lecture_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License \n",
    "\n",
    "Copyright 2020 - 2023 Patrick Hall (jphall@gwu.edu)\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISCLAIMER:** This notebook is not legal compliance advice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 2: Global and Local Explanations of a Constrained Model\n",
    "This notebook presents a combination of global and local explanation techniques to paint a detailed portrait of a constrained model's behavior. Explanations help summarize model mechanisms and predictions. This is important for model documentation, model debugging, human appeal of model decisions, and human learning from machine learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Global hyperpameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 12345 # global random seed for better reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports and inits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/patrickh/Workspace/GWU_rml/env_rml/lib/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"1.8.0_252\"; OpenJDK Runtime Environment (build 1.8.0_252-8u252-b09-1~18.04-b09); OpenJDK 64-Bit Server VM (build 25.252-b09, mixed mode)\n",
      "  Starting server from /home/patrickh/Workspace/GWU_rml/env_rml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmp91ychf7x\n",
      "  JVM stdout: /tmp/tmp91ychf7x/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmp91ychf7x/h2o_patrickh_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n",
      "Warning: Your H2O cluster version is too old (9 months and 4 days)! Please download and install the latest version from http://h2o.ai/download/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.26.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>9 months and 4 days !!!</td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_28iu80</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>21.33 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>24</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>4</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.9 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.26.0.3\n",
       "H2O cluster version age:    9 months and 4 days !!!\n",
       "H2O cluster name:           H2O_from_python_patrickh_28iu80\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    21.33 Gb\n",
       "H2O cluster total cores:    24\n",
       "H2O cluster allowed cores:  4\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.9 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rmltk import explain        # simple module for explaining\n",
    "import h2o                       # import h2o python bindings to h2o java server\n",
    "import numpy as np               # array, vector, matrix calculations\n",
    "import pandas as pd              # DataFrame handling\n",
    "from scipy.special import expit  # for inverse logit\n",
    "import shap                      # for visualizing Shapley values\n",
    "import time                      # for timers\n",
    "\n",
    "import matplotlib.pyplot as plt      # plotting\n",
    "pd.options.display.max_columns = 999 # enable display of all columns in notebook\n",
    "\n",
    "# enables display of images in notebook\n",
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "\n",
    "# in-notebook display of matplotlib charts\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(SEED)                     # set random seed for better reproducibility\n",
    "\n",
    "h2o.init(max_mem_size='24G', nthreads=4) # start h2o with plenty of memory and threads\n",
    "h2o.remove_all()                         # clears h2o memory\n",
    "h2o.no_progress()                        # turn off h2o progress indicators    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start global timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_tic = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download, Explore, and Prepare UCI Credit Card Default Data\n",
    "\n",
    "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
    "\n",
    "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input features: \n",
    "\n",
    "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n",
    "* **`SEX`**: 1 = male; 2 = female\n",
    "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n",
    "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n",
    "* **`AGE`**: Age in years \n",
    "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n",
    "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n",
    "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n",
    "\n",
    "The steps below import a pre-trained model and explain it for the credit card validation data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data and reformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import XLS file\n",
    "path = 'default_of_credit_card_clients.xls'\n",
    "data = pd.read_excel(path,\n",
    "                     skiprows=1) # skip the first row of the spreadsheet\n",
    "\n",
    "# remove spaces from target column name \n",
    "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign modeling roles\n",
    "Selected features are carried forward from the best model in the previous lecture. Notice again that demographic features are not used as model inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_name = DEFAULT_NEXT_MONTH\n",
      "x_names = ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'LIMIT_BAL', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs for models\n",
    "y_name = 'DEFAULT_NEXT_MONTH'\n",
    "x_names = ['PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'LIMIT_BAL', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT4']\n",
    "print('y_name =', y_name)\n",
    "print('x_names =', x_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into training and validation partitions\n",
    "Explanations will be calculated for the validation data to give a better idea of how explanations will look on future unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data rows = 20946, columns = 25\n",
      "Validation data rows = 9054, columns = 25\n"
     ]
    }
   ],
   "source": [
    "split_ratio = 0.7 # 70%/30% train/test split\n",
    "\n",
    "# execute split\n",
    "split = np.random.rand(len(data)) < split_ratio\n",
    "train = data[split]\n",
    "valid = data[~split]\n",
    "\n",
    "# summarize split\n",
    "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n",
    "print('Validation data rows = %d, columns = %d' % (valid.shape[0], valid.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Pre-trained Monotonic GBM\n",
    "Load the model known as `mgbm5` from the previous lecture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Details\n",
      "=============\n",
      "H2OGradientBoostingEstimator :  Gradient Boosting Machine\n",
      "Model Key:  best_mgbm\n",
      "\n",
      "\n",
      "Model Summary: "
     ]
    },
    {
     "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></th>\n",
       "      <th>number_of_trees</th>\n",
       "      <th>number_of_internal_trees</th>\n",
       "      <th>model_size_in_bytes</th>\n",
       "      <th>min_depth</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>mean_depth</th>\n",
       "      <th>min_leaves</th>\n",
       "      <th>max_leaves</th>\n",
       "      <th>mean_leaves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>46.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6943.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.369565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     number_of_trees  number_of_internal_trees  model_size_in_bytes  \\\n",
       "0               46.0                      46.0               6943.0   \n",
       "\n",
       "   min_depth  max_depth  mean_depth  min_leaves  max_leaves  mean_leaves  \n",
       "0        3.0        3.0         3.0         5.0         8.0     7.369565  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on train data. **\n",
      "\n",
      "MSE: 0.13637719864300343\n",
      "RMSE: 0.3692928358945018\n",
      "LogLoss: 0.4351274080189972\n",
      "Mean Per-Class Error: 0.2913939696264273\n",
      "AUC: 0.7716491282246187\n",
      "pr_auc: 0.5471826859054356\n",
      "Gini: 0.5432982564492375\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.21968260039166268: "
     ]
    },
    {
     "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></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>13482.0</td>\n",
       "      <td>2814.0</td>\n",
       "      <td>0.1727</td>\n",
       "      <td>(2814.0/16296.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1907.0</td>\n",
       "      <td>2743.0</td>\n",
       "      <td>0.4101</td>\n",
       "      <td>(1907.0/4650.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>15389.0</td>\n",
       "      <td>5557.0</td>\n",
       "      <td>0.2254</td>\n",
       "      <td>(4721.0/20946.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0       1   Error               Rate\n",
       "0      0  13482.0  2814.0  0.1727   (2814.0/16296.0)\n",
       "1      1   1907.0  2743.0  0.4101    (1907.0/4650.0)\n",
       "2  Total  15389.0  5557.0  0.2254   (4721.0/20946.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>max f1</td>\n",
       "      <td>0.219683</td>\n",
       "      <td>0.537474</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max f2</td>\n",
       "      <td>0.127859</td>\n",
       "      <td>0.630227</td>\n",
       "      <td>329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>0.583033</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.446699</td>\n",
       "      <td>0.821493</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max precision</td>\n",
       "      <td>0.950247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>max recall</td>\n",
       "      <td>0.050609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.950247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.325159</td>\n",
       "      <td>0.413494</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.177542</td>\n",
       "      <td>0.698495</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.219683</td>\n",
       "      <td>0.708606</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        metric  threshold     value    idx\n",
       "0                       max f1   0.219683  0.537474  248.0\n",
       "1                       max f2   0.127859  0.630227  329.0\n",
       "2                 max f0point5   0.446699  0.583033  147.0\n",
       "3                 max accuracy   0.446699  0.821493  147.0\n",
       "4                max precision   0.950247  1.000000    0.0\n",
       "5                   max recall   0.050609  1.000000  395.0\n",
       "6              max specificity   0.950247  1.000000    0.0\n",
       "7             max absolute_mcc   0.325159  0.413494  194.0\n",
       "8   max min_per_class_accuracy   0.177542  0.698495  281.0\n",
       "9  max mean_per_class_accuracy   0.219683  0.708606  248.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 22.20 %, avg score: 22.00 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>cumulative_data_fraction</th>\n",
       "      <th>lower_threshold</th>\n",
       "      <th>lift</th>\n",
       "      <th>cumulative_lift</th>\n",
       "      <th>response_rate</th>\n",
       "      <th>score</th>\n",
       "      <th>cumulative_response_rate</th>\n",
       "      <th>cumulative_score</th>\n",
       "      <th>capture_rate</th>\n",
       "      <th>cumulative_capture_rate</th>\n",
       "      <th>gain</th>\n",
       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.010074</td>\n",
       "      <td>0.813927</td>\n",
       "      <td>3.607883</td>\n",
       "      <td>3.607883</td>\n",
       "      <td>0.800948</td>\n",
       "      <td>0.843446</td>\n",
       "      <td>0.800948</td>\n",
       "      <td>0.843446</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>0.036344</td>\n",
       "      <td>260.788259</td>\n",
       "      <td>260.788259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.020338</td>\n",
       "      <td>0.795575</td>\n",
       "      <td>3.519808</td>\n",
       "      <td>3.563432</td>\n",
       "      <td>0.781395</td>\n",
       "      <td>0.805153</td>\n",
       "      <td>0.791080</td>\n",
       "      <td>0.824119</td>\n",
       "      <td>0.036129</td>\n",
       "      <td>0.072473</td>\n",
       "      <td>251.980795</td>\n",
       "      <td>256.343177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.030316</td>\n",
       "      <td>0.763679</td>\n",
       "      <td>3.405328</td>\n",
       "      <td>3.511394</td>\n",
       "      <td>0.755981</td>\n",
       "      <td>0.783970</td>\n",
       "      <td>0.779528</td>\n",
       "      <td>0.810905</td>\n",
       "      <td>0.033978</td>\n",
       "      <td>0.106452</td>\n",
       "      <td>240.532798</td>\n",
       "      <td>251.139446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.040008</td>\n",
       "      <td>0.715138</td>\n",
       "      <td>3.261891</td>\n",
       "      <td>3.450954</td>\n",
       "      <td>0.724138</td>\n",
       "      <td>0.739815</td>\n",
       "      <td>0.766110</td>\n",
       "      <td>0.793684</td>\n",
       "      <td>0.031613</td>\n",
       "      <td>0.138065</td>\n",
       "      <td>226.189099</td>\n",
       "      <td>245.095388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.050081</td>\n",
       "      <td>0.664416</td>\n",
       "      <td>3.116869</td>\n",
       "      <td>3.383755</td>\n",
       "      <td>0.691943</td>\n",
       "      <td>0.686695</td>\n",
       "      <td>0.751192</td>\n",
       "      <td>0.772164</td>\n",
       "      <td>0.031398</td>\n",
       "      <td>0.169462</td>\n",
       "      <td>211.686898</td>\n",
       "      <td>238.375473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.100019</td>\n",
       "      <td>0.543384</td>\n",
       "      <td>2.859463</td>\n",
       "      <td>3.121984</td>\n",
       "      <td>0.634799</td>\n",
       "      <td>0.601794</td>\n",
       "      <td>0.693079</td>\n",
       "      <td>0.687101</td>\n",
       "      <td>0.142796</td>\n",
       "      <td>0.312258</td>\n",
       "      <td>185.946339</td>\n",
       "      <td>212.198445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.150005</td>\n",
       "      <td>0.366237</td>\n",
       "      <td>2.224293</td>\n",
       "      <td>2.822849</td>\n",
       "      <td>0.493792</td>\n",
       "      <td>0.446951</td>\n",
       "      <td>0.626671</td>\n",
       "      <td>0.607076</td>\n",
       "      <td>0.111183</td>\n",
       "      <td>0.423441</td>\n",
       "      <td>122.429306</td>\n",
       "      <td>182.284922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.205672</td>\n",
       "      <td>0.292765</td>\n",
       "      <td>1.595510</td>\n",
       "      <td>2.490659</td>\n",
       "      <td>0.354202</td>\n",
       "      <td>0.312777</td>\n",
       "      <td>0.552925</td>\n",
       "      <td>0.527422</td>\n",
       "      <td>0.088817</td>\n",
       "      <td>0.512258</td>\n",
       "      <td>59.551043</td>\n",
       "      <td>149.065864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.301251</td>\n",
       "      <td>0.196648</td>\n",
       "      <td>1.174504</td>\n",
       "      <td>2.073077</td>\n",
       "      <td>0.260739</td>\n",
       "      <td>0.234499</td>\n",
       "      <td>0.460222</td>\n",
       "      <td>0.434485</td>\n",
       "      <td>0.112258</td>\n",
       "      <td>0.624516</td>\n",
       "      <td>17.450421</td>\n",
       "      <td>107.307684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.400029</td>\n",
       "      <td>0.173817</td>\n",
       "      <td>0.864327</td>\n",
       "      <td>1.774604</td>\n",
       "      <td>0.191880</td>\n",
       "      <td>0.184844</td>\n",
       "      <td>0.393961</td>\n",
       "      <td>0.372842</td>\n",
       "      <td>0.085376</td>\n",
       "      <td>0.709892</td>\n",
       "      <td>-13.567284</td>\n",
       "      <td>77.460410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.500286</td>\n",
       "      <td>0.151431</td>\n",
       "      <td>0.701418</td>\n",
       "      <td>1.559537</td>\n",
       "      <td>0.155714</td>\n",
       "      <td>0.161335</td>\n",
       "      <td>0.346216</td>\n",
       "      <td>0.330455</td>\n",
       "      <td>0.070323</td>\n",
       "      <td>0.780215</td>\n",
       "      <td>-29.858249</td>\n",
       "      <td>55.953665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.600306</td>\n",
       "      <td>0.131214</td>\n",
       "      <td>0.619237</td>\n",
       "      <td>1.402870</td>\n",
       "      <td>0.137470</td>\n",
       "      <td>0.140709</td>\n",
       "      <td>0.311436</td>\n",
       "      <td>0.298841</td>\n",
       "      <td>0.061935</td>\n",
       "      <td>0.842151</td>\n",
       "      <td>-38.076342</td>\n",
       "      <td>40.286982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.700659</td>\n",
       "      <td>0.114794</td>\n",
       "      <td>0.559314</td>\n",
       "      <td>1.282050</td>\n",
       "      <td>0.124167</td>\n",
       "      <td>0.122817</td>\n",
       "      <td>0.284614</td>\n",
       "      <td>0.273630</td>\n",
       "      <td>0.056129</td>\n",
       "      <td>0.898280</td>\n",
       "      <td>-44.068568</td>\n",
       "      <td>28.204987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.800821</td>\n",
       "      <td>0.102226</td>\n",
       "      <td>0.369293</td>\n",
       "      <td>1.167887</td>\n",
       "      <td>0.081983</td>\n",
       "      <td>0.108062</td>\n",
       "      <td>0.259270</td>\n",
       "      <td>0.252921</td>\n",
       "      <td>0.036989</td>\n",
       "      <td>0.935269</td>\n",
       "      <td>-63.070697</td>\n",
       "      <td>16.788724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.904564</td>\n",
       "      <td>0.091861</td>\n",
       "      <td>0.402152</td>\n",
       "      <td>1.080066</td>\n",
       "      <td>0.089277</td>\n",
       "      <td>0.097524</td>\n",
       "      <td>0.239774</td>\n",
       "      <td>0.235099</td>\n",
       "      <td>0.041720</td>\n",
       "      <td>0.976989</td>\n",
       "      <td>-59.784808</td>\n",
       "      <td>8.006633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034810</td>\n",
       "      <td>0.241112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053527</td>\n",
       "      <td>0.076989</td>\n",
       "      <td>0.221999</td>\n",
       "      <td>0.220010</td>\n",
       "      <td>0.023011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-75.888783</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                  0.010074         0.813927  3.607883   \n",
       "1         2                  0.020338         0.795575  3.519808   \n",
       "2         3                  0.030316         0.763679  3.405328   \n",
       "3         4                  0.040008         0.715138  3.261891   \n",
       "4         5                  0.050081         0.664416  3.116869   \n",
       "5         6                  0.100019         0.543384  2.859463   \n",
       "6         7                  0.150005         0.366237  2.224293   \n",
       "7         8                  0.205672         0.292765  1.595510   \n",
       "8         9                  0.301251         0.196648  1.174504   \n",
       "9        10                  0.400029         0.173817  0.864327   \n",
       "10       11                  0.500286         0.151431  0.701418   \n",
       "11       12                  0.600306         0.131214  0.619237   \n",
       "12       13                  0.700659         0.114794  0.559314   \n",
       "13       14                  0.800821         0.102226  0.369293   \n",
       "14       15                  0.904564         0.091861  0.402152   \n",
       "15       16                  1.000000         0.034810  0.241112   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          3.607883       0.800948  0.843446                  0.800948   \n",
       "1          3.563432       0.781395  0.805153                  0.791080   \n",
       "2          3.511394       0.755981  0.783970                  0.779528   \n",
       "3          3.450954       0.724138  0.739815                  0.766110   \n",
       "4          3.383755       0.691943  0.686695                  0.751192   \n",
       "5          3.121984       0.634799  0.601794                  0.693079   \n",
       "6          2.822849       0.493792  0.446951                  0.626671   \n",
       "7          2.490659       0.354202  0.312777                  0.552925   \n",
       "8          2.073077       0.260739  0.234499                  0.460222   \n",
       "9          1.774604       0.191880  0.184844                  0.393961   \n",
       "10         1.559537       0.155714  0.161335                  0.346216   \n",
       "11         1.402870       0.137470  0.140709                  0.311436   \n",
       "12         1.282050       0.124167  0.122817                  0.284614   \n",
       "13         1.167887       0.081983  0.108062                  0.259270   \n",
       "14         1.080066       0.089277  0.097524                  0.239774   \n",
       "15         1.000000       0.053527  0.076989                  0.221999   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.843446      0.036344                 0.036344  260.788259   \n",
       "1           0.824119      0.036129                 0.072473  251.980795   \n",
       "2           0.810905      0.033978                 0.106452  240.532798   \n",
       "3           0.793684      0.031613                 0.138065  226.189099   \n",
       "4           0.772164      0.031398                 0.169462  211.686898   \n",
       "5           0.687101      0.142796                 0.312258  185.946339   \n",
       "6           0.607076      0.111183                 0.423441  122.429306   \n",
       "7           0.527422      0.088817                 0.512258   59.551043   \n",
       "8           0.434485      0.112258                 0.624516   17.450421   \n",
       "9           0.372842      0.085376                 0.709892  -13.567284   \n",
       "10          0.330455      0.070323                 0.780215  -29.858249   \n",
       "11          0.298841      0.061935                 0.842151  -38.076342   \n",
       "12          0.273630      0.056129                 0.898280  -44.068568   \n",
       "13          0.252921      0.036989                 0.935269  -63.070697   \n",
       "14          0.235099      0.041720                 0.976989  -59.784808   \n",
       "15          0.220010      0.023011                 1.000000  -75.888783   \n",
       "\n",
       "    cumulative_gain  \n",
       "0        260.788259  \n",
       "1        256.343177  \n",
       "2        251.139446  \n",
       "3        245.095388  \n",
       "4        238.375473  \n",
       "5        212.198445  \n",
       "6        182.284922  \n",
       "7        149.065864  \n",
       "8        107.307684  \n",
       "9         77.460410  \n",
       "10        55.953665  \n",
       "11        40.286982  \n",
       "12        28.204987  \n",
       "13        16.788724  \n",
       "14         8.006633  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "ModelMetricsBinomial: gbm\n",
      "** Reported on validation data. **\n",
      "\n",
      "MSE: 0.13326994104124376\n",
      "RMSE: 0.3650615578792757\n",
      "LogLoss: 0.4278285715046422\n",
      "Mean Per-Class Error: 0.2856607030196092\n",
      "AUC: 0.7776380047998697\n",
      "pr_auc: 0.5486322626112021\n",
      "Gini: 0.5552760095997393\n",
      "\n",
      "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.27397344199105433: "
     ]
    },
    {
     "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></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Error</th>\n",
       "      <th>Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6093.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>0.1379</td>\n",
       "      <td>(975.0/7068.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>863.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>0.4345</td>\n",
       "      <td>(863.0/1986.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total</td>\n",
       "      <td>6956.0</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>0.203</td>\n",
       "      <td>(1838.0/9054.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0       1   Error              Rate\n",
       "0      0  6093.0   975.0  0.1379    (975.0/7068.0)\n",
       "1      1   863.0  1123.0  0.4345    (863.0/1986.0)\n",
       "2  Total  6956.0  2098.0   0.203   (1838.0/9054.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Maximum Metrics: Maximum metrics at their respective thresholds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>threshold</th>\n",
       "      <th>value</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>max f1</td>\n",
       "      <td>0.273973</td>\n",
       "      <td>0.549951</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>max f2</td>\n",
       "      <td>0.147835</td>\n",
       "      <td>0.634488</td>\n",
       "      <td>307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>max f0point5</td>\n",
       "      <td>0.436620</td>\n",
       "      <td>0.590736</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>max accuracy</td>\n",
       "      <td>0.456963</td>\n",
       "      <td>0.825271</td>\n",
       "      <td>147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>max precision</td>\n",
       "      <td>0.947069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>max recall</td>\n",
       "      <td>0.045106</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>max specificity</td>\n",
       "      <td>0.947069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max absolute_mcc</td>\n",
       "      <td>0.347246</td>\n",
       "      <td>0.429999</td>\n",
       "      <td>184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>max min_per_class_accuracy</td>\n",
       "      <td>0.181585</td>\n",
       "      <td>0.709970</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>max mean_per_class_accuracy</td>\n",
       "      <td>0.230518</td>\n",
       "      <td>0.714339</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        metric  threshold     value    idx\n",
       "0                       max f1   0.273973  0.549951  217.0\n",
       "1                       max f2   0.147835  0.634488  307.0\n",
       "2                 max f0point5   0.436620  0.590736  153.0\n",
       "3                 max accuracy   0.456963  0.825271  147.0\n",
       "4                max precision   0.947069  1.000000    0.0\n",
       "5                   max recall   0.045106  1.000000  397.0\n",
       "6              max specificity   0.947069  1.000000    0.0\n",
       "7             max absolute_mcc   0.347246  0.429999  184.0\n",
       "8   max min_per_class_accuracy   0.181585  0.709970  275.0\n",
       "9  max mean_per_class_accuracy   0.230518  0.714339  240.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gains/Lift Table: Avg response rate: 21.94 %, avg score: 22.52 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>cumulative_data_fraction</th>\n",
       "      <th>lower_threshold</th>\n",
       "      <th>lift</th>\n",
       "      <th>cumulative_lift</th>\n",
       "      <th>response_rate</th>\n",
       "      <th>score</th>\n",
       "      <th>cumulative_response_rate</th>\n",
       "      <th>cumulative_score</th>\n",
       "      <th>capture_rate</th>\n",
       "      <th>cumulative_capture_rate</th>\n",
       "      <th>gain</th>\n",
       "      <th>cumulative_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>0.011155</td>\n",
       "      <td>0.815010</td>\n",
       "      <td>3.295055</td>\n",
       "      <td>3.295055</td>\n",
       "      <td>0.722772</td>\n",
       "      <td>0.839858</td>\n",
       "      <td>0.722772</td>\n",
       "      <td>0.839858</td>\n",
       "      <td>0.036757</td>\n",
       "      <td>0.036757</td>\n",
       "      <td>229.505549</td>\n",
       "      <td>229.505549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>0.020543</td>\n",
       "      <td>0.795575</td>\n",
       "      <td>3.700764</td>\n",
       "      <td>3.480460</td>\n",
       "      <td>0.811765</td>\n",
       "      <td>0.805631</td>\n",
       "      <td>0.763441</td>\n",
       "      <td>0.824217</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>0.071501</td>\n",
       "      <td>270.076417</td>\n",
       "      <td>248.045999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>0.030042</td>\n",
       "      <td>0.783550</td>\n",
       "      <td>3.604721</td>\n",
       "      <td>3.519749</td>\n",
       "      <td>0.790698</td>\n",
       "      <td>0.792441</td>\n",
       "      <td>0.772059</td>\n",
       "      <td>0.814170</td>\n",
       "      <td>0.034240</td>\n",
       "      <td>0.105740</td>\n",
       "      <td>260.472142</td>\n",
       "      <td>251.974853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>0.040093</td>\n",
       "      <td>0.743192</td>\n",
       "      <td>3.005876</td>\n",
       "      <td>3.390927</td>\n",
       "      <td>0.659341</td>\n",
       "      <td>0.761335</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.800925</td>\n",
       "      <td>0.030211</td>\n",
       "      <td>0.135952</td>\n",
       "      <td>200.587630</td>\n",
       "      <td>239.092657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>0.050033</td>\n",
       "      <td>0.697702</td>\n",
       "      <td>3.444512</td>\n",
       "      <td>3.401573</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.723091</td>\n",
       "      <td>0.746137</td>\n",
       "      <td>0.785461</td>\n",
       "      <td>0.034240</td>\n",
       "      <td>0.170191</td>\n",
       "      <td>244.451158</td>\n",
       "      <td>240.157260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>0.101281</td>\n",
       "      <td>0.553193</td>\n",
       "      <td>3.104777</td>\n",
       "      <td>3.251394</td>\n",
       "      <td>0.681034</td>\n",
       "      <td>0.614736</td>\n",
       "      <td>0.713195</td>\n",
       "      <td>0.699075</td>\n",
       "      <td>0.159114</td>\n",
       "      <td>0.329305</td>\n",
       "      <td>210.477654</td>\n",
       "      <td>225.139444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>0.150320</td>\n",
       "      <td>0.383564</td>\n",
       "      <td>2.187046</td>\n",
       "      <td>2.904171</td>\n",
       "      <td>0.479730</td>\n",
       "      <td>0.466067</td>\n",
       "      <td>0.637032</td>\n",
       "      <td>0.623061</td>\n",
       "      <td>0.107251</td>\n",
       "      <td>0.436556</td>\n",
       "      <td>118.704581</td>\n",
       "      <td>190.417123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>0.200022</td>\n",
       "      <td>0.296915</td>\n",
       "      <td>1.580423</td>\n",
       "      <td>2.575244</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.327817</td>\n",
       "      <td>0.564881</td>\n",
       "      <td>0.549698</td>\n",
       "      <td>0.078550</td>\n",
       "      <td>0.515106</td>\n",
       "      <td>58.042296</td>\n",
       "      <td>157.524427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>0.301303</td>\n",
       "      <td>0.203539</td>\n",
       "      <td>1.133514</td>\n",
       "      <td>2.090616</td>\n",
       "      <td>0.248637</td>\n",
       "      <td>0.250648</td>\n",
       "      <td>0.458578</td>\n",
       "      <td>0.449174</td>\n",
       "      <td>0.114804</td>\n",
       "      <td>0.629909</td>\n",
       "      <td>13.351366</td>\n",
       "      <td>109.061561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>10</td>\n",
       "      <td>0.403468</td>\n",
       "      <td>0.176970</td>\n",
       "      <td>0.961068</td>\n",
       "      <td>1.804595</td>\n",
       "      <td>0.210811</td>\n",
