SVM_Postive_Negative.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, cross_val_score\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, accuracy_score, f1_score\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, recall_score, precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../../dataset/deceptive-opinion.csv/deceptive-opinion.csv')\n",
    "# Keeping only the neccessary columns\n",
    "data = data[['text','polarity','deceptive']]\n",
    "data['text'] = data['text'].map(lambda text: text if type(text)!=str else text.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\varun\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n",
      "C:\\Users\\varun\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Splitting the dataset into train and test set\n",
    "train, test = train_test_split(data,test_size = 0.2)\n",
    "# Removing neutral sentiments\n",
    "train = train[train.polarity != \"Neutral\"]\n",
    "train.loc[train.polarity == 'negative', 'polarity'] = 0\n",
    "train.loc[train.polarity == 'positive', 'polarity'] = 1\n",
    "test.loc[test.polarity == 'negative', 'polarity'] = 0\n",
    "test.loc[test.polarity == 'positive', 'polarity'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train['text'].values\n",
    "X_test = test['text'].values\n",
    "y_train = train['polarity'].astype(int)\n",
    "y_test = test['polarity'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tokenize(text): \n",
    "    tknzr = TweetTokenizer()\n",
    "    return tknzr.tokenize(text)\n",
    "\n",
    "def stem(doc):\n",
    "    return (stemmer.stem(w) for w in analyzer(doc))\n",
    "\n",
    "en_stopwords = set(stopwords.words(\"english\")) \n",
    "\n",
    "vectorizer = CountVectorizer(\n",
    "    analyzer = 'word',\n",
    "    tokenizer = tokenize,\n",
    "    lowercase = True,\n",
    "    ngram_range=(1, 1),\n",
    "    stop_words = en_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kfolds = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:   47.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=Pipeline(memory=None,\n",
       "     steps=[('countvectorizer', CountVectorizer(analyzer='word', binary=False, decode_error=u'strict',\n",
       "        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None,\n",
       "        stop_word...',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))]),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'svc__C': [0.01, 0.1, 1]}, pre_dispatch='2*n_jobs',\n",
       "       refit=True, return_train_score='warn', scoring='roc_auc', verbose=1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "pipeline_svm = make_pipeline(vectorizer, \n",
    "                            SVC(probability=True, kernel=\"linear\", class_weight=\"balanced\"))\n",
    "\n",
    "grid_svm = GridSearchCV(pipeline_svm,\n",
    "                    param_grid = {'svc__C': [0.01, 0.1, 1]}, \n",
    "                    cv = kfolds,\n",
    "                    scoring=\"roc_auc\",\n",
    "                    verbose=1,   \n",
    "                    n_jobs=1) \n",
    "\n",
    "grid_svm.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9789833438089253"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'svc__C': 0.01}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svm.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736636669124981"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_svm.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def report_results(model, X, y):\n",
    "    pred_proba = model.predict_proba(X)[:, 1]\n",
    "    pred = model.predict(X)        \n",
    "\n",
    "    auc = roc_auc_score(y, pred_proba)\n",
    "    acc = accuracy_score(y, pred)\n",
    "    f1 = f1_score(y, pred)\n",
    "    prec = precision_score(y, pred)\n",
    "    rec = recall_score(y, pred)\n",
    "    result = {'auc': auc, 'f1': f1, 'acc': acc, 'precision': prec, 'recall': rec}\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acc': 0.91875,\n",
       " 'auc': 0.9789833438089253,\n",
       " 'f1': 0.9265536723163842,\n",
       " 'precision': 0.9010989010989011,\n",
       " 'recall': 0.9534883720930233}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_results(grid_svm.best_estimator_, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_roc_curve(model, X, y):\n",
    "    pred_proba = model.predict_proba(X)[:, 1]\n",
    "    fpr, tpr, _ = roc_curve(y, pred_proba)\n",
    "    return fpr, tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "roc_svm = get_roc_curve(grid_svm.best_estimator_, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAAPdCAYAAACN3SvUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xvc33PBP/DXeweb2bQVhVY0xzVR\nDjkmbjmUU0kHaQ6VkkO4idnMLmw2MrqJJJGikNud0I8o3IvcClEzw5YkIjkOG9s+vz+ua9q1fS+2\nua7v9zo8n4/H5/G5vp/3+/u5Xt/Lw/bYXnt/3qWqqgAAAAAAAMACvRodAAAAAAAAgM5FgQQAAAAA\nAEArCiQAAAAAAABaUSABAAAAAADQigIJAAAAAACAVhRIAAAAAAAAtKJAAgAAAAAAoBUFEgAAAAAA\nAK0okAAAAAAAAGhFgQQAAAAAAEArCiQAAAAAAABaUSABAAAAAADQigIJAAAAAACAVhRIAAAAAAAA\ntKJAAgAAOqVSyg9LKVWN48VSytRSyrmllOGNzgkAANAdKZAAAIDO7rUkT7YcTyUZkOT9Sb6e5I+l\nlM80MBsAAEC3pEACAAA6u9urqlql5XhXkv5JPp7kkSTLJbmolLJyIwMCAAB0NwokAACgS6mq6rWq\nqq5Psk/LpRWSfLqBkQAAALodBRIAANBV/S7JrJav39/WpFLK20opJ5VS7iulzGo57i2lNJVSVnyj\nb1BKGVhKOaaU8rtSyjOllNmllBmllJ+XUvYupfRZ2tCllJVKKSeXUu4upTxfSnmplPJgKeWnpZTd\nF5k7vmXfpwve4H6XtMw5fpHrH2u5/nDL611KKdeXUp4qpcwvpRy60D5Tl71J5rEt8+5sY3z3Usov\nSin/KKW8Wkp5suX1Dkv+kwEAADqTpf7DDgAAQCdSWs69aw6Wsk6SG5O8t+XSyy3nDVqO/UopH6uq\nakaN966f5LqF3js3zYXVsJZjjyRTkjy2xGFL2TbJVUmGtFx6NcnsJGu3HJ9JB/w5rZRybJJJSaok\nzyeZ3zL0kyT7JdmtlDKwqqpZbdxi74XmL3zf5ZJcnOTzC11+Ick7k+zWct+JVVWNbpcPAgAA1I0V\nSAAAQFe1ZZofX5ckMxcdLKX0S3NZ894kf02yfZKBLceOaS5+1khyVUsRsvB7V0pyfct7ZybZPckK\nVVUNSbJikm3SXJzMW9KwLWXWL9JcHt2dZLsky1dV9bYk70iyc5L/WdL7LYXVkpyS5Owkq7R8hkEt\n3+vXSZ5MMiDNhVit3BsmGZ7m0unyRYYnp7k8mtlyHtTyeQYl+XqSF5McV0r5TDt/JgAAoIMpkAAA\ngC6llNK3lLJTkktaLr2WxYuNJPlCkhFpXuXz8aqqflP9241JdknzqqIN0noFTZKMTvLuJE8l2bqq\nqmuqqno1SaqqerGqqilVVe1fVdUTSxF9UpqLlWlJPlpV1S1VVc1vueczVVXdUFVVRxQtyye5tKqq\nb1RV9VTL93ulqqq/V1U1L8kVLfO+0Mb7F6w+unnhz1tKWS/JIUmeSfIfVVVdvmAFU1VVs6qqOi/J\nQS3Tx7TvRwIAADqaAgkAAOjstmzZW+cfpZQn0/zIt+vTvHpofpKDqqqq9Ri5vVrOV1VVNW3Rwaqq\n7su/V/x8dsH1UkpJMrLl5WlLWRLVVEp5W/69wmfsGzwqrqN86w3GFjyWbodSyjsWHmj5WXx+kXkL\n7JfmRwheVVXVX9u495VpLvg2LKWsvHSRAQCARlIgAQAAnV3fJO9qOd6Zf/855pkkm1dVdWEb79uo\n5XzzG9z7N4vMTZI1k6zU8vUvlzptbZumOff8JDe00z2X1Kwkf25rsKqqO5LMSPPPedEVUFsmWT3J\nnCT/XWMsST63UMHX6kjyaP69P9V73uLnAAAA6kiBBAAAdHa3VlVVqqoqSfon+WCaV7a8PckFpZQh\nbbxvQQn09ze494KVSwuvjnnXQl8/ugx5a1lwz2casPro6aqqqjeZc1nLee9Fri94/cuqqp5fZGzV\nlvOg/Lvgq3Us+HPngKXMDQAANJACCQAA6DKqqppTVdW9aX7k3A1p3r/oe2/ytn5L+W3KsmRrwD2X\n1LwlmHNpy/kjpZShSVJK6Z1/r0ha9PF1yb//PHnIgoLvTY7fvrWPAQAA1JMCCQAA6HJaVtR8I83l\nyGdKKR+tMe3plvPqb3CroS3nfy507R8Lff1G710aC+759lLKwKV439yWc/83mPO2ZYv0by17RN2b\n5qJrwZ5H26f5kYEvJLm2xtuebDm//61+fwAAoPNRIAEAAF1SVVUPJrm85eWEGlPubjlv9wa3+Y9F\n5ibN+wEtKJ8+scwBW/t9msuuXkl2Xor3PddyHlprsJTSK633b3orFqwy+kLLecHj6/6nqqrZNeb/\nruW8eymlTztlAAAAOgkFEgAA0JV9q+W8VSll20XGrmw571pK+cCibyylbJDkUy0vr1hwvWV1049b\nXn6zlLLqou9dWi37B/2i5eVJS7EK6U8t581LKe+qMb5vktXear4WP01SJflQKWXD/PtnU+vxdUny\nw5b570lyzBvd+A32qQIAADopBRIAANBlVVX1xyQ3tbw8fpHhnySZmubHsv2ilLJdkpRmOyS5Lkmf\nJPcluWyR905M8kSaH+E2pZSyWymlb8v7B5VS/qOUcsVSlkvHJXkpyfAkt5ZSPtqygiillCEt32PR\nR8X9b5ofFdcvyU9LKau3zB9QSjk4yXlJnl2KDG2qqupvSRbsU3Rhmh+N92SSX7cx/89Jzm55OaGU\nclYp5X0LxkspA0spO5ZSLklzOQUAAHQhCiQAAKCrO63lvH0pZYsFF6uqmpNkzyR/S7JGkt+UUmYl\nmZXkV2l+LNwjST5dVdWrC9+wqqp/Jvl4kr8nWTPNq4deKqU8m+Y9gX6d5DNJei9pyKqqpifZI8nz\naX7s3C1JXi6lPJ/kmZbvsfMi73ktyaFJ5qf5UXyPtMx/Psk5SS5O8sslzbAEFqw2WvBYvCuqqpr3\nBvOPSvL9lq8PSzKzlPLCQj+nG5Lsk6X4OQEAAJ2DAgkAAOjSqqq6Mck9LS+PX2TswSQbJBmf5M9p\nXo2Ulq9PTLJhVVUPt3Hfe5OMSDI2yV1JZqd5JdCMJP+T5PNJ/rGUWX+dZL00l15Tk8xNc7nyYJJL\n01wwLfqeK9NcLN2S5MWW+fckOaCqqq8tzfdfAj9L8tpCr9t6fN2CbHOrqvpqkm3SnP+vSZZLsnyS\nR5NclWRkkk+3c04AAKCDlebHewMAAAAAAEAzK5AAAAAAAABoRYEEAAAAAABAKwokAAAAAAAAWlEg\nAQAAAAAA0IoCCQAAAAAAgFYUSAAAAAAAALSiQAIAAAAAAKAVBRIAAAAAAACtKJAAAAAAAABoRYEE\nAAAAAABAK30aHaC7KqX8JcmKSR5pcBQAAAAAAKBrWiPJC1VVva/e31iB1HFWXH755d8+fPjwtzc6\nCAAAAAAA0PVMmzYtr7zySkO+twKp4zwyfPjwt991112NzgEAAAAAAHRBG2+8ce6+++5HGvG97YEE\nAAAAAABAKwokAAAAAAAAWlEgAQAAAAAA0IoCCQAAAAAAgFYUSAAAAAAAALSiQAIAAAAAAKAVBRIA\nAAAAAACtKJAAAAAAAABoRYEEAAAAAABAKwokAAAAAAAAWlEgAQAAAAAA0IoCCQAAAAAAgFYUSAAA\nAAAAALSiQAIAAAAAAKAVBRIAAAAAAACtKJAAAAAAAABoRYEEAAAAAABAKwokAAAAAAAAWlEgAQAA\nAAAA0IoCCQAAAAAAgFYUSAAAAAAAALSiQAIAAAAAAKAVBRIAAAAAAACtKJAAAAAAAABoRYEEAAAA\nAABAKwokAAAAAAAAWlEgAQAAAAAA0IoCCQAAAAAAgFYUSAAAAAAAALSiQAIAAAAAAKAVBRIAAAAA\nAACtKJAAAAAAAABoRYEEAAAAAABAK12uQCql7FVKObuUMqWU8kIppSqlXLKM9xpaSrmwlPJ4KWVO\nKeWRUsq3SylD2js3AAAAAABAV9Gn0QGWwfFJNkwyK8ljSdZblpuUUtZMcnuSdya5OskDST6c5PAk\nO5dStqqq6l/tkhgAAAAAAKAL6XIrkJIcmWSdJCsm+fpbuM+5aS6PvlFV1SerqhpVVdV/JDkzybpJ\nJrzlpAAAAAAAAF1QlyuQqqq6uaqqh6qqqpb1HqWUYUl2TPJIknMWGR6X5KUkI0spKyxzUAAAAAAA\ngC6qyxVI7eQ/Ws6/qqpq/sIDVVW9mOS2JAOSbF7vYAAAAAAAQM9WVVWuvfbavIW1NG9ZV9wDqT2s\n23J+sI3xh9K8QmmdJL9+oxuVUu5qY2iZ9mYCAAAAAGhl8uSkqSmZNavRSYA6+HWS45L8Psl7G5ij\np65AelvL+fk2xhdcH1yHLAAAAAAAbVMeQY/w+yQ7JPlYy9dJ8kTj4vTYFUhvprSc33RtWFVVG9e8\nQfPKpI3aMxQAAAAA0AMpj6BbeyDJ8Un+u8bYa3XOsrCeWiAtWGH0tjbGV1xkHgAAAABA4zVwPxSg\n/R1zzDGZPHly5s+fX3N88ODBee655+qcqllPfYTd9JbzOm2Mr91ybmuPJAAAAAAAgLdklVVWqVke\nbbfddrnjjjsybNiwBqRq1lMLpJtbzjuWUlr9DEopg5JsleSVJHfUOxgAAAAAANAzHHzwwRk6dOjr\nrzfeeOP86le/yq9//etsttlmDUzWzR9hV0rpm2TNJK9VVTVjwfWqqmaUUn6VZMckhyQ5e6G3nZhk\nhSTfq6rqpXrmBQAA6mTyZJtRAwAAdTFnzpzMnDkzw4cPX2ysf//+aWpqymmnnZYJEybk05/+dEop\nDUi5uFJ1sWdmllI+meSTLS9XSbJTkplJprRce7qqqqNb5q6R5C9J/lpV1RqL3GfNJLcneWeSq5NM\nS7JZku3S/Oi6Lauq+tdbyHnXRhtttNFdd921rLcAAAA6yqBByiMAoOsZODB58cVGpwCW0Lx583Lp\npZfmhBNOSFVVefDBB9OvX7+a86qqSp8+i6/52XjjjXP33XffXVXVxvXIvLCu+Ai7DybZr+XYqeXa\nsIWu7bUkN2lZkbRJkh+muTg6Ks2rlc5KssVbKY8AAIBOTnkEAHQ1Awc2r6AGOr2qqnL11Vdnww03\nzH777Ze//vWvefTRR3PeeefVnN+7d++a5VGjdbkVSF2FFUgAANCJLfxICH8mAgAA2sktt9yS4447\nLnfcccdiYyuvvHJmzJiRQYMGLfH9rEACAAAAAADoou6+++7svPPO2W677WqWR3379s3nP//5zJ07\ntwHplk3nWxMFQPdgc3IAAAAAurmHHnooY8eOzeWXX15zvJSSkSNHpqmpKe973/vqnO6tUSAB0DGU\nRwB0BQMHNjoBAADQBf3973/PSSedlB/84AeZN29ezTm77757JkyYkPXXX7/O6dqHAgmAjqE8AqCz\nsxE1AACwDH7yk5/ky1/+cmbPnl1zfJtttsmkSZOyxRZb1DlZ+1IgAdDxbE4OAAAAQDex0UYb5dVX\nX13s+gc/+MFMnDgxO+20U0opDUjWvno1OgAAAAAAAEBXsd566+WAAw54/fVaa62Vn/70p7nrrruy\n8847d4vyKFEgAfQckycngwYlpdTnAAAAAIAuav78+bn11lvbHB83blyGDRuW8847L/fff38+//nP\np1ev7lW5dK9PA0Dbmpoasy+RzckBAAAA6CKqqsp1112XD33oQ9l2221z55131pz3nve8Jw899FC+\n9rWvpW/fvnVOWR8KJICeolHlkc3JAQAAAOgCfvvb32abbbbJrrvumvvuuy9JMmrUqFRt7O/d3VYc\nLapPowMA0ABt/KYHAAAAAD3NvffemzFjxuS6665bbOzmm2/OTTfdlB122KEByRqre9djAAAAAAAA\nNcyYMSP77LNPPvShD9Usj0op2WeffbLWWms1IF3jWYEEAAAAAAD0GE888UROPvnkfP/738/cuXNr\nztlll10yYcKEbLjhhnVO13lYgQTQ3iZPTgYNSkrpXAcAAAAA9GDPPfdcRo8enTXXXDPf/e53a5ZH\nW221VaZMmZJrr722R5dHiRVIAO2vqSmZNavRKdo2cGCjEwAAAABAXd1///3Zeuut8+yzz9Yc32CD\nDXLKKafkE5/4RIp/jJ3ECiSA9tfZy6OmpkanAAAAAIC6WnfddfPud797sevDhg3LpZdemnvuuSe7\n7LKL8mghViABdKSqanQCAAAAAOjxevfunVNOOSW77757kuRd73pXTjjhhHzlK1/Jcsst1+B0nZMC\nCQAAAAAA6PKqqsoNN9yQzTffPIMHD15sfNddd80nPvGJbLXVVjn88MOzwgorNCBl1+ERdgBLY/Lk\nZNCgpJS2DwAAAACgrn73u99lu+22y8c//vGcfvrpNeeUUnLttddm9OjRyqMloEACWBpNTUu+x9HA\ngR0aBQAAAAB6uj//+c/ZY489suWWW+bWW29Nkpx55pn5xz/+UXO+PY6WnAIJYGksTXnU1NShUQAA\nAACgp/rLX/6SfffdNxtssEF+8YtftBp7+eWXM2HChAYl6z7sgQSwrKqq0QkAAAAAoEd58sknM2HC\nhJx33nl57bXXas7Zeeed86UvfanOybofBRIAAAAAANCpPf/88zn99NNz5pln5qWXXqo5Z4sttsjE\niRPz0Y9+tM7puicFEgAAAAAA0Cm98sorOeecczJx4sQ888wzNeeMGDEip5xySnbbbTd7HLUjBRIA\nAAAAANDpzJ07Nx/84Afz4IMP1hxfY401ctJJJ+ULX/hCevfuXed03V+vRgcAAAAAAABYVJ8+fbLn\nnnsudn3llVfOWWedlQceeCAjR45UHnUQBRIAAAAAANApHXPMMRk8eHCSZNCgQTnppJMyY8aMHHbY\nYenXr1+D03VvHmEHAAAAAAA0zJ133pm5c+dmyy23XGxsyJAhGTduXB577LGMGjUqK620UgMS9kwK\nJAAAAAAAoO6mTZuW448/PldddVU22GCD3HPPPenVa/EHpx1xxBENSIdH2AEAAAAAAHXz6KOP5ktf\n+lLWX3/9XHXVVUmS++67L5dddlmDk7EwBRIAAAAAANDh/vnPf+bII4/M2muvnYsuuijz589vNT52\n7NjMnTu3QelYlEfYAQAAAAAAHebFF1/MGWeckdNPPz2zZs2qOWfTTTfNxIkT06eP2qKz8F8CAAAA\nAABod3PmzMl3v/vdTJgwIU8//XTNOeutt14mTJiQT33qUyml1Dkhb0SBBAAAAAAAtJt58+blxz/+\nccaNG5dHH3205pz3vOc9OfHEEzNy5Eirjjop/1UAAAAAAIB289WvfjUXXnhhzbGVVlopo0ePzte/\n/vX079+/zslYGr0aHQAAAAAAAOg+vvKVryx2beDAgRk3blxmzJiRI488UnnUBSiQAAAAAACAdrPF\nFltkjz32SJIst9xyOeKIIzJz5sw0NTVlxRVXbHA6lpRH2AEAAAAAAEtl+vTpmTJlSs3VRkkyYcKE\nDBkyJE1NTVl99dXrnI72YAUSAAAAAACwRB577LEceOCBGTFiRA466KA8+OCDNeeNGDEiF110kfKo\nC1MgAQAAAAAAb+hf//pXjj766Ky11lq54IILMm/evMybNy9jx45tdDQ6iAIJAAAAAACoadasWRk/\nfnyGDRuWyZMnZ86cOa3Gr7jiivzpT39qUDo6kgIJAAAAAABoZc6cOTn77LOz5pprZuzYsXnhhRcW\nm7P22mvniiuuyIgRIxqQkI7Wp9EBAAAAAACAzmHevHn5yU9+khNOOCGPPPJIzTmrrbZampqasv/+\n+6dv3771DUjdKJAAAAAAAKCHq6oq11xzTcaMGZM///nPNecMGTIko0ePziGHHJLll1++zgmpNwUS\nAAAAAAD0cJdeemlGjhxZc2zAgAE58sgjc/TRR2fw4MF1Tkaj2AMJAAAAAAB6uL322itDhw5tda1v\n37459NBDM2PGjIwfP1551MMokAAAAAAAoIfr379/TjzxxCRJKSVf/OIX88ADD+Tss8/OKqus0uB0\nNIICCQAAAAAAeoDHH388xx57bGbPnl1zfN99983Xv/713Hvvvfnxj3+cYcOG1TkhnYk9kAAAAAAA\noBt79tlnc+qpp+ass87KK6+8klVXXTVHHHHEYvP69OmTc889twEJ6YysQAIAAAAAgG7opZdeysSJ\nE/O+970vp556al555ZUkyYQJE/Liiy82OB2dnQIJ6JwmT04GDUpK6VwHAAAAAHRyr776as4999ys\ntdZaGT16dJ5//vlW408//XS+//3vNygdXYVH2AGdU1NTMmtWo1O0beDARicAAAAAgFbmz5+fyy67\nLGPHjs3MmTNrzllllVUybty4fPnLX65zOroaBRLQOXX28qipqdEpAAAAACBJUlVV/t//+38ZPXp0\n7r333ppzBg8enFGjRuWwww7LgAED6pyQrkiBBHR+VdXoBAAAAADQKf32t7/Ncccdl9/+9rc1x5df\nfvkcfvjhOeaYYzJkyJA6p6MrUyABAAAAAEAX9Nhjj2XbbbfNvHnzFhvr06dPDjzwwIwdOzarrrpq\nA9LR1fVqdACgG5k8ORk0KCnlrR8AAAAAwBsaOnRoDjjggMWu77333pk2bVrOPfdc5RHLTIEEtJ+m\npvbfu2jgwPa9HwAAAAB0I+PGjUu/fv2SJJ/4xCdyzz335Cc/+UnWWmutBiejq/MIO6D9dER51NTU\nvvcEAAAAgC7kueeey7e+9a3svvvu2WyzzRYbHzp0aL797W/n/e9/f7bZZpsGJKS7UiABHaOqGp0A\nAAAAALqsl19+Od/5zncyadKkPPvss7n99tvzm9/8JqXG9g8HHXRQAxLS3XmEHQAAAAAAdBKvvfZa\nzj///Ky99to59thj8+yzzyZJbrnlltx4440NTkdPokACAAAAAIAGmz9/fi6//PK8//3vz9e+9rU8\n/vjji8359re/3YBk9FQKJAAAAAAAaJCqqnLDDTdkk002yec///k8/PDDi81ZccUVM378+FxxxRUN\nSEhPZQ8kAAAAAABogDvuuCPHHXdcbrnllprj/fv3z2GHHZZjjz0273jHO+objh5PgQQAAAAAAHU0\nderUjBkzJldffXXN8d69e+fLX/5yTjjhhLz73e+uczpopkACAAAAAIA6qaoq++yzT+69996a45/9\n7Gdz8sknZ5111qlzMmjNHkgAAAAAAFAnpZScfPLJi13fcccd84c//CGXX3658ohOQYEEXdHkycmg\nQUkpnesAAAAAAN7Urrvumi233DJJstlmm+U3v/lNbrjhhmy88cYNTgb/5hF20BU1NSWzZjU6RdsG\nDmx0AgAAAABomNmzZ+fcc8/NvHnz8s1vfnOx8VJKzjzzzDz++OPZY489UvzjbDohBRJ0RZ29PGpq\nanQKAAAAAKi7uXPn5kc/+lGampryt7/9LQMGDMjIkSOzyiqrLDb3wx/+cAMSwpJTIEFXV1WNTgAA\nAAAAPVpVVbnqqqty/PHH54EHHnj9+ssvv5zx48fnO9/5TgPTwbKxBxIAAAAAACyjm266KR/+8Iez\n1157tSqPFrjssssyqzM/UQjaoEACAAAAAICl9Pvf/z4f+9jHssMOO+QPf/jDYuP9+vXLf/7nf+aB\nBx7IQHuG0wV5hB0AAAAAACyhadOm5fjjj89VV11Vc7xXr17Zf//9M27cuLz3ve+tczpoPwokAAAA\nAAB4E48++miamppy8cUXZ/78+TXnfPrTn87JJ5+c4cOH1zkdtD8FEgAAAAAAvInvfe97ueiii2qO\nbb/99pk4cWI23XTTOqeCjmMPJAAAAAAAeBPf/OY3M2TIkFbXNtlkk9x444256aablEd0OwokAAAA\nAAB4E4MHD86xxx6bJFl33XVz5ZVX5s4778zHPvaxBieDjuERdgAAAAAA9Hjz5s3LJZdckiuvvDJX\nX311evVafP3FYYcdllVWWSX77LNP+vTx1+t0b1YgAQAAAADQY1VVlZ///OfZYIMNsv/+++faa6/N\nT3/605pzBwwYkP322095RI+gQAIAAAAAoEe65ZZbssUWW+RTn/pU7r///tevn3DCCXn11VcbmAwa\nT4EEAAAAAECPctddd2WnnXbKdtttl//7v/9bbPyxxx7LnXfe2YBk0HkokAAAAAAA6BEefPDBfO5z\nn8smm2ySX/3qV4uN9+rVK/vtt1+mT5+erbfeugEJofPwoEYAAAAAALq1v//97znppJPygx/8IPPm\nzas555Of/GTGjx+fESNG1DkddE4KJAAAAAAAuqVnnnkmkyZNytlnn53Zs2fXnLPttttm4sSJ2Xzz\nzeucDjo3BRIAAAAAAN3S9OnT861vfavm2Ic+9KFMmjQpO+ywQ0opdU4GnZ89kAAAAAAA6Ja22GKL\n7LHHHq2urb322rn88svzhz/8ITvuuKPyCNqgQAIAAAAAoEurqqrNsQkTJqSUktVWWy3nn39+pk6d\nms9+9rPp1ctfj8Mb8X8I1MvkycmgQUkpb/0AAAAAAFJVVa699tpsvPHGefDBB2vOGTFiRK655po8\n/PDDOfDAA9O3b986p4SuSYEE9dLUlMya1b73HDiwfe8HAAAAAF3ElClTsvXWW2e33XbLPffck7Fj\nx7Y5d5dddsnyyy9fx3TQ9SmQoF46ojxqamrfewIAAABAJ/fHP/4xu+yyS7bZZpvcfvvtr1+/4oor\ncvfddzcwGXQvfRodAHqkN3gmKwAAAACwuIcffjgnnHBCfvrTn9YcL6Xk5ptvzkYbbVTnZNA9WYEE\nAAAAAECn9cQTT+Tggw/O8OHD2yyPdt111/zxj3/MUUcdVed00H1ZgQQAAAAAQKfz7LPP5rTTTst/\n/dd/5ZVXXqk5Z+utt86kSZOy1VZb1TkddH9WIMFbNXlyMmhQUsobHwAAAADAm5o9e3ZOPfXUDBs2\nLJMmTapZHm2wwQa57rrr8r//+7/KI+ggViDBW9XUlMyateTzBw7ssCgAAAAA0B2cc845ee655xa7\nPmzYsIwfPz6f+9zn0quX9RHQkfwfBm/V0pZHTU0dFgUAAAAAurr+/fvnxBNPbHVtlVVWybnnnptp\n06Zl7733Vh5BHViBBO2pqhopMozYAAAgAElEQVSdAAAAAAC6hKqqMn/+/PTu3XuxsZEjR+a0007L\nP/7xjxx77LE57LDDssIKKzQgJfRcaloAAAAAAOrqtttuy0c/+tGcffbZNcf79OmTyy+/PDNnzsyo\nUaOUR9AACiR6rsmTk0GDklLe2gEAAAAALJE//elP2X333bP11ltnypQpmTBhQl588cWaczfYYIMM\nGTKkzgmBBRRI9FxNTUu3f9GbGTiw/e4FAAAAAN3IzJkzM3LkyGy44Ya55pprXr/+9NNP54wzzmhg\nMqAt9kCi52rv8qipqf3uBwAAAADdwJNPPpnx48fne9/7Xl577bWac+6///46pwKWhAIJkqSqGp0A\nAAAAALqN559/Pt/61rfy7W9/Oy+99FLNOVtssUUmTpyYj370o3VOBywJBRIAAAAAAO3ilVdeyTnn\nnJOJEyfmmWeeqTln/fXXz4QJE7Lbbrul2GMcOi0FEgAAAAAAb9lFF12UsWPH5u9//3vN8TXWWCMn\nnXRSvvCFL6R37951TgcsLQUSAAAAAABv2e23316zPHrnO9+ZsWPH5sADD0y/fv0akAxYFr0aHQAA\nAAAAgK5v3LhxrQqiFVdcMSeffHJmzJiRQw89VHkEXYwCCQAAAACAJTZ79uya14cOHZrDDjss/fr1\ny1FHHZUZM2bk+OOPz8CBA+ucEGgPCiQAAAAAAN7U1KlT86lPfSqf+MQnUlVVzTmjR4/OQw89lNNP\nPz0rrbRSnRMC7ckeSAAAAAAAtOmvf/1rxo0blx//+MeZP39+kuTGG2/MjjvuuNjcIUOGZMiQIfWO\nCHQAK5DoeJMnJ4MGJaV0rgMAAAAAaNNTTz2VI444Iuuss04uvvji18ujJDnuuONavQa6HyuQ6HhN\nTcmsWY1O0TbPYAUAAACA173wwguZPHlyzjjjjMxq4+/1+vTpk6effjrvfOc765wOqBcrkOh4nb08\nampqdAoAAAAAaLjZs2fnzDPPzLBhw3LSSSfVLI+GDx+eq666KnfccYfyCLo5K5CorzY21wMAAAAA\nGmPu3Ln50Y9+lKampvztb3+rOee9731vTjzxxIwcOTK9e/euc0KgERRIAAAAAAA91E033ZTDDjss\nDzzwQM3xlVZaKWPGjMlBBx2U/v371zkd0EgKJAAAAACAHmr27Nk1y6OBAwfm6KOPzpFHHpkVV1yx\nAcmARrMHEgAAAABAD7XLLrtkyy23fP31csstlyOOOCIzZ87MuHHjlEfQgymQAAAAAAC6ueeee67m\n9VJKJk2alF69euWAAw7Igw8+mDPPPDMrr7xynRMCnY0CCQAAAACgm/rb3/6Wr3zlK3nve9+bJ554\nouacj3zkI5k5c2YuvPDCrL766nVOCHRWCiQAAAAAgG7m6aefzlFHHZW11147P/jBD/Liiy9m/Pjx\nbc5XHAGLUiABAAAAAHQTs2bNysknn5xhw4bljDPOyJw5c14fO//88zNz5swGpgO6EgUSy27y5GTQ\noKSUNz4AAAAAgA41Z86cnHXWWRk2bFhOOOGEvPjii4vNGTZsWJ566qkGpAO6IgUSy66pKZk1a8nn\nDxzYYVEAAAAAoCeaN29efvSjH2XdddfN4Ycfnn/+85+LzRk6dGguuOCCTJ06NZtvvnkDUgJdUZ9G\nB6ALW9ryqKmpw6IAAAAAQE9SVVV+8YtfZMyYMZk6dWrNOW9/+9szevToHHzwwVl++eXrnBDo6hRI\ntI+qanQCAAAAAOgRHnrooey777654447ao6vsMIK+c///M8cddRRedvb3lbndEB3oUACAAAAAOhC\nVl555UyfPn2x63379s1BBx2UMWPG5F3velcDkgHdiT2QAAAAAAC6kMGDB2fUqFGvvy6lZN999830\n6dNz1llnKY+AdqFAAgAAAADohJ544onMmzev5tihhx6a1VZbLbvvvnvuu+++XHzxxXnf+95X54RA\nd6ZAAgAAAADoRJ555pkce+yxGTZsWC677LKacwYMGJD77rsvV199ddZff/06JwR6AgUSAAAAAEAn\n8NJLL+WUU07JsGHDctppp2X27NkZO3ZsXn311Zrz3/GOd9Q5IdCTKJAAAAAAABro1Vdfzbnnnps1\n11wzY8aMyfPPP//62F/+8pd8//vfb2A6oKdSIPU0kycngwYlpbz1AwAAAABYZvPnz8+ll16a4cOH\n55BDDsmTTz652JzVVlstQ4YMaUA6oKfr0+gA1FlTUzJrVvvec+DA9r0fAAAAAHRjVVXluuuuy+jR\no/OnP/2p5pwhQ4Zk1KhROfTQQzNgwIA6JwRQIPU8HVEeNTW17z0BAAAAoJuaMmVKjjvuuNx22201\nxwcMGJAjjjgi3/zmNzN48OA6pwP4NwVST1ZVjU4AAAAAAD3CnDlzsueee+aXv/xlzfE+ffrkq1/9\nao4//visuuqqdU4HsDgFEgAAAABAB+vXr1+WW265xa6XUvKFL3whJ554YtZcc80GJAOorVejAwAA\nAAAA9ATjx49PKeX117vsskvuueeeXHLJJcojoNNRIHUnkycngwYlpbR9AAAAAAAd5rnnnsvjjz9e\nc2zEiBHZd999s9VWW2XKlCm59tprs+GGG9Y5IcCSUSB1J01NyaxZSzZ34MAOjQIAAAAAPcnLL7+c\n0047LcOGDcsRRxzR5rzvfve7mTJlSrbeeus6pgNYegqk7mRpyqOmpg6NAgAAAAA9wWuvvZbvfe97\nWWuttXLsscfm2Wefzc9+9rPcddddNecvv/zyrR5jB9BZ9Wl0ADpIVTU6AQAAAAB0W/Pnz88VV1yR\nsWPH5uGHH15sfPTo0bnhhhsakAygfSiQAAAAAACWUFVVueGGG3Lcccflj3/8Y805K664YrbZZpvM\nmzcvvXv3rnNCgPahQAIAAAAAWAK/+93vctxxx+XWW2+tOd6/f/984xvfyLHHHpu3v/3tdU4H0L4U\nSAAAAAAAb+DPf/5zxowZk1/84hc1x3v37p0vf/nLOeGEE/Lud7+7zukAOoYCCQAAAACgDaNGjcpp\np52Wqo09xz/3uc/lpJNOyjrrrFPnZAAdS4EEAAAAANCGddddt2Z5tNNOO+WUU07JRhtt1IBUAB2v\nV6MDAAAAAAB0ViNHjszw4cNff7355pvn5ptvzvXXX688Aro1BRIAAAAA0KO98sorueuuu2qO9enT\nJxMmTMiIESPy85//PLfffnu23Xbb+gYEaAAFEgAAAADQI82dOzcXXHBB1llnney888554YUXas77\n5Cc/mXvvvTd77LFHSil1TgnQGAokAAAAAKBHqaoqV155ZdZff/0ceOCBeeyxx/L000/njDPOqDm/\nlJLevXvXOSVAYymQAAAAAIAe46abbsqmm26az3zmM5k+fXqrscmTJ+epp55qUDKAzkWBBAAAAAB0\ne3feeWe233777LDDDjX3O+rXr1+++tWvpm/fvg1IB9D59Gl0AAAAAACAjjJt2rQcf/zxueqqq2qO\n9+rVKwcccEDGjRuX97znPXVOB9B5dckVSKWUoaWUC0spj5dS5pRSHimlfLuUMmQp77N1KeXqlvfP\nLqU8Wkr5ZSll547KDgAAAAB0vEcffTRf+tKXsv7667dZHu21116ZOnVqLrjgAuURwCK63AqkUsqa\nSW5P8s4kVyd5IMmHkxyeZOdSylZVVf1rCe7z9STnJnkpyf8keSzJ0CR7Jvl4KeX4qqomdMynAAAA\nAAA6ys9+9rN88YtfzKuvvlpz/GMf+1hOOeWUbLrppnVOBtB1dLkCKc2lzzuTfKOqqrMXXCylnJHk\nyCQTkhz0RjcopfRNMjHJ7CQbV1U1faGxU5Lck2RMKeX0qqrmtP9HAAAAAAA6yhZbbJFSymLXN910\n00ycODHbb799A1IBdC1d6hF2pZRhSXZM8kiScxYZHpfm1UQjSykrvMmt3p7kbUkeXLg8SpKqqqYl\neTDJ8kkGtkNsAAAAAKCOhg4dmsMOO+z11+utt17++7//O//3f/+nPAJYQl2qQEryHy3nX1VVNX/h\ngaqqXkxyW5IBSTZ/k/s8leSfSdYppay98EApZZ0kayf545I8Cg8AAAAAqL+5c+fmmmuuSVVVNcdH\njRqVD3zgA7nwwgvzpz/9KXvuuWfNVUkA1NbVHmG3bsv5wTbGH0rzCqV1kvy6rZtUVVWVUg5JckmS\nu0op/5Pk8STvTvKpJFOTfH5JApVS7mpjaL0leT8AAAAAsOSqqsrPf/7zjBkzJtOmTcv111+fnXba\nabF573jHO3LvvfcqjQCWUVdbgfS2lvPzbYwvuD74zW5UVdXP0ryi6bkk+yYZlWRkmh+Dd1GSmW8p\nKQAAAADQrn7zm99k8803z5577plp06YlSY477rjMnz+/5nzlEcCy62oF0ptZ8DtC7XWrC08s5YtJ\nbkoyJcnwND/6bniaVy59J8llS/INq6rauNaR5IFl+QAAAAAAQGt33XVXdtxxx2y//fa58847W43d\nc889ufLKKxuUDKD76moF0oIVRm9rY3zFRebV1LLP0YVpflTdyKqqHqiq6pWqqh5I8yqku5J8ppSy\n7VuPDAAAAAAsi+nTp+ezn/1sNtlkk9x4442Ljffq1Sv77bdfNttsswakA+jeulqBNL3lvE4b42u3\nnNvaI2mBHZP0TXJrVVWt1re2vP7flpcbL0tIAAAAAGDZPfbYYznwwAMzYsSI/OxnP6s555Of/GTu\nu+++/PCHP8zqq69e54QA3V+fRgdYSje3nHcspfRauPwppQxKslWSV5Lc8Sb36ddyXrmN8QXXX13W\noAAAAADA0vnXv/6ViRMn5jvf+U7mzJlTc862226biRMnZvPNN69zOoCepUsVSFVVzSil/CrNK4gO\nSXL2QsMnJlkhyfeqqnppwcVSynot7114T6IpLee9SimnV1V130LzP5hkrzTvo/SbDvkgAAAAAEAr\nM2bMyEYbbZQXXnih5vhGG22UiRMnZocddkgppeYcANpPlyqQWhyc5PYkZ5VStk8yLclmSbZL86Pr\nxiwyf1rL+fXfVaqqurOUclGSA5L8vpTyP0n+mmSNJJ9MslySb1dVNbUDPwcAAAAA0GLYsGH5wAc+\nkNtuu63V9bXXXjvjx4/PXnvtlV69utqOHABdV5f7FbeqqhlJNknywzQXR0clWTPJWUm2qKrqX0t4\nqy+nuUD6XZKdWu6zQ5LfJtm7qqoj2zc5AAAAANCWUkomTpz4+uvVVlst559/fqZOnZrPfvazyiOA\nOuuKK5BSVdXf0lz+LMncmutZq6qq0lxC/bDdggEAAAAAbaqqKtdcc0023XTTrLrqqouNf+QjH8kX\nv/jFbLjhhjnkkEOy/PLLNyAlAEkXXIEEAAAAAHQ9t956a7baaqvsscceGT9+fJvzfvzjH+foo49W\nHgE0mAIJAAAAAOgw99xzTz7+8Y9n2223ze9+97skyfnnn58ZM2Y0OBkAb0SBBAAAAAC0u4cffjh7\n7713Ntpoo1x//fWtxubOnZsTTjihQckAWBJdcg8kAAAAAKBzevzxx3PSSSflBz/4QebOnVtzzm67\n7ZZRo0bVORkAS0OBBAAAAAC8Zc8++2xOPfXUnHXWWXnllVdqzvnIRz6SiRMnZquttqpzOgCWlgIJ\nAAAAAFhmL730Us4666yceuqpef7552vO2XDDDTNx4sTsvPPOKaXUOSEAy0KBBAAAAAAsk/nz5+fD\nH/5w7r///prja665Zk4++eR87nOfS69etmMH6Er8qg0AAAAALJNevXplv/32W+z6Kqusku9+97uZ\nNm1a9t57b+URQBfkV24AAAAAYJkdeuihWW211ZIkgwcPzqRJkzJjxowcdNBB6du3b4PTAbCsPMIO\nAAAAAHhDt912W1566aXsuOOOi40NGDAgEyZMyPTp03PMMcdkyJAhDUgIQHtTIAEAAAAANd13330Z\nM2ZMrr322qyxxhqZPn16lltuucXm7b///vUPB0CH8gi7rmDy5GTQoKSUNz4AAAAAoB3MnDkzI0eO\nzAc/+MFce+21SZJHHnkk559/foOTAVAvCqSuoKkpmTVryecPHNhhUQAAAADovv7xj3/k0EMPzXrr\nrZdLLrkkVVW1Gh8/fnxmz57doHQA1JMCqStY2vKoqanDogAAAADQ/Tz33HMZM2ZM1lxzzZxzzjl5\n7bXXFpuz5ZZb5oorrkj//v0bkBCAerMHUlezyL/6AAAAAIBl9fLLL+c73/lOJk2alGeffbbmnPXX\nXz+nnHJKdt111xTbKAD0GAokAAAAAOhhXnvttVx00UU58cQT8/jjj9ecs8Yaa+Tkk0/O3nvvnd69\ne9c5IQCNpkACAAAAgB7miCOOyLnnnltz7F3velfGjh2bAw88MMstt1ydkwHQWdgDCQAAAAB6mIMP\nPji9erX+q8EVV1wx48ePz8MPP5xDDjlEeQTQwymQAAAAAKCHGTFiRPbdd98kSf/+/fPNb34zM2fO\nzJgxYzJw4MAGpwOgM/AIOwAAAADohqZOnZrrr78+Rx11VM3xpqam9OvXL8cff3yGDh1a53QAdHZW\nIAEAAABAN/LII49k//33zwc+8IEcffTR+cMf/lBz3uqrr57zzjtPeQRATQokAAAAAOgGnnrqqRx+\n+OFZZ511cvHFF/9/9u40vMrqbtv4uSDMoIiA4IAKIopiRcGiVkXFeRZxqnVEedCKVRFISBgCSWQQ\nUess1UrrLL4qlVbEoaWURylFnCpDxJGCaGUUCGS9HxJ5gGwEyWbvDOfvOHKEfa9/7nW1n8Ar676J\nMQKQlZWV5mSSpMrIAkmSJEmSJEmqxJYtW8bgwYNp3bo1d999N0VFRZusT548mX/84x9pSidJqqws\nkCRJkiRJkqRKaPXq1dxxxx20bt2a3NxcVq5cWWamffv2vPDCC3Tp0iUNCSVJlVlGugNIkiRJkiRJ\n2nbr1q3j97//PUOGDOGLL75IONOqVStyc3O57LLLqFmzZooTSpKqAgskSZIkSZIkqRKIMfL888+T\nnZ3Nxx9/nHCmWbNmZGdn06tXL+rUqZPihJKkqsQCSZIkSZIkSaoEXnjhBXr06JFwrVGjRvTt25eb\nb76ZRo0apTiZJKkq8h1IkiRJkiRJUiVwzjnncOCBB25yrU6dOtxyyy0UFhYyaNAgyyNJUtJYIEmS\nJEmSJEmVQM2aNcnLywOgRo0aXH311cyZM4c77riDpk2bpjmdJKmqsUCSJEmSJEmSKojPPvuMG2+8\nkWXLliVcP/fccxkwYADvv/8+48aNo1WrVilOKEmqLnwHkiRJkiRJkpRmS5YsoaCggHvvvZc1a9bQ\npEkThg4dWmYuhEBBQUEaEkqSqhtPIEmSJEmSJElpsnz5cnJzc2ndujVjxoxhzZo1AIwZM4bFixen\nOZ0kqTqzQJIkSZIkSZJSbM2aNdx99920adOGwYMHs3z58k3WV6xYwQMPPJCmdJIk+Qg7SZIkSZIk\nKWXWr1/PH/7wBwYPHsynn36acGbPPfdkyJAhXHHFFSlOJ0nS/7FAkiRJkiRJknawGCMvvfQSWVlZ\nfPjhhwlndt11V7Kysrj++uupW7duihNKkrQpCyRJkiRJkiRpB3rzzTfJzMxk+vTpCdcbNGjALbfc\nwq233srOO++c4nSSJCVmgSRJkiRJkiTtIEuWLOG0005j9erVZdZq1apF7969GThwIM2bN09DOkmS\ntqxGugNIkiRJkiRJVVXTpk258cYbN7kWQuCKK65gzpw53HXXXZZHkqQKyQJJkiRJkiRJ2oEGDBiw\n4dF055xzDrNnz+axxx5jn332SW8wSZJ+hI+wkyRJkiRJksrhm2++YcSIEZx00kmcdNJJZdabNGnC\n/fffz7777kuXLl3SkFCSpJ/OAkmSJEmSJEnaDitXrmTs2LGMHDmSZcuW8dprr3HiiSdSo0bZh/5c\ncsklaUgoSdL28xF2kiRJkiRJ0k+wdu1a7r33Xtq0aUN2djbLli0D4F//+hfPPvtsmtNJkpQcFkiS\nJEmSJEnSNli/fj1/+MMfOOCAA/j1r3/NokWLyszcd999aUgmSVLyWSBJkiRJkiRJPyLGyMSJE+nY\nsSO/+tWv+OSTT8rM7LLLLowYMYJJkyalIaEkScnnO5AkSZIkSZKkLfjb3/5GZmYmf//73xOu169f\nn9/85jfcdtttNG7cOMXpJEnacSyQJEmSJEmSpM28++67ZGVl8corryRcz8jIoFevXmRnZ9OiRYsU\np5MkacezQJIkSZIkSZI206dPH/7617+WuR5C4Je//CVDhw6ldevWaUgmSVJq+A4kSZIkSZIkaTN5\neXllrp155pnMmjWL8ePHWx5Jkqo8CyRJkiRJkiRpM7/4xS8444wzNvx56tSpvPzyyxxyyCFpTiZJ\nUmr4CDtJkiRJkiRVO6tWreLuu+9m6dKlFBQUJJwZMWIE119/PaeddhohhBQnlCQpvSyQJEmSJEmS\nVG0UFRUxbtw4cnNzWbhwIRkZGfTs2ZM2bdqUmT3ooIM46KCD0pBSkqT08xF2kiRJkiRJqvKKi4t5\n8sknOfDAA+nduzcLFy4EYN26dQwaNCjN6SRJqngskCRJkiRJklRlxRiZNGkShx12GJdeeinz588v\nMzNp0iS+/fbbNKSTJKniskCSJEmSJElSlTRt2jS6du3K6aefzrvvvltmvW7duvTv35958+bRpEmT\nNCSUJKni8h1IkiRJkiRJqlLee+89Bg4cyMsvv5xwvWbNmvTs2ZOcnBz22GOPFKeTJKlysECSJEmS\nJElSlfDJJ58wePBg/vCHPxBjTDhz8cUXk5ubS9u2bVOcTpKkysUCSZIkSZIkSVXCc889x/jx4xOu\nnXrqqeTn59OxY8cUp5IkqXLyHUiSJEmSJEmqEn7961+z++67b3LtyCOP5M0332TSpEmWR5Ik/QQW\nSJIkSZIkSaoS6tWrx+DBgwE46KCDePHFF/n73//Occcdl+ZkkiRVPj7CTpIkSZIkSZXCunXrePTR\nR3n88ceZMmUKtWvXLjNz1VVX0bhxY7p3707NmjXTkFKSpKrBE0iSJEmSJEmq0IqLi3n22Wc56KCD\nuO6665g6dSoPPfRQwtlatWpx4YUXWh5JklROFkiSJEmSJEmqkGKMvPrqq3Tu3JkLL7yQOXPmbFgb\nNmwYK1asSGM6SZKqNgskSZIkSZIkVTj/+7//y4knnsgpp5zCzJkzy6wvXbqU6dOnpyGZJEnVgwWS\nJEmSJEmSKowPP/yQ8847jy5duvDGG2+UWa9RowY9e/Zk7ty5dOvWLQ0JJUmqHjLSHUCSJEmSJEn6\n9NNPGTJkCI8//jjFxcUJZ3r06MGwYcNo165ditNJklT9WCBJkiRJkiQpbZYsWUJeXh733Xcfa9eu\nTThz0kknkZ+fT6dOnVKcTpKk6ssCSZIkSZIkSWmzePFi7r777oSnjo444ggKCgo44YQT0pBMkqTq\nzXcgSZIkSZIkKW3at2/P5Zdfvsm1Aw88kAkTJjB9+nTLI0mS0sQCSZIkSZIkSTvclt5rBDBkyBBq\n167NXnvtxaOPPsp7773HeeedRwghhQklSdLGLJAkSZIkSZK0w8QYmTBhAh06dGDGjBkJZ/bee29e\nffVV5syZw5VXXknNmjVTnFKSJG3OAkmSJEmSJEk7xOuvv06XLl3o3r07H374IVlZWVucPe6446hb\nt24K00mSpB9jgSRJkiRJkqSkmjFjBieddBInnngib7/99obrkydPZsqUKWlMJkmStpUFkiRJkiRJ\nkpLi3//+Nz169KBz58689tprZdZr1KjBO++8k4ZkkiTpp7JAkiRJkiRJUrl8/vnn9OzZk4MOOojn\nnnsu4cx5553He++9x4ABA1KcTpIkbY+MdAeQJEmSJElS5bRkyRIKCgq49957WbNmTcKZ448/noKC\nAn7+85+nOJ0kSSoPCyRJkiRJkiT9JGvWrGHkyJGMGjWK5cuXJ5w5/PDDKSgooFu3boQQUpxQkiSV\nlwWSJEmSJEmSfpKMjAyeeuqphOXR/vvvT15eHt27d7c4kiSpEvMdSJIkSZIkSfpJatasSV5e3ibX\n9thjDx555BE++OADLrjgAssjSZIqOQskSZIkSZIkJRRjpKioKOHaOeecw89//nOaNGnC6NGjmTt3\nLtdccw0ZGT7wRpKkqsACSZIkSZIkSWW89dZbHHXUUQwfPjzhegiB8ePHU1hYyK233kq9evVSnFCS\nJO1IFkiSJEmSJEnaYObMmZx66ql07dqV6dOnc8cdd7B48eKEs23btmXnnXdOcUJJkpQKFkiSJEmS\nJEli7ty5XHzxxRx++OH85S9/2XB95cqVZd53JEmSqj4fSitJkiRJklSNffnll+Tm5jJu3DjWr1+f\ncGbhwoXEGAkhpDidJElKF08gSZIkSZIkVUPffvst/fv3Z7/99uOhhx5KWB4de+yxTJs2jWeeecby\nSJKkasYTSJIkSZIkSdXIypUrueuuuxg5ciRLly5NONOxY0fy8/M55ZRTLI4kSaqmLJAkSZIkSZKq\niXHjxjFw4EAWLVqUcH2//fZj+PDh9OjRgxo1fHCNJEnVmQWSJEmSJElSNTFnzpyE5dHuu+/O4MGD\nueqqq6hVq1YakkmSpIrGXyWRJEmSJEmqJvr378/OO++84XPjxo0ZMWIEc+fO5brrrrM8kiRJG1gg\nSZIkSZIkVTHLly9PeL1Jkyb069ePevXqkZmZSWFhIf369aN+/fopTihJkio6CyRJkiRJkqQqYvbs\n2Zx55pkce+yxFBcXJ5y56aabmD9/Pvn5+eyyyy4pTihJkioLCyRJkiRJkqRKrrCwkMsuu4xDDz2U\nP/3pT8yaNYtnn3024WyDBg1o2bJlihNKkqTKxgJJkiRJkiSpklq4cCE33HAD7dq1449//CMxxg1r\n2dnZFBUVpTGdJEmqzDLSHUCSJEmSJEk/zXfffcfIkSO56667WLVqVcKZ3XbbjcWLF7PHHnukOJ0k\nSaoKLJAkSZIkSZIqidovr9kAACAASURBVFWrVvHb3/6W22+/nf/+978JZzp06EB+fj5nnHEGIYQU\nJ5QkSVWFBZIkSZIkSVIFV1RUxKOPPsrQoUP56quvEs7su+++DBs2jIsvvpiaNWumOKEkSapqLJAk\nSZIkSZIqsNdff51evXoxb968hOu77bYbgwYNomfPntSuXTvF6SRJUlVlgSRJkiRJklSB1alTJ2F5\ntPPOO9OvXz9uuukmGjRokIZkkiSpKquR7gCSJEmSJEnasqOPPpozzzxzw+e6devSr18/CgsLycrK\nsjySJEk7hAWSJEmSJElSBbBo0aItruXl5VGrVq0Nj7IbMWIETZo0SWE6SZJU3VggSZIkSZIkpdGC\nBQu44ooraNWq1Rbfc3TIIYfwxRdf8MADD7DHHnukOKEkSaqOLJAkSZIkSZLSYNGiRfTp04f999+f\nxx9/nLVr1zJo0KAtzjdv3jyF6SRJUnVngSRJkiRJkpRCS5cuJScnhzZt2nDPPfdQVFS0Ye3JJ59k\n1qxZaUwnSZJUIiPdASRJkiRJkqqD1atXc++995Kfn8+3336bcKZ9+/asXLkyxckkSZLK8gSSJEmS\nJEnSDrRu3TrGjRtH27Zt6du3b8LyaO+99+axxx5j9uzZHH300WlIKUmStClPIEmSJEmSJO0AMUae\nf/55srOz+fjjjxPONGvWjOzsbHr16kWdOnVSnFCSJGnLLJAkSZIkSZKS7JNPPuHCCy9kxowZCdcb\nNWrEbbfdxm9+8xsaNWqU4nSSJElbZ4EkSZIkSZKUZC1atGDhwoVlrtepU4df//rXDBgwgKZNm6Yh\nmSRJ0rbxHUiSJEmSJElJVq9ePQYPHrzhc40aNejZsydz585l9OjRlkeSJKnCs0CSJEmSJEnaTp99\n9hlr1qxJuHbVVVfRrl07LrjgAj744AMefvhh9tprrxQnlCRJ2j4WSJIkSZIkST/R119/zc0330zb\ntm156KGHEs5kZGQwY8YMnn32WQ444IAUJ5QkSSofCyRJkiRJkqRttHz5coYOHUrr1q0ZO3Ysa9eu\nZfjw4axYsSLhfMOGDVOcUJIkKTkskCRJkiRJkrZi9erVjB07ltatWzNkyJBNCqPFixczduzYNKaT\nJElKvox0B5AkSZIkSaqo1q9fz/jx4xk8eDCfffZZwpm99tqL1q1bpziZJEnSjuUJpHS74w5o1AhC\n2PKXJEmSJElKqRgjL7zwAocccghXXXVVwvJo1113ZcyYMcyZM4dLL700DSklSZJ2HE8gpduQIbCF\n5ySX4XOTJUmSJEna4d544w0GDBjA22+/nXC9YcOG3Hrrrdxyyy3stNNOKU4nSZKUGhZI6fZTyqMh\nQ3ZoFEmSJEmSqrP169dz1llnMWnSpITrtWvXpnfv3mRlZdG8efMUp5MkSUotC6SKJMZ0J5AkSZIk\nqdqqWbMmLVq0KHO9Ro0aXH755QwZMoS99947DckkSZJSz3cgSZIkSZIklRoyZAi1a9fe8Pncc89l\n9uzZPProo5ZHkiSpWvEE0o40axaEkO4UkiRJkiRpI9988w1Lly6ldevWZdZatWrF9ddfz6xZsygo\nKKBLly5pSChJkpR+Fkg7UnHxts82bLjjckiSJEmSJFasWMHYsWMZNWoURxxxBJMnT044N2LECGrV\nqkXwl0IlSVI15iPsKoKGDWHIkHSnkCRJkiSpSlqzZg333HMPbdq0IScnh2XLlvHaa6/x2muvJZyv\nXbu25ZEkSar2PIGUCjGmO4EkSZIkSdXO+vXreeKJJxg0aBALFiwos56ZmcmJJ55oWSRJkpSABZIk\nSZIkSapSYoy8/PLLDBw4kPfffz/hzC677MJFF13E+vXrycjwP49IkiRtzr8hSZIkSZKkKuOvf/0r\nAwYM4B//+EfC9fr163PzzTfTt29fGjdunOJ0kiRJlYcFkiRJkiRJqvRmzZpFVlYWkyZNSrheq1Yt\nevXqxcCBA2nRokWK00mSJFU+FkiSJEmSJKlSy87OJi8vL+FaCIHLLruMoUOHsu+++6Y4mSRJUuVl\ngSRJkiRJkiq1zp07J7x+1llnkZeXR4cOHVKcSJIkqfKrke4AkiRJkiRJ5XH22WfTpUuXDZ+POeYY\npk6dyksvvWR5JEmStJ0skCRJkiRJUoW3atUq/va3vyVcCyFQUFDAz372M1555RXeeustjj766BQn\nlCRJqlp8hJ0kSZIkSaqwioqKeOSRR8jNzWX58uUUFhbSvHnzMnNdu3Zl5syZ1Kjh78pKkiQlQ1L/\nVhVC2C+EkB1C+GMI4f9tdH3PEMLpIYSdkrmfJEmSJEmqmoqLi3niiSc48MADuf766/nPf/7DypUr\nycvL2+LPWB5JkiQlT9L+ZhVC6Ad8COQClwBnbbRcD3gZ+GWy9pMkSZIkSVVPjJFXXnmFww47jF/+\n8pfMnz9/k/X777+fBQsWpCecJElSNZKUAimEcB5wOzAN+AVwx8brMca5wL+Ac5KxnyRJkiRJqnqm\nTp3KscceyxlnnMG7775bZr1evXrceuutNG7cOA3pJEmSqpdkvQPpZmABcGqMcXUI4aQEMx8AxyZp\nP0mSJEmSVEXMnj2bgQMHMnHixITrGRkZXHvtteTk5NCyZcsUp5MkSaqeklUgHQqMjzGu/pGZr4Dd\nkrSfJEmSJEmq5AoLCxk0aBBPPPEEMcaEM5dccgm5ubnst99+KU4nSZJUvSWrQKoJrN3KTNNtmJEk\nSZIkSdXAiy++yAUXXMC6desSrp9++unk5eVx6KGHpjiZJEmSIEnvQALmA122tBhCCMBRwEdJ2k+S\nJEmSJFVixx57LA0aNChz/aijjuKtt97iT3/6k+WRJElSGiWrQHoOOCKE8D9bWP8NcADwdJL2kyRJ\nkiRJldguu+xC//79N3w++OCDeemll5g6dSrHHusrlCVJktItWQXSHcC/gXtDCFOAEwFCCENKP48G\nZgH3JWk/SZIkSZJUwRUVFfHUU09RXFyccL1Pnz4ceeSRjB8/nlmzZnHWWWdR8hATSZIkpVtS3oEU\nY1wZQjgOeAA4D/jhb3uDSr+/AFwbY/QdSJIkSZIkVXHFxcU8++yz5OTkMHfuXAAuvvjiMnMNGjRg\n2rRpqY4nSZKkbZCUAgkgxrgEuCCEsAcl70PaFVgKTI8xfpqsfSRJkiRJUsUUY+TVV18lMzOTf/3r\nXxuuZ2dn0717d2rVqpXGdJIkSfopkvUIuw1ijF/GGJ+PMT4UY3za8kiSJEmSpKpv+vTpnHDCCZx6\n6qmblEcA8+fPZ9y4cWlKJkmSpO2RlAIphLAshNB/KzO3hRCWJmM/SZIkSZJUMXzwwQece+65HHnk\nkbz55ptl1mvWrMl1113HWWedlfpwkiRJ2m7JeoRdQ6DOVmZql85JkiRJkqRKbsGCBQwePJjx48cT\nY0w4c+GFFzJs2DD233//FKeTJElSeSXtHUjbYGdgTQr3kyRJkiRJSbZ48WLy8vK4//77KSoqSjhz\nyimnkJ+fz2GHHZbidJIkSUqW7S6QQgib/y1w9wTXAGoCrYBLgLnbu58kSZIkSUqvr776inbt2rFi\nxYqE6126dKGgoICuXbumNpgkSZKSrjwnkGYAP5xRj8C1pV9bEoDB5dhPkiRJkiSl0e67787xxx/P\nyy+/vMn19u3bk5+fz9lnn00IIU3pJEmSlEzlKZDGUFIcBeAW4B/AtARz64FvgNdjjP8sx36SJEmS\nJCnN8vLymDhxIjFGWrVqRW5uLpdddhk1a9ZMdzRJkiQl0XYXSDHGvj/8OYRwBfBCjHF0UlJJkiRJ\nkqS0iDEyYcIEDj30UNq0aVNmvUOHDtx0003su+++9OrVizp16qQhpSRJkna08pxA2iDG2CwZ95Ek\nSZIkSekzZcoUMjMzeeedd7jkkkt44oknEs7deeedKU4mSZKkVKuR7gCSJEmSJCm93nnnHbp160a3\nbt145513AHjyySeZNWtWmpNJkiQpXZJaIIUQTgshjA0hPBtCeCnB14tJ2mfPEMLvQghfhRDWhBAW\nlO67y3bcq0MI4fEQwuel91ocQngrhHB5MrJKkiRJklRR/fvf/+aCCy7giCOOYMqUKWXWs7Oz05BK\nkiRJFUFSHmEXQsgAXgBOBwIQS7//IG50vbx7tQGmAc2BF4F/A0cANwGnhhCOjjF+s433uhJ4BFgF\nTAQWAI2Bg0v/tzxe3rySJEmSJFU0n3/+OUOGDOGxxx6juLg44cz555/P8OHDU5xMkiRJFUVSCiSg\nL3AGcCdwFyVFTD7wGNAVyAXeBHolYa/7KCmP+sQY7/nhYghhDHAzkAf8z9ZuEkLoQkl59D5waozx\nP5ut10pCVkmSJEmSKowlS5ZQUFDAvffey5o1axLOnHDCCRQUFHDEEUekOJ0kSZIqkmQ9wu4SYHaM\n8dYY42el19bGGOfFGB8BjgfOAa4ozyYhhNbAyZQUVPdutjwYWAn8KoTQYBtuNxKoCVy2eXkEEGMs\nKk9WSZIkSZIqiuXLl5Obm0vr1q0ZM2ZMwvKoU6dOTJ48mSlTplgeSZIkKWknkPYDfrfR5whsOMET\nY/w4hDARuBb4bTn2OaH0+6sxxk3O2McYl4cQ/k5JwdQFKPvw5lIhhD2BY4AZwAchhOOBw0tzzwLe\n2Pz+kiRJkiRVRjFGunbtysyZMxOut2vXjry8PM4//3xCCAlnJEmSVP0kq0BaD6zY6PMKYNfNZj4B\nziznPu1Kv8/ZwvpcSgqk/fmRAgnovNH865Q8Zm9j74UQzo8xzttaoBDCP7ewdMDWflaSJEmSpB0t\nhEDv3r259tprN7m+5557MnToUC6//HIyMpL1nwckSZJUVSTrEXZfAntu9HkeJaeANnYw8F0599m5\n9PvSLaz/cL3xVu7TvPT7hcCBwPml994PGA90AP4UQqi9/VElSZIkSaoYrrzySvbff38Adt11V+64\n4w7mzp3L1VdfbXkkSZKkhJL1t8RplDwS7gcvAYNCCHcBEyg54XMq8FyS9tuSH87ax63M1dzoe88Y\n48TSz8tCCFdQUip1AroDT/7YjWKMhycMUnIy6bBtCS1JkiRJUnm9+eabfPPNN3Tv3r3MWkZGBiNH\njmTmzJnceuut7LTTTmlIKEmSpMokWSeQngIWhRD2Kf08BvgAuJGSR8QNpuSU0oBy7vPDCaOdt7C+\n02ZzW/Lf0u9rgFc2XogxRuDF0o++NVSSJEmSVKH985//5JRTTuH444+nd+/eLF++POHcOeecw9Ch\nQy2PJEmStE2SUiDFGCfHGI+JMS4o/byckvcMXQXkA9cCh/ywXg4fl37ffwvrbUu/b+kdSZvfZ3mM\nsTjB+g8FU72fkE2SJEmSpJSZM2cOF110EZ06deLVV18F4Ouvv2bs2LFpTiZJkqSqIFknkMqIMa6J\nMf4+xpgTYxwXY9zaqaBt8Ubp95NDCJtkDyE0Ao4Gvgemb+U+s4ElQNMQwm4J1g8u/b5g+6NKkiRJ\nkpR8X3zxBddddx3t27fnmWeeKbM+evToLZ5CkiRJkrbVDiuQNhdKXFGee8QY5wOvAvsAN2y2PBRo\nADweY1y50b4HhBAO2Ow+64AHSz+O3LiMCiF0AK4E1rHj39kkSZIkSdI2+fbbb+nXrx9t27bl4Ycf\nZv369WVmjjvuOP785z/TqFGjNCSUJElSVZKRik1CCN2BXOAA4PflvN31wDTg7hDCicBHwM+B4yl5\ndN3AzeY/+iHGZtfzgROBy4EOIYQ3gWZAd6AucGuMcV45s0qSJEmSVC4rV65k7NixjBo1iqVLEz/c\no2PHjhQUFHDyyScTwub//JUkSZJ+unIVSCGEhpS836gzUAT8DXis9IQPIYSuwGigIyUFzqvl2Q9K\nTiGFEDpRUkidCpwOLATuBobGGL/dxvusKi2g+gEXU3KiaTUl5dQdMcZJ5c0qSZIkSdL2Wrt2LQ8/\n/DDDhg1j0aJFCWfatm3LsGHD6NGjBzVqpOwhI5IkSaoGtrtACiE0Bv4B7M//ne65DDgPOCOEcCfQ\np3TtTSAnxvj3cqUtFWP8HLhqG2e3+KtXMcZVwJDSL0mSJEmSKoyBAwcyevTohGu77747gwcP5qqr\nrqJWrVopTiZJkqTqoDy/ntQfaAfMpeQ00DBgPnBqCGECcBPwPtAtxnhCssojSZIkSZKqgxtvvJHa\ntWtvcm2XXXZh5MiRzJs3j+uuu87ySJIkSTtMeQqkM4EvgY4xxiExxsHA4cB/gHOA/wccHmN8vfwx\nJUmSJEmqXlq1asUNN9wAQP369cnKyqKwsJDbbruNevXqpTmdJEmSqrryvANpX+DxGOP3P1yIMS4P\nIbwEXAcM+OFdSJIkSZIkqaxZs2bx/PPPM2zYsITrmZmZrF+/ngEDBtCyZcsUp5MkSVJ1Vp4CqT4l\np40298O1eeW4tyRJkiRJVda8efMYNGgQTz75JADHHXcc3bp1KzPXrFkz7rrrrlTHkyRJksr1CLsf\nFWMs3lH3liRJkiSpMvrqq6/o3bs3Bx544IbyCEpOGsUY05hMkiRJ2lR5TiABtA8hnL/5NYAQwnlA\n2PwHYowTyrmnJEmSJEmVyn//+19GjhzJXXfdxffff19mfcaMGUyZMiXhKSRJkiQpHcpbIPUo/dpc\nAJ7bws/ULOeekiRJkiRVCqtWreKee+7h9ttv57vvvks4c8ghh1BQUMCJJ56Y4nSSJEnSlpWnQJoA\neL5ekiRJkqTNFBUVMW7cOHJzc1m4cGHCmdatWzNs2DAuvvhiatTYYU+YlyRJkrbLdhdIMcYLkhlE\nkiRJkqTKrri4mKeffpqcnBzmz5+fcKZFixYMGjSIa665htq1a6c4oSRJkrRtyvsIO0mSJEmSVGrK\nlClceumlCdd23nln+vfvT58+fWjQoEGKk0mSJEk/jWfkJUmSJElKkm7dutGlS5dNrtWtW5f+/ftT\nWFhIZmam5ZEkSZIqBQskSZIkSZKSJIRAQUEBADVr1qRXr17MmzeP22+/nSZNmqQ5nSRJkrTtLJAk\nSZIkSfoJCgsLufrqq1m0aFHC9a5du1JQUMBHH33EAw88wB577JHihJIkSVL5+Q4kSZIkSZK2waJF\nixg+fDgPPvggRUVFNGzYkLvvvjvh7IABA1KcTpIkSUouTyBJkiRJkvQjli5dSnZ2Nq1bt+a3v/0t\nRUVFADzwwAN88sknaU4nSZIk7RgWSJIkSZIkJfD9998zevRoWrduTV5eHqtWrdpkvaioiAcffDBN\n6SRJkqQdy0fYSZIkSZK0kXXr1vHoo48ydOhQvvzyy4Qz++yzD7m5uVx66aUpTidJkiSlRlILpBDC\n8cAvgQOBBjHGQ0uv7w90A56PMSZ+y6gkSZIkSWlUXFzM888/T3Z2NnPmzEk407x5c3Jycrj22mup\nU6dOihNKkiRJqZO0AimEcB/QCwjAOqDmRsurgLuB+sDoZO0pSZIkSVJ5xRiZPHkymZmZzJw5M+HM\nTjvtxG233cZvfvMbGjZsmOKEkiRJUuol5R1IIYSewP8ATwN7Avkbr8cYvwCmA2ckYz9JkiRJkpJl\n+fLlXHTRRQnLozp16tC3b18KCwvJzs62PJIkSVK1kZQCiZKTRx8Al8UYvwJigpk5QJsk7SdJkiRJ\nUlLstNNO9OvXb5NrNWrUoGfPnsydO5dRo0ax6667pimdJEmSlB7JKpDaA6/FGIt/ZOY/QPMk7SdJ\nkiRJUtL06dOHFi1aANCjRw8+/PBDHn74Yfbaa680J5MkSZLSI1nvQFoP1NrKTEtgZZL2kyRJkiRp\nmy1evJj8/HyOPvpoevToUWa9QYMGPPzww7Ro0YJOnTqlIaEkSZJUsSSrQPo3cOyWFkMItYGuwLtJ\n2k+SJEmSpK1atmwZY8aM4Y477mDFihVMnDiRc889l1q1yv4O5JlnnpmGhJIkSVLFlKxH2P0RODiE\nkLeF9QKgFfB4kvaTJEmSJGmLVq9ezZ133knr1q0ZOnQoK1asAGD+/PmMGzcuzekkSZKkii9ZBdJ9\nwN+AASGEuUB3gBDCY6WfbwYmxxgfS9J+kiRJkiSVsW7dOn73u9+x//77c8stt/DNN9+UmXnssceI\nMaYhnSRJklR5JKVAijEWAacAdwItgIOBAFwO7F56/exk7CVJkiRJ0uZijEyYMIEOHTpwzTXX8Pnn\nn5eZadq0KWPHjuWtt94ihJCGlJIkSVLlkax3IBFjXA30DSFkAocAuwJLgXdL1yRJkiRJSropU6aQ\nmZnJO++8k3C9YcOG9O3bl1tuuYVGjRqlOJ0kSZJUOSWtQPpB6Wmkfyb7vpIkSZIkbeydd94hKyuL\n1157LeF67dq1ueGGG8jMzKRZs2YpTidJkiRVbkl5hF0IYUoI4VchhPrJuJ8kSZIkSVszbNiwhOVR\njRo1uOqqq5g7dy5jxoyxPJIkSZK2Q1IKJOB44DHgPyGER0MIxyXpvpIkSZIkJZSXl1fmXUbnnXce\n7733Hr/73e9o1apVmpJJkiRJlV+yCqS2QB6wBLgCeD2E8EkIYWgIYb8k7SFJkiRJ0gYdOnTgl7/8\nJQDHH38806dPZ8KECbRv3z7NySRJkqTKL8QYk3vDELpSUiJ1BxoCEfg78Hvg2RjjsqRuWEGFEP55\nGBz2T4Ak/38sSZIkSdXBihUruPPOO/niiy948MEHE84sWLCAuXPn0q1btzKnkSRJkqTK7vDDD2fm\nzJkzY4yHp3rvpBdIG24cQj3gAkrKpK5AAL6PMTbcIRtWMBZIkiRJkrR91qxZw4MPPsjw4cP5+uuv\nAZg5cyYdO3ZMczJJkiQptdJZICXrEXZlxBi/jzGOB84EMoF1QL0dtZ8kSZIkqXJbv349jz/+OO3a\nteOmm27aUB4BZGVlpTGZJEmSVP1k7KgbhxCOpuT0UQ9gJ0pOIE3fUftJkiRJkiqnGCMvvfQSAwcO\n5IMPPkg48/bbb7Nw4UJatmyZ4nSSJElS9ZTUE0ghhL1DCDkhhLnAX4GewApgBHBgjPGoZO4nSZIk\nSarc3nrrLY466ijOPffchOVRgwYNyMnJobCw0PJIkiRJSqGknEAKIVxJyWmjYygppb4HngJ+D0yO\nO+pFS5IkSZKkSmnmzJlkZWXxl7/8JeF6rVq16N27N1lZWey2224pTidJkiQpWY+w+13p92mUlEZP\nxxiXJenekiRJkqQqYu7cueTk5PD0008nXA8h8Ktf/YqhQ4eyzz77pDacJEmSpA2SVSDlA4/FGOcl\n6X6SJEmSpCror3/96xbLo7PPPpu8vDwOPvjgFKeSJEmStLmkvAMpxphteSRJkiRJ2porrriCdu3a\nbXLt2GOPZdq0abz44ouWR5IkSVIFkZQCSZIkSZKkjW3pVbgZGRkMHz4cgI4dOzJp0iTefPNNjjzy\nyFTGkyRJkrQV2/UIuxDCbCACZ8cYPy39vC1ijPFn27OnJEmSJKniW7t2LY888ggPPvggU6dOpVGj\nRmVmunfvzsSJEznttNOoUcPfa5QkSZIqou19B9LulBRINTf7LEmSJEmqhoqLi3nyyScZNGgQhYWF\nAIwdO5acnJwysyEEzjjjjFRHlCRJkvQTbFeBFGNs+mOfJUmSJEnVQ4yRV155haysLGbP3vThFKNG\njaJ37940beo/GSVJkqTKxmcFSJIkSZK2y9SpUzn22GM588wzy5RHAOvWrePtt99OQzJJkiRJ5ZWU\nAimE8FII4eKtzFwYQngpGftJkiRJktJn9uzZnHnmmRxzzDFMnTq1zHpGRga9e/dm/vz5nH766WlI\nKEmSJKm8tvcdSJs7E5ixlZm2gA+5liRJkqRKqrCwkEGDBvHEE08QY+LX4F566aXk5ubSpk2bFKeT\nJEmSlEzJKpC2RV1gXQr3kyRJkiQlwddff82QIUN46KGHWLcu8T/rzjjjDPLy8vjZz36W4nSSJEmS\ndoRkFkiJf/0MCCHsCpwMfJXE/SRJkiRJKfD9998zbty4hOXR0UcfTUFBAcccc0wakkmSJEnaUbb7\nHUghhGU/fJVeGrjxtY2+VgKLgU7As8kILUmSJElKnVatWnHDDTdscu2QQw5h4sSJ/O1vf7M8kiRJ\nkqqg8pxAmsP/nTo6DPiGxCeM1peuTQHuKcd+kiRJkqQdaN26dWRkJP5nYmZmJg8//DDNmjVj2LBh\nXHzxxdSosd2/kyhJkiSpgtvuAinG2OmHP4cQioEHY4y5SUklSZIkSUqZ4uJinnnmGXJycrj//vvp\n1q1bmZmmTZvy+uuvc8ghh1C7du00pJQkSZKUSsn6dbEOwP1JupckSZIkKQVijPz5z3/m8MMP55JL\nLmHevHlkZmYSY+JX3Hbq1MnySJIkSaomklIgxRg/iDF+nYx7SZIkSZJ2vGnTptG1a1dOO+00Zs2a\nteH6jBkzmDBhQhqTSZIkSaoItusRdiGEW0r/+LsY43cbfd6qGOOY7dlTkiRJklR+77//PgMHDuSl\nl15KuF6zZk0++uijFKeSJEmSVNFs7zuQRgMRmAh8t9HnsJWfi4AFkiRJkiSl2IIFCxg8eDDjx4/f\n4iPqLrroInJzc9l///1TnE6SJElSRbO9BdJZpd8/3+yzJEmSJKkCWbRoEXl5eTzwwAMUFRUlnDnl\nlFPIz8/nsMMOS3E6SZIkSRXVdhVIMcY//dhnSZIkSVJ6FRUVMWzYMMaMGcPKlSsTznTp0oWCggK6\ndu2a2nCSJEmSKrztPYEkSZIkSarAMjIyeO211xKWR+3btyc/P5+zzz6bELb2JHJJkiRJ1VGNZNwk\nhLBHCOHYEEL9ja7VCCHcFkL4ewjh1RDCycnYS5IkSZK0dSEECgoKNrm2995789hjjzF79mzOOecc\nyyNJkiRJW5SUAgkYCrwIrNvoWn9gBHAk0A2YGELwgdqSJEmSlEQxRr7//vuEa8cddxynnHIKzZo1\n46677uLjjz/miiuuoGbNmilOKUmSJKmySVaBdBQwJca4FiCU/BpbH2A+0B44AVgD3JKk/SRJkiSp\nWosxMnnyZDp3uuvXIQAAIABJREFU7kz//v23OPfwww8zf/58+vTpQ506dVKYUJIkSVJllqwCqQXw\n6UafDwF2A34bY/x3jPFNSk4oHZmk/SRJkiSp2nr77bfp1q0bJ598Mv/85z954IEH+OSTTxLO7rXX\nXjRq1CjFCSVJkiRVdskqkOoARRt9PhqIwJSNrn0KtEzSfpIkSZJU7Xz44Yecf/75/PznP+f111/f\ncL2oqIjBgwenMZkkSZKkqiZZBdIXQIeNPp8GfBtjfH+ja02BFUnaT5IkSZKqjc8++4yrr76aDh06\n8MILLyScWb16NevXr09xMkmSJElVVUaS7vNn4PoQwhBgNXAq8IfNZtoBnyVpP0mSJEmq8r7++mvy\n8/O57777WLt2bcKZbt26kZ+fT+fOnVOcTpIkSVJVlqwC6XbgfGBQ6eevgSE/LIYQ9gB+AdyTpP0k\nSZIkqcpavnw5Y8aMYfTo0axYkfhBDp06daKgoIBu3bqlOJ0kSZKk6iApBVKMcWEIoT1wRumlyTHG\nbzYaaQoMBl5Mxn6SJEmSVFU98sgjZGZmsmTJkoTrBxxwAHl5eZx33nmEEFKcTpIkSVJ1kawTSMQY\nlwNPbWHtXeDdZO0lSZIkSVXVkiVLEpZHe+21F0OGDOHyyy8nIyNp/5STJEmSpIRqJPuGIYQmIYTj\nQwjnhRBOCCE0SfYekiRJklRV9enTh5YtW274vOuuuzJmzBjmzJnD1VdfbXkkSZIkKSWSViCFEFqE\nEJ4DFgGvAc8Bk4HFIYTnQggtf/QGkiRJklSNfPPNNwmv169fn0GDBtGgQQMGDRpEYWEhN998M3Xr\n1k1xQkmSJEnVWVJ+dS2E0BT4O7Av8B9gGrAQaAkcCZwPHB5C6BxjTPwgb0mSJEmqBmbMmEFWVhbz\n58/no48+onbt2mVmrrnmGs4//3yaN2+ehoSSJEmSlLwTSAMpKY+GA/vGGC+IMd4YY7yg9HousHfp\nnCRJkiRVOx9//DE9evSgc+fOTJ48mcLCQsaNG5dwtlatWpZHkiRJktIqWQXS2cAbMcZBMcY1Gy/E\nGNfGGIcAbwDnJGk/SZIkSaoUvvjiC6699loOOuggnnvuuU3WcnNzWbVqVZqSSZIkSdKWJatA2gOY\nvpWZ6cDuSdpPkiRJkiq0b775hr59+7LffvvxyCOPsH79+jIzBxxwAEuW+JRvSZIkSRVPUt6BBCwH\n9tzKzB6lc5IkSZJUZa1YsYKxY8cyatQoli1blnDmsMMOo6CggJNOOokQQooTSpIkSdLWJesE0jSg\nRwihY6LFEMIhQI/SOUmSJEmqctasWcM999xDmzZtyMnJSVgetW3blqeffpp33nmHk08+2fJIkiRJ\nUoWVrBNItwOnAf8IITxKyfuOFgItgK7AVaV73Z6k/SRJkiSpwnjjjTe4+uqrWbBgQcL13XffnSFD\nhnDllVdSq1at1IaTJEmSpO2QlAIpxviPEMJlwCNAL+C6jZYDsAK4Osb4j2TsJ0mSJEkVSbNmzfj0\n00/LXN9ll13IysrihhtuoF69emlIJkmSJEnbJ1knkIgxPhNCmEzJo+oOA3YGlgL/Ap6JMf43WXtJ\nkiRJUkVy8MEHc9lllzF+/HgA6tevz80330zfvn1p3LhxmtNJkiRJ0k+XtAIJoLQkeiiZ95QkSZKk\niuLTTz9l7733Trg2dOhQJkyYwJVXXkl2djYtWrRIcTpJkiRJSp5yF0ghhPOAI4AI/G+M8cVyp5Ik\nSZKkCmTevHnk5OTwzDPPMGPGDDp27FhmZt999+WLL77wxJEkSZKkKmG7C6QQQm1gEtB1s+tvAKfF\nGIvKF02SJEmS0uurr75i2LBhPPLII6xbtw6ArKwsJk2alHDe8kiSJElSVVGjHD97I3A88B3wB+CP\npX8+HuhT/miSJEmSlB7//e9/GTBgAPvttx8PPPDAhvII4M9//jNvvvlm+sJJkiRJUgqU5xF2FwHL\ngENjjJ8DhBD2BmaXrt1R/niSJEmSlDqrVq3i7rvvZsSIEXz33XcJZ372s59Rq1atFCeTJEmSpNQq\nzwmkdsDzP5RHADHGT4EJpWuSJEmSVCkUFRVx//3306ZNGzIzMxOWR23atOHJJ59k5syZHH300WlI\nKUmSJEmpU54TSA2BzxNc/6x0TZIkSZIqtOLiYp566ikGDRrE/PnzE860aNGCwYMHc80113jySJIk\nSVK1UZ4CKQDFCa4nuiZJkiRJFcrnn3/OWWedxbvvvptwvXHjxgwYMIAbb7yR+vXrpzidJEmSJKVX\neQokgN1DCIdtfg0ghNCRkpJpEzHGmeXcU5IkSZLKrWXLlqxevbrM9Xr16nHTTTfRr18/dtlllzQk\nkyRJkqT0K2+BdG3p1+YCMCPB9ZiEPSVJkiSp3DIyMsjLy+OCCy7Y8Pnaa68lJyeHli1bpjmdJEmS\nJKVXecqcmZQUQpIkSZJUYRUWFtKsWTMaNWpUZu3888/niCOOoE2bNuTm5rLffvulIaEkSZIkVTzb\nXSDFGDslM4gkSZIkJdN//vMfhg8fzkMPPUROTg45OTllZkIIvPXWW9StWzcNCSVJkiSp4qqR7gCS\nJEmSlEzfffcdAwcOpE2bNtx7770UFRUxatQolixZknDe8kiSJEmSyrJAkiRJklQlfP/994waNYrW\nrVuTn5/PqlWrNqwtX76cgoKCNKaTJEmSpMqlPO9AkiRJkqS0W7duHY8++ihDhw7lyy+/TDizzz77\n0Llz5xQnkyRJkqTKywJJkiRJUqVUXFzMc889R3Z2NnPnzk0407x5c3JycrjuuuuoXbt2ihNKkiRJ\nUuVlgSRJkiSpUokx8uqrr5KVlcXMmTMTzuy0007069ePm266iYYNG6Y4oSRJkiRVfhZIkiRJkiqN\nGCP/n707D9NrsNsHfp9syKIqRBevkkjsa1RRtYSiaO1ea4uWt3aNLZM9k0zGWqqWUjR9KW8V5a2l\neInWUnspIUTSUrvGEksiyeT8/qB+1XliSSbPM8vnc125nmvO9zvn3PnLmDvnnB122CE33nhjxfni\niy+eI444IkOGDEnv3r2rnA4AAKD96FTrAAAAAJ9WURRZbbXVmh3v3LlzDj744EyZMiWnnnqq8ggA\nAGAhKZAAAIA2pa6uLr169frw6z333DOPP/54Lrjggiy//PI1TAYAANB+tOgj7IqiWDnJXklWS9Kj\nLMudPzi+fJK1k9xZluWMlrwmAADQ/rzyyit59dVXs8YaazSbLbPMMjn++ONz5513Zvz48Rk4cGAN\nEgIAALRvLVYgFUVxQpJx/3LO8l/GSyT5XZIjkpzXUtcEAADalxkzZuT000/P6aefnlVXXTX3339/\niqJotjd06NB07ty5BgkBAAA6hhZ5hF1RFLskOSnJ3Uk2TXL6v87LspyS5M9JdmqJ6wEAAO3LrFmz\n8uMf/zh9+/ZNfX193nnnnTz44IO56qqrKu4rjwAAABatlnoH0o+S/C3JdmVZ3p3k7Qo7k5Ks0kLX\nAwAA2oG5c+fm4osvTv/+/XPsscdm+vTpH5kPHz48c+fOrVE6AACAjqulHmG3bpJLyrKc9TE7LyRZ\nroWuBwAAtGFlWebqq6/O8OHDM3ny5Io7yyyzTA499NCUZVlxDgAAwKLTUgVS5ySzP2FnmU+xAwAA\ntHO33npr6urqcv/991ec9+rVK8cdd1x+9KMfpVevXlVOBwAAQNJyBdLUJBvNb1i8/9bbTZI80ULX\nAwAA2pj7778/dXV1ufXWWyvOu3XrlsMPPzx1dXVZdtllq5wOAACAf9VS70C6MsmGRVH8cD7zY5Ks\nmuTXLXQ9AACgDRk9enQ23HDDiuVRp06dctBBB2XKlCn58Y9/rDwCAABoBVqqQDo9yeQk5xRFcWuS\nrZKkKIrRH3x9WpKHk5zbQtcDAADakEGDBlU8vttuu+Wxxx7LRRddlBVWWKHKqQAAAJifFimQyrJ8\nJ8nmSX6bZIskmyYpkoxMsmWSa5J8syxL70ACAIAOaLPNNsu3vvWtD7/eaqutcu+99+bKK6/Maqut\nVsNkAAAAVNJS70BKWZb/SLJ7URRfzvvvQ+qd5M0k95Rl+UxLXQcAAGid3nrrrdx5550fKYr+1fjx\n4zN9+vQ0NDRk6623rnI6AAAAPosWK5D+qSzL55Nc1dLnBQAAWqf33nsvP/vZz9LQ0JA33ngjTz75\nZFZaaaVme+uuu27uueeeFEVRg5QAAAB8Fi31DiQAAKCDaWpqyoQJEzJgwIAcc8wxefXVVzNnzpyM\nGjVqvt+jPAIAAGgbWuQOpKIozvqUq2VZlke3xDUBAIDaKMsy1157bYYNG5bHH3+82fzSSy/N8ccf\nn7XWWqsG6QAAAGgJLfUIuyM+YV4mKT74VCABAEAbdfvtt2fIkCG59957K8579OiRwYMHZ4UVVqhy\nMgAAAFpSSxVI8/unhUsl+WqSIUkmJhnXQtcDAACq6MEHH8zQoUNz8803V5x37do1hx56aIYNG5Y+\nffpUOR0AAAAtrUUKpLIsJ33M+K6iKP43ySNJrkvycbsAAEAr8tRTT2XEiBG54oorKs6Losj++++f\nMWPGZMUVV6xuOAAAABaZlroD6WOVZTmtKIprkxyb5FfVuCYAALBwfv/732fHHXdMU1NTxflOO+2U\ncePGZc0116xyMgAAABa1TlW81otJVq3i9QAAgIWw2WabVXwc3WabbZa7774711xzjfIIAACgnapK\ngVQURZFksyRvV+N6AADAwuvevXtGjhz54dfrrbdebrzxxtx+++3ZeOONa5gMAACARa1FHmFXFMX6\nH3P+/0jy/SQbJPllS1wPAABoGbNnz84ll1yS/fffP926dWs2//73v59rr702BxxwQPbYY4906lTN\nhxgAAABQKy31DqQHkpQfMy8+2Dm+ha4HAAAshKamplx++eUZOXJk/vrXv2b27Nk59NBDm+117do1\nN954Yw0SAgAAUEstVSD9OJULpHlJXk9yX5KJZVl+XMkEAAAsYmVZ5rrrrsvQoUPz2GOPfXi8vr4+\n3/3ud9OjR48apgMAAKC1aJECqSzL41riPAAAwKJzxx13ZMiQIbn77rubzV566aX89Kc/zZAhQ2qQ\nDAAAgNamRR5gXhTFWUVRNH/eBQAAUHMPP/xwdthhh2y22WYVy6MuXbrk8MMPzwEHHFD9cAAAALRK\nLfUG3P9K8pUWOhcAANACnn766eyzzz5Zb731csMNNzSbF0WRfffdN08++WTOPvvsfOELX6hBSgAA\nAFqjlnoH0rNJerfQuQAAgIXw4osvZuzYsfn5z3+euXPnVtzZcccd09DQkLXXXrvK6QAAAGgLWqpA\n+nWS7xZF0assy7da6JwAAMBnNH369AwYMCBvv/12xfmmm26axsbGbLrpplVOBgAAQFvSUo+wG5fk\nqSS3FEWxRVEUPVrovAAAwGfQu3fv7Lrrrs2Or7322rn++uvzxz/+UXkEAADAJ2qpAumVJBsl2TDJ\nrUlmFEXxVlEUM/7tz5stdD0AAGA+xowZk65duyZJ+vbtm1/96lf585//nO233z5FUdQ4HQAAAG1B\nSz3C7qkkZQudCwAA+Bjz5s3Lr3/966y++upZZ511ms1XXHHFDB8+PMsss0x+8IMfpFu3bjVICQAA\nQFvWIgVSWZYbtMR5AACA+SvLMjfeeGOGDh2aRx55JNtuu21+//vfV9wdOXJkldMBAADQnizwI+yK\novhuURRrt2QYAACgsrvuuiubb755dthhhzzyyCNJkptuuim33357bYMBAADQLi3MO5AmJNm5hXIA\nAAAVPProo/nOd76TTTfdNHfccUez+YgRI2qQCgAAgPZuYQokAABgEZk2bVr233//rLPOOvnd735X\ncWevvfbKxRdfXOVkAAAAdAQt8g4kAACgZbz88ssZN25czj///MyZM6fiznbbbZfx48dnvfXWq3I6\nAAAAOgoFEgAAtAJvvvlmTj311Jxxxhl59913K+5svPHGaWxszOabb17ldAAAAHQ0C1sgLVUUxQqf\n5RvKsnx2Ia8JAADtzg477JC77rqr4mzNNddMQ0NDvv3tb6coiionAwAAoCNa2ALp6A/+fFplC1wT\nAADanR/96EfNCqQVV1wx9fX12WeffdK5c+caJQMAAKAjWtgyZ0aSN1oiCAAAdGS77rprNthggzzw\nwANZdtllM2LEiBxyyCFZbLHFah0NAACADmhhC6QzyrKsb5EkAADQjpVlmVtuuSXPP/98DjzwwGbz\noihy6qmn5s4778wxxxyTnj171iAlAAAAvM/j5AAAYBG79957U1dXl4kTJ6ZXr17Zcccds+yyyzbb\n22KLLbLFFltUPyAAAAD8m061DgAAAO3V448/nl122SUbbbRRJk6cmCR566230tjYWONkAAAA8PEU\nSAAA0MKeeeaZHHjggVlrrbVyzTXXNJufe+65mT59eg2SAQAAwKejQAIAgBbyyiuv5JhjjsmAAQMy\nYcKEzJs3r9nONttsk7vuuiu9e/euQUIAAAD4dBb4HUhlWSqfAAAgyYwZM/LjH/84p59+et5+++2K\nOxtuuGEaGxszaNCgKqcDAACAz26BCyQAAOjoZs2alfPOOy8NDQ3zfSTdaqutloaGhuy8884piqLK\nCQEAAGDBKJAAAGABnXTSSRkzZkzF2QorrJAxY8Zk//33T+fOnaucDAAAABaOx9ABAMACOvLII7Pk\nkkt+5NgyyyyTM844I08++WQOOOAA5REAAABtUpsskIqiWL4oiouLonihKIr3iqL4W1EUZxZF8fmF\nOOdmRVE0FUVRFkUxriXzAgDQPvXu3TvHH398kqRnz54ZPXp0pk6dmmOOOSaLL754jdMBAADAgmtz\nj7AriqJfkruT9ElybZLJSTZMcnSS7Yqi+HpZlpUfQD//c/ZK8ssk7ybp2bKJAQBoyx544IH88pe/\nzFlnnVXxHUbHHHNM3nnnnQwePDjLLrtsDRICAABAy2uLdyCdm/fLo6PKsty5LMshZVkOSnJGklWS\nNCzAOX+S5HNJGlsuJgAAbdnkyZOzxx575Ktf/WrOPvvsXHXVVRX3evbsmcbGRuURAAAA7UqbKpCK\nouibZJskf0tyzr+NRyV5J8n+RVH0+Azn3CnJgUmOSvJCyyQFAKCt+vvf/54f/OAHWWONNXLllVd+\neHzYsGGZO3duDZMBAABA9bSpAinJoA8+by7Lct6/DsqyfCvJXUm6J9no05ysKIo+SX6e5JqyLC9t\nyaAAALQt06dPz3HHHZf+/fvnoosuyrx5H/lxM0899VSuv/76GqUDAACA6mpr70Ba5YPPp+Yzn5L3\n71AakOTWT3G+C/J+ifbDBQ1UFMWD8xmtuqDnBACget5+++2cccYZOe200zJjxoyKOwMHDkxjY2O2\n3nrrKqcDAACA2mhrBdLnPvh8cz7zfx5f6pNOVBTFQUl2SvKfZVm+3ALZAABoQ957771ccMEFGTdu\nXF555ZWKOwMGDEhDQ0N22223FEVR5YQAAABQO22tQPok//y/+vJjl4pixSRnJvlNWZZXLMwFy7Ic\nOJ9rPJhk/YU5NwAALa+pqSmXXXZZRo4cmb/97W8Vd7785S9n9OjROeCAA9KlS3v7kRkAAAA+WVv7\nv+F/3mH0ufnMl/y3vfm5OMnMJIe1RCgAANqO++67L9/97ncrzpZeeukMHTo0hx12WJZYYokqJwMA\nAIDWo1OtA3xGT37wOWA+8/4ffM7vHUn/tH6SPkleLYqi/OefJL/4YD7sg2PXLFxcAABam4033jjf\n+ta3PnKsR48eGT58eKZNm5Zjjz1WeQQAAECH19buQJr4wec2RVF0Ksty3j8HRVH0SvL1vH9n0T2f\ncJ7/TtK9wvH+STZL8nCSB5P8eaETAwDQ6owfPz433nhjunbtmh/+8IcZNmxYlltuuVrHAgAAgFaj\nTRVIZVlOLYri5iTbJDk8yU//ZTwmSY8k55dl+c4/DxZFseoH3zv5X85zVKXzF0VxQN4vkK4vy3J4\ni/8FAACoiilTpmTUqFEZN25c+vbt22y+7rrr5rzzzsu2226blVZaqQYJAQAAoHVrUwXSBw5LcneS\ns4qi2CrJE0m+lmTLvP/oumH/tv/EB59F1RICAFATzz//fOrr63PRRRelqakpnTt3ziWXXFJx94c/\n/GGV0wEAAEDb0dbegZSyLKcm2SDJhLxfHB2bpF+Ss5JsXJbl9NqlAwCgFl577bWceOKJWXnllXPB\nBRekqakpSfKrX/0qjz76aI3TAQAAQNvTFu9ASlmWf09y4Kfc/dR3HpVlOSHvF1MAALQB77zzTn7y\nk5/klFNOyZtvvtlsXpZlLrroopx55pk1SAcAAABtV5sskAAA6Nhmz56dCy+8MPX19Xn55Zcr7qy8\n8soZO3Zs9txzzyqnAwAAgLZPgQQAQJsxb968XH755Rk5cmSmTZtWceeLX/xiRo0alYMOOihdu3at\nckIAAABoHxRIAAC0emVZ5oYbbsjQoUPzl7/8peLOUkstlbq6uhxxxBHp3r17lRMCAABA+6JAAgCg\n1Zs1a1YOPvjgvPjii81mSyyxRI455pgcf/zx+fznP1+DdAAAAND+dKp1AAAA+CRLLLFERo0a9ZFj\nXbp0yWGHHZapU6dm/PjxyiMAAABoQQokAADahIMOOigrr7xyiqLIvvvum8mTJ+ecc87JF7/4xVpH\nAwAAgHbHI+wAAGgVXnzxxYwdOzbrrbdeDj744Gbzrl275sILL8xSSy2VddZZpwYJAQAAoONQIAEA\nUFNvvPFGTjnllJx55pmZOXNmvvCFL2SfffZJjx49mu1uvvnmNUgIAAAAHY9H2AEAUBPvvvtuTjnl\nlPTt2zeNjY2ZOXNmkuSll17KWWedVeN0AAAA0LEpkAAAqKo5c+bk/PPPz8orr5wTTzwxr7/+erOd\