcOHQohBPbu3QsgqyRHo0aNjMJnALhy5QoqV66MY8eO5d4B5LHXGQl98OBBODk5oW7dutymWABltL8ip8D4s88+gxACH3/8sey3ZZ/WCwAVKlSQ63MIIbB9+/a3st+M5Ufq74IyqKZFixaIiIh45bbqELp9+/YyWGKMFQwGgwGZmZlygJ6np6cs26NevHfhwoWynzZ+/Hj5uPrcrfx88+ZN+Pr6QgiBDRs25NKR5G8LFy6Ei4sLXF1deZYWY3nsjQfQOdXounfvHsqVK4dGjRrJgBnIalwbN24sgwDlokjd6DJWUGQPAXIqm7F161bNwoSvczLlEWmWT2l3f/vtNzki8fbt2ya3jYqKQmBgIBwcHFCsWDFNSRdz+qwsW7ZMM0ume/fu8jnlXDNx4kQIITBlyhRkZmZqzjnq8BkA/P39odPpcOrUqVw9jryWPYQ+e/asfC4iIgLNmjWDra0tNm3alId7yd6EAwcOwMbGBqNGjcKWLVs0zynffeV/Y2NjUa5cOdjY2CAoKEiG0OqFe3755RfY2dnh7NmzWLVqlVzgJy0tjUdysgJl+fLlEELAz8/P5KKcL168MLq+iYqKkiMWGzdujLt37+bW7jLG8omnT5/C29tbjlxWQmh1ZqIOoZcuXSofNxVCX79+XdaKLqiSk5Px4MED9OrVC0WLFkX58uU1izcyxvLGGw2g58+fj5UrV8rQTH3hERYWBiEEZs6cKR/LzMw0OQotPT0dO3bsMFpxnS9kmCVTh8379+/HjBkz8Mknn6Bv375YtmwZ4uLiNNtnD6Gzlw1glit7QGyqZmSXLl1k6KouvQEAqampWLRoEYQQaNq0KX7++ecc3zs/y8zMlGUhKleuDFtbWwgh8Pnnn2u2O3fuHIoUKYLKlSvD3d0dQghMmDDBaBRoSEgI7O3t0atXL6NguiDIHkJfvXoVUVFRaN26tVHdeT4fm6fY2FgUKlQIQgjY2NhAp9Ph008/xebNmxEfH6/ZNjMzE8nJyZgxYwYcHR3h4OCAr7/+GomJiXKbEydO4MMPP8S7776LK1eu4O7du3B2doa7u7vmwpkxS/f48WO0bt0aRYoUMRr5fO7cOSxduhQNGzZE69atsWLFCs3zZ86cQbNmzTB37txc3GPGWH6gzNh79uwZ6tWrByEE6tSpI8txqM+l6kXF/yqEzum/C4Lbt2/jiy++QIkSJSCEQJs2bfDHH3/k9W4xxvAGA2glBKhfvz42bNggwzSl0fv5558hhMCQIUMAZDW2OU2BTkpKQu3atREYGFggG01W8Kg/5xMmTECRIkU0ozqFEHBzc8Px48c1r1MvTDho0CBcvnw5t3ed5TJ18HfhwgWjEFUZmRgREQEHBwe0adPG5PukpKTg2LFjmg6ZOba3T58+hbu7O6pVq4YFCxbAwcHBKIR++vQpBg0aJAPqTz/91GgRvUWLFqF06dKoVq0arl27ltuHkW+oQ+gOHTrAy8sLQgjMmjVLbmOOnxOW5f79+7JWc9WqVeV3wsbGBu+99x7Wr19vVGLlv//9L8aPHw9XV1d5s+fzzz9Hv3798M4770AIgZCQEADArVu34ODggGbNmuXF4TGWa7LfhLtz5w5KliyJ5s2bax5fsWIFatSoAb1er+nTqcMjAJqFgfkGH2MFR2ZmpuY736BBAwgh4OHh8Y9D6ILuwYMH8Pf3R9OmTbFkyRKu+cxYPvLGAui9e/fCw8ND1i9av369ZkRnbGwsihQpgq5duyItLS3H8BkAvv76a+h0Oq4hyAqc8ePHQwiBJk2a4KeffkJ4eDgmT56Mli1bQggBOzs77Nu3T/Oabdu2yTvmvXr1MgokmWVS6h43aNAAmzdvNppWpl6MUL1AX07M8YJXOccoC4vMnz8fp0+fRrFixYxC6OPHj8PPzw/W1tbw8PDAnDlzEBsbi2PHjqF3796wsbGBs7MzLl26lFeHk2+cOXNGjpIVQmD27NnyOb7AMV/K327WrFnQ6/UYPnw44uLiMGnSJFl/0tbWFmXLlkVwcDAuXrwov2P379/H5s2bUadOHfm5sLW1hZubm6Z2/ODBgyGEwJgxY4xmXjBmKUyVR4uLi5M3d3788UeEhYWhT58+EELAwcEBM2fOxLFjxzB9+nQ5zT4xMdHkDCbGWMGgbkvCw8Oxe/dujBs3TvbBatWq9Zch9PLly3N9v83Bs2fP8PTp0xzLWTLG8sYbLcFx5MgR1K5d22QIfevWLdSqVQtCCLz77rvQ6XSYMGGCZionACxZsgROTk5o164dHj169CZ3j7F8bdeuXShUqBCaNWtmFII9e/ZMLgZVtGhRoxFqmzdvRvny5eUoNGbZEhMTMWjQIDRs2BBCCOj1eri5uSEoKAinT59GamoqAODHH3+EEAJffvklAMsND6Ojo2Fra4tKlSrh5s2b2L17N4oWLQohBHr06CG3Cw8PR+/evWXHvnDhwhBCwNraGq1ateIyNioREREQQmDevHnyMUv9/BQ04eHh8sJVKRWQkpKCadOm4aOPPpLPVapUCR07dkRMTIzsqyUlJWHfvn344YcfcPjwYcTExMj3Xbx4MYoWLYqqVavixo0beXJsjOWmb775BkOHDpX/rQ6FhBAoVqwY/P39NbPXnj17BldXV9SsWdNoQXbGWMGh7lONGzdO3sBq164dnJyc4OzsLMtxmKoJrW5v5s+fn+v7zxhj/8QbCaDVd+sPHz4sg+bsIfSOHTtkQ9m8eXOj+oAzZ86Eq6srypUrx3V6WIGjjH7etm2bfCwzM1PTQencuTOEEOjUqROSk5M1z6m/MzyCxnJl/9uuXr0aX3/9NaysrOQFb7NmzbBz505s3LgRdevWReHChS1m4Y2cPttBQUHQ6/Vy0ZUdO3aYDKHv3buH8PBw9OvXDwEBAfjmm2+wZ88evuFpwv379+XPHD5bluHDh0MIgWHDhmlGB6WmpuLw4cNo2bKlvEHj6uoKHx8fbNmyRdaqzG7cuHFwcnKCk5MTzyJgBUJMTAyEEGjfvr3m8dDQUPTo0QPdunVDeHi4prQGAGzZsgV6vR4jRowAwP01xgq6b7/9FkIIdO7cWd4U/v3337Fr1y45uzynEHrBggUQQmDatGl5su+MMfZ3vbER0OoO1P79+1G3bl0IIeDt7Y21a9fKi5YlS5bIELpPnz6YNWsWFi9eDD8/P1nnli9eWEHz4sULtG7dGjqdTq6crg4FlPDnxIkTcHV1RaVKleRFzV8tSMfMW07BX/YpZWfPnsX8+fPlFHlra2uUL18exYsXhxACU6dOfeX75XeHDx82GlWprpt38OBBWFtbw8vLS47W3L17tyzHoQ6h2d9jrp8ZZkz5vuzYsQO2trYoXbo07ty5A+DPhZBSUlLQpk0bCCFQrVo1VKtWTfbbmjZtKoMzIKvubZUqVSCEQL169fDrr7/m/kExlgfUZa527dqleU5dfkbdfp46dQpeXl5wcnLC/v37c3V/GWP5z+XLl1GqVCmUKVNGDhRRX8fduXNHZiq1a9eWZRbVIfT//d//5eo+M8bY/+KNluAwGAzyAub06dNycRpvb2+sW7dOBiabN2+Gm5ubZkEOZ2dndOnShUc+swIpPT0dvr6+EEJgzpw5OW738OFD1KxZUzN1mlkudcgcERGBAwcOYMOGDZqaZtmD6OfPn2Pbtm0YMGAAihcvDmtra1lXPKfRi/ndsGHDZPmmZcuW4ezZsya369ixI4QQCAsLk4+pQ+iePXvKx5UyJQDftGEFT2ZmJlq3bg0hBAIDA+XF7J07d+RMG19fX6SnpyMuLg7jx49H6dKlIYTAjBkzNO81b948jB07Frdu3cqLQ2Esz8yZMwdCCHz99dcAoDnHZj+vhIWFoWnTphBCaOqmM8Ysm3J+NXUz/9ChQxBCoH///gCy2pDsN69+++03VK5cGUII1KxZU4bQSj/W1M0uxhjLr/52AL1x40acOXPG5HPqIERZeMPR0VGW41i3bp3snF25cgXHjh3DsmXL8OOPP+LatWtyagljlkrdOch+cTJ//nxYWVmhS5cuiI+P1zyn3rZ169awt7fHtWvX3u7OslyXkpIif1ZfyE6dOtUoTF68eLFsM3MKo0+dOoUVK1bA1dUVQgisWrXq7R/EGxYUFAQhBHQ6nQySS5UqhWHDhuHSpUuaIPnYsWMoVKiQJmgGtCF07969c/kIGMtflHbil19+QbFixdC0aVMAQHx8vLyJ07ZtW6PXnT17VnPjU91GmevNLcb+CaVP9ttvv6Fs2bIoWbIkbt++bXLbqKgoBAYGwsHBAcWKFcOCBQvkcxwYMWbZDh8+DD8/P1y9etXk8zt37jQaIKFmMBiQlpaG/v37y0F71apV4wXnGWNm628F0KNGjYIQAr169cL58+c1z6mDjx49ekAIgX/961/YsGGDnDri6emJ0NBQvlBhBVL2lY5/+OEHnDt3Tj524sQJuLi4QAiBcePGISUlxeiu9uHDh2FnZwcfHx+5KjKzDIcPH4a/v79R26rUBi9ZsiT69u2LGjVqwM7ODk5OThg7dqz8HLzq5sa2bdug0+kwaNCgt38gb9jx48dhbW0NnU6Htm3bYvz48Xj33XchhICTkxM6dOiAyMhIJCYmIiUlRZYg2bt3L4A/fxe7d++W5UiURRkZK8iuXbuGihUrQgiBSZMmoWvXrkbhc3p6usmQjIMzVhD8VYkzg8GALl26QAiBKVOmaEpvAFkjFBctWiTL1/z88885vjdjzLIkJSWhUaNGskazqdl20dHREELAxcXF5MxW5TXLly/Hu+++i3LlykEIgXXr1r31/WeMsbfhbwXQJ0+elHffevfurQnPFD179oQQAl988QVu3rwJIKs2Z/aFCZWOF3fAWEGgDp+nT58uVzZeuXKlJkhetWqV/I4NHToUx48fl52PY8eOoWXLlhBC4Keffsr1Y2BvT2JiouykBgQEyHpu0dHRcHFxQadOnWR7e/PmTfznP/9BmTJlYGdnh8DAQJMhtCIzMxMXLlyAra0tnJ2dcfPmTbMrOXHmzBlZsmnOnDmIiIjA+vXr0bhxYwghULhwYXzwwQc4dOgQli1bBiEEJk+eDEAbGGzbtg1ly5Y1ee5irCBasWIFhBBywc42bdrI53iwACvI1OeOCxcuGI04fPnyJYCs8lgODg6a745aSkoKjh07pikxyNc+jFm2uLg4pKWl4fjx4wgKCpJrkyjtiLKGSUpKCj7++GPo9XoEBgZqFi1Vn4MnTJiAxo0b48GDB9i0aVOuHgtjjL1Jf7sER1RUlCaEjoqKks8pI5/79esnw2elA3f48GEZQjdq1AirVq3iDhgrENQXMcosgoYNG2Lnzp0mt1+6dKn8jrm6uuKDDz5Ax44dZU312bNnm3xvZt527dol60P6+/vjjz/+wMaNG2Fvby9rHitt5pMnT7B69Wq8++67KFq0qCaEVn8m1G1snTp1zHra3pkzZ+T3YuLEiXj+/DkyMjKwadMm9OrVSz6njOi0s7NDXFyc0fskJyfnwd4zlj9dvHhRrivQrFkz+TiHz4xlGT16NIQQaNCgATZv3iwXClOoFyN8nVGJ3G9jzLIdPHgQzs7OuHPnDgwGg+yLDx06FI0bN8b169cB/NkWrF69Gra2ttDpdJgyZYp8XhEREYFatWrh448/1jyeveweY4yZg3+0CKE6CPjiiy/w+++/mxz5rG50gawQun79+hBC4IMPPuCaz6xAWbJkCYQQ+Pjjj43KLADasHDbtm3o0KED7OzsIISAXq9HkyZNEBoaanJ7Zhn27dsHb29vORK6f//+aNWqFQDtwiQA8OzZM6xatcpkCJ39s6GMcmzRooXZBtCA9twzatQoJCUlyef27duHkSNHytkF7dq14+8IY6/hX//6F4QQaNmyJZ4/f87fG8b+v8TERAwaNAgNGzaUfTE3NzcEBQXh9OnTcg2CH3/8UVPeib9DjBU8BoMBN27cgL29PUqUKAHgz7bg1q1bcHd3hxACHTp0MAqZg4OD5XonH330ERYsWIALFy4gNDQU9erVg16vx/r163P7kBhj7I0TAED/QFRUFHl7exMRUcWKFenatWvUr18/mjx5MpUqVYoAkBCCiEjz8/79+2nq1Km0dOlSql69+j/5v2bM7Dx69Ig6dOhAly9fpoMHD1K9evWISPvdICIyGAyk0+mIiCgpKYni4+Ppzp075OzsTC4uLlS8eHGj7Zh5unv3Ll27do1iYmLI2tqaOnToQG5ubnTq1CkaMmQIRUdHk729PVWvXp1OnTpl8j2SkpJo69atNHHiRHr27Bn179+fgoKC6J133pHbXLx4kXr37k1xcXEUGRlJ1apVy6UjfDvU557AwEAaMmQIubi4yOdv3LhBu3btoq+++or0ej1lZmaSXq/Pq91lLN9Szj9xcXHk6+tLqamptG3bNvL09ORzDCvwsvfPfvzxRzpz5gwtW7aMMjMzqWjRolS3bl0aMWIEpaam0owZM+jy5csUFRVFNWrUyMM9Z4zllcTERKpSpQpVrlyZIiMj5eMAKDo6moYNG0YnT56k9u3b04IFC6h8+fJym7lz59KSJUvojz/+ICIiKysrysjIkM8NHjxYvpe6bWKMMXPyjwNooqwg4P3336eMjAyqUqUKzZs3j9q2bUtExo2j+r+fP39ORYoU+R93nbH8JSMjg6ysrEw+d/bsWfL09KSAgABauXLlK0Oxv+pYcMfD/J0+fZqGDRtGsbGxlJycTEREtWvXpm7dutHw4cMpKiqKAgMDKTIykmxtbSk0NJQ6dOhg8r2SkpJo+/btNGrUKHr48CGdOHGCGjdurNlm5syZ5OfnZzE3/dQh9JgxY2jIkCHk7Oxs9N3g8Jmxv/bs2TPq0aMH/fLLL/TZZ5/R+vXr83qXGMt1Od10yX4eiYmJoZMnT9LKlSvpwoULZGVlRW5ubvTs2TN6/PgxTZkyhcaNG8c3cRgrgK5du0ZVqlShunXrUnR0tKb9UELowYMH0+nTp02G0CdPnqSYmBjavXs3paenU7169ah58+b00UcfEREPQGKMmb//KYAmIoqMjKSmTZtSRkYG9evXjwYOHChHd2bHwRmzVCdOnKDIyEjy9/fXjMZUbNu2jbp06UKff/45rVmzxuR7KN+Phw8f0qNHjywmLGRahw4dog4dOlBqaio1b96c7Ozs6OjRo/T8+XNydXWlQYMG0dixY2nfvn00bdo0On78OHXp0oXGjx9PHh4eJt8zKSmJ1q9fT9bW1tS3b1/5uCV3VLOH0MOGDaMSJUoQEZ9rGPu7zp49Sw0bNiRra2uKiYkx+5kSjP0d6pDo9OnTlJycTAkJCeTr60t2dnYmZ9OkpqbS3r17ae/evbRlyxZKSkqijIwMev/99+nIkSM5DkhgjFkmAHTp0iWqVasWNWrUiE6ePEnp6elkbW2t2eavQmgiohcvXpBOp9O81pL79IyxguN/7h15e3vTyZMnydvbm3744Qd6+fIlDRkyhOrUqWO0LQcCzBLdvn2bunTpQg8fPiS9Xk89e/YkZ2dnzTalSpUiIQQlJCTIx9QdCXVg9t1339GDBw9o4cKFmlIKzPwdPHiQ2rRpQ5UqVaKgoCAKCAggoqwyGQMGDKDIyEjauHEjdezYkdq0aUMGg4HS09Npy5YtZGtrSyNHjjQZQhcrVoz69u0rO6rKZ8uSO6qenp4UGRlJ3t7eNH36dCIiGULzuYaxv6datWrk6elJ3bt35/CZWTz1TEz17LXg4GCaO3euJkzu0aMH9ezZk+zt7WUInZmZSYULF6ZOnTpRp06dKCAggH777TcKCgqikydPUmhoqDy/M8Ysn3IdZ2NjQ0QkrwPVATJRVhbSoEEDmjdvHg0ePJj27NlDRCRDaKWNKVSokNH/hyX36RljBccbacmUIICIaM2aNRQSEkLnz59/E2/NWL5XsmRJ+uabb6h06dI0efJkWrNmDcXHx2u2cXFxIScnJwoLC6Nly5YRUVZHwmAwUGZmpgzMDh06REuXLqXk5GT6HycnsHzmyJEj1LZtW6pSpQrNnj1bXpymp6eTh4cHLV68mBwcHCg2NpbWrl1LRES+vr40adIk8vb2ptDQUJo1axbFxsbK91R/RtSd3ILSSVWfe6ZPn07z5s0z+u4xxv6anZ0d7dmzh/79738TUdZNLMYs0ZEjR2jgwIF04cIFIiIZPk+YMIEmTJhANjY25O/vT9WrV6dz587RhAkTaObMmfT06VPS6/VkMBg0U+qJiBo1akR9+/alJUuWkBCCzp49mzcHxxjLNZcvX5Z5h3Idl5KSQkQkg2hT13LqELphw4by3Hvjxg3ZxjDGmKV6YylF9hB64cKFFB0d/abenrF8yWAwkJWVFY0aNYqGDh1KhQoVouDgYKMQulKlSjR+/HgiIho0aJAsw6HT6eSFTEREBE2aNIl0Oh316dOHHB0dc/+A2FsRHh5OH3zwAen1evryyy9lLbfMzEyytrYmg8FAdevWpc8//5yItAGyj48PTZ48mRo2bEihoaE0c+ZMGULzSN8/zz1WVlYUHBxMS5cu5c47Y/+Acs4BUGBuYrGC5fHjxzR27FgKDQ2lkJAQunjxIhFllaBZunQpdezYkcLCwmjFihUUFhZGkydPJltbWwoJCaEZM2bQ06dP5eABIjJaRLpSpUpkY2NDGzdupFu3bvFAAsYs1NGjR6l69eo0bdo0eTOLKGsRQiKSI5gzMzNNvl4dQjdq1Ij27NlDX375JV27do3Pv4wxi/ZGWzglCLCxsaGVK1fSqlWr6MWLF2/y/4KxfEW5ELGysqLBgwfTyJEjcwyh+/XrR8OGDaOMjAwKCAigcePG0Z49e+j8+fO0dOlSCggIoJMnT9K0adPo448/JiLTd86Z+SlcuDARZY12jo2NlQGyup4kALp//z4R/Tl1T7nI9fHxkSOhf/rpJ/r22295lomKp6cnhYeHk4ODA3Xq1Ik774z9D/jGFrNUjo6OFBQURE2aNKEff/yRZs2aRXFxcRQXF0dpaWk0btw4qlOnDhkMBipbtiz169ePgoODqUSJErRw4UJNCK3unyllr2rVqkXVqlWjEiVKULFixfi7xJiFsrOzIyKiLVu20Ny5c+ncuXNE9OdsRAcHByLKuk7MzMyU7UVmZqYMpYUQVKtWLZo/fz7Vq1eP9u/fr5nlyBhjluiNr5Dh6elJR44cIT8/P/r6669N1jBizFyZWtwsewhNRDRr1iwKDg4mIqJevXqRs7Mz2dra0ujRo6lYsWI0efJkmjZtmuZ9ihcvTosWLaKvvvqKiHixCUvi5eVFZ86cIS8vL1q1ahVlZGTQ0KFDqU6dOnK04c2bN+n06dPUsGFD6tatGxGRvMgVQpCPjw8JIWjMmDG0ceNG6tOnTx4fVf7SqFEjunfvHtna2ub1rjDGGMun/Pz8yMbGhiZMmEChoaGk1+vJ2tqaGjRoQPXq1aOMjAx5c9jBwYE6d+5MAGjixIm0cOFCIiIKDAwkBwcHo/UWVq5cSRcuXKDmzZvn2fExxt4+T09P2a9fs2YNGQwGGj16NKWmphLRn6V9sl/HqQeeEGWNlK5VqxbNnTuXHj16RB06dMidA2CMsTwi8JaGWKalpXEQwCzOkydPNAsDqkNi5eeMjAyaN28ezZo1i168eEHjxo0jf39/cnFxka/bvXs3HTt2jH799VcSQlDr1q2pQYMG1LhxY6P3ZZYjKiqKvL29iSjrxsSgQYOoQYMGlJaWRvXr16dbt27R7NmzqW/fvkajo5UbH3v27KGUlBQZUjPGGGPMtLt379K1a9coJiaGrK2tqUOHDuTm5kanTp2iIUOGUHR0NNnb21P16tXp1KlTJt8jKSmJtm7dShMnTqRnz55R//79KSgoSNMfvHjxIvXu3Zvi4uIoMjKSF/NkrABQ9+u/+OILsre3p7lz51Lt2rXJ3d2dMjMzyWAwkBCChBCUkZEhFxo0GAz09OlTSktLo/nz58v34WtAxpgle2sBNGOW5uDBg9S+fXsaMmQIeXl50SeffCKfUzoLSqfidUJo5XXK6GmFqVHWzHKoO6sDBgwgf39/GjhwIF29epUmTZpEw4YNo0KFChl9Dkx9LriTyhhjjJl2+vRpGjZsGMXGxlJycjIREdWuXZu6detGw4cPp6ioKAoMDKTIyEiytbWl0NDQHEcgJiUl0fbt22nUqFH08OFDOnHihBw0oJg5cyb5+flR9erV3/qxMcbyB3W/vnz58vTf//73b70+ODiYgoKC3sKeMcZY/sMBNGOv4dKlS1S/fn16+fIlWVtbU0ZGBnXt2pW6dOlCLVq0ICcnJ7ltenq63CZ7CK2U4yDShoccOhcs6s6qo6MjJScn06RJk2jkyJFkZWXFwTJjjDH2Pzh06BB16NCBUlNTqXnz5mRnZ0dHjx6l58+fk6urKw0aNIjGjh1L+/bto2nTptHx48epS5cuNH78ePLw8DD5nklJSbR+/Xqytramvn37ysf5nM1YwRYVFUXvv/8+ZWRkUIUKFejTTz8lLy8vsrKyovT0dLKysiIrKyt5vWdtbU1CCLKzs6OGDRsSEV8LMsYKhjdeA5oxS+Tk5ESOjo704MEDqlChAt24cYM2bdpE27dvp0qVKtH48eOpRo0aVKtWLbkARU41oXv37k1OTk6aixXucBQsyoKtTZo0ocePH1PdunWpVatWOdaMY4wxxtjrOXjwILVp04YqVapEQUFBFBAQQERZZTIGDBhAkZGRtHHjRurYsSO1adOGDAYDpaen05YtW8jW1pZGjhxpMoQuVqwY9e3bV/bzsteAZowVTJ6ennTixAlq0qQJXb9+nZ48eULu7u6vPRuCb2IxxgoKbukY+wsGg4FcXV1p+PDhpNPpyM/Pjy5dukQTJ04kDw8Punz5MvXt25f8/Pzo22+/pUuXLpHBYCCiP0Po4cOHk42NDc2YMYNWrlxJDx8+zOOjYnnN09OTjh8/Tnq9ns6dO0fLly+ns2fP5vVuMcYYY2bryJEj1LZtW6pSpQrNnj1bhs/p6enk4eFBixcvJgcHB4qNjaW1a9cSEZGvry9NmjSJvL29KTQ0lGbNmkWxsbHyPdWTRZXwmYhvFjPG/uTl5UUnT54kIqLvv/+eZsyYQTExMfL5zMxM+XP2CejcljDGCgpu7Rj7C0qnwMvLiwwGA82ZM4cePnxIEydOpGPHjtG3335LrVu3ptu3b9P48ePp448/pq5du9L58+fp3r17ZGVlRUOGDKExY8bQixcvKDAwkG7fvp3HR8XyAy8vL7no0cqVK2nBggV0/vx5+TxXSGKMMcZeT3h4OH3wwQek1+vpyy+/pI8++oiIsoIfa2trMhgMVLduXfr888+JSBv6+Pj40OTJk6lhw4YUGhpKM2fOlCE0z1JjjL0OZYYjEVFoaCjNnz9f9uv1er3s13ObwhgrqDiAZuw1NWvWjIYNG0ZERJs3b6bMzEwqUqQIjRkzhjZt2kSHDh2iFi1a0N27d2n79u3k6+tLAQEBtG3bNrK2tqYhQ4bQ5MmTafHixVSvXr08PhqWX6g7q2vWrKEFCxbQuXPniIg7qIwxxtjrKly4MBFljXaOjY2VAbJer5fbAKD79+8TEWnW5CDKCqGVkdA//fQTffvtt5qbwowx9le4X88YYznjRQgZew3KwhA7d+6kTz/9lIoXL05RUVFUunRpysjIICsrK3r+/Dl17tyZ9u/fT1WrViUioitXrhARUaNGjcjHx4cmTpwo35PrfTG1qKgoatKkCaWnp9Mnn3xCS5Ys0SxuyRhjjLFXi46OJi8vLyIi8vf3p6FDh1KdOnVkP+7mzZv0/vvvU9myZWnz5s3k5uZGRNoFwA4cOEBjxoyhmJgY2rt3L/n4+OTZ8TDGzBP36xljzBinX4y9BuWixM/Pj5o2bUr37t2jBQsW0IsXL8jKyoru3r1L/v7+tH//fmrbti1dvHiR9uzZQ+PGjaNSpUpRREQE2djYaN6Tw2emptSEJiJq3Lgxd1IZY4yxv6lBgwZy9OHatWspJCSEoqOjSQhBaWlp5OvrS0+fPqU+ffpQyZIl5euEEHJ6fOvWrWny5Mm0YcMGDp8ZY/8I9+sZY8wYj4Bm7DVlZmaSXq+nsLAw+vTTT6lOnTp07NgxevToEfXv35927NhBbdq0obCwMM3rYmJi6OXLl9SwYcM82nNmTh48eECurq5EpB2RxRhjjLHXExUVRd7e3kRENGDAAPL396eBAwfS1atXadKkSTRs2DAqVKiQ0XnW1HmXZ6wxxv4p7tczxtifOIBm7G+6fv06ffjhh3T9+nWaOHEiXbp0ibZs2aIJnzMyMkin0xldsPBFDHtd/FlhjDHG/jl1CO3o6EjJyck0adIkGjlyJFlZWfF5ljGWa7i9YYwxDqAZ+0dWrlxJ/fr1Izs7O0pJSSEfHx/au3cvEZGsCc0YY4wxxvKOug5r3bp1afHixTKUZowxxhhjuYdvwzH2D3h5eVGNGjUoJSWFmjZtyuEzY4wxxlg+o9Rh1ev1dO7cOVq+fDmdPXs2r3eLMcYYY6zA4QCasX+gZs2a1Lx5cyIi0uv1lJqaSgaDgcNnxhhjjLF8xMvLi06dOkVEWTPYFixYQOfPn5fP82RQxhhjjLG3jwNoxv4m5UJl6NChVLlyZfr9998pNjaWdDodGQyGPN47xhhjjDGm5unpSZGRkUREtGbNGlqwYAGdO3eOiIgXBWOMMcYYywUcQDP2NykXKs7OzuTu7k537tyhuXPnEhHx4hKMMcYYY/mQEkJbW1vTqlWraNq0afTo0aO83i3GGGOMsQKB0zLG/iF7e3uaPHky6fV6+vnnn+ny5ct5vUuMMcYYYywHSk1oIqLGjRuTk5NTHu8RY4wxxljBwAVrGfsfVKtWjTw9Pal79+5UrVq1vN4dxhhjjDH2Cl5eXnTv3j1ydXUloqzSalyGgzHGGGPs7RLglTcY+588fvyYHB0diYjIYDBwGQ7GGGOMMTPA/TbGGGOMsdzBATRjbwiPoGGMMcYYY4wxxhhjTItv+TP2hnD4zBhjjDHGGGOMMcaYFgfQjDHGGGOMMcYYY4wxxt4KDqAZY4wxxhhjjDHGGGOMvRUcQDPGGGOMMcYYY4wxxhh7KziAZowxxhhjjDHGGGOMMfZWcADNGGOMMcYYY4wxxhhj7K3gAJoxxhhjjDHGGGOMMcbYW8EBNGOMMcYYY4wxxhhjjLG3ggNoxhhjjDHGGGOMMcYYY28FB9CMMcYYY4wxxhhjjDHG3or/B1LohEb5pBRKAAAAAElFTkSuQmCC","text/plain":["<Figure size 1600x500 with 3 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["msno.bar(train, figsize = (16,5),color = \"#7B68EE\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"zqpDIM2EAaGg"},"source":["There are no missing values!"]},{"cell_type":"markdown","metadata":{"id":"1d849003","papermill":{"duration":0.039936,"end_time":"2022-04-15T07:42:21.329174","exception":false,"start_time":"2022-04-15T07:42:21.289238","status":"completed"},"tags":[]},"source":["Let's look at the distribution of the target variable"]},{"cell_type":"code","execution_count":7,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:40.971684Z","iopub.status.busy":"2022-08-29T07:55:40.969840Z","iopub.status.idle":"2022-08-29T07:55:41.417704Z","shell.execute_reply":"2022-08-29T07:55:41.416189Z","shell.execute_reply.started":"2022-08-29T07:55:40.971632Z"},"id":"64bdc5a1","outputId":"b81538dd-356d-453e-879d-c3c173ad9f12","papermill":{"duration":0.602813,"end_time":"2022-04-15T07:42:21.972450","exception":false,"start_time":"2022-04-15T07:42:21.369637","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/var/folders/n0/3c71vqs570b8l21fmnvb5k640000gn/T/ipykernel_5338/1674861069.py:1: 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='HeartDisease',data=train,palette='pastel')\n"]},{"data":{"text/plain":["<Axes: xlabel='HeartDisease', ylabel='count'>"]},"execution_count":7,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.countplot(x='HeartDisease',data=train,palette='pastel')\n"]},{"cell_type":"markdown","metadata":{"id":"nANq5hK6A7vu"},"source":["We are dealing with an unbalanced sample, where the majority of people are healthy."]},{"cell_type":"markdown","metadata":{"id":"2045df17","papermill":{"duration":0.048087,"end_time":"2022-04-15T07:42:28.694076","exception":false,"start_time":"2022-04-15T07:42:28.645989","status":"completed"},"tags":[]},"source":["Let's look at unique values in categorical objects"]},{"cell_type":"code","execution_count":8,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:41.420814Z","iopub.status.busy":"2022-08-29T07:55:41.420040Z","iopub.status.idle":"2022-08-29T07:55:41.814432Z","shell.execute_reply":"2022-08-29T07:55:41.813318Z","shell.execute_reply.started":"2022-08-29T07:55:41.420750Z"},"id":"F74CoCB9BZ5a","outputId":"20144a30-a483-4f34-fcee-d1ad35d83204","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Feature 'HeartDisease' has '2' unique values\n","Feature 'BMI' has '3604' unique values\n","Feature 'Smoking' has '2' unique values\n","Feature 'AlcoholDrinking' has '2' unique values\n","Feature 'Stroke' has '2' unique values\n","Feature 'PhysicalHealth' has '31' unique values\n","Feature 'MentalHealth' has '31' unique values\n","Feature 'DiffWalking' has '2' unique values\n","Feature 'Sex' has '2' unique values\n","Feature 'AgeCategory' has '13' unique values\n","Feature 'Race' has '6' unique values\n","Feature 'Diabetic' has '4' unique values\n","Feature 'PhysicalActivity' has '2' unique values\n","Feature 'GenHealth' has '5' unique values\n","Feature 'SleepTime' has '24' unique values\n","Feature 'Asthma' has '2' unique values\n","Feature 'KidneyDisease' has '2' unique values\n","Feature 'SkinCancer' has '2' unique values\n"]}],"source":["for column_name in train.columns:\n","    unique_values = len(train[column_name].unique())\n","    print(\"Feature '{column_name}' has '{unique_values}' unique values\".format(column_name = column_name,\n","                                                                                         unique_values=unique_values))"]},{"cell_type":"markdown","metadata":{"id":"7onKBNPoBiHL"},"source":["Numeric variables are BMI, PhysicalHealth, MentalHealth, SleepTime. The rest are categorical."]},{"cell_type":"code","execution_count":9,"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:41.816956Z","iopub.status.busy":"2022-08-29T07:55:41.815949Z","iopub.status.idle":"2022-08-29T07:55:41.825460Z","shell.execute_reply":"2022-08-29T07:55:41.824109Z","shell.execute_reply.started":"2022-08-29T07:55:41.816890Z"},"id":"ak2oiscfCD-b","outputId":"aebfb6bf-cae0-43fc-94c7-d7db72764a4a","trusted":true},"outputs":[{"data":{"text/plain":["Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n","       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n","       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n","       'Asthma', 'KidneyDisease', 'SkinCancer'],\n","      dtype='object')"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["train.columns"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:55:41.828480Z","iopub.status.busy":"2022-08-29T07:55:41.827149Z","iopub.status.idle":"2022-08-29T07:55:41.837833Z","shell.execute_reply":"2022-08-29T07:55:41.836391Z","shell.execute_reply.started":"2022-08-29T07:55:41.828430Z"},"id":"8CmxEwy2B_oV","trusted":true},"outputs":[],"source":["numeric_features=['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n","categorical_features=['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory',\n","       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth','Asthma', 'KidneyDisease', 'SkinCancer']"]},{"cell_type":"markdown","metadata":{"id":"6Y5PfeouCns0"},"source":["Let's analyze numerical variables."]