       "      <td>0.187190</td>\n",
       "      <td>0.395839</td>\n",
       "      <td>0.382836</td>\n",
       "      <td>0.098187</td>\n",
       "      <td>0.728097</td>\n",
       "      <td>-3.893198</td>\n",
       "      <td>80.459549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>0.500221</td>\n",
       "      <td>0.152028</td>\n",
       "      <td>0.655734</td>\n",
       "      <td>1.582382</td>\n",
       "      <td>0.143836</td>\n",
       "      <td>0.163566</td>\n",
       "      <td>0.347096</td>\n",
       "      <td>0.340424</td>\n",
       "      <td>0.063444</td>\n",
       "      <td>0.791541</td>\n",
       "      <td>-34.426603</td>\n",
       "      <td>58.238248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>12</td>\n",
       "      <td>0.599956</td>\n",
       "      <td>0.133009</td>\n",
       "      <td>0.555349</td>\n",
       "      <td>1.411651</td>\n",
       "      <td>0.121816</td>\n",
       "      <td>0.141651</td>\n",
       "      <td>0.309647</td>\n",
       "      <td>0.307381</td>\n",
       "      <td>0.055388</td>\n",
       "      <td>0.846928</td>\n",
       "      <td>-44.465076</td>\n",
       "      <td>41.165144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>13</td>\n",
       "      <td>0.702231</td>\n",
       "      <td>0.115062</td>\n",
       "      <td>0.492323</td>\n",
       "      <td>1.277757</td>\n",
       "      <td>0.107991</td>\n",
       "      <td>0.123549</td>\n",
       "      <td>0.280277</td>\n",
       "      <td>0.280607</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>0.897281</td>\n",
       "      <td>-50.767685</td>\n",
       "      <td>27.775745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td>0.801966</td>\n",
       "      <td>0.102380</td>\n",
       "      <td>0.353404</td>\n",
       "      <td>1.162802</td>\n",
       "      <td>0.077519</td>\n",
       "      <td>0.107834</td>\n",
       "      <td>0.255061</td>\n",
       "      <td>0.259121</td>\n",
       "      <td>0.035247</td>\n",
       "      <td>0.932528</td>\n",
       "      <td>-64.659594</td>\n",
       "      <td>16.280206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>15</td>\n",
       "      <td>0.905346</td>\n",
       "      <td>0.091861</td>\n",
       "      <td>0.379909</td>\n",
       "      <td>1.073405</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.097585</td>\n",
       "      <td>0.235452</td>\n",
       "      <td>0.240675</td>\n",
       "      <td>0.039275</td>\n",
       "      <td>0.971803</td>\n",
       "      <td>-62.009063</td>\n",
       "      <td>7.340501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034810</td>\n",
       "      <td>0.297899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065344</td>\n",
       "      <td>0.076884</td>\n",
       "      <td>0.219351</td>\n",
       "      <td>0.225172</td>\n",
       "      <td>0.028197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-70.210141</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      group  cumulative_data_fraction  lower_threshold      lift  \\\n",
       "0         1                  0.011155         0.815010  3.295055   \n",
       "1         2                  0.020543         0.795575  3.700764   \n",
       "2         3                  0.030042         0.783550  3.604721   \n",
       "3         4                  0.040093         0.743192  3.005876   \n",
       "4         5                  0.050033         0.697702  3.444512   \n",
       "5         6                  0.101281         0.553193  3.104777   \n",
       "6         7                  0.150320         0.383564  2.187046   \n",
       "7         8                  0.200022         0.296915  1.580423   \n",
       "8         9                  0.301303         0.203539  1.133514   \n",
       "9        10                  0.403468         0.176970  0.961068   \n",
       "10       11                  0.500221         0.152028  0.655734   \n",
       "11       12                  0.599956         0.133009  0.555349   \n",
       "12       13                  0.702231         0.115062  0.492323   \n",
       "13       14                  0.801966         0.102380  0.353404   \n",
       "14       15                  0.905346         0.091861  0.379909   \n",
       "15       16                  1.000000         0.034810  0.297899   \n",
       "\n",
       "    cumulative_lift  response_rate     score  cumulative_response_rate  \\\n",
       "0          3.295055       0.722772  0.839858                  0.722772   \n",
       "1          3.480460       0.811765  0.805631                  0.763441   \n",
       "2          3.519749       0.790698  0.792441                  0.772059   \n",
       "3          3.390927       0.659341  0.761335                  0.743802   \n",
       "4          3.401573       0.755556  0.723091                  0.746137   \n",
       "5          3.251394       0.681034  0.614736                  0.713195   \n",
       "6          2.904171       0.479730  0.466067                  0.637032   \n",
       "7          2.575244       0.346667  0.327817                  0.564881   \n",
       "8          2.090616       0.248637  0.250648                  0.458578   \n",
       "9          1.804595       0.210811  0.187190                  0.395839   \n",
       "10         1.582382       0.143836  0.163566                  0.347096   \n",
       "11         1.411651       0.121816  0.141651                  0.309647   \n",
       "12         1.277757       0.107991  0.123549                  0.280277   \n",
       "13         1.162802       0.077519  0.107834                  0.255061   \n",
       "14         1.073405       0.083333  0.097585                  0.235452   \n",
       "15         1.000000       0.065344  0.076884                  0.219351   \n",
       "\n",
       "    cumulative_score  capture_rate  cumulative_capture_rate        gain  \\\n",
       "0           0.839858      0.036757                 0.036757  229.505549   \n",
       "1           0.824217      0.034743                 0.071501  270.076417   \n",
       "2           0.814170      0.034240                 0.105740  260.472142   \n",
       "3           0.800925      0.030211                 0.135952  200.587630   \n",
       "4           0.785461      0.034240                 0.170191  244.451158   \n",
       "5           0.699075      0.159114                 0.329305  210.477654   \n",
       "6           0.623061      0.107251                 0.436556  118.704581   \n",
       "7           0.549698      0.078550                 0.515106   58.042296   \n",
       "8           0.449174      0.114804                 0.629909   13.351366   \n",
       "9           0.382836      0.098187                 0.728097   -3.893198   \n",
       "10          0.340424      0.063444                 0.791541  -34.426603   \n",
       "11          0.307381      0.055388                 0.846928  -44.465076   \n",
       "12          0.280607      0.050352                 0.897281  -50.767685   \n",
       "13          0.259121      0.035247                 0.932528  -64.659594   \n",
       "14          0.240675      0.039275                 0.971803  -62.009063   \n",
       "15          0.225172      0.028197                 1.000000  -70.210141   \n",
       "\n",
       "    cumulative_gain  \n",
       "0        229.505549  \n",
       "1        248.045999  \n",
       "2        251.974853  \n",
       "3        239.092657  \n",
       "4        240.157260  \n",
       "5        225.139444  \n",
       "6        190.417123  \n",
       "7        157.524427  \n",
       "8        109.061561  \n",
       "9         80.459549  \n",
       "10        58.238248  \n",
       "11        41.165144  \n",
       "12        27.775745  \n",
       "13        16.280206  \n",
       "14         7.340501  \n",
       "15         0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Scoring History: "
     ]
    },
    {
     "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></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>duration</th>\n",
       "      <th>number_of_trees</th>\n",
       "      <th>training_rmse</th>\n",
       "      <th>training_logloss</th>\n",
       "      <th>training_auc</th>\n",
       "      <th>training_pr_auc</th>\n",
       "      <th>training_lift</th>\n",
       "      <th>training_classification_error</th>\n",
       "      <th>validation_rmse</th>\n",
       "      <th>validation_logloss</th>\n",
       "      <th>validation_auc</th>\n",
       "      <th>validation_pr_auc</th>\n",
       "      <th>validation_lift</th>\n",
       "      <th>validation_classification_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.173 sec</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.415591</td>\n",
       "      <td>0.529427</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.778001</td>\n",
       "      <td>0.413815</td>\n",
       "      <td>0.526105</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.780649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.197 sec</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.407822</td>\n",
       "      <td>0.511864</td>\n",
       "      <td>0.716131</td>\n",
       "      <td>0.534717</td>\n",
       "      <td>3.474912</td>\n",
       "      <td>0.236370</td>\n",
       "      <td>0.405538</td>\n",
       "      <td>0.507496</td>\n",
       "      <td>0.726731</td>\n",
       "      <td>0.537125</td>\n",
       "      <td>3.444264</td>\n",
       "      <td>0.187652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.218 sec</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.401483</td>\n",
       "      <td>0.498746</td>\n",
       "      <td>0.744646</td>\n",
       "      <td>0.532172</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.228731</td>\n",
       "      <td>0.398808</td>\n",
       "      <td>0.493698</td>\n",
       "      <td>0.752909</td>\n",
       "      <td>0.534588</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.232825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.242 sec</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.396471</td>\n",
       "      <td>0.489013</td>\n",
       "      <td>0.748189</td>\n",
       "      <td>0.535621</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.228636</td>\n",
       "      <td>0.393394</td>\n",
       "      <td>0.483273</td>\n",
       "      <td>0.756448</td>\n",
       "      <td>0.535692</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.214491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.268 sec</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.392442</td>\n",
       "      <td>0.481430</td>\n",
       "      <td>0.750121</td>\n",
       "      <td>0.535358</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.210780</td>\n",
       "      <td>0.389030</td>\n",
       "      <td>0.475135</td>\n",
       "      <td>0.758511</td>\n",
       "      <td>0.536095</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.217915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.291 sec</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.389141</td>\n",
       "      <td>0.475375</td>\n",
       "      <td>0.750058</td>\n",
       "      <td>0.535198</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.245059</td>\n",
       "      <td>0.385453</td>\n",
       "      <td>0.468630</td>\n",
       "      <td>0.758505</td>\n",
       "      <td>0.535659</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.214270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.313 sec</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.386399</td>\n",
       "      <td>0.470332</td>\n",
       "      <td>0.756986</td>\n",
       "      <td>0.535024</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.243961</td>\n",
       "      <td>0.382447</td>\n",
       "      <td>0.463157</td>\n",
       "      <td>0.764722</td>\n",
       "      <td>0.536039</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.229843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.338 sec</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.384191</td>\n",
       "      <td>0.466316</td>\n",
       "      <td>0.757005</td>\n",
       "      <td>0.535418</td>\n",
       "      <td>3.529706</td>\n",
       "      <td>0.243961</td>\n",
       "      <td>0.380045</td>\n",
       "      <td>0.458834</td>\n",
       "      <td>0.764634</td>\n",
       "      <td>0.536411</td>\n",
       "      <td>3.422307</td>\n",
       "      <td>0.220013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.363 sec</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.382341</td>\n",
       "      <td>0.462760</td>\n",
       "      <td>0.761106</td>\n",
       "      <td>0.540176</td>\n",
       "      <td>3.514359</td>\n",
       "      <td>0.247446</td>\n",
       "      <td>0.378063</td>\n",
       "      <td>0.455049</td>\n",
       "      <td>0.770340</td>\n",
       "      <td>0.542043</td>\n",
       "      <td>3.457524</td>\n",
       "      <td>0.204330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.389 sec</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.380701</td>\n",
       "      <td>0.459589</td>\n",
       "      <td>0.762515</td>\n",
       "      <td>0.540880</td>\n",
       "      <td>3.518279</td>\n",
       "      <td>0.235654</td>\n",
       "      <td>0.376184</td>\n",
       "      <td>0.451464</td>\n",
       "      <td>0.772358</td>\n",
       "      <td>0.543522</td>\n",
       "      <td>3.457524</td>\n",
       "      <td>0.223548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.415 sec</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.379202</td>\n",
       "      <td>0.456705</td>\n",
       "      <td>0.762522</td>\n",
       "      <td>0.541424</td>\n",
       "      <td>3.518279</td>\n",
       "      <td>0.235606</td>\n",
       "      <td>0.374583</td>\n",
       "      <td>0.448380</td>\n",
       "      <td>0.772982</td>\n",
       "      <td>0.543893</td>\n",
       "      <td>3.457524</td>\n",
       "      <td>0.226309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.439 sec</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.378052</td>\n",
       "      <td>0.454467</td>\n",
       "      <td>0.761648</td>\n",
       "      <td>0.541505</td>\n",
       "      <td>3.521332</td>\n",
       "      <td>0.231023</td>\n",
       "      <td>0.373354</td>\n",
       "      <td>0.445973</td>\n",
       "      <td>0.772925</td>\n",
       "      <td>0.544553</td>\n",
       "      <td>3.460882</td>\n",
       "      <td>0.228960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.466 sec</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.377043</td>\n",
       "      <td>0.452420</td>\n",
       "      <td>0.762767</td>\n",
       "      <td>0.541658</td>\n",
       "      <td>3.521332</td>\n",
       "      <td>0.229972</td>\n",
       "      <td>0.372199</td>\n",
       "      <td>0.443670</td>\n",
       "      <td>0.773412</td>\n",
       "      <td>0.543195</td>\n",
       "      <td>3.460882</td>\n",
       "      <td>0.224542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.492 sec</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.376137</td>\n",
       "      <td>0.450517</td>\n",
       "      <td>0.764795</td>\n",
       "      <td>0.543264</td>\n",
       "      <td>3.525899</td>\n",
       "      <td>0.234317</td>\n",
       "      <td>0.371369</td>\n",
       "      <td>0.441932</td>\n",
       "      <td>0.774161</td>\n",
       "      <td>0.543632</td>\n",
       "      <td>3.448038</td>\n",
       "      <td>0.227413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.521 sec</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.375357</td>\n",
       "      <td>0.448963</td>\n",
       "      <td>0.765145</td>\n",
       "      <td>0.543113</td>\n",
       "      <td>3.525899</td>\n",
       "      <td>0.235654</td>\n",
       "      <td>0.370549</td>\n",
       "      <td>0.440335</td>\n",
       "      <td>0.774176</td>\n",
       "      <td>0.543202</td>\n",
       "      <td>3.448038</td>\n",
       "      <td>0.228076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:11</td>\n",
       "      <td>41.563 sec</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.374699</td>\n",
       "      <td>0.447543</td>\n",
       "      <td>0.766118</td>\n",
       "      <td>0.544037</td>\n",
       "      <td>3.528417</td>\n",
       "      <td>0.233219</td>\n",
       "      <td>0.369999</td>\n",
       "      <td>0.439161</td>\n",
       "      <td>0.774592</td>\n",
       "      <td>0.543709</td>\n",
       "      <td>3.448038</td>\n",
       "      <td>0.228297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:12</td>\n",
       "      <td>41.624 sec</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.374098</td>\n",
       "      <td>0.446341</td>\n",
       "      <td>0.766529</td>\n",
       "      <td>0.543896</td>\n",
       "      <td>3.560713</td>\n",
       "      <td>0.229161</td>\n",
       "      <td>0.369390</td>\n",
       "      <td>0.437926</td>\n",
       "      <td>0.775021</td>\n",
       "      <td>0.544851</td>\n",
       "      <td>3.424855</td>\n",
       "      <td>0.226751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:12</td>\n",
       "      <td>41.672 sec</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.373534</td>\n",
       "      <td>0.445115</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>0.544208</td>\n",
       "      <td>3.568370</td>\n",
       "      <td>0.231452</td>\n",
       "      <td>0.368810</td>\n",
       "      <td>0.436669</td>\n",
       "      <td>0.774927</td>\n",
       "      <td>0.545957</td>\n",
       "      <td>3.442929</td>\n",
       "      <td>0.225425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:12</td>\n",
       "      <td>41.714 sec</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.373121</td>\n",
       "      <td>0.444171</td>\n",
       "      <td>0.766785</td>\n",
       "      <td>0.544720</td>\n",
       "      <td>3.568370</td>\n",
       "      <td>0.229352</td>\n",
       "      <td>0.368496</td>\n",
       "      <td>0.435909</td>\n",
       "      <td>0.775256</td>\n",
       "      <td>0.545586</td>\n",
       "      <td>3.442929</td>\n",
       "      <td>0.226530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td></td>\n",
       "      <td>2020-05-25 11:49:12</td>\n",
       "      <td>41.769 sec</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.443360</td>\n",
       "      <td>0.767145</td>\n",
       "      <td>0.545059</td>\n",
       "      <td>3.568370</td>\n",
       "      <td>0.226439</td>\n",
       "      <td>0.368047</td>\n",
       "      <td>0.435006</td>\n",
       "      <td>0.775474</td>\n",
       "      <td>0.545922</td>\n",
       "      <td>3.442929</td>\n",
       "      <td>0.224652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp    duration  number_of_trees  training_rmse  \\\n",
       "0     2020-05-25 11:49:11  41.173 sec              0.0       0.415591   \n",
       "1     2020-05-25 11:49:11  41.197 sec              1.0       0.407822   \n",
       "2     2020-05-25 11:49:11  41.218 sec              2.0       0.401483   \n",
       "3     2020-05-25 11:49:11  41.242 sec              3.0       0.396471   \n",
       "4     2020-05-25 11:49:11  41.268 sec              4.0       0.392442   \n",
       "5     2020-05-25 11:49:11  41.291 sec              5.0       0.389141   \n",
       "6     2020-05-25 11:49:11  41.313 sec              6.0       0.386399   \n",
       "7     2020-05-25 11:49:11  41.338 sec              7.0       0.384191   \n",
       "8     2020-05-25 11:49:11  41.363 sec              8.0       0.382341   \n",
       "9     2020-05-25 11:49:11  41.389 sec              9.0       0.380701   \n",
       "10    2020-05-25 11:49:11  41.415 sec             10.0       0.379202   \n",
       "11    2020-05-25 11:49:11  41.439 sec             11.0       0.378052   \n",
       "12    2020-05-25 11:49:11  41.466 sec             12.0       0.377043   \n",
       "13    2020-05-25 11:49:11  41.492 sec             13.0       0.376137   \n",
       "14    2020-05-25 11:49:11  41.521 sec             14.0       0.375357   \n",
       "15    2020-05-25 11:49:11  41.563 sec             15.0       0.374699   \n",
       "16    2020-05-25 11:49:12  41.624 sec             16.0       0.374098   \n",
       "17    2020-05-25 11:49:12  41.672 sec             17.0       0.373534   \n",
       "18    2020-05-25 11:49:12  41.714 sec             18.0       0.373121   \n",
       "19    2020-05-25 11:49:12  41.769 sec             19.0       0.372722   \n",
       "\n",
       "    training_logloss  training_auc  training_pr_auc  training_lift  \\\n",
       "0           0.529427      0.500000         0.000000       1.000000   \n",
       "1           0.511864      0.716131         0.534717       3.474912   \n",
       "2           0.498746      0.744646         0.532172       3.529706   \n",
       "3           0.489013      0.748189         0.535621       3.529706   \n",
       "4           0.481430      0.750121         0.535358       3.529706   \n",
       "5           0.475375      0.750058         0.535198       3.529706   \n",
       "6           0.470332      0.756986         0.535024       3.529706   \n",
       "7           0.466316      0.757005         0.535418       3.529706   \n",
       "8           0.462760      0.761106         0.540176       3.514359   \n",
       "9           0.459589      0.762515         0.540880       3.518279   \n",
       "10          0.456705      0.762522         0.541424       3.518279   \n",
       "11          0.454467      0.761648         0.541505       3.521332   \n",
       "12          0.452420      0.762767         0.541658       3.521332   \n",
       "13          0.450517      0.764795         0.543264       3.525899   \n",
       "14          0.448963      0.765145         0.543113       3.525899   \n",
       "15          0.447543      0.766118         0.544037       3.528417   \n",
       "16          0.446341      0.766529         0.543896       3.560713   \n",
       "17          0.445115      0.766312         0.544208       3.568370   \n",
       "18          0.444171      0.766785         0.544720       3.568370   \n",
       "19          0.443360      0.767145         0.545059       3.568370   \n",
       "\n",
       "    training_classification_error  validation_rmse  validation_logloss  \\\n",
       "0                        0.778001         0.413815            0.526105   \n",
       "1                        0.236370         0.405538            0.507496   \n",
       "2                        0.228731         0.398808            0.493698   \n",
       "3                        0.228636         0.393394            0.483273   \n",
       "4                        0.210780         0.389030            0.475135   \n",
       "5                        0.245059         0.385453            0.468630   \n",
       "6                        0.243961         0.382447            0.463157   \n",
       "7                        0.243961         0.380045            0.458834   \n",
       "8                        0.247446         0.378063            0.455049   \n",
       "9                        0.235654         0.376184            0.451464   \n",
       "10                       0.235606         0.374583            0.448380   \n",
       "11                       0.231023         0.373354            0.445973   \n",
       "12                       0.229972         0.372199            0.443670   \n",
       "13                       0.234317         0.371369            0.441932   \n",
       "14                       0.235654         0.370549            0.440335   \n",
       "15                       0.233219         0.369999            0.439161   \n",
       "16                       0.229161         0.369390            0.437926   \n",
       "17                       0.231452         0.368810            0.436669   \n",
       "18                       0.229352         0.368496            0.435909   \n",
       "19                       0.226439         0.368047            0.435006   \n",
       "\n",
       "    validation_auc  validation_pr_auc  validation_lift  \\\n",
       "0         0.500000           0.000000         1.000000   \n",
       "1         0.726731           0.537125         3.444264   \n",
       "2         0.752909           0.534588         3.422307   \n",
       "3         0.756448           0.535692         3.422307   \n",
       "4         0.758511           0.536095         3.422307   \n",
       "5         0.758505           0.535659         3.422307   \n",
       "6         0.764722           0.536039         3.422307   \n",
       "7         0.764634           0.536411         3.422307   \n",
       "8         0.770340           0.542043         3.457524   \n",
       "9         0.772358           0.543522         3.457524   \n",
       "10        0.772982           0.543893         3.457524   \n",
       "11        0.772925           0.544553         3.460882   \n",
       "12        0.773412           0.543195         3.460882   \n",
       "13        0.774161           0.543632         3.448038   \n",
       "14        0.774176           0.543202         3.448038   \n",
       "15        0.774592           0.543709         3.448038   \n",
       "16        0.775021           0.544851         3.424855   \n",
       "17        0.774927           0.545957         3.442929   \n",
       "18        0.775256           0.545586         3.442929   \n",
       "19        0.775474           0.545922         3.442929   \n",
       "\n",
       "    validation_classification_error  \n",
       "0                          0.780649  \n",
       "1                          0.187652  \n",
       "2                          0.232825  \n",
       "3                          0.214491  \n",
       "4                          0.217915  \n",
       "5                          0.214270  \n",
       "6                          0.229843  \n",
       "7                          0.220013  \n",
       "8                          0.204330  \n",
       "9                          0.223548  \n",
       "10                         0.226309  \n",
       "11                         0.228960  \n",
       "12                         0.224542  \n",
       "13                         0.227413  \n",
       "14                         0.228076  \n",
       "15                         0.228297  \n",
       "16                         0.226751  \n",
       "17                         0.225425  \n",
       "18                         0.226530  \n",
       "19                         0.224652  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "See the whole table with table.as_data_frame()\n",
      "\n",
      "Variable Importances: "
     ]
    },
    {
     "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>variable</th>\n",
       "      <th>relative_importance</th>\n",
       "      <th>scaled_importance</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>2794.444824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.693347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_2</td>\n",
       "      <td>307.237366</td>\n",
       "      <td>0.109946</td>\n",
       "      <td>0.076231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_3</td>\n",
       "      <td>215.152893</td>\n",
       "      <td>0.076993</td>\n",
       "      <td>0.053383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_4</td>\n",
       "      <td>155.434448</td>\n",
       "      <td>0.055623</td>\n",
       "      <td>0.038566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>127.986313</td>\n",
       "      <td>0.045800</td>\n",
       "      <td>0.031755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAY_5</td>\n",
       "      <td>127.538628</td>\n",
       "      <td>0.045640</td>\n",
       "      <td>0.031644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAY_6</td>\n",
       "      <td>102.351601</td>\n",
       "      <td>0.036627</td>\n",
       "      <td>0.025395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>82.432350</td>\n",
       "      <td>0.029499</td>\n",
       "      <td>0.020453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>58.934135</td>\n",
       "      <td>0.021090</td>\n",
       "      <td>0.014623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>58.858047</td>\n",
       "      <td>0.021063</td>\n",
       "      <td>0.014604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    variable  relative_importance  scaled_importance  percentage\n",
       "0      PAY_0          2794.444824           1.000000    0.693347\n",
       "1      PAY_2           307.237366           0.109946    0.076231\n",
       "2      PAY_3           215.152893           0.076993    0.053383\n",
       "3      PAY_4           155.434448           0.055623    0.038566\n",
       "4   PAY_AMT1           127.986313           0.045800    0.031755\n",
       "5      PAY_5           127.538628           0.045640    0.031644\n",
       "6      PAY_6           102.351601           0.036627    0.025395\n",
       "7  LIMIT_BAL            82.432350           0.029499    0.020453\n",
       "8   PAY_AMT2            58.934135           0.021090    0.014623\n",
       "9   PAY_AMT4            58.858047           0.021063    0.014604"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load saved best model from lecture 1 \n",
    "best_mgbm = h2o.load_model('best_mgbm')\n",
    "\n",
    "# display model details\n",
    "best_mgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Main Drivers of Model Decisions: Global Shapley Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate Shapley values for the MGBM predictions\n",
    "In `h2o`, for GBM and random forest models, the `predict_contributions` function is used to calculate Shapley values. Notice the Shapley values are the same size as the input feature matrix, plus one constant column for the Shapley intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.32592475  0.19044518 -0.24705228 ...  0.09026365  0.13503307\n",
      "  -1.43555605]\n",
      " [-0.32468784 -0.04222467 -0.02269689 ... -0.36542782 -0.10665477\n",
      "  -1.43555605]\n",
      " [-0.26399514 -0.0377474   0.3863875  ...  0.12269209 -0.02331262\n",
      "  -1.43555605]\n",
      " ...\n",
      " [-0.25929636 -0.03532003 -0.08576533 ...  0.12963703  0.17130034\n",
      "  -1.43555605]\n",
      " [ 1.51308882  0.21253292  0.21623014 ...  0.07868841  0.07920039\n",
      "  -1.43555605]\n",
      " [-0.32426992 -0.04222467 -0.03084931 ... -0.34815508 -0.10122114\n",
      "  -1.43555605]]\n",
      "\n",
      "Shapley contributions rows = 9054, columns = 11\n"
     ]
    }
   ],
   "source": [
    "# must convert from Pandas to H2OFrame ... and back again ... wasteful\n",
    "shap_contribs_hf = best_mgbm.predict_contributions(h2o.H2OFrame(valid))\n",
    "shap_contribs_matrix = shap_contribs_hf.as_data_frame().values\n",
    "print(shap_contribs_matrix)\n",
    "print('\\nShapley contributions rows = %d, columns = %d' % (shap_contribs_matrix.shape[0], shap_contribs_matrix.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's crucial to note that Shapley values are calculated on a per-feature, per-customer basis. They are a *local* explanation technique that is aggregated up to a *global* explanation technique."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot global Shapley feature importance\n",
    "Global Shapley feature importance is calculated as the average absolute Shapley value for each input feature. The values in the chart below can be interpreted as the mean absolute contribution a feature makes on a model prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa57377fba8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap_contribs = shap_contribs_matrix[:,:-1] # remove constant intercept column\n",
