[E_01]_iris_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ca6eb78",
   "metadata": {},
   "source": [
    "# 루브릭 평가 기준\n",
    "\n",
    "### 평가 문항\n",
    "1. 3가지 데이터셋의 구성을 파악하고, 데이터를 이해하는 과정이 포함되어있는가?\n",
    "2. 3가지 데이터셋에 대해 각각 5가지 모델을 이용하여 학습을 수행하고 결과값을 얻었는가?\n",
    "3. 3가지 데이터셋에 대해 모델의 평가 지표를 선택하고, 그 이유를 근거를 바탕으로 서술하였는가?\n",
    "\n",
    "### 상세 기준\n",
    "- feature와 label 선정을 위한 데이터 분석과정을 전개함\n",
    "- 모델학습 및 테스트가 정상적으로 수행되고, 결과값을 얻었음\n",
    "- 모델학습을 통해 얻은 결과물을 바탕으로 평가지표를 선택하고, 본인의 의견을 서술하였음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1c175f",
   "metadata": {},
   "source": [
    "# 모듈 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "453b79ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "408826fc",
   "metadata": {},
   "source": [
    "## 손글씨 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70407d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b58d355c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 1797\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR) #데이터 설명서"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4685a550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_data = digits.data\n",
    "digits_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19a9b636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f1ad71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target_names #총 10개의 label로 구성되어 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f43e0431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_label = digits.target\n",
    "digits_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f7c41a0",
   "metadata": {},
   "outputs": [
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       "      pixel_0_0  pixel_0_1  pixel_0_2  pixel_0_3  pixel_0_4  pixel_0_5  \\\n",
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       "1796        0.0        0.0       10.0       14.0        8.0        1.0   \n",
       "\n",
       "      pixel_0_6  pixel_0_7  pixel_1_0  pixel_1_1  ...  pixel_6_6  pixel_6_7  \\\n",
       "0           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "2           0.0        0.0        0.0        0.0  ...        5.0        0.0   \n",
       "3           0.0        0.0        0.0        8.0  ...        9.0        0.0   \n",
       "4           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "...         ...        ...        ...        ...  ...        ...        ...   \n",
       "1792        0.0        0.0        0.0        1.0  ...        4.0        0.0   \n",
       "1793        1.0        0.0        0.0        0.0  ...        1.0        0.0   \n",
       "1794        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1795        0.0        0.0        0.0        0.0  ...        2.0        0.0   \n",
       "1796        0.0        0.0        0.0        2.0  ...        8.0        0.0   \n",
       "\n",
       "      pixel_7_0  pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  \\\n",
       "0           0.0        0.0        6.0       13.0       10.0        0.0   \n",
       "1           0.0        0.0        0.0       11.0       16.0       10.0   \n",
       "2           0.0        0.0        0.0        3.0       11.0       16.0   \n",
       "3           0.0        0.0        7.0       13.0       13.0        9.0   \n",
       "4           0.0        0.0        0.0        2.0       16.0        4.0   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1792        0.0        0.0        2.0       14.0       15.0        9.0   \n",
       "1793        0.0        0.0        6.0       16.0       14.0        6.0   \n",
       "1794        0.0        0.0        2.0        9.0       13.0        6.0   \n",
       "1795        0.0        0.0        5.0       12.0       16.0       12.0   \n",
       "1796        0.0        1.0        8.0       12.0       14.0       12.0   \n",
       "\n",
       "      pixel_7_6  pixel_7_7  \n",
       "0           0.0        0.0  \n",
       "1           0.0        0.0  \n",
       "2           9.0        0.0  \n",
       "3           0.0        0.0  \n",
       "4           0.0        0.0  \n",
       "...         ...        ...  \n",
       "1792        0.0        0.0  \n",
       "1793        0.0        0.0  \n",
       "1794        0.0        0.0  \n",
       "1795        0.0        0.0  \n",
       "1796        1.0        0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "digits_df = pd.DataFrame(data = digits_data, columns=digits.feature_names)\n",
    "digits_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbe49142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pixel_0_0</th>\n",
       "      <th>pixel_0_1</th>\n",
       "      <th>pixel_0_2</th>\n",
       "      <th>pixel_0_3</th>\n",
       "      <th>pixel_0_4</th>\n",
       "      <th>pixel_0_5</th>\n",
       "      <th>pixel_0_6</th>\n",
       "      <th>pixel_0_7</th>\n",
       "      <th>pixel_1_0</th>\n",
       "      <th>pixel_1_1</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel_6_6</th>\n",
       "      <th>pixel_6_7</th>\n",
       "      <th>pixel_7_0</th>\n",
       "      <th>pixel_7_1</th>\n",
       "      <th>pixel_7_2</th>\n",
       "      <th>pixel_7_3</th>\n",
       "      <th>pixel_7_4</th>\n",
       "      <th>pixel_7_5</th>\n",
       "      <th>pixel_7_6</th>\n",
       "      <th>pixel_7_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1797.0</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "      <td>1797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.303840</td>\n",
       "      <td>5.204786</td>\n",
       "      <td>11.835838</td>\n",
       "      <td>11.848080</td>\n",
       "      <td>5.781859</td>\n",
       "      <td>1.362270</td>\n",
       "      <td>0.129661</td>\n",
       "      <td>0.005565</td>\n",
       "      <td>1.993879</td>\n",
       "      <td>...</td>\n",
       "      <td>3.725097</td>\n",
       "      <td>0.206455</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.279354</td>\n",
       "      <td>5.557596</td>\n",
       "      <td>12.089037</td>\n",
       "      <td>11.809126</td>\n",
       "      <td>6.764051</td>\n",
       "      <td>2.067891</td>\n",
       "      <td>0.364496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.907192</td>\n",
       "      <td>4.754826</td>\n",
       "      <td>4.248842</td>\n",
       "      <td>4.287388</td>\n",
       "      <td>5.666418</td>\n",
       "      <td>3.325775</td>\n",
       "      <td>1.037383</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>3.196160</td>\n",
       "      <td>...</td>\n",
       "      <td>4.919406</td>\n",
       "      <td>0.984401</td>\n",
       "      <td>0.023590</td>\n",
       "      <td>0.934302</td>\n",
       "      <td>5.103019</td>\n",
       "      <td>4.374694</td>\n",
       "      <td>4.933947</td>\n",
       "      <td>5.900623</td>\n",
       "      <td>4.090548</td>\n",
       "      <td>1.860122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel_0_0    pixel_0_1    pixel_0_2    pixel_0_3    pixel_0_4  \\\n",
       "count     1797.0  1797.000000  1797.000000  1797.000000  1797.000000   \n",
       "mean         0.0     0.303840     5.204786    11.835838    11.848080   \n",
       "std          0.0     0.907192     4.754826     4.248842     4.287388   \n",
       "min          0.0     0.000000     0.000000     0.000000     0.000000   \n",
       "25%          0.0     0.000000     1.000000    10.000000    10.000000   \n",
       "50%          0.0     0.000000     4.000000    13.000000    13.000000   \n",
       "75%          0.0     0.000000     9.000000    15.000000    15.000000   \n",
       "max          0.0     8.000000    16.000000    16.000000    16.000000   \n",
       "\n",
       "         pixel_0_5    pixel_0_6    pixel_0_7    pixel_1_0    pixel_1_1  ...  \\\n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000  ...   \n",
       "mean      5.781859     1.362270     0.129661     0.005565     1.993879  ...   \n",
       "std       5.666418     3.325775     1.037383     0.094222     3.196160  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       4.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%      11.000000     0.000000     0.000000     0.000000     3.000000  ...   \n",
       "max      16.000000    16.000000    15.000000     2.000000    16.000000  ...   \n",
       "\n",
       "         pixel_6_6    pixel_6_7    pixel_7_0    pixel_7_1    pixel_7_2  \\\n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000   \n",
       "mean      3.725097     0.206455     0.000556     0.279354     5.557596   \n",
       "std       4.919406     0.984401     0.023590     0.934302     5.103019   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     1.000000   \n",
       "50%       1.000000     0.000000     0.000000     0.000000     4.000000   \n",
       "75%       7.000000     0.000000     0.000000     0.000000    10.000000   \n",
       "max      16.000000    13.000000     1.000000     9.000000    16.000000   \n",
       "\n",
       "         pixel_7_3    pixel_7_4    pixel_7_5    pixel_7_6    pixel_7_7  \n",
       "count  1797.000000  1797.000000  1797.000000  1797.000000  1797.000000  \n",
       "mean     12.089037    11.809126     6.764051     2.067891     0.364496  \n",
       "std       4.374694     4.933947     5.900623     4.090548     1.860122  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%      11.000000    10.000000     0.000000     0.000000     0.000000  \n",
       "50%      13.000000    14.000000     6.000000     0.000000     0.000000  \n",
       "75%      16.000000    16.000000    12.000000     2.000000     0.000000  \n",
       "max      16.000000    16.000000    16.000000    16.000000    16.000000  \n",
       "\n",
       "[8 rows x 64 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396b7e67",
   "metadata": {},
   "source": [
    "- 모델의 값들이 상대적으로 크게 차이 나는 것들이 없기 때문에 표준화 변환 작업은 하지 않아도 될것으로 생각된다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "31f36647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    183\n",
       "1    182\n",
       "5    182\n",
       "4    181\n",
       "6    181\n",
       "9    180\n",
       "7    179\n",
       "0    178\n",
       "2    177\n",
       "8    174\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits_label_df = pd.DataFrame(digits_label)\n",
    "digits_label_df.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202698ad",
   "metadata": {},
   "source": [
    "- 클래스의 숫자가 대체적으로 골고루 나뉘어져 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a581d8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i+1) # 10개의 이미지를 2행 5열로 표시\n",
    "    plt.imshow(digits.data[i].reshape(8, 8), cmap='gray') \n",
    "    # plt.imshow()는 (m,n) 형태의 데이터를 입력으로 받기 때문에\n",
    "    # 데이터셋 내의 1열로 펼쳐진 64개의 데이터를 원래의 이미지 형태인 (8,8)로 복원\n",
    "    # cmap은 해당 이미지의 색상을 지정(gray는 흑백으로 출력)\n",
    "    plt.axis('off') # 축을 보이지 않게 해줍니다. \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576e6faa",
   "metadata": {},
   "source": [
    "### train_test_split\n",
    "- train_test_split(arrays, test_size, train_size, random_state, shuffle, stratify)\n",
    "- arrays : 분할시킬 데이터를 입력 (Python list, Numpy array, Pandas dataframe 등..)\n",
    "- test_size : 테스트 데이터셋의 비율(float)이나 갯수(int) (default = 0.25)\n",
    "- train_size : 학습 데이터셋의 비율(float)이나 갯수(int) (default = test_size의 나머지)\n",
    "- random_state : 데이터 분할시 셔플이 이루어지는데 이를 위한 시드값 (int나 RandomState로 입력)\n",
    "- shuffle : 데이터를 랜덤으로 섞을지 말지 결정(default = True)\n",
    "- stratify : 지정한 Data의 비율을 유지한다. 예를 들어, Label Set인 Y가 25%의 0과 75%의 1로 이루어진 Binary Set일 때, stratify=Y로 설정하면 나누어진 데이터셋들도 0과 1을 각각 25%, 75%로 유지한 채 분할된다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5dee3595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train 개수:  1437 , X_test 개수:  360\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(digits_data,\n",
    "                                                   digits_label,\n",
    "                                                   test_size=0.2,\n",
    "                                                   random_state=42,\n",
    "                                                   stratify=digits_label)\n",
    "\n",
    "print('X_train 개수: ', len(X_train),', X_test 개수: ', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3488544c",
   "metadata": {},
   "source": [
    "### 모델 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec45c16",
   "metadata": {},
   "source": [
    "#### Decision tree\n",
    "- criterion : 분할 품질을 측정하는 기능 (default : gini)\n",
    "- splitter : 각 노드에서 분할을 선택하는 데 사용되는 전략 (default : best)\n",
    "- max_depth : 트리의 최대 깊이 (값이 클수록 모델의 복잡도가 올라간다.)\n",
    "- min_samples_split : 자식 노드를 분할하는데 필요한 최소 샘플 수 (default : 2)\n",
    "- min_samples_leaf : 리프 노드에 있어야 할 최소 샘플 수 (default : 1)\n",
    "- min_weight_fraction_leaf : min_sample_leaf와 같지만 가중치가 부여된 샘플 수에서의 비율\n",
    "- max_features : 각 노드에서 분할에 사용할 특징의 최대 수\n",
    "- random_state : 난수 seed 설정\n",
    "- max_leaf_nodes : 리프 노드의 최대수\n",
    "- min_impurity_decrease : 최소 불순도\n",
    "- min_impurity_split : 나무 성장을 멈추기 위한 임계치\n",
    "- class_weight : 클래스 가중치\n",
    "- presort : 데이터 정렬 필요 여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2b1b6b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.95        36\n",
      "           1       0.84      0.72      0.78        36\n",
      "           2       0.78      0.80      0.79        35\n",
      "           3       0.79      0.81      0.80        37\n",
      "           4       0.86      0.86      0.86        36\n",
      "           5       0.88      0.95      0.91        37\n",
      "           6       0.81      0.83      0.82        36\n",
      "           7       0.84      0.86      0.85        36\n",
      "           8       0.71      0.69      0.70        35\n",
      "           9       0.82      0.75      0.78        36\n",
      "\n",
      "    accuracy                           0.82       360\n",
      "   macro avg       0.82      0.82      0.82       360\n",
      "weighted avg       0.82      0.82      0.82       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier(random_state=42, max_depth=20) #가지치기 최대 깊이 20\n",
    "decision_tree.fit(X_train, y_train) #학습\n",
    "\n",
    "y_pred = decision_tree.predict(X_test) #예측\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8f185d",
   "metadata": {},
   "source": [
    "- 모델의 정확도는 82%가 나왔음\n",
    "- DecisionTreeClassifier의 인자인 max_depth를 3부터 여러가지의 숫자로 실험을 해봤는데 낮을수록 나무의 깊이가 깊지 않아 성능이 낮았으며, 10이상부터는 82%로 수렴하였음\n",
    "- 다른 인자를 섞어서 써보면 성능을 올릴 수 있을것으로 생각됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4e537a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iUvGUvMm4ZGvv04A5wHnAEkLC5ip3XxEzNhGRcpQMOj6EkMSZCywnJHIed/e/xYxNRERERCqTkjcZZ2Y/Aj4MfImw1e2CuBGJiFQOM6sCjid8Dp8J7Ozuy+JGJSIiIiKVRsmbjEuuAFdpiKaISFxmVu3urbHjEBEREZHKMzh2AOWgvrZmRVNL68Q0j1lXU72ysbmlIRmYqcSNiEhE9fX1K4CJIZ9eXHV1dSsbGxsbir6wiIiIiGSGKm9SYGa+8db/TvWYI0/5BO5e/LMEERF5AzOLtvmUmenvgYiIiEiFU+VNAd379EsATJ04lpXrNzG4qoqqQcbEMSNYvmYjrW1tHLrXtLhBiohIqu6++24Apk+fzpo1a2hpaaG+vp6GhgZeeeUVzIxZs2ZFjlJERERESsmg2AGUs6P3353FK9bx7KLljB0xjG3NzTS1tLJ4xVpGDx+ixI2ISBmaM2cOCxcu5Omnn2bUqFG0trayfv16nnvuOaZNm6bEjYiIiIj0mZI3BTZ90lgam1tZtmY97e3OhNEjmDFlIhu3NPLyslWxwxMRkZTNmzePYcOGMWzYMJYsWcKUKVOYOXMmo0aNYvHixSxatCh2iCIiIiJSYjTzJgWaeSMiUt4080ZEREREYtLMm5QsWbmO5Ws2sNtO42lta+fVletpbW9n4ujh1NZUs3TVeqoHVzFxzAgAVqzdSMPYkSxdtZ5hQ2qZ2jCWpavW09LWTnOLdqIVEcmaxYsXs3TpUmbMmEFraytLlixh9OjRNDY2Mm7cOJYsWUJbWxsNDQ1s2LCBtrY2Ro0axdq1aznwwANZtGgRW7ZsYdy4cWzbto26ujq2b9/Oli1bOOCAA3jooYcYM2YM1dXVrF27lp133pmpU6fGvtsiIiIikgGqvElBIbcKT/OYIiLSP/X19auamprGx1hbW4WLiIiIiGbepKCxuaXB3a3zf0AV8BRweqeffwn4bVe3yf2nxI2ISFxmNtHMPmpm85qamgYBvwBOAAb39Pk90P+AycAngAeANU1NTTea2bFmpr/ZIiIiIhVKlTcFZGbnAp8Ejug4LMHMRgILgGPd/blY8YmIyOuZ2WjgHcBc4DDgD8CVwO3u3hIhnunAeUk844GrkngejjaER0RERESKTsmbAjGzwcAzwMfd/bYu/v3TwMHufl7RgxMRkX8ws2HA6YQEybHA7YQEyZ/cfVvM2Doys70JMZ5PqOy8ErjS3Z+OGpiIiIiIFJySNwViZu8CLiZU17zhQTazocDLwCnu/mSx4xMRqWRmVgecSkiEnArcC1wB3OTum2LGtiNmZsDBhETOXGAT+UTOSzFjExEREZHCUPKmAMysGngeeI+7393D7/0LcLy7v71YsYmIVKqkIvJEQsLj7cCThKTHde6+JmZs/ZXMwTmSkIQ6B1hCSEJd7e5LY8YmIiIiIulR8qYAzOx9wDnufvIOfq8OeAk4090fLkpwIiIVJEluzCYkbM4GXiEkbK5x92UxY0tbkpw6jpDIOZPQunsFcK27r44YmoiIiIgMkJI3KUsSMi8SkjcP9uL3Pwic4e6nFjw4EZEKkLQVzSQkMc4D1hGSGFe5+ysxYysWM6sFTiEkrd4CPEh4DG509w0RQxMRERGRflDyJmVm9jHgZHd/Wy9/vwZ4AXiXu99T0OBERMqYme1Lfg4M5OfAzI8XVXzJjLXTCMms44E7CY/NH7I0kFlEREREuqfkTYrMbAihDeqt7v54H273HuDdhPk3ekJERHrJzHYln7AZTX4r7Uf1efpGZjYKOIOQyJkF/IlQkXObuzfHi0xEREREeqLkTYrM7FPALHc/p4+3Gww8C3zI3f9akOBERMqEme0EnEtI2EwHriEkbO519+0xYyslZjYBOIuQyNkXuJGQyPm7u7dFDE1EREREOlHyJiVmNpxQdXO8uz/bj9ufD3wcOEpXi0VEXs/MxpFPNBwA3ERINNypRMPAmdkU8gmxKeQTYvcrISYiIiISn5I3KTGzzwF7ufs/9fP2g4CngP9w9z+mGpyISAkysxGEFp+5wNHAnwkJhb+4e1PE0Mqamc0gDHo+HxhGaEW7AnhCFxdERERE4lDyJgVmNhpYABzp7gsGcJx3AJ8DDtWVThGpRGZWTxiuOxc4Cfg7+eG6WyKGVnGSXbv2IzwX5wOthOfiCnd/PmZsIiIiIpVGyZsUmNnXgAZ3v3iAxzHgUeDr7n5dKsGJiGRcsuvemwgJgtOAhwmVHje4+/qYsUmQ/H06jPz266vIb7++KGJoIiIiIhVByZsBMrPxwPPAIe6+OIXjvQX4NnCgu7cP9HgiIllkZlXAsYRkwJmEz9ErgWvcfWXM2KRnyXN3DKEi5yxC5emVwNXuviJmbCIiIiLlSsmbATKz7wBD3P0jKR3PgPuA/3X3y9M4pohIFiSfb0cQTvrPBZYTTvqvcvclMWOT/jGzakJ721zgdOAxwnN6nbuvixmbiIiISDlR8mYAzGwSMB/Yz92Xp3jcE4GfAntrFxURKWVJwuZAwsn9XKCRfLvNCzFjk3Ql84reTKimOhm4h/Bc3+zum2PGJiIiIlLqlLwZADP7X6DF3T+Z8nENuBP4nbv/X5rHFhEpBjPbg3ASPxeoI1RjXAk8pR2Lyp+ZDQfeTnj+jwFuJTz/f9JOYSIiIiJ9p+RNP5nZLsDjhOqYVQU4/mzg98Ae7t6S9vFFRNKWfC7mtpieRNhi+krgQSVsKpeZjQXeQUjkHALcTHhd3OHurTFjExERESkVSt70k5n9HFjt7p8t4Bp/JpSb/6RQa4iIDISZTQTOISRs9gSuJ7TK3K2h69JZ0m6ce73sDlxHeL3c4+7bY8YmIiIikmVK3vSDme0OPECoiinYQEYzOwy4Edjd3RsLtY6ISF+Y2WjylRSHAX8gVFLcrkpB6S0zm06o1JoLjCdfqfWwKrVEREREXk/Jm34ws98CL7n7V4qw1o3AXe7+g0KvJSLSHTMbRthNaC5hi+/byc8w2RYzNil9ZrY34bV1PlBFMiPJ3Z+OGpiIiIhIRih500dmtg/wd0I1zKYirHcAcFuy3pZCrycikmNmdcCphBPqU4F7CS0uNxXj808qTzKw/2Dyu5NtIp/IeSlmbCIiIiIxKXnTR2Z2NfCIu3+7iGteCTzp7t8s1poiUpnMbDBwIuHE+e3Ak4ST5+vcfU3M2KSymNkg4EjCa/Ec4FVC8vBqd18aMzYRERGRYlPypg/M7EDgL4QqmK1FXHcv4J5k3Y3FWldEKkNykjybcJJ8NvAKIWFzjbsvixmbCPwjqXgcoQrsDOAZwmv0WndfHS8yERERkeJQ8qYPzOwm4E53/+8Ia/8GWOjuXyr22iJSfpL2lJmEk+HzgHWEqoar3P2VmLGJ9MTMaoGTCa/dtxA2ELgSuEEXOERERKRcKXnTS2Z2OGFL0xnu3hRh/d2Ahwg7XK0t9voiUh7MbF/y80QgP09kfryoRPrHzIYCpxFezycAdxJe03/QIG0REREpJ0re9JKZ3Uq4qvfTiDH8DFjv7p+OFYOIlB4z25V8wmYMScIGeFRbMku5MLNRhJaq84FZwJ8I1WS3agt7ERERKXVK3vSCmR0D/AbYK+YXQDObQhgeure7r4wVh4hkn5ntBJxLSNhMB64hJGzudfftMWMTKTQzmwCcRUjk7AvcQHj9/93d22LGJiIiItIfSt7sQDIX4u/Ar9z913GjATP7H6Dd3f81diwiki1mNo4wcHgucABwE6Hy4E6dsEqlSi585BKZU8gnMu9XIlNERERKhZI3O2BmJwE/AvbNwsmPmU0C5gMHaKtUETGzEeRbRY4C/kw4Mf1LjPlcIllmZjMIA7rPB4YBVxESnE+ohVBERESyTMmbHiRVN/cDl7j7lbHjyTGzbwPD3f1DsWMRkeIzsyHAWwmVBCcRqgNzQ1q3RAxNpCQkf9/3I7yHzgdaCe+hK9z9+ZixiYiIiHRFyZsemNlpwDeBA7NUWp20RrwAHOruC2PHIyKFZ2Y1hO2R5xJ213mYUDFwg7uvjxmbSClLEjmHEZI45wGrCO+tq9x9UcTQRERERP5ByZtumNkg4BHgq+5+Q+x4OjOzrwA7u/t7Y8ciIoVhZlXAsYSTyncAzxGqA67R0HKR9CXvuWMISdKzgAWE99zV7r4iZmwiIiJS2ZS86YaZnQX8J6G6JXMPUrIl6gLgaHd/MXI4IpKSpArgCMLJ47nAcsLJ41XuviRmbCKVxMyqCW2Jc4HTgccI78Xr3H1dzNhERESk8ih504XkyttTwKfc/c+x4+mOmX2WMEj5gtixiEj/JQmbAwkniXOBRvJtGy/EjE1EwMzqgTcT3p+nAPcQ3qM3u/vmmLGJiIhIZVDypgtmdiHwYWB2FqtucsxsOPAScKK7PxM7HhHpGzPbg9ASNReoI1zVvxJ4KsufPSKVLPnbezrhvXsMcCvhffsn7fAmIiIihaLkTSdmNpgwV+L97v632PHsiJl9EjjK3c+KHYuI7JiZ7UJ+q+JJhK2KrwQeVMJGpLSY2RjCbJy5wCHAzYT38x3u3hozNhERESkvSt50YmbvBf7J3U+IHUtvJFsGLwBOd/dHY8cjIm9kZhOBcwgJmz2B6wktF3e7e3vM2EQkHWY2ifA+nwvMAK4jvM/vydKOlSIiIlKalLzpwMxqCVtwX+ju98aOp7fM7KPAm939rbFjEZHAzEYTdoiaS9iG+A+EK/K3u3tLzNhEpLDMbBr5Crvx5CvsHlaFnYiIiPSHkjcdmNmHgdPc/S2xY+mLJOn0IjDX3e+PHY9IpTKzYYRZGHMJW3zfTn4WxraYsYlIHGa2N+Ez4XygimS2lbs/HTUwERERKSlK3iSSnSReokTbj8zsYkLy5qTYsYhUEjOrA04lnJidCtxLaJW4yd03xYxNRLIj2VXuIPJDyjeRT+S8FDE0ERERKQFK3iTM7N+AY9z9zNix9IeZVRMGLb+vFAYti5Sy5P12AuEk7O3AE4STsOvcfU3E0ESkBJjZIOBIQhLnHOBVwmfIVe6+NGZsIiIikk1K3vCPVoeXgDeVchmzmf0T8EFCEkpPrEiKkpOt2YSTrbOBVwgnW9e4+7KYsYlI6Up2uTyOkAw+E3iGUL13rbuvjhiaiIiIZIiSN4CZfQY4wN3Pjx3LQJhZFfA08G/u/pfY8YiUuqTN4VBCwuY8YB3hpOoqd38lZmwiUn6SGXYnExI5bwEeJHzm3OjuGyKGJiIiIpFVfPLGzEYRttqe7e4vRA5nwMzsHODfgcNVfSPSP2a2L/m5FE5+LsX8qIGJSMUws6HAaYTPoROAOwmfRX/QAHQREZHKo+SN2ZeBqe7+z7FjSUPS2vEY8EV3vyl2PCKlwsx2I7+17yjyW/s+qkSoiMSUXGg6g/D5NAv4E6Ei5zZ3b44XmYiIiBRLRSdvzGwsYYvtw8qpBcLM3gZ8HTjI3bfHjkckq8xsJ+BcwpXt6cA1hITNvXrviEgWmdkE4CxCImdf4EZCIufv7t4WMTQREREpoEpP3nwLGOXuH4wdS5qSOR0PAN9396tixyOSJWY2jjBweC5wAHAT4cTnTp34iEgpMbMp5BPQU8gnoO9XAlpERKS8VGzyxswagPnAgeW4LaeZvQn4X2A/nZBKpTOzEeRbDo4C/kxI2Nzq7k0RQxMRSYWZzSDf+jmM0Pp5BfCEWj9FRERKXyUnby4BcPd/iRtJYSTVN3cBl7r7b2PHI1JsZjYEeCvhivRJwN/JD/vcEjE0EZGCMrP9CZ99c4E2wmffFe7+fNTAREREpN8qMnljZjsDTwH7uPuK2PEUipnNAX4N7OnurZHDESk4M6shbLM7l7BLy0OEk5Yb3H19zNhERIotuZBzGOEz8TxgNaEa5yp3XxQxNBEREemjSk3e/ATY5O7/ETuWQjOz24Br3f3nsWMRKQQzqwKOJbQKnAk8Tzg5udbdV8aMTUQkK5LPytmEz8qzgAWE5PbV5XwhS0REpFxUXPLGzKYDjwB7uPva2PEUmpnNIgww3EOzPaRcJFeTjyBcTT4XWE5I2Fzt7ktixiYiknVmVk1oJ50LnA48RkjkXOfu62LGJiIiIl2rxOTNr4Al7v7F2LEUi5ndDNzu7v8bOxaR/koSNgeSn+PQSEjYXOnuL8aMTUSkVJlZPfBmwufqKcA9hM/Wm919c8zYREREJK+ikjdmdgTwB2CGu2+IHE7RmNlBhN11DnT3VZHDEekTM9uTfMKmjnB1+ErgKe2gIiKSHjMbTqjEOR84BriV8Hn7Z3dvjBmbiIhIpau05M0qYLW77xs7lmIzszbgL+5+WuxYRHbEzKYShmvOBSYRtry9EnhQCRsRkcIzszGE2ThzgUOAmwmfw3doEwQREZHiq7TkzQzg1Uqc/WJmEwFX5Y1klZkdR2iLOhfYE7ieULp/t7u3x4tMRKSymdkk4BxCImcGcB1wH2EnP7VWiYiIFEFFJW9EJJvMbDDQCvwN+B5hRlNL3KhERKQzM5tGqIz8FnC5u18YNyIREZHKoOSNiGSCmQ1y9+2x4xARkR3TZ7aIiEhxDY4dQH/VV1etaGrbPjHNY9YNHrSysbW9Ic1jFlt9dc2KprbWlB+X6pWNrS0l/bhI/9XX169oampK9TXVWV1d3Up312tMRKREuPv2Yvx96Kiurm5lY2Oj/laIiEhFKtnKGzPzFT94e6rHbPjXm3B3S/WgRWZmvuF7v0r1mKM++Z6Sf1yk/8zMV69qK+ga4ycM1mtMRKTEmJlv316875GDBpn+VoiISMUq2cqb7tz/8hoAdhkzlHVbm2lt305ddRXjh9exZO1WzOCQqWMiR1l89778AgBTx4xj7dYttLa3UVddw8ThI1m8bjUt7e0ctesekaOUUnTf/XcDMHWX6axYsZzqmhqqBlUxYUIDLy54jrraOmbOnBU5ShERieXuu8PfienTp9PU1MSWLVuoqqqioaGBjRs3sm7dOmbN0t8JERGRngyKHUDajtxtHEvWbuP51zYxor6a1nanqbWdBSs3s+v4YRWZuAE4erc9WbxuNc+uWMbI+nra2ttpam3hhVXL2WXMOCVupN+OOnIORx05h9tu+yMbN25kwviJNDZuY+PG9QypH6LEjYhIhZszZw4LFy7kmWeeZvDgwWzbtpX169ezaNEiZsyYocSNiIhIL5Rd8gZg2rihNLa2s3xDI+3bnfHD6zhwyiheWLGZV1ZviR1eNNPHTqCxtYVlG9bT5tuZMHwkB+w0leUb1tPc1ho7PClhDzwwj3HjJlBXX8fLryxg4sRJTJw4iUGDBvH44w/HDk9ERCKaN28ew4YNY+jQYSxZsoSdd57CzJkzaWxsZMGCBbHDExERKQmaedOBZt50TTNvKptm3oiISFc080ZERKR4SnrmzX0vrWHKmCG8tqGR6eOH0da+naXrG2lt386EEbXUDq5i5aYmDJgwog6AV9dtY2R9NVPGDOH51zYxaJAxcUQdgweVz3eBeS8/zy6jx7F843p2GzeR1u3tLF2/FjNjv8lTWL15Eys2baCuuoapY8Yx/7WlHDxlGkvWrWFj4zYaRoxicFUVqzZtZMzQYbHvjmTAvffdxS5TprF8+VJ23XUGbW2tLF26hNq6OgZXDaa5uYltjduYMH4ia9auZujQYQyuGkx1dTXr169j0qSdeO21ZbS2tbLLlGlU19Tw6pJFjBo1mmHDhse+eyIi0gdmNgg4BGDx4sUsXbqUGTNm0NraypIlSxg9ejQrV65k2rRpNDY2snHjRkaNGsWGDRtoa2tj7NixrF+/nv3224/58+ez9957s2TJEgYNGsTatWs58MADefbZZ2lpaaGhoYEVK1YwePDg3NrmpXrlUUREZABKtvJGW4V3TVuFS9qKtVW4tn8VEckuM5sInAycCrwJWFNXVze1qalpSLFiqK2tbW9ubl4J3Jr8d7u7ryvW+iIiIjGVbPJmR8xsZ+BRYC9gCTDS3bfHjSouM6sGXgMOdfdFZnYhcL67nxY5NClRZrYHcC9wqrs/2sW/z0j+/a3uruE3IiIlIvnOcBRwCiFhMx24E/gLcKu7L4kQkwG7d4hpDvBsEtNfgIfdvb3YcYmIiBRDOSdv3g580N3fbGYvE04en48dV0xm9mbg8+5+VPK/hwNLgem6ciV9ZWZDgAeAH7n7z3r4vXcA3wNm6nUmIpJdZjaNkBQ5BTgeWECocPkL8KC7Z2p3AzOrBWaTj3kn4A7yCablEcMTERFJVTknb74MVLn758zsGuAGd788dlwxmdlvgEfd/X86/OwawhecS+NFJqUmufr5K6AKeNeO5g+Y2feAPYHTK70CTkQkK5Ik/LHkkx+jeX1L0qqI4fWZme1EuB+nEFq7lpJPPs1z9+aI4YmIiAxIOSdv/gD8yt2vN7PPAGPd/VOx44rFzOqA5cC+7v5ah5+fBXzI3U+KFpyUHDO7CPhXYJa7b+3F71cDfwducfdvFjg8ERHpQpJ434d829GRwGPk246eLJcEu5lVAYcR7uephPt9N8l9dfeXIoYnIiLSZ+WcvFkGHJ3MdjkF+A93PyF2XLGY2RnAJ9z9+E4/ryfMwdnT3VfGiE1Ki5kdDNwGHNOXVsTkiugjwAXu/rdCxSciInlmNgo4iXzCpp18suZOd98UL7riMbOxhMchV2XUSNJeBfzN3TdHDE9ERGSHyjJ5Y2YNhAF2Y93dzWw8oW97dKVuL2lmVxK+nLxhNomZ/Q54wN1/VPzIpJQkJwGPAp919yv7cfuTgN8ShmZrFoGISMqSbbxnkq842R+YRz5R8WKlfhfKSSqQ9ief0DoceJh8i9VTlf4YiYhI9pRr8uYtwL91bAUysyXA8e7+crzI4jCzoYSWqd3cfU0X/34aoTLpmKIHJyUj+bJ7A/Cqu39sAMf5PGG72ROyNvxSRKQUJRetOm7jvYp8dc097t4UMbzMM7NhwHHkE15DySdybnf3tfGiExERCco1efM5YLi7/0eHn90IXObu10QLLBIzmwu8293f3M2/1xBapw5y91eLGpyUDDP7FHAOMGcgQx+Tq8J/IlzZ/Pe04hMRqRTJ3+2jyLcATSPssnQrYRMC/S0fADPLbUd+CiGp8xz5yqWH3L0tXnQiIlKpyjV5cz1wlbtf1eFnXwDq3f0z8SKLI0lc3eDuv+nhdy4FnnP37xUtMCkZZnYMcC1wuLsvTuF44wjtV59w9xsHejwRkXJnZruSb/M5DniBfELhQSUUCiPZjvxo8o/9FOCv5LcjXxoxPBERqSDlmrxZBLzJ3Rd0+NlpwMfc/ZRogUVgZiOBJcAu7r6xh987Cfimux9WtOCkJJjZREKi5X3u/ucUj3sEcDNwZCW2M4qI9CRpeT6OfNJgBK9v5XlDG7QUnplN5vUtaq+Rb1GbpxY1EREplLJL3iS7CSwERnXc7tLMJgFPARMqaQidmb0bONPdz9jB7w0Gcjt0aftMAf6x1erthC+kXyjA8T8GvBc4yt0b0z6+iEipSOaK7Uu+FeoIQuI8lxh4qly28S4Xyd/IQ8k/Z/sB95BPsi2opO+cIiJSWOWYvHkT8Dl3P7aLf3sNmOXuS4ofWRxm9mfgN73ZGcjMfgi85u5fL3xkUgrM7GuEE4hT3L29AMc34Epgo7u/P+3ji4hkmZmN5vXbV7cCfya/fXVFbONdLsxsDK/flr2ZfCLnTm1HLiIiA1GOyZv/ABrc/V+7+Lc/Ar+olBkbyVyRl4HJ7r61F78/G/ixux9Q8OAk88zsrcBPgZnuvqqA6wwnbNH6zZ7mMomIlLoOlRq5k/tcpUZudo0qNcpEp0qqU4FZ5CupbgWeVCWViIj0RTkmb64C/uDuv+/i374KeCHaP7LIzD5A2B59bi9/fxCwGDjV3ecXNDjJNDObCjwEvMPd7y3CevsBfyNsH/50odcTESmWpG07l6w5iTAjJVeNoRkpFaLDDKNclVVuhtGthBlGq+NFJyIipaAckzcLgLe7+7Nd/NuZwEXuflrxIys+M/sb8N99qTQys+8BWyslwSVvlOysMQ+4wt2/X8R13wl8DjhMrQIiUqq62Z2o4zbe2p1Iuts9LJfU0+5hIiLyBmWVvEl2VloGjOxqPoeZ7UL4gzip6MEVWXKlbz6hZarXV/XM7DDgMmBPlW5XJjP7ETAJOKvYrwEz+xkwBjhXrz8RKRVmthv5iopjgefJt8c8pBNx6YmZ1QBHkW+xmsrrtyN/NWJ4IiKSEeWWvDkO+Ia7H9XNvxuwGtjf3V8rYmhFZ2YfJ8wqeXcfb2fAS8A57v5YQYKTzDKzC4CvEF473W4tX8D164B7gd+6+38Xe30Rkd4ws2G8vgVmGPmqiTu0jbcMRHIB7mTCa+tkYCX5ZODdarUTEalM5Za8+TdgV3f/aA+/cxuhleiPxYus+MzsPuCr7v7nftz260C1u/97+pFJVpnZPsBdwJvc/YmIcUwHHiS0P94fKw4RkZzkwsb+5NtcDicMWs8lbJ5StaAUQjLkeib5194BhNbm3BbyL+q1JyJSGcotefN74K/u/qsefudbhJkuXy1eZMVlZtOAR4BJ7t7aj9vvD9wCTNMXgsqQXEV+CPiuu/9fBuI5HfghoQJIQxxFpOiSbZ/fRDhpPgVoIn/C/Hdt+ywxJNvLn0i+6qud/OvyTs2MExEpX+WWvHkWON/dn+zhd84BLnD3M4sXWXGZ2b8Du7n7B/p5ewOeAS5W5UP5S57vy4Amd39v7HhykkTrwcBbupphJSKSpqTC4TDyc0f2Ae4mOTF295cihifyBsnf733IJ3KOBB4jXxH2hLYjFxEpH2WTvEkqB1YRhhV3W22SDBX8m7vvUrTgiszMHgM+6e5/G8AxPg+Mc/dPpBeZZJGZfQj4IHCEuzfGjifHzAYTBjbe6e5fjh2PiJQfM5vM67fxXkZ+tsg8d2+OGJ5InyTbkR9L/jU9CriN8Jq+TZWsIiKlrZySN0cDl7j7YTv4PQPWAzPK8Y+Yme1BmFuy80CqFdI6jmRbsrvYH4Gj3X1B7Hg6S4Y2Pgr8s7vfFjseESltyTbes8lXKuwM3E7+5HZZxPBEUpXMkMslco4HFpBPTj7Qn9Z6ERGJZ1DsAFJ0CKFUtEfJDJfHCe0Y5eg84JqBJlzc/UXgNWBOKlFJ5iTzHK4GPpjFxA1AsivcBcBvzWxK7HhEpPSY2e5m9lEzu4Ww4+TXgK3A+4Hx7n6eu/9KiRspN+6+0N1/6u5nAOOBTwJVwH8Dq83sOjN7v5lNjRmniIj0TjlV3vwKuN/df96L3/0esNrdv1X4yIonqSqaT5hVc18KxxvQ7BzJLjMbBNwMvODun4wdz46Y2aeB04Hj3L0ldjwikl1mNpxQZZCrOKgnX21wh7uvjRieSCaYWQNhIPephO3IV5OflXN3ltqoRUQkKKfkzZPARe7+SC9+9wLgDHc/t/CRFU+HXaKmpzGgbqC7Vkl2mdlngNMIyZDMP7dJsukm4CV3/9fY8YhIdiQXLg4g3wp1GGH3vFzC5mntnCjSveRv7CHkh3UfCNxL/j30vN5DIiLxlUXyxszqgbXAGHdv6sXv7wX80d13K3hwRWRmXweq3f3fUzzmfcBX3f3PaR1T4jKz44HLgUNLqU0gafN6FPh3d78mdjwiEo+ZjSVfNXAKoQ2q4zbeWyKGJ1LSzGwUYTvyXPWak0/k/NXdN8aLTkSkcpVL8uZw4OfuflAvf78K2ADs4u7rCxha0SRXHl8CznH3Hc7+6cNxPw7MdPd3p3VMiSfZWeUR4F3ufkfsePrKzA4F/gzMdvcXYscjIsWR7D53OPmTyb2BvxNOJm/VNt4ihZF8v9yb/HvvKOAJ8snSx7UduYhIcZRL8uaDwCx3f08fbjMP+PxAttPOkuSk9gpgjzRLW5PdfuYDk3tT1STZZWbVwJ3A7e7+ldjx9Ffyfv8I4T2/LXY8IlIYZrYz4YTxFMI23q+Sv/p/r7bxFik+MxtC2Mwi12I1htdvR74qYngiImWtXJI3Pweecvcf9uE2/wMsdvfvFS6y4jGz7wLb3P0LBTj2ncD/uPuNaR9bisfMvk2YC/GWUr5KllwF/B3QTthCvPQ/xEQEM6sjv433qcAkXr+N9/KI4YlIF5L5iLkk6wnAy+QHH99fCnP1RERKRbkkbx4FPuru9/fhNu8GTnH3CwoXWXEkg+YWA6e6+/wCHP8DwPHuPjftY0txmNkZhK1BZ7r7msjhDJiZDSUMJP2Bu18aOx4R6bskETuD/NyaY4BnyJ/4PeLu7fEiFJG+SCp8jyTfYrUb8DeSijl3XxQvOhGR0lfyyRszqyHMrxnv7lv7cLv9gWvcfa9CxVYsZjYb+Im771+g448jXEmZ3JfHWLLBzHYD7gfe5u4Pxo4nLcng8XuAk9398djxiMiOJdt4n0A+YVNLfnbGX919XcTwRCRFZjaR129Hvo586+Ndan0WEembckjeHAz83t337ePtBgMbgQZ331yQ4IrEzH4IvObuXy/gGn8GfuPuVxZqDUlfshPbfcAv+9JWWCrMbC7wdUJF0YbI4YhIJ0ll6IHkr8TPBB4gX10zX62PIuUv+Sw4mHzi9mDC95PcZ8Fz+iwQEelZOSRvLgKOc/d39uO2DwKfdPd56UdWHEkSahlwdCF320jazM509zMKtYakL5kHNQI4v1y/FJnZ/wJTCK/PsryPIqXEzMYTrrbn5mBsIn+1/e+q4BQRMxtJvgrvVMDIJ3L+qgsyIiJvVA7Jmx8BC9z9kn7c9sfA8+7+P6kHViRmdiLwLXc/rMDrjASWELZX31jItSQdScLtM8BhpV5d1hMzqwXuJrRBfjd2PCKVJrmIMIv8FfU9Cdt45+ZcvBIvOhHJumT+1Z7kEzlHA0+ST/o+WsobLYiIpGVw7ABSMBO4qp+3fYwwILGUzQUK3srk7huTXafOAH5T6PVkYJKZTt8lVKWVbeIGwN2bzexc4CEze8jd744dk0i5M7Mp5FuhTgQWEU6y/h24z91b4kUnIqUkqZp9PvnvkqTlew7hM+Y3wHgzu43wGXObu6+IFqyISEQlXXnTYW7NJHff1I/bH0KY41KQQb+FlgxrXg4c7O6vFmG98whbM7+50GtJ/5nZCOAR4Kvu/rvY8RSLmb0ZuJQw/0Zf7ERSlGzjnTuZOhWYCHQ8mXotYngiUsbMbBfynz0nEJLFuUHn9ytZLCKVotSTN/sB17v7Hv28fS2wHhhXihPvzeytwGfcfXaR1htKSBbtVg7bTZejpPT4amCtu38wdjzFZmZfJZRbn+zubbHjESlVyWfJHry+jeFp8idMj2kbbxEptmQ78o5tmnvw+u3IF0YMT0SkoEo9efMu4C3uPncAx3gM+FApbqFsZr8DHizmLkJmdiXwN3f/WbHWlN4zs08A7yIMsG6KHU+xmVkVoRLgQXf/bOx4REpJUrXXcYBoFfmZE3919/URwxMReQMzm8DrB6RvID/4+O+leHFWRKQ7pZ68uQRY5u7fGcAxfkG4gviT1AIrgqQf+DVgr2K2iJjZGcAn3P34Yq0pvWNmRwI3AkdU8pWn5Ivco8AH3f2PseMRyapk696DyF/BPgS4n3x1jbbuFZGSkXymHUj+M20m8AD5JPR8faaJSCkr9eTNPcCX3P2vAzjGhwgzMi5OL7LCM7OzCBVDJxV53TpC0mhfd19ezLWle8nWvI8CH3H3P8SOJzYzOxq4Hpjl7osihyOSGclnxcmEk5uTCa3DuavUd+kqtYiUiw7VhLl5OdXkP+/uUDWhiJSakk3eJNn1jcBUd183gOPMAn7i7oekFlwRmNnVhCGRl0ZY+9eEaqWS3WK9nCStQn8iPCefiR1PVpjZvwHnA7PdvTl2PCIxJPMhjiB/8rI7r58PsShedCIixdFhjlfus3A28Az5SsNHNcdLRLKulJM3ewJ/cffpAzxOPbAWGF0qJ3hmNhxYCkwfSOJqAOufCnzB3Y8q9tryRmb2ReB44CQN6c1LvqhdB7zm7h+JHY9IsZjZVPLzH04AFvL6nVlaI4YnIhJdUkl+DPkWqwbgdkJlzq3aQU9EsqiUkzfnA2e7+1kpHOtp4N3u/tjAIys8M7sAuNDd3xpp/WrCrlOH6aptXGZ2MvArtD12l8xsJKGd7AvufnnseEQKIbkIMYf8Sch4khMQQoXmyojhiYhknplNIZ/0PglYTL7F6l5tRy4iWVDKyZvvAOvd/RspHOvXwLwYLUj9YWY3A9e4++8ixvAz4GV3/3asGCpd8kXjYeA8d78rdjxZZWYHEa6mHevuz0YOR2TAkqqyvciX/x8NPEF+KOdj7r49WoAiIiXMzAYDh5PfeW9P4C7y7aYvRwxPRCpYKSdv7gS+7e5/SeFYHwf2dvcPDTyywjKz0cAiYIq7b4oYx/HA90ptVlC5MLMawheJG939v2LHk3Vm9l7gU8Dh7r4ldjwifZVUkZ1IPmFj5Fuh7nT3DfGiExEpX2Y2jrAdea66cTP5ZPnf3H1rxPBEpIKUZPImueq4HtgzjXJwM5tNSETMGnBwBZachL41jXaxAcZRRZi7c6y7vxgzlkpkZpcA04EzdYW9d8zsl8AQ4AJtFSpZlwzlP5j8ld+DgHvJl/E/r9exiEhxJZ/NB5BPpB8KPEj+s/kZfTaLSKGUavJmV+Bud985peMNB1YAo7I+yNHMbgUudfdrMhDLfwNr3P2rsWOpJGZ2DvBfhDk32uayl5K5IA8AP3P3H8eOR6QzM5vA67fxXkv+6u7d2sZbRCRbknOI48kn2mt5/XbkRd9YRETKV6kmb84G3uXup6d4zOeBc9z96bSOmbbki/2LwOQsfIk3syOBXwL76ipDcSS7rM0DTimVAdtZYmYzgPsI1WsPxY5HKlsy/P1I8qX4uwF/Jb/byeKI4YmISB8knQG7k/9MnwPMJ5+Ef1jbkYvIQAyOHUA/HULYQSZNjybHzWzyBjgL+GMWEjeJBwhtKPuR7cetLJjZEOBa4LNK3PSPuy8wsw8AV5vZTHdfGzsmqSxmNo18uf3xwEuEL/b/AjyQ9epPERHpWnIhc0Hy3/8m25HPJnzm/wKYbGZ3kB98vDxasCJSkkq18uZW4Ifu/ocUj/lJYKq7fzytY6bNzO4izOa5OXYsOWb2baDV3T8bO5ZyllzN+TVhSOm7Vek0MGb2PcJuPW/TzCAppCTpeiz5hM1o4DbCl/fb3X1VxPBERKRIzGxn8q2xJxFmR+YGz9/r7s0RwxORElByyZvkJHYVcJC7L0vxuMcDX3X32WkdM03JB/5TwKQsfbib2SHANcDuSigUjpldTLgyP0u7Ggxc0q7yd0Il2zcihyNlJPkbtTf5svmjgMfIz0B4QglDEZHKlmxHfhj5vxX7EHYRvRX4i7u/FDE8EcmoUkzeTAEeARrSTBaY2SjgVcLQ4sz1o5rZvwL7u/t7Y8fSUXKi8gJwobs/HDuecmRmBxOu1M929xdix1MuzGwnwmfJhe5+Z+x4pHQlfz9OJP8lfDv5GQd3uvvGeNGJiEjWmdlYQjVObvDxVvJJ/7+5+5aI4YlIRpRi8ubtwAfd/c0FOPbLhEGmz6d97IEysweBz7v7bbFj6czMvgIMdfdPxo6l3CQnhY8Cn3H3qyOHU3bM7CTgt8Ch6j2X3kq2ip1JvhXqAMI23rny9xdViSgiIv2RXBjdn/wFgcOBh8lfFHhKf2NEKlMpJm++DFS5++cKcOyrgRvd/fK0jz0QZjYdeJCwy1Rb7Hg6M7N9CH9MpqodID3JH+8bgMXu/onY8ZQrM/sc4cvRCRoWK90xswbCrIJTkv+7ivxV0XvcvTFieCIiUqbMbBhhwH3ugsEQXj87TZsviFSIQbED6IeZhPkBhfBYcvysOQ+4NouJGwB3fxZYT5jtIOn5FNAA/L/YgZS5bwCbk/8rAoCZ1ZjZsWb2TTN7HHgOOB34G3CIu+/r7v/m7rcpcSMiIoXi7lvc/Q/u/lF3352wBfnDwIXAQjN70My+bGZHJrN0RKRMlVTljZnVA2sJX5xTb20ys/OAH7j75LSP3V9mVgO8QpjLcVfseLqTtE4d7e4nxo6lHJjZHOBq4HB3XxI7nnKX9Jo/BvyLu98QOx6JI6ly7LiN9wvkq2sezGoCXUREKpOZ1QJHk2+xmgLcQfjbdau7L40YnoikrNSSN6OBZYRhxZsKcPxDCR94o7PSS2pmuwIvA2PcfX3seLpjZhcAv3P3qtixlDoz2xt4FjjL3a+PHU+lMLOjCHNLTnL3v8aORwov2cb7OPJfekeST9bc7u5r4kUnIiLSN2Y2mfx25G8ClpP/uzbP3ZsihiciA1RSyRuRSmBm4witUp/OShKxUpjZF4DL3P3l2LFI+pI5UvuQ383jCMJA8Nyg4ac0t0tERMqBmVUBh5L/m7cvcA/5wcdLlMwRKS1K3oiISFkzs98CdYS5XK3AnwlfXP9WiCpOERGRrDGzMYTtyE8BzgDGAIN0oVCkdCh5IyIiZc3MlgM3ApcAC/RFVUREKlkyR/RAd38gdiwi0nuZSN7U19evaGpqmpjW8erq6lY2NjY2vOHn1VUrmtu2p7YOQO3gQSubWtvfsFZPBtfWr2hvSe/+VtXUrWxrfuP9LYS6qsErmre3pxZ77aCqlU3tbUWJvZhq6upXtDan9xx3VF1bt7KlqTjPdzkbPLh+RXt7us9RVVXdyrY2PTd9UVtdv6KlLf33Ss3gupXNrXouREREeiPt87HudHeeJiI7lonkjZn52m3tqR1v7JAq3N26WmfZV2entg7ATp+f1+VaPTEz/+Rta1OL4Xsnj+1zDP1lZr78xH9O7XiT//rrosVeTGbmP3lkY0GO/aFDR5blY1ZsZuYf/fDqVI/5wx+P13PTR2bm938z/VnsR35mtJ4LERGRXjIz9+2FPy+0Qaa/zyL9NDh2AN25b97dAEydNp3ly5dhGPX19UyY2MCqlStoampi8ODBHHjwIQNe64FF4SR7yug6Vm5qoabKGDTImDCsmqUbmtnucMiU4QNepydLn7oPgBENU2ncFBI7gwZVMXT0BLauX0VbcyODqgYzcY+DChpHfzywfgUAU+qHs6p5Gw7UDapiQm09Sxq30Lp9O7NGFzyRXzIWPHYvAGMnT2XD6tcwM6pr6xgxdiLbNq2nccsmpu93aOQoK9ey5cl7cfhUtm1bCUBVVS1DhkyguXkDzS2baJg4M2aIFePxheG9Mnn0VFZveg3DqK2uY8zwiSxftwjD2HcXvVdEREQK7e67w7nZ9OnTWbNmDfX19bS0tNDQ0MDixYvZvn07s2bNihylSHkbFDuA7hw1ew5HzZ7DrX+6hc2bNrHTlClsa9zGhg3raWxsZMaee6WSuAE4YtpIjpg2kjueX8empjbGDa2msbWdl9Y0st1h51G1qazTk50POIqdDziKVx68lW0b1jB09Hham7exccVi2pobGTFxSiYTNwBHjG7giNEN3LF6CRtbW5hcO4TG7W28tDUkxXaqGxo5wmyZccjRrFm+mOUvP8vwMeNpb2ulrbmZLevXMH7n6UrcRLbT5KPYtGkJa9c9R139GNq3t9LW3sT69S9SWztSiZsiOnj60Sxft4SXVzzH6KHjadveRnNrE4tWvcjk0VOVuBERESmSOXPmMGfOHG655RZWrV7FiBEj2LZtG8899xzbtm1j2rRpsUMUKXuZTd7k7LPf/mzZspmFL79ES3MzdXV17LXPvrzw3HxaW1tTW+ehxRsZN6yauupBLFzXxMThNew/aRi1g40NjW2prbMj46bvQ2vjFjYsW0h7awtDx0xk7LS92LhiCe1t6d3ftD24YSXjauupq6piYeNmJtYMYZ/hY6gdVKXkTRfG7zydlqZGNqxcxvb2doaPHc+YSVNY/OzjvPjovNjhVbyRI6fR1tbIli3L8e1tDBkygfETDqSxaR0rVj4aO7yK8eSi+xlSO5T62iGs3LiMCSMnM33iXlRXVfPa+iWxwxMREako8+bNY8KECdTX1bNgwQImTZrEwQcfTF1dHc8//3zs8ETKXmbbpnKOOKrrGTWHzToy1XUOnzqyy5/vO2lYquvsyM77HdHlzyfvc1hR4+irWaO6bovad/iYIkdSGnY/qOvX764HHF7kSKQrkyd1/T4cO2avIkdS2Q6c1vX7ZP+pep+IiIgU2+zZXZ+XqV1KpDgyk7y595672GXqNJYvW8quu82gtbWVzZs3sWnTRqZMmcrGjRvYtHEDVVWD2XPvfXjmqSeorx9C/ZAh1NXVseK15WDGTjtN6XGd+xduZMroWl7b2ML0sXW0bneWrm+mbbszPqm8WbGpBTOYOLyGxpbtbGxqo656ELuMruOFldswg4YRNVQN6v+srVefupeRE3dh85rljJ68K+3trWxetQwMxu+6H40b1rBl7QoG19YxsmEqG1csYfTk6WxavZTmLZuoGz6KYWMbWL3w2X7H0F9LG7ewvHkruw4ZQev27Sxr3ooBE2tCu9TGthbqBlWxS/1wVjZvY2NbC6MG11I3qIrG7W1sbW+jsb2NI0eX96D5Fx+dx9jJu7Bh5XLG77Ib29taWbdiKdU1tTQ1bqWufiiNWzczYuwENq9fQ3V1LZN334dF8x9l+JjxNG/bQsO0PVizbBEtTY0MGTGauiGqYkrTps2vsnXLckaO3JXt29vYvGUpg6tqGT16BitXPsbwEbvQ1tpIc8tGhg5tYMvmpdTXjw//e0gDW7YuZ/DgOkYMn8q6dc/Fvjsl67FX7mXS6Cms2ricKeN2o629lZUbljLIBjFm+AQAtjZvxn0725q3MqR2KJNG78LLK55jRP0oWtqa2WnsNFZtXI5hbGrcEPcOiYiIlKDFixezdOlSZswI52JLlixh9OjRANTX17NixQp22WUXXnzxRerq6hg1ahQrVqygoaGBIUOGsHTpUkaPHs369esZPXo0Gzdu/MfvT5s2jalTp0a+hyKlLRO7TWmr8IHRVuHZo63Cs09bhWeDtgoXERGJr76+fmVTU9OEQq+jrcJF+i8TyZveMLO1QDNwpLsvLuA6JwPfAFrc/ahCrbODGD4HjAAWAjPd/eIYcfSHmb0D+H/AZnc/OXY8WWVmtwLXu/vPOvzso8Cp7n5avMgkx8xmAT939wPN7DfA/e7+09hxVQIzGwrMA37j7pd08zunAT8DDnf3ZUUMT0REpGyY2UHAx4B3ADcC/+vuj6V4/Ork2B8DdgF+DFzq7mvSWkOkUmR+YDGAmdUCw4FlwKQCLzcJWFqEdXpyCPBY8l86W2oVTxYev0wzs6OAPYFfdfqnS4EDzSzbA44qxxzgruT/vyv531JgZmbA/wFPAv/d3e+5+y3AD4EbzKyuSOGJiIiUPDMbbGZnm9ndwC3Ay8Ae7v6eNBM3AO7e6u5Xufts4ExgL2CBmf0ySRyJSC+VRPIGaABWAa9RnOTNImBSchIRQy558xSwV5K8KhWTgFdQ8qYnXwK+7u4tHX/o7k3AN5N/l/iOBe5O/v+7gWMjfiZUkk8D04AP+o5LQ79F+Lz5uZ4bERGRnpnZODP7DKG6/xPA/wLT3f0b7r660Ou7+6Pu/s+Ei5ivALeY2V1JIikzs1hFsqpUkjeTCImb5RQnebOY0KI1qsBrvYGZjQXGAC+5eyMhE75vseMYgEmEmEeYWU3sYLLGzI4GZgC/6eZXfgkckLTsSCRmVgXMJp+8eTn5v7vFiagyJK1QHwHOTJKZPUqSOxcB+wP/UtjoRERESpOZHWRm/wcsIHwPPd3dj3H3a9y9tdjxuPsqd/86MB34EeFv+Ctm9mkzG1fseERKRSklb5ZTvMqb14q0VlcOBh539+3J/36U0mqdyj1XqwgVU/J6X6KLqpscd28mzFz6YjGDkjc4EFju7qvgH0mCuwjVOFIAZrY3oV3qbHdf3tvbuftW4Azg383sTQUKT0REpKQkrVHnJK1RfyAkbvZw9/e6++ORwwP+0VJ1ddJSdQahIkctVSLdKKXkTbESKrGTN7mWqZxSm3sT+/HLLDObDexO91U3Of8H7GdmRxQ+KulGx3k3OZp7UyBmNgq4CfgPd3+gr7dPhtjPBX5vZrunHJ6IiEjJ6NQa9TFCa9Su7v7NYrRG9Ze7P+bu7wH2IFQ832Jmd6ulSiRPyZu4a3VlJm9M3syMEEd/xX78suzLwNd2VJ7aofrmS8UISrrUcd5Nzt2o8iZ1SYvaFcCf3b3zEO9ec/e7CO+xm8xseFrxiYiIlAIzO7iL1qg5sVqj+svdV7v7NwgtVT8kzOZ5xcw+o5YqqXRK3nSQDLyMnXzoXHnzJKEKozpCLH2SZMXHUrzh0iXDzOYQ/gj9tpc3+T9gbzM7snBRSVfMbBBwDG+svHkeqDezqcWPqqx9HagBPpXCsX4C3Av8NnkeRUREypaZVSetUfcAN5MkbrLUGtVfHVqqjiG0VO2BWqqkwpXKl9tiJVSGE8ZbbC7CWm9gZiOTNZ/P/SyJ5VXCtnpZNxFY6+5tKHnT2ZfoRdVNTjITR9U3cewLrO88dyWZe6PqmxSZ2fnAucC5aVwVTJ6jjwLjgS8M9HgiIiJZZGbjzew/CTs2fQz4b8KuUd909zVxo0tfFy1Vf0haqs5RS5VUklJL3qwExhXwTTo5WYfk/04u0DrdOQh4yt3bO/28VObe5J4nUPLmH8zsWGAq8Ls+3vRXwJ5mdlT6UUkPupp3k6O5Nykxs5nA/wBvd/e1aR03SXyeBbzXzN6R1nFFRERi69Aa9SJhB8y3Ja1R1yYXT8tah5aqXQmzfD4OLFRLlVSKkkreJB9K64AJhVwn+f9jJB86t0zllMqOU7mdpkDJm46+BHy1r5UFyUno11H1TbEdS8/JG1XeDJCZTQSuBz7o7k+nfXx3Xwm8A/iZme2f9vFFRESKpYvWqBcJrVEXufsTcaOLI2mpuiZpqTqdMONngZn9n5kdHDk8kYLJfPImqbIZR6i6gcImBWInbzoPK84plaHFsR+/zDGz44ApwO/7eYjfADPM7Oi0YpLuJXOvuhpWnDMfGGNmxa7KKxtmVgNcC/za3a8r1Dru/gjwL4QBxmMLtY6IiEghdGiNWkhoCc61Rn2rHFuj+svdH3f395LMxAFuNrN7koRX5meGivRF5pM3hCqbdR1KAZdT2ORNrnKkkOt05xBClU1njwMHJruyZFnH5E2Mxy9TkkTAlwlVN/0qZe1QffPlNGOTbu0JNCZbT7+Bu28H7kHVNwPxP8BaivCadvfLCImiq9UTLyIipSBpjfoVocJmV+Ct7n5spbRG9VfSUvVNwgYh/02YBfSKmf2nmY2PG51IOkohedMxIQDFq7zZBFSZ2bACrfU6ZjYUmAY82/nf3H0DYQenGcWIZQA6Pn4rgfElkHAqpOMIc5MuG+BxfgPsZmbHDDgi2ZGeWqZy1DrVT2b2QWA28M4kEVYMnwFagO8WaT0REZE+SVqjzk1ao24CXiC0Rl3s7k9GDq+kuHtbkuiaA7yNMBvoRbVUSTlQ8qabtZJdS4rZ+nMgML+HuSilMLS44+NX6PlEmZZG1U1O8pr4Gpp9Uww9DSvO0dDifjCzOYT3xBnJLnpFkQyAvwB4q5m9p1jrioiI7EjSGvVZQmvUR4BLgF3VGpUOd3/C3S8imYmDWqqkxCl5E2+tzrobVpxTCkOLYz5+WXM80ABcntLxfgtMS06ApQB6Me8m50lgsplVZGKyP8xsF+BKQsXNS8Ve393XA28H/svMZhV7fRERkY7M7JAOrVHTybdGXafWqPS5+5ouWqoWqqVKSk2pJG+Wd/jf5Zq86W5YcU4pDC3uuNU6VGjypkPVzVfS+gOs6pui2BUw4OWefimp5LgXVd/0ipkNAW4Evuvut8WKw92fBS4CrtPAaRERKbYOrVHzCH8Xnwd2V2tU8XRqqTqNfEvVr9RSJaWgVJI3lVJ509Ww4pzHgYPNLJPPWRLXBGBFhx9XZPIGOJHwWFyZ8nF/D0w1M81bKYxjgbuSlskd0dybXkgSmb8EngF+EDkc3P0PwE+A682sLnY8IiJS/jq1Rn0Y+D6hNeq/3H1t3OgqV6eWqhfIt1Sdq5YqyapMJgI66SqhkvpV0+TqcB2wvtBrdbF2HeGD45nufsfdVxOGKE8vdDz9NA7Y7O7NHX5Wccmb5GT1S6RYdZOTVN98FVXfFEpv5t3kaO5N7/w74arWB3qZFCuGbwBLgJ8m71cREZHUJa1Rv+b1rVHHufv1ao3KjqSl6lvkW6o+glqqJKNKMXmzAphYgAqUScCKTicYxUo+7A+86O5NO/i9LA8t7tzeBhWYvAFOIiSy0q66yfk9MMXMjivQ8StZb+bd5DwGTDezMQWMp6SZ2VuAjwNnuntj7Hhyks/49wAHE+ITERFJRdIadV7SGnUD8BxqjSoJHVqqjgXeSminz7VUZfX8SypMySVvkgTHFmBsIddJFCv5sKNhxTlZHloc8/HLhE6zbtoLsUZypearwJdVNZCeZKDuEEL/+Q4lVVAPANq+vQtmtifwa+Bsd18WOZw3cPethAHGnzGzk2LHIyIipc3MJpjZ5witUR8itEbtptao0uTuT7r7xeRbqm40s3lqqZLYMp28SU5OJ/L6OSoQKjzSTgp0VTlSiHW60tvkTdYrbzonb4r1+GXFm4BRwFUFXucywuN6XIHXqSTHAnf3sbVHc2+6YGajgJuBT7v7/ZHD6Za7LwLmApeZ2W6RwxERkRJkZjOT1qgXgKnAW9QaVT46tFTtSpjdl2up+qxaqiSGTCdvCNU1W7toJypERUfMypGZ9DysOOcxYGZGKy4quvKmGFU3Oaq+KYi+zLvJ0dybTsysipBcvNXd/y92PDvi7n8HvkK4ojYscjgiIlICOrRG3QtcDzxLaI16n7s/FTk8KYCkpeq6Di1V0wktVb9WS5UUU9aTN523ns4pVvJmLTC0kLuSmFkNsA+www97d38NaAWmFCqeAejq8VsBNFRIguEUYCRwTZHWuwJoAE4o0nrlri/zbnIeBvYys5EFiKdUfRWoBz4ZO5A++DGhBe63Wd3NT0RE4uvQGrUI+CDwXUJr1LfVGlU5OrRU7U6YaZRrqTpPLVVSaFn/otpVQgCKlLxJWihWEk6SC2UfYGEyg6E3sto69YZEW1IxtRUo66GuHXaY+nKhq25ykuqbrwBfqpDkWMGY2SRClV+3u711JdlZ7WHg6ELEVWrM7DzgfODcZCZQSUg+5z9K+Jz/XORwREQkY5LWqN8QWqN2Ad7s7se7+w1qjapc7r7W3f+L0FL1fcKsI7VUSUEpeRNnrY56O+8mJ6vJm1iPXxacCgyneFU3OVcAE4ATi7xuuTkWuMfdt/fjtpp7A5jZwcAPgTPcfU3sePoqScSdBbzPzM6IHI6IiESWtEbN7dAaNZ/QGvV+tUZJR0lL1fXufhxvbKmaGTc6KTdK3sRZq6ND6N28m5xHCTNysqYikzedqm76c/Lfb0mVz1fQ7JuB6s+8m5yKT96Y2QTCdqgfLuVtUJO21HcAvzCz/WLHIyIixdepNeoDqDVK+qCLlqrrzexetVRJWko5eTM55bV6mq+T9lodzQQe78PvZ67yJkkcFPO5ypI3A0OBayOtfyWh5UfbHfdff+bd5DwA7GdmQ1OMp2QkM7uuBX7v7sWuPEuduz8M/Cuhf72s2z1FRCSvU2vUVOBUtUZJf3VoqdoN+B75lqrPJRe9RPqlFJI3nbfvhpSrOZITkJHA6kKv1WndwcAB9C158ypQnczpyIpRQHM3c3vKtvKmww5TRa+6yVH1zcAkPck7AU/05/bu3pjc9qj0oioplwAbgC/EDSM97v574EbgquQzWkREylCH1qj7gOsIs+92S3aNejpyeFIGOrVUvYWQGHxBLVXSX6WQvOm2FSfFk9UGYFU3J+CFTD7sCSxz9029vUEyXDNr1TfdPU9QxskbQl9rLeEPfkxXERJoJ0eOoxTNAe4d4KDpimydMrMPAMcB/xQreVlAnwa2A9+OHYiIiKTLzCaa2ecJrVHvB75DmGfzHXdfFzU4KVvu/pS7v4/QUvUs+ZaquWqpkt4qyeSNu28hbJmd1ha93VX4kPy8UMmHvg4rzlHyJrKYs24661B9o52n+m4g825y7kqOUzHMbDbhNff2viSfS0VSIj8XeJuZvSt2PCIiMnBmdqiZ/RZ4HtiZ0Bp1glqjpJiSlqpvE1qqvkuYrbRILVXSG5lN3uxgjgqkmxSIlXzo67DinEcpneRNIZNfMZ0GVBMGtWbBNYRk5imxAykxA5l3k3MfcIiZ1acQT+aZ2RTgauBd7r4gdjyF4u7rgbcD3zWzw2PHIyIifWdmNWZ2ftIadS3wNKE16gNqjZKYkpaqG9z9eMIMzV0ILVW/UUuVdCezyRtgBNCeVNl0pRySNzPpf+VNlt7UFVV5k6Wqm5yk+ubLqPqm18xsNLAr/Uug/kPyGTUfmJVGXFlmZkMI82C+7+63Rg6n4Nz9WeBi4LqMzRkTEZEedGiNWgi8j9AGu5taoySLkpaq9xNaquYTWqruU0uVdJbl5E1PCQEoXvJmFTAm7cGVZjYIOIi+DSvOeQUYkQxbzYIdJm/KLKHwNqCKcBKbJdcAw4FTYwdSIo4BHnD31hSOVfZzb5L38C8IW19+L3I4RePuNwM/J3yRqo0dj4iIdM/MDuumNerGAc63Eym4Ti1V3yHfUvV5M5sYNzrJgiwnb7rbujunKMmb5IN+DZD2G2Z3YG1/sv/J0OLHgYNTjqm/un2ukqqE7YSkQsnLYtVNThLPl9HOU711LAOfd5NTCXNvPkUYsv6+5DOoknwdWAb8RO8tEZFs6dAadT/hQtZTqDVKSlinlqpTgSnA82b2WzM7NHJ4ElGWkzc9DRGG4lXepL1WTn+HFedkaWhxjMcvltMBI3tVNznXAvWE3lnp2RwGPu8mZx5wuJnVpHS8TDGzU4F/Ac5ItkevKEli9J+BQ4GPxo1GRETgH61RXyDsGnUx8C1C0ua7ao2ScuHuTyctVbsRZjZdm7RUnV+u3zule1lP3uwoITA5pbWKWeWT099hxTlZGlpczOcqmg5VN1/KauWBqm96x8xGAHsBD6VxPHffCCwADkvjeFliZnsAvwHOdfelseOJJakifDvwWTM7IXY8IiKVKmmN+h2hNWon4GR3P9Hdb1JrlJQrd1/n7t8hdG98hzDLSS1VFabUkzelXHnT32HFOVkaWlwplTdnAA7cHDmOHbkeqAXeGjuQDDsaeMTdm1M8Ztm1TpnZSOAm4HPufm/seGJz94XABcDlZjY9djwiIpUiaY26wMweIOx4+CT51qhnIocnUjQdWqpOIOwyuzP5lqqyu4gor1fxyRszqwLGASsLvVaHNY1QNdOfYcU5C4AJyY450ZjZMGAwsLGHXyv55E0yYPqLZLjqJqdD9Y12nupeGluEd1ZWQ4uTz8bLgDvd/Rex48kKd7+TMAPnxuTzT0RECsTMGszsi4TWqIuAbwK7qzVK5B8tVR8g31J1jZndr5aq8lXKyZvlpJMQmACs28GOM2mtlTMN2OruPSWMepSUhT5J2LEqpknA8h0kNEo+eUOoumkH/hA5jt66AagGTosdSEbNIb1hxTn3AEelvTNdRF8hDBr/l8hxZNEPgUeAXytBKiKSPjM7PGmNeo7wHVKtUSLd6NBStRvwX+Rbqr6glqryUsrJm41AtZkNLfA6kP7MloEOK87JwtDi3j5+JZu8SapuvkQJVN3kqPqme8lnxgHAA2ke193XAouJ/54cMDM7F7gQODulrdTLSvI58GHCrIXPRg5HRKQsJK1RFyatUVcBTwC7uvsH1RolsmPu3u7uN3ZoqdqJ0FL1O7VUlYeSTd4kX57TSArESD4MdFhxThaGFpd98gY4E2gBbokdSB/dCFQBb4scR9YcCTzh7tsKcOySn3tjZgcBPyLsLLU6cjiZlcxLegfwATN7e+x4RERKVYfWqMXAe8i3Rn3P3dfHjU6kNHVqqXqSfEvVBWqpKl2ZTN6Y2RCgBtiwg18t1eTNQIcV52RhaHFZJ29KseomR9U33SrEvJuckp57Y2bjCS13H3H3JyKHk3nu/hpwFnCpme0TOx4RkVJiZrPM7PfkW6Pe5O4nqTVKJD1JS9V3ybdUXURoqfqimTXEjU76KpPJG5KEQC9OlouVvFlBGA484Merw7DiNJI3zwFTzGx4Csfqr7JO3hBOzBqBP8YOpJ9uBIywxbEEhZh3k3M3MDsZ9ltSzKwauAa43N2vjh1PqXD3h4BPAjfFHiAvIpJ1HVqjHgSuIGzeodYokQLr0FJ1InAyYSTIc0lL1eGRw5NeynTyphe/V5Tkjbu3AJsIu1IN1E6E7aaXD/RA7t4GPAMcONBjDUBvnqv1QF1SUVUySmmHqe4kcX8J+KKqb8DM6gjVavcV4vjJEPKVhJk6peYHwBbg87EDKTXu/lvCMPMry2hgtYhIasxskpl9iXxr1NeBGWqNEik+d3+mU0vVVWb2QJJYVUtVhpVD8magg4Qn92GtNKpHDgEeSzEZEHtocW+SX2nNJyq2s4GtwJ9jBzJANxMShmdEjiMLZgHz3X1zAdcoubk3ZvY+4CTgwqTdTvru3wl/U78ZOxARkaxIWqMuA54FJgInJa1RN6s1SiSuDi1VuxO+v7wXWKyWquzKavKmmAmVYlb5QLjqn8aw4pxHiTv3ptjJr6Ioh6qbnE7VN1l9zxdLIefd5JTU3BszO5pwBfTt7r4xdjylKqmEPA8408z+KXY8IiKxmFmtmf1Th9aoRwmtUR9y9/mRwxORTpKWqpuSlqo3Ec7ZnjOz36ulKluyeiKXqbapFNeC9Obd5GS+8iZRUskb4BxgM/CX2IGk5A9AO6q+KeS8m5y7gTmlkCgzs50Jc27e7e4vxI6n1Ln7OsJ8qe+b2aGx4xERKaakNerLwCLg3eRbo76v1iiR0pC0VH0Q2JUwk+oqM3tQLVXZkNWTi94mBJYzgIRAMgNkYjHW6iDt5M18YLcY82SS+SHDgLW9+PWSSd4kw2bLouomp0P1zZdKIalQCMkfnFnAvEKu4+5LgY3A3oVcZ6DMrJ6ws9Ql7l7qrYGZkVxVfj9wvUqORaQSdGqNmgCc6O5vUmuUSOly9/Xu/j1CS9U3CLOqFpvZl8ysJM7pylFWT+KKVc0xFtjq7k1FWIvki3wdYVhbKty9GXge2D+tY/ZBA7CylzMySiZ5Q6i62QjcGjuQlN0CNANnxg4kkkOBBe6+oQhrZbp1Kklc/xxYAHwncjhlx91vBH4JXGdmtZHDERFJXYfWqId4Y2vUs5HDE5GUdGipOonQUtUAPJu0VM2KHF7FKfXkzVpg+AC+HPd2HUgn+XAw6Q4rznmUOK1Tk+j9rlklkbzpUHXzxXKpuslR9U1R5t3kZDp5A/wbsA9wcbm9zjPkq4Sdx36ond5EpFx0aI1aTGiN+ipqjRKpCF20VF2hlqriyuoJXK+SKknFx0pCBrBg6yTSSD7MJN2WqZzHiDO0uNiPXzGcR9ja/PbYgRTIn4BG4KzYgURwLIWfd5NzF3BsFk/azewU4FPAme6+LXY85Sr5+/Ru4AjgQ5HDERHpNwuOMLPLCa1R44ETktaoP6g1SqSydGipmkGYbaWWqiLJXPImydqNANb08iYDSQoUO/mQ9rybnFhDi8sqeZNU3XyBMqy6yanUnafMbDBwFHBPMdZz90WEFrUZxVivt8xsBvBb4Fx3XxI7nnKXbEn/duALZnZc3GhERPomaY16J/AQcBnwCDDd3T+s1igRSVqqbk5aqk4izJJ91swuU0tVYWTx5K0vc1SgyMmbAV5JL1Ty5ilgrwizFcoqeQPMJSQN74gdSIH9GdgKnB07kCI6GFji7r1NCqfhbjLUOmVmI4CbgC+4e1GSWALu/gpwIaG0eFrkcEREdsjMJpvZVwitUe8EvgLskbRGbYganIhkkrvPd/cPEVqqHiV873komY2l+X8pyWLypi8JAZLfndzPtSb3dq2kvaAFGNWfhcxsLDAaeLk/t++Juzcmx9037WPvQF+eq9XAqKz2QyaVGV+gjHaY6k6n6puqyOEUSzFbpnIyM/cmqbL6PXCXu/8sdjyVxt3/CnwLuNHMhsaOR0Sks6Q16sikNWo+MI7QGnWyWqNEpLeSlqrvE6rPv0ZoIV9sZl9WS9XAZTF5cwahl7a3ilV5M9C13gds6ENFUV9tBv6lQMfuztuAXm1RntzvVfR/PlGhfYaQYPpr7ECK5C/AFuD/xQ6kSD4OPF3kNR8EzjSzXr1HCuzLhMTzJyLHUcn+hzDc71dZnIUkIpUpaY16F6E16vfAw6g1SkQGqENL1ZuAE4AJhJaqy5MZWvou1A9ZTN4sB57pw++vBKb2c60p9C15swrYpZ9r1QAv9fO2vfF8AY/dncV9XHcV4THPoq8AD5d71U1Ocj8fBb4ZO5YiqSdcSSymhcB2IGqlhZn9HPgP4Gx3b4kZSyVL3nMfAo4jvPdERKLp1Br1T4Qk/x7u/gO1RolImtz92Q4tVY8AlwMPmtk71VLVN1bq56pm9gvCdrd9yt4l2b7twPnufmUvb9MO3OjulbhTz4CZmQOXuvv7YsfSmZkNqcSddyr1flcSMzsLqHH3K2LHImBmxwAHufv/xo5FRCqPmV0A/CewE+EE6ofu/lzcqESkkiRjG95CqIzfH9gAzC7ybMqSVA7JmxpgH3d/oh+3PRR4tLfVFma2J7DC3Tf2dS0BM9sXWOTuW2PHIiIiIlJpzOw7wDHAqaqwEZHYzGwm8Afg7e7+cOx4sq7kkzciIiIiIiIiIuWsqDNvauvrV5iZp/FfbX39imLG3p2qmrrU7pOZeVVNXbf3q762NrW16mtru18nxefJzLy+yM9V2vHHuA9pSvs1uqPXabHU1Kd7v2rq+36fqqoK8NhWdR1Hdcr3t7of91f6rnZw+p9HtYNL9/NIRHasEN9jyuU7jYiUr7S/65bj992iVt6Ymf9x68o+3eade8xk3bKlfbpNrQ2iuY+bOtVWGc3t/XssTvl9X2Ye9+zWf5pEd/N7zMy3/fX+VNYZcuKRPa7T19fF1Mk7seS15anEBlBrVSubtrf1a2cqM/PG7VtSiwWgftCwbh+vrDMzP+436T03AH9/9+Toj4eZ+Y82LkrteB8ZOa3P98nM/NQ3pff+B/jL7V1/BpiZf3fDy6mt86lRu0V/DiuBmfmtH1qd6jFP+cl4PXciZaw/38P6eHx9hohI5piZ/7+16Y7g+s7Yvcvq825w7ACemReSEROn7cLa11ZgZtTU1jJ64gRWLFrMumVLOfKXy7q87ap7r6Zu/FTqG3ajafVivK2VEXsewf0X7cSyI+d2u+bVqxYytW4ou9WPYHHTFlp9O2fNv5NlXz6y+9s8sYqpo+vYbWw9i9c30druHDFtBDt98Y3JlHXPh5/Vj9+F5g2rMIxB1TXUjJxA69b1bG9twbe3MXL6gX15qLo076nHAZjWMJnla9dgBnXVtUwcM4ZFK8JJ5eF77zvgdQDuvvtuAKZPn86KFSsYPnw4LS0tNDQ0sOS15X1+zI8YMaHb39/p/isnphJ0B/PungfA1OnTWLkiJBFra2uZ2DCBNavXsGXLVgYPHswhMw9Oe+lM2vD8AwDUjZ9C8/oVDKquwayKmlETaFy1GDNjxG6HRI6y7xbc+yAA46ZOoXnbNlobmxhUVcWIieNZs/hV2lta2f2owwsaw7r1yWdA/S60t4d50L69lZraCTQ1LqV9exNjRh8x4HVevvchAMZO3ZmWbY20NjZhVVUMnziOpk2b2bZ+I1MPPWjA60j6nl5+HwATR0xl3dYVmA2iuqqGMfUTWLllKe7b2WvizMhRikgWdfw+tnz5cgYNGkRtbS0NDQ2sW7eOjRs3MmvWrMhRioik49X7wiickVN3ZuvKMNO4qraGoRPG0bh2HS1btjFo8GAaDkrnnDfLom8Vvt/sI9lv9pE89Kfb2LpxE+N2mkxzYyMrl7yKDeo5vLoJ02jdtIbG115ie1sLtWN3pq1xc4+3eWjTaoZWDcaBBds2MbGmnn2GjOr5Nos3MbSmCndYsGYbE4fXsP+koTzyatdrjdnrSMbsdSSrH7+d1q0bqR3TQHtLIy0bV9HevI3qoSNSSdwAzD7gYBa9tpz5C19m/KhRtLa10dTazKIVrzFj5ympJW4A5syZw5w5c7jllltYt24dI0aMYNu2bSxcuLDH23X1mO87ZDRPbVlH6/a+VUgNxOw5s1m0cDHzn5nP2HFjaGlpoba2hheee4HRY8Zw+KzDKiZxAzBqryMYtdcRrH3iDtoaN1M9YjztLY1sW/4S3tZC3bis7uresxlHz2Lt4ldZ9uwLVNXU0LytkZamJtYsfpVx03YpeOIGYMzoI2lsfJXNm59nkNXS0rIegK1bF1BXNzmVxA3Abkcfzm5HH86zf7mT9a8uY9iEcbQ2NrJucahWHDutNJ/DSrD/5KNYsWkJi9Y+x8j6sbS1t9DS1sSKzUvYc8LBStyISLfmzJnDwoULefrpp5kwYQLNzc2sX7+e5557jtGjRytxIyJlZcpRh7Fx8TJWP/si9WNG0d7SQltjE+tefJn6MaOZfOiBFZG4gQxU3gDMv+9BRk0YT21dHctfeoWJU6cwfMwYFj/bc9nUiBl9Pwk7fMT4vt9m6oguf37olOHd3mb9Cw9SM2IcVTW1bFvxCvXjp1A9bAybFz1N3bSd+xxDT3advDOr1q9j2epVuDs7j5/AyKHDWbJyBaOHdx17f82bN48JEyZQX1/PggULmDZtGqNGjerxNt095gcMG5NqbL0xfbfprFq5imWvLsPdqa2r46BDDuK5Z5+noWEiZmVTVdcrG15MXqfVdTStXEjduCkMHjaaplWLaFq7jJqRfX+/xPbS/Q9TO3QotUOHsGHpcsbssjNDRo1kxYsvMWxscV5z69c/SFXVEAYPHkJT01Lq66dQXT0Gb1zEtsbF1NZ2X3XWVwvvf4Rh48cxuK6ONS8vYvQuOzNk1AhWvvgy43adlto6kq5nXnuAuuqh1FcPYfWWZUwcvgsj6kazcO1zvLDqcfacUDmJZBHpm3nz5jFs2DCGDRvGkiVL/vFd7IknnmDRokVMnJh68bKISFSjpk9h66q1bF6+AtwZulMD9aNHsWHRq2xcsozJh6ZTGJF1mUje7HtU11cI9jr8UAA2vnA/dWOn0Lz+NeonTmd7eysta5eBGTUjJ9Le0kj7to0MqqmjdtwuANy/cRVT6obyWvM2ptcPp3X7dpa1bMOAiTX1NLa3sbG9lbpBVexSOzTcZtFGpoyq47VNzUwfU0/r9u0s3dBM23Zn/LAa6gYPYsXmlnCM4TU0trZ3e59G79n1fRq9VzpX3Ds6av+uX6z7D9s99bVmz57d7b/19TF/YdtGzIxRg2uoG1TFipZGBpvR2N794zpQR88+qsufHz7rsIKtmWWj9uj6dTpsl9LNXu9+ZNfP5fRDi3cyPHp014/riOHpP67Tjzy0y5+rXSrb9pvU9d+CfRoq87NIRHqvu+9ixx57bJEjEREpjp2P6LoiecJ+exU5kriKOrC4uqZmbVtra58ufdvgWrytuU/rFHNgsVVV4+2tfb5ddwZV165sb2nqclBvTXX12ta2tlRKB+pqalY2Njd3uU59ff2KpqamPl226c9j3vPx+j+wuKamZm1rH19nO1JXV7eysbGxX/HEVlVTt2J7a3Oql+F6ep0Wy+CamrXtKT7P1XW1K1sa+3afBg2qWeue7mtt0KDale3tb4yjqqZm7fYU7+/gutqVrX28v9J31VU1a9u2p/saqamqW9ncVpqfRyKyY/35HtYXpfydRkTKV3V93Yq2pnTPWcrt+25RZ960trSMdXfL/QeM6fS/vwjMB96d+9n21ibr+Dtd3a7zf03b27v9t25v07a9z7dxd9ve1tJjbMAhwBLg2p6Ok7tdTyfELa2tO3r8vgM8B7xjR2t1l7gBaGxsbNjRYw4sB5qBQT095jt6rrp/DvuXuAFo6eF1BowENgA7J//7A8BNO3y8SvhLTntLU2+ez9uAs4FGoHZHj0fsxA1A244/Tz4G/AK4HXjbjt57fU3cAGzf3nMMyc9uAJYCB/bqM6CLxA1A+47vbz3QBjzbm3XK6Q9ZlrW2d/28AfsAa4Gpnf79EuB3PT1/StyIlLeO38MInxV/A54Bjurt9yhgPPBLwve180m+r1Hi32lEpHy1NvZ8zgLcDZyT/P/vBG7d0Xfecvu+G3Vgsbuv7/SjScBryf/ty+0yo5v7tIIC3KdYa5nZIMKXgmZgVNprpa1TDO8E7nD33BZmlwHHmNkuvbx9Wej0fOaSjPcDC4Eee3uy+nh0EdchwGPJf91um5Xm/enmWAX5XOvi9xuAlWmvI+ly9/XJZ+gvgC+7+5JOv/J5wmfSqd3dvtAxikh8ZjbEzL4B3APcCBzs7m/c4rQb7r7G3S8GzgE+A9xuZnvqM0RESknuM8vM9gD2Am5O/uk64DAzm7qj25aT6LtNdTKJcIW6x5OPElPM+zQJeLUIa40DNtGLE9IsSZIUHwF+lPuZu28FfkeowKlUOwPthOfzUXpIdJSYQwj3J/Z9KtZnwCRgGTDMzOoKvJYMzAcJf39/3Pkf3H0L4fPop2Y2rNiBiUh8ZnY68CwwDTjA3f/H3dv6cyx3vw+YCdwC3GtmXzezIakFKyJSHBcBv3X3FgB3bwQuB94TNaoiy2LyZiEllBDohUnAK8AkK/xWRv9YqwjrvEaJJW+A4wAH7ur0858AF5lZbdEjyoZDgEfd3dlBlUqpSJIXewBPE/E+Je/5BorzuZZ7X65I1pQMMrMpwFeAi929y+ns7n4b4XPqa8WMTUTiMrPpZnYz8G3gIne/wN2XD/S47t7m7pcABwK7AfPN7G0DPa6ISDGYWQ3wbuDSTv/0C+C9ZlZV/KjiyGLy5kVKKyGwI5MIM2/agXT37e56rWI8fqWavPkw8OMkSfEP7v4C4ST/7ChRxTeTkOAg+b9dj3MvLfsDC9y9iZDQHGFmMfY9Hw00UZyKuFJ9X1aMJJn3E+B/3P3ZHfz6vwHnmVn6WxSKSKaYWa2Z/SfwMPAAYUbaX9Nex92Xuftc4H3Ad83sJjOblvY6IiIpOw14MTln+wd3f4pw0fLkKFFFkJnkTTIDoIEwsLicTjyKckKVVBoMRcmbLpnZTsCJhBaprvyIkNypRLnZMABPAPub2eB44aTiH/cpSdY9Tpzqm2K+V0rufVmBziO0QXxrR7/o7muBfwUuTa44iUgZMrOTgKeAI4BD3f0b7t63bVb7yN3vAA4AHgIeMbPP6HNGRDLsYt5YdZNzKSEhXREyk7wBxgJbgMWU14lHsU6ocsOKdZLYtfcDV7j7pm7+/RZgipkdVLyQMqNjomMTYT7LXlEjGrjcvJucWHNvlLwRAMxsLPADQrtUSy9vdhWh5e7TBQtMRKIws8lmdgXhxOP/ufvp7r6oWOu7e7O7fx04DDgaeNLMTijW+iIivZFsKjMLuLabX7kCON7MKmJkQJaSN7kTj82E6vLhkeNJy2SKl7x5DVgFjCtw5UTH+zS5gOukwsyqCRnZNwwHzUkGAf6MMNC4YpjZJKCW0NqXUw5zbzpWE0G8+9QxoVLo90qxPmukf34AXOXuD/T2BknV2IeAj5nZPgWLTESKxswGm9knCNU2rwD7uPvNO7hZwbj7QuBthCTxr8zssuS7gYhIFrwHuNLdt3X1j+6+GbieMBOn7GUueZN8WS2Lk48Ow0qLlrxJkhBrgQmFXovSeZ7OJPRJzt/B710KnG1mowofUmYcTH5YcU5JJ2+SZN2+wJMdfpyF5E2hh5aX2vuyYpjZKcAc4HN9va27LwW+SGifqpiBfCLlyMyOBB4BTgdmu/tnuzshKSYPbgL2IVzMecrMPl4GLdQiUsKS7z0XEQYT9+QXwMVF2Bwouqwlb3IT9cvl5GMM0JhsZVasyhuKuFapPE+v2x68O+6+EvgT8M+FDihDOg4rzin1ocX7AouSbeBzFgATzGx0kWPJJVW3AG3AyEKvRYlUxFWKZLvvnwIfSF4H/fFTwtD7D6UWmIgUjZmNM7NLCWX//wWc5O7PRw7rDdx9q7t/hpBsPgN4WEPTRSSiNwGr3P2JHfzeg0Az4bOrrGUteZNLPiynNJICO9IxIVXo+1Ts5M1ySuB5MrP9gBnAjb28yY+BDycDtCtB5/YiCMN9Dyrhx+AN9ynZkvlJQqVRMRXlMyC5OjqG0DZZKknVSvE14B53v7W/B3D37YTWzy8mvd8iUgLMbJCZXUzYjGMroUXqis67XmaNuz9H2OThO8D1ZvbzZG6XiEgxXcyOq25ybea/oAIGF2fp5Kxz8qEcrhwX8z4VJXnTqRVsE1CVXFnOqg8DP3f31l7+/n3ANsKXlkrQVaJjHbCakPQqRZ2HFefEGFqcm0MDhU2qTATWJG2TSt5khJnNIuww9a8DPVZylf4S4KeVUBYsUuqSDRDuJZT8n+run3D3jXGj6r2klepyQitVE/CsmV1Uwhd2RKSEmNlE4CTCQOLe+D1wWoQq+6LK0gdwMStHiiVGK1Oh1xoFtLj7tqzPJzKzEcD5wM97e5vkPv2IChhcbGbjCG08r3Txz6U896araiKIc5+K9b7suM4qYIxmFcSVbLt7KfCvybbfafgOsDNwQUrHE5GUmdkIM7sEuBX4JXC0uz8eN6r+c/cN7v5x4M2EnTvnmdmBkcMSkfL3LuCGHnYKfp3ku9afgX8qaFSRKXlTWOWYvOm4TqHXGqh3Ane4+/Id/ubrXQ4cUwHtCQcDjyctGZ2VZPImGWx2APBEF/9cEcmbpPpmDaEaR+L5NLCYsN13KpItxi8Cvmdm49M6rogMnAXnA88Bw4B93f3Sbv7Glhx3fww4Evg1cLuZ/SC5SCYikqqkwrhXLVOd/AJ4XzlXKCt5U1gd79MGoMbMhhRwrWIMfC6J5E3ypu3VoOLOkkG3vwM+mHZcGdPVsOKcUh1avBdhQHBXpenPAVPMbHgxAknWMWBz8qOKf19WimRb748BH0p7toW7PwxcRth6XEQywMz2Au4gJG3PcfeL3X1N5LBS5+7b3f3nhI0BRhBaqc4r5xMlEYniGMJGDff38XZ/B4YCh6UdUFZkInmTfOgXazZEMXW8Gu7ACgpwv5KtkccQ5pSAThIBjgMcuKuft/8JcJGZ1aYWUfZ0115E8vNDSvALWbf3KalIeQY4qEix5Haayp28631ZAZJ5EL8AvujurxZomS8AR5nZmwt0fBHpBTMbYmZfB+YBfwBmuvt9kcMqOHdf7e4XEWZ6fRa4zcz2iByWiJSPi4Ff9PUCWFLpeGly+7KUieQNYe5GW4dtVMvlxKNjQgoKd78mAquTHXUgVOAUajhyV/cpi8OlPwz8uL9Xvd39BeAp4OxUo8qWnhIdqwgVI9OLGtHAdTesOKeYQ4u7SqgU831ZDp+hpejDhMTxTwu1QFId+H7C8OKiVJKJyOuZ2dsIu0jtChzg7pckFwkqhrvfS/ib+ifgPjP7agErzEWkAiQDh08ndEH0x2+AczK+oU6/ZSV507HlB2AdMMTM6iPFk5ZiXQ3vvM4KYGKBdgTI/BV+M9uJsFtUf9/0OWU7uNjMRhGSfi/28GulOPemp2oiKO59KuZ7JfPvy0qQzMn6EnBxoedcuPsdwJ3A1wu5joi8nplNM7ObgO8C73P38/sxW69suHubu/8AOBDYA3jGzE6LHJaIlK4LgFv723qafB7fTagMLDtZSt7848SjQ4tRQ7SIBihpN4mSvHH3ZkLVxNhCr0U2TxLfD1zR2+nkPbgF2NnMDk4hpqw5CHiyQ7VWV0oqeZMkKw8GetrVQ8kbKYjkM/8nwCXJtt7F8EnC1aUji7SeSMUys1oz+0/gEeAhQrXNHZHDygx3X+bu5wEfAL5vZjea2dTYcYlI6Ui+S72Pvg8q7uwXlGnrVCaTN4nllPbJx3BC6fzmDj8r1H3q6vErZKKo4xWmTD1Pyfyf9wE/HuixkvLnnxHaIMrNjipUoMSSN8BuwLodbMs8H9itSGXdnd8rG4BqMxtahLWUvCm+84EpwLeLtaC7rwM+AfyyzOdziURlZicCTxJ2WzrM3b+eXCiTTtz9dmB/QpLrUTP7tJnVRA5LRErDTMIw9DsHeJy/EDYp2W/gIWVLlpM3pX7y0XlYKRSvbaqYa2XteToTeNHd56d0vEuBs5P+y3LS005TOY8CM0toaPFMep53k6tKe46wnXihdVVRmPr7Jak4mkCoVszJ2vuyrJnZOOD7hHapliIvfw2wAPhMkdcVKXtmNsnMLgd+Cfy7u7/N3RfGjivr3L3Z3b8GHE7YNeZJMzs+clgikn0XA78caOt5cgH+V5Rh9Y2SN4UTM6FSzLXWAkPNrK4Aa/VHv7YH7467ryQM4nt3WsfMiN5U3rxG2KZv58KHk4re3CcoXkVRsd6X44BNnZIGWR0kXq5+AFzu7g8Ve+EkKfhh4CNmtm+x1xcpR2Y22Mw+DjwNLAL2dfeb40ZVetz9FeA0QnL512b2ezMr2ZEIIlI4yYDhc4Ffp3TI/wMuzNA5aiqUvCmcskveJG+qKuAfs2SSE4eVZGA+UVIatztwY8qH/hHw4QINgC665HmcSqhA6Vby3JZS61SlJm+6WmcFMKFcXrNZZmanAkcDn48Vg7svS9b/pZlVxYpDpByY2RHAw8AZwDHu/p/JDm/SDx7cCOwDLAWeNrOPmdnguJGJSMacA8xLvtMMWFIl+TihK6NsZOWLfedtbkHJm74o1uPXVStYodbqjw8Dv3D31pSPez+wDTgp5ePGciDwTC8fp5JI3iStXUreJDoMLR+X8lrSQbJN90+B92fg5O7nQDPw0chxiJQkMxtrZr8Arge+A5zo7j1e5JDec/et7v5p4FjgHcBDZjYrclgikh0XE8ZVpOlSyqx1KivJm+5Ockq57L+r+7QGGFmAwW0xr/AXaq0+MbMRwFzCCUyqkmTVjyifwcWHsIPZMB2URPKGUEnUmLS57chTwF6FHPBqZvXAUGBdp3+qqPdlBfga8Lcs7DiT9Ie/H/i8mU2LHI5IyTCzQWZ2EfAs0Ajs7e6Xd3GhSlLg7s8CJwDfA24ws5+ZWSF2RxWREpG0fU8njKpI003A/ma2W8rHjSbryZtSPvHo6mr4dmAVKbYYJSXy43n9sFKovJPEdwJ3uPvyHf5m/1wOHGNmuxTo+MXUm2HFOY8mv591OxxWnOPujcBLQCEn0DcAK4pUpdbd+zJTO8GVm2R77nMI23Vngru/QBic/NMSGjQuEo2ZHQjMI+xSeaq7f9zdN0YOq+wlrVSXEVqpWoD5ZvYetfqKVKyLgF8lg4ZTk1Si/y45flmI/iGZbJtbTdhGt6MsJAQGoqtWJkj/fo0DNnaxw0khKpd6uk/RqqSSk5SPkML24N1JWiJ+B3ywUGsUUW/biwCWALVmlvX3Yl/uExS+oqinRGcx35dZf95KUlK1dSnwL8l23VnyHULy8J9iByKSVWY2wsx+ANxO2JHkKHd/PHJYFcfdN7j7x4C3Er5f3W1mxdgNUkQyIvlO9U7CgOFCuBT4ZzOrLtDxiyp68obu56isBkaX8ANdrCqVHtdJ+eprVitvjgO2A3cVeJ0fAxcVst2m0JJ2nt2BZ3rz+yU0tLg/yZtCVhQV872S1fdlOfs0oXrrmtiBdJbMsroY+K6ZTYgdj0iWWDCX0CI1grCL1C8Gui2tDIy7PwocSbhIdoeZfT+ZKSYi5e8M4El3f7kQB09ml71CSBKXvMwkbzr/0N3bCQmciUWPKB1RkzfuvgVoA0YWei3inyR+GPhJofvT3f1F4Eng7EKuU2D7Ay8kZYS99RhwcIHiGbAkQdmXVjAo/H1S8qZMJX3ZHwU+ktWZGO7+CPBb4JLIoYhkhpntSai0+U/gPHe/yN1XRw5LEu6+3d1/BuwLjAaeM7Nz1QIqUvYuBn5Z4DUupUxapzKbvEmU5MyGpLqhjjcOK4X071MxH79JyTELvU6vmdlOwImEqzXF8GNCi1ap6suw4pxHyXblzWTAgb5sLfgEsF8BK/u6e6+sAYanXL3V3VpK3qQsmTF2KfB5d18aO54d+CIwy8zK4kqTSH+Z2RAz+xpwL3ALcIi73xs5LOmGu6929/cQNqH4PHCrme0ROSwRKQAz2xU4CLihwEtdAxxtZjsXeJ2Cy3ryplRPPibR9bBSKF7bVDHXivk8vR+4wt03FWm9W4CdzSyzlSg70NcKFSh8i9FAzQQe60sVRFKZtgTYu0AxdVcRtx1YSUpDy5Mrkg1drUXpfn5m2UcIwzVT39Uube6+jfD5+JNkNz6RimNmpwHzCe3CB7r7JWkPxJTCcPd5hAtHfwHuM7OvJBdHRaR8vBf4vbs3FXKRZHbpVcA/F3KdYlDypjCykFAp5lqrgDFmNjjFtXYoqZp4HwUcVNxZ8qXvp5TutuF9nQ0DoU90lJmNK0A8aejPfYLCzvIp1vtyFNCSnKh3tU60QeLlxsymAl8A3l8q8zHc/a+ENpFvxI5FpJjMbKqZ3UjYfe397j7X3ftSnSkZ4O6t7v59wpX5vQi7UqmaUKQMJOeN7yFUNBfDpYTZpVnIf/RbFoLvbqcUUPKmN4ry+JlZHTAUWNv535L5RGso/nyiM4EX3X1+kdf9JXC2mY0u8roDYmY1hEqTp/pyu+RE9XGyO/emkpM3O1xH8wIGLnkMfwp8P9mOu5R8CniHmR0dOxCRQjOzGjP7DOHz/RFgf3e/PXJYMkDuvtTdzwU+BFxiZjeY2S6x4xKRAXkzsLhY53HJYPT1hHEbJSsLyRtV3pTGWg3Ayh5aU2I8Vx8BflTkNXH3lcCfKL3Su32BV7qp0tiRLO84peRNF5IS0RZCdY4MzIWEx/o7sQPpK3dfD3wcuDRJwouUJTM7gbCpwNHAYe7+tT4O55eMc/dbCRsvPA48Zmb/kVyYEpHSczHFq7rJuTRZt2QpeVMYPd2nlcC4ZPBlWmt1NawUineFP+21dsjM9iP0sN9YrDU7+RHwoRIrvevPsOKcTA4tNrOJwBBgUT9u/jhwYIrvxVxM1YSdMrrbxaSY78uSHPqeJWY2HvgucHGyDXcpug54nrDLjkhZMbNJZnYZ8Cvg08Db3P2VyGFJgbh7k7t/BZgFHAs8YWbHRQ1KRPrEzCYDxwBXF3npy4FTku92JSkLJ547St6U4syGbluZkpkp64EJA11kB8NKId3HL1PJG8LMmV9EPJm6H9gGnBRp/f7oz7DinKwOLT6EPg4rznH3DYRkatq7WEwE1iTthF1J833ZU9tkbi0lbwbmEuB3yfbbJSl5f3wE+KCZ7R87HpE0mNlgM/sYoRV4CbCPu9/Un78HUnrc/WXgrcBngd+a2e/MLJXNAESk4P4ZuCbZQKRoku/+NwHvLOa6aYqavEm2yx1OmJfSlVI98ShWomM00OTujQVeBzKUvEl2TplLxB1fki+HP6K0tg3vb3sRwItAg5mNSi+cVAzkPkFhWqeK+V7JzPuyHCWDMY8gbLtd0tx9OeEk59K0q81Eis3MjgAeAt4BHOvun0laRaWCeHADsA+h0vRpM/uoPuNEsivpWojRMpVzKXBxqc6EjF15k5uj0t3OHSuB8SX4IVysE6pinrj15gp/saqk3gXckZyMxHQ5MDvZhSbTkonu+wNP9Of2SRXJk4QdH7JEyRslbwrCzIYTdrJ7fz/nRGXRL4FGwgwckZJjZmPN7OfA9cD3gBPc/dnIYUlk7r7F3f8DOA44G3jIzA6PG5WIdOP4/8/eeYfZdVV3+11TbtFIMyNpNDOSLI1kudtykVwkF5lmik0goYQWDAFMQiDtSyVOCCWEkEJCEkgChoANAQwhhNjG2BhcJWNwL7It2aojTVUZSdNn1vfH2deMR/feuWWfevf7PHrA0j1nrdP3Xnut3wKG8ETlw+BevBjIxSHZr4qwgzdFJx6qOg4cBqLaorgQxXRoILjgzSGgUUSaLNkqdkyBaGuYKOlvEWB78EKYVb4bgN8I25cSOA3Yq6pHqthHFEWL4xi8sfmsBPWuqUU+hRckviNsR2xhFkquAa4VkdVh++NwlIqI1InIe4CngFG8EqmvuxIpx0xM15qXAv8I/K+I/LuILArZLYfD8WKuAa4L6/1t7MZWuDjSwRtDrCYfRvW+hcJipRBQ8MbcnHHM8inGS4Bp4K4AbJXC54H3mhLAKFONWHGOSIkWmwHZImB7Fbt5CDjPsvD0XM9KH7DYZEP5bSuuumGhYtpqvwGvzXaiUNVteF2z/iOuKcOO2kJEzgbuwVsoeY2q/o7RLXA4jsOUUn0NOB2YBJ4SkXfHrMGEw5FIRKQNeDXw9ZBduR74ZRFpCdmPsgn7RZa44A2eWGl/kVIwCC6gEqStoK7TB4HPR2W1TVWfxSsnenPYvsxBNWLFOaImWrwOeGSOZ60oqjqAl913ojWv5g6qTgKDWBAtn8sW8Xt/ho4JxH4R+B3TZjuJ/ANeRuvVYTvicBRCRJpF5DPAj4CvAhtVtdrvmKNGUNVDqvoh4LV4Gdt3OcF2hyN0fg34v7DHV6rah/dteVuYflSCC97YJ0oBlSBt9QDtfq5siMhy4GXA1/yyUSGfxxsYRJlqy4sAtgJdIjLfgj82sHFMYL90KpDn0lyHery6Yd/s1CDXAs/gtddOJCaA+D7gb0WkI2x/HI6ZiMev4pVItQJnquoXqgnUO2oX0ylwI95K/49F5B+MppnD4QgQk+17DeEJFc8mlqVTcQjeBKKlYpEgj2kuvQuwM0lswCtP6Sv0G6NPNIS/+kTvB76hqsUmq2FwE3CCiJwXtiP5MAG1c4GHq9mPacv+BHCOBbdsEOXgje/Ppdl+/xxZaHF7f4aKWZX9APDBqGT3+YXJYPgK8NmQXXE4XkBETgF+CPw58FZVfY+qFitDdzjmRFWnVPXfgTOBxXilVG92paMOR6BsABqBu8N2xHA70BbV+Vsh4hC8idvKcZSyYWzZ6gAGTMchv23lxWgJXUMEhIpnY1ax/4Potg0/Ga+Uz0aKYpREi6McvAniuSzFzhBQH6FsqchiuhpeB1wbgU52QfFRYL2I/FLYjjhqGxHJisgngM3ArcB6Vb03ZLccCUNV+1T13cDbgY8At4rIyeF65XDUDKEKFc/GZHN+mZhl34QdvJmr/TTET3CzlGPqATotRPyDOn+lTBJztvwKtP0y8KzpJBBFrgPeKCILw3YkDzbEinNEQrRYRJrx7uunLezuQWCdjRU4EwBoB3rn+KmN53LO59+yaHnS+W28NtpRSef1HVUdwcto/HwcRfscyUBErgKeBE4BzlHVz5hMT4fDF1T1HryxzG3AFhH5mIhkQ3bL4UgsZtz+Bjz9sijxn8BbRWRe2I6UStjBm5rMvFHVUeAYXuqmr7YIboXflq1CfBD4nE/7rhpV7QVuAd4dsiv5sCFWnCMqosXnAY+VkA02J6raA4wDK6v2yisbPGzKCIuRlOcyEZi22X8OXFNruhqq+hO8TIdPhe2Lo7YQkS4R+R/gn4DfVNW3qGp3yG45agRVnVDVf8ArKz8DeEJErgzXK4cjsbwNuMPMlyKDqu4B7gfeFLYvpRJa8MboqCymtBXqOE08AplQGbE1AY74accQ6iTR6FCcDHzP9r4t83ngtyLYjtJWeRF4mjcnRWCFyuYxgb3SqSCfFRe8sYDJuPoP4O9MG+1a5I+A14nIZWE74kg+IpISkT/By3p8CFirqreF7JajRlHVvar6ZrzGE58Vke+KiI3FHIfD8QveR3Qzm2MlXBzmJLMdOGD0QoqxHzslRkFRilgpVD+hKkWsFOwIlpZ6TH6Jo/4W8IUYpFFvxsuouiJsR3KY52YdVYoV51DVMbxOPGG324x78CbI59IFb4rzTmAJ8JmwHQkLVT2EVzZ2nYhkQnbHkWBE5KXAI8Am4EJV/YTJRnY4QkVVf4g3tnkEeEhE/tjoLTocjioQkXPx9FOjGqS/CThZRE4L25FSCDN4U9LEw9TkjwJR1BLJR1Cr4aXaGQQWiEg6AFvWJ4mmRvKtwBds7tcPTCDtc0Srbfhq4IiqFuwUVgFREC2Oe/Am6MybOOmGBYaItAN/B7w3BsFhX1HV/wEexysfczisIiKdIvI1PL2Da4HXqurzIbvlcLwIVR1V1Y8DFwEvBR4WkctDdsvhiDvvA75sQ+rAD8z476vAe8P2pRTCDt6UMvGAmKwcG7HSJcxdCgYBBW+MfkMv0Om3Lfy5TlcDP4pR95f/Ai4Vka6wHTHYFCvOEaposYg0AavwBC5tYeuYSn1WeoCOKkvsXNlU9fwz8BXTNtvhZd+8X0TODtsRRzIQkXoR+RBeYHAvcLqq/k9Uuo04HPlQ1eeAK4G/AG4QketFpCNktxyO2GFkFt6GJwwcZb4EXB2HbDsXvLHLEuBgiSu4NoI3pQY0qrVVSlernB1rK/ym5Oe3iLBQ8WxU9RhwA/CbYftisClWnCNs0eJzgKcsZ0rsBRpEJJDObKb87AjViZYHWaKVOEx77POBj4XtS1RQ1f3Ah4EvGV06h6NiROQi4Gd4QpCXq+qfmm+kwxF51OO7eGLGPXiCxh80C7UOh6M03gQ8oKq7wnakGEbz8CngdWH7MhcueGOXII8pirZs6xO9BFDgLkv7C4p/A95TZamaLWyXFwE8BpweYnTa+jGZVWAbpVOBPJdGl6QJryzSNztJxZRjfh6vu9Rw2P5EjC8DQ8Dvhu2II56IyCIR+Xe8JgOfAV6qqk+F65XDURmqelRV/xivjOotwE9F5IKQ3XI44kKUhYpnEwvhYhe8sUsUAypV2TJlHe14qw5FMZOgcaC1Elt5+CDw+bilV6vqM3gBjjeH6ccMsWLbgY5h4HngTJv7LQM/AlIQo+ANXhlkb4nPRlzen0HyN8Ctpk22Ywbmnno/8GEROTFsfxzxQUTqROTX8VYvJ/FKpL4Wt2+4w5EPVX0CuBz4LPB9Efk3EYmLHqfDETgicipwGvB/YftSIt8FzheRVWE7Uow4BW/iILhZankRVH9MQZ2/NmBIVcfLsFX1RFFETgBehleCFEc+hxd8CpMTgClKv0/KIUzR4igHb4J6B5RjZxBocl2EPEw77Nfjtcd25MHoPXwa+I8YdXp0hIjRSbob+ABwlap+yHQxczgSgymlugGvlGoa2Coi73LvSYcjL+8FvlrGHDJUTJOk/wJ+PWxfihGn4E0cVo7LPqYqXvhBnb9y7FRraybXAN9Q1SEL+wqDm4DlIhJmV6Z1wIM+rXqGIlpsAhCn4Ilf2qaqYzLPcicRey7N9a9WtDwRmPvnOuC33cRyTv4Rr8vju0P2wxFhRGSBiPwD8CO8xZaNqmpbJN/hiBSqelBVPwi8FvgQcJeInBWyWw5HZDDSCu/CEwKOE9fhSV9EVtsqzOBNOSvHcRHcLGdCdQQvK6K5QlvlrvDHJnhjHvhr8DQpYomqTgL/Qbhtw/0QK84RlmjxWuBZVR31Yd87gAUisqTC7RcCo2VoqMTquUwIfw48YUQoHUUw77D3AZ8WkZoP/DlejHi8GdgKLALOUtX/iGorWIfDD1T158AG4BvAT0Tk70RkfshuORxR4JeAp42URGxQ1cfw4g6vCtuXQoQSvBGRxXglHaV2HYjLxKOcDlBQ4XGJyGo8XZlSz181wa9yj8lGoO2TQLeq2mwFHQbXAW8TkbUh2f8AMODTvrcCF4QwqftjPF0l65gMlWN47ZIrodyASpDP5XI8ocWaRURegqfl8qFwPYkPqvoI3nvs3133KUcOETkF+CHwEeBtqvrrqtoXslsORyio6pSq/htwFp5G5FYReZMrpXLUKkYv9W+Ar4btS4VcD/xDVJ/hsDJvxvHKHkotidkPLDM3Q5RZRQnCvjMYAFZXYOcY8AhQaqvk/cAJFd6EJ1NaR5scA8BJFdiZyeuBbVXuI3RUtRfvGr0xJBf2AT/3ad+HgZ1A0BO6XuB+H/f/M7xjq4QTKS9Y1oP3zqiENUB/Gb//CfBEhbaSwk+Ae007bEfpfALvnfx7IfvhCBkRWSwiHwc24wVv1qnqPSG75XBEAlXtVdV3Ae8APgr8IMTFO4cjTNJ4c8G4fh/uxhNajuSilcShCYCItAIHgVNV9dmQ3cmLCYxMA7+iqt8rcZsp4Fuq+naffVsB7AbaVbWcCR8i0gfsUtWS2iKKyP8AV6pqFNpkOxyBISI3Am9Q1ZJe9iLyJuDbQF25ukQiosAXVPU3yve0NjGD6KdcWUf5mEyLblUtNdvTkTDM4tkU3mD87aq6N2SXHI7IIiKNwO8Cfwf8nqp+NmSXHA5HQohF8AZARE5W1UhnY5Tro+moNGjUrX2l0vMnIsuAw6UO2o1ezVJV3VWuLYcjzohIFlhczqSmiufyRLygqgtEOByOQDClh/caPSSHwzEHInIh8IyqVprR63A4HC8iNsEbh8PhcDgcDofD4XA4HI5axLqGTDab7RERtfEnm82Wox+TCJJ4/tLZlLVjEhFNZ1OROK6ZZLMNVo8xm20oeIypbMaarVQ2U9BOfcqeHRHR+lQRW5m0XVuZdEFbSXzGknhMxai14w2SdL29cysimq535zcsMha/FbP/ZIp8OxwOh93vlPtmOcImyHF6UAQ5d7OJ9cwbESlXvoFlXSvZv3uPPR/Sqd7p0TFrXXDqUpkenRjrKNsPSaM6Vra9cs9f17Ll7N5fTuMZj7Q0Mqalah57SEManSz/mO48Yk9w/CUL3oWqRkoBXES0b+D3rO2vve2fCh6jiOh1Q7ut2Hlf88qidi75crcVOwD3vWd5UVsv32dPh/iOZRuK2irnGVu2rIv9+8s/3/VpYWqsvGdZ0il0rLJmWkOTZclZFaS5YUnknq/ZiIhOD9upNq2bl4388QaJiOh3f9ne+OMN3+t05zckRESfGn/el32fkTrRXVeHowgiopP95Y+X56JhSdo9e47AERF99f4HrO3v1qUXhn4fi4j29r/f2v46lnwhkGMKREX57rvvBmD16tXs27ePVCpFfX09nZ2dbN26lf2797Cx+76C2/fdeAuZrmVk13QxuqsbnZikecO5BX+/ZfklZQdaiqETYx0bv1R4Ett3341klnSR7VzDaP8udHKC5lM3sOW9y7l0Y/7tevtuJJPpIptdw+joLlQnaGnewL1blh/325nnr7u7m9bWVsbHx+ns7OTAgQPs3r+P7o1vLejfjX076Mo0sSbbzK7Ro0zoNBua21m+5Zt0b7yzwDa30pVZxprsCnaN7mNCJ9nQfA7Lt7yEQuei2HnIx6P3Pg3A0lVLODx4lInxSdLZRhZ1tDKw7yAjR0c5+5JTCx5X1Nmy2ZM+WdnVwoHBEcYnpshkGmhvb2L3bq/8ef36aruqezx7308BaOtawcToGGPHjiH19bR0LOHYwUNMjo0zNTnJqvPOrtrW4We8IEtmyQomjhwEQOrqaGxpZ/xgDzo5zoI166q2c/D+hwHIrljGWN8AdQ0NUF9Hun0xk0NHmTg0RMu6s6q2Ay9+xnp6vIlrOp2ms7OT/ft3F3yOofizfG33xrzbPHZjH61dGRavyXJo1yhTE8rKDc18cvmWit6FW5Zfctxv77t7MwBdq7sYHBhkcnKKdDpFR2c7O3d4wagLLlpf2gmKAXffey8Aq1d1sW/f/l98Zzo62PrMM9TVCZddcmnIXsaTJwe2ANAxbyVD4weY0kka69K0ZpZwZOwAI5PHqK+rZ03rOSF76iiVn9/jDcKXrzqBgd5+pqeVTCZNW+cSDg4c5NjRYzQ01HPmOtcsx+Gwyd2bvQY8q7tW0dPXC0A6laazvYPDQ4c5cOggF62/MEwXHY6SOLDlIQCyK5cz1ttPXWMjUl9HyozTx/oGWbThvJC9LJ8tm72mpCu7FnBgcJSJyWnS6Xra2+dxZGicg4fGWL++PTT/Amm9vWnTJjZt2sRNN93EoUOH6OjoYHh4mJ07d9Lc3Fx026EHHqW+KQsKw9t2kupoY94ZJ3H0saeDcH1OhrY9QH26CVQZ3r+NVEsHTV1rObbnyYLbHB56gPr6JkAZGd5GKtXB/Ka1DI9sz/v7mefv8OHDNDc3Mzw8zI4dOzjttNOK+vfAUD9N9Q0osG14iI5UljPmtbL12KEi2zxGU30WRdk2vIuO1GLWNp3M9pHCGsSFzsPI/vzHBHDOpaexf9cAzz+5l6aWLJOTU4yPTtC7e4AlyxbGOnADsPHiE9h48Qnc9sPn6R8YZvmyBYyMTLJ792HGRqc48cSF1mydcslF9O/cTfeTT1PXUM/Y8AgTI6Mc2LuPVCbDirVnWAncALScuoGxgd0c2/s0DfOamR4bZnJ4iInDfaRa260EbgAWbjiP0d37OPr0czQubmVyZITJoaOM7N5HZsUya4Eb8J6xHTt28Pjjj9PW1sbY2BjpdJqdO3cW3a7Qszx0pHiH9oWrMgwPTDC4fYTJ8WlaTkgzfqy49nChd+GxJ/PrHV+y6WJ27tzNk09spaW1mfHxcdLpNM9sfZYVK5cnKnADsOnSS9mxcydPPPkk7UuWMDw8zMFDh9j+/HOs6lrpAjdVcGbbRs5s28jPe27n8NggizNLGZsaoe/YHobGD7Bs/okucBMzzr/sQvbu2MO2J5+ldfFCJicnGRsbZ+/OvWSb5nHOhee6wI3D4QObLr6MTRdfxs233cLBgwdZvnQZwyPDHDx8kENDh13gxhEbFm1cx6KN6+i//R4mho7SuGQhUyOjjPcNMnFoiOYzTw7bxYrYePFSdu8+wtatB2huTjExMc3o6BS7dx9h/vzGUAM3EHD/8rVr19Lb28v27dtRVVatWkVra2vRbZovzD8gnH928aBFUDSfnP8l27TizILbtDTn32Ze9qSC29x77720t7eTzWbZtm3bC+fu8ccfL+rfhc1L8v59c0OqyDb5J/knZbsKblPoPGSXFj6mxzY/S3Z+mmxTmr69B+hc2cb8lnlsf3w381vnFdwuTtx/fzdtS+aRzTTw/POHWLGymZaWNE89OcD8+Y1WbbWf2MVQ3wAHu/ejqixavpR5rS30Pb+TxStPsGZn6NkHqEs3UZ9uYuxAN+nFK2icv5DRvp2M9u8m1WLvpZbtWs74wAHG9vWBKpkVS2lsXsCxZ3cw/4yTELGXnbhmzRp6e3vZs2cPqkomk2H58vxZYzkKPcvNC84vut2KC4sHrfPus8C7sKHIx/HEE1fR19dP9559qCrpTJpz15/Dk49vpW1JGw0NgX4CfGfNiavp7etjT3e3943pWklrSyuPPfE4y5Yuo7HR7jNXS2wd/Ckt6TZS9Wn2H3ueJfNWsCC1iN1DW9l/7HlOTtkJ2jqCY8WalQz2DtC7twdVZfHypTS3LqB7ZzeP9vZzzoXnhu2iw5FI7r3/Ptrb2slkMmx7bjurVnaxsHUhhw4f5q777ubySzaF7aLDURIHf/oIqbZF1KdTjDy/l+yKpTS0LODY9p3Uz28K272KWbWqmf7+YfbtO4aqsmxZEy0tafbsPkLbkix1deFVfAU6cr/00sIrn4e3PExmxVLG9veRXb2C6YlJxrt7QSDV0cbUyChTh48iDfVkTlzB8DPPI1JHQ+sC6jJpxnsGvN8us1ox9Qv/ntlCZvEKxg7uJ9uxmumpCcYHu0GEVEsHU+MjTA0fpi6VId22krFBT8NndGwv42P7yWRXo9MTjI13A0Iq1cH01AiTU4epq8uQSa9kePiZgvYLnbu1a72VsS2H+1iRaWL/2DCrswuYmJ6me3wYATpSWUamJjk8NUGmrp6V6SaeGfbKdvaO9bB/rJ/V2ROYmJ6ke7zPbLOYkakxDk8dJVOXYmV6Kc8M7wBgbGBvyedh/FBvwWM6++JT8v79uZdGIzBngw0b8k/+N2wsHhSohJM35g8kdJ1rd/W0+ZQCAcuVhQOWldJ60bl5/36BD9H8ou+nw1tIZ1aU/Szv2nKY1hUZhvaPsWh1lqmJaYa6xxGB5hPSDA9MMDEyTUOmjtaVac9Wie/CumyaqeER6rOZgn5vvHRD3r+/aOMFlZ+oCHPpxceXjwEu68YCpy++KO/fn7qoeKDSEV3WX5L/PXDaOeUHlx0OR+lcuiH/t8pl3TjixsIC4/RWi9nxYXDRhvzSuWeetThgT47HumBxNpvtGR0dLSuCUo1IZ4H9xVawuLGxkYmJ8kSE01LHmE6XtY23XTCCxfWN9UxNFC8JKYdUprF3bGTc2vW1QSpVPzgxMb3I1v4ymfrekZHJvMfYkEoNTk1MWLHVmEn3jo+M5rVT15Aa1Ck7dgDqGtO9U+MFbKUaB3Vi0p6tdKp3qsA7IJVKDU6Ucf4qFR4PUrC4kvdGITKZTO/IyEiknq/ZlHsNixGH4w2SxrrU4KTae+5TdZnesSl3fsMgk830jI2WP3YphXQm3Tta4NvhcDjsfqdm4r5ZjjAIcpweFNlsQ8/o6JS1b2SxuZtNrAdv8hoRWaiqB83/Px24HXhUVa8qdbtaZfY5EJELgG8BP1PVt5S6XZTIc0z/BPQAQ8D5qvqeUraLMnmO8ZXAPwN3qupvlrpdhbb+E7gEeJ+q3m3LVh47AowAg6paNJXIgq03AB8HblXVP7Rlp4Ct7wDnA29Q1Yds2gqKPMd0NfCHwH+r6sdK3S4u5Dne3wQ+AHxdVf+21O0cx5Pn3G4B/gKYD3xQVa8odVtH+Mwaj/0S8CngHFWdMn/3PeBuVf1MKftwOBylISILgUuB64ArVfXBPL8R4HPAucCrVXVo9j7cs+eIAnnGBp8GjgBvBt6T7/7Ot13UmPWNvBT4B7y4xZSq/mUp2/lNIILFsw5mKdBr/rec7WqSPOegpPMX5XOXx7fLgbvNn8vL2C6yFLhuPfhw3YKylef3C4FRYImI1Ptsy7f7PshrFRRJPKZiJPE9GRVmDc4WA2cC9wA/BjaISMGidnd+o8eMQWkd8Eng2lzgxvDnwJ+ISMHaKXddHY6KuAQvcPPaQhNb9VbUPwQ8CvxARBbM+nf37DkiQZ57cR3wkPlTsCNG1O/hWf7NPKaiwn5BHlcgwZtZLAX2UELwxpGXRJ0/sxJxEvBz4CmgRUTsqetGhyCvW1C2lgL7gcNAWwC2knb+giSJx1SMWjveoHgFcJeqjpkV4Z8DLwnXJUeFvBU4Bnx/5l+q6hPAD4H/F4ZTDkcSEZGrgC8Dv6SqPyv2W1WdBj4IPEGeAI7DETVMxljJgY4YEcljCit4swNom2u13pGXpcDzJGdScgnwU1UdNx+su4Ekyuzn7vuggg9B2MoFb/YHZMv3YzIfoCCvVVAk8ZiKkbT3ZFR4DXDrjP++1fydI0aISCNeGeqfaf7a+b8EfltE8rerdDgcJSMirwH+Ey9w80Ap25jx8AeArcDNIjLfRxcdjmpZCYypag8RC3RUSS54swvIikgktKbCCt7sAw4A4TZKjydL8W4iSUg0/nLgrhn/fRdFSqdizFLgWaDDpKv7ghmUtwLbSV7w5pkA7LQAE8DuAGwFSVDnLyrknrdaOV7fMe+tV3N88ObV4XjkqIL3As+r6k/y/aOq7gC+AfxpoF45HAlDRF4NfBV4var+tJxtTQDnN4BteAGc+PZddiSddUCuFPBR4EwzH4ktIpLFqwx5wixyPAScF65XHmEFb3ITvmUh2I87ywhuwhwEOb2bHEV1b2LMMrwyjiOAn33mOoF+vACp38/XzHsxCFvbgIx5ofpFUt9Py/DKEn0NHkaIpcCTJOsahs3ZwBFVfW7G3z0GNInISSH55CgTEZmHJzj9Z3P89K+Ad4vICv+9cjiSh2lUcT3wy6q6pZJ9mADONXiZpDe5AI4joqzHC26gqkfxkgxOD9Wj6jkbeFp/0W72QYpo+QRJ2MGbJAQfgiYx589kDp0BzFyNeAxvgulLe9MQCeq6BZ0NE7StOUV3LdmJ/fOVY0Yp2G68jm5+6xOFiinHbcfLPqtLSIZiFHg18IOZf2FWo1z2Tbz4EHC/qv682I9M+vsXgI8E4pXDkSBE5Arga8CvqOrmavZlAjjvw5sQ/58JwDocUSJXXpSjqGhxTMh3TJEoBwsjeLMMLysgMZOjgEnS5PJi4EFVHc39hel6cS8J0r2ZMXkOIqMjccEbEWkAFgF9ftsieZltAAvw5tlHSNZxFWIJcEhVx6mN4w2K2SVTOVzwJiaISCvwR3gdpUrhb4FfFpFTfHPK4UgYIvIK4OvAG1T1Phv7NGPj9wJ7cQEcR4Qwc5wXMm8MkQl0VIEL3swgN+HbhxtUl4VZUV6Cl30QRFmM38zWu8mRNN2bZmAqoMlz7vkaxCtnyPhsax/+P8sdwICqTuL/fT8zINVhPkpxJ3dMUBvBjJnH674zFjBto9eT/319O7DJ53eNww5/CNykqltL+bFpffoZ4BO+euVwJAQReTmeXtQbVfVem/s2AZxfx/uufd/nEnKHo1SW4sUT9s74u8gEOqpgdvDmOWCRiPgpfVESgQZvTK1mI15r4VqYRNimDTicoBXl2Xo3OZKmexPk5HkpsN+UM/TiaeD4aovgAlIEZctkgx3DX32ioKjl4E0tHG8QvBzYoqrHZv+Dqh7Aa2l7WeBeOUrGlCJ/APhomZv+M3CZiMR9IO5w+IqIvBT4JvAmVb3HDxsmgPNuvPHd/7oAjiMCrMeropjZufBh4Jy4dpUWkTSeZs9jub8z5YsPE4GgVNCZNzMnlm5QXT6JmZSYlM9zgHwibg8BXVGIbloi8OBDwLaSeExB2AqKJB5TMWrteIOgUMlUDlc6FX2uBb6mqrvK2cgE7D5p/jgcjjyIyEuAG4E3q2q+DEVrmADOu4AB4Hsu69ERMrMzVFDVXJJGXEtuzwSeU9XhWX//ILUavDH/3w2qyydJ528D8FiBldxJYDPJWckNsowjkHvEiMAKXves/UCnjyVGQQdv9gVkKyhqrYwoidcwNMxzfZxY8Sxc8CbCiMgq4B1UHoD5InCqiCRGi87hsIV5Lr4NvEVV7wzCphknXw0cBP7HBXAcIXJc8MYQZ9HiYsfkgjcB2487STp/hfRuciRJ9yaJmSMvZNGp6ggwCiz005b5/0k5f0GSxGMqRq0dr9+cZv736SK/eRBPI2plAP44yuejwOdVta+SjU2p9l8Cn0qIDpjDYQURuQz4b+CtqvrjIG2bAM6v4S2ifdeUejgcQRPpQEeFRPqYwgze9OCt1ochmhxXkjQpKaR3kyNJujdhZY74mWUx85gggEBRAHaCthUUszNR4i50PhdJvIZh8mrg1ln17C/CpPHfBrwqMK8cJSEiZwBXAn9f5a7+C2gBrqraKYcjAYjIpcB3gbep6h1h+GACOO/A0+hzARxHoIhIOzAf2JHnnyMR6KiQQsGbZ4ClItISsD8vIrTgjREEPUoyBEGDItfGGOAAMC+OYmXm43I+UKyF4s+Ak8N+QCwx87rtx3vwra9eGmGwdjwhu5wtvybqM48pSFt+loLNFFTP2UpCoCOQ8xchZgdvknANw2QuvZscP8CVTkWRTwB/ZzQIKsYE6K4FPukW3Ry1johcghe4eYeq/ihMX1R1Ang7MAL8twvgOAJkHfBQgcWdh4Hz4va9EJEGYC3wyOx/M9/Bx4DzAnbrRYQRvNk3479rYSJhk5nBL8VkL4XqUWVcCGxV1aFCPzBp2g8AlwbmlX/MvG7HgAm8FUzbLAEOmg85BJcNE6StfqBFRFJ+2ZnxEUrK+2l2MMNPfaIo4DJvLGGE5S8GSllVvg14uYg0+uuVo1RE5ALgIuBfLe3y+8Aw8FZL+3M4YoeIXAz8D/BOVb0tbH/ghQDO24Bx4Ns+jZEcjtkUylBBVQeAQ8CJQTpkgdOBvap6pMC/h55RFGbZFLiBdbkk5fzNpXeTIym6N0Fdt7ACKoHZMq36+oEOP+0Y4vp8zWbm+RvGG9y1humQX5igVCe/uI6DxDRDMSK8BK8FaMFAew5V7QWeAzb67ZSjZP4a+ITRJasaE9j+M+DjLkjnqEVEZAPwPeBqVf1hyO68CBPAeSswBdzoAjiOACgYvDFEojtTmazD87sQoR9T2MGbWuh8YpPZmUtxPX+bKK53k+Nu89u4E1TGWdABFd/vRZNu2Y6XZearLRL4fjJBiwxeR4ocSQlK5WMhMJqbrMY8QzEKlFoylcN1nYoIIvIyYDXwZZv7VdWfAM8D77W5X4cj6ojIRXjZZ+9W1XLei4FhstbfgtcN9FsuyOrwmbmCN3HsOFXKMdVU8CafTkZSJxFWybOiDDE8f+ZDsgG4p4Sf/xQ4S0Tm++uVf5iygxRe6mAOv3Q4Zgcf+oBFpn7Tb1t+3YttwJCqjgVgK+/7KeYlRkuBnln1yLF7b5TB7GsIyT5ev3HBmxhi3lmfAj4yo4zWJtcCf2G+bw5H4hGRC4H/A35dVW8J259imADOm4F64JsugOPwAxFZhDdG31bkZ6EHOipgPcWDN08Bq8KcmwYWvDECWgvw0thzuEF16bxoRdkQx/O3HnheVQ/O9UNzrA/haS7ElSAnzy8KqBhhrQHiXWI0205gtlT1KF76cbMPtoIiyPMXBWrteH1DRNbgfbMfLWOz+4HVIuIyncLl9XgZd9/0Y+eq+jO8a/0hP/bvcEQJox11E/BeVb05bH9KYUYAJw18wwVwHD5wHvCIkTMoxEPAurgsgprGL+fgiS3nxSyIPGl+FwpBZt50Ar2zLrIbVJdOUiYlperd5Ii77k1owYeAbSXxmPy0FRRJPKZi1Nrx+smrgB8WaxE+GzOouQN4pW9eOYpiBp+fBK6dY1BdLX8O/JGItPpow+EIFRE5H7gZeJ+q/l/Y/pSDyVh+I5AF/ssFcByWmau8CFXtAcaAlYF4VD0nA/0lJBiEmlEUZPDGDaqro9D5i1sb3FL1bnLEXfcmccEHo6MyD69dvW92DEHe90l8RyXlvVEqSbyGYVFuyVQOVzoVLu/AK9P1NUNAVbfiZSP8oZ92HI6wEJF1eM/R+1X1+2H7UwkzAjjzga/5VEbvqE3mDN4YQhf4LYO5xIpzhHpMLngTH2J//syK4CWUF7zZDJwX424x+a5bUIK74M890snxpWBH8KQWFli2FXTwa9+sv4vVM5aHIO+/KJDEaxg4psz5cuD2Cjb/IfBK8753BIjpLvMx4MPlZExVwUeBD4iIH6W5DkdoiMh5wA+A31TV74XsTlWo6ijwK0ALcIML4DgsUWqgI06ixaUGpGo+82ZZXGrhQiYJQpznAt2q2l/qBqp6DHgcT+Q4jgR53QrZsp1lcZwdM1EIxBbJyPIJiiS8N8ohidcwDC4FnlLVwTl/OQtV3YPX4et861455uL9wNOqWs4CScWo6i7ga3gCxg5HIhCRc/ECNx9Q1f8J2R0rmADOLwOLgOtdAMdRDSLSDCwHninh53ESLZ5LrDjHE8DJYSUWBB28edGKqBEEncSLBjuKk29S0g8sjFEda7l6NznirHsTVClTvm5kvtgi/zEFacuP85dPUN0XWwET+4y9Mqm14/WLSkumcrjSqYARkSa8IMqfBWz6k8A7RKQrYLsOh3VE5By899eHVPW7YftjkxkBnCXAV1x2pKMKzgUeV9XJEn77ELA+6okaIlKHJ8JcUKw4h3mWngXW+u1XPsLOvAE3sC6V486f6SbUjz/dhPxgE5UHb+Kqe5Pvvj8MNJrBti0WAcOzupGBPyUyYQdveoEllgce+QTVIf7vp1orI3LBGzu44E38+F3gblWdc+BpE1XtAz6PV0LlcMQWETkbr+zzt1X1O2H74wdmjPh6vDGPC+A4KqXU8iKAvXjxhqiPw1YDR8w3rRRCyyiKQvAmyfoLNsk3CYOYnD8T0byM8vRuctwHXGCyI+JGvoyzXImRzesWdEAlqHsx3/mbwBNLXmLZThLfT/mOawioF5H5IfjjN/mOt494ZSiGioicgHcef17Fbu4BzhSRxXa8chRDRBYCvw/8RUgu/D1wlYicEZJ9h6MqRGQtcBvwu6r67bD98RNVHQZeh1f28p8ugOOogJKDN2bOEwfR4lI1fHKEdkxRCN64VdHSiPv5OwsYVNV8x1AUVR3Cq6u8wLpX/hPUdQs7G8a6rSKlYNZtEf/n6ziMeGkLXnbeC/gUPAwdI5YteOLZLxDDDMWweRVwmzlvFWE6nNwFXGHNK0cx/hj4nqo+G4ZxVT0M/B3wiTDsOxzVICJn4gVufl9VvxW2P0FgAji/BKwAvuQCOI4yWU95gY44iBaXqneTI7RjcsGb+BD381ep3k2O2OnemMlzMzCQ55+DCj70AO0m88lvW7aPqRUYN4MMv23F/fnKRwfQn6cUDOJ9XIVYCuwv0GUnicfrF6/GKx2ollvxAkEOHxGRpXhCxR8P2ZV/BS4SkTgusjhqFBO4+RHwh6r6jbD9CRLTEOS1wCrgi5bHiY6EYiQfVgNPlbFZHESL11GC3s0MHgNON3O9QAnkQTWq5m146euzcYPqOSi0omyIy/mrVO8mRxx1bzqBviKTZ5sdcPIGH1R1HK9Mps1vWwQXUPHDVr6uTACHgJSIzLNoKyjmOn9J68BU6BpCfN6ToWK+1a/AXvDm1W5C4Dt/DnzFdPkKDaOl8Qngr8P0w+EoFVPmdzvwR6r69bD9CYMZAZyTgC+497WjBM7G60Y5XsY2kQ7emEz/ssqmzLOzAzjTL78KEdRD2o5XMpNPldoNqucm1ivK5qHYRGV6NznuBTbGTLciyOBDFGwl7pjMM9dj2VZQBHn+okCtHa8fbAB2qGpPtTtS1eeAo3gDPYcPiMiJwFuBT4Xti+HLwGoReVnYjjgcxRCR0/ACN3+iql8L258wMZ1/rwROBf7DBXAcc1COWHGOHcB8EWn3wR8brMDrfl2utEcoQamgHlA3qK6OuJ+/0/A6Ie2udAeqegDv4Y9s5DYPoQcfbNsywbOFzNJRsW3HkLjzFzBJPKZi1Nrx+kG1XaZm47pO+cvHgH9R1XyluYFjxOQ/Anwq6m1hHbWLiJwK3AH8mareELY/UWBGAOd04N9cAMdRhHKFfXMLoQ/hteKOIuuABwskSRQjFNFiF7yJB3OVA0S9/KFavZsccdO9KXbf2+5iFNQ9ktNRySdmegCYJyJZS7aCvO8LddDyw1ZQ1FoZURKvYdDYDt78ABe88QXTHeeVwGfC9mUW3wQyeO2IHY5IMSNwc62qfjVsf6KEqh4BXoPXYORzLoDjKEC5wr45oixaHKtjCjJ4U2xQnbRJhG3iHvyqtmQqx93ES/cmiZkjBe34UGKUxPMXJEEGD6NAEq9hYJh05jXAFou7vQtYLyLNFvfp8Pgr4NOmG2NkMBpv1wKfdB1sHFFCRE7BC9x8RFW/ErI7kWRGAOdc4F9dBp1jJiKSAU4BHq9g8yjr3lRSCgbwCLDW6AUGRhQybw4BaaNe7chPsfPXCyyJ6iDJvPhtZd7cDVwa1WPNQyCZD+YcBzVxLXZMtm0FVQrWACwmv6C6VVsBU2vBjFo7Xtu8EvixKX2xghH02wI4DRSLiMhGvMHm58P2pQA3443t3hGyHw4HACJyMl7g5qOq+uWw/YkyJiD8arx3zL+4AI5jBmcB21R1tIJtExe8Mc9KN548SGCEHrwxq/VJXAW2ScHMJaP2fRi73YRssgZQ4Plqd6SqfXjn4Zxq9xUQxTLOBoEFIpK2YGcB3jnO140M7D5fxY4pSFv7gU5Lg4piguoQ3/dTrWU81lqmkW1eg92SqRy3mn07LGDeeX8NfKzCAbTvmHHdh4GPhdFG1eGYiYichBe4+biqXhe2P3FAVQ8DrwIuAD7rAjgOQ6UZKgDbgDYRWWjRn6oRkaVACqhUlzXwoFTowRtDEicSNonz+bscuKsCEahCxEn3pljQchova6rTlp0i5ziobJjAbJlJyzCwyE87hig/X8UodlyDQJNJgU0Ksc1QDBtzXl6JnRbhs8m1DHeDfztcgZcB+ZWQ/SiKqt4NPA28P2xfHLWLiKwBfgz8lap+MWx/4sSMAM4G4B/dO9xBBWLFOcy85xGiJ1pcqVhxjsBFi4MK3gRZapFE4jy5tKV3kyNOujelXDcbIqqRCKjE2Facn6+8mMn4ErygxXGYj5St4GHoGJHseXii2ccRgwzFsFkH9FXTEbAIW83/BppWnERmZN38RZFMwSjxZ8C1rizeEQYiciJe4OavVfULYfsTR1T1EF5g/xLgMy6AU/NUKuybI4qixbE7Jpd5Ew/ifP5s6d3kuAu4LOoq+Gby3EaBybMhyOCDrRKjQO5FEZkP1APFxDhd8KYwS4CDc+iXxPG4CtEJ9MyxcpKk47WN7S5TL2CuiWsZboc34I3bvhO2I6Wgqg/jLbj8Tti+OGoLEVmFF7j5tKr+e8juxJoZAZxNwN+7AE5tIiKNwJnAo1XsJoq6N9WUggE8DJwb5LzUd0PmYDrwutAUwg2qCzDXirIhknoOItKF1zL0GVv7VNV9wEG8F0iUaQcOzLE6GkjwQVWHgXGg1W9bWD6mOSbjtu77uY5pAGiOmXbDXMcEyXrv1trx2ubVeG29/cIFb6rECKv/FfBnJv08LvwF8P+ipnPgSC4mcPMT4O9VNaqi3rFCVQ/ilWy+FPhbF8CpSc4AdppGBJWSuOCNqh7AmyecbM2jOQgiSrQYOKKqY0V+4wbVhYnzivLlwN0W9W5yxEH3ppTJZFDBB4hflkrQx1RQhNlMlPqIV4lRrQUz5hLShmQdrzXMpHotcK+PZn4MXCwi83y0kXTeifce8kOXyDdU9Vnge8Afh+yKowYwi4Y/Af5RVf81bH+ShJmkvsL8+bQL4NQc1WaogKeDtlxEmi34UzUisgRoofqmOoEGpYII3tTaJMI2cT5/tvVucsRB9yZKARUrtubSUbFlxxDk+ZtLk8umraAo9fzZ0FyKAkm8hkFxBV6Q3bfORUb48iHgJX7ZSDKmK+FHgQ/7sBgSBB8H3m+6ejgcviAiK/ECN/+kqv8ctj9JZEYA55XAp1wAp6aoWKw4h6lGeJzodA0+D3jIQjZroKLFLngTfUqdlERxEmZb7ybHXcDlEf9oRDH4UO090gYcNuKvhegHFlooMQryvk9ioKPWghlJvIZB4ZvezSxc6VTl/CbwmKpuDtuRSlDVPcBXgT8P2xdHMhGRFXiBm39R1c+G7U+SUdVBvADOa4BPRnws7rBHtcK+OaIkWhzLY3LBm+gTy/MnIsuAhcCTtvetqruAEeBU2/u2SOIyb0qxo6pTeAGcDr9tEb/zFyRJPKZi1NrxWsEMuoMK3vwAF7wpGxFZAHwYuDZsX6rkU8BbTQcgh8MaInICXuDmc6r6j2H7Uwuo6gDwcuC1wCdcACfZmMz7s/FafVdLlHRvbJSCgSdavC6o5yAqwZs4CoIGRamTElvdhGxxOXCPj8KKUde9SWLwoRQ7QdqyUQpWiqC6FVsBU2vBjFo7XlusBYZVdXsAth4FFojImgBsJYnfA+5Q1cfCdqQaVLUf+BfgY2H74kgOIrIcL3Dz76r6mbD9qSVmBHBej1fW6Ugup+I1ETlsYV+JC96oai9wFFhdtUclEETwZhlzCEmaCX4v8RIEDYo5hThVdQQYxct0iQqX44/eTY67iX7wZi4B1T5gsekiUhGmG1mG4t3IwI44cinHFKSt/cDSKoOWpQiqQ0Q7uhWhlPMXt2MqRpAC4UkiqKybXMvwHwKvCsJeEhCRxcDvAh8J2xdLfAZ4pYisDdsRR/wxGd4/Ab6oqn8ftj+1iAnKvhx4k4j8Zdj+OHzDVoYKeBUZJ4bdwMA0a2gHnrW0y8CCUlHJvAG3KlqIuJ6/Tfijd5PjLmBTxLKNZlJKidEkMEh1JUZLmbsbGSQw80ZVjwAKLPDTjiFqz9dclHJcfcCiaoKHESKuGYph43eL8Nk43Zvy+FPg26r6XNiO2EBVh4BP47U8dzgqxohf/wT4sqr+bdj+1DKq2ge8DHiLiCQl0Ox4MVWLFecwuplP4ZVhhcm5wKNG7sEGgYkWRy1448Qkjyd2k0sRace7lo/6aOY5QICo1s+XIhgL1V+3IO+PoI4Jgjuu2D1fc2GCE52Upk80QPX6RKEiIo14WYf9xX4X0QzF0DBaKhcAdwZo9nY8sfl0gDZjiSkHeQ/wibB9sczn8bQBNobtiCOezAjcfFVV/yZsfxwvlI28DHibiDhh8uRhS9g3RxREi2N7TFEL3sRichQwcTx/m4D7LEYzj8NkmkSydMpMnkvRUYHqSzkilQ1jw5aIZIAmvKwkX21ReilYlJ6vuViEp2NSSuvnOB1XITqAvhLfN0k4Xlu8DLhfVY8FZdB0KXkKuDQomzHmI8B1qlrK+yk2mPfSx4C/dllwjnIRkU7gx8ANqvrXYfvj+AWq2gO8FHiHiMRdYN1hMNqQ5+GJ8toiCro3NkvBMPsKRLTY1+CNOYA4Bh8iQakryoYonT+/WoTPJqqixYuBYwFNnkt9voaAehGZH4Ctao+pE+gtoRTMhq1Sj6kXaDOK+1Gn1GOCaL03KqXWjtcWgendzMKVTs2BiJwEvBGvxCiJfAUvk/MVIfvhiBEi0oEXuPkvVf1k2P44jscEcF4GvFNEPhy2Pw4rrAEOmMUXWyQxeLMPmAZOsLjPvPidedMCTJS4sucG1cfTAfTHcEXZb72bHHcZW1EjyMlkSbZMICQuJUZRPH+TeKLQ7VXYCopaC2bU2vFWTcAtwmfjgjdz83Hgn1R1LiH6WGLep3+By75xlIgpx/8x8C1VTVopYaJQ1f14AZxfF5E/CdsfR9XYDnIAPA6cGlYJtSkbXwFstbVPM88KJCjld/DGDaqro1SNEYiIZpCILMJrlWb7Qc/H00CTiKwMwFY5BHnfB3KPlKqjUq0dQ5D3fbnXKvRnrATKPX9xf+8m8Rr6zSlAA14JU9D8HK9LnO+rU3FERM7BKz34p5Bd8ZvvAPXAG8J2xBFtRGQJXuDmO6rqWs3HAFPu+VLgfSLyR2H746gKa2LFOYwG4TbgLJv7LYNzgCdUdcLyfgMRLXbBm2hTzvmLShvcy/B0FGw/EMcRYd2byGWOWLC1EBg1L9y56AWWVFFiFOR9n8RAR60FM5J4Df3m1cCtJZYmWsVkkt6OaxleiE8Cn1LVo2E74ieqOg38GfBXCel45/ABE7i5A/gf4KPheuMoB1Xtxgvg/IaI/EHY/jgqxrawb44wRYtjfUwueBNt4nj+gtK7yRFF3ZskBm9KtmPaAB4Clvhti/icvyBJ4jEVo9aO1wZBtwifjSudyoOIXIK3EvkfYfsSED/E0/R7Z9iOOKKHiLQBPwL+D/hIGMFmR3Wo6l68AM4HROT3w/bHUR4m696PsikIV/cm1sfkd/CmnBXRXmBxTARBg6LUTjgQnUlJUHo3OaKoe1Pudau27KdUW9VkqZRjJ0hbFd/3ZQqqQ3Sy2+Yiju+NakjiNfQNEckCl+CtZofFD4FXuIyLX2DeR58CPqqqY2H7EwRmMv5h4KOufbxjJiKyGC9wczPw5y5wE19UdQ9eAOdDIvJ7IbvjKI8uYMS0grdNEoM3u4CMiPg6zoxM5k3MBEGDopxJyRG88d8CH/0pioi0AKcBPwvQ7BN4Qb8oTcjKuW49QLtpxVcWIpLCEwUvpRsZBJR5E7CtauyUI6hera0gqbVMlFo73mq5HHhEVQ+H5YDpSLID2BCWDxHk1UAbcEPYjgSJqt6HJ175m2H74ogGMwI3twLXusBN/JkRwPkdEfmdsP1xlIxfQQ6AR4EzTWflwBCReXgdtJ6wve8ZosXn2d73TPwO3vwanppzqbiB9YspJ/hlo5tQtVwLHApy1dDUzQ8AfxWUzRIoZzI5iSccekEFdnLdyKZL/H0SgzcH8aLc2QrsvAIoZ7uwn69SKef8HQOWm04escOs1q/Da89YCnG5hn7yD8AzYTuBJ1b4T2E7EQXMosu/4mUYlNJdMmlcC3zEtEh31DCm6cXt5s+HXeAmOajqbrwAzu+LyIfC9sdREn8B+PIMztB1+zU/9l+E9wKTPs5VFe+8+YbfwZvb8FIeS+UwcKpPvsSRNUBfGb8/BJzsjyslcYBgS6Zy/ITSs098xaS+r8LLqCmFaTz/S/39TE7CC16USi+wusLWrGeUaeuw2aYsTAbSCsoLWg4CJ5ZrC9hOea2S+/CeychiylCWU3rw5jBwH14QJ45M4Pm/vcTfHwHqzcpurXKIcN7Ts7kT7/5zwOvx3mH/G7YjYaCqjwLzgT8M2xdHeJjOof14jSj+xAVukoeq7sIL4HxcRO4N2x/HnDwG3OLj/u8k+PnbfvwtG78FL5vUNyRK70YROQrcoaqvD9uXsDGT2Cng1ar6wxK3mQSuV9X3+OqcoyAi0gQcBc5WVX8fXpF/An5XVUsKxojIBmALkDaiwuXYGgZuUdU3lfj7zcCJqtpZpp3FeJlUK4zQXSnbKPB7qvrZcmyVi4hcBdwE1EV1UCkia/ACGYtUtZxgW00w4736WlUtZ2HB4fAVEZGovleCoNaP3wEisgr4HPCGWtF9qlVE5G3Axar622H74nDEjagFb1qB4XInlklFRJaoaskRSZNuOmT0gxwhUe51q8JOHbBQVQfL2KYi30xQ5VCpKf1Gj2eeqh6qwFa59/0SYCCIgb+ItKtqOdlwgRPU/RdX3PlxOBwOh8PhcMSRSAVvHA6Hw+FwOBwOh8PhcDgcL6YizZt0NtUjImrrTzqbqkTvwxEAjZl6q9e6MVNf0bVOZzPW/EhnM4Heb6ls2prvqWzaPSslkMlkrZ3zTCYb63OetXgusgGei2zWnt8iotlsvK9jMVL19t6PIqKpejvvyGy20fI1bIz8NWywfMy5Pw0xOPZSyDbYPz/ZhmScmySQyjbYfRdlG9y1TQA2xyEvevZjPj4LC5vjq2Jjq/qMvfmPiGh9pvAcKJO1O1/NZAvPV1MW56SpCuakFWXeiIjeMXRd2dv96ikfZrDHTrZ6naR7p6ZHy9LTiAqZunTPmI53lLtdfVqYGrOTKdWQlt6J0ek5z5+I6Cf3v9qKTYBrl95KqRots/3YPPqoFR8uzpxTkQ+ZTF3P2JiWfd0Arhv6blm//5NTPsBgT29Z20g6hY6VX3Eo6Tp0rNRmPSANaXSy/HJ0kTTllrFXekwAe/ZMlPX7jRtXsndvOQ21ipNOS+9oCc9YqTRm6nomK7z/hso8F4VoXtFY0bNTCSKiU0fK11FeffKp7O4pSS5pTtKS6h2dHov8d0ZE9NtX2rt333zLUivXWUR04MC1NlwCoG3RJwO7/ypFRPQD/Z+0vt9/W3Jt5I+9FERE+3/rY1b3ueTzf5mIc5MERET/c+B3re3v19s+665tAhARHXvCvj59+qwWd39UgIg9iTERKXgNRERfsm+LFTsAdy7bWNRWd8/rrNla3vn9orauG9ptxc77mleWfQ83WLEMPHbfswB0drUxPjrByLEx6uqFRR0tHB44wsjRMQZ7+rl0Y3fe7Xv7biST6SKbXcPo6C5UJ2hp3lDQ3r1bllc0iYkCYzre0b3xzoL/fmPfrXRllrEmu4Jdo/uY0Ek2NJ/D8i0v4drujXm3eezGPlq7Mixek+XQrlGmJpSVG5oL2vjk8i1Vnb8dWw4AsHBllqGeMRpSddTVC/PbUwztH2NqYpoV61qrMTEnD9/zIADLVi1nfGyMI4eOkEqnWNSxmEMDB5manGJqcorT1pXd9CgvY2PasbvA+f/2jX2s7MqwZk2W3btGmZhQLjLnf+Xy419cz973JABtXR2MDY8yMTKO1NfR0tHKkYEhBnt6uaHAs3JP3420Z7rozK6hf3QXkzrBac0beOeW5Wzsvi/vNn033kKmaxnZNV2M7upGJyZp3nAuAFuWX8LG7qsLbPccma75ZNc0M7rrKDoxzZNv/CEbv5TfN4C++24ks6SLbOcaRvt3oZMTNJ+6gS3vXV72839vkWMqdlxbll9y3G/vv/8eAFauXEVv7z4aG1PU19ezZEkn/f097N27n0qubyFWVvmMzWZyTDsqef4/mef+u8+ci66Vq9jfuw+AbCZLx5JODh46wMDgABdfdKlN961w971ec4rVq1YxMDjAxMQkmUyazo4Otj7zDJl0ht09eyn0fi30bi3E8i0vie135qkD3nVvz67k4FgfoKTqMrSm2+kb2c3k9DinLyr8jfWLzZu9AU/XylZ6eo7QmKqnvl5ob5/PoYMjjE9MMTk5zTnnJKuj+77NOwBo7lrIyOAwAHX1dWTb53N07yF0WulYvyJMF0Nl876dAHQtWMj+Y0PUSx2p+nra581n19BBRIT1HSeE66SjYp7Z7H3727qaOTo4AkBdvdDS3sTAniEmx6Y4ZePyMF10hMA9P/fGd6uWdzF4yJNvrK+rp6Otg4GDAxwdPkZDfT3rzjwvTDdrgrvvvhuA1atXc+zYMY4cOUI6naazs5MDBw4wNjbG5OQk69evr8rOofsfBiCzYhlTwyNMHTlGXSZNqn0REwcOM3V0GGmoZ8HZp1V9TPdv8e6pFSvnMTI8yejoNHX10L4kw6FD44xPTDM1qaw9u7VqW8/e91MA2rpWMDY8zMjhIzRmM7R0LKF/x25EhBMvsHMfW2sVfvYlp3D2Jadw/w8eZf+ufha2L2BseJyenQOMj06wqKOl4LaHhx6gvr4JUEaGt5FKddA07wyOHdtqy73Y8MDQYzTVZ1GUbcO76EgtZm3TyWwf2VVwmz0PDJFqqgeFgW3DzO9I0bm2iYHtI775uXrjIg7uGaH36SPMX5JifMTTSD64e4SFK7K+B24AzrtsPeddtp77brmLfTu6WbKsndHhUY4cOsLI0WEWtS+2FrgpxgMPDNFkzv/2bcO0d6Q4/Yx5PPXkMaan80e2T7nkTPp39tH95G4aUg2MDY8xMTrB/me6aelYWNDWM0MPkKlvQlH2DW+jNdXBqqa17Bsp3ik5s2o5EwMHGdm+i+nxCdIndDI5dJRjW58ruM3QA73UNzWAwvC2w6Q6sjStXVTUztC2B6hPN4Eqw/u3kWrpoKlrLUeee7C4f5lVTEwMMDKyHZ0eJ50+gamp4lkXQw88Sn1T1vi3k1RHG/POOImhnz6S9/cbNlzGhg2X8aMf3czQ0GHa2joYGRnm8OGDnH762UVtda3KMDgwwXPbRxgfn2b5CWmGhibZ+lQ0OmwvXJVheGCCwe0jTI5P03JCmtGhSXoL+HfJhsvYuWcHTz79BEsWtzM5OUk6lWbXnp0saeuIZOAGYNOll7Lp0ku56Qe30N8/wAnLlzM8PMLBg4fobO9g3bnnFtw237v1jHlreOzoM8EdQICcsWgjZyzayIN9t3Ns4jCL08sYmxrh0Jint93ZdGIofl188UouvnglP7xtG4cPj7FkSRMjw5Ps3nUIRFi+vDlxgRuAZRevZtnFq9l529OMDhxlXnsTEyPjHNl9kMnhCVrXtIXtYqhcvGwVu4cOsfVAL23zmhifnmR0apJnDw5wwoJWF7iJOadevJz+3Yfp3jpItjnF2Mgk46NT7Hv2AItPWOACNzXKZedfwmXnX8Itd91K/4EB2he3Mzw6wq59uxmfGOfCs893gZuA2LRpE5s2beKmm25i165dLF++nOHhYQ4ePMjhw4c555xzqg7cALRuOI/WDecx+KN7Ge3uIbWsnamRUYa372Lq2DDzTl5lJXADsGHjYjZsXMyPbu9hb/cIbUtSjIxM0dc/Sn//GEuXZq0EbgBOueQi+nfupvvJp2lIpZianGRiZJQDe/fR2tluLXADFjNvcqw+6wQO9g2x7/l+VJXOlW3Mb51H9/OFG7S0NF+Y37mG4ivbSeTC5vyTyJOyXQW3WXFh/vPUdlLWik+FWNQ1j6P9YxzuHgWF9IIGWk/I0PPUEVasb6W+0VpssCCP3vcQC9sXk8qk2bt9N51dy2hZ3MrQgcO0LC4cMLTJhQXO/xlnFn+82k/sZKjvEAe7B1GFRctbaFq4gKH+wqmlpxZ4VpZlTypqq/nC/NkFDc3zi2xTftJB88n5/VuwpvgLv9A7oKitAsfUfNG5Bbd54IF7aWtrJ53OsmPHdlas6GLhwsU89ND9RW0VusbNZ1h/hVZEoXdApoh/J3adSN9AH9379qKqpNMZTl7TyfM7t7OwtfoPtF/cu3kz7e3tZDIZtj23nVUru2htaeGxJ55gzYmFAxKF3q1nzz/VL1dDZ+uBn9KSaiNVn2b/8PO0z1vB/MZF7D6ylcGRfSxMt4fm2xlntNPfd4wdOw6iqqxc2UpLS5o9ew7T2urvtyss9t+/k2zbfOozjRx+7gALVraSWTiPwa29DD7Zw7JLVoftYqisallI//Ax9h0dQhWWzZ/PwkyWJwd66Zg3HxFXHRFXnr2/m0xTI+l5jRzoPkrbymbmL8zQ/fQgh/uGaWlvCttFR0jc99AW2hcvIZPOsH3Xc3QtX0nrghZ2de9m267nOLlrTdgu1gz33nsv7e3tZLNZtm3bxqpVq2htbWVsrHyphLloOu0kJgYOMLpzL6iSWbGUhoUtHHtyG80XnG3tff/ATwdpa0uTSdez4/ljrFgxj+aWRlSHaW1ttGIjR/uJXQz1DXCwez+qyqLlS5nX2kLf8ztZvNLeAkSgmje/suoPGDpgp+axFjVv6hph2o50hdO8qcCHVEoGJyYonnpSgHI1b/7fqvcydOBgWdtUrA/TKDBR+nvAad6UhtO8qY5KNW+Wdq2i74AdbTWneVMdTvPGHk7zpjBO8yY6OM0bRz6c5k20cJo3cxNlzZuKgjfpbKpnfHTCmhZAKtPYOzYyXvIAWUQWqmp5M9uIU8kxichCgEq2K3Wbxkx9z+TYtLVr3ZCu650YnSp7MtSYSg1OTkxUFDiZTSqT7h0bqT7wV+p5bEg1Dk5NTFrxvTGT6h0fKX0yKSKrVHVnOTYqua+quRf9sJVKpQYnLN0v6XSmd3R0pKz7Jah3VCl2bJ6LTDrTO1LmuaiUbDbbMzo6au3dk8lkekdGSvc9Tt+ZhrrU4JTaucYAjXXp3vGp6t+R2Wxjz+jopMVr2NA7MjIR6WBafap+cHpi2tq1eGG/mYbeyYgfeylkGxp7Rqfs3RMAmfqG3pHJ+J+bJJDKNvRMjE5Zu76Nmfre8ZFJd21jjs1xyEyCHJMkCavjwiJjq/pMumd6rPxkhULUpVO9U6P550CpVN3gxIRau8fSmbre0ZH889WGVGpwytL5a8yke8fLnJNWFLzJu6NZA10ReQJYAyxR1aOlbueIPnmu9WeBlwPXqur/lrqdZR++D9wAfBlYWchOFO63PL5/CbgE+KCq3lHqdo7SyXPOtwAfBb4LtKjqZCnbJYE85+IWYC3wKlV9qtTtwmCmDyLyCUCBK4A/UdW7S9muFph1ngTYA7wMeAeQUdU/KWVbv30z//0QcDLQpaoHwvLLL/Ic7/uBK1T1zSJyCnA3cEItvYNmM+t+/T3gTFW9RkSeAt6lqj8rZVtHNMldIxHJAHuB81V1p4j8BbBUVX+r2HaBOusIhFnP/DrgFmCtqvbP+M3f4r0b3z7XPhzVkec79TvAe4DrVfUzpW5Xoa0twKnAGapasG12ubby2FkN3A88paovtWWngK0/A94CfE5Vv2DTFlgULM5jfCnQY/63nO0cEafAte4lwGudZ1/rgAeBR4CCqlBRuN/csxI8s16qDXjBii14A8mCymhJPOdxvv9m+bAOeMj8WVfGdoln1vGeBkwB24Db8YJdpW5rnTjff5WQx++rgevNvz0L7ABeWcb2iWPWMb4WuNn8/5vNf5e6rSOCzLhGbwAenJEN/BXgrSKSV/DGXdvkMiNw0wBcB/zxzMCN4aPARSLymmL7cFRPkN/loGwl8Zhy+KIoKyJpYAHQzRyOOxLBUryV3VCutYh0AE14g+AHmWMiF0GWEeL5q0FOBbpVdQjvfomuOm8wLMULYsXt/ssFbN01LM4VwO2mwP2nwBoRWRKyT8ALA/c24nn/lY2IrMHLMrp1xl9/FS+gU/OIyALgIuBH5q9uAq4KzyOHZd6HN1EHQFX34C2ivCk0jxxh8/vAAF7m/ItQ1WHgN4F/E5HC3TUcfhDIuNBkBnfizYGW+WmLYOeqvtryqx1QJ14mxn78vxiO8FkKPE94g+91wENmcvIQ8ZvIhX3+ao31ePcJlJC1kWTM5HkxXuAzNvefiCzD65a4lxq/hiVwBV7GDao6AdyFV+YaBdqBA8A+YnT/VcE7gW+Y65DjRuDVItIajkuR4gpg84xS+83AiSJSC/dGohGRk4Azgdml9V8ErgneI0fYiMiJwJ8Av1lIPVdVb8f7Zv1VkL45WEYw85JFwAjBJHssBXYD6ULZfhbx9fz5FbxZihe42U9tDMhqFhM1XQY8Q8jBG/P/YzWRM5PnRXglDe5ZCYbY3i8+0AEM4k2e4xRonxmwfQpYHcDHOHaISCOwCZippTVn6VSA1MxYwXwrXyiZymF0fn6Eyz6AF5dM5YKNtwNXhuaRwxbvBW5Q1dntI2/GywY8PQSfHCFh3of/AXxaVZ+f4+d/gFded6H/njkMS/HmdUFkwwQ1Bgja1rN+2fEreLOMGhmQOWjG01PYQXiTv5mT8aeBE0SkOSRfyqUDL2U0bpPnODPzfnkYOFdE/HoXRp3cxyxumQ8vXEMzGXgKODtUj6LJBmC7qg7M+LvbgSvM4DlsZg6mkv7+uwQYxnvnzOZ6arx0yryDr2RG8MbgSqdijgkivxv40ux/MwG6r+AFdxy1wzvxsn7/ca4fmu/X/wOuM/eSw3+WAk+QrIBKILEJM7bKnT9fxjV+Zt7sI34TAkf5RGHyl9O+wHTseBw4JyRfyqVmVp6jgJkgnIuZQBmxsD48HYpaJPeujtv9NzMAB/HUugqCF0qmZvAMIMApwbtzHLU0VngXXueOfOUBtwKnmTKCWmUdcFBVn5v197cCLzNaio54chVeEHlrgX//MnC1u8a1gdFc+1vgfYW67OXhG3ilNX/om2MOAEQkhbcwvw3IiEjWR3NBZ8MEMd5oBcbwkhra/VgcdmVTjmrJXeteYImI1AdpXEQW4UXvt8/46ziVwrhnJVhOAgZntSSOo06SLeJ6/80O3tTyNSzGccEbEzyISulUXO+/sjCD3zcCX8/37yZ77Jt4q9G1ylUcn3WD6UCzFbgscI8ctniRUPFsVHUb8CTwusA8coTJP+KV0D005y8N5rv1AeAPRKRWF9uCohPoU9VpSuiYVCVJLJtaCuxX1TFgCK8pg1Vc8MZRLbmbdAI4CATdxWQd8Ih5yeSIU/eZXCT4MNDgFPV9Zz0mS2sGcQr22SZ272qzarcATwwuRy1fw7wYAdyzgPvy/LML3gTL64Cfq2p3kd9cj5d9EIVytjB4kd7NLG5ijpbhjmgiIicAG4Fvz/HTL+IFeRwJxrT9vhj4y3K3NS3mPwl8oYbfk0GQ+y5DQIEOgsm+DTR4Y/6/L7Zc8MZRLUE+5PmYvQIP8ZrI5YJfintegiDu94ttcs/vEFAfk+DhOuDhWeUnjwOniEgmJJ+iyEvxOveM5vm3O4DLI6AfUCtjheOEivPwIF6q9cX+uxMtRKQTLyvy3gI/uRmnexNXfh34lmn7XIzvAutFZJX/LjnCwIwv/g34jRLuh0L8MzAfeI81xxyzCSN40wN0+hWUM2OdhUA/LnhTkJzjg0CTG1AnmigGb57Eay86L2BfKmHm+asF3YewKRi8qdGVnDgGD4+7hiZAsQ0v08ThkU/vBgBV7cOrxw67e0fu/XcET+dvQcj+WMcEJi4G/qfY78wz+FVqU7j4NcDts1qoz+RRICsiUdBpcpSI0Xp4L0VKpnKYd/jXcZPyJPNx4G7T/rsiVHUKL0PrU+bd6rBP0MGbfeb5P4bXfdcPOoB+c/+44E0BZk4IevHq5xzJJArBmxeVwRj9gK3Eo/tMTv0c4jN5jiUmOLOOWd1ejKbCEFCLYqFhP7+VkC8AB/EqlwyCgsEbQxRKp+IYPCyXtwHfU9VjJfz268CbanDBq1jJVC6wdTOudCpuvAJPY65UbZPrgPeISIOPPjlCQEQuAN6O1zWqKlT1UTyR689Wuy9HXsLIvMnZ8qvrZJjHFP3gjXnpLsbr4AK10QK0lglt8mfagS/H654ym7iUwsRx8hxXVgHHVLU3z7/F5X6xTRyDh4WCN7V6DY/DlB4045WTFSLU4I1Zle/ES5eG+Nx/5VJKyRQAqroX7z7+JV89ihCms8nLgR/M8VNXOhU/igoVz0ZVHwf2Aq/yzSNH4JiSlS8Cf2DaftvgY3gZ0y6ga59AxoVmQTWoMWhQQSII4Jj8yLxpx4u059q/JXVA5vDICe5C8Nf6XOCxAq0G49I62AVvguO4LK0Z1NzE30ye2/nF5DnyZXsishBPFP3ZPP9cc9ewCFcAP5ol5D6be4GzRaQlIJ9msxg4OkOTJ3HvPxE5G+847ypjs+uprdKpy4CnTSlfMe4ALjCLNo6IY4TlrwD+q8xNr8MJFyeNP8AbZ5R7LxREVUeA3wA+n8Ry25AJSs5hAV5i5RHz30EFbwaABSKSDsBWbII3M52GBA7IHC8izODDevKvwEMMWgfnmTy7Z8Vf5rpfam3i3wYcMe0MIR7333nAowWCEo8CZ0ZAhDcKzFUylRv83g+8JAiH8lALY4Wr8VriFguizeZ/gMtEpMMnn6JG0ZKpHKbs7D7glb575LDB1cD/qurhMrf7FvASEUnau6AmEZGTgD8EfnNWk4GqUdUfAz/C60DlsEdQ87ogxwAv2DLf4z78k3SJbfBm34z/jvxqrqMyjCBwGjhk/iroa12ofAK8coFTfYys2qANGDIaPZDMyUuUKHa/1KJo8ex3dRxKXAteQzO52wmcEaRDUUNE6vHKUEoRhQyzdCrRYwVTQv4O4IZytlPVo8D/4mnl1AJXUULwxuBKp2KA+Y5eQxklUznMKvx3gHfZ9ssRLOY++A/gr02bbz/4Q+DNIrLBp/3XIrMrKoLQocnZ8jN4E9R4Y6YtX+y4zBtHNSwFemZE04O+1gXLYMyq8nai3X3GPSsBMUOsuNDEfx8wCawI0q+QieP9VywAB060GLxz1Kuq3SX89nbCy2SI4/1XDq8Adqvq0xVsWxOlU6Z7VBOzROSLcDNwpcladUSXSwDFy5SqhOuA97nrHHveDbTgtff2BVU9APw+cJ3Rz3JUgVn8acNrNgRea+0Wn85tKJk3ftoSkflAPV4TlBfs2F4YdsEbRzXku9adQWQviEgTsBp4qsjPol4K456V4FiON5jcV+Q3Ub9fbBPH+2+u4E2tXcN8XAHcVuJvHwVaRaTLR38KEcf7rxxKFirOw53AEhFZa8+dSHIVcEup5RSqugNPr+B8X71yVMs1wHVVlMk8AAwDl9tzyREkpuzz08D7CuhS2uRbwC7gj322Uwu0Awdy18yUGPXjtdq2TeKCNzk7uXefqg4D43hBTGv4EbyZqbIMyRuQOX7Bix4GIzw5AiwKwPbZwFMzSo7yEfWJ3OyXySCeiFattYkNgnXAQ3MMJqN+v9gmVpNnI0q4Atha5Ge1dg3zMafeTQ4zMLuDcEqn8t1/US/bKwkjqnsl8M1KtlfVKbxyq3fa9CuClFMylcOVTkUYEWkFXk/lgctca3gnXBxvPgv8p6o+4rchc798APg9ETnVb3sJZ/Z3GXwOdARgB4KLTRQ6f1bHNi7zxlENQT7ksykmPpsj6iUUy5iRCWImUr34J6JVy6yncKepHLU28c8XPGyKcPDwXODxOVbxHsHroFQfiEcRw2Qknk953Y3C0r1J8ljhTcCPVXWwin3cALwjqfeyCXBdhCc4Wg4ueBNt3gbcpqr9Ve7na8BVIhLEYqDDIiJyFd536GNB2VTV3cDHgS+4cruqCDt4s8x29Yb5hi7hF6VgOVtBBm+s2goieNMHLHIdQBJJmMGbYm2fczwKnBXhey/M81drzFVuA9EP9tlmduacEu3g4ZzX0HQ22QecFohH0WMTXobZ0TK2uR14eQgD3tnvvwPAPBHJBuyHH1RTMgWAqm4FuvHEp5PIFcDmMu9VgM3Aia4bUWSpSKh4NkbL5Gbg16r2yBEYJkP288BvmJKRIPkcXhOV9wZsN0mEFrwxYuVTQLNlO23A4VmVGi54M4vZF2MKr0a5Vtpe1hKz1bsh2ODNXBO5o3h1sKcH4E8luOBNcJQSvNkDNNbQpGB2GilEu+NPKdcQai+DaiYll0zlUNU9eN/o83zxqDAvuv9M8LCH6AYPS0JEVgFnArdY2F2ShYsrKZlCVSfwNJ2utO6RoypEZB1e2Xy52VSFuA64psa6QMadv8LLOrwjaMNmvnkN8MkaGsfZJshxYT5bfsyBAillMgRyTFaDN2blrgNvADYTNyFNJqEEH0xZxyl47cDnIsoTORe8CQAR6QSyeIG8gpjJY5TvF9vE7f5zwZu5KTt4Ywi0dMpMxuJ2/5XKO4FvzaHHVirfBF5rSowSgxkrXkkFwRuDK52KJtcAXzIl4Da4E8gAF1ran8NHROQi4Ffx2neHgqo+DnwR+JewfIg5YZZN+WUrccdkO/NmMXBEVcdm/X0SBmSO4wlr8H0WsM0IJM9FlCdy+c5flDMf4sp5zC1WnCPK94s14jZ5FpF5wBrgyRJ+XhPXcDZmpXE58PMKNg9a96YFmMxTMhPJ+69UzHNVdclUDlUdwJvAvtHG/iLEOuCgqj5X4fY/AF4mImmLPjmqwOhtvQX4T1v7NN/sL+GEiyOPaSV9HfD/qtT6ssEn8LTvXh+yH3EkkHGhKY/O4JVL+2qL/MfUCywWkYYAbEU+eJPPaYj5gMxRkLAmf6WIFeeIpI6JGeR3EpPJc8wpRaw4R61M/FuBcVU9Nuvvo3r/nQ1szbMwkI+HgXNrULTwFcBPTOp4udwJXGSCZEGQ1LHCBrya/Z9Z3GcSS6deS+VZN7mg1lN4Gk+OaPAmPA2jvZb3+xXgTUZLxRFd/giv9LyiDns2MQu77wf+NWlZiwEQZDZMT55F1UCCN6bxxQG81ui+2iImwZvZGijgsgkSh4myt+BpJcwkiGtdilhxjkeAcyLYsaMVb/I8W9At7pOXKFJquQ1ENNjnA4Xe1VG9/0q+hmbV7wBwkq8eRY9KS6ZQ1SG8d+VlNh0qQlLHClcDXy0xy69UbgbWikiXxX2GTUV6N7NwpVPRwopQ8WxUtQcvuPwW2/t22EFETgF+H/iA5XdfxajqncAPgU+F7ErcCErLNMgFnCDHG/lsWbfjMm8cldIJ9OWpbQ7iWpczkTuEp8F0ip8OVYB7VoKjnODN80CziCzx0Z8oUOz+80PErVrKuYZQOxlUwAuZfK+gwuCNIcjSqcS9/0wJz68CX7e5X5Nt9i0S0nXHaJCdBNxb5a5uwtMDcmK2ISMip+OVtVYbkCvEdbjSqUhiMly/APyVqhbVFQyBPwJ+RUQuDtuROGDepfl0a3uBdsuL4EEHb3y3ZfRYm4DZZYOxyLxJ1IDMUZBQrrVp+30mXhvwUnmI6GVT5FMkB/esWEVEFuN1vyhJW8GsGj1M8if+cXtXr8O7LqUSxWfeT84ERqvQEAEXvKmW1wKPqOpuH/Z9PfCuhAQqrgRuN12jquExvLbAUVuYqUXeB3zFwjUtxK3AchFZ69P+HZXzHmAeERQIVtWDwO8CX3T6WCWxGDg2W0/UPNcHAZuLmkEuIAY13ugEevNknw0BDUYXzAoueOOolLzXWlWPAPhYn3wGsDOPVkcxHiL4NrhzUehZ6cMfEa1a5Tzg4TK7X0TxfrFNbN7VZtB1GuUHbJN+DWdSccnUDB4AukSkw4I/cxGb+68MrgZu8GnfDwBKMrru2CiZygXab8GVToWKeT+/E09Y2BeMjtd/4rJvIoURyf8UcE2FWmtB8B28xbs/DduRGFDouwz2v82Jy7wpZMd8q6zacsEbR6UE+ZDPpNzyCYhmCUWhh3wSL+XOtohWrZKU+8U2xYKHiyIWPDwTeE5VR8rY5iFgXUIyFUqh6uCNeffciVd+5TeJGiuYMsvLgf/2Y/9m8Hc98C4/9h8URivv5XjdomzgdG/C5/XAE6q63Wc7XwbeYUoTHNHgn4Evqmo5CyuBYt6dHwR+25T3OQoTheCNVX2YIs1hILhjsm4rqOBND169XK11/0gyhQSgwN8BeDmdg3LkJnJRuv/COn+1RiX3Sy2IFhcKHk4B/Xh1z1Gh7Guoqr3AMLDKD4eihFn5vhT4sYXdBVU6VWis0A8sNOWxceJtwP/lMk994mvAr8Y8/X8T8LSq9lna3x3ABSLSYml/jvJ5Hz4IFc9GVXfifQfe4Lctx9yIyOuAc/HackcaVd0DfBSvfCpK84CoUSz4YFt0t5CtQ0DKYufLVmCswOKfC94Y8up4qOo4Xs1X0kVAa4m5blK/RE/LzqQwbUUPASf64VCFhHX+ao1KMm+24QWbF/rgT1QopLkE0QseVnINoTYyqAA24k2ID1jY1+3AFQFkLBUaK0zhdTCMUvCwFK7Gy4zxDSMG+jjxzjSxUjKVw5RP30dwWk2OGYjIarx37HcDMvlFXOlU6Jj2258D3l9mRmyY/BtQj9dC3JGfIMeFhcYAipfwYcvWXMdkc64VmC1rwRsz2AurlMYRPIFfa6N0fjZeS9tyidpEzj0rPmNWY5cCz5SznZlAPoq3qpRU4nT/ueBNcWzo3eTYBkwCfqeXx+n+K4qInImXlm0j82kurscLFMUVq8EbgyudCo/3AF+fLXDqI98HzhSRkwKy58jPXwO3qepPwnakVMy47hrgEyKyPGx/IkoUyqZs20riMVnNvGkBJooIycZqQOaYkzAG36cC+1X1cAXbRm0il5jJS4Q5F3isQiG9qN0vtonF/WfKZ84iGQFbv7AWvDGrXr6WTpmOCym8bMh8ROb+K5F3Al8LSLDzv4GXiEhbALasIiKn4LVRLadrXCncDFzpyiGCxeii/ToBlEzlMFn8NwDvDcqm48WIyEa80rU/CtuXclHVJ/AycCLXGSsiBDIuNNpnrXhl0r7aovgx9QAdFr8dsQzeFHMa7NfLOcIljMlfJfolOaKmYxJkbWmtkqT7xRoiMh8vfbhQEDRKk+fTgd2qerSCbR8E1idZtFhEFuF14tpicbd+694sBfblaaeZI0r3X1FMNuiv4V+XqRehqkPATcBbg7BnmauAW4pc94pQ1R14pXbn29yvY05eBexV1ccDtnsd8O4Y6mLFHjPpvg74PUtlumHw18AZIuK0k44nKC3ODqCvyIJHIMEbkzF4BK9Fui1bgWiZBhm8ic2AzFEcM2BdAvQW+Ilf17rS8gmIUPeZGZPnoQI/cc+KHaq+Xyz6EiWW4mWwFZs8R0VzqZpruA+vvXKSU6RfBtyrqmMW93kHsMkM1P0gSWOFlwI9qvpkgDbjWjrlR8lUDlc6FTzXEGDWTQ5VfRrYjrveYfCnwPPAt8N2pFLMhP0a4J9FpDVkd6JGUFqcQY4BomIr0sGbQhEniNeAzFGcduCAaS2bD78yRyqeyKlqDzAGrLTqUWWUMnl2z0r1VDPx3wqsEJEFFv2JCnGaPFfzzCvJDsKBXb0b4AWB923ABpv7nUGSsnTfhc9CxXm4A1gep7a3RuD0IuBHPpm4CTeZDwwRWQpcDnwzJBe+iDcBdwSEed/8DvBbtrPngkZV78EL+P5N2L5EhRJ1azstLYAHHVDxPTZhykgXAYU6KVod17jMG0clBH6tTU3ieVRXLx+ViZx7VnzG6GqsAp6qZHsTmHwCOMeiW1EhTvdfNQE4iM4z7xfWgzcGP0un4nT/FcQEdn+JgCewJtX868Qr++YKYHOF5Y+lsBk40QQVHP7zbuA7Pl7PufgOsFFEVoRkv6Yw4+8vAB8zbbeTwJ8AvyQil4XtSERoBqYKPdMmY2kYL0BRLVHJhrFpqwMYKFIKNgjMF5G0BVsueOOoiLmu9UEgKyJZizbX4GX7DFaxj6hM5Iq1kwP7Ilq1yDnAk6o6UcU+onK/2CYW72pTnnkOyQjYWkdE1gAZwI+SHRe8mZs3AHeraqGVNj+5Hvg184zEAT9LpnLB9tuAK/2y4fAw45L3EkLJVA5VHcYLmr47LB9qjGuARuDzYTtiC1U9hJdJ9AVbE+qYM9d3Gex9m0vJvo1biVZROyZbrRevM2XVuOCNoxJKuUl7sHSTGqoRn80RFRHauc7fGHZFtGqRJN0vtpnrXd0DtEcgeHgK0GsGWZWS1GsIXnDlRz6lsN+H15J3oQ/7TspY4Wrgq2EYNl1T+oGXhGG/HMx75Ep8DN4YnO5NMFyOtwL/QMh+fBF4bwS+U4lGRJYBfwW8L6COekHyXeBp4M/CdiQCRCl4E8fMmyDPn9XgzVzZBPuBpVEQjHVUTak3qU3R02rLJzDbR6H7TBjnr9awdb8kMWtjruDhOJ6YdtjtiG1cw114WYA2A8lRwa+SqVyK9GY8QV7bzDVW6MULHkY2q0REVuJlhd0UohtxES5eDxxU1ed8tvMD4OVuFd13rgGuC1v3RFUfxitFeEWYftQA/wL8uwkYJwpzD38I+C0ROTNsf0ImyODDXGOAAaC52qYJprS5Dm8xvBC25lpzHZNNW8Fl3pg0xzHAj5U8R7AEGmE02JjI7cW758Ne1Q3j/NUaNu6XJ4E1IjLPgj9RIi73X9XXcIZo8XlWPIoIRhzvZfgnAAv+lU6VEjw8TPjBw2K8A/i25S5f5fIN4PWme2GU8bVkKocR2n4S2OS3rVpFRBbjZVF9LWxfDE642EdE5FeAM4FPhu2LX6hqN/AR4Is1nsUVmcwbVZ3GE/6tdtFtruYw4DJvYjMhcFTPXOrdYPFam0wZmxO5sLMpAj1/tYaIZICT8QSHK8ZMzJ4G1trwK0KU8q6OQscfGwE4iMYzb5vzgT2qOtd1rIbbCCF4Y4js+898j64m+C5TL0JVe4F7gV8J048SCCR4Y3ClU/7ya8DNqnogbEcM3wBeISLtYTuSNESkBS/r5v0mEzPJ/AcwDXwgbEdCJMhxYVBjgMgEpCzbshO8MZ1dGvFWy4oRhQmBo3pKTQ+zda27gBEzWK2WKEzk4jJ5jitrgWctDTiicL/YptTgYWhlezO6y7ngTX58K5maweN43RFW29qhKWlZgJcWXYzIBm+AC4AG4P6wHcHT3HlX2E4UwnR/OgkvyBQENwGvjUBpdOIw5/R9eNkukUBVDwPfIx7lg3Hjb4BbVPXusB3xG5Pp8X7gYyJyQtj+hEQg40JTDr0ET1txLluBBW8sfDMCXZS3lXlTSmoSRHtA5iidoFdObYjP5oiCgGmsV55jQNLuF2uYrKT5eFoBxQj7/jsROGxKIaolUdfQ4HvwxnzPf4Td7JtOPBHq6Tl+F/b9V4yrgevD1vww/B9wXoRbJr8GuL3Krn/l8BiQxhM7d9jlQrzudneF7cgsrgPe5wJ29hCRS4HXAX8cti9BoapPAf8KfK5G76WgFuWX4GmgzfVNCCR4Y1qjTwAtftsiqsGbEn4X5QGZowTMS62DYKKmOWyVT0DIq/Bm8txE9CfPcSYx94sP5CbPUQ+027yGzwGLjF5D7DEifOcBQayI2ta9ifVYwQgovgW4IWxf4AVh6e/gafBEkSBLpnIBx1twpVN+cA3wpYgELWeyGa/k5ZKwHUkCJjvyi8DvVNnpMY58Cq/k/o1hOxICUSplSqotF7xxhMZi4FgJJSlRDd7sABaEWCMdl8lznLF5vzwGnJagDiZxeVdbu4Ymy+NhkiNafDnwM9MEwG9+BLzMYuenuNx/hbgSeFJVd4btyAyuB66O2mqxCXS9HK8LVJDchAveWMUEjN8IfCVkV47DjKWuwwkX2+LDwDN4bbRrCqNzeA3wWRGpteY6QZUYlToGsCEdEch4w5T5txNgUoML3jjKJdBrbV4S67E3kQu7+4x7VnzETBjOwAu6VI2qjuBlbiSljWRc7j+bAThIVgZVEHo3wAudOHqwd+7icv8VInSh4jxsxisVilpp4CbgaVXtC9juj4ELjOCqww5vBe5U1bkmJ2FxA17ntdawHYkzInIG8EHggxHMsAoEVb0P+F/g02H7EhSmo2oKOFTsd6p6BFA83bpKiVo2jA1bbcCQ6ZRZjD5gsekWWhUueOMol1KvdT+wUEQaq7SXE8fqrnI/MwlzIlfWsxK11dQYcAawQ1WPWdxnkib+kX9X2+ouN4skXcPAgjcGm6VTkb//CmHK7l6GV6YUGcwk63qiJ9oaaMlUDvPuvw9/OqXVKpESKp6NqvYDPwTeHrYvccVkD3wR+EsTtK9lPgy8RkQuD9uRgFgK9JQYsKv225zE4E1JdlR1Ek8yo+rKD1vBm2XMrbIMIXcwcVih1Jt0Ci/K2FmlvfXAg5ZXAcIUMC1FFCw3AJ0AWv12KGHYFCvOkSTB23I+Zp0hBQ9XAuOW22An4hqaThjteGVgQWEzeFPS+w9vPBG1scJb8LqvDIXtSB5uAN5qMg+jQijBG4NrGW4JETkb71n8Ydi+zMF1eEEmR2X8pvnffw/Viwhgupj9NvAFo1OZdEodF0L1gY5SxwA24gVBxSZKPSawNLZxmTeOcgnyIQf7K/AQj8wbcM9LJSTtfrFNqcHXYWCccIKHflzDZ/Ay2eJeSvEK4McmOB4UdwHni0iThX3FIXhYiHcRvZIpAFT1ebx7/DVh+wIgIqfgCfMHGWScyc3AlSabwFEd7wO+HPA7pxLuwMv2Tsq3OjDMosDHgGtK6ARYE6jq94AngD8P2ZUgCHJeUqqtXqCtUr09EckC84ADJfw8qGOyYQsIPngzBNQZ8TNHPCknwhjV4M02YElIgmQueOMvftwvjwJnWSgBjALl3n9hZD9Yv4Zm4vEYcK7N/YZA0CVTuVaaD+FpmFRLqcHDEWAUiIRopIichpcR9qOwfSlClEqnrsLLUgpFN0NVdwADwPlh2E8KZgL0duA/w/ZlLkzQ4Us44eKyMAHyfwU+Z9plO37BbwO/ISJrw3bEZ4IcF5ZTYnSAykuMOoleKZgNW0DAwRtzEm0oSDvCI/aZN+Yj/wjhiBaXc/7cs1IGRgTsbLxraw0j0rYHOM3mfkNiKaWlkUJ4wUM/AnAQ8wwqk0XwCgIO3hhslU7F4f7LxzuBr5sBZVS5EXiFiCwK2xHCLZnK4UqnqudX8MrWd4btSIl8BXiLpSzBWuENwCl4bbIdM1DVfcC1wBctdlyMIkF+l4OyFeRcK/BxTdXBG9NCdwGeCE8pRGlA5iifwG5SEenAS3vbWek+ihDWRC6uk5c4cCrQ7ZMmRawn/jOIQ/DQBW/ysxavo8HOEGxXHbwxwdXFeFpopRCJ958Jmr0T+GrYvhTD6DT8AE+bJzREpBm4iPCzlFzL8Oq5hggLFc9GVffiiVW/OWxf4oDpzvXPwPtNm2zH8VyHV0L+W2E74iOBjAtNlldnGbaCCt7UZOZNJ9BbRp1kJAZkjooJ8iZdBzzkU+p1WAKmrmzKP/wQK84Re8FbM3leRIQnzyKyDGjAy3SyTdyvYeAlUzP4ObBcRKq5H9qBwTKyV6Ly/rscz+/Hw3akBKJQOnUFsNmU24XJZuDEKu/ZmkVETsbr3vj9sH0pEydcXDqfBr6vqveG7UhUMXPb9wN/KSIrwvbHJ4KalywChlV1tMTfV7OAWM4xHQYaq8jYi2XwphynIToDMkeZmKhp4MGbKrYvRuCr8HGYPMecRN0vPpCbPJcqPBnG/ednwPYpYFWMU+pDC96Ye+YneGVblRLXscLVRFSoOA+3AauNYHBYRKFkKqeZcBtwZdi+xJT3Ater6njYjpTJzXhBuzPCdiTKiMgmvGf1T8P2Jeqo6tPAZ4HPR0xE3xZBzeuCHAOUbMuMN+OS5QO44I2jPM4GskBQK6d/hNcu2w+2A6eIyDk+7T8fl+FlFbgsNX/4AHDQp31vBy4xpXxxpdx39Yl4HXaC5GNA2o8dq+oE3rMXu/RnEVmDF7y5L0Q3dgB/V8X2sRsrGKHidwDfDNOPUjEBizuBr4dh3zQBeCdeoC8KPAr8bdhOxA2TAfm7eBoyscI8AzcB/xW2L1HFZKN9BfhtU27pmJtPA6uBPw7bER8o59s8DZxmymP9tAOezmSlWXSBjDcCLgV7ARvBm3OA4TJ+fxgvCOCIH3vxVjVKrY3txVvprlTo6078S9kdwxtgHvJp//nYBnyvjKyCfmBNQiP9fnA3/k0aevAmzuW866LGWcCxMn7/LTwR1CC5C38nnt/Ba6kcNxRPiHsgRB9uwcteqpRyxwqH8HR+wmQp0I33/MeFHwFhfTPq8YJ8z4Zkfza3AbvCdiKGLAeOAE+H7UiF/BiYH7YTEeYzwGpV/Z+wHYkLJgPtK8DfhOyKVUz351ZKH1s8j/deLWcsmeNsyhsDXE/lY9AzKW8x9xiVxSaWAVPASIm/7wE6jV5wxUi12eki8giQUtWSUhRF5G+AP1LVJCt3OwARWYk3cGp10f3yEZFzgYeBbBk1og5HXkTk28AVqtoati+O2kNEfgYsVNWTSvz9R4G/VFUXvHY4HA5LiEgj3vwvbiVxoSMiC0wH0kQgIhcCPwUyfotWi8jdwCpVXemnHWNLgd9Q1S+U+Pse4FFVfVWZdq7Cy/SrK2Vh3pTtHwXOUtUny7H1ov1YCN404pWMlVRKY7IIMqpaapTKEWNEJOuudeW48+ewhXn3plxXCUcYGM0vSh0rmG3c+8/hcDgcDp8I6jtrxgBiStj9tlXWMYlICpgqQxOyGltVn++qgzcOh8PhcDgcDofD4XA4HA7/sKF540gA2Wy2R0TUxp9sNltUHyBt0Va6iK2GbKM1OyKiDdnGgraCPH+1Rjpj8X7JFLlfGuzZERFtaIj2dbR5z85132YzGXvPRyZT/P1i8TqmfbyGqXp75yT3J1Vf/NzMRZD3RFjUZxusHmN9tiGUY8ymLV+rdJHn1+I5y1o+X9kGu9cz2xDO9fSbxky91fPUmKkP/TxlLY4NRESzRcYHUSXbYPc7km2o7huSFLKW5w/eu6/wHCJKBDWfyVi0k4ngWMNPimbeZBrresYmtazuKvVpYWrMTjZPQ1p6J0anO63szFEUESm7O+8JXcvo3l2OmPcvuGPYju7my+e1UUgTQUT0mv6PW7ED8MUlHylqq9zzt6xrOft377PiG4Ck63unRycT97yIiN7xvB2d4JefOK/oNfzt37KnB/svny98b9omVZfpmdCxsjth2cy8FJGi53b0oYrLe19EZt2ZRc+riOj3395rxdbr/qvDt2soIvqtV1b2/izEW25bWpW/lbzHALq6lrG7zG9BWuoY01Ib7821r/re0enS3n0ioq/r+V0rdgG+3/nZwJ7zmYiIjm4etLa/zMWLiz6/vQN2mrR1tH3e6vkSEe19y+/b2h0d3/rHUK6n34iI/tX+V1rb358vvS308yQiOv7kkLX9pc5sDv2YykVEtP+9D1rb35IvrY/dOfADEdGBA39hdZ9tiz4R6LltzNT1TI6VN4fPUf58ZiX7d+8p285Do4fK+v1rV53Fvp69ZdtJpYXxMmMTImnKVRmQdAodsyclJelU7/To2HFjm4ZiG41Nakf3xzbm/bcbH+mja2GGNYuz7Do4ysSUsmFVM8v/cgvXduff5rEb+2jtyrB4TZZDu0aZmlBWbijcbeyTy7fEuS1v7Ln77rsBWL16NQMD3qS2vr6ezs5ODhw4QPfu/QWvNRS+3p9cvuX43967GYDOVV0M7uuhIdVIXX09izraOTx4gNGjx6hrqOeU86rv7L1/804AFnQtZLjX0x2rTzcwr30+R3Z74uTt61dUbafY+evp6WH/7n1s7L4677Z9Nz5Hpms+2TXNjO46ik5M07yh+OOwZfn1NfO8PPbAvQB0nrCKA/091Dc0UFdXz6IlHfR270JVOf3cC6u2073Puy+bF3Rx7Nh+ROqor08zb147I6ODTE9PotOTtLefW7WtapjQsY4bNnbn/bd7+m6kPdNFZ3YN/aO7mNQJTmvewDu3LD/utzPv2e7ubtLp9Av3bHd3N+Pj41x00UVWfL7nwZ8DsGr5cvb19ZFOpaivq6OjrY1d+7oZG5/g0nXrrdh6os9753Q0reTgSC8iQkNdioXZdvqP7WVapzm1zY6tannqgOdr+7yVHBrrJ9vQxOT0BK2pdvpH96CqnNy6LjB/Zt4TPT09NDQ0vOg7MD4+zu7d+9ld4Fvw7Rv7WNmVYc2aLLt3jTIxoVy0oZmVy7fQvfGtebe5sW8HXZkm1mSb2TV6lAmdZkNze0Efl2/5ZlXvvsEt3rMzb2Uzo33HqGuog3ohs6SJ0f5jTI9OIQ11tJ5d2IcocM/D3vtq1bIu9g/sJ9WQor6+no5F7ezu2cO0TnPhmedbsbVls7fwsLJrAcPDk4yOTFJXL7S3z2NwcJSpyWkmJ6c559xwztmWPm+Av3J+C30jx2ioq6NOhPZME7uPef0T1i8OtRN96OzYcgCAhSvncbRvjLoGoa5emN+e4uCuERBYsa41XCcr4J6f3wfAquVdDBwaZHJignQ6Q2dbBzv27kBEuPDsC0L20j82738IgK4Fyxg0k+P6unras4vZf6yPiekJ1reH3cgvnmze7DWv61rZSk/PURpTddTX19HePp89uw+hCuvPP35sFQaTY9pRyXw83xxt9tiwrq6OTCZDZ2cnO3bsYP/uPWzsvi+vrb4bbyHTtYzsmi5Gd3WjE5M0bziXLcsvOe63D97j7WP5qi4GenppaGygrr6eto4ODg4MsK9nL90b7yx4zDf23UpXZhlrsivYNbqPCZ1kQ/M5LN/ykorGKJcWGFf39t1IJtNFNruG0dFdqE7Q0ryBe7csL3geip2LQmxZfknesU1FZVMP7BqiKVWPKmwbGKZjQYq1S5vY3l9cf2fhqgzDAxMMbh9hcnyalhPSjA5N0re1ko5jDr/ZtGkTmzZt4qabbqKvr4+Ojg6Gh4fZsWMHp512WsHt9jwwxNM3D9K6MoNOK5Oj0yw+OQsCUxP5V1rPvvRi9u/czY4nt9K6ZDFjwyNMjI7Rs3M3rW2LOe2CdVYCNwBLL17Fkd0HObC1l8yieUxPTDE1OsGx/UdoX7/CSuAGCp+/nTt3MjpavHlUZtV8JgZGGdk+xPT4FOkTmpgcGufYU+V0vksuZ194Kfv37GTHs0/SsnAxYyPDjI+NsPu5ZzjtnAusBG4Ali+7mOXLLmbnrh8yNj5E0/ylTE6OMHRkN9NT4yxpOyv0wE0xnhl6gEx9E4qyb3gbrakOuuadyc6jj+f9/cx79vDhwy/cswcPHmRoaIiTTz7Zmm+XrT+fnfv28uT2bbQvXsTwyAij42Ps7O7mpJVd1gI3AGe1b6T36G52HdpKc3oxE1PjjE+N0nt0N0sXrI5M4AbgjEUb6R/Zw54jT9PcuIgj494z333sWdoyJwQauIEX3xMHDhx40XtsaGiIVatWFdz2gQeGaGqqB4Xt24Zp70hxxpnzePzxo0VtrsrMZ2BijO0jQ4xPT3NCuoljUxNsH7G3yj6TxRuXs3jjcnpu38HE4VFSS+YxNTL5QuBm4brOyAduAC4772IuO+9ibrnvhxw+OkT7oiUMjw6zq2c30zrNyStKavRVEhsvXsbGi5dx2w93sXfPEZYsmcfIyCS7dx9hYnyKM89qCy1wA7Cx/QQ2tp/Abd3PcWh8lCXpeYxMTrL72GEEqfnADcDqjYtYvXERz9zex8jhCZqWpJgYmeJo3zh1DXXMX1JVN9vQuOz8S9ixdydPbnuS1vktTExOkE6lefr5Z1i5bGWiAzcAFy9dx8VL13Hb7nsYGD1Ie3YRI5Oj7D7SzZGJY5zY4nuzn8Ry8cVd7N51iK1b+1ncNo8RE7jes/sQy09oiUzgZi7yzcfHj00xsD3/PH7Tpk3s2LGDxx9/nI6ODsbHxxkdHWXnzp2cfvrpBe0MPfAo9U1ZUBjetpNURxtNa0/h8JaH8/5+/WWXsP6yS7jnlls5cugQi9rbGR0eYejQISbGi2e0PDD0GE31WRRl2/AuOlKLOWPeGrYee77wNnnGKGetbeLBnxdvKJbJrGJiYoCRke3o9Djp9AlMTRWPZeQ7F/POOIljW58rul0+imbeFOLCrvzZMictyRbdbsWF+bfLNFfkhiMA7r33Xtrb28lms2zbto1Vq1bR2trK44/nn/xB4eu88qLCWVYAy05cxcG+fvq796GqLDlhGc2LFvL8E0/RuqStquOYSc/9u2hsStE4L8Wx7sPMX7mQzMIsB57uY2pskvq0vfsx3/lbuHAh27dvL7pd84X5F5IbzkhZ8y3uLFt5IgcH+ujv6fbul6Un0LSghWcff4hT1tqd4C5efAbDw/0cPrwDVFnQvJJ0qoW+vkciHbw5tTl/EGvV/OKrbmvXrqW3t5ft27ejqmQyGc477zy2bdvGokWLrPl34gkr6DswyN6eXhRl+aJOFre28MS2baw/40waGxut2Vo6fxWHRvsZGNkHqrRllzE/tZDDowPMT7Vas2ODjnldHB7vZ2C0G1DmNSygI9tFz8hO+kf2BB7AgePviVLeZRcW+BasXTu/qK0Lm5fk/fuTsvbuh9kM/rSbdFuW+nQjx54/xLwVzaQWZTi28zADm/fSdvEJvtm2yX2P3k/7wjYyqQzb9z5PV+dKWhe08Pj2J5mXKT5GK5ef3r+fJUuyZDINPP/8IVasbGbRwgxbnx5kbGyKdLreqr2y/evvZkmmiUx9A88fPcSKpmYWpjIMjA2jqojUfHUIO396kPltaRrSdRx4fpjWFVnmLWzk6MA4rSdkwnavOYL5EwAArQJJREFUIu57cAvz582nad589vTspWu59wyMjo3SsTj6QVgb3N/zMG3ZRWTq0zw3tIeV85exMNPCzqG9PHVgO5csjc6CRdxYtXoh/X3H2LdvCFVYtnwBLS0Znn/+IIsWZWlsDPe9VwqF5mltJxX+RqxZs4be3l727PEygDs6OuYcAzRfmH/RvWXjeUX9O+msMznQ18ee554HVVLpNIs7ij+7Fzafnd+HhsLjjUJjlPXnLyhqq6XA2LoYhc5FQ3Px8VA+imrepOplcGKaskbqNjVv6lP0T45pbbxpQ6YSrYMlHYsZ6DtQkT2neeM0b0rFad7MTYOkBqeYKDuq4jRviuM0b0qjo2MxfWV+C2xq3qSo6x/TqZLGCk7zJj9O88bDad6UhtO8iQZO88YfnOZNeeOAxR3tHOjrL9tOUJo3DY0wWWaD8ihr3pTVKlxEFqpq2XUbIrJKVXdWsF1F9hzlk0qlBicmyp/85SOTyfSOjIwUDCI0plKDk5ZspTKZ3rECthqyjT1To5PWdGDqMw29kyMTeW0Fef7ykeRnxer9ks70jo0WuF8asj1TU6P27pf6TO/kZHnX0Ral3A/ZbLZndNTe8Ra7b60+H+l078joaOH3S31qcHLa0v1Sn+kd8+kapuozPRPT5YtMF6OxLt07PlX43MxFkPdEPoIYK9Sl6gd1Ytpa+lhdpr53aiT4oHmqMTU4MWnnPgfIpDK9I2OFnt/6wQlL5yyTqe8dsXi+sg0NPaNTU/bu2fr63pHJ5C2C1KfqBqcn1Nr90pCu650YnQr1PGUz2Z7RMYvvq3Smd6TA+CCqpOpTgxOWvncAmfp078hk5d+QpGDznZcjk2noHSkwhwiKUr6VQc1nMtlsz5il8UYqne4fGx0tOdlDRBYClDtvqmSMUoWtwtdKVd0f9+e4P8DCWf99KfAs8I1ytqvQ1ueBJ4BfCsDWQeBoudtVYOc1wFPAF2wfU639yXNuvw1sBzb4eb8AKWAC2GH7fonKn1nH2wwMA0/aPt481/A3gIeBP/fzGpr/vgvoAdZE7RrOOv8rgX5AzP+uCMPfPOfvQeAQ0B6182fp+J4C1gFfA94bl2PMcxzrgB3A/9o+hjy2/h54DHhLFM7XrOfoT4B/Nf//SeDCsP2Lwp881/A24JeAo0BLXM/RrGv/28C/mzHCO8o5H3H9M+v4Xwo8Yv5/Bhgs9h1Jyjnw+7ya/z4H2Gb+/2bgFeVsH6c/eY59s19jkln3bxfQB9xbro9J/1ORYLEj+ejx0b5leBOeoip/ebarxNZSY2uZn7ZEJAvMA+pEpGiBY7m2gjymWiPEe7MT74O1VOYQS4jrdZzld0n3bJ7tyrUD4bxfrNuqllk2NwF3qzcyudv8d6nb+uUTJOxdNuub0I53XI/infPLS902bCLyTEXinphl5yrgZvP/bzb/Xcp2iWbWfS94wb6f4wXhzi1luygyy791wEPmT1GhsKgfV6nMOo6rgevN34/iBbF+rcRtHTPIc26uAG43//9289/lbB8bwpqjUeI3rFJbccYFbxylshTYQwkPUYxs5V5A+wOyFdT5qzWCvF/2AeNAq8+2osBSoBuYZwKdfttK2vulWi7HyxLC/G/RQEIQiEg9sIR4nL9K2ATcp6pTROScV0HNP1MisggvEHGn+auiwZsaZgUwoar7KSHQESNKDt4kDRFpAn4Z+K8Zf309cPVci0+OknglLw7e2BOUijDm3glyzL2HEhZMaw0XvHGUylLgeYIbCAZhayle4GZfQLaCOn81w4wPSZD3SxDBviiQO95evKwjv235fg1FZD5el8XdftuyQOSCN3iBm0ME884Mg5nn/FkgLSKrwnOnKnLPVIeI+D3Wi+r37VXAnaqa63+7GThRRKLmZ9jkghyQkECHiGSAk/FK8B8CzgvgOYgSvwxsVtWeGX+3Be/7d34oHiUEc29tBH5i/uqnwGqTuZl0WoExYC/BBW+m8Mr4HYZaepE5qiM3OGs0EX1fMJPxTuBpgsukCCrz5llgiVm9dtihBZgEdhHc/ZLUietsgn4+ngnITuSvoZlctgGPm796Au/dEbaQZJD3RBhswgRvSi1XizBL8QbYR4DFAdgK4ptdLjNLplDVCTxtlytD8yiazAzePEgCgjfAWuBZVR1V1X5gCFgdsk9B8kLJVA7zTrve/Jujci4FHlfVQ/DCe+Uu4OVhOhUQQS96J3m8UTEueOMolaCyDhbhiaQGsTIeZCZFLoJ8EG/12mGHoK9hLWbeBPWRfgLo9Dk9Ni7XcBOeSN80gCnjuZfwAwlB3hOBYkpsVvOLSSxEJ+OpEgIZ+JqSygze4kRk7gmzSPJq4JZZ/+RKp45nHV7QBjzB7lV+LtIFxMxjgoRkFJWCiCwHLgC+n+efvwa8VURSwXqVKGbq3eSYU/cmIbgxdwRwwRtHqQT1ECX1xeBeQv4QZCZFrV3DQI53ho7KLmAEWOiXLeJzDWeW7+SIQiAhLuevEi4D7jerqDmicM4rJchvdlDaceWwAdirqntm/f0PgJeLSDoEn6LKekzQ0tz/T+F104kzLxyT4SHzd7XA24H/nlEu+AKqugPv+r4mcK+SQ8HgTQ1os7h5UwRwwRtHqQQdvOkB2n2uUQ5qcporBXMvIfu4D4l/BHW8bcBhVR0PwFZcrqEL3gRPvnP+FNBqVrLjRtDf7EGgyehBRIEXlUzlUNUBvJbhYWexRQJTotmIlxmcIwlZKjNLwSAZxzQnZrz5LmaVTM3ClU5ViIgsAdbg6dzM5FlAgVMDdypY3Jg7ArjgjWNOTHplMzBAQANBM5EbwpvY+WoL/4+pFRhX1eEAbNUauWvYByz2WU+o1j4kQU/+CNBWZK+hGRyeADwy658eAVaIiJ/vxLmI/Pmrghf0bnKYsrV7iNlE30zgOggmIyb3zVaCETcvlbzBG4MrnfoF64CHzPXLEetAhxmznoHX9jzHQ8C6GsiMOA+YB9xX5Dffwcs+81sLK4m8HLhrVoZmTk+oFkqnXPAmArjgjaMUOoE+M5D1+yFaRjgTuWUB2AH3ErJNbuIwCRwA/FT7r7UPSVjBmyCexX6gJaJ1/5fhdQmZnPmX5r83m38Pi5n3hN/6RIEhIi3AacDP8vxzFDKeymUxcExVR0lW8LUkRGQlsJzjV8dz3Ay8Nin3b5XMzlABTysmziVGZwA7VPVY7i9UdR8wgdcWPclcDdyQ00vLh6oexisffEtgXiWHfCVTOWoheJObo/XiYwMWEWnA+4714f+4MHa44I2jFMKYXOVsJWEVPsiAVK0RyL05Q5el1087UcGIkM7DC4glafK3DC/YN40XwIlKlsBM8pXv5Ag7kJA7f8PAOF5WYRK4BPiZqo7l+bewz3klBPnNjuL37UrgB0boOx+P4oksnxKcS5ElX/DmCeDkCJXAlUu+Y4KYZxTNhYg0Am8Dbijh5650qkxMsLdY8OYO4HJzHZJKbsF0An8bsLQDg2bRKirflcjggjeOUgg6oLLPb1vm5boQbwI3CMwzE1Y/iNzKZIII6ty2AwfNB6sWruFSoMekAicpeBOHZzHKwZs4nL9KKHbOH8PLMuoI0J9qqfVnqljJVK7EwZVOeaznxV2ZMBlbz+K1244jxx2TIemixa8Ctqnq9hJ+ezvQJSJJ12ixySmAAM/k+0fTkv454KIgnQqYQOZovPi7krjultXigjeOUkjiQLAD6FfVKTOQ68G/VfggV0FrjaDul5l2DgMNIjLfJ1tRYObx9gGLfFxNSuL7pSJEZCGeGOLPC/zkQeAkEWkNzCnDLOF1SNa77Di9mxwme+M+wi1XK5eafabMIszlwA/n+OnNwGv99yi6GH2tZuD5PP8c5yyVmsy8wcukKSZU/AImo+HrwDt99ShZXAHcPksfajZJL50KY8wd+nclarjgjaMUZkZa/Y6ABhVtnXlM4P9LKIhIdS0S5IdkH7ywapv0lYCZxzuFJ1buV+ZBINfQlADMx8u0g2hew0s5vl31Cxgh95+a3wXNImB4RvvZRLzLRKQJL8Pg/iI/CzvjqVyOK2XyUd8lyPFBKbwUeFhVD87xuzuA843eUa1yHt65yjcZfZAYBjqMVsbZHC/4DjE9plIwgf9XAt8uY7PrgXf63NU1SRQrmcqR2OCNWbCsx2smA8HN0Q4BKRGZ55Ot2OEeWEcpzBwIDgILRCRt24gZXAaVpTLzmHK2googJ0boM0zMpCuN92KH4K6h37aiQJDHG5RmRideKVhOyDGK1/By4O45fhNWICGpz8DFwCMzglL5iFvw5oVrpapH8YRa/QpSRC2ztGjJVA4jZnsfCZ1olUihDBWIb4nRqUC3qg7l+bc9QKNpj5403gzcVkLQ8gVU9TE83ZJYddMLA5N5fDle0LcY9wJrExoUzund5IK9gczRZlRHJPG5rQgXvHGUwsyB4DT+tQNd4JnQI+a/kzIZn3n+RoARvFVsR3Xk+5Ak4X6JAqE8HwHa8dtWpRTTXslxNy54Y5NSzvnDwKoYtdYN5FqZbm0teNpxvtkpwx/BK4WaM3hjqPXSqWLBm8eAMyLaka8YBY/JjBWSWjr1LkosmZrF9WZbR3EuxOtg1lfsR0YvagteBmDSCGtc6Let2OGCN45SCOohSuqLwb2E/MFdQ/8IavI3W0fliPnrBbZtEfFrKCLNwOnAA3P89Kd4kyo/zlExIn3+qmDO4I0pY9tCfHRvgrpWOe24qGSznQlMA0+V+PubgdfUcNnIOvIL++Yyk3bgtd2OEwWPyZC44I2InAScxNw6T/n4L+CXTSazozCllEzlSGrplBtzR4Ra/WA5yiOohyhfKZNfJUb5jsmv9L/ZtqKgC5AE3IfEP4K6ZxcCY6b19Ew9IT+exahfw4uBn5uVu4KYf3/Q/D5IEvceM+K264DNJfz8LuJTXhCUptvseyInbt7gg61SuAq4eQ5B0RdQ1R14el7n++pVBDGi5x3AtiI/i2OgYz2Fs4kgvuVgxXgn8I1CWmnFUNUevMD0L9t2KmG44E14chM5W2GX5EYGF7xxFEVE6oE2vFKpHIEMBM2Ebhxo9dsW/mUWLODFAl++2apBZl/DHrxgnx/vtahP/G0TVrZdkLaidg1L0bvJEYYGS9TPXyVcBDxhdGHmIha6N3m04yC4b7bf4uZzUU7JVI5aLZ06D3jUXLNCPEiMAh3m238uXpljIRIlWmyOueQuUwW43uzDkQejX3M2np5NKTwGtIpIl39ehYJbMI0ILnjjmIt24IBpK5jDrwhoEidys3VZ/LRVa7xoddlkJBwB/NClyLfikORVgKBWWGZnCPhpa/Yx9QJLTIA6CpSivZIjDN2bIFfdgqKcc/5z4NQYCFE2A1OzAlJ+va9m3xM5W4HfFyKyCDgHuLPMTW/Gy9ipNYrp3eSIW+bNScCgqh4o8pvngWbTJj0JXAIcpXjAai7+F7hARJbbcSlxvASvC2QxUfsXMGWkPyJ52Tf55k3WqyNMQLIDb0F2pq24jzes4YI3jrkIOqAS1EQuqBKAIM9frRHIuc2jy+KLnahgBCpb+YUIKSQw88akmB/EC1CHitEbOAcvfb0UtgDnBtw6c/a1GgLqTfvQuFJy8EZVx/D0iMJo014OYS6C+GlrLl4F3FnqBGsGm4HVIpLkYHw+SgnePAKcHWIZXLnMeUxmIe1hvMyjJHA1cH2ppYL5MM/MfwNvt+ZVsiinZCpHEkun8i2YDmO/Acti4Ij55uaIfZm2TVzwxjEXiRsImpX2Jby4FKwfWGjaAdokSoPbpBHUuV0MHJulRZLka5gTIZ2ZTh/rZz4EW+WyAa9d9XApPzZioo+a7YJidvBLic75KxsTpLyQ0lPhIR66N7X6TFVSMpUL4t4GXGndo2gzl7Avpt12N1777Tgw5zEZ4pZRlBej2fVG4OsWdnc98C6fNCbjTqXBm5cnTAw9qPd9lL4rkSRJN5XDH/JlwwSZpeKHrTbgkKqO5/7CTFT7sV+rH2Q2Ua0R5Idk9jUcBOaLSMayrSiQ73h7gHYfBiJhZ/ZFZTWnHL2bHIFpsMzQUUnSu+wC4FlVPVzGNnHQvQnqO5qzFfozZRZkXgXcUuEuaqp0ymjxrQS2lvDzOGnEzCVWnCNWWj5FeB3wM1Wd/QxWwn3APDzNIIdBRFbiNVZ4tJztVHUv3pwiKRleENy3JWljDeu44I1jLpK4ipfPTpC23EvIDqHdL6amuQevnCpp5DvecbwymTa/beFPtl0DXmpvn9+2KqQc7ZUcQereLMBLtjky6++jcv4qoZJz/lPgrIiXiiXxmz0XG4C9qrqnwu1/ALxMRNIWfYoy5+AJdU/O+cuYdGcyAeZ1lKb9kojMG+BdVCdU/AJmTHOD2afjF1wB3GHOT7kkpnTKLFQ24S1cziSoedMAnlZVyrKtWOKCN465yPcQ9QGLfaiDrqXgzTKXnlo55gXejPdCn0nc75coUOx4betCBPXMdwADeTqrhH4NzaBoPd7KZzncB5wfUPZXkPdEUJQdvDHaEA8TfJv2csgnInwYaDTaSjaJSvCmopKpHKo6ADxJ9LOqbFGK3k2OuAQ6VuGVN/fO9UO89ujtIrLQX5f8Q0Q6gY3A9yzu9gbgbT7IB8SZSkqmciQmeIO3UNmbR1spyAXTPpK5YFo2LnjjmIt8D9EkXvTVmtCnEd7M4AmIziToyXgQk9Pc6vUCy7ZqiU6gL89qiAveVE8kgpsB2MnZCvsaXgg8lSerpShGj2IrXvmP30T5/JWNWXjYCNxTweZR173J9822rk9UQDsO23ZK5CqqCN4Yaql0qpzgzcN44uhRny+UfEwmiP8o8S4RejvwPaN/ZgVV3Q5sxytBrHnMPf9yKg/e3AlcGHBjAb/ItygA8Z+jxZKov4wd4RPUoH0p0BNWVDdIW3EX+owIYQcY/LIVBcI+tweArBFj9NMOROMaVqJ3kyOo0qkon79KWAfsUtXZKeClEEab9nII6lotAQ4awV8/7RTFaFIswytpq4abgdfWSEZsOYGOA3iLdSf56lH1lBOQgvhkFBXiaiyVTM3ierNvhxfcG6y0HNMsyDwCXGbRp7AIe1zol61Y4oI3jrkIMniTxBeDewn5g7uG/hHI8RrRzHo8LZ0XMMHNHpu2iPY1rER7JUdQArpRPn+VUM053wycZzm4aJOwv9k9QEeAmRpXAbfmKYksl0eBNPHprFQR5r49CXiijM3iIPBbbvAmDseUFxE5G09Et9J3WDFuBF4Z55Iyi1RTMpUjKaVTbswdIVzwxlEQswLViTcYm43t9LV86uLgTewaLAtEFkr/s6qabrQo5nO8wJd1WzVIsRRO23pCRW1ZtBMVAnk+zL7258m288NWJK+h0W66iMrKdzDbbQhAoyCoeyIoKg7eqOpRvIlvkG3ay6HQt9T2vZ73nvBR3LwQVwE3VbsT8x6qhdKps4GnVXWsjG0inaVivvfrKa1NeI5IH9McXA18rUIR3aKo6kHgNuDNtvcdQ1zw5hcE9V2B5I03rOOCN45iLMYTgBvN82+BrOL5VGIUdimYH7ZqjUL3y1FgEmjx2xbJvYZhr9wHaWs/0BliqcR6YLuqHqpkYzPQfg7/V5AT8wwYrZZLqbxUDSKqe2O0FVLAoTz/HOfnNy8mi2QT8ENLu6yF4E25GSoQ/UDHckDJP7ksxFZghckAjQ1Gr+sd+FMylaPmS6fMu3QDnm5NNfwMWCkiHVU7FS5FxwC2xlBmP4kZb/iFC944ihGFyVWQtuJ8TLVGIOe21j4kRURIId7PR6Fg3yhwDC9QHQbV6N3kCEKDpdC1GgSaAup4ZYuz8YLq+e7xUomq7k2QCwZR+L69FHjYBDFtcAdeBzebwf+oUUnw5mFgXYT1gNYBDxW47/NiGm88gdc2PU68Ak+v6xkfbfwQOFlE1vhoI+pcBjxiGgNUjLnP7sS7bnGm0BjqCF7g1FYQtAWYKCDEnbgxd6W44I2jGMUGZ76UUBT4N9uT8c4CtnqBJWYCa4NCaYbgXkLVUuzc2rw3m4GpAp2AkpjCmRMhHc/zb35M/oJ6PoK6X8qlGu2VHEHo3uQ9f2ay1Eu82nfaOOf34nURSVvwxyZBf7PD/r5V1SJ8NmbCcB/wSlv7jCBlB29MoPMYXjvuKFJJQAqin1GUj3fhb9YNRoT8G9R29o2NkqkcSSidCipYH4VFgcjjgjeOYgQ9uQpi0LkQGFHVkdn/YCash7FXq+9eQv4RhQ9JH7DIpDEnhTnPq8XV19Azb3yyVRLmvrkYO5k3l/h8H0bu/FVB1cEbVT0MPEMwbdrLISrPlO8BUfMestEifDaJLZ0yGlun44kzl8uDRDfQsY7y9G5yRPmYjsNkhF0JfCsAc9cDV8egRbxfWA/eRDhzrRSCet9HYVEg8tTqQ+kojagMBIOajAdpK6lit0ER+v1iupsMAHGvZZ5JseMdBsbwAqC+2sJutl0d0E5+4XWrtsrkXGCPqg5UsxNV7Qf24lP6v9EVyQCFSlNiM6Ay98Jl2OnSEkXdm0JCjxDfb3YhzgSmgacs7/dm4DUJnbSeBTyXb/GqBB4iut2Z1lN55k1UjykfbwLuUNV8TTBs8zBettUlAdiKFEafpgt4wNIutwMTeIHT2GEWhhbhLVjmI6h5Uy/QZrE6IrYk8ePksEexh8h2O9DQJ+MB24rNhCdqmBd3G/l1WSC+90sUSOLz0QYMFSgFs22rHGyU7+Tws3SqmI4KxOsZOAM4pKrdFvYVVJv2cij2TA0CCyyWeoX9fXstcHM5OieloKo78ILyUcuqskGl5UUQ0RIjEekEssCuCjZ/ElhjxGnjwNX4XDKVwzxXtSpc/ArgTqNXUzXmXN5OfMsxO4ABs2CZj6AWTCeBA3iLcTWNC944ilHsIRoDjmBB6NOk8rYC/QV+EvRk3FZGTDFbB4CsWdV2lEc7cKDIh9UFbyonSsGbIJ7DnK2wgjfVlkzl8FNAN8h3pt/YPOf3AhsDaNNeDsW+2dNY0ieaQzsOgnmm/CiZypHU0qmqgzcRLP04jzLFinOYcezTwFrrXllGRFbjBZ9vCdDs14E31uA41WbJVI44694Uy+iE+M7RYosL3jiKEdSkpxPoM4NLP+1ARCanPrVArxUicQ19sBUF5jpe27XNhWz1A60msOunHQjhGpqMxUuxm3lzmU+lHkHeE36zCUvn3JQu7CRa2QjF9ALA3r2+CBg23dr8tJMXEVmEVyb4E59M3IQL3sxmH15XmeX23LFCNccEEc0oysOvAd8skkFqHZOh+HPgdUHZDBsTnPQjeHMH3jfaxpgmaNyYO2K44I2jGKVEW21EQIOMtEYtglzzL6EKCOq+hNq7hoE8H2YlrwmvlOM4THpuP3b0hKJ4DdcC/apaSIenLFR1P16phx8ryEG+M33DDMptlqpB9EqngvqWlnRP+Jil8Wq8soZCwaNq2QysFpHErPAa3Yq1wCOVbG8WnKIo8FupWHGOKB7TizDPUWAlU7P4Kl6Hq1rhdGAS2GZzpybY/yyw0eZ+AyKpc7TY4oI3jryYj0VQk5657AwCTSKSCcCWrcnpXAJf1mzVIG4VwD+CzLbrnSPVPaj3SxjX0HYQAfwT0I3i+auEU4AxVd1pcZ9REy2OxDfbiJuP45VD+4GfJVM5bYXb8Dr7JIXT8QTSj1SxjygK/FYqVpwjisc0m414AYWfh2D7e3jloVWXW8aEK4DbbWtpGeJaOuXG3BHDBW8chWgGplT1aJHfBDUQVCzV6s9lC3vHNJfAl01btcZc1/AQkBKRpgBsJe0aRmLyF7Atv7ME8mFTeyWHX7o3SXkG/Djn9wCXRqH7hUnHb8bLwCpEHJ/fF2HO9avwMXhjSFrpVLXlRRCxEiMRWYy3SPZcFbt5DDjNopC3H1wNXO9TQKEoqnoML4Dz9qBth8QVeIFbP7gNF7yJkq3Y4oI3jkKUMjizpXUwV50+BDfojOMx1RpFz60Z4AR1HeOk91GUEkRIwe5zONfzEcg1NCvR08ACC7bmxJxna9orM7gL2ORDEGqua9UHLDLZhlHG+jlX1V68Z8KXNu1lMpd2HCTj+7YB2Kuqe33Y90xuBV4W8Ul9OSQueIMnVvzwHPd8UUzb9OfwWs9HDpNx/mY88eCwqImuUyYAfhmePo0fbAZOF5GFPu3fL+Z63x8EMtUKW5sF10bgcJGfJWbMXQ0ueOMoRJRWxoO0tR/otDABmqtuM2er5l9CFRDI/WLah6bxMnmK2UmKLkJOhHSkyG/i+MyX+iwGdR1PB46o6h6bO1XV3cAxs3+bzJUZOYWX7WFDn8gXfNK7yREV3ZsofUdt2prNa/E/6wZVHcBrJR2Fa2sDG8GbXXiTtKiU0Ng4JoheUGomrwUeMe/3sLgLL0AfhSC1n2wAthl9GuuY7mb3AS/zY/8+Ukp1RA/Vv++XAvsDKqePNS544yhE4gaCIrIAELwW53kxE9dRoNrIeKnHlJSJf5AEdW8uBXrm+JD0AO0+dfkJmiDv2cS9X8rAryAC+KPBErXzVwmrgXpguw/7joruTZALBmHeE1fhlTQFQSJKp8z36VyqDHSYb+FDeBkvUaBaseIcURYtvhpPNDg0TGbTDcA7w/QjAPzoMjWbOOreBDnmjvtYIxCSMOFw+EMSJ1elRHWt2grATi0SmQ+Jads5BLRVaSsKlHJeh4A6Ewj125aNgG0ppWBWbJWBH9orOazq3pg08ha8zl/FiPq77HLgbp80I+7Gvzbt5ZDEb/aLEJGVZp8P2NxvEW4GXhuwHpYfnIJXUnfIwr6iJPBbrVhxjigd0wuISDteYPi7YfuCF7x5RwzKY6vBBW9mYb5r7XgLlcUIat7UA3Qk4J1cFWEPNhzRpeTBmYWHKDKT8YBtudrNMjH3WgfR+ZDYshUFSglW2dITCuqZbwXGTfcbv23NiY96NznuAi63OLDpwGtpPpemRNTfZb6dc1XtxiutDFszoxQdmj5gsYUJWFiLE1cBt87RCMAmjwEp4NSA7PmFrfIiiEiJkYi04N1fz1jY3aPAWSLSaGFfNnkb8P05GocEgqo+DewmRoGHcvj/7f17nBzXed8Jf093T1/mPgDmBnAwgwsJShTvIgnauliWbEeS7fguOY6VOHbiOG+ycZxkN3m98eskdjb77q5fO5t3s/E6iSXbMUXZcRTrxrUsSxQpULRJSbyBJEASAAlgrsDMADPTMz0zZ/+oamCmp6u7qrrq1Onq5/v58COhp6ufU+d+fuc8z3Hj0NyB49YUJy8A3UqpozHbiYoDwLK7UdkIUxumZRz38P0t2mprRLwRvPDTiFaACs7ObCjcmyOGcW6TakS7Lcb92JoDBtzdbcEf+4EVtwNvRLvVFxuwrX2ksQyPA1vAGzH9/us4wZePR/R7tuVfWOJ0VQM74t74GbM3gQWcndRQuMJgkuJN7PFuqrhi9edof9ep1Ik3OG5gz0Uh5LlB698Ebm/1tyLmYzjBgm0hzYGL3wc86caliQ23T/ki7SOC2TYvjMpWWyPijeDFQZrv4kHrMTCGgata60qT711q0Q4EeycTCvI2joBjbaBPC/ET1wGiic3SaQNJkLxt9X392JrBiSfUyjXMtpXhe4GvxHXlq/u7UcZgMVknYkEpNYFzk9jpGM3YEPcmSF1vpW/sx6lqnrHjXKIYs2/gBpB/D/BYVL/pkzTEvYlSvHkNGHKv6U6SKN8J7BGlAFBKvQNnbvhnSadlB48AH3JPPaUNEy5TVdrJdcrvuNJua7S2RsQbwQtTix6TiwNTk9ugtjq6EwqI7ALEh5H3dY+mD+G4cHjiHtNdxBF4w2JbGb6H+OLdVIky7o3JPjMu3kN88W6qPE607mphMFXXk2pT78O5FvpqhL/phy8BD7TrgtWtk5EJHe6m0zdIXui4n2iCFVd5Brvi3vwk8LsGXQSb4t7C9KfAjySdlhgwKd58EfjOFjemTJHWNVpbI+KN4IVtE8FZnKsKW/HVN7U49Rvgq2VbHYiIN/Fh6n1HgXmfk9J2XWh6Ebf7DkTrwmNb/oUh9jzXWp/DuaUwydgoto3Zy0BWKdXbgq2dGHWZquK6hz8BfLdp2xFxFCdmRbOg40Gw4ZRKak/euIv6v4oTJNg2Uuc6pZQ6AvQCz5uwp7W+hHPCxCax0AuZc1uIiDfCHtzjyXmcXe9mtBqo0ldjdRd687TmYmSqYzgALPkI8BWFrU7DT1BOcOpKn1KqYMCW7cFa/eL3faNY/PmxY9JW7GWolJoCCsCrcdrBCeBZcO21iqk6EScmBDNIMO6Nu6lxgOax4yCaMbtpnXBPOkVSL9zTIyavCK+lnV2nohY5IGGhQynVA0wBL0X4s98A7rbkNMR3Ape11i8mnZA6fA54uyt4pIXvAr4Y8+nMWtrFdUrEGwsR8Uaoxzgw7bMjM7WLZ9KW6Xfq6ON/AfEr9m3jLGTG4rZFCgaSAEFIofU6a2ObX8QRPEot2GpGrPFuqri//zjRxGDxm3/TOPGJrJpTKKXGcdzuTOyoJhn3ZgS44gYkbkY7jdlV7sAJxB1n3KJGfBb4oG312yepE2+Au4EXfcRK9I17jfoscGtUv9kCHwM+nnQi6uFuSn4S52RQWjDpMlUlbeJNSxewuButfTgB9ZvR9nPuVmnHgUiIH9OLK5O78H4Db7Xb5LZTMJK37gA0gHOCJzY7FtGHvyCkYHbxZ+pkn8YRIOIsxzivCK8lqlMgfvNvA8dN5kAENqPk3cBXfVx1HgVRX9MeBBsF0ShsVfle4LOGd8Zv4LrFzQMPJGG/Re4j2tgw4JzuG0swDlAc74T7m4m6Timl+oDvwwkObCufAD6WcIyvSHBPWn0nThwak3wFuD9Ct9K48HvSstULWMaAGZ9jdVpOu4dGxBuhHqmbCLo76t3AFR9fv+Y8ovrC2ELEmzgxlbejwKzPgeQyMN7mE5nUtfkEbDXjvcQfrLhKVEGLbcq/MJjM89dw5lRHDdnbSdrbVJIuU1XaznXKHZPuJ+KTN64b+3PAvVH+bgAifyeXZ0k+DskP45zQjDJGUdT8ObAFPJx0QiLgfhxPg4smjbqxtJ4h+VsKm2Gqv2/3uYZRRLwR6pHGieAYPl3BIvDVN3myoNOwbiDRWq8CGzgnddqVIPm6AHS34GKUxv6lIUqpQ8Ag0cZoaMSLwKBrNxTujuQw/uKogJ19mal4NzuvaU8i7o2NJ1ijsIVSah+Om8yXW/mdCPgsbSbeABNARWvtt7yCkKTrVByuYJC8Oxg4LlOfSDgNDXH7urQELk7CZaqK1a5Trvg7hmVz7qqdNt8wbQkRb4R6pHFxFcROq7b8Xq3Xqp2OImBcFjBXhlVb7Ry7KIhYVXUxChtPyNRpu14gh+PO49dWXGX4Xpzrqk2471SPMH+V1oSEYeBqgLgSVvVlSqkDwC3ANw2aTVK88dumpoHRFuK3mBZE/xLwZa11ucXfaZWvAUeUUu3Uz8clckBCQodSqogTl+aFGH7+G8C9ScU2UkpN4giVSZ8y88PvAj/qlkc7I+KNN4PAhrtB6QdTG6bXcU5+9Ye01faIeCPUI8jCdQnoCuO3GVLVDTtxCrMYNyEUzQDDltxwYDsDwJbbcfuhlfpiUuyzAZPtw5S4OY5zY4ffOBlxlqHJeDdVWg2ga7JOxMG7ga/5DOIbFUkFLQ4y8V3HcQ0OG58oaPttVeywwWUKtx49Bnwo6bQEIHXiDXAn8GocYp7rqrQMJHWT0k8Aj7pt1Gq01heAb+HEo2pL3HXLOzE/Nld5BjhosSAcZh7cDmu0tkfEG6EeQXfhwzaiB3CuJDexuAraCd0F/JwBW9s4pwO+PaStTsKkoNJp4k3Q9z0B/HUDtirApHuKIk47EFMZKqW6gL9F/FeE1/IK8Ldc+2EImn93Er7PjINfA/zuGEbFy8BhpZTpBc3HgGMBvh+qriulunGuu78ap50d9kaAHyd5l6kqL+HUq3bhHwNxiZevAW9TSt0R0+978c+Bnhh/Pw/8jzH+fl3ctvVLwB+att0CnwN+M+lEtMC/AMoBNgQjxY0ddRX4jSTs++CjwFSA75uccx/GSV9HIuKNUI/b8T85A1jDETuC8irwn3HihfhhHjga8pTKg0CQ3YxfA347qBH3uO1x/F13V+28fwdzsTDambtw6ppfrgJvC2nrgYC2NnDqWLtyP8EWur9OiEmmO0EdJVgclT/Auco7KPfjv28BZ8f1nhB2mrEJPIHjdmGSr7l2wy7eHsARz/zyb7FrIv8lDF+3625m/Fcc1yST/F8Ei5OxTri6fjuwHOA02xyO+BpWQNzG2Z1+I+TzUfPfiOeWo7j4I+BTMf32CvDHOGVski8A//8Yf///IBmxMIezeH0qAdth+Rzmyz9KXsZx/0qS/4TjrmcjnwX+9wDfv0b4IOb34N/FHZx0fT6krbZHJXTzomAprjCyCTystfY1iCilVoB/r7X+hZjTNoUziesLqpQrpd4E/kJr/YNxpG2HnRLOIvg2rfWZOG11GkqpfwP8lNba1y1gSql349w0kw0aa0QpdQ34D1rrn/f5/a8C+7TWpnchI0EpVQb+v1rrX4rZzu3AaaDXvW0hTlufBu7VWh/2+f2/Bvy21rpjg+DtRCn1ZWBUax1WABUsRSl1BfgjrfVPB3zup4D/6LeNKKWGgVlgWGs9HzylgiAIQjuglPrbwL8LM4dSSmngZ7XWNm0AWYuIN8IelFKZIIvdoN9vhbC23BMxOsCOYWhM5kenYapuBq0vbvwm1a7l3g5tOIQdBTdOQ/h9RtquS5j8E9qDVuq5zfMDQRAEITlamXPLOOEfEW8EQRAEQRAEQRAEQRAsRmLeCLFQLJSmlVI6iv+KhVLk8QNKpejSVyo1Tp9JW4IQBaUI22+pSfst5QvR2coXjLWPfLYYWbqVUjqfLYZKu6n+xYZ+LFvMRZrn2WIuXJ7n8xHW2bx3nkfYNky3j0bkS9GVY74Urgy9KGajrWPFbLTpAygVI2yLRe+22FXMRpoXXcVsg7oeXX9ayjfuS0vFCG0VvW3lS9G233wpmfZbykbcD2Xje4+uUsR1tuRdZ/1SjLC/U0rpYgR9ng3jeTunL2nk5E0Hkc0Up7f1+mjQ5wqqi3UdJG6lw7k/3R3/9OvPPQHAxNgUMwuXACjmSwzvG+XayjI/9A+/i6vzfuOY3kQVMuj14Kftauv+448/DsCRI0eYnnbaeqFQYGxsjPn5ed773g8wPx8kGLo/W+VymevXr5PNZhkbG2Nubo7NzU02Nze5//77dz2nlCKNMTm6ipnpzXUdqG6qXAG9GfxGTaUKBL2JUxXy6PUgsW8b/tbMdnl9LJIf80EmX5zWleDtfvVPZ3f9+4nnTgEwNXaY8sY619dWyGYyjO4b4eq1RT70338/l+d8xenew7Xf/+xuW6dfcGyNjDKzeJVcJuvYGhzi/NwMP/z/++dcubIUyEZOFdiM+AbW//wdl3b9+/SiEyZspDjBxvY623qLLV1hID/C7Np5lFIc769/u+5f+fLBUG1bKbXLs6m2b1lcXNzVj12/fp0P/+D3MX9p1usnPWnUj83Pz7OxsUGpVGJsbIylpSWuXLnCQw89VC/Nod/1A5d+dtdnV59yyqA00c/GlTJ6a5tMPkthpJuNhTX0pkZvbdN/1/Ce3/viwX8fOh3Lv/UHuz578lUn5vzkgRHKlQor62tkVYbRgSFeufwWha4uHjh6257f6v+ZH/FMg1JKX390b0zGJ156HoCpkTEWrjntIJvJMjI4xLf9w7/L9HLwdlhQGdYDnlrPFxQb6+HmkL81/3d2/fvVrznleGCyj6uXV1AZRVchy8BIN3Pnl1FKcfT+vd3Yzxz4PyIdE5VS+vKH/taez09dccb+w6U+5taduU0+k2Wk0M3VyjrXNje4b3Bkz3Pjn/vNyMdspZSefnb3Tdmnnvmqk75DU0zPXiKTzVLoKjB8YJQ3L54D4L4798bUH7uv2LD+/bOL79r12fmnnPo2OFHk+owzJuaKGXpHulhZqKC3YHtLM35n757f+5eHnmhoa+k3zu767MmzTwMwuf8WFlacOzSyKsto/zAzy3OUK2Wy2Rz3Trxj13MDf/94wzxXSunKm7vncl99yh3fDh/m8ozTNxaLBcaGR7g4fZmVlRXe9dDJPb/VNTHe8J1+/9re9nv6Caf9jkyNceXSPLlCF5lslsHRIebOTbO5scnt3/6OPc/9eN8HE5n/KaX09A+c2vXZqXknxu7h7oNc2VikojcpZgoMF/fx1uo0Wmvu21c/BODYf304tvdQSulfm/nRXZ+9dsqJqbzvcA8rV9bZ2timq5ilb6TI6uIGWxvbbG9pbrlraM/v/cLop1pOq1JKX579iV2fnTrlrHUOH+5ldXWTcnmLbEYxMlJi4co6K9crZHOKu+/ev+f3xkd+L5I0NRvPNzc3b8wdzp8/z4e+7y8zPxN8DRR2/txsjZbL5W6sm65cucLGxgabm5vcd9/uOVYa100i3nQQSin9rocvev59ZvZRisVJSqVjlMvn0brCQP9Jnjh1iIsPf7nuM4/OfoHJ4kGOlSY4X75ERW9ysv9uDp36jj3iTZWl64vksjkK+SK5bO7G51Pv7+bhi096pm/20c9RnDxI6dgk5fMX0ZVN+k/ew6lD387DFz/m8cxrFCd7KR3rp3z+OrqyTf/JUU4d+sSejqHK4uIiuVyOYrFILnczfUopvPLPK+8Anjh1KLAtL9LYCYFTN3/x4sN1//bco7MMThbZf6zE4vkyWxXN4ZP9/OqhUzz8H+qXx+yTj1IcnqQ0dozy3Hn0ZoX+E055nPrpQ4HL8YlThzzrple99OLUoW83WoZKKR00n0799KE94k2VxetL5LJZijXtt/v9I1zwKMNPPTrL4ckix46VuHC+TKWieehkPwCHD53aI97csLVy3bHVlSeXvXnJXN+PfzhUm/8dj3L/6uyjjBQnGSsdY658nk1d4fb+vRP1nfzkqUN7xJsqK5UlsipHV6ZANtO8XUN04k2VRn2LUspIn9kgzZGJN1UqS+uoXIZMIUsm5+9QcZTiTZXF1RVymcyeOutFGPHmhq067aP3xz7oOV5D4zH74sP1b159dPYNJos9HCv1c758nYre5mT/CIdOPeLZ5sG73R8+dGqPeFNldWmdTDZDVzFL1kc5mhJvqixV1smpDIVMllymefpMiTc30ndtx/zKR1sMKt5UKS9tkskpcoUMmZy/1wsq3lRZXF126nmusGvM8SKMeHPD1tKS05cVCr7yL4x4U2Vl8TrZXJauYp5srnlfYZN4U2Vp4xq5TJZCJk/O51hnWrypsra0QSaXIVfI+OpbID7xpsrS0ga5rKJQzJLzmaa4xJsqjdZAYefcQefP7jw5UPoavGvq1k3+WpqQepaWnyab7QE0a6tnKBQn6MoNsbpWfzAFeHr5OXqyJTSaM6vnmSiOMZTr5+za+Ya2/uTJz3B4/AhHJ27lwuVzZDLKs5FWWX76W2R7SqBh9cw5ihPjZPt7WXnJO33LT8+Q7cm5zyxRnOglN1Tg2p833nH+9Kc/zdGjRzlx4gSvv/46Sqm6u8c7KRanqFTmAA1aUyhOsLW1wvpGY5U6jK1O4s2nl8n3ZEHD/JlVBieKFPqzzJ5ufFFRcWSKytIcaI1GU9w/webaNcqz5zyfqdcGctl+Vla8b3GvVy9zQ/2snT1P6fhkyLc2w/KZp8kWekBrVi+fobh/gmx3PytvnfZ85ncfe4Qj41PcOnGMl954GZXJoLXm1luONbQ1OVVkfq7iNg/NLRNFlpc3ufiW90mY3/vKF5kaHeO28Vt46c3zKAXrGxVuO3iL9zt5tPm1s41P6YwUp1iuzKHRaK0ZLk6wtnmN6fIbHOm9q+Gz9Xhm4TFGipOMdx9jds0Rg0AzVjrKUCHwIahQ1PYtGXehefvtt3s+Ezb/bOjH5h47R2myn55jg6ydXyZTyrF1vUJpso/CaI+RNPzek1/myPAIt44d4qWLb6IU5DJZ9vf1c3j/3pM/Ldn68p8wNTrOrQdv4aUL51BKkW8ykfUas5+59mKDZ+boyebQwJnVZSaKPfRnuzi9stjY1tPL9Lh999kzq9wyUWRoKMdf/Pm1hs994/NvMDzZz9jxwRsnbnJdGQ7XOTllmkffepXD3X0c7xnk5WtXKGVzlLe3uLVnkL6ufNLJA+ALX/5jJg8d4djUbVx46w0G+odYK69y5+33RGrnlccWGHI3Va66myq5fIYDt5Yo9EW7tPjcC19kav8Et44c5dzCBRSKrlwXhwbHONC792RCWD7xqUc5evgwtx07zguvvExGZdBoJg/dwtjI3lNVrfCV3/sTRqfGGb/tFt586RwqkwGtOXB4hMHRfZHaipMvTD/OZPchjvUd5sLKJRSK8vY6t/ZOMVy06z1e+MIl9k/2MHysj4XzKygFlfVtRo/30TdSTCRNj33+LQ5P9XL8WD/nz19HuRLDrbcN0NfXlUiaQq2B6sy5t8orbFz1XgN5rutOv+b5zMc//vEbaXvhhRd2zWv6+/vDvXAbIuKNAMBA/97jtADdpeOezzzYX39Rc7zkvWj98+e/Rk+pF43m7IVXuGVskv6eAS7OXGiYvv4H7677ee7t3unrf7D+IqnvAe9B+IknnqC3txetNadPn2ZqaorBwUGef/75hukLk3+NbN15550N7XUKEw/W74yL/Y27rv5b65dH76R3vnqVYS73dm87HvXSduEGvPMo1+09AB49OMXs1Zsix+TYBAM9A1yYebOhrQc9yrH/7d7leGR0nLmlRbQGtObw8Cj7+vp4o8GxXa82Xzo+0DB9JzzKPoxw8/Li1ylme9BoLq2e4UBxgr7cEDPlcyysXzQm3hw7doyZmRm0dspqamqKoaEhzpw54/lM2PyrZ2tpaYkLFy4Y6csWv36ZbE8XaM3KmasUJ/roGiqydm6Z9dk1Y+LN0ZFRZpeXcKqs5vCBEQa7e3ht5nLk4s2RsXFmFxedPEczOTzKvr7Gk1evMfv+vvruDc4z9dPdn2ssVni1+Xc+0Of5zJmnLlPs6QINl1+9yoHD/ZQG8sy+HsxFMi4mu/uZW1+9Ub4jxW4OdxU4v3qNt+X2kVHJbu5+/RtP0tPtzCnOvPEyE+OTDPQN8sab3ouhMFx4eol8TxatYf7MGgMTBUqDOa6eLzubLRFy6rW/oCffjdaaV2bOcnjfLezrGeSlS68wWGrcLwXl6OQks/NzN/qxyYkJ9g/t47Xz5xgdHkZFVL4vf+0Fir0ltNZcfOUCw4dH6dvXz8wblxkY2eu6YytfX/gWPdluRwy+do6J7nH25Qd4efl1lirXrBNv9k/1cm2ujHb2WNk30U3PvgKXX1qieyhPtstsGNivPzVLd08OrTWvnlliYqKHfUMFXnzxKqVStO3IL2HXQF7zydJ4ozWax7quf6/LZRWvec3Zs2f3uEulGXGb6iDCxrzpIkuFrcD2vNymvHjwJ04wO914EViXLgWV4PU4aN0/eHCSy5cbi0xR2fIijcf/ALJ5tbBdIdBIHzbmDXQBAWM4deWgshnC1l5Mx7xRufwCW5XAsygvtykvTvzEnbw5HTxmFeyNedOMyZ/7ca4sLgd6xkTMm1aI2m2qEftHD3BlNnhclKT7sUZuU2GIw20qKK24TdXjbT/713nzavB22IWiQrDyjTLmTVhMu00FxbTbVFDCuk2FIazbVFBacZsKSituU0Gx0W0qDEm5TYUhbrepMMTtNuXFwYlJLr8VYg0Ucv6c9HzDZkS86XCUUkNa66tBnwFo9Fy+K79Q2Qy+YKxHIV+cKa+v+V7s+kpfPr9QqUSTvmKxOLO25p0+k7bSQph66T43pbU+F9QWNK4vUTxTfS7Me0WJnzRE2X6L+eLMWoP2m891LVS2NqOx1ZWfWdvwL4yFqS/uc0NZ1XV2S0eTRwBdmcLMxlY5cNs21b+USqXpcrkcybGhQqEwVy6XA/shZPLZBV3ZjizPM4XszFZ5M3ie53ILla2tiOps18zaxkb9PM8XpsuVjciOagVpHy30cU3bVC6fXdiKqBy7itmZjbXgZehFMZubXt/eiizPC5nsTHkruvRBxG2+UJxZK9dv89l8ZmG7oiNrb7lCZqZS3qprK5/LL1RCbDLUo9hVmFnb8O5Lo82/wsxaub6tfKkwXSlH1367ivmZjTVzGz9V8pmuhYqOZowGKGbyM2tb8bxHVyk7vVnejizPc8XMTGWtfp31Sz6fXahEOG4VitmZcot9XpLrEtvWaG1J9eiR/Cf/xfkfMFTz7/cBLwO/F+Q5g+n7HuAl4D9Fnb46tn7btfXdNuSF/Cf/1f5Xp87+TeAbwC8HeS6krT8DLgInorYVcx79GvA/AE8B7zaV9jrp+HNgATgUZ1kBtwKXgK8ETWOE7/o9wJeAfw78SoJ5/ivAN4G/EWeeu/9+DdgASqbeN+r/6rzTrwPPAT9mwzvV1PNfAv439/+/ADxkU/rcf/8W8CLwwSjzr44dBawCi1GXUx1bfxl4Hvh3Bmx9Eng1jn67pi7lcI4BvxU0jUn+V/MO7wAuABlgGFgEemx6hzrl+yfAm8AdSeR5nfR8Ffgu4H8B/lkSdaBOmv4BzhzvHyeRRz7S9z+46fv7NqTP9H9mHfyEjkXvVVjHgcvu/wZ5LhZMps/D1nQctgQhCixoH9b0FQHs3wc86/7X0Bk7yrQnWFa++rGwtnz+jk15fomY81w5QTiq+d5wZzHp9tEIk/kXhho7Hwaqfp6fdf/t57nYMNXm63y/H9gCikqpUlR2PJ6JbZ6UYJ85CswDI0qphusxm9pvTVo+AnxSa72ttZ4Dvo5Hm0jqHWzrX2r68BJwL/A14CvAe02nx+O3be6DoU3mhXEh4o2QFOM4an3TiX5CmEyf7XkhCLVI+2iAOxG/F0dEeIYmQkLM6RjBTP7ZUE5V8eYZ4H4VVYTR4Izj7OzGnRfVxfNFA7ZMYir/AqGUGgVOAE+4HzUUbxLEVFusLqB8ibYR2ErbmFPNv2XgQMy2IsftXz+Kc1KpyiPuZzZjU/9yEnhBa72C0688pJSy4eo6m/KoHjbMNxJDxBshKcaB17G34ZlM3zjO0Xdb80IQajFSZ5VS3UCe9hukjwFXtdYLOGLC/Qml4wDOwqDpDloEVCdTGaWU93UR8XI/jnBzEcel42BC6TA1flR3R5vuQLYZts4PPgj8idZ6w/3314AjSqmk6pkXpuYUVfHBRP0zOU8y2X5N5V8c3IfTzz6z47M/At6vlBpIJkmNUUoVgD7gHHbk+XtxTtygtV4EzgLvTDJBLravS2wdI4wg4o2QFNWG1/C4bYKMA2eAIaVUV1xGXIV9AKfD7shOSGhLxoFXMDO5nab9JrfVEyDgxJ446gpRpjG9uEpsIeIGQRwGzmjH2b2p61SMjOPEdEt1nseIqf4lKDtdptBabwKPAR9KLEU1KKWywH6c+Uua6l+1TozGeaJOKdWHI0icI135FwcfBR5x+1vghgDxZZwYRTYyBsxgT56/B1e8cfmK+1nS2NoHVzE1xlqJiDdCUpg8bhuG6o7mHI5fclyMArOY2RkXhKgYxwmy3dRXPwI77Ti5vSHeuLv0p4G7EkhHx4g3OG5q39Rab7v/TkS8UUrlcBbPp0l/nkfOjsWzVbu+7kbLB4Dau59tc50aAa5gxpXOdP9yAVjBaV9x2umUPjM07rj/ERw3qVoecf9mI9bkuXsK6EHgyR0fN4x7Y5BxnIDsttZL29MXKyLeCElhTQfqgan02Z4PgrALd9I2huMPvUS8vvrt2j52nryB5OLedNJCxJY8H8G53estYDzmuDs789w2152wJF2PvHgXzqmumZrPvwB8p7sQswGTdSKN/YvJdzpo0FbUPAwsa61fqPO3Pwa+XSkVp8gWFpv6lweAV7TWSzs++yrwbe4mQCIopYpAD/AG0JWgG3Rdak7H9VsSI8goIt4ISWHtoOVOtnemL84J0K58SDDApiD4ZT9wTWu9juH2EaOdyHDb8P3sFhKSintjMv+SFhKsynOt9SqwDgzFbYs2ah8+qL7TPHZNzD8MfKb2Q631PM4OsA275bC7TozFPKcwMk9yXcFGMOPuklbxK2q8Tt2gtb4O/N/ADxlNkT9Mze39cCPeTRW3P3kT5yRpUowDM647nI11szrGbuN4LjS8aTGNiHgjGMeN/VAArmJnxzAAbLoDkJGJgtb6GqBxAqkJgs1UJ5yQrl3QqJgE1rTW0zs+Syr+Shp3xr24j92BM88BPe4NQSaR9tEa1THRton5rng3NdjkOlXNvzWgTLzioan6N4wTAL5iwFb1neaBvphPVLVl+3XFtB9l9y1Ttdh665RNeV4b76ZK0nFvTI5hYbA9fbEj4o2QBOPAtKvqXiJ59buWJCbfJmwJQhQk0T6uACVLg5vXUuu+A/A8cCIB14qdscVG44pPtOMGjwUS6MeUUv3ALTgBDAHYEbTY9A6miDetYd2YqJQ6jrOp8w2Pr3wW+F5LTs6msf6ZfCeTu/rV2IpW1PMAvBe4qLU+0+A7nwfuU0rZ9l7VurSMczNiIhum7kUoD+O4SdWSdNwb6/rgGmrTZ9saMnZEvBGSoDpggb0dQzV9cQcStr2TFIRaTC8OLrkLcVuDm9eyR7xxd8HPAu8wnJZq/q0D14gvPtEYzjHrbZLpx+4Gnndv/9lJEieekhg/5oj5ZkSDmMw/v3wY+NyOYNi1PAfkgRPmkuSJ8f6ZdM2TdtqK7b1cIX0EZ1yzpZ775aM0PnVTHfP+GPgRIynyz845RZL5fh9wTmt9pc7fHgfe7Z5wSgIb++Cd2L6GjB0Rb4QksF2wSPLkTccpyELbISfTGlPv5A0kE0DXVP5V4wjEbceLTszzG7a01lvEfzOiKWwcE+vGu6niLgRtcZ0yUv9c9/c8sIhT9wZijE9ksk6Yar8HcAL+blTtWHJyqyFuGf8Q8KiPr9t465Qtcwovlylcl+tZzG/2VLElj7ywPX2xI+KNkAS2NzxZnAqCN9I+PPAIVlzFaABdNy1jmMm/pMvJijx3SWLxHKstwyRdl3bhulU8DHyxyVc/C3xv/Clqiqn8u+H+7p5IilM8TOOYc8OOG9x8AxiMyVaUfADnhqTzPr77ReB2pdThmNMUBFv6lz3BimtI0nXKljzywvb0xY6IN0IS2N7w0jhREISoMLU4zQP9OIEjY7UVIQdxrrB8q87fTLvwDAIb7sIAzIk3CziBgosx2apHbbDiKmeB/UqpfQbTYnzxbMCWSWwbEz8APOVeKtCILwHvVEoNGEhTI4yLD4ZtpWVOZjL/ouSjeNwyVYt7qui/AD8Wa4p84l6/vR/nVAsklOeuO9S7cNyjvBDxxhvb0xc7It4ISbCz4cV93DYMO9M3AwzH4Xvq/uawawM6tBMS2g5T/sZjwOyOOBPt0D7uA57dsaDeybeAOwzGJaldHMTpu75zF1nj9GlGbglyT6AcBV6s/Ztbd76J2aDFSS3+bIxNEAbbJuYNXaaqaK1XcIKPfnfsKfLAPW03ihNHBQy1eRej4k0cLkZuQPxuHAH6hq2o7bi0nXjjCvLfB3wqwGOfxJ5bp0aAhR2x0ZLK87tx3F1nG3znceA9CbnS2dYH17IzfWkZ9wIh4o2QBDsn+nEftw3DzvRtAEvEE+hz5/WXYGcnKQi17IlvEtMEo+0mt3jHXkFrfR04D7zNUFqSWlzFbauWu4DTbl9dD2Nxb9wgpDsXz6bz3Ib4MKHZsXiuBvFMtM275dnoivBaknad2g+saK3L7r/jrBOJ9C/uCahtnFOZUTOGudNsO8fRuG1FxQdxNiemm37zJl8GblFK3RpPkgJhy5zCM95NFa31mziXDJiaL+ykncQbG9MXOyLeCElg+6BVL31xTIAOcvMEA3Sogiy0D65IU+urXwaGYjBnez9RD6/YK1VMxmAxmX87T2Ph/n9TQoJNeX4AuObe7gXxLp5rx492aB/NsM0V7F6coLJnfX7/s8AHXdEnCUzWCVPzpKotE3Mlk4KoLUJCEHy7TFVxg6l/CjsCF5uss41oFu+minHXKfdk8D5uupYtAH2G3aA9qSPwzwAHEryZKxFEvBGSwPbj3qYG1XYcvIXOZgCouC4CVUy1D9v6iXp4nrxxMRn3plNO3tic58tAxg16G7etNIwfte+U9MQ8yKkbtNbncE4SPxBXgpqQujZfJ/B6bLYwn39tI74qpXqB78GJYROUR7DDdSrxPtMVdt+NpeINzsnReVd0q3pHGHOD9sEugd91gbuC4xLXMYh4IxjFjW0zgDPBqWLNoKWU6mH3DR5gbqJwFSi6yrIg2EhtnYV0TKRbRik1CvQAbzT4mog30dNMvHkFOKSUisPNopZd+eBOMKV9+Kc2/5KemPuKd1PDZ0juyvDaOnENR/9oZ/FwH7C6wxUsTlv1xMP9bqDbuG3Z3n6/D/ia1nqh6Tf3cgroV0oldfV1FRvy/A6ccAmXmn7TFW8Mx70xOccLg+3pM4KIN4JpxoCZHUFIwa6GNw5cqgk4auSIrmtzOiZbghAFpsWbnROcOWDQYMDfoNyLd7DiKt8A7jZ0kqDuzm7UE8E6N3jcsBWlHQ/bBeAE8JzXd1wB4DngnrjTw948B7MuHu0+dpjMv4a4YuwJ4ImAjyYZ98a0eJiEKxMYEm/cvmOBeMTDdjtZGthlqoo73/8kybtO1dbZBaDb8Ibpe2h8y9ROzgGbwPHYUrMXa/pgD+qlLw1jXyBEvBFMY7tqmuTJgjhtCUIUJDmRtjG4+U6anQBBa72E8063GUhPbf6tABWck49RUnuDB5jrx94BnNVarzX5nqmgxUmOHzPEdDOiQWwaEz8IfLFBIGwvvgZMKaWSiKWRxpORqZuTGXYFaxml1BDwHcCnW/iZTwIfTej2pCpeG6YmXYL8xrupps+065RNfXA9bE+fEUS8EUzj1fBsuSUjdRMFQYgQG9qHLX1FLc0C51YxFUDXVP4l2Y+1S56bEDfjvBnRFDaNiWFcpqqnNR4DPhR5ippjSnzI49z2NL/j47jEQ5N1ojagbVy2BoENN+B/nHai4geAP3U3H8LyDKAw5zZcj0T7F1e48i3euIh4s5t2mxfGgog3gmnatWMQ8UYQpH00ounJGxdTcW9M5V+S5dSRee6xeI7FlmGsaPNu/n4X8PmQP5GU65Sp/BsDZne6v2utKzhx+4YjtpXGMaeenWUg58ZdtI3QLlNV3FMkSQcuTrp/uQ1Y01qfD/DMV3BcrUyRdB41w/b0GUHEG8E0tje8NE4UBCEqjOxMuru3B3B2c2O1FQVKqX04cV/O+Ph67EKCezNIFmdBsBNTC5FZYF9MgT534le8eQk4YmBhlNjieYetdt6BtGVMfBfwqta6tv/xyxeA97kxmUyS5Gm7qq12FodN2dozjsYcnyg0Sqlh4CEC3LrWgEeAj7g3LhnFtTmK4ya1E5N5/l78x7upcgYoKKWmok9OXWzpg72wPX1GEPFGME29hmeTr75nxxClr66Hz3PVVjtPvoV0Y2rgHAGu1MRRictWFNwLfLPOYroezwL3xjyBHQcu1wmebGohsoVzKmQ0Yls3cANXvwP4ZrPvui5FLwF3xZUelyR37uOyZRJbjsQHuiK8Fq31PPAiBt0d3DlFGuufkTrh9idD7A68XrVlMv9sm//9MPA5N2ZaS2itX8DZUDjZcqqCsx+4prVer/nctHgTxGUqibg3tosjtqfPCCLeCKapN9GP67htGOqlbw1Yw7myMir2s/f6S+jATkhoK8apH+k/6glnvRM+VVs2tg+/sVdwr1q9ChyLMT1e+RfHrRFJLUTeDpzXWl/3+f1niDHujbt49jqZZqp92HQrSCBcV6UhnKDkO0mizX8vIeLd1PAZzLpODQBbddpDHHWiUf9soq5fxTmN0B2hnVFgzhWed9LuJ5dapWWXqRqScp0yWWf3EDLeTRWT4o2pMSws9dLXtuNeWES8EUzjNWjZ0vhMDartNHgLQhUvX/2MUqovZjtgTz9Ri1/3nSpxu04lvTMel62d2JbngzhBSGt3qNOU53HSaPFs7J2UUsdx4gl9o8Wf+izwYYO366S1ze+xteOWoHadk9XbBInLVmiUUodwTis+FuHPfhL40QRO2ifdZx7BCdj8WohnjcS9cctkhL2uZabcoBviCvyD7BX4p4HRhG8yM4qIN4Jpku5AmyHijSDUwY0X0oVzo80NYvLVb7f2cR/BFntx336U1oXcTtolzxeAbqVUyYAtW9uHH7zeyfTE/MM4biJ+XCAb8RyQB25vPUm+SF2bb+AKFrktg3ZM22qFHwX+Wx1Xo9Borc/gCFffEdVv+iTpPH8v8HgdV2Y/nAYGlFK3RJymWg4AS66b8Q1c9/UFYnSD9kldgd/1YFjB8WjoCES8EYzRIAgpWDBoucEF+9h7gweYmyjMAYOuwiwINuEVRwXaeyLdEkqpfuAQzgTLL8/ixMmJi05YiNyH4wrll+eBW5VSxZjSUzcf3PYygxPjLFZbWNg+AuCVf6Yn5lG4TFXL/bM4YpAJvOrEFdpXPKye5rxmwFajdxqNOEZZu7TfjwK/H8PvPgJ8JIbfbUTSeR7WZQpXSP4q8Z++8cojsKNu2p4+Y4h4I5ikGoS0UudvNjS8MWDGY8ctaneNusdmXUV5luQVbkGopdHAaaR9YFdw8yr3AM/VCa7ciGeA+2I8TWDkWP6OGzyMuri55X83AU7euCLAqzhBjuPAK8/BXPuwYRwNi8n8q4vr+nkS+GJEP2lavKk3p6i6GEUtHtYrq1jquceGQRy26omH6zjiUZTiofVuwUqpI8BR4Esx/PyjwA+5QaJN4VVnZ4EhA2kJLd64mIh7k3gf3IRG6WvnsS8wIt4IJrFdNTWZPtvzQhBqSbx9uMLvIs4JPlvwHay4insFcRmYjCVF3mW1BHS5V4lHgdcNHhBvP3YbMK21Xgz4XJyuU4m3D/ezsTb1/bdhTPwA8JTWut5JjzB8CXinUmowot9rhC31r13nSTbYsmnu92PAH3pstraE1vo88ApOezOF15xiC+fEe2wbpkqpw0AP8HILP2Mi7o0NfXAjbE+fMUS8EUzSrOElHc3chsE7DluCEAU2tY+k+4qdBA2cWyXOALqNXHiiLKuk+rHAgpmL8Tx3MSVuruGIgkMR2jKFDWNiJC5TVdzg1V8Fvjuq32yAkfzz4f4epXhosk543UYUqS33dFcWJ9B/bHYiIOpbpmoxfetUkv3Lewgf76bK8zhtK85T+Tb0wY1op3lhrIh4I5ik3TsGEW+ETsbI5NalndpH24g3LmkQbzo2z5ssnqu22nESm2ibd10AP4Tj6hQlplynTC1squ7ve9xEXdfEVWBfRLbSOCcbxzt23ALQ68ZfTAyl1O045fxEjGY+BXx/jDHIakmyf2nVZap6QugJ4j19Y/u8y/b0GUPEG8Ektje8NE4UBCEqTC1OFc4R5trrKiO31SruDVxHgBdDPB6LkOBOhntwFgL1MCXeTAMjEQf6rBJWvPkW8PaY4huY6tM9F88x2DJJ0mPivcCy1vpsxL/7WeCDMbWDndgg2Jq01a5zMk87MQU3D8NHgE/V3uoTJVrraZyYZR+My0aVJreWQRuINy5xx71Jug9uhu3pM4aIN4JJmjW8pH31TS5OpRMS2o1xzASL2w+suLu4cdtqlbuAl2qv1vTJs8D9MfR51cDrXke0oww82GghsoHjGhBpfCJ3EXwvIcQb143lHPD2KNPkkvjiLwZbJkl6TPwwEbpMVdFan8MJivpA1L9dQxrrn8nTbMOY2TCwuv2641HcLlNVTLlODQAVt/+vR2x5rpQax5nTvBDBz8Ud9ybpPrgZjdJnQ0BlY4h4I5ik0UQ/6uO2YWjUMVzDGdf6PP4ehH6cTRavoIjteuxdSDemjuVbPbmtIWzsFYC3AEX0bb2RextEm39+bEX9fkeBRa31fMjn4wpabMqt0GT5GsFdPI+QrCvY9xK9y1SVz7q/HwvuCcACTjD3erSr+NCors8Bg0qpfAR2DgBLDUR40+NbkvO/u4AS8HUDtv4L8JciDKDvRZJ95nuAJzxusQ3KN4BJpVSUN5/txHbxxqTrvtWIeCOYxPZFWbPjrFGlz/Z8EIR6NKq3C0C3UqoUsx2wq32Edd+p9ilxuE7ZsjMeta0qrQhmEEOeu4uPHM5tXvVo9zyPm2EcQa7R4jm2d3KDgN5GfDE+4o570yiOCrRv/Ws0J9vGOdEUhYuRFe8Ug60wfBR4pMXgur5wBfgnge+L2VSSeR6VyxSuq+wp4N1R/N5OfHgETAOjBtw/67LjdFwjgX+8TW9aDIyIN4JJmqnfSR97M7WLbPvgLQi7cAMo9uERRyViX33b+4mdhBZvXES8CY61ed5gwTML7Iso1k6zPLepffjFVz2KcWL+QeBPQro/+uFrwJRS6lBMv2+yTpg8bWeqfzHdZ3q5H0dtKxBu+/oIZlymqjzi2oyTJE87RSbeuMQV92YIWNdar9b7o9Z6HccDIVI36AA0FPi11teBLRzPhtQj4o1ghB1BSK0ULZRSORy/VC9VF6KbADUbvKeBYVdpFgQbqMZRaXT011T7sELcdAMD34ZzhWdY4hISTOVfM1txCAmtijffBO6KuH9tmA9u4M85nDEwVltY0j4C0iz/4p6Yx+kyVd0xfwznNqs4aFYn5oChCMXD2Nu8e4qzCFyJ2xZmxUObN+8eADZwArub4tPA+5RSgzHaSGKcQik1DNxCtPn5OPGIN83yCJLdGPCTvnYc+0Ih4o1gimZBSCFZX98RYKHBDR5gaJdHa10BruIozYJgA80mnGB4F9SC47HvAM5ordda+I2kTt603M/6OGZdtRXZZMq1eR/wTNjf0Fov4UwCT0SVLixsHxHYMYnJ/NuFGzPlA8Dno/7tGuJ0nWo2p4haPDR1Gma6iftOVLYaniZyxcMKTuDbVrG5/X4U+KQJl6kqbn/8JeAHYjTTLM9niGfD9N3Ak03WFUH5c+BWpVQUdXEnifXBPvGbvo6IFyrijWCKtHQMJibfUdoShCiwpn1YEtwcWo+9AvAG0KuUGokgPVWaldWCa7PYop1mN3hA9P3YYZyj3V63wvgl6qDF1rSPCO2YJMkx8V3AK1rrRqduo+ALOCcMCjH8dhrrn03vZNJWIu3XjWXyEeCTpm0T/61TzeYUGzjxyqJ2CYraZaqa1qdx+q0osX1dYnv6jCLijWAK2xueTYN3lLYEIQqkfeylVfeduIIWN5uobhNNfKIkyqnlPHcxmucuptpHlDcjmiLJNv9hYnSZquIGZ32B+FweYs+/He7vjcTTqE5G2tSmIrHlCuY9eMSOi8pOSN4FzGutX0rA9meAh103ozhIqn+JXLxxiSPuje3zLtvTZxQRbwRT2N7wUjdREIQIMelv3C7to52FhCh815MopyhOO0Gbijd+Fs8R34xoClN1th6xxrupIa4rw021xabu71rra4DGCXDfCqbnZCbGt2rsuEZuSdXg5rkWbQXlo5gNVHwD9/Tm54AfjsmE8bFKKTUEHKMFF98GxBH3xvZ5V5JjhHWIeCOYIg0dQ6ctTgWhisnFqfXtww38eQfRBCKMTEhw07UPZwHQiCjyz3c5RRifKCrB7BvAPRFee2rN4jlCWyZJpM0rpY7jiAzfiPJ3G/AZ4MMxxOsylX9+7Ji01W5zsqZ23PgoCzhxGI3gCkU/QjIuU1XidJ1Kon95F/CUG8Myar4O3KGU6o3wN22fd9mePqOIeCOYIomJfhBMdgzNrtqs2uqIwFtCW2CqzvYDW26AyGa2khyk3w6c85FOP0R5CmQU5+j7VpPvRZF/TeuEe+3oOs41pC3hjgv3E8FOptZ6AecWm2Ot/paL3/YRe57vsNVO40dSY+KHgc81uUUvSp4H8kQbLBvM5Z9J8cbIO7n9ypgJW9jbft8HvKG1ft2gzVq+gHMLYKTv7QocXTgxbRoR9ZwiLpcp3EsSngW+LcKftV0cMTXGtgUi3gim8LPjENVx2zD46biuAN3uFZahUEp1AwWc26Qa0TGdkNAWWLMzGaGtVojKfQfgDM5NF1EEYLZtZzwqW1V7GeCtCH4Log1aLO0jJAEXz1G/k0mXqapLW6SuU24A5D5gvslX263++bE1Axxo8ZagIaDs49bAdsu/ICTmMlVFa72Oc234j0b80+PAZR83aLWNeOMSddwb28Ub29NnFBFvBFPYOmhV8SMuaZxYA60E+hyn+fWX0EGdkNAW+Gm/s8CQ67oTpx1Ivn1E5b5TDSD8TeDeCH7OtsVVVLbAzfMIr7GN5MSTG4S0l8ZBSKE989wE+4A1Q4vnG7gBnR8CvhjVb/rkM0R7ZfgYMOvj9FC71T8/c7IKzkZYKy5GVr1ThLZ84Yp/PwB8yoS9JnyS6F2njOe5UqofeBvOtd5xEXXcG2u9IxIW+K1ExBvBFH4CwkGy4o2f9LUaEMuUHUGIBNcffj9N4qi4rjpzOK47YbG9n6gSmXjj8gzRuE6ZzD/TfVnUeR6Vu9oYjiDfbPE8DYy0GGenXdpHEJJ6pw/gxKS4FuFv+uFLwP1KqYGIfi+tbd7knMzP4t5UkHcw236/G3hRax3VicZW+FPguFLqSIS/mcSc+9uAv/ARm6wVvoYTty20J0AV17UsAyw3+p4bWHoDiKrv8ss+YNWHwL8I5JVSPfEnKVlEvBFiJ0AQUnA6UKO++j6vv6zS6qAaZPAeSyj+jyDsZARYcAMpNsNU+zDeT1Rxj+jfRbRBTqMSEoL0L+0U/wKidVXD/a37DF1pjNZ6AyfuwoG4bdFe4r/fd1rEmZh3R2TXyBXhtbhxoJ7AWTRHgd/8m8Zxz2zFxchIm1dK5XEWiHNx2yJYHBpTgpRJ8SZxl6kq7kmqPwR+LMKfTUIwi9tlqiqkPA+cjODn/LqWQTIbA37H2Kp3RLuMfaER8UYwQR+w7TO4ZxIdw37guutz2wwji1NXsV/FUZwFIUn8Tn7ArLiZVHDzEzgTnWYBEIOQhHjTbrvI9xHhtata6xmcPnayxZ+ytn20YMckxifm7umnD+O4MCXBZ4gu7o2N4mGr9W8UmPMZSNpUm1oCulrc1beq/bpC6IeBP4jbVgCivnUqiTnFe3HcmuImKtcpk2NYGGxPn3FEvBFM4HdnA5JpeEHT18qOtXRCQrvhd7cQDO2CJhzcPOoTIAAvA7e4vvKt4Ld/mQX2uS5xgXEXMH5u8IAI+jGl1AhOWb/Ryu/UIYqgxUH7dBPjRzuNHUmMifcCS1rr1yL4rTB8FvhgRFfVm8w/U7clmRZEm45vrniYNvH1Q8DTWuuGLtGG+SowqpQ6EdHv+RU314AyLd6M6I6NdwOnWvkdn0QVtNjkGigMQfqDdjp1GhoRbwQT2C5Y2LhzGoUtQYgCaR+7iTr2Cq5L2vPAPS3+lN+J6ibO7TRh4xOZPmZ9L9EGK64SxYknG9tHyzcjGiSJNp+Iy1QVrfV5HAH1gQh+zkj+BXR/b6dxwFT+5XBOUvsRSkyNbda4TFVxY+c9Cnwkop803b+cBL7pukfGzRPAA27Q6Vawfd5le/qMI+KNYALbG17qJgqCECHSPnYTuXjj0k5Cguly6vg8D7J4juhmRFMk0ea/l+RcpqpE5TplKv/6gS2f7u9XgWIL4mEax5xRYN4VJ5oxjXP6JLY1mnvK87uAP4rLRgs8Avx4RC5MpvuX2OPdVNFaL+Oc2m1VBLZ93mV7+owj4o1gAtsbno3Hjqu2EgnKKgg7sNEtpGrLaF/hTqbvJR4hoaUbp9xApKP4C7wOreWf6X4sTvHm/hYXCabcggfwv3hu1ZZJjNYlpdQocCvOrnWSfJZorgw3Vf98980RxCey2ZU9rK0g+bcOXKO1+ETN+H7gca311RhthOXrQAm4M4LfMh22wVS8mypRxL2xet6F/aE3jCPijWAC2zuGNO7yCEJUmDpZ0A3kcW6VidVWCxwDrmitF2L47VZPgRzAieOx4fP7pk7eLAMZpVQr8YnuJ8JgxTu4iBM7qR0ExyB2WrVlEtNj4geBL7o32yTJKWBSKWXq1jdon/qXxjmZbe3XOpepKq7490laDFyslCoCvYDf8bqlPHft3Q88GfY3QhBF3Bvb1yW2p884It4IJgjS8KrHbaO6DtQPQdI3BwwppbqCGgl4/SV0SCckWI/Jnd3pAHFNkmgfcQQrrvIicLSF20ysXFy1GuhTKTUEDANnwjzfCDdtrQYttnnxZ/XJzYBxVCCaYJRJ3jJ1Azfu1GM4QWND4cZR2Q/M+HzEyMmRHbZMiTdjYU7PuYKywjnl4teWidNEEGPgVaXUPuDdwH+L4/cj4hHgoy2eihwDZnzeWgatzykeBF5yL1QwxVeBk2HWJDuwXRyxPX3GEfFGMME4Pm+rcSfTBeB7Yk3RboKkbwsn4NxoCDtBB5KOiJou2Iu7OHgA8CumTgPDrgtPUHy3Q5c7gJ8NYacVfgUYjOOH3RMzOeDvhvyJoPl3D/B3DNlqpS/7h0AuQL8ZlCHgX4V5MGAQUmgtH4Lm+f3A3wtpyxR9OCefTCyeUUpNAD+CoZgUPjgL/EYLz48AC64Q5IeDwA+GtGWyzQeZk5WB6zgiVlA+BHQH2DBo5Z3+HvDOAN+PcxH6CeCSYZEhKN/EmWf/oxZ+w2SdBfg3QFzjVF201ldw1kw/38LPBMkno+uSHQK/3/TlgHcopXrjS1XyhLomVBD84ja8t+PvStkqv4GhY4duDIvb8e+qAbCOs/B5K6C5ewC/Lg3gpOl2pVQmxoWLIHiitd5USv17fF57qbXeUEqtA8eBVwKaO4mzkPPLr4ew0Sq/TTzuO1V+nfALy28L+P3/GXgopK13Euz0ggYeJty7/WmIZ4LwP+PU1zCcAFYDLJ7ngENKqZJ7NW0Q3hXw+/8Cy0/eAO8AVgIsnheAI0qpfAD3wJ2s4tSnN0M8GwefBO5q4fm7ceYjfnkUmAxp611AkKvVNc7pjv89iBFXED2OcwrbL2s4+filILZw4oX8eoDvLwF3KKVUiJvv/lccF9IgvBv4TwGf8cNL2CNg1kVrrZVSj+D/pHo9HiLYnOI6rbku/1ecvDXNvyPkvEQpdQBHRPfb3q7heEeMaa39xtdrhUM4WoXfWG/PAb8FrMSWIgsQ8UaIm2oQTd/+5Vrrn48tNXvpwtmxCTIBGsaZdAY9ev0OggWgW3e/30Ww9AlCZGit/3bAR3qBowQXVu4lwI6O1vpZ4nNh8rL5KzH//n/fwuP3EeB2Ia31E4QP2nobTt/ulzGc8g2M1vrPgD8L86zP32/ltpWjOLfw+DaH058fILiAcD/+J7BorRO7CjsAJ3BOLvllHejBOQkYWLxxY1V9IOhzcaG1fhH4gRZ+4u0EmFNorS8DvxDS1r0EE1T2EW4hXMRpU+WAtt5OQPHGzY9/EOCRDZwxKgP4uTVqp63fDvJ9nHI9FPAZX7Q4zhhDa/3TLf7EPQQ7Ja8JL+Sjtf7lsM+2QotrpltwtIAs/k4NKZwxbBL/lyO0wmGcWIi+2pwby+xvxp2opFHBxWNBEARBEARBEARBEATBFBLzRhAEQRAEQRAEQRAEwWJEvBGMUyqVppVSOor/SqWSiWN7gpA6omyHcbTFUq4r2vTluuqmL58tRmpHKaXz2aJnXpjq/2zvZ7PFXKT5ni3mGuRFIcK8KFgx5pRK0bWPUql+22gXbK7rxWy09byYbVTP7e7TTVLMR5cXxXzjfLCh/hWz0fVxTj0z188VS9GNwcWS99gbKm3FbLT5WsyGSl+pK7o8KnVFm0cmkT7OQdymhNDkM8Xpil4Pc+sStfXu8ccfB+DIkSPMz8+zsbFBqVRibGyMK1eusLGxwebmJvfdt9uFWimF1rruVYKZfHFaV4Knr6C6WNe+Q/Q46cgV0JvBwtIUVIb1COMQF1R2pry96TvmhdDZKLU35mKjdvjGG2+wsbHBX/3IT3DhcrBwHaqQQa8Hr+vzv/Cvd/37a2+9DsDkwD4W1laobG1RzHUx0tPH8nqZxfIq948frvtbB37tn9TtK5RS+vfedXHP908vPQXASHGCa5WrbOlNujJ5BrpGWNlcZHVrmeN99UM6/MQThzz7pWb5fvnyZfr7+9nY2LjR/73rO9/HwuXgc4xGdi5dukQmk6FQKDA2Nsb8/Dzv+8B7mb08H9hOvqDYWA8+l3j3xZ/a9e+lp5x3LE70sr3uuLdvl7foGimxMb3K9sY2/fcN1/2trx76Tw3zfGV1d9zjJ554DoCpqTHm5514+tlshtHRfczPL/H9f+kfcXk+SIiPcOMAhB8L5q/84q5/f+1rFwCYPDzIzOx1crkM2axiZKSXNy8sojXc/869YTQO7PtVz7wLS1cxM725rgONv2HG3irN2lRXVxfZbJaxsTFef/11lFI89NDeeN2N5hRhUErpS+/9yT2fP7Xo3O49UexlfXuLla1Nskoxki+yUFnnI899hdmNoPFt9+YD7O3XS6XSjf7lHfe8nbnLC4HthOnTswXFVoh+Imy9+NZv7c6/Z1517sE4dGCS2cXLdOXyZFWW/QOjXJw/xy/8mx9j7nrwvIDmc9pKpUKxWGRsbIyLFy/y3R/+HuYvtxKL9yaqkJ3R61ujl79ndwizU1e+CcDh0jhXKktUtisUswVG8vu5vD7HytYaJ4furvub44+9K/I+wQullH62fPrGv5/56p8DcGjqFjbW11m9vkomm+XA6AGuzl9h9foqv/CRn2PhcrD+OWw9euvyB/d89tSpKwAcPlxiZnadrpwik1WMjBS4cGGNysY2D53cG87rlvHPh8pXpZS++q93hxF88nUnnyb33cL08iz5bBeZTIbRvmHOX3mLj/znv82VxStBTbXUD+/9rfxMeXu97rok7BqyWR936dIlBgYGbvRxJvt7U4h4I4RGKaV/5+G9i54qX519lJHiJGOlY8yVz7OpK9zef5KfPHWobuMDWFxcJJfLUSwWyeWax9Nu1PCUUvrh/+CdvtknH6U4PElp7BjlufPozQr9J05y6qcPcfHhL9d95tHZLzBZPMix0gTny5eo6E1O9t/NoVPfgZetxnY+6mHnDSaLPRwr9XO+fJ2K3uZk/0jDvDh06pG27ISEZKgnIlRp1A6VUuHax8WP1X1m9tHXKE72UjrWT/n8dXRlm/6To5w69Ik94k2VpfIa2UyGYi5HLuMvbm5Q8abKyuYSWZWjK1Mgq/zF+A8q3lTxynelFA9frH8B3+yjn6M4eZDSsUnK5y+iK5v0n7yHU4e+PXA/q5TiFy8+7Plezz06y+Bkkf3HSiyeL7NV0Rw+2c+vHjrFBY/nPvXoLIcnixw7VuLC+TKViuahk/0cPnRqj3hTZXNpHZXLkClkUTl/B4SDijdVFhevk8tlKRbz5HI361JP9/sD98+HTj3iOQ5AtGPBoVOP7BFvqiwtlclmMxSLOXI+8i8O8UYppb3qUqN65NW3QOP+xcScIgxe4k2Vpc0NckpRyGTJqZtldfArv4PX/CrM3Arq50UrbT5Mnx60TgAN60WjOlEr3lRZXl0kl8mR7yqSy96sE3f/TH+oOZk796r7XOM+PVj+eXHq0CcAqBVvqixVrpFTWQqZPLmMvzEsSfGmyrXFZbK5LPliYU/bva/4tsB1KWw9qifeVFlaqpDLKQqFjK++Nkrx5kYa1pbJZrIUc4Vd9Xnon5wIVcdOHfpE4Hzywu2bPcdlE31cI9pVvJHbpoRYeGX5aYrZHjSaS6tnGC5O0J3t582VvR30Tj796U9z9OhRTpw40VAtbZXlM0+TLfSA1qxePkNx/wS53iHWLp/1fObp5efoyZbQaM6snmeiOMZQrp+za+cjtQMwVexlrlJGs4zWMFHsYWWrwuWNNY6XglxuIgjBCdMO67WP/mwvp1de93xm+ekZsj050LB6ZoniRC/ZgTzXn2+8+/n5115icmAfx/cNc37xCkpBVzbLwd4BDnT3hnrnejw+40wgxkvHeHPlZTZ1BdCMlY4ylA916LAhQfN9+elvke0pufl3juLEONn+XlZON77RN0z5vvn0MvmeLGiYP7PK4ESR0lCO+bPeN14//fQyPe4zZ8+scstEkaGhHGcbPAOw8NgFipN9lI4NUD5/DV3ZJlPMUpjoI7+/2PDZIPzu7z7GkSPj3HrrBC+99AYZlUFrzfFbb2n4XL3+eXmz8eVH9caCbHc/K295j4lPL8/Rk82hgTOry0wUexjI5nnueuPd1M9/7lUmpwY5fnw/588tohT0DxQZHu5mcLDU8Nm4qVePCv1ZZl5qfLNrmPEX9tb1TCZDV1cX994b6vKzSHl0+jUmi70c6+7n5ZVFKtvb5DMZbil692Fh51Yf//jHb+TDCy+8QCbTfKFZr6yKgzkWGrTfen16bqjA2tmlhraGpoqszFVAOzvrgxNFysubLL3lfZLNa8x57nrjiw6//I3PcsvwESbHbuXi3BugVFNRw6vNnyt7XwBXL8+b1T3PMfG5BXrv2t8wjfV49OLnOdx9kOPdh3n5+uuUskXKW+vc2X+CLp9CTlJ8+Y+/xKEjtzB12xHeeuNN+ocGKK+ucfs9b/d8xmucmnmxcf8yVTzIXOUqGo3WmoniGMubjS/3+9Sjb3H4cDfHjvfw8svXKJWyLC1WOH5rL8PDhVDvHJTff+aPmNw3wa3DRzg9cwaFYn1zg+PDUw2fK071Upkr32hvxYletlYqbFxe9XymUR98vDQZ6XuNFKdYrszdKI/h4gTlrea3f5taQyaN3S1XaFtO9D9Y9/PunLfw8MQTT9Db24vWmtOnTzM1NcXg4CDPP/88d955Z6Tp67+1fvpK4963BD7Yf1fdzxt1WmHsOLbquwYcL3U1fE4QouDYsWPMzMygtTNwTk1NsbS0xIULFzyf8Wof/TnvhUj/g/UFkN47G09Spwb3M7dyzR3Y4fDAEAOFEq8vzkcq3owWp1hyJxCgOVCYoK9riJm1cyysX/R0mwqDV//33HPPeT7T/2D9Ha9cf+M88CrfRkw8WL/vPnDcWwx40OOZ4w2eWXp6hmxP167FS9dQgZWXr7L66iL5h6PzDD169CCzs1fR7gR2cnKMgYEeLlyYaficV//cCK+xINftPSZ62bmr1/t27aeeepOenjxaw6uvznP48CADAwXefHMpceEGvOtR8e2Np6Nhxl+vNnX2bOPNE1NMlvqY31hDrzp3FE8UexjI5Xlj7ZrnM2HmVlC/zQ8NDTV8xqusCg3ar1efXjo+EMpWo3rhVSfu6j3h+cyzZ07RXexFo3nj8iscPHCY/u5BLsw0FrzDtMV6eT44OMgzzzzj+YznmBhCuAGY7D7I3PpVJw1oRgoHmCwd5Lnll1Eo7hu8I9TvmuCWoxNcmV1wTlpoTb6QZ//oAV569kXPZ7zq0egdPQ1tedWlRkxOdjM3t3Fj/BgdKXL4cDevv7bC4GAXXV3xh5Wd2j/B3DU3j9BMDB1iqHuA+SYCf5h2GqYPDotXP9cIk2vIpBG3KSE0Uca8CUscMW+6yFJhK9hD2S7YCuYjKjFvhCRp5L7TiNH9I8xeCeir36WgEtyWl9tUGMK6TYUhrNuUF/tHR7gyGzw+QlA7t0we5OKFy4Ht5LpgM4SLvJfbVBjCuk15cfvRj/DmdLD4P2Fj3nShqBCifXi4TQUlDrepbF4tbFfwXtnWIcqYN2Ex7TblxZ1f+y8sVJrvNNdiqs2H6dPDxrwJNSdjb8ybZnzwH53g0mKIvCB4vu8fPcCV2XDxdWrxinnTCja4TTXi/YdPcnW28YZDLVHGvAlLHG5TXrztf3kP0wuNNyLqEba91cNUzJuwtKvblIg3QqQopYa01g2jiOXz+YVKpRJoUudFsVicWVtb8y1Y+ElfvWcAgjwX5hn3uSmt9bkgz1TtBbUldDalUmm6XC5H5vsTpC36aR+lXNd0eWszuvRlczNrm5U96ctni9OV7XAitBddmcLMxla5/oTFUP8XZfkWCoW5crncOOjWDvyUbyafXdCV7UjyASBTyM5sleuL1/l810KlshlRnudn1tbqT0brEdf4kc9nFyoR5V+xmJtZW9vbNqIk7BjlZ0xMck7R9Peyuen17a3I+pdCJjtT3qpfz23v0z2eCzzn8dU+cvmFylY0daLQVZwpb3jngw1z2nyma6Gio+njAAqZ/Ex5y38/1wpR5l+hWJgpr9Ufe+vRrP4Vi9np9fXt6NpvITNTLm8Fztd8Nr9Q2Y6ojuUKM2uVQHkUtm2HWm81nBcm2MfZhIg3QuLUNlal1P8JfBvwS1rr/+r3OUEQWmNnm1JKDQJvAW9prW/3+5zB9I0ArwBvaq0bnnkOmr46fdLPAz8JfEpr3fA4UAS2vg5MAQ9qrT0DerQ6MVJKHQWeAN7QWn97kDTGQZ18eBvwfwNntdbvizJ9dWz9C+B7gf9Ta/2bUdkxSZ13egD4feC01vr7/D6XBurkxb8F3gX8S631H/p9zmD6fhM4Cfyi1vqPo0xfHVvXAbTWDX0r01Yv6uTDDwP/DHhCa/13/T4X0tYfAu8A/obWun7k+ZC2AqThW8DfAf4EGNJa1z0iaEO510n7Y8BtwA9orb/l9zkT6VNK3Q/8R+DfAQ9orX/aRPrq5NHfBz4G/IHW+n8ykQZbqJMXzwKHgTu11p5H6do5L+J3yBOEJtRpPOPAZfd/gzwnCEIL1LQpX+2wznOxUSd908SQvrB9kklbYfLc5vJNa56bwuOdmrYPm98pLLbnRVJtXinVByjn/6q+qG3ZjMk6YUP9qyn3InAr8AzwGuAZ6MaGcrch/wLYuR941v2vYRC8uMrXpe3HsLB0Yl6IeCPYyDjwJj4m7YIgxMY4cBHIK6W6k05MHar9xIBSKm/IVqx9klJKAaMmbLHjnVy7tjEOXMDMOGDSlinS+E5hsT0vTM15qosaX6Joyumk/uVO4FWtdRkfIoOFJJ1/jbgPJ0+fB04opcxcM7UXWTcBSqkssB/n1Hhq80LEG8FGxnF2B1Lb8AShDahO9H2dbkmAavrmcASPuG2Z6JP2Ays4opmJhdxbwAbQ+CqYZKhO2E2Ih+PA69hZz8NSfadhd0Lbydg+pzCVPhFvbmIkz11hfIxk+5eqwABtJt64GzMDwBvYWWfvA57VWq8BZ3Hc45LA9j7OFCPAFeASKc4LEW8Eq1BKZXAWYq+Q4oYnCG2A7RN9k+kbx0yfZPqd2qF8TYiH48DLBuyYpHpybgk4kHBaEsNdPFfb78GEk7MHV1gbAV5F2rxJbvTpMZ88HMQRyJM8OXIfjssUtJl4g7MemMPCOquU6sJxQavG4XmG5PLW1BzFdjqijxPxRrCNA8A15PifICTNQeweBE2mbxznWPTBmCf6O98p7oWmSVth2DkJiy197uJ5GHgpTjsJYHv5mmIIKAPnsbMfGwau4pyCi7ucjLSpNuEgTp0o49SRuLAhz6txWQC+CdyplMollJag2JB/XrwNOK+1vu7++1mcvE6Cg5iZo9hOR4x7It4IttERqqkgtAG2t0Uj6XODPfbh7JxuA/1x2UJO3uzEVPpGuLl4HnFPf6YB28vXFLbng8n02S7Im8RUviea567b0duB5wC01tXN0beZTktIbK6zO0UxSOhUkxtnpzpH2cRON2hT2N7fR0JaJilCeqg2vBnEV18QksTmHS8wl74xYEZrvU38EwIRb25iatI+Dlxyr869RnpcjGwvX1PsjI015Lo62MRO98C4xUOpE9xwdxnCjDtO0nn+duANrfXKjs/ayXVqHCd+iY11dmcsIXDcp+5IoI8ZA2YNzVFsJ+n2ZgQRbwTbqE6kKzhBp0YSTo8gdCrVSZOtgd9Mpa86GcCQLVN5bvskJ43la4QdrmDTpOSdWmAcuKy13sJMcPOgVNO3ASwTr3hYreudXidGgTm3TpgSb+aAQQM3I9ayM95NlXYTb26MU5a5BO0Sb1z3qfOYP9VUHStB2rbt85pIEPFGsI2dE+lUNz5BsBxrB8EdN3iYOplhqk+q2poF9scVl2DHDR62BoLsAbpwgu2mqXxNcQBYcgWBtLxTWGwv3yT6FxvzwSTG89wVimYxLx7WuvZAsrFZglLNvxVgHScAdOK4AvndwDdq/pRE3trex5lk50nGUcvEvsgQ8UawDemEBCFhDC+ewzAIbGitVzHkVuP+f1MT/U1ggfhOHlZ3nm09Zl3NB42IN2FI4zuFxfaTVab7F1tdUExiWrwxVb71qHXtwf333W0SlsDWvuw2nDFqqebzJE412ZpHSVCdO5SBFWB/wumJBRFvBNuQTkgQksfk4jkMSexWp8mW7f1sGvPcJGl8p7DYnhdG0qeUKgHdOO7oV4Bu97NOpCP6F/fk5l04N0zdQGu9iHMK6FZTaWkBW9tvvRNNIOJN0nREXoh4I9hGRzQ8QbCcne1wHuhPwFe/ETvTN0O8wc1NLa6UKVvY7yPfEYurGEnjO4Wl9uSDbcHXq4G5Id62OAZMaxcct4KxmGzZjsn+L8m2eAK4qLVervO3dol7Y2tfVu9EEzhuVKZPNdmaR0Zx51Cj2H3SMhJEvBFsQzohQUieG+3Qda2Zxa6J/s70VXCueh6O2xbx9kn9wJYb9DBuWzvfaRnIKaV6Y7IVhnriYcGArbSMOXveKa2+/z6wvXyTOG0Xty3b6RRxuF6w4irWizc7Aq/PuB/ZVGfrijeuG9VlHLcqU9jex5liP3DdvTkSUpwXIt4I1uBOLnfuQqW24QmC5exsh2BfWzSZvtqJUVw79/XeKXZblrrGmRQPTZWvSXaW7xqwBuxLNEXJYe2coibwOsRb/+qJN2mo62GorROx5IMriGdxBPJYbXng5doD7RG0eBi46m7QgCV1VimVAe5lb7DiKqbz1kh9bgNMzqESRcQbwSYGcYKQrrj/tmqiJQgdhO27tCbTl8adcSlfPBfPNuVDWGwvXyMopfoABVxzP7ItH/YBq25wTejsNm+SnXlxDacr6IvLjiuQQzInbxqJN/e6QoSt7HRvA3vq7FFgUWs97/F306ea5OSNQ8f0cTY3WqHz6JiGJwiWY3tbNLW4z+EcxZ11P7oKFJRS3VHbQsSbnZhK3z5gpXbxnAIXI9vL1xRJL56bIW0+GXae7Ivz5GFiee6KMvfgcTpEaz2HcyLoiIn0hMTWOtvIHQ0Mijc75ihV17JFIO/eGNpp2FpfIkfEG8Em6qnsYymYSAtCu1HbFm0L/GYqfSPAgnt1t4mJvqk8t32SYyovduWDe+pzHecUaDtje/s1RW09nyHe4OZBqdcO45rz1NrqyDpRJ44KmBNvTOb5cZyx60qD79ge98bWOtvIHQ3MnmqqN0exJZ9MY/u8JjJEvBFsonYiXQZW6FxffUFICtsHQVPpq/WhjtOW7MLfJI3la4Q6rmDQ5u/UArVzig1gCTiQWIp2U5u+VWCDeMRD2+OYmeIAsOTWhSqm+heT4uH9ND4dAvbHvbG1zjZyR0NrvYBzSveYgbTUjpVgTz6ZxvZ5TWSIeCPYhHRCgmAHtg+CptJnsk+qtTUNjEa9e2d45zkw7q1SfcDCjo/TUL6mGALKbqDiKu3+TmGxvXyT7F9sygeTJJbnBm5G3ElDgcGl3U7eLAPZJG9GdMVxm/LW9j7OJHX7uDR6b4h4I9iEVyeUymjhgmAjDRbPVrTDOjd4QHzpS3Kiv44TTHN/xHZqb/AAuyZ7Y8Cse8tUFRFv/JPGdwqL7Xkh4o15kszzOG3V4ltgsHhxWzsm2nAz4mFgXWs93eR7It6Yp7a+XAe2gP7EUhQTIt4INiGdkCAkzxgwY2jxHIbaIKSQjsm3KVu297NpzHOTpPGdwmL7hpARtz2lVBfOiazZHR/PAUPu3zqJ1Pcvfk+HaK0vARVgIs70tICNfVmzYMVVRLwxT8fkhYg3gk10TMMTBIup1w5ngAOWBPr06ifiCPRZG/i1aqudJ/q297OpX1zFTBrfKSz18sKmYJ6m+pdRYE5rvVX9wP3/c+7fOgnT/Yup8WMnU8B1rfVssy9it+uUjX1Zs2DFVZ7BzKkmG/PIOG4+d0xeiHgj2ETqgkcKQhuypx26NxlcwbnZIGnqpa8MrBJ9cHMjO/futZ4FnGs+Y7VF/X52AehTShUjthWGRuJhLgZb9RZXtpzMCIPXOHrQYveIuLB9TmHqZFA9O3HZsh3P9hGDraROfvkVGMDSoMUegdch+Trrxx0NrfUMUAYmY06PyfpsMwPAlusqtZNU5oWIN4JNdIxqKggW02iib0NbNJk+o6dhalzBYrW18wPXRW4GZ7KcNPXSF5d4mMYxp947XXP/t89wWpLG2vI1vFNse59uEiN57grhPeyOHReLrTr4EhhcbD15sw9YcTdmdpJYnXXbbFBhLO68tbaPM0xH9XEi3ghW4AYhzeFc47mTVDY8QbAY2wfB1Io3dT6Py1btaROwx53ESF6k+Jh1PfHLhkCfRlFKlYBuklk8+6EPp2iu1XweRzu0vU83Sb3+bwHodutMVFRjx5kQ5GtJg3hjY50dx1k3v+Xz+yLemMP2eU2kiHgj2MI4cKnOQJfKhicIFmP7IGgkfe4V3aM4V3bvZA7oV0rlo7KF2Ty3cVK8E1N50Qds1zlmbUs9D4vt7dcUY8B0QotnPyQtQkPn1QnwFjenifbkYSJ5HuAq6yoXgC6llG31wMY6ex/wTJ0+xYtYxZsdc5TafJrHcYMuxGXbQmyf10SKiDeCLTRseB3oqy8ISWH7IGgqffuBa+6V3TdwXYxmMTPRb/dTPmEwlT4vO8tA1j0N2o7YXr6msH1OIW3eMA3iqIC5/iXuPL8F0NQXcPfgChE2xr2xsc7eD3wjwPefAe6Psa9pNEexxQ3aFDbWl9gQ8UawhboNz90V3abzfPUFISlsHwSTXtybtNWJC7lEyzcFLka2l68pvMp3DVjDuTo7SUyKh1InHIaAslsHajHZp8dxM2KVoKdDwE7XKRvrrN9rwqtcBBTxBcw1OUexHRvrS2yIeCPYgnRCgmAHtg+CptJX7xaHuGwZOSVgeOc5MO5V9PtxTjbV0s7lawSlVB/OYqE2jgq06Tu1QLM5RdI3kNStfzGJh151XerETYzkeYw3I1YJ4jJVxUbxxqvOLgA9Cd2MGChvd5xqiitvZd10E9vnrZEi4o1gC9IJCULCuFcxm1o8B6bBDR6QwpM3WusVoIJzDWYU7ANW69zgARaUL85tUlfc26VqaefyNcU41L21DNr3ncJie/km3r/EYMd2bMjzOGztJC3iTaOTkcZdgpRSI0Av8EbAR0W8MYNXXiwCeaVUt9nkxIuIN4ItSCckCMkzAiwYWjyHwesGD2jvybcpW7b3s2nMc5Ok8Z3CYnteGEmfe5pthL2B18FZBI+43+kEOqF/CeraA/AaThD+4RjSExbb2u+9wLMB3dHAKQsRb+Knkdg3TcryQsQbwRaaHWFP+oizIHQCjdrhNPH66vuhaT8RYfrG8Q76GHWfZMqFx/Z+1mT60jjxtd1VyCSN6pINtyyZKqsDwKLWeqP2D+5ni4BNi/Y4Md2/mBo/AFBKjQFFnBukfOMucL+BXadvbOuf7yf4iSaINxi0bXmUJLbPbSJFxBvBFqQTEoTk8WyHrqvNCo5bVVI0St81nFs2ogpubmpnvICT5vm4bdH4nWaBfa7rXFI0St80MOpejxqVrUaLq3Ycc2QcvYnteWGq/jXKh6ht2U7aT97cR7jTIWCR65S7AWNb+w3jjgZwDidGz2i0yQHsy6NEUEr1AHkcIboeqcsLEW8EW5BOSBCSx/aJvsn0meqTxoBZ93pPL1tR7Ro1Er82cWIJxTHJ9Esz8fA60YmHaTyl0mj38QpQUkqVDKYnSWyfU9jgKhm1LdsxJcjncOKL1YsdF6mtGsIKDGCReIOzmbHtbsjUI4n+OVTe7ghafG/kKeqw0yYNaBTrDVLYx4l4IySOG4S0l/pBSCGFDU8QLKXRbjAk3xZNpq/RRD9Kt4tm79SutsLQbKEZdV7Y7FYTBs/yTcEV6L5RSnXhXAttevHsC1dAKwJXPb5iqp5Hbct2GvV/s8CQW3daZRSY11pvefw9rjxPi3hjVZ1VSg3huB+eCfkTceVto/os7fomqcsLEW8EGxgDppvsPKeq4QmCpdi+S2skfT6Obc8AByIK9GnLaaKobYXBVPl2AwXSd8za9vI1xSgw12DxnHQ+jOPMeUzsFEuduEmjk31bwBzRnDxMKs/DBCuu8ipO8OqhCNMTFtvq7L3ANxusUZoRedBiH3OUWWB/wm7QprCtvsSOiDeCDXRcwxMES7G9LZpK3wBQca/q3oPrYnQF5xaXVhHx5iam0tds8byAE6egGIEtk9hevqawPR9Mpq+Ra0XUtmzHVL4bz3Ol1H6c02avh3neFa++RTzuPUGxrc6GDVZcJY6gxX7mKAtEM0exHdv7+8gR8UawgWYN7ypQ7CBffUFICquOK9fBVPqa2YFohQQRbxxMucU1zAdX1JnBORXaTthevqZolg/XcDavowpuHpRm6YtSPJQ6AbhlrXDK3gujfXrENzfeB3yjhdMhYI/rlG11thV3NICzOKdg9kWUHjA7R7Ed2+pL7Ih4I9iAn4l06hqfIFiI7YOgyZMZqRJvfByzjsxWGNz0jeLcKuVFO5avEdzNjW6cE2FetNU7tYDtcwqT4qG1bd4w4zQOagrmxOGob0aE1gUGaB/xxvTNiC3lrSuofZNoTzWlbgxrgY7r40S8EWxAOiFBSBj3CmZTi+fAuIEkG93gAdFOvhudAInaVqP+bwnocq/DbIU+nHWhiZ3nMOwHrmut1xt8R8Qbb6qx40wsTm3HT/kmeYrQtDhscxB6U9iU51HaqtIx4o3r4jWPgZsRlVL9wCHg5RZ/Kuq8TeMYFpZmeTEP9CulCobSEzsi3gg20My/FffvnXLtnSAkwQHgmo/Fc1LtcJTGN3hAdOkz2Sc1tBXhKQHb+1mT6UvjxNfvO3XCOCp1nRun2caa2IrDhcdGbOxfoqx/rQQrrvISMJGgO2EVm/rnu4Hn3RgyrRB10GKb8ihpms2htnE2/drNDdoTEW8EG5BOSBCSx3c7TGiib5MrUzvasr2fTWOemySN7xQW2/PCVPqGgLLWes3rC+7f1nBONaaZ1PYvSqkBnIXpq638jitQvADcE0GyWsGm9ttqsOIqUQcttimPkqbj8kLEG8EGOq7hCYKFNG2HWuvrwBbQbyRFu/HTT0QV3NxIn+T67O/HiW8Rqy38vdM0znWxScwNTIqHaRxz0vhOYbE9L2wSbKOyZTupFW9wYqk81+RUql9scJ2yqf1G4Y4G8ApwyHXDigKb8igxXFeoPhzXqEakKi9EvBFsIAn/YEEQduOnHUJybbFp+lwXo2nMLHqiiJkxAiz4OJIdhS0/+bcOLOO40JnGr3hYwbkmNVZbJH+zWlD8tN85YFAplTeQniSxfU5hqv75FW/ara6HwU+daDkfXOF7hMax4yKxtYOoBAZIWLxRSnUDRZyNmEaYqrOR5K07xj9HdKeajNTnNmAMmPFxy1qq8kLEGyFR3J3nZkFIQcQbQYgb23dpTaYvjTvjUr43OYjdi/sw+BG/tnDG2tgDfSaFUioLDGPmNFtgXOFsEEdIa4SpOFdR2bIdP/3LDDDs1qGwDAPLWuuNJt+LMs9TI97gllOTwOtgoM66QtJR4MWIfjLKvJWTNw62z2tiQcQbIWlGaR6EFFLW8ATBQmwfBG0TOqaB0RZdeGx7p6hshcFI+tzFcz/pO2Zte/ma4gCwZHjxHIRRYNbQnEfqxE38iJsbOLf7tXLyMIk8jyJYcZUXgGOucJEENtXZu4CXfPQlfokyaHGQOUqa1/o21RdjpLlAhfagIxueIFiI7W3R1OK+B+jCmcR7orUuAys4MWvCEuSdWr2ZRMrXYQxn8dzsmPUssN89HdoOyCkLB6nnydiyHVN5YTTPlVK9wCRwutXfghuusy/jCBdJYHJMbEZUwYqrRBK02J2j5Gk+R1kHrtPaHMV2OrKPE/FGSBq/DW8OGOgAX31BSArbB0GTk+9LPo5tR2VLFnIOVi2u3JMR87SPi5Ht5WsKv/lwBShFENw8KH7TNwvsa1E8tD2OmRHcMu7GKfNmRDJ+GLBT5W7gRa11JYLfqpKk65RN/ViU7mjgXMV+xBVfWsGvaxmkvG1jV30xhog3QtL4nUhv4wg47TKRFoR2w/ZB0KrFvWFbqRZvXNezNJavEZRSXTjXQjeLowJt8k4t4HdOUQ1uPhZ7inZjUjy0ts0bZgyYtkyQr96M2Kp7UtQCA7SHeGPiZsRI89Z1v3qJ1k81pW4Ma4GO7ONEvBGSxu9RbzBzTFIQOg538RzE7cJoO3QDSI7S/AYPaD19QSdGrdjym+cLQJ97LWZgAtzgAcn0swPAlnubVDNMl287TPhGgTmfVwWnfRwNOqcwXb4m+xebXFCSxOQ8M6h42Gr960jxxhVCYrsZ0R1rTwDPR/zTUeRtGsewsPht2zPAgTZyg26IiDdC0gTphFJ11ZsgWMQgsKG1XvHx3STaod8gpNB6+qw7eeOePJwh/CmBccztPIfBr6sByMmbeqTxncJie15Y179U7bQYfN1mbMzzKGxBtMGKq3wLuD3sZkGL2NJ+3wGc0VqvRfy7UQQttiWPbMDvHGoTx21yJPYUGUDEGyFppBMShOSxvR2aTJ/JnXtT7yXlm4wtU6TxncJi8mRLGIz0L0qpPkAB13x8vfqdvjC22oBU9i9uLJ9bcW6IigxXsHgNR8AwjS19WdTBiqtEEbTYljyygY7MCxFvhKQxueMqCEJ9grTDRSBv+CrRIOlrNbi5kdOArq/+qAlbhOhnDe/CmzyBmcbTnkHKt13eKSy2zylM1T/fQU3d76S5XphsHyb7lzuBV9xbhaImKdcpW/rnONzRwHHDuk0pVWzhN6S/B1wXqP04p5L9kJq8EPFGSJqOVE0FwTJ8t8MIffWDECR9rQY3N9Un7QeuBZh4Gzl547rOreO40pkiSJ4vAV0t3Nhhe0yUMATJvxlg2I0jlUZsn1OYSl+Qet6qLdsxkucBA6+3ZMslLoEBEhBv3A2XQfwFXod462wseau1LgNnaO1Uk+19nClGgAXXJcoPqckLEW+ExHB3nkfwF4QUUtTwBMEygkwGwHxbNJk+G12Z2slWGIKKh+1QviYJkn8VnMDVw7GmKAHcxfMYlpavK5gN43+nOM1t3iRB2/xYyJOHQ8C61no1gC0Rb24yBsy6GzB+iKXOurf33YET+ycOWs3bNI5hYejYPk7EGyFJDgDLPoOQQooaniBYhu2DoJH0uac57gD8uly14mIk4s1NTJXvIZwFgp9bmULbSQDby9cUQ0A5QJDRYeB7YkxPLe8CcoCfwOHQWjn9d8BDAb6f1joBwcTNNaCMU5eC8n1Af4Dvt5rnfxMnrXFwAXhQKTUR0+/X40cIFoMqrjr700C3z9sPwzAE/KsWng8s3qQ0GHnHjnsi3ghJch9QCfD9RRxf0a54kiMIHct78b+gAEfc+M6Y0lKPd+N/wQ1O+t4Xws4q8L8C3wjw/SxwOISt78C/SAROX/ntIewAfBvB+tounPSZ4iTBFiE54D0h7FzGKV+/pz3ncSa+tgdyvR+nLvolQ/i6ZDP34rj8+eWPgF+JKS31+HPgX7mnn/ywBJwIeb3tvwB+PsD3c8D7Q9ixGtcV5zjO/NEv6zh1KSifA34pwPevA/e0sLD+t8B/CflsM94E/i+CLY5b5Q+Afxrg+2vAAzGk40vAr8bwu1V+FfjfwjyolDqA41q26PORNZz+PswcxXbeR7A51CaOgN72pOK+c6FtmQCCBO1aAXpwGmuQhYggCI25BfB7VBmgAByNKS31uIVg4k2o9LkuOf844GMZnNMc5wM+dwQoBfh+hfA344wDfk84gpOuIyFthWGMYIvuUOlzj+OHKd8h/N3aYxx34TdMMPHGdPma4hYCzCm01svAP4svOXvsrQK/GOCR60AvzpzHb1yHqq3PB/k+jgg9FfCZdiCPU99XAjxTJMRiV2s9B/zLAI+s4MQ+UwTbPKna+3tBnwnw29vA34rr9z1sXgD+dYBH1ggf265ROl4F/seof3fH7/85jpAbhmGc+hJE8MsSbo5iO0HnUJs4Y0Tbo3wEohcEQRAEQRAEQRAEQRASQtymBEEQBEEQBEEQBEEQLEbEG0EQBCH1FAulaaWUjuq/YqHkN26KYIBSKbryLZWkbNNAvlSItM3nSwXPelHM5qLtX7I5T1tS181TKuUjzPN8wzw3ZauYjbZ9FLPe7cOGOlvqKkb6vqWuonfeFrOR2SoWs9JGhV2I25QQC8ViZnp9XQfyRVWqgNZBwh40JqMKM1vb5bHIflAQ2pAwbbGgMqz7vq2z2W9lZ8rbm57tMJMvTuvKupH0nfvT3WFBvv7cEwBMjE1xZWkegGwmy/C+Ua4uLfBXfuF7mVsKHq9RFTLo9WDpy6kCmyH6vzB5EdZWPbpUYWbDo5/NFHPTen0rcEyCbEGxtR58blI7n3n88ccBOHLkCCsrK1y7do1CocDY2BhXrlzh/e9/D5cuzQW240WhoGbK5e3Ixpyw+ZcvKDZC5F89on6nsITpxwB+a3lvPNdXn3wRgAOToyxOXyXXlUVlMwyMDrK2tMrS7CK3fdvb9zz3M/0/hNa6bqwJpZS++K6f2PXZU0vOjeATxV5mNtboz3axobcZ6SoxvbHKR195gqtr4UIpNarr8/PzbG5u7qrr3/H+9zATUV3PFdRMxYI6kSkWpvX6RuA6Ebb/W1z+rV3/fvLJVwGYnDzA7MwS2VyWbFYxOjrA/Pw1/vKHfoPZhSuB7TSzNTO9iFKKfCHH6OgACwvXuX6tTC6X4Z57p3Y9N9j/Mw3r7KX3f3XXZ09d/SYAE6Vx5jactOdVFyOF/ZxZOUchk+e+gTvqpvngn767oa1Gdfby5ct0dXWRzWYZGxvjjTfe4MM/+L0sXJ6vayssV37xxV3//tqFvwDg8OAhLl+bpZDNk1UZRnoPcH7xIpWtCg8fvr/ub+371Tsavu+bl96767Onnlp0bE0UmZnZoK8vR6WyzfBInjNnVvnbf+dlrsyEmQN0se47Bnqz38rPlLfXE2/boeatWcX6VkTjXk7NlCvJ93FeiHgjxIJSSl+4+HDdv33q0VkOTxY5dqzEhfNlKhXNQyf7OXzoFO96+KLnb87MPkqxOEmpdIxy+TxaVxjoP+n5/SdOHfLsWAWhUwjbFi8+/NG6zzw6+waTxR6Olfo5X75ORW9zsn/E0/6hU480bIdKKf3wf6jf7meffJTi8CSlsWOU586jNyv0nzjJqZ8+FDh9h049ske8qbJ0fZFcNkchXySXvRnHf+r93fxOgz7pq7OPMlKcZKx0jLnyeTZ1hdv7T/KTpw7x8MWP1X+nR1+jONlL6Vg/5fPX0ZVt+k+OcurQJzxtedkB+MlT4fIijK16/GSDflYppb3yoVle/KJHnX3u0VkGJ4vsP1Zi8XyZrYrm8Ml+fvXQqT0L2iqLi4vkcjmKxSK53M3yVUrh1TbAu314cdhJQ2RjTiv5F7TNexH1O4UlbD9WT7ypsrq4QiaXoauYJ5vLNk1DUPGmytLmBjmlKGSy5NTNA++Hnvg9vPo+aNz/hanrQduUF79qUZ14+OKTdf82++jnKE4epHRskvL5i+jKJv0n7wHg1KFvD9z//eSpQ3sElSqLi6vkchmKxS5yO+rRYP/PhO7Tg9ryIqh4U2Wpco2cylLI5Mll/N1tE1S8qdKozgYdRxtx6tAn9og3VZbKy2QzWYq5gu/3DSre3LC1tEkupygUMuRyNx+fOPiVUO976tAnuPjwl+s+9+jsF5gsHuRYaYLz5UtU9CYn++/2fKdDp77DmrYdat76z+s/8+g3Z5kcKnJsf4nzV8tUtjQnp7z7uEP/Hzv6OC/ktinBKE8/vUxPTxY0nD2zyi0TRYaGcpw9u9bwuaXlp8lmewDN2uoZCsUJunJDrK6dpbt03EziBSFFhGmLTy/P0ZPNoYEzq8tMFHsYyuU5u7bM8ZL3QBiG5TNPky30gNasXj5Dcf8E2e5+Vt46HSh9g7k858qNd7f/5MnPcHj8CEcnbuXC5XMoBesb3jtgryw/zXJljuHiYbb1NpXtMrd0385M+RznVl7wfqenZ8j25EDD6pklihO9ZPvzrJy+2jB9I8UplitzaDRaa4aLE6xuLrOw7r3wq5cX/dkuTq8sNnyvYrYHjebS6hmGixN0Z/t5c+U0Ez1va5jGICw/PUNlrkzxcC96W7Nd3qL79kFWX17k2rPepwPefHqZvFtn58+sMjhRpNCfZfZ044tkPv3pT3P06FFOnDjB66+/jlKKhx56qOEz9dpHf3+Wl0+vcPvbekK9d5TUq0u5oQJrZ5c8n6n3TgMDWZ5//jp33tlrMPXR0aicGvHk7/0Zw0dGGbv1IBdfuoBSikwuS9/+PvYf9hajw/DYwltMFnt3iKhbFJssEOv1f7neIdYun234XNC6Xq9NlYZyzJ9d48DxIBe52EVx6hCVuSugnVNKxYlxNpevs35xxvMZr/7vwspLns/85997kqkjw9x66xinX7qIUopcLsO+/X0N0xemT29k6/Dh/c0zJQCPzT/hLPp7DnN+7RKV7Qr5TBe3FMcYKURn6+Mf//iN+vrCCy+QyTji5u23397wueJUL5W58o7y7WVzeYP1iyv0vG0ocDo+9+qXmBqc4Pj+Kc4tvoVCMVDsY7h7H4OlgVDvVo9PPTrtCg/dvPLyChuVbQr5DMMjjW+8bvS+Xjy9/Bw92RIazZnV80wUx+jP9vLc9Ve4q/dEZO9kkrBryKmhInMrFbQGjWZisMjK+haXlzc4Ptx+/ZyIN4JRHnyw/gLveJNJwkD/g3U/F+FGEMIRpi0+2D9c/5lSVyRp2kn/rfXbfK7bWyTySl9vtnH6Jg8eZf7q7I2J9C1jk/T3eE/YTnj0R5M99Y+TV+l/sP7OYK6/8cTNy153Lnhe9Oe8bYWxEwavfOi7v36aq0x41Nlif+OpzLFjx5iZmUFrp3ynpqZYWvIWOcC7ffQ3sWUKrzwsHfeut17v1K7CDYQvp5GjYyzPLoLWaA0HDg/TPdjL7OuXIxVvnl6adUVUzZm1JSYKPQzkCrxRXm74nFf/Vxr3nvM88cQT9Pb2orXm9OnTTE1NMTg4yPPPP+/5jFebamfhBqD/wfqnC3L93nXdq/87nNvrQlflyNERZmeXnUWh1hw+fIDBwW5ef322YfrC9LWNbEUp3nx9cceif+UcE8Vx+rt6Obd6kaGu6IQMqN83Dw0NcfZsY5Ey7FjqxZGhCWZXFm7MAQ4PHmKg0MebS5ciFW8mJ0vMz2+g9Spaw8REkcHBHBfOlxs+F+Z9H+y/q+7n7SrcQPg15IOTHs+1oXAD4jYlxEQ+rxYqFfYFeUZi3ghC9IRpi10oKkTkO9wk5o3K5RfYqhhJn5fblBf3/eAUV5YbT8Lr0qWgEix9YeMwhMmLLF1sEY2PfKOYNyqfXaCyHahsATJdsB0ieUHnM5OTB7lwIXhMIy9siXmT64LNaIrXmpg3YfoxqB/zJixh3aa8uO/ZzzCz2lhE9CJoXR8e3c/8bLj4K7W0e8ybsP2flyuTF3fc9k+5OB0uzlBQW16EdZsKQ1i3Kc/fmzzE5QuXIklbFS+3qTCEdZvy4v4Hv87sW41FnHp0kaXCVuDn6mFLzJsw/X2UMW/yWebWN3W0RzAjRMQbwQhKqSGtdWPfgPrPTWmtz5myJwhpJ0zbUEoNAYR5ztQzzdJXLJSm1zfKgSf6XhTyxZny+pqvSU4L+Re4/wtjy1T5tjAONE1fqVSaLpejKd9CoTBXLpcDTdxMjTm2t19T+ElbvlSYrpSDL+696CrmZzbW6i9s8pnsQkUHFyq9KGSyM+Wt+qJ3Pp9fqFSCCd5eFIvFmbU1f/0Y2F0nIL72kc/nFiqVrYjyvGtmbW3DM89N2cpnuhYqejPCOpufKW95tI8E6yw4ZVzMFU6XN4NdjtAwHbnCzFql/sZFPp9ZqFR0JO9bKGRmyuWtQG0UOq+/93gulWtIEW8EQRCEjmTnAK2UOgg8B7yltb7H73OCvdSU753AHwOvaa3f7/c5IV3Ulq1Sah7oB0paa8/t61aFSqXUdwG/DnxTa+15RCcKwVsp9R+BdwL/VGv92ShtCQ518vwjwD8Bvqq1/u/8PhfS1qeBtwF/TWt9Kipbdex0A1eAaa31VFR2PGz9DPBzwGNa6/93lLb8pkMptR94Hbiotfb2lQuRjjrv+/eAvwZ8Wmv9L6OyI3QGmeZfEQRBEIT0UTMpGgcuu/8b5DnBUqR8hVpqFlB5HOFmCTjg97mgdlx81b8wda/OMwfjsiU4JFy+sdjysDMNjCulPG/esfmdAqYjtjHClvcV0oGIN4IgCILgTKLeBPYppaKPwCwkzThwAWdhKwgAY8AsziIq7nphsv5JXTdPGst3HLgErEHweFMhbCVdZ8eBt4CSUiruSLY2vK/Qpoh4IwiCIAg3d6vngcj84gVrOAicx8zEXGgPqm3e1257BLZeM2AH14YpW4KDkfJVSmVwxqfX47aF+fZh4p2apeEy7mkjA7aSfl+hTRHxRhAEQRBu7jKamKgK5qkeUzcxMRfag2qdMNHmx3EWawU3lkgsuKcGh4CzSD03yTjwMk1cjCLgAHAN54SIiTpraky8kX8x22mWBpP9QdLvK7QpIt4IgiAIgtmJm2AeKV+hFtOLNRPi4SgwB1yM2Y6wm3HgHFABBmK2Y7rOmrJ1Ghh0Y1Elgen3fQEYi1nsE1KIiDeCIAiCcHPidglZ9KSR6i6ylK9QxWSdMGVL6nkypLF8jYyJrngxiiM4zpKc27KRvHXFqUGc01PXgf1x2RLSiYg3giAIgiAnM9KOlK9Qi5E64S5OTdW/nSd8RmVXP36UUkWgB+dabVPlm6aTN/uBFa112YCtRph631FgVmu9ZcCWkEJEvBEEQRAEWdynFsOLZ6F9MFUn+gCttb5mwNY4cFlrvY7s6ptiDJjWWmvMiTezODcj5gzYMvVOGLDlJx2d8r5CmyLijSAIgtDR7LjBYxqZTKWRfmBLa30dKV/hJmlcrFVvzDFhS3AwWb5VcW6L+G9GTGP7qEtCp+MwYEtIISLeCIIgCJ3OfuCau1stk6n0IZNloR47F2txBg41vrg3ZEtwSF35unFZ+nEEolS8UxP6gG1DAr8N7yu0MSLeCIIgCJ2OTKbSjZSvsAulVBbn2uUZrfUaUMa5YjsOUre4F3aRxvIdw4nLsh2zHbCjzqaxDIWUIuKNIAiC0OlUb5kAx3VqxHWlEtKBTJaFWkaAK1rrTfffcdYLWRimmzSW784x8RqOZ1FfjLaSrrM70zCHc2V5lwFb0kaFwMjkVBAEQeh0bsSJ0FpvAMvAcKIpEqJkZxyQOWDIdQsQOpeddQL3/x80YCtOO7B3YRinLcHBSPm6bn2m6tLOMbEaiDkN7aNpGtzTRnM4p49itYW0USEEIt4IgiAInc7OBQ/IbljauFG+bqDPOeIN9CnYj8k2X7urPxCHeOi6gg0DM+5H0o+ZwdRJikFgQ2u9YsBWUu3DhpM3cafDhvcV2hgRbwRBEIROR8SbdCPlK9SSyGLN3dWfJZ5d/QPAknt6EKSem2JnXVoCupRSPTHbgXSKNzPAsCtEmiRR8SbGYOlCChHxRhAEQeh0aidul5BFT5rYGb8BpHwFs3XClC2p58lwI99dF6M0lK+RMbHmiu6q2/JVzLstG8nbHafjpgG01teALZybvQTBFyLeCIIgCJ2OnMxIN1K+Qi1pPMVQL46P7OrHiFIqh3NL2dyOj+Mq3zTW2X5gyxUx4rbVCFPvOwxc1VpXDNgSUoqIN4IgCEKnI4v7dCPlK9RipE4opUpAEec0Qay2qHknNzbKJrKrHyejwLwbS6uKKfEmzpsRTfWZtXbitBUkHWl/X6GNEfFGEARB6Fhqj227yGQqJSiluoE8sLjjYylfweRibdp1pzFhSxaGZjGZ57XiXPVmxAMx2drpRpT2OivijdA2iHgjCIIgdDID7L7BA2QylSZMLp6F9iGNizVZGJondeXrxmXZjxNYOzY7LonXWfd0XAEzAn/i7yu0PyLeCIIgCJ2MTKbSjZSvsAv3tN0obtBQl2vun/oiNpe6xb2wizSW7whwRWu9GbMdsKPOyuk4oa0Q8UYQBEHoZDwnUxLoMxXIZFmoZT+worUuVz9wF25x1Is0Lu6Fm6SxfOvZuQJ0u6dU4raVhHhTm4YZ4rmy3Ib3FdocEW8EQRCETqb2hha01qvAOs4tIkJ7s6d8cSbmIzFMzIX2oF6dwP3soAFbcdgB74VhHLYEB5Pla8pWvTFR45xUi1pkMJl/vtPg3ga1SPRXltvwvkKbI+KNIAiC0MnUW/CA7IalhT3l6wb6XCT6ibnQHphs8167+geiFA89Aq+D9GNxYyoOTS+QA5bitkXy7cOGkzdxpcOG9xXaHBFvBEEQhE5GxJt0I+Ur1JLoYs3d1b+KE1skKvYBa1rrtZrPpZ7HS726tAD0KaUKUdupicsC6RVvxgy6LdvwvtJGBd+IeCMIgiB0Ml4Tt0vIhCoNSPkKtZisE6ZsST1Phnri3DaOi9FYnHZc2rnO1rXlCpCrOIKkCYy8rytGjdWxdRUoKKW6o7IlpBsRbwRBEIROZhxnklaL7IalAylfoRaTdcKULannhlFKZXBOT03X+XM7l68RW65YUcARL2K11QRTebsPWK09HRdjsHQhpYh4IwiCIHQy4laTbqR8hVqM1AmlVB4YAObjtoX3Oy0BednVj4UDwJIbQ6sWU+Ubx82IpvpML1ewOGw1TYeBNHjZicOWkGJEvBEEQRA6GVncpxR38dyPmcWz0D6YavOjwJzWesuArbrvJLv6sWJyMe5VvtWbEQfjtkV6xYxOe1+hzRHxRhAEQehIlFI9ODd4LNf5s0ym2p8xYNaNQVGLlG/nksbFmiwMzZO68nVP8IxizhUs0Tpryem4OGwJKUbEG0EQBKFTseXYthAPMlkWdtHgSm2AK0C3UqoUkbnULe6FXaSxfPcD17XW6zHbATvq7CjmBH4b3ldIASLeCIIgCJ2KTKbSjZSvUEs/sKW1vl77B1fEjfKWIJsW9wcjtCU4HMRcnpuqS43szAFDSqkuA7ZM9c/NyjDKeEI2vK+QAkS8EQRBEDqVRhO3ZSCrlOozmB4hWkxOzIX2oFGdgGgX3SYX903reoS2BAeTi3FTdcnTjhu7aY7oxE2T7cMLzzLUWpeJ9spyG95XSAEi3giCIAidSqOJmwT6bH8ale8asEZ0E3OhPWi04AZzpxguA2NRiIdNXMGqtqQfix5TcWiKQC+wELct7GofJupsp72vkAJEvBEEQRA6FZMTN8E8zcr3ElK+nYbJOjHu/t4e3F39FZwYI61SPR14zePv0o/FQ6O6NAvsV0rlIrAzBsx4xGWBdIo3lzBzMtKW95U2KvhGxBtBEAShUxHxJt1I+Qq12LJYi9JWo8DrUdoRdtPoZN8mzkmZkTjtuLRjnW1oS2t9DdDcFCbjwsj7+jgdNwcMuLdfCUJDRLwRBEEQOhXPnXEXOZnR3jQrX1nUdh4m64SpUz7SjxnGXYyPkb7yNWLLFSn6cUQLL0z0z6bytg9HjKp7Os49VTVDdPGEhBQj4o0gCILQqcjJjHQj5SvUYmqnPQsM4yzIYrVF83daAPpkVz9SBoF1rfVqg++YKt92PHkzhvcV3VHbaoQtp+OitCWkHBFvBEEQhE5FFvcpxV08H8DM4lloH0y1+WHgqta6YsBWw3dyF8izyK5+lDSrR2BOvKnejNhrwJapd4rSVivpSNv7CilAxBtBEASh41BKFWh8gwfIZKqdGQGuuLEnvJDy7TzSuFjza0uuIo6OZlfOQ3R53kyci+RmRB9xWYjCjkviYkaA03Gxl+EOWzIeCU0R8UYQBEHoRKo3eMgx5nQik2WhHs3qxRwwpJTqitkOpGgh3IGksXz7gU2t9UrMdsCOOmtS4LfhfYWUIOKNIAiC0InIZCrd+ClfCeTaQSiluoE8sOj1Ha31Fo6L0WiL5poFQgVZGLYzaRRv/NiZAYbdUytx24q7f/adrxFcWS5tVIgMEW8EQRCETsTPZGoB6FFKFQ2kR4gWkxNzoT0YB6abnLYDcwvhdlvcCzdJY/k2taO13gCWcOKJxWqL+Ousn/eN6spyG95XSAki3giCIAidSNOYBe4ibwaZULUjfsq3em1rqxNzoT3wE6cEoolz4TsmSgTioV9b0o9Fh5HFuFIqB+zHOQ3WzJaJOmvSVtxxmvyUYTUdrbYdG95XSAki3giCIAidiMmJm2AeKV+hFpN1wu+u/jZOrJFQKKVKQAm40uSrUs+jxU9dmgZGlVKtrLVGgYUmcVnA3Gkxk7YSP3kTYTpseF8hJYh4IwiCIHQisrhPN1K+Qi22LdaisGXSFUy4iR9xbh24hnNyJjY7Lu1UZ/3augoUXYEyDmx73xngQATxhISUI+KNIAiC0In4CSgKEtS2XfFbvrKo7RxM1gm/C8NW+xfpx5LBVL6bLF8jtlxx4gCNr+iuui1Pt2KrCabetwQUaXI6Tmtdcb8zEtaW0BmIeCMIgiB0InIyI91I+Qq1GKkTbgybMRO28P9Oszi7+rkWbAmAUqoPZ/10rdl3aT2OiW2nQ6KwVb2iu2LAViNMxfjxezquakvGI6EhIt4IgiAInYgs7lOKu3gexdm1bYaUb+dgqs3vA1a11mUDtny9kxszZQHZ1Y+CceCyocW43zobxc2IJl39/NiJwlYU6UjL+wopQcQbQRAEoaNwd5/30fwGD5DJVDuyH1jxuXgWd5LOwTZXJpCFYTtiMs/9inPVmxHH4raF2TobS/9sWOCXNipEiog3giAIQqcxgnODx5aP78pkqv2QybJQD7/1YgYYbiFwqHWL+4hsCQ6pK1+lVDeQB5bitONiQ50NIvCn4X2FFCHijSAIgtBpyGQq3Uj5CrtQSuVxruSeb/ZdNxbHIk5Q1TCkbnEv7CKN5RvUFWzMPb0S2pbP78ZVZ9NYhkKHIOKNIAiC0GkEmUzNAvuUUl0xpkeIFpksC7WMAbNa622f3295IWzAjmlbgkMay9e3Ha31GlAGhuK2hR3iTatXltvwvkKKEPFGEARB6DT83jKB61o1j+MfL7QHvssXZ2JeamFiLrQHQeoEtHbDTBBbrd5kE9SWLAxbx5QrUwb/cVmqtkzUWZO2Wm0fXgQRq1q9styG9xVShIg3giAIQqcRZPINsuhpN0xOzIX2wGSbD7qrX3BjjgTCdQUbBOZ8PiILw2gIvBgP6WJ0ALimtV4PYMtEnTVpK66x16RYZcP7CilCxBtBEASh0xDxJt1I+Qq1WLk4bVE8HAXmfAZeB6nnURGkfFeADRyRLTY7LmkUb+aAAVeojBJb37fVeEJCByDijSAIgtBp/CzwzgDffzfwD2JKixA9PwK8LcD3HwJ+Lqa0CHbwj4HvDPD99wL/MKgRd9H1LoK5WU4CPxHUFvBjBFtQDgEPtHCLVsfjnpB6GxDkpFQf8P0hzP0UcEeA7x8G/lIIOwB/H6fe+uU7gH8U1IhSaj8wAfitgwroAt4f1FYT/l/AyQDfD9sfHMY5QeUnEDTcfN8gaRM6jFzSCRAEQRAEw/wQ8HiA738YWIgpLUL0/DjwxwG+/yHgdExpEezgrwArAb7/MWAqqBGttVZK/XXgDwM89iPAl4LaAn4TeDPA978A/PUAJ3WEGrTWq0qpvwE8HeCxv0qw+lDlfwL+NMD3fwtYDmEHnDHx+QDf/8uA3+DfN9BaLyilfkprfdbn97eUUh8D/iSorSb8IPD1AN//PuBaCDtvAj+ltZ7x82Wt9ZrbfwRJm9BhKH+3wgmCIAiCIAiCIAiCIAhJIG5TgiAIgiAIgiAIgiAIFiPijSAIgtARdHV1TSuldPW/rq6uplew1j7j9znBPFK+Qi1h6kTY50w9Y9qW4JDG8jXZZ9pQZzvtfYV0Im5TgiAIQkeglNK//Mu/fOPfv/zLv4zWuuGtDrXP+H1OMI+Ur1BLmDoR9jlTz5i2JTiksXxN9pk21NlOe18hncjJG0EQBEEQBEEQBEEQBIsR8UYQBEHoGFZWVtjc3GRpaSnWZ4RkCFNOUr7pJmz5mnrG9vQJN0lj+ZocE23onzvtfYX0IeKNIAiC0DHk83kymQylUinWZ4RkCFNOUr7pJmz5mnrG9vQJN0lj+ZocE23onzvtfYX0ITFvBEEQhI6gq6trenNzc7T671wuN1OpVMaCPOP3OcE8Ur5CLWHqRNjnTD1j2pbgkMbyNdln2lBnO+19hXQi4o0gCIIgCIIgCIIgCILFiNuUIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxYh4IwiCIAiCIAiCIAiCYDEi3giCIAiCIAiCIAiCIFiMiDeCIAiCIAiCIAiCIAgWI+KNIAiCIAiCIAiCIAiCxfw/whKHQ1A5J/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure( figsize=(20,15) )\n",
    "plot_tree(decision_tree, max_depth = 10, filled= True, feature_names=digits.feature_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9f30c5",
   "metadata": {},
   "source": [
    "#### Random Forest\n",
    "- Random Forest는, 수많은 의사 결정 트리(Decision Tree)가 모여서 생성\n",
    "- 수많은 요소(Feature)들을 기반으로 Target을 예측한다면, 분명 오버피팅이 일어날 것\n",
    "- Random Forest 는 전체 Feature 중 랜덤으로 일부 Feature만 선택해 하나의 결정 트리를 만들고, 또 전체 Feature 중 랜덤으로 일부 Feature를 선택해 또 다른 결정 트리를 만들며, 여러 개의 의사 결정 트리를 만드는 방식으로 구성\n",
    "- 이렇게 여러 결정 트리들이 내린 예측 값들 중, 가장 많이 나온 값을 최종 예측값\n",
    "- 여러 가지 결과를 합치는 방식을 앙상블(Ensemble)\n",
    "- 하나의 거대한 (깊이가 깊은) 결정 트리를 만드는 것이 아니라 여러 개의 작은 결정 트리를 만드는 것\n",
    "- Random은, 각각의 의사 결정 트리를 만드는데 있어 쓰이는 Feature들을 무작위로 선정한다는 의미"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fead249",
   "metadata": {},
   "source": [
    "#### 장점\n",
    "- Classification(분류) 및 Regression(회귀) 문제에 모두 사용 가능\n",
    "- Missing value(결측치)를 다루기 쉬움\n",
    "- 대용량 데이터 처리에 효과적\n",
    "- 모델의 노이즈를 심화시키는 Overfitting(오버피팅) 문제를 회피하여, 모델 정확도를 향상시킴\n",
    "- Classification 모델에서 상대적으로 중요한 변수를 선정 및 Ranking 가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "24fc6d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97        36\n",
      "           1       0.90      0.97      0.93        36\n",
      "           2       1.00      0.97      0.99        35\n",
      "           3       0.97      0.97      0.97        37\n",
      "           4       0.97      0.97      0.97        36\n",
      "           5       0.97      1.00      0.99        37\n",
      "           6       1.00      0.97      0.99        36\n",
      "           7       0.92      1.00      0.96        36\n",
      "           8       0.94      0.86      0.90        35\n",
      "           9       0.97      0.92      0.94        36\n",
      "\n",
      "    accuracy                           0.96       360\n",
      "   macro avg       0.96      0.96      0.96       360\n",
      "weighted avg       0.96      0.96      0.96       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(random_state=42)\n",
    "random_forest.fit(X_train, y_train) #학습\n",
    "\n",
    "y_pred = random_forest.predict(X_test) #예측\n",
    "print(classification_report(y_test, y_pred)) # 결과 지표를 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af495d93",
   "metadata": {},
   "source": [
    "- 정확도는 확실하게 높아져서 96%가 나왔으며 나머지 성능지표도 골고루 높게 나왔다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7252770",
   "metadata": {},
   "source": [
    "#### svm\n",
    "- Support Vector와 Hyperplane(초평면)을 이용해서 분류를 수행하게 되는 대표적인 선형 분류 알고리즘\n",
    "- 결정 경계(Decision Boundary), 즉 분류를 위한 기준 선을 정의하는 모델로서 데이터 군으로부터 최대한 멀리 떨어지는 게 좋다\n",
    "- Support Vectors: 결정 경계와 가까이 있는 데이터 포인트\n",
    "- 마진(Margin): 결정 경계와 서포트 벡터 사이의 거리\n",
    "- 최적의 결정 경계는 마진을 최대화\n",
    "- 매우 빠르다는 장점이 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81107a14",
   "metadata": {},
   "source": [
    "- Margin이 넓을수록 새로운 데이터를 잘 구분할 수 있다. (Margin 최대화 -> robustness 최대화)\n",
    "- Kernel Trick: 저차원의 공간을 고차원의 공간으로 매핑해주는 작업. 데이터의 분포가 Linearly separable 하지 않을 경우 데이터를 고차원으로 이동시켜 Linearly separable하도록 만든다.\n",
    "- cost: Decision Boundary와 Margin의 간격 결정. cost가 높으면 Margin이 좁아지고 train error가 작아진다. 그러나 새로운 데이터에서는 분류를 잘 할 수 있다. cost가 낮으면 Margin이 넓어지고, train error는 커진다.\n",
    "- γ: 한 train data당 영향을 미치는 범위 결정. γ가 커지면 영향을 미치는 범위가 줄어들고, Decision Boundary에 가까이 있는 데이터만이 선의 굴곡에 영향을 준다. 따라서 Decision Boundary는 구불구불하게 그어진다. (오버피팅 초래 가능) 작아지면 데이터가 영향을 미치는 범위가 커지고, 대부분의 데이터가 Decision Boundary에 영향을 준다. 따라서 Decision Boundary는 직선에 가까워진다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed847d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        36\n",
      "           1       0.95      1.00      0.97        36\n",
      "           2       1.00      1.00      1.00        35\n",
      "           3       1.00      1.00      1.00        37\n",
      "           4       1.00      1.00      1.00        36\n",
      "           5       1.00      1.00      1.00        37\n",
      "           6       1.00      1.00      1.00        36\n",
      "           7       0.97      1.00      0.99        36\n",
      "           8       1.00      0.94      0.97        35\n",
      "           9       1.00      0.97      0.99        36\n",
      "\n",
      "    accuracy                           0.99       360\n",
      "   macro avg       0.99      0.99      0.99       360\n",
      "weighted avg       0.99      0.99      0.99       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_model = svm.SVC(random_state=42)\n",
    "svm_model.fit(X_train,y_train)#훈련\n",
    "y_pred = svm_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a365dfe7",
   "metadata": {},
   "source": [
    "- 99%의 정확도가 나왔음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26617282",
   "metadata": {},
   "source": [
    "#### Stochastic Gradient Descent Classifier (SGDClassifier)\n",
    "- SGD (Stochastic Gradient Descent)이란? 배치 크기가 1인 경사하강법 알고리즘\n",
    "- 확률적 경사하강법은 데이터 세트에서 무작위로 균일하게 선택한 하나의 데이터 포인트를 이용하여 각 단계의 예측 경사를 계산\n",
    "- alpha : 값이 클수록 강력한 정규화(규제) 설정 (default=0.0001)\n",
    "- l1_ratio : L1 규제의 비율(Elastic-Net 믹싱 파라미터 경우에만 사용) (default=0.15)\n",
    "- fit_intercept : 모형에 상수항 (절편)이 있는가 없는가를 결정하는 인수 (default=True)\n",
    "- max_iter : 계산에 사용할 작업 수 (default=1000)\n",
    "- tol : 정밀도\n",
    "- shuffle : 에포크 후에 트레이닝 데이터를 섞는 유무 (default=True)\n",
    "- epsilon : 손실 함수에서의 엡실론, 엡실론이 작은 경우, 현재 예측과 올바른 레이블 간의 차이가 임계 값보다 작으면 무시 (default=0.1)\n",
    "- n_jobs : 병렬 처리 할 때 사용되는 CPU 코어 수\n",
    "- random_state : 난수 seed 설정\n",
    "- learning_rate : 학습속도 (default='optimal')\n",
    "- eta0 : 초기 학습속도 (default=0.0)\n",
    "- power_t : 역 스케일링 학습률 (default=0.5)\n",
    "- early_stopping : 유효성 검사 점수가 향상되지 않을 때 조기 중지여부 (default=False)\n",
    "- validation_fraction : 조기 중지를위한 검증 세트로 설정할 교육 데이터의 비율 (default=0.1)\n",
    "- n_iter_no_change : 조기중지 전 반복횟수 (default=5)\n",
    "- class_weight : 클래스와 관련된 가중치 {class_label: weight} or “balanced”, default=None\n",
    "- warm_start : 초기화 유무 (default=False)\n",
    "- average : True로 설정하면 모든 업데이트에 대한 평균 SGD 가중치를 계산하고 결과를 coef_속성에 저장 (default=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3278d47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        36\n",
      "           1       0.89      0.89      0.89        36\n",
      "           2       1.00      1.00      1.00        35\n",
      "           3       0.95      1.00      0.97        37\n",
      "           4       0.97      1.00      0.99        36\n",
      "           5       1.00      1.00      1.00        37\n",
      "           6       0.95      0.97      0.96        36\n",
      "           7       0.90      0.97      0.93        36\n",
      "           8       0.91      0.83      0.87        35\n",
      "           9       1.00      0.89      0.94        36\n",
      "\n",
      "    accuracy                           0.96       360\n",
      "   macro avg       0.96      0.96      0.95       360\n",
      "weighted avg       0.96      0.96      0.96       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_model = SGDClassifier(random_state=42)\n",
    "sgd_model.fit(X_train,y_train)#훈련\n",
    "y_pred = sgd_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bffda68",
   "metadata": {},
   "source": [
    "- 96%의 정확도가 나왔음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29e53fee",
   "metadata": {},
   "source": [
    "#### Logistic Regression\n",
    "- 소프트맥스(softmax) 함수를 사용한 다중 클래스 분류 알고리즘이며, 다중 클래스 분류를 위한 로지스틱 회귀를 소프트맥스 회귀(Softmax Regression)라고도 함\n",
    "- 소프트맥스 함수: 클래스가 N개일 때, N차원의 벡터가 각 클래스가 정답일 확률을 표현하도록 정규화를 해주는 함수\n",
    "- 결과값을 확률로 간주학 위해 0~1사이의 값을 반환하는 시그모이드 함수를 이용\n",
    "- 시그모이드 함수: x값이 아무리 커지거나 작아져도 항상 0과 1사이의 값을 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6c8d5840",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        36\n",
      "           1       0.89      0.89      0.89        36\n",
      "           2       0.97      1.00      0.99        35\n",
      "           3       0.95      1.00      0.97        37\n",
      "           4       0.95      1.00      0.97        36\n",
      "           5       0.97      0.97      0.97        37\n",
      "           6       1.00      0.97      0.99        36\n",
      "           7       1.00      0.97      0.99        36\n",
      "           8       0.86      0.86      0.86        35\n",
      "           9       1.00      0.94      0.97        36\n",
      "\n",
      "    accuracy                           0.96       360\n",
      "   macro avg       0.96      0.96      0.96       360\n",
      "weighted avg       0.96      0.96      0.96       360\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "logistic_model = LogisticRegression(random_state=42)\n",
    "logistic_model.fit(X_train,y_train)#훈련\n",
    "y_pred = logistic_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb1c238",
   "metadata": {},
   "source": [
    "- 96%의 정확도가 나왔음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7419ad8",
   "metadata": {},
   "source": [
    "## 전체 평가\n",
    "- digits dataset의 경우 SVM 알고리즘이 가장 정확하였다.\n",
    "- 모델의 전체적인 정확도도 99%로 가장 높았으며, 실제 1이라는 숫자를 1이라고 예측한 Reall도 점수도 1에 거의 가까운 값을 보였기 때문에 digits dataset의 경우 SVM 알고리즘이 가장 적합하다고 생각한다.(f1-score 또한 가장 높음)\n",
    "- 모델의 평가 지표로는 accuracy를 써도 될것으로 생각한다.feature값 또한 이상치로 생각되는 값들이 없었으며, target class의 분포 또한 비슷하게 분류되어 있었기 때문에 imbalance한 데이터도 아니었기 때문이다.\n",
    "- 추가적으로 accuracy를 뒷받침 해줄 수 있는 Confusion Matrix도 함께 사용해도 된다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e170e6b2",
   "metadata": {},
   "source": [
    "## 와인 분류"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d051c06",
   "metadata": {},
   "source": [
    "### 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1a889b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "\n",
    "wine = load_wine()\n",
    "wine.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2704e40a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1ede9da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "03cba841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fb40126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names']\n",
    "wine_data = wine.data\n",
    "wine_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6c4ee782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_label = wine.target\n",
    "wine_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2f00b87b",
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "0                            3.92   1065.0  \n",
       "1                            3.40   1050.0  \n",
       "2                            3.17   1185.0  \n",
       "3                            3.45   1480.0  \n",
       "4                            2.93    735.0  \n",
       "..                            ...      ...  \n",
       "173                          1.74    740.0  \n",
       "174                          1.56    750.0  \n",
       "175                          1.56    835.0  \n",
       "176                          1.62    840.0  \n",
       "177                          1.60    560.0  \n",
       "\n",
       "[178 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df = pd.DataFrame(data = wine_data, columns=wine.feature_names)\n",
    "wine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "72c2680c",
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>178.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.000618</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>746.893258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.811827</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>314.907474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.362500</td>\n",
       "      <td>1.602500</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>1.742500</td>\n",
       "      <td>1.205000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.782500</td>\n",
       "      <td>1.937500</td>\n",
       "      <td>500.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.050000</td>\n",
       "      <td>1.865000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2.355000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>1.555000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>0.965000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>673.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.677500</td>\n",
       "      <td>3.082500</td>\n",
       "      <td>2.557500</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>3.170000</td>\n",
       "      <td>985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.830000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.230000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
       "\n",
       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
       "count     178.000000  178.000000            178.000000       178.000000   \n",
       "mean        2.295112    2.029270              0.361854         1.590899   \n",
       "std         0.625851    0.998859              0.124453         0.572359   \n",
       "min         0.980000    0.340000              0.130000         0.410000   \n",
       "25%         1.742500    1.205000              0.270000         1.250000   \n",
       "50%         2.355000    2.135000              0.340000         1.555000   \n",
       "75%         2.800000    2.875000              0.437500         1.950000   \n",
       "max         3.880000    5.080000              0.660000         3.580000   \n",
       "\n",
       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \n",
       "count       178.000000  178.000000                    178.000000   178.000000  \n",
       "mean          5.058090    0.957449                      2.611685   746.893258  \n",
       "std           2.318286    0.228572                      0.709990   314.907474  \n",
       "min           1.280000    0.480000                      1.270000   278.000000  \n",
       "25%           3.220000    0.782500                      1.937500   500.500000  \n",
       "50%           4.690000    0.965000                      2.780000   673.500000  \n",
       "75%           6.200000    1.120000                      3.170000   985.000000  \n",
       "max          13.000000    1.710000                      4.000000  1680.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08454996",
   "metadata": {},
   "source": [
    "- 특성별로 평균의 차이가 많이 나는 것들이 있어 표준화 변환을 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ee6ce97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    71\n",
       "0    59\n",
       "2    48\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_label_df = pd.DataFrame(wine_label)\n",
    "wine_label_df.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94537e0d",
   "metadata": {},
   "source": [
    "- 클래스의 숫자가 대체적으로 고르게 나뉘어져 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9187fdc",
   "metadata": {},
   "source": [
    "### train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ad5d8149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train 개수:  142 , X_test 개수:  36\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(wine_data,\n",
    "                                                   wine_label,\n",
    "                                                   test_size=0.2,\n",
    "                                                   random_state=42,\n",
    "                                                   stratify=wine_label)\n",
    "\n",
    "print('X_train 개수: ', len(X_train),', X_test 개수: ', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3433a6e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229214b4",
   "metadata": {},
   "source": [
    "### 모델 학습"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b284b0",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f3bcceef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96        12\n",
      "           1       0.88      1.00      0.93        14\n",
      "           2       1.00      0.90      0.95        10\n",
      "\n",
      "    accuracy                           0.94        36\n",
      "   macro avg       0.96      0.94      0.95        36\n",
      "weighted avg       0.95      0.94      0.94        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier(random_state=42)\n",
    "decision_tree.fit(X_train, y_train) #학습\n",
    "\n",
    "y_pred = decision_tree.predict(X_test) #예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2df28e",
   "metadata": {},
   "source": [
    "- 94%의 정확도가 나왔음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "921090d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PDzpSpqepTyIiIhJu1FEjIiKSSVzd/VJ2bN/B/v37GTj0laDjiIiIiEgGUEeNiIjIYYRLR42ceOqoERERkXCjxYRFRESykW+//oa7+/bL8Ovc3bcf1UpXOOi1Xuj/LI1q1ANgx/btdGnejnaNW9C20YUsnDs/w7OJiIiIhDMVakREROSEu+Xu23l95BsHPL9t61b++H1R2uOYHDl4efhgJk//jBeGvMyj9zx0MmOKiIiIhB0VakRERMLIyuUraHXeBXS6oA29OnQHYNzoMXS6oA0tzjmfpx56HAh1xnRt0YEru/WiwZlnMWnsBHp16E6jGvXSulIaVj+bu/v2o22jC3ngtrsPuNY306bT/vyWtGvcgmcffRKAzyZ/yoXnNKbTBW146sHHjvl1FC9Z4qDjwgc9/QLX33pT2uO4uDhKlCoJQHRMNFFRkcd8TREREZGsQIsJi4iIhJFZ02fSvmsn+tx6IykpKQC0bN+GLj264+60bXQhV/bpDUD8vn18MGUiUyZ/wnOPPcXU76cz86sZjBj6FgNee5G9e/bS88rLOLNWDS7r3CPdbUXuzmP3PsiELz8hV65cXNX9Upb+sYRJ4ybw3KsvUr12zbTr/9vFbToTvy8+3XOde3Tjst5XHva1rftzLRs3bKB67ZoHbHN3Hrz9Xm68/ZYj/2KJiIiIZEEq1IiIiISR9t068cITz/K/S6+mWo3q3Hj7zcycNoPXB75MSkoKK5evYMPadQBUrVENM6NEyRJUrlaFiIgISpQqyfat2wCIio7izFo1AKhVtzbLly6jSNGiAGz5awurlq+kZ7tuAOzYsYM/V6+h3/138cqAQezZvZvOF3Xlwrat0uV77+Pxx/zaBjzxDDffdfD1ce679U7Oa9yABk0aHfP5RURERLICFWpERETCSEREBA88+QgAXS5sT6v2rXnq4ccZP3Uy+QsUoE3D5vw9sfHftxb9++9/b09KTOKXBT9TrcaZzJ8zj6YtmrF7124AChUuRNkK5Rn98Tji4uJISUnB3UlISGDAay+SkJBAoxr1DijUHE9HzarlK3nw9nsAWLNqDY/f+zD393+YgU8+R2RkFNfdfMPRfrlEREREshwVakRERMLI1I+nMPSl14mMjKRYiWKcVrYMnbp3oVOztlSsXJG4nDmP+Fw5c+Xk/RGjuOvHOdSoU5MadWrx7dffAKHCzr2PPUiPNl0wMyKjonht5DBef+Flfpz9PYmJifS86vIDznmkHTUv9H+WjydO5q9Nm1m+ZBljPp3I2M8+TNveqEY97u//MGvX/MlTDz1OvfPOpWPT1hQvVeKgixCLiIiIZBf296duIiIicnBm5puSdgYd46g1qlGPGQu+DzpGWCsalRd3P3DVYxEREZGAaOqTiIiIiIiIiEiYUEeNiIjIYWTWjho5PHXUiIiISLhRR42IiIiIiIiISJhQoUZERCSM3HTV/5j/09wMv84zj/SnYfWz+XrqtLTndu7YQaVipzN53EQAxo0eQ8emrenYtDVnV6zBA/3uOeT5vv36G6qfWjFt/40bNgJwwxXXpj13er4S/Lrwl0Oe466bbqNDk1ZceE5jhr70Wtrzd/ftR+sGzWhVv2naYsij3nybOuWqpWUVERERySo09UlERCSbuvOhezm/edO0x68OGETts+umPe7SoztdenQH4OqLLqNt5w7/eb7WHdvy1KAB6Z57ZfgQALZv20b781tStXq1Qx7/2ICniImJISkpiYbVz+aya69i7eo1LPl9MZ/M/IK1a/6kz6XXMOnrKfS86jLWrvnzqF+ziIiISLhTR42IiEgGe6DfPWmdK+vXrqNri1DBo1vLDnRs2prWDZqxfMnSdMd8+/U33N23HwBb/tpCx6atAfhlwc90ad6Ojk1b06/PzZyoteY2bdzEyhUrqVm39gHbdu/axaJff+Ps+vX+8xxTP/6Mto0upP/9jx6Qa/K4D2nTqf1/Hh8TEwNAfHw8p5U5nZiYGIoUK0psXCxJSUns2L6DQkUKH+UrExEREclcVKgRERHJYN16XsS40WMAGP/eWDpf3BWA4ePeZeK0T7j9gbt5beArR3SuB267m1dHDmPitE/IlSsXX33+ZbrtE8eMS7vV6N9/9u/f/5/nHfjks9x0x60H3fbRhEm0bNcGs0OvuVujbi1m/TaHSV9PYcP69UwcMy7d9gnvjaXzxd0O+/puuOJa6lWsSe2z62Jm5M6Th9KnncK5VWrTvVVHbujX97DnEBEREcnMdOuTiIhIBqteuyZLFy1m3759fDJxMu99PI69e/dy9039WLlsBUmJiQd0ivy7KPLv7pRFv/7GdZdcCcCePXuoXK1KuuM6du9Cx+5djirfqhUr2bF9B1WrV+PjCZMO2D7hvbE88OSj/3mO3Llzp/29fZeOzJrxLZ0uChWkNqxbz65du6hQ6YzDZnll+BDi4+Pp0rwdHbp1Zt2fa9m2ZSvfL5rP5o2b6NmhO1/8MOOoXp+IiIhIZqJCjYiIyEnQol1rXnrmBUqWLkWevHn5aMIk8uXPx+TpnzFtylTeeHVIuv3zFcjPuj/XAfDzvAVpz1euVpWh742gUOFCACQmJqY7buKYcQx//Y0Drj9mysS0W4v+v5/nL2TlshVc1LoTK5YtJ3eePFSoVJFKVSuzedNmNm7YSLUaZ6btv3nTZvLmy0uOHDnSntu5Ywd58+UDYNaMbylboVzatgnvj6NDt87prrl2zZ+UOqV0uucSEhLIkSMHOXLkIDYujti4WDwlhfwFCxAREUHuvHnYu2fPQV+DiIiISFahQo2IiMhJ0OWS7pxTqRZvjBkJQN16Z/HiUwPo3qojFatUPmD/KmdWJSIigs7N2lKn3llpzz864Emu63kVyUlJRERE8MTAZ6hU9Z/jj6Wjpm2n9rRNXT/mmUf6U7lalbRzTho7gfZdOqbb/8Hb7+GWu2+nYpVKac9NeH8c77wxgtjYWE4vV4Z7HnsgbdvE98fy5gfvpD1OSkriqm69+Oy7r9Od95qLLmPXzl3s37+ftp07cFqZ0yl96imMHT2Gdo1bkJAQz2333XlUr01EREQks7ETtQihiIhIVmVmvilpZ9AxTqjBL77C+NEfcM9jD6ab/HQkbrji2rRpTsfip+9+YOG8BVzVp/cxn2PUm28z7JXB3P/4Q1zQ6sJjPk/RqLy4+6EX3xERERE5yVSoEREROYysWKiREBVqREREJNxo6pOIiIiIiIiISJhQoUZEREREREREJEyoUCMiIiIiIiIiEiY09UlEROQwYmNjNxaNylss6Bxy4sXGxm4MOoOIiIjIv2kxYRERkQCYWU7gWaAtcJm7Tw84Utgxs4LA60AVoKe7Lwg4koiIiEiG061PIiIiJ5mZ1QXmAvmAGirSHJy7bwUuAp4GvjCzO8wsMuBYIiIiIhlKHTUiIiInSWqR4W7gZqCvu78XcKRMw8xOB94GUgh1IK0ONpGIiIhIxlBHjYiIyElgZmWA6UBToI6KNEfH3VcCTYApwE9mdkmwiUREREQyhjpqREREMpCZGXAZ8BzwJDDQ3VOCTZW5mVlt4B1gAXC9u28LOJKIiIjICaOOGhERkQxiZoWAD4B+wAXu/ryKNMfP3ecCdYDNwAIzaxpwJBEREZETRoUaERGRDGBmFxLq+FgFnO3uCwOOlKW4+z537wv0Bkaa2XNmliPoXCIiIiLHS7c+iYiInEBmFgc8BXQCrnT3LwOOlOWZWWFgMFCB0BjvnwOOJCIiInLM1FEjIiJygphZLeAnoBihsdsq0pwE7v4X0BV4AZhmZreZmX7GERERkUxJHTUiIiLHKXXs9u2E1qK5FXjX9QYbCDMrC4wE4oHL3f3PgCOJiIiIHBV92iQiInIczOw0YBrQGjjL3UepSBMcd18ONCb032SumV0UcCQRERGRo6JCjYiIyDGwkF7Aj8DHQFN3XxVwLAHcPcndnyBUPHvUzEaaWb6gc4mIiIgcCRVqREREjpKZFQBGA/cALdz9GXdPDjiW/D/u/hNQG9hFaIx344AjiYiIiByWCjUiIiJHwcwuABYCG4G67j4v4EjyH9x9j7tfD9wAjDazpzXGW0RERMKZFhMWERE5AmYWC/QHugNXufvnAUeSo2RmRYChwGlAL3f/NeBIIiIiIgdQR42IiMhhmFl1QmvRnEpo7LaKNJmQu28GOgGvANPNrK/GeIuIiEi4UUeNiIjIIaT+En8rcDeh8dtva6JT1mBm5YF3gB3Ale6+LuBIIiIiIoA6akRERA7KzE4BviDUgXG2u49QkSbrcPelQANgFqEx3l0CjiQiIiICqFAjIiJyADO7GJhDqFDT2N1XBBxJMkDqGO9HgA7AU2b2lpnlDTqXiIiIZG8q1IiIiKQys/xmNgp4GGjl7v01djvrc/fvgVrAfmC+mTUIOJKIiIhkYyrUiIiIAGZ2PrAA2AbUdvc5gQaSk8rdd7v7dcAtwAdm9oSZxQQcS0RERLIhLSYsIiLZmpnlAB4DegHXuPsnAUeSgJlZMeANoDihMd6LAo4kIiIi2Yg6akREJNsys2rA90AFQmO3VaQR3H0j0A4YBnxjZtebmQUcS0RERLIJddSIiEi2kzp2uy9wH6HR229qopMcjJlVJDTGezNwlbtvCDiSiIiIZHHqqBERkWzFzEoBnwEXAee4+xsq0sihuPsfQH3gJ0ILDXcMNpGIiIhkdSrUiIhItmFmXYG5wAygobsvCziSZALunujuDwKdgQFmNszMcgedS0RERLImFWpERCTLM7O8ZjYC6A+0c/fH3D0p6FySubj7LKAmYIS6a84NNpGIiIhkRSrUiIhIlmZmDQmN3d4H1HL3HwKOJJmYu+9y96uBO4AJZvaImUUHnUtERESyDi0mLCIiWZKZxQAPA1cC17r75GATSVZjZiWAN4FChMZ4Lw44koiIiGQB6qgREZEsx8wqA7OBM4GaKtJIRnD39UBrYATwrZldqzHeIiIicrzUUSMiIllG6i/J1xPqpLkPGKqJTnIypBYH3wHWAVe7+6aAI4mIiEgmpY4aERHJElJvQ/kEuAw4z92HqEgjJ4u7/w6cC/xMaKHhtgFHEhERkUxKhRoREcn0zKwTMA/4AWigtUIkCO6+393vBS4CXjKz180sV9C5REREJHNRoUZERDItM8tjZm8AzwKd3P0hd08MOpdkb+7+DVADiAXmmdnZAUcSERGRTESFGhERyZTMrD4wH0ghtGDw7GATifzD3Xe6+xXAvcBkM3vQzKICjiUiIiKZgBYTFhGRTMXMooEHgGuB/7n7xGATifw3MysFvAXkITTGe1nAkURERCSMqaNGREQyDTM7A/gWqEuoi2ZisIlEDs/d1wItgfeA78zsao3xFhERkUNRoUZERMKehfyPUJFmONDG3TcEm0rkyLl7iru/CJwP3ASMN7MiwaYSERGRcKRCjYiIhDUzKwZMBq4BGrr7qxq7LZmVu/8K1AMWExrj3SrgSCIiIhJmVKgREZGwZWbtCS0YPB+o7+6LAg0kcgK4e4K73wVcArxuZq+YWc6gc4mIiEh40GLCIiISdswsN/A80Ay41N2/DTiSSIYws/zAy4TWXerp7nOCTSQiIiJBU0eNiIiEFTOrB8wDogktGKwijWRZ7r7d3XsBDwOfmtm9ZhYZcCwREREJkDpqREQkLJhZFHAfcD1wvbuPCziSyEllZqcQWiw7B6FOshXBJhIREZEgqKNGREQCZ2blgZlAfaCWijSSHbn7GqA5MB74wcwu1xhvERGR7EeFGhERCUzq2O1rgNnAu0Ard18XcCyRwKSO8X4euAC4HfjAzAoFHEtEREROIhVqREQkEGZWBJgI3AA0dvdB7p4SbCqR8ODuC4GzgFXAAjO7MOBIIiIicpKoUCMiIiedmbUGFgC/A/Xc/beAI4mEHXePd/d+wOXAMDN70czigs4lIiIiGUuLCYuIyEljZjmB54DWwGXuPiPgSCKZgpkVBF4DqhEa4z0/2EQiIiKSUdRRIyIiJ4WZ1QXmArmBGirSiBw5d98KXAw8CXxuZndqjLeIiEjWpI4aERHJUKm/TN4N9AX6uvv7AUcSydTM7DTgbcCBy919VcCRRERE5ARSR42IiGQYMysDTAeaAnVUpBE5fqmFmabAJ8CPZtZTY7xFRESyDhVqRETkhEsdu30F8AMwDmju7n8Gm0ok63D3ZHd/BmgB3AuMNrMCAccSERGRE0CFGhEROaHMrBAwFrgNaOruL2jstkjGcPd5QF1gI6Ex3k0DjiQiIiLHSYUaERE5YcysBaGx2yuAs93954AjiWR57r7P3W8GrgHeNrMBZhYbdC4RERE5NlpMWEREjpuZxQFPAx2BK9x9WrCJRLKn1I62wcAZhMZ4q1gqIiKSyaijRkREjouZ1QLmAEUIjd1WkUYkIO6+BegGDACmmdltZqaf90RERDIRddSIiMgxSR27fTvQD7jF3d8NOJKI/Evq1LWRQAKhTrc1AUcSERGRI6BPWERE5KiZ2enAV0AroK6KNCLhx91XAI2BL4E5ZnZxwJFERETkCKijRkREjpiZGdCL0G0VzwLPu3tysKlE5HDMrA4wCvgJuNHdtwebSERERA5FHTUiInJEzKwg8B5wF3Chuz+rIo1I5uDuc4DawHZCY7zPDzKPiIiIHJoKNSIiclhm1ozQ2O31wFnuPj/YRCJytNx9r7vfCPQB3jWzp80sR9C5REREJD3d+iQiIodkZrFAf6A7cKW7Tw04koicAGZWBBgClCE0xvvXgCOJiIhIKnXUiIjIQZlZDeBH4BSguoo0IlmHu28GOgMvAV+b2c0a4y0iIhIe1FEjIiLppP6ydhuhtWj6ASNdbxYiWZaZlQPeAXYR6pxbG3AkERGRbE2fnIiISBozOwX4AuhAaC2at1WkEcna3H0Z0BD4BphrZl0DjiQiIpKtqVAjIiIAmFkPYA7wOXC+u68MNpGInCzunuTujwHtgP5mNsLM8gadS0REJDtSoUZEJJszs/xm9i7wINDS3Z/S2G2R7MndfwBqAfsIjfFuGHAkERGRbEeFGhGRbMzMmhAau70FqOPucwOOJCIBc/c97v4/oC8wxsz6m1lM0LlERESyCy0mLCKSDZlZDuBx4BLganefEnAkEQlDZlYMGAaUBHq5++8BRxIREcny1FEjIpLNmFk14AegHFBDRRoRORR33wi0BwYDM8zsBjOzgGOJiIhkaeqoERHJJlLHbvcF7gPuBIZropOIHCkzOwMYCWwFrnL39QFHEhERyZLUUSMikg2YWWlC05y6AfXc/S0VaUTkaLj7YqABoY68eWbWKeBIIiIiWZIKNSIiWZyZdSc0dvtroLG7Lw82kYhkVu6e6O4PAZ2AZ83sDTPLE3QuERGRrESFGhGRLMrM8pnZ28BjQFt3f9zdk4LOJSKZn7vPBmoCKcB8M6sfbCIREZGsQ4UaEZEsyMwaAvOBPUBtd/8x2EQiktW4+2537w30A8ab2aNmFh10LhERkcxOiwmLiGQhZhYDPAJcDvR2948DjiQi2YCZFQfeBAoTGuO9OOBIIiIimZY6akREsggzqwJ8B1QBaqpIIyIni7tvANoAw4Fvzex/GuMtIiJybNRRIyKSyaX+MnQj8CBwLzBME51EJChmVgl4B9gAXO3uGwOOJCIikqmoo0ZEJBMzs5LAp0BPoL67D1WRRkSC5O6LgPqE1smab2btg00kIiKSuahQIyKSSZlZZ2AuMBto4O5LAo4kIgKAu+939/uBrsBAMxtiZrmDziUiIpIZqFAjIpLJmFleM3sLeBro4O6PaOy2iIQjd/+W0BjvaGCemdULNpGIiEj4U6FGRCQTMbPzCN1OkAjUcvfvg00kIvLf3H2nu18J3ANMMrMHzSwq6FwiIiLhSosJi4hkAmYWDTwEXA1c5+6TAo4kInLUUtfVGg7kAS5196XBJhIREQk/6qgREQlzZlYRmAXUItRFoyKNiGRK7r4OaAmMBmab2TUa4y0iIpKeOmpERMJU6i8v/wMeBR4ABmuik4hkFWZWldAY71VAb3ffHHAkERGRsKCOGhGRMGRmxYGPgKsITXR6XUUaEclK3P1X4BxgEbDAzFoHHElERCQsqFAjIhJmzKwDMI/Q6O367v5HwJFERDKEuye4+91AD+BVM3vFzHIGnUtERCRIuvVJRCRMmFlu4AWgKaFFNmcFHElE5KQxs3zAy8BZQC93/yngSCIiIoFQR42ISBgws3MIddFEAjVVpBGR7Mbdd7j7pYQm3H1sZveZWWTQuURERE42ddSIiAQodez2fYQWDb7e3ccHHElEJHBmVhoYAeQg1GG4IuBIIiIiJ406akREAmJmFYCZhBbTrKUijYhIiLv/CTQHxgE/mNkVGuMtIiLZhQo1IiInmYVcC8wCRgKt3H19wLFERMKKu6e4+9/rdt0GjDWzQgHHEhERyXAq1IiInERmVhT4kNCtTo3c/WWN3RYROTR3/xk4G1hBaIx3i4AjiYiIZCgVakREThIzawvMB34BznH334NNJCKSObh7vLvfDlwGDDWzQWYWF3QuERGRjKDFhEVEMpiZ5QIGAC2Ay9z9m4AjiYhkWmZWAHgVqAH0dPd5AUcSERE5odRRIyKSgczsLGAuEEdo7LaKNCIix8Hdt7l7D+Bx4DMzu0tjvEVEJCtRR42ISAYwsyjgHuBG4EZ3/yDgSCIiWY6ZnQq8TejDx8vcfWWwiURERI6fOmpERE4wMysHzAAaAbVVpBERyRjuvhq4AJhMaIz3pRrjLSIimZ0KNSIiJ0jq2O2rgO+A94EW7r424FgiIlmauye7+7PAhcBdwHtmVjDgWCIiIsdMhRoRkRPAzAoD44G+QBN3f9HdUwKOJSKSbbj7fOAsYD2hMd7Ngk0kIiJybFSoERE5TmbWClgALAHqufsvAUcSEcmW3H2fu98CXAUMN7PnzSw24FgiIiJHRYsJi4gcIzPLCTwDtAMud/evg00kIiJ/S739aTBQCejl7gsCjiQiInJE1FEjInIMzKw2MAcoANRQkUZEJLy4+1agO/As8IWZ3W5m+tlXRETCnjpqRESOgplFAncCtwI3u/vogCOJiMhhmNnpwEggkVAH5JpgE4mIiByaPlUQETlCZlYG+BpoDtRRkUZEJHNw95XA+cDnwBwz6xFoIBERkf+gjhoRkcMwMwMuA54DngJe0EQnEZHMKfXW1VHAPOB6d98ebCIREZH01FEjIvIfzKwQMAa4HbjA3QeoSCMiknm5+1ygDrCF0BjvJgFHEhERSUeFGhGRQzCzCwmN3V4NnOXuCwOOJCIiJ4C773X3m4DrgHfM7FkzyxF0LhEREVChRkQkjZn9z8zOM7M4M3sReIPQopP93D0+6HwiInJiufsUoAZQDvjBzKoBmNlQM4sNNJyIiGRbKtSIiABmVhJ4AsgD/AQUJzR2+8tAg4mISIZy97+ALsBA4CszuwUoAtwYYCwREcnGtJiwiAhgZkOAM4AqwG3AKNc3SBGRbMXMyhIa421ARaC8u28LNpWIiGQ36qgRkWzPzM4FegOVgd+ARwAtLikikv28R+jn4zggPzA00DQiIpItRQUdQEQkDJQCZgITgD+AxcDSQBOJiEgQWhEq2p8BNCJUsBERETmpdOuTiIiIiIiIiEiYUEeNSBYSGRu9ISUhqVjQObKKiBxRG5PjE4sHnUNERI5fXFzshvj4BL1HhonY2Bwb9+2L13usiMhBqKNGJAsxM2+yrn/QMbKMr0rei7tb0DlEROT4mZknbV0bdAxJFVWwlN5jRUQOQYsJi4iIiIiIiIiECRVqRERERERERETChAo1IiIiIiIiIiJhQoUaETmsbbOWs/jeSRl+nRXPfcGctq8xp+1rrHplxgHbt3y1mDntX2du5yEsvPxtkvYkAPBb3w+Y12Uo87oMZUb5h9n923oAppd7KO35LV8tzvD8IiKSudzQ726atO3C9G9nU6N+05N+/dvueZAdO3eme27l6jW0v/iyk54FYNmKlTRt15UGLdozbMSoQ+533c13pGVcunwFjVt34vw2nWnesTsrV68B4IVXBtO0XVeatuvKaVXr8NLgN07KaxARyQo09UlEMkzSngSicuU44v2Lda5Jmdub4e7MbT+YYp1qEFsyX9r2AueVpVCTM4BQUWfTxIWU7HkWVQZ1AyBx+z7mdRpM7iolAIg7tQC1xvU+ga9IRESyklnf/8S8mV8Edv3nn3z0hJ4vOTmZ/fv3ExcXd0zH3/tIf55+5H5q1ziTBi060Ll9awoWKJBunz+WLGXTX3+lPS6QPx8TRr1JwQIFmPLFVzw5YBCDX3yWW2+4jltvuA6Ac5u1pVPbVsf+wkREshl11IhkY/u37GFe92GhrpNuw9i/ZTcAi/qNZ26HwczrOpTtP6wCYO+KLfzSexQ/NBnIttnLD3nOxB37WDvye+Zf9Cbr3vnxqPLkLFsYADPDoiOwyPTDICJi/qktJ+9NJNcZRdNt3/zxLxRpXS3tcfy6HcztNITfbnifxK17jyqLiIhkbbfd8yDLVoY6SDZs3JT2/OixE7igfVfOadaGh/o/C0C/+x5m6lehTs+169bTotPFALTs3COtA2XJstB7Y9N2Xel338M069CNbpeFPixISUmh9039OL9NZ5q268ripcvS9v1ry1Z2795D+4svo0Wni3lm4MtpWR5+8jnqN29Lsw7dGD/p40O+ll9+W8RdDz3O+W06s2LVmmP+mixZtpyzatckMjKSxg3O5Yc58w/Y58nnB3FH3+vTHhcqWDCtmBMTE01UVOQB54yNzUHpUiWPOZeISHajjhqRbCwqbyw1Rl1JRHQka0d+z/rRczjlugbs+nkddadcj0VE4CkpbP9uJSl791P13SvY/fM6Vg36mgLnlk13rq1fL2H9+3NI2p1A0bbVqPZGT6Jyh7pp1gz7lr8+/S39tfPHceYbvQ6aa9NHPxN3akFyFMt7wLaN4+ez6tUZROSI4tQbGqbfNnEBZ/Rvn/b4nFm3E1MoF+tH/8Typz+n4tMdj+XLJCIiWdDzTz7Kl9NnMm3y2HTPt2/Vgh5dO+HuNGrVkT5XX07P7l14ecgbNG/SiPfGfcjFXToCMG7kG+TKlZPPvvyaga8O4ZUBTwHQ4oImDHjiYTpecgU///Y7y5avJGfOOL7+eDw/zJnHPY/0Z9zIf24FGvb2KM5vUJ/bbvwfb48ew9gPPwLgoymfM2vqR8TExJCSkpIu5+7de3hr1HtMnvI55U4/jR7dOvPUw/dhFvqQo023XuyLj093TI8uHel9xcHfewHc//l7/nx52bZ9e7rtP81bQNEiRShZovgBx8bHx/Po08/z+gvPpHt+9NiJXNylwyGvKSIiB1KhRiQbS9q+l8X3TGL/lj0k70ogX73TiIiO5NQbGvH7zWOJiI2mTL8LAMhdpThmRo5S+UncfmB3yobx80lYv4PS19SncPPKROT459vLKdecxynXnHdEmbb/sIq1I76n+tuXH3R7sc41Kda5Jn8Om8XqV7+h/AOhVuqEDTtJ3pVArgr/dNnEFMoFQNEO1Vn7zg9H9kUREZFsbdo3Mxn46hBSUpzlK1exdv0G6tSszqLFS9m3bx8TP/6Uj8e8w969+7jpzntZtmIViYmJFClcKO0ctaqHujtPLV2Krdu2s3jZcurVrQ3A2XVqsXzFqnTXXLJsOZd065K6vXZaoebJh+/nulvuwMy4o+8NVK5YIe2YtevXM2zEKBo3OJcre/VIu+bfPv7gncO+1unfzuaRpwYQGRnB1IljsH81su7YuYsC+fOn2//J5wcxeOCz7N6zJ93zycnJXHbdTfS97moqnVE+3bbxkz7mi0kfHDaLiIj8Q4UakWxsw/gF5Kt3Oqf0Po+1b3/PnkUb8eQUirSqQrEO1dkwbh5/vjWbgo0rkO6nNz/wXFUGdWP/X7vZOH4+C3q+RVyZwpS69GzyVC91xB01e/7YyLJHP+HMEZcRGRd9wDVSEpLSCkBR+WKJ3PZPwWjjhwsp2v7MtMfJe/cTkSMKi4xg+3cryFmm8LF8iUREJJt5uP+zTP1wDAXy56dhyw54aptJu1YX8syLr1K6ZAny5s3DhMmfkD9vXqZ/MoEpX3zFq8PeSjuH/es9092pULYMX8+cxSXdOvPDnHmUOf3UdNcsX7YMP86dx3nnnMWPc+elPX9evbNo3qQRM7/7gSeeG8g7Q19J21axQnkWzJrGD3Pm8dY7o7n1199p2awJfa6+nHx58x5RR03j885N11FUoVxZ5i74mRrVqjDj29nc0bdPuuOXr1jFZdfdxL74eBYtXsKLrw3l5j69ufH2e2hY/xw6t2+Tbv+5C37mlNIlKVyo4BF//UVERIUakWytYMNy/HbTB2z7Zik5iufFoiJJ2p3Az1eMxCKMlMRkzniiPUm74g9/MiCmcG5OubYBp1zbgN2/byD+z+3k4cg7ahY/MJmkHfv49dp3AajweDtyVy7O77eMpfLArqx790c2f/QLELptq9ILXdOO3fThQqoNvSTt8d6lm1nUbzyReXIQERNFpec6H8VXRkREsqvunTvQrH03Klc8g5w5/1mU95JunalUtwFjhg8BoF7d2jz1wku06nIJVSqd8Z/n7NCmJZOnfE7j1p0wswNuD7rmsp70uLoPn0z9kioV/zlXl0uvJmH/fhISEnjo7tsPeu6z69Ti7Dq12L9/P598/iV/bdlKvrx5j6ij5v974sF76N33dhITE7n8ku4ULFCADRs3MWjwG/R/8J60hZdXrl5D3zvv4+Y+vflm9ve8/d5Y6tWtzYSPPqV2jTN57vGHAHhv3MS028REROTImftBPhoXkUzJzLzJuv5Bx8gyvip5L+5uh99TRETCnZl50ta1QceQVFEFS+k9VkTkEDT1SUREREREREQkTKhQIyIiIiIiIiISJrRGjYj8p/XvzyH21AIHjOM+0u2Hs27Uj6x/bw4RMZFUer4LcaelX3Bw5/w/WfLAR4BzWt8mFG5eiW0zl7HyhWkAJG7dQ1zZwpz5Ri9WPPcFmz76hZhCuYgpkpuqr/c4pkwiIiInwoh33+f0006l8XnnHtP2wxk2YhTD332fmJgYhr00gLKnn5Zue9N2XUlMTCQ6OpoWF5zPXbfcyLQZM3n82YEAbNmylQrlyzL27WH0H/Ain0+bTkLCfpo0Oo/+D95zTJlEROT4aY0akSwks61Rk7htLwt6Daf2h9ex+5f1rH5tBtUGX5Jun7kdBlP19YuJzBvLvI5DqDvlBizyn2bApY9+Su5qJSjeuSYrnvuCXJWKUbTtmf//UsdEa9SIiGQdWW2Nmq3bttGm26V8M2Ui8xb+woCXXuO9twan26dpu66MGT7kkFOX7nzwMWqeWZVLunVm//79xMTEpB035MVnKV+2TIbl1xo1IiKHplufRASAlKRkfv3faOZ1Gcrypz7nhyYDAVK7VH5m35ptzGn7Gr9e/x4/XDCIjRMXpNt+LHbO/5MC55YlIiqSvDVLs3fZX+m2J8cn4skp5CiRj6hcOchZtjB7l/+zj7uzZervFGlZJe25VQO/Zm7HwWycsOCYMomIiBytpKQkLrm6D03bdeX+x5+iRv2mADzy1ADGffgRK1evoX7ztvTqfQO1GjTj/XEfptt+LH6YM5/GDc4lKiqKs2rXZPHSZQfsYxaaHNWqyyXM//mXdNvcnY8/m0qH1i0B0oo0iYmJ5M2Tm2JFihxTLhEROX669UlEAPjrs9+JKZqHqq/3YNvs5fz12W8H7LN/825qje9N0q4EFvYcTrGONQ56rr1LN/PHXRMPeL7c/S3JW+uUtMdJ2/YSle+f0aekpO/wS9q+j6i8sWmPo/LFkrR9X9rj7d+tIHfVEkTmDP1wWfqq+pS5vRlJO+OZ120Y+c4+jdhS+Y/k5YuIiByzSZ98RvGiRXj3jdeY/u1sJn/y+QH7bNr8F19/PJ6du3bTpltPLurS4aDn+mPJUvrcdvcBzz/18H2cXadW2uNt27dTIH++tMcpKQd2yb//Vqib5tff/+Dy/93ET9P/yTVj1nfUqFaVXLlypj1310OP88GESTQ7v1G650VE5ORSoUZEANi3fAt5apYGSFdM+becZxQlIiaKmEJRpCQkHvJcOcsXoda43oe9ZlT+OJJ+35D22CLSN/lF5YsjaWd82uOknfFE5f+nsLNp4kKK/qtYFF0w9ENlVN5YCjQox54/NqpQIyIiGW7J8hXUrV0TgHr/Kqb8W+WKFYiJiaFwoYLExycc8lwVK5Rn2uSxh71m/nz5WPjr72mPIyMPbJT/+5anqpUrEhUdzb59+4iLC72Pvj9uIhd36Zhu/6cfuZ/+D95Dz2uuZ8qXX9G6+QWHzSEiIieebn0SEQDiyhRk14LQvfu7Fvx50H3sCO8k37t0M/O6DD3gz855a9Ltl7fWKWz/bgWenMKuhWuJK1so3fbIuGgsMoKEjTtJ3rufvSu2EFcmtE9KYjLbZi6jUNMz0vb/u6iTkpTMzjmriTst/flEREQyQvkyp/PTvNAttz/OO/itt3aEb6J/LFlK03ZdD/jzw5x56farV7cWM76dTXJyMnMX/HzQ9WR27twFwIaNm9i7Z29akSYxMZFpM2bSslmTtH0TEkLFo8jISHLnykVcbOwB5xMRkZNDHTUiAkDhllXYNPln5nUZSp7qpbDoY//2cKQdNdEFclK8W23mdhyCRUdQaUAXIP0kqXIPteaXa94FnDK3XUBEVCQAW6cvIf85ZYiI+Sfn0kc+Yc/iTXhKCsU61SBnucLH/BpERESOVIc2Lflg4mSatutKnZrViYmJPuZzHWlHTcECBbisRzcat+5EdHQ0Qwc9B/wzSarhufVo1qEbcXFxJCUlMfDpx9KO/fyr6TSqf27aujQAfe+8nyXLV5CYmMg5Z9WhScPzjvk1iIjI8dHUJ5Es5HinPqUkJhMRHcm22ctZN/x7qg7O3uOtNfVJRCTryOipT3+PwZ7+7Wxef2MEo998PcOulRVo6pOIyKGpo0ZE0vza+10Sd+7DE5OpNKBz0HFEREQyje5XXMv2HTvZv38/QwcNCDqOiIhkYirUiEiaM4dfGnQEERGRTGnCqLeCjiAiIlmEFhMWEREREREREQkTKtSISIbZNms5i++dlOHXmddlKHPav868LkNZ9dJ0ABI27OTHFi8zo/zD7DzEFCsREZFw9fXMWfS9874Mv86rw4Zz3oXtaNK2C3MX/AzAvIW/UKtBM3KVKMtfW7ZmeAYREUlPtz6JSJZw5luXElMoV9rjqPxx1Bx9FUsf/STAVCIiIuFr46bNvPvBeGZ8OpG/tmzlkmv68OWksZQvczozPp1Ih0uuCDqiiEi2pEKNSDa2b9VWfrvxfSJyRBGZMwfV376MjePns+7dH0nem0jB8ytQ9s7mbJu1nFUvfk1UnhzsWbKJMrc3Y8MH84hfs5XKL3YjT/VSfH/+QArUL8vuX9eTp2ZpKjzSJt21ts1cxooBX4I7BRqWp0y/C/jr899Z+cI0InPFkO/s0yl7Z/NjeyEGv1z9DhFxMZS7rwV5qpUkMjaayNhjH48qIiLyX5avXMWl195IbGwOcufKxYejRzB67ASGvT2KPXv30aLp+Txy7x18PXMWTw4YRN48eVi0eAkP3d2Pke+PZeWqNbz12ovUrnEm1c9twvkN6zP/51+pW6sGz/d/JN21ps2YyaNPP4+7c0Hjhjx4121MnvI5Tzw7kFy5cnJevbN59L47j/o1rFy9hqqVKhIZGUmxokVYu249CQkJ5MmT+0R9mURE5BioUCOSjW2ftZwibc/k1Osa4CkpABRuWYVinWvi7sztMJiEK84BICU+karvXclfn/3OyuenUXfK9Wz7djnr3vmBis90ImXvfkr0qEueM0vy85Uj2bXwnxGo7s6yJ6ZQa1xvInPG8EvvUexduplNH/1Cxac7kqd6qbTr/9uCnsNJiU9M91yxTjUo2evsdM9VHXwJMYVyseePjfx24xjOmnrTif5SiYiIpDN95my6dmjLrTdcR0rqe1j7Vi3o0bUT7k6jVh3pc/XlAOyLj2fK+NFM/vRzHnvmeb6f9ilfffMtQ4eP5LUXnmHP3r1c2asHtapXo3Ovq9JuQYLQe+i9j/Tny0ljyZUrJ90v780fS5Yy7sOPePX5p6ld48y06/9bm2692Bcfn+65Hl060vuKXmmPy5c9nbkLFrJ37z7+XLeOVWvWsm37DooXK5oRXzIRETlCKtSIZGNF2p/JqoFf8dsN75O7aglOvb4R22YuY82QmXiKE79qK/s37AQgd5XimBk5SuQld+ViWEQEOYrnJXH7PgAsOpI8Z5YEIE/N0uxdsYWYIqFP5BK37mHf6q0svHQEAEk74olfu53Tb23KmtdmkLxnP0U71qBw80rp8tUYdcURvY6/b3nKVbEYFh1J8r5EIuPUTSMiIhmnW8d2PDHgRS699kZqVKvK7X37MO2bmQx8dQgpKc7ylatYu34DADWqVcHMKFmiONWqVCIiIoJSJUqwddt2AKKjo6hVvRoAdWvVYOmy5RQtWgSAv7ZsZfnKVbS7+DIAduzYweo/13H/Hbcy4KXX2L1nLxd16UDbFum7Uj/+4J3DvoZCBQty58030KZ7L047pRTVq1amcKGCJ+pLJCIix0iFGpFszMwod19LAOZ3f4PCLaqw4pkvqPnB1UTlj2Nu+8G4+987//vAf/6eut0Tk9n963pyVy3BrgVrKdTkDJJ2JwAQXTAXOcsWpvo7VxAZFx3qnnFISUgKdeMkJPFD0xcPKNQcaUdN0q54ovLEkrBpF8l796tIIyIiGS4iIoInH7oXgAs7XUT71hfycP9nmfrhGArkz0/Dlh3S3kPtX++b//7739sTE5NY8Muv1KhWlTnzF9Ligibs2r0bgMKFClKhXFk+HjOSuLg4UlJScHcSEvbz2gvPkJCQQI3zmh5QqDmSjhqAbp3a061Te5atWMkjTw0gKkq/HoiIBE3fiUWysS1fLOLPN2ZDpJGjaB5iTytA0Q7VmddtGLkqFD2qgkdEzhjWj5nLznlryFO9FHmql2LbrOVA6IfSsnddyMJew8HAIiOp8kp31gyeyY6fVuNJyZToUfeAcx5JR42npDCv67BQASg5hTMebwdASmIyC3sOZ8+STexZsomi7atz6nUNjvj1iIiI/JePP5vKS0PeJDIykhLFilL29NPo3rkDzdp3o3LFM8iZM+6Iz5UrZ05GvDuGH+fOp07N6tSpWZ2vZ84CQu+hj91/F226X4oZREVGMXLIy7zw6hBm//ATiYmJXHXpJQfmO4KOGoBLr72R9Rs3UiBffl569gkA1vy5lqtvuo2Fv/xGt8t70/vynlzSrfMRvx4RETk+lvZpuYhkembmTdb1D+TaPzQZyNlf3RLItTPKVyXvxd3t8HuKiEi4MzNP2rr28DsGoEb9piyYNS3oGCdVVMFSeo8VETmEiKADiIiIiIiIiIhIiAo1InJCZLVuGhERkZMlu3XTiIjIf1OhRkQA+P2Wsexc8GeGX2fFc1/w/fkD2Tp9CYk79jGv+zDmdhzM3A6D0430PpR9q7fy9ekPpGXds2QTczsPYW7nIawc9PV/Hrt/yx4W9BzO3A6DWfHcFwB4cgrzugzlhyYDj/eliYhINnLVDbfw07wFGX6dR54aQPVzmzD1qxlpz+3YuZNi5asx7sOPAPh65ixOrVqHpu260rRdVzZs3HTI823fsYOWnXvQtF1XmnXoxrrUyVQHM+WLr2jYsgNN2nah4yVXsHv3nrTnz7mgNQ1atOeuhx4HYO269dRv3pb2qdOpRETk2KlQIyInXZnbL6Bg4wpExERR5cVu1J54HRWf68SyJz477LGrXvya/GefnvZ42eNTOOPxdtQefy27F65lz5JD/3C6+pUZlLi4DrU/vI6dC9ayZ/FGLDKCWuN6n4iXJSIikiEeursfzZs0Sns84KXXObtOrXT7dGzTkmmTxzJt8liKFyt6yHONnfgRDc49m2mTx3LFJRcx+K2Rh9y3aaPz+GbKh3z10ThqVT+T98d/CMATzw3kgxFDmfnZJOYuWMjylasoVbIE777x2nG+UhERARVqRLK0JQ9/zNbpSwBIWL+D+Re9CcD8i99kXpehzGn3OnuX/5XumG2zlrP43klAqANlXpehAOz+dT3zug9jXpeh/HHnBE7EQuSRcdHkKJEPgIiYKCzqv78l7f59A5F5cpCjZL605+L/3E7uKiUAyF2tBNu/W3HI43f8uJJCzUIjwAs3q8j271Ye5ysQEZGspN99D6d1rqxdt54WnS4GSOtAadCiPUuWLU93zNczZ9H3zvsA+GvLVpq26wrAgl9+pXnH7jRt15U+t955Qt43ATZu2syKVaupW6tmuuc//uwLGrXqyP2PP/Wf16pUsQK7Ujtjtu/YSZHChQ65b0xMTNrf9+zdS+WKFQCoWqki23fuJCkpicTEJPLmyXMcr0hERP4/FWpEsrDinWuycUKoLXvjxIUU61gdgDPf7EWtcb05vV9T1gyZeUTnWvLQR1R5+SJqjetNZK4caQWgv238cCHzugw94E/K/qTDntvdWfrwx5x6fcP/3G/VwK847YZG6Z7LWb4w275dRkpSMttmLSdpe/whj0/em5g2cjwqbxyJ2/cdNpuIiGQfPbt3YfTY8QC8N+5DLu7SEYBxI99g2uSxPHDnbQx8dcgRneu2ex9i5OCXmTZ5LLly5eLzadPTbR8z/sO0W5X+/Wf//v3/ed4nnx/EHTdfn+65ujVr8NsPM/j64/GsX7+RMeMnHfL4apUr8u13P1DzvAt4ddhb9Oz+32O3R4+dQO2Gzfhm1vdUKFcWgG6d2tGmWy+qnN2IOjWrU7hQwf88h4iIHJ2ooAOISMbJU70Ue5duJnlfIps//ZUao64gee9+Ft87iX0rt+BJKUQXzJX+oHSDMv/5RG7Poo381uc9AJL37CdXxWLpDivWoTrFOlQ/ppxLHviI/PXLUuC8cofcZ/v3K4k9rSAxRdJ/alf+gdYsvm8Sq16aTmzJ/MQUO/SnepFx0STHJxIZG03Srnii88cdU14REcmaatc4k0WLl7Jv3z4mfvwpH495h71793HTnfeybMUqEhMTD+hAMfvnjfPfnSy//v4Hl1wTKqjs2bOHapUrpTuue+cOdO/c4ajyrVi1mu07dlC9ahUmTP407fncuf95L+/SsS0zvv2Oi7oc/NzPDnqVnt070+fqK/hk6pfc/dATDH7x2UNes0fXTvTo2omXBr/Bcy+9xtOP3M/Nd97P919+QrGiRejV+wa+/e5HzjvnrKN6LSIicmgq1IhkcYUurMzqV6YTWyIfUXli2fzJL0TljaX2xOvY8tVi1r41O93+0fniSFi/A4DdP69Pez5X5eJUfb0HMYVCPwymJCanO27jhwtZ9/b3B1y/xugriYg59LealYO+xqIiOKX3eWnPJe9LJHnv/rRrhbKsY+ec1Sy45C12L9rI3mV/cebwS4ktnZ/qIy7Dk1P49brRFGp6BgDxa7cTWyp/umvlO/t0tk77gyKtq7F12mLK3nPhf33pREQkG2rX6kKeefFVSpcsQd68eZgw+RPy583L9E8mMOWLr3h12Fvp9i+QPx9/rgu9X85b+Eva89WqVOK9NwendZskJiamO27M+A95/SDrw0wZ9266W47+bf7CX1i2YhWtu/Zk2YqV5Mmdi0pnVKB0qRLky5sXgBnffkeFsmUA2LT5L/LlzUOOHDnSzpGS4hQuGMpUqEABtm3ffsh9ExIS0h7nz5eXLVu3ARAZGUm+vHmJiIigQP58bNux/T++oiIicrRUqBHJ4op3rsF35z1PtSGXAJC39qmsGjSd+T3eIlfFAxcbzFW5OEQY87oNI2/tU9Ker/BwG367/n08ORkijDMea5euq+ZYOmri125nxTNTyXf2aczrMpQcxfNS5ZWL2PHjKrZOX0L5B1ql7Vv6mvqUvqY+EJpQVerKc4gplIsNY+ex/r2fwIzSV9cnpnBuAH6+YiRnTb0p3fVOvaEhv/cdy5rB35L/vLIHdAWJiIhc0q0zleo2YMzw0C1O9erW5qkXXqJVl0uoUumMA/Y/s0plIiIiaNahG/Xq1k57fsATD9PzmutJSk4iIiKCgU8+RtXKFdO2H0tHTad2renUrjUQmgZVrXJFqlauyJDhI3nj7XeJjY2lXJnTeOy+OwG4/f5HuPvWm9LlvrH3lVxx/c289ubb7N+/n5ef63/Ifd8YOZqxqZOl8ufLyxsvPw/AfbffzAXtuxEdHcWppUvR8oImR/U6RETkv9mJWthMRIJnZt5kXf+gY/ynNUO/ZeOEBZS9qzkFG1c46D6rXp5O4eaVjrmQkrBpF2sGz0xX6DkUT05hfvc3sJgoao6+Mt22r0rei7vbIQ4VEZFMxMw8aevaoGMctRdfG8rocRN57L670k1+OhJX9OnL8NcGnfB9D2btuvX0uLoPFcuXY+hLAw67f1TBUnqPFRE5BBVqRLKQzFCoyUxUqBERyToya6Emq1KhRkTk0DT1SUREREREREQkTKhQIyIiIiIiIiISJlSoEREREREREREJE1qjRiQLiYyN3pCSkKRRRidIRI6ojcnxicWDziEiIscvLi52Q3x8gt4jw0RsbI6N+/bF6z1WROQgVKgRkaNmZnHA78AV7v71SbpmFLAAuMfdJ52Ma4qIiByMmUUDrwK1gbbuvj7gSBnCzAx4CLgMaOXufwQcSUQkW1ChRkSOmpndC9Rx9y4n+boXAq8AVd19/8m8toiICICZ5QHGAA50d/fdAUfKcGZ2FfAk0MXdZwadR0Qkq9MaNSJyVMysBNAPuPNkX9vdPwf+AG482dcWERExs5LADOBPoH12KNIAuPubhLpqxptZt6DziIhkdeqoEZGjYmZvApvd/a6Arl8J+Aao4u6bg8ggIiLZj5lVBT4GhgBPejb8IdrMagIfAS8Az2fHr4GIyMmgQo2IHDEzq0PoB7SK7r4zwBwDgRh3vz6oDCIikn2YWRPgfeA2d38n6DxBMrNTgU+AacCt7p4ccCQRkSxHhRoROSKpCwpOB0a6+9CAsxQktJjxBe7+S5BZREQkazOzS4CBwMXuPi3gOGHBzPID44EdQE933xtsIhGRrEVr1IjIkeoC5APeDDqIu28FHgNeSC0giYiInFAWcg+hRXSbqkjzD3ffDrQEdgPTzKxIsIlERLIWFWpE5LDMLBZ4FrgljFqcBwOlgDZBBxERkazFzKKA14CLgPrq3jxQ6vTFy4AvgNlmViHgSCIiWYYKNSJyJG4B5rv7V0EH+Zu7JwK3AQPMLCboPCIikjWYWW7gQ6AM0Mjd1wYcKWx5yP3AM8A3ZnZu0JlERLICFWpE5D+ZWXHgduCOoLP8f+4+BVgGaFFhERE5bqnveV8DG4G2QS6cn5m4+xDgKmCSmXUOOo+ISGanxYRF5D+Z2TBgm7uHXaEGwMwqAzOAyu7+V9B5REQkc0p9P/kEeAt4TKOnj56Z1QYmA8+4+4tB5xERyaxUqBGRQzKzWsCnhMZx7wg6z6GY2SAgwt1vDDqLiIhkPmbWCPgAuNPdRwSdJzMzs9MJFbw+A/q5e0qwiUREMh8VakTkoFKnKX0FjHb3wUHn+S9mVojQuO4m7v5r0HlERCTzMLOLgUGExkxPDTpPVmBmBYAJwF/Ape6+L+BIIiKZitaoEZFD6QQUBN4IOsjhuPsW4HHgeY3rFhGRI5E6fvsOQlMNm6lIc+K4+zagBZAIfGFmhQOOJCKSqahQIyIHMLMchH5wvdXdk4LOc4ReA04DWgUdREREwpuZRQIvA5cC57r7woAjZTnungD0BL4BZplZuYAjiYhkGirUiMjB3Az84u5fBh3kSP1rXPfzZhYddB4REQlPZpaL0G05FYGG7v5nwJGyLHdPcfe7gReAmWZWL+hMIiKZgQo1IpKOmRUD7iQ0kjuz+RRYCfQJOIeIiISh1Pe4r4BtQOtwXig/K3H314DewEdm1iHoPCIi4U6LCYtIOmY2BNjl7v2CznIszKwqoR/CK6euXSMiIoKZVSQ0jegd4GGN3z75zOws4EOgv7u/HHQeEZFwpUKNiKQxs5qExmlWdPftwaY5dmb2MpDi7n2DziIiIsEzs/OAccB97h72i+RnZWZWhlAH7GTgLo3vFhE5kAo1IgKkjeOeBoxJbVHOtFKnS/wONHL334POIyIiwTGzrsCrhMZEfxZ0HgEzKwRMBNYBl7t7fLCJRETCi9aoEZG/dQCKAEODDnK83P0v4AlgQNBZREQkGKnjt28DBgItVKQJH6m3JjdPfTjVzAoGmUdEJNyoUCMif4/jfo7MNY77cF4FypuZxnWLiGQzqeO3BwJXAfXdfV6wieT/S+2i6QF8R2h8d5mAI4mIhA0VakQE4Cbgd3efGnSQE8Xd9wP9gAEa1y0ikn2YWU7gA6A60MDdVwccSQ4hdXz3HcDLwLdmVjfoTCIi4UCFGpFszsyKAneTOcdxH85HwFrguqCDiIhIxjOzIsCXwF6gZWZeGD87SZ0A1Qf41MzaBp1HRCRoWkxYJJszs9eBfe5+a9BZMoKZnUnoh/ZK7r416DwiIpIxzKwCofHbY4D7NX478zGzeoQWGX7E3V8POI6ISGBUqBHJxsysOjCVUBFjW9B5MoqZvQYkuPstQWcREZETz8zOBcYDD7n7kKDzyLEzs3KExnePB+7V+G4RyY5UqBHJplLHcU8FJrj7K0HnyUiprfC/AQ3dfVHQeURE5MQxs07AEEJjnj8JOo8cPzMrDEwCVgJXuntCsIlERE4urVEjkn21A0oAg4MOktHcfTPwFKHJViIikkWYWV9CC9G2VJEm63D3v4ALgBzAZ2ZWIOBIIiInlQo1ItmQmcUAA4DbstA47sN5CahoZi2CDiIiIsfHzCLM7HlCC9Ce5+5zgs4kJ5a77wO6A/MITYQ6LeBIIiInjQo1ItnTjcBid/8s6CAnS+q47tuB580sKug8IiJybMwsFngfqAPUd/eVwSaSjOLuyanDDoYAs8ysdtCZREROBhVqRLKZ1PVa7gH6BZ0lAJOADcC1QQcREZGjZ2aFgC+AJODCrLwQvvzD3QcCfQndBtUq4DgiIhlOiwmLZDNm9iqQ6O43B50lCNll0pWISFZjZmUJTQOagKYBZUtmVp/QNKgH3H1o0HlERDKKCjUi2YiZVQOmESpSbA06T1DMbDCwx91vCzqLiIgcnpmdDUwEHnf3VwOOIwEyswqECnbvESrY6JcZEclyVKgRySZSx3F/Dkxy95eCzhMkMytKaFx3fXdfHHQeERE5NDNrDwwDrnb3yUHnkeClvo9PApYQ+nexP+BIIiInlNaoEck+2gClgdeDDhI0d98EPI3GdYuIhDUzu57Q+1YbFWnkb6nv402BPMAUM8sfbCIRkRNLhRqRbOD/jeNODDpPmBgEVDWz5kEHERGR9FLHbz8N3Aw0cPcfg84k4cXd9wJdgF+BmWZ2SsCRREROGBVqRLKH64Hl7v5p0EHChbsnoHHdIiJhJ3X89rvAeYRuUV0ecCQJU+6eTGga1FvAbDOrGWwiEZETQ4UakSzOzAoD95E9x3EfzkTgL+CagHOIiAhgZgWBzwj9jNrM3bcEHEnCnIcMAG4FpppZi6AziYgcLy0mLJLFmdnLhH6OuSnoLOEo9dO3KYQmYW0PNo2ISPZlZqcTmubzMXCnxm/L0TKzBsBYQuPb3ww6j4jIsVKhRiQLM7OqwFdAZX0qeWhmNhTY4e63B51FRCQ7MrM6hKb4PJXdJxPK8TGzioQKfiOBhzW+W0QyIxVqRLKo1HHcU4BP3P3FoPOEMzMrRmgxwnPdfUnQeUREshMzawMMB3q7+8Rg00hWkPq+/hGh9/ZrNb5bRDIbrVEjknW1Ak4DXg06SLhz943As6l/RETkJDGza4FhQDsVaeRESX1fPx8oBHxsZnmDTSQicnRUqBHJgswsGngejeM+Gi8C1c3sgqCDiIhkdanjt/sDdwAN3f27oDNJ1uLue4BOwBLgGzMrHXAkEZEjpkKNSNbUB1hJ6B5tOQLuHk/oF4YXzCwy6DwiIlmVmeUgtH7I+YRuOV0abCLJqtw9CbgBGAXMMrPqAUcSETkiWqNGJIsxs0LA70ATd/816DyZSeq6Pl8Do9x9SMBxRESyHDPLD0wAtgE93X1fsIkkuzCzHoS6Zy9x9y+CziMi8l9UqBHJYsxsEBDp7jcEnSUzMrPawCdARXffEXQeEZGswsxOJdTpORXo5+7JAUeSbMbMGgNjCI1/HxF0HhGRQ1GhRiQLMbPKwAxC47j/CjpPZmVmbwBb3f2OoLOIiGQFZlYLmAw85+4DA44j2Vjqz0qfAG8Cj2t8t4iEIxVqRLIQM/sEmOruLwSdJTMzs+LAL8A5WjtBROT4mFlL4G2gj7uPCzqPiJmVIDS+ex6hf5cavCAiYUWLCYtkEWbWCigPvBJ0lszO3TcAA4Bngs4iIpKZmdnVwHCgo4o0Ei7cfT3QGCgBTDazPAFHEhFJR4UakSwgdRz3AEL3/O8POk8W8QJQy8yaBB1ERCSzsZBHgXuARu4+K+hMIv/m7ruBDsBqYIaZlQw4kohIGhVqRLKG64C1hNp45QRIHdd9JxrXLSJyVMwsBhgBtADqu/vigCOJHFTq+O7rgA8Ije+uGnAkERFAa9SIZHpmVhBYBFzg7j8HnScrSR3XPQMY4e7Dgs4jIhLuzCwfMA7YTWgM8t6AI4kcETPrRag7+WJ3/yroPCKSvalQI5LJmdlAIIe79wk6S1ZkZnUIdSpVdPedQecREQlXZnYKoWk604GbNX5bMpvU253fA25193eDziMi2ZcKNSKZmJlVAr4Bqrj75qDzZFVm9haw0d3vDjqLiEg4MrMahIraA4HnNfJYMiszqwZ8DLwOPKV/yyISBBVqRDIxM/sI+MrdBwSdJStLXWDwZ+Asd18edB4RkXBiZs2BUcCN7j4m6Dwixyv1ff9j4HtC/66TAo4kItmMFhMWyaTMrAVQEXgp6CxZnbuvA55H47pFRNIxsyuAkUAXFWkkq0h9328EnA58aGa5g00kItmNCjUimZCZRREqHNyucdwnzfNAXTNrHHQQEZGgpY7ffgh4EDjf3b8JOpPIieTuu4B2wAbgazMrHnAkEclGVKgRyZyuJfSDw6Sgg2QX7r4PuAuN6xaRbM7MooE3gLbAue6+KOBIIhnC3ROBa4APgdlmVjngSCKSTWiNGpFMxswKEBrH3dzdFwadJztJHdc9E3jD3d8MOo+IyMlmZnmBsUACoTHGewKOJHJSmNnlhG6B7ubuM4LOIyJZmwo1IpmMmT0P5HL364LOkh2Z2VmEPlmrmNoWLSKSLZhZKUILrM4GbtICq5LdmFkz4F2gr7u/F3QeEcm6VKgRyUTM7AxgFqFx3JuCzpNdmdkI4E93vy/oLCIiJ4OZnUlo/ParwDMaWSzZlZlVJ/T/hZeA5/T/BRHJCCrUiGQiZjYJ+Mbdnw06S3aW+qnyAqCuu68MOI6ISIYys6bAe8DN7j466DwiQTOz0sAnwDeEumuSA44kIlmMCjUimYSZNQdeJ9RNkxB0nuzOzB4Aqrn7RUFnERHJKGZ2KfAc0N3dpwedRyRcmFk+Qus17QN6aL0mETmRNPVJJBP4f+O4VaQJDwOAc82sQdBBREROtNTx2/cBjwFNVKQRSc/ddwBtgK3AV2ZWLOBIIpKFqFAjkjlcA/wFTAw4h6Ry972ExnUPNDN9LxWRLCN1/PYQoAuh8du/BRxJJCy5+37gSuBTYJaZVQw4kohkEbr1SSTMmVl+QuO4W7r7/GDTyL+ljuueBbzu7iOCziMicrzMLDfwAeCEbnfaHXAkkUzBzK4C+gNd3P3boPOISOamQo1ImDOz54B87t476CxyIDOrB4wnNK5bv9CISKZlZiUITbOZC/TR+G2Ro2NmLYCRwPXuPjboPCKSealQIxLGzKwCMBuo6u4bg84jB2dmI4GV7v5A0FlERI6FmVUBPgaGAk9q5LDIsTGzmoQKns8DL+j/SyJyLFSoEQljZjYRmO3uTwedRQ4tdUznfKCOu68KOI6IyFExs8bAGKCfu78TdB6RzM7MTiU0vvtL4DaN7xaRo6VCjUiYMrOmwDBC47jjg84j/83MHgIquXuPoLOIiBwpM+sBDCQ0XnhawHFEsozUNQbHA9uBXqlDCEREjogmlYiEITOLBF4A7lCRJtN4FmhgZvWDDiIicjip47fvAp4CLlCRRuTEcvftQEtgD/ClmRUJNpGIZCYq1IiEp6sIfQIzPuAccoRSPym7G43rFpEwZ2ZRwCtAD6C+u/8ScCSRLCl1fPdlhG6BmpW69qCIyGHplwmRMGNm+YBHgVu1AF2mMxpIAXoGHURE5GDMLBcwASgHNHL3tQFHEsnSPOR+Qp23M8zs3KAziUj40xo1ImHGzJ4GCrv71UFnkaNnZucAYwmN694TdB4Rkb+ZWTFC02h+Bq5z98SAI4lkK2bWGhgBXOvuE4LOIyLhS4UakTBiZuWA74Ez3X190Hnk2JjZKGCpuz8UdBYREQAzq0RoCs1w4DF1bIoEw8xqA5OBp919UNB5RCQ8qVAjEkbMbBzwk7s/GXQWOXZmdgqhcd213H11wHFEJJszswaEOv3ucvcRQecRye7M7HRChdMpwO3unhJsIhEJNyrUiIQJMzsfeAuorElPmZ+ZPQKUd3etVyMigTGz7sDLQE93nxp0HhEJMbMChNaL+gu41N33BRxJRMKIFhMWCQP/Gsd9p4o0WcYzQGMtGigiQUgdv3078BzQTEUakfDi7tuAFkAi8IWZFQ44koiEERVqRMLDFcBuQq3pkgWkLiR8D/CCxnWLyMmUWvwfRGgscH13XxhwJBE5CHdPIDQp8hvg29S1CkVEVKgRCZqZ5QUeA27R4o5ZzihC32d7BB1ERLIHM8sJjAMqAw3d/c+AI4nIf3D3FHe/GxgIzDSzswOOJCJhQIUakeDdA3zm7nOCDiInVurigLcAT5lZroDjiEgWZ2ZFga+AHUBrd98RcCQROULu/hrQG/jIzNoHnUdEgqXFhEUCZGZlgB+B6u6+Lug8kjHMbDSwyN0fCTqLiGRNZnYGoSkyo4CH1aEpkjmZ2VnAh8AT7v5K0HlEJBgq1IgEyMw+AOa7+xNBZ5GMY2anAXOAmroNQURONDOrD4wH7nP3N4LOIyLHJ/WDvE+BScDdGt8tkv2oUCMSEDNrBLxNaBy3RjJmcWb2GHC6u18adBYRyTrMrAvwGqHxvp8FnUdETgwzKwRMBNYCV2gqqEj2ojVqRAKQOpFjIHCXijTZxtNAEzOrF3QQEckazOwW4EWghYo0IlmLu28BmgMGfG5mBQOOJCInkQo1IsG4DNgHjAk6iJwc7r4buA8YaGYWdB4RybzMLNLMBgLXEBq/PS/gSCKSAVK7aHoA3wOzUm+JEpFsQIUakZPMzPIAT6Bx3NnRSCAauDjoICKSOZlZHKEifw2ggbuvDjiSiGSg1PHddwAvExrfXTfoTCKS8VSoETn57gamuvuPQQeRk+tf47qfNrOcAccRkUzGzAoDXxLqyGzp7tuDTSQiJ4u7vwxcD3xqZm2DziMiGUuFGpGTyMxOB64D7g04igTE3WcCs4F+QWcRkczDzMoDs4CvCC0cnBBwJBE5ydz9Q6AtMNTMrgs6j4hkHE19EjmJzOx94Bd3fyzoLBKc1ILdT0ANd18bcBwRCXOpi5BPBB5y9yEBxxGRgJlZOULju8cB92l8t0jWo0KNyEliZg2Ad4FK7r436DwSLDN7Aijt7pcHnUVEwpeZdQSGEBrP+0nAcUQkTKTeCjkJWAFcpS47kaxFhRqRk8DMIoAfgAHuPjroPBK81EWl/wA6aL0iETkYM7uJ0Lpm7d19TtB5RCS8pC4u/g5QCOjk7tsCjiQiJ4jWqBE5OS4FEoH3gg4i4cHdd6Fx3SJyEGYWYWbPEVo4tIGKNCJyMO6+D+gOzCc0Eeq0YBOJyImiQo1IBjOz3EB/NI5bDjQCiCP0Q5aICGYWS6iofzZwnruvCDiSiIQxd09291uAocAsM6sdcCQROQFUqBHJeHcB09z9+6CDSHj5f+O64wKOIyIBM7NCwBdACnChu28NOJKIZBLuPhDoC3xmZq0CjiMix0mFGpEMlNqC2ge4J+gsEp7cfQbwI3Bb0FlEJDhmVhb4NvXPJe4eH3AkEclk3H0c0AF4y8yuCTqPiBw7LSYskoHMbDSwyN0fCTqLhC8zK0OoWFPd3dcFnUdETi4zOwv4EHjC3V8JOo+IZG5mdgbwCTAaeFC33otkPirUiGQQM6sPvA9U1DhuORwzexIo7u5XBp1FRE4eM2sHvAFc4+6Tgs4jIlmDmRUFJhOaMHmNu+8POJKIHAUVakQyQOo47u+AF919VNB5JPyZWV5gEdBOE15Esgcz6wM8AHR09x+CziMiWYuZ5QTeBfIAnd19R8CRROQIaY0akYzRk9BikKODDiKZg7vvJPQLm8Z1i2RxqeO3nya0mHgDFWlEJCOkdnR3AX4nNL77lIAjicgRUqFG5AQzs1zAk4TGcacEnUcyleFAbqBrwDlEJIOYWQ5gFNAAqO/uywOOJCJZmLsnAzcR+hljlpnVCDaRiBwJFWpETrw7genu/l3QQSRzSf1h6lbgGTOLDTqPiJxYZlYA+ByIApq5+5aAI4lINuAhA4B+wFQzax50JhH5b1qjRuQEMrNTgXlATXdfE3QeyZzMbDzwo7s/GXQWETkxzOw04NPUP3eo41JEgmBmDYEPgLvdfXjAcUTkEFSoETmBzGwUsNTdHwo6i2ReZlYO+B44093XB51HRI6PmdUmNH3laXcfFHQeEcnezKwSofHdI4BHNb5bJPyoUCNygpjZuYQ+oajo7nuCziOZm5k9AxRy96uDziIix87MWhH6Zeg6d58QdB4REQAzKwZ8BPxM6PtTYsCRRORfVKgROQFSx3HPAl5197eDziOZn5nlA/4AWrv73KDziMjRM7PewKNAJ61bJiLhJnUAxntADqBr6gRKEQkDWkxY5Dj8a4xyDyASeCfAOJKFuPsO4EHgBY3rFslcLORxQovLN1SRRkTCUWoHeCdgGTDDzEoFHElEUqlQI3J8ZppZFeApNI5bTrw3gPxA54BziMhhmFlc6v/GAG8DFxAav7000GAiIv/B3ZOA64HRhMZ3nwmh72VmFhloOJFsTIUakeNTCugNfAskmllUwHkkC/nXuO5nNa5bJHyZWRlgoZnlB6YAuYAL3H1zoMFERI5A6vjup4G7gS/NrClwO6DhGCIBUaFG5PjkA64AYoFRQJ5A00iW4+7TgIXAzUFnEZFDug74CphJaGHObu6+N9hIIiJHx91HA90IddckA9emdgmKyEmmQo3IMUpdNyQfEA2sBGq4+7ZAQ0lWdQdwh5kVDzqIiKRnZjkIdVa2Az4HlhN6bxARyYxKAo8BNwB7gQ7BxhHJnlSoETl2BqwBOrv7Lfr0VDKKuy8BhhP6wUlEwsutQEFCHZUXAmUBrVcmIplVNNCe0Pe1U4Hngo0jkj1pPLeISCaQuvbFIqAloUX/Hnb3dYGGEhHMrBlQFxju7huCziMiciKkdgs2Bc519weDziOS3ahQIyKSCZhZbqAXcDGQA7jN3WcHm0pERERERE40TagREckchhP6nl0MiAfyBppGREREREQyhAo1YSQyNnpDSkJSsaBziJwMETmiNibHJ2px3CPXE3gQaAzkBgoFG0fCUXRk7IaklAS9j2RTURE5NiYmx+v7qogcUlxM1Ib4xGS9T5wgsdGRG/ftT9L3XTnhdOtTGDEzb7ju8aBjiJwU35S8H3e3oHNkNmZWF5gKPOfuTwSdR8KLmfnjjbR0UXZ1/4yS+r4qIv/JzHzjKxcHHSPLKHbDe/q+KxlCHTUiIpmIu/9kZgVdVXYRERERkSxJ47lFRDIZFWlERERERLIuddSIiIhkM8MWdKFHlSHkij7ypY4G/dSEvnW/Yu6G9ykQeypl8p970P3+3l48dxUWb/2SGkU7H3fepdumM3XFU5Qr0JALy9x7TOeYu+F99iZupcEpfY47z5FKTN7HuMW3sCthI8VyVaJt+f5E2D+fkW2LX8Orc1tSLFclANqUe4QSuauxdtdCJi+9l0iLJn9sabpUHEiERfLXvuV8uuxh9ifvpUTuarQu9/BJey0iIofS6PFPmXF/K6b9up6d8Yl0rHNq0JHSrNy8i1tH/cD+pBQuPrcsl55XLt32ffuT6Dvyezbu2EelEvl56qI6REQYnQZ+SWKyEx1pNKlSgr4XVgnoFUh2pUJNJvfHLeMoeeU5eGIyyx/5FIuOJDJXDJVe7kZUvjh2LVzLsnsnY9GRxJbOzxkDu2CREWyZuog1g6YDUO6xNuSpWRqAPb9tYOOYuZR9uPVhrz2nySDqfNWXrV8tZs0LX0NUBFF5Yqn0ajcic+Vg9aCv2TDyR3JVLk7Vty8FIH7NNua1fJVclUJrmJV9pA25q5U44te78YN5rHvrO4p2rkGpa+ofcr/ts5bz10e/Ur5/O5Y9+DGn3X4Bu39Zl/bcwez+ZT0p+xLJe9aRvblsfH8uiVv3UrpPgyPOD7D1q8Uk70ygSIczj+q4jJawYSe/Xf4Oe5f9RfVxV5OnRqkD9tk1709WPj0VT0qhYNMzKH19Q1a/+DXbvl6C708m33llKXPvhQD8eO4AcpTMB0Cx7rUpdlHtk/p6ghAbHbEhIcm1QN9RyhFlG+MTU7QQXyZRu/hFR7R9W/waFm6aeEIKNb9s/pg25R/j1Lx1j/tcRysheQ85InMd07FzNr5Hqdw1aFj5eiYvuYcl276iYsEL0u1zat46XFrt7XTPffvnYFqWfYDT89Vj/B+3sWL7bMoVaMCUZY/QpeJAckYXPObXIyJyLP5u5jU79HIsTase+c/0Ryo5JYX9SSnExRzbr62Pf7iQBzvVpPopBWjz3Be0rVmaArlypG0fPXsFNU8tyA3NK3P3+z8x7bf1NKtWEoAR1zWkUO4chzq1SIZSoSaLyFE6P2e+fyWROWNY//YPrBv+PafefD5rB39LmQdakq/e6Sy+bTw7Zq8g37llWPXMl1SfcA3JuxJY9L/3qPHhtQBs+WIRBZtVPKpr5z+vLAWbnAHAque+ZPPEnynesy7FL65DkfZnsvzBT9Ltn7fOqWmFm6O1afwCqr7Vi5hieY74mHKPtjmi/fb8up7ErXuPuFBzrP7+WmW05D0JROY68jeX6PxxVBt9Bcsf/fSg21MSklj1/DSqvNmTyJwxac+X7tOAU28+H4CFXYaxb8UW4soUIiI2murjrjmu15DZJCR5sbWPHLzLQA6t1EOzVdzKIHsSt/D+731I8WSMCC6u/Dq5Yv7poklKSWDi4jvYFr+GCIukW6WX2Lx3GV+vHkiSJ1AkrgKdKz6f7pxfrnyOYrkqUSpPDd7/vQ8FY09l494/aHzKTVQv2jFt++qdP7J65xyGLejCBafdzpernuOaGuMAmLC4H7WLXcRp+c5Od+4UT2Hi4tvZsm8FZhF0rPAMW+NXs2jLZ6zbvYBzSl51QKFo057FTF56LymeTFx0Pi6uPBgjgtG/9yY+aSdGBB3PeA6A1bvmMOrXK9kWv4bulV6laK6Dfz/etX8TCzdN4Pctn3FWiUupUbTTMX39V+34nian9QOgYqHmrNgx+4BCzZ+75jN0fkeK5apMq7IPEh0ZR9FcFYlP2oG7k5C8m1zRBdkW/yf7U/YxcfEd7EvaQZPTbqVs/vOOKZeIyP/37eKNvDDlN3LGRLF22x4e6VKLBmcUo9PAL6lxakF++XM7L/Q8m4fGz2Pr7gRioyMZdNk5FM0bm3aO92YvZ+ue/VzfrBL1HvqI+mcU5dc/t9Gxzmlc36wS2/fup9+oH9i2dz+xUZEMuqwehfPEHjTP7+u288H3K/lh+V8MuOQsKpbId0yva9mmXdQ6LfS+d94ZRZm3cmu6gtJ3yzZze+uqADSvVpJZSzanFmqMK4d8Q1xMFA90qEG1Uwoc0/VFjpUKNWHMU1JYcvtE9q3YgkUY5Z/pSM5yhdk8+RfWDJpO7Cn5SdqxD4AcxfOmHWfRkVhkqLU6Z8WiJO2Ix91J3p1AdMFc7Fu+hbiyhYjKnYOo3DnwpBRS4hOJiI1m5w+rKd2nIfs37eKPm8eRkpBEdKFcVHyxC5E5Y1j+6BR2/riKnOWL4IkpAET8q8KdvDeRnGcUBSCmaB7i12w74HXtmv8nCzoOJVflYpR5sBWRcdEH7JO8dz9/9B1L4ta9RMZGc8aLXdg2bTG75v3J771Hc8qt5x9Q8Ni/cReLrn+fiBzRRBfORWRqBXxhl2FUHtIj3b5/dwMB/HrZSMo90Za1w2aTvDuerV8sospbvdg6bTHrh3+PJ6dQ/OI6FO9Zlz2LN7Hk1vFEFchJZM5o8tQ65aD/7ea3G0zNydexYdRPrH/nR2p92odVz31JnpqlSdyyJ60T58f6z5Pv3DLs+XU9RTpUp3SfBiRu38eSOyaStH0vETlCr90iI/j9mnfTzl/17UvTFUv+lpKYzLYvF7Np/HwsOpJKr3Q/aL6DiYiNJiL2wP8Wf9s5dw0RsdH8fu17eFIyZR9sRa4qxdP++6ckJhOZO5boIrkB8MRkFnYZRmSeWMo92prYU/UJsMjJFhuZl8urjSIyIpof1o1kzsbRNDrlxrTtP60fRcG40+laaRAQKpTERuXj6hpjAXj/9z6s2TmXU/IevCNu9/7NXFNjPAnJuxjxc0+qF+2Ytu3cUtewZd+KtG6RPOuLsHnvEvLnKM2GPb8fUKQBWLTlM2Ii4+hdcwJ/7pzHZyueoGfVN6lQsAnnlLySUnlqHHBMgdhTuLL6GCIsgs9X9Of3v6ZQKk9NcOeaGuPTXteqHd8TZTF0r/Iqv/71CXM2jKZVuYfSnevnTR+ycPOHmEVQvUgHLj/zXaIjQr9EfL6iP6t3zkm3f9GcZ9C+wpOH/PrvTdxOXFTol4vYqLzsS9yebnuemKLcdtYsckTl5ouVzzBr7TAan3oTlQo2451fr+CziMcpkusMiuWqzJpdc9iw+1durjsDx3lzYVduqvPVf366LSJyNHbs3c/7Nzbmr10JXDb4Gz67M9QlfXa5IjzcuRavT/uDs8oUpk+zSnw4ZzUvff4bj3U9+PvD5l3xPNixBrlyRNH4iSlc36wSL33+G93qlaFl9VJ89vNaXp/2B/d3+Of7+p74RN6dvYLPfl7L6YVz0+Ws03igY42073M9XplOfGJSuut0qnsalzUof8jX9O9l/fLGxbBtb0K67dv3JJA/LiZt+/bU7cOuOY9CuXOwaN0Obhgxmy/vaXmkX0aRE0KFmjC25bNFRMTFUGNC79AtJ098RuWhPVj13JfU+rQPFhXB3KYvpzsmcete1o/4nmqjLgegULNK/HrFO6x4/DNynlGEnJWLseunNUTlj0s7JjJvLInb9xERvZ+oPDmIiI5kzUszKNHrLAq3qcqaV75hw7s/ka/e6exbupmak69j38otzGv5Wto5No1fwJ+vfkNEjihK39DwkK8ppmge6s66jajcOVj5zBesGzaLU25qfMB+G975kbx1T6X0/xqwedLPrHl5BuUeac3GMXOpPKQH0YUObENf8/IMSl51LoXbVGXVgGkkbtlzVF/vUtecm1ZASdy2l/Vv/0D1cVeDwcLOb1C4fTVW9v+cck+2I0/1Uiy+ZdwhzxVXrjB7l2xmx4+riCmah6Sd8ez8cTWlrjuPLZ/8lrZf4ubdlLm/BZG5Ypjb9CVK92nAny/PoFjXmhRqUZktny9i7eBvKdCoHLkqFaPc42052Dqye/7YyIaRP7Jn0UYKnF+Bsg+3TrvtaPvsFax+7ssDjjnjxa7Els5/xF+f/Rt2snfxJmp92oeEtdtZcvvEtE6sFY9NYfOkn8nfqDyROUPFnhofXkt0oVxs+2YZS++ZnPZvUkROnn1J25m09B727N9CQvIuTs9XL932TXuXpCuuRFgEG/b8xpcrnyXZE9ke/yc7C7c95PmL5jqDqIgYoiIKkZSScMj9AOoU78HcjWMolrMyVQsf/Pbav/Yto3SeOgCUzluLrfGrDvsatyes5dPlj7A/eQ+7928mZ3RBzozrQPmC5zPm9xvIFV2QC06/E4ASuasBkD9HKRYlfX7AuX5YP5LoyDjOKXEV5Qs0IsIi07Ydydo48Uk7eefXKwFoW/5x4qLzEZ+0gzwxRYlP2kVcdP50+0dF5CAqIvShQvUiHZm2egAAk5bezaXVRlIsV0WmLH+MBZsmUCrPmZTIXS2tIyp3TFH2JG4hd0zhw+YSETkS/8fefcdHVaV/HP+cmUmZ9EIChN5DL1ZURLC7ImDvrv1nb1vtrmXXLrZVrNhRFF17RVHAggJC6CR0CJDek5k5vz8GAyGUEJLcZPJ9v16+lrlz7r3PZCdP7jxz7nkGdkrE7XLRNt5LeZW/evv+3YJ5JjO7iFMP7ALAAd3bMPnHrF0eq2NiVPUtRmFbv0BevL6An1Zs4dlvFuPz21qzZDYUlPHqjBUc2iuVsw/pzsAdZrG8eVXtzww7mrlsEw9+PB+XMbx73egaxezCsioSo2rONo+PCqegrIrUeC9FZVUkbH3+j1ue0tPi8bhdlFX66n37lUh96N3WjJWt2ELcfsG1Y2KHdqRsVS5VuaVEtIurnk0Rvd36Lv7SShZd/iY97j6xupCx/B//o/+r5xHdp23ww/TUecQMTMNXUL5tv6JywhK8bP5oAYmjegXPnbmFjlcGCy5x+3di8/u/E54SS+x+wRkk3q7JhCVFVR8j9eTBpJ48mHXPz2Ld09/T7badV51dER5cEcG3Xcq4Qax++JudjivNzCH15MFbz9+Z7Lfn7PnntV3MscM6kvvlkj3uA+y08FG+KpfylTnMP/1FAHwFZVRuLKR8TR6xgzpsPUcn/CWVOz1mwvBu5M/MxF9YTtIxfcibvhzr8+PZYXpnRIcEwhKDP0cTFvxAULIkm8KfV7Fu4gysL0BUn1Tih3ejcPZqFl/9DpEd4un8lyOrxwMUzMgi/4dM2l9wICknDaxRyEoY3o2EBrgFyZPgJf6AzrijwonqlYqvaNuHsm63HUfXm49h8ZVvkzdtGUlH9qmOIXFEDzJv/3ifzy8wec4mOidGMLzrzqf/7un5PXl9djaT524i3O3i4bE96JJU8/06d10xt3+ahbVw7eEdObqPpgE3d/M2vUfXuIM4pOOl/Lz+FbJLF9d4PjW6N1n5s6oLOAEbYPrqJziu+220jxnA5EX/h2XXTcYMu57N4TZhBOy2bz67J4zg61UPsaF4Aaf0fmyn+yR7u5OVP4MhbU9mbeEckiL3fCvqT+tfZv92Z9OvzfF8nnUvWIsvUMGB7S/g4LQLmbbqMeZv/gC3CdttvAAXD55CXvla5ma/w3erJ5AWO4iD0v5MG2/3Os2oifTEVd/eBdA1/mCW5n5DSlQvluZ+Ta/EI2rsX+4rItITvJU3q2AWyZHdAbBYosKCv19RYYmU+/JJ9nan3F9Elb8Mi6WoMrt6jIhIQ1iwNo9AwLKluIJIz7brTLcrmDu7p8bwa1YOB/ZI4ZfMLXTf3VIEO5nt17tdPIent2VUv+Dnl0qfv8bzPdvGMf3W4/ltZQ5vzMxk4bp8Rvdvz4WH9yTOG16nGTWH9Epl6vXbbjHtkRrL76tz6d8xgVnLN3H10ek19h/eM5WvM9bTq10cX2Wsr46tqKyKWG8YmwrKKKtQkUaant5xzZi3ezL5M7JIPXkIRXPW4u2cRFhSFBUbC/GXVmLC3JRkbADA+vwsvuJt0i4aXmONFWttdSHAkxiFL78cb7dkyjK34C+txF9cgXG7cEWGkffNMrrfHVzPxdstmaLf1hDxp/4Uzl6Dt3sbvN2S2TR1HgDlq4NFIwiuXfJH8cUTH4kvr3SXr8lXVF5drCiYlYW3+847jvxx/vgDu1A4e/Uux9XaZ+5aItr3o2jO2t2OdUV4qMorxR0dTunSTUCwUGK3/sGI7JxEVM8UBrz1Z4zLRaDKjyvMTWTHRIrnrydmYBpFc9ZW3+a1o/hDurHosreIP7grCcO7sfiqd0g4rHvtgTv5zBDVK5XEw3uQeESwaBao9BGo9NP5+lEALL1pKgUzMqufB0i76GDanrUfOZ8tZOmNUzFuQ+ppQ2lzfL8Gm1ETO7QTax7/DhsIULWlpPr/8z/+/zduF+6ocFyRYQQqfGAtrsgwShZn40mM2sPRpS7OGLrz91tdn9+dvNIq3vxtE+9fPIAFG0u476vVPHt6zdsL7/xsJc+e3pu4CA/jX1zA6F4J1Rdv0jz1SBjBO0uuYUX+98SGt8Ptqvlnf/92ZzN16V94bu54XMbDaemP0z9lDO8svpo2UT1hHzrBx4QH349vLryUwzr+H53i9qNX4hGsK5pHbMTOlyXqm3wci3O+4Lm54wDDuN4P7PE86clH8/GK2/ktezKR7jiiPUnkl69l6tKbcBkPlgCn9nmczPwZdYo7MbIjo7rcwKguN7C64BcKKzbSxtu9Xt2mhrU9g/eW3MBzc8eTEtWLXkmjAXh3yfWc0ucxVhb8yDerHiLcHY3XE8/JfR4D4Oiu/+CNjItxu8IIc3k5o+8zuIybI7v8hZfmn4nfVnFkl7/WmPEjIrKv2sREcsHE71mfV8od44fUev68Q3tw5aQf+ez3dUSEuXjy/IP36vjXHdePv77xC09+uYiAhXMP7c4pB3StNW5Y12SGdU2m0ufnqwUbyC2uIM4bXqcZNTu65aRB3PD6z1T5A5x5UDcSoyPYVFDGxG+XcuvYwZw5vBvXvfITYx/5ml7t4jiyX3sCAcvJE77BG+7GF7Dce/p+e31ekX1ldjabQJxhjLEj1t9T/dgGAiy9cSrlK3PBQK8HxhHVK4VN7//O2qe+J7JTAv6SSrrefAylyzax4taPiekfbJySdGQfOl45gvwZmaz8z5fBblDeMNKfOQNPbCQ5ny9izZPTAeh+1wnEDkoj44LXqm9PqdhYyNLr3iVQ5ScsMYo+T5waXKPmrk8pnL2a6L5tKZi1kv2/v571L/3Ilo8WAOCJ89Lr0ZMJS/Cy8Y3ZZE/+jdIVW4jp1470/55B4W9rWP3QN7iiwwmL99L7sZPxxHvZkb+kgiVXT6EqvxRXhIc+j59KeGps9XozO7v1qWJDIYuvehtXhIeItHhcER563jemep+SJdnVXZ82vTePNU9NJ6Z/e8pX5tLnqdMAWHLVO4S1jaX3oyeT9/VS1r/8Y7CQFeGh/6vnUbp0M0tvfI+wxCg8iVHE9G+/y65PPx/4ED3uPZHko9P5ab8H6PXQOJJG9a7RLWr7tXL++HdVfhnL//4BVXmlELC0O3t/ItLiWPnvLzGeYFEt/b9n4Inb+eJrEFyvJ3faUtqdWfc/LIEqPxnnTKJ02WYi0uJpc9JAOl5+aHXXLE9cJBvf/JXsyb9hfX663Xos8Qd3Y9lfplKWlYOtChC7Xye633E8FRsLyTjvVdwxwZlfPe8dQ3S/mk19vk+7FWttSH3KN8bYhlhM2Oe3XPPeMjYXV7F/p1i+WJLLN1cN4eFpa+iTGsXgtGiumLKMLokRLNlUxjUjOjB2YJvq50/sX/eWy3+YtiyPmVmF3HJMcErzUU/P46srt903Xl4V4NSXM/jo0mC3sv97eyl/GdWJnim1f3/3Voc7ZoXce8Epxhh7z+HrnQ5jl6atepS20en0a3O806GEpFunp+l3SUR2yxhjs586s9b2GUuz+WjOWv59hooSe6PtVW8p70qjUKGmGdmxUNOUKrcUU7JgQ41ZGiKNSYWaXft4YQ4/rSrkX8d3Y9bKAm75OKtWoea0lxcy/ZohFFf4Oee1RXx6+aCdFmqWbynjHx9m1jrHLUd3ZmjHbVOWp/6+mXUFlVw9Inhr35FPzeXrq4ZUP7+xsJKbPljO6+f1A+Bv/1vBaUNSOaBz3Tuw7YoKNQ2nORdqPllxB1tKV3DugEm4jJt5m97jlw2v1xhzdr/nd3s7z4y1z7IoZ9vaMm4TxoWD3mq0mFsaFWpEZE9UqGlYKtRIY9GtTwJAeJsYwh0q0qx9dga5XyyqfmzC3Ax868Ld7pN192cUzd12e1NEhwT6PH5qo8W4O0uunULFuvzqx7FDOu5yjR6RusjKKWdIh2D3rKEddl4I6ZXiJdzjIsnjosIX2OWxerbxMuXC/ns8Z7zXw6Lsbbctuna4pSne66agfNu95EUVfhK9+hMidXdCj7tqPB6cejKDU0/eq2Mc2vFyDu14eUOGJSIiwKG923Jo753flioiTU9X2eK4jpcfSsfLD63T2OzJvxHROXGXhZA/nk8Y3q1esWx4/Rey3/oNV7ibXo+cjLdLzZbSRXPXsuK24MK4na4dSfLR6XS4/FCW/+N/uMLd4DKkXXoIAAU/rSTrns8xbhftztqPtmfsvH2hyI66JkXyy+oiTh6Uwrz1xTsdU9eOvHWdUTO0QwyPfrsWf8CycGMJ3XZYSNgb5sbjMmQXVRIb4SYrp5yuSbu+/U5kb/y2cTKJkZ3plrDzGWl7en5PftnwOr9lv4XbhHNy70dI8nap8fzaorl8vOI2sDCy87WkJx9NXvkanv7tONpGBxee/FOPu6q7RomIyDZvzcqkc5sYDum187Xy9vT8nrw6YwVvzcok3OPi0XMPomubmOrnCkoruei5H6jyB7AW7j1tGIM6J/FNxgYe+SwDj8sQ6w3jmT8PJzoyjH9Mns3i9QWUVfo59cCuXDqq927OLOIcFWqkRdlTsWNfiiFVeaVkv/Ergz+4lOIFG1h53xf0fbbm1NDMOz6h78Sz8MRFMG/c8ySN7k1U71SGfBj8hjd78m9smPQTXf9+NFn/+ox+L59LWFIUv5/6AknH9iUsYd/X85DQd1x6Eh9m5HDqSxkMSouubmtZH3WdUZMYFcZpQ1IY/+ICwlyGh8b2AGp2krr92C5cOnkJ1sKNR3TE49ZMX2kYw9qdsU/P705pVR6/bnyDS4d8wIbiBXyRdR9n9nu2xphPVtzBWX0nEuGJ4/m54+i9ddHfznH7cd6AV+p9bhGR1uDM4TtpmLEXz+9OXkkFr89YwUc3HcX8tXnc+/48nrtk2xe84R4XT15wMO0Toli2sZCb3/mVd64ZxWF9UhndP9jB6cGP5zP119Wce2gP/nXKUMI9bnz+AIff8ynnH9aDiDAtzC7Njwo10ixZn5/FV0+hanMRcQd0IefzRew37VpWPfQ1UeltiR3cgcVXTCaycxIlS7LpdM1IUscNqn4+5cS9/9azaO5a4od3w3jcxA7pSNmKLTWeD5RXYf0BItrHAcGuXGWZOUT1Sqke4ysqJ7pvcNFcf3kV4SkxW8e2oXjuWq0BJHXicRuePKUnYW4Xs1YWsL4g2Ab+plGdqse8ck7f6n9/s3Utme2fr4/zD2jH+QfUXPR5+05SwzrG8r9LBu7TOaR181sfUxZfTVHlZrrEHcCinM+5dv9pfL3yIdpGp9MhdjCTF11BUmRnskuXMLLTNQxKHVf9/ICUE/f6nGuL5tItfjhu46Fj7BC2lK2o8XxVoJyA9RMXEbygT47qTk5ZJh5XJGuL5vLc3HG0je7L8d1vJ8ytYruItF4+f4ArJ/3I5sIyDuiewme/r2P6rcfz4MfzSW+fwOAuiVz+4iy6tIlm8foCrju2H+P371L9/Jhhe3+dMmdlLof2TsXjdjG0SzLLNxXVeN4b7qlunR3mceFxBb/cCt+uvXhphZ8+7eNrbK+o8tOlTQzhnvp/GSbSmFSokWYp5/PFhKfG0PeZM8iflUXO54tqjancXMyg9y7BX1TBgnMmkTpu0E6PVbp8M8v//kGt7d1uPY7YoR2rH/vyyvDEb7uVwwZqLrRdlV+GJ27bRbonPhJffhkA+T+sIOu+L/AXVtD/1fOCz0dHULo82EGpcPZqEkb02IufgLR2l01eSmG5j0q/5eGxeu9IaFi85XNiwlM5o+8zZOXPYlHO57XGFFdu5pLB71HhL2LS/HMYlDpup8faXLqcD5b9vdb247rdSse4odWPy3x5RHriqx9bW3NNp7KqfLyeuOrHke54yqrySYsdxI0HzCTCE8NXKx9g5rrnGdn5mr19ySIiIeOz39eRGhfJxIsOYeayTXz2+7paYzYXlfPBDaMpKvdx1lPfMn7/Ljs5EizPLuSvb/5Sa/tt44YwrOu2pgh5pRXER4VXPw7sohGOtZY73p3DVUenV29795eVPPnlYiI8rhrbr570I98u2sgFI3pg6novuUgTU6FGmqWyzC3EDgl2n4nbrpiyvajeqbjCPbiSPQQqfLs8VlTPFAa9e8kez+lJ8FKyaGP1Y7PDYqqeeC++wrLqx77CCjxbb2VKOKwHQz+5gpzPF7HyP1/S99kz6fmfk1hx60eYMDdRvVMJb7vv3XGk9Xjp7PQ9DxJpYbaUZdIhZghAjWLK9lKje+NxheNxJeMLVOzyWClRPblk8Lt7PKfXk8BG/3YL1hvXDs/HU+YrrH5c4S/EG5aAxxWBxxUBwKCUcXyz+uE9nktEJJRlbi5iSOfg+o3bF1O216ddHOEeN8kxbsqrdtPsoG0cU68/co/nTIgKZ+G6gurH7l0UVm555zcO6ZXKYdstiHzKAV055YCuPDdtKU9/tZjbxw8B4MkLDqa8ys+pj0/jpGGdq2fbiDQnKtRIsxTZLZnCX1aTevIQiubVrtYDda6A13VGTezQjqx+dBrWH6AkYyPe7jX/ALm9YRi3i8rsItyxEZRn5eDtlkSgwocrIvir5ImPxBUZ/Hd0v3YMfOtC/KWVLLrsLeLqMd1TZF/MzCrg44U53Pun+t8bXhe3fJzJJwtzOaFfUqOfS1q2ZG83Vhf+wpC2J7OuaN5OxxjqltvrOqOmY+xQpq16lID1s7E4g2RvzfdomNuLy7gpqsgmwhNLTlkWSd5ulPuKiPQEC+xZBbNIjtR7W0Rat25tYvk5czOnHtiVuatydj6ojtfndZ1RM6xrMg9/moE/ECBjbT7dU2t/8fnYZxl43C4uH92neltFlb967Zn4qDDyStw1tkd4XESGuYnU+jTSTKlQI81Sm+P6suXDBfx+yvPEDOqA2YckWtcZNWGJUbQ9bSjzxj2HCXPT++HxQM1OUt3uOJ6Fl7wBQOcbR2E8bnK+yGDd87MwLoPxuOj14DgA1j79PbnfLMW4DF3+dlR1MUck1FwzoiMn9Evmk4W7uGgT2apvm+NYsPlDnp93Ch1iB+F2hdX7WHWdURMVlsjQtqfx3LxxuE0Y43sHZ8Zs30nq+O538MaiS8DCqC434jYelhX8yDerHiLcHY3XE8/JfR6rd6wiIqHg+MEd+N9vqxn/2NcM7py0T+u71HVGTWJ0BGcc1I2THvmaMLeLR885ENjWSapLm2ju/2gBB/Vow/jHvqZdfBT/vXA4r8/M5MM5qwGI84Yz4byDALjkhRkUl1dR6Qtw4pBOdNmug5RIc2LsLu7zk6ZnjLEj1t/jdBjNRqDKjyvMTf6sLDa8/FOtDkzSsn2fdivW2pC6MdgYY9fdVff2watyy7n63WVEeFxEh7uZdE46U3/fzOu/bqKsys8RPRP46+jOzMwq4PHp64iNcLNsSxk3jerElLmbWZNfzoTxPRmYFsOoJ+dySLc4MjaWMjgthruO71pjRs0PmQU88u0arIUR3eO5cVQnvliSy2PfriUq3M2BnWP525Gd6/3a92X2Toc7ZoXce8Epxhh7z+HrnQ5jt/yBKtyuMLLyZ/HThpc5s++ze95J6uTW6Wn6XRKR3TLG2Oyn6n9NXeUPEOZ2MXPZJl6avoznLj50zzuFsLZXvaW8K41CX/FLs7Xo0jfxFZZjq/z02jq7RSSUzFpZyIn9k7n8kDQCWxevPjY9ifGDUrDWMu6FDC44INjxqdwX4M3z+/LFkjwe+XYtn142kBkrC3l1djYPnBRDaVWAs4a1ZUD7aC56czHz1xdXn8day31frmLKhf2JCndz6eQlLN9SxkcZOdw/pjsD02Kqz7+9c19dRLmv5v3l4wa24dz929YaK1JXby66lHJfIf5AVfXsFhERaRkufm4GhWWVVPoD1bNbRKThqVAjzVb/l891OgSRRjWmfzITpq/l6inL6N8uiisO68APWQVMnLkBay2r8srZWBQs1PRrG4Uxhnax4aSnRuFyGdrHhpNfFlxI2+MyDGgfDcDgtBgyc8tJiQ7eVpJb6mNVXgXnv74YgIJyH+sLKrhhZEf+O2M9pZUbGDuwDUf3SawR32vn9UWkoZ3b/2WnQxARkXp65f9GOB2CSKugQo2IiENcBm4+Oti28oxJCzkmPYkHv1nD2xf0I8HrYewLC/jj7tTt1+bb/t9/zIPxBSwZG0vo3y6a39cXM6pXAsUVfgCSojx0T47k1XPT8Ya5CQQsFqj0BXjgpB5U+AKMfmperUKNZtSIiIiIiDQ9FWqk1cifmcmWjzLoed+YRj3P76c8j60KYMJcJB7Ri07XjARg1UNfk//9CtwxEfR5/FTCkqMbNQ5p/r5cmseLP23EbSA1NpwuiZGMHdCG015eSO8UL969WEQ7KszF23M2M3ddFoPSohmUFsPMrGA7S2MMfz+yM+e9FpxR43EZnjylFxNnrWf2miKq/Jazh6XWOmZdZ9RM+G4tny7KZUtJFVk5C3nj/H51jlukPjLzZ5Kx5SPG9LyvUc/zxsJLKK3KpcpfxohOVzIgZQyV/lKmLL6GMl8BcRHtGNf7IcJckY0ah4hIqJqxNJuP5qzl32fs16jnGf/Y11T5LWFuw6h+7bn2mH58Om8tEz5fSITHTbsEL09ecDBh7vovkCzSkFSoEWkE/V46p0YhpmRJNkXz1jH4g8vY/OEC1j79Pd1uO87BCKU5OGlAG04a0KbGtqtHdODqER1qjT2kWzwAgzvE8Nj4ngD0TPHy3BnBVpTGwF3Hd621zx/7HdY9nsO6x9d4/o/ZPPvqupEduW5kxz0PFGlhTk9/Go8rnApfMc/MOYEBKWP4ZcOrdEsYzvAOlzBr3fPM2fg2B6ad73SoIiKyB5MuH0FyTET14wEdE/nwpqMIc7u4+/15fPDrak49sKtzAYpsR4UaaTbKVuWy5Op3cEV4cEeF0/+V89j03jw2vjEbf2kliUf0ouvfjiJ/ZiZrJnyHOzaC0mWb6fKXI9n0zhzK1+TRZ8KpxAxK49cjHif+kG6UZGwgZkhHetx1Qo1z5f+wglUPfwMWEkb0oMtNo8n5YjGrH52GOzqcuAO70PVvR9XvhRjDwovfwOUNo9stxxIzoD2FP60i6ajgB+qko/qwbuKMff1xiYg0W7llq3hn8dV4XBGEu6M4b8ArzNv0HrM3vEFloJReiUdwVNe/kZk/k+9WTyDCE8vm0mUc2eUvzMl+h7zyNZzaZwJpsYN4fPYRdEs4hA3FGXSMHcIJPe6qca4VeT/wzaqHAUuPxBGM7nITi3O+YNqqRwl3R9Ml/kCO6vq3er0OjyscgEp/CanRwRyeU5bF4NSTAWgfM5Cf1r+sQo2IhKyVW4q58uVZRHjcREd4eO2Kw3n3l5W8NiOT0kofo/u15+8nDmTG0mwe+3whsZFhLNtYyF//NIC3f1rJ6pwSnjz/IAZ1TuLwuz/h0N5tWbA2jyFdkrj71GE1zvX9kmwe+ngBFsvhfdrxlz8N4PP563jkkwyiIjwc1KMN/xgzqJ6vxHDhxO/xhnu4bexgBnRKpNN2X6qGe1x4NJtGmhEVaqTZKJiZRZsTB9Dx8kOxgeC6GMnH9SX15MFYa/l97HNU/rkIgEB5FQPe+jO5ny9m1SPfMPSzK8ifkcWG136m1wPj8JdW0u6s/YgZmMbCC1+n+Pdt7WqttWTd+wWD3r0Yd1Q4Cy99k9Llm9ny0QJ63T+WmEFp1eff3oJzJhEor6qxLWX8YNqfe0CNbX2fPZOw5GhKlmSz5OopDPvyKqryS4nsnASA2xuGv6SyQX92It9cNcTpEESqZRXMZEDKiRza8XICNphP+yYfx+DUk7HW8ty8sRRV/hmAqkA5f+77FotzPueb1Y9wxdDPyMqfwc8bXmNc7ANU+kvZr91ZpMUM5PWMC1lf9Hv1eay1fJF1LxcPfpdwdxRvLryUzaXLWbD5I8b2up+02EHV59/epPnnUBUor7FtcOp4DmhfexH7l34/g40lCzm66z8AaBudzvK87+gSfyAr8qZT7itoqB+biEizM3PpJk4a2on/OzK9ukPkcYM6csoBXbHWMuaRr7lwRHCmb3mln7evPoLPfl/Hw59m8MXfjuGHpdm88sMKHjo7idJKH2cf0p2BnRK54Nnv+X11bvV5rLXc8/483rt+NNERHi5+7geWZxfy4W9rePCs/RnUOWmnHSrPeuo7yqt8NbaN378L5x/Ws8a25y85lOSYCBavL+CqSbP4+p/bZrav3FzEtIUbuOE43botzYcKNdJspJw0gNWPfcviq94mpn97Ol45gvwfMlk3cQY2YClblUvFxkIAovu1xxhDePs4ovu2xbhcRLSLw5dfBoAJcxMzMA2AmCEdKMvKISwlWDWvyi2lfHUuGee9AoCvoJyKdQV0vmEUa//7Pf6SSlLGDSL56PQa8Q14/YI6vY4/bnmK7tMWE+bCX1aFJ96LvzD4oSBQXoU7Onwff1oiIs3XgJST+Hb1Y7y96Crax/RnRKcrycz/gRlrJ2IJkFu2isKKjQC0j+6HMYa4iPa0jeqLy7iIi2hHmS8fALcJIy1mIAAdYoeQU5ZFdHgKAKVVueSWr+aVBecBUO4roKBiHaO63MD3a/5L5doSBqWOIz356BrxXTDw9Tq/lgsHTabMV8Czc/7EgJQx7NfuLD5ZcQcvzDuNtNgBxITXXt9JRCRUjB3WiUc+W8gVL81iQMcErjq6Lz8szeaZr5dgrWXl5mI2FASvv/t3TMAYQ/uEKPqmxQc7VCZEkV8a/ILS43YxsFOwccGQLklkbi4mJTZ4K1JOcQWrcoo597/fAVBQWsW63FJuOr4/T321mJKKJYzfvzPHDKx5e/ibV42s0+v445an9LR4PG4XZZU+vOEeNheVc+WkH3n6z8MJ99R9bUCRxqZCjTQfxtDtlmMBmH/6iyQd25dVD3zFwHcuwpPgZd5JE7e1uDE19/vDHx1ybJWf4owNxPRvT/G8dSSO6oW/uAKAsKQovN3b0P+1C3B7w4KzZywEKnz0emAcgQofv45+olahpq4zanxF5XhiI6ncVESgtAq3N4z4g7uy8t9f0v78A8n9ZilxBzTM2iASOq6fupwLD2zH4A4xjXqeh6et4aOMHO46viuH90jglo8z+WRhLif0S+LeP3Xf7b5fLsnj8elrMQb+dXw3huwm1p2N/WppHv/6fCVnD2vL/x2a1tAvTZoRg+HYbrcA8OLvp9M3+Vi+WvkAFw16B68ngYnzTqI6oW+Xw832Lc22Pu+3VWwozqB9TH/WFc2jV+IoKvzFAESFJdHG250LBrxGmNu7dfaMxReoYFzvB/AFKnhi9uhahZq6zKgJ2ADW+nG7wghzefG4IvG4IvC4Ijip138A+Hb1BLrFD2+An5iISPNkXIbbxg0G4NTHp3HcoA7c/+F83r1uFAlR4Zz48FfbOlRud4FeI5tvHeDzB8hYm0f/jonMW53L6H7tKd56bZ0cE0H31FjeuHIk3nDP1g6VlgpfgIfOPoCKKj8j7/20VqGmrjNqisqqiPWGsamgjLKKYJGmpLyKi5/7gX+dMpTuqbEN8eMSaTAq1EizkfvVEta/MAvcLsJTY/F2SSRl7EDmn/YiUb1ScHvD6nwsd1Q42W/PYcWcD4kZlEbsoA7kz8wEgh8Euv79KDLOnQTGYNwu+jx1GuuenUHh7NVYX4B2Z9Veeb4uM2psIMD8U1/E5Q3D+gP0uOdPQHB2TXS/dswbOxF3TAS9Hz+1zq9FpKHdNKoTh/dIAOCaER05oV8ynyzM2e0+/oDlwW9W895FAyiq8HHFO8t4/+IBezX2qN6J5JRUkVfq2+l+EjqW5H7FrHUv4DJuYsNTSfR2YWDKWF78/TRSonoR5vLW+Vjh7ijmZL/Nh8vnkBYziA6xg8jMnwkE8/lRXf/OpAXnYjC4jJvT0p9ixrpnWV0wm4D1sV/7s2odsy4zanyB8uqZOn5byWEdr8TjimBjySI+Wn4rLuOma/xBpCcfU+fXIiLS0nw5fz3Pf7sUt8uQGu+lS5sYxu3fmZMnTKN3uzi84XX/OBkV7uGtH7OYs+pXBnVKZHDnJGYszQaC+fyfYwZx9tPfAQaP2/D0n4fzzNdLmJ25hapAgHMO6VHrmHWZURMIWE6e8A3ecDe+gOXe04PX+c9OW0LmpmLu/WAeAGcP785pB3Wr8+sRaUzmjwqnOM8YY0esv8fpMELCr6MeZ79p1zodhuzG92m3Yq01ex7Zchhj7Lq7tn27fudnKxndK4HDeySwobCCG6au4K0L+nHWKwup8lsq/QEeG9+T7sne6hk1JZV+Pl6Yw71/6k5uSRWXvb2UKRf2J2NjCXd9tpKAhe7Jkdw/pvsOsw/q5uFpa+iTGsWJ/ZOrt83MKqg+564s31zGQ9PW8MzpvQE4ceJ8plzYn8iw2gvv7W7s5DmbyCv11ZhR0+GOWSH3XnCKMcbec/j6PQ9sQR6fPYpr95/mdBgtwq3T0/S7JCK7ZYyx2U+d6ci5D7/nU6bferwj524sba96S3lXGoVm1IiINJJTBrXhhZ82cniPBD6Yn8O4gcFW3C+c2YeocDffLs9n4swN/GfM7m85Arjj05U8fWovUmPDueuzlXy3ooAjeiZUP//Bgi28+kt2rf3eOK8v4Z5972KQV+Yj3rvtT0ZcpJv8Mh/twmqvt7Q3Y0VEREREpCYVaiQkaTaNNAcD02JYvqWMsio/ny7K5bVz0ymr9HPzx1msyi2nKmBJjqp5S9/2k2S2n++4ZFMpV05ZBkBJpZ/0tlE19hs7oA1jB7RprJdCgtdNYfm2W5YKK/wkeHf+J2RvxorsiWbTiIiEhlCbTSPSmHTlLM3SkuvfJe3Cg4kd3GHPg/fBqoe+ZstHGXS/6wQSR/Zk+c0fsuWTDNqc0J+e943Z4/7lq3OZffgEBn9wGbGDO1C6bDPL/v4BAIlH9KTztUfsct+qnBKWXDsFf3EFCSN60OUvR2L9Aeaf/iJVuaUqNoWIY/ok8vQP62kfF05spIdPFuYQF+lm6sUDmLYsj5d+3lhjfHykhw2Fwe4ICzaUVG9PbxvFs6f1Jik6WNip8tdsOdyQM2rKqvyUVQaqzwXQLclLZk45pZV+iiv8eFyGyDDXXo2V1undJddzcNqFdIgd3Kjn+XrlQ2Rs+YgTetxFz8SRrC2ay8crbgMLIztfW2tB4e19uPxmMrZ8Qv82JzCm5327Pc+zc8fgNuH4AhUc2+0WuiUMp8xXwFsLL8dvqzC4OC39CeIi2vHR8luYm/0uNxw4g+iw5N0eV0Skubv2lR+5aGRvhnRJatTzPPjxfD78bQ13nzqMkX3bAVBYVslBd3zEA2cewJhhnZixNJsrX/6R7qnBxgbPXngIqfE7X//s3V9W8tqMFQCszyvj2EEd+NcpQ3c6NhCw/GPyryzekE/beC+Pn3cQ3nAP/3z7V6b8vJIf7zyxuoOUSGNSoUZavc5/GU3iyODK8J2uHUmbP/Vny8cZddp39YTviD9wWwenrHs+p+c9JxLdrx0LL3mD0mWbieqVstN91zz1PW3P3I+UMQNYcN4rlCzdRHTvVAa9ewm/jnp831+YNAsnD0rhsMfnMHHrei3DOsbyxPfrOPuVhfROjao1vm/bKFzGcNrLGQzruK0DwZ3HdeXKKcvwBSwuA3ef0I0+2+1f3xk1E75by6eLctlSUkVWzkLeOL8fv6wuYvqKAm49Ztt72+M23HRER86YtBBjgvEAezVWpLGN7vIXeiYGF5b8ZMUdnNV3IhGeOJ6fO47eSaNxmZ23Xh3Z6Vr6t/kTGVs+3uM5Lh70Lh5XOHnla3hn8dVcNuQDFmz+iK7xBzGqyw3MyZ7Czxte4aiuf+PEnveysWRxg75GEZHW4K9/GlhdpAF4+qvFDOtas+B9wuCO/PuM2g1AdnTKAV055YCuAFzy/AxOHNJxl2O/XrgBlwv+d+NRPPXlIt76MYsLD+/Fv0/fj8Xr8+v1WkTqQ4UaaTKZd35C4qjeJI7sScWGQpZe/y4DJ1/I/DNfwlb5CVT66TPhFLzdt33YzJ+ZyZaPMuh53xiqckpYdNmbDHr3EoozNpB516fgD+Dt0Yae94+t18KqO4poF0dZ5pY6jS1ZtBFPbAQ2Lb56W/naPKL7Bf+oxAxoT8GPWbss1BT+sooufz0SgKSj+lD440qie6fu4yuQ5qZjQgQrbz+4+nG7uHA+vXxQrXGPjd/WRvL5M/vUer5/u2jeuqDfPscTF+nhvzPWERfp5vAeCVw3siPXjax5wTJ/fQmnDan9vj0mPYlj0pPqPfarpXm8/PNGzt2v7T6/DnHWJyvupHfSKHomjqSwYgPvLrmeCwdN5qXfz8Rvq/DbSk7pM4E23m3rL2XmzyRjy0eM6XkfJVU5vLnwMi4Z/C4bijP4NPMuAtZPG28Pxva6f5/zeVWgnID1ExfRHoDkqO7klGWSEtVrp+PjItqxpSyzTsf2uIJrLZX7CmgfHfydTInqSc7W/ct9BZo9IyItxu3vzuHIfu0Z2bcdG/JLufbVn3jnmlGc/sQ0qvwBKn0Bnjj/4Brtq2cszeajOWv59xn7kVNcwSXP/8DU648kY20et787h4C19EiN48Gz9m+Q6/NNheWs2lJSaybPlwvWsWBtHsN7pfLPMQP3eK7i8ioWry/ggO67/mLrx+WbOXpAsOHB0QPTeOiTDC48fOd/O0Qakwo10mRSTx7CuhdnkTiyJ5vf/52UccEPq/1ePAd3VDh53y5j7cSZ9PrPSXs8VuYdn5D+9OmEp8aSeden5H+3nMQjtiXRzR/MZ8MrP9Xab8Cbf8a1F20Ed2f1Y9/S454Tybr38+ptUT1TyJ+RSfxBXcifmUXiiNptBP/gL62sbjnuiYukfE1eg8QlsjuXDm/PpcPb73bMVSPqfsvh3ow9qnciR/VOrPN4ab6GpJ7MrPUv0jNxJL9vep9BqeMAOKf/i4S7o1iW+y0z107kpF7/2eOxPllxB6f3fZrY8FQ+XXEXy/O+o1fSEdXPz9/0AT9teKXWfn8e+GZ10WRHZVX5eD1x1Y8j3fGUVeXv1WvcldKqPF7PuJAtZZmc3PsRANpGp/N51j08MXs0flvJ5UM+apBziYg0tlMP6MLz3y5lZN92TJ29mvH7BWfIvnTZCKIjPExbuIFnvl7CA2ftv8dj3TplDs9eOJzUeC93vDuHbxdtZFS/bdcc7/+6mknfL6u13+SrjyDcs/MZjwCPfZbB1cf05ZO5a6q3DemcxIzb/0SY28UNr//M+7+uZvz+XXZ5DICP567l2EEddlvQyS+tID4q+Lcl3htOfknFbo8p0lhUqJEmEzMojbLlm/GXVZHz6UL6v34+/tJKlt/8IeUrc7E+P2FJ0TV32i6Rbr+wasnibBZfMRkAf0klUX1qzkRJGTuQlLEDG+ulUPDTSiK7JBGeElNje7fbjmPFLR+x5onviEiLJ7xt7C6OAG5vGIHyKlyRYfiLKghLqH0bjIhIc5QWO4jNpcup8pexMOdTzh/wOpX+Uj5cfjO5ZSvxWx/RYTW/+TTsfKXs7NLFTF50BQCV/hJSo2vOKBuYOpaBqWP3Kj6vJ54yX2H14wp/Id6whL06xq5EhSVy6ZD3KahYzwvzTqVP8lF8v+ZphqSewkFpf2ZJ7td8lnUP43s/1CDnExFpTIM6J7Esu4iySh+fzFvLm1eOpLTSxz8nz2bllmKq/LbWmizbFzqs3ZbQl2wo4PKXZgJQUuEjfbtZ5wDj9uvMuP0671V8q7YUU1hWRf8OCTUKNdGR29bGGzO0E7OWb9pjoWbq7FXcNm7366UlRIVTWFoFQGFZFQnRWo9GnKFCjTSppGPSWfvUdMLbx+GJjWTLJxl44iIZ/P6l5E5byoaXas6CCYv3UrkheLFdMn999fbovm3p+8yZhCUHCzuBKn+N/RpyRo2/rIpAaWX1uQCK52+g6NfVLDh7EiWLN1K2Ygv9Xz6XyI4J9J90LtYfYNHlb5E4OrguScW6fCI6JNQ4btyBXcn9ZiltTuhP7jdL6frPXS90KSLS3KQnH8P0NU8RF9GeSE8sGVs+IdITx6VD3mdp7jR+Wv9SjfFeTzyFFRsAWF88v3p726i+nNnvmerbhfyBqhr71WdGTZjbi8u4KarIJsITS05ZFknebvitj9LKHGIj9nz7XZW/jMpAaY3bmPyBKoxx4zIuItwxRLiDfxcsAaK2FqaiPImU+wr2eHwRkebi2IFpPPnlItoneIn1hvHx3DXERYXzvxuP4puMDbwwveYsmPiocNbnlwKwYO22GeHpafE8d/Gh1YWdHRsf1GdGzYK1eazcUsyZT35L1uZiYiLX0atdHGmJXuK8wb8Bs5ZvoltK8MvRzUXlxEWGERFW83ibi8rJLiynf8fEGtt2HHtwz1S+yljP6P7t+TpjPQf32PkSBiKNTYUaaVKpJw9m9qGP0XfimQDEDuvEmse/Y/5ZLxPdp/b6LFF924LL8PtpLxA3rFP19u53nsDiK9/G+gPgMvS4+09E99l24V3fGTWrJ3xLzicLqdxSTFlWDgPf/DOFv6wi/7vldLvtuOpxHS4ZTodLhgPbOlSFJUeTPWUO2W/9BgY6XHwI4W2CM24y/vw6w768qsa5Ol41gqXXTmHdszOIP7R7jfilZYrwmOwOd8zS/5F7KcJjarerkmZvcOrJPPbLoZzZbyIAnWKH8d3qx3l5/lmkRtVeZ6ltdF8MLl6Ydxqd4oZVbz+hx528vehKAtaPMS7+1ONu2m43q6Y+M2oAju9+B28sugQsjOpyI27jIacsiy+z/sOZ/Z6tMfbb1RNYuOUTiiu3kFOWxZ8Hvsmqwl9Ynvcdx3W/rXpcQcUG3lt6PQYXflvFsd1vBWB42kVMWXIdP62fhN9WclLPf+91vCIiTjnlgK4Mv+tjnr/kUAD269qGCZ8v5Iwnv6VP+/ha4/ulxeMyhpMnfMN+2y3we/cpQ/m/l2bi8wcbH9x72n41ZtXUZ0bNn4Z04k9Dgp8BHvx4PuntE0hPi+eV75fz2swVRIa56domhn+MCS6pcMe7c7ju2H614v7wtzWMGdqpxradjT2yX3s+/30dJz3yFW3jvEw4/6C9ilekoZjtp6uJs4wxdsT6e5wOo1VZ99xMNk2dR9e/H13d+WlHa56cTtLRfepdSKncVMS6Z2fUKPTsyh/tuU24h4Fv/rle52spvk+7FWvtvq8wJyLVjDH2nsPX73lgCJq59jnmbZ7K0V3/Xt35aUe/b3qfCE8sfZKO3OPxpq95kj5JR9coGu2tj5bfwvK86Vw+5MMGu/Vqd26dnqa8KiK7ZYyx2U+d6XQYu/XsN0t4b/Yqbh4zqEbnp7q4etKPPHnBwXseuJdj//n2r0xfvJGP/3I0CVHbZnO2veot5V1pFCrUNCMq1EhrokKNSMNrzYUaUaFGRPasJRRqWhIVaqSxuJwOQEREREREREREglSoERERERERERFpJlSoERERERERERFpJrRGTTPijgzbGKjwqWOMtAquCE+2v7xq71aIE5HdCnNHbvQFKvR3pJXyuCKyq/zlyqsiskvecM/G8iq//k40kMgwd3ZZpU95VxqcCjXS4hljwoAFwA3W2k+a6JwRwELgMmvt101xThGRUKd8LiISGpTPRfaNbn2SUHAlkAV82lQntNZWAH8FHjXGuJvqvCIiIU75XEQkNCifi+wDzaiRFs0YkwwsBo6w1mY08bkNMA14w1o7sSnPLSISapTPRURCg/K5yL5ToUZaNGPMEwTfx1c7dP6hBL8p6GOtLXAiBhGRUKB8LiISGpTPRfadCjXSYhlj+gHfAX2ttVscjON5INda+zenYhARacmUz0VEQoPyuUjDUKFGWixjzKfA59baxxyOox3BxdIOttYudzIWEZGWSPlcRCQ0KJ+LNAwtJiwtkjHmeKA78LTTsVhrNwIPAQ84HYuISEujfC4iEhqUz0UajmbUSIuztd3f78BfrbUfOR0PgDEmElgEXGStneZ0PCIiLYHyuYhIaFA+F2lYmlEjLdH/AWuAj50O5A/W2nLUDlBEZG8pn4uIhAblc5EGpBk10qIYY5IItvsbba1d4HQ829vaDvA74BVr7fNOxyMi0pwpn4uIhAblc5GGp0KNtCjGmAlAmLX2Sqdj2RljzH7ARwTbARY6HY+ISHOlfC4iEhqUz0Uango10mIYY/oC04F+1trNTsezK8aYF4FN1tp/OB2LiEhzpHwuIhIalM9FGocKNdJiGGM+Br621j7idCy7Y4xpD8wHDrTWZjodj4hIc6N8LiISGpTPRRqHFhOWFsEYcyzQG3jS6Vj2xFq7AXgEtQMUEalF+VxEJDQYY45D+VykUWhGjTR7xhgPMA/4p7X2f07HUxfGGC/BdoAXWGu/czoeEZHmQPlcRCQ0bM3nvwP/UD4XaXiaUSMtwWXABuBDpwOpK2ttGfA31A5QRGR7yuciIqHhcmA9yucijUIzaqRZM8YkEmz3d7S19nen49kbW9sBfg+8aK190el4REScpHwuIhIalM9FGp8KNdKsGWMeAaKstf/ndCz1YYzZH/gfwXaARU7HIyLiFOVzEZHQYIx5FPAqn4s0HhVqpNkyxvQGZgD9rbWbnI6nvowxLwPrrbU3Ox2LiIgTlM9FREKDMaYPwXzeT/lcpPGoUCPNljHmf8B0a+1DTseyL4wxaQQXWzvAWpvldDwiIk1N+VxEJDQYYz4EvlM+F2lcWkxYmiVjzNFAP+AJp2PZV9ba9cBjwP0OhyIi0uSUz0VEQoMx5higL8rnIo1OM2qk2dna7m8OcJu19n2Hw2kQW9sBLgbOtdZ+73Q8IiJNQflcRCQ0bM3nc4Fblc9FGp9m1EhzdAmwGfjA6UAaytZ2gH8HHjPG6PdORFoL5XMRkdBwKbAJ5XORJqEZNdKsGGMSCFa2j7PWznU2moa1tR3gDGCitfZlh8MREWlUyuciIqFhaz5fAhxjrZ3ncDgNSvlcmisVaqRZMcY8BMRbay91OpbGYIw5CHiPYDvAYqfjERFpLMrnIiKhwRjzMBBrrb3M6Vgag/K5NEcq1EizYYzpBcwi2L412+l4Gosx5lVglbX2VqdjERFpDMrnIiKhwRjTG5iJ8rlIk1KhRpoNY8z7wCxrbUivvm6M6QjMA4ZZa1c5HY+ISENTPhcRCQ3GmA+AGdbaB5yOpTEpn0tzo0WTpFkwxhwJDAImOB1LY7PWrgUeR+0ARSQEKZ+LiIQGY8xRwACUz0WanGbUiOOMMW6C7Vvvsta+63Q8TcEYE0VwUbYzrbUznI5HRKQhKJ8rn4tIaNjajnsOcIe19j2n42kKyufSnGhGjTQHFwN5BBfxahWstaXAP1A7QBEJLcrnIiKh4WIgB5jqdCBNRflcmhO9AcVRxph44F/ADbb1Te96E/AD5zodiIjIvlI+Vz4XkdCwNZ/fhfK5iGN065M4yhjzAJBsrb3Y6VicYIw5GJgCpKsdoIi0ZMrnyuciEhqMMQ8CidbaS5yOxQnK59IcqFAjjjHG9AR+BAZaazc4HY9TjDGvAyustbc7HYuISH0onwcpn4tIS7ddPh9grd3odDxOUT4Xp6lQI44xxrwH/GKt/bfTsTjJGNMJmAsMtdaudjgcEZG9pnwepHwuIi2dMWYq8JO19j9Ox+Ik5XNxmgo14ghjzCjgRaCvtbbc6XicZoy5C+hlrT3b6VhERPaG8nlNyuci0lIZY0YDL6B8Diifi7NUqJEmt7V966/Avdbad5yOpzkwxkQTbAd4urV2ptPxiIjUhfJ5bcrnItISbc3nvwF3W2unOB1Pc6B8Lk5S1ydxwoVAEcFFugSw1pYA/wQeVTtAEWlBlM93oHwuIi3URUAB8K7TgTQXyufiJL3hpEkZY+KAu4HrW2G7vz15HTCApleKSLOnfL5byuci0mJsbcf9L1pnO+49UT4XR+jWJ2lSxpj/AG2ttRc6HUtzZIw5BJhMsB1gidPxiIjsivL57imfi0hLYYy5H0ix1l7kdCzNkfK5OEGFGmkyxpjuwC8E27eudzqe5soY8yawxFp7p9OxiIjsjPJ53Sifi0hzZ4zpAfxMsB33Bqfjaa6Uz6WpqVAjTcYYMwWYY6291+lYmjNjTBeCi7kNsdaucToeEZEdKZ/XjfK5iDR3xph3gV+ttfc5HUtzpnwuTU2FGmkSxpiRwCSC7f7KnI6nuTPG3A10s9ae63QsIiLbUz7fO8rnItJcGWOOAF5G+bxOlM+lKalQI41ua7u/X4D7rbWTnY6nJTDGxBBsB3iKtfZHp+MREQHl8/pQPheR5mhrPp8N/Nta+7bT8bQEyufSlNT1SZrCBUAZoD8CdWStLQZuBh4zxhin4xER2Ur5fC8pn4tIM/VnoAR4x+E4Wgzlc2lKKtRIozLGxAL3oPat9fEq4AHOcjoQERHl832ifC4izYYxJg64G+Xz+lA+lyahW5+kURlj7gM6WGsvcDqWlsgYcxjwBsF2gKVOxyMirZfy+b5RPheR5sIY82+gvbX2z07H0hIpn0tTUKFGGo0xpivwKzDIWrvO4XBaLGPMZGChtfYup2MRkdZJ+bxhKJ+LiNOMMd0IrjU2yFq73ul4Wirlc2lsKtRIozHGvA0ssNb+y+lYWrLtPiANttaudTgcEWmFlM8bhvK5iDhtaz7/3Vp7j9OxtGTK59LYVKiRRmGMGQG8jqYENghjzL1AJ2vt+U7HIiKti/J5w1I+FxGnbM3nrxHM52rHvY+Uz6UxqVAjDc4Y4yI4pfIha+2bTscTCrYu4rkEGGet/dnpeESkdVA+b3jK5yLihO3y+YPW2recjicUKJ9LY1LXJ2kM5wOVgP4INBBrbRFwC2oHKCJNS/m8gSmfi4hDzgcqgMlOBxIqlM+lMalQIw3KGBMD3Iva/TWGSUAkcIbTgYhI6FM+b1TK5yLSZJTPG5XyuTQK3fokDcoYcw/QxVp7ntOxhCJjzOHAq+jeYhFpZMrnjUv5XESaytZ83llrqTQO5XNpDCrUSIMxxnQBfkOrnzcqY8w7wDyt1i8ijUX5vGkon4tIY1M+bxrK59LQVKiRBmOMeQtYZK29y+lYQpkxphswGxhorV3vdDwiEnqUz5uG8rmINLat+XyhtfZfTscSypTPpaGpUCMNwhhzKMHFJvuofWvjM8b8G2hvrf2z07GISGhRPm9ayuci0li25vM3Cd6So3zeyJTPpSGpUCP7bGu7v5+Ax6y1rzsdT2tgjIkDFgMnWWtnOx2PiIQG5fOmp3wuIo1hu3z+qLX2DafjaQ2Uz6UhqeuTNIRzAT/Bir00AWttIXAbagcoIg1L+byJKZ+LSCM5F/ChfN5klM+lIalQI/tka7u/+wi2+ws4HU8r8zIQDZzmcBwiEgKUzx31MsrnItJAtsvnN6gdd5N7GeVzaQC69Un2iTHmX0APa+05TsfSGhljjiD4B6Gv2gGKyL5QPneW8rmINJSt+by7tfZcp2NpjZTPpSGoUCP1ZozpDMwBhlhr1zgdT2tljHkX+NVae5/TsYhIy6R83jwon4vIvlI+bx6Uz2VfqVAj9WaMeQNYZq29w+lYWjNjTA/gZ2CAtXaD0/GISMujfN48KJ+LyL7ams+XWmvvdDqW1kz5XPaVCjVSL8aYQ4C3CbZvLXE6ntbOGHM/kGKtvcjpWESkZVE+b16Uz0Wkvrbm88kE23ErnztM+Vz2hQo1ste2tvubBTxprX3V6Xikuh3gEuBEa+2vTscjIi2D8nnzo3wuIvWxXT5/wlr7mtPxiPK57Bt1fZL6OBswwOtOByJBW9sB3g48qnaAIrIXlM+bGeVzEamns7f+7xuORiHVlM9lX6hQI3vFGBMN/Bu1b22OXgTigVOcDkREmj/l82ZN+VxE6kz5vFlTPpd60a1PsleMMXcSXMfgLKdjkdqMMaMI/kHoa60tdzoeEWm+lM+bN+VzEamrrfm8t7X27D2NlaanfC71oUKN1JkxphMwFxhmrV3lcDiyC8aYqcBP1tr/OB2LiDRPyuctg/K5iOzJdvl8qLV2tcPhyC4on8veUqFG6swY8xqQZa29zelYZNeMMT2BHwm2A9zodDwi0vwon7cMyucisidb83mmtfZ2p2ORXVM+l72lQo3UiTHmYOBdgtPki52OR3bPGPMgkGitvcTpWESkeVE+b1mUz0VkV7bm8ykE23ErnzdzyueyN1SokT3aukr5LOC/1tpJTscje2aMiSfYDvB4a+0cp+MRkeZB+bzlUT4XkZ3ZLp8/ba19xel4ZM+Uz2VvqOuT1MVZgAd41elApG6stQXAHagdoIjUpHzewiifi8gunAW4gdecDkTqRvlc9oYKNbJbxpgo4D+o3V9L9AKQBIx3OhARcZ7yeYumfC4i1ZTPWzTlc6kTFWpkT/4CzLLW/uB0ILJ3rLU+4AbgQWNMhNPxiIjjlM9bKOVzEdnBX4CZ1toZTgcie0f5XOpKa9TILhljOgLzgP2stSsdDkfqyRjzATDDWvuA07GIiDOUz0OD8rmIbJfPh1lrVzkdj9SP8rnsiQo1skvGmFeANdbaW5yORerPGNOL4GJz/a212U7HIyJNT/k8NCifi8jWfL7aWnur07FI/Smfy56oUCM7ZYw5EHifYPvWIofDkX1kjHkYiLXWXuZ0LCLStJTPQ4vyuUjrtTWfTyWYz9WOu4VTPpfdUaFGatm6CvkM4Dlr7UtOxyP7zhiTQLAd4LHW2rnORiMiTUX5PPQon4u0Ttvl84nW2pcdDkcagPK57I4WE5adOQOIBCY5HYg0DGttPnAnagco0toon4cY5XORVusMIAJ4xelApGEon8vuqFAjNRhjvMD9qN1fKHoOSAHGOh2IiDQ+5fOQpnwu0ooon4c05XPZKRVqZEc3AT9ba6c7HYg0rO3aAT6kdoAirYLyeYhSPhdpdf7I5987HYg0LOVz2RWtUSPVjDFpwHxgf2ttltPxSOMwxnwIfGetfcjpWESkcSiftw7K5yKhT/m8dVA+lx2pUCPVjDEvAxustf90OhZpPMaYPgQXo+tnrd3kdDwi0vCUz1sH5XOR0Kd83joon8uOVKgRAIwx+wP/A9KttYVOxyONyxjzKBBprb3C6VhEpGEpn7cuyucioUv5vHVRPpftqVAjf7T7+x54yVr7gtPxSOMzxiQCi4GjrLXznY5HRBqG8nnro3wuEpqUz1sf5XPZnhYTFoDTgGjgZYfjkCZirc0D/oXaAYqEGuXzVkb5XCRkKZ+3Msrnsj0Valq5re3+HgBusNb6nY5HmtSzQHvgRKcDEZF9p3zeqimfi4QQ5fNWTflcABVqJNgO7ldr7bdOByJNa2s7wBuBh40x4U7HIyL7TPm8lVI+Fwk5yuetlPK5/EFr1LRixpj2wALgQGvtCqfjEWcYYz4GvrLWPup0LCJSP8rnAsrnIqFA+VxA+VxUqGnVjDEvAputtX93OhZxjjEmneBidX2ttVucjkdE9p7yuYDyuUgoUD4XUD4XFWpaLWPMfsBHQB+1+xNjzATAY629yulYRGTvKJ/L9pTPRVou5XPZnvJ566ZCTSu0dRXx74BXrbXPOR2POM8Yk0SwHeBoa+0Cp+MRkbpRPpcdKZ+LtEzK57Ij5fPWTYsJt06nAPHAi04HIs2DtTYXuBt4RO0ARVoU5XOpQflcpMVSPpcalM9bNxVqWhljTCTwIHC92v3JDp4BOgEnOB2IiOyZ8rnshvK5SAuifC67oXzeSqlQ0/pcD8y11k5zOhBpXqy1VQTbAT5ijAlzOh4R2aPrUT6XnVA+F2lxrkf5XHZC+bz10ho1rYgxph3Bdn8HW2uXOx2PNE/GmE+Bz6y1E5yORUR2Tvlc6kL5XKT5Uz6XulA+b31UqGlFjDHPA3nW2r86HYs0X8aYfsC3BNsB5jgcjojshPK51IXyuUjzp3wudaF83vqoUNNKGGOGAp8SbPdX4HQ80rwZY54AsNZe43QsIlKT8rnsDeVzkeZL+Vz2hvJ566JCTSuwdZXwacCb1tpnnY5Hmj9jTDKwCDjCWrvQ6XhEJEj5XPaW8rlI86R8LntL+bx10WLCrcN4IAl4welApGXYOqXyXuBhp2MRkRqUz2WvKJ+LNFvK57JXlM9bFxVqQpwxJoJgu78brLU+p+ORFuVpoJsx5ninAxER5XPZJ8rnIs2I8rnsA+XzVkKFmtB3HbDAWvu104FIy7K1HeBNqB2gSHOhfC71onwu0uwon0u9KJ+3HlqjJoQZY9oCGcBwa+0yp+ORlmfr/dOfAR9Za59wOh6R1kr5XPaV8rlI86B8LvtK+bx1UKEmhBljJgJF1tqbnI5FWi5jTH+Ci92lW2tznY5HpDVSPpeGoHwu4jzlc2kIyuehT4WaEGWMGQJ8TrDdX76z0UhLZ4x5CvBZa69zOhaR1kb5XBqS8rmIc5TPpSEpn4c2FWpC0NbpcN8Ab1tr/+t0PNLyGWPaEGwHeLi1dpHT8Yi0Fsrn0tCUz0WcsTWffw28o3wuDUH5PLRpMeHQNBZIAZ5zOhAJDdbaLcB9wENOxyLSyiifS4NSPhdxzElAKsrn0kCUz0ObCjUhZmu7v4dQuz9peE8BvYwxxzkdiEhroHwujUj5XKSJGGM+Nsb0RPlcGofyeYhSoSb0XAMsstZ+6XQgElqstZVsawfocToekVZA+VwahfK5SJPqB1wILAYyjTH6/CUNRvk8dClRhBBjTCrwD+AvTsciIesjYB1wudOBiIQy5XNpAsrnIk0jHvg/IBf4Hkh0NhwJQcrnIUiLCYcQY8wzQJm19ganY5HQZYwZCHxFsB1gntPxiIQi5XNpCsrnIo1r6wLCPqAY+BC4Vq2UpTEon4ceFWpChDFmEPAl+uWUJmCM+S9Qrg+RIg1P+VyakvK5SOPZepvTZuAaa+0bTscjoU35PLSoUNPCGWPuAZ4BXgamWmufcjYiaQ2MMSnAQuAw4CLgYWvtJmejEmnZlM/FCcrnIiKhQfk8tGjBoZZvLJANtAeedTgWaT22AP8h2MEgBZgK6A+ByL5RPhcnKJ+LiIQG5fMQosWEW7444EbgfWCtMaajs+FIKzGVYBeDfkAYwfehiOwb5XNxgvK5iEhoUD4PISrUtHwpQAwwHjjJWrvW4XikdTif4OJ4scAAIMHRaERCg/K5OEH5XJqM2xu+0Rhj9V/T/Of2hm90+v9zaVLK5yFEa9S0cMYYH/ASwUXKyp2OR1oXY8zRwP+Ah6y1tzkdj0hLpnwuTlI+l6ZgjLGnZT/hdBitxjttr8Faa5yOQ5qW8nlo0Bo1LV+UtbbS6SCkdbLWfmmMiSNYvReRfaN8Lo5RPhcRCQ3K56FBhZoWThf14jRrbZXTMYiEAuVzcZryuYhIaFA+b/m0Ro2IiIiIiIiISDPRambUuCLDNtoKX1un4wgVJsKTHSivaud0HLJrkWGujRU+q/f8XorwmOzyqoDe2y2Ecnv9KIc3f2GuyI0+W6H3dgPxmIjsqkC53vMhYOVbP1KRW0KfK4/kl+te44AJ5zb4OXZ23E0zlrH2ozkM+/fpDX6+rDd/ZPHjX+AK83Ds9Jt3OW72jW9Qnl3IYa//X/W2klVb+Oywexn14Q0kDenc4LHJvtM1ecNqLdfqraZQYyt8bYevu8vpMELGrA53KNk0cxU+23bdXcOdDqPF6XDHLL23WxDl9vpRDm/+fLai7V3D1zkdRsi4Y1YHvedDUGMUaRriuL6SCjzREXUen3Z0f7qcegBfHnn/LscULc+mYktxre2LHvuCNgd1r1ec0jR0Td6wWsu1eqsp1IiIiIiISPOwacYyFj32OWGxkRQuy6b/X09g1ds/U7I6hwOfPI/EQZ34/V/vkztnNVXF5fS/6XjSjhtY4xifH34fx06/mcq8En65/nWqCsswLhcj3roSV5i7xtiKnGJ+vOwlbCAAxjB84oVEtIkl+7vFZDzwCa5wD21H9qHv9cdWH7csu4Cf/u9lXBFhRLaJwRMbucvXU76pkNXvzWbdZ/Ppcf6hdD55/zr/LCLaxO5xzKLHPqfPNUex+LEvqrcVLFyPJzYSb1pCnc8lIi2DCjUiIiIiItLk/OVVHP72Vaz/bD4LH/6Uo774K5t+WEbmKzPY76Ez6XfT8XiiI6jML+W7056sVaj5w6IJX9DhT4PpevpB2EAA46q9DGdYnLe6gLPilR/IevNH+lx1JHNve5cj3r+eiKRorD9QY5/Fj39Jz4tH0vHEIWQ89AkVObVntKx5/1dWv/8bxmXoNG4/Dn/rStyRYQDMv+d/5PyaVWN8XO92DLv/jL36OeXOXU1EShzetvE1ti989DOG3nsqv9/zwV4dT0SaPxVqGsGmyXOI6JxI/PCu9Xp+T7Jfn82myXNxhbvp8fBYIrsk1Xi+eO46sm7/FKyl47WHk3h0n3qdR2R3Js/ZROfECIZ3ja/X83vy+uxsJs/dRLjbxcNje9Alqea3WHPXFXP7p1lYC9ce3pGj+yTW6zwiu6N8Lq3BnE2TSYzoTNf4nU/N39PzezI7+3XmbpqM2xXO2B4PkxTZpcbz64rn8mnW7Vgsh3e8lj6JR9frPNLyJPTvgDEGb/sE4vumYVwuvO3jqcwvAWDZ89+x4asMXB4XpevydnmcwqUb6XXpEQA7LdIAVOaV8Ns/3qYip5iqonJSDupBRU4JkSlxRCRFB/d119y3OHMT6VcfBUDysK6s/3JBreOumDQDd1QYvS4eSduR6TWOMfDWk+r+w9iNRY99zv4Pn4WvpKJ625afVhDTpQ2RqXENcg4JDbo+Dx0q1DSC1DOG7tPzu1OVV8qmN39jwPsXU7JgI6vv+4rez9Zc1GzlnZ/R+9nT8cRFsGD8iySM7lXrD4/IvjpjaOo+Pb87eaVVvPnbJt6/eAALNpZw31erefb03jXG3PnZSp49vTdxER7Gv7iA0b0ScLtMvc8psjPK59IaDE3d/bf7e3p+d0qr8vht05tcPOB9NpYs4KvV93F672drjPls5Z2c3vtZIjxxvLhgPL0SRuMy7l0cUUKK2fm/rQ0WVtZ+OIejvvgrVQVlfHbYvbs8TFyf9myetZwupx6wyxk1q9+bTZuDetD78lGsmPQDBYvXE5EcTfnmIirzSwlPiKq1b0y3VHLnrKJD+wRy56za6bmPmHotJWtyWfXOzyx67HMSB3Wmx0UjiO2e2mAzakpWbuGnq17BX15F0dKNLH12GsZlyPk1i+lnPk3BovUUr9jMoa9cRkRyzF4dW0KLrs9Dhwo1+8D6/Cy75j2qNhcTu38ncr9YwpBvrmLNw9OI6pNK9OA0ll0xhYguiZQt2USHa0bQZuzA6ueTT+y/1+csnruOuOFdMR43MUM6ULZiS43nA+VVWF+AiPbB6rq3ezLlWTl4e6Y0yGuW1sfnt1zz3jI2F1exf6dYvliSyzdXDeHhaWvokxrF4LRorpiyjC6JESzZVMY1IzowdmCb6udP7J+81+ecu66Y4V3j8LgNQzrEsGJLWY3ny6sC+AKW9nHBhfq6J3vJyimnZ4q3QV6ztD7K59Ia+K2P95ZdQ3HVZjrF7s+S3C+4asg3TFvzMKlRfUiLHsyUZVeQGNGFTWVLGNHhGga2GVv9fP/kE/f6nOuK59I1bjhu46FDzBC2lK2o8XxVoJyA9REX0R6AZG93csqzSPH2bJDXLC1XWEIUUR2TmDZ2AokDOhIWv+u/8X2vPZpfrnudzNdm4nLvfI2a1BF9+PmqV9g0fQne9gmYMBfG5WLI3Sfz/dn/xR0RVr1GzR/SrzmKH6+YxIpJPxCVlogrcucfnaI7JdHvxuPod+NxbPklk7INBcR2T63zjJr1Xyxg2cRplKzJ4btTn2DI3acQkRzDsonfMvDWkzjm238CULI6hzn/fIfel48CqJ5F9PO1r9LzopEq0rQiuj4PfSrU7IPcz5cQlhJD7/+eRsGsleR+saTWmKotxfR/70L8xRUsOuc12ozd+b21Zcu3kPmPD2tt73zL0cQO7Vj92Jdfhme7P1TW2hrjfflluOO3TUFzx0Xiy6v5SySyNz5fkktKTBj/Pa03s1YW8MWS3FpjthRX8d6F/Smu8HPOa4sYO7DNTo+1fEsZ//gws9b2W47uzNCO2xbSyy/zEe/dlp52fJ/nl/mIj9x2ARYX6SavzLfXr03kD8rn0hosyf2cmLAUTuv9X1YWzGJJ7he1xhRXbeHC/u9R4S/mtUXnMLDN2J0ea0vZcj7M/Eet7Ud3voWOsdtmmpX58vF6tk2x3/F9XubLJ9K97flIdxxlvl3f4iKhI/XQXqQe2guApCGdOfDx8wCI69WOQ164GIBDX7601n5dzzy4+t9/tLIOT4zm0Fcu2+35Evp3qC54bK/tyHTajkyvse2P43rbJzDq/evq+pIAaHPA3ndgSjtmAGnHDKi1fcdCT3Tn5Bqtuf/wx89OWg9dn4c+FWr2QXlWDjFDOgAQO7TDTsd4e6XgCvfgSvIQqNj1G9Xbsw39p1y4x3N64r2ULsqufrzj1E53vBd/QXn1Y39RBZ5EVTGl/rJyyhnSIfgNzdAOO+9K0CvFS7jHRZLHRYUvsNMxAD3beJly4Z5nHsR7PSzKLq1+7NphymS8101Bub/6cVGFn0Sv0pnUn/K5tAY55Vl0iBkCQIfYnd+2l+LthccVjseVhC9QsdMxAG28Pbmw/5Q9ntPriSe7dFH1Y5ep+T73uuMp9xdUP67wF+H1aE0D2Ter351N5mszamw75MVLCE+MdigikYal6/PQp5/cPojsmkTRL6tJOXkQxfPW73yQqds9eXX9BjZmaAfWPvot1h+gZOFGIrvVXHjS7Q3DeFxUZhfhjo2gPCuHyK5JOx5WpM66JkXyy+oiTh6Uwrz1tbsdQJ3f5nWu2A/tEMOj367FH7As3FhCtx0WKvOGufG4DNlFlcRGuMnKKadr0q5bZorsifK5tAZJkV1ZXfQLg1JOZn3xvJ2OMdTtfV7XGTUdYoby7dpHCVg/G0sWkhTZrcb4MLcXl/FQVJlNhDuWnPIskiK71v1Fiexg5Vs/Et05mSOm7nwmzB/PpxzSq17Hz3x1Bivf+glXuJv9Hz2HmK7bZilUFpQy66IXCFT5sBaG3nsqiYM6kTd/Db9c8xpFmZs4cc7d1bcoLX9xOqum/IIr3MOQf51M4qBO9YpJWh9dn4c+FWr2QdJx6eR8mEHGqS8RPSit1r2we6Ou38CGJUaRctoQFox/ERPmosdDwSnJ23ce6XL7sSy5dHKwS8iNR2A8WpBP6u+49CQ+zMjh1JcyGJQWTdg+LGRa14p9YlQYpw1JYfyLCwhzGR4a2wOouVL97cd24dLJS7AWbjyiIx63FiqT+lM+l9YgPek4MnI+5KWMU0mLHoTbFVbvY9V1Rk1UWCJDUk7jxQXjcZkwxvZ4CKjZSerYLrczecmlWCxHdLwRt9HlqdTf9rdG1ef53anMKyHr9VmM+ugG8uevZf69/2P4cxdVP+8O93Dgk+fhbZ9A4bKNzLl5CiPfuZqYbimM+ugGZpy3bSHt8k2FrH53NqM/vIGKnGJ+vPxljph6bb1jk9ZF1+ehz+x4b1moMsbY4evuavDjBqr8uMLcFMxaSfakn+n9zOl73ikEzOpwB9Za/eY1Y8YYu+6u+rVS3VGVP0CY28WslQVM+jmbZ3ZY4T2UdLhjlt7bLUhD5vbWlM+Vw5s/Y4y9a/i6Bj+uP1CF2xXGyoJZ/Jw9idN7P9Pg52iO7pjVQe/5ZsAYY0/LfqJBjhXw+fn5ylco31xI8gHdWf/ZfI6dfjMZD35CfHp7Egd35sfLXya6SzKFizeQft0xdB6/X/XzHcfsfde+jd8sZNOMZQy6LVhY/2LUvzlmWu11bwCKV25hzj/fZsSbV1Zv+3b8BIY/fzERyTHk/LqSrNdnsv8jZwPw6cH/4pjv/ok7ov4F1B290/Yave+bgYa8Jt9ea7o+315ruVbXVxb7aOllk/EVlmMr/fR4eOcL7om0dJdNXkphuY9Kv+XhrdVzkVCjfC6tweSll1HuK8RvKxnb42GnwxGpt/WfzScyNY6DJ17I5pnLWP/Z/FpjyjcXMuqD66gqKuf7s56m8/j9dnqsouXZ/PrXt2ptH3TbWJKGda1+XJlXSvj2i8AHdv6Ft7WWeXe8R5+rjtpl/DHdUsj7fQ2+0krKNuRTsjaXqoIy3KkNV6iR0Kbr89CmQs0+Sn/pbKdDEGl0L52dvudBIi2c8rm0Bmenv+R0CCINojhzE4lDOgPUKKZsL65PO1zhHiKSY/CX73oR+NiebXe5ps32whK8VC7c1n3P7OJ2k7m3vEvKIb1IPWzXMxwikqJJv+Zovj/7v0R3TCShXxrhSVrsWOpO1+ehrf43s8k+KZiZReYtHzfJuXyF5fwy4H5yPsoAoHJTEQvPfoWMU18i67ZParVWE2kMM7MKuOXj2guVNbRbPs5k6IOzm+RcIjtqqtyeecvHzB76YJP9HRHJKpjJx5m3NPp53lpyKS8uOIVnfz+BjJzgotz+QBWTl1zGCwvG88KCcWSXLm70OKT5i+mWQt681QDkzl210zF1XRy7aHk2346fUOu/3N9W1hiXPKwrm2ctx/oD5P2+htjuKbWOteixzzEeF70vH7XH83YaO4xR719Hv78cT2zPtri0Dpk0gaa6JgcoLPcx4P5f+Cgjp8b2Cd+tZfRTc5skhpZKM2pagfXPzCBmu3az6574nnZ/PpCkY9JZeedn5H+7nMRR9Vv5XqS5uWZER07ol8wnC3P2PFikhep4zQiST+hHzicLnQ5FpEGd2uspPK5wKvzFTJz/J/onj2Fl4Swi3LGc0Wciqwp/Ysa6pzm51+NOhyoOSzt+EGv+N4dvx08gcXBnXOH1L3LUdUZNeGI0Xc84iGknPYYJc3PAo8GZmH90koru0oYF939Mm4N68O34CXjbJXDQfy+gdF0ev1z3GvkZ65h58Qt0P/cQupx6AD9dMYmy7ALCE6IY9p/QXRdNWq9nZqxn6NY24n/IK61i2eayXewhf1ChZhfKV+Wy7Op3cUV4cEeHkz7pHDZP/Z1Nr/+Kv6yKhCN60vmvoymYmcW6x6fjjo2gbNkWOt00is1T5lK+Jp+eE8YTMzCNuaOeJO6QbpRmbCRmcBpd7zq+xrkKfshkzSPfgrXEj+hOpxtHkfvFEtY+9i3uqHBiD+xM578dWa/XUbm5mIrVecQM2VaoKc/KIe3KwwCIHtiewh9XqlAjrMot5+p3lxHhcREd7mbSOelM/X0zr/+6ibIqP0f0TOCvozszM6uAx6evIzbCzbItZdw0qhNT5m5mTX45E8b3ZGBaDKOenMsh3eLI2FjK4LQY7jq+a41z/ZBZwCPfrsFaGNE9nhtHdeKLJbk89u1aosLdHNg5lr8d2bler6NdXDiZOUr+snOhktvD28VRlqlipGyTW76Kd5ddjccVQbg7mnPSJ/H75qn8uul1qvxl9Ew4gtGd/0pWwUymr3ucCHcsW8qWMarTTczdPIX88jWM7zmBtJiBPDl3FN3iDmFjaQZpMYM5vmvNBbszC37g2zWPYLF0jx/BqE43siT3C75d+xjh7ig6xx7IkZ3/Vq/X4XGFA1DpLyHVG7xtJDGyM35bCUC5r4CosOR9+ElJqHB53Bz09AW4wtxsnrmM0nV5APT/6wnVYw57/f+q/33s9JtrPV8fPS44jB4XHFZj2/adpE7bULuIGNUhkZFTrqm1/aD/XrBPsUhoCpVr8s3FlazOq2DIDoWap35Yz+WHtOe6qcvr+yNqFVSo2YXCWStJPrE/aZcfgg0EAEg6Np2U8YOw1pIx7gUqLzgAgEC5j75vnk/eF0tY+8i3DPz0MgpnrCT71dnEPHASgdIq2p41jOgB7Vl80ZsUz19ffR5rLavu+5L+Uy7EHRXOkksnU7Z8CzkfZdD9/jHEDEyrPv/2Fp37KoEd7rVtM24gbc/dv8a2dY9PJ+3Kw8j9dFH1tqj0thRMX0HqGUMpmL4CV6QWLROYtbKQE/snc/khaQS2Lo53bHoS4welYK1l3AsZXHDAHxfKAd48vy9fLMnjkW/X8ullA5mxspBXZ2fzwEkxlFYFOGtYWwa0j+aiNxczf31x9Xmstdz35SqmXNifqHA3l05ewvItZXyUkcP9Y7ozMC2m+vzbO/fVRZT7av4ujBvYhnP3b9uIPxUJNaGS20V2tLJwFv2TT+SQtMsJ2OB7Kz3pWAaljMdaywsZ4zigMvih0Bco5/y+b7Ik7wu+XfsIlw38lJWFM5id/SonxTxAVaCUYW3Pon30AN5cfBHri7ct0mqt5ctV93Fh/ymEu6OYvORStpQtJyPnI8Z0v5+0mIHV59/eq4vOxRcor7FtYJtx7N/23FpjJy08k+yShRzZ+R8AxIW3p9JfyhNzR1LpL6lTW3BpHWZd/AKVhWUEKn3Vs1tEWrpQuSZ/fPo6rjwsjU8X5VZvW19QQXZRJQPTahZvpDYVanYheUx/1k6YzrKrpxDVvx0drjiMgh+y2DBxJtZaylflUbmxCICofm0xxhDeLpao9FSMy0V4+1h8+cFv9Y3HRfSA9gDEDE6jPDOXsJTgYmG+3FIqVuWx+PzXg48LyqlYX0DHG0ay/r8z2FBaSZuxA0k8uk+N+Pq+dt4eX0P56jx8BeVE92tXo1DT4eoRZN7yMVve+53I7snVsUjrNqZ/MhOmr+XqKcvo3y6KKw7rwA9ZBUycuSH4oTOvnI1FwT8K/dpGYYyhXWw46alRuFyG9rHh5JcFP2B6XIYB7YPvq8FpMWTmlpMSHSwI5pb6WJVXwfmvB9cYKCj3sb6gghtGduS/M9ZTWrmBsQPbcHSfxBrxvXZe36b6UUgIC4XcLrIz/ZPHMH3tBKYsu5p2Uf05rMMVZBX8wMwNE7HWkle+iqLKjQC0jeqHMYbY8HakRqXjMi5iw9tT5ssHwGU8tI8eAEBazGByyzOJDguuxVHqyyWvYhWvLz4fCM5wKahYz8iONzBj/X+p3FDKwDZj6ZN4dI34zuv7Wp1fywX93qLMV8Bz80+kf/KJLMj5H8nebpyV/gKbSpfwUeY/Ob/fm/v6I5MQcOgrlzkdgkiDC4Vr8tV55RSU++jXLrpGoeax79ZyzYgOu9lT/qBCza64DF1uDl5kLDxjEknHpLPmwW/o9/YFeBK8LBj7AvyxCK/ZbqGy7f+99WnrC1CSsZHo/u0o/n09CaN64S+uAMCTFEVk92TSXz0Xtzcs+A2rhUCljx4PnESgwse80U/Vupivy7euJQs2ULEql4XnvEr5ylzc0eF4e6UQ1SeV3k+dCsCKv/2PpOP0AVjAZeDmo7sAcMakhRyTnsSD36zh7Qv6keD1MPaFBXV9y+MLWDI2ltC/XTS/ry9mVK8Eiiv8ACRFeeieHMmr56bjDXMTCFgsUOkL8MBJPajwBRj91LxafxQ0o0YaRAjkdpGdMbg4ukvw1o5JC88gPekYvlnzIBf0exuvJ4EXFozFbn3zbr/Aas3FVoPPB6yPjSUZtIvuz/ri3+mVMIoKf/Bb2ChPEsmR3Tk3/VXC3N6ts2csvkAlJ/V4AF+ggqfmja5VqKnLjJqADWCtH7crjDCXF48rAo8rAmsDRHmSAIj0xFPuK2yQn5mISHMUCtfkCzaUsCq3gnNeXcjK3HKiw930SvGyOq+Cuz5fCcDa/Ar+/eUq/rn1tUpNKtTsQt6XS9n44k/gNoSnxhLZJZE2Ywew8LSX8fZOwe2t++1CrqgwNr89h6y564gelEbMoDQKZmYBYIyh89+PZPF5wW+ajMdFrydPYf3EWRTNXoOt8pN69rBax6zLt67JJ/Qj+YR+AKx5eBpRfVKJ6pNK/vQVrHt8OhhDm5P6E92vXZ1fi4SuL5fm8eJPG3EbSI0Np0tiJGMHtOG0lxfSO8WLN6zui/RFhbl4e85m5q7LYlBaNIPSYpiZVQAE3/N/P7Iz570WrN57XIYnT+nFxFnrmb2miCq/5exhqbWOWdcZNRO+W8uni3LZUlJFVs5C3ji/X53jltAXCrkdYO2E78j9dBFVW0pYmJVDvzfOr3PcEpqW5n3JTxtfxOAmNjyVxMguDGgzlpcXnkaKtzdhbm+djxXmimLO5rdZlzWXtOhBpMUMIqtgJhB8bx/Z+e+8tjj4XnUZD6f0epJZ6yeypmg2flvFsNTat6DUZUaNL1DB61uP6w9UcljalXhcEQxKOYUpy65kWcY3VPnLOLLz3+v8WkTqa9OMZaz9aA7D/t24i/yunPwTy1+cHvy7cfL+9LrsiEY9nzR/oXBNfkK/ZE7oF1xP7OFpa+iTGkWf1CjeumDbdfnop+aqSLMbprW0ZjbG2OHr7trzwEYwd/RTDPnmKkfO3VhmdbgDa23deh6KI4wxdt1dwx059+in5vLNVUMcOfe+6nDHLL23WxDl9vpRDm/+jDH2ruHrHDn3U3NHc9WQbxw5d2O5Y1YHveebAWOMPS37CafDqJOmKtR8dujdHPXF33B7w/h85L855uu/4wpvmO/S32l7jd73zYCuyRtWa7lW14waERERERFpUYpXbuGnKyfhjgjDEx3BYa9dzup3Z5P52kx8pRW0G92PAX//E5tmLGPRY58TFhtJ4bJs+v/1BFa9/TMlq3M48MnzSBzUic8Pv5eUQ3uRv2AdSUM6M+TuU2qca9P3S8h46FOwltTD+9D/Lyew/vP5LHzkMzxREbQ5qDsD/nFivV5HbM+2+EqCt826I8Mwbtc+/2xEpOVToaYJtNRvXEXqK9Qq9yI7o9wuoSrUZtNIaNo8cxmdThpK7/8bXd1FL+24gXQ+ZX+stUwb8yjlF44AwF9exeFvX8X6z+az8OFPOeqLv7Lph2VkvjKD/R46E19pJd3OHk7iwE7MuGAieb+vqT6PtZbf7/kfR7x3LZ7oCGZe/AJFy7NZ++Ec9nvwTBIHddppF7/vz3oaf3lVjW2dx+9P9/MPrbGt45ihfHnU/Ri3iz5XHqlCjTQoXZO3XCrUiIiIiIhIi9Jp7DAWPvIZP10xiYQBHehz1VFs+mEpS5+ZhrWW4pVbKNuQD0BC/w4YY/C2TyC+bxrG5cLbPp7K/BIAXB43iQM7AZA0pAvFmZuISIkDoDKnmJJVW/jh3GeCjwvKKF2XR7+bjmfJU1/jK6mg0/j9SDtmQI34Rrx55R5fQ1VxOYsmfMFxM2/DHe7hu9OfosPxg4jqmNRQPyYRaaFUsq2D5ddPpXhe498nvubhacwd9ST501cAUDx3HfNPep75Y54j78sle9zfV1jOLwPuJ+ejDAAqNxWx8OxXyDj1JbJu+4TdrUfkL6ti6f+9zYLxL5D5jw+rvxlYctlkfhlwfwO8OmlJrp+6nHnrihv9PA9PW8OoJ+cyfUU+ALd8nMnQB2dzy8eZe9z3yyV5jHluPic9P5+5e4h1Z2O/WprH4U/M4ZkZ6/f5dUjL1BJye+YtHzN76INk3vLxHs+T9+US5o95jvknPU/x3ODryvtqKXMOf4L1z8zY9xciLcbU5dezrnheo59n2pqHeXLuKFbkTwdgXfFcnp9/Es/NH8OSvC93u+/ejH1ryaW8uOAUnv39BDJyPgTAH6hi8pLLeGHBeF5YMI7s0uBimN+tncCDs4c0yesXhxnDoNvGctB/L2DjtEUUrdjEgvs/5pAXL+aIqdcS06VNdWecGs3Ntu+Ms/X5gM9PfsZaAHLnrSamW0r1mPDkGGK6pzLijSs4Yup1HP3V30g9rDeRbePZ76Ez2f+xs5l3+3u1wvv+rKf5dvyEGv9lvlIzFxtjcIW58USF4wr34PGGUVVUXutY0no5dU0OUFjuY8D9v/BRRk6NsRO+W8vop+bu9nhlVX7+7+2ljH9hAf/4MJNAYNefQ3c19rLJSxhw/y/1fk0tnWbUNDOdbhpFwuE9AFh552f0fvZ0PHERLBj/Igmje+12OuT6Z2YQM3RbX/p1T3xPuz8fSNIx6ay88zPyv11O4qheO9138+TfiB6cRu8rDiPz5o/In7acxCN702fiGcwd/VTDvkiR7dw0qhOH90gA4JoRHTmhXzKfLMzZ7T7+gOXBb1bz3kUDKKrwccU7y3j/4gF7Nfao3onklFSRV+rb6X4iDam+ub3jNSNIPqEfOZ8s3O3xrT/A6ge/YcB7F+ErqmDZFe8w4P2LSTyqN1U5JfjyShv8NYkAjOp0Ez0SDgfgs5V3cnrvZ4nwxPHigvH0ShiNy+y8O8nejD2111N4XOFU+IuZOP9P9E8ew8rCWUS4Yzmjz0RWFf7EjHVPc3KvxxnZ8Tpyy7Ma7fVK87HhywUse/47jNuFNzWO6C7JdB43jG9Pfpy43u32qoufJyqclW/9RO6ct0kc1JnEwZ3ZNGMZECymDPzniXx/9n8Bg/G4OOjpC1j6zDfkzM4iUOWn2zm1F4qty4waT3QEXc84iG9OeASMIXn/rsT3Tatz3CINaftrcoBnZqxnaIeYGmPySqtYtrlsj8ea/NtmBqdFc8Vhvbn5o0ymLc/nyN6JezV24hl99lgQCmWttlCz8s7PSBjdi4TDe1CxoZAVN0yl31sXsPCsV7BVfgKVfno+Nh5v9+TqfQpmZpHz8UK63/snqnJLWHrZ2/SfciElGRtZeddnELBEdk+m+/1jMGbfFqIOlFdhfQEi2genXXq7J1OelYO3Z8pOx1duLqZidR4xQ7YVasqzcki78jAAoge2p/DHlbss1BT+tJqONx4BQOJRfYJjj+y9T69Bmpc7P1vJ6F4JHN4jgQ2FFdwwdQVvXdCPs15ZSJXfUukP8Nj4nnRP3tbCdWZWAR8vzOHeP3Unt6SKy95eypQL+5OxsYS7PltJwEL35EjuH9N9n9/zAO3iwsnM2XPyz8opp3uyl5gINzERbnx+S3lVgMiw2h9292astHyhltvD28VRlrn7wiUE8723ezLumAjcMRFYn59AeRWuyLp/UJHm7bOVd9IrYTQ9Eg6nsGIDU1fcwAX93uKVhWfht1X4A5WM7/kYyd7u1ftkFcxkYc7H/Kn7vZRU5fL20su4sP8UNpZk8NnKu7AESI7szpju9+/ze7sqUE7A+oiLaA9Asrc7OeVZpHh77tNYAI8rHIBKfwmp3uC1SWJkZ/y2EoByXwFRYck73VdCV6exw+g0dliNbenXHkP6tcfUGpt6aPD6N2lIZw58PNgCPq5XOw554eKtI0ytBYRTD+1VvV/qiD6kjuhT4/lBt41tiJdB78tH0fvyUQ1yLGkZWsI1+ebiSlbnVTBkh0LNUz+s5/JD2nPd1OW73f+n1YXceERHAI7qk8iPKwt3WajZm7GtSast1LQ5ZRAbX/iJhMN7kPPBfNqMGwhAnxfOxB0VTv63y9kwcSbd/zNmj8daecen9Hr6VMJTY1l512cUfLeChCO2XWxs+WAB2a/WnrbV943zdtl+z5dfhjs+svqxOy4SX96uP8Cue3w6aVceRu6