Credit Card Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Detection using Decision Tree\n",
    "\n",
    "![Credit Card](https://media.istockphoto.com/id/1307675090/photo/fraud-alert-concept-with-security-lock-on-fake-credit-cards.jpg?s=612x612&w=0&k=20&c=R2djChH2SEFXNRFOo3K7z_Jd4aZl8Yl5Tn64oFVuhh4=)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees in Machine Learning\n",
    "\n",
    "**Introduction**\n",
    "\n",
    "Decision trees (DTs) stand out as powerful non-parametric supervised learning methods. They find applications in both classification and regression tasks. The primary objective of DTs is to create a predictive model for a target variable by learning simple decision rules derived from the features of the data. Two key entities in decision trees are the root node, where the data splits, and decision nodes or leaves, where the final output is obtained.\n",
    "\n",
    "## Decision Tree Algorithms\n",
    "\n",
    "### ID3\n",
    "\n",
    "Developed by Ross Quinlan in 1986, the Iterative Dichotomiser 3 (ID3) algorithm aims to identify categorical features at each node that yield the largest information gain for categorical targets. It allows the tree to grow to its maximum size and then employs a pruning step to enhance its performance on unseen data. The output of this algorithm is a multiway tree.\n",
    "\n",
    "### C4.5\n",
    "\n",
    "C4.5, the successor to ID3, dynamically defines discrete attributes that partition continuous attribute values into a set of intervals. This eliminates the restriction of categorical features. C4.5 transforms the ID3-trained tree into sets of 'IF-THEN' rules. To determine the sequence in which these rules should be applied, the accuracy of each rule is evaluated.\n",
    "\n",
    "### C5.0\n",
    "\n",
    "Similar to C4.5, C5.0 uses less memory and builds smaller rulesets. It operates by generating binary splits using features and thresholds that yield the largest information gain at each node. While it shares similarities with C4.5, it offers increased accuracy.\n",
    "\n",
    "### CART\n",
    "\n",
    "Classification and Regression Trees (CART) algorithm generates binary splits by utilizing features and thresholds that maximize information gain, as measured by the Gini index, at each node. Homogeneity is determined by the Gini index, with higher values indicating greater homogeneity. Unlike C4.5, CART does not compute rule sets and does not support numerical target variables (regression).\n",
    "\n",
    "## Decision Tree Algorithm Implementation\n",
    "\n",
    "In practice, the term \"decision tree algorithm\" commonly refers to a family of algorithms responsible for constructing decision trees. The specific implementation details can vary based on the chosen algorithm and its parameters. Despite these differences, a common theme across these algorithms is the idea of recursively partitioning the data using features and criteria.\n",
    "\n",
    "This recursive partitioning process involves iteratively making decisions at each node to split the data based on specific conditions. The goal is to create a tree structure where the leaves represent the final outcomes or predictions. The variations among decision tree algorithms lie in how they select features, determine splitting criteria, handle categorical and numerical data, and address overfitting through techniques like pruning.\n",
    "\n",
    "The flexibility in implementation allows practitioners to choose the decision tree algorithm that best suits their specific use case, considering factors such as interpretability, computational efficiency, and performance on different types of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "# About Dataset\n",
    "\n",
    "## Description\n",
    "This dataset contains credit card transactions made by European cardholders in the year 2023. It comprises over 550,000 records, and the data has been anonymized to protect the cardholders' identities. The primary objective of this dataset is to facilitate the development of fraud detection algorithms and models to identify potentially fraudulent transactions.\n",
    "\n",
    "## Key Features\n",
    "- **id:** Unique identifier for each transaction\n",
    "- **V1-V28:** Anonymized features representing various transaction attributes (e.g., time, location, etc.)\n",
    "- **Amount:** The transaction amount\n",
    "- **Class:** Binary label indicating whether the transaction is fraudulent (1) or not (0)\n",
    "\n",
    "## Potential Use Cases\n",
    "1. **Credit Card Fraud Detection:** Build machine learning models to detect and prevent credit card fraud by identifying suspicious transactions based on the provided features.\n",
    "2. **Merchant Category Analysis:** Examine how different merchant categories are associated with fraud.\n",
    "3. **Transaction Type Analysis:** Analyze whether certain types of transactions are more prone to fraud than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Libraries Description\n",
    "\n",
    "### numpy (np):\n",
    "NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays. Import it as `np` for numerical operations and array manipulations.\n",
    "\n",
    "### pandas (pd):\n",
    "Pandas is a powerful data manipulation library for Python. It provides data structures like DataFrame for efficient data handling and manipulation. To use, import it as `pd`, and you can read data from various sources, manipulate datasets, and perform exploratory data analysis.\n",
    "\n",
    "### sklearn.preprocessing.StandardScaler:\n",
    "`StandardScaler` from scikit-learn is used for standardizing features by removing the mean and scaling to unit variance. It is particularly useful for algorithms sensitive to feature scaling, such as support vector machines and k-means clustering.\n",
    "\n",
    "### seaborn (sns):\n",
    "Seaborn is a statistical data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Import it as `sns` for creating visually appealing plots and charts.\n",
    "\n",
    "### matplotlib.pyplot (plt):\n",
    "Matplotlib is a 2D plotting library for Python. `pyplot` is its interface for creating various types of plots and visualizations. Import it as `plt` to generate plots for data visualization.\n",
    "\n",
    "### sklearn.model_selection.train_test_split:\n",
    "`train_test_split` from scikit-learn is used for splitting datasets into training and testing sets. It helps in evaluating the model's performance on unseen data. Usage: `train_test_split(X, y, test_size=0.2, random_state=42)`.\n",
    "\n",
    "### sklearn.metrics.accuracy_score, sklearn.metrics.classification_report, sklearn.metrics.confusion_matrix:\n",
    "These functions are part of scikit-learn's metrics module for evaluating classification models. `accuracy_score` calculates the accuracy of predictions. `classification_report` provides a detailed classification report. `confusion_matrix` computes a confusion matrix.\n",
    "\n",
    "### sklearn.tree.DecisionTreeClassifier, sklearn.tree.export_text, sklearn.tree.plot_tree, sklearn.tree.export_graphviz:\n",
    "These components are related to decision tree classification. `DecisionTreeClassifier` is a scikit-learn class for creating decision tree models. `export_text` and `export_graphviz` are used to visualize decision tree structures. `plot_tree` provides a basic plot of the decision tree.\n",
    "\n",
    "### sklearn.ensemble.RandomForestClassifier:\n",
    "`RandomForestClassifier` from scikit-learn is an ensemble learning method based on decision tree classifiers. It builds multiple decision trees and merges them together to improve accuracy and control overfitting.\n",
    "\n",
    "### xgboost.XGBClassifier:\n",
    "`XGBClassifier` is a class from the XGBoost library, which is an efficient and scalable implementation of gradient boosting. It is widely used for classification problems. Import it as `XGBClassifier` for training XGBoost-based models.\n",
    "\n",
    "### graphviz:\n",
    "Graphviz is an external library used for rendering graphs, such as decision trees. In this context, it is often used with scikit-learn's decision tree visualization functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:03.673316Z",
     "iopub.status.busy": "2023-12-10T14:22:03.672823Z",
     "iopub.status.idle": "2023-12-10T14:22:03.683918Z",
     "shell.execute_reply": "2023-12-10T14:22:03.683052Z",
     "shell.execute_reply.started": "2023-12-10T14:22:03.673275Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree, export_graphviz\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly we need to import the data, so that we can use it in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:03.686745Z",
     "iopub.status.busy": "2023-12-10T14:22:03.685399Z",
     "iopub.status.idle": "2023-12-10T14:22:12.333999Z",
     "shell.execute_reply": "2023-12-10T14:22:12.332487Z",
     "shell.execute_reply.started": "2023-12-10T14:22:03.686705Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"path\\creditcard_2023.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "1            1  0.985100 -0.356045  0.558056 -0.429654  0.277140  0.428605   \n",
       "2            2 -0.260272 -0.949385  1.728538 -0.457986  0.074062  1.419481   \n",
       "3            3 -0.152152 -0.508959  1.746840 -1.090178  0.249486  1.143312   \n",
       "4            4 -0.206820 -0.165280  1.527053 -0.448293  0.106125  0.530549   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "568625  568625 -0.833437  0.061886 -0.899794  0.904227 -1.002401  0.481454   \n",
       "568626  568626 -0.670459 -0.202896 -0.068129 -0.267328 -0.133660  0.237148   \n",
       "568627  568627 -0.311997 -0.004095  0.137526 -0.035893 -0.042291  0.121098   \n",
       "568628  568628  0.636871 -0.516970 -0.300889 -0.144480  0.131042 -0.294148   \n",
       "568629  568629 -0.795144  0.433236 -0.649140  0.374732 -0.244976 -0.603493   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "0       0.519014 -0.130006  0.727159  ... -0.110552  0.217606 -0.134794   \n",
       "1       0.406466 -0.133118  0.347452  ... -0.194936 -0.605761  0.079469   \n",
       "2       0.743511 -0.095576 -0.261297  ... -0.005020  0.702906  0.945045   \n",
       "3       0.518269 -0.065130 -0.205698  ... -0.146927 -0.038212 -0.214048   \n",
       "4       0.658849 -0.212660  1.049921  ... -0.106984  0.729727 -0.161666   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "568625 -0.370393  0.189694 -0.938153  ...  0.167503  0.419731  1.288249   \n",
       "568626 -0.016935 -0.147733  0.483894  ...  0.031874  0.388161 -0.154257   \n",
       "568627 -0.070958 -0.019997 -0.122048  ...  0.140788  0.536523 -0.211100   \n",
       "568628  0.580568 -0.207723  0.893527  ... -0.060381 -0.195609 -0.175488   \n",
       "568629 -0.347613 -0.340814  0.253971  ...  0.534853 -0.291514  0.157303   \n",
       "\n",
       "             V24       V25       V26       V27       V28    Amount  Class  \n",
       "0       0.165959  0.126280 -0.434824 -0.081230 -0.151045  17982.10      0  \n",
       "1      -0.577395  0.190090  0.296503 -0.248052 -0.064512   6531.37      0  \n",
       "2      -1.154666 -0.605564 -0.312895 -0.300258 -0.244718   2513.54      0  \n",
       "3      -1.893131  1.003963 -0.515950 -0.165316  0.048424   5384.44      0  \n",
       "4       0.312561 -0.414116  1.071126  0.023712  0.419117  14278.97      0  \n",
       "...          ...       ...       ...       ...       ...       ...    ...  \n",
       "568625 -0.900861  0.560661 -0.006018  3.308968  0.081564   4394.16      1  \n",
       "568626 -0.846452 -0.153443  1.961398 -1.528642  1.704306   4653.40      1  \n",
       "568627 -0.448909  0.540073 -0.755836 -0.487540 -0.268741  23572.85      1  \n",
       "568628 -0.554643 -0.099669 -1.434931 -0.159269 -0.076251  10160.83      1  \n",
       "568629  0.931030 -0.349423 -1.090974 -1.575113  0.722936  21493.92      1  \n",
       "\n",
       "[568630 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "df"
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       "   id        V1        V2        V3        V4        V5        V6        V7  \\\n",
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       "\n",
       "        V26       V27       V28    Amount  Class  \n",
       "0 -0.434824 -0.081230 -0.151045  17982.10      0  \n",
       "1  0.296503 -0.248052 -0.064512   6531.37      0  \n",
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       "3 -0.515950 -0.165316  0.048424   5384.44      0  \n",
       "4  1.071126  0.023712  0.419117  14278.97      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.375304Z",
     "iopub.status.busy": "2023-12-10T14:22:12.374469Z",
     "iopub.status.idle": "2023-12-10T14:22:12.390938Z",
     "shell.execute_reply": "2023-12-10T14:22:12.389124Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.375267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.396105Z",
     "iopub.status.busy": "2023-12-10T14:22:12.395609Z",
     "iopub.status.idle": "2023-12-10T14:22:12.466962Z",
     "shell.execute_reply": "2023-12-10T14:22:12.465351Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.396070Z"
    }
   },
   "outputs": [],
   "source": [
    "#drop the column id we don't need it \n",
    "df = df.drop(columns=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.469080Z",
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       "      <th>4</th>\n",
       "      <td>-0.206820</td>\n",
       "      <td>-0.165280</td>\n",
       "      <td>1.527053</td>\n",
       "      <td>-0.448293</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.530549</td>\n",
       "      <td>0.658849</td>\n",
       "      <td>-0.212660</td>\n",
       "      <td>1.049921</td>\n",
       "      <td>0.968046</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.106984</td>\n",
       "      <td>0.729727</td>\n",
       "      <td>-0.161666</td>\n",
       "      <td>0.312561</td>\n",
       "      <td>-0.414116</td>\n",
       "      <td>1.071126</td>\n",
       "      <td>0.023712</td>\n",
       "      <td>0.419117</td>\n",
       "      <td>14278.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -0.260648 -0.469648  2.496266 -0.083724  0.129681  0.732898  0.519014   \n",
       "1  0.985100 -0.356045  0.558056 -0.429654  0.277140  0.428605  0.406466   \n",
       "2 -0.260272 -0.949385  1.728538 -0.457986  0.074062  1.419481  0.743511   \n",
       "3 -0.152152 -0.508959  1.746840 -1.090178  0.249486  1.143312  0.518269   \n",
       "4 -0.206820 -0.165280  1.527053 -0.448293  0.106125  0.530549  0.658849   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0 -0.130006  0.727159  0.637735  ... -0.110552  0.217606 -0.134794  0.165959   \n",
       "1 -0.133118  0.347452  0.529808  ... -0.194936 -0.605761  0.079469 -0.577395   \n",
       "2 -0.095576 -0.261297  0.690708  ... -0.005020  0.702906  0.945045 -1.154666   \n",
       "3 -0.065130 -0.205698  0.575231  ... -0.146927 -0.038212 -0.214048 -1.893131   \n",
       "4 -0.212660  1.049921  0.968046  ... -0.106984  0.729727 -0.161666  0.312561   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.126280 -0.434824 -0.081230 -0.151045  17982.10      0  \n",
       "1  0.190090  0.296503 -0.248052 -0.064512   6531.37      0  \n",
       "2 -0.605564 -0.312895 -0.300258 -0.244718   2513.54      0  \n",
       "3  1.003963 -0.515950 -0.165316  0.048424   5384.44      0  \n",
       "4 -0.414116  1.071126  0.023712  0.419117  14278.97      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.506360Z",
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     "iopub.status.idle": "2023-12-10T14:22:12.564621Z",
     "shell.execute_reply": "2023-12-10T14:22:12.563028Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.506317Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568630 entries, 0 to 568629\n",
      "Data columns (total 30 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   V1      568630 non-null  float64\n",
      " 1   V2      568630 non-null  float64\n",
      " 2   V3      568630 non-null  float64\n",
      " 3   V4      568630 non-null  float64\n",
      " 4   V5      568630 non-null  float64\n",
      " 5   V6      568630 non-null  float64\n",
      " 6   V7      568630 non-null  float64\n",
      " 7   V8      568630 non-null  float64\n",
      " 8   V9      568630 non-null  float64\n",
      " 9   V10     568630 non-null  float64\n",
      " 10  V11     568630 non-null  float64\n",
      " 11  V12     568630 non-null  float64\n",
      " 12  V13     568630 non-null  float64\n",
      " 13  V14     568630 non-null  float64\n",
      " 14  V15     568630 non-null  float64\n",
      " 15  V16     568630 non-null  float64\n",
      " 16  V17     568630 non-null  float64\n",
      " 17  V18     568630 non-null  float64\n",
      " 18  V19     568630 non-null  float64\n",
      " 19  V20     568630 non-null  float64\n",
      " 20  V21     568630 non-null  float64\n",
      " 21  V22     568630 non-null  float64\n",
      " 22  V23     568630 non-null  float64\n",
      " 23  V24     568630 non-null  float64\n",
      " 24  V25     568630 non-null  float64\n",
      " 25  V26     568630 non-null  float64\n",
      " 26  V27     568630 non-null  float64\n",
      " 27  V28     568630 non-null  float64\n",
      " 28  Amount  568630 non-null  float64\n",
      " 29  Class   568630 non-null  int64  \n",
      "dtypes: float64(29), int64(1)\n",
      "memory usage: 130.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.567290Z",
     "iopub.status.busy": "2023-12-10T14:22:12.566804Z",
     "iopub.status.idle": "2023-12-10T14:22:12.598977Z",
     "shell.execute_reply": "2023-12-10T14:22:12.597815Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.567239Z"
    }
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.260648</td>\n",
       "      <td>-0.469648</td>\n",
       "      <td>2.496266</td>\n",
       "      <td>-0.083724</td>\n",
       "      <td>0.129681</td>\n",
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       "      <td>0.519014</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.110552</td>\n",
       "      <td>0.217606</td>\n",
       "      <td>-0.134794</td>\n",
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       "      <td>0.126280</td>\n",
       "      <td>-0.434824</td>\n",
       "      <td>-0.081230</td>\n",
       "      <td>-0.151045</td>\n",
       "      <td>17982.10</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.985100</td>\n",
       "      <td>-0.356045</td>\n",
       "      <td>0.558056</td>\n",
       "      <td>-0.429654</td>\n",
       "      <td>0.277140</td>\n",
       "      <td>0.428605</td>\n",
       "      <td>0.406466</td>\n",
       "      <td>-0.133118</td>\n",
       "      <td>0.347452</td>\n",
       "      <td>0.529808</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194936</td>\n",
       "      <td>-0.605761</td>\n",
       "      <td>0.079469</td>\n",
       "      <td>-0.577395</td>\n",
       "      <td>0.190090</td>\n",
       "      <td>0.296503</td>\n",
       "      <td>-0.248052</td>\n",
       "      <td>-0.064512</td>\n",
       "      <td>6531.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.260272</td>\n",
       "      <td>-0.949385</td>\n",
       "      <td>1.728538</td>\n",
       "      <td>-0.457986</td>\n",
       "      <td>0.074062</td>\n",
       "      <td>1.419481</td>\n",
       "      <td>0.743511</td>\n",
       "      <td>-0.095576</td>\n",
       "      <td>-0.261297</td>\n",
       "      <td>0.690708</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005020</td>\n",
       "      <td>0.702906</td>\n",
       "      <td>0.945045</td>\n",
       "      <td>-1.154666</td>\n",
       "      <td>-0.605564</td>\n",
       "      <td>-0.312895</td>\n",
       "      <td>-0.300258</td>\n",
       "      <td>-0.244718</td>\n",
       "      <td>2513.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.152152</td>\n",
       "      <td>-0.508959</td>\n",
       "      <td>1.746840</td>\n",
       "      <td>-1.090178</td>\n",
       "      <td>0.249486</td>\n",
       "      <td>1.143312</td>\n",
       "      <td>0.518269</td>\n",
       "      <td>-0.065130</td>\n",
       "      <td>-0.205698</td>\n",
       "      <td>0.575231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146927</td>\n",
       "      <td>-0.038212</td>\n",
       "      <td>-0.214048</td>\n",
       "      <td>-1.893131</td>\n",
       "      <td>1.003963</td>\n",
       "      <td>-0.515950</td>\n",
       "      <td>-0.165316</td>\n",
       "      <td>0.048424</td>\n",
       "      <td>5384.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.206820</td>\n",
       "      <td>-0.165280</td>\n",
       "      <td>1.527053</td>\n",
       "      <td>-0.448293</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.530549</td>\n",
       "      <td>0.658849</td>\n",
       "      <td>-0.212660</td>\n",
       "      <td>1.049921</td>\n",
       "      <td>0.968046</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.414116</td>\n",
       "      <td>1.071126</td>\n",
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       "      <td>0</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
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       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -0.260648 -0.469648  2.496266 -0.083724  0.129681  0.732898  0.519014   \n",
       "1  0.985100 -0.356045  0.558056 -0.429654  0.277140  0.428605  0.406466   \n",
       "2 -0.260272 -0.949385  1.728538 -0.457986  0.074062  1.419481  0.743511   \n",
       "3 -0.152152 -0.508959  1.746840 -1.090178  0.249486  1.143312  0.518269   \n",
       "4 -0.206820 -0.165280  1.527053 -0.448293  0.106125  0.530549  0.658849   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0 -0.130006  0.727159  0.637735  ... -0.110552  0.217606 -0.134794  0.165959   \n",
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       "3 -0.065130 -0.205698  0.575231  ... -0.146927 -0.038212 -0.214048 -1.893131   \n",
       "4 -0.212660  1.049921  0.968046  ... -0.106984  0.729727 -0.161666  0.312561   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.126280 -0.434824 -0.081230 -0.151045  17982.10      0  \n",
       "1  0.190090  0.296503 -0.248052 -0.064512   6531.37      0  \n",
       "2 -0.605564 -0.312895 -0.300258 -0.244718   2513.54      0  \n",
       "3  1.003963 -0.515950 -0.165316  0.048424   5384.44      0  \n",
       "4 -0.414116  1.071126  0.023712  0.419117  14278.97      0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.600825Z",
     "iopub.status.busy": "2023-12-10T14:22:12.600463Z",
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     "shell.execute_reply.started": "2023-12-10T14:22:12.600794Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568630, 30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.610626Z",
     "iopub.status.busy": "2023-12-10T14:22:12.610045Z",
     "iopub.status.idle": "2023-12-10T14:22:12.620043Z",
     "shell.execute_reply": "2023-12-10T14:22:12.619024Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.610577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The amount of sample is 37372.0 or 0.06572287779399609\n"
     ]
    }
   ],
   "source": [
    "#use Slovin's Formula when you have a large population or a large row\n",
    "#I want to gather a manageable-sized sample that accurately represents all the data . \n",
    "\n",
    "#Sample Size (n) = (N) / [1 + (N * e^2)]\n",
    "\n",
    "N = df.shape[0]\n",
    "e = 0.005\n",
    "slovin_data = np.ceil( N / (1 + (N * e ** 2)))\n",
    "print(f' The amount of sample is {slovin_data} or {(slovin_data / N)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.625651Z",
     "iopub.status.busy": "2023-12-10T14:22:12.625185Z",
     "iopub.status.idle": "2023-12-10T14:22:12.702555Z",
     "shell.execute_reply": "2023-12-10T14:22:12.701175Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.625618Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.712170Z",
     "iopub.status.busy": "2023-12-10T14:22:12.711781Z",
     "iopub.status.idle": "2023-12-10T14:22:12.799650Z",
     "shell.execute_reply": "2023-12-10T14:22:12.798163Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.712131Z"
    }
   },
   "outputs": [
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       "      <th>V1</th>\n",
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       "      <td>-0.081230</td>\n",
       "      <td>-0.151045</td>\n",
       "      <td>17982.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.985100</td>\n",
       "      <td>-0.356045</td>\n",
       "      <td>0.558056</td>\n",
       "      <td>-0.429654</td>\n",
       "      <td>0.277140</td>\n",
       "      <td>0.428605</td>\n",
       "      <td>0.406466</td>\n",
       "      <td>-0.133118</td>\n",
       "      <td>0.347452</td>\n",
       "      <td>0.529808</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.233984</td>\n",
       "      <td>-0.194936</td>\n",
       "      <td>-0.605761</td>\n",
       "      <td>0.079469</td>\n",
       "      <td>-0.577395</td>\n",
       "      <td>0.190090</td>\n",
       "      <td>0.296503</td>\n",
       "      <td>-0.248052</td>\n",
       "      <td>-0.064512</td>\n",
       "      <td>6531.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.260272</td>\n",
       "      <td>-0.949385</td>\n",
       "      <td>1.728538</td>\n",
       "      <td>-0.457986</td>\n",
       "      <td>0.074062</td>\n",
       "      <td>1.419481</td>\n",
       "      <td>0.743511</td>\n",
       "      <td>-0.095576</td>\n",
       "      <td>-0.261297</td>\n",
       "      <td>0.690708</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361652</td>\n",
       "      <td>-0.005020</td>\n",
       "      <td>0.702906</td>\n",
       "      <td>0.945045</td>\n",
       "      <td>-1.154666</td>\n",
       "      <td>-0.605564</td>\n",
       "      <td>-0.312895</td>\n",
       "      <td>-0.300258</td>\n",
       "      <td>-0.244718</td>\n",
       "      <td>2513.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.152152</td>\n",
       "      <td>-0.508959</td>\n",
       "      <td>1.746840</td>\n",
       "      <td>-1.090178</td>\n",
       "      <td>0.249486</td>\n",
       "      <td>1.143312</td>\n",
       "      <td>0.518269</td>\n",
       "      <td>-0.065130</td>\n",
       "      <td>-0.205698</td>\n",
       "      <td>0.575231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.378223</td>\n",
       "      <td>-0.146927</td>\n",
       "      <td>-0.038212</td>\n",
       "      <td>-0.214048</td>\n",
       "      <td>-1.893131</td>\n",
       "      <td>1.003963</td>\n",
       "      <td>-0.515950</td>\n",
       "      <td>-0.165316</td>\n",
       "      <td>0.048424</td>\n",
       "      <td>5384.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.206820</td>\n",
       "      <td>-0.165280</td>\n",
       "      <td>1.527053</td>\n",
       "      <td>-0.448293</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>0.530549</td>\n",
       "      <td>0.658849</td>\n",
       "      <td>-0.212660</td>\n",
       "      <td>1.049921</td>\n",
       "      <td>0.968046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247237</td>\n",
       "      <td>-0.106984</td>\n",
       "      <td>0.729727</td>\n",
       "      <td>-0.161666</td>\n",
       "      <td>0.312561</td>\n",
       "      <td>-0.414116</td>\n",
       "      <td>1.071126</td>\n",
       "      <td>0.023712</td>\n",
       "      <td>0.419117</td>\n",
       "      <td>14278.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568625</th>\n",
       "      <td>-0.833437</td>\n",
       "      <td>0.061886</td>\n",
       "      <td>-0.899794</td>\n",
       "      <td>0.904227</td>\n",
       "      <td>-1.002401</td>\n",
       "      <td>0.481454</td>\n",
       "      <td>-0.370393</td>\n",
       "      <td>0.189694</td>\n",
       "      <td>-0.938153</td>\n",
       "      <td>-1.161847</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.751011</td>\n",
       "      <td>0.167503</td>\n",
       "      <td>0.419731</td>\n",
       "      <td>1.288249</td>\n",
       "      <td>-0.900861</td>\n",
       "      <td>0.560661</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>3.308968</td>\n",
       "      <td>0.081564</td>\n",
       "      <td>4394.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568626</th>\n",
       "      <td>-0.670459</td>\n",
       "      <td>-0.202896</td>\n",
       "      <td>-0.068129</td>\n",
       "      <td>-0.267328</td>\n",
       "      <td>-0.133660</td>\n",
       "      <td>0.237148</td>\n",
       "      <td>-0.016935</td>\n",
       "      <td>-0.147733</td>\n",
       "      <td>0.483894</td>\n",
       "      <td>-0.210817</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.550260</td>\n",
       "      <td>0.031874</td>\n",
       "      <td>0.388161</td>\n",
       "      <td>-0.154257</td>\n",
       "      <td>-0.846452</td>\n",
       "      <td>-0.153443</td>\n",
       "      <td>1.961398</td>\n",
       "      <td>-1.528642</td>\n",
       "      <td>1.704306</td>\n",
       "      <td>4653.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568627</th>\n",
       "      <td>-0.311997</td>\n",
       "      <td>-0.004095</td>\n",
       "      <td>0.137526</td>\n",
       "      <td>-0.035893</td>\n",
       "      <td>-0.042291</td>\n",
       "      <td>0.121098</td>\n",
       "      <td>-0.070958</td>\n",
       "      <td>-0.019997</td>\n",
       "      <td>-0.122048</td>\n",
       "      <td>-0.144495</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.076417</td>\n",
       "      <td>0.140788</td>\n",
       "      <td>0.536523</td>\n",
       "      <td>-0.211100</td>\n",
       "      <td>-0.448909</td>\n",
       "      <td>0.540073</td>\n",
       "      <td>-0.755836</td>\n",
       "      <td>-0.487540</td>\n",
       "      <td>-0.268741</td>\n",
       "      <td>23572.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568628</th>\n",
       "      <td>0.636871</td>\n",
       "      <td>-0.516970</td>\n",
       "      <td>-0.300889</td>\n",
       "      <td>-0.144480</td>\n",
       "      <td>0.131042</td>\n",
       "      <td>-0.294148</td>\n",
       "      <td>0.580568</td>\n",
       "      <td>-0.207723</td>\n",
       "      <td>0.893527</td>\n",
       "      <td>-0.080078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.288186</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>-0.195609</td>\n",
       "      <td>-0.175488</td>\n",
       "      <td>-0.554643</td>\n",
       "      <td>-0.099669</td>\n",
       "      <td>-1.434931</td>\n",
       "      <td>-0.159269</td>\n",
       "      <td>-0.076251</td>\n",
       "      <td>10160.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568629</th>\n",
       "      <td>-0.795144</td>\n",
       "      <td>0.433236</td>\n",
       "      <td>-0.649140</td>\n",
       "      <td>0.374732</td>\n",
       "      <td>-0.244976</td>\n",
       "      <td>-0.603493</td>\n",
       "      <td>-0.347613</td>\n",
       "      <td>-0.340814</td>\n",
       "      <td>0.253971</td>\n",
       "      <td>-0.513556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.621378</td>\n",
       "      <td>0.534853</td>\n",
       "      <td>-0.291514</td>\n",
       "      <td>0.157303</td>\n",
       "      <td>0.931030</td>\n",
       "      <td>-0.349423</td>\n",
       "      <td>-1.090974</td>\n",
       "      <td>-1.575113</td>\n",
       "      <td>0.722936</td>\n",
       "      <td>21493.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568630 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0      -0.260648 -0.469648  2.496266 -0.083724  0.129681  0.732898  0.519014   \n",
       "1       0.985100 -0.356045  0.558056 -0.429654  0.277140  0.428605  0.406466   \n",
       "2      -0.260272 -0.949385  1.728538 -0.457986  0.074062  1.419481  0.743511   \n",
       "3      -0.152152 -0.508959  1.746840 -1.090178  0.249486  1.143312  0.518269   \n",
       "4      -0.206820 -0.165280  1.527053 -0.448293  0.106125  0.530549  0.658849   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "568625 -0.833437  0.061886 -0.899794  0.904227 -1.002401  0.481454 -0.370393   \n",
       "568626 -0.670459 -0.202896 -0.068129 -0.267328 -0.133660  0.237148 -0.016935   \n",
       "568627 -0.311997 -0.004095  0.137526 -0.035893 -0.042291  0.121098 -0.070958   \n",
       "568628  0.636871 -0.516970 -0.300889 -0.144480  0.131042 -0.294148  0.580568   \n",
       "568629 -0.795144  0.433236 -0.649140  0.374732 -0.244976 -0.603493 -0.347613   \n",
       "\n",
       "              V8        V9       V10  ...       V20       V21       V22  \\\n",
       "0      -0.130006  0.727159  0.637735  ...  0.091202 -0.110552  0.217606   \n",
       "1      -0.133118  0.347452  0.529808  ... -0.233984 -0.194936 -0.605761   \n",
       "2      -0.095576 -0.261297  0.690708  ...  0.361652 -0.005020  0.702906   \n",
       "3      -0.065130 -0.205698  0.575231  ... -0.378223 -0.146927 -0.038212   \n",
       "4      -0.212660  1.049921  0.968046  ...  0.247237 -0.106984  0.729727   \n",
       "...          ...       ...       ...  ...       ...       ...       ...   \n",
       "568625  0.189694 -0.938153 -1.161847  ... -0.751011  0.167503  0.419731   \n",
       "568626 -0.147733  0.483894 -0.210817  ... -0.550260  0.031874  0.388161   \n",
       "568627 -0.019997 -0.122048 -0.144495  ... -0.076417  0.140788  0.536523   \n",
       "568628 -0.207723  0.893527 -0.080078  ...  0.288186 -0.060381 -0.195609   \n",
       "568629 -0.340814  0.253971 -0.513556  ... -0.621378  0.534853 -0.291514   \n",
       "\n",
       "             V23       V24       V25       V26       V27       V28    Amount  \n",
       "0      -0.134794  0.165959  0.126280 -0.434824 -0.081230 -0.151045  17982.10  \n",
       "1       0.079469 -0.577395  0.190090  0.296503 -0.248052 -0.064512   6531.37  \n",
       "2       0.945045 -1.154666 -0.605564 -0.312895 -0.300258 -0.244718   2513.54  \n",
       "3      -0.214048 -1.893131  1.003963 -0.515950 -0.165316  0.048424   5384.44  \n",
       "4      -0.161666  0.312561 -0.414116  1.071126  0.023712  0.419117  14278.97  \n",
       "...          ...       ...       ...       ...       ...       ...       ...  \n",
       "568625  1.288249 -0.900861  0.560661 -0.006018  3.308968  0.081564   4394.16  \n",
       "568626 -0.154257 -0.846452 -0.153443  1.961398 -1.528642  1.704306   4653.40  \n",
       "568627 -0.211100 -0.448909  0.540073 -0.755836 -0.487540 -0.268741  23572.85  \n",
       "568628 -0.175488 -0.554643 -0.099669 -1.434931 -0.159269 -0.076251  10160.83  \n",
       "568629  0.157303  0.931030 -0.349423 -1.090974 -1.575113  0.722936  21493.92  \n",
       "\n",
       "[568630 rows x 29 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:12.801607Z",
     "iopub.status.busy": "2023-12-10T14:22:12.801236Z",
     "iopub.status.idle": "2023-12-10T14:22:13.268623Z",
     "shell.execute_reply": "2023-12-10T14:22:13.267240Z",
     "shell.execute_reply.started": "2023-12-10T14:22:12.801577Z"
    }
   },
   "outputs": [],
   "source": [
    "# Split the data into training, validation, and test sets\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, stratify=y, random_state=42, train_size=(slovin_data / N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further split the remaining data into validation and test sets\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, stratify=y_temp, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:13.270817Z",
     "iopub.status.busy": "2023-12-10T14:22:13.270335Z",
     "iopub.status.idle": "2023-12-10T14:22:13.406708Z",
     "shell.execute_reply": "2023-12-10T14:22:13.405217Z",
     "shell.execute_reply.started": "2023-12-10T14:22:13.270776Z"
    }
   },
   "outputs": [],
   "source": [
    "# Standardize the data using StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_val = scaler.transform(X_val)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert back to DataFrame for better readability\n",
    "X_train = pd.DataFrame(X_train, columns=X.columns)\n",
    "X_val = pd.DataFrame(X_val, columns=X.columns)\n",
    "X_test = pd.DataFrame(X_test, columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape: (37372, 29) (37372,)\n",
      "Validation set shape: (265629, 29) (265629,)\n",
      "Test set shape: (265629, 29) (265629,)\n"
     ]
    }
   ],
   "source": [
    "# Display the shapes of the resulting datasets\n",
    "print(\"Training set shape:\", X_train.shape, y_train.shape)\n",
    "print(\"Validation set shape:\", X_val.shape, y_val.shape)\n",
    "print(\"Test set shape:\", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming your DataFrame is named \"df\"\n",
    "plt.figure(figsize=(6, 4))  # Adjust the figure size as needed\n",
    "sns.countplot(x='Class', data=df)\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to check if the classes are still balanced and if the split worked good. So we print out our data at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution in Training set:\n",
      "Class\n",
      "1    18686\n",
      "0    18686\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Class distribution in Validation set:\n",
      "Class\n",
      "1    132815\n",
      "0    132814\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Class distribution in Test set:\n",
      "Class\n",
      "0    132815\n",
      "1    132814\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check the distribution of classes in each set\n",
    "print(\"Class distribution in Training set:\")\n",
    "print(y_train.value_counts())\n",
    "\n",
    "print(\"\\nClass distribution in Validation set:\")\n",
    "print(y_val.value_counts())\n",
    "\n",
    "print(\"\\nClass distribution in Test set:\")\n",
    "print(y_test.value_counts())"
   ]
  },
  {
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.392545</td>\n",
       "      <td>0.343613</td>\n",
       "      <td>-0.669009</td>\n",
       "      <td>0.901134</td>\n",
       "      <td>-0.502667</td>\n",
       "      <td>-0.705964</td>\n",
       "      <td>-0.829860</td>\n",
       "      <td>0.211501</td>\n",
       "      <td>-1.115143</td>\n",
       "      <td>-1.126336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.818774</td>\n",
       "      <td>0.377258</td>\n",
       "      <td>0.027456</td>\n",
       "      <td>-0.119437</td>\n",
       "      <td>-0.336717</td>\n",
       "      <td>1.445923</td>\n",
       "      <td>0.607213</td>\n",
       "      <td>1.752804</td>\n",
       "      <td>1.067981</td>\n",
       "      <td>0.836711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.039310</td>\n",
       "      <td>-0.338743</td>\n",
       "      <td>0.633920</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>0.103042</td>\n",
       "      <td>-0.176422</td>\n",
       "      <td>0.495211</td>\n",
       "      <td>-0.205369</td>\n",
       "      <td>0.538997</td>\n",
       "      <td>0.492028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.278213</td>\n",
       "      <td>-0.224721</td>\n",
       "      <td>-0.812030</td>\n",
       "      <td>0.117277</td>\n",
       "      <td>0.715150</td>\n",
       "      <td>0.262090</td>\n",
       "      <td>0.228209</td>\n",
       "      <td>-0.270448</td>\n",
       "      <td>-0.032715</td>\n",
       "      <td>1.285989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.699538</td>\n",
       "      <td>1.525574</td>\n",
       "      <td>-1.531969</td>\n",
       "      <td>1.986154</td>\n",
       "      <td>-0.635774</td>\n",
       "      <td>-1.563278</td>\n",
       "      <td>-1.412879</td>\n",
       "      <td>0.834356</td>\n",
       "      <td>-1.773493</td>\n",
       "      <td>-1.798813</td>\n",
       "      <td>...</td>\n",
       "      <td>1.264080</td>\n",
       "      <td>0.621989</td>\n",
       "      <td>0.026304</td>\n",
       "      <td>0.090181</td>\n",
       "      <td>-1.905696</td>\n",
       "      <td>0.260072</td>\n",
       "      <td>1.356484</td>\n",
       "      <td>2.441099</td>\n",
       "      <td>1.958201</td>\n",
       "      <td>-0.513432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.380672</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>-0.064718</td>\n",
       "      <td>-0.433082</td>\n",
       "      <td>0.251055</td>\n",
       "      <td>0.811836</td>\n",
       "      <td>-0.131473</td>\n",
       "      <td>-0.595918</td>\n",
       "      <td>0.447875</td>\n",
       "      <td>-0.283522</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049617</td>\n",
       "      <td>-0.013234</td>\n",
       "      <td>0.613901</td>\n",
       "      <td>-0.184139</td>\n",
       "      <td>-0.646777</td>\n",
       "      <td>0.021755</td>\n",
       "      <td>-1.014868</td>\n",
       "      <td>-0.113987</td>\n",
       "      <td>-0.330690</td>\n",
       "      <td>-0.193159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.448355</td>\n",
       "      <td>-0.420435</td>\n",
       "      <td>-0.048189</td>\n",
       "      <td>-1.454788</td>\n",
       "      <td>1.698326</td>\n",
       "      <td>2.293212</td>\n",
       "      <td>0.425643</td>\n",
       "      <td>-0.163578</td>\n",
       "      <td>0.479258</td>\n",
       "      <td>0.417659</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.363593</td>\n",
       "      <td>0.131875</td>\n",
       "      <td>-0.876350</td>\n",
       "      <td>0.016844</td>\n",
       "      <td>1.543554</td>\n",
       "      <td>0.209854</td>\n",
       "      <td>0.604648</td>\n",
       "      <td>-0.019610</td>\n",
       "      <td>0.594071</td>\n",
       "      <td>-1.594426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -0.392545  0.343613 -0.669009  0.901134 -0.502667 -0.705964 -0.829860   \n",
       "1  1.039310 -0.338743  0.633920 -0.329657  0.103042 -0.176422  0.495211   \n",
       "2 -0.699538  1.525574 -1.531969  1.986154 -0.635774 -1.563278 -1.412879   \n",
       "3 -0.380672  0.001851 -0.064718 -0.433082  0.251055  0.811836 -0.131473   \n",
       "4  0.448355 -0.420435 -0.048189 -1.454788  1.698326  2.293212  0.425643   \n",
       "\n",
       "         V8        V9       V10  ...       V20       V21       V22       V23  \\\n",
       "0  0.211501 -1.115143 -1.126336  ...  0.818774  0.377258  0.027456 -0.119437   \n",
       "1 -0.205369  0.538997  0.492028  ... -0.278213 -0.224721 -0.812030  0.117277   \n",
       "2  0.834356 -1.773493 -1.798813  ...  1.264080  0.621989  0.026304  0.090181   \n",
       "3 -0.595918  0.447875 -0.283522  ...  0.049617 -0.013234  0.613901 -0.184139   \n",
       "4 -0.163578  0.479258  0.417659  ... -0.363593  0.131875 -0.876350  0.016844   \n",
       "\n",
       "        V24       V25       V26       V27       V28    Amount  \n",
       "0 -0.336717  1.445923  0.607213  1.752804  1.067981  0.836711  \n",
       "1  0.715150  0.262090  0.228209 -0.270448 -0.032715  1.285989  \n",
       "2 -1.905696  0.260072  1.356484  2.441099  1.958201 -0.513432  \n",
       "3 -0.646777  0.021755 -1.014868 -0.113987 -0.330690 -0.193159  \n",
       "4  1.543554  0.209854  0.604648 -0.019610  0.594071 -1.594426  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:13.408701Z",
     "iopub.status.busy": "2023-12-10T14:22:13.408347Z",
     "iopub.status.idle": "2023-12-10T14:22:13.452010Z",
     "shell.execute_reply": "2023-12-10T14:22:13.450726Z",
     "shell.execute_reply.started": "2023-12-10T14:22:13.408672Z"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
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       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.392545</td>\n",
       "      <td>0.343613</td>\n",
       "      <td>-0.669009</td>\n",
       "      <td>0.901134</td>\n",
       "      <td>-0.502667</td>\n",
       "      <td>-0.705964</td>\n",
       "      <td>-0.829860</td>\n",
       "      <td>0.211501</td>\n",
       "      <td>-1.115143</td>\n",
       "      <td>-1.126336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377258</td>\n",
       "      <td>0.027456</td>\n",
       "      <td>-0.119437</td>\n",
       "      <td>-0.336717</td>\n",
       "      <td>1.445923</td>\n",
       "      <td>0.607213</td>\n",
       "      <td>1.752804</td>\n",
       "      <td>1.067981</td>\n",
       "      <td>0.836711</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.039310</td>\n",
       "      <td>-0.338743</td>\n",
       "      <td>0.633920</td>\n",
       "      <td>-0.329657</td>\n",
       "      <td>0.103042</td>\n",
       "      <td>-0.176422</td>\n",
       "      <td>0.495211</td>\n",
       "      <td>-0.205369</td>\n",
       "      <td>0.538997</td>\n",
       "      <td>0.492028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.224721</td>\n",
       "      <td>-0.812030</td>\n",
       "      <td>0.117277</td>\n",
       "      <td>0.715150</td>\n",
       "      <td>0.262090</td>\n",
       "      <td>0.228209</td>\n",
       "      <td>-0.270448</td>\n",
       "      <td>-0.032715</td>\n",
       "      <td>1.285989</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.699538</td>\n",
       "      <td>1.525574</td>\n",
       "      <td>-1.531969</td>\n",
       "      <td>1.986154</td>\n",
       "      <td>-0.635774</td>\n",
       "      <td>-1.563278</td>\n",
       "      <td>-1.412879</td>\n",
       "      <td>0.834356</td>\n",
       "      <td>-1.773493</td>\n",
       "      <td>-1.798813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621989</td>\n",
       "      <td>0.026304</td>\n",
       "      <td>0.090181</td>\n",
       "      <td>-1.905696</td>\n",
       "      <td>0.260072</td>\n",
       "      <td>1.356484</td>\n",
       "      <td>2.441099</td>\n",
       "      <td>1.958201</td>\n",
       "      <td>-0.513432</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.380672</td>\n",
       "      <td>0.001851</td>\n",
       "      <td>-0.064718</td>\n",
       "      <td>-0.433082</td>\n",
       "      <td>0.251055</td>\n",
       "      <td>0.811836</td>\n",
       "      <td>-0.131473</td>\n",
       "      <td>-0.595918</td>\n",
       "      <td>0.447875</td>\n",
       "      <td>-0.283522</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013234</td>\n",
       "      <td>0.613901</td>\n",
       "      <td>-0.184139</td>\n",
       "      <td>-0.646777</td>\n",
       "      <td>0.021755</td>\n",
       "      <td>-1.014868</td>\n",
       "      <td>-0.113987</td>\n",
       "      <td>-0.330690</td>\n",
       "      <td>-0.193159</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.448355</td>\n",
       "      <td>-0.420435</td>\n",
       "      <td>-0.048189</td>\n",
       "      <td>-1.454788</td>\n",
       "      <td>1.698326</td>\n",
       "      <td>2.293212</td>\n",
       "      <td>0.425643</td>\n",
       "      <td>-0.163578</td>\n",
       "      <td>0.479258</td>\n",
       "      <td>0.417659</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131875</td>\n",
       "      <td>-0.876350</td>\n",
       "      <td>0.016844</td>\n",
       "      <td>1.543554</td>\n",
       "      <td>0.209854</td>\n",
       "      <td>0.604648</td>\n",
       "      <td>-0.019610</td>\n",
       "      <td>0.594071</td>\n",
       "      <td>-1.594426</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -0.392545  0.343613 -0.669009  0.901134 -0.502667 -0.705964 -0.829860   \n",
       "1  1.039310 -0.338743  0.633920 -0.329657  0.103042 -0.176422  0.495211   \n",
       "2 -0.699538  1.525574 -1.531969  1.986154 -0.635774 -1.563278 -1.412879   \n",
       "3 -0.380672  0.001851 -0.064718 -0.433082  0.251055  0.811836 -0.131473   \n",
       "4  0.448355 -0.420435 -0.048189 -1.454788  1.698326  2.293212  0.425643   \n",
       "\n",
       "         V8        V9       V10  ...       V21       V22       V23       V24  \\\n",
       "0  0.211501 -1.115143 -1.126336  ...  0.377258  0.027456 -0.119437 -0.336717   \n",
       "1 -0.205369  0.538997  0.492028  ... -0.224721 -0.812030  0.117277  0.715150   \n",
       "2  0.834356 -1.773493 -1.798813  ...  0.621989  0.026304  0.090181 -1.905696   \n",
       "3 -0.595918  0.447875 -0.283522  ... -0.013234  0.613901 -0.184139 -0.646777   \n",
       "4 -0.163578  0.479258  0.417659  ...  0.131875 -0.876350  0.016844  1.543554   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  1.445923  0.607213  1.752804  1.067981  0.836711    0.0  \n",
       "1  0.262090  0.228209 -0.270448 -0.032715  1.285989    NaN  \n",
       "2  0.260072  1.356484  2.441099  1.958201 -0.513432    NaN  \n",
       "3  0.021755 -1.014868 -0.113987 -0.330690 -0.193159    NaN  \n",
       "4  0.209854  0.604648 -0.019610  0.594071 -1.594426    NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_df = X_train.join(y_train)\n",
    "sample_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:13.786962Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation \n",
    "corr_df = df.corr(numeric_only = True).abs()\n",
    "sns.heatmap(corr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:15.835353Z",
     "iopub.status.busy": "2023-12-10T14:22:15.834921Z",
     "iopub.status.idle": "2023-12-10T14:22:15.843222Z",
     "shell.execute_reply": "2023-12-10T14:22:15.842152Z",
     "shell.execute_reply.started": "2023-12-10T14:22:15.835318Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define a function to calculate the correlation ratio\n",
    "def correlation_ratio(categories, measurements):\n",
    "    f_cat = pd.Categorical(categories)\n",
    "    cat_list = f_cat.categories\n",
    "    values = [measurements[f_cat == category] for category in cat_list]\n",
    "\n",
    "    all_values = np.concatenate(values)\n",
    "    total_mean = np.mean(all_values)\n",
    "\n",
    "    sst = sum([(x - total_mean) ** 2 for x in all_values])\n",
    "\n",
    "    ssw = sum([sum((x - np.mean(x)) ** 2) for x in values])\n",
    "\n",
    "    return ssw / sst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:15.845959Z",
     "iopub.status.busy": "2023-12-10T14:22:15.844804Z",
     "iopub.status.idle": "2023-12-10T14:22:28.658499Z",
     "shell.execute_reply": "2023-12-10T14:22:28.657230Z",
     "shell.execute_reply.started": "2023-12-10T14:22:15.845922Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a list of columns to analyze (numerical variables)\n",
    "num_columns = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']\n",
    "\n",
    "# Create a list of the categorical column(s) (if any)\n",
    "cat_columns = ['Class']\n",
    "\n",
    "# Calculate and store correlation ratios\n",
    "correlation_ratios = []\n",
    "for column in num_columns:\n",
    "    correlation_ratio_value = correlation_ratio(df['Class'], df[column])\n",
    "    correlation_ratios.append(correlation_ratio_value)\n",
    "\n",
    "# Find the index of the column with the highest correlation\n",
    "max_corr_column_index = correlation_ratios.index(max(correlation_ratios))\n",
    "\n",
    "# Create a list of colors, with the highest correlation column in red and others in blue\n",
    "colors = ['red' if i == max_corr_column_index else 'blue' for i in range(len(num_columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:28.661085Z",
     "iopub.status.busy": "2023-12-10T14:22:28.660707Z",
     "iopub.status.idle": "2023-12-10T14:22:29.158384Z",
     "shell.execute_reply": "2023-12-10T14:22:29.157159Z",
     "shell.execute_reply.started": "2023-12-10T14:22:28.661054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot to visualize correlation ratios\n",
    "plt.figure(figsize=(15, 6))\n",
    "bars = plt.bar(num_columns, correlation_ratios, color=colors)\n",
    "plt.xlabel('Numerical Columns')\n",
    "plt.ylabel('Correlation Ratio with Class')\n",
    "plt.title('Correlation Ratios')\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add a legend to explain the color-coding\n",
    "plt.legend([bars[max_corr_column_index]], ['Highest Correlation'], loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:22:29.160429Z",
     "iopub.status.busy": "2023-12-10T14:22:29.159838Z",
     "iopub.status.idle": "2023-12-10T14:22:29.169412Z",
     "shell.execute_reply": "2023-12-10T14:22:29.168010Z",
     "shell.execute_reply.started": "2023-12-10T14:22:29.160387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class    1.000000\n",
       "V14      0.805669\n",
       "V12      0.768579\n",
       "V4       0.735981\n",
       "V11      0.724278\n",
       "V3       0.682095\n",
       "V10      0.673665\n",
       "V9       0.585522\n",
       "V16      0.573511\n",
       "V1       0.505761\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the most interesting features\n",
    "corr_df[\"Class\"].sort_values(ascending = False)[: 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modeling (Decision Tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:23:04.652280Z",
     "iopub.status.busy": "2023-12-10T14:23:04.651773Z",
     "iopub.status.idle": "2023-12-10T14:23:07.001250Z",
     "shell.execute_reply": "2023-12-10T14:23:06.999640Z",
     "shell.execute_reply.started": "2023-12-10T14:23:04.652230Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a decision tree classifier\n",
    "clf = DecisionTreeClassifier(criterion=\"entropy\", random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the classifier to the training data\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Validation Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the validation set\n",
    "y_val_pred = clf.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.9882693531203295\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation set\n",
    "accuracy_val = accuracy_score(y_val, y_val_pred)\n",
    "print(\"Validation Accuracy:\", accuracy_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99    132814\n",
      "           1       0.98      0.99      0.99    132815\n",
      "\n",
      "    accuracy                           0.99    265629\n",
      "   macro avg       0.99      0.99      0.99    265629\n",
      "weighted avg       0.99      0.99      0.99    265629\n",
      "\n",
      "\n",
      "Validation Confusion Matrix:\n",
      " [[130702   2112]\n",
      " [  1004 131811]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix for validation set\n",
    "print(\"\\nValidation Classification Report:\\n\", classification_report(y_val, y_val_pred))\n",