    "shap.summary_plot(shap_contribs, x_names, plot_type='bar', color='k') # use shap package for nice bar plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PAY_0` dominates the global feature importance, which is actually problematic. This means the model is placing too much importance on one variable, at least on average. What happens if values for this variable drift in the future? What happens if someone learns how to manipulate the predictions of the model based on `PAY_0` values? What if this variable is missing for a customer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. The Behavior of the Main Drivers: Partial dependence and ICE\n",
    "\n",
    "Partial dependence and individual conditional expectation (ICE) help paint a more localized portrait of model behavior. Partial dependence can be interpreted as the estimated average output of a model across the values of some interesting input feature. ICE is simply the prediction for an individual of interest when changes are made to a model input feature. Together, these two techniques can describe not only how a model is behaving for some feature of interest, but also, if partial dependence and ICE diverge, whether partial dependence is failing in the presence of strong interactions or correlations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate partial dependence for each feature in best MGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init dict to hold partial dependence and ICE values\n",
    "# for each feature\n",
    "# for mgbm\n",
    "mgbm_pd_ice_dict = {}\n",
    "\n",
    "# calculate partial dependence for each selected feature\n",
    "for xs in x_names: \n",
    "    mgbm_pd_ice_dict[xs] = explain.pd_ice(xs, valid, best_mgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find some percentiles of yhat in the validation data\n",
    "ICE can be plotted for just one or many individuals. Since no particular individual is interesting, yet, under the MGBM, individuals at deciles of `p_DEFAULT_NEXT_MONTH` are selected for ICE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 6593,\n",
       " 99: 17863,\n",
       " 10: 23504,\n",
       " 20: 15919,\n",
       " 30: 1919,\n",
       " 40: 22205,\n",
       " 50: 12297,\n",
       " 60: 1847,\n",
       " 70: 12644,\n",
       " 80: 29479,\n",
       " 90: 7682}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge MGBM predictions onto test data\n",
    "mgbm_yhat_valid = pd.concat([valid.reset_index(drop=True),\n",
    "                             best_mgbm.predict(h2o.H2OFrame(valid))['p1'].as_data_frame()],\n",
    "                            axis=1)\n",
    "\n",
    "# rename yhat column\n",
    "mgbm_yhat_valid = mgbm_yhat_valid.rename(columns={'p1':'p_DEFAULT_NEXT_MONTH'})\n",
    "\n",
    "# find percentiles of predictions\n",
    "mgbm_percentile_dict = explain.get_percentile_dict('p_DEFAULT_NEXT_MONTH', mgbm_yhat_valid, 'ID')\n",
    "\n",
    "# display percentiles dictionary\n",
    "# key=percentile, val=ID\n",
    "mgbm_percentile_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate ICE curve values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop through selected features\n",
    "for xs in x_names: \n",
    "\n",
    "    # collect bins used in partial dependence\n",
    "    bins = list(mgbm_pd_ice_dict[xs][xs])\n",
    "    \n",
    "    # calculate ICE at percentiles \n",
    "    # using partial dependence bins\n",
    "    # for each selected feature\n",
    "    for i in sorted(mgbm_percentile_dict.keys()):\n",
    "        col_name = 'Percentile_' + str(i)\n",
    "        mgbm_pd_ice_dict[xs][col_name] = explain.pd_ice(xs, \n",
    "                                                        valid[valid['ID'] == int(mgbm_percentile_dict[i])][x_names], \n",
    "                                                        best_mgbm, \n",
    "                                                        bins=bins)['partial_dependence']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assess partial dependence and ICE for each feature in best MGBM\n",
    "The partial dependence and ICE curves for the monotonic GBM show that it picks up on discrete jumps in the probabilities of default, say at `PAY_* >= 2`. Of all the features, `PAY_0` has the steepest partial dependence and ICE curves, indicating that the model is very sensitive to `PAY_0` values and confirming the findings of the global Shapley feature importance chart above. The partial dependence and ICE curves also confirm the monotonicity of the constrained GBM model on average and for many individuals at tested deciles of `p_DEFAULT_NEXT_MONTH`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56e83a438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa579902470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAEzCAYAAADHKNteAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl4VPXVwPHvyQIhAcQQdhCQRbYQZBFr0VCXCiqooC0CFbRVWrUqbelLfUGQqq0LglRErFoVWbQglCJgsZVgrfLKmgYICBI2QbawJoEs5/3jdycMQ3aSTJbzeZ55mLnrucPk3nt+2xVVxRhjjDHGGGOMMWUjJNgBGGOMMcYYY4wxVZkl3sYYY4wxxhhjTBmyxNsYY4wxxhhjjClDlngbY4wxxhhjjDFlyBJvY4wxxhhjjDGmDFnibYwxxhhjjDHGlCFLvEtIRDaJSN9gx1ERiMgTIvJGAfNHisi/yzMmY4wjIv1EZHuw4zAmWAq7RgUs+7aIPF3WMZUHEUkRkRuDHUdxFXZ/JSIrReRn5RiSMcaUCku885DXxSoweVTVzqq6spDttBIRFZGwMgq1QlDVZ1X1Z1A6x+x9/2dFJCZg+npv260uLuJix9NXRHJE5JT32isiH4hIr4DlVERO+y13SkR+682bKCKZec3zW/89b5lGeUyfGDCtrYio3+e9gTcqIvJTv32lBxzD4UKO+YCI7BORCL9pj4jIcu99RD7H+6g3f7qILA7Y5msiMr8U4koXkUsCpm/x4mnsN+06EUnwtntMRBaKSHu/+f28dV4K2NYaERkiIk/5xZUhIll+n9f6fQfNA9b/YyEFUbVE5AUR2eMdy1YReVxEpKBjN6aq8s756d7f1nde8lu7hNvqKyJ7/af5X6MuMs6RIpLtdx7YKSJ/8T+vmIvnf3/lXTvfK+m28vo9iMjNIrJKRE6KyCHvOjHQmxf4f+x7NS3i/vyvi/tE5CURCQ1YZqWIpIpITb9pP/OuY/7T6ovIQRHpV8D+OnnXrFTv9YmIdCrq92OMKV+WeFdiUrUT+p3APb4PIhILRAYvHL5V1dpAHeBqIBn4TERuCFguTlVr+72e95v3fn7zRKQOcCdwAhhaGgGr6pu+fXnb/sZv3zGFrQ9EAA8VsswVAcc0zZs+FogTkXvA3fwAdwMPl0Jcu4Ef+T6IyFUEnMu8/S0F5gGNgTbAduA/InKZ36IngJ+JSLPAnajqBL84HwdW+sXZowhxXsBLrhcB3wd+iPs9/RR4DHiuJNssZH9V+RxhqpYB3t9ad6AnMK64Gyin3/sXXpyXADcC6cBaEelSDvs2F0lE7gL+CrwLNAcaAU8CA/wW+yLgulZbVb8txm7ivN/IDbjr+QN++28FXAsoMNA3XVXfAPZ5sfhMBZaq6vIC9vUtcBcQDcQAi3HXPWNMBWSJdwmJX624iFzllTie8ErrfTVoq7x/j3mln98TkRARGSciu7ySzHf9a+9E5F5v3hERGR+wn4niagzfE5ETwEhv31+Iq9HbLyKviEgNv+2piDwkIl97pbu/F5E2IvIfL94P/JcPOMZdItLDez/M21Zn7/NPRWSRX1y+EukLjtlvey96JbI7RaR/IV/xLOBev88jcBdK//hqetvc7X3vr4lILW/epSKyxCvNTvXeN/dbd6X3XXzufS//kIAa9ryos1dVnwTeoPSSpbuBg8Cz3rFWBM8DY6UENU+qehIYBbzsJbpvAL9S1e9KIa7A38a9BPw2gBeA11V1hqqeUtUjqjoG+C/n39AfAt6nBDf5JXQL7qZrkKpuUdUsVf03MBL4lYhcJiIjJKBrhoj8TkQ+8N7XEpGp4mrMD4jIn3y1JOI1K/fOHd8BMwIDEJEnvb/BkyKSJCK3etMjvXNCO79lm4tImojUK6svxBh/qroPWAZ0ARCR+8TVBJ4UkW9EZJRvWfFqM0Xkf0TkADDXW7ep+NVUBlyjEJG/en87x8XVfHYuQZzZqrpDVR8CEoCJftu/2rvGHhORjeLXGsm79vxBRP7P+3v7m4hEF2PdfK9bIvITOXf/8L/+8Yq79xgrIju8+R/49ivnWqqN8K6nh/3XF5FQcc31d3j7XSsiLbx5HURkhYgcFdd650fkQUR+ICL/9fu8QkS+8vv8mYjc4b1PEZEbxdX0PgH82Pu/3Oi3yZbFvX6LiAAvAb9X1TdU9biq5qhqgqo+UNj6xaWqycBneL9lz73Al8DbXHit/xnwkIh0E5GbcYn76EL2cUxVU1RVAQGygbalcwTGmNJmiXfpeBl4WVXr4mrWPvCmX+f9W88rMf0Cd4M9EvgBcDlQG3gFXJMh4FVgGNAEV6IeWBN3OzAfqAfMxp1kR+NKOr+HO1EH1lLeDPTA1dT+FngdGA60wF0Q7iFvCUBf73088I3fMcV78wPldcwAvYGtXpzPA296F8H8fAnUFZGO4pppDQECm5v9EWgPdMNdaJpxrrQ4BPgL0BK4DFcr8UrA+kOB+4CGQA3gNwXEk5cPge4iElXM9fIyAnfTOBeIFZG4UtjmxfocWIOr7S02r5R+GbAO2K6q75RSXAlAcxFpLSLhwGBgjm+miFyKqzH7ax7rfgDcFDBtEjBcRFqXUnwFuQn4TFUP+E9U1QTgCO68sBD3u/KvmR/KuWN8CVdTEwtcgfsbGOu3bCsgHPf3/WgeMWwFrsGdX54D5olIjKqm4c4tw/2WHQZ8pKrHin2kxpSAl9DdAqz3Jh0EbgPq4s7XU0Sku98qjXG1fS1xSU1/vBZKBdRULgPa4c7963DX0ovxIa5ADXGtZz4Cnvbi+g2wQEQa+C1/L3A/7jqfBUwrxrp5Xre8+4cZwE+ApkB93HnC55fAHbhrd1MgFZgecBx9cOeUG4AnRaSjN/1XuPuEW3D/D/cDad61bwXu3NQQd51+VfJu6vwl0E5EYrzzdldcAUkdcQXmPXFJai7vGvIs51qL+V8XS3L9vgJ3XpxfhGXzJCKvisirRVy2E+53sd5v8r2439ts4Gbx61qmqim4e5i3gNeAh1Q1tYj7OgZkAH/CfWfGmArIEu/8LfJKnI95J7SCTrSZQFvv5vWUqn5ZwLLDgJdU9RtVPQX8DhgironcXcDfVfXfqnoWdwLWgPW/UNVFXiltuqquVdUvvZqzFGAm7sLq73lVPaGqm4Ak4B/e/o/jbkCuzCfWBL9tXQv8we9zfol3fnap6p9VNRt4B3fD0aiQdXw1mzcBW3DNsIDckusHgdGqetSrYX0Wd+HHq+FcoKpp3rxnuPB7+YuqblPVdFxC1q0YxwOuiZfgCkF81vn/brxSa58fBcxr6h1La9z3O8e7SVzJ+TW6wTQOVxObX43npoBjCvyOP8PdAF7sja0/9bZ3L3ArrnDgkN/8+t6/+/NYdz+u8OfcxlT34AppJpYwnk0B54qCCipi8okrNzZVPYFrJj8EcrtZNAeWeueJnwKPeTUdx3EFUEP8tnMGV6Nz1vttn0dV31fV/d45ZBbu78rXdP4d3DnKZzju79CYsrbI+/v5N+7a8iyAqn7k1SyrV0D1D7wk15MDTFDVM3n93vOiqm+p6klVPYP7u4+TgHEjiulbXKIM7m9mqaou9f7GVuDOUbf4LT9LVZNU9TQwHndtCC3iuvldt+4ClqjqKu+4xuO+G5+fA//rtdjyHfddcn7z/Ke8+4qNwEbAl+j+DBinqlu9/4eNqnoEVyCSoqp/8e5B1gMLcC24zuPF+xWucL6Ht/3Pcd1urga+9rZZVCW5fhd0bfB3dcB1bYffcTzktXIoyDoRSQX+jmvt9RcAEemDKyD6QFXXAju4sGvZK7h7yg2quqgIx+SLqx6uMPURzk/0jTEViPX/y98dqvqJ74OIjMRdfPLyU1ytWbKI7MRdvJbks2xTYJff5124/4dG3rw9vhmqmiYigReiPf4fxA3q8hKutDjS29bagHX8m/em5/G5MXlLAF4UkSZAKO7iNkFcH6VLgA35rJeX3Bo+77jA1fYXZBau6XprLmxK3AB3vGv9Ks7FixMRiQSmAP2AS735dUQk1Ev+z4sJSCtCPIGa4ZJA/9rA7qqa3wjSH6jq8Dym3wv8V1WTvM+zgWdF5H9UNQtXIxIesE64N71Mqeo6EfkUGEPeNyudVXVvHtPxSvL/gGsR8oyILPIKQUrDu7iaoTgu/G34/maaACkB85oAeQ3g9iywTUT+UIJYzvsOROSPBCT3fg4DHfOZ5x/bHNyN8/O4G7P5qnrWqwUPxyX7ubvk/N/CAVXNzC9YEfH1KffVqNf2i3cVECqui0i6F9Oy/LZlTCk675rrI65b0gRcy44Q3Hn/v36LHFLVjKLuxEtwn8Elhw04l5zGAMdLFjrNgKPe+5bA3SLi32c4HPjU77P/dXyXNz+miOvmd90KvH84HXD/0BJYKCL+yXg25xeA57ftFrgkMVBLoLdXYOITRv6Fdb5WdHu996m4AvEzFK8gv6BYC+J/bdhZwHJfqmqfYsbjL7/7gBG4ig//8/wI3L0K4LqzicgW3HdULN7/+WvAIRHpqKoHSxC7MaYMWY13KVDVr1X1HlyTp+eA+V4TrMDaanAl4y39Pl+Gu2n+DpfY+PdDrsW5Etrc3QV8noEb6KuduqbuT+BuxC+ad+FIwzVRW+XVxB3A1TT/W1Vz8lqtNPbt7X8X7uJ4C64pn7/DuMSgs6rW816XqBvQBODXuGZlvb3vxdcEvjRHjr4TWOfVWpSIV3N/L9BeXJ/DA7hkqxGuiwC4wcRaBazamvMLcMrSk7hS9IbFXO8VYIGqPo4rgS9JUpsnVd2Gu4mKxw0m4z8vFVf4dEGtC25Qtn/msb0DuFYtk0orxnx8AvSRC0euj8fdeK/0Ji0FWntNPYdwrpn5ftz5ok3A797/PJHv36BXUPcn3N9wtFdLsh3v70JVFVeQMRzXZHVeQUm8MWVJ3NgFC4AXgUbe73Up55/HA3/vhV2DhuK6bN2IK0Bu5dvdRYR6J+eaSe/B1WjX83tFqeof/ZZv4ff+MlwN5+Eirpuf/f7b9Qqf/c8Le4D+AduOUNenvjB7cN3o8pqeELDN2qr6i3y240u8r/Pe+1rVFdSCrtTuKXDdbPbguieVK+9+7kdAvN+1fjSutUVpdi3zFU5dMGCoMSb4LPEuBSIyXEQaeImor+Q3B9f8NQfXl9tnLjDa659am3P9l7Jw/Y4GiMg14gY8m0jhNwN1cCMznxKRDkB+F7ySSsAlXb6L4sqAz4HyOuaL8VPg+sDk1vuu/4zr79cQXP84v6bddXCJ+TFxA8hMKI1gxGkmIhNwLSCeuMhN9sHdLPXENZXrhut3/wHnmpvPBwaKG2wmRNwgcf/LhSOX1hD3iCvfq1RatKjrorAYeLio64jIINyYA75Hpj0EDBORa0ojJs9PgBu9ZpOBfguMEpFfiEiUuMeyvIDrV5jfM3qfx3VrKK3fbl6WAl8AH4oblChMRL6PG2hnqlfYhFeDtxDX9zMc7+/NS4Lfwg1aF+P9HluISGC/9fzU5ty5KUREfs6FA/G8i7tBvIcLWxMYU55qADVxv9csr/b7h4Ws8x1Qv4Cm43VwNaxHcAlKifrDihtwrLWI/AmXTD7lzXoPdx2/2VsmQtwgcP79rYeLewxUJK6wb77XEqso6+ZnPnCbiPTx7h8mcf493mu4lkctvfgbiMjtRTzcN4Dfi0g775zTVUTqA0twhcY/EZFw79VLzvUND/QfXIH4VcD/edeWlrgxYFbls853QCsRuej7Va9g8VfAeHGD9tX1rql9ROT1i91+Ie7AtTDoxLlrfUdcgU2Ju5aJyE0icqX3e6mLawGZiuueZ4ypYCzxLh39cE0/T+Ga1Q7x+kml4Zq0fS6un9DVuJtmXxPqnbjBMH4JuQnOL3EJ1X7gFG5gmbwSC5/f4ErwT+IS0fdL+dgScDcqq/L5fJ58jrnE1PXtW5PP7P/B1dZ9KW6U909wF3Vwj+GohatF+BIo6HEcRdHU+/89heunFgv0VdV/BCy3Uc5/9ufUQrY7AlioqptU9YDvhfsdDRSReqqaiEsyX8BdUP/tvQITyI9xhQ2+V2mO1D0RN6hOoK0Bx/ucuMHNpuMGhjkBuSMVjwXekHxG0S8ur6VJnn3ZVPVfuP7fQ3E3bt/gmqp+X91YCHmtcxTX5O/SvOaXBq/AaCDuN/lP3O/pbVzrgMDBgebgauXeD2hd8jiu5cwaXNPY5RRxFFtVXYe7AV+DO8e09t77L7MDVzN0UlX/r+hHZ0zp8rqmPIoriEzF/T0vLmSdZFwB9zfiN5aGn3dxrYX2AZtxf4vF8T3vWnACVxBdF+ilqv/19r8HV6P+BK7AYA+uq47//dYs3N/9AdxjGx8txrp58u4fHsadN/bjvi//5sov4767f4jISe+4exfxmF/C/R/8wzvuN4Fa3v/PD3Gtcr71juc5XGFJXjGexg1mt0ndODbgCiJ3FdAs2jdI5hERWVfEePOlqvOBH+MGiPsWd314Gvib32Lfkwuf490LQNzTU14rwa5H4Pql7w641r+CK5QuaUF5Pdzv/TiuO0AboF9xul8YY8qPuAJAUxF5NeLHcM3IC+qPZIwxpUZE5gCbVTW/1gHGmBIQkZXAe+qe22yMMaYasRrvCkZEBoh7nm4Url/bf7lwgChjjCkTItIWN1rxX4IdizHGGGNMVWGJd8VzO67507e454wOUWuWYEqZiLTPoymd71XcQdSqfFzVhYg8jxsIb1IRB10qrf2+JSIHRSQpn/kiItNEZLuIJMr5z3GucnEYY4yPiDyRzzXRnjhhTCVjTc2NMcYElYhch+vv/q6qdslj/i248S9uwfVLfVlVi9o/tdLFYYwxxpiqx2q8jTHGBJWqruLcc5DzcjsuGVZV/RKoJyJNqmocxhhjjKl6SuVxQxVNSEiI1qpVK9hhGGNMpSaqtE9P50RYGPtrlHww+rS0NMWNZuzzuqoW5/E9zXAjPPvs9abtL3FQJVPkOETkQdzz0omKiurRoUOHcgnQGGNMwdauXXtYVRsEOw5T/VTJxLtWrVqcPn268AWNMcbkb948uOce+OQTiI8v8WZEJF1Ve5ZiZBWeV7DwOkDPnj11zZr8nopojDGmPInIrmDHYKona2pujDEmb3PmQLNmcO21wY5kH9DC73Nzb1p1jcMYY4wxlYwl3sYYYy505AgsW+ZqvEOCfqlYDNzrjSp+NXBcVcu7mXlFisMYY4wxlUyVbGpujDHmIs2fD1lZMHRome9KROYCfYEYEdkLTADCAVT1NWApbiTx7UAacF9VjsMYY4wxVU+VfJxYVFSUWh9vU9FkZmayd+9eMjIygh2KMYU7cABycqBp0yKvEhERQfPmzQkPDz9vuoikqWpUaYdYWVgfb2OMqThEZG11G3fEVAxW421MOdm7dy916tShVatWiEiwwzEmf2fPwunTLukuYuKtqhw5coS9e/fSunXrMg7QGGOMMaZyCXrHPWOqi4yMDOrXr29Jt6n4jnqPso6OLvIqIkL9+vWtRYcxxhhjTB4s8TamHFnSbSqFo0chKgoiIoq1mv2+jTHGGGPyZom3McaYc9LTIS2tWLXdxhhjjDGmYNbH2xhjzDklaGZujDGmKssCjgDZwQ7EUxeoHewgjCk2q/E2phoREYYPH577OSsriwYNGnDbbbeV6X5HjhxJ69atiYuLo3379tx7773s3bs3d36rVq2IjY2lW7dudOvWjUcfffS89XzTp02blrvOhg0bEBGWL1+eOy0lJYUuXbqct++JEyfy4osv5m5v/vz5523Dt+3o6Ojcfd188815Hsf27dsREWbMmJE77ec//znvvfceAMOHDz8v3muvvRaA559/ngcffDB3nXfeeYfbb7+dSZMm5S4bGhqa+3769Ol57n/cuHGICCkpKbnTXnzxRUSEDRs2AJCamsrw4cNp27Ytbdq0YeTIkZw4caLQ+H/+85/TrVs3Ol13HbX69KFbr15069aNhQsXMnz4cBYtWpS7TlZWFvXq1cszRmOMMZVBJrAPWId7UuJfgD8Co4GhwA1AF6AB7qmKjYFmFeT1ahl8H8aUPavxNnmqCH01q+Kj7oItKiqKpKQk0tPTqVWrFitWrKBZs2blsu8XXniBu+66C1Vl6tSpXH/99SQlJVGjRg0APv30U2JiYvJdL9DcuXPp06cPc+fOpV+/fiWKqVu3brkJ6/Dhw7nrrru44447ClynUaNGTJkyhQceeICwsAtPoVOmTLlgG6NHj6Z79+6sXr2adu3aMWHCBBISEmjZsiVPPvkkWVlZxMTE5MZSkNjYWObNm8fYsWMBWLBgAR07dsydf99999GzZ8/cwoBx48YxatQo5s6dW2D8r732Gpw6xfYVK7hr/PjzYlmwYEGhcRlTeSgu6bjY2jvfdjK81xm/9xlFmO6bV1FqEU3VlQUcAr7zex3NZ9naQCPv1R641nvvS8ArgquCHYAxJWKJtzHB8PjjUIQkq1i6dYOpUwtd7JZbbuGjjz7irrvuYu7cudxzzz189tlnAJw+fZpf/vKXJCUlkZmZycSJE7n99ttJSUnhJz/5CadPnwbglVde4ZprrmHlypVMnDiRmJgYkpKS6NGjB++9916BBTciwujRo1m4cCHLli3j9ttvL/ahqip//etfWbFiBddeey0ZGRlEFHMgsJJq3LgxPXr0YNasWdx3331FWic8PJzp06fz0EMP0a1bN0aNGkXLli1LtP9BgwaxcOFCxo4dy7Zt24iJiSE0NBSArVu3kpSUxIcffpi7/MSJE7n88stza8kLjP/oURCBPAoUjCmeLOCU3+uk9+9pIOcit61Auretgl5pBcyrCMluCBCB3YqZshcCxOBqrTsBP+Bcch34igpSjMZUfXa2N6aaGTJkCJMmTeK2224jMTGR+++/PzfxfuaZZ7j++ut56623OHbsGFdddRU33ngjDRs2ZMWKFURERPD1119zzz33sGbNGgDWr1/Ppk2baNq0Kd///vf5/PPP6dOnT6FxdO/eneTk5NzE+wc/+EFuAjlixAhGjx4NwJgxY3j66acBmDVrFrGxsfznP/+hdevWtGnThr59+/LRRx8xePDgUv+u8jN27Fhuv/12RowYccG80aNHM3HiRAC6du3Ku+++C8B1113H5ZdfzqpVq1ztcgnVq1ePxo0bk5yczPvvv8+QIUNym45v2rSJK6+8kpCQc72IwsLCiIuLY/PmzbRv3z7/+FVd4l2nTp779T8uU562An8NdhCeHFwy60uiA5Nq//fl/Vg5wSUMeb1i8pgWSencAoXjkue8XjULmWe3YMYYU53YWd+YYChCzXRZ6dq1KykpKcydO5dbbrnlvHn/+Mc/WLx4cW6f6IyMDHbv3k3Tpk155JFH2LBhA6GhoWzbti13nauuuormzZsDrul2SkpKkRLvwK4ExWlqPnfuXIYMGQK4goR3332XwYMH51vTXtpdJ9q1a0e3bt14//33L5iXV1NzgBMnTrB+/XrOnDnDoUOHaNq0aYn3/+Mf/5h58+bx97//nYSEhPP6bJc4/hMnICsL8um77X9cvqbxpjwkA+ODHYSfmrimqL5XHc41Ta2Tx/TAz1FAaCnEEcH5iXQELvk2xhhjKiZLvI2phgYOHMhvfvMbVq5cyZEjR3KnqyoLFizgiiuuOG/5iRMn0qhRIzZu3EhOTs55zbpr1qyZ+z40NJSsrKwixbB+/XpuuOGGYseenZ3NggUL+Nvf/sYzzzyDqnLkyBFOnjxJ/fr1SU1NPW/5o0eP0rp162LvpzD/+7//y9ChQ7n66quLtPz48eO57777uOSSS/j1r3+d2+e6JAYOHEjHjh255pprqF373MiunTp1Yv369ahqbmFDdnY2iYmJdOrU6bz/mwviP3oUQkPd87tNBTIA14+4orDbBmOMMaYkbFRzY6qh+++/nwkTJhAbG3ve9Jtvvpk//elPubXR69evB+D48eM0adKEkJAQZs2aRXZ2yftHqirTpk1j//79JRoU7Z///Cddu3Zlz549pKSksGvXLgYPHszChQupXbs2TZo04V//+hfgku7ly5cXqQa+uDp37kybNm1YtmxZoctu2LCBFStWMGbMGH7xi1+wdetWPv300xLvu3bt2jz33HP87ne/O296hw4d6NSpE3/4wx9ypz311FNcffXVtGrVKv/4c3IgNRUuvRRC7LJQsYTgkt2K8jLGGGNMSdgdljHVUPPmzXMf2eVv/PjxZGZm0rVrVzp37sz48a6J60MPPcQ777xDXFwcycnJRJWgVnTMmDG5jxP76quv+PTTT3NHNAfXx9v3OK1777033+3MnTuXO++887xpgwcPzq1Bfvfdd/n9739Pt27duP7665kwYQJt2rTJXXbUqFE0b96c5s2b873vfa/Yx+Fv3Lhx7Nmz57xpo0ePzj2Obt26kZ2dzS9+8QtefvllatSoQWhoKDNmzODRRx8lM7PkNZlDhw6lW7duF0x/++23SUpKok2bNrRp04aUlBRef/31guNPS3PJtz272xhjjDGmTEhVfGRTVFSU+kZfNiVjjxMrfVu2bDnvsU/GVBjbt8Pp09C1qxvV/CLk9TsXkTRVrbZt2Hv27Km+wQiNMcYEl4isVdWewY7DVD9W422MMdVZVhYcP+5quytAgZsxxhhjTFVkHbaMMSbAhg0bGDly5HnTIiMj+c9//lMu+580adJ5z+IGN3r72LFjS39nqanuUWLWzNwYY4wxpsxY4m1MOfIfbdpUXN26dWPDhg1B2/+TTz7Jk08+WT47O3oUIiIgMvKiN1XVuocYY4wxxpQWa2puTDmJiIjgyJEjlpyYiuPsWTh5slSamfse6+b/qDljjDHGGONYjbcx5aR58+bs3buXQ4cOBTsUY5wTJ1xT8xo1XD/vixQREUHz5s1LITBjjDHGmKrFEm9jykl4eDitW7cOdhjGnNOjh3tu91dfBTsSY4wxxpgqzZqaG2NMdZScDOvWwbBhwY7EGGOMMabKs8TbGGOqo7lzXb/uH/842JEYY4wxxlR5lngbY0x1owqzZ8P110OTJsGOxhhjjDGmyrPE2xhjqpuvvoIdO2Do0GBHYowxxhhTLVjibYwx1c2cOW4k80GDgh2R/b3iAAAgAElEQVSJMcYYY0y1YIm3McZUJ9nZMG8e3HYb1KsX7GiMMcYYY6oFS7yNMaY6+fRT+O47a2ZujDHGGFOOLPE2xpjqZPZsqFsXbrkl2JEYY4wxxlQblngbY0x1kZ4OCxa4vt21agU7GmOMMcaYasMSb2OMqS6WLoWTJ62ZuTHGGGNMObPE2xhjqos5c6BRI/f8bmOMMcYYU24s8TbGmOrg2DH46CMYMgRCQ4MdjTHGGGNMtWKJtzHGVAcffghnzlgzc2OMMcaYILDE2xhjqoM5c6BNG+jVK9iRGGOMMcZUO5Z4G2NMVbd/P/zrX662WyTY0RhjjDHGVDuWeBtjTFX3/vugas3MjTHGGGOCxBJvY4yp6mbPhu7doUOHYEdijDHGGFMtWeJtjDFV2bZtsGaN1XYbY4wxxgSRJd7GGFOVzZ3r+nUPGRLsSIwxxhhjqq0yT7xFJFRE1ovIEu9zaxFZLSLbReR9EanhTa/pfd7uzW/lt43fedO3isjNZR2zMcZUCapuNPO+faFZs2BHY4wxxhhTbZVHjfdjwBa/z88BU1S1LZAK/NSb/lMg1Zs+xVsOEekEDAE6A/2AV0UktBziNsaYym3dOtfUvBI0MxeRfl7h6nYRGZvH/MtE5FOvIDdRRG6pynEYY4wxpmop08RbRJoDtwJveJ8FuB6Y7y3yDnCH9/527zPe/Bu85W8H5qnqGVXdCWwHrirLuI0xpkqYPRvCw2Hw4GBHUiCvMHU60B/oBNzjFbr6Gwd8oKpX4gpjX62qcRhjjDGm6inrGu+pwG+BHO9zfeCYqmZ5n/cCvvaPzYA9AN78497yudPzWCeXiDwoImtEZE1WVlbgbGOMqV6ys2HePLjlFrj00mBHU5irgO2q+o2qngXm4Qpd/SlQ13t/CfBtFY7DGGOMMVVMmSXeInIbcFBV15bVPvyp6uuq2lNVe4aFhZXHLo0xpuJKSID9+ytFM3OKVsA6ERguInuBpcAvK3Ic/oXBhw4dKoNQjTHGGFOZlGWN9/eBgSKSgqs1uB54GagnIr7MuDmwz3u/D2gB4M2/BDjiPz2PdYwxxuRlzhyoXRsGDAh2JABhviTUez1Ygm3cA7ytqs2BW4BZIhKMJ3MUKQ7/wuAGDRqUe5DGGGOMqVjK7KZFVX+nqs1VtRWuH9y/VHUY8Clwl7fYCOBv3vvF3me8+f9SVfWmD/FGPW8NtAP+r6ziNsaYSu/MGZg/HwYNglq1gh0NQJYvCfVerwfML0oB60+BDwBU9QsgAogJ3JGIpIrI0TxeqSJytJA4Sy0OY4wxxhh/wWiT/T/APBF5GlgPvOlNfxNXc7AdOIpL1lHVTSLyAbAZyAIeVtXs8g/bGGMqiaVL4fjxytLMHOAroJ1XuLoPd/4PDH43cAPwtoh0xCW8ebXh9iXB4m23Z5DiMMYYU4WtXbu2YVhY2BtAF8rnSVGm4ssBkrKysn7Wo0ePg4EzyyXxVtWVwErv/TfkMSq5qmYAd+ez/jPAM2UXoTHGVCFz5kCDBnDDDcGOpEhUNUtEHgE+BkKBt7xC10nAGlVdDPwa+LOIjMYNcDbSaxUVuK3cglkR0eIU1JZmHMYYY6q2sLCwNxo3btyxQYMGqSEhIXYdMOTk5MihQ4c6HThw4A1gYOB8G4XMGGOqkhMn4O9/hwcegEo00KSqLsUNVuY/7Um/95txY4dUiziMMcZUeF0s6Tb+QkJCtEGDBscPHDjQJa/5leeuzBhjTOEWLnR9vIcNC3YkQSEi/iXM9QI+49VaG2OMMRcrxJJuE8j7TeTZ9cASb2OMqUrmzIHWraF372BHEiz+XZY+D/jsG7DTGGOMMaZcWeJtjDFVxYED8Mkn8LvfgUiwowmW+ar6t8IXM8YYY4wpPzYCnzHGVBUffAA5OZVpNPOyMCHYARhjjDHlITQ0tEeHDh06tWvXrnP//v0vP3nyZFByu0mTJjX033d8fHzbw4cPhwJERkZeWZJtzp8/v26rVq26XHbZZV2eeOKJxqUVazBZ4m2MMVXFnDkQFwedOgU7EmOMMcaUsZo1a+YkJydv/vrrrzeFh4fr5MmTGxR13aysrFKLY+bMmY1OnTqVm1cmJCRsj4mJKfHjn7Oyshg9evRlS5cu3bZt27ZNCxYsiF67dm1E6UQbPNbU3BhjqoIdO2D1anj++WBHEmwdRGRdHtMFUFXtXt4BGWOMqdruv//+FklJSZGluc0uXbqkvfXWW3uKunyfPn1OJSYm1gJ49dVXo2fMmNEoMzNTunfvfvrdd9/dFRYWRmRk5JXDhg07tGrVqrrTpk3bHRERkfP4449flpaWFlKjRg1dtWrV1jp16uQ8/PDDzT///PM6Z8+elQceeODgmDFjDi9ZsqTOpEmTmkZHR2du3bq1VmxsbNqiRYt2Pvvssw0PHjwYHh8f3/7SSy/NWr169bZmzZrFrlmzZkuTJk3Oy+7Hjx/faOHChdFnz56VW2+99diUKVO+zetYVq5cGdWyZcsznTp1OgswaNCgo/Pnz6/Xo0ePAxfznQabJd7GGFMVzJnj/h0yJLhxBN9Ozh9QzRhjjKnSMjMz+fjjj+v+8Ic/PLFu3bqI+fPnR69Zsya5Zs2aOnz48Mtee+21+o888siR9PT0kN69e5/+85//vDcjI0Patm3bZfbs2Tvi4+PTjh49GlK7du2cqVOnxlxyySXZSUlJW9LT06VXr14dBgwYcAJgy5YttTZs2PBNq1atMnv06NFhxYoVtceNG3dwxowZjRISErYFJtr+Pvzww7rbt2+PSExM3KKq3HjjjW2XLVtWu3///qcCl92zZ0+NZs2anfV9bt68+dnVq1fXLptvr/xY4m2MMZWdKsyeDdddBy1aBDuaYDurqjuCHYQxxpjqozg106XpzJkzIR06dOgE0Lt375OPPfbY4ZdeeikmKSkpMi4uriNARkZGSMOGDbMAQkNDGTlyZCpAYmJiRMOGDTPj4+PTAKKjo3MAPvnkk7rJycmRixcvvhTg5MmToZs3b46oUaOGxsbGnm7Tpk0mQOfOndN27NhRo6ixLl++vO6qVavqdurk+sOlpaWFJCcnR+SVeFdVlngbY0xlt2EDbN0Ko0cHO5KK4MtgB2CMMcaUB18fb/9pqip33333kenTp+8LXL5GjRo5YWEFp3+qKpMnT949ePDgE/7TlyxZUqdmzZq5zy0PDQ0lKyuryI9QUVUef/zx/WPGjDlc2LItWrQ4u2/fvtykfu/evefVgFdWNriaMcZUdnPmQFgY3HVXsCOpCP4tIkPzewU7OGOMMaYs9evX78SSJUsu3bdvXxjAd999F7pt27YLaqa7du2acfDgwfCEhIRIgNTU1JDMzExuuumm4zNmzGhw5swZAUhMTKx54sSJAnPGqKio7OPHjxe4TP/+/U/MmjUrxrfczp07w30xBoqPjz+dkpISkZycXCMjI0M+/PDD6MGDBx8r2jdQcVmNtzHGVGY5OTB3LvTvD/XrBzuaiqBPPtNvAZoDc8oxFmOMMaZc9ejRI2PcuHH7brjhhvY5OTmEh4frtGnTdrdv3/68GuOIiAidPXv2jkcfffSyjIyMkIiIiJxVq1ZtGz169OGUlJSasbGxHVVVoqOjM5cuXVpgF64RI0Yc7tevX/tGjRqdXb169ba8lhk0aNCJTZs2RfTq1asDQGRkZM7s2bN3NmvW7IJ+4eHh4UyePHl3v3792mdnZzN06NDDPXv2zLiY76UiEFUtfKlKJioqSk+fPh3sMCo1kSK3HCkzVfG3aUypW7kSfvADl3xX0IHVRCRNVaOCtO8hwFhgB/C0qq4v7xh69uypa9asKe/dGmOMyYOIrFXVnhe7nY0bN6bExcUV2mzaVD8bN26MiYuLaxU43Wq8jTGmMpszB6KiYMCAYEdSYYhICHAvMAZYDwxV1c0Fr2WMMcYYU3Ys8TbGmMrqzBmYPx/uuMMl3wYRGQWMBj4DBtoI58YYY0zFdODAgdC+ffteETh95cqVWxs3bpwdjJjKkiXexhhTWX38MaSmwlAbM8zPDOA7oCfwV79uMwKoqnYPVmDGGGOMOadx48bZgaOyV2WWeBtjTGU1Zw7ExMBNNwU7koqkXbADMMYYY4wJZIm3McZURidPwuLFcN99EB4e7GgqDF/TchG5DOjkTd6sqruDF5UxxhhjqjtLvI0xpjJatAjS062ZeQARqQPMBL4PbPQmdxWRL4AHVfVk0IIzxhhjTLVV4IPOjTHGVFBz5kDLlvC97wU7kormZdyjwy5X1YGqOhC4HPga+FNQIzPGGGNMtWWJtzHGVDYHD8KKFXDPPRBip/EA16rqeFXNHQ1VVXNU9UlcLbgxxhhTJYSGhvbo0KFDp3bt2nXu37//5SdPngzKTcGkSZMa+u87Pj6+7eHDh0MBIiMjryzJNu++++5W0dHRce3atevsP/27774Lveaaa9q1bNmyyzXXXNPu0KFDoRcXffmxpubGGFPZ/PWvkJ0Nw4YFO5LKRgpfxBhjjCmeP93/zxa7k45EluY2L+tSP+2Xb92wp6BlatasmeMbFXzgwIGtJ0+e3GDixInfFWX7WVlZhIWVTio4c+bMRg888MDROnXq5AAkJCRsv9ht3n///Ycfe+yxg/fdd19r/+kTJkxo0rdv35PPPvvs10888UTjJ598svGMGTP2Xez+yoNVlRhjTGUzezbExkKXLsGOpCL6UkSeCJwoImOB1UGIxxhjjClzffr0ObV9+/aaAK+++mp0bGxsxw4dOnQaOnRoy6ysLMDVPj/wwAPNr7jiik7//Oc/ayckJEReeeWVHa644opOsbGxHVNTU0OysrIYNWpU8y5dunRs3759pxdeeCEGYMmSJXWuuuqqK/r163d569atOw8cOLB1Tk4OTz/9dMODBw+Gx8fHt+/du3d7gGbNmsXu37//gqx+/PjxjXzbHT16dNOCjqd///6nGjRokBU4ffny5fVGjRp1BGDUqFFHli1bdulFf3nlxGq8jTGmMvnmG/jiC/jDH4IdSUX1S+AvIrINWO9N6w5sAkYGKyhjjDFVV2E102UtMzOTjz/+uO4Pf/jDE+vWrYuYP39+9Jo1a5Jr1qypw4cPv+y1116r/8gjjxxJT08P6d279+k///nPezMyMqRt27ZdZs+evSM+Pj7t6NGjIbVr186ZOnVqzCWXXJKdlJS0JT09XXr16tVhwIABJwC2bNlSa8OGDd+0atUqs0ePHh1WrFhRe9y4cQdnzJjRKCEhYVuTJk0uSJR9Pvzww7rbt2+PSExM3KKq3HjjjW2XLVtWu3///qeKc6xHjhwJa9myZSZAixYtMo8cOVJp8tlKE6gxxhhg3jz375AhwY2jglLVY8CdItIe8PULe1JVtwYxLGOMMabUnTlzJqRDhw6dAHr37n3yscceO/zSSy/FJCUlRcbFxXUEyMjICGnYsGEWQGhoKCNHjkwFSExMjGjYsGFmfHx8GkB0dHQOwCeffFI3OTk5cvHixZcCnDx5MnTz5s0RNWrU0NjY2NNt2rTJBOjcuXPajh07ahQ11uXLl9ddtWpV3U6dOnUCSEtLC0lOTo4obuLtLyQkBJHK04vMEm9jjKksVF0z8+9/H1q1CnY0FZKIdPX7uMP7t6Zvuqomln9UxhhjTOnz7+Pto6py9913H5k+ffoF/Z5r1KiRU1i/blWVyZMn7x48ePAJ/+lLliypU7NmTfV9Dg0NJSsrq8hZr6ry+OOP7x8zZszhoq6Tl/r162ft2rUrvGXLlpm7du0Kj46OzreWvaKxPt7GGFNZJCbC5s02qFrBNgBzgFe813S/1ytBjMsYY4wpc/369TuxZMmSS/ft2xcGbhTwbdu2XVAz3bVr14yDBw+GJyQkRAKkpqaGZGZmctNNNx2fMWNGgzNnzghAYmJizRMnThSYM0ZFRWUfP368wGX69+9/YtasWTG+5Xbu3Bnui7E4br755mMzZ86sDzBz5sz6/fr1O1bcbQSL1XgbY0x+vvwS9u+HH/4QoqKCHY17dndYGNx9d7Ajqch+CwwCjgFzgb+palpwQzLGGGPKR48ePTLGjRu374Ybbmifk5NDeHi4Tps2bXf79u3P+i8XERGhs2fP3vHoo49elpGRERIREZGzatWqbaNHjz6ckpJSMzY2tqOqSnR0dObSpUt35Lc/gBEjRhzu169f+0aNGp1dvXr1tryWGTRo0IlNmzZF9OrVqwNAZGRkzuzZs3c2a9YszxrrAQMGtP7yyy/rpKamhjVq1Kjr2LFjvx09evThp556av+dd97ZpmXLljHNmjU7u3DhwgJjq0hEVQtfqpKJiorS06dPBzuMSq0i9Jeoir9NU4mkpUGLFnD0KEREuOT7jjtgwACIiSn/eHJyXPPy2Fj46KPy3/9FEJE0VS3XkgsRaQfcAwwAvgb+GKxm5j179tQ1a9YEY9fGGGMCiMhaVe15sdvZuHFjSlxc3EU1mzZV08aNG2Pi4uJaBU63pubGGJOXt992Sfef/gQPPADr18P990OjRvCDH8C0abB7d/nF8/nnsGcPDB1afvusxFT1a+B9YAnQB+gU3IiMMcYYU51ZU3NjjAmUnQ0vvQRXXw0PPwwi8PLLsG4dLFzoXo895l7du8Odd7pXp05u2bIwZw5ERsLtt5fN9qsIEbkMGALcCRzAJd8dVdWaQRljjDEVyIEDB0L79u17ReD0lStXbm3cuHF2MGIqS5Z4G2NMoL/9DXbsgOeeO5dIi0CPHu719NOwbRssWuSS8PHj3att23NJeO/eEFJKjYrOnoUPPnBJd+3apbPNqisFSAQW4vp5NwR+6us+o6rTghaZMcYYY3I1btw4O3BU9qrMmpobY0ygF1+Eyy93fbrz0749/Pa38MUXsG8fzJjh1pkyBa65Bpo1g5//HD7+2CXOF+Mf/3DN3q2ZeVE8C/wdV7AcAzQIeBljjDHGlDur8TbGGH//+Y9Lpl95BUJDi7ZO06Yuyf75z+HYMVi61NWEv/cezJwJdevCrbe6mvD+/Ytfaz1nDkRHuwHeTIFUdVxRlhOR36rq82UdjzHGGGMMWI23Mcac78UXXZI7cmTJ1q9Xz9VM//WvcOgQ/P3vcNddsGIF/OhHbkT0226DN9908wtz6pRr+n733VDjgsdwmpIbEuwAjDHGGFN9WOJtjDE+X3/t+m0/9FDpPLe7Vq1zSfb+/bByJfziF5CUBD/7GTRuDPHxMHUqpKTkvY3Fi92jzayZeWkL/jMTjTHGmIsQGhrao0OHDp3atWvXuX///pefPHkyKLndpEmTGvrvOz4+vu3hw4dDASIjI68s7va2b98e3rt37/Zt2rTp3LZt286///3vG/rmfffdd6HXXHNNu5YtW3a55ppr2h06dKiIzRODzxJvY4zxmTIFwsPhkUdKf9thYS7JnjIFdu50I6SPGwepqTB6NLRuDVdeCZMmQWIi+J5jP3u2e554nz6lH1P1psEOwBhjjLkYNWvWzElOTt789ddfbwoPD9fJkycXeSyTrKysUotj5syZjU6dOpWbVyYkJGyPiYkp8ajk4eHhTJ48ee+OHTs2ffXVV1vefPPNhmvXro0AmDBhQpO+ffue3LVrV1Lfvn1PPvnkk41L4xjKg/XxNsYYcM2+//IXuPde96zusiTikuwrr4SnnoLt211N+6JFMHEiTJjgBmobONANrParX5XeCOnGx2q8jTHGlIp5P81ocWBTTmRpbrNx55C0IW9G7Cnq8n369DmVmJhYC+DVV1+NnjFjRqPMzEzp3r376XfffXdXWFgYkZGRVw4bNuzQqlWr6k6bNm13REREzuOPP35ZWlpaSI0aNXTVqlVb69Spk/Pwww83//zzz+ucPXtWHnjggYNjxow5vGTJkjqTJk1qGh0dnbl169ZasbGxaYsWLdr57LPPNjx48GB4fHx8+0svvTRr9erV25o1axa7Zs2aLU2aNDkvux8/fnyjhQsXRp89e1ZuvfXWY1OmTPk2r2Np2bJlZsuWLTMBLr300pw2bdqk7969u0aPHj0yli9fXi8hIWErwKhRo47Ex8dfAewr8RddjuxOzhhjwI1KnpHhktzy1rYt/OY38O9/w7ffugHZrrgCpk+HrCwYNqz8Y6qkROQXRVz0wzINxBhjjCknmZmZfPzxx3VjY2PT161bFzF//vzoNWvWJCcnJ28OCQnR1157rT5Aenp6SO/evU9v3bp1c3x8/Olhw4a1mTp16u6tW7duTkhI2Fq7du2cqVOnxlxyySXZSUlJWzZu3LjlnXfeaZCcnFwDYMuWLbWmT5++Z/v27Zt2795dc8WKFbXHjRt3sGHDhpkJCQnbVq9evS2/GD/88MO627dvj0hMTNyyZcuWzRs2bIhctmxZoaPNbt26tcbmzZsj4+PjTwEcOXIkzJeUt2jRIvPIkSOVpiK50gRqjDFlJj3djWJ+223QsWNwY2ncGB580L1OnIDdu6FLl+DGVLk8AMwobCFV/X05xGKMMaYaKE7NdGk6c+ZMSIcOHToB9O7d++Rjjz12+KWXXopJSkqKjIuL6wiQkZER0rBhwyyA0NBQRo4cmQqQmJgY0bBhw8z4+Pg0gOjo6ByATz75pG5ycnLk4sWLLwU4efJk6ObNmyNq1KihsbGxp9u0aZMJ0Llz57QdO3YUedTX5cuX1121alXdTp06dQJIS0sLSU5Ojujfv/+p/NY5fvx4yKBBg9r88Y9/3OOLz19ISAgilacBW5kl3iISAawCanr7ma+qE0SkNTAPqA+sBX6iqmdFpCbwLtADOAL8WFVTvG39DvgpkA08qqofl1XcxphqaNYs19T8N78JdiTnq1vXku5yJiL9gJeBUOANVf1jHsv8CJiI6ye+UVVt5DtjjDHlztfH23+aqsrdd999ZPr06Rc0v65Ro0ZOWFjB6Z+qyuTJk3cPHjz4hP/0JUuW1KlZs2bu+CihoaFkZWUVOetVVR5//PH9Y8aMOVyU5c+cOSO33nprm7vvvvvoiBEjjvmm169fP2vXrl3hLVu2zNy1a1d4dHR06XVWL2Nl2dT8DHC9qsYB3YB+InI18BwwRVXbAqm4hBrv31Rv+hRvOUSkE+6xL52BfsCrIlJpRq8zxlRwOTkweTL07AnXXRfsaKotEeknIltFZLuIjM1nmR+JyGYR2SQic/LZVFcROZrHK1VEjhYSQygwHegPdALu8a5B/su0A34HfF9VOwOPF/dYjTHGmLLSr1+/E0uWLLl03759YeBGAd+2bdsFNdNdu3bNOHjwYHhCQkIkQGpqakhmZiY33XTT8RkzZjQ4c+aMACQmJtY8ceJEgTljVFRU9vHjxwtcpn///idmzZoV41tu586d4b4YA+Xk5DBkyJCW7du3z5g4ceJ3/vNuvvnmYzNnzqwPMHPmzPr9+vU7ltc2KqIyq/FWVQV8TQfCvZcC1wO+2oF3cLUGM4DbvfcA84FXxLUduB2Yp6pngJ0ish24CviirGI3xlQjS5bAtm0wb54b9MyUO7+E9yZgL/CViCxW1c1+y/gnvKki0jDvrfFfoGcJQ7kK2K6q33j7nIe7BvnXJjwATFfVVABVPVjCfRljjDGlrkePHhnjxo3bd8MNN7TPyckhPDxcp02btrt9+/Zn/ZeLiIjQ2bNn73j00Ucvy8jICImIiMhZtWrVttGjRx9OSUmpGRsb21FVJTo6OnPp0qU7CtrniBEjDvfr1699o0aNzubXz3vQoEEnNm3aFNGrV68OAJGRkTmzZ8/e2axZswtqrFesWFF70aJF9du1a5fua0r/1FNP7fvxj398/Kmnntp/5513tmnZsmVMs2bNzi5cuLDA2CoSUS27J6p4N1Nrgba4m6oXgC+9Wm1EpAWwTFW7iEgS0E9V93rzdgC9ccn4l6r6njf9TW+d+fntNyoqSk+fPl1mx1UdVIT+EmX52zQm13XXuX7U27e7R36ZUiciaaqa74PRReR7wERVvdn7/DsAVf2D3zLPA9tU9Y1C9rVeVYv9zFBv3btw16GfeZ9/AvRW1Uf8llkEbAO+j2uOPlFVl+exrQeBBwEuu+yyHrt27SpJSMYYY0qZiKxV1ZIW0ObauHFjSlxcXJGaTZvqZePGjTFxcXGtAqeX6ajmqpqtqt2A5riahA5ltS8ReVBE1ojImtJ8Lp0xpgpbvRo++8w9R9uS7mBqBvgPTLPXm+avPdBeRD4XkS+9vth5yXe08lLqphQGtAP6AvcAfxaReoELqerrqtpTVXs2aFDkx6oaY4wxpooq0p2miMSq6n9LuhNVPSYinwLfA+qJSJiqZuEScl/H/31AC2CviIQBl+AGWfNN9/Ffx38frwOvg6vxLmmsxphqZPJkqFcP7r8/2JFUdWEissbv8+veObtY2+BcwtscWOVdmwL7dvUQkea+1lM+ItIXN2haXAH7KMr1Zi+wWlUzcd2ftnlxfVW8wzHGGGOqtwMHDoT27dv3isDpK1eu3Nq4cePsYMRUlopaxfOqN+r428BsVT1e2Aoi0gDI9JLuWri+e88BnwJ34UY2HwH8zVtlsff5C2/+v1RVRWQxMEdEXgKa4m5w/q+IcRtjTN6++QYWLIDf/hbq1Al2NFVdViHN+koz4V0IfCYirwEvAg2Al3A15g8UEudXQDvv6Rv7cAN7Bo5YvghX0/0XEYnxtvtNIds1xhhjTIDGjRtnB47KXpUVqam5ql4LDMPdGK0VkTkiclMhqzUBPhWRRNzNzApVXQL8D/Arb5C0+sCb3vJvAvW96b8Cxnr73gR8gBvcZjnwsKpWuRIQY0w5mzoVQkPhl78MdiTGL+EVkRq4hHdxwDKLcLXdFJTwquo7QHdcUr4ZWA38G7hKVQsstPVaYj0CfAxsAT5Q1U0iMklEBnqLfQwcEZHNuILkMap6pPiHbIwxxpjqpMidGlX1axEZB6wBpgFXeqOOP6GqF/SpU9VE4IIBbrzRYq/KY+K76e4AACAASURBVHoGcHc++34GeKaosRpjTIGOHoU334Rhw6Bp02BHU+2papaI+BLeUOAtX8ILrFHVxd68H3oJbzYFJ7xtcY+x3Ii7Dl2CK2jOKUIsS4GlAdOe9HuvuMLhXxXvKI0xxhhTnRW1j3dX4D7gVmAFMEBV14lIU1zT8HwHszHGmArntdcgLQ1+/etgR2I8pZXwishM4GrgEVX9TETqAL8HNojIo6r6r1IP3hhjqgjVbDRjLznpO8hJ/wZN30FO2g5yMnaBVozBi2u0eJTwpiOCHYYxxVbUGu8/AW/garfTfRNV9VuvFtwYYyqHM2dg2jTo1w+6dAl2NKb0bcd1ScoCUNWTwOMi0g14FbgmmMEZY0ywaXYaOek7zyXVXpKdk74DTU8B9Xvcs4QjtVoREtEKQmoGK+TzhdYOdgTGlEhRE+9bgXRf32oRCQEiVDVNVWeVWXTGGFPaZs+G776D3/wm2JGYMqCqL+QzfYOI9CnveEzFoTmZaPpOctK2kpP2NZqTXvhKxlQFOWfJydiVm2jr2f3nzw+tS0hkG0JrdyWkwZ1IZBtCarUhpNblSEQLSudJjKYshIaG9mjXrl16dna2tG3bNv2DDz5IqVOnTqHdqkrbpEmTGo4ePfqwb9/x8fFtFyxYsDMmJiY7MjLyyrS0tPXF2V5aWpr07t27w9mzZyU7O1sGDBiQOmXKlG8BkpOTa/zoRz+6/NixY2GxsbFpCxYs2BkREVEpnmglrvVeIQuJfAncqKqnvM+1gX+oaoWsOYiKitLTp08HO4xKzXXfD66i/DaNKZacHFfLXbMmrFsHFeB3Xh2ISJqqRpXTvhJUNd57/7aqjvSbt05Vu5dHHP569uypa9asKXxBc9FUFT170Euut5KTto2c0+69pn9TYZrKGlPepGYzQmq1QbyEOiQ3uW4D4dEV4r6vvIjI2kKetFEkGzduTImLiztcGjGVlH9SO3DgwNbdu3dPmzhx4ndFWTcrK4uwsCIP91WgZs2axa5Zs2ZLkyZNLjjJliTxzsnJ4eTJkyGXXHJJzpkzZ6RXr/9n797joqzyP4B/ztwZbjIgoCigKCKIJOjimjWmqZCXUrOb7mrtam26iu26bS2W8mvddjfUKEW3XdssftWuaRmlrv5EKDM2MiGEQUEuXrmJMDAMczu/Px4oRMFBLg8M3/fr9bwYzpzneb5MJPOdc873TBqzdevWCzNmzGh44IEHRj700EM1K1eurHniiSf8IyIiGp9//vnKbvlBukl2drZXREREYNt2e19tVUvSDQCc83rGmLq7giOEkF5x6BCQnw+89x4l3Y7LrdXj8W2eo//oDoJbDbAZzgnJdUNzgt38GNa6HztKlJA4jYbUJRwS74chUQdD4jwGEqfRgMyt/RsQ4lAYmKR7Eixya4dWGodX5dm6NTfyCpUYYv6mumBv/6lTp9bn5OQ4AcCOHTs0ycnJPmazmUVGRjbs2bOnVCaTQa1WT1iyZEllRkaGW1JSUplKpbLFxcX5GwwGiUKh4BkZGQWurq62VatWDTtx4oSryWRiK1asqFi/fn1Vamqqa0JCwlCNRmMuKChwCg8PN3z88cfFmzdv9q6oqJBrtdpgDw8PS2Zm5tn2EvENGzb47N+/X2MymdicOXOut4xityWRSODu7m4DAJPJxCwWC2OMwWaz4eTJk66ffPLJeQB46qmnqjdu3Di0ryXe7bH3/8IGxlgk5/wUADDGogDQHC1CSP/y2mvAsGHAI4+IHQnpOR1NlaFpNCKymarQ+O194OYuDhBxK7j5xvdYTOUPiXoM5EN+Bol6DCTOwZCox4Cp/CGsjiOEEMdlNptx+PBht1mzZtWdOnVKtXfvXk1WVpZOqVTypUuX+u/cudNz9erV1Y2NjZLo6OiGt95666LRaGSjRo0al5KSUqTVag3Xrl2TuLi42LZt2+bl7u5uzc3NzW9sbGSTJk0KmTdvXh0A5OfnO50+ffp8YGCgOSoqKuTIkSMu8fHxFcnJyT7p6elnbzXi3WLfvn1uhYWFqpycnHzOOe6///5RBw8edImNja2/VX+LxYJx48aFlpWVKZctW1Yxffr0hitXrshcXV2tcrkcABAYGGgqLy9X9MiL2gPsTbzjAPybMXYZwoiBL4BHeywqQgjpbt9+C6SlCcl38z/YxCENYozNg7B9mHur/bcZhG3FiEhMpX+GrSEP8qFPAl1aM8rAlMOEkWv1GEjUo8CkNAmPECKezoxMd6empiZJSEhIKABER0fr165dW7Vlyxav3NxcdURExFgAMBqNEm9vbwsASKVSLF++vAYAcnJyVN7e3matVmsAAI1GYwOAo0ePuul0OvWBAwc8AECv10vz8vJUCoWCh4eHNwQFBZkBICwszFBUVGR30nvo0CG3jIwMt9DQ0FAAMBgMEp1Op2ov8ZbJZNDpdHlVVVXSOXPmBH3zzTeqYcOG9ev1QnYl3pzzbxhjIQDGNDcVcM7NPRcWIYR0s8REwNUV+OUvxY6E9KwTAFqmNHwFYHGr577q/XAIANiarsB84U3IhiyFKvTvYodDCCEOQalU2nQ6XV7rNs45W7x4cfX27dsvte2vUChst1vXzTlniYmJZYsWLapr3Z6amuqqVCp/mDkmlUphsVjsXsLFOUdcXNyV9evXd2rak5eXl/Wee+7Rf/rpp+4bN24s1+v1UrPZDLlcjpKSEoWPj4/p9lfpGzoz/2oShPVykQAeZ4z9vGdCIoSQblZaCvzrX8DKlYA7DXo6Ms75zzo6xI5voDIVbwa4BcoRL92+MyGEkDsWExNTl5qa6nHp0iUZAJSXl0vPnj1708j0+PHjjRUVFfL09HQ1ANTU1EjMZjNmzpxZm5ycPLipqYkBQE5OjrKurq7DnNHZ2dlaW1vbYZ/Y2Ni6d99916ulX3FxsbwlxrYuX74sq6qqkgJAfX09S0tLcxs7dqxRIpFg8uTJ+rffftsDAHbv3u05d+7c67d/VfoGu0a8GWPvAggCcBqAtbmZA9jTQ3ERQkj3ef11oZja2rViR0J6GGPsiQ6e5pzz93stGAIAsDWWwnxpF+RDn4JEHSR2OIQQ4tCioqKM8fHxl2bMmBFss9kgl8t5UlJSWXBw8A0jwyqViqekpBStWbPG32g0SlQqlS0jI+PsunXrqkpKSpTh4eFjOedMo9GYP//886KO7rls2bKqmJiYYB8fH1NmZubZW/VZuHBh3ZkzZ1STJk0KAQC1Wm1LSUkp9vPzu2n6+IULF+TLly8fYbVawTlnDz744LXHH3+8FgASExMvPvroo0GvvPKKX1hYmGHt2rWiVpbvDHu3E8sHEMr7yf5OtJ1Y1/WFbSX6ya8b6euuXweGDwcefFCoZk56XS9vJ5bczlMPABjGOe/1DWkH+nZixrwVMF/ZA+e7CyFRDRc7HELIAOdI24mRvqmr24nlQiiodqU7gyKEAGhqAi5eBMrKbjzKy4EFC4BlywAJVeW9Y3/7G1BfD/zmN2JHQnoB5/xXrb9njD0G4PcAsgA8JEpQA5jNcA7mK29DPmwVJd2EEEIGNHsTby8AeYyx/wJoamnknM9v/xRCCDgHqqtvTqpbjtJS4OrVm8/z8QGcnIBPPwXeeAPYuhXQans//v7OZBKmmc+YAUyYIHY0pJcwYf+onwNYD+A7AE9wzvM6Pov0hKbzmwCmgCLwBbFDIYQQ0sdcvXpVOm3atDFt248fP17g6+trvdU5/Zm9iffGngyCkH6rqQm4cKH9xLqsDGhss+W9SgX4+wMBAcCcOcLj1sewYUIfzoEPPgCefx6YNg1YuBD4y1+AIFojabcPPgAuXwb+8Q+xIyG9hDH2NIB1AL4AMJ9z3uG6NNJzrPVnYLn6v1AE/A4Spa/Y4RBCCOljfH19rW2rsjsye7cTS2eMBQAYzTk/yhhTA+j1dXKE9CrOgaqqjpPqW41W+/oKCXR4+K0Tay8vodDX7TAGPP448NBDwlZYr74KpKYCa9YA8fFUnft2OBf27B43Dpg9W+xoSO9JBlAOYCKAf7eqV8EgFFeLFCuwgcZ0/mVA6gJFwHqxQyGEEEJEZ29V8xUAVgLQQKhu7gdgJ4AZPRcaISL64APgF78ADIYb252cfkyg58699Wi1Utm9sTg5CYn2U08JXxMTgXfeARIShD2pb7Mf44B15Ajw/ffA22/b90EHcRSjxQ6AANa6U7BUfATFiJfBFJ5ih0MIIYSIzt537KsA/ARAJgBwzs8xxrx7LCpCxPbee8CgQcCf/nRjYu3pKV4SN3QosHs3sHo1sG4d8KtfAW++CWzZAsyaJU5MfdlrrwFDhgizBsiAYe/UcsbYl5zzqT0dz0DVdP4lQOYBRcA6sUMhhBBC+gR7SyU3cc5/2PuNMSaDsI83IY7HYgEyMoB584Rp3Q89BERG2j9FvKdFRgLHjwMffSSsH589Wxh91+nEjqzvyM4WRrzXrOn+GQjEUfTK9mYDkfX6SVirPoMi8HdgMloSQwghhAD2J97pjLEXATgxxmYC+DeAT3suLEJE9O23gF4P3Hef2JG0jzGh2FpenlBw7YsvhDXla9YIVdQHusREwNkZePppsSMhfRd9eNxDmoriwRTeUAz/tdihEEKIQ5NKpVEhISGho0ePDouNjR2p1+tF2X82ISHBu/W9tVrtqKqqKikAqNXqO95WxmKxYOzYsaH33XffqJY2nU6nGD9+fIi/v/+4OXPmjDQajX1gVMw+9v7H+T2ASgDfA3gawOcA4nsqKEJElZYmfJ02TdQw7KJUAuvXA+fOCeu9t28HRo8WttAym8WOThwXLwLvvy+8Hh4eYkdDyIBiuXYM1ppjUAS+ACalSQWEENKTlEqlTafT5Z07d+6MXC7niYmJg+0912KxdFscu3bt8qmvr/8hr0xPTy/08vLq8nZgr7zyis+oUaNu2B7oueeeG7Z69erysrKyXHd3d8vrr7/u1dX79BZ7q5rbALzVfBDi2I4dA8LChL20+wtvbyA5GXj2WeA3vwHi4oAdO4R1znPn9o0p8r0lKQmw2YTXgJD2DaD/KXoH5xxNRRvAlH6Q+z0jdjiEENJrTqxOG349/5q6O685aKzGcPeb912wt//UqVPrc3JynABgx44dmuTkZB+z2cwiIyMb9uzZUyqTyaBWqycsWbKkMiMjwy0pKalMpVLZ4uLi/A0Gg0ShUPCMjIwCV1dX26pVq4adOHHC1WQysRUrVlSsX7++KjU11TUhIWGoRqMxFxQUOIWHhxs+/vjj4s2bN3tXVFTItVptsIeHhyUzM/Osn59feFZWVv6QIUNuyO43bNjgs3//fo3JZGJz5sy5vnXr1svt/TxFRUXyw4cPu7/wwgtXtm7d6gMANpsNJ0+edP3kk0/OA8BTTz1VvXHjxqHPP/985Z29yr3LrhFvxlgxY+x826OngyOk15lMwIkTfXuaeUfCw4HDh4VtxxgD5s8HZs4EcnLEjqx31NUBu3YBixcDgYFiR0NEwBg7aGfX5T0Zx0BkrT4EW+1XUIzYACZViR0OIYQMGGazGYcPH3YLDw9vPHXqlGrv3r2arKwsnU6ny5NIJHznzp2eANDY2CiJjo5uKCgoyNNqtQ1LliwJ2rZtW1lBQUFeenp6gYuLi23btm1e7u7u1tzc3Pzs7Oz8d955Z7BOp1MAQH5+vtP27dsvFBYWnikrK1MeOXLEJT4+vsLb29ucnp5+NjMz82x7Me7bt8+tsLBQlZOTk5+fn593+vRp9cGDB13a679q1arhf/nLXy5KJD+mq+Xl5TJXV1erXC4HAAQGBprKy8sV3fU69jR7q5pPbPVYBWAxhK3FCHEs//2vsIXY9OliR3LnGBP2D581SxgF37gRmDBBmHr9P/8jjI47qr//XUi+f/tbsSMh4vG1pxPnPLunAxlIhNHueDCnEZAPfVLscAghpFd1ZmS6OzU1NUlCQkJCASA6Olq/du3aqi1btnjl5uaqIyIixgKA0WiUeHt7WwBAKpVi+fLlNQCQk5Oj8vb2Nmu1WgMAaDQaGwAcPXrUTafTqQ8cOOABAHq9XpqXl6dSKBQ8PDy8ISgoyAwAYWFhhqKiIruT3kOHDrllZGS4hYaGhgKAwWCQ6HQ6VWxsbH3bvu+//767l5eX5Z577jGkpqa6duU16kvsnWretlrTNsbYtwBe6v6QCBFRWpqQuGq1YkfSdXK5UGxt6VJhz+/t24W1z/HxwNq1jlft22wGtm0T/ttNnHj7/sRRuTPG5rf3JOf8QG8GM1BYKvfDpj8FVeg/wST9ZvCBEEL6tZY13q3bOOds8eLF1du3b7/Utr9CobDJZB2nf5xzlpiYWLZo0aK61u2pqamuSqXyh8KkUqkUFovF7mVbnHPExcVdWb9+fdXt+n755ZcuR44cGeTn5+fe1NQkaWhokDz44IMj9u/fX6zX66VmsxlyuRwlJSUKHx8f0+2u11fYO9U8stUxkTH2DOwfLSek/zh2DIiIADQONKFDoxES0txcISl9/nlg7Fhg716AO1Bh53//G7hwgUa7iTuAhyHMzGp7PCxiXA6LcytMRS9Boh4Dme8SscMhhJABLSYmpi41NdXj0qVLMgAoLy+Xnj179qZPRMePH2+sqKiQp6enqwGgpqZGYjabMXPmzNrk5OTBTU1NDABycnKUdXV1HeaMzs7O1tra2g77xMbG1r377rteLf2Ki4vlLTG2tX379kvl5eU5ly5d+v6f//zn+cmTJ+s/+eSTYolEgsmTJ+vffvttDwDYvXu359y5c6/b87r0BfYmz4mtHlsAlAB4pNujIURMRiNw8iSwapXYkfSMMWOATz8Fjh4F1q0T1kHfcw+wdSsQFSV2dF3DuVBILiQEeOABsaMh4irlnP9c7CAGEkv5h7A1nIFq3AdgEvpMnhBCxBQVFWWMj4+/NGPGjGCbzQa5XM6TkpLKgoODbxgZVqlUPCUlpWjNmjX+RqNRolKpbBkZGWfXrVtXVVJSogwPDx/LOWcajcb8+eefF3V0z2XLllXFxMQE+/j4mNpb571w4cK6M2fOqCZNmhQCAGq12paSklLs5+fXqfLqiYmJFx999NGgV155xS8sLMywdu3a246g9xWMO9KIVzNnZ2fe0NAgdhj9GusDVbB7/XczLU1Y2/3pp0IlcEdmsQD/+AewYQNQVQX8/OfA5s3A0KFiR3Znjh0DZswA3npLWMtO+hTGmIFz3it7SzHGvuOc3/GeoT1h4sSJPCsrS+wwegS3WdBwciyYVA119HdgTJQtZAkhxG6MsW85511ek5adnV0SERHRb5I+0nuys7O9IiIiAtu22/XRNGPsuY6e55xvucO4COk7jh0DJBJhFNjRyWTA008Djz0mJNzbtglTtX//e2E7MnW37ojR8157TSgat3Sp2JEQ8S1v7wnG2AnO+d29GIvDs1zZA95YCFXEJ5R0E0IIIR2w96/kRAC/AuDXfDwDIBKAa/NBSP+XliYU5XJ3FzuS3uPuDvz5z0B+PhAbC7z0kjAlPSVF2Au7P8jNBQ4eBH79a0BFWxgNdLepVu7fa4EMANzWhKbiTZC4TYLUa57Y4RBCCOlnrl69Kg0JCQlte1y9elUqdmw9wd7FWMMARHLO9QDAGNsI4DPOOQ0vEcfQ0ABkZgqjvQPRyJFCsbWMDGH999KlQFIS8PrrwOTJYkfXsS1bACcn4Fe/EjsS0vc53toqEZkv/R3cWAbV2Lf6xPIkQggh/Yuvr6+1bVV2R2Zv4u0DoPWCfFNzGyGO4csvhXXP990ndiTiuvde4JtvgD17gBdfBKZMAZ55BvjTn/rmTIArV4D33gNWrgQ8PcWOhvQBjLGF7T0FwKk3Y3Fk3GqAqfgVSAfdA6lmptjhEEIIIX2evYn3HgD/ZYztb/7+IQDv9ExIhIggLU1Y9zx1qtiRiE8iAZYvBxYtEqaeJyUBH38MvPEGsHChsM95X/HGG8IHJuvWiR0J6Ts6mvOc2mtRODjzxWRw01Uowj+k0W5CCCHEDnYl3pzzPzLGDgJoqTr1JOf8u54Li5BelpYGREcDzr1SeLl/cHUVthpbsgRYsQJ4+GFg3jxg+3Zg+HCxowPq64HkZOHDgKAgsaMhfQTn/EmxY3B03KKHqeRVSDUzIfO4V+xwCCGEkH6hMxtuqgHUcc7fZowNZoyN4JwX91RghPSa2logK0uYWk1uNnGiMP389deFEfCxY4E//hFYvRqQilj7Yvdu4Pp14Le/FS8G0ufcYhcODqAKwJf0N6t7mC4kgZuroAx6RexQCCGEkH7DrqrmjLGXATwP4IXmJjmA93oqKEJ61RdfCBW8p08XO5K+SyYTCs+dOSOsA4+LE4qufSfSxBeLRRiNv/vuvl/8jfQ21zaHG4SdOQ4yxh4TMzBHwM01MJX+FTKv+ZC6/0TscAghZECTSqVRISEhoaNHjw6LjY0dqdfrRdnXMSEhwbv1vbVa7aiqqiopAKjV6gl3ck0/P7/w4ODg0JCQkNBx48aNbWkvLy+XTpkyZXRAQMC4KVOmjK6srOw3FdDt/Y+zAMB8AA0AwDm/DNpGjDiKtDRAqQR++lOxI+n7AgOBzz4DPvgAKCsDJk0C1q8XqsL3pn37gJISGu0mN+Gcb7rFsRbAFAC/Ezu+/s5UmghYaqEIShA7FEIIGfCUSqVNp9PlnTt37oxcLueJiYmD7T3XYrF0Wxy7du3yqa+v/yGvTE9PL/Ty8rJ29brp6elndTpdXm5ubn5L28svvzxk2rRp+tLS0txp06bpX3rpJd+u3qe32DvV3MQ554wxDgCMMVoISxzHsWNC0k17QNuHMeDRR4FZs4Dnnwdee03Yiiw5GYiJ6fn7cy7cc/RoYc05IXbgnF9jVAWsS2ymSpgubIPM5xFIXSPEDocQQvqMnN89NVxfkKvuzmu6jhlnGP+X3Rfs7T916tT6nJwcJwDYsWOHJjk52cdsNrPIyMiGPXv2lMpkMqjV6glLliypzMjIcEtKSipTqVS2uLg4f4PBIFEoFDwjI6PA1dXVtmrVqmEnTpxwNZlMbMWKFRXr16+vSk1NdU1ISBiq0WjMBQUFTuHh4YaPP/64ePPmzd4VFRVyrVYb7OHhYcnMzDzr5+cXnpWVlT9kyJAbsvsNGzb47N+/X2MymdicOXOub9269XJnX5dDhw4NSk9PLwCAp59+ulqr1Y4BcKmz1xGDvSPe/2KM7QIwiDG2AsBRAG/1XFiE9JJr14DsbJpmfic8PIC//U3Y+1ulAmJjgccfB8rLe/a+X3whrDl/7jlx15iTfoUxdh+AGrHj6M9MJX8GrI1QjNwodiiEEEJaMZvNOHz4sFt4eHjjqVOnVHv37tVkZWXpdDpdnkQi4Tt37vQEgMbGRkl0dHRDQUFBnlarbViyZEnQtm3bygoKCvLS09MLXFxcbNu2bfNyd3e35ubm5mdnZ+e/8847g3U6nQIA8vPznbZv336hsLDwTFlZmfLIkSMu8fHxFd7e3ub09PSzmZmZZ9uLcd++fW6FhYWqnJyc/Pz8/LzTp0+rDx486NLRzzVjxozRYWFhY1977TWvlrbq6mpZQECAGQCGDx9urq6u7kzNMlHZW9X8NcbYTAB1AMYAeIlzfqRHIyOkN6SnCyOoA33/7q645x7g9Gng1VeBzZuBQ4eAv/4VeOopYWuy7vbaa4CXF/Dzn3f/tUm/xxj7HkJBtdY0AC4DWNb7EXWN5dr/oUm3SuwwAAC2xvOQDVkKqfPY23cmhJABpDMj092pqalJEhISEgoA0dHR+rVr11Zt2bLFKzc3Vx0RETEWAIxGo8Tb29sCAFKpFMuXL68BgJycHJW3t7dZq9UaAECj0dgA4OjRo246nU594MABDwDQ6/XSvLw8lUKh4OHh4Q1BQUFmAAgLCzMUFRUp7I310KFDbhkZGW6hoaGhAGAwGCQ6nU4VGxtbf6v+X375pW7EiBHmS5cuyaZPnx4cFhZmbNtXIpH0qy0tb5t4M8akAI5yzu8DQMk2cSzHjgFqNfATKhLUJUol8PLLwhT0p58Wth97911g1y4gJKT77qPTAZ9+KtxL3a0zuojjmNvmew6gmnPey4UIugeTuUPiepfYYQAAJG4/gTLoj2KHQQghpFnLGu/WbZxztnjx4urt27ffNP1aoVDYZLKO0z/OOUtMTCxbtGhRXev21NRUV6VS+cMH21KpFBaLxe6sl3OOuLi4K+vXr6+yp/+IESPMAODn52eZM2fO9ZMnTzrHxsbWe3p6WkpLS+UBAQHm0tJSuUaj6b7F6j3stok359zKGLMxxtw557W9ERQhvSYtDZg6FVDY/YEd6UhIiPCavv22UHQtIgJ44QXhUCq7fv0tW4Rp7c8+2/VrEYfEOS9t28YYc2aMLQXwOOd8TkfnM8ZiALwOQArg75zzV9vptwjAXgCTOOdZXY/81qRuE+EU/kFPXZ4QQoiDiYmJqVu4cOGoF198sdzPz89SXl4ura2tlQYHB5ta9xs/fryxoqJCnp6ertZqtYaamhqJi4uLbebMmbXJycmD586dq1cqlTwnJ0cZGBho7uiezs7O1traWsmQIUPa7RMbG1u3cePGoStXrrzm7u5uKy4ulisUCu7n53dT4lxXVyexWq3w8PCw1dXVSdLS0tz+8Ic/XAaA2bNnX9+1a5fn5s2br+7atcszJibm+h2+VL3O3nmg9QC+Z4z9gzGW1HJ0dAJjbDhjLI0xlscYO8MYW9vcrmGMHWGMnWv+6tHczpqvW8gYy2GMRba61rLm/ucYY/1uqiDpo8rLhe2xaJp595JIgF/8AsjPBxYtAjZtAu66S1gL3hXl5cCePcCyZYC3d/fESvoMxlgMY6yg+W/A7zvot4gxxhljE29zPQVjbAFj7N8ArgCYAWDnbc6RAtgOIBZAKIDHGWOht+jnCmAtgMzb/mCEEEJIL4qKijLGx8dfmjFjRnBwcHDo9OnTgy9cuCBvbKk+ZgAAIABJREFU20+lUvGUlJSiNWvW+I8ZMyZ02rRpwQaDQbJu3bqqkJAQY3h4+NjRo0eHrVixIsBsNnc4sr1s2bKqmJiY4Ojo6OD2+ixcuLBu8eLF1yZNmhQSHBwcumDBgqDr16/fsljPxYsXZZMnTw4ZM2ZMaGRk5NhZs2Zdf/jhh+sAYNOmTVfS0tLcAgICxh0/ftxt06ZNVzr7GomFcd52KdwtOrWT7HLO3+ngnCEAhnDOTzW/SfkWwEMAlgO4xjl/tfnNlQfn/HnG2AMAfg3gAQDRAF7nnEczxjQAsiDsw8qbrxPFOW+3SI6zszNv6O3tjRxMX1gvYc/vZpd8+CHw2GPA118D0dE9e6+B7NAh4Fe/Erb/+uUvgb/8RSjM1lkvvQS88oqQ0I8Z0+1hkp7DGDNwztvdDaM54T0LYCaAiwC+gTA6ndemnyuAzwAoAKy+1UgzY2wWgMcBzAKQBuBDAG9wzgPtiPOnADZyzmc3f/8CAHDO/9Sm3zYIS6/WA/jt7Ua8J06cyLOyemxQnBBCSCcwxr7lnHf44a09srOzSyIiIuyaNk0GluzsbK+IiIjAtu0djngzxvwBIcG+1dHRuZzzK5zzU82P9QDyAfgBeBBAy7nvQEjG0dy+hwu+hlBBfQiA2QCOcM6vNSfbRwD0wp5FxOGlpQGurkBUlNiROLaYGCA3V9hz++23henoH3wgFLWzl8EAbN8OzJ9PSbdj+gmAQs75ec65CcAHEP4mtPU/AP4MwNjBtQ4BGAlgKud8Kef8UwA2O+PwA9C6QM7F5rYfNM/GGs45/6yjCzHGVjLGshhjWZWVlXbenhBCCCGO6nZTzT9uecAY++hOb8IYCwQwAcK0PB/OecuUgKsAfJoft/eG57ZvhJrv8cObnO7cEJ44sLQ04N57gdsUmSDdwNlZqHSelQX4+wvbjj3wAFBcbN/5//ynsPXbb3/bo2ES0XRbwgsgEsBJAEeblzP9AsJ67S5jjEkAbAHwm9v15Zz/jXM+kXM+cfDgwd1xe0IIIcShXL16VRoSEhLa9rh69apD7hd7u4yj9XzjkXdyA8aYC4CPAMRxzutaT2HmnHPGWLfMJ+ac/w3A3wBhqnl3XJM4sEuXgLNngZUrxY5kYLnrLmFq/5tvAn/4AxAWBiQkAHFx7X8AYrUKRdWio4G77+7deEl3kTHGWs+1/lvzv9l2aZXwLr9dX875aQCnAfyeMTYFwrRzOWPsIID9t7nvJQDDW30/rLmthSuAcQCON/8t8wVwgDE2vycLrBFCCCGOyNfX19q2Krsju92IN2/nsV0YY3IISXcK53xfc3N58xTylnXgFc3t7b3hud0bIUI6Ly1N+Dp9urhxDERSKbB2LZCXB9x/v1D9fNIk4Jtvbt3/k0+AoiJhtLsP1B4gd8TSMvrbfLRNfjuT8JYAmAwh4e1wjR7n/CvO+a+br7e1+byOfANgNGNsBGNMAeAxAAdaXa+Wc+7FOQ9sXjP+NQBKugkhhBByW7dLvCMYY3WMMT2A8c2P6xhjesZYXUcnMmE44B8A8jnnW1o9dQBAS7G2ZQA+adX+8+bq5pMB1DZPST8MYBZjzKO5Avqs5jZC7lxamlDgKyJC7EgGLn9/Ianeu1eoWD55sjDyrdff2O+114ARI4AFC8SJk/SGbkt4GWOrWz0Oaz7fxjn/D+f8qY6C4JxbAKyG8DcmH8C/OOdnGGMJjLH5Xf8xCSGEEDJQdZh4c86lnHM3zrkr51zW/Ljle7fbXPtuAD8DMJ0xdrr5eADAqwBmMsbOAbi/+XsA+BzAeQCFAN4C8GxzDNcgFNT5pvlIaG4j5M4dOwZotcLWV0Q8jAlbjuXnA08/DSQlCdPPP/1UeP6rr4CTJ4HnnhNGyolD6uaEt3Vy/e4dxPI55zyYcx7EOf9jc9tLnPMDt+g7jUa7CSGEEGKPHqsqxTn/EjeuEW9txi36cwCr2rnWbgC7uy86MqCVlAjHc8+JHQlp4e4O7NgBLF0qrLufP19IyOvqhJkJTz4pdoSkh3HOP4fwAWzrtpfa6TvNzsvS2gRCCCGE9Ak03EcGnpb13ffdJ24c5GZTpgCnTgF//COQmgocOQI8+6xQFZ0Q+wxijC1gjC0C4MYYW9j6EDs4QgghpLtIpdKokJCQ0NGjR4fFxsaO1Ov1ouR2CQkJ3q3vrdVqR1VVVUkBQK1WT7iTa1ZVVUljYmJGjhgxImzkyJFhR48edQaA8vJy6ZQpU0YHBASMmzJlyujKysp+MyWSEm8y8Bw7BgweLExpJn2PQgG8+CLw/ffC19/cducmQlrLADAfwNzmx/NaHXNFjIsQQgjpVkql0qbT6fLOnTt3Ri6X88TERLv3r+zO7Zd37drlU19f/0NemZ6eXujl5WXtyjVXrlw5fNasWXXFxcVn8vLy8u666y4jALz88stDpk2bpi8tLc2dNm2a/qWXXvLtavy9hTYwJgML58KI9333UYXsvm70aGHkm5BO4JwvFzsGQgghA0vhK08NNxTlqrvzmuqgcYZR8bsv2Nt/6tSp9Tk5OU4AsGPHDk1ycrKP2WxmkZGRDXv27CmVyWRQq9UTlixZUpmRkeGWlJRUplKpbHFxcf4Gg0GiUCh4RkZGgaurq23VqlXDTpw44WoymdiKFSsq1q9fX5WamuqakJAwVKPRmAsKCpzCw8MNH3/8cfHmzZu9Kyoq5FqtNtjDw8OSmZl51s/PLzwrKyt/yJAhN2T3GzZs8Nm/f7/GZDKxOXPmXN+6devlW/0s1dXV0szMTNe9e/eWAIBKpeIqlcoKAIcOHRqUnp5eAABPP/10tVarHYN+suMVjXiTgaWwUNjDm6aZE+KQGGPbWj1e2+a5f/Z6QIQQQkgPM5vNOHz4sFt4eHjjqVOnVHv37tVkZWXpdDpdnkQi4Tt37vQEgMbGRkl0dHRDQUFBnlarbViyZEnQtm3bygoKCvLS09MLXFxcbNu2bfNyd3e35ubm5mdnZ+e/8847g3U6nQIA8vPznbZv336hsLDwTFlZmfLIkSMu8fHxFd7e3ub09PSzmZmZZ9uLcd++fW6FhYWqnJyc/Pz8/LzTp0+rDx486HKrvgUFBQqNRmNZvHhx4NixY0MfffTRgLq6OgkAVFdXywICAswAMHz4cHN1dXW/GUjuN4ES0i2OHRO+UuJNiKO6t9XjZQBeb/X9+F6OhRBCyADQmZHp7tTU1CQJCQkJBYDo6Gj92rVrq7Zs2eKVm5urjoiIGAsARqNR4u3tbQEAqVSK5cuX1wBATk6Oytvb26zVag0AoNFobABw9OhRN51Opz5w4IAHAOj1emleXp5KoVDw8PDwhqCgIDMAhIWFGYqKihT2xnro0CG3jIwMt9DQ0FAAMBgMEp1Op4qNja1v29disbD8/Hz166+/XjZ9+vSGJ598cviGDRt8X3/99RtGyCUSCVg/msFKiTcZWNLSgKFDgeBgsSMhhPQM1s5jQgghxKG0rPFu3cY5Z4sXL67evn37TdOvFQqFTSbrOP3jnLPExMSyRYsW1bVuT01NdVUqlbzle6lUCovFYvffWc454uLirqxfv77qdn0DAwNNPj4+punTpzcAwKOPPlrz6quv+gKAp6enpbS0VB4QEGAuLS2VazSa7lus3sNoqnkfxRgT9XBItL6bkIFAwhjzYIx5tnqsYYxpAPSbyqeEEELInYiJialLTU31uHTpkgwQqoCfPXv2ppHp8ePHGysqKuTp6elqAKipqZGYzWbMnDmzNjk5eXBTUxMDgJycHGXLNO/2ODs7W2trazvsExsbW/fuu+96tfQrLi6Wt8TYlr+/v8XX19eUnZ2tBID//Oc/bmPGjDECwOzZs6/v2rXLEwB27drlGRMTc/12r0lfQSPeZODIywMqKmiaOSGOzR3At/hxtPtUq+f4zd0JIYQQxxEVFWWMj4+/NGPGjGCbzQa5XM6TkpLKgoODTa37qVQqnpKSUrRmzRp/o9EoUalUtoyMjLPr1q2rKikpUYaHh4/lnDONRmP+/PPPizq657Jly6piYmKCfXx8TO2t8164cGHdmTNnVJMmTQoBALVabUtJSSn28/O75Yj1G2+8UbZkyZKRJpOJ+fv7N73//vslALBp06YrCxYsCAoICPDy8/Mz7d+/v8PY+hLGueO9D3F2duYNDQ1ih9ElDjvq3And/rv55pvAr38NnD8PjBjRvdcmhLSLMWbgnA/YzdgnTpzIs7KyxA6DEEIIAMbYt5zziV29TnZ2dklERMRtp02TgSc7O9srIiIisG07jXiTgSMtDQgIoKSbEAfHGJMBiAUQ0tyUB+Aw57zfrAMjhBBCiGOhxJsMDDYbcPw4MH++2JEQQnoQY8wPwDEAVwB8B2HK+VwAWxhj93HOb7lnKCGEEEJ619WrV6XTpk0b07b9+PHjBb6+vlYxYupJlHiTgSEnB7h2DZg+XexICCE9648Akjnn21o3MsbWAPgThC3GCCGEECIyX19fa9uq7I6MEm8yMKSlCV+psBohjm4y53x520bOeRJjrECEeAghhBBCaDsxMkCkpQGjRgHDhokdCSGkZzV28Jyh16IghBBCCGmFRryJ47NYgPR04LHHxI6EENLz3BljC2/RzgC49XYwhBBCCCEAJd5kIPjuO6CujqaZEzIwpAOY185zGb0ZCCGEEEJIC5pqThxfy/ruadNEDYMQ0vM45092dIgdHyGEENJdpFJpVEhISOjo0aPDYmNjR+r1elFyu4SEBO/W99ZqtaOqqqqkAKBWqyd09nrZ2dnKkJCQ0JbDxcVlQkJCgjcAlJeXS6dMmTI6ICBg3JQpU0ZXVlZKu+8n6VmMcy52DN3O2dmZNzQ0iB1GlzDGxA5BdN32uxkTA1y4AJw50z3XI4R0CmPMwDl3FjsOsUycOJFnZWWJHQbpY2xNdbBUnwU36cUOhZB+ReoRBJm7/x2fzxj7lnM+satxZGdnl0RERFR19TpdoVarJxgMhu8AYP78+SMiIyMNGzduLLfnXIvFApmseyY/+/n5hWdlZeUPGTLE0lGMd8JiscDX1zfiq6++yg8ODjY988wzwzQajWXz5s1XX3zxRd+amhppcnLypa79BN0rOzvbKyIiIrBtO001J47NbAa+/BJYvlzsSAghhAww3GqG5fp5WKrPwlJdAMu1H7/a6q+KHR4h/ZLb9D/DdcrvxA7jBpd3PjW86WKuujuvqRw2zjD0md0X7O0/derU+pycHCcA2LFjhyY5OdnHbDazyMjIhj179pTKZDKo1eoJS5YsqczIyHBLSkoqU6lUtri4OH+DwSBRKBQ8IyOjwNXV1bZq1aphJ06ccDWZTGzFihUV69evr0pNTXVNSEgYqtFozAUFBU7h4eGGjz/+uHjz5s3eFRUVcq1WG+zh4WHJzMw8214ivmHDBp/9+/drTCYTmzNnzvWtW7devt3PdeDAATd/f/+m4OBgEwAcOnRoUHp6egEAPP3009VarXYMgD6VeLeHEm/i2L75BmhooPXdhBBCegTnHLb6qzck1ZbqAiHZvn4esP34vlOi9oJMMwaqoFjIPMdApgmGxEkjYvSE9D/SQSPEDqHPMZvNOHz4sNusWbPqTp06pdq7d68mKytLp1Qq+dKlS/137tzpuXr16urGxkZJdHR0w1tvvXXRaDSyUaNGjUtJSSnSarWGa9euSVxcXGzbtm3zcnd3t+bm5uY3NjaySZMmhcybN68OAPLz851Onz59PjAw0BwVFRVy5MgRl/j4+Irk5GSf9PT0s7ca8W6xb98+t8LCQlVOTk4+5xz333//qIMHD7rExsbWd/Szvf/++5qHH364uuX76upqWUBAgBkAhg8fbq6uru43+Wy/CZSQO3LsmPCV1ncTMuAwxqYACESrv3Wc8z2iBUQchrlKh4Zv3oDp8n9huXYWvKnuxydlKsg0oyHzGQ/V2Ichb06wZZ6UZBPiqDozMt2dmpqaJCEhIaEAEB0drV+7dm3Vli1bvHJzc9URERFjAcBoNEq8vb0tACCVSrF8+fIaAMjJyVF5e3ubtVqtAQA0Go0NAI4ePeqm0+nUBw4c8AAAvV4vzcvLUykUCh4eHt4QFBRkBoCwsDBDUVGRwt5YDx065JaRkeEWGhoaCgAGg0Gi0+lUHSXeRqORHT161H3Lli0Xb/W8RCLpV8tzKfEmji0tDYiIADw9xY6EENKLGGPvAggCcBqAtbmZA6DEm9wRzjlMF06g/uu/wnj2ACBTQel/L9TjlzUn1kKCLXUfDsaodi0hpOcplUqbTqfLa93GOWeLFy+u3r59+03TrxUKhe1267o55ywxMbFs0aJFda3bU1NTXZVK5Q8FmKRSKSwWi91ZL+cccXFxV9avX2/3uvi9e/e6h4aGGoYPH/7DSLqnp6eltLRUHhAQYC4tLZVrNJp2R9n7Gkq8ieMyGoGvvgKeeUbsSAghvW8igFDuiBVESa/iNiuMZz+B/uRfYb70NSROnnC952U4T1wFqfPgTl3HXFUGU8kZmEvOwFqqg+VyEbihfxeDJf0A52BWG5il5eDCV3PrtlsfaHlstQkfXfYBco0fZO/tBUJCxA6lT4qJialbuHDhqBdffLHcz8/PUl5eLq2trZW2rJFuMX78eGNFRYU8PT1drdVqDTU1NRIXFxfbzJkza5OTkwfPnTtXr1QqeU5OjjIwMNDc0T2dnZ2ttbW1kiFDhrTbJzY2tm7jxo1DV65cec3d3d1WXFwsVygU3M/Pr93E+YMPPtA88sgj11q3zZ49+/quXbs8N2/efHXXrl2eMTEx1+18aURHiTdxXF9/LSTf06eLHQkhpPflAvAFcEXsQEj/xM2NMOS8A/3XibDWFEI6aCTcY96EOuJJSIxW4MRJ4MoVQK//4eB1deDV5bBVlYNfqwKvuw6mrwcMjZA0WSA3A4o+krwQ0m8VXwfq6m7fb4CKiooyxsfHX5oxY0awzWaDXC7nSUlJZW0Tb5VKxVNSUorWrFnjbzQaJSqVypaRkXF23bp1VSUlJcrw8PCxnHOm0WjMn3/+eVFH91y2bFlVTExMsI+PjykzM/PsrfosXLiw7syZM6pJkyaFAIBarbalpKQUt5d419XVSb788ku3d955p7R1+6ZNm64sWLAgKCAgwMvPz8+0f//+DmPrS2g7sT6qP61X6Cld/t18+WXglVeAa9cAd/fuCYoQ0mlibCfGGEsDcBeA/wJoamnnnM/vzTgA2k6sv7EaqtGQtR0NWW/CZqiEfOgkuEatg6rOD+xYGnD0qPDBrqXfzG4kxPFkZgI/+ckdnepI24mRvom2EyMDT1oaEBlJSTchA9NGsQMg/Yul5jzqM7fAcHo3uLkRatW9cDU9Aum+ErC1K4H6Dgvv2k+hAFxdfzwUdtcmIuTOqVT2HU5Ot25XKABJH6pdMGaM2BEQ0mmUeBPHZDAIIxLr1okdCSFEBJzzdLFjIP2D6fI3qD/5V5i+3gvleQk8qwOgyKsFq8wAkNHueZaRQ8EmTIJk8BAwN7cbk+nWR9vnKNEmhBAAwNWrV6XTpk276VOU48ePF/j6+lpvdU5/Rok3cUwnTgBmM+3fTcgAxRibDOANAGMBKABIATRwzt1EDYz0CZzb0PTtv9G0JwHSzDy4nZdAVsUhFMA/f8tzTGqgaewQyBYugeoXv4HMx7dXYyaEEEfj6+trbVuV3ZFR4k0cU1oaIJMBU6eKHQkhRBxvAngMwL8hVDj/OYBgUSMi4tPrYd3xZ9h2bYWyxADVD6VEbDd1tarlaPA0o2GoFJI5D8J9aTxcR0zozWgJIYQ4EEq8iWM6dkwouuHiInYkhBCRcM4LGWNSzrkVwNuMse8AvCB2XEQE338PvmMHsOdtSA1NkN6iC1epYAkbiTrnatSpymEOGASPWasw+P5nIBvk0+shE0IIcSyUeBPHo9cDWVnA738vdiSEEPEYGGMKAKcZY3+BsK1YH6oMRHpcUxOwbx+QnAx88QVu2itEIgEmToT17mjUudSg6uJBWBrzoAyIgCb2z3Cb8hgkcqUYkRNCCHFAlHgTx/PFF4DVSuu7CRnYfgYh0V4NYB2A4QAWiRoR6R2lpcCuXcA//gFUVNz0NA8JAXv2WTTeHYZrJ99G3cmdwFULXKIehCY2Duqx9/bIlp6cc1hqq2G8dB5Nl4vRdPk8jJeLYW3spmrphAwQXjMfh+aeeWKHQUinUeJNHM+xY0LV2ClTxI6EECISznkpY8wJwBDO+Sax4yE9zGYDDh8WRrc/+0z4vhUuAfjc2WBrf4d6Vz2qP3sNjX/9EhInV2hmrYLH7NVQ+AR1OQyrsRFNV0p+SKqbLp2H8YrwtelyMawG/Q39ZR6DIXMZ1OX7EjKQuEfSwEoLqVQaNXr06Ear1cpGjRrV+K9//avE1dX15qIVPSwhIcF73bp1VS331mq1oz766KNiLy8vq1qtnmAwGL7r7DU3bdrk/e677w5mjCEkJMTw4YcflqjVaq7T6RSPPPLIyOvXr8vCw8MNH330UbFK9WPFjr6Mcd4v4uwUZ2dn3tDQ0KVr9MSn3aRz7vh3MypK2LLl+PEu3b8v/A444v+fZOBhjBk45869fM95AF4DoOCcj2CM3QUggXM+vzfjAICJEyfyrKys3r7twFBVBezeLYxwn7+5GrnVDTAvmAL5ht2oK/0C1z5LhOmyDnKvAHjErsWgab+AVN35Qvc2swk1Jz6D4dzpH0awjZfPw1x15YZ+EqUTlH4joRoyQvjqNxLKISOavwZCqqY6JIT0NsbYt5zziV29TnZ2dklERERVd8R0p1ontfPnzx8RGRlp2LhxY7k951osFshk3TMG6+fnF56VlZU/ZMgQS0cx2qu4uFg+derUkIKCglwXFxf+wAMPjIyJialds2ZN9QMPPDDyoYceqlm5cmXNE0884R8REdH4/PPPV3bLD9JNsrOzvSIiIgLbttOIN3EsNTXAd98BL78sdiSEEHFtBPATAMcBgHN+mjE2QsyASDfhHPj6a2DHDuDf/xbWcrdhHMVguHcQVGv/BsP5c7j22jRYr1+FMnAChv76fbhFPwwm7fxboMbSApQf+DsqP38HlppKgDEofIZDNWQEBk2OgWroSCiHNifWQ0dArvHpEx/iEkJ6Vs2nTw03V+aqu/Oa8sHjDB7zdl+wt//UqVPrc3JynABgx44dmuTkZB+z2cwiIyMb9uzZUyqTyaBWqycsWbKkMiMjwy0pKalMpVLZ4uLi/A0Gg0ShUPCMjIwCV1dX26pVq4adOHHC1WQysRUrVlSsX7++KjU11TUhIWGoRqMxFxQUOIWHhxs+/vjj4s2bN3tXVFTItVptsIeHhyUzM/Nse4n4hg0bfPbv368xmUxszpw517du3Xq5vZ/HarWyhoYGiVKptDY2NkqGDRtmttlsOHnypOsnn3xyHgCeeuqp6o0bNw7ta4l3eyjxJo4lPV14UzZ9utiREELEZeac17ZJemgKSW+yWIRRaJ1OOAoKgKIiwGzu2nWrq4VrtcEHDYLxp26oHVMGadRMWC0jUbn1SdiM9XAePxueq96DOmx6pxNhq7ER19L2ovzA36H/LgNMKoPHPfPh/eAKuE+cDolc0bWfhxBCushsNuPw4cNus2bNqjt16pRq7969mqysLJ1SqeRLly7137lzp+fq1aurGxsbJdHR0Q1vvfXWRaPRyEaNGjUuJSWlSKvVGq5duyZxcXGxbdu2zcvd3d2am5ub39jYyCZNmhQyb968OgDIz893On369PnAwEBzVFRUyJEjR1zi4+MrkpOTfdLT08/easS7xb59+9wKCwtVOTk5+Zxz3H///aMOHjzoEhsbe1OhixEjRphXrVp1dcSIEeOVSqXtnnvuqVu4cGHdlStXZK6urla5XA4ACAwMNJWXl/ebf4Qp8SaOJS0NcHISthIjhAxkZxhjTwCQMsZGA1gD4CuRY3JM168LiXDrBFunAwoLu55k22PSJJgWTEY1S4GNVwDKaNQc+j+A/x/cpjwOz7m/hSogotOXbTiXg4pP3kLl4fdg1V+Hatgo+D/7KgbPWQaFp28P/CCEkP6qMyPT3ampqUkSEhISCgDR0dH6tWvXVm3ZssUrNzdXHRERMRYAjEajxNvb2wIAUqkUy5cvrwGAnJwclbe3t1mr1RoAQKPR2ADg6NGjbjqdTn3gwAEPANDr9dK8vDyVQqHg4eHhDUFBQWYACAsLMxQVFdmd9B46dMgtIyPDLTQ0NBQADAaDRKfTqW6VeFdWVko/++yzQYWFhd97enpa58yZM3LHjh2aBQsW1HXl9RIbJd7EsaSlAXffDShpCxhCBrhfA/gDgCYA7wM4DOB/RI1IDBaLsMVifb0wG6grTCYhmW5JsFuS7KtXuyfWznByAh5/HLZfLkfttXdh+O4NcLii4ZwRkJyBZvYaaGLjIPfy79RlrYZ6VB35ABWfvIX6vP+CyRXQ3LcIPg+ugNsELZiEdqQjhPQdSqXSptPp8lq3cc7Z4sWLq7dv336pbX+FQmG73bpuzjlLTEwsW7Ro0Q1JbmpqqqtSqfzhD4lUKoXFYrF7ChHnHHFxcVfWr19/23Xxn376qZu/v3/T0KFDLQDw0EMPXf/qq69cnnnmmWt6vV5qNpshl8tRUlKi8PHxMdkbg9go8SaOo7IS+P574PHHxY6EECIyzrkBQuL9h86cxxiLAfA6ACmAv3POX23z/HMAfgnAAqASwFOc89JuCbotqxW4dAmoqxOO2trbP277fRcLjXaZnx8QEiIcY8YAwcGASxcLikkkQGgomuoLUP3hQ7A1XIGpGrAYneC54AV43P8MpC4edl+Oc46G/CyUf/IWqo68D5uhHk4jQhEYtxVesT+D3N2za/ESQkgviomJqVu4cOGoF198sdzPz89SXl4ura2tlQYHB9+QoI4fP95YUVEhT09PV2u1WkNNTY3ExcXFNnPmzNrk5OTBc+fO1SuVSp5sbwy0AAAgAElEQVSTk6MMDAzscPqSs7Oztba2VjJkyJB2+8TGxtZt3Lhx6MqVK6+5u7vbiouL5QqFgvv5+d00PT0wMNB06tQpF71eL3F2drYdO3bMNSoqyiCRSDB58mT922+/7bFy5cqa3bt3e86dO/f6Hb9YvYwSb+I4WqqY0/7dhAxYjLEDHT3fUVVzxpgUwHYAMwFcBPANY+wA57z1aMJ3ACZyzg2MsV8B+AuAR7se+S1cuQIEBPTIpbuVUikk1GPG/Jhkh4QIba6u3X47a6MeVR8uhfnCAXALYDEOg8f8l+E+dSkkCpXd17Hor6PqcArKP3kLhnPZkCid4Hn/o/B5cAVcwn/aY0XROOcwXauC8XIZGi+XwWoQ+cMRQvoZt7AJcA0OEzuMPisqKsoYHx9/acaMGcE2mw1yuZwnJSWVtU28VSoVT0lJKVqzZo2/0WiUqFQqW0ZGxtl169ZVlZSUKMPDw8dyzplGozF//vnnRR3dc9myZVUxMTHBPj4+pszMzLO36rNw4cK6M2fOqCZNmhQCAGq12paSklJ8q8R7+vTpDfPmzasZP378WJlMhrCwMMNzzz1XCQCJiYkXH3300aBXXnnFLywszLB27VpRK8t3Ro9tJ8YY2w1gLoAKzvm45jYNgA8BBAIoAfAI57yGCX/dXgfwAAADgOWc81PN5ywDEN982Vc45+/c7t60nZhj6PTv5rPPAu++C1y7BjQXXeiKvvA7QNuJEUfQm9uJMcYqAVyAML08E8CN1dU4T+/g3J8C2Mg5n938/QvN5/ypnf4TALzJOb+7o5jueDuxujrA3b3z57XCAXAFYFOgzStxBxhgcQfMGsDsKXy1eAIWNwC9OAObyQCpCrDZPOF2/za4TX7C7ing3GpF7bdpqDy4B9eO7YWtqRHOwRPg/eAKeM1+AjKXrr3eAGBtMsJ4+QIamxPrlgS78dKPj21Nxi7fh5CBaszzf0bQM7+74/MdaTsx0jeJsZ3YPwG8CWBPq7bfA/g/zvmrjLHfN3//PIBYAKObj2gAyQCimxP1lwFMhPD+4dvm0YeaHoyb9FfHjgH33tstSTchpHd14xRvXwgj1o8DeALAZwDe55yfsSMMPwhJe4uLEP4mtecXAA7acd074+ICPsQXVokFXK2EzUkJ7qQAV0lgU3JwpRU2hRU2uQk2hRE2WSNssgZwJYdNCXAVwOUA5M6AzBXCgH73YgB6/19cBqdxP8Og2X+06wNSzjkM57JReeg9VP3nf2GuugKpizsGP7AM3g+ugEtIZKcjsDY14drXx1FfmHdjgn2pDKbqipv6K72HwGmoP9xC74L3/fPhNNQfTkP9oRo6HDKXzu8jTshApvCg5R+kf+qxxJtznsEYC2zT/CCAac2P34Gwv+rzze17uDC89zVjbBBjbEhz3yOc82sAwBg7AiAGwkgGIT+6fFko8vPLX4odCSGkk7pzijfn3ArgEIBDjDElhAT8OGNsE+f8zW6MeSmED4W17Ty/EsBKAPD371yBrx9IJDB/+xkast6E5XoJrLUlsNYVArYbZ+VJXHwhdQ+CzD0A8kGBkLkHQjooEFL3AEjdAyCRd+vWsv1G09UyVP3nf1F56D00nj8DJpNj0N1zMHj2UnjcPQcSpf3T0gHAUq9H5fGDuPqf/ahM+wyWej0AQOqkhpNfAFRD/eEWOqE5ofaHk5+QXCt9/CClgp+EEHKTq1evSqdNmzambfvx48cLfH19rWLE1JN6e423D+f8SvPjqwB8mh/fapTBr4N2Qm5E67sJ6c9+AqCQc34eABhjH0D4QPaHxJtzntaq/9cAlrZ3seaEew6EpDsQQBLw/+3de3zT5d3w8c+VpEl6LqW0xQItWAq0lNKWg4qzCIJFEBXHHhVPm/OwyRA22XPPISp6O92GMpziYXrfD8rzcCsHD0xAGAjTKVIY1CItlDOFtrTQY9qmSa7nj6RYoYVCD2mb7/v1yivJL79cv29+TXrlm+vE6hbEUQD0bXS/j2fbueXfgHvStgytdV1TBWmt3wTeBHdX8xYcu0mumlJqD67HFBqHOeYqjIl3YAqLwxgahzEsFlNIP5Sf/+UW3+04Ksso3bSCknXvUfFv96iC4GFj6P/bJfQc/xP8QsMvqTz76RKKNn5M0frVlHyxAZe9DnPPXkTf9BOib7yNsNSr8AsL7xRDk4QQoquJjo52njsre3fmtcnVtNZaKdVmA1gbty6YzV1mHXXRVjZvhrAwGD7c25EIIc5nUko1HuT8picxbdBmXbyVUkuBocCnwDNa65xLiHM7MFAp1R93wn0H7u7qjctPBd4AMrXW5/cpbmPWARPo/dh5ub9oxFVvp+yrtZxa9x5nvvgEba/D2i+Bvg89S8SNd2GNGXBJ5dUUHKXosw8pXL+K09v/CS4X/jGx9Lv7F0RPvI0eI8agjG3fbV8IIUT31tGJd5FSqrfW+qSnK3nDl5bmWhkK+L5resP2z5squHHrQmBgoMxI5Ws2bYKMDJAvQ0J0Ro62mMgGLt7FG3dLeDXwGDCrUUukwv2bb7MDarXWDqXUTNxrfhuBd7TWe5RSC4AsrfXHwJ+AIOADT9lHLzRTumgfWmsqs/9Fybr3KP3H+zgqTmPq0YuoWx+mV+bdBA4ZcUmt0FX5eylcv5rC9auo+HYHAEEJScT/8gmiMqcRkjhcWrWFEEK0Skcn3h8D9wEveK4/arR9pqd74Wig3JOcrweeV0o1LMg5EfhdB8csOrujR+HgQZg1y9uRCCEuT1t28W7V/Npa609xt5Y33ja/0e0bWlN+R9AuF/Wni7AXH6eu6Bj24uPUny5EO7vHcDlXXQ1nvlxD3YlDGCz+hGfcRkTm3YSOugGDqWVTvWmtKd+9ncLPVlO0fjXVB/MACEu9ikH/+0Wib7yNwP4D2/NlCCGE8DHtlngrpf4f7tbqCKXUcdyzk78AvK+UegA4AvzEs/unuJcSy8e9nNhPAbTWp5VSz+Lu/gewoGGiNSHO2uwZ+jlunHfjEEJcrk7Xxbuz0i4X9WdOYS8+djapthcdo+6U+9pedAz7qQK084cTsCmjCUxeG13WphSK4JRr6fvzpwnPuA1jYMvWCnc5HJzJ+oLCdaso+mw1tSePo0wmwkePJe7+WURNuAVrtEwjI4QQon2056zmdzbz0Pgm9tXAo82U8w7wThuGJrqbTZsgIgKSkrwdiRDiMkgX7+ZVfbedk8sXUVfsSbJPFaDr7T/YR/mZMUf2wRLZl+CUazFH9cUS2QdzZF8sUX0xR/bBFBbhk12lnXV1lP7rHxSuW0Xxxo+wny7BYLHSKyOThN/8J5Hjp2AOu7QJ1+prNEc3OTnwqYOaEhnZJkRHS7zLj4G3dI8fElvLaDSmDxw4sMbpdKr4+Pia999//3BwcLCro+NYsGBB5Jw5c0oajp2RkRG/cuXKQxEREc6AgIBUm83270st89lnn41cunRpL601995776n58+cXAxQVFRlvu+22AQUFBZaYmJi6jz766GCvXr26RJcuedeKrk1rd4v32LFgaFUPUyGEF3WHLt7twVldQWXOV5gj+xA89GrMnkS6IaG2RPbF1KOXTybVzXFUV7mX/Vq/6uyyX6bgECLH3Ux05jQirrsRU0DgJZVZU6o58KmDA2scHNrgxGEDcwiE9JN6R4iOVlsmP3g1sFgsroZZwadOndp/4cKFvZ5++umiljzX4XBgaqOeUG+88UbUgw8+eLoh8d6yZUt+a8rbvn27denSpb127ty512q1ujIyMhKmTZtWPnTo0Lqnnnqq99ixYyuff/75/U888UT0/Pnzo5csWdIlZiGVxPtchw/D/Pn8BShvdClr5n6tl8JsLRMQDIR4Lo1vhwAVwAdAp//XdvAgHDsGv5Oh/0KI7id05HjSVh30dhidnr3sNMX/+ITCdaso2br+7LJfvafcQXTmNHpePQ7DJa54UnbIxYE1DvZ/7KDgSxfaBUExiqH3+BF/s5G+1xkxmuUHDyEE1Hz3s76uqpyAtizTEDTU5p/4zrGL7+l27bXXVmVnZ/sDvPbaa+FLliyJqq+vV2lpadVLly49YjKZCAgISJ0xY8aprVu3hixevPio1Wp1zZ49u5/NZjOYzWa9devWvODgYNejjz7a58svvwy22+3qwQcfLJ47d27JmjVrghcsWHBFeHh4fV5enn9ycrLtww8/PPT8889HFhcX+2VkZCT06NHDsW3btn0xMTHJWVlZe3v37v2DcU9PPvlk1OrVq8PtdruaPHly2csvv3yiqdfy7bff+qemplY1JPJjxoypXL58edhzzz1XtG7durAtW7bkATz88MOlGRkZg2hiXpjOSBLvc50+Df/8J/fgTkAvNke2neaT8qbun/tYOVB/CeEF0nSifKnbWvKfIQF47hJi84pNm9zXsn63EEL4lNrik+5lv9at4vTXm9FOJ9Yr+tJvxiNE3zjtkpf90lpT9G8X+Z84yP/ESUmOu7dmxFADo3/rR/zNJqLSDNK7QAjR6dTX17N+/fqQiRMnVuzcudO6YsWK8KysrFyLxaLvvvvufq+//nrPmTNnltbU1BhGjx5d/dZbbx2vra1V8fHxQ5ctW3YgIyPDdvr0aUNQUJBr0aJFEaGhoc6cnJy9NTU1auTIkYNvvvnmCoC9e/f679q162BcXFx9enr64A0bNgTNmzeveMmSJVFbtmzZd26i3diqVatC8vPzrdnZ2Xu11txwww3xa9euDZo0aVLVufsOHz68ZsGCBTGFhYXGwMBAvWHDhtCUlJRqgNLSUlNsbGw9QN++fetLS0u7TD7bZQLtMGlpcOgQ4Z6KNQgIBcI816EtuJ/Q6H6z69Y0YuP8xBzOT56DufgPAeD+MaAcd6t1BVAJnATyztlWcc6l8bbngWdwz3i0vgXH9JrNmyE6GgYN8nYkQgjRKWiXC3vpKepOnaS2+CR1xSexlxajXR0+7K9duOx1lH65kTM7vwKtCRwwiAEP/5aoG6cRmpx+SYmx0645ttVJ/honBz5xUFmgUQaIucbA2D+aiZ9iImyAdCcXQlzYpbRMt6W6ujrD4MGDEwFGjx5d+dhjj5W89NJLETk5OQEpKSlDAGpraw2RkZEOAKPRyP33338GIDs72xoZGVmfkZFhAwgPD3cBbNy4MSQ3Nzfg448/7gFQWVlp/O6776xms1knJydXX3nllfUASUlJtgMHDrS4K9G6detCtm7dGpKYmJgIYLPZDLm5udamEu+0tLTaxx57rHD8+PEJ/v7+rqSkJJuxiR9SDYau9WOoJN4XUeW5XG7/BQPuhPlSEvcw3AvOlnuO25JEufG2H067c3keAVKAZUA67inoO52G8d3jxkEX+tAJIcTlcDkc2EuLqSv2JNRFJ75Prj2364pPUldShHY02+jQLYQMTSPh188SnTmNoPghl/Rcl0Nz4O9O8lY6OLTeQV05mPwhboKRMU+ZGHCTiYCIpusUl0tzYreLvM+c7NvoxFba6QdkCdHt/GiWH6Pub9nSgd1d4zHeDbTWavr06aWvvvrqeemL2Wx2XWxct9ZaLVy48Ojtt99e0Xj7mjVrgi0Wy9l/ekajEYfD0eIv4FprZs+efXLu3LklLdl/zpw5JXPmzCkBmDlzZkyfPn3sAD179nQcOXLELzY2tv7IkSN+4eHhXabCk8S7nbn4vjW7K6kBbgeygJXAGKDJRXO9KTcXCgulm7kQotsq/Woze//zN+6EurQYmmi1NvfshaVXbyyRvQlKGIo18goske771sjeWKKuwNIz0r2kWHegFAa/S//SXW/T5LzrIGuRnfJDGv8IGHirifibTcSOM+IX0PT3x6pTmn0bHOSud7Jvg5PKIvf3zitSDPSIlR99heho1hD53F1IZmZmxbRp0+KfeOKJopiYGEdRUZGxvLzcmJCQ8IO2uWHDhtUWFxf7bdmyJSAjI8N25swZQ1BQkGvChAnlS5Ys6TVlypRKi8Wis7OzLXFxcRccGRsYGOgsLy839O7du9l9Jk2aVPH0009f8dBDD50ODQ11HTp0yM9sNuuYmJgmE+eCggJTTEyMY//+/ea///3vYdu3b88FuPHGG8veeOONns8//3zhG2+80TMzM7PsMk6TV3STWli0hwPAPcAnwF+BB70bzvka1u+WxFsI0U0ZA4KwRF1B6ND0Rsn094m1JSLqkicN8zU1pZpdb9Sz8zU7NSXQe5SBsS9YuHKKEYPx/C/wznrNka9d5H3mIO8zJ8d3uNAaAnrCoAkmBt9oJGGikZBo6YIuhOh80tPTa+fNm1cwfvz4BJfLhZ+fn168ePHRcxNvq9Wqly1bdmDWrFn9amtrDVar1bV169Z9c+bMKTl8+LAlOTl5iNZahYeH13/66acHLnTM++67ryQzMzMhKirKvm3btn1N7TNt2rSKPXv2WEeOHDkYICAgwLVs2bJDzSXeU6dOvbKsrMxkMpn0okWLjkZERDgBnnnmmZO33XbblbGxsRExMTH21atXXzC2zkS5l9DuXgIDA3V1dXWryuhK4wXa27PAPODnwNsdeNyLvjenT4dvvnHPRN8Of6/O8B7ojp9P4XuUUjat9aWt39SNjBgxQmdlZXk7DJ9TfsTFjsX1ZP9XPQ4bDJhkZOSvzfS59vwxgaePuMhb7yRvvYP9m5zUVoDBCP2uMjB4oolBNxrpk2ZoMlEXQnQtSqkdWusRrS1n9+7dh1NSUlrUbVr4lt27d0ekpKTEnbtdWrzFRT0FjMLd6r0L2OHdcNxcLneL95QpMr5bCCHEWcXZTra/VE/uBw6UgiF3mBgxx49eSd9PzFNfozmwxUnueid5nzkoznX/yBnWV5Ey3cTgG00MHG/EP+yH9UvpwXKy/28exzYdw7a/DOzODn1tQgiIuT2em18b5+0whLhkkniLi3IBd+FOuFfgnmzttFcjAnJyoLRUupkLIYRAa82xLU6+WVjP4Q1O/IIgbaYf6TP9COlrOLvPka9dbHunnt3vO6irApMFBlxn5KoHjQyaaCJqiDrbGu5yudi/8Sh7Vx6geFshzmOV+DvcY+xdWoPVhAqWbv5CdDRrD6u3QxBtpLCw0Dh27Njzlib6/PPP86Kjo7vdL5uSeIsWKQV+DHyBe6bzybgTcq+R8d1CCOHzXE7N/g+dfPOSnaIdLgKiFD9aYCblQT+sPdwJdNUpzY736tn2joOi71yYAyDlJyaGTzcx4DojZs+kajVltXzz5n4OrT9CeXYJxtIaGtJqBdDDil9yT+ImxjLsjgQCe/p75TULIUR3ER0d7Tx3VvbuTBJv0WJZwEzgLdzdz5/yZjCbNsGVV0K/ft6MQgghhBfU12j2vOcg62U7ZQc1PeIVE/5qIeluEyarwuXU5K5zsO3tevZ84sRZD/1GG5j+hoXh/8uENVhRuKeULc/ncWJrATX5ZVhs9Rgbhi4ZFfQLIXRUFIOmxTNwQj+MJplMTQghxOWTxFtckr8BVwHzgW+Av3sjCKcTtmxxT64mhBDiotpkokYNjjpw1oGzVn9/u85zu7bR7Tpw1Gr34/ZGt+taH4u9Cr77vw5qTmmiRxqY+ryF+JvdM5SfPuzim/+2s/2/HZQd0wT0hDGP+jH6Zyaik4zk/+MYK3+yg6qsokbdxoFAP4zDI4kZ25fkOxOIHNSj9edLCCGEaEQSb3HJZgKpwLvACOBgRwewaxeUl8M4mVhDCOF7nPWa2tOamtNQU6KpKdXUlrqva07rs9sa367rMquctoCC/hONjPqNmT4/MuC0w+4VDra97SB/k3tIYMIEI1P/5EfSVCOn9pXyz2e3U/r5MQLqnGit0cEWLKOjGXBTHMk/Hog1RMZqCyGEaF+SeItLVgvcjnuytZXANUBNRwawaZP7euzYjjyqEEJ0uBNfO/nqBbs70W5Iosub398UAP491dlLaD8D/hEKSxhtshSW0eK+mKwKoxlMVjBaFEYrmMxgtCr34xbP7Ub7mKxgNIMytM1KFCe/dfLRHDs7ltVjOw09+ikmzDcz8j4TBkM1//zDV3z1m0P4V9S5J0wzG7FOiGXMf4wgJi2yTWIQQgghWkoSb3FZDgMzcHc1XwLc35EH37wZBg+G3r078qhCCNHhnPVgK9LuJDrOnUS7k2pPgh2uzm6z9lT4+Xeu5RXtNk1loabigIvKQk1VscblbGVXcxtkr3RwbLsLoxmG3mJi9M9M9B3p5MuFWayYsB+/YhtGBQaDwjCqN6N+ncagzNg2elVCCNE5GI3G9IEDB9Y4nU4VHx9f8/777x8ODg7u8PmPFyxYEDlnzpyShmNnZGTEr1y58lBERIQzICAg1Waz/ftSy3z22Wcjly5d2ktrzb333ntq/vz5xQBfffWV/y9+8YtYm81m6NOnj33FihUHw8PDvTrnc0upNhn31ckEBgbq6urqVpWhZG3oFpkPPAM8ArzRxmU3+d6sr4fwcLj3Xnj11TY+4g91hvdAd/x8Ct+jlLJprQO9HYe3jBgxQmdlZXk7jDbjdLgT6MpCT1JdqKks+v5+ZZHLva1QU1fZPjFEDzUw+mcmUqYrcj7Yw97//g51qBw/BXbAODicYY8kk3rPYAwGmRRNCPE9pdQOrfWI1paze/fuwykpKSVtEdPlapzUTp06tX9aWprt6aefLmrJcx0OByZT27TBxsTEJGdlZe3t3bu340IxttT27dutd91115U7d+7ca7VaXRkZGQlvvvnmkaFDh9YNHTp0yIsvvnhs8uTJVYsWLep56NAhy1/+8pcTbfJC2sju3bsjUlJS4s7dLi3eolWeBUYBi4F/455wrV1lZUFVlSwjJoTwafW1mpozGtsZqDnTcFtTU9ZoW5n+/rocaOXveC4nVJ/SVJdomvpN0BoKwdGKkGhFn1QjwdHKfYlybwuOVgRFKozm1v6o6SJ/Yz67/ppN3jMlWDQYNDj7BtP/3iFc9asU/Kzy9UYI0ZF+1hdyAtq2zKE2eOdYS/e+9tprq7Kzs/0BXnvttfAlS5ZE1dfXq7S0tOqlS5ceMZlMBAQEpM6YMePU1q1bQxYvXnzUarW6Zs+e3c9msxnMZrPeunVrXnBwsOvRRx/t8+WXXwbb7Xb14IMPFs+dO7dkzZo1wQsWLLgiPDy8Pi8vzz85Odn24YcfHnr++ecji4uL/TIyMhJ69Ojh2LZt277mEvEnn3wyavXq1eF2u11Nnjy57OWXX24yYf7222/9U1NTqxpa0MeMGVO5fPnysOeee67oyJEjlkmTJlUBTJkypeLGG29M6GyJd3OkZhKtooF7cI/3XgGkAe3601/D+t0yvlsI4QMOf+XkH3+wYyvT1HgSatsZjaP2ws+zBENAD4W/5xIeCwZjK4NREHStwZNMG84m2cHRCmuok/Lj5ZTml1F2qILK41VUnaymILsG+5lanBV2tK0eVeektWm3cmksAFrjigggalo81/7vdFlXWwjhs+rr61m/fn3IxIkTK3bu3GldsWJFeFZWVq7FYtF33313v9dff73nzJkzS2tqagyjR4+ufuutt47X1taq+Pj4ocuWLTuQkZFhO336tCEoKMi1aNGiiNDQUGdOTs7empoaNXLkyME333xzBcDevXv9d+3adTAuLq4+PT198IYNG4LmzZtXvGTJkqgtW7bsa6rFu8GqVatC8vPzrdnZ2Xu11txwww3xa9euDWpIohsbPnx4zYIFC2IKCwuNgYGBesOGDaEpKSnVAPHx8bXLli0Lu+eee8ree++98MLCwi4zO6Yk3qLVzgDTgH8By4EbAWd7HWzzZhg2DCIi2usIQgjRaTjtUH5C499DETUY/HsY3Ml0mCIgTOHfA/x7KAJ6KMzBGqfdRn2VjariKqqLaqg+ZaOmpJa6sro2aPF2UXe4loodtTjK63BV16NqnRidLpr71uPSGq0U+BnAakKFWlBtsB52yKgofvT7UfQcENrqsoQQovVa3jLdlurq6gyDBw9OBBg9enTlY489VvLSSy9F5OTkBKSkpAwBqK2tNURGRjoAjEYj999//xmA7Oxsa2RkZH1GRoYNoGGc9MaNG0Nyc3MDPv744x4AlZWVxu+++85qNpt1cnJy9ZVXXlkPkJSUZDtw4ECLk95169aFbN26NSQxMTERwGazGXJzc61NJd5paWm1jz32WOH48eMT/P39XUlJSTaj0f3r8TvvvHN45syZfV944YXemZmZZX5+fl1mXKYk3qJN7AJ+Afw37u7nT7THQerq4Isv4OGH26N0IYTodIx+RQxI2UNdWR11p+1UHbVTXu3AZatH1zlR9U6UQ2N0aUzoDpmbwgVokwEsRlRPK4ZQC8aeVvx7BRAUE0hIv2B69A8lIiGMsH7BGNsg0RZCCHE+i8Xiys3N/a7xNq21mj59eumrr75acO7+ZrPZdbFx3VprtXDhwqO33357RePta9asCbZYLGeTXKPRiMPhaHGlo7Vm9uzZJ+fOnduizrFz5swpmTNnTgnAzJkzY/r06WMHSE1Nrf3yyy/3A2RnZ1s+++yzsJbG4G2SeIs283+Aq4HfAduAj9r6ANu2QW2tjO8WQviMA58do2JVPuBuPXYphTYod+JrNkKIBUOACUOgH4ZgM5YwC5YwC/49rQT08icw0p+g6AACIwMw+rUuAVYGAz1ig2X8tBBCdGKZmZkV06ZNi3/iiSeKYmJiHEVFRcby8nJjQkKCvfF+w4YNqy0uLvbbsmVLQEZGhu3MmTOGoKAg14QJE8qXLFnSa8qUKZUWi0VnZ2db4uLi6i90zMDAQGd5ebmh9wVWHJo0aVLF008/fcVDDz10OjQ01HXo0CE/s9msY2JimuyeXlBQYIqJiXHs37/f/Pe//z1s+/btuY23O51Onnrqqd4PPPBA8WWcJq+Q2lO0qVlAKu4kfCSwvy0L37wZDAbIyGjLUoUQotO66lfDKJvan5CYQAJ7+csM3UIIIS4oPT29dt68eQXjx49PcLlc+ELEKaAAABUpSURBVPn56cWLFx89N/G2Wq162bJlB2bNmtWvtrbWYLVaXVu3bt03Z86cksOHD1uSk5OHaK1VeHh4/aeffnrgQse87777SjIzMxOioqLs27Zt29fUPtOmTavYs2ePdeTIkYMBAgICXMuWLTvUXOI9derUK8vKykwmk0kvWrToaEREhBPgnXfeCX/77bcjAW666aYzs2bNKr2c8+QNspxYMzrDUlJdVV9gJ1AIjAZsl1nOee/NjAyornbPbN4BOsN7oDt+PoXvkeXEutdyYgJKSkrYtWsX5eXl3g5FCJ8zdOhQBg0adNnP707LiYnOSZYTEx3mGHAnsB54C5jRFoXW1MDXX8OsWW1RmhBCCNEiRUVF7Nixg507d569Pnr0qLfDEsJnvfjii/z2t7/1dhhCXDJJvEW72AjMA54HvgL+2toC//UvsNth3LjWliSEEEKcR2vNiRMnzkuyT5z4fnnYhIQExowZw69+9StSU1OJjIz0YsRC+Kbo6GhvhyDaSGFhoXHs2LHndV/4/PPP86Kjo9ttkSRvkcRbtJsXcHc1fwl31/N/taawTZvAaIRrr22L0IQQQvgwrTVHjhxh586dP0iyi4vdc/QYDAYGDx7MuHHjSE9PJy0tjeHDhxMSEuLlyIUQovuIjo52njsre3cmibdoNxq4D8gC3gfSgaLLLWzzZhg5EoKD2yg6IYQQ3ZnWmuLiYvLz88+77N+//+z4bKPRSFJSEjfddNPZJDslJYXAQJ+dlkAIIUQ7kMRbtKtyYBrwNfA/wA1Ak1MXXkhVFWzfDjKeRwghRCMul4uTJ082mVzn5+dTVVV1dl+DwUBcXBzx8fHcddddDBs2jLS0NJKTk/H39/fiqxBCCOELJPEW7e5b4CHgPeAPwNxLLeCLL8DhkPW7hRCiiykrK+PIkSMcOXKEw4cPc+zYMerrL7gc7EU5nU6OHz9Ofn4+Bw4coKam5uxjfn5+9O/fn/j4eK677jri4+PPXmJjYzGbza19SUIIIcRlkcRbdIhlwFXA47hbv1deypM3bQI/P7jmmvYITQghxGXQWlNSUnI2qW6cYDfcPne5LavVisViadVxlVJcccUVxMfHM3HixB8k13379sVkkq82QgghOh+pnUSH+TXucd7/BewBclv6xM2b4eqrISCgvUITQohOSWuNw+HAbrdTV1fX5KW5xxpvt9vtrY7F4XBQUFDwg8TaZrP9YJ+QkBDi4uKIjY3luuuuO3u74ToiIgKlVKtjEUII0XrvvvtuWGJiYm16enotwOzZs68YO3Zs5a233lrZ3HNuv/32uClTppT/9Kc/PXOx8tesWRO8cOHCqM2bN+e3ZdwtFRAQkGqz2f7tjWM3RRJv0WHqgem4ZzhfBYwCqi74DKCsDHbuhCefbOfohBCi8/nwww+ZNm2at8M4q2fPnsTFxTFkyBAmTZpEbGzsDxLrsLAwb4cohBAdT6n0ditb6x3tUWx9fT0ffvhhmMPhKG9IvBctWnTiYs8Tl08Sb9GhCoD/hXud77c9ty9o61ZwuWR8txDCJyUmJrJgwQIsFkuTF7PZ3KLHzGZzq1uaDQaDjJEWQohOJC8vz5yZmTkwOTnZlpOTE5CQkFDzwQcfHH7mmWei1q1bF1ZXV2cYMWJE1bJly44YDAZGjRo1aOjQobZvvvkmaPLkyWc2btwY9vXXXwe/+OKLvVeuXHlg/vz5vRtasx9//PHeTZVxMStWrAiZO3duX39/f9eoUaPOtrFVVFQYHnjggX65ubn+DodD/f73vz9x9913ly1evLjnRx99FFZZWWkqKiry+/GPf1y6cOHCkwCvvfZa+JIlS6Lq6+tVWlpa9dKlS4+YTCYCAgJSH3jggeLPPvss1Gq1utasWZPft29fR25urvmOO+4YYLPZDJmZmWWN43ryySejVq9eHW6329XkyZPLXn755RN5eXnmSZMmDRw1alRVVlZWUFRUlH39+vX5QUFBOicnx/LQQw/FlpaWmoxGo/7ggw8OJiUl1TVVTkv/XpJ4iw73OfAfwJ9wj/d++UI7b94MVitcdVUHRCaEEJ3LoEGDeFJ6/AghhGjG4cOHrW+88cbhiRMnVk+fPj3uT3/6U6+5c+cW//nPfz4JcOutt/Zfvnx56F133VUOYLfbVU5Ozl6A/Px8a3Pdxi9URnNsNpuaOXNm3IYNG/KSkpLqpkyZMqDhsSeeeKL39ddfX/HBBx8cLikpMY4YMWLI1KlTKwCys7MDv/322z1BQUGu1NTUxFtuuaU8KCjItWLFivCsrKxci8Wi77777n6vv/56z5kzZ5bW1NQYrr766qpXXnml4JFHHunzyiuv9PrjH/948pe//GW/n//856dmzpxZ+oc//KFXw7FXrVoVkp+fb83Ozt6rteaGG26IX7t2bdCAAQPsR48etb733nsHr7nmmiM33XTTgKVLl/b45S9/efquu+7q//jjjxfee++9ZTabTTmdTtVcOZMmTbpoJ16QxFt4yZ9xT7b2R9zrfP+zuR03b4YxY6CVk/EIIYQQQgjRLtqpO3hLREdH2ydOnFgNcM8995QuXrw4csCAAXUvvfRSdG1traGsrMyUmJhYg3uVX+68887TLSl37dq1wc2V0Zxdu3ZZ+/TpU5ecnFwHMGPGjNK//e1vvQA+//zzkPXr14ctXrw4GqCurk7l5+ebAa699tqK6OhoJ8DkyZPPfP7550Emk0nn5OQEpKSkDAGora01REZGOgD8/Pz0HXfcUQ6Qnp5evXHjxhCAnTt3Bq1du/YAwMMPP1z67LPP9gFYt25dyNatW0MSExMTAWw2myE3N9c6YMAAe0xMTN0111xTA5Cammo7fPiw5cyZM4aioiLzvffeWwYQEBCgAd1cOZJ4i07vp8B24H0gDTh57g4lJbB7Nzz3XEeHJoQQQgghRKd37jAipRS/+c1vYrdt2/ZdfHx8/a9//esramtrz/YRDw4Odl2sTJvNpi5UxuXQWrNixYr8lJSUusbbv/jii8CmXoPWWk2fPr301VdfLTi3LJPJpBu6vZtMJhwOx9kCDAaDburYs2fPPjl37tySxtvz8vLMZrP57P5Go1HX1NQ0+zqbK6elWnUChWiNSmAaEAR8APidu8OWLe7rceM6MiwhhBBCCCG6hJMnT5o3btwYCLBs2bLwa665pgogOjraUV5ebvjkk096NPfcoKAgZ0VFxXn5oM1mM7S0jMaGDx9eW1BQYN6zZ48FYPny5eENj11//fUVCxcujHK53Hn/l19+6d/w2BdffBFSVFRkrKqqUp9++mlYRkZGVWZmZsWaNWt6FBQUmACKioqM+/btu+BEI2lpaVVvvfVWOMBbb73Vs2H7pEmTKt59992I8vJyA8ChQ4f8GsptSo8ePVzR0dH2d999NwygpqZGVVZWGi61nHNJ4i286jvgAWAM7jHfP7B5MwQGwogRHR6XEMI3KaUylVJ5Sql8pdR/NPG4RSn1P57Htyml4jo+SiGEEMItLi6u9pVXXokcMGBAUllZmenxxx8/NWPGjFNDhgxJuv766xNSUlKqm3vujBkzTi9evDh6yJAhiQ3JMkBERISzpWU0FhAQoF955ZUjU6ZMiU9MTBwSERHhaHjshRdeOOFwONTgwYMT4+Pjk+bNmxfT8NiwYcOqp06demVSUlLSzTfffOa6666zpaen186bN69g/PjxCQkJCYnjxo1LOHbs2HntdI299tprR998883IhISExIKCgrP7Tps2rWL69OmnR44cOTghISHxtttuu7KsrMx4obLee++9Q6+++mpkQkJC4ogRIwYfO3bMdDnlNKa0Pq81vssLDAzU1dUten80S9YZ7VgvAXOAO4Hlnm16yBCIjYW1a70SU2d4D3THz6fwPUopm9Y60NtxXIxSygjsAyYAx3GPhrlTa/1do31+CQzTWj+ilLoDuE1rfcEFGkaMGKGzsrLaMXIhhBAtpZTaobVudavO7t27D6ekpFxWl+O2kpeXZ54yZcrA/fv37/FmHK2xePHinllZWYFLly496u1Y2sru3bsjUlJS4s7dLi3eolP4Le4J1v4GJAFRAHv3+nw3c6WUVy9C+JhRQL7W+qDW2o77d8BbztnnFuD/eG6vAMYr+bAIIYQQ4iK6TOJ9se5/omtzAD8BKoBVNPqmK+t3C+ETOkkX7xjgWKP7xz3bmtxHa+3APcNrz3P2QSn1kFIqSymVderUqXYIVQghhK8bNGiQ3Vut3RMmTLhy8ODBiY0vK1euDLnUcmbNmlXanVq7L6RLzGru6f73Ko26/ymlPm7c/U90fYW4k+/NwCsAoaGQmurVmIQQ7a+F/+MfAM5oreM9XbxfBC7YxdubtNZvAm+Cu6u5l8MRQgjR9lwul0s1NYu2L9iwYcMBb8fQGblcLgU0OXN8V2nxbkn3P9ENfAE8DpgBrrsOjC2er0C0A293de8MF2/z9uvvoHPQWbp4FwB9G93v49nW5D5KKRMQCpS2cRxCCCE6v5xTp06FehItIXC5XOrUqVOhQE5Tj3eJFm+a7v43uvEOSqmHgIc8d7VSqqaVxzTh7gHtq7z2+v/iufDJJ+DdxMfX3wMg56AhuZJz0Lpz4K+Uajy72JueFuEGF/0fzzldvJVSDV2823Jim+3AQKVUf9wJ9h3AXefs8zFwH/AV8GNgk77ILIg7duwoUUodaUVcEbTt6+yK5BzIOQA5Bw3kPLTuHMS2RQAOh+PnhYWFfyssLBxK12nMFO3LBeQ4HI6fN/VgV0m8L6pxt762oJTKaosZD7sqX3/9IOcA5ByAnAPwnXPgSehnAusBI/CO1nqPUmoBkKW1/hh4G3hXKZUPnMadnF+s3F6tictXzv+FyDmQcwByDhrIeegc5yA9Pb0YmOrNGETX0lUS75Z0/xNCCNE1XUoX7+Pt2cVba/0p8Ok52+Y3ul0LTG/r4wohhBCie+sq3SLOdv9TSplxtzB87OWYhBBCtI2W/I9v6OINLeziLYQQQgjRWXSJFu/muv+182HbrNt6F+Xrrx/kHICcA5BzAO18Dtqri3c3Iu9BOQcg5wDkHDSQ8yDnQHRBShoMhBBCCCGEEEKI9tNVupoLIYQQQgghhBBdkiTeQgghhBBCCCFEO5LEuxlKqT8ppXKVUtlKqdVKqTBvx9RRlFKZSqk8pVS+Uuo/vB1PR1NK9VVKbVZKfaeU2qOUeszbMXmDUsqolPq3UmqNt2PxFqVUmFJqhed/wV6l1NXejqkjKaXmeD4DOUqp/6eUsno7Jl8ldZLUSb5eJ4HUS75eJ4HUS6Jrk8S7eRuAoVrrYcA+4HdejqdDKKWMwKvAJCARuFMplejdqDqcA/iN1joRuAp41AfPAcBjwF5vB+FlfwHWaa0HAyn40PlQSsUAs4ARWuuhuCc986UJzTobqZOkTvL1OgmkXvLZOgmkXhJdnyTezdBaf6a1dnjufo17XVlfMArI11of1FrbgeXALV6OqUNprU9qrXd6blfirthivBtVx1JK9QEmA3/zdizeopQKBa7DPZs2Wmu71rrMu1F1OBPg71k3OwA44eV4fJbUSVIneW77ZJ0EUi9JnXSW1Euiy5LEu2V+Bqz1dhAdJAY41uj+cXywgm+glIoDUoFt3o2kwy0Cfgu4vB2IF/UHTgH/5ena+DelVKC3g+ooWusC4M/AUeAkUK61/sy7UQkPqZN8lA/XSSD1kk/XSSD1kuj6fDrxVkpt9IwROfdyS6N9fo+7m9cy70UqvEEpFQSsBGZrrSu8HU9HUUpNAYq11ju8HYuXmYA0YInWOhWoBnxmfKlSqgfulsX+wBVAoFLqbu9G1b1JnSQuxFfrJJB6ycOn6ySQekl0fSZvB+BNWusbLvS4Uup+YAowXvvOgucFQN9G9/t4tvkUpZQf7i84y7TWq7wdTwcbA0xVSt0EWIEQpdR7Wmtfq9yOA8e11g0tSyvwrS85NwCHtNanAJRSq4BrgPe8GlU3JnVSk6ROwufrJJB6CaROAqmXRBfn0y3eF6KUysTdpWmq1trm7Xg60HZgoFKqv1LKjHvSio+9HFOHUkop3GOo9mqtX/J2PB1Na/07rXUfrXUc7r//Jh/7cgOA1roQOKaUGuTZNB74zoshdbSjwFVKqQDPZ2I8PjaRT2cidZLUSfhonQRSL4HUSR5SL4kuzadbvC/ir4AF2OD+bPO11voR74bU/rTWDqXUTGA97tki39Fa7/FyWB1tDHAP8K1Sapdn2xNa60+9GJPwjl8Byzxf+A8CP/VyPB1Ga71NKbUC2Im7a/O/gTe9G5VPkzpJ6iSpk4TP1kkg9ZLo+pTv9FYTQgghhBBCCCE6nnQ1F0IIIYQQQggh2pEk3kIIIYQQQgghRDuSxFsIIYQQQgghhGhHkngLIYQQQgghhBDtSBJvIYQQQgghhBCiHUniLYQQQgghhBBCtCNJvIXoAEopp1Jql1IqRyn1gVIqoNFjtyqltFJqsOe+VSmVq5RKbrTPXKXUG82UHauU2ukpf49Sqtuv7SuEEOLySZ0khBAdT9bxFqIDKKWqtNZBntvLgB1a65c89/8HuALYpLV+yrMtE/g9cJ3nsa3ACK31mSbKNuP+LNcppYKAHOAarfWJDnhpQgghuhipk4QQouNJi7cQHe+fQDyA50vJtcADwB0NO2it1wEngXuBl4Gnm/qC49nXrrWu89y1IJ9rIYQQLSd1khBCdAD5ZyhEB1JKmYBJwLeeTbcA67TW+4BSpVR6o91nA/8J9NJav3uRcvsqpbKBY8CL0rIghBDiYqROEkKIjiOJtxAdw18ptQvIAo4Cb3u23wks99xe7rkPgOeLyiZgycUK11of01oPw91qcZ9SKqoNYxdCCNG9SJ0khBAdzOTtAITwETVa6+GNNyilwoFxQLJSSgNGQCul5urvJ19weS4torU+oZTKAX4ErGib0IUQQnQzUicJIUQHkxZvIbznx8C7WutYrXWc1rovcAj3F5QWU0r1UUr5e273wD0+L6/NoxVCCNGdSZ0khBDtSBJvIbznTmD1OdtW0qhrXwsNAbYppXYDW4A/a62/vchzhBBCiMakThJCiHYky4kJIYQQQgghhBDtSFq8hRBCCCGEEEKIdiSTqwnRRSilkoFzl3Cp01qP9kY8QgghfJfUSUIIcWmkq7kQQgghhBBCCNGOpKu5EEIIIYQQQgjRjiTxFkIIIYQQQggh2pEk3kIIIYQQQgghRDuSxFsIIYQQQgghhGhH/x8tsUG/yuhkzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa50779d470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5795295c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa57975e588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5795f1550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa57943a780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAEzCAYAAADHKNteAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl8FPX5B/DPk3NJOEMIRwIBAyEEQpCgWMWGikfiWUFaRBS0VawHkv7Kr9SCIlpqtShSEbBWK4ioRUBKAYv+JPGkhisNOSBIuOQKBhISQhLy/P74zobJkptsFsLn/XrtK7sz35l5ZrO7M898jxFVBRERERERERG5h5enAyAiIiIiIiJqyZh4ExEREREREbkRE28iIiIiIiIiN2LiTURERERERORGTLyJiIiIiIiI3IiJNxEREREREZEbMfFuJBHZLiLDPR3HhUBEnhSRN2qZP0FEvmjOmIjIEJEEEcnxdBxETaGu441L2b+LyHPujqk5iEiuiFzv6Tgaqq5zJRHZICK/bMaQiIg8hol3Nao7wLkmj6raX1U31LGeniKiIuLjplAvCKo6S1V/CTTNPlvvf6mIBLtM32Ktu+f5RdzgeIaLSIWInLQe+0XkAxG5wqWcikiRrdxJEflfa94MESmrbp5t+XesMp2rmT7DZVpvEVHb6/2uJzci8gvbtk657ENeHft8SEQOiIjDNu0xEVlnPXfUsL+TrPnzRGSVyzoXiMiyJojrlIi0c5meacXTxTbtxyKSbK33uIisEJFI2/wEa5mXXNaVKiJjROQZW1wlIlJue73J9h6EuSz/fB0XolqJyIsiss/al2wRmSwiUtu+E10MrN/vU9b35LCV/LZu5LqGi8h++zT78eY845wgImds3+ndIvKW/TeCzp/9XMk6Dr7T2HVV93kQkZtEJEVECkXkqPWbf7s1z/V/7Hx0q+f27Me4AyLykoh4u5TZICL5IuJvm/ZL65hkn9ZRRI6ISEI9ttvLOi7OryGmI2I7xxIRX2uaWq+32/b1jHX8cr5+UkS6isgqEflePHBORXQpY+J9EZOWndDvBnC384WIxAAI8Fw4+F5VWwNoA+AqAFkAPheRES7lYlW1te3xgm3e+zXNE5E2AO4EUABgbFMErKp/c27LWvd3tm0H17U8AAeAR+oo09dln+Za06cCiBWRuwFzwgRgNIBHmyCuvQB+5nwhIlfC5bfM2t4aAO8B6AIgAkAOgK9EpIetaAGAX4pIqOtGVPVpW5yTAWywxRlXjzjPYSXXKwFcA+BGmM/TLwA8AeBPjVlnHdtryb8RdOG6zfreDAYwBMC0hq6gmT67X1txtgNwPYBTADaJyIBm2DadJxG5C8A/ACwCEAagM4CnANxmK/a1yzGqtap+34DNxFqfkREwx+YHbdvvCeBaAArgdud0VX0DwAErFqc5ANao6rp6bPM+APkAfm5P3m3yASTaXida05zb7287dn0O4DHbvs8CUAFgHYBR9YiFiJoQE+9GElutuIhcadWSFVhX+J01aCnW3+PWlcYfiYiXiEwTkT3WFcpFYqu9E5H7rHnHRGS6y3ZmiKkxfEdECgBMsLb9tZgavYMi8qqI+NnWpyLyiIjstK4IPysiESLylRXvB/byLvu4R0TirOf3WOvqb73+hYistMXlvIp9zj7b1vdn68rwbhGxHzSqsxjm4OM0Hubgao/P31rnXut9XyAirax5HURktZgr4PnW8zDbshus9+JL6335t7jUsFdHjf2q+hSAN9B0ydJoAEcAzLL29ULwAoCp0ojaKlUtBDARwCtWovsGgF+r6uEmiMv1s3EfXD4bAF4E8LqqzlfVk6p6TFWnAPgvqiYBRwG8j0YkBo10M8yJ2khVzVTVclX9AsAEAL8WkR4iMl5cumaIyO9E5APreSsRmSOmxvyQiPzFeXImVrNy67fjMIDqakyesr6DhSKSLiK3WNMDrN+EPrayYSJSLCLt3fWGUMulqgcArAUwAABE5H4xNYGFIvKdiEx0lhWrNlNEfisihwAstZbtJraaSpfjDUTkH9b34ISYms/+jYjzjKruUtVHACQDmGFb/1XW8fK4iGwTW8si6zjyRxH5j/Xd+UhEghqwbI3HIBG5V86eC/zeHq+Y84ipIrLLmv+Bc7tyttXZeOvYmGdfXkS8xdR67rK2u0lEulvzokRkvYj8IKYlzs9QDRH5iYj81/Z6vYh8a3v9uYj81HqeKyLXi6npfRImmTwpIttsqwxv6LFYRATASwCeVdU3VPWEqlaoarKqPljX8g2lqlkwSaz9osx9AL4B8Hece9z+JYBHRGSQiNwEk7gn1bUda7/ugzkmlaHqRQSn+hwDa6Sqh1X1NQDf1lmYiJoUE++m8QqAV1S1LUzN2gfW9B9bf9tbVxq/hjnBngDgJwAuA9AawKsAICLRAF4DcA+ArjBX4V1r4u4AsAxAewBLAJyB+TEPBvAjmB9311rKmwDEwdTU/i+A1wGMA9Ad5iByN6qXDGC49TwewHe2fYq35ruqbp8BYCiAbCvOFwD8zTrA1OQbAG1FpJ+Ypl1jALg2UXseQCSAQQB6w7xXzivMXgDeAhAOoAdMTcarLsuPBXA/gBAAfgB+U0s81VkOYLCIBDZwueqMhznRXAogRkRim2Cd5+tLAKkwtb0NZl3ZXwtgM4AcVX27ieJKBhAmpjmeL8xV+3edM0WkA0wt2z+qWfYDADe4TJsJYJyI9Gqi+GpzA4DPVfWQfaKqJgM4BvO7sALmc2WvmR+Ls/v4EkztTgyAvjDfgam2sj0B+MJ8vydVE0M2gKthfl/+BOA9EQlW1WKY35ZxtrL3APiXqh5v8J7SJc9K6G4GsMWadATArQDawvz2viwig22LdAEQBPO7fR9MTd73ddRUrgXQB+Z3fDPMcfF8LIe5OAYxLWH+BeA5K67fAPhQRDrZyt8H4AGYY3Y5gLkNWLbaY5B1LjAfwL0AugHoCPOdd3ocwE9hjsPdYGo757nsxzCY34cRAJ4SkX7W9F/DHPNvhvk/PACg2DqOrYf5nQmBOea+ZsXi6hsAfUQk2PoNHghzgaSNmIvfQ2CS1ErW8WAWzrb8sh/jGnMs7gvzG7esHmWrJSKvichr9SwbDfO52GKbfB/M520JgJvE1k1MVXNhzkfeBLAAwCOqmo+6DYP5X78Hc7yq7kL8SgA/FpH21vHuWgAf1Wc/iMizmHjXbKV1lfq4iByHSYhrUgagt3XyelJVv6ml7D0AXlLV71T1JIDfARgjplndXQD+qapfqGopzI+2uiz/taqutK7snlLVTar6jVVzlgtgIczB2O4FVS1Q1e0A0gH829r+CZiTlstriDXZtq5rAfzR9rqmxLsme1T1r6p6BsDbMCcpnetYxnlV9wYAmTBNtwBUXhV+CECSqv5g1bDOgjlZgFXD+aGqFlvz/oBz35e3VHWHqp6COcANasD+AMD3AATmIojTZvvnxrrS7fQzl3ndrH3pBfP+vmudWG5A1avZnjQNpia2phrP7S775Poefw5z0ni+J8N2aq3vPgC3wFwcOGqb39H6e7CaZQ/CXPw5uzLVfTAXaWY0Mp7tLr8VtV2oCK4hrsrYVLUAppn8GKCym0UYgDXW78QvADyhqset7/DzzrKW0zC1QKXWZ7sKVX1fVQ9avyGLYb5Xzqbzb8P8RjmNg/keEjXESuu78AXMcWIWAKjqv6yaZbUuNv0bVpJrqQDwtKqeru6zWx1VfVNVC1X1NMx3OFZcxoBooO9hEmXAfP7XqOoa6/uyHub35mZb+cWqmq6qRQCmw/zOe9dz2ZqOQXcBWK2qKdZ+TYd5b5weBvB7q/WVc7/vkqrN85+xzhG2AdgGwJno/hLANFXNtv4P21T1GMwFkVxVfcs6n9gC4EOY1lhVWPF+C3OhPc5a/5cwXWiuArDTWmd9NeZYXNvvvN1VLseoXbb9eMRq5VCbzSKSD+CfMC233gIAERkGc4HoA1XdBGAXzu0m9irM+eFWVV1Zj30CTKK91krS3wWQICIhLmVKrHh+bj1WWdOI6ALHxLtmP1XV9s4Hau/r+guYWqcsEflWRG6tpWw3AHtsr/cA8IFJQrsB2OecYdVAuR689tlfiEikmGbUh8Q0P58Fl8QCgL1576lqXtfUlDgZwLUi0hWAN8wB8Rox/ZraAdhaw3LVqazhs/YLtWzXaTHMgWwCzm1G1Qmmz/cmW8KzzprubDa7UExTvQKYJvDtperAKPZax+J6xOMqFCYJtNcGDrZ/blT1Y9u8D1zmOWtv7gPwX1VNt14vAXCP7SSqHKYG087Xmu5WqroZwGcAptRQpL/LPlVejLGu/v8RpkXIH8T0Y28qi2BObKtrYuf8znStZrmuAKobwG0WgDtFJKoRsfR3+a2YU0vZvBrico3tXZxtiTIWwDLrYlw3mP/9dtvnfiVMTZHTIVUtqykAMd1E0mzL98bZ34wUAN5iusUMsmJaW8v+EFXHefwMt5KbUwAgIoki8o3VlPk4TBJqP14dVdV6JxBimk0/L6bZdAGAXGtWfcaKqEkogB+s5+EARrtcWBuGqt9h+zF5D8z3M7iey9Z0DHI9FyhC1XOBcAArbOvNhGn9Zr+YXdO6u8Mkia7CAQx1ifcemFYI1XG2iPux9XwDzMXthl6Ury3W2tT2O2/3jcsxKqKBsQ1W1Q6qGqGq01TVeQFkPEwlhv03u0rttKoqzP9me302ZLUWGA3rQrWaFoN7Uf24L4tgjn8NamZORJ7FxLsJqOpOVb0b5uT3TwCWWc22XGurAXM1Pdz2ugdMAnUY5sqtvR9yK5y9qlu5OZfX82EG+uqjpqn7kzC1sOdNVXNgDoKPA0ixauIOwdQ0f2E7ANUW3/lsfw/MIGs3wzT/s8uDuWhgT3raqRlMBAD+B6Yp2lDrfXE2gW/KkaPvBLDZOilqFFt/rkjr4skhmKb4nWG6CADmwNvTZdFeqHoBx52eAvAYqiZ39fEqgA9VdTJM87w/NlVAqroD5sQrHuZqv31ePoBNqKamBmZQtk+rWd8hmFYtM5sqxhp8AmCYnDtyfTzMyfoGa9IaAL2s5qFjcLaZ+UGY34sIl8+9/Xeixu+gmBGb/wLzHQ6yLhTkwPpeWCeKzosa9wJ4r7Yknqi+xIxD8CGAPwPobH321qDqb7LrZ7eu48lYmO5X18NcDO7p3Nx5hHonzjaT3gdTo21P3AJV9Xlb+e625z1gajjz6rlsTQ7a1ysiAah6LrAPQKLLuh1q+tTXZR9Ml7jqpie7rLO1qv6qhvW4Jt7OFnK1Jd5Ndn4A02VmHzwwQJh1bvYzAPG243YSTGuL8+kmdidM8//XbOsNRfXNzT/H2ZaDvF0r0UWCiXcTEJFxItLJSkSdtZ8VMM1fK2D6cjstBZAkpn9qa5zt81QO01fpNhG5WsyAZzNQ9wlEG5iRmU9atXU1HSQbKxkm6XIeSDe4vHZV3T6fj18AuM41ubXe67/C9BEMAUyfOlvT7jYwiflxMYPOPN0UwYgRKiJPwzTZe/I8VzkM5gRrCEzzukEw/e4/wNnm5ssA3C5mgBovMYPE/R6mD5idn5hbXDkfTTIqsJouCqsAPFrfZURkJMyYA85bpj0CU4t/dVPEZLkXwPVWU0tX/wtgooj8SkQCxdzK5UWYvog13df3BZhuDU312a3OGgBfA1guZiAjHxG5BmZwnjnWxSZYtX4rYPqL+sL6vllJ8Jswg9YFW5/H7iLi2m+9Jq1x9rfJS0QehqnxtlsEc1J5N1iTQk3HD4A/zGevXMwAmzfWscxhAB1raTreBqZrxTGYFlCzGhOYVXPeS0T+ApNMPmPNegfmmHyTVcYhZhA4e3/rcSISbSXHM2Fap5yp57I1WQbgVhEZZp0LzETV87UFMK2Iwq34O4nIHfXc3TcAPCsifazfj4Ei0hHAapgLwPeKuT2Vr4hcIWf7hrv6Cubi9pUA/mMdJ8JhxnNJqWGZwwB6ish5n3taFwl/DWC6mEH72lrHx2Ei8vr5rr8OP4VpYRCNs8ftfjDJ8Pl0ExsP8/seY1vvNTAJfYy9oLX/twG43XreIGJuFeocMd1fbLcOJSL3YeLdNBJgmn6ehGlWO0ZN36pimL7FX1pNt66C+VFdDHNg2g3TL+dxoDLBeRwmoToI4CTMYDTVJRZOv4G56l8Ik4i+38T7lgxzcpNSw+sqatjnRlPTHzC1htm/hamt+8ZqZvgJzIkAYJr7toKpefgGphn6+ehm/X9PwvRtiwEwXFX/7VJum1S9X2htzY4Bc6BdoarbVfWQ8wHzObpdRNqrahpMkvkizCA6X1gP1wTyY5iLDc5HU47UPQPmSryrbJf9/ZOYwV7mwQwmUwBUjm48FcAbUsMo+g1ltTTZUsO8/4Pp/z0W5mTvO5juINeoGQuhumV+APAygA5NEV8N26iAue3MNzA17ydhku5Xce6AQu/C1OS979K6ZDJMy5lUACdgPtuuyXNN298Mc9KeCvMb08t6bi+zC6Y2qVBV/1P/vSOqmZqxNibBXFTMh/lurqpjmSyYi9XfiW1cDJtFMC1/DgDIgPleNcSPrN/1ApiLym0BXKGq/7W2vw+mRv1JmAsG+2C63djPnRbDfIcPwdyCcVIDlq2WdS7wKMxvwEGY98t+/+pXYN67f4tIobXfQ+u5zy/B/A/+be333wC0sv4/N8K0sPne2p8/4Wxy5hpjEcxgdtutbjCAuai4R1WP1LBt54CXx0Rkcz3jrZGqLoPp4/yAFfNhmOOifaCxH8m59/G+AgDE3AllQSM2PR6mX/pel+P2q6jaTazexAzGNwLmAuwh22MTzG/8ObXe1nlDvZqxV+MUzPEHMK0m6zWmAhGdH2nEhTJqJlaN+HGYZuS7PR0PEV0aRORdABmqWlPrAKJLnohsAPCOmvs2ExER1Yo13hcYEblNzMBggTB94f6LswPGEBG5lYj0hhnh+C1Px0JERETUUjDxvvDcAdNk6nuYe5OOaUz/HaLaiBkN37X5nfPR0EHUWnxclwoReQFmILyZ9Ryoqam2+6aIHBGR9Brmi4jMFZEcMSOyD66uHBHRhUxEnqzh+Ma7RxBdAtjUnIiIPEpEfgzT33CRqg6oZv7NMONf3AzTl/UVVa1vn1YiIiIij2ONNxEReZSqpuDsvZOrcwdMUq6q+g2A9iJS1/17iYiIiC4YTXK7oQuNl5eXtmrVytNhEBERgOLiYoUZAdnpdVVtyC1/QmFGhXbab0072AThuV1wcLD27NnT02EQERGATZs25alqJ0/HQZeeFpl4t2rVCkVFRXUXJCIitxORU6o6xNNxeErPnj2RmlrTXRGJiKg5icgeT8dAlyY2NSciogvdAQDdba/DrGlEREREFwUm3kREdKFbBeA+a3TzqwCcUNWLopk5EREREdBCm5oTEdHFQ0SWAhgOIFhE9gN4GoAvAKjqAgBrYEY0zwFQDOB+z0RKRERE1DhMvImaSVlZGfbv34+SkhJPh0LkFg6HA2FhYfD19W3Qcqp6dx3zFcCj5xMbERERkScx8SZqJvv370ebNm3Qs2dPiIinwyFqUqqKY8eOYf/+/ejVq5enwyEiIiK6oLCPN1EzKSkpQceOHZl0U4skIujYsSNbdBARERFVg4k3UTNi0k0tGT/fRERERNVj4k1ERERERETkRky8iYiILlhFAL4HoJ4OhIiIiM4DE2+iS4iIYNy4cZWvy8vL0alTJ9x6661u3e6ECRPQq1cvxMbGIjIyEvfddx/2799fOb9nz56IiYnBoEGDMGjQIEyaNKnKcs7pc+fOrVxm69atEBGsW7euclpubi4GDBhQZdszZszAn//858r1LVu2rMo6nOsOCgqq3NZNN91U7X7k5ORARDB//vzKaQ8//DDeeecdAMC4ceOqxHvttdcCAF544QU89NBDlcu8/fbbuOOOOzBz5szKst7e3pXP582bV+32p02bBhFBbm5u5bQ///nPEBFs3boVAJCfn49x48ahd+/eiIiIwIQJE1BQUFBn/A8//DAGDRqE6OhotGrVqjKWFStWYNy4cVi5cmXlMuXl5Wjfvn21MVJT+wxAKIA2AAYDGANzt7UlAL4FcMJzoREREVG9cVTzGjRFX0VzBxyiC0dgYCDS09Nx6tQptGrVCuvXr0doaGizbPvFF1/EXXfdBVXFnDlzcN111yE9PR1+fn4AgM8++wzBwcE1Ludq6dKlGDZsGJYuXYqEhIRGxTRo0KDKhHXcuHG466678NOf/rTWZTp37oyXX34ZDz74IHx8zv0Jffnll89ZR1JSEgYPHoyNGzeiT58+ePrpp5GcnIzw8HA89dRTKC8vR3BwcGUstYmJicF7772HqVOnAgA+/PBD9OvXr3L+/fffjyFDhlReDJg2bRomTpyIpUuX1hr/ggULAJjk/K677qoSy4cfflhnXOQuAwDMA5ANYAdMsv0PABW2MiEA+gKIdHlEAPBvzmCJiIioBky8iTxh8mSgHklWgwwaBMyZU2exm2++Gf/6179w1113YenSpbj77rvx+eefAwCKiorw+OOPIz09HWVlZZgxYwbuuOMO5Obm4t5770VRUREA4NVXX8XVV1+NDRs2YMaMGQgODkZ6ejri4uLwzjvv1HrhSkSQlJSEFStWYO3atbjjjjsavKuqin/84x9Yv349rr32WpSUlMDhcDR4PY3RpUsXxMXFYfHixbj//vvrtYyvry/mzZuHRx55BIMGDcLEiRMRHh7eqO2PHDkSK1aswNSpU7Fjxw4EBwfD29sbAJCdnY309HQsX768svyMGTNw2WWXVdaSNyZ+8qSeAB5xmXYawHc4m4w7H6sBHLaV87KWjwRwGZiEE1HLcCuA6zwdBFGDMfEmusSMGTMGM2fOxK233oq0tDQ88MADlYn3H/7wB1x33XV48803cfz4cVx55ZW4/vrrERISgvXr18PhcGDnzp24++67kZqaCgDYsmULtm/fjm7duuGaa67Bl19+iWHDhtUZx+DBg5GVlVWZeP/kJz+pTCDHjx+PpKQkAMCUKVPw3HPPAQAWL16MmJgYfPXVV+jVqxciIiIwfPhw/Otf/8KoUaOa/L2qydSpU3HHHXdg/Pjx58xLSkrCjBkzAAADBw7EokWLAAA//vGPcdlllyElJaWydrkx2rdvjy5duiArKwvvv/8+xowZU9l0fPv27bj88svh5XW2F5GPjw9iY2ORkZGByMjIOuOviX2/yNP8AfSzHq5OoGoy7nxsBFDeXAESEblRDzDxposRE28iT6hHzbS7DBw4ELm5uVi6dCluvvnmKvP+/e9/Y9WqVZV9oktKSrB3715069YNjz32GLZu3Qpvb2/s2LGjcpkrr7wSYWFhAEzT7dzc3Hol3q5dMRrS1Hzp0qUYM2YMAHMhYdGiRRg1alSNNe1NfZurPn36YNCgQXj//ffPmVddU3MAKCgowJYtW3D69GkcPXoU3bp1a/T2f/7zn+O9997DP//5TyQnJ1fps32+8dfEvl/OpvF0IWoH4ArrQURERBcKJt5El6Dbb78dv/nNb7BhwwYcO3ascrqq4sMPP0Tfvn2rlJ8xYwY6d+6Mbdu2oaKiokqzbn//s81Xvb29UV5ev1q1LVu2YMSIEQ2O/cyZM/jwww/x0Ucf4Q9/+ANUFceOHUNhYSE6duyI/Pz8KuV/+OEH9OrVq8Hbqcvvf/97jB07FldddVW9yk+fPh33338/2rVrh//5n/+p7HPdGLfffjv69euHq6++Gq1bt66cHh0djS1btkBVKy82nDlzBmlpaYiOjq7yv2lo/ERERETUeBzVnOgS9MADD+Dpp59GTExMlek33XQT/vKXv1TWRm/ZsgUAcOLECXTt2hVeXl5YvHgxzpw50+htqyrmzp2LgwcPNmpQtE8//RQDBw7Evn37kJubiz179mDUqFFYsWIFWrduja5du+L//u//AJike926dfWqgW+o/v37IyIiAmvXrq2z7NatW7F+/XpMmTIFv/rVr5CdnY3PPvus0dtu3bo1/vSnP+F3v/tdlelRUVGIjo7GH//4x8ppzzzzDK666ir07Nmz0fETERER0flh4k10CQoLC6u8ZZfd9OnTUVZWhoEDB6J///6YPn06AOCRRx7B22+/jdjYWGRlZSEwMLDB25wyZUrl7cS+/fZbfPbZZ5UjmgOmj7fzFlb33XdfjetZunQp7rzzzirTRo0aVVmDvGjRIjz77LMYNGgQrrvuOjz99NOIiIioLDtx4kSEhYUhLCwMP/rRjxq8H3bTpk3Dvn37qkxLSkqq3I9BgwbhzJkz+NWvfoVXXnkFfn5+8Pb2xvz58zFp0iSUlZU1ettjx47FoEGDzpn+97//Henp6YiIiEBERARyc3Px+uuv1zt+IiIiImp60hJveRUYGKjO0Zcbi7cTo6aWmZlZ5bZPRC1RdZ9zESlW1YZfrWkhhgwZos7BCImIyLNEZJOqDvF0HHTpYY03ERERERERkRtxcDUiIhdbt27FhAkTqkwLCAjAV1991SzbnzlzZpV7cQNm9PapU6c2y/aJiIiIqGmxqXkN2NScmlpmZiaioqKa/NZWRBcKVUVWVhabmrtgU3MiogsHm5qTp7CpOVEzcTgcOHbsGC/IUIvkvK2b/VZzRERERGSwqTlRMwkLC8P+/ftx9OhRT4dC5BYOhwNhYWGeDoOIiIjogsPEm6iZ+Pr6olevXp4Og4iIiIiImhmbmhMRERERERG5ERNvIiIiIiIiIjdi4k1ERERERETkRky8iYiIiIiIiNyIiTcRERERERGRGzHxJiIiIiIiInIjtybeIpIkIttFJF1EloqIQ0R6ichGEckRkfdFxM8q62+9zrHm97St53fW9GwRucmdMRMRERERERE1Jbcl3iISCmASgCGqOgCAN4AxAP4E4GVV7Q0gH8AvrEV+ASDfmv6yVQ4iEm0t1x9AAoDXRMTbXXETERERERERNSV3NzX3AdBKRHwABAA4COA6AMus+W8D+Kn1/A7rNaz5I0RErOnvqeppVd0NIAfAlW6Om4iIiIiIiKhJuC3xVtUDAP4MYC9Mwn0CwCYAx1W13Cq2H0Co9TwUwD5r2XKrfEf79GqWISIiIiIiIrqgubOpeQdECqkzAAAgAElEQVSY2upeALoBCIRpKu6u7T0kIqkiklpeXl73AkRERERERETNwJ1Nza8HsFtVj6pqGYDlAK4B0N5qeg4AYQAOWM8PAOgOANb8dgCO2adXs0wlVX1dVYeo6hAfHx/X2UREREREREQe4c7Eey+Aq0QkwOqrPQJABoDPANxllRkP4CPr+SrrNaz5/6eqak0fY4163gtAHwD/cWPcRERERERERE3GbVXDqrpRRJYB2AygHMAWAK8D+BeA90TkOWva36xF/gZgsYjkAPgBZiRzqOp2EfkAJmkvB/Coqp5xV9xERERERERETUlMpXLLEhgYqEVFRee1DlNJf35a4ntLRNRQIlKsqoGejsNThgwZoqmpqZ4Og4iIAIjIJlUd4uk46NLj7tuJEREREREREV3SmHgTERERERERuRETbyIiIiIiIiI3YuJNRERkEZEEEckWkRwRmVrN/B4i8pmIbBGRNBG52RNxEhER0cWFiTcREREAEfEGMA9AIoBoAHeLSLRLsWkAPlDVy2HuvvFa80ZJREREFyMm3kRE5HEXSE3zlQByVPU7VS0F8B6AO1zKKIC21vN2AL53QxxERETUwjDxJiIij7qAappDAeyzvd5vTbObAWCciOwHsAbA426Ig4iIiFoYJt5ERORpF1NN890A/q6qYQBuBrBYRM45lorIQyKSKiKpR48ebfYgiYiI6MLCxJuIiDztQqlpPgCgu+11mDXN7hcAPgAAVf0agANAsOuKVPV1VR2iqkM6derkhlCJiIjoYsLEm4iI3M3HWftrPR5qxDrqVdN8nr4F0EdEeomIH0yT9lUuZfYCGAEAItIPJvFmlTYRERHVysfTARARUYtXrqpDaplf35rmBMDUNIuIs6b5iL2QiOTDNEt3JWZRDaopCFUtF5HHAHwMwBvAm6q6XURmAkhV1VUA/gfAX0UkydrOBFWtbntERERElZh4ExGRp1XWNMMk3GMAjHUp46xp/nsdNc3OZt9irbe2hP8cqroGpim7fdpTtucZAK5pyDqJiKjl2bRpU4iPj88bAAaArYjJqACQXl5e/su4uLgjrjOZeBMRkUc1ZU2zqp5xPhcRtb8mIiJqKj4+Pm906dKlX6dOnfK9vLzY8olQUVEhR48ejT506NAbAG53nc/Em4iIPI41zUREdJEZwKSb7Ly8vLRTp04nDh06NKC6+Uy8iYioxRAR+xXm9i6vYdWeExERnS8vJt3kyvpMVNv1gIk3ERG1JKNtz790ea04d5RyIiIiIrdj4k1ERC3JMlX9yNNBEBEREdlxBD4iImpJnvZ0AERERM3B29s7LioqKrpPnz79ExMTLyssLPRIbjdz5swQ+7bj4+N75+XleQNAQEDA5Y1Z57Jly9r27NlzQI8ePQY8+eSTXZoqVk9i4k1ERERERHSR8ff3r8jKysrYuXPndl9fX509e3an+i5bXl7eZHEsXLiw88mTJyvzyuTk5Jzg4OBG31WkvLwcSUlJPdasWbNjx44d2z/88MOgTZs2OZomWs9hU3MiImpJokRkczXTBYCq6uDmDoiIiFq2Bx54oHt6enpAU65zwIABxW+++ea++pYfNmzYybS0tFYA8NprrwXNnz+/c1lZmQwePLho0aJFe3x8fBAQEHD5PffcczQlJaXt3Llz9zocjorJkyf3KC4u9vLz89OUlJTsNm3aVDz66KNhX375ZZvS0lJ58MEHj0yZMiVv9erVbWbOnNktKCioLDs7u1VMTEzxypUrd8+aNSvkyJEjvvHx8ZEdOnQo37hx447Q0NCY1NTUzK5du1bJ7qdPn955xYoVQaWlpXLLLbccf/nll7+vbl82bNgQGB4efjo6OroUAEaOHPnDsmXL2sfFxR06n/fU05h4ExFRS7IbVQdUIyIiatHKysrw8ccft73xxhsLNm/e7Fi2bFlQampqlr+/v44bN67HggULOj722GPHTp065TV06NCiv/71r/tLSkqkd+/eA5YsWbIrPj6++IcffvBq3bp1xZw5c4LbtWt3Jj09PfPUqVNyxRVXRN12220FAJCZmdlq69at3/Xs2bMsLi4uav369a2nTZt2ZP78+Z2Tk5N3uCbadsuXL2+bk5PjSEtLy1RVXH/99b3Xrl3bOjEx8aRr2X379vmFhoaWOl+HhYWVbty4sbV73r3mw8SbiIhaklJV3eXpIIiI6NLRkJrppnT69GmvqKioaAAYOnRo4RNPPJH30ksvBaenpwfExsb2A4CSkhKvkJCQcgDw9vbGhAkT8gEgLS3NERISUhYfH18MAEFBQRUA8Mknn7TNysoKWLVqVQcAKCws9M7IyHD4+flpTExMUURERBkA9O/fv3jXrl1+9Y113bp1bVNSUtpGR0dHA0BxcbFXVlaWo7rEu6Vi4k1ERC3JN54OgIiIqDk4+3jbp6mqjB49+ti8efMOuJb38/Or8PGpPf1TVZk9e/beUaNGFdinr169uo2/v3/lfcu9vb1RXl4u9Y1VVTF58uSDU6ZMyaurbPfu3UsPHDhQmdTv37+/Sg34xYqDqxERUUvyhYiMrenh6eCIiIjcKSEhoWD16tUdDhw44AMAhw8f9t6xY8c5NdMDBw4sOXLkiG9ycnIAAOTn53uVlZXhhhtuODF//vxOp0+fFgBIS0vzLygoqDVnDAwMPHPixIlayyQmJhYsXrw42Flu9+7dvs4YXcXHxxfl5uY6srKy/EpKSmT58uVBo0aNOl6/d+DCxRpvIiJqSYbVMP1mAGEA3m3GWIiIiJpVXFxcybRp0w6MGDEisqKiAr6+vjp37ty9kZGRVWqMHQ6HLlmyZNekSZN6lJSUeDkcjoqUlJQdSUlJebm5uf4xMTH9VFWCgoLK1qxZU2sXrvHjx+clJCREdu7cuXTjxo07qiszcuTIgu3btzuuuOKKKAAICAioWLJkye7Q0NBz+oX7+vpi9uzZexMSEiLPnDmDsWPH5g0ZMqTkfN6XC4Goat2lLjKBgYFaVFR0XusQqXfLiRq1xPeWiKihRKRYVQM9tO0xAKYC2AXgOVXd0twxDBkyRFNTU5t7s0REVA0R2aSqQ853Pdu2bcuNjY2ts9k0XXq2bdsWHBsb29N1Omu8iYioRRERLwD3AZgCYAuAsaqaUftSRERERO7DxJuIiFoMEZkIIAnA5wBu5wjnREREF6ZDhw55Dx8+vK/r9A0bNmR36dLljCdicicm3kRE1JLMB3AYwBAA/7B1GxIAqqqDPRUYERERndWlS5czrqOyt2RMvImIqCXp4+kAiIiIiFwx8SYiohbD2bRcRHoAiLYmZ6jqXs9FRURERJc6Jt5ERNRiiEgbAAsBXANgmzV5oIh8DeAhVS30WHBERER0yar1RudEREQXmVdgbh12marerqq3A7gMwE4Af/FoZERERHTJYuJNREQtybWqOl1VK0dDVdUKVX0KphaciIioRfD29o6LioqK7tOnT//ExMTLCgsLPZLbzZw5M8S+7fj4+N55eXneABAQEHB5Y9Y5evTonkFBQbF9+vTpb59++PBh76uvvrpPeHj4gKuvvrrP0aNHvc8v+uYjqurpGJpcYGCgFhUVndc6bCPhNlpLfG+JiBpKRIpVNbCZtrVTVasdYE1EclS1d3PEYTdkyBBNTU1t7s0SEVE1RGSTqg453/Vs27YtNzY2Ng8A/vLAp933ph8LOP/ozuoxoGPx42+O2FdbmYCAgMuLi4u3AMDtt9/ea/DgwcUzZsw4XJ/1l5eXw8enaXodh4aGxqSmpmZ27dq1vLYYG2Lt2rWt27RpU3H//ff32rlz53bn9IcffjgsKCiofNasWYeefPLJLvn5+d7z588/cL770JS2bdsWHBsb29N1Omu8iYioJflGRJ50nSgiUwFs9EA8REREbjds2LCTOTk5/gDw2muvBcXExPSLioqKHjt2bHh5ucmHAwICLn/wwQfD+vbtG/3pp5+2Tk5ODrj88suj+vbtGx0TE9MvPz/fq7y8HBMnTgwbMGBAv8jIyOgXX3wxGABWr17d5sorr+ybkJBwWa9evfrffvvtvSoqKvDcc8+FHDlyxDc+Pj5y6NChkYBJxA8ePHhOVj99+vTOzvUmJSV1q21/EhMTT3bq1OmcRH7dunXtJ06ceAwAJk6ceGzt2rUdzvvNayYcXI2IiFqSxwG8JSI7ADivsA8GsB3ABE8FRURELVddNdPuVlZWho8//rjtjTfeWLB582bHsmXLglJTU7P8/f113LhxPRYsWNDxscceO3bq1CmvoUOHFv31r3/dX1JSIr179x6wZMmSXfHx8cU//PCDV+vWrSvmzJkT3K5duzPp6emZp06dkiuuuCLqtttuKwCAzMzMVlu3bv2uZ8+eZXFxcVHr169vPW3atCPz58/vnJycvKO6Gm+n5cuXt83JyXGkpaVlqiquv/763mvXrm2dmJh4siH7euzYMZ/w8PAyAOjevXvZsWPHLpp89qIJlIiIqC6qehzAnSISCcDZL+wpVc32YFhERERN7vTp015RUVHRADB06NDCJ554Iu+ll14KTk9PD4iNje0HACUlJV4hISHlAODt7Y0JEybkA0BaWpojJCSkLD4+vhgAgoKCKgDgk08+aZuVlRWwatWqDgBQWFjonZGR4fDz89OYmJiiiIiIMgDo379/8a5du/zqG+u6devapqSktI2Ojo4GgOLiYq+srCxHQxNvOy8vrybpHtxcmHgTEVGLISIDbS93WX/9ndNVNa35oyIiImp6/v7+FVlZWRn2aaoqo0ePPjZv3rxz+j37+flV1NWvW1Vl9uzZe0eNGlVgn7569eo2/v7+lQNYeXt7o7y8vN5Zr6pi8uTJB6dMmZJX32Wq07Fjx/I9e/b4hoeHl+3Zs8c3KCioxlr2Cw37eBMRUUuyFcC7AF61HvNsj1c9GBcREZHbJSQkFKxevbrDgQMHfAAzCviOHTvOqZkeOHBgyZEjR3yTk5MDACA/P9+rrKwMN9xww4n58+d3On36tABAWlqaf0FBQa05Y2Bg4JkTJ07UWiYxMbFg8eLFwc5yu3fv9nXG2BA33XTT8YULF3YEgIULF3ZMSEg43tB1eAprvImIqCX5XwAjARwHsBTAR6pa7NmQiIiImkdcXFzJtGnTDowYMSKyoqICvr6+Onfu3L2RkZGl9nIOh0OXLFmya9KkST1KSkq8HA5HRUpKyo6kpKS83Nxc/5iYmH6qKkFBQWVr1qzZVdP2AGD8+PF5CQkJkZ07dy7duHHjjurKjBw5smD79u2OK664IgoAAgICKpYsWbI7NDS02hrr2267rdc333zTJj8/36dz584Dp06d+n1SUlLeM888c/DOO++MCA8PDw4NDS1dsWJFrbFdSHg7sRrwdmJERE2jOW8nZttmHwB3A7gNwE4Az3uqmTlvJ0ZEdOFwx+3EiOx4OzEiIrpkqOpOAO8DWA1gGIBoz0ZERERElzI2NSciohZDRHoAGAPgTgCHYJLvfqp6fs2giIiIqEkdOnTIe/jw4X1dp2/YsCG7S5cuZzwRkzu5NfEWkfYA3gAwAIACeABANsyJUE8AuQB+pqr5Ytp2vwLgZgDFACao6mZrPeMBTLNW+5yqvu3OuImI6KKVCyANwAqYft4hAH7h7D6kqnM9FhkRERFV6tKlyxnXUdlbMnc3NX8FwDpVjQIQCyATwFQAn6pqHwCfWq8BIBFAH+vxEID5ACAiQQCeBjAUwJUAnhaRDm6Om4iILk6zAPwT5sJyMIBOLg8iIiKiZue2Gm8RaQfgxwAmAICqlgIoFZE7AAy3ir0NYAOA3wK4A8AiNSOSfSMi7UWkq1V2var+YK13PYAEmNFqiYiIKqnqtLpLASLyv6r6grvjISIiIgLcW+PdC8BRAG+JyBYReUNEAgF0VtWDVplDADpbz0MB7LMtv9+aVtP0KkTkIRFJFZHU8vKL5j7qRETkGWM8HQARERFdOtyZePsAGAxgvqpeDqAIZ5uVAwCs2u0mueeWqr6uqkNUdYiPD8eMIyKiWp3/PSOJiIg8yNvbOy4qKiq6T58+/RMTEy8rLCz0yB2rZs6cGWLfdnx8fO+8vDxvAAgICLi8oevLycnxHTp0aGRERET/3r1793/22WdDnPMOHz7sffXVV/cJDw8fcPXVV/c5evSod9Pshfu585+zH8B+Vd1ovV4Gk4gftpqQw/p7xJp/AEB32/Jh1rSaphMRETVWk1z0JSIi8hR/f/+KrKysjJ07d2739fXV2bNn13ssk6ZsIbxw4cLOJ0+erMwrk5OTc4KDgxs9Krmvry9mz569f9euXdu//fbbzL/97W8hmzZtcgDA008/3XX48OGFe/bsSR8+fHjhU0891aUp9qE5uK1qWFUPicg+EemrqtkARgDIsB7jATxv/f3IWmQVgMdE5D2YgdROqOpBEfkYwCzbgGo3Avidu+ImIqJLAmu8iYioSbz3i5Luh7ZXBDTlOrv09yoe8zfHvrpLGsOGDTuZlpbWCgBee+21oPnz53cuKyuTwYMHFy1atGiPj48PAgICLr/nnnuOpqSktJ07d+5eh8NRMXny5B7FxcVefn5+mpKSkt2mTZuKRx99NOzLL79sU1paKg8++OCRKVOm5K1evbrNzJkzuwUFBZVlZ2e3iomJKV65cuXuWbNmhRw5csQ3Pj4+skOHDuUbN27cERoaGpOamprZtWvXKtn99OnTO69YsSKotLRUbrnlluMvv/zy99XtS3h4eFl4eHgZAHTo0KEiIiLi1N69e/3i4uJK1q1b1z45OTkbACZOnHgsPj6+Ly6SSll3N0d4HMASEUkDMAhmtNnnAdwgIjsBXG+9BoA1AL4DkAPgrwAeAQBrULVnAXxrPWY6B1ojIiKyE5Ff1bPocrcGQkRE1EzKysrw8ccft42JiTm1efNmx7Jly4JSU1OzsrKyMry8vHTBggUdAeDUqVNeQ4cOLcrOzs6Ij48vuueeeyLmzJmzNzs7OyM5OTm7devWFXPmzAlu167dmfT09Mxt27Zlvv32252ysrL8ACAzM7PVvHnz9uXk5Gzfu3ev//r161tPmzbtSEhISFlycvKOjRs37qgpxuXLl7fNyclxpKWlZWZmZmZs3bo1YO3ata3r2rfs7Gy/jIyMgPj4+JMAcOzYMR9nUt69e/eyY8eOXTR9jN0aqKpuBTCkmlkjqimrAB6tYT1vAnizaaMjIqIW6EFYt6Osjao+W910EUmAuRWmN4A3VPX5asr8DMAMmObq21R17PkETEREF7eG1Ew3pdOnT3tFRUVFA8DQoUMLn3jiibyXXnopOD09PSA2NrYfAJSUlHiFhISUA4C3tzcmTJiQDwBpaWmOkJCQsvj4+GIACAoKqgCATz75pG1WVlbAqlWrOgBAYWGhd0ZGhsPPz09jYmKKIiIiygCgf//+xbt27fKrb6zr1q1rm5KS0jY6OjoaAIqLi72ysrIciYmJJ2ta5sSJE14jR46MeP755/c547Pz8vKCyMXTgO2iuUJARETkTiLiDWAegBtgxin5VkRWqWqGrUwfmO5O16hqvoiEVL82IiIi93L28bZPU1UZPXr0sXnz5p3T/NrPz6+irkGoVVVmz569d9SoUQX26atXr27j7+9fOT6Kt7c3ysvL6531qiomT558cMqUKXn1KX/69Gm55ZZbIkaPHv3D+PHjjzund+zYsXzPnj2+4eHhZXv27PENCgq6aG5n5ZGR74iIiOxEJEFEskUkR0Sm1lDmZyKSISLbReTdGlY1UER+qOaRLyJ1dVO6EkCOqn6nqqUA3gNwh0uZBwHMU9V8AFDVIyAiIrpAJCQkFKxevbrDgQMHfAAzCviOHTvOqZkeOHBgyZEjR3yTk5MDACA/P9+rrKwMN9xww4n58+d3On36tABAWlqaf0FBQa05Y2Bg4JkTJ07UWiYxMbFg8eLFwc5yu3fv9nXG6KqiogJjxowJj4yMLJkxY8Zh+7ybbrrp+MKFCzsCwMKFCzsmJCQcr24dFyLWeBMRkUc1cU3zf1F9F6f6CAVgby64H2awT7tIK54vYZqjz1DVdY3cHhERUZOKi4srmTZt2oERI0ZEVlRUwNfXV+fOnbs3MjKy1F7O4XDokiVLdk2aNKlHSUmJl8PhqEhJSdmRlJSUl5ub6x8TE9NPVSUoKKhszZo1u2rb5vjx4/MSEhIiO3fuXFpTP++RI0cWbN++3XHFFVdEAUBAQEDFkiVLdoeGhp5TY71+/frWK1eu7NinT59Tzqb0zzzzzIGf//znJ5555pmDd955Z0R4eHhwaGho6YoVK2qN7UIipmt1yxIYGKhFRUXntY6m6C/QEt9bIqKGEpFiVQ2sZf6PYBLYm6zXvwMAVf2jrcwLAHao6ht1bGuLqjb4nqHWsncBSFDVX1qv7wUwVFUfs5VZDaAMwM9gbm+ZAiBGVY+7rOshAA8BQI8ePeL27NnTmJCIiKiJicgmVW3sBdpK27Zty42Nja1Xs2m6tGzbti04Nja2p+t0NjUnIiJPq66mOdSlTCSASBH5UkS+sQZBq06No5VbNeu1OQCgu+11GM69Rcl+AKtUtUxVdwPYAaCP64pU9XVVHaKqQzp1qvdtVYmIiKiFqldTcxGJUdX/ujsYIiJqkXxEJNX2+nVVfb2h64BJcIfDqmm2jk2ufbviRCRMVffbJ4rIcJjRymNr2ca3APqISC+YhHsMANcRy1cCuBvAWyISDHNB4LsG7gsREdEl79ChQ97Dhw/v6zp9w4YN2V26dDnjiZjcqb59vF8TEX8AfwewRFVPuC8kIiJqYcrraNZX35rmjapaBmC3iDhrmr91KbcCwOcisgDAnwF0AvASTIL8YG1Bqmq5iDwG4GOY/ttvqup2EZkJIFVVV1nzbhSRDABnAExR1WO1rZeIiIjO1aVLlzOuo7K3ZPVqaq6q1wK4B+bEaJOIvCsiN7g1MiIiulRU1jSLiB9MTfMqlzIrYWq7UVtNs6q+DWAwTFKeAWAjgC8AXKmq/6krEFVdo6qRqhqhqn+wpj1lJd1Q49eqGq2qMar6XuN2mYiIiC4l9e7jrao7AUwD8FsA8QDmikiWiIx0V3BERNTyqWo5AGdNcyaAD5w1zSJyu1XsYwDHrJrmz1B7TXNvAIMAbANQCqAdLuIxTfTM+Q0WSkRERJ5XrxMRERkoIi/DnBBdB+A2Ve1nPX/ZjfEREdEloKlqmkVkIYA3ACSp6s9gar87A9gqItc10+40mfL8z1H0RThKD7wB1QpPh0NERESNVN8agL8A2AwgVlUfVdXNAKCq38PUghMREV0IcgDEqernAKCqhao6GcA4AM95NLJGEL9O8Arsj9OZD+LUpnicObnd0yERERFRI9Q38b4FwLuqegoARMRLRAIAQFUXuys4IiKihlDVF62m667TtwIY5oGQzot3YBRaxW2AI/pNVBRlonjjIJzO+R30TLGnQyMiIg/z9vaOi4qKiu7Tp0//xMTEywoLCz3SrWrmzJkh9m3Hx8f3zsvL8waAgICAyxu6vuLiYomJienXt2/f6N69e/dPSkrq5pyXlZXlN3DgwKgePXoMuOWWWy4rKSmRptkL96vvP+cTAK1srwOsaURERBcMEUm2Pf+7y+xUXIREBL7d7kfAj7Lg0+VelOY+j6JvBqA8b62nQyMiIg/y9/evyMrKyti5c+d2X19fnT17dqf6Lltefs416kZbuHBh55MnT1bmlcnJyTnBwcGNvh2Yw+HQL774Ijs7Oztj+/btGZ9++mnbTz/9NBAAfv3rX4c99thjh/fu3Zverl278ldeeSW4KfahOdT3dmIOVT3pfKGqJ5013kRERBeQtrbnA13mXTRXxavj5ReMVv3fRHm38Tid+TBObb0ZPiGj4d93Drz8u9W9AiIicot1D5V0z8uoaNLcKDjaqzjhdce++pYfNmzYybS0tFYA8NprrwXNnz+/c1lZmQwePLho0aJFe3x8fBAQEHD5PffcczQlJaXt3Llz9zocjorJkyf3KC4u9vLz89OUlJTsNm3aVDz66KNhX375ZZvS0lJ58MEHj0yZMiVv9erVbWbOnNktKCioLDs7u1VMTEzxypUrd8+aNSvkyJEjvvHx8ZEdOnQo37hx447Q0NCY1NTUzK5du1bJ7qdPn955xYoVQaWlpXLLLbccf/nll7+vbl+8vLzQrl27CgAoLS2V8vJyERFUVFTg66+/bvPRRx99BwAPPPDAsRkzZnT77W9/e7Tx73TzqW+Nd5GIDHa+EJE4AKfcExIREVGjaSPnXTR8OsQj4Kqt8LvsWZTnrULRV/1Quu9VqDa6coGIiC5iZWVl+Pjjj9vGxMSc2rx5s2PZsmVBqampWVlZWRleXl66YMGCjgBw6tQpr6FDhxZlZ2dnxMfHF91zzz0Rc+bM2ZudnZ2RnJyc3bp164o5c+YEt2vX7kx6enrmtm3bMt9+++1OWVlZfgCQmZnZat68eftycnK2792713/9+vWtp02bdiQkJKQsOTl5x8aNG3fUFOPy5cvb5uTkONLS0jIzMzMztm7dGrB27drWNZUvLy9HVFRUdOfOnWPj4+MLrrvuuqLDhw/7tGnT5oyvry8AoGfPnqWHDx/2a+K3023qW+M9GcA/ROR7mBqDLgB+7raoiIiIGqe9iNwGc2G5ne12ZAJzW7EWQbz84X/ZNPh2GYOSrEdwOvtxlB1cBEfUAni3HVz3CoiIqMk0pGa6KZ0+fdorKioqGgCGDh1a+MQTT+S99NJLwenp6QGxsbH9AKCkpMQrJCSkHAC8vb0xYcKEfABIS0tzhISElMXHxxcDQFBQUAUAfPLJJ22zsrICVq1a1QEACgsLvTMyMhx+fn4aExNTFBERUQYA/fv3L961a1e9k95169a1TUlJaRsdHR0NAMXFxV5ZWVmOxMTEk9WV9/HxQVZWVkZeXp73LbfcEvHtt986wsLCmq59vAfUK/FW1W9FJPw2YRwAACAASURBVApAX2tStqqWuS8sIiKiRvkSwM+s518BGG2b91Xzh+NeXgG90eryj1F++D2c3pGE4v9cAd/uk+AfMRPi08bT4RERkRs5+3jbp6mqjB49+ti8efMOuJb38/Or8PGpPf1TVZk9e/beUaNGFdinr169uo2/v39lyzFvb2+Ul5fXuwuXqmLy5MkHp0yZklffZQAgODj4zLXXXlv4z3/+s92MGTMOFxYWepeVlcHX1xe5ubl+nTt3Lm3I+jypISPfXQHTX24wgLtF5D73hERERNQ4qnpvbQ9Px+cOIgLfLncj8EdZ8A19CGX7XkHR19EoO7ICqi2idT0REdVTQkJCwerVqzscOHDABwAOHz7svWPHjnNqpgcOHFhy5MgR3+Tk5AAAyM/P9yorK8MNN9xwYv78+Z1Onz4tAJCWluZfUFBQa84YGBh45sSJE7WWSUxMLFi8eHGws9zu3bt9nTG6+v77732co6KfPHlSPvvss7b9+vUr8fLywlVXXVX41ltvdQCAN998s+Ott956vO535cJQrxpvEVkMIALAVgDOTmQKYJGb4iIiImowERlby2xV1aXNFkwzE9/2cPSbD99u41GSORElaSPhHXwbHH3/Aq9W4Z4Oj4iImkFcXFzJtGnTDowYMSKyoqICvr6+Onfu3L2RkZFVaoYdDocuWbJk16RJk3qUlJR4ORyOipSUlB1JSUl5ubn/z969xzV5nn0A/90JORhOEjBgUVARRGikKo7OWsO0rjB7UmtPbNMetN1rp9J3rOuKJ2Zd2w1FO0S61latbddZtY56mG41eWtbWmqFckgQy8FSBUHOAZKQ+/3jCZZSDkECAby+n09mcud+7ud60n1Iruc+lcjUavVUzjlTKpXmo0ePXujpnMuWLauKiYkJ8fX1NXU3z3vx4sX1eXl58lmzZoUCgEKhsO7fv7/Y39//R8PHL168KFm+fPnEtrY2cM7Zvffee/Xhhx+uA4Dk5ORvH3zwwaDNmzf7h4eHG9esWdOnHnRnYvbcDWeMFQAI48Pk1rmrqytvamrqVxuM9X/x22HycRFCyIBijBk5566DdK60bt76BYBxnHPxYMTRUWRkJM/KGtydzLjVDPPF7Wi9sAEAIJu0EZLx/wMmHpT/DIQQMmQxxr7knEf2t53s7OySiIiIYZP0kcGTnZ3tExERMaFzub1DzXMhLKhGCCGEDFmc8990fADQAvgphD28+/1Da7hgIgmkgb+D60/zIVbOR2vR79GoG4uWgqfQVp9FN4YJIYSQQWbvquY+APIZY58DaG0v5Jzf0/0hhBBCyOBjjIkA/BpAAoCvADzCOc/v+aiRSTQqEKMiPkBb3RmYy1+D+dJemMvTIXKLgMT/CUj84sAkXs4OkxBCyA3o8uXL4ujo6Cmdy0+fPm3w8/MbcXtk2pt4bxzIIAghhBBHYIw9CSAewP8BuIdz3uO8tBsBYwwuo+fAZfQc8CnbYb78Dszlr6HV8Fu0nk+Ai2oJJDc9AbGXxiHTrAghhBB7+Pn5tXVelX0ks3c7MS1jLBBAMOf8FGNMAWDQ58kRQgghvUgDUAFhWPk/OySSDMLiajf0JtfMxRPScU9BOu4ptNV/BfN3r8N8+S1YLu8HGzUZEv/HIRm7DCLZWGeHSgghhIwo9q5qvgLASgBKCKub+wPYBWD+wIVGCCGE9FmwswMYLsQe0yH2+BtkwS/DUvk+zOWvwVT0HEwXEuHic5fQC+4dAyayd3AcIYQQQrpj77fpKgA/AZAJAJzz84wx1YBFRQghhFwHe4eWM8Y+5pzPGeh4hgMmVkAy9leQjP0VrE2FQi/4pT2wXPkATHYTJGMfheSmxyBSTHJ2qIQQQsiwZW/i3co5N7UP2WOMuUDYx5sQQggZjmhfrS6IXEMgC34J0qDNsFR9CPN3r8FU8meYSl6AeLQGTHaTs0MkhNzgJH4Pw2XM3c4Og5A+szfx1jLG/ghgFGNsAYD/AfCvgQuLEEIIGVB087gHTCSBRHUfJKr7YG35Fubv3oSl8gCspu+cHRoh5AYnVv7M2SEMGWKxeGZwcHBzW1sbmzx5cvN7771X4u7ubh3sOJKSklTx8fFV7efWaDST33///WIfH582hUIx3Wg0fnU97VosFqjV6jA/Pz/TRx99VAQAer1e+sADD0yqra11UavVxvfff79YLpcPi+90exPvPwB4HMDXAJ4EcBTAawMVFCGEEEKGBpF8HGSTEiGblOjsUAghhHQgk8ms7auC33PPPROTk5PHbNy4scKeYy0WC1xcHLOGR3p6uu+KFSuutifeWq22yBHtbt682Xfy5MnNjY2N1xb1fuaZZ8Y9/fTTFStXrqx55JFHArZv3+7z7LPPXnHE+QaavauaWwH83fYghBBChjvaN4sQQohDnHn6o/G1BVcVjmxz9FSl8ba//eyivfXnzJnTmJOTMwoAdu7cqUxLS/M1m81sxowZTXv37i11cXGBQqGYHhcXd0Wn03ns2LGjTC6XW9euXRtgNBpFUqmU63Q6g7u7u3XVqlXjzpw5424ymdiKFSsqExISqjIyMtyTkpJuUiqVZoPBMEqtVhsPHz5cvGXLFlVlZaVEo9GEeHl5WTIzMwv9/f3VWVlZBWPHjrV0jHHdunW+hw4dUppMJrZw4cLabdu2dTuM6sKFC5ITJ054Pvfcc5e2bdvmCwBWqxWffvqp+wcffPANADz22GPVGzduvGm4JN4ieyoxxooZY990fgx0cIQQQkhfMMaO2Vl1+UDGQQghhAwWs9mMEydOeKjV6uazZ8/KDxw4oMzKytLr9fp8kUjEd+3a5Q0Azc3NoqioqCaDwZCv0Wia4uLiglJSUsoMBkO+Vqs1uLm5WVNSUnw8PT3bcnNzC7Kzswv27NkzRq/XSwGgoKBgVGpq6sWioqK8srIy2cmTJ90SExMrVSqVWavVFmZmZhZ2F+PBgwc9ioqK5Dk5OQUFBQX5586dUxw7dsytu/qrVq0a//LLL38rEn2frlZUVLi4u7u3SSQSAMCECRNMFRUVUkd9jgPN3vEFkR2eywEshbC1GCGEEDKU+NlTiXOePdCBEEIIuTH0pWfakVpbW0WhoaFhABAVFdWwZs2aqq1bt/rk5uYqIiIipgJAS0uLSKVSWQBALBZj+fLlNQCQk5MjV6lUZo1GYwQApVJpBYBTp0556PV6xZEjR7wAoKGhQZyfny+XSqVcrVY3BQUFmQEgPDzceOHCBbuT3uPHj3vodDqPsLCwMAAwGo0ivV4vj42Nbexc95133vH08fGx3H777caMjAz3/nxGQ4m9Q82rOxWlMMa+BLDe8SERQggh182TMXZPd29yzo8MZjCEEELIQOk4x7sd55wtXbq0OjU1tbxzfalUau1tXjfnnCUnJ5ctWbKkvmN5RkaGu0wmu7aImVgshsVisXvaFucca9euvZSQkFDVW92PP/7Y7eTJk6P9/f09W1tbRU1NTaJ777134qFDh4obGhrEZrMZEokEJSUlUl9fX5O9MTibvUPNZ3R4RDLGnoL9veWEEELIYPEEcD+EkVmdH/c7MS5CCCFkwMXExNRnZGR4lZeXuwBARUWFuLCw8Ec909OmTWuprKyUaLVaBQDU1NSIzGYzFixYUJeWljamtbWVAUBOTo6svr6+x5zR1dW1ra6ursc6sbGx9fv27fNpr1dcXCxpj7Gz1NTU8oqKipzy8vKv33zzzW9uvfXWhg8++KBYJBLh1ltvbXjjjTe8AGD37t3ed911V609n8tQYG/ynNzhuQVACYAHHB4NIYQQ0j+lnPNfOzsIQgghxBlmzpzZkpiYWD5//vwQq9UKiUTCd+zYURYSEvKDnmG5XM73799/YfXq1QEtLS0iuVxu1el0hfHx8VUlJSUytVo9lXPOlEql+ejRoxd6OueyZcuqYmJiQnx9fU3dzfNevHhxfV5ennzWrFmhAKBQKKz79+8v9vf3t3RVvzvJycnfPvjgg0GbN2/2Dw8PN65Zs6bXHvShgnE+LLY96xNXV1fe1NTUrzYY6/+CtyPxsyWEkL5ijBk5566DdK6vOOfTB+Nc9oqMjORZWVnODoMQQggAxtiXnPPI3mv2LDs7uyQiImLYJH1k8GRnZ/tERERM6FxuV483Y+yZnt7nnG+9zrgIIYQQR1re3RuMsTOc89sGMRZCCCGEEAB9W9V8FoD2RWnuBvA5gPMDERQhhBByPXpZrTxg0AIhhBBCSI8uX74sjo6OntK5/PTp0wY/P782Z8Q0kOxNvMcBmME5bwAAxthGAB9yzn85UIERQgghDkbzfwghhJAhws/Pr63zquwjmb2Jty+AjhPyTbYyQgghZMhgjC3u7i0AowYzFkIIIYSQdvYm3nsBfM4YO2R7fR+APQMTEiGEEHLd7u7hvYxBi4IQQgghpAO7Em/O+QuMsWMAbrcVPco5/2rgwiKEEEL6jnP+qLNjIIQQQgjpzN4ebwBQAKjnnL/BGBvDGJvIOS8eqMAIIYSQvupiFw4OoArAx/SdRQghhBBnEdlTiTG2AcCzAJ6zFUkAvDVQQRFCCCHXyb3TwwPCzhzHGGMPOTMwQgghxJHEYvHM0NDQsODg4PDY2NhJDQ0NduV2jpaUlKTqeG6NRjO5qqpKDAAKhWL69bTp7++vDgkJCQsNDQ27+eabp7aXV1RUiGfPnh0cGBh48+zZs4OvXLki7v8VDA57/+MsAnAPgCYA4Jx/B+EHTa8YY2LG2FeMsQzb64mMsUzGWBFj7B+MMamtXGZ7XWR7f0KHNp6zlRsYY3faf3mEEEJuJJzzTV081gCYDeD3zo6PEEIIcRSZTGbV6/X558+fz5NIJDw5OXmMvcdaLBaHxZGenu7b2Nh4La/UarVFPj4+/d4OTKvVFur1+vzc3NyC9rINGzaMjY6ObigtLc2Njo5uWL9+vV9/zzNY7B1qbuKcc8YYBwDGmGsfzrEGQAGEXgcAeAnANs75u4yxXQAeB5Bm+7eGcz7Z1ivxEoAHGWNhAB4CEA7gJgCnGGMhnPMRt7cbIYSQgcE5v8oYY86OgxBCyMiT8/vHxjcYchWObNN9ys3GaS/vvmhv/Tlz5jTm5OSMAoCdO3cq09LSfM1mM5sxY0bT3r17S11cXKBQKKbHxcVd0el0Hjt27CiTy+XWtWvXBhiNRpFUKuU6nc7g7u5uXbVq1bgzZ864m0wmtmLFisqEhISqjIwM96SkpJuUSqXZYDCMUqvVxsOHDxdv2bJFVVlZKdFoNCFeXl6WzMzMQn9/f3VWVlbB2LFjf5Ddr1u3zvfQoUNKk8nEFi5cWLtt27bv+vq5HD9+fLRWqzUAwJNPPlmt0WimACjvazvOYG+P93uMsXQAoxljKwCcAvD33g5ijI0DsBDAa7bXDMA8AAdsVfZAWCEdAO7F9yulHwAw31b/XgDvcs5bbfPzigD8xM64CSGEEDDGfgagxtlxEEIIIY5mNptx4sQJD7Va3Xz27Fn5gQMHlFlZWXq9Xp8vEon4rl27vAGgublZFBUV1WQwGPI1Gk1TXFxcUEpKSpnBYMjXarUGNzc3a0pKio+np2dbbm5uQXZ2dsGePXvG6PV6KQAUFBSMSk1NvVhUVJRXVlYmO3nypFtiYmKlSqUya7XawszMzMLuYjx48KBHUVGRPCcnp6CgoCD/3LlzimPHjrn1dF3z588PDg8Pn/rXv/7Vp72surraJTAw0AwA48ePN1dXV/dlzTKnsndV878yxhYAqAcwBcB6zvlJOw5NgTC0r31YujeAWs55+92PbwH42577A7hoO5+FMVZnq+8P4LMObXY85hrG2EoAKwFAKpXac1mEEEJGGMbY1xAWVOtICeA7AMsGPyJCCCEjXV96ph2ptbVVFBoaGgYAUVFRDWvWrKnaunWrT25uriIiImIqALS0tIhUKpUFAMRiMZYvX14DADk5OXKVSmXWaDRGAFAqlVYAOHXqlIder1ccOXLECwAaGhrE+fn5cqlUytVqdVNQUJAZAMLDw40XLlywO+k6fvy4h06n8wgLCwsDAKPRKNLr9fLY2NjGrup//PHH+okTJ5rLy8td5s2bFxIeHt7Sua5IJMJwGszWa+LNGBMDOMU5/xkAe5Lt9uPuAlDJOf+SMRZ9/SHah3P+KoBXAcDV1bXzjy5CCCE3hrs6veYAqjnnTfYczBiLAbAdgBjAa5zzF7uptwTC6KxZnPOsfsRLCCGEXJf2Od4dyzjnbOnSpdWpqak/Gn4tlUqtLi49p3+cc5acnFy2ZMmS+o7lGRkZ7jKZ7FqOJRaLYbFY7M56OedYu3btpYSEhCp76k+cONEMAP7+/paFCxfWfvrpp66xsbGN3t7eltLSUklgYKC5tLRUolQqHTdZfYD1OtTcNpfayhjz7GPbtwG4hzFWAuBdCEPMt0MYrt7+X3wcvh+TXw5gPADY3vcEUN2xvItjCCGEkGs456WdHmUAwBj7JWPsw56Otd1oTgUQCyAMwMO2dUY613OHsH5JpuOvgBBCCLl+MTEx9RkZGV7l5eUugLAKeGFh4Y96pqdNm9ZSWVkp0Wq1CgCoqakRmc1mLFiwoC4tLW1Ma2srA4CcnBxZfX19jzmjq6trW11dXY91YmNj6/ft2+fTXq+4uFjSHmNn9fX1opqaGlH7848++shj2rRpzQBw55131qanp3sDQHp6undMTExtb5/JUGHvmPhGAF8zxk7CtrI5AHDOV3d3AOf8Odi2H7P1eP+Ocx7HGPsngPshJOPLAHxgO+SI7fWntvf/a1vQ7QiAtxljWyEsrhYM4HO7r5AQQsiQ5+ieZtuOGQsBPALgTgDvA9jVSxg/AVDEOf/G1sa7ENYZye9U708QFgBN6KU9QgghZFDNnDmzJTExsXz+/PkhVqsVEomE79ixoywkJMTUsZ5cLuf79++/sHr16oCWlhaRXC636nS6wvj4+KqSkhKZWq2eyjlnSqXSfPTo0Qs9nXPZsmVVMTExIb6+vqbu5nkvXry4Pi8vTz5r1qxQAFAoFNb9+/cX+/v7/6jH+ttvv3VZtGjRZABoa2tjS5Ysqb7//vvrAWDTpk2XFi1aFBQYGOjj7+9vOnToUI+xDSWM895HZTPGupwXxznf01V5F8dHQ0i872KMTYKQdCsBfAXgl5zzVsaYHMA+ANMBXAXwUIcfP88DeAyABcBazvmxns7n6urKm5rsGlXYU8z9Oh4QhlQQQsiNjjFm5Jx3uxuGrae5EMACCOt4fAHgYc55fqd67gA+BCAF8HRXiTdj7OcAHgbwcwAfAfgHgFc45xPsiPN+ADGc8ydsr38FIIpz/nSHOjMAPM85X8IYOw3hu62rOK6tOxIQEDCztLS0t9MTQggZBIyxLznnkf1tJzs7uyQiIsKuYdPkxpKdne0TERExoXN5jz3ejLEAznmZvQl2dzjnpwGctj3/Bl2sSs45bwGwtJvjXwDwQn9iIIQQMmQ5sqf5OID/AzDHthMGGGPbHREkY0wEYCuA5b3V7bjuSGRkJN2FJYQQQm5wvQ01PwxgBgAwxt7nnC8Z+JAIIYTcYK7tamHzLYCojhVsPc3jOecfMsZ6SrxnAHgIwCnG2DcQRliJ7YyjtzVF3AHcDOC0bVSUH4AjjLF7aIE1QgghpG8uX74sjo6OntK5/PTp0wY/P782Z8Q0kHpLvDuOt540kIEQQggZsVwYYx0T01dtPcJ26WNP8zkA5wD8gTE2G8Kwcwlj7BiAQ72c9wsAwYyxiRAS7ocgzBFvb7sOwLW9RHsaak4IIYSQnvn5+bV1XpV9JOst8ebdPCeEEELsZellPt2A9DRzzj8B8AljbA2AOyAk0t0m3pxzC2PsaQAnIPSS7+ac5zHGkgBkcc6P9HSRhBBCCCHd6S3xjmCM1UPo+R5lew7ba8459xjQ6AghhNwIHNbTzBh7mnP+N9vzcM55HufcCuDftkePOOdHARztVLa+m7rRvV4ZIYQQQgh62cebcy7mnHtwzt055y625+2vKekmhBDSb5xzC4D2nuYCAO+19zQzxu7pY3OPdXi+z1ExEkIIIYT0h737eBNCCCEDZoB6mvu/LyQhhBBCiAP02ONNCCGEDDOjGWOLGGNLAHgwxhZ3fDg7OEIIIcRRxGLxzNDQ0LDg4ODw2NjYSQ0NDU7J7ZKSklQdz63RaCZXVVWJAUChUEy/njarqqrEMTExkyZOnBg+adKk8FOnTrkCQEVFhXj27NnBgYGBN8+ePTv4ypUr9u5c4nSUeBNCCBlJdADuAXCX7fndHR53OTEuQgghxKFkMplVr9fnnz9/Pk8ikfDk5OQx9h5rsVgcFkd6erpvY2PjtbxSq9UW+fj49Gs7sJUrV47/+c9/Xl9cXJyXn5+ff8stt7QAwIYNG8ZGR0c3lJaW5kZHRzesX7/er7/xDxYaak4IIWTE4Jwvd3YMhBBCbixFmx8bb7yQq3Bkm4qgm42TE3dftLf+nDlzGnNyckYBwM6dO5VpaWm+ZrOZzZgxo2nv3r2lLi4uUCgU0+Pi4q7odDqPHTt2lMnlcuvatWsDjEajSCqVcp1OZ3B3d7euWrVq3JkzZ9xNJhNbsWJFZUJCQlVGRoZ7UlLSTUql0mwwGEap1Wrj4cOHi7ds2aKqrKyUaDSaEC8vL0tmZmahv7+/Oisrq2Ds2LE/yO7XrVvne+jQIaXJZGILFy6s3bZt23ddXUt1dbU4MzPT/cCBAyUAIJfLuVwubwOA48ePj9ZqtQYAePLJJ6s1Gs0U/HAnlCGLerwJIYSMGIyxlA7P13R6781BD4gQQggZYGazGSdOnPBQq9XNZ8+elR84cECZlZWl1+v1+SKRiO/atcsbAJqbm0VRUVFNBoMhX6PRNMXFxQWlpKSUGQyGfK1Wa3Bzc7OmpKT4eHp6tuXm5hZkZ2cX7NmzZ4xer5cCQEFBwajU1NSLRUVFeWVlZbKTJ0+6JSYmVqpUKrNWqy3MzMws7C7GgwcPehQVFclzcnIKCgoK8s+dO6c4duyYW1d1DQaDVKlUWpYuXTph6tSpYQ8++GBgfX29CACqq6tdAgMDzQAwfvx4c3V19bDpSB42gRJCCCF2mNvh+TIA2zu8njbIsRBCCLkB9KVn2pFaW1tFoaGhYQAQFRXVsGbNmqqtW7f65ObmKiIiIqYCQEtLi0ilUlkAQCwWY/ny5TUAkJOTI1epVGaNRmMEAKVSaQWAU6dOeej1esWRI0e8AKChoUGcn58vl0qlXK1WNwUFBZkBIDw83HjhwgWpvbEeP37cQ6fTeYSFhYUBgNFoFOn1enlsbGxj57oWi4UVFBQotm/fXjZv3rymRx99dPy6dev8tm/f/oMecpFIBMaGzzqqlHgTQggZSVg3zwkhhJARpX2Od8cyzjlbunRpdWpq6o+GX0ulUquLS8/pH+ecJScnly1ZsqS+Y3lGRoa7TCbj7a/FYjEsFovd37Occ6xdu/ZSQkJCVW91J0yYYPL19TXNmzevCQAefPDBmhdffNEPALy9vS2lpaWSwMBAc2lpqUSpVDpusvoAo6HmhBBCRhIRY8yLMebd4bmSMaYEMGxWPiWEEEKuR0xMTH1GRoZXeXm5CyCsAl5YWPijnulp06a1VFZWSrRarQIAampqRGazGQsWLKhLS0sb09raygAgJydH1j7Muzuurq5tdXV1PdaJjY2t37dvn097veLiYkl7jJ0FBARY/Pz8TNnZ2TIA+Pe//+0xZcqUFgC48847a9PT070BID093TsmJqa2t89kqKAeb0IIISOJJ4Av8X1v99kO7/EfVyeEEEJGjpkzZ7YkJiaWz58/P8RqtUIikfAdO3aUhYSEmDrWk8vlfP/+/RdWr14d0NLSIpLL5VadTlcYHx9fVVJSIlOr1VM550ypVJqPHj16oadzLlu2rComJibE19fX1N0878WLF9fn5eXJZ82aFQoACoXCun///mJ/f/8ue6xfeeWVsri4uEkmk4kFBAS0vvPOOyUAsGnTpkuLFi0KCgwM9PH39zcdOnSox9iGEsb5yPsd4urqypuamvrVhiPmC4zEz5YQQvqKMWbknLs6Ow5niYyM5FlZWc4OgxBCCADG2Jec88j+tpOdnV0SERHR67BpcuPJzs72iYiImNC5nHq8CSGEjCiMMRcAsQBCbUX5AE5wzofNPDBCCCGEjCyUeBNCCBkxGGP+AP4L4BKAryAMOb8LwFbG2M84513uGUoIIYSQwXX58mVxdHT0lM7lp0+fNvj5+bU5I6aBRIk3IYSQkeQFAGmc85SOhYyx1QD+DGGLMUIIIYQ4mZ+fX1vnVdlHMkq8CSGEjCS3cs6Xdy7knO9gjBmcEA8hhBBCCG0nRgghZERp7uE946BFQQghhBDSAfV4E0IIGUk8GWOLuyhnADwGO5j+MlcVoObIMnjM3QhZUKxDdtwghBBCyOCjxJsQQshIogVwdzfv6QYzEEewNlXCaqxC9bsLIR33U3hoNkM2cZ6zwyKEEEJIH9FQc0IIISMG5/zRnh7Ojq+vZIEa+P7GgNG/SEdb3UVU7Z+PK/vmofXiGWeHRgghxMnEYvHM0NDQsODg4PDY2NhJDQ0NTsntkpKSVB3PrdFoJldVVYkBQKFQTO9re9nZ2bLQ0NCw9oebm9v0pKQkFQBUVFSIZ8+eHRwYGHjz7Nmzg69cuSJ23JUMLEq8CSGEkCGMiSVwnbESvqvOw/Pn22GpykfVnjmoeucXMF360tnhEUIIcRKZTGbV6/X558+fz5NIJDw5OXmMvcdaLBaHxZGenu7b2Nh4La/UarVFPj4+170dWERERKter8/X6/X5ubm5+XK53PrQQw/VAsCGDRvGRkdHN5SWluZGR0c3rF+/3s8R1zAYaKg5IYQQMgwwFzncfrIailseR1NWKho+fQlXXo+EfMp98NAkQaJSOztEQgi5IX2367Hxrd/mKhzZpmzczcabntp90d76c+bMaczJyRkFADt36gIBzQAAIABJREFU7lSmpaX5ms1mNmPGjKa9e/eWuri4QKFQTI+Li7ui0+k8duzYUSaXy61r164NMBqNIqlUynU6ncHd3d26atWqcWfOnHE3mUxsxYoVlQkJCVUZGRnuSUlJNymVSrPBYBilVquNhw8fLt6yZYuqsrJSotFoQry8vCyZmZmF/v7+6qysrIKxY8f+ILtft26d76FDh5Qmk4ktXLiwdtu2bd/1dl1HjhzxCAgIaA0JCTEBwPHjx0drtVoDADz55JPVGo1mCoDyPn24TkI93oQQQsgwIpK6wn327+H3dDHc525Ca8l/UflqBK4efBjmatoxjRBCbjRmsxknTpzwUKvVzWfPnpUfOHBAmZWVpdfr9fkikYjv2rXLGwCam5tFUVFRTQaDIV+j0TTFxcUFpaSklBkMhnytVmtwc3OzpqSk+Hh6erbl5uYWZGdnF+zZs2eMXq+XAkBBQcGo1NTUi0VFRXllZWWykydPuiUmJlaqVCqzVqstzMzMLOwuxoMHD3oUFRXJc3JyCgoKCvLPnTunOHbsmFtv1/bOO+8o77///ur219XV1S6BgYFmABg/fry5urp62HQkD5tACSGEkL5gjM0GMAEdvus453udFpCDiWQe8Ji7Hm6znkbDZ8lo+nw7mgveg0L9a7jfvh4uXhOdHSIhhNwQ+tIz7Uitra2i0NDQMACIiopqWLNmTdXWrVt9cnNzFREREVMBoKWlRaRSqSwAIBaLsXz58hoAyMnJkatUKrNGozECgFKptALAqVOnPPR6veLIkSNeANDQ0CDOz8+XS6VSrlarm4KCgswAEB4ebrxw4YLU3liPHz/uodPpPMLCwsIAwGg0ivR6vTw2Nraxu2NaWlrYqVOnPLdu3fptV++LRKJhtdsHJd6EEEJGHMbYPgBBAM4BaJ9nxgGMmMS7nWiUEp4/ewFuP1mDhk9eRFPWThhz34LrLU/Afc7zEHuMc3aIhBBCBkD7HO+OZZxztnTp0urU1NQfDb+WSqVWF5ee0z/OOUtOTi5bsmRJfcfyjIwMd5lMxttfi8ViWCwWu7NezjnWrl17KSEhocreYw4cOOAZFhZmHD9+/LUh697e3pbS0lJJYGCgubS0VKJUKh03WX2A0VBzMnwsWwY8/7yzoyCEDA+RAG7jnP8P5/y3tsdqZwc1kMSuKoxesBV+qy7AdfpKNJ17HZdTJ6P23/Foa6xwdniEEEIGQUxMTH1GRoZXeXm5CyCsAl5YWPijnulp06a1VFZWSrRarQIAampqRGazGQsWLKhLS0sb09raygAgJydHVl9f32PO6Orq2lZXV9djndjY2Pp9+/b5tNcrLi6WtMfYnXfffVf5wAMPXO1Yduedd9amp6d7A0B6erp3TExMbU9tDCXU402GB6MReOcdwNcX2LwZGEbDSgghTpELwA/AJWcHMtjEHv4YHZsKt58moOH//oSmL15BU9bfwFxGOTs0QgjpNw9NEtyi1jo7jCFr5syZLYmJieXz588PsVqtkEgkfMeOHWXti5O1k8vlfP/+/RdWr14d0NLSIpLL5VadTlcYHx9fVVJSIlOr1VM550ypVJqPHj16oadzLlu2rComJibE19fX1N0878WLF9fn5eXJZ82aFQoACoXCun///mJ/f/8ue6zr6+tFH3/8sceePXtKO5Zv2rTp0qJFi4ICAwN9/P39TYcOHeoxtqGEcc57rzXMuLq68qampn614Yj5AiPxs3Wajz4C5s0Tnn/zDTCR5i4SMlwwxoycc9dBPudHAG4B8DmA1vZyzvk9gxkHAERGRvKsrKzBPu015upCGLPfBG9rcVoMhBDiKKMm3wXZxHnXfTxj7EvOeWR/48jOzi6JiIiwe9g0uXFkZ2f7RERETOhcTj3eZHjQ6X74nBJvQkjPNjo7gKFC4h0Cz3lbnB0GIYQQckOjxJsMD1otEBEBfPut8HzZMmdHRAgZwjjnWmfH4DBNTYDroA4YIIQQQgbc5cuXxdHR0VM6l58+fdrg5+fX1tUxwxkl3mToM5mATz8FnnwSKC39Ye83IYR0gTF2K4BXAEwFIAUgBtDEOfdwamB99cYbQFIScOAAMHOms6MhhBBCHMbPz6+t86rsIxmtak6GvqwsoKUFmDtXeFy4AJT/aIcEQgjp6G8AHgZwHsAoAE8ASHVqRH319dfAqlVASQkwezawcydAa4cQQgghwxIl3mTo09pGjN5+O6DRCM+p15sQ0gvOeREAMee8jXP+BoAYZ8fUJ+XlgEQiPDeZhCT8kUeAhgbnxkUIIYSQPqPEmwx9Oh0QFgaMGSPM83Z3p8SbENIbI2NMCuAcY+xlxlg8htt3XkwMcPYsMH3692XvvgvMmgXk5jovLkIIIYT02fD6EUJuPBYLcOaMMMQcAMRiYM4cSrwJIb35FYTvuKcBNAEYD2CJUyO6HkFBwCefACtXfl9mMAA/+Qmwd6/z4iKEEEJIn1DiTYa27GxhWGX7EHNAeJ6fD1y54ry4CCFDGue8FAADMJZzvolz/oxt6PnwI5cD6enAvn2AQiGUNTcLuzs88YTwnBBCyA1HLBbPDA0NDQsODg6PjY2d1NDQ4JTcLikpSdXx3BqNZnJVVZUYABQKxfTuj+zepk2bVJMnTw4PDg4Ov/vuuycajUYGAHq9Xjpt2rTQgICAmxcuXDippaWFOeYqBh4l3mRo6zi/u1177/f//d/gx0MIGRYYY3cDOAfguO31LYyxI86Nqp9++Uvg88+B0NDvy15/HfjpT4Hz550XFyGEEKeQyWRWvV6ff/78+TyJRMKTk5PH2HusxWJxWBzp6em+jY2N1/JKrVZb5OPjc93bgRUXF0teffVV33PnzuWfP38+r62tjb322mtKAHjmmWfGPf300xVlZWW5np6elu3bt/s44hoGA20nRoY2nU4Yaunv/33ZzJnAqFHCe4sXOy82QshQthHATwCcBgDO+TnG2ERnBuQQ4eHAF18I2yu+/bZQlp0t/F184w1gyfAbTU8IIcNdzb8eG2++kqtwZJuSMTcbve7efdHe+nPmzGnMyckZBQA7d+5UpqWl+ZrNZjZjxoymvXv3lrq4uEChUEyPi4u7otPpPHbs2FEml8uta9euDTAajSKpVMp1Op3B3d3dumrVqnFnzpxxN5lMbMWKFZUJCQlVGRkZ7klJSTcplUqzwWAYpVarjYcPHy7esmWLqrKyUqLRaEK8vLwsmZmZhf7+/uqsrKyCsWPH/iC7X7dune+hQ4eUJpOJLVy4sHbbtm3fdXc9bW1trKmpSSSTydqam5tF48aNM1utVnz66afuH3zwwTcA8Nhjj1Vv3LjxpmeffXZYDIOlHm8ydFmtQq92x2HmACCVCj08NM+bENI9M+e8rlPZyNiLy80NeOstIC1N+HsICFNy7r8fWLtWWAGdEELIDcNsNuPEiRMearW6+ezZs/IDBw4os7Ky9Hq9Pl8kEvFdu3Z5A0Bzc7MoKiqqyWAw5Gs0mqa4uLiglJSUMoPBkK/Vag1ubm7WlJQUH09Pz7bc3NyC7Ozsgj179ozR6/VSACgoKBiVmpp6saioKK+srEx28uRJt8TExEqVSmXWarWFmZmZhd3FePDgQY+ioiJ5Tk5OQUFBQf65c+cUx44dc+uq7sSJE82rVq26PHHixGkqlSrC3d29bfHixfUVFRUu7u7ubRLbjh8TJkwwVVRUSAfgIx0Q1ONNhq68PODq1e+Hlnek0QAbNwK1tcDo0YMeGiFkyMtjjD0CQMwYCwawGsAnTo7JcRgDnnpKWOF86VKguFgo374dyMwE/vEPICDAuTESQsgNoi89047U2toqCg0NDQOAqKiohjVr1lRt3brVJzc3VxERETEVAFpaWkQqlcoCAGKxGMuXL68BgJycHLlKpTJrNBojACiVSisAnDp1ykOv1yuOHDniBQANDQ3i/Px8uVQq5Wq1uikoKMgMAOHh4cYLFy7YnfQeP37cQ6fTeYSFhYUBgNFoFOn1enlsbGxj57pXrlwRf/jhh6OLioq+9vb2blu4cOGknTt3KhctWlTfn8/L2QYs8WaMjQewF4AvhF6GVznn2xljSgD/ADABQAmABzjnNYwxBmA7gF8AMAJYzjk/a2trGYBEW9ObOed7BipuR2CwbRbb0CBsfUWuT3uPdleJ99y5AOfCiucLFw5uXISQ4eC3AJ4H0ArgHQAnAPzJqRENhJkzgS+/BB59FPjgA6Hss8+ELcjeeguIjXVufIQQQgZM+xzvjmWcc7Z06dLq1NTU8s71pVKp1cWl5/SPc86Sk5PLlixZ8oMkNyMjw10mk10bOSYWi2GxWOxe2IxzjrVr115KSEio6q3uv/71L4+AgIDWm266yQIA9913X+0nn3zi9tRTT11taGgQm81mSCQSlJSUSH19fYfNMK+BHGpuAfC/nPMwALcCWMUYCwPwBwD/4ZwHA/iP7TUAxAIItj1WAkgDAFuivgFAFIT5ehsYY14DGHe/3QLgKADs3u3kSIY5nQ4YPx6YMOHH70VFCUMsabg5IaQLnHMj5/x5zvksznmk7XlLb8cxxmIYYwbGWBFj7A9dvP8MYyyfMZbDGPsPYyxwYK6gD7y8gEOHgL/8RdhyERBGC/3iF0BiorAtIyGEkBtCTExMfUZGhld5ebkLAFRUVIgLCwt/1DM9bdq0lsrKSolWq1UAQE1NjchsNmPBggV1aWlpY1pbWxkA5OTkyOrr63vMGV1dXdvq6up6rBMbG1u/b98+n/Z6xcXFkvYYO5swYYLp7Nmzbg0NDSKr1Yr//ve/7lOnTm0RiUS49dZbG9544w0vANi9e7f3XXfdVWvP5zIUDFjizTm/1N5jzTlvAFAAwB/AvQDae6z3ALjP9vxeAHu54DMAoxljYwHcCeAk5/wq57wGwEnYOpSHqq8AnAGEIX9t172g342Nc2FF87lzhSGVnY0aJexj277qOSGEAGCMHenp0cuxYgCpEG4EhwF42HbDuKOvAERyzqcBOADg5YG4jnbc2ob6z/4JznuZns4Y8LvfAadPAzfd9H35Cy8ACxYAly8PZJiEEEKGiJkzZ7YkJiaWz58/PyQkJCRs3rx5IRcvXpR0rieXy/n+/fsvrF69OmDKlClh0dHRIUajURQfH18VGhraolarpwYHB4evWLEi0Gw299izvWzZsqqYmJiQqKiokO7qLF68uH7p0qVXZ82aFRoSEhK2aNGioNraWnFXdefNm9d0991310ybNm3qlClTwq1WK3vmmWeuAEBycvK3r7zyil9AQMDNNTU1LmvWrOm1B32oYL1+mTviJIxNAKADcDOAMs75aFs5A1DDOR/NGMsA8CLn/GPbe/8B8CyAaAByzvlmW/k6AM2c8792OsdKCD3lkEqlM1tbW/sbc7+OXwzgfQB4/31aeft6FBYCU6YIe9euXNl1neefB15+GaipERYbIoQMSYwxI+fcdZDOdQXARQjDyzMhzP65hnPe7d06xthPAWzknN9pe/2c7Zg/d1N/OoC/cc5v6ymmyMhInpWV1ZfLuKZWuweXdi2HV8xq+P5qG5jIjvvllZVAXBxw6tT3ZX5+wkrogYHC3O+AAGFEkVx+XXERQshwxRj7knMe2d92srOzSyIiIoZN0kcGT3Z2tk9ERMSEzuUDvrgaY8wNQg66lnNe3zGh5ZxzxphDMn/O+asAXgUAV1dXp69cexgQhkhv20aJ9/VoH0LeeUXzjubOBbZsEeYz3nHH4MRFCBkQjLEYCOt8iAG8xjl/sdP7zwB4AsI0pisAHuOcl3bRlB+ABQAeBvAIgA8BvMM5z7MjDH8ISXu7byFMc+rO4wCOdXM9124GB/RjkTPP23+F1rJsXD26DW0N1bjpqTfAXH7UcfFDKhVw/DiweTOwaZMwgujyZeF5Z76+3yfi7Y+OybmPT9ejjgghhBDSJwOaeDPGJBCS7v2c84O24grG2FjO+SXbUPJKW3k5gPEdDh9nKyuH0Ovdsfz0QMbtCFYAWLMGiI8X9lydNcvZIQ0vWq3w4zGk2xErwOzZwnxGrZYSb0KGsQ5DvBdASHa/YIwd4Zx3XDCmfYi3kTH2GwhDvB/s3BbnvA3AcQDHGWMyCAn4acbYJs753xwY8y8BRALo8u5gx5vBkZGR130zuK2pHs21LvBetB7Vh5JgNdbCf817EMl62S5WLAY2bBD+Tj7yCFDVTadMRYXw+OKLrt+Xy3/YQy6TXe+lEEKI4zz7bNdrAJFh5fLly+Lo6OgpnctPnz5t8PPzG3HzdQdyVXMG4HUABZzzrR3eOgJgGYAXbf9+0KH8acbYuxB6GOpsyfkJAFs6LKj2cwDPDVTcDvXYY8D69UKv99tvOzua4UWn635+dzt3d2DGDFpgjZDh7ycAijjn3wCA7XvgXgDXEm/O+Ucd6n8G4JfdNWZLuBdCSLonANgB4JAdcXR3A7hz+3dAWDFdwznv37ymXtR9fhKX3v4rJF4q+MY8jsbPdqPsz3difMK/IHa1YyvFBQuAggJh8bVvvgHKyr5/lJf3vg5JS4sw9aew261ZCSFk8D36KCXeI4Cfn19b51XZR7KB7PG+DcCvAHzNGDtnK/sjhIT7PcbY4wBKATxge+8ohK3EiiBsJ/YoAHDOrzLG/gSg/XZ8Euf86gDG7TgeHsCKFcIiay+9JPQWkN6Vlgo/ChMSeq87dy7wt78JPw5priIhQ5ULY6zjJOdXbT3C7Rw5xHsvhPVEjgLYxDnP7UOcXwAIZoxNhJBwPwRhuHrH9qcDSAcQwzmv/HETjuU9fynU/pPwzUtP4du3X4en+mY0F2WiNEmDgOdOwGW0X++N+PgI30WdWSzApUvC39v2v7udH3V1jr8oQggh5AY0YIm3bZG07ror53dRnwNY1U1buwEMz725fvtbICVFSA5fesnZ0QwP7SuVd7V/d2caDZCcDHz+uX31CSHOYHHEQjZA70O8IfSENwFYA2B1h3VFGISvGo/u2uacWxhjT0PY81sMYDfnPI8xlgQgi3N+BMBfALgB+Ket7TLO+T39v7LuuQbfAvVrn6Hi8KsoS3sOInCAF6Bk/WwEPH8KUt9J19ewi4twQ3j8eOC2btaHq6sDLl4UEnN7esgJIWQwBDp/J0dC+mrAF1e74U2YACxZArz6KrBuHa2+bQ+dTtiX9uabe687Z44wHL19aDohZDhy2BBvznm/tsnknB+F0FvesWx9h+eDuqBEXd5XOLf6YUx7eTf8lvwGyujFKH3ld6j56C24tZWi+I+RCFx/GvLAaQMTgKen8LDn7zEhhBBCujVg+3iTDuLjgdpa4M03nR3J8KDTAbffDtizbY6XF6BW0zxvQoa3a0O8GWNSCEO8f7Dndoch3vcMxhDvocJqNsPS2IzPHpyL8zuS4OLpjeCN+zBl639h5oGw1Neg+I+RqPv0YO+NEUIIIcRpKPEeDD/9KXDrrcJc7+E4TK+2FjCbB+dcly4B58/3rfdaowE++WTwYiSEOBTn3AKgfYh3AYD32od4M8bah3F3HOJ9jjF2pJvmRpSa86PxXeEaMK+5OL9tAzIfjkbzt6XwnPkzqN/Sw2N+AtpMFpRvX4LSrU/AaqG/g4QQcqMQi8UzQ0NDw4KDg8NjY2MnNTQ0OCW3S0pKUnU8t0ajmVxVVSUGAIVCMf162vzTn/6kCg4ODp88eXJ4UlKSqr28oqJCPHv27ODAwMCbZ8+eHXzlyhVx/69gcFDiPVji44GiIiAjw9mR9E15ORAUJGzbMBjae677knjPnQs0NQFnzw5MTISQAcc5P8o5D+GcB3HOX7CVrbfNqwbn/A7OuS/n/BbbY0DnVQ8VAQsn4pb10aipjEOD8VHUnPsKutgIfJfxD4gkUox/6mVM3JwJJvVE0+evI+/XQajPPuPssAkhhAwCmUxm1ev1+efPn8+TSCQ8OTl5jL3HWiwWh8WRnp7u29jYeC2v1Gq1RT4+Ptfd2/jFF1/I9+7dO+bs2bMFBQUFecePHx+dm5srA4ANGzaMjY6ObigtLc2Njo5uWL9+vR2rjA4NNMd7sCxeLOyDum0bcO+9zo7GPpwDTz4JXL0K7N4NbN4MKHrZO7a/dDphHvz0Ptwcu/3274+N6mkhZEIIGV5cFBKo42cgeFkYcrdNReFrQXAzvY5zv30IFf/+EOotqXCdMgvBaaUoWX8bTOV5OP+7OfCMfgyBq16CZLSPsy+BEEJGvOb8x8ZbG3Md+iNZ5HazcVTY7ou91xTMmTOnMScnZxQA7Ny5U5mWluZrNpvZjBkzmvbu3Vvq4uIChUIxPS4u7opOp/PYsWNHmVwut65duzbAaDSKpFIp1+l0Bnd3d+uqVavGnTlzxt1kMrEVK1ZUJiQkVGVkZLgnJSXdpFQqzQaDYZRarTYePny4eMuWLarKykqJRqMJ8fLysmRmZhb6+/urs7KyCsaOHfuD7H7dunW+hw4dUppMJrZw4cLabdu2fdfVtXz99dejpk+f3uju7m4FgNtuu63h3XffHb158+aK48ePj9ZqtQYAePLJJ6s1Gs0UdLEuzFBEPd6DxcUFWL1aWLF7uPTMvvUW8OGHwNKlwsq2Bw4M/Dl1OmF1XZc+3BPy9QWmTPl+NXRCCBlh5Eo5Iv80G/d8vhrKX+yCsSUW3x15C/+dE47qzE/h4uqJSX/OgtuMu6DwARo+fhPnHghBxZHXwa1WZ4dPCCFkAJnNZpw4ccJDrVY3nz17Vn7gwAFlVlaWXq/X54tEIr5r1y5vAGhubhZFRUU1GQyGfI1G0xQXFxeUkpJSZjAY8rVarcHNzc2akpLi4+np2Zabm1uQnZ1dsGfPnjF6vV4KAAUFBaNSU1MvFhUV5ZWVlclOnjzplpiYWKlSqcxarbYwMzOzsLsYDx486FFUVCTPyckpKCgoyD937pzi2LFjXa46fcsttzR//vnn7pcvXxY3NDSITp486Xnx4kUpAFRXV7sEBgaaAWD8+PHm6urqYdORPGwCHRGeeALYuFHo9d63z9nR9OzSJeFGwezZwDvvAOfOAX//O/DrXw/cOauqgNxc4OGH+36sRgP84x/CHHrxsJnqQQghPeKcoyrXijFq4e+a23h3zEn7OWqenokvE15Hc96L+Oyh2+Fzx28R+beXMf5/D+HS31cA2jdhlY7CN1uewJV/7cbEhFSMmjDVyVdDCCH9x0RisL500AyCvvRMO1Jra6soNDQ0DACioqIa1qxZU7V161af3NxcRURExFQAaGlpEalUKgsAiMViLF++vAYAcnJy5CqVyqzRaIwAoFQqrQBw6tQpD71erzhy5IgXADQ0NIjz8/PlUqmUq9XqpqCgIDMAhIeHGy9cuCC1N9bjx4976HQ6j7CwsDAAMBqNIr1eL4+NjW3sXHfGjBkta9asuTx//vyQUaNGWcPDw43iLn7fi0QidNg2dMgbWv+vHek8PYHHHwdSU4EXXwT8/Z0dUdc4B556CmhpEYaYi8XCTYNnnwX0eiA0dGDO+/HHwr/Xsy3Y3LnClm1ffw3ccotj4yKEECfJe8uC4ytaMe1xF8x9QQb5aOEHhle4N+44+nuUn7wL2c88gepTKTim/jemrv87Jq54DWI3b1z9MBmqn92Kq18ZkPPr61rbhhBChpyAVS/B/1e/d3YYQ0L7HO+OZZxztnTp0urU1NQfDb+WSqVWl15uWnDOWXJyctmSJUvqO5ZnZGS4y2Qy3v5aLBbDYrHYnfVyzrF27dpLCQkJVfbUj4+Pr4qPj68CgKefftp/3LhxJgDw9va2lJaWSgIDA82lpaUSpVLpuMnqA4wS78G2ejXwyitC8r1li7Oj6do77wBHjgB/+YswhBsAli0Dnn8eeO014K9/HZjz6nSAXA7MmtX3Y9uTdZ2OEm9CyIjR1MrR2AbkvG5B4SEL7tghw5QlLtfu8PsvCMPY7I/x9fq/4tu316Pg+Z/DsHMVZvwlHmMe8sGVd5+Das4dkEz8ObgDF9IhhBBn8Zh+HR00N5CYmJj6xYsXT/7jH/9Y4e/vb6moqBDX1dWJQ0JCTB3rTZs2raWyslKi1WoVGo3GWFNTI3Jzc7MuWLCgLi0tbcxdd93VIJPJeE5OjmzChAk9bpnh6uraVldXJxo7dmy3dWJjY+s3btx408qVK696enpai4uLJVKplPv7+3f55VReXu7i7+9vOX/+vPTDDz8c/cUXX+gB4M4776xNT0/33rJly+X09HTvmJiY2uv4mJyCEu/BNmkScN99wK5dQiLr6ursiH6oogL47W+F7c/i478v9/UVFoXbswd44QVAJnP8ubVa4bzX0/b48cDEiUIbq1c7PjZCCHGCqCekUHiLcPg3LTBXAxm/bMXXb1qw4BUZRk8UlmkRiUSI2Px7TFp2Dz5ffj9av3sZZ5Z+gtG3xSP03q2oP/o7jDI1wnPuckhUkyD1DYLEezyYi8TJV0cIIcTRZs6c2ZKYmFg+f/78EKvVColEwnfs2FHWOfGWy+V8//79F1avXh3Q0tIiksvlVp1OVxgfH19VUlIiU6vVUznnTKlUmo8ePXqhp3MuW7asKiYmJsTX19fU3TzvxYsX1+fl5clnzZoVCgAKhcK6f//+4u4S73vuuSeotrbWxcXFhaekpJS1r5K+adOmS4sWLQoKDAz08ff3Nx06dKjH2IYSxjnvvdYw4+rqypuamvrVhiPmC3T72Z45A8yZA+zcCfzmN/0+j8NwDtx/v7Cg2ldfAVM7zQc8fhyIjRXmUj/wgGPPXVcHKJVAYiKwadP1tbF8uRB7ZSUwjOZ7EDLSMcaMnPMhdpdx8ERGRvKsrKx+tdFcx/Hhc63I+bsF7i6AWALM2SjFzN9KIJZ8//fOajJB/9IfUbI7GW3cD/WNjyL4Pgk8LNvAWxu+b1AkhsQnEFLVJEh8g2z/ToJUFQSJ7ySIFZ79ipcQQoYqxtiXnPPI/raTnZ1dEhERYdewaXJjyc7O9omIiJjQuZwSK+cqAAAgAElEQVQS724MaOLNubDtVW2tMGdaNEQWl3/vPeDBB4X5513t293WJvTYh4QAJ0869tzHjgG/+AXwn/8A8+ZdXxu7dwtz6PPzf3zTgBDiNJR4X3/i3VTXiozt2bgn/haMcpei+JM2/POxFpi+4ZCLAO+pDDHpcoz9yQ8Xnan6+BTOxf8KpqvVaGq5Dy0mDbynAl6TW+Du2wC5exVcRBXgjWUwV36DtoYf/nYUu3n/IBGXqoIgGTMBTDIAo50IIaQPJD6BkHiPu+7jKfEmA627xJuGmjsDY8Azzwirdx89Ctx1l7MjAq5cAVatEuZX/+//dl1HLBYS2w0bgG++EZJwR9FqhS3Ebr31+tvQaL5vixJvQsgIcPZYKd7d8DmOpX6NhzZFYcETYXgmR4GP/mKG9k8mWPUc+zXNuGWlC27/kwwyD+Gmsc+cOzD3xNfIefZxVJ76J7wCv0GbORCVHzejvOH7qXoi6WjIlbMh9xFDobRA5tYCibwZQAPaKirRcvE8YKoHQFuSEUKGBtfbViLg6XRnh0Ec4PLly+Lo6OgpnctPnz5t8PPza3NGTAOJery7MaA93gBgNguJa3Aw8N//9vtc/fbgg8Dhw8CXXwI339x9vW+/BQIDgT/8QZjr7SizZwv/fvLJ9bfBOTBunJCAv/22Y+IihPQb9Xj3b6h5UVYF3vjfM8jXfYdxU73w65dnI3LhBFwp5HjviWZUf8bhKgZGqRgW7JAh+F7xte8wzjnK3k7H+a3r0NZstJUJ/8OtALdywMqF76uOX1nM9j0oYmAiQCLnEMsAEc3iIYQ42bhf/g5Bqzdf9/HU400GGvV4DzUSibAI2O9/L+yR7cyVuN9/Xxhmvnlzz0k3ICS2sbHAG28Ic7EdsY+i0Qh88QXwu9/1rx3GhNXNdTrhlyXN8yaEjACTI32x+fQifPGvEuxJOIMtd38I9bxxWP7X2/A/Wh98vtuCY79rhbmS48hDLZgUK8Yd22XwCBD2Nw2MewqBcU/1eA7OOVquNKO24CpqC66iJr/62nNLE62GTggZOrx5P0ZHEuJE1OPdjQHv8QaEOd7jxgFLlgirhTtDVRUQHi7E8dlnwg2B3nzwgbAy++HDwkrn/fWf/wB33CEMu4+N7V9bu3YJC9YVFQFBQf2PjRDSb9Tj3f/F1dpZzG3496t5+MfGz9FQ3QLNr6YgbvOtkEpccXhNK4oOtsHdBXCRAbcnSTHjfyQQuVz/9xm3cjRebEBt/lXUna8BN9OQ8/9v797joqr2/oF/vtzlpqIiiCYpIkKKgpc0k8oySLOOaT9M00qxnvLxdrSTZVZ6up2yDFMzzd9zvPzyHM3KY6lpx+ToUyaWIiooKnkDFZSL3IdZvz/2xkYaYJQZZsDP+/XaL/asvffaa5bbWfOdtfbaRGRfAXcHoU3fgJs+nj3eZGvs8XZELVoAzzyjBYvvvAPU8uw7m5k6FbhyRZsszZKgGwCGDtXKumKFdQLvpCRtgrm77qp/XqbP82bgTURNjIurMx56oQdixnbFxnf2418fHsT//jMDD8/oiZHLo5E5zhUbnytF2QXghxfLcXiNAQ9+4o6AKOe6MzdDnAQ+HX3h09EXHeKCrftmiIiIbiEOMp32LWzqVMBgABYvbvhzf/21di/0nDlAjx6WH+fiAjz9tNZDffZs/cuRlAT06gX4+tY/r27dgNattTyJiJoor+buePLtAfg4fQzufKwzvnhrP57vshqnz6RhxqFmuOMFF1ypBC6kGrHmrhJ8P70MJ7cacPm4EZUVTW+kGxERkaNj4G1vnTsDw4drvd7FxQ133suXgeeeAyIjgdmzb/z4CRMAo1G717s+ysq0Ie5VPdX1JQLcfbc2szkRURPn39EX09cMwXv7RiEorCWW/dcPeLH/OgQNzsKkPR5wChEUGYBfl1Zg46OlWNm9GAtbFGF5tyJsGFaCHdPKsH9ROU58a8DlY0ZUljMoJyJqLJydnaPDwsLCu3TpEhEXF9epsLDQLrHdvHnz/E3PHRMTE5KTk+MMAJ6enr1uJs/58+f7d+nSJSIkJCRi3rx5/lXpP/74Y7OePXuGhYaGht93330hly9fbjTxbKMpaJM2YwaQmwusXt1w55w2Tbu/+3/+x/Ih5qY6ddLuy/7sMy0Av1n79gGlpdYLvAFtVvNTp4AzZ6yXJxGRA6uagG3210OhjApvPfwNVrz0LwxfUYS733LDFXcgxwDkGYCCcuDCSYWTSZU4sLwCO2eV48sRpVjZQwvKP+1ahPVDS7B9SimSE8tx4hsDctOMMJQxKCciciTu7u7GtLS0I8ePHz/s6uqqFixY0MbSYw0G602cuWzZsrZXr169Flfu2rUro3Xr1jf9OLB9+/Z5rFq1qs0vv/xy9OjRo4e3bt3aIjU11R0AEhISgt98882zx44dOzJ8+PArb7zxxs3f8N/AeI+3I7j7biAqCli4EEhI0O53tqXNm7Ugf+7c+s2mPnEiEB+v3R/+4IM3l0fVkPC77775clRXFcT/5z/AE09YL18iIgcmIug7/HZExd12bQK2v/T7J+55siumpdwJV2cv5J5QyDlhRO5JI3IyFHJOVOLKCYWKAu0LgbMAxacUrpyrhPMPAJrcU1SJqLHrN9sVd7/mbu9iVPNMByDV07p53lEMrLS4F2ngwIFXU1JSmgHAkiVL/JYuXdq2oqJCoqKiilatWvWbi4sLPD09e40ZM+ZSUlKSb2Ji4mkPDw/jtGnTbisuLnZyc3NTSUlJ6T4+PsYXXnih/Z49e3zKy8slISHh4qxZs3I2b97sM2/evHZ+fn4V6enpzbp371781VdfnXrrrbf8L1686BoTExPasmVLw969e48FBQV1T05OPhoYGHhddP/qq6+2/fLLL/3Ky8tl6NCheR9++OF5c+/l0KFDzXr16nXVx8fHCAB33XVX4bp161r89a9/vfDbb7+5x8XFXQWAYcOGFTz44IOhH330kdl8HA0Db0cgovV6jx0LbNtW/5m9a5OXBzz7LNC9O/DKK/XL69FHgVatgOXLbz7w3rVLe4RZq1b1K4upHj2A5s21vBl4E9EtxtwEbP/5/DgCOvvCP9gXbTr6wD/YB7c/4ou+wT7wD/aFm2szXD5ZFZQr5GRowXnucSNKLgLOAOoxOToRkdVcucTRN9VVVFRg27ZtvkOGDCn45ZdfPDZs2OCXnJyc5u7ursaOHXvbJ5980mry5Mm5JSUlTv369Stavnz52dLSUgkJCblj7dq1J2JiYoovX77s5O3tbVy4cGHr5s2bV6amph4tKSmRPn36hD388MMFAHD06NFmBw4cOBkcHFwRHR0dtn37du85c+ZcXLp0adtdu3Ydqx5om9q4caNvRkaGR0pKylGlFO6///6QLVu2eFcF0aZ69uxZMm/evKDs7GxnLy8vtX379uaRkZFFABASElK6du3aFk8++WTemjVr/LKzs91sV7PWxcDbUYwapT3T+4MPbBt4z5gBXLgAbNoEuNXzOnV3B8aPBxITtTzbtr2x4w0GYM8eLQ9rcnYGBg7kBGtE1OhdvnwZ8+fPx9y5c9GyZcsbOrZqArYHn7sD3y07jKyMfFzMLMCJXy6h4FLJdfu6ujtfC8j9g33hH+qDyCG+aBPsgxb+Pqi82gz5ZxQq+UhvIrKzgAhHvFPW8p5payorK3MKCwsLB4B+/foVTp06NeeDDz5onZqa6hkZGdkNAEpLS538/f0NAODs7IynnnrqCgCkpKR4+Pv7V8TExBQDgJ+fnxEAduzY4ZuWlua5adOmlgBQWFjofOTIEQ83NzfVvXv3os6dO1cAQERERPGJEycsDia2bt3qm5SU5BseHh4OAMXFxU5paWke5gLvqKio0qlTp2YPHjw4tFmzZsaIiIhiZ2ft6RwrV67MnDx5cod33nknMDY2Ns/V1bXR/BLDwNtRuLkB//3f2kRnhw5pPdLWtmWLNhnayy8D0dHWyXPiRO3Hgr//Xfvh4Eb8+itQVKTdk21tgwYB33wDXLwI+PvXvT8RkQPauXMnFi1ahM8//xwfffQRHn/8cYjcWNezf0dfjH2r/3VppUUVuPRbIS5mFuBi5vV/T/560mxg3qq9N1zdb+6xZERE1jJ8Rk/c3yXc3sVwCFX3eJumKaVk1KhRuYsXLz5XfX83Nzeji0vt4Z9SShYsWHD6scceKzBN37x5s4+7u/u1INfZ2RkGg8HiBkkphWnTpmXNmjXLomefT58+PWf69Ok5ADB58uSg9u3blwNAr169Svfs2XMcAFJSUty/++67FpaWwd4YeDuSSZOA+fOBDz8EVq60bt75+Vr+4eHavd3W0q2b1ru8YgUwa5Y2bN5Stri/u4rp87xHjrR+/kREDeCxxx5DcnIyEhISEB8fj1WrVmHJkiXo2LFjvfL18HJFh3A/dAj3M7u9tKgCl04X4mJmIS7pQfml04WoNNRjMk0iIivwae1h7yI4tNjY2IIRI0aEvPzyyxeCgoIMFy5ccM7Pz3cODQ0tN92vR48epRcvXnTdtWuXZ0xMTPGVK1ecvL29jQ888ED+0qVL2wwbNqzQ3d1dpaSkuAcHB1fUdk4vL6/K/Px8p8DAwBr3iYuLK3j99dfbTZo06XLz5s2Np06dcnVzc1NBQUFmx1KdO3fOJSgoyHD8+HG3b775psW+ffvSTNMrKyvx2muvBU6YMOHiTVSTXTDwdiR+fsBTT2lB7Ntv3/jQ7drMnAmcPw9s3KgNEbemhARtuHhS0o31Xu/aBXTpAtTyn/SmRUcDnp4MvImo0evZsyd++uknfPzxx3jllVcQHh6O+fPnY8qUKair5+JmeXi5okM3P3ToZj4wJyIixxQdHV06Z86cc4MHDw41Go1wdXVViYmJp6sH3h4eHmrt2rUnpkyZcltpaamTh4eHMSkp6dj06dNzMjMz3bt3795NKSV+fn4V33777Ynazjl+/Pic2NjY0LZt25bv3bv3mLl9RowYUXD48GGPPn36hAGAp6ence3atadqCryHDx/eOS8vz8XFxUUtXLjwdNUs6StXrvT77LPP/AHgoYceujJlypTcm6knexClGs2weIt5eXmpoqKieuVxo0P5zLmpuj12DOjaVeuVfuONepcBgDbr+JAhwF/+ArzzjnXyNFVcDLRrBwwbBqxZY9kxRqM2odrIkdrkbLbwwAPApUvAgQO2yZ+ILCIixUopL3uXw1569+6tkpOTrZLX6dOn8cILL2Dz5s2IiorC8uXLERUVZZW8iYhuBSKyXynVu775HDx4MDMyMtKiYdN0azl48GDryMjI4Orpjjg7wa0tNBR4+GFg6VKgpKTu/etSWKjdhx0WBrz+ev3zM8fTU5uRfcMG4PJly45JTdVmWLfm87urGzQISEkBrlyx3TmIiBrQbbfdhk2bNmH9+vU4f/48+vTpg5kzZ6K+PzYTERGRbTHwdkTTp2s9tWvX3vixRqN2bEoK8N132n3dZ89qk6p52PCemIQEoKzM8h7vXbu0v7YMvGNiAKWA3bttdw4iogYmIhg5ciSOHj2KhIQELFiwABEREdiyZYu9i0ZERGSx7Oxs57CwsPDqS3Z2dpOcyZNDzWtgt6Hm2oFAVBRQXq71DItovd9ZWUB2traYW8/K0h7rVVl5fX6zZwNvvVXv91OnPn2A0lIt6K+r/kaNAvbtAzIzbVee0lLted5TpgDvvWe78xBRrTjU3HpDzc3ZvXs3Jk2ahKNHjyI+Ph4LFy5EW2vOEUJE1IRwqDnZWk1DzTm5miMS0Xq9x48HQkKAnBygoOCP+zk5aY/KCgwEAgKAHj1+Xw8I0NbbtQM6dWqYcickAM8+C+zdC9x5Z837KaVNevbgg7Ytj4cH0K8fn+dNRE3awIED8euvv+Ldd9/Fm2++ia1bt+L999/HM888Y5UfkYmIiKj+GHg7qvh4YMcObfi2aSBtut66NeDsQCMxRo8GZszQJkurLfBOT9eer23LYeZVYmK0GeILCwEfH9ufj4jIDtzd3TF37lw8/vjjePbZZzFx4kSsXr0ay5YtQ9euXe1dPCIiolse7/F2VG5uwKpVwD/+AXz0kTZc/KmngNhYoGdP7VFjjhR0A1pgGx8PrFtnvoe+SlUP9I08euxmDRqkDb3/8Ufbn4uIyM7CwsKwc+dOrFixAgcPHkSPHj0wb948lJWV2btoREREtzT2eJN1JSQAn32mBd+TJpnfJylJ67UPCbF9efr3136gSErSHqlGRNTEOTk5YcKECRg6dCimT5+O1157DWvWrMGAAQPQrl07BAYGol27dtfWAwMD4e7ubu9iExERNWkMvMm6+vYFunfXhpubC7yV0mY0HzSo7gnYrMHbG4iO/n0WdSKiW0RAQAA+//xzjBs3Dm+++Sa+//57ZGdnw2Aw/GFfPz8/s0G56d+AgAC4urra4Z0QEf1ORODkxEG71rB69eoW4eHhpdHR0aUAMG3atHb33HNP4aOPPlpY0zGPPfZY8LBhw/KffvrpOp/Xu3nzZp8FCxa03blzZ4Y1y20pT0/PXsXFxb/a49zmMPAm6xLRer2nTAEOHNCGxZvKzNQeb9YQw8yrxMRow/VLSoBmzRruvEREDiAuLg5xcXEAAKPRiJycHJw/fx5ZWVlm/6alpSErK8tsgE5EZG/vvvsuXnzxRXsX43oi0TbLW6n9tsi2oqICX331VQuDwZBfFXgvXLjwvC3ORRoG3mR9Y8cCL76o9XovXnz9tqr7uxtiYrUqgwZpjxP7+eeGDfiJiByMk5MT/P394e/vj57Vfxg1URWgmwbl2dnZqKz+uEoiogY2cOBAexfBYaSnp7vFxsZ26d69e3FqaqpnaGhoyfr16zPfeOONtlu3bm1RVlbm1Lt376tr1679zcnJCX379u16xx13FP/888/eQ4cOvbJjx44WP/30k8+7774b+MUXX5yYO3duYFVv9syZMwPN5VGXDRs2+M6aNatDs2bNjH379r1alV5QUOA0YcKE29LS0poZDAZ55ZVXzo8dOzYvMTGx1ddff92isLDQ5cKFC64jR47MXbBgQRYALFmyxG/p0qVtKyoqJCoqqmjVqlW/ubi4wNPTs9eECRMufvfdd809PDyMmzdvzujQoYMhLS3NLT4+vlNxcbFTbGxsnmm5Xn311bZffvmlX3l5uQwdOjTvww8/PJ+enu4WFxfXpW/fvleTk5O927ZtW75t27YMb29vlZqa6j5p0qSOubm5Ls7Ozmr9+vUnIyIiyszlY+m/FwNvsr6WLYGRI4G1a7WA19Pz9227dgF+fkB4eMOVZ+BArSd+1y4G3kREFjAN0CMjI+1dHCIiqkFmZqbHsmXLMocMGVI0atSo4Pfee6/NrFmzLr7//vtZAPDoo4/evm7duuZPPPFEPgCUl5dLamrqUQDIyMjwqGnYeG151KS4uFgmT54cvH379vSIiIiyYcOGXXum8csvvxx47733Fqxfvz4zJyfHuXfv3t2GDx9eAAApKSlehw4dOuzt7W3s1atX+COPPJLv7e1t3LBhg19ycnKau7u7Gjt27G2ffPJJq8mTJ+eWlJQ49e/f/+qiRYvOPffcc+0XLVrU5m9/+1vW888/f9vEiRMvTZ48Offtt99uU3XujRs3+mZkZHikpKQcVUrh/vvvD9myZYt3p06dyk+fPu2xZs2akwMGDPjtoYce6rRq1aqWzz///OUnnnji9pkzZ2aPGzcur7i4WCorK6WmfOLi4q6aq4/qGHiTbUycCKxZA6xfrz2PvEpSktYD3ZD35rRoAURG8nneRERERGR9NhoObomAgIDyIUOGFAHAk08+mZuYmOjfqVOnsg8++CCgtLTUKS8vzyU8PLwEQD4AjB49+rIl+W7ZssWnpjxqcuDAAY/27duXde/evQwAxowZk7tixYo2APDDDz/4btu2rUViYmIAAJSVlUlGRoYbAAwcOLAgICCgEgCGDh165YcffvB2cXFRqampnpGRkd0AoLS01Mnf398AAK6urio+Pj4fAKKjo4t27NjhCwC//PKL95YtW04AwLPPPps7f/789gCwdetW36SkJN/wcK3nr7i42CktLc2jU6dO5UFBQWUDBgwoAYBevXoVZ2Zmul+5csXpwoULbuPGjcsDAE9PTwVA1ZQPA2+yr0GDgNBQbbh5VeB97hxw4gTwwgv2Kc/y5UB5ufaoNiIiIiKiRk6qTVYsIvjzn//cce/evUdCQkIqZsyY0a60tPRaj5ePj4+xrjyLi4ultjxuhlIKGzZsyIiMjLzu+Za7d+/2MvcelFIyatSo3MWLF5+rnpeLi4uqGvbu4uICg8FwLQMnJydl7tzTpk3LmjVrVo5penp6upubm9u1/Z2dnVVJSUmN77OmfCzFKQHJNkS0Xu89e4AjR7Q0e9zfXWXQIG1ytf12+0GSiIiIiMiqsrKy3Hbs2OEFAGvXrvUbMGDAVQAICAgw5OfnO/3rX/9qWdOx3t7elQUFBX+IB4uLi50szcNUz549S8+dO+d2+PBhdwBYt26dX9W2e++9t2DBggVtjUYt7t+zZ8+1GY93797te+HCBeerV6/Kt99+2yImJuZqbGxswebNm1ueO3fOBQAuXLjgfOzYsVp7z6Kioq4uX77cDwCWL1/eqio9Li6uYPXq1a3z8/OdAODUqVOuVfma07JlS2NAQED56tWrWwBASUmJFBYWOt1oPtUx8CbbGT8ecHUFVqzQXiclAT4+f5zpvCFUBfscbk5EtRCRWBFJF5EMEXnJzHZ3EfmHvn2viAQ3fCmJiIg0wcHBpYsWLfLv1KlTRF5ensvMmTMvjRkz5lK3bt0i7r333tDIyMiimo4dM2bM5cTExIBu3bqFVwXLANC6detKS/Mw5enpqRYtWvTbsGHDQsLDw7u1bt362uMx3nnnnfMGg0HCwsLCQ0JCIubMmRNUta1Hjx5Fw4cP7xwRERHx8MMPXxk0aFBxdHR06Zw5c84NHjw4NDQ0NPy+++4LPXPmTK3PtFyyZMnpTz/91D80NDT83Llz1/YdMWJEwahRoy736dMnLDQ0NPxPf/pT57y8POfa8lqzZs2pxYsX+4eGhob37t077MyZMy43k48pUeoPvfGNnpeXlyoqsuj6qFH1IQ83oynW7Q0bNQrYuVMbZh4VBXTsCHz7rX3KEh4O3H478M039jk/0S1KRIqVUl72LkddRMQZwDEADwA4C2AfgNFKqSMm+zwPoIdS6jkRiQfwJ6XU/6kt3969e6vk5GQblpyIiCwlIvuVUr3rm8/BgwczIyMjb2rIsbWkp6e7DRs2rMvx48cP27Mc9ZGYmNgqOTnZa9WqVaftXRZrOXjwYOvIyMjg6uns8SbbSkgAcnO1Xu8jR+wzzLzKoEHA7t0AH4dDROb1BZChlDqplCoHsA7AI9X2eQTA3/X1DQAGizV+qSUiIqImrdEE3nUN/3NEIlLvpdG7/34gOBiYPVt7bc/HecXEAAUFwMGD9isDEZnlIEO8gwCcMXl9Vk8zu49SygBthtdW1faBiEwSkWQRSb506ZINikpERLe6rl27lturt/uBBx7oHBYWFm66fPHFF743ms+UKVNym1Jvd20axazm+vC/xTAZ/icim0yH/5GDcnICJkwAXn0VaNYMiI62X1nuvlv7m5SkDXsnIodg4Wf8BABXlFIh+hDvdwHUOsTbnpRSnwL4FNCGmtu5OEREZH1Go9Eo5mbRvhVs3779hL3L4IiMRqMAMDtzfGPp8bZk+B/VwO49708/rQXg/fvb91Fe7dsDnToBu3bZrwxEZI6jDPE+B6CDyev2eprZfUTEBUBzALlWLgcRETm+1EuXLjXXAy0iGI1GuXTpUnMAqea2N4oeb5gf/tfPdAcRmQRgkv5SiUhJPc/pAsBQ51425ijDzWsph+X19O9/a48Zs7eTJ+1RDoe4nhoJ1pVlGlM9NRMR09nFPtV7hKvU+RmPakO8RaRqiLc1J7bZB6CLiNwOLcCOB/BEtX02ARgP4EcAIwH8W9Uxk+b+/ftzROS3epSrNaz7Ppsy1pVlWE+WY11ZpjHVU0drZGIwGCZmZ2evyM7OvgONpzOTbMsIINVgMEw0t7GxBN51Mh3WZw0ikmyNGQ+bOtaTZVhPlmNdWYb1ZH16QD8ZwDYAzgBWKqUOi8g8AMlKqU0APgOwWkQyAFyGFpzXlW+b+pSL/9aWY11ZhvVkOdaVZW7FeoqOjr4IYLi9y0GNR2MJvC0Z/kdERI3TjQzxPmvLId5KqW8BfFstba7JeimAUdY+LxERETVtjWVYxLXhfyLiBq2HYZOdy0RERNZhyWd81RBvwMIh3kRERESOolH0eNc0/M/Gp7XasPUmjvVkGdaT5VhXlmky9WSrId5NSJP5t24ArCvLsJ4sx7qyDOuJqA7CDgMiIiIiIiIi22ksQ82JiIiIiIiIGiUG3kREREREREQ2xMC7GhGJFZF0EckQkZfsXR5bEZEOIrJTRI6IyGERmaqn+4nIdhE5rv9tqaeLiCTq9ZIiIlEmeY3X9z8uIuNN0qNF5JB+TKLoDwOv6RyOTEScReRXEdmsv75dRPbq7+0f+oRQEBF3/XWGvj3YJI/Zenq6iDxokm72mqvpHI5MRFqIyAYRSRORoyLSn9fUH4nIdP3/XaqIfC4iHrymyBy2Sfz8MIdtkmXYJlmGbRJRA1FKcdEXaJP6nADQCYAbgIMAwu1dLhu910AAUfq6D4BjAMIB/A3AS3r6SwDe1dcfArAFgAC4E8BePd0PwEn9b0t9vaW+7Wd9X9GPjdPTzZ7DkRcAMwD8PwCb9df/BBCvr38C4L/09ecBfKKvxwP4h74erl9P7gBu168z59quuZrO4cgLgL8DmKivuwFowWvqD3UUBOAUgGYm/85P8ZriYuZaYZvEz4+a6ottkmX1xDap7jpim8SFSwMtdi+AIy0A+gPYZvJ6NoDZ9i5XA733rwE8AEeSc/gAAAZ6SURBVCAdQKCeFgggXV9fBmC0yf7p+vbRAJaZpC/T0wIBpJmkX9uvpnM46gLtmcLfA7gPwGa9gc0B4FL9uoE2K3N/fd1F30+qX0tV+9V0zdV2DkddoD1X+RT0SRurXyu8pq6VOwjAGWhf4lz0a+pBXlNczFwrbJP4+WGubtgmWVZPbJMsqye2SVy4NNDCoebXq/rwqXJWT2vS9GFCvQDsBdBWKZWlb8oG0FZfr6luaks/ayYdtZzDUS0E8CIAo/66FYA8pZRBf2363q7Vh749X9//RuuvtnM4qtsBXALwf/UhkCtExAu8pq6jlDoH4H0ApwFkQbtG9oPXFP0R2yR+fpjDNskybJMswDaJqOEw8L7FiYg3gC8ATFNKFZhuU0opAMqW52+Ic9SHiAwDcFEptd/eZWkEXABEAViqlOoFoAjaELtreE0B+r1+j0D7UtgOgBeAWLsWishBsE2qHdukG8I2yQJsk4gaDgPv650D0MHkdXs9rUkSEVdoX3DWKqU26skXRCRQ3x4I4KKeXlPd1Jbe3kx6bedwRHcBGC4imQDWQRva9xGAFiLiou9j+t6u1Ye+vTmAXNx4/eXWcg5HdRbAWaXUXv31BmhfenhNXe9+AKeUUpeUUhUANkK7znhNUXVsk/j5UR3bJMuxTbIM2ySiBsLA+3r7AHTRZ1l0gzZpxCY7l8km9Jk3PwNwVCn1gcmmTQDG6+vjod1nV5U+Tp/1804A+fowqm0AhohIS/1X0yHQ7tHJAlAgInfq5xpXLS9z53A4SqnZSqn2SqlgaNfDv5VSYwDsBDBS3616PVW9t5H6/kpPj9dnA70dQBdok7KYveb0Y2o6h0NSSmUDOCMiXfWkwQCOgNdUdacB3Ckinvr7qKonXlNUHdskfn5ch22S5dgmWYxtElFDsfdN5o62QJvV8hi0GRhfsXd5bPg+B0Ib+pQC4IC+PATtnpvvARwHsAOAn76/AFis18shAL1N8noGQIa+PG2S3htAqn7Mx9AnOKnpHI6+ALgHv88g2wlag5IBYD0Adz3dQ3+doW/vZHL8K3pdpEOf+bS2a66mczjyAqAngGT9uvoK2gywvKb+WE9vAEjT38tqaLPA8priYu5aYZvEz4+a6uwesE2qq47YJllWT2yTuHBpgKXqA4KIiIiIiIiIbIBDzYmIiIiIiIhsiIE3ERERERERkQ0x8CYiIiIiIiKyIQbeRERERERERDbEwJuIiIiIiIjIhhh4ExEREREREdkQA2+imyQilSJyQERSRWS9iHiabHtURJSIhOmvPUQkTUS6m+wzS0SW1XGOaSJSKiLNTdLu0fOeaJLWU0+bKSKL9XIdEZESff2AiIwUkVEiclhEjCLS27o1QkRE9sI2iYjIsTHwJrp5JUqpnkqpOwCUA3jOZNtoALv1v1BKlQKYBmCJaIL0/V+q4xyjAewDMKJaeiqAx6vtd1A/1wtKqZ4AHgJwQi9jT6XUBv24EQCSbvjdEhGRI2ObRETkwBh4E1nHfwCEAICIeAMYCGACgPiqHZRSWwFkARgH4EMAryulrtSUoYh0BuANYA70L0smfgPgISJtRUQAxALYUlchlVJHlVLpN/C+iIio8WGbRETkYBh4E9WTiLgAiANwSE96BMBWpdQxALkiEm2y+zQAbwJoo5RaXUfW8QDWQfsC1VVE2lbbvgHAKAADAPwCoKxeb4SIiBo9tklERI6JgTfRzWsmIgcAJAM4DeAzPX00tC8n0P9e6xlQSp0H8G8ASy3IfzSAdUopI4AvoH2hMfVPPW00gM9v8j0QEVHTwDaJiMiBudi7AESNWIl+39o1IuIH4D4A3UVEAXAGoERkllJK6bsZ9aVG+oQ3XQBs10btwQ3AKQAfV+2jlMoWkQoADwCYCq2XgYiIbk1sk4iIHBh7vImsaySA1UqpjkqpYKVUB2hfTu6+wXxGQ7vfLlhf2gFoJyIdq+03F8BflFKV9S86ERE1MWyTiIgcBANvIusaDeDLamlf4I8T0dQl3kw+X8JkYhwAUEr9r1LqK0szFZE/ichZAP0BfCMi226wXERE1HiwTSIichDy+0gjIiIiIiIiIrI29ngTERERERER2RAnVyOyI33CmuqPcClTSvWzR3mIiOjWxTaJiMh2ONSciIiIiIiIyIY41JyIiIiIiIjIhhh4ExEREREREdkQA28iIiIiIiIiG2LgTURERERERGRD/x92JnDkwmMySQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa578f36f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5783bc390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAEzCAYAAADHKNteAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xt4FOXZBvD7yZmEYwjhkECCnEIggATEIjZUPCRasIJYVAQ8YhUV/OQrtaCILW1VBFEMqLUVRNQiIKWART9JFJUaTmmAgEHOAoEQSEgIyZLn++OdDZMl52SzEO7fdeVid+admWeW3Zl55j2MqCqIiIiIiIiIyD28PB0AERERERERUUPGxJuIiIiIiIjIjZh4ExEREREREbkRE28iIiIiIiIiN2LiTURERERERORGTLyJiIiIiIiI3IiJdw2JyHYRGezpOC4FIvKsiLxTwfxxIvJ1fcZERIaIxItIhqfjIKoLlZ1vXMr+XUT+4O6Y6oOI7BORGz0dR3VVdq0kIutF5KF6DImIyGOYeJehrBOca/Koqj1UdX0l64kUERURHzeFeklQ1Zmq+hBQN/tsff6FIhLiMn2Lte7I2kVc7XgGi0ixiJyx/g6JyMci0t+lnIpInq3cGRH5X2vedBEpKmuebfn3rTKty5g+3WVaZxFR2/tDrhc3IvKgbVtnXfbhRCX7fFREDotIgG3aBBFZa70OKGd/n7TmzxORlS7rnC8iS+sgrrMi0sxl+k4rnja2aT8XkSRrvadEZLmIdLXNj7eWedVlXSkiMkpEXrDFVSAiDtv7TbbPINxl+T9XciOqkYi8LCIHrX3ZJSITRUQq2neiy4F1/D5r/U6OWclv4xqua7CIHLJPs59vahnnOBE5b/tN7xWRv9mPEVR79msl6zz4fk3XVdb3QURuEZFkEckVkePWMX+YNc/1/9j5166K27Of4w6LyKsi4u1SZr2IZIuIv23aQ9Y5yT6tpYhkikh8Fbbb0TovJpYTU6bYrrFExNeaptb77bZ9PW+dv5zvn3VZ37vWOjtX5TMhotph4n0Zk4ad0O8FcLfzjYjEAAj0XDj4SVUbA2gC4FoA6QC+EpEhLuV6q2pj299LtnkflTdPRJoAuANADoB76iJgVf2rc1vWun+0bTuksuUBBAB4rJIy3Vz2aa41fQqA3iJyN2AumACMBPB4HcR1AMBdzjcicg1cjmXW9lYD+BBAGwCdAGQA+EZEOtiK5gB4SETCXDeiqs/b4pwIYL0tztgqxHkRK7leAeA6ADfDfJ8eBPAUgL/UZJ2VbK8hHyPo0jXU+t30BdAPwNTqrqCevrvfWnE2A3AjgLMANolIz3rYNtWSiNwJ4B8AFgIIB9AawHMAhtqKfetyjmqsqj9VYzO9re/IEJhz88O27UcCuB6AAhjmnK6q7wA4bMXiNAfAalVdW4VtjgGQDeDX9uTdJhtAgu19gjXNuf0etnPXVwAm2PZ9pi3+QTDnRiKqJ0y8a0hsteIico1VS5Zj3eF31qAlW/+esu40/kxEvERkqojst+5QLhRb7Z2IjLHmZYnINJftTBdTY/i+iOQAGGdt+1sxNXpHROQNEfGzrU9F5DER+cG6I/yiiHQSkW+seD+2l3fZx/0iEmu9vtdaVw/r/YMissIWl/Mu9kX7bFvfK9ad4b0iYj9plGURzMnHaSzMydUen7+1zgPW5z5fRBpZ81qIyCoxd8CzrdfhtmXXW5/FButz+be41LCXRY1DqvocgHdQd8nSSACZAGZa+3opeAnAFKlBbZWq5gIYD+A1K9F9B8DTqnqsDuJy/W6Mgct3A8DLAN5S1URVPaOqWao6GcB/UToJOA7gI9QgMaihW2Eu1Iar6k5Vdajq1wDGAXhaRDqIyFhx6ZohIr8TkY+t141EZI6YGvOjIvK68+JMrGbl1rHjGICyakyes36DuSKSJiK3WdMDrWNCF1vZcBHJF5Hm7vpAqOFS1cMA1gDoCQAicr+YmsBcEflRRMY7y4pVmykivxWRowCWWMu2E1tNpcv5BiLyD+t3cFpMzWePGsR5XlX3qOpjAJIATLet/1rrfHlKRLaJrWWRdR75k4j8x/rtfCoiwdVYttxzkIjcJxeuBX5vj1fMdcQUEdljzf/YuV250OpsrHVuPGFfXkS8xTTX32Ntd5OItLfmRYnIOhE5KaYlzl0og4j8QkT+a3u/TkS+t73/SkR+Zb3eJyI3iqnpfRYmmTwjIttsq4yo7rlYRATAqwBeVNV3VPW0qharapKqPlzZ8tWlqukwSaz9pswYAN8B+DsuPm8/BOAxEekjIrfAJO6TKtuOtV9jYM5JRSh9E8GpKufAyrbjA+B1AE9UZzkiqh0m3nXjNQCvqWpTmLuHH1vTf27929y60/gtzAX2OAC/AHAVgMYA3gAAEYkG8CaAewG0hbkL71oTdzuApQCaA1gM4DzMwTwEwM9gDu6utZS3AIiFqan9XwBvARgNoD3MSeRulC0JwGDrdRyAH237FGfNd1XWPgPAAAC7rDhfAvBX6wRTnu8ANBWR7mKado0C4NpE7c8AugLoA6AzzGflvMPsBeBvACIAdICpyXjDZfl7ANwPIBSAH4BnKoinLMsA9BWRoGouV5axMBeaSwDEiEjvOlhnbW0AkAJT21tt1p39NQA2A8hQ1ffqKK4kAOFimuP5AhgB4APnTBFpAVPL9o8ylv0YwE0u02YAGC0iHesovorcBOArVT1qn6iqSQCyYI4Ly2G+V/aa+XtwYR9fhandiQHQDeY3MMVWNhKAL8zv+8kyYtgFYCDM8eUvAD4UkRBVzYc5toy2lb0XwL9U9VS195SueFZCdyuALdakTAC/BNAU5tg7W0T62hZpAyAY5rg9BqYm76dKairXAOgCcxzfDHNerI1lMDfHIKYlzL8A/MGK6xkAn4hIK1v5MQAegDlnOwDMrcayZZ6DrGuBRAD3AWgHoCXMb97pCQC/gjkPt4Op7Zznsh+DYI4PQwA8JyLdrelPw5zzb4X5f3gAQL51HlsHc5wJhTnnvmnF4uo7AF1EJMQ6BveCuUHSRMzN734wSWoJ63wwExdaftnPcTU5F3eDOcYtrULZMonImyLyZhXLRsN8L7bYJo+B+b4tBnCL2LqJqeo+mOuRdwHMB/CYqmajcoNg/q8/hDlflXUjfgWAn4tIc+t8dz2AT6uyHzaTACSramo1lyOiWmDiXb4V1l3qUyJyCiYhLk8RgM7WxesZVf2ugrL3AnhVVX9U1TMAfgdglHX38U4A/1TVr1W1EOagrS7Lf6uqK6w7u2dVdZOqfmfVnO0DsADmZGz3kqrmqOp2AGkA/m1t/zTMRcvV5cSaZFvX9QD+ZHtfXuJdnv2q+raqngfwHsxFSutKlnHe1b0JwE6YplsASu4KPwJgkqqetGpYZ8JcLMCq4fxEVfOteX/ExZ/L31R1t6qehTnB9anG/gDATwAE5iaI02b798a60+10l8u8dta+dIT5fD+wLizXo/TdbE+aClMTW16N53aXfXL9jL+CuWis7cWwnVrrGwPgNpibA8dt81ta/x4pY9kjMDd/LqxM9SDMTZrpNYxnu8uxoqIbFSHlxFUSm6rmwDSTHwWUdLMIB7DaOk48COApVT1l/Yb/7CxrOQdTC1RofbdLUdWPVPWIdQxZBPO7cjadfw/mGOU0GuZ3SFQdK6zfwtcw54mZAKCq/7JqltW62fRvWEmupRjA86p6rqzvbllU9V1VzVXVczC/4d7iMgZENf0EkygD5vu/WlVXW7+XdTDHm1tt5Repapqq5gGYBnOc967isuWdg+4EsEpVk639mgbz2Tg9CuD3Vusr537fKaWb579gXSNsA7ANgDPRfQjAVFXdZf0/bFPVLJgbIvtU9W/W9cQWAJ/AtMYqxYr3e5gb7bHW+jfAdKG5FsAP1jqrqibn4oqO83bXupyj9tj24zGrlUNFNotINoB/wrTc+htQ0kw7AsDHqroJwB5c3E3sDZjrw62quqIK+wSYRHuNlaR/ACBeREJdyhRY8fza+ltpTasS64bYeJRuCk9E9YCJd/l+parNnX+ouK/rgzC1Tuki8r2I/LKCsu0A7Le93w/AByYJbQfgoHOGVQPlevI6aH8jIl3FNKM+Kqb5+Uy4JBYA7M17z5bxvrymxEkArheRtgC8YU6I14np19QMwNZylitLSQ2ftV+oYLtOi2BOZONwcTOqVjB9vjfZEp611nRns9kFYprq5cA0gW8upQdGsdc65lchHldhMEmgvTawr/17o6qf2eZ97DLPWXszBsB/VTXNer8YwL22iygHTA2mna813a1UdTOALwFMLqdID5d9KrkZY939/xNMi5A/iunHXlcWwlzYltXEzvmbaVvGcm0BlDWA20wAd4hIVA1i6eFyrJhTQdkT5cTlGtsHuNAS5R4AS62bce1g/u+32773K2BqipyOqmpReQGI6SaSalu+My4cM5IBeIvpFtPHimlNBftDVBbn+TPCSm7OAoCIJIjId1ZT5lMwSaj9fHVcVauTQHiLGcxwj3Wc32fNqspYEeUJA3DSeh0BYKTLjbVBKP0btp+T98P8PkOquGx55yDXa4E8lL4WiACw3LbenTCt3+w3s8tbd3uYJNFVBIABLvHeC9MKoSzOFnE/t16vh7m5Xd2b8hXFWpGKjvN237mco6rbp7mvqrZQ1U6qOlVVnTdAxsJUYtiP2aVqp1VVYf5vtldlQ1ZrgZGwblSraTF4AGWP+7IQ5vxX7WbmMOeoGdaNWyKqR0y864Cq/qCqd8Nc/P4FwFKr2ZZrbTVg7qZH2N53gEmgjsHcubX3Q26EC3d1Szbn8j4RZqCvLmqauj8LUwtba6qaAXMSfAKmSVIOzAnyEQBf205AFcVXm+3vhxlk7VaY5n92J2BuGtiTnmZqBhMBgP+BaYo2wPpcnE3g63Lk6DsAbLYuimrE1p+rq3Xz5ChMU/zWMF0EAHPijXRZtCNK38Bxp+cATEDp5K4q3gDwiapOhGme96e6CkhVd8NceMXB3O23z8sGsAll1NTADMr2RRnrOwrTqmVGXcVYjs8BDJKLR66Pg7lYX29NWg2go9U8dBQuNDM/AnO86OTyvbcfJ8r9DYoZsfl1mN9wsHWjIAPW78K6UHTe1LgPwIcVJfFEVSVmHIJPALwCoLX13VuN0sdk1+9uZeeTe2C6X90IczM40rm5WoR6By40kz4IU6NtT9yCVPXPtvLtba87wNRwnqjisuU5Yl+viASi9LXAQQAJLusOUNOnvjIHUfaAWgcBJLmss7Gq/qac9bgm3s4WchUl3nV2fQDTZeYgTFejemVdm90FIM523p4E09qiNt3E7oBp/v+mbb1hKLu5+Ve40HKwuo9rHQLgZds2AOBbEamTgV2JqHxMvOuAiIwWkVZWIuqs/SyGaf5aDNOX22kJgEli+qc2xoU+Tw6YvkpDRWSgmAHPpqPyC4gmMCMzn7Fq68o7SdZUEkzS5TyRrnd576qsfa6NBwHc4JrcWp/12zB9BEMB06fO1rS7CUxifkrMoDPP10UwYoSJyPMwTfaerWyZSgyCucDqB9O8rg9Mv/uPcaG5+VIAw8QMUOMlZpC438P0AbPzE/OIK+dfnYwKrKaLwkoAj1d1GREZDjPmgPORaY/B1OIPrIuYLPcBuNFqaunqfwGMF5HfiEiQmEe5vAzTF7G85/q+BNOtoa6+u2VZDeBbAMvEDGTkIyLXwQzOM8e62QSr1m85TH9RX1i/NysJfhdm0LoQ6/vYXkRc+62XpzEuHJu8RORRmBpvu4UwF5V3o/o1KUTl8QPgD/Pdc4gZYPPmSpY5BqBlBU3Hm8B0rciCaQE1s5xyFbJqzjuKyOswyeQL1qz3Yc7Jt1hlAsQMAmfvbz1aRKKt5HgGTOuU81VctjxLAfxSRAZZ1wIzUPp6bT5MK6IIK/5WInJ7FXf3HQAvikgX6/jRS0RaAlgFcwP4PjGPp/IVkf5yoW+4q29gbm5fA+A/1nkiAmY8l+RyljkGIFJEan3tad0kfBrANDGD9jW1zo+DROSt2q6/Er+CaWEQjQvn7e4wyXBtuomNhTm+x9jWex1MQh9jL2jt/1AAw6zX1dEVpuuBcxuw1rW85qETUVUw8a4b8TBNP8/ANKsdpaZvVT5M3+INVtOta2EOqotgTkx7YfrlPAGUJDhPwCRURwCcgRmMpqzEwukZmLv+uTCJ6Ed1vG9JMBc3yeW8L6Wcfa4xNf0BU8qZ/VuY2rrvrGaGn8NcCACmKVUjmJqH72CaoddGO+v/9wxM37YYAINV9d8u5bZJ6eeFVtTsGDAn2uWqul1Vjzr/YL5Hw0SkuZrBT+6DGak7G+bu9te4OIH8DOZmg/OvLkfqng5zJ97VLpf9/YuYwV7mwQwmkwOUjG48BcA7Us4o+tVltTTZUs68/4Pp/30PzMXejzAXG9epGQuhrGVOApgNoEVdxFfONophHjvzHUzN+xmYpPsNXDyg0AcwNXkfubQumQjTciYFwGmY73aVnsFqdR2Yby17BKblRIpLmT0wtUm5qvqfqu8dUfnUjLXxJMxNxWyY3+bKSpZJh7lZ/aPYxsWwWQjT8ucwgB0wv6vq+Jl1XM+BuancFEB/Vf2vtf2DMDXqz8LcMDgI0+3Gfu20COY3fBTmEYxPVmPZMlnXAo/DHAOOwHxe9udXvwbz2f1bRHKt/R5QxX1+Feb/4N/Wfv8VQCPr/+dmmBY2P1n78xeYmyVlxZgHM5jddqsbDGBuKu5X1cxytu0c8DJLRDZXMd5yqepSmD7OD1gxH4M5L9oHGvuZXPwc7/4AIOZJKPNrsOmxMP3SD7ict99A6W5iVSZmML4hMDdgj9r+NsEc4y+q9bauG6rUjN1luUyXuAHghFZxXAUiqjmp/o0yqi9WjfgpmGbkez0dDxFdGUTkAwA7VLW81gFEVzwRWQ/gfTXPbSYiIqoQa7wvMSIyVMzAYEEwfeH+iwsDxhARuZWIdIYZ4fhvno6FiIiIqKFg4n3puR2mydRPMM8mHVWD/jtEFRIzGr5r8zvnX3UHUWvwcV0pROQlmIHwZlRxoKa62u67IpIpImnlzBcRmSsiGWJGZO9bVjkiokuZiDxbzvmNT48gugKwqTkREXmUiPwcpr/7QlXtWcb8W2HGv7gVpi/ra6pa1T6tRERERB7HGm8iIvIoVU3GhWcnl+V2mKRcVfU7AM1FpLLn9xIRERFdMurkcUOXGi8vL23UqJGnwyAiIgD5+fkKMwKy01uqWp1H/oTBjArtdMiadqQOwnO7kJAQjYyM9HQYREQEYNOmTSdUtZWn46ArT4NMvBs1aoS8vLzKCxIRkduJyFlV7efpODwlMjISKSnlPRWRiIjqk4js93QMdGViU3MiIrrUHQbQ3vY+3JpGREREdFlg4k1ERJe6lQDGWKObXwvgtKpeFs3MiYiIiIAG2tSciIguHyKyBMBgACEicgjA8wB8AUBV5wNYDTOieQaAfAD3eyZSIiIiopph4k1UT4qKinDo0CEUFBR4OhQitwgICEB4eDh8fX2rtZyq3l3JfAXweG1iIyIiIvIkJt5E9eTQoUNo0qQJIiMjISKeDoeoTqkqsrKycOjQIXTs2NHT4RARERFdUtjHm6ieFBQUoGXLlky6qUESEbRs2ZItOoiIiIjKwMSbqB4x6aaGjN9vIiIiorIx8SYiIiIiIiJyIybeREREl6xCAD96OggiIiKqJSbeRFcQEcHo0aNL3jscDrRq1Qq//OUv3brdcePGoWPHjujduze6du2KMWPG4NChQyXzIyMjERMTgz59+qBPnz548sknSy3nnD537tySZbZu3QoRwdq1a0um7du3Dz179iy17enTp+OVV14pWd/SpUtLrcO57uDg4JJt3XLLLWXuR0ZGBkQEiYmJJdMeffRRvP/++wCA0aNHl4r3+uuvBwC89NJLeOSRR0qWee+993D77bdjxowZJWW9vb1LXs+bN6/M7U+dOhUign379pVMe+WVVyAi2Lp1KwAgOzsbo0ePRufOndGpUyeMGzcOOTk5lcb/6KOPok+fPoiOjkajRo1KYlm+fDlGjx6NFStWlCzjcDjQvHnzMmOkurYWQCcA1wFIBJDl2XCIiIioRjiqeTnqoq+ieQIO0aUjKCgIaWlpOHv2LBo1aoR169YhLCysXrb98ssv484774SqYs6cObjhhhuQlpYGPz8/AMCXX36JkJCQcpdztWTJEgwaNAhLlixBfHx8jWLq06dPScI6evRo3HnnnfjVr35V4TKtW7fG7Nmz8fDDD8PH5+JD6OzZsy9ax6RJk9C3b19s3LgRXbp0wfPPP4+kpCRERETgueeeg8PhQEhISEksFYmJicGHH36IKVOmAAA++eQTdO/evWT+/fffj379+pXcDJg6dSrGjx+PJUuWVBj//PnzAZjk/M477ywVyyeffFJpXOQu/QD8CcAiAI8BeApAAoD7APwSQIDnQiMiIqIqY+JN5AkTJwJVSLKqpU8fYM6cSovdeuut+Ne//oU777wTS5Yswd13342vvvoKAJCXl4cnnngCaWlpKCoqwvTp03H77bdj3759uO+++5CXlwcAeOONNzBw4ECsX78e06dPR0hICNLS0hAbG4v333+/whtXIoJJkyZh+fLlWLNmDW6//fZq76qq4h//+AfWrVuH66+/HgUFBQgIqJ8EpE2bNoiNjcWiRYtw//33V2kZX19fzJs3D4899hj69OmD8ePHIyIiokbbHz58OJYvX44pU6Zg9+7dCAkJgbe3NwBg165dSEtLw7Jly0rKT58+HVdddVVJLXlN4idPagdgCoDfAtgG4H0AHwBYCaApgJEARgP4OdiIjYiI6NLFszTRFWbUqFH48MMPUVBQgNTUVAwYMKBk3h//+EfccMMN+M9//oMvv/wSkydPRl5eHkJDQ7Fu3Tps3rwZH330UUlTcADYsmUL5syZgx07duDHH3/Ehg0bqhRH3759kZ6eXvL+F7/4RUnz5tmzZ5dMnzx5csn0//73vwCAb775Bh07dkSnTp0wePBg/Otf/6rtx1ItU6ZMwcsvv4zi4uKL5k2aNKkk3jFjxpRM//nPf46rrroKycnJeOaZZ2q87ebNm6NNmzZIT0/HkiVLMGrUqJJ527dvx9VXXw0vrwuHdh8fH/Tu3Rs7duyoUvzlse9Xv379ahw/1ZQA6APgFQAHAawD8CsAHwH4BYBImAR9u4fiIyIiooqwxpvIE6pQM+0uvXr1wr59+7BkyRLceuutpeb9+9//xsqVK0v6RBcUFODAgQNo164dJkyYgK1bt8Lb2xu7d+8uWeaaa65BeHg4ANN0e9++fRg0aFClcbh2xahOU3N7wjlq1CgsXLgQI0aMKLemva4fc9WlSxf06dMHH3300UXzympqDgA5OTnYsmULzp07h+PHj6Ndu3Y13v6vf/1rfPjhh/jnP/+JpKSkUn22axt/eez75WwaT57iDeBG6+9NmNrv92GS8r/AJOijAdwNU2NOREREnsbEm+gKNGzYMDzzzDNYv349srIuDNakqvjkk0/QrVu3UuWnT5+O1q1bY9u2bSguLi7VrNvf37/ktbe3NxwOR5Vi2LJlC4YMGVLt2M+fP49PPvkEn376Kf74xz9CVZGVlYXc3Fy0bNkS2dnZpcqfPHkSHTt2rPZ2KvP73/8e99xzD6699toqlZ82bRruv/9+NGvWDP/zP/9T0ue6JoYNG4bu3btj4MCBaNy4ccn06OhobNmyBapacrPh/PnzSE1NRXR0dKn/m+rGT5eqIJgE+24AmTA14IsAPANgMoAhMEn4cABNPBQjERERMfEmugI98MADaN68OWJiYrB+/fqS6bfccgtef/11vP766xARbNmyBVdffTVOnz6N8PBweHl54b333sP58+drvG1Vxeuvv44jR47UaFC0L774Ar169cJnn31WMm3s2LFYvnw5xowZg7Zt2+L//u//cMMNN+DkyZNYu3YtnnrqqRrHW54ePXqgU6dOWLNmTcno5eXZunUr1q1bV9Ji4N1338WXX36JX/ziFzXaduPGjfGXv/wF0dHRpaZHRUUhOjoaf/rTn/Dss88CAF544QVce+21iIyMREZGRo3ip8tFKIAnrL9dABbD1ISPA/AgAP9ylyQiunz8AcAkTwdBVG1MvImuQOHh4aX6aTtNmzYNEydORK9evVBcXIyOHTti1apVeOyxxzBixAgsXLgQ8fHxCAoKqvY2J0+ejBdffBH5+fm49tpr8eWXX5aMaA6YPt7OQcJ69eqFhQsXlrmeJUuW4I477ig1bcSIEUhMTMSYMWOwcOFCPP7443j66acBAM8//zw6depUUnb8+PGYOHEiAKB9+/b49ttvq70vTlOnTkVsbGypaZMmTcL06dNL3m/atAm/+c1v8Nprr5Xsb2JiIh566CFs3rwZvr6+Ndr2PffcU+b0v//975gwYULJPl933XV46623qhw/NRTdAMwA8AKAbwGsBnDOoxEREdWNPp4OgKhGpCE+8iooKEidoy/XFB8nRnVt586dpR77RNQQlfU9F5F8Va3+3ZoGol+/fpqSkuLpMIiICICIbFJVjhJK9Y6jmhMRERERERG5EZuaExG52Lp1K8aNG1dqWmBgIL755pt62f6MGTNKPYsbMKO3T5kypV62T0RERER1i03Ny8Gm5lTXdu7ciaioqDp/tBXRpUJVkZ6ezqbmLtjUnIjo0sGm5uQpbGpOVE8CAgKQlZXFGzLUIDkf62Z/1BwRERERGWxqTlRPwsPDcejQIRw/ftzToRC5RUBAAMLDwz0dBhEREdElh4k3UT3x9fVFx44dPR0GERERERHVMzY1JyIiIiIiInIjJt5EREREREREbsTEm4iIiIiIiMiNmHgTERERERERuRETbyIiIiIiIiI3cmviLSKTRGS7iKSJyBIRCRCRjiKyUUQyROQjEfGzyvpb7zOs+ZG29fzOmr5LRG5xZ8xEREREREREdcltibeIhAF4EkA/Ve0JwBvAKAB/ATBbVTsDyAbwoLXIgwCyremzrXIQkWhruR4A4gG8KSLe7oqbiIiIiIiIqC65u6m5D4BGIuIDIBDAEQA3AFhqzX8PwK+s17fTMQ/MAAAgAElEQVRb72HNHyIiYk3/UFXPqepeABkArnFz3ERERERERER1wm2Jt6oeBvAKgAMwCfdpAJsAnFJVh1XsEIAw63UYgIPWsg6rfEv79DKWKSEij4hIioikOBwO19lEREREREREHuHOpuYtYGqrOwJoByAIpqm4W6jqW6raT1X7+fj4uGszRERERERERNXizqbmNwLYq6rHVbUIwDIA1wFobjU9B4BwAIet14cBtAcAa34zAFn26WUsQ0RERERERHRJc2fifQDAtSISaPXVHgJgB4AvAdxplRkL4FPr9UrrPaz5/6eqak0fZY163hFAFwD/cWPcRERERERERHXGbW2yVXWjiCwFsBmAA8AWAG8B+BeAD0XkD9a0v1qL/BXAIhHJAHASZiRzqOp2EfkYJml3AHhcVc+7K24iIiIiIiKiuiSmUrlhCQoK0ry8vFqtw1TS105D/GyJiKpLRPJVNcjTcXhKv379NCUlxdNhEBERABHZpKr9PB0HXXnc/TgxIiIiIiIioisaE28iIiIiIiIiN2LiTURERERERORGTLyJiIiIiIiI3IiJNxERkUVE4kVkl4hkiMiUMuZ3EJEvRWSLiKSKyK2eiJOIiIguL0y8iYiIAIiIN4B5ABIARAO4W0SiXYpNBfCxql4N89jLN+s3SiIiIrocMfEmIiKPu0Rqmq8BkKGqP6pqIYAPAdzuUkYBNLVeNwPwkxviICIiogaGiTcREXnUJVTTHAbgoO39IWua3XQAo0XkEIDVAJ5wQxxERETUwDDxJiIiT7ucaprvBvB3VQ0HcCuARSJy0blURB4RkRQRSTl+/Hi9B0lERESXFibeRETkaZdKTfNhAO1t78OtaXYPAvgYAFT1WwABAEJcV6Sqb6lqP1Xt16pVKzeESkRERJcTJt5ERORuPs7aX+vvkRqso0o1zbX0PYAuItJRRPxgmrSvdClzAMAQABCR7jCJN6u0iYiIqEI+ng6AiIgaPIeq9qtgflVrmuMBU9MsIs6a5kx7IRHJhmmW7krMohpcXhCq6hCRCQA+A+AN4F1V3S4iMwCkqOpKAP8D4G0RmWRtZ5yqlrU9IiIiohJMvImIyNNKapphEu5RAO5xKeOsaf57JTXNzmbfYq23ooT/Iqq6GqYpu33ac7bXOwBcV511EhFRw7Np06ZQHx+fdwD0BFsRk1EMIM3hcDwUGxub6TqTiTcREXlUXdY0q+p552sRUft7IiKiuuLj4/NOmzZturdq1Srby8uLLZ8IxcXFcvz48eijR4++A2CY63wm3kRE5HGsaSYiostMTybdZOfl5aWtWrU6ffTo0Z5lzWfiTUREDYaI2O8wN3d5D6v2nIiIqLa8mHSTK+s7UWbXAybeRETUkIy0vd7g8l5x8SjlRERERG7HxJuIiBqSpar6qaeDICIiIrLjCHxERNSQPO/pAIiIiOqDt7d3bFRUVHSXLl16JCQkXJWbm+uR3G7GjBmh9m3HxcV1PnHihDcABAYGXl2TdS5durRpZGRkzw4dOvR89tln29RVrJ7ExJuIiIiIiOgy4+/vX5yenr7jhx9+2O7r66uzZs1qVdVlHQ5HncWxYMGC1mfOnCnJK5OSkjJCQkJq/FQRh8OBSZMmdVi9evXu3bt3b//kk0+CN23aFFA30XoOm5oTEVFDEiUim8uYLgBUVfvWd0BERNSwPfDAA+3T0tIC63KdPXv2zH/33XcPVrX8oEGDzqSmpjYCgDfffDM4MTGxdVFRkfTt2zdv4cKF+318fBAYGHj1vffeezw5Obnp3LlzDwQEBBRPnDixQ35+vpefn58mJyfvatKkSfHjjz8evmHDhiaFhYXy8MMPZ06ePPnEqlWrmsyYMaNdcHBw0a5duxrFxMTkr1ixYu/MmTNDMzMzfePi4rq2aNHCsXHjxt1hYWExKSkpO9u2bVsqu582bVrr5cuXBxcWFsptt912avbs2T+VtS/r168PioiIOBcdHV0IAMOHDz+5dOnS5rGxsUdr85l6GhNvIiJqSPai9IBqREREDVpRURE+++yzpjfffHPO5s2bA5YuXRqckpKS7u/vr6NHj+4wf/78lhMmTMg6e/as14ABA/LefvvtQwUFBdK5c+eeixcv3hMXF5d/8uRJr8aNGxfPmTMnpFmzZufT0tJ2nj17Vvr37x81dOjQHADYuXNno61bt/4YGRlZFBsbG7Vu3brGU6dOzUxMTGydlJS02zXRtlu2bFnTjIyMgNTU1J2qihtvvLHzmjVrGickJJxxLXvw4EG/sLCwQuf78PDwwo0bNzZ2z6dXf5h4ExFRQ1Koqns8HQQREV05qlMzXZfOnTvnFRUVFQ0AAwYMyH3qqadOvPrqqyFpaWmBvXv37g4ABQUFXqGhoQ4A8Pb2xrhx47IBIDU1NSA0NLQoLi4uHwCCg4OLAeDzzz9vmp6eHrhy5coWAJCbm+u9Y8eOAD8/P42Jicnr1KlTEQD06NEjf8+ePX5VjXXt2rVNk5OTm0ZHR0cDQH5+vld6enpAWYl3Q8XEm4iIGpLvPB0AERFRfXD28bZPU1UZOXJk1rx58w67lvfz8yv28ak4/VNVmTVr1oERI0bk2KevWrWqib+/f8lzy729veFwOKSqsaoqJk6ceGTy5MknKivbvn37wsOHD5ck9YcOHSpVA3654uBqRETUkHwtIveU9+fp4IiIiNwpPj4+Z9WqVS0OHz7sAwDHjh3z3r1790U107169SrIzMz0TUpKCgSA7Oxsr6KiItx0002nExMTW507d04AIDU11T8nJ6fCnDEoKOj86dOnKyyTkJCQs2jRohBnub179/o6Y3QVFxeXt2/fvoD09HS/goICWbZsWfCIESNOVe0TuHSxxpuIiBqSQeVMvxVAOIAP6jEWIiKiehUbG1swderUw0OGDOlaXFwMX19fnTt37oGuXbuWqjEOCAjQxYsX73nyySc7FBQUeAUEBBQnJyfvnjRp0ol9+/b5x8TEdFdVCQ4OLlq9enWFXbjGjh17Ij4+vmvr1q0LN27cuLusMsOHD8/Zvn17QP/+/aMAIDAwsHjx4sV7w8LCLuoX7uvri1mzZh2Ij4/vev78edxzzz0n+vXrV1Cbz+VSIKpaeanLTFBQkObl5dVqHSJVbjlRrob42RIRVZeI5KtqkIe2PQrAFAB7APxBVbfUdwz9+vXTlJSU+t4sERGVQUQ2qWq/2q5n27Zt+3r37l1ps2m68mzbti2kd+/eka7TWeNNREQNioh4ARgDYDKALQDuUdUdFS9FRERE5D5MvImIqMEQkfEAJgH4CsAwjnBORER0aTp69Kj34MGDu7lOX79+/a42bdqc90RM7sTEm4iIGpJEAMcA9APwD1u3IQGgqtrXU4ERERHRBW3atDnvOip7Q8bEm4iIGpIung6AiIiIyBUTbyIiajCcTctFpAOAaGvyDlU94LmoiIiI6ErHxJuIiBoMEWkCYAGA6wBssyb3EpFvATyiqrkeC46IiIiuWBU+6JyIiOgy8xrMo8OuUtVhqjoMwFUAfgDwukcjIyIioisWE28iImpIrlfVaapaMhqqqhar6nMwteBEREQNgre3d2xUVFR0ly5deiQkJFyVm5vrkdxuxowZofZtx8XFdT5x4oQ3AAQGBl5dk3WOHDkyMjg4uHeXLl162KcfO3bMe+DAgV0iIiJ6Dhw4sMvx48e9axd9/RFV9XQMdS4oKEjz8vJqtQ7bSLg11hA/WyKi6hKRfFUNqqdt/aCqZQ6wJiIZqtq5PuKw69evn6akpNT3ZomIqAwisklV+9V2Pdu2bdvXu3fvEwDw+gNftD+QlhVY++gu6NCzZf4T7w45WFGZwMDAq/Pz87cAwLBhwzr27ds3f/r06ceqsn6HwwEfn7rpdRwWFhaTkpKys23bto6KYqyONWvWNG7SpEnx/fff3/GHH37Y7pz+6KOPhgcHBztmzpx59Nlnn22TnZ3tnZiYeLi2+1CXtm3bFtK7d+9I1+ms8SYioobkOxF51nWiiEwBsNED8RAREbndoEGDzmRkZPgDwJtvvhkcExPTPSoqKvqee+6JcDhMPhwYGHj1ww8/HN6tW7foL774onFSUlLg1VdfHdWtW7fomJiY7tnZ2V4OhwPjx48P79mzZ/euXbtGv/zyyyEAsGrVqibXXHNNt/j4+Ks6duzYY9iwYR2Li4vxhz/8ITQzM9M3Li6u64ABA7oCJhE/cuTIRVn9tGnTWjvXO2nSpHYV7U9CQsKZVq1aXZTIr127tvn48eOzAGD8+PFZa9asaVHrD6+ecHA1IiJqSJ4A8DcR2Q3AeYe9L4DtAMZ5KigiImq4KquZdreioiJ89tlnTW+++eaczZs3ByxdujQ4JSUl3d/fX0ePHt1h/vz5LSdMmJB19uxZrwEDBuS9/fbbhwoKCqRz5849Fy9evCcuLi7/5MmTXo0bNy6eM2dOSLNmzc6npaXtPHv2rPTv3z9q6NChOQCwc+fORlu3bv0xMjKyKDY2NmrdunWNp06dmpmYmNg6KSlpd1k13k7Lli1rmpGREZCamrpTVXHjjTd2XrNmTeOEhIQz1dnXrKwsn4iIiCIAaN++fVFWVtZlk89eNoESERFVRlVPAbhDRLoCcPYLe05Vd3kwLCIiojp37tw5r6ioqGgAGDBgQO5TTz114tVXXw1JS0sL7N27d3cAKCgo8AoNDXUAgLe3N8aNG5cNAKmpqQGhoaFFcXFx+QAQHBxcDACff/550/T09MCVK1e2AIDc3FzvHTt2BPj5+WlMTExep06digCgR48e+Xv27PGraqxr165tmpyc3DQ6OjoaAPLz873S09MDqpt423l5edVJ9+D6wsSbiIgaDBHpZXu7x/rX3zldVVPrPyoiIqK65+/vX5yenr7DPk1VZeTIkVnz5s27qN+zn59fcWX9ulVVZs2adWDEiBE59umrVq1q4u/vXzKAlbe3NxwOR5WzXlXFxIkTj0yePPlEVZcpS8uWLR379+/3jYiIKNq/f79vcHBwubXslxr28SYiooZkK4APALxh/c2z/b3hwbiIiIjcLj4+PmfVqlUtDh8+7AOYUcB37959Uc10r169CjIzM32TkpICASA7O9urqKgIN9100+nExMRW586dEwBITU31z8nJqTBnDAoKOn/69OkKyyQkJOQsWrQoxFlu7969vs4Yq+OWW245tWDBgpYAsGDBgpbx8fGnqrsOT2GNNxERNST/C2A4gFMAlgD4VFXzPRsSERFR/YiNjS2YOnXq4SFDhnQtLi6Gr6+vzp0790DXrl0L7eUCAgJ08eLFe5588skOBQUFXgEBAcXJycm7J02adGLfvn3+MTEx3VVVgoODi1avXr2nvO0BwNixY0/Ex8d3bd26deHGjRt3l1Vm+PDhOdu3bw/o379/FAAEBgYWL168eG9YWFiZNdZDhw7t+N133zXJzs72ad26da8pU6b8NGnSpBMvvPDCkTvuuKNTRERESFhYWOHy5csrjO1SwseJlYOPEyMiqhv1+Tgx2za7ALgbwFAAPwD4s6eamfNxYkRElw53PE6MyI6PEyMioiuGqv4A4CMAqwAMAhDt2YiIiIjoSubWpuYi0hzAOwB6AlAADwDYBXMxFAlgH4C7VDVbTBXzawBuBZAPYJyqbrbWMxbAVGu1f1DV99wZNxERXZ5EpAOAUQDuAHAU5nzTXVVr1wyKiIiI6tTRo0e9Bw8e3M11+vr163e1adPmvCdicid39/F+DcBaVb1TRPwABAJ4FsAXqvpnEZkCYAqA3wJIANDF+hsAIBHAABEJBvA8gH4wyfsmEVmpqtlujp2IiC4/+wCkAlgO0887FMCDzu5DqjrXY5ERERFRiTZt2px3HZW9IXNb4i0izQD8HMA4AFDVQgCFInI7gMFWsfcArIdJvG8HsFBNx+jvRKS5iLS1yq5T1ZPWetcBiIcZNIeIiMhuJsxNWh8AIR6OhYiIiAiAe2u8OwI4DuBvItIbwCYATwForapHrDJHAbS2XocBOGhb/pA1rbzppYjIIwAeAQA/vyo/y52IiBoQVZ1aeSlARP5XVV9ydzxEREREgHsHV/MB0BdAoqpeDSAPpll5Cat2u06G/lbVt1S1n6r2q+zB8EREdMUb5ekAiIiI6MrhzsT7EIBDqrrRer8UJhE/ZjUhh/VvpjX/MID2tuXDrWnlTSciIqqp2j8zkoiIyIO8vb1jo6Kiort06dIjISHhqtzcXI88sWrGjBmh9m3HxcV1PnHihDcABAYGXl3d9WVkZPgOGDCga6dOnXp07ty5x4svvhjqnHfs2DHvgQMHdomIiOg5cODALsePH/eum71wP7f956jqUQAHRcQ5Ut0QADsArAQw1po2FsCn1uuVAMaIcS2A01aT9M8A3CwiLUSkBYCbrWlEREQ1VSetrYiIiDzF39+/OD09fccPP/yw3dfXV2fNmtWqqss6HI46i2PBggWtz5w5U5JXJiUlZYSEhNR4VHJfX1/MmjXr0J49e7Z///33O//617+Gbtq0KQAAnn/++baDBw/O3b9/f9rgwYNzn3vuuTZ1sQ/1wd1tsp8AsNga0fxHAPfDJPsfi8iDAPYDuMsquxrmUWIZMI8Tux8AVPWkiLwI4Hur3AznQGtEREQ1xBpvIiKqEx8+WND+6PbiwLpcZ5seXvmj/hpwsPKSxqBBg86kpqY2AoA333wzODExsXVRUZH07ds3b+HChft9fHwQGBh49b333ns8OTm56dy5cw8EBAQUT5w4sUN+fr6Xn5+fJicn72rSpEnx448/Hr5hw4YmhYWF8vDDD2dOnjz5xKpVq5rMmDGjXXBwcNGuXbsaxcTE5K9YsWLvzJkzQzMzM33j4uK6tmjRwrFx48bdYWFhMSkpKTvbtm1bKrufNm1a6+XLlwcXFhbKbbfddmr27Nk/lbUvERERRREREUUA0KJFi+JOnTqdPXDggF9sbGzB2rVrmyclJe0CgPHjx2fFxcV1w2XSGtqtibeqboV5DJirIWWUVQCPl7OedwG8W7fRERFRQyMiv1HVxCoUXeb2YIiIiOpBUVERPvvss6Y333xzzubNmwOWLl0anJKSku7v76+jR4/uMH/+/JYTJkzIOnv2rNeAAQPy3n777UMFBQXSuXPnnosXL94TFxeXf/LkSa/GjRsXz5kzJ6RZs2bn09LSdp49e1b69+8fNXTo0BwA2LlzZ6OtW7f+GBkZWRQbGxu1bt26xlOnTs1MTExsnZSUtNs10bZbtmxZ04yMjIDU1NSdqoobb7yx85o1axonJCScqWjfdu3a5bdjx47AuLi4MwCQlZXl40zK27dvX5SVlXXZDO512QRKRERUBQ8DqDTxVtUXy5ouIvEAXgPgDeAdVf1zGWXuAjAdprn6NlW9pzYBExHR5a06NdN16dy5c15RUVHRADBgwIDcp5566sSrr74akpaWFti7d+/uAFBQUOAVGhrqAABvb2+MGzcuGwBSU1MDQkNDi+Li4vIBIDg4uBgAPv/886bp6emBK1eubAEAubm53jt27Ajw8/PTmJiYvE6dOhUBQI8ePfL37NlT5UdJrV27tmlycnLT6OjoaADIz8/3Sk9PD6go8T59+rTX8OHDO/35z38+6IzPzsvLCyKXTwM2Jt5EREQARMQbwDwAN8EMEPq9iKxU1R22Ml0A/A7AdaqaLSKhZa+NiIjIvZx9vO3TVFVGjhyZNW/evIuaX/v5+RVX9vQnVZVZs2YdGDFiRI59+qpVq5r4+/uXjI/i7e0Nh8NR5axXVTFx4sQjkydPPlGV8ufOnZPbbrut08iRI0+OHTv2lHN6y5YtHfv37/eNiIgo2r9/v29wcHDddVZ3M4+MfEdERGQnIvEisktEMkRkSjll7hKRHSKyXUQ+KGdVvUTkZBl/2SJS2fgg1wDIUNUfVbUQwIcAbncp8zCAeaqaDQCqmgkiIqJLRHx8fM6qVataHD582Acwo4Dv3r37oprpXr16FWRmZvomJSUFAkB2drZXUVERbrrpptOJiYmtzp07JwCQmprqn5OTU2HOGBQUdP706dMVlklISMhZtGhRiLPc3r17fZ0xuiouLsaoUaMiunbtWjB9+vRj9nm33HLLqQULFrQEgAULFrSMj48/VdY6LkWs8SYiIo+q45rm/6LssUWqIgyAvbngIQADXMp0teLZANMcfbqqrq3h9oiIiOpUbGxswdSpUw8PGTKka3FxMXx9fXXu3LkHunbtWmgvFxAQoIsXL97z5JNPdigoKPAKCAgoTk5O3j1p0qQT+/bt84+JiemuqhIcHFy0evXqPRVtc+zYsSfi4+O7tm7dunDjxo27yyozfPjwnO3btwf0798/CgACAwOLFy9evDcsLOyiGut169Y1XrFiRcsuXbqcdTalf+GFFw7/+te/Pv3CCy8cueOOOzpFRESEhIWFFS5fvrzC2C4lYsY0a1iCgoI0Ly+vVuuoi/4CDfGzJSKqLhHJV9WgCub/DCaBvcV6/zsAUNU/2cq8BGC3qr5Tyba2qGq1nxlqLXsngHhVfch6fx+AAao6wVZmFYAimCdyhANIBhCjqqdc1vUIgEcAoEOHDrH79++vSUhERFTHRGSTqtb0Bm2Jbdu27evdu3eVmk3TlWXbtm0hvXv3jnSdzqbmRETkaWXVNIe5lOkKoKuIbBCR76xB0MpS7mjlVs16RQ4DaG97H46LH1FyCMBKVS1S1b0AdgPo4roiVX1LVfupar9Wrar8WFUiIiJqoKqUeItIjLsDISKiBstHRFJsf4/UZB0wCe5gAHcDeFtEmpdRLlZEwl0nishgAJsr2cb3ALqISEcR8QMwCsBKlzIrrBggIiEwNwR+rPJeVJM6clB4cB7UUeHTVoiIiC47R48e9Y6Kiop2/Tt69GhlN8ovS1Xt4/2miPgD+DuAxap62n0hERFRA+OopFlfVWuaN6pqEYC9IuKsaf7epdxyAF+JyHwArwBoBeBVmAT54YqCVFWHiEwA8BlM/+13VXW7iMwAkKKqK615N4vIDgDnAUxW1ayK1lsbjuMrcW7XBJzbMxV+YY/At/0T8Aq46L4CERHRZadNmzbnXUdlb8iqVOOtqtcDuBfmwmiTiHwgIje5NTIiIrpS1FlNs6q+B6AvTFK+A8BGAF8DuEZV/1NZIKq6WlW7qmonVf2jNe05K+mGGk+rarSqxqjqhzXb5arxbTsagf2+gU/wTSjc/wryNnTE2bR7cT5nkzs3S0RERHWsyn28VfUHAFMB/BZAHIC5IpIuIsPdFRwRETV8quoA4Kxp3gngY2dNs4gMs4p9BiDLqmn+EhXXNHcG0AfANgCFAJrhMh7TxLv5z9Co18cIui4Dvu2fgOP4P5H/n37IT4lDUeanUD3v6RCJiIioElXt491LRGbDXBDdAGCoqna3Xs92Y3xERHQFqKuaZhFZAOAdAJNU9S6Y2u/WALaKyA31tDtu4dWoIwK6vorG1x+Ef5dZKC7Yh4LUXyHvmyjTD/x87Z7mQURERO5T1RqA12EGpemtqo+r6mYAUNWfYGrBiYiILgUZAGJV9SsAUNVcVZ0IYDSAP3g0sjoiPs3gF/E0ggbuQUDMRxDfYJzbNQFnvmqPcxm/Q3GBa/d4IiIi8rSqJt63AfhAVc8CgIh4iUggAKjqIncFR0REVB2q+rLVdN11+lYAgzwQktuIlw98W9+FwP7fIbDfBvgE34DCfS8hb0Mkzqbdh/M5lQ3iTkRElzNvb+/YqKio6C5duvRISEi4Kjc31yPdqmbMmBFq33ZcXFznEydOeANAYGDg1dVdX35+vsTExHTv1q1bdOfOnXtMmjSpnXNeenq6X69evaI6dOjQ87bbbruqoKBA6mYv3K+q/zmfA2hkex9oTSMiIrpkiEiS7fXfXWan1G809UNE4N18IBr1WoqggT/AN/xxOI6vQP5/YpGfMhiO4yuhWuzpMImIqI75+/sXp6en7/jhhx+2+/r66qxZs1pVdVmH46J71DW2YMGC1mfOnCnJK5OSkjJCQkJqPABJQECAfv3117t27dq1Y/v27Tu++OKLpl988UUQADz99NPhEyZMOHbgwIG0Zs2aOV577bWQutiH+lDVx4kFqGrJQ0RV9YyzxpuIiOgS0tT2upfLvMvmrnhNeQVehYBuc+B/1XQU/fQOCg/Mxdltt0MCIiB+bTwdHhFRrfm1fxy+be/zdBilrH2koP2JHcV1mhuFRHvlx78VcLCq5QcNGnQmNTW1EQC8+eabwYmJia2Lioqkb9++eQsXLtzv4+ODwMDAq++9997jycnJTefOnXsgICCgeOLEiR3y8/O9/Pz8NDk5eVeTJk2KH3/88fANGzY0KSwslIcffjhz8uTJJ1atWtVkxowZ7YKDg4t27drVKCYmJn/FihV7Z86cGZqZmekbFxfXtUWLFo6NGzfuDgsLi0lJSdnZtm3bUtn9tGnTWi9fvjy4sLBQbrvttlOzZ8/+qax98fLyQrNmzYoBoLCwUBwOh4gIiouL8e233zb59NNPfwSABx54IGv69Ontfvvb3x6v+Sddf6qaeOeJSF9n324RiQVw1n1hERER1YjWcF6DIr7N4RfxDHzbPwVH5idwHPsQWlzg6bCIiGrPK8DTEVxyioqK8NlnnzW9+eabczZv3hywdOnS4JSUlHR/f38dPXp0h/nz57ecMGFC1tmzZ70GDBiQ9/bbbx8qKCiQzp0791y8ePGeuLi4/JMnT3o1bty4eM6cOSHNmjU7n5aWtvPs2bPSv3//qKFDh+YAwM6dOxtt3br1x8jIyKLY2NiodevWNZ46dWpmYmJi66SkpN2uibbdsmXLmmZkZASkpqbuVFXceOONndesWdM4ISHhTFnlHQ4HevbsGX3gwAH/sWPHZt5www15R44c8WnSpMl5X19fAEBkZGThsWPH/NzyobpBVRPviQD+ISI/wdQYtAHwa7dFRUREVDPNRY9begwAACAASURBVGQoTFeqZrbHkQnMY8WuKOLlC982o+DbZpSnQyEiarCqUzNdl86dO+cVFRUVDQADBgzIfeqpp068+uqrIWlpaYG9e/fuDgAFBQVeoaGhDgDw9vbGuHHjsgEgNTU1IDQ0tCguLi4fAIKDg4sB4PPPP2+anp4euHLlyhYAkJub671jx44APz8/jYmJyevUqVMRAPTo0SN/z549VU56165d2zQ5OblpdHR0NADk5+d7paenB5SXePv4+CA9PX3HiRMnvG+77bZO33//fUB4eHjdtY/3gCol3qr6vYhEAehmTdqlqkXuC4uIiKhGNgC4y3r9DYCRtnnf1H84RERE7uHs422fpqoycuTIrHnz5l30iAs/P79iH5+K0z9VlVmzZh0YMWJEjn36qlWrmvj7+5e0HPP29obD4ahyFy5VxcSJE49Mnjz5RFWXAYCQkJDz119/fe4///nPZtOnTz+Wm5vrXVRUBF9fX+zbt8+vdevWhdVZnydVZ+S7/jD95foCuFtExrgnJCIioppR1fsq+vN0fERERO4UHx+fs2rVqhaHDx/2AYBjx4557969+6Ka6V69ehVkZmb6JiUlBQJAdna2V1FREW666abTiYmJrc6dOycAkJqa6p+Tk1NhzhgUFHT+9OnTFZZJSEjIWbRoUYiz3N69e32dMbr66aeffJyjop85c0a+/PLLpt27dy/w8vLCtdf+P3v3H9fUdf8P/HXym/BLAgIaBRRBBAMVdDjrFlprhart1Np2pau2m2330Ql2ZV03rEqd67qhyKZIf9ip41vXWrUOFafrTFrXsaIVihAQ5Ye1AoLIrxBIyPn+kWApFYwSCOD7+XjcR5Nzz733fePj0fDO+9xzZja/++67HgCwc+dOzwULFly/9acyNNhU8WaM7QEQCOAsgK4Z6jiA3QMUFyGEEHLbGGNP9rGbc87fG7RgCCGEkEEWFRVlSE5Ovjxnzpxgs9kMsVjM09PTq4KDg79VGZbJZDwrK+vC6tWr/QwGg0Amk5m1Wm3pmjVr6ioqKqQqlWoK55wpFArjkSNHLvR1zWXLltXFxsYG+/j4dOTm5pberM/ixYubzp07J5sxY0YIAMjlcnNWVla5Uqn8zvDxS5cuiZcvXz6hs7MTnHP2yCOPXPvxj3/cCACpqalfPf7444EbN25UhoWF6RMSEm6rgu5IjPNbzzXDGCsGEMpt6TwEODs789bW1n6dg7H+T347TD4uQggZUIwxPefceZCuldHLrocAjOOcCwcjju6mT5/O8/JG5EpmhBAy7DDGTnPOp/f3PPn5+RURERHDJukjgyc/P98rIiIioGe7rZOrFcIyodoVewZFCCGE2BPn/Ofd3zPGngDwa1jW8P6RQ4IihBBCyF3P1sTbC0ARY+x/ANq7GjnnD/d+CCGEEDL4GGMCAE8DSALwBYAnOedFfR9FCCGEkMFUXV0tjImJmdyz/eTJkyW+vr6dNztmOLM18V4/kEEQQggh9sAYex7AGgCfAHiYc97nc2mEEEIIcQxfX9/OnrOyj2S2LiemYYz5AwjinJ9gjMkBDPpzcoQQQsgtZACoATAdwAfd5utgsEyuFumowAghhBBy97J1VvMVAJ4DoIBldnMlgB0A5gxcaIQQQshtC3J0AIQQQgghPdk61HwlgO8ByAUAzvl5xpj3gEVFCCGE3AFbh5Yzxj7lnM8e6HgIIYQQQgCgz4XOu2nnnN9Y+40xJoJlHW9CCCFkOBqU5c0IIYQQQgDbE28NY+w3AJwYY3MBfADgHwMXFiGEEDKg6MdjQgghw5pQKIwKCQkJDQoKCouLi5vY3Nxsa25nVykpKd7dr61WqyfV1dUJAUAul0+70/OaTCZMmTIl9L777pvU1abT6STh4eEhfn5+U+fPnz/RYDCwvs4xlNj6j/NrAFcBfAngeQBHACQPVFCEEEIIIYQQQnonlUrNOp2u6Pz58+fEYjFPTU0dbeuxJpPJbnFkZmb6tLS03MgrNRpNmZeXV7+XA9u4caPPpEmT2rq3vfjii+NWrVpVU1VVVeju7m7aunWrV3+vM1hsndXcDOAt60YIIYQMd8PmF3JCCCFD26lV/x5/vfia3J7nHDVFob/3L/ddsrX/7NmzWwoKCpwAYPv27YqMjAwfo9HIIiMjW3fv3l0pEokgl8unxcfHX9VqtW7p6elVMpnMnJiY6KfX6wUSiYRrtdoSV1dX88qVK8edOnXKtaOjg61YsaI2KSmpLjs72zUlJWWsQqEwlpSUOKlUKv3BgwfLN23a5F1bWytWq9XBHh4eptzc3FKlUqnKy8srHjNmzLey+7Vr1/ocOHBA0dHRwebPn399y5YtX/d2PxcuXBAfO3bM/ZVXXrmyZcsWHwAwm8347LPPXD/66KOLAPDss8/Wr1+/fuzLL7989c4+5cFlU8WbMVbOGLvYcxvo4AghhJDbwRg7amPX5QMZByGEEDJYjEYjjh075qZSqdrOnDkj27dvnyIvL0+n0+mKBAIB37FjhycAtLW1CaKjo1tLSkqK1Gp1a3x8fGBaWlpVSUlJkUajKXFxcTGnpaV5ubu7dxYWFhbn5+cX79q1a7ROp5MAQHFxsdO2bdsulZWVnauqqpIeP37cJTk5udbb29uo0WhKc3NzS3uLcf/+/W5lZWWygoKC4uLi4qKzZ8/Kjx496tJb/5UrV45/4403vhIIvklXa2pqRK6urp1isRgAEBAQ0FFTUyOx1+c40Gyd1Xx6t9cyAEthWVqMEEIIGUp8benEOc8f6EAIIYTcHW6nMm1P7e3tgpCQkFAAiI6Obk5ISKjbvHmzV2FhoTwiImIKABgMBoG3t7cJAIRCIZYvX94AAAUFBTJvb2+jWq3WA4BCoTADwIkTJ9x0Op380KFDHgDQ3NwsLCoqkkkkEq5SqVoDAwONABAWFqa/cOGCzUlvTk6Om1ardQsNDQ0FAL1eL9DpdLK4uLiWnn3fe+89dy8vL9MPfvADfXZ2tmt/PqOhxNah5vU9mtIYY6cBvGr/kAghhJA75s4Ye7i3nZzzQ4MZDCGEEDJQup7x7t7GOWdLly6t37Zt2+We/SUSiVkk6jv945yz1NTUqiVLljR1b8/OznaVSqU3JiYVCoUwmUw2P7bFOUdiYuKVpKSkulv1/fTTT12OHz8+SqlUure3twtaW1sFjzzyyIQDBw6UNzc3C41GI8RiMSoqKiQ+Pj4dtzrfUGHrUPPIbtt0xtgLsL1aTgghhAwWdwCPwjIyq+f2qAPjIoQQQgZcbGxsU3Z2tsfly5dFAFBTUyMsLS39TmU6PDzcUFtbK9ZoNHIAaGhoEBiNRsydO7cxIyNjdHt7OwOAgoICaVNTU585o7Ozc2djY2OffeLi4pr27Nnj1dWvvLxc3BVjT9u2bbtcU1NTcPny5S//+te/Xpw5c2bzRx99VC4QCDBz5szmd9991wMAdu7c6blgwYLrtnwuQ4GtyXNqt9cmABUAHrN7NIQQQkj/VHLOn3Z0EIQQQogjREVFGZKTky/PmTMn2Gw2QywW8/T09Krg4OBvVYZlMhnPysq6sHr1aj+DwSCQyWRmrVZbumbNmrqKigqpSqWawjlnCoXCeOTIkQt9XXPZsmV1sbGxwT4+Ph29Pee9ePHipnPnzslmzJgRAgByudyclZVVrlQqb2t69dTU1K8ef/zxwI0bNyrDwsL0CQkJt6ygDxWM85G3lKmzszNvbW3t1zkY6/+EtyPxsyWEkNvFGNNzzp0H6VpfcM7veM3QgTB9+nSel5fn6DAIIYQAYIyd5pxPv3XPvuXn51dEREQMm6SPDJ78/HyviIiIgJ7tNlW8GWMv9rWfc775DuMihBBC7Gl5bzsYY6c45/cOYiyEEEIIIQBub1bzGQC6JqVZCOB/AM4PRFCEEELInbjFbOV+gxYIIYQQQvpUXV0tjImJmdyz/eTJkyW+vr6djohpINmaeI8DEMk5bwYAxth6AIc5508NVGCEEEKIndHzP4QQQsgQ4evr29lzVvaRzNbE2wdA9wfyO6xthBBCyJDBGFvc2y4AToMZCyGEEEJIF1sT790A/scYO2B9/yMAuwYmJEIIIeSOLexjX/agRUEIIYQQ0o1NiTfn/HeMsaMAfmBteoZz/sXAhUUIIYTcPs75M46OgRBCCCGkJ1sr3gAgB9DEOX+XMTaaMTaBc14+UIERQgght+smq3BwAHUAPqXvLEIIIYQ4isCWToyxdQBeBvCKtUkM4G82HitkjH3BGMu2vp/AGMtljJUxxv7OGJNY26XW92XW/QHdzvGKtb2EMTbP9tsjhBByl3HtsbnBsjLHUcbYE44MjBBCCLEnoVAYFRISEhoUFBQWFxc3sbm52abczt5SUlK8u19brVZPqqurEwKAXC6fdifnVCqVquDg4NCQkJDQqVOnTulqr6mpEc6aNSvI399/6qxZs4KuXr0q7P8dDA5b/3EWAXgYQCsAcM6/huUPGlskACju9v4PALZwzicBaADwU2v7TwE0WNu3WPuBMRYK4AkAYQBiAWxnjA2bD5gQQsjg4ZxvuMmWAGAWgF85Oj5CCCHEXqRSqVmn0xWdP3/+nFgs5qmpqaNtPdZkMtktjszMTJ+WlpYbeaVGoynz8vLq93JgGo2mVKfTFRUWFt7IJdetWzcmJiamubKysjAmJqb51Vdf9e3vdQaLrUPNOzjnnDHGAYAx5mzLQYyxcQDmA/gdgBcZYwzA/QCetHbZBWA9gAwAj1hfA8A+AH+x9n8EwF7OeTuAcsZYGYDvAfjMxtgJIYTc5Tjn16zfKYQQQohdFfzq2fHNJYVye57TdfJUffgbOy/Z2n/27NktBQUFTgCwfft2RUZGho/RaGSRkZGtu3fvrhSJRJDL5dPi4+OvarVat/T09CqZTGZOTEz00+v1AolEwrVabYmrq6t55cqV406dOuXa0dHBVqxYUZuUlFSXnZ3tmpKSMlahUBhLSkqcVCqV/uDBg+WbNm3yrq2tFavV6mAPDw9Tbm5uqVKpVOXl5RWPGTPmW9n92rVrfQ4cOKDo6Ohg8+fPv75ly5avb/dzycnJGaXRaEoA4Pnnn69Xq9WTAVy+3fM4gq0V7/cZY5kARjHGVgA4AeAtG45Lg6XCYLa+9wRwnXPe9Y/wFQCl9bUSwCUAsO5vtPa/0X6TY25gjD3HGMtjjOXZ8xccQgghwx9j7D5YRlkRQgghI4rRaMSxY8fcVCpV25kzZ2T79u1T5OXl6XQ6XZFAIOA7duzwBIC2tjZBdHR0a0lJSZFarW6Nj48PTEtLqyopKSnSaDQlLi4u5rS0NC93d/fOwsLC4vz8/OJdu3aN1ul0EgAoLi522rZt26WysrJzVVVV0uPHj7skJyfXent7GzUaTWlubm5pbzHu37/fraysTFZQUFBcXFxcdPbsWfnRo0dd+rqvOXPmBIWFhU3505/+5NXVVl9fL/L39zcCwPjx44319fW3M2eZQ9k6q/mfGGNzATQBmAzgVc758b6OYYwtAFDLOT/NGIvpd6S3jvFNAG8CgLOzMx/o6xFCCBl6GGNfwjKhWncKAF8DWDb4ERFCCBnpbqcybU/t7e2CkJCQUACIjo5uTkhIqNu8ebNXYWGhPCIiYgoAGAwGgbe3twkAhEIhli9f3gAABQUFMm9vb6NardYDgEKhMAPAiRMn3HQ6nfzQoUMeANDc3CwsKiqSSSQSrlKpWgMDA40AEBYWpr9w4YLE1lhzcnLctFqtW2hoaCgA6PV6gU6nk8XFxbXcrP+nn36qmzBhgvHy5cui+++/PzgsLMzQs69AIMBwGsx2y8Tb+jz1Cc75fQD6TLZ7uBfAw4yxhwDIYJngZissVXORtao9Dt8MDbgMYDyArxhjIgDuAOq7tXfpfgwhhBDS3YIe7zmAes55qy0HM8ZiYfmuEgJ4m3P+ei/9lsDyWNQMznleP+IlhBBC7kjXM97d2zjnbOnSpfXbtm37Tr4kkUjMIlHf6R/nnKWmplYtWbKkqXt7dna2q1QqvfHDtlAohMlksjnr5ZwjMTHxSlJSUp0t/SdMmGAEAKVSaZo/f/71zz77zDkuLq7F09PTVFlZKfb39zdWVlaKFQrFsBnqfMuh5pzzTgBmxpj77ZyYc/4K53wc5zwAlsnRPuacxwP4N4BHrd2WAfjI+voQvqlGPGrtz63tT1hnPZ8AIAjA/24nFkIIIXcHznllj60KABhjTzHGDvd1rPWH5m0A4gCEAvixdYLPnv1cYZk4NNf+d0AIIYTcudjY2Kbs7GyPy5cviwDLLOClpaXfqUyHh4cbamtrxRqNRg4ADQ0NAqPRiLlz5zZmZGSMbm9vZwBQUFAgbWpq6jNndHZ27mxsbOyzT1xcXNOePXu8uvqVl5eLu2LsqampSdDQ0CDoev3vf//bLTw8vA0A5s2bdz0zM9MTADIzMz1jY2Ov3+ozGSpsHRPfAuBLxthxWGc2BwDO+eo7uObLAPYyxjYC+ALAO9b2dwDssU6edg2WZB2c83OMsfcBFAEwAVhp/TGAEELICGHvSrN1qcr5sEzmOQ/AhwB23CKM7wEo45xftJ5jLywTfBb16PcaLCtvJN3ifIQQQsigioqKMiQnJ1+eM2dOsNlshlgs5unp6VXBwcEd3fvJZDKelZV1YfXq1X4Gg0Egk8nMWq22dM2aNXUVFRVSlUo1hXPOFAqF8ciRIxf6uuayZcvqYmNjg318fDp6e8578eLFTefOnZPNmDEjBADkcrk5KyurXKlUfqdi/dVXX4kWLVo0CQA6OzvZkiVL6h999NEmANiwYcOVRYsWBfr7+3splcqOAwcO9BnbUMIsReVbdGLsps/Fcc532T0iO3B2duatrTaNKuyVPZ4XsOWzJYSQkY4xpuec97oahrXSXApgLiwTaH4O4Mec86Ie/VwBHAYgAbDqZok3Y+xBAD8G8CAsI6z+DuDP1tFXt4rzUQCxnPOfWd//BEA053xVtz6RAH7LOV/CGDsJ4KVe4ngOwHMA4OfnF1VZWXmryxNCCBkEjLHTnPPp/T1Pfn5+RUREhE3DpsndJT8/3ysiIiKgZ3ufFW/GmB/nvGqoJtiEEEJGBHtWmnMAfAJgNue83Hq+rfYIkjEmALAZwPJb9e0+4ef06dPpV1hCCCHkLneroeYHAUQCAGPsQ875koEPiRBCyF3mZstGRnfvYK00j+ecH2aM9ZV4R8LyqNIJxthFAHthGb5ui1tN5ukKYCqAk9ZRUb4ADjHGHqYJ1gghhJDbU11dLYyJiZncs/3kyZMlvr6+I+7R4lsl3t3HW08cyEAIIYSMWCLGWPfE9E1rRdgmt1lpPgvgLIBfM8ZmwTLsXMwYOwrgwC2u+zmAIOtEnpdhSeCf7HbuRgA31hLta6g5IYQQQvrm6+vb2XNW9pHsVok37+U1IYQQYivTLZ6nG5BKM+f8PwD+wxhLAPAALIl0r4k359zEGFsF4BgsVfKd1gk+UwDkcc4P9XWThBBCCCG9uVXiHcEYa4Kl8u1kfQ3re845dxvQ6AghhNwN7FZpZoyt4pz/xfo6jHN+jnNuBvBP69YnzvkRAEd6tL3aS9+YW94ZIYQQQghusY4351zIOXfjnLtyzkXW113vKekmhBDSb5xzE4CuSnMxgPe7Ks2MsYdv83TPdnu9x14xEkIIIYT0h63reBNCCCEDZoAqzf1fF5IQQgghxA76rHgTQgghw8woxtgixtgSAG6MscXdN0cHRwghhNiLUCiMCgkJCQ0KCgqLi4ub2Nzc7JDcLiUlxbv7tdVq9aS6ujohAMjl8ml3cs66ujphbGzsxAkTJoRNnDgx7MSJE84AUFNTI5w1a1aQv7//1FmzZgVdvXrV1pVLHI4Sb0IIISOJFsDDABZYXy/sti1wYFyEEEKIXUmlUrNOpys6f/78ObFYzFNTU0fbeqzJZLJbHJmZmT4tLS038kqNRlPm5eXVr+XAnnvuufEPPvhgU3l5+bmioqKie+65xwAA69atGxMTE9NcWVlZGBMT0/zqq6/69jf+wUJDzQkhhIwYnPPljo6BEELI3aVs47Pj9RcK5fY8pzxwqn5S8s5LtvafPXt2S0FBgRMAbN++XZGRkeFjNBpZZGRk6+7duytFIhHkcvm0+Pj4q1qt1i09Pb1KJpOZExMT/fR6vUAikXCtVlvi6upqXrly5bhTp065dnR0sBUrVtQmJSXVZWdnu6akpIxVKBTGkpISJ5VKpT948GD5pk2bvGtra8VqtTrYw8PDlJubW6pUKlV5eXnFY8aM+VZ2v3btWp8DBw4oOjo62Pz5869v2bLl65vdS319vTA3N9d13759FQAgk8m4TCbrBICcnJxRGo2mBACef/75erVaPRnfXgllyKKKNyGEkBGDMZbW7XVCj31/HfSACCGEkAFmNBpx7NgxN5VK1XbmzBnZvn37FHl5eTqdTlckEAj4jh07PAGgra1NEB0d3VpSUlKkVqtb4+PjA9PS0qpKSkqKNBpNiYuLizktLc3L3d29s7CwsDg/P794165do3U6nQQAiouLnbZt23aprKzsXFVVlfT48eMuycnJtd7e3kaNRlOam5tb2luM+/fvdysrK5MVFBQUFxcXF509e1Z+9OhRl5v1LSkpkSgUCtPSpUsDpkyZEvr444/7NzU1CQCgvr5e5O/vbwSA8ePHG+vr64dNIXnYBEoIIYTY4IfdXi8DsLXb+/BBjoUQQshd4HYq0/bU3t4uCAkJCQWA6Ojo5oSEhLrNmzd7FRYWyiMiIqYAgMFgEHh7e5sAQCgUYvny5Q0AUFBQIPP29jaq1Wo9ACgUCjMAnDhxwk2n08kPHTrkAQDNzc3CoqIimUQi4SqVqjUwMNAIAGFhYfoLFy5IbI01JyfHTavVuoWGhoYCgF6vF+h0OllcXFxLz74mk4kVFxfLt27dWnX//fe3PvPMM+PXrl3ru3Xr1m9VyAUCARgbPvOoUuJNCCFkJGG9vCaEEEJGlK5nvLu3cc7Z0qVL67dt2/ad4dcSicQsEvWd/nHOWWpqatWSJUuaurdnZ2e7SqVS3vVeKBTCZDLZ/D3LOUdiYuKVpKSkulv1DQgI6PDx8em4//77WwHg8ccfb3j99dd9AcDT09NUWVkp9vf3N1ZWVooVCoX9HlYfYDTUnBBCyEgiYIx5MMY8u71WMMYUAIbNzKeEEELInYiNjW3Kzs72uHz5sgiwzAJeWlr6ncp0eHi4oba2VqzRaOQA0NDQIDAajZg7d25jRkbG6Pb2dgYABQUF0q5h3r1xdnbubGxs7LNPXFxc0549e7y6+pWXl4u7YuzJz8/P5Ovr25Gfny8FgH/+859ukydPNgDAvHnzrmdmZnoCQGZmpmdsbOz1W30mQwVVvAkhhIwk7gBO45tq95lu+/h3uxNCCCEjR1RUlCE5OfnynDlzgs1mM8RiMU9PT68KDg7u6N5PJpPxrKysC6tXr/YzGAwCmUxm1mq1pWvWrKmrqKiQqlSqKZxzplAojEeOHLnQ1zWXLVtWFxsbG+zj49PR23Peixcvbjp37pxsxowZIQAgl8vNWVlZ5Uql8qYV6z//+c9V8fHxEzs6Opifn1/7e++9VwEAGzZsuLJo0aJAf39/L6VS2XHgwIE+YxtKGOcj7+8QZ2dn3tra2q9z2ON5gZH42RJCyO1ijOk5586OjsNRpk+fzvPy8hwdBiGEEACMsdOc8+n9PU9+fn5FRETELYdNk7tPfn6+V0REREDPdqp4E0IIGVEYYyIAcQBCrE1FAI5xzofNc2CEEEIIGVko8SaEEDJiMMaUAD4GcAXAF7AMOV8AYDNj7D7O+U3XDCWEEELI4KqurhbGxMRM7tl+8uTJEl9f305HxDSQKPEmhBAykvwOQAbnPK17I2NsNYDfw7LEGCGEEEIczNfXt7PnrOwjGSXehBBCRpKZnPPlPRs55+mMsRIHxEMIIYQQQsuJEUIIGVHa+tinH7Qo7MRsaETjv16G2TjsQieEEEJIN1TxJoQQMpK4M8YW36SdAXAb7GD6q/3iP9Hy2R9huJADz0f3Q6QIdHRIhBBCCLkDlHgTQggZSTQAFvayTzuYgdiDU+hSeEpdce3Ak6h9Jwoej+yBU3Bvt0cIIYSQoYqGmhNCCBkxOOfP9LU5Or47IQuMhfdPT0PkMRHX3n8YTSfXgptH3GSvhBBCbpNQKIwKCQkJDQoKCouLi5vY3NzskNwuJSXFu/u11Wr1pLq6OiEAyOXyabd7vvz8fGlISEho1+bi4jItJSXFGwBqamqEs2bNCvL39586a9asoKtXrwrtdycDixJvQgghZIgTeUzA6GWnII94Bs2fbkT93ofQqa93dFiEEEIcSCqVmnU6XdH58+fPicVinpqaOtrWY00mk93iyMzM9GlpabmRV2o0mjIvL687/oU4IiKiXafTFel0uqLCwsIimUxmfuKJJ64DwLp168bExMQ0V1ZWFsbExDS/+uqrvva4h8FAQ80JIYSQYYCJnTBqwTuQKL+P68dW4eo7UVAs2QfJ2OmODo0QQu5qX+94dnz7V4Vye55TOm6qfuwLOy/Z2n/27NktBQUFTgCwfft2RUZGho/RaGSRkZGtu3fvrhSJRJDL5dPi4+OvarVat/T09CqZTGZOTEz00+v1AolEwrVabYmrq6t55cqV406dOuXa0dHBVqxYUZuUlFSXnZ3tmpKSMlahUBhLSkqcVCqV/uDBg+WbNm3yrq2tFavV6mAPDw9Tbm5uqVKpVOXl5RWPGTPmW9n92rVrfQ4cOKDo6Ohg8+fPv75ly5avb3Vfhw4dcvPz82sPDg7uAICcnJxRGo2mBACef/75erVaPRnA5dv6cB2EKt6EEELIMMEYg3PkCoxe9inAzbi66160fvG2o8MihBDiQEajEceOHXNTqVRtZ86cke3bt0+Rl5en0+l0RQKBgO/YscMTANra2gTR0dGtJSUlRWq1ujU+J2bspAAAIABJREFUPj4wLS2tqqSkpEij0ZS4uLiY09LSvNzd3TsLCwuL8/Pzi3ft2jVap9NJAKC4uNhp27Ztl8rKys5VVVVJjx8/7pKcnFzr7e1t1Gg0pbm5uaW9xbh//363srIyWUFBQXFxcXHR2bNn5UePHnW51b299957ikcfffTGEK/6+nqRv7+/EQDGjx9vrK+vHzaF5GETKCGEEHI7GGOzAASg23cd53y3wwKyI8nYGRj9szNoOPAkrh9egY7L/8Wo2L+AiWSODo0QQu46t1OZtqf29nZBSEhIKABER0c3JyQk1G3evNmrsLBQHhERMQUADAaDwNvb2wQAQqEQy5cvbwCAgoICmbe3t1GtVusBQKFQmAHgxIkTbjqdTn7o0CEPAGhubhYWFRXJJBIJV6lUrYGBgUYACAsL01+4cEFia6w5OTluWq3WLTQ0NBQA9Hq9QKfTyeLi4lp6O8ZgMLATJ064b968+aub7RcIBGCM2RqCw1HiTQghZMRhjO0BEAjgLICu58w4gBGReAOAUO4Fzx8fRbNmHZpP/Q7GmrNQLNkH0agAR4dGCCFkEHQ94929jXPOli5dWr9t27bvDL+WSCRmkajv9I9zzlJTU6uWLFnS1L09OzvbVSqV8q73QqEQJpPJ5qyXc47ExMQrSUlJdbYes2/fPvfQ0FD9+PHjbwxZ9/T0NFVWVor9/f2NlZWVYoVCYb+H1QcYDTUnhBAyEk0HcC/n/P8457+wbqsdHZS9MYEQbvdthOKxj2C6dh6170TBcOGYo8MihBDiILGxsU3Z2dkely9fFgGWWcBLS0u/U5kODw831NbWijUajRwAGhoaBEajEXPnzm3MyMgY3d7ezgCgoKBA2tTU1GfO6Ozs3NnY2Nhnn7i4uKY9e/Z4dfUrLy8Xd8XYm7179yoee+yxa93b5s2bdz0zM9MTADIzMz1jY2Ov93WOoYQq3oQQQkaiQgC+AK44OpDB4BT8MMQ/zUP9viWofy8OruoNcJ39WzBGv68TQsjdJCoqypCcnHx5zpw5wWazGWKxmKenp1d1TU7WRSaT8aysrAurV6/2MxgMAplMZtZqtaVr1qypq6iokKpUqimcc6ZQKIxHjhy50Nc1ly1bVhcbGxvs4+PT0dtz3osXL246d+6cbMaMGSEAIJfLzVlZWeVKpfKmFeumpibBp59+6rZr167K7u0bNmy4smjRokB/f38vpVLZceDAgT5jG0oY5/zWvYYZZ2dn3tra2q9z2ON5gZH42RJCyO1ijOk5586DfM1/A7gHwP8AtHe1c84fHsw4AGD69Ok8Ly9vUK5l7mjF9SPPo60wC9JJ86F4ZA8ETh6Dcm1CCBkOGGOnOef9Xg4iPz+/IiIiwuZh0+TukZ+f7xURERHQs50q3oQQQkai9Y4OwBEEEmd4PLIHknHfR+M/E1H7znR4Lt0PsU+Eo0MjhBBC7mqUeBNCCBlxOOcaR8fgKIwxuExfCbHPNFzbvxS1787EqHl/gWT8vY4OjRBC+k0gHw2h3NPRYRA7qK6uFsbExEzu2X7y5MkSX1/fzpsdM5xR4k0IIWTEYYzNBPBnAFMASAAIAbRyzt0cGtggko6fBe+fnsG1/Y/j+uGfOTocQgixC7f7/wDXWb9ydBjEDnx9fTt7zso+klHiTQghZCT6C4AnAHwAywznTwMIdmhEDiB08YHXUydgKDsKbuzf3CeEEDIU0KMzZLiixJsQQsiIxDkvY4wJOeedAN5ljH0B4BVHx3VbPvwQaGgAfvITQCq9o1MwgQhOwQvtHBghhBBCbgetM0IIIWQk0jPGJADOMsbeYIytwXD7zjOZgJdfBlasAAIDgS1bgJYWR0dFCCGEkDswvP4IIYQQQmzzE1i+41YBaAUwHsASh0Z0uz78ELhgXZ708mXgxRcBf38gJQW4ds2xsRFCCCHktlDiTQghZMThnFcCYADGcM43cM5f5JyXOTqu2xIXB7zxBuDr+03btWvAunWWBPyll4Cvv3ZcfIQQQhxKKBRGhYSEhAYFBYXFxcVNbG5udkhul5KS4t392mq1elJdXZ0QAORy+bQ7OeeGDRu8J02aFBYUFBS2cOHCCXq9ngGATqeThIeHh/j5+U2dP3/+RIPBwOxzFwOPEm9CCCEjDmNsIYCzAHKs7+9hjB1ybFS3yc0NSEoCysuBjAxgwoRv9rW0AKmplrbnn/+mMk4IIeSuIZVKzTqdruj8+fPnxGIxT01NHW3rsSaTyW5xZGZm+rS0tNzIKzUaTZmXl9cdLwdWXl4ufvPNN33Onj1bdP78+XOdnZ3s7bffVgDAiy++OG7VqlU1VVVVhe7u7qatW7d62eMeBgNNrkYIIWQkWg/gewBOAgDn/CxjbEJfBwxZMhnwwgvAz34GvP8+8PvfA4WFln0dHcCbbwJvvw089hjwyitAeLhj4yWEkLtMwz+eHW+8Wii35znFo6fqPRbuvGRr/9mzZ7cUFBQ4AcD27dsVGRkZPkajkUVGRrbu3r27UiQSQS6XT4uPj7+q1Wrd0tPTq2QymTkxMdFPr9cLJBIJ12q1Ja6uruaVK1eOO3XqlGtHRwdbsWJFbVJSUl12drZrSkrKWIVCYSwpKXFSqVT6gwcPlm/atMm7trZWrFargz08PEy5ubmlSqVSlZeXVzxmzJhvZfdr1671OXDggKKjo4PNnz//+pYtW3odttXZ2claW1sFUqm0s62tTTBu3Dij2WzGZ5995vrRRx9dBIBnn322fv369WNffvnlq3f6OQ8mqngTQggZiYyc88YebdwhkdiLSAQ8+SSQnw8cOgTMnPnNPrMZ2LsXiIgAFiwATp1yXJyEEEIGldFoxLFjx9xUKlXbmTNnZPv27VPk5eXpdDpdkUAg4Dt27PAEgLa2NkF0dHRrSUlJkVqtbo2Pjw9MS0urKikpKdJoNCUuLi7mtLQ0L3d3987CwsLi/Pz84l27do3W6XQSACguLnbatm3bpbKysnNVVVXS48ePuyQnJ9d6e3sbNRpNaW5ubmlvMe7fv9+trKxMVlBQUFxcXFx09uxZ+dGjR11u1nfChAnGlStXVk+YMCHc29s7wtXVtXPx4sVNNTU1IldX106xWAwACAgI6KipqZEMwEc6IKjiTQghZCQ6xxh7EoCQMRYEYDWA/zg4JvsQCICFCy0JtkZjqYD/85/f7D982LL98IeWCvi8eQAbNo/AEULIsHM7lWl7am9vF4SEhIQCQHR0dHNCQkLd5s2bvQoLC+URERFTAMBgMAi8vb1NACAUCrF8+fIGACgoKJB5e3sb1Wq1HgAUCoUZAE6cOOGm0+nkhw4d8gCA5uZmYVFRkUwikXCVStUaGBhoBICwsDD9hQsXbE56c3Jy3LRarVtoaGgoAOj1eoFOp5PFxcV9Z7mOq1evCg8fPjyqrKzsS09Pz8758+dP3L59u2LRokVN/fm8HG3AEm/G2HgAuwH4wFJleJNzvpUxpgDwdwABACoAPMY5b2CMMQBbATwEQA9gOef8jPVcywAkW0+9kXO+a6DitofxADYDQEkJMHmyg6MhhJC70i8A/BZAO4D3ABwD8JpDI7I3xoCYGMt2+jTw+uuWmdC5tbCv1Vq2adMsy5JNnerIaAkhxH4mTADkdh3ZPSx1PePdvY1zzpYuXVq/bdu2yz37SyQSs0jUd/rHOWepqalVS5Ys+VaSm52d7SqVSm+MHBMKhTCZTDb/qss5R2Ji4pWkpKS6W/X9xz/+4ebn59c+duxYEwD86Ec/uv6f//zH5YUXXrjW3NwsNBqNEIvFqKiokPj4+HTYGoOjDWTF2wTgl5zzM4wxVwCnGWPHASwH8C/O+euMsV8D+DWAlwHEAQiybtEAMgBEWxP1dQCmw5LAn2aMHeKcNwxg7P1igOVm8NprwN/+5uBoCCHk7sM518OSeP/2do5jjMXC8iOwEMDbnPPXe+x/EcDPYPmOuwrgWesM6o4VFQV88AGg01lmQt+zx7IOOAB88QXwxBOOjY8QQuwpNxf43vccHcWQFBsb27R48eJJv/nNb2qUSqWppqZG2NjYKAwODv5WghoeHm6ora0VazQauVqt1jc0NAhcXFzMc+fObczIyBi9YMGCZqlUygsKCqQBAQHGvq7p7Ozc2djYKBgzZkyvfeLi4prWr18/9rnnnrvm7u5uLi8vF0skEq5UKr8zy1tAQEDHmTNnXJqbmwXOzs7mjz/+2DUqKkovEAgwc+bM5nfffdfjueeea9i5c6fnggULrt/xhzXIBuwZb875la6KNee8GUAxACWARwB0Vax3AfiR9fUjAHZzi/8CGMUYGwNgHoDjnPNr1mT7OIDYgYrbHq4C2AYA771n+SOIEELIoGCMHepru8WxQlj+9x0HIBTAjxljoT26fQFgOuc8HMA+AG8MxH104Z0mNP33A3Bu4+PpISHAzp2WWc5XrwacnAYyPEIIIUNMVFSUITk5+fKcOXOCg4ODQ++///7gS5cuiXv2k8lkPCsr68Lq1av9Jk+eHBoTExOs1+sFa9asqQsJCTGoVKopQUFBYStWrPA3Go19VraXLVtWFxsbGxwdHR3cW5/Fixc3LV269NqMGTNCgoODQxctWhR4/fp14c363n///a0LFy5sCA8PnzJ58uQws9nMXnzxxasAkJqa+tWf//xnXz8/v6kNDQ2ihISEW1bQhwpm85d5fy7CWAAALYCpAKo456Os7QxAA+d8FGMsG8DrnPNPrfv+BUslPAaAjHO+0dq+FkAb5/xPPa7xHIDnAEAikUS1t7f3N+Z+He8F4KpcDjzyCPD//l+/zkUIIcMZY0zPOXcepGtdBXAJluHlubCs5X0D51zTx7HfB7Cecz7P+v4V6zG/76X/NAB/4Zzf21dM06dP53l5ebdzGzdc1+zClR3L4RK5EGOefwciN5tXirG4ehVIT7c8893P70VCCBky9u4FVKo7OpQxdppzPr2/IeTn51dEREQMm6SPDJ78/HyviIiIgJ7tAz65GmPMBcCHABI5503dE1rOOWeM2SXz55y/CeBNAHB2dnb4zLV1ALBqFfDHPwJr1wJTpjg6JEIIGbLsOMTbF8BcAD8G8CSAwwDe45yfsyEMJSxJe5evYHn0qTc/BXC0l/u58WOwn5+fDZe+Ofcf/ATm1gbUvvcyLr4cjrEv/BUuEfNsP8Ho0ZbHnl4bWY+3E0IIIcPNgC4nxhgTw5J0Z3HO91uba6xDyGH9b621/TIs85J1GWdt66196EtKskz8kJLi6EgIIWTIsucQb855J+c8h3O+DMBMAGUATjLGVtk55qdgmXvkj73E8SbnfDrnfPro0bdZpe5+HYEAiocSEbDxc4hcPHHp9VhU70qEucNwx+ckhBBChoLq6mphSEhIaM+turr6pkPQh7sBS7ytw8jfAVDMOd/cbdchAMusr5cB+Khb+9PMYiaARs75FVhmon2QMebBGPMA8KC1bejz8gJ+8Qvg738HztlSbCGEkLvS9wCUcc4vcs47AOyFZd6PGzjn/7ZOmAYA/4XlR9ibYoxJGWOLAfwNwEoA6QAO2BCHTT/0MsYegGXStoc554MyflvmH46A330Oj9jVaMjZiorkGTBUfTkYlyaEEEIGhK+vb6dOpyvqufn6+nY6OraBMJAV73sB/ATA/Yyxs9btIQCvA5jLGDsP4AHrewA4AuAiLNWJtwD8HwBwzq/BsgTM59Ytxdo2PLz0EuDsTFVvQsjdTMQYy+u2Pddj/82GeCv7OF9fQ7x3A/gMQCSADZzzGZzz1zjntoyU+hxAEGNsAmNMAuAJWH4U7n7+aQAyYUm6a29yDrvqbG1Gbfa76DToIZA4wXfZVox/+QhMTVdRkTwD13LSbZ94jRBCCCEOM2DPeFsnSetthrI5N+nPYalM3OxcOwHstF90g8jT0zKz7O9/b3nWm9ZRJYTcfUz2mMgG+NYQb3UvXZ4C0AogAcDqbvOKMFi+atx6Ozfn3GQdkn4MlmfNd3LOzzHGUgDkcc4PwTK03AXAB9ZzV3HOH+7/nd3cNe1BXNj4LCq2vgjv+c/AZ/ELcLknDhP/UIArb/4UNbsS0HL2KMa+8C5Eo3wHKgxCCCGE9NOAPuNNrH75S8DFBdiwwdGREELIUGS3Id6ccwHn3NW6uXXbXPtKursdf4RzHsw5D+Sc/87a9qo16Qbn/AHOuQ/n/B7rNmBJNwB4xT6F0O0nMSp6Hqo/+DPOPjYZRb+Yi8YvTkGZuB++z26HvugkLv5KhebT/xjIUAghhBDSD5R4DwaFAkhIAPbtAwoKHB0NIYQMNUNuiPdQwRiDe6QawRv3IvLQJYx/fiPaqkpQ+uvF+GLxBLRUXcW4pByIFOPw1Z8expV3fg5zu/7WJyaEEELIoKLEe7C8+CLg5kZVb0II6YFzbgLQNcS7GMD7XUO8GWNdFeXuQ7zPMsYO9XK6EUvi6Ytxz/wWkR9exOQ3PoJT4FR89dY6FL7wADrYBDjPeBzXT+xA+W+iYCj/wtHhEkIIGWBCoTAqJCQkNCgoKCwuLm5ic3OzQ3K7lJQU7+7XVqvVk+rq6oQAIJfLp93JOV977TXvoKCgsEmTJoWlpKR4d7XX1NQIZ82aFeTv7z911qxZQVevXh02M6BT4j1YPDyAxERg/37g7FlHR0MIIUPKUBviPVTor7TiP7/4NxrO1d9oYyIRFD98GKFpObjng/PwfSIRzac1uPz+39FhHg/jtSsoXxuN+uw/gZvNDoyeEELIQJJKpWadTld0/vz5c2KxmKemptq8fqXJZLJbHJmZmT4tLS038kqNRlPm5eV1xzOTf/7557Ldu3ePPnPmTHFxcfG5nJycUYWFhVIAWLdu3ZiYmJjmysrKwpiYmOZXX3112ExwMmCTq5GbWLMG2LrVUvU+YMvKNoQQQu5mV09fQcWH/0PZ33QYc984hP5fBMbOGY+uSeOcxk9CwC/+iPErUnDt4w9Q/eF2XC++BLm3ELVZSWj8z/sY/8v9EHv2uvoaIYSQfmorena8uaVQbs9zClym6p1Cd166dU+L2bNntxQUFDgBwPbt2xUZGRk+RqORRUZGtu7evbtSJBJBLpdPi4+Pv6rVat3S09OrZDKZOTEx0U+v1wskEgnXarUlrq6u5pUrV447deqUa0dHB1uxYkVtUlJSXXZ2tmtKSspYhUJhLCkpcVKpVPqDBw+Wb9q0ybu2tlasVquDPTw8TLm5uaVKpVKVl5dXPGbMmG9l92vXrvU5cOCAoqOjg82fP//6li1bvr7ZvXz55ZdO06ZNa3F1dTUDwL333tu8d+/eURs3bqzJyckZpdFoSgDg+eefr1er1ZNxk3lhhiJKvAfTqFGW5Hv9euCLL4BpdzTyghBCyF1C5qTDKKffQDb5XlwvjMK/llbBPcQToT8Px4SlwRA5Wb7GhTInjH7oaYx+6Gm06M6g+sPtaPpkN7j5c5xfFQCX7z8D2YQoB98NIYT0nzx4Olwm22WhjBHDaDTi2LFjbg8++GDTmTNnZPv27VPk5eXppFIpf+qpp/x27NjhuWrVqvq2tjZBdHR061tvvfWVwWBgkyZNmpqVlXVBrVbrr127JnBxcTGnpaV5ubu7dxYWFha3tbWxGTNmhCxcuLAJAIqLi53Onj17MSAgwBgVFRVy/Phxl+Tk5NqMjAwfjUZT2jPR7m7//v1uZWVlsoKCgmLOOR544IFJR48edYmLi2vp2feee+5pS0lJUVZXVwudnZ358ePH3SMiIloBoL6+XuTv728EgPHjxxvr6+uHTT47bAIdMRISgC1bLFXvgwcdHQ0hhJAhzC30HgT+/BV89f47kBk/gYu/Lzr0P0Bu4kV88doYBP90KiY/GwYn728KLS4hkZj027dhav4Tqt//ExqPp6L1s7fR+tnbDrwTQgixj5bJcXBZf8TRYXzL7VSm7am9vV0QEhISCgDR0dHNCQkJdZs3b/YqLCyUR0RETAEAg8Eg8Pb2NgGAUCjE8uXLGwCgoKBA5u3tbVSr1XoAUCgUZgA4ceKEm06nkx86dMgDAJqbm4VFRUUyiUTCVSpVa2BgoBEAwsLC9BcuXJDYGmtOTo6bVqt1Cw0NDQUAvV4v0Ol0spsl3pGRkYaEhITqOXPmBDs5OZnDwsL0QuF3H+UWCATotmzokEeJ92AbNcoy0dq6dcCZM0BkpKMjIoQQMkQ5jRmHyS9tRFDCOtR+nI2q/5eJuk/2QTHqQwhcpqM4NQqFaeGYuHQypvw8HB6hnjeOFbmOwrifbsTYp1/FtWM70NnW7MA7IYQQ+3CeMtvRIQwZXc94d2/jnLOlS5fWb9u27TvDryUSiVkk6jv945yz1NTUqiVLljR1b8/OznaVSqW8671QKITJZLI56+WcIzEx8UpSUlKdLf3XrFlTt2bNmjoAWLVqlXLcuHEdAODp6WmqrKwU+/v7GysrK8UKhcJ+D6sPMEq8HaGr6r1+PXDorpuYlxBCyG0SiMXwnbcIvvMWQX+pHJf2vo2vPtgJd5f/QeDkjSsHo3ExayZ87gtH6P+FY+z93zwHLhBL4LVgtYPvgBBCyGCIjY1tWrx48aTf/OY3NUql0lRTUyNsbGwUBgcHd3TvFx4ebqitrRVrNBq5Wq3WNzQ0CFxcXMxz585tzMjIGL1gwYJmqVTKCwoKpAEBAca+runs7NzZ2NgoGDNmTK994uLimtavXz/2ueeeu+bu7m4uLy8XSyQSrlQqb5o4X758WaRUKk3nz5+XHD58eNTnn3+uA4B58+Zdz8zM9Ny0aVN1ZmamZ2xs7PU7+JgcghJvR3B3B375S2DtWiAvD5hOz6kQQgixjXz8BExO+h2CEtej9l//QNV7b8L8STacRh1G29kIaJ+YCefA2ZiychomLg2CUEZf9YQQcreIiooyJCcnX54zZ06w2WyGWCzm6enpVT0Tb5lMxrOysi6sXr3az2AwCGQymVmr1ZauWbOmrqKiQqpSqaZwzplCoTAeOXLkQl/XXLZsWV1sbGywj49PR25ubunN+ixevLjp3LlzshkzZoQAgFwuN2dlZZX3lng//PDDgdevXxeJRCKelpZW1TVL+oYNG64sWrQo0N/f30upVHYcOHCgz9iGEsY5v3WvYcbZ2Zm3trb26xz2eF6gz8+2qQmYMAH4/veB7Ox+X4sQQoYqxpiec+7s6DgcZfr06TwvL29Ar2Gpgr+FS+/vREddDbjQE/qW7wMu9yF4xQ8Q/GwYnEbbdcJdQggZlhhjpznn/a565efnV0RERNg0bJrcXfLz870iIiICerbTz+CO4uZmqXr/9rfA//4HfO97jo6IEELIENNQZsYnr7YjZKkYE2KFEDvd/EdhSxV8E4ISN6DmxCFceu9N1H2SDXQewcU/T0Vx6kxA4D7I0RNCiP1NWf0AIn7zoKPDIOS2UeLtSL/4BbB5s+VZ7yNDa3ZGQgghjld7rhOVH3eidH8nxC7ApAUihDwmQsADQggl303CBWIxxsQtwZi4JdBXXcSlv7+NqvfegbThTQdETwghA6BZAoAS75GgurpaGBMTM7ln+8mTJ0t8fX07HRHTQKLE25FcXYGXXgJeeQX473+BmTMdHREhhJAhRG8AKq4Co7wA1zEMZYdNKN5rgnQUEPSICCFLRfCLEUIg+m4SLvebaKmCJ6xHw5nPYO5od8AdEEKIfTlPCHJ0CMROfH19O3vOyj6SUeLtaKtWAamplqp3To6joyGEEDKETHlIhPg9QP4+E3Q5nTC1Ax6egIsXg+4DEwp3meA0miF4kRAhS8UYd68ATPDtJFwgkcBzptpBd0AIIYQQgBJvx3NxAZKSgJdfBj77zDLZGiGEEAJALOeIeEyIyCfFMDRzFGWbULDPhOKcTpgMgIcCcHYFCneZkP+mCS5KhsmLLZVw3xkCu0wUSgghhJD+Ezg6AAJg5UrAy8tS9R6KSksBvd7RURBCyF2n+JOvsdz7HaT++Bj++1EpJs01YfmHTthQ7YynsqQYpxaiopLjcgtgcAHMIuCLHUZk/bANb4XooU1uR21+Z9+rbBBCCCFkwFHFeyhwdgZ+9SvL9p//ALNmOTqibxw5AjzyCBAbC/zjH46OhhBC7irOHjJ875EJOH2kEp/uPQ/GgKBoH0Q9FICo+f54+v3RMOqBomwT8veZUHy0E50GYNQowKkT+HyzEf/7kxHuAQwyBVW/CSHD37SfizH1J2JHh0HIbaN1vHsx4Ot499TaCkycCISHA8eP9/vadnHqFDB3LiCRAI2NwNGjlgScEEJuA63j3f91vM1mjotnanH6cCVOH6lE2ec14Bzw8JUj8iF/RD3kj4i5fhAKxCg6bB2OfrQTpjZLEq7wZGBCO90QIYQ4UGi8COpk6R0fP5LW8RYKhVFBQUFtnZ2dbNKkSW3vv/9+haurq3mw40hJSfFes2ZNXde11Wr1pA8//LDcy8urUy6XT9Pr9V/c7jlfe+017927d4/mnOPpp5+++uqrr9YCwGeffeb085//3F+v1wvGjRvXsW/fvosKhWLQ77kvva3jTYl3LwY98QYsk6y99BLwySfA7Nn9vn6/fPkl8MMfAqNHAx9/DNx/PyAQWNrF9CsjIcR2lHj3P/Hu6XqtHl/kVOH04Qp8cawK+sYOCEUCTPnBGEyfH4DIh/wxetwo6I52In+fCaXHO2HqsGsIhBDiEA9tlEC9RnLHx4+kxLt7Uvvwww9PiIyM1K9fv77GlmNNJhNEIvsMflYqlaq8vLziMWPGmPqK0Vaff/657Mknnww8c+ZMsUwmM6vV6uA333yzcurUqe1Tp06d8oc//OHS/PnzW9LS0jzLy8ulW7du/douN2InvSXeNNR8KPn5z4E//hFYtw74178cF8fFi8C8eZYh8MePA+PGAVu2AAsWAH/5C7BmjeNiI4QQglHectz3dAjuezoEnSYzSj6rRt7hCpw5Uom/vnQKf33pFHwmuFmq4c8E4IldSkid6CuDmgkxAAAdOElEQVSfEEIGxrPjgUK5fc85VQ/svGRr79mzZ7cUFBQ4AcD27dsVGRkZPkajkUVGRrbu3r27UiQSQS6XT4uPj7+q1Wrd0tPTq2QymTkxMdFPr9cLJBIJ12q1Ja6uruaVK1eOO3XqlGtHRwdbsWJFbVJSUl12drZrSkrKWIVCYSwpKXFSqVT6gwcPlm/atMm7trZWrFargz08PEy5ubmlvSXia9eu9Tlw4ICio6ODzZ8///qWLVtumjB/+eWXTtOmTWvpqqDfe++9zXv37h21cePGmsrKSmlcXFwLACxYsKBp3rx5wUMt8e4NfQsPJXK5ZXbzF18EtFpLxXmwVVcDDz4IGAyWyru/v6X9oYcsw8w3bADi4wFv78GPjRBCyHcIRQKE/mAsQn8wFk+/PgtXq5px5mglTh+uwMfvFuPoti8hEDKIJDTWnBAy/D25MRqPvDjN0WEMKUajEceOHXN78MEHm86cOSPbt2+fIi8vTyeVSvlTTz3lt2PHDs9Vq1bVt7W1CaKjo1vfeuutrwwGA5s0adLUrKysC2q1Wn/t2jWBi4uLOS0tzcvd3b2zsLCwuK2tjc2YMSNk4cKFTQBQXFzsdPbs2YsBAQHGqKiokOPHj7skJyfXZmRk+Gg0mtKbVby77N+/362srExWUFBQzDnHAw88MOno0aMuXUl0d/fcc09bSkqKsrq6Wujs7MyPHz/uHhER0QoAkyZNMmRlZY36yU9+cv1vf/uborq6+s6HPwwySryHmhdeAN54w1L1/ve/B/fa169bkusrVywV97Cwb/YxZql6q1TA2rVAZubgxkYIIcQmo/1cMe/5qZj3/FR0GEwo0n6Nc9qvYerodHRohBDSb4GRox0dwk3YXpm2p/b2dkFISEgoAERHRzcnJCTUbd682avw/7d37+FVFWffx793EkKI4SiHUMBDlAgJyCFI0VLRUngToHiCFsRqLVy0r0UMKj5qqT617aPYohhEq1b7FKSlBaq1UUBoFYp9i6JFDDSBUCIYICCSBAwhJJn3j7VCNyEJAXbI3tvf57rWlbVnrT0zN1lk9uw1ayYnJ75fv369AcrLy6M6d+5cCRAdHc13vvOdgwCbNm2K69y587Fhw4aVAdQ8J7169eo2ubm58a+99lp7gEOHDkVv2bIlLjY21vXt2/fzSy655BhAampq2fbt2xvd6V2xYkWbtWvXtklJSUkBKCsri8rNzY2rq+M9cODA8rvuumvv8OHDk1u1alWdmppaFh3tfXn80ksvFUybNq3HY4891jU9Pb24RYsWYfPctDreoaZVK7j/fsjMhLffhmuuOTflHjkCY8fCli3e7OVDhpx8Tq9ecOedMHeu9wXBAH3bKCISymLjYug/8gL6j7yguasiIiJB1rJly+rc3NwtgWnOORs/fvyB+fPnF9Y+PzY2tvpUz3U752zOnDk7b7rpptLA9Ozs7NYtW7Y83smNjo6msrKy0ZNiOefIzMzcM3PmzEY9Fz9jxoxPZ8yY8SnAtGnTunXv3r0CYMCAAeXvvPPONoBNmza1fPPNN9s1tg7NTet4h6KpU6FrV++u97mY/K6yEr71LVi3DhYu9J7vrs9DD3lrjt9117mpm4iIiIiINEp6enppdnZ2+8LCwhiAoqKi6K1bt550Z/ryyy8v37dvX4s1a9bEAxw8eDDq2LFjjBgxouTZZ5/tdPToUQOvc1taWtpgn/G8886rKikpafCcjIyM0oULF3asOW/Hjh0taupYl5pj27Zti3399dfbTZky5bPA9KqqKh5++OGukydP3tdQuaFEd7xDUatW8MADMH06zJ8Pd9zhzSjeFKqrYcoU7y73M894HfCGtGsHP/uZ9+XAkiXwzW82Tb1EREREROS0pKWllc+aNatw+PDhydXV1bRo0cJlZWXtTE5OPmFti7i4OLdo0aLt06dPv6C8vDwqLi6ueu3atVtnzJjxaUFBQcu+ffv2ds5Zhw4djr3xxhvbGyrztttu+zQ9PT25S5cuFevXr99a1zk33nhj6ebNm+OuuOKKXgDx8fHVixYt2tGtW7c6nwsfO3bsJcXFxTExMTFu7ty5Ozt27FgF8NJLL3V48cUXOwOMGjXq4PTp0w+cyb9Tc9ByYvVoluXEApWXe3ee166FwYMhKwu+/OWzrtMJnPOWL3viCXjkEe/Z7caoqoJBg+DAAcjN9SaFExGph5YTC/5yYiIicmYiaTkxCU31LSemoeahKi7Om1xtwQLYtct75vo73/FmHQ+W2bO9Tvedd8KsWY1/X3S090XArl3e8mciIiIiIiJSL3W8Q1lUFHz725CX5y0z9tvfQnIy/OIXUFFx6vc35IUXvOHsN9/sTZZ2unf4v/pVb1j67Nmwc+fZ1UVERERERL5Q9u7dG92rV6+U2tvevXsjcv1LdbzDQevW8NhjsHkzDBsGM2d6y3otX35m+S1b5s1KnpEB//u/Z/78+OOPe8PV77vvzN4vIiINOnr0KLt3727uaoiIiARdYmJiVW5u7pbaW2JiYkSuf6mOdzjp2dObBO2NN7zXo0bBmDGwbVvj8/jLX7y73EOGwNKl0KLFmdfnggu8O/G//z387W9nno+IiNRp9erVdOvWjbS0NB5++GE2bNhAdXV1c1dLRERETpM63uEoIwM++sh7vnrtWkhN9db+PnSo4fdt2ADXX+8NV8/ODs6kaPfdBz16eDOwV0Xkl1MiIs2mT58+PProo7Rq1Yqf/vSnXHHFFXTr1o0pU6bw6quvcvjw4eauooiIiDSCOt7hKjbWm5F861aYNMl71vqyy+Dll+teXzs31+uwd+oEK1dC+/bBqUd8vPcFwMaN8NJLwclTREQAuPDCC7n//vtZt24dRUVFLFy4kGHDhrF06VJuuOEGzj//fNLT03n66afZsWNHc1dXRERE6qGOd7hLTIRf/xr+8Q/o3t2bjO0rX/HubtfYtQtGjvRmI3/zTfjSl4Jbh29+05ts7cEHobg4uHmLiAgAHTt25JZbbmHx4sXs37+ft956izvvvJOCggLuvPNOkpKS6NOnz/GOemVlnUujioiISDPQOt71aPZ1vM9EdbW3/Nh//Rfs3w/f/S7ccw/ceCPs3g1r1kD//k1T9j//CWlpkJnpLVEmIuLTOt5Nv473tm3beP3118nOzmbNmjVUVlbSoUMHMjIyGDNmDCNHjqRt27ZNWgcRkXPBzIg604mB0TregRYuXNguJSWlPC0trRwgMzPzS9dcc82h66+/vt7nV2+66aaLxowZU3L77bcfPFX+2dnZrefMmdPlrbfeyg9mvRsrPj5+QFlZ2T/Pdbn1reMdc64rIk0oKspb6/uGG+AnP4GnnoIXX/TWBF+5suk63QADBsCUKTBvHkydCr16NV1ZIiJygp49e5KZmUlmZiYlJSWsWrWK7OxsXn/9dRYtWtTc1RMRCZrZs2dzX6itqGOW1mR5O/d+U2R77NgxXn311XaVlZUlNR3vuXPnahmNJqSOdyRq29Zb63vKFHj0UZg4Ea6+uunL/dnP4A9/gBkzvJnXgzBqQERETk/btm0ZN24c48aNo6qqivfee481a9ZQUVHR3FUTETlrQ4cObe4qhIy8vLzY9PT0nn379i3LycmJT05OPrJkyZKCH//4x11WrFjR7ujRo1GDBg06vGjRoo+joqIYPHjwZX369Cl79913E0aPHn1w9erV7f7xj3+0nj17dtdly5Ztf+ihh7rW3M2+9957u9aVx6ksXbq0zcyZM3u0atWqevDgwcdnAC0tLY2aPHnyBbm5ua0qKyvthz/84e5bbrmlOCsr6/w//elP7Q4dOhRTVFTUYty4cQfmzJmzB+CZZ57p8Oyzz3Y5duyYDRw48PMFCxZ8HBMTQ3x8/IDJkyfve/PNN9vGxcVVZ2dn5/fo0aMyNzc3dsKECUllZWVR6enpJzz/+qMf/ajLK6+80qGiosJGjx5d/OSTT+7Oy8uLzcjI6Dl48ODDGzZsSOjSpUvFypUr8xMSElxOTk7LqVOnXnjgwIGY6Ohot2TJkn+npqYerSufxv6+1PGOZL16wW9+c+7K69QJHn4Y7r7b63iPHn3uyhYRkZNER0czZMgQhgwZ0txVERGRJlBQUBD33HPPFYwcOfLz8ePHX/Tzn/+808yZM/f94he/2ANw/fXXX7x48eK2N998cwlARUWF5eTk/AsgPz8/rr5h4w3lUZ+ysjKbNm3aRatWrcpLTU09OmbMmKSaYw8++GDXa6+9tnTJkiUFn376afSgQYN6jx07thRg06ZN53300UebExISqgcMGJBy3XXXlSQkJFQvXbq0w4YNG3Jbtmzpbrnllgt++ctfnj9t2rQDR44cibryyisPz5s3r/D73/9+93nz5nV6/PHH99xxxx0XTJkyZf+0adMOPProo51qyv7jH//YJj8/P27Tpk3/cs7x9a9//dLly5cnJCUlVezcuTPu5Zdf/vdVV1318ahRo5IWLFjQ/o477vjs5ptvvvjee+/de+uttxaXlZVZVVWV1ZdPRkZGo5YYUcdbgusHP4DnnvPueo8Y4c2+LiIiIiISqZpoOHhjJCYmVowcOfJzgG9/+9sHsrKyOiclJR194oknEsvLy6OKi4tjUlJSjgAlABMnTvysMfkuX768dX151Gfjxo1x3bt3P9q3b9+jAJMmTTrwq1/9qhPA22+/3WblypXtsrKyEgGOHj1q+fn5sQBDhw4tTUxMrAIYPXr0wbfffjshJibG5eTkxPfr1683QHl5eVTnzp0rAVq0aOEmTJhQApCWlvb56tWr2wB88MEHCcuXL98O8L3vfe/AT37yk+4AK1asaLN27do2KSkpKQBlZWVRubm5cUlJSRXdunU7etVVVx0BGDBgQFlBQUHLgwcPRhUVFcXeeuutxQDx8fEOcPXlo463NI/YWJg711u6LCvLW/JMRERERESCrvaE0GbGPffcc+H69eu3XHrppcfuvvvuL5WXlx8fI966devqU+VZVlZmDeVxJpxzLF26NL9fv35HA9PXrVt3Xl0xOOds/PjxB+bPn19YO6+YmBhXM+w9JiaGysrK4xlERUWdNLu1c47MzMw9M2fOPGEyvLy8vNjY2Njj50dHR7sjR47UG2d9+TSWlhOT4EtP94aZP/IIFBU1d21ERERERCLSnj17YlevXn0ewKJFizpcddVVhwESExMrS0pKov785z+3r++9CQkJVaWlpSf1B8vKyqIam0eg/v37lxcWFsZu3ry5JcDixYs71By79tprS+fMmdOlutrr97/zzjutao6tW7euTVFRUfThw4ftjTfeaDds2LDD6enppdnZ2e0LCwtjAIqKiqK3bt3a4FDagQMHHn7hhRc6ALzwwgvn16RnZGSULly4sGNJSUkUwI4dO1rU5FuX9u3bVycmJlYsXLiwHcCRI0fs0KFDUaebT23qeEvTeOIJKC/31vYWEQkTZpZuZnlmlm9m99dxvKWZ/d4/vt7MLjr3tRQREfFcdNFF5fPmzeuclJSUWlxcHHPvvffunzRp0v7evXunXnvttcn9+vWrd43lSZMmfZaVlZXYu3fvlJrOMkDHjh2rGptHoPj4eDdv3ryPx4wZc2lKSkrvjh07VtYce+yxx3ZXVlZar169Ui699NLUWbNmdas5dvnll38+duzYS1JTU1O/8Y1vHLz66qvL0tLSymfNmlU4fPjw5OTk5JSvfe1rybt27WrRUPnPPPPMzueff75zcnJySmFh4fFzb7zxxtLx48d/dsUVV/RKTk5OueGGGy4pLi6Obiivl19+ecf8+fM7JycnpwwaNKjXrl27Ys4kn0Bax7seYbmOd6iZORPmzIF334VBZ71cooiEqXBZx9vMooGtwAjgE+A9YKJzbkvAOXcAlzvnvm9mE4AbnHPfaijfc7GOt4iINE4kreOdl5cXO2bMmJ7btm3b3Jz1OBtZWVnnb9iw4bwFCxbsbO66BEt963jrjncTMrOz3sLarFneTOd33QVf9C8hRCQcDAbynXP/ds5VAIuB62qdcx1Qs1zEUmC4hf0faxEREWlqYdPxPtXwPwlBbdvC//wP/P3v8LvfNXdtRCSEhcgQ727AroDXn/hpdZ7jnKvEm+H1/FrnYGZTzWyDmW3Yv39/E1RVRES+6C677LKK5rrbPWLEiEt69eqVErgtW7aszenmM3369AORdLe7IWExq7k//G8+AcP/zOy1wOF/EqJuvx2efRbuuw+uuw7OC/nRpiJyjjXyb/xk4KBz7lJ/iPdsoMEh3s3JOfc88Dx4Q82buToiIhJ81dXV1VbXLNpfBKtWrdre3HUIRdXV1QbUOXN8WHS8CRj+B2BmNcP/Ir7jHSojGM/4efWoKHjqKRg6FJKTIT4+uBULZ126wLp1zV0LkVDQmL/x1wH/7e8vBZ42M3PBnUyjEOgR8Lq7n1bXOZ+YWQzQFjgQxDqIiEh4yNm/f39Kp06dSr6onW85UXV1te3fv78tkFPX8XDpeNc1/O/LgSeY2VRgqv/SmdmRsywzBqg85Vnh46ziCcoXALt3n30e/xH+v5/8fDjx3zX8YzqR4gl95yqmVmYWOLvY8/4d4Rqn/BtPrSHeZlYzxDuYE9u8B/Q0s4vxOtgTgJtrnfMacBvw/4BxwF9P1fl///33PzWzj8+iXh0JbpyhINJiUjyhL9JiirR44NzFdGEwMqmsrJyyd+/eX+3du7cPYfT4rjSpaiCnsrJySl0Hw6XjfUqBw/qCwcw2BGPGw1CheEJfpMWkeEJfJMZ0NvwO/TRgJRANvOSc22xmjwAbnHOvAS8CC80sH/gMr3N+qnw7nU29IvH3FGkxKZ7QF2kxRVo8EH4xpaWl7QPGNnc9JHyES8e7McP/REQkPIXMEG/n3BvAG7XSHgrYLwfGB7tcERERiWzhMizi+PA/M4vFu8PwWjPXSUREgqMxf+NrhnhDI4d4i4iIiISKsLjjXd/wvyYuNmjD1kOE4gl9kRaT4gl9IRFTUw3xjiAh8XsKskiLSfGEvkiLKdLigciMSeQ40w0DERERERERkaYTLkPNRURERERERMKSOt4iIiIiIiIiTUgd71rMLN3M8sws38zuD4H6vGRm+8wsJyCtg5mtMrNt/s/2frqZWZZf901mNjDgPbf5528zs9sC0tPM7CP/PVnmL9hdXxlBiKeHmb1lZlvMbLOZ3RUBMcWZ2btm9qEf04/99IvNbL1fj9/7k0ZhZi391/n+8YsC8nrAT88zs/8TkF7ndVlfGUGKK9rM/mlm2eEej5kV+NfERvPXkw7na87Pu52ZLTWzXDP7l5ldGe4xycnq+7/SjPVRmxTCMVmEtkd+/mqTQvCa8/NVeyTSGM45bf6GN6nPdiAJiAU+BFKauU5XAwOBnIC0x4H7/f37gdn+/ihgOWDAEGC9n94B+Lf/s72/394/9q5/rvnvzWiojCDE0xUY6O+3BrYCKWEekwEJ/n4LYL1f/h+ACX76L4H/6+/fAfzS358A/N7fT/GvuZbAxf61GN3QdVlfGUGK627gt0B2Q2WFQzxAAdCxVlrYXnN+fr8Bpvj7sUC7cI9J20m/Y7VJapNON56IbI/8PNUmheA15+el9kibtkZszV6BUNqAK4GVAa8fAB4IgXpdxIkfcvKArv5+VyDP338OmFj7PGAi8FxA+nN+WlcgNyD9+Hn1ldEEsf0JGBEpMQHxwAfAl4FPgZja1xbezM1X+vsx/nlW+3qrOa++69J/T51lBCGO7sBfgK8B2Q2VFSbxFHDyh5ywvebw1rDegT9BZiTEpK3O37PaJLVJZxNLRLRHfn5qk0L0mkPtkTZtjd401PxE3YBdAa8/8dNCTRfn3B5/fy/Qxd+vr/4NpX9SR3pDZQSNP/xrAN438mEdkz8EbiOwD1iF9+15sXOuso56HK+7f7wEOP8UMdWVfn4DZZytucB9QLX/uqGywiEeB7xpZu+b2VQ/LZyvuYuB/cCv/aGXvzKz8xooLxxikpOpTVKbdCZxRFp7BGqTQvmaU3sk0kjqeIc555zD+wMeVmWYWQKwDMh0zpU2dXm1BbsM51yVc64/3rfyg4Fewcr7XDOzMcA+59z7zV2XIBrqnBsIZAA/MLOrAw+G4TUXgzfc91nn3ADgc7xhdk1VXp3ORRkSXsL1uoukNimS2iNQm9RUgliG2iORRlLH+0SFQI+A1939tFBTZGZdAfyf+/z0+urfUHr3OtIbKuOsmVkLvA84i5xzf4yEmGo454qBt/CGpLUzs5g66nG87v7xtsABTj/WAw2UcTa+Aow1swJgMd7QvqfCOB6cc4X+z33AK3gfRsP5mvsE+MQ5t95/vRTvg084xyQnU5ukNumMRUh7BGqTQv2aU3sk0kjqeJ/oPaCnebNYxuJNyvFaM9epLq8Bt/n7t+E9k1aTfqs/Y+QQoMQfgrMSGGlm7f0ZH0fiPae0Byg1syH+DJG31sqrrjLOil/Oi8C/nHNPREhMncysnb/fCu/5wH/hfeAZV09MNfUYB/zV/6b2NWCCeTOyXgz0xJtQpM7r0n9PfWWcMefcA8657s65i/yy/uqcmxSu8ZjZeWbWumYf71rJIYyvOefcXmCXmV3mJw0HtoRzTFIntUlqk043nohqj0BtEiF+zak9EjkN7hQPgX/RNrzZFrfiPRP1wxCoz++APcAxvG8VJ+M9d/QXYBuwGujgn2vAfL/uHwGDAvL5LpDvb7cHpA/C+4O/HXgaf3KM+soIQjxD8YYCbQI2+tuoMI/pcuCffkw5wEN+ehJeo54PLAFa+ulx/ut8/3hSQF4/9Oudhz9rZ0PXZX1lBPH6u4b/zCAblvH4eX7ob5trygvna87Puz+wwb/uXsWbBTasY9JW5+9ZbVITXndEWJtEBLdHfhnXoDYppK45P1+1R9q0NWKruXBFREREREREpAloqLmIiIiIiIhIE1LHW0RERERERKQJqeMtIiIiIiIi0oTU8RYRERERERFpQup4i4iIiIiIiDQhdbxFREREREREmpA63iJnyMyqzGyjmeWY2RIziw84dr2ZOTPr5b+OM7NcM+sbcM5MM3vuFGVkmlm5mbUNSLvGz3tKQFp/P+1eM5vv12uLmR3x9zea2biA8+/xz+8YrH8PERFpPmqTRERCmzreImfuiHOuv3OuD1ABfD/g2ERgnf8T51w5kAk8Y55u/vn3n6KMicB7wI210nOAb9Y670O/rB845/oDo4Dtfh37O+eWAphZD2AksPN0AxYRkZClNklEJISp4y0SHH8DLgUwswRgKDAZmFBzgnNuBbAHuBV4Evhv59zB+jI0s0uABGAW/oelAB8DcWbWxcwMSAeWN7KuTwL3Aa6R54uISHhRmyQiEmLU8RY5S2YWA2QAH/lJ1wErnHNbgQNmlhZweibwM6CTc27hKbKeACzG+wB1mZl1qXV8KTAeuAr4ADjaiLpeBxQ65z481bkiIhJ+1CaJiIQmdbxFzlwrM9sIbMAbIveinz4R78MJ/s/jdwacc7uBvwLPNiL/icBi51w1sAzvA02gP/hpE4HfnSoz/3m/B4GHGlG2iIiEF7VJIiIhLKa5KyASxo74z60dZ2YdgK8Bfc3MAdGAM7OZzrmaYXTV/lYvf8KbnsAqb9QescAO4Omac5xze83sGDACuAvvLkNDLgEuBj708+wOfGBmg51zexsRr4iIhC61SSIiIUwdb5HgGgcsdM59rybBzNYAXwXWnkY+E/Get3s0IJ8dZnZhrfMeAjo756r8Dy71cs59BHQOyK8AGOSc+/Q06iUiIuFDbZKISIjQUHOR4JoIvFIrbRknT0RzKhPqyOcVAibGAXDO/d059+pp5i0iIl8MapNEREKE/WekkYiIiIiIiIgEm+54i4iIiIiIiDQhPeMt0oz8CWtqL+Fy1Dn35eaoj4iIfHGpTRIRaToaai4iIiIiIiLShDTUXERERERERKQJqeMtIiIiIiIi0oTU8RYRERERERFpQup4i4iIiIiIiDSh/w9JszCFES9LswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5781e10b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for xs in x_names: \n",
    "    explain.hist_mean_pd_ice_plot(xs, y_name, valid, mgbm_pd_ice_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Main Drivers Interacting: Surrogate Decision Tree \n",
    "A surrogate model is a simple model of a complex model. A simple surrogate model can be extracted from a more complex model and used to explain the more complex model, IF the surrogate model is accurate and stable. When a decision tree is used as a surrogate model, it creates a summary flow chart of the more complex model. It can also highlight interacting features. Features in parent-child node relationships in decision trees are likely to have interactions that affect model predictions. If a feature has divergent partial dependence and ICE curves and is in a parent-child node relationship in a surrogate tree model, this is particularly interesting evidence that an interaction is at play. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train surrogate decision tree \n",
    "A simple `depth=4` regression tree is trained with the predictions of the MGBM as the target, and with the original inputs to the GBM as the inputs to the decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated MOJO path:\n",
      " /home/patrickh/Workspace/GWU_rml/best_mgbm.zip\n",
      "Discovered H2O jar path:\n",
      " /home/patrickh/Workspace/GWU_rml/env_rml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "\n",
      "Calling external process ...\n",
      "java -cp /home/patrickh/Workspace/GWU_rml/env_rml/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/Workspace/GWU_rml/best_mgbm.zip -o best_mgbm.gv --title Best MGBM Flowchart\n",
      "Calling external process ...\n",
      "dot -Tpng best_mgbm.gv -o best_mgbm.png\n"
     ]
    }
   ],
   "source": [
    "surrogate = explain.get_cv_dt(x_names, 'p_DEFAULT_NEXT_MONTH', mgbm_yhat_valid, 'best_mgbm', SEED, 'Best MGBM Flowchart')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Visualize surrogate decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image(('best_mgbm.png')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The displayed tree serves as a flowchart summary for the `best_mgbm` model. The square nodes represent decision cut points and the circular terminal nodes are estimated predicted probabilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assess fit of surrogate decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>cv_1_valid</th>\n",
       "      <th>cv_2_valid</th>\n",
       "      <th>cv_3_valid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>0.031291604</td>\n",
       "      <td>0.0021172413</td>\n",
       "      <td>0.028846923</td>\n",
       "      <td>0.03253302</td>\n",
       "      <td>0.03249487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean_residual_deviance</td>\n",
       "      <td>0.0017139268</td>\n",
       "      <td>1.2270037E-4</td>\n",
       "      <td>0.0015745525</td>\n",
       "      <td>0.001761558</td>\n",
       "      <td>0.00180567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mse</td>\n",
       "      <td>0.0017139268</td>\n",
       "      <td>1.2270037E-4</td>\n",
       "      <td>0.0015745525</td>\n",
       "      <td>0.001761558</td>\n",
       "      <td>0.00180567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>r2</td>\n",
       "      <td>0.95078444</td>\n",
       "      <td>0.0022910195</td>\n",
       "      <td>0.9532509</td>\n",
       "      <td>0.9503795</td>\n",
       "      <td>0.94872284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>residual_deviance</td>\n",
       "      <td>0.0017139268</td>\n",
       "      <td>1.2270037E-4</td>\n",
       "      <td>0.0015745525</td>\n",
       "      <td>0.001761558</td>\n",
       "      <td>0.00180567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rmse</td>\n",
       "      <td>0.041381575</td>\n",
       "      <td>0.0014960271</td>\n",
       "      <td>0.03968063</td>\n",
       "      <td>0.04197092</td>\n",
       "      <td>0.042493176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmsle</td>\n",
       "      <td>0.033517655</td>\n",
       "      <td>0.0014647902</td>\n",
       "      <td>0.031826653</td>\n",
       "      <td>0.034331735</td>\n",
       "      <td>0.03439458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   mean            sd    cv_1_valid  \\\n",
       "0                     mae   0.031291604  0.0021172413   0.028846923   \n",
       "1  mean_residual_deviance  0.0017139268  1.2270037E-4  0.0015745525   \n",
       "2                     mse  0.0017139268  1.2270037E-4  0.0015745525   \n",
       "3                      r2    0.95078444  0.0022910195     0.9532509   \n",
       "4       residual_deviance  0.0017139268  1.2270037E-4  0.0015745525   \n",
       "5                    rmse   0.041381575  0.0014960271    0.03968063   \n",
       "6                   rmsle   0.033517655  0.0014647902   0.031826653   \n",
       "\n",
       "    cv_2_valid   cv_3_valid  \n",
       "0   0.03253302   0.03249487  \n",
       "1  0.001761558   0.00180567  \n",
       "2  0.001761558   0.00180567  \n",
       "3    0.9503795   0.94872284  \n",
       "4  0.001761558   0.00180567  \n",
       "5   0.04197092  0.042493176  \n",
       "6  0.034331735   0.03439458  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surrogate.cross_validation_metrics_summary().as_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the surrogate decision tree is accurate (`R2 = 0.95`,`RMSE = 0.04`) and stable (small standard deviations for the fit measures), it is **likely** a good summary of `best_mgbm`. Potential interactions between `PAY_0` and `PAY_5` are likely worth investigating. Also, the decision policies in the surrogate tree can be indicative of the decision policies in `best_mgbm`. For instance, the lowest probability of default occurs in the surrogate model when `PAY_0 < 0.5` and `PAY_3 < 1` and `LIMIT_BAL >= 75742.5`. The highest probability of default occurs when `PAY_0 >= 1.5` (as in partial dependence) and `PAY_5 >= 1` and `PAY_6 >= 1` and `PAY_3 >= 1`. When combined with global feature importance and partial dependence/ICE, a fairly clear picture of global model behavior can be presented, especially for constrained models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Customer-level Summaries: Local Shapley Explanations\n",
    "With the exception of ICE, the methods in this notebook have been focused on global summaries of models, or summaries across entire partitions of data. Shapley values and a few other techniques, allow for the construction of highly localized summaries of the customer-level behavior of the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create data structure for local coefficients\n",
    "Shapley values at the 10th, 50th, and 90th percentiles of `p_DEFAULT_NEXT_MONTH` will be displayed to give some flavor of how `best_mgbm` treats different kinds of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_coef_dict = {10: pd.DataFrame(columns = ['Shapley Contribution'], index=x_names),\n",
    "                   50: pd.DataFrame(columns = ['Shapley Contribution'], index=x_names),\n",
    "                   90: pd.DataFrame(columns = ['Shapley Contribution'], index=x_names)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create mapping between validation set ID and Shapley value array indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     7\n",
       "2     9\n",
       "3    11\n",
       "4    12\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validation data IDs are from credit card data\n",
    "# Shapley contribs are indexed from 0, like any numpy array\n",
    "valid_idx_map = valid['ID'].copy(deep=True)\n",
    "valid_idx_map.reset_index(drop=True, inplace=True) # Pandas index will now start from 0\n",
    "valid_idx_map[0:5] # Shapley array index on left, ID on right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find local contributions for best MGBM at three percentiles of `p_DEFAULT_NEXT_MONTH`\n",
    "Using the mapping created above, Shapley values for three individuals are extracted from the numpy array of Shapley contributions and stored as DataFrames. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop through features and percentiles\n",
    "for name in x_names:\n",
    "    for percentile in [10, 50, 90]:\n",
    "    \n",
    "        # use mapping find correct row of Shapley array\n",
    "        # create a Pandas DataFrame from row of Shapley values\n",
    "        idx = valid_idx_map[valid_idx_map == int(mgbm_percentile_dict[percentile])].index[0]\n",
    "        s_df = pd.DataFrame(shap_contribs[idx, :].T, columns=['Shapley Contribution'], index=x_names)\n",
    "        local_coef_dict[percentile]['Shapley Contribution'] = 0\n",
    "        local_coef_dict[percentile].update(s_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot Shapley contributions at three percentiles of `p_DEFAULT_NEXT_MONTH`\n",
    "Shapley values can be interpreted as the contribution that a feature makes to a single prediction, above or below the mean prediction of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa56e32fda0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize a three-pane plot\n",
    "# with shared y-axis\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(ncols=3, sharey=True)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0, right=2, wspace=0.1)\n",
    "\n",
    "# pane 1\n",
    "_ = local_coef_dict[10].plot(kind='bar', color='k', ax=ax0,\n",
    "                             title='10th PCTL of p_DEFAULT_NEXT_MONTH')\n",
    "\n",
    "#pane 2\n",
    "_ = local_coef_dict[50].plot(kind='bar', color='k', ax=ax1,\n",
    "                             title='50th PCTL of p_DEFAULT_NEXT_MONTH')\n",
    "\n",
    "# pane 3\n",
    "_ = local_coef_dict[90].plot(kind='bar', color='k', ax=ax2,\n",
    "                             title='90th PCTL of p_DEFAULT_NEXT_MONTH')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, `PAY_0` is exerting the most influence over the model predictions, but this time at the per-customer level. For the lowest probability of default customer, all feature contributions are negative. For the median risk customer, some contributions are positive and some are negative, but `PAY_0` is making the strongest contribution. For the high-risk customer at the 90th percentile of  `p_DEFUALT_NEXT_MONTH`, a large contribution from `PAY_0` seems to be driving them toward predicted default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data values for individual at 90th percentile of `p_DEFAULT_NEXT_MONTH`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEFAULT_NEXT_MONTH</th>\n",
       "      <th>p_DEFAULT_NEXT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>7682</td>\n",
       "      <td>60000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24541</td>\n",
       "      <td>25838</td>\n",
       "      <td>27022</td>\n",
       "      <td>28357</td>\n",
       "      <td>28992</td>\n",
       "      <td>24505</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.553193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  \\\n",
       "2341  7682      60000    2          2         3   64      2      0      0   \n",
       "\n",
       "      PAY_4  PAY_5  PAY_6  BILL_AMT1  BILL_AMT2  BILL_AMT3  BILL_AMT4  \\\n",
       "2341      0      0      0      24541      25838      27022      28357   \n",
       "\n",
       "      BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  \\\n",
       "2341      28992      24505      2000      2000      2000      1000      1000   \n",
       "\n",
       "      PAY_AMT6  DEFAULT_NEXT_MONTH  p_DEFAULT_NEXT_MONTH  \n",
       "2341      2000                   1              0.553193  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mgbm_yhat_valid[mgbm_yhat_valid['ID'] == mgbm_percentile_dict[90]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shapley values for individual at 90th percentile of `p_DEFAULT_NEXT_MONTH`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Shapley Contribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PAY_0</th>\n",
       "      <td>1.721030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_2</th>\n",
       "      <td>-0.030681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_3</th>\n",
       "      <td>0.009926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_4</th>\n",
       "      <td>-0.030656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_5</th>\n",
       "      <td>-0.016284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_6</th>\n",
       "      <td>-0.002939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <td>0.061148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <td>-0.000628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <td>-0.025099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <td>-0.036680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Shapley Contribution\n",
       "PAY_0                  1.721030\n",
       "PAY_2                 -0.030681\n",
       "PAY_3                  0.009926\n",
       "PAY_4                 -0.030656\n",
       "PAY_5                 -0.016284\n",
       "PAY_6                 -0.002939\n",
       "LIMIT_BAL              0.061148\n",
       "PAY_AMT1              -0.000628\n",
       "PAY_AMT2              -0.025099\n",
       "PAY_AMT4              -0.036680"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_coef_dict[90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairing the data values for the individual with the Shapley values allows it to be seen that:\n",
    "\n",
    "* Most recent payment status is two months late\n",
    "* Credit Limit of NT$60000 is low \n",
    "\n",
    "are the main causes of high prediction for this individual. These types of explanations are sometimes known as \"reason-codes\" or \"adverse action notices.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shapley values sum to model prediction in logit space\n",
    "Shapley values plus the Shapley intercept (-1.43555605) will add up to the model prediction for each row before the application of the logit link function. This is one of the most desirable properties of Shapley values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inverse logit of sum of Shapley contributions and intercept: 0.553193\n",
      "MGBM prediction for individual: 0.553193\n"
     ]
    }
   ],
   "source": [
    "# should also match PAY_0 ICE plot above ... it does\n",
    "print('Inverse logit of sum of Shapley contributions and intercept: %.6f' % expit(local_coef_dict[90].sum()[0] + shap_contribs_matrix[0, -1]))\n",
    "print('MGBM prediction for individual: %6f' % mgbm_yhat_valid[mgbm_yhat_valid['ID'] == mgbm_percentile_dict[90]]['p_DEFAULT_NEXT_MONTH'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Customer-level Summaries: Counterfactual or Prototype-based Explanations\n",
    "Counterfactual and prototype explanations attempt to tell customers what they could change to affect a more positive outcome for the model. Some researchers believe these types of explanations are better suited for human understanding than Shapley values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select the individual at the 90th percentile of `p_DEFAULT_NEXT_MONTH`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_row = mgbm_yhat_valid[mgbm_yhat_valid['ID'] == mgbm_percentile_dict[90]].copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use Shapley values and ICE plots to perturb row\n",
    "Using Shapley and ICE, the features that are likely to have the greatest effect on an individual's prediction can be seen. For `best_mgbm` and this individual, `PAY_0` is the most important feature. By changing it to a lower value, this individual has a much lower probability of default, likely below the cutoff to receive the credit product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th style=\"text-align: right;\">  predict</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td style=\"text-align: right;\"> 0.169017</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_row['PAY_0'] = 0 # create a new prototype customer, based on info from SHAP and ICE\n",
    "best_mgbm.predict(h2o.H2OFrame(test_row)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The prototype-based explanation for this individual is:** By changing your most recent payment to on-time, from two months late, you would receive the credit product. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### End global timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All tasks completed in 64.23 s.\n"
     ]
    }
   ],
   "source": [
    "big_toc = time.time() - big_tic\n",
    "print('All tasks completed in %.2f s.' % (big_toc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shutdown H2O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y\n",
      "H2O session _sid_b411 closed.\n"
     ]
    }
   ],
   "source": [
    "# be careful, this can erase your work!\n",
    "h2o.cluster().shutdown(prompt=True)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}