},{"cell_type":"code","execution_count":11,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:41.843695Z","iopub.status.busy":"2022-08-29T07:55:41.842939Z","iopub.status.idle":"2022-08-29T07:55:59.672303Z","shell.execute_reply":"2022-08-29T07:55:59.671133Z","shell.execute_reply.started":"2022-08-29T07:55:41.843643Z"},"id":"d3bffec3","outputId":"34a3005f-5cc0-4e59-b2da-1382a3c0d630","papermill":{"duration":6.262687,"end_time":"2022-04-15T07:42:28.452620","exception":false,"start_time":"2022-04-15T07:42:22.189933","status":"completed"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["Text(0.5, 0.98, 'Visualizing continuous columns (train dataset)')"]},"execution_count":11,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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r/8BZYsiZy2PnUqjB8fdLJdsrguSZIkSZIkSVIMOuOMMzj99NM59NBDOeyww7j77rupWbMmH374IZmZmTzxxBOMGTOGrl270r59e5588knef/99Pvzww6CjS1K5UVBQwIgRI8gvyKf9+vb8Lut3VC+oHnSsImrl1+LY9cdyVNZR5OTkcP/99wcdSZIkSZL2WqiEX2pV0nVSzFm1Ci67DNq3h3ffherVI6etf/cd9O5d7v/HYXFdkiRJkiRJkqQYl5+fz5QpU8jOzqZDhw7MnTuXvLw8unXrVrimZcuWNGvWjA8++GCnz5OTk0NWVlaRmyTFstdee42vvvqK/TfvT6PcRkHH2akQIZpsbkK93HrMmDGDjz/+OOhIkiRJkiSpNOXlwdixcOih8M9/Rr524C9/gfnz4ZZboFq1oBOWiMV1SZIkSZKkGJWamrrLsSQp9n311VfUrFmTpKQkrrjiCp5//nmOOOIIMjIySExMpE6dOkXWN2rUiIyMjJ0+38iRI0lOTi68NW3aNMq/A0kKTk5ODvfddx9xxHHYxsOCjrNbIUK02tgKgFGjRlFQUBBwIkmSJEmSVCrefBPatoXBgyEzE44+GubMgcmToYK9R5sQdABJkiRJkiSVnvT09CIF9Z2V1dPT08sqkiQpQIcffjhffPEFmZmZTJ06lX79+jFr1qy9fr5hw4aRlpZWOM7KyrK8LilmfffddyxbtozqW6pTJVwl6DglklSQRGJBIl9//TUZGRnsv//+QUeSJEmSJEl764cfYMgQ2PpzvQYNYMQIuOgiiI8PNtte8sR1SZIkSZKkGLO7UrqldUmqPBITE2nRogXt27dn5MiRtG3blnHjxpGSkkJubi7r168vsn7FihWkpKTs9PmSkpKoXbt2kZskxao2bdrQu3dvNiVs4pM6n5AXygs60i7lxOXwUd2PyI3L5YorrrC0LkmSJElSRbVhAwwdCkceGSmtJyTAoEHw/fdw6aUVtrQOFtclSZIkSZJi0s7K6ZbWJalyKygoICcnh/bt21OlShVmzJhReG3BggUsXryYDh06BJhQksqPuLg47rrrLnr16sX6KuvLdXl9a2k9Oz6byy+/nEGDBgUdSZIkSZL2SjhcuuukCqWgACZNgsMPh1GjIDcXevSAefPggQegTp2gE+6zhKADSJIkSZIkKTosqUtS5TZs2DB69uxJs2bN2LBhA5MnT2bmzJm88cYbJCcnc/HFF5OWlka9evWoXbs211xzDR06dOD4448POroklRtxcXHcfffdAEybNo2P63zMUVlHUTu//HzjxLqEdXyV/BUb4zdy2WWXMXjwYEKhUNCxJEmSJGmv+M8ZVVoffwzXXgsffRQZH3JIpKz+xz/G1P8wLK5LkiRJkiRJkhSDVq5cSd++fVm+fDnJycm0adOGN954g1NPPRWABx54gLi4OHr37k1OTg49evTgkUceCTi1JJU/W09ej4uLY+rUqbxb/10O+O0ADss+jGoF1QLLtTF+IwtqLGBF1RUAXH755ZbWJUmSJFVoZ55Z8rV33w033xy9LFKZyciAYcNg4sTIuGZNuOUWGDQIkpKCTBYVoXDYL0zYXlZWFsnJyWRmZlK7dvk5LaGsPTb3sRKvvaz9ZVFMIkmSFNvcf5aMf06StOdSU1N3mPMUdkkqGfefJeOfk6TK5t133+W+0ffx3fzviCeeA7MP5JBNh1AlXKXMMuTE5fB99e/5tfqvhAnTvn17rr/+eo4++ugyyyBJQXH/WTL+OUmSKqo9/Ryu7VdVaDk58OCDcOedsGFDZK5vX7jnHmjcONhse2hP9p+euC5JkiRJkhSDiiutb523vC5JkiTtnRNPPJETTjiBl19+mbEPjOXHZT/ya/VfOWTjIRz424HEEx+1194S2sKP1X/kpxo/kU8+hxxyCNdddx0nn3yyp6xLkiRJklRRhMPwyisweDD88ENk7thjIyX2444LNlsZiAs6gCRJkiRJkkrXzkrrJb0uSZIkaefi4uJITU3l9TdeZ9iwYdSoU4P5teYzp8EcViSuKPXXCxNmSdUlzGowix9q/ED9hvW5++67SU9Pp2vXrpbWJUmSJEmqKObPh9NPhzPOiJTWU1Jg4kT44INKUVoHi+uSJEmSJEkxZftSenp6euFtV+skSZIk7ZnExEQuvPBC3prxFpdddhl5iXnMrTOXT5M/ZVPcplJ5jaz4LD6s8yHzas8jrlocaWlpvPnmm/Tp04eEBL9cW5IkSVJs2ZMfXdx1V/RySKUuMxOGDIGjjoLXX4cqVeCGG2DBAujXD+IqT5278vxOJUmSJEmSKpnty+rbjyVJkiTtu1q1ajFkyBBeevklTjjhBFYmrWR2g9l8X/178snfq+fMC+Xxbc1vea/+e6xLXEfPnj15/Y3Xufzyy6lWrVop/w4kSZIkqXx48cWSr7355ujlkEpNQQE88QQcdhiMGQNbtsAf/wjffAOjRkHt2kEnLHN+DF+SJEmSJEmSJEmS9lHz5s2ZMGECb7zxBiNGjOB7vmdptaUcueFI9svdr8TPsyxpGd/V+o6cuBwOOugghg8fTseOHaOYXJIkSZIqlnA46ARSCbz3HgwcCHPnRsaHHw5jx8JppwUaK2ieuC5JkiRJkiRJkiRJpSAUCnHaaafx2muvcckll5CTmMMndT5hadWlJXr8j9V/5IvkL6AapKWl8dJLL1lalyRJklRp9OtXuuukQCxZAn/9K5x4YqS0Xrt25LT1r76q9KV1sLguSZIkSZIUs1JTU3c5liRJkhQdNWrU4Prrr2fq1KnUSa7DvNrzWJa0bJeP+bnaz8yvOZ/GKY15+eWXufzyy0lMTCyjxJIkSZIUvEmTSnedVKY2b4a7746crD55MoRCcMkl8P33MHgwVKkSdMJyISHoAJIkSZIkSSo96enpRQrqOyurp6enl1UkSZIkqdI64ogjmPDkBPr168eXfEnc+jhSclN2WLe46mK+rfUtDRs2ZNLTk2jWrFkAaSVJkiRJ0h4Lh+GFF2DIEPjpp8hcx44wbhy0bx9otPLIE9clSZIkSZJizO5K6ZbWJUmSpLJz5JFHMmHCBKpXr84Xdb5gdZXVRa4vS1rG17W/pn69+jz11FOW1iVJkiRJqii++QZOPRV69YqU1g84AP79b5gzx9L6TlhclyRJkiRJikE7K6dbWpckSZLKXps2bfjn4/8kFB9ifs35hfMFFDC/1nxqVK/BU5Oe4uCDDw4wpSRJkiQFq2/f0l0nRc3atXDNNdC2LcyYAUlJcMstsGABnHcehEJBJyy3EoIOIEmSJEmSpOiwpC5JkiSVH+3bt6dbt2688cYbZCZkkrwlmVWJq9gct5kLel/AoYceGnRESZIkSQrUpEklW/fUU9HNIe1Ufj489hjceiusWROZ69UL7rsPmjcPNlsFYXFdkiRJkiQpRvXp04fc3NzCcWJiIlOnTg0wkSRJklS5nXPOObzxxhssrraYozYcxa/Vfi2clyRJkqTKzAOqVe7NmgXXXgvz5kXGRx4J48bBKacEm6uCiQs6gCRJkiRJkkpfampqkdI6QG5uLqmpqQElkiRJknTCCSdwwAEHsLzqcjbFbWJV0iratWvHYYcdFnQ0SZIkSQrMqafu2XpL7ipTv/wC55wDXbpESut168Lf/w5ffGFpfS9YXJckSZIkSYoxuyunW16XJEmSghEXF0fnzp3ZEtpCRlIGYcJ06dIl6FiSJEmSFKi33go6gVSMTZvgb3+Dli3huecgLg6uvBK+/x6uvhoSEoJOWCFZXJckSZIkSYohffr0KbzfpEkT0tPTC29NmjQpdp0kSZKksrN1X74mcU2RsSRJkiRJKgfCYXjmmUhh/fbbYfPmyGnrn38OjzwC9esHnbBCs7guSZIkSZIUQ3JzcwvvP/LII0WubTvedp0kSZKksrO1qL62ytoiY0mSJEmSFLAvvoiU1M89F379FZo1i5y2/vbb0KZN0OligsV1SZIkSZIkSZIkSSoj+++/PwD5cflFxpIkSZJUWXXrFnQCVXqrV8MVV0D79jB7NlSrFjltff586NMHQqGgE8aMhKADSJIkSZIkSZIkSVJlkZycvMuxJEmSJFU206fvWS84HI5eFlUyeXkwfjzcdhusXx+Z+/Of4d57I6etq9R54rokSZIkSVIMSUxMLLx/1VVXFbm27XjbdZIkSZLKTvXq1Qvvx8XFkZSUFGAaSZIkSapYLK2r1EyfDu3awcCBkdJ6u3aR09anTLG0HkWeuC5JkiRJkhRDpk6dSmpqKgBLliwpvF/cOkmSJEllr0aNGoX3q1evTsivG5ckSZJUyV1wQcnWnX9+dHOokli0CIYMgRdfjIwbNIC774aLL4b4+GCzVQKeuC5JkiRJkhRj0tPT9+m6JEmSpOipWrVqYVl929PXJUmSJKmy+te/SnedVKyNG+Gmm+CIIyKl9fj4yGnrCxfCZZdZWi8jFtclSZIkSZJiUHp6OomJiUXmEhMTLa1LkiRJAQuFQlStWhWAatWqBZxGkiRJkqQYFw5HPvVw+OEwciTk5sKpp8K8eTB2LNStG3TCSiUh6ACSJEmSJEmKjqlTpwYdQZIkSVIxqlatym+//WZxXZIkSZKkaPr0U7j2Wvjgg8j44INhzBhITYX/fhuaypbFdUmSJEmSpBg1ZswYZs6cWTju0qULaWlpwQWSJEmSBFB44vrWXyVJkiSpMjv//MiB2CVZJ5VIRgbcdBNMnBg5cb1GDbjlFhg8GJKSgk5XqcUFHUCSJEmSJEmlLzU1tUhpHWDmzJmkpqYGE0iSJElSoaT//pA8MTEx4CSSJEmSFLynny7ddarEcnPhvvvgsMPgyScjpfULLoCFC2HoUEvr5YDFdUmSJEmSpBizu3K65XVJkiQpWHFxkR/TeuK6JEmSJEWEw/t2XeLVV+Goo+D662HDBvj97+H992HSJNh//6DT6b8srkuSJEmSJMWQMWPGFN4/55xzSE9PL7ydc845xa6TJEmSFIwkT3qTJEmSJABCoX27rkps4UL4wx8it4ULoVEjmDABPvoIOnQIOp22Y3FdkiRJkiQphsycObPw/vnnn1/k2rbjbddJkiRJCkZiYmLQESRJkiQpcCNHlu46VRJZWZHT1Vu3jpy2XqUKXHddpLzevz/EWZEuj/xPRZIkSZIkSZIkSZICEB8fH3QESZIkSQrcTTeV7jrFuIKCyInqhx4K990HeXmR09a//hpGj4batYNOqF1ICDqAJEmSJEmSJEmSJEmSJEmSJO3SBx/AtdfCp59GxocdBmPHQs+egcZSyXniuiRJkiRJUgzp0qVL4f1//etfRa5tO952nSRJkqSyFQqFivwqSZIkSZJ2YdkyuOACOOGESGm9Vq3IaetffWVpvYLxxHVJkiRJkqQYkpaWxsyZMwF49tlnefbZZ3e6TpIkSVKwwuFw0BEkSZIkKXAjRsBNN5VsnSqZzZvhgQfg7rshOxtCIejfP/JfhkaNgk6nveCJ65IkSZIkSTEmPT19n65LkiRJkiRJkiSVlWHDSnedYkA4DC+8AEceGflUQ3Y2dOgAH38MTzxhab0Cs7guSZIkSZIUg9LT0+nSpUuRuS5dulhalyRJkiRJkiRJUvn17bfQowecfTb8+CPsvz/861/w3nvw+98HnU77qNwX12fPns0ZZ5zB/vvvTygU4oUXXtjtY2bOnMnvfvc7kpKSaNGiBRMnTox6TkmSJEmSpPImLS2N9PT0wltaWlrQkSRJkiRJkiRJkoo45ZTSXacKat06GDgQ2rSB6dMhMTFy2vqCBfDXv0IoFHRClYKEoAPsTnZ2Nm3btuWiiy6iV69eu13/008/8Yc//IErrriCf//738yYMYNLLrmExo0b06NHjzJILEmSJEmSVD6kpqbuMOeJ65IkSVLwwuEwAHl5eQEnkSRJkqTgvf126a5TBZOfD48/DjffDGvWRObOOgvuvx8OPjjQaCp95f7E9Z49e3LXXXdx9tlnl2j9o48+SvPmzbn//vtp1aoVV199NX369OGBBx6IclJJkiSp8ho5ciTHHHMMtWrVomHDhpx11lksWLCgyJouXboQCoWK3K644oqAEktS7CuutL6reUmSJEllb9OmTUFHkCRJkiQpOLNnw+9/D1dcESmtH3FE5LT155+3tB6jyn1xfU998MEHdOvWrchcjx49+OCDDwJKJEmSJMW+WbNmMWDAAD788EOmT59OXl4e3bt3Jzs7u8i6Sy+9lOXLlxfe7r333oASS1Js21053fK6JEmSFKytJ61v3Lgx4CSSJEmSJAVg8WL485+hc2f44guoUwcefBC+/BK26wArtiQEHaC0ZWRk0KhRoyJzjRo1Iisri99++41q1art8JicnBxycnIKx1lZWVHPKUmSJMWS119/vch44sSJNGzYkLlz59KpU6fC+erVq5OSklLW8SSpUtm+lJ6enl7stdTU1CLXJEmSJJWdjRsihXWL65IkSZIEXbvC22+XbJ0quE2bYPRoGDUKfvsN4uLgssvgzjuhQYOg06kMxNyJ63tj5MiRJCcnF96aNm0adCRJkiSpQsvMzASgXr16Reb//e9/06BBA1q3bs2wYcN2+XXYOTk5ZGVlFblJkvbM9sV0i+qSJElS8PLz88nMirx3snbt2oDTSJIkSVLwZswo3XUqh8JheO45aNUK/va3SGm9UyeYOxfGj7e0XonEXHE9JSWFFStWFJlbsWIFtWvXLva0dYBhw4aRmZlZePv111/LIqokSZIUkwoKChg0aBAdO3akdevWhfPnnXce//rXv3jnnXcYNmwYTz/9NOeff/5On8cPmEqSJEmSpFi0du1aCgoKAFizZg35+fkBJ5IkSZKkYIVCQSdQVM2bByefDOecA4sXQ9Om8MwzMHMmtGsXdDqVsYSgA5S2Dh068OqrrxaZmz59Oh06dNjpY5KSkkhKSop2NEmSJKlSGDBgAF9//TXvvvtukfnLLrus8P5RRx1F48aNOeWUU1i0aBGHHHLIDs8zbNgw0tLSCsdZWVmW1yVJkiRJUoW3evXqwvsFBQWsW7eOBp4sJ0mSJKmS2pPSeigUObhbFcTq1TB8OPzjH1BQAFWrwo03wg03QPXqQadTQMr9iesbN27kiy++4IsvvgDgp59+4osvvmDx4sVApMzSt2/fwvVXXHEFP/74IzfccAPz58/nkUce4dlnn2Xw4MFBxJckSZIqlauvvpqXX36Zd955hyZNmuxy7XHHHQfADz/8UOz1pKQkateuXeQmSdozqampuxxLkiRJKnsrV64sMl61alVASSRJkiRJioItW+Dvf4fDDoPx4yOl9XPOgQUL4G9/s7ReyZX7E9c//fRTTj755MLx1hMX+/Xrx8SJE1m+fHlhiR2gefPmvPLKKwwePJhx48bRpEkTHn/8cXr06FHm2SVJkqTKIhwOc8011/D8888zc+ZMmjdvvtvHbP1wauPGjaOcTpIql/T09CIF9Z2V1dPT08sqkiRJkqRtbC2q19pSiw0JG1i5ciWtWrUKOJUkSZIkSaVgxgwYOBC++SYybtsWxo2Dzp2DzaVyo9wX17t06UJ4F9/tMHHixGIf8/nnn0cxlSRJkqRtDRgwgMmTJ/Piiy9Sq1YtMjIyAEhOTqZatWosWrSIyZMnc/rpp1O/fn3mzZvH4MGD6dSpE23atAk4vSTFnu3L68VdlyRJkhSMFStWAJCcl1xYXJckSZIkqUL76ScYMgSefz4yrl8f7roLLr0U4uODzaZyJS7oAJIkSZIqvvHjx5OZmUmXLl1o3Lhx4e2ZZ54BIDExkbfeeovu3bvTsmVLhgwZQu/evXnppZcCTi5JsWtn5XRL65JUeYwcOZJjjjmGWrVq0bBhQ8466ywWLFhQZE2XLl0IhUJFbldccUVAiSWpcthaVE/eklxkLEmKfSXZo2/evJkBAwZQv359atasSe/evQs/9CRJUixp2hRCoaBTaJ9t3Ai33AKtWkVK6/HxcM01sHAhXHGFpXXtoNyfuC5JkiSp/NvVtyQBNG3alFmzZpVRGknSVpbUJalymzVrFgMGDOCYY45hy5Yt3HTTTXTv3p1vv/2WGjVqFK679NJLueOOOwrH1atXDyKuJFUa69atA6DmlppFxpKk2FeSPfrgwYN55ZVXeO6550hOTubqq6+mV69evPfeewGnlySp9OxtYX03P5ZWWQqHYfJkuPFGWLo0MnfKKTB2LLRuHWg0lW8W1yVJkiRJkmLU/fffX+SDQ507d2bIkCEBJpIklaXXX3+9yHjixIk0bNiQuXPn0qlTp8L56tWrk5KSUtbxJKnSys7OBiCpIKnIWJIU+3a3R8/MzOSJJ55g8uTJdO3aFYAnn3ySVq1a8eGHH3L88ccHEVuSpFJlaT0GzJ0L114L778fGTdvDmPGwJlneoy+disu6ACSJEmSJEkqfampqTt828WsWbNITU0NKJEkKWiZmZkA1KtXr8j8v//9bxo0aEDr1q0ZNmwYmzZtCiKeJFUamzZtIo44EgsSC8eSpMpp+z363LlzycvLo1u3boVrWrZsSbNmzfjggw8CyShJUmlq2nTvH2sfuhxYuRIuuQSOOSZSWq9eHe6+G779Fs46y/+QVCKeuC5JkiRJkhRjdldOT01NJT09vYzSSJLKg4KCAgYNGkTHjh1pvc1X9Z533nkceOCB7L///sybN48bb7yRBQsWMG3atGKfJycnh5ycnMJxVlZW1LNLUqzZtGkT8eF44sPxgCeuS1JlVdwePSMjg8TEROrUqVNkbaNGjcjIyNjpc7lPlyRVFEuWBJ1AeyU3Fx56CG6/HbbuM/76Vxg1Cg44INhsqnAsrkuSJEmSJMWQ+++/v/D+n/70Jy644ILC8dNPP81zzz1XuG7IkCFlnk+SFIwBAwbw9ddf8+677xaZv+yyywrvH3XUUTRu3JhTTjmFRYsWccghh+zwPCNHjuT222+Pel5JimVxcUW/FDs+Pj6gJJKkIO1sj7433KdLkqSoee01GDwYFiyIjNu3hwcfhBNOCDaXKqy43S+RJEmSJElSRTFr1qzC+9uW1rcfb7tOkhTbrr76al5++WXeeecdmjRpssu1xx13HAA//PBDsdeHDRtGZmZm4e3XX38t9bySFOuqVq1Kfiif/FB+4ViSVLnsbI+ekpJCbm4u69evL7J+xYoVpKSk7PT53KdLkqRS9/338Mc/wumnR0rrDRvCE0/Axx9bWtc+sbguSZIkSZIkSVIMCofDXH311Tz//PO8/fbbNG/efLeP+eKLLwBo3LhxsdeTkpKoXbt2kZskac9Ur16dAgrIi8srHEuSKofd7dHbt29PlSpVmDFjRuHcggULWLx4MR06dNjp87pPlyRVFLs5U0HlQVYW3HADHHkkvPIKJCTAkCGwcCFcdBHEWTvWvkkIOoAkSZIkSZIkSSp9AwYMYPLkybz44ovUqlWLjIwMAJKTk6lWrRqLFi1i8uTJnH766dSvX5958+YxePBgOnXqRJs2bQJOL0mxq1q1agDkxOUAnrguSZXJ7vboycnJXHzxxaSlpVGvXj1q167NNddcQ4cOHTj++OMDTi9J0r779VcIhfbuseFw6WbRdgoKYNIkGDoUVqyIzPXsCQ88AIcfHmw2xRQ/+iBJkiRJkhRDOnfuXHj/6aefLnJt2/G26yRJsWn8+PFkZmbSpUsXGjduXHh75plnAEhMTOStt96ie/futGzZkiFDhtC7d29eeumlgJNLUmyrX78+AFkJWQA0aNAgyDiSpDK0uz06wAMPPMAf//hHevfuTadOnUhJSWHatGkBppYkKXiW1qPso4+gQwfo3z9SWj/0UHj5ZXj1VUvrKnWeuC5JkiRJkhRDhgwZwqxZswB47rnneO6553a6TpIU28K7+Yle06ZNC/8/Q5JUdho2bAj8r7i+dSxJin2726ND5Js4Hn74YR5++OEySCRJUtl64ok9f4yl9ShatixywvrWg49q1YJbb4WBAyExMdhsilmeuC5JkiRJkhRj0tPT9+m6JEmSpOjZWlTPTMgsMpYkSZKkWHfJJSVfGw5bWo+anBy4557IaepbS+v9+8PChXD99ZbWFVUW1yVJkiRJkmJQeno6nTt3LjLXuXNnS+uSJElSwLYW1TdU2VBkLEmSJElSVIXDkJ4ORx4Jw4bBxo1w/PHw8ccwYQKkpASdUJVAQtABJEmSJEmSFB1DhgxhyJAhQceQJEmStI1GjRrtcixJkiRJUqn77jsYNAjefDMybtwYRo2Cv/4V4jwDW2XH/7ZJkiRJkiTFqEmTJpGamlp4mzRpUtCRJEmSpEpv2xPWExISqFOnTnBhJEmSJKkMPf546a5TCaxfHymsH3VUpLSemAhDh8KCBXDBBZbWVeb8b5wkSZIkSVIMSk1NZerUqUXmpk6dSmpqakCJJEmSJAHUqVOHhITIF2M3aNCAOEsCkiRJkiqJiy8u3XXahfx8eOwxOPRQGDcuMk5NhW++gZEjoVatoBOqkvJdEEmSJEmSpBizfTm9Ro0au7wuSZIkqezExcVRt25dAOrXrx9wGkmSJElSzHn3XTjmGLj8cli9Glq1gjfegBdfhBYtgk6nSs7iuiRJkiRJUgyZNGlS4f2rrrqK9PR0/u///o/09HSuuuqqYtdJkiRJKltbP1yanJwccBJJkiRJKjuhUOmu03Z+/RX+8hc46ST4/HNIToaxY+HLL6F796DTSYDFdUmSJEmSpJgyderUwvunnXZakWvbjrddJ0mSJKlshf7bwqhdu3bASSRJkiRJFd5vv8Gdd8Lhh8OUKZHm/2WXwfffw8CBUKVK0AmlQglBB5AkSZIkSVLp23qC4/aqVq3K5s2byziNJEmSpG1tLa5XsTwgSZIkSdpb4TD85z9w3XXwyy+RuRNPhAcfhKOPDjabtBMW1yVJkiRJkmJQdnZ2sfOW1iVJkqTyY2uBXZIkSZKkPfLVV5HT1N95JzJu0gRGj4Y//zly4rpUTsUFHUCSJEmSJEmlp0+fPoX3X3/99SLXth1vu06SJEmSJEmSJEkVwJo1MGAAtGsXKa1XrQrDh8P8+XDuuZbWVe554rokSZIkSVIM6du3L1OnTgXgkUce4ZFHHqFq1ao7nLTet2/fIOJJkiRJkiRJkqRKKhwuWa86HI5+lgpnyxb4xz8iJfW1ayNzffpETlk/6KBAo0l7whPXJUmSJEmSYkx6enqR8fal9e2vS5IkSSpbof82NcK2MSRJkiRJu/P223D00XD11ZHS+lFHReaee87Suioci+uSJEmSJEkxKD09nT59+hSZ69Onj6V1SZIkqRwJ+RXukiRJkiqRNm1Kd13M+/ln6N0bTjkFvv4a6tWDhx+Gzz6Dk08OOp20VxKCDiBJkiRJkqTo6Nu3L3379g06hiRJkiRJkiRJEl99VbrrYlZ2NtxzD4weDTk5EBcHV14Jd9wRKa9LFZjFdUmSJEmSJEmSJEkKQDgcDjqCJEmSJKm8CIdhyhS44QZYsiQy17UrjB0LRx0VaDSptFhclyRJkiRJilE33ngj3333XeG4VatWjBo1KsBEkiRJkuB/hfVQKBRwEkmSJElSufDZZzBwILz7bmR80EFw//1w9tngvx0VQ+KCDiBJkiRJkqTSl5qaWqS0DvDdd9+RmpoaUCJJkiRJkiRJklSZlfTQ8Ep1uPjKlXDppfD730dK69Wrw513wrffQq9eltYVcyyuS5IkSZIkxZjdldMtr0uSJEmSJEmSpLI2b17prqvQ8vLggQfgsMPg8cchHIa//AUWLIBbboFq1YJOKEWFxXVJkiRJkqQYcuONNxbe79y5M+np6YW3zp07F7tOkiRJUjDC4XDQESRJkiRJZe2NN6BNG0hLg8xM+N3vYM4cmDwZmjQJOp0UVRbXJUmSJEmSYsh3331XeH/IkCFFrm073nadJEmSpGCE/Mp3SZIkSZXIfvuV7roK54cfIDUVTjsN5s+P/Eb/+U/4+GM48cSg00llIiHoAJIkSZIkSZIkSZIkSZIkSYptq1eX7roKY8MGuPtueOAByM2FhAS45hoYPhzq1Ak6nVSmLK5LkiRJkiRJkiRJUhnaetK6J65LkiRJUgwrKICnn4ahQyEjIzLXo0ekwN6qVbDZpIDEBR1AkiRJkiRJpafVNm903n///UWubTtu5RuikiRJUuDC4XDQESRJkiRJ0fDxx3DCCXDhhZHS+iGHQHo6vPaapXVVap64LkmSJEmSFENGjRpFamoqALNmzWLWrFk7XSdJkiQpGBbWJUmSJFUGe/slUw0alG6OMrV8OQwbBk89FRnXrAm33goDB0JSUrDZpHLAE9clSZIkSZJiTHp6+j5dlyRJkiRJkiRJ2hd7W1oHWLWq9HKUmZwcuPdeOOyw/5XW+/WDhQvhhhssrUv/ZXFdkiRJkiQpBqWnp9Nqu6+abNWqlaV1SZIkSZIkSZIUVftSWq9wwmF4+WVo3RpuvBE2boRjj4UPP4SJE6Fx46ATSuVKQtABJEmSJEmSFB2jRo0KOoIkSZKkXcjJyQk6giRJkiSVqtIordeoAdnZ+/48UTd/PgwaBG+8ERmnpMA998AFF0Cc50pLxbG4LkmSJEmSFKNSU1N3mPPEdUmSJCl4BQUFAGRlZQWcRJIkSZLKn02bgk6wG5mZcPvt8Pe/w5YtUKUKDB4Mt9wCtWoFnU4q1/xIhyRJkiRJUgwqrrS+q3lJkiRJZWfjxo2AxXVJkiRJqlDy8+Hxx+HQQ+GBByKl9TPOgG++gVGjLK1LJWBxXZIkSZIkKcbsrpxueV2SJEkKTjgcLiysr1+/PtgwkiRJkqSSee89OPZYuPRSWLUKWraE11+H9PRIkV1SiVhclyRJkiRJiiHbl9LT09MLb7taJ0mSJKlsZGZmkpubC8CqVasIh8MBJ5IkSZKk8qV69aATbGPJEvjrX+HEE+Gzz6B2bRgzBubNgx49gk4nVTgW1yVJkiRJkmLU9mX17ceSJEmSyt7y5csL7+fm5rJ27doA00iSJElS6QmFSud5srNL53n2yebNcNddcPjhMHly5Dd3ySXw/fcweDBUqRJ0QqlCSgg6gCRJkiRJkiRJkiRVFtsW17eO69evH1AaSZIkSSodpVVaD/xLqcJheP55GDIEfv45MtexI4wbB+3bBxpNigWeuC5JkiRJkiRJkiRJZWTJkiUA1M2tW2QsSZIkSRVVaZTWq1cvB6X1r7+Gbt2gd+9Iaf2AAyKnrc+ZY2ldKiUW1yVJkiRJkmJUamrqLseSJEmSyt7ChQsBaJzTuMhYkiRJkiqbcPh/t+zsAIOsXQvXXAPt2sHbb0NSEtxyCyxYAH/5S+kdJy+JhKADSJIkSZIkqfSkp6cXKajvrKyenp5eVpEkSZIkbWPBggXEEUdKTgrf1vqWBQsWBB1JkiRJkiqnLVvgscfg1lsj5XWAXr3gvvugefNgs0kxyhPXJUmSJEmSYszuSumW1iVJkqRgFBQUsHDhQmrm1aRqQVWSCpKYP39+0LEkSZIkqfKZORPat4cBAyKl9datYcYM+M9/LK1LUWRxXZIkSZIkKQbtrJxuaV2SJEkKzsKFC9m8eTO1t9QGoHZebZYsWcKaNWsCTiZJkiRJlcQvv8Cf/gQnnwzz5kHduvD3v8Pnn0PXrkGnk2JeQtABJEmSJEmSFB2W1CVJkqTy5eOPPwagfl59AOrl1WNV0io++eQTTjvttCCjSZIkSdJeC4chFNrzx5SpTZtg1Ci4917YvBni4uCKK+COO6B+/TIOI1VenrguSZIkaZ+NHDmSY445hlq1atGwYUPOOussFixYUGTN5s2bGTBgAPXr16dmzZr07t2bFStWBJRYkiqH1NTUHW6SJEmSgvPJJ58AUC+3HgD1c+sXmZckSZKkyqBMS+vhMDzzDLRsGSmpb94MXbpETlh/+GFL61IZs7guSZIkaZ/NmjWLAQMG8OGHHzJ9+nTy8vLo3r072dnZhWsGDx7MSy+9xHPPPcesWbNYtmwZvXr1CjC1JMW2nZXULa9LkiRJwSgoKODjjz6men51qhVUA6D2ltokhBP46KOPAk4nSZIkSXvv5JNLvrZMS+uffw6dO8O558Kvv8KBB8Jzz8Hbb0ObNmUYRNJWCUEHkCRJklTxvf7660XGEydOpGHDhsydO5dOnTqRmZnJE088weTJk+natSsATz75JK1ateLDDz/k+OOPDyK2JMWs3ZXTU1NTSU9PL6M0kiRJkgAWLlzI+sz1NMltUjgXRxx1c+vy/fffs2bNGup70p8kSZKkCmjmzKATbGfVKrjlFvjnPyNN+WrVYOhQuP76yH1JgakQJ64//PDDHHTQQVStWpXjjjuOjz/+eJfrx44dy+GHH061atVo2rQpgwcPZvPmzWWUVpIkSVJmZiYA9epFvvZ67ty55OXl0a1bt8I1LVu2pFmzZnzwwQeBZJSkWLV9aT09Pb3wtqt1kiRJkqLrww8/BKB+btFy+taxp65LkiRJ0j7Ky4Nx4+Cww+CxxyKl9T//GebPh+HDLa1L5UC5L64/88wzpKWlcdttt/HZZ5/Rtm1bevTowcqVK4tdP3nyZIYOHcptt93Gd999xxNPPMEzzzzDTTfdVMbJJUmSpMqpoKCAQYMG0bFjR1q3bg1ARkYGiYmJ1KlTp8jaRo0akZGRUezz5OTkkJWVVeQmSdoz25fVPWVdkiRJCk5hcT1vu+L6f8dbr0uSJEmS9sL06dCuHQwaBOvXR+7Png1TpkCzZsFmk1So3BfXx4wZw6WXXkr//v054ogjePTRR6levToTJkwodv37779Px44dOe+88zjooIPo3r07f/nLX3Z7SrskSZKk0jFgwAC+/vprpkyZsk/PM3LkSJKTkwtvTZs2LaWEkiRJUuUwcuRIjjnmGGrVqkXDhg0566yzWLBgQZE1mzdvZsCAAdSvX5+aNWvSu3dvVqxYEVBiSYpd4XCYuXPnUmNLDaoWVC1yrfaW2lQJV2Hu3LkBpZMkSZKkfdOlS+mu2yOLFsFZZ0H37vDtt9CgAfzjH/Dpp3DSSVF4QUn7olwX13Nzc5k7dy7dunUrnIuLi6Nbt2588MEHxT7mhBNOYO7cuYVF9R9//JFXX32V008/vUwyS5IkSZXZ1Vdfzcsvv8w777xDkyZNCudTUlLIzc1l/fr1RdavWLGClJSUYp9r2LBhZGZmFt5+/fXXaEaXJEmSYs6sWbMYMGAAH374IdOnTycvL4/u3buTnZ1duGbw4MG89NJLPPfcc8yaNYtly5bRq1evAFNLUmzKzMwkKyuLmvk1d7gWIkT1LdX5dfGvFBQUBJBOkiRJkvbNO++U7roS2bgRhg2DI46AF1+E+HgYOBAWLoTLLouMJZU7CUEH2JXVq1eTn59Po0aNisw3atSI+fPnF/uY8847j9WrV3PiiScSDofZsmULV1xxBTfddNNOXycnJ4ecnJzCcVZWVun8BiRJkqRKIhwOc8011/D8888zc+ZMmjdvXuR6+/btqVKlCjNmzKB3794ALFiwgMWLF9OhQ4dinzMpKYmkpKSoZ5ekWJaamkp6enqRsSSp8nj99deLjCdOnEjDhg2ZO3cunTp1IjMzkyeeeILJkyfTtWtXAJ588klatWrFhx9+yPHHHx9EbEmKSYsXLwagen71Yq9Xz69OZm4mq1at2uFno5IkSZJU3oVCZfhiBQXw73/DjTfC8uWRuVNPhbFjIyV2SeVauT5xfW/MnDmTESNG8Mgjj/DZZ58xbdo0XnnlFe68886dPmbkyJEkJycX3po2bVqGiSVJkqSKb8CAAfzrX/9i8uTJ1KpVi4yMDDIyMvjtt98ASE5O5uKLLyYtLY133nmHuXPn0r9/fzp06GAZRpJK2bZFdYiU1bfedrVOkhT7MjMzAahXrx4Ac+fOJS8vr8i3nrZs2ZJmzZrt9FtPJUl7Z8mSJcCui+uA3zgnSZIkqcLZk9L6PhfcP/kEOnaEvn0jpfWDD4YXXoA33rC0LlUQ5bq43qBBA+Lj41mxYkWR+RUrVpCSklLsY2699VYuuOACLrnkEo466ijOPvtsRowYwciRI3f61XrDhg0jMzOz8OYbQpIkSdKeGT9+PJmZmXTp0oXGjRsX3p555pnCNQ888AB//OMf6d27N506dSIlJYVp06YFmFqSYtfuSumW1iWp8ikoKGDQoEF07NiR1q1bA5CRkUFiYiJ16tQpsrZRo0ZkZGQU+zw5OTlkZWUVuUmSdi87OxuAhILivxC7SkEVADZu3FhmmSRJkiSpwsjIgIsugmOPhQ8/hBo1YORI+PZbOPPMMj7yXdK+KNfF9cTERNq3b8+MGTMK5woKCpgxYwYdOnQo9jGbNm0iLq7obys+Ph6AcDhc7GOSkpKoXbt2kZskSZKkkguHw8XeLrzwwsI1VatW5eGHH2bt2rVkZ2czbdq0nX4gVZK073ZWTre0LkmV04ABA/j666+ZMmXKPj2P32AqSXunfv36AOTE5RR7PSc+Mt+gQYMyyyRJkiRJ5V5uLtx3Hxx2GDz5ZGTuggtg4UIYOhSSkoLNJ2mPFf+R/nIkLS2Nfv368fvf/55jjz2WsWPHkp2dTf/+/QHo27cvBxxwACNHjgTgjDPOYMyYMRx99NEcd9xx/PDDD9x6662cccYZhQV2SZIkSZKkysCSuiQJ4Oqrr+bll19m9uzZNGnSpHA+JSWF3Nxc1q9fX+TU9V196+mwYcNIS0srHGdlZVlel6QS2G+//YBdFNf/O791nSRJkiRVeq+8AoMHw/ffR8bHHAMPPgjHHx9sLkn7pNwX1//85z+zatUqhg8fTkZGBu3ateP111+nUaNGACxevLjICeu33HILoVCIW265haVLl7LffvtxxhlncPfddwf1W5AkSZIkSQrEa6+9xvjx4wvHV155JT179gwwkSSpLIXDYa655hqef/55Zs6cSfPmzYtcb9++PVWqVGHGjBn07t0bgAULFrB48eKdfutpUlISSZ5kJUl7rGHDhgBsjt9c7PXNcZsJhUKFJ7NLkiRJUqW1YEGksP7aa5Fxo0YwciT06wfbdEUlVUzlvrgOkdNgrr766mKvzZw5s8g4ISGB2267jdtuu60MkkmSJEmSJJVPqampO8yNHz+e8ePHexK7JFUSAwYMYPLkybz44ovUqlWLjIwMAJKTk6lWrRrJyclcfPHFpKWlUa9ePWrXrs0111xDhw4dON6TqySpVO23337UrVOXdWvXESZMiFDhtXzyyaySySGHHEJCQoX48a0kSZIkFQqHIRTa/bqta3cqMxPuvBPGjYMtW6BKFRg0CG65BWrXLo2oksoBP34iSZIkSZIUY7YvraekpOzyuiQpNo0fP57MzEy6dOlC48aNC2/PPPNM4ZoHHniAP/7xj/Tu3ZtOnTqRkpLCtGnTAkwtSbEpLi6Ojid2ZHPcZjbGbyxybV2VdeSH8unUqVNA6SSp8vn1119ZsmRJ4fjjjz9m0KBBPPbYYwGmkiQptu20tF5QABMmwGGHwf33R0rrf/gDfP013HuvpXUpxlhclyRJkiRJiiGvbf3qTOD6668nPT2dxx57jPT0dK6//vpi10mSYlM4HC72duGFFxauqVq1Kg8//DBr164lOzubadOm7fCBJ0lS6dhaTF+VuKrI/KqkVUWuS5Ki77zzzuOdd94BICMjg1NPPZWPP/6Ym2++mTvuuCPgdJIkVSw9euzDuvffh2OPhYsvhpUrI+X1V1+Fl1+O3JcUcyyuS5IkSZIkxZDx48cX3j/ppJOKXNt2vO06SZIkSdF34oknEgqFCovqW61KXEW1atVo3759QMkkqfL5+uuvOfbYYwF49tlnad26Ne+//z7//ve/mThxYrDhJEmqYN58cy/WLV0K558PHTvC3LmRU9Xvuw+++gp69oxKTknlg8V1SZIkSZKkGLSz03Lr169fxkkkSZIkQWQv3qZNG9YmriUvlAdAdnw2GxM2ctJJJ5GYmBhwQkmqPPLy8khKSgLgrbfeIjU1FYCWLVuyfPnyIKNJkhTbNm+GESPg8MPh3/+GUChy2vrChTBkCPjvIinmWVyXJEmSJEmKQRkZGcXOr1mzpoyTSJIkSdqqW7duhAmzKjFy6vqKxBUAnHLKKUHGkqRK58gjj+TRRx9lzpw5TJ8+ndNOOw2AZcuW+aF/SZKiIsyZvABHHgk33wzZ2XDCCfDxx/D449CoUdABJZURi+uSJElSJbZixQouuOAC9t9/fxISEoiPjy9ykyRVPFdeeWXh/Tlz5hS5tu1423WSpPLFfbokxa6uXbsCsCJpReGvcXFxdOrUKchYklTpjBo1in/84x906dKFv/zlL7Rt2xaA9PR0jj322IDTSZJU/oVC/7vtTiu+5U268wJnw48/wv77w7/+Be++C7//ffTDSipXEoIOIEmSJCk4F154IYsXL+bWW2+lcePGhEryzoIkqVzr2bMn48ePB2D06NGMHj2a+vXr73DSes+ePYOIJ0kqAffpkhS7DjnkEJo0acLKxSvJJ5/1ietp17Yd9erVCzqaJFUqXbp0YfXq1WRlZVG3bt3C+csuu4zq1atH9bVnz57N6NGjmTt3LsuXL+f555/nrLPOKrweDoe57bbb+Oc//8n69evp2LEj48eP59BDD41qLkmSSqqkb1XVYR1/428M4GESyIekJBgyBIYNg5o1oxtSUrllcV2SJEmqxN59913mzJlDu3btgo4iSSpF6enppKamFo63L62np6eXdSRJ0h5wny5JsSsUCtG2bVteWfIKK5JWECbs3/eSFJBwOMzcuXNZtGgR5513HrVq1SIxMTHqxfXs7Gzatm3LRRddRK9evXa4fu+99/Lggw/y1FNP0bx5c2699VZ69OjBt99+S9WqVaOaTZKk3SlJaT2OfC7ln9zFLTQg8vOJ5zmLs7+9Hw4+OMoJJZV3FtclSZKkSqxp06aEw+GgY0iSoiA9PZ3XXnut8PR1gCuvvNKT1iWpAnCfLkmx7aijjuKVV17hl2q/FI4lSWXrl19+4bTTTmPx4sXk5ORw6qmnUqtWLUaNGkVOTg6PPvpo1F67Z8+eO31/JhwOM3bsWG655RbOPPNMACZNmkSjRo144YUXOPfcc6OWS5Kk3SlJaf0kZvMg19KOLwH4miMZxFhm0A0OAd/ykhQXdABJkiRJwRk7dixDhw7l559/DjqKJCkKevbsSXp6euHN0rokVQzu0yUptrVu3RqAdYnriowlSWVn4MCB/P73v2fdunVUq1atcP7ss89mxowZgeX66aefyMjIoFu3boVzycnJHHfccXzwwQc7fVxOTg5ZWVlFbpIklaWmLGYKf2Y2nWnHl6yjDtfwIO34IlJal6T/8sR1SZIkqZKpW7cuoW0+Dp+dnc0hhxxC9erVqVKlSpG1a9euLet4kqRSlJqausNcenp6AEkkSbvjPl2SKo9DDjmk8H5CQgJNmzYNMI0kVU5z5szh/fffJzExscj8QQcdxNKlSwNKBRkZGQA0atSoyHyjRo0KrxVn5MiR3H777VHNJklScaqxiesZzY2Mojq/kU8cj3EZt3Ina2gQdDxJ5ZDFdUmSJKmSGTt2bNARJElloLjS+tZ5y+uSVP64T5ekyqNOnTrEx8WTX5BP/Xr1iYvzS7IlqawVFBSQn5+/w/ySJUuoVatWAIn2zbBhw0hLSyscZ2Vl+cEoSVKUhfkTzzGa6zmQxQDMohPX8iDzaBtwNknlmcV1SZIkqZLp169f0BEkSVG2s9L6ttctr0tS+eI+XZIqj7i4OOrWq8vq1atpsJ8nEEpSELp3787YsWN57LHHAAiFQmzcuJHbbruN008/PbBcKSkpAKxYsYLGjRsXzq9YsYJ27drt9HFJSUkkJSVFO54kSQC04UvGMZAuzAJgMU25jvt4jj8BoV0/WFKl58f3JUmSpEosPj6elStX7jC/Zs0a4uPjA0gkSdpX25fW09PTC2+7WidJKj/cp0tS7Nt6mm+9evUCTiJJldP999/Pe++9xxFHHMHmzZs577zzOOigg1i6dCmjRo0KLFfz5s1JSUlhxowZhXNZWVl89NFHdOjQIbBckiQBhFet5hGu5DN+Rxdm8RtVuY2/0ZL5PMc57K60Hg6XTU5J5ZsnrkuSJEmVWHgn7w7k5OSQmJhYxmkkSaVt+7J6enq6hXVJqgDcp0tS7AuFIoWO6tWrB5xEkiqnJk2a8OWXXzJlyhTmzZvHxo0bufjii/nrX/9KtWrVovraGzdu5Icffigc//TTT3zxxRfUq1ePZs2aMWjQIO666y4OPfRQmjdvzq233sr+++/PWWedFdVckiTt1JYtMH48DB/OlawH4BnO4XpG8yvNSvQUltYlbWVxXZIkSaqEHnzwQSDyQ9LHH3+cmjVrFl7Lz89n9uzZtGzZMqh4kiRJUqXkPl2SKp8qVaoEHUGSKq2EhATOP//8Mn/dTz/9lJNPPrlwnJaWBkC/fv2YOHEiN9xwA9nZ2Vx22WWsX7+eE088kddff52qVauWeVZJknjrLRg0CL75BoAvaMtAxjGbziV+CkvrkrZlcV2SJEmqhB544AEgcpLjo48+Snx8fOG1xMREDjroIB599NGg4kmSJEmVkvt0Sao8tp64vu3f9ZKksjNp0qRdXu/bt2/UXrtLly47/ZYliPx/xB133MEdd9wRtQySJO3Wjz/CkCHwwguRcf36XLHmLv7JpRSw+3/HWFaXtDMW1yVJkqRK6KeffgLg5JNPZtq0adStWzfgRJKkaEhNTSU9Pb3IWJJUfrlPl6TKZ1fFRUlS9AwcOLDIOC8vj02bNpGYmEj16tWjWlyXJKlc27gRRo6E+++HnByIj4cBA+Bvf+Mf9XyvStK+s7guSZIkVWLvvPNO0BEkSaUsPT29SEF9Z2X1bQvtkqTyxX26JMU+C+uSFKx169btMPf9999z5ZVXcv311weQSJKkgIXDMHky3HADLFsWmevWDcaOhSOPDDSapNhicV2SJEmqZNLS0kq8dsyYMVFMIkmKlu3L68VdlySVL+7TJUmSpGAdeuih3HPPPZx//vnMnz8/6DiSJJWduXPh2mvh/fcj4+bNYcwYOPNMCIWCzSYp5lhclyRJkiqZzz//vETrQr4JIUkV2s7K65bWJal8cp8uSZIkBS8hIYFlW0+ZlSQp1q1YATffDBMmRE5cr1EDbroJ0tKgatUdlofDJeux+wVTknbF4rokSZJUybzzzjtBR5AklRFL6pJUcbhPl6TKKWyjQ5ICsf17JuFwmOXLl/PQQw/RsWPHgFJJklRGcnPh73+HO+6ArKzI3Pnnwz33wAEH7PRhnqcgqTRYXJckSZIkSZIkSZKkAPhNGpIUjLPOOqvIOBQKsd9++9G1a1fuv//+YEJJklQWXnsNBg+GBQsi4/bt4cEH4YQTdvmwPfmnSyjkqeuSds7iuiRJklTJffrppzz77LMsXryY3NzcItemTZsWUCpJUmm46KKLWL16deG4QYMGTJgwIcBEkqSScp8uSZIkRU9BQUHQESRJKlvffx8prL/ySmTcsCGMHAkXXghxcYFGk1S5+DeOJEmSVIlNmTKFE044ge+++47nn3+evLw8vvnmG95++22Sk5ODjidJ2gepqalFSusAq1evJjU1NaBEkqSScp8uSbHPk9YlSZIklYmsLLjhBjjyyEhpPSEBhgyBhQvhoossrUsqc564LkmSJFViI0aM4IEHHmDAgAHUqlWLcePG0bx5cy6//HIaN24cdDxJ0l7aXTk9NTWV9PT0MkojSdpT7tMlKfZZXJekspeWllbitWPGjIliEkmSykBBATz1FAwbBitWROZ69oQHHoDDDw82m6RKzeK6JEmSVIktWrSIP/zhDwAkJiaSnZ1NKBRi8ODBdO3aldtvvz3ghJKkPXXRRRcV3j/iiCO45557CsdDhw7l22+/LVw3YcKEMs8nSdo99+mSJElS6fv8889LtM4PF0mSKrwPP4Rrr4VPPomMDz0Uxo6F00/fq6fz/xollSaL65IkSVIlVrduXTZs2ADAAQccwNdff81RRx3F+vXr2bRpU8DpJEl7Y/Xq1YX3ty2tbx1vPY1923WSpPLFfbokSZJU+t55552gI0iSFF3LlsHQofD005FxrVowfHikxJ6YuFdPuTel9XB4r15KUiURF60n/vHHH6P11JIkSZJKSadOnZg+fToAf/rTnxg4cCCXXnopf/nLXzjllFMCTidJkiRVTu7TJSn2hf/b5Ajb6JAkSZK0r3Jy4J574LDD/lda798fFi6E666ztC6pXInaiestWrSgc+fOXHzxxfTp04eqVatG66UkSZIk7aWHHnqIzZs3A3DzzTdTpUoV3n//fXr37s0tt9wScDpJkiSpcnKfLkmVR2hvmiCSpFLx6aef8uyzz7J48WJyc3OLXJs2bVpAqSRJ2gPhMLz0EqSlwaJFkbnjj4cHH4Rjjtmnp97bf6qEQpbXJe1a1E5c/+yzz2jTpg1paWmkpKRw+eWX8/HHH0fr5SRJkiTthXr16rH//vsDEBcXx9ChQ0lPT+f++++nbt26AaeTJO2NBg0aFN4fOnRokWvbjrddJ0kqX9ynS5IkSdE1ZcoUTjjhBL777juef/558vLy+Oabb3j77bdJTk4OOp4kSbv37bdw2mlw5pmR0nrjxpHT1t97b59L65IUTVErrrdr145x48axbNkyJkyYwPLlyznxxBNp3bo1Y8aMYdWqVdF6aUmSJEl7YNGiRdxyyy385S9/YeXKlQC89tprfPPNNwEnkyTtjQkTJhTe//bbb0lNTS28ffvtt8WukySVP+7TJUmSpOgZMWIEDzzwAC+99BKJiYmMGzeO+fPnc84559CsWbOg40mStHPr18OgQdCmDbz5JiQmwrBhsHAhnH8+xEWtEipJpSLqf0slJCTQq1cvnnvuOUaNGsUPP/zAddddR9OmTenbty/Lly+PdgRJkiRJOzFr1iyOOuooPvroI6ZNm8bGjRsB+PLLL7ntttsCTidJ2lvp6en7dF2SFCz36ZIU+8LhMAA5OTkBJ5GkymnRokX84Q9/ACAxMZHs7GxCoRCDBw/mscceCzidJEnFyM+Hxx6DQw+FceMi4zPPjJy8PmIE1KwZdEJJKpGoF9c//fRTrrrqKho3bsyYMWO47rrrWLRoEdOnT2fZsmWceeaZ0Y4gSZIkaSeGDh3KXXfdxfTp00lMTCyc79q1Kx9++GGAySRJ+yo9PZ0GDRoUmWvQoIGldUmqANynS1Lsy8/PB2DdunUBJ5Gkyqlu3bps2LABgAMOOICvv/4agPXr17Np06Ygo0mStKM5c+CYY+Dyy2H1amjVKnLa+gsvwCGHBJ1OkvZIQrSeeMyYMTz55JMsWLCA008/nUmTJnH66acT99+vomjevDkTJ07koIMOilYESZIkSbvx1VdfMXny5B3mGzZsyOrVqwNIJEkqTRMmTAg6giRpL7hPl6TYt379+iK/SpLKVqdOnZg+fTpHHXUUf/rTnxg4cCBvv/0206dP55RTTgk6niRJEb/+CjfcAFOmRMbJyXD77XDVVVClSlRfOhyGUGjvHidJuxK14vr48eO56KKLuPDCC2ncuHGxaxo2bMgTTzwRrQiSJEmSdqNOnTosX76c5s2bF5n//PPPOeCAAwJKJUkqLampqTvMeeK6JJV/7tMlKbZt2bKFrKwsANasWRNwGkmqXL7++mtat27NQw89xObNmwG4+eabqVKlCu+//z69e/fmlltuCTilJKnS++03GD0a7rkncj8UgssugzvvhP32K5MIltYlRUvUiuvTp0+nWbNmhSesbxUOh/n1119p1qwZiYmJ9OvXL1oRJEmSJO3Gueeey4033shzzz1HKBSioKCA9957j+uuu46+ffsGHU+StA+KK61vnbe8Lknlm/t0SYptixYtIvzfRseaNWtYt24ddevWDTiVJFUObdq04ZhjjuGSSy7h3HPPBSAuLo6hQ4cGnEySJCLN7//8B667Dn75JTJ30knw4IPQrl2ZxbC0Lima4na/ZO8ccsghxX5l6dq1a3c4JUaSJElSMEaMGEHLli1p2rQpGzdu5IgjjqBTp06ccMIJniojSRXYzkrrJb0uSQqW+3RJim3ffPMNAEn5SUXGkqTomzVrFkceeSRDhgyhcePG9OvXjzlz5gQdS5IkmDcPunaFP/0pUlpv2hSmTIFZs8q0tL43LK1L2hNRK66Hd/K30caNG6latWq0XlaSJEnSHkhMTOSf//wnixYt4uWXX+Zf//oX8+fP5+mnnyY+Pj7oeJKkvbBtKb1u3bqkp6cX3rY9xdHyuiSVX+7TJSm2bS2qN93ctMhYkhR9J510EhMmTGD58uX8/e9/5+eff6Zz584cdthhjBo1ioyMjKAjSpIqmzVrYMAAOPpomDkTqlaF4cNh/nz485/37vhzSSrHEkr7CdPS0gAIhUIMHz6c6tWrF17Lz8/no48+ol05/wSQJEmSVNk0a9aMZs2aBR1DklTKnnrqqR3GFtYlqeJwny5JsSccDvPRRx8RRxxNfmvCDzV+4KOPPuLyyy8POpokVSo1atSgf//+9O/fnx9++IEnn3yShx9+mFtvvZXTTjuN9PT0oCNKkmLdli3wj3/ArbfCunWRuT/9CUaPhgMPDDabJEVRqRfXP//8cyDypstXX31FYmJi4bXExETatm3LddddV9ovK0mSJGkPbP3A6e6MGTMmykkkSZIkbeU+XZJi3zfffMP3339P482NqV5Qnbq5dXn//fdZvnw5jRs3DjqeJFVKLVq04KabbuLAAw9k2LBhvPLKK0FHkiTFurffhoED4euvI+M2bWDcOOjSJZA4HuouqSyVenH9nXfeAaB///6MGzeO2rVrl/ZLSJIkSdpHWz9wutW7775L+/btqVatWuFcyHcoJEmSpDLlPl2SYt9//vMfAJpsblL467rEdTz//PNcddVVQUaTpEpp9uzZTJgwgf/85z/ExcVxzjnncPHFFwcdS5IUq376Ca67DqZNi4zr1YO77oJLL4WEUq9ylsi+vtUUDpdODkmVR9T+tnvyySej9dSSJEmS9tHWD5xuVatWLSZPnszBBx8cUCJJUjT069ePp556qshYklR+uU+XpNi2efNmXnrpJaoWVKVBbgMAGuc05rvwd0z7zzSuuOIK4uLiAk4pSbFv2bJlTJw4kYkTJ/LDDz9wwgkn8OCDD3LOOedQo0aNoONJkmJRdjbccw+MHg05ORAfD1deCbffHimvB8TSuqQglGpxvVevXkycOJHatWvTq1evXa6dtvVTQ5IkSZIkSSo16enppKamArBu3brC+8WtkyRJklR2pkyZwoYNG2jxWwtCRBoiCeEEGm9uzK9LfuX111/n9NNPDzilJMW2nj178tZbb9GgQQP69u3LRRddxOGHHx50LElSrAqHYcoUuOEGWLIkMte1K4wbB61bBxqtNL7Ub+NGqFlz359HUuVSqh/ZT05OLvya0uTk5F3eJEmSJEmSFB27K6VbWpckSZLKVnZ2No8++ihVwlVovql5kWuHZB9CHHGMGzeOLVu2BJRQkiqHKlWqMHXqVJYsWcKoUaMsrUuSouezz+Ckk+C88yKl9YMOgmnT4K23Ai+tl5YLLgg6gaSKqFRPXH/yySeLvS9JkiRJkqSyte3J69vPS5IkSSpbkyZNYt26dRyWfRhVwlWKXKteUJ0mm5rw888/8+KLL9K7d++AUkpS7PN9EUlS1K1cCTffDE88ETlxvXp1uOkmGDIEqlYNOl2pWrQo6ASSKqJSLa5v67fffiMcDlO9enUAfvnlF55//nmOOOIIunfvHq2XlSRJklQC8+bNKzIOh8PMnz+fjRs3Fplv06ZNWcaSJJUyfxgrSRWL+3RJik0bNmzgiSeeIDGcyEG/HVTsmhabWrC0+lIeeughUlNTqVKlSrHrJEmSJJVTeXnw0ENw++2QmRmZO+88GDUKmjQJNluUHHJI0AkkVURRK66feeaZ9OrViyuuuIL169dz7LHHkpiYyOrVqxkzZgxXXnlltF5akiRJ0m60a9eOUChEOBwunPvjH/8IUDgfCoXIz88v0fPNnj2b0aNHM3fuXJYvX87zzz/PWWedVXj9wgsv5KmnnirymB49evD666/v+29GkrRTnrguSRWL+3RJik1Tpkxhw4YNHJ59OAnh4n88W7WgKk03NeXnZT/z6quvcuaZZ5ZxSkmSJEl77Y03YNAgmD8/Mv7d7+DBB6Fjx0Bj7UyrVqXzPE8/XTrPI6lyiVpx/bPPPuOBBx4AYOrUqaSkpPD555/zn//8h+HDh1tclyRJkgL0008/lerzZWdn07ZtWy666CJ69epV7JrTTjuNJ598snCclJRUqhkkSUUVV1rfOm95XZLKJ/fpkhR7cnNzeWriU1QJV6HZb812ubb5pub8Uv0XHn/8cVJTUwmFQmWUUpIkSdJe+eEHSEuDl16KjPfbD0aMgP79IT4+2Gw7UVr/zDjmGKhZs3SeS1LlErXi+qZNm6hVqxYAb775Jr169SIuLo7jjz+eX375JVovK0mSJKkEDjzwwFJ9vp49e9KzZ89drklKSiIlJaVUX1eSVLydlda3vW55XZLKH/fpkhR7XnzxRVatXsXBmw6mSrjKLtdWK6jG/r/tz8KFC5k9ezadO3cuo5SSJEmS9siGDXDXXfDAA5CXBwkJcO21MHw4JCcHnW6nSrO0/vHHpfNckiqfqBXXW7RowQsvvMDZZ5/NG2+8weDBgwFYuXIltWvXjtbLSpIkSdqNefPmlXhtmzZtSu11Z86cScOGDalbty5du3blrrvuon79+qX2/JKkiO1L69sW1Le9ZnldksoX9+mSFJuee+454ojjoN8OKtH6gzcdzNJqS3n22WctrktSKduT90F2dyiAJKmSKiiAp5+GoUMhIyMyd9ppkQJ7y5bBZtuNVq1K53k2bPCkdUn7JmrF9eHDh3PeeecxePBgTjnlFDp06ABETl8/+uijo/WykiRJknajXbt2hEIhwuHwLteFQiHy8/NL5TVPO+00evXqRfPmzVm0aBE33XQTPXv25IMPPiB+J1+Tl5OTQ05OTuE4KyurVLJIUmWy/Q9k09PT/cGrJJVT7tMlKfbk5ubyzTffUDuvNlULqpboMbXya1Etvxrzviz5B5okSSVz1llnFRlvv/8ObXMMbWntuSVJMeSjjyKnqm89arxFi0hh/Q9/KL2jzKNo/vy9f+xu3q6SpD0SteJ6nz59OPHEE1m+fDlt27YtnD/llFM4++yzo/WykiRJknbjp59+KvPXPPfccwvvH3XUUbRp04ZDDjmEmTNncsoppxT7mJEjR3L77beXVURJkiQpUO7TJSn2LFy4kC1btpCcl7xHj0vOSyZjVQYrVqygUaNGUUonSZVPQUFB4f233nqLG2+8kREjRhQexPjBBx9wyy23MGLEiKAiSpLKo+XLYdgweOqpyLhmTbj1Vhg4EJKSgs0mSRVQ1IrrACkpKaSkpBSZO/bYY6P5kpIkSZJ248ADDww6AgcffDANGjTghx9+2GkhZtiwYaSlpRWOs7KyaNq0aVlFlCRJksqU+3RJij1fffUVAMlb9rC4viWZDDL4+uuvLa5LUpQMGjSIRx99lBNPPLFwrkePHlSvXp3LLruM7777LsB0kqRyIScHxo2DO++EjRsjcxdeCCNGQOPGgUaTpIosasX17Oxs7rnnHmbMmMHKlSuLfHIV4Mcffyzxcz388MOMHj2ajIwM2rZty9///vddFuDXr1/PzTffzLRp01i7di0HHnggY8eO5fTTT9/r348kSZIUy7799lsWL15Mbm5ukfnU1NSovN6SJUtYs2YNjXfxpk5SUhJJnlIgSfskNTWV9PT0ImNJUsXhPl2SKraMjAwAqudX36PHbV2/9fGSpNK3aNEi6tSps8N8cnIyP//8c5nnkSSVI+EwvPwypKXBDz9E5o47Dh58ECrwob0tW8L8+UGnkKQoFtcvueQSZs2axQUXXEDjxo0JhUJ79TzPPPMMaWlpPProoxx33HGMHTuWHj16sGDBAho2bLjD+tzcXE499VQaNmzI1KlTOeCAA/jll1+K/QeHJEmSVNn9+OOPnH322Xz11VeEQiHC4TBA4f49Pz+/RM+zceNGftj6xg3w008/8cUXX1CvXj3q1avH7bffTu/evUlJSWHRokXccMMNtGjRgh49epT+b0qSKrn09PQihcadlRu3LbRLksoX9+mSFBvq1asHQF4ob48elxuXW+TxkqTSd8wxx5CWlsbTTz9d+O0WK1as4Prrr9/lQYqSpBg3fz4MGgRvvBEZp6TAqFFw/vkQFxdotL2xl5XNIv77tpQklZqoFddfe+01XnnlFTp27LhPzzNmzBguvfRS+vfvD8Cjjz7KK6+8woQJExg6dOgO6ydMmMDatWt5//33qVKlCgAHHXTQPmWQJEmSYtXAgQNp3rw5M2bMoHnz5nz88cesWbOGIUOGcN9995X4eT799FNOPvnkwnFaWhoA/fr1Y/z48cybN4+nnnqK9evXs//++9O9e3fuvPNOT2qUpCjZvrxe3HVJUvnlPl2SYkP9+vUByInL2aPH5YZyizxeklT6JkyYwNlnn02zZs1o2rQpAL/++iuHHnooL7zwQrDhJEllb/16uOMO+PvfYcsWSEyEwYPh5puhVq2g0+0VS+uSyquoFdfr1q27z6cA5ObmMnfuXIYNG1Y4FxcXR7du3fjggw+KfUx6ejodOnRgwIABvPjii+y3336cd9553HjjjcTHxxf7mJycHHJy/veGUVZW1j7lliRJkiqKDz74gLfffpsGDRoQFxdHXFwcJ554IiNHjuTaa6/l888/L9HzdOnSpfAUyOK8sfVUAklSmdlZed3SuiSVf+7TJSk27G1xfet6i+uSFD0tWrRg3rx5TJ8+nfnz5wPQqlUrunXrVvhNR5KkSiA/H558Em66CVatisydcQaMGQMtWgSbbR9YWpdUnkWtuH7nnXcyfPhwnnrqKapXr75Xz7F69Wry8/MLv5Zpq0aNGhX+w2F7P/74I2+//TZ//etfefXVV/nhhx+46qqryMvL47bbbiv2MSNHjuT222/fq4ySJElSRZafn0+t/54S0KBBA5YtW8bhhx/OgQceyIIFCwJOJ0naV5bUJalicp8uSbHh4IMPBiCzSuYePW59lfUkJSZxwAEHRCOWJOm/QqEQ3bt3p1OnTiQlJVlYl6TK5r334Npr4bPPIuOWLWHsWOjRI9BY+8rSuqTyLmrF9fvvv59FixbRqFEjDjroIKpUqVLk+mdb/8IvZQUFBTRs2JDHHnuM+Ph42rdvz9KlSxk9evROi+vDhg0r/IpUiJy4vvWroCRJkqRY1rp1a7788kuaN2/Occcdx7333ktiYiKPPfZY4Q9XJUkVlyeuS1LF5D5dkmJDo0aNOPTQQ/lx4Y8UUEAccbt9TE4oh6wqWXQ8piNVq1Ytg5SSVDkVFBRw99138+ijj7JixQoWLlzIwQcfzK233spBBx3ExRdfHHRESVK0LFkCN9wA//d/kXFyMvztbzBgAGzXcZQklb6oFdfPOuusfX6OBg0aEB8fz4oVK4rMr1ixgpSUlGIf07hxY6pUqUJ8fHzhXKtWrcjIyCA3N5fExMQdHpOUlERSUtI+55UkSZIqmltuuYXs7GwA7rjjDv74xz9y0kknUb9+fZ555pmA00mS9kVxpfWt85bXJal8c58uSbGjY8eOfP/996yrso76efV3u35N4prCx0mSoueuu+7iqaee4t577+XSSy8tnG/dujVjx461uC5Jsei33+D++2HkSNi0KXI0+SWXwF13QcOGQaeTpEojasX1nZ1uvicSExNp3749M2bMKCzCFxQUMGPGDK6++upiH9OxY0cmT55MQUEBcXGRUwsWLlxI48aNiy2tS5IkSZVZj22+6q5FixbMnz+ftWvXUrduXb8WVZIqsJ2V1re9bnldksov9+mSFDs6duzIxIkTWZW4qkTF9ZVJKwsfJ0mKnkmTJvHYY49xyimncMUVVxTOt23blvnz5weYTJJU6sJheP55GDIEfv45MtexIzz4IPzud4FGk6TKaPffR7cP1q9fz+OPP86wYcNYu3YtAJ999hlLly4t8XOkpaXxz3/+k6eeeorvvvuOK6+8kuzsbPr37w9A3759GTZsWOH6K6+8krVr1zJw4EAWLlzIK6+8wogRIxgwYEDp/uYkSZKkGJCZmVm4V9+qXr16rFu3jqysrIBSSZL2xfal9fT09MLbrtZJksoP9+mSFDuOP/54ateuzbJqywgT3uXaLWxhZdJKmjVrxuGHH15GCSWpclq6dCktWrTYYb6goIC8vLwAEkmSouKrr6BbN+jdO1JaP+AAmDwZ5syp0KX1UGjnN0kq76JWXJ83bx6HHXYYo0aN4r777mP9+vUATJs2rUjRfHf+/Oc/c9999zF8+HDatWvHF198weuvv06jRo0AWLx4McuXLy9c37RpU9544w0++eQT2rRpw7XXXsvAgQMZOnRoqf7+JEmSpFhw7rnnMmXKlB3mn332Wc4999wAEkmSStP2ZXVPWZekisF9uiTFjsTERHr27MnmuM2srbJ2l2tXJK1gS2gLZ555pt+wIUlRdsQRRzBnzpwd5qdOncrRRx8dQCJJUqlauxauvhratYO334akJLj1VliwAP7ylwrd8I529PCuP28rSfssIVpPnJaWxoUXXsi9995LrVq1CudPP/10zjvvvD16rquvvpqrr7662GszZ87cYa5Dhw58+OGHe/QakiRJUmX00UcfMWbMmB3mu3Tpws033xxAIkmSJEnu0yUptqSmpvLMM8+wtOpS6ufV3+m6ZVWXAXDGGWeUVTRJqrSGDx9Ov379WLp0KQUFBUybNo0FCxYwadIkXn755aDjSZL21pYt8NhjkZL61m+z690b7rsPDjoo0GilwdK6pFgQtRPXP/nkEy6//PId5g844AAyMjKi9bKSJEmS9kBOTg5btmzZYT4vL4/ffvstgESSJEmS3KdLUmz53e9+F/kZadUMtrDj3+8Am+M2szppNe3atePAAw8s44SSVPmceeaZvPTSS7z11lvUqFGD4cOH89133/HSSy9x6qmnBh1PkrQ3Zs6E3/0OBgyIlNZbt4YZM2DqVEvrJWBpXVJZiVpxPSkpiaysrB3mFy5cyH777Retl5UkSZK0B4499lgee+yxHeYfffRR2rdvH0AiSVJpSk1N3eVYklQ+uU+XpNgSFxdHr1692BLaQkbV4g/4WlJ1CWHC9OnTp4zTSVLls2XLFu644w6aN2/O9OnTWblyJZs2beLdd9+le/fuQceTJO2pX36BP/0JTj4ZvvoK6taFhx6Czz+Hrl2DTleuhcP/u0lSWUmI1hOnpqZyxx138OyzzwIQCoVYvHgxN954I717947Wy0qSJEnaA3fddRfdunXjyy+/5JRTTgFgxowZfPLJJ7z55psBp5Mk7Y309PQiBfWdldXT09PLKpIkaQ+5T5ek2NO7d28eeughfq36K002NylyLUyYJdWWUK1aNXr27BlQQkmqPBISErj33nvp27dv0FEkSfti0ya45x4YPRo2b4a4OLjiCrjjDqhfP+h0kqSdiNqJ6/fffz8bN25kv/3247fffqNz5860aNGCWrVqcffdd0frZSVJkiTtgY4dO/LBBx/QtGlTnn32WV566SVatGjBvHnzOOmkk4KOJ0naS7srpVtal6TyzX26JMWexo0bc+KJJ7IucR0b4zcWuba2ylo2xW/i9NNPp2bNmgEllKTK5ZRTTmHWrFlBx5Ak7Y1wGKZMgcMPhzvvjJTWu3SJnLD+8MOW1iWpnIvaievJyclMnz6d9957jy+//JKNGzfyu9/9jm7dukXrJSVJkiTthXbt2vHvf/876BiSpFK2/cnr285Lkso/9+mSFHt69erFnDlzWJ60nEM3HVo4v6zqMgDOPvvsoKJJUqXTs2dPhg4dyldffUX79u2pUaNGkes7+wY7SVLAPv8cBg6EOXMi4wMPhPvvh169IBQKNlspi7HfjiQVikpxvaCggIkTJzJt2jR+/vlnQqEQzZs3JyUlhXA4TMi/VSVJkqTAZGVlUbt27cL7u7J1nSSpYrKkLkkVh/t0SYp9nTt3JikxiYyqGYXF9QIKWJG0gv0a7Ef79u0DTihJlcdVV10FwJgxY3a4FgqFyM/PL+tIkqRdWbUKbrkF/vnPyInr1arBsGFw3XWR+zGmLOqV4XD0X0OSilPqxfVwOExqaiqvvvoqbdu25aijjiIcDvPdd99x4YUXMm3aNF544YXSfllJkiRJJVS3bl2WL19Ow4YNqVOnTrEfLN36gVPfnJekiu36669nwYIFhePDDz+c0aNHB5hIkrQz7tMlKfbVqFGDzl068+abb7IxfiM182uytspacuNy6XFaD+Li4oKOKEmVRkFBQdARJEklkZcHDz8Mf/sbZGZG5s49F+69F5o2DTRatFhalxTrSr24PnHiRGbPns2MGTM4+eSTi1x7++23Oeuss5g0aRJ9+/Yt7ZeWJEmSVAJvv/029erVA+Cdd94JOI0kKVqK+0rrBQsWkJqa6knsklQOuU+XpMqhR48evPnmm2QkZdBiUwtWJK0onJckBWPz5s1UrVo16BiSpO29+SYMGgTffRcZH300jBsHJ50UaKxosrQuqTIo9eL6//3f/3HTTTftUFoH6Nq1K0OHDuXf//63xXVJkiQpIJ07dy72viQpdhRXWt/+uuV1SSpf3KdLUuVw3HHHAZBZJXJa5Poq60mskki7du0CTCVJlU9+fj4jRozg0UcfZcWKFSxcuJCDDz6YW2+9lYMOOoiLL7446IiSVHktWgRpabD1PewGDWDECLjoIoiPDzZbBWE5XVJ5VurfNzdv3jxOO+20nV7v2bMnX375ZWm/rCRJkqS98Prrr/Puu+8Wjh9++GHatWvHeeedx7p16wJMJknaW9dff33h/ZNOOon09PTC20nbnESz7TpJUvniPl2SYtd+++1HSqMUMqtkUkABG6psoGWrliQmJgYdTZIqlbvvvpuJEydy7733Fvk7uHXr1jz++OMBJpOkSmzDBhg2DI44IlJaT0iInLj+/fdw6aWW1iUpRpR6cX3t2rU0atRop9cbNWrkG+uSJElSOXH99deTlZUFwFdffUVaWhqnn346P/30E2lpaQGnkyTtjQULFhTe376cvu1423WSpPLFfbokxbYjWx/J5rjNrElcQwEFHHnkkUFHkqRKZ9KkSTz22GP89a9/JX6bImTbtm2ZP39+gMkkqRIqKICnn4bDD4d77oHcXOjeHebNgwcegDp1gk4oSSpFCaX9hPn5+SQk7Pxp4+Pj2bJlS2m/rCRJkqS98NNPP3HEEUcA8J///IczzjiDESNG8Nlnn3H66acHnE6SJEmqnNynS1Jsa9WqFTNmzCAjKQOAli1bBpxIkiqfpUuX0qJFix3mCwoKyMvLCyDRjh5++GFGjx5NRkYGbdu25e9//zvHHnts0LF2Kj8f5syB5cuhcWM46aTdH468q8dsf+2EE+D994sfN2wYeUxGBqxaBfXrw5o1xf+6alXktmQJNGkC9erB2rWweDGEw5H+7OrV8NtvULVq5Hl/+w02b4akpMivOTmQnf2/30c4HJnfsiWSOz8/cn/r8+Xn/2+dpKJ+zyc8yLV04EMAfuAQ0hjDS2+eAUeEAk4nSYqGUi+uh8NhLrzwQpKSkoq9npOTU9ovKUmSJGkvJSYmsmnTJgDeeust+vbtC0C9evUKT3iUJEmSVLbcp0tSbKtXrx4A2fGRxluDBg2CjCNJldIRRxzBnDlzOPDAA4vMT506laOPPjqgVP/zzDPPkJaWxqOPPspxxx3H2LFj6dGjBwsWLKDh1pZ2OTJtGgwcGCmDb9WkCYwbB7167fljYMdr8fH/K4AXN5ZUsTQig5EMoz8TAdhATe7iFsYyiFyK7x2qZPyQjKTyrtSL6/369dvtmq1vskuSJEkK1oknnkhaWhodO3bk448/5plnngFg4cKFNGnSJOB0kqS9cfjhh7NgwQIARo8ezfXXX194bfTo0UXWSZLKJ/fpkhTb6tSpA8Cm+MiHlJKTkwNMI0mV0/Dhw+nXrx9Lly6loKCAadOmsWDBAiZNmsTLL78cdDzGjBnDpZdeSv/+/QF49NFHeeWVV5gwYQJDhw4NOF1R06ZBnz47FiWXLo3MT526Y3l9V4/p3bv419m+pG5pXaqYqpDLQMZxK3dSmw0APEVfhjGS5ewfcLqKz9K6pIqg1IvrTz75ZGk/pSRJkqQoeeihh7jqqquYOnUq48eP54ADDgDgtdde47TTTgs4nSRpb4wePZrU1FQA5syZw5w5c3a6TpJUPrlPl6TYVrt2bQA2x28uMpYklZ0zz/x/9u48PorCfuP4M7ub3SSb++YI941yCIJovSot2lZEq7XWk/rTakVFPNEKQrV4oCL1QK3Wq/VqFWNVPPBWBEVR5AbDnYTcd3azu/P7Y81KyEGAZCfH593XvHZndmb2yTbAmDz73dP0+uuva+7cuXK73Zo1a5aOOOIIvf766/rFL35haTav16uVK1dq5syZoW02m00TJ07UsmXLLEzWkN8fnIzeWFHSNCXDkKZPl047LTghvSXHAOi8fqU3dL+u0SBtkiSt0JG6Sgu1XEdZnKxz4O9QAB1FqxfXAQAAAHQcvXr1anR6zP33329BGgBAa8nKygqV15t6HADQfnGdDgCdW1RUVL316Ohoi5IAQNd27LHH6t1337U6RgMFBQXy+/1KT0+vtz09PV3r169v9BiPxyOPxxNaLysra9OMdT75RNq5s+nHTVPasSO43wkntOwYAJ3PIG3Q/bpGv9JbkqRcpesm3alndIFM2SxO13FRVAfQUfE3PwAAANCFHX/88XrmmWdUXV1tdRQAQCvLysrS4MGD620bPHgwpXUA6AC4TgeAzs0wDKsjAAA6mXnz5ik+Pj60ZGZmhuV5c3IOfL+WHgOg44tTqebrWn2vw/QrvSWvInS3rtcgbdTTuojSOgB0UUxcBwAAALqw0aNH67rrrtOVV16p3/3ud7r44ot11FF8HB8AdBb33HOP1REAAAeB63QAAACg9SUmJrb4zUNFRUVtnKZpKSkpstvtysvLq7c9Ly9PGRkZjR4zc+ZMzZgxI7ReVlYWlvJ6t24Hvl9LjwHQcRkKaKr+qb/pZqVrjyTpdf1G1+pebdIgi9MBAKxGcR0AAADowhYsWKD58+crKytLTz/9tI477jgNGDBAf/zjH3X++ec3+ChSAEDHMnny5AbbmLgOAO0f1+kAAABA61uwYEHofmFhoW6//XZNmjRJEyZMkCQtW7ZMb7/9tm699VaLEgY5nU6NGTNGS5cu1ZQpUyRJgUBAS5cu1bRp0xo9xuVyyeVyhTFl0LHHSj17Srt2SabZ8HHDCD5+7LEtPwZAxzZBn2uhrtJYrZQkrddgTdcCva2TLU4GAGgv+LwNAAAAoItzOBw644wz9Nprr2nnzp36wx/+oFtvvVWZmZmaMmWK3n//fasjAgAOQmOl9ea2AwDaF67TAaDzMvdp6e27DgBoGxdeeGFo+eyzzzR37lw9//zzuuqqq3TVVVfp+eef19y5c/XRRx9ZHVUzZszQ448/rqefflrr1q3T5ZdfrsrKSk2dOtXqaPXY7dIDDwTv7zvMvm59wYLgfgdyTGOPAWjfumuXntV5+lzHaKxWqlRxmqF7NULfUVpvA/wnBICOjOI6AAAAAEnSihUrNHv2bN17771KS0vTzJkzlZKSot/85je67rrrrI4HADgA+yunU14HgI6D63QA6Hz2LaoHAgGLkgBA1/X222/r5JMbFilPPvlkvffeexYkqu/ss8/W/PnzNWvWLI0aNUqrVq3SkiVL2uWnL51xhvSf/0g9etTf3rNncPsZZxzYMf/9b3DZ97G9y++NrQOwhks1ull3aKMG6Tz9SwEZ+ocu1iBt1P2aoVo5rY7Y6VBaB9DROawOAAAAAMA6e/bs0bPPPqt//vOf2rRpk0499VQ9//zzmjRpkowfx5lcdNFFOvnkkzV//nyL0wIAWmLfUnpWVlajj02ePLneYwCA9oPrdADo3Px+f711iusAEH7Jycl67bXXdO2119bb/tprryk5OdmiVPVNmzZN06ZNszpGi5xxhnTaadInn0g5OVK3btKxxzZfLt/fMfs+dvTR0uefN76elhY8JjdXys+XkpOlwsLGb/Pzg8vOncGifFKSVFQkbd8eLIMGAlJBgVRdLUVGBs9bXS3V1EguV/DW45EqK3/6WkwzuN3nk/z+4OLz/XS+un/6KZuiczE1RYt1r65VP2VLkj7T0bpKC/W1xlicrfPi7xEAnQHFdQAAAKAL69mzp/r3768//vGPuuiii5SamtpgnxEjRujII4+0IB0A4FDtW0zPyspi2joAdABcpwNA57ZvUX3fIjsAoO3NmTNH//d//6cPP/xQ48ePlyQtX75cS5Ys0eOPP25xuo7JbpdOOKH1jmnssf2tAwiTNWukq6+Wli4NrvfoId19t4455xyt/PEN9wAANIXiOgAAANCFLV26VMcee2yz+8TFxemDDz4IUyIAAAAAXKcDQOdWW1tbb93n81mUBAC6rosuukhDhw7VwoUL9corr0iShg4dqk8//TRUZAcA7KO4WJo9W3r44eBHCbhc0nXXSTfdJMXEWJ0OANBBUFwHAAAAurD9lWEAAAAAhB/X6QDQudUV1+0Bu/w2v7xer8WJAKBrqa2t1Z/+9Cfdeuut+te//mV1HABo//x+6bHHpFtvlQoLg9tOP12aP1/q18/abACADsdmdQAAAAAA1snLy9P555+v7t27y+FwyG6311sAAB3b5MmTm10HALRPXKcDQOdWV1R3mMEZY/tOYAcAtK2IiAj997//tToGAHQMH30kHXGE9Oc/B0vrw4dL770nvfIKpXUAwEFh4joAAADQhV100UXavn27br31VnXr1k2GYVgdCQBwiLKysuoV1Jsqq2dlZYUrEgDgAHGdDgCdW2lpqSQpMhApj92jkpISawMBQBc0ZcoULV68WNdcc43VUQCgfdq2Tbr+eunll4PrCQnS3LnS5ZdLDiqHAICDx78iAAAAQBf26aef6pNPPtGoUaOsjgIAaEX7ltcbexwA0H5xnQ4AnduuXbskScneZJVGlGr37t0WJwKArmfgwIGaO3euPvvsM40ZM0Zut7ve41dddZVFyQDAYlVV0t13S3fdJdXUSDab9Kc/BUvrKSlWpwMAdAIU1wEAAIAuLDMzU6ZpWh0DANAGmiqvU1oHgPaP63QA6NzqiutJtUn6QT+E1gEA4fPEE08oISFBK1eu1MqVK+s9ZhgGxXUAXY9pBqerX3+9tH17cNvxx0sPPCCNHGltNgBAp0JxHQAAAOjCFixYoJtuukmPPvqo+vTpY3UcAEAro6QOAB0T1+kA0Llt375ddtOuhNoESdK2bdusDQQAXVB2drbVEQCg/Vi1Srr6aunjj4PrvXpJ8+dLZ54pGYal0QAAnQ/FdQAAAKCLSUxMlLHXD5kqKyvVv39/RUdHKyIiot6+RUVF4Y4HAAAAdElcpwNA11BQUKA1a9YosTZRTtMpt9+t5cuXy+PxyOVyWR0PALqcgoICSVJKSorFSQDAAgUF0l/+Ij3+uBQISFFR0o03BqeuR0dbnQ4A0ElRXAcAAAC6mAULFlgdAQAQJrfccotWr14dWj/88MN1xx13WJgIANAUrtMBoGtYunSpTNNUhidDkpRek64f7D/o888/14knnmhxOgDoGkpKSnTLLbfoxRdfVHFxsaTgG0l///vf6/bbb1dCQoK1AQGgrdXWSo88Is2eLZWUBLf97nfSPfcEp60DANCGKK4DAAAAXcyFF14ov9+v+fPnKysrS16vVyeddJJmz56tqKgoq+MBAFrJ5MmTG2xbvXq1Jk+erKysLAsSAQCaw3U6AHQN77zzjiQp3ZMuScrwZOgH9w965513KK4DQBgUFRVpwoQJ2rVrl84991wNHTpUkrR27Vo99dRTWrp0qT7//HMlJiZanBQA2sh770lXXy2tXRtcHzlSWrhQOu44a3MBALoMm9UBAAAAAITf3/72N918882KiYlRjx499MADD+iKK66wOhYAoJU0Vlo/kMcBANbgOh0AOrfdu3dr2bJliq+NV1Qg+KakeF+8ovxReuedd1ReXm5xQgDo/ObOnSun06ktW7bo0Ucf1fTp0zV9+nQ99thj2rx5syIiIjR37lyrYwJA6/vhB+n006Vf/CJYWk9OlhYtklaupLQOAAgriusAAABAF/TMM8/o4Ycf1ttvv63Fixfr9ddf17/+9S8FAgGrowEADtEtt9wSun/SSScpKysrtJx00kmN7gcAaB+4TgeAzu2xxx6T3+9Xn6o+oW2GDPWu6q2Kigo9++yz1oUDgC5i8eLFmj9/vtLT0xs8lpGRobvvvluvvvqqBckAoI1UVEg33ywNHSotXizZ7dJVV0mbNkl/+lNwHQCAMKK4DgAAAHRB27dv169+9avQ+sSJE2UYhnbv3m1hKgBAa1i9enXo/tVXX13vsb3X994PANA+cJ0OAJ1Xbm6u/vOf/8jtd6u7p3u9x3pV95LTdOqf//ynKioqLEoIAF1DTk6Ohg8f3uTjhx12mHJzc8OYCADaiGlKzz0nDR4szZsneb3SxInSt99KDzwgJSZanRAA0EVRXAcAAAC6IJ/Pp8jIyHrbIiIiVFtba1EiAAAAAFynA0Dn9dhjj6m2tlYDKgfIkFHvMYcc6lvZV2VlZXruuecsSggAXUNKSoq2bt3a5OPZ2dlKSkoKXyAAaAtffSUdc4x0/vnS7t1S377Sq69K77wjNfPmHQAAwsFhdQAAAAAA4Weapi666CK5XK7QtpqaGl122WVyu92hba+88ooV8QAAAIAuiet0AOicioqK9J+X/6Nof7S61XRrdJ/e1b2V7c7W008/ralTp9b7twAA0HomTZqkW265Re+++66cTme9xzwej2699VadfPLJFqUDgEOUlyfdfLP0z38GJ6673dItt0jXXCPt80Z5AACsQnEdAAAA6IIuvPDCBtvOO+88C5IAAFrb4YcfrtWrV0uSHnjgAV199dWhxx544IF6+wEA2heu0wGgc3rhhRfk8Xo0rGqYbE18ILbDdCizKlNbjC16/fXXdeaZZ4Y5JQB0DXPnztXYsWM1cOBAXXHFFRoyZIhM09S6dev08MMPy+Px6Nlnn7U6JgAcGK9X+vvfpblzpbKy4LbzzpPuvFPq0cPabAAA7MMwTdO0OkR7U1ZWpvj4eJWWliouLs7qOGFX7inXypyVWl+wXjaj8R+e7evSMZe2cSoAAIDOq6tff7YUrxMAtNzkyZP3u09WVlYYkgBAx9UZrj8//vhj3XPPPVq5cqVycnL06quvasqUKaHHTdPU7Nmz9fjjj6ukpETHHHOMHnnkEQ0cOLDFz9EZXicAaEter1cnnnCiSgtKdWLBiXKYTc8Vq7HV6MOUD9V/YH+9/vrrMgwjjEkBoGNojevP7Oxs/fnPf9Y777yjusqMYRj6xS9+oQcffFADBgxozciW4Dod6ELefDM4UX3jxuD62LHSwoXShAnW5gIAdCkHcv3ZslYyupRL/3epTnz6RD3+9eOq9ddaHQcAAAAAAByg/ZXSKa0DQNdQWVmpkSNH6qGHHmr08bvvvlsLFy7UokWLtHz5crndbk2aNEk1NTVhTgoAndfbb7+tgsICZVZlNltal6TIQKS6VXfTpk2btGLFijAlBICup2/fvnrrrbdUUFCgL774Ql988YXy8/O1ZMmSTlFaB9BFbNwo/frXwWXjRiktTXriCWn5ckrrAIB2jeI66tlRukMvr3lZkvR1ztd68MsHVePjlxQAAAAAAHQ0WVlZOvzww+ttO/zwwymtA0AXcsopp+j222/X6aef3uAx0zS1YMEC/eUvf9Fpp52mESNG6JlnntHu3bu1ePHi8IcFgE5q3bp1kqQMT0aL9k/3pNc7DgDQdhITEzVu3DiNGzdOSUlJVscBgJYpK5Ouv1467LDgtPWICOm664Ll9T/+UbJRBwQAtG/Nv60fXc6jKx+V3/RrSMoQZRdna33Bet237D5dPf5quZ1uq+MBAAAAAIADcMcdd1gdAQDQTmVnZys3N1cTJ04MbYuPj9f48eO1bNky/f73v2/0OI/HI4/HE1ovKytr86wA0JHl5eVJCk5Tb4m6/eqOAwAAACRJgYD09NPSzJlS3bXir34l3X+/NGiQtdkAADgAvMUKIR6fR4+tfEySdMfP79CMCTPkjnBrW+k2Lc1eanE6AAAAtGcff/yxTj31VHXv3l2GYTSY0GiapmbNmqVu3bopKipKEydO1KZNm6wJCwBdyOTJkxssAABIUm5uriQpPT293vb09PTQY42ZN2+e4uPjQ0tmZmab5gSAjm7Pnj0yZMgZcLZo/7ri+p49e9oyFgAAADqSL76QjjoqOFE9Ly9YVH/jjeBCaR0A0MFQXEfIS2teUn5VvnrG9dTkwZPVJ6GPzhh6hiRpfcF6i9MBAACgPausrNTIkSP10EMPNfr43XffrYULF2rRokVavny53G63Jk2apJqamjAnBYCuo6mSOuV1AMChmDlzpkpLS0PLjh07rI4EAO1acXGx7KZdhowW7e8IOELHAQAAoIvbvVs6/3xpwgTpyy+l2Fjpnnuk1auD09YBAOiAKK4j5MEvH5QkXT72cjlswR+KDU4eLEnaWrJVHp+nyWMBAADQtZ1yyim6/fbbdfrppzd4zDRNLViwQH/5y1902mmnacSIEXrmmWe0e/fuBpPZAQCtY3/ldMrrAICMjAxJUl7dx4v/KC8vL/RYY1wul+Li4uotAICmDR8+XD7Dp3JHeYv2L3IWhY4DAABAF1VTI82bF5ym/txzwW1Tp0obN0rXXSc5W/ZpPgAAtEcU1yFJWrFrhVbsWiGn3an/O+L/QttTolOUGJkov+nXluItFiYEAABAR5Wdna3c3FxNnDgxtC0+Pl7jx4/XsmXLLEwGAJ3TvqX0rKys0NLcfgCArqVv377KyMjQ0qVLQ9vKysq0fPlyTZgwwcJkANC5/PznP5ck5Tnz9rNnUJ4rr95xAAAA6EJMU3rtNWn4cOnmm6XKyuC09RUrpCeflJp5ozkAAB0FxXVIkh796lFJ0tnDz1aaOy203TAMDU4JTl3fULjBkmwAAADo2HJzcyVJ6enp9banp6eHHmuMx+NRWVlZvQUAcGD2Lavvuw4A6NwqKiq0atUqrVq1SlLwTaWrVq3S9u3bZRiGpk+frttvv11ZWVlavXq1LrjgAnXv3l1TpkyxNDcAdCbHHHOMIiIiQoX05pgyle/KV1JSkkaMGBGGdAAAAGg31q6VJk2SpkyRfvhB6t5devZZ6bPPpCOPtDodAACthuI6JEkfbftIknTeiPMaPDY4+cfiegHFdQAAAITPvHnzFB8fH1oyMzOtjgQAAAB0KF999ZVGjx6t0aNHS5JmzJih0aNHa9asWZKkG264QVdeeaUuvfRSHXnkkaqoqNCSJUsUGRlpZWwA6FRiYmJ0zDHHqCyiTKWO0mb33ePcoxpbjSZOnCi73R6mhAAAALBUcbE0fbo0YoT07ruS0ynNnClt2CCdd55kGFYnBACgVVFch0prSrWleIskaWz3sQ0eH5Q8SJK0rXSbanw1Yc0GAACAji/jx48tzMurP1ksLy8v9FhjZs6cqdLS0tCyY8eONs0JAAAAdDYnnHCCTNNssDz11FOSgp+4OXfuXOXm5qqmpkbvvfeeBg0aZG1oAOiE/vjHP0qSNrs3N7mPKVOb3ZtlGIYuuuiiMCUDAACAZfx+6dFHpUGDpAceCK6fdlpw8vrf/ibFxFidEACANkFxHfo271tJUq/4XkqKSmrweEp0ipKjkhUwA9pc1PQP1AAAAIDG9O3bVxkZGVq6dGloW1lZmZYvX64JEyY0eZzL5VJcXFy9BQBwYCZPntzsOgAAAIC2N27cOI0ZM0Z5rjyV2csa3afAWaDSiFKdfPLJ6t+/f5gTAgAAIKw++UQaO1a67DKpoEAaOlR65x1p8WKJa0EAQCdHcR36OudrSdLojNFN7jM4ZbAkaUPhhrBkAgAAQMdSUVGhVatWadWqVZKk7OxsrVq1Stu3b5dhGJo+fbpuv/12ZWVlafXq1brgggvUvXt3TZkyxdLcANAZZWVl1VufPHlyaGluPwAAAABtwzAMXX755ZKkLe4tje6zJTq4/bLLLgtbLgAAAITZjh3S738vHXectGqVlJAQnLb+7bfSL35hdToAAMKC4jr0Te43kvZTXE8OFtc3FmwMSyYAAAB0LF999ZVGjx6t0aOD15QzZszQ6NGjNWvWLEnSDTfcoCuvvFKXXnqpjjzySFVUVGjJkiWKjIy0MjYAdFr7K6VTWgcAAADC62c/+5mGDh2qvMg8eQ1vvccq7BUqchbpuOOO05AhQyxKCAAAgDZTXS3NnSsNHiy9+KJkGNKf/iRt3ChddZUUEWF1QgAAwobiOvRNzo/F9W5NF9cHJQ+SJG0r3abq2uqw5AIAAEDHccIJJ8g0zQbLU089JSk4WWzu3LnKzc1VTU2N3nvvPQ0aNMja0ADQyTVVTqe0DgAAAISfYRj67W9/q4AC2hW5q95jOyN3SpLOPPNMK6IBAACgrZim9J//SEOHSrNnBwvsxx4rff21tGiRlJpqdUIAAMKuQxTXH3roIfXp00eRkZEaP368VqxY0aLjXnjhBRmGoSlTprRtwA6sxlejtflrJTU/cT0pKkmp0akyZWpz0eZwxQMAAAAAAIcgKyurwQIAAADAGr/5zW8UERGhnZE7ZcqUpGCRPWqXEuITdOKJJ1qcEAAAAK3mu++kn/9cOussads2KTNTeuEF6aOPpFGjrE4HAIBlHFYH2J8XX3xRM2bM0KJFizR+/HgtWLBAkyZN0oYNG5SWltbkcVu3btV1112nY489NoxpO57v93wvv+lXclSyesb1bHbfwcmDlV+Vr41FG3V4+uFhSggAAAAAAA7W5MmTG2yjvA4AAABYIzExUT//+c/19ttvq9JeqRh/jIoiiuSxefS7yb+T0+m0OiIAAAAOVWGhdOut0qOPSoGAFBkp3XCDdOONUnS01ekAALBcu5+4ft999+mSSy7R1KlTNWzYMC1atEjR0dF68sknmzzG7/fr3HPP1Zw5c9SvX78wpu14vsn5RpI0uttoGYbR7L69EnpJknaX727zXAAAAAAA4NA0VlpvbjsAAACAtjdmzBhJUoWjot5t3XYAAAB0UD6f9OCD0sCB0iOPBEvrZ50lrV8vzZlDaR0AgB+16+K61+vVypUrNXHixNA2m82miRMnatmyZU0eN3fuXKWlpeniiy9u0fN4PB6VlZXVW7qKb3J/LK5njN7vvt1iukmScspz2jQTAAAAAAA4NPsrp1NeBwAAAKyRmZkpSaqyVQVv7VX1tgMAAKADev99afRo6corpeJiacQI6YMPpJdeknr3tjodAADtSrsurhcUFMjv9ys9Pb3e9vT0dOXm5jZ6zKeffqonnnhCjz/+eIufZ968eYqPjw8tXekHQwdTXC+qLpLH52nTXAAAAAAA4ODsW0rPysoKLc3tBwAAAKDt9eoV/ITjusJ6tb263nYAAAB0INnZ0hlnSCedJH3/vZSUJD38sLRypXTCCVanAwCgXWrXxfUDVV5ervPPP1+PP/64UlJSWnzczJkzVVpaGlp27NjRhinbD3/Ar29zv5Ukje62/+J6rCtWMc4YmTKVV5nX1vEAAAAAAMAh2resvu86AAAAgPBKTk6WJHlsntBtRESE4uLirIwFAACAA1FZKf3lL9LQodKrr0p2uzRtmrRpk3T55ZLDYXVCAADarXb9r2RKSorsdrvy8uqXpPPy8pSRkdFg/y1btmjr1q069dRTQ9sCgYAkyeFwaMOGDerfv3+D41wul1wuVyunb/82FG5Qta9a0RHRGpg0sEXHdIvppk1Fm7S7fLd6xTP5AQAAAAAAAAAAAGipkpISSZLTdAZvA06V1JaosrJSbrfbwmQAAADYL9OUnn9euuEGadeu4Laf/1x64AHpsMOszQYAQAfRrieuO51OjRkzRkuXLg1tCwQCWrp0qSZMmNBg/yFDhmj16tVatWpVaJk8ebJOPPFErVq1SpmZmeGM3+59k/ONJGlk+kjZbfYWHdMtppskKbcit81yAQAAAAAAAAAAAJ1RQUGBJMkVcNW7rdsOAACAdurrr6Vjj5XOPTdYWu/bV3rlFem99yitAwBwANr1xHVJmjFjhi688EKNHTtW48aN04IFC1RZWampU6dKki644AL16NFD8+bNU2RkpA7b50IgISFBkhpsh/RNbrC4PjpjdIuP6RYbLK7nlOe0SSYAAAAAANB6Jk+erKysrHrrAAAAAKyTn58vSXL56xfX8/Pz1bt3b8tyAQAAoAl79ki33CI98URw4np0tHTzzdK110qRkVanAwCgw2n3xfWzzz5b+fn5mjVrlnJzczVq1CgtWbJE6enpkqTt27fLZmvXg+PbrVBxvdsBFNd/nLieU0FxHQAAAACA9igrK6teQb2psvrehXYAAAAA4bFmzRpJktvvDt763KHtY8eOtSwXAAAA9uH1Sg8+KM2ZI5WVBbf94Q/SXXdJPXtamw0AgA6s3RfXJWnatGmaNm1ao499+OGHzR771FNPtX6gTmJ9wXpJ0mFpLZ9GXzdxfU/lHtX6axVhj2iTbAAAAAAA4ODtW15v7HEAAAAA4ffZZ5/JLrsSaxMlScm1yaHtF154oZXRAAAAUGfJEmn6dGnDhuD6EUdICxdKxxxjaSwAADoDRpV3UZXeSu0u3y1JGpg0sMXHxbviFemIlClTeyr3tFU8AAAAAABwiJoqp1NaBwAAAKxRWFiodevWKdGTKLvskqTIQKRifDFasWKFvF6vxQkBAAC6uE2bpFNPlU45JVhaT02V/vEPacUKSusAALSSDjFxH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Of/aXP5McmkgMwA/eH4h+Li4tSpU6diTAgAAAAA12fNGslkKtzarl0LsSgnR5o9W4qKkk6fzpt7/HHp3XelqlVvOCeA0oviOgAAAICbFhMTI0kKCQmxmZ8zZ44GDBggSZo8ebIcHBwUHh4us9ms0NBQzZw5s4STAkD5cHl5Pb/zAIDyJTc3V8OHD1f79u3VpEkTSVJycrJcXFzk6+trs9bf39/6ZtTLmc1mmc1m6zg9Pb3YMgNAWZeSkiJJ8rrgdV3388rxsrk/AAAAAJQWhS2tF8rWrVJEhLR9e964aVNp+nSpY8cifBAApQ3FdQAAAAA3zWKxXHONm5ubZsyYoRkzZpRAIgBAQeV1SusAUD5FRERoz549+uGHH27qOtHR0Ro/fnwRpQIA+xYYGChJOu94Xsou/P3OOZyzuT8AAAAA2JWTJ/N2WJ89W7JYJG9v6fXX80rsTlRaAXvHf+UAAAAAAAB2ipI6AECSIiMj9dVXX2nTpk02JciAgABlZWUpNTXVZtf1lJQUBQQE5HutqKgojRw50jpOT09XUFBQsWUHgLLs4p+P5xzPXdf9Lq7nz1cAAAAAdiUnR/rwQ+mVV6S//86b69dPevttqYC/iwJgfxyMDgAAAAAAAIDisXv3boWFhVmP3bt3Gx0JAFCCLBaLIiMjtWzZMq1fv161atWyOd+qVSs5Oztr3bp11rnExEQlJSUpODg432u6urrK29vb5gAA5O9i8fyM05nrut9Zp7NydHAs8E1EAAAAAFDmbNkitWkjPfdcXmm9WTPp+++lefMorQPlDDuuAwAAAAAA2KGwsLAr5l555RVJ7MQOAOVFRESEFi5cqOXLl8vLy0vJycmSJB8fH1WoUEE+Pj4aOHCgRo4cKT8/P3l7e2vIkCEKDg5Wu3btDE4PAGVf5cqV1ahRI/2691eddTwrzxzPa97nlPMppTmn6e5Od8vZ2bkEUgIAAABAMTp+XHrpJWnOnLyxj4/05pvS4MGSE/VVoDxix3UAAAAAAAA7c3lp/c4777zqeQCAfYqJiVFaWppCQkJUtWpV67F48WLrmsmTJ+v+++9XeHi4OnbsqICAAC1dutTA1ABgP0wmk4YOHSqLLPrd4/dC3efiusjIyOKMBgDl3uHDh/XXX39Zx1u3btXw4cP14YcfGpgKAIDSz2Ip5LrsC9L06VL9+v+U1p96SvrtNykyktI6UI7xXz8AAAAAAIAd2b17t/X2W2+9pUaNGlnHe/fu1UsvvWRd17Rp0xLPBwAoOZZC/Euim5ubZsyYoRkzZpRAIgAof0JCQtS0aVPt3r1bdTPqyivHq8C1p5xP6bTLad1zzz1q0qRJCaYEgPLnscce06BBg/Tkk08qOTlZXbt2VePGjbVgwQIlJydrzJgxRkcEAKDMulM/Sq0jpJ078yZuv12aMUMKDjY2GIBSgR3XAQAAAAAA7Mgrr7xivX1paf3y8aXrAAAAABQPk8mkIUOGSJISPRMLXGeRRfs890mSdT0AoPjs2bNHbdq0kSR9/vnnatKkiTZv3qwFCxZo7ty5xoYDAKAU69Gj4HNVlKK56q8fdVdead3XN6+wvm0bpXUAVhTXAQAAAAAA7NCdd96Z73zr1q1LOAkAAABQvnXs2FFt27bVcdfjOul8Mt81R12PKs05TT179lTDhg1LOCEAlD/Z2dlydXWVJK1du1ZhYWGSpAYNGujYsWNGRgMAoFRbufLKOUdd0BC9r99UT/31iSTpvxoo/fab9NxzkqNjCacEUJpRXAcAAAAAALBDmzdvznd++/btJZwEAAAAKN9MJpNefvllmUwm/er1qyyy2Jy/oAtK9EqUq6urRo0aZVBKAChfGjdurFmzZun777/XmjVr1L17d0nS0aNHVbFiRYPTAQBQdnTQJu1QS72vYfJRurarldpqi57Rf6XKlY2OB6AUorgOAAAAAABgRyZMmGC9vXfvXptzl44vXQcAAACgeDVo0EB9+/bVGaczOux22ObcQfeDynTI1DPPPKOqVasalBAAype3335bH3zwgUJCQvToo4+qefPmkqTY2Fi1adPG4HQAAJR+ATqm+XpCm9RJzbRbp+Snf2uW2uonbVVbo+MBKMWcjA4AAAAAAACAotO0aVPr7ZdeekmS1Lp16yt2Wr90HQAAAIDiN2zYMC3/crkOuh9UUGaQTDIpV7n60+NP+fn5aeDAgUZHBIByIyQkRCdPnlR6erpuueUW6/ygQYPk7u5erI+9adMmvfvuu4qPj9exY8e0bNky9erVy3reYrFo7Nix+uijj5Samqr27dsrJiZGt912W7HmAgDgIpOp4HNOylakpmu8xspbZ5Qrkz7SM3pZ/9Fp/fOpJf//YSYAcAV2XAcAAAAAALAzsbGxNuPLS+uXnwcAAABQ/CpVqqTQ7qHKcMrQ385/S5JSXFOUZcpS7969i70oCQCwZbFYFB8frw8++EBnzpyRJLm4uBT7n8cZGRlq3ry5ZsyYke/5d955R++//75mzZqln376SR4eHgoNDVVmZmax5gIAQLp6ab2jNupn3a7JGilvndFW3aG2+kmD9YFNaV2Svv22mIMCKLPYcR0AAAAAAMAOxcbGavfu3XrllVescxMmTGCndQAAAMBAffr0UWxsrA67HZZftp8Oux2WJIWHhxucDADKlz///FPdu3dXUlKSzGazunbtKi8vL7399tsym82aNWtWsT12jx491KNHj3zPWSwWTZkyRa+++qoeeOABSdInn3wif39/ffnll3rkkUeKLRcAAAWV1qvqqN7TaD2mzyRJJ1VRL+ktfax/ycLeyQCuE8V1AAAAoBzLycnR3LlztW7dOh0/fly5ubk259evX29QMgBAUWjatCm7qwNAGcTrdACwX23atFH16tV15M8jqnuurk66nlTr1q1Vu3Zto6MBQLkybNgwtW7dWjt37lTFiv/sEPvggw/qmWeeMSzXwYMHlZycrC5duljnfHx81LZtW8XFxVFcBwAUm/xK607K1jBN1ViNl5fOKlcmzdJgvao39bf8rnq9V16RJkwoprAAyjSK6wAAAEA5NmzYMM2dO1f33XefmjRpItPVPvsNAFDmhIWFXTFHkR0ASj9epwOA/TKZTOrcubPmzp2rv9z+kiTdfffdBqcCgPLn+++/1+bNm+Xi4mIzX7NmTR05csSgVFJycrIkyd/f32be39/fei4/ZrNZZrPZOk5PTy+egACAcuNurdd0RaqRfpUkxamdIjRDP6tloe7/n/9QXAeQP4rrAAAAQDm2aNEiff7557r33nuNjgIAKGL5ldYvzlNeB4DSjdfpAGDf6tWrJ0k66nZUklS/fn0j4wBAuZSbm6ucnJwr5v/66y95eXkZkOjmREdHa/z48UbHAADYgVv1lyZqlB7W55KkE6qkF/SO5qm/LHIwOB0Ae8CfJAAAAEA55uLiorp16xodAwBQxAoqrRf2PADAWLxOBwD7drG4ft7xvM0YAFByunXrpilTpljHJpNJZ8+e1dixYw19A2lAQIAkKSUlxWY+JSXFei4/UVFRSktLsx6HDx8u1pwAAPvzf+zdd3iVhd3/8ffJBAKETViyh6ACLpxVK4rYijjqqFbEjeIALYoKKqIodeCgUq2I9OfjqFqMrUURdxkKiqhsZENYAUISss/vj1NSI0NGTu6M9+u67svc+4NPn3B7zud8Tzx5DOFRFtCJi3mDQmJ4lpvowCIm0N/SuqRS428TSZIkqQq7/fbbeeqppwiHw0FHkSSVkp+X0lNTU4uXvR0nSSo/fE6XpMrtpx9OqlWrFo0aNQowjSRVTY8//jj/+c9/6Ny5Mzk5Ofz+97+nVatWrFmzhkcffTSwXK1btyYlJYWpU6cWb8vIyGDmzJkcf/zxezwvMTGR2rVrl1gkSdpnU6YwlyN4lLuoSRb/4QSOYjY38yxbqXtAl7z77lLOKKnSiAs6gCRJkqSydf7555dY/+ijj/j3v/9Nly5diI+PL7Hv7bffLstokqRS9vOyempqqoV1SSqnfE6XpKqjevXqVKtWjZycHOrVq0coFAo6kiRVOc2bN+fbb7/ltddeY+7cuWRmZnL11Vdz2WWXUb169ajeOzMzkyVLlhSvL1u2jDlz5lCvXj0OOeQQbrvtNkaOHEn79u1p3bo1w4YNo2nTpvTt2zequSRJVdDKlTB4MLz1Fp2A9TRiCKP5G3846AnrDz1UOhElVT4W1yVJkqQqJjk5ucT6eeedF1ASSZIkSTv5nC5JVUtSUhI5OTkkJSUFHUWSqqy4uDguv/zyMr/vrFmzOO2004rXBw8eDEC/fv2YMGECQ4YMISsri+uuu46tW7dy0kknMXnyZKpVq1bmWSVJlVRuLjz+eKRdnp0NMTE8VTSQ+3iAbdQ56Mv7JYKS9sbiuiRJklTFvPTSS0FHkCRJkvQzPqdLUtVSs2ZNNm/ebHFdkgIyceLEve6/4ooronbvU089lfBeGn2hUIgRI0YwYsSIqGWQJFVhkyfDLbfA4sWR9ZNOgrFjua3rEQd96bvvdtK6pF9mcV2SJEmqwn7961/z9ttvU6dOnRLbMzIy6Nu3Lx999FEwwSRJpaJPnz6kpqaWWJcklX8+p0tS5RcbGwvg9FxJCsitt95aYj0/P5/s7GwSEhKoUaNGVIvrkiQFYsUKGDQI/vGPyHpKCvzpT3DZZRAK7delnKgu6WBYXJckSZKqsE8++YS8vLxdtufk5PD5558HkEiSdLBSU1NLFNT3VFb/aaFdklS++JwuSVXHzgK7JKlsbdmyZZdtixcvZsCAAfzxj38MIJEkSVGSkwOPPQYPPww7dkBsbGTi+v33Q+3aQaeTVAVZXJckSZKqoLlz5xb/PG/ePNLS0orXCwsLmTx5Ms2aNQsimiSpFPy8vL67/ZKk8sfndEmqOkL/nWgYExMTcBJJ0k7t27fnkUce4fLLL2fBggVBx5Ek6eC9916kpL50aWT9lFPg2WfhsMOCzSWpSrO4LkmSJFVB3bp1IxQKEQqF+PWvf73L/urVq/PMM88EkEySVFr2VF63tC5J5ZfP6ZJU9ThxXZLKl7i4ONauXRt0DEmSDs6yZXDbbbDz/YAmTeDxx+GSS+C/H6L9uXB4j7t2OU6SDobFdUmSJKkKWrZsGeFwmDZt2vDll1/SsGHD4n0JCQk0atTIN04lqRKwpC5JFYvP6ZJU9ThxXZKC8fPXTMLhMOvWrePZZ5/lxBNPDCiVJEkHaccOGD0aHnkEcnIgLi5SYB8+HGrV2uup+1Jal6TSYHFdkiRJqoJatmwJQFFRUcBJJEnR1Ldv3xK/62NiYpg0aVJwgSRJe+VzuiRJklQ2+vbtW2I9FArRsGFDfv3rX/P4448HE0qSpIPx7rtw662RaesAp50Gzz4LnTv/4qn7U1r/3e/g738/wIyShMV1SZIkqcrZn+m7ffr0iWISSVI07e53eFFREX369HESuySVQz6nS1LVEg6HgUhRUpJU9vywqCSp0li6NFJY/9e/IuvNmsHjj8NFF0VljPqbb5b6JSVVMRbXJUmSpCrm55Nk9iQUClFYWBjdMJKkqPilQqPldUkqf3xOl6SqyeK6JEmSpAOSnQ2PPAKjR0NuLsTHw6BBMGwY1KwZdDpJ2iOL65IkSVIV4yQZSarcflp8TElJ4fnnny9ev+6660hLSys+btKkSWWcTpK0Jz6nS5IkSdE1ePDgfT72iSeeiGISSZIOQjgM77wDt90GK1ZEtvXsCc88A506BRpNkvaFxXVJkiRJkqRK5KfFx5+W1neu75zGbkFSkiRJCl44HA46giRVGd98880+Hee3YUiSyq3Fi+GWW2Dy5Mh6ixbwxBNwwQVwAH9/HchfeRdeuP/nSNJPWVyXJEmSqrisrCw+/fRTVq5cSV5eXol9t9xyS0CpJEmSpKrN53RJkiSpdH388cdBR5Ak6cBkZcHDD8Njj0FeHsTHwx13wD33QFLSAV3yQD+n9fe/H9h5krRT1IrrP/74I23atInW5SVJkiSVgm+++Yazzz6b7OxssrKyqFevHps2baJGjRo0atTIQowkSZIUAJ/TJany2znNt7CwMOAkkiRJksqtcBjefhsGD4aVKyPbevWCp5+GDh0O+LIHWlr3C6MklYaYaF24Xbt2nHbaafy///f/yMnJidZtJEmSJB2EQYMGcc4557BlyxaqV6/OjBkzWLFiBUcddRSPPfZY0PEkSQcgJuZ/L/dcd911Jfb9dP2nx0mSyhef0yWp8gv/t/Hh+6iSFJxZs2YxZMgQLrnkEs4///wSiyRJgVu4MFJSv/DCSGn9kEMiJfZ//zuQ0jpAjRoHfq4k7RS1dyi//vprjjjiCAYPHkxKSgrXX389X375ZbRuJ0mSJOkAzJkzh9tvv52YmBhiY2PJzc2lRYsWjB49mrvvvjvoeJKkAzBp0qTin9PS0ujTp0/xkpaWttvjJEnli8/pklT5FRQUALBjx46Ak0hS1fTaa69xwgknMH/+fP7xj3+Qn5/PDz/8wEcffURycnLQ8SRJVVlmJtx1Fxx+OEyZAgkJcO+9MH8+nHfewTXPD5L/+SKpNEStuN6tWzeeeuop1q5dy/jx41m3bh0nnXQShx12GE888QQbN26M1q0lSZIk7aP4+PjiibuNGjVi5X+/Yi45OZlVq1YFGU2SdBBSU1MPar8kKVg+p0tS5bezsG5xXZKC8fDDD/Pkk0/y7rvvkpCQwFNPPcWCBQu46KKLOOSQQ4KOJ0mqisJh+Pvf4dBD4dFHIT8fzj4bfvgBHnzQceeSKo2ofyd0XFwc559/Pn//+9959NFHWbJkCXfccQctWrTgiiuuYN26ddGOIEmSJGkPunfvzldffQXAKaecwvDhw3nllVe47bbbOOywwwJOJ0k6GKmpqcWlx51iYmIsrUtSBeBzuiRVbnl5eaRvTgco8a1IkqSys3TpUn7zm98AkJCQQFZWFqFQiEGDBvH8888HnE6SVOXMnw9nnAEXXQSrV0OrVvDOO/DPf0K7dkGnk6RSFfXi+qxZs7jxxhtp0qQJTzzxBHfccQdLly5lypQprF27lnPPPTfaESRJkiTtwcMPP0yTJk0AeOihh6hbty4DBgxg48aNvjgvSZXApEmTSE1NLV4mTZoUdCRJ0j7wOV2SKrfly5dTWFQIwObNm9myZUvAiSSp6qlbty7bt28HoFmzZnz//fcAbN26lezs7CCjSZKqku3bYcgQOOIImDoVEhNh+HCYNw/69IFQKOiEJVSvHnQCSZVBXLQu/MQTT/DSSy+xcOFCzj77bCZOnMjZZ59dPOmrdevWTJgwgVatWv3itcaOHcuf/vQn0tLS6Nq1K8888wzHHnvsHo/funUr99xzD2+//Tbp6em0bNmSMWPGcPbZZ5fWH0+SJEmqFI4++ujinxs1asTkyZMDTCNJkiQJfE6XpMpuyZIlAMQWxVIYU8jSpUtL/O6XJEXfr371K6ZMmcLhhx/O7373O2699VY++ugjpkyZwumnnx50PElSZRcOw+uvw+23w9q1kW3nnANPPglt20b91gfah/ezXZJKQ9SK68899xxXXXUVV155ZfFkmJ9r1KgRL7744l6v8/rrrzN48GDGjRtHjx49GDNmDL169WLhwoU0atRol+Pz8vI444wzaNSoEW+++SbNmjVjxYoV1KlTpzT+WJIkSVKlU1BQwCeffMLSpUv5/e9/T61atVi7di21a9emZs2aQceTJB2EPn367LItNTU1gCSSpP3lc7okVV4//PADACm5Kaypvobvv//e4roklZHvv/+eww47jGeffZacnBwA7rnnHuLj45k2bRoXXHAB9957b8ApJUmV2g8/wM03w8cfR9bbtIGnn4bf/KZMbn+gpfVwuHRzSKq6olZcnzJlCoccckjxhPWdwuEwq1at4pBDDiEhIYF+/frt9TpPPPEE1157Lf379wdg3Lhx/Otf/2L8+PHcdddduxw/fvx40tPTmTZtGvHx8QD7NNVdkiRJqopWrFjBWWedxcqVK8nNzeWMM86gVq1aPProo+Tm5jJu3LigI0qSDtDuSus7t1tel6Tyzed0Saq8cnNzeeutt0gIJ9AhqwPrqq/j9ddfp1+/foQOtEEiSdpnRxxxBMcccwzXXHMNl1xyCQAxMTG77Z9IklSqMjLggQciJfWCAqhWDYYOhSFDIj+XgQP5T47q1Z20Lql0xfzyIQembdu2bNq0aZft6enptG7dep+ukZeXx+zZs+nZs2fxtpiYGHr27Mn06dN3e05qairHH388N910E40bN+awww7j4YcfprCwcI/3yc3NJSMjo8QiSZIkVQW33norRx99NFu2bKF69erF28877zymTp0aYDJJ0sHYU2l9X/dLkoLlc7okVV7/+te/2LJlCy2yW1C9qDpNdjThxx9/5D//+U/Q0SSpSvj000/p0qULt99+O02aNKFfv358/vnnQceSJFVm4TC88gp06gRPPBEprZ97LsybB8OHl1lp/UCEw5bWJZW+qBXXw3v4bojMzEyq7eMv202bNlFYWEjjxo1LbG/cuDFpaWm7PefHH3/kzTffpLCwkPfee49hw4bx+OOPM3LkyD3eZ9SoUSQnJxcvLVq02Kd8kiRJUkX3+eefc++995KQkFBie6tWrVizZk1AqSRJB+PnpfTU1NTiZW/HSZLKD5/TJalyCofDTJw4kRAhDtlxCACtdrQCYOLEiQEmk6Sq4+STT2b8+PGsW7eOZ555huXLl3PKKafQoUMHHn300T12USRJOiDffQenngqXXw7r1kG7dvDeezBpEuzj8F9JqmziSvuCgwcPBiAUCjF8+HBq1KhRvK+wsJCZM2fSrVu30r5tsaKiIho1asTzzz9PbGwsRx11FGvWrOFPf/oT9913327PGTp0aHFugIyMDMvrkiRJqhKKiop2++1Eq1evplatWgEkkiSVpp+X1VNTUy2sS1IF4HO6JFVOL774IvPnz6dJThOqF0W+USO5IJl6efX49NNPeeeddzj33HMDTilJVUNSUhL9+/enf//+LFmyhJdeeomxY8cybNgwzjrrrF1eU5Ekab9s2wb33QfPPguFhVC9OtxzD9x+e9QnrIdCUb28JB20Ui+uf/PNN0BkYsB3331XYiJMQkICXbt25Y477tinazVo0IDY2FjWr19fYvv69etJSUnZ7TlNmjQhPj6e2NjY4m2HHnooaWlp5OXl7TKhBiAxMZHExMR9yiRJkiRVJmeeeSZjxozh+eefByIfQM3MzOS+++7j7LPPDjidJEmSVDX5nC5Jlc8nn3zCY489RvWi6nTe3rnEvsO3H860etO49557adWqFV27dg0opSRVTe3atePuu++mZcuWDB06lH/9619BR5IkVVThMPztbzBkCOzsPJ5/PjzxBLRsGfXbW1qXVBGUenH9448/BqB///489dRT1K5d+4CvlZCQwFFHHcXUqVPp27cvEJk0M3XqVAYOHLjbc0488UT+7//+j6KiImJiYgBYtGgRTZo02W1pXZIkSarKHn/8cXr16kXnzp3Jycnh97//PYsXL6ZBgwa8+uqrQceTJEmSqiSf0yWpclmyZAmDBw0mJhzDkVuPJDFccqBWUmES3bd256u6X3HTjTfx1ttv0bhx44DSSlLV8tlnnzF+/HjeeustYmJiuOiii7j66quDjiVJqoi+/RZuugn+85/IeocO8MwzcOaZZXL70i6th8Olez1J2qnUi+s7vfTSS6VyncGDB9OvXz+OPvpojj32WMaMGUNWVhb9+/cH4IorrqBZs2aMGjUKgAEDBvDss89y6623cvPNN7N48WIefvhhbrnlllLJI0mSJFUmzZs359tvv+W1115j7ty5ZGZmcvXVV3PZZZdRvXr1oONJkg5Snz59Sny1dZ8+fQJMI0naVz6nS1LlsX79em644QaysrPovq07yQXJuz2uQX4DDt1+KPOYxw033MCECRNITt79sZKkg7N27VomTJjAhAkTWLJkCSeccAJPP/00F110EUlJSUHHkyRVNFu3wvDhMHYsFBVBjRowbBgMGgSJib94emlw0rqkiqRUi+vnn38+EyZMoHbt2px//vl7Pfbtt9/ep2tefPHFbNy4keHDh5OWlka3bt2YPHly8ZSBlStXFk9WB2jRogXvv/8+gwYN4ogjjqBZs2bceuut3HnnnQf+B5MkSZIqsbi4OC6//PKgY0iSSklqamqJgvqeyuo/LbRLksofn9MlqeJbvnw5/a/sz9p1a2mf2Z4muU32enzLHS3JjM1k3rx5XH755bz44os0atSojNJKUtXQu3dvPvzwQxo0aMAVV1zBVVddRceOHYOOJUmqiIqKYOJEuPNO2LAhsu13v4PHH4cWLYLNJknlWKkW15OTkwn99+M7pTkBYODAgQwcOHC3+z755JNdth1//PHMmDGj1O4vSZIkVTb7WlZ0Mq8kVUw/L6/vbr8kqfzxOV2SKo958+Zx1VVXsWXLFjpmdqRtdttfPCdEiC6ZXYghhkWLFnHpJZfy0oSXOOSQQ8ogsSRVDfHx8bz55pv89re/JTY2Nug4kqSK6uuvYeBAmD49st6pEzzzDPTsGWwuSaoASrW4/tJLL+32Z0mSJEnlS9++fUush0IhwuHwLtsKCwvLMJUkqTTtqbxuaV2Syi+f0yWpcpg5cyYDbhhAdnY2h2ccToucfZ+2GCLEoZmHklCUwKI1i7jk4ksY/9J4OnXqFMXEklR1+LqIJOmgbNkC994L48ZFJq4nJcF998Gtt0JCQtDpJKlCKNXi+k/t2LGDcDhMjRo1AFixYgX/+Mc/6Ny5M2eeeWa0bitJkiRpHxQVFZVYr1WrFt9++y1t2rQJKJEkKRp8M1aSKhaf0yWp4ps1axbXXH0NBfkFdNvWjSa5Tfb7GiFCtMtuR3xRPD/wA5dffjmvvvoq7du3j0JiSZIkSb+oqAheegnuugs2bYpsu+QSeOwxaNYs0GihUKC3l6T9FhOtC5977rlMnDgRgK1bt3Lsscfy+OOPc+655/Lcc89F67aSJEmSAvDZZ59xzjnn0LRpU0KhEJMmTSqx/8orryQUCpVYzjrrrGDCSlIV0qdPn10WSVLV4XO6JJWt5cuXc+OAGynIL+CorUcdUGn9p1rmtKTrtq5s376da6+5lo0bN5ZSUkmSJEn7bPZsOOEEuOaaSGm9c2f46CN49dVKW1r/2RcASlKpilpx/euvv+bkk08G4M033yQlJYUVK1YwceJEnn766WjdVpIkSVIAsrKy6Nq1K2PHjt3jMWeddRbr1q0rXl599dUyTChJVc+eSuqW1yWp6vA5XZLKTnp6Otdccw3bMrZxWMZhNMxrWCrXbZbbjI6ZHVmXto7rr7+e7OzsUrmuJEmSpF+weTPccAMccwzMnAk1a0YmrM+ZA6edFnQ6S+uSKqy4aF04OzubWrVqAfDBBx9w/vnnExMTw3HHHceKFSuidVtJkiRJAejduze9e/fe6zGJiYmkpKSUUSJJqtp+qZzep08fUlNTyyiNJCkoPqdLUtnIzc3lxhtvZNWqVbTPbE/znOalev022W3Ijs3mhx9+YPDgwYwdO5bY2NhSvYckSZKk/yoshBdfhKFDIT09su33v4c//QmaNg02239ZWpdUkUWtuN6uXTsmTZrEeeedx/vvv8+gQYMA2LBhA7Vr147WbSVJkiQdgFAoRChar3D81yeffEKjRo2oW7cuv/71rxk5ciT169eP6j0lqSr6eWn9pwX1n+6zvC5J5Z/P6ZJUMUyePJlvvvmGpjlNaZfdrtSvHyJEl+1d2BGzg48//pgvv/yS448/vtTvI0mV3f68DuI31klSFfXll3DTTTBrVmT9sMPg2WfhlFOCzVWKLKdLClrUiuvDhw/n97//PYMGDeL0008vfvHkgw8+oHv37tG6rSRJkqR9ULdu3RIFmMzMTLp3705MTEyJ49J3ThE4SGeddRbnn38+rVu3ZunSpdx999307t2b6dOn73FCWG5uLrm5ucXrGRkZpZJFkqqSn78hm5qa6huvklSO+ZwuSRXTokWLAGiZ3ZIQ0fnAUQwxtMhpwabETSxatMjiuiQdgL59+5ZYD4VChH/S3vvps3hhYWFZxZIklQebNkUmrL/4YqTZXbs2PPBApMQeHx90OkmqVKJWXL/wwgs56aSTWLduHV27di3efvrpp3PeeedF67aSJEmS9sGYMWPK9H6XXHJJ8c+HH344RxxxBG3btuWTTz7h9NNP3+05o0aN4oEHHiiriJIkSVLgfE6XpIpp2bJlACQVJkX1PjULapa4nyRp/xQVFRX//OGHH3LnnXfy8MMPF38YaPr06dx77708/PDDQUWUJJW1wkJ44QW4+27YsiWy7Q9/gNGjISUl2GySVElFrbgOkJKSQsrPfoEfe+yx0bylJEmSpH3Qr1+/QO/fpk0bGjRowJIlS/ZYiBk6dCiDBw8uXs/IyKBFixZlFVGSJEkqcz6nS1LF9OOPP5JQlEBCOCGq96lRWIMQIX788ceo3keSqoLbbruNcePGcdJJJxVv69WrFzVq1OC6665j/vz5AaaTJJWJGTMiE9W//jqyfsQRMHYs/OTvhvIiVEpf7DRtWulcR5IORtSK61lZWTzyyCNMnTqVDRs2lPjkKuALKpIkSVIVtnr1ajZv3kyTJk32eExiYiKJiYllmEqSKp8+ffqQmppaYl2SpD3xOV2SDkxiYiL5MflkxmZSs7Bm1O6zLX4bYcJUq1YtaveQpKpi6dKl1KlTZ5ftycnJLF++vMzzSJLK0IYNcNdd8NJLkfXkZHjwQRgwAOKiOgf4gJRWaR3gv18yIkmBitpv2muuuYZPP/2UP/zhDzRp0oRQaf4GlSRJknTA6tatu8/P5+np6ft0XGZmJkuWLCleX7ZsGXPmzKFevXrUq1ePBx54gAsuuICUlBSWLl3KkCFDaNeuHb169TqgP4Mkac9SU1NLFNT3VFb/aaFdkhQ8n9MlqeK67bbbuOGGG5hfcz7HbDsmKvcIE2ZezXkA3HzzzVG5hyRVJccccwyDBw/mb3/7G40bNwZg/fr1/PGPf+TYY48NOJ0kKSoKC2HcOLj3Xti6NbLtyivhkUfgv38XlDelWbkMh0vvWpJ0MKJWXP/3v//Nv/71L0488cRo3UKSJEnSARgzZkypX3PWrFmcdtppxeuDBw8GoF+/fjz33HPMnTuXl19+ma1bt9K0aVPOPPNMHnzwQSc1SlKU/Ly8vrv9kqTyxed0Saq4Tj31VE466SS++OILNiRsoFFeo1K/x+pqq8mIz+D888/n8MMPL/XrS1JVM378eM477zwOOeQQWrRoAcCqVato3749kyZNCjacJKn0TZsGN90Ec+ZE1rt1g7Fj4YQTgky1V6VVWp82zUnrksqXUDgcnc/StG7dmvfee49DDz00GpePqoyMDJKTk9m2bRu1a9cOOk5gnp/9/D4fe91R10UxiSRJUuXm8+e+8d+TJO2/3ZXXLa1L0r7x+XPf+O9JkiKWLl3KOeecQ2JeIiekn0BCOKHUrp0dk820+tOIT4rn/Q/ep2HDhqV2bUmqaErz+TMcDjNlyhQWLFgAwKGHHkrPnj33+ZuQyjOf0yXpv9avhzvvhJdfjqzXqQMPPQTXXw+xsYFG+yWl9deRk9YllYX9ef6M2sT1Bx98kOHDh/Pyyy9To0aNaN1GkiRJUinJyckhLy+vxDZf0Jakis2SuiRVfD6nS1LF0LZtW2688UaeeeYZZtWZxbFbjiWuFN6KzQvl8VXdr8gL5TFs6DBL65JUikKhEGeeeSa/+tWvSExMrBSFdUnSfxUUwJ//DMOHw7ZtkW1XXQWPPAI+U0tSoKJWXH/88cdZunQpjRs3plWrVsTHx5fY//XXX0fr1pIkSZL2UVZWFnfeeSdvvPEGmzdv3mV/YWFhAKkkSaXFieuSVDH5nC5JFdNNN93E2rVreeutt/gm+RuO2nYUMcQc8PUKKGBWnVlkxWZxww03cNFFF5ViWkmq2oqKinjooYcYN24c69evZ9GiRbRp04Zhw4bRqlUrrr766qAjSpIO1Oefw8CBMHduZP3II2HsWDjuuGBzSZKAKBbX+/btG61LS5IkSSolQ4YM4eOPP+a5557jD3/4A2PHjmXNmjX85S9/4ZFHHgk6niTpIOyutL5zu+V1SSrffE6XpIopFAoxYsQINm/ezCeffML3tb7n8O2HE2L/J/gWUcQ3yd+wNX4rF1xwAbfddlvpB5akKmzkyJG8/PLLjB49mmuvvbZ4+2GHHcaYMWMsrktSRbRuHQwZAv/v/0XW69aFUaPgmmsgNjbYbHvgl31IqoqiVly/7777onVpSZIkSaXk3XffZeLEiZx66qn079+fk08+mXbt2tGyZUteeeUVLrvssqAjSpIOwJ5K6z/db3ldksovn9MlqeKKi4vjySefpF+/fsydO5ekwiTaZrfd7+vMrzmfjYkbOeWUU3jggQcI2WiRpFI1ceJEnn/+eU4//XRuuOGG4u1du3ZlwYIFASaTJO23/Hx49lm47z7Yvj3SBr/mGnj4YWjQIOh0e1QWj/jhcPTvIUn768C/m24fbN26lb/+9a8MHTqU9PR0AL7++mvWrFkTzdtKkiRJ2kfp6em0adMGgNq1axc/t5900kl89tlnQUaTJB2gn5fWU1NTi5e9HSdJKj98Tpekiq1GjRr85S9/oUlKExbWXMiGhA37df6qaqtYUWMFHTt05MknnyQ+Pj5KSSWp6lqzZg3t2rXbZXtRURH5+fkBJJIkHZBPP4Ujj4TBgyOl9WOOgZkz4fnnLa1bWpdUTkWtuD537lw6dOjAo48+ymOPPcbWrVsBePvttxk6dGi0bitJkiRpP7Rp04Zly5YB0KlTJ9544w0gMuGxTp06ASaTJJWGn5fVnbIuSRWDz+mSVPHVq1ePPz/3ZxITE/k2+VsyYzP36bwtcVv4ofYP1Emuw9g/jyUpKSnKSSWpaurcuTOff/75LtvffPNNunfvHkAiSdJ+WbsWfv97OPVU+P57qF8/UlafMSNSXi/HLK1LquqiVlwfPHgwV155JYsXL6ZatWrF288++2wnwkiSJEnlRP/+/fn2228BuOuuuxg7dizVqlVj0KBB/PGPfww4nSRJklQ1+ZwuSZVD586defjhh8kP5fN1na8pCBXs9fjcUC7f1PkGYmDMU2No0aJFGSWVpKpn+PDhDBw4kEcffZSioiLefvttrr32Wh566CGGDx8edDxJ0p7k58Njj0HHjvDqq5EW+A03wMKFcO21EBO1OmS5Fg6XXCSpPIuL1oW/+uor/vKXv+yyvVmzZqSlpUXrtpIkSZL2w6BBg4p/7tmzJwsWLGD27Nm0a9eOI444IsBkkiRJUtXlc7okVR6//e1v+eGHHxg/fjxLayylY1bHPR67oOYCcmJyGHrnUI4//vgyTClJVc+5557Lu+++y4gRI0hKSmL48OEceeSRvPvuu5xxxhlBx5Mk7c5HH8HAgTB/fmS9Rw8YOxaOOirYXJKk/RK1jxglJiaSkZGxy/ZFixbRsGHDaN1WkiRJ0n6YOHEiubm5xestW7bk/PPPp1OnTkycODHAZJKk0tCnT5+9rkuSyief0yWpcrn11ltp2qQpy5KWkR2TvdtjtsZtZU31NXTp0oUrrriijBNKUtVSUFDAiBEjaN26NVOmTGHDhg1kZ2fzxRdfcOaZZwYdT5L0c6tXwyWXwOmnR0rrDRrAiy/CtGkVqrQeCkUWSarqolZc79OnDyNGjCA/Px+AUCjEypUrufPOO7nggguidVtJkiRJ+6F///5s27Ztl+3bt2+nf//+ASSSJB2s1NTUEut9+vQpXvZ2nCSp/PA5XZIql2rVqjHkziEUUcSCmgt22R8mzPyakamRd999NzExUXsLV5IExMXFMXr0aAoKCoKOIknam7w8GD0aOnWC11+HmBi46SZYtAiuuiqyXkFYWJek/4nab+/HH3+czMxMGjZsyI4dOzjllFNo164dtWrV4qGHHorWbSVJkiTth3A4TGg3r5SsXr2a5OTkABJJkkrDL5XSLa1LUvnmc7okVT5nnXUWRx99NGnV0tgSt6XEvvWJ69mSsIXevXtz9NFHB5RQkqqW008/nU8//TToGJKkPfnwQ+jaFe68E7Ky4PjjYdYsePZZqFs36HT7Jdql9XA4uteXpNIWF60LJycnM2XKFP7zn//w7bffkpmZyZFHHknPnj2jdUtJkiRJ+6h79+6EQiFCoRCnn346cXH/+0+DwsJCli1bxllnnRVgQknSwUpNTd1lyvrO7ZKk8snndEmqvEKhELfeeit/+MMfWF19NXW3/69ss6raKgBuvfXWoOJJUpXTu3dv7rrrLr777juOOuookpKSSuzf3WsqkqQysGoVDB4Mb74ZWW/UKDJ1/Q9/qFAT1neytC5Ju4pKcb2oqIgJEybw9ttvs3z5ckKhEK1btyYlJWWPk2IkSZIklZ2+ffsCMGfOHHr16kXNmjWL9yUkJNCqVSsuuOCCgNJJkkqLJXVJqlh8Tpekyu3oo4+mcePGpKWl0WV7F2KIIS+Ux6bETRx++OG0bt066IiSVGXceOONADzxxBO77AuFQhQWFpZ1JEmq2nJz4YknYORIyM6OlNQHDoQHHoA6dYJOVy5ZWpdUUZV6cT0cDtOnTx/ee+89unbtyuGHH044HGb+/PlceeWVvP3220yaNKm0bytJkiRpP9x3330AtGrViosvvphq1aoFnEiSFA1XXXUVmzZtKl5v0KAB48ePDzCRJGlvfE6XpMotJiaG3/zmN4wfP56NCRtpnNeYtMQ0woT57W9/G3Q8SapSioqKgo4gSdrp/ffh5pth8eLI+kknwbPPQteuweYqByynS6qMSr24PmHCBD777DOmTp3KaaedVmLfRx99RN++fZk4cSJXXHFFad9akiRJ0n7q168fAHl5eWzYsGGXF+sPOeSQIGJJkkrB7r7SetOmTfTp08dJ7JJUzvmcLkmV129/+1vGjx/P+sT1NM5rzPrE9QD07t074GSSVHXl5OT4oVFJCsKKFTBoEPzjH5H1xo3hT3+Cyy+HUCjYbJKkqCn14vqrr77K3XffvUtpHeDXv/41d911F6+88orFdUmSJKkcWLx4MVdddRXTpk0rsT0cDvt1qJJUge2utP7z/ZbXJan88jldkiqvTp06kZiQyPb87QBsj99Os2bNaNy4ccDJJKlqKSws5OGHH2bcuHGsX7+eRYsW0aZNG4YNG0arVq24+uqrg45Y4RQWwuefw7p10KQJnHwyxMYe+Dk/33fCCTBt2u7XGzWKnJOWBhs3Qv36sHnz7v+5cWNkWb0amjeHevUgPR1WroxMNi4qgk2bYMcO2Pl5hh07ICcHEhMj/8zNhays//05wuHI9oKCSO7CwsjPO6+38z/hnJwsRSSSwx08xt08TA12UEAsz3Az96+/n4wrksFaoSRVaqVeXJ87dy6jR4/e4/7evXvz9NNPl/ZtJUmSJB2AK6+8kri4OP75z3/SpEkTQk4vkKQK76qrrir+uXPnzjzyyCPF63fddRfz5s0rPm78+PFlnk+S9Mt8Tpekyis2NpbWbVqzZP4S8kP55MTk0LZt26BjSVKV89BDD/Hyyy8zevRorr322uLthx12GGPGjCkXxfWxY8fypz/9ibS0NLp27cozzzzDscceG3Ss3Xr7bbj11kgZfKfmzeGpp+D88/f/HNh1X2zs/wrgu1uXVDH05j2e5hbasRSAT/kVNzGWHzgs4GTljx92kVRZlXpxPT09fa8TARo3bsyWLVtK+7aSJEmSDsCcOXOYPXs2nTp1CjqKJKmUbNq0qfjnn5bWd67vnMb+0+MkSeWLz+mSVLm1bduWBQsWsDl+c/G6JKlsTZw4keeff57TTz+dG264oXh7165dWbBgQYDJIl5//XUGDx7MuHHj6NGjB2PGjKFXr14sXLiQRjvHi5cTb78NF164a8FyzZrI9jff3LW8vrdzLrhg9/f5eUnd0rpUsbRiGWO4jXOJfBPoWppwB4/xKpcCfmD/5yytS6rMYkr7goWFhcTF7bkPHxsbS0FBQWnfVpIkSdIB6Ny5s8VFSZIkqZzxOV2SKrcWLVoAkJ6QXmJdklR21qxZQ7t27XbZXlRURH5+fgCJSnriiSe49tpr6d+/P507d2bcuHHUqFGj3H17XmFhZDL67gqWO7fddlvJkvm+nCOp8qjGDobzAPPozLmkkk8cj3E7nVjAq/weS+u78nehpMqu1Ceuh8NhrrzyShITE3e7Pzc3t7RvKUmSJOkAPfroowwZMoSHH36Yww8/nPj4+BL7a9euHVAySZIkqeryOV2SKrfq1asDkB/KL7EuSSo7nTt35vPPP6dly5Yltr/55pt07949oFQReXl5zJ49m6FDhxZvi4mJoWfPnkyfPn235+Tm5pbo42RkZEQ9J8Dnn8Pq1XveHw7DqlWR4049dd/OkVR5/IZ/8hS30pYfAfiI0xjIs8ync8DJyicL65KqilIvrvfr1+8Xj7niiitK+7aSJEmSDkDPnj0BOP3000tsD4fDhEIhCv2uTUmqcBo0aFA8pfeuu+7ikUceKd531113lThOklQ++ZwuSZVbQkICAAWhghLrkqSyM3z4cPr168eaNWsoKiri7bffZuHChUycOJF//vOfgWbbtGkThYWFNG7cuMT2xo0bs2DBgt2eM2rUKB544IGyiFfCunX7f9y+niOp4mrDUsZwG+cQ+X26mmbczuO8wUU4YV2SVOrF9Zdeeqm0LylJkiQpSj7++OOgI0iSStn48ePp06cPAPPmzSv+eXfHSZLKJ5/TJalyKy6ux1hcl6SgnHvuubz77ruMGDGCpKQkhg8fzpFHHsm7777LGWecEXS8/TZ06FAGDx5cvJ6RkUGLFi2ift8mTfb/uH09R1LFU51s7uIRhjCaauSSTxxPMJgHGUYWNYOOJ0kqJ0q9uC5JkiSp4jjllFOCjiBJioLU1NQ9FtZ37pcklV8+p0tS5RYKOWVSksqDk08+mSlTpgQdYxcNGjQgNjaW9evXl9i+fv16UlJSdntOYmIiiYmJZRGvhJNPhubNYc0aCId33R8KRfaffPK+nyOpIgrTh1TGcButWQ7AFHpyM8+wkE7BRpMklTsxQQeQJEmSFKzPP/+cyy+/nBNOOIE1a9YA8Le//Y0vvvgi4GSSpIORmppKgwYNSmxr0KCBpXVJqiB8Tpekyis3NxeAuHBkxlheXl6QcSRJ5UxCQgJHHXUUU6dOLd5WVFTE1KlTOf744wNMtqvYWHjqqcjPP/9c1s71MWMix+3PObvbJ6l8assS/sVveIe+tGY5q2jOhfydM/nA0vp+8IM8kqoSi+uSJElSFfbWW2/Rq1cvqlevztdff138xum2bdt4+OGHA04nSTpY48ePJzU1tXgZP3580JEkSfvA53RJqtx2FtXjiiyuS1JZqlu3LvXq1dunJWiDBw/mhRde4OWXX2b+/PkMGDCArKws+vfvH3S0XZx/Prz5JjRrVnJ78+aR7eefv3/nvPVWZPn5vp+W33e3LqlsVSebB7mXH+jC2fybPOIZxV10YgFvcSHgp0/2laV1SVVNXNABJEmSJAVn5MiRjBs3jiuuuILXXnutePuJJ57IyJEjA0wmSSoNffr02WWbE9clqfzzOV2SKrfi4roT1yWpTI0ZM6b4582bNzNy5Eh69epVPMV8+vTpvP/++wwbNiyghP9z8cUXs3HjRoYPH05aWhrdunVj8uTJNG7cOOhou3X++XDuufD557BuHTRpAiefvPdy+S+d8/N9J5wA06btfr1Ro8g5aWmwcSPUrw+bN+/+nxs3RpbVqyNF+Xr1ID0dVq6MlEeLimDTJtixA6pVi1x3xw7IyYHExMg/c3MhK+t/f5ZwOLK9oAAKCyNLQcH/rldY+L/jpIovzHn8gycZREtWAvA+Z3ILT7OIjgFnq3j8vSCpKrK4LkmSJFVhCxcu5Fe/+tUu25OTk9m6dWvZB5IklZrdldZ3bre8Lknlm8/pklS5WVyXpGD069ev+OcLLriAESNGMHDgwOJtt9xyC88++ywffvghgwYNCiJiCQMHDiyRr7yLjYVTTy29c3a375fWJUXZokVw883wwQeR9UMOgTFj6NW3LwtDTliXJO2bmKADSJIkSQpOSkoKS5Ys2WX7F198QZs2bQJIJEkqDXsqre/rfklSsHxOl6TKLTc3F4D4ovgS65KksvP+++9z1lln7bL9rLPO4sMPPwwgkSSVY1lZMHQoHHZYpLSekAD33APz58N554GldUnSfrC4LkmSJFVh1157LbfeeiszZ84kFAqxdu1aXnnlFe644w4GDBgQdDxJ0gH4eSk9NTW1eNnbcZKk8sPndEmq3IqL62GL65IUlPr16/POO+/ssv2dd96hfv36ASSSpHIoHIa//x06dYJHHoH8fOjdG77/HkaOhBo1gk4oSaqA4oIOIEmSJCk4d911F0VFRZx++ulkZ2fzq1/9isTERO644w5uvvnmoONJkg7Sz8vqqampFtYlqQLwOV2SKrcdO3YA/5u4np2dHWQcSaqSHnjgAa655ho++eQTevToAcDMmTOZPHkyL7zwQsDpJKkcWLAAbr4Zdn4LRatWMGYM9OnjhHVJ0kGxuC5JkiRVYaFQiHvuuYc//vGPLFmyhMzMTDp37kzNmjWDjiZJkiRVWT6nS1LltmXLFgBqFtYssS5JKjtXXnklhx56KE8//TRvv/02AIceeihffPFFcZFdkqqk7dvhwQfhySehoAASE+HOO+Guu6B69aDTSZIqAYvrkiRJUhV01VVX7dNx48ePj3ISSZIkSTv5nC5JVUN6ejpx4TiqFVUDLK5LUlnLz8/n+uuvZ9iwYbzyyitBx5Gk8iEchtdfh9tvh7VrI9t++9vIlPW2bQONJkmqXCyuS5IkSVXQhAkTaNmyJd27dyccDgcdR5IUJX369CE1NbXEuiSp/PI5XZKqhvT0dOKL4okNxxIbjmXz5s1BR5KkKiU+Pp633nqLYcOGBR1FksqHH36Am2+Gjz+OrLdpA089FSmuS5JUyiyuS5IkSVXQgAEDePXVV1m2bBn9+/fn8ssvp169ekHHkiSVgtTU1BIF9T2V1X9aaJcklQ8+p0tS5bd+/XrWrVtHw4KGhAhRo6AGCxcuZMeOHVSvXj3oeJJUZfTt25dJkyYxaNCgoKNIUnC2b4cHHoiU1AsKoFo1GDoUhgyJ/CxJUhTEBB1AkiRJUtkbO3Ys69atY8iQIbz77ru0aNGCiy66iPfff9/JjpJUCfxSKd3SuiSVTz6nS1Ll9/F/p1g2ymtU/M/c3FymT58eZCxJqnLat2/PiBEjuPDCCxk1ahRPP/10iUWSKrVwGP7v/6BjR3j88Uhp/dxzYd48GD7c0rokKaqcuC5JkiRVUYmJiVx66aVceumlrFixggkTJnDjjTdSUFDADz/8QM2aNYOOKEk6CD+fvP7T7ZKk8svndEmq3KZOnQpAo9xIcb1xbmOWJi1l6tSp/PrXvw4ymiRVKS+++CJ16tRh9uzZzJ49u8S+UCjELbfcElAySYqy77+HgQPh008j6+3aRSaun312sLkkSVWGxXVJkiRJxMTEEAqFCIfDFBYWBh1HklRKLKlLUsXmc7okVS6rVq1i+vTp1M6vTfWi6gAkFySTWJTIh1M+5I477qBu3boBp5SkqmHZsmVBR5CksrVtG9x/PzzzDBQWQvXqcM89cPvtTliXJJWpmKADSJIkSQpGbm4ur776KmeccQYdOnTgu+++49lnn2XlypVOcZQkSZIC4nO6JFVOBQUF3HHHHeTn59Mmu03x9hAhWme1Zuu2rdxzzz2Ew+EAU0pS1bNp0yY2bdoUdAxJip5wGP72N+jYEcaMiZTWzz8f5s+PFNctrUuSypgT1yVJkqQq6MYbb+S1116jRYsWXHXVVbz66qs0aNAg6FiSpFI2bNgwvv322+L1rl278uCDDwaYSJK0Nz6nS1LlNXbsWObMmUOzHc1omtu0xL7WO1qzMXEjU6dO5bXXXuPSSy8NKKUkVQ1bt0Y+LPT666+zZcsWAOrWrcsll1zCyJEjqVOnTrABJam0fPstDBwIX3wRWW/fPjJxvVevYHNJkqq0UNiP7e8iIyOD5ORktm3bRu3atYOOE5jnZz+/z8ded9R1UUwiSZJUuQXx/BkTE8MhhxxC9+7dCYVCezzu7bffLpM8+8LndEnaP3369NnjvtTU1DJMIkkVk8/p+8bndEn6ZV9++SX9ruhHtYJqnJh+IvHh+F2OyYnJ4Yv6X0AivPXWW3To0CGApJJU/h3s82d6ejrHH388a9as4bLLLuPQQw8FYN68efzf//0fLVq0YNq0adStW7e0o5cpn9OlKm7rVhg+HMaOhaIiqFED7r0XBg+GxMSg00mSKqH9ef504rokSZJUBV1xxRV7LcJIkiq2vZXWd+63vC4rkotxAAEAAElEQVRJ5Y/P6ZJU+cyaNYsbbriBcDhMt23ddltaB6hWVI3Dtx3O7Dqzuar/VYx/abzldUmKghEjRpCQkMDSpUtp3LjxLvvOPPNMRowYwZNPPhlQQkk6CEVFMHEi3HknbNgQ2XbhhfD443DIIcFmkyTpvyyuS5IkSVXQhAkTgo4gSYqSYcOGFf98xhlncPPNNxevP/PMM0yZMqX4uAcffLDM80mS9szndEmqXD799FNuvvlm8nPz6batG3UK6uz1+MZ5jem8vTPzmMdlv7+MF/76At26dSuTrJJUVUyaNIm//OUvu5TWAVJSUhg9ejQ33HCDxXVJFc8338BNN8H06ZH1jh3h2WehZ89gc0mS9DMxQQeQJEmSJElS6fn222+Lf/5paf3n6z89TpIkSVLp+uc//8mAAQMoyC3gqK1H0SS3yT6d12pHK7pu68r27dvp168f//nPf6KcVJKqlnXr1tGlS5c97j/ssMNIS0srw0SSdJC2bIkU1o8+OlJaT0qCRx+FuXMtrUuSyiWL65IkSZIkSZIkSZJUSt544w3uuOMOYgpiODb9WBrmNdyv85vlNuOorUeRn5PPddddx4cffhilpJJU9TRo0IDly5fvcf+yZcuoV69e2QWSpANVVATjx0OHDvDnP0fWL74YFiyAIUMgISHohJIk7ZbFdUmSJEmSJEmSJEkqBZMmTWLYsGEkFCXQI70HdQvqHtB1GuU14pgtx0A+3HrrrXz22WelnFSSqqZevXpxzz33kJeXt8u+3Nxchg0bxllnnRVAMknaD7NnwwknwNVXw6ZNcOihMHUqvPYaNG8edDpJkvbK4rokSZIkSVIl0rVr1+Kfn3nmmRL7frr+0+MkSZIkHbz333+foUOHkhBO4Nj0Y6ldWPugrlcvvx5Hbz2acEGYm266iS+//LKUkkpS1TVixAgWLlxI+/btGT16NKmpqbzzzjs88sgjtG/fnvnz5/PAAw8EHVOSdi89HQYMgGOOgZkzoWZNeOwx+PZb+PWvg04nSdI+sbiuXWTnZ/Plmi8Jh8NBR5EkSZIkSfvpwQcfLP55ypQp9OnTp3iZMmXKbo+TJFVOn332Geeccw5NmzYlFAoxadKkEvvD4TDDhw+nSZMmVK9enZ49e7J48eJgwkpSBffpp58yePBgYgtjOWbLMdQqrFUq162XX48jtxxJQV4B1193Pd9++22pXFeSqqrmzZszffp0OnfuzNChQ+nbty/nnXce99xzD507d+Y///kPLVq0CDqmJJVUVAQvvAAdOsC4cRAOw+9/DwsXwu23Q3x80AklSdpnFaK4PnbsWFq1akW1atXo0aPHPk8TeO211wiFQvTt2ze6ASuZAf8aQI+/9mD8nPEUFBUEHUeSJEmSJO2n1NTUg9ovSaocsrKy6Nq1K2PHjt3t/tGjR/P0008zbtw4Zs6cSVJSEr169SInJ6eMk0pSxbZu3TpuHngz4YIwR209iuSC5FK9fsP8hnTf2p0dO3Zw7TXXkpmZWarXl6SqpnXr1vz73/9m06ZNzJgxgxkzZrBx40YmT55Mu3btgo4nSSV99RUcdxxcdx1s3gyHHQaffAKvvAJNmwadTpKk/Vbui+uvv/46gwcP5r777uPrr7+ma9eu9OrViw0bNuz1vOXLl3PHHXdw8sknl1HSymHd9nX833f/B8CXa75k7FdjySnwTQpJkiRJkiqa1NRUunbtWmJb165dLa1LUhXSu3dvRo4cyXnnnbfLvnA4zJgxY7j33ns599xzOeKII5g4cSJr167dZTK7JGnvpk+fTm5eLh22d6Befr2o3KNxXmNaZ7VmW8Y25syZE5V7SFJVU7duXY499liOPfZY6tWLzu9vSTpgmzZFyuo9ekTK67VqwZNPwtdfwymnBJ1OkqQDVu6L60888QTXXnst/fv3p3PnzowbN44aNWowfvz4PZ5TWFjIZZddxgMPPECbNm3KMG3F9/zs5ykoKqBdvXYkxCYwb+M8Hp/+OFl5WUFHkyRJkiRJ++nBBx8kNTW1eHnwwQeDjiRJKieWLVtGWloaPXv2LN6WnJxMjx49mD59+h7Py83NJSMjo8QiSVXdt99+C0D9/PpRvc/OUvzO+0mSJKkSKiyEceOgY0d44QUIh+Hyy2HhQrjtNoiPDzqhJEkHpVwX1/Py8pg9e3aJF85jYmLo2bPnXl84HzFiBI0aNeLqq68ui5iVRn5hPn+Z/RcAHjztQQYfN5iaCTVZuW0lU5dNDTidJEmSyrPPPvuMc845h6ZNmxIKhXaZ0BgOhxk+fDhNmjShevXq9OzZk8WLFwcTVpKqkD59+uyySJIEkJaWBkDjxo1LbG/cuHHxvt0ZNWoUycnJxUuLFi2imlOSKoJvv/2W2HAsNQtqRvU+dfLrFN9PkiRJldCMGZEJ6wMGQHo6HHEEfPYZ/O1v0KRJ0OkkSSoV5bq4vmnTJgoLC/frhfMvvviCF198kRdeeGGf7+OEmIh/LPgH6zLX0TipMecfej6t67bm/E7nAzB/0/yA00mSJKk8y8rKomvXrowdO3a3+0ePHs3TTz/NuHHjmDlzJklJSfTq1YucnJwyTipJVceeSuqW1yVJB2Po0KFs27ateFm1alXQkSQpcKtWriIuHEeYcNTvFRuOZeXKlVG/jyRJksrQxo1w9dVw/PEwezbUrg1PPx35+eSTg04nSVKpKtfF9f21fft2/vCHP/DCCy/QoEGDfT7PCTERz375LADXH3U9CbEJAHRs0BGA5VuXk1NgqUiSJEm717t3b0aOHMl55523y75wOMyYMWO49957OffcczniiCOYOHEia9eu3WUyuySpdPxSOd3yuiQpJSUFgPXr15fYvn79+uJ9u5OYmEjt2rVLLJJU1V108UXkxuSyOCm63y43r9Y8CkOFXHTRRVG9jyRJkspIYSGMHQsdOsD48ZFt/frBokVw880QFxdsPkmSoqBcF9cbNGhAbGzsPr9wvnTpUpYvX84555xDXFwccXFxTJw4kdTUVOLi4li6dOlu7+OEGJi7fi6fr/ycuJg4rj/6+uLtDWo0oH71+hSFi1iSviTAhJIkSaqoli1bRlpaGj179izelpycTI8ePZg+fXqAySSpcvp5KT01NbV42dtxkqSqpXXr1qSkpDB16tTibRkZGcycOZPjjz8+wGSSVPHceuuttGrVimVJy9gStyUq91ifsJ611dbSvXt3+vXrF5V7SJIkqQxNmwZHHw0DB8LWrdCtG/znPzBhAjRuHHA4SZKip1wX1xMSEjjqqKNKvHBeVFTE1KlTd/vCeadOnfjuu++YM2dO8dKnTx9OO+005syZs8dJ6k6Igee+eg6A8zqdR9NaTUvs2zl1feHmhWWeS5IkSRVfWloaAI1/9iJb48aNi/ftTm5uLhkZGSUWSdL++XlZ/efrkqTKLTMzs/i1coh8qHTOnDmsXLmSUCjEbbfdxsiRI0lNTeW7777jiiuuoGnTpvTt2zfQ3JJU0VSrVo1Ro0ZBCOYmzyU/lF+q18+NyeX75O9JSEjg4YcfJjY2tlSvL0mSpDK0YQP07w8nnghz5kCdOvDsszBrFpxwQtDpJEmKunL/fSKDBw+mX79+HH300Rx77LGMGTOGrKws+vfvD8AVV1xBs2bNGDVqFNWqVeOwww4rcX6dOnUAdtmukj5a/hEA/bruOqGhY/2OTFs1jUWbFpV1LEmSJFVho0aN4oEHHgg6hiRJklRhzZo1i9NOO614ffDgwQD069ePCRMmMGTIELKysrjuuuvYunUrJ510EpMnT6ZatWpBRZakCuvII4/kqquu4sUXX2RmnZkcs/UYEsOJB33d7Jhsvqz7JbmhXIbePpQ2bdqUQlpJkiSVuYICeO45GDYMtm2LbLvqKhg1Cho1CjabJEllqNwX1y+++GI2btzI8OHDSUtLo1u3bkyePLl4WuPKlSuJiSnXg+PLvcy8TBZvXgzAMc2O2WV/x/qRiesrtq1gR/4OqsdXL9N8kiRJqthSUlIAWL9+PU2aNCnevn79erp167bH84YOHVpcrAHIyMjY47coSZIkSdrVqaeeSjgc3uP+UCjEiBEjGDFiRBmmkqTK64477iA7O5tXX32VGfVmcOyWY6ledODvq22P3c5Xdb8iJyaHW265hX79dh1AJUmSpArg889h4ECYOzeyfuSRMHYsHHdcsLkkSQpAhWh8Dxw4kBUrVpCbm8vMmTPp0aNH8b5PPvmECRMm7PHcCRMmMGnSpOiHrMC+TfuWMGGa1mpKo6RdP8FXt3pdGtVoRJgwi9Kdui5JkqT907p1a1JSUpg6dWrxtoyMDGbOnMnxxx+/x/MSExOpXbt2iUWStH/69Omz13VJkiRJpScmJob77ruPG264gazYLGbUm0FmbOYBXWtr3FZm1ptJTkwO9957LzfddBOhUKiUE0uSJCmq0tLgiivgV7+KlNbr1o1MXf/yS0vrkqQqq9xPXFf0fZP2DQDdU7rv8ZgODTqwYeUGFm5aSNfGXcsqmiRJkiqIzMxMlixZUry+bNky5syZQ7169TjkkEO47bbbGDlyJO3bt6d169YMGzaMpk2b0rdv3+BCS1IllZqaWqKgvqeyempqallFkiRJkqqMUCjEoEGDSE5O5tFHH2VmvZkcl34cSYVJ+3yNrXFb+bLelxTFFDH6kdGce+65UUwsSZKkUldQAM8+C/fdBxkZEArBNdfAww9DgwZBp5MkKVAVYuK6ouubdb9cXO9YvyMAizY7cV2SJEm7mjVrFt27d6d798gz5eDBg+nevTvDhw8HYMiQIdx8881cd911HHPMMWRmZjJ58mSqVasWZGxJqrR+qZRuaV2SJEmKrquuuooRI0aQG8plVp1Z5IXy9um87JhsZtedTVFMEc8884yldUmSpIrms8+ge3cYNChSWj/6aJgxA55/3tK6JElYXBc/mbje5JeL66szVpOVl1UmuSRJklRxnHrqqYTD4V2WCRMmAJFpYyNGjCAtLY2cnBw+/PBDOnToEGxoSark9lROt7QuSZIklY2LL76YgQMHkhWbxezk2RRSuNfj80P5zKo7i9xQLvfffz89e/Yso6SSJEk6aGvXwmWXwSmnwPffQ/36kbL6jBlw7LFBp5MkqdyICzqAgpVXmMf3G74H9j5xPblaMik1U0jLTGPR5kV7LblLkiRJkqTywZK6JEmSFKyBAweyatUq3nnnHebWnku3jG6ECO1yXBFFzE6eTWZsJtdeey0XX3xxAGklSZK03/Lz4emn4f77ITMTQiG4/noYOTJSXpckSSVYXK/i5m2cR35RPnWq1aFVnVZ7PbZj/Y6kZaaxOH2xxXVJkiRJkiqAPn367LLNMrskSZJUdkKhECNHjiQtLY2ZM2fSKLcRzXKb7XLc8hrLSU9Ip3fv3gwePDiApJIkSdpvH38MAwfCvHmR9R494Nln4eijg80lSVI5FhN0AAXrm3XfANAtpRuh0K7THX7qkORDAFiXuS7quSRJkiRJ0sHZXWl9b9slSZIkRUdCQgKjRo0iFAqxqvqqXfaHCbOq+iqqVavGyJEjiYnxLVxJkqRybc0auOQS+PWvI6X1Bg3gr3+FadMsrUuS9At81aOK+yYtUlzvnvLLE9Sb1GwCwLrtFtclSZIkSSrPfqmcbnldkiRJKlvNmjXjxBNPJD0hnazYrBL7tsRvISs2i969e1OzZs2AEkqSJOkX5eXB6NHQsSO8/jrExMCNN8LChXD11ZF1SZK0V/5tWcXtT3E9pWYKAFtytrAjf0dUc0mSJEmSpAPz81J6ampq8bK34yRJkiRF1wUXXADAqmolp67vXL/wwgvLPJMkSZL20YcfQteucOedkJUFxx8Ps2bB2LFQr17Q6SRJqjAsrldhReEi5qTNAaB7k18uriclJFE7sTYAaZlp0YwmSZIkSZJKwc/L6j9flyRJklR2evbsSUxMDFvjt5bYviV+C3Xr1uWoo44KJpgkSZL2bNUq+N3v4IwzYMECaNgQXnoJvvgCuv9y30qSJJVkcb0KW5q+lMy8TKrFVaNTg077dE6Tmk0AWJe5LprRJEmSJEmSJEmSpEolJiaGoqIiYsIl36KN+e9btqFQKIhYkiRJ2p3cXBg1Cjp1gjffhJgYuPlmWLQIrrwysi5JkvZbXNABFJyv130NwOGNDicuZt/+p9CkVhMWbl7Iuu0W1yVJkiRJkiRJkqR9lZOTA0AssSW2x4Zji/dJkiSpHPjgg/+V1AFOOgmefRa6dg02lyRJlYAf/arCvkn7BoDuKfv+tTVNazYFnLguSZIkSVJF0KdPn72uS5IkSSo72dnZQKSo/lM7i+tFRUVBxJIkSdJOK1fCBRdAr16R0nrjxjBxInz2maV1SZJKiRPXq7Di4nqTfS+uN6nVBLC4LkmSJElSeZWamlqioL6nsnpqampZRZIkSZIEzJ49G4CkgqQS25MKk0gPp/PNN99w1FFHBRFNkiSpasvNhcceg4cegh07IDY2MnH9/vshOTnodJIkVSpOXK/C5m+cD8DhjQ7f53Oa1IwU1zdnbya3IDcquSRJkiRJ0sH5pVK6pXVJkiSp7E2ePBmAlNyUEttTclJK7JckSVIZ+ve/4bDD4N57I6X1X/0KvvkGnnzS0rokSVFgcb2K2pG/g1UZqwBoX7/9Pp9XK7EWNRNqEibM+qz10YonSZIkSZIO0p7K6ZbWJUmSpLKXnZ3NJ598Qs2CmtQqrFViX/38+iSEE5j878kUFRUFlFCSJKmKWb4c+vaFs8+GJUugSRN45RX45BM4fN+HgEqSpP0TF3QABWPZ1mUA1E6sTcMaDffr3CY1m7A4fTFrt6/lkORDohFPkiRJkiSVAkvqkiRJUvnwwQcfkJOTQ7vcdrvsiyGGxjmNWbVxFTNmzOCEE04IIKEkSVIVkZMDo0fDqFGRn2Nj4dZb4b77oHbtoNNJklTpWVyvopakLwGgXb12hEKh/Tq3Sa1IcX1d5rpoRJMkSZIkSaWkT58+u2yzzC5JkiSVrcLCQp577jliiKHFjha7PeaQHYewqvoqxo4dy/HHH7/f799JkiRpH/zzn5GS+o8/RtZPPRWefRa6dAk0liRJVUlM0AEUjJ8W1/dXk5pNAFi33eK6JEmSJEnl1e5K63vbLkmSJCk6/vWvf7F8+XKaZzenelH13R6TXJBM49zGzJo1i5kzZ5ZxQkmSpEruxx/hnHMiy48/QtOm8Oqr8NFHltYlSSpjFterqOLiet0DKK7X+m9x3YnrkiRJkiSVS79UTre8LkmSJJWNwsJC/vznPxNDDG2z2+712HZZkfftnnnmmbKIJkmSVPnt2AH33QedO0emrcfFwR//CAsWwCWXgN9yI0lSmbO4XkUtTl8MHNzE9Y1ZG8kvzC/VXJIkSZIk6eD8vJSemppavOztOEmSJEml7/3332fZsmV7nba+U3JBMo1yGzFr1ixmzZpVRgklSZIqoXAY3nknUlgfMQJyc+H002HuXBg9GmrVCjqhJElVlsX1Kqp44voBFNeTE5OpHledMGHWZ60v7WiSJEmSJKmU/Lys/vN1SZIkSdETDod5/vnnCRGiTXabfTpn59T1559/PprRJEmSKq8lS+A3v4G+fWH5cmjeHN54A6ZMgUMPDTqdJElVnsX1Kii3IJeV21YC0L5++/0+PxQK0aRWZOr6usx1pZpNkiRJkiRJkiRJqgy++OIL5s+fT5OcJtQoqrFP59QpqEP9vPp8+umnLFiwIMoJJUmSKpHsbLj3XujSBf79b4iPh7vugvnz4Xe/g1Ao6ISSJAmL61XS8q3LKQoXkRSfROOkxgd0jSY1/1tc325xXZIkSZIkSZIkSfq5F154AYA2Wfs2bX2nndPZ//rXv5Z6JkmSpEonHIa3345MU3/oIcjLgzPPhO++g1GjoGbNoBNKkqSfsLheBS1JXwJAu3rtCB3gpwl3FtfTMtNKLZckSZIkSSpdffr02eu6JEmSpOjIzs7mq6++om5eXWoX1t6vcxvkNaBGYQ0+/fRTwuFwlBJKkiRVAosWwVlnwQUXwMqVcMgh8NZbMHkydOwYdDpJkrQbcUEHUNn7aXH9QDVKagTAhqwNpZJJkiRJkiSVjtTU1BIF9T2V1VNTU8sqkiRJklTlzJs3j6KiIurm193vc0OEqJNfh7UZa1m5ciUtW7aMQkJJkqQKLCsrMl39sccgPx8SEuCPf4S774YaNYJOJ0mS9sKJ61VQaRbXN2ZvdNKDJEmSJEnlzC+V0i2tS5IkSdH13XffAZBckLzb/XNqz+GLul8wp/ac3e6vk1+nxHUkSZXDQw89xAknnECNGjWoU6fObo9ZuXIlv/nNb6hRowaNGjXij3/8IwUFBWUbVCqvwmF480049FAYNSpSWu/dG77/HkaOtLQuSVIFYHG9ClqyJVJcb1+v/QFfo0GNBoQIkVOQw/a87aUVTZIkSZIklZI9ldMtrUuSJEnRN2/ePACS83dfXM+MzSQjPoPM2Mzd7t953s7rSJIqh7y8PH73u98xYMCA3e4vLCzkN7/5DXl5eUybNo2XX36ZCRMmMHz48DJOKpVDCxbAmWfC734Hq1ZBy5YwaRL861/Q/sA7UJIkqWzFBR1AZW/x5sXAwU1cj4+Np271uqTvSGdD1obSiiZJkiRJkkqRJXVJkiQpGDExkflhIUIHdP7O82JjY0stkyQpeA888AAAEyZM2O3+Dz74gHnz5vHhhx/SuHFjunXrxoMPPsidd97J/fffT0JCQhmmlcqJzEx48EF48snIhPXERLjzzsjihHVJkiocJ65XMfmF+Szfuhw4uOI6QKOkRgBszNp4sLEkSZIkSZIkSZKkSqNu3boA5MXkHdD5O8/beR1JUtUwffp0Dj/8cBo3bly8rVevXmRkZPDDDz/s8bzc3FwyMjJKLFKFFw7D669Dp04wenSktP7b38IPP8ADD1halySpgrK4XsWs2LaCwnAh1eOq06RWk4O6VqMakeL6+qz1pRFNkiRJkiRJkiRJqhSKi+shi+uSpH2XlpZWorQOFK+npaXt8bxRo0aRnJxcvLRo0SKqOaWomzcPevaESy6BNWugdWtITYV334W2bYNOJ0mSDoLF9SpmSfoSANrWa0tM6OD+z98wqSEAG7OduC5JkiRJkiRJkiTtlJycDEB+TP4BnZ8fyi9xHUlS+XXXXXcRCoX2uixYsCCqGYYOHcq2bduKl1WrVkX1flLUbN8Od9wBXbvCRx9BtWpw//2RKevnnBN0OkmSVArigg6gsrWzuN6+XvuDvlajpMjE9Q1ZGw76WpIkSZIkSZIkSVJlUa1aNQAKQ4UHdP7O86pXr15qmSRJ0XH77bdz5ZVX7vWYNm3a7NO1UlJS+PLLL0tsW79+ffG+PUlMTCQxMXGf7iGVS+EwvPpqpLS+bl1kW58+MGZMZNq6JEmqNCyuVzE7i+vt6rU76Gv9tLgeDocJhUIHfU1JkiRJ0oHLzc1l9erVQceQVME0b97cN7clSZJK2c7CeVGo6IDO31lc9zlNksq/hg0b0rBhw1K51vHHH89DDz3Ehg0baNQo0smYMmUKtWvXpnPnzqVyD6nc+f57GDgQPv00st62LTz9NJx9drC5JElSVFhcr2JKs7jeoEYDAHIKctiUvYmGSaXzH2KSJEmSpAOzevVqBg0aFHQMSRXMk08+Sdu2bYOOIUmSVKkUT1znwCau7yy877yOJKlyWLlyJenp6axcuZLCwkLmzJkDQLt27ahZsyZnnnkmnTt35g9/+AOjR48mLS2Ne++9l5tuuskPM6ny2bYN7r8fnnkGCguhenW4++7I1HWfgSRJqrQsrlcxO4vrbese/JuRCbEJ1K1Wly05W1iSvsTiuiRJkiQFrHnz5jz55JNBx1A5s2rVKp544gkGDx5MixYtgo6jcqh58+ZBR5AkSap0vv76awASiw6sZJhQlFB8HSfsSlLlMXz4cF5++eXi9e7duwPw8ccfc+qppxIbG8s///lPBgwYwPHHH09SUhL9+vVjxIgRQUWWSl84DK+8Eimor18f2XbeefDkk9CyZbDZJElS1Flcr0KKwkUs27oMgLb1SmeKVqOkRmzJ2cLi9MUc3+L4UrmmJEmSJOnAJCYmOjVZe9SiRQv/9yFJkiSVgaysLF555RUSixJJyU05oGu02NGCJTWX8NL4l7jkkkuIi/NtXUmqDCZMmMCECRP2ekzLli157733yiaQVNbmzoWBA+HzzyPr7dtHJq736hVsLkmSVGZigg6gsrN2+1ryCvOIDcXSvHbpTNJqlNQI+N8kd0mSJEmSJEmSJKkq+/vf/8727dtpmd2SWGIP6BoJ4QSaZzdn9ZrVfPDBB6WcUJIkqYxt3Qq33gpHHhkprdeoAQ8/DN99Z2ldkqQqxuJ6FbJsS2Taess6LYmLKZ2pDA2TGgIW1yVJkiRJkiRJkqRly5bxwgsvEBeOo+WOlgd1rdbZrQkRYsyYMaxfv76UEkqSJJWhoiJ4+WXo2BGefhoKC+HCC2H+fBg6FBITg04oSZLKmMX1KmTZ1khxvXWd1qV2zUY1IhPXF6cvLrVrSpIkSZIkSZIkSRXN999/z6WXXsqmTZvokNmB+HD8QV2vRlEN2mS1YcWKFVx6yaUsX768dIJKkiSVhTlz4OST4corYcOGSHn9gw/g73+HQw4JOp0kSQqIxfUqZOfE9VItrif9t7i+eTHhcLjUritJkiRJkiRJkiRVFNOnT+cPl/+BrVu2cnjG4bTa0apUrtshqwMdMjuwZu0aLr30Un744YdSua4kSVLUbNkCAwfCUUfBtGmQlASPPgpz58IZZwSdTpIkBcziehVSPHG9bukV1xsmNQRgW+420nekl9p1JUmSJEmSJEmSpIrg/fff59prriVnRw7dt3anRU6LUrt2iBDtsttxWMZhbEnfwh8u/wMzZswotetLkiSVmqIiGD8+Mll97NjI+kUXwYIFMGQIJCQEnVCSJJUDFterkOLieilOXE+ITaBOtToALE5fXGrXlSRJkiRJkiRJksqzwsJCxowZwy233AL5cMyWY0jJS4nKvQ7JOYTu27qzI3sHV111FS+//LLfhixJksqP2bPhxBPh6qth40Y49FD48EN4/XVo3jzodJIkqRyxuF6FLNtS+hPXARolNQJgSfqSUr2uJEmSJEmSJEmSVB6lp6dzzTXX8Nxzz5FUmESP9B7Uz68f1Xum5KZw7JZjic+P5+GHH2bw4MFkZWVF9Z6SJEl7lZ4OAwbAMcfAjBlQsyb86U8wZw6cfnrQ6SRJUjlkcb2KyCvMY3XGaqB0J64DNKphcV2SJEmSJEmSJElVw7fffst5553HtGnTaJzbmBPST6B2Ye0yuXe9/HqcmH4i9fLq8d5773HhhReydOnSMrm3JElSsaIieOEF6NABxo2DcBguvRQWLoQ77oCEhKATSpKkcsriehWxcttKwoSpEV+jeEJ6aWmY1BCAxemLS/W6kiRJkiRJkqTouf/++wmFQiWWTp06BR1Lksq1SZMm8fvf/571aevpmNmRI7cdSXw4vkwzJBYlcuzWY2mT1YYff/yRCy+4kE8//bRMM0iSpCrsq6/guOPguutg82bo0gU+/hj+7/+gadOg00mSpHLO4noVsWzLMgBa1WlFKBQq1WvvLMI7cV2SJEmSJEmSKpYuXbqwbt264uWLL74IOpIklVvff/8999xzDzH5MRy75VjaZrclROm+77avYoihU1Ynjtx2JHk78hg0aBCrVq0KJIskSaoiNm+G66+HHj0i5fVateCJJ+Cbb+DUU4NOJ0mSKgiL61XEj1t+BKB1ndalfm2L65IkSZIkSZJUMcXFxZGSklK8NGjQIOhIklQuZWZmMmjQIAoKCui2tRv18+sHHQmAlNwUumzrQlZWFoMHDyYvLy/oSJIkqbIpLIRx46BDB3j+eQiH4fLLYeFCGDQI4sv222ckSVLFZnG9ili2NTJxvU3dNqV+7YY1GgKQviOd9B3ppX59SZIkSZIkSVJ0LF68mKZNm9KmTRsuu+wyVq5cudfjc3NzycjIKLFIUmUXDoe57777WLlyJe2y2tEgv3x9yKdZbjOa72jO3LlzefLJJ4OOI0mSKpOZMyMT1gcMgPR0OPxw+Owz+NvfoEmToNNJkqQKyOJ6FbGzuB6NieuJcYk0rdUUcOq6JEmSJEmSJFUUPXr0YMKECUyePJnnnnuOZcuWcfLJJ7N9+/Y9njNq1CiSk5OLlxYtWpRhYkkKxmeffcY///lP6ubVpV1Wu6Dj7Fbn7Z2pWVCT8ePHM2/evKDjSJKkim7jRrjmGjjuOJg9G2rXhqeegq+/hpNPDjqdJEmqwCyuVxHLtvy3uF639IvrAO3qRV6ks7guSZIkSZIkSRVD7969+d3vfscRRxxBr169eO+999i6dStvvPHGHs8ZOnQo27ZtK15WrVpVhoklKRibNm0CoHlOc2LK6durccTRJCcy9XRnXkmSpP1WWAh//jN06AAvvhjZ1q8fLFoEt9wCcXHB5pMkSRWeTxNVRDQnrgO0q9uOz1Z8xuLNi6NyfUmSJEmSJElSdNWpU4cOHTqwZMmeB5QkJiaSmJhYhqkkKXht27YFIDM2M+Ake5cVlwVAu3blcyq8JEkq56ZPh5tugm++iax36wbPPgsnnhhoLEmSVLmUz5EAKlWZeZlsyo5MVojWxPX29dsDsGSLE9clSZIkSZIkqSLKzMxk6dKlNGnSJOgoklSuFBfX48p3cT0zNpPq1av7e1ySJO2fDRugf3844YRIaT05GZ55Br76ytK6JEkqdRbXq4BlWyLT1utVr0ftxNpRuUe7epHJDUvSLa5LkiRJkiRJUkVwxx138Omnn7J8+XKmTZvGeeedR2xsLJdeemnQ0SSpXKlVqxYpKSlsTdhKXigv6Di7lR2TTWZ8Ju3atSMUCgUdR5IkVQQFBZGCeocOMGFCZNtVV8GiRTBwIMTFBRpPkiRVThbXq4BlWyPF9dZ1ojNtHf5XXF+8eXHU7iFJkqSK6/777ycUCpVYOnXqFHQsSZIkqUpbvXo1l156KR07duSiiy6ifv36zJgxg4YNGwYdTZLKnf79+5MfyueHWj8EHWUXYcLMrT2XIoq46qqrgo4jSZIqgi++gKOOgltugW3b4MgjYfp0ePFFaNQo6HSSJKkS86NxVcDOieut60a/uL55x2a27NhC3ep1o3YvSZIkVUxdunThww8/LF6Pc1KHJEmSFKjXXnst6AiSVGFcccUVTJkyhVmzZpGSm0KT3CZBRyq2ovoK0hPS6dWrF2effXbQcSRJUnmWlgZDhsDf/hZZr1sXHnoIrrsOYmODzSZJkqoEJ65XAWUxcb1mQk1SaqYAsHTL0qjdR5IkSRVXXFwcKSkpxUuDBg2CjiRJkiRJkrRPYmJiePjhh0lMTGRe7XnkhnKDjgRAVmwWC2stpG6dutx3331Bx5EkSeVVQQGMGQMdO0ZK66EQXHMNLFoEAwZYWpckSWXG4noVUBbFdfjf1PXFmxdH9T6SJEmqmBYvXkzTpk1p06YNl112GStXrgw6kiRJkiRJ0j5r2bIlt99+O7mhXH5M+jHoOAAsSlpEIYUMv2849evXDzqOJEkqjz77DLp3h0GDICMDjj4aZsyAF14AhwxJkqQyZnG9Cli25b/F9brRLa63r9cegCXpS6J6H0mSJFU8PXr0YMKECUyePJnnnnuOZcuWcfLJJ7N9+/Y9npObm0tGRkaJRZIkSZIkKUiXXnopTZs2ZWWNlYFPXc+MzWRdtXUcdthh9O7dO9AskiSpHFq7Fi67DE45Bb7/HurVg7/8JVJaP/bYoNNJkqQqyuJ6JRcOh8t84vqSLRbXJUmSVFLv3r353e9+xxFHHEGvXr1477332Lp1K2+88cYezxk1ahTJycnFS4sWLcowsSRJkiRJ0q4SEhK4/vrrKaSQZTWWBZplSVLkPbmbbrqJUCgUaBZJklSO5OfD449Dx47wf/8HoRBcfz0sWgTXXQexsUEnlCRJVZjF9UpuQ9YGMvMyCRGiVZ1WUb3XzuL64s2Lo3ofSZIkVXx16tShQ4cOLFmy5w89Dh06lG3bthUvq1atKsOEkiRJkiRJu3f++eeTkpLCyqSV5IfyA8mwI2YH66qt49BDD+W0004LJIMkSSqHPvkEunWDO+6AzMzIZPUvv4Rx46B+/aDTSZIkWVyv7JZuWQpAi+QWJMYlRvVe7eu1B2BJuhPXJUmStHeZmZksXbqUJk2a7PGYxMREateuXWKRJEmSJEkKWkJCAieccAIFFARXXI/dQZgwp5xyitPWJUkSrFkDl14Kp50G8+ZBgwbw17/C9Olw9NFBp5MkSSpWIYrrY8eOpVWrVlSrVo0ePXrw5Zdf7vHYF154gZNPPpm6detSt25devbsudfjK7ul6ZHietu6baN+r7b1IvfYmL2RbTnbon4/SZIkVRx33HEHn376KcuXL2fatGmcd955xMbGcumllwYdTZIkSZIkab9t374dgPhwfCD3TyhKKJFDkiRVUXl58Kc/QadO8NprEBMDN94ICxfC1VdH1iVJksqRcv908vrrrzN48GDuu+8+vv76a7p27UqvXr3YsGHDbo//5JNPuPTSS/n444+ZPn06LVq04Mwzz2TNmjVlnLx82DlxvV29dlG/V+3E2jRKalTivpIkSRLA6tWrufTSS+nYsSMXXXQR9evXZ8aMGTRs2DDoaJIkSZIkSftt27ZthAgRF44L5P47C/PbtjlMSpKkKmvqVOjaFYYMgcxMOP54mDULxo6FevWCTidJkrRb5b64/sQTT3DttdfSv39/OnfuzLhx46hRowbjx4/f7fGvvPIKN954I926daNTp0789a9/paioiKlTp5Zx8vJhZ4G8LCauA7Sv1x6AxZsXl8n9JEmSVDG89tprrF27ltzcXFavXs1rr71G27Zl84wqSZIkSZJUmvLy8li5YiXx4XhChALJEFcUKcwvXbqUcDgcSAZJkhSQVavgoougZ09YsAAaNoSXXoIvvoDu3YNOJ0mStFfluriel5fH7Nmz6dmzZ/G2mJgYevbsyfTp0/fpGtnZ2eTn51NvL58kzM3NJSMjo8RSWSxN/29xvV7ZlIJ2TnZfkr6kTO4nSZIkSZIkSZIklaW//e1vpK1Po+mOpoFliCWWJjlNmD9/Pv/85z8DyyFJkspQXh488gh06gR//zvExMDNN8OiRXDllZF1SZKkcq5cP7Fs2rSJwsJCGjduXGJ748aNSUtL26dr3HnnnTRt2rRE+f3nRo0aRXJycvHSokWLg8pdnpT1xPXi4voWi+uSJEmSJEmSJEmqXDZs2MCzzzxLQjiB9lntA83SKbMTscQy+tHRZGVlBZpFkiRF2QcfwOGHw9ChkJ0NJ54Is2fD009DnTpBp5MkSdpn5bq4frAeeeQRXnvtNf7xj39QrVq1PR43dOhQtm3bVrysWrWqDFNGz/bc7WzI2gCU3cT19vUiL9At2ryoTO4nSZIkSZIkSZIklZXHH3+c7B3ZdNzekfhwfKBZqhdVp01mGzZs3MC4ceMCzSJJkqJk5Uq44ALo1SsyWb1xY3j5Zfj8c+jWLeh0kiRJ+y0u6AB706BBA2JjY1m/fn2J7evXryclJWWv5z722GM88sgjfPjhhxxxxBF7PTYxMZHExMSDzlve7Jy23qBGA2on1i6Tex7a8FAA5m2cRzgcJhQKlcl9JUmSJEmSJEmSpGj6+9//zqRJk0jOT6Z5TvP9Pv+zep+RF8oDID8mH4Dtcdv5sP6HJIQT+FX6r/b7mm2y27Cm+hr++sJfOeqoozj11FP3+xqSJKkcys2Fxx6Dhx6CHTsgNhZuvhnuvx+Sk4NOJ0mSdMDK9cT1hIQEjjrqKKZOnVq8raioiKlTp3L88cfv8bzRo0fz4IMPMnnyZI4++uiyiFouLU2PFNfb1i2baesAHep3ICYUw9acrazLXFdm95UkSZIkSZIkSZKiZebMmdx3330khBPoltGNEPs/vCkvlEdebGQJh8IAhEPhyLb/Ftr3VyyxdN/WnVA4xKDbBrFgwYIDuo4kSSpH/v1vOOwwuPfeSGn95JPhm2/gySctrUuSpAqvXBfXAQYPHswLL7zAyy+/zPz58xkwYABZWVn0798fgCuuuIKhQ4cWH//oo48ybNgwxo8fT6tWrUhLSyMtLY3MzMyg/giB2TlxvW29siuuV4urRrt67QD4YcMPZXZfSZIkSZIkSZIkKRqWLVvGwJsGEi4Mc+TWI0kqTAo6UgnJBcl03dqV7B3ZXH/99WzcuDHoSJIk6UAsXw59+8LZZ8OSJZCSAv/v/8Gnn8LhhwedTpIkqVSU++L6xRdfzGOPPcbw4cPp1q0bc+bMYfLkyTRu3BiAlStXsm7d/yZ7P/fcc+Tl5XHhhRfSpEmT4uWxxx4L6o8QmJ0T19vVbVem9+3SsAsA8zbOK9P7SpIkSZIkSZIkSaUpPT2d66+/noztGRy+7XDq5dcLOtJupeSl0CmzE2lpaQwYMIDs7OygI0mSpH2VkwMPPgiHHgrvvAOxsTB4MCxcCJddBqH9/6YXSZKk8iou6AD7YuDAgQwcOHC3+z755JMS68uXL49+oAoiiInrECmu/2PBP/hhoxPXJUmSJEmSJEmSVDFlZ0cmmK9YsYJ2We1oltss6Eh71Tq7NVmxWXz33XfcdtttjB07lvj4+KBjSZKkvfnXv+DWW2FppOPDqafCs89Cly6BxpIkSYqWcj9xXQeuuLhet2yL650bdgawuC5JkiRJkiRJkqQKqaCggEGDBjF37lya72hO+6z2QUf6RSFCdNnehYa5Dfn000+57777CIfDQceSJEm78+OPcM458NvfRkrrTZvCq6/CRx9ZWpckSZWaxfVKKq8wj5XbVgIBTFxvFHmA/mHDD74YJkmSJEmSJEmSpAolHA4zfPhwPvnkExrmNuSw7YcRIhR0rH0SQwzdt3WnTn4d3nrrLZ566qmgI0mSpJ/asQPuvx86d4Z//hPi4uCPf4QFC+CSSyBUMZ45JEmSDpTF9Upq+dblFIWLSIpPonFS4zK9d8f6HYkNxbItdxvrMteV6b0lSZIkSZIkSZKkg/H888/z1ltvkZyfTPdt3YmpYG+pxhHH0VuPJqkwieeee4633nor6EiSJCkchtTUyDT1Bx6A3Fw4/XSYOxdGj4ZatYJOKEmSVCYq1qss2mdL05cC0KZuG0Jl/GnMxLhE2tVrB0SmrkuSJEmSJEmSJEkVwWeffcaTTz5J9aLqHL31aOKICzrSAUkIJ3DMlmNICCdw/33389133wUdSZKkqmvJEvjtb+Hcc2HZMmjWDN54A6ZMgUMPDTqdJElSmbK4Xkkt3RIprret1zaQ+3du2BmAHzZaXJckSZIkSZIkSVL5t3LlSgYPHkxMOIYjtx5JYjgx6EgHpUZRDbpt7UZ+fj4DBw5k8+bNQUeSJKlqyc6GYcMiU9bfew/i4+Guu2DBAvjd76CMB1FKkiSVBxbXK6mdE9fb1g2muN6lYRfAieuSJEmSJEmSJEkq/7Kzs7npppvYvn07h2UcRnJBctCRSkWD/AZ0yOxAWloat912GwUFBUFHkiSp8guH4R//gM6dYeRIyMuDM8+E776DUaOgZs2gE0qSJAXG4nolVTxxPajieqP/FteduC5JkiRJkiRJkqRy7qmnnmLRokW0ym5Fs5xmQccpVW2y25CSk8KXX37Jyy+/HHQcSZIqt0WLoHdvOP98WLECWrSAt96CyZOhY8eg00mSJAXO4noltbO43q5eu0Duv3Pi+ryN8wiHw4FkkCRJkiRJkiRJkn7JvHnzmDhxIkmFSXTMrHyFshAhDt9+OInhRJ5++mnWrFkTdCRJkiqfrCy4+244/HB4/31ISIisz58fKbGHQkEnlCRJKhcsrldCReEiftzyIwBt6wUzcb1D/Q7EhmLZlruNtdvXBpJBkiRJkiRJkiRJ2pvCwkKGDx9OUVERXTK6EEts0JGiIj4cT6eMTuTk5DBy5Mig40iSVHmEw5GJ6oceCqNGQV4enHUWfP89PPQQJCUFnVCSJKlciQs6gErfym0rySnIISE2gUOSDwkkQ2JcIu3qtWPh5oX8sPEHmtWuXF+pKEmSJAVt48aNZGRkBB1DUgWwatWqEv+UpF9Su3ZtGjZsGHQMSZLKxPjx4/nuu+9omtOUBvkNgo4TVU1zm7I6bzUfffQR77zzDueee27QkSRJqtgWLIBbboEpUyLrLVvCmDFw7rlOWJckSdoDi+uV0LyN8wDoWL8jcTHB/Z+4S6MukeL6hh84s+2ZgeWQJEmSKpuNGzdy4403kJubH3QUSRXIE088EXQESRVEYmI8f/7zOMvrkqRKLRwO8+c//5mnn36axHAih24/NOhIURciRJftXZhWbxp33XUXOTk5XHzxxUHHkiSp4snMhAcfhCefhPx8SEyEIUPgrrugRo2g00mSJJVrFtcroZ3F9c4NOweao0vDLrw9/+3iPJIkSZJKR0ZGBrm5+fzhD6tISckJOo4kSapE0tKq8be/tSAjI8PiuiSp0iosLOShhx7ilVdeoUZhDY7ZegyJ4cSgY5WJmoU16bGlB7PqzmL48OFs2rSJG2+8kZBTYSVJ+mXhMPz97zB4MKxZE9n2m9/AU09B27bBZpMkSaogLK5XQjuL4oc2CHYyRJeGXQD4fuP3geaQJEmSKquUlBxatLC4LkmSJEnSvsrLy2PIkCH8+9//pnZ+7SpVWt8puSCZ49KP46s6X/H000+zefNm7rnnHmJjY4OOJklS+TV/PgwcCB99FFlv3TpSWD/nnGBzSZIkVTAW1yuh+ZvmA8FPXD+i8REAfJv2LfmF+cTHxgeaR5IkSZIkSZIkSVVLOBzmxx9/ZMaMGXz55ZfMnDmTLVu2UD+vPkduO5L4cNV8/yqpMInj04/nqzpf8corrzDlgyn0OK4HPXr04LjjjqN58+ZOYZckCWD7dhgxAsaMgYICqFYN7roLhgyB6tWDTidJklThWFyvZMLhcPHE9aCL6x0bdCQ5MZltudv4fsP3dG/SPdA8kiRJkiRJkiRJqtzC4TArVqxg5syZxcumTZuK91cvrE6r3FZ0zOxILFV7wnhiOJEeW3uwoOYCNqzfwLvvvsu7774LQJMmTejRo0fx0qxZs4DTSpJUxsJheO01uOMOWLs2sq1PH3jySWjTJthskiRJFZjF9Upm7fa1ZORmEBuKpX399oFmiQnF0KN5Dz5Y+gEzVs+wuC5JkiRJkiRJkqRSFQ6HWb16NTNmzGDmzJl8OfNL1m9YX7y/WlE1muU2o15+Pern1adGUY0A05Y/8eF4Dt9+OOHtYbJis9gcv5n0hHTS16QzadIkJk2aBEDz5s2LS+zHHXccjRs3Dja4JEnR9P33MHAgfPppZL1tW3j6aTj77GBzSZIkVQIW1yuZndPW29dvT0JsQsBp4Lhmx0WK62tmMOCYAUHHkSRJkiRJkiRJUgUUDofZunUrGzduZNOmTaxZs4bZs2czY8YM1q1bV3xcYlEiTfKaUD+vPvXz61OjsAYhQgEmrxhChKhZWJOahTVpmdOSMGEyYzPZnLCZzfGb2bBqA2+tfou33noLgJYtW9KjRw+OPPJIUlJSaNSoEQ0aNKB27dqEQv77liRVUBkZcP/9kZJ6YSFUrw533x2Zul6tWtDpJEmSKgWL65XMzuJ654adA04ScVzz4wCYsXpGwEkkSZIkSZIkSZJU3uTl5bF582Y2bty422XTpk1s2LCBTZs2UVBQsMv5CUUJpOSlUD+/PvXz6pNUmGRRvRSECFGrsBa1dtSi1Y5WhAmzPXY7mxMiE9nXLl/LGyve4I033ihxXkJCAg0aNKBRo0Y0bNiQBg0a0LBhw+Jie8OGDWnYsCH169cnLs63qiVJ5UQ4DK+8An/8I6SlRbb17QtPPgmtWgWZTJIkqdLx1YBKpri43qB8FNePbXYsAIs2L2Jz9mbq16gfcCJJkiRJkiRJkiRFUzgcJisrq7hwvsdC+voNbN22da/Xig3HkliUSK3CWiQWJZZY6uTXoWZhTYvqZSBEiNqFtam9ozatd7QmTJiMuAy2xW0jNyY3ssTmkpufy5YdW0hbm0YRRXu+XihE3bp1i4vsPy22/7z4npSUVIZ/UklSlTN3LgwcCJ9/Hllv1w6eeQbOOivYXJIkSZWUxfVKZt6m8jVxvX6N+nSo34FFmxfx5Zov6d2+d9CRJEmSJEmSJEmSdAAKCwtJT0/fpYC+u2J6Tk7OXq+VEE4goTCB+kX1qVZYbZdS+s4lLhxnMb0cChEiuSCZ5ILk3e4PE6YgVEBOTM7/iu07y+07f96Qy4+bf2ThwoV7vVeNGjWKC+67K7bvLL7XqVOHmJiYaPxxJUmV0datcN99MHYsFBZC9epw771w++2QmBh0OkmSpErL4nolEg6H+WHDD0D5Ka4DHNf8OBZtXsSM1TMsrkuSJEmSJEmSJAUkHA6Tm5tLZmbmbpesrKxdtm3bto2NGzeyYcMG0tPTKSra8xTtGGJIKEwgsSiR2kW1i8vnuyumx2DBuDILESI+HE98YTy1Cmvt9dhCCvdcbo/JJSc/h/WZ61m5YiVhwnu8TmxsLA3qN6BBw0ixvXbt2tSsWbN4SUpKKrG+c6lVqxY1a9YkISGhtP81SFIJy5cv58EHH+Sjjz4iLS2Npk2bcvnll3PPPfeU+B00d+5cbrrpJr766isaNmzIzTffzJAhQwJMXskUFcHf/gZDhsCGDZFtF1wATzwBhxwSbDZJkqQqwOJ6JbIhawNbcrYQE4qhQ/0OQccpdlyz45j47URmrJkRdBRJkiRJkiRJkqQKJxwOk52dvdeC+S8u2zPJzMqksLBwv+8fF44jsTCROkV1SpTPqxVVI6EwgWpFkWJ6fDje6ejab7HEUqOoBjWKauz1uDBh8kJ5uy+3/3ey+/a129m8fjM/hH7Y7xzx8fHUTKpJzVo196nwvqf91atXJxTy/w8k7WrBggUUFRXxl7/8hXbt2vH9999z7bXXkpWVxWOPPQZARkYGZ555Jj179mTcuHF89913XHXVVdSpU4frrrsu4D9BJTBnDtx0E0ybFlnv2BGeeQbOOCPQWJIkSVWJxfVKZN7GeQC0qduG6vHVA07zP8c1Pw6AmatnUhQuIibkBA1JkiRJkiRJklT5FRYWliic727C+fbt2/c4+Xznvuzs7L1OOt+TnVOvY4tiiSuKo3a4NnHhuOIlvii+xHpcOI64orhdjoklNgr/dqT9EyJEYjiRxMJE+IXPXxSECkos+aF8CmIKdtleYlteAQU7Cti0eRNpMWkUhAr2OuF9T2JjYkmquX9l990tNWrUICbG91WlyuSss87irLPOKl5v06YNCxcu5Lnnnisurr/yyivk5eUxfvx4EhIS6NKlC3PmzOGJJ56wuH4wtm6FYcP4/+zdd3xUdf798TMlM+kJIR1ClV6ligVQUZoKggXEFdCVVUFFUFZcRVB3WTsWyvpbBfQLyq7SLCBFQVEEBAGVIiCdJNQkpE6m/P6YzcikQIAkNwmv5z7mkbmf+7n3vu9lllzGM+/RtGnejushId7lxx6T+MYNAACACkVwvRopCK43j2lucCX+WsW1UpA1SOl56dp5fKeaxTQzuiQAAAAAAAAAAIAS5efnF+lqXjhMfs7u56czlZ2TfUHHt8gbNLe4LbJ6rIr0RJYcLPcEFBkrWDbLTAd0XJIK/r9wMTzyyC23nOb/Bd9LCrwXN5bnVNqpNB03H5fT5JRb5//BE0kKCQ7xdYAPCws7Z+i9uHUhISGyWokFAJVVenq6oqKifMtr165V165dZTsjTN2zZ0+9+OKLOnXqlGrUqGFEmVWX2y3Nni399a/SsWPesTvukF59Vapd29jaAAAALlH8C7Ua8QXXoytXcN1qtqpjrY76Zv83+uHQDwTXAQAAAAAAAABAmfF4PHI4HMrKyvI9srOz/X6WNFZ4XVZWlrKzspXnyLugWqweqyweb+g8wBOgmp6aRUPlhcLnAZ6AImNm0WUZMJpJJllkkcVtkV32i9qXS67SBd4LjeXn5+t0xmmlmdPkNDnlMp2j1XwJ7Ha7QkJC/B7BwcF+P8+17sznNroTA2Vi9+7deuutt3zd1iUpJSVF9evX95sXFxfnW1dScD0vL095eX/cv2RkZJRDxVXMpk3SyJHSDz94l5s1k956S7r+emPrAgAAuMQRXK9Gth33BtcrYzD8ilpX+ILrwy8fbnQ5AAAAAAAAAADAIB6Pp0iIvLiAebGh8jPmF3Q7z87Olst1YWFOSTLL7A2cuy2yeCwK8gQpzBNWYhfzwmMFwXOLx0LgHECxLPL+/WJ3XVwA3i33+XV+NzmVb86XK98lR5ZDOaYcpZhT5DQ55ZHnguuwWq0KCQ5RcEiwQkNDiw24lxR6L25eUFCQTCa+HQJV15NPPqkXX3zxrHO2b9+upk2b+pYPHz6sXr166fbbb9f9999/0TVMnjxZkyZNuuj9VAsnT0pPPy3NmCF5PFJoqPTss9Ijj0h88AYAAMBwBNerEV/H9ZjK1XFdkq6of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size 3000x2000 with 16 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import warnings\n","warnings.filterwarnings('ignore')\n","fig,ax = plt.subplots(len(numeric_features),4,figsize=(30,20))\n","for index,i in enumerate(numeric_features):\n","    sns.distplot(train[i],ax=ax[index,0],color='green')\n","    sns.boxplot(train[i],ax=ax[index,1],color='yellow')\n","    sns.violinplot(train[i],ax=ax[index,2],color='purple')\n","    stats.probplot(train[i],plot=ax[index,3])\n","    \n","fig.tight_layout()\n","fig.subplots_adjust(top=0.95)\n","plt.suptitle(\"Visualizing continuous columns (train dataset)\",fontsize=30)"]},{"cell_type":"markdown","metadata":{"id":"R7EbAvUnC2mC"},"source":["The analysis showed that only the BMI variable is close to the normal distribution, the rest are close to bimodal."]},{"cell_type":"markdown","metadata":{"id":"bad50f58","papermill":{"duration":0.048821,"end_time":"2022-04-15T07:42:30.478725","exception":false,"start_time":"2022-04-15T07:42:30.429904","status":"completed"},"tags":[]},"source":["Let's look at the distribution of the number of people with heart disease from various factors"]},{"cell_type":"code","execution_count":12,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:55:59.675158Z","iopub.status.busy":"2022-08-29T07:55:59.674231Z","iopub.status.idle":"2022-08-29T07:56:09.275552Z","shell.execute_reply":"2022-08-29T07:56:09.274308Z","shell.execute_reply.started":"2022-08-29T07:55:59.675110Z"},"id":"wrzs0VZwfw7t","outputId":"7572f52d-ccdc-4953-88e0-6bfbdfc2509e","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 2000x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for feature in categorical_features:\n","  fig, ax1 = plt.subplots(figsize=(20,10))\n","  graph = sns.countplot(ax=ax1,x = feature , data = train,hue='HeartDisease',palette='pastel')\n","  graph.set_xticklabels(graph.get_xticklabels(),rotation=90)\n","  for p in graph.patches:\n","    height = p.get_height()\n","    graph.text(p.get_x()+p.get_width()/2., height + 0.1,height ,ha=\"center\")"]},{"cell_type":"markdown","metadata":{"id":"d4fc9577","papermill":{"duration":0.050553,"end_time":"2022-04-15T07:42:41.884124","exception":false,"start_time":"2022-04-15T07:42:41.833571","status":"completed"},"tags":[]},"source":["I propose to look at the distribution of categorical variables depending on gender"]},{"cell_type":"markdown","metadata":{"id":"ae399357","papermill":{"duration":0.054155,"end_time":"2022-04-15T07:42:55.991530","exception":false,"start_time":"2022-04-15T07:42:55.937375","status":"completed"},"tags":[]},"source":["Let's look at outliers in numerical variables depending on several factors"]},{"cell_type":"code","execution_count":13,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:09.277551Z","iopub.status.busy":"2022-08-29T07:56:09.277186Z","iopub.status.idle":"2022-08-29T07:56:10.890516Z","shell.execute_reply":"2022-08-29T07:56:10.889507Z","shell.execute_reply.started":"2022-08-29T07:56:09.277515Z"},"id":"b032c70e","outputId":"d177e216-aef4-49b4-a61d-7338152db37a","papermill":{"duration":2.035858,"end_time":"2022-04-15T07:42:58.083881","exception":false,"start_time":"2022-04-15T07:42:56.048023","status":"completed"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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size 1500x2500 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["i=1\n","plt.figure(figsize=(15,25))\n","for feature in numeric_features:\n","    plt.subplot(6,3,i)\n","    sns.boxplot(y=train[feature], x = train['AlcoholDrinking'])\n","    i+=1"]},{"cell_type":"code","execution_count":14,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:10.892750Z","iopub.status.busy":"2022-08-29T07:56:10.892361Z","iopub.status.idle":"2022-08-29T07:56:12.760344Z","shell.execute_reply":"2022-08-29T07:56:12.759587Z","shell.execute_reply.started":"2022-08-29T07:56:10.892700Z"},"id":"4608cb9d","outputId":"a5996633-9b17-47f0-b647-a53fb2b3fac0","papermill":{"duration":2.204804,"end_time":"2022-04-15T07:43:00.347774","exception":false,"start_time":"2022-04-15T07:42:58.142970","status":"completed"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1500x2500 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["i=1\n","plt.figure(figsize=(15,25))\n","for feature in numeric_features:\n","    plt.subplot(6,3,i)\n","    sns.boxplot(y=train[feature], x = train['Diabetic'])\n","    i+=1"]},{"cell_type":"markdown","metadata":{"id":"r_1adLMGD94q"},"source":["From all the graphs presented, it can be concluded that alcohol consumption and smoking are not the main factors in heart disease, as scientists testify to this."]},{"cell_type":"markdown","metadata":{"id":"Beedh0qn4svB"},"source":["<a id=\"title-four\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>STATISTICAL TESTS</center></h1>"]},{"cell_type":"markdown","metadata":{"id":"RYzsJPAUCm6O"},"source":["Let's transform our dataset using the OrdinalEncoder method."]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:56:12.762184Z","iopub.status.busy":"2022-08-29T07:56:12.761730Z","iopub.status.idle":"2022-08-29T07:56:14.774679Z","shell.execute_reply":"2022-08-29T07:56:14.773895Z","shell.execute_reply.started":"2022-08-29T07:56:12.762126Z"},"id":"dUuI-ZQxBqzv","trusted":true},"outputs":[],"source":["from sklearn.preprocessing import OrdinalEncoder\n","enc = OrdinalEncoder()\n","enc.fit(train[categorical_features])\n","train[categorical_features] = enc.transform(train[categorical_features])"]},{"cell_type":"markdown","metadata":{"id":"BK9xuz6j5t-u"},"source":["Let's carry out the Shapiro-Wilk test to check for the normality of the distribution of numerical variables."]},{"cell_type":"code","execution_count":16,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:14.776496Z","iopub.status.busy":"2022-08-29T07:56:14.776048Z","iopub.status.idle":"2022-08-29T07:56:14.853008Z","shell.execute_reply":"2022-08-29T07:56:14.851977Z","shell.execute_reply.started":"2022-08-29T07:56:14.776448Z"},"id":"fPJlf6bG50AC","outputId":"0726b46b-b7d6-4287-c517-9696323bd207","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Statistics=0.928, p=0.000\n","Sample does not look Gaussian (reject H0)\n","Statistics=0.476, p=0.000\n","Sample does not look Gaussian (reject H0)\n","Statistics=0.551, p=0.000\n","Sample does not look Gaussian (reject H0)\n","Statistics=0.892, p=0.000\n","Sample does not look Gaussian (reject H0)\n"]}],"source":["# Shapiro-Wilk Test\n","from scipy.stats import shapiro\n","# normality test\n","for feature in numeric_features:\n","\tstat, p = shapiro(train[feature])\n","\tprint('Statistics=%.3f, p=%.3f' % (stat, p))\n","\t# interpret\n","\talpha = 0.05\n","\tif p > alpha:\n","\t\tprint('Sample looks Gaussian (fail to reject H0)')\n","\telse:\n","\t\tprint('Sample does not look Gaussian (reject H0)')"]},{"cell_type":"markdown","metadata":{"id":"zXCPxTRX6GUj"},"source":["The test for normality has not been passed, therefore, to find the relationship of our variables, it is necessary to use non-parametric tests. Since we mostly have categorical variables, we use Pearson's Chi-square test to check."]},{"cell_type":"markdown","metadata":{"id":"0tY7kjt47o8m"},"source":["The purpose of the test is to determine if two variables are related to each other.\n","\n","Null hypothesis:\n","We start by defining the null hypothesis ( H0 ), which states that there is no relationship between the variables.\n","An alternative hypothesis could argue that there is a significant relationship between the two."]},{"cell_type":"code","execution_count":17,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:14.854696Z","iopub.status.busy":"2022-08-29T07:56:14.854427Z","iopub.status.idle":"2022-08-29T07:56:15.438931Z","shell.execute_reply":"2022-08-29T07:56:15.437666Z","shell.execute_reply.started":"2022-08-29T07:56:14.854664Z"},"id":"Ebae8wLa7oPA","outputId":"14801a7c-b4bd-4d3a-9014-dc2b0f6d2e5b","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 1.892352227090306e-73\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 2.9886131565632597e-180\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 2.2386144306283177e-121\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n","p value is 0.0\n","Dependent (reject H0)\n"]}],"source":["from scipy.stats import chi2_contingency\n","# defining the table\n","for feature in categorical_features:\n","  stat, p, dof, expected = chi2_contingency(pd.crosstab(train[feature],train['HeartDisease']))\n","# interpret p-value\n","  alpha = 0.05\n","  print ( \"p value is \" + str (p))\n","  if p<=alpha:\n","    print ( 'Dependent (reject H0)' )\n","  else :\n","    print ( 'Independent (H0 holds true)' )"]},{"cell_type":"markdown","metadata":{"id":"d5mWCxU4Dp_7"},"source":["In our dataset, there is a statistically significant relationship between the target variable and categorical features. This means that you should try to make a small prediction of the target feature."]},{"cell_type":"markdown","metadata":{"id":"2405ae86","papermill":{"duration":0.059806,"end_time":"2022-04-15T07:43:10.497063","exception":false,"start_time":"2022-04-15T07:43:10.437257","status":"completed"},"tags":[]},"source":["Let's build a correlation table"]},{"cell_type":"code","execution_count":18,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:15.441085Z","iopub.status.busy":"2022-08-29T07:56:15.440540Z","iopub.status.idle":"2022-08-29T07:56:15.798119Z","shell.execute_reply":"2022-08-29T07:56:15.797035Z","shell.execute_reply.started":"2022-08-29T07:56:15.441035Z"},"id":"6cdbfd86","outputId":"d5ab5ca2-e8e3-4381-afd4-66f02872664f","papermill":{"duration":2.514981,"end_time":"2022-04-15T07:43:13.073719","exception":false,"start_time":"2022-04-15T07:43:10.558738","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["HeartDisease        1.000000\n","AgeCategory         0.233432\n","DiffWalking         0.201258\n","Stroke              0.196835\n","PhysicalHealth      0.170721\n","Diabetic            0.168553\n","KidneyDisease       0.145197\n","Smoking             0.107764\n","SkinCancer          0.093317\n","Sex                 0.070040\n","BMI                 0.051803\n","Asthma              0.041444\n","Race                0.034854\n","MentalHealth        0.028591\n","SleepTime           0.008327\n","GenHealth          -0.011062\n","AlcoholDrinking    -0.032080\n","PhysicalActivity   -0.100030\n","Name: HeartDisease, dtype: float64 \n","\n"]}],"source":["correlation = train.corr()\n","print(correlation['HeartDisease'].sort_values(ascending = False),'\\n')"]},{"cell_type":"code","execution_count":19,"metadata":{"_kg_hide-input":true,"execution":{"iopub.execute_input":"2022-08-29T07:56:15.800632Z","iopub.status.busy":"2022-08-29T07:56:15.799863Z","iopub.status.idle":"2022-08-29T07:56:17.753358Z","shell.execute_reply":"2022-08-29T07:56:17.752340Z","shell.execute_reply.started":"2022-08-29T07:56:15.800583Z"},"id":"326070fc","outputId":"41edd6d7-3a87-41d3-b3e3-5434a4f678ae","papermill":{"duration":1.660685,"end_time":"2022-04-15T07:43:14.796548","exception":false,"start_time":"2022-04-15T07:43:13.135863","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Index(['HeartDisease', 'AgeCategory', 'DiffWalking', 'Stroke',\n","       'PhysicalHealth', 'Diabetic', 'KidneyDisease', 'Smoking', 'SkinCancer',\n","       'Sex', 'BMI', 'Asthma', 'Race', 'MentalHealth', 'SleepTime',\n","       'GenHealth', 'AlcoholDrinking', 'PhysicalActivity'],\n","      dtype='object')\n"]},{"data":{"text/plain":["<Axes: 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","text/plain":["<Figure size 1400x1200 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["k= 18\n","cols = correlation.nlargest(k,'HeartDisease')['HeartDisease'].index\n","print(cols)\n","cm = np.corrcoef(train[cols].values.T)\n","mask = np.triu(np.ones_like(train.corr()))\n","f , ax = plt.subplots(figsize = (14,12))\n","sns.heatmap(cm,mask=mask, vmax=.8, linewidths=0.01,square=True,annot=True,cmap='viridis',\n","            linecolor=\"white\",xticklabels = cols.values ,annot_kws = {'size':12},yticklabels = cols.values)"]},{"cell_type":"markdown","metadata":{"id":"NXcVnQDmkOt0"},"source":["<a id=\"title-five\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>MACHINE LEARNING</center></h1>"]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:56:17.754759Z","iopub.status.busy":"2022-08-29T07:56:17.754529Z","iopub.status.idle":"2022-08-29T07:56:19.380637Z","shell.execute_reply":"2022-08-29T07:56:19.379612Z","shell.execute_reply.started":"2022-08-29T07:56:17.754727Z"},"id":"e54dddb6","papermill":{"duration":1.444846,"end_time":"2022-04-15T07:43:17.493879","exception":false,"start_time":"2022-04-15T07:43:16.049033","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["from sklearn.model_selection import train_test_split, KFold, GridSearchCV\n","\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.ensemble import ExtraTreesClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.ensemble import GradientBoostingClassifier\n","from sklearn.svm import SVC\n","from sklearn.metrics import accuracy_score\n","# not working on ARM from lightgbm import LGBMClassifier\n","from xgboost import XGBClassifier\n","\n","from sklearn.metrics import precision_score,recall_score\n","from sklearn.metrics import f1_score"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:56:19.382249Z","iopub.status.busy":"2022-08-29T07:56:19.382008Z","iopub.status.idle":"2022-08-29T07:56:19.405685Z","shell.execute_reply":"2022-08-29T07:56:19.404706Z","shell.execute_reply.started":"2022-08-29T07:56:19.382200Z"},"id":"Oa7DT8XQlgAu","trusted":true},"outputs":[],"source":["y=train['HeartDisease']\n","train.drop('HeartDisease',axis=1,inplace=True)"]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:56:19.407607Z","iopub.status.busy":"2022-08-29T07:56:19.407335Z","iopub.status.idle":"2022-08-29T07:56:19.495484Z","shell.execute_reply":"2022-08-29T07:56:19.494562Z","shell.execute_reply.started":"2022-08-29T07:56:19.407574Z"},"id":"1a45f940","papermill":{"duration":0.235328,"end_time":"2022-04-15T07:43:17.798803","exception":false,"start_time":"2022-04-15T07:43:17.563475","status":"completed"},"tags":[],"trusted":true},"outputs":[],"source":["X_train, X_test, y_train, y_test=train_test_split(train,y,test_size=0.1,random_state=42)"]},{"cell_type":"markdown","metadata":{"id":"3f1cba0d","papermill":{"duration":0.069262,"end_time":"2022-04-15T07:43:17.937288","exception":false,"start_time":"2022-04-15T07:43:17.868026","status":"completed"},"tags":[]},"source":["Let's build simple models and evaluate the quality of their training"]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{"iopub.execute_input":"2022-08-29T07:56:19.497664Z","iopub.status.busy":"2022-08-29T07:56:19.496856Z","iopub.status.idle":"2022-08-29T08:01:11.633663Z","shell.execute_reply":"2022-08-29T08:01:11.632373Z","shell.execute_reply.started":"2022-08-29T07:56:19.497630Z"},"id":"78a8c0e5","papermill":{"duration":250.019534,"end_time":"2022-04-15T07:47:28.027076","exception":false,"start_time":"2022-04-15T07:43:18.007542","status":"completed"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["model: KNeighborsClassifier()\n","Accuracy_score: 0.9027517198248906\n","Precission_score: 0.3271428571428571\n","Recall_score: 0.0798465829846583\n","F1-score: 0.12836322869955158\n","------------------------------ \n","\n","model: LogisticRegression()\n","Accuracy_score: 0.9115697310819262\n","Precission_score: 0.5423728813559322\n","Recall_score: 0.08926080892608089\n","F1-score: 0.1532934131736527\n","------------------------------ \n","\n","model: XGBClassifier(base_score=None, booster=None, callbacks=None,\n","              colsample_bylevel=None, colsample_bynode=None,\n","              colsample_bytree=None, device=None, early_stopping_rounds=None,\n","              enable_categorical=False, eval_metric=None, feature_types=None,\n","              gamma=None, grow_policy=None, importance_type=None,\n","              interaction_constraints=None, learning_rate=None, max_bin=None,\n","              max_cat_threshold=None, max_cat_to_onehot=None,\n","              max_delta_step=None, max_depth=None, max_leaves=None,\n","              min_child_weight=None, missing=nan, monotone_constraints=None,\n","              multi_strategy=None, n_estimators=None, n_jobs=None,\n","              num_parallel_tree=None, random_state=None, ...)\n","Accuracy_score: 0.9114133833646029\n","Precission_score: 0.5337186897880539\n","Recall_score: 0.09658298465829847\n","F1-score: 0.16356657809270742\n","------------------------------ \n","\n","model: ExtraTreesClassifier()\n","Accuracy_score: 0.892714196372733\n","Precission_score: 0.3035589672016748\n","Recall_score: 0.151673640167364\n","F1-score: 0.2022785398744478\n","------------------------------ \n","\n"]}],"source":["models = [KNeighborsClassifier(), LogisticRegression(), XGBClassifier(),ExtraTreesClassifier()]\n","scores = dict()\n","\n","for m in models:\n","    m.fit(X_train, y_train)\n","    y_pred = m.predict(X_test)\n","\n","    print(f'model: {str(m)}')\n","    print(f'Accuracy_score: {accuracy_score(y_test,y_pred)}')\n","    print(f'Precission_score: {precision_score(y_test,y_pred)}')\n","    print(f'Recall_score: {recall_score(y_test,y_pred)}')\n","    print(f'F1-score: {f1_score(y_test,y_pred)}')\n","    print('-'*30, '\\n')"]},{"cell_type":"markdown","metadata":{"id":"a908cdd1","papermill":{"duration":0.073199,"end_time":"2022-04-15T07:47:28.174834","exception":false,"start_time":"2022-04-15T07:47:28.101635","status":"completed"},"tags":[]},"source":["High accuracy is explained by a large proportion of healthy people. When dealing with imbalanced classes, this metric is useless. More informative metrics are precission,recall. Precision can be interpreted as the proportion of objects called positive by the classifier and at the same time are really positive, and recall shows what proportion of objects of a positive class out of all objects of a positive class the algorithm found. The balance between these two metrics is F1-score. It is the introduction of precision that does not allow us to write all objects into one class, since in this case we get an increase in the False Positive level. Recall demonstrates the algorithm's ability to detect a given class in general, while precision demonstrates the ability to distinguish this class from other classes. To solve our problem, you should choose the algorithm with the highest recall, but the choice of the final algorithm is up to you. It would be possible to search for the optimal hyperparameters of the model, but since Since the problem is exploratory in nature, we will leave the search for hyperparameters for competitions."]},{"cell_type":"markdown","metadata":{"id":"FcnQQjHugf6_"},"source":["<a id=\"title-six\"></a>\n","<h1 style='background:#4682B4; border:2; border-radius: 10px; color:black'><center>RECOMENDATIONS</center></h1>"]},{"cell_type":"markdown","metadata":{"id":"-AJgXAw-gqJe"},"source":["10 Essential Measures to Prevent Cardiovascular Disease\n","1. Healthy nutrition. The composition of the daily diet significantly affects the state of blood vessels and the heart. Frequent and excessive consumption of fatty and fried foods, coffee, chicken eggs, salt and sugar is a sure way to worsen the condition of blood vessels and develop heart attacks, strokes, hypertension and other dangerous ailments.\n","The increased content of saturated fats, caffeine, salt and sugar increases the level of \"bad\" cholesterol and blood sugar. Under their influence, atherosclerotic plaques that calcify over time are formed on the vascular walls. There is a narrowing of the lumen of the vessels, leading to their wear. This factor increases the load on the heart, arterial hypertension develops. Hypertension, in turn, leads to the development of many serious diseases that can lead to disability and death.\n","\n","2. Fighting excess weight. Obesity always increases the risk of vascular and heart pathologies - every extra 10 kg can increase blood pressure by 10-20 mm Hg. Art. All people need to be regularly weighed and have their abdominal circumference measured to determine abdominal obesity.\n","\n","3. Fight against physical inactivity. Hypodynamia is one of the common causes of diseases of the heart and blood vessels. This is confirmed by the facts about the low physical activity of citizens and the elderly. Remember! Physical activity should be appropriate for age and general health. Be sure to check with your doctor if you have any contraindications for physical education, and what loads are acceptable for you!\n","\n","4. Giving up bad habits. All studies on the effects of smoking, alcohol and drugs point to one indisputable fact - giving up these bad habits can reduce the risk of heart and vascular diseases by dozens of times. If you cannot get rid of addiction yourself, then to give up addictions, you should use the following methods: to quit smoking - acupuncture, nicotine patches or chewing gums, hypnosis;\n","to refuse alcohol or drug addiction - a course of treatment and rehabilitation by a professional narcologist.\n","5. Fighting stress. Frequent stressful situations lead to wear of blood vessels and myocardium. During nervous tension, the level of adrenaline rises. In response to its impact, the heart begins to beat faster, and the vessels are constricted by spasm. As a result, there is a jump in blood pressure, and the myocardium wears out much faster.\n","\n","6. Self-control of blood pressure and its timely reduction\n"," An increase in blood pressure leads to the development of coronary artery disease, heart attacks, strokes and other pathologies of the heart and blood vessels. That is why all people should regularly monitor pressure indicators.\n","\n","7. Systematic preventive examination. Scheduled preventive examination and timely visits to a cardiologist should become the norm for people at risk for the development of pathologies of the heart and blood vessels. The same applies to people who report an increase in blood pressure when measured independently. Do not neglect the recommendations of your doctor!\n","\n","\n","8. Controlling blood cholesterol levels\n","It is necessary to start annually to control the level of cholesterol in the blood after 30 years. In healthy people, its level should not exceed 5 mmol / l, and in patients with diabetes - 4-4.5 mmol / l.\n","\n","9. Blood sugar control\n","It is necessary to start monitoring blood sugar levels annually after 40-45 years. Its level should not exceed 3.3-5.5 mmol / l (in the blood from a finger), 4-6 mmol / l (in the blood from a vein).\n","\n","10. Taking blood thinners\n","For people at risk, a cardiologist may recommend taking blood thinners. The choice of the drug, its dose, the duration of the course of administration is determined only by the doctor, guided by the data of analyzes and other examinations.\n","\n","* Compliance with these rules for the prevention of cardiovascular diseases will significantly reduce the risk of their development. Remember this and be healthy!"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"}},"nbformat":4,"nbformat_minor":4}
diff --git a/framingham-cvd-risk/pipeline.py b/framingham-cvd-risk/pipeline.py
new file mode 100644
index 000000000..85ed6d264
--- /dev/null
+++ b/framingham-cvd-risk/pipeline.py
@@ -0,0 +1,157 @@
+from kfp import dsl
+from kfp import kubernetes
+
+
+@dsl.component(base_image='registry.access.redhat.com/ubi8/python-39', packages_to_install=['kaggle', 'pandas', 'scikit-learn'])
+def kaggle_dataset():
+    from kaggle.api.kaggle_api_extended import KaggleApi
+    kaggle = KaggleApi()
+    kaggle.authenticate()
+    kaggle.dataset_download_files(dataset='kamilpytlak/personal-key-indicators-of-heart-disease',
+        path='archive/',
+        force=True,
+        quiet=False,
+        unzip=True
+    )
+    import pandas as pd
+    train = pd.read_csv('archive/2020/heart_2020_cleaned.csv')
+    numeric_features=['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']
+    categorical_features=['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory',
+        'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth','Asthma', 'KidneyDisease', 'SkinCancer']
+
+    from sklearn.preprocessing import OrdinalEncoder
+    enc = OrdinalEncoder()
+    enc.fit(train[categorical_features])
+    train[categorical_features] = enc.transform(train[categorical_features])
+
+    y=train['HeartDisease']
+    train.drop('HeartDisease',axis=1,inplace=True)
+
+    from sklearn.model_selection import train_test_split
+    X_train, X_test, y_train, y_test=train_test_split(train,y,test_size=0.1,random_state=42)
+    print(X_train)
+
+    import pickle
+    def save_pickle(model, object_file):
+        with open(object_file, "wb") as f:
+            pickle.dump(model, f)
+    save_pickle(X_train, '/data/X_train.pkl')
+    save_pickle(X_test, '/data/X_test.pkl')
+    save_pickle(y_train, '/data/y_train.pkl')
+    save_pickle(y_test,'/data/y_test.pkl')
+    import os
+    print(os.listdir('/data'))
+
+
+@dsl.component(base_image='registry.access.redhat.com/ubi8/python-39', packages_to_install=['pandas', 'scikit-learn', 'xgboost'])
+def train_model_using(module_name: str, class_name: str):
+    print(f'Using model_name: {module_name} {class_name}')
+    import os
+    print(os.listdir('/data'))
+    import pickle
+    def save_pickle(model, object_file):
+        with open(object_file, "wb") as f:
+            pickle.dump(model, f)
+    def load_pickle(object_file):
+        with open(object_file, "rb") as f:
+            return pickle.load(f)
+    X_train = load_pickle('/data/X_train.pkl')
+    X_test = load_pickle('/data/X_test.pkl')
+    y_train = load_pickle('/data/y_train.pkl')
+    y_test = load_pickle('/data/y_test.pkl')
+    
+    from sklearn.metrics import accuracy_score
+    from sklearn.metrics import precision_score,recall_score
+    from sklearn.metrics import f1_score
+    import importlib
+    def instantiate_class(module_name, class_name, *args, **kwargs):
+        try:
+            module = importlib.import_module(module_name)
+            class_ = getattr(module, class_name)
+            instance = class_(*args, **kwargs)
+            return instance
+        except ImportError as e:
+            print(f"Error importing module {module_name}: {e}")
+        except AttributeError as e:
+            print(f"Error getting class {class_name} from module {module_name}: {e}")
+        except Exception as e:
+            print(f"Error instantiating class {class_name} from module {module_name}: {e}")
+    m = instantiate_class(module_name, class_name)
+    m.fit(X_train, y_train)
+    y_pred = m.predict(X_test)
+
+    result = {
+        'model': str(m),
+        'Accuracy_score': accuracy_score(y_test,y_pred),
+        'Precission_score': precision_score(y_test,y_pred),
+        'Recall_score': recall_score(y_test,y_pred),
+        'F1-score': f1_score(y_test,y_pred),
+    }
+    print(result)
+    save_pickle(result,f'/data/{class_name}_results.pkl') # save result statistics, space-friendly for constrained environments
+    
+
+@dsl.component(base_image='registry.access.redhat.com/ubi8/python-39', packages_to_install=['pandas', 'scikit-learn'])
+def select_best():
+    import os
+    import pickle
+    def load_pickle(object_file):
+        with open(object_file, "rb") as f:
+            return pickle.load(f)
+    all_files = os.listdir('/data')
+    print(all_files)
+    result_files = [file for file in all_files if file.endswith("_results.pkl")]
+    results = []
+    for result_file in result_files:
+        file_path = os.path.join('/data', result_file)
+        single_result = load_pickle(file_path)
+        print(f'Result from file {file_path}: {single_result}')
+        results.append(single_result)
+    best_result = max(results, key=lambda x: x['Accuracy_score'])
+    print("Best result:")
+    import json
+    print(json.dumps(best_result, indent=2))
+
+
+@dsl.pipeline
+def framingham_cvd_risk_pipeline():
+    pvc1 = kubernetes.CreatePVC(
+        pvc_name_suffix='-mypipeline-pvc',
+        access_modes=['ReadWriteMany'],
+        size='500Mi',
+        storage_class_name='standard-csi', # might need to change
+    )
+
+    dataset_task = kaggle_dataset().set_caching_options(enable_caching=False)
+    kubernetes.use_secret_as_env(
+        dataset_task,
+        secret_name='kaggle-api',
+        secret_key_to_env={'KAGGLE_USERNAME': 'KAGGLE_USERNAME', 'KAGGLE_KEY': 'KAGGLE_KEY'})
+    kubernetes.mount_pvc(
+        dataset_task,
+        pvc_name=pvc1.outputs['name'],
+        mount_path='/data',
+    )
+
+    KNeighborsClassifier_task = train_model_using(module_name="sklearn.neighbors", class_name="KNeighborsClassifier").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(KNeighborsClassifier_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+    LogisticRegression_task = train_model_using(module_name="sklearn.linear_model", class_name="LogisticRegression").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(LogisticRegression_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+    XGBClassifier_task = train_model_using(module_name="xgboost", class_name="XGBClassifier").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(XGBClassifier_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+    ExtraTreesClassifier_task = train_model_using(module_name="sklearn.ensemble", class_name="ExtraTreesClassifier").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(ExtraTreesClassifier_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+    RandomForestClassifier_task = train_model_using(module_name="sklearn.ensemble", class_name="RandomForestClassifier").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(RandomForestClassifier_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+    GradientBoostingClassifier_task = train_model_using(module_name="sklearn.ensemble", class_name="GradientBoostingClassifier").after(dataset_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(GradientBoostingClassifier_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+
+    select_best_task = select_best().after(KNeighborsClassifier_task,LogisticRegression_task,XGBClassifier_task,ExtraTreesClassifier_task,RandomForestClassifier_task,GradientBoostingClassifier_task).set_caching_options(enable_caching=False)
+    kubernetes.mount_pvc(select_best_task,pvc_name=pvc1.outputs['name'],mount_path='/data')
+
+    delete_pvc1 = kubernetes.DeletePVC(pvc_name=pvc1.outputs['name']).after(select_best_task)
+
+
+if __name__ == '__main__':
+    from kfp import compiler
+    compiler.Compiler().compile(framingham_cvd_risk_pipeline, __file__+'.yaml')
diff --git a/framingham-cvd-risk/pipeline.py.yaml b/framingham-cvd-risk/pipeline.py.yaml
new file mode 100644
index 000000000..6a4713d58
--- /dev/null
+++ b/framingham-cvd-risk/pipeline.py.yaml
@@ -0,0 +1,770 @@
+# PIPELINE DEFINITION
+# Name: framingham-cvd-risk-pipeline
+components:
+  comp-createpvc:
+    executorLabel: exec-createpvc
+    inputDefinitions:
+      parameters:
+        access_modes:
+          description: 'AccessModes to request for the provisioned PVC. May
+
+            be one or more of ``''ReadWriteOnce''``, ``''ReadOnlyMany''``, ``''ReadWriteMany''``,
+            or
+
+            ``''ReadWriteOncePod''``. Corresponds to `PersistentVolumeClaim.spec.accessModes
+            <https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes>`_.'
+          parameterType: LIST
+        annotations:
+          description: Annotations for the PVC's metadata. Corresponds to `PersistentVolumeClaim.metadata.annotations
+            <https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
+          isOptional: true
+          parameterType: STRUCT
+        pvc_name:
+          description: 'Name of the PVC. Corresponds to `PersistentVolumeClaim.metadata.name
+            <https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaim>`_.
+            Only one of ``pvc_name`` and ``pvc_name_suffix`` can
+
+            be provided.'
+          isOptional: true
+          parameterType: STRING
+        pvc_name_suffix:
+          description: 'Prefix to use for a dynamically generated name, which
+
+            will take the form ``<argo-workflow-name>-<pvc_name_suffix>``. Only one
+
+            of ``pvc_name`` and ``pvc_name_suffix`` can be provided.'
+          isOptional: true
+          parameterType: STRING
+        size:
+          description: The size of storage requested by the PVC that will be provisioned.
+            For example, ``'5Gi'``. Corresponds to `PersistentVolumeClaim.spec.resources.requests.storage
+            <https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.
+          parameterType: STRING
+        storage_class_name:
+          defaultValue: ''
+          description: 'Name of StorageClass from which to provision the PV
+
+            to back the PVC. ``None`` indicates to use the cluster''s default
+
+            storage_class_name. Set to ``''''`` for a statically specified PVC.'
+          isOptional: true
+          parameterType: STRING
+        volume_name:
+          description: 'Pre-existing PersistentVolume that should back the
+
+            provisioned PersistentVolumeClaim. Used for statically
+
+            specified PV only. Corresponds to `PersistentVolumeClaim.spec.volumeName
+            <https://kubernetes.io/docs/reference/kubernetes-api/config-and-storage-resources/persistent-volume-claim-v1/#PersistentVolumeClaimSpec>`_.'
+          isOptional: true
+          parameterType: STRING
+    outputDefinitions:
+      parameters:
+        name:
+          parameterType: STRING
+  comp-deletepvc:
+    executorLabel: exec-deletepvc
+    inputDefinitions:
+      parameters:
+        pvc_name:
+          description: Name of the PVC to delete. Supports passing a runtime-generated
+            name, such as a name provided by ``kubernetes.CreatePvcOp().outputs['name']``.
+          parameterType: STRING
+  comp-kaggle-dataset:
+    executorLabel: exec-kaggle-dataset
+  comp-select-best:
+    executorLabel: exec-select-best
+  comp-train-model-using:
+    executorLabel: exec-train-model-using
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+  comp-train-model-using-2:
+    executorLabel: exec-train-model-using-2
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+  comp-train-model-using-3:
+    executorLabel: exec-train-model-using-3
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+  comp-train-model-using-4:
+    executorLabel: exec-train-model-using-4
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+  comp-train-model-using-5:
+    executorLabel: exec-train-model-using-5
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+  comp-train-model-using-6:
+    executorLabel: exec-train-model-using-6
+    inputDefinitions:
+      parameters:
+        class_name:
+          parameterType: STRING
+        module_name:
+          parameterType: STRING
+deploymentSpec:
+  executors:
+    exec-createpvc:
+      container:
+        image: argostub/createpvc
+    exec-deletepvc:
+      container:
+        image: argostub/deletepvc
+    exec-kaggle-dataset:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - kaggle_dataset
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'kaggle' 'pandas'\
+          \ 'scikit-learn' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef kaggle_dataset():\n    from kaggle.api.kaggle_api_extended import\
+          \ KaggleApi\n    kaggle = KaggleApi()\n    kaggle.authenticate()\n    kaggle.dataset_download_files(dataset='kamilpytlak/personal-key-indicators-of-heart-disease',\n\
+          \        path='archive/',\n        force=True,\n        quiet=False,\n \
+          \       unzip=True\n    )\n    import pandas as pd\n    train = pd.read_csv('archive/2020/heart_2020_cleaned.csv')\n\
+          \    numeric_features=['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n\
+          \    categorical_features=['HeartDisease', 'Smoking', 'AlcoholDrinking',\
+          \ 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory',\n        'Race', 'Diabetic',\
+          \ 'PhysicalActivity', 'GenHealth','Asthma', 'KidneyDisease', 'SkinCancer']\n\
+          \n    from sklearn.preprocessing import OrdinalEncoder\n    enc = OrdinalEncoder()\n\
+          \    enc.fit(train[categorical_features])\n    train[categorical_features]\
+          \ = enc.transform(train[categorical_features])\n\n    y=train['HeartDisease']\n\
+          \    train.drop('HeartDisease',axis=1,inplace=True)\n\n    from sklearn.model_selection\
+          \ import train_test_split\n    X_train, X_test, y_train, y_test=train_test_split(train,y,test_size=0.1,random_state=42)\n\
+          \    print(X_train)\n\n    import pickle\n    def save_pickle(model, object_file):\n\
+          \        with open(object_file, \"wb\") as f:\n            pickle.dump(model,\
+          \ f)\n    save_pickle(X_train, '/data/X_train.pkl')\n    save_pickle(X_test,\
+          \ '/data/X_test.pkl')\n    save_pickle(y_train, '/data/y_train.pkl')\n \
+          \   save_pickle(y_test,'/data/y_test.pkl')\n    import os\n    print(os.listdir('/data'))\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-select-best:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - select_best
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef select_best():\n    import os\n    import pickle\n    def load_pickle(object_file):\n\
+          \        with open(object_file, \"rb\") as f:\n            return pickle.load(f)\n\
+          \    all_files = os.listdir('/data')\n    print(all_files)\n    result_files\
+          \ = [file for file in all_files if file.endswith(\"_results.pkl\")]\n  \
+          \  results = []\n    for result_file in result_files:\n        file_path\
+          \ = os.path.join('/data', result_file)\n        single_result = load_pickle(file_path)\n\
+          \        print(f'Result from file {file_path}: {single_result}')\n     \
+          \   results.append(single_result)\n    best_result = max(results, key=lambda\
+          \ x: x['Accuracy_score'])\n    print(\"Best result:\")\n    import json\n\
+          \    print(json.dumps(best_result, indent=2))\n\n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using-2:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using-3:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using-4:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using-5:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+    exec-train-model-using-6:
+      container:
+        args:
+        - --executor_input
+        - '{{$}}'
+        - --function_to_execute
+        - train_model_using
+        command:
+        - sh
+        - -c
+        - "\nif ! [ -x \"$(command -v pip)\" ]; then\n    python3 -m ensurepip ||\
+          \ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
+          \ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.6.0'\
+          \ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"'  &&\
+          \  python3 -m pip install --quiet --no-warn-script-location 'pandas' 'scikit-learn'\
+          \ 'xgboost' && \"$0\" \"$@\"\n"
+        - sh
+        - -ec
+        - 'program_path=$(mktemp -d)
+
+
+          printf "%s" "$0" > "$program_path/ephemeral_component.py"
+
+          _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main                         --component_module_path                         "$program_path/ephemeral_component.py"                         "$@"
+
+          '
+        - "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
+          \ *\n\ndef train_model_using(module_name: str, class_name: str):\n    print(f'Using\
+          \ model_name: {module_name} {class_name}')\n    import os\n    print(os.listdir('/data'))\n\
+          \    import pickle\n    def save_pickle(model, object_file):\n        with\
+          \ open(object_file, \"wb\") as f:\n            pickle.dump(model, f)\n \
+          \   def load_pickle(object_file):\n        with open(object_file, \"rb\"\
+          ) as f:\n            return pickle.load(f)\n    X_train = load_pickle('/data/X_train.pkl')\n\
+          \    X_test = load_pickle('/data/X_test.pkl')\n    y_train = load_pickle('/data/y_train.pkl')\n\
+          \    y_test = load_pickle('/data/y_test.pkl')\n\n    from sklearn.metrics\
+          \ import accuracy_score\n    from sklearn.metrics import precision_score,recall_score\n\
+          \    from sklearn.metrics import f1_score\n    import importlib\n    def\
+          \ instantiate_class(module_name, class_name, *args, **kwargs):\n       \
+          \ try:\n            module = importlib.import_module(module_name)\n    \
+          \        class_ = getattr(module, class_name)\n            instance = class_(*args,\
+          \ **kwargs)\n            return instance\n        except ImportError as\
+          \ e:\n            print(f\"Error importing module {module_name}: {e}\")\n\
+          \        except AttributeError as e:\n            print(f\"Error getting\
+          \ class {class_name} from module {module_name}: {e}\")\n        except Exception\
+          \ as e:\n            print(f\"Error instantiating class {class_name} from\
+          \ module {module_name}: {e}\")\n    m = instantiate_class(module_name, class_name)\n\
+          \    m.fit(X_train, y_train)\n    y_pred = m.predict(X_test)\n\n    result\
+          \ = {\n        'model': str(m),\n        'Accuracy_score': accuracy_score(y_test,y_pred),\n\
+          \        'Precission_score': precision_score(y_test,y_pred),\n        'Recall_score':\
+          \ recall_score(y_test,y_pred),\n        'F1-score': f1_score(y_test,y_pred),\n\
+          \    }\n    print(result)\n    save_pickle(result,f'/data/{class_name}_results.pkl')\
+          \ # save result statistics, space-friendly for constrained environments\n\
+          \n"
+        image: registry.access.redhat.com/ubi8/python-39
+pipelineInfo:
+  name: framingham-cvd-risk-pipeline
+root:
+  dag:
+    tasks:
+      createpvc:
+        cachingOptions:
+          enableCache: true
+        componentRef:
+          name: comp-createpvc
+        inputs:
+          parameters:
+            access_modes:
+              runtimeValue:
+                constant:
+                - ReadWriteMany
+            pvc_name_suffix:
+              runtimeValue:
+                constant: -mypipeline-pvc
+            size:
+              runtimeValue:
+                constant: 500Mi
+            storage_class_name:
+              runtimeValue:
+                constant: standard-csi
+        taskInfo:
+          name: createpvc
+      deletepvc:
+        cachingOptions:
+          enableCache: true
+        componentRef:
+          name: comp-deletepvc
+        dependentTasks:
+        - createpvc
+        - select-best
+        inputs:
+          parameters:
+            pvc_name:
+              taskOutputParameter:
+                outputParameterKey: name
+                producerTask: createpvc
+        taskInfo:
+          name: deletepvc
+      kaggle-dataset:
+        cachingOptions: {}
+        componentRef:
+          name: comp-kaggle-dataset
+        dependentTasks:
+        - createpvc
+        taskInfo:
+          name: kaggle-dataset
+      select-best:
+        cachingOptions: {}
+        componentRef:
+          name: comp-select-best
+        dependentTasks:
+        - createpvc
+        - train-model-using
+        - train-model-using-2
+        - train-model-using-3
+        - train-model-using-4
+        - train-model-using-5
+        - train-model-using-6
+        taskInfo:
+          name: select-best
+      train-model-using:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: KNeighborsClassifier
+            module_name:
+              runtimeValue:
+                constant: sklearn.neighbors
+        taskInfo:
+          name: train-model-using
+      train-model-using-2:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using-2
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: LogisticRegression
+            module_name:
+              runtimeValue:
+                constant: sklearn.linear_model
+        taskInfo:
+          name: train-model-using-2
+      train-model-using-3:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using-3
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: XGBClassifier
+            module_name:
+              runtimeValue:
+                constant: xgboost
+        taskInfo:
+          name: train-model-using-3
+      train-model-using-4:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using-4
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: ExtraTreesClassifier
+            module_name:
+              runtimeValue:
+                constant: sklearn.ensemble
+        taskInfo:
+          name: train-model-using-4
+      train-model-using-5:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using-5
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: RandomForestClassifier
+            module_name:
+              runtimeValue:
+                constant: sklearn.ensemble
+        taskInfo:
+          name: train-model-using-5
+      train-model-using-6:
+        cachingOptions: {}
+        componentRef:
+          name: comp-train-model-using-6
+        dependentTasks:
+        - createpvc
+        - kaggle-dataset
+        inputs:
+          parameters:
+            class_name:
+              runtimeValue:
+                constant: GradientBoostingClassifier
+            module_name:
+              runtimeValue:
+                constant: sklearn.ensemble
+        taskInfo:
+          name: train-model-using-6
+schemaVersion: 2.1.0
+sdkVersion: kfp-2.6.0
+---
+platforms:
+  kubernetes:
+    deploymentSpec:
+      executors:
+        exec-kaggle-dataset:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+          secretAsEnv:
+          - keyToEnv:
+            - envVar: KAGGLE_USERNAME
+              secretKey: KAGGLE_USERNAME
+            - envVar: KAGGLE_KEY
+              secretKey: KAGGLE_KEY
+            secretName: kaggle-api
+        exec-select-best:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using-2:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using-3:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using-4:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using-5:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
+        exec-train-model-using-6:
+          pvcMount:
+          - mountPath: /data
+            taskOutputParameter:
+              outputParameterKey: name
+              producerTask: createpvc
diff --git a/framingham-cvd-risk/requirements-ipynb.txt b/framingham-cvd-risk/requirements-ipynb.txt
new file mode 100644
index 000000000..0bedef9ad
--- /dev/null
+++ b/framingham-cvd-risk/requirements-ipynb.txt
@@ -0,0 +1,10 @@
+pandas
+numpy
+matplotlib
+seaborn
+missingno
+scipy
+jinja2
+scikit-learn
+xgboost
+kaggle
diff --git a/framingham-cvd-risk/requirements-kfp.txt b/framingham-cvd-risk/requirements-kfp.txt
new file mode 100644
index 000000000..af2d803da
--- /dev/null
+++ b/framingham-cvd-risk/requirements-kfp.txt
@@ -0,0 +1,2 @@
+kfp
+kfp-kubernetes
diff --git a/framingham-cvd-risk/simpified-for-pipeline.ipynb b/framingham-cvd-risk/simpified-for-pipeline.ipynb
new file mode 100644
index 000000000..fc3e23fc7
--- /dev/null
+++ b/framingham-cvd-risk/simpified-for-pipeline.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading personal-key-indicators-of-heart-disease.zip to archive\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 21.4M/21.4M [00:04<00:00, 5.39MB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ['KAGGLE_USERNAME'] = '<replace-with-your-username>'\n",
+    "os.environ['KAGGLE_KEY'] = '<replace-with-your-key>'\n",
+    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
+    "kaggle = KaggleApi()\n",
+    "kaggle.authenticate()\n",
+    "kaggle.dataset_download_files(dataset='kamilpytlak/personal-key-indicators-of-heart-disease',\n",
+    "    path='archive/',\n",
+    "    force=True,\n",
+    "    quiet=False,\n",
+    "    unzip=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "train = pd.read_csv('archive/2020/heart_2020_cleaned.csv')\n",
+    "\n",
+    "numeric_features=['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n",
+    "categorical_features=['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory',\n",
+    "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth','Asthma', 'KidneyDisease', 'SkinCancer']\n",
+    "\n",
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "enc = OrdinalEncoder()\n",
+    "enc.fit(train[categorical_features])\n",
+    "train[categorical_features] = enc.transform(train[categorical_features])\n",
+    "\n",
+    "y=train['HeartDisease']\n",
+    "train.drop('HeartDisease',axis=1,inplace=True)\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test=train_test_split(train,y,test_size=0.1,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model: KNeighborsClassifier()\n",
+      "Accuracy_score: 0.9027517198248906\n",
+      "Precission_score: 0.3271428571428571\n",
+      "Recall_score: 0.0798465829846583\n",
+      "F1-score: 0.12836322869955158\n",
+      "------------------------------ \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mmortari/git/demo20240120-cvd-risk/venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model: LogisticRegression()\n",
+      "Accuracy_score: 0.9115697310819262\n",
+      "Precission_score: 0.5423728813559322\n",
+      "Recall_score: 0.08926080892608089\n",
+      "F1-score: 0.1532934131736527\n",
+      "------------------------------ \n",
+      "\n",
+      "model: XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+      "              colsample_bylevel=None, colsample_bynode=None,\n",
+      "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+      "              gamma=None, grow_policy=None, importance_type=None,\n",
+      "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+      "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+      "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+      "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+      "              num_parallel_tree=None, random_state=None, ...)\n",
+      "Accuracy_score: 0.9114133833646029\n",
+      "Precission_score: 0.5337186897880539\n",
+      "Recall_score: 0.09658298465829847\n",
+      "F1-score: 0.16356657809270742\n",
+      "------------------------------ \n",
+      "\n",
+      "model: ExtraTreesClassifier()\n",
+      "Accuracy_score: 0.8925578486554097\n",
+      "Precission_score: 0.3046767537826685\n",
+      "Recall_score: 0.15446304044630405\n",
+      "F1-score: 0.2049976862563628\n",
+      "------------------------------ \n",
+      "\n",
+      "model: RandomForestClassifier()\n",
+      "Accuracy_score: 0.9024702939337086\n",
+      "Precission_score: 0.3660618996798292\n",
+      "Recall_score: 0.1195955369595537\n",
+      "F1-score: 0.1802890932982917\n",
+      "------------------------------ \n",
+      "\n",
+      "model: GradientBoostingClassifier()\n",
+      "Accuracy_score: 0.9126328955597248\n",
+      "Precission_score: 0.5876777251184834\n",
+      "Recall_score: 0.08647140864714087\n",
+      "F1-score: 0.15075987841945288\n",
+      "------------------------------ \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import ExtraTreesClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from xgboost import XGBClassifier\n",
+    "from sklearn.metrics import precision_score,recall_score\n",
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "models = [KNeighborsClassifier(), LogisticRegression(), XGBClassifier(), ExtraTreesClassifier(),\n",
+    "          RandomForestClassifier(), GradientBoostingClassifier()]\n",
+    "\n",
+    "from collections import namedtuple\n",
+    "import importlib\n",
+    "\n",
+    "ClassInfo = namedtuple(\"ClassInfo\", [\"module_name\", \"class_name\"])\n",
+    "models = [\n",
+    "    ClassInfo(module_name=\"sklearn.neighbors\", class_name=\"KNeighborsClassifier\"),\n",
+    "    ClassInfo(module_name=\"sklearn.linear_model\", class_name=\"LogisticRegression\"),\n",
+    "    ClassInfo(module_name=\"xgboost\", class_name=\"XGBClassifier\"),\n",
+    "    ClassInfo(module_name=\"sklearn.ensemble\", class_name=\"ExtraTreesClassifier\"),\n",
+    "    ClassInfo(module_name=\"sklearn.ensemble\", class_name=\"RandomForestClassifier\"),\n",
+    "    ClassInfo(module_name=\"sklearn.ensemble\", class_name=\"GradientBoostingClassifier\")\n",
+    "]\n",
+    "models = {class_info.class_name: class_info for class_info in models}\n",
+    "\n",
+    "def instantiate_class(class_info, *args, **kwargs):\n",
+    "    try:\n",
+    "        module = importlib.import_module(class_info.module_name)\n",
+    "        class_ = getattr(module, class_info.class_name)\n",
+    "        instance = class_(*args, **kwargs)\n",
+    "        return instance\n",
+    "    except ImportError as e:\n",
+    "        print(f\"Error importing module {class_info.module_name}: {e}\")\n",
+    "    except AttributeError as e:\n",
+    "        print(f\"Error getting class {class_info.class_name} from module {class_info.module_name}: {e}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error instantiating class {class_info.class_name} from module {class_info.module_name}: {e}\")\n",
+    "\n",
+    "for v in models.values():\n",
+    "    m = instantiate_class(v)\n",
+    "    m.fit(X_train, y_train)\n",
+    "    y_pred = m.predict(X_test)\n",
+    "\n",
+    "    print(f'model: {str(m)}')\n",
+    "    print(f'Accuracy_score: {accuracy_score(y_test,y_pred)}')\n",
+    "    print(f'Precission_score: {precision_score(y_test,y_pred)}')\n",
+    "    print(f'Recall_score: {recall_score(y_test,y_pred)}')\n",
+    "    print(f'F1-score: {f1_score(y_test,y_pred)}')\n",
+    "    print('-'*30, '\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}