ni6q3RaW3pWD6ClLPGErB9BW7vWD35Zfh2Xo+T1wkvvw9f1iWluWUQW144aeNHN4jgQ/m5zBuYBsAXjizD1Hhbr5dns/EmRv4z5juezgS3PHpSp4+tRepseHc9dlKvltRwBE9E6qf/2DBFl79JbvWfm+c15dwz74XSPLKfMR7t/3uxEW6yS/z0S4sfJ/GSssXarm9rnx5ZXjit13Qubfm8fB2KtSEikFtTuGnjS/QI+Fw5ud8wMA24wA4s88LhLujWJ7/LTM3TGRM9//s8VifrryDU3s9TWx4Kp+tvIsVBd/RM+GI6ucXbPmAX7JfrbXfeX3fqC6a7KjMl0+kO776caQ7jjJf3j6P/cOkhWeSXbKQIzv/A4C48PZU+kt5Yu5IKv0lXNh/ym73FxFpLlrCNfnj09dx5WFpfLoot3rb+oIKsosqGZgWs8v9/pBf5iM+MngtFBfpIb9s1zPY92Zsa9JqCzUxA9MoW74Ff1kVuZ8uIv21c/GXVZJ188eUr8rFVgUIS46qudP21cntbrMrXbKJZVcGLxD8JZVEpbetsVubsQNoM3bnt2Xsijvei79g2z2q/qIKPInenY4tX52Hr6Cc6H7tahRqOlw9gsxbPmbLe78T2T2ZsJToXZ7PEx+Jv7AcUmPxFZXjSdj5uaTlGpgWw/ItZZRV+fl0US6vnZtOWaWfmz/OYlVuOVUBS3JUzQ91u3jLs2RTKVdOCU4JLqn0k9625u/K2AFtGDugTWO9FBK8bgrLtyXxwgo/Cd6dp7O9GSstXyjl9r06boIXX+F2xy2sUB4PMWkxA9lStpwqfxmLcj/l3PTXqPSX8XHWzeSWryJgq2rNKjE1FubY9ubeVLqEKcuCt2VU+ktoG5VeY78BbcYyoM3ezRbwuuMp9xdUP67wF+H17Pwb0b0Z+4cL+r1Fma+A5+afSP/kE1mQ8z+Svd04K/0FNpUu4aPMf3J+vzf3KmaRPxw7/WanQ5BWpLlfk6/OK6eg3Ee/dtE1CjWPfbeWa0Z02M2e28RHeigs95MaC0Xlvt1ee+/N2NakVf8UEo/pw/qnfyC8fRye2EhyPlmIOy6SAVMvJm/aMja+9HON8Z74SCo3FAJQsmBD9fao9Lb0fvY0wpKChZBAlb/GfvX51tXtDcN4XFRmF+GOjaA8K4fIrklYn5+qnFLC28ZWjy1ZsIGKVbksPOdVylfm4o4Ox9srhag+qfR+6lQAVvztfyQd1xeAig2FhLeNwbi2VVHjDu5C3tfL8PZMIf+bZSSM3Pn0Y2nZjumTyNM/rKd9XDixkR4+WZhDXKSbqRcPYNqyPF76eWON8fGRHjYUBqeWL9hQUr09vW0Uz57Wm6To4B+RKn/NtpQNOaOmrMpPWWWg+lwA3ZK8ZOaUU1rpp7jCj8dliAxz7dVYCV2hktt3xV9WRaCssjouAG+3JMozc/CXVuIvrsB4XLrtKQT1STyGH9Y/TVx4eyI9sSzM+YRIdxwXD5jKsrxp/LzxpRrjIz3xFFYG39MbShZUb28blc5pvZ8lOizYWcYfqNlCuD4zasLcXlzGQ1FlNhHuWHLKs0iK7Irf+iityiE2vG29xgZsAGv9uF1hhLm8eFwReFwRWBsgypNU/TrLfYV786OUEPPzta/S86KRJA3p3KjnyXjwE9Z+OIchd59C25Hp/PbPt1n30Tw6nDiYYf8+fZf7VeaVMPOi5wEI+AIULt3IuCW7btax/osFLHrsc4wxDLnnFJKGdmHDlwuYd+dUup1zCH2uPLLBX5s0reZ8Tb5gQwmrcis459WFrMwtJzrcTa8UL6vzKrjr85UArM2v4N9fruKfR3dhQ2EFbWPCcbm2VZMO7hLH18vy6Jni5Ztl+YzcOstnb8a2dq26UJNy8iDmHPY4vScGE2vssI6se+J7Fp79ClG9U2uNj+rbFuMyZJz2MrHDOlZv73rncSy7cgrWFwCXodvdJxDVZ9v+9fnWFaDL7cey5NLJYC0dbzwC43FTlpXDmv98Te9nt/0xSD6hH8kn9AOC3UWi+qQS1SeV/OkrWPf4dDCGNif1J7pfOwCWXT2FvpPOwR0Tse1nccZQVlz/PgtOfhFvzzYkjFahJhSdPCiFwx6fw8TTg/f4D+sYyxPfr+PsVxbSOzWq1vi+baNwGcNpL2cwrOO2D5B3HteVK6cswxewuAzcfUI3+my3f31n1Ez4bi2fLsplS0kVWTkLeeP8fvyyuojpKwq49Zgu1eM8bsNNR3TkjEkLMSYYD7BXYyV0hUpuB1g74TtyP11E1ZYSFmbl0O+N8yn6ZTUF01fQ5dZt6zAYj5uONx3BwjMmgTF0vfO4vY5Lmr9BKSfz+JzDOL33RAA6xg7j+3VP8MrCs0mNqr2uXNuovhjj4uWM0+gYu20tj+O63smUZVcSsD4MLk7odjepUdvW36jPjBqAY7vczuQll2KxHNHxRtzGQ05ZFl+v+Q+n9362XmN9gQpeXxxcU8QfqOSwtCvxuCIYlHIKU5ZdybKMb6jyl3Fk57/vdbwi9dH/ryfQdmRwFlrf646l45+GsPbjubvdJzwxmiOmXgcEF0Fe9+nvuxxr/QEy7v+YIz64Dl9RObMue4nRH95A+6MHUJFTTEVuyS73lZajOV+Tn9AvmRP6BWdoPjxtDX1So+iTGsVbF/SrHjP6qbn88+jg9fbVU5Yx6Zy+xERsWxD+jKEpXP/+Ck5+cQE923gZvbX4sjdjWzuzu5bNocQYY4evu8vpMHZrw3Oz2Pz+fDr//cjq7iA72vLBfNwxEfVe6Nf6/Kz424f0fGRcncYvuWwy5Zk5DP6q5sr1szrcgbV231eqkkZjjLHr7qrdhaA5eW7WBt6fv5m/H9m5xirz23vq+3Uc1Sexxh+dXdmbsV8tzePhaWs4d7+2nLP/tm9vO9wxS+/tFqQ15vZ1T31P4lF9ahSNdiXvq6WseXgabc/dj7bn7F+9XTm8+TPG2LuGN377+H0xa8NzzN/8Pkd2/nt156cdzd/yARHuGHon7nkGwN6M3ZXv1k5gwZYPOLXXU7SN7lu9/Y5ZHfSebwaMMfa07CfqNHbu7e/R/sh+tB2ZTtmGfH6+9jVGvnM1009/ikCVj0ClnwOeOJfY7qnVM2p8JRWs/WgOw/59OhU5xcy65AWOmHod+RlrmXf7VGwgQGyPtgx78Ix6Lbia8eAnxKe3p+OYodXbNs1YVn3Ouvjpikl0O2c4qYftPN8XLttIxgOfMPy5iwD4+riHOOL963BHhrHyrR+pyC2p84yad9peo/d9MxAq1+Q74/Nb/vbhCh4Zt+cv+fdmLATbc2fmlPPVlYNrbG8t1+qtekZNc9P+0uG0v3T3v8Rtxg7cp3MYj7vORRqAPhPP2KfziezOpcPbc+nw9rsdc1Ud74Xd27FH9U7kKK0oL02goXN7h6tG1Hls4lG9STxKHfykcQxvfynD21+62zED92Jmzt6M3ZWRHa9jZMfr9vk44rwupx7Asue/pe3IdFZP/ZXO4/cD4JCXLsETHcHGaYtY+sw09ntgz9eqc299j4Of/TORqXHMu+M9sr9dTLtR2wp5a97/lRWTfqi13+GTr9rlraz14SutJHfuKg586rxdjqnMLyU8YdsXTmHxXirzS/G2i9/lPiL7qi7X5DvjcZs6F172ZizAxDP67HlQCFOhRkREREREmpXEQZ0oWrYJf1kl6z6Zx4g3r8BXWslv/3ybkpVbCFQFiEiu2X2mxiSZ7e4aKFiygR8vD67h5CupIC49rcZ+ncbtR6dx+zXaa/nD+s/n0/6o/jXWidxReHwUVQXbugFWFZbXKNyISOugQo2IiIiIiDQ7accOYPGTX+Ftn0BYrJe1H88lPC6KA/93Axu/WcjyF6bXGB8WH0XZ+nwA8hasrd4en96e4c9dVF3Y2XFx+IacUeMvq8RXWlmriASw+r3Z9LvxuN2OjemeQlHmJnwlFfhKgovDu7U4vEiro0KNiIiIiIg0O51POYBPh/+LQ56/GIDk/bqxeMIXTD/jKeL61L5NI75fGsZl+Pbkx0ner2v19iF3n8yP//cy1hfAuAxD7j2V+PRt+9d3Rs2iRz9n7SfzqNhSRHHmZg6ffBVbfs4k+7vFDLp9XI2xlfmllKzaQtLQbQ0PdjbW5XHT/y8n8N1pT2KMYfC/Tt7ruESk5Ws1iwm7IsM22gpf2z2PlLowEZ7sQHlVO6fjkF2LDHNtrPBZvef3UoTHZJdXBfTebiGU2+tHObz5C3NFbvTZCr23G4jHRGRXBcr1nnfY3iwm3BwtfXYaq9+bzcCbx1R3ftrR4ie+pP3RA2oUgnZlb8Zu+HIBGQ9+QvfzDqX7eYfWKV4tJtw86Jq8YbWWa/VWU6gRERERERHntPRCTUujQo1Iy7XrlaxERERERERERKRJqVAjIiIiIiIiItJMqFAjIiIiIiIiItJMaI0aERERERFpdG5v+MZAeZUWVW0irsiwbH9ZZcgvuioSilSoERERERGRkGWMcQOzgX9ba99uonPGAIuB06y1s5rinCISOnTrk4iIiIiIhLI/AyXAO011QmttMXAz8KgxRp+5RGSvKGmIiIiIiEhIMsbEAfcA19umv5XgNcANnNXE5xWRFk63PomIiIiISEgyxvwbaG+t/bND5z8UeAtIt9aWOBGDiLQ8KtSIiIiIiEjIMcZ0I7g2zUBr7XoH43gLWGStvcupGESkZVGhRkREREREQo4x5h1gnrX2Hofj6AL8Bgy21q51MhYRaRlUqBERERERkZBijDkceJXgLUdlzSCee4Au1trznI5FRJo/FWpERERERCRkbG3H/QvwgLX2Lafjgep23UuAk621Pzkdj4g0b+r6JCIiIiIioeR8oByY7HQgf9jarvsW4DFjjHE6HhFp3lSoERERERGRkGCMiQXuxZl23HvyChAOnOl0ICLSvOnWJxERERERCQnGmHuBTtba852OZWeMMSOA1wmunVPqdDwi0jypUCMiIiIiIi2eMaYr8CvNvLuSMeZtYL619m6nYxGR5kmFGhERERERafGMMZOBDGvtv5yOZXeMMd2A2cAga+06p+MRkeZHhRoREREREWnRjDGHAW/QQm4pMsbcB3Sw1l7gdCwi0vyoUCMiIiIiIi2WMcYF/Aw8Yq19w+l46mLrosdLgLHW2l+cjkdEmhd1fRIRERERkZbsPKAKeNPpQOrKWlsE3IradYvITqhQIyIiIiIiLZIxJga4D7ihGbbj3pNJgBc43elARKR50a1PIiIiIiLSIhlj7ga6WWvPdTqW+jDGjAReIbi2TpnT8YhI86BCjYiIiIiItDjGmC7Ab8AQa+0ap+OpL2PMFGCOtfZep2MRkeZBhRoREREREWlxjDFvAkustXc6Hcu+MMZ0B34BBlpr1zsdj4g4T4UaERERERFpUYwxhwCTCd4yVOJ0PPvKGPMfoK219kKnYxER56lQIyIiIiIiLcbWdtw/Ao9ba19zOp6GYIyJI9iu+0Rr7a9OxyMizlLXJxERERERaUnOASzwhtOBNBRrbSFwG2rXLSKoUCMiIiIiIi2EMSYa+DdwvbU24HQ8DewlIBY41elARMRZuvVJRERERERaBGPMXUAva+3ZTsfSGIwxo4AXgb7W2nKn4xERZ6hQIyIiIiIizZ4xpjMwBxhqrV3tdDyNxRjzHvCLtfbfTsciIs5QoUZERERERJo9Y8zrwApr7e1Ox9KYjDE9gJ+AAdbajU7HIyJNT4UaERERERFp1owxw4F3CLbjLnY6nsZmjHkASLLWXuJ0LCLS9FSoERERERGRZmtrO+6ZwNPW2lecjqcpGGPigcXACdbaOU7HIyJNS12fRERERESkOTsLcAOvOR1IU7HWFgB3AI+qXbdI66NCjYiIiIiINEtb23H/h9Bsx70nLwCJwHinAxGRpqVCjYiIiIiINFd/AWZYa2c4HUhTs9b6gRuAB40xEU7HIyJNR2vUiIiIiIhIs2OM6QjMA4ZZa1c5HY9TjDHvAzOttQ84HYuINA0VakREREREpNkxxrwKrLLW3up0LE4yxvQCZgH9rbXZTscjIo1PhRoREREREWlWjDEHAe8BfVpDO+49McY8BMRZay9zOhYRaXwq1IiIiIiISLOxtcvRTOBZa+3LDofTLBhjEgi26z7OWjvX2WhEpLFpMWEREREREWlOzgTCgVecDqS5sNbmA3eidt0irYIKNSIiIiIi0iwYY6KA+2md7bj35HmgDTDW6UBEpHGpUCMiIiIiIs3FTcCP1trvnQ6kubHW+oAbgYfUrlsktGmNGhERERERcZwxJg34HTjAWpvldDzNlTHmf8B0a+1DTsciIo1DhRoREREREXGcMeZlYL219manY2nOjDG9gRkE23VvcjoeEWl4KtSIiIiIiIijjDH7A/8j2I67yOl4mjtjzCNAlLX2/5yORUQango1IiIiIiLimK1djL4HXrTWvuh0PC2BMSaRYLvuo621vzsdj4g0LC0mLCIiIiIiTjoNiAImOR1IS2GtzQPuAh5Ru26R0KNCjYiIiIiIOMIY4wUeAG6w1vqdjqeFmQi0B8Y4HYiINCwVakRERERExCk3ALOttd85HUhLs7Vd9w3Aw8aYcKfjEZGGozVqRERERESkyRlj2gPzgQOttZlOx9NSGWM+AqZZa/+/vXuPrqq81z3+vFkrV5IQQsIlQUCu4SpYu/ehp1rRTbUXQCvS1qodtrUdWj3bYqvdek6V09OeVov1fkEQ8VIHPdRbK1LoBrSW7LZ2g4YEY5AEIRAgFwKBrCQr63f+WNmBmEAScpkzyfczhmO4st4557MypmO4nrzzfZd5nQVA96CoAQAAANDrnHPPSDpsZnd6naUvc85NlvSOpKlmdtjrPAC6jqIGAAAAQK9yzn1K0h8U3Y77qNd5+jrn3IOS4szsZq+zAOg6ihoAAAAAvaZpl6K3JD1nZiu8ztMfOOfSJe2UdKmZ7fA6D4CuYTFhAAAAAL3pKkmpklZ5HaS/MLNKST+V9Gu26wb6PooaAAAAAL3COZcg6X6xHXdPeEpStqQveR0EQNdQ1AAAAADoLbdJ2mZmm70O0t+YWYOkJWK7bqDPY40aAAAAAD3OOTdC0g5J/2xmH3mdp79yzq2TtMHMHvQ6C4CzQ1EDAAAAoMc551ZIqjSzO7zO0p8556ZIelvSFDMr9zoPgM6jqAEAAADQo5xzsyWtk5RjZtVe5+nvnHMPS4oxs1u8zgKg8yhqAAAAAPSYpl2INkv6jZkt9zrPQOCcG6rodt1zzSzf6zwAOofFhAEAAAD0pCslDZG00usgA4WZVUj6P5IeYLtuoO+hqAEAAADQI5xz8WI7bq88IWmMpC94HQRA51DUAAAAAOgp/yopz8w2eR1koDllu+4HnHOxXucB0HGsUQMAAACg2znnhkvKlzTHzIq8zjMQNT329KakdWb2sNd5AHQMRQ0AAACAbuecWy7pqJn90OssA5lzbpqiizlPaVq7BoDPUdQAAAAA6FbOuVmS1iu6HfcRb9PAOfeopIiZ/Q+vswBoH0UNAAAAgG7T9LjNJklrzOxJr/NAcs5lKLpd90VmttPrPADOjMWEAQAAAHSnhZIyJK3wOgiizKxc0s8kLfM6C4D2UdQAAAAA6BZN23H/StISMwt7nQctPC5pvHOO7boBn6OoAQAAANBdbpVUYGYbvQ6ClsysXtLtkpaxXTfgb6xRAwAAAKDLnHPDFN2O+7+b2Yde50FrTesH/VHS62b2qNd5ALSNogYAAABAlznnnpR0wsyWeJ0Fp+ecm67oYs85ZlbpdR4ArVHUAAAAAOgS59xMSRsV/fJf5XUenJlz7nFJ9WZ2m9dZALRGUQMAAADgrDU9TrNR0stm9rjXedA+51ympAJJF5rZB17nAdASiwkDAAAA6Ir5kkZKWu51EHSMmR2W9H8V3aELgM9Q1AAAAAA4K865OEnLJP2A7bj7nEclTXbOXeZ1EAAtUdQAAAAAOFu3SPrQzDZ4HQSd07Rd9w8lPeCcC3qdB8BJrFEDAAAAoNNY56Tva1pf6E+Sfsf6QoB/UNQAAAAA6LSmnYMazOxfvc6Cs8eOXYD/UNQAAAAA6BTn3HRJmxT9cl/pdR50jXPuKUnHzWyJ11kAUNQAAAAA6ISmx2U2SHrdzB7xOg+6zjk3TNHH2D5jZh96nQcY6FhMGAAAAEBnfEnSKElPeh0E3cPMDkn6pdiuG/AFihoAAAAAHXLKdtxLzKzB6zzoVg9Lmuacm+d1EGCgo6gBAAAA0FE3S9ptZm96HQTdy8zqxHbdgC+wRg0AAACAdjnnMiTtlPQ5MyvwOg+6X9P6Q5skrTEzHm0DPEJRAwAAAKBdzrlHJZmZ3ep1FvQc59wsSesV3dHriLdpgIGJogYAAADAGTnnpknaLGmKmVV4nQc9yzn3tKRqM/uh11mAgYiiBgAAAMBpNT0Os17SOjN7yOs86HnOueGS8iXNMbMir/MAAw2LCQMAAAA4ky9IGiPpca+DoHeY2UFJ9zf9A6CXUdQAAAAAaJNzLlbSA5JuZzvuAechSTOdc5d6HQQYaChqAAAAAJzOTZL2SFrndRD0LjMLSfqRpF875wJe5wEGEtaoAQAAANCKc26oottxzzWzfK/zoPc1rU+0RdKLZrbc4zjAgEFRAwAAAKAV59zDkgJm9n2vs8A7zrnzFZ1RNdnMqr3OAwwEFDUAAAAAWnDOTZH0tqLbcZd7nQfecs6tlFRhZnd4nQUYCChqAAAAALTgnFsnaaOZ/drrLPCec26kpDxJ/83MdnmdB+jvWEwYAAAAQDPn3BckTZD0mNdZ4A9mdkDSMkn3eZ0FGAiYUQMAAABAUvN23O9JutPMfu91HviHcy5B0cWlv2Vmm73OA/RnzKgBAAAA8F++J6lU0h+8DgJ/adqu+w6xXTfQ45hRAwAAAEDOuXRJH0i61MzyvM4D/2narvttSavNbIXXeYD+iqIGAAAAgJxzD0qKN7ObvM4C/3LOfUrRGVeTzeyo13mA/oiiBgAAABjgnHM5kv4saaqZHfY6D/zNObdK0kEz+7HXWYD+iKIGAAAAGOCcc3+QtNnMlnmdBf7nnMtSdLvuT5vZbq/zAP0NiwkDAAAAA5hz7jJJkyU94nUW9A1mtl/SA2K7bqBHMKMGAAAAGKCcc0FFt+O+y8xe8zoP+g7nXKKi23V/08ze8joP0J8wowYAAAAYuL4rqUzS614HQd9iZrWS7hTbdQPdjhk1AAAAwADknBui6Hbc88zsfa/zoO9p2q77HUkrzewZr/MA/QVFDQAAADAAOecekDTIzL7ndRb0Xc65T0t6TdHtuo95nQfoDyhqAAAAgAHGOTdJ0lZFt+M+5HUe9G3OudWSSs3sLq+zAP0BRQ0AAAAwwDjnXpf0ZzO73+ss6Pucc9mKLkp9gZmVeBwH6PNYTBgAAAAYQJxz8yRNk/Sw11nQP5hZqaSHJP3S6yxAf8CMGgAAAGCAaNqOe5ukn5jZK17nQf/hnEtSdHHqa8zsHa/zAH0ZM2oAAACAgeM7ksolvepxDvQzZnZC0e26H3TO8T0T6AJm1AAAAAADgHMuTdEZD5eb2XZv06A/atque6ukJ81stdd5gL6KogYAAAAYAJxzv5I02Mxu9DoL+i/n3D9LelnR7bprvM4D9EUUNQAAAEA/55ybKClX0jQzO+h1HvRvzrnnJZWY2f/yOgvQF/HsIAAAANBPOed+6JwbLul+SfdT0qCX/Jukm5xzY5xz/+acS/c6ENCXBL0OAAAAAKDHXC+pWtJMSV/zOAsGCDPb55x7RNIvJI2TtEnSX71NBfQdzKgBAAAA+q9USUskPSdpm3NulMd5MAA4516SNEjSZyU5Re9DAB1EUQMAAAD0X5mS0iV9S9JtZrbP4zwYGG6TNEHRkma6pMGepgH6GIoaAAAAoP9KVHQR4Rlm9kevw2BgaFoL6SpJd0iKlTTb20RA38KuTwAAAEA/5Zw7x8z2ep0DA5dzbpikajOr8zoL0FdQ1AAAAAAAAPgEjz4BAAAAAAD4BNtzAwAAAKeISYgts7rwcK9z9DUuPngwEmoY4XUOdExCbExZXdi4zzspPugOhhoi3OfoUTz6BAAAAJzCOWdzSpd6HaPPyc2+R2bmvM6BjnHOWenSOV7H6HOy78nlPkeP49EnAAAAAAAAn6CoAQAAAAAA8AmKGgAAAKAXHVqzTdW5JWf9fnsOvviu8hasUP6iVQrtqWz1fs32UuUtWKG8+U+ramPhWV8HOJM12w4pt6T6rN9vz4vvHtSCFXlatCpfeypDrd7fXlqjBSvyNP/pPG0srDrr6wBeYDFhAAAAoBcN++rsLr1/Jg1VJ3Topf/U9Fe/reM7yvTxz/+kSU8tbjGm5N71mvTUYgVT47XjymeUdslEuQB/v0X3+ursYV16/0yqTjTopf88pFe/PV07yo7r53/6WE8tntRizL3rS/TU4klKjQ/qymd26JKJaQrEsLQM+gaKGgAAAKAHWLhRRbe+rIbDNUq54BxVbijUrE3f195lm5U0eZgGnZelopvWKn7MENUWHlL2rRcqY+GM5veHfnlap69Zs71UqXPGygUDSp6VrdqPylu8Hwk1yMIRxY9MlSQljhuqUHGFEidkdstnxsATbjTd+nKRDtc06IJzUrShsFKbvj9Lyzbv1eRhSTova5BuWlukMUPiVXioVrdemK2FMzKa3//ytKGdvub20hrNGZuqYMBpVnayPiqvbfF+qCGicMQ0MjVekjRuaKKKK0KakJnYLZ8Z6GkUNQAAAEAPqPxjoWIzkzXpiatVnVuiyg2tHzNqKK/RtJdvUGNNnXZ+4wVlLJzR5rlqd5Vr949/3+rno++ep5TZo5pfh4/UKjj45JfRT+7wGj5Sq8DghObXgdQEhatafskFOuOPhZXKTI7VE1dPUm5JtTYUtn7crrymQS/fME01dY36xgs7tXBGRpvn2lVeqx//fnern989b7Rmj0ppfn2kNqzBiSe/yn7yPj9SG9bghEDz69SEgKpqw53+bIBXKGoAAACAHhAqrlDyrGxJUsrs7DbH9a0YxgAAEWxJREFUJE7MVExcUDHpQUXqTv9FMnFChqatvaHdawYHJ+rEzoPNr11My0eaAoMT1Vh9cj2PxmN1Cg5hlgHOXnFFSLOykyVJs7NT2hwzMTNRccEYpQdjVBeOnPZcEzIStfaG9meSDU4MaufBE82vYz7xSNPgxICqQ43Nr4/VNWpIIl990XdwtwIAAAA9IGFsuo79/WNlfmWmat7b3/Yg17E1Mzo6oyZ5drb2/XqLrDGi4wVlSjg3vcX4QGKsXDBG9QePKZASr1BxhRLGpn/ytECHjU1P0N8/PqavzMzUe/tr2hzTwdu8wzNqZmcn69db9qkxYiooO65z0xNajE+MDSgY43TwWL1S4gMqrghp7CfGAH5GUQMAAAD0gPTLc1Tx+3zlL1qlQTOzFBMbaP+g0+jojJrYIUnKvHqWdlz5jFxsjMb/aqGk6E5S8aOHaPCcsRrzk8tUeOMayUyjllwsFzz7XMDlOen6fX6FFq3K18ysQYrtwsLUHZ1RMyQpVlfPytSVz+xQbIzTrxaOlxTdSWr0kHjNGTtYP7lsjG5cUygzacnFoxQMsJAw+g73yef5AAAAgIHMOWdzSpd2y7kiDY2KiQ2oOrdEB1f/TZOeXNz+QX1UbvY9MjO+DfcRzjkrXTqnW87V0BhRbCBGuSXVWv23g3ryEzsw9SfZ9+Ryn6PHMaMGAAAA6CEffneNwkdDsvpGjV+20Os4QI/47poPdTQUVn2jaVnT7BYAZ4+iBgAAAOghOauu8ToC0ONWXZPjdQSgXzn7BwgBAAAAAADQrZhRAwAAAPhY9dZiVbxRoHE/+1KPXqfg68/JGqJbGh/7x15dsO1HCqaxdTd6x9biar1RUKGffWlcj17n7jd2a11Bpb44Nb3HrwWcLYoaAAAAAJr60vWSpBOFh1Ry73pKGvRLt144Sl+cOlTrCiq8jgKcFkUNAAAA0AWhPZUquuV3iokPKjAoTjmrv6HDr7yvQy/+Q421DUq7eIJG/+gSVW8tVunDbyuQEq/aonKdc/tcHV67XaG9RzThoSuVPCNL2+c+qtTPnKsT+WVKPi9LY5d+ocW1qt/Zrb0PbJHMNPjCcTpnyVxVbijUvge3KJAUp5R/Gq3Rd1zapc9T/sr7yrhiRpfOgf5nT2VIt/yuSPHBGA2KC2j1N3L0yvuH9eI/Dqm2oVEXT0jTjy4Zra3F1Xr47VKlxAdUVF6r2+eeo7XbD2vvkZAeunKCZmQla+6j2/WZc1OVX3ZC52Ula+kXxra41ju7q/XAlr0yky4cN1hL5p6jDYWVenDLPiXFBfRPo1N0x6Wjz+pzjEiN0+6K2m74jQA9h6IGAAAA6IKjuSUa+uVpyvreZ2SRiCQp/bIcZV45U2am/CtWqv6bn5YkRUJhTXnpelVtKNS+B7Zoxpvf1dG/lOjg8+8q+b4Fipxo0PCvn69B00fqg2+9pJq8/c3XMTPt+flGTVt7gwJJcSq8cY1qd5Wr4g/5GvfL+UqekdV8/VPtvPZ5RULhFj/LuGKGhl97QZufp3L9B5r++ne669eDfiK35Ki+PG2ovveZLEUiJkm6LCddV87MlJnpipX5+uan6yVJoXBEL10/RRsKq/TAln1687sz9JeSo3r+3YO6b0GyTjRE9PXzh2v6yEH61ksfKG9/TfN1zEw/37hHa2+YpqS4gG5cU6hd5bX6Q36Ffjl/nGZkJTdf/1TXPr9ToXDL+/+KGRm69oLhPfhbAXoGRQ0AAADQBUPnT9O+h95W0S1rlTRthLJv+qyq3ynWgeVbZWYK7alSfdkxSVLS1OFyziluRIqScobJxcQobmSKwkeif+F3wRgNmj5SkpR8XpZCuysVmzlIkhSuPKG6PVX64PoXo6+rQ6rbX61RP/ic9j/xFx04Ua+MhTM0ZN7kFvmmvHBdhz/LsX/sVcL4oQqmJnT594L+Zf60oXro7X26ZW2Rpo1I0k2fzdY7xdVavvVAtESsCqnsWLSomTo8Sc45jUiJU86wJMXEOI1MidOR2mhhGIxxmj4yel+fl5Ws3ZUhZQ6KlSRVnghrT1Wdrn/xA0lSdSis/dV1+sHnRumJv+zXifoDWjgjQ/MmD2mR74XrpvTWrwLocRQ1AAAAQFfEOI25a54kqeCrq5X++RztvX+Tpv72mwqmJWrHwpWSNc0AcO7kcaf+e9PbFo7oeH6ZBk0boZr39ytt7kQ11tRJkoLpSUoYN1Q5z1+rQGJsdPaMSZH6sMbft0CRurDeu+SxVkVNZ2bUlL+ap8wrZnbxF4L+KMZJd80bI0n66uoCfT4nXfdv2qvffnOq0hKDWrhyR0dvc4Ujpvyy45o2YpDe31+juRPTVFMXXcg6PSmocUMT9Py1OUqMDSgSMZmk+nBE9y0Yr7pwRJc89l6rooYZNehPKGoAAACALqja+KHKnvmrFHCKG5aihDFDlLFwugquflaJkzIVSIzt8LlikmJ1+LfbVLy9VINmZil5ZpaqtxZLkpxzGn3npfrguheir4MxmvjoVdq/PFfH3t0ra2jUsGvOb3XOjs6oscaIjmwq0uim0gk41cYPq/TMX8sUcNKwlDiNGZKghdMzdPWzBZqUmajE2ECHz5UUG6Pfbjus7aXFmpk1SDOzkrW1uFpS9D6/89LRuu6F6IyaYIzTo1dN1PLc/Xp37zE1NJquOX9Yq3N2dEbNQ2/t05s7K1V+vEHFFQX6zfVTO5wb6C3OrPXzfQAAAMBA5ZyzOaVLPbn29kse06xN3/fk2l2Vm32PzMy1PxJ+4Jyz0qVzPLn2JY9t16bvz/Lk2l2VfU8u9zl6XIzXAQAAAAAAABBFUQMAAAD4RF+dTQN0Rl+dTQP0FooaAAAAoJvsuu0V1bxX2uPX2btss7bPfVRH3v5IklSzvVR5C1Yob/7TqtpYeMZjC77+nPIXrVL+olX6j3P/d/OOU21p67w17+/Xe/Oe0O673+i+D4Q+5bZXdum90pr2B3bRss17NffR7Xr7oyOSpLvf2K3Z97+ru9/Y3e6xGwurNP/pPC1Ykaft7WRta+yfPqzSRY9s05N/2d/lzwF0FosJAwAAAH3QObfPVdpF4yVJJfeu16SnFiuYGq8dVz6jtEsmygXa/pvs1JeulySdKDykknvXK5iWeNprtHXe5JlZGrv0clW8UdD9Hwr4hNvnnqOLxqdJkm69cJS+OHWo1hVUnPGYxojp/k0f6+VvTdexurBu+n9FevXb0zs19l8mDVHF8QZVnQi3eRzQk5hRAwAAAJxByb3rm2eu1B04qoKvrZZ0cmZK3oIVqt3d8otj9dbi5hknDZXHlb9olSTpeH6Z8hc/q/xFq/TRHa+rOzb2iIQaZOGI4kemKjAoXonjhipUfOYvspJU/sr7yrhiRrefF33TvetLmmeuHDhap6+tjhZxX3+uQItW5WvBijztrmg5+2prcXXz7JbK4w1atCpfkpRfdlyLn83XolX5uuP1j7rlPpekEalx6sgqvsUVIY0bmqjk+IBGpsYr3GgKNUS6PBboLcyoAQAAAM4g46qZKlv5V6VdNF4Vr+U1lxuTV35NgaQ4HdmySweWb9W4X8xv91wl97ypiY8vUtywFJUsXa/qtz5S2sUTmt8vf22HDj7/91bHTfnNdYqJa/t/3cNHahUYnND8OpCaoHDV6R9n+i+V6z/Q9Ne/c9r3z/a86JuumpmhlX8t00Xj0/RaXoWumJEhSVr5tclKigtoy64jWr71gH4xf1y757rnzRI9vmiihqXEaen6Er31UbUunpDW/P5rO8r1/N8PtjruN9dNUVyw63MJqmrDGpx48r+X1ISAjtSGNSI2rktjgd5CUQMAAACcQfKMLNXuKldjbYMq39ypnBeuVWNtvYrvekOhPZWyhohihya1PMid8nf/UyYTnCg8pKKb10qSGo/XKylneIvDMhZOV8bCth/ROJ3A4EQ1VoeaXzceq1NwyOkfZ5KkY//Yq4TxQxVMTTjtmLM5L/quGVnJ2lVeq9qGRr25s1IvXJuj2vpG3fVGsfZUhtQQMQ1Nim1xzGlucxUeOqGb1xZJko7XNypneMv/PhZOz9DC6Rk99VGUlhjQ0dDJR5aO1jUqLbHtr76dGQv0Fu5AAAAAoB1DPj9Z+x9/R3EjUxVMSVDFugIFUhM0/ZVvq2pzkcpW/a3F+ODgBNUfOCpJOr7jQPPPk3KGa9JTVys2fZAkKdLQ2OK4s5lRE0iMlQvGqP7gMQVS4hUqrlDC2HRZuFENFScUNzyl1THlr+Yp84qZza/bGnu686L/+vzkIXr8nf0amRqnlISg1hVUKDUhoFe+PV2bi6q06m9lLcYPTgjqwNF6SdKOA8ebf54zPElPXT1J6YOixU5DY8tHibpzRk1tQ6Nq6yPN15Kkc9MTtbsipBP1jaqpa1QwxikhNqZTYwEvUdQAAAAA7cj8ykxt++zDmrR8sSQp5fxRKn3kzyq45jklTRrWanzSlOFyMU75Vz+rlPNHNf987L2Xq+jmtbJwRIpxOvenX1TS5JPHn82MGkka85PLVHjjGslMo5ZcLBcMqLa4Qnt/8e+a9NTiFmOtMaIjm4o0+q55zT8L7T3S5ti2zov+6yszM/XZh7dp+eJJkqTzR6XokT+X6prnCjRpWFKr8VOGJynGOV39bL7OH3Wy5Lv38rG6eW2RwhFTjJN++sVzNfmU4892Rs1Db+3TmzsrVX68QcUVBfrN9VP194+P6e2PqvU/Pz+meVww4HT7xaP01dUFci6aR1KnxgJect21sBMAAADQHzjnbE7pUq9jnNGBp3N1+NU8jb7z0uadnz6p/LU8BZLjNeTSSe2erzNja97fr+K73lDaxeN1zg8vaf55bvY9MrOOrPUKH3DOWenSOV7HOKOncw/o1bzDuvPS0c07P33SY38u1b9MHtKiCDqdzoz904dVWrZ5r6791HB944KTjyhm35PLfY4eR1EDAAAAnKIvFDV+RFHTt/SFosaPKGrQG3j4DgAAAAAAwCcoagAAAAAAAHyCogYAAAAAAMAnWKMGAAAAOEVMQmyZ1YWHtz8Sp3LxwYORUMMIr3OgYxJiY8rqwsZ93knxQXcw1BDhPkePoqgBAAAAAADwCR59AgAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ+gqAEAAAAAAPAJihoAAAAAAACfoKgBAAAAAADwCYoaAAAAAAAAn6CoAQAAAAAA8AmKGgAAAAAAAJ/4/5Z1z3DXwu75AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "\n",
    "plt.figure( figsize=(20,15) )\n",
    "plot_tree(decision_tree, filled= True, feature_names=wine.feature_names)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b95e3ad",
   "metadata": {},
   "source": [
    "#### random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ca6aa98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        12\n",
      "           1       1.00      1.00      1.00        14\n",
      "           2       1.00      1.00      1.00        10\n",
      "\n",
      "    accuracy                           1.00        36\n",
      "   macro avg       1.00      1.00      1.00        36\n",
      "weighted avg       1.00      1.00      1.00        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(random_state=42) # RandomForest분류기 객체를 생성\n",
    "random_forest.fit(X_train, y_train) # 훈련\n",
    "y_pred = random_forest.predict(X_test) # 예측\n",
    "\n",
    "print(classification_report(y_test, y_pred)) # 결과 지표를 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bde002",
   "metadata": {},
   "source": [
    "- 와인을 100% 정확하게 분류했음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34fca7cf",
   "metadata": {},
   "source": [
    "#### svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "08cd9bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        12\n",
      "           1       0.93      1.00      0.97        14\n",
      "           2       1.00      0.90      0.95        10\n",
      "\n",
      "    accuracy                           0.97        36\n",
      "   macro avg       0.98      0.97      0.97        36\n",
      "weighted avg       0.97      0.97      0.97        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_model = svm.SVC(random_state=42)\n",
    "svm_model.fit(X_train,y_train)#훈련\n",
    "y_pred = svm_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d829e0",
   "metadata": {},
   "source": [
    "- 97%의 정확도를 보였음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37112457",
   "metadata": {},
   "source": [
    "#### SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f8db766f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96        12\n",
      "           1       0.93      1.00      0.97        14\n",
      "           2       1.00      1.00      1.00        10\n",
      "\n",
      "    accuracy                           0.97        36\n",
      "   macro avg       0.98      0.97      0.97        36\n",
      "weighted avg       0.97      0.97      0.97        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_model = SGDClassifier(random_state=42)\n",
    "sgd_model.fit(X_train,y_train)#훈련\n",
    "y_pred = sgd_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011a5249",
   "metadata": {},
   "source": [
    "- 97%의 정확도를 보였음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb33e67f",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4fa5b9cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        12\n",
      "           1       0.93      1.00      0.97        14\n",
      "           2       1.00      0.90      0.95        10\n",
      "\n",
      "    accuracy                           0.97        36\n",
      "   macro avg       0.98      0.97      0.97        36\n",
      "weighted avg       0.97      0.97      0.97        36\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logistic_model = LogisticRegression(random_state=42)\n",
    "logistic_model.fit(X_train,y_train)#훈련\n",
    "y_pred = logistic_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce17d9f",
   "metadata": {},
   "source": [
    "- 97%의 정확도를 보였음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058f1a72",
   "metadata": {},
   "source": [
    "## 전체 평가\n",
    "- wine dataset에서는 random forest 알고리즘의 성능이 가장 좋게 나왔다.\n",
    "- 모든 데이터의 파라미터를 random_state만 고정시키고 돌렸을 경우, random forest는 모든 데이터를 정확하게 분류하였다.\n",
    "- 모델의 평가 지표로는 정확도와 Confusion Matrix를 사용하면 될 것으로 생각된다. 데이터 전처리에서 표준화 변환을 진행하였고, 불균형한 데이터셋이 아니었기 때문에 해당 지표로도 모델을 충분히 설명할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010f49d7",
   "metadata": {},
   "source": [
    "## 유방암 여부 진단"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "359394d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d8b09d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7262ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_data = cancer.data\n",
    "cancer_label = cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f9bb3b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</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>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "0                   0.07871  ...        25.380          17.33   \n",
       "1                   0.05667  ...        24.990          23.41   \n",
       "2                   0.05999  ...        23.570          25.53   \n",
       "3                   0.09744  ...        14.910          26.50   \n",
       "4                   0.05883  ...        22.540          16.67   \n",
       "..                      ...  ...           ...            ...   \n",
       "564                 0.05623  ...        25.450          26.40   \n",
       "565                 0.05533  ...        23.690          38.25   \n",
       "566                 0.05648  ...        18.980          34.12   \n",
       "567                 0.07016  ...        25.740          39.42   \n",
       "568                 0.05884  ...         9.456          30.37   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0             184.60      2019.0           0.16220            0.66560   \n",
       "1             158.80      1956.0           0.12380            0.18660   \n",
       "2             152.50      1709.0           0.14440            0.42450   \n",
       "3              98.87       567.7           0.20980            0.86630   \n",
       "4             152.20      1575.0           0.13740            0.20500   \n",
       "..               ...         ...               ...                ...   \n",
       "564           166.10      2027.0           0.14100            0.21130   \n",
       "565           155.00      1731.0           0.11660            0.19220   \n",
       "566           126.70      1124.0           0.11390            0.30940   \n",
       "567           184.60      1821.0           0.16500            0.86810   \n",
       "568            59.16       268.6           0.08996            0.06444   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.7119                0.2654          0.4601   \n",
       "1             0.2416                0.1860          0.2750   \n",
       "2             0.4504                0.2430          0.3613   \n",
       "3             0.6869                0.2575          0.6638   \n",
       "4             0.4000                0.1625          0.2364   \n",
       "..               ...                   ...             ...   \n",
       "564           0.4107                0.2216          0.2060   \n",
       "565           0.3215                0.1628          0.2572   \n",
       "566           0.3403                0.1418          0.2218   \n",
       "567           0.9387                0.2650          0.4087   \n",
       "568           0.0000                0.0000          0.2871   \n",
       "\n",
       "     worst fractal dimension  \n",
       "0                    0.11890  \n",
       "1                    0.08902  \n",
       "2                    0.08758  \n",
       "3                    0.17300  \n",
       "4                    0.07678  \n",
       "..                       ...  \n",
       "564                  0.07115  \n",
       "565                  0.06637  \n",
       "566                  0.07820  \n",
       "567                  0.12400  \n",
       "568                  0.07039  \n",
       "\n",
       "[569 rows x 30 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df = pd.DataFrame(data = cancer_data, columns=cancer.feature_names)\n",
    "cancer_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "089d7e9b",
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  ...  worst radius  \\\n",
       "count     569.000000              569.000000  ...    569.000000   \n",
       "mean        0.181162                0.062798  ...     16.269190   \n",
       "std         0.027414                0.007060  ...      4.833242   \n",
       "min         0.106000                0.049960  ...      7.930000   \n",
       "25%         0.161900                0.057700  ...     13.010000   \n",
       "50%         0.179200                0.061540  ...     14.970000   \n",
       "75%         0.195700                0.066120  ...     18.790000   \n",
       "max         0.304000                0.097440  ...     36.040000   \n",
       "\n",
       "       worst texture  worst perimeter   worst area  worst smoothness  \\\n",
       "count     569.000000       569.000000   569.000000        569.000000   \n",
       "mean       25.677223       107.261213   880.583128          0.132369   \n",
       "std         6.146258        33.602542   569.356993          0.022832   \n",
       "min        12.020000        50.410000   185.200000          0.071170   \n",
       "25%        21.080000        84.110000   515.300000          0.116600   \n",
       "50%        25.410000        97.660000   686.500000          0.131300   \n",
       "75%        29.720000       125.400000  1084.000000          0.146000   \n",
       "max        49.540000       251.200000  4254.000000          0.222600   \n",
       "\n",
       "       worst compactness  worst concavity  worst concave points  \\\n",
       "count         569.000000       569.000000            569.000000   \n",
       "mean            0.254265         0.272188              0.114606   \n",
       "std             0.157336         0.208624              0.065732   \n",
       "min             0.027290         0.000000              0.000000   \n",
       "25%             0.147200         0.114500              0.064930   \n",
       "50%             0.211900         0.226700              0.099930   \n",
       "75%             0.339100         0.382900              0.161400   \n",
       "max             1.058000         1.252000              0.291000   \n",
       "\n",
       "       worst symmetry  worst fractal dimension  \n",
       "count      569.000000               569.000000  \n",
       "mean         0.290076                 0.083946  \n",
       "std          0.061867                 0.018061  \n",
       "min          0.156500                 0.055040  \n",
       "25%          0.250400                 0.071460  \n",
       "50%          0.282200                 0.080040  \n",
       "75%          0.317900                 0.092080  \n",
       "max          0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74cb56e",
   "metadata": {},
   "source": [
    "- 특성값에 차이가 많이 나는 변수들이 존재해서 표준화 변환 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "245d1eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_label_df = pd.DataFrame(cancer_label)\n",
    "cancer_label_df.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1b6006",
   "metadata": {},
   "source": [
    "- 클래스별로 적절하게 분류되어 있는것으로 생각됨"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6751b0cc",
   "metadata": {},
   "source": [
    "### train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5acadaaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train 개수:  455 , X_test 개수:  114\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cancer_data,\n",
    "                                                   cancer_label,\n",
    "                                                   test_size=0.2,\n",
    "                                                   random_state=42,\n",
    "                                                   stratify=cancer_label)\n",
    "\n",
    "print('X_train 개수: ', len(X_train),', X_test 개수: ', len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2060021",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae95e27",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "46ae4a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.93      0.89        42\n",
      "           1       0.96      0.90      0.93        72\n",
      "\n",
      "    accuracy                           0.91       114\n",
      "   macro avg       0.90      0.92      0.91       114\n",
      "weighted avg       0.92      0.91      0.91       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier(random_state=42)\n",
    "decision_tree.fit(X_train, y_train) #학습\n",
    "\n",
    "y_pred = decision_tree.predict(X_test) #예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6856e264",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[39  3]\n",
      " [ 7 65]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3b7ca4",
   "metadata": {},
   "source": [
    "- 91%의 정확도를 보였음\n",
    "- TN 39 / FN 7 / FP 3 / TP 65"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79c0a29",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8acb1fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.93      0.94        42\n",
      "           1       0.96      0.97      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.95      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(random_state=42) # RandomForest분류기 객체를 생성\n",
    "random_forest.fit(X_train, y_train) # 훈련\n",
    "y_pred = random_forest.predict(X_test) # 예측\n",
    "\n",
    "print(classification_report(y_test, y_pred)) # 결과 지표를 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "412b9997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[39  3]\n",
      " [ 2 70]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c0bcfe9",
   "metadata": {},
   "source": [
    "- 96%의 정확도를 보였음\n",
    "- TN 39 / FN 2 / FP 3 / TP 70"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a7be5e",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a6145a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98        42\n",
      "           1       0.99      0.99      0.99        72\n",
      "\n",
      "    accuracy                           0.98       114\n",
      "   macro avg       0.98      0.98      0.98       114\n",
      "weighted avg       0.98      0.98      0.98       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_model = svm.SVC(random_state=42)\n",
    "svm_model.fit(X_train,y_train)#훈련\n",
    "y_pred = svm_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9729d8e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41  1]\n",
      " [ 1 71]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257f19c1",
   "metadata": {},
   "source": [
    "- 98%의 정확도를 보였음\n",
    "- TN 41 / FN 1 / FP 1 / TP 71"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd29cfee",
   "metadata": {},
   "source": [
    "#### SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f5888a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.95        42\n",
      "           1       0.99      0.96      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.96      0.97      0.96       114\n",
      "weighted avg       0.97      0.96      0.97       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sgd_model = SGDClassifier(random_state=42)\n",
    "sgd_model.fit(X_train,y_train)#훈련\n",
    "y_pred = sgd_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b01df50c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41  1]\n",
      " [ 3 69]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb5f565",
   "metadata": {},
   "source": [
    "- 96%의 정확도를 보였음\n",
    "- TN 41 / FN 3 / FP 1 / TP 69"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1491a0b9",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9f878992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98        42\n",
      "           1       0.99      0.99      0.99        72\n",
      "\n",
      "    accuracy                           0.98       114\n",
      "   macro avg       0.98      0.98      0.98       114\n",
      "weighted avg       0.98      0.98      0.98       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "logistic_model = LogisticRegression(random_state=42)\n",
    "logistic_model.fit(X_train,y_train)#훈련\n",
    "y_pred = logistic_model.predict(X_test)#예측\n",
    "\n",
    "print(classification_report(y_test, y_pred))#결과지표 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4648e71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41  1]\n",
      " [ 1 71]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1f296fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e5615df",
   "metadata": {},
   "source": [
    "- 98%의 정확도를 보였음\n",
    "- TN 41 / FN 1 / FP 1 / TP 71"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ce4267",
   "metadata": {},
   "source": [
    "## 전체 평가\n",
    "- 유방암 데이터 셋에서는 SVM 모델과 Logistic Regression 모델이 가장 좋은 성능을 보였다.\n",
    "- 두 모델은 정확도 98% / confusion matrix TN 41 / FN 1 / FP 1 / TP 71를 보였으며, Recall값도 가장 높게 측정되었다.\n",
    "- 평가지표로는 Recall을 사용하는것이 적합하다고 생각한다. Recall은 실제 양성을 양성이라고 예측한 비율을 뜻하는데, 암 같은 경우에는 실제 발생한 것중 암이 발생했다고 정확하게 예측하는것이 중요하기 때문이다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b70036",
   "metadata": {},
   "source": [
    "## 참고 자료\n",
    "### train_test_split\n",
    "- https://blog.naver.com/PostView.naver?blogId=siniphia&logNo=221396370872\n",
    "- https://wikidocs.net/43332\n",
    "### Decision Tree\n",
    "- https://inuplace.tistory.com/548\n",
    "### Random Forest\n",
    "- https://wooono.tistory.com/115\n",
    "### SVM\n",
    "- https://hleecaster.com/ml-svm-concept/\n",
    "### 표준화변환\n",
    "- https://alex-blog.tistory.com/entry/Machine-Learning-Random-Forest-%EB%9E%9C%EB%8D%A4-%ED%8F%AC%EB%A0%88%EC%8A%A4%ED%8A%B8-%EC%98%88%EC%8B%9C-feat-python\n",
    "- https://bskyvision.com/entry/python-SVM-%EB%B6%84%EB%A5%98-%EB%AC%B8%EC%A0%9C%EB%A5%BC-%ED%86%B5%ED%95%B4-%EB%B0%B0%EC%9A%B0%EB%8A%94-%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-%ED%9B%88%EB%A0%A8-%EB%B0%8F-%ED%85%8C%EC%8A%A4%ED%8A%B8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fcd3d36",
   "metadata": {},
   "source": [
    "# 회고\n",
    "- 오랜만에 지도학습 모델들을 돌려봐서 헷갈렸지만 재미있었다.\n",
    "- 아직은 기본적인 모델과 간단한 데이터로 실험을 했기 때문에 할만 했다.\n",
    "- 하지만 fundamental을 통해 배웠던 것들을 시도해보려고 했지만, 제대로 활용을 하지 못했던 것이 아쉽다.\n",
    "- 다른 모델들은 다 써봤는데, SGD 모델은 처음 써본것 같아서 새로웠고, logistic 모델에 오즈비나 기타 모델들의 파라미터 같은 것들은 정리를 해두는것이 좋을것으로 생각된다.\n",
    "- 앞으로 계속 진행될 Exploration을 깃허브에 올리는 것까지 모든 과정들을 철저하게 연습해서 푸른 잔디밭으로 만들고 싶다."
   ]
  }
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