nK6+8MmVZ1iAdAAAAkCiQAACoknnz5uV//ud/svrqq+eHP/xhXnzxxWY7yy23XM4555z86U9/SlEU\nNUgJAAAAJN6BBADAIlaWZW666abU1dXl4Ycfrriz5JJL5sQTT8zRRx9d8d1HAAAAQHUpkAAAWGT+\n9Kc/pa6uLn/4wx8qzhdffPEcddRROfHEE7P00ktXOR0AAAAwPwokAAAWmZ/97GcVy6POnTvn+9//\nfkaOHJkvf/nLNUgGAAAAfBzvQAIAYJEZM2ZMunbt+pFje+65Zx5//PGcf/75yiMAAABopRRIAAAs\ntLIsKx5fccUVc+ihhyZJtt122zz44IP59a9/nQEDBlQzHgAAAPAZeYQdAAAL7M0338xpp52WJ554\nIldeeWXFnWHDhmXnnXfOlltuWeV0AAAAwIJSIAEA8JnNnDkz55xzThobG/Paa68lSSZOnFixJOrT\np0/69OlT7YgAAADAQvAIOwAAPrW5c+fmwgsvTP/+/XP88cd/WB4lSV1d3XwfZQcAAAC0LQokAAA+\nUVmWufLKK7Pmmmvm4IMPzvPPP99sZ9q0aXnmmWdqkA4AAABoaQokAADmqyzL3HLLLfnqV7+aPfbY\nI08++WSznV69eqW+vj5Tp07NiiuuWP2QAAAAQIvzDiQAACq67777UldXl9tuu63ifLHFFsvhhx+e\nurq6LLPMMlVOBwAAACxKCiQAAD7iySefzNChQ3P11VdXnHfq1CkHHnhgRo0alf/4j/+ocjoAAACg\nGhRIAAB8xKRJk+ZbHu22224ZN25cVl111SqnAgAAAKrJO5AAAPiIXXbZJV/96lc/cmzrrbfOfffd\nlyuvvFJ5BAAAAB2AAgkAoIOaN29exeNFUaSxsTFJssEGG+SWW27JLbfc0qxUAgAAANovBRIAQAfz\n3nvv5Sc/+Un69++fV199teLOVlttlYkTJ+a+++7L1ltvXeWEAAAAQK0pkAAAOoimpqZMmDAhAwYM\nyDHHHJNp06Zl/Pjx893fYostUhRFFRMCAAAArYUCCQCgnSvLMr/97W+z9tpr58ADD8yzzz774ezc\nc8/9yNcAAAAAiQIJAKBdmzhxYjbaaKPsuuuuefzxx5vNu3Xrlj//+c81SAYAAAC0ZgokAIB26MEH\nH8w222yTQYMG5b777ms279atW44++uhMnTo1O+20Uw0SAgAAAK1Zl1oHAACg5Tz55JMZMWJEfvOb\n31Scd+rUKd/97nczevTofOUrX6lyOgAAAKCtUCABALQDr776aoYOHZpf/OIXaWpqqriz8847Z9y4\ncVljjTWqnA4AAABoaxRIAADtQKdOnXLFFVdULI+22GKLNDY2ZqONNqpBMgAAAKAt8g4kAIB2oHfv\n3jn++OM/cmz99dfPTTfdlNtuu015BAAAAHwmCiQAgDZk9uzZmTdvXsXZMccckz59+qR///759a9/\nnfvvvz/bbLNNiqKockoAAACgrVMgAQC0AU1NTbn00kuz6qqr5sorr6y407Nnz0ycODGTJk3Knnvu\nmU6d/KgHAAAALBi/VQAAaMXKsszvfve7rLvuutl///3z17/+NcOHD8+cOXMq7q+++urp2rVrlVMC\nAAAA7Y0CCQCglfrjH/+YTTfdNN/5znfy2GOPfXh8ypQpmTBhQu2CAQAAAO2eAgkAoJV5+OGHs/32\n22fzzTfP3Xff3WzetWvXvPjiizVIBgAAAHQUXWodAACA9z399NMZMWJE/ud//qfivCiK7Lfffhkz\nZkxWWmmlKqcDAAAAOhIFEgBAjb3wwgupr6/PRRddlLlz51bc+fa3v52GhoastdZaVU4HAAAAdEQK\nJACAGmlqasqwYcNy1llnZebMmRV3vvGNb+Skk07KJptsUuV0AAAAQEemQAIAqJHOnTvn0UcfrVge\nrbPOOmlsbMx2222XoihqkA4AAADoyDrVOgAAQEc2fvz4j3zdr1+/XHbZZXnooYfyrW99S3kEAAAA\n1IQCCQBgEZs3b15mzJhRcbbOOutkn332yRe+8IWcd955eeKJJ7L33nunUyc/pgEAAAC14xF2AACL\nSFmWufGh1TZPAAAgAElEQVTGGzN06NCsueaaufTSSyvunXnmmenRo0e6d+9e5YQAAAAAlfmnrQAA\ni8Bdd92VzTffPDvssEMeeeSRXHbZZfnLX/5ScXfZZZdVHgEAAACtigIJAKAF/eUvf8m3v/3tbLrp\nprnjjjs+PF6WZYYNG1bDZAAAAACfngIJAKAFTJs2Lfvvv3/WXXfdXHfddRV3evXqldmzZ1c5GQAA\nAMBnp0ACAFgIL730Uo444oisuuqqufTSS1OWZbOd7bffPn/+859z2WWXpVu3bjVICQAAAPDZdKl1\nAACAtujNN9/MqaeemjPOOCPvvvtuxZ1NNtkkjY2N2WyzzaqcDgAAAGDhKJAAAD6jCy+8MCeeeGJe\ne+21ivM111wz48ePz4477piiKKqcDgAAAGDhKZAAAD6jpqamiuXRiiuumLFjx2bvvfdO586da5AM\nAAAAoGV4BxIAwGd00EEHZeWVV/7w6+WWWy5nn312nnzyyey3337KIwAAAKDNUyABAFRQlmWef/75\nirOuXbtm3LhxWXLJJTNu3Lg8/fTTOfzww9OtW7cqpwQAAABYNDzCDgDg39xzzz2pq6vLE088kalT\np6ZHjx7NdvbYY49885vfzNJLL12DhAAAAACLljuQAAA+MGnSpOy8887ZeOONc/vtt+fll1/OT37y\nk4q7nTp1Uh4BAAAA7ZYCCQDo8P72t7/lgAMOyFprrZVrr732I7NTTjklr732Wo2SAQAAANSGAgkA\n6LBeeeWVHH300RkwYEB++ctfpizLZjtf+9rX8uabb9YgHQAAAEDteAcSANDhzJgxI6effnpOP/30\nvPPOOxV3vva1r6WxsTFbbrllldMBAAAA1J4CCQDoMGbNmpVzzz0348ePz/Tp0yvurL766mloaMhO\nO+2UoiiqnBAAAACgdVAgAQAdwh//+Mfst99++fvf/15xvsIKK6S+vj777bdfOnfuXOV0AAAAAK2L\nAgkA6BBWWGGFvPzyy82OL7vsshk+fHj+67/+K4sttlgNkgEAAAC0Pp1qHQAAoBpWXHHFHHrooR9+\n3atXr4wZMyZTp07NUUcdpTwCAAAA+BcKJACgXZk8efJ8Z0OHDs0yyyyTwYMHZ9q0aRk5cmR69epV\nxXQAAAAAbYMCCQBoFyZPnpzdd989q622Wm677baKO3369MkzzzyT008/Pcsss0yVEwIAAAC0HQok\nAKBNe/bZZ/P9738/a6yxRq666qokSV1dXcqyrLjfvXv3asYDAAAAaJMUSABAm/SPf/wjgwcPzoAB\nA3LxxRdn3rx5H87uu+++XHvttTVMBwAAANC2dal1AACAz+Ktt97KGWeckdNOOy1vvfVWxZ2BAwdm\n2WWXrXIyAAAAgPZDgQQAtAnvvfdezj///IwbNy6vvvpqxZ1VVlklDQ0N2XXXXVMURZUTAgAAALQf\nCiQAoFVramrKpZdemlGjRuWZZ56puLP88stn9OjR+d73vpcuXfx4AwAAALCw/IYFAGi1XnrppWy1\n1VZ5/PHHK8579+6doUOH5rDDDsviiy9e5XQAAAAA7ZcCCQBotZZbbrn06NGj2fEePXpk8ODBOfbY\nY/O5z32uBskAAAAA2rdOtQ4AADA/RVHkpJNO+vDrrl275qijjsrUqVNTX1+vPAIAAABYRNyBBADU\n3FNPPZWll146yyyzTLPZoEGDsu2222a55ZbLmDFjsuKKK1Y/IAAAAEAH4w4kAKBmnnvuuRxyyCFZ\nffXVM27cuPnuXXfddfnlL3+pPAIAAACoEgUSAFB106dPzwknnJD+/fvn5z//eZqamnLeeeflmWee\nqbjfpYubpgEAAACqSYEEAFTN22+/nYaGhvTt2zennnpqZs2a9eFs9uzZGT16dO3CAQAAAPAh/5wX\nAFjkZs+enZ///OcZO3ZsXn755Yo7/fv3z/bbb1/lZAAAAABUokACABaZpqamXH755Rk5cmT++te/\nVtz50pe+lFGjRuXAAw9M165dq5wQAAAAgEoUSABAiyvLMtdff32GDh2aRx99tOLO5z//+dTV1eWI\nI47IEkssUeWEAAAAAHwcBRIA0KLKssyOO+6YG264oeK8e/fuOeaYY3L88cdnqaWWqnI6AAAAAD6N\nTrUOAAC0L0VRZJNNNml2vEuXLjnssMPy9NNPp6GhQXkEAAAA0IopkACAFnf00UenT58+Sd4vlPbd\nd99Mnjw555xzTr74xS/WOB0AAAAAn0SBBAAskBdffDEPPPBAxVnPnj0zYsSI7Ljjjnn44Ydz6aWX\npl+/flVOCAAAAMCCUiABAJ/JG2+8kaFDh6Zfv37ZZ599MmfOnIp7hx9+eH73u99l7bXXrnJCAAAA\nABaWAgkA+FTefffdnHzyyVlppZXS2NiYmTNnZsqUKZkwYULF/aIoqhsQAAAAgBajQAIAPtacOXPy\ns5/9LCuvvHKGDBmSN9544yPz0aNHZ+bMmTVKBwAAAMCi0KXWAQCA1mnevHn59a9/nREjRmTq1KkV\nd77whS9k+PDh6dLFjxQAAAAA7Ynf9gAAH1GWZX7/+9+nrq4ujzzySMWdz33ucznxxBNz1FFHpUeP\nHlVOCAAAAMCipkACAD501113pa6uLnfccUfF+eKLL56jjz46J5xwQpZeeukqpwMAAACgWhRIAECS\npKGhIcOHD68469y5cw4++OCMGDEiX/rSl6qcDAAAAIBq61TrAABA67DjjjtWPL7XXnvliSeeyHnn\nnac8AgAAAOggFEgAQJJknXXWyT777PPh19ttt10eeuihXH755enfv38NkwEAAABQbQokAOhA3nzz\nzfzmN7+Z77y+vj6bbbZZbr/99tx4441Zb731qpgOAAAAgNbCO5AAoAOYOXNmzjnnnDQ2Nub111/P\ngAEDss466zTb69evX/7whz/UICEAAAAArYk7kACgHZs7d24uvPDC9O/fP8cff3xee+21lGWZYcOG\n1ToaAAAAAK2YAgkA2qF58+blN7/5TdZYY40cfPDBef755z8yv/7663PnnXfWKB0AAAAArZ1H2AFA\nO1KWZW655ZbU1dXloYceqriz5JJL5vjjj8+6665b5XQAAAAAtBUKJABoJ+69997U1dVl4sSJFeeL\nLbZYjjzyyAwZMiS9e/eucjoAAAAA2hIFEgC0cZMmTcrw4cNzzTXXVJx37tw5Bx10UEaOHJnll1++\nyukAAAAAaIsUSADQhk2cODFbb7115s2bV3G+xx57ZOzYsVlllVWqnAwAAACAtqxTrQMAAAtu0003\nTb9+/Zod/+Y3v5n7778/V1xxhfIIAAAAgM9MgQQAbVjXrl0zbty4D7/ecMMNc+utt+bmm2/OBhts\nUMNkAAAAALRlCiQAaOVmzZqVs88+O2+//XbF+e6775699947V199de65554MGjSoygkBAAAAaG+8\nAwkAWqm5c+fmkksuyahRo/L3v/89b775ZoYNG9Zsr1OnTrnssstqkBAAAACA9sodSADQypRlmauv\nvjprr712DjrooPz9739PkpxyyimZPn16jdMBAAAA0BEokACgFbntttuy0UYbZbfddssTTzzxkdmM\nGTNy2mmn1SgZAAAAAB2JR9gBQCvwwAMPZOjQobnlllsqzrt165bDDjssgwcPrnIyAAAAADoiBRIA\n1NCTTz6Z4cOH58orr6w479SpU773ve9l1KhR+cpXvlLldAAAAAB0VAokAKiB5557LmPGjMkvfvGL\nNDU1VdzZZZddMm7cuKy++upVTgcAAABAR6dAAoAqe+utt7LGGmtkxowZFedbbrllGhsb87Wvfa3K\nyQAAAADgfZ1qHQAAOppevXrlwAMPbHZ84MCBufnmm3PrrbcqjwAAAACoKQUSANTA0KFD07NnzyTJ\ngAED8pvf/Cb3339/vvnNb6YoihqnAwAAAKCj8wg7AFgEmpqa8qtf/Sr9+vXL17/+9WbzPn36ZPz4\n8enevXu+973vpUsX/0kGAAAAoPXw2yoAaEFlWeZ///d/M2zYsEyaNCkbbrhh7rnnnop3FR155JE1\nSAgAAAAAn8wj7ACghfzhD3/IJptskp133jmTJk1Kktx333255pprapwMAAAAAD4bBRIALKSHHnoo\n2223XbbYYovcc889zeb19fUpy7IGyQAAAABgwSiQAGABTZkyJXvttVcGDhyYm266qdm8KIrsv//+\nufrqqys+wg4AAAAAWivvQAKAz+iFF15IfX19LrzwwjQ1NVXc+c53vpNx48ZlrbXWqnI6AAAAAFh4\nCiQA+JRee+21nHzyyTnrrLMya9asijubbbZZGhsbs8kmm1Q5HQAAAAC0HAUSAHxKe+21V2655ZaK\ns3XXXTeNjY3ZdtttPa4OAAAAgDbPO5AA4FMaMmRIs2P9+vXL5ZdfngcffDDbbbed8ggAAACAdkGB\nBACf0qBBg/LNb34zSfLFL34x5513Xp544onstdde6dTJf1IBAAAAaD88wg4APlCWZW644YY89dRT\n+dGPflRxp7GxMVtttVWOPPLIdO/evcoJAQAAAKA6FEgAkOTOO+/MkCFDctddd6Vbt27Zdddd85Wv\nfKXZ3sCBAzNw4MAaJAQAAACA6vG8HQA6tEceeSQ77rhjvvGNb+Suu+5KksyePTujR4+ubTAAAAAA\nqCEFEgAd0tSpU7PvvvtmvfXWy/XXX99sfskll+SFF16oQTIAAAAAqD0FEgAdyosvvpjDDz88q666\nai677LKUZdlsZ4cddsiDDz6YL33pSzVICAAAAAC15x1IAHQIb7zxRk455ZT85Cc/ybvvvltx5+tf\n/3oaGxvzjW98o8rpAAAAAKB1USAB0K69++67Ofvss3PSSSfl9ddfr7iz1lprpbGxMdtvv32Koqhy\nQgAAAABofRRIALRr5557bk488cSKs5VWWiljx47N3nvvnU6dPNUVAAAAAP7Jb8sAaNd++MMfZrnl\nlvvIseWWWy5nn312Jk+enH333Vd5BAAAAAD/xm/MAGjXevbsmREjRiRJllxyyTQ0NGTq1Kk5/PDD\n061btxqnAwAAAIDWySPsAGjz/vSnP+W8887LRRddlK5duzabH3zwwXn99ddz6KGHpnfv3jVICAAA\nAABtizuQAGizHnvssey0007ZZJNNcskll+QXv/hFxb1u3bpl+PDhyiMAAAAA+JQUSAC0OX/961/z\n3e9+N2uvvXb+93//98PjY8aMycyZM2uYDAAAAADaBwUSAG3Gyy+/nKOOOiqrrLJKLrnkkpRl+ZH5\nCy+88JFCCQAAAABYMN6BBECr9+abb+a0007LGWeckXfeeafizkYbbZTGxsZsscUW1Q0HAAAAAO2Q\nAgmAVmvmzJk599xzM378+Lz22msVd9ZYY400NDTkO9/5ToqiqHJCAAAAAGifFEgAtDpz587NhAkT\nMmbMmDz33HMVd77yla+kvr4+++67bzp37lzlhAAAAADQvimQAGh1Jk+enEMOOaTZO46SZNlll82I\nESNyyCGHZLHFFqtBOgAAAABo/zrVOgAA/Ls111wze++990eO9erVK/X19Zk6dWqOPPJI5REAAAAA\nLEIKJABapfr6+nTp0iWLLbZYBg8enGnTpmXEiBHp1atXraMBAAAAQLunQAKgJp544onsvvvueeSR\nRyrO+/XrlwkTJmTKlCk5/fTTs8wyy1Q5IQAAAAB0XN6BBEBVPfvssxk9enR++ctfZt68eZk1a1au\nu+66irv77rtvldMBAAAAAIk7kACokldffTWDBw9O//7984tf/CLz5s1Lklx//fW54447apwOAAAA\nAPhXCiQAFqm33norY8aMSd++fXPGGWdk9uzZzXYuu+yyGiQDAAAAAObHI+wAWCRmzZqVn/3sZ2lo\naMg//vGPijurrrpqGhoasssuu1Q5HQAAAADwcRRIALSouXPn5pJLLsno0aPz7LPPVtz5j//4j4wZ\nMyb7779/unTxnyIAAAAAaG3a5CPsiqJYviiKi4uieKEoiveKovhbURRnFkXx+U/5/T2Koti3KIrL\niqKYXBTFO0VRvFUUxQNFURxbFEW3Rf13AGhvyrLMb3/726y99to56KCDKpZHvXv3zo9//OM89dRT\nOfDAA5VHAAAAANBKtbnf3BVF0S/J3Un6JLk2yeQkGyY5Osl2RVF8vSzL6Z9wmm8kuTTJa0kmJrkm\nydJJvp3ktCS7FkWxVVmWsxbN3wKg/WlqasqJJ56YKVOmNJv17Nkzxx57bAYPHpwll1yyBukAAAAA\ngM+iLd6BdG7eL4+OKsty57Ish5RlOSjJGUlWSdLwKc7xUpL9knyxLMvdPzjHIUkGJHkoySZJDl80\n8QHapy5dumTcuHEfOdatW7ccffTRmTp1akaPHq08AgAAAIA2ok0VSEVR9E2yTZK/JTnn38ajkryT\nZP+iKHp83HnKsny4LMtflWU5+9+Ov5Xk9A++3KIlMgN0JLvvvnvWX3/9dOrUKQcccECeeuqpnHnm\nmenTp0+towEAAAAAn0Fbe4TdoA8+by7Lct6/DsqyfKsoirvyfsG0UZJbF/Aacz74nLuA3w/Qbj33\n3HOpr69P3759M2TIkGbzTp065fzzz88SSyyRNdZYowYJAQAAAICW0NYKpFU++HxqPvMpeb9AGpAF\nL5AO+uDz959muSiKB+czWnUBrw/Q6kyfPj0nnXRSfvrTn+a9997LkksumYMPPji9e/dutrvBBhvU\nICEAAAAA0JLa1CPsknzug8835zP/5/GlFuTkRVEckWS7JA8nuXhBzgHQnrz99tsZN25c+vbtm9NO\nOy3vvfdekmTGjBk56aSTapwOAAAAAFhU2todSJ+k+OCz/MzfWBS7JjkzyUtJdivLcs4nfMv7FyrL\ngfM534NJ1v+sOQBag/feey8XXHBBxo0bl1deeaXizi233JKmpqZ07ty5yukAAAAAgEWtrd2B9M87\njD43n/mS/7b3qRRFsXOS/0nySpItyrKctmDxANq2pqamXHLJJVl11VVz1FFHVSyPvvzlL+fnP/95\nHnjgAeURAAAAALRTbe0OpCc/+Bwwn3n/Dz7n946kZoqi2CPJZXn/zqNBZVlOWfB4AG1TWZb53e9+\nl6FDh2bSpEkVd5ZeeunU1dXl8MMPzxJLLFHlhAAAAABANbW1AmniB5/bFEXRqSzLef8cFEXRK8nX\nk8xMcs+nOVlRFPsk+e8kzyfZ0p1HQEf0xz/+MUOGDMmf/vSnivPu3btn8ODBOe644/K5z83vBlAA\nAAAAoD1pU4+wK8tyapKbk6yY5PB/G49J0iPJf5dl+c4/DxZFsWpRFKv++7mKovhekkuSPJtkM+UR\n0FH99re/rVgede3aNUcccUSmTp2asWPHKo8AAAAAoAMpyrKsdYbPpCiKfknuTtInybVJnkjytSRb\n5v1H121SluX0f9kvk6Qsy+Jfjm2Z5P/yfoF2cZK/V7jUG2VZnrkQOR9cP1n/wfcvvqCnAVjkXn31\n1fTt2zdvv/12kqQoiuy3334ZM2ZMVlpppRqnAwAAAICOa+DAgXnooYceKstyYLWv3dYeYZeyLKcW\nRbFBkvok2yXZPsmLSc5KMqYsy9c+xWm+kv9/99VB89l5JskCF0gArU1ZlimKotnxZZddNscee2zG\njBmTb3/722loaMhaa61Vg4QAAAAAQGvR5u5AaivcgQS0Fq+//npOPvnk3HvvvbntttsqlkgzZszI\no48+mq9//es1SAgAAAAAVOIOJABa3LvvvpuzzjorJ598ct54440kyTXXXJNddtml2e6SSy6pPAIA\nAAAAPtTpk1cAaEvmzJmT8847L/369UtdXd2H5VGSDBs2LE1NTTVMBwAAAAC0BQokgHZi3rx5ufzy\ny7PaaqvlsMMOy0svvdRs5/XXX8/TTz9dg3QAAAAAQFuiQAJo48qyzA033JD1118/++yzT6ZOndps\nZ6mllkpjY2OefvrprLLKKjVICQAAAAC0Jd6BBNCG3XXXXamrq8sdd9xRcb7EEkvk6KOPzgknnJDP\nf/7zVU4HAAAAALRVCiSANuiJJ57ICSeckOuuu67ivEuXLvnBD36QESNG5Etf+lKV0wEAAAAAbZ0C\nCaANeumll+ZbHu29996pr6/PyiuvXOVUAAAAAEB74R1IAG3QlltumW222eYjx7bffvv8+c9/zmWX\nXaY8AgAAAAAWigIJoBWbM2fOfGfjx49PkmyyySb5wx/+kOuvvz7rrrtutaIBAAAAAO2YAgmgFZo5\nc2ZOPfXUrLDCCvnb3/5WcWfgwIG5//77c+edd2azzTarbkAAAAAAoF1TIAG0InPmzMkFF1yQlVde\nOSeccEJeeumljB49er77G2ywQYqiqF5AAAAAAKBDUCABtALz5s3LFVdckTXWWCP/9V//lRdeeOHD\n2SWXXJJJkybVMB0AAAAA0NEokABqqCzL3HTTTfnqV7+a//zP/8yUKVOa7fTs2TOTJ0+uQToAAAAA\noKNSIAHUyD333JNBgwZlu+22y0MPPdRsvvjii+e4447LtGnTsttuu9UgIQAAAADQUXWpdQCAjmbS\npEkZNmxYrr322orzzp0756CDDsrIkSOz/PLLVzkdAAAAAIACCaBq/vGPf+S4447Lf//3f6csy4o7\ne+65Z8aOHZsBAwZUOR0AAAAAwP+nQAKoksUXXzy///3vK5ZH22yzTcaPH5+BAwfWIBkAAAAAwEd5\nBxJAlfTs2TMjRoz4yLENN9wwt956a2666SblEQAAAADQaiiQAFrYrFmzMmfOnIqzgw8+OCuttFJW\nW221/Pa3v80999yTQYMGVTkhAAAAAMDHUyABtJC5c+fmoosuSv/+/XPxxRdX3OnWrVv+7//+L48+\n+mh23nnnFEVR5ZQAAAAAAJ9MgQSwkMqyzFVXXZU111wzP/jBD/Lcc8+lvr4+7777bsX9vn37pnPn\nzlVOCQAAAADw6SmQABbCrbfemg033DC77757nnzyyQ+Pv/DCCzn77LNrmAwAAAAAYMEpkAAWwP33\n35+tt946W2+9dR544IFm827dumXmzJk1SAYAAAAAsPC61DoAQFsyefLkDB8+PFdddVXFeadOnXLA\nAQdk1KhRWWGFFaqcDgAAAACgZSiQAD6FZ599NmPGjMmECRMyb968iju77bZbxo4dm9VWW63K6QAA\nAAAAWpYCCeBjlGWZE044IT/96U/z3nvvVdwZNGhQGhsbs+GGG1Y5HQAAAADAoqFAAvgYRVHkxRdf\nrFgeDRw4MCeddFK23nrrGiQDAAAAAFh0OtU6AEBrV19fny5d/n/fvsoqq+TKK6/M/fffrzwCAAAA\nANolBRJAkqampvzjH/+oOOvbt28OOeSQLL/88rnwwgvz2GOPZbfddktRFFVOCQAAAABQHQokoEMr\nyzLXXHNN1l577Xzve9+b715DQ0OeeuqpfP/73//I3UgAAAAAAO2RAgnosG6//fZsvPHG2WWXXfL4\n44/nhhtuyB133FFxd6mllsoSSyxR5YQAAAAAALWhQAI6nAcffDDbbrttttxyy9x7770fmdXV1aUs\nyxolAwAAAABoHRRIQIfx1FNP5T//8z+zwQYb5Oabb242L4oiK6+8cmbOnFmDdAAAAAAArYcCCWj3\nnnvuuRxyyCFZffXVc8UVV1Tc2WmnnfKXv/wlEyZMSPfu3aucEAAAAACgdfEmeKDdmj59ek4++eT8\n9Kc/zaxZsyrubL755jnppJOy0UYbVTkdAAAAAEDrpUAC2qULL7wwxx57bGbMmFFxvt5666WxsTHb\nbLNNiqKocjoA4P+1d+dhclZ13rg/J2GHsG+iMJgMYVFEYUAQVEARGUUd2QRlURThRXYGwQQSiCQB\nBASUUUFkHfkNg2wqyM8XEUQBRWSTLYkgaGTfl5DAef+oCnYnFdJJqrvSnfu+rlwP/ZxT5/lWpXjS\nXZ8+5wAAADB/s4QdMCAtvfTSLcOjtdZaKxdffHH+8Ic/ZNtttxUeAQAAAAC0IEACBqQdd9wxG264\n4Ztfr7baavn+97+fe+65J7vssksGDXL7AwAAAACYFZ+gAv1WrTUTJ05s2TZo0KCMGzcuyy23XE48\n8cRMmDAh++yzTxZeeOE+rhIAAAAAoP+xBxLQL91444056qijctddd2XSpElZYYUVZuqzzTbb5OGH\nH86QIUM6UCEAAAAAQP9lBhLQr9xxxx35xCc+kQ996EO56aab8vzzz2f8+PEt+5ZShEcAAAAAAHNB\ngAT0CxMnTsznP//5vPe9783Pf/7zbm1nnHFGHn300Q5VBgAAAAAw8FjCDpivTZ48OWPGjMlZZ52V\nadOmteyzzTbb5LXXXuvjygAAAAAABi4BEjBfeuaZZ3LiiSfmtNNOyyuvvNKyzxZbbJHx48dn8803\n7+PqAAAAAAAGNgESMF95+eWXc/rpp+eEE07Is88+27LPe97znowbNy7bbbddSil9XCEAAAAAwMAn\nQALmGzfddFN22mmnTJ48uWX70KFDM2bMmHzuc5/LoEG2cAMAAAAA6C0CJGC+MXz48LzwwgsznV91\n1VVzzDHHZO+9984iiyzSgcoAAAAAABYsfoUfmG+stNJKOfzww9/8eplllsnYsWMzYcKE7LfffsIj\nAAAAAIA+IkAC+twf//jH1Fpbth166KFZY4018vWvfz2TJk3KUUcdlSWXXLKPKwQAAAAAWLAJkIA+\nc9ddd+VTn/pUNtpoo1x22WUt+wwZMiQPPvhgxo8fn+WXX76PKwQAAAAAIBEgAX3gL3/5S/bYY49s\nsMEGueqqq5IkI0aMyLRp01r2t1QdAAAAAEBnCZCAXvPYY4/lgAMOyNprr50LLrig27J19913Xy64\n4IIOVgcAAAAAwKws1OkCgIHnueeey7e+9a2ceuqpeemll1r22WyzzbL22mv3cWUAAAAAAPSEAAlo\nm1deeSXf/e53M27cuDz99NMt+7zrXe/K2LFjs/3226eU0scVAgAAAADQEwIkYJ5NmzYt5557bkaP\nHoN5uaoAACAASURBVJ2//e1vLfusueaaOe6447Lbbrtl8ODBfVwhAAAAAABzQoAEzJOnn346m222\nWR544IGW7SuttFKOPvro7LPPPll00UX7uDoAAAAAAObGoE4XAPRvyy+/fNZcc82Zzi+99NIZM2ZM\nJk2alAMOOEB4BAAAAADQjwiQgHk2duzYN/970UUXzWGHHZaJEydm5MiRWWqppTpYGQAAAAAAc8MS\ndkCP/PnPf87SSy+dd7zjHTO1bbTRRtl1112z5JJL5phjjsnqq6/egQoBAAAAAGgXM5CAt/Twww/n\ni1/8YtZff/2MGDFilv0uvPDCnHXWWcIjAAAAAIABQIAEtPTEE0/kkEMOyfDhw3PuuefmjTfeyAUX\nXJC77767Zf9Bg9xOAAAAAAAGCp/4At08//zzGT16dIYOHZpvf/vbee21195sq7Vm5MiRHawOAAAA\nAIC+YA8kIEny6quv5nvf+16OP/74PPnkky37rLPOOtljjz36uDIAAAAAAPqaAAkWcNOmTcsFF1yQ\nUaNG5ZFHHmnZZ/XVV8+xxx6b3XffPQst5LYBAAAAADDQ+SQYFlC11lx22WUZOXJk7r333pZ9Vlxx\nxYwYMSL77rtvFltssT6uEAAAAACAThEgwQLqM5/5TK688sqWbUsttVQOP/zwHHLIIVl66aX7uDIA\nAAAAADptUKcLADpj2223nencIosskoMPPjiTJk3KqFGjhEcAAAAAAAsoARIsoL785S9n6NChSZJB\ngwbli1/8Yh544IGceuqpWWmllTpcHQAAAAAAnSRAggHskUceyfXXX9+ybZFFFsmYMWPyH//xH7nr\nrrtyzjnn5F/+5V/6tkAAAAAAAOZLAiQYgJ588skcdthhWWuttbLbbrvl5Zdfbtlvt912y09+8pOs\nt956fVwhAAAAAADzMwESDCAvvvhixowZk6FDh+aUU07JlClTMnny5JxxxhmdLg0AAAAAgH5EgAQD\nwJQpU3LGGWdk2LBhOeaYY/LCCy90ax8/fnyeeeaZDlUHAAAAAEB/s1CnCwDm3uuvv56LLrooxxxz\nTB5++OGWfd7+9rdn9OjRGTJkSB9XBwAAAABAfyVAgn6o1porr7wyI0aMyD333NOyz/LLL5+jjjoq\n+++/fxZffPE+rhAAAAAAgP5MgAT9zK9//esceeSRufnmm1u2L7HEEjn00ENz+OGHZ5lllunj6gAA\nAAAAGAgESNCPnHTSSTniiCNati288MLZd999M2LEiKyyyip9XBkAAAAAAAPJoE4XAPTcDjvskIUW\n6p77llKy++675/7778/pp58uPAIAAAAAYJ4JkKAfGTp0aL761a+++fWnPvWp3HHHHTn//PPzzne+\ns4OVAQAAAAAwkAiQYD7z9NNP5+yzz55l+8iRI7PtttvmpptuyhVXXJH111+/D6sDAAAAAGBBYA8k\nmE+89NJLOe2003LiiSfmueeey1prrZUPf/jDM/VbddVVc80113SgQgAAAAAAFhRmIEGHvfbaaznz\nzDMzbNiwjBgxIs8991yS5KijjkqttcPVAQAAAACwIBIgQYe88cYbueiii7Luuutm//33z2OPPdat\n/Xe/+11+9rOfdag6AAAAAAAWZJawgz5Wa83Pf/7zfOMb38idd97Zss+yyy6bI488MltvvXUfVwcA\nAAAAAAIk6FO/+c1vcuSRR+amm25q2b744ovn4IMPzn/+539mueWW6+PqAAAAAACgQYAEfeCOO+7I\niBEjZrkk3UILLZSvfOUrOfroo/O2t72tj6sDAAAAAIDuBEjQy26++eZ84AMfSK21Zftuu+2W4447\nLsOGDevjygAAAAAAoLVBnS4ABrpNNtkkG2644UznP/GJT+RPf/pTLrroIuERAAAAAADzFQES9LJB\ngwZl7Nixb369+eab54YbbshPf/rTbLDBBh2sDAAAAAAAWhMgQRu8/PLLOemkk/Lkk0+2bN9mm22y\n33775aqrrsqNN96YD37wg31cIQAAAAAA9Jw9kGAeTJ06Neecc06OPfbYTJ48OY899li+9a1vzdSv\nlJIzzzyzAxUCAAAAAMCcMwMJ5sIbb7yRiy++OOutt1723XffTJ48OUnyne98J4888kiHqwMAAAAA\ngHkjQII5UGvNNddck4022ii77rprJkyY0K19ypQpGTduXIeqAwAAAACA9rCEHfTQb3/72xx11FG5\n4YYbWrYvtthiOfDAA/P1r3+9jysDAAAAAID2EiDBbNx9990ZMWJErrzyypbtgwcPzt57751jjjkm\nb3/72/u4OgAAAAAAaD8BEszCX/7yl4waNSoXXnhhaq0t++yyyy457rjjMnz48D6uDgAAAAAAeo8A\nCVp49dVXs/HGG+epp55q2b7ttttm7Nix2XDDDfu4MgAAAAAA6H2DOl0AzI8WW2yxHHDAATOd33TT\nTfOrX/0q11xzjfAIAAAAAIABS4AEs3DooYdmxRVXTJKst956ufzyy/Pb3/42W265ZWcLAwAAAACA\nXmYJOxZY06ZNy3nnnZfVVlst22233UztQ4YMycknn5xaa77whS9k8ODBHagSAAAAAAD6ngCJBU6t\nNZdeemlGjhyZ+++/P+uss0622WabLLTQzP877LHHHh2oEAAAAAAAOssSdixQfvnLX2aTTTbJTjvt\nlPvvvz9Jct999+X888/vcGUAAAAAADD/ECCxQLj11lvzkY98JNtss03+8Ic/zNQ+bty4vPHGGx2o\nDAAAAAAA5j8CJAa0e++9NzvssEPe//7357rrrpupfdCgQdl7771z3XXXZdAg/zsAAAAAAEBiDyQG\nqL/+9a8ZPXp0zjvvvFnOLNpxxx0zZsyYrLPOOn1cHQAAAAAAzN8ESAwoTzzxRMaOHZszzzwzr732\nWss+H/3oRzN27NhsvPHGfVwdAAAAAAD0DwIkBpSvfvWrueyyy1q2bbzxxhk3blw+8pGP9HFVAAAA\nAADQv9j0hQFl5MiRM51bZ511cumll+aWW24RHgEAAAAAQA8IkBhQNtxww+y8885JktVXXz0//OEP\nc9ddd+Wzn/1sSikdrg4AAAAAAPoHS9jRr9Rac8UVV+T222/Pscce27LPmDFjsummm2a//fbLYost\n1scVAgAAAABA/ydAot/41a9+laOOOiq33HJLSinZaaed8u53v3umfsOHD8/w4cM7UCEAAAAAAAwM\nlrBjvnfbbbdl2223zdZbb51bbrklSWMm0ogRIzpcGQAAAAAADEwCJOZb999/f3beeef827/9W669\n9tqZ2n/6059m4sSJHagMAAAAAAAGNgES851HH300++yzT971rnflkksuadnn05/+dO64444MGzas\nj6sDAAAAAICBzx5IzDeeeuqpnHDCCTnjjDPy6quvtuyz5ZZbZty4cdl00037uDoAAAAAAFhwCJDo\nuBdffDGnnXZaTjzxxDz//PMt+2y44YYZN25cttlmm5RS+rhCAAAAAABYsAiQ6Lgf//jHGTlyZMu2\ntdZaK9/85jez4447ZtAgKy4CAAAAAEBf8Ik8Hbfnnntm6NCh3c6tttpq+cEPfpB77rknO++8s/AI\nAAAAAAD6kE/l6bhFFlkkY8aMSZIst9xyOemkkzJhwoR85StfycILL9zh6gAAAAAAYMFjCTv6xA03\n3JBTTz01F110UZZYYomZ2j/3uc/l8ccfz1577ZVll122AxUCAAAAAADTmYFEr/rTn/6Uf//3f8+H\nP/zhXH755TnjjDNa9hs0aFAOPvhg4REAAAAAAMwHBEj0igkTJmTXXXfN+973vlx99dVvnh8/fnye\neeaZDlYGAAAAAADMjgCJtvr73/+e/fbbL+uuu24uvvjimdqfffbZXH755R2oDAAAAAAA6Cl7INEW\nzzzzTE488cScdtppeeWVV1r22WKLLTJ+/PhsvvnmfVwdAAAAAAAwJwRIzJOXX345p59+ek444YQ8\n++yzLftssMEGGTt2bLbbbruUUvq4QgAAAAAAYE4JkJgrU6dOzdlnn50xY8Zk8uTJLfsMGzYsY8aM\nyS677JJBg6yWCAAAAAAA/YUAibny6KOP5qCDDsrUqVNnalt11VUzatSo7L333ll44YU7UB0AAAAA\nADAvTAthrrzzne/MV7/61W7nll122YwfPz4TJ07MvvvuKzwCAAAAAIB+SoDEXBs5cmSWXHLJLL74\n4jnyyCMzadKkfP3rX88SSyzR6dIAAAAAAIB5IEBilu68885sv/32+fWvf92yfZVVVsmPf/zjTJw4\nMePGjctyyy3XxxUCAAAAAAC9wR5IzGTSpEkZNWpULrrootRa89RTT+Wmm25KKWWmvttvv30HKgQA\nAAAAAHqTGUi86R//+Ee+9rWvZZ111smFF16YWmuS5He/+12uuuqqDlcHAAAAAAD0FQESefbZZzNi\nxIgMGzYs3/3udzN16tSZ+lx++eUdqAwAAAAAAOgES9gtwF555ZV85zvfybhx4/LMM8+07PPud787\nY8eOzSc/+ck+rg4AAAAAAOgUAdICaNq0afnRj36UY489Nn/7299a9llzzTUzZsyY7Lrrrhk8eHAf\nVwgAAAAAAHSSAGkBUmvNJZdckpEjR+bBBx9s2WfllVfO0UcfnX322SeLLLJIH1cIAAAAAADMDwRI\nC5gTTzyxZXi09NJL54gjjshBBx2UpZZaqgOVAQAAAAAA84tBnS6AvlNKybhx47qdW3TRRXP44Ydn\n0qRJGTFihPAIAAAAAAAQIA1UtdaW5z/60Y9m6623zuDBg/OVr3wlEyZMyEknnZQVVlihjysEAAAA\nAADmV5awG2AeeuihjB49OiuttFJOOumkmdpLKTnjjDMyePDgrL322h2oEAAAAAAAmN8JkAaIxx9/\nPMcff3z+67/+K1OnTs2iiy6aAw88MKuvvvpMfddbb70OVAgAAAAAAPQXlrDr555//vmMGjUqQ4cO\nzemnn56pU6cmSaZMmZJjjz22w9UBAAAAAAD9kRlI/dSrr76aM888M2PHjs1TTz3Vss+tt96aKVOm\nZNFFF+3j6gAAAAAAgP7MDKR+Ztq0aTnnnHMyfPjwHHbYYS3DozXWWCM/+tGPcvvttwuPAAAAAACA\nOWYGUj9Ra81PfvKTjBw5Mvfdd1/LPiuuuGJGjhyZfffdV3AEAAAAAADMNQFSP3D99dfniCOOyO9/\n//uW7UOGDMnhhx+eQw45JEOGDOnj6gAAAAAAgIFGgNQP3HDDDS3Do0UWWST7779/jjrqqKy00kod\nqAwAAAAAABiI7IHUDxxyyCHdAqJBgwblS1/6Uh588MGccsopwiMAAAAAAKCtBEjzkddff73l+SFD\nhmTEiBFJks9+9rO5++6788Mf/jBrrLFGX5YHAAAAAAAsIARI84Enn3wyhx12WDbaaKNMmzatZZ99\n9903t956ay699NKsu+66fVwhAAAAAACwIBEgddALL7yQ4447LkOHDs0pp5ySO+64I+edd17Lvosu\numg23njjPq4QAAAAAABYEAmQOmDKlCk5/fTTM2zYsIwaNSovvPDCm22jR4/OK6+80sHqAAAAAACA\nBV2/DJBKKe8opZxTSvl7KWVKKeWhUsq3SynLzeE4yzcf91BznL83x31Hb9T9+uuv57zzzsvaa6+d\ngw46KE888UTLfhMmTOiNywMAAAAAAPRIvwuQSinDktyW5ItJbk1yapJJSQ5K8rtSygo9HGeFJL9r\nPm5ic5xbm+PeVkoZ2q6aa625/PLL8573vCd77bVXHn744Zn6rLDCCjn55JPz4IMPZv3112/XpQEA\nAAAAAObYQp0uYC6cmWTlJAfWWs+YfrKUckqSQ5Icn2TfHowzNsnwJKfWWg/tMs6BSU5rXufj81Tp\nhhvm+pNPzpGbbZZbbrmlZZcll1wyhx56aA477LAss8wy83Q5AAAAAACAdii11k7X0GPNWUETkzyU\nZFit9Y0ubUOSTE5Skqxca33pLcZZMskTSd5I8rZa6wtd2gY1r7Fm8xqT5rLW25ZeeukNn3/++Zbt\nCy+8cPbbb7+MGDEiK6+88txcAgAAAAAAGMA22mij/PGPf/xjrXWjvr52f1vCbuvm8dqu4VGSNEOg\nm5IskWTT2YyzWZLFk9zUNTxqjvNGkmubX241L8W2Co9KKdlzzz3zwAMP5LTTThMeAQAAAAAA853+\ntoTd2s3jA7NofzDJx9JYmu7/zuM4aY7zlkopt82iaYPBgwfn9ddff/PEMsssk9VWWy133XVXdthh\nh9kNDQAAAAAALMDuvffepLFiWp/rbwHS9E2CnptF+/Tzy/bROG9l0OuN9OiONwd97rk899ysLgnQ\nr6zTPN7X0SoA2s/9DRjI3OOAgcr9DRjINkiyVCcu3N8CpNkpzeO8buzU43Fmte7g9JlJnViXEKC3\nuccBA5X7GzCQuccBA5X7GzCQvcUqaL2uv+2BNH36zjKzaF96hn69PQ4AAAAAAMCA098CpPubx1nt\nTbRW8zirvY3aPQ4AAAAAAMCA098CpF81jx8rpXSrvZQyJMnmSV5JcvNsxrm52W/z5uO6jjMoycdm\nuB4AAAAAAMACo18FSLXWiUmuTbJmkv1naD42yZJJzq+1vjT9ZCllnVLKOl071lpfTHJBs//oGcb5\nWnP8X9RaJ7WxfAAAAAAAgH5hoU4XMBf+T5LfJjm9lPKRJPcmeX+SrdJYcm7EDP3vbR7LDOe/kWTL\nJIeWUt6b5NYk6yb5dJLHM3NABQAAAAAAsEAotdZO1zDHSimrJzkuyceTrJBkcpLLkxxba316hr41\nSWqtMwZIKaUsn2RUks8keVuSp5JcneSYWuujvfkcAAAAAAAA5lf9MkACAAAAAACg9/SrPZAAAAAA\nAADofQIkAAAAAAAAuhEgAQAAAAAA0I0ACQAAAAAAgG4ESAAAAAAAAHQjQAIAAAAAAKAbAdIcKKW8\no5RyTinl76WUKaWUh0op3y6lLDeH4yzffNxDzXH+3hz3Hb1VO8Bbmdf7WyllyVLK50sp/11Kua+U\n8lIp5YVSyh9KKYeVUhbp7ecAMCvt+h5uhjE/VEp5vZRSSynfbGe9AD3VzvtbKWX9Usr5pZRHmmM9\nXkr5dSllj96oHWB22vg53BallCuaj3+1lPLXUsrPSykf763aAWallLJjKeWMUsqNpZTnmz9TXjiX\nY7X9Z92ZrlFrbddYA1opZViS3yZZOckVSe5LskmSrZLcn2TzWutTPRhnheY4w5Ncl+T3SdZJ8ukk\njyfZrNY6qTeeA0Ar7bi/Nb/xvjrJ00l+lWRCkuWTbJ9k1eb4H6m1vtpLTwOgpXZ9DzfDmEOS3Jlk\nxSRLJTm+1jqynXUDzE4772+llL2SnJ3k5SQ/TfJQkmWTvDvJ32utn2tz+QBvqY2fw+2X5MwkLyW5\nLMmjSd6R5LNJlkgystZ6fG88B4BWSil/SrJBkhfTuCetk+SiWusX5nCctv+s2/I6AqSeKaX8IsnH\nkhxYaz2jy/lTkhyS5Pu11n17MM73k+yT5NRa66Fdzh+Y5LQkv6i1+g0IoM+04/5WSnlvkncluaTW\n+lqX80OSXJ9kwySH11pPbv8zAJi1dn0PN8OY5yT5TJJvJTk+AiSgA9r4M+qmSX6T5O4kH6+1/mOG\n9oVrrVPbWjzAbLTp59SFkzyRZNEk76213t+lbd0ktyd5I8lytdYp7X8WADMrpWyVRnA0IcmH0/hF\n7LkJkNr+s27L6wiQZq+UMjTJxDR+C2tYrfWNLm1DkkxOUpKsXGt96S3GWTKNf7jeSPK2WusLXdoG\nNa+xZvMaZiEBva5d97fZXGO3JBcl+Wmtdft5Lhqgh3rjHldK+XSSy5PsnmShJD+KAAnoY+28v5VS\nbkjywSTr11rv7rWiAXqojZ/DrZLkH0nurLVu0KL9ziTrJ1mxHb+lDzCnSilbZi4CpL74PG86eyD1\nzNbN47Vd/zKSpBkC3ZTGtNdNZzPOZkkWT3JT1/CoOc4bSa5tfrnVPFcM0DPtur+9lem/sTptHsYA\nmBttvceVUlZOclaSy2utc7VGNUCbtOX+1tyH94NJ/pDknlLKVqWUw5t7WH6k+YuOAH2tXd/DPZ7G\nL3IPL6Ws1bWhlDI8yVpJ/iQ8Avqhvvg8L4kAqafWbh4fmEX7g83j8D4aB6Bd+uK+9KXm8Zp5GANg\nbrT7HveDNL5/nudlAADmUbvubxt36X9d889JaSzR+cskfyql/Os81AkwN9pyj6uNZZf2T+P7t9tK\nKeeVUsaVUs5PcluSe5Ls1IZ6Afpan+UMC83rAAuIZZrH52bRPv38sn00DkC79Op9qZTytSQfT/Kn\nJOfMzRgA86Bt97hSypeSfDrJLrXWx9pQG8C8aNf9beXmceckT6axqfz/TbJSklFpLNf5s1LK+l33\nuQToZW37Hq7Wekkp5e9Jfpxkjy5Nj6WxFLEtJID+qM9yBjOQ2qM0j/O6oVS7xgFol7m+L5VSPpvk\n22msOb2DzZeB+VCP7nGllDXTuJ9dUmv9n16uCaAdevo93OAuxy/XWi+rtT5fa52YZM80lrYbnmSH\n3ikTYK70+OfUUsoX0phReWOSddNY0mndNMLy7yS5uJdqBOiktuUMAqSemZ7YLTOL9qVn6Nfb4wC0\nS6/cl0opn0njG/HHk2xZa/VbXUAntOsed06SV5L8n3YUBdAG7bq/PdM8Tkny864NzaWfrmh+ucmc\nFggwD9pyj2vuc3ROGkvV7V5rva/W+kqt9b40ZljelmSn5ib2AP1Jn+UMAqSeub95nNWagdM34pvV\nmoPtHgegXdp+Xyql7JTkkjSWBPhwrfX+2TwEoLe06x63YRrLPD1RSqnT/6Sx7EmSjGieu3zeygXo\nsXb/jPrCjBswN00PmBafg9oA5lW77nEfS7Jwkl+32GT+jSQ3NL/caG6KBOigPssZ7IHUM79qHj9W\nShnU9R+dUsqQJJun8VupN89mnJub/TYvpQyptb7QZZxBafzD1vV6AL2tXfe36Y/ZLcn5Sf6WZCsz\nj4AOa9c97vw0ljuZ0VpJPpTGPm+3Jbl9nisG6Jl23d/uTGPvoxVLKau02OPt3c3jQ/NeMkCPtese\nt2jzuNIs2qeft8cb0N+09fO8t2IGUg8013++NsmaSfafofnYJEsmOb/W+tL0k6WUdUop68wwzotJ\nLmj2Hz3DOF9rjv8LH7gCfaVd97fm+T3TuMf9NcmH3MuATmvj93AH1lq/POOf/HMG0s+a577ba08G\noIs23t+mJfl+88sTm7/YOL3/+kn2SjItyf+2+SkAzFIbf069sXncsZTynq4NpZT3Jtkxjf1Brmtf\n9QDtU0pZuHl/G9b1/NzcJ+e6hsayxsxO8y/pt2ksX3JFknuTvD/JVmlMBftArfWpLv1rktRaywzj\nrNAcZ3ga/0DdmsbmfZ9OY6+QDzTfAAB9oh33t1LKVmlsTDoojTWmH2lxqWdrrd/upacB0FK7voeb\nxdh7pREiHV9rHdn24gHeQht/Rl0ijc3kN01jJuX1afxW/g5pLF13WK31lF5+OgDdtPEed06SL6Yx\ny+iyJA+n8YHrZ5IskuTbtdZDevnpALypuW/4Z5pfrppk2yST8s/Q+8la6+HNvmsm+UuSh2uta84w\nzhzdJ+e6XgFSz5VSVk9yXJKPJ1khyeQklyc5ttb69Ax9Z/nhQyll+SSj0nijvC3JU0muTnJMrfXR\n3nwOAK3M6/2ty4eob2Wmf+wA+kK7vodrMe5eESABHdTGn1GXSHJEks8leWeSV5P8PsnJtdare/M5\nAMxKO+5xpZSSZM80ZlRukGRIkufTCMzPqrVe3LvPAqC7UsroNLKBWXnz87O3CpCa7T2+T851vQIk\nAAAAAAAAurIHEgAAAAAAAN0IkAAAAAAAAOhGgAQAAAAAAEA3AiQAAAAAAAC6ESABAAAAAADQjQAJ\nAAAAAACAbgRIAAAAAAAAdCNAAgAAAAAAoBsBEgAAAAAAAN0IkAAAAAAAAOhGgAQAAAAAAEA3AiQA\nAKBPlFI+WkqppZSRna5lflFKWaj5mvxyDh93YfNx7+it2gAAgAWbAAkAAHhTM5R4qz97dbrGdiil\nfLPFc3u5lHJ/KeU7pZS3zyf1bdHJOnqqlPLlFq/nlFLKw6WUC0op67fpOkJIAADoIwt1ugAAAGC+\ndOwszv+pT6vofb9KckPzv1dKsm2S/ZPsXErZpNb6UG9evNY6rZSybpKX5vCh/5nkm0n+0f6q5snt\nSa5s/vcySbZI8oUkO5ZStqq13tyxygAAgDkiQAIAAGZSax3d6Rr6yHW11m9O/6KUsnCSa5NsmWRE\nkq/0dgG11vvm4jGTk0zuhXLm1R9nfO+UUs5OsneSMUm26URRAADAnLOEHQAAMFdKKWuXUk4opfyh\nlPJEc8myh0op35+TJeBKKcNKKWeXUiaWUl4ppTxVSrmrlPJfpZTlWvT/fCnl+lLKs6WUV0spfy6l\nfKOUssi8Pqda69QkZzW/3GSG667WrOnh5nN9vJRyaSnlfS1qXLSUcnAp5fZSyjOllJear83lpZSt\nu/SbaQ+kUsqjaYRXSXJjlyXhpnXp020PpFLKB5tf/8+snlsp5cHm67vsDOe3K6Vc3XzdpzT/Hk4s\npSzd81fuLf2wedy4RU09fg+VUi5M8v83vxwzw3J5W8zQt9feIwAAsKAwAwkAAJhbOyXZJ41l4G5K\nMjXJ+mnM2vlkKeXfmjNlZqkZEvw+yVJJfp7kf5MsnuSdSfZIclqSZ7r0P695/q/Nvs8l+UCS45Ns\nXUrZttb6+jw+r9I81i7XHZbkN0lWTfLLJP+dZI00XoNPlFL+o9Z6dZcxLmi23ZnkvCSvJnl7kg8m\n+ViS697i+qck+Uyz74/SeK5J8sasHlBrvbGUMjHJp0opy9Van+naXkr5QJJ/TfL/1Vqf7XL+uCRH\nJ3kqyVVJnkiyQRpL5G1XSvlArfWFt6i1J6a/nlNbtM3Je+gnabwGu6f70oPJP1+jvnqPAADAdWuo\n0AAAB3NJREFUgCdAAgAAZlJKGd3i9EO11nO7fH1ukpNqrVNmeOx2SX6W5BtJDpjNpXZOslySr9Va\nvzvDOEsl6Trr5stpBAOXJNmj1vpql7YxSUYm2TdJt3HmRHMJu+nL1t3SpekHaYRHR9ZaT+jS/3tJ\nrk9yfinlX2qtL5dSlk+yY/PxH6i1dgt+SikrvFUNtdZTmmN8MMk5tdbf9LD885Icl2SXJN+boW3P\nLn2m17FNGuHRb5J8stb6XJe2L6cxE+uYNMKkeTH99Wz1PM5ND99DtdaflFKeTyNA6rb04Ax19+p7\nBAAAFhQCJAAAoJVRLc79Oo0P/JMktdZHWz2w1np1KeW+JNvOwfVeaTHOizOcOijJa0m+3DUYaDo2\nydeSfD5zFg5sXUqZ/nPRikk+nmRYGjNxxiVJKWXNJFsn+UuSk2eo8cbmsnGfS2PW0H+nMXOpJJky\nY3jUfMxTc1DfnDg/jddhz3QJkEopi6UR1E1OY3+n6Q5sHr/cNTxq1nh2KeWgNF7POQmQNuwSPi6T\nRgi2UZJHW43T5vdQ0jvvEQAAWCAJkAAAgJnUWsvs+pRSShqzQfZM8p40ZhIN7tLl5R5c6ookY5J8\nr5Ty70l+kcZSZvfWWrsuITckybuTPJbk0MalZ/JqknV7cM2utmr+SRrBw1+TnJlkbK31b83z0/c4\nuqHWOi0zuy6NAOl9Sf671vpMKeXqNJaAuz2NpdduTHJLrXWmoKxdaq0Pl1KuT7JVKWV4rfWBZtOn\nkyyb5KwZlm7bLMmUJLvO4vVcKMnbSinLzBgwvYX35Z+v13QPJflgq7CoTe+h6WP11nsEAAAWSAIk\nAABgbp2exoyOvye5Jsnf0viAPkm+lGS12Q1Qa51USnl/GjOetk2yQ7Ppr6WUk2qt32l+vXzzuEpa\nz46arlXA81aObrUU2gyWaR5ntZ/T9PPLdjm3Y5Ijk+yaxrJySfJKKeWSJIfXWp+Ywzp76tw0ArE9\nk4xonptp+bqm5dOYKfVWr2fS2J+qpwHSD2utX24GQ6uksXzdcUmuLKVs3iJAm+f3UBe99R4BAIAF\nkgAJAACYY6WUtyXZP8kdSbaYcbm5UsruPR2r1npPkp2bS8ltkORjaex7c0Yp5YVa63n5Z4Dx+1rr\nJu14DnNg+rVXnUX722bol1rry2nsH3RMKWWNJB9K8sU09udZI/+c9dRul6axPNvupZSjk6ycxut5\nW/N17ur5JK/VWldudxHN2WP/SDKmuZ/TwWksIXfE9D7tfA81dfI9AgAAA86gThcAAAD0S8PSmL3y\nixYf/P9LkjXndMBa67Ra62211nFp7FOTNPYVSq312ST3J1m/lLLsrMboJbc3jx8spQxu0T49DPpj\nqwfXWv9aa70wjSDnL0m2LKUs06pvF9OXmmt1vVmqtb6U5H+TrN6s6wvNMWacfZQkNydZqZSy9pxc\nYy6MSvJUkgObYdp0c/MemuXr0uH3CAAADDgCJAAAYG481Dx2C1Wa+9D8ID38WaOUskkppdUMmFWa\nx6574JySZLEkP2wVwJRSli+lzLj/zjyrtT6U5FdpBB4HzHDNzZPskkZAckXz3CqllI1bDLVk88/U\nzH4ZtaeaxzXesldr5zaPezT/TE3y3y36ndI8nt2cDdRNKWWp5vKC86TW+nySk5Ismu5Lyz3UPM7J\ne2h2r0tH3iMAADAQWcIOAACYY7XWR0sp/5vGXj9/LKX8Mo29gj6W5MUkdyVZrwdD7ZFkn1LKr5NM\nSPJskn9Nsn0ae+Gc1uWaPyilbJRknyQfLqVcm+Svaex9MzTJB5OclcaeOu321SS/SXJqKWW7JLel\nEWLslEYYtFdz9k/SmP1zaynlz2nMSno0jdfmk2ksKXdKl76zcl2SmuSEUsoGabwub9Rax/ag1hvS\nmOm0a5KFk1xWa31qxk611mtLKSOTjEnyYCnl6ubjlkpj9s+H0wjOPtmDa87Od5IcmmTPUsoJtdYH\n5vI99Oc09pz6fCnl9SSPpPE6nVdrfaTD7xEAABhQBEgAAMDc2iuN0GenNPayeTzJlUmOTnJVD8e4\nKI2fSz6QZKMkiyf5WxozZr5Va/1z18611q+WUn6WRqCzTZJl05iV8nCSE5NcOE/PaBZqrQ82g4mR\nSf49jeXhnk/ysyRja61/6NJ9YpLRSbZMsnWSFZM8neS+JP+Z5H96cL27SylfTCN02T+NWTWvJ5lt\ngFRrraWU8/PP2T6tlq+b3vf4UsqNSQ5MsnmST6exl9CjSb6Xxt/PPKu1vlRKGZ/GDKExaczaSubw\nPVRrnVZK+Y8k45N8LsmQZtP1aYRJHXuPAADAQFMae5sCAAAAAABAgz2QAAAAAAAA6EaABAAAAAAA\nQDcCJAAAAAAAALoRIAEAAAAAANCNAAkAAAAAAIBuBEgAAAAAAAB0I0ACAAAAAACgGwESAAAAAAAA\n3QiQAAAAAAAA6EaABAAAAAAAQDcCJAAAAAAAALoRIAEAAAAAANCNAAkAAAAAAIBuBEgAAAAAAAB0\nI0ACAAAAAACgGwESAAAAAAAA3QiQAAAAAAAA6Ob/AUfqf0Wh2pfNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fb715f8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr = roc_svm\n",
    "plt.figure(figsize=(14,8))\n",
    "plt.plot(fpr, tpr, color=\"red\")\n",
    "plt.plot([0, 1], [0, 1], color='black', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Roc curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "train_sizes, train_scores, test_scores = \\\n",
    "    learning_curve(grid_svm.best_estimator_, X_train, y_train, cv=5, n_jobs=1, \n",
    "                   scoring=\"roc_auc\", train_sizes=np.linspace(.1, 1.0, 10), random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_learning_curve(X, y, train_sizes, train_scores, test_scores, title='', ylim=None, figsize=(14,8)):\n",
    "\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABpAAAALoCAYAAAB/HbZ+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJktTCBJIkSZIkSZIkSZJa/AOdR66KFUZjEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2023d438>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 372,
       "width": 840
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(X_train, y_train, train_sizes, \n",
    "                    train_scores, test_scores, ylim=(0.7, 1.01), figsize=(14,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}