{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{"id":"QMRYNYL7eTsD"},"source":["# Medical Insurance Cost prediction\n","\n","This exercise is about performing some of the steps described in the notebook for the California Housing Data on another dataset for Medical Insurance Cost prediction."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"LAHyKeqahC0j"},"source":["# Get the Data"]},{"cell_type":"code","execution_count":572,"metadata":{"executionInfo":{"elapsed":398,"status":"ok","timestamp":1682900855852,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"YDh9HJxCZIlZ"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","medical = pd.read_csv(\"https://bit.ly/44evDuW\")"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"lV47LyGfhPwR"},"source":["# Take a Quick Look at the Data Structure"]},{"cell_type":"code","execution_count":573,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1682900855853,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"3JL7eYP3ZfsX","outputId":"ce8eb8cb-0cb9-4216-bf31-0fe43c912934"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>bmi</th>\n","      <th>children</th>\n","      <th>smoker</th>\n","      <th>region</th>\n","      <th>charges</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>19</td>\n","      <td>female</td>\n","      <td>27.900</td>\n","      <td>0</td>\n","      <td>yes</td>\n","      <td>southwest</td>\n","      <td>16884.92400</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>18</td>\n","      <td>male</td>\n","      <td>33.770</td>\n","      <td>1</td>\n","      <td>no</td>\n","      <td>southeast</td>\n","      <td>1725.55230</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>28</td>\n","      <td>male</td>\n","      <td>33.000</td>\n","      <td>3</td>\n","      <td>no</td>\n","      <td>southeast</td>\n","      <td>4449.46200</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>33</td>\n","      <td>male</td>\n","      <td>22.705</td>\n","      <td>0</td>\n","      <td>no</td>\n","      <td>northwest</td>\n","      <td>21984.47061</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>32</td>\n","      <td>male</td>\n","      <td>28.880</td>\n","      <td>0</td>\n","      <td>no</td>\n","      <td>northwest</td>\n","      <td>3866.85520</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   age     sex     bmi  children smoker     region      charges\n","0   19  female  27.900         0    yes  southwest  16884.92400\n","1   18    male  33.770         1     no  southeast   1725.55230\n","2   28    male  33.000         3     no  southeast   4449.46200\n","3   33    male  22.705         