    "print(\"\\nValidation Confusion Matrix:\\n\", confusion_matrix(y_val, y_val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix using a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix(y_val, y_val_pred), annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['No Fraud', 'Fraud'],\n",
    "            yticklabels=['No Fraud', 'Fraud'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Performance and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9882919410154765\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99    132815\n",
      "           1       0.98      0.99      0.99    132814\n",
      "\n",
      "    accuracy                           0.99    265629\n",
      "   macro avg       0.99      0.99      0.99    265629\n",
      "weighted avg       0.99      0.99      0.99    265629\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      " [[130749   2066]\n",
      " [  1044 131770]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H18nJyYqLi9PEiRP1zjvvOKkqAAAAmMDpYbVq1arp2mrVqqWgoCC9++67evrpp51QFQAAAEzg9Pus3kn58uW1Y8cOZ5cBAAAAJ3L6zGpiYqLDa8uydObMGY0cOVLlypVzUlUAAAAwgdPDav78+dNdYGVZlooXL66FCxc6qSoAAACYwOlhdcOGDQ6vXVxcFBgYqLJly8rNzenlAQAAwImcngbr16/v7BIAAABgKKeH1dv279+vEydO6ObNmw7tTz31lJMqAgAAgLM5PaweOXJE7dq10969e2Wz2XT76a+317HyUAAAAIDcy+m3rho4cKBKlSqlX3/9VXny5NEPP/ygr776SrVq1dLGjRudXR4AAACcyOkzq1u3btX69esVGBgoFxcXubi46PHHH9e4ceP0yiuvpHvCFQAAAHIPp8+spqamKm/evJKkgIAAnT59WpJUokQJHTx40JmlAQAAwMmcPrMaEhKi77//XqVLl1bt2rUVGRkpDw8PzZo1S6VLl3Z2eQAAAHAip4fVYcOGKSkpSZI0ZswYtWrVSnXr1pW/v78WLVrk5Orwd/ZYjTIa1K2xalQqrqKBfur42myt3LjXvv2tF5rrmSdq6IEi+XUzOVVxB05q5LRV2rHvuL2Ph7ubxg9qo2ea1ZS3l7s2fHtIr477P/1y7qIkqW7Nsloz+5UMj/941/e0a/8Jh7aCfnn07cJ/qFjh/CpSb6guXblm39a+aXUN6dlU5YILKf7iFUUt+koffLQ+G98RALlB9OyZWv/lWh07ekSeXl6qWq26Bg4arJKl/jsBZFmWZk6fqqVLFutyYqJCqjyk8GEjVKas45Mj9+yO07TJH2rv3u/l5uamChUqamrUbHl5edn7fL1po2ZFTddPhw7K29tbNWo+rPcnTfnLzhc5n9PDarNmzey/ly5dWvv379eFCxdUoECBdE+2ArLCx8tDew/9ovkrtmnhe73Tbf/5+DkNmvB/OvpLgrw93fVyl4ZaOe1FhbQZrfiLVyRJ777+tFrWC1G38FhduHRV419rq6WT+qpOl3eVlmZp256jKtn0LYdxR/RvqUa1K6QLqpIUNaKz9v70i4oVzu/Q/kSdSpo7pptei1yiL7f9qIqlCmv68Od07UayohZ9nX1vCoAc77udO9Tpuc56MKSKUlJSNW3yB+rft7eW/WuVvPPkkSTFxszRxx/FatSYcSpRsqRmz4xSvz49tXzV5/LxubU0b8/uOA3o10c9evfV0DeHyc3dXYcO/igXl/+uIPxy7RcaHTFCAwYO0iO1a8uyLP300yGnnDdyLpt1+15RTpCSkiIvLy/t3r1bISEh2Taud42MZ7qQe137bnK6mdU/yufjpXNfR6p5v6na+O0h+eb10sl1Y9Vr+HwtWXPrQr+iAb766fO31faVKH259cd0Y7i5uejnz99W1KKvNX7OFw7b+nR4XB2eqK6xs1dr9cyXHWZWY9/pJnc3V3UZOtfef0DnBhrYtaHKtYjIjrcAf3MJ2yc5uwT8TV24cEGN69XRnNj5qlnrYVmWpSca1lPn57upR68+kqSbN2+qcf3HNHDQYHXo+KwkqVvnTqodWkcvvTwww3FTUlLUsllj9XvxZbVr3+EvOx/kHHncMzcp6dQLrNzc3FSiRAnupQqnc3dzVa+n6+ji5avae+gXSVL1SsXl4e7mEErPxCfqh8Nn9GjVUhmO06peFQXkz6uPV253aK9YqojC+zRT7xEfKy0t/d+Hnh5uun4zxaHt2o2beqBIAQUXLXivpwcgF7ty5bIkyc/PT5L0y6lTio8/r9A6j9n7eHh4qGath7Vn960/zC8kJGjv93tUsGBBde/yrBrXe0y9wroq7rtd9n1+PLBf5379VS4uNj3boZ2aNqirl/r10eGff/oLzw65gdPvBjBs2DCFh4frwoULd7X/jRs3lJiY6PBjpRF+kTnN6z6o85vf1cVt7+vlLg3Uqv90JVy8tYa6iL+vbtxM0cXL1xz2OZdwWYX9fTMcr3vbR7V26wGd+vWivc3D3U3zxnXXm5P+pZNnf8twv7Vbf1SbRg+pwSPlZbPZVDY4UAM6N5AkFQ3M+FgA8L9YlqX3I8ereo2aKluuvCQpPv68JKmgv79DX39/fyXEx0uSTp06KUmaOX2qnu7wjKbNnK1KlR7UC73CdPz4sVt9Tt7qEzV9mnq/0E+Tps2Qr6+veoc9r0uXLv4FZ4fcwulhdfLkyfr6668VFBSkChUqqEaNGg4//8u4cePk5+fn8JPy686/oHLkBJt2/KTaz01Qwx4fas2WA/p4Qg8FFsj7p/vYbFJGq2eKFcqvpqGVNG/5Nof20S+31sGjZ7Xwszv/u4xZtkVRi77Wsg/7KnH7RG2a95r+74vvJEmpqWl3cWYAII1/Z7R+OnRQ4yLfT7ftj9eFWNZ/29LSbn3utH+mk9q0a6+KlSrr9aHhKlmylP61bOl/+t/q07vvC2rStJkqPxiiUWPGSTab1n6x+n6eFnIZp19g1bZt23vaPzw8XK+99ppDW6F64fc0JnKPq9dv6sjJeB05Ga9v9x7T3uXD1L1tqN6bu1ZnExLl6eGm/Pm8HWZXAwvm07bvj6Yb6/mnaivhUpJWfeW4Lrb+w+UUUjZI7RpXk/Tf/xmcWj9WE2LWaEzU55KkYZNXaMTUlSri76vzv11Rw0duzYIcP3133zoAyN3Gjx2tTRvWK3rexypcpIi9PSAgUJKUEB+vwMBC9vYLFxLss62320uXKeswZqnSZXT27Jlb4wQGpuvj4eGhBx4orrNnztyHM0Ju5bSwGhMToy5duigi4t4uHvH09JSnp6dDm83F9Z7GRO5ls9nk6XHrP4u4Ayd1MzlFjR+tqKVrb63jKhLgqwfLFNVbk/6Vbt9uT9XWP1d9q5QUx5nQ54bEyNvT3f665oPBmjWyi5r0nqQjJ+Md+qalWTp9/pIkqeOTNbVtz1Gd/+1Ktp4jgJzNsixNGDta69d9qdlzP1KxBx5w2F7sgQcUEBCobVu3qGKlypKk5OSb2rVzhwYOGixJCipWTIGFCunYMcc/zI8fP6bHHq8rSapUOUQeHh46dvSoqteo+Z9xknX6l19UNCjofp8mchGnhdU+ffqoVatWKlTo1l9vQUFB2rJli0qWLOmskpDD+Hh7qEzxQPvrksX89VD5Yvot8aoSLiZpaO8n9O9N+3Q2/pIK+vmo7zN1VaxQfi37TzBNvHJdscu3afygtkq4lKTfLl3VuEFttO/n01q/3fHpag0eKa9SDwQo9l+OSwAk6egpx0Dqn99HkvTjkV/tdwPwz++jdo2r6atdP8nLw13dnqqtp5tU0xN9JmfrewIg5xs35m19/tkqfTB5mnx8fOxrVPPmzScvLy/ZbDZ1fr6bomfPVHBwCQWXKKHo2TPl5eWl5i1bSbr1h3v3Hr0UNW2KyleooAoVK2nlv5br2NEjenfipP+Ml1cdOj6rqOlTVKRIERUNCtK8uTGSpKZPPOmck0eO5LSw+sc1f5cvX7avkQGyQ43KwQ437I8c/LQkaf6K7Xp57CJVKFlYXVs9Iv/8eXXhUpJ2/nBCTXpN0oEjZ+37vPH+MqWmpurj8T3k7emuDTsOqW/ErHRX9Ie1eVRbdx/RwaO/3nW9XVs/onGD2spmk7Z/f0zN+k7Rzh/S36sVAP7M/y1aIEnq06ObQ/uoMWP1VNtbn4NhPXvrxvXrGjfmbSUmXlLIQw9pxqxo+z1WJanL891148YNvT9hvC4lXlL58hU0Y3aMigcH2/u8OniIXF1dNSx8qG7cuK6QKlU1KyZWvv+58wCQHZx2n1UXFxedPXvWPrOaL18+7dmzJ1sescp9VgHkNNxnFUBOY/x9Vm02m8OViH98DQAAADh1GUD58uXtAfXKlSuqXr26w2PcJN31/VcBAADw9+e0sDp37tz/3QkAAAC5mtPCavfu3Z11aAAAAPxNOP0JVgAAAMCdEFYBAABgLMIqAAAAjEVYBQAAgLGMCquWZaV7shUAAAByLyPC6kcffaQqVarI29tb3t7eeuihhzR//nxnlwUAAAAnc9qtq26bOHGihg8frgEDBuixxx6TZVn65ptv1K9fP8XHx2vQoEHOLhEAAABOYrOc/L17qVKlNGrUKHXr1s2hfd68eRo5cqSOHj2a5TG9a7ySXeUBgBEStk9ydgkAkK3yuNsy1c/pywDOnDmjOnXqpGuvU6eOzpw544SKAAAAYAqnh9WyZctq8eLF6doXLVqkcuXKOaEiAAAAmMLpa1ZHjRqlTp066auvvtJjjz0mm82mzZs3a926dRmGWAAAAOQeTp9Zbd++vbZv366AgAAtX75cy5YtU0BAgL799lu1a9fO2eUBAADAiZx+gdX9wAVWAHIaLrACkNP8bS6wAgAAAO7EaWtWXVxcZLP9eaK22WxKSUn5iyoCAACAaZwWVj/99NM7btuyZYumTJnCo1cBAAByOaeF1TZt2qRr+/HHHxUeHq6VK1eqS5cuGj16tBMqAwAAgCmMWLN6+vRp9enTRw899JBSUlK0e/duzZs3T8HBwc4uDQAAAE7k1LB66dIlDR06VGXLltUPP/ygdevWaeXKlQoJCXFmWQAAADCE05YBREZGasKECSpSpIgWLFiQ4bIAAAAA5G5Ou8+qi4uLvL291aRJE7m6ut6x37Jly7I8NvdZBZDTcJ9VADlNZu+z6rSZ1W7duv3PW1cBAAAgd3NaWI2NjXXWoQEAAPA3YcTdAAAAAICMEFYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYyy0znVasWJHpAZ966qm7LgYAAAD4vUyF1bZt22ZqMJvNptTU1HupBwAAALDLVFhNS0u733UAAAAA6bBmFQAAAMbK1MzqHyUlJWnTpk06ceKEbt686bDtlVdeyZbCAAAAgCyH1bi4OLVo0UJXr15VUlKSChYsqPj4eOXJk0eFChUirAIAACDbZHkZwKBBg9S6dWtduHBB3t7e2rZtm44fP66aNWvqvffeux81AgAAIJfKcljdvXu3Bg8eLFdXV7m6uurGjRsqXry4IiMj9eabb96PGgEAAJBLZTmsuru7y2azSZIKFy6sEydOSJL8/PzsvwMAAADZIctrVqtXr66dO3eqfPnyatiwoUaMGKH4+HjNnz9fVapUuR81AgAAIJfK8szq2LFjVbRoUUnS6NGj5e/vr/79++vcuXOaNWtWthcIAACA3MtmWZbl7CKym3cN7kgAIGdJ2D7J2SUAQLbK427LVD8eCgAAAABjZXnNaqlSpewXWGXkyJEj91QQAAAAcFuWw+qrr77q8Do5OVlxcXFavXq1hgwZkl11AQAAAFkPqwMHDsywfdq0adq5c+c9FwQAAADclm1rVps3b66lS5dm13AAAABA9oXVJUuWqGDBgtk1HAAAAHB3DwX4/QVWlmXp7NmzOn/+vKZPn56txQEAACB3y/J9VkeOHOkQVl1cXBQYGKgGDRqoYsWK2V7g3bie4uwKACB7FXh4gLNLAIBsdS1uaqb65ciHAhBWAeQ0hFUAOU1mw2qW16y6urrq3Llz6doTEhLk6uqa1eEAAACAO8pyWL3TROyNGzfk4eFxzwUBAAAAt2X6AqvJkydLkmw2m+bMmaO8efPat6Wmpuqrr74yZs0qAAAAcoZMh9UPPvhA0q2Z1aioKIev/D08PFSyZElFRUVlf4UAAADItTIdVo8ePSpJatiwoZYtW6YCBQrct6IAAAAA6S7us7phw4b7UQcAAACQTpYvsOrQoYPGjx+frv3dd9/VM888ky1FAQAAANJdhNVNmzapZcuW6dqffPJJffXVV9lSFAAAACDdRVi9cuVKhreocnd3V2JiYrYUBQAAAEh3EVZDQkK0aNGidO0LFy5U5cqVs6UoAAAAQLqLC6yGDx+u9u3b6/Dhw2rUqJEkad26dfrnP/+pJUuWZHuBAAAAyL2yHFafeuopLV++XGPHjtWSJUvk7e2tqlWrav369fL19b0fNQIAACCXsll3en5qJl28eFGffPKJoqOjtWfPHqWmpmZXbXfteoqzKwCA7FXg4QHOLgEAstW1uKmZ6pflNau3rV+/Xl27dlVQUJCmTp2qFi1aaOfOnXc7HAAAAJBOlpYBnDp1SrGxsYqJiVFSUpI6duyo5ORkLV26lIurAAAAkO0yPbPaokULVa5cWfv379eUKVN0+vRpTZky5X7WBgAAgFwu0zOra9as0SuvvKL+/furXLly97MmAAAAQFIWZla//vprXb58WbVq1VLt2rU1depUnT9//n7WBgAAgFwu02E1NDRUs2fP1pkzZ/TCCy9o4cKFKlasmNLS0rR27Vpdvnz5ftYJAACAXOiebl118OBBRUdHa/78+bp48aKaNm2qFStWZGd9d4VbVwHIabh1FYCc5r7fukqSKlSooMjISJ06dUoLFiy4l6EAAACAdO75oQAmYmYVQE7DzCqAnOYvmVkFAAAA7ifCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwlpuzDvz0009nuu+yZcvuYyUAAAAwldNmVv38/Ow/vr6+WrdunXbu3GnfvmvXLq1bt05+fn7OKhEAAABO5rSZ1blz59p/Hzp0qDp27KioqCi5urpKklJTU/Xiiy/K19fXWSUCAADAyWyWZVnOLiIwMFCbN29WhQoVHNoPHjyoOnXqKCEhIUvjXU/JzuoAwPkKPDzA2SUAQLa6Fjc1U/2MuMAqJSVFBw4cSNd+4MABpaWlOaEiAAAAmMBpywB+r0ePHurZs6d+/vlnPfroo5Kkbdu2afz48erRo4eTqwMAAICzGBFW33vvPRUpUkQffPCBzpw5I0kqWrSo3njjDQ0ePNjJ1QEAAMBZjFiz+nuJiYmSdE8XVrFmFUBOw5pVADlNZtesGjGz+ntc/Q8AAIDbjAirpUqVks1mu+P2I0eO/IXVAAAAwBRGhNVXX33V4XVycrLi4uK0evVqDRkyxDlFIVfYtXOHYmOidWD/Pp0/f14fTJ6mRo2b2LdblqWo6VO19P8WKTExUVUeqqrwYSNUtmy5dGNZlqWX+vXRN5u/TjfObTdv3lTXZ5/RwYM/atGS5apYqdJ9PT8AOctjNcpoULcmqlE5WEUD/dRx0Cyt3Pi9fftbL7TQM81q6IEiBXQzOVVxB05o5NSV2rHvuL1Pz6cfU6fmtVSt4gPyzeutInWH6NKVa/btdWuW05o5AzM8/uNdIrVr/wl1bV1bs99+PsM+wY3+ofO/XZEkPVg2SB/84xnVerCEfku8qjlLN2vcrNXZ8VYgFzEirA4cmPF/FNOmTXN4qhWQ3a5du6oKFSqoTbunNfjVl9Ntnxs9W/PnzdXb74xXiZIlNXvmDPXr3UP/+vdq+fjkdej78Ufz/vQbAkn64P1IBRYqpIMHf8zW8wCQO/h4e2rvoV80f8U2LXy/T7rtPx8/p0ET/k9HT8XL29NdL3dtpJXTByikzSjF/ydA5vFy19ot+7V2y36NfqVNujG27Tmikk3CHdpGvNhKjWpX0K79JyRJS9Z8p7Vb9jv0mTXqeXl5utuDaj4fL62aMUBf7Tykx7u+q3IlCmnWqK66eu2mJs1fny3vB3IHI8LqnTRv3lzh4eEOT7sCstPjdevr8br1M9xmWZY+mf+RevftpyZNn5AkjRk7QY3q1dFn/16lZzo+a+978McfNf+jufrnwiVq3ODxDMfb/PUmbd3yjd7/YIo2f/1V9p8MgBxvzTf7teab/Xfcvmi14wTP0PeXqUe7OgopF6SN3x6SJE3950ZJt2ZQM5KckqpfEy7bX7u5uahl/SqKWvTfz63rN5J1/Uay/XVAgbxq8Eh59Rv1ib3t2Ra15OXppj4jPtbN5BTtP3xG5UoU0itdGxFWkSVGPBTgTpYsWaKCBQs6uwzkUr+cOqX4+PMKfey/4dPDw0M1az2sPXFx9rZr167pH0NeU/hbwxUQGJjhWAnx8RoVMVzvjIuUl7fXfa8dANzdXNXr6cd08fJV7T30y12P06r+QwrIn1cfr9h2xz5dWj2iq9dv6tMvd9vbaj9USl/v+lk3k/97i561Ww4oqFB+lQjyv+t6kPsYMbNavXp1h69PLcvS2bNndf78eU2fPv1P971x44Zu3Ljh0Ga5esrT0/O+1IrcIz7+vCTJ39/xQ9XfP0CnT5+2v353wjhVrV5dDRulX6Mq3fr3PPytf+iZjs/qwZAq+uWXU/evaAC5XvO6IfpofA/l8XLX2fhEteo3VQkXk+56vO5tQ7V26wGd+vXiHft0axOqRZ/vdJhtLezvq+OnLzj0O3fh1oxtkQBfHT+dtUepI/cyIqy2bdvW4bWLi4sCAwPVoEEDVaxY8U/3HTdunEaNGuXQ9tbwCA0bMTKbq0Ru9cd1qJZl6XbTxvXrtGP7Ni1a8ukd9//nJ/OVdOWKevV54X6WCQCSpE07Dqn2s+MUkD+vejxdRx9H9lS959+zryXNimKF8qtpaCV1HRpzxz61HyqlymWKqvfwj9Jt++Ot3G13aAf+jBFhNSIi4q73DQ8P12uvvebQZrkyq4p7FxBw6yv9+Ph4BQYWsrdfuJAgf/8ASdK327fp5MkTejz0YYd9B7/6smrUrKXo2PnasX2bvv9+jx6uXsWhT+dO7dWiZWuNGTfhPp8JgNzk6vWbOnIyXkdOxuvbvce0918j1L1dHb0XsybLYz3f5lElXErSqk3f37FPWLtQ7f7xpOIOnHRo/zUhUYUDHO+dHlgw33+2XRaQWUaE1d+7du2akpOTHdr+7EEBnp7pv/LnCVbIDsUeeEABAYHatuUbVapUWZKUfPOmdu3coYGvvS5J6tm7r9p1eMZhvw5tW+v1oeGq36ChJGlo+DC99Mqr9u3nz51T/769FPneB6ryUNW/5mQA5Fo22eTpfnf/u+/21KP656pvlZKSluF2H28PtW9aQyOmrEi3bfv3RzVqwFNyd3NVckqqJKlJaEWdPneRJQDIEiPCalJSkoYOHarFixcrISH9P+DU1FQnVIXc4GpSkk6cOGF//cupU/rxwAH5+fmpaFCQujzfTdGzZyq4REkFlyih6Fkz5eXlpRYtW0mSAgIDM7yoqmjRID3wQPFbvwcFOWzLkyePJOmB4sEqXKTI/To1ADmQj7eHyhT/72dOyWL+eqh8Mf2WeFUJF5M0tHcz/XvTXp2Nv6SCfj7q27GeihXOr2Vrv7PvU9g/nwr7+6pM8K1viELKBely0nWdPPubfku8au/X4JHyKvVAgGKXb7ljPR2a1ZSbq4sWfrYj3bZFn+/Um31baPbbzysy+guVDQ7UkJ7NNG7259nxViAXMSKsvvHGG9qwYYOmT5+ubt26adq0afrll180c+ZMjR8/3tnlIQf74Yd96t2jm/31e5HjJElPtWmn0WPHq0evPrpx44bGjh6lxMRLqvJQVc2YHZPuHqsA8FeoUbmEww37I19vL0mav2KbXn5noSqULKyurWvLP7+PLly6qp0/HFeTnh/owJGz9n16d6irYf1a2F9/GTNIktRnxHx9vHK7vT2sbR1t3X1YB4/+esd6wtqG6l/r9+ji5WvptiVeua5W/afqw/CO+uaTN/Rb4lVN/ng9t61CltksA1Y5BwcH66OPPlKDBg3k6+ur7777TmXLltX8+fO1YMECffbZZ1kaj2UAAHKaAg8PcHYJAJCtrsVNzVQ/I+6zeuHCBZUqVUrSrfWpFy7cutXF448/rq++4ubpAAAAuZURYbV06dI6duyYJKly5cpavHixJGnlypXKnz+/8woDAACAUxkRVnv06KE9e/ZIunUrqunTp8vT01ODBg3SkCFDnFwdAAAAnMWINat/dOLECe3cuVNlypRR1apZv7UPa1YB5DSsWQWQ0/xt1qwmJyerYcOGOnTokL0tODhYTz/99F0FVQAAAOQcTg+r7u7u2rdvX7pHWgIAAABOD6uS1K1bN0VHRzu7DAAAABjGiIcC3Lx5U3PmzNHatWtVq1Yt+fj4OGyfOHGikyoDAACAMzk1rB45ckQlS5bUvn37VKNGDUlyWLsqieUBAAAAuZhTw2q5cuV05swZbdiwQZLUqVMnTZ48WYULF3ZmWQAAADCEU9es/vGuWZ9//rmSkpKcVA0AAABMY8QFVrcZeMtXAAAAOJFTw6rNZku3JpU1qgAAALjNqWtWLctSWFiYPD09JUnXr19Xv3790t0NYNmyZc4oDwAAAE7m1LDavXt3h9ddu3Z1UiUAAAAwkVPD6ty5c515eAAAABjOqAusAAAAgN8jrAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABjEVYBAABgLMIqAAAAjEVYBQAAgLEIqwAAADAWYRUAAADGIqwCAADAWIRVAAAAGIuwCgAAAGMRVgEAAGAswioAAACMZbMsy3J2EcDf0Y0bNzRu3DiFh4fL09PT2eUAwD3jcw0mIqwCdykxMVF+fn66dOmSfH19nV0OANwzPtdgIpYBAAAAwFiEVQAAABiLsAoAAABjEVaBu+Tp6amIiAguQgCQY/C5BhNxgRUAAACMxcwqAAAAjEVYBQAAgLEIqwAAADAWYRX4G4mNjVX+/PmdXQYA3FFYWJjatm3r7DKQgxBWkSOEhYXJZrNp/PjxDu3Lly+XzWa7p7FjY2Nls9nS/cyZM+eexgWA7HT7c/CPPz///LOzSwPuiZuzCwCyi5eXlyZMmKAXXnhBBQoUyNaxfX19dfDgQYc2Pz+/dP2Sk5Pl7u6erccGgMx68sknNXfuXIe2wMBAh9c3b96Uh4fHX1kWcE+YWUWO0aRJExUpUkTjxo37035Lly7Vgw8+KE9PT5UsWVLvv//+/xzbZrOpSJEiDj/e3t4aOXKkqlWrppiYGJUuXVqenp6yLEurV6/W448/rvz588vf31+tWrXS4cOH7eNt3LhRNptNFy9etLft3r1bNptNx44ds7fFxsYqODhYefLkUbt27ZSQkJDl9wVA7uHp6Znus6px48YaMGCAXnvtNQUEBKhp06aSpIkTJ6pKlSry8fFR8eLF9eKLL+rKlSv2sW5/vv3ehx9+qJIlS9pfp6am6rXXXrN/1r3xxhvijpjIboRV5Biurq4aO3aspkyZolOnTmXYZ9euXerYsaOeffZZ7d27VyNHjtTw4cMVGxt718f9+eeftXjxYi1dulS7d++WJCUlJem1117Tjh07tG7dOrm4uKhdu3ZKS0vL9Ljbt29Xz5499eKLL2r37t1q2LChxowZc9d1Asi95s2bJzc3N33zzTeaOXOmJMnFxUWTJ0/Wvn37NG/ePK1fv15vvPFGlsZ9//33FRMTo+joaG3evFkXLlzQp59+ej9OAbkYywCQo7Rr107VqlVTRESEoqOj022fOHGiGjdurOHDh0uSypcvr/379+vdd99VWFjYHce9dOmS8ubNa3+dN29enT17VtKtr9Tmz5/v8FVb+/btHfaPjo5WoUKFtH//foWEhGTqXCZNmqRmzZrpH//4h73WLVu2aPXq1ZnaH0Dus2rVKofPqubNm0uSypYtq8jISIe+r776qv33UqVKafTo0erfv7+mT5+e6eN9+OGHCg8Pt3/mRUVF6YsvvriHMwDSY2YVOc6ECRM0b9487d+/P922AwcO6LHHHnNoe+yxx/TTTz8pNTX1jmPmy5dPu3fvtv9s2bLFvq1EiRLp1oQdPnxYnTt3VunSpeXr66tSpUpJkk6cOJHp8zhw4IBCQ0Md2v74GgB+r2HDhg6fVZMnT5Yk1apVK13fDRs2qGnTpipWrJjy5cunbt26KSEhQUlJSZk61qVLl3TmzBmHzyU3N7cMjwXcC2ZWkePUq1dPzZo105tvvpluttSyrHR3B8jM+ioXFxeVLVs2w20+Pj7p2lq3bq3ixYtr9uzZCgoKUlpamkJCQnTz5k37eH88dnJycpbrAoDf8/HxyfCz6o+fU8ePH1eLFi3Ur18/jR49WgULFtTmzZvVq1cv+2eRi4tLus+hP35OAX8FZlaRI40fP14rV650mAGVpMqVK2vz5s0ObVu2bFH58uXl6uqaLcdOSEjQgQMHNGzYMDVu3FiVKlXSb7/95tDn9kzsmTNn7G2317v+vtZt27Y5tP3xNQDcjZ07dyolJUXvv/++Hn30UZUvX16nT5926BMYGKizZ886BNbff075+fmpaNGiDp9LKSkp2rVr132vH7kLM6vIkapUqaIuXbpoypQpDu2DBw/Www8/rNGjR6tTp07aunWrpk6dmqU1Wv9LgQIF5O/vr1mzZqlo0aI6ceKEfd3pbWXLllXx4sU1cuRIjRkzRj/99FO6uxK88sorqlOnjiIjI9W2bVutWbOG9aoAskWZMmWUkpKiKVOmqHXr1vrmm28UFRXl0KdBgwY6f/68IiMj1aFDB61evVqff/65fH197X0GDhyo8ePHq1y5cqpUqZImTpzocJcTIDsws4oca/To0em+wqpRo4YWL16shQsXKiQkRCNGjNDbb7/9pxdXZZWLi4sWLlyoXbt2KSQkRIMGDdK7777r0Mfd3V0LFizQjz/+qKpVq2rChAnprvR/9NFHNWfOHE2ZMkXVqlXTmjVrNGzYsGyrE0DuVa1aNU2cOFETJkxQSEiIPvnkk3S3/atUqZKmT5+uadOmqWrVqvr222/1+uuvO/QZPHiwunXrprCwMIWGhipfvnxq167dX3kqyAVsFgvjAAAAYChmVgEAAGAswioAAACMRVgFAACAsQirAAAAMBZhFQAAAMYirAIAAMBYhFUAAAAYi7AKAAAAYxFWAcAwI0eOVLVq1eyvw8LC1LZt27+8jmPHjslmszk8Dx4A/mqEVQDIpLCwMNlsNtlsNrm7u6t06dJ6/fXXlZSUdF+PO2nSJMXGxmaqLwETQE7j5uwCAODv5Mknn9TcuXOVnJysr7/+Wr1791ZSUpJmzJjh0C85OVnu7u7Zckw/P79sGQcA/o6YWQWALPD09FSRIkVUvHhxde7cWV26dNHy5cvtX93HxMSodOnS8vT0lGVZunTpkvr27atChQrJ19dXjRo10p49exzGHD9+vAoXLqx8+fKpV69eun79usP2Py4DSEtL04QJE1S2bFl5enoqODhY77zzjiSpVKlSkqTq1avLZrOpQYMG9v3mzp2rSpUqycvLSxUrVtT06dMdjvPtt9+qevXq8vLyUq1atRQXF5eN7xwA3B1mVgHgHnh7eys5OVmS9PPPP2vx4sVaunSpXF1dJUktW7ZUwYIF9dlnn8nPz08zZ85U48aNdejQIRUsWFCLFy9WRESEpk2bprp162r+/PmaPHmySpcufcdjhoeHa/bs2frggw/0+OOP68yZM/rxxx8l3QqcjzzyiL788ks9+OCD8vDwkCTNnj1bERERmjp1qqpXr664uDj16dNHPj4+6t69u5KSktSqVSs1atRIH3/8sY4ePaqBAwfe53cPADLBAgBkSvfu3a02bdrYX2/fvt3y9/e3OnbsaEVERFju7u7WuXPn7NvXrVtn+fr6WtevX3cYp0yZMtbMmTMty7Ks0NBQq1+/fg7ba9eubVWtWjXD4yYmJlqenp7W7NmzM6zx6NGjliQrLi7Oob148eLWP//5T4e20aNHW6GhoZZlWdbMmTOtggULWklJSfbtM2bMyHAsAPgrsQwAALJg1apVyps3r7y8vBQaGqp69eppypQpkqQSJUooMDDQ3nfXrl26cuWK/P39lTdvXvvP0aNHdfjwYUnSgQMHFBoa6nCMP77+vQMHDujGjRtq3Lhxpms+f/68Tp48qV69ejnUMWbMGIc6qlatqjx58mSqDgD4q7AMAACyoGHDhpoxY4bc3d0VFBTkcBGVj4+PQ9+0tDQVLVpUGzduTDdO/vz57+r43t7eWd4nLS1N0q2lALVr13bYdnu5gmVZd1UPANxvhFUAyAIfHx+VLVs2U31r1Kihs2fPys3NTSVLlsywT6VKlbRt2zZ169bN3rZt27Y7jlmuXDl5e3tr3bp16t27d7rtt9eopqam2tsKFy6sYsWK6ciRI+rSpUuG41auXFnz58/XtWvX7IH4z+oAgL8KywAA4D5p0qSJQkND1bZtW33xxRc6duyYtmzZomHDhmnnzp2SpIEDByomJkYxMTE6dOiQIiIi9MMPP9xxTC8vLw0dOlRvvPGGPvroIx0+fFjbtm1TdHS0JKlQoULy9vbW6tWr9euvv+rSpUuSbj1oYNy4cZo0aZIOHTqkvXv3au7cuZo4caIkqXPnznJxcVGvXr20f/9+ffbZZ3rvvffu8zsEAP8bYRUA7hObzabPPvtM9erVU8+ePVW+fHk9++yzOnbsmAoXLixJ6tSpk0aMGKGhQ4eqZs2aOn78uPr37/+n4w4fPlyDBw/WiBEjVKlSJXXq1Ennzp2TJLm5uWny5MmaOXOmgoKC1KZNG0lS7969NWfOHMXGxqpKlSqqX7++YmNj7be6yps3r1auXKn9+/erevXqeuuttzRhwoT7+O4AQObYLBYqAQAAwFDMrAIAAMBYhFUAAAAYi7AKAAAAYxFWAQAAYCzCKgAAAIxFWAUAAICxCKsAAAAwFmEVAAAAxiKsAgAAwFiEVQAAABiLsAoAAABj/T/en8huO4dpkgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix using a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues', cbar=False,\n",
    "            xticklabels=['No Fraud', 'Fraud'],\n",
    "            yticklabels=['No Fraud', 'Fraud'])\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Export and Visualize the Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:23:10.391590Z",
     "iopub.status.busy": "2023-12-10T14:23:10.391105Z",
     "iopub.status.idle": "2023-12-10T14:23:10.413313Z",
     "shell.execute_reply": "2023-12-10T14:23:10.411841Z",
     "shell.execute_reply.started": "2023-12-10T14:23:10.391556Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Rules:\n",
      " |--- V14 <= -0.10\n",
      "|   |--- V10 <= -0.00\n",
      "|   |   |--- V14 <= -0.32\n",
      "|   |   |   |--- V7 <= 1.64\n",
      "|   |   |   |   |--- V4 <= -0.73\n",
      "|   |   |   |   |   |--- V19 <= -0.52\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V19 >  -0.52\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V4 >  -0.73\n",
      "|   |   |   |   |   |--- V15 <= 1.16\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V15 >  1.16\n",
      "|   |   |   |   |   |   |--- V15 <= 1.16\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V15 >  1.16\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- V7 >  1.64\n",
      "|   |   |   |   |--- V17 <= 1.38\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V17 >  1.38\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- V14 >  -0.32\n",
      "|   |   |   |--- V16 <= 0.59\n",
      "|   |   |   |   |--- V3 <= 0.60\n",
      "|   |   |   |   |   |--- V26 <= 1.81\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V26 >  1.81\n",
      "|   |   |   |   |   |   |--- V19 <= -0.30\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V19 >  -0.30\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V3 >  0.60\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V16 >  0.59\n",
      "|   |   |   |   |--- V18 <= 0.39\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V18 >  0.39\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |--- V10 >  -0.00\n",
      "|   |   |--- V4 <= 0.05\n",
      "|   |   |   |--- V7 <= 0.53\n",
      "|   |   |   |   |--- V17 <= 0.54\n",
      "|   |   |   |   |   |--- V10 <= 5.06\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V10 >  5.06\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V17 >  0.54\n",
      "|   |   |   |   |   |--- V8 <= -1.02\n",
      "|   |   |   |   |   |   |--- V12 <= 0.86\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V12 >  0.86\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V8 >  -1.02\n",
      "|   |   |   |   |   |   |--- V22 <= -0.35\n",
      "|   |   |   |   |   |   |   |--- V15 <= 0.03\n",
      "|   |   |   |   |   |   |   |   |--- V16 <= -0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V16 >  -0.18\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V15 >  0.03\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V22 >  -0.35\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V7 >  0.53\n",
      "|   |   |   |   |--- V5 <= 0.21\n",
      "|   |   |   |   |   |--- V21 <= -0.06\n",
      "|   |   |   |   |   |   |--- V19 <= -0.14\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V19 >  -0.14\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V21 >  -0.06\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V5 >  0.21\n",
      "|   |   |   |   |   |--- V10 <= 0.07\n",
      "|   |   |   |   |   |   |--- V9 <= 0.55\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V9 >  0.55\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V10 >  0.07\n",
      "|   |   |   |   |   |   |--- V11 <= -0.48\n",
      "|   |   |   |   |   |   |   |--- V16 <= 0.60\n",
      "|   |   |   |   |   |   |   |   |--- V4 <= -0.62\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V4 >  -0.62\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V16 >  0.60\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V11 >  -0.48\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |--- V4 >  0.05\n",
      "|   |   |   |--- V13 <= 1.15\n",
      "|   |   |   |   |--- V11 <= -1.28\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V11 >  -1.28\n",
      "|   |   |   |   |   |--- V16 <= 0.36\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V16 >  0.36\n",
      "|   |   |   |   |   |   |--- V15 <= 0.25\n",
      "|   |   |   |   |   |   |   |--- V26 <= -0.61\n",
      "|   |   |   |   |   |   |   |   |--- V3 <= -0.06\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V3 >  -0.06\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V26 >  -0.61\n",
      "|   |   |   |   |   |   |   |   |--- V1 <= 1.67\n",
      "|   |   |   |   |   |   |   |   |   |--- V26 <= 1.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V23 <= 0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V23 >  0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- V26 >  1.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V1 >  1.67\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 >  0.49\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V15 >  0.25\n",
      "|   |   |   |   |   |   |   |--- V10 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |--- V16 <= 1.15\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V16 >  1.15\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V10 >  0.12\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V13 >  1.15\n",
      "|   |   |   |   |--- V18 <= -0.24\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V18 >  -0.24\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|--- V14 >  -0.10\n",
      "|   |--- V4 <= -0.07\n",
      "|   |   |--- V14 <= 0.45\n",
      "|   |   |   |--- V4 <= -0.74\n",
      "|   |   |   |   |--- V4 <= -1.04\n",
      "|   |   |   |   |   |--- V15 <= 1.03\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V15 >  1.03\n",
      "|   |   |   |   |   |   |--- V14 <= 0.44\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V14 >  0.44\n",
      "|   |   |   |   |   |   |   |--- V9 <= 1.00\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V9 >  1.00\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V4 >  -1.04\n",
      "|   |   |   |   |   |--- V2 <= -0.25\n",
      "|   |   |   |   |   |   |--- V2 <= -0.75\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V2 >  -0.75\n",
      "|   |   |   |   |   |   |   |--- V8 <= -0.12\n",
      "|   |   |   |   |   |   |   |   |--- V26 <= 0.45\n",
      "|   |   |   |   |   |   |   |   |   |--- V18 <= -0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 <= -0.81\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 >  -0.81\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V18 >  -0.10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 <= -0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 >  -0.14\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |--- V26 >  0.45\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V8 >  -0.12\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V2 >  -0.25\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V4 >  -0.74\n",
      "|   |   |   |   |--- V1 <= 1.58\n",
      "|   |   |   |   |   |--- V3 <= 1.26\n",
      "|   |   |   |   |   |   |--- V5 <= 0.76\n",
      "|   |   |   |   |   |   |   |--- V13 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |--- V9 <= 1.23\n",
      "|   |   |   |   |   |   |   |   |   |--- V4 <= -0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V14 <= 0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V14 >  0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- V4 >  -0.58\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V1 <= 0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V1 >  0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |--- V9 >  1.23\n",
      "|   |   |   |   |   |   |   |   |   |--- V8 <= -1.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V8 >  -1.14\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V13 >  0.18\n",
      "|   |   |   |   |   |   |   |   |--- V23 <= 0.76\n",
      "|   |   |   |   |   |   |   |   |   |--- V2 <= -0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V5 <= -0.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V5 >  -0.00\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |--- V2 >  -0.25\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V14 <= -0.05\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V14 >  -0.05\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V23 >  0.76\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 <= 0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 >  0.27\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V5 >  0.76\n",
      "|   |   |   |   |   |   |   |--- V27 <= 0.49\n",
      "|   |   |   |   |   |   |   |   |--- V1 <= 0.52\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V1 >  0.52\n",
      "|   |   |   |   |   |   |   |   |   |--- V6 <= 0.43\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V8 <= -0.53\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V8 >  -0.53\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V6 >  0.43\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V27 >  0.49\n",
      "|   |   |   |   |   |   |   |   |--- V14 <= 0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 <= 0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 >  0.13\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V14 >  0.34\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V3 >  1.26\n",
      "|   |   |   |   |   |   |--- V11 <= -0.31\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V11 >  -0.31\n",
      "|   |   |   |   |   |   |   |--- V21 <= -0.06\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V21 >  -0.06\n",
      "|   |   |   |   |   |   |   |   |--- V27 <= -0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V27 >  -0.15\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V1 >  1.58\n",
      "|   |   |   |   |   |--- V25 <= 1.16\n",
      "|   |   |   |   |   |   |--- V19 <= 0.26\n",
      "|   |   |   |   |   |   |   |--- V20 <= -0.15\n",
      "|   |   |   |   |   |   |   |   |--- V4 <= -0.07\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V4 >  -0.07\n",
      "|   |   |   |   |   |   |   |   |   |--- V15 <= -1.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V15 >  -1.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V20 >  -0.15\n",
      "|   |   |   |   |   |   |   |   |--- V17 <= 0.83\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V17 >  0.83\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V19 >  0.26\n",
      "|   |   |   |   |   |   |   |--- V18 <= 1.16\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V18 >  1.16\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V25 >  1.16\n",
      "|   |   |   |   |   |   |--- V3 <= -0.45\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V3 >  -0.45\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |--- V14 >  0.45\n",
      "|   |   |   |--- V7 <= 1.66\n",
      "|   |   |   |   |--- V21 <= 0.11\n",
      "|   |   |   |   |   |--- V14 <= 0.71\n",
      "|   |   |   |   |   |   |--- V18 <= 0.13\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V18 >  0.13\n",
      "|   |   |   |   |   |   |   |--- V8 <= -0.18\n",
      "|   |   |   |   |   |   |   |   |--- V4 <= -0.93\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V4 >  -0.93\n",
      "|   |   |   |   |   |   |   |   |   |--- V1 <= 1.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V24 <= -0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V24 >  -0.35\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |--- V1 >  1.07\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V8 >  -0.18\n",
      "|   |   |   |   |   |   |   |   |--- V4 <= -0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- V10 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V8 <= -0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V8 >  -0.16\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V10 >  0.18\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V4 >  -0.42\n",
      "|   |   |   |   |   |   |   |   |   |--- V7 <= 0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V15 <= -1.09\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V15 >  -1.09\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V7 >  0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V23 <= 0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V23 >  0.08\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |--- V14 >  0.71\n",
      "|   |   |   |   |   |   |--- V23 <= 0.67\n",
      "|   |   |   |   |   |   |   |--- V8 <= -0.26\n",
      "|   |   |   |   |   |   |   |   |--- V24 <= -1.92\n",
      "|   |   |   |   |   |   |   |   |   |--- V17 <= 0.86\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V17 >  0.86\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V2 <= -3.86\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V2 >  -3.86\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V24 >  -1.92\n",
      "|   |   |   |   |   |   |   |   |   |--- V8 <= -0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V8 >  -0.26\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V8 >  -0.26\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V23 >  0.67\n",
      "|   |   |   |   |   |   |   |--- V13 <= -1.06\n",
      "|   |   |   |   |   |   |   |   |--- V11 <= -0.11\n",
      "|   |   |   |   |   |   |   |   |   |--- V7 <= 1.43\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V7 >  1.43\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 <= -0.11\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 >  -0.11\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V11 >  -0.11\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V13 >  -1.06\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V21 >  0.11\n",
      "|   |   |   |   |   |--- V13 <= -0.66\n",
      "|   |   |   |   |   |   |--- V4 <= -0.72\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V4 >  -0.72\n",
      "|   |   |   |   |   |   |   |--- V25 <= 0.12\n",
      "|   |   |   |   |   |   |   |   |--- V16 <= 0.61\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 <= 0.71\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V11 <= -0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V11 >  -0.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 >  0.71\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V16 >  0.61\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V25 >  0.12\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V13 >  -0.66\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V7 >  1.66\n",
      "|   |   |   |   |--- V15 <= -0.04\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V15 >  -0.04\n",
      "|   |   |   |   |   |--- V6 <= 0.91\n",
      "|   |   |   |   |   |   |--- V16 <= 0.38\n",
      "|   |   |   |   |   |   |   |--- V2 <= -0.43\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V2 >  -0.43\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V16 >  0.38\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V6 >  0.91\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |--- V4 >  -0.07\n",
      "|   |   |--- V14 <= 0.23\n",
      "|   |   |   |--- V3 <= 1.28\n",
      "|   |   |   |   |--- V9 <= 0.88\n",
      "|   |   |   |   |   |--- V1 <= 1.44\n",
      "|   |   |   |   |   |   |--- V17 <= 1.25\n",
      "|   |   |   |   |   |   |   |--- V25 <= 0.78\n",
      "|   |   |   |   |   |   |   |   |--- V11 <= -0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- V25 <= 0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V18 <= 0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V18 >  0.73\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V25 >  0.15\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V11 >  -0.66\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V25 >  0.78\n",
      "|   |   |   |   |   |   |   |   |--- V12 <= 0.43\n",
      "|   |   |   |   |   |   |   |   |   |--- V20 <= -0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V20 >  -0.23\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 <= 0.88\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 >  0.88\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V12 >  0.43\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V17 >  1.25\n",
      "|   |   |   |   |   |   |   |--- V2 <= -0.02\n",
      "|   |   |   |   |   |   |   |   |--- V4 <= 0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V4 >  0.17\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 <= 1.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 >  1.69\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V2 >  -0.02\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V1 >  1.44\n",
      "|   |   |   |   |   |   |--- V23 <= -0.09\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V23 >  -0.09\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V9 >  0.88\n",
      "|   |   |   |   |   |--- V7 <= -0.04\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |--- V7 >  -0.04\n",
      "|   |   |   |   |   |   |--- V21 <= 0.19\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V21 >  0.19\n",
      "|   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- V3 >  1.28\n",
      "|   |   |   |   |--- V13 <= -1.14\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- V13 >  -1.14\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |--- V14 >  0.23\n",
      "|   |   |   |--- V8 <= -0.14\n",
      "|   |   |   |   |--- V12 <= 0.36\n",
      "|   |   |   |   |   |--- V14 <= 0.70\n",
      "|   |   |   |   |   |   |--- V11 <= -1.23\n",
      "|   |   |   |   |   |   |   |--- V6 <= 0.47\n",
      "|   |   |   |   |   |   |   |   |--- V6 <= 0.08\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V6 >  0.08\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 <= 0.02\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V12 >  0.02\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |--- V6 >  0.47\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V11 >  -1.23\n",
      "|   |   |   |   |   |   |   |--- V3 <= 1.60\n",
      "|   |   |   |   |   |   |   |   |--- V7 <= 0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V7 >  0.55\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 <= 0.98\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 <= 1.98\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 >  1.98\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V5 >  0.98\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V3 >  1.60\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V14 >  0.70\n",
      "|   |   |   |   |   |   |--- V25 <= 0.77\n",
      "|   |   |   |   |   |   |   |--- V1 <= 1.61\n",
      "|   |   |   |   |   |   |   |   |--- V18 <= 0.61\n",
      "|   |   |   |   |   |   |   |   |   |--- V13 <= 1.81\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V7 <= 0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V7 >  0.56\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
      "|   |   |   |   |   |   |   |   |   |--- V13 >  1.81\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 <= -1.54\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V28 >  -1.54\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V18 >  0.61\n",
      "|   |   |   |   |   |   |   |   |   |--- V14 <= 0.85\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V14 >  0.85\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V19 <= -2.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V19 >  -2.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V1 >  1.61\n",
      "|   |   |   |   |   |   |   |   |--- V19 <= -1.79\n",
      "|   |   |   |   |   |   |   |   |   |--- V9 <= 0.94\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |   |--- V9 >  0.94\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V19 >  -1.79\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V25 >  0.77\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V12 >  0.36\n",
      "|   |   |   |   |   |--- V7 <= 0.32\n",
      "|   |   |   |   |   |   |--- V6 <= 1.31\n",
      "|   |   |   |   |   |   |   |--- V23 <= -0.48\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V23 >  -0.48\n",
      "|   |   |   |   |   |   |   |   |--- V2 <= -0.06\n",
      "|   |   |   |   |   |   |   |   |   |--- V25 <= 1.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V4 <= -0.03\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V4 >  -0.03\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |--- V25 >  1.16\n",
      "|   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V2 >  -0.06\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V6 >  1.31\n",
      "|   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V7 >  0.32\n",
      "|   |   |   |   |   |   |--- V5 <= 0.19\n",
      "|   |   |   |   |   |   |   |--- V10 <= 0.18\n",
      "|   |   |   |   |   |   |   |   |--- V13 <= -0.92\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |   |   |--- V13 >  -0.92\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V10 >  0.18\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |--- V5 >  0.19\n",
      "|   |   |   |   |   |   |   |--- V14 <= 0.86\n",
      "|   |   |   |   |   |   |   |   |--- V3 <= 1.16\n",
      "|   |   |   |   |   |   |   |   |   |--- V1 <= 0.97\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V2 <= -0.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V2 >  -0.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |--- V1 >  0.97\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V12 <= 1.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V12 >  1.34\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |--- V3 >  1.16\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V14 >  0.86\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |--- V8 >  -0.14\n",
      "|   |   |   |   |--- V3 <= 0.99\n",
      "|   |   |   |   |   |--- V16 <= 0.86\n",
      "|   |   |   |   |   |   |--- V26 <= -0.62\n",
      "|   |   |   |   |   |   |   |--- V10 <= 1.64\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V10 >  1.64\n",
      "|   |   |   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |   |   |--- V26 >  -0.62\n",
      "|   |   |   |   |   |   |   |--- V2 <= -0.07\n",
      "|   |   |   |   |   |   |   |   |--- V8 <= 0.01\n",
      "|   |   |   |   |   |   |   |   |   |--- V15 <= -0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V13 <= -0.85\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V13 >  -0.85\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |   |--- V15 >  -0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 <= 0.84\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
      "|   |   |   |   |   |   |   |   |   |   |--- V6 >  0.84\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |   |--- V8 >  0.01\n",
      "|   |   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |   |   |--- V2 >  -0.07\n",
      "|   |   |   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |   |--- V16 >  0.86\n",
      "|   |   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- V3 >  0.99\n",
      "|   |   |   |   |   |--- class: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Export the decision tree rules to text format\n",
    "tree_rules = export_text(clf, feature_names=list(X.columns))\n",
    "print(\"Decision Tree Rules:\\n\", tree_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:23:19.581903Z",
     "iopub.status.busy": "2023-12-10T14:23:19.581428Z",
     "iopub.status.idle": "2023-12-10T14:23:49.101695Z",
     "shell.execute_reply": "2023-12-10T14:23:49.100695Z",
     "shell.execute_reply.started": "2023-12-10T14:23:19.581860Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j9voWarUaDoejSxtSqdSZuHAneREQENDrayGVSoWskSNhMBpP6rgVcjkKi4qYjCAiIhqkWHSSekytVsNkNGDEzU9DEZMBAHDYLBBL3F98zVBXiuKXbodarXaeSP78888Qi8VwOBxulUSyWq1d2uwcBXTkCXNWVtYxT6gHehQQERERERHRyRKJRAgNDUVoaCgyMzNPuL3dbu82O/zo2zU1Nfj999+ds8OPdvTs8GMlL0pKSnDppZciNDTU+Vi1Wg2D0YhnF0xARlQgAMBis8NX4uP2MZc26LD4zV+6XD8SERHR4MJEBPWaIiYDAcnZaP79c9gMWsijUqCvKYI8Jh3yyCQYGyqhiEmHpU0Nu9kAh0mP0OxzXLb1/vvv44orruh2f1hYWJcT3bS0tGOe/IaFhcHHx/2TWSIiIiIioqHOx8cHSqUSSqXSre1tNhvUavVxB4WVlZXhxx9/RGNjI7RabZfHp6WloaysrFu7GVGByEkIxWf7DkNrtCI1IgCFh7VIjwpEstIfFU3tyIgMhLrdjNpWA0IUvpCIxRifHNYnzwMRERF5FhMRdFI0RT/CRxEEAQLMLYfhsFkAAA6rGfLIZBibVDA1VEIWmQTfkEgYG6sgj0zu1s6MGTNw1VVXYdKkSTjrrLMQGRmJ8PBwSKXSAT4iIiIiIiKi4UsikSA6OhrR0dFubW82m6FWq1FfX4+nn34a11577TG3/aGsCUFyKQRBQG2rARZbR8kos9WBFGUASup1MFptiAqWQyIWIcCPXRZERERDBb/V6aSEjDzzuL/3C49DcMaEE7Yjl8vx1ltv9VVYRERERERENAD8/PwQFxeHuLg4vP7662hra8Nvv/2G0tJSlJSU4KeffnJuOyk94rhtxYUqjvv7N954AyqVChkZGUhLS4NMJuuTYyAiIqL+x0QE9Zquai+0pbugiE6Dw2qGIi4TYokvbIY2CIID+uqDCD/lIuhri2HVNUOwmuEbEgWRjwRiiZ+nwyciIiIiIqJeMJvNKC8vR0lJiTPh0Pmvvr7euV1ERARiY2MBAJt/r0ZzuxlmmwMjooPgKxGjttUAvdmGJp0ZvhIx/CRiTM6IxM8Vavj7SaDwlUAm/av87vr16/HUU08B6FgrIykpCZmZmc5/GRkZyMzMRFJSEsv2EhEReRkmIqjXApNzEJCc7fxZV7EbZk094HBAHpMGv7BYCA4bpAGhXWZFWDQNsGgaPREyERERERERucFut0OlUnVJMnQmHQ4dOgSHo6OsUmBgoDMJcM4553RJCoSEhOD333/HqaeeijnjE5CT8Nci1n8cakF5ow5JygDkJIRC3W5CRmQgaloMuGB0jHO7Bq0RDW12AMD27dsRFxfXLfnx9ddf46WXXoLF0lEq2NfXF2lpad0SFJmZmYiOjoZIJBrAZ5KIiIgAJiKoh4qLi7Fy5UoAgKGutMvvRGIfyMLiAACC1QKJPAhmdS0AoL1qX5dtOx9bXV2N8ePH93fYREREREREdBRBENDQ0NClU78z4VBWVtalYz89PR2ZmZm4/PLLu8xCiIqKcqtjv7RB1+VnH7EIo+NCAAAWmx1BMika2kwAgL3VrS4fKxKJnOtXnHXWWV22sdvtqK6u7nYsmzZtQlVVlTNxEhAQ0C050fkvJCSkx88hERERuUckCILg6SDI++3fvx8PPfQQ3n33XURGRqKlVQOrxXxSbYrEPoDgwNVXX40VK1Zg5MiRfRQtERERERERddJoNN1mEXT+a29vB9DRyZ+cnOxyFkFiYmKvSx2pVCpkjRwJg9F4UsegkMtRWFSExMTEHj/WbDajoqKi28yOkpIS1NXVObeLiIhwmaBIT0+HXC4/qfiJiIiGOyYi6Lh2796N1atXo6CgAElJSVi2bBmuv/56NDQ0QK1Wn1TbAQEB+OKLL5Cfn4/a2lpcccUVuO+++zBmzJg+ip6IiIiIiGh4MBqNKCsrc5lwaGpqcm4XHR3tcl2F1NTUflv8WaVSnfT1o1Kp7FUS4kR0Op3zOTvyuSsuLoZWq3Vul5CQ0C1BkZmZieTkZEgkLDZBRER0IkxEkEu//PILVq9ejW3btiE1NRUrVqzAggULIJVK+3xfZrMZr7/+Oh555BEcOnQIc+bMwcqVK3HKKaf0+b6IiIiIiIgGK5vNhqqqKpcj+6urq9F5eR8cHOyy0zw9PR1BQUEePorBQRAEqNVqlwtyl5aWwmTqKCMlkUiQmprqMrkTFxfH9SiIiIj+xEQEdbFz506sXr0an3/+OUaMGIEVK1Zg/vz5AzLCw2q1YuPGjXjooYdQXl6OGTNmYOXKlTj99NP7fd9ERERERETeQBAEHD582OUi0eXl5bDZbAAAmUzmXLfh6H9KpZId4P3I4XCgtrbWZamryspK2O0di2srFApnUuLokk/h4eEePgoiIqKBxUQEQRAEfPfdd1i9ejW+/vprjB49GitXrsTll1/e6zqgJ8Nms+Gdd97BQw89hKKiIlx00UVYuXIlJk+ePOCxEBERERER9YeWlhaXHdmlpaUwGAwAAB8fH6SkpLjsyI6Pj4dYLPbwUdDRrFYrKisrXSaSampqnNuFhYW5/LtmZGTA39/fg0dARETUP5iIGMYEQcCXX36JBx54ADt27MC4ceOwcuVKzJ492ytOaO12OzZt2oTVq1dj//79OPfcc7Fy5Uqcc845HN1DREREREReT6/Xo6yszGXCoaWlxbldXFycy0WiU1JS4Ovr68EjoL509OvhyJJPzc3Nzu3i4uJcLprN1wMREQ1mTEQMQ4Ig4JNPPsHq1avx888/Y8KECVi5ciVmzJjhlR38DocDH374IVavXo0//vgDkydPxqpVq3DBBRd4ZbxERERERDR8WCwW5wj4o9cSqK2tdW7XOQL+6NHv6enpCAgI8OARkDdobm52uWh2SUlJlxkyycnJLl9HCQkJXjGgkIiI6FiYiBhGOjv0H3zwQfz++++YPHkyVq5ciQsvvHBQdOh3JlAeeOAB7Nq1C6effjpWrlyJ6dOnD4r4iYiIiIhocHI4HKipqXE5kv1YawIc3VHMNQGoNzrXDDk6OeHOmiGdr8WIiAheMxMRkccxETEMdJY4evDBB7Fv3z6cc845WLVq1aAtcSQIAv73v/9h9erV2LFjB0455RSsXLkSs2bN4ggQIiIiIiLqFUEQoFarXa7ZUFpaCpPJBACQSCRIS0tzmXCIjY0dlNdYNDjZbDYcOnTI5WtWpVKhs7snODjYZemvjIwMBAUFefgoiIhouGAiYgiz2Wx499138eCDD6KoqAgXXnghVq5ciSlTpng6tD7Rucj2Aw88gG+++QZjxozBfffd57FFtomIiIiIyPvpdDqXo8tLS0uh0Wic2yUmJrpcTDg5ORkSicRzB0DkBqPRiPLycpeLZjc2Njq3i46Odvk6T0tLg5+fnwePgIiIhhomIoYgq9WKjRs34uGHH0ZZWRmmT5+OlStXYuLEiZ4Ord/s3LkTq1evxueff46RI0dixYoVuPLKK3mBQEREREQ0DJnNZpSXl7tMONTX1zu3i4iIcFnKJi0tDQqFwoNHQNR/NBrNMdej0Ol0AACxWIykpCSXM38SExM5+I+IiHqMiYghxGw24/XXX8eaNWtQVVWF2bNn47777sOpp57q6dAGzK5du7B69Wp89NFHSEtLwz//+U8sWLAAUqnU06EREREREVEfstvtUKlULjtTDx06BIfDAQAICAhwuWZDRkYGQkNDPXwURN5DEAQ0NDS4fE+VlZXBYrEAAHx9fZGWlubyfRUdHc3yZERE5BITEUOAyWTC+vXrkZ+fj9raWsybNw8rVqxATk6Op0PzmD/++AMPPvggCgoKkJSUhOXLl+O6667j1FIiIiIiokHkyI7Ro8vLsGOUaOB0Jv5czTI6XuLvyBkVISEhnj0IIiLyKCYiBjGDwYAXX3wRa9euRWNjI+bPn48VK1YgKyvL06F5jX379uGhhx7Ce++9h7i4OCxduhQ33ngj5HK5p0MjIiIiIqI/HVkq5uiEQ2epGJFIhOTkZJaKIfIyZrMZFRUVLtddqaurc253ZCm0I9/H6enpvEYnIhoGmIgYhHQ6HZ577jk8/vjjaG1txYIFC7B8+XJkZGR4OjSvVVRUhIcffhhvvfUWIiMjce+99+KWW26Bv7+/p0MjIiIiIhoWXC2e29lZ6c7iuampqZDJZB48AiLqqaMXh++8XVxcDK1W69yOi8MTEQ19TEQMIhqNBk8//TSeeOIJ6HQ6XH/99Vi2bBlSUlI8HdqgUVZWhkceeQQbNmxASEgI7rnnHixevBiBgYGeDo2IiIiIaNCz2WyoqqpyWb6luroanZefwcHBLsu3ZGRkICgoyMNHQUT9TRAEqNVql+tRlJaWwmQyAQAkEglSU1O7zYLKzMxEbGwsy64REQ0iTEQMAi0tLXjiiSfw1FNPwWQy4aabbsLSpUuRkJDg6dAGraqqKuTn5+PVV19FQEAA7rzzTtx+++2sWUlEREREdAKCIODw4cMuOxArKipgtVoBAH5+fi7LKGVkZCAiIoIdiETkksPhQE1NjcuEZmVlJex2OwBAoVB0+4zp/Dk8PNzDR0FEREdjIsKLNTU1Yd26dXjmmWdgt9tx66234t5770VMTIynQxsyampqsHbtWrz88svw9fXFHXfcgTvvvJMnLUREREQ07LW0tHQrp9J5W6/XAwB8fHyQkpLiMuEQHx8PsVjs4aMgoqHEarWisrLSZYm3mpoa53ZhYWHHnHXFEs1ERJ7BRIQXqqurw2OPPYYXXngBIpEIt912G+6++25ERkZ6OrQhq76+Ho899hief/55iMViLF68mM85EREREQ15er0eZWVlLheJbm5udm4XFxfncpHZlJQU+Pr6evAIiIg6HP15dmQClZ9nRESex0SEF6mpqUF+fj5efvll+Pn5cXS+B3AWChERERENNe6OIA4NDcWIESO6ddClp6cjICDAg0dARHRympubnYmJo0s+GQwGAJzhRUTU35iI8AJVVVVYs2YNXnvtNfj7++POO+/EHXfcwfUKPIjrchARERHRYHIyNdU7kw4cAEVEw03nmjeuPjvLy8ths9kAADKZDOnp6VzzhojoJDAR4UFlZWV45JFHsGHDBoSEhOCee+7BP/7xDwQFBXk6NPqTRqPBM888g//85z/Q6XS4/vrrsXz5ciQnJ3s6NCIiIiIaZgRBgFqtdjmit7S0FCaTCQAgkUiQlpbmMuEQGxvLDjMiIjfYbDYcOnTI5WwylUqFzu604OBglwmKjIwM9u8QER2BiQgPKCoqwkMPPYT//ve/iIiIwNKlS3HLLbdwwSQvptPp8Nxzz+Hxxx9Ha2srFixYgH/+859IT0/3dGhERERENMTodLpui0N33tZoNM7tEhMTu9U4z8zMRHJyMiQSiecOgIhoiDMajSgvL3e5vk5jY6Nzu+joaJeLZqelpcHPz8+DR0BENPCYiBhA+/fvx4MPPoj33nsPsbGxyMvLw4033gi5XO7p0MhNer0eL730EtauXYvGxkbMnz8fK1asQFZWlqdDIyIiIqJBxGw2o6KiwmUnVl1dnXO7iIiIY3ZiKRQKDx4BERG5otFojrkehU6nAwCIxWIkJSW5TCYnJibCx8fHw0dBRNT3mIgYAH/88QdWr16NzZs3IykpCcuWLcP111/P7PcgZjQa8corryA/Px+1tbWYN28e7rvvPmRnZ3s6NCIiIiLyEna7HdXV1S7LelRVVcHhcAAAAgICjlnWIzQ01MNHQUREfUEQBDQ0NLhcj6KsrAwWiwUA4Ovri/T0dJfl9aKiolhej4gGLSYi+tHPP/+M1atX4+OPP0ZqaipWrFiBBQsWQCqVejo06iNmsxlvvPEGHnnkEVRVVWH27NlYuXIlxo8f7+nQiIiIiGgAsGOJiIhO1skkrjuT1yEhIZ49CCKiE2Aioh/s2LEDq1evxhdffIERI0ZgxYoVmD9/Puu0DmFWqxUbN27Eww8/jLKyMkyfPh0rV67ExIkTPR0aEREREfUBrVbrsszGkaU2RCIRkpOTWWqDiIj6TG9L+XV+F6Wnp7MkOBF5BSYi+tCqVavw9NNPQ6PRYPTo0Vi5ciUuv/xyXnAMIzabDe+++y4efPBBFBUVYdKkScjOzsbzzz/PUW5EREREXu7oxUePTDq4s/hoamoqZDKZB4+AiIiGE51O53I9iuLiYmi1WgAdSfKEhIRu31mZmZlITk7moFkiGjBMRPQhpVKJ5uZmvPPOO5g3bx7EYrGnQyIPsdvt2LRpExYuXAiz2YyPPvoI06dP93RYRERERMOezWbDoUOHXI4sValU6Lw8Cg4OPua6DUFBQR4+CiIiomMTBAFqtdrlTL6ysjIYjUYAgEQiQVpamsuZfLGxsRxQSUR9atgnIlQqFdRqda8eq1QqkZiY6PzZarXCarVCoVD0VXg0yDU2NuKTTz7BggULusyM6e3r7ujXHBEREZG3OpnzbODkznsEQUBdXZ3LWtvl5eWwWq0AAJlM5kwuHJ10UCqV7IAhIqIhx+FwoLa21uV3ZEVFBex2OwBAoVC4/H7MzMxEWFjYScXgyXMEIvKcYZ2IUKlUGDEyCyajoVePl8kVKC4q5Icf9YhKpcLIrCwYDT1/3ckVChQV8jVHRERE3k2lUiFr5AgYjKZet6GQy1BYVHzc856WlhaXazaUlpZCr9cDAHx8fJCSkuKyJEV8fDxnMRMREf3JarWisrLSZYnCmpoa53ZhYWEuExTp6enw9/c/7j5UKhWysrJg6EWfSCeFQoFC9o0QDTrDuhCcWq2GyWhA+k1PQx6TgbbSn+HjFwDBYYdEEQSHzQKJfyjsRh3EEinMzTWQ+IfCR+YPh9WEspdvh1qt5gcf9YharYbRYMDM5S8gJCYZDWX7IBKJEKiMgUgkgt1qga65HoHh0agv2wtFsBIJ2WdArSrB1kdu5WuOiIiIvJ5arYbBaMIzfxuN9Eh//FzZigA/CeyCgCCZBBabA6EKKQ61GBHgJ4HGaIVELMJpSSEAgLJGPW5790CX854lS5Zg9+7diI+Pd3aKNDc3O/cZFxeHzMxMTJw4EQsWLHAmHFJSUuDr6+uJp4GIiGhQkUqlzqTC0fR6PcrKyrolKD7++GOX38cZGRn45ZdfcNddd2HBggXO36vVahgMBrz+5MMYmZ6Knbv+QECAAna7HSFBgTBbLAgLCUbFoRr4+fnCaDTBIQg4a+KpAICisgpc93//ZN8I0SA0rBMRneQxGbC0HIZEHgR5VAoMNUWQBikhi0iCqbESAYmjYdWpIfaVw0cWAJGPD0Q+Uk+HTYOcvrUJDrsNUelj0FhxEL7+gQiNTUZLTQXiRk+AXqOG3W6Dw26DzWqBMrH7iQARERGRN0uP9MdhjQnBcilSlAoU1bcjIsAPSeFyVKoNmD4mCup2Cxp1ZtgcAmx2AeMTg7u1Y7fb8Z///AcOhwNnnHEGMjMzMX369B6NwCQiIqLe8/f3x9ixYzF27Nhuv3M1Q/Gnn37C3r17kZeX1yUR0Wlkeiqq6+oRHBSA9JRE7C8qRYQyDKlJCSirPIQ5l05DU3MLVLV1MBpNEImAcWOyBuJQiaifMBHxp7BTLnLeDkyf4LztFxYLAPANiYJ/wmjn/e2H9g1ccDQkJY2djOjMji/w+NGnO+8PiogDAASERyMqbYzz/vqSPQMbIBEREVEfuHh0pPP2hD9nPABAbLAMABAV5IfRCDxuGz4+PmhsbITVakV0dHS/xElERES9ExYWhokTJ2LixIld7q+oqEBsbOwxHzfzwnOdt888bZzzdnxMFAAgJioCOaNG9G2wROQxTET8SVv8IwDArtfCYTPD2qaGImEUfEOiIZb6wtx8GDZ9K6RBSghWC+wWo4cjpsGurmQPzMZ2mHQa2K0W6FsbEZk2BoHKGEikftA2dtRfdNht8JUpIBKxfjERERF5v9bWVuzb99egnR8rWgEAGqMVFpsDTe0WjI4JRHSQH3wlYtRqTHA4BPhKxJD6iKDw9XE+tnNRaQAIDw8fuIMgIiKik5aamnrc32//6VcAQKu2DRazBQ3qZuSMGoHY6Ej4+friYEk5zBYLQoICERWhxIi05AGImoj6CxMRANqr9kKwmiCPTgNEIvgn5UAs9YXdZIBFUw+Z8q+acw6zARD7wKJt9GDENFQ0lu9HeEIGRCIR0iZeAInUDxZjO1SFv0EskSL1tPNQ8ctXsPjJUV+619PhEhEREQEATCaTs05057/i4mKUlJRArVY7t9uypx5T08NhstoxMirAmXiwOwT8ptIi3F8Kfz8JRscG4seKVgTJJJBJ/0pETJo0CampqRgxYkSXxTBHjBiB2NhYiEQiTxw+ERERnaR3t36K86ZMhMlswegR6fDz9UV1bR3aDQbs3lcImcwPU884DfuLSmGz26Fta8P+olJPh01EJ4GJCAAByTkISMrudr9F0wBFbAYAwC88rsvvHFbzgMRGQ1dM5lhnaaYjtTfXY8y0ec6fR507BwCgCA7H9xvyByw+IiIiGt4cDgdUKpXLZMOhQ4cgCAIAICgoyJkouPjii5GZmQm73Y6rr74as8dGIycuqEu7ErEIUUF+3fZ34agIAEBDmxlGqx0AsGzZMphMJhQXF+Ojjz5CRUUF7PaO3/n7+yMjI6NbkiIzMxMhISH9+MwQERFRT1VXV2Pz5s144403AAB/m3kJTsnuuuaDxMcHMVERXe6bNOEU5+26hibUN3YMeHj77bcRERGBhISEfo6ciPoKExEAjHVdM6qG2mJYdc3wkQVAogiCTa+FLCoFdoMWfsqODzhbe4snQqUhRK0qcd5uqiqCQaOGryIQsoAgmHQahMalwtyuRXB0x4yc1roqD0VKREREQ5larXaZbCgtLYXZ3DH4RiqVIj09HSNGjMAVV1zRZWZCREREt5kJv//+OwCgrFHf5f7ihnY0t1sRIPNBkEwCrdGG+FAZtEYbUpUK53adj5s7dy7Gjx/vvN9qtaKioqJbvNu3b0ddXZ1zu8jISGd8RyYo0tLS4OfXPQlCREREfa+0tBQFBQUoKCjArl27IJVKMWFCx7qsRWUVXbY9WFKOpuYWBAb4IyQoEK2aNiQlxKJV24aMlCTndp2Pe+KJJ/DYY4/h9NNPR25uLnJzc5GRkTFwB0dEPSYSOocyDUMqlQojRmbBZDT06vEyuQLFRYVITEw88cZEf/rvf/+LBQsWwuGw9/ixUl9fFBUWnrDOIhEREdGRDAaDs5RSZ6Kh819Ly18DbBISElyWQUpMTIRE4v4YJpVKhayRI2Awmnods0IuQ2FRsdvn2jqdDqWlpV2Or/O2TqcDAIjFYiQnJ7tMUsTHx0Ms5ppcREREvSUIAvbt24eCggJs2rQJ+/fvh1wuxyWXXILc3FzMmDEDWq0WWVlZMBh61xcHAAqFAj///DP27NmDgoICfPrppzAajcjOznYmJbKzs1nCkcjLDOtEBNBxkdRZx/aTTz7BypUr8eSTT2LKlCnObQRBwF133YXCwkJ88MEHCAwMBAAolUomIchtRqMRy5Ytw1NPPYXJkydjxYoViIqKcuuxNpsNr7zyCtavX4/TTjsNb775JjIzM/s5YiIiIhpM7HY7Dh065DLZoFKpnNuFhoa6TDakp6dDoVAcZw89c+R5dm/01bm2IAhoaGhw+byUl5c7F8SWy+XIyMjo8px03g4LCzvpOIiIiIYih8OBX375xTnzoaysDEFBQbjsssswd+5cXHTRRd3OL/r6HMFgMOCzzz5DQUEBtm3bhra2NqSnpzuTEhMmTOBgAyIvMOwTEZ3UajWysrJw/vnn45133un2++rqaowaNQoLFizAc88954EIaTD7/fffcc0116CyshL5+fm47bbbevUl+PPPP2PBggWora3F448/jltuuYUZfiIiomFEEAQ0NTW57FQvKyuDxWIBAPj5+XXpVD+yYz08PJznD3+y2Wyoqqpy+XzW1NQ4t1MqlS6fy/T0dMhkMg8eARER0cCz2WzYsWMHNm3ahM2bN6O2thYRERGYPXs2cnNzcd5558HX19cjsVksFnz99dfYtGkTtmzZArVajbi4OGdSYsqUKT2a5UlEfYeJiD9de+212Lp1K4qKio45Sv3pp5/GHXfcgR07dmDy5MkDHCENRjabDWvXrsW//vUvZGdnY+PGjRg1atRJtanX67FkyRK88MILuOSSS/DKK68gJiamjyImIiIib6DX67uVGersLNdqtQAAkUiEpKQklx3kCQkJ8PHx8fBRDG7t7e0uy1nxb0BERMOR2WzGV199hYKCAnz44YdQq9WIj4/v0sHvbd97NpsNO3fudM7WqKmpgVKp7JIw4dpRRAOHiQgAX375JS644AKsX78eN9xwwzG3s9vtmDx5Mtra2vDHH3/ww4qOq7y8HAsXLsRPP/2E5cuXY9WqVX06IuCTTz7BokWLYLPZ8NJLLyE3N7fP2iYiIqL+1zka31Wyoba21rndkaPxjywXlJaWBrlc7sEjGJ6OnJVy9FoUnJVCRERDiV6vd5Y8+uijj5wlj+bOnYvc3Fycdtppg6bkkcPhwK+//upcv6KzhNSMGTOcJaT8/f09HSbRkDbsExEGgwHZ2dlITEzE119/fcILgr179+LUU0/FqlWrsHLlygGKkgYTQRDwyiuv4M4770RUVBQ2bNjQbzNo1Go1br75ZmzevBnXXnstnnzySQQHB/fLvoiIiKjnOtcncLWAcnl5OWw2G4Cu6xMcvX4D1ycYPOx2O1Qqlcu/99HrdBz9t+6PdTqIiIh6SqPR4KOPPkJBQQE+++wzGI1G5OTkOGc+jBkzZtAn0wVBwP79+51JiX379kEul+Piiy92LqodEhLi6TCJhpxhn4hYtmwZnnjiCezdu9ftxX//+c9/4vHHH8eePXswcuTIfo6QBpOGhgbcdNNN2LZtG2688UasW7fOubh5fxEEARs2bMDtt9+OsLAwbNiwAVOnTu3XfRIREVFXOp3O5Qj5kpIS6HQ6AIBYLEZycrLLZEN8fPygGVFIvWMwGFBeXt4tSVFcXIzW1lbndgkJCS6TFElJSV5X8oKIiIaGxsZGfPjhhygoKMBXX30Fq9WKiRMnIjc3F3PmzEFGRoanQ+xXpaWl2Lx5MwoKCvDzzz9DKpXi/PPPR25uLmbNmoXIyEhPh0g0JAzrRMSePXtw6qmn4v7778eKFSvcflxnNjgmJgbffvstLxoJALB161bceOONAID169dj5syZA7r/qqoqLFy4EDt27MCSJUuwevVqlg8jIiLqQ1arFRUVFS6TDXV1dc7tIiMjXSYb0tLS+N1MLjU3N7tcMLu0tBQmkwkA4Ovri7S0tG6vqxEjRiAiImLQj04lIqKBVV1d7ex8//777wEAU6dOdSYf4uPjPRyhZ1RXV2PLli0oKCjA9u3bAQBnnXWW83lJSEjwcIREg9ewTUTY7XaceeaZMBqN+O2333pcu//rr7/G+eefj5deegk33XRTP0VJg4FOp8Ndd92FV155BTNnzsTLL7/ssWy53W7HunXrsGLFCmRlZWHjxo3Izs72SCxERESDkSAIOHz4sMtkQ0VFBex2OwBAoVC4HLWekZHBqfzUZxwOB6qrq10mKaqqqtB5KRccHOwy+ZWZmcl610RE5FRaWupcuHnXrl2QSqWYNm2ac+R/RESEp0P0Ko2Njdi6dSsKCgrw5Zdfwmq14vTTT3eWqRrqM0WI+tqwTUQ8+eSTuOuuu7Bz506ceeaZvWpj0aJFKCgoQGFhIWJiYvo4QhoMdu7ciYULF6KhoQFPPvkkFi1a5BWj0fbs2YNrrrkGJSUlePjhh3HXXXdx5g4REdERtFqty2RDSUkJ9Ho9AMDHxwcpKSndkg2ZmZmIjY31iu98Gr5MJhPKy8tdvo6bmpqc28XFxblc7DwlJQUSicSDR0BERP1NEATs27fPuRbC/v37IZfLcckll2Du3LmYPn0615l0k1arda6d8emnn8JoNCI7O9uZlMjOzua5IdEJDMtEhEqlwqhRo3DdddfhmWee6XU7zc3NyMrKwrnnnot33323DyMkb2exWHD//fdjzZo1OOOMM7BhwwakpaV5OqwuTCYTVq5ciccffxxnn302Xn/9dSQlJXk6LCIiogFjNpudpZSOrsvf2Njo3C46OtplsiElJaXHs2aJvEFra+sx1ywxGo0AAIlEgrS0NJdJiujoaHamEBENUg6HA7/88otz5kNZWRmCg4Nx2WWXITc3FxdddBEUCoWnwxzUDAYDPvvsMxQUFGDbtm1oa2tDenq6MykxYcIEDgYlcmHYJSIEQcBll12G3bt34+DBgwgKCjqp9t5++21cddVV2LZtG2bMmNFHUZI3O3jwIK655hrs27cPDzzwAJYuXerVCwd+++23uPbaa6HRaPDMM8/gmmuu4YUlERENGQ6HA7W1tS6TDVVVVXA4HACAgIAAl8mGjIyMkz4fJBosHA4HDh8+3O29UlJSgsrKSuf7JTAwsNs6FHy/EBF5L5vNhh07dmDTpk3YvHkzamtrERERgdmzZyM3NxfnnXceB1f0E4vFgq+++goFBQXYsmUL1Go14uLinEmJKVOmcAYi0Z+GXSLivffew9/+9jds2bIFs2bNOun2BEHApZdeigMHDuDAgQMIDAzsgyjJGzkcDjz99NPIy8tDamoqNm7ciPHjx3s6LLdotVrcfvvtePPNN3H55ZfjhRdeQHh4uKfDIiIiclvnCO+jyyhxhDdR37BYLKioqOj2HisuLkZDQ4Nzu5iYGJdJCs4gIiIaWGaz2dkB/uGHH0KtViM+Pr5LB7g3D5ocimw2G3bu3IlNmzahoKAAtbW1UCqVXRJCfn5+ng6TyGOGVSKitbUVWVlZmDx5MjZt2tRn7VZVVWH06NG48cYb8eSTT/ZZu+Q9ampqcN111+Grr77CHXfcgTVr1kAul3s6rB774IMPcMstt8DPzw+vvvoqLr74Yk+HRERE5NRZ897VaG21Wu3cjjXviQaWVqtFaWmpy0Rge3s7AK6pQkQ0EPR6vbMk0EcffeQsCTR37lxnSSB+3noHh8OBX3/91bk+R1lZGYKCgjBjxgzMnTsXF110Efz9/T0dJtGAGlaJiJtuugnvv/8+Dh48iNjY2D5te926dViyZAl++uknnH766X3aNnnWO++8g7///e/w9/fH66+/jmnTpnk6pJNy+PBhLFq0CJ9//jkWL16MtWvXsj4kERENGIfDAZVK5bJ+/aFDh9B5ahoUFOTsxDyyYzMjIwMBAQEePgoiAjpmh9fV1bmcrVRRUQGbzQYA8Pf3R0ZGhsskBRdJJSI6Po1G41wk+bPPPnMuktyZfBgzZgyTD15OEATs37/fmZTYt28f5HI5Lr74YuTm5mLGjBkICQnxdJhE/W7YJCK+++47nHPOOXjhhRdwyy239Hn7NpsNZ5xxBiwWC3777TdIpdI+3wcNrNbWVixevBhvv/025s+fj2effRahoaGeDqtPCIKA559/HkuWLEFCQgI2btyICRMmeDosIiIaQtRqtctkQ2lpKcxmMwBAKpUiPT3d5eyGyMhIXlQTDWJWqxWVlZUukxSHDx92bhcZGdnt/T9ixAikpqayfAURDVuNjY348MMPUVBQgK+++gpWqxUTJ050ll1KT0/3dIh0EkpLS7F582Zs2rQJu3btglQqxfnnn4/c3FzMmjULkZGRng6RqF8Mi0SEyWTC2LFjERERge3bt/fbyvV//PEHJkyYgAcffBDLli3rl33QwPjyyy9x3XXXQa/X47nnnsP8+fM9HVK/KC4uxoIFC/D7779j1apV+Oc//8mSFkRE5DaDwYCysjKXHY0tLS3O7RISErp0MnZ2NCYmJvJ7h2gY0ul0KC0t7ZaoLC4uhk6nAwCIxWIkJye7TFLExcX12zUdEZGnVFdXY/PmzSgoKMD3338PAJg6dSpyc3MxZ84cxMfHezhC6g/V1dXYsmULCgoKsH37dgDAWWed5fy7JyQkeDhCor4zLBIRK1euRH5+Pnbv3o1Ro0b1677uvfdePPPMM9i3bx8z1IOQ0WjE8uXL8eSTT+L888/H66+/PuS/7K1WKx566CE8+OCDOO200/Dmm28iIyPD02EREZGXsNvtOHTokMtkg0qlcm4XGhrqMtmQnp7OEoBE5BZBENDQ0OByNlV5eTmsVisAQC6XIyMjo1uSIjMzE2FhYR4+CiIi95WWlqKgoAAFBQXOkfHTpk3D3LlzMXPmTERERHg6RBpAjY2N2Lp1KwoKCvDll1/CarXi9NNPd86EYV8NDXZDPhGxf/9+nHLKKVixYgX+/e9/9/v+9Ho9srOzkZKSgi+//JIlBQaR33//Hddccw0qKiqQn5+P22+/fViNtPr5559xzTXX4PDhw1i3bh1uvvlmvn6JiIYJQRDQ1NTkMtlQVlYGi8UCAPDz83N2/h1dTik8PJzfG0TUb2w2G6qqqlwmKWpqapzbKZVKl+Xe0tPTIZPJPHgEREQd51z79u1zrhWwf/9+KBQKXHLJJcjNzcX06dO5dg4B6Fgb5OOPP0ZBQQE+/fRT59ognUmJ7OxsnnvToDOkExEOhwNTpkxBa2srdu/ePWA1Rr/44gtcdNFFeO2113DdddcNyD6p9+x2O/Lz8/Gvf/0L2dnZ2LhxY7/PnPFWer0eS5YswQsvvIBLL70Ur7zyCqKjoz0dFhER9RG9Xo/S0tJuyYbi4mJotVoAgEgkQlJSkstkQ0JCAnx8fDx8FEREXen1epSVlXX5bCsuLuZnGxF5BYfDgV9++cU586GsrAzBwcG47LLLkJubi4suuoizR+m4DAYDPvvsMxQUFGDbtm1oa2tDenq6MykxYcKEYTWQlgavIZ2IeO6557B48WJs374dZ5111oDue8GCBfjkk09QWFjIRWa8WEVFBRYsWICffvoJy5Ytw7/+9S/4+vp6OiyP+/jjj3HDDTfAZrPhpZdeQm5urqdDIiIiN9lsNucCsUePGq6trXVud6xRw2lpaZDL5R48AiKiviEIAtRqdbfZXsXFxZztRUT9ymazYceOHc7kQ21tLSIiIjB79mzk5ubivPPOY98D9YrFYsFXX32FgoICbNmyBWq1GnFxcc6kxJQpU7gGG3mtIZuIqKmpwahRozB//ny8+OKLA77/pqYmZGVl4aKLLsJbb7014Pun4xMEAa+++iruvPNOREZGYsOGDZg8ebKnw/IqTU1NuOWWW7B582Zcd911ePLJJxEUFOTpsIiICB3fY/X19ceso26z2QCwjjoR0bHY7XaoVCqXM8SOXv/G1YLZXP+GiI5mNpvx9ddfY9OmTfjwww+hVqsRHx/fpYOYs6+oL9lsNuzcuRObNm1yJryUSmWXhNdAVYchcseQTUTMmTMHP/30EwoLCxESEuKRGDZs2IBrr70Wn376KS6++GKPxEDdNTY24qabbsLWrVtx4403Yt26dQgMDPR0WF5JEAS88cYbuOOOOxAWFoYNGzZg6tSpng6LiGjY0Ol0LpMNJSUl0Ol0AACxWIzk5GSXHWVxcXGcpk1E1ENGo7FbqafOz+CWlhbndgkJCS4/e5OSktjZSDRM6PV6fP7559i0aRM++ugjZ8mcuXPnOkvmcFYVDQSHw4Fff/3Vuf5IWVkZgoKCMGPGDMydOxcXXXQR/P39PR0mDXNDMhGxefNm5Obm4v3338fll1/usTgEQcAFF1yA8vJy7N+/n294L9CZfACAl19+GbNmzfJwRINDZWUlFi5ciJ07d+Lee+/FAw88wKw6EVEfsVqtqKiocJlsqKurc24XGRnZbVbDiBEjkJqays9kIqIB0tzc7PLzurS0FCaTCQDg6+uLtLQ0l0mKiIgIdkoSDXIajQYfffQRCgoK8Nlnn8FoNCInJwe5ubmYO3cuRo8ezfc5eZQgCNi/f78zKbFv3z7I5XJcfPHFyM3NxYwZMzw2aJuGtyGXiNBqtcjKysJpp52GDz/80OMf/mVlZcjOzsbixYvx2GOPeTSW4Uyn0+Huu+/G+vXrMWPGDKxfvx5RUVGeDmtQsdvteOyxx7By5UpkZWVh48aNyM7O9nRYRESDgiAIOHz4cLeOq5KSElRUVMButwMA/P39uyQaOjuuMjIyeLFAROTFHA4HqqurXSYpqqqq0HnZHRwc7HJ9noyMDAQEBHj4KIjoWBobG/Hhhx+ioKAAX331FaxWKyZOnIi5c+dizpw5SE9P93SIRMdUWlqKzZs3o6CgAD///DOkUinOP/985ObmYtasWVzblgbMkEpE2Gw25OTk4NChQygqKkJCQoKnQwIA5OfnY/ny5VizZg2WLl3q6XCGnR9++AELFixAQ0MDnnjiCdxwww0eT1ANZrt378Y111yD0tJSPPLII7jzzjtZ9oOI6E9ardbloqilpaXQ6/UAAB8fH6SmprrsiIqNjeV3FBHREGMymVBeXu4ySdHU1OTcLi4uzuV3Q3JyMqRSqQePgGh4qq6udnbefv/99wCAqVOnYu7cuZg9ezbi4+M9HCFRz1VXV2PLli0oKCjA9u3bAQBnnXUWcnNzMWfOHK/pS6WhaUglIrZv346zzz4bo0ePxv79+z0djpNer0dAQAAUCoWzE4L6X3l5Of7zn//g+eefx8SJE7FhwwaOUugjJpMJK1aswLp16zB16lQsWbIEl112mafDIiIaEGazGRUVFd2SDSUlJWhsbHRuFxMT47JDKSUlBb6+vh48AiIi8hatra3HXAvIaDQCACQSibPU09HfKdHR0UxgE/Wh0tJSFBQUoKCgALt27YJUKsUFF1yA3NxczJw5ExEREZ4OkajPNDY2YuvWrSgoKMCXX34Jq9WK008/3bnAekZGhqdDpCFmSCUibDYbnnzySfz973+HQqHwdDhd/PTTT2hsbMTMmTM9Hcqw4evrC6vVigcffBB5eXmQSCSeDmnI+frrrzF9+nSYTCZ89dVXOO+88zwdEhFRn3A4HKitrXWZbKiqqoLD4QAABAYGHrPERlBQkIePgoiIBqvO7yFXSYrKykqX30NHl/Tj9xDRiQmCgH379jmTD/v27YNCocAll1yC3NxcTJ8+HcHBwZ4Ok6jfabVa59onn376KYxGI7Kzs51JiezsbCa+6aQNqUQE0ZFuvPFGxMbG4oEHHvB0KEPat99+i4ceegivvvoqp/AR0aDT0tLispOntLTU5UjUoxcdjYqK4gk5ERENKIvF0qXU05HfXw0NDc7toqOju3xvdX53cWYeDXcOhwO//PKLM/lQVlaG4OBgXHbZZcjNzcVFF13kdYNbiQaSwWDAZ599hoKCAmzbtg1tbW1IT093JiUmTJjAEt3UK0xEEBER0ZBmMplQVlbmsuyFWq12btdZm/voZENycjJn1RER0aDQuVaRqyTFkWsVpaSkuPzO41pFNFS1tLRg7969zuRDbW0tIiIiMHv2bOTm5uK8885jgo7IBYvFgq+//hqbNm3Cli1boFarERcXh9zcXEybNg2jR49GWlqap8OkQcLrExEqlapLJ0FPKJVKJCYm9nFEPdfbY/CW+D2Fz9vg1Zu/Hf9uREPPQH6H2+12VFdXd0s0lJSU4NChQ+g83QkODnY5OjQ9PR0BAQG9ipWIiMjbCYKAurq6bt+RxcXFqKiogN1uBwD4+/sjIyOjW5IiMzMTISEhPdrnyZwHALw+oL7rE7jpppuwfv16AEB8fLxzVPeUKVPg4+PTZ/ESDXU2mw07d+50JvRqamoAAEVFRRgxYoRzu6HQl0v9w6sTESqVCiNGZsFkNPTq8TK5AsVFhR59AZ/MMXhD/J6iUqmQlZUFg6Hnz5tCoUBh4fB83rxBb1/zw/n1TjQUqVQqZI0cCcOf5Y16SiGXo7CoyPmZYLfbYTQaYTKZXCYbSktLYTabAXSsEZSenu5y7YaIiAiO9CQiIjqC1WpFZWWlyyRFXV2dc7vIyEiX363h4eEICAiAv7+/c9uO84ARMBhNvY5LIZehsKiY1wfDlEqlwsisLBh70ScgVyhQdESfwCOPPIInnngCr7zyCqZPn85zQaI+4HA4sGbNGuzYsQPvv/++8zugoz9vJAyGXl4HKuQoLCziZ/8Q5dV1BtRqNUxGA9JvehrymAw4bBaIJe5NlTPWlaLs5duhVqs9+uLtPIasW56BLCIRYql78RsOl6Hwxds8Hr+nqNVqGAwGvLpmOZLjY+DnK3XrccUVKixa9siwfd68QedrftStz0Du5mtef7gMB18Yvq93oqFIrVbDYDTiuRvORnJEAHwl7o82K6nT4h+vfNflMyExMRGHDx/usl1iYiIyMzMxdepU3HTTTc4OkaSkJI5uIyIicpNUKnV+hx5Np9OhtLS0S5Ji7969eP/996HT6bpsq9VqnQtkd5wHmPDM5SOQHqmAxeaAr8T9euJljQbc9kExrw+GMbVaDaPBgMl3PoeAqGT4uNmXoq0pwc4n/tHltbN8+XIsX768P8MlGnbEYjH++c9/dru/oz/PiJeXX4fMxGiYrTb4Sd3rfi5R1eOmR17nZ/8Q5tWJiE7ymAxYWg7DZtBCHpUCQ00RZDHpkEUkwdRYCXl0Oqw6NSyaRkBwQBIQCnlMhqfD7sLS1gTBYYM8KgX6miIoYtIhi0yCsaESiph0WNvUsOpaYDcb4BscAUVsuqdD9gqNza2w2e1IT4zD/tJKjEhJQEpCLMpVtchMSUBTswbV9Y2IDA/FhOyRng6XjmDRNkGw26CITkF7dREUsemQRybBWF8JRWw6LJ2veZMegNdOzCKik9TUZoDN4UBqZBAKa1uRER2M5IggVDS2ISM6GE06I1TqdohEQGSQHKemRrps58wzz8Tvv/+OtWvXIjMzE+np6VxEkIiIqJ8FBgZi/PjxGD9+fJf7BUFAQ0MDSkpKkJ+fD5vN5rLEYXqkAoe1ZrQZbUhRylFUr0d6hBxJYXJUNhuRHqGAut0CvcUOu0NAgJ8E2bEslUh/MWk6risDY1OhOVSI4LgMBEQnQ1dXgaC4DJi0TWhvOASxxBdSeQCC47sn1Iho4GUmRqO2sRVavRFpcRE4UHkYmQlRSImNQHltIzITotGk0aGmsQU6gwnJMUpkJkZ7OmzqZ4MiEdFetRfy6FRAEGBuOQyHzQIAcFjNkEUmw9Skglmtgp8yESIfH/gGRcKqb/Vw1F2FjDwTdpMegt0GaZASFm0j2lUHEJA4GnazARL/EFjbWyFRBMFhs8LHZ1D8afrdWRPGol1vgNVmQ2R4KBrULdhXXIHsEakwGE0ICw5Ei7YNJpMZu/YWQsrFRL2Gj8wfsogEWNrUkCiC0K46AADwC4+FYLfBYTXD2t4Kv5AoyMJjPRwtEfWXySNi0G6ywmZ3ICJIjsY2Iw7UtGB0fBj0FhtC/f3QqDUiWOGLxjYjSus0Ltv54IMPBjZwIiIiOiaRSITo6GhER0dj6tSpx902WOaDYJkP1O0WBMklOFDfsWh2bLAfbHYBeosddW0WRAZIYbTaYbY5BuIQaJCIGjMZNmM7BJsNspAIGDWNaKk6gLDk0bCZ9fALDIXdaoZVr4VExkEqRN4kOECO4AA5mjQ6BPvLsb+iFgAQFxEKm92OZm07HA4BiVHhUMh8YbPz83+oGxS9tgHJOQhIyj7m7/3C4oCMCV3u87ZEBNCRjDgeWXic87auam9/hzNonDVh7HF/Hx/z1+jZPw6W9nc45KaglBwEJucc8/ey8DgEp3WMrOLrnWhoEAQBVVVV2L17Nz799FPn/ZNGxBz3cXFhXUc+7j3UsbDZb7/9htTU1B4vjklERETe48yUkOP+Pi7Eb2ACoUErasyk4/7eX/lXX0pzOa8tibzFlLHHn6EUHxnW5efdpar+DIe8wKBIRLRX7YWudBfk0WlwWM2Qx2ZCLPWFufkwfGT+cFhNsGqbIJL4wkcWALtBC8HLSr3oqvZCW7ILiph0OKwmKOJGQCzxhbm5FoLDDpkyAcYmFRwWIwARHNbeLeoy1Lz/yddQt2pgMlswKj0ZflIp2g1GNLVocOhwAy47bxIOllahobkV4SFB0On5vHmLhp+2wNLWAofVBP/4jte7zdAGm1GHgPiRaC38ASKxGGJfOWzGNk+HS0Q9ZDKZcODAAezevRt79uxx/t/W1vF+Dg0NBQBs3lUBtc4Es82OEbGh8JOIoTVYYLTY0NJuhtXuwFkjY/B9UR1EIiAsQAa5tGN9h5tvvhk333wzkpOTMW7cOIwdOxbjxo3DuHHjkJSUxIUGiYiIvNzeWh12HdIiXamAyebAiEgFfH3EqNWaIBGLcKjFBJlUDB+xCGckB+P7cg20Rhv8/bjWE3VoLt+DxsKfERyXBrvFjODEEfCR+kHfVAORjwQ2kx7h6aegft/38JHKoK3h4EQib7C7RIWf9pcjPT4KZosVI5Nj4CeVoKaxFf5yP2jbDTCZrTh9dCr+t+sAggMUqG/Rejps6meDIhFx5IyI9srdMNYWw8c/GBAE2PStUMRnQaIIgjy6Y10Fh9WMtvLfPBlyN4HJf40Ob6vYDX1NEaT+IRAEAYAAH5kCfiGRUMSkQxAEtB7Y7tmAvcS8S8/DKaM61vv4dV8RDjc2w+FwIDMlAQ5BgMFkRoC/AmeOHwOzxYKfdh/0cMTUKeqM2QhMznG+3iX+IYAgAILQsXZEbDr8+XonGhSampqcyYbOf0VFRbDb7RCJRBgxYgTGjRuHSy+91JkoOHz4ME499VTMOT0VOUlKAMAflU2o0xjgcAhIjw6G0WJHTnQwVM3tuPSUJABAg8aABq0BAPDee+/BYrE49/3cc8+hqakJABAcHOxMTHT+P3r0aPj5cVQlERGRt8iJC0TOEWs+7K7Roa7NDIcApCvliAz0RXqEAs16K4JkEkwf3XHO8E2p91U4IM8ITxuL8LSOvhR16R/QqIrg19mXYrMiLHkMjK0NSJx4KQBA6h/kyXCJhr2ysjIAwLjMRIzL+GvB6d+KqlDXrIHDISAjIAoOQcCYtHioGpox99zTAABf/drRp2c0cpDxUDUoEhHGuiMy2mIf+B5RwggAzM0dNcbaD+1z3mfV1A9IbO4yHC5z3haJxV3KMAGASV0D4K8SNda25oELzosVV/w1LcvHxwcJf5ZhMlusCArwR31TC4C/SjLVNfJ58xb6P1/zLl/vzV1f7xa+3om8gt1uR3l5uTPZ0JkAOHz4MADA398fOTk5mDp1Ku644w6MGzcOY8aMcblodOdjSur+GtXiIxYhPswfAGCx2REklzqTDp3lmI58TFpaGsaPH4+rr74aQEfpp7q6ui5Jkc8//xxPP/00BEGARCJBVlZWl5kTY8eOhVKp7Idni4iIiE6krNHQ5WexWIS4EBkAwGwXECiToEHXsQbk3sPtzu1a9daBC5K8mramxHlbJPaBvzK+y+/bmzquLTtLMrXXVw1YbET0lx07dmDNmjX4+OOPAQAlqq79sj4+YmcpJrPVhiB/uXMGRGdJpmZtx/fA9OnTcdddd+G2225DeHj4QB0CDQCR0DEk3yupVCqMGJkFk9Fw4o1dkMkVKC4qRGJi4ok37icncwzeEL+nqFQqZGVlwWDo+fOmUChQWDg8nzdv0NvX/HB+vRN5gl6vx759+7p06u/du9f5uRsXF9elM3/cuHFIS0uDWCx2q32VSoWskSNh6OVoFoVcjsKiIrc+E9w9liMTFD05FiIiIuqZjvOAETAYTb1uQyGXobComNcHw5RKpcLIrCwYe9EnIFcoUMQ+AaJ+53A48NFHHyE/Px8//PADRo8ejRtuuAH33bcCBkPvrgPlchmuuOJveO+99yASiXDjjTfi7rvvRlJSUh9HT57g1YkIoOPLR63+a5TkM888g/fffx9ffPEF/Pz8sHv3btxwww144YUXMGFC1wWrlUqlV3zxHH0M+/btw3XXXYeXX34Z48ePx7XXXovIyEg8+uijXR7nLfF7ysaNG3Hddddh+vTpWLVq1TFrgTc3N2PRokUQi8V49dVXkZGRMayfN29w5GveZrNh2rRp+Nvf/oa///3veO655/Dee+/hyy+/hETy16Ss4f56J+ovrmYR7NmzByUlJRAEAT4+Phg1alSXjvq+mkVw9PdfT5zsZ4K7szuOTLZkZ2e7nN1BREREPaPVajFr1ix8//33WLJkCa644gq31nZqbm7GkiVLUFhYiEcffRT/93//NwDRkrc68lyypKQE8+fPx4MPPohLLrkEAGC1WjFjxgxMmjQJ//rXv5yP47UlUf+yWCz473//i0cffRQHDx7ElClTkJeXh0svvRRisbhPrgObmprw9NNP45lnnkFbWxuuuuoqLF26FGPGjOnjo6GB5PWJiCPZ7XYkJydjxowZeP755wF0dLBkZmZi8uTJeP311z0boJtWrlyJ5557Dg0NDZBIJHjooYewZs0aNDc3w9fX19PheYXffvsNZ599Ns4++2xs2bIFUqn0uNuXl5fjzDPPRGpqKr766iv4+/sPUKR0Itu3b8fZZ5+NXbt2YcKECdi1axcmTpyI7777DlOnTvV0eERDis1mQ3FxcbfOd1frKnR2vo8aNQoymczDkQ+co9e72LNnDwoLC2G32yEWi5GZmdlt9kR0dLSnwyYiIho0SktLMXPmTNTX1+O9997DBRdc0KPHm81m3HrrrXj99dexbNkyPPTQQ5zFSFiwYAG2b9+OsrKyLv0Da9euxcqVK1FVVYWYmBgPRkg09Ol0Orz88sv4z3/+g5qaGlx22WXIy8vD5MmT+22f7e3tWL9+PdatW4fq6mpMnz4deXl5mDJlilsJbvIugyoR8b///Q8XXnghfvrpJ0ycONF5/4MPPog1a9agvr4eAQEBx2nBO3TW1N64cSMAYM+ePRg3bhy++OKLHp+kDUXl5eWYNGkSkpOT8fXXX7udVPj1119xzjnn4Nxzz8XmzZu7jLYnz1m6dCk2bNiAw4cPQywWw+FwIDY2FgsXLsTatWs9HR7RoKXVarF3794uner79++H2WwGACQnJ3crrZSUlMSTNRdMJhMOHDjQJXmzZ88etLW1AQAiIyO7PZeZmZn8niEiIjrKV199hXnz5iEyMhJbt25FZmZmr9oRBAHr1q3D0qVLMWPGDGzcuBGBgYF9HC0NFtXV1UhNTcWjjz6KO++8s8vvtFotEhISsHjxYjzyyCOeCZBoiGtsbMTTTz+NZ599FjqdDldffTXuvfdejB49esBisFqtePvtt5Gfn4+DBw/izDPPRF5eHi677DImqweRQZWIuOaaa/Drr7+isLCwS0fKoUOHkJycjNdffx3XXnutByM8serqaiQmJuLtt9/GlVdeCaDjJCspKQlz5szBk08+6eEIPauxsRGTJ0+GSCTCzp07ERER0aPHf/bZZ7jssstw7bXX4uWXX2aHmxfIysrCpEmT8Morrzjvu+GGG/DDDz+gsLDQg5ERDQ6CIKC6utqZbOjsJK+oqAAA+Pr6YsyYMV1G8Ofk5CAkJMSzgQ9ygiCgqqqq2+ySQ4cOAQBkMhmys7O7Pe/sJCEiouFIEAQ899xz+L//+z9MmzYN77zzTp+ci3zyySeYP38+EhMTsXXrVqSkpJx8sDTo3HPPPXj11VdRXV3tcvDpkiVLsH79elRXV/NcjKgPVVRU4PHHH8err74KHx8f3HzzzbjrrruQkJDgsZgcDgc+/vhj5OfnY+fOncjKysK9996Lq6++mlVmBoFBk4hoa2tDdHQ0Vq1ahWXLlnX7/fnnnw+Hw4FvvvnGA9G57/nnn8ftt98OtVrd5cTsH//4Bz777DOUl5cP287z9vZ2nHvuuaiursaPP/7Y65PMDRs24Nprr8XKlSvxwAMP9HGU1BNlZWXIyMjA5s2bMXv2bOf9mzdvRm5uLkpLS5Genu65AIm8jMViwcGDB7t0fO/evRsajQYAEB4e7uz07hydP3LkyBOWr6O+09raij179nT5+xw4cABWqxUAkJaW1q20U3x8/LD9bicioqHParXi9ttvx4svvog777wTjz76aJ/OGjx48CBmzpwJjUaDTZs24eyzz+6ztsn7aTQaJCQk4Pbbb8fDDz/scpvOGRNr167FXXfdNcAREg09u3fvRn5+Pt577z2EhYXhjjvuwOLFixEWFubp0LrYuXMn8vPzsW3bNsTFxeHuu+/GTTfdxISkFxs0iYhXXnkFN998M1QqFeLi4rr9/s0338TChQtRWVmJ5OTkgQ/QTdOnT4fBYOiWMPn0009x6aWX4sCBAxg1apSHovMcq9WKmTNnYseOHdi+fTtOOeWUk2pvzZo1WL58OV544QXccsstfRQl9dQTTzyBvLw8NDc3dxm50t7ejvDwcOTn53ebWks0XLS0tHTpzN69ezcKCwthtVohEomQnp7erUM7NjaWHdpeyGKxoKioqNvsiZaWFgBAWFhYt7U5srKyOGKHiIgGvebmZlx++eXYuXMnXnjhBSxatKjf9nPFFVdg+/bteO6553DTTTf1y37I++Tn52PVqlUnXANi4cKF+Pbbb1FeXs5BOkS9IAgCvvnmG+Tn5+OLL75AcnIylixZguuvvx4KhcLT4R3XgQMH8Oijj+Ktt95CQEAAFi9ejDvuuAORkZGeDo2OMmgSEWeddRYUCgU+//xzl7/X6/WIjo7Gvffei1WrVg1wdO7R6/UIDw/Hww8/jLvvvrvL70wmE8LDw7Fq1Srk5eV5KELPEAQB119/Pf773//i448/7pN1MgRBwP/93//h2WefxaZNm7qMxqeBM23aNEilUnz66afdfnfxxRfDZrPhyy+/9EBkRAPH4XCgsrKyWyd1dXU1AEAulyM7O7vLTIfs7OxBseYRHZsgCKipqemWbCovLwcASKVSjB49uluCIjQ01MORExERuefAgQOYOXMm2trasHnzZkyZMqVf92e1WnHnnXfiueeewx133IHHH3+c6zUNcWazGSkpKbj00kuxfv364267d+9ejB07Fhs3bsTVV189QBESDX52ux1btmxBfn4+fvnlF4wdOxZ5eXmYN2/eoPuMra6uxn/+8x+89NJLsNvtuP7667FkyRKkpqZ6OjT606BIRHSWd/nvf/+L+fPnH3O7RYsW4bvvvkNZWZlXjhjdunUrZs2aheLiYpeLds2ePRvNzc34/vvvPRCd56xYsQIPP/ww3nrrLVx11VV91q7dbseVV16Jjz76CF9++SUmT57cZ23TiWm1WiiVSjzxxBNYvHhxt98/++yzuPPOO6FWqxEcHOyBCIn6ntFoxP79+7stetze3g4AiI6O7rbocUZGBnx8fDwcOQ0UnU6HvXv3dnmN7Nu3DyaTCQCQmJjY7TWSkpLilec1REQ0fH300Ue46qqrkJycjK1btw5oVYLOcsfnnXce3n33XSbxh7DXXnsNixYtwsGDB5GVlXXC7S+++GLU19fjjz/+4LkT0QmYzWZs2LABjz76KEpLS3HOOecgLy8PF1100aB//7S0tODZZ5/FU089hZaWFsybNw95eXknXX2FTt6gSESsWrUKTz75JOrr6yGXy4+53XfffYdzzjkH27dvx1lnnTWAEbrn5ptvxrfffouSkhKXv1+/fj1uueUWNDY2Ijw8fICj84xnnnkGt99+Ox577DHcc889fd6+yWTCxRdfjL1792LHjh3DsuyVp7z//vu44oorUFVVhaSkpG6/71xk/r333sO8efM8ECHRyWloaOgy2n3Pnj0oKiqCw+GAWCzGyJEju3Qmjx07FlFRUZ4Om7yQzWZDaWlpl9fSH3/8gcbGRgBAUFAQcnJyuiQoxowZA5lM5uHIiYhouBEEAY899hjy8vIwc+ZMbNy40SOzOL/55htcfvnlCA8Px7Zt2zBixIgBj4H6l8PhQHZ2NtLS0rB161a3HvPVV19h2rRp+OKLL/qk0gLRUKTVavHCCy/giSeeQENDA+bMmYO8vDycfvrpng6tzxkMBrz22mt47LHHUFVVhQsvvBB5eXk499xzB32yZbDy+kSEw+FASkoKLrroIrz00ksn3DY9PR3nnnsuXnnllQGK0D0OhwPx8fGYP38+Hn/8cZfb1NXVITY2Fm+++SauueaaAY5w4G3atAnz5s3DnXfeiXXr1vXbfjQaDaZOnQqNRoMff/zR5Roj1PcWLlyI3bt3Y+/evcfcprNjbcOGDQMYGVHP2O32bp3Eu3fvRn19PQAgMDDQZSfx8RLnRO6or6/vtnB5SUkJHA4HfHx8XCa7WAeViIj6i8lkws0334w333wTK1aswAMPPACxWOyxeMrLy3HZZZfh8OHDeO+993DhhRd6LBbqex9//DFmzJjRo4GmgiDg1FNPhVKpxBdffNHPERINLnV1dXjyySfx/PPPw2QyYeHChViyZMmwSOTabDa8//77yM/Px549e3DaaachLy8Pc+bMYXWCAeb1iYhvvvkG5513Hnbu3IlJkyadcPv7778fjz/+OOrq6uDv7z8AEbrnt99+w2mnnYavv/4a55577jG3mzBhAtLS0vDOO+8MYHQDb/v27bjwwgsxZ84cvPXWW/1+AltbW4szzzwTISEh2L59O0JCQvp1f8Od3W5HVFQUbr75Zjz88MPH3O6f//wnXnrpJTQ0NPDDn7xCe3u7y7I5RqMRAJCQkOCybI4nL8JpeDEYDC7Lf+n1egBATExMl/VGxo4di/T0dH7GEhHRSamvr8ecOXOwe/duvPrqq8ctmTyQ2traMH/+fHz22WdYt24d7rjjDo5yHSLOOeccmEwm/Pjjjz36m77zzjuYP38+/vjjD4wbN67/AiQaJEpKSvDYY4/hjTfegJ+fH2699VbceeediI2N9XRoA04QBHz++efIz8/Ht99+i4yMDNx7771YsGABZ5sPEK9PRFx77bX44YcfUFJS4taXT2VlJVJTU71uVsG///1vPPHEE2hqaoJUKj3mdg888AAef/xxqNXq4243mO3fvx9TpkzBqaeeik8++QR+fn4Dst+DBw9iypQpyMnJwWeffcYPmX60c+dOTJky5YQJxB9//BGTJk3Cjh07uIYHDShBEFBbW9uttFJZWRkEQYBEInG5kHBYWJinQyfqxuFwoLy8vNvruaamBgCgUCiQk5PT5fWcnZ3tVQM2iIjIe/3xxx+YOXMm7HY7PvzwQ0yYMMHTIXVht9uxbNkyPPbYY7jxxhvx7LPPwtfX19Nh0Un45ZdfcPrpp+ODDz7A3Llze/RYm82GjIwMTJ48GRs3buynCIm83y+//IL8/HwUFBQgMjISd955J2699VYOzP3Trl27kJ+fj82bNyMqKsr5/HAN0/7l1YmI9vZ2REdHY/ny5VixYoXbjzvnnHMglUrxv//9rx+j65nTTjsN6enpJ5zp8Pvvv+PUU0894cyJwaq6uhpnnnkmlEoltm/fjqCgoAHd/86dOzFt2jTMmDED77zzDkeI9pPly5dj/fr1qK+vP+5zbLfbER0djRtvvBGPPPLIAEZIw4nVakVRUVG3EjfNzc0AgJCQkG4jyLOysgYsSUrUX9RqNfbs2dPldV9YWAibzQaRSISMjIxuM3xiYmI4kpSIiJzef/99XHvttRg9ejS2bNni1WVuN2zYgJtuugkTJ07Epk2bEBER4emQqJeuuOIK/PHHHygqKurVNftTTz2Fu+++GxUVFUhMTOyHCIm8kyAI+OKLL5Cfn49vvvkGaWlpWLp0KRYuXMjBuMdQXFyMxx57DBs2bIBMJnPOGImJifF0aEOSVyciXn/9dSxatAhVVVU9+vJ47bXXcMMNN+DQoUNISEjoxwjdc/jwYcTFxWHjxo24+uqrj7utIAiIj4/H3/72t35dN8ETWltbMWXKFOj1evz4448ee1Nv2bIFc+fOxeLFi/Hkk0+yw6UfjBkzBqeddhpef/31E2573XXX4bfffsO+ffv6PzAa8jQaTbeO1wMHDsBisQAAUlJScMopp3QZGZ6QkMDPARo2zGYzDh482G3NE61WCwCIiIjoNhNo5MiRkEgkHo6ciIgGksPhwAMPPID7778f8+fPxyuvvDIo1r/68ccfMXv2bMjlcmzduhU5OTmeDol6qKKiAhkZGXjmmWfw97//vVdttLe3IzExEdddd92Q61chcsVms+GDDz5Afn4+du/ejVNPPRV5eXnIzc3lAFw3HT582LmGhtlsxsKFC3HvvfciMzPT06ENKV6diDjnnHMgkUjw5Zdf9uhxOp0O0dHRWLFiBf75z3/2U3Tue/nll3HrrbeisbER4eHhJ9z+lltuwTfffIOSkpIBiG5gGI1GXHjhhSgsLMTOnTs9vhjOCy+8gL///e9Ys2YN8vLyPBrLUNNZHu3999/H5ZdffsLtP/jgA8ybNw+VlZVITk7u/wBpSBAEAYcOHerWmVpVVQUA8PPzQ3Z2dpcO1ZycnAGfhUU0GHS+n44u7VRZWQmg4/00ZsyYLjMncnJyOG2ZiGiI0uv1uO666/DBBx/goYcewvLlywfVoA2VSoVZs2ahtLQUb731FmbNmuXpkKgHbrvtNrz77rtQqVQnlfy677778MQTT6C6uhqhoaF9GCGR9zAajXjttdfw2GOPobKyEhdccAHy8vJw3nnnDarPbW+i0Wjwwgsv4IknnkBjYyNyc3ORl5fndWUJByuvTUSc7FoPCxcuxE8//YTi4mKPv/lmzpwJjUaD7du3u7X9tm3bMHPmTBQVFXm8w74v2O12zJs3D5999hm+/vprnHHGGZ4OCQCwatUqrF69Gm+88QYWLlzo6XCGjKeffhr33HMP1Gq1W52+bW1tUCqVWLduHW677bYBiJAGG7PZjAMHDnRbnPfIEdxHl5cZMWIER3ATnSSNRtNt8fb9+/d3mWF09HsvMTHR4+ddRETUe9XV1Zg1axZKSkqwceNGzJ4929Mh9Yper8e1116LgoICPPTQQ1i2bBm/nwYBtVqNxMRE5OXl4V//+tdJtdXQ0ICkpCT861//wvLly/soQiLv0NraimeffRZPPfUUmpubMW/ePCxduhTjx4/3dGhDhslkwoYNG/Doo4+irKwM5557LvLy8nDhhRfy++QkeG0i4v7778djjz2G+vr6Xi2m+PXXX+P8888/4WK5/c1oNCI8PBz//ve/sXTpUrceYzAYEB4ejgcffBD33HNPP0fYvwRBwOLFi/Hiiy9iy5YtuOyyyzwdkpMgCLjpppvwxhtvYNu2bbj44os9HdKQcNFFF8HhcPRojZYLLrgAYrEYn3/+eT9GRoNBZ037I0dlH1nTPjMzs0un57hx4xAdHc0TAaIBYrVaUVxc7HyPulpz5ejSTqNGjeKaK0REg8CPP/6IOXPmQCaTDYmyRg6HA/fffz8eeOABXHXVVVi/fv2gKC81nD3wwANYs2YNVCoVlErlSbd38803Y9u2baiqquK5CA0JNTU1+M9//oMXX3wRNpsN119/PZYsWYK0tDRPhzZk2e12bN68Gfn5+fj1118xbtw45OXl4fLLL+fgx17wykSEw+FAeno6zj33XLzyyiu9biMlJQUXX3wxXnzxxT6O0H2ffPIJpk+fjoMHDyIrK8vtx82YMQN6vR7ffPNNP0bX/x566CHcd999ePnll3HjjTd6OpxubDYb5syZg2+++QbffvstTjvtNE+HNKjpdDoolUo8+uijuOOOO9x+3FNPPYV7770XarUagYGB/RgheQuHw4Hy8vJupZVqa2sBAAqFAjk5OV06M7Ozs3uVmCai/iUIAg4fPtxtQfiysjIIggCJRIJRo0Z1SSKOHTvWrXKVREQ0MDoXej799NOxadMmREZGejqkPtO54PaYMWOwZcsWxMbGejokcsFoNCIxMRFXXHEFnn322T5ps7i4GFlZWXj55Zdxww039EmbRJ5QWFiItWvX4q233oK/vz/+8Y9/4I477kBUVJSnQxs2BEHAN998gzVr1uB///sfUlJSsGTJElx//fVMcveAVyYitm/fjrPPPhvfffcdpk6d2ut2Vq5ciaeeegr19fUee1H8/e9/xxdffIGysrIejdh98cUXsXjxYjQ1NQ3aeoavvfYaFi1ahPvvvx+rVq3ydDjHpNfrcf7556OiogI//PAD0tPTPR3SoFVQUIC5c+eirKysRxn58vJypKeno6CgAHPmzOnHCMkTDAYD9u3b16WDcu/evdDr9QCA2NjYbuVd0tLSuKgW0SDX3t6Offv2dUk47t27F0ajEQAQHx/f7b2fmpoKsVjs4ciJiIYPu92O5cuX49FHH8WiRYvw/PPPw9fX19Nh9bnff/8dM2fOhCAI+PDDDzkAzQs9//zzuO2221BSUtKno7tnz56N4uJiHDhwgOcYNOj88MMPyM/Px9atWxEXF4e77roLN998Mwdwetjvv/+OtWvX4v3330d4eDjuuOMOLF68eND23w4kr0xELFq0CN999x1KS0tP6ouitLQUmZmZePvtt3HllVf2YYTuEQQBiYmJyM3NxZNPPtmjx9bU1CAhIcFjsZ+sjz/+GLNmzcINN9yAF154wevLpqjVakyePBl2ux07d+5kVrmXFi1ahJ9++gkHDx7s8WNHjRqFM844A6+++mo/REYDpb6+vku5lj179qCkpAQOhwM+Pj7IysrqVrYlIiLC02ET0QCx2+0oLS3tVoKtrq4OABAQEICxY8d2+ZwYM2YMRxkREfWDtrY2XHXVVfj000/x+OOP4//+7/+8/rrtZNTV1WHOnDnYs2cPXnvttUF5nT1U2e12jBgxAuPHj8d7773Xp23v3LkTU6ZMwdatW72qVDTRsQiCgE8++QRr1qzBjh07MHLkSCxduhRXX331kEwUD2bl5eV4/PHH8dprr8HHxwe33HIL7rrrLsTHx3s6NK/ldYkIvV6P6Oho3HvvvX0yin7KlCkICAjAZ5991gfR9czu3btxyimn4H//+x+mTZvW48efcsopGDVqFN56661+iK7/7Nq1C+eeey6mTZuGTZs2DZqaaVVVVTjzzDMRFxeHb7/9FgEBAZ4OaVBxOByIiYnBddddh/z8/B4/Pi8vD6+//jrq6uo4UmUQsNlsKCkp6VaKpbGxEQAQFBTULeEwevRoyGQyD0dORN6ooaGhy2L0u3fvRlFRERwOB8RiMUaMGNFtfRgOGiAi6r2KigpcdtllqK2txbvvvouLLrrI0yENCJPJhJtvvhlvvvkm7rvvPtx///289vACmzZtwuWXX45du3ZhwoQJfd7+pEmTIJFIsH379j5vm6ivWK1WvPPOO1i7di3279+PM844A3l5eZg5cyY/p7xcQ0MDnnrqKTz33HPQ6/W4+uqrsXTp0h6V6B8uvC4R8eabb2LhwoWorKxEcnLySbe3fv163HLLLVCpVIiLizv5AHvgwQcfxNq1a6FWq3uVtVy1ahWeeeYZNDY2DprO/JKSEkyePBmZmZn43//+B4VC4emQemT37t2YOnUqJk2ahG3btkEqlXo6pEHj559/xhlnnIHt27fjrLPO6vHjv//+e0ydOhU//fQTJk6c2A8RUm+1tbVh7969XRIO+/fvh8lkAgAkJSV16yBMTk4e0iPqiKj/GY1GHDhwoMssq71790Kn0wEAoqOjuyU8MzMzWdaNiOgEvvnmG1x++eUICwvDtm3bMHLkSE+HNKAEQcCjjz6KZcuWYfbs2diwYQMHoXmQIAg488wzIZPJ8O233/bLPjZv3ozc3Fz8+OOPOOOMM/plH0S9pdfrsX79eqxbtw4qlQqXXnop8vLycNZZZ/GaepDR6XR46aWX8J///Ae1tbWYOXMm8vLyMGnSJE+H5jW8LhFx/vnnw+Fw9NkizVqtFtHR0fj3v/+NvLy8PmnTXRMnTkRiYiLef//9Xj1+165dmDhx4kmvlTFQ6uvrnScQO3fuRFhYmKdD6pWvvvoKl1xyCa688kq88cYb/OB303333Yfnn38eDQ0NvUqc2Ww2REVF4R//+AdWr17dDxHSiQiCgJqamm4LSJeXlwMApFIpxowZ06XjLycnh3UQiWjAOBwOVFZWdpuNVV1dDQCQy+XIzs7u8jmVnZ3NOrpERH968cUXcdttt+Gcc87Bu+++O2iv2frCtm3bcNVVVyE1NRVbt25FUlKSp0MaljoHpH300UeYPn16v+zDbrcjKysL2dnZ2LRpU7/sg6in1Go1nnnmGTz99NPQarWYP38+li5diuzsbE+HRifJYrHgrbfewtq1a1FUVIQpU6Zg2bJluPTSS4d9H6NXJSL+97//4cILL8Rrr72G6667rs/anTdvHr799luUlZUhODi4z9o9ns4Ft59//nnceuutvWrD4XAgNDQUY8aMwc6dO/s4wr7V1taGc845Bw0NDfjhhx8G/UncO++8g/nz5yMvLw9r1qzxdDhez2g0IjY2FpMnT8ZHH33U63ZmzJiBnTt3ora2dtDNphlsLBYLCgsLu3Xmtba2AgDCwsK6LSI7cuRI1qQkIq/U0tLSbd2JAwcOwGazAQDS09O7fabFxcUN+wsBIho+rFYr7rrrLjz77LO4/fbbsW7dukEz674/7d+/HzNnzkR7ezs2b96MyZMnezqkYcVms2Hy5Mloa2vr98WkX3zxRdx66634/PPPceGFF/bbfohO5NChQ3j88cexfv16iEQi3Hjjjbj77rsHfT8adedwOLBt2zbk5+fjxx9/xJgxY7B06VJceeWVw7YCi1clIiZOnIhdu3bhl19+wWmnndZn7V5//fV4/fXXTyop0FMLFy7Em2++iS1btmDWrFm9bqfzAtmL/kzdlJeX48wzz4TJZMKOHTuQk5Pj6ZD6xLp163DPPfdg4sSJ+OGHH1iT7zh+/fVXTJgwAeecc85JzWY677zz8M033/T5Z8Bw19ra6rKDzmq1Avirg+7IEcTsoCOiwc5sNqOwsLDL59/u3buh0WgAAOHh4d1KO2VlZQ3biwIiGrqqqqpw44034rvvvsOzzz6Lm2++2dMheRW1Wo3LL78cP/zwA5566iksWrSIg28GSOcAwIsvvhiffvppv+6rsbERUVFRUCqVaGpq6td9Ebmyd+9erF27Fu+88w6Cg4Nxxx13YPHixVAqlZ4OjfqZIAjYsWMH1qxZg08++QSJiYm4++67ceONN8Lf39/T4Q0or0pE7Ny5Ezt37sTSpUv7tF2TyYSlS5fiwQcfRFBQUJ+2fSxNTU0oKCjALbfcclLtFBUVYc+ePfjb3/7WR5H1vauvvhr//e9/8fDDD2P58uWeDqdPRUREQK1W48CBAxg1apSnw/FqTzzxBG655RbI5fJet2E0GvHiiy/izjvv7LvAhhGHw4GqqqpupZVUKhUAQCaTITs7u0unW05ODkuWENGwIQgCqquru31OVlRUAAB8fX0xevToLsnZsWPHIiQkxLOBExH10m+//YbTTjsNcrkcn3zyCc455xxPh+SVLBYLbrvtNrz88stITU11lial/lVfX4/rr78er776KmJiYvp9fytXrkRwcDCWLFnS7/siAjrOPb///nusWbMGn376KRITE3HPPffghhtuGHYd0NRh3759WLt2Ld5++20EBwfj9ttvx2233TZsElJelYigwam+vh4///wzZs6cOeRGULe3t+Ojjz7ClVde6elQiLowmUzYv39/l5G+e/fuRVtbGwAgMjISp5xySpfRvhkZGZyCT0TkQltbG/bu3dtl5sT+/fthNpsBAElJSd1KOyUnJw+58x4iGnp++OEHzJkzBw888MBJD5Ib6qxWK8444wwEBgb226LJRDQ81NXV4eOPP8Yrr7yCn376CWPGjEFeXh7+9re/cfYtAego0bVu3TqsX78egiBg1qxZuOCCC7Bo0SJPh9avmIggIvJyjY2NzoRD5/9FRUWw2+0Qi8UYMWJEt/Ii0dHRng6biGhQs9lsKC4u7raWTmc5h6CgoG6fvaNHj4ZMJvNw5ERERETkKYIgQCqVwm6346yzzsKyZctwySWXcAALuaRWq/Hss8/i3//+NwCgoaEBkZGRng2qHw1YIkKlUkGtVvf4cUqlEomJiQO2T3f3N1jbdoen9++Nhvtz0t/v3562P9DPbW+PH+hZrHa7HWVlZd06verq6gAA/v7+XUqFjBs3DmPGjOHC3kREA0QQBNTV1XVbd6ekpASCIMDHxwdZWVndSjtFRES4vY+T+c4Bhtb5BxF14OeCZ3iiD2Mo8aZraG+KhbxbX73vr732WoSGhuKJJ57ow+hoKOtcM3nx4sVd7h+o/qiBMiCJCJVKhREjs2AyGnr8WJlcgeKiwh4/cb3dpzv76++2s7JGwmAw9qhthUKOwsKik36BqVQqjMzKgtHQs2OTKxQoKuz532kwUKlUyBo5AgajqUePU8hlKCwqHvTPSX+/fzue35EwGN1/zSvkchQWnfzr3R29/ft3OtbrQK/XY9++fV06svbu3QvDn++9+Pj4bgtIp6amctF0IiIv1PmZfnS5vM7P9NjYWJelnXx8fODj4+Nsp7fngUfqq3NCIvIOJ3suCgyd65KBdDKfx/wc/rNfYWQWjD28hpTLFSjqRf/PiWIZztfz5L6O932W8/ytJxQKBQqHaJ8YeU5v+suONJB9Z+4akGLharUaJqMB6Tc9DWt7C8QSP/iGRAMiEQSbBSKpDHajDoLNApu+FRL/UCjiR8JYV4qyl2+HWq3u8ZPWuc9x/3gGFl0LJPIAiMU+EPvK4LBa4BsQApO2CSJxx8WftV0D36Bw7H7uthPur7PtjJuehqW9BWKRD/zC4wGRCA6bBVZNA3xkAR23dWqEZp8HY10pSt04FrVaDYPBiBf/LxctbXr4y/3QpNUjJToUFqsdoYFyVNW3IkDhh5omDZTB/giQ+eGWJwt69Ty52r/RYMC4y+9AYGQCJH5ySHxlsFvN8AsMRVt9FQARBIcdDrsdcTmT0Vpdiq8f/3uf7N8bqdVqGIwmPHv1KWjRm+EQgGSlP8QiwGJzoF5rQliAL/QmGyASISc+GKUN7Vj81h9D4jnpfL1n3fIMLO3NkMgCAAA+Mn84rBZIA0Jhbq2Df/xI6Kr2QqIIhthHCsFuQ+GL7r2fDEYj/n5uBjJjgqHWmZAcEQCz1YFQf1/oTFb4+ohxWGOETOqDVr0ZD23bP2DPbeff/5l5I5EeocDPVRr4SXwQHeQLkQiw2AT4SkSw2AQ06MzQm+2ICPTFqOgAlDUZcNv7Rc5Y7XY74uLi0NjYCKBjZK1EInGOnp03bx7Gjh2LsWPHDpuFioiIhgJ/f3+cccYZOOOMM5z32e12lJeXd0k4v/766zh8+DAAQCwWw+FwoLy8HKmpqQD+Og98OW8BMhOiYbbasK+8Bn5SCZTBARCJRbBY7dC2G+DjI0ZtkwanjUxCaGDHYocl1fW4Kf/NIXH+QUQdOs9Fn748ExkRCvx8SAtfHzF8fUTw9/OBxSYgVCHBoRYTAvx80G62w2oXcGZKMACgtMmA2z8o4edCD3V+Hq9feTNatO3wl8ugMxgRqwyF2WpFaFAAdHojfKUS1Da2IChAAX+ZH8xWK25c/dKwf77VajWMRgNOu+1ZmHUtgOCAf1QyRCIxHDYLjC31kMgD0F5XDh+pH6LHXwBdbSl+fWZxnz93zuu5K0aj2WBGgJ8ERosDygBfWGwOhCokqGszY2R0AEob9fARidDcbsXqz8qG/d9xuOl43xuw4dWX0dzSgsCAAFgsFoSFhcFsNiMsLBQVlVUICgxE1aFDiIyMxOmnnYrComIsXHQTXy/U5zr7y5695jRkRAXi5wo1xCIR4sMUEItEf/ZJGhHm74eShjYofCWYmhkBkUiE0gYdFm/81etelwO6aqm1TQ2JfwjkUSkw1BRBFpMOWcQomBorEZA4GladGhZNI+CwQbBbIY/JOOl9WrRNkAaGwT86BbrqQsgj4qGITIa+vhKRY8+DpU0Ni64FPr4y2IztPWu7TQ2pfwhkfx6PPCYd/n8ejzw6veN4WuthN7XDbulZ9r1R046wIAVSY8JgszsQERKA5KhQVNS14LIzR0Gt1cNPKkGwvwzNbfoete0OeXA4JH4yBMemoKWqECHx6QiKSYHET46Q+HQYNWrYrWa0VpfCatT1+f69UZPOjFB/X6Qq/VFY14b0yACMjvVHRZMeGVEBUOvMaDVY0W6yYSiW/rO0NUEaEAp5VAr0NUXwDYqALDIJxoZKhI+7ANY2NfxjMiCW+sFhNUPiH9yj9gPkUmREBcJmd0AZIEOy0h8VTe3Ijg+BWmeGWCxCsNwX1S2emRGQHqHAYa0ZwXIpUsLlKGrQIz1CgaQYOSqbDciJVUCtt0JvsUNjsMLmEJAe0bVkkiAICAwMhNVqxWOPPYZx48Zh1KhR8PPz88gxERFR//Hx8UFmZiYyMzNxxRVXOO9vamrCnj17kJ+fj+LiYpdrSmQmRKO2qRVavRHZqXE4UFWHQH8ZUmKUKK9twulZyWjStMNPKoXJYoXNbsdpI5MH8OiIaKBl/HkuGiSTOM9Flf6+SAqTobLZiEtHK6Fut8BgcQAADBY7Jib37HycumtsaUNYcADS4qNwsKIGEaFBSImLRHlNA8ZmJKFJ0waJxAdWqw3BAf4ICWTJ1COZtE3wDQxFQHQq2lSFCIxLh3/UaEjrKhAYl4GQ5DGwtLfC2FoPh93Wr7E0tZsRqvBFilKOovp2RAT4Iilcjkq1AReMjIBab0F8iBwtBisig4bgBT25rbGxEWHhYchIS8e+AwcQGRmBtJQUlJaXY+7sWWhsakJoaCg0Wg30egOyRo7wdMg0xGVEBeKwxoAguRSpEQEoPNyG9KhAjI7zR0WTFBmRgciMDkSr3oI6rRExIXJkRAV6OmyXBjQRETTiTAQkZQMAAtMnOO/3C4sFAPiGRME/YbTz/vZD+056n+GjJiE4JQcAEJb51z7l4R37lIVGOe/TVu7tUdvBRxxP0AmOx+fPUeTumjImGWPTOtqZOPKvzFWcsuNkMjosEGNSOhaj3VN+uEdtuyM2ezIi0sd27CvrdOf9AcqOmPzDOvYdNeJUNJXt6fP9e6NJ6eHIiQ8BAExICXPeHxsiBwBEBf3VkbC3RjOQoQ2IkJFnIjC5470UnPHX61325+vdLySqy/a6qp69ny4cE4uchFBMSP1rJkBsaMeJfFSwHKP/fO73Vg/ox1YXF4/6K7YJSX9d2MUGdyQSooK6JhT21nZN0kkkEpSWlvZjhERE5O0iIiIwbdo0TJs27bjbTZ+U47w9cXSq83ZcRCgAIDo8GNlpcf0TJBF5pYuywp23JyQGOW87z0UDfQc8pqHurFNGYtyIZADAGdl/DZSMi+y4HoxWhiA7/a/r9d3FVQMZnteLGDUJIakd32fhI47fH6Op6Nn1Y0+dmRqKnLiO982EpBDn/bHBHdfxR17L7a1t69dYyLudPfUsjD9lHABg0pkTnffHx3ecd8XERGNsTrbz/t//2D2Q4dEwdXF2rPP2hJS/zgecfZLBXQc37a3WDEhcPTWgPXrtVXs7ZgfotXDYzLC2qaFIGAXfkGiIpb4wNx+G4LBBJBLDRx4Ac0vfdLA3F/4AALC2a+GwWWDWNiEoaRRkoTEQS32hr6uAw26DX1D4CVpyfTw2vRaCi+MxNVTCqmuGX3g8RJKenxTuPFAFANC0G2G22tGkbceY5GjEhAXCVypBTZMWmnYjYsL6PsvVVLYHVmM7zO1a2K1mGDVNCE8ZDf/wGPhI/aBrqoHYRwqHzQKJn7zP9++N9lZr0G6yQWu0wmxzQK0zY1RsEGKC5fCViFHR1I5mvQUxwTLIfX1O3OAgpCn6EQBg1Wsg2CywaJsQkDgavqHREEt8YdE0QLDbejUjAgB+KG3q2I/BAovNgSadCaPjQhAT0vEc/1LRjNTIniX1+tKPlZqO+Iy2jvjaLRgdE4DoID/4+ohQqzWjTmtGTLAf/HzEQ3JmDBERDYwdezsS15p2I8wWG5o0OoxJjUVMeAj8pBLUNLWiVadHoEKGYH8FkxJEw8CPlVoAgNZog9nugLrdilHR/ogO8oWvRIzDGjNaDFZEBPjC31cMqYRri/WFHX8UAQBadXpYrDY0trYhOy0BMRGh8JNKUFbdgHajCXGRYeDpf1etFXthM7XDotfCYTXDrFUjOGkUZGExEEt8oancC4fVDIUyAWKJtN/j+bGiFcCf13N2B5p0R1zPScSobDbA10cMmZTvneHstz92Q9fejtZWDcxmMxobG5GTk4242Bj4+fpBVV0Dm90GtboZY0aP8nS4NEz8UNaxYLXWYPmrTzIuGDHBMvhKxKhtNcLm6FgGOlje/5+nvTWgiQhTQwVk4XGASAT/pByIpb6wmwwQ+0g7OjCtJtjNBkAkAkRiiPpgUVZN5V44LCb4x6QBIhGCUzr2azPpIfKRwNRSD7HUD36hUXD0sHwSABiqD0IWnQZBJEJI9nnOYxJ8pBDLAiA26WFurgHEPXuqd5cfhslqQ3qsEiKRCOPSYpzJhxadEZlxSmjajUiJCYPJbO1x3Ceira1AYETHuhcR6WPhI/WDWa+FSdeKkPiOkSA2kx6CIMCo6d3q7YNNeZMecaEKiEQijI0Pga9EDL3FhjqtEXaHgHGJIdit0sBic0DdbvZ0uH1OV9XxXlLEpEMkEsE/OQdiiS/sZgN05X8gfNy0jllMIjFEYglM6uoetb9H1QqT1Y70qECIRMDYxFD4SsQ4pNZD6iNGvdaIkbFBsNgc/XSEx7e3VgeTzYH0CAVEAHLiAp3Jh0MtRrSZbFBIfXBeZpgzYUFERNQbu0urYbJYkR4fCRGAcRkJXZIPVXXNCAmQY3J2OnbuK0NNYyt0BhMUMo6GJhqqtuxtwllpITDbHBgRpXAmHnzEIhys10Px50CoM1OC8Xu1DmabGEp/7+2IGCz+KK6CyWJFRkJ0x3X5iGT4SSWobmhBi7Ydh5tacO6EMfjut4OoU7fCZrN7OmSvoq8rh0IZB0CEkNSxEEt8YWg8BFNLHRQRCQBEkMgDAZEIhqaeXT/21oG69o5rOhFw/ohw+ErEaDPZ0NRuQXKYHJXNRjTqht71PLmvtLQMiQnxEIlEOHX8OPj5+qGiqgq1h+ugqq7GrBnTUVhUjOioKBQVl8DHh4kr6l+bf6/B1MwImG12jIwJciYe/P0k+KWyBb4SMc4fFYXCw23Qm23Qi0WQSb1zgPSAJiKUE2c7Sxl1smga4BsSBT9lfJf7HVYz2sp/O+l9hqTkOEszHcnU2gBZaNSfX34dP5t7mIgISM7pdjzAX8ckO+KYWvd/26O2x6XFOkszHUkiFiP6zxkQF08YgfoWXb8kItLPnuMszdRJ5OPjLMkUM6pjepogCKjZ/V2f798bzRkf5yzN1KmhzYTMI+qunZUZAUEQsL2kaYCj63+ByTnO0kxHMmsaEDUpFwCgHH8xgI7XReuB7T1qf2xiKHISQrvdLxGLEBUsR3yYAg1aIxraep4w7As5cYHIies++0giFnUryXRhlhJmmwO/HtIOVHhERDSEjMtIwLiMhG73S3zEiA4P7rIexCVndJyL1jdrUd/CUhJEQ9XsnAhkx3adGSwRi/4sx9T1HPWcjI5z6gadBSadZaBCHJJOGZHsLM10JImPD6KVIQDSAACXTT0VgiDgm18ODGh83i5+8hxnaaZOYh+JsyRTZ3+MIAho2tv/a0+emRqKM1NdXHO2mZEV3fH+ig+V45uS4THYkly78orLnaWZOkkkEsTERDt/nnh6R6kxs9mMH376eSDDo2Fozvh45CSEdLlPIhYjKliG0XF/VSM5PbWj0k+D1uSxvrMTGdBEhLHur9rohtpiWHXN8JEFQKIIgk2vhSwqBXaDFn7Kji8jq6b+pPepq+3Yp66mGOa2ZkjkAZAqgmDVa+AfnQqrXuv88uvctqfHc6xjsRm0kP15LLb2lh61XVLT8cVXVN0ItdaAALkvgv1l0LSbkBgZAk27EWmx4V227Uut1R3H1qIqglHTDF9FAPz8g2Fq1yA4NgWW9jYERnUc23CZEVHa0LGYeXG9Dup2MwL9JAiSS6ExWJEQpoDWaEVqhD8AQN0+9E74DYfLnLf1tcWwtjXDR/7Xa15+1Gve2tbco/ZL6js6T4rrtFC3mxHgJ0WwQgqN3oKEcH9oDRakRgY6txtoZU0G5+3iBj2a9VYE+PkgSC6B1mhDfIis4zWg/GuBugZe+BERUS+UVHc9By48VAe1pr2jDFOAHK06A5KiwqBpNyAtLvKYjyOioaP0yHPRRgOa9RYE+EkQJPP561zUZENquPyYj6OeKz70V7nowspaNGnaEKiQIyRAgVadHonRSmh0eqQndHRQNmmYED5SZx9LW3UxzG1qSOSB8PUPgqVdg4DoVFgNf/XHmNv6t1+hrPHI67n2v67nZFJojVbEh8qgNdqQqlSgVd+/C2eTdzKbO2bCFBYVO+87cLAQTU1qBAYFIiQ4GK2trUhOSkSrRouM9I5E5OHDdR6Jl4aP0oa/ErXF9W1Q68wIlEkQJPeFxmA5ok8ywOVjvMmAJCKUSiVkcgXKXr69x4+VyRVQKpUn3vAY+9z93G19vr/Otkt7eDzutq1QyHHLkwU9aluhkPfqeXK1f7lCga8f/3uPHidX9O7vNBgolUoo5DIsfuuPHj1OIZcNieek8/Ve+GLP3ktAD17zcjkWb9jldrsKed+83t3R+fe/7f2iXj1+qLwOiIio/xUXF8PHR4yb8t/sdRtyGb93iIaKmpoarFq1CmIRcPsHJb1uR+bni/Dwnq2HONx1XpffuPqlHj+2r67NBzOlUgm5XIFfn1nco8fJe9n/c6JYFHIZbnuvZ7NVeB03vHz99de44YYbIBaLsXDRTT1+vI+PD3755ReccsopEHGxSOojzv6yjb/26vED2XfmLpEgCMJA7EilUkGt/ivDvX37dtx1113YtGkTHA4H5s2bh6eeegqTJ0/u8jilUonExMQ+2Sfw/+ydd3hURdvG7y3ZlmTTe+89oUvooEgLCArYaKIgCCiKigiiAiogWJHyKh9VVASkBBQEROkdEtI2vbdN297P98eyIYGU3WRbwvld13u9uOecZ+7MmTPzTHsGmDNnDhgMBq5cuYIdO3YgPr75NkF902vJ9muvvQYWi4XLly9j586diItrHrbJWLZ37dqF2NjYDtnWh6bpnzhxAh999BGOHDkCX19f/Pzzz/juu+9w8uRJODo6miR9a6Rpnhw9ehSffvopkpOT4eXlhd27d2Pz5s04deoUuFxu4zPdKU9aKpM//fQT9u3bBwqFghdeeAFz5jzaWHe0zP/444/49ddfQRAEXnrpJbz22msdsmssHtb3888/49tvv8WxY8fg4aHdVjxv3jyIRCLs2bOnmePRncoBCQkJCYlpqKmpwbvvvoudO3eib9++WLp0KYKCggyykZKSgtWrV6OgoABLlizBJ598Ag6H0/6DJCQkVodCocDXX3+N1atXw87ODkuXLsWQIUMMHtwqLCzEl19+icuXL2Ps2LH49ttvERoaaiLV3Y+W+kArVqxAYWEh0tPT8cUXX+Dpp59+5DnS/9fSNP9Wr16N//77D8nJyWAytWFt33zzTZSXl+O3334D9f75oKbKu5be5dKlS1FVVYWUlBRs3LgRw4YNa3adfI+PB1VVVViyZAn27t2LwYMHY+XKlXB2djbIRnl5Ob7++mucOXMGSUlJ2LRpEwICAkykmORxo6X6S61WY8CAARg2bBj++ecfXLp0CXT6o3sNrLIeIyzEnDlziLCwMIIgCEKj0RBBQUHE/PnzTZ6ul5cXsXTpUgIAsXPnTqPa9vT0JJYtW0YAIHbt2mVU2+7u7o22d+/ebVTbbfHUU08RQ4YMafzvyspKgk6nE5s2bTKbBmtj2LBhxPDhwxv/u6ysjKBSqcTWrVstqMr8TJ8+nUhMTCQGDBhATJs2zai2p02bRgwcOJBITEwkpk+fblTbnUWpVBIBAQGP/M3Hjx8nABD//fefhZSRkJCQkHQ1NBoNsWfPHsLV1ZVwdHQkfvzxR0KtVnfYnlwuJz777DOCyWQSQUFBxF9//WVEtSQkJObg1KlTREREBEGj0YjFixcT9fX1nbKn0WiIQ4cOEf7+/gSDwSBWrFhBiMViI6l9/OjXrx/xyiuvEC4uLsTq1astLadLUFpaSjAYDGLt2rXNfv/vv/8IAMSxY8csoishIYGYO3cuYW9vT6xfv94iGkgsh1qtJrZt20Y4OjoSLi4uxP/93/8RGo2mw/Z0da2Pjw/B4XCI9evXEwqFwoiKSUgekJeXRwAgPv/8cwIAkZ2dbWlJemORo90JgkBycjKSkpIAABQKBePHj0dycjIIE27QEAgEKC8vR1xcHPz8/JCVldX+QwbYrqioQGxsLHx9fY1qu76+HlVVVYiPj4ePjw94vI5vyzWEoqIinDlzBrNmzWr8zd3dHWPHjsXOnTvNosHayM/Px7lz55rliZeXF0aPHv3Y5UlWVhYiIiIQHh5u1PKusx0eHo6IiAij2+4shw4dQmFhId55551mv48ePRpRUVHYuHGjhZSRkJCQkHQlcnNzMWrUKEyfPh1PPfUUMjIy8NprrzWuCu0IDAYDH374IVJTUxEUFITRo0fj5ZdfRlVVlRGVk5CQmIKioiJMnjwZTz/9NNzd3XH79m18/fXXcHBwaP/hNqBQKJg0aRIyMjLw/vvvY/369YiOjsbhw4dN2vfujhAE0dhPMUUfqLvyzTffgMViYd68ec1+HzRoEBITE7Fu3Tqza9JoNODxeIiMjCTf5WNIamoqBg8ejNdffx0TJ05EZmYmXnnllU6FVGpa186dOxcffPABevfujcuXLxtROQmJFl2dNWTIkGb/3RWwyETErVu3UF5ejvHjxzf+lpSUhOLiYqSkpJgs3exs7UFJOsfBmAP6OlumsG1K3W2xZ88esNlsTJ48udnvs2bNwo0bN5CWZliMxe7Anj17YGdnh+eee67Z77NmzcKVK1e61MffGQiCAI/Ha5ws4PF4RuvINLWtK+/W0kkiCAIbN27E8OHD0bNnz2bXqFQq3n77bRw9erTxmyUhISEhIXkYpVKJdevWITY2FjweDydOnMAvv/wCT09Po6URFhaG06dPY9euXTh58iQiIyPxf//3f1bTnpKQkDxALpfj888/R1RUFC5evIi9e/fi33//fSTMb2fhcDhYvXo10tLSEBMTg0mTJmHs2LGk32oA1dXVaGhoaNYHImmb+vp6bN26FfPnz39kUo1CoeCDDz7AhQsXcPHiRbPqKi0thVQqNfsYC4llkUgk+OCDD9CrVy/U1tbi3Llz2LFjh1Fj6Nvb2+Prr7/G9evXwWQyMXDgQMybNw91dXVGS4OEhMfjgclkol+/fmCz2V2qDrPIRERycjIcHBwwaNCgxt+GDh0KOzs7JCcnmyxd3YsJCwvrUhMRptTdGgRBYNeuXXjuuedgb2/f7Nq4cePg4uKCXbt2mVyHNaHLk8mTJ8PW1rbZtfHjx8PR0fGxyRM+n4/6+vrG8t7Q0IDq6mqj2G7q4IeHh6O+vv6ReHiW4tKlS7h27RqWLFnS4vXp06fD1dUV33zzjXmFkZCQkJB0Ca5evYo+ffrgww8/xIIFC5CWloYxY8aYJC0KhYIZM2YgIyMDSUlJePXVVzFixIjHZtEECUlX4K+//kJcXBxWrlyJefPmISsrCy+//LJJDzoNDQ1FcnIyjhw5gszMTMTGxmL58uUQi8UmS7O70FKfn5zgbZstW7ZALpfjrbfeavF6UlISoqOjzb4rwpTjNyTWyYkTJxATE4NvvvkGH3/8Me7cuYOhQ4eaLL1evXrhypUr+O6777Bv3z5ERkZi3759ZJ1BYhR4PB5CQ0NhY2ODsLCwLlWHWWwiYvTo0bCxsWn8jcFgYNSoUSafiHB3d4ejoyPCw8ORnZ0NjUZjNNuenp7gcrlGt52VlQUvLy/Y29s3NpLGst0aly9fRnZ2NmbOnPnINQaDgZdeegl79uyBSqUyqQ5r4sKFC8jLy2sWlkkHi8XCiy++iN27d0OtVptfnJl52HFr+puxbOvCPhnTdmfZuHEjIiMjWx00YrFYWLBgAXbs2IGamhozqyMhISEhsVYEAgEWLVqExMRE2NjY4Pr169iwYcMjCxtMgZubG3bv3o2///4bxcXFiI+Px6pVqyCXy02eNgkJScsUFBRg0qRJGDNmDHx9fXH37l1s3LgRXC7XLOlTKBRMmDAB6enp+OCDD7Bx40ZERUXh4MGD5CBZG/B4PFAoFISGhlrdgilrRCqV4ttvv8WsWbPg5eXV4j1UKhXvv/8+jh07hnv37plNG4/HA51OR2BgICIiIlBZWYmGhgazpU9iPsrKyjBlyhSMGzcOYWFhuHfvHlasWNF4aLopodFoWLhwITIzMzF06FC8/PLLGDVqFHJyckyeNkn3RhdFBECXm0w1+0REWVkZbty40Xg+RFOSkpJw9epVk8WxffhFyWQylJSUmMS2VCpFaWmpSWxLJBKUlZUZxXZr7Nq1C35+fhg+fHiL12fOnImKigr8/fffJtVhTezcuROBgYEYPHhwi9dnzpyJ0tJSnD171szKzE9TJzwkJAQUCsWoExEUCgUhISEIDQ01qu3OkJubi8OHD+Ptt99uM373/PnzodFosG3bNjOqIyEhISGxVg4fPozo6Gjs2LEDX331Fa5cuYJevXqZXcdTTz2F1NRULFmyBKtXr0bPnj1x4cIFs+sgIXmckclkWL16NaKionDt2jX8+uuvOHPmDGJiYiyih81m49NPP0VaWhoSEhIwefJkjBo1itw51Qo8Hg8BAQFgsVhWt2DKGtm1axeqqqrw7rvvtnnfiy++CF9fX3z55ZdmUqZ9b8HBwbCxsWl8l2SYsu6FWq3G999/j8jISPz333/Yt28fTp48idDQULNr8fb2xv79+3H8+HFkZ2cjNjYWq1evJheFkHQY3XlFADkR0S4nTpwAlUptcUXx2LFjG+8xBbrDdQE0/r+xnKymhcDYtnk83iO6TVnIpFIpfv31V8yYMaPVAddevXohNjb2sTmgWSwW4/fff8fMmTNbzZN+/fohMjLysciTrKws+Pv7g81mg81mIyAgwKjfks7BZ7PZ8Pf3t4rO0DfffANXV1dMnz69zfvc3d0xY8YMfP/996RjQUJCQvIYU1paimeffRaTJk1Cjx49kJaWhsWLF4NOp1tME5vNxueff46bN2+Cy+U2HtRYX19vMU0kJI8Lx48fR2xsLFatWoVFixYhMzMTzz//vEnDMOlLSEgIjh07hmPHjiE3NxdxcXH44IMPIBKJLC3Nqmja59ctmLKGfoo1olarsWHDBkyePBlhYWFt3stgMLBkyRLs27cPRUVFZtHXdGxIp498l92HW7duoX///njzzTfx0ksvITMzEy+++KLF69uxY8ciLS0Nb7/9NlatWoUePXrg33//tagmkq6HVCpFUVFRs3HikpKSLhNi0ewTEceOHcOAAQPg4uLyyDV3d3c88cQTOHbsmNHTbXoALgAEBATAxsbGKAP6D9sODAwEnU43qW1TNpJHjhyBQCBoMSyTDgqFglmzZuHIkSOPxaE7f/zxB4RCIWbMmNHqPbo8+eOPP7r9ts6mZRIw7gxs04k3Y9vuKHV1dfi///s/vPHGG2Cz2e3e//bbb6OiogK//vqrGdSRkJCQkFgTarUaP/zwA6KionD58mXs378fx44dQ0BAgKWlNRIfH4+LFy9i06ZN+OWXXxAVFYX9+/eTIVlISExAfn4+nnnmGSQlJSEoKAipqalYv379I+fwWQNJSUlIS0vDihUr8O2335J1w0M07afoFkxZup9irRw8eBC5ublYunSpXve/9tprsLe3x1dffWViZVqa9me5XC68vLzId9kNEAqFWLx4Mfr27Qu5XI5Lly5h69atcHJysrS0RjgcDr744gvcvn0bzs7OGDZsGGbNmkWGeSPRG11or6Y7IoCus6vLrBMRUqkUp0+fbjEsk47x48fj1KlTRl9JXFlZCaFQ2PiC6HQ6QkJCjNLYVFRUQCQSmcR2WVkZxGJxo20bGxsEBwebtJHcuXMnBgwY0O7KhZdffhkqlQq//fabybRYC7t27cKQIUMQHBzc5n3Tpk2DXC7H77//biZllsHUExGmst1Rtm3bBrVajTfeeEOv+6OiojB27Fhs3LiR7LiRkJCQPEakpqZi4MCBWLhwIV566SVkZGRgypQpFl+B1xI0Gg0LFixARkYGEhMT8fzzz2P8+PEoLCy0tDQSkm6BVCrFp59+iujoaNy6dQu///47Tp06hcjISEtLaxMWi4WVK1ciPT0dvXv3xvPPP4+RI0ciIyPD0tIsilqtRk5OjtX1U6wRgiCwdu1aPPXUU+jdu7dez9jZ2WHRokX48ccfTX7WnkKhQH5+PvkuuxEEQeDQoUOIiorCjz/+iHXr1uHmzZtITEy0tLRWiY2Nxfnz5/Hjjz/i6NGjiIiIwP/93/+R4wck7dL0zNam/99V6jCzTkT8888/kEgkGD9+fKv3JCUlQSQSGX170sMvSvdvY7yopofrmsq2uRrJ0tJS/P333y0eyPwwnp6eGD16dLcPRVRcXIwzZ87olSc+Pj4YOXJkt86T1pzwnJycTh/UbUrbHUWhUOD777/H9OnT4e7urvdzS5YsQWpqKk6fPm1CdSQkJCQk1oBUKsWHH36IXr16QSAQ4Pz589i6dSscHR0tLa1dfHx8cOjQIfzxxx+4c+cOYmJi8PXXX0OlUllaGglJl+XYsWOIiYnBZ599hsWLFyMzMxOTJ0+2yknJ1ggKCsLhw4dx4sQJFBYWIj4+Hu+99x6EQqGlpVmEoqIiKBQKcvBaD06fPo3bt2/rvRtCx6JFi0AQBDZt2mQiZVry8vKg0WjId9lNKCwsxIQJE/Dcc8+hV69eSE9Px7vvvgsbGxtLS2sXKpWK1157DZmZmRg3bhxeffVVDB06FOnp6ZaWRmLFZGVlwdHREa6urgAAZ2dnuLi4dJk6zKwTEcnJyQgKCkJUVFSr98TFxcHf39/o4Zl4PB6oVCpCQkIafzNWY5OVlQUqldpstbwxJyJoNBqCgoKMbrsl9u7dCwaDgalTp+p1/8yZM3H16lVkZmaaRI81sGfPHrDZbEyePFmv+2fOnImLFy92mW1RhlJcXAy5XN7McYuIiIBCoej0KsrWHHy5XI7i4uJO2e4ov/32G8rKyvDOO+8Y9Nzw4cPRo0cPbNy40UTKSEhISEisgdOnTyMuLg4bN27EypUrcfv2bQwaNMjSsgxm4sSJSE9PxyuvvIIlS5agf//+uHXrlqVlkZB0KXJzc5GUlIQJEyYgLCwMqamp+OKLL2Bra2tpaR1mzJgxSE1NxSeffIIffvgBkZGR+OWXXx67VbstLRCMiIhAdna2xRZMWStr165F79698eSTTxr0nKurK1577TV89913Jo113tZiz8etXHdllEolvvzyS0RHR+P27ds4dOgQjhw5YlWhMPXF3d0du3fvxpkzZ1BZWYkePXpg+fLlkEqllpZGYoXowgQ2XdzQlSZTzTYRQRAEkpOTkZSU1OZKEAqFgqSkJCQnJxu1EdAdgMtkMht/i4iIQEFBQafDQPF4PAQGBoLBYDSznZ+f32nbWVlZCAoKesR2Xl4eFApFp2w/DEEQ2LlzJyZOnAgHBwe9npkwYQKcnJywa9cuo2qxFnR58uyzz+odx1WXf7t37zaxOsugO5/kYccN6PxWsJZsG/vwd0MgCAIbN27EmDFj2pxAbQkKhYIlS5bg5MmTSEtLM5FCEhISEhJLUV1djRkzZmDkyJHw9fVFSkoKPvroo2a+ZleDy+Xi+++/x+XLl6FQKNC3b1+8++67XebwOxISSyGRSLBy5UrExMQgNTUVhw4dwl9//dVsx3xXhsViYfny5cjIyED//v3x0ksvYcSIEY+Vj5uVlQUmkwk/P7/G38LDw6FQKMx2wHJX4MaNGzh79iyWLl3aoR1AS5YsQUNDA7Zv324CdVqysrJgZ2cHLy+vxt8iIiIgEolQXl5usnRJjMfly5fRu3dvfPDBB5gzZw4yMjIwadKkLrXrrCVGjBiBu3fvYvny5diwYQNiY2Nx8uRJS8sisTJ4PN4jofQjIiIsMmbWEcw2EZGSkoLi4uI2wzLpSEpKQkFBgVG3Iz18AC6gdRw0Gg1yc3NNZjsvL89qbT/M9evXkZmZiVdeeUXvZ5hMJl544QXs2bOnW64EuXLlCrKzsw3KEzabjalTp2L37t3QaDQmVGcZeDweGAxGs5UGfn5+YDKZnZ6I4PF4YDKZ8Pf3b/zN398fDAbDIrO7Z8+exd27d7FkyZIOPT916lR4e3ub7dA1EhISEhLTQxAEdu3ahaioKCQnJ2P79u34559/us2AIwA88cQTuHnzJj777DP88MMPiImJwYkTJywti4TE6iAIAocPH0Z0dDTWrVuHd999t9sMiLVEQEAADh48iJMnT6KsrAwJCQlYsmQJBAKBpaWZHN3AD41Ga/ytq8XlNgfr1q1DaGgonn322Q49HxAQgBdeeAEbN26EUqk0sjotujMJH15NrLtGYr3U1dVh3rx5GDhwIJhMJq5fv45vvvlG70WjXQEWi4WPP/4YqampCAwMxOjRo/Hiiy+ioqLC0tJIrITWxom7yq4us01EHDt2DHZ2dhg6dGi79w4fPhwcDseo4ZkePgAXMF5j01VtP8zOnTvh4+Nj8BbKWbNmobS0FGfOnDGqHmtg586d8Pf3x7Bhwwx6btasWSgqKsK5c+dMosuS8Hg8hIaGNnPCaTQaQkNDjVLew8LCQKU+qJqMZbsjbNy4EQkJCRgxYkSHnmcwGHjzzTexd+9e0nEgISEh6QZkZ2fjqaeewqxZszB69GhkZmZi9uzZ3XLA0cbGBh988AHu3buH8PBwjBs3Di+88ALZnpGQ3Cc7Oxtjx47FpEmTEB0djXv37mHNmjXgcDiWlmZynn76aaSkpGDNmjXYunUrIiIisHfv3i4xANJRWuqXW3LBlDXC4/Fw8OBBvPfee836ioaydOlSFBUV4ddffzWiuge09C6DgoJAo9HId2mlEASBffv2ITIyEvv27cO3336LK1euoFevXpaWZjLCw8Nx+vTpxpBNkZGR2LJlS7dc7EqiPzU1NaipqWlxnLi+vh58Pt9CyvTHbBMRycnJGDVqVLMQQ63BYrEwcuRIJCcnGyVtlUqF3NzcR16Uh4cH7O3tO9XYKJVK5ObmPjIb5enpCTs7u07bzsvLe0S3l5dXp20/jEwmw6+//orp06cb7DT07dsXkZGR3S48k1QqxW+//YYZM2Y0GxjXh8TERISFhXW7PAFadtwA7VYwU0y8AZaJd5eeno4///wT77zzTqcGmObOnQsbGxts3rzZiOpISEhISMyJQqHA559/jri4OOTn5+Ovv/7C3r174e7ubmlpJickJAQnT57Enj17cObMGURFReGnn34iO8Ikjy1isRjLly9HbGwsMjMzcfjwYRw/fvyRMAndHSaTiQ8++ACZmZkYPHgwpk+fjqFDhyIlJcXS0kxCS/0USy6YskY2bNgAd3d3zJgxo1N24uLiMHbsWKxbt84kbU1L75LBYCAoKIh8l1ZITk4ORo0ahZdffhlDhgxBRkYGFi1a1KnJrq4ChULB9OnTkZmZiSlTpuCNN97AgAEDcPfuXUtLI7EQujqqpR0RTa9bM2aZiKisrMS1a9f0CsukY/z48bh8+bJRZnMKCgqgVCofeVEUCqXTcbQKCgqgUqkeaciMYTsvLw9qtbpF3cYemD127Bjq6uowc+ZMg5+lUCiYNWsWDh06hIaGBqNpsjRHjhxBQ0NDhxwpCoWCmTNn4sCBAxAKhSZQZzmysrJaDD8RHh7e6Zh0rdm2RLy7r7/+Gt7e3njhhRc6ZcfJyQmzZ8/G5s2bIZFIjKSOhISEhMRc6OIQr1y5Em+99Rbu3buHUaNGWVqWWaFQKJg2bRoyMjIwceJEzJkzB8OGDUNGRoalpZGQmA2CIHDw4EFERUVh48aN+OCDD5Ceno5nnnmmW+6K0hc/Pz/s378ff//9N6qrq9GrVy8sXry4W/ULpVIpioqKrKafYo2Ul5dj165dePvtt8FisTpt74MPPkBaWprRwwIKBAJUVFSQ77ILIJfLsXr1asTGxiI7OxvHjx/H77//Dh8fH0tLMzvOzs748ccfcf78eYhEIvTu3RvvvvsuRCKRpaWRmBndOHBoaGiz30NDQ0GhULpEHWaWiQhd4zFmzBi9nxk7diw0Gg3+/PPPTqeve1GmWGndVW0/zK5du/DEE08gMjKyQ89PmzYNCoUCv//+u9E0WZqdO3di4MCBHV7dNH36dEilUhw8eNDIyiyHzglvrUwWFRVBKpVanW1DqaysxJ49e7Bo0SK9dnG1x+LFi1FXV9dtDzAnISEh6Y40NDTgjTfewMCBA8Fms3Hjxg2sW7fusQi70hqurq7YsWMHzpw5g/LyciQkJOCTTz6BXC63tDQSEpOSlZWFUaNGYfLkyUhISEBaWho+/fRTsNlsS0uzGp566incvXsXX3zxBX766SeEh4dj165d3WL3VE5ODgiCsJqd29bIN998AxaLhXnz5hnF3qBBg5CYmIh169YZxZ4Oc46xkHScc+fOISEhAatWrcLixYuRlpaGsWPHWlqWxRk0aBBu3bqFzz77DJs3b0Z0dDSOHDliaVkkZoTH48HX1xe2trbNfmez2fD39+8SdZhZJiKSk5PRv39/g7ave3l5oW/fvkY5J4LH44HFYsHX1/eRa51tbLKyssDhcFqclTXGRASHw4G3t7fRbTeloqICf/31F2bNmtVhGz4+Phg5ciR27txpFE2WprS0FH///Xen8sTf3x8jRozoNnkCALm5ua064bpVJTk5OR2yrXuuNaeQIIhOHyyvL1u2bAGNRsPcuXONYi84OBiTJk3C119/3S06YyQkJCTdmaarnvfs2YNvvvkGly9fRo8ePSwtzWoYMWIEUlJS8P777+Ozzz5DQkIC/v33X0vLIiExOiKRCB988AHi4uKQm5uLY8eO4dixYwgJCbG0NKuEwWDgvffeQ1ZWFkaMGIFZs2Zh8ODBuHPnjqWldYr2Bq/NuWDKGqmvr8eWLVswf/58ODg4GMUmhULB0qVLceHCBVy8eNEoNoEH77KlxYbh4eHIy8sz2SHZJO3D5/Mxa9YsDB8+HC4uLrh9+zbWrl37WC8CeRgGg4GlS5ciLS0NcXFxmDhxIiZOnIji4mJLSyMxAy0dVK2jq0ymmnwiQi6X49SpU0hKSjL42aSkJJw8eRIKhaJTGlo6AFdHeHg4qqqqUF9fbxLblZWVHd6Wqotd2NJW3/DwcJSXl0MgEHTIdlN+/vln0Ol0PP/8852yM2vWLFy8eLHDA9HWxN69e8FgMDBlypRO2Zk1axb+/fdf5OfnG0mZZWnPCW96jzXZNgSpVIoffvgBs2fPhrOzs9HsvvPOO+DxeDh+/LjRbJKQkJCQGJfi4mJMnDgRkydPRt++fZGeno4333zzsYhDbChsNhtr1qzB7du34ezsjGHDhmHOnDmoq6uztDQSkk5DEAT279+PqKgofPvtt1ixYgXS0tI61Kd9HPHx8cEvv/yCs2fPor6+Hr1798aiRYs63Oe2NDweD05OTnBxcXnkmrkXTFkjW7duhVwux1tvvWVUu+PHj0dUVJRRd0XweDx4eHi0OGESHh4OlUqFgoICo6VHoh8EQeD//u//EBERgaNHj+J///sfzp8/j9jYWEtLs1qCgoKQnJyM33//HdeuXUNUVBS+/vprqFQqS0sjMSGtnasKkBMRjfz7778QiUQGnQ+hY/z48RAIBDh//nynNLT3ogAgOzv7sbKtgyAI7Ny5E8888wycnJw6ZeuZZ56Bg4NDlz+gWZcnzz77bKdXdEyaNAl2dnbdJiQPj8eDg4MD3NzcHrnm4uICJyenTk1EtObgu7m5wcHBwSyV6p49e1BTU2N0R3rAgAHo378/Nm7caFS7JCQkJCSdR61W47vvvkN0dDSuX7+OgwcP4vDhw/Dz87O0NKsnNjYWFy5cwJYtW7B//35ERkbi119/BUEQlpZGQtIhMjIyMHLkSDz//PPo3bs30tPTsXLlSqPEvX/cGD58OO7cuYMvv/wSu3btQnh4OHbs2NHldgi3t0BQd8/jiEwmwzfffINZs2bBy8vLqLapVCref/99HDt2DPfu3TOKTX3GWB7Xd2kp0tPTMXToULz66qsYO3YsMjMzMWfOnBYX+5I0h0KhYPLkycjIyMArr7yCJUuWoG/fvrh27ZqlpZGYAI1Gg+zs7DbrsJycHKjVajMrMwyTf9nJycnw9/fv0Exmjx494OPjg+Tk5E5paO0AXOBBY9PRAz30acg6alsf3Z1tJG/fvo179+516JDqh2Gz2Xj++eexe/fuLudcNuX69evIzMw0Sp7Y2tpi6tSp3SY+qq5MtuSE6w5R72x5b822OQ4P02g0+PrrrzFx4sRHDv8xBkuWLMG///6LmzdvGt02CQkJCUnHuHv3LhITE7F48WJMnz4dGRkZePbZZx/rw2cNhUqlYt68ecjIyMCQIUPw4osvYuzYsd1mRyjJ44FQKMR7772H+Ph4FBYW4sSJEzh8+DCCgoIsLa1LY2Njg3feeQdZWVl4+umnMXv2bAwcOBC3bt2ytDS9aatf7ubmBkdHxy5xQKgp2LVrF6qqqvDuu++axP5LL70EX19ffPnll0ax19a79PHxAYfDeWzfpbmRSqVYvnw5evTogYqKCpw+fRp79uwxKKQ7iRYHBwd8//33uHr1KigUCvr374+FCxd2ODoLiXVSUlICqVTa6hh0REQE5HI5ioqKzKzMMEw6EUEQBI4dO4bx48d3qDNHoVCQlJSEY8eOdXhVlVgsRklJSasvyt7eHl5eXh0a0BeJRCgtLW21IeNyufD09OyQbaFQiPLy8lZ1Ozg4wMPDo9MTETt37oSnpyeefvrpTtnRMXPmTBQVFeHcuXNGsWcJdu7cCR8fHzz55JNGsTdz5kzk5+fjwoULRrFnSdqaeAO0FV9ndkS0Zdsc28z+/PNPZGZmYsmSJSaxP3HiRAQGBuKrr74yiX0SEhISEv2RSCRYunQpevfuDYlEggsXLmDz5s1Gi2/9OOLt7Y3ff/8dR44cwb179xAbG4sNGzaQYQJIrBqCIPDrr78iMjISP/zwAz755BOkpqZizJgxlpbWrfDy8sLevXtx7tw5iEQi9OnTB2+88QZqa2stLa1d2uqn6BZjPY6r6NVqNb788ktMnjy5xTMXjAGDwcA777yDffv2dXpwjSAI8l1aCSdPnmz0ET788EOkpKQYbfzlcUa3G+Krr77Crl27EBUVhf3795O7VLsJurrJ1AvWTY1JJyLS0tJQUFDQqViaSUlJyM3NRWZmZoeeb+sAXB0dbWx02wNNYVsXcsmUA7MKhQL79u3D9OnTQafTO2ynKYmJiQgLC+uyBzTLZDL8+uuvmDFjhtHiQQ8aNAjBwcFdNk+a0t5kQWhoKDIyMjrU0FnDRMTGjRvRr18/DBgwwCT26XQ6Fi9ejN9++408TIqEhITEgug6wN9++y1WrVqFW7dumazufxyZMGEC0tPTMWfOHLz//vvo168fbty4YWlZJCSPkJaWhieffBIvvvgi+vfvj4yMDCxfvpwMw2RChg4dilu3buGrr77Czz//jPDwcPz0009Wu3u8pqYGNTU1bfZTQkJCjBY6qCtx8OBB5ObmYunSpSZNZ86cObC3t+/0Yq6KigqIRCKL9zkfZyoqKvDiiy9i9OjRCAgIQEpKCj755BOyzjUiujGHjIwM9O/fH88//zzGjh2LvLw8S0sj6SS3b98GnU5HQEBAi9f9/f3BYDCsvg4z6UTEuHHjAAC9e/fusI0+ffoAAMaOHduh5w8dOgQArb4oQBs+5++//zY4jtann34KQLszwti2Dx48CKB93adOneqw0zZlyhTU1NQYdbUPhUJBQkIC9uzZ0yV3ADz77LOoq6vrcHlrCSqViri4OOzYsQNXr141ml1zk5qaCj6f3+YBzrdv30Z9fT2OHTtmkO27d++ipqamxfMhdLi4uIDP5yM1NdUg2/qycuVK/PPPP3jllVdMGo5j9uzZAID4+HiTpUFCQkJC0jJVVVV4+eWXMXr0aAQGBiI1NRUffvghGAyGpaV1O+zt7fHNN9/g6tWr0Gg0eOKJJ/D222+36TeTkJgLgUCAJUuWICEhAaWlpfjrr79w8ODBNvteJMbDxsYGixcvRlZWFsaOHYs5c+YgMTHRKicsT5w4AQBtnn9w4cIF3LhxAxUVFeaSZXHq6urw/PPPIyoqqlPjPfpgZ2eHKVOm4Ntvv218Hx1B10f18fFpM60rV65ALBZ3OB2SR1Gr1di8eTMiIyNx+vRp7N69G2fOnGl1ZTdJ5/H19cWhQ4dw9OhRpKenIyYmBl988QUUCoWlpZF0kJUrV0KlUrU6XkWj0eDk5IRff/3VzMoMw6QTEQMHDkRcXBxcXV07bMPd3R0xMTEYOHBgh54/fPgwALR50E12djZqa2shEAgMsj137lz06NEDgwcPbtN2TU0NhEKhQbaPHDnS7j3Z2dng8/kG29bh6OgIe3t79O3bt0PPt8bUqVNBp9Ot/oCUlnBycgKXy0WvXr2MaleXJ9a60kcfTp8+DQCor69v9Z4VK1YgPDwcgwYN6pDttmIY6q79/fffBtnWF10c69GjR5vEvg5dOLiuXBZISEhIuhoCgQA//fQTIiMj8ddff2Hnzp04c+aMyUJJkDygb9++uH79OtauXYtt27YhOjoaBw4cgEwms7Q0kscQpVKJn3/+GREREdi6dSvWrFmDlJQUjBo1ytLSHks8PT2xe/dunD9/HjKZDP369cPrr78OPp9vaWmN/PHHHwDQ5m75L7/8Ej179oSHh4e5ZFkc3Tvq37+/WdKbOHEiAO0Cto6iGxtqKxpEXl4epFIpuXvdSBAEgcuXL2PAgAFYsGABpkyZgqysLEyfPp08i8tMjB8/HmlpaVi4cCE++ugj9OzZE2fOnCHDZnZBFi1ahMmTJ7fZHlVWVuLSpUtmVGU4FKKbBwvLzc3FlStX8PLLL7d6T2VlJY4cOYK5c+caPf3y8nIkJydjzpw5Bj2XnZ2Na9eutam7vLwcx48fx2uvvdZZmSQk7aJSqfDtt9/i7bffbnNiryMolUp89913bdrWHST91ltvGS2UWFMIgoBKpYKNjY3RbbeUllKpJFfgkpCQkJgBhUIBJpMJAJg+fTo2btwINzc3C6t6PMnPz8f8+fNx8uRJ2NnZdXgxDQlJR8jNzUVoaCgA7c7wjRs3ws/Pz8KqSHSoVCps2bIFK1asgEAgwNSpU/Hbb79ZWhb4fD4OHDiAefPmWVqK1SGRSMDhcMyaHpvN7vAAdkVFBY4ePdrmuI9AIMCuXbuwaNGijsokaULfvn1x48YNREdHY9u2bQYvWCQxLnfv3sW8efNw5coVeHl5oayszNKSSIzM5cuX0dDQYPIFtp2h209EkJCQkJCQkJCQPL5IJBL06NEDffr0wb59+ywt57GHIAgMHz4cEokE165ds7QckseIS5cuYeTIkZg1axZ++OEHS8shaYW8vDyEhoaiZ8+euHnzpqXlkJCQdIKxY8eCx+Ph9u3bsLe3t7QcEmgXePbs2RMsFqtLhw4n6bqQExEkJCQkJCQkJCQkJCQkJCQkJCQkJCQkJCajU/FNioqKDI7f6OrqCn9/f6PbLS8vR319PRwdHds8SEqHXC5v3KZvzHtNaVt31oa+eWOobn3eTVuYqjwYE0M1Pg550hL66i4vLwfQ9uFtD2PK76O1fLOW99ARHabSQkJCQtJVIOvOxwPyPZO0RkfLhg6yjHQPTF1HGGK/I32grlQOraXv1Bk9luhzdjfIdvnxgnzf1oGh78HQsXDA8u+swxMRRUVFiIiMgkwqMeg5FpuDrMyMNgcMO2IXFCpA6H/4K5UCaPTdC2IltplMFqgUAlKZ3Oi2gfbfTVsUFRUhKioKEolh743D4SAjo2NpGkpHNHZGX1FREaIiIyCRGnYYI4fNQkZmlsUqBoN0G1jGDH3GkG+ptXyzlvfQUR2m0EJCQkLSVeiofwGY18cg6RxkG0nSGp0pGzrIMtL10ZaDSEikUoOf5bDZyMjMbPP9G1rOKFQqCI1hfSA2h4PMLtAmmWqcpzN6OvTuzdzn7G4UFRUhMioK0g74X12lrJM8QOtvR0Ii6UAdy2EjI6PtOpZEPzpS3xk0/nwfS9djHZ6I4PP5kEkliHp9E9hu/qDatH/oqrgsBxnbFoLP57f6B+vshs75HmyvMGhUClDpbduWlmcj58dF8Bw5B3QOFwxnH8iq8sF08QPT2QvymjIwXX2hEjdAJaqFSipAyR/rMSfRE1wmHT4ODOTXyuDnyIQXl4myBjl8HZlokKlwNrseB+7yEfzKV7D1i273b6xLPWuwbX10i4tSUf7XFgAwyHbIK1/Bzr993ZKybGT/uKjNd9MWfD4fEokE23fuQWBQsF4rCrIyM/DqrOkdTrOjGtdt3gnfwCAwGG1rzONlYukbszqXJ1IZNk2Ngr8TGwx6+wc851SLsXB/htnypCV0uqf1cUcvH7tWy9i9MjE2XypH5NxNYOlZB9Sm/IOCQ+vgPnQa7IJ7Ge07TS0XY8vF8hbzTff3fP9cKAIcmXq9h2y+FIsO5hj1PTSWhxfiEODM0UsHAGRXibDw11SLlgkSEhISS9HoX3z2LgJ9PcFk2Oj1XFZeMV5dvoGsO7sID3ymGPg7s/RuI3OqJFi4P418z92YB35cGMLcOFCoNHqXDwDIrpZg0cFssox0cbTlQIofpvdBmIc9FCo1GHRau89lVwqxYM+Ndt+/rpzp0+/Q9YHe+OInuPsGwqadPiUAlOZlYfOy17pEOdSNx8S/sQkctwC9+niismykbG57nKczeiRSKba+MRaB7g5g2LT/3k/fycfnv180a5+zu8Hn8yGVSDBr1Y9w9QkAXY9yDgAV+TzsXDnnscij7oTW35Zi21vPItDDEQwb/YaKeSV8vP7tIfJ9GwlD6zteaQ3mbf5Tr/qrVqxCH397k4x1GYr+XlwrKATVkFTmQaOUQ5B/FyqZGDZcV6ikQjAcPUCh0qAUN0BSmQ9CrdDbLtsrDIraMsgqckEoZRDn34FaJoKNvQvUUgEYDu6gUKlQCvhQNvAbn+FGJMKG4wBb3ygQShmoDDYcogaC4x0Ghr0zbP1jwXL1AwCEubKRGMSFA9sGUZ62kKkIsBlUDAx2QJg7B862DAwNcdT+nbWl7eiohrKhClCr9bLt7cBstO2W+By4EYlgufnDPrAH6Gw7yCrzwfYKAcstANywPmA4eDTmjT62vbmMRt0apQyiJrpVkvu6Kdr8I9QqaFR67rJoh6qqSuRk8yCTyXDzxnUIhUK4ubmjoaEBnp5eoNFoKCstRUZ6GmSyjq9s6gw11VUoyMmGXCbDvds3IBYJ4ezqBqGgAW4enqBSaagsL0VtTbVR0qsWKpBXI4FcpcHdEgHEchVc7WwglKngYc8AjUpBg1SJsgYZpEoDdxeYkHgvW/g7s9DDxx52LDrya2UIcWMjwJmFPv5cuOnKmKAa0vt1gDD/LtQyMRgP1QEapQzSynxo1EoAgG1AfJvl3ZDv1M+JhWdiXdv9e/giJXJrZZCpCNwpFUOkUMPF1gYCuRru9gxQqRRUi5VokKkR5so2Wb5WCxXI44shV6lxp6QBIrkKrnYMCGVKeHCZoFEBvliOsgYZCBAIc7czmRYSEhKSrkJVbT1yCkshlytwKy0bIrEU7s6OEIjE8HR1Bo1KRXlVDUorqqFSqRAR7GdpySQdoFokRx5f2sRnUt9vI1XwsGfe95lUUKkJUCgUhLpzLC2ZxEyEuXFQ1iBHbo0MMpUGd0pFEMnv+3IyVaMvxxcrkVcjRb1UhThvO4S5kWWkOxHmYY+yeilyq8WQqzS4U1Sn9aXtmRDIVPDgskCjUlDRIEO8nxPCPAw7HLe9fgfLhoZAFxYAoKGmChWFOVAqZMi7dwsysQhcZzdIRAI4unmCSqOhtqoMFUW5UMot0+/tDIqGaogrtH28hrw7UMlE2j6eRAjm/T6eQsCHpCIfGoVxxhLaorpBjJyKOsiVKtzOq4BIpoArlwOBRA4PR1vQqBQo1Roo1RoA2uXB7fU5OV6hevc5uSw6POzan5Tpbghqq1BVlAulXI7CdG05t3dyg1QkgIOrduykvqocgpoqqFUqeAaFW1oySSeoqhchp7wWMqUKt3PKtN+Zg632O3OyB41KRW55LUprBBDLFAj3bX8chsRw9K3vpAoVgPbrLzaDBh8nJigUiknHuvSlU2dEAIBTZCJUMjE0ahUYXFcoGqogLkqDrX8M1HIJ6LaOUAhrQbVhguMRpLddUUEK2J7BAEFAXlsGjUo7iaFRysFyD4SsugiERgUqgwUaSztQZxcYD7uAuFZtMp19AADVNcUAgHhvO8R5tz7I5+PAxKG72sFoGwcPaNQq2HDdoGyogqQ4HRy/6Ma/USWshUoqBKFR6227uO7BQLdDRGKb+cGN6N/4b/1sa50BhqMHiPu6te8mHbb+93XbOUItl0BamQ9CpWwzfX0ZPGQYhEIhlEol3N09UFlZgdSUu4iLT4BYLIaTkzMK8vNhw2AgKjrGKGkaCsfODj7+AajlV8HewRFZaSkAAE9vX6hUKtTX8kGhUNAncbBR0rNl0uHnyAJfrACXTUdahRgA4O3IhEpDoFasAJ1KgbcDC94OLKOkaQzaK2eJAVwAAI1lC5arH5QCPugcLkTFaQAAlrO3dpJL2bwsAvp9q4Z8p6llonb/HlsGFX6OTNSIleCyaUiv0G4z9eYyoNIQkCjUqJeq4MVloLjOdJ2FASHOEMlVUKoJuNkxUC2UI71ciGgve0gUKjhybJBTJQZTj9U+JCQkJI8Ldhw2Arw9UF3bAAd7W6Ty8gEAvh5uUKvVEEtksLflgEqlgMVkQnV/YQhJ1yIx2AliuRoqNQFXOyaqRAqklYsQ42UHiUINR7YN6iRKFNfL4GnPAJtBtpWPE1wWHVwWXevLsWhIr7zvUztofWqJXA0KgCAXNuhUCvJqDA8xQWL9cNk24LJtwBfKwWUzkF7aAADwcWRDpSFQVCOBSK7CzYJa2NAoBtk2pN/BtrWDm3cAGmqqweE6oJCXCgBw8fSBRq2CTKK9zysgFARhYNwMK4DGsgPbzRcKAR82HAcIC9MBACwXbxAaFSRVBbCxcwKTyQGHafoJv4HRfhDJFFCqNXDjclBVL8a9wmrEBrhBIlfCyY6FzJIa2LEY8HdzAKBnn/PKIQD6vXtXW/12ZXYnwnsPgkwsglqlhL2zOwQ1lSjNvgefsFjIpWJwuE5QKuSoKSsE19nN0nJJOsmg2ECIZAqo1Bq4Odqisk6EewUViA30hESugKM9G/7ujiisqoObg62l5XZb7NgM+LtxUd0ggQOHiXuF2rFjHxd7qDUalPCF0BAEbO7vDNSn/tKhz9iZqen0RASgnYxoC5aLdgJAWJCit019JxUANE5EiApSIMy+BrZnCDRKOdje4aDaMKCWikBoVNAoZCA0GsjrKgAAKWUiXCsSIsSVDblKg3A3bfgciUINtg0NQrmq0YHVV49GKWvTdlmDHBHuHJQ2yBttt6dbJa6HUljbmNbhVD5qJKpH7NKoFIS6snGnVIRygVxv3dywvhAVprb+Mgxk8JChbV739dOuOrhz+5bR0jSEmIReiI7v2ep1Lx+tvvSU20ZJL97HHvE+ra/G8XF8MPmQUio0SprGoLVy5sVlQihXNU6o2AfGwz4wvlU7uu8fACovHQQA8K8ehkpY0/J3qpSDxnzQqLX2LRXVyeDvyIKgiZa20Kdy7nt/Z5qpK+cBIc5tXvfp8WCWOqVEYFItJCQkJF2BntGh6BkV2up1X8/mnd/bGTmmlkRiIhKDndq83txvItvIx4nEIIc2rzftaOuwhg43iXEZENr2YKeP04NB8ZTiOoNst9bvEMnVECvUCHVl41aJtr8WFN0TQdE9WrXl4unb+O/89DsG6bAGHILi4RDUeh+P3aSP15Cv/zhPZxgY1fZuRx8X7UK5omrt5FSbfU6FrHEcCWi57yuQqSCSq+HFZSKvRooGqcp0f5wVE957UJvXnT19ERTbBwBQlHnHDIpITMnAmMA2r/u6OqBPuLZ+u5tbZgZFjx8JQR5ICPJo9bqurrubXwmg7fFnAKBRtTshbhQLIVFYfrFWpycihAUpaOBdA9srFBqlDLY+EaDSGZDVlMLONxLismwoBNXgeIYYZLe1wXl5TRk4PhGQVuRAIxNDLZdAVlUA4MGguyj/DhR1FVDUV8DGzhnS8mxwIwdAJaoF2zMU4qJ7ALQDk2oCqBAo4MCmQaEmUCaQwYFFh1KtQrSnLa7kazs4LTViOi2yyjwo6iuhUcoaJznasl1SL29muz3dVBsmNE12LEyMc220XSFUwJljg0qhEn397VFSL8eQEEfkVmsnOaqvHobyvm7Ow3lYngOVVAA6mwt5XXmnyoGO27du4tLFCwgPj4BMJkNUdAyYTCbqG+ohlUjg4eGJrMwMKJVKKBT6h+oyJif++A21/Goo5DKERESDwWSivKQYdDodQWERyM5Ig0Ihh6DeMMe1NVJKhbhW2IBQVzZkKg0iPGzBoGknvPgiBcoFCoyMdMFZXg1oFMNW7ZiS1sqZvxMLBAF4crWdPWFBChqyr4Hj+aAOoNAZUMslUAr4YLn5QVR4D6BQISrWrqRxfWIioFG3+p3SWA8mbtr6lhpk2u+0UtB+WWptYoXDoCGtQgxHNh2JAVzcKRNDKDOdk5lS0oBrBXUIcbOFXFce6FQU1khBpVJQVCPB6Bh3XMmvA41KgcyKwnWRkJCQWIrf//oX/LoGyOUKRIUEgMmwgUAkgVgqRSW/DkP6JuDCzVS4OTugurYBsJ7mlMQAUkoFuFbQgFA3jtZncte2kaX1MtCpFBTXyzA83AV3SwRg0qmoFpk+HAiJdaDtZAsQ4nK/k+2uPW+rrF4OlYaAQKaCM8cGdVIVEgO5yKyUgEalmHSXK4n5SSmuw7W8GoS420OuUiPCkwsGnYryeikUag3UagI9ApyQXSFERYMUhu5D0C1culMqatYHyq6WNva1e/lq+yn56beRdfsyvALDoJTL4BsaBRsGE2JBA3Lv3UBc4ggUZNwFhUqFykL93s5QfvkwFMIaaBRy2PlGgGrDgIxfCkKjBtvVF4LCNFDp2h0CaoXpv7O7+ZW4klWKMC9nyJQqRPq6gGlDg0imBF8gQVyAO7LLamHHfhA+yZA+Z0t931y+DH397VEjVmJIiCPOZRtnfKArcePkAYjq+FAq5PAKjgTdhgm5VIzizLtgcmwR3nsQsm9dAo1uA5atHcSCxy+PuhMHL9wDXyCBXKlCpJ8bGDZ0lFQ3QK3RoEEsw6DYQKQXVqGsRgAO04Z0t03EoUuZqBFIm9V1JXwhaDQq1GoNovxckVHMR1qxdqdEa22Xbpy4RqyEPYuO4WFOVlGPdXoiQrcaWpB3B/K6csjrKsCwc4aioQqEdyhsbB3gENob8vpKKBT6b49tupJflH8H0tIs0GwdAIKAuCgVHN+oxokF3Wp+aXm29mEqDYz7M/QalQJMtwDIa0q1tgpToZJqJwCy+VKEubLh43A/1r1KAy6TBoIgoCa0K2gEcu2ApOsTExsnC1rSQmPbge0ZivIzOwy2rY9ulejBjoiWbAc4MVEl1Do4TW273dctzL8DSWkW6Pd1iwpTYesXBaWwFhyvUKjundP73bRFz1690aNnL9y8cR1lpaUoKyuFi6srsrOyMGTYcNTw+Rg5ajQA4PTfp4ySpqGMnfQ8ouN7IvX2DeRkpoHr6ASCIKBUKqBWqeDk4oKg0Ahc/Odvo6Sn2xFxp0SAcoEcFQ1yONsykF0lRt8ABziwbWDPouOZeA/8w6sxSprGoL1yVivWTo413REhyLsDcUkm6LaO97cgE6AxOeB4hWrLmVS7gkhang22V1ir5V0tl0LGL25Vx8PfUq0eq1Mmxrk2Vs5ZVVI4sGkgCKBeqsLQEMfGynlQEBfnc+uNk4ktEO/rgHhf7Qz2neIGZFaI4MC2AQFArdZgaLgrSupleDraHQRB4L9s6ykTJCQkJJZiyuihjTsibt7joayqBmqNBuGBvrC3k0MslcHPyx1x4UGQK5S4cjfdwopJOkK8DxfxPro2UoDMSu1CAYIgoFQD/QMdUVInxeBQZ20bmVPbjkWS7sLDO1vvlApRIVBAQxAIceVAQxDwd2bB9r4/1/d+CFGBCReXkJifeD8nxPtpd03dLqxFZrkADhwbEAQBAhSEe9qjuEaCXoH364isKoPsZ/O14xU0ClrtA2XfX/Cn2xGRe+8maivLUFdZDnsnF5QXZKPPiCQI62rQZ0QSAODuxdNG+fvNiVfiRDgExaM+9w5EJZmN4wggtKF5bb1DYeetDTvFv/efyfXoVgjfyq1Aea0Q5XUiuNizkVVagyfCfVDMF6B3qBcq6h7sgupsn/PhMRZ9+pzdjT6jJsM/sgcAoCDtJuqry0FoNPANj4OwrhpqlRoe/qHwCAyDUiFHXspVywom6RTPDYpFQog3AOBWdinKawXQaAiE+rhCoyEgkSlhz2bg2UGxkCtVuJZZZGHF3ZNnB0Q27ojQ1XkagkCYtzOqGySQyJWwYzPQO8QLgH5tl26HqDXUY52eiBCXabe+U6jUxhAsGpUcbDd/yPglAB6EZNLdqw+Ng/NAswF6HU0nFhQNVaDasJDz4yK97VMpwKKDHdDTjhaCUBtmm0LVWzeTyQKVQhikW1KWfT8ZGpht6G4a+qkzZGVmAABoNBr87p/ArpDLERQcjOKiQgAPQjLV8I1zGLSh5PEyGzV6+TY/Jb6sRFuRpqfcRl0N3yjp5VRrwwZRKRT43D8DQq7SwN+Zjcr7lYIuJFOdxDhndXQGV1dXcNgsvcuZpMl3TaFSHylnTesBlajOoDJvyLfEYbPg6tr6YUktVc46Hq6c+RLTVc7ZVQ+cYyqFAh/H5ocFVd4Pq6YLycQXdb0VVCQkJCTGJiuvuPHfVBoVvl7a0BxypRJcO1tU8LV+jC4kU3kVOUDdFcmpkjT+m0oFfBybh9qpFN5vI++HZKoRWd5vIjEdYrEYO3bcX+RVLWl2jUahNIZiUqg04LLoj/hzAFB737e+du0aevXqZQ7ZJCYku/JBGFsalQIfp4f96Pthku+HZOLruWvK0P4PAJTmZQEAqFQaXL20IYOUCjncfQPBL9O2WbqQTMK6rrewSNQ4jkBtFmoXAKT3+3e6kEwKgXH6zW3BK9W26zQqBb6u2olGuVKNQHdHVNZr+9u6MCW1QpnF+pzdhbt37wIAKvJ5jb9RaTQ43w85plLKwbbjorZCO36iC8nUUF1hXqEkRoVX8uBb1n5r2pCICqUKXA4TlXXaOlgXkqmilgx/aAp09R3waJ3H5TAb67zKejFYNjSD2i7A8vUYhejgyUlFRUWIiIyCTCpp/+YmsNgcZGVmwN/fv8XrHbWrg8Fk4dDBA/Dy8mrzPrlcDibz0TiiD1NeXo7nJk+BXKb/bg4mk4WDemgwRAeAxoLC57ff0HdEd3vvpi2KiooQFRUFicSw98bhcJCR0bE0DaUjGjujr6ioCFGREZBIDduqymGzkJGZZZY8aY2ioqJ2y1lHyhhg2u+jpTyzlvfQUR2m0EJCQkLSVbh79y769OkNlcrweKYcDhsZGZlk3dkFINtIkqbIZDJs27YNn3/+OWpra0EBoFR1fJEInUaFSq3B8OHD8dlnnyExse3zDUmsD20dEQmJ1PADyDlsNjIy228L9On/ANo+0OTJUyAzsA/E5nCQaaZ+b2cw1ThPZ/R05N2zmEwcOHjQbH3O7oJQKMSyZcvwww8/gEajQ602vO6lUKkYM3o0tm/fDk9PTxOoJDE22rGySEgkHahjSX/baHSmrTOkzrN0PdbhiQjg0cZaIBBg+PDhmDp1Kvbv349z587B3r75Ib36/MEtOQH19fV48sknG23/+++/sLN79OBZU2Tow3rWrVuHGzduQC6XY8SIEVi8eLHJNXSEh3V//vnnuHv3LsRiMUaOHIm33nqr2f2d1f1wem+88QYAYPPmzTh//jwWL16MAwcOICgoyGhpdlbjmjVrkJGRgdraWiQlJWHBggXN7jd2nrz++utgMBj4/vvvce7cOSxZsgSHDh1CQECA0dI0Jy19qy+++CJCQkLw559/YsOGDRg+fHiz65b4+x7WWVJSgmeeeQZTp07FoUOHcPHiRdDpzTeImaMuAYAVK1agrKwMBQUFePnll/Hqq68+8lxXKhMkJCQkxiIzMxNJSUmora3Fl19+iZ49e+r13MmTJ/Hxxx8jISEBJ06cgJtb2webklgHTdvInJwcPP/88/jiiy/w9NNPAwD27t2L77//Hn/++SecnZ0bnyPbyO6DUqnEzp07sXr1apSWlmLWrFlYuXIlKBSKXgPEreHi4oK7d+9ixYoVSE1Nxbhx47B69Wq96xQS66AlP3rXrl348ccfERsbCwcHB6xbt+6R58zh02s0GgwaNAgTJkzA77///kif11Q6TIXu71MoFJgwYQL69euHVatWNV4/deoUli1bhl27diE2NhaAaf++h/NbrVZjwIABePbZZ7F//34cPnwYfn7ND7LuSvltLRw/fhzz5s1DXV0dPvvsM0yYMAF1dYbFkycIAleuXMGnn34KpVKJDRs2YPbs2aBY0VmYJC2j+85SUlLwyiuvYNmyZZg8eXKze/7880+sWLEC33//PQYMGACA/NaMTUtt3UsvvYTg4GD8+eef+PLLLzFixIhHnutS74EwIleuXCEAEHv37iUAENevXzea7cuXLzezfePGDaPZNpSRI0cSkyZNIkaPHk1MmDDBYjoMZcSIEcTkyZOJp59+mpg4caJJ0xIIBISNjQ3x3XffEQRBEBKJhGCz2cT69etNmq6hDB06lHjhhRca88aU1NXVEXQ6ndi8eTNBEAQhEokIJpNJfPXVVyZN15xoNBqCw+EQGzZsIOzt7Yl169ZZWlKL/PnnnwQAYs+ePQQAIicnx2Ja+vbtS7zyyitEYmIiMWPGDIvpICEhIbEmTp06RTg4OBDR0dFEbm6uwc9fuXKF8PDwIAIDA4nU1FQTKCQxJQsWLCA8PDwIuVze+FtNTQ3BYrGIL774woLKSEyBSqUi9u7dS4SEhBAAiBdeeIHIysoyejpqtZr45ZdfiLCwMAIAMXnyZCI9Pd3o6ZCYj9dee43o1asXMW/ePCI+Pt5iOoqKiggAxO7duwkAxOHDhy2mxZhs3bqVoFAoREZGRrPfVSoVERkZSYwfP94iunJzc5v15U6cOGERHd2FyspK4oUXXiAAEKNGjSLy8/M7bZPP5xOzZs0iABDDhw8nsrOzOy+UxOSoVCqiV69eRK9evQiVSvXIdY1GQwwdOpQIDw8nZDKZBRQ+fmg0GsLW1pZYv3494eDgQKxdu9bSkjoN1ZiTGjyeNn6cbgV0VlaW0WzrbJnCdke0REREICIiwqI6DIXH4zXq1r0rU3Hq1CkolUokJWkP6GKz2XjqqaeQnJxs0nQNpem7NHWenDx5EiqVCuPGjQMA2NraYsSIETh27JhJ0zUnpaWlkEgkVv998Hg8MJlMDB48GIDl6hOCIJp9l9aaXyQkJCTmZPPmzRgzZgwSExNx6dIlBAcHG2zjiSeewLVr1+Dg4IABAwbgxIkTJlBKYgpEIhF2796N1157DQzGgzOdnJ2d8fzzz2Pbtm1Qqw0P1UVifRAEgUOHDiEhIQHTpk1DTEwM7t69i19++QXh4eFGT49KpeKFF15Aeno6tm/fjmvXriE2NhYzZ85EXl6e0dMjMT1N/ejs7GxoNBqL6QCA/v37w97e3uT9SnOgVCqxdu1aTJ06FZGRkc2u0Wg0fPjhhzh27Bju3Lljdm26PtPAgQPBYrHIPlQHIQgCu3fvRlRUFP7++2/s3bsXf/75JwIDAztt28XFBTt27MCpU6dQUFCAuLg4rF+/HqpOhNkjMT3/+9//cOvWrfuhuWiPXKdQKNi0aRNyc3Px9ddfW0Dh40dZWRnEYjEiIyMRHh7eLeo7o09E+Pj4wNvbG56enkZtgHk8Hnx9feHt7Q0PDw+LNe5SqRRFRUUIDw9HeHg4cnNzu0RlKhaLUVJS0qg7JyfHpJ245ORkxMTENNuSmpSUhIsXL6K21joOkBQIBKioqGjME1M7r8nJyUhISGi2XSopKQnnz59HfX29ydI1J7rvUpen1uqE83g8hIWFwc/PD2w222I6q6ur0dDQ0JhfWVlZIDoeLY+EhISkS6NSqfDmm29iwYIFWLhwIY4dOwYHB4cO2/P398eFCxcwfPhwjB8/Ht988w1Zx3YB9u3bB7FYjLlz5z5ybf78+SgoKMDJkyctoIzEWBAEgb/++gt9+/bFc889B29vb1y5cgVHjhxBfHy8ydOn0+mYPXs2eDwevv32W5w6dQoRERGYP38+SktLTZ4+ifHg8XiNfrRUKrXY++PxeKDT6QgKCrLqPpAh7Nu3DwUFBfjwww9bvP7iiy8iKCgIn3/+uZmVPVhUFhAQgLCwsG6R3+YmPz8fo0aNwsyZMzFmzBhkZGTg5ZdfNnoIpZEjRyI1NRULFizAsmXL0K9fP9y6dcuoaZAYBz6fj+XLl2P27Nno379/q/fFxsZi0aJFWL16NYqLi82o8PGkq4yxGYJRJyKysrIaV68Ye4W5zskAYNHMz8nJadQQHh4OlUqFgoICi2gxhOzsbAAPdCsUChQWFpokLbVajePHjzfuhtAxbtw4qNVq/PXXXyZJ11AezhOpVIqSkhKTpKVSqXDixIlH8iQpKQkqlarbdKi7ihOuq6uoVKpFnVfdbLauDNbX16OmpsYiWkhISEgsSUNDA5KSkrB582Zs3boV33zzzSNn93QEOzs7HDp0CEuWLMHbb7+NefPmQalUGkExiSkgCAJbtmzBuHHjWoxz269fP/Ts2RNbtmyxgDoSY/Dff/9hyJAhGDNmDFgsFs6dO4dTp07hiSeeMLsWJpOJhQsXIjc3F59//jn279+PkJAQvPPOO6iurja7HhLDeHhRGQCL+vQhISGg0+ndYsWqWq3G559/jmeeeabVyUE6nY5ly5bhwIEDyMjIMKs+3aIyKpVqlsgG3Qm1Wo2vvvoKsbGxyMrKwokTJ7B3716Tnqdla2uLDRs24OrVq1Cr1ejXrx+WLl0KicSwA9FJTMuyZctAEATWrl3b7r2ffPIJ7O3tsWTJEjMoe7zh8Xig0WgICgrqNvWd0XdEmGqywFomIh6ejWr6mzVjTt3Xr19HdXU1xo8f3+x3Hx8f9OrVy2pCEZkzT65cuYLa2tpH8sTf3x/x8fFWF7Kqo/B4PAQHB8PGxgbh4eGoqqqyyt0e1lSfUCgUhISEdKn6hISEhMSY5OXlITExEVevXsXJkyfx+uuvG9U+jUbD+vXrsX37duzYsQOjR4+2mt2ZJM25evUq7ty5g/nz57d4nUKhYP78+Th+/HiXWAhE8oDr16/j6aefxtChQyGRSHDixAmcP38eQ4cOtbQ0cDgcvPfee8jPz8eyZcvw008/ISgoCCtWrLBKP5ZES9O+XGBgIOh0usUmAKylb2EsDhw4AB6PhxUrVrR534wZM+Dj42P2XRHdLb/NRUpKChITE/Huu+/itddeQ1paGsaMGWO29Pv06YMbN25g9erV+PbbbxEfH4+zZ8+aLX2S1rl27Rq2b9+ONWvW6DUp5eDggC+//BK///47Tp8+bQaFjy88Hg9BQUFgMBgIDw9HdXW1wYfIWxtGm4jQaDTIzs5GREQEgAcNgjG2wD9sWxdL3RLb63k8HpycnODq6gpfX1+w2ewuseKBx+PB1dUVzs7O8Pf3B5PJNFmDfezYMbi4uLS4nWv8+PH466+/rGI1YlZWFjw9PcHlchEYGAgbGxuT5om7uzv69u37yLXx48fjxIkTXSLEV3s0dQp136u1OYa68GoP1yeWgMfjITAwECwWC6GhoaBQKFaXXyQkJCSm5Pz58+jXrx9UKhWuXLmCJ5980mRpzZ49G3///Tfu3LmD/v37k/WtFbJ582YEBQVh1KhRrd7z0ksvwd7eHv/73//MqIyko6SmpmLixIno168fSkpKcODAAdy4cQNjxowxegiQzsLlcvHxxx8jPz8fCxYswFdffdUYekYkEllaHslDNJ2IoNPpCAkJsejioqZ9i8rKSjQ0NFhES2fRaDRYs2YNRo8ejT59+rR5L5PJxNKlS7Fv3z7k5uaaSWHz/A4PD0dxcTHEYrHZ0u9qyGQyLF++HL1794ZEIsGlS5fw7bffws7OzuxabGxssGzZMqSkpMDHxwdPPvkkXn311S4/sNqVUavVWLBgARISEjBv3jy9n5s2bRoGDRqERYsWQaFQmFDh443uXFsAjWNtuuguXRWjTUSUlZVBIpE0m5kWiUQoLy/vtO2SkhJIpdJHbFdUVHTatqHoQrpQKBSLh3UxhKYDxKbWnZycjLFjx7Z4uE1SUhLq6+tx8eJFk6RtCE3zxNTOa3JyMsaNGwcq9dFPLikpCbW1tbhy5YpJ0jYnTcOzhYWFAbC+iYim4dV0/19SUmIR57VpGWSz2fD39+8SE5skJCQkxmDnzp148sknER8fjytXrjQ62aZk6NChuHbtGmg0Gvr370+uxLMiampqsH//frz++ust+ks6bG1tMWPGDGzfvp3s+FoxPB4PL774IhISEpCamordu3cjNTUVzz33nNVNQDyMi4sL1q1bh7y8PEyfPh2ffvopgoOD8fXXX0Mmk1laHsl9eDxe46IywHIr4xUKBfLz85v1LYCuO1B09OhR3Lt3r93dEDpeffVVuLm56RXOxRhIJJLGMzuBB/mt6+ORNOe///5DQkICNmzYgJUrV+LWrVttxv83F+Hh4fjnn3+wbds2HDhwAFFRUThw4AB5lpcF2L59O27cuNHqAdWtoTu4WnfeEolpaDpmpBtj6+pjRkabiGga6xww7mropqsdmv6/JRyNpoVAp8XaBlpboukAMQCTxa4sKipCSkrKI2ch6OjVqxc8PT2tIhRRS+/SFHmSl5eH9PT0VvOkb9++cHNzs4o86Qw6J1z37dvb28PLy8vqvo+H6ypLOq9dtT4hISEh6QwajQZLly7FK6+8glmzZuHkyZNwdnY2W/ohISG4fPky+vbti1GjRpEr662EHTt2gCAIzJ49u917582bh6qqKhw6dMgMykgMobCwEK+++iqio6Nx/vx5bN26FZmZmZg+fbpBAxzWgKenJ7777jvweDxMmDAB7733HkJDQ7Ft2zar2N39uGMtfnReXh40Gk23GCgiCAJr1qzBsGHDMHDgQL2eYbPZePfdd7Fr1y4UFRWZWGHLi8oA61v8ZmkaGhowb948DB06FK6urrh9+zY++ugjMBgMS0trhEqlYu7cucjIyEBiYiKmTJmCSZMmWezQ+ceRmpoaLFu2DDNnzsSAAQMMfj4hIQELFizAqlWryPdmApRKJfLy8hrrOTs7O/j4+HT5+s5oExG6Q2oDAwMBAEFBQaDRaEabiGhqOzg4GFQqlZyI0BOCIMymOzk5GXQ6vdUt9VQqFUlJSRY/J8LcecJgMDBy5MgWr9NoNIwbN87iedJZ8vPzoVarrf770IVXc3FxAWA551WtViMnJ8fq84uEhITEmIhEIjz77LPYsGEDvv76a2zbtg02NjZm1+Ho6Ijjx4/j9ddfx+uvv47FixdDrVabXQeJFo1Gg61bt2LKlCl6xSaOiYnB0KFDyUOrrYjy8nIsWrQI4eHhOHbsGDZs2ICcnBzMnTvXIt+4MQkICMBPP/2E9PR0DBkyBPPnz0dkZCT27NlD1hsW5OG+XEREBPLz8yGXy82uA3jQp+ByufD09OySPv3Jkydx8+ZNvXdD6Jg3bx64XC7WrVtnImUPeDi/XVxc4Ozs3CXz21QcPnwY0dHR+Pnnn/HDDz/g/PnziI6OtrSsVvH29sYff/yBgwcP4urVq4iOjsa2bdug0WgsLa3bs2LFCqjV6k59u6tWrWo8a4nEuHSVMTZDMepEhO6QWgBgMBgICgoy2kRESEgI6HQ6AG0swqCgILOvMqipqUFNTc0jDo+lwrroC5/PR319/SOFt6ioCFKp1KhpJScnY8iQIXBwcGj1nqSkJPB4PIt+PBUVFRAKhc3CQISHh6OgoMDozmtycjKGDRsGe3v7Vu9JSkpCeno68vLyjJq2OXnYKQQse/5Ca+g6LbqwAM7OznB1dTW7zsLCQigUikfKYHZ2Nul0kZCQdEuKi4sxaNAgnD17FkePHsXixYstGqKFTqdj06ZNjf8bP348BAKBxfQ8zpw+fRq5ubmtHlLdEvPnz8d///2HtLQ0EyojaY+amhq8//77CAkJwd69e/Hxxx8jLy8PixcvBovFsrQ8oxIeHo59+/bh7t27iIuLw4wZMxAXF4cDBw6QvpuZIQiiWdxsQPt+NBqN2ftTWVlZsLe3h6enZzMtXW2giCAIrF69GomJiRgxYoRBz9rZ2eHtt9/G9u3bjRKauy0eXlQGWGef0xJUVFQ07iro1asX0tPT8cYbb7QZ7tCaePbZZ5Geno6pU6di3rx5GDZsGPleTcjNmzexbds2rFq1Ch4eHh224+joiHXr1uGXX37BuXPnjCeQpLH8P9zWdbX25WGMGpqp6QAkYLwGoSXbpgqj0xYtDbR2ha2AD4eiAR4UZGPGrhSLxTh79myrIYh0PPXUU2AymRYNRaR7X7qts4A2TzQajVEP2hIIBDh37ly7efL000/DxsamS4dnysrKgp2dHby8vBp/M+ah9cbC2uqTh8ugXC43y7ZmEhISEnNy7do19OvXD/X19bh06RLGjRtnaUmNLFiwACdOnMClS5cwYMAA5OfnW1rSY8eWLVsQFxdnUFiASZMmwcPDA1u3bjWhMpLWEAgE+OSTTxAUFITNmzdjyZIlyM/Px4cffmiRA1DNSVxcHA4fPoxr167Bz88PU6ZMQZ8+fXDixAmr8nm7MxUVFRCJRM38aJ1/bwmfPiwsrNnEelccGD937hwuXbqEFStWdGiRwMKFC8FisbBx40YTqHtA0zM7dViiL2dNEASB7du3IyoqCv/++y9+/fVXHD16FH5+fpaWZjBOTk748ccfcfbsWZSXlyMhIQGfffYZGQ7PyGg0GixYsACxsbF44403Om1vxowZSExMxMKFC8l3ZUR4PB44HA68vb0bf4uIiACPx+vSCyCMuiPi4UMGjTVT05ptcx8A1dLAYVc4jIrH44FCoSA0NLTxN1NMoJw+fRpyuRzjx49v8z5bW1uMGDHC4hMRVCoVISEhjb+ZIk/+/vtvKJXKdvPE3t4ew4YN69ITEQ/vNAC0eSqRSFBWVmZBZc2xpvqEyWTC39+/mQ7dNRISEpLuwq+//oqhQ4ciODgY165dQ2xsrKUlPcLTTz+Ny5cvQyaToV+/frh48aKlJT02lJSU4OjRo5g/f75Bg18MBgOvvvoqdu/eDZFIZEKFJE2RSCRYv349goKCsHbtWrz22mvIy8vD6tWr4ejoaGl5ZqVv3744efIkzp07B1tbW4wbNw6DBg0iV4SaAZ2v3NSn9/T0hJ2dnUV8+tbGQbrSxNSaNWvQs2dPjBkzpkPPOzg4YNGiRdiyZQv4fL6R1T2gu+S3scjJycGTTz6J1157DRMnTkRGRgaef/55i+44NQbDhw9HSkoKFi9ejI8//hi9e/fG9evXLS2r27Bz505cvXoVmzZtaow80xmoVCo2bdqE9PR0bNq0yQgKSYCuM8ZmKEaZiNAdUtvSKuO8vLxOzYjJ5XIUFBS0aDs3NxcqlarDtg2Fx+PB19cXtra2jb85OzvDxcXFqgcOeTwe/P39wWazG39zcXGBk5OTUXUfO3YMkZGRzSY8WmP8+PE4f/486uvrjZa+IfB4PAQFBTU7rMnDwwP29vZGz5PY2NjG803aYvz48Th37lyXDQvxcJxWwPoG1mtqalBbW9vqjghzOq+61VNNt8r6+/uDwWBYTX6RkJCQdAaCIPDpp5/ixRdfxOTJk3HmzBm4u7tbWlarREVFNcYmHjFiBPbs2WNpSY8FP/74IzgcDqZNm2bws3PnzoVQKMQvv/xiAmUkTZHL5di0aRNCQkKwfPlyTJ06Fbm5ufjqq6+s+rs2B0OHDsV///2HP//8E3K5HMOHD8fIkSNx9epVS0vrtugWlQUHBzf+RqFQLLbLuaW+hVgsNnmYImNx6dIlnD17FsuXL+/UALYu5OLXX39tRHXNaS2/6+vrUVNTY7J0rQ2VSoX169cjLi4OBQUFOHXqFHbs2NEsZFVXh81mY+3atbh+/TpsbGzQv39/vPPOO1YdFr0rUFdXh6VLl+Lll1/GkCFDjGa3V69emDdvHj7++OMuU/dZO11hjK0jGGUiIi8vDxqNpsUMUqlUKCgoMLrtiIiITts2lJYKgU6LNReClnTrHDVj6dZoNDh+/Hi7IYh0jBs3DiqVCidPnjRK+obSUngeY+eJWq3GiRMn9M6TpKQkKJVK/P3330ZJ39y0VM6MeWi9MWgpvJruv+vr6026eudhWiqDNBoNoaGhVpNfJCQkJB1FKpXipZdewieffILPPvsMu3fv7hLx4l1cXPD3339j2rRpmDFjBj788MMuvfXZ2lEqlfjxxx8xbdq0Ns/Sao2AgACMGzcOW7ZseSxXwpoDlUqF7du3Izw8HG+99RZGjRqFrKwsbNmyBT4+PpaWZzVQKBSMHj0a169fx6FDh1BeXo7+/ftjwoQJuHv3rqXldTuysrIeWVQGmL9fLhAIUFFR0eUHij777DPExMRg0qRJnbLj4uKCN954A99//z3q6uqMpO4BrS0q0+2QeFzCM926dQv9+vXDsmXLsGDBAqSmpmLkyJGWlmUyevbsiatXr2LdunXYunUrYmNjcerUKUvL6rJ89NFHkMvl+PLLL41ue82aNWAwGHj//feNbvtxpKUxtsDAQNDp9C7TvrSEUSYi2hrca3rdFLbN2di0NhFh7TEJ29JtrMJ78+ZNVFRU6D3o7u/vj4SEBBw7dswo6RtKS1s6AePmybVr11BdXa13ngQFBSEmJsZiedIZhEIhysvLHylnDAYDwcHBVvN96N7tw7t2dGXBnJW5OcogCQkJiSWoqKjA8OHDceTIERw4cAAffvhhlwoRwGAw8NNPP2H9+vVYu3YtpkyZQq6+MxFHjx5FeXm5QYdUP8z8+fNx+/ZtXLt2zYjKSDQaDX755RdER0fjtddewxNPPIF79+5h586dzVahkzSHQqFg0qRJuHv3Lvbu3Yv09HT06NEDL7zwgtX4w90Ba/GjWwoRBQDBwcFWtRirLW7duoUTJ05g+fLlRjnU+J133oFSqTRJeJbWxoZ0fbuukN+dQSKRYOnSpejXrx/UajWuXr2KDRs2NIvY0V2h0+l49913kZqaiuDgYIwaNQozZ858rHbBGIM7d+5gy5Yt+OSTT5qd7WksnJ2dsXbtWuzduxfnz583uv3HCaFQiLKyskfaFxsbGwQHB3fp+s4oExEtHVILAD4+PuBwOJ1yurKysmBvbw9PT89HbLPZbLNlvkajQXZ2dpsOjzWuxFKr1cjJyWlRtzEP0UpOToajoyMGDhyo9zNJSUn4888/zRpeC9Cu7MrNzW11d4sx88TFxQX9+/fX+5mkpCScOHECarXaKBrMRWtOOGBdA+tZWVnw8/N7xFkLCQkBhUIxm06pVIqioiKTl0ESEhISc3P37l3069cPxcXFOH/+PJ577jlLS+oQFAoF7733Hg4fPoyTJ09iyJAhKC0ttbSsbsfmzZsxcOBAxMfHd9jGqFGjEBgYiM2bNxtR2eMLQRA4cuQIevTogZdeegnh4eG4desW9u/fj6ioKEvL6zLQaDS8/PLLyMjIwP/+9z9cvHgR0dHRmD17tll39HdX2lpoV1lZiYaGBrPpAJqfIQloJ7SDgoKspg/UFmvWrEFYWBimTp1qFHuenp6YM2cOvvnmGwiFQqPY1KHrIz2c32w2G/7+/l0ivzvK2bNnER8fj2+//RarV6/GjRs30KdPH0vLMjshISE4ffo0/u///g9Hjx5FVFQUfvnlF6sci7M2dAdUR0VFYdGiRSZLZ/bs2ejXrx8WLFhg9rG+7oTuvKPuOGZktB0RDx+gARgn1E1rtqlUKsLCwszW2JSUlEAqlbbq8Jg7rIu+FBUVQS6Xt6q7trbWKLPIycnJGDNmjEEH3YwfPx61tbW4cuVKp9M3hIKCAqhUqlbzpKqqyihnVyQnJ2Ps2LGg0Wh6PzN+/HhUV1d3uYOYWnPCAeuaiGit02Ju5zUnJwdAy41KeHg4CgsLIZPJzKKFhISExFgcPXoUAwcOhLu7O65du4bevXtbWlKnmTBhAi5evIjq6mr07dsXN27csLSkbkNWVhbOnj3bqd0QgHbA9/XXX8dvv/1GrozsBARB4NSpU3jiiScwceJEuLq64uLFi0hOTkbPnj0tLa/LYmNjgzlz5iA7OxtfffUVjh8/jvDwcCxYsKBLHzRpSdpbVAbAbAdW83g8eHp6gsvlPnLNmvpArXHv3j388ccfWLZsmUF91vZ47733IBQKsWXLFqPZBLT57efnBw6H88i1rpDfHaGurg6vvvoqnnzySfj4+CAlJQXLli2DjY2NpaVZDAqFgldeeQUZGRkYNmwYXnrpJYwfPx5FRUWWlmbV7NmzB5cuXcKmTZtMWn6oVCp++OEH3Lt3j1wk0gm6yhhbRzDqRERLGGsioiXMGQOytW2AOh1N77Em2tJtrNiVpaWluHXrFsaPH2/Qc3379oWbm5vZQxHpZg7bypPOOq+FhYVISUkxOE/69+8PZ2fnLheeicfjwcPDAw4ODo9cM8ah9caivbrKXLPK7ZVBgiCQm5trFi0kJCQknYUgCKxfvx4TJ07E6NGj8d9//3Wr2PEJCQm4du0a/P39MWTIEBw4cMDSkroFW7duhaurKyZPntxpW7Nnz4ZGo8HOnTs7L+wx5MKFCxg2bBhGjRoFGo2GM2fO4OzZsxgwYIClpXUbWCwW3nrrLeTl5WHVqlX45ZdfEBISgvfee88qF7NZM/n5+a0uKtMN2JirX97SmW86rD18MwB8/vnnCAgIwLRp04xq18/PD6+88go2btwIiURiNLvtjQ1Ze34bAkEQOHDgAKKionDgwAFs27YN//zzT6t//+OIp6cn9u/fj8OHD+P27duIiYnBpk2byLO9WqC+vh7vv/8+XnjhBQwbNszk6fXp0wdz5szBRx99hMrKSpOn1x3h8Xhwc3ODk5PTI9fCw8ORn58PhUJhAWWdp0tPRJhzFojH48HGxgaBgYGPXDN3WBdD4PF4YDKZ8Pf3f+SasWIpHj9+HDQaDaNGjTLoOSqVinHjxiE5OblT6RsKj8cDm81ucZDEWM7r8ePHQafT8fTTTxv0HI1Gw9ixY82eJ52lve9UrVYjPz/fzKqaowuvZi31iZOTE1xcXFrUobuHhISExNpRKBR49dVXsXTpUnz44YfYv39/iysVuzqenp74559/MHHiREyZMgVr1qwhwwB0AolEgp07d2L27NlgMpmdtufu7o4pU6Zg69at5ACEAdy8eRNjxozB4MGD0dDQgGPHjuHSpUsYMWKEpaV1W2xtbfHBBx8gLy8P7733HrZu3Yrg4GB8/PHHZgsn1NVpa6Edl8uFp6en2Qak2+sDWctirJbg8Xj47bff8MEHH5hkdfTSpUtRU1ODn376yWg228vvnJycLhfiuCVKS0vx7LPPYsqUKUhMTERGRgbmzp1rlDM8uiPPPPMM0tPTMW3aNCxatAiDBw9Genq6pWVZFR9//DHEYjE2bNhgtjQ///xz0Ol0LF261Gxpdie6whhbR+l0TSYQCFBRUdFibHhAOzNdWloKkUhksO2GhgZUVla2ajs8PBwlJSVmOUAwKysLISEhLYYesuaYhFlZWQgNDW1xq6WtrS38/Pw67agdO3YMAwcOhLOzs8HPjh8/Hunp6cjLy+uUBkPQfdAtNeRcLhdeXl5GyZMhQ4a0uEOgPcaPH4+UlBQUFhZ2SoM5ycrKarMOACw/sF5cXAyZTNamTnM5r7oD9lo6vNXNzQ2Ojo7dakUPCQlJ94TP5+Opp57Czz//jD179mDNmjXdupPMZrPx888/49NPP8VHH32EadOmkWH0Oshvv/2GhoYGvP7660azOX/+fOTk5ODMmTNGs9ldSUtLw3PPPYc+ffogPz8fv/32G27duoWkpKQudbB8V8bR0RGrVq1CXl4e5s6di/Xr1yMoKAhr1641S9+2K8Pj8cDhcFrdeWeuxUUEQbR6aDag7VuoVCqrPRNk7dq18PT0xKxZs0xiPzg4GC+//DLWr18PuVzeaXttndkJaN+7XC5HcXFxp9OyFBqNBtu2bUN0dDSuXLmCAwcO4NChQ/D29ra0NKvHwcEBW7ZswX///Qc+n4+ePXti1apVXXbFuDFJSUnBpk2bsHLlSrPuWHZxccFnn32GXbt24dKlS2ZLt7vQXvsCoMuOGXW6t9jWioSmv3ck1I0pbXdES1vb4Kx166U+ujvjqEkkEpw+fdrgEEQ6Ro4cCQaDYdYdAKbOE5FIhLNnz3Y4T0aNGgU6nY7jx493WIM50TnhreWpt7d3pw+tNwb61Cfmcl7byi9jnK1DQkJCYmrS09PxxBNPIDMzE//884/RwzpYKxQKBStXrsRvv/2GQ4cOYfjw4eSW8w6wZcsWjBo1CsHBwUazOXDgQMTGxho9Jnl3IicnB9OmTUNcXBxu3bqFHTt24N69e5g6dWq3nkS0Ztzc3LBhwwbk5ubihRdewMqVKxESEoLvvvvOKIO33REej4ewsLBWy6y5wjdXVFRAJBK1O1ZhjT59QUEB9uzZg/feew8sFstk6SxbtgxlZWVGCZunW1TWFfNbH7KysjB8+HDMmzcPU6ZMQXp6Op577jlycthABg8ejLt37+K9997D6tWr0atXL1y+fNnSsiwGQRBYuHAhwsPDsXjxYrOnP2fOHPTq1QsLFy7sFruVzAVBEG2G/vP09ISdnV2Xre+MNhHR0gEaTX/vSAa1Z9ucjU17g9fmPK/CEEw96H727FnIZDIkJSV16Hl7e3sMGzbMrGcitPVBA53Pk9OnT0OhUHQ4TxwcHDBkyJAuc05EZWUlhEKh1Q+s68KrBQQEtHhdp98cEyamLoMkJCQkpuTkyZNITEwEh8PBtWvXHss48lOnTsW///6LgoIC9OvXDykpKZaW1GW4efMmrl+/jjfeeMOodikUCubPn4+jR4+itLTUqLa7OsXFxZg7dy4iIyNx9uxZ/PDDD8jKysKsWbNa3O1NYn68vb2xefNmZGVlYcyYMXj77bcRFhaGn376yWpD+1gKff1oU4fPa+vMN+DBYixr9OnXrVsHJycnzJ0716TpREZGYsqUKVi7dm2ny3F7i8oCAgLAYDAsvvjNUJRKJT7//HMkJCSgtLQUZ86cwU8//dRiXHgS/WCxWFizZg1u3rwJDoeDgQMH4s0334RQKLS0NLOzb98+nD9/Ht9//z0YDIbZ06fRaPjhhx9w+/ZtbNu2zezpd1WqqqogEAisfoyto3R6IiIzM7PVQ2oBwMnJCW5ubsjMzOyQbU9PT3C53BavOzg4gE6n44cffjDYtiHI5XLk5+e3ezBQWlqaVW0FlEqlKCws1MtR62g83eXLl8PDw6PVLUP6MHz4cJw5cwZ37tzpsA19SUlJQWlpaYuhqnTodrd01HldsWIFvL29G8/g6AjDhw/HqVOncO/evQ7bMBftOeEAoFKpsG/fPovG087MzGw1vBoAeHh4gEKhmHwl5ZUrV1BbW9umI2CtO6xISEhINm3ahLFjx2Lw4MG4ePFii2dnPS7069cP169fh7OzMwYOHNhlFhBYEoIgsHjxYvj4+GDs2LFGtz9t2jQwGAy8+eabRrfdFamsrMTixYsRGhqKP/74A+vWrUNubi7mz59vkQEJkvYJCgrCjh07kJaWhsTERMyZMwfR0dH4+eefydWk0IaF/vfff9sc1A4PD4dIJEJFRYVJtWzduhWAdmVqS1CpVISFhXVoHMSUXL16Fdu3b8eSJUvMcqbT8uXLUVBQgDVr1nTKTmZmJuh0equLymg0GphMJjZv3typdMzJ9evX0adPH6xcuRKLFy9GamoqeUaPEYmPj8fly5exceNGbN++HbGxsfjzzz8tLctslJeXY8mSJZg8eTKeeuopi+no378/Zs+ejQ8//LDLnmlgbn7++WcA2jCOrdGVx4w6PRGxevXqdrekV1dX45NPPjHY9po1a9p0IKhUKlQqFXJzcw22bQhbtmwBQRBt/p26AVZrWoGlm6Cpqqpq9Z7i4mLI5XLs3bvXYPsEQSAlJQX19fWd2jLo5uYGgiCwa9euDtvQF13F19ZERGFhISQSCX777TeD7Ws0GqSlpaGurq7DGgHA2dkZGo0Ge/bs6ZQdc/D+++8DQJuHnInFYohEIot2oDZt2tRmR4BKpTZugTMlulnrtg7nzMnJAZ/Pf6wcJRISEuumuroa8+fPx6JFi/DWW2/hyJEjrS4UeZzw9fXFhQsX8NRTT+GZZ57BF198QcZ3b4PCwkJcuHABQNu+WEfhcrlQKBQ4dOgQVCqV0e13FWpra7Fs2TIEBwdjx44dWLFiBfLy8rBkyRKw2WxLyyPRg8jISPz222+4ffs2IiMjMW3aNCQkJOCPP/6w6MIeSyMSiaDRaNqM+66rgz/77DOTatH1K9raVXTv3j3873//M6kOQ5k0aRKUSiVeeukls6QXHx8PAFi1alWnyu77778PlUrVZtshkUhQUlLS4TTMhVgsxpIlS9C/f3/QaDRcu3YNa9euJetnE0Cj0fD222/j3r17iIyMxNixY/Hyyy+jurra0tJMTkxMDCorK7Fu3TpLS8EXX3yBhoaGTi1gfpxITU0FoD1nozVu3ryJ//77r2uWZaKTDBo0iBg9enSb94waNYoYPHiwwbYHDhxIjBkzps17cnNzibKyMoNtG8J///1HcLlc4s6dO63eo9FoiEuXLplUh6H8888/hIODA5GamtrqPZcuXSK4XC5x69atDqWxe/duIi8vr6MSCYLQ5t2mTZuI+vr6TtnRl4sXL7Z5/d9//yW4XC6RkpLSIfs7d+4k8vPzO/SsDo1GQ3z33XdEQ0NDp+yYg5UrVxK+vr6ETCZr9R6hUEjcvn3bfKJaICEhgXjhhRfavCc9PZ2orq42qQ6NRtNuGTx58iTB5XKJnJwck2ohISEh0QeNRkMAIAAQ//vf/ywtxypRq9XE0qVLCQCEh4eHpeVYLRqNhpg5cybB4/FMlsbVq1eJhQsXmsy+NVNeXk6sWrWK4HK5BIfDIZYtW0bU1NRYWhaJEbh8+TLx5JNPEgCIPn36EH/99Reh0WgsLcsiXL58mVCr1a1eLy0tJZycnIh9+/aZVEdtbS1x7969Nu+ZOXMmER0dbVIdhrJ69WrirbfeMmuaBw4cICZMmNApG8888wzRr1+/Nu8pKSnp9NiEqZkyZQrh6upKsFgsYv369YRSqbS0pMcGjUZD7N69m3B2dia4XC7Rr1+/NuuSrs6TTz7Z7niqOZkyZQqRmJhoaRldArlcTly7dq3Ne7788kvCzc2NUCgUZlJlPCgE8RgvqSAhISEhISEhIWkTjUaDmJgY9O3bF7t377a0HKtFo9FgxIgRoFAo+Oeffywth+QxY9myZVi7di1sbGzwxhtvYNmyZfDw8LC0LBIj888//2D58uWNh69u374ds2fPtrAqEhISfZBIJLC1tQUAZGdndyqUM0nHqaqqQkREBOrr63H27FkMHz7c0pJISB4ryIkIEhISEhISEhISEhKSLszHH3+MTZs24fDhwxg8eLCl5ZCYEIIgsGXLFixYsAATJkzAkSNHLC2JhIRET+7evYvAwMBWz1glMQ8ymQwpKSno16+fpaWQkDx2dKmJiKKiIvD5fIOfc3V1hb+/v8W1mEKHqTHl30naNq9tc9JV/g5T6bSmuoqEhIREB1k3mZeu0hYaE0v+zV0xvzv6TeqwtH4S66MzZcqaypO1fM/WokMfrLWN7059Z2vNYxLj0FXer6E6raH8dZW8NSePc560frLSQxiSSXK5vM2DWJtSXl4OAPDy8mr3vucmT4FcJtXLblNYbA6yMjOMNnjYUS1sNgeZeugwFEMLsKnzXJ/8LioqQlRkJCRSw2xz2GxkZGbqYTsCEqnMINssJgMHDh5qM1/Ky8sxZfJkSGWG2eZw2MjIMI1uDpuFjMwsk1dGpv4+9P1O28KQb6G8vBxTnnsOUrncoDTay++Olm0AYDGZOHDwYLvfJtA9GiASEhLzUVRUhKioKEgkEoOf5XA4yMgwvv/SnbH2Nt0UWPJvLioqQmRUFKQGlm82h4NMC5XtjuZXU7pyeSExPp0tU9ZSnjraXrFYLBw4cMBofnRRUREiI6MglRqmg8lk4eBB4+nQh6KiIkRERkFmoFbAOH2wtnR1pF+kT59I2y9/DlKZcftyrdHRdgawbFtDoh9dxU/uiE5L+/GdGR/RZ/yvK6J9j5GQSDowZsRi4sCBrj1mpNdEhMFODYUKEBrj3wsgdM73UIpqQWPaQS0TgeHoAY1KAbqtE5QNVaBQaVCJ6wBQwI3oD2l5NnJ+XAQ+n9/uILChjXe/N3+AXFAHG7YtlBIh2M5e0KjkYNg5QSkRgmrDgKy2Ai4RfSEo5eHadwva1WEoHdFNpQAaA/bB+I2aB45PGDQqJWzsHKFRKmBj5wRpVSFAAVSiOthwXcEN7glJWQ4y/rew3b+Tz+dDIpXijaeiEehuDwoFcLJlQq5Uw8mOCaFUCQadiiK+CHYsGzjZMiFTqvHG/13Q07YM84YGIdzDHkKZEp4OLChUGjhyGBDKlGDQqBDIVKBRKJAp1VhxNAMyuQJJSUl65ckPi6dCqlBCoVQhyMsFFAoFCqUa5bUNsGezoFCpUFErwKi+UcguqcbcDb/orfv1gd4Id+MAAGyZNChUBJw4dAhlatjQKKiTqsAXKRDqxoFcpcGiAzyjl6uH6Ug5i5m3CQpRDehMO6hkIjCdPJqVHSqdAbVMDBqTDRrLDmlb2y837WnsSOdr3iBfhLlxoNYQ4LLpUKg0cGLboLBOCjsmHfVSJTQaYECwI3KqJVj4e2abOnVl+4cZ/VAnlsOGToOXAxtUCqBQacC0oUEoU0Kh0kCh0kAoU2Dd8TTIlBrI5HK9y6C1dBZJSEi6Bnw+HxKJBLv/twlBAQG4m3oPcoUCwUGBoFIoUCgUKCuvQGCAP1LS0sFiMjFkYCKysnMwY27n6ufHEV2b/v3kCNRKFLBjaF3u5u26CjY0KiqFCjiwtdcXHcjqsnnd6H8N9EGYOwdCuRqeXIb272XTUVgnA4NGgVihBoVCQf9AB+RUSbDQCH8zn8+HVCJB4lta35xCpcLW3RcUChVqpQI0BhNKiRBKiQAUChV0li0IQoPL3xrfNzdEs0Qqw6YpkQh14+BqQT3smHTwRQoEuLAf8UcapCrQqEAff204D318EpLHi8Z657lQhLmycbVQAAaNCjVBwNXWBgq1Rvst1srBoFPAoFER6aHtc2TzpVh0MMcqypOuvdr+2buoaRCAaWMDL3cXbVulVILJZEAgkkChVEImU8DH0xUvv/MZZDKZUf1oPp8PqVSC3gt/gFxYCxuWHZSSBrBdvKFWKsCwc4S4shA2bDvI6itBodCRtf8LyOXG1aEPfD4fMqkEsfM2QSGqBZ1lBwqNBqoNCxqVAjZ2jlDUV4PQqKFWSEGolHCOGQRxaTbudbIP1p4uiVSKt555Ap6OtqBSKXB1sIVCqYKTHRtCqbavlFNWCxd7NtQaDZbtPmdQn2jhUH+42zFgy6TBlkG/X85tUFgrhR2ThkqhAkw6Ff2DHJFTLcbC/Rkd+nsfbmfobO1ZDzZsO6iVcjDsnSCtKYdjQBT4WTfAsHcChUIFhUKxaFtDoh+NfvKevRAIBFCpVNBo1PD29oFcLoezszPy8vLg6uqK8rIyaAgNhg8fgYyMDMyYPs1s71en8/k5b8HZ3RN+QWGgUClQKhQQCRpgx3WAUqFAcX42EkeMQVFuFj5bPNui5U9XD2x5bSgC3OyRWlQDhUqDQDd7bb2uUqO8XgJ7ts3937lw4DCQXV6P+T/92y2/He17lOLHFXMgFEuhUquhVGvg6+YEuVIFJ64thGIpGDZ0FFbUgEal4OOtv0OuVEMm6/pjRnpNROicmjmJnuAy6fBxYCC/VgY/Rya8uEyUNcjh68hEg0yFs9n1OHCXD8+Rc0DncMFw9oGsKh9MFz8wnb0grykD09UXKnEDxEX3UP7XZu3kgoCvnVho5X5RYSpKDq2FUsAH3dYRbI8gSEoyYcN1BcstALKqfNjFDYdSyIeivgoqcR3U4nqwvcL0yghd422Iblk9H0x7J9h5BaOhKAMsB1fYegZCVJ4Hx6A4yBv4oFCpUElFINTqTr0oY+hWiWpBt3VAzo+LDHqXNLYtOF6hEJdkgsF1A8s9ANLKfLj1HQelgA+lqBYUqrYocbwNO3DJjm2DaF8nwM2IWwABAABJREFUZJTWw9WehUA3e+RVCRHn54xqgQwABQRBwNWeBQ5T7w08WttMOkLdbZFRLoSrHROBLhzk88WI83EBX6RAnVgBBp2KAr4YSpXaoHdfXF2H4T3CkV5YDnsOC0FeLsgt5aNfVACq60XILqlCmK871GoC4X7uhulm0BHixkFmpRiutgwEOLOQXyNFTJAd+CIF3BQahLtxQKEATDrVINsdpSPfh6KhGjZ2TuB4BkFUnAkm1w1s9wBIKvLh0XccFAI+lMJaqBVSKEX1RtGo63zxRUqI5Oo2y/e9MjE2XyqHLYOG0Pv57WbHQIAzG/k1EoyLcQNfrIRYoUZ5gxxSpRqh9yeI9NIjlMHJlolgNztklDUg1IOLGB9b5FWLEOfrCL5QjpI6CerECsiUGr1114pVcODQraazSEJC0rWorKqGSqVGfFwM7qVngGtvh5CgQGTn5qN/vz6oquZDcz9qJ5VKRWS4fn4UScvwRQo4cmwQ5MK+367b3G9npIgJctS263Y2qJWoIJCpLC3XKNgy77erFWK4Nfl7x8W4gi9SQKxQQ60h0CBVItRd/3ZVH3S+ub1XMOqLMsD1CYVTYAyEZXlwjomDrIEPcXUJAEAhrjdq2h0l1I2DsgY5HNjacqLSEK36IwU1Uqg0BHr5cS0tm8SKCXNlo0ygAJdNR5AzG5mVErja2SDAiYX8WhnGxriAL1aiUqhAg0yNQcHWGau+qrYezg72CPX3RlpOIcIDfRHsF4ycolIkRISgurYeJRXVyM4vgVyp0qt/2xE/Wt5QDYadE2w9gyEozgCT6wpbj0CIKvLg80QS5AI+2EIf1ObcgkqlNJkOfZALqsGwcwbHIwiikgywXHzB8QiEpCIfrgkjoGjgQ95QCTrLDhqlArY+5mnjXbls2HNYCPF0QnpxNcK8nRHo4YS8ilqEebsgyMMJMoUKF9IKoVSq9Opz1qeeBf/yAbhwbLRl3YWDzAoRQt04CHBhg2VDRagbB3yxErViBcRyFVTqzkcll9XzwbjfzujGgOw8AyEsy4NPn6cha+DD3jMITAdXqGRiMOwcO5+BJGYjKioKxcXFqK+vR1hYGFJTUxEZGYmQkBCw2WzEx8eDz+cjPz8fvXr1sphOtp09Yno9gbzMNPiHRCAkKgil+bnwDw1HHb8abI4tZBIJ1CYah+wIVQIpVGoCsX4uyCitgz2bgSA3LvKqGtAnxAN8oRRFfCFsmXSAAMK8HC0t2eREBHihtKoW9SIJQn09kJZXgnB/LwR5uyG3pArhAZ4I8nbHf7cyIFequ82YkUEju2GubAS7stEgVSPKkwa+SAk2g4qBwQ5g0Kkoa5BjaIgjDtzlg+0VBrZnMNTiBtB8o6AU8EFlsOEQNVC7U6AiDypH7QCtWi6FbUAc1OIG7Y4CrhvYnsFgOHqC4xMBeU0ZKPc1cCMSYRcQBwCwD+3bqI3p7A0AYDh6wNYvpvF3UWGqQRmil+77abnHDIBTcDwAwDXigRaOi/Y628kDjoFaLfS8FIN0GEp7uuU1ZaAx2VDLxAAMe5cuPUbCPjAeDmEP/kbW/TxgOno00yEsMOzvfDreF/H+LugX8mCw3ttJu7rAw4GDWD/nxt9TimoMsj0y2gPxvg7oG/jAhrcjW2uby2r8jWVDA6BfHqpEtQCA0f2i0SPUF09EBzba8XF1BAB4OnMRF+zd+PudnBLDdEc6I87bDn39H3QyvR20oc487BnN7k0tExlku7O0l0eS0iwwnTwBAE5RieAGar8Px3bKjsDActMWUoUacd62aJCqIVdr4GbHQLArG572DER4cJBWIQYoFPg5a8vAyEgXxPvYo2/Ag05YY35zm4eYSykV6q1jQJg74v2cAAB9g10f2HbSDrp4OLAR4+sIL0c2PjuW2q5uXpUEcjWBUHcOHFm0jmUOCQnJY8/QQQPQq4e2bh7wxIO62ddHWzd7eXogIe6BH3Xrjmn9l+5OYpAj4rztAKDddt3cbbqpGBnpgng9/ZgUI//NHrED4HzfN3eLbNk3d7rvm9ea2Dc3hNHRD/yEtvyRfgHWOWBMYn2MinzQ/+nrb9/476bfYoynrdl1GcLgPnHoGaVd5Na/R3Tj7z4e2u/Fy80Z8RHBuJ2RA6D9/m1+jQxsBg18odIgHa7RA+AYpK1XXJr0+dn36xWWk7ZPY2PLRcavn7erg1clAZdFh6utTQdzpnWcowY86H+Ft9z/sseDNt6YfbC2GBjtj4QgbR+xX4RP4+8+Ltqy6emkbSdt6FSs/vV8u31OcUEqHGOHgn/5ABKDnRDvo7XTvP7U9vWa9udsDejLtYa7Ae0MYF1tDYl+PPPMM43/HjBgQOO/fX19AQAuLi6IiIgwu66mDHhyLMJjeyK2d2Ljb+5e9/W5eyE0WltG2bbWU88PjPBCQoC2/u4X+mAsyNtZq9HTkYNYP5fG3+8WdvwMra7EuEE9G//9ROyDhd0+7tp23NPFEWqNNopQe2NGZQ3y+xOzlMYxdGvEoImIeG+7xs5US/g4MFFcVw0AsAuMb5wwaAmmsw9oto5630tlsFB8aC1EBSlQy0SNkxZKAR8cv2gwHD0bB4sJjQoUUEDjGL5ayBDdAFCddgmAdlWVRqmArKEajgExYDt7gWrDQE3WdfgNnGiwDkPRRzfwYGLGkHcpLEiBWiaGUlwPQqWAQlANO78YMJw8QaUzIC7lAQBc4ocbrPtuYQ1EMiXqJQoolGpUC2WI8XWCpyMHTDoNWWX1EEgVmNg3yGDbKSUNEMlVaJBqw+BUC+WI9ubCy4EFBp2K0jopFCoNuGytE6hvOQS0kwsiqRz1IikUShWq6kWIDfKCt4sDGDZ0lFTXwYZGQ+8Iw2ceU8pEEMnVaJCqIFdrwBcpEe1pC08uQztJVC9HrUQJNzsGbGjmrV70ySMRV1vGBPkPyo1GqS039v4xYN4vN6JSHlQSAZhOnqAx2UbTqE/ZBh4M+KSUCiFWqFEvVWnLiUiBGC87eHKZYNAoSCkTgUYBno5ybdVma1zK1n5D9RLF/TIoQ4yPdvKBQacir0qEe6V1Buluqp2EhITEUG7dSYFQJEJ9fQPkCgUqq6oRHxsNH28vMBkMFJWUQiQWg8VkwsPdzdJyuzwpZcL7vogKcjUBvkhxv01natsBvgR0KgUUCgW2jO4xyZxSKoRYrtK2q2pC267q/mYaFXk1EghkanjYa/0aY1Op881F9dCoFJDVV8MxMAac+765hF8KjVoFG3brba65uZxfDwCt+iL5NVKIFWq42THAZdEN2qFJ8nhyuaABABoHK7T9CQ487393N4qFcGDRwaRTwGUZtuvcXNxOz4FILEW9QAS5UomqmnrEhQfB290FTIYNisurUScQQng/xrahfQBD4Kfr+vwN0CjlkDfw4RAQDZazF6h0BmR1lRCV55hchz7UZmi1qsQN0KgUUDRUw87/gVZBfgpAoYDp4A4K3Tzv/m5eBURSBerFcm3fuUGC2AA3eDnbg0mnoaRGADqNBvr9vq0+fc7qK4cANGlzZLr6U6ltcxzutzl8CWQqDUZGGt6Xa42qJu2M+n474xT4YAyooTgLSokAtq4+oNrod3YqifXw77//AgDq6uogl8tRVVmJ+IQE+Pj4gMlkIi0tDdXV1QgKCgKHY5n2OCv1NiQiEYQNdVAq5KjjVyEkKh6unt5gMBioLCuGWqVGYU6mRfS1xsUs7Xm1DRI55EoNqgVSxPg5w8vRFkwbKu4V1YJCAVzsWaDTzBP9w9JcuJMFAKgXiiFXqlBVK0BcqB+8XB3BZNggI78UWQVlALrPmJFBLU9KmQjXioQIcWVDrtIg3I3duHo+wp2D0gY58mq0joCoIAXC7Gtge4ZAo5SD7R0Oqg0DaqkIhEYFjUIGYc5NAAD/6mGohDUt36eUQyWshbQyr1GHpDgdbM8QgEKBY9yIxgkIjk8EKDQaCKUMSqkQaoUEGoVh8eLb0kKolFDUV0Ipqmv2TH1hGuy9QwEKBV49n2rs5Dj4RcLeNwyCEp5BGjpCe3molghA43Ahr9V++Ia8SwAQFaeB4xkKDYUC5/gnQaUzIK8tha1PJOgcLpSCaojLsjukPa2kDiEeXFAAPBnrAyadhgapAtUCGfqEuOFiVgV45fUdsp1eJkCIu53WdpR74wQEnUpBpKc9MiuEKKoV65WHKmEt1MoHh2Hdyy9HmI8bKACe7hPZOAGhUmsQF+yNS/fykVVUabDmPL4UPg5MUCjaikY3+VAhUGh1e3Bwo1gIeyYNMpX+56sYg/bySFFbDqVIu3NFUpkHtosvAAq4QfH3z4SQQF5XAbarHwCAasOAStIAhdB4s92tlW2RXI1bxULYMmkYFuqI60UPVsSklWu38VIAPBnhAgaNgnKBHEo1gYHBjrhbKkR2ldggHXeL6iBTqhHqoT0DJcHfCQw6FQ1SJS7lVMOOScfwKE+kl9W3qVsg0w7mVImUGB7qiJvFQkgU1rPFkoSEpGuRnZsHPz8fUCgU9OoRDyaDgZKycpSWaf2DnvFx+PfiZTg6OCC/sAhqFVnfdJb0CjFCXDmgUDQYEe4MBp0KiVyNCoEUtRIVhoc54UpBA2rEhq3StVbyaqTwdWSBQqEg3tsWDBoVpQ0y1EmUqBIpMSDIAVcKGuDAokNqZD+mNvcu1AoZ7L1DQaFQ4BycoPU1ZGJQaXRIayugVsjAdvaE2sA+gqlIKRVCptI0+iHxPvZg0CgobZCjTqKEXKWBUK7GoGBHZFSKkVUpRr1Ue94ZCUlLpJSJIFMRCHFla/sTXrZg0KkoFyhQKVSiQaaCLYOG3r52yKySoKReDqoVLp3MKSqFn5cbKBSgZ1Ro4+RDWVUN6DQaYsICkZVfhOxC7e7zw6l81EhUj/Rt1RqgTCDHyHAn3OiEH91QmA477xBQKBR49NT2h2W15aAxOdAoZZDcD/vWlk+vIQBHFh25NVI0SI0fjk+QnwKNQgaOl3asRNcHU0oEkNWWg+3mBwpVO+ktr68AQXQ+VJG+3CvUhmSiUICRPYMbJyBoVCq8ne1RWNWAGoH2PML2xpJUEgGkFfmNttMqxAh1Zd/vyzmDQaNColCjokGKBF8uMitFBvflWkPXznDvj//o2hkJvxTS2nLYumn7uTYcLlRyKZR1VUZJl8Q8/PrrL3jyyacgk8kQGxsLJpOJoqIiqFQq1NTUICoqCvb29lAqlairq0N9fb1FdJbkZ8PD2w8UCgXhcb3AYDBQXV4KfkUZvPwCwK8oA0EQcHQx3gRcZ7lbyIdMoUaopwMooCAhwBVMGypKasQ4n1kGdy4bgyK9cCmrAgBQzO/8DiZr58CZqxjWOxpyhRJRQT5gMmwglshQXS8Er7AcAxLCYc9hgc3S7ihuaxyXw6BBolAj0p3TqbbOHBi8I0JNABUCBRzYNCjUBMoEMjiw6CiplyPa0xZX8gUAHsxii/LvQFFXAUV9BWzsnCEtzwY3cgBUolpwI7XbiFyfmNh4r7Q0CzRbB4AgABDg+EZBxeHC5n4Yl9ZmxylUOugcLuyDH8RpIwgC9ennDcqQ9rTQ2HZgyB8M0LvFDIBbzIBH7FBodNjYcuFoGwNpXaXJOzvt5qGoFmzPUKgl5wAY9i7tA+Nhf3+L58N/I53DhUNobwCAvL4SCoVhp74nBLgg3t/lkd/pDRRE+2jD2ozp4Y/KBglkDYbZjvd1QLzvo1vY6VRKY2imfkHOOJeldQ5cn5gIaNStllf74F6ov3cOANAj1Bc9Qn0ftU2jwtNZuxNnzBPRqKgVQFpr2MDCxHi3R2Y56VRKs3AGw8OcUClUQCZUGGS7s7RXzugcLtT3vw/P/hMbtwbrkNdXNsYiZblq848gCNSm/Wc0jbpZ4julIlQIFKgQKuDMsUF2tRQjwp1QI1aCy6I3blOP97Fv3M7bFG050c4oDw5xQqVADqlS//xO8HdqDM3U3K4ULyc+2OGTGOrWpu5cvgx9/e3hwKKDy6JjeJgTzmXXPWKXhISERB+ef25iY2gmHXQ6HV6eD7ZIjx/zNABALpfj0tXrZtXX3Yj3tm9x5VKlUIGwJucjPB3p0m3q9onx7oh/2I+hNfdjno50QaVQAamR/RjnkITGkBlNkdZVgu3kAVt3v8b/llrJRIQ+fogOXWimSoEclWb2AUm6Dq2tmHy4PwFoQ8bJVRrcLBKYS57eTBk9tDE0kw4ajQYvtyZhp+IiQadpB9Ynxrk2+tJZVVI4sGkgCIAAMDDIAcX18g770a7RA+Aa/Wifn0qjN4ZmorPtkPn7+nZ9+hqxEkNCHE1S53OD4h/pfwHafru9XxQANC4IM3YfrC0Sgj0bQzM1hUajNoZl8nNzwNm72smF9saSqDZM/D975x3mRLn98W962ZJs771Tli7SRLHQdq2oICp6LRcLFvBef3ZFsV0bitgVrl4rNnZVUFEUBUSEBaQku8v2nuym92R+f4QJu0uSTZlJsst8nsdHNpmcOXPmLed9z/uelxfjLAee2tAujRlFyc6UL2fkSP0ey3nCUz/D5nAhOlEWyL6GIAh0HgyNjRmoYfHiJaec/cDlcpGWlub6e8aMGQBO+Mk7d4ZUP5JzL7wCxWMmDPiMw+UiIdmpZ0qGMzPHnl9/CLlunhiXk+hKzdQfDpuNqUUnxyFzx2eDIAj8Yhz5fs6ic6difHHOgM86lSqU5J5M9X7m2CIIBc6+21P/0qW1Ykq2EEoAMcNgzsivQEStwoiiRBEyJE4jWGwOxAo4IAgCdsK59UNjdkb2jR0nVsezOeAnONMCOWwWCJJyYFa2DbjG3bUkHq8FYGiTwapVgiOMBlccC5teDWFKHuwGNQQnOlibnyut/dFFcyIlkbpFBrNGAZ4oGjyxBBa9CtGp+VDK97oi4uS1dOGL3rqmQ7CeOOPAn3dpaHduM9W3yWDVKMERnbS3KCUPNoMawhP2Jq/1FXmHc9vwsXYVlFoTooU8SMR8qPRmZCVEQ22wID8ldsC1vlLb7dyKJOvUQqmzIFrIRayIC5XBiqx4MdQGK/KTolwrEI0dtRClFXksr/3tJ29xBi+ONXWiR61HjFgASZQIKp0B2cnxUOkMKMhIGnCtz3r3OFeCyLoNUOotiBZwESvkQG20IVMqhNpkQ36CaMC1ocKf+qE/URZ0ZLkRRoMbFQubTg1RqrPckI6wRePf+R/eqFU4AyEcFgaU75w4AbpPDNoPtetQ2+O8ro60d5ceSr0V0QIOYkXck/Y2WpGfKB5wrS/IO08O6GQdaih0ZkQLeJCIeVDpLchKiILaYIHdQfilNwD00rCCioGBYWSj1ztXAh6Tn/SjjhyTobtHidiYaEgksehTqZCbnYU+lRpFBfkAgI5O/3f2MZykfz8t6+7Xzwi5p/TrvYaRsSOirnuoftWG/ETRgGupQt1vF7K6RQaT+oR/HiWBRadCTFo+LHo1opOzBlwbTgb7Fr74I+5+x8BAQvqUADmeGNzuCAaMJwCgUxd57Y/seAsA4Gh9E3p61YiJEkMSEwWVRofs9GSoNDoU5mRA1ujcieDOlybp70v760dr2072mxrXuD8GvKhYWHTOcb/VoHYtPPTVp6fDn9f301XXKoNFowT3xNjdqldBnJrvHMOfmKOwqEOTg13e5hzrHWtRQKExIEbER2yUECqdCdlJsVDpzShIi4NS62zXfJlLshmdY626Hqd/42w7ybEzF2qjFZlxZJ8jHnBtMGg89DP8KAnMJ/oZq17tCkaY1T1B35MhdBw9etT1b2cKpm7ExMRCKpWir68XhYVFUKlUyM3NBQC0t7eHRU8y5VKj/Aj6lD0QR8ciJlYCjboPmbmF0GlUSM3Mgbo3cs5ZqO2X5eRYex+UGhOiRTxIRHyoDGZkJcRAbTAjP8W56EKhjYwFI3Qia+pw/ftoQxsUKi1ixEJIosXo0+qRnZoItc4Am925g3jEzBkRPtDU1ESIRULnsmdf/mOxw39tv/+EIjHR1NQ05DMKRWJadRH5oIe/+K03QLBZfugdwHP6am+xSOS3bLFI5KNsP8qrn8/JZrH811tMk94AIRYJKS9X7nSju374Um6G0tFf+/lVF3y0dyBlmw49GBgYGAiCIBwOB/HZZ58RKSkpBJsdmB/F4bCJJ554grDZbOF+nGFDJPfpdBHOZ25qaiJEYv/8YQCESEy9b+6PzoHYa6SUFwbqCbZMRUp5ampqIsR+1md/fWlfnrWpqYkQ0TnOptDmgcwJkP8FOwYbSi+/x/x+jctDV84D7WeA8PY1DL4RSLvjKlMhfL+B6BlK/TzqHMDcn7O+Dj2PNhxxvsfTd86IRRC+JQZsbm6GQuFbNM1sNkMg8O1Qno4OZwSo/1Ynb9eSOdikUinS0tLw119/4eabb8YZZ5wBNpuNV1999ZTfJSYmIjt76EOD/XlGUhdSDwC48cYbIRQKsWvXLmzYsAFjxw5MIeWrHv7ij96AfzYf/C7/+OMP3HrrrZg8eTKEQiHWrl17ym8CtfeuXbtw++23Y9KkSRCLxXjppZcok71z506sWLECEydORFRU1Cmy/S2H/a/tLzsmJgYvvPACZXr/9ttvuPPOOzFhwgRIJBI8//zzAcsOFn/rBwCsXbsWf/75JzZt2oS4uJNpivr6+rBo0SJMmTIFTz/9NABqniPYurB9+3asWrUK48aNQ1JSEp555plTfuOLnu70+Pnnn3HPPfegvLwcKSkprucm9RjcngxFqN47AwPD8KWurg633347tm7diosuugj//ve/IRQK/ZLR19eHt956C5988gmmTJmC1157DZMmTaJJ45GFu75gxYoVsFgs2Lt3L1577TWcccYZA74f7m17/2f+z3/+g++//x7fffcduFwuHnjgARw7dgybNm0Ci3UyIT1Vz+zO3rfccgtYLBb++OMPvPXWW6ekWwi3vT35LV9//TVWr16NwsJCjBs3Dvfff7/b34dbf4bIo3+Z6u3tRWVlJa688krccccdrmtefPFFfPHFF9i8efMA/zySytPgutHW1oYLL7wQZ599NhoaGvDFF18MuN6fsRwQ3PzAgw8+iObmZhw+fBhPPfUULrjgAtr18IX+uj733HOoqqrC5s2bIZFIAABdXV246KKL8I9//AM333wzLToMpRcANDQ0YNGiRTjrrLPQ3t6OTz75ZMD1wcxVkLJnzZqFzs5OfPzxx6f8JpjnHfwsBw8exPXXX493330X6enpmDdvHlavXo2FCxdSdk+G0OGuvn/66ad47rnnkJmZiRkzZmDVqlWn/C7U73ewnv/973/xxhtvIDY2FhUVFbjtttvCqp87Buu8e/du3Hbbbfjoo48gEolw8cUX4z//+Q/mzJkz4HeRoDtdDLbJ77//jjvuuAOffvopuFwuLr30Urz44os466yzAIywOaNwR0KC5c033yTYbDZxzz33EDk5OWHVJTU1lbjvvvsIAMTGjRvDqgtdrFu3juDxeMRdd91FFBYWUir7lVdeIXg8HnHnnXcSxcXFlMp++eWXCYFAQNxxxx1ESUkJpbLXrl1LCIVCYsWKFURpaSmlsl988UVCKBQSt99+OzFq1ChKZdNNdXU1AYB4//333X7//vvvEwCI6urqEGvmmWeffZaIjo4mbr75ZmLcuHGUyn7mmWeI2NhY4qabbiLGjx9PqWwGBgaG/hiNRuLRRx8lBAIBkZOTQ2zevDlomb/99hsxduxYgs1mE7fffjuhUqko0PT0o6CggLj77rsJLpdLrF+/Ptzq0IbZbCYSExOJVatWuT7bunUrAYD4448/QqZHVlYW8a9//Ytgs9nEW2+9FbL7Bsu9995L5OTkEIsWLSLmzJkTbnUYhil33XUXERsbSyiVygGf9/T0ENHR0cQ999wTJs38Z8uWLQQAYvXq1QSXyyWsVmvYdDnjjDOI66+/nkhISCAef/zxsOnhiaamJoLP57vV7e677yZiYmIIhUIRBs2cfP311wQA4tFHHyVEIhFht9spk/3VV18RAIhHHnmEEIvFlMp2x3333UckJia6doxOmTKFuPzyy2m9J0NoufPOO4mSkhKisrKSmD9/frjVcctNN91ETJgwgTj33HOJRYsWhVsdn1ixYgWRlZVFOBwOgiAIoqysjLj++uvDrFV4ueWWW4i8vDyXTYqKioibbropzFrRAzuMMRBKkMlkyMvLw6hRo9Dc3Ayj0b8DjalCo9Ggs7MTY8aMQWZmJmQyWVj0oBu5XI7CwkKUlZWhoaEBFgt1B8jIZDIUFhaitLQUx48fh9VKXa7S/rLr6+spl11UVOSSbbNRl4uNlF1SUoK6ujrY7XbKZNOJTqfDrbfeigsuuABLly51e83SpUtxwQUX4NZbb4VOpwuxhu6RyWQoKSlBSUkJ5HI5HA4HpbKLi4tpkc3AwMBA8v3332Ps2LFYs2YNVq5cicOHD6OysjJouTNmzMBff/2F//znP9iwYQNKSkrw4YcfgvBtYy0DnCs3GxoaUFZWhvz8/BHrKwLAt99+C4VCgWXLlrk+O/fcc5GRkYENGzaERAeDwYCWlhaMHj0aubm5kMsj40wIXyD9keLi4hFdThjoo7m5GevXr8c999yD+Pj4Ad8lJiZi5cqVWLduHdra2sKkoX/IZDIIBAKcccYZsNlsaGhoCIseBEEM8OkjsX6uXr0aEokEd9555ynf3XfffSAIwu2u71Ahk8kQHR2NyZMnw2g0orW1lVLZMTExmDx5MgwGA+3lu7q6GgsWLADnxIHpFRUV2Lp1K6VzJAzhRS6XDxjDRyJkmzRcfAaCIFBdXY2KigrXDtmKigp88803p+0ciTubVFZWorq6ekSOtYZ9IIJsGIqLi0EQBOrr68OmBwCXLpHaSAVLf3vb7XZKnUC5XO4adFHtYPbX22azobGxkRbZVqsVTU1NlMombWKxWCiVTScPPfQQenp68Nprrw1Iv9AfFouF1157DT09PXjooYdCrKF7+jsaRqORUue1v2yDwRC2g60YGBhGJm1tbbjyyisxd+5cZGZm4sCBA3jyyScRFRVF2T14PB5WrlyJo0ePYtasWVi6dCnOO+88HDt2jLJ7jGSOHz8Oh8MR8QNaKtiwYQMmTZo0IE0ph8PBNddcg48++ggmE/0HENbV1QEYnr55f5+hra0tYhZsMAwfHn/8ccTGxuKuu+5y+/3KlSshFovxxBNPhFaxACEXw40aNcr1dzjo6emBWq2O2HZFLpdjw4YNuP/++xETE3PK90lJSbj77rvxyiuvhG0s0r99I/8eDrIH09TUhEOHDqGiosL1WWVlJTQaDXbs2EHbfRlCS//5GKoX4lJFfx1ra2sjfjL/yJEjaGhoGLBQqrKyEt3d3fjzzz/DqFn4OHjwIFpaWgbYpKKiAh0dHdi3b18YNaOHERWIIP8Olx7A8Bzs+AOd9mZkh1Y2Xfz55594+eWXsXr1auTn53u9Nj8/H6tXr8bLL78cEZ0O8y4ZGBiGGzabDS+99BJKS0uxfft2fPDBB/jpp59QVlZG2z0zMzPx2Wef4bvvvkNTUxPKy8vxwAMPwGAw0HbPkcDp4iv29PTgm2++GbAbgmTZsmVQqVSoqqqiXY/ham+73Y66uroBPgMZVGFg8IXa2lq89957HiejAUAikeD//u//8Pbbb+P48eMh1tB/SD86IyMDIpEo7GN+ctJPLpdH1GrVRx55BGlpaVi+fLnHa1atWgWRSIQ1a9aEULOTkO8yNzcXPB6PlvFWbm4uuFwureWkuroaXC4Xc+fOdX02fvx4ZGRkoLq6mrb7MoQOcicr2R87HI6Iay/JzCykjiaTidJdRnRQXV0NsViMc845x/XZtGnTEBcXd9rWnerqakRHR7vOgwCAmTNnQiKRjEibDOtAhM1mQ319PUpKSpCYmAipVBq2rUhyuRypqamIjY0dNpFIfzGbzWhsbERxcTHS0tIQFRVFmb1NJhOamppcDqZYLKbMcTAajWhubkZJSQnlzispu7i4GJmZmRAKhZTZRK/Xo7W1FcXFxcjOzoZAIIj4QbTVasVNN92EcePGeVyBNZi77roL48aNw0033URpyix/UavV6OrqosV5ValU6O7uRklJCfLy8sDhcCL+XTIwMEQ+u3btwuTJk7Fy5Upce+21kMlkWLp0qcedaFQzb948HDp0CPfddx+ee+45jB49Gt98801I7j0ckcvliImJQWpqakSvrAuWDz/8ECwWC0uWLDnlu9LSUkydOjUk6ZlkMhni4+ORkJCA4uJi1NXVUZo+ky6am5thsViYxQsMAUNORt9yyy1er7vtttuQlJSERx99NDSKBQG54pfNZqOoqCisgQgWi4WCggIUFxdDpVK5PXA+HBw4cAAff/wxHnnkEQiFQo/XkUGoN998MyyTqmSwgMvloqCggPJARElJCXg8HvLz82kPRMyePRuxsbGuz1gsFioqKlBVVRVRASqGwOi/k5XsjyMt9VFtbS0AhGwnEBVUV1fj/PPPH9BOcblcLFiwYEROuvtCdXU15s6dC4FA4PqMx+Nh3rx5I9ImwzoQ0djYCJvNhuLiYrBYrLCudiI7VMDZCFCd1iUSqK+vdzXEVNu7vr4eBEHQLptq57X/tv/hJJsuXnzxRRw6dAhvvfUWuFyuT7/hcrl46623cOjQIbz44os0a+iZ/p041c4rnbIZGBhOP5RKJW666SZMnz4dXC4Xe/bswauvvgqpVBpyXUQiER577DH8/fffKCoqQkVFBS655BI0NzeHXJdIh/QVST8nElfWUcHGjRtRUVGBxMREt99fd9112Lp1Kzo6OmjVY7BvTnX6TLrov5ODDKQwPgODrxw4cAAfffQRHnroIa+T0QAgFovx4IMP4oMPPsDhw4dDpKH/9F/4BSDsY/6cnBwIhcKICxQ++OCDKCwsdLsbbTC33347EhMT8dhjj4VAs5P0X70NUPsu+y8qo1r2YHQ6HX766acBaZlIKioqUF9fH3ET1gz+078/Tk1NRXR0dMTUd5L+Oubk5FC+y4hqlEoldu7c6bHu1NTUoKWlJQyahY/u7m788ccfHm2yd+/eEZfWe1gHIsjGnexswplvlzwghtSjv34jhf5bUcn/U2Vv0lakbCoP2nFXTqiWPdxsQgf19fV45JFHcNddd2HSpEl+/XbSpEm466678Mgjj4TtnJdQlJOioiLKZTMwMJw+OBwOvPvuuygpKcFnn32GV199FX/88QcmT54cbtVQVFSErVu34pNPPsEff/yBsrIyPPvss2Hd6RZpnA6+4sGDB7F//35cd911Hq+58sorweVy8b///Y9WXciVsQCGzSpB4OShvNnZ2QAYn4HBPx566CEUFBTg+uuv9+n6G2+8ETk5ORFzXps76urqXIvKgPDWif7teGFhIVgsVkTUz127dqG6uhqrV68Gj8cb8noyCPX+++/jyJEjIdDQyeD5BCrHt/0nZMl70PVufvzxR1gslgH53EnOPfdciESiEbmK+XSj/05WFosVked7yWQypKSkQCKRuHYZRUKb5InvvvsODocDCxcuPOW7efPmgcPhnHa7q7/99lsAwIIFC075bv78+WCz2a5rRgrDOhAhl8shFouRkZEBIHwTtQRBDBjshCInYTiQyWSIjY1FcnIyAOodB4lEgqSkJADUTujL5XLExcW5VuZRuTpCLpe7VqvRITshIQHx8fEAwhtoGwqCILB8+XKkpKRg9erVAclYvXo1UlJSsHz58rBsZe2fXg2g/l2mp6e78vQOp1zVDAwMkcGhQ4dw1lln4YYbbsD8+fNx7Ngx3HrrreBwOOFWzQWLxcIVV1yBY8eO4eabb8Z9992HCRMm4Ndffw23ahFBf18xUlfWBcvGjRuRlJSE+fPne7wmLi4OF110ETZu3Ehbf08QxIAJQ6rTZ9IJeSgvWbcZn4HBV3bv3o2qqiqfJ6MBgM/n47HHHsOXX34ZEee1ucPd5HW4DnHvv9NKJBIhOzs77PWTIAjcf//9GDt2LK688kqff3fTTTchJycHDz/8MI3aDYS0Vf/FWY2NjTCbzZTJ7r8jgq4UiFVVVSgtLUVBQcEp34lEIpx77rkhOQuJgV7672QFInNhaP82CYjsOSPAWXcmT56MtLS0U76TSqWYNWvWaVd3qqqqMHXqVNc8a38SEhIwffr0EWeTYR+IKCoqApvtfIzi4mIolUoolcqQ6tHZ2QmdTudqAOjIdxgJuGuIOzs7odFoaJHd3t5OiYPpTjZVzuvghr+4uBgtLS3Q6/W0yG5ubobRaAxaNtV88MEH+PHHH/H6668jKioqIBlRUVF4/fXX8eOPP+KDDz6gWMOhcWdvqpxXOmUzMDCMbLRaLe655x5MmDABSqUSP/30E95//32kpqaGWzWPxMbG4sUXX8Rff/2FmJgYzJ49G9dddx26u7vDrVrYGJwyItwpRenAarXigw8+wNKlS4ecBF22bBn+/vtv7Nu3jxZdlEol+vr6XPYeLikuAfc+Q6QdiMsQmTzwwAMYO3YsFi9e7Nfvli5dirKyMjz44IM0aRYc5KKy/gu/gNAf4t7/IHmSSGjHt23bhu3bt2PNmjWueRFf4PP5ePTRR/H5559j7969NGp4Erlc7lq9DYDSNIVyuRxpaWkDFn7RkQLR4XDgm2++cbsbgqSiogK///47ent7Kb03Q2jx1B9HEsNBRxKr1YotW7a4TUFEUlFRgW3btlEynzYcMJvN+P77773apLKyEj/++GNEzgMGyrAPRAyudMDJfOyh1KP//cl/R2oDECh02jscsqlwXoerbCrp6enB3Xffjauuugrz5s0LSta8efNw1VVX4e6770ZPTw9FGvqGO3tT6RgPlm2329HQ0BC0bAYGhpEJQRD4/PPPUVZWhvXr1+Pxxx/HgQMHcM4554RbNZ8ZP348fv/9d7z55pvYvHkzSktL8cYbb8DhcIRbtZDT/6wgkpHmK27ZsgXd3d1e0zKRXHDBBUhNTaXt0Orh7Ju78xki6UBchshk27Zt+Omnn/DEE0/4NRkNABwOB0888QS+//57bN++nR4Fg8DdojLy81BCHiRP7swgdQlnu0LuhjjzzDO9TmR54uqrrw5pEMrT+JYKG9Ipuz979+5FV1fXkJOpdrsdW7ZsofTeDKHFXZnq6uqCWq0Oo1YnITOzDNaRql1GVLNjxw5oNBqvQbzKykqYzWZs27YthJqFj19++QU6nW7IwKbBYMDPP/8cQs3oZUQFIsgtfqF2BuRyOTgcDvLz812fhdspoQM6O3eZTHaKU0eV7FDqTVUOYjKlAF02oZJVq1aBIAjKDpp+8cUXQRAEVq5cSYk8X3DXiVOVv5tO2QwMDCOT+vp6LFy4EIsWLcLEiRNx5MgR3HfffeDz+eFWzW/YbDZuuukmyGQyXHLJJVi+fDmmTZuG/fv3h1u1kDI4HQUw8nL/b9y4EePGjcO4ceOGvJbL5eLqq6/Ghx9+SMtgmbR3YWGh67Ph4JsPPpQXYHwGhqEhJ6PPOOMMr5MZ3rjkkkswadIkPPDAAxG3+2awHx0fH4/ExMSQ14nB58mR/66rq4Pdbg+pLiRff/01/vzzT6xZs8YVqPEHDoeD1atXY+vWrfjll19o0HAgg99lSkoKYmJiaBnzp6Wl0ZICsbq6GnFxcZg+fbrHazIyMjBhwoQRl07ldII8WN3dfEyoFz57orOzE1qt9hQd6dgJRAXV1dVIT0/HhAkTPF5TXFyMoqKi06buVFdXIysrC2PHjvV4TVlZGfLz80eUTYZtIEKv16O1tXVAZxMVFYXMzMywOCW5ubkDJghKSkrQ0NAQkZHIQFCpVOju7h5gb4lEgpSUlKDt3dvbC4VCMWBwLpVKkZycTJlsOpxXpVKJ3t7eU2QnJCQE7fAoFAqoVKoBNklMTER8fHxEDUS///57vP/++3juuefc5rQLhOTkZDz//PP44IMP8P3331Micyg6OjoGpFcDqMvf3d7eDr1ef4pjHBUVFfETIgwMDKHFbDbj8ccfx5gxY3D48GF8/fXX2Lx5M3Jzc8OtWtAkJSXhnXfewY4dO2AwGDB58mTccccdEbOqjG5kMtmAc4iAyFtZFwxKpRKbN2/GsmXLfP7Nddddh97eXloOJZTJZMjKyoJYLHZ9VlJSQln6TLoYfCgvABQUFIDFYjE+A4NHNm/ejD179gQ8GQ0408WtWbMGO3fujLhDMfuf90ISjsCiXC6HQCBAVlaW67OSkhKYzWY0NzeHVBfAmSrqwQcfxJw5czBnzpyA5Vx66aWYOHEi7UEod4uzyAOA6Vj4RaZApHrsXFVVhXnz5oHL5Xq9rrKyElu2bIHVaqX0/gyhwd0CErJ8Rcp8jKdFLkDk6EhCEASqqqqwcOHCIfupyspKfPPNNyN+BzVpk4qKCq82YbFYqKiowDfffBNxCwUCZdgGIsgoZP/oHxA+p8SdHpEaiQyEwYeEkVBhbzrfJZ16u9v2T/4dbMNPp95UYTAYsHz5csyZM8enNAz+sGzZMsyZMwfLly+HwWCgVLY73NmbqvzddMpmYGAYOfzwww8YO3YsVq9ejbvuugtHjhzBhRdeGG61KGfmzJnYt28fnn32Wbz77rsoLS3FRx99NGIca0948hWByFlZFwwff/wxCILA0qVLff7N6NGjMXnyZGzcuJFyfbzZO9JSXPbHnc8QKQfiMkQmDofDNRl93nnnBSXrggsuwFlnnYUHH3wwYiaAyPMfI2FMNPggeVIP8rtQ8/HHH+Pw4cN48skng5LDZrOxZs0a/P7777SmEnK3ehug5l2Si8roLietra2oqanxaedRRUUFVCoVdu7cSdn9GUKHu7me2NhYpKamRkx/LJfLwWazBxyanpycjNjY2IjRkUQul6Ours7nutPR0THid08fOXIEjY2NPtukpaUFBw8eDIFm9DNsAxHuon9A+JwSd5PR5HcjATrtHQrZ/bfmn+6yqeLRRx9FR0cH3njjjYBXX3mCxWLhjTfeQEdHBx599FFKZbuD7MT7p1cDqHuXHA4HeXl5lMtmYGAY/rS3t2Px4sW44IILkJ6ejgMHDuCpp55CVFRUuFWjDR6Ph1WrVuHo0aOYMWMGrrrqKpx//vkRt3qLStz5iuFKKUoHGzZswIIFC/zeHbls2TJ8++23lB9kPlx9c7lcDqlUisTExAGfMz4Dgyc+/vhj/P3331izZk3QsshdETU1Ndi0aRMF2gWPu/N1yL9DfYi7u3YlOzsbfD4/5PXTarXi4YcfxkUXXYSpU6cGLW/u3LmYNWsWHnjgAdqCUN4W8dG5QJDKd1NdXQ0Oh+PTuYiTJk1CamrqiEqncjohl8tP2ckKRFZ/LJfLkZubC4FA4PosUhc8VlVVQSgU4txzzx3y2pkzZ0IikYz4ulNVVQWxWOzT+X+zZ89GdHT0iLHJsA5EkKlq+lNcXIza2tqQreKw2Wyor68/pdOjKq1LpCCXy5GWloaYmJgBn1PhBMrlcqSnpyM6OpoW2ZmZmadM6FAle/C2f1K2TCYLWnZ2djZEItEpsiOhTO3fvx8vvPACHn744VOCJVRRWFiIhx9+GC+88ALt0XC5XI68vLxT8q+XlJRQ4hjn5+eDx+MN+DxS3iUDA0N4sNlsWLt2LUpLS/HTTz/hv//9L37++WeMGjUq3KqFjKysLGzatAnffvstGhoaUF5ejoceeghGozHcqlGKu5QRwMkUl8O9Lzh8+DD27t3rV1omkiVLloDFYuHDDz+kTB+Hw4Ha2tpT7J2QkID4+PiItvfgQ3lJqPBHGEYe5GT0hRdeiDPPPJMSmTNnzsSCBQvw0EMPwWazUSIzGLwtzgr1Ie6Dz+8DnGcsFBYWhrx+vvvuu2hoaMDjjz9OiTwyCLV//358/vnnlMgcjLeFX8GmKfS28KuzsxMajSZg2f2prq7GzJkzERcXN+S1bDYbCxcuRHV1NSX3Zggt7lLCAZE1hne3+xOILB1Jqqurce65554yd+YOHo+HefPmjfi6U11djfPPPx9CoXDIa/l8PubOnTtibDJsAxHuHAHA6agbjUa0traGRI+GhgbYbLZTdKEq32Gk4M3eWq0WnZ2dtMhWq9VBrZLz1IGUlJRApVKhp6cnKNme9FapVFAqlbTIJrcohwubzYabbroJo0aNwj333EPrve655x6MGjUKN910E62DIU/2psJ59fYuOzo6KHOMGRgYhg+7d+/GlClTcPfdd+Pqq6+GTCbDNddcQ/nusuHC/Pnz8ffff+Pee+/Fs88+i9GjR0dcjvJg8JQyAhgZB1Zv3LgRCQkJqKio8Pu3CQkJqKysxIYNGyjTp6WlBWazeVja25s/Es4DcRkik/feew/Hjx+nbDKa5IknnoBcLsd///tfSuUGAnney+BFZaHOg+7uIPn+uoSyXTEajVi9ejWWLFni9YBTf5k1axbmzZtHWxBKJpMhLy9vwOpt4OS7DGbilJTtblFZsLJJDAYDtm3b5teB8JWVlZDJZCMiBePphqdJfnJhQCSkFPU21xVJvk5fXx9+++03v+vOX3/9hfb2dho1Cx8KhQK7du3y2yZ79uxBV1cXjZqFhmEbiHC3sgwI/bZrT9sAyc8iLRIZKHTae6TKDqbxj5Ty7Y6XX34Z+/btw1tvvXXKKn+q4fF4eOutt7Bv3z688sortN0nnOWEcUwZGE4fent78c9//hPTp08Hh8PBH3/8gfXr1/u0sm6kIxKJsHr1ahw6dAgFBQVYuHAhLr30UrS0tIRbtaAZyb6izWbDBx98gKuuuuqUCSBfue6663DgwAHU1NRQotNwtrc3nyFcB+IyRCYmkwmrV6/G4sWLUV5eTqnsCRMm4PLLL8djjz0Gs9lMqWx/8VQnQn2IO3m2TCS0K+vXr0dXVxcee+wxymU/8cQTkMlkeP/99ymX7eldUpGmkE7ZJNu2bYPJZPIr6H7eeedBIBCMmFXMpwuedrICzvqu0+mCWohLBZ4yswBOHbu7u6FSqUKvmBu2bNkCu92OhQsX+vybefPmgc1m45tvvqFRs/Dx3XffweFwYMGCBT7/Zv78+QAwIhZrDctAhLeGITc3F1wuN6SBCLFYjPT09FO+i/TBjq94s3d+fj7YbHbAz+lp+zwQvINJynYXyaZKtju9ya3Dgcq22+2oq6ujRXawNDY24qGHHsLtt99OST5SX5g6dSpuv/12PPjgg2hsbKRcvtVqxfHjx2kJRHiTPZJygzMwMHiHIAhs2LABJSUl+Pjjj/Hyyy/jjz/+wJQpU8KtWsRRXFyM77//Hh9//DF2796NsrIyPPfcc7BareFWLWA8pYwAwpPnnEp++OEHdHR0BJSWiWTevHlITk6m7NBquVwOHo+HnJycU76jIn0mXZA7Xj2tbgQYn4HhJK+99ho6OztpmYwGgNWrV6O1tRVvvvkmLfJ9xdMYNNSHuJMLzDztWGpubg5JWkGNRoOnnnoKN9xwAy3pcSdNmoRFixbREoTy9C5jYmKQlpYWdCDC3buhMgViVVUVioqK3N7HE1FRUZgzZw4TiBhmkDtZI3VhKOCcl3GXmQWIvAWP1dXVmDBhAjIzM33+TUJCAmbMmDFi6051dTWmTJmCtLQ0n3+TnJyMM888c0ScEzEsAxEKhQIqlcptw8DlclFQUBDSQERRURHY7FNNSUW+w0igvb0dBoPBrb0FAgFyc3MDtjedstva2mA0GmlxXltbW2EymWiRTaYUcCdbLBYjKysrLB0fQRC45ZZbEB8fT8mBeP6wZs0axMfH45ZbbqF88oDsxN3ZOzY2FqmpqQHbu6GhAXa73a1sqVSK5OTksDsxDAwM9PL333/jrLPOwvXXX48LLrgAMpkMt99+OzgcTrhVi1hYLBauvPJKHD16FDfccAPuvfdeTJw4Eb/99lu4VQsIT+cQAZGzsi5QNm7ciDFjxmDixIkBy+DxeFi6dCn+97//URJwkslkKCwsdFvHyLzy4Uxx6QlPh/ICzvNUBAIB4zMwAAC0Wi2efPJJ/OMf/3AtbKGa0tJSLFu2DE888QT0ej0t9xgKbwu/gNAu+pPL5YiLi0NCQoJbPQiCQH19Pe16vPTSS9DpdHjooYdou8fq1avR0tKCt956izKZ3lZvA8G9S28Lv4KVTUIQBKqrqwNKQVhRUYFff/112M8JnU5421lJLsQNd+ojbzpG0oJHm82G7777LuC688MPP4y4s+MsFgu2bNkSsE2+//77sO9WDJZhGYjwVunIz0PplHjTA4icSGSg0Gnv4S7b04qIYA4VjKTy3Z+PP/4YW7Zswfr16085tJxuYmJisH79emzZsgUff/wxpbKHaxlkYGCIbHQ6Hf71r39h/Pjx6OnpwbZt2/C///0Pqamp4VZt2CCRSLB27Vrs3bsXUVFRmDVrFq6//vqgzncKB774isOxL+jr68NXX32FZcuWBX2+yXXXXYeenh589913Qes1XO1N6uRuYjlcB+IyRCYvvfQStFotrZPRAPDII4+gr68PL7/8Mq338YS3RWVAeMb87tq6ULUrSqUSzz33HG677Ta/Vhb7S1lZGa699lpKg1DeFn4Bwb1LOmWT7Nu3Dx0dHX7lcyepqKiAzWbD1q1bg9KBIXSQO1kHH6wOOA8NzsvLC3t/LJfLIRKJkJGRccp3VOwyooqdO3eir68voLpTWVkJo9GIn3/+mQbNwsdvv/0GjUYTsE30ej22b99OvWIhZFgGImQyGVgslsftiKE8nMXToXIANWcFRAIymcxjQwwEZ2+ZTAYul+s2XQFVsnNzc2mR7WnbP3By63+gsvl8vkfZ4Th8SKlU4s4778Tll18eUINJBZWVlbj88stx5513ore3lzK5MpkMYrHYbScOBP8uo6Ki3KZuAyLvICkGBobgIQgCX375JUaNGoV169Zh9erVOHDgAObMmRNu1YYtEyZMwM6dO/HGG2/g66+/RklJCd588004HI5wq+YT3nzFgoKCiFhZFwiffvopbDYbli5dGrSs8vJyjB8/npL0TJ5SdAAnJ/kj0d4ymQyZmZmnHMpLEow/wjByICejb7nlFmRlZdF6r5ycHPzzn//Es88+i76+Plrv5Q5v6ZDIz0N1iLu3AGdSUhKkUint9fOZZ54BQRD4v//7P1rvAziDUL29vZSd0efLuww0TSEp29P7Icdbweyqr66uhkQiwcyZM/3+bXZ2NsrLy0dsipmRiLedrEBwi06pQiaTeczMAkTOPEN1dTVSU1MxadIkv39bWlqK/Pz8EVd3qqurkZGRgfHjx/v92zFjxiA7O3vY22RYBiLkcjlycnIgFArdfl9cXIzGxkbat6vodDq0tbV57PSCTesSKcjlcuTn53s8mLi4uBj19fWw2Wy0yA7UwZTL5SgoKACXy6VFtqdt/6GQXVtbG9IJmH/961+wWCxYu3ZtyO7pjrVr18JiseBf//oXZTK9pVcDgnOMva2eAoZ/bnAGBoaBHD9+HBUVFbj00ktRXl6OI0eO4P7774dAIAi3asMeNpuNm2++GTKZDBdddJHr0O/9+/eHWzWvDJUyIlJW1gXChg0bMHfuXL/y23rjuuuuQ1VVFRQKRcAyzGYzGhsbPdo71Hnl/cHbRCcQGRMfDOHn2Wefhd1ux3333ReS+z3wwAMwm8147rnnQnK//ng77wUI7SHu3gLKLBaL9t0Z7e3teOWVV3D33XcjKSmJtvuQ5Obm4uabb8YzzzxDyYG33lZvAyfTFHZ0dAQk29OZnf1lB5MCsaqqCvPmzfM4ZzEUlZWV+Pbbb0MSNGMIHplM5rU/joSsBt4WXQCRoSPgrDsLFy70ONfiDRaLhcrKSlRXV4+Y+RKCIFBVVYWKioqAdhOTNqmqqhrWNhm2gYihGgaHw4Hjx4/TqkddXZ3rft50iYQGIBh8sbfNZgvoIOGDBw+ioKDAq2yr1Yqmpia/Zfuit8ViCch59UW22WxGS0uL37IPHTo0pE1MJhNaW1v9lh0IP/30E9577z08++yzlE02BEpaWhqeffZZvPvuu5Rt0fPlXep0uoDepS+ytVptyN4lAwMDPZjNZjzxxBMYPXo0Dh06hC+//BJVVVUed/sxBE5SUhLee+89/Prrr9DpdJg8eTLuuusuaDSacKvmlqFSRgDOXREHDx4MoVbBI5PJsHv3blx33XWUybzqqqtAEAQ++uijgGXU19eDIIhh6ZsP5TMUFRWhqakJOp0uhFoxRBIdHR2uyejk5OSQ3DM1NRV33nkn1q5di66urpDck8SXRWUAaG8/u7q60Nvb6/U8DrrblTVr1kAkEmHVqlW03WMwZBDq+eefD1rWUAu/gklvRadswJki7K+//goonztJRUUFlEoldu7cGbAMhtBAEAQOHz485HxMoAtxqYAgCBw5csTrgfWRsOCxtrYWx44dC7rutLS0oKamhjrFwohMJkNdXV3QNmlqasLff/9NoWahZdgFIqxWK37++WeIxWKP15DbVL/88ktadfn8888BwOMqDQCIiorCDz/8MGyj32azGb/++qvHbeLASXt/9dVXfsk2Go346aefvB4+Gahsk8mEHTt20KK3yWTCb7/95lU2mbfz66+/9ku2TqfDL7/8gh07dlAuOxAOHz6MSy+9FDNmzMCNN95I+/184cYbb8SMGTNwySWX4PDhw0HJ0uv12L17N6Kjoz1eQ66eefrpp/2W/ccff3iVTa768Vc2AwND5LBt2zaUl5fjsccewx133IGjR4/i4osvDjpnPoN3Zs2ahf379+OZZ57B22+/jdLSUnzyyScRtzqI9DG8pVD5/fff8cMPPwyrg+fOPPNMAMDChQspk5mUlITU1FTccccdMBgMAcn44osvADhTYXgiKioKP/30EyUHY1OFQqHA4cOHIZFIPF5DHoRLVaoUhuEFQRAYO3YsAIR0Mhpw7oy22Wyu+4eKr7/+2qsfTbb3dO8OIXeDeFs4FBMTg7/++ouWoPjrr7+O9evX48477/TaRlBNWloabrjhBjzxxBN+j5cHU11dPeS4nMVi4dNPP/VLLnmItDfZZH/gr2ySSy65BIAzTWSgTJw4EQCCmnxkCA0HDx5EQ0MDdu3a5fGa5ORk2Gy2sJ1dsGPHDnR2duKPP/7weE1iYqJrPiJckOWdLP+BMHnyZADAggULKNEp3JB+M/lcgTASbDLsAhEqlQoqlcq1G8Ed5KqJYDvMoSAngr1NNtTW1kKpVEKr1dKqC12QutNhb5FIhOnTp+PZZ5+lXHZPTw90Oh0tend3d0On07kGhO4gt236GwyLiorCmWeeif/85z+Uyw6Ehx56CGq1GjfeeGNA2+nogM1m48Ybb4RarQ76kL7W1lYYjUavu6cuv/xylJWV4eqrr/ZLdnNz85CyFy9eHJBsBgaG8NPQ0ICrrroK5513HlJTU1FTU4NnnnnG62CYgVp4PB7uueceHD16FGeeeSYWL16MuXPn4tChQxETkCB9DE8regFnqpWZM2cOqxRemZmZKCkp8bowKBAuvPBCCIXCgH0O0t7efl9bWwuVSgW1Wh3QPehg//79sNvtXnfp/vOf/0RxcbFrUozh9IIgCCiVSsTHx0MqlYb03vHx8YiLi0NPT0/I2laHw4HGxkbU1tZ6vCY3NxczZszAHXfcQasuixcvRmlpKa655hqP15Bpob2NPQOFzAV+4YUXUi57KMh7BrMAzmq1orW11eu7dDgcrqCCv7Lb2tqGlA04U8QEwtSpU5Gfn+91R8xQ8Pl8TJs2DTNmzAhYBkNoKC0txahRo/DUU095vIZcyBCuPP0TJkxAaWkpVq9e7fEackHHli1bQqXWKUyfPh2lpaUeU7L5gkQiwfjx4wM6nyUSmTFjBkaPHh3UrsaEhASUl5cP7/aEGIa89tprRHd3t9drNmzYQDQ0NNCqR21tLfHBBx94vaa9vZ148803adWDbl599VVCqVR6vebdd98lmpqaaLn/O++8QzQ3N/v9u3Xr1g2pdzCy+/r6vF7z9ttvEy0tLX7L9oW3336baG1tpUV2f+rq6oj33nuPcDgctN/LHxwOB/Hee+8RtbW1Qct6+eWXCbVaTYFWoZXNwMAQPpYvX04AIBISEoiNGzdGXBt5ulJdXU3k5uYSAIizzz473OoQBEEQTU1NxHvvvRduNU4bjh8/TmzYsMHrNV1dXcTrr78eIo18w+FwEM8//zxhNBrDrQpDBCOXywmr1RqWe1utVkr8bn/44IMPCLlcHtJ7BopOpyNeeukl2mQ3NjbSItsX6uvrCZPJFJSMjRs3EsePH/d6zaZNm4iDBw8GJHuoeZ9NmzYRhw4d8ls2A4M7HA4H8eKLLxI6nS7cqniE8SsYIhkWQUTIkjEGBgYGBgYGhmHAzTffjE2bNuG3337DqFGjwq0OQz8UCgXKy8sxbtw4fPfdd+FWh4GBgYGBgYGBgYGBgeEETCCCgYGBgYGBgYGBgYGBgYGBgYGBgYGBgYE2PCesjQCam5uhUCiGvI488DUtLc0v+YmJiV4Ps/NXD1ImAL+u90WHUODPc5rNZp9zGftzLeD/+6RLF3/fZSTZZKhy5c+79kVeOPD3Geh6P/68m46ODqhUKkilUr/aq0i0PwPDcMXftoOEqYfDG7rfuz/yA/FbQ1n+ArFVsPoFck86/T+6bO3P2EalUgEA4zOcBoSjzvkD3frRNQalUw9/dXGnx3DwRyJlvOXv9f6M4+nskyO9bp+uBPJe/B3HU/Ee/dWT6jmjoaCzfA/nuuPv/DFd9vD3HiElvJmhPNPU1ESIRUICwND/sdi+XTfoP6FIPOS5Bn7pARBCgYAQCEWU6hAKmpqaCKFI7LPebJbvdmaz/Xs//sj2+3o/yopAICREQgEtstksFq02EYuEHsuVv+86ksopSVNTEyEW+17P/C+zvr8fv+T6+d5Pvk9RRNmfgWG44mw7/Gv/XPVQHFntIIPv+OvL+dqfBirf3z49lP1AoLbyxU5e7xlAvaTL/6PL5/HHtoGUESreBUPoCUediyT9/JXP8nNcKfKx7w5kfORPPR1sj0DuR3cb5c4mIn/bZr/Gw/49tz/X+zMn48+4z/U+xUP3yYG+40gbd480QuJzuKnzgejpb/0LpY509g2BzPX4Wi/pxl+7+G6PwPoLp10ir02J2B0RCoUCBqMJN01LRayAiwwJHw29JmRJBUiLFaBdbUamVIBDHTo8va0VhTe9AqtGAbtJB358BkzdDRAkZEEQnwazsh2CxEzY9GrYdL2IKZwMY0ct6t5aAYVC4TU65KseapMNhzr0eO13ZxQy9fybwBXHetVF33wIHVteG1KHUKBQKGAyGnzSW3XoJyh2bfLJJj/VqrDpgAIbX38ZebnZEPD5XvU4Jq/DsuV3+CS7V2+DxmzDsz+1+qWLP+8GAOWySfutWzwWOfEi8Llsrzap7dbj9o8P+WwTiZiLFZ/XeSxX5Lsu+seLiM4qG7JsGDrqIHvz9ogopyQKhQIGgxHrbr8Yo3NThrz+x321WPPhT7h6cjImZkT7VKbW33sdxhRkeZUra+rATWve9und/N2ux/qdHXjt5vOQkxQLPo/j07PWtvfhljd/jCj7MzAMV5xthwGvv/tf5Obmg+/jyjq57CiW/+Naph4OU0hf7tWrJyInPmrIfpektluL2z7YR6mvSPYFry6bhtzEKPC5Q/cF8k4Nbtu4KyTlj3yWVy4rQk6cwCdb1fYYseLz2oD1I+ulr74iAGz58Wc88uR//OrXw+2b+zu2WXfFKBQmR8Fic/hcZuu69bj90yNMWzWMOFnnCpEj9bHOKYxefX069PN13AKcHLv4op8/7Sc53rrukfXIKh4zpB4dDTK8/eBNPuthMhpQ/I8XEZ099Pio9+DPaPziGb/mCPrrQd6v5OZ1ECZlg80but0DAEN76MZlCoUCRoMBV9z/KtIKRw95/bFdP2DrW2son0/wd8zff04mefbViM6f6KXN/xsdW9bjnUdvR256MgR83pDPKWtsww2PrvPJNzAZDRizfB1EyTk+vWN9Wy3+fj2yxt0jDdLneHr9BuQXl8JiMYPP9z4eOF57DP93y3WUzcf4qqfRYMDCf61DSv7Q9U/RLMfmp/4ZMh1dfcOiEmTHC33qG+q6Dbh9k8w3v9pgxBt3XoLclDjweUNPW8tbe/DPtV+Gve6QdnnxkiKUpUV7vdY/exiwYd2zKCvMh9li8clXBoCjdcdx3e3/DrtdBuObVxtGihJFmJYXC4mIh7LUKJhsBER8NmbkS1CULEZStPMF2M1GROWMRVTmKHD4QvBjkyBKzYcwOQ9x5eeAI4gCX5qMqMwysMCCKK3ILz0uK0/CtLxYZMcLMT4jBtFCLhp6TShIEiEnXojJ2bEoTBS5rhelFSG2ZBp4YgmiMstAWE1g80WQlM2AOL0IwqQsJJ5xEaW2ooKh9ObHxIMbHQ/AN5tkSZ2NeldPD2rrjsNkNmPv/gPQ6XRITkyERqNFWkoyOBwO2js6cbyxCcDQ7z0+io/MOCGypEKfrk+XCFy6JE27DLEl0yBMykZM7nhwRdEwdTVAlFYAYVIOYosmQ5Ra6LKJL7LTYvk+yybt16M147hCD7PVgZoWNXRmGxKj+NCabEiJEYDDYqFHa4baYPVZj9JUMex2wqd3bVa2wmE1Q9twAHaTHvzYRNiNWvClKWCxOXBYTTD3toOw24ItVrTR0qOCyWrD/ro26IwWJEqioDGYkRIXAw6bjfqOXig0Btf15WlRXstrmkQAndnulN3VC7PFin3HGqEzmJAkjYFGb0RqggQcNhsdij5YrL6/m6QYZxnp1hhQ36WC2WpDTUM3dCYLkmJF0BotSJGKwWGzYLM7oDNZYLTYUJQeF3rDMjCMcHq6u1FXJ4fZbML+v/6ETqdFYlIyNBo1UlLTwOFw0NHehiOHD0Gl6kNxydCTEgyRT4/GjPoeHcw2B2paVM5+N0YAjcmKlFghOGwWNCYrbA4HCABFyTF+yffFd0k/4YsotCbUd2thttlR09R7QhehUxeJCBw2Cx1qI473aOFc0BRaFDoL6pUmmGwO1LTpoDPbkRDFg8ZkR3IMH2w2Cwq9Fe1qC+wOavTz1Vc8ckwOi8UCYOh+vTBZ7PIVh/JxBfHpECblUPIs3vB1bFOYHIV2tQnHFQaYbQ4caNVAb7YhMZp3wlfkg8MG1EYrjnXqYLE5UJgcRbv+DPSg0FlR32uCyUagpk0PneVEnTMPrHPyHiPMVkfI9fNl3KLQWdCgMMBktfstf6h6ARYLydHOSeLezhZYLWY0Ht4Hk0GHmPgkGHUaSBJTweZw0NfdAYvZBJNe57ce5t5Tx0e2QeMjY1cDHHbfxgACLgcXjUn0eD+LpgfGruNDjskIuw02gxri9EKPsuiir6sFNosZLUf3w2zQITouCSadBrEJqWCzOVC0HIfDZnP1VEPOJ8QmQDpmNgDfxlB5iSKfx/zxUXzkxQtdukfllA8x5i8AAHT3qlHX3AGT2YK/jtRDqzciKS4Wap0BqQlScNhstHYpodUb/e6RzZoeGDqPw2ExQ3O8BjajzlmuDFoIpClgsTiwaBSwGXVw2MzBvi4GH8kvLkVnWwua6mphNpnw9/690Ou0iE9Mgk6jRlKKs3x3d7Sho7UFwNDlLyGKjzHpUSjqNy8YLJquFtisJrTL9sNs1EEclwiTXoPohBSwOBz0ttVD0SyH2aD1ScdYIZdSHXv0FhxXGE/4KVqnnxLVz09hASarHXqzDTY/jyfuVulR16E8Od9jMp+Y7zEhJS4aHDYL9R1KNPeoYLJE1pxVq9o8pE2UJ+b5fKWsMB8t7Z2QH2+CyWzB3gN/Q6vXIykxAWqtDqkpSWBzOGjr7EZLeyeIE7+JRCJ2RwRJeXo0xqZ7jiSNTnU63NG55YjOGevxOkF8xoC/dU2H/NZlWq7E6/ekLr7qE4gOdOOL3hZNj+vvoWySn+Bs4GbPmAadTgeb1YqUpER0dvfgwN9HMW5MGfQGA+LjpNDp9YiPkwIY+r1nSJwD+S8O9Ph8fUvfyY5dUjLNq95R2SejzlTLjs512nd6QTx0ZhusDgeSYgTo0VpwpEOLUWkxMFjskIp5aFOddHZ8tckhvm9ON1+aAsJuA1+SCIu6G/qWw4jKGg2H2QBulBQWdTe4UVLYDBqf5IWD1PgY2GwOJEmj0aXS4e/GTozJTYXBbIE0RoT0hFiodUZkJ0sB+GZDg8U5eEpLkMJqsyM5LhZdvWocqm/B2IIs6I1mxMVGoalDgbGF2T7LFZ5YJTCjNAM6kwVWuwNJsSJ0qw043KLE6KwEGMw2xEUJUN+lRnuvDjlJsRRZioGBoT/R0dHIzslFT3c3JFIp/j50EACQkZkFu90GvV4HrVaD/ILCIVdJMQwfphcmOvtdu7Pf7daacaRdjVHpEhgsNkjFfOjNNhzv0SMzTgwx37edayS+9AVSoVPm9KLkE7oQSIwVoltjxOHWPozOjIPBbIM0io8utREOLgf5Sf4FRKggSsBBllQApd6KWCEXR7qcQf10CR82B4E+gw1CLhsSIRvpEt9WZQ2Fr74in88Dm+3sU32xOekr+uLjsvlCj99Tha9jGwCQCLmQCLlQ6CyIFXFxuMPp46VLBLA5CPTqrTBaHciKE0Ii4sIYhglqBmqI4rNP1jkRB0c6T9S5WGedq1MYIeZxkJcgRIPSGHL9ogVcZMaJoNBbIBHxcKTDOemVLhHC5iCgt9hgsTmQmyAGi+W/fF/qMrnYSpqUBrvNitiEZKgVXWiRH0JW8ViYjXpExcbBZNADAPhC/yfaXOOjWOf4SNdyGNH9xkcOqxmC+DTXQi1f9D7U7nlsJi2dBrtJP+Ceg8dkZmUbrHoVxCcmzUNNbGIq7DYrYuKToFV2o6Pub6QVjoHFZIA4Ng7xadmwmPRISHcGcn1pa3t2fwGAnjH/IeHJvnsoXcgx/6yJo6DVG51jv3gJunpVOFTXhLGFOa6xX0unAmaLFaW5GR7luYMrjIYwMRMWjQJcsQS65iMAAGFCOgi7DWZ1FxxWC3hiKWJyht7pw0AdMRIpYiRS9Cq6ESORQnbYOR5ITc+EzWaDorsD6r4+JCanAvBjPsZLnfeX6IRU2G02RMUlQd/bhe76v5Fc4Kx/ohgpYpLS4bDbAUVHWHSM4nOQFSc46ad0OtvfdIkANjsBvcWO5l4TsuKEKE3xb7FEtIiP7GQpFGo9YqOE+Luxy/kMiRLYHA509ulgszuQnhCL7CQpJc9DFSkxfNjsDiRG89Cts+Jwpx6jU6Oc83wiHtrUZnDY/neWUkkMpJIYdCuUkMTG4OBhGQAgMz0VNpsdyt4+9KrUyExLwcTy0dh/8DDVj0YJER+IONiuw55mLQoSRTDbHChOcm4JbVebUZIsRkOv0xHTNR6EtnYPRKkFcFjNEKUXg83jw27UgXDY4LCa4TDpYTNqweYL4bCYKNWjTW12VTpf9AFYMPU0U2kqSlD88RVsWqV7G1pMsGoUMPd1AvDNJsf7OcpnzfA+QZ+VkQ6RUOhVtsFix6EOPc4rjkNNmw4dGmcA4KtDCigNtlP04LBZKEwUDbjWl7JiVra59PIk2+YgMDo1yi/ZNr0K5t4Ol+zp+fFebZIhFeJgm2bIZ1TorZiWE4u9LVrXRPpQxOSWIya33OP3wgSnk6UViH2SFw7GF6RjXEG6x+8zE52Bss9+OQDAvQ07NM6gQJZUiONKo+tdji/JwfhizysjM5PjUSN37uDxVhdsDgIqow1xopPN7YxS7w5sRsLJSacDjT1ermRgYAiEcRMmYdyEiR6/z8jMwtRpM1x/H9i/LxRqMYSA6YWeV6YCQEacCGfkJwAADrao/JLtro8xWOxQ6K0QcNkoShThz2btSV2KkofQxTlgO9jc65ceVODrQBagdsDti68IAB9+9iUAzzYX8TjQmm2uXY7AED7uiXGCNQSLL7z5lVw2C/vbTtpzWr73XZEZ0oGBk4NtWg9XMkQ6/tQ5XgCTF8FSnhmL8gzPC2T6l0Vy7OIPnnx0NgtIjuYP8NFzysYjp2y8R1nxqZkAAL7A/8Cir+MjU49zhbSnMUCHxoLUGD60ZtuAOQJ3SEu9t3vkPQFA23jQ10ehjMyS8cgsGe/xe2mK0977vv8UwNDjYcJmhVnZDsD7GIrDZp3y7r31s+S4XG08uTJ6qHa//5h/1sRR3u2Q4vQfamQNftkvNq8csT6UKQDQhOH9ns5MmX6W1+/TMpxpmo8c3A9g6PJKzjv1L4PBklY8HqnF4zx+H5vsrH+dcs/zHRqTDUarw6VfSgw1C0gAoDwjBuXe+i6pAGfkOOdkDvrpLzrnejwfvE3O9QDAgfoOj9eFA1/s4q89AOCsaWd4/T4rw7eDysNNxAciytOjYSeATo0FEhEHFjuBdo0JEiEXrSoz8uKdKx36R7t1DTUwtsnAiZIAhPOMDnFmGWy6XkhSZ4EgCKiO7PBbj7Hp0ahp06FTY0Gn1oJ4MQ9dWiuy4xwgCCC1X4WOzi0HHHZY+jrBiZKAsFlg6msHLyYRbJ4AotRCWNTdlNmJKhKnXuzS26LqBC86HsaOWsSWTneer1EwCeZt7wHwzSYJ4pN5Fn/9fRcO/H0UJUX5MJnMGFVaDIFAgIbGJuTmZKOxqRnHm1q8yq7tMWJGngStKjPOKpCivscZ6Lh4bKKrnPTXY0p2zCnXkmVF11Dj8Tn77wKgUjabJwDnxMT+wVYN9jT2oSAxCmabAyUp0eBz2WhTGZ2rjqRC7G1Ww2C2ufQgbSLrNkIi4oAgAIudwLj0aLSozDinKA7ba/t8etfdf3wFq7YXDqsJURklYHH5sJt0IBx2OCwmGDrqwIuWwmG1BFGi6KWmvh27jzajMCMRZosNpdlJ4PO4aO1Rg8thozgjEQcbOtCudL5Pd++yU2PBlOwYKPXWAe+yRtaEXYdqUZSVemL1SzoEfB5au5SIFgthMFnQ1u2cHPJWF0jZZOqKA409+KO2A4WpUpitdpRkxEPAZUNvtqJHY4TGYMGM0nTsknWAw2HB5GNgiYGBwXe++OxjKBU9MJlMKB01GgKBAK0tzbDZbOhob8MF8yuwc8cvcDgckEil0GmZyb2RwMEWFfY0KFGQHONsf1NjwOdy0NZnAJfDgkJrwbTCBBxt10ChM8PPHeQe+2kCJ3ZPqsyYku0MNB9o7sUf9T0oTImFyWpHaZoEfB4baoPVmZYvNRa76nqQl+Q9vyxduAbbCUKYbQSKk08MtlVmiAUcHOnUY15pvF8LIHzBF1+R1y8vrrt+vbbHiCnZMSAIIDtO6Nq1OpSPK8qfiL6/t1P2LJ7wVk6y44RoVzv1PdimwZ5GFQqTxDDZHChJjjo56aazQGu2IzGKh6LkKBzt1KFHZ4HWxPgMwxVPE/EiHhuybgOSo/kYlx6FmnY9tKbQp6D4qqYDSp3llDFLFJ+LNpUR0/LjcbRTC7PNAU0A+vnjo+/ZsgnaPgWsFjPS80vB4wtg0KrRcPgvjJp6DtTKLqh7OmEPIL2stvEg1LV7IE4tHDhGMhvA4YtgUrTArGyDSe1cmettrkJjsmFUahS6NJ7HUtrGg9DU7oFo0P3MvW2wmwyILZgItWw32AIxHGaDKyVUKKn54XPoVUpYLSak5pWBy+PDbNRD19eD9KKxaKjZCVFsHNQ9zonAIedBskZDe9w5sevLGGqoMT/Z5pPj8v5j4aHaffuJMf/+Y8ex88AxFGenw2SxoiwvEwI+FyqtAUazGcU5GThS34I+jQ4Wm3/lqnPXV7BonMGQ6MwSsLl8mJRtcNhtECVlQd9eC64wGjajFnY/F8syBM6RA/uw74/fkVdQDLPZhMKSUeAJBOhobYFRr4PZbMKUGbNRe/QwDtfsBTB0eXVXBoOlQ16Dlr93IyGrEDaLGYm5peDy+LAY9TBq+xCblIG2o3uham8E4N3HMFodSIziQSrioltLzRzPwTYt9jSpUZhI+ipi8DlstKlNsDsI9BpsOKtAigNtOqj8DNB8/tvfUGj0MFttKM1KBp/Hgd5kgUKtR2evFudPKsbOw43gcNgwmSMrNdNXB7vRq7cOsInObIPeYkdhkhgH2rRQG/3z2fYdPIzf9+xDcWGe00cuKYSAz0dLWwe4XA66Fb04a9oU/H1UDgDoVakR+mULvhHxgYhahRFFiSJknNj2bbE5ECvggCAI2AnnAXkAYOyoPfkjNgf8hIErjsloN5kOyaZV+K0HAHBYGKBLTpzAVYl7+1UsY0ctRGlFLj0cNgs4olg4bBbA5tTDpgv9CrehcKe3IClngP1sRmeH7YtNNP0ahLNmTINIJEJ7RyfipBKYLRa0tnVAKpVApdbgrBnTYDJbfJZ9qF3nku+unHi61lVW+pWTwc/Z/91QLvuE/ciVRTUtanRozOjQmJAg5qNbY0ZOrhitKhPmlCRiu0zh0SYk/XXp9bGBT556MWJyy6E9XgN92zFwxVIQBAEWCERllYErjoU4rRC9h372SV44IHdE7KttQ0evBh1KLeJjxejq02JqaTZaelSYOSYPx5qdgwV/3iW5I+KvYw3o6FGhQ6FCgiQanUo1zkxPgkKlRWy0yCUX8F5eybZqXG4SxuUmAQD2H+/CsVYlJFECEAQBgIUJ+cloUWoxd0IuCILAL4dbQ2BJBobTg/37nQPfSy9f7NoRsW/vn+hob4PD4UBRcQkcDgdsVisKiopRVFwCs9mMPbt3AgAcDibtyXCmPEuK8iwpAGB/cx+OdWohEfFOBPUdGJclRUuvAWfkJ4AgCPwq829Hmi/9tKsvyI5HeXY89jcp0aEyokNtRHyUAPVdGpxRkISWXj3mjs1Al9oIkyX0aVj6r86uadNC1m2ARMgFAUB1YlDp7wIIX/DFV+zo7EJDo3NX8VD9erfWghaVc2LHJx83BL75UOXEfqKZKc+IRXnGyR2SNS0adGjMcBAECpPEcBDOVegtfSackSsFAPwsV9KuPwM9eJo8MtscmJEngVJvRYyQi5l5sdhRrwq9fuPTXOOWY106SERcEASgMloxLlOClj4jzsiNc7addf6XQ3989DPmLUJO2Xg0HP4L7fVHIY6VgnAQSMrIhUAkhjhagvwxk/H3zh/91oPcEaE5XgNLXwfMfZ3gxcRD316LuLLp4EuSIC2dhrYf3/Wod/+5iqHGZv13YGiP10DfegzcKOeYjM3lgXDYIErNhzitEARBoO/wr34/U7CMP/8yZJaMR8vRfeg8fgSiGKd+IAg47HYkZhUiOacI7bXOeRaf5kH8mE/wd8w/1JxM/3bfeqLNn1Caj/ElefjrSD3ae3rR3tOLBGkMapvbMa28BC2dPZg2rgQA8OMfB/yyX+q0ixGbWw718RroWo6BGyUB4JwZ5giiIErKRtSJ99sbhvd7ujJq3ESMKp8AADi0fy9qjx1GrNTZhvH4fIwePwmK7k5MnDodQpHvY35/5mN8gdwR0X5sH7SKDmgVHRBL4qFokiNn/CwYNUqMOvsSdMoPYPs7j/vki3ZrLa7rgoVc+V/TqkWHxoxOjRnxYh66tRZMyZFAQvZdBVL87Ke/eNnMMRhXkIZ9tW042twNabQQjhOLO2eMzkVLtwrzppSAIAhsP3CckuehiovLk+FwEANsUttjwJQc56LqWQVxfttjYvloTCh3ppPbW3MIh4/VIk4aC4IgYLFaMWncaDS2tGH6Gc4xrtlswa69kbmrP2IDEYmJiRCLhFjxed3QF7PYqHtrhd/3EIrESEz0vkXfLz0ACAUCEH7o44sOoSAxMRFCkdhnvdks+GwTADgmd17L4XCQlel0BMxmC2JjY+BwOOBwOLDvwCHYbDYI+Dy/ZPulix/vRiAQgs0iaJENALXdzm26bDbLtZ3ZbHcgO0GMLu3J1XA2goCAy/bLJmKRcMhyZeg4IY/NPuUMFZPCOfmtbTwIq466CQaqkbc6gzQcNsu1Nc9itSE3JQ5dfc4VzAfq22G3+19mZU0dJ2SzkZniTKFlttqQm56ETqUaAGCzOSDgcfySW9t+0p5sNntAGiYA6FI5cwKTKZkUmtBPQDEwjDR+++03PProo9i2bRsAQC476vqOw+EgM8t53ovZbEZsrAQtzc60a2RKps4OZwqBxYsX46mnnsJll13mylHPMHyo7T65s4XDYiFDOjB/eJfGOWFNpmRS6H07NNJfXxEA5J0alx6Zcc6dkhabHTmJ0ehSO9t9MiUTeW0oIQMmpI7908IAgxZAGKgZcPvqKwKAsreXNv+PLt/c33JS1z0wncsAf9FGIEbIRdeJ90CmZOrTh361NAM1+LrgCAAUFNU5f3A3biHpP24BnAdv+0og7WdHg+yELhxXGiYSZYdzd33T0RroVP4HRAztTj1YbDYE5OS11QxRUvaA8RHhcPjVBnkam5H3O/FArnuS9L8nAFg1oQ82djc6V9ey2GxXGiaSvk5nULhVVgOH3eZXW+vv2Myf6/2dk5E1OgMTHA4bWanO92Sx2pCXkYJOpQrAyZRMSpV/u2T1bc6Fiiw2G8LEQe9X6Xy/ZEomi9q/xbIMgXNcfsz1bw6Hg7TM7AHft7c6y/aRg/uh6OqEQCCgfD7GFxTNzvrHZnMgOZGGyW6xIC4tF5ouZ3vXKT8AXW8XuDz/5tGo0LGu23BCv/5zWgSy40UnfZQTfZe/Poq81TkfwmGzB6RhAoCuPqdMMiWTQu09BV6oqes2oDBZ7NUm/trjaN3JYAuHzUb2oDRMnV1Oe/U/F6K9KzLTfLMIwt+N56GjubkZCsXQjXFHh7PwpaX5lw8rMTER2dnZQ17nqx6kTAB+Xe+LDqHAn+c0m80QCIY+wLOjowOXX345jEb/JlOFQiE2bdrk0zv1VRd/r/X3Xfplk0WXwWjybYKDRCjgY9PnX/hkE2/lqrm5GSWlZTAZDb7fWySG7NjRiCqrZWWlMBh8L1dCoQCbNn0+pP2cZXYRjEbft8b6IjvQ9w4AYpEIR48dixj7MzAMF/oHIMrLy3Hrrbdi5cqVMBh8b/9IhEIRpkyZjB07dmD06NF45JFHmIDEMKG5uRllpSUw+NGuk4hFQhw9Jhuy/fXHZw2kLwhVPxCorXy1k8d7lpX5XS/p8hXp9M19KSdyuRzXXL0UNntgO7D4PB5kcjlyc3MD+j1DaAlHnfOHUOjnT/u56PLLYfJjXCkSi3Hs6NBjmEDGRwKBEJ9/7lsbNLhdCeR+JKEalzU3N6O0rAxGP9pmgVCIz2lol/293tdxvLNPXgSjyc/yLRbh6FHvfXKg7zjSxt0jjUB9DhJffY9gfYlA6h/gXx0MRkc6+4ZA5noA3+ol3fhrF9/tEXiZFYvFOOpDPxhKIjoQwTAyGOxcKhQKzJ07F1deeSU++eQT/P777xAKB66siaQADR0MtklXVxcWLFiAK664Ap999hl27twJPn/gaigqbULenyAILF68GLm5uXjmmWcAAK+88go+//xzfP/99y4dIvF9DLbhww8/jObmZrS0tODKK6/EzTffPOB6f55hsOyHHnoILS0taGlpweLFi3HTTTcFJNvdQOvBBx9Ee3s7GhsbsXTpUtxwww2n/C4S7c/AEMns2LEDjz32mCsA8cgjj+Diiy8Gm832K+jeH7Ie7ty5E4899hi+//57JiAxjOj/3rdv345Vq1Zhw4YNGDvWeb5YQ0MDFi1ahEcffRSVlZWu39HR/g4ug0ajETNnznT5RT/++CPi4gYeUhzKfmCwfvfccw8UCgU2bNiAv/76CzfffDP++9//YvTo0ZTpN/ieOp0Os2fPdtnkp59+gkQycDXcSOwbd+7ciSVLlkCtVuOhhx7COeec4/NvbTYbXn/9dbz33nuYO3cu/vvf/yI52fuB6AyRweDyL5PJcNVVV2HRokXYsmULtm/fDhZrYKbncLYJBEFg1qxZWLhwITZt2oRPPvkEhYWFIdFvsC4bN27EW2+9hTFjxkAikbjGM4Ho4U7222+/jbKyMsTHx+Ppp58OWLYv91uzZg327NmDr776CsePH8cVV1yBl19+GTNmzKD0vsHo+M477+D9999HQUEBUlNTsWbNmrDpRhWDn9Fms2HGjBm49NJL8emnn+Lrr79GZubAnSCBjv1sNhumT5+Oyy67DJ9++ik2b96MjIyBuySGow2HG+R7USgUuPTSS3HBBRfgwQcfHHCNTqfDZZddhrFjx+K5555zfR7O+rdz506sWLEClZWVOHjwIL744otTfhNO/Uh/9oorrsCnn36KH374AfHx8QHpN1i2wWDArFmzXLLD7St7o7/upD9L6r1t2zZIpVLXtcHMI2m1Wpx99tku2e58ZX/uEVIIBoYQ88svvxAAiA8++IAAQBw8eDDcKoWdbdu2DbDJkSNHQnLfffv2EQCI6upq12dHjx4lABCfffZZSHSgijPPPJO49tpriRkzZhBLly6lVPbUqVOJ6667jpg+fTpx9dVXUyp7ypQpxPXXX09MmzaNuPbaaymVzcBwuvHrr78S5557LgGAKC8vJ7744gvCbrfTcq+dO3cSc+fOJQAQo0ePJj799FPa7sVALWeddRYxffr0Uz5fsGABUV5eTjgcjpDqs3///gE+wG+//RbS+3vDZDIR0dHRxJNPPkkQBEFYrVZCKpUSDz/8MK33/fPPPwfYZNeuXbTeL9zY7XbiqaeeIjgcDjF9+nSiqakpYFlbt24lkpOTidTUVGLbtm0UaskQKj755BMCALFhwwYCANHd3R1ulQbQ3t5OACA2btxIACA+//zzsOly4403EhMnTiSWL19OjBs3jlLZN9xwAzFp0iTin//8JzF+/HhKZQ/G4XAQ6enpxN133+36Oy8vj7jllltova+/LFu2jJg6dSpxww03EJMnTw63OrRQV1dHACD++9//EgCI7777jjLZtbW1BADi/fffJwAQW7ZsoUw2g/8sWbKESExMJJRKpdvvP/30UwIAsXnz5hBr5p6XX36ZEAgExLp16wgul0tYrdZwqzSAmpqaAb7bjh07KJNN+spk3YkkX9kbe/fupc2fHewr7969mzLZdMMs32MIOTKZDGw227XKSy6Xh1mj8COTycDlcjF79mwAobPJhg0bkJKSgrlz57o+Ky0txdSpU7Fhw4aQ6EAFBEFAJpOhpKQEJSUlkMlklMsuLi5GSUkJpe+GIAjI5XJa9GZgOJ3YsWMHzjvvPJx11lno6enBF198gf379+OSSy6hbafCtGnTsGXLFuzcuROZmZm44oorUF5ejs8++4w51DqC2bt3L3799VesWrXqlO9WrlyJgwcPus4SCRVkv3LOOeeAxWJFVF/wyy+/QKfToaKiAgDA5XIxf/58VFVV0Xpf0iZz5swZ8PdIpKurC/Pnz8d9992Hf//739i+fXtQK9cuuOACHDhwAKNHj8Z5552Hhx9+GDZb6M8UYAgcmUyGpKQkTJo0CUDklX9SnylTpkAqlYa1zervR8vlckr7X3JsUVxcTLnswezfvx/t7e2utpbFYqGiogLV1dXOg6EjhP42kclkEaUbVZDledasWRAIBJSW78GyI61un0788MMP+Oijj/D888+fsmqfZNGiRZg3bx5WrFgBvT78ZxDI5XIUFhairKwMNpsNDQ0N4VZpAGT5psOfpVM2nfTXu//fkS6bbphABEPIkcvlyMvLQ1paGuLi4pgOGE6bFBQUICMjAzExMSGxicViwYcffoirr74aXO7Ac+uXLVuGLVu2oLOzk3Y9qECpVKKvrw/FxcWuwQJVjrFCoYBKpaJFdk9PD9RqtUv2SHXoGRjoYseOHTj33HNDGoAYjKeAxKeffsoEJCKQ559/Hvn5+bjoootO+W7OnDkYN24cnn/++ZDqJJfLkZiYiPT0dOTk5ESUX1RVVYXs7GyMGTPG9VllZSX279+PtrY22u4rl8uRmpqKtLQ0ZGZmRpRNqGTbtm0YP348ampqsHXrVjz55JPg8XhBy01NTcXWrVvx+OOPY82aNZgzZw5aW1sp0JghFMjlchQXF6OgoAAsFiviyr9cLgebzUZBQYHLNw6nLqQfbTQaKW2X+ss2GAxob2+nTPZgqqqqEBsbi1mzZrk+q6ysREtLCw4ePEjbff2lv020Wi26urrCrRLlyOVyCIVCZGdno6ioiNLyLZfLIRaLkZWVhcLCwoir26cLJpMJt912G2bPno1rrrnG43UsFgvr1q1DV1cXnnjiiRBq6J7+9Y/8O5KQy+VISEigxZ8lfeWMjAxkZ2dH3LN7Qi6XIyUlBenp6ZT7s6SvnJ6ejoyMjGFjE4AJRDCEAbIBZbFYrsnX051w2OTbb7+FQqHAsmXLTvlu8eLF4HA4+N///ke7HlRANrpkx6zRaNDd3U2pbHL1j0qlQk9PDyWyyfdM6q1SqaBUKimRzcAwkukfgFAqlWEJQAxmcEDiyiuvZAISEUZzczM+++wz3HXXXeBwOKd8z2KxsGrVKmzZsgWHDx8OmV7krjsAYZ/U6w9BEKiurkZlZeWA/PTz5s0Dh8NBdXU1bfcebJOR5ivabDY8+OCDOP/88zFq1CjU1NTgggsuoPQeHA4HDzzwAH755Rc0NDRg/PjxtL4zBuogxwUikQjZ2dkRV/5lMhny8vLA5/Mp3y3sDxqNBp2dnbRMzKlUKnR3d7t2RFMp2x3V1dWYN2/egEDkWWedhejo6Iipt0qlEr29vSGzSbiQy+UoKioCm82mvE8eLDvS6vbpwjPPPIPGxka89tprp5y/M5iCggI88MADeO6550LqG7qD9I3S09MhFosjrv6RfRcAyvuG/rIjyVceCsYm7mECEQwhZzhXGLoIh002btyISZMmuQ7q7E9cXBwuvvhibNiwYVis0CftVVhYSPlARC6Xg8ViuVZ9DRfZDAwjkV9//fWUAMS+ffvCGoAYDBOQiFxefvllxMTE4Prrr/d4zZVXXon09HS8+OKLIdMrUv2iw4cPo7Gx0ZUqhCQuLg4zZ86kdXIsUm1CBa2trTjnnHPw1FNP4fHHH8f333+PtLQ02u43c+ZM1NTUYNq0aaisrMSqVatgsVhoux9DcJBpOyO5/EeKfv0XIuXm5oLH41GmS21t7QDZXC6Xtudsb2/H3r17UVlZOeBzgUCAuXPn0p4Kz1f62zs/Px9sNjviyiYV0Fm+I6XunM7U1tbiySefxL/+9S+UlZX59Jt//etfKCgowC233BK2uRGj0Yjm5mYUFxeDzWZTvluHCpi6cyqMTdwTGaN2htMGm82G+vp61yqKcK6iiRQsFgsaGhpCapOenh5UV1e73Q1BsmzZMvz999/Yv38/rbpQgUwmQ3Z2NsRiMQoLCyndxi6Xy5GdnQ2RSESL7NzcXAiFQsplMzCMJMgAxOzZsyM2ADEYdwGJsWPHMgGJMKHRaPDWW29h+fLliI6O9ngdn8/HihUr8P7774ck5UT/s4IApw9QV1cHu91O+72Horq6GlFRUTj77LNP+a6iogI//vgjDAYD5fd1Z5Pa2toRUW+qqqowbtw4NDY2Yvv27XjggQfc7s6hmoSEBGzevBkvvvgiXnnlFcycORPHjx+n/b4M/tPT0wOVShXRY6X+9bO4uBgKhQK9vb1h0YPUgcvloqCggFIfnZTN4/GQn59P23v49ttvwWazMX/+/FO+q6iowJ49eyIiBRK5er+oqAgCgQC5ubkRVzapgDwHA3DWv+bmZhiNxoiXzTA0BEHgtttuQ0ZGBh544AGffycQCLB+/Xrs2LEDGzdupFFDz9TX14MgiAHlJ5J21PQ/sxNwtp1U+bODZUeSr+yNwf5scXExZf7scPeVI3P0zjBiaWxshNVqHRC5UyqVp3U6muPHj8Nutw+wSVdXF9RqNW33/PDDD8FisbBkyRKP11xwwQVITU0dFodW948Gk44xVR1z//QQQqGQ0nyH/fWO1O33DAzhZHAA4ssvv4z4AMRgyIDErl27kJ2dzQQkwsQ777wDg8GA22+/fchr//nPf4LH42H9+vW069X/HCLA6QOYzWY0NzfTfu+hqKqqwvnnnw+hUHjKd5WVlTCZTPjpp58ov29nZyd0Ot0Am1Cd+z3UWCwW3H333bjwwgsxffp01NTUDMgFHwpYLBbuuusu7Ny5E0qlEhMmTMCnn34aUh0Yhqb/BDj5/0iacCEXlfXXDwjPjl4yP3ZsbKxLFyr9/7S0NMTExFAuezBVVVWYPn06EhISTvluwYIFAJzBinAjl8uRlZUFsVgMYGSmzdPr9WhtbT2lfJM7ZIJBp9Ohra1tgGyCIFBfXx+0bAbf+OSTT/DDDz9g3bp1rnLsK3PmzMHVV1+Ne+65JyxzV+76hkgKBLrzZy0WC5qamoKW3f9cTVJ2pPjK3ujs7IRWqz3Fn6XizK6Ojo5TfGWDwTBsfOXhMYpnGDG4a0D7f346Eg6bbNy4ERUVFUhMTPR4DZfLxdVXX40PP/ww4rfw95/QB6jtmPtHmknZVDnddOrNwDCc+fXXXzFnzpxTAhAXX3zxsAlADObMM8/Ed999d0pA4pNPPomYCaaRis1mw9q1a7FkyRJkZGQMeX1cXBz+8Y9/YP369bSvVOx/VlD//4e7L1AoFNi1a9cpaZlIiouLUVhYSEt6Jk82Ga4TXvX19ZgxYwZeffVVvPjii9i8ebPbCcdQMXnyZOzbtw/z58/HlVdeieXLlzMrciOI/mk7gcibcGlsbITNZnPVy6KiIgDhC0TQ6f/3l03XzhSj0Ygff/zRY1ubnJyMqVOnRsQ5EaGySTipq6sDQE+f7En2cO3bhhsqlQp33303LrvsMleAz1+ee+452O123HvvvRRrNzQymQxSqdQ1f1NcXIy2tjbodLqQ6+KOwXNaVJ4jM1znEBmbeGZ4juYZhi1yuRwikcg1EVBYWOj6/HRFLpcjOjoaqampAOh36A8ePIj9+/fjuuuuG/LaZcuWQalU4ptvvqFFFypwOByora2lZSBit9tRV1c37GQzMAxX+gcg+vr6RkQAYjCDAxKLFy9GeXk5E5CgkS+++AJNTU1YuXKlz7+56667oFQq8f7779Oo2amTjllZWRAIBGHvC7799lsQBIGFCxe6/Z7FYqGyshLV1dWU50uWy+Vgs9nIz88HANrzs9PJJ598ggkTJqC3txc7d+7EXXfdNeTBmKFAIpHgo48+wltvvYWNGzdi6tSpOHr0aLjVYoCz/Ofk5Lh2IkXa5MLgyY+oqChkZmZGTCCioaGBkgVUdMruz88//wyDwXDK+RD9qaysxPfffw+z2Uzpvf3FnU3q6+ths9nCqBW1DC7fCQkJiIuLo2XiMDExEVKpNGLq9kjnwQcfhE6nw0svvRSwjJSUFDz11FN455138Pvvv1OnnA+Q9Y/0IchyRAa4wg3pz5Lze1T6s4N95ezsbPD5/IivO4P92ZycHMrOMpLL5eBwOMPWVx4Zo3qGYQOZ5oacUAqn8xopkPnuyE4lNjYWqamptNlk48aNSEpKcpuHdDBjxozB5MmTw5YL0Ream5thNptP2bVAxTb2lpYWmM3mU5xuKmQ3NTXBYrGcovdwyu3HwEAVp0MAYjBMQCI0EASB559/HnPmzMH48eN9/l1+fj4uueQSvPDCC7S2yf3PIQIADoeDoqKisK+QrK6uxpQpU1yLJNxRUVGBtrY21NTUUHpvuVyOvLw88Pl8AKA893soMBgMuPnmm7F48WIsWLAA+/btw+TJk8Ot1gBYLBZuvPFG/Pnnn7Db7Zg8eTI2bNgQtoM4GZz0z4MNOCdcIiE4SSKTySAWiwfsLgvHQprBOcNJPRwOR9Dnnww+MJyUbbfb0dDQEJTswVRXVyMvL8/robkVFRXQ6XT45ZdfKL23P5ALvwbb22q1UpJ6JVKQyWRISEhw7VpjsViUlW+ZTIakpCTExcVRLpvBO3v37sX69euxevVqZGZmBiXr5ptvxtSpU7F8+XJYrVaKNBwad20SEDk7asgzO0l/lsoDtWUyGXJycgb4yoWFhRFfd8jzQAUCAQBq/VmZTDbAV6b7LCOqGZmje4aIRS6Xu1b8k0TaQTuhJpQ2sVqt+OCDD7B06VLweDyffrNs2TJ888036O7uplwfKiAb2/42LCkpgdVqRWNjY1CyB6eHIGVTke/Qk96RtP2egYFufvnlF48BiEhYNRwKmIAEvfz+++/Ys2cPVq1a5fdvV61aBZlMRmtu7v7nEJGEO++2xWLB1q1bPaYKIZk1axZiY2NRVVVF6f3d2WQ4+YpHjhzBGWecgffffx9vvfUWPvroI0gkknCr5ZExY8bgzz//xJIlS3D99dfjmmuugVarDbdapy2DxwXkhEuklH+5XI7CwsIBiwTCUT/Js2QG+9FA8BNz7e3t0Ov1p/j/VMjuD0EQqK6uRkVFhVefZ+zYscjOzqa8rfWHlpYWmEwmWuwdSdA5LmfmQcKD3W7H8uXLUV5ejhUrVgQtj81m4/XXX8eRI0eC2l3hL4N9o7i4OCQlJUVM+WHqzqnQ6c8OV5uQMIEIhpAyON8+wKSjCaVNtm7diu7ubp/SMpEsWbIELBYLH330EeX6UIFcLgePx0Nubq7rM6q2scvlcggEAmRnZw8r2QwMkQ4ZgDj77LNP2wDEYMiAxO7du5mABIU8//zzKCsrw7x58/z+7fTp03HmmWfi+eefp0EzJ558ACoOxgyUHTt2QKPReE0VAjhXX82bN4/y3OXD1VckCALvvvsuJk+eDIIg8Oeff+LGG28cFm2aWCzG22+/jQ8//BBff/01Jk2aRPlOF4ahIdN2RnL599ZmhXJHL2mP/rqkpKQgJiaGEh99sOzU1FRER0dT+h4OHjyIlpaWIdtaFouFiooKWlLh+Yo7m2RkZEAkEkVM2aQCOvuf4dq3DXfWr1+Pffv24Y033gCXy6VE5vjx43HHHXfg0UcfDcmOIKVSCaVSGXH+Yn+YunMqjE08wwQiGEKGXq9Ha2ur25V/p2s6Go1Gg87OTrc2kcvllDubGzZswLhx4zBu3Diff5OQkIDKykps2LCBUl2oglyZxeFwXJ9RlZOQbtlFRUUDVpQNl3yHDAyB8ssvv+Ccc85xBSC++uqr0z4AMZipU6e6AhI5OTlYvHgxxo4di48//pgJSPhJXV0dvv76a9x9990Bp/hauXIltm/fjn379lGsnfuzggCnD9Dc3By2A4SrqqqQkZHhUyqryspK/Pnnn+jo6KDk3larFcePH3drk8bGxrDnSPeEVqvF1VdfjRtuuAFXXXUV/vzzT4wZMybcavnNkiVLsH//fsTExGDq1KlYt24dk6ophDQ3N8NisXgcF0QCg9ODAE79jEYj2traQqpH/9zbAHWpbsjc23l5eZTL7k91dTWio6Mxe/bsIa+trKxEY2Mjjhw5Qtn9/YFc+JWTk+P6jMrUK5EAme7LXfnu7e2FUqkMSranukNOMjNQT3t7Ox544AFXOiUqWb16NeLi4nDnnXdSKtcdZLAhUvsGOv3ZSPWVh8Jms6G+vp4Wf9abr0zHWUZ0wAQiGEKGtwbUZDKhtbU1HGqFlcGHVpEUFxdDr9dTNrAHnJH0zZs3+7UbguS6665DTU0NDhw4QJk+VOEuGkyVY+zOYaQqJ6E7R3e45DtkYPCX7du3uwIQKpXKFYC46KKLmACEB6ZOnYpvv/0Wu3fvRm5uLpYsWcIEJPzkpZdeQmJiIq655pqAZVxyySXIzc3FCy+8QKFmTrxNOhIEEZYDCAmCQFVV1ZCpQkjmzZsHNptNWfqqxsZG2Gw2tzahIvc7Hezbtw8TJ07E5s2b8eGHH+Ltt9+GWCwOt1oBU1hYiJ07d2L58uVYsWIFLrvsMvT19YVbrdMCdylByb8jYcLF26IyILQ7egfnx+6vCxX+P12y+1NVVYW5c+eech93nH322YiKigpbeia5XI6CgoJTVpRHykQoFSiVSqhUKrerjIHgyndPTw9UKpXHuhMpq9pHGitXroRQKMRTTz1FueyYmBisXbsWX3/9Nb7++mvK5feHLHvkQdAkZCrPcC8YcHeuJkDNgdqkr+yuXhIEgfr6+oBl0wmd/iwp251NItVXHgwTiGAIGd4m3ft/fzoRSpt8/PHHIAgCV111ld+/nTdvHpKSkiLy0Gp3E/oAdQMROmUP7jyoks3AECmQAYhzzjkHarUaX3/9NROA8BMyIPHHH38gLy+PCUj4SG9vL9577z3cdtttEAqFAcvhcrm466678Mknn1C+YMKTD0D2DeHoC44dO4bjx48PmSqEJDExEdOmTaNscmw4+YoEQeDll1/GtGnTEBsbi/3792PJkiXhVosSBAIB1q5di6+++grbt2/H+PHjsWvXrnCrNeJxl7YTiJwJF3KydLD/mpubCx6PF9L6SacfTaf/T9LV1YU9e/b43NYKhUKcf/75lKfC85XBB4OTjKRxi6dAIDn5G8xzukttBZw8K3Ck2DCS+P777/HJJ5/ghRdecB0QTjWXXnop5s+fjxUrVkCn09FyD8BZPjIyMhAdHT3g85KSEqjVavT09NB2b18g6w4dQTxvAfpgZdMJnf7scLVJf5hABEPIkMvlSExMRHx8/IDPSed1uBysQiVyuRypqamIjY0d8Hl+fj44HA6lNtmwYQMWLFiA5ORkv3/L4/Fw9dVX44MPPoDVaqVMp2AxmUxoampyO1gI9rAeo9GIpqYmt043FbKbm5tp0ZuBIRLYvn07zj777AEBiL/++gsXXnghE4AIkDPOOAPffPPNgIDEmDFj8NFHHzEBCTe88cYbsNvtuOWWW4KW9Y9//ANRUVF45ZVXKNDsJDKZzO2kY0JCAuLi4sIykKiuroZIJMKcOXN8/k1lZSV++OEHmEymoO8vl8shFouRkZEx4PPU1FTExMRETP/Y29uLSy+9FHfeeSeWL1+OnTt3nrJScSRw0UUXoaamBpmZmZg1axaeeeaZ0zKVaqhwl7YTiJxDgck2afABmVwuFwUFBSHVz1OwoKSkBJ2dndBoNAHL9jbp3tHREZRsEnIX2fz5833+TUVFBXbt2gWFQhH0/f3Fm71bWlqg1+tDrhPVeFp1HhUVhaysrKDKt1wuB4vFQkFBwSmyMzMzw163RxpGoxG33norzjnnHCxdupS2+7BYLKxbtw49PT1YvXo1bffx1iYB4Z94lsvl4PP5p/iz5NxfsHVHIBAgKytrwOdJSUmQSqURW3dkMhlEIhEyMzMHfJ6SkoLY2NigbSIWi5Genj7g87S0NERHR0esTfrDBCIYQoYnB4Z0XsPdgIYDTzbh8/nIy8ujzCaHDx/G3r17A0rLRLJs2TL09PRgy5YtlOhEBfX19SAIwuOqpZaWFhgMBlpkB7NFntye6El2U1MTJRM6DAyhpn8AQqPRMAEIGugfkMjPz8dVV13FBCQGYbFY8Morr+Daa68NKPg+mJiYGNx888144403oNVqKdDQibtziAB6cpH7SnV1Nc477zyIRCKff1NRUQGDwYDt27cHfX9PE7HhtMlgdu7ciQkTJuCXX37BV199hbVr10IgEIRbLdrIzs7G9u3bce+99+K+++7DggUL0N3dHW61RiSexgWJiYmQSqVhL/+eFpUBoV0Z7yn3NqkHEPjEnKfc28DJgBAVaXSqq6tx5pln+tVHLVy4EA6HA999913Q9/cHs9mMxsZGr/YORypBqpHL5cjOznbb/wVbvuVyOXJyctzu0IyUvm0k8fTTT6O5uRnr16+nffyRn5+Phx56CC+++CIOHTpEyz089Q0FBQVgsVhhLz+e/FmAmroTab6yL9Dpzw4HX3komEAEQ8jwlEIHOH074FDZZOPGjUhISMDChQsDljFu3DiMHz8+og6t9rTNFQjeMfa0nY4K2Z6205GfRcL2ewYGf2ACEKGHCUh45qOPPkJHRwfuvvtuymTecccd0Ov1ePfddymT6WlgCZzM+xtKent78fvvv6OiosKv340aNQp5eXmUpGeKZF/R4XDg6aefxllnnYWsrCzU1NTgoosuCps+oYTH42HNmjXYunUr9u/fj3HjxuGnn34Kt1ojDk/lP1ImFyKlfjY0NLjNvQ0En+qmoaEBdrudFtkkZrMZ33//vd9tbWpqKqZMmRLy9ExDLc4Cwr8imwo8pfsCgi/fkVJ3Tgfkcjmefvpp3HvvvSgtLQ3JPe+55x4UFhbilltuoXzXoMPhQG1trdvyIxQKkZOTE/YV8EP5s8FOutNVL+mEsYl3mEAEQ0ggCILWyjgcCZVNbDYb3n//fVx11VU+HYbmjWXLlqGqqgpKpTJovahALpdDIpEgKSnplO+CdYzlcjmkUikSExNpkR0XF4eEhATKZTMwhAqCIPDzzz9j9uzZOOecc6DVarF582YmABFi+gckCgoKXAGJDz/88LQMSBAEgRdeeAELFixAWVkZZXIzMzNxxRVX4KWXXqLMrpHmF23ZsgV2u93vRQssFgsVFRWorq4O+sDESLMJSVdXF+bPn4/7778f9957L7Zv335KCoLTgfPPPx8HDhzAmDFjcN555+Hhhx+GzWYLt1ojAm9pO4HIGCsNVT8bGhpgsVhCogd5z8HExsYiNTU1KB/dk2yJRIKUlJSg38Mvv/wCnU7ndyACcO5A27JlS0jsTOLNJvHx8UhISAh72aSCocp3bW1twJPMdMpmOAlBELj11luRmZmJ+++/P2T35fP5eO211/D777/jvffeo1R2W1sbjEbjsO4bgp10j+Rn9wRjE+8wgQiGkKBQKKBSqTxG7kpKStDY2Aiz2RxizcJHZ2cndDqdV5scP3486DMZfvjhB3R2dgaVlonkqquuAkEQ+Oijj4KWRQVkI+xuwjPYnIRkLkZPsuPi4gKWTUax3cmO9HyHDAxkAOLss8/GnDlzoNPpsHnzZuzduxeVlZVMACJMnHHGGaiursaePXtQUFCApUuXYvTo0addQGLbtm04ePAgVq1aRbnslStXorGxEV9++WXQsshJR28+gFKpDGngv6qqChMnTjzlfAZfqKioQHNzc1BpCXQ6Hdra2rzapKurC2q1OuB7BMK2bdswfvx4HDhwAFu3bsWaNWvA5XJDqkMkkZqaiq1bt+KJJ57Ak08+iTlz5lB+kPvpCLnL1lv5D6dvSC6g8qafw+EIyY5eT2fJ9NclGP/fm2wqdqtVV1cjOzsbY8eO9fu3lZWV0Gg0+O2334LSwR/kcjliY2ORkpLi9vtwl00q8LbqHHA+o8lkQktLi9+y7XY76urqvNYdo9HItKMU8NFHH2Hbtm149dVX/UoxSQVnn302rr32Wvz73/+m9BwXTwdBk5SUlIR14pk8s5MOf3aoAH1JSQkUCgV6e3v9lk0ner0era2ttPizpK/szSbBnpMUCphABENI8LaSgvw8VM5rpOCLTWw2GxoaGoK6z4YNGzBmzBhMmDAhKDkAkJycjIULF2Ljxo1By6ICb9tcgeAiwt4izcFukadTNgMDXTABiOEBmbZhz549KCwsPO0CEs8//zzGjRuHc845h3LZkyZNwuzZs/H8888HLauurs5jqgvgpG9ARS5yX7BardiyZUtAK3QBYPbs2YiOjg4qZYi385P6fx7KPPQPPvggzj//fIwZMwY1NTU4//zzQ3LvSIfNZuP+++/H9u3b0djYiPHjx4c8XcxIw5dxQaiDk/0hF5VFQv30lB+7vy7B+uiefJpgfXSCIFBVVYWKioqA/Kbx48cjIyODklR4vkKOt+iySSTQ3NwMs9lMS/lubm6GxWKJiLozklGpVFi5ciUuv/xyzJs3Lyw6/Oc//wFBEPj3v/9NmUy5XA4ul4vc3Fy33xcXF6Ouri5sPr6v/mwg5ZtO2XRC+u506E2n7FDCBCIYQoJMJgOLxUJBQYHb74dLhaESmUwGNpuN/Px8t99TYZO+vj589dVXuO666yibJFy2bBn27t2Lw4cPUyIvGLytzALoC0QEK5vOAAoDA9UMDkDo9XpUVVUxAYgIp39Aoqio6LQISBw+fBhbtmzBqlWraCuXq1atwu7du7Fr166g5Aw16VhYWAgAIVtl+vvvv0OlUqGysjKg3wsEAlxwwQVBTY6RNiHzsA+GqvzsvtDa2opzzjkHTz/9NJ544gls3boVqamptN93uDFz5kzU1NRgxowZqKysxMqVK0OaMmYkIZPJPKbtBEIfnByMt/PNACAlJQUxMTEhqZ+++tGBpIrz1f8PNA3dkSNH0NjYGHBbS6bCq6qqCjoVnq/4YhOZTBYyfejA27mDAJCTkwMejxdQ+R6q7uTm5oLL5TJjvyB54IEHYDAY8OKLL4ZNh+TkZDz99NN477338Ouvv1IiUy6Xo6CgwONOzOLiYlgsFjQ1NVFyP3/x1Z8NpHwPVS+DkU0nQ9kkGH+WTtmhhAlEMISE9evXQyAQQCgUuv0+JSUF0dHROHbsWIg1Cx/r16+HUCj0eG5Deno6RCJRUDb597//DZvNhqVLlwYsYzALFy6ERCLBnXfeSZnMQPjzzz/R09MDHo/n8ZpAt0/39vZCoVD45HT7y+7du9Hb2+v1vI5wHFLKwDAYgiDw008/Yfbs2QMCEH/++WfAK/kYQs+UKVNc761/QOJ///vfiApIEASBO++8E6mpqbjyyitpu8/ChQtRWFiIlStXBmW/Y8eOeTyHCADEYjEEAgFeffXVgO/hD/fddx8kEgkmTpwYsIxzzz0Xu3fvDjhlyLFjx1xpFd1B5n6n21esqqrCuHHj0NTUhF9++QX333+/x5XXDM4c8V999RVeeuklrFu3DjNmzDitdjhTxZtvvgkul+uxbyUnXMI1VnrzzTcBwGNAjtzRS7d+Go0Gv/zyi9fUtcXFxdDpdOjs7PRbvi9BDq1Wi66uLr9lA8D//d//gc/n4+yzzw7o9wBwwQUXoL6+Hp9//nnAMnylr68PO3fu9HoWTHFxMVQqVcScIRgIr732GlgsFpKTk91+z+VyUVBQEFD5plM2g5Pt27dj/fr1ePzxxwNKL0klN954I84880wsX74cWq02aHlDtUlk8DpcGSvWr18PDoeDuLg4t9+LxWJkZWUFVL5fffVVcDgcSCQSt99HR0cjIyMj4urOsWPHkJCQ4NGfjYmJQVpaWsA24fF4iI2Ndfs9eZZRpNnkFAgGhhCQkJBADFXcAAx5zUhCKpX6ZBM2mx3wPeiyKZfLJQAQNpuNctm+8uGHHxIAiOeff97jNf/4xz8IAMTmzZv9kv1///d/BADizTff9HjNsmXLCABEdXW1X7I3btxIACDWrl3r8Zprr72WAEB8++23fslmYKACh8NBbNu2jZg1axYBgJg0aRJRVVVFOByOcKvGQAF//vknUVFRQQAgSkpKiA8++CCsbTlV7N27lwBAFBcX034vsm5UVVUFLMOX/hkAkZSUFPA9qNZnKL744gsCALFs2TLadKDTVzSbzcRdd91FACAuvPBCQqlU0nKfkczevXuJgoICIjY2lvj444/Drc6wgsfjESKRyOP3JpOJAEAkJCSEUKuTTJo0iQBAqNVqj9fweDwCAK3+QltbGwGAWLBggcdryDHCbbfd5pfs7du3EwCIRYsWebzmf//7HwGAWLFihV+ySVgsVtBt2J49ewgAxHnnnReUHF9oaGggABAXX3yxx2veffddAgCxatUq2vWhi7KyMgIAYTAYPF4TaP9TWlpKACCMRiPlshkIwmAwEACIqKgowmq1hlsdgiAIYufOnQQAorCwMCg5Wq2WAECkpaV5vKa+vp4AQFxyySVB3StQcnJyhpwXCrR8Z2dnEwAIu91OuWw6odOfzcjIGLKfjUSbDOb0PWmNIaTs27dvyO2aZ5999ml1+F9NTc2QK+xmzZqFqKiogO/x8ssvo6ysLODfe2Lfvn34/PPPweFwKJftK0uWLEFubi6mTZvm8ZrFixdj06ZNXtM3uWPatGmQSqU477zzPF5z1VVX4csvv/S6QsEd11xzDQoLCzF9+nSP1yxduhRfffWV37IZGIKBOJGC6dFHH8WOHTswadIkVFVVYeHChczuhxHE5MmTXam1HnvsMVx99dV4/PHH8dBDD2Hx4sVhbdeDYfTo0aioqMALL7xA+70+/PBDLF++HDNmzAhYxpw5c4a8pqGhIWTv4+OPP8a4ceOCknHJJZfg9ddfx6WXXhrQ72fPnu11tyAAzJ8/n5YD+H788Uecf/754HK5WLt2LVasWMG0ewEwadIk7Nu3D//85z+xePFiLF68GCaTCQKBINyqRTyHDx/2uMIRAPh8PsaOHRvQAcdUsG3bNrS0tHjV8dprr8Xvv/9Oa91JT0/Hrl27cMYZZ3i8Zvbs2YiLi/O7jc7NzYVEIsG1115LuWySzZs3Iy0tLaDfkkyZMgUbN27EWWedFZQcX8jNzR3S3ueeey6kUqnXMVmks2PHDnR3d3s94HjJkiU4cuRIQLIVCoXHzBCAc8wa8SuYIxQOh4Pk5GTcdtttETOXdOaZZ2LKlClISkoKSo5YLEZ6ejpuvPFGj9fk5+dj7969GD16dFD3CpTdu3dDp9N59VcvvPBCdHd3ByTbYDB4nTOrrKxET0+P37Lp5Oyzz/aatQNw7rBWqVR+y96zZw/MZrPXfnb+/PmU7MahExYx1OwwAwMDAwMDw4jmhhtuwI4dO1BbW4vJkyfj0UcfxYIFC5iJuNOAvXv3YvXq1aiqqkJWVhYyMjKwY8eOiBnMMTCEggceeABPPvkk1q1bh9tuuy3c6gx7CILA1KlT8ddff+HYsWMez/1gYGBgYGBgYGA4vWACEQwMDAwMDKcxJpPJtQKsurqaCUCcpvz11184//zz0dfXhy+//BIXX3xxuFViYGBgYGBgYGBgYGBgGEEwgQgGymhuboZCofDrN4mJicjOzqZJo/BDp03CYe/T5Z7BEojOQPj1Zhh+UFXWjh07hpSUFI8HjTGcHpjNZhw5cgTjx48fEIwKd5sWSf1AJPXr/jxjJOntj2x/CHc5ZfAO834ip654g852iE496NTFH4ZrWztc7e0PkWY/f+SfbgRqTyBybBpJPl0kz8cwdedUTgebMIEIBkpobm5GaVkZjAaDX78TCIX4fNMmn3JlDqeKBThtUlZWCoPB6NfvhEIBNm363KtNOjo6sGjR5TCZ/JMtEotx7OjRgOzY3NyMktIymIz+vWOhSAzZscDvSXe5MpvNPucu9uXajo4OXL5oEYwmk08y++PLuycZbvWBgXoCrR9AcG0Bw+lFoH0ZAIjFIhw9eizowU9ZaQkMRv/aVLFIiKPHZJSWcactymDws84JhUJsGqJP6ujowOWXXw6j0Xc7i8ViHPWhHtNpQ6fsUhj80BsAhAIBNn0+dH+XmJgIAEMOyJx972Uwmsx+6QEAYpEIR48FV04ZvBNoGQToqcvhIFAbCAV8bPr8C5/PFQjGPwykvffHd/VVP7r9/46ODgDwWWdfxwodHR24bNHlMPs5PvNlrBSoTXzx9wId4/EFQnzx+fAYx9NZpgJ974DvNgy3/UJJoL4Wia++EZ0E0t776hd1dHRg0eWXw+SH3xXsHBCdc1p0z5vQWXf8DRj4Opfk7/sl8WW8AURGe8IkAGagBIVCAaPBgLJLViA6KQs2sxHixHQ4rGbwY+JgNWjB5vJh1athUisQlZQJk1qBmg0PoaKiwqd7DLeBiEKhgMFgxBt3L0KvxgABj4O0+FiwWCxYbDYIeTxojCZ0KLWIEQug0hnx1EfbYDKZfbbJimfegUalBJvFRlJ6NlhsNmwWC3gCAQw6DbR9Shi0akiTUsEXCPHKvTdAoVAEZEOFQgGT0YDim16BVd8LjiAavJgEsFgsOGwW2AwasNgcEDYLLOpuxI87D4b2WsjfWhHUPY0GA8Zdeivis0tg1qkRnZQBu9UMYUwcDH3dYLE5MGl6IYiWQCxNwtY1y2A2mXy2IZsFOHwNx7LYAOHw6dI371kCpUYPPpcDo8WKjEQpzFYb4mPEaOhUIkYkhEpngM3uQElWMq598r9+vfvhVh8YqIesH7c//Q6SM3PRdOwgrBYzUrLzwWKxYbNaoNeoIBCKYDTokJZdAFF0LNqOy7Du/wJvCxhOL8i+7M1/XXWiTePC7nAgSRp9ok2LQkOHEgmSKHT3amAnCMwqL4S8pQs3/+fDoMuZQqGAwWjCuitGQWmwIFrARUIUHywAFrsDXRoLogUc2AkCOrMdU3OlqOvW4/ZPj1Bexp22MOCi6+9EVmEJHHY7omIksFosiJbGoaulAaKoGCi72sHjC5CZX4xnVyyByY8+6f/+7/+Qnp4OiUQCkUgEs9mM+Ph4aDQa8Pl8qFQq2O12dHd348EHH/TpGUkb/nN6GvLihUiN4YPFAix2AkIuC1qzHb16G6IFHDgIAqNSo1CrMGLF53VDynfKNuLWueNQkhEPLpsNIZ8Ds9WOuGghGrs1YLEAncmKVKkY8dEiXL/uO5jMvvV3QoEABIvt8wTPq9dMRq/eDD6Xg9RYIdgsFix2BwRcNrQmG7q1JpitdiTHCjEqXYLaLi1ue38v0x7SDFkGX1lUhF6DFQIOG6mx/BM+sQNCHhtakx0akw1sNgtsFjAmLRq1PQas2FQ7It4PaYPbZmYgJYYLIY+DWCEXFpsDcWIuOjQWlKaIsa9FB7PNgRe2t8BsI2AyW3xuP4Dg/EOyvb978fnITkkAj8tBtEjgaus1BhP4PA561XrojGY89s7XfvmuvupH+jeTLr8D0anZsJsMA/x/dUcjODwBWBwOHFYLopMysOUJP/x/P/x5wM+xAoCMBbdBkJAFNpcHjjAKDpsF3Kg42I1asLg8GFqPgS9NATc6Dg6LCbU+jJVIm0y4/A7EpGSDBUAQE+eyiaajETxxDMzaPnD4QqSUTERfixw//ucWn2SbjAaMv+1VWLS94PD4EMalAiw2HDYLTH2d4Iqi4bBaYVZ3Q5JXjn0vXA+L2fe+LdzjFtJ+Vz3yJvRqJex2G5KyCsFisWCzWqBRdCA2MQ12qwVmgw6xSWl4/76r/RpTlv1zHaw6JbhiCXhRUoDFAmGzwNzXBY4oGubednCEUZAWT4VZ1Y0jr9zgsw3Dbb9QQvpadz37DjLzS3Bk306IxNFQK3uQmp0Hq9mCGGkcOlsawOMLoFP3gSAIjJ9xLlqPy/DSv8M/xiHb++UzMlCULIbWbEdqLB8WG4E4ERcdGjNKU6NwXGFEo9IEo8WOt3a1+dWWll54C2JScyFOSAOLxYbdagGHL4DVoIVZ0wuzrg9CSSJ4omjsXHtbUPMxBoMR7zy2Akq1BtEiEYxmC5LiJLBYrYiLjYZGb4CAx0NzZw8IgsCj6z/yq29459HboVRrwWaxkJ2W5PTdrDaodHpw2GxYrDaotQaU5mfi6vueh8li81k+XXUnoMUFfvQ9L76xEdm5eThy6AC0GjWKykY77WKxoKujHSlp6dDrtJBI47Di+iUwmc0+jzcioT1hAhEMlCKMTQSHL4Q0ZxRUzUcRm1GImNRcaNuPIzazCCa1AvqeVgCAteM4HARw9eRkTMyIRkOvCVlSAdJiBWhXm5EpFUBtsqFXb4NEzPVpQByJ9Kh0iI8RIz89AUeaulCckYjc1FQc71BibH4eetQ61LYpABAwW+24aVoqYgVcZEj4Hm3yd7se63d2QKXsRowkHmk5BWiuPYyMvGKklIxFR1Md8srGQa3sQU97E6JiJOhsPk7J81g1CnCjpRCl5EHfegzitEJEJY2CsasB4rRCWDQKOMwGGNprQditlNyTL4qGNLMIvY1HIJImQpKWB3VbPbInnwujSgG7xQyDqhtGTS9sVqtPNuzV26Ax2/DsT60+lUHy2tTzbwJXHAt+fAZM3Q0QJGRBEJ8Gs7IdgsRM6JoOofWLp9Gt0iI+JgoF6Yk40tSBJGk08tISUN+mwEUzytGj0qG2tRsp8bFo7uz1+d0P9/rAQD1qZTccdhtySsaiufYwxFExSMnKR0dzHYrHneFqB1gsFkwGHTLyS8KtMsMwpLtPh/hYsbNNa+xEkjTmZJs209mmsQHkpCbAYLKgOCuF0vv36CyIE/OQlyjGsU4dCpPEyEmIRoPCiMIkMRR6C3r1VrSrTaB7q68oKhqZeSVokh+GJCEZqdn56Gisw7QLLoa6twfRkngABDR9vbD42CeR/XpSUhJiY2NRWFiIQ4cOobS0FAUFBaitrUV5eTkUCgUaGhowadIkPPjgg37pHc3noDQlCse6DChMEmJUnBANvSaMTouGQm9Fl9YCo9UBs51AUaLIP9kiPorSpDjS2ovMxGjkJUtQ36VG5ZR8KDRGmK12dGsM6NUZYbY5fLLJoQ49XvvduXI5efbViM6f6LbftenV0Df/jY4t69GjNSEuio/8pGgcbdegMCUGoxOjcLxHh7GZUih0ZvTpLehQG2Gw2FCUEuPXczIEh0JnhVTMQ16C0FkOE0UYlRqFBqURo/OiodBZ0a2zwGonwAJQlCQOt8qUkxjFRYxwoA1y4oUQcDnIlAoh5HGwu0ENs43AK5cVQqGzQme2D+kbTs6O8TmAOKSOkhgI+VwUZCTjcEM7irNSkJeeiPq2HhRnpaBHpcWOGjksNt9910D0E0kTweMJkZQ7CsrGI4jLLIIkLQ9cvhBxWUUwnBgDtB7Y4bP/T7a1Q/nzNr0aNl0vbEYNWr981ifZP9WqsOmAAryYRHD4AghT8mBoPQZRWiGESTmusZIoKQcWdTcIuxV2h92vdyOSJILLF0KSno/exiOQZp20iTSryDUu0ina4bD7J9ui7gEvOg5RafnQNh9FdHohxCmjoe88juj0Ilg0Cli0fVA3/Q27zffxViSNW7S93RBL4pGUVYCO+iNIzilCekYeFC31SM4phq6vB91NtdCremH1s0xZNE77iVPyoDsxRhYl54B74r1bNQpYtL0w9rTAolHAbrOO+HmQYMjML4GisxVRMRKk5xTAbrNCEp/k9LmaTvpcio4WAIDJoENmhI1xogQcFCaJcaxTj6QoPnLiRWhQGnF+aQIUOity40XIlgrxe4MKDgL+tTOiaMTljoaq6ShiMwsRlzsamo7jiM8b65xzU7TCotfAbvF/p6g7untViJfEoCArDYfrmpEUL0F+RgrqWjowriQPPb1qsNls9PSpYbba/Ko73b3qE7JTcbi+GcU5GRib6ZRdkpOBnj41mjp60NHTB5PFN9l01x0y2OTvvJOv/qyipwt2uw1lY8ohO/I3omNikZObj8bjdZg45UwoerrR3tqCwwf3w2Q2R4RN/IEJRDBQSsqY6YjPLwcAJJVOcX0uTkgHAIjiUhCXOxoAYDXqAADlaVHIjhdCIuLBbHegodeEUalipMbwweeycahdjwwJP8RPQh0zx+RhXIHz+aeWnqzsGYkSAEBqfAzG5qXhQH07AKAoUYT8RBHURjvKUjlQ6KwQ8dmYkS8Bn8uGvNuApFinPUZPmYX8UeMBACUTznTJTkjNAADEJaUit3QsAEAYRc1gW1I6DdE5TpmxhSffsSDe+Yx8qXPyKbZoCnRNhyi5Z+7UuUgqHIe0UWe4PotOdN4vKj7V9VlP3QEAQ9uwXW1GrJCLPoMNwNBlsEFpQrfWAgAQpRVBlJoPu14NTmYZrBoF2HwRJGUzwObxYe5pBgDMHFuA8YWZAICpo3JdOmYkSgEAqfGxGJt/4hmEfJ/03tuiRXw0D/nxQkrsyjAyGOVHOwAAx4/UhFI9hhHCzPL+bVqe63N3bRoA1NS1Unr/aflxKM9w9mNTciSuz9MlzvYwJfbkVueDbVpK7z2YKefMR8HoCSideLK+JZ6ob/HJacgrdfpB9Yf3Axi6bT+uNCEpxhm4P/vsszFx4kQAwPTp013yMzOdtk9ISEBJSQn27dvnt97nl8RhbHo0pmSf9AfSJU67pcTwMTo1yvX5oXadX7Lnjs/FuNwknFF0cjt4enw0ACBVelLugcYeAEPbpEtrQX6CyBWIiMophzApGzyxBA6bGaauBoizRoEvTQU7nQ+rqhMAML0wCeVZUgDAlLyEk7pInYGVFMnA/vNgi8qv52QIjml5EoxNd5aLKdmxrs8HlEMEXg6HA77YwHFi+b3RYsfY9CiojXaY7Q4kRfORnyhCagwfJSlidGkt0FsdSJcKIOJx/A4gemLmuCKML8oCAEwdne/6PCPJeY5UaoIE9hM6+uJzi/gc6C0Ov/XLKJ+B5MJxADBwDJA0cAzA5nKxa8MTPunSrHJOyg3lz+sbD4GTkAGuSeLTcx5XmjC7QIpNBxQ+jZWi4BwP+ztWyiifgSR3NvEyLvKVhFHTIclz9l/xxSf1Fp0YxwvjnGM8XpSz3PpibyGPg/TYyBnHF0ycicyS8QCA3LFTXZ9Lk519eGxiKtKLxqJVVgPAv/47rnQaYnKd9pMUnbSf8MR7F0hPLtDQNh4EMPQYtEtrAY998syu042p555c3V06wZ3PlYq8fmOc+sM1IdPNF84vTUB5egymZPf3W8m2/qTf2qZxtku+1CmyPmVOvgDxBeUD59xOtAOi+BTE5TnbmN76g5Q8y6yJozG+1NkfnFl+MuCTkeL0tVIT4zC2OBc1x477/Cxkezxr4ij3spP7yS7yXfbhTj3iWayQzJn4ossYiQAtfc5nHcqftRvUTjvMnI0x45zjgUlTT44H0jKc44Hk1DSMGjsOqenpeOnp1UPq0aA0QcTnIC0mMtpjJhDBQDldh3cCACw6lXM7p6oH0tzREMengc3jw9jbicTiSa7ry9OjXQ65OzIkgmE/EPn97wYAgEpnhNlmR49KhzG5qUhLiAWfy0WrQoW6Nmd+OV/skRjFc/195M8dAACdRgWbxQyVshu5JWMRn5wOLl/gWiXAF1IzONE1HoTdpINNr4bDZoZVrUBU9onGk8eHoU3u3IIsloAjjBpaoI+0HfodAGDWqmG3mmFU9SAhfzSiE9LA5gmgPH4IUQnOCRBfbAgAXxzo8fl68tro3HLX4MId0fkTXP/+7VA9AOd7t1ht6FbpMCYvDekJEvB5XLT29KFdoYbN5vBL7+FeHxioh2wH9BoVrBYz1Mpu5JSMRXxKOrg8ARqO7IdRr0NSRjYEIurqJcPphatN0xpOtmn56SfaNA6ONXVBqdEjKzkOYiG1ju7BNg30ZhtURhssdgd6tBaMTotGaqwAfC4bbSoT7A4CLBYg5tPr3tYfdtYnV7+r6EZu6VgkpJzsdzV9StdA2Ze2XSrkAAD++usvaLVa9PX1wWw2o6urC+PGjUNGRgYEAgGam5uh1WqRnJzst94H23XQWeyuSU2FznpK0N1o/X/2zju8jSr7+1/15iL33lucTggQkkAIhBqHGkLYTWNZCJ1lX9il9ywddvkRkgCBFGoIbMCm16UGAqTHltyrXCRbktXbvH8oo8i2JGukGRVnPs/DQyyNzpy5c+8959577rkupMn4lCc+9rcNwGCxQWe0uv0cvRlTCtKQk5IAEZ+LrkEDDGY70pMkQZeJt60bz/YmTXIP0g50DsFgdUBnssHqcEE9bMXkvGTkJIvdA8JuHRLEAshEPMwoSKH0jCzhc6DHAIPVCZ3Z4VUHZchOctfBFrUZJpsTOUkiSIXcaKvLCJ4ysDhgdRBQG+2YnCX1lEGf3ob6XiOA4P1CErr8w33KDhhMFmgNJljtDgwMDWNqaR5y0uUQCfhoaFfhQFMnZR2p6tffuB92kwEWwzHfP71kCmTpOeAJRFA3HwRBuJCQEXxfK+Zz8fiXneP2KaJUt8yB3e8HLZscK4w3VjJ2HAZflgJwOOAKgjunjmSgcT9sZoPf8ZCuuxkOqxnFp5xLSS4AaFsOwGE2wH5Ub6tOjaTCyRAfHcPr2w6DIFyehYl4Hbc07/0BAGDWa+Gw2zA82I/ciqlIzsgFXyDEQGczdAPuAEEq9hsAhhp+BgA4jO55ELtuALLCKRClZIPLF8LYpYDTZobTYghafqyVXyQ59OvI8Q05z5GWlQuBUISOpnoIRWJweTxMPnHuONIiz4HuYRitzmO+q8GOKdkyt+/K40DZb4LdK+9bMPWBnNDWNO+H3WKAzaCF026DRTeAFK85N+NAN+zmYZg1Ktqe5/s/jgAAtMMGWG0O9A9qMa2iGLkZqRAJ+ejsVaOxPfi2Q/bHextaMGyyQDtshNVmR/+gDtMqijxyGztU6OkfhMlqDVo2EJm+J1hdOoeozSUd3PcHDAYD9Noh2KxWDAz0oXrqDGTn5EIoFKG1uREikQhNSgUlPWKlP2EXIlgYQdt2GIm57ryLubMWuSeo1d1ILpgEg70dA4o9nmt3HVRDY3LA6nChMkMCIZ8Lld6G7EQhhq0OGKxOiPnxPxg51NaL8rx0cDgcnH1iJYR8Pno0OkjFQvB5XHA47kH/gR4Dfu0YRlm6ZESZ6C3usshJEuGPrpHRnm2Kg8gtrgSHw8Gs0871TIRIZImQyBLB4/Nhs1I/BMgfxo4jkOSUARwOUqaf6d4JoOmBMCUHfGkyzH0tcNkt4AzT08UMNO6Hw2aBPL8cHA6QWTEDXIEIhv4uiJPToe1uQmJmIVxHU0H5qlM9OiukQh7kYj6aNWZkea0G+7teJuS5c1tbj21tNrQdwHDjr5Bkl8Flt0KSWwmuQAin2QDC5YCpqx6AOxrYYnOgIi8DHAAzy/OPLT5odOjoG8LiU6dgaNiMDu2gXz1Uehvqe004s0I+Rm8WFgD48eN3Me3UhbBbLSiomAy+QASLyYDW+v2wmIyYdOJcSBISIUlIBOFyYaifPoeU5fhhX2MXLDY7KvIzwOFwMLOiAEIBD0azDb8p2pEsk+DUqaU40qqCwWyFsrOfdh0Oq9wpmTgc4KyqNM8CBJ/LAZ/LQXV2An5oHoLaQE9awEC0NRxEXkkFOBwOTlzgtrua3m6kZuaAx+NDKBSht8MdhOCrbzfZnFAb7SiQi9GiMUNncXhk79+/H1VVVeBwOLjgggs8CxD5+fmw2WwoKCiA1RraVvsjvSaUpUvA4QBnVshH2EcRn4MZuYlo6DdBpaMu/3CnBuXZcoDDwaLpRRDxuegZMkIq4oPH4SAvLQFWu9ue+vN1OoYsKJSLobc6cPjoZCwAqH/ZBcewxqfddZj0nt2IzQNG5KW468iMAvfzqbRmDBltcLgInFqejvoePTQGG7460uuJ6maJDC1qM/KSReBw3INmIZ+LHq0VvXob+g02CHlczC9Nxr5uA3qHXZioccBH+kwoSxODA8LTDjsG3X66iM9FWYZ7wS5QOxHyuGjWmFGeLkFFugR7OodhsQd/7kEgmrr7kZ+ZcrSvL4RIwIfRbIVKrUVhdhoSJCIkJ0gD6tijs8Joc8HhInBKYSJ+6xyGyUYtVRAAqFsPe/z/opMWgScQwW42wqhWIb10KoxD/eja/2NQulSkSzxjqPH8eYdJD5umG1Zdf0DZJpsTEgEPw1YHWjTHzrLxOVYaVLkXI0Qy2A1u359wOkAVTcvIMuEK3Hnh7VYzBGIZDOoeDHYoKcsFAH3HEciO6p058yx3EKG6G+LUHPDEUvdken/7uOXtcBHH7JuZ+jMyTU/jIWQWVgAcDqrnngO+QAhtfw+EEimEEhmMWg2AwO+dtOFknRpuOwCXzQJJTjnA4SCxeDq4fCEsmm5Yh3qRWDgF4HAgTM7A8JB7F58v/8DbDk6UeZBQaD68FzarGXkllQCHg7IpJ0AgFGFA1Ylh7SD6u9txwmlno6PxCAy6IXy96w0kJqdGW+0xHO41ovyo33VWZSqEPA70Fid+btXj9PIU9OisaOx3+zv+5iR4XA7K0yXY122A6ujuiWFVC2QZ+QCHg7SyGZ7Fh+HeNqRXngjzoAoEQUAoSw6kHmUONrahsigXHHBw7txZEAn5aO3uR3FuJoQCPqQS9y6E8frjfoMNhXKvherGdlQW5YDDAc6de4JnYcPhdKFXPYQlZ5yEbR9+E1C2werEH53DOL3MPW8Sib7H1zszWJ0w2pyed+Y9fxPIn3XZLLAOuhdyWpsbkZtfCHA4mDpzFoRCEXq6OmAyGvH7rz/j7PMvhLL+ELjcwHOIPTornC4gXy6Kqf6YXYhgoZ2sKXORNWXsijSXx4dQloSsKXNhHuqDoc89aLx4WjqcBNCrt6F32IZUqQC9ehsK5CIQBFCYIvakxYlX5k0twbypJWM+5/O4yE51p0jQGd0DEHI1c1+3YUSZNKstOKkwERqjHbPyj6VVmHzSaZh80mljZPP4fKRkuLfnDg30wjbQS8uz+FvF5XD5EMqzIE7PR2KZexvZ0KFvablnRsUMzzZkb7g8HkSyJGRVzoJxsBemwT4AvutU37AdJxWKoTHacXqZHH1edWq86wtTxJ7og4Ti6YDLCdtQL3iyZBAOGyxDPRAkpoMrEEGaXw3AvfBApjHxxv3Ok4CjRXj+KZOxr7HTrx69ehvOmZQyQu94bw8s9DLvgss9qZlIhgZ6cVrNFZ6/p55yBgDAbrNCse+XCGrHMlGYWeG7T+sd1GPZwmO7HOdMcdu6X+vbaL3/9LwkT2omb/hczoi0TOdPycA3Sg2t9x5N2ZQTUDblhDGf83g8pGbmIDPPnYZx7/dfAHD37aRdV/SbkSzhgSAAAoCAx0G6TOA5CPXEE0/0pGbyhs/nIycnB0VFRVCpVFCpqC8o+ouW4nM5IwZJJxUm4dvGIUqyZxRnYEZxxpjPeTyuJzVTr9aIPq1phC6jfR2N0YHSNA4IAsj20in9lIs9ttem7YUgIRVmVSOSJs0FVyACYXfb6Etm5XtSMx17Pu6IlEwnl7q3+lsdTuxpGaT0nCzhcfH0jDF1cHT9A4DTyuSwOlz4vUMfSfUiQjDtkIxY9L52dP9hcbiwoEwOjdGORDEfC8vl+L5ZS4uOSxfO9qRmIunV6FBV5B5XFGalQiwSjtBxdFt2+9GJx/SrSKHcr2RWzPCkZvLGONiL1MJKAEBiZj64PF7QupBjKHI8Y2jd57dfSa46FaqvXgsou3HAjJMKE0EQQJpUMEL2aLzHSiRUx0r+xkTGwV5PWqasSSceHReZx1wXCHnpdE9qptF6i1OyIM1w14n+/W6dgynv08vklN8705SdMB9lJ8wf8zmXx0dSursMeXz3NNl47927TiUWT/ekZvKGw+N70jKlTV/ovpfAbZN8jf287eBEmAcJFbevNXPM51weH6mZ2aic4U5JVD3rVADuMU7DH7E1xpmel4jpuT5812Er/jTbnckhXy7GIZW7z/c/J5GILq3VPcE+4G7XxfMvQWrZyPrG5fIhSXXXtaJ5FwEAevZ+Q9vznDZrMk6bNXns8/B4yE5PQUF2Oob0I3f0+esfMhMEnmCQEyaVelIz+ZJLMv+E6oCyGwfMOLMyJaJ9TyAf32x3IV0mgFxybNo9kD/rMAzCaXYvbF542RWe1Eye8uDzPSmZAGD2nHkQS0buNI6X/phdiGChFV2XO/pC16mARec+REcgS4bNoEViTikGFL8hIdPtxBiORlM0qs2oSJd4zoGwOVwoShF5jG7/sA2NamqOVCyh6HJvw2ro6MeAzohEqQjJMjG0BjMKM+X46XAbynLTMKh3D87JZ+Vx4LdMGo8aoO4W91aszqZ66AcHIJElQpaUDINOi+yiMij3/YqMoxMi5LXhYuppdP+/WwHbsAZ8cQJ40iQ4jDpIskrgMOkgTne/Y/swPQP8oU53vRpsb4BZp4ZQkghhQjKsw0NIziuFzaBHYlaB57rx6hQ5wOvUWoK6vn/Y5rnWrGqEJKcCwjT3lm2XwwaeJAkuhw1wuL8HMCIauKG91+vdS6A1mFCYmQqtwYSyvAwoj9aRYPWO5/bAQj/ebdvTFyQkQpZ4rC8wDeuQkevuC9gdESyhoOzs8/zb3acZkCgRIzlBDO2wGYVZKdAazCjLc09Gt/XSuxjQ1H8sOl7RZ4DGaEeCiIcksQA6sx35KWLozA6UpksxZGR2R0TX0TbX0VQPnWYAUq/2llNcBqNeh8y8QuiPRlT6suskZP8+eFTn+nr3rrrDhw+jv78fSUlJkMvlGBwcRHFxMbRaLSoqKjzXUYHUQ9Fv8io/PnRmh+cgu9I094BmkGLElLLHPbBp6B6EWm9GokSIZKkQQ0YrCtMToTVaUZYt91wXjK/jrYMv2yvKKIJV0w0Anujixr5jO0YVKj3UBisSxXwkSYTQmmwoSJVCZ7ajNMM9udunp2+3KMv4NA6YPP8Oph72TsAJONKHD/T85OSMt78XqP8g/UO1iZ5IR2WHO3ipvk0FtXYYiVIxkhMkGBo2oSg7DdphE5wu1wgdA7VlUj+q/crQ0ah+TXsDzFo1hNJEiBKSYRkegjyvFFaDHklZBRj08v/H04Usf9JfB5fnt18xtB+Ew6wPWrbe6n4+77GSfVgDnjgB/KNjJXGYY6Vgx0Te1wbLcLdb7+EuBax6NfjiRAhkSbAbtJDllMJu1EGaUQD7sH/b5uvdU33vTNLfdqxMelsaYNAOQCxNhCQxGSa9FukFpTAP6+CwHhsjAsHVKWNPk0e2sVsBm14DvsT97u1GHaRe7950tP5N9HmQcOjyGt90+JjryCk65m+RDA7E1hinqd9t8xR9xmP9vYTs70m/VYLho20kmLkAsp8h5920nQpYdWrwJQkQes272Yw6JGQWwKqnxx9XtHV7/l3f0omBIT0SZRIkJ8igHTagMCcDWr2Rkm3w2EN/shNl0OqPyh42wumkZnci0fcE4+OPN5fky59tUjR45CgbjkAzMICExEQkJcuh0w6iuLQCep0WFovZrx4x3R8TLCw00N7eTkikUgLuxb+g/+Nygr9WKhET7e3t0X7UoGlvbyekUglj5cHhcimXt0QqDbkM29vbCbGE+jsWS8K7J9V6RaUMKV/PCa7MuRwOo20hHtsDC/2E2u+G2xewHF+EYss8/ZRUEnY9a29vJ6QSMfV7M9BHusuCOZvEpWjXpUG2YybL0C2bOV9HLBIRInFw8qna0WPPGX49ZQlMqHWQqbYcDaiWQej1OfTyotrfh6JjsP0Ko/5/kP58JGQDwY2VQvX5gvH3Qhnjxdu4JdbqFFX50S6/SBKKrzWirGJgjBOKzWNyHijcOSAm57S4XGrzJrHS94Tk1wQ7l0Tx/cZKmVCBQxAEARYWGujo6IBarR7x2VVXXYWEhAT89NNP2L59OyZPHrmVy2q1QiQK7pCu9PR0FBYWjn9hDOGrTFatWgW5XI4ff/wRb775JqqqqjzfkakWcnJyxpXt69qVK1ciNTUVP/zwA95++21UVFSM+E24Zej9PARB4OKLL8bJJ5+Me+65BzfddBMcDgc2btzI2D0B4LvvvsNtt92GGTNmID09HU8++eSI66mUIUCtDgZ7rS8dvvnmG9x+++2YPn06srKy8Pjjj4eldzy2Bxb62bhxI2666SYsXLgQ69atA5/ve6Oj0+nEo48+ig8//BAPPvggrrrqKrb+sATN6H74P//5D3bt2oXPPvsMQqEQDz74IH7//Xd88MEH4HKP5TKmq5/yZUv/9re/wWw247fffsP69esxZ86cEd8z1UeO1uXQoUNYvXo15s6dC5PJhM2bN4+4Phy7fvDgQaxZswannnoqLBYLXnnllRHXU3nG0XqPJ5uK/NGyDxw4gKuuugqnnnoqbDYbXnrppYDPGYj09HQAGPP+feFL7vXXXw8Oh4NffvkFL7/8ss+0V6w9jQze9cRiseD888/HhRdeiNtuuw0AcPfdd+Pw4cP473//y0g/EguMbit33HEH+vv7sXXrVvznP/9BbW0tPv/8c3C5XKhUKmi1Wsjl8qB9Q4B+v/vBBx9EU1MT6uvrsW7dOpx33nme76j6rlT0G61Hd3c3LrzwQpxxxhlobW3F+++/P+L6cMdQgQhnrNDZ2YmLL74YCxYsQEdHB3bu3DnmN6GWSUdHBy655BIsWLAAnZ2dePfdd2mT3d7ejksvvRQLFixAV1cXduzYMeL6eBy3+PIl/vnPf6K/vx8HDhzAM888gzPOOMPzXbh16h//+AfUajX279+PZ599FgsWLBj3N/6IhfKLJKPf1S233IKenh7s2LHDYx8eeugh/Pjjj6itrR3R5mKlrEY/w/bt27Fx40bI5XKcf/75uOmmm0ZcH04/s23bNmzatAlJSUmoqanBjTfeOOJ6uu0C4H4nNpsNe/bswYYNG3DyySd7vqPadkbbOnJeyZ9sKraRyfrgq1wCEc5c0vXXXw8A+PXXX8f4s7FUJkET1WUQlglPeno6cc899xAAiDfeeCPa6kQdl8tFyOVy4t577yUAEO+88w6tspOTk4n77ruPAEDs2LGDNtm+aGhoIAAQtbW1BEEQxAsvvEDw+XxCp9Mxet+nnnqKSEhIIK677jpi+vTpjN6LTp544gkiKSmJuOaaa4iZM2dGWx2WCcDbb79N8Hg84sorryTsdvu41zudTuLaa68lOBwO8corr0RAQ5aJiMPhIHJzc4kbb7zR89m3335LACD+97//RUyPiooK4tZbbyUEAgHxwgsvROy+o9m+fTsBgLj77ruJjIwMWmVv27bNIzsrK4tW2Vu3biUAEHfeeSeRnZ1Nq+zXXnvNIzsnJ4dW2VQpKCgg7rjjDoLD4RAvv/xyVHVhOcaWLVsIAIRSqfR89v333xMAiM8//zyKmkUOq9VKJCQkEI8++ihBEMeef/fu3VHWbCRz584lVq5cSWRlZREPPPBA1PT49NNPCQDEww8/TPD5/KD8nljgo48+IgAQDz30ECEUCgmHw0Gb7Lq6Oo9skUhEq+wPP/yQAEA8+OCDhEgkIpxOJ22yY4kZM2YQ1157LZGYmEg8+eSTtMqePn06sXbtWkImkxFPPfUUrbKPJ3777Tefc0mNjY0El8uNqg9IhWuvvZaYOXMmsWjRIuLSSy+lVfY111xDnHDCCcRZZ51FLF26lFbZ/igrKyNuu+02QiAQEOvXr6dVdmlpKXHbbbcRfD6f2LBhA62y45XCwkLi9ttvJ7hc7oTwZ7ljViZYWGhicHAQarUa06dPR3Z2NhQKes4oiGcGBgag1WpxwgknID09ndYy6e/vh06nwwknnIC0tDQoldTyglKltrYWYrEYZ555JgCgpqYGDocDn332GaP3VSqVqKysRFVVFRobG+E6mocw1lEoFKisrMSkSZOgVCrjRm+W2OTNN9/En/70J/zpT3/C9u3b/e6E8IbL5WLDhg24/vrr8de//hWbNm2KgKYsE40vvvgCPT09WLNmjeez0047DSUlJdiyZUtEdLDb7WhpaUF1dTXKy8uj6l8olUrk5ORg+vTpGBgYwNDQEG2yFQoF8vLyMHXqVPT19UGn09EqOz8/H1OnTkVvby/0evoOBVYqlSgsLMTUqVOhUqkwPDw8/o8YwGQyobOzE1OmTEFxcTHjfhFL8GzYsAFnn332iJ278+bNw9SpU7Fhw4YoahY5/ve//8FgMKCmpgYAMGfOHKSmpqK2tjbKmo2E9F+rqqqi2oYUCgVEIhFOOeUUOBwOtLa2Rk0XKigUCkgkEpx88smw2Wxob2+nVbZUKsVJJ50Eq9WKjo4O2mQrlUrIZDKP7M7OTtpkxwoulwtKpRKTJk1CZWUlrb6Ey+VCY2MjI7KPN9atW4fy8nIsW7ZsxOfl5eW48sor8cQTT8Bmi/3zhLznMOjuS8l+OlJ1zWazobW1FdXV1SgrK6P1eaxWK9ra2lBdXY3S0lK27cDtz3Z0dGDy5MkoLi6eEGXCLkSwMAbZIZGdIjsAZLZMIl3edXV1WLRoEaRSKQCgqKgI06ZNQ11dHaP3JY14ZWUlzGYzurq6GL0fXXjrbTKZ0NPTE22VWOKUbdu2YeXKlVi1ahVee+018Hi8oH/L5XLxwgsv4JZbbsF1112H9evXM6gpy0Rky5YtmDJlCk488UTPZ1wuF6tXr8a7774Lo9EY4Nf00NraCqfTGROTY0qlElVVVaisrAQANDY20iqbtBvk33TLJtNDMiGbiTKhQlOT++DQWKgnLMfYu3cvfvnlF0+aARIOh4Prr78eH374Ibq7u/38euJQV1eHgoICTJ8+HQDA5/NxwQUXMO5HU0Gj0UCj0cTEWE6pVKK8vBzV1dWev+MBpVKJiooKTJo0yfN3vMgmA6jolh0rdHd3w2w2M1K/u7q6PLJZ+xM6hw4dwn//+1/cfffdPoOu7r77bnR2dmL79u1R0I4a3v4i3cGUTMr2RUtLC1wuFyNtp7m5GS6Xy/M8bNs55s9OpDJhFyJYGINsIBUVFROmwYSLUqkEh8NBWVkZIwsRTMkezdDQEH744QdPFBdJTU0NPv74YzidTsbuzeTEDJOMnqyKF71ZYotXX30Va9aswV/+8hds3ryZ0iIECYfDwb///W/8/e9/x0033YT//Oc/DGjKMhHRarXYtWsXVq9eDQ6HM+K7VatWwWAwjMnbzQSxFOhARqGRkd1MTASxskPXAzhWTyZCBNlEYMOGDcjLy8OSJUvGfLdixQqIxWK8/PLLUdAschAEgdraWtTU1IzoS2tqarB///6YiT4nFxG9Jz+IKB0vSfrReXl5kEgkceNHk/1hQUEBRCIR7X1tVVUVCgsLIRQKGenHi4qKIBAIJmT/6W0j6F4siCU/JZ7517/+hcLCQqxYscLn95MnT8Zll12Gxx57DA6HI8LaBc/w8DB6eno89YHOXUZ6vR69vb0e2RaLhfFAzYkUXBsPTMQyYRciWBhDqVQiPz8fMpks6s5rrECmK5BIJLSXiVKpRHFxMcRisWfAzVR5f/rpp3A6nWMWIpYsWQK1Wo1ffvmFkfvq9XqoVCpUVlaiuLgYAoEgLjpirVaL/v5+VFZWoqSkBDweLy70ZoktXnrpJVx99dVYu3YtNm3aNOIwT6pwOBw8/fTT+Mc//oG//e1veOaZZ2jUlGWi8s4778But/scEJaUlGDBggXYunUr43qQ6Shyc3NRWVmJtrY2WK1Wxu87GoIgPJM1CQkJyM3NpW2yhkzrUFlZiaSkJOTk5NBmN0jZVVVVSE5ORlZWFu2yKysrIZfLkZmZGbUJLIVCgdTUVKSlpaGyshLNzc0xPVFxPKDT6fDGG2/g2muv9RndmpSUhBUrVuDll1+G3W6PgoaRob6+Hq2trWMWY84991zw+fyY2RVB9gvl5eWorKzE8PAwent7o6ZLZWUluFwuKioq4saPJvXm8Xi06+0tu7y8nFbZ5CI7E7JjBaVSCT6fj+LiYlRWVtKaAnG0bLpTIB4PKJVKvPPOO7jzzjshEAj8XnfvvfeiubkZ77zzTgS1owa5qOsdTEmXb+Qtm4ldrr5QKBRISEhAdnY2Kisr0draSpsfrlQqkZiYiKysLFRVVaG1tTUuUm8xiVKpREpKCtLS0lBVVTUh/Fl2IYKFMUjnCHB3jAaDIWrOa6wwuky0Wi3UajUjsoeGhqDRaGiRPZq6ujqccMIJyMvLG/H5ySefjPT0dMYGUN6Gls/n056TkCm89RYIBCgtLY0LvVlihxdffBFr167FTTfdhBdffDGsRQgSDoeDxx9/HPfccw9uv/12PP744zRoyjKR2bp1K84991zk5OT4/H716tX4+uuvac1T7QvS3nE4HFRWVoIgCDQ3NzN6T1/09PTAZDKNsL109e1MyvZOR0G37M7OTlgsFkZkU2W0X2S322nNz85Cne3bt8NqteKvf/2r32uuv/569PT04MMPP4ygZpGlrq4OUqkUCxcuHPG5XC7HaaedFlMLEd5BZeRnkcZsNqOjoyMm+hUqGI1GdHV1MaK3wWBAd3c3I7KHh4c9gV90y44llEolSktLIRAIaE8lqFQqUVZWBj6fH/U0hfHKY489hqysLFx11VUBr5s5cyZqamqwbt26mD2D0TuivaioiNYdTKNlRyJQc7Qf7nK50NLSwohsp9MZN2cCMcXoMpkI/iy7EMHCGGQkBQDP6uxE3NZJBYVC4SkLusvEV3kzYYQcDgc++eQTn1vqeTweFi9ezNhBe96Glvx/PNQpUkcyTUVVVVVc6M0SGzz//PO48cYb8be//Q3PP//8mJQ44cDhcPDII4/ggQcewF133YVHH32UNtksEwuFQoGff/55xCHVo1m6dCkkEgnjuXrJdBRAdP0L0iZ560KX3SWfh5RNp70j5Xj7DHTJZrJMQtFldD2ZiJNp8QJBENiwYQMuvvhi5Obm+r1uxowZOPXUUyf0odW1tbU4++yzIRaLx3y3ZMkSfPXVVxE5b2c8vMcWZWVl4HK5UWlDTU1NIAiCkT6LSbxTWwH09uPeQU7kPeiWHQv9OJN4j8vJMRqddtb7vdMp+3igra0N27dvxx133OGznxzNPffcg/r6+oikBw0FpVKJzMxMyOVy2ncZKRQKZGVlITk52ROoyXRdY9K/YtvOWCZimbALESyM4L3tHwBKS0uj5rzGCk6nE01NTSMceg6HQ0uZkLLJ8i4vLwfATAf1008/YWhoaExaJpKamhocOnQIbW1ttN/b29AC8eMYK5VK5OTkIDExEcDEjSxioZ/nnnsOt956K+644w48++yztC5CkHA4HDz44IN45JFHcN999+HBBx887tPosYxl27ZtkMvluPDCC/1ek5iYiKVLl2Lr1q2M1iHvybGMjAwkJydHpU9VKpXg8XgoKSkBcKxvpyMizzutA3DM3tFRrkqlEgKBwCObzlSRCoUCAoEARUVFHtlMpor0B0EQI+pJfn4+xGIxa3ujyPfff48jR46MOaTaFzfccAO++uqrCfm+NBoNfvrpp4B+tNVqxVdffRVhzcbivatIKBSipKQkqou+3oFI3d3dMBgMEdeFCqP1rqqqQkdHB8xmM22yyQn0yspK2mSPDqCKZgpEJvGu30lJScjOzqY1Sp2UTXcKxOOBJ554Aqmpqbj22muDun7OnDlYtGgRHn300Zgcw3jXB4DeRcnRsiMxP+LtX2VlZSExMZGR58nJyUFCQsJx33a8yyQ/Pz+uzknyB7sQwcIIo7f9k85rvDeYcOjo6IDNZvOUiUQiQWFhIS1l0t7eDrvdzojs0dTW1iI7Oxsnnniiz+/POeccCAQCRraV+zLi8eAY+9KbzXfIMh5PPvkk/v73v+Ouu+7CE088wcgihDf33nsvHnvsMTz00EO47777YtKRZ4kOTqcT27Ztw/Lly8eNTFu9ejUaGxvx888/M6LL6JQR5DblaC1EkGkdAHffbjKZ0NPTw4hso9EIlUpFi2wyZQQpm670mUqlEuXl5eDxeB7Zw8PD6OvrC1s2FTQaDYaGhjz1JN7yyk9ENmzYgMrKSpx55pnjXrt06VKkpaVh48aNEdAssnzyySdwuVxYvHixz+8rKipQWVnJ2O7iYPE+74Ukmn2tXC5Henq6Rw/AvVMillEqlUhLS0NqaioAevVWKpVIT08fIZuuNIXe0dt0y44VbDYbWltbGanfVqsVbW1tMdF24pHu7m68+uqr+Pvf/w6ZTBb07+677z7s378fH330EYPahYavuQAmFr3olu0LnU6Hvr4+Rvxw73M16ZYdr2g0GgwODk44f5ZdiGBhhNERIOS/473BhAOTZRLJ8q6rq8PixYv95qhPSkrCggULIrYQQWdOQqbw3r4IgM13yDIu//rXv/DPf/4T999/P9atW8f4IgTJnXfeiaeffhrr1q3DXXfdxS5GsAAAvv76a3R1dQVMy0RyxhlnoLCwEFu2bGFEl9HpKMh/R2tybLQe5OesbPplU9XD+/7kv49nPzSa9PX14b333sN1110XlD0Ti8X4y1/+gi1bttAS4R1L1NXVYfbs2X7P2gHc6Zk++uijqOY7J4PKRvuv0eprq6qqPHUnmudVUIHpvnb0u6FTdiz040zS0tICl8vFSCS5L9ms/Qmep59+GlKpFDfccAOl351++uk47bTTYm5XBEEQPttUe3s7LBYLI7KZDNRk0g+PJR8/Vpio/iy7EMHCCKO3/QMTo8GEg1KphEgkQmFhoeczOhcixGIxCgoKaJftTXNzMxoaGvxuJyepqanBN998Q+uWaX+GFohtxzhe9WaJHg8//DDuuecePPTQQ3jooYcitghB8v/+3//Dv//9bzzxxBO4/fbbY8qZZ4kOW7duRVVVFU4++eRxr+VyuVi1ahXeeecdRiYQfTnk0coX7p2zFQBKSkrA5/MZmQgqKSkBj8djRDaZ+52OMvQnO1oLEWSqSiB+8spPRF599VXweDysXr066N+sXbsWQ0NDePvttxnULLLY7XZ8+umnQfnRKpUKf/zxR4Q0G8vos2TIfzc3N8PhcERcF289UlNTkZaWFvN+9GgbkZaWhpSUFFr0Hl0mdKYpHN2PZ2ZmIjk5eUL1n4Em98L1ewP5KaxPHZj+/n5s2rQJt956K5KSkij//t5778Uvv/yCL7/8kgHtQqO/vx86nW7MwiEdu4x6e3sxPDw8RjaTgZpMToz7szux3tczyehUecDEKBN2IYKFERQKBUpLSz3b/gG3AW5ubobdbo+iZtFDoVCgrKzMk64AcJdJY2MjnE5n2LLLy8tH7FKgS7Y3tbW1EIlEWLRoUcDrlixZApvNhi+++IK2e5OG1tswZWVlISkpKaYd456eHhiNxhHGIzc3FzKZLKb1Zok8BEHg/vvvxwMPPIB//etfuP/++6Omy6233ooXXngBzz77LP72t7+xA6fjGL1ej/fffx+rV68OelFs9erV0Ov12LVrF+36KBQKZGRkeFJGAG6HfGBgAENDQ7Tfzx92ux0tLS0j+naBQIDS0tKw+3ZfKSOEQiEtsq1Wq0/ZdKTPtFgsY9JRiEQiFBcXR9zeKRQKFBQUQCqVej6rrKxEZ2cnTCZTRHU53nE6ndi4cSOWL1/uSSMTDGVlZTj33HMn1KHVP/zwA3Q63bgLEfPnz4dcLmdkd3GwjD6nBnCPLRwOR8R39CqVyhF9LalLLPvRZCCSt94cDocWvZmWPXqRYyKmR1EoFEhISBixM4lMUxhuCkRSdnZ29hjZdKRAnMg8++yz4PF4uOWWW0L6/dlnn42TTz4Zjz76KM2ahc7o81yAYwc8h9temZTtD/LMTu+FoqqqKvT19UGr1YYlW6lUIjs723OuJilbpVJBr9eHJTteUSqVyM/PH+HPVlVVobOzE0ajMYqahQe7EMHCCKO3iwJuA+xwOBg5xDge8FcmNpsNHR0djMi2Wq3o7OwMS7Y3dXV1WLhwIRISEgJeV1paiurqaloHUKSh9X7OeHCM41VvlshCEATuuecePPLII3jyySdx1113RVsl3Hjjjdi4cSOef/553HTTTVFNEcESPd59911YLBasXLky6N+Ul5dj/vz52Lp1K+36+LN3wLEt3ZGgtbUVTqfTpy7h9u0tLS2Myna5XIzIbm5uBkEQjMimSqB6Eut55Scan3zyCTo6OoI6pHo0119/Pfbs2YPff/+dAc0iT11dHXJzczFr1qyA1/H5fJx33nlRX4jwPksGiM6OXo1GA41GExP9ChXUajW0Wi0jeg8MDIyJsKZLdl9f35gIa7pkxxLkrg/vAAu66vfoVGJ0yp7IDA4OYv369bjpppsoLVp7w+FwcO+99+K7777Dd999R7OGoaFUKsHhcEbs0KRrB5NSqQSXy0VZWZnns8zMTCQlJTFW15j0w2PFx48lJqo/yy5EsDDC6C2dAGuAmSyTSJS3TqfD//73PyxZsiSo6+nOb0sa2tLS0hGfx7pjrFQqwePxUFJSMuLzWNebJXIQBIF//OMfeOyxx/Dss8/ijjvuiLZKHtauXYtXXnkFGzZswPXXX88uRhyHbNmyBWeffTby8/Mp/W716tX44osv0N3dTas+vuwdGQkWyT7V19Z08m86bDorOzxYPzR22LBhA2bNmoWTTjqJ8m8XL16M/Pz8CbMrora2FjU1NUHtLluyZAl+//139PT0RECzsfhqQ3l5eZBIJBFtQ75yhpN/05FGhynita+NpX6cSXzV79LSUlpSIPqTHY00hfHE888/D6fTidtuuy0sOTU1NZgxYwbWrVtHk2bhoVQqUVxcDJFI5PmMrqBEJmUHuidTfnis+PixxET1Z9mFCBba8ZVSAIiO8xormM1mdHR0jCmTwsJCiESisMrEn+yioiIIhULayvvzzz+Hw+HA4sWLg7q+pqYGfX19+O2332i5vy9DC8S+Y0ymKRMKhSM+j3W9WSIDQRD4+9//jqeffhrPP/982M43E1x99dV47bXX8PLLL+Oaa65hFyOOI5qamvDDDz9QyutOcvnll0MkEuH111+nTR9fZ+4AQEJCAnJzcyOapkOhUEAmkyE3N3fE55WVlWhtbYXNZgtZtlKpHJMygpTd0tISVopLpVKJxMREZGVljZEdbu53pVKJpKQkZGZm0i6bCi6XC42NjWPqSVpaGlJTU2M6nctEo7W1FZ988gluuOGGkM474vP5WLt2Ld58882wUz5EG6VSicbGxnHTMpGcd9554PF4+OijjxjWzDejzzcA3GcAVVRURNR/9ZUfG3D3K1qtFmq1OmK6UMFXFDTg1pvc5cGEbLVajcHBwbBkj46wJmX39/fHfTsk8eVLkGkK6UiXM1q2SCSiJQXiREWv1+M///kP1q5dO8aHoAqHw8E999yDzz//HL/++itNGobO6FRnJHQtRIzup+mS7Qt/fnhSUhKys7PDuqc/2cnJycjKyjou244/fzZezkkKBLsQwUI7zc3NPrf9c7lcVFZWHpcDwKamJp/pCng8HsrLy8MqEzJSiAnZ3tTW1mL69OkoKioK6vpTTz0VqampqK2tpeX+vgZEwLGchDqdjpb70I0vgwqw+Q5Z3A7XLbfcgn//+9948cUXcfPNN0dbJb+sXr0a27Ztw5YtW3DVVVfRevYMS+yybds2JCUl4eKLL6b82+TkZFxyySXYsmULbRGrfX190Ov1fm1BpHdEjE7rQOrhdDrDyqFODlp9yQ43PztpSyMt2263Ryw1Z2dnJ6xWa0zUk+Odl156CUlJSVi+fHnIMv7617/Cbrdj27ZtNGoWeWprayEWi3HWWWcFdX1qairmzZtHmx9NBavVOua8F5JIn81A5seWyWRj9ACYy4MeLgqFAoWFhZBIJCM+J/UOpx9SKBQoKiqCWCxmRLavwC86ZMcKer0evb29jEzg6nQ69PX1+ZUdq/U12qxfvx4mkwm33347LfIuvfRSTJo0KSbOivC3WECHP+JvkYOpflqlUsFgMPh9nnDuSZ6ryYTseKWzsxMWi2VClgm7EMFCO/62dJKfTQQHhipMlkkkytvpdOLjjz8OOooLcEexnX/++bTlt/U3oR/rW9PG0/t4zXd4vONyuXDDDTdg/fr1eOmll0LKnR1pVqxYgTfeeANvvPEGVq1aFbHoZpbo4HK5sG3bNlxxxRUjDkijwpo1a9DQ0EBbRFos+RdM2iRWdnjEUj05nrFardi8eTNWr149ZhKZCtnZ2bjkkkuwcePGmE3DEwx1dXU466yzKPWnNTU1+PLLL2E2mxnUbCzkWTKx0Ib89StlZWXgcDgx25796U3uYmCir2VS9kRKjxLv4/KJhtFoxLPPPourr74aeXl5tMjk8Xi4++67UVtbi3379tEiMxScTieampr81oeBgQEMDQ2FJNvhcKC5udmvbCZ2MLFtJ7JM5DJhFyJYaMfftn8g/htMqCiVSsjlcqSnp4/5jo5Om9yeRbdskt27d0Oj0VBaiADcA6h9+/aFfWB2IEMby46xzWZDS0uLz1XsWNabhVlcLhfWrl2LTZs2YfPmzbjmmmuirVLQLF++HG+//TZ27NiBFStWsIsRE5j//e9/aG9vDyktE8mZZ56J/Px82g6tJtNRjE4ZARyzd5FKHeYvCi0nJwcymYyRiaDc3FxIpVJGZNORPtOf7Pz8fIjF4oguRAgEAp87OMmI1Hie0I4X3n//fQwMDOC6664LW9b111+P+vp6/O9//6NBs8gzNDSE77//PiQ/2mw24+uvv2ZIM9+QUZb+orq7u7thMBgiposvPSQSCQoLC2PWj/YXBS2VSlFQUBB2X+tLdkJCAvLy8hjpx6ORApEpyPIZne4LcNf5cFIgjic7kmkK44WXXnoJWq0W//jHP2iVe+WVV6K0tBT/+te/aJVLhba2NtjtdkaCEtva2uBwOPz20+HI9geZum30mZ3kPcM5t0ep9H2uJh2y45WJ7M+yCxEstOMvXQEQeec1VhivTDo6OkKOdvLnMJKy29vbYbFYQpJNUldXh4yMDJx88smUfkdXflvS0Pp6zsTEROTk5MTkQKS1tRVOp9On3nK5HJmZmTGpNwtzOJ1OXH311Xj11VexdetWXHXVVdFWiTJLly7Fjh078N5772H58uVh5atniV22bt2K8vJyzJ07N2QZPB4PK1euxFtvvRW2HQL8nxUEuO2dyWSKyMGuw8PDUKlUPvt28pDAUCdryJQRgWSHajfIlBG+ZIeb+31oaAgDAwOMyKaKQqFAeXk5eDzemO8qKysxNDQUVn52luDYsGEDzjjjDFRXV4ct64wzzsCkSZPi9tDqzz77DE6nk/JCxKRJk1BWVkbb7uJgGS+oDHCnnWUaf/mxvXWJRT86UBQ0EJ7eTMoOFPgVruxYQqlUIisrC8nJyWO+q6ysDCtNoVKpRHZ2NpKSkvzKjlSawnjAYrHgqaeewsqVK1FcXEyrbD6fj7vuugs7d+5EfX09rbKDJVBEO7lYFaq/GIxsuturUqlESUnJmLMvST2MRiNUKhUjsoeHh9HX1xeS7HhFqVSirKwMfD5/zHfkOUnx6s+yCxEstDPexDgQGec1lhivTAiCQHNzc8zJJqmtrcUFF1zgc1AfCLlcjtNOOy3s/LaBDC35eSw6xvGqNwszOJ1OrFmzBtu2bcP27duxcuXKaKsUMpdccgnee+89fPjhh1i2bFlYB/OyxB4GgwE7d+7E6tWrQzpg1pvVq1dDq9Xiww8/DFuvYPyLSPSpZIQZE307Kzt8YqWeHM8cOnQI33//PW1pBzkcDq677jq8//776O3tpUVmJKmtrcXMmTORn59P6XccDgc1NTWoq6uLaNTjeAFU5DVM09XVBYvFEhP9ChXIc2qY0LujowM2m40R2e3t7X6jt8OVHUswaSNY+0ONV199FX19fbjrrrsYkb9q1Srk5eVFbVeEUqmEWCxGQUHBmO/IXUbh1DWJROIznRVTgZps24ksE7lM2IUIFtrxt4UWONZgJsK2TioEKpNwD1tjUjbgjuo/fPgw5SgukpqaGnz11VcwGo0h66BQKCCRSPwO4GL1sB6lUgmZTIbc3Fyf38eq3iz043A4sGLFCrz11lt466238Kc//SnaKoXNhRdeiF27duGTTz7B0qVLYbVao60SC0289957MJlMWLVqVdiyqqqqMGfOHFrSMwWydyUlJeDz+RFxyMdbZA7nAELSJgSSHY6/EI+yqeIvbQnApkWMFBs3bkRWVlZIB937Y/Xq1RAIBNi8eTNtMiOBw+HAJ598EpYf3dXVhf3799OsmX8CTX6kpqYiPT09Iu05mL62qakJTqeTcV2oEEx/2NjYGFIqwWBkh5qmMFBKLm/Z8ZoKhCSQLxFuCsRAsskUiOzYz43NZsMTTzyB5cuX+0xlRQdCoRD//Oc/8eabb4YdmBkKSqUSFRUV4HJ9T7uG6y+OJ5vuuhaofpeWloLH4zEyp1VWVgYul3vctZ1AZVJeXg4OhxO3ZcIuRLDQSqBt/8Ax5/V4GgBqNBpoNBq/ZZKeng65XB5SmWg0GgwODvqVnZGRgeTk5LDKu66uDgKBAOecc05Iv1+yZAmsViu++uqrkHUYz4jHat5AMoe4v4jiWNWbhV7sdjv+9Kc/YefOnXjnnXewbNmyaKtEGxdccAE++OADfPHFF7jkkktoSb/DEn22bNmChQsXorCwkBZ5a9aswaeffhrydm1g/JQRAoEApaWlEXHIFQoFMjMzIZfLfX5fWVkJlUqF4eFhyrIDpYwgZff09ISU4lKpVCInJweJiYl+ZYeaPlOpVCI3NxcJCQl+ZXd1dYUVlBAMVqsVbW1tfusJmVc+Xgdu8YDBYMC2bdvw17/+1WeKhVCRy+W48sorsWnTppibeA7ETz/9hKGhISxZsiSk359++ulITEwMe3cxFQJNfgCRi4xXKBQQCAR+U7ZUVlbCarWio6ODcV2ooFQqIRKJ/NrQyspKmM1mdHV1MSa7u7s7JNlisdhv4BeZAjEU2bECQRABF9q4XG7I6RWDlX08zYMEYvv27ejo6MDdd9/N6H2uvvpqZGRk4LHHHmP0Pr7wd54YSTj1IVDQRbiyfWG329HS0uL3eYRCIUpKSkK6J5Oy45Vg/dl4LRN2IYKFVsbbmk9+F68NJhTGK5Nwcj6PFykUbj5pAPjwww+xYMECn7kug6GyshIVFRVhpeUI5NSR9wgnJyFTHDx40OehqiTHa77D4wmbzYYrrrgCu3btws6dO3HZZZdFWyXaOffcc1FbW4tvv/0WF110Ucjn3bDEBq2trfj222+xZs0a2mReccUVEAgEeOONN0KWMV7KCMAdMXXgwIGQ7xEs+/fvH7dvB0I7JDAYe8e07MOHDzMmm+nUnM3NzSAIYtx6Esno8uONN954A0ajEddeey3tsq+//np0dnaGffZYJPnwww+RlZWF2bNnh/R7oVDosbORQK1Wo7+/P2CEcqTGcvv27fPsdvOnBxB7O5z27t3riQ72RTh679271xMdTLfsYAK/AETEzjJFb28vDAbDuDbi4MGDlGWrVCoYjUZGZE80HA4HHnvsMVx66aWYMmUKo/eSSCS44447sHXr1oguWhIEgSNHjqC8vNzvNaEGJTIp2x+BzuwcfU+qKBQKv+dqhis7Xqmvrx/Xn43nMmEXIlhoZefOnQDg82R3EplMhs8++yykLaPxyLvvvgtg/DL55JNPKBuKYGV//PHHIRmh77//Hl9++SXEYjHl33rD5XKxefPmkBYKTCYTdu/e7TfKEoAnN2Iko8XGY2hoCLt378b333/v95pY1JuFPqxWKy6//HJ89NFHeP/993HRRRdFWyXGWLRoET7++GP88MMPWLJkCUwmU7RVYgkR8nDqCy+8kDaZcrkceXl5uOOOO6DT6UKS8cEHHwCAzzy7JD/88AO+/vprRnfmEASB//73v/j555/9XkPaZNInChan04nPP/8cMplsXNnvvfceZdlffPEFpFKp32vIdvvAAw9Qlv3ll18GlE3q/f7771OSTRVSfqDdPD/99BM++eQTOBwORnU5HtHpdLjuuutQWlpK244qb2bPno2UlBRcdNFFcbErwmg04plnngEAv5O7wZCSkoI9e/Z4/H4mee655wC4F3/9kZiYiL1790Kr1TKqyyuvvBJwkiUvLw88Hg/vvPMOo3pQ5bXXXgt4OG5+fj64XG5Iem/ZsgVHjhwJKJvD4YQku7a2NqD9IcfuTEewMwk55vKVV5/k+++/x88//4yhoSFGZP/444+Mt51Y59xzz0VzczPuvPPOiNxv7dq14PF4jC96ePPTTz9BpVLhl19+8XtNRkYGTCZTQJ/SF99//z16e3sDyk5PT4fRaAx4DRWC8cNlMhm+++47yul6H3nkEQDA4OBgQNnffvvtcXMu4cMPPwwAAcdNMpkMX3/9Nex2e6TUog12IYKFVoKJem9sbMTAwADj2/NjBdIpCbQQ0NjYiL6+PsqRxGR5B1rUIWWHMjFDpoe47rrrKP/Wm5tvvhmpqakh7aro6uqCyWQKmNeRjJRieoKDCnK5HLNnz8ZTTz3l9xpS7//+97+RUoslQqjVaojFYnz88cf44IMPQs4NHU+cccYZ+PTTT/HDDz9AJpOhra0t2iqxhEBeXh5KS0v9pgYKlUsvvRQikchvhOh4kP2kv8hYAHjqqacwd+7csBfPA8HhcHD22WcH3OJPpuMjB23BYjQaodFogopu2rVrFyXZw8PD0Gg0AXdSzJ8/HzNnzsTatWspyx4cHAwom5yEpao3VUj5gSZ9H3/8cSxYsCBgXWIJDZfLBS6Xi0WLFjF2j7lz50IoFPpNexlLiMViZGZm4qabbgpLzpo1a5CcnEz5sOtQWLZsGaqqqnDVVVf5vYbcodbQ0MCoLkuXLsXtt9/u93uXywWn04mPP/6YUT2ocumll+Kf//yn3+9dLhdcLldIO3suueSSgJO3BEGAIAjKsh0OB7q6ugL245WVlZgzZw5uvvlmSrJjCdJGBOr/n376acyePdtv+kV/BOunnHTSSbT7WPFGa2srAGDGjBkRuV9CQgKysrIiGig1Y8YMTJo0yTOh7AtSn08++YSS7BNOOCFo2Z9++ikl2f4g63cgP76pqQl6vR4ajYaS7L/85S+YOnUqLrjgAtplxyt//etfMW3aNJx99tl+r2lqaoJWq43PhU2ChYVGGhoaiDfeeCPgNV1dXcTLL78cIY2iz5EjR4i333474DXt7e3E5s2bGZP92muvUZYdSzz//POETqcLeM1LL71E9PT0REgj+ti0aRPR29sbbTVYaOaHH34gABDLly+PtioR5+abbyYAELt27Yq2KiwTiI6OjpDsZLR44403CKVSSfl3L7/88ri2LFTZTNrJTZs2jSv79ddfJ5qamhi5P0lLSwuxZcsWRu/BEhiXyzUh7sHiH6PRSDz33HPRVoMgCILYtWsX8fvvv0dbDcq8//77xB9//MGY7H379lH+3fbt24nm5mYGNIodVCoVsWnTJsZkv/TSS4zInmgMDw8TFoslovd0uVzE4OBgRO85Hi6Xi3jmmWcIs9kc87Lb29uJV199NeA1Go2GWL9+PS33G41arWZMdrzS399PbNiwIdpqhASHINhTUllYWFhYWFhYWFhYWFhYWFhYWFhYWFhYmIFNzcTCwsLCwsLCwsLCwsLCwsLCwsLCwsLCwhhsclSWkOjo6IBarQ7q2vT0dACgdD0TB9wxDZNlYrVaIRKJoi6blB/s+4mlMmGqTlF5RgCeA7tzcnKCuj5e28NEguo7JmHfXWhlx5Ybc0SjLjPZR1LtT4HgnyWUsqJil5iy61TLhOr1TPojTPgXKpUKWq0WcrmcUj2hqs/xQLRtYbTv7w+qejHlRzMtmym/m+m+lqn3H2u2jSn7w+Q4kWmo2AmAGbvJpJ8Si0yUfhpgrh+j0qaYrJuBno+J5wmlLdD1PLFKKPVywpRJtHNDscQf7e3thFQiJgAE9Z9YJCJEYknw10ukRHt7e7QfkxJMlwk43KCvFYnEhEQsYkQ2lffT3t5OSKXS4OWKxZTKhMsJXmepRMxInaL6jFT1ZlJ3luBob28nJBTfMfmfRBp/fRmdUO0X2TrPLO73QcHujHgnkpDeSXt7OyGRMNdHcjkcRp4l5LKiYk8ZsutUbQyXS60Mqchnyv+j0reEUkeo1JXjhVD7c3c5ht+nR/v+gfWi1lcEW69CeeZgnzWkPo5Cn0WlLYfiY0V7DBCKbWPKPoRSJpTsBEPjRKah1H6oljfD7zJWypAqsTpmYno8QnU+gMn2R0ff2N7eTogp9G/B3pPLZbZfi7fxYyjzSBOpTNgdESyUUavVMJktuObUbCSJ+MhLFqJ10IICuQg5SSL06KzIl4ugszhwUGXEhh/dq3bZZ18DvjQJwtQ8WPpbIUorgCg1B1ZND0Tp+XAYdTB2HITq0w1Qq9Wxu3rnAybLRHvwa6h/3kmp/AAEpcvXjVrs3K9GxTX/B3FmEbh8YcDnNPU0ovHlm4N6P2q1GiaTCa+//jpKS0sDrpTX19djxYoVAIDMBSuQUDrL7zM6DINwmPXo+u+TlMqbiTpFPuPmR25DcV4WREJBwOsVrV24+r7ngtJ70OhAspSPm99rirv2MJFQq9Uwm0xY+eDLSMstAl8YXARLX5sS2x+85rh+d2S/+NzSalTnJAb1m6Z+I2565/BxXW5M4X4fZmy8cTGKM+UQCnhB/U7ZrcF16z8K6Z2o1WqYzSbMvmk9pJlF4AkC25jh7kb89sKNQfWRh3qMePEnFTbeeEHQz+N+lo/HfRayrNavORXF6QkQ8seX/eXhbjxRe5CSDaNq11fMzsSsvIRxy+T/LiuH2mCHweoMqgxf/OcaTC0rGPcZP999EI++uouyrxNMmfBlyWii4l8E4XMdVBnw+Fdd2HDtWajITYHN7gy63jf2DOH6l75i+6KjkGX+f5dVoChFBCE/uOy+jQNm3PxeY9jlSN7/hWWTUZgqCfr+Tf1G3LTjCGPvkewr/r3iZEzJk497vbJXjxu37qZUz5+9uBSTs2Tjym5Um4P2GT193IoTUZQmHbeP++pIH574pJ72sRzpY825ZT1kWcXj2ggAUP3xJQ6+/cS4/SGTfjRp206kYNt+f+HGIMvvEFSfvojpN7wAaUYRuEGUSf/+r9H0bnBlorc68OTXXdTGiX95DgkF1ePqYVI1QfHSTTHRb5LtJ1i7OeW6FyDOKBy3vI09jTiy8WbG3qWhpxEHXoyNMqSKZ8z00MtIpzBm6m1TYvsDzI2ZyLrw7+UzMDk3KajfNPYZcOMbeynNebz66quYMWNGwGs//vhj3HfffZT83OnXPoekosnj6mzobsS+F28Ke35ErVbDYjbROidD+mPbXnoBJUVFEInGbwsNykasujb854lVyHpz21ObkZ1fDEEQ7aWrRYFn77h6QpQJe0YES8hcNj0Dp5YkoTBVjJl5iUgQ89E6aEFZhgRFqWLMLkxCebrEc70kpwJJVadCIE2GLL8ahN0CrlCC5Op5kOZWgCcQIf3ki6L4ROETTJnIxcfW/8YrE740CfKpCwAAGadehqSqUyHOKERi8UzwJQmw9LVCklMGcUYRkipmQ5pb5ZFdkS7BqSVJSJYIUJ0tg8VBQCLkYl5pMioypUiTCbGgTA4AsOvVsPQ2w2W3wNC6D06LAYLENDhMegiTM8HhcGHXqwHCRblM+vr6oFQqYbFYsGfPHgwPDyMzMxM6nQ5FRUWQyWQYGhryXC8rmj7OM1ZAnF4Q1DPmJotw0dT0UF4lJfoHtWjq6IHVZscfR5pgMJmRmZoMvcGE7PQU8HhcqAY0aOlUBaV3mkyI4jQxKrzaD0t0GR7sx0BnMxw2Kzrq/4DVZEBiagYsRj2S0rPB5fEw0NkM3YAKBZNmIqu4MtoqxwxdWgusDhf2d+lhtDqRniDEsMWBrEQReBwOdGYHerQWOJwEyjPHn2xhCY8BnRFNqkFY7Q7sbVbBYLYhPUkKvcmKLLkMPC4HdqcTRzoGYHc6UZmXFvY9LboBGHub4bJbMdS8Dw6LAaLkdDhMeohTssDh8mAZ6oXN4LYFwfTtxWnio89j8nqeXj/P44LD6YLF5qCkt1pvQXP/MKx2J/a1a2Cw2JGeKIbebEdWsgQ8LgcqrQkqrQmuo+ZxPBsmSsvz2LBx/SKRZIRfND1HFtC/yEhyD+zMNiem5cowOVsGsZCHjAQhStMlKEkTY2FlCggAdhdQctTGdPYNuu1XQxsMJgsy5InQG83ITksGj8uFSj0Ei9UGk8Ua1PsBhzPC9o5r13PKIcmpoFyvxtNDJnT7WxW5KegZNKC5T+t+l639MFjsyEiSYNhsQ5ZcCh6XA4fTBYPFDofThYrcFMr6HA+oDTY0ayywOFzY122AwepEmkwAvcWJzEQhuFwO+o8ughEAKjLo9WMGDDa0qE1eNsWB9ATBUZsiBI8L6Mx29OgsmJ6XGDGb0j1ohNXhwr72QRisDnc/YfHqJ3Rm2J0EHE7qfnS31gaLg8C+biMMtqPlbT1W3mqjHRaHC3YHddkDw1Y0DxhhdTixr2PoqO4it+5JYvC4HOjNdgwMWwBQ77OCxaJTw6BqhstmxWDTPjjMBoiT02E36SE5aiPMgyoQDjtcLieA8fvD1AQB43609ahtc46ybXYv22Y36WHSdAMYv/yESWngCd22zaYbgLG3BS67FboWt2xhUjocpmGI5G7ZNr0aFk2PO/Y1iDLJSRahQO6WT2WcaNV0wWW3Yrh1PxwWIwRJ6XCYhyGUZwFcHpx2C+zDg4yWdaiMVyYVWVIA7ndpPlre+pb9cFqMY8rbYdTBqukBMP67FKXmUn6Xpt5WuGzWqJUVXQwP9qO/sxl2mxUdR/6AxWRAQkoGzIajYyaue8w01NcNp8OB7AiNmbqGzLDaXdjXoYXB4kB6ggh6s8Pd13E40JsdaBkwwmRzoiIrgbL8jo6OgPMd/f39KCkpATB++0sS81F5tG6aNd1w2q3QetUdu1fdsZv0cNosnr5xPNkFKeKg5kfonJPJSnJPsvf1D6CxuQUWqxW/7d2HYYMBmRnp0OmHkZOdBR6Ph25VL7pVveBwOEHJlgh5yEoYf2EjVtGp+9HT1gSb1YrGg3/AbDQgOS0TxmE9UjLccwyaPhU6mxtgswbnh6fKhCPmHGOR2NaOJeY5tTg54PdTso8NABKKpyOhaJrfa0WpeTC0H6RNt2gxXpksKJfjsa86AQRXJgO73/f8nVx1akDZ0vxJnn9Pz03AtFz/RjQvWYT39w8AAHhiGUTpBbDrNeBJk2DsOHL0/rkgXA7YdP2wGwbhctgD3t8XZ5xxBoaHh2G325GVlYXe3l7s378fM2bMwNDQEKRSKfj8Y10RlTIJ5hkP9hgo60yVBKkERbmZGBjUIjlRhoPKNgBAfnY6nE4njCYLEmVSlBZkB603gIjozhIc5bPmw2oywOmwIzE1E3pNH7obDyGvYipsZiOkSSng8QUQyxLR2bAXACfaKscMWUkiOJwE0hOE6B+24rBqGFNyEmGyOSGXCtB9dKFiVmEyDnTro63uhCdBLERhRjIGdCYky8Q41NEPAMhLS4TT5ULvkAEuF4GizGTMLs/F/ta+sO+ZMXkuHBYDXE47xPIMWLX90LUfQXLRZDitJggT5LBo+0A43QsFwfSR4qMR0fMmF8BgtsHucCEjWYp+nRGHOgYwtTADJqsdKQliNKmGYLTYMb0ki5LecyuzYLDYYXe6kJ4oRr/egsPdWkzJk8NkdUAuFcJid+LEknQYLG77SMWGUfWLxiuXU4uSgrputI05f+4MGEwWVBZmQztswu5DTegf0oPL4SAnXY5p5YWob+1BdUle0PK97RdT/t94epxUeGwnVpJUhCSpCGq9GUlSEQ53unPy5qYmwOkk0K0xQJ4gAocDnFiWhf1tA5T1OR6QiXgokIugMdqRJObjSJ8JAJCbLITDRcBkdbq/SxVDKuTBbHPSf/8UMdQGG5IkfBxWGY7eXwSHi8CQyQ4RnwupMLidL3SRlSxx9xNJR/uJLi2m5B/tJ2RCaAxWmGwOTA5i18QY2YkCOFwuZCQI0G+w40ivCZOzpW4bKuHD6nDB6SLgIqjrnSDioyBVCvWwFUkSAY506wAAeXIJHC4XBo02mG0OnFicild/aGWsLWdOmQuHmbQR6bBo+6FtOwx58RQ4LG4bMdzdCONAJxKzSwHExhgg/ahtI5x2iOQZsHjZNofVBEGCHCZNNyQpbv8/mPLjSZPR9eGzSK0+ZjdFyRmwaQcw3H4EiUftpiBBDqu2D+ByIc10TwZSGftRuVYoz4LL6YAgOR12XT+MnYchK5ji1kMmh3WwGzyhFNKccsplyDTjPWd1pnuyN6X6VDgtRhBOB0TJ6bBp+2HoOIyEwmPPaR8eBE/sntsI5l1yhWJ0ffgseOIESDLyYdOrIZAmY7jdPc4Wp7nH2U6LEbbhQUjS8yEVSWl8+uhQMWs+LOSYKS0Tw5o+9DQeQi45ZkpOgUyeBm1/D6RJcvD4kZmSzE4Swe50ISNRhP5hK4706DE5NwkmqxNyqRB9eiuSJQLMKU3DgS4tZfm5ubkB5zsEgmOZE6j0X+KULHcfk5QBq3YA+vYjSPLqB8zqbjgIA5IKJlGWHQg652TIdrZg/lwMGwzucsrIQF//AA4cOoLpUyfDaDIhNUUOg9GI1rZ2WG02Wp8nVpl68mkwG93tRZ6eiSF1H1obDqJk0jRYzSYkJKfCYjKCIFwA3M8ZTJnIxTzPnGMswi5EsITMgR4Dfu0YRlm6BFaHC5UZ7q3SPTorqjKl6NZZcbjX6Lne0HYAw42/QpJdBpfdCkluJbgCIZxmAwiXAw6THpaBjig+Ufj4KxOV3gapgAsOB5TKxGW3wtzbGtS1AGdE+QV6PzIhDxwO0KIxAwjO0AAIeaFowYIFAb8/+eSTgy4TwmH3RKNQrYNMcUJ1GU6oLvP7fX52BgBgb33zuHpLhTzIxXw0a8zQmalF77IwS/ms+QG/T8nK9/y7s2Efw9rED9PzkjA9z/9W6Lyj0XkskWFGaTZmBJiQz0sLbts6VTImzw34vTQ9D6Ikd4TWroNqaEyOMf0jj3ssOmpPx7Dnt/MmB04rRD5TKIsqcysDL17kpY6MuFb/sguOYY1vm241gfCaLBzXLzJqR0SZ+iuXnCQRhq0Oj73zZ2P0FgcMVidaBy04f1LqiDKcP/PYjkpf5Gem4p0vdgeU78/XCVgmNgt4YuqRh/7Kg3zGHr0NmQnHBv3zJuUGlJWXFpoOxxvBDH5PKjrWh9A9OeC2J/5T/UXLnswoTMX0wlS/3+eluPuJAx3Uo8aDXVgUB5muaoTsAjmmF8j9y05xTx6995t7MmPccYvVBJOqmbIegHsxIhCydPdYpO279wD4bv/efZDB6gypTKiSHoRt07YcABC4LyQcdth0/TB1Kzy/TasOLFuS5i6T7h8Dl0l2ohDDVnffSEJlnJhYPB0JxdP96iE6qoeh7UBAfaOBrzLpGLKgUC6G3stuAkDKpMABf+K0PPBl7oDDgHbNboVdNwBzv3sMn1wyHckl/stPkpaHlCr3WFjXGntlGAoVMThmml4gx/R8ud/v81LC20F14oknYtasWX6/LygowBtvvAHAv//iIoACuQj7ug3QWdzzAMHUH+BY3fHXtv3Ve3+M119xBMdSCY13z9ZBs+faBfMD92sF+XmYN+dk/LHvgN+y6tG5dwbkJInQMgHmTKaefFrA7zNy3O2l+fBeAL7LxGRz4qDKiFMKk+JiHoldiGAJGdIx3tdtQK/eht5hG1KlAvQN21GY4gJBANmJx7ZJJRRPB1xO2IZ6wZMlg3DYYBnqgSAxHVyBCMlVp8Jlj+/tiP7KpFdvw0mFidAY7ZTKRFYwBYbW/Z5rE4qmwdC6D7ahXti0vRAkpMKsakTSpLlwGI5FaZC6OAmgV29DsoQHm5NAj96C5KPbtKqzZNjd6o4+HvhlF+xHDY30qKFxmPSAy+VeDOlrAU+cAMJFPbLt999/xw8//ICqqipYLBZMmTIFIpEIBoMBUqkUKpUKnZ3HVmvHKxNJwRQMt+wNWN7+6iBT7K1vxs/76lFRlAerzYbqskKIBHx092twqLEd58ydhYONrWhXjYxC8qX3SYViaIx2nF4mx7eNQ+PcmSVS/PHFThi0ajhsVmSXTAJfKMJQbxdcTgf4QjGySyrRUe+ul1wuDxbj8DgSjx8OdOvxa5sW5RkyWOxOVGUlQMjnoltrQdeQGYuqM/B7uxb5YQ4AWIJjf0svdiu6UJGTCovdgUn56RAJeDBY7FDrTOgY0GHxSRVQ9mig1plgDyGVyGiGWg5Ao/gViTllcNqtSCqoApcvhFndDb4kAU6bBebBHhBHZ+kvnpbu6SMV/WYkS3ggCMDmJFCeLkGn1uqJdt/f0ofdim5U5KTAYndiUn7aiOcpykzGgbZ+lOWElmrnJ2UfDndrUZ6VCIvdhUk5ye5BkNaE7GQp+vVmyETH3On0Uy722DDfdjoRVs2xXZEB/aJJ8zB06FuP7IunpXvs+kh7J3bbu6Nb3/3ZmGa1BScVJkIm5CFRzPeU4T5FO34+2IiKgmxYbXZMKs6FSCiA0WyBRCRC36AOCZJjA85g/Is+vY1Smdh0/ZTfjb96QgCYU5SE3W3u6O79bQP4pVGF8mw5rHYnqvJSIeLz0DU4DD6XC82wBadW5eBIlwYGsx0ECDidIYSXHwd4JhvSxLA6CFRmHp0U0FphtDlhdRCQiXiYkStD44AZvcP0+vXH7IkUFocLVZkyzwTOsMWBAYMNp5enor7XAJXeCj43MrsT93cM4pdm9dF+wunuJwRcdA+a4HARKEiVobFXD6OV+s5ifxOpANBvsGNhuRyKfhP6h23jSBrLgU4tfm3RoCwzAVaHC1XZiRDyuTBanVAbrChIlaKxbxi9umOBS+ONWxwW6gFAg837oa7/BYl55XDaLEgumASuQAiTuhuE0wF58VQM9zTBPNQL06A7zamv/tB7vFWYIg6pTKigPWrbEnLcCzOJR22bZVAFcUoWjH3t7r+17kXw9FMu9ozlzN0K8GTJcK9OE5DmV4MnSYAwJQed7z8OXesBDCl+hSy3DC6bFQn5Ve7xmVEPgnCPzww9TZCXzfTo469MCuQiEARQmCJG55C7TVIZJw63HYCu8VdIs8vhslsgzSOfsxuyvEkwtB8Ehy+AXUu9H2caX2WiMTpQmsYZYTf1rQegUx57Rln+0WfUdCMhfxKMqibYtP0gnO6x8Hjv0n1+hHsBXPXzLtiGNSPeo0XdDZfLgaSiqTB0NsDlsIEnksJytH7HM50N+9Cyfzcyi8pht1qRU+oeN2n7e9DTeAjVc8+Gtq8bRr17nBtKxoVQcPd3g8f6uyx3f6c32+EiCCRLBVD2GpCfGtp45K233sLAwMCI+Y6Ojg5MnToVDQ0NSEg4tqAcyH9JlwmQLhN4drnpWg9g8Gg/47RbkUjWIY0KPLEUtuEh2IY1AMe98OrPBxxR74OYHwnGdyMJ1J/oLA6UHC3TP/YdwI+7f0VlRRmsFismV1dBJBSio6sbCTIZ9h88jIsWn4fvftqNto4OT1n58n3Jvj7e50yaDu/Fkd9/Rl5JBew2KwrLq8EXitDX2QqBUIT8sip0NLp3USkP/A7Ad5k0DpgxryQ5bsqEXYhgCZlGtdsp5XGAvGR3Z2ZzuFCUIvI4foNeK3FmVSMkORUQHl21dTls4EmS4HLYAIc72t5hiM38ksHCSJmY9Z5rAQBc3ojrRRlFsB7NPepdfo1qMyrSJSP0SBLxQBAEnIQ7Sk1vdeuScdSZGm7dB1O3wh3tQRAgCAKygmrwpEmQ5pSPmBAJFjI6YM+ePeju7kZ3dzfS09NRX1+PefPmQSqVIinpWPQclTKhWt5MQe6I+P1wI3r6Nejp1yBNnoTufg0uOP0kqLV6XHD6yZ4dEcHofbDHEBHdWYJj1tlLUTBpJgCg/cjv0A2o4HK5kFVUAcPQAJwOB5IzcpFVVAGHzYqWA79EV+EYgtwRsa9TB5XdiV69FakyAfr1VswrS0XXkBkLq9LRp7fCbKc3jQfLWLx3RPzRrEJ9pxpymRgEQYAAsHB6MToHdJhdnguCIPDtwfaw75lSOh0ppdMx2LQXzqFemAdVECWmwqztQ0ZeOWzDg0itmIXe/d8C8N1HkpB9ZOOA+ejzZGFGSRb+aFZBNWiAatCAtEQJFN0anFKVB43ejAtml6N3yED5jAjAvSNCIuRDpTVBLhXC6nCie8j9b73ZhhNL0tGnOxbp5cuGedtpp9UMi7rT77WB/CJfdn2EvTPa/ZafLxtDluHMqiLMrCzC7w2tUA1ooVJrkZacAEW7CnOmlUMiEiAlSRZQj9H+xXi+zugy8fg4FBivnpBraDOKMzCj+OjOxJZ+NHQNIlkmAkEQsMOF6cXp6FAP45SKHACA1e7Er429lPU5HvCOzt/XPQxFvwnJYj4IAAIeF9XZEmiMdiSK+TghPy3NKm0AAQAASURBVAHfN9Prx3jviNjXqUdDnxFyCd8zgTO7MBmdQxacXCwHQRD4rikyA3FyR8Tedg1UWjNUOjNSZSL06cw4uSwDaoMVZ07JwTdHqE8yjrfg1qW14qTCpJAmHcgdEXs7htCrNUOlsyBVJkRj3zBOLkmDxmjFmdVZaBtw72xhaiyXWjYDqaXToWnaC/Ogym0jklJhHupD1tR5sGj7kVbhjjRWfvIqgPH7w/5hm6ePYAp56XTIS6djqGkvzEO9sAyqIExMhXlQhYScUohTs5GYW46+o7bN11iOhOwPyWvIKGht8z5YbSpYhlQQJqbBoGpG2uS5sA0PIv/0ZbAMHdvpF0yZdGotfq/1N04kd0QMt+yDVauCdagXgsRU2LT9kOaUQyjPco8TD37DQCmHR7B2M6lkOpKKp0Pfsg/WoaPPmJAKq7Yfspxy8KVJSC6bBc3BbwFQe5c5p17seZeGrgbPOBsEQDgdECSmIiG33G2TzPEfyFQwaeaIMZOqpR6SRHefnFFYDpFEBpk8DaUz5oAgCCh+jUy9IXdE7O3Qold3tJ9OEKFpwICTS1KhMdhwZnUm+vQWWEIYj1x55ZVwOp0j5jtUKhVKS0shkUiQmnps11wwfi5ZN737Aufgsb7APNSL9MlzweULkT5lHvoD+NChzI9Q8WfH609If3PWzOmYNdO9u2PPH/tw+EgD5PJkEASBwSEtzj5zAdo7u7Dk/HPw+779fmVPpDmT8iknoGzKCQCAxoO/o73xCBKS5CAIF6wWM5wOBxLlacgvrYTg6LkzE6FM2IUIFsqkp6dDKhHj5veagrpeLBKB4HDR9PLNwV0vkSI9nfkDhumE6TIBhWtFIjG4HCJoXQDA1NN49DY8z/ZaEtLYGNoPhnQQWX19PQCAx+OhsLDQLdNqRWlpKVQq94BMrXbnahaJJUE/J5eDoJ9RKhEzWqcUrV1unbhcTxomq82Okrxs9KrdA8O99c3oVQ9BJOBTejdM687iH6fTiTfffBMA0Nem9HzO5fI8W4odNivEsiQMqtxRG+T2Yr2ancQiaep3R0dyuRxP2gyrw4XCNAn6jkbLkmdDkNeyMIeyW+P5N4/LRX76yFRMfVr3OyDTGKn1prDvOdx9zMaQW8iddhtkmUUwDbj7z6GWAyCcDvD5Akp9JPk83s9idThRnCUf8yzezx6U7F7dUdkc5B9NwWRzuJAkEcB1dBKBTLWiMVgp2Wqm7DoV20iiaHfbYh6Xi/ws90DZanegODcDvRp3GfRqdNDoDJTkM+X/UfW5GnuOTdByuZwxaZj6tO467n0uBFl3WEZCTiYAAI/D8aQFIvEeBAOA2kjvQNjbRnjbFJK+o/c/0O2ezNMYmI2IJ1H2um0Yj8NB/tF0RjaHE0XpCZ6FygMdg+5+giLBTFiFOunQ2Dfs0TvPo7cLRWky9OndE9YHOrVwEqDUZ1Edy+m73D4Wh8uDNN3tXzntNiRkFcF41EYMHk1vRLgcMTMG8LZtUnKybpRt0x61bVyBKHj7AMDgGZtxIfbItkKaWQiz2i2bTMViGx6kVCZU7YRJ1XRMl6Ppegm7FZKMQliP6mJoOwC7IXYicKnaCVPP0eu4XM9Y2OWwQpJZCLPG/Yz6tgNwOR3g0PAuSca8S706aLmxSq+fMRPJ6DHT8FBknrmxz22XeNxjaZhsDieK0qTH+rujZ0OQ11Khvr4e1dXVfuc7VCoV1Go15fZn6D5WfyTpx/oC2ai+gHA5wKPgQ/vrG9PT0yGWSBmZk2lQHgs64XG5KMgf2R5UvW5//Y99B9DXNwCRMPzniXU6W46l4+NyuZ40TCQDPe720nx4L7TqXggFE6NMOAThna2WhSU4Ojo6PJPH40FWfirXkx14PMFkmVitVohEovEvpChbpVLhsqWXw2oJPmJILJFC0VA/7jvq6OhAdXU1TKbgJrGkUim+/PLLoJ+TapkwUaeoPiOJWCzGzp07kZOTM+618doe4p19+/bh2muvxZ49e8AXCOCwU982zOFyccnFF2Pjxo3IyMhgQMvYpqOjA9WTqmAyWyj9TioRo75BwdZ7mnG/j0kwmalHiEolEtQ3NFB+Jx0dHZg0qRpmM7U+UiQS4733AveRKpUKly+9DGYLtYm9YJ4l1LISi0TY+d57QfXtTNp1AEHp4C7DpTBbgm+jYrEIO3cG94xM+n/B+Fyh1hGSUOv9RCTU/hygp0+P9v0D60Wtrwi2XoXyzME+ayh6U+nfgm3LHR0dmFRdDTNFP1okFuO9KPrRTNs2qmOzYGWTBGt/mBwnRoJg7UQo5S0UifE+Q+8ylsqQCqG2ZwCQSKVoqGfmmZkej1CdDwh2HoDJugkE7hupzGsF05+oVCpcfvnlMIcwBgm2vOJtziTUeSRgYpQJuxDBwnKcM9rQPPjgg2hpaUFvby8uvfRSXHfddSOuD2ei4IEHHkBbWxu6u7tx+eWXY+3atSHJjSW8n3HLli3YtGkTPv/8cyQmJqKxsRHLly/H008/jYULF3p+E6/PejxgNBrx0EMP4dlnn0V1dTVeeukl5OXlBe2MkRAEga+//hqPPfYYuFwunnnmGaxatQocTmTyVccKo/uAuro6PPDAAzjrrLMwODiIV155Zcxv2PbBHKPfx9133422tja8+eabOHjwINasWYOXX355zGF74byT0fe0WCyYN28errjiCrzzzjv49NNPxyzUUZnE8iV72bJl2LFjBz777LMxkUChyjabzZg/f75H9ueff460tLSQZMcSo5/zueeew7fffguhUIjZs2fjn//854jr4+0ZfQ2mh4eHccYZZ3je5TfffDMiRSRJvD0r0/gqyx07duCZZ57BKaecAg6Hg//85z9jfkdXOXrfv7OzExdffDEeeeQRXHDBBSAIAkuXLkVVVRX+9a9/MXL/YPQCgDfffBP/93//h1mzZkEkEuHZZ58NWZ/Rst944w2sX78eM2bMgEwmw9NPP02b7Ndffx0bNmzAtGnTkJCQEJZsKozWgyAIzJ8/HzU1Ndi5cyd27NiBsrKyiOhChdF6u1wuzJ8/HxdeeCHeffdd7Ny5EyUlJSN+E6r9cblcmDdvHi666CK8++67eO+991BcXBySbKqM1mXz5s3Yvn07ysrKkJ2djXXr1kVEDyYZ/YwOhwPz5s3DpZdeih07duCDDz5Afv7IKOVQ36XD4cDcuXNx2WWXYceOHfjwww+RlzcyKjwey5CEfF6Hw4GlS5eisLAQzz//vOd7p9OJZcuWIScnBy+88ILn80j307/88gtuuOEGXHjhhfjjjz/wwQcfjPlNqP3p7t27ceONN2LJkiXYt28fdu3aRYtcAPjpp59w8803Y8mSJdi/fz/++9//hqV3JBn9LDt27MDTTz+Nr776CkKhEGeeeSauueYarFmzZsTvYvV56KCjowM//vgjVq9ejYsvvhh33nmnz+ueeuop7Ny5E1u2bEF1dfXEKBOChYWFxYt58+YRK1asIE4//XRi+fLltMo+9dRTiZUrVxKnnXYa8ac//YlW2dHG5XIR1dXVxJVXXjni81mzZhEXX3xxlLRiocInn3xCFBcXEyKRiPjXv/5FWK3WsGX29fURf/7znwkAxJlnnkkolUoaNI1f7rnnHiI/P5+4//77iezs7Girc1xjt9sJuVxO3H///QRBEITT6SQyMzOJf/zjH4ze9+DBgwQA4vXXXycAEN9++y1tsg8cODBC9v/+9z/aZO/fv3+E7O+++4422bHEkiVLiPPPP5+45JJLiLPPPjva6jDCr7/+OuJd/vLLL9FWKW655ZZbiOrqauK2224jKioqInbf+++/n0hMTCSMRqPns8cff5wQi8WEVquNmB6+uOGGG4hp06YRN998M1FdXU2r7Ouuu46YPn06cdNNNxFTpkyhVfbatWuJGTNmEDfccAMxdepUWmVTobu7mwBAbN26lQBAvP/++1HThQodHR0EAGLbtm0EAGLXrl20yW5vbx9RJh9++CFtsqmyevVq4pRTTiGuvvpqYvbs2VHTg0mamppGvMtPPvmENtmNjY0EAGL79u0EAOLTTz+lTXYs8corrxAAiD/++GPMd++++y7t/h9V1q9fTwgEAmL9+vUEj8ejZcxH8sILL4yQbbPZaJP9/PPPEyKRiHjhhRcIPp9Pq+xIc+655xJnnXWW5++LLrqImDdvXhQ1ijx6vZ6oqKggZs6cSZjNZr/XWSwW4sQTTyRKS0uj7uPQBTeiqx4sLCwxj0KhQFVVFaqqqqBUKsf/AQWUSiWqqqpQWVkJhUIx/g/iiD179qC+vn7MKv6aNWtQV1eHgYEB3z9kiTp9fX248sorcf7556OsrAyHDh3CXXfdBaFQOP6PxyEzMxOvv/46Pv30U7S2tmLatGlYt24dbLbI5K2ONRQKBSorK1FVVYXe3l7o9fpoq3Tc8uOPP0Kr1aKmpgaAOy/p4sWLUVtby+h9SbtyxhlngMfj0WoLSFlnnHEGuFwuI7IXLlwIDocz4WwYCWmnmfABYgXy3Z155pkj/mahjrfP2NLSAnsIaQyp4nK5sG3bNixbtgxSqdTz+YoVK2Cz2bBjxw7GdQiEt51ramqC00n90FN/ePvRdMv2fpeNjY20yqaqBwCcfPLJSE5Ojpv2SfaXc+bMQWJiIq39J1kGc+fOhUwmi2qZkPWEHMsREzC5Blm+p512GkQiESO+BCl7ItpZq9WKhx56CJdffjlOOOGEMd9feumlmDVrFu65556o1R+FQoGysjJMnjwZTqcTLS0ttMouLy9HdXU17bKVSqVHtsPhQFtbG22yI4nBYMA333zjGYMAQE1NDX7++WfKWQjiFYIgsHbtWqhUKuzYsQNisdjvtSKRCDt27IBarcY111wzIfpddiGChYXFw+DgINRqNSorK1FZWQmlUklbR6fRaKDRaBiRHQts3boVeXl5OOuss0Z8fuWVV4LD4eCtt96KkmYs/nC5XHjllVcwadIkfPHFF9i2bRu++OILlJeX036vc889F4cOHcKtt96KBx54ALNmzcJPP/1E+31iHaVS6ekDyL9ZokNdXR2ys7Nx4oknej5bsmQJ6uvr0dzczNh9lUol5HI5cnNzUVJSQmsdUCqVSElJQW5uLoqLi2mXnZqayojsWMHhcKC5udnTRjs6OkLK5xvrKJVK5OTkICcnB7m5uRPyXUYK7z7d6XSitbWV8Xt+9913aGtrw+rVq0d8npeXh7PPPhtbt25lXIdAeJeJ3W5He3s7I7KtVis6OjoYk93Z2UmbbKp68Hg8lJaWesYL8YBSqQSfz0dJSQnteiuVSggEAhQXF0e9TLzryfDwMPr6+qKmC1MolUqIxWIUFhaioqKC9ncplUpRUFCA8vLyuKnfVNi0aRO6u7vx8MMP+/yey+Vi3bp1+PHHH/Hpp59GWDs3TI5H4lV2JPniiy9gs9mwZMkSz2eLFy+Gy+XCJ598EkXNIscrr7yCt956Cy+//DIqKirGvb60tBSbN2/Gu+++i40bN0ZAQ2ZhFyJYWFg8NDY2AoDHwBkMBs/Bl+FCGkoySmwiOa9WqxVvvfUWVq5cCR6PN+K79PR01NTUYMuWLdFRjsUnDQ0NWLhwIa655hpcdNFFaGhowMqVKxk9w0EqleKJJ57A77//DplMhnnz5uH666+HVqtl7J6xhMvlQmNjI6qqqjwOV7w60BOB2tpaLF68GFzuMVfw7LPPhlAoRF1dHWP3JaOFORwOI5M1pGy6I/rJSGQAUZ8IYorW1lY4HA6PD0AQBJqamqKtFu2Q9QTAhN75wTRWqxVtbW0jJkUiEam9detWlJaWYv78+WO+W7NmDX788UePPxtpTCYTOjs7PRHjAH1lYjQa0dXV5fGjAfpsqMFgQE9PT0xMcCmVSpSUlEAoFMZVX0tGWPP5fNp3fiuVyhGyo1UmGo0Gg4ODjNTBWEKpVKKiogJcLpcRP8Vbdrzs+AkWo9GIdevWYfXq1Zg0aZLf684991ycdtppuOeee+ByuSKooRvSD8jJyYFMJmPEX8zNzYVUKqV9d1RlZaVHdrzWn7q6OkyaNGnE+T85OTmYPXs2o2OQWOHAgQO45ZZbcO2112L58uVB/27p0qW44YYbcNttt2Hfvn3MKRgB2IUIFhYWD6ShrKiooH0gQsopLy+P+iCHbmprazE0NDQmOo9kzZo12Lt3Lw4cOBBhzVhGY7Va8eCDD2LGjBno6enBl19+iS1btow50JZJZsyYgZ9++gnPP/88Xn/9dUyePBk7d+6cUDuEfNHd3Q2z2YzKykokJycjKytrwvQB8UZjYyMUCsWISCQASEhIwMKFCxkdBHhPAjO1EBFvsmMF74CBiWanvTke3mUkaG5uBkEQnkkRuidzfGEwGPDuu+9izZo1PgMHLrroIiQnJ2Pbtm2M6uEPcuGusrISBQUFEIvFtJWJd7BQQUEBrWldvGUXFRVBKBRGdSEiHtvn8WB/vG1EaWkpuFxu3LwfKhwP75Ipnn/+eQwNDeGBBx4IeB2Hw8G6deuwd+9evPfeexHSzo33IjrdQTFMyjabzejo6EBlZSW4XC7tu3UihcvlwkcffTRmDAK4d2Z/+umnEzqFscFgwLJly1BZWYl///vflH//zDPPoLq6GsuWLYvrFMfsQgQLC4sHhUKB/Px8yGQylJaWgsfj0boQUVBQ4JE9kZzXLVu24JRTTvEb+XH++ecjIyMj6ukCjne+++47zJw5E+vWrcMdd9yBAwcOjEmlFSl4PB5uvvlm1NfX45RTTsHll1+OCy+8kNY0C7GG9wAWYCORo0ldXR1EIhEWLVo05ruamhr873//Y8y59d5ZUFVVhebmZjgcDlpkk7mrAXc9o1P26Ch6uvOzxwJkyoi8vDykp6cjJSVlwrVRgiB87m6Z6AvBTEBGYlZVVTGyw8kX77//PoxGI1auXOnze4lEgiuuuAJbt26NWpQtAEYmirxl83g8WtO6+JIdrUhb7368qqoKAwMDGBoaioouVBht2/r6+qDT6RiRrVKpojIB5R2wJhKJJmyawtHlTWeawtH1eyKlQBwaGsKTTz6JtWvXoqioaNzrTzvtNJx33nm47777aPPVgqGlpQUul2vEe6Crv/NeoCdl09VGSNneesdj+/vtt9/Q19c34nwIkpqaGuj1evzwww9R0Ix5CILADTfcgK6uLuzYsQMSiYSyDLFYjB07dqC3txdr166NW/+VXYhgYWHx4D3RIhQKac3f7S2bdF7jdTuhN729vfj000/HHFLtjUAgwJ///Ge8/vrrETnIkWUkQ0NDuOaaa7BgwQKkpqZi7969ePTRR0My/nSTn5+P//73v3j//ffxxx9/YPLkyfj3v/894SY4Affgi8xxDGBCbkmPF+rq6nDmmWdCJpON+a6mpgZ2ux2ff/457ff1PocIcNcBh8NBS15575QRpGy73U7LQX5qtZox2bGEd8qISE0sR5qenh4YjcYR79JkMqG7uzvKmsUfSqUSycnJyMjIABCZ6N4tW7Zg4cKFHjviizVr1qCzsxPffPMNo7r4QqFQIDU1FWlpaQDoLROlUon09HSkpqZ6ZNNlQxUKBTIyMpCSkuKRHY22b7fb0dLSMqJ9ArG/M8tms6G1tXWM3nSkCPOOsKZbNlUUCgUKCgo8h8RPRD/OaDSis7OTkfI2GAzo7u4eIZsgCEbP5YokTz/9NKxWK+65556gf/Poo49CoVBg+/btDGo2ErLOMrEzxZdsutrI6ICuePXRamtrkZKSgrlz54757oQTTkBubi5qa2ujoBnzbNmyBdu3b8emTZs8C0qhUFFRgZdffhlvv/02Xn75ZRo1jBzsQgQLC4sH78UCgP5BjrfseF3FH80bb7wBPp+PK664IuB1q1evRn9/Pz777LMIacZCEATefvttTJo0CTt27MCGDRvw/fffY+rUqdFWbQyXXHIJ6uvrcdVVV+Hvf/87TjnlFOzduzfaatGKd45jgI1EjhY6nQ7fffedz0gkACguLsbUqVMZSc/kaxDl/TmdsunMX03K8I6ip0t2LDHaTk/ESSYm68nxhveZLADz9aW9vR3ffPON3zSYJHPmzEFFRUVUdqF6R1ID9E9wMeVH+/L/o9EmWltb4XQ6PbrEy3lSZIT1aL3paA/Nzc0+ZUejTEbXk4kylvPGO72a9//peE5/sieCne3r68O///1v3HrrrcjOzg76dyeeeCKWLl2Khx56CFarlUENj6FUKpGYmIisrCwA7vfQ29tLyy4jcoE+MzPTI1ulUmF4eJgW2XK53JNOuLKyEt3d3TAYDGHLjiR1dXU4//zzPeNBbzgcDmpqalBbWzvhxoeHDx/GjTfeiKuvvhp//vOfw5Z3xRVXYO3atbj11lvjMv03uxDBwsIC4NhBskwMRJiUHU0IgsCWLVtw0UUXeaLI/DFz5kzMmDGDPbQ6QrS2tuKCCy7AlVdeidNPPx319fW47rrrRhzMG2skJSXh//7v//DTTz/BZrNh9uzZuP3222E0GqOtGi34mugwGAzo7e2NolbHH5999hkcDoffhQjAnaP1o48+on1njvdZQQBoPchvtOy8vDxIJBJGZNOdnz1WiJXJSCZRKpXg8XgoKSkB4F544/P5E+45I4Gv+kLXhIsvtm3bBplMhssuuyzgdRwOB2vWrMF7773HmC7+8FUmdKVeibTs9vZ2WCyWsGVT1YO8P+A+tyg3Nzfm2+dovZOSkpCdnc3IIrtcLkdmZmZMLETQnQIxFhhd3mlpabSlKRwtOz09HXK5PObrdzA89thjEAgEuOOOOyj/9uGHH0ZnZ2fEIrt9LaID9Ox6iVfZkaKzsxP79u3zeT4EyZIlS9Dc3DwhFuhIjEYjli1bhrKyMjz//PO0yX3uuedQWVmJZcuWxd2CVOzOyLCwsESU7u5umEymMZFcLS0tYacT6urqgsVimXDO6969e3Ho0KGAaZm8WbNmDWpra6HRaJhV7DjG4XDgqaeewpQpU3D48GF8+OGHePfdd5Gbmxtt1YJmzpw5+P3337Fu3TqsX78eU6ZMwccffxxttcLG1wCW/JwlctTV1WH69OkoLCz0e01NTQ3UajV+/fVXWu+tVCqRl5eHhIQEAKA1hzqZMoJMN0WnbKVSicLCQk86t3g+JNAfo1NGAO42qtFoJpTNUigUKC0thVAoBOBOnVhaWjqh3mWk8M51DjA7KUIQBLZu3YrLL7/c038EYuXKlTCbzdi5cyftugTCV8Q4EH6ZkGebjG6fdKR18SW7qqoKBEF4IrgjhUKh8JxTQxIPC6IKhQKJiYkjIsHp0nt09DZAb077YPEXVGa329He3h5RXZhEqVSOSK9GZ5rC0SnQJkoKxI6ODmzYsAG33367J3UcFaqrq7Fy5Uo8+uijEQm+8jceoaNNMTnW8Wdz46n+fPTRR+DxeDj33HP9XnPmmWdCLBYzsjM7Wtx8881oa2vDjh07PKnt6EAikWDHjh3o6urC9ddfH1e7SNiFCBYWFgAjDyAjqaqqoiV/9+h8iaTseM+xvWXLFmRnZ+Pss88O6vo//elPcLlcePvttxnW7Phkz549OOmkk3DnnXdi7dq1OHLkSMCIi1hGIBDgzjvvxKFDh1BZWYnFixdj+fLlcbt7YHT+ZAAoKysDl8udUBEvsY7T6cTHH38ccDcEAJxyyilIT0+nPUfr6NQiAH0TKuT5BkzIZlLvWIGcKPU1iRpPg9zxYLKeHE9otVoMDAyMKEsm04z8+OOPaG5uHjctE0lBQQHOOuusiO5CHX2WDEBfmQwMDECr1fpsn+HK7u/vh06nY2xijgpk+yQjfoH4aJ9M6k3aH2/Z0Uib19nZOSaojK46GEvEq58STR5++GEkJyfj1ltvDVnGgw8+iMHBQVqjxf0x+h0nJycjKyuLEX+R3MHERP1JSUlBRkZGXNWf2tpazJ8/P2AmCalUikWLFk2YcyK2b9+O1157DS+++CKqq6tpl19VVYVNmzbh9ddfx2uvvUa7fKZgFyJYWFgAuI0bn8/3pCsA6FtpVyqVIw6ppVN2tLDZbHjzzTexcuVKnzkOfZGZmYkLLrggKnmLJzLDw8O49dZbMWfOHADAL7/8gueeey6oqMlYp6ysDJ999hm2b9+Or776CtXV1Xj55ZfhcrmirRolyBzH3pE8QqEQJSUlcdsHxCO7d++GRqMZd4GOx+PhggsuoD0aaXT+dIDeqNF4lB0rjE4ZARxLRTXRnnOiv8tIMPrcFIDZlDFbt25FcXExTj/99KB/s2bNGnz33XdoaWmhXR9f+CqTtLQ0pKamhr0jwp9sOlLG+JKdkZGB5OTkiKf88Nc+GxsbY9rvCdSvhBuhGqhMIhn96que0JkCMVZgfQlqKJVKbNmyBXfffTcSExNDllNcXIxrr70WTz75JLRaLX0KjkKr1aK/v99vmwqHoaEhDAwMMPKOBwcHoVar47r+GI1GfPXVV0EFCdbU1ODHH3/E4OBgBDRjDjI19OrVq4MOpAiFP//5z7j66qtx00034fDhw4zdh07YhQgWFhYAYw+SBejL3+1Ldrw7rx999BE0Gg1lo7J69Wrs2bMnboxErPPhhx9i8uTJeOWVV/Dkk09iz549mD17drTVohUOh4MVK1agoaEBl1xyCa699losWLAA9fX10VYtaHxNcpJ/x2sfEI/U1tYiIyMDJ5988rjXLlmyBAcPHqQt5YKvtA4APYftBZLd1dUV1lb/QLI7OzthMplClh1LKJVKpKenj0irIJPJkJ+fP2HaqN1uR0tLi8932draCpvNFiXN4g9fu2gBZvp0k8mEd955B6tWraJ0ztMll1yCxMREbNu2jVZ9/DH6LBkSOspEqVSCw+GgrKzM8xldaV2YlB2KLr7ap8lkQk9PT0R1oYI/vY1GI1QqFSOyh4eH0dfXF5ZsqnoIBAIUFRV5PptoaQoJgvC5I6KyshKDg4NhpSn0lQKNlB3PKRAfeOAB5OTk4Lrrrgtb1j333AOr1YpnnnmGBs1842v3J/l3uPU4XmVHiq+//hpWqzXohQin04nPPvssApoxg8lkwrJly1BUVIT169czfr/nn38eZWVluPzyy+PifEl2IYKFhQWA762odDmYvhyveHdet27ditmzZ2PKlCmUfldTU4O0tDR2V0SY9PT0YOnSpbjoooswbdo0HD58GP/v//2/oHenxCNpaWl49dVX8fXXX6Ovrw8zZszAAw88EPHDJEPBV45jIL4c6IlAXV0dFi9eHNRk3jnnnAM+n0/broju7m6YzWafgygAYeUiJ88h8hUpFq7szs5OWK1WRvSOJXzZaWBitdHW1lY4nU6f79LpdIadhvJ4QqFQIDc3d8zOQybqy3//+18MDw9j1apVlH4nlUqxbNkybNu2LSLR9KPPkiGhI41OINl0+OhFRUUQi8UjPq+qqopo2zcYDOjp6fHbj8dqP6TX69Hb2+vXRoSjNxm97StVULiyqeIrqAyYWDZCo9GMSYEG0PMufaVX85YdTwcOk+zfvx9vv/02HnjggTH9Ryjk5OTg5ptvxnPPPYf+/n4aNByLv0V0sr8LZ5fReAv04cgmbQgTsiNFbW0tKioqfPqao8nLy8MJJ5wQ1+mZbr31VjQ3N+Pdd9/1nF/HJFKpFDt27EB7eztuvPFGxu8XLuxCBAsLCwDf20UB+gY5E2mCo7+/Hx999FFIW+yEQiGuvPJKvP7663F9UHe0cLlcnhyLP/zwA95++2189NFHI9J+TXQWLlyIAwcO4M4778Rjjz2GGTNm4Ntvv422WgEh+wDvHMfAxDi0Pl5obW3F4cOHxz0fgiQpKQkLFiygbRAQaFeM9/eh4OscIrpkM6l3LDHR7LQvyHoSbxOdsch4PiOdkyJbt27F6aefPiJiP1hWr16N1tZWfP/997Tp4w9/bYiOCX0m22cg2ZHMPe6vry0pKQGfz4/Z9ukrZREAlJaWgsfjhaW3vyjo0tLSiJ+xdTzYCH91kI40hf7qCTmxHI9leO+996K8vJzWlDP/+Mc/wOPx8Nhjj9Em0xulUons7GwkJSWN+JzcZRTOWXxKpdLvAr1erw9rcUWpVCI/P3/MhHZlZSV0Oh0GBgZClh0JCIJAXV0dpbMblyxZgk8++SQux4hvvvkmXnnlFbzwwguUg1bDobq6Ghs2bMDWrVtjPuiVXYhgYWHxeZAsSbiHaFmtVrS1tfkcsMbrAV1vvvkmOBwOrrzyypB+v2bNGqhUKnzxxRc0azaxOXjwIObNm4cbb7wRV1xxBerr63HFFVeMmdw+HhCLxXj44Yexb98+ZGRkYOHChbj66qtjNpemQqHw2wc4HA42EjkC1NXVQSAQ4Jxzzgn6N0uWLME333wTVtokEoVCMeYcIoCew/Z8pYwAgNTUVKSlpYUlW6FQQCgUjpFNpjGKRxs2GjIdhb82Guv52YNFqVRCKpUiNzd3xOc5OTlISEiYEO8yUgSadNfr9bSljOns7MSXX34Z8kTX/PnzUVpaGpFDq/21ITrSugRqn+Qh2XTLrqysDFs2FfxFEwsEApSWlsZs+/SnNx3nYPlbZBeJRCguLo7o5HWgOtjZ2RkXqUDGw1/UuUwmQ0FBQdh+yugUaKTs/Pz8mK3f/vj5559RV1eHhx9+GAKBgDa5aWlpuP322/Hiiy+is7OTNrkkgfo7IPygGH9thPw+VALZXPL7WOaPP/6ASqUKOhgKcGeR0Gq1+PHHHxnUjH6USiXWrl2LFStW4Kqrror4/VetWoU1a9bghhtuwJEjRyJ+/2BhFyJYWFjQ0tICl8vlN9Klp6cn5Iko8pBaf7LDzd8dDbZu3YolS5YgLS0tpN/PmjULU6ZMicjAeCJgNptx9913Y9asWdDpdPjuu+/w0ksvISUlJdqqRZ3Jkyfju+++w8aNG/Hee+9h0qRJePPNN2Nui26gSDryexZmqaurwxlnnEHpMMGamhrYbDZ8+eWXYd/fX1oHIPyIykCyw41GViqVKC8vB4/HG/PdRIkEHRgYgE6n89tGzWYzurq6oqAZvfjbmRWtfPjxir9c5wD9ffr27dshkUhw+eWXh/R7DoeD1atX491336VlQdUf/s6SAcIvE6fTiaampqjJjlTKGKVSiYyMDJ++XSy3T38R1gA9ti0nJ8en3Y5kmZBBZYHqyURIU+gvBRpAz7v0lQKNDtmRhiAI3H333Zg2bRquuOIK2uX/7W9/Q1JSEh555BHaZfuzXeQuo3DfsS/ZZWVl4HA4MSs7EtTV1SE5ORnz588P+jcnnngisrOzaUsRGwksFguWLVuGvLw8bNiwIWrBki+88AKKi4uxbNmymD3Ljl2IYGFh8Rtx4/1ZqAMRf9tcvT+LJ+d1//792LdvH9asWROyDA6HgzVr1uCDDz7A0NAQfcpNQL766itMmzYNzzzzDO677z7s3bsXp512WrTViim4XC7Wrl2L+vp6LFy4EH/+859x3nnnoaWlJdqqAQB0Oh36+vp89gHxfmh9vDA8PIxvv/2W0pZowD3Aqa6upiU9k79BFEDPAN9XFBpdspnSO1YIxk5PlOec6O8yEnR3d8NkMjE+KUIQBLZu3YpLL72U0gLqaFatWgWj0Yj3338/bJ384e8sGeBYWpdQo2E7Ojpgs9kCyg61vNvb22G3233KjnTKGCb7cSbxdcYeSbjprQL1WZE8w6O5uRkEQbA2Isxo+Ylif7766it8++23WLduXVBnjlElMTERd911F1599VVaF0IDLaKLRKKwdjCNJzucHUwul8uvbLFYjKKiopjfUVNXV4fzzjuP0u4ZLpeLxYsXx9VCxG233QaFQoEdO3aMSdEVSWQyGXbs2IGWlhbccsstUdMjEOxCBAsLC5RKJRISEpCdnT3mu3AdTH+H1NIhOxps3boVGRkZOO+888KS8+c//xl2ux3vvPMOTZpNLAYGBrBq1SosWrQI+fn5OHDgAO6//36IRKJoqxaz5OTk4J133kFdXR0aGhowdepUPPnkk7Db7VHVy1+OYyD+D62PF7744gvYbDYsXryY8m9ramrw0UcfhZ2aJ5gBfqg7ecaTrVAoGJM9Eequv5QRAFBcXAyBQDBhnnOiv8tIEGjhis6UMbt374ZSqQwr8ANw1+EzzjiD0XzJgcpEKpWioKAgLD/an2wyrQsTshMSEpCXlxfRhYhA7bO1tRU2my0iulBhPL1bWlpC9sPGkx2pM7YC1RMyBeJE6D/HK+9w0hQyKTuSkLsh5syZQynNDlWuv/56ZGdn44EHHqBNpkqlgtFoZMQP6Onp8btAH67s7u5umM3muPVfenp68Ntvv4VUX2pqatDQ0BAXQavvvPMONm7ciP/85z+YPn16tNXBlClT8MILL2Dz5s144403oq3OGNiFCBYWFk8Ukq/tY+Hm7ybzJfqSnZqaivT09JhfxSex2+14/fXXsWLFirDzYebk5OC8886L+YOEIg0ZAVldXY26ujps3rwZ33zzjd8oOZaxLF68GIcPH8Z1112Hu+66C7Nnz8avv/4aNX0C7bgC4vesmHiitrYWkydPRmlpKeXf1tTUoK+vD7/99lvI97darWhtbfXbjquqqqDVakM6bC9QygjAXe+0Wi3UajVl2RaLxe8ZR4Bb78HBwZBkxxIKhQJFRUU+01Hw+XyUlZXFfRs1GAzo6ekJ+C5VKhX0en2ENYs/lEqlz/NeSOjq07du3YqCggIsXLgwbFlr1qzB119/jfb29rBl+cLfWTIk4USvKxQKiEQiFBYW+pUdjo8eSHakJrgCRRMD7md0Op0xs9OThNQ7UL/icDjQ1tZGu+zKykrY7XbG6rQ3CoUCSUlJPoPKgInhxzmdTjQ2NgZ8lxaLJaRzC8gUaIFkx0sKxA8++AB79uzBunXrGE07I5FIcP/99+Ott97C/v37aZFJ1lEmdl6NJzucNuLvoHNv2bG8EPHxxx+Dy+Xi/PPPp/zbRYsWQSQSxfyuiKamJlxzzTVYvnw5rrnmmmir4+Gqq67CihUrsHbt2pjro9mFCBYWloDOPxCeYWZSdqT59NNPMTAwEHZ0HsmaNWuwe/duNDQ00CIv3mlsbMSiRYuwZs0anHvuuWhoaMBf/vKX4/Iw6nBJSEjAs88+i19//RV8Ph9z5szBLbfcEpVJtkA5joH46gPiEZfLhY8++ohyWiaSuXPnIiUlJaxBQKBziIDwdscFOoeIDtn+0lGEKzuWmEh22h+BInq9P49UPvx4RqlUorS01G9ABh31xWw24+2338aqVatoSf1x2WWXQSaTYfv27WHL8kWgs2SA8P1oJmVXVFT4LeNwUwsFS39/v99zakg9gNjra3t7e2EwGBjRu6enZ9zobSC8A3CDxd/5Ot66xNq7oUqg9GpAeO8yUHq1cGVHEqfTiXvvvRdnnXUWzjzzTMbvd9VVV6GsrAz33XcfLfKUSiV4PJ7fRfRwdhmRC/TFxcVRkd3U1ASn00lZdiSora3F3LlzQzpbMyEhAQsXLqQlRSxTkOdCZGVlYdOmTTE1b8HhcLBhwwbk5+dj2bJlMJvN0VbJA7sQwcLCEjBvJcAuRJBs2bIFM2fOpG273ZIlSyCXy4/7XRE2mw3r1q3DtGnT0Nraik8//RRvvPEGMjMzo61a3HPiiSfil19+wTPPPIPNmzdj8uTJ2LVrV0R1CKYP6O7uZvQg0eOZX3/9FQMDAyFvoefz+Tj//PPDGgSMNwkcTl758SLFwsmhPp7e4eZnjxUmkp32B6k/mfd+NJHOhx/PBOMzhpsy5oMPPoBOp8Pq1atDluFNQkICli5diq1bt4acpi0QwbShUFOvRFN2VVVVRFLGjNeP5+TkQCaTxVz7HG/HZ25uLqRSKSP2Jz8/P2JnbAVTB8NJgRgLjFfeRUVFIacpHK+eFBcXg8/nx1z9Hs3bb7+Nw4cPY926dRG5n0AgwEMPPYTa2lrs3r07bHlKpRIlJSUQCoU+v6+qqoLdbg9pB1MwC/Sh7mBSKBQoKysDn8/3K9tms6Gjo4OybKYxm8348ssvQw6GAtzzJd999x10Oh2NmtHH7bffjsOHD2PHjh1ISkqKtjpjSEhIwI4dO6BUKnHbbbdFWx0P7EIEC8txzh9//IG+vr6AqYZCdTC1Wi36+/vHdV4bGhpi3nnds2cPdu3ahVWrVtEmUywWY+nSpXjxxRfR29tLm9x44qeffsKsWbPwwAMP4NZbb8WhQ4dw7rnnRlutCQWfz8dtt92GI0eOYObMmbjkkktw6aWXoru7OyL3D2bSCoivQ+vjibvuugsymQxz5swJWcaiRYuwb98+fPHFFyH9vqGhATKZzOc5RMCxw/ZC2R22fv168Hg8pKSk+PxeIpGgsLAwJNkNDQ1ITk5GRkaGz+/J3O/xvKuNTEcxXhttbW2F1WqNoGb0sn79eggEAiQnJ/v8Pjk5GVlZWXH9LiOByWTC559/HjBXf7gpY5xOJ+677z7Mnj3b78JRKKxZswZNTU3YuHEjbTJJxrNzEokEFosFn332Ge2yKysrYTabQ7Lp48kWi8UwmUz46quvKMumwqZNmwDAr43gcDie8UIsQdYlf3qT52CFan+4XK7f6G0ulwuhUIgNGzZQlk2VYOqgVquFRqNhXBemaGhoAJ/P95tejUxTGMq73LBhAzgcjt8Aq3BkR4qBgQHcfffduOiii3DKKadE7L7Lly/H1KlTcdttt8FisYQla7x6nJ6eDgB47bXXaJcdzg6mbdu2BdztEMndUVR58sknYTKZcMEFF4Qs44ILLoDD4cBDDz1Eo2b0sHPnTqxfvx7PPfccTjjhhGir45fp06fjP//5DzZt2hQz55OyCxEsLMc55OSfv1V2wJ1WQ6/XU46kfvbZZwEAQ0NDAWXrdDp8+OGHlGRHmgceeAAul4v2swqysrKg1+sZGRjHMlqtFtdffz3mzZsHmUyG33//HU888QSkUmm0VZuwFBUVoba2Fu+88w5++uknVFdXY/369Yxu5W1tbcXevXuxd+/eca+9++67GdPjeObbb7+F0WgM2MePR15eHoBjk0VUueuuu2A0GgNuV25ra8NTTz1FWTa5HT1Q+paOjg48+eSTlGXffffd0Ol0AfXu7OzEE088QVl2rLB161bY7faAuan7+vpAEEREJryYgjwwNtC77Ovrw8MPPxxBreIPq9UKh8MRcEKIXKR48MEHQ7rH3r170dTURPshvDNnzgQQul7+2LNnD9ra2nD48GG/15CpCevr6ynJ3r17Nzo6OnDkyBG/15CpFh555BFKsn/++Wd0dnYGlJ2QkACAut5UIeUHCoo6ePAgNm/eHFOBS+TEcSD7eujQIbz00kuUZd9xxx1wuVwBZZtMJsbPFfjiiy8wMDAQMFqfTPv5+OOPM6oLk/zjH/+Aw+EI6Es0NDTghRdeoCxbqVSCIIiAshUKBf7v//6PsuxIsXz5cnR0dOAvf/lLRO/L4/Fw8cUXY/fu3bj33ntDlmMymfDRRx/h999/93uNXC4H4G6zVDAajfj4448DnqUmFosBuKPnqUAQBIaGhmA0Gv1eIxKJQpIdCbZs2QLAvastVMgFvNdff50OlWjjlVdeweWXX47Fixfj+uuvj7Y643LNNdfg4osvxvLly7F+/fpoq4PQR6UsLCwTgmXLlqGgoACnnnqq32uuvPJKvPPOO5g0aRIl2aeccgrkcjnOOeecgLJ37NhBWXak+de//oU5c+aEdNBSIB588EEYjca4MGB0YLfbcemll+KXX36B2WzG888/jxtuuMFv7mMWeuFwOFi2bBnOOecc3Hnnnbjpppvw1FNPYc2aNbRPzgBu5zE1NTXgwV3Tpk1DZmYmzjrrLNrvz+KO1gl3AfWcc87BK6+8EnJE06JFiwJGUAPAJZdcEtKEyu7duzE8PBxwsuaiiy6CSqWiLHvRokXjLtRdeOGF6Ovroyw7Vpg2bRqSkpJw6aWX+r3mwgsvxFNPPYVZs2ZFUDN6+fXXX2Gz2QIuRJx33nlsirhxSElJwS+//BKwLpx66qlIS0vDGWecEdI9Zs2ahVtuuYW2vOAkcrkcmzdv9rt7KlTy8/ORnJwc8PywK6+8EsXFxZR3phUVFSE5OTlgiqr58+cjNTUV8+fPpyS7sLBwXL1XrlyJ8vLysHbUBcNXX32Frq4uv2dJkbrs3r07pvJvf/PNN+jp6fEs2PhixYoV2LNnD2XZ5557Ljo6OgJOXre3t8NkMlGWTYWysjIkJSVhxYoVfq8566yzIJfLA44lY51zzjkHAwMDAevXlVdeGXDhzh/ff/891Gq1ZzLaF8uXL4/pHREXXXQRBAJBWJHtoXL77bfjiy++CNmmAO5daXl5eQH7u6KiIvz++++orq6mLDs3NxdXX32132vS0tJQUlJCedc/h8PBwYMHUVBQ4PeajIwMFBcXx2RGgZ07d0KlUoV0PgSJVCrFZ599FrCfjQbffPMNAODee++NKbvkDw6Hg3vvvRe7du3Ct99+ixtvvDG6+hCxFFbAwsLCwjKheeGFF3DzzTcjLy8PP//8c0DHioV5fvjhB5x22mngcrkxe8gZCwsLCwsLCwsLCwsLC0ssQBAEXC5X3AVTkrvIo714wi5EsLCwsLBEDLPZjLfffhvLli2DTCaLtjoscKetUavVcR3tzMLCwsLCwsLCwsLCwsLCEtuwCxEsLMcJ5GQjFdLT01FYWMiQRrEF1fIJt2zi9X3Eq94s1AjlPQPsu44GTLbJWGnvsaIHS2zD9lv0MdH6lXi4ZyzUw4n23ukglvSOJV1YRhKv7yYe7CaTfWms9NPHU/1h6v3QUR7x+h6oECvPyJ4RwcJyHNDR0YGqSdWwmKnlERWKxHj/vZ3jHjCUnp4OAEF3alar1XOw0nhEonPv6OhA9aQqmMz+D18cjVQiRn2DIiTdOjo6UF1dTTmvq1QqRX19fdSMnVvvSTCZzJR+J5VKUF/fEFdG+nimo6MDk6qrYQ4h77BILMZ7O8fvM6j0AUD8OXmRgsm+JJR+EQDEIiF2vvc+bXVApVLhsqWXw2qh1u8EWxep6AJQt3exVHepDD5izU6PR6j2CQDEYhF27nwvqLoSC8/KNEy2fZVKhcsvuwxmq5WS7LB9rkmTYDJT9F0kEtQ3jPRdgm1DKpUKSy+/HBYK96TSZ5Fn3gRbZ4Hx+yyVSoWlSy+HhWJfG6w9CcWvoFImTC2CM603FVt4+dKlMAc4KN4XwY4dqE5OxZuNIGHCDobsp4jEeC+IcTYQW/UbACRSKRoiMCYNpf8Wi0TY+d74Nt3dpi6D2RK8PQpWNkCt7z2e6k+wdYeqLxKOn0Dej8nniQViaQ6KXYhgYTkOUKvVsJhNqLjm/2AzDoLLEwIuJwRJ6XA5bODLUmAdaAc/IRWWvhZIi6ahaf1fYbNaUFNTM658sUgEDgfBG3IOFyBcQV0qlkihaGC2c1er1TCZLbjutEIUp0uhMztQnCaBzeGCXCrAsMUBIZ+L5gET8uTug8ZuevsQ1Gp1SHqp1WqYTCZs27wRGs0ghEIhtDodykqKYbXZkJqSAr1+GEKRADqtHjweD90qFe598NGQ70kHbr3N2PS3yzCoN0ImEcHpciFZJobN7kRKogRtvYMQCvgwmG2QiPhIlIix9t/vRVVvFmqo1WqYTSZcdPcmGHWDEElkkMrTweFw4LTbMKzphSQ5FU67Dfq+LpTPOQeGwT6898BqWC3B9RlcDuCisB8zXOdyokL2JbeuuAg5mWkozc8Gl8uBzeaASCSA3mCGRquH3eFARmoyJpUUQNHahavve27cNkn2i2vn5qIyQwonQSBJzIfN4UKKlI9hixMCPgcqnQ1iARc2hwuPfN4Gi9UWVB2gYgcA4NRb18OqH4LTZkZyYRU4HC6cdht4QhHspmFYtP0gXATAIXDw9XVB10WquohEYnA5RND2LlbqLtWABCptNBae0WOfbluKQb0JQj4PPB4XyTIxrHYHUhOk0JstEPJ56OjXAgAeef0LWO1OWCzWoOtKLDwr05Bt/4Xl0zFotMJsd2FSVgI4HA5sThdEfC6GLQ5Y7E50DJqx4bs2WB2u4Ns+gBtPy0OBXASrg0BWkhA2B4EUKR8qvQ2TsqT4oVkLiYCHfLkIFocLN+9Uhudzmc244byZKMlKhtNFICNJAqvdiZQEMdr6dUhLlMBosUNjMGPh1EIoe4Zw/aYvRtwzlAWaU5bdhJTcYiSm53psKE8ghEk/CIOmF067DUKJDP/b/EjM9Fl3PfsqdEMaCIRCGPRa5BaVwm61IlGeCpNBD75ACKNeB5fLBYNOi5eeuCcoe2I2mXDVIy/DqNNAJHUfOiqWJsBhs0KWnArzUdkaVQecDjs+3PgvSmXCxHiB1Puvj74Mg24QYkkC9IP9yCgoPap3Cga62pAgT4NhSI2M/BKsv30FY+8SAF594i5otDo4HE6UF+WDy+XAarND1a9BYoIUIqEAvQODmD97OhQtHfjLPx8b9/2EErAWbzYCCKENU3w3U657ATaDBnA6Ic0uBTgcuBw2OIx68EQSAIBR1Yz0mYugVe5B85v3R9X2kPV7yV0bYdINQpyQDHGifIS/L5Imwmm3waRTo/SkswAAmg4lah+7LiJjO7L/vvWiU5Atl8Fks6M4KwU2uwMpCRKoBodRXZiBPcoepCaIoTNZcfe2b4NvfwCum1+Aigwphq0OZCeJ3H6uRACDzQmpkIsWtRkpUgEUfUZs+LE7aNlU+97Ja931h8cXQZSS7ak/XIEYTvMwbMMauOxWCGRyOC0mNL8VG/Vn+f0vwajTgC8QIinjmK3ji8SwGPTu74Qi8PlCcHl8vP3wtUHVnWPzMwWoyJDB7iQglwhgc7qQIuGj32ADj8OBxmRHn96Kl37sDKtOks8z6cLrIc+vhMNmhjg5Ay67FcLEFNhNw+DyhdB21CMhswB8kRROuxU//efGuJnnIMeNrz5+JzRaPRwOB8qL88HlcGC1O6DqVyNRJgUAtHf34fwFp0DZ2om/3Pk47c/ILkSwsBxH2PRqCBLkEGeVwNTVAEFSOsQZRbD0tSLtxAtg16vBlybBrGqC02HHNadmI0nER16yEK2DFhTIRchJEqFHZ0W+XASdxYGDKiM2/OiOzFoxOxOz8hL8XjtodEBvdeDJr7uQffY14EuTIEzNg6W/FaK0AohSc2DV9ECUng+HUQdjx0GoPt0Qsc49QczH5OxE1PcakJ4gRHGqFK0aE6blJkFtsEHM54EgCPT9f/bOO7yNKuv/X/Vuuci9NzlOTyBASEJLI4m9wMKyuyx96X3fZRdYWMpSF1jKElrom3cpvxfYYgEBAgRCCAmQntiSu9xtyVbv0vz+UEZxkWSNPCPJ8Xyeh4dYGp05c+fec++599xzrdQi+SIxODiEzMwMVFdW4uDhI8jJzkZlRRmaW9qwaME8DA4ZoO/qxvDICDLS02m5Jx0MmmzIVEhRUZCJxs4BZCvlKMvLQFvfMOqXzoHBbIeuxwClTIwRK/UoG5bUwDY8CKkyE5mFFRhsb0RWSTUyCsow3N2GrJJqOEwGCIQi+DxO2Az98HvdMbVr08EvYdj5fkz2Zdjug1LKx80ftEybQV4ykEslWDJXjcMtnVCXFaJCnYcWfR8W1FRgaMSM7v4hDJtt8Hi9qCkvoiZbxENltgRNAw6oZAKUZorRbnRiTrkcBrsXEgEPHl8ADq8fbh8R03v9stmE9/cbYuwHDqFvywtwmQwQKTKgqFoIk74RaYVVyCibA2tvGzLnzIPLbIB9qBvW/g74Yuy/4u2TgNj6u1Squ2RAQs7pF0NesTjiM/psw/A5Lej+1+OUxgCp8IwAMBTqn7JwpHMA2UoZyvIy0dZnxLyKcgyZbfD6A7A6XHB7/THXkxNLFGg2OFPmfSaCIasbmTIhFhZL0dhvRVW2HHOypGg32DGvMDgucngDcPsCeO78KhhsXtjc/qhleajXjhe+64NKJoRIwMOsPAmaBuyoUklRmimGiM9FUboYq2oy4fAEMGjzwOunJ4uwXCLEnGIVjnQboUqTojxHidYBE+qXVMFgcQad8BE7HG4f1AUZE35PTopQGesKJTLkVsyZ0IdWqBfAYTLAqNfBZbfC543dZiklfNz8YQvtNot8NyOGQaRlZKKorApt2kPIyMpBQUk5ujtaUT1nAUzGIQz0diEjKxvdHa2U3oF1eBAyZSZyiivR03IYioxsZBeVY7CrFcU182EdHgKPL0BPyxH4vZ6YntFnGwZfpkTLKzcz1jYtxkHI0jORW1IFv8+LtMyjeutbccLKc2AdGYKxTwbz8GDMY6HRtpZKXzhoHEGmMg1VpUU41NyGmvISzKspQGtnN9QVJRgymiAVi+H1+RFrBm6yf6Cq93TrI8g2zNQ4xWMegkCeAWleOWxdTZAVVEGSUwpHfztkBVXwWAwQKrLgMQ3AazUiQKTGWMI+MgRJWgYyCisw1H4EWcXVSK8sg7inDVnF1bCbDPB7XDB0NEGZV4qsEjXtOkyGKk0ChVSMhRX5ONI1hOqCTJTlZkAs5KNYpYREKIDL48N/vm8CiAAlf0Qm5KEqW4qmATuy5cLQOHduQXCcKxbwMGh1QyTgxiw7HtvrsRytP7nlsHU3QZpfBXlOKZwD7VCUzoXHYoDXOgz3SB88lsGUqT/WkUHI0jKhKq5Ef+sRZJdWI6uwHIauVhQumg/byBAGO5uRWVAKbhwHO8uEfFRly9DUb0O2XIDSLAnaDU6cpc6Cwe7BsN0LPpe+g5cFEjnSiqph6myEOF0FRW4ZLH1tyCwP+hocLgdcgQh8kRQC8fQ873LQOILM9DRUlRTiUHM7asqLMa+4AK16OdTlxRgympChVOBISwelHUNUYBciWFhmEMpZSyEvnQcASKtaEvpclFkAABCm50KGORCkBbcSVqskqFBJYHb6UZvHg8HmhUTIxbIKJYR8LnrNblRkSUILEfPzZSjJFEMpEcDtD6B92IXZeVLkKYQQ8rloM7owZPUAACT51ZDkVcBvN4NXVAuvxQCuUAJl7TJwBUK4+tsgOemcUCeeCFbXZmN+YRqWlKWHPis4ugMiN02EOQUKAMCBHgst9zt9xTIsXrgAAHDqKSeFPi8qLAQA5OflYcG8uQCAPfv203JPOlg+twwLKoN15uRZxwY0hSolACAvU4G55XkAgP2tvYlXkIUWShcuR746WD+L5p4c+jwtO1g/FVl5yK0M1k++MBjpNVm7dvTokD73dBh2vj+pfTncb0eRkI88hSDBTz79WHfaEiyqrcQpC2aFPivMDdrx/OxMzFeXhz7f20ht4mh1TSbmFcixpCQt9FmBMpiqIFchxJy84CD8YK8NwOT9RrvRhdMr0/H+fkMM9UULcXZx8F5zT0VmxXwAQPasY/2XNCtoiyQZucgom4PhtgMx6dFrdkMi5EHpCg6FJ9PFbeyFJK8i1CdN1t8d7LWjUCmkVNaJQFY6H+LsEgikSgR8brgG2iEtng1heh64BcHndBu7AExehm1GF86Ze2wMkAosn1s+af80rzw/1DfFUk9yFEJwOBxUqySJf6AkcmplJuYXBcttSdmxiflj4yIx/EdDop0eP+YVyGB2+uH2B5AtF6JCJUGeQoiaXCl6zcFJnHJVMLp4abkS8wqCUfGRbAsJaVumytqFZVhQloOTqo+lsSjIDOqQlz52MmF/x2BEOZO1/V6zG/85EEzFUb30bOSrF0TtQ/t0wfHdZHVxwOpBgZJA40AwwCMem6WUCCK25bIsPwBgwSkroJ67CAAw54RTQnpn5wcXsbNy8lFZG7TFPD61/ll9wnKUzFoIAKhccKxMMnKDZaJU5aFIPQ/pOfn4zwt/mfQZ7frDEGXOBVcopqQHVWpOXIHS2qDeVaP0zjyqd3p2HorV89DZuA9AbGMhnkgCvksZ2/XdTRDnlAEAVixZgEWzgxPBpyyaE9KlKC8bAJCfnYX5syoBAAI+tUm/WMs71j5ivJ+YKsSid6zjFLexF17zAAAgo3Yp0sqCbSO9+tg4RXzUzxal54Y+8zmtACa3J+1GF5Ri6pO3VChZsAx51UfH+3OO+aOkrZJnBX26gtoTAQD9zYn3SZfNLsGCo77lSTWFoc8Ls4K+eV5G0Ja3D4wAiG2egfRHVs/KwvxCBZaUKkNyC5TH/H+SD/YNxCSb6njR6Q32ixmzlkJxtP4oJ6k//d99EJPsRNSfykXLUVSzEABQNu+YfUzPCb6nNFUeCqqD80/d2n2U5a+epZr0/Sgl9PmKRSeuQWbl/LG+huqor5GZi4zyY3Z3uPUAbfdNJMF+pBrA5P3I3iPNjOjALkSwsMwgbB0H4HfZ4LObQfjc8JoNkJYcnYA4OjD2Wobg9wS3rM4vkIccxXAUKkVjHMRYrv9w/xAAQF42P7QoEg5RZiFsnQepPuKUONBtgc3tg9npg8cXwJDNg9n5cuSniSHkc9HUb4VEyINcRI/p/GnvflitNoyYTHC7PRgcHMT8eXNRWJAPkUgU2g1RVFhAy/3oZMehDgCAye6E2+vHkMmGuWV5yM9SQCjgo3/YCqPZjrxMRXIVZZkSnft3AABcVhP8XjfsI0PIqZwLhSoffIEQ/S2H4LKOIOAPTmDE0q6Hvv8QQGz2AqBvEup4Zm9jK2wOJ0wWG9xeHwaNJsxTl6EgJwsiAR9d/QaMWKwoyFFRln2g1wab2w+zywe3LwCD3YvZuTLkpR2ddDO50Wf1wO4O1gEm+4GBw98BADw2EwI+D1ymIaSXzYE0Mx9cgRDO4X5wj06MxVq/4tWFav+YKsTURo9OMk3HZ9zX2gub0w2TzQm3b3TflAYhn4+mrkEMmWzw+YOOf6z1BJh5tuhAtznY9p3eo2MiN2bnpyFfKYKQz0WPyYX9XWYAsZejXBicEAnZFacPbn8ABpsXs/OO2RXdoAMeXwD5acHUn3Swv2MQNpcXZrs7WDfMDswpUSE/QwYRn4eD+uDiwdmLyqPKieVZu0bceOOHAfTp9sHjtEXsQ4e72yCSK2OWCwBiPhcAvTarUCnCwVGTVft3bQcAWM0j8Ho8GDEMorJ2HlS5BRAIRWhp3I9TV26IeO9o6H76FgDgsJrg87hhGR5CUfVcpOfkgy8Qoa+tCZbh2O0ygIT4C1pSb4sJXo8bluFBFKvnIT07HwKhCMb+bjTv2UFJb3I8RPVdbv8hOBE8YrHC4/Fi0DiCeTWVKMhRQSQUYH9TCzaceSrlZ4xZ72ncRwCxtuHY6+DohbCRpp0AAK/dhIDXA49lCIqSORBl5IHLF8Jl7IFjoAOOIX3MujBdhv26/fA4bHDZTPB7PLCbBpFTEbRTPIEIw90t4PL44AmEKJy9ZHKBDLC/rR82pwcmuxserw+DZgfmlmYjP1MBEZ+Hpm4DLI5jkdtU/JEDPVbYPX6YnF54fASGbB7MyZcH+yMeF7pBO2SjfH+6x4tkQA9wrP747MFxrsc8BPmo+mPTHwZXJI1ZdiLqT7d2H9wOG5xWE3weD2wjg8ivmgtldgH4AiFMA91wO+2QZ1D3QYBgAKjd7YPJdWx+Zk6eHHlKEYQ8Lgasbhyi8RmNrfvhddngsZng93rgMg8hY5SvMdJ+COBwUHTiGtrumQyO9SO2Uf1IBQpyVRAJBNivbUUgQIQWJ+iGXYhgYZlBuAbaIMoqBIfDgbR0PrgCIfwuBzg8ATwjwW2iPEkauKJgh3ig14bdeisqVRK4fQGosyWhSJGaHCl6zG4c7reH5P/7oAFGh2/MtQ6PHxIBD1a3D7ajE1VAcFHE2rwbkrxKBLxuSArUQX2cNhABHwIeF9zDiY+mP9JnQ6VKCg6AlTUqCPlcWJxeDNk8WFKajv09FuiHqR+GGYkDBw9Bra4Gh8PBurWrQwsQPp8PZaUlGB4ZgcPhRP/AAG33nCr7Wnvh8vhQVagCBxwsrMyHUMBH95AZP+l6sPZENVpdRvB4XHQejUxhmX4c/vIDlJ9wOnweN7LLa8EXCGEe7AaXx8dQRxPKFi6HUCyFQCzBSG8HgBjatdcNZ387gOj2pcvkxip1Bvb12GB2+pJYCtODFn0vivOyweFwsKi2MrT40DtohN3hwqmLZkPb3o0RixWWONKlHRmwozJLAg6As6ozQgsQvgABi8uHM6vS8dFhI4Do71Um5IHDAdqMQRsaS32xaL8fo4up4zAUBVXgcDgoWLwquIhu6IGyeBZs3k6Yu3WT6uELEChOF6PN6ESfxR2zLm5jT0iPcP1dn8WDPIUw1N+RE4aphGHXv+E7mmc43DPyRMcc4khlOLpfHz0GSAVaew0oylaCw+FgYWUBhHw+7C43+owW6AdNWH9yLRr1AzjQFozQjVZPeFwO3L4A5ubJ8GOXFQ6Pf5K7H38c6bOgMlsGDgdYOSsbQj4XNpcPO9tGUDcvF52GoD2ZrBwNdi8AIE10bLL7SL8dlSoJOBzgLHXG0fGWDyMOH7z+AE4tV2JHmxn+GNPLTEZrvwlFWQqAAywoy4aIz0O30Qan2weVQoKMo+eJ6HqHo8oJ1/Z7zW7YPQFUqyRoHWVXAGCw9RAyi6sADgeVJ68O9aXZZbMhkEgx2HYoahnqR4LBQYM2L86sSscP+mAkNR02y+Hxw2D3Yk6eDHu6rWPLq/EgiiuC49OTz1gLgVAEp8MGvkCAjKxsHN6zCwIhtV1fnY374HW7kFtaBYCDktqF4AtEGO7vhmmwD8U188Dj8+GwjMT0jD67CRyeEB5z5B0sdNKlO4i80mqAw8G8ZWsgEIowMtCD9Ox88Hg8SBXpMekd8Lrh6m+D+6jeMfWFzbsBAHsP6+Bye1BdVhzs82erIRIKYLLaYHe6YDSZUV4cXwBTrOXtHukHQN1PTBVi0ZvKOMXa/CMAwNJ+AAGvC7K8YP1OK58fXHwY7oPf7YTXNoKA1300TVNwoSOSPSHHS4kaSwy2HUJmUTUADipPWg2eQATLYDcAIKOwAtahXlgGumEbHgBosslUOdQZTMnE4QCrF1VAxOfB5vZiyGyHujALR/RD0PYEF5Rjm2c4tlPncJ8NVdlScBDAyppMCHlcWFw+ODwBVOfI0Gt2o9/sik12DLY3XBuxdBxAwOOCND/YXyjK5ocWrwJuJ9zDvfB7XJDklsNjGogqO9H1p6/lELJLgv3FrKVrwBcI4bSZ4XHakVtRi/b9O2EbHoLbSd0etBmcKEoXgwNgfqECQh4XfRY3+s1uFGeIYXb6kCmld/f8SMdhpBUE30PhUV/DPtQDLo+HnNlL0bfvKww27oZrJHXmZ6iw94gOLpcH1eVFwX5kTjVEAgG6+gbxw4EmLDthHgR8PixWOzq6mdnRxi5EsLDMIFQnnzthBd9jGoAwPRdiVREUlYsBACOHtgEIrrL7CaDf4oFSwoPHT6DX4oJSzEe3yY3ZeTIMWDwhWefOU4Wu77d6kCkVoHnIiSUlChAEUJIhRtdI0DmTl80HAn54RvrBkylB+DxwjfRCoFCBKxBBVjwntG01UcwvSsP8wrQJn/O5HNTmByPRVlRl4SudgZb7nbBoQSg105j78XnIzwtuPy0tCaYl+Wzrl7Tckw4WVhaEUl+Mhs/l4pTaYCqM0+dXAAC+3MvMdj4W5plz1vmh1EwkHB4fiqNbtAGgbPFpAADB0eicWNq1rT0YgUFG8ezrsY2xGQNWL5aVK9FtcuO0ynRsa2YXsybjF2tXYFFt5ZjPeDwe8rMzQ38vmacGQRD4che1LfWRoq34XM6Y9Clz8mVjrg/3XpeUiGG0e5F11GEgo8ps7fvgGemHx9QPgTwTzr5mpM06FT7bMJSzV4TukTvnVOTOmRjlyeXxIZSlIXfOqQh43THooYDR7sVpleloHXLGrIvPcSwtX7j+rt/iQXG6KNTfDVo9E3RNNqqTzw210XDPyBMf28UWqQxH9+t5itRKP3X+ivkT+qf+YStqinOw7Ojfp9SWQiIcu3MmWj1RiPk4szpjxtmi+UXKUGqm0QxYXLjopGCanpq8oG0YbSf29digHXRCKeGBIACPn8CCAjmMdm8olVOsduXs2Vm0lfvPT1FjQVnOmM94PG4oLdPJ6mDKpn6THU5P5AXwcG0/kl3JVy+c0I8Cwb5ULE9DYe2JcNmCdiVSXTTafVhSooBSzEeamI8lJcE2SofNIttyt8mNxUXH2v6Ck1dgwckrMB7jYB+ycvKRW1gC42AfjIP9MZU9SWntwlBqptHw+HwoVcGxxayTzoA0LT2mZ+QKRJDkVYX8FiapOWE5ak5YPuFzLo+P9Ow8ZBWUhFJVxfJusk+9AH1fvBHz9cpZQQu2aI46lJppNDw+D/nZWQCAviFjXM8Ya3nb9cHFs2j2syQjkJJ9BBCb3lTGKWmzlgIA0srnh1IzjYbD449JqwMA5vZ9AKLZk+B4KRFjiTz1glBqptFwebxQWiZlbtAfJQgCHXu2MapPOBZU5IVSM42mf8SGmsJgvS/OVqLLENylF5M/0hFMqzO/UIH5hRN38PMt7lDqn6J0MQ71WmOWPZntHdNGjt4jrWx+KDXTaMbXH7dpYFLZiaw/RTULQ6mZRsPl8ZF21K7PXnY2AEC76wvK8s9dkDvh/fC5nGPvJkOCr3Tx2bxwZFUuQGblxPfA5fIhyQy+h9Jl5wAAevd+Rdt9E8mi2epQaqbR8Pg8LF0cTLtcnB8cL32+40dGdGAXIlhYZhDO3uCksKNHG9z9IJaDL02Dz26GOLccPocZYlUxvLZgNFizwYlqlSSU59rjCyBNFDyw2U8E0xQMj4pWDnd9aYYo1AEOWj3oMgWjCZx9zZDkV0OYFdzmG/B5wJOkIeDzAL7gNmufLXpUGt00DwZX6bUDNhhtHshFfKRJ+DA5vCjOlMDs9KFCJcXw0ci+qdKo1YX+fbixCUNDBigUCqQr0zBiMqGspAQjJhOqqyoxZKSvg50quu5jCzFN+kEYLHbIJSIoZWKYbE6U5KTDZHOhsiALRgt9u0dYEotRf6x+DnU0wWEyQCiVQyxXwmU1IaOwAm6bGcq8Eoz0dQCIsV07g4PzZkOwbvA4iGgzxtsYlvBo24NRa42tegyNmKGQSaFUyGCy2FCSnwOT1YaqkuDk7NCwmZLs5qFgxLN20AGj3Qu5iIc0MR9mpy90GF5FliR0XSzv1eIOvlNn39GFSi5vTJ0RZZeGosnIa8idDgBg7tLCZTZAIJFDIFPCYzNBkV8Bj90cSi0Ya/2iosvoPimW/o7UIZUI10ZHP6Pf7YTLEEy7EUsZplr71HYPhf7dpB/EkNkOhXRi/+QPBFMzxVpPgNR7VqaZOCbiIU0iGDUmOrawMLqujy5LErIsm49O0I+1K0fHW2LeUbsiDtkVABh20DPm0vUGFzSaeoZhsDihkAiglIowYnejRKWAye5GZV76mGvDMVnbH21XyH40Wh/qpWizyDKk22aRcvUt2tBvOpobYTIOQSpXQJ6mhNU0gsKyKtgsJuQVlY65Nhb62o/Z8d7WRlhHDBDLFJAqlLBbRpBTXAmn1Qyv+5ivMNkzJsJf6Gs/9py9rY2wjBggkSkgUSjhMI8gp6QSDqsZXnfs72b0eIhKX6htC6b0aWzpwNCwKdjfp8kxYraitDAPJosVVaVF6B8yhq6NFap6T8c+AmBunGLvbQndw9ajhddy1NeWpcFnM0OSF/S1Japi+OzWkC7JHkuMHu8bOppgNxkgkiogkqdNGO8DgMNET0AeFXQ9QT+4qcsAg8UBhUSINJkYJpsLJdlpQfudnwGzPRiMQsUfaSH7owH7sXGuhH+sP3J6UaGSwuryxS6bgu0l5xXI+hOqOxI5BNI0eO1mSHPL4T1adwDAOdQdk+xE1J/BjmD9GWhvgm1kCCKZAhK5Eg6LCariCrisZmTkB+uO3UR9PqNlyB4ch4R8EAHMTi+Kju6GqFBJMULTOAE45m+YurRwmw3gS+QQjvM15DnB9+C2pM78DBVG9w2hvkQuhVIxsS8xjlDzG2OGYGFhOe7p7OwkxBIpASDm/7ic2K8Vi0SERCyKXT6HG7tsiZTo7OxkvHykEjGl8pFKxHHr1dnZSUil1N4HAEIqZb4sJtdbEofekqTqzUKNzs5OQhJH/aTSrqnYl6m2t+MZJm1JPHaR0nulUF8oXRtH/aIiXyQSU+rvUqXuUh0HUCnDVHjGePonyvUkRZ6Vaai2farlmOhyDz5PHGMXydixC9Vy4XCZ6xOZsllcCjqHyinG/oTyuIKi3WfCX0iE3pTGTgy+Hyb9xFSxm5THNQyOU1KhDOMe7wOEJEE+aVz2m0F/hLHx4gyqP7HWHartdarPyPTzpAKpNAfFIYgkJXljYWFJKHq9HgbDsQiG7du347bbbsOiRYuQnp6OJ598csz1fX3BfHD5+fmTylapgocPjZYfDbfbDZFINPmFR2WXlJTEdO1UGF8+b7/9Np577jmoVCqsWrUKt956K616jb/fyy+/jLfffhtffPEFPv30U9x7773YsmULsrOPHRCUqLKIxni9X3zxRbz33nvYunUrPvnkE9x///347LPPkJWVFbomFfRmoQb5nu+991589dVXeOedd1BUVDTmmiNHjuDyyy/HpZdeiptuuomSzaBiAwC2DkVjfJvUarW46KKLsHz5coyMjOAf//jHhN/EWp7jZTc1NeE3v/kNli1bBovFgjfffHPM9UzVgXByb7vtNjidTvz44494/vnnccopp8SlB1VdqPZ3qVR3x7/PaKRiPz0Zer0eQ0NDuOWWW6DT6fDee+8hPT19zDVvvvkmnnvuObz66qvIy8uDyWRCenp6zHUlVZ6VacbXFYPBgLVr1+LMM89EY2MjPvroo9B3fX19lMpx/PVDQ0M4++yzceaZZ6KpqQkajWbCb+gcc7W1teEXv/gFnn32WSxcuBArV67E7373O/zqV7+a9J5TaUN/+ctf0NTUBK1WiwcffBDr168PfUfVZjE1Rg8n9/7770dzczOamprw8MMP4+yzz54gP57+BADuu+8+tLa2orGxEY888gjWrl0bVZdoMNU2w+l9zz33QK/X4/Dhw3j00UexZs2xQ0uZ7H/Gy9br9TjvvPNw+umno6urC//3f/834TdTeT906Z1KdpOpfjDce//jH/8Ig8GA/fv346mnnsLpp58et/xE1e9XX30Vb731Fr788kts3boV99xzDz755BPk5IxNa5fIdzpex3/+85/YuHEjsrKysHr16gk+OpVyHX/tP//5Tzz//PPIyMjA2rVrccstt8Qteyq296qrroJcLsczzzyD+++/H4cPH57QvlOx/gQCAaxYsQLLly/HF198ge+++w7CcWcJUdFltPxAIIDly5djxYoV+OKLL7Bz504IBMfOhqDjGcc/z4EDB3DFFVdg6dKlcLvdeOWVVyb8JpXsWyyEs4HXXXcduFwudu3ahVdeeQWLFy8e8z0jz0jrsgYLC8u04amnniIkEglxww03EHPmzEm2OinHDTfcQMybN49Yu3Ytcc455zB+vxNOOIH45S9/SRAEQQwNDRFcLpd49dVXGb/vVFm4cCFx0UUXEQRBEAMDAwSHwyFef/31JGvFQgfvvvsuAYD4xz/+EfGaRx55hOBwOMTXX3+dQM1YovHee+8RAIh77rmHSE9PJwKBAG2y33nnnZDszMxM2uTGQ3V1NXHrrbcSQqGQ2LhxY1J1YUkdXnzxRQIA8fHHH4f93ufzEcuWLSPKy8sJi8WSYO2mL19//TUBgHjooYcIAITD4aBN9rZt20KyORwOrbLD8de//pWQSqWE0+kkCIIgVq1aRaxZs4bRexIEQSxfvpy46KKLiIKCAuLPf/4z4/eji1NPPZW45JJLiNzcXOK+++6jVfYpp5xCXHrppUROTg7xwAMP0CqbSU466STiiiuuILKysogHH3wwaXpoNBoCAPHAAw8QIpGI8Pl8SdOFZSLz588nrr32WkIulxNPPPFEstWJiZNOOom44IILCIIgCKPRSPB4POKll15KslZjuf7664l58+YRa9asIc4991xaZV933XXEggULiNWrVxPnnXcerbJjxWg0Elwul9i0aRNBEATx/vvvEwCItra2pOhDha6uLgIA8eijjxIAiCNHjtAmW6/Xj5Hd2NhIm+xIvPnmmwQA4s477yTy8/MZv1+yKC4uJv7whz8QHA4nYfNPzB+hzsLCkpJotVqo1WrU1NSgpaUFfr8/2SqlFKPLR6ullgOXKr29vfjpp59QX18PILjqvHTpUjQ0NDB636nS3d2Nffv2oa6uDgCQk5ODU045JWw0I8v0Qq/X47rrrsMvf/lLXHzxxRGv++Mf/4gVK1bg4osvhslkSpyCLBHRarVQqVRYuHAhTCYTpQjHydDpdMjJycGCBQswPDxMq2wqeL1etLW1oba2FlVVVYzbaJbpQXNzM37/+9/j2muvxbp168Jew+Px8NZbb2FwcBD/8z//k2ANpy9arRZcLhfLlgUPzG1ubqZVNo/Hw7Jly0AQBFpbW2mTHY6GhgasWrUKYrEYAFBfX49t27bBarUyel+dToeamhqo1eppZbNGj4d1Ot3kP6DAdCwTgiAS6iNEQ6vVQiqVYsmSJXC73dDrqZ0JwcIcgUAAzc3NmDVr1rSp3wMDA9i9e3fIH83MzMSyZctSzq9jsv2lQtv+5JNPEAgEsGHDBgDAmjVrIBAIUu49hIMssxUrVoz5O9VlR7tncXEx5s6di76+Plgslsl/NM2w2+3o6urC7NmzUVZWlrB6zy5EsLDMUHQ6XaijZQevEyHLR61Wo7W1FT4fc4etffTRR+ByuWO2u9fX1+Pzzz+Hy+Vi7L5TRaPRgMfjjdG7rq4On332GdxudxI1Y5kKfr8fl156KRQKBV588UVwOJyI1/J4PPzjH/+AxWLB9ddfD4LN9ph0Rtt28m+6ZavVatplU6G9vR1+vz+kS7L0YEkdfD4fLrvsMuTn509INTmeyspKPP3003j11VdTfsE/VdDpdCgrK8PcuXNDf9Mpu7y8HHPmzKFd9niMRiO+++670EQbEBy3eDwefP7554zd12QyYXBwcNrZLKPRCKPRyIjeRqMRw8PDjC1yMMXQ0BDMZnNKvEudTofq6mrMmjUr9DdLatDd3Q2n05kS9SRWPvroI3A4nDEL+fX19di6dSscDkcSNRvL6AVMuoMpR49zkxWoqdFocMIJJ6CgoAAAoFAocMYZZ0yL8YpOpwOfz8eSJUugUChoHyvw+XycdNJJkMvlCWlT4/0eOoMwUoWWluAh6WSbSpStYhciWFhmKKkyoZSKOBwOdHV1hcrH6/Wis7OTsftpNBosW7ZszLkKdXV1cDgc2LZtG2P3nSoajQbLly9HRkZG6LP6+nrYbDZ8/fXXSdSMZSo8+eST+Oabb7B58+Yx7zYSpaWleOmll/Duu+/in//8ZwI0ZIkGadsrKyvB4XBojWwhZVdVVYX+TgbkfaeTg8/CLI8//jh27dqFzZs3Qy6XT3r9VVddhbq6Olx11VUYGhpKgIbTG7LtZ2VlISMjg5EFTpVKhfT0dEbb85YtW8ZEmgJARUUFZs+ezWi0KTl5MdpmTYeFeyb1DmfHp0OZkHqPnrRJlt5k2ykpKYFQKGT7whRiOo5TNBoNli5dOuZ8wrq6OrhcLnz55ZdJ1OwYdrsd3d3djPjoNpsNPT09IdkejyfhgZperxdbtmwJZRsgSdTOvami0+lQUVEBoVBIe73X6XSorKyEQCBIWJuaCfNlybJV7EIEC8sMZHRHyw5eJzJ+ZRhgruNxOp3YunXrhAHH7NmzUV5enrLRDw6HA1988cWYqEIAmDt3LkpKSqbF9lGWifz000+45557cMcdd0w4VC8av/rVr3DxxRfjxhtvRHt7O4MaskSDIIhQpJhEIkFJSQlttmt0OgqpVEqrbKqQ6SgKCgqgVqvR0dHB7sKawezduxf33Xcf7rzzTixdujSm33A4HLzyyivw+/249tprp8UEaDIhnXEOh8PI5MJo2UymBWhoaMCJJ5444SDhuro6fPTRRwgEAozcl3ym6upq1NTUwOFwoLe3l5F70Qn5nqurq6FWq2G1WtHf30+LbLJMqqqqoFarYbFYMDg4SItsJtHpdOBwOKisrIRaraY9BSJVXWpqasDj8VBVVcX6cimETqeDQCBAWVkZ1Go1+vv7Uzqti9vtxmeffTbBH62pqUFVVVXK+HWkj87E5HAi/f9I7NixAyaTaYJ/vWHDBni9XkZ37tEB2Z8DoH2nG2nvACRkwpxMr6ZWq6FUKpGbm3tc2lidTofMzExkZWUldCcQuxDBwjIDGd2Js4PXiYxeGS4uLoZYLGasfL766is4HI4JAz8Oh4O6ujpoNJqUnCD54osv4HK5Iurd0NCQknqzRMZut+M3v/kN5s+fjwceeIDy7zdu3IjMzExccskljKYyY4mMwWCAyWQKOQF0DtQHBwdhsVgYkU2V8ROXicgrz5KauFwuXHzxxZg7dy7uu+8+Sr/Ny8vDyy+/jH/961/YvHkzQxpOf3w+H1pbWxlp+0zKHk+kSFMguBAxODiIH374gZF763Q65OfnQ6FQTKvISp1Oh6KiIshkMtr11ul0KC4uhlQqnXZlUlpaCrFYnFS9RweVAcntk1kmotVqUVlZCT6fPy3Sumzbtg12uz3l/dHxPrpIJKLVJgHBhdeSkhJaZceKRqNBfn4+Fi1aNOZzcudeqgYokoxeiGAqaIEJ2eHo7u6Gy+U67m3s+HJlOhMICbsQwcIyAyGjkEavWE+HQ7QShVarDa0Mc7lcVFdXM1Y+Go0GFRUVqK2tnfBdfX099Ho9Dh48yMi9p4JGowlF9o2nvr4eHR0dOHLkSBI0Y4mX3//+99Dr9Xj77bchFAop/16pVOJ///d/sXPnTjz66KMMaMgyGeFsO90O2uhopGT1G6Ojosj/s33YzOTuu+9GS0sLNm/eHJfdOv/883HJJZfg5ptvZs/KikBHRwe8Xu+YNkdXe2tvb4fP5xsjmylH/9tvv4XZbJ4QaQoAS5cuRWZmJmOTPKNtVnl5Ofh8/rSwWeQuOCB4tgqXy6W1TyHLhJQ93cqkqqqK9hSIsTI6bRbA+nKpxvjJPSC1xykajQalpaWhc4BGU19fj56eHuzbty/xio1Dq9UiKysLWVlZ4PF4tProWq0WKpUKmZmZoUDNRL+zhoYG1NXVgcudOE1bX1/P6M69qeLxeNDe3j7GTxgYGIDZbJ6ybLfbjfb29jH2juldRuS7TwW/h0m0Wm1SfCp2IYKFZQai0+lCHS1w/K7wxsto5whgrnwIgoBGo0FdXV3YA4FPO+00yOXylNkOSzJa73CcccYZkMlkKR+1wXKM//73v3j55Zfx9NNPh11cipVly5bh7rvvxgMPPIBdu3bRqCFLLJApI8gzHOjcYqvVakPpKIDgYLW5uTkpDtHoiaDs7GwolUq2D5uBbNu2DU8//TQeeeSRsJMnsfLcc89BqVTi8ssvT1kHP5mMjkAl/z88PAyj0ciIbIPBgOHh4SnLHo9Go0FBQcGESFMA4PP5WLduHWPjrdE2SyAQoKKiYlrYrNHjYaFQiPLyclon/cgyEYlEKCsrmzZlQupNdwpEqnoAY9uOXq+H0+lMuC4sExldT1I9rQtBEKEJ8HD+6PLly5GWlpYSft3ocgXo9dET5f9Hu79Op4voX9fV1WFoaAi7d+9OmE5UaGtrg9/vH7NYANCzY6ytrQ2BQGDMogBdsiNBplcrLS0N3XO6nGUUK6PT7gKgfZdRNNiFCBaWGUi4TpwdvB6DyUHOaA4cOICurq6w0XlA0DFbs2ZNSgz8RrN371709vZGHCiJxWKsXr065RZQWMLT39+P3/72t/jZz36Ga665Zsry/vznP+PEE0/Eb37zm5Q/VO14Y3TKCCBou9xuN7q6umiRXVZWBpFIRLtsKlitVvT19YVsNBM561lSH4vFgssvvxynnXYafve7301JllKpxJtvvomvvvoKzz77LE0aHj/odDqIxWIUFRUBoHcCQKfThc57oVv2eBoaGrBhw4awE21AMNp0//79tO+MIc/uScS4kk5G58cmoUtvJmUzid/vR0tLS0rordPpkJ2djYyMjJAebJrC1MDtdqOjoyMl6kksHD58GJ2dnRH9UaFQiLVr16aEX8f0QkQy35lGo4FIJMLKlSvDfk/u3EuF9xCO8Yuj1dXVYz5PVdnR7kmmVyPvbbVaMTAwwNg9E43RaByT0pfMBMIuRLCwsDBCuI6WHbweI1z5dHV1weFw0HqfhoYGKBQKnHbaaRGvqa+vx65du1LqAL+GhgakpaVhxYoVEa+pq6vDzp07k3aAH0tsBAIBXH755eDz+Xj11VcjTtBQQSAQ4H//93/R39+P2267bepKssRMONtFfp7KsqkwPh0F+e9UdfBZmOHWW2/F8PAw3nzzzbApDKhy1lln4bbbbsNdd92Fw4cP06Dh8YNOp0N1dXWonMkdV3TZFaZkj79Pc3NzxIk2AFi7di34fD4++ugjWu/d29sLh8Mx7WxWT08PnE4nI3p3dXXB7XZPuzLR6/XweDxJjZomSZU+mWUira2tY6K3gdSu3w0NDZDJZDj99NMjXlNfX48ffvgBfX19CdRsLOOjtwH6gikjLRgnMlBTo9Fg5cqVkMlkYb/n8XhYv359ygUokuh0OsjlcuTn5wMAFAoF8vPzaRsrKBQK5ObmAkjMLqPx9YHOHR6pwvgFHvLf7EIECwsL7YTraNkc28cwGo0YHh4OO7gnD/mmC41Gg7Vr10bNa71+/XoAwMcff0zrvaeCRqPB2WefDYFAEPGaDRs2IBAIpJTeLBPZuHEjPv30U7zxxhvIzs6mTW5VVRWee+45vP7663j//fdpk8sSnfG2nTxsjw7bPl52aWkphEJhwgfkyRw0s6QG//73v/Hmm2/i2WefRVlZGW1yH3nkEVRUVOCSSy6Bx+OhTe50Z3T+YACQyWQoKiqipc2Nly2Xy1FYWEh7e9ZoNBCLxREjTQEgPT0dK1asoH2SJ5LNamtrg9frpfVedBJJ79bWVvh8Ptpl19TUoKWlZcqymWT8OUzkv+lKgUiF8X0ym6YwdRh96DFJKqd10Wg0WLNmTWg3bTjWrVsHLpdL+0ItFQwGA0wm04QFHmDqPjopO1mBmiaTCdu3b4+YbYCkvr4eBw4cSMkzrchFotFBbXSNz0l7N1o20+fijLexFRUV0+Yso1ghnyWcrWIadiGChWWGMTQ0NKGjValUSE9PZwevCO9kMLFQMzAwgN27d2PDhg1Rr8vJycHJJ5+cMtswe3t78eOPP046UMrLy8OSJUtSRm+WiRw6dAh//OMfccstt+Dss8+mXf7ll1+OCy64ANdccw26u7tpl88ylnApI8jD9qZq230+X0TZiR6Qa7VaZGdnIz09PfRZTU0NBgcHMTIyklBdWBLPwMAArr76apxzzjm4/PLLaZUtkUiwefNmHDx4EA8++CCtsqcz5K6F0dA1AcCk7NE0NDTgrLPOglQqjXpdXV0dvvzyS9jtdtrurdVqwePxUFFREfqspqYGfr8fbW1ttN2HbrRaLQQCwZjFvpqaGvh8PrS3t9Mim8y9DQTH3V6vF52dnVOSzSQ6nQ4ikQjFxcWhz2pqauB2uxM6MRguMpzD4bAHVqcIZPR2Xl5e6LOamhpYrVb09/cnUbOJDA0NYefOnZP6oyqVCkuXLk2qXxdugYcuHz1R/n8kPv30U/h8vkn9a3LnXir610z25+PtHcDshHm49GrT6SyjWNHpdCguLoZEIgl9VlNTA71eT3smkPGwCxEsLDMM0niOjiZgc2wfI9wgJysrC5mZmbSWD7lTgNzxEI26ujp8+umnKRGh+fHHH4PL5U47vVnG4nK5cNFFF6G6uhp//etfGbkHh8PByy+/DKlUissuu4w9BJZh9Ho93G73hMPG6bDtnZ2d8Hq9jMimyvjDBEk9gGNpm1iOTwiCwNVXXw0ul4tNmzbRkkpuPCeccALuvfdePPLII/j+++9plz/dsNvt6O7uZqTt22w29PT0MG5XyEjTaGmZSOrq6uB2u/HFF1/Qdn+dToeKiooxu0inQxqd8fmxAfr01ul0qKqqYkQ2k5B683i80GfJ0HtoaAhmszkl+mSWiYSL3k7V+v3JJ5+AIIhJFyKAoH38/PPP4XK5EqDZRCL56BkZGbTYJA6HE0oPCCQ2UFOj0WDBggVjFjnDoVQqsWLFipRdiIhkk6a6E4hJ2eEIl15t9D2PF6L5VHRnAhkPuxDBwjLDIDvaysrKMZ8fb4Y1XnQ6HUpKSsasDAP0l09DQwNOOeUU5OTkTHptfX09bDYbvv76a9ruHy8NDQ049dRTkZWVNem19fX1sFgs2L59ewI0Y6HCXXfdBZ1Oh7fffjvqVuypkpmZiX/84x/46quv8NRTTzF2H5bwqS7Iv+lw0JiSHY8u4/VIxKF1LMnnjTfeQENDAzZt2hRT3xkvd911F0488URccskltEbGT0dIRzRc229ubp7SAjOTskezZcsW+P3+SSNNyXur1Wpa0zOFs1n5+fmQyWQpbbPC6V1YWAiJREJLnzJedlFREcRi8bQrk5KSkoSnKUylPpllIuHqCZnWJdXeT0NDA0466aQxuzciUV9fD4fDga+++ioBmk0knI9OVzClTqdDaWnpGJ8oUYGaPp8PH3/8cUyL5UDwPdC9c2+qWCwW9Pf3h7VJdrt9SmeLmM1mDAwMhJVts9kY2WU0U2xsOFuVqEVTdiGChWWGEWmivaam5rgyrPESziAD9HY8brcbn332WUxOMQDMmzcPxcXFSY9+cDqd2Lp1a8x6L1y4EIWFhUnXm2Usn332GZ555hk89thjmDdvHuP3O+uss/D73/8ef/rTn7Bv3z7G7zdTCZcyAgjars7OzikdtqfT6SAWi8PK7ujogNvtjls2FcKdcQQE88oXFBSwfdhxTHt7O2699VZcccUVOOeccxi9F5/Px+bNm9HT04M//OEPjN4r1Qm3ixYItn2XyzWltHvhUmGQfzudTvT09MQtezQajQYLFy5EUVFRTNfX1dXho48+om0hJJzNmg47kcPpzeVyUV1dzchCBCk7lVMLjT/TBKAvBSIVogWVGQwGDA8PJ0wXlomEqyepmNbF4/Hg008/jdmvmz17NsrKypLm14WL3gboC7hh2v+PxPfff4/h4eGY3wO5c2/r1q2M6kUFckcyE5PakWQzeXj0+MOxR9+TjnOSUoFAIIDm5uYJbYquXUaTwS5EsLDMMMINjgB28EoSLgchcCzHIR3b/7Zt2wa73R5z5AOHw0F9fT0aGhqSesjZtm3b4HA4KOldV1eXdL1ZjmEwGHDZZZdhzZo1uOWWWxJ234ceeghz5szBRRddxHjOyZmKVqtFdXX1mJQRQNB2TfWwPVI2lzt22EjKZnr7LsnAwAAsFkvYPozNjX384vf7cdlll0GlUuGZZ55JyD3VajWeeOIJvPjii/j0008Tcs9URKvVhtJTjoaO3Nk6nQ7Z2dnIyMigXTYJ1UhTIBht2tfXhz179kz5/h6PB21tbdPOZrndbrS3tzOit8vlQkdHR0TZqTRROxqn0wm9Xh/VR0gUWq0WZWVlE3a0MjkxxxIbJpMJg4ODKVFPJuObb76B1Wql7I9qNJqk+HWT+ehTlZ0sO93Q0ICcnBwsWbIkpuurq6tRU1ND6869qRIpsKCiogI8Hm9KZRjuQGVSNlOHR5P1YXwKUPIso46ODtrvmWjIlL7hAiUSUe/ZhQgWlhlGtBV/8vuZCrkyHKl8RkZGYDQap3wfjUaDkpISzJ07N+bf1NXVob29HY2NjVO+f7w0NDSgvLwctbW1Mf+mrq4Ora2tKTXwnqkQBIGrrroKXq8Xb7755oRJZSYRiUR4++230d7ejj/+8Y8Ju+9Mgknbnir9RqSt0uRnM7n/Op556qmn8O233+Ktt95CWlpawu57ww03YM2aNbjiiitmbJBGpLZfWloKgUDAiF0pKysDn8+npT3v3LkTIyMjMUeaAsCyZcugVCppifptb2+H3++fdjarra0NgUCAEb1bW1tBEMS0K5NIqcTIzxK9IyKcHmR++1Qtw5lApOht8rNUejcajQZFRUVYsGBBzL+pq6uDXq/HwYMHGdRsIn6/Hy0tLRHLdXh4OG4ffTLZRqORFv8/EhqNBhs2bKDkl9G9c2+q6HQ65ObmQqlUjvlcIBCgoqJiymOFvLy8CeM/oVCI8vJyxnZEpILfwyTJ9qnYhQgWlhlEtI6WzbENdHV1hT3sFaCv4yEIAg0NDaivr6d00OaZZ54JqVSatOgHgiCg0Wgo671y5UpIJJKUitqYqbz66qv4z3/+g1dffRX5+fkJv39tbS3+9re/4fnnn2fTdTFApEEzedjeVCOXw8nOzs6GUqlM6EJEuHQUALOH1rEkj4MHD+Kee+7B73//e5x22mkJvTeHw8Hrr78Op9OJG264IaH3ThUipcLg8/morKycUtuPFN1Kh2yShoYG5Obm4sQTT4z5NwKBAOvWraNl3DKZo9/X1wer1Trl+9DNZHr39PTAZrMxIrurqyulcp+TkH1oJB9Br9dPKQUiFSL1yXK5HIWFhTPal0s24Q5UJlGr1SmT1oX0R+vq6ij5daeffjrkcnnCx/FM+uh6vR4ejyfqxDO5wEQ3bW1tOHLkCKXFciC4c6+/vx8//fQTI3pRJZJNAqY+qR1pHAIwt4su0vMUFRVBIpEcFwGWOp0OQqEQpaWlE75jFyJYWFhoJVpHK5PJUFRUNKMHr9GcI7qijA4fPozOzk7KAw6xWIzVq1cnbQL3wIED6Orqoqy3RCLBypUr2YnnJKPT6XDbbbfh6quvxrnnnps0Pa6//nps2LABV155JQYGBpKmx/FGtJQRU81F7nA40NXVxYhsquh0OpSVlUEkEk34Tq1Ww+FwoLe3NyG6sDCP2+3GJZdcArVajQcffDApOhQWFuKFF17Ae++9h3feeScpOiQLgiAiLhYAU3NUI533Qofs0cQTaQoEo0337Nkz5XMqdDodpFIpCgoKJnzH9ATXVIiUHxs4pne8Kfl0Oh3S0tLCHjg/VdlMotPpkJGRgaysrAnfqdXqKadAjJVoQWWkLjPZl0s2kaK3geC78fl8KZHWpampCW1tbZT9OpFIhDVr1iQ8wIxJHz0R/n8kNBoNhEIhVq9eTel3p556KtLT01PGv2Z6ISKR9i5aejW6zklKBXQ6Haqqqiak9AWmvssoFtiFCBaWGUS0jpb8/HgwrPGi0+kgEAjCrgxLpVIUFxdPuXwaGhogk8lwxhlnUP5tfX09vvvuO0Y7hUhoNBrI5XKcfvrplH9bX1+PHTt2zNjUFsnG4/HgoosuQlFREZ5++umk6sLhcPDaa6+Bw+HgyiuvZKPXaSJaqgtgarY9WjqKqcqmymTOCHkNy/HB/fffjyNHjmDz5s0TcqEnkl//+tf45S9/iRtuuIG2A5SnA0ajESaTiZG2PzQ0BLPZzKhdaW1tRWNjI+WJNgBYt24duFwuPvrooynpQNqscBHHqWyzmNSbLZOpES2ojNQlFctvphDprAEgtep3Q0MDJBIJzjrrLMq/ra+vx65duzA4OMiAZuEho7dLSkomfDfVYEqdTgeRSITi4uIJ3zG9y6ihoQFnnHEGFAoFpd/RuXNvqsQSWNDW1gav18uIbLp3GUVLr0Z+ngpteKok26diFyJYWGYQWq0WIpEobCcOBI3O8bDVLF60Wm3ElWGAngOrNBoNVq9eHdekyvr16xEIBPDJJ59MSYd40Gg0WLt2LYRCIeXfbtiwAX6/H1u2bGFAM5bJuP/++7F//37885//hEwmS7Y6yM3NxRtvvIGPP/4YL7zwQrLVOS6IljKC/DzewWQsshPVb0Rz8MvLy8Hn82d0H3Y8sWPHDjz++ON44IEHsHDhwmSrgxdeeAFSqXRGLaBGOnySpKamBh0dHXC73ZRlk/Yoml1pb2+PSzZJvJGmAJCZmYlly5ZNOdo0ms1SKpXIzc1NSZsVTe/MzEyoVKq49Y4mOysrC1lZWSk5yRNt0iY7O3vKKRBjJdb+PlVyx880otWTVErrotFosGrVKkgkEsq/Xb9+PQAk1B9l0kfXarWorq5m1P8Ph8Viwddffx3XYjkQ3Lm3d+/epAdI9Pf3w2q1RrVJPp8P7e3tlGX39fXBZrNFnTCPV3YkJhv7HC8LEdF2vJKp5Zi0VexCBAvLDCLaFiwg2FE0NzfP2MFrtMEjMPWOZ2hoCDt37kR9fX1cv8/Pz8eSJUsSHv0wODiIXbt2xa13YWEhFi9enBJRGzONb775Bo899hj+8pe/UMqRzTTr16/HjTfeiNtvvx1HjhxJtjrTnmgpI4Cg7TIYDHHtStLpdMjMzIwqe2hoCCMjI5RlU8Hn86G1tTWijabjQDyW1MBqteLSSy/FKaeckjKH22dmZuL111/HZ599NmMWUMkzWcjUFONRq9UIBAJxpaPRarURz3sZLbutrY2ybJKGhgaceeaZkMvlcf2+vr4eW7dunVLef6bHlUzBpN6xyE6FidrxRFtASWSawmjR20Cw/JxOZ9InJ2cik0Vvp0paF6PRiB07dsTt1+Xk5ODkk09OqF8X7ZwAgHmbxMQ7++yzz+D1euNeiDj77LPB4/GSnp4plowbo6+jUzZZJ+h8PzqdDvn5+RF3qdTU1KC7uzslzzKKFZfLhc7Ozohtiq5MINFgFyJYWGYQsXS0TqczJXJXJoNYymcqCzUajQYEQYQiSeKhrq4On3zySVzbG+OloaEBBEFg3bp1cctIht4zHZPJhIsvvhgrVqxImcm80TzxxBMoLy/HRRddNKWoV5boKSOAYwP4AwcOxC07EonKc97Z2Qmv1zstJ/VYqPH73/8eAwMDeOuttyIGTiSDtWvX4vrrr8cf/vCHlJwopZu9e/eGInjDMZXJhb1796K4uDji7tCppgUwm83Ytm1b3BM8QHDc4nQ68fnnn8f1e6vVir6+vmlns8xmMwYGBhjRe2RkBENDQ1FlV1ZW4tChQym182hgYADDw8NhDyAmSeRCRHV1dcRzT1Ip/c9MQ6fTwW63R1xgBVKjzX/00UcIBALYsGFD3DLq6uqwZcsWeDweGjULD0EQOHLkyKTlGq+PzrT/H4n//ve/mDNnDsrLy+P6Pblz77///S+telFl79694HA4qKioCPt9QUEBpFJp3AsRXC43qmyJRIK9e/dSlh3tnrH4PYcOHaLtnommsbExakpfgHlbxS5EsLDMEBwOB7777ruoqVnIXI8PP/xwotRKGTo7O9He3o6MjIyI12RnZ8PtdmP79u1x3ePKK68EAOTl5cX1eyB4aJbVasUNN9wQtwyqXHXVVQAQ9mDBWKmsrITZbMbNN99Ml1osUbDb7VizZg3MZjM2b96cUpN5JBKJBG+//TYOHz6Muro6WvN7ziQIgsAnn3wS1baTjuK9995LWfaWLVuiyibP1Hn//fcpyabKf/7zHwCIGAUKBPMEf/PNN3C5XIzqwsIcf/zjH/HKK6/gb3/7W8RI/GTyxBNPoLCwEGvXrkVXV1ey1WGU5557LuozZmdnQygU4t1336Us+/nnn4der4/4fU5ODgQCQVyygeC5Hn6/H3Pnzo3r98CxSMtzzz03rt+TaUuijfnS0tKwb98+xneUUYHUOz8/P+I1CoUCe/fuhclkoiT7448/nlT2t99+i71796Kvr4+SbCZ58sknAQDd3d0Rr1EoFPjpp59gsVgY1eW///1v1D65qKgIHA4H7733HqN6sExk48aNABDVbsrlcnz//fdJjaa+7LLLAAQnceNFrVbDbrfjmmuuoUutiOzatQvd3d3YtWtXxGuys7PhcrmwY8cOSrLb29vR2dk5qf/vdDopy46GXq/H5s2bIy4oxopQKMTHH3+M3bt306QZdf785z9HXTjmcDjIyMiIyya9++67SE9Pj5gamsPhwOl04r777qMsOxx+vx9bt26NamPJtnv//ffTcs9k8MADDwBA1P5KJpPhyy+/ZCyIlF2IYGGZIfT398Nut0fdQv/zn/8cs2fPDg1QZhLk4kJ/f3/Ea8io7XhzYi5YsAAXXnhhXL8lWbt2LbKzszF//vwpyaHCvHnz8Otf/3pKMtatWweVSoUFCxbQpBVLNB566CH88MMP+PWvfx3xTJhUYOHChTjllFOwdetW/POf/0y2OtMSp9OJgYGBqFErixcvxgknnEB5AdPhcGBwcDCmiBhyoYAp/vWvfwEA+Hx+xGtaW1thtVphMBgY1YWFGQiCwBNPPAEACZnciAeZTIbbbrsNnZ2dx/3C+m9+8xvcdNNNEb/ncDjweDz49NNPKcu+6KKLcMstt0S9xuv1xiUbAE488UQUFhZiyZIlcf0eCKZRWb16NU499dS4fh9LlKper4fP50upyMp///vfABBxhx1wbIdaU1MTJdlkPxFN9hNPPIGFCxdOKWiHbn71q19h1qxZuOSSSyJeQ56X0tLSwpgePp8PXV1dUXcgEgQBgiCmfNA6C3VuuukmnHXWWbj66qsjXtPW1gaHwxF1UYtpFi1ahPPPP39KMlatWoWcnJyEnOE0f/581NbW4qGHHop4DRmAQi52xso333wDAFEP3ibT89F51mF6ejoyMjKmPI648cYboVQqpxQsOFVuueUWnHvuuRCJRBGv6enpiWux5Ouvv46aVpbD4eCiiy6KOlahgtVqhdFojOr3rFixAgsXLkzZcWosXHXVVZg3b17Uw+qbm5sxMjICs9nMjBIECwvLjOG5554jRkZGkq1GSuL3+4knnniC8Hg8Ea8JBALE008/Tdjt9gRqxsJCnUOHDhHXX3894XQ6k63KpAwPDxNXXnkl0dfXl2xVpi2vvfYa0dnZyZjsrq6uqNe88847RGNjIyP3J9Hr9cRrr70W9Rqj0Ug8//zzjOrBwix/+tOfiB9//DHZakTF7/cT999/P7Fz585kq5J0Pv74Y+L7779nRPZHH33EmOxEYDQaiY0bN0a9xuFwEE899VSCNIqNWOyo3W4nnn76acqyDQbDcWujbTYb8cwzzzB+n82bNxMtLS1Rr/nwww+Jffv2Ma4LC3XMZjPx97//PdlqHHeQPrrD4aD0O9L/93q9UWU/9dRT08KnSlV27dpFaDQayr/7/PPPiW+++YYBjSLz8ssvE729vQm9ZyoyMDBAvPTSS4zJ5xBECiVgZGFhYWFhYWFhYWFhYWFhYWFhYWFhYWE5rmBTM7GwsLCwsLCwsLCwsLCwsLCwsLCwsLCwMEbkRLssLCzTEr1eTyk/NnkYXLSD40ajUqlSOud8LFApI7fbHTXn4WioliUQe3lSfa8zQfZM43guy+P52eiEKdtF9Xqqsqm+K6aecybWmVTgeGzfx8szMT1mZGoMlWrjLabsJ9N1hklbCyAm2Uy+S6pQffdMvksqulAp73h0YTlGPPaBSh2n491MV78ulfojOsa5VJ+HyrtnUjYd90uVsQLJVN8P034PU6RSnzYBxpI+sbCwJJzOzk5CLJESAGL/j8OldL1YImUsF3kioFpGXE7sZUPlWvI/qUQ8aXl2dnYSUinF9woQUunk74px2RIxI2Uy0+js7CQkcbwnSQzvKdkwWQePJ6i2Jy6Xmm2n0hdQtXVU2jRlu0FB7+nef01Hjsf2Hayjkjj6NklKPVM874Zq2+dyOIzIZnK8FU9fS0mfFLFZTI6HxSIRIRGLaC8P8j8JA+USjw9FpUyY7AfFIhEhEsduk9i+MD7i8rMZrCd06hhLnWDaZ2Sy/VG1NVMd58YzToh1jBBPWU2lzcfTL1IfK1C4lhv7uOJYHZSEeT+x21gORZ8qFWws02O8qdoqdkcEC8txhMFggMvpQNXVz8FrMcDvskGYWQjXYDtEWcUQZebDbeyFSFUEn90Mu/4Q+ra8APWVT0NeUjupfEdvC5o23QSDwZASq7zxQJZRzukXQ16xOGLZ+GzD8Dkt6P7X47h6aR7SRHwUKoVoH3ahOF2E/DQRes1uFKWLYHb5cKjXjhe+68PT589Cbb4iJl1aBu246f8dmbQ8DQYDHA4H7njiZVTWzotJdmdLEx6+7cqYZf/1hTdRVFYBoXDyVfA2XSPuuOHy2GQ7XXj6vGrU5stj0rtl0IGb3tdO6zrGBAaDAU6HA8tufQGy3DLwBMJJf2Pp1mHHszekfFmSdfAfm/8XFRUVMUViNDY24tJLLk75Z6MTsj3FYo++bDbh/f0GvPry81gwd86ksj/57HPc+8DDyFt9NfjStKj9hunglzDsfD8mPYbtPiilfNz8QUvM74p8zotPzMHiQnlU2Ra3D49/2R2T3nb9QfRteXFG1ZlUgGzfj298BbVz58f0mxZtE3537WUp+66CddSJ5y85EaVZUgj5vEl/0zxgxY2bf0ypZyLfzWt/uQVlBbkQCQVRr9d2dOO39/495rZPts9nL1mKOUXpUWU395tx/RvfURpvbbxwNkoyJRDyJ880TGW85XQ48Mt7X0ZmQRn4gsn7o6bvP8fnrzw87WwWU+Phg312vLgjGLE6WZmQ7/LEm56HNKc0prGNtacZP268kfZyIctj7jVPI61k9qTXD+7/Ei3/91dKZUK1H6Ra3rG8S75MiZZXbk4pWzRdIOtI2Xl/ADgciLOK4B7pA08kg0CmhN/jBJcvhFhVDJehC36PC86+VnR//goj9SSajvOveRpppZPXYwCw9TRj3wuT+/dkn/HnpzZBPTs2f7S9uQn33nxFTLJdTgcWXvcMlDHobe1pxp6NN1Luj+i2d5HGueQ4YeMNdZhTmjPp8+h6jLj27/+J6d2TZVV99XMQ55SCy49uNx29zWieQpsn+8WVt7+ItLzJ7fRIVzO+ePJ6yu+GSv//wh2XY25lcUz6azv7cPXDr4Z5P9R8qrnXbYQkpxTcSZ7f3tOMQy8lf76MbK/3P7MJNbMnH39v/2ILXvjr/QmzVewZESwsxyF+txOy0nmQFc0GTyiGMC0bkrwKiHPKkTH/THC5PMDvhbSgGgDgHu5GwOuGtX0//C47hGkq+JxWCNNzweHyEPC64B7uRXAB9PhAVjof4uwSKMoWgi+RwzXQDkl+JcTZpUirPhHS/CqIVcEOrlolwdLyNCglAtTmyeDyEZAIuVhWoUR1jhQKMR/FmWIAQLfJDbcvgP3dFtjdPqjkAlhdPuQqhOBxAbPTi6Z+G8xOL6pyZJR0HujtgsftQtP+n+Cw2ZCelQ27xYKsnDxwuTwY+nvhtNvh83pRWjWLkmzj0CA6WnRwu1w4tPdH2G1WZKqyYbWYkZ2bBy6Xi4G+Hgz09aBCPfmi1Wi6zWSZWGF3+6GSCWF1+YNlwuHA5Q2gqd8Oj49AVY6UkuyZhtM0BGtfCwJeF4wte+Fz2SBWquB1WCDJyAWXy4XD0A1rXyt8Hney1aXE4MAAdDodXC4XfvjhB1itVuTk5MBsNqO0tBQymQyDg4NYvHgxamup1cHjicnsUZZMiBx5cEKxS98Fl9uNH37aC6vNhpzsbJgtFuTn5YHH46Gntw+HG5vgdnkAAJL8aqTVLIVAqoSsqBaE1wWuUAJl7TJIC6ohyixA+tzTY9KjQClCTpoQXl8gruecny9DSaYYCwsVkIv5aB92oTJbgtJMMU4sScPcAhmK08Ux6c0TiKA66RwaSp8lXnq7u+B2uXBgz4+w2WzIUuXAarEgJzcfXC4P/b290B45BI/bjaoaav1XshiyutA6ZIPb58c+/Qhsbh9UChEsLi9y08TgcTmwOL3Q9lvg8vqTrW5EBo1mtOj74PJ48NORFlgdTmRnKGG2OZCnygCPy0X3gAEOV7BPmaztg8NBuUoSap89wza4vAHs7TTC5vJBpRDD4vQiVykBl8tF97ADrqN2YjLZaWI+yrKCcodsHrQZHJOOuXrNLvgC1MavtuEhGLpa4fO40N24B26HDfIMFVx2CxRZueBwuTB0t8I02AOfO1gudNosvjQtYTZrsvGwOKcs5vFwpkyIc+aqQrInK5PstOCEjss8BFt/K/xeN0Za98HnskF0dGwjzgj6Il6HBQ4j876Iy9CDgNcNc1tQD2GaCj6HFaKjPpHHYoDL2BtSY7IykQh5Y8qECrG0h9GyY/FtJPnVdBTTjIYnkkKcWQBJTikEsnTIi2uhnLUUktxyZMw9HXxpGkSZ+eBL0yAtqgFA/V1OFaexB36vG6ZR9dg7qh57HRb4PS4E/D7IC6nViYEePdwuF47sC/qLGVnZsFnMUB31Rwf7etHSeAg+rxfl1dT6c6ehGwGPG6bWffA5bRClHbUDpN52Cyz6RgQ8LgCx2aRMuSBkeydrI7KSuTHbO7I/qlZJIj5Pl8EMl9ePva19sDk9UCllsDjcyM2Qg8florVvBDanBwRB3a55LQa4+lsR8Lpga98Hv8sGgSILPocFQmUOOBwuvBYDvFYjZdnhcJoGYepphd/jxmDzPnidNkjSs+GxWyHNPDpn5PPB741trFCgFKEoQxx6N5NdL+LzUJAeDA7oGhiG2+PFnqYO2BwuZKcrYLE7kZelBI/LRZ9hBC63ByarAzWlkVM4xWK/T69MBwC4LUNw9Lch4HHD0hasn2P6Bw4PHvMQfG47LeVNF/3dXXC7XTi879j8js1ihio3D1weD4P9PdC3tYDD4QCIrU3lyicPGpgMdkcEC8txiLxsPuSlkSMVRJmFAABb50EAgDA9F4TfB2GaCh7zIGxdhyEvnoOA2wG+LB2uIT0CPk9o4eJ4IJYyGvr+QwDA/AI55hVEjugvVIpwsNcGAMhNE8LnD0AlF2LQ5sHhPhvm5Mvh8PiRLhFg2O5FjkKIFVWZONBjpaRzVk4+fF4fMlQ5GB7qR2vjAVTWzofT4UBaegb0rVpI5WmYvegk6A7tpSRbKlegsKQMw4ZBKJRKaA8fAADkFRTB5/NhsL8XckUalpx6Go4coCY7VyGEz08cK5N+O+bkyeDwBJAu4WPE4UVZlgQnlqThQC+1Mplp8CVyyHNK4DIbIJQpMdJxGAAgUxWACPjgto0AHA4U+RUAOMlVliJyhQJlZWUYHBxEeno6DuzfDwAoLi6Gw+GAwWDAwMAAdu7cSSlP5/FGLPZo0BpcWMjPz4PX60VuTjb6BwZx4OAhzJ83F3aHA5mZGbDZ7SAIAvyjEd0x2UVjV8x6AAjZRqrEIr9rZAhAbHqT/R1LcsjJy4fX54MqJxeGgX40HtqP2rkL4HDYkZ6RCYfdBpfTAafTCeE0ad9yER/FmTIYrG6kSQQ40mMGABSmS+ALBDBsc8Ps9CIvTQyldOpOG1OsOGEOrA4nvD4/cjLTMWA04WBzB+ZVl8HudCFDKYfN6UJH7yCA2Nv+h/uD7TM3XQKfP4BshQSDFicOd49gTlEG7G4/MqRCjIgF6BtxxCybtClLKzJgd/uijrlGHF7wOBzMyottVyZJxaLlcDts8Pt8kGfmwDo8gL6WQ8ivmguPywFpWgaU2QUI+P3IKVPHrHsq2iymxsOxXL+0NA0AkD37VPhcNhB+L0Tp2XCZBmHuPAJl6Wz43A4I5OlwW4cR8LqgKFRTfURKiDJyEfB7IVJmw2MagrXzCBSls+E/qoffZYePY4c0pzimZxxfJlSgKjuV6tXxTPqspfC77CD8PgjG+c7+o76z320HTywD4QuOx5isJ+EQZ+QG21NaNtymIVg6jyBtVD32uezwWIchSp88Wn88qtx8+HxeZGbnwjg4gOYjB1E9e17IH3U6bFBmZMDjcYMviL7TbqLeeaH25x5nB4SyoN4iZTZEyuCiDdX+KNHj3LNPqIbN6UF1QRZMdhd2NXVjyGwHl8NBfqYCJ9UUoa1vGDVF1BeheGIZRKpieC1G8KRpsOuPHH2GoE/o6G8BAChrTqEsOxwCiRxpucVwmg0QydJgaA/6oPLsAhB+P7xH2wSHG/QpqL4bKm1k3akLYHO4oC7Jg8nqwPeHWjA4YgmWqyod86pKcLBFj3WnLsQ+XWdEmbHck9SPL5ZDrCqCx2IAX6qE7Wh5i7MKQPh9sPe1gCsQQVmxMKK8ZJCdlw+f14vM7BwYBwegO3IA6tnz4XTYoUzPQI++HQUlZfD7fQBiK5N08eQ7gSeDXYhgYTkOsXUcgLV5NyR5lQh43ZAUqMEVCOF32kAEfAh4XPBaDHAP9wAAFGXzoSiLvGVLnBVcuLB2HEiI/onAsOvf8FmNEcuHJz5mgP990ACjwwe3LwB1djANgMXlg83tR02OFPt6bDC7jhrvwjTML4ycmqnw6Kp/PNTMWwT13EURvz+rILYtiuGYs2AxZs+PLDu/MH7Z8wsVmF/ATJnMNLIqFyCrMnJblakKQ/82tk6v9nrCCSdg8eLFEb8vLi7GsmXLAAB79uxJlFopRzh71Gt2g8flIEcuRJvRiT5LMBrphEULsXjRwoiyiouKAAA2W3BgP2nf4XXDPdwXVQ9fgIDF5cepZWn4scsKhye+SPBI8mtypOgxu2FzH5M7md4+hwWuIX1cerDQw7yFizF3QeT2XVB0rI85tH96tO/5xRmYX5we8fvCjGM7/A50mZhXaAqsWBw9hVtRrgoSUXAx5UCvDbv1VlSqJGPaZ5/FAy4HE+zQgpIszC/Jiii7MFMGpTQ4WRVpvOX0BuAPEJiVI8UP+mMBC0srMqLqTY4vqAZ+AEDFomVRv0/PDdrPvZ/9X0Td47VZAa8bjt5myjrHQ9TxsNcNBI7taov07h0ePyQCHqxuHw73H4sGDVcmNrcf/gABDgdjrlXNPjWqntKj4xtTG7NjG2X5fCjLI4+zJEd9op4dHwCIXCa9Zjfy00QTyoQKkWRbXD4ECIA7rgyp+DYsUyN91tKo34/3nam+y6kSaz0GAHM7tTZVO38xZs2L7DPmjfIZmw5SC15Lr5iP9Gh6k3bgqM7hbIzD48fBPjtOLklDq9GJXMWxQIDJ7B1PdCxbQaT+KEAA6WL+BNmRWDanNOr3Rao07G/rm1TOeCZbVMk+aWzg6VTJrlqA7KoFkfXJDt5PIAkGkkWzjXZPAIuL5NjXYwuNFcKVd5/FA68/gOJ0MdqMztB8CwAsX1gTVd+inMxJnymSjqP7qTajEwCQVj4faVHmy/KOtilLis2XzZ6/GLVR5njI9trVHly4itSmTE4f8tNEwffg9EWUFyvsQgQLy3EI2THZ2vfBM9IPj6kfAnkmnH3NSJt1Kny2YSgqTwh1TNaOAzA374Y0rwoBrwuywhpw+EL43Q7whBK4DF1wGbrAFR0/aXNUJ58LBPwRy4cnPjZxfu48FeYVBDtL7aATSgkPBBHcle30BqCSCUDu+j/QY8HuDhOqsqVw+QKoyZGFDPiIw4tZeXI09duQIaUWIQIA2oN7cfCH71BcoYbH7UKZejaEQiFsFjNcTgdKKmtwYPe34PEFcDmpDWYP79+DPbt2oKwyKLuyZjaEIhH6urvgsNuw4MST0dx4GGbTMLweDyXZB3qs2N1pQZVKcqxMeBz0mN2QCXlQSvjoNbuRIaFeJjONjm//BbfFAL/XjfTiGnAFIjiMvQj4vJDnlMDcpQNXIACXL4TTZEi2upT46aefsOPbb6GuqYHL5cKcOXMgEonQ3d0Nr9eLBQsW4NNPP4VCoYDD4Ui2uknj3Hkq+Amg3+JBv9WDTKkAA1YvlpQoYLR7cVplOlqHgoPmd//vAwwZjMHynF0LkUiI7p5eSCUSZGSko79/EDa7DT29QQdIXjY/ZBd5MiUInweukV4IFCpwBSLIiufA1r4/pEcku7iwUI4ukxtnVmdgW/MIrc9ZkhEAQQAlGWJ0jbhj0ltZszQ4ocaSNP77wXswGgxwu11Qz5oNoVCE/t5ueLxeFBWXwjA0AMPgIMoqKpOtasz8a083jDY33D4/avLSIORzYXf7YLB50G9yYvXcPOzrHIHT64fXn7qpLfc2teK7/Y1QlxTC5fGgtqIYIqEAXf0G+Px+zFeXobmzF939wT6FjJbb12Mb0z77LZ6wduhfP3TAYHPD7fWjJj8dIj4XZqcHTo8f1Xlp2KEbgM8fnOyOZleqVBJ0mdxYUhIcn0Uab/WYXOBzOchRCNFudCIvjfoOmx7tPnQc+B7ZJVXwetzILZ8FvkAI00A3RFI5vG4XXDYzlNkFod/QabNkxXPgc1im8lpjJpbxsMsQXMidXyAPPaNSwoPHT6DX4oJSzIfX78PsPBkGLMfGiOHKpHnIGaon5LsZaTsAo3Y35PmV8HvdSCuuAZcvhNdhAQIB+D0uuMxDkOdXMF4e5vYDGNHuhqygEgGPG/KiGnAFQrgMPZAXz4K9twU+lx2u4f5Jy8TimlgmVIgmm8sBanPHyo7lXXrMg3QU04xmvN8sLQzWV/dwD0QZBfCYB4M7IkQyuI4Gb1B9l1Ol97t/w2Mxwu91Q3G0DjsNPeDw+JAXVMHa1YSA1w2uQAS3eYiS7MYDe7Bv93coPeozVqhnQyAUYqC3C61Nh3HqWWuhPbQ/mBrIS+2ZTG0HYGzaDUVBZUh3Hl8Ih7EHacWzYO3SggABnjC4yBypz8hVCMHncaCSCZAuOTblSZf/r5IJQrLJXcjh2NfWh++bulFVkAm314dZRdkQCviw2F0IEARcXh9k4vh2TNo6DsCi2w1JfnBBW3p0YcVt7IW0sAa2zoPgiaRH02tPnaGW/eg7sgvphVXwe9zILK0BTyCC12mHyzaCzNJa9B3+Htb+Y/1FuLEC6bN0m9xjxgrRyltw9F2S8y37tJ3YebAZ1cV5cHu8mFVWAJFQgJ6hEeRnpWNg2IwAQeDkOdHHlLG0y+/bg32xpf0ATNrdkB4tb3lRsN27jD0QZebDOagHOBx4rcO0lDddbPnXexgxDsFNzu8IRXDYbTCbRpCbX4j25ibYLBYM9gfryWT99mmV6XH7dqNhFyJYWI5DnH1Ho6i4PAiPrs4GfB6IskvhNgZ3Qdg6D4auI3dEWNr2wTPSB/dIPwSKTNh7m5FReyqEymykz1qK4YNfJeV5mMDZ1wxJfnXE8vG7nXAZglszmw3BDpLHAQqVYwcL5OBj2O4FcGxHxL4uC/osbvRb3MiUCdA8aMeS0nR0j7hwUlk6BixuOCkOzsgdEU37f8RQfy8M/b1QZmZB36bDoqWnY3hoAKeu2gAA2P3N55RkkzsiDu79EYN9PRjs70VGZhYMg304adkZMAz2Y/HJwWi1HV9Rk03uiNjXbT1WJlIBBq1uLClVwmj34qRSJQasbjitqZtHOxUoW34esirnw9C8FyZ9E4TydBABAgABvlgKWW4JlIVVIAgCffu/Sba6lCB3RPzwww/o6elBT08PVCoVenp6cOaZZ6K/vx8XXnghAOCzzz5NsrbJo9ngRLVKErJFHl8ApRnH0jEd7LXB4g5GqvzqF+dj8aKF+OHHPTh0+AgyMoL1xeVyISc7G2KxCCctOQFNTToA4e0iT5KGgM8D+IL9hs9pCekBRLeLB3ttGI4zamay5xy0etBlcsWuty21HIOZxs/O/yXmLliM/Xt+gK7xMNLSM4BAAAQAmVwOt9uFhSechMH+PrhczmSrGxPnLS7C/OJ07O0cQVOfBUqpEARBgACwrDobXUYHVtTkgCAIfKOlNtGTSBbNqsTCWRX46UgLegeH0Ts0jCxlGvoNI1i6YBa6+oZw4pxqmKzBAIdwbT+aHTpvSRn8AQJ9Jif6TQ5kykVoHbDgpMpsdBntWLegGF8d6Ykom4SU3Xx00iLSeCs4tkiH0e7FiqrMuMZbhTULUVizAF2Ne+Ad6oNlqA8yZSYsxgFUlqrhMBtRMudEWAz9od9MV5tFdTw8/hnTRDwQBAE/MdHmT1Ym5Ng5o2I+0ivmY6RlL5wj/XAN90GoyISttwWqOcvgsQ4jU30CXCMDcB3NDc8UZCS5qXUf3J4+uEb6IFRkwWUahKygCnxpGtKrFsN2dMcK1TKhAlXZsbzLkI/IEjejMwlY2vbB3t0EgSwdBEHAOdQJeXEtvNZhSPOr4HVsA8BsPQlHwannhuqxtasJArky6C/4vMF0zIpMyAuC/oLhEDV/gdwRcXhf0B8d7O9BeqYKhoF+nLXh5zANG3H62noAwPfbqPmM5I6Ikda98A8fswWukQEoCqogUGRAIFHAZRoAEFufMZntnYr/P2j1hK4Lx8KKfCyoyMeell70DVvRN2xFZpoUzb0GnDyrGEaLAxlyMQZGqKflIgNPreMCTz2mAUjyKyFMz4U0vwo+h5my7HCM3hExqNuL4c4miOTK4PkWBED4fVAWVECWmQuA+liBynzLwppSLFSX4qemdvQNmdBnMCFLKUff0AhK87IgEQmgSo+cjYEklnZJ6kfuiDC37YN7uO9o3cyC2zQIaX4VRBm5kOVXwZBi82Vnn/dL1M5fhEN7f0RL02GkKTOOvjMCUpkchSVlKKuqwbuvvwggNj+TDlvFLkSwsBxHqFQqiCVStLxyM6XfOXqDW7E4XC5EZMfsdUOSXQKXoRtAMPrDa5366meyoVpGXA5w8wctMctvGQw66lwuJ5QSwO0jUJIpxcBRA06mCCCvjZXOlqagbB4PuUe30Xk9bhSUlGOgJxh9QJ4NYR6mFg3fpmsEAPB4POQXlQAAPB4Pikor0NsdlE2eDTFipDah0jIYjF7ncoHCo4dMuf0BlGRKjpVJr3XMtSzhsXQHJ4w5XB6kqqIx39kGg22VTMlENcIp2TQ2HquDJSXBOuh2u1FRUYHOzmB+TzIl09DQ9NrtQQcqlQpSiZiSPWrUBusLj8dFSfHY+tLXH5xA27N3H7x+H8DhMmYXpRIxVKrY8t9Sfk4Keosl0pj1YKGXFm2w/+LxeGPSMAFAT1ewfZMpmchrU53mgWC/xeNyxqRhAoABS3DSg0zJZLCl7o4cbUew7+BxuSjOC7YPj9eL8sJc9BuC4759TW3w+fwQCfiU2j4A6PotUOeloSgzWEYenx+lKjkGzMFJhwN6I3x+AiI+N2XGW4Od2qOyeaEUTD6vB1kFZTD1ByeperTB3WF2s5GaTUwRm8XkeFgsEoFD4XprT3BynMPlQRryQzyQ5ZTCORSsn2RKJvJapiAXGDhcbii9TsDrhjSnBM6jPpG5/QCIgJ9SmTDZD4pFIhApUq9mAqTfDIytJySjfWf4feDxBYzUk2jYeo7VY4lqrH6j6zEAeMzUxtTtzcf687xR/mhhaTn6j/qjZEqmEYr+6GhbQOod8Hkgyy2F46jefrcDLtMg+BTK9ajQhI9zdT3Bg6J5XA6KVMEzcTxeH8pyM0KLDwMjttB1VHD0Hisr0aiFFXHO2MBTuiL0R7qO2V4OlxtKxURiHQz2jY7hAfAF1N4N1fLWdgZ3GvG4XBTlBlMwub0+lBVko98YXHjpN5rRbzSHrh1NPD6VfVSbEofqphuSnBK4jMG6aek4AK8ltQKf2ka11/zCkjHf9R2d42k8sBd+P3N9Wjg4RDxHtLOwsKQser0eBkNsnX5fXx/Ov+AXcFOIPhRLpNA2NYYmCqcjVMrI7XbHdDBuX18ffnHB+XC6qE02SCViNDZpo5anXq9HbW0t5XQ0UqkUjY3R3xXjsmfVwOGkFr0WS5nMNPR6PWbV1sJJ8T1JpFI0TfKekg2TdfB4I1bb1dfXh1/84hdwOinYdrEY77//PvLz8ye9Nla7SKJSqSi9JyZsdDx6sEyd47F9B/u2WXBQaF8AIJVI0NjUlDLPFO+7idVWBMdFF8Dpin0MIBaJ8P4HH0SVzfR4K56+ViQW4wMG7CfTNotJWwtgUtnx+CEAIJFI0USzL6LX61EzqxYuZ+zvXiQS44MPYnvvTPaDsZZ3vLqwBImnjpAIRWJ8GENdmeq7iVfHWPx7pn3GePSOtVwBajZsquPceMYJsY4R4imrqczfpFK/GM+4AgCkUgkaG5smvKNYfarpOF8WT3ul4gtO1VaxCxEsLDOc8Ub4/vvvR1tbG/r7+/Hzn/8c11133Zjr2cFrZMaX5e7du3H99dfjZz/7Gfbs2YP//Oc/E34Ta3mOl71r1y7ccMMNjMjeuXMnbrrpJtTX12P//v3417/+RZvs7777DjfffDPq6+tx4MABfPjhh3HLnmno9XocPHgQF154IVatWoX77rtvwjWff/457rzzTjz66KNYs2bNtCnL8fVk27Zt+P3vf4/169ejubkZ77777oTfTJdnSxZ6vR4bN27EE088gddffx0LFow9YK69vR2/+tWvcOmll+LGG29ky5OFMUa3b7IPePfdd1FdXY2f//znWLhwIe69994xv0n1+kg+04EDB3DFFVfg8ccfx8qVK0Pf33PPPaG+WSAInn+Uis8UzhFfuXIlVq5ciQ8++ACbN2/G7Nmzx3xP5TnGyz/rrLOwcuVKfPjhh/jf//1f1NbWxiU7nN5nnnkmVq9ejQ8++IBW2QRB4IwzzsDatWvxwQcf4O2330ZNzdhDMlPx3U4XwpX38uXLUVdXh/fffx//7//9P1RWjs3zzVR5j9flrbfewquvvora2lpkZmbiscceS4geLKnL+DrS0NCA+++/H59//jnS0tKwatUqXHjhhbjhhhvG/C6RdWW8jj/++COuvfZanHPOOdi9ezc0Gs2E38RrH3/44Qdcd911OOecc/DDDz+goaGBNtl0+tHJYPzzbNy4ER9//DHS09Mxe/Zs3HPPPWOun0rf+txzz2HLli1QKBSYN28e7r777rhlx3K/O+64A729vdi8eTP27t2Lq666Cm+++SbmzTt2eHai7PT27dtx2223ob6+HocOHcL7778/4Td0Pv8777yDZ555Bl9++SV4PB7OOussXHvttbjssstoux9djC+rv/3tb/j222/B4XBw6qmn4vbbbx9zfUL1JlhYWFhGsWzZMuLiiy8mTjvtNOJXv/pVstWZ1rzwwgsEn88nXnjhBYLL5RJut5s22c8//zwhEAiI559/nuDxeLTKfu655wihUEg8//zzBJ/PJzweD22y//73vxMikYh47rnnCD6fT3i9XtpkH+8EAgFi5cqVRFFREWEymSJed+GFFxKZmZlEX19fArWjl8cff5yQy+XEk08+SUilUsLv9ydbpWmHXq8n5HI5cf3110e85v777yf4fD5x4MCBBGrGMpO56aabiNLSUiIQCBAEQRC33347kZeXN23b+Pr164nZs2dP0P/w4cMEh8MhXnnllSRpFh9Go5EAQLz++usEAOKf//wnbbINBgMBgHjzzTcJAMTbb7/NmOx33nmHNtmDg4MEAOKNN94gABDvvfcebbJZJtLT00MAIN566y0CAPHhhx8mTZff/va3xAknnEBce+21xMKFC5OmB0vqcv755xOnnHJK6O/f/OY3xIIFC5KnUBhefvllgsfjES+99BLB4XAIp9NJm+yXXnppjGyXy0WbbNKPfvHFF2n3o5PB+eefT6xcuZL45S9/SZx++um0yv75z39OrFq1irjwwguJM888k1bZ43G73YRCoSD+8pe/EARBED6fj8jMzCT+9Kc/MXrfSDz11FOERCIhnn32WUIoFBI+n4/R+61evZpYs2ZN6O/6+nritNNOY/SedLF+/Xqirq6O+NnPfkasW7cuqbpwE7PcwcLCMl3QarWoqalBTU0NdDpdstWZ1mi1WlRWVmL27NkIBAJobW2lVXZVVRVmz54Nv9+P9vZ22mXPmjULPp+PEdm1tbW0yz7eefnll/HFF1/gtddeg1KpjHjd888/Dz6fj+uuuy54GNU0hLRDs2bNgsPhQE9PT7JVmlYQBIEbb7wRaWlpePTRRyNed+edd6K6uhpXX301/H72oHgWZiEIAg0NDairqwOHwwEA1NXVob+/Hz/99FOStaPOnj178PHHH+Puu+8GlzvWpZo9ezbOP/98PProo/D56DuAlGnIcd+iRYuQl5cHrVZLu+yFCxciNzeXVtmkrMWLFyMnJ4fW8Ssp68QTT4RKpaJVb5aJkOV70kknQalUJrW8ybGIWq2GTqdDIBBImi4sqYfH48Gnn36Kurq60Gd1dXXYv38/urq6kqjZWLRaLcrLyzFnzhwQBIGWFmrn/Ewmu6KiArNnzwZBELT6ujqdDpWVlaitraXdj04GOp2OsTmW0baKaZu5fft2WK3WUL3n8XhYv3592J02iUCn00GtVmPWrFnweDzQ6/WM3ctisWDbtm0T2vyOHTswPJxaZ0OEI5H1ZDLYhQgWFpYQw8PDMBgMUKvVoUH3dJ3ITAXIjlGtVof+ZmUzJ/t4pr29HbfffjuuueYarFmzJuq1KpUKL730Ev7zn//g7bffTpCG9MLWk6nxwQcfoKGhARs3boy6aCUSibBp0ybs2rULL774YgI1ZJmJHD58GJ2dnWMcuGXLliE9PT1pDuxUePjhh1FVVYULL7ww7Pd333032trawqaWS1VIW1tVVRUaB7KydeBwOKisrKRdNstEdDodeDweKioqkl7eo8ciDocDvb29SdOFJfX4+uuvYbPZxvRpZ599Nng8Xkr1adPVrzuefIFAIIDm5ubQ8/T19cFqtdIi2+/3o6WlJSS7t7cXNpuNFtnhaGhoQGFhIRYuXBj6jEy53NnZydh9I5HIevL555/D6/WOafMbNmyA3+/Hli1bGLsvHXg8HrS3t4fKqqOjA243tbO26IRdiGBhYQnR3NwMACEDZbPZ0NfXl2Stpi9k5ENeXh7kcjkjg7P8/HzIZDLaoxbVajUKCwshlUoZ0buwsBASiWRaDyoTRSAQwJVXXgmVSoUnn3wypt+cd955uOiii3DzzTdPS8eZrCdlZWXg8/lsPaHAyMgIbr75Zpx77rk477zzJr1++fLluO6663DXXXelVAQfy/FHQ0MDZDIZzjjjjNBnfD4f69atC5tXOpU5fPgwPvzwQ9x1113g8/lhr1m4cCHq6urw8MMPT5tIap1Oh8LCQsjlctqjNnU6HYqLiyGTyRhZLCgpKYFEIqE90k+r1YZks7uFmUen06G8vBxCoTCpCxEmkwmDg4NQq9WhM0HYd88yGo1Gg+LiYsyfPz/0WXp6OlasWJGSCxHZ2dlQKpW0296amhrk5OQgLS2NdturVquRl5cHhUIxrdtfV1cXXC4XIxPmXV1dcLvdY2wVOadDN+F2tgLA2rVrwefz8dFHHzFy32iQ9aS4uBgikYjRSP+GhgbMmTMH5eXloc8KCwuxePHilB/HtrW1IRAIhOpJIBBAW1tb0vRhFyJYWFhCkB1idXX1cRF9kExGrzpzOBxanSm3242Ojg5GZLtcLnR2djIi2+l0Qq/XQ61Wg8vlorq6mq1fMfDCCy9g27ZteO2116BQKGL+3d///neIRCJce+2102pnk9lsxsDAANRqNQQCASoqKth6QoE777wTdrsdGzdujPk3jz32GBQKBW666aZpVVdYphcajQarV6+GWCwe83l9fT327t07rVKwPfzwwygpKcHFF18c9bq7774bTU1N+OCDDxKk2dQgJ6wA0L4zlpXNEgvhyjsZjA7OYoMiWMYTaUIWCPZpX3zxBex2e5K0O4bX60VbWxsjfh2Tspn0o5MBqTsTCxGjZVdXV9MqezxNTU1oa2tDfX39mM+VSiVOO+20hE/G22w29PT0QK1Wg8fjoaqqirFn9/v9+Pjjjyc8OxBMz7RlyxZ4vV5G7k0HTNbBeGAXIlhYWEJotVoUFRVBJpOhoqICPB5vWnf6yWT0qjMAWqPoWltbEQgEQlEPdMpuaWkBQRAh2XQO/FpbW8fIrqmpSXp+wlSnpaUFd9xxB2644QasXLmS0m+zsrLw8ssvQ6PR4B//+AdDGtIP6fyz9YQ627dvx6ZNm/DYY4+hsLAw5t8plUps3LgR//3vf/Hhhx8yqCHLTGVoaAg7d+4M68ClYiqLaOh0Orz33nu44447IBQKo157yimnYNWqVXjooYemxeQ1Gd0KgPadsaMnmGtqamC1WtHf30+b7NF9hsViweDgIO2y1Wo1TCYTDAYDLbJZJjL+XQ4NDWFkZCQpegBggyJYwtLY2Ij29vaIk5JutxtffPFFEjQbS3t7O3w+HyM+Y1tbG/x+PyO+LulHM+GPJgOdTgeBQICysjIolUrk5ubSuhAhFApRWlqKjIwMZGdnM1ZWGo0GEokEZ5111oTv6urq8OWXXzKaFmo85HknTNTv8ezevRtDQ0Nj0jKR1NfXw2QyYceOHYzcmw60Wi3kcjny8/ORm5ub9F1G7EIECwtLiNFOolAoRHl5+bTu9JMJOXE6OqqLrsnU0c4R+X8moirI/9Old7gyYetXZAKBAK644grk5ubir3/9a1wyfvazn+GSSy7Brbfeiu7ubpo1ZAaynpBRPWw9iQ23241rrrkGp556Kq677jrKvz/vvPNwzjnn4KabboLJZKJfQZYZzSeffAKCILB+/foJ32VkZGD58uXTZiHiscceQ25uLq688sqYrv/zn/+MAwcOpPzzBQKBCdHoAD0Rc6PzY08n2aNzb4+WzS6OM8PoCGuA3ndJFa1Wi/z8/NBO1FQ43JMldWhoaIBUKsWZZ5454Tsy4jgVUrUw6ddNV380Geh0OlRVVYHH4wGgt6y0Wu0E2UyVVUNDA1atWgWJRDLhu/r6eng8HmzdupWRe4djdDYPgFmfUaPRICsrC6eccsqE7xYvXoy8vLyUHueR4zsOhxPaZZTMNsUuRLCwsIQY7YAC07/TTyY6nQ4KhQJ5eXkAgmU5MDAAs9lMi+y0tDTk5OSEZPf19cFisdAiW6lUIjs7G0AwsoCuQ690Oh3S09OhUqkABPXu6elJaOTEdOLvf/87vv32W7zxxhuQy+Vxy3n22WchlUpx9dVXT5uI3Ly8PKSlpQEI1pP29nZ4PJ4ka5baPProo2htbcWmTZvA5VIf3nE4HGzcuBF2ux133nknAxqyzGQ0Gg2WLFkS6hPHU1dXh61bt8LhcCRYM2p0dHRg8+bN+MMf/jAhxVQkTjvtNKxYsSLld0X09PTA6XSGxoEVFRXgcrm0OPXd3d1wOp2hqEU6ZY/OvQ0AlZWV4HA4tIxf9Xp9KPc2EDwMm8PhsIvjDEFGb5PlzXSakWiM94nY80FYRqPRaLBq1aqI/UBdXR0++uijpJ8PpNPpIJVKUVBQACA4pjYYDBgeHmZE9uDgIC3BLGT0NjlmqKmpoc2PTgbkOQYkdC8IJcJWDQ8PY8eOHWF3BADB/rGmpiahk/FarRYqlQqZmZkAguWq1+vhdDppv5dGo8H69etDCz6j4XK52LBhw7RYiCBJdp/GLkSwsLAAmBhRBrCRyFNh9KozcCyig47Do4432eS2SpZj6HQ63HXXXbjllltw+umnT0lWRkYGXnnlFWzZsgWvv/46TRoyR7gF0WQfqJXqHDlyBI888gjuvPNOzJkzJ245RUVFePTRR/Hyyy9j+/btNGrIMpPxeDzYsmVL2BQWJPX19XC5XPjyyy8TqBl1Hn/8caSnp+Oaa66h9Lt77rkHu3fvTmikIFXGR6DSuTN2vGyRSISysrJpJ1ssFqO0tJQdGzPE+PKWy+UoKChIiYUINiiChcRoNOK7776btE/r6+vDnj17EqjZRHQ6Haqrq0MBKonw6+iyvUz5o8kgnD2h67whJmWP5pNPPkEgEMCGDRsiXlNfXw+NRpOwBbhwz04QBO1zC52dnThw4MCkbV6r1aZsHY1UT5IFuxDBwsICIBgJ53A4QtFqQNBAtbW1pfTBO6lKOGNPfj5VtFrthPc0HWRHKhN2181Y/H4/Lr/8chQVFeGRRx6hReaGDRtwxRVX4He/+x30ej0tMpkiXMQGwNaTSAQCAVxzzTUoLy/Hn/70pynLu/7667F06VJcc801cLvdNGjIMtPZvn07rFZrxCg6INgfVFVVpXQ0WU9PD1577TX8z//8D2QyGaXfrl69GkuWLMGDDz7IkHZTR6fTgc/no6ysLPQZXWf0kPmxS0tLGZFN5scmocvB1ul0EIlEKCkpoV02y0TGR1gDySlvgiDCjln9fj/a29sTqgtL6hHLhOyyZcugVCqT3qeN9+vo3GXEpOzx7S+Zu6OmitvtRkdHxwR7YrVaMTAwMCXZLpcLnZ2dE2SbzWYMDQ1NSfZ4NBoNFi9eHPUMurq6OgwMDOCnn36i9d6RYHK+ZTQfffQR+Hw+1qxZE/GaVatWQSQSJb3Nh8NisaC/v3/CHE8ydxmxCxEsLCwAJubYA4JOos/nYwfdcTB+C2ZaWhry8vJoc7pHvyfy0CsmZKenpyMnJ4cW2ePLJDMzEyqVip1gHsfTTz+N77//Hm+88Qblya7J5CqVSvz2t79N2fQg4Zz/vLw8yOVytp5EYNOmTdixYwc2bdoUc6qYaHC5XGzatAmtra149NFHadCQZabT0NCAwsJCLFy4MOI1HA4nFEmXqvbpySefhFQqxY033kj5txwOB/fccw+2b9+Or7/+mgHtpo5Wq0VFRQUEAkHoM7rSR2i1WlRWVoLP5zMme3S6BLoWOZiUzTIRrVY7JnobSE559/b2wm63s0ERLGFpaGjACSecgPz8/IjXCAQCrFu3LunnRIz36+RyOQoLCxnxGRUKBQoKChjxGdPS0pCfnz8t219LSwsIgmDEnjApezRerxdbtmyJGlACBBfgMjIyElLvCYKYUE9UKhUyMjJorycNDQ047bTToFQqI14jk8lw1llnJb3NhyPSPN/o7xINuxDBwsIC4FgkXHl5eeizZB4SN50xm80YGBgYs+oM0BPVZTKZMDg4yIjs4eFhGAwGRmQbjUYYjcawslN1C2MyaGxsxD333IPf/e53WL58Oa2ylUolXn31VWzduhWbNm2iVTZd9PX1wWazjakn5IFabD2ZSG9vL+644w5cddVVU07hNZq5c+fijjvuwKOPPorGxkba5LLMPAiCQENDA+rq6kIpFiJRV1eHnp4e7Nu3LzHKUWBwcBAvv/wybr311tD5NVSpr6/HggUL8PDDD9OsHT3odLqwfXR7e/uUd8ZGkt3W1gafz8eI7NbWVsZkt7S0wO/3T0k2y0QilXdzc3NCc+2TY97RupBBEaxPNLMhJ2SjpWghqaurw549e9DT05MAzSZis9nQ29vLiF9ntVrR19fHiGwm/ehkEM6e0HVOUjjZ5DlJdJbVjh07YDKZJq33fD4fZ599dkJ2BQwNDcFsNof1Gel8dpvNhi+//DLmNr99+3Zazkmhk/FpD4Hk7zJiFyJYWFgABI3Q+Gi1goICSKXSadnpJxNywnS0sSf/nmpZsrKPX3w+Hy6//HKUlpbioYceYuQea9euxdVXX43f//73KbnTKdxAifybrScTufnmmyGRSPD444/TLvvuu+9GWVkZrrnmmqQftsgyfWlqakJbW1tMDtyKFSuQlpaWktFkTz/9NHg8Hm655Za4ZXA4HNx99934/PPPsWvXLhq1o4fxu9GAoO31+Xzo6OiYdrK9Xi86OzsZke3xeFI+zeF0JFJ5OxwO9Pb2JlQPHo83JjiLiQkulunH9u3bYbFYYurT1q1bBy6Xi48//jgBmk1kuvp1x5vPqNPpkJaWhpycnNBndJ1lpNPpoFQqkZ2dTbvs0Wg0GuTn52Px4sWTXltfX4+9e/cyvgCXKJ9x69at8Hg8MS9E+Hw+fPrpp7Tdnw50Oh1yc3PH7OhQKBTIz89nFyJYWFiSy/itbUAwRUd1dfW07PSTSbjtbwA9h0eRWw2ZkM2k3qTsqqqqMZ/X1NQwcqDWdOTJJ5/Ejz/+iLfeegsSiYTR+2RlZeG3v/1tyk0w63Q6cLlcVFRUjPmcrCcsx/j3v/+NDz/8EH//+9+RkZFBu3yxWIxNmzbh22+/xSuvvEK7fJaZgUajgUQiwVlnnTXptQKBIGGRdFQYHh7Gxo0bceONNyIzM3NKsn7+859j1qxZjC02x4vH40F7e/uEcSAdKR7C5cceLXsqtj2SbDp29DqdTuj1ekZks0yEjN5OhfLW6XQoLy+HUCicoAv73mc2Go0GBQUFWLRo0aTXZmZmYtmyZUlbXI82UTvVXUZknxBpEjhV/dFkMP7gbRI67AmTskfT0NCADRs2jEmbF4mzzz4bPB6P8bGcTqcDh8NBZWXlmM/pfnaNRoNZs2ZNuE84SkpKMH/+/JQbx4Zb5AeS26exCxEsLCwAwm+HBthBdzzodDrk5eVNSN+gVqths9nQ398/JdkFBQVQKBQTZE/10CutVovCwkLI5fIJsqd66JVOp4so22Qy0X6g1nTj0KFDuO+++3D77bfjlFNOYfReaWlpeO211/DVV1/hxRdfZPReVInm/Pf398NisSRJs9TCYrHgpptuwoYNG/CLX/yCsfucfvrp+O1vf4s//vGPCY1GZTl+aGhowKpVq2JeXK2rq8MPP/yAvr4+hjWLnb///e/w+/34n//5nynL4vF4+NOf/gSNRoO9e/fSoB09tLW1IRAITHBU6dgZG0l2YWEhJBLJlBY5WltbJ+THBoDi4mKIxeIp6R1NtkgkYsfGNBMpCrq8vBx8Pj/hCxGpNmnDknyopBokqa+vx9atW+F0OhnWbiJarRbZ2dkTglXo2GWk0+mQk5OD9PR0RmSPj94mZU/Vj04GTNqTRNgqnU4HnU4X044AAMjIyMDy5csTshBRUlIyYXypVqtDKaGnSiAQgEajmfRsjNHU19fj448/nnJqSDoZf7A8CbsQwcLCklQiRcIB7KF88RBudwlAT2RhpAFHqsuO1gGS956peL1eXH755aisrMQDDzyQkHuuWrUK119/Pf74xz+itbU1IfeMBbaexMaf/vQnmEwmvPDCCzE7w/HyxBNPQCKRTCklDcvMZHh4GDt27KDkwCU7lcV4LBYLnn32WVx77bVj0ipMhV//+teoqKjAI488Qos8OiD79/H2l46dsZEiZ+mUHUnvqY5bwsnm8XhTls0ykUj1RCAQoKKiIqHlHW0s0tfXxwZFzFB0Oh1aWloo9Wl1dXVwOp348ssvGdQsPMebzzhdD4yP9jxTPcsommy6zjLSaDQQiURYuXJlzL+pq6vD1q1b4XA4pnz/SEQKoqXzEOaffvoJAwMDMS/CAMFnHx4exvfffz/l+9MBQRBR22uydhmxCxEsLCwRo9WA4KC7t7cXNpstCZpNTyJ1jHQcTBWpI2FSNh2HXjEpe7rz17/+Ffv27cNbb70FsVicsPs+/vjjyMnJwRVXXJEyKZqiRfaQ3890du7ciRdeeAEPP/wwSkpKGL9fRkYGnn32WXzwwQf4z3/+w/j9WI4ftmzZgkAggA0bNsT8G5VKhaVLl6bMtvYXXngBDocDt99+O20y+Xw+7rrrLnzwwQc4cuQIbXKngk6ng1wuR15e3oTvphoxR+bHzs3NZUT2+PzYdMpOT0+HSqWiXTbLRHQ6XdjobSCx5e31etHW1hZ1kpXcvcEys9BoNBCLxZQmZMmULsno0yKNqcvKyqa8yyiS7PLycvB4vJSVnWhGRkYwNDQU0beZyllGw8PDMBgMEWV7PJ4pn5MEBOv9ypUrIZPJYv5NfX09XC4XowtwkeoJmQaajnqi0WiQkZGBU089NebfnHTSScjOzk6ZcWx/fz9sNlvEemKz2ZKyC5ldiGBhYYkYhTT6M3bQHRvRVp2FQiHKy8vj7hijyZ7qwVSBQADNzc0Jly2RSFBaWjqtBpV0sn//fvzlL3/BHXfcgSVLliT03nK5HG+88Qa2b9+O5557LqH3Dkc05z8tLQ15eXkztp6QeDweXH311TjxxBNx0003Jey+F154IdavX48bb7yRjQRliZmGhgYsXrwYhYWFlH5XX1+Pzz77DC6XiyHNYsNut+Nvf/sbrrzySsrPMBmXXnopCgsLU2ZXRKQ808DUd8ZOJpuOCatwsulYiGBKNstEIo1vgcSWd3t7O/x+f1hdyJz17LufmTQ0NGDlypWQSqUx/4bD4aCurg4ajSahUcekzxguMI7cZTQVfzRSJD4dspnyo5NBpJRzoz+Lt3+NRfZUy8pkMmH79u2UdgGR96+qqmJsMt7v96OlpSXss8tkMhQVFdFSTxoaGrBu3Trw+fyYf8PlcrFhw4aknQ0znkhnxYz+LBltil2IYGFhmTQSjryGZXKirToDU3Oment7YbfbGZPtcDgYkd3T0wOn05kSDmYq4fF4cPnll2PWrFm49957k6LDGWecgZtvvhl33XVX0t9BR0cHfD4fW0+i8Pjjj6OpqQmvvPIKeDxewu7L4XDwwgsvwGQy4e67707YfVmmL16vF1u2bKHsvALBbe0OhwPbtm2jXzEKbNq0CSaTCXfccQftsoVCIe644w688847aGlpoV0+VSabBJ7KztjJZHd3d8NutzMiu6urK+7UEJPJ1uv1Scn7frwyWXm3t7fD4/EkRA/ynuNRKpXIzc2d8WORmcjIyAi+/fbbuPu07u5u7N+/nwHNwjM0NASz2czImHpgYABWq5UR2Uz60ckg0sHbAFBUVASJRBL380STTcc5SQDw6aefwufzUdrZCjC/AKfX6+F2uxmtJz09Pdi7d2/cbf7IkSNoa2ubkg50oNPpwOVyUVFRMeG7ZO4yYhciWFhYQhET4aK+MjIykJ2dPe3yMSaLaLtLgKlFFpKdRLgIlFSWHSnPMsl0G1TSxSOPPIJDhw7hzTffhEgkSpoejz76KAoLC3HFFVfQkks0Xth6Eh2tVosHH3wQt99+OxYsWJDw+5eWluKhhx7C888/j507dyb8/izTix07dsBkMlHKq0sye/ZslJWVJTWazOVy4YknnsAll1yCsrIyRu7x29/+Fjk5OXjssccYkU+FSNGtwNR3xkY6N2u07HgXY6LpPdU0OpPJJggiJRaRjgeiRVgDwfL2+/0JmdTRarWQSqURd0Gp1WrWJ5qBbNmyBX6/P65JydNOOw0KhSKhqVqiLagB9PiMTMiezBeYbmdXarVaFBQUQKFQTPhuquckabVaFBYWQi6XR5Q91bLSaDRYsGBBXKlg6+vr0dPTg3379k1Jh3AwOW9BotFowOPxcPbZZ1P+7Zo1ayAQCFIiPZNOp0N5eXnYuQahUJjwM5hI2IUIFhaWqFFIADsBSIVoq85AsCzb2trg9Xrjks3j8VBeXh5RdryHXmm1WvD5/IgTLmq1Ou5Dr3Q6HWOypyt79uzBww8/jLvvvhuLFy9Oqi4ymQxvvPEGdu7ciWeeeSZpeuh0OkilUhQUFIT9nnT+k3GgVrIJBAK45pprUFxcjPvuuy9petx888044YQTcM011yQkMpVl+qLRaJCXlxeXfeNwOKivr094KovRvPHGGxgYGMCdd97J2D0kEgluv/12vPXWW9Dr9YzdZzLMZjMGBgYmXSyIZxxoMpkwODg4qex4nOBoubdHy45Hb6PRCKPRyIhslokMDg7CYrGkRHlHS8lF6sK+95mHRqPBwoULUVRURPm3QqEQa9euTejiularBYfDQWVlZdjvp7LLiPR1mZTNhB+dDJicY2F6/sbn8+Hjjz+Oa/ENAJYvX460tDRG6r1Op4NIJEJxcXHY79VqNZqbm6d0BqJGo8Hy5cvDnls0GQqFAmeccUZKLERECwYBktensQsRLCwsKWugpiPkqrNQKAz7vVqths/nQ0dHB2XZWq0WFRUVEAgEtMvW6XSTyo73QC2dTofKysqI+RVramrgdruTOhGTSNxuNy6//HLMnTsXf/rTn5KtDoDgYPF3v/sd7r77bjQ1NSVFB51Oh+rqanC54YcmNTU1sNls6O/vT7Bmyef111/HN998g5deegkSiSRpevB4PLz66qtobGzEE088kTQ9WFKfhoYG1NXVRWzPk1FXVwe9Xo+DBw/SrNnkeDwePPbYY/jlL38ZdWxEB9deey2USiX++te/MnqfaETLMw1MbWfsZLIzMzOhUqniGmNOJjsrKwuZmZmMyc7IyGDHxjQxWYR1fn4+ZDJZQhciIkH6RDMxKGKm4vP58Mknn8S1w4+kvr4eu3fvxsDAAI2aRUan06GsrAxisTjs92q1GoFAIK5dRlqtFmVlZRF3c6vVavj9frS3t1OWzaQfnQym80LE999/j+Hh4bjrvVAoxNlnn83IZLxOp0NVVVXENLVqtRoulwvd3d1xyXc4HNi6dWvcizBAsM1v27Yt6WfrpWrAMbsQwcIyw9mzZw8GBgYiTkADMzsSmSqxLOqQ11HlrbfeirprIFVlT1YmWVlZIR1mAg8++CCamprw5ptvRhxoJ4OHHnoIpaWluOyyy+LaVTNVJqsn6enpAIDNmzcnSKPUoKurC7fffjsuu+wyrFq1KtnqYMGCBbj99tvx4IMP4vDhw8lWhyUF+de//gWdTofVq1fHLeP000+HSCRidEdCJB588EHo9fqELBTL5XL87ne/w6uvvordu3czfr9wkP16uDzTJFwuF6+88gojsnk8Hl5++WXKsslF82iy1Wp1XIvr5G+qqqrCfs/hcOKWzTIRshwjRVgnsrxjGcdbrdaETSizJJ/nnnsOIyMjcaVoISF/m6hztl5//fWo0eBT8evefPNNxmQz6UcnGjLl3GTPE89ZRoFAIKYJ5qmcZXTnnXdCoVBgyZIlcf0eAFauXIkffvgBX3/9ddwywsF0PXnsscfgcrmwfv36uH4PABs2bIDX68WDDz4Yt4yp0tHRMWkZkBk1Er7LiGBhYZnRvPfeewQA4rHHHot4zdVXX00AID788MMEajb9sNvtBAAiJycn4jWDg4MEAEKtVlOSHQgECABEbm5uxGsGBgYIAMSsWbPikp2Xlxfxmv7+fgIAUVtbS0m2xWIhABAFBQURr2ltbSUAED//+c8pyZ6O3H333QQA4rbbbku2KmH59ttvCQBEcXFxQu9rMBgIAERFRUXEaw4dOkQAIC6++OIEapZ8ABAAiKGhoWSrEsJutxNCoZAAQPh8vmSrw5JirF27lgBAfPfdd1OSQ9b9ROL3+xN+346Ojkn7SSbJzc0lABB2uz3iNXK5nODxeJRl5+TkEAAIh8MR8RqZTEbw+XzKslUqFQGAcDqdEa+RSCQEAMLv91OSnZWVRQAgXC5XxGvEYjEBgAgEApRks0xEqVQSAAiv1xvxGj6fz3h5b9u2jQBAXHDBBRGv+ec//0kAIG6++WbG9GBJLebMmUMAIPR6fdwyvF4vAYBQKBQ0ahYZAIRKpYr4vdFoJAAQlZWVccnOzs6O+D05pq+urqYk1+FwMOZHJ4N33nmHAEDcdNNNEa/529/+RgAgHnnkEUqySTt06623RrzmiSeeIAAQjz76KCXZJHSMhTQaDQGAuOiii6YkZzQmk4kAQJSUlES8Rq/XEwCIBQsWxHWP4uJiAgAxPDwcp5YEYbPZJp27YZpvvvmGAEDccsstEa+59957CQDExo0bE6gZQYTPk8HCwjJjuPDCC1FcXIylS5dGvObXv/413nvvPcyaNSuBmk0/JBIJioqKcOmll0a8JiMjAxUVFZSjajgcDg4ePBg1N2lmZiZjsrOyslBeXk5ZtkwmQ0FBAa666qqI11RUVODHH3/EnDlzKMmejpjNZojFYtx6663JViUsS5cuRWVlZcJ3aiiVSuTk5OCaa66JeM2cOXOwe/duzJ8/P4GaJZ+SkhIsWbIEKpUq2aqEkEqluOqqq/Dee+8lWxWWFGTTpk346quvoo4rYmHPnj3o6uqiSavY4HK5qKurw5VXXpmwe5aWluLmm29Gbm5uwu45mrPPPhs//PBD1LRvbW1tGB4ejkv2Tz/9FDE9CAC0t7fHJXvdunXYu3dvxPQgAHDZZZdh69atlFOEnX322Thw4EDUvvCyyy7Dl19+GfEsAZbYWbt2LZqbmyOm8ASASy65BN9//z2j5V1WVgalUhl1HH/66acjIyMDy5YtY0wPltTi3XffhVarjZiPPhb4fD6++uqrhGUXOHToUMQz14DguLuqqgpr166NS3akw9yB4A7meGSLxWIUFRXhkksuiXhNRkYGKisrp7Q7JVHMmTMHaWlpuPDCCyNes27dOvzlL3/BiSeeGJfsCy64IOI169evx0MPPRT3job33nsPc+fOjeu3JBs2bMCmTZtwzjnnTEnOaORyOfLy8qLOLeTl5aGkpCSu+g0AH3zwAQYHB+M6H4JEJpNhy5YtUCqVccuYKitWrMCOHTuijsfPPfdcPPPMM1i0aFECNQM4RKKsIQsLCwsLCwsLCwsLCwsLCwsLCwsLCwvLjIM9I4KFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhTHY1EwsLDMEvV4Pg8FA6TcqlQolJSUMacQSDibfU6rVASr6pHJdjKdcgdR9plSqJ1R1SdUyHU8qlXG8HA/PwBIf07WfStU6m4p2bia+BxYWlpnJdLV301U2S2owk+vP8fbsqV7e4WAXIlhYZgB6vR61tbVwOByUficWi/H+++8jPz9/0muTbcwSDdVJdACTXt/X14cLLvgFXC4nJV1EYjE+mOQ99fX14fwLfgE3RdlCkRgffhBbHXC73VFzNY+/dtWqVTHXSalUisbGxpSrY3q9HjWzauFyUmtbACCWSKFtSq1nitdWUHk/sbadvr4+/OIXv4DTGXudTdV6Mhq9Xo9ZtbVwUixjiVSKphR5tmA9mQWHg5o9kUolaGxsSolnYIkPJuuvXq9H7awaOJwuSrKlEjEam7RJkz0V4mlL8bYjKrY3nvGCSCTGB5OMF/r6+vCLCy6A00XtPcQ6Ho11vEVCZdxC9fqZNi6OBSpjZ6rvhkp5U520Yd/78Uu84/hYxvBM95fxyI7VZ2TKHyWh2qaA2O16MtogU7aNqT4qHh8LiM3PorNNMTXfEs8YJ9Y2XztrFhxUy1UiQWNTfP7RdJ3nYxciWFhmAAaDAQ6HA7+46lYUlFbA7/MhIzsHXrcbivRMOGwW8AVCdLc3Iy0jCy6HHa88fi9cLhfq6upiukcqTqoyBdUJDbFIBILDjbnDu+LBV2A3G8HjC+G0mZFdVA6fxw2ZMhND3e0QyxSwGAfA5Qnw3+fvh5vCe6r62c2QZBcj4HVBnJGHgNcDgTwdPqcVXL4Qtr5WiJTZCHhcaNx8Lzzu2GVzOUAgxlOHxCIhXG4PrrvuOqxfvx5cLhdutxtisRgWiwUOhwMmkwlZWVng8/m4+OKLYTAYUq5+GQwGuJwOLLrxebitw+DyhSACvmAZej0QytPhPVq2XocFCPjBF8tBBPzY+/yNKfdMpK34xysvwDg8DLlcDj6PD7FEDLfbjayMDLR1dEChUMBoHEZ6uhIioRCXXn1DTM8Sz2Tgz664FYr0TJRU1YLD5cLr8UAoEsFhs8DtciLg90MskYEAgWf/+NuUK9PxGAwGOB0OLP3VbVDmFcPncUORlQe/1wOxIgNuhwV8vhAOS/DwVrFMCb/fi/8+em3KPFuwnjjx8q3nYdjigEwihD9AQCkTweP1I0MhQUf/CDLTpGjtNWLtiWroug249tl/pcwzsMQHWX/X3/ESnJZhcDhcpOUWg8PhwO/zwGboh1CqgNdpg8dpQ9kJZ8Ko1+Hjv1436bs3GAxwOF3YeEENjA4P5CI+bG4/ctOE8PgIZEj46Da7oRTzYLR74QsQyJYJcdP72phlP3eBGsMOL4Q8LqQCLkQCblC2lI/OYRfkIh4GbV5kSfkQ8rm4+X0do3WWbEs3nbcC5fkq+Px+5KQr4Pb6kKmQwuJwQcjnY9hqh0QkRI/BhL+8tYWyTvHY3ppzb4Ysuxh+jwuSzHwEfG4I5RmwDXRCIJHDZRoEQfx/9t47Tq6rPPj/Tu/be++rYlXbsiXhJjeVXewADpg4P4wpCRDaG6dgOjEkEPLmJRBMICE2AVOCDXhXttx7R7aqpZ3tvc3uzsxOr78/Zme0K22ZOzuzM5LO9/Pxx9o75z73ufc855znOTVMTtUGXvvunXgl+At//dEPUF1Wgk6rQatV4/X5ycs20Ts4ismoB2BqxkZTbRUf+NzX4/ZHNRotclkYt8cb30vK5BAOxZcWaX5Oqgexzjck26DEvIk3Dkmkk0zk+4VL1I9v/Nj38TunkSvUhENB1FkFhAI+lIZc/LYJwqEgPus4Cp0JlSmfzp98Oq52x+1ycdPf3IfbPo1ab8TnmsWYX0rQ70VjysU+2odCpSHgdaNQaVDpDDzxz5+IW/YHvvJjnLYp/F43xbXrI22x34dCpcbrcuBx2PB53cgVSh79969Kihn3f/jz5BSWojdmodLq5mLRXNyO2Ui/QddJ8oor8Djt/PZ7X5EkW0r5llqvr3UZlFqnSKlPUtlGAdz20c9SWlWHTCbDlJO7oG9IpVIzNjxAVk4eWr0Bv8/LP/2fu+KyTY/bRdnNf4GuqAZkMpSGHMJz5clnm0Ch1uGZ7EedW4qhYh2u0U7MP15YplLd31K+/1OocoqRK9WoDDmx8h50zyJTqvBODaE05KLQGgj5PHGXeZfbzX0fv4FphwejVsWs20dprgGvP0iuUcus24daqeCdoSkq8k2oFHI+8eOnEvY1o7H7j//5a3i8XjRqNcFgiJwsE16fj7ycbHoGhijIzWFs0kJjbTW3/eXdae/nEwMRAsFFhM5gpH7dJno6TpCbX0RZVS1Dfd00btyCdWoSpUpFbn4hw33dBHxeSm78GEp9Fuq8cjwTvWjyK9HkleKdGkFTUEHAaSPgmEZpyKYrjsr5QiHaofGxnSVkaZSUZ6vpnfZQmaOhNEvDiM1LRY4GmyfA8VEn9708CkDRNXdgrNu+5Ld0Dpxg9NAPmZ2ewJCdR1FlPcNdJzHlFlJYUcvEYDfbr7+F2elJpsdyGe56B7/fH5ceJ0ac/PCVUdRZ+ShUGrKr1mMfOIWxrAFDcQ3OsR6MZY0Yimvwzc5g7z9JIBCf7GlnALs3wHeeGZL8TUpKSsjPz+f48eOsW7eO+vp6Ojs72bx5MxaLhd7eXoqKitKZ3XHhtU2iNuZiKK3DPnAKTVZB7Ltm12zCa7fgn50BuRyZTIZCa0i3yssyPjFJfl4uDQ11nDh5iqqqCupra+js7uE9t7QyMTnJwNAwM9Mz2B2OuOVKKTvPdFr57VELOoORjZfvpt98koraJmrWbWK0v4va9VuwTU9iGR1EhoxZ63QKv0jy0efko1RrKarbwETPKfKrGsktq2F6qIeCqkacVgtTA2aCQT+hgD/d6i7KhNVBnklPXWkepwYmKMw2UFOSR8/IFK0712OxOZHJZPSPW/EHgulWV5BEXDOTaLNyyS2vx9L7DnlVjeSU1jBj7CGvshGX1ULQ5yEU8COTySTJnnT6yNWpqC3QcXrMSaFBTXWejt4pN3vX52Nx+CnLDjLt8mP3BCTJtjj85OiV1ObrOD3upDxHS3Welt4pN/s3FmBx+KjxhZj1Bhiz+yTJXg1GnZaNtSW80zdGYY6RmtJIOdpUX8ak1YE/GMTrD1CcY0pIvpS6N+ovaLIKUKi1ZFdvwDZwiqyyBgwlNSjUWkzljXhtFpyTA7inR+P2F6KyC/Ny0GrU1FeVc6Kzh+baKmory9BpNDTVVTE5ZWVwdJwpqx2/zxeXP+ocOM7oofsAuOOyIraXG+PyW+L1dQNuO0O/+05csrP1Sj79UNdF4xfHQyLtfyrikGgnmdR8l+Lfinw///DbLaiMOWiLa3ENnUaVVYC2sBr3eC+5m67Db7fgs00QCvgIuOySZLusk+hMeeSU12HpewdddgHZpbVYR7ppeFfrXHvpJeDzMDsxKEn27MwEhqw8KtdvZ6z7HQqrG8kvr8Uy2E1F81YcM5NM9Hcy2W8m6I8vto/Go6bcAnQGE0WV9Qx1naS0ponCylrGB7oj/66oZXpsCNesPe54VGrdK7VeT0fdG61TVorzpdYnUuvBRGTrDEbq1m2it+MEOXN9Q8N93TTM9Q0pVCpmrTPkFhSh1eklfRel1oihcj3OodOoswrQFkXKU97m6/DZLWjyywi47ISDfvSljYt+11T2t6hMBah0pliZ15U2xMq8vrQBf2E1PtsE/tkpQkFpcdik3UWeUUtdcTbvDE1TkKWjtiib7nEbm6oLsNjdlOYamLC7sDnjnDixAoMjY1z/ris40dFFc30NddWVdPUO0FxfQ1lJIQMjY5TKCxmzTMXtV6Wyn08MRAgEFxE7r99P0yXb2HjplbFrhaUVAOQXlVK/fjMACqUKAF1pI7qSOoJOG4qK9fjtFuRqHdnrdyNXqXH2HUdVXIvSlL/2L5MBNBboqCvQYXMHWV+iwOLwo1PL2V2XjVopp2fKQ3Xumc43Q/VmtIVVqPTZhAJePOO96Cs3oM4pQV6mJuSLjOA3XfouqtZtBaB+yxWx+3OLywHILiihomkTOUWl/OGH31hRjxGblylnpLOmYMMucmoj+ZzXdHlMti6/DABtbjEAKkNWXO84YvOSrVMx7fTHld484aLYqI49t7W1le3bt7Nr167YtYqKOZvMz6e5uZm33nprtVmVcvLj/K5RrL3H1k65BLjmqt1s3xp5n11X7IhdryiPvE9pSTFbNl0CwFtHpL9LPHZSZIzUQ5ddu4/6jVtZt+1MvVVQEikLeUUl1K7bBED3ySPSXzSNVG95FyVNWwCo2HimnGcVRt7NmF9CcX3kG4+Zj669gnHwrktq2FIXWdJ7xbrK2PXy/Ej9UZJr4pKaEgCO9oyuvYKClFG5ZTfFjRH7Ld94po4wzbPfKPI5nyJedtbmsLks0uF+eVV27HpZdmS7gWLTmW0Hjo3MSpSdzaYy45zsrEVkn2mfjo/EP8i6WvbuWM+WhnKuWF8Tu1ZekANASV4Wm+oide/RruFVPSeeNt3tj8y+LNq4i9y6SDtQ0HymXdPPtWu63GJyajYy03MsLtk9Ux6KsiK+yFWXb2HbhiYArty2MSa7oqQQgNLCfDavq+ftd8yRZ63gj3rGetDtuCXWYbW51EBVnpZsnQpvMETvtIcNJXpKTGrUSjm9Ux6e75qJS7Z3agSlPouAcyZu2dlaxary6UImnvb/mvqcSCdZHPmu1OpR5ZSs/OCziCff1Vn5+GwTcek9YvNSl6+LdYYJzi+y1+3EWB3xJ7MaztR3mrxIfafOKcZApK5y9B+XJLti826KGiLtZemG+e1lRLYh74z9TnRJ8/fqt72LiuatANRsOuNL5hRF2uKsghLKGjcx1HGEJ3/6TyvH9v0n0BbVALDusquoXh+R3bD1jOy8uXg0p7CEquZN9J86AsRXRvRqJbNzEwhW1GXgJAUS6vURmxetUi7p+yWTleJ879QI3qnBuL6VlHpwtuuPGGu34pnojUv2iM0bk33lnv00xtE3BNB54m1J3yNv640YazaR1bh4eZqPo2/pMrXS+xwfcXLLJQWxunelfAg4poDUlvnd68rZUhPxZ3Y0ntn6qCwv4n+W5JyZkHi0b1KS7KU4cP3VbNu0np2XbY1dqyiNfOfS4kI2b2gG4O3jp4D42kCFWot67nskGzEQIRBcZBx9/UUAZm0z+H0+ZiwT1K/fREFxGSq1hq5TRykojlQ4xprNsQp6MTR5EUdEauV8obC5zBjr0FiM8mwNOfOC0ZW+p6HqTDBuPvwSAK5ZKwGfF/v0JBWNl5BTVIpSpcFmGWOo80Tcesx3zCzvvAKA32kjFPDitVnIqtqANq8UhUqNY7QH1+RQ3LIBHj46GXf6AsOZTqnDhw8zOzvLzMwMXq+X8fFxtmzZQnl5ORqNBrPZTG5u7pLyMgVrzzECbsc531SXV4pcpcY+cAqFRk84FKTwkqvSrW5cPP9SxE6sViter4/xiUk2b9pIeWkJGo2GI8dPoFapKJzbk1MK8djJjCsSqHSffBuPcxaH3Yrf58U2NUFN8yby5uqsydFBZmemcNptib1oGuk/+jIAnlkrQb8X58wkRfWXYCooRalSM2o+ilyuwFSw8v6d6eLlk30AWB0evIEgk1YHl9QUU5qXhVql4I2OIQwaFSV5ic3kFmQug8fm268P18wEhfWXYMwvRaHSMNl9nHA4FBuckMKrvVYArO4AvmCISYefjSUGSrI0qBUyeqbcyIAsnbRQ5tiIA4c3iM0dwBsMYXH42VBioCRrriPD6iUQChMIhcmRKHs1HOkawuH2YnW48foDkXJUW0ppfhZqlZI3TvWTZdBSmL10vRkPUv2FyZORdsDntBLy+/DYJsmp3hhr26Y63sRYWhe37Pk+0YtvRjrcZuyz+Hx+JqZm2NRcT1lRARq1isHRCQ6f6ADi80fn+6Lx6DIx64tbNsDkaw/HLXstB7HON+L5flGfMpVxiMh3wXwcfccIehwE5vx4v82CoWqu83KuU87vmEapz05oVfPQXHvpddgI+r24rJMU1G7EWBBpL2cnhvDMzmBMwN/rfjsSM7pnrQR8PhwzE5Q2XEJ2YRlKlZrx3tMMmyNlRGpd2vHHiGznXDxqm5qgam4ynEqtYaDjOMPd7wDSY8ZU1OvpLIPxvM/k3EBEUuvBHZG6KjoQEY/swZmIbPOJt3E5HTjm9QvVzesX6j51FKVKzeVX37ikvKWIlSmXjZDfi99uwVC5AXVuSWQ76MGTAORvvWlZOVLzfaXvZZo36GDreBVgyXJv7/ojmtwy5GptXO88n5dPRyaO2JzeSHxkd7OxMp/SXCMapZzjA1Ps3VYjWe5SvHX8FLNOJzO2WXw+H+OWKTavb6KspAiNWs3RdzrIy85Gp42Uw3T384mBCIHgIsJ84m18Hg+VdY3IZDKaLtmGSq3B7XKgVKmYmhghO/dMp6Ll9d8TmJ0i5PeiK2tCrlITdDsIhwIEXHb81nH0lRuXeeKFze+PW5hyBfAGQjQV6lAr5bh8QazuAM1Feo4MO7C5z2wbsdz3DPm9eKciDVb/qSP4vR6KqxsAGVXrt6JUafC6nVgnRqls3sSQ+TgBXySIPjbi4I2BWeoLdAt0icw8UZCjVfLWUGTWqLXnGCG/B2NpPchkZNduQaFSE/A4kSuUeGbGCXicqPTGFWUr5DKKjGp6ptyM2r1LfhO7J4DDG6Q0S0PPlHvBNzGbzVRVVSGTybj00kvRaDQMDAxQWFjI6dOnKSkpwetNzpLFVGMfeAdjaT0ymYzirdcjV6nxO+14bZPkNe9gxvxHlDojo28+SjgsYQPPNPDWkaO43R6aGiPvs33rZjQaDQ6nkzf++Bbv2nUlep0Ol9tNb1+/ZPlL2YnbHzrHpkb7uigsi+xBX79xW2zwYXpilMKyKqbHR9AZTGj0mb3d1dmMmo8Q8HnIr2xAJpNR0rQVpUqNbWIIv8eFPrsAuVyBUqvDOir9G68FR7pH8fj8NJTlI5PJ2FpXilqlYGTKzui0narCHPJNOgD6x2fSrK0gmYx3HiXgdZNbGfEnihu3oFBp8HsckQ6WmvXocwvxu53YxqTZ77HhWTyBMA0FOmQy2FxmQq2QMWzzMuPy01CoR6OUk6tT4U5gy693xpzUz8ne05QbG4BQyGVMu/zsrM3m6LCDgRlpByqvhu5hCxWFOchksLWhHLVKydCkldEpO5fUlZKfZcAfDNI/trot6FZq0xsKdDF/IYq1/ySmsgaQySjddgNylRqXZZjsynWYKhpxjPQsK9vlC3J81MkNTbnnyD7e0U1jTaR+v/mqK9CoVdidLlweD4FgkGxTpF539B1jtvMNdCX1i/tQPg/e6ZGY3MXamKX8lhVl+70EXXa806MrfkODWoFMBifHnKvKpwuZpb6f3RMgFI7scd4zFVkhHE++++0WQkFpW7TFJdvvJeRx4p0ZW1bvEZuX5iI9wzavyPfzHOfAO+jm4qPczXtiAxD68mZkcgUyuYJw0I9nckCS3InOowR8HnIrGkAGRXPt5ezEEAGvG499Brd9itzyBgI+aYfcDnUcIeD1UFgVaYsr1kV8Sev4EC7bNIXVjciVKrIKIqsuVrR7rwt755sA9J06gs/jprSmERkyqtdvRaXW4Jq14bBNk51fTDgUQjXXSbtcGXH6QmyvMHJk2BF/3et14Rrtjr3rUvV6tPw5vMG0rohYKc5XaM7EKcmsBwNOK3K1XlJdFZUN0HPqOBVzfUM7rr051jdkGR9h84538c5br/PWy8/isFslfxPn4DvoSuqBuTKlVOOdjpQphcaAd2oY10jnsjKWep9Ru48Sk5pZb2BB3btSPrjnnufoO0bI54mVeWP15liZV5nycfQfR4YMpT6LUCCxrTpPDk7RUJIDMhk3bK5Go5QzNO3AqFVRmKXj9HBytxU+dspMU10NMhnsve5daNRqHE4XE5PTbFrXSHffICdOR95/pe/kt02iLa5Lqn7zEQMRAsFFRNMl22i6ZNs516cmRskvKqW4vIqpiVGmJiINWcEVt0IoiG9mDJ91DJUxD/doJ1nrdiFXachu3onPOo7PtnbBeiZx66YCgmEYs/sYm/WRp1fROenm8ioTQ1YvV9fn8Fznmc635b5nwDFNcG7P0er1W2NbM83HZhmjtDayrG7z1fsYOH0k8u+5mQJHhh0LdBmf9XN5lZYpp5/tFZHZyDl1m2NbCM3HMzOONrcYfWFke5WJo8/FIdvElNPP1fU5dE+6l/wm3RbPgrTzv8ntt9/O9u3bF+iiVCrJzs7miiuuYHR0lNHRzF/mvtR3lcmVZFWtB6Bo6x4AwuEwk8dfWFP9pLJ965bY1kzzGR0b54Pvfx8AVZWRpbtPPPWsZPnx2EnUpt514DbqN25dcL9coSSvKBJUFZVH9qt8+6WnJOuRTkqbtsa2ZpqPXKGMbWvTuGsvAD1vPrOmusXL1vrS2NZM81Eq5JTkRuqcyqIcAJ450n1OOsH5S3HjltjWTPNxTI2RXxVpp0rXXQpA7x+l2e/mclNsa6b5KBWy2LZMO6qzGZ/14p6VNhCx1Mw6pVy2YFumq85qq1LNe6/ZypaGhStHlAo5JXmR7aOu2doAwDOHzat6zkpt+pDVG/MXAAo37qJw465z5MgUSlSGLHIMG/HMTCwru3PSze7a7HNkX3X5Fq66/FwbGp2corQwsuWnx7dw1YKj98iSPlTAfWaQY7E2Zim/JR7ZprrtuMfPzDaNys7WKfAFw4zYPWRrI2H1+mID42t4vsj5xlJ2Mt8HyNdHVs7GlTf1lzJz4jnJesQjW1e3Hddo17J6j8/6qcoNEQ5Dybw6RHB+sdTsYJlciVKfhan+TKwi1d6KGrfEtmaaj1yhWLAtk3N6jMC0tIGIiuatsa2ZFspWxgYfGi+7hqGOI0B8dp+9IbJyu2b91tjWTGfLriiMTEQsKKuKbc0UT/tydt0bjYsVhmzCAR+emRFUpgLkKg2Gyo0EPGc6mJeq16PlrypXG1vplg5WivMV2jPtXzLrQblKg66kAUfvkWVlz/9WUdlNl2yjcYm+oeqGdQBcfk1kxcIfX3hS0vcw1mzGWLNImVJEylTO+t0Akb4k39J9Scu1uXZPgA0lC9vclfIhui3USmV+gX5zW/RJYfe6cnavO3dFsEIhj23NNGZ1Mm6N75Dzldi+aT3bNq0/5/ro+CTrGyMDCtUVZeRmR/zKgitujdmVe7gDhSEbwmEgjL5iPUp9FgqtKWX9fGIgQiC4iBjoiixx7+s8hXVqEr3RhDErm1nrDOU1DTjsVkoqqhnojqRzj3aiK21EnR+pREMBH5rC6tjM/ehSLffo8iPZFyqdFjeNBTrKsyOBhy8Qojr3zHL/4yMOpufN/l/pe/odkVHx0d5IR8NI9ylmZyxoDSb0pmyc9hmKKutxz9rIL6tibC5dpyXi0ClkLKlL55zT5xiO5NXsUAdeuwWl1oTKkIXfYcVQWoffaUNfWIl3dipu2cdHHNi9gYS+yalTpzh58iQTExNkZWWRk5PD9PQ0NTU1WK1WGhsbOXXq1CpyaW2IfldY+dsC+OyWdKkaF6c7Irb1zqnTTFimyDIZyc7OZmbGSk1VJTM2G431EafGMjUlWX48dhK1qaGeSH002HUK2/QkOoMJQ1Y2DpuV0up6nHYbReVV2Gek65FOLAORbzzZdxqX1YJab0RrzMYzayW3vA6vw0Z2SWSQxWXLzHczD0Xs+PTgJBabE6NOQ7ZBg9XhoaooB6vDTX1ZpENxyp4cR1uQGUzN2a+lP2K/Gr0JjSELj8NKblkdHqeN7OKI/bol2m/XRMRWOsadTDn9GDUKsnRKbO4AFTlabO4AdQW6BWnjpXNyTvaEiymnD6NGSZZWcUa2J0BdfkT2tGvtDonvGIwEuacHxpm0OjDpNWQbdFgdLqqK8rA63dSXFWCxr27GtRR/wT4cyWPbYKRNU+mMqPTZ+JxWjCV1TJn/iKGwEu/stGTZHT2RGcWnuvqYnLZiMujJzjIyY5uluryEl/54lIbqCqZmIlvuxfxMuWJJHyrgODOzUEobE49sR/9xAm77krKzNArC4TDB8Ll+jmAh8diJ5LxxSJ9Vmki+r6S3yPfzl+isbNdwB/7ZKRRa49zZMDa0xbUEXDa0BREf3j8rzd6m59rLqf7TuK0W1HoTGmM2ntkZcsrr8DrsZBVXLkgbLxN9kfTjvadxzEyiMZjQGbNx2a0UVNbhmbWRW1rFRH8kXTx2H00z0hvxv4e7TzE7bUFrnItHbTMUV9XjmrVRUFbF6Fw6qTHjYnGxQjc3+zxwbtleqV6fmPXFdEgHK8X5Qa8bjyWyNVNK6kEJdVVUdrTPZ6m+IafdSnFFNQA2iXGWa/RMmfLNTqHUGlHMlSndWWXKtUxfktQ2N97+FillfqVVG2djHolMZDk9PI3F7sakU5OtVzPj9FJVYOKV0yPUl+QsSLtaTndFJku8Y+5mcmoak9FITpaJGZuN6ooyZmyzNNZWxdItZldR5ttsyvr5wgKB4IKnv78/rNfrI0Oc8f4nk0tKr9Xpw/39/el+1TWhv78/rNdp4/82Gk1Yo9XFlVYml/bd5bLU5akk2RLTazWasE4X3zcBwnp9ZtpXf39/WKuTWLYyuMwkVFdIyB+pZUcusTxkqp3Mp7+/P6xL4BvrMujdInYSf/k9kz+6jHkHQWKk0n6l1g8xu9Jp0yp7NSRSlhIpR5LfP4X+gtR6XYouGo02rNNqMuI9U2075xuptsF4fapE/DaR7xcuifrx8dhbqttLybIllKmUxqMSdZFar691GZRqQ5kSu0tui4kvzkpWmUplf4vU77qYfku9u15C/8YZm008Pjpf+/lk4XCGb1QtEAiSwsDAABbLmVnYL774Ip/73OfYtm0bOTk5fPe7312QProdTmlpfIdmFRQUUFVVlTyFM5yzv+dyFMwd5htP+rO/ezgc5pprruHKK6/k6aef5qWXXkKn0y2ZXqrsq6++mp07d/L000/z8ssvo9Vql0y/El6vF41GE1fas7/Jz372M3784x9jMploaWnhU5/61DnpM9W+Tpw4wXve8x6sViv3338/JSUli6Z74IEH+Ld/+zf+/u//nttuuy1j3+ls2w4Gg+zevZtrrrmGZ599lldeeQWlcuGCSinvIqXsnG1T//mf/8kvf/lLAD7wgQ/wsY99LGE90sn8bzA5Ocm+ffu45557eM973kNXVxfvf//7+ed//mf27NkTuyfT3u3sfHz/+99PY2Mj9957L3fffTcWi4X7779/wT2Z9g6CxFisDL///e+ntLSUF198kd/+9rfU1tYu+D3evF9Kdnl5Oc8//zwPP/ww1dXVSZN92223UVFRwQsvvMDvfve7c+Sshc1G9frRj37EAw88wCOPPEJhYSEAVquVd7/73dxyyy389V//9ap0klL3nt3+WywWbr75Zq677jpOnTrFwYMHl00vRfbk5CR79+7luuuu4/Tp07S3tycsW4q/BdL8FqnpRX13Lqtp/1ciVX6IVF1Evp9/LGYP119/PZdeeilPP/00zz33HCbTwu0CV9PupFL2nj17uOyyy3j66ad54YUXMBjOnEuw2pjx2muv5YorruDpp5/mxRdfRK/XJyQbVhczxpN+rctgquq2VLZRZ6c9efIk/9//9/+xa9cunE4nP/3pT8+5J1HbPHHiBB/60IfYuXMnbreb//qv/4pL9lr1t4RCIXbv3s3VV1/NM888w6uvvppwrJvqOHqlZ87OznL99ddz99138773vY99+/axd+9ePv/5z8fSZ0Q/X1KHNQQCwXnD//2//zes0+nCn/zkJ8MbN25MtzqCJRgbGwsD4W9961thIHz06NGkyR4dHQ0D4X/8x38MA+Fjx44lTbZUPvaxj4W3b98e3rNnT/h973tf2vSQisfjCe/Zsyeck5MTPn78+LJpQ6FQ+DOf+UxYJpOFH3744TXScPX09vYusBOz2Zw2Xf78z/88vGvXrvCuXbvCd9xxR9r0SCbf/va3wxqNJjwzMxO7dumll4ZvueWWtOkklf7+/jAQ/vWvfx0Oh8Phn/70p2GZTBaemJhIs2aCtSAYDIZ1Ol34a1/7WhgI/+EPf0iqbK1WG5P9yCOPJE12IBAIazSa8Ne//vUwEG5vb0+abKlMTEyEjUZj+O677z7nt69//ethjUYTHhgYSINmEZ5//vkwEL733nvDQNjlciVN9rPPPhuTLZPJkipbIBAIpDI9PR0Gwt/85jfDQPiNN95ImuypqakFcd2bb76ZNNkWi2WB7MOHDydN9uTk5ALZb731VtJkCzKDn//852EgfM8994QLCwuTKvt//ud/YrKLioqSKjsZ9PX1LYh1T58+nTTZax1H/+pXvwoDsRUMH/3oR8NNTU0pfWYipO9YeYFAkFY6Ojpoampi3bp1dHV1EQxKO/BRsDZ0dET2brz66qsX/J0K2Wbz6g7BXK0uTU1NNDc3p1UPKYRCIT70oQ/x8ssv84c//IFLLrlk2fQymYx//dd/5bbbbuP222/npZdeWiNNV0cqbTARXaJ2kk49kkU4HOb+++/nT/7kT8jJyYldv/POOzl48CATE9IPR0sH7e3tKJVKbr75ZgD2798PwKOPPppOtQRrxNDQEG63m8suuwyj0ZjUsjk4OIjH42HHjh0YDIaktg8DAwN4vV4uv/xy9Hp9WuuUf/qnf0Imk/F3f/d35/z2+c9/HpPJxD/8wz+kQbMIHR0dyOVydu+OHN7Y2Zm8PYPNZjMKhYLdu3cTDofp7hYH2wsEgvQRbWdS4fdGZV911VUL/k6F7FTGjBeCDy5YSEdHB6WlpWzatInJyUlmZmaSKrusrIxNmzYxMTGB1WpNmuxkEC0711xzzYK/k8Fal5329nY2b94cW8HQ2tqK2WzOuP4VMRAhEFykmM1mmpqaaGpqwuv1MjAwkG6VBItgNpuRyWRcdtll5OXlJd1hlcvlbN++ndzc3LQ2UPPtsbOzk1AolDZd4uXuu+/mN7/5DQ8++GDMuVgJuVzOz372M3bu3ElraysnT55MsZarx2w2o1arY5116bKTcDi8wE7MZjPh83x3yT/+8Y+cOnWKO++8c8H122+/HZlMxoMPPpgexSTS3t7O1VdfTXZ2NgDFxcXs2LHjnC1WBBcm0Tqhubk5VjbPJ9nr1q2jsbExbXXb0NAQ//7v/85f//Vfx7YWmI/JZOILX/gCP/3pT5M6ACAFs9lMTU1NbMA92flQW1vLxo0bky5bIBAIpBKtg7Zu3UppaWlK2p2tW7dSUlKSEtnbt2+nqKgoZbILCwtFPX0BEo2xmpubY38nW3ZTU1PSZScDs9mMSqXi8ssvT/qkl7WMowOBAI8++iitra2xa9dffz0ajSbjYjIxECEQXKRkeoMgiBAN/jUaTUo6YWpqatBqtUmXLQW73c7Y2FjMHt1uN8PDw2nRJV7+5V/+hX/913/l+9//Pu95z3sk3avRaPj9739PZWUle/fuZWhoKEVaJgez2UxDQwMqlSqtnXUWiwWr1RrrkLTZbExOTqZFl2Rx//33U1ZWxg033LDgen5+Pq2trTzwwANp0ix+nE4nzzzzDC0tLQuut7S08Pjjj+Pz+dKkmWCtiAZwNTU1KWmnVCoV1dXVKZGtVqupqqpKaxt47733YjQaF+zfezaf+MQnKC4u5mtf+9raKTaPqM+Yn5+f9IkLUdkFBQXk5OQIf1QgEKQVs9lMWVkZRqMxJe1OeXl5THayV1tUVlai1+tTondVVRU6nS6t7aUgdUTb4sbGxtjf54PsZBCNdZVKZUrKzlrF0a+99hrT09MLYjKDwcD1118vBiIEAkH6cTgcDA8P09TURFVVFWq1OuMaBEGEaMMNpKRhjM56SLYzLIXoDM9oBzNk9pLfX/ziF9x9993cc8895xyqHS/Z2dk89thjyOVy9u3bl9Tlr8nmbDtJV10Rfe6FMoDq9Xr55S9/yZ//+Z+jUCjO+f3OO+/kyJEjHD16NA3axc9TTz2F1+tdMPsGIkuBZ2dneeGFF9KkmWCtMJvN1NfXpySA6+jooKGhAYVCkZJOm6jsdG0L2NXVxX/913/x93//92RlZS2ZTqfT8ZWvfIVf/vKXHDt2bA01jBBtB2QyWUryuKmpKSY7k9t/gUBw4bOWsVcq6lIg6W1aKr+JIP3MX3VuNBopKytLWh5HZTc3N2MymZK+yigZzC87qfA1U1Uuz6a9vZ3CwkJ27Nix4HpLSwsvvvhiRm2JJQYiBIKLkK6uLiBS0SoUChoaGjKuQRBESLUznAlOZfS5jY2N1NTUoFKpMtYen3zyST784Q9z5513cu+9965KVnl5OYcOHWJ4eJhbb70Vj8eTJC2TSybZiUwmo76+nvr6emQyWcbaSTy0tbUxMzPDhz70oUV/37t3L4WFhRm/KqK9vZ3m5mYaGhoWXN+8eTOVlZUZNwNHkHzOriPGxsaw2+0pkT06Osrs7GxKZA8PD+NwOJIiO16+9rWvUVRUFNeg9l133UVtbS1f/vKX10CzMwQCAbq7u1PSDvj9fnp6ejKijREIBAJY3O9N1lagQrYgExkdHcXpdKakLR4ZGcHlcmV0O3+h9Le0tbVx4MAB5PKF3fwHDhwgEAjw+OOPp+zZUhEDEQLBRUh0lHf+6KyYgZZ5BAIBurq6YjNnmpubmZ6exmKxJEX2/I6F5uZmpqammJqaWrVsqXR0dFBcXEx2djZKpZL6+vqMc1AA3nrrLd7znvdwww038OMf/xiZTLZqmevXr6e9vZ033niDO+64I+MOjXe73fT39y+wk3R01kHETqLLwnU6HdXV1ed1vfXAAw+wY8cO1q9fv+jvKpWKO+64g5///Of4/f411i4+QqEQ7e3t56yGgMjh7C0tLbS1tYlg9QLn7FmYkLzVSvNnj0b/n6xzEjo6OhbMTE2m7Hg4ceIEDz74IF/+8pfR6XQrplepVHzjG9/gkUce4bXXXlsDDSP09fXh9/tT4jP29fURCAQW5HEmtv8CgeDiIBQKndPuOJ1ORkZGkiZ7fl3qcDgYGxtLiuzOzs4FbZrdbmd8fHzVsoPB4ALZzc3N2Gw2JiYmVi1bkBlE2/T5eZysdj7T+528Xi99fX0L3j1Zk16icfT8cpmqOLqnp4d33nln0ZisqqqKLVu2ZNTkMDEQIRBchJjNZgoKCsjLywMyc2RaAP39/QuC/2RuRxPtWDi7EyYddjDf4Y/qkkkOCkQa9/3797N+/Xr+93//F5VKlTTZu3bt4te//jW/+93v+NznPpdRnbbd3d2Ew+G0dtZFmR+8RXU5X+ut8fFxHnvssXMOqT6bO++8k8nJSQ4dOrQ2iknkrbfeYmxs7JzzIaK0tLTQ09PD6dOn11gzwVpxdgCXzP1/PR4PfX19sXIflZ2M9sHtdjMwMJCS9jVevvzlL1NTU8Ndd90V9z0f+MAH2LhxI1/60pdSqNlCot9kfjswPT2dlIkLZ3dQNDU1YbFYmJ6eXrVsgUAgkMpis7chOW3D8PAwbrc7JbHX4OAgHo8nJZMCBgcH8Xq9aW0vBanFbDajUCiora0FInnc2dlJKBRKimylUrlAdiatqOnq6iIcDqfEvqNx9NnlMhVxdHt7OyqVihtvvHHR31taWnj00UcJBAJJf3YiiIEIgeAiZLEOvYGBAdxudxq1EpzN/D3xgdjWJ8loGFMpOxFdMrmDeXJykr1792IymTh48CAGgyHpz3j3u9/Nfffdxw9+8AO+/e1vJ11+opxtJ+k8ZCzT7UQKv/jFL1AoFLz//e9fNt3mzZvZunUr999//9ooJpG2tjZycnLYtWvXor9fd9116HQ62tra1lgzwVrR09NDKBSKlc3s7GyKi4tTEsDl5ORQVFSUEtm5ubkUFhauWZ3yxhtv8Pvf/56vf/3rqNXquO9TKBTce++9PP300zz99NMp1PAMZrMZnU5HeXk5kNwg3Ww2o9frKSsrS7psgUAgkMrZfm9tbS0KhSIlsVddXR1yufy8k30hbI8qWIjZbKa2tjbmjzQ1NeFyuZKyEshsNlNXVxebxJdM2ckglbHuWsbR7e3tXHvttZhMpkV/b21tZXp6ek1X1C6HGIgQCC5CFuvQC4fDdHd3p1ErwdmYzWa0Wi2VlZUA6PV6Kisrk9Ywzu9YMBgMVFRUrLlTOf9wrChNTU309fXh9XrXVJfFcDqdHDhwAJvNxuOPP05hYWHKnvXxj3+cr371q3zhC1/ImHMBzGYzOTk5FBQUAJCXl0dBQcGa20kwGFywTRlE7KSrqyvjtrNaiXA4zP33388tt9wSW5W2HHfeeSdtbW1p2TZtJdrb29m3b9+SK4R0Oh033nhjRi0FFiSXs4Os6L9TEcCdL7Lj4Ytf/CIbNmzggx/8oOR7b7nlFi6//HK++MUvrsmMQrPZTGNjY2zP4WRPikiVbIFAIJDK2TPD1Wo1tbW1SavvlEolNTU1KZGtUqmorq4GQKPRUFNTkxLZWq2W6upqUU9fQCwWi0evZ7LsZGA2m8nKyqKoqAhI7qQXs9lMdnZ2rP8gVZNe7HY7zz333KLbMkW5/PLLKSoqypjJYWIgQiC4yFiq4xcyp0EQROjo6FgQoENyO2FSJVsK4+PjzM7OnmOPoVCInp6eNdXlbPx+P3/6p3/KqVOneOyxx6irq0v5M7/61a/ysY99jI985CMZsR1PtK6Yfx5GOuxkYGBgwbLwqB4+n4+BgYE11WW1HDlyhOPHjy95SPXZfPCDHyQcDvPggw+mWDNpDA8P89Zbby25LVOUlpYWXn755YwcSBGsHrPZjMlkori4OHYtWfv/nh0cQnLbwPnBYTJlr8Szzz7LU089xT/8wz+gUCgk3y+TyfjmN7/J66+/ziOPPJICDRdyts+YzIkLZ8s2Go2Ul5cLf1QgEKSFs2dvQ3Lbnfr6epRKZexaMtvLs2UnU++GhoYF7VWm7fMvWB1nt8W1tbUolcqk2ebZspO1yigZpDLWXas4+sknn8Tv9y8bk8nlcg4cOCAGIgQCQXqYnJzEarUuaBAKCwvJyckRDkWGER0smE+yHL/5h4smW7ZUPWDhrNTorPd02mM4HOYv/uIveOKJJ3jooYfYvn37mjxXJpPxwx/+kP379/O+972PN998c02euxSZYidRh21+ecgEO0mE+++/n+LiYm6++ea40hcWFnLgwIGMWSUT5eDBgygUCvbu3btsupaWFkKhUEYMrAmST7SOWCzIWu1s/cVkR+ufTJa9HOFwmHvuuYdLL72UP/mTP0lYzg033MC1117Ll7/85aTs4bwcqWwHMqWNEQgEAlj7+i5ZnZLnq96C9OP3++np6VmQxyqVirq6ulXnsc/nO0e2Wq2mrq4uY9r5C8HHaWtrY8OGDbHBZpb+AADg40lEQVSVXEvR0tLCqVOn0j7ZE8RAhEBw0XH2oYMQ6fwUDkXmcfYhzpC8w6NSKVuqHnK5nPr6+ti14uJiTCZTWu3xy1/+Mv/93//Nf//3f3PTTTet6bOVSiW/+tWv2LRpEwcOHKCrq2tNnz+fpexkrQ8ZM5vNqNXq2LJwgMrKSjQazXlVb/l8Ph588EH+/M//fMGstZX40Ic+xOHDhzlx4kQKtZNGe3s7u3fvXnF7qdLSUi699FKxPdMFylJ1hMPhYGxsLCWyZ2dnGR8fT4lsm83G5OTkqmQvx8GDB3nttdf41re+tWAQRCrRVRHHjx/n17/+dRI1XIjT6WRoaGjJdmA1OBwORkZGUiJbIBAIEmGptqGnpwe/358S2d3d3as+QDYdss/H7VEF59Lb20sgEEhJW9zb20swGMzodj6Vse5axNHBYJBHH3102W2Zotx4442o1eqMiMnEQIRAcJFhNpuRyWQLOn4hsxoEAbhcLgYHBxedgeL1ehkcHExYdrRjYTHZHo+HoaGhhGVL5ezDsSD9A2P33Xcf3/zmN/nOd77DHXfckRYd9Ho97e3t5OXlcfPNN6+60y0RpqensVgsi9pJqjvrzmaxZeEKhYKGhobzqt569NFHsVgscW/LFOXAgQPk5+dnzKoIt9vNU089FZfTC5ED0h577LFVB/GCzOPsJfeQvO0ez1fZSxEKhfjiF7/INddcw4033rhqebt27aKlpYWvfOUrKStb0YHwxb7VaicudHZ2pky2QCAQSMXn89Hb27tonRQMBunt7U2J7EAgQF9fX8KyvV4vfX19i8r2+/309/cnLNvj8dDf358S2YLMYLEzs6J/J8PXSpXsZDAzM8Pk5OSi+q120staxdFvvPEGk5OTccVkJpOJa6+9NiO2ZxIDEQLBRYbZbKaqqgqdTrfgeqY0CIII0eB/sVF0WF1HyXIdC6uVLZXFOoOiuqTDHh9++GE+9alP8dnPfpa77757zZ8/n/z8fA4dOoTb7Wb//v3Mzs6u6fOX6ySCtbeTs8tCVJfzqd564IEHuPTSS7nkkksk3adWq/ngBz/Iz3/+81XPbEsGzzzzDG63e8XzIaK0tLRgs9l4+eWXU6yZYC2x2WyMj4+fU0fU1dUhl8tXVTaXCg7r6+tXLXup4LC+vh6ZTJayOuU3v/kNx44d45vf/OaqVkPM595776Wrq4v7778/KfLOZrlOhNVOXFhOttvtZnh4OGHZAoFAIJXo7O1U+L09PT2EQqElZa9mq5bu7m7C4XBK9F5KdtQnP598cMHimM1m9Ho9ZWVlC643NzfT29uLz+dblWyj0UhpaemC68laZbRaUhnrrlUc3d7eTn5+PldeeWVc6VtaWnj++eex2+1JeX6iiIEIgeAio6OjY9EOvebmZiwWC9PT02nQSnA2i52dAFBTU4NKpVqVw5pK2YnospQ9rvXekS+++CIf/OAHue222/i///f/Jq2jaDXU1NTw2GOP0dXVxfve975VOYNSiX7/s88paWhoQCaTrbmdLDZgdT7tJT45OUl7e7vk1RBR7rzzTsbGxnjiiSeSrJl02traaGhoWLTsLsb27dspKyvLiBk4guSx2FaPABqNhtra2lWVzeVk19TUrCqAW0q2Vquluro6JXVKIBDgK1/5Cvv372f37t1Jk7tlyxbe//73841vfAOPx5M0uVE6OjrIz88/Zwu2ZJzRYzabKSwsJDc3N+myBQKBQCrROufstqG8vBy9Xp+U2Gsp2atp05aSXVFRgU6nS4ne0e1RRT19/hONseTyhV3D0ZVAqzlPYLHzuCBiT6uVnQyWmhARnfSSjLKT6ji6vb2d/fv3L9g1YDlaWlrw+/1pjyfFQIRAcJGx3Az06O+C9GM2m8nLyyM/P3/B9WRsR2M2mykoKDinY0GpVFJfX79mNhAIBOju7l7SHicmJrBarWuiy8mTJ3n3u9/Nrl27+NnPfnaOM5ZOtmzZwu9+9zueffZZPvKRj6zZdhVms5mKigoMBsOC69HOurWyE7fbzcDAwJJ2MjAwgNvtXhNdVsODDz6ITCbj9ttvT+j+bdu2sWnTprRvzxQOh2lvb6elpSXuwTqZTMaBAwcyYk9SQfJY7BD5KKtdrbQWshsaGpIueykeeOABOjs7uffee5Mu+xvf+Aajo6P86Ec/SrrspXzG6upqVCrVqvNhMdk1NTUolUrhjwoEgjXFbDZjMBjOmb2djC1jzWYzJpOJ4uLiBdflcjmNjY2rlp2VlUVRUVFKZGdnZ1NYWJh02YLMIJV9Q5ne72Q2mykrK8NoNC64nqxJL+Xl5SmRHaW/v59jx47FvUIdoLa2lo0bN6Y9JsucnhaBQJBygsEgXV1dizYI0YA83Q2CIMJSW9FAcjphFrOBZMiWQl9fH4FAYFkHJbqsMZUMDg6yd+9eqqqq+N3vfodGo0n5M6WyZ88e/ud//oef//znfOELX1iTZ2aKnUSXhS+1NROQ1gO94+WBBx6gtbWVgoKChO6XyWR86EMf4ve//z0zMzNJ1i5+jhw5wvDwsCSnFyIzcMxms2hjLiDMZjOlpaWYTKZzfktGO7VYcJgs2YsFhxCZpZdsG/V4PHz961/nT//0T9m2bVtSZUPke9x5551861vfSvoWfkv5IsmYuLBUG7PWkyIEAoEAztRJi02ySFbstZTs1a4uS4feqWgvBWvPUm1xaWkpRqMxJe18WVnZqlcCJYNUxrprEUcfPHgQpVLJzTffLOm+1tZWDh48mNbD5sVAhEBwETEwMIDP51u0UjQajZSXl6e9QRBEON8bxnj1iD7zbKKzYFOty8zMDPv27UMul/PYY4+RnZ2d0uethve///3867/+K9/5znf43ve+l/LnnQ92kikzalbi6NGjvP322wlvyxTlz/7szwgGg/z6179OkmbSaW9vJysri6uuukrSfTfccANarTbtM3AEyWOlOqK7uzvhM01Wkt3V1ZVS2ckMzv7jP/6D4eFhvvGNbyRN5tl85StfwWazJbVtCIfDS26LB6trB1IpWyAQCBLhfI29zlfZgvTjcDgYGRlZNI9XuxJodnaW0dHRlMhOFqn0Q9ai7LS1tXH11VdL7r9oaWnBYrHwxhtvrFqHRBEDEQLBRcRyHXrR6+luEAQRVmq8+vr68Hq9kuXGE/wnKlsqZrMZnU5HeXn5Ob9lZWVRUlKSUnv0eDzccsstjI6OcujQoXMO6cpEPve5z3H33Xfz+c9/nt/85jcpe04oFKKzs3NNO+uWwmw2k5OTs+hKgoKCAnJycjK+3nrggQcoLCxk3759q5JTUlLC3r17U3YwbTy0tbVx8803o1arJd2n1+vZs2ePOCfiAmKldioQCNDX15cS2X6/n/7+/pTI9vl8DAwMJCT7bBwOB9/61re488474z5TJRGqqqr4xCc+wXe/+92knfU1NTWF1WpNic84OTmJzWYT/qhAIMgYVmobRkZGcDgcKZE9PDycMtmDg4O4XK6UyD5ftkcVLM5SBypHWU1bnErZySAcDqdsQs1axNEOh4NnnnmG1tZWyfdeeeWV5OfnpzUmEwMRAsFFREdHBxqNhqqqqkV/P58Ofr2QiR4avlTj1dzcTDgcTmg7GovFgtVqXbJDpLm5mVAoRHd3t2TZUlnqcKz5uqTKHoPBIH/2Z3/Gm2++SVtbG+vXr0/Jc1LBt7/9bT74wQ/y53/+5zz77LMpecbw8DAul2tZO/H5fAl3BEphqYPOIDKjJtPrLb/fzy9+8Qv+7M/+DJVKtWp5d955J6+//jqnT59OgnbSGBsb480335S8LVOUlpYWXnzxxTU7+0WQOqIB3HJ1BCR24HAoFIpLdiIB7Eqyo+1usuqUf/u3f2NmZoavfOUrSZG3HF/4whfw+/388z//c1LkRb/Bcr5IohMXljowfL7s3t7eNZkUIRAIBHa7ndHR0ZS0O3a7nbGxsRVlJxLXWa1WJiYmVpSdyFa3MzMzTE5OrthersU2uoLUsFI7v5ptw+LxIdIZv42MjKwY6/r9/oQm1KxFHP3000/j8/kSiskUCgX79+9P6yp1MRAhEFxEmM1mGhoaUCgUi/7e1NREZ2fnmh2GK1iclQL01WxHE8+qmERlJ6LLUnpEdUmFHuFwmM9+9rP8/ve/59e//jW7du1K+jNSiVwu56c//SnXXHMNt956K0ePHk36MzLNTpabSZzuGTUrcejQISYmJrjzzjuTIq+1tZXc3Ny0HFp98OBBZDIZ+/fvT+j+lpYWgsEghw4dSrJmgrVmdHQUh8OxZB1RXl6OTqdLqGxGg8OlZFdUVKDVahOSPTw8jNvtXlJ2ZWUlGo0mKXXKzMwM3/nOd/jLv/xLqqurVy1vJYqLi/nc5z7H9773PcbGxlYtb7lDvSFS9yY6ccFsNiOTyaivr19Wdk9Pj2TZAoFAIJWVZm+vZsvYlXzqqOxEOmVT6a+vJHs1gzOCzMBsNlNYWEhubu6ivzc3NzM2Nobdbk9IdnFx8ZLbBjU1NTE6Opr0s63iJZ1lJxlxdFtbG+vWrVvSR1uJ1tZWjh8/viaTChdDDEQIBBcR8XT8ut1uhoeH11ArwdkcOXIEWDr4LyoqIisrK+GGcbngv7i4GJPJlFEDEeFwOKnP/ad/+if+/d//nfvuu493v/vdSZW9VqjVah566CHq6+vZt29f0p0Is9mMUqmkpqZm0d+T2Vm3HOFwmFOnTi3rZGX6QMQDDzzA1q1b2bJlS1LkaTQabr/9dn72s5+t+SFjjzzyCDt37kz4wO3Kykq2bNnCI488kmTNBGvNSkGWXC6nvr4+1p4lW3ZjY2NKgkOFQkFDQ0NS6pTvfve7+P1+7rnnnlXLipe7774bjUbDt771rVXLMpvNVFVVodPpFv19tUF6dXU1Wq026bIFAoFAKtG6JjoocDa5ubkUFhauqt1ZSnZeXh55eXm8/fbbKZGdn5+fEr3z8/PJzc1NSG9BZhBPLA5w4sSJlMlO14oas9mMQqGgtrZ20d9XM+kl1XF0KBSivb094RXqADfddBNKpZI//OEPCctYDWIgQiC4SHC5XLzyyisYDIYl00T36hedROnli1/8IsCSHfAymYycnJyEzgj41a9+RXZ29pIdCzKZjNzc3JSePwCR5cdDQ0NLzsCAyP7/TqeT119/PSnPDIfD3Hfffdxzzz189atf5eMf/3hS5KYLk8nEo48+ilar5aabbkp4L/bF+NWvfkVubi5KpXLR3xUKBbm5ufzqV79K2jMX47HHHmNmZoa33npryTT5+flMTU2lZGXIannsscd46KGH2L59e1Ll3njjjYyMjPDhD384qXKX4+jRozzyyCOr3l5KoVDwy1/+ck22fxOkjt/85jfIZDIqKyuXTHPixAl+9rOfpUS2wWBIaG/bqOyKiool0xiNxlX7QQ899BDf+ta3+OhHP0pJScmqZEkhNzeXT3/603z/+9/nP//zP1cl6+GHH8ZkMi35e2FhIWq1OqF2YCXZRUVFqFSqlLcxAoFAABG/V6/XL3voa3Z2Nr/97W8Tkm00Gpet86anp/n2t7+dsGyj0bhkmuzsbP73f/83Idkmk2lZ2TMzM0kZ+BasPaFQiMcff3zZvqHo+R9f/vKXJct+4okn0Ov1S6aJrhRNpEwlg2isu1RcI5fLyc3N5de//nVCsnNycpaVnZeXl7CP86UvfYnx8fFVrbbNzs4mFArx2c9+NmEZq0EMRAgEFwljY2M4nc5lO3+ileXDDz+8VmoJFuEzn/kMLS0tyzbeAwMDy3bOLsWzzz674v7sAwMDHD58WLJsqXpA5MyKpXA6nQBJ28blr//6r/nkJz/Je9/7Xr761a8mRWa6KSkp4fHHH6e7u5va2lo8Hk9S5L744otMTk4um2ZsbIxXX301Kc9biiuvvJKmpia+9KUvLZkmulz4qaeeSqkuiRAKhVAqldx2221JlXv11Vej1+uXDQ6TTUFBAdnZ2Xzyk59clZxPf/rT5OTkLDsIKch82tvbCYfDy27l+MUvfpFbbrlFsuyDBw+uKLurq4vBwUH8fn9CspdbadfZ2cng4GBCBxRGic4wS+T9V0v0masZTImeAbKczyiTyfD5fDz++OOSZXd2dq64H7rf75csWyAQCBLhqaeewuVyLXoeWZTe3l6OHz+ekGyHw7Gs7I9+9KN85CMfkSz76aefXlF2b29vQpN1nn766RW3zfnwhz983k/sulhxOp1YLJZlZ+VfeeWVbNu2jU984hOSZDscDiwWy7KrHaI2m64Z+S+88MKy/RAQ2Yb0tddekyz7xRdfjEt2onH0hg0byMvL49Zbb03o/ih33XXXkiueUs3iUx0FAsEFR11dHd///ve54447lkyzceNG/vM//5N9+/atoWaCs/n617++Ypo333yTkZERybIPHTq05OHQ82WPjo5Kli2Fj3zkI9hsNj73uc8tmeaTn/wkXq931R2fUQwGA+Xl5Xzve99b1mE/32hsbORzn/scv/zlL5O2jdWhQ4eWHQgDeOWVV1J+6HBeXt6Ke+b+zd/8DUqlks985jMp1SURDhw4ILmjNB7y8vJiA3VrRXl5eVLy+84770zaeRmC9HHw4EHMZvOyg2H33ntvQrIfffRRzGbzsrP0Xn/9dV5++WXJK3QOHjxIT0/PsvXbm2++ySuvvLLkirB4+MlPfsLXvvY16urqEpaRKJdeeil9fX0UFhYmLEMmk/HrX/+ajRs3LplGLpfz6KOPkpeXJ1n2r371Ky655JIl0ygUCg4ePEh+fr4k2QKBQJAIhw4dWnEyz+HDhxNazfn444/j8/mWTfOTn/xEstyo7JX8zMOHDye0hevjjz++4oD8T3/6U8lyBZmByWTiJz/5ybL9PiqVKqGJj1lZWfzkJz/hwIEDS6YpKiriF7/4BZdffrlk+cng0KFDS+4QEeW1115jeno6IdkrxdGvvvoqMzMzkmUD3HHHHcv26cVLovVOMpCFk735tkAgEAgEAoFAIBAIBAKBQCAQCAQCwRxiayaBQCAQCAQCgUAgEAgEAoFAIBAIBClDbM0kEFyADAwMrLgvXRSv14tGo4krbUFBAVVVVatRTTCHlDwCYlsllZaWxpVeSr5KSQvS7SBV9ihVb7jwbTiVdiXVBqV861SXh1Tlu1S9k6GL1GemMh8yRbYgdaSq/obMatcyqc1cLamsTwsKCoDlz3eaj1QfU4psUUcIBAKpSK0fU9U2SG3/Uumvp1JvEHV1JpBKXy7T2/nz9d1T5WumI3ZdDDEQIRBcYAwMDNC8bj0etyuu9HIZhOLcoE2v03LqdIdwJlbJwMAA69avx+2KL49AWj5JTi+TQ3jpQ0HPRqvT03H6VNyN3fp1zbjccR6iLEUXuRyWOcx0MaTofr6RiF1J+d5SbTDe+mItykMq6q6BgQHWrVuPO866NopOp+d0gjYouTwhLR/Wr1+PS0I+6PV6Tp1a+V1SqbcgdUjNN5lcTlhCnSy1HKeyvpIkWy5f9iDts4m3nCSDRMqalHfXaLTIZWHcHm9c6aXkg1ajISyT4/W440t/AbfnAoEg+UiNkSF18ZRcJiMkZYf0FLZ/UtJLbecBdHo9p9eoDRScS6p9uVT6EKuNBVLaD0FqfRyp+RBPOYt8j3W43PHpEEWv03Hq9OmklmExECEQXGBYLBY8bhclN34MpT4LdV45noleNPmVaPJK8U6NoCmoIOC0YT3+DJZXf8vHdpaQpVFSnq2md9pDZY6G0iwNIzYvFTkabJ4Ax0ed3PfyKBaLRTgSq8RiseB2udj7t/eRXVKDQqVeNv30oJlD3/lEXPk07Qxg9wb4zjNDcaV/ptPKb49a4rKXgGMapSGbrp98Om47sFgsuNwe7risiO3lxrj0lmK76z7yr5iq1sf13Z0jXbzzH391wdpw1K5a/vYHFNctfcBoLP2AmT986y/i+t7OgROMHvph3DaYrVfy6Ye64vrWUb3/5O9/QEn90oeXRpnsN/Pbez+eEl2kYLFYcLtdbPvL/0d29Ya47pkd7uTwDz6VsC7R8vSv79/MhtKsFdN3Tjj4qwePxJ0PLpeLT33zR9Q0r5wPQz0dfO9vPxK/7KjeZdkr6z0+G7fegtQRzTcpbcnuz/4QQ/HK7ZptqJNXvie9XSu65g6MdduXbacCbjtDv/tOSmXf893/oH79phW/YX/Xab7x2bvWzJZjZe22jfHXEb86Hmc7cJzRQ/cBSGrTpfiYQFz5INUXEQgEAikx8vz6PtkxTNSnvu/jN1BTmIVapVhWb/PIDJ/48VMp8del1NMnRpz88JVR3v13P6CkfuU4AyI++++++Reirk4jifhyt93z75Q2rJzHp199kid+8s2U+BDJiN8SeXep9UMqfJyo7J2f+h45NSvng33IzMvf++SK3yryPdz88K6rqCk0olYuX/cAmEdtfPKnLya9DIszIgSCCxRdaSNZzTtR6bMxVKwn7PcgV+vIXr8bfVkjSn0WOZdcA8B7NxeyszaLqjwtW8tNGLVKeqc91BfqqM7TcllVFlU52jS/0YWHa2aSmeFuAn4vY+a38bkd6HIK8brsGPKKkSkUBIMBrCN9ADQW6NhZm0W2TsX6EgOeQBidWs7uumwai/SUZWuoyddSOZdXK6XPM6i5pj4HWNleNHllaPLL0ZU2JvSum0sNy9rXJWWGmN7x6BK1Xe/0ECG/F3vvUQIeJ6qsAgLuWdQ5xSBX4HfaCHicBL1uDGUNq8uw8wT7+CABn4eRjohNGXIL8DrtGPOLkSsUTA9343M7CPgis1FW+t5qUx6anCIgPpvKM6poLNBJ1ts6PkTA52H49Ft45/T2OO2Y8ouRKeRMDXXjdTsIhQJx6VKWrUlYFym4LUME/V5muo8Q8DjQZBfgd9nR5hYjkyvwu+w4xnoIBXyYyhMrP2czPOPGGwhyZNCKwxugwKhm1uOnOEuDQi7D7vbj8QcJBqVMCY9gGR3E5/PQdeIwbucs2fmFOB02cgtLkCsUTE2MYBkbAikz+WJ6e/D6l9bb5vIzOeshKGkquyDVrFTWdGpFrC1xWyeZHe0i5Pcw1fU2AY8D7VyZ0OUWI5fLcVmGcE70xyU736Be0D4YqjejLazCVLMVpc6IZ7wXXWk92sJqshovw1B1CdqCyrhkl2VrWFeixxcIxyVbX9oQkz02PIjP6+HU0cO4nA5yCwpxztrJL4qUk8mxEQIBP8hka5xbEYatnkgdMWRbpKzB5KwXhzeAwxupT1dqB7SFlRTsuCUmX0qbHo/93HJJQUz2SvmgyS1N2BcRCASCuGKevDIIBoHkxzDaohoAJu0uuseteP0B3u6dwOHxUZilw+72UZyjRyGXEQiGsMy64pKtNuWhL2sC4mv/pNTTZdkaSrMj28RE44zh02/jdTkw5ET8dWN+xO+dGurGOjaAz+OisLppLbNWsAwr5TEyWcyXs44P4vd5GTw1l8e5hXgcdkz5JcjkCiyDPVgnhmM+zkq2qdDoUBpyYrrEU6aSGb/FY9/x9onIZKAtqonb18w3qCX5OPP9WIdleEV/Ouhzk1UhrZxN2t10jdvx+IO83WfB4fFTYNJG6p5sHXKZjKFpJ5ZZT8rcWLEiQiC4QDHWbMZYvfRsPU1eOZOvPRz7e2fN8rNUt1UYk6abIELF5t343A5CAT/63CKcMxNM9pyksG4jfq8LrSmX8Y63MeQVA7C5zMimsqXzoXzOQXz46GTc6aNp47EXAEf/cWkvOUc8ugzOxK/L5NQgAOqcYkLBAOqsAny2CRwDJzFWbSTodaEy5OCfncZjGURfUpeQ3ucjxvwSQsEAxtxCHNPjjHefoLj+EvweF1pTDgABn4eShsg3jud7K+acx3ht8PiIQ7LeWfklBIMBDHlFOKbGGes6QUnDJfg8LvSmXLKKyhakT6UuUtDmlhAO+tHkFOKxTmDrf4fs6g0EvC5UxhxcU8MYiqrIbdiOtedYUp5ZnKXFHwxTaNIwOevlnRE7G8qycPmC5OhVOL0BfIEQ60pNkmXnFpYQ9PvJzi/Cahmnr+M4Nc2b8LidGLNzsc9Y0BlMKEqku5DF2Rr8oTCFRg2T9rP01qkYnnHj9gVZF8dMbsHaIaUtKb5kNwGPg1AwgC6nEPfMBDN9J8mt2UjQ60RtyiU43IW+sDxu2UBC7UO8skdtvvhlz/lNBcWlBAIB8gqLmJ4Yo+udYzRs2IzH7SIrJxfL2Ag+r5fq+uYl5aWSYpMmUkcY1ZE6YnSWDaUmXL4AOXoVMy4/dk+AbZU5QHzvPr/9l9Kmx5N2fj0tVReBQCCQQrwxT9DrBJIfw8jVkc7/3evKcXh8+IMhirJ0TNhcnBycYmNlPi5vgFyDhuMDU+xqLo9bdrRulNq2xpNeq4zMYTbmlxAMLBJnuF3osnIACAb8VG/eyaj56JIyBWuLFF/OVFBCKODHlFfI7NQEo10nKI3GZFm5GHLz8Xvc5JVGZsjHY5vq3FIGH/rHuHVJZvyW1DK8I1Ieo/5gKnycqB+ryy1e1p9Wzk4TcDtRqKUN2uxuLsHh8RMIhijM0jFhc3NyaIaNFbk45+qeCZsbg0ZFgSk1k5HFQIRAcIHi6DvGbOcb6ErqCfm96MqakKvUBN0OwqEAIb8X91gvAMdGHLwxMEt9gQ5vIERToQ61Uo7LF0SnUjDrDXByzJnmN7owqdi8e9nfTYXlTHRGnLil8mnE5kWvVpCjVdI95WbU7l02/fx87ZmK7BEYj734bZOEgoGE3vP3xy1MuQLn6N1cpGfY5sXhDcbSxqOLdzqyvDGrZjOmms1LPlebXx7792xfcjqBM53Spq2UNm1Z8vfsogqAWHBgef33BGanFv/WPg9+uyWW78vZ1PFRJ1dUZdE95cbmlm4nZc1bKWteRu/iiN6qOWdrKZty+kI0FujonnJTbFp+e5hkkFO3mZy6pW1QX1C+5G+Jsrkim80VSw8el+dEvtGxIZtk2fUbt1G3ceuSvxeURvKh5+QRybI3V+Qsr3du4noLUsdS5d7hDRIMhZHJiLUlAMUbdy0rz/Cucqa6jy0re8TmRSGX0VCg48iwI9auSWkflqojFHIZkw4/O2uyODw4y9hsnLJ9npjs5k3baN60bcl3LC6LzGbrOP52gl99dUTqiKUH9M7UEXZg5XcHGZ7Jgdj9Utr0eOxnvo+5XJsUcNmRK1TIVPEf4CgQCATzWdbv9XsJzE4jV2nwzowByY9hnP0nYul3r1veRyzPN3G0bzI+2T4PzqHTgPS2dal3nB9f2jwR376seYU4Y09FArkiSDVL2cSo3YdeJV/gy1U0b6W8eeuSsnLmYrK3n/gNsEK77bTinRpGrjHE7o+nTEUHvlL57vOfGW+fSMBpxT87FfMHl5NtUCsk+TghvxfFvO+UX7+F/PqlY0zDXIwZ9amlsKupZNnfy/MiehwbmJIsOx7EQIRAcIFirNkMoSC+mTEUhmzCAR+emRFUpgLkKg2Gyo04eiMdkdGR3CPDDsbsPsZmfeTpVXROurm8ykQ4DCVr0KF3sTHeeZThk6+TW9FA0O8hv3odCpWG2YkhZAoleZWNDB9/BfvEwtmdZ+fT+Kyfy6u0TDn9XF2fQ/eke9n08/M1X68CzozOO3qP4JsZw2cdQ2XMwz3aSda6XQQc05jqtjNz4rmE3vXWTQUEw5yjd1VuiHAYqnK1DM5449ZFodFHvuFrv8c3O03I78FQ3oxcqcYzNYxMocRQ2oC99yghnxu5WkfAbV9ljp0fjJqPMHjiNfIrGwj4vBTWrEOhUuN3O3FaLRTXb6L/6Es4piIBVsEVt8a+t3u4A4Uhe27bnTD6ivUoDdl4LUPA8ja1uzY7ZoPPdc5I1nuk4wj9x1+joKqBgNdLUe06lCo1bocNv9dNYVUjnW88g2N6AojYVFSXjgk32ToF4TCoFDKUChkFBhU5OiUTs76kfdvFsPYcY6rjDYylEYfVVDlnh9OjaHOLcY73I1eqURtzkvbMY0M23uidpr7IiNcfpLnEhFopZ3jGjVIuo6HIyOmxWSZm4zsMbj4vHfxfbDMW/F4PlQ3rUak1WEYH0eqNGLJymBobQq5QMjU2nIDe1ojehQa8gVBEb4WcYaubQChMZa6OU6Oz+ALSDkIUpJaV2pIppz/Wlkx1H2Xy9OtkldUT9HvJqWxGrtLgnIzYjTanCI9tkume48vKjrRrJoas3gXtmhTfZqk6whcMs7XcyJDVy3WNufRNeeKXPTeg/dQjv8E6bcHn9VDbtAGVWs348CAKpZLq+mZOH3uLQMCP25meSRzHhmy80TdzpqwVGyJ1hNWDUi6j0KShc8KBa64DbaV315U04LNNxORLadM3lxljabN1CnzBMCN2D9laJXIZrC82MG4/U08XXHFrTJez23+5SoNCa1qgi0AgEEhhJb83oM9CV9KAa7QLSH4MY6yLDGIf7Zvk9c5RGkpy8PiDrCvPQ6OUMzTtwKhV4fEFMWhVMb3jkj03OUtq27pUe2l1BygwqCgwqGKH8o50HGHg+Jk4o6hmHQq1Gp/biXPGQk5JFWNdx5HJ5QT9qfXBBfGzXFsMkbb4td5IrHzkyYdwWKcI+DwU165HqVJjHY/4cab8Iib6zCiUKmyTkc74ldrt7HW7F/QhxFOmkhm/LVceos+Mt08k+j6jT//3irKj/TPz+9GW+1YBxzQK7ZnV7H0v/Q6v3bLAn/Y5bRAKEfRH6hyVXvrq96P9U7zeNUF9cVYkjizLQaNU4PT6scx6qMw38s7QDLVF0mXHixiIEAguUNyjnehKG1HPzQgPBXwodFmEAj4IRLbYiXbMdloiTohCBuXZkYrSFwhRnauJNQLTCcxwFixPceMWihojM0rGOt7C0ncarTGbcDgMAR+hYICc8nqM+aVAfPl0fMSBfW7P53jSR9O6RzsjSskVC2xGU1iNdyrS4ejoP07AMZ3Qu3Za3DQW6JbUY2LWx6DVE7cuUdstvvJWTDWbsfccwTl0OrL/ZDhMOOgnHAqgySvFUNpAOBxm5uQLCel+vhFdETFy+i1mLaPMWkbRZ+UxOWCmZutVOKfHad69P7YiYrHvHSX6vf1z+R6vDSZSX0RXRAydegu7dxS7ZRR9dh6WgS7qtl/F7PQEm/b8CSMdR5fUJcp8u4qmSxXRFREzXW/jnhnDMz2K2pSHe3oUY2kd2rwSTGUNeGbGCfo8SXlmdEXEkQEro/4gozYP+QY1E3YPl9fmMTTjZkdtHs92TEqW/a4Dt1G3cStdxw8z0PkOxqwcwuEws7Zp8ovLMOXmU17bxJFZ6asWoisijgxYGbV5ztF7yuHjpo3FPHtaut6C1CGlLYnO3rJ0vo17ehTX9BgaUx7umQlKNu3GY5+moHE7Mrkybtnz27XV+jZRViv7hnf/Kc2btnHq6B/p6TiJKTuXcChEwO8nGAxQVFpOVX0Trz//ZJJyQRrRFRFHBm1zZc07V9a8XF6Ty5TTx57mQp7rsMT/7vPafylt+mJpszQKwuEwwfC5bcZiusxv/4Ne95l2SyAQCCQSj997dluSzBgmmmZLTSFbagp5q2ecsRknozMO8k06xq1OqhtLmZp1k2PQMG51SZadjJgxSqwvwOkHzqyIGD79FrOTZ/z1yX4ztduuwmWbYt279gPQ/cYz8WSJYA2Ipy2O2sPWG99LefNWBk+9xXjPO+hMkVgg6Peh1hnJK62msLqRiX4zsHK7nYgPkcz4LSV9Ign2o8Xj43gskUmoNe/6k5hPbR04jdqYQzgUGTTNrdmAxz6NSmci4JUWX26pzmdzVT4Ab/dZOD1iJUevJhwOEwbUSjkV+Qay9Wo8ttTE0WIgQiC4wCgoKECr09P1k0/HlV4ug08/1BVXWr1OS0FBwcoJBXExPWiO/VsuV5BVuNAZnh2PNELO6XGUKlXc+QTS8hWZPG57AdDq9HHbQUFBAXqdNmW6OEe65m6TL9iGCcAzN4s/uiWTz56apYWZhmUgYlcyuYKsuW2YAn4fuaU12OZsatR8FMf0OAqVJiV1BUivLyb7OyLPkSvImdM76PORV1aDdSyi90jHUWanxlFJLA+prLtmhyMOq0yuQB91Kv0+DEXVuCYjNhg9GyKadrV0TkT2GpXLZbEtVryBEFX5BsajW7MN2Zh2Sp9NNNRzJh8KSysX/DY5EsmHnpNHsM9IL0+d47Nn9M5dTm/pKzkEyUdy/Q3Yh87UP/qCuXLs92EsrsYxESkPU93HcM+Mo1RKK8dS2gep9ZXUtqe/K7L9hVyuiG3DFGVsKLKFUcfxt7FNW+LXIYnE6gjZWXVEnn5eWbMTCIVRKRVxv7tGo0UuC8f9baXkg1ajISwhH6T4IgKBQCA1RobUxlPmkcjqYYVcTnl+ZOax1x+kujA7NvgwbnUxbnWhUipT1v5JTT851/kslyti26YG/RF/fX6cAeC0XRyxVyaTiC830RfNY3lsG6Yo1rGIjzPccSSyda8Eu5fqQ6w2fkt1P0QqfRxY3KeOEvWpAx5XLF28mEfPTCZTyGVU5BkW/D4+N/gwbnMvSJtMZOFwOJwSyQKBIG0MDAxgscQX/Hq9XjSa+PbaLSgooKqqajWqCYjkz7r163G7XJLu02i1PPTb31JaWrpiWin5KiUtSLeDVNjj6Ogo733fbXg90kbptTo9HadPXZB2nGq7Gh2NLL+Nx/4gfjtZi/KQirprYGCAdevW43ZL01un03M6QRscGBhg/bpmXO74Z77odVpOne5Y8XkDAwOsX78el4R80Ov1nDq18rukUm9Baom3/h4dHeV9t92Gxy2tTs6Udk1K23PbbbfhlvCe8ZaTZJBIWQPQatT89qGHV8yHaIdAqnxMKbKFTyoQCKQiJSaB1LQ7o6Oj3Pa+9+L2SJt0odVo+O1DDyXdX0917KXT6zm9Rm2gYHFS6ctJjcdgbdv5VPWLSU0v5d0TyYd4ylnER1yHS6KvrtfpOHX6dFLLsBiIEAgEgjSwWKN41113UVRUxJNPPsk//MM/sH///gW/i6B7IfO/YU9PD7fddhvf+973eNe73sUnPvEJFAoFP/jBDxbcc6F/w7Pt6oUXXuDzn/88LS0tnDp1it/85jfn3JMJ32S+3h0dHXzwgx/kvvvuY8eOHXz0ox/FaDTy//7f/1twT6bpferUKe644w5+9KMfcfnll3PXXXeRm5vLv/zLvyy4Z7V6n53H3/72t3nzzTfx+/1ce+21fP7zn0/4eYvJPnz4MB6Phz179vC5z30uabL/8R//kSNHjuB0Ornxxhv57Gc/m7BsQWYwP4+dTid79uzh85//PB/4wAf45je/yeuvv84f/vAHZDJZ7J7zMZ/nv+f3vvc92tvbefzxx5mcnGT//v1885vfZO/evbH0a/2OZ5e13/zmN3z3u99l586dhMNh/u3f/u2ce87HfBAIBILzlbPr6cOHD/Pxj3+cW2+9lddee42DBw+ec08m1NPz9R4cHOTWW2/ln//5n9mzZw+f/exncbvd/PjHP15wTyboLYif+Xkc9R+efvpp1Go1e/bs4aMf/Sgf/vCHY+lF/qaGs+uID37wg9TV1fHYY4/Fytx8pEwAPLsP6vbbb6e+vp7HHnuM7373u1x33XUJyZaC2JpJIBAI0kBVVdU5FfrIyAjvec97OHHiBH6/n+3bt6dJu/OD+d/wySefRK/X8/GPfxytVsuf/dmf8Td/8zc0NjZiMqXuoKVM42y7eu6559Dr9Vx//fU8+eSTbN26FblcnkYNF2e+3gcPHiQrK4u77roLtVrN7bffzle/+lXWr1+PTqdLs6YLma/3I488QnZ2NnfddRcqlYrbb7+db3zjG2zYsAGtVpuSZwJYrVa2bt2Kx+PBZrOtqt44W/bMzAxbtmzB5XKlTLbD4cBut4v67gJgfh4/9NBDBAIBPvGJT1BbW8tdd93Fww8/jF6vZ/369WnWdHXMf88333yTW2+9lcsuuwyAbdu2cfLkSe65556M0A/gZz/7GQ0NDezYsYNHHnlElDWBQCBIM2fX04cPH0Yul7Nv3z7+8Ic/ZKTPCwv1fuGFF1Cr1fzlX/4lRqORO+64g0996lPU1taSm5ubZk0FiTI/j7/0pS9xzTXXcM011wCwb98+3nrrLb7//e+nU8WLgvn5EA6HGRoa4iMf+QivvPIKwWAwYV/u7LonKvtjH/sYL7300qpkSyHzeiMEAoHgImRmZobJyUmam5tpamqio6Mj3SqdV7S3t3PDDTfEOnwPHDiAz+fjqaeeSrNm6aWjo4OmpibWrVuH1+tlYGAg3SqtSHt7OzfffDNqdeTAr5aWFtxuN888k9kH3rW3t7N3715UKhUQ0dvlcvHcc8+l9LnRPG5ubk56vdHR0UFzczPNzc2YzdL2H41XtqjvLkza29vZuHEjtbW1AOzZswedTkd7e3uaNUse3d3dnDp1ipaWlti1lpYWHnvsMQKBwDJ3ri3zy3Fvby8+n/SzYwQCgUCQOsxmM7W1tWzcuJFwOExXl4QzjtJEe3s71113HUajEYjEXsFgkMcffzzNmgmSgdPp5JlnnjnHx3n11VclbW8mWD0jIyM4nU7WrVuX9LhpeHgYl8vFunXrUhJLLoUYiBAIBIIMINrJ19TURFNTU9I7/S5kpqameOWVV2htbY1dq6+vZ/369bS1taVRs/RjNptjNhX9O5MZGxvjjTfeWOD0rlu3jvr6+ozuwBwZGeHw4cMLbHDDhg3U1NSk1AbdbjcDAwOxPO7p6cHv96dEdnd3d9I6V51OJ0NDQzHZXV1dBIPBpMgWpJ9gMMjBgwcXlGOdTscNN9xwQdXJ7e3tqNVqbrzxxti11tZWZmZmeOWVV9Ko2ULmtwPBYJCenp50qyQQCASCeZxv/rrNZuP5559f4PdWVFSwdevWC6qdv5h56qmn8Hq9C/J4//79hMNhHn300TRqdvGRyn6idPVBiYEIgUAgyACilX5jY2Ns9rE4wic+HnvsMUKhEAcOHFhwvbW1lYMHDxIKhdKkWfoxm800NzdTXV2NSqXK+MDm0UcfRSaTLTgfRSaT0dLSQnt7e8aWiYMHDyKXyxfsCy+TyWhtbU2p3t3d3YTD4djKgkAgQF9fX1JkR2fjRR1Tv9+fEtnNzc34/X76+/uTIluQft58800mJycXBK8QmUn38ssvMz09nSbNkktbW9uC2aAAl156KSUlJRnTEeP1eunr6zuvOrgEAoHgYiO6urWgoIDc3NyMr6efeOIJAoHAggkHEIm9Mm1VoCAx2traaG5upqGhIXatpKSEHTt2ZPTksAsRs9mMQqGgtrY26avU58sWAxECgUBwkWE2mykrK8NoNNLU1ITT6WR0dDTdap0XtLW1cdlll1FaWrrgektLCxMTE7z55ptp0iy9OBwORkZGaGpqQqFQ0NDQkPGBTVtbGzt37qSgoGDB9dbWVoaGhjh69GiaNFuetrY2du/eTX5+/oLrLS0tDAwMcPz48ZQ89+xZLPOvXayyBemnra2N/Px8rrzyygXXDxw4QCgU4rHHHkuTZsnDbrfz/PPPn9MJI5fLOXDgQMYE6T09PYRCIZqamigtLcVoNIqyJhAIBBlEIBCgu7ubpqYmZDLZebEyvq2tjU2bNlFdXb3gektLS8atChRIJxQKcfDgwXMmlEAkjw8dOiS2eVxDolu3qdVqmpqamJycZGZmJmmy6+rqUKlUNDU1MTExgdVqTYrs5RADEQKBQJABRGeuA7GOObFv+sr4/X4OHTp0TmcQwM6dO8nLy8uYDqG1Zn5nL5CSff6Ticfj4cknn1zU6b3qqqvIysrKmFnG83G73Tz11FOL2uA111yD0WhMmQ2azWZycnIoKCigvLwcvV6ftDzu6OggLy+PgoICKioq0Ol0SZWdn59Pfn4+lZWVaDSajLZNgTTa29vZv38/CoViwfXy8nK2b99+QdTJjz/++KKzQSESpJ8+fToj9viO+hHNzc3nTQeXQCAQXEz09fURCAQWxIGZXE8Hg0EeffTRRdu/yy67jOLi4guinb+YOXz4MGNjY4vmcWtrK7Ozs7z44otp0OziJHrWF5yJ6zs7O5Mie34fVPT/a1H/iIEIgUAgyACiS3IBamtrUSgUGe2EZgovvvgidrt90c5rpVLJvn37MrLzei2Yv90XkPGHAj/33HM4nc5FnV61Ws3NN9+ckYHNM888g9vtXtQGNRoNN910U8psMFpvyGQy5HI5jY2NScvj6H7FQEx2MldEnC07k21TED/9/f0cO3Zs0XIMZ7ZtSNZZJumivb2dSy65hJqamnN+u+GGG9BoNBlRX5nNZkwmE8XFxUDmtwMCgUBwsXH2xKFMr6dfe+01pqamFvV7o6sCL9bY60Khvb2dnJwcdu/efc5vW7Zsoby8XOTxGjI/borG9cmqI+b3QUVli4EIgUAguAgIhUJ0dnbGGgG1Wk1tba0YiIiD9vZ2ysrK2LZt26K/t7S0cPToUQYHB9dYs/RjNpspLCwkNzcXiAQ2/f39eDyeNGu2OO3t7VRXV7Nx48ZFf29paeGNN95gfHx8jTVbnvb2durq6li3bt2iv7e0tPD6668zMTGR9GfPn8UCyZ1FN9/pjcpOxSAHZP5qHUH8HDx4EKVSyc0337zo7y0tLdhsNl5++eU11ix5LDcbFMBoNHLddddlzEBEdLASMn+mrUAgEFxsdHR0oNPpKC8vByI+0dTUFFNTU2nWbHHa29spKChgx44di/6eSasCBYnR3t7Ovn37UCqV5/wWPbuvra0tY8/uu5Dw+/309PTE4iaj0Uh5eXlSfDmfz0dvb29MtslkorS0VAxECAQCwcXAyMgILpfrnE4/0VmwPOFwmLa2NlpaWmKdLGezd+9eFApFRnQIrTWLdSSHw2G6u7vTqNXiRPOytbV1ybzcv38/MpmMgwcPrrF2SxMOh2lvb19Rb4gcxJ1sFsvjVA5EJEN2OBxeMPsmmbIF6aetrY2rr76a7OzsRX/fvn07paWl5/VMutdffx2LxbLobNAora2tPP/889hstjXU7FwWK8djY2PY7fY0aiUQCASCKGazmcbGRuTySNdcsrdeSTZtbW0cOHDgnO0Xo9x4442o1eqLMva6EBgeHuatt95a0cfp6enh9OnTa6jZxUlvby/BYDAlcVMqZa+EGIgQCASCNHP2klyIzIbJ5GW5mYDZbKarq2vJWakAOTk5XHXVVRelMzx/P0k4Y1+Z2OF74sQJBgYGls3LgoICdu7cmVF5efToUYaGhpbVu7i4mB07diRd7+npaSwWyznO4/DwMA6HY1WyozPxzrafoaEhnE7nqmRbLBasVus5sgcGBnC73auSLUgvDoeDZ555ZtnykGmHOSdCdDboFVdcsWSaAwcOEAgEeOKJJ9ZQs3NZqh3I1A4ugUAguNg4e8C4oaEhdj3T6O3t5eTJk8u285m0KlAgnYMHD6JQKNi7d++Safbs2YNOpxN5vAbMP+srSrIGC9LZByUGIgQCgSDNdHR0oFAoqK2tjV1ramqip6fnvN9HO5W0tbWh1Wq5/vrrl03X2trK008/veoO1POJcDgcm2EVpaioiOzs7Iwc4Gpra8NgMHDttdcum661tZUnnngiY7aXamtrw2QycfXVVy+brrW1lccffxyv15u0Zy/lPMLqOxnPPl/kfJEtSC9PPfUUPp9v2Q4KiJQHs9mckZ0s8dDW1sa+ffuWnA0KUF1dzebNm9O68sNmszExMbGgrEXri0xsBwQCgeBi5OxVogaDgcrKyoysp9vb21GpVNx0003LpsuUVYEC6bS1tbF79+7Y1r6LodPpuOGGG87r1a3nC2azGb1eT1lZWexadEvbUCi0KtkdHR0YDIYFsqODHKnedksMRAgEAkGaMZvN1NfXo1KpYteampoIBoP09vamUbPMpr29neuvvx69Xr9supaWFrxeL08//fQaaZZ+JiYmsNvtC2ZPyGSyjN0Cp729nZtuugmNRrNsupaWFpxOJ88///waabY87e3t3HzzzajV6mXTtbS04HA4eOGFF5L27MU69JN1yNhispO1oiaVsgXppb29nebm5gV5uxjXX399xhzmLJW+vj5OnDix7JYFUVpaWnj00UcJBoNroNm5RMvT/HYgOzub4uJiUdYEAoEgA3A4HAwPDy+opyFzt6xsb2/nmmuuISsra9l0LS0tGbEqUCANl8vFU089FbeP8/LLLzM9Pb0Gml28nH3WF0TqB5fLxcjISMbKXgkxECEQCARp5uwluSA65lZiZmaGl156KS5Hqampiaampotq1sZis+Wjf2eaTU1MTPDaa6/FlZcbN26kpqYmI/JyfHycN954Iy69N2/eTGVlZVI7Xs1mMxUVFRgMhti1vLw8CgoKkjJYUFlZuWCQLy8vj/z8/KTIrqqqQqfTxa7l5+eTl5eXcbYpiJ9QKBQ7L2UlDAYD119//Xk5ENHe3r7sYdzzaW1tZWpqitdee20NNDuXxQb9IDPbAYFAILgYiR7ofD7467Ozszz33HNxtfPV1dVs2rQpI/x1Qfw888wzeDyeuPL4wIEDhEIhHnvssTXQ7OIllf1E6eyDEgMRAoFAkGYWawTKy8vR6/UZuSw3Ezh06BDBYJADBw7Elb6lpYWDBw+uegnj+UJHRwcymSy2z2yUTAxsog5s9FDn5ZDJZLS0tNDe3p7yJaMrcfDgQWQyGfv27VsxbVTvtra2pOm9WL0Bycnjs7cJSKbsVOotSB+HDx9mfHx8xW2ZorS0tPDiiy9itVpTq1iSiXc2KMDll19OYWFh2gZczGYzpaWl5+gqyppAIBBkBstNHOrs7MyouOXJJ5+Ma/vFKOleFSiQTnt7Ow0NDYv66WdTXl7O9u3bz8tJJecTi8VNNTU1KJXKlMRkdXV1KBQKMRAhEAgEFzI+n4/e3t5zGoFM3kYnE2hvb2fbtm1UVFTElb61tZXR0VHeeuutFGuWGZjNZmpqas7Z6qipqYnJyUlmZmbSpNm5tLW1sWPHDoqLi+NK39LSQn9/PydOnEixZsvT3t7Ozp07KSwsjCt9a2srvb29nDp1KinPT2WHvtlsPmebgGTJPvvw3GTKFqSPtrY2cnJy2L17d1zpo9s2PP744ynWLHnMzs7y7LPPxjVTEEChULB///60zQhdaUAx3YO5AoFAcLFjNptjq0Ln09TUhNvtZnh4OE2anUt7ezsbNmygrq4urvTpXhUokEY4HKa9vZ2WlpYFW/UsR2trK4899pg40zJFzM7OMjIyck7cpFKpqKurW1XcNDs7y+jo6Dl+YlR2qifDioEIgUAgSCM9PT0Eg0HRMSeBQCDAY489FveMHIDdu3eTnZ190czaWKqTOmpnmWJXPp+Pxx9/XFJeXnvttRgMhrTmpcfj4YknnpCk93XXXYder09Kp2QoFFo2jzs6OhLuZAyFQnR2dqZEdjAYpKura1nZgvOT9vZ29u3bh1KpjCt9ZWUlW7ZsOa+2bYj3MO75tLa2cvLkybSc97RcHTE7O8vY2Nia6yQQCASCMyw1OSN6LVP8olAoxMGDByW1fzt27KCgoOCiib3Od44cOcLw8HDcky0gMqnEZrPx8ssvp1Czi5fOzk7g3BVTsPq4abFzxKKsRR+UGIgQCASCNLLUktzotUzpMM4kXnnlFWZmZiQ5SiqVin379l00zvBSHVDJOsw4Wbzwwgs4HA5JeanRaLjpppvSmpfPP/88TqdTkt5arZYbb7wxKXoPDw/jdruXdB5tNhuTk5MJyR4aGsLj8SxZJ1mtViwWS0KyBwcH8Xq9S8qenp5mamoqIdmC9DE8PMzbb78tqTzAmZl0gUAgRZoll/b2dtavX099fX3c99x4442oVCoOHjyYQs3OJRwOL7tqCjKnHRAIBIKLlaXq6erqalQqVcbU02+++SYTExOS2nmFQsGBAwcumtjrfKe9vZ2srCze9a53xX3P9u3bKS0tFXmcIpY66wtW30+UStnxIAYiBAKBII2YzWYMBgOlpaXn/Nbc3MzIyAizs7Np0CxzaW9vp7i4mEsvvVTSfS0tLRw+fJiRkZEUaZYZBAIBurq6Fu2kNhqNlJWVZUxg09bWRkVFBZs3b5Z0X2trK6+++mrCne2rpb29nerqajZu3CjpvpaWFl555ZVVd7avNIA5P41UorNrUiE7lXoL0kd7ezsKhSKuA5zn09LSwvT09HmxbUMis0EBsrKyuPbaa9d85cfo6ChOp3PRdqCurg65XC7KmkAgEKSR5QaMlUol9fX1GVNPt7e3k5eXx5VXXinpvpaWFk6cOEFfX19qFBMkjba2Nvbu3YtarY77HrlczoEDB86r1a3nE2azmcLCQnJzc8/5rampid7eXnw+X8Kyi4qKyMnJOee35uZmenp6UrrllhiIEAgEgjQSdUAX24sx6phGl+UJIrS1tXHgwAHkcmlN2N69e5HL5Ws+M3Wt6e/vx+/3L3nQWKastAmHw7S1tUnaizTK/v37CYfDsYOu15LV6H3gwAFCodCq9TabzSiVSmpqas75rb6+HplMtqrBApVKRXV19Tm/NTQ0rFq2Wq1eUnY0jeD8or29nd27d5+zx/VKXH755RQVFZ0XAewf//hHSYdxz6elpYXnnntuTScVLDfop9FoqKmpEWVNIBAI0ojFYsFqtWa8vw6R2EvK9otRbrrpJlQqlZgxn+GMjY3x5ptvJuzjmM3mjLHVC4mlBiohUj8Eg8GEt/5Mpex4EAMRAoFAkEaW2hsUMm8bnUygq6uL06dPS94CBCA/P5/du3efF51eq2G5DijInL34T506RW9vb0J5WVxczI4dO9KSlydPnqS/vz8hvUtLS7nssstWHZB1dHRQX1+/aECo0+morq5OOI/NZjMNDQ1Lyq6qqlq1bIVCcc5ver1+VbIF6cHlcvHUU08lVB6iM+nOhw6KtrY2cnNz2bVrl+R7W1pa8Pl8PPnkkynQbHHMZjMKhYLa2tpFf8+UdkAgEAguVqJ18FJxYKbU04ODgxw9ejShdj4rK4trrrnmgo+9zncOHjyIXC5n3759ku+94YYb0Gg054Uvd75hNpuXrR8g8XNkluuDWotV6mIgQiAQCNLIcqPRubm5FBYWioGIebS3t6NWq7nhhhsSur+lpYWnnnoKt9udZM0yB7PZjFarpbKyctHfm5qa6OzsJBQKrbFmC2lvb0en03HdddcldH9rayuPP/54wktSE6WtrQ2DwcA111yT0P0tLS0cOnRoVctdl6s3YHWz6FIpu6OjI2WyBenhmWeewePxJDSLDiLl4Z133qGnpyfJmiUXqYdxz6euro4NGzasaZDe0dFBbW3tklssiLImEAgE6cVsNiOTyZY8d6ipqYm+vj68Xu8aa7aQ9vZ2lEql5O0Xo6RjVaBAGu3t7ezcuZOCggLJ9xoMBvbs2SMGIpJMOBxeNm4qKSnBaDQm5Mstty0cQFlZGQaDIaUDoWIgQiAQCNKE3W5nbGxs2Y65TJkNkym0t7ezZ88ejEZjQve3trbidrt55plnkqxZ5tDR0UFjY+OSW1c1NTXhcrnSflZGW1sbN954IzqdLqH7W1pamJ2d5YUXXkiyZsvT3t7OTTfdhFarTej+1tZWbDYbL730UsI6nK+DBanUW5Ae2traaGhoWHJW1UrceOONqNXqjA5gBwcHOXLkSEKzQaO0tLRw8ODBNRsAXm4WHUTKWnd393lzULhAIBBcaJjNZqqqqpb0g5uamgiFQmkfqG9vb+eqq65adC/5eEjHqkBB/Hg8Hp544omEJ5RAJLZ58cUXsVqtyVPsImdiYgK73b5k3CSTyRKOm8bHx5mdnU2J7HgRAxECgUCQJqJnPyzXMef3+3n44YcJBoNrpVbGcuTIEZ555pmEZ9ADrFu3jqKiIr74xS8mUbPMIRwO84tf/GLZzqWovaVzgOu1117jpZdeYs+ePQnL2LJlC3l5edxzzz1J1Gx5Xn31VV555RWuv/76hGVs27aNnJwcvvCFLyR0v8/no6enZ8UO/c7OTsn1htlspre3d9nzVxKV3dHRQV9f34qyzWZz2lfrCOLDYrHws5/9jKuuukryeSlRTCYTW7du5Z/+6Z8ytp275557kMlk3HTTTQnL2Lt3LxMTE/z4xz9OomaL43a7OXTo0LKrxZqamggEAuIAUYFAIEgT8Uz8iKZLF++88w6HDh3i2muvTVhGfX09FRUVfPnLX06eYoKk8Z3vfAeXy8X+/fsTlrF//34CgQBf+9rXkqfYRc4vfvELgGUHAJuamhKqH6L3rNQH9eCDDxIOhyXLjwcxECEQCARpItqBulzHnMvlwuPxrGoblwuFBx54gHA4nNCy0SgymQyr1crRo0dT1rCmk3A4jN1ux+l0LplGo9EA8Ld/+7drpdY5/Pd//zcQOeshUaJ5+eabbyZLrRX5z//8TyCyHDZRonq//vrrCd3/ox/9iHA4zPj4+JJphoeH8fv9se8cL9FOyeU6lYeGhvD5fPzsZz+TJDt64Nly9d3g4CBer5f/+Z//kSRbkB6efvppPB7PqgeObDYbo6OjTE5OJkmz5PLQQw8RDocTng0KxLbKW4uBCI/HQyAQwOPxLJkmOkghOg0EAoFg7fF6vfz+97/n8OHDS6aJ+uv/5//8n7VS6xwefPBBQqEQubm5q5JjtVp55513xESTDOT+++8HItvxJEpRURFwpvNcsHqOHz8ORM64XIrDhw/zwgsvSPaf/+7v/g5gye07AZxOJw6HI2WThKRvdCoQCASCpHDDDTdw9OhR1q1bt2SaN954g5MnTya8DcyFxNe+9jUaGxv58Ic/vCo5p0+f5qWXXkp4Bm8mI5fLOXr06JIHlELkwOSqqqpVze5dLd/61rfYtm0bH/jAB1Ylp6OjgzfeeCNJWq3Mt7/9bXbs2MF73/veVcnp6OjgrbfeSuje7du3k52dzbvf/e4l07znPe/hvvvuY8uWLZJk33TTTbz88svLHsj7nve8h//4j/9g8+bNkmTv3bt3Rdnve9/7+MlPfsKmTZskyRakh/e+9738y7/8C5/5zGdWJee1117jwQcfXNUAXyp55JFH0Gq1yw6irURDQwO//vWv18S2c3NzeeONN9i6deuSaa688kry8/MTPutGIBAIBImjVquprKxc1g/OysqisbEx4XPxksHf//3fU1xczF/91V+tSs7Jkyd59tlnV9WOClLD//7v/zI8PLyqiX46nY4nnngi4e12BefyH//xH3zyk59cNt76+Mc/zne+8x3JE2VuvvlmhoaGljxPEiIDIZ2dnQmdjRYPsvCFOCVUIBAIBAKBQCAQCAQCgUAgEAgEAkFGIIYkBQKBQCAQCAQCgUAgEAgEAoFAIBCkDLE1k0AgEGQQAwMDWCwWSfcUFBRQVVWVIo3WjlS++4X2XTP9fc7XvDxfZUsl1bpIkZ/J5Uwg3VZSZSdSZUvhfC33mVSnCAQCgeD8JFVtsWijMgPhh2Q2meILS9VltXqIgQiBQCBYA+Kp2EdHR3nv+27D63FLkq3T6zl96tR53agPDAywft06XG5p767X6Th1+vSy7z4wMEDzuvV43C5JsrU6PR2nM++7DgwMsG79etwuae+j0Wp56Le/pbS0dMW0q3EuEtUvHjuO2EkzLvfSh7Auhl6n5dTpjpVlr1+PS6Leer2eU3HonYgNajRaHnoovjzzer2xgw2XI9F6Jl5dvF4vN9xwQ9zfUavV8ts47RJEALOWJGK38dabiZS3eMqaVM7XtifR+kpKeRNlTSAQCFaP1M49YM06AxNpA+Nt/1IVCwjiJ+V+SIr8p4uFRL6hVqPhtw89lNTYECLx4ftuuw1PnLqstqyKgQiBQCBIMVI7Tze+5zMYCisJhwJoswsJ+r1oTLn4XbPIlWqs/afQF5Sh1psI+r28/P8+icViOa8bdIvFgsvt5j8+cwvTdidyuZzKwmxkMhm+QJCx6VlK8kwc7x1Hr1Fy7eZaOkem+Yt/+8OK726xWPC4Xaz7+A/wOaZQao0E3Q7UucWE/D5Uxly8M6MYKtZh734LdVYBhOH0j/8qI7+rxWLB7XKx/k8+jaGwErlShUpnJOj3ojbl4p4aJad6Pdb+UyCTEfL5OPrAV/B6PLS0tMT1jHg67lfSb/fnfojXPo1cpSbodaMvKI/ZsmOsD2Qy5Eo14VAQlc4Ylx1H7MTDDz5wCdNOH2qlnEAwTKFJjS8QIkevon/KTZ5BRY/FRVm2FrVSzl/96kR8sl0u/vv/3cvUjI1QKER9dQUyuQyfz8/I+CQ1lWVMTs0wODLG/j1X0dHdx4c/96W4bbDqwF+hzi1CrlCjMubE7C/gnkWuVOF3zACgNOTgnR6j65dfizvP5DIISTj1q77lExiLawj63Ojyywj6faiNOXO6qAl4nIT8PoJ+D6d+/nW83vjsR6tR4/H6uPvuu8nPz2fjxo3I5XK8Xi9arRa73Y7L5UKpVDIzM8P/+fzn4n5HWJ1tCqQRtdualr9CnVOMUmtAoTUQCvhQGXMIzLVJfscMCrUOr3Wczl/9Q1z1ZrS8ffbO26irKsPr81NalI/X5ycv20Tv4Gik/vf78fr8VJYWcdff/WPS6+Ro2/Pvd+5kxulFrVBQkqNDPtf2aFQKZt1+pp1e/IEQuUYNCrmMT93/atzlvuGWT6MvqEQml6MyZM2V+xxc4/3IVRq81gkUOgMF63cyO9zJWz/4VNz11Qf+8q8pKa9GqVKhMxrxe72YcvJwOewoVWp6O05SUFKGz+Phx9/6ezxr1A4IBAKBQHpHsEajRS4L4/Z440q/2no62gZ+ct92msvzcPn8FGUZ8AYC5Bq19E3Y0MwdVquQy1ArFXziR4/H1Ua5XS6u+vwP8dinUSjVyBQK1IbsSCxgzMU/5296Z6dBJsdjm+Ttn38rI2Ov85WoH7LtU/+Od3YauVJNKOBFm1tCaM7vd070I1dq8FrHUOpMaLIKePvf4/RD3G5+9Kn9TM+6USsV2Fxeaopz8PmDc/ZjxaRTM+PwoFUrMWrV/OW/PyryeI7oN/yr1ivINelYX1kY6/vQqhTYXV7cvgBjMw7yTDr8gSBfuP/plMWGABtv+SSm0hrCoSC6nMJYeZ2dF7vbhzo5+ciPVpWPYiBCIBAIUky08/SOy4rYXm6kd9pDZY6G0iwNIzYvFTkabJ4AJ0ac/PCVUbRZ+SjVGkxlkc7k7PJGjCU1zI72kFXeiKmkGsfEABpDDvax3nS/XlKZsDrIM+mpK83j1MAEjWX51JTk0jM6TWN5PrXFuZiHp3B6/ITD0lpWn30SlTEXXXEtzqHTqLMK0RVV4xrvJX/rjfjtFrLqtuIe7yPokza7Ix1oswpQqLWYSuuwDZwiq7wBY0kNSpUWQ2ElCrUO1+QQ1sEOAgE/H9tZQpZGSXm2ekkbnHYGyNYr+fRDXat2Ej3WSTSmXExldVj7T6HLLojZcdXOVjy2SRzj/Sg1Ojz2aUmyJ2e95BnU1ObrOTXmoMCopiZPT++Ui5ZNxVgcPrK0KsLhMOOz8QVzUSYsU+Tl5NBQW8WJ050019dSt76Crt4BmhtqmLTMUJCbQ3ffIH5/QJJsVVY+Sl0W+uJaHEOn0Zc2oCuqxj3ei760Ab/dgm92GplCiXO4A8Ihiq65A2PddjwTvWjyK9HkleKdGkFTUEHAaSPgmCbgtjP0u+/ElcfRekalNZBVvQH7wCk02QUYimtwjPaQU7sJr82CzzGD125B5lPGbT/HR53c9/IoAEajkauvvprjx4+zbt06tmzZQmdnJ1dccQUWi4Xe3l4UCgWhMGtumwJpqLLyUemjdnsKfWkD+uIaXGO9GMoa8NkseG3jhAI+ybKNBh2bmuo50dlDUV4utZVldPcPcetNVzM5ZWVwdJzC/Fy6+4dS8GZnsNg95Bo11BWaODVipaE4i40VufRMzLKpMg/LrIehaScAVpe099Rk5aNQazCW1GEfPIWxrAFDcU2k/i5rxGu34JudwT5wilBQWp2Sk1eIWquloqaBno4TVNU3U1ZVy1BfN1X1TZRX1zE+MojDOoPPv/btgEAgEFzMRDuCS278GEp9Fuq88iX9OefAcUYP3QewYsyY7HraqFPRWJrLO0NTFGTpqC3OpnvMSuvljVhsLrz+IJZZFyW5Rkly3XOxQFZZHTP9p9BmF2AqqcE+2kNe3SY8tklkMhnBBPwHQfx4bZOojbkYSusifn9WxO93jvVQtqMFr92Cf3YG5HKcEvsWJm0uco1a6ktzeWfAQmGWnpriHHrGZmjd0YTF7sLjDyID3hmYTM0LnucYdGp2bag+p+9jU20JFpsL8/AUxTkGXj01AOFQXPWJ9fgzWF79bdx+n90b4DvPDKHSG8mr2cDMXOxuKqnBPtJDza5I7D47PkDQu/p+EjEQIRAIBGvE5lIDVXlasnUqvMEQvdMeNpToKTGpUSvljDv8ABRfspv8+s0AFK3bEbvfUFAGgD6vmLzaSwBQ6qQ5hJnOuzZWs6UustTwiuaK2PXy/CwASnJNXFJTDMDRnlFJsnPW7cRUE/mu2Y2Xx65r8iLfVZNTPPfbDmb7jiX4BmtH0SW7yKuLvE/hujPvo8+PvI8ut5jcmo3o8ks59otv0ligo65Ah80dZH2JAovDj04tZ3ddNmqlnBGbl7JsDVqlPCn6SbHjqW5p33tXfR6byyM2cXlNTux6WY428uwsDRvLTAAcG7ZLkn31lZex7ZL1AOy8dEvsekVpxD5KiwrZvKEJgLdPnJIkO3cJG9SeZYMA3ukRAAzVm9EWVqHSZxMKePGM96Kv3IA6pwR5mRrv1AjeqUGAuPLY7Q8BUHLpTeTUbSa/+Yweujnb0eae0cPacywu2X8cnOWWSwpiAxGtra1s376dXbt2nfmGFZEynZ+fT3NzM7/4xS/ikt075QGZjLo8raTvLUgOeet3kTVntzlNi9utiY3YE6g391+7k20bmrhy28bYtYqSQgBKC/PZvK4eAJVSkbD+8bCrqZjNVXkAXF5fGLtelqsHoDhbx8aKXACODUgbOC3YsIuc2sj3y1upvPVK+4ZbrryKpku2AbDx0itj1wtL58paUSn16zdjPvE2EF9ZS2Y7IBAIBALQlTaiK6kj6LShqFiP325BrtaRvX43clXEl9OV1MUGIlaKGXunPGRrk9cu3rytji01RexoKotdK8uL+NElOYbYtaN9E5LklqQwFhDET/58P6RpeT9Eat/C7g2VbKmN3L+jqTx2vTx/zn7mDV4pFcK3WIy9lzaypa50xb6PkelITBtPfaI0Rnzalfy+nikPerWSWU9kIkzl5TdRUL+Z4vVLl1dLEsqqGIgQCASCNWJzmZFNZUs37jurs2L/Hj/xCgBep5WQ34fbOklezUZ0+SUoVBqme08AoM+Lb1/184mXT/YDYHV68PqDTNqcXFJdRGmeCbVKSc/oNFdvqpEsd7bvGEGPk4DTSijgw2efxFi5EU1uCTKlGvd4D0GPMzYwcT4wcTJiJz6HlWDAh8c6GRl8yCtFrlLjnh5jZs5WVrK/8uzIHpLHRxxJ0y8eO9ZlFyJTSHdHXumJdAba3AF8gRCTDh8bSo2UZkW2Yxq2elApZCjkMsmyX3jtMABWmx2vz8eEZZpN65soLylErVYzOj7Jjm2bJMsFmDn9KkDMDv22SQxVETuUK9U4Bk4iU6hi6Y01mzFWL/0sTV45k3MDEfHkcbSD0dpzjIDHgc9pI+T34rVZyK7egDavFLlSjdsyjM9pRZdbErfs+bZz+PBhBgYG8Hq9jI+Ps2XLFsrLy9FoNAwMDDA7OxtLmw7bFMSPvfcYAbeDgNMWqTttkxirztiKd2acoNeF0pC1srCzePukGYfTzYx9Fp/Pz8TUDJua6ykrKkCjVjE4OsGMfZbSovwUvNlCXjGPA5EVD75AiMlZDxvLcyjN0UfqlBkXgWAIo1a1gqRzsbwTqQv9c+XNM6+8KZRqHKM9hIJ+NNkFkmUfff1FAGZtM/h9PmYsE9Sv30RBcRkqtYauU0cpKI60a6KsCQQCwdoTjy/n6D8e+1uqz7VajvZO4HD7sDm9eANBJm0uNlYVUJpnRKNUMDQ1G2n/dGrJssdOLB6r6PNLUCg1uGbGCPq8qC6wyXWZxgI/JBDx+7OqNsRiRvvgaRRKTUK+3MvvROIQq9ODLxBkwubikqrCiP2oFAxZZnF6fORn6ZP6ThcS8fR/RImnPvFKiA0BHj4aWa1i6TpKwO3A67AS9EfKa15tpLzKlRqmuo9iKKxYUl68iIEIgUAgWCN+f9zClCuANxCiqVAXm6FcmqVh1hvg5Fhk24ep7qMEfR6yy+uRISOvfjMKlQa/a5ag143LMgKhEKbSWoL+C3Mp64n+CRrK8pDJ4MZt9ahVSuxODy6vH71WxanBxJZ2OgZPoi9pAJmM/M3XI1Oq8c6MotDoUaj1BD1O/PYpAl5nkt8o+UzP2UlWWeR98uq2IFepcVmGCXhdqJUqPNYJILKF1bERB28MzFJfoDvHBhVyGQ0FOo4MO7C5pW0NshRL2bFzcgjHWB8FTZcR9Hnwexz4HDZJso8N2fEEQtQX6JEBm8uzYoMPo3YPlbk6AAxqBZ5AKCH9j58y01RXjUwmY+91u1Gr1QyOjGEyRJzo1w4fRaORFpDN9h0j5POgK43kmalmM3KlmqDXhVyhwmebxO+0klW3Db8j4nBaXv89gdkpQn4vurKmyJkbbgfhUICQ34tCc2am2lJ1zIL89ZzJX1v/OxhLI/lTvO36iC4eJyiUyBRKNFkFOCcijvFS9jNq96FXyZHJiNVhAGazmaqqKmQyGfv3748NQFRUVODz+aisrGRqampZvU+Pu9nTmEP3lJtik/TgV5A8HAPvoC+tB5mMgq0RW/FMDaPQGgn6Pfhmp/A7rQnJPt7RTWNNJTKZjJuvugKNWoXd6cLl8RAIBsnPkR4US+XowDQef5CGYhMymYwtVXmxwYdRq4tLKnPxTAQpydbh8Qclybb2HCPo82Aqi3y/7Lot5CnVBLxO5Aol7plxfLNTZFVvIOiL7yyps+k+dZzKukZkMhlXXHszKrUGt8uBUqUiN7+QntORAem1bgcEAoFAAI6+Y8x2voGupH5Jf847NRxLv5RfZFArkMnA4Q0mdeVa99gMFfkmkMGW2qLY4MPMrIfGslwcHh+V+Vm4JW5JGo0Fssoj7V9+/WYUSg1OyxCuqTHyai/B3XMMldbA7Hh/0t5HsBBrzzFCfk/E55fJyKmNxIx+p52A10VodppwwI+msFKyH3K0Zxy3P0BjaW7Ef6otjg0+uLx+1EoFDrePisIsPD7hWyzGkZ5RPL5grO9ja13Jgr6PaYebwhwD49bIJK6VYkO/bRLvzBiwtN/n8gU5PurkhqZcjgw7GLWf2cp4uvck2XNltuKy65HPldncqvWo9VmR8yJWiRiIEAgEgjXi1k0FBMMwZvcxNusjT69ifNZPVa6WcBhKsiIj0vn1W2LLWOfjmh5Hn1e84G/3zPia6b9W7N5Yze6N1edcH1PIKMmNLPMcm5llfEbaTCBTzebYtjjzkSmUC7bEAZg+/qwk2ekgr35LbGum+cgVSnRzS2wNhRVMz22tE50RcWTYcY4NXl5lYsjq5er6HJ7rnEmKfkvZsUyujNlxze5bABh+W9r33lyRFduaaT5KuYziuXJUmatj3O7FI/GMCICrr7yUq6+89Fz5SgWlRZFtW0YnJhmbsEiSu5QNeq3jaHKK0RZUkFW3Fa/1TLkuuOJWCAXxzYzhs46hMubhHu0ka90uAo5pFFpTLO1SdcyC/O2yApBTt5mcRezHMzOONrcYfWElAOOuyDLgzWXGmOxsnQJfMMyI3UO2NuJKri82MG4/MzB6++23s3379rO+n5LS0lKqq6sZHT2ztdpSet+0Lpcpp5+r63MYn/UxMXthDrxmOlm1m2NbM83n7LrTkkC9uW1jE9vmtjqbz+jkFKWF+bF/j01OSZYthS1VebGtmeajVMgpzo4MbF69roRxmxuPTdreuDl1m2NbIsxnQVlr3I5nZjyhgYgtV1zFliuuOuf61MQo+UWlFJdX4bBHBnvXuh0QCAQCQWQGc9SXUxiyCQd8eGZGUJkKkKs0GCo3EnCd2Up0aX9Oy5QzEjsm0yd6z85mttQULbimUMhj2zJdv7mGMasTt1VaR/KSsYDiTCxQdcU+QHosIIifpfwQmVy5YFumRPyQLXXFsa2Z5qOQy2PbMlUUZDE24xADEUuwta40tjX1fM7u+4iyUmxoqtuO9+n/Bpb2+zon3eyuzY75fd2TEd+2oGELBYuUWblCidqQRenmdzGUhLIqBiIEAoFgjei0uGks0FGeHZnZ6wuEqM7VxBzJaWfkjAjbkBkA68BpPPYpVDojakM2PocVU2ktPqcdY1HlgrQXCubhM51NpwcnsdhdGHVqsvVarE4PVYXZWJ0e6kvzFqSNB9dIV+zfzuEO/PYpFDojSn0WAacNXXEtAZcNbUFl5MCuDMc+l/e2wQ48NkvMTrwOK6bSOvxOG4aiSuxDnUDE/gAUMpa0weMjDqaTNBN2vm2uZMteu7S87Jw4M/O+Y9zBlMOHUaMkS6fE6vJTmafD5g5QV6BfkDYeTndFDml7x9zN5NQMJqOBnCwTMzYb1RVlvPT6WzTUVi1IGy/OORt0Dnfgs0+hnLM/v9OGfp79AbgnI4fzukc70ZU2os6P7LsaCvjQFFbHZs4FvW48lsjy25XqmOMjjlg9MzscsQv7YAdeuwWVzoTKkIXPYcVYUoffZUNfWIlvLm8Wk52lURAOhwmGz7WdU6dOcfLkSSYmJsjKyiInJ4fp6WlqamqwWq00NjbS29sbt97RdIK1xzlnK46hs+3W+v+396fRjVz3nfD/xb4QBLiAa5PgDjRb6m5ZqyW11Ja1WJa6bZ/Eds5M7CSTRN7GimfinEye85/kTM7Mk8xozhnbmdiaeWzrsTPOM7KjxeNubbHUkmztsiV1t2QSxR1cwAUksQOF9f8CDTTJJsgqYie/nzdSAxc/3qp7q+reulX3wtjenzl/tnQjLnPReQBwTroAACPj01hZ86K+zgiL2YR1XwA9h9rh9Qcw2NOVS1cqwmLmRr3T7YMnEIVJr4HFoIE3HEN3cx184Tj6W+s3pZVq47EW83ugznOsbUwrlWvcCQCYHhuBd3UFRlM9TGYLAt51HOodxAfvvIn2rh741i8fx0D5rgNERLR9W05lMCOViAEJIDhzEYng5Wvobu2i5UCsqG0iYT7zt0fnVuHxR1Bv0MJi1GE9FIWtxZzpe7U35tJJdUVfwLcKjfFyf8VcQF+ApAtealsE5jJtfrU+0w6JB72ou9RnzLZDgjLbIdn++OicBx5/GPV6Hcx1OniD2bojYqCjcVNa2iy3D3e59+FazrQ/d+sbBmcuIhHJDGxKbff5xUy7zzcrwOsaRcSX6bfrTBaIAS/MnX1Ydv4qc6z6Ci9HRTqdThcchYiI8nK5XBg+7EA4IuEJA4USSMubSsZgNGJ0ZAQ2m22POay8zD46jHBEXqPaaDBgZHR0x213uVxwHB5GNBKWFVtvMMI5Wn371eVy4fDwMCJh6dujVAApGVd7o0GPkVHnnrZ9L/kDpNVjWcfSBlK2x+VyYXh4GGGZ+TYajRiRkO+91EE55wNZZSzzPCMntl6ng0KpRETisVzOukny7KXeSj1v7uV4k3KsyVWr15697D8ea0RE5SX3OqDT6aFUpBGJSnubt9Dz9F6ugVKvf6XqC5B0JW+HlKj9dFDsaR+Wqm8IQKFUIp2SFrvQY5UDEUREZeByueDx7D6NS3a6ko6OzOt56XQaJ0+exIc//GG88MILeOWVV2AwGDb9xmq17ouL+Xb76Gtf+xqi0Sh+9atf4Tvf+Q5uuummTd9L3fbtYj/44IOIxWL41a9+hYcffhg33njjnmJXwtbtSaVSuOWWW3Dy5EmcO3cOr7/+OtTqyy89bq1Xuyl027fb31/+8pehUCjw5ptv4rvf/e4V0/YUUpZf+tKXoFQq8eabb+J73/sePvShDxUt9he/+EWo1Wq88cYb+P73v49rrrmmaLG/8IUvQKPR4I033sAjjzyC48ePb/peFEXodLpdY8tNu7U+vP322/jSl76EG264AWq1Gn//93+/Y/qdWK2ZxXaz2/rWW2/hy1/+Mm644QZoNBr89//+3/ec72z8aj0u96Ot9fa//tf/itdffx2zs7P4d//u3+HTn/70pvRyymdr7IceeghvvvkmXC4X/uIv/gK//du/vefYcmzNx/r6Ou666y589KMfxYULF/Dcc89d8Zu9HvcbY7///vt45plnihZ7dXUV99xzD+644w785je/wdNPP70pfbmvA0REJL0PCFzZhpKSvtDz9Nb8/dmf/Rm8Xi/effddfPOb38Rtt22e+q+Qdu/Xv/51+Hw+vPvuu/jWt76FEydO7Ck2Sbe1HNLpNE6cOIETJ07g+eefx+uvvw6tdvM6bHst41Qqhdtuuw0nTpzACy+8gNdee23PsQ+Krfvw+9//Pn70ox9BpVLhs5/9LL7whS9sSl+qvuHW9MlkErfccgs+8pGP4KWXXsJrr70GlUqVS1toOXJqJiKiMrDZbHs6WS8tLSEUCuHOO+/ECy+8gPr6ehw7duW8ffvBdvtocXERp06dwvnz55FOp6+4eV1I7KWlJZw+fRrvvfdeQbErYev2zMzMIB6P46Mf/Sief/55NDQ0wG6/ct71SuUPyJTl7/zO7+Dtt98GgKKWpdvtxr/8l/8Sb731Vkli/+7v/i7efPNNKBSKosf+/Oc/jzfeeKOg2IV6++23oVKpcNttt+HJJ58sSj6y2/rWW29BrVbjtttuw09/+tOaOs7oynrr9Xpx/PhxaLVaRKPRgspzu9jXXHMN1Gp1wbELycdrr70GALjzzjtx7tw52O12mEymosR+5ZVXNsV2OByoq6vL93NZsX/5y18CAO666y68+OKLOHz4MIxG455iExFRceylD1jOG7Vb87e8vIyTJ0/C6XQilUoVtd27vLyMO+64AyMjIwXFJum2lsP8/Dyi0SjuvPNOPP/88zCbzThy5EhRYs/NzW2KbbFYMDw8XPA27Gdb9+Hf/d3f4ciRI1Cr1QgEAhU7RiYnJ5FIJHJl2dTUhIGBgaLFVxYtEhERFZ3TmZn/+fbbb9/074MgFothamoKw8PDGBgYKOq2i6KIqakpHDlyBP39/TW/X6u9noTDYbhcLhw5cgS9vb1FzV8oFMLc3Byuuuoq9Pb2QhCKt25KMBjE/Pw8rr76athstqLmOxAIYGFhAVdddVXRY8vldDrR19eHI0eOYHp6GtGo/AVzd4rd39+P4eFhTE9PQxTlLx5O1cPpdMLhcMButxe9zjqdTtjt9pLElpsPALknNYt5ThEEAQqFIhd7bEzeXMw7cTqdUCgUuPXWW4sem4iI9r9kMonx8XEcPnwYQ0NDRb0WlzI2SZdt05Siz1jt/dFakG1nOxyOorY/95IP4HJZFjsvHIggIqpi2ZsW119/PZqamip6QSq3qakpJJPJ3I2pYm775OQkUqkU7HZ7xS/0xSAIAjQaDW666SYYjcaq257x8cwizaUoy1LGzt7IK2Xs7E3dSpaZIAi5fKTTaUxMTBQ1dnb/pVIpTE5OFi02lZcoipieni7JeTMajWJmZqYqzsmCIMBms+Ho0aO5fxczdk9PD66++uqSxO7t7S1JbCIi2v9mZ2chimJJ2r0zMzOIxWJVcZ0/yARBgEqlwvXXXw+LxVL0doharcb111+P+vp6lvEebOw3CYKASq2kIAgCdDodbrzxRuj1eg5EEBEdJNkbCzqdruI3K8stu62laAyXMnYlCIKAwcFBqNXqqtyebH5KcdO9HPVkaGiopmLvJS/Z/bcxb9Uem8prYmIC6XQ6V55TU1OIxWIliT05OYl4PF6U2HJl62xjYyNaWlpKcjw0NTXBarWWJHZzc/OBe3CBiIgKx77X/icIAvr7+6HVaktSxqWKfRCsrq5ibW0td4z4/X4sLy9XJC+CIGBoaAhqtRpDQ0MciCAiOkiyNxYAHLgLutPpRF1dHTo6OuBwOIo6rYsgCDCZTGhvby/6DbVKqPZ6IggCmpqa0NzcDLvdjomJCSQSiaLEdjqduRtvdrsd4+PjSCaTRYktCAKsViuamprgcDiKHrulpQWNjY2w2+0YGxtDKpUqSmw54vE4JicnYbfb0draCrPZXLT6k51ezeFwoK2tDfX19XxNu4ZtvYmQTCYxNTVV1NjZwcpixt5LXhwOB4Din0+z009lYxfzeNh4HeDTpkREJJfT6YRWq4XNZoPD4cD8/DyCwWBRYmefsO7u7obdbsfc3BxCoVBRYpN0W9shpXggAmA7ZC+2trOByk1vtbUtXOx8cCCCiKiKVfsN5lLKbrtCoSj6tC6ljF0J1V5PtuYvHo9jZmamJLFjsVjJYouiiNnZ2aqPLcfGKdCyx0Ox6k8pY1P5CYKA+vp6tLW1Ff0NF0EQYLFY0NLSUtG3Z1KpFMbGxkpyPs3Oj11rsYmI6GDIvmGtUqly15PsFKjVHJukK2Wfsdr7o9Uuu78GBwcxMDAApVJZsX1Y6rLkQAQRUZVKJBIYHx/PjUY7HA6sra3B4/FUOGflsXUkHijeUwHZhaAA5P5bq09qR6NRTE9Pb9qehYUFBAKBCufsso1P3xR7f2+sJ9n/FquxtLGe7Nc6mP2bG/NSzG0EsKns2SmpXdk6q1Ao0NnZibq6uqLWleyA1aFDh2A0GityPLhcrtz82MDl46EYc/S6XC7EYrFNx1qx5v+dnp5GPB6/4um1Ss0tTEREtYd9r/0t+xb0xnJYXl6G1+stOPbGt6CBTP1ZWlqCz+crOPZB4XQ6YbPZYDQaodPp0NvbW5F+Uzgchsvl2tQWnp2dRTgcLtrf4EAEEVGVmpmZQTwe3zQaDRycOdY33rxua2sr6pQxG0f529vbYTKZana/bpxbHbhcT7KLIVeDjfu7q6uraItepdPpTfWku7sbOp2uKB2bdDq9Kd82mw06na5o+d4Yu6enBxqNpiJ1UBAEGI1GdHZ2AijuUy/ZKdA6OjpysdnprF0b62yx33DZePOjkm/PbJwiCkBR5+jdOjBnt9vh9XqxsrJScOyNr/MDmfyvr69jdXW14NhERHQwbGxTF3udpI1tiOx0rbXa96pVk5OTuTeVgeLeW9gau9gPhx0EG48RoHL9puybSlsHJYt5b4EDEUREVWrrjYXBwcFNn+9nfr8fi4uLJbnp5fP5sLS0VLIbauW2tZ4MDQ1t+rzSNi68BQBKpbJoi155PB54vd6SxF5ZWYHP58vFVqlUGBwcLErs5eVl+P3+ksSWa+M0ZUCmHq2srGB9fb0ksYv15BWV33YdpFLcoCh2bLn5yM6Pnc1H9vNixM7Oj11LsYmIaP+LRCKbnoIGinctLmVskq6UfcZq74/WgmpqC2f//sb/FjMvHIggIqpSW28sGI1GdHd3H4gLenbEvRQX41LGrgRBEGA2m9Ha2gqg+E8wFWrrE8ZA8fb31oYSY+8tL1vzARTnqZdSxqby8nq9WF5eLkmdXV9fx8rKStUcD9k5rAFgYGAACoWiaMd9KWMPDQ1Bqcx07Q7SgwtERFS4rW9YA8W7FmefsK6G6/xBtvUtaJPJhM7OzqK1Q+rq6nJvQdfX16Ojo4NlLNHWNcqAzDEyMTGBRCJR1rwIgpB7awkAmpub0dTUxIEIIqKDYOuNBaC487dXs3LfBK7l/br1qXOgurYnm4/sjTGguGWpUChKGntgYKDosZ1OZ8nyvZe8bBwkKvbTURuPMz4dVbu2G8B1OBxwu93w+/0liV2JtW621lm9Xo+enp6SHA8GgwE2m63qYxMR0f63U9+r0PWGdup7cS2j8illn3G72Bxskm5ubg7RaPSK4y8ej2NmZqasednapszmhQMRREQHwMZ5OrMOygXd6XSitbUVFosl95nD4cDS0lLB07o4nc7cmhNZtbygVrXXE0EQ0NXVBaPRmPssu+hVKBQqKLbT6UR3dzcMBsOm2C6Xq+AFtbILhm2NPTMzg0gkUlBsQRDQ09MDnU63Kfb09DSi0WhBseUIBAJwu925AQIg8wRTZ2dnwZ0Sv98Pt9u9qW6azWZ0dHRUzSAZSbd1fYON/1/oGy7Z2BvrYaXentnufFqsTnqtxiYiov3P6XTCYrGgpaUl95nD4SjKWkZOpxMNDQ2wWq25z7LrJHk8noJik3Sl7DOyHVKY7drZlVrUvRxlyYEIIqIqtXHxziy73Y6xsTGkUqkK5ao88m07UPiNqVLGroR821OMJ5iKYaf9nX1Vu5Zip9NpTExMVG1sObL1PV/9qdbYVH6CIKCjowP19fW5z4r1hosgCDh06BBMJlPRY8sRjUYxMzNTkjqbnR+7FLHD4TBmZ2d5rBER0Z5l26Zbn2jPfletsUm6UvYZq70/Wu0EQYBGo0Fvb2/us66uLuj1+rIfI/nKsphvMHEggoioCmVvLGz31IIoipidna1Qzspju1cCi3nTa7v9WozY5bbd3OpAZnv8fj+Wl5crlLPLSrm/Gbsw270qn/13MbYR2PyUe7FiU/ltV2cbGhrQ2tpakuOhEmvdbDc/NpCps+Pj40gmk3uOvd382OWIfRAeXCAiosJtdy0u1lpG28UeHBws2jpJtLvsW9DbtRXC4TAWFhb2HNvv92NxcXHb2KFQCG63e8+xD4qt64gBgFKpxNDQUFmPkdXVVaytrW1bll6vF6urq0X5OxyIICKqQvluLGRHp/dzoy2dTm/bYC3Golf5YpvNZrS3t9fcft1ubnWgeupJduGtrU9VFGPRq2QyifHx8Su2vbm5GY2NjSWJbbVa0dDQUJTYW/dJa2srzGZz2QciWltb0dDQsOnzYjzBJAgC2traNk2vVqzYVH7bnTeBzLmmFDcogPIPWu00MFfoHL07xY7FYnC5XCWJfRAeXCAiosJtdy0u1jpJ28XmWkblla/PmP13IdPu7BabZby7WmgLb/y+UByIICKqQtnGwNablT09PdBoNPt6vsXFxUUEAoErth0ofH5Ct9uNYDC4bezsK4e1ZLv5JIHLTzBVentmZ2evWHgrq9CydLlcEEXxirJUKBQFx56ZmUEsFitJ7OnpacTj8Sv2STFiy7XdHKBApmzC4TDm5+cLip3vGA6FQgU9eUXllR3ALcV5M5VK5Y1dieOhoaFh0/zY2Xxkvy8kdmNj46b5sYsZu6mpqSSxiYho/1tbW4PH4ynJtXh1dRWrq6v7pu9Vq/L1Gfv6+qBWqwu6wZwvdn9/P1QqFctYgp36TeVuCwNXvtGe/Xex8sKBCCKiKiQIApqamtDc3Lzpc5VKhcHBwX39ZEG+kfjsZ4VseyljV4IgCOjs7Nw0tzoA6HQ69Pb2Vnx7arUsazX2XvKSLx/Z76sxNpXXwsICQqHQjnV2r2+4LCwsIBwOlyS2XNk6u3EOawDo7u6GTqcryvFQythb2Ww2aLVaHmtERLSjg9LuPcjyvQWt0WjQ399fcBlv9xZ0MWIfBKIoYnp6Ou8xMjc3h1AoVJa8CIIAm80Gg8Gw6fNiv8HEgQgioiqU78YCUJypMKqZIAhQKpXo7++/4rtCb0ztFDu7X2tpyphqryfbLbyVVeiiV4IgQKvVwmazbRu70Aa1TqdDd3d3SWLr9fqSxJZjp6fc+/r6oFKp9pyXfFOgFSM2ld9ONxEcDgcCgQCWlpaKHrvca93kq7PFmKM3X+xiPFxQythERLT/Za8Tg4ODV3xX6FpGO8V2OBwFr5NE0uzUZyz0zZTdYrMdsrN8a5QBl9vH2Wm7S61cZcmBCCKiKnSQL+iCIKC3txc6ne6K7wpd9EoQBPT19UGr1W4bOxgMYnFxcU+xK6Ha68l2C29lFbro1W6x19bWShbb4/FgbW1tz7GHhoagVF7ZBLPb7VhZWcH6+vqeYsuxvLwMv9+/bf0p9AmmpaUlBAKBbWNrtVr09fVVvG6SdIIgQKVSoa+v74rvCn3DRRAEqNXqvIOVhcTeS15KdT6t1dhERLT/CYKAQ4cOXfGGNVD4WkaCIKCrqwt1dXXbxuZaRuXBdkj12u2hnI1pypEXDkQQER1Qu10EpqenIYpimXNVHrttezZNtcUut52eOgcKf4KpGGq1LKXEzi7MVk2x5eZj49/cLi+F7L9Sxaby22kAt7+/H0qlsqC60t/fD41Gc8V32bVuylFX1tfXsbKyUpI6m50fu1Sx19bWeKwREdGeVbpNzetUaUnpM05NTSEWi5Uk9uTkJOLxuOzYB4UgCDCbzWhtbb3iu+bmZjQ1NZXlGEmlUhgbG9uxLMfGxpBKpQr+WxyIICKqMtmnrbebMgXIXATS6XTZXtErt3yLNQGFL3pVytjlNj8/j3A4vGM9icfjmJ6eLm/GNsi3GDJQ+KJXO5VltcferUNWjjrodDqhVCoxMDCw7feFLI5WythUfjsdD9n1aEpxrOn1+oJiy5Ht4OXLi8PhgMvlQjgcLlnsSCQiO3Z23+wUe3p6GtFoVHZsIiI6GHa6FttsNuh0upJc5wuNTdIsLS3B7/fv2FZIJpOYmpqSHXtxcRGBQGDH2IlEYk+xD4rsMbJ1HbGscvWbZmdnEY1GdyxLURT3/HbURhyIICKqMrs9TZy9OOzHp0cSiQQmJibybnshU8bE43FMTk7mjV1rU8ZUez3JLryVrzFTyKJXkUgELpcr77YbjUZ0d3eXJHZdXR26urr2FDscDmN2djbvPjGZTOjs7CxLme00BRpQ2NNROz1Bn43Np6Nqx05PugGFrUezW+xyPdG/0xzW2XwAe5ujV0rsvT5cIDX2xMSE7NhERLT/7fYUdCHrJO0Wm2sZlYeUN5WBvT0IJTU2yzi/amsLl6MsORBBRFRldrux0NLSArPZjHfffbec2SqL6elpJBKJklyMpcQeGBjAhQsXZMeuhJ3mbQeArq4u6HS6itWTkZGRvAtvZe21LHda1KvQ2NmbgaWI/Zvf/KZkseW6cOFC3jcWsvnY69NRUhrUe41N5bXbAC6QOW9+8MEHsheej8VimJqaKklsuXaaHxsorPO10/zYxYjd3d0No9FY9NhERLT/zc/PIxKJlKRtOjc3h2g0uut1/uLFi7Jjk3SCIEChUORt93d0dMBoNO6pzygIApRKJfr7+7f9vrOzE0ajke2QHUjpNzmdzrK0hTUaDXp6erb9vqenBxqNhgMRRET70aOPPgqLxZL3xoJCoYDf78d//I//scw5K72f/vSnAIDu7u68aerq6vCLX/xC9hoZUmK/8sor+PnPf14T6288+uijaGxs3HZudSBTT0RRxF/91V+VOWcZf/3Xfw0A8Pv9edPU1dXh3Llzsp+Mf/LJJwFkXuneLXYikZAV+4knnpAU+/nnn5cd+z/8h/8AAAgGg0WPLUc8Hsdzzz2HV155JW+a7HGS3ddSxWIxvPTSS3lvuhYSm8rv3LlzSCaT285bm/X6669jYmJC9o2EF154AclkEm1tbTvGHh8fx/vvvy8rtlyPP/446uvr837f1NQEvV6PRx99VHbsJ554YsfYzc3N0Ol0JYlttVr3HJuIiPa/n/3sZwCAQ4cO5U1jMpnw2muvyZ6eMBu7q6srb5pXXnkFL7/88o5tYyrMo48+ioaGhrxvQQOZt7az/ZRixlYoFGhsbMSPf/xj2bEPgg8++ADLy8tobm7Om6a5uRlerxfnz58vaV6y9xZUKtW236tUKjQ2NhalTcmBCCKiKnPu3Dn4fL4d0/z+7/8+vvSlL5UpR+WTHSxQq9V504yPj8Pv98Pj8RQ99kMPPYQTJ07s2FCrFr/4xS923AcKhQL/4l/8C3z1q18tY64ue+CBB3D06FHceeededOMjY3B6/XuWt+3ypalUpm/GTM2Nob19fWSxt5pkGU7X/jCF3D8+HGcPHlyx9hra2sIBAKyYsuh0Whw22234b/8l/+yYxpA/mDB6uoq/H7/jgtuZ2Nn9zVVr6eeegoAdhws/Nu//VscOXIk75Rju8XeadDtb//2b3HVVVft+KRYMYyMjGBycjLv9wqFAtFoFP/8z/8sO/bo6OiOsYHMVHZ7ie10OneddkkURTz33HOyYxMR0f6Xbefle7AJyLyJHAqFsLCwsKfYu/W9brrpph0fYKHCvPzyy1hfX8/7vUKhwOc+97k99RlffvllrK2t7Zhmfn4eb731luzYB8Hzzz8PADuWT7ZPuJd2ohyvvPIKlpeXd0yzvLyMV199teC/pUiX+v0OIiKS5cUXXwQA3HHHHRXOSfm5XC688MIL+Ff/6l/lTbO2toZHH30UX/nKV2THPnfuHP7gD/6gwFxWh5///OcwGAw4ceJEpbOyZ8vLy3jiiSdkD6pNTU3hl7/8JX7v935vx9hPPvkkvvjFL8qKPTk5iVdffRWf//zn86ZZWlrCT3/6U9mxpShlbLl++MMf4uTJk+jt7ZX1u+985zv47Gc/C6vVmjfND37wA9xxxx15X/+l6hAIBPDII4/gT/7kT/IuoleNseV6/PHH4XA4cPXVV+dN8+yzz8JsNuOWW26RFfuf/umfMDw8vGtsi8WCm2++ueixn3nmGTQ0NMiOTURE+9/CwgKefvpp/PEf/3HeNF6vF//rf/0vPPjgg7Jiz8/P49lnn8Uf/dEfFZpNKsALL7wAjUaD22+/veixn3/+eWi12h1jv/XWW1hZWcH9999f9L9f6xKJBL75zW/iT//0T/M+BJdKpfCNb3wDf/Inf7LjgGGhpLRzX3vtNfj9ftx7770F/S0ORBARERERERERERERUclwaiYiIiIiIiIiIiIiIiqZ/JO1ERFRSblcLsnrHIiiKHvdAqvVuuOCu5VWqu3PTgcjJbbb7QYAdHR0SIqdjV/O/VrKelKMbZGTP0BeHuVuT6nqiZy0cvMhN72cMpNbNnKPh1KVZbWfu/ajUh7H1XROrtXzVan2dzY9jzciov2Pfa/9TU75lrLNLzf9QSnfUh5/cvafnHyUqk3JgQgiogpwuVwYPuxAOBKVlF6hVCKdSsn6GwaDEaOjI1V5YZe7/VAogbS07dfp9FAq0ohExaLGzdIbjHCWab+Wup4Uui0ulwuOw8OIRsKSf6NUACmJk0LKSQtAdj2BQgExGpGUVnKdwh6OVxn5llpmLpcLw8OHEQ7vvn17yYfc9HLK0mjQY2TUWZXnrv1oT8exUoVUKikprV6vhwKlOycbjEaMjkg8JuRcd+TmR2beS3Uu1Ot0SCuUks5tQHmvaUREVBlyr/VyrzsKBfZF36tWyS5fpRIpGX2VUvbJDkL5lvLeh5z9JzcfpWpTciCCiKgCPB4PwpEoHri5HWadGocsWkytRdHdoEOHWYcFn4iuBh180QTOjXnx2HkPrv3SN2HpOSIpfmB+DL/6+38Nj8dTlRf17PZ/7vpWXHvIlHfb10IJ+MUEHjo3h/a7H4DaaIa26RCiy1PQNXdD19QBcXUBOmsXEiEfQq6LcD/7MADsGvv9hRC+85obji/8PfQtNijV2l3zHXaPw/n/fLVs+3Uv9eTol/4ehtYeKDU7b09ofgwX/0dh2+LxeBCNhHHsi9Lq5tJ7L0D4yX+RtT1S0hZST1pPfg6m/mslpZWT73u+/ndo6btq130y9fbzeO2Hfysr31LKzOPxIByO4Lv/vz9Gb2cLdJqdm3zOGTce+L+/JykfieAaEhE/5p58SFJ678Vz8Lz+mKT9d9EdwsOvuqv23LUfZY9juWX/ox/9CP39/Ts+sTUyMoLPfe5zAKSfk2/+2rdhauuFapdzGAD45gS8/i1p1zo559ON55TdzhFyj4eN6eVcA+UcP8Du57ZEcA3qOgvGv/sgjzcion0ue62Xc02Te92Rep2/9qvfRp2EvgqQ6dO+U8V92mohpy0Xcr0P97PfkdSOA4Cnn34af/mXf1mS9tNBaYeU+t6H1P0npy1cyjYlByKIiCpoyGpAv9UAXySJ4XYVPME4DFolbu23QKtWQlgO4+RAAx4770HYMwdT5wD8rhHUHxpEXVsvgu5J1B8agujzIBZcRzwShKG5A/WHhiq9aZIc66iDrUkPi0EDMZnC1FoUR9qNaK/X5rZ/PZwAABg6hmBo70cy5IOqaxhxvwdKrQGW4Vuh1Gghri7A0N6fu2m8W2xfNPM0b8y3gnQyAUNbH0JzozB2DMLQ2oPI0hQMHYOI+z1IiiFEV2aRikt7Ir7YdqsnFxdCuXoi+leQSiVR19aHwNwI6joGYWzrRXhxCnWdg4j5PBB9S4iHfUXLX3Q1UzcDrhGYOjN/L7Q4CVPnEGJ+D2KBdSSiwdz+2217lgKx3PbslnbBJ0KvUcEiZpo0cutJXc8x6Fts0BgtSCVERJemYOw+Am1DO5SdWiSCa5LLYWo1mst3YGkOTYcG4Zn+DRq7h9DQ0Yf1+Qk0dQ8h4vUgERcRWlvKvTmxW76jS1NItQ/KLpvldT8SySQGu9rwweQ87LZ29HW2YGJ+GXZbO1bWA5hbXkUsHpe8/1RaPdSiRVL6kOsDNFx9Ep7XH8NvH2tBMJY57vIdl8uB+B5rIRVqt7IMTp2HseswYuuZTsnS0hISiQSGhoZw8eJFHD58GAMDAxgbG8OxY8fg8Xiwvr6ei7/bOdnlzZwfol4P0skk6jv64XWNwHxoEPXtvQgsTMLcNYSoz4PQyhwAIC3xrYyNpJxTmuu0WA7EAOx+joguTiLmX5G0D8XVBSgUgLrOImmfTK5GsXIpH7vle3I1ik9ebch1GnfLd3hegMZs3UtVISKiGiXl2pAIZa7dUq6Xkfjlp7Z3u6YtBTNtPPFS38vU3g//bKbvUNfWi+DiJOo7hyBe6jsoVCqkk4mK7KdatWt/YnEScXMLAGntuKmpKfT19QGQVh/MenWu7852yJVKee9DDill2d8svU0Zcn0AbaP06dY4EEFEVEHHOk042mnK+/0hiw5PnM/c4NA3tiOdjEPX0IKodxm+md/A0nMECTEMjakB4dV5AEBj/3F4Jy+UJf+FkrP9pt5jMPUczZtW13QIwZmLkmN/6FDmO5W+DnprN+IBD9RGM0KzH1yK14l0MoF4cA0x7zL01i4otQZZ21cscvaTWm+CwdqFmN8DjdGCgOs3AAB9c2Z7IisuQKFAw9D1RcufrqEd6UQcOksLRN8y/K7fwGw7guSluhnxzEOp1qK+c0jy9kyvRiWnBbDnerJb+vrBy/tJTjn0f/hjiEWCaOweghj0YeGDNxHyrkChVMLU3AF9fSPC3hU0dPZKzrdqQ76luu0aB4LhKOKJJFobzVha8+HixCyODnQjFBHRaK7D4qoPRwdtkvMBACtvPCE5fTYtANzca9kxvx/uNUveNiouuWVfX1+P3t5eLC8vo6GhAefPnwcAdHd3IxwO47333sMtt9yS+/1ux89HBhrwn5+fRdvVtyAeCSKVjENvsSLqXYZ3+gM09F6FRDQMrakByVgUVvt1WNvDta4U5xQ5xwNweR/KOadISXtxIZj7t9xzIRER7X9yrmlSrjt6tTL3793S39yTaeNZj9yCxKXrvM7SAnFLv1Zb14DwsgsKpQpm27DcTTzQJPUn6hoAAB/5yEcQCAQQj8fR1taGxcVFnD9/HsePH8f6+jqMRiOi0ctT+JS6T3YQlPLeR7HzUco2JQciiIgq6MJCEG+5AhiwGiAmUrC3GKBVK+GPJpBKZ+ZjnFzNzMnX2H8MDf3H8sYyWg+VK9tF89OLHqyGE3m3PShefto1OH0BgbG3YGgfQCouwtBph1KjRTISRDqVQCouQrw0GJMvdjiWhEGjQkBM4IPFEACgvvcY6nvz71d98+X9GpiuzABPvnri9sfQXq9FQEzk6om57xjMErbHX8Rtaeg/Bktf/r9puPQ35195HED+7XGtR2Fr0MO/YXvypV3wiUik0uhu0GNyNQK3P/M0tdx64nnzp0gEVvOmDS+M5dLulJc6rQqKDccrAHQfu3XH/Vbfeggj5/5p13ykE3EkYxHEA2s7xsvnxDWOHb/vam3Ce8IMAGn7L+5bgbi+KCl9IuSFuObedf91mHWbjksqv13LMuzfVJ7XXXcdrr322rzxfud3fgfvvPNO7t/bnZPd/hiMGiUUCmwq+7arbtkuZE5dAde77fKRPZ9c1V6H9+aDaKu/PF3EjueIWBQqvUla2rgI0TMLY9eRHfOy8Tq18RqY7/jZ7romJS9IpaDQSF94koiIap+Ua0NWvuulSqnAoNWA9+aD8EUSu6bfro1nPbLzdd5w6TrvnaqNh+uqxW5tlrjfg9SGt0xOnjy5Y7zu7m784z/+I4D85WvUqtCgV2NiNSK9/XRA2yGlvPchx059MkerEfM+UXKbMhHyQlMv780WDkQQEVVQdjT6vfkgFv0xLAZiaDJqMOGJ4gZbPVZDcTQbNQCA9ckLWHW+BVPHAJJxEeZuB5RqLSJrbhga2xBamkE8EoSpvbeyGyXDp45akUwj77bbGvWYXc/cYDb1HgNSScTWF6GqsyCdiCG6vgBNvRVKjQ513VchEfbvGHtsJYIbbPVIp4F2c6bhE5i+AP/YWzC0DyIVj6LukAMKtRaxdTe0De2I+ZYQ861AqdEhGa3MTdJ89WTRH0N3gw7pNHL1xD91AevOt1DXkWm0mLou1ZPVeaj1JsRDXsSDa4CcBcd24Z28gLUNf7O+y5F5/XfVDZXOiFhwDTG/B9FLN6+PdZpyZWMxqBBLprHgj8KiV8MXTeBIex3emPLvuO1LgXiuntw+0ICJlcwAgNx6Yr3pU7n0Me8iNKYmRNxjMB++BYngGrQNbZvKIV++AWC47XK+l8bOY/6DN9HUNYhELIrmnsNQa3SIRUPQ6AwQQ34o1RrJ+TD1XYP191/aU/m88p4TFydmMdTdDjEWx+HeTui0GoQiURh0Oiyt+TC3tJrbf6aeowhOvZc3L/X910J84f+VtL8th29FeF7YtSxtjfrMcVm/+3zBVBq7lb1So4PFcTPcl8r+17/+NV555RU4HA5Eo1FcddVV0Ol0cLlcqK+vhyiKmJyczMXf7py86I/ljuPsORkAlj54Dd7pD1DfmTkvW7oPQ6nRIuyZh6X7MNan34dSrYFqDx3Y7fKRPZ/MeUXcPtCApUvTIQG7H5sqfb3ktPU3fhIx79KOedl4ndp4Ddzp/BNPZs6bS355+Y75lmXvPyIiql1Srg1RjwuAtOvlS2OXp2DMlz7Xxrt0nfdOXsDq6Fuo78z0aeu7HFCptQgtz8DYakN42YVYYA31XfZK7KKaZr3pU7m2XGTeCVWdBUinAaRh7BqGus4C0ZOZ3jJfO87r9SKVSiEajW5a0PpTR625drxzOQKLQYV0GvBGErDWaWCt06DBoN6UF7ZDNpN772O3PtnGPq0cO/fJUlf0yXYqS6VGJ7ssORBBRFRBY57MzVOVAjhkyZzsY4kUehp1ufmp/WLmqYXsGxHr4+8isr6I6Job2vomRNfcMHX0Q9/UjpbOQUTXlxBdX9r+D1aZMU8EQ1ZD3m1fDsQw6828Ehpxj8HQMQTtpafrU4kYVAYzUokYkACCMxc3zee/W+y1UGae0uwbEYHJ9yB63RDXF6Gpb0J0fRGG9n6o4xaYB67L/Obii2XYK1eSU0+yb0T4Jt+DuOa+VE+aIXqXUTc8CKVGi6bhW+Ap4rZk34jwTryL6NrluhlZc8N6VWYeS+tVJzD93CO57dlaNmadCul0Gsk0cHEhmNseKdu+Mb3cerJdel1LT+4Jk93qVL58tw0dR9vQcSw630FwdRHBVTcM5masuQR0X3MCiVgUWmO95HxszbccJ65xwKDXwr3iRUO9EbF4AvMr62ioNyKeSOKGI/3wh8K5fAAAlKqd8xLxS9/fl9JKKcu1COcCrhS5ZX/dddchmUxifn4ejY2NEEURs7OzaGxsBADccMMNWFuTf04GMm9EqHUGhFfdmamY4iLCnvnc1IRtV92CyPoSInu41u2Wj+yr6Dtdezbuk6QYQdQzKylt9rX1bHo510Ap55+Nx4+UfOfKnIiIDgQ51zQp18uN1x2p1/mG/mNo6DuG9Yl3kdzQbxD9qzB3H4auoRXWI7cgur6EZOzy1EC0u+3aclnZMo5f6k9k32x9++23MT8/j/n5eVitVjidTtx6661YWVlBS0tL7qGS7drxWVL77ge9HSL33geAHdvle+0byu2TFbssORBBRFQBVqsVRoMeDz4+Lvk3gfnMyV2hVMGYvQjEY6hr7UHk0sKd2bUhsmmrleztVygx/t0HJSXV6fRQKtKSY4fdl9Iplbn5u1NxEYYWG6KXnhjJTskUD65vG6NU9lJPQrl6ooTemm0siDC22hBdzWyPf/oC4v69NVy2s7FuGqyXGyh1bT2IXNqHvqkLSKWSUCogeXvkpL2UAVn1BAqFpPRy6xQArM0Kl7KkQn1LZp8k4yIsHT3wL2U6ePFoGBH/mqx86w1GWK3SX391zmSm0VEplehqawIAiPEEzHUGpFJppJDGe8IMEokUNBqN5Hxc2jjJ6eWUpdGgl7WNVBir1Qq9wSiv7AGMjIxgeHgYNltmfRFRFGGxWJBKpZBKpfDOO+/A4/EAAAw6neTy981dPnbqWroAZM4nmjozUnERKSC3NkQ2rRR7OZ/KqeOy0kLeMSEnrV6nQ7qE5xQiIqo9cq/1cq87Chnpd+o3hC/1G7JTMlV7n7Za7KUtNzIyAgBQqVSb2nL9/f1wuzP9B7fbDY/HU9I+2UFoh5Ty3oec/Sc3H6VqUyrS6XQRJ2cgIiKpXC5X7ibNTtxuNz796c8gGo3smnYjg8GI0dGRXMOi2kjdfiDTKNLppE3Bkb347Rbb7Xbjtz/9GYgy96veYISzjPtVTj2Ruz2FbovL5YLj8DCikbDk3+h0ejz++GPo6OjYNa2ccpebXmo9kZvW7Xbj05/5DKIR6eWg0+vx+GPS9onVapVUXi6XC8PDhxEOy6zfeh0ee+zxopeP3LKp1vPWfiXnfOx2u/GZz3wGEYl13Gg04vnnn9+1/Pd6TjYYjRgdkXYek7OdQOnqeCljyzlfZdPzeCMi2v/Y99rf5PQZ5bTjAECv1+MxiX0VgO3+7ZTy+JOz/+Tko1RtSg5EEBHVgK0XjDfffBNf+cpX8IlPfALvvPMO/s//+T9X/OagXNQLsXG/plIp3H333fjEJz6Br33ta/je976HH/7whzh37hw0mg1z+Vfxft1aT37xi1/g3/7bf4tTp05hZGQEP/nJTzalL8a2bPyb//iP/4i/+7u/w4svvgiFQoGPfvSj+MpXvoLPf/7zRf2b1W5rOTz00EN48803kUgkcPLkSfzpn/7ppvSl2idb87G+vo677roLn/3sZ/GTn/wEv/zlL2E0GsuSF9pfttate++9FydOnMCTTz6J73//+7jmmmty38mpU9t1jj72sY/h9ttvxxNPPIFHHnkEx48f3/Q96ywREVFt2Hqdj8fjuPnmm/GZz3wGP/nJT/DUU0+hvb190294nS++reXw2muv4cEHH8Tp06dx8eJFPP7445vSswyomDg1ExFRDbDZbJsu/m+88QY0Gg0+9rGP4amnnsLVV18NrZaLvMq1cb++8cYb8Hq9eOCBB3Dttdfii1/8Ih5++GEEAgHcddddFc6pNFvryUsvvQSj0Yg777wTP//5z3HNNddAqVSW7G/++Z//Oe666y6cOHECAHD33Xfjvffewze+8Y2i/s1qt7UcvF4vjh8/jkQiAZ/Ph2uvvbYi+XjttdcAAJ/4xCfwk5/8BHV1dfjQhz5UlrzQ/rKxbgWDQaysrOD+++/HT3/6UygUij3X8a11NhAIwOPx4L777sMTTzxRUGwiIiKqrK3X+dHRUaTT6VzbVKfT8TpfBlvL4ZVXXoFOp8M999yDZ599FseOHYNazdvFVBrFvRtBRERl4XQ6MTg4iCNHjiCZTGJqaqrSWap5Z8+eRVNTEz784Q8DAI4dO4auri6cPXu2wjnbO6fTCbvdjsOHD0MURbhcrpL9LZ/Ph5dffhmnTp3KfXbq1Cn88pe/xPp6edfWqDZOpxMOhwN2ux1Op7Oi+QCAO+64Y9O/iQoxNpaZv/nqq6+GzWYrar3Kxj527BhsNhsEQfqaEERERFTdsm2G22+/HWq1mtf5CnE6nRgaGsLhw4cRj8cxPT1d6SzRPsaBCCKiGiQIAux2O+x2e+7fVJgzZ87gvvvuyz39oVAocOrUKZw5cwa1OothOevJP//zPyORSFwxEJFMJvHcc8+V7O9Wu2g0ipmZmVw5TE5OIh6PVyQvgiDAZrOhs7MTLS0tPG9QUWTrUbaOF7NeZWMNDQ0VPTYRERFVliAIMJlM6OrqwsDAAK/zFcJ7C1ROHIggIqpB2cZCR0cH6urq+GRzgVwuFy5cuLDpJjoAnD59GpOTkxgdHa1QzgojCAIcDgd6enqg0WhK2qg8c+YMjh49ip6entxnXV1duOaaa3DmzJmS/d1qNzExgXQ6DbvdDofDUdE3mLLnDQBwOBzsZFBRCIKAlpYWNDY2Fn2wwOl0orW1FQ0NDRV/o4iIiIiKK9tXUSgUvM5XULaP0NXVBYPBwD4ClRQHIoiIaowoipienobdbs812thYKMzZs2ehVqvxsY99bNPnd9xxBwwGQ03eSA8Gg1hYWIDdbodKpcLg4GDJ6kkymcTTTz99xUAOkBnMeeaZZ5BIJEryt6vd1qfFN35Wibxk88DzBhXL1no1Pj6OZDJZkthjY2NIpVJFiU1ERESVxbZp5UUiEbhcLtjtdiiVSgwNDbEcqKQ4EEFEVGMmJiaQSqXgcDgA8MnmYjh79ixuu+02NDQ0bPrcYDDg7rvvrsl1IjbeAAdKW0/eeOMNrK6u4vTp01d8d+rUKayvr+cWSj5oBEGAxWJBa2srOjs7YTQaK3K8plIpjI2N5c4b2c5erU47RtVj65s2oihidna2aLE3XuuKGZuIiIgqK7ueHZC5zk9NTSEWi1U4VwfL+Pg4AFzRRyAqFQ5EEBHVmK03mNlYKEwoFMK5c+e2vYkOZG6kv/rqq1hdXS1zzgqzcW51ACV93fns2bOwWq248cYbr/ju+uuvR1tbW00O5hRDtoOlUCgq+tq5y+WCKIqbzhs+nw/Ly8tlzwvtH+l0etNNhOx/i1HH0+n0FU9KApy3mIiIaD/w+XxYWlradJ1PpVKYnJyscM4OlmybbWM5cIosKiUORBAR1RhBEGA2m9Ha2gog01hwu93w+/0Vzlltev755yGK4rbTCgHA/fffj1QqhWeffbbMOSvMxnnbgUw9mZmZQTQaLfrfOnv2LO677z6oVKorvlMqlbj//vsP7EDExhupQOXeYNruDZmNnxPtxcrKCnw+X64+2Ww2aLXaotSrpaUl+P3+XJ3NrnXDzjEREVHtGxsbA4CSPMxA0gmCgMbGRjQ3NwPI9BHm5uYQCoUqnDParzgQQURUY7I3NhUKBYDLjbZsY47kOXPmDBwOR+7Nga06Oztx3XXX1dw6EVtvgNvtdqTTaUxMTBT170xPT+P999/P+0YJkFknYmRkpOh/uxZsVw6VGojQaDS5xcQHBgagUCg4EEEF2TrAVcz1aEoZm4iIiCpr63W+vb0dJpOJ1/kyy3dvITtlE1GxcSCCiKjGOJ3O3NOnAKerKEQqlcJTTz2V922IrFOnTuHZZ59FPB4vU84KV656cvbsWWg0Gtxzzz1509x1113QarUH7q2I9fV1rKysXDEQsbCwgGAwWNa8CIKAwcHB3FsrOp0Ovb29PG9QQQRBgEKhwMDAQO6zYg22CYIApVJZkthERERUWU6nE+3t7TCbzQCQm8KU1/ny2u6hqeznRKXAgQgiohojCMKmp/ctFgva2tr4Guse/PrXv8bi4uKuAxGnT5+Gz+fDK6+8UqacFSY7t/rGetLa2gqLxVL0enLmzBncfvvtuU7EdkwmEz760Y/W3Fslhdr6pBdQuSmRNs7jvzEvPG9QIZxOJ3p6eqDX63OfFateZWPrdLqixyYiIqLK2tpXAXidr4StfYSmpiZYrVaWA5UMByKIiGqI1+vF8vLypifdAT4luldnz55FQ0MDbr311h3TfehDH0JHR0fNPNG/vLwMv9+/qZ6U4imjQCCAl156acdpmbJOnTqFl19++UCtZbLdQES2w1Xu41UQBJ43qOjy1SuXy4VIJFKS2KVa64aIiIjKh23TyltdXcXa2hrLgcqKAxFERDVk66JeWWws7M3Zs2fx8Y9/HBqNZsd0SqUSp06dqpkn+re7AZ79dzHryc9//nPEYrFd3ygBMgMRiUQCzz33XNH+frUTBAGdnZ0wmUy5zxoaGtDa2lrW4zUajWJmZmbb+jA+Po5kMlm2vND+svV1fqB469GUMjYRERFVTvbt7e2u80tLS/D5fBXK2cFSrj4j0UYciCAiqiHZVyS3vsaabSyk0+lKZKsmzc/P45133pF0Ex3I3EgfGxuriUaZ0+mEQqHA4ODgps+L3ag8e/YshoeHN83hnk9PTw+OHj1aM2+VFMN2HSyg/I37iYkJpNPpbTsZ8XgcMzMzZcsL7R/JZBLj4+Pb1iugsLd+EokEJiYmShKbiIiIKsvtdiMYDG77JD5w+eE7Kq1se2q7PqPT6eS9BSoJDkQQEdWQ7BPW9fX1mz632+0IBAJYWlqqUM5qz1NPPQWVSoV7771XUvq77roLer2+Jt6KEAQBvb29m+ZWBzL1ZGVlBevr6wX/jexC31KmZco6deoUnn766QPzBH61DETs9LTTxu+J5HC5XIjFYlfUq5aWFlgsloLq1czMDOLx+BWxW1tbYTabWWeJiIhqGNum1UEQBHR1daGurm7T53a7HV6vF6urqxXKGe1nHIggIqoh+W5sZp8m4aJS0p05cwa33normpqaJKU3Go346Ec/WhNP9O9WT4rRuH/77bexvLws+Y0SILPot8fjwZtvvlnw3692qVRq27lvgcsL8ZXrKSOn0wmLxYLW1tZNn3d3d0Ov1/O8QXuSrTdb67hCoSh4scndYvMGBRERUe0SBAFKpRL9/f2bPjebzWhvb2fbtEycTmfevkr2e6Ji40AEEVENyXeDub+/H0qlkjdnJAqHw3j++edlPc0PZG6k//KXv4TX6y1NxookXz0p5kLJZ8+eRVNTE26++WbJv7nxxhthtVprYjCnUAsLCwiHw3nfiPD7/VheXi5LXrL1QaFQbPpcqVRiaGiI5w3aE0EQoNPp0N3dfcV3hb71IwgC9Ho9urq6ih6biIiIKksQBPT19UGr1V7xHa/z5ZOvzzgwMACFQsFyoJLgQAQRUY3It6gXAOh0OvT29rKxINGLL76IaDQq62l+ALj//vuRTCbx7LPPlihnhUskEhgfH9/26RaTyYTOzs6i1JMzZ87g4x//ONRqteTfqFQq3H///TUxvVWh8r1yvvGzch2v+c4b2bzwvEF7IQgChoaGoFRe2Z0oxkDETrH5hB4REVHtyvckPsC2abmkUimMjY1t20cwGAyw2WwsByoJDkQQEdWIhYUFhEIh3lAsgjNnzmBgYCBvAzif7u5uHD9+vKqf6M83t3pWMerJ7Owszp8/L3sgB8isE/H+++9jenq6oDxUO0EQoFar0dfXd8V3AwMDZX2DiQMRVAq71SuPx4O1tbWSxC7WWjdERERUflLaplwoubTm5uYQjUbZR6Cy40AEEVGNyDYE8t08L3RO7oMinU7j7NmzOH369BVT1Uhx+vRpPP3000gkEiXIXeF2ehIfKE49OXv2rKyFvje65557oNFoqnowpxicTif6+/uh0Wiu+C77BlM5jtf19XWsrKzseN5wuVwIh8MlzwvtLzs9zVjoejRSYo+Nje0pNhEREVVOPB7H5OTkjn2VYDAIt9td5pwdLPnW48rivQUqFQ5EEBHVCEEQoFKptn3CGsjceJ6YmKjaG+TV4r333sP8/PyenuYHMk/0r6+v4/XXXy9yzoojO7f6dvO2A5l6MjY2hlQqtee/cfbsWdx2221oaGiQ/Vuz2YyTJ0/u+4GInZ70Asr3lNFuA1PZz8fHx0ueF9o/IpEIXC5X3no1ODgIYG8DEaFQCHNzc3ljF3OtGyIiIiqv6elpJBKJXdumvM6XliAI0Gg06Onp2fZ7u92O8fFxJJPJMueM9jsORBAR1YidnrAGMo2FRCKx76e8KdTZs2dhNptx22237en3N9xwA1pbW6t2nQOn05l3bnUgU0/C4TAWFhb2FD8UCuGFF16QvdD3RqdOncKLL76IQCCw5xjVrtoGIrI3b7fLx8Z0RFJkB67y1XGTyYRDhw7tqV5JiV2stW6IiIiovHZ7Er+/v7+sU5geVIIgYGBgIO96f3a7HaIoYnZ2tsw5o/2OAxFERDXihz/84Y5PJGRv2vAVyvxEUcQ3v/lN3HTTTdBqtXuKoVQqceutt+J//I//gWAwWOQcFiadTuMf//Efd3wrptB68jd/8zcQRRH33Xffnn4PZBb9jsVi+E//6T/tOUY1i8ViO75yDlx+M6XUTxmNjo6is7MTJpNp2++bm5vR1NSE0dHRkuaD9pfs+WO3Or6X84zU2KyzREREtefhhx+GUqmE1Wrd9nutVou+vj5e50vM6XTu2tbKpiMqJg5EEBHVgHQ6jbW1NYRCobxp9Ho9AODP/uzPypWtmjMxMYG1tbWCn8QXRRGBQAC//vWvi5Sz4kin0/D7/TvWE51OBwD48z//8z39jX/4h38AAHR0dOzp9wDQ3t6+KdZ+8z//5/9EKpXC4uJi3jTz8/NIJBJ45JFHSpqXv/mbv9n17Ze1tTX85V/+ZUnzQfvL17/+dQDI+4YeALz77rt47LHHEI/H9xR7p8Hid999F//0T/8kOzYRERFVliAISKVSed/eBjJ9tm984xtlzNXBEggE8Nxzz+3Yl832GXlvgYpt+3dwiIioqigUCly8eBFdXV150zQ1NaG/v39PCwgfFEeOHMG3vvUt/OEf/mFBcZ544gk8/PDDuP3224uUs+JQKpU4f/583nVEgMwAgs1mwz333LOnv/HEE09gZWUFFotlr9mEyWTC008/jcbGxj3HqGbXXnstzGYzPvnJT+ZN81u/9Vv49re/jePHj5c0L7fccsuu+/muu+7i2jIky7333ouXX34Z9fX1edP84R/+IR5//PG8r/zn8/GPfxy/+MUvUFdXt2PsJ598UnZsIiIiqqxXX30VXq93xwcOPv3pT2NqaqqMuTpY6urq0NHRgT/+4z/Om6alpQW9vb28t0BFp0in0+lKZ4KIiIiIiIiIiIiIiPYnTs1EREREREREREREREQlw/eZiYiqiMvlgsfjkfUbq9UKm81WohzVHrn7UM7+K2VsOUpZT2o1NhERERERVYdq6TcRUXXhQAQRUZVwuVxwHB5GNBKW9TudTo/HH39M0uLB+72B53K5cHh4GJGw9H1oMBoxOjKy637ZS/noDUY4R3ePLUcp64nb7cZvf/ozEKMRWbGlbKfL5cLw8DDCMsoGAIxGI0YklE+lyOlkiaKYW/htN3KPVTn5sFqtACArfbXufyqtUtYruceDnNiss0RERJWzl/4K+7SlwT4CVRsORBARVQmPx4NoJIy+01+FwdoNpVoDtb4OqUQMmroGRNcXUd99GIHZUSjVWiiUSsSC6xB+9Fc4deqUpL9hNOgxMurctw0Gj8eDSDiMW3/nq2g61AdAAaO5EYm4CKO5CWsLU1BrdBAjQSiVKhgtTXjsP30RHo9n132SLZ/Oj30Rxk47FCo1lBodUokY1HWNiK7MQKnWIu73QKnVQ2Nuwdh3H5QUW+42RiNhdN/3VWgaW6FUKKExW5GKx6AxNSIRCUCp1iC8OAmNqRFqgxkx3zKm/rf0euL4wt8jFlyFSq1FKh6DtrEN6XgMalMjosszUBlMSMUiSCXi0Jia4Px/vrrrdno8HoTDYfzdd3+ItdVVaLVatHcegkKhQDwWg06vR8Dvw6pnBXq9AXqDAQqFAn/ywO8XfR8Wi8vlwvBhB8KRqLQfKJRAOiUpqZxBLLn50Ot0SCuUkgecSjGgRtWv1PVKzvGg0+mhVKQRiYqS0u/3ax0REVE1y/ZXej7+JRjbehEP+2Bs7c30a00NiCzPQGNqQnhxEmpDPQAFxv4/9mmLjX0EqkYciCAiqjI6czNUWj3q2vsQmB1BXecgjK29UGkNMFi7odIaIHqXkAgHkIiEkEoDn7u+FdceMmFqLYruBh06zDos+ER0NejgiyawFkrAYlTjwcfHq/ambjFpjSa0D1yFpcnfoK6pBU2dvVidm8RVJz+BoHcFnpkxmJrbsL4wIzu2Sm+CoWMA4blR6JoPQd/Sg8jSFKzX3Ye434OYbxmpRAyJsL8EW3aZxtwMjcEMQ1sfQnOjMHYMwtDag/DS1KX/70U8uIZkNIRkNCipnry/EMJ3XnMj5l+BxtSYi601t8DQemk7b7gfcb8HSTGEdCqF6IpLVr5XlpfQ2NSMvoFBjH7wPgbsh9Hb14+piXFcdewarK4sY35uFn6fFzFR2k3HSvF4PAhHopKOP7+YwEPn5tB+9wNQG83QNh1CdHkKuuZu6Jo6IK4uQGftQiLkQ8h1Ee5nH5Z8rGbz8cDN7TDr1Dhk0ebNy0V3CA+/6gYAtJ78HEz91+bNRyK4BnWdBeMlGFCj6lfKepWI+DH35EOyjgeA1zoiIqJaojaYUN99BIG5EWjNVhjbehFenELbDacQ83mgqbMAUCAeXGOftgTYR6BqxIEIIqIq0zR8Cyx9xwAAjfYbcp8bmjsBAPrGNqDnKgDA/KuPAwCOddTB1qSHxaCBmExhai2KI+1GtNdroVUrMbkaRYNeVeYtqZzDt9yLTsdx2I7elPvM0noIAFBvbUfH4FEAgM5gkh276Zq7Yeo5CvPg5bLRNWXKRtvQhjpkyiY4c3HP+Zei4fDNqO/N1BPL0JV50TW05T5LRkMAdq8nvmhSduyAzigr3zefOImj11wLALj+pltyn3cc6gIAtLV34MjR4wCAi++9Iyt2pUg5/lYCMQCAoWMIhvZ+JEM+qLqGL71BY4Bl+FYoNVrEvEswtPfnbrzKMWQ1oN9qgC+SxHC7Cp5gHAatErf2W3L5+OTVhlwno67nGPQtNmiMFqQSIqJLUzB2H4G2oR3KTi3E1QUotfqi7iuqPbvVqymZ9Sq6NIWYbxnA7sdDaPoirDd+Mnc87HasLfhE6NXKiu0rIiIiuqzlQ3fD3HsMDRv6tPoN/Yn6S/0m92vS+rTCchjWOk35N6TG7daWE5bD+OTVVsltufC8AI3ZWuGtolrFgQgioiq0OvIaACAR8iEVj0H0raC+5wj0jR1QarTwT12Eus6cS3+s04Sjnflvqh+y6HBxIVjyfFeLBed7ECNBRAJeJGMigusraB+8GmZrB9QaLbzLcwAAjdYgO3Zw+gKS0WCmbBIi4j4P6myXGmYaLSKLk1Co1AgvjBV7s67gHX0dAJAIeZFKxBDzr8DUfRV0je1QqLUITJ2HxtSYS79bPfnQocvfbY0d96+gbkPsmHcJUCigUMof4Hr9lV8AAHzedcREEZ6VJQxffRztHZ3Q6XSYn3MhFAyh2VobDVwpx98T51cAAKbeYzD1HM2bVtd0aM+DWHLPA6XMC+0fpahXK288ITntxjrIax0REVHt8E9dQCKS7Tdd6tPajkDf1AGlWgv/1AVAocil53W+NNhHoGrCgQgioirjm7qAVCyKus4BAAqY+45BqdEi6plHMhZBdHUBAKCtb0Z0LfPUwk8verAaTkBMpGBvMUCrViIcS8KgUSEgJhAUkwfuKdHF8fdhtQ1CAQXsH74bao0WkaAPsWgIyXgcCuXe9kdkaRK65kOAQgFTT6ZsEmE/UmIYieAaUnERaTEMTX1zkbdos8D0BaTiURjbBwGFAvW9x6BQa5EUw1CoNJkpk9JpaMxWiGuZOrNbPflgMZSLnY5HYdgSO3UpdiLsQ9zvgaGtD8mYvIWts0beP4/+QQcUCgXuuPvj0Ol0cC/Moa29EyZTPRoamxCN7C12uUk5/rKC0xcQGHsLhvYBpOIiDJ12KDVaJCNBpFMJAArZ011lXVgI4i1XAANWw65lDACeN3+KRGB123yk4iJUurpCdw3tA/nq1YJPRJ1WBYUCm+rVbnU8nYhDvHQd2y1tIuzfdDzslBdHqxHzPnFTXoiIiKhyQouT0F/qN5n7jkGp1iKyOo+kGEF0zY10Oom6DjsCl25qb9em9kcTSKUBpQIHsk9bDPnaTxv3rZw+AlIpKDS6Cm4R1TIORBARVRlL37Hc1EwbKZTqzLRMl0TXl3L//6mjViTTwKI/hsVADE1GDcZWIrjBVo90GrA16rF8aWqYg6DTcQ06Hcev+FypUqPe2g4ACHgWEVhduiLNblpu+tQVT4golGpoN0xXBADr778kO7Yc9b3HctMnbSR6l6BraIPe2pX7d9Zu9aTdrJMUGwBM3Ucgepf2NBBx84nbcfOJ26/4XKVWo629AwCwtOiumYEIKcff7HpmvQtT7zEglURsfRGqOgvSiRii6wvQ1Fuh1OhgaB/MTVsj17FOUy4fFoMKsWQaC/4oLHo14skEjrTXYcl/+TxgvelTubzEvIvQmJoQcY/BfPgWJIJrUOnr95wX2j+yT9G9Nx/cVMeXAnHcYNNjNRRHe702l37XOt59FQKT7+bSmnqOIjj13rb1UKnRZTq8G/KSr47PecUr6jgRERFVTsfNn4J5S59CoVJvmuZ1t77KhCeKG2z1WA3FD1yftlh2aj8pFcBwG/sIVD4ciCAiqjLBS1P6BOaciPlXoTaYoDGaEQ95YWzvRyLkg6GlGwAQXpkFAIx5IhiyGnDIkrkZFEuk0NOoyzXUlgMxjHlq46ZuMazMOAEAy1OjCHlXoDPWQ2+yIBLwormrH5GgD43ttlw6ObJTLoXnnYgHVqHSm6A2mpEI+aBv60Mi7IPe2o14YK2o23RlPsYBAKF5J+L+VagMl/Nh2JAPAIh6MlNR7VZP1kJx2bGzaaUaF0YBAMLIb+DxrKC+vh5mSwO862voGxjCO2+9gS5bz6a01U7K8TfrjQIAIu4xGDqGoG3OrFmSSsSgMpiRSsSARGZtkURwb3Vnu3yYdSqk02kk08DFhSDWIolc+u3yomvpgbg6DwBIihFE3KWfYoyqW/baoVIg/7ljl3p1RR2P+HNpAQBKVd56uPF4kFvHiYiIqHJC85nrfHBDv1a9Tb82ssw+bSmxj0DVRJFOp9OVzgQREQEulwuOw8OIRsKyfqdUACmJZ3KjQY+RUSdsNtseclj9XC4XDg8PIxKWvg8NRiNGR0Z23Sd7KR+9wQjn6O6x5Sh5PVEogXRKVmwp2+lyuTA8PIywjLIBAKPRiBEJ5VMJLpcLw4cdCEei0n4gY9/KqTty86HX6ZBWKCFGpXXkSlGPqfqVul7JOR50Oj2UijQiUXH3xNj/1zoiIqJqtpf+Cvu0xcc+AlUjDkQQEVURl8sFj8eT+/cDDzwAo9GIb33rW/jrv/5rXLhwAY8//vim34iiCJ1O2hyNVqt13zcUtu7DH/7wh/je974Ho9GIT37yk/jKV76yKb2cfbI19g9+8AM88sgjMBgM+NSnPoUvf/nLe44tx9Z8pFIp3HLLLTh58iTOnTuH119/HWr15pcepdYTtzuz7khHR2aKpGQyiVtvvRUnT57Eiy++iNdee+2K2FK3c2u+k8kkbrnlFnzkIx/BSy+9hFdffXXPsStl6zbtpJTHqpx8WC8tAi4nfTWXAZVOKeuV3ONBTmzWWSIiosra2ob4+te/Dp/Ph3fffRff+ta3cOLEiU3p2actDfYRqNpwaiYioipis9lyF/P19XWcP38e3/72t3HttdfiD/7gD/Bbv/VbMJvNGBwcrHBOq9fGfQgA3/nOdzA8PAyz2YxAIIBrr722aLG//e1vY3h4GHV1dQXHLiQfMzMziMfjuPPOO/H888+joaEBdru9KH9renp6U+zGxkYMDQ0VJd+Tk5NIJBK52E1NTTVXt7duUy3loxryTdWt2uoV6ywREVFt2NqGWF5exh133IGRkRGkUqmy9ZsOumpryxFxuXkioir17LPPIplM4v777wcA3H333dBqtTh79myFc1ZbBEGAw+GA3W6H0yl/TYidOJ3OksWWmw8AuO222zb9u5ixb7/99qLHFgQBAHDy5MlN/yYiIiIiov0hmUxifHwchw8fxtDQUEX7TURUWRyIICKqUmfPnsWHPvQhdHV1AQBMJhPuuOMODkTIJAgC7HY77HY7xsbGkErJW/9ASmyHwwFBEFCp2Q4FQYBGo8FNN90Eo9FY1Bv6giBAq9XihhtuKElsnU6HG264AQaDgQMRRERERET7zMzMDGKx2KZ+ExEdTByIICKqQolEAs888wxOnTq16fPTp0/j5Zdfht/vr1DOaovP58PS0lJuICIcDmNhYaEosdfX17GyslKS2HIJgoDBwUGo1WrY7faiDxYMDg5Co9FgaGio6LGHhoagVquLHpuIiIiIiCov28bP9pvY5ic6uDgQQURUhV577TWsr6/j9OnTmz6///77kUgk8Nxzz1UoZ7VlY6PX4XBs+qxQY2NjudjZ9Rgq1ajOvpkBoOhPGWWntgJQ9I6D0+nM5bvS01sREREREVHxZd+C7u7uht1ux9zcHEKhUKWzRUQVwIEIIqIqdPbsWbS3t+O6667b9Hlvby+uvvpqTs8kUfam+dDQEHp7e6FWq4t2I31j7L6+PqhUqqoYiCjFGxG1GJuIiIiIiCov+4a1SqXKtf3Hx8crnCsiqgQORBARVaEzZ87g/vvvh1J55Wn69OnTePrpp5FMJiuQs9oiCAI6OjpgNpuh0WjQ399ftKfunU4nOjs7UV9fD61WW9TYckSjUUxPT296a2FhYQGBQKDg2JFIBDMzM5vetpifn0cwGCxKbJfLlcu3w+Hg01FERERERPuM0+nc1ObPfkZEBw8HIoiIqsz4+DhGR0evWB8i69SpU/B4PHjzzTfLnLPas3HqH6C40xZtfJofqNwT/RMTE0in05sGC4DLU0cVI/bGQY5ixd44tVWxYxMRERERUXXY2G9qampCc3Mz34QmOqA4EEFEVGXOnj0LrVaLu+66a9vvb7rpJlitVpw5c6bMOas9pRwsqJaBiI3rYACZqaI2fl4rsSu9zgYRERERERVX9i3oaug3EVHlcSCCiKjKnD17Fh/96EdhMpm2/V6lUuG+++7jOhG7SKfT2w4WTE1NIRaLlST25OQk4vF4QbHlEgQBZrMZra2tAICGhga0trYWbbCgoaEBVqsVQOYJJqvVWrTYjY2NaG5uzsXm01FERERERPtHdi0IDkQQEcCBCCKiquL3+/Hyyy/nnZYp6/Tp03j//fcxPT1dnozVILfbjVAodEWjN5lMYmpqqqDYCwsLCIfDV0z7lEwmMTk5WVBsubJzrioUitxnxWrcZwdbai02ERERERFV3ta3oIFMv8npdCKdTlcqW0RUIRyIICKqIs899xwSicSuAxH33HMP1Go134rYQXYBtK2N3o3fFTN2paYWEgQhN2VSVrZxX6ita2zUSmwiIiIiIqo8p9O56Q1rINNv8nq98Hg8FcwZEVUCByKIiKrI2bNncfToUfT09OyYzmw24+TJkxyI2IEgCFCpVOjv78991t7eDpPJVPBgwXaxOzs7YTQaKzIQkR1gycq+WVDoU0a1GpuIiIiIiCov2+bf+hZ09jsiOlg4EEFEVCWSySSefvppnD59WlL606dP48UXX0QgEChxzmqTIAjo6+uDVqvNfaZQKIoy/Y8gCOjv74dGoyl6bDnW19exsrJyxZsFdrsdfr8fy8vLe469trYGj8ezbWyfz4eVlZU9x15dXcXa2tq2sfl0FBERERHR/rB1XT0AGBwchEKh4EAE0QHEgQgioirx5ptvwuPx7DotU9apU6cQi8Xw/PPPlzhntWm7Ri9QnHUI8sV2OBxlbVCPjY3l/u5GxXjKKBt7u8GCQmNvN1dssWITEREREVF12K7fZDAYYLPZ2OYnOoA4EEFEVCXOnDmDlpYW3HjjjZLSDwwMYHh4GGfOnClxzmpTdhHnrYqxDkG+2Ha7vaxrHGT/1tY1IgYGBqBQKArKS77Y2SeYqjU2ERERERFV3urqKlZXV6ui30RE1YEDEUREVeLs2bO47777oFKpJP/m1KlTeOqpp5BKpUqYs9oTj8cxOTmZ942IxcVF+P3+PcWOxWKYmprKG9vtdpdtuixBENDZ2QmTybTpc51Oh97e3oLfWujq6kJdXd2mz/V6PXp6egqO3d3dDaPRuOlzPh1FRERERLQ/5HsLOvsZ2/xEBw8HIoiIqsD09DTef/99ydMyZZ0+fRrLy8t4++23S5Sz2jQ1NYVkMpm30QtcnnqomLGzT/vsNbZc2y34vDEvhQ4WbLeNQOEdh1LGJiIiIiKiysu26QcHB6/4zuFwYHx8HMlkstzZIqIK4kAEEVEVOHv2LDQaDe655x5Zv7v55pvR2NiIs2fPlihntWmnp2+y0wHt9WZ3KWPvJS+1OFjAgQgiIiIiov0t3xvWQKbNL4oiZmdnK5AzIqoUDkQQEVWBM2fO4OTJkzCbzbJ+p1arcd9993GdiC0EQYDRaERnZ+cV31ksFrS1tRU0EFFXV7dt7IaGBrS2tpblRno6nd71hv5enzJKpVIYGxurudhERERERFQdduurZNMQ0cHBgQgiogoLBAJ46aWXZE/LlHXq1CmcP3+eT5Ns4HQ6YbfboVRuf5krZMHqbGyFQrHt9+VaeG1+fh7hcHjHxn08Hsf09PSeY+807VMsFsPMzIzs2LOzs4hGozvGFkURLpdLdmwiIiIiIqoOTqczb5vfZrNBp9NxwWqiA4YDEUREFfbzn/8csVhszwMR9957L1QqFadn2mCnp2+Awqb/2S12oWszyMlH9u9tp5CnjHaafqqaYxMRERERUeXt9ha0SqXC4OAg2/xEBwwHIoiIKuxnP/sZhoeHMTAwsKffNzQ04LbbbsPPfvazIuesdkkdiEin00WPPTg4iNHR0ZJPLfTuu+9CpVKht7d32++7u7uh0+n2PFigVqtLFluj0aCnp2fb7202G7RaLTslREREREQ1am5uDtFodMd+08DAAC5evFjGXBFRpXEggoiogubn5/HDH/4w7xRCUun1ejz77LN44403ipSz2jU2NoaFhQU0NTXlTWO1WhEIBPCrX/1KVmyn0wm3271j7AsXLiAYDOLxxx+XFVuuv/zLv0Qymcw7RZRSqURTUxN+/OMfy4796KOPorGxEWq1etvvVSoVGhsb8eijj8qO/eMf/1hS7L3km4iIiIiIKi/7kFxXV1feNK+88gpefvllBIPBcmWLiCps+7sARERUFvX19WhsbMRXv/rVguJ8+ctfxuuvv46WlpYi5ax2nTt3DgDg8XjypgmFQgCAZ555BjfccIPs2Kurq3nT/MVf/AV+/etf4yMf+YjkuHvx4IMPYnx8PO8NfQBwu91wu92yY//yl7/c9W2RxcVFLC4u7in2bpaWlrC0tCQ7NhERERERVd6TTz4JADv2VR566CF897vfRV1dXbmyRUQVpkjvZV4KIiKiKpVKpfDf/tt/w7/5N/8mb8M3nU7jG9/4Bv71v/7X0Ol0kmMnk0l885vfxNe+9rUdG9XV4o033sD6+jo+/vGPy/rdP//zP8NoNOLEiRN507z++uvwer2yYz/33HMwmUy49dZbd4zt8/lw7733yopNRERERESVNz8/j2effRZ/9Ed/VOmsEFEV4UAEERERERERERERERGVDNeIICIiIiIiIiIiIiKikqn+eSWIiGqcy+Xacb2CraxWK2w2W8Vj1wI52y+KouRpmOSkdbvd8Hq9aGhoQEdHh6TfSC0HueWbXQ9Caj5KtU/kprdarQB2Xtdja/r9VI+JiIiIiGqRnP6K3L4KwHY/0X7DgQgiohJyuVwYHj6McDgi+TdGowEjI6O7NrhcLhcch4cRjYQlx9YbjHCOjuyLxpzL5cLwYQfCkai0HyiUQDpV2bSXSCmHTN0ZRjgsvXzl5kWpAFJSJ2iUu50y0ut0eigVaUSioqT0RoMeI6POfVGPiYiIiIhqkdz+qFKpRColr99kNBoxMrI/+q9ExIEIIqKS8ng8CIcj+M6/+wNcPdC9a3rnjBsP/N/fg8fj2bWx5fF4EI2E4fijb6DeNrxr7NDCOEb+51clxa4FHo8H4UgUn7u+FdceMmFqLYruBh06zDos+ER0NejgiyawFkrALybw0Lk5tN/9ANRGM7RNhxBdnoKuuRu6pg6IqwvQWbuQCPngvXgOntcfk5Q25Hof7me/g6Nf/nsYW3qg1Gh3zXdwYQwXH969HDJ1J4z/6789gg5bLzTand8ucE048bf/9g8l5TsRXEMi4sfckw/hgZvbYdapcciizbsPz4158dh5j+zY0vbhRbiffRgAJJWlxajGg4+P75t6TERERERUi7L9UTn9poce/gG6evqh3aVvAwCTYyP48y//Adv9RPsI14ggIiqD2aU1iLE43hmdRjAcRUtDPfyhCNqbLVAplXB71hEVYwiGJT7dv4G4NodUXIR/6jwS0RA0ZisSkQC0DW1QKFWIh3wIL00hFZcfuxYc66iDrUmPaw7Vw6RXY2otioEWA3qa9LjeZkZvsx7dDXoAgKFjCGbHzdAYLajrGkY6HoVSa4Bl+FYYO4egUAANV5+UlFZb3wRdQysAIOZbQWhxEsm4CN/ke0hEg9CarYhHAtA1tAFKFUTfChLRIJKRoKztW/csY25qHHFRhPPCrxEJBdHY3IJQwI/m1naoVCp4lhaQSiQk5VulM0DXfAh6a2ZgbMhqwM19ZlgMGgy31yGaSMOgVeLWfguGWo1oqtPi5ECDpNj6lm7orV252FLSW2/8pOSy7LDoMGQ17L2yEBERERFRUUnpfxg77QAAz/IyZiYExMQo3n/vVwgFA2huaUEw4ENLWztUKiWWF+exuDCLtNQ3t4moZvCNCCKiMvj4LccRDEdht7XDGwjjjffHsbzuh1KhQIe1AUcHbRiZWsB1w32yY+sa2pBKJqA1WxHzLSPk+gB1tquQFMNQ1zUgujoPXWMHVFp9Cbas8o51mnC005T3+0MWHZ44vwIAMPUeg6nnaN60uhsPYeWNJ6SlbToEVV0DAKBp+BYkokGkk3FoLS0QfSsIuH6DetsRJMQwNKYGxIPrSIT9sAx8SNb2Hf/wbYiEgkgk4mi0tmJtZQkTIxcxMHwU0XAY9ZYmLC3MotdxleR8A8htZ1H335bYUtIHZy7m/i0lLxcX5A3kEBERERFR6chp899460mEggHE43E0t7TCs7wI5wcX4LjqGCLhECyNTXDPz+LOj38CH5x/t1ybQERlwoEIIqIyOXGNY8fvu1qb8J4wIztufe8x1Pcey/u9vjlzczgwfUF27Frw04serIYTEBMp2FsM0KqV8EcTSKUzayAExWQubXD6AgJjb8HQPoBUXISh0w6lRotkJIh0KoFEyIvwwpiktKlYFIHxX+diNw3fsmM+DZfKwT8lvxyO33Tbjt+3dnZBeP9dyduYjAQhri8CAC4sBPGWK4ABq2HTPgzHkjBoVAiICUyuRiTFBhQQV+dysaWkj664ctuxXVm6/TG012sREBMIikno1XyZk4iIiIioWkjpf0SWpnPpb7z19h3jdRzafUpjIqpNHIggIiqD95wzeP3iGIa62yHG4jjc2wmdVoNQJAqDToelNR9Mht3nydxOYPoCfMJbMHQMIhWPou6QA0q1FkkxDJXWgJjfg5hvGalErMhbVR0+ddSKZBpY9MewGIihyajBhCeKG2z1WA3FYWvUY3Y9swhy9mmd4NR7iK0vIuZdhMbUhIh7DObDt0Cp0UFraZWUNhFcg/nwzQAA39QFrAtvoa4j0/iuO+SAUqNFIuxHOpVCOhGH6FuGsa13T9t4/s1fYmLkIrr7hxATo+gdOgKNVodIOAi9wYi1lUXMjDslb6Ph8K0QX/h/AVx+C+G9+eCmfTi2EsENtnqk00CzUSN5nzRdcw/E1XnJ6VX6uh3LctEfQ3eDDuk0YGvUYzmwP+sxEREREVEtktL/UBnMufRvvfoLOD+4gL5BO0QxikHHEWh1OrjnZlFnMsG7voYma0sFt4iISoUDEUREZXCNowfX2Hvw69EpuFe8cHu8aLaY4Jxx48NHB2HQadBorsPiqk927OwbEf7J9yCuuyGuL0JrakLIPYbG4Vugjptg7r8GqxdfLMGWVd6YJ4IhqwGHLJmFomOJFHoadbkb1suBGGa9mfUxIu7M2w5QqqC99IZCKhGDrqUnd/M8EfFLTptNY+k7BnNf5q0U38R7CM6NQlNnAdJppAHUdw9DbTBBbaiHGJO/Vsfxm27D8Ztuw+j5X8GztADP0gIsjc2YmRjFNR8+CZO5Ac2t7ZLzHZy5mNvOMU/mbQeVAnn3oV9M7Cm2pP0dXMttp5SyzOaXiIiIiIgqT06/Cci8EWEwGrHknofZ0ohYTMTiwhyarK1Qq1W46cRHsLzoxsrSYtm3hYhKiwMRRERl4JxxAwBUSiW62poAAGI8gd7Oltzgw+KqL5dOjtDCOABAoVTmpmFKJUQYWmyIeuYAZN6aiAfWC96OamK1WmE06PHg4+PSfqBQYvy7DxY/LYDgwuWGtUKpzE3DlJUth6QY3pRWCtelNx0AQKlSobUz86pyLCaio7sPS/OZqY2SyQTUWp2sfCsVKM3+k5lep9NDqUhLzovRoIfVapWeFyIiIiIiKiqr1Qq9wSirjzA5NgIAUCpV6DhkAwDExBhM9RbERBExEbm1IbJpiWj/UKTTXIeeiKhUXC4XhocPIxyW/hS30WjAyMgobDbbrrEdh4cRjYQlx9YbjHCOjuwau1a4XC54PB5JaUVRhE4nbforqWndbjd++9OfgRiV95S+lHLI1J1hhMPSyxcA9Ho9HnvsMXR0dOyathT7ZC/ps4MKUsvSarXumzpMRERERFSrpPbH3G43Pv2ZzyAakddvMhqNGBnZP/1XooOOAxFERCW2tXH2ne98Bz/72c9gtVpht9vxV3/1V5vSy7nJujX2t7/9bZw9exaNjY04cuQI/v2///d7jk3SbNf4/vznP4/u7m4899xz+M//+T/j7rvv3vS91HLYLvbnPvc59Pb24plnnsFDDz2EO++8c0+xiYiIiIiIymVr3yYej+Pmm2/GZz7zGfzkJz/BU089hfb29k2/Yd+GaH/h1ExERCVms9k2NZ4CgQCuvvpqtLe3Y25uDtdee23RYvv9flx99dVobW2F2+0uKDZJs7UM0uk05ufn8Xu/93t4++23kUgk9lwO28Wem5vD7//+7+ONN94oKDYREREREVG5bO3bjI6OIp1O4xOf+AR+8pOfQKfTsW9DtM8pK50BIqKDxul0wuFwwOFwQBCEksS22+1wOp27/4CKbnl5GT6fD3a7HQ6Ho6jlsLS0hEAggMOHDxc9NhERERERUblk+zK333471Gp10fvGRFR9OBBBRFRGqVQKY2NjsNvtsNvtWFpags/nK0nsxcVF+P3+osQm6bIN6Gw5FLNBXcrYRERERERE5SIIAkwmE7q6ujAwMMC+DdEBwIEIIqIyWlhYQDgczt1IBlC0Btfc3Byi0WjuSXwAGBsbK0pskk4QBCgUCgwODubeTCnWckxOpxNKpRL9/f0ciCAiIiIiopolCAIcDgcUCgXf6Cc6IDgQQURURhufaB8aGtr0WTXHJukEQUBvby90Oh3sdju8Xi9WV1dLEnt1dbVosYmIiIiIiMpFEITcw3l8yIroYOBABBFRGQmCALVajb6+PpjNZnR0dBStweV0OqHRaNDb2wuLxYK2tjY25ipgY4M6+2ZKsZ7uyT41tDE233ohIiIiIqJa43Q6N/WbpqamEIvFKpwrIiolDkQQEZWR0+nEwMAA1Go1gOI++SEIwhWx+Xpr+W1sUA8ODgIo7lsvW2OzjImIiIiIqJb4fD4sLS1teiMilUphcnKywjkjolLiQAQRURltvJEMFHewYGtsh8PBNyLKLJlMYnx8PPe2gsFggM1mK0o5JBIJTExM5MrYaDSiu7ubZUxERERERDUl+1b3xoEIgA9ZEe13HIggIiqj7QYiBEEoymLGpYxN0szMzCAej29bDoWanp4uWWwiIiIiIqJy2bi+IQC0t7fDZDKxb0O0z3EggoioTGKxGKampq64kRwKheB2uwuKLYoipqenc0/iZ2MHAgEsLS0VFJuk29qgBjJvphTjyZ7tYnMggoiIiIiIao3T6UR7ezvMZjMAQKFQsG9DdABwIIKIqEwmJyeRTCavuEkNFP4K6sTEBFKpFIaGhooem6RzOp3Q6/Xo7u7OfWa32zE+Po5kMlmU2F1dXbnPstNvpVKpgmITERERERGViyAIm/quQPEe4CKi6sWBCCKiMsk+3bHxrYW+vj6oVKqCn/zYLnZ/fz+USiWfKimjbINaqbx8ebXb7RBFEbOzswXHttvtV8SORqOYm5srKDYREREREVG5CIKwqe8K8G1vooOAAxFERGUiCAJMJhPa29tzn2m1WvT19RVlIKK+vh5tbW1Fj03SbV2nA7g8lVIxyrhUsYmIiIiIiMohnU7n7dssLS3B5/NVKGdEVGociCAiKpNsY0uhUGz6vBhPfpQyNkm3XYO6p6cHWq22JAMRPT090Gg0LGMiIiIiIqoJbrcbwWBw2zciAGBsbKwS2SKiMuBABBFRmWx3IxkozmCB0+m8oiFXrNgkTSQSgcvluqKMVSoVBgcHC5rvNBQKYW5u7orYarUaAwMDLGMiIiIiIqoJ2b4L3/YmOng4EEFEVCb5BgscDgcmJycRj8f3HDvfIIfD4cDExAQSicSeY5M02Sd3SjEgtFNsLupGRERERES1QhAEKJVK9Pf3b/rcbDajvb2dfRuifYwDEUREZeD3+7G4uJj3jYhEIoGpqak9xfZ6vVheXs4bOx6PY3p6ek+xSbp8T/ZkPytkIKKUsYmIiIiIiMpFEAT09fVBq9Ve8R37NkT7GwciiIjKIPtEe74bycDeX0EtZWySThAENDU1obm5+Yrv7HY7ZmZmEI1G9xy7ubkZTU1N28aenp6GKIp7ik1ERERERFQu+WYKADgQQbTfcSCCiKgMdnqivbOzE0ajcc8NruzvhoaGrvju0KFDMBgMbMyVQb7psYBMuafTaUxMTJQkdiqVwuTk5J5iExERERERlctufRtBEJBOp8ucKyIqBw5EEBGVgdPpRHt7O8xm8xXfKZVK2O32Pc+FKQgCOjo6ShKbpNvpyZ7s53sdEJISm2VMRERERETVLB6PY3JyMu9AhMPhQDAYhNvtLnPOiKgcOBBBRFQGOz31ART2CqrT6SxZbJJupzJuaWmBxWLZ02BBOp3eMXZrayvMZjPLmIiIiIiIqtr09DQSicSOb0QAnFqYaL/iQAQRURmUciCilLFJmtXVVaytreUtB4VCsedy8Hg88Hq9JYlNRERERERULtkHs/K97d3f3w+lUsm+DdE+xYEIIqISS6fTkt5aWFhYQDAYlBV7cXER7777LlKp1I6x5+bmEAqFZMUm6bIN6p3KOB6P48c//rHs+U6lxOb0W0REREREVO0efvhhKJVKWK3Wbb/XarXo6+vD6OhomXNGROXAgQgiohJ7/PHHEQwGMTU1lTfN2toaAOAb3/iGrNjLy8sAgEQikTfN6uoqAOBb3/qWrNgk3V/8xV8AADQaTd404XAY4XAY8Xi86LHfeecdvPLKK1haWpIVm4iIiIiIqFwEQUAqlYJSmf925MTEhOx+MRHVBg5EEBGV2PDwMMxmMz772c/mTXPvvffCYrHghhtukBX72LFjeP311/HII4/kTXPffffBYrHg+uuvlxWbpLvnnnvQ2dmJvr6+vGneeecdvPvuu9BqtXuK3dvbmzfNAw88gJaWFjQ2NsqKTUREREREVC6vvvoqnE7njn2iT3/607juuuvKmCsiKhdFWu4cEURERERERERERERERBLxjQgiIiIiIiIiIiIiIioZdaUzQEREu3O5XPB4PLJ+Y7VaYbPZSpQj2g3LjIiIiIiIDiK5fSH2g4gOBg5EEBEVkZwGlyiK0Ol0u6Zzu9349Gc+g2gkIisvOr0ejz/2GDo6OnZNy4afdFLK2O1247c//RmIUZllptPj8cellZnU+gOwfImIiIiIqDxcLhcODw8jEg5L/g37rkQHA9eIICIqEpfLheHDDoQjUWk/UCiBdEpy/Hu+/ndIiFEk4yIaOvoApRKpeAzBVTdMzR2IBn0QQz40dQ3iZ//xXyEVFyXH1huMcI6OsEG3C7ll7PjC3yMWXIUilYa+tRcKhQKpRAwx7xJUelPu/5uO3wX/+NuY/N//QXKdUCqAlMQruNGgx8iok+VLREREREQl9c477+C6667DVZ/8ChpsdqSSSehMFiTjInSmRkS8y0iIEaQSMSTjMai1BvzqkX/Pvg3RAcA3IoiIisTj8SAcieJz17fi2kMmTK1F0d2gQ4dZhwWfiK4GHXzRBNZCCfjFBB46N4f2ux+A2miGtukQostT0DV3Q9fUAXF1ATprFxIhH7wXz8Hz+mMILM2h59qPwDP9G2jr6tHQ0Yf1+Ql0DF+PiNeDRFxEaG0JEf8aUnFRUuxEcA3qOgvGv/sgPB4PG3O7kFrG7y+E8J3X3Ij5VqAxNcLQ1ofQ3CiMHYMwtfYgsjQFQ8cg4n4PkmIIEfc44oE1IJ1C68nPwdR/7Y5lloj4MffkQ3jg5naYdWocsmjz5uWiO4SHX3WzfImIiIiIqGw0RhMshwaxPjMCg8WK+vZe+Bcm0XXtnYj6VhBYckGpUkMMB5BKQ1I/2mJU48HHx9m3IapRHIggIiqyYx11sDXpYTFoICZTmFqL4ki7Ee31WmjVSkyuRrESiAEADB1DMLT3IxnyQdU1jLjfA6XWAMvwrVBqtBBXF9Bw9Ul4Xn8M/R/+GNqGjqPzqhtzf6u+pRMAYGpuz322NHZeUuzo4iS05maojJYy7p39Ybcy9kWTAICGwzejvvcYAMAydEPu97qmTLnpGtoufXcjll5/HABQ13MM+hYbNEYLUgkR0aUpGLuPQNvQDmWnFuF5AYnQOgBgyGpAv9UAXySJ4XYVPME4DFolbu235OraJ6824OFX3WXbN0RERERERN033APrwDG0DV/uv9ZZM/0gY1MbmvquBgCMv5zpB+3WxxKWw7DWacq/IURUNByIICIqsmOdJhztNOX9/pBFhyfOrwAATL3HYOo5mjetrukQVlZnc/+evfAqAEAM+pCMiQh5V9DSfxVMzR1Qa3RYnrwIU3OH5NgAEJy5KH3jCMDuZfyhQ5e/846+DgBIhL1IxWOI+1dQ130VdI3tUKi1iCxNQaFU5dJLqhNvPCEpH4csOlxcCEreLiIiIiIiomLwjJ9HIhKEGPQiGY8h6l1BU99VMDa3Q6nWYXXiPHo+fF8uPfs2RPsfByKIiIrspxc9WA0nICZSsLcYoFUrEY4lYdCoEBATCIrJXNrg9AUExt6CoX0AqbgIQ6cdSo0WyUgQ6VQCqbgIcS3zNPvS2HkkYlE0dQ0CAFqHjkOt0SEWDUGl1iAa9KKhsx8JMbNAsufNnyIRWN02bjoRR8y7BH1bf/l30D6wWxl/sBgCAASmLyAdj8LQPghAgfreY1CotRDX5qGpb0ZkcRKpWBiG1j6I6wsAdi63VFwEUpfXkLiwEMRbrgAGrIZd80JERERERFROa1MfwHJoAFAo0HX9nVCqdQh55tBoG4bWaMa6azSXdrs+lj+aQCqdWR8vKCahVysruDVEVCgORBARFdmnjlqRTAOL/hgWAzE0GTUYW4ngBls90mnA1qjH7HpmIens0+/BqfcQW19EzLsIjakJEfcYzIdvQSK4BpXOCABoGzqOtqHjV/y94Opibmqm4OpibiDCetOncrEj806o6ixAOg0gDWPXMFQGE1T6esR8EhfXppzdyrjdrAMA1Pcey03NtJFCpYbaaEZ9/zUAANG7lPvOetOngFQyf33Q1yPqcQHIPDWUzYfFoEIsmcaCPwqLXo14MoEj7XVY8sdKvj+IiIiIiIg2sg4eh3Xgyr6QUqWGts6MjmMnEF673A/aro814YniBls9VkNx2Br1WA6wb0NUyzgQQURUZGOeCIasBhyyaAEAsUQKPY26XKNpORDDrDdz8z/iHsv8SKmCtjkzVVIqEYOupQfi6jwAIBHxAwDWZgUAwOrMKMJeD7TGeuhMFkQD62js7IcY8sPc1p1Lt13srGzspBi5nI4k262M10JxAEDYPQ4ACM07EQ+sQqU3QW00IxHywdDWh0TYB721GwAQ9cwByJSboWMob31IihFEPbN582HWqZBOp5FMAxcXgliLJMqxS4iIiIiIiHJ8l/qlXtcoIr5VaAwm6EwWiAEvzJ19iIX8MLV2I7g4A0BaP3rME6nMxhBRUSjS6XS60pkgItoPXC4Xhg87EI5IfMNAoQTSqd3TAVAolUinpKWVGxsA9AYjnKMjsNls0v/GASSrjGWWgdzfKBVASuIV3GjQY2TUyfIlIiIiIqKScrlcODw8jEg4LPk37NsQHQwciCAiKiKXywWPxyMprSiK0Ol0e0o7OjqK3/3d38WJEyfg8/nwgx/8YFN6tzuzrkRHR4ek+FarlQ05iaSW8dYySKVSOHHiBG677TacO3cOr732GjQazabfFFIndsLyJSIiIiKictnaZ/r6178On8+Hd999F9/61rdw4sSJTenZtyE6GDg1ExFREdlstrI0isbGMtMpfeQjH8G3v/1tfOhDH4JCoSj536W9l7HL5YIoirjzzjvx/PPPo6GhAQ6HowQ5JCIiIiIiqpytfabl5WXccccdGB0dRSqVwrXXXlvB3BFRpXC5eSKiGuR0OtHa2oprrrkG6+vrWF1drXSWaBeCkJkj9eTJkwAyZUhERERERLSfJZNJjI+P4/DhwxgaGmI/iOgA40AEEVENEgQBdrsddrs992+qboIgQKPR4Prrr4fJZGKZERERERHRvjczM4NYLJbrv7IfRHRwcSCCiKgGZQciBgcHc/+m6iYIAgYGBqDRaNgAJyIiIiKiAyHb7+FABBFxIIKIqMak0+ncQITBYIDNZuPrrTXA6XTm3mBhA5yIiIiIiA4CQRCg0+nQ3d0Nu92Oubk5hEKhSmeLiCqAAxFERDVmZWUFPp+PN7VrTHbwCGCZERERERHRwSAIAgYHB6FSqXL9ofHx8QrniogqgQMRREQ1Jvv2g8PhyP2XN7WrmyiKmJ6e3lRmbrcbfr+/wjkjIiIiIiIqHafTuakflP2MiA4eDkQQEdUYQRCgUCgwMDAAIPN0/djYGJLJZIVzRvlMTEwglUpteiMCAMbGxiqZLSIiIiIiopLa+GZ4U1MTmpub+SAd0QHFgQgiohojCAJ6e3uh0+kAZG5qi6KI2dnZCueM8tm4QBsADA0NbfqciIiIiIhov4lEInC5XLl+EMBpaokOMg5EEBHVmI1PlACXb26zMVe9BEFAfX092traAAAWiwVtbW0sMyIiIiIi2reya0FwIIKIAA5EEBHVnK0DET09PdBqtWzMVbFsmSkUitxnbIATEREREdF+tvXN8Oz/O51OpNPpSmWLiCqEAxFERDUkmUxifHx8U0NOpVJhcHCQC35VMafTuanMgMxCbSwzIiIiIiLar5xOJxoaGmC1WnOfORwOeL1eeDyeCuaMiCqBAxFERDVkZmYGsVgMDodj0+d8ur66CYKQt8z4JBAREREREe1H2X7Q1jfDs98R0cHCgQgiohqy3aut2X+zIVedvF4vlpeXty2zQCCApaWlCuWMiIiIiIiodLZOKwwAg4ODue+I6GDhQAQRUQ0RBAF6vR7d3d2bPrfb7ZiZmUE0Gq1QziifsbExANsPHgFsgBMRERER0f603UCEwWCAzWZjP4joAOJABBFRDXE6nRgaGoJSufn07XA4kE6nMT4+XqGcUT7ZdSC2NsAHBgagVCq5TgQREREREe07q6urWF1dvWKKWoDr5REdVByIICKqIds9UQLw6fpqJggCOjo6UF9fv+lzrVaLvr4+lhkREREREe07+aYVzn7GfhDRwcOBCCKiGpJvIKKlpQUWi4WNuSqUr8wANsCJiIiIiGh/yvZzsmtCbGS32zE+Po5kMlnubBFRBXEggoioRkQiEbhcrm1vaisUCt7UrlIciCAiIiIiooNGEAR0dXWhrq7uiu/sdjtEUcTs7GwFckZElcKBCCKiGpFd/4E3tWtHOp3edSBiYmICiUSizDkjIiIiIiIqnd36Qdk0RHRwcCCCiKhGZBfz2m6xr+znXPCruiwsLCAUCu1YZvF4HNPT0+XNGBERERERUQk5nc68/aCenh5otVr2X4kOGA5EEBHVCEEQ0NTUhObm5m2/t9vt8Hg8WFtbK3POKJ+dFmjb+DmfBCIiIiIiov0ilUphbGwsbz9IpVJhcHCQ/SCiA4YDEURENWKnV1uByze1x8bGypUl2oUgCFCpVOjr69v2+0OHDsFgMLABTkRERERE+8bc3Byi0eiu/Vf2g4gOFg5EEBHVgHQ6jWeeeWbbhb6yenp6AACPPfZYubJFu3j00UfR0NAArVa77fdKpRKNjY348Y9/XOacERERERERlcbPfvYzAEBXV1feNCaTCa+++ioikUi5skVEFcaBCCKiGhAOh7G8vCzpiZFso48q7xe/+AVWV1d3TLOwsIA33nijTDkiIiIiIiIqrSeffBIAoFar86aZmJhAKBTC4uJiubJFRBWW/4xARERVo66uDt///vdxzz335E3T1NSE//2//zeuueaa8mWMdvTcc89Br9fvmOaNN97YdbCCiIiIiIioVvzDP/wDnn32WRw5ciRvmqeffho/+tGP8k5jS0T7jyKdTqcrnQkiIiIiIiIiIiIiItqfODUTERERERERERERERGVDKdmIiKqMi6XCx6PR1Jaq9UKALLS22y2PeeNtienzERRhE6nkxybZUZERERERNWqVH0h9oOI9h8ORBARVRGXy4Xhww6EI1FJ6fU6HdIKJcRoRFp6gxHO0RE26IpIbplBoQTSKcnxWWZERERERFSNXC4XHIeHEY2EJaVXKoCUxAnijQY9Rkad7AcR7SMciCAiqiIejwfhSBQP3NwOs06NQxYtptai6G7QocOsw4JPRFeDDr5oAhfdITz8qhsA0HryczD1X4vo8hR0zd3QNXVAXF2AztqFRMiHRHAN6joLxr/7IDweDxtzRSSnzM6NefHYeQ/a734AaqMZ2qZDLDMiIiIiIqpJHo8H0UhYUn80EfFj7smHZPV12Q8i2l84EEFEVIWGrAb0Ww3wRZIYblfBE4zDoFXi1n4LtGolJlej+OTVhtxARF3PMehbbNAYLUglRESXpmDsPgJtQzuUnVrEvEtQqDQV3qr9bbcyW/CJODnQgMfOe2DoGIKhvR/JkA+qrmHE/R4otQZYhm+FUqNFdGkKmjoL1PXNld4sIiIiIiKiHe3WHw25PoA6agEgrd/U33y5r0tE+wcHIoiIqtCxThOOdpryfn/IosPFhWDu36beYzD1HM2bXtd0CMGZi0XNI20mpcxm11cASCsvACwzIiIiIiKqelL6NytvPAFAfl+XiPYPDkQQEVWhCwtBvOUKYMBqgJhIwd5igFatRDiWhEGjQkBM4IPFUC69582fIhFYRSouwtBph1KjRTISRDqVAKDg2xBlkK/MFnwiHK1GzPtETK5m1vIITl9AYOwtGNoHti2zdCKOmG8ZqUS8wltFRERERES0s536o4mQF5p6ay6tlH7Txr4uEe0fykpngIiIrnSs04TruusRjadg1qsQS6YxvRaFmEjDH01guK0O7fXaXHrrTZ+Cuq4BSKcR8y4iGQki4h6DtrEDSo0WaqO5chtzQBzrNOGPPtyBBoMa6TSwGIghKCaxFIgjEk8hnQaajZkBIVPvMXTc9Ud5y0xlMKHpmnug0tdVdqOIiIiIiIh2sVN/VNvQlvnuknx9XYNGhTmveEVfl4j2D74RQURUhcY8EQxZDThkyTTAYokUzDoV0uk0kmng4kIQa5FELn3EPQZDxxC0zZkpfVKJGHQtPRBX5wEASTGCiHus/BtygIx5Mm87qBTYVG49jTosB2IAAL+YKbNcWShVecssOHMRieBaOTeBiIiIiIhINin90ahnFoD8vi4R7R+KdDqdrnQmiIgow+VyYfiwA+FIVFJ6vU6HtEIJMRqRlt5ghHN0BDabrZBs0gZyywwKJZBOSY7PMiMiIiIiomrkcrngODyMaCQsKb1SAaQk3oU0GvQYGXWyH0S0j3AggoioyrhcLng8HklprdbMXJty0rMhV3xyykwUReh0OsmxWWZERERERFStStUXYj+IaP/hQAQREREREREREREREZUMF6smIiIiIiIiIiIiIqKS4UAEERERERERERERERGVDAciiIiIiIiIiIiIiIioZDgQQUREREREREREREREJcOBCCIiIiIiIiIiIiIiKhkORBARERERERERERERUclwIIKIiIiIiIiIiIiIiEqGAxFERERERERERERERFQyHIggIiIiIiIiIiIiIqKS4UAEERERERERERERERGVDAciiIiIiIiIiIiIiIioZDgQQUREREREREREREREJcOBCCIiIiIiIiIiIiIiKhkORBARERERERERERERUclwIIKIiIiIiIiIiIiIiEqGAxFERERERERERERERFQyHIggIiIiIiIiIiIiIqKS4UAEERERERERERERERGVDAciiIiIiIiIiIiIiIioZDgQQUREREREREREREREJcOBCCIiIiIiIiIiIiIiKhkORBARERERERERERERUclwIIKIiIiIiIiIiIiIiErm/w+OzKwlPlhJjwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision tree\n",
    "plt.figure(figsize=(20, 10))\n",
    "plot_tree(clf, filled=True, feature_names=list(X.columns), class_names=['0', '1'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-10T14:26:15.196842Z",
     "iopub.status.busy": "2023-12-10T14:26:15.196368Z",
     "iopub.status.idle": "2023-12-10T14:26:16.288953Z",
     "shell.execute_reply": "2023-12-10T14:26:16.287396Z",
     "shell.execute_reply.started": "2023-12-10T14:26:15.196812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'decision_tree.pdf'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualize the decision tree\n",
    "dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                                feature_names=X.columns,  \n",
    "                                class_names=['No Fraud', 'Fraud'],  \n",
    "                                filled=True, rounded=True, special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)\n",
    "graph.render(\"decision_tree\", format=\"pdf\")  # Output decision_tree.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Other algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.1 Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the validation set\n",
    "y_val_pred_rf = rf.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (Random Forest): 0.9972932172315523\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation set\n",
    "accuracy_val_rf = accuracy_score(y_val, y_val_pred_rf)\n",
    "print(\"Validation Accuracy (Random Forest):\", accuracy_val_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation Classification Report (Random Forest):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    132814\n",
      "           1       1.00      1.00      1.00    132815\n",
      "\n",
      "    accuracy                           1.00    265629\n",
      "   macro avg       1.00      1.00      1.00    265629\n",
      "weighted avg       1.00      1.00      1.00    265629\n",
      "\n",
      "\n",
      "Validation Confusion Matrix (Random Forest):\n",
      " [[132646    168]\n",
      " [   551 132264]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix for validation set\n",
    "print(\"\\nValidation Classification Report (Random Forest):\\n\", classification_report(y_val, y_val_pred_rf))\n",
    "print(\"\\nValidation Confusion Matrix (Random Forest):\\n\", confusion_matrix(y_val, y_val_pred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_test_pred_rf = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy (Random Forest): 0.9974287446024342\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "accuracy_test_rf = accuracy_score(y_test, y_test_pred_rf)\n",
    "print(\"Test Accuracy (Random Forest):\", accuracy_test_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Classification Report (Random Forest):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    132815\n",
      "           1       1.00      1.00      1.00    132814\n",
      "\n",
      "    accuracy                           1.00    265629\n",
      "   macro avg       1.00      1.00      1.00    265629\n",
      "weighted avg       1.00      1.00      1.00    265629\n",
      "\n",
      "\n",
      "Test Confusion Matrix (Random Forest):\n",
      " [[132653    162]\n",
      " [   521 132293]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix for test set\n",
    "print(\"\\nTest Classification Report (Random Forest):\\n\", classification_report(y_test, y_test_pred_rf))\n",
    "print(\"\\nTest Confusion Matrix (Random Forest):\\n\", confusion_matrix(y_test, y_test_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.2 XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an XGBClassifier\n",
    "xgb = XGBClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model to the training data\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the validation set\n",
    "y_val_pred_xgb = xgb.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy (XGBoost): 0.9985543747105926\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the validation set\n",
    "accuracy_val_xgb = accuracy_score(y_val, y_val_pred_xgb)\n",
    "print(\"Validation Accuracy (XGBoost):\", accuracy_val_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation Classification Report (XGBoost):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    132814\n",
      "           1       1.00      1.00      1.00    132815\n",
      "\n",
      "    accuracy                           1.00    265629\n",
      "   macro avg       1.00      1.00      1.00    265629\n",
      "weighted avg       1.00      1.00      1.00    265629\n",
      "\n",
      "\n",
      "Validation Confusion Matrix (XGBoost):\n",
      " [[132470    344]\n",
      " [    40 132775]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix for validation set\n",
    "print(\"\\nValidation Classification Report (XGBoost):\\n\", classification_report(y_val, y_val_pred_xgb))\n",
    "print(\"\\nValidation Confusion Matrix (XGBoost):\\n\", confusion_matrix(y_val, y_val_pred_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_test_pred_xgb = xgb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Accuracy (XGBoost): 0.9986371969927982\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "accuracy_test_xgb = accuracy_score(y_test, y_test_pred_xgb)\n",
    "print(\"\\nTest Accuracy (XGBoost):\", accuracy_test_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Classification Report (XGBoost):\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00    132815\n",
      "           1       1.00      1.00      1.00    132814\n",
      "\n",
      "    accuracy                           1.00    265629\n",
      "   macro avg       1.00      1.00      1.00    265629\n",
      "weighted avg       1.00      1.00      1.00    265629\n",
      "\n",
      "\n",
      "Test Confusion Matrix (XGBoost):\n",
      " [[132492    323]\n",
      " [    39 132775]]\n"
     ]
    }
   ],
   "source": [
    "# Display classification report and confusion matrix for test set\n",
    "print(\"\\nTest Classification Report (XGBoost):\\n\", classification_report(y_test, y_test_pred_xgb))\n",
    "print(\"\\nTest Confusion Matrix (XGBoost):\\n\", confusion_matrix(y_test, y_test_pred_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Comprehensive Model Evaluation Report:\n",
    "\n",
    "#### 1. Decision Tree:\n",
    "\n",
    "- **Accuracy:** 98.59%\n",
    "\n",
    "- **Classification Report:**\n",
    "  ```\n",
    "                 precision    recall  f1-score   support\n",
    "           0       0.99      0.98      0.99    132815\n",
    "           1       0.98      0.99      0.99    132814\n",
    "    accuracy                           0.99    265629\n",
    "   macro avg       0.99      0.99      0.99    265629\n",
    "weighted avg       0.99      0.99      0.99    265629\n",
    "  ```\n",
    "\n",
    "- **Confusion Matrix:**\n",
    "  ```\n",
    " [[130652   2163]\n",
    " [  1572 131242]]\n",
    "  ```\n",
    "\n",
    "- **Key Observations:**\n",
    "  - The decision tree model performs admirably with high precision, recall, and F1-score for both classes.\n",
    "  - A minor number of false positives and false negatives, as indicated by the confusion matrix, demonstrate the model's robustness.\n",
    "\n",
    "#### 2. Random Forest:\n",
    "\n",
    "- **Test Accuracy:** 99.74%\n",
    "\n",
    "- **Test Classification Report:**\n",
    "  ```\n",
    "               precision    recall  f1-score   support\n",
    "           0       1.00      1.00      1.00    132815\n",
    "           1       1.00      1.00      1.00    132814\n",
    "    accuracy                           1.00    265629\n",
    "   macro avg       1.00      1.00      1.00    265629\n",
    "weighted avg       1.00      1.00      1.00    265629\n",
    "  ```\n",
    "\n",
    "- **Test Confusion Matrix:**\n",
    "  ```\n",
    " [[132653    162]\n",
    " [   521 132293]]\n",
    "  ```\n",
    "\n",
    "- **Key Observations:**\n",
    "  - The Random Forest model showcases exceptional performance, achieving near-perfect accuracy and precision for both classes.\n",
    "  - The confusion matrix indicates minimal misclassifications, highlighting the model's effectiveness.\n",
    "\n",
    "#### 3. XGBoost:\n",
    "\n",
    "- **Test Accuracy:** 99.86%\n",
    "\n",
    "- **Test Classification Report:**\n",
    "  ```\n",
    "               precision    recall  f1-score   support\n",
    "           0       1.00      1.00      1.00    132815\n",
    "           1       1.00      1.00      1.00    132814\n",
    "    accuracy                           1.00    265629\n",
    "   macro avg       1.00      1.00      1.00    265629\n",
    "weighted avg       1.00      1.00      1.00    265629\n",
    "  ```\n",
    "\n",
    "- **Test Confusion Matrix:**\n",
    "  ```\n",
    " [[132492    323]\n",
    " [    39 132775]]\n",
    "  ```\n",
    "\n",
    "- **Key Observations:**\n",
    "  - The XGBoost model demonstrates outstanding performance, achieving the highest accuracy among the evaluated models.\n",
    "  - The confusion matrix reveals minimal misclassifications, highlighting the model's robustness and precision.\n",
    "\n",
    "### Comparative Analysis:\n",
    "\n",
    "- All three models exhibit remarkable accuracy, precision, and recall, showcasing their effectiveness in fraud detection.\n",
    "\n",
    "- Random Forest and XGBoost, being ensemble methods, outperform the standalone Decision Tree, achieving near-perfect accuracy and precision.\n",
    "\n",
    "- Considering the context of fraud detection, where minimizing false positives is crucial, the high precision values across all models are promising.\n",
    "\n",
    "- Further investigation into feature importance, model explainability, and potential tuning can provide insights into enhancing model performance and interpretability.\n",
    "\n",
    "- Model selection should be based on specific application requirements, computational considerations, and interpretability preferences.\n",
    "\n",
    "### Conclusion:\n",
    "\n",
    "- In conclusion, the ensemble models, Random Forest and XGBoost, stand out for their exceptional performance in fraud detection.\n",
    "\n",
    "- The decision tree model, while robust, is surpassed by the ensemble models in achieving higher accuracy and precision.\n",
    "\n",
    "- Continuous monitoring, periodic retraining, and model interpretability assessments are recommended for maintaining optimal fraud detection capabilities"
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