Simi_ML_Guassian,Random Forest,Decision Tree_breast cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2851e3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Naive Bayes Gussian\n",
    "#Breast Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed65eede",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "data=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd7ae012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names=data['target_names']\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "078f002b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=data['target']\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9323610a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=data['feature_names']\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8372229f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
       "        1.189e-01],\n",
       "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
       "        8.902e-02],\n",
       "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
       "        8.758e-02],\n",
       "       ...,\n",
       "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
       "        7.820e-02],\n",
       "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
       "        1.240e-01],\n",
       "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "        7.039e-02]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features=data['data']\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ac1309c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 30)\n",
      "*********\n",
      "(569,)\n"
     ]
    }
   ],
   "source": [
    "print(features.shape)\n",
    "print('*********')\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fa87cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(features,labels,test_size=.33)\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d0f6811",
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "869b9d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7111c946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.254e+01, 1.632e+01, 8.125e+01, 4.763e+02, 1.158e-01, 1.085e-01,\n",
       "       5.928e-02, 3.279e-02, 1.943e-01, 6.612e-02, 2.577e-01, 1.095e+00,\n",
       "       1.566e+00, 1.849e+01, 9.702e-03, 1.567e-02, 2.575e-02, 1.161e-02,\n",
       "       2.801e-02, 2.480e-03, 1.357e+01, 2.140e+01, 8.667e+01, 5.520e+02,\n",
       "       1.580e-01, 1.751e-01, 1.889e-01, 8.411e-02, 3.155e-01, 7.538e-02])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f91dba91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "429f8329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign\n"
     ]
    }
   ],
   "source": [
    "p=gaussian.predict([[9.567e+00, 1.591e+01, 6.021e+01, 2.796e+02, 8.464e-02, 4.087e-02,\n",
    "       1.652e-02, 1.667e-02, 1.551e-01, 6.403e-02, 2.152e-01, 8.301e-01,\n",
    "       1.215e+00, 1.264e+01, 1.164e-02, 1.040e-02, 1.186e-02, 9.623e-03,\n",
    "       2.383e-02, 3.540e-03, 1.051e+01, 1.916e+01, 6.574e+01, 3.359e+02,\n",
    "       1.504e-01, 9.515e-02, 7.161e-02, 7.222e-02, 2.757e-01, 8.178e-02]])\n",
    "print('Malegnant'if p[0]==False else 'Benign')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8dbca323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes score:  0.9627659574468085\n"
     ]
    }
   ],
   "source": [
    "print(\"Naive Bayes score: \",gaussian.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "762774c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9627659574468085\n"
     ]
    }
   ],
   "source": [
    "y_pred=gaussian.predict(xtest)\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(accuracy_score(ytest,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112e2daa",
   "metadata": {},
   "source": [
    "# Decision tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a7c0d76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a1d32db",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=DecisionTreeClassifier (criterion='entropy', max_depth=30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "454b948d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=30, random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c0fdc047",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_en = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2829aef0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with criterion entropy: 0.9149\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Model accuracy score with criterion entropy: {0:0.4f}'. format(accuracy_score(ytest, y_pred_en)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a3704ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       1, 1, 0, 0, 0, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train_en = model.predict(xtrain)\n",
    "\n",
    "y_pred_train_en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b107a6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(ytrain, y_pred_train_en)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e0c27dfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.9149\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(model.score(xtrain, ytrain)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(model.score(xtest, ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5a69c9d",
   "metadata": {},
   "outputs": [
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       " Text(0.7272727272727273, 0.2777777777777778, 'entropy = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(0.7272727272727273, 0.5, 'entropy = 0.0\\nsamples = 6\\nvalue = [6, 0]'),\n",
       " Text(0.8181818181818182, 0.6111111111111112, 'entropy = 0.0\\nsamples = 17\\nvalue = [17, 0]'),\n",
       " Text(0.9090909090909091, 0.7222222222222222, 'entropy = 0.0\\nsamples = 109\\nvalue = [109, 0]')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/DScnJ/j6+gqrYjHGKhbPs8oYY7VcdZhntbSVq2oqnmeVMeVwn1XGGGOMMVZtccsqY4zVctWhZbU24pZVxpTDLauMMcbeWUJCApycnKrseoVzthYOdPLy8kKrVq2KnY7q6dOn0NPTE/qZZmdnY8iQIbC1tUWPHj1w4sSJUq+1YcMG9OjRA3Z2dvD09IRMJgMAfP/997Czs4ONjQ1GjhyJnJwcAAWzCUgkEvTo0QMzZ84UzuPt7Q1dXV252REYY8rjyipjjDG1EhoaKixzunz5coXBWIWWLFkCsVgsPD5+/DgaNGiA6Oho/P7775g9e3ap13FycsKFCxdw+vRpfPzxx0Kl99tvv8Xp06dx9uxZEBGOHz8OAFi9ejWioqJw6dIlXLx4EbGxsQCALVu2oEuXLu/7tBmrtbiyyhhjTM6MGTPw+++/AwDy8vJgZmaG7OxszJkzB1KpFN26dcOGDRsUjhs1ahTOnDkDAIiMjBRaO2/evAlnZ2c4ODhg8ODBePXqVYWVtaQBWffv38fTp0+F+V0BwMjICDKZDESEzMxMuRWyimNiYgINjYKvSS0tLWhqagL4d7Wr/Px85ObmChXnwu3Z2dmoX78+Pvroo/d7cowxAFxZZYwx9pbRo0dj+/btAICwsDA4ODigXr16mDdvHiIjI3H+/Hn8+OOPws/fZZk4cSICAgIQHh4OqVSKTZs2ye3Pzs5WWGVKKpXC19f3nZ/D/Pnz4efnJ7etXbt2ePXqFTp06AAnJyeF/SWJjY3FkSNHMHjwYGGbr68vPvnkEzx//hwff/yxsN3b2xvt2rWDvr4+mjRp8s7lZ4z9i+dZZYwxJqdjx45IS0tDWloatm/fjlmzZgEANm7ciH379kFTU1PYX1RJk/3HxsZixIgRAACZTKYwH2m9evUQGRlZYeU/f/48mjVrBiMjI7nt27ZtQ+vWrbFv3z4kJCTA3d0d169fL/VcCQkJGDVqFEJDQ9GgQQNh+5IlS7B48WJMmjQJ27Ztw8SJEwEU/OSfk5ODgQMHIiwsDL17966w58VYbcWVVcYYYwqGDx+OdevW4cGDBzA3N0dGRga2bt2KmJgY5OTkoH379nh7BoGmTZsiMTERAHD58mVhu5mZGXbu3Al9fX0A/64+VSg7OxsuLi4KZRCLxViyZEm5y3758mXcuHEDrq6uiIuLQ4MGDYSKa4sWLQAUrFKVlZUFAMjNzUVSUhLatGkjd57k5GR4eHhg69ataNeunbC9cLUrkUiEJk2aoH79+nLb69ati4YNGwrbGWPvhyurjDHGFAwbNgwGBgZYtGgRAEBXVxdmZmYQi8UwNTVFs2bNFI7x9vbGsGHD8Msvv6Bt27bC9vXr12P06NHIzc0FAMycOROurq7C/vdpWZ0zZw6OHz+OlJQUODk5ITQ0FFOnTsXUqVMBAAsWLICxsTGsrKzw6aefwtPTExKJBK9evcL3338PAHjw4AHc3d1x8+ZNhXOnpaUJ5xo2bBjGjRuHiRMnIj4+Hnl5eTAxMRFeo0GDBuHly5fIzs6GnZ0dr2jFWAXheVYZY6yWEolE+gBGA1isLt8Fn3/+OZKSkhAYGCgMbHpfO3fuRG5uLkaOHFkh53ubt7c3zpw5g8jISHz44YfC9v/vNjEEwH4iyi7peMZqO66sMsZYLSISiTQB9AIwFoAtgN8ATODvgqr3/5XVSACmALYD2EJEd1VZJsaqI54NgDHGagGRSGQgEokWAEgA4AvgAAADIvpKleWq7YjIHgV/NABAtEgkihCJRJ4ikUhbleVirDrhllXGGKuhRCJRXQB9UNCKagngFwCbiejGW/l4uVUVeHu5VZFIVA9AfwDjAHQBEISC+/WXakrIWPXAlVXGGKthRCJRWwBjAHwJIB7AZgChRFTsbPxcWVWNtyurb+0zQsE9HA0gDsAmALuJ6HUVFpGxaoErq4wxVgP8f6tcPxS0opqjoFVuCxHFKnEsV1ZVoLTKapE8dQG4oaC1tQeAnShobY2pgiIyVi1wZZUxxtSYSCQyBuANYBSA2yhoRf2diN4oew4dHZ2UN2/etKycErKSaGtrp75+/frDsnMWEIlEbfBvi/kjFLS27iKil5VURMaqBa6sMsbKjSs3Fae8FRYAEIlEWgDcUdCK2gn/jiS/UwlFZNWMSCSqg4IZHcYBsAEQAmATEf3xVr4Sm8w5hivXu8Q1KxlXVhlj5cY/G1ccZX4KLpK3PQoqqCMA3EBBy9p+IpJVYhFZNSYSiVqjoKXVG0AqCt4TIUT0QiQSrQOQQEQrizmOY7gSlSeuWdm4ssoYKzf+oqs4xX2piUSi+QAyichfJBLpABiEgkpqewCBKGhFvV/1pWXV1f/Pn+uCgtZWKYBQAPsAbAAwj4iC38rPMVyJuLJasbiyyhgrN/6iqzjFTF/0FYBvUNA30QPAcABXUNBidpCIclRSUKY2RCJRKxTMIjAWwBsA+gC+IKL9RfJwDFcirqxWLF4UgDFWIeLi4mBubo7s7IJVI1esWAE/Pz8AgI6ODtzc3AAAZ8+ehZWVFezs7ODg4ID4+HgAwKZNmyCVSiGVStGhQwcMGjQIALB792506NABS5YsqbSyP3r0CE5OTrC1tYWPj0+xeWbOnAkDAwM4OTnJbX/48CEGDBgABwcH9OrVS9hub2+P5s2bl6vcIpFoCIClAF6goB/icwCfEZErEe3hiipTUhqANgDuAqgLQAcFK5UprbbGc9u2bYVyL1q0SNj+LvHMKhARceLEiVO5UsFHh6Jly5bRggULKD4+nrp160Zv3rwhIiIjIyMhz+PHjykrK4uIiA4fPkxeXl4K5xk7diz99ttvwuPAwEBavHhxsdd824sXL5TKV5SnpyedOnVK+H94eLhCnqSkJLp//z45OjrKbe/duzc9fvxYIX9iYqJS5f7/1xIAGgPIB/AawGUA2wB8TdXgfnNSrwRABGAAAFcAXVHQslrvrTxUltoYz0WfW1HKxnOhwrjmVDGJW1YZYxXGx8cHR44cgaenJ9asWQMtLS2FPPr6+mjQoAEAQEtLC5qamnL7ZTIZjh8/jn79+il93VevXiEkJAQDBgzA+PHjy13ua9euwcHBAQAwYMAAREREKOT56KOPoKEh/5GZmJiIV69eYcaMGZBIJAgICBD2ffzxx+UqAxE9B2AMQAJgIYDTKGghY6xc/r++tI+IwojoOhElEVF2ec9T2+IZKGjAs7e3R8+ePXHt2jVhe3njmVWsOqouAGOs5qhTpw4kEgn2798PsVhcat6srCzMmzcPP//8s9z2AwcOwNnZGdraZS+NfuLECQQFBSE1NRV9+vTBxo0b8eGHBbPFJCYmYsSIEQrHeHh4YPLkyXLb8vPzhf/r6enh6dOnZV4bAJKSknDlyhXExsbigw8+gFQqhZ2dHUxMTJQ6/m1EFI+CFacYU7naFs8AcPHiRTRv3hw3b97EkCFDcOvWLaWPZZWHK6uMsQrz119/ISoqCj179sSmTZtKbBWRyWTw8PDAvHnz0LlzZ7l9O3bswMyZM5W6XnBwMO7cuYPJkydjwIABaNSokbDPwMAAkZGRSp2naAtLZmYmmjVrptRxTZs2hZmZGQwMDAAAUqkUMTEx71xZZaw6qW3xDADNmzcHAHTs2BG6urp48uQJWrRoofTxrHJwZZUxViGICBMmTMDatWthamoKa2tr9OvXD61atZLLl5ubi6FDh8LT0xN9+/aV2/fkyRPcuXOnzFacQtu3b0d6ejpCQ0Ph4eGBxo0bw8vLC/379y9XS0zXrl0RFRUFiUSCAwcOYPTo0Upd39jYGDKZDM+ePUPjxo1x5coVDB8+XKljGavOamM8y2QyEBG0tbWRnJyMJ0+elKuiyyqRqjvNcuLESf0SihmcsW7dOpo6darw+ODBgzR48GAikh+0EBgYSI0aNSKJREISiYS++uorYd9PP/1E8+fPVzi3sgMbEhMTKSAgoMx8b0tISCAHBwcSi8U0bdo0ys/PJyKir7/+mq5fv05ERGvWrCEbGxtq2rQpOTo60q1bt4iIKDw8nMRiMVlaWsqVcfTo0WRqakpGRkbUq1evEq8NHojBSQWpuBguqjbGc2JiInXt2pXEYjFZWVnRiRMnhHMqG8+FOK4rNvE8q4yxcivvHI0mJiZo3749Dh06VO5r7d69G8uXL8eoUaMUWlBqAp6PkanC+8yzyvFcNo7risWVVcZYufGE4hWHv9SYKnAMVy6O64rFU1cxxhhjjLFqiyurjLEqt2/fPiQmJqq6GAqCg4NhZWUFKyurYudmLGm1nkLh4eEQiUR4/PgxACAqKgqdOnWCtra2sI2xmkRdY7nQnTt3ULduXZw5cwZAyStvPXnyBH369IG9vT08PDzw/PnzKnke7P+putMsJ06c1C9BidVvSjNy5EiKjo5W2J6bm/te530fGRkZ1LlzZ3r9+jU9efKEPv30U8rLy5PLU9pqPfn5+dS7d2/67LPP6NGjR8I5X7x4QRKJRNj2NvBADE4qSO8bw4XUNZYLff755+Tk5FTscyi68ta0adMoODiYiIiCg4PJ19e31DJwXFds4pZVxliFmDt3LiQSCaysrLB//34AwIIFCzB27Fj0798fnTp1wrlz53Dr1i2EhYVhypQpwvribdq0waRJk+Di4oInT57Azc0NEolErvVSKpXCx8cHLi4ucHR0RFpaGo4ePYpJkyYJZRg4cCD++OOPdyr/xYsXIZFIoK2tjebNm0NfXx8JCQlyeUpbrScoKAh9+/YV9gOArq4uGjZs+E7lYUxVakMsA8Dp06dhaGgIfX19hX1vr7x1584dWFhYAAB69OiB8PDwdyobezdcWWWMvbewsDCkp6cjKioKERER8PPzE1aR0dHRwf79++Hv74+ffvoJpqamcHV1hb+/vzCa+O+//8a3336LU6dOYenSpfDw8EBUVBTmzJmD2bNnC9fp2rUrjh8/juHDh2PlypXo2bMnzp07h9evXyM1NRUpKSno0qWLXNmK/qxXNMXFxcnle/r0KfT09ITHpa18U7hazzfffAMAeP36NXbs2AFvb+/3fi0ZU6XaEstEhKVLl2LWrFnFvg5vr7zVuXNn4TkePny4XKtisffHiwIwxt5bTEwMoqKiIJVKARS0Sjx58gQA0L17dwCAoaFhiR/wrVu3hqGhIQDg9u3bQguLWCzGjBkzhHyWlpYAAGtra+zduxcaGhoYMmQIQkNDkZycXOzk3+PGjcO4cePKfA7NmjVDRkaG8LiklW+KW61nzZo1mDBhAurU4Y9Upt5qSyyHhITAyckJurq6xZ7j7ZW35syZg6lTp8LBwQHW1tbFtsayysOfrIyx92ZmZgZnZ2f89NNPAIDs7GzUq1cPQMEULoUKunIB9erVQ25urrC96M/pHTp0QHR0NIyNjXHmzBm0b99e2Hfx4kUYGRnhwoULwvYvv/wSnp6eePbsGaKiohTKtmnTJvzyyy8K27ds2QJjY2PhsYWFBebMmQOZTIaXL18iKSlJ+NItVNJqPbGxsYiKisKWLVtw48YNfPHFFzh06JBclwDG1EFtieU//vgDV65cwcmTJxETE4Pbt2/jl19+Qbt27Ypdeatx48bYtm0bAGD9+vX4/PPPS38hWcVSdadZTpw4qV9CMYMzfH19ydbWlqRSKXl6ehIRkZ+fHwUFBRER0YMHD8jR0ZGIiA4cOEBisZhGjBhBRPIr4qSmppKrqyvZ2dmRVCqle/fuERGRRCKhb775hpycnMje3p5SUlKEYwYPHkzjxo1TKFN5bd++nSwtLcnS0pJOnjxJRETJyck0dOhQIip9tZ5CRQdT3bp1ixwdHUlXV5fEYjGtWbNGIT94IAYnFaTiYrhQbYjlot4eJFbcylunTp0iiURCTk5ONGvWrDIHkHFcV2ziRQEYY+WmignFpVIpgoOD0bp1a4V9n3/+OWbNmoWuXbtWaZkqAk8ezlRBlYsC1NRYLorjumLxACvGmNrKyclBnz59oK2trfZfbozVZhzLrDTcssoYKzdeqrHicAsMUwWO4crFcV2xuGWVMaaWig6oqEwbNmxAjx49YGdnB09PT8hkMgDA/PnzYWFhARsbG0ydOrWwHyBev36NCRMmwMnJCVKpFHfu3KmScjKmrqoqlktaUW7BggX4z3/+I0yFlZ2dDQAYNWoUunbtCqlUioEDB1ZJGVnxuLLKGGOlcHJywoULF3D69Gl8/PHHCA4OBgB4eXnh4sWLOHv2LFJSUoRJwhctWoR+/frh5MmTiIyMlBsBzRhTnc6dO+P8+fPCtFlFzZs3D5GRkYiMjBRmPwAAf39/REZGYs+ePVVZVPYWrqwyxipUQkICbGxsYG9vD6lUioyMDJw+fRp2dnawtbVF37598fr1awAFAy3mzJmDnj17wsXFBbt27ULPnj3RpUsX3L17V8jz9mo3ReXk5GDs2LFwcHCAWCzG+fPnAQBLlixBjx494ODggNWrV7/z8zExMYGGRsFHZdFVq4pWQotuP3HiBCIiIiCVSjF9+nS5aX0YUyc1LZZLW1FuxYoVEIvF+PHHH+W2T58+Hba2tsVOmcWqkKqnI+DEiZP6JZQy7U1AQAD5+fkJj/Pz8ykrK0t4PHPmTNq2bRsRFUxhs3fvXiIiGjNmDE2bNo2IiIKCgmjWrFlCnsI1uQMCAsjHx4eI/p0iZ8OGDbRs2TIiIkpLSyNLS0siIurQoYNw3eLWBXd3dxemoCpMY8aMKfF53bx5k7p16yb3XIgKprSRSqXCNerVq0d79uwhIqLJkydTQEBAieckIp7ihpNKUmkxXKimxnLR6eWIiP755x/Kz8+n169fk4uLC4WHhxMR0ZMnT4iIKCMjg7p160Z3794t8zUrxHFdsYkXBWCMVaghQ4Zg6dKlGD58ONq0aYMFCxYgNjYWvr6+kMlkSE1NRePGjYX85ubmAApWvjEyMhL+X3RS8LdXuykqJiYG586dQ1hYGICC1WoAYO3atZg0aRJyc3MxYcIEuQm+AZTrZ72EhASMGjUKoaGhchP9X7t2DfPmzcOhQ4eE1temTZuiV69eAIA+ffrgyJEjSl+HseqkJsZycQpXt9LW1sagQYNw5coV2Nvbo3nz5gAKWmR79uyJP/74AyYmJu91LfZuuLLKGKtQGhoaWLp0KYCCFWmOHTuGLVu2YOHChbCyssLMmTMLW3YAyK+KU9wKOUDxq90UMjMzg5GREaZPnw4AwuAIKysrODo6IjExEe7u7rh69arccQMHDkR6errcNmNjY2zZskVuW3JyMjw8PLB161a0a9dO2H7r1i2MHTsW+/fvl1vK0dHREVeuXIFYLMbFixfxySefKPGqMVb91LRYLklmZiZ0dXVBRIiIiICnp6fc9pycHJw5cwZDhw5V6nys4nFllTFWoQ4dOgR/f39oampCR0cHYrEYWVlZGDNmDDp06IDGjRvLtcYo48qVKwgMDEReXh5+/fVXuX1jx47F5MmTYW9vDwDo2rUrVq9eDXd3d7x58wYymQxTpkxROKeyrTFz5sxBWloapk6dCgAYNmwYxo0bh4kTJ+LFixfw8vICUNC3rV+/fli+fDm8vb3x5s0bfPDBB9ixY0e5nitj1UVNi+W//voLU6ZMwZ9//glPT094eHjg66+/xrRp03Dnzh0QEWxtbdGvXz8AwNChQ5GVlYWcnBx4enqic+fO5XqurOLwPKuMsXKryjkaS1vtpibg+RiZKlS3VehqGo7risWzATDGGGOMsWqLW1YZY+XGq99UHG6BYarAMVy5OK4rFresMsYYY4yxaosrq4yxKpWQkAAnJ6cqu55UKoWVlRXi4uIAAAEBAbCwsICtrS127dpV6rFnz56FlZUV7Ozs4ODggPj4eADA7du30a1bNzRs2BBnzpwR8i9atEhYstHQ0BAzZswAUDD1jqGhobD6FWPqTNUx7OXlhVatWsHb21vIk5qaCmtra0ilUlhYWODUqVMACmYi+Oqrr2BrawsHBwfExsaWeq2SlmTNy8vDnDlzhGWUC6fj8vX1xYcffij3OcAqgaoneuXEiZP6JSgxoXhJHjx4QI6Oju98fHkVnQA8LS2NunTpQjKZjGQyGfXo0YMyMzNLPPbx48fCZOSHDx8mLy8vIiLKysqi9PR0GjlyJEVHRxd7rLOzM128eFF47OfnR0FBQQr5wJOHc1JBUtcYJiJ69OgRRUREyE38n5ubS7m5uUREdP/+ffrss8+IiGjfvn3k7e0tlNve3r7Ua2VkZNCLFy8Urrlx40Zat25dsccU9znAcV2xiVtWGWPvbcaMGfj9998BFLRAmJmZITs7G3PmzIFUKkW3bt2wYcMGheNGjRoltEhERkYKLSU3b96Es7MzHBwcMHjwYLx69apCyvngwQOYmZmhXr16qFevHoyNjXHp0qUS8+vr6wuLABRdUrVBgwbQ09Mr8bi///4bSUlJ6NGjR4WUm7HKpi4xDKDY2QQ0NTWF+MzMzMSnn34KALhz5w4sLCwAAIaGhrh7964wf2txSlqS9bfffkNKSgocHBzw5Zdf4sWLFxXxVJiSuLLKGHtvo0ePxvbt2wEAYWFhcHBwQL169TBv3jxERkbi/Pnz+PHHH5GTk6PU+SZOnIiAgACEh4dDKpVi06ZNcvuzs7OFn9uLJl9f31LPa2xsjOvXryMzMxOZmZk4d+4cnj59WmZ5srKyMG/ePHzzzTdKlX/nzp0YNmyYUnkZqw7UJYZLExcXB7FYDFdXV7i7uwMAOnfujKNHjyI/Px9Xr15FcnKywgICykhKSoKenh7Cw8NhamqKlStXvnM5WfnxogCMsffWsWNHpKWlIS0tDdu3b8esWbMAABs3bsS+ffugqakp7C+qpFVuYmNjMWLECACATCaDVCqVO65evXqIjIwsdzmbNm2KpUuXYsCAAWjSpAm6dOkCfX39Uo+RyWTw8PDAvHnzlJ4UPDg4GAcOHCh3+RhTFXWJ4dIYGxvjzJkziI+Ph4ODA9zc3NCzZ09cunQJ9vb2MDExQadOndCiRYtyn7voMspubm6YOXNmhZadlY4rq4yxCjF8+HCsW7cODx48gLm5OTIyMrB161bExMQgJycH7du3l/syAwq+ABITEwEAly9fFrabmZlh586dQkXy7Z/tsrOz4eLiolAGsViMJUuWlFrO/v37o3///nj27BkGDx4srFX+8OFD6Ovro06dfz8Wc3NzMXToUHh6eqJv375KvQ5//PEH9PT00KZNG6XyM1ZdqEsMF0cmk0FLSwsA0LhxY7mf8ufPn4/58+fjzz//xI8//ghNTU3k5uYiKSlJ6TgtXEa5Q4cOvIyyCnBllTFWIYYNGwYDAwMsWrQIQEHfLzMzM4jFYpiamqJZs2YKx3h7e2PYsGH45Zdf0LZtW2H7+vXrMXr0aOTm5gIAZs6cCVdXV2H/+7TKjBgxAo8fP0a9evWwYsUK1K1bFwBga2uLy5cvo2XLlkLe4OBghIeHIzMzE4GBgTA1NcX69euRkZGBwYMH49atW4iNjYWTkxOWLVsGAAgKChJalBhTJ+oSw3PmzMHx48eRkpICJycnhIaGIjY2FnPnzoWmpiZycnLwv//9DwCQkZEBd3d3aGpqomXLlvD39wdQ0H/d3d0dN2/elDt3SUuy+vj4YMyYMQgICICOjo7QZYJVDV4UgDFWbuo0ofjnn3+OpKQkBAYGwtjYuNg8SUlJ8PX1RWBgYKWUYe3atdiyZQsWLFiAAQMGyO3jycOZKtS0GC6vnTt3Ijc3FyNHjnyv8/j6+mLfvn3Ytm0bPvvsM2E7x3XF4soqY6zc1OmLrrrjLzWmChzDlYvjumLxbACMMcYYY6za4soqY4wxxhirtriyyhhjjDHGqi2eDYAxVm7a2tqpIpGoZdk5WVm0tbVTVV0GVvtwDFcujuuKxZVVxt6Tjo5Oyps3b/hDvwJoa2unvn79+kNVl+N9iEQiLQD/A2AHIBbATSJaqNpSsbJwHFcOdY9pkUhkA2AXgBAAIwG0I6Lnqi1V7cOzATD2nnhUbcWpCSNoRSKRGEAfAI0BDAMgAqBPRC9VWjBWKo7jyqHOMS0qWJ7rewBZAIYCaAFgPRGVf9UC9l64zypjjFWsxwDeADD8/8dNAHDLKmPq6SaAwtUOPgQwT4VlqbW4ZZWx98QtMhVHnVthivP/LTNmAFKI6B9Vl4eVjOO4ctSkmBaJRA0A/IeIrqi6LLUNV1YZe0/8JVdxatIXG1MvHMeVg2OaVQTuBsBYJYiLi4O5uTmys7MBACtWrICfnx8AQEdHB25ubgCA9PR0WFhYQFdXF8HBwcLxGRkZcHFxgUQigbW1Na5fvw4A2L17Nzp06IAlSyqvy9SjR4/g5OQEW1tb+Pj4FJvnxo0bEIvFsLa2xqpVq4TtXl5eaNWqFby9vYVtly5dglQqhVQqhaWlZbHri6szHR2dFJFIRJwUk46OToqq78+7UjaGz549CysrK9jZ2cHBwQHx8fEAgD179sDCwgISiQR9+vTB06dPAVRNDK9ZswYmJiYKS5P27t0bNjY2sLCwkFvb/tSpU7CysoK1tTV+/fVXhfOlpqbC2toaUqkUFhYWOHXqFAAgISEBenp6QnwfOHCg0p5TReO4VbMYJyJOnDi9RyoII0XLli2jBQsWUHx8PHXr1o3evHlDRERGRkZCnuzsbEpJSSE/Pz8KCgoStvv7+9OCBQuIiCg6OpoGDhwo7AsMDKTFixcXe823vXjxQql8RXl6etKpU6eE/4eHhyvkEYvFdPfuXcrLyyNbW1uKi4sjIqJHjx5RREQEjRkzpthz79y5k7766qsSr/3/r6XK72l5Ukn3n6nX/SzuPioTw48fP6asrCwiIjp8+DB5eXkREVF8fDxlZ2cTEdG6devI19dXOKayYzg5OZlycnLkyklEdOfOHSIiev36NRkZGdHr168pNzeXPv30U/rnn3/o1atX1LlzZ3r+/Lnccbm5uZSbm0tERPfv36fPPvuMiIgePHhAjo6OpZalur4HOG4rRlXdX25ZZayS+Pj44MiRI/D09MSaNWugpaWlkKdu3bpo2VJxtpz//Oc/eP68YHaU9PR0fPDBB0pf99WrVwgJCcGAAQMwfvz4cpf72rVrcHBwAAAMGDAAERERcvtlMhmeP38OExMTaGhowM3NDVFRUQCA1q1bl3ruHTt2YMSIEeUuE2OqoEwM6+vro0GDBgAALS0taGpqAgDatm2LunXrKmxXxvvG8Icffog6dRRnpvzkk08AAPXq1YOGhgZEIhHi4uJgaGiIZs2aQUdHB9bW1rh8+bLccZqamkL5MzMz8emnnwr7/vzzT9jZ2cHLywtPnjwpd1kZUwbPs8pYJalTpw4kEgn2798PsVhcrmO7deuG+fPno2PHjsjMzMTp06fLPObEiRMICgpCamoq+vTpg40bN+LDDwumN0xMTCy2kujh4YHJkyfLbcvPzxf+r6enJ/x8Wejp06fQ09MrNU9xUlJS8PDhQ1haWpaZl7HqoDwxnJWVhXnz5uHnn3+W256cnAx/f38cOXKkzOtVVAyXZcmSJfDw8ICWlpbS8RwXF4dRo0bh7t272Lp1KwCgVatWiI+PR6NGjbB161b4+PjIdS9grKJwyypjleSvv/5CVFQUevbsiU2bNpXr2BUrVmDgwIG4efMmdu/ejUmTJpV5THBwMO7cuQMvLy+MHj1a+JIDAAMDA0RGRiqk4r7kNDT+/VjIzMxU6GParFkzZGZmlpqnODt37sSwYcPKzFfb7Nu3D4mJiaouhoLg4GBYWVnByspKoXW90MqVK2FjYwNbW1vExMRUcQkrn7IxLJPJ4OHhgXnz5qFz587C9oyMDAwaNAibN2/GRx99VOb1KiqGS7Nlyxbcvn1b6DPbrFkzZGRkCPtLimdjY2OcOXMGFy5cEK6ppaWFRo0aAQCGDx+Oq1evlqssNQ3HcuXhllXGKgERYcKECVi7di1MTU1hbW2Nfv36oVWrVkqfo0WLFsK/RSuHJdm+fTvS09MRGhoKDw8PNG7cGF5eXujfv3+5WmW6du2KqKgoSCQSHDhwAKNHj5bbX/gFFR8fj7Zt2+LIkSPYvHlzmeULDg7Gnj17ysxX2+zbtw/NmzeHgYGB3Pa8vLxy/XRckTIzM/HDDz/gwoULyMrKgqOjI65fvy73h8zdu3dx5MgRnDlzBnfv3sWECRNK/CJUR8rGcG5uLoYOHQpPT0/07dtX2J6VlYV+/fph0aJF6N69u1LXrKgYLklISAj27NmD/fv3C/fS2NgYCQkJyMjIQP369XHu3DksW7ZM7jiZTCZ0gWjcuDEaNmwIAHj27BmaNGkCAIiIiBC6GdRWHMuVqCo6xnLiVJMTiumov27dOpo6darw+ODBgzR48GAiIoVBD7169aK2bduSmZkZjR49moiIkpKSyMHBgSQSCfXo0YMiIiKE/MoOzkhMTKSAgIAy870tISGBHBwcSCwW07Rp0yg/P5+IiL7++mu6fv06ERFdu3aNbGxsyMrKilasWCEcO3v2bOrWrRt99NFH5OjoSOnp6UREdOPGDbK1tS3z2qimgzFKS8Xd/zlz5pCdnR1ZWlrSvn37iIjIz8+PvL29qV+/ftSxY0c6e/YsxcbGUsuWLalLly7Up08fIiIyMDCgiRMnkoODA6WlpVGfPn3Izs6O7O3t6f79+0REJJFIaMaMGeTs7EwODg6UmppKR44coYkTJwplcHd3F+5XeYWFhcm9f3v16iVcu9DGjRtp9erVwmMzMzPKycmRy6NO9/Pt+6hsDAcGBlKjRo1IIpGQRCIRBhD6+flRixYthO0LFy6UO6YyY/jXX38lR0dH0tHRIUdHR4qIiCCZTEZ169al7t27C2V6+PAhEREdP36cLC0tydLSkoKDg4XzSCQSIioY5Glra0tSqZRsbGzo5MmTRES0d+9e6tq1K9nZ2ZGzszM9ePBAoSzV9T1QXNwWh2O5dFV1f1X+huHESd2Tsh96hYyNjYUPs/IKDQ0lc3Nz8vf3f6fjq7vq+sVWWnr7/h89epTGjx9PRAWjrjt37kx5eXnk5+dHU6ZMISKiiIgIGjp0KBERjRw5kqKjo4Xj69SpI3zpT5s2jQIDA4mooEJRWFmSSCRCpSIgIIB8fHwoLy+PunTpQq9evaKUlBSysrJSeH1//vlnoaJSNN27d08u386dO8nPz094PGzYMLp06ZJcnu+//14oGxGRtbU1paWlyeVRp/tZnjjmGFZedX0PKHO/OZbLVlX3l7sBMFbF7t27987Henh4wMPDowJLwypaTEwMoqKiIJVKART8hFo4Srrw52BDQ8MSB6W1bt0ahoaGAIDbt28L/ZXFYjFmzJgh5CscqGZtbY29e/dCQ0MDQ4YMQWhoKJKTkxW6bwDAuHHjMG7cuDKfgzL9GN/O8/z5c7mBOjUZx3DtwLFcffAAK8ZUTF075Ze0oEFkZCRatWolTBR+6dIlAMC2bdvQtm1bYXt1fM4VwczMDM7OzsIAmD///FOYnkwk+nchn4JGiYJphHJzc4XtRfu2dejQAdHR0QCAM2fOoH379sK+ixcvAgAuXLggbP/yyy+xbds27Nq1C56engpl27Rpk/D6F01xcXFy+SwsLBAdHQ2ZTIb09HQkJSUJX7qFpFIpDh8+DCLCvXv30KxZs2KnS6pN1DWWiQhfffUVbG1t4eDggNjYWABAWFgYbGxsIJVK4eDggEePHgEANmzYgB49esDOzg6enp6QyWRV+nyqCsdyNVIVzbecONXkhPecXPrtn44KFU7CrQoZGRnUuXNnev36NT158oQ+/fRTysvLk8tT0oIGJS0KoEw/PVTTnwxLS8Xdf19fX6GPn6enJxGR3OtUdDL1AwcOkFgsphEjRhCRfH/I1NRUcnV1JTs7O5JKpcJPfBKJhL755htycnIie3t7SklJEY4ZPHgwjRs3rtTXWRnbt28X+jEW9lFMTk4WfvIkIlq+fDlZW1uTjY0N/fHHHwrnUKf7+b5xTKS+sbxv3z7y9vYmooL3pr29PRERyWQyIU/hT9REJCwIQkT07bff0pYtW0q8fnV9Dyh7vzmWS1dV91flbxhOnNQ9FfehVxs65RcqrrL68ccfk42NDX311Vf08uVLIiqorJqYmJCNjQ3NnTtX4QuTiKrtF1tpqSIqOeUlkUjo0aNHxe4bOnQoXbt2rYpLVDx1up8l3cfaEMv//e9/afPmzcJjfX19uYoqEdGqVatox44dCuf39fWV6+/4tur6HlBF3BZHXWK5JFV1f7kbAGMVLCwsDOnp6YiKikJERAT8/PyEifZ1dHSwf/9++Pv746effoKpqSlcXV3h7++PQ4cOAQD+/vtvfPvttzh16hSWLl0KDw8PREVFYc6cOZg9e7Zwna5du+L48eMYPnw4Vq5ciZ49e+LcuXN4/fo1UlNTkZKSgi5dusiVTdmfjt514n8AMDc3x927d3HmzBm0aNEC//3vfwEA/fv3F+at/Pvvv3ny8AqWk5ODPn36QFtbG127dlV1cWqE2hLLnTt3xtGjR5Gfn4+rV68iOTkZ6enpAIDdu3fD3NwcGzduhJWVldxxsbGxOHLkCAYPHvx+LzSTw7GsqJp1SmBM/dWWTvklKZwkHCiYKPybb74BALkvTE9PT+zfv7/YMrKyRUZGKmyrW7cuDh8+XPWFqcFqSyz37NkTly5dgr29PUxMTNCpUydhnufCAWEhISGYO3cudu3aBQBISEjAqFGjEBoaKiw3y8qPY1k53LLKWAWrLZ3yS/Ls2TPh/+Hh4ULZii5sUHQ7e3/GxsZVer2AgABh3fuarDbF8vz58xEVFYUpU6agS5cu0NTUxJs3b4T9enp6qF+/PoCCJWQ9PDwQEBCAdu3aKfFKsrdVVcxGRUWhU6dO0NbWxuPHj4XtmZmZ6N+/P2xtbTFq1ChkZ2cDAC5dugQbGxvY29tj1qxZVVJGZXDLKmMVrHfv3jh//jzs7OygqamJVq1a4Zdffikxf9++fTF//ny0a9dO4afxOXPmYOTIkdi2bRs0NDTkVoq6cuUKAgMDkZeXh19//RUA0LJlSzRv3hwmJibCKjNFKdsao6uri2nTpgktSqtWrYKGhgZSUlIwbdo0hISECM/19u3bqF+/PsLDw7F161bs3LkTW7duRf369dGsWTMEBAQAKFjO7+TJk6hbty6MjY2xaNGiMsvBqp9Xr17h999/V1ilpyaqLbGckZEBd3d3aGpqomXLlvD39wcAbN68Gb///jtEIhG0tLTw888/C88lLS0NU6dOBQAMGzZMqbKwqte5c2ecP38ebm5ucttXrFiB/v3748svv8TcuXOxc+dOjB49GpMmTUJoaCgMDQ0xduxYnDp1Co6OjioqfRFV0TGWE6eanMADbCoMqulgjNJSaff/wYMHZG1tTVKplCQSCaWnp1NUVBTZ2tqSWCwmNzc3evXqFREV3NPZs2eTi4sLOTs702+//UYuLi7UuXNnunPnjpDn7cE4RP+OOs7OziZvb2+yt7cnGxsbOnfuHBERLV68mLp370729va0atWqct4VeQsXLqSjR48qrMRWHHW6n6qIY6KaG8uFqut7oKT7XRNjtrAcRd9nlpaWwgqDFy9epC+++IKICha8KLRp0yaaO3duqeetqvvL3QAYqyG4U371Ex4eDmdnZ0RERCAyMhK6urowNzfH6dOnER0dDVNTU6EPIFDwk+2xY8dgYGCA8+fP49ixY/Dx8cHWrVuFPG8PxikqICAARkZGCA8Px969ezF9+nQAwM6dOxEREYHw8HBMmzZNoZwDBw5U+DnZ29tbIV9qaiquXr0KV1fXCnqFWHE4llWnpsVsSdLT06GrqwtAftDdBx98gGvXriE/Px9hYWFKD6ytbNwNgDE1xJ3y1cOQIUOwdOlSDB8+HG3atMGCBQsQGxsLX19fyGQypKamonHjxkJ+c3NzAAUDc4yMjIT/R0VFCXneHoxTVExMDM6dO4ewsDAA//YTXrt2LSZNmoTc3FxMmDABYrFY7rg9e/Yo9Xz8/Pzg6+tbjleAlYVjuXqpaTFbkqZNmyIzMxN6enpyg+62bNmCGTNmIDc3F8bGxmjVqtV7XaeicGWVMcYqiYaGBpYuXQqgYMDMsWPHsGXLFixcuBBWVlaYOXNm4U+SAOQH7RQ3gAcoGIxjZGQkNxinkJmZGYyMjITWmcJBE1ZWVnB0dERiYiLc3d1x9epVueMGDhwoTFVUyNjYGFu2bJHbFhcXh/nz5wP4d5DN7t27y/eiMFaN1bSYLYlUKsWBAwcwcuRIHDhwQOjT/J///AdHjhxBfn4+RowYIfRLVjWurDJWAxgbGyuMAq4MGzZsQGBgILS1taGvr49t27ZBS0sL8+fPx/Hjx1GnTh2Ym5vjf//7H0QiEcaMGYNbt26BiDBgwAC5uSVrg0OHDsHf3x+amprQ0dGBWCxGVlYWxowZgw4dOqBx48ZyrTTKKG4wTqGxY8di8uTJsLe3B1Dw8+Pq1avh7u6ON2/eQCaTYcqUKQrnVLaV5uTJk8L/jY2NuaJayaoqrgsFBARgwoQJyMnJAQDk5eXB19cXly9fRm5uLhYuXAiJRFJl5VGFmhazf/31F6ZMmYI///wTnp6e8PDwwNdff42ZM2di5MiRCAgIgKGhofBH6OrVq3Hw4EFoaGjA29sbHTp0KNdzrSyiorV/xlj5iUQiUnUcVdWX2r1792BkZAQNDQ3MnDkT7du3x5gxY3Dnzh2hxWDIkCEYP348HB0dcffuXXzyySfIz8+HjY0NgoODhZ/KiiMSiUBEohIzVENVef+lUimCg4PRunXrKrne+1Kn+1kd4vhtVVlZffXqFTw8PHDnzh3cv38fAPDzzz8jLy8PEydOfOfzVtf3QFXdb3WL2fKqqvvLA6wYq0QJCQnCnHVSqRQZGRk4ffo07OzsYGtri759++L169cACj7U5syZg549e8LFxQW7du1Cz5490aVLF9y9e1fI4+PjAxcXFzg6OiItLU3uejk5ORg7diwcHBwgFotx/vx5AMCSJUvQo0cPODg4YPXq1e/8fExMTKChUfCxoaWlJcwjWfSnraLbP/nkEwAFP63VqVNHbt5JxtRVTYtrAPjhhx8wdepUuZ+yf/vtN6SkpMDBwQFffvklXrx48V7XYOydVcWUA5w41eSEUqa8CQgIID8/P+Fxfn4+ZWVlCY9nzpxJ27ZtI6KCqUX27t1LRERjxoyhadOmERFRUFAQzZo1S8gTHBwsnNvHx4eI/p0GZcOGDbRs2TIiIkpLSyNLS0siIurQoYNw3by8PIVyuru7k0QikUtjxowp8XndvHmTunXrJvdciIhOnTpFUqlU4Rrbt28nT0/PEs9XCNV0mpvSUmn3v7ZTp/tZnvtY0+I6JSWF+vXrJ3dNIqJPPvmEVq9eTUREK1eupPnz5yv9GhWqru8BjtuKUVX3l/usMlaJauLI0pKWWbx27RrmzZuHQ4cOCa2vAHD06FEEBQXh4MGDSl+DseqspsV1SbM8NG3aFL169QIAuLm5YebMmUqdj7GKxt0AGKtEhSNLd+7ciZSUFBw7dgzff/89Fi5ciKioKPTr16/wr3wAyo8sBVDiyNIvvvhCbnlIoGBk6bZt27B06VJ8/fXXCuVUds6+kpZZvHXrFsaOHYvQ0FC5dcejoqKwZMkS7N69G9ra2kq/brVFQkICnJycqux6UqkUVlZWcv0gs7OzYWxsjCVLlpR5/MqVK2FjYwNbW1vExMQAKFg6tEuXLuWa41Hd1bS4LpzlwdXVVYhxAHB0dMSVK1eE8hV266nNVB2zXl5eaNWqlcJ9DA4OhpWVFaysrBAREQGg4P311VdfwdbWFg4ODoiNjS31WjKZDCNGjICtrS0GDBggLJ29du1aGBoaIjg4uBKeoXK4ZZWxSlTTRpaWtMzixIkT8eLFC3h5eQEApk+fjn79+mHkyJFo3Lgx+vfvD6Bgib8ePXqU6/myihUaGio32GPdunUwNTUt87i7d+/iyJEjOHPmDO7evYsJEyYgIiICYrEYa9asUekXWVWraXFd0iwPPj4+GDNmDAICAqCjo6OwhCyrGkVjdvny5YiLi5OLt8zMTPzwww+4cOECsrKy4OjoiOvXr+PgwYPIzc1FdHQ0EhIS8OWXXyI8PLzE62zfvh2GhobYsWMHNm3ahNWrV2PhwoWYPHky/vnnn0p/nqWqir4GnDjV5IQq7PtU2tKMNQGqaf+20tLb93/69Om0e/duIiLKzc0lU1NTkslkNHv2bJJIJNS1a1dav349ERUs7ejo6EhERCNHjqTo6GgiIoqIiBD6FsbExJCTkxPZ29uTh4cHvXz58p1f37ffPxkZGdS7d28KDAykxYsXl3rsxo0bhf6LRERmZmaUk5OjUN6i1Ol+VmUcv60mx3V1fQ8Uvd/qFLNvX4uIKCwsjKZOnSo87tWrF92/f5/++9//0ubNm4Xt+vr6JJPJSrzW559/Lizvm5qaSra2tsI+Pz8/CgoKUjimqu4vdwNgjLEKNHr0aKEFKiwsDA4ODqhXrx7mzZuHyMhInD9/Hj/++KMwl2VZJk6ciICAAISHh0MqlWLTpk1y+7OzsxV+6pVKpUqtNPX9999j1qxZSpXj6dOn0NPTEx43adIEGRkZSh3LWHWmTjFbnLdjs3D51M6dO+Po0aPIz8/H1atXkZycrLCQQEnnKboEa3XA3QAYUyPFLc3IqpeOHTsiLS0NaWlp2L59u1AZ3LhxI/bt2wdNTU1hf1El9WWMjY3FiBEjABT0KStcaaZQvXr13ul9kZCQgAcPHsDOzg7x8fFl5m/WrJlc5fT58+dyX5Ds3XFcq5a6xGxJ3o7NwuVTu3fvjkuXLsHe3h4mJibo1KkTWrRoUeZ5DA0N5ZZgrQ64ZZUxFVJ1Z/22bdsKf9UvWrSo1GPPnj0LKysr2NnZwcHBQajg3L59G926dUPDhg1x5swZIf+iRYuEcxsaGmLGjBkAqkdn/co2fPhwrFu3Dg8ePIC5uTkyMjKwdetWREVF4dixY2jSpInclxtQMPI6MTERAHD58mVhu5mZGXbu3Cm08CxcuFDuuHdtpbl27Rr+/vtvuLq6YtWqVdixYwf27dsHAHj48CFyc3Pl8kulUhw+fBhEhHv37qFZs2aoU4fbO4pSdTxbW1tDIpGge/fuCAkJEfIVN/imJCXFMwD8+OOPcHJygr29PX777TcANSee1SFmS2JhYYHo6GjIZDKkp6cjKSkJhoaGAID58+cjKioKU6ZMQZcuXaCpqYnc3Fw8fPhQ4TyFS7ACkFuCtTrgTxrGapminfU1NTWV/gvf0NAQJ0+eRIMGDXDkyBH4+fkhKCgIH3/8MU6dOoVvvvlGLv93332H7777DgDg4uKCoUOHAkD16KxfyYYNGwYDAwPhDwBdXV2YmZlBLBbD1NS02BYLb29vDBs2DL/88gvatm0rbF+/fj1Gjx4tVB5nzpwJV1dXYf+7ttIMHDgQAwcOBABs27YNjx8/xoABAwAAtra2uHz5Mlq2bCnkb9++PZydnSEWiyESibBu3bpyX5NVvKLxHBkZiXr16uH58+fo3LkzPv/88xIH3xSdXq6okuL52LFjSE5OlhuMBdSceFaHmAUKBrkeP34cKSkpcHJyQmhoKPT09DBt2jShcrlq1SpoaGggIyMD7u7u0NTURMuWLeHv7w8AePDgAdzd3XHz5k25c48aNQre3t6ws7ODnp5e9RpQVxUdYzlxqskJajzApl27diSVSsnFxYWuXr2q9HlOnjxJI0eOlNtWtPxFJSUlkampqdw2VXfWr8j09v2vzoYOHUpisZju3btXYp7Hjx/TqFGjlD5ndHQ0WVhYCBPcF6VO97Ok+6hO8Vzo8ePH1KdPHyIqefBNWd6O59GjR5OPjw85OjqSh4cHJScnC/tKimei6vseUJe4VSZmyys4OFhYtEIZ/v7+1LlzZ2Fxi6Kq6v5yyypjFWz06NGYO3cuBg0apNBZv2HDhpDJZOjUqZPS81JOnDgRwcHBMDAwwLp167Bp0yZMmzZN2J+dnQ0XFxeF48RicZlzZ168eBHNmzfHzZs3MWTIENy6davM8mRlZWHevHn4+eeflSr/zp07MWzYMKXysspV9Kfhkujr6yMwMFDpc4rFYly4cOF9ilWtqVM8Z2VloXfv3rh16xaWL18OoOTBN0XnSVZGUlISGjVqhJMnT2L37t2YOXMmduzYUa5zsPJTJmbLa/jw4eXKP3nyZEyePLnCy1EeXFllrIKpU2f95s2bC2XW1dXFkydPSu2AL5PJ4OHhgXnz5qFz585KXSM4OFjoB8WYulGneG7YsCFOnz6Nf/75B927d8fgwYNLHHxTXm+vZlVWH3fGKhJXVhmrBCV11o+JiUFOTg7at28v9wUGlN1ZX19fH0BBy0tR79oSI5PJQETQ1tZGcnIynjx5InyJPXz4EPr6+nIDaHJzczF06FB4enqib9++Sr0Of/zxB/T09NCmTRul8jNWHalDPOfk5KBOnToQiURo0KABtLW1oa2tDQsLC8yZMwcymQwvX76UG3wTFxcHY2NjpV6DwtWsXF1deTUrVuW4sspYJVCHzvppaWno378/GjRogLy8PGzYsEEYdFHcAJvg4GCEh4cjMzMTgYGBMDU1xfr165GRkYHBgwfj1q1biI2NhZOTE5YtWwYACAoKElqRGFNX6hDPDx8+xJgxY6ChoQGZTAY/Pz9oaWlBS0ur2ME3OTk56NKlC7KysuTOU1I8jxgxAhMmTBBW0dq8eXO5y8jYO6uKjrGcONXkBDXpqE9UOQNsyqs6dNavyKStrZ0CgDgpJm1t7RRV3x9lkzrFcaH3GXxz5swZWrBgwXuXobR4Jqq+Mc1xq14xLip4LzHG3pVIJCKOo4ohEolARKKyczJWsTiOKwfHNKsIvCgAY4wxxhirtriyyhhjjDHGqi2urDLGGGOMsWqLZwNg7D1pa2unikSilmXnZGXR1tZOVXUZWO3EcVw5OKZZReDKKqtQOjo6KW/evOEP/Aqgra2d+vr16w9VXY73IRKJWgP4HcBDALkAbhLRUtWWiimL47niqHs8iwpWOZgMwBfAXADLARgS0UuVFozVCjwbAKtQPKK24tSEUbQikagxgK4ARgEQA9AFYEBEr1VYLKYkjueKU0PiuTOATwD8D0A6gM1E9D/VlorVBlxZZRWKv9wqTg36ctsCwAzAUwCtAawiIh+VFowpheO54tSQeP4RwOcA6gKo//+bGxFRnupKxWoDrqyyCsVfbhWnJny5FRKJRFoAOgPwAPCAiDaouEhMCRzPFaemxPP/dwfQB2AFYCiAz4koV7WlYjUdV1ZZheIvt4pTU77cmPrieK44HM+MvTueuopVuri4OJibmyM7OxsAsGLFCvj5+QEAdHR04ObmBgBIT0+HhYUFdHV1ERwcLBxPRJg+fTpsbW3h5OSEx48fAwB2796NDh06YMmSJZVW9kePHsHJyQm2trbw8Sn9l2s7Ozt4e3sLj728vNCqVSu5bQBgb2+P5s2bV2q5K5uOjk6KSCQiTu+fdHR0UlR9P8tD2Xg+e/YsrKysYGdnBwcHB8THxwMoWHvexcUFEokE1tbWuH79OoDqE8+DBg2CRCJB9+7dsWbNGmF7cfF86dIlSKVSSKVSWFpaolmzZpVW9qrCsV2z41dtqXp9Xk41K6GE9bWXLVtGCxYsoPj4eOrWrRu9efOGiIiMjIyEPNnZ2ZSSkkJ+fn4UFBQkbD927Bh98cUXwv9HjBgh7AsMDKTFixcXe823vXjxQql8RXl6etKpU6eE/4eHhxebb8+ePdS3b18aM2aMsO3Ro0cUEREht42IKDExUalyo5quqU2l3GdWfup4n5WJ58ePH1NWVhYRER0+fJi8vLyIqGAt+cI16aOjo2ngwIHCMdUhnmUyGRER5eTkkJGRET1//pyISo7nQjt37qSvvvqqxGtX5/tcNHFsl4+63Fd1T9yyyqqEj48Pjhw5Ak9PT6xZswZaWloKeerWrYuWLRVnyYmIiIC7uzsAwNnZGZcvX1b6uq9evUJISAgGDBiA8ePHl7vc165dg4ODAwBgwIABiIiIUMiTm5uLDRs2YNKkSXLbW7duXew5P/7443KXg7HqRJl41tfXR4MGDQAAWlpa0NTUBAD85z//wfPnzwEU/JrywQcfKH3dqojnevXqCddq06YN6tcvGEdUUjwX2rFjB0aMGFHuMjHGysbzrLIqUadOHUgkEuzfvx9isbhcxz59+hR6enoACvp95eaW3Zf/xIkTCAoKQmpqKvr06YONGzfiww8LpjhMTEws9kvFw8MDkydPltuWn58v/F9PTw9Pnz5VOG7jxo3w8vIq9gubsZqoPPGclZWFefPm4eeffwYAdOvWDfPnz0fHjh2RmZmJ06dPl3m9qoxnAHBzc8OlS5cwceJEoZJdmpSUFDx8+BCWlpZl5mWMlR+3rLIq8ddffyEqKgo9e/bEpk2bynVss2bNkJGRAaCg20qdOmX/jRUcHIw7d+7Ay8sLo0ePFr7YAMDAwACRkZEK6e0vNgDQ0Pg3RDIzMxX6pD1//hz79u2Dl5dXuZ4TK96+ffuQmJio6mIoCA4OhpWVFaysrIptjQOAlStXwsbGBra2toiJianiElYtZeNZJpPBw8MD8+bNQ+fOnQEU9HEdOHAgbt68id27dyv8IlGcqornQocOHcKDBw9w8OBB3Lp1q8zy7dy5E8OGDSszX23CscwqEresskpHRJgwYQLWrl0LU1NTWFtbo1+/fmjVqpVSx0ulUoSEhMDd3R3h4eH47LPPyjxm+/btSE9PR2hoKDw8PNC4cWN4eXmhf//+5WqJ6dq1K6KioiCRSHDgwAGMHj1abv9ff/2F58+fo3fv3khPT0dycjJ+/vnnd/qJkhV8wTVv3hwGBgZy2/Py8pRq4aoMmZmZ+OGHH3DhwgVkZWXB0dER169fl6v43L17F0eOHMGZM2dw9+5dTJgwocQvQnWnbDzn5uZi6NCh8PT0RN++feX2tWjRQvg3MzOzzGtWVTzn5+cjLy8PdevWhY6OjpDKEhwcjD179pSZrzbhWGYVStWdZjnVrIRiOuevW7eOpk6dKjw+ePAgDR48mIjkB2QQEfXq1Yvatm1LZmZmNHr0aCIiys/Pp6lTp5JYLCZHR0dKTEwU8is7ICMxMZECAgLKzPe2hIQEcnBwILFYTNOmTaP8/HwiIvr666/p+vXrcnnfHnwxe/Zs6tatG3300Ufk6OhI6enpREQ0evRoMjU1JSMjI+rVq1eJ10Y17rhf3H0uyZw5c8jOzo4sLS1p3759RETk5+dH3t7e1K9fP+rYsSOdPXuWYmNjqWXLltSlSxfq06cPEREZGBjQxIkTycHBgdLS0qhPnz5kZ2dH9vb2dP/+fSIikkgkNGPGDHJ2diYHBwdKTU2lI0eO0MSJE4UyuLu7K9wvZYWFhcm9f3v16iVcu9DGjRtp9erVwmMzMzPKyclR6vzqdp+VjefAwEBq1KgRSSQSkkgkwuCjpKQkcnBwIIlEQj169KCIiAi5Y1QZz8+fPxfKa2lpSWvWrBGOLSmeb9y4Qba2tmVeuzrf56KptNjmWFakLvdV3ZPKC8CpZqXyVGKIiIyNjYUPs/IKDQ0lc3Nz8vf3f6fjq7vq/CGo7H0+evQojR8/noiIXr9+TZ07d6a8vDzy8/OjKVOmEFFBJX/o0KFERDRy5EiKjo4Wjq9Tpw49ePCAiIimTZtGgYGBRER0/PhxoYIkkUgoODiYiIgCAgLIx8eH8vLyqEuXLvTq1StKSUkhKysrhbL9/PPPQsWkaLp3755cvp07d5Kfn5/weNiwYXTp0iW5PN9//71QNiIia2trSktLU+o1qgn3uRDHc8mq830umkq65xzLxVOX+6ruibsBMJW6d+/eOx/r4eEBDw+PCiwNq2gxMTGIioqCVCoFUNCH8cmTJwCA7t27AwAMDQ1LHOjSunVrGBoaAgBu374t9G8Ui8WYMWOGkK9wYIu1tTX27t0LDQ0NDBkyBKGhoUhOTlb4uRcAxo0bh3HjxpX5HIr2mQaK7+v4dp7nz58LgwJrE47nmotjmakSV1YZY5XGzMwMzs7O+OmnnwAA2dnZwtRAItG/i/kUNFAUTBtUdLaHon3bOnTogOjoaBgbG+PMmTNo3769sO/ixYswMjLChQsXhO1ffvklPD098ezZM0RFRSmUbdOmTfjll18Utm/ZsgXGxsbCYwsLC8yZMwcymQwvX75EUlKS8KVbSCqVYtKkSZg2bRri4uLQrFkzpQYCMqYuOJaZSqm6aZdTzUp4zwml9+7dSw8fPnyvc1SGoKAgsrS0JEtLy2InEv/jjz/IysqK7OzsyMbGhv744w+5/adOnSIA9OjRI7ntW7ZsoTp16hR7TVTjn5fKc599fX3J1taWpFIpeXp6EhHJLfzw4MEDcnR0JCKiAwcOkFgsFhZ+KNoHMjU1lVxdXcnOzo6kUqnwE59EIqFvvvmGnJycyN7enlJSUoRjBg8eTOPGjVO6rCXZvn27cP9PnjxJRETJycnCT55ERMuXLydra+ti739pasp9Lo66xnN+fj5NmDCBxGIx2dvb082bN4mI6NWrVzRkyBCSSCTk6upKjx8/JiKi9evXU/fu3cnW1pY+//xzYZGEoqrzfS6aSrvnHMuK1OW+qntSeQE41az0vl9ub/dzKpSbm/te530fGRkZ1LlzZ3r9+jU9efKEPv30U8rLy5PLk52dLQzWOHXqFHl4eAj78vPzqXfv3vTZZ5/JVVZfvnxJvXr1onbt2hV73er8Ifi+97kiSSQShT8CCg0dOpSuXbtWxSUqn5p8n9U1nvft20fe3t5EVFABs7e3JyKiNWvW0JIlS4iI6MyZM8KqXHfv3hXO8e2339KWLVsUrlud73PRpMrYVsdYVpf7qu6J51lllW7u3LmQSCSwsrLC/v37AQALFizA2LFj0b9/f3Tq1Annzp3DrVu3EBYWhilTpgjri7dp0waTJk2Ci4sLnjx5Ajc3N0gkErm1xqVSKXx8fODi4gJHR0ekpaXh6NGjcvM3Dhw4EH/88cc7lf/ixYuQSCTQ1tZG8+bNoa+vj4SEBLk8devWFX4Ky8zMxKeffirsCwoKQt++fYXVfAr98MMPmDp1qtxPaKxi5OTkoE+fPtDW1kbXrl1VXZwapTbE8507d2BhYQGgoB/m3bt3kZ2dLbe9R48ewpRGJiYmwvRHRVfrYu+PY5kB4JZVThWb8NZf5bVlBClRwTrnFhYWpK+vTxcvXiSigp8NHR0dKScnR67VICUlhfr160dEitN3FUI1/ov97fvM3p063efaEs9hYWE0cOBAysvLoytXrpCGhgYlJyfTxo0b6euvvyaigi4OWlpacsfdvHmTunXrRllZWQrlq873uWji2C4fdbmv6p641zCrVLVlBGlhmS5cuIALFy5g8uTJuHTpEtasWYMJEyYodND38/ODr69vmddmrDqpLfHcs2dPXLp0Cfb29jAxMUGnTp3QokULfPnll/j2228hlUrRvXt3dOzYUTgmISEBo0aNQmhoqMKvKIyx98PdAFilKhxBWrgE4p9//omWLVsCePcRpACKHUEKQGEE6bZt27Br1y54enoqlG3Tpk2QSqUKKS4uTi6fhYUFoqOjIZPJkJ6eXuwI0jdv3gj/19PTQ/369QEAsbGx2LJlC1xdXXHjxg188cUXePnyJeLi4jB//ny4uroiOTmZp+x5B0VH+VamqKgodOrUCdra2nj8+LHC/vnz5xdbli+++AJOTk5VUcQqU1viGSi4r1FRUZgyZQq6dOkCTU1N1K1bF2vWrEFkZCTs7e3Rs2dPABBiOCAgAO3atVP+BWUKVB3XEydOFN47H374Ifz9/aukPKx03LLKKlXv3r1x/vx52NnZQVNTE61atSp2ipFCffv2xfz589GuXTts375dbt+cOXMwcuRIbNu2DRoaGti8ebOw78qVKwgMDEReXh5+/fVXAEDLli3RvHlzmJiYoGHDhgrXUrYlRldXF9OmTRNak1atWgUNDQ2kpKRg2rRpCAkJwcGDB7F27Vqh39qaNWsAFCzDWEgqlSIoKAgNGjTAyZMnhe3GxsbYvXt3meVgqtG5c2ecP39e6HdZ1N9//61QGQKAa9eu4cWLF1VRvCpVW+I5IyMD7u7u0NTURMuWLYUKy61btzBx4kTUqVMHxsbG+PHHH4XnkpaWhqlTpwIAhg0bplRZmOqUFNfr168X/m9qasoNCdWFqvshcKpZCSro76SOI0iVgWrcF6q0+/zgwQOytrYmqVRKEomE0tPTKSoqimxtbUksFpObmxu9evWKiAru3ezZs8nFxYWcnZ3pt99+IxcXF+rcuTPduXNHyPP2EoxE//b1zc7OJm9vb7K3tycbGxs6d+4cEREtXryYunfvTvb29rRq1ap3ugdFFfc+8/b2pps3byr0O+7fvz9dunRJmManNOp6nysLx3P1vOe1Ka6JiM6fP089e/Ys83h1ua/qnrgbAKuReASp6oSHh8PZ2RkRERGIjIyErq4uzM3Ncfr0aURHR8PU1BS7du0S8ltYWODYsWMwMDDA+fPncezYMfj4+GDr1q1Cnq5du+L48eMYPnw4Vq5cKXe9gIAAGBkZITw8HHv37sX06dMBADt37kRERATCw8Mxbdo0hXIOHDhQ4Sdjb29vpZ/njRs3ABT8NF7UoUOH0K1bN7Ro0ULpc7HScTyrXm2J60JBQUH44osvyn0cqxzcDYCpvcjISIVtdevWxeHDh6u+MAxDhgzB0qVLMXz4cLRp0wYLFixAbGwsfH19IZPJkJqaisaNGwv5zc3NARQMvjEyMhL+X3SlmrcH3BQVExODc+fOISwsDEDBgBkAWLt2LSZNmoTc3FxMmDABYrFY7rg9e/a81/P87rvvsHbtWrlteXl5WL16NQ4ePCgMPGLlw/FcPdWWuAYK/jg6cuSIQgWaqQ5XVlmNZmxsXGyfwoq2aNEihIeHAygYFTxo0CCsWrUKV65cweTJk6GlpYX69esjJCQETZo0qfTyqJKGhgaWLl0KoGBQzLFjx7BlyxYsXLgQVlZWmDlzZuHPjQDkB+YUN0gHKH4JxkJmZmYwMjISWl6ys7MBAFZWVnB0dERiYiLc3d1x9epVueMGDhyI9PR0uW3GxsbYsmWLUs/z/v37QotNcnIyJk2ahLlz5yIjIwODBg3C69evERsbi0WLFuG7775T6pysdFUVzxs2bEBgYCC0tbWhr6+Pbdu2QUtLC3FxcRg7dixEIhHatm2LDRs2CEuO1nS1Ja4B4PDhw5BIJMJAWaZ6XFllrAJ89913QoXExcUFQ4cOBQAsX74c//3vfyGRSLBkyRLs2LEDU6ZMUWVRK92hQ4fg7+8PTU1N6OjoQCwWIysrC2PGjEGHDh3QuHFjuRYYZRQ34KbQ2LFjMXnyZNjb2wMo+Glx9erVcHd3x5s3byCTyYp9zZVtgfnrr78wZcoU/Pnnn/D09ISHhwe+/vprxMTECHmMjY2xbt06AMD169cBFPzR4u3tzRVVNeTk5ITx48dDQ0MDM2fORHBwMMaMGYNvv/0WS5YsgY2NDb7//nsEBQVhzJgxqi5ulagtcQ0UdAEouggFUz1R0b9yGHtfIpGIlHlPJSQkYPjw4ahXrx6ICHv37kVMTAx8fX1BRNDV1cWuXbugo6MDqVQKKysrXLt2DUQEb29vBAQEIDU1Fbt27cInn3wCqVSKzz77DDdu3BA++D744AOhJSYnJwcTJ07E/fv3kZ2djZUrV8LKygpLlizBgQMH0LBhQ7i5uQl/xb+rv//+G87OzoiNjQVQMJ9qt27d0L9/f8yYMQM9evQQKrJKvJYgomq5vJWy97kiSKVSBAcHo3Xr1lVyvapWE+5zTY1noGAKKyMjI4waNQqmpqb4888/UbduXZw4cQLbtm3Dzp07lTpPdb7PRVVVbNeUuFaX+6rueIAVU4ma2ll/586dGDZsmPDY3d0dU6ZMQceOHXHu3DkMHDjwXV8yxqqtmhrPsbGxOHLkCAYPHgygYLqjwr6zhw8fLnHxA8ZYxeJuAEwlampn/eDgYBw4cEB4/NVXX+H3339H9+7dsXz5cqxevRqzZs0q1zlru+IG3LDqpSbGc3ErUq1atQpTp07F2rVr0aVLF+jr6yt9PiaP45qVB7esMpUo7Ky/c+dOpKSk4NixY/j++++xcOFCREVFoV+/fu/UWR9AiZ31v/jiC7mVd4CCzvrbtm3D0qVLhf5KRZWnJeaPP/6Anp4e2rRpI7f9gw8+AAC0aNFC+FKtzRISEqp0ZafCn50LB+acOnUKVlZWsLa2Vugn97b09HRYWFhAV1dXboGHQuHh4RCJRAorWwUEBKBu3brC47Vr18LQ0LDYc9QENS2eS1qR6qOPPsLu3btx8uRJaGpq4vPPPy/vS1VjqTquvby80KpVK7n7eenSJeE+W1paFrtMdlFEhOnTp8PW1hZOTk5CXO/evRsdOnTAkiVLKu8JsVJxyypTiZrWWR8o6JQ/YsQIuW3Lly/H0KFDoa2tDQ0NjRpbWanuQkND0bp1a+Tl5WH69OkIDw9H/fr1YWVlBTc3NzRq1KjY4xo1aoQDBw5gw4YNCvuICKtWrcJnn30mt/3Vq1f4/fffYWBgIGybPHky/vnnn4p9UtVITYvnklak+uWXX7B582ZoaGjAzc0Nzs7O5XpOrGIVxjVQ8FkbFxcn9xnbo0cPoQX3l19+wZkzZ0o934kTJ/DPP/8gOjoax48fx7x587B9+3Z4eHggKyur2OWWWRVR9aoEnGpWggpWvCEqfdUbdYVqvDLK2/d5+vTptHv3biIiys3NJVNTU5LJZDR79mySSCTUtWtXWr9+PREVrIRTuLLTyJEjKTo6moiIIiIiaMyYMUREFBMTQ05OTmRvb08eHh708uXLd34di743bt++Tf369RP2ffXVV3Tq1Kkyz+Hn50dBQUFy27Zv304bNmxQeO8tXLiQjh49qrCqVXHnIFKv+1xVOJ6rxz1Xl7guVPRab+vZsyedP3++1HPOnj2b9uzZQ0RE+fn59J///EfYFxgYSIsXL1Y4Rl3uq7on7gbAGHtvo0ePFtZ+DwsLg4ODA+rVq4d58+YhMjIS58+fx48//oicnBylzjdx4kQEBAQgPDwcUqkUmzZtktufnZ2t8HOuVCqFr69vqed9+vQp9PT0hMd6enrvNEjm9evX2LFjh8JPyKmpqbh69SpcXV3LfU7Gqht1ieuypKSk4OHDh0I/6JIU/XwQiUTIzc19r+uyisPdAFiNwJ31Vatjx45IS0tDWloatm/fLgwi27hxI/bt2wdNTU1hf1El9VeMjY0VulTIZDJIpVK54+rVq/dO97xZs2bIyMgQHmdmZpbZj604a9aswYQJE1CnjvxHqJ+f33t/sTKO5+pCXeK6LG/P0lKSop8PRKQQ30x1uGWVVUuq7qzftm1b4a/6RYsWlXrsgQMHhLxdunQRRjqHhYXBxsYGUqkUDg4OePToEYCa21l/+PDhWLduHR48eABzc3NkZGRg69atiIqKwrFjx9CkSRO5Ly4AaNq0KRITEwEAly9fFrabmZlh586dQuvNwoUL5Y571xYYY2NjJCQkICMjAzKZDOfOnRP6nD58+FDplpTY2Fhs2bIFrq6uuHHjBr744gu8fPkScXFxmD9/PlxdXYVBOrWVqmO4uAE3qampsLa2hlQqhYWFBU6dOlXqOW/fvo1u3bqhYcOGCv0df/zxRzg5OcHe3h6//fYbgJo5kE4d4roswcHB8PLykttW3EpoUqlUmM0lPDxcoT86Ux3+s4Gx/1e0s76mpqbSf+H369cP/fr1AwAsXbpU+GvcwcFB+Dl469at+Omnn7By5coa21l/2LBhMDAwECr3urq6MDMzg1gshqmpabEtmN7e3hg2bBh++eUXtG3bVti+fv16jB49Wqg8zpw5U+6n9XdtgdHU1MQPP/yA3r17AwB8fHyEgT+2tra4fPkyWrZsKXdM7969cfv2bdSvXx/h4eHYunWrXGVEKpUiKCgIDRo0wMmTJ4XtxsbG2L17d7nLyN5dWQNumjdvjujoaGhqaiI+Ph5Dhw6Vq0y97eOPP8apU6fwzTffyG0/duwYkpOT5e43UDMH0qlDXAMFg+KOHz+OlJQUODk5ITQ0FHp6eoiJiUGjRo3kypGTk4MuXbogKytL7hwuLi44cuQIbG1toaWlhcDAwHcqC6sEqu40y6lmJZQyIEOdOuu3a9eOpFIpubi40NWrV5U+j5mZGT1+/Fhh+6pVq2jHjh3C45I66xeFatxxv7T7XN0MHTqUxGIx3bt3r8Q8jx8/plGjRlVaGfz9/alz5860d+9ehX3qdJ/VKYbfvtbbrl69Sl9++aVS5y5afiKi0aNHk4+PDzk6OpKHhwclJycL+9RxIF3RpC6xrUxcl+TMmTO0YMECpfOHhoaSubk5+fv7K+xTl/uq7olbVlmVGT16NObOnYtBgwYpdNZv2LAhZDIZOnXqVOqKMkVNnDgRwcHBMDAwwLp167Bp0ya5VWuys7Ph4uKicJxYLC7zJ/iLFy+iefPmuHnzJoYMGYJbt26VWZ7Lly+jVatWchOF7969G8uWLcOLFy9w5MgRpZ4Xq1ghISFl5tHX16/UVpTJkydj8uTJlXb+qqJOMVySuLg4jBo1Cnfv3pVbMas8kpKS0KhRI5w8eRK7d+/GzJkzsWPHjnc6F3s3ysR1SWxsbGBjY6N0fg8Pj1rdpac64MoqqzLq1Fm/efPmQpl1dXXx5MkTtGjRotRjduzYoTDPauGHXEhICObOnSu35CRj6kadYrgkxsbGOHPmDOLj4+Hg4AA3N7dyn6Np06bo1asXAMDNza3Mfu2MsffDlVVWpUrqrB8TE4OcnBy0b99e7ssMKLuzfmFLZnZ2ttxx79oqI5PJQETQ1tZGcnIynjx5IvTLevjwIfT19RVGiebk5ODQoUNYvny5sO3NmzfQ1tYGUDBFUv369ct8fRir7tQhhksik8mgpaUFAGjcuDEaNmwo7IuLi4OxsbFS53F0dMSVK1fg6uqKixcv4pNPPil3WRhjyuPKKqtS6tBZPy0tDf3790eDBg2Ql5eHDRs2QEOjYOKMkgbhhIWFwdbWVlhDHAA2b96M33//HSKRCFpaWvj555/LXRbGqht1iGGg+AE3sbGxmDt3LjQ1NZGTk4P//e9/AEoecJORkYHBgwfj1q1biI2NhZOTE5YtW4YRI0ZgwoQJwgpamzdvfqcyMsaUI3r7L2DG3odIJCJ1fE99/vnnSEpKQmBgYImtK0lJSfD19X3vvo27d+/G8uXLMWrUqFL7MYpEIhCRqMQMKqSu97k64vtcMZSJ4ZKcPXsWJ0+ehJ+f33uVYe3atdiyZQsWLFiAAQMGyO2rzve5KHW659WButxXdceVVVah+IOu4lTnD0EdHZ2UN2/etCw7JyuLtrZ26uvXrz9UdTmKw/FccapzPBfFsV0+1Tl+axKurLIKxV9uFUddvtxYzcXxXHE4nhl7d7yCFWOMMcYYq7a4ssoYY4wxxqotrqwyxhhjjLFqi6euYhVKW1s7VSQScef8CqCtrZ2q6jKw2o3jueJwPDP27riyWkl4RGXlUPeRlyKRyADAHgD/AOgEoB0RyVRbKlYajuXKUQNiWQTgawCzAcQDCCKiDaotFWM1E88GUEl4FG3lUOcRtSKRqC2A8wBiATQH8B8A84novyotGCsVx3LlUOdYBgCRSLQRgC2ARAASAK8AfEhEuSotGGM1EFdWKwl/wVWOGvAF9wGA7gB6APAA8ISIpCotFCsVx3LlqAGxXBdARxTEshSAKwAJEd1QZbkYq4m4slpJ+Auucqj7FxxTPxzLlYNjmTGmLJ4NoIrExcXB3Nwc2dnZAIAVK1YIS/vp6OjAzc0NQMGyf1ZWVrCzs4ODgwPi4+MBALdv30a3bt3QsGFDnDlzRjjv7t270aFDByxZsqTSyj558mRIJBL06NEDM2fOFLYPGjQIEokE3bt3x5o1a4o91svLC61atYK3t7fc9uDgYFhZWcHKygoREREAgISEBOjp6UEqlUIqleLAgQOV9pwYe1fqHMtjxoyBlZUVLC0tsXz5cmH7jRs3IBaLYW1tjVWrVhV77MOHDzFgwAA4ODigV69ecvuePn0KPT09BAcHA+BYZoxVMCLiVAmp4KWVt2zZMlqwYAHFx8dTt27d6M2bN0REZGRkJOR5/PgxZWVlERHR4cOHycvLi4iIsrKyKD09nUaOHEnR0dFy5w0MDKTFixcrXK84L168UCpfUTKZTPi/nZ0d3bx5U257Tk4OGRkZ0fPnzxWOffToEUVERNCYMWOEbRkZGdS5c2d6/fo1PXnyhD799FPKy8ujBw8ekKOjY6ll+f/XVeX3V5mkra2dAoA4KSZtbe0UVd8fZVNNiuU7d+4QEVFeXh5ZWlpSXFwcERGJxWK6e/cu5eXlka2trbC9qN69e9Pjx4+LPe+0adPIzc2NgoKCiIhqXCwTx3ONiWdO6pl4NoAq5OPjAxsbGxw9ehRr1qyBlpaWQh59fX3h/1paWtDU1AQANGjQAA0aNHin67569QoHDhxASEgIGjRogJ07d5br+Hr16gEAsrOzUb9+fXz00Udy21+9eoU2bdqgfv36Cse2bt0acXFxctsuXrwIiUQCbW1taGtrQ19fHwkJCdDQ0MCff/4JOzs7GBgY4Mcff0SLFi3e5SlXC2/evGlJRKouRrWk7tMhqWssf/LJJwAADQ0N1KlTB5qampDJZHj+/DlMTEwAAG5uboiKioKRkZFwXGJiIl69eoUZM2YgOTkZI0aMwJgxYwAA9+/fx9OnT2Fubi53rZoUywDHc2nUPZ5Z9ceV1SpUp04dSCQS7N+/H2KxuNS8WVlZmDdvHn7++ed3vt6JEycQFBSE1NRU9OnTBxs3bsSHHxbMFJOYmIgRI0YoHOPh4YHJkycrbPf29kZYWBhcXV3RpEkTYbubmxsuXbqEiRMnCl/GZSn8ybCQnp4enj59ik8//RTx8fFo1KgRtm7dCh8fH2zfvr28T5uxSqfOsQwAO3bswMcffwxDQ0P8/fffxcZjUUlJSbhy5QpiY2PxwQcfQCqVws7ODiYmJpg/fz4WL16MoKAgIX+rVq04lhljFYYrq1Xor7/+QlRUFHr27IlNmzZh/PjxxeaTyWTw8PDAvHnz0Llz53e+XnBwMO7cuYPJkydjwIABaNSokbDPwMAAkZGRSp9ry5YtyMnJwcCBAxEWFobevXsDAA4dOoSXL1/Czs4OQ4YMgampaZnnatasGTIyMoTHmZmZaNasGbS0tIQWquHDh2P16tVKl4+xqqTOsXz06FEEBQXh4MGDAAriMTMzU9hfGI9FNW3aFGZmZjAwMAAASKVSxMTE4J9//kGzZs3kWmEBcCwzxioUD7CqIkSECRMmYO3atVi2bBnWr1+P5ORkhXy5ubkYOnQoPD090bdv3/e65vbt23H06FG8evUKHh4eGDx4MPbv3w+goDWmcPBD0bR27VqF87x58wYAULduXTRs2BD169dHfn4+cnJyABQMKilMyrCwsEB0dDRkMhnS09ORlJQEQ0NDPHv2TMgTEREh/GRZW+3btw+JiYmqLoaC4gbHvW3lypWwsbGBra0tYmJiqriElUudYzkqKgpLlizB7t27oa2tDaCgYtmoUSPEx8eDiHDkyBHY2dnJHWdsbAyZTIZnz56BiHDlyhWYmJjg8uXLuHHjBlxdXREcHIyVK1fi/PnzHMtv4Vhm7D2putNsTU14a1DGunXraOrUqcLjgwcP0uDBg4lIflBGYGAgNWrUiCQSCUkkEvrqq6+IiCg9PZ0cHR2pVatW9Nlnn9Hs2bPljlFmUEZiYiIFBASUme9tvXv3JolEQlZWVjRr1iwiInr+/LlQRktLS1qzZo2Qf+jQoZScnExERLNnz6Zu3brRRx99RI6OjpSenk5ERNu3bydLS0uytLSkkydPEhHR3r17qWvXrmRnZ0fOzs704MEDhbJAjQZlvP0eKK/iBuAQEeXm5r7Xed9HSYPjirpz5w5JpVLKz8+n27dvk1QqVTiPOt9HdY7lNm3aUKdOnYQyXbx4kYiIrl27RjY2NmRlZUUrVqwQ8heN5fDwcBKLxWRpaVlsGf38/IQBVjUtluk947kmxzKR+t1LTuqXVF6AmprK88FmbGxMffr0UTp/UaGhoWRubk7+/v7vdLy6UacPxeLeA3PmzCE7OzuytLSkffv2EVHBl7y3tzf169ePOnbsSGfPnqXY2Fhq2bIldenSRXhvGBgY0MSJE8nBwYHS0tKoT58+ZGdnR/b29nT//n0iIpJIJDRjxgxydnYmBwcHSk1NpSNHjtDEiROFMri7u9P169ff6fUPCwuTq6j16tVLuHahjRs30urVq4XHZmZmlJOTI5dH3e9jSTiWladO7wEq5n3AsfwvdbuXnNQvcZ/VauDevXvvfKyHhwc8PDwqsDSssoSFhSE9PR1RUVF48+YNLC0thZ+HdXR0sH//fkRGRuKnn35CSEgIXF1d4e3tLQzg+fvvv/Htt9/C0NAQ33zzDTw8PDBq1CicOHECs2fPxq5duwAAXbt2xQ8//ICtW7di5cqV+O9//4u5c+fi9evXeP78OVJSUtClSxe5sm3atAm//PKLQpm3bNkCY2Nj4XFJg+PatWsnl6dwxggAaNKkCTIyMtR+NLgyOJZrB47lmh/LrHrhyipjVSQmJgZRUVGQSqUACgbfPHnyBADQvXt3AIChoaHCSOxCrVu3hqGhIYCCieUnTZoEABCLxZgxY4aQz9LSEgBgbW2NvXv3QkNDA0OGDEFoaCiSk5MxevRohXOPGzcO48aNK/M5lDQ4rrQ8z58/l/tSZEzdcSwzVrV4gFU1pq6d8tPT02FhYQFdXV1hRRug4C/+wsEfHTp0wKBBgwAAixYtErYbGhrKfVjXJGZmZnB2dkZkZCQiIyPx559/omXLgukJRaJ/V50kKpjLsV69esjNzRW2F50arEOHDoiOjgYAnDlzBu3btxf2Xbx4EQBw4cIFYfuXX36Jbdu2YdeuXfD09FQoW9F7UzS9PUduSYPjipJKpTh8+DCICPfu3UOzZs1Qpw7/Xayu8ZydnY0hQ4bA1tYWPXr0wIkTJwAAGRkZcHFxgUQigbW1Na5fvw6gdsQzxzJjVUzV/RBqasJ7Dq4hUt9O+dnZ2ZSSkiI34OJtY8eOpd9++01hu7OzszDoozhQo75Rxb0HfH19ydbWlqRSKXl6ehKR/MCUoiv/HDhwgMRiMY0YMYKI5AfvpKamkqurK9nZ2ZFUKqV79+4RUUE/t2+++YacnJzI3t6eUlJShGMGDx5M48aNK/G1VVZxg+OSk5Np6NChQp7ly5eTtbU12djY0B9//KFwDnW/j+9CXeP54MGDNGrUKCIqGNjVrVs3IiLy9/enBQsWEBFRdHQ0DRw4UOH8pcWzOr0HqJj3Acfyv9TtXnJSv6TyAtTUVNIXXG3olF+opMrqmzdvqE2bNvT69Wu57UlJSWRqalrq9dXpQ7GiKjnlIZFI6NGjR8XuGzp0KF27dq2KS1S8mnIfa0M837p1izw9PSk/P59u3LhBrq6uRER08uRJmj59OhER7d+/nyZMmCB3XFnxrE7vAVJBPKtLLBOp373kpH6J2/OrUG3plF+WAwcOwNnZWZjnsdDOnTsxbNgwpc/DlJOTk4MBAwagRYsW6Nq1q6qLU2PUlnhu164dXr16hQ4dOiAzM1OY37Vbt26YP38+OnbsiMzMTJw+fVruWhzPFY9jmdVWXFmtQrWlU35ZduzYgZkzZypsDw4OxoEDB8p1LiavuJWM6tati8OHD1d9YWq42hLP27ZtQ+vWrbFv3z4kJCTA3d0d169fx4oVKzBw4ED4+PjgwoULmDRpEo4ePSocx/H8fjiWGfsXV1arUGGn/J9++glAwcCFevXqAXj3TvnGxsbFdso3MjJS6JTv6emJZ8+eISoqSqFsyrbEWFhYYM6cOZDJZHj58mWxnfJL8+TJE9y5c0dhPfU//vgDenp6aNOmjdLnYmUzNjZWGFhRGbZu3YqtW7dCQ0MDTZo0wc6dO9G4ceNKv64q1aZ4LpyqSE9PD1lZWQrbW7RoIbdkK8dzxauqWAaAH3/8EYcPH0ZeXh4mTJiAoUOHVsl1GSsJV1arUO/evXH+/HnY2dlBU1MTrVq1KvYLpVDfvn0xf/58tGvXDtu3b5fbN2fOHIwcORLbtm2DhoYGNm/eLOy7cuUKAgMDkZeXh19//RUA0LJlSzRv3hwmJiZo2LChwrWUbYnR1dXFtGnThNakVatWQUNDAykpKZg2bRpCQkKE53r79m3Ur18f4eHh2Lp1KwAgJCQEn3/+udyXOQAEBQVhxIgRZV6fVU9eXl748ssvAQDfffcdgoKChJbCmqq2xLOXlxc8PT0hkUjw6tUrfP/99wCAKVOm4IsvvkBgYCBev36N//73v8J5OZ7V17Fjx5CcnIyTJ0+quiiMCUSFf/WziiUSiUgVr61UKkVwcDBat26tsO/zzz/HrFmz1Lqvk0gkAhGJys6peqW9BxISEjB8+HDUq1cPRIS9e/ciJiYGvr6+ICLo6upi165d0NHRgVQqhZWVFa5duwYigre3NwICApCamopdu3bhk08+gVQqxWeffYYbN24IlZoPPvhAaI3JycnBxIkTcf/+fWRnZ2PlypWwsrLCkiVLcODAATRs2BBubm6YPn36ez/v6dOno1+/fkIFqITXpkbcx8pWk+NZnd4DQMnvg5oWy19++SWaNWuG69evQ09PD/7+/vjwww/Lem3U6l4y9cPzrNYCOTk56NOnD7S1tdX2i62mCQ8Ph7OzMyIiIhAZGQldXV2Ym5vj9OnTiI6OhqmpqTDABij4ufbYsWMwMDDA+fPncezYMfj4+Agt1kDBQJzjx49j+PDhWLlypdz1AgICYGRkhPDwcOzdu1f4Itu5cyciIiIQHh6OadOmKZRz4MCBCvM1ent7F/uc1q5di44dO+Ls2bMwMzOrgFeJFYfjuXqpabGclJSE7OxsnDx5EkOHDi12fAFjVY27AdQw3ClfPQwZMgRLly7F8OHD0aZNGyxYsACxsbHw9fWFTCZDamqqXJ9Pc3NzAAWDcoyMjIT/F+2v+PZAnKJiYmJw7tw5hIWFAYDQv3Dt2rWYNGkScnNzMWHCBIW+xHv27FH6OU2ePBmTJ0/G8uXLsXLlSqxYsULpY1nxOJ6rv5oWy02bNkWvXr0AAG5ubli0aJGyLwVjlYYrq7VIVXXQX7RoEcLDwwEU/EQ2aNAgrFq1CgsWLMBvv/0mrPRy/PhxYUBKbaOhoYGlS5cCKPjZ7dixY9iyZQsWLlwIKysrzJw5E0V/cizax7e4wTtA8QNxCpmZmcHIyEhohcnOzgYAWFlZwdHREYmJiXB3d8fVq1fljhs4cCDS09PlthkbG2PLli1y2968eSNMRaanp4c3b96U7wVh5VZV8bxhwwYEBgZCW1sb+vr62LZtG7S0tPD999/j2LFjyMvLE94TdevWrfTyVDc1LZYdHR1x5coVuLq64uLFi/jkk0/K/ZowVtG4ssoq3HfffYfvvvsOAODi4iI3knTevHnw8vJSVdGqjUOHDsHf3x+amprQ0dGBWCxGVlYWxowZgw4dOqBx48blHk1f3ECcQmPHjsXkyZNhb28PoOBnxtWrV8Pd3R1v3ryBTCbDlClTFM6pbGvMokWLcO7cOQAFLTNFf9Jk6s3JyQnjx4+HhoYGZs6cieDgYIwZMwbffvst5s2bBwAYMWIEjh8/jj59+qi4tFWvpsXyiBEjMGHCBOH8RQf7MaYqPMCqkpRnUEZN66Bf6O+//4azszNiY2MBAAsWLMCePXvQuHFjDBo0CN988025z6lOHfmrcmBOaQNxqqOafB9rajwDwPz582FkZIRRo0YJ2/Lz8+Hl5QU/Pz+FVsDSqNN7AKi6eFa3WAbU714yNaTqJbRqakI5luYLCAggPz8/4XF+fj5lZWUJj2fOnEnbtm0jooIl+Pbu3UtERGPGjKFp06YREVFQUBDNmjVLyBMcHCyc28fHh4j+XY96w4YNtGzZMiIiSktLI0tLSyIi6tChg3Ddt9cHJypY1lEikcilMWPGlPi8VqxYQUuWLBEe//PPP5Sfn0+vX78mFxcXCg8PV/IV+hfUaFm/8rwH3ldpSzNWRzX5PtbUeL558yZ169ZN7rnMmzePjIyMqE+fPvTy5UslX6EC6vQeoCqMZ3WLZSL1u5ec1C9xN4BqoKZ10C/09go2hSvjaGtrY9CgQbhy5YrwUxN7P8UNxGGqURPjOSEhAaNGjUJoaCgaNGggbF+yZAkWL16MSZMmYdu2bZg4caLS52TF41hmTBFXVquBmtZBHyh+BZvMzEzo6uqCiBAREQFPT0/lXiDG1EhNi+fk5GR4eHhg69ataNeunbC9cFCdSCRCkyZNUL9+feVfJMYYKweurFYDNa2DPlD8CjbTpk3DnTt3QESwtbVFv379yvWcapOEhAR4e3tX2SoyUqkUMpkMQUFBMDY2hrW1NerWrYtXr15hxowZ+Pzzz0s9fuXKldi3bx80NDSwfv16dOrUCWfOnMHkyZPx2WefFfsHTU1V0+J5zpw5SEtLw9SpUwEAw4YNw7hx4zBx4kTEx8cjLy8PJiYmPMVRCVQdyw8fPsTXX3+N58+fQ0tLC0ePHi31eI5lVi2puh9CTU2owv6Kb1PHPk/Kghr1jXqf98CDBw/I0dHxnY8vr7ffMzKZjIiInj17RoaGhqUee+fOHZJKpZSfn0+3b98mqVQq7IuIiCi2H2RtuY8VoabGszq9B+g93geqjuXevXvT48ePlTr2XWKZSP3uJSf1S7yCFWNVZMaMGfj9998BAHl5eTAzM0N2djbmzJkDqVSKbt26YcOGDQrHjRo1CmfOnAFQ0J+tcNWZmzdvwtnZGQ4ODhg8eDBevXpVYWUtnP/2xYsXZa5GFRERgX79+kEkEqF9+/Z48uQJcnNzK6wsjFU36hLLiYmJwq8jEokEAQEBpebnWGbVFXcDqIG4g371NHr0aMydOxeDBg1CWFgYHBwcUK9ePcybNw8NGzaETCZDp06dSlzO9G0TJ05EcHAwDAwMsG7dOmzatElumcXs7Gy4uLgoHCcWi7FkyZJSz52VlYXevXvj1q1bWL58eal5nz59io8++kh43KRJE2RkZKBFixZKPQ9WOo7n6kddYjkpKQlXrlxBbGwsPvjgA0ilUtjZ2cHExKTY/BzLrLriyipjVaRjx45IS0tDWloatm/fjlmzZgEANm7ciH379kFTU1PYX1RJg25iY2OFfsEymQxSqVTuuHr16r1zRadhw4Y4ffo0/vnnH3Tv3h2DBw9GkyZNis3brFkzZGRkCI+fP38OPT29d7ouY+pAXWK5adOmMDMzg4GBAYCC/qwxMTElVlY5lll1xd0A1EBCQgKcnJyq7HqFE5XHxcUhPT0djo6OEIvFsLS0LLNz/oEDByCVSiGVStGlSxdhWp6wsDDY2NhAKpXCwcEBjx49AgDs3r0bHTp0KLOlr6YYPnw41q1bhwcPHsDc3BwZGRnYunUroqKicOzYMTRp0kTuSwwo+MJJTEwEAFy+fFnYbmZmhp07dyIyMhLnz5/HwoUL5Y7Lzs4W7kXR5OvrW2oZc3JyhDI0aNAA2trawlKqDx8+VPhZUCqV4vDhwyAi3Lt3D82aNUOdOvx3cElUGc+JiYly74W6devKVU7elp6eDgsLC+jq6iI4OFhuX0hICBwdHWFvb4///e9/AGpXPKtDLBsbG0Mmk+HZs2cgIly5ckWoqHIsM3XC70JWrNDQULRu3RovX77E9u3b0bp1a/zzzz+wsbFBr169SjyuX79+wij/pUuXCh90Dg4OcHV1BQBs3boVP/30E1auXAkPDw9kZWXh8ePHlf+kqoFhw4bBwMBAGDmtq6sLMzMziMVimJqaCnPRFuXt7Y1hw4bhl19+Qdu2bYXt69evx+jRo4UvnJkzZwqvMfDurTEPHz7EmDFjoKGhAZlMBj8/P2hpaQEAbG1tcfnyZbRs2VLI3759ezg7O0MsFkMkEmHdunXlviarXIXxDPzbreDcuXNYsmRJqS1njRo1woEDBxT6X/711184ePAgTpw4AQ2Nf9s8alM8q0Msa2pqYvXq1XBzc0Nubi769OmDTp06AeBYZmpG1SO8ampCGSNHp0+fTrt37yYiotzcXDI1NSWZTEazZ88miURCXbt2pfXr1xOR/GjSkSNHUnR0NBHJj86MiYkhJycnsre3Jw8Pj3KvJlNUSaOPs7KyqH379kqfx8zMrNhRqKtWraIdO3YIjwMDA2nx4sVKnRNqNOq0rPdAdTJ06FASi8V07969EvM8fvyYRo0apfQ5o6OjycLCQliJqaiadh/VMZ7Hjx9Pv/76q1Ln8PPzo6CgIOHxwoULaeLEieTs7Ey9e/em27dvC/uUjWd1eg+QGsVzVccykfrdS07ql7hlVUXUpYN+UVOnTlV6ffHLly+jVatW0NfXF7bt3r0by5Ytw4sXL3DkyBGlzsOqRkhISJl59PX1ERgYqPQ5xWIxLly48D7FUhvqFs/Z2dkICwvDjz/+qPRzLCopKQlpaWkICwvD1atXMX78eB4IVk1wLLOaiCurKqIuHfQL+fr6onnz5hg3bpxS+Xfs2KGwKICHhwc8PDwQEhKCuXPnYteuXe9cHsaqE3WL54MHD8LR0RE6OjrvdHzTpk3RtWtXaGhooHv37khKSnrnsjDGWFm4sqpCJXXQj4mJQU5ODtq3by/3BQaU3UG/sCWzcMnFQu/TErNixQokJycrzNH38OFD6OvrK3TAz8nJwaFDh+SmPCpcmhEA9PT0eGlGVuOoSzwDBSvMFW2pBUqO5+I4OjoKLXjx8fFo2rRpmccwxti74sqqCqlDB/0HDx5g9uzZwkh+ADh+/Djq1atXbAd9oGDkv62tLRo0aCBs27x5M37//XeIRCJoaWnh559/LndZGKvO1CGegYK5NGNjYyGRSOS2lxTPvXv3xu3bt1G/fn2Eh4dj69atcHR0xPHjxyGVSpGTkwN/f/93KgtjjClF1Z1ma2qCmnTGL05ldNAvSWhoKJmbm5O/v79S+aFGHfm1tbVTABAnxaStrZ2i6vujbFLnWCaqvvGsTrFMHM81Jp45qWcSERFYxROJRMSvbcUTiUQgIlHZORmrGBzLlYNjmTGmLF4UgDHGGGOMVVtcWWWMMcYYY9UWV1YZY4wxxli1xbMBVBJtbe1UkUjUsuycrDy0tbVTVV0GVrtwLFcOjmXGmLJ4gJWKiEQiPQD3AfwKoA+AQUR0VbWlen86Ojopb9684S/2CqCtrZ36+vXrD1VdDlY2kUj0PQBjAJ8B2A9gJhHlqrZU74/jueJwPDP27riyqiIikWgxgHEAHgM4CEAXwDfqPuyYR05XHB4trR5EIlFjAEkA8gDsBJALYC8RRaqyXBWB47nicDwz9u64z6rqfAugGQAZAD0AkfytwJhaWgigIYBXAD5AwR+g91RaIsYYq0G4ZVVFRCLRJwAeEFGOqstSkbglpuJwS4x6EIlEjQDoEVGiqstS0TieKw7HM2PvjiurrELxl1vF4S83pmoczxWH45mxd6eybgA6OjopIpGIOCmXdHR0UlR1r95XXFwczM3NkZ2dDQBYsWIF/Pz8AAA6Ojpwc3MDAJw9exZWVlaws7ODg4MD4uPj5c4THh4OkUiEx48fAwB2796NDh06YMmSJZVW9kePHsHJyQm2trbw8fEpNs/MmTNhYGAAJycnue3W1taQSCTo3r07QkJChO1//vknXF1d4eDggNGjR1da2VWB47pmxnBRFRHPISEhcHR0hL29Pf73v/8BqD7xPGjQICFu16xZI2xv27YtpFIppFIpFi1aJGzX0dERtm/atKnSys5YraaqdV6h5uttVzWoyTraJd3XZcuW0YIFCyg+Pp66detGb968ISIiIyMjIc/jx48pKyuLiIgOHz5MXl5ewr78/Hzq3bs3ffbZZ/To0SNhe2BgIC1evFip1/DFixdK5SvK09OTTp06Jfw/PDxcIU9SUhLdv3+fHB0d5bbLZDIiInr27BkZGhoK25ycnCgzM7PMa6vLPS+aOK5LVpPu5/vE861bt2jYsGGUl5encN7qEM+FcZuTk0NGRkb0/PlzIpJ/bkWVtP1t6nj/OXGqLokHWLEq4ePjgyNHjsDT0xNr1qyBlpaWQh59fX00aNAAAKClpQVNTU1hX1BQEPr27SvsV9arV68QEhKCAQMGYPz4/2vv7uNqvv//gT9O0cqwks3H+MWU5VYuslydnJxTyWUzLBfJXDbMxfSxhshcLDHNxUcYJrEKn9jHVQjTxcr1xdCyEBJRWWpGOV09f3/0Pe91nBNdn4ue99vtdeO8z/vidc6z1+nV+7yer9fUStf7ypUrcHZ2BgAMHToUMTExKvu8//77MDBQbUpGRkYAgL///hu2trYAgHPnzqFJkyYYN24cZDIZIiMjK10nxjStOu157969MDU1xYABAzB48GDcvHmzwteti/asaLd5eXlo06YNGjVqBKD0xo6TkxP69++PK1euCPtnZGRAKpVi6NChKt8GMcZqBi8KwOpEgwYNIJVKcfDgQUgkktfu+/z5cyxcuBBbtmwBAOTn5+Onn35CVFSU0tfpr3Py5EmEhoYiMzMTgwcPxubNm/Gvf5VOcZiWloZx48apHOPu7o6ZM2cqbSspKRH+b2Zmhuzs7ApdX/E6Bg0ahBs3bmDlypUAgPT0dFy5cgVXr14FEaF3797o06cPmjZtWuHzMqZp1WnP6enpyMrKQlRUFC5fvoypU6ciNjb2teeo6/bs5uaGCxcuYPr06UIn+/z582jevDl+//13jBw5Ejdu3AAApKamonnz5jh16hQmTZr0xtfCGKs8nb+zeuDAAaSlaV8SblhYGMRiMcRisdq/3gEgMDAQvXv3hqOjIxITE+u4hnXrjz/+QFxcHPr37//acV1yuRzu7u5YuHAhunTpAgBYt24dpk2bhgYNKv63VVhYGG7evImxY8di4sSJwi82ALCwsEBsbKxKefUXGwClO6a5ubkwNzevcB0aN26MX3/9FcnJyVi+fDn++usvNGvWDL169YKpqSnMzMzQuXNnpKSkVPic+o7bs26oTntu1qwZ+vfvDwMDA3Tv3h3p6elvvF5dt+fIyEjcu3cPhw8fFjqlzZs3BwB07NgRpqamePLkidJ2FxeXCr0WxlgVaGr8AWpobNv48eMpPj5eZXtRUVGNnL8qcnJyqEuXLpSfn09Pnjyhzp07q4zPunnzJslkMiopKaHk5GSSyWSvPSd0ZLyTuriWlJRQnz596MKFC/T8+XPq3LkzPXr0iIiUx3sVFhbSJ598Qjt27FA63tPTk/r370/9+/cnMzMzkslkwli4141xy87Ops2bN1O/fv3I3d2dDhw4QERE9+/fJ6lUqlKCgoJUzjF69GiKjY0lIqKxY8cK491ede/ePaUxqwUFBVRSUkJERHl5edShQwd6+fIl5ebmUrdu3aigoIDkcjl17tyZ/vzzT7Xn1JWYly3Vbdf63J71JZ7Vbc8nT56kyZMnExHRnTt3qEePHsJzmm7PxcXFVFBQIPy/d+/edPfuXXr58iXl5+cTEdGjR4/IysqKiouL6e+//xZ+NhMTE6l79+5q606km/HnwkVbiuYu/Jpfar6+vtSnTx/q1auX8IG0ePFi8vLyoiFDhlDHjh3p9OnTlJSURC1atCA7OzsaPHgwERFZWFjQ9OnTydnZmbKysmjw4MHUp08fcnJyojt37hARkVQqpa+++opcXV3J2dmZMjMz6ejRozR9+nShDsOGDaPffvut3Dq+TlRUFH355ZfC44EDBwrXVti8eTOtWbNGeGxra0uFhYXlnlNXPujUxXXjxo1K78fhw4dpxIgRRKT8yy0kJISaNGki/LL54osvVM4llUqrlGCVlpZGwcHBb9zvVampqeTs7EwSiYS8vb2FDujs2bOFn49169ZR7969qVmzZuTi4kI3btyg27dvU58+fUgmk5FYLKbdu3cL59y9ezf17t2bevTo8do66UrMyxZ18ef2XEpf4lnd9lxSUkJff/01SaVScnBwoPPnzysdo8n2/OzZM6G+vXr1onXr1gnX69q1K0kkEhKLxXTy5EkiIjp//jzZ2dmRo6MjOTo60pUrV8q9ti7GnwsXbSmau3A5ndVjx47R1KlTiYgoPz+funTpQsXFxbR48WKaNWsWERHFxMTQqFGjiEj1TkyDBg3o3r17RETk7e1NISEhRER04sQJ4QNVKpVSWFgYEREFBweTj48PFRcXk52dHeXl5VFGRgaJxWKVum3ZskXtX/C3b99W2i88PJwWL14sPB4zZgxduHBBaZ/ly5cLdSMicnBwoKysLLXvCZW+YUQailVlSmXvrFlZWQkdk8rau3cv2dvbq72Dog90JeZly6vx5/b8D32I55twey6fLsafCxdtKVqXYJWYmIi4uDjIZDIApWOeFGODunfvDgBo27ZtuQPjW7dujbZt2wIAkpOTMWPGDACARCLBV199JezXq1cvAKVzYe7fvx8GBgYYOXIk9u7di8ePH6ud/3LKlCmYMmXKG1+Dubk5cnJyhMfqxka9us+zZ89gZmb2xnPrm9u3q74qpbu7O9zd3WuwNqymcXuuX7g9M8Zqg9YlWNna2sLV1VUYJH/t2jW0aNECQOkKIApEpauqGBkZoaioSNhedrqjDh06ID4+HgCQkJAAa2tr4bnz588DKJ1KSLF90qRJ2LFjByIiIuDh4aFSt61btwqTP5ctrybI9OzZE/Hx8ZDL5Xj69CnS09OFX7gKMpkMR44cARHh9u3bMDc3r1QCkb7S1QSbQ4cOCT8PdnZ2sLe3F55TNwG6QnBwMBo2bFjr9dcUbs/1m662ZyLCF198AUdHRzg7OyMpKQlA6cwko0aNgkwmw8CBA4WEqmXLlgk/P23btlX6Q4oxVgM0dUsXr/l6yc/PjxwdHUkmk5GHhwcRlY5xCw0NJSLlZJZDhw6RRCKhcePGEZHymKnMzEwaMGCAMHZQ8fWeVCqlf//739S3b19ycnKijIwM4ZgRI0bQlClTyq1bRe3cuZN69epFvXr1ol9++YWIiB4/fix83UlEtHLlSnJwcKDevXvT1atXX3s+6MhXSK+La0XoaoJNWcuXL6fvvvuOiF4/AfqLFy9o4MCB1K5dO7Xn0ZWYly3q4s/tuZS+xLMydLU9HzhwgLy8vIio9OfTycmJiErHp/v7+xMRUUJCgtLCJQqurq5K43AVdDH+XLhoS9HchTW40s2rSTpljRo16rWD5DVFVz7o1MW1PiTYlGVra0sPHz4kIqKlS5fS9OnTydXVlQYNGkTJycnCfkuXLqVjx46VuwKOrsS8bNFEu9aV9qwv8awP7fm7776jH3/8UXjcqlUrksvl9MUXXwjJVQUFBdSqVSul49LT08nGxkbtdXUx/ly4aEvRumEAmlJYWIjBgwfD2NgYXbt21XR19EZUVBSePn2KuLg4xMTEYPHixcLE3CYmJjh48CCCgoKwfv162NjYYMCAAQgKChJWdnr06BG+/vprnDp1CgEBAXB3d0dcXBx8fX0xf/584Tpdu3bFiRMn4OnpicDAQPTv3x9nzpxBfn4+MjMzkZGRATs7O6W6VfRr4OzsbKXxh6+bTPzixYto2bIlWrVqBaB0AvRHjx4hKioKS5YsEVbdyczMxOXLlzFgwIDqvcFMLW7PtaO+tOcuXbrg2LFjKCkpweXLl/H48WM8ffoUXbp0EV7LkSNH8OeffyodFx4ejjFjxlTvTWaMqaiXg6rUrTDSsGFDHDlypO4ro+fqS4KNwk8//aS0mk6zZs3QtWtXlQnQFy9eDD8/vzdem70Zt+e6U1/ac//+/XHhwgU4OTmhffv26NSpE959911MmjQJX3/9NWQyGbp3746OHTsqHRcWFoZDhw69sQ6Mscqpl51VALCysqqzlYPWrl2LI0eOoLi4GNOmTcOoUaNQXFwMPz8/XLx4EUVFRVi6dCmkUmmd1KcuKRJs1q9fDwAoKCgQ1t6uaoKNlZWV2gQbS0tLlQQbDw8P/PXXX4iLi1Op29atW7Fr1y6V7du2bYOVlZXwuGfPnvD19YVcLseLFy/UJtgApXfzIiMjhaVVgdJVbRRLxN69exfNmjUDAKSkpGDRokUAgMePH8Pd3R379u1T+x6yyqmrtn337l1MmDBBWBVp586daNOmTa1fV5PqU3tetGgRFi1ahGvXrmHt2rUwNDSEoaEh1q1bBwA4evSo8NoB4OrVqzAzM9P7nwHGNKHedlbryvHjx/H48WP88ssvStu3bduG//f//h9WrFihoZrVjUGDBuHs2bPo06cPDA0N0bJlS7W/UBQ+/vhjLFq0CO3atcPOnTuVnvP19cX48eOxY8cOGBgY4McffxSeu3TpEkJCQlBcXIzdu3cDAFq0aIHmzZujffv2aNy4scq1KnonxtTUFN7e3sLdpNWrV8PAwAAZGRnw9vYWOqNRUVFwdHTE22+/LRzr4uKCEydOQCaTobCwEEFBQQCg9PNgZWXFHVUdtGnTJkyePBnjx49HWFgY1q9fj9WrV2u6WrWqvrTnnJwcDBs2DIaGhmjRooXQbm/cuIHp06ejQYMGsLKywtq1a4XzhoaGKn2rwhirOSLFX8B1fmGRiNRdOzU1FZ6enjAyMgIRYf/+/UhMTISfnx+ICKampoiIiICJiQlkMhnEYjGuXLkCIoKXlxeCg4ORmZmJiIgIfPjhh5DJZOjWrRuuX78ufPC99957wt2XwsJCTJ8+HXfu3EFBQQECAwMhFovh7++PQ4cOoXHjxnBzc8OcOXOq9DonTZoEc3Nz/PbbbzAzM0NQUBD+9a9/wdnZGRKJBAkJCWjbti3+85//oEmTJq97v0BEonJ30BLlxbU2yWQyhIWFoXXr1irPjR49GvPmzdPJcYu6EvOyXhd/fWvbISEhePbsGWbPno3169cjPz8f8+bNe917o1fxrC3cnhljr9K6BKvo6Gi4uroiJiYGsbGxMDU1hb29PX799VfEx8fDxsYGERERwv49e/bE8ePHYWFhgbNnz+L48ePw8fHB9u3bhX1eHaxfVnBwMCwtLREdHY39+/cLv7jCw8MRExOD6OhoeHt7q9Rz+PDhKgP5vby8VPZLT09HQUEBfvnlF4waNQpz584VtpuZmSE6Oho2NjYq9WLVwwk22kff2raLiws2b96Mzp07Y8OGDULyHKt53J4Zq9+0bhjAyJEjERAQAE9PT7Rp0wZLlixBUlIS/Pz8IJfLkZmZiaZNmwr7KyZfb926NSwtLYX/lx3T9Opg/bISExNx5swZREVFASgdbA8AGzZswIwZM1BUVIRp06ZBIpEoHfe///2vQq+nWbNmGDhwIADAzc0Ny5YtU7td0YlllccJNrpB39r2vHnz8O2338Ld3R179uyBr68vfvjhh0q8I0wdbs+MsVdpXWfVwMAAAQEBAEq/Qj9+/Di2bduGpUuXQiwWY+7cuSj7tVTZQf3qBvgD6gfrK9ja2sLS0lK461JQUAAAEIvFcHFxQVpaGoYNG4bLly8rHTd8+HA8ffpUaZuVlRW2bdumtM3FxQWXLl3CgAEDcP78eXz44YdK2zt06KC0nTF9pW9tGwDeffdd4V9FZ5gxxljN0rrOamRkJIKCgmBoaAgTExNIJBI8f/4ckydPRocOHdC0aVOluy8VoW6wvsLnn3+OmTNnwsnJCUDp14pr1qzBsGHD8PLlS8jlcsyaNUvlnBW9+zJu3DhMmzZNOL8iicDHxweTJ09GcHAwTExMVJIPWM2rqyzxJ0+eYMKECcjLy4O5uTm2b99e6Z9ZfaRvbdvPzw9Tp05FgwYNUFhYiC1btlSq7qzy6qoNx8XFYebMmbh9+zZSUlKE8bNLlizBf//7X2HJ4BMnTijNCMAYqx1al2BV0143WF+X6MrgfE0kZFRUXf2i+/e//41u3brB09MT4eHhSE5Oxrffflvp8+hKzMuqy/jrWtvmeFZfXbXh3NxcNGjQAG5ubko/Y0uWLIGVlRXGjh1b6XPqYvwZ0xZal2DF6p/U1FT07t0bTk5OkMlkyMnJwa+//oo+ffrA0dERH3/8MfLz8wGUdlB8fX3Rv39/9OvXDxEREejfvz/s7Oxw69YtYR8fHx/069cPLi4uyMrKUrpeYWEhPv/8c2FGhrNnzwIA/P390aNHDzg7O2PNmjVVfj03b95Ez549AQA9evRAdHR0lc/FmC7QtzZsamqqdnosAFi1ahUkEonStFWMsVqmqXVeoYE1xHUZdGRd6arENTg4mBYvXiw8LikpoefPnwuP586dSzt27CCi0nXD9+/fT0REkydPJm9vbyIiCg0NpXnz5gn7hIWFCef28fEhIiJLS0siIvrhhx9oxYoVRESUlZVFvXr1IiKiDh06CNctLi5WqeewYcNIKpUqlcmTJ6vsN3/+fFq7di0REa1du5asra0r/Z4Q6U7MyxZu1+XT53jqWxtWkEql9ODBA+Hxn3/+SSUlJZSfn0/9+vWj6OjoCr0/RLoZfy5ctKVo3ZhVVv/oW5a4r68vvvzySzg7O8PBwQGtWrWqxLvBmO7RtzZcHsWyrMbGxvj0009x6dIlYUw0Y6z26NwwgNTUVPTt27fOrqeYnFwxTurUqVMQi8VwcHBQSehQJzo6Gn379oWTkxN8fX2F7Xv27IGLiwucnJzwn//8BwCwb98+dOjQAf7+/rXzYrSUIks8PDwcGRkZOH78OJYvX46lS5ciLi4OQ4YMAVHls8QBlJsl/tlnnyE2NhaxsbG4du0agNIs8R07diAgIACzZ89WqWdF599s2rQpduzYgejoaLz//vsYPXp0Fd+Z+kWX2nZBQQFGjhwJR0dH9OjRAydPngQAJCQkwM7OTu3PhT7TtzZcHkWnmIgQExOjUi/GWO3gO6sVsHfvXrRu3RrFxcWYM2cOoqOj0ahRI4jFYri5uZW78lR2djZWr16NI0eO4K233hK2//HHHzh8+DBOnjwprCsOAO7u7nj+/DkePnxY669Jm+hblnh0dDSWLVuGhg0bwt7eHsuXL69U3VndqWrbPnHiBN5++23Ex8fjwYMHGDp0KFxdXSGRSLBu3TqEhYXV8SvRLH1rw3/88QdmzZqFa9euwcPDA+7u7pg9eza8vb1x8+ZNEBEcHR0xZMiQSr0mxlgVaWr8AcqMhZozZw7t27ePiIiKiorIxsaG5HI5zZ8/n6RSKXXt2pU2bdpERET37t0jFxcXIiIaP348xcfHExFRTEyMMPYoMTGR+vbtS05OTuTu7k4vXrygqio7Zik5OZmGDBkiPPfFF1/QqVOnyj12586dNH78eBowYAC5uLjQ2bNniYho6dKlNH36dHJ1daVBgwZRcnKycExISAh9++23KueCjox3ghaMWXx1nJmu0pWYly2vxl9f2/aNGzfIw8ODSkpK6Pr16zRgwADhubL1LUsf4llX9KUNl6WL8efCRVuKVtxZnThxIhYsWIBPP/0UUVFRcHZ2hpGRERYuXIjGjRtDLpejU6dOFf66Zvr06QgLC4OFhQU2btyIrVu3Ki2rWFBQgH79+qkcJ5FIXvsVfHZ2NszMzITHZmZmyM7OLnf/9PR0/PHHH4iLi0NmZiYGDhyIGzduID09HVlZWYiKisLly5cxdepUtau2MKbr9LVtt2vXDnl5eejQoQNyc3Nx8ODBCtWfMcZY5WlFZ7Vjx47IyspCVlYWdu7ciXnz5gEANm/ejAMHDsDQ0FB4vqzyxjolJSVh3LhxAAC5XA6ZTKZ0nJGRUZU6h+bm5sjJyREe5+bmCgPu1WnWrBmcnJxgbGyMNm3aoGnTpnj69CmaNWuGrl27wsDAAN27d0d6enql68LKxx1/7aGvbXvHjh1o3bo1Dhw4gNTUVAwbNgy//fZbpa/L1OM2zBgrSys6qwDg6emJjRs34t69e7C3t0dOTg62b9+OxMREFBYWwtraWumXFlDaGUxLSwMAXLx4Udhua2uL8PBwIQtbscyiQlXvvlhZWSE1NRU5OTlo1KgRzpw5gxUrVgAA7t+/j1atWqFBg3/eUmdnZ8yYMQNEhL/++gs5OTkwMzODi4sL9uzZAwC4e/cumjVrVpm3qt5JTU2Fl5cXfvnllzq5nkwmg1wuR2hoKJo1a4YRI0ZALpejqKgIixcvxsCBA8s99tChQ8L8jrm5uTA0NMTly5eRkJCAmTNnolu3bmqX7dRn+ti2gX+WWjUzM8Pz588r+nbUe5psz0ZGRsIfOwBw+vRpZGVlKd1VL6ugoABjx47F48ePIZfLsXz5cri6utbr9syYJmhNZ3XMmDGwsLDAsmXLAJROymxrawuJRAIbGxu1dzm8vLwwZswY7Nq1Cx988IGwfdOmTZg4cSKKiooAAHPnzsWAAQOE56t698XQ0BDff/89Bg0aBKB0yVRF0oCjoyMuXrwoLMMHAO3bt8fAgQMhlUohl8uxZs0aiEQiuLi44MSJE5DJZCgsLERQUFCl68JqlyLx5sWLF9i5cydat26NP//8E717935tZ3XIkCFC0kVAQIDQwamviTeAfrbtsWPHwsPDA1KpFHl5eZxEp+UU7Rn4567tmTNn4O/vX25HFeBEOsa0hqYGy0ILEnEqYtSoUSSRSOj27dvl7vPw4UOaMGFCta+1d+9esre3p6CgIJXnoCOD8ysaV11JvCnr+fPnlZrg39bWlh4+fCg8Li/xpjy6EvOyRVfaNVHttO34+Hjq2bOnMLl9WfocT11sz1OnTqXdu3e/9tiqJNKVRxfjz4WLthStubOqrRRf179Oq1atEBISUu1rubu7w93dvdrn0QW6knhT1pdffok5c+ZUaN+LFy+iZcuWvCCAFquNti2RSHDu3LnqVEsn6Vp7LigoQFRU1BuXTOVEOsa0A3dWmUboSuKNgp+fH5o3b44pU6ZUaP+ffvpJaWwcY/pM19rz4cOH4eLiAhMTk9fux4l0jGkH7qwyjdGFxBsAWLVqFR4/fozg4GCl7eUl3hQWFiIyMhIrV658wzvAmP7QlfYMAKGhoUp3agFOpGNMm3FnlWmMLiTe3Lt3D/Pnz0fv3r2FuzsnTpyAkZGR2sQbAIiKioKjoyPefvvtSl+PMV2lC+0ZKJ1TNykpCVKpVGk7J9IxpsU0NVjW2Ng4AwBxqVgxNjbO0PQA54oU6FCCTXnqOvGmPNDBhAxu17rfhssWbs/q1Zf2zIWLthQREYGxmiISiYh/pmqGSCQCEYnevCdjtYPbc83h9sxY1RlougKMMcYYY4yVhzurjDHGGGNMa3FnlTHGGGOMaS2eDYDVKGNj40yRSNTizXuyNzE2Ns7UdB1Y/cbtueZwe2as6jjBitUZkUjUFMBdAAsBfAtgPhFt12yt6o5IJGoCIARAawC/AniXiCZqtlasokxMTDJevnzJHbcaYGxsnJmfn/8vTdejOkQi0WCUtucAAH4AuhPRPc3WijH9xJ1VVmdEItHXAMYDaArAB8B9Ijqv2VrVHZFIZAJAAkAKYDKAtwHYENFDjVaMVQhnxtccfciMF4lEXQC0QWlntRjAGSL6QrO1Ykw/cWeV1QmRSNQAwN//97AIQAaAvUS0QHO1qlsikaglSu/E9ABQCOBdAEeJyE2jFWMVwp3VmqMnndUvAUwB0BZACYDGANoQ0QNN1osxfcSdVVYnRCKRIYDtAP4L4DwRZWu4ShojKl0Q3QrAIABGRBSo4SqxCuDOas3Rh86qwv8N77EHMA6ALxHx2FTGahh3VhljrAK4s1pz9KmzyhirfTx1lQaYmJhkiEQi4qJaTExMMjQdn5rCcdb/OKekpMDe3h4FBQUAgFWrVmHx4sUAABMTE7i5lY7wOH36NMRiMfr06QNnZ2fcvXsXAJCfn49Ro0ZBJpNh4MCBSE9PBwDs27cPHTp0gL+/f63V/cGDB+jbty8cHR3h4+Ojdp/r169DIpHAwcEBq1evFrbfv38fQ4cOhbOzMwYOHAgAuHDhAmQyGWQyGXr16gVzc/Naq7smcHvW//bMtJim13utjwV6sN52bYEerZ/NcS6fLsa5vHiuWLGClixZQnfv3qWPPvqIXr58SURElpaWwj4PHz6k58+fExHRkSNHaOzYsUREtG7dOvL39yciooSEBGE7EVFISAh9++23FXo///777wrtV5aHhwedOnVK+H90dLTKPhKJhG7dukXFxcXk6OhIKSkpREQ0aNAgevjwYbnnDg8Ppy+++KLc5/Up/kw348lFtwrfWWWMsWrw8fHB0aNH4eHhgXXr1uGtt95S2adVq1Z4++23AQBvvfUWDA0NAQA3b95Ez549AQA9evRATExMha+bl5eHPXv2YOjQoZg6dWql633lyhU4OzsDAIYOHapybblcjmfPnqF9+/YwMDCAm5sb4uLikJaWhry8PHz11VeQSqUIDg5WOfdPP/2EcePGVbpOjDGmDi8KwBhj1dCgQQNIpVIcPHgQEonktfs+f/4cCxcuxJYtWwAAXbp0QWRkJPr27YsjR47gzz//fOP1Tp48idDQUGRmZmLw4MHYvHkz/vWv0ilL09LS1HYS3d3dMXPmTKVtJSUlwv/NzMyQna2c85idnQ0zMzOVfdLT03Hp0iUkJSXhvffeg0wmQ58+fdC+fXsAQEZGBu7fv49evXq98bUwxlhF8J1VHXTgwAGkpaVpuhoqwsLCIBaLIRaLy71DFBgYiN69e8PR0RGJiYl1XEPdwnHWDX/88Qfi4uLQv39/bN26tdz95HI53N3dsXDhQnTp0gUAMGnSJACATCbD6dOn0bFjxzdeLywsDDdv3sTYsWMxceJEoaMKABYWFoiNjVUpr3ZUAcDA4J+P/9zcXJUxpubm5sjNzVXZp1mzZrC1tYWFhQWMjY0hk8mUYhweHo4xY8a88XXUJ9yWGasmTY9DqI8F1Rz7NH78eIqPj1fZXlRUVK3zVkdOTg516dKF8vPz6cmTJ9S5c2cqLi5W2ufmzZskk8mopKSEkpOTSSaTqZwHejT2ieOsX3FWF8+SkhLq06cPXbhwgZ4/f06dO3emR48eEZHymNXCwkL65JNPaMeOHeW+t0eOHKEFCxYIj183ZjU7O5s2b95M/fr1I3d3dzpw4AAREd2/f5+kUqlKCQoKUjnH6NGjKTY2loiIxo4dK4xfLUsikdCdO3eopKSEZDIZ3b59m4qKisjOzo5yc3OppKSEXFxc6Pr168IxdnZ2dPfu3XJfJ5H+xL+i9LktE+lmPLnoVuFhAFpiwYIFOH36NAoKCjB//nx88sknWLJkCdLT05GVlYW7d+9iy5YtMDU1RVRUFK5du4ZWrVohMjISbdq0gZubG5KTk7Fnzx5MnDgRf//9NwwNDbFt2za0a9cOMpkM3bp1w/Xr11FcXIzdu3fj8uXLiIyMxMaNGwEAw4cPxzfffAM7O7tK1//8+fOQSqUwNjaGsbExWrVqhdTUVLRr107YJyYmBkOGDIFIJIK1tTWePHmCoqIiNGhQf34MOc765YcffoCdnR26d+8OAFi+fDlmz56NiIgIpf3CwsIQHR2N3NxchISEwMbGBps2bcKNGzcwffp0NGjQAFZWVli7dm2FrtusWTNMnToVU6dOxYMHD3Dy5EkA/9xZrYiVK1di0qRJKCgoQLdu3eDk5AQA8Pb2xoQJE2BnZ4f169dj3LhxKCkpwbBhw2BlZQUAWLNmDdzc3FBUVITBgwejU6dOAIDExEQ0adIEH3zwQYXqoMu4LTNWhzTdW66PBa/8hX7s2DGaOnUqERHl5+dTly5dqLi4mBYvXkyzZs0iIqKYmBgaNWoUEan+ld6gQQO6d+8eERF5e3tTSEgIERGdOHGCRowYQUREUqmUwsLCiIgoODiYfHx8qLi4mOzs7CgvL48yMjJILBbTq7Zs2aL2Ts3t27eV9gsPD6fFixcLj8eMGUMXLlxQ2mf58uVC3YiIHBwcKCsrS2kf6NFf6BznUvoS51fj+SZWVlY0ePDgSh2jsHfvXrK3t1d7R1Qf6Hr8uS0r08V4ctGtwn8eaYHExETExcVBJpMBKB3b9uTJEwAQ7ti0bdtWJQFCoXXr1mjbti0AIDk5GTNmzAAASCQSfPXVV8J+ioQHBwcH7N+/HwYGBhg5ciT27t2Lx48fY+LEiSrnnjJlCqZMmfLG12Bubo6cnBzhcXlj4Mru8+zZM6UEDn3Hca5fbt++XeVj3d3d4e7uXoO1YTWJ2zJjdYs7q1rA1tYWrq6uWL9+PQCgoKAARkZGAEpXelEgKl09x8jICEVFRcJ2xTQ4ANChQwfEx8fDysoKCQkJsLa2Fp47f/48LC0tce7cOWH7pEmT4OHhgb/++gtxcXEqddu6dSt27dqlsn3btm3CV4IA0LNnT/j6+kIul+PFixdIT08XPowVZDIZZsyYAW9vb6SkpMDc3LxefZ3EcWZMP3BbZqxu8U+dFhg0aBDOnj2LPn36wNDQEC1btlT7YaPw8ccfY9GiRWjXrh127typ9Jyvry/Gjx+PHTt2wMDAAD/++KPw3KVLlxASEiKMfwKAFi1aoHnz5mjfvj0aN26scq2K/pVuamoKb29v4U7D6tWrYWBggIyMDHh7e2PPnj2wtraGq6srJBIJRCKRMO6qvuA4128HDhzARx99BAsLC01XRUlYWJgQo4CAAGHsqgIRYfr06fj999/RsGFDBAUFwdbWVng+OjoaLi4uePDgAVq3bg0AWLt2LY4cOYLi4mJMmzYNo0aNqrsXVAe4LTNWxzQ9DqE+FmhgJRSpVEoPHjxQ+9yoUaPoypUrdVwj9aBHY584zuXTxThXN566mhF+4MAB8vLyIiKie/fukZOTk/BcSUkJDRo0iLp16yb83EVFRdHXX3/92uvWx/hXlq60ZSLdjCcX3So8z2o9VlhYiMGDB8PY2Bhdu3bVdHVYLeE4154FCxZAKpVCLBbj4MGDAIAlS5bg888/xyeffIJOnTrhzJkzuHHjBqKiojBr1iy4ubkBANq0aYMZM2agX79+ePLkCdzc3CCVSuHs7Iy7d+8CKP0a1sfHB/369YOLiwuysrJw7NgxYYwjUJoRfvXq1SrVv2xGePPmzYWM8LLKrrLVtm1b3Lp1CwUFBQCA0NBQfPzxx8LqXADw3//+F0SEvn37YsSIEcjI4GXjawq3ZVZvabq3XB8LeI3pckGP/kLnOJdPF+P8ajzrS0Z4VFQUDR8+nIqLi+nSpUtkYGBAjx8/pry8PHJxcaHCwkKlu4D9+vWjL7/8kohKZzX47LPPVOqnD/Fn/9DFeHLRrcJjVhljrArqS0Z4//79ceHCBTg5OaF9+/bo1KkT3n33XaxatQrTpk1TSbhp1qwZBg4cCABwc3PDsmXL3lgPxhh7HR4GUE+VzQqtTcuWLYNMJoNMJkPbtm2Vfgmz2ldXcf7hhx/Qo0cP9OnTBx4eHpDL5XVyXU1SZIQrljS9du0aWrRoAaDqGeEA1GaEA1DJCN+xYwciIiLg4eGhUretW7cK7a5sSUlJUdqvZ8+eiI+Ph1wux9OnT9VmhAPAokWLEBcXh1mzZsHOzg6GhoZISkrCtm3bMGDAAFy/fh2fffYZXrx4ARcXF1y6dEmo+4cffljxN5WVq67askJwcDAaNmxYp9dkrDx8Z5XVqm+++QbffPMNAKBfv356lxXMSvXt2xdTp06FgYEB5s6di7CwMEyePFnT1apV9SUjPCcnB8OGDYOhoSFatGiBoKAgAKWzCCjIZDKEhobi7bffxrhx4zBt2jRhVoGyr4Xphry8PPz8889aN3MFq79Eir/6Wd0RiURU3vuempoKT09PGBkZgYiwf/9+JCYmws/PD0QEU1NTREREwMTEBDKZDGKxGFeuXAERwcvLC8HBwcjMzERERAQ+/PBDtUv2vffee7CyskJKSgoKCwsxffp03LlzBwUFBQgMDIRYLIa/vz8OHTqExo0bw83NDXPmzKnWa3706BFcXV2RlJT0pvcGRCR67U46oj7GGSi9C2dpaYkJEya87r3RuTi/Lp61RSaTISwsTJgSqqzRo0dj3rx5Oploo0/x18e2vGzZMvTo0QMzZ85UuRtfznujc/FkOkbTg2brY8FrBuoHBwcrJTyUlJTQ8+fPhcdz586lHTt2EFFp8sX+/fuJiGjy5Mnk7e1NREShoaE0b948YZ9XEzSIiCwtLYmI6IcffqAVK1YQEVFWVhb16tWLiIg6dOggXPfVqWyIiIYNG6aSvDF58uRyX9eqVavI39+/3OcVoEcD9etjnH///Xf66KOPlF6LOroY59fFs7aom76ooKCABg0aROPHj6/z+tQUfYq/vrXljIwMGjJkiNI130QX48lFtwoPA9AyI0eOREBAADw9PdGmTRssWbIESUlJ8PPzg1wuR2ZmJpo2bSrsb29vD6A0WcPS0lL4f9mVTV5N0CgrMTERZ86cQVRUFIDSBAsA2LBhA2bMmIGioiJMmzYNEolE6bj//e9/lXpdYWFhOHToUKWO0Wf6GOfU1FRMmDABe/fuVZrKiFVdbGysyraGDRviyJEjdV8Zppa+teXFixfDz8+vEu8AY7WPO6taxsDAAAEBAQBKkyiOHz+Obdu2YenSpRCLxZg7dy6I/vkqqmwih7qkDkD9kn0Ktra2sLS0FL4yUsyfKBaL4eLigrS0NAwbNgyXL19WOm748OF4+vSp0jYrKyts27ZN5TVdvXoVZmZmaNOmTaXeC32mb3F+/Pgx3N3dsX37drRr167S7werPMXXwrUtLi4OM2fOxO3bt5GSkiIMSfjvf/+LJUuW4N69e3j58mWt10Nb6VtbTklJwaJFiwD806737dtXuTeFsRrGnVUtExkZiaCgIBgaGsLExAQSiQTPnz/H5MmT0aFDBzRt2lTpr/SKUJegofD5559j5syZQjJE165dsWbNGgwbNgwvX76EXC7HrFmzVM5ZmTtuoaGhGDduXKXqrO/0Lc6+vr7IysrCl19+CQAYM2ZMhRJ8mPbr0qULzp49KyxmoODi4oJPPvkEHTt21FDNtIO+teVffvlF+L+VlRV3VJlW4AQrDajLRI3XJWhoI30aqM9xLp8uxrmi8dTHhBug/J+xqtzh1ef4V5eutWVAN+PJdAvPs8oYYzUoOjoarq6uiImJQWxsLExNTWFvb49ff/0V8fHxsLGxQUREhLB/z549cfz4cVhYWODs2bM4fvw4fHx8sH37dmGfrl274sSJE/D09ERgYKDS9YKDg2FpaYno6Gjs379f6JSGh4cjJiYG0dHR8Pb2Vqnn8OHDVeZh9fLyqp03hTHGqoGHAeg5dQkaTP9wnLWHviXcsLrFbZkxVXxnVcekpqaib9++dXY9xdeUiq/5Tp06BbFYDAcHB5WxVK86dOiQcMfGzs5O+KWckJAAOzs7vovzGpqM89OnT+Hi4gKJRIJevXrh2LFjbzw+MDAQvXv3hqOjIxITEwHU3zgrEm7Cw8ORkZGB48ePY/ny5Vi6dCni4uIwZMiQKiXcACg34eazzz5TWkkLKE242bFjBwICAjB79myVevKd1bqhS5/ZCjdv3kTDhg2RkJAAoP62ZaY9+M4qe6O9e/eidevWKC4uxpw5cxAdHY1GjRpBLBbDzc0NTZo0UXvckCFDMGTIEABAQECAsIa4RCLBunXrlFbAYZqniPOLFy+wc+dOtG7dGn/++Sd69+4trPWuzq1bt3D06FEkJCTg1q1bmDZtGmJiYuptnPUt4eaPP/7ArFmzcO3aNXh4eMDd3R2zZ89GbGws/P398ejRI/Tt2xdeXl4YPXp0pV4Xqx1V/cxWWLJkibCqGcCf2UwLaHqi1/pY8Mrk0nPmzKF9+/YREVFRURHZ2NiQXC6n+fPnk1Qqpa5du9KmTZuIiOjevXvk4uJCRETjx4+n+Ph4IiKKiYkRJnhOTEykvn37kpOTE7m7u9OLFy+oqspOSp6cnCxMFk1E9MUXX9CpU6cqdB5bW1t6+PCh8LhsfcuCHk0uratxLuv58+dkbW392mM3b95Ma9asER7b2tpSYWGhSn3L0sU4vxrPulJebHSZrsdfV9pyVT6z4+LiaP78+Up1fbW+r9LFeHLRrcJ3VrXAxIkTsWDBAnz66aeIioqCs7MzjIyMsHDhQjRu3BhyuRydOnWq8Fcw06dPR1hYGCwsLLBx40Zs3bpVKcGioKAA/fr1UzlOIpHA39+/3PNmZ2fDzMxMeGxmZobs7Ow31ufixYto2bIlWrVqVaH66ytdiXNZX3755RuzyLOzs/H+++8Lj9955x3k5OTg3XffrdA1GNM1utKWK/uZTUQICAjAnj171CblMaYp3FnVAh07dkRWVhaysrKwc+dOzJs3DwCwefNmHDhwAIaGhsLzZZU3vi0pKUmY11Qulyt9nQMARkZGVRrEb25ujpycHOFxbm4uzM3N33jcTz/9xPOsQnfirODn54fmzZu/cb7UV38unj17pvQLklUPJ9xoH11py5X9zN6zZw/69u0LU1PTSl+LsdrEnVUt4enpiY0bN+LevXuwt7dHTk4Otm/fjsTERBQWFsLa2lrpww0AmjVrhrS0NACldy8VbG1tER4eLtzJVKxwolDVv9KtrKyQmpqKnJwcNGrUCGfOnMGKFSsAAPfv30erVq2EcakKhYWFiIyMxMqVKyvxbugvXYgzAKxatQqPHz9GcHCw0nZ1cZbJZJgxYwa8vb2RkpICc3NzlZ8Dpl5qaiq8vLyUJmKvTTKZDHK5HKGhoTAyMlL6I/L06dPIysp64x8aN2/eRMeOHYVxyQkJCZg5cya6deumdgU7faULbbmyn9lXr17FpUuX8MsvvyAxMRHJycnYtWsXr0rHNI5/o2iJMWPGwMLCAsuWLQMAmJqawtbWFhKJBDY2Nmr/Gvby8sKYMWOwa9cufPDBB8L2TZs2YeLEiSgqKgIAzJ07FwMGDBCer+pf6YaGhvj+++8xaNAgAICPj4+QKOLo6IiLFy+iRYsWSsdERUXB0dGR14r/P7oQ53v37mH+/Pno3bu3cIfnxIkTMDIyUhtna2truLq6QiKRQCQSYePGjZW+Jqs7iuQb4J+7tmfOnIG/v3+F7ohz8k0pXWjLlf3M/u6774T/T5gwAV5eXtxRZdpB04Nm62OBhhI1qmLUqFEkkUjo9u3b5e7z8OFDmjBhQoXPGR8fTz179qR58+apPAc9GqjPcdavOFc0nrqSfFPW1KlTaffu3W88virJN+roc/w1ra7bMpFuxpOLbhWNV6A+Fl350NMEffrQ4ziXTxfjXNF4JiYm0scff0xERJGRkTRz5kwiIvr777+JiOjly5fUvn17KigoqFBn1dHRke7fv09ERBs2bKC1a9cqXU8ul5NUKlUpCxcuVKmbus6qXC6nNm3aUF5e3mtfV0lJCfXv359ycnK4s8qU6GI8uehW4WEAjDFWg3Ql+Ubh8OHDcHFxgYmJyWv34+QbxpimcGeVMcZqmC4k3yiEhoaqTFPEyTeMMW3CnVXGGKthupB8A5TOw5mUlASpVKq0nZNvGGPaRPTqX/es9olEIuL3XT2RSAQiEr15T+3HcS6fLsZZH+I5evRopKenIyQkBFZWVmr3SU9Ph5+fH0JCQip0zoSEBPj4+EAmk1V4ijqOv37RxXgy3cKdVQ0wMTHJePnyZYs371n/GBsbZ+bn5/9L0/WoCRzn8ulinLmzUnN0sXPD7bl8utiemW7hzipjjFUAd1Zrji52VhljmmOg6QowxhhjjDFWHu6sMsYYY4wxrcWdVcYYY4wxprV46irGGKsAY2PjTJFIxAk2NcDY2DhT03VgjOkOTrBijLFqEolEB1D6x38XACOJ6Kxma1QzOAO+5nDGPGNVx51VxhirBpFI1B3AeQCPANwEYAfgSyIK12S9agLPgFBzeAYExqqOhwEwxlj19AOQBmAXgHMALhLRY81WiTHG9AffWWWMMaYW31mtOXxnlbGq49kAGGOMVVhKSgrs7e1RUFAAAFi1ahUWL14MADAxMYGbmxsA4OnTp+jZsydMTU0RFhYmHJ+fn49Ro0ZBJpNh4MCBSE9PBwDs27cPHTp0gL+/f63V/cGDB+jbty8cHR3h4+Ojdp/r169DIpHAwcEBq1evFrafOnUKYrEYDg4O2L17t9Ix2dnZMDMzU3qdjLEaRERcuHDhUiPF2Ng4AwBxUS3GxsYZmo5PZUvprwhVK1asoCVLltDdu3fpo48+opcvXxIRkaWlpbBPQUEBZWRk0OLFiyk0NFTYvm7dOvL39yciooSEBBo7dqzwXEhICH377bdqr/mqv//+u0L7leXh4UGnTp0S/h8dHa2yj0QioVu3blFxcTE5OjpSSkoKFRUVUefOnenPP/+kvLw86tKlCz179kw4xtvbm9zc3JRe56v+773UeEy5cNHFwndWGWM15uXLly00/aGmrUWfsup9fHxw9OhReHh4YN26dXjrrbdU9mnYsCFatFB9yTdv3kTPnj0BAD169EBMTEyFr5uXl4c9e/Zg6NChmDp1aqXrfeXKFTg7OwMAhg4dqnJtuVyOZ8+eoX379jAwMICbmxvi4uKQkpKCtm3bwtzcHCYmJnBwcMDFixcBAHfu3EF2djbs7e0rXR/GWMVwZ5UxxlilNGjQAFKpFDk5OZBIJJU6tkuXLoiMjAQAHDlyBH/++ecbjzl58iTGjRuHYcOGISsrC5s3b0Z4eOlkC2lpaZDJZCplw4YNKucpKSkR/m9mZobs7Gyl5xVf57+6T3nbAWDRokXCMAjGWO3g2QAYY4xVyh9//IG4uDj0798fW7durdRdzkmTJuHrr7+GTCZD9+7d0bFjxzceExYWhps3b2LmzJkYOnQomjRpIjxnYWGB2NjYCl3bwOCf+zO5ubkwNzdXet7c3By5ubkq+5ibmyMnJ0dl+9mzZ2Fubg5LS8sKXZ8xVjV8Z5UxpjUOHDiAtLQ0TVdDRVhYGMRiMcRicblfWwcGBqJ3795wdHREYmJiHdew7hARpk2bhg0bNmDFihXYtGkTHj+u+ExdDRs2xLp16xAbGwsnJyf079//jcfs3LkTx44dQ15eHtzd3TFixAgcPHgQQOXurHbt2hVxcXEAgEOHDkEmkyk9/9Zbb6FJkya4e/cuiAhHjx5Fnz59YGVlhdTUVOTk5EAul+PMmTPo1q0bLl68iOvXr2PAgAEICwtDYGAgzp7Vi/UgGNMumh7HxYULF/0pKCchp6LGjx9P8fHxKtuLioqqdd7qyMnJoS5dulB+fj49efKEOnfuTMXFxUr73Lx5k2QyGZWUlFBycjLJZDKV80AHE2zUxXPjxo305ZdfCo8PHz5MI0aMICLlBCsiooEDB9IHH3xAtra2NHHiRCIiSkpKIqlUSi4uLjR16lTKy8sT9q9oglVaWhoFBwe/cb9XpaamkrOzM0kkEvL29qaSkhIiIpo9ezb99ttvRER05coV6t27N4nFYlq1apVw7IkTJ6hXr17Uq1cvCgsLUzn3q4lkr9LF+HPhoi2F51lljNUYdfNyLliwAKdPn0ZBQQHmz5+PTz75BEuWLEF6ejqysrJw9+5dbNmyBaampnB2dkbLli3RqlUrREZGok2bNnBzc0NycjL27NmDiRMn4u+//4ahoSG2bduGdu3aQSaToVu3brh+/TqKi4uxe/duXL58GZGRkdi4cSMAYPjw4fjmm29gZ2dX6dd0/PhxHD16FP/5z38AAIMGDcKGDRvQrl07YZ8tW7YgLy8P//73vwEAHTt2xNWrV9GgwT8jrXRxns3KzrPavn17WFtbC2NSK2Pfvn1YuXIlJkyYgJkzZ1b6eG2ni/FnTFvwmFXGWK2JiorC06dPERcXh5cvX6JXr174+OOPAZTOyXnw4EHExsZi/fr12LNnDwYMGAAvLy8haefRo0f4+uuv0bZtW/z73/+Gu7s7JkyYgJMnT2L+/PmIiIgAUPr17vfff4/t27cjMDAQ3333HRYsWID8/Hw8e/YMGRkZKh3VrVu3YteuXSp13rZtG6ysrITH5SXXlO2sZmdn4/333xcev/POO8jJycG7775b/TdRh9y+fbvKx7q7u8Pd3b0Ga8MY0xfcWWWM1ZrExETExcUJYwPlcjmePHkCAOjevTsAoG3btipZ2QqtW7dG27ZtAQDJycmYMWMGAEAikeCrr74S9uvVqxcAwMHBAfv374eBgQFGjhyJvXv34vHjx5g4caLKuadMmYIpU6a88TWUl1zzun2ePXum1MFljDFWdZxgxRirNba2tnB1dUVsbCxiY2Nx7do1Ye5Nkeifb0QVXzUbGRmhqKhI2G5oaCj8v0OHDoiPjwcAJCQkwNraWnju/PnzAIBz584J2ydNmoQdO3YgIiICHh4eKnXbunWr2sSclJQUpf169uyJ+Ph4yOVyPH36FOnp6UIHWkEmk+HIkSMgIty+fRvm5uZKQwDqK11NmEtNTYWZmZnwM3Ho0CGl56OjoyESifDw4UOl7cHBwWjYsGGt1p2x+og/TRljtWbQoEE4e/Ys+vTpA0NDQ7Rs2VLtV+8KH3/8MRYtWoR27dph586dSs/5+vpi/Pjx2LFjBwwMDPDjjz8Kz126dAkhISHCmFUAaNGiBZo3b4727dujcePGKteq6J1VU1NTeHt7C3eHV69eDQMDA2RkZMDb2xt79uyBtbU1XF1dIZFIIBKJhLGy9d2BAwfQvHlzWFhYKG0vLi5W+kOkLuXm5uL777/HuXPn8Pz5c7i4uOC3335TmtYKAOzt7fHLL7+oHE9EWL16Nbp166a0PS8vDz///LPKa2WM1QBNZ3hx4cJFfwqqORtAVUilUnrw4IHa50aNGkVXrlyp4xqpBx3MBlcXT19fX+rTpw/16tWLDhw4QESlmfBeXl40ZMgQ6tixI50+fZqSkpKoRYsWZGdnR4MHDyYiIgsLC5o+fTo5OztTVlYWDR48mPr06UNOTk50584dIiqN51dffUWurq7k7OxMmZmZdPToUZo+fbpQh2HDhgnZ+5UVFRWlNJvBwIEDhWsr3Lt3j5o3b06Ojo7k6elJWVlZwnM7d+6kH374QeXnbunSpXTs2DGVGREUdDH+XLhoS+FhAIwxvVNYWIjBgwfD2NgYXbt21XR19EbZhLmYmBgsXrxYWBVKkTAXFBSE9evXw8bGBgMGDEBQUJAwO4AiYe7UqVMICAiAu7s74uLi4Ovri/nz5wvX6dq1K06cOAFPT08EBgaif//+OHPmDPLz85GZmVluwlxFhnW8bjUqhZYtW+Lu3bv49ddf4ezsDB8fHwBAfn4+fvrpJ3h5eSntn5mZicuXL2PAgAHVe4MZY2rxMADGmE5Tt3pRw4YNceTIkbqvjJ6rLwlzb731Ft566y0AgKenJ9asWQMAWLduHaZNm6YyHnnx4sXw8/N747UZY1XDnVXGmF6xsrJSuZtWG7Zv347t27fDwMAA77zzDsLDw9G0adNav64mKRLm1q9fDwAoKCiAkZERgKonzFlZWalNmLO0tFRJmPPw8MBff/0lrEJVVkWnIuvZsyd8fX0hl8vx4sULtQlzf/31F9555x0AQExMDD788EMAQFJSEuLi4rBt2zZcv34dn332GSIjI5GSkoJFixYBAB4/fgx3d3fs27fvTW8nY6yCuLPKGGNVMHbsWEyaNAkA8M033yA0NFS4U6iv6kvCXExMDJYtW4YmTZrgrbfewtatWwGUziKgIJPJEBoairffflspEcvKyoo7qozVMF7BijFWY1634lFqaio8PT1hZGQEIsL+/fuRmJgIPz8/EBFMTU0REREBExMTyGQyiMViXLlyBUQELy8vBAcHIzMzExEREfjwww/Vrlz13nvvCXdWCwsLMX36dNy5cwcFBQUIDAyEWCyGv78/Dh06hMaNG8PNzQ1z5syp9uueM2cOhgwZorLW/CvvDUjHVjCq7ApWNUEmkyEsLAytW7dWeW706NGYN2+eTo5D1sX4M6YtOMGKMVYnoqOj4erqipiYGMTGxsLU1BT29vb49ddfER8fDxsbG2FFKqD069rjx4/DwsICZ8+exfHjx+Hj44Pt27cL+7yaiFNWcHAwLC0tER0djf379wud0vDwcMTExCA6Ohre3t4q9Rw+fLhKks6rCTUKGzZsQMeOHXH69GnY2trWwLvE1OGEOcbqNx4GwBirEyNHjkRAQAA8PT3Rpk0bLFmyBElJSfDz84NcLkdmZqbSmE97e3sApUk5lpaWwv/Ljld8NRGnrMTERJw5cwZRUVEAShNpgNIO5owZM1BUVIRp06YJS7sq/O9//6vwa5o5cyZmzpyJlStXIjAwEKtWrarwsUw9TphjjL2KO6uMsTphYGCAgIAAAKXJMsePH8e2bduwdOlSiMVizJ07F2W/ci6bsKMueQdQn4ijYGtrC0tLS+GOakFBAQBALBbDxcUFaWlpGDZsGC5fvqx03PDhw/H06VOlbVZWVti2bZvStpcvX8LY2BhA6fRHL1++rNwbwqqlrhLp7t69iwkTJgiLBuzcuRNt2rSp9esyxv7BnVXGWJ2IjIxEUFAQDA0NYWJiAolEgufPn2Py5Mno0KEDmjZtWulsenWJOAqff/45Zs6cCScnJwClQwbWrFmDYcOG4eXLl5DL5Zg1a5bKOSt6Z3XZsmU4c+YMAKBZs2ZKwxOY/ti0aRMmT56M8ePHIywsDOvXr8fq1as1XS3G6hVOsGKM1Zi6TMh5XSKONtLFBJuqxFPfEulCQkLw7NkzzJ49G+vXr0d+fj7mzZtX6fPoYvwZ0xZ8Z5UxxliNUSTSLVmyBEDpsA1FIh0AzJs3DxERERg/fjyA0kS6FStWwMvLS0ikCwsLw/bt27Fy5UoApXfFv//+e2zfvh2BgYFKyXSKRLoff/wRT548wZAhQ3D27FmEh4fj0qVLePvtt4VVtsqq6HAPFxcX9O/fH8HBwXj58iUuXLhQY+8VY6xiuLPKGNNJ6hJxmObpWyLdvHnz8O2338Ld3R179uyBr68vfvjhh0q8I4yx6uKpqxhjWiE1NRV9+/ats+spvoJWJOmcOnUKYrEYDg4OKuNfX3Xt2jU4ODhAKpVCIpHg2rVrAICEhATY2dmVO9VVfaBIpAsPD0dGRgaOHz+O5cuXY+nSpYiLi8OQIUOqlEgHoNxEus8++wyxsbGIjY0VYiEWi7Fjxw4EBARg9uzZKvWszBRl7777rvCvojPMGKs7fGeVMVZv7d27F61bt0ZxcTHmzJmD6OhoNGrUCGKxGG5ubmjSpIna42xsbHD69GmIRCJER0fD398fe/fuhUQiwbp165RWOqpv9C2Rzs/PD1OnTkWDBg1QWFiILVu2VKrujLEaQERcuHDhUiOl9CPlH3PmzKF9+/YREVFRURHZ2NiQXC6n+fPnk1Qqpa5du9KmTZuIiOjevXvk4uJCRETjx4+n+Ph4IiKKiYmhyZMnExFRYmIi9e3bl5ycnMjd3Z1evHhBVSWVSunBgwdERJScnExDhgwRnvviiy/o1KlTFTrPzz//TMuWLRMel61vWf/33mg8RpUpr8ZTE8rGSZfpYvy5cNGWwndWGWO1ZuLEiViwYAE+/fRTREVFwdnZGUZGRli4cCEaN24MuVyOTp06Vfhr8+nTpyMsLAwWFhbYuHEjtm7dqrQKVUFBAfr166dynEQigb+/f7nnzc7OhpmZmfDYzMwM2dnZr61LQkICfHx88PDhw0otJMAYY6xyuLPKGKs1HTt2RFZWFrKysrBz505hyp/NmzfjwIEDMDQ0FJ4vq7yxi0lJSRg3bhwAQC6XQyaTKR1nZGRUpcQrc3Nz5OTkCI9zc3Nhbm7+2mMkEgnOnTuHc+fOYebMmZwlXks4kY4xxp1Vxlit8vT0xMaNG3Hv3j3Y29sjJycH27dvR2JiIgoLC2Ftba3UIQVKJ9lPS0sDAFy8eFHYbmtri/DwcLRq1QrAP6tSKVT1zqqVlRVSU1ORk5ODRo0a4cyZM1ixYgUA4P79+2jVqhUaNPjn4/LV1asaNWpUmbeEMcZYJXBnlTFWq8aMGQMLCwssW7YMAGBqagpbW1tIJBLY2NiovYPp5eWFMWPGYNeuXfjggw+E7Zs2bcLEiRNRVFQEAJg7dy4GDBggPF/VO6uGhob4/vvvMWjQIACAj4+PkATk6OiIixcvokWLFsL+hw8fxoYNG4QlONetW1fpa9ZXqamp8PLywi+//FIn15PJZJDL5QgNDYWRkZFwZx4ATp8+jaysLKUhIGU9ffoUAwcOxM2bN7FhwwaMHTsWQOkQkJkzZ6Jbt24q87IyxmqBpgfNcuHCRX8KtCAhp6JGjRpFEomEbt++Xe4+Dx8+pAkTJlT4nPHx8dSzZ0+aN2+eynPQwQSb2ohn2US6ulBegtbp06dp4MCBrz22oKCAMjIyaPHixRQaGqr0XHmJdOXRxfhz4aIthedZZYzVS3v27EF8fDysrKzK3adVq1YICQmp8DkV41gVKy/VF1999RV+/vlnAEBxcTFsbW1RUFAAX19fyGQyfPTRR2on0p8wYQISEhIAlI5NVSTa/f7773B1dYWzszNGjBiBvLy8Gq/zTz/9pHSXVZ2GDRsq3VFnjGkGDwNgjDFWLboy60PZ46OiorB27doKv0bGmOZwZ5Uxxli16MqsDwqHDx+Gi4sLTExMqnwOxljd4c4qY4yxatOFWR8UQkNDle7UAupnfWCMaQdulYwxxqpNF2Z9AEoXgEhKSoJUKlXarm7WBwAYNGgQkpOT0ahRI0RHR2P79u1Vui5jrOpEr/6lyxhjVWViYpLx8uVLzkhRw9jYODM/P/9fmq5HZYhEItL13xGjR49Geno6QkJCyk2mS09Ph5+fX4WT6RSrl8lksgon04lEIhCR6M17MsZexZ1VxhhjaulDZ1VbcGeVsarjqasYY4wxxpjW4s4qY4wxxhjTWtxZZYwxxhhjWotnA2CMMaaWsbFxpkgk4oS5GmBsbJyp6Towpqs4wYoxxliliUSiCAC5AIYCWAxgc33KxhKJRGYAwgE0AtACgDcRHddsrRjTTzwMgDHGWKWIRKIPAXwMYBiAMAA9AHTRaKXq3gQAyQCeAmgJYLlGa8OYHuPOKmOMscoKAmAMIAPA2wBOA0jRaI3q3kkAivVjiwDYi0Qiew3WhzG9xcMAGGOMVYpIJGoKoISInmu6LtpCJBK1JKLHmq4HY/qIO6uMMcYYY0xr8WwAjDFWDbzEbPl0cYnZ8nCcy6dPcWbaie+sMsZYNfCSpOXTpyVGOc7l06c4M+3ECVaMMcYYY0xrcWeVMcYYY4xpLe6sMsaYBh04cABpaWmaroaKsLAwiMViiMVixMTEqN0nMDAQvXv3hqOjIxITE+u4hrqDY8xY9XCCFWOMadCBAwfQvHlzWFhYKG0vLi6GoaGhRuqUm5uL77//HufOncPz58/h4uKC3377DQYG/9zfuHXrFo4ePYqEhATcunUL06ZNK7fDU99xjBmrHr6zyhhjNWzBggWQSqUQi8U4ePAgAGDJkiX4/PPP8cknn6BTp044c+YMbty4gaioKMyaNQtubm4AgDZt2mDGjBno168fnjx5Ajc3N0ilUjg7O+Pu3bsAAJlMBh8fH/Tr1w8uLi7IysrCsWPHMGPGDKEOw4cPx9WrV6tU//Pnz0MqlcLY2BjNmzdHq1atkJqaqrRPTEwMhgwZApFIBGtrazx58gRFRUVVup4u4hgzVne4s8oYYzUoKioKT58+RVxcHGJiYrB48WKUlJQAAExMTHDw4EEEBQVh/fr1sLGxwYABAxAUFITIyEgAwKNHj/D111/j1KlTCAgIgLu7O+Li4uDr64v58+cL1+natStOnDgBT09PBAYGon///jhz5gzy8/ORmZmJjIwM2NnZKdVt69atkMlkKiUlRXnxqezsbJiZmQmPzczMkJ2d/dp93nnnHeTk5NTIe6jtOMaM1S0eBsAYYzUoMTERcXFxkMlkAAC5XI4nT54AALp37w4AaNu2rUrHQKF169Zo27YtACA5OVm4kyaRSPDVV18J+/Xq1QsA4ODggP3798PAwAAjR47E3r178fjxY0ycOFHl3FOmTMGUKVPe+BrMzc2VOiW5ubkwNzd/7T7Pnj1T6tjoM44xY3WLO6uMMVaDbG1t4erqivXr1wMACgoKYGRkBKB0PkoFxZydRkZGSl+tlh3D2KFDB8THx8PKygoJCQmwtrYWnjt//jwsLS1x7tw5YfukSZPg4eGBv/76C3FxcSp127p1K3bt2qWyfdu2bbCyshIe9+zZE76+vpDL5Xjx4gXS09OFzpWCTCbDjBkz4O3tjZSUFJibm6NBg/rxK4VjzFjd4p86xhirQYMGDcLZs2fRp08fGBoaomXLlmo7Dwoff/wxFi1ahHbt2mHnzp1Kz/n6+mL8+PHYsWMHDAwM8OOPPwrPXbp0CSEhISguLsbu3bsBAC1atEDz5s3Rvn17NG7cWOVaFb3rZmpqCm9vb+HO4erVq2FgYICMjAx4e3tjz549sLa2hqurKyQSCUQiETZu3FiRt0cvcIwZq1u8ghVjjFWDJlY2kslkCAsLQ+vWrVWeGz16NObNm4euXbvWaZ3U0aeVjeo6zroSY0C/4sy0EydYMcaYHigsLMTgwYNhbGysNZ0YVrM4xqy+4jurjDFWDbxmfPn06Y4bx7l8+hRnpp34zipjjOm4sokztWnZsmXCVEht27ZVylxntauuYhwXF4dOnTrB2NgYDx8+rJNrMvYm3FlljDFWId988w1iY2MRGxuLDz/8EKNGjdJ0lVgN69KlC86ePStMm8WYNuDZABhjrJakpqbC09MTRkZGICLs378fiYmJ8PPzAxHB1NQUERERMDExgUwmg1gsxpUrV0BE8PLyQnBwMDIzMxEREYEPP/wQMpkM3bp1w/Xr14UM8ffee0+4XmFhIaZPn447d+6goKAAgYGBEIvF8Pf3x6FDh9C4cWO4ublhzpw51Xpdjx49Qnp6Onr06FHdt0jn6VuMTU1Na+idYazm8J1VxhirJdHR0XB1dUVMTAxiY2NhamoKe3t7/Prrr4iPj4eNjQ0iIiKE/Xv27Injx4/DwsICZ8+exfHjx+Hj44Pt27cL+7y6qlFZwcHBsLS0RHR0NPbv3y90WMLDwxETE4Po6Gh4e3ur1HP48OEqKx55eXmV+7rCw8MxZsyYar47+kFfY8yYNuE7q4wxVktGjhyJgIAAeHp6ok2bNliyZAmSkpLg5+cHuVyOzMxMNG3aVNjf3t4eQOkKR5aWlsL/y07+/uqqRmUlJibizJkziIqKAlC6KhEAbNiwATNmzEBRURGmTZsGiUSidNz//ve/Sr2usLAwHDp0qFLH6Ct9jTFj2oQ7q4wxVksMDAwQEBAAoHTloePHj2Pbtm1YunQpxGIx5s6di7IZ5mVXP1K3EhKgflUjBVtbW1haWgp32woKCgAAYrEYLi4uSEtLw7Bhw3D58mWl44YPH46nT58qbbOyssK2bdtUXtPVq1dhZmaGNm3aVOq90Ff6GGPGtA13VhljrJZERkYiKCgIhoaGMDExgUQiwfPnzzF58mR06NABTZs2VbrrVhHqVjVS+PzzzzFz5kw4OTkBKP06ec2aNRg2bBhevnwJuVyOWbNmqZyzMnfdQkNDMW7cuErVWZ/pW4z/+OMPzJo1C9euXYOHhwfc3d0xe/bsStWfsZrG86wyxlg11OX8m69b1Ugb6dP8m3UVZ12LMaBfcWbaiROsGGOMMcaY1uI7q4wxVg28slH59OmOG8e5fPoUZ6ad+M4qY4wxxhjTWtxZZYwxDUlNTUXfvn3r7HqKSelTUlKQlpamNOdmw4YNkZOT89rjAwMD0bt3bzg6OiIxMREAkJCQADs7O56zsxyajDEAnDp1CmKxGA4ODirJWq96+vQpevbsCVNTU4SFhQnbOcZM07izyhhj9cjevXthZWUFCwsLYenUgIAAuLq6wszMrNzjbt26haNHjyIhIQHbtm3Dl19+CQCQSCRYt25dHdWeVYQixsXFxZgzZw4iIyNx6tQpfPfdd/j777/LPa5JkyY4dOiQyqICHGOmadxZZYyxGvTVV1/h559/BgAUFxfD1tYWBQUF8PX1hUwmw0cffYQffvhB5bgJEyYgISEBABAbGyvcxfr999/h6uoKZ2dnjBgxAnl5eTVe559++umN01HFxMRgyJAhEIlEsLa2xpMnT1BUVFTjddEFuhLjlJQUtG3bFubm5jAxMYGDgwMuXrxY7v4NGzZEixYtauTajNUknmeVMcZq0MSJE7FgwQJ8+umniIqKgrOzM4yMjLBw4UI0btwYcrkcnTp1qvBXqtOnT0dYWBgsLCywceNGbN26VenOV0FBAfr166dynEQigb+//xvPX1BQgKioKKxdu/a1+2VnZ+P9998XHr/zzjvIycnBu+++W6HXoU90JcbZ2dlKd8vNzMyQnZ1d8RfKmJbgzipjjNWgjh07IisrC1lZWdi5cyfmzZsHANi8eTMOHDgAQ0ND4fmyylvNKCkpSbjrKZfLIZPJlI4zMjJCbGxslet7+PBhuLi4wMTE5LX7mZubK41pffbs2WuHDegzXYnxqzHLzc2Fubl5pc/DmKZxZ5UxxmqYp6cnNm7ciHv37sHe3h45OTnYvn07EhMTUVhYCGtra7w6DVKzZs2QlpYGAEpf1dra2iI8PBytWrUC8M/ymgrVvbMaGhqqMkbx/v37aNWqFRo0+OdXhEwmw4wZM+Dt7Y2UlBSYm5srPV/f6EKMrayskJqaipycHDRq1AhnzpzBihUrAKiPMWPain9KGWOsho0ZMwYWFhZYtmwZAMDU1BS2traQSCSwsbFRe3fLy8sLY8aMwa5du/DBBx8I2zdt2oSJEycK40Pnzp2LAQMGCM9X585qdnY2kpKSIJVKlbY7Ojri4sWLSuMXra2t4erqColEApFIhI0bN1bpmvpCF2JsaGiI77//HoMGDQIA+Pj4CEu/qosxAAwaNAjJyclo1KgRoqOjsX379kpfl7GaxosCMMZYNejSZPGjR49Geno6QkJCYGVlpXaf9PR0+Pn5ISQkpELnTEhIgI+PD2QyGVauXKn0nD5NFq8rca7rGAP6FWemnbizyhhj1aArnRhN0KdODMe5fPoUZ6adeOoqxhhjjDGmtbizyhhjjDHGtBZ3VhljjDHGmNbi2QAYY6wajI2NM0UiES/7o4axsXGmputQUzjO5dOnODPtxAlWjDHGGGNMa/EwAMYYY4wxprW4s8oYY4wxxrQWd1YZY4wxxpjW4s4qY4wxxhjTWtxZZYwxxhhjWos7q4wxxhhjTGtxZ5UxxhhjjGkt7qwyxhhjjDGtxZ1VxhhjjDGmtbizyhhjjDHGtBZ3VhljjDHGmNb6/7Qy6TXDPXxKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(model.fit(xtrain, ytrain)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "48cd0f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.92      0.88        64\n",
      "           1       0.96      0.91      0.93       124\n",
      "\n",
      "    accuracy                           0.91       188\n",
      "   macro avg       0.90      0.92      0.91       188\n",
      "weighted avg       0.92      0.91      0.92       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(ytest, y_pred_en))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae1a1e0",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f072a09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model2=RandomForestClassifier(n_estimators=100,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2a3a06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f6e5f69e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with 100 decision trees :0.9681\n"
     ]
    }
   ],
   "source": [
    "y_pred_100=model2.predict(xtest)\n",
    "print('Model accuracy score with 100 decision trees :{0:0.4f}'.format(accuracy_score(ytest,y_pred_100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb5cfc1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}