0     no  northwest  21984.47061\n","4   32    male  28.880         0     no  northwest   3866.85520"]},"execution_count":573,"metadata":{},"output_type":"execute_result"}],"source":["# display the first 5 rows of the dataset by calling the head() function on medical\n","\n","medical.head()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"lCYIq-ONmCdl"},"source":["Each row represents one patient. \n","There are 7 attributes."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"CrGKBp2SiNxz"},"source":["The `info()` method is useful to get a quick description of the data, in\n","particular the total number of rows, each attribute’s type, and the number\n","of non-null values:"]},{"cell_type":"code","execution_count":574,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1682900855854,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"4OxHMT6gZvKP","outputId":"26ace350-d719-4dc4-92a5-074d821b1709"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1338 entries, 0 to 1337\n","Data columns (total 7 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   age       1338 non-null   int64  \n"," 1   sex       1338 non-null   object \n"," 2   bmi       1338 non-null   float64\n"," 3   children  1338 non-null   int64  \n"," 4   smoker    1338 non-null   object \n"," 5   region    1338 non-null   object \n"," 6   charges   1338 non-null   float64\n","dtypes: float64(2), int64(2), object(3)\n","memory usage: 73.3+ KB\n"]}],"source":["# get the number of rows, columns, and data types by using the info() method\n","medical.info()\n","\n"]},{"cell_type":"code","execution_count":575,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1682900855854,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"yMepsRzoitgm","outputId":"9bfe9356-be96-4900-cf9e-d06a16622dcc"},"outputs":[{"data":{"text/plain":["region\n","southeast    364\n","southwest    325\n","northwest    325\n","northeast    324\n","Name: count, dtype: int64"]},"execution_count":575,"metadata":{},"output_type":"execute_result"}],"source":["# show the number of patients in each region by using the value_counts() method on the \"region\" column\n","medical[\"region\"].value_counts()\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"F6tnR5ibi4D2"},"source":["Let’s look at the other fields. The describe() method shows a summary\n","of the numerical attributes."]},{"cell_type":"code","execution_count":576,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1682900855854,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"kGTXFG_AaMt2","outputId":"d9846637-6e7c-4ac8-8f82-465b6d25645f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>bmi</th>\n","      <th>children</th>\n","      <th>charges</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>1338.000000</td>\n","      <td>1338.000000</td>\n","      <td>1338.000000</td>\n","      <td>1338.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>39.207025</td>\n","      <td>30.663397</td>\n","      <td>1.094918</td>\n","      <td>13270.422265</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>14.049960</td>\n","      <td>6.098187</td>\n","      <td>1.205493</td>\n","      <td>12110.011237</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>18.000000</td>\n","      <td>15.960000</td>\n","      <td>0.000000</td>\n","      <td>1121.873900</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>27.000000</td>\n","      <td>26.296250</td>\n","      <td>0.000000</td>\n","      <td>4740.287150</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>39.000000</td>\n","      <td>30.400000</td>\n","      <td>1.000000</td>\n","      <td>9382.033000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>51.000000</td>\n","      <td>34.693750</td>\n","      <td>2.000000</td>\n","      <td>16639.912515</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>64.000000</td>\n","      <td>53.130000</td>\n","      <td>5.000000</td>\n","      <td>63770.428010</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               age          bmi     children       charges\n","count  1338.000000  1338.000000  1338.000000   1338.000000\n","mean     39.207025    30.663397     1.094918  13270.422265\n","std      14.049960     6.098187     1.205493  12110.011237\n","min      18.000000    15.960000     0.000000   1121.873900\n","25%      27.000000    26.296250     0.000000   4740.287150\n","50%      39.000000    30.400000     1.000000   9382.033000\n","75%      51.000000    34.693750     2.000000  16639.912515\n","max      64.000000    53.130000     5.000000  63770.428010"]},"execution_count":576,"metadata":{},"output_type":"execute_result"}],"source":["# show descriptive statistics for the dataset by calling the describe() method on medical\n","medical.describe()"]},{"cell_type":"code","execution_count":577,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":699},"executionInfo":{"elapsed":1363,"status":"ok","timestamp":1682900857210,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"gpSxGghzhZ7y","outputId":"46cdbb4f-4557-4369-a1c2-2207f96123db"},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'age'}>, <Axes: title={'center': 'bmi'}>],\n","       [<Axes: title={'center': 'children'}>,\n","        <Axes: title={'center': 'charges'}>]], dtype=object)"]},"execution_count":577,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA9oAAAKqCAYAAADFQiYyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACET0lEQVR4nOzde3yT9fn/8Xda0pQCaSlCS6VAp8hBjoJABBWhtCIylX7n2NBVxlc2VlCo81C/cvRQZJsyXAF1rLhN5oYbqIBARYUpBaHKJuAYTBQHtGxgKVAbYnP//vDXaOgxzd3m0Nfz8cgDct+f+851fZI0n+s+WgzDMAQAAAAAAEwREegAAAAAAAAIJxTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARBTaAAAAQBiZP3++LBaL/vvf/zbp69x1113q3r17k74GEKootAEAAAAAMFGrQAcAAAAAIPQ8//zzcrvdgQ4DCEoU2gAAAAB8ZrVaAx0CELQ4dBwIAZ9++ql+8pOfqGfPnmrdurU6dOig73znO/rkk0+qtf373/+u66+/Xq1bt1aXLl302GOPKT8/XxaLpVr7119/Xddee63atGmjdu3aafz48dq/f3/zJAUAAJrUf//7X91+++2y2+3q0KGD7r33XlVUVHjmWywWzZgxQ2vWrFGfPn3UunVrORwOffjhh5KkZ599Vpdffrmio6M1atSoauMIztEGascebSAE7N69Wzt27NCkSZPUpUsXffLJJ1q+fLlGjRqlAwcOKCYmRpJ07Ngx3XDDDbJYLMrJyVGbNm3061//Wjabrdo6f/e73ykzM1Pp6el68sknVV5eruXLl2vkyJH64IMP+OEEACDE3X777erevbtyc3O1c+dOLV26VJ9//rl++9vfetr89a9/1auvvqqsrCxJUm5urm6++WY98MADWrZsmX7yk5/o888/1+LFi/XDH/5Qb775ZqDSAUIKhTYQAsaPH6//+Z//8Zo2YcIEORwO/fnPf9add94pSXryySf1+eef6/3339fAgQMlSVOmTFGPHj28lj137pzuuece/e///q+ee+45z/TMzEz17NlTTzzxhNd0AAAQelJSUvTKK69IkrKysmS327Vs2TL99Kc/Vf/+/SVJBw8e1D/+8Q/PBvb27dvrRz/6kR577DH985//VLt27SRJlZWVys3N1SeffMLGeKABOHQcCAGtW7f2/N/lcunUqVO6/PLLFRcXp/fff98zb9OmTXI4HJ4iW5Li4+M1efJkr/UVFBSotLRU3/ve9/Tf//7X84iMjNSwYcP01ltvNXlOAACgaVXtpa4yc+ZMSdLGjRs908aMGeNVOA8bNkySlJGR4Smyvzn9448/bqpwgbDCHm0gBHzxxRfKzc1Vfn6+jh07JsMwPPPOnDnj+f+nn34qh8NRbfnLL7/c6/mhQ4ckSaNHj67x9ex2uxlhAwCAALr4iLbLLrtMERERXudad+3a1atNbGysJCk5ObnG6Z9//nkTRAqEHwptIATMnDlT+fn5mjVrlhwOh2JjY2WxWDRp0qRG3Vajapnf/e53SkxMrDa/VSv+NAAAEG4sFku1aZGRkTW2rW36Nzf2A6gdo2kgBLz88svKzMzUL37xC8+0iooKlZaWerXr1q2bDh8+XG35i6dddtllkqROnTopNTXV/IABAEDAHTp0SCkpKZ7nhw8fltvt5hxroBlwjjYQAiIjI6ttQX7mmWdUWVnpNS09PV2FhYXau3evZ9rp06f14osvVmtnt9v1xBNPyOVyVXu9//znP+YFDwAAAiIvL8/r+TPPPCNJGjduXCDCAVoU9mgDIeDmm2/W7373O8XGxqpPnz4qLCzUG2+8oQ4dOni1e+CBB/T73/9eY8eO1cyZMz239+ratatOnz7tOWTMbrdr+fLluvPOO3XVVVdp0qRJ6tixo44ePaoNGzZoxIgR+tWvfhWIVAEAgEmOHDmib3/727rxxhtVWFio3//+9/r+97+vAQMGBDo0IOxRaAMh4Je//KUiIyP14osvqqKiQiNGjNAbb7yh9PR0r3bJycl66623dM899+iJJ55Qx44dlZWVpTZt2uiee+5RdHS0p+33v/99JSUladGiRfrZz34mp9OpSy+9VNdee62mTJnS3CkCAACT/fGPf9TcuXP10EMPqVWrVpoxY4Z+9rOfBTosoEWwGFzRAAh7s2bN0rPPPqtz587VenETAAAAAObgHG0gzHzxxRdez0+dOqXf/e53GjlyJEU2AAAA0Aw4dBwIMw6HQ6NGjVLv3r1VUlKilStXqqysTHPmzAl0aAAAAECLQKENhJmbbrpJL7/8sp577jlZLBZdddVVWrlypa677rpAhwYAAAC0CJyjDQAAAACAiThHGwAAAAAAE1FoAwAAAABgopA8R9vtduv48eNq166dLBZLoMMBAIQowzB09uxZJSUlKSKCbc/hinEDAMAMvowbQrLQPn78uJKTkwMdBgAgTHz22Wfq0qVLoMNAE2HcAAAwU0PGDSFZaLdr107SVwna7faAxeFyubRlyxalpaXJarUGLI5QRf/5h/7zD/3nn3Dpv7KyMiUnJ3t+VxCeGjJuCIfPdDjkIIVHHuQQPMIhj3DIQQqPPHwZN4RkoV112Jfdbg94oR0TEyO73R6yH5ZAov/8Q//5h/7zT7j1H4cTh7eGjBvC4TMdDjlI4ZEHOQSPcMgjHHKQwicPqWHjBk5IAwAAAADARBTaAAAAAACYyPRCu3v37rJYLNUeWVlZkqSKigplZWWpQ4cOatu2rTIyMlRSUmJ2GAAAAAAABITphfbu3bt14sQJz6OgoECS9J3vfEeSNHv2bL322mtas2aNtm3bpuPHj2vixIlmhwEAAAAAQECYfjG0jh07ej1ftGiRLrvsMl1//fU6c+aMVq5cqdWrV2v06NGSpPz8fPXu3Vs7d+7U8OHDzQ4HAAAAAIBm1aRXHb9w4YJ+//vfKzs7WxaLRUVFRXK5XEpNTfW06dWrl7p27arCwsJaC22n0ymn0+l5XlZWJumrK9e5XK6mTKFOVa8dyBhCGf3nH/rPP/Sff8Kl/0I9fgAAEJyatNBet26dSktLddddd0mSiouLFRUVpbi4OK92CQkJKi4urnU9ubm5WrBgQbXpW7ZsUUxMjJkhN0rV4fFoHPrPP/Sff+g//4R6/5WXlwc6hBave/fu+vTTT6tN/8lPfqK8vDxVVFTovvvu00svvSSn06n09HQtW7ZMCQkJAYgWAICGadJCe+XKlRo3bpySkpL8Wk9OTo6ys7M9z6tuFJ6Wlhbw+2gXFBRo7NixIX8vuECg//xD//mH/vNPuPRf1RFSCJzdu3ersrLS83zfvn0aO3as17VdNmzYoDVr1ig2NlYzZszQxIkT9e677wYqZAAA6tVkhfann36qN954Q3/5y1880xITE3XhwgWVlpZ67dUuKSlRYmJireuy2Wyy2WzVplut1qAY4AVLHKGK/vMP/ecf+s8/od5/oRx7uODaLgCAcNRkhXZ+fr46deqk8ePHe6YNHjxYVqtVW7duVUZGhiTp4MGDOnr0qBwOR1OFAgAAQkAgr+0SDtcdCIccpPDIgxyCRzjkEQ45SOGRhy+xN0mh7Xa7lZ+fr8zMTLVq9fVLxMbGaurUqcrOzlZ8fLzsdrtmzpwph8MRsK3S3R/aUOf8TxaNr3M+AAAwRzBc2yXUrzsghUcOUnjkQQ7BIxzyCIccpNDOw5druzRJof3GG2/o6NGj+uEPf1ht3tNPP62IiAhlZGR4XdQEAAC0bIG8tks4XHcgHHKQwiOPqhzm7ImQ022psc2++enNHJVvwuF9kMIjj3DIQQqPPHy5tkuTFNppaWkyDKPGedHR0crLy1NeXl5TvDQAAAhBwXJtl1C/7oAUHjlI4ZGH022Rs7LmQjtUcguH90EKjzzCIQcptPPwJe6IJowDAACgQeq7tksVru0CAAgFTXp7r3BQ1znchx5Na8ZIAAAIT6F0bRcAABqCQhsAAAQU13YBAIQbCm0AABBQXNsFABBuOEcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIm4GBoAAADCWl23a5WkTxaNr3M+APiKPdoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCLO0QYAAAAaobZzv22RhhYPbeZgAAQV9mgDAAAAAGAiCm0AAAAAAEzEoeMAAABAkKnrlmTcjgwIfuzRBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBH30QYAAADCRF3335a4BzfQXNijDQAAAACAiSi0AQAAAAAwUZMU2seOHdMdd9yhDh06qHXr1urXr5/27NnjmW8YhubOnavOnTurdevWSk1N1aFDh5oiFAAAAAAAmpXphfbnn3+uESNGyGq16vXXX9eBAwf0i1/8Qu3bt/e0Wbx4sZYuXaoVK1Zo165datOmjdLT01VRUWF2OAAAAAAANCvTL4b25JNPKjk5Wfn5+Z5pKSkpnv8bhqElS5bokUce0S233CJJ+u1vf6uEhAStW7dOkyZNMjskAAAAAACajemF9quvvqr09HR95zvf0bZt23TppZfqJz/5ie6++25J0pEjR1RcXKzU1FTPMrGxsRo2bJgKCwtrLLSdTqecTqfneVlZmSTJ5XLJ5XL5Fa8t0mj0slWv7W8MLRX95x/6zz/0n3/Cpf9CPf5wcOzYMT344IN6/fXXVV5erssvv1z5+fkaMmSIpK820M+bN0/PP/+8SktLNWLECC1fvlw9evQIcOQAANTO9EL7448/1vLly5Wdna2HH35Yu3fv1j333KOoqChlZmaquLhYkpSQkOC1XEJCgmfexXJzc7VgwYJq07ds2aKYmBi/4l08tPHLFhQUeP2LxqH//EP/+Yf+80+o9195eXmgQ2jRqk43u+GGG/T666+rY8eOOnToUI2nm73wwgtKSUnRnDlzlJ6ergMHDig6OjqA0SOc1HVLLG6HBaAxTC+03W63hgwZoieeeEKSNGjQIO3bt08rVqxQZmZmo9aZk5Oj7Oxsz/OysjIlJycrLS1Ndrvdr3j7zt/c6GU/+L/RKigo0NixY2W1Wv2KoyVyuVz0nx/oP//Qf/4Jl/6rOkIKgcHpZgCAcGV6od25c2f16dPHa1rv3r315z//WZKUmJgoSSopKVHnzp09bUpKSjRw4MAa12mz2WSz2apNt1qtfg/wnJWWRi9b9dpmxNGS0X/+of/8Q//5J9T7L5RjDwdNcbqZ1LhTzsLhdIhwyEFqmjzMOFXQl/XaIgyvf31db13rbmxMDX3di9vxeQq8cMhBCo88fInd9EJ7xIgROnjwoNe0f/7zn+rWrZukr7ZUJyYmauvWrZ7CuqysTLt27dL06dPNDgcAAASppjjdTPLvlLNQPx1CCo8cJHPz8OdUwY0bNzZ6vY8OcTdqvfWt25+Y6nvdi/F5Ch7hkIMU2nn4csqZ6YX27Nmzdc011+iJJ57Q7bffrvfee0/PPfecnnvuOUmSxWLRrFmz9Nhjj6lHjx6e862SkpJ06623mh0OAAAIUk1xupnUuFPOwuF0iHDIQWqaPPw5VXDf/HSf12uLMPToELfm7ImQ013z0ZN1rbeudTc2poa+bhU+T8EjHHKQwiMPX045M73Qvvrqq7V27Vrl5ORo4cKFSklJ0ZIlSzR58mRPmwceeEDnz5/XtGnTVFpaqpEjR2rTpk1c1AQAgBakKU43k/w75SzUT4eQwiMHydw8zDhVsDHrdbottbapL7e61u1PTL72KZ+n4BEOOUihnYcvcZteaEvSzTffrJtvvrnW+RaLRQsXLtTChQub4uUBAEAI4HQzAEC4apJCGwAAoD6cbgYACFcU2gAAICA43QwAEK4otAEAQMBwuhkAIBxFBDoAAAAAAADCCYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIi6EBAAAAzaz7QxsCHQKAJsQebQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNxH20AAACgFtzvGkBjsEcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJuL2XgAAAEALUdftyj5ZNL4ZIwHCG3u0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEpp+jPX/+fC1YsMBrWs+ePfWPf/xDklRRUaH77rtPL730kpxOp9LT07Vs2TIlJCSYHQoAAADCCOcXAwgVTbJH+8orr9SJEyc8j3feecczb/bs2Xrttde0Zs0abdu2TcePH9fEiRObIgwAAAAAAJpdkxTarVq1UmJioudxySWXSJLOnDmjlStX6qmnntLo0aM1ePBg5efna8eOHdq5c2dThAIAAILY/PnzZbFYvB69evXyzK+oqFBWVpY6dOigtm3bKiMjQyUlJQGMGACA+jXJ7b0OHTqkpKQkRUdHy+FwKDc3V127dlVRUZFcLpdSU1M9bXv16qWuXbuqsLBQw4cPr3F9TqdTTqfT87ysrEyS5HK55HK5/IrVFmk0etmq1/Y3hpaK/vMP/ecf+s8/4dJ/oR5/uLjyyiv1xhtveJ63avX18GT27NnasGGD1qxZo9jYWM2YMUMTJ07Uu+++G4hQEcTqOqwcAJqb6YX2sGHDtGrVKvXs2VMnTpzQggULdO2112rfvn0qLi5WVFSU4uLivJZJSEhQcXFxrevMzc2tdt63JG3ZskUxMTF+xbt4aOOXLSgo8PoXjUP/+Yf+8w/9559Q77/y8vJAhwB9fSTcxaqOhFu9erVGjx4tScrPz1fv3r21c+fOWjfQAwAQaKYX2uPGjfP8v3///ho2bJi6deumP/3pT2rdunWj1pmTk6Ps7GzP87KyMiUnJystLU12u92vePvO39zoZT/4v9EqKCjQ2LFjZbVa/YqjJXK5XPSfH+g//9B//gmX/qs6QgqBZfaRcAAABFqTHDr+TXFxcbriiit0+PBhjR07VhcuXFBpaanXXu2SkpIat2RXsdlsstls1aZbrVa/B3jOSkujl616bTPiaMnoP//Qf/6h//wT6v0XyrGHi6Y4Eq4xp5yFw+kQ4ZCDVHce/pzy15xsEYbXv2ar6z0247TIb/4/nD9PoSIccpDCIw9fYm/yQvvcuXP617/+pTvvvFODBw+W1WrV1q1blZGRIUk6ePCgjh49KofD0dShAACAINMUR8L5c8pZqJ8OIYVHDlLNefhzyl8gPDrE3STr3bhxY63z/OmjmtYbzp+nUBMOOUihnYcvp5yZXmj/9Kc/1YQJE9StWzcdP35c8+bNU2RkpL73ve8pNjZWU6dOVXZ2tuLj42W32zVz5kw5HA4O/wIAAKYcCdeYU87C4XSIcMhBqjsPf075a062CEOPDnFrzp4IOd2NP3qyue2bn+75f0v4PIWKcMhBCo88fDnlzPRC+9///re+973v6dSpU+rYsaNGjhypnTt3qmPHjpKkp59+WhEREcrIyJDT6VR6erqWLVtmdhgAACAEmXEknD+nnIX66RBSeOQg1ZyHP6f8BYLTbQmpmGv63ITz5ynUhEMOUmjn4UvcphfaL730Up3zo6OjlZeXp7y8PLNfGgAAhBiOhAMAhKMmP0cbAACgNhwJBwAIRxTaAAAgYDgSDgAQjiICHQAAAAAAAOGEPdoAAAAA6tT9oQ11zv9k0fhmigQIDezRBgAAAADARBTaAAAAAACYiEIbAAAAAAATcY42AAAAgkL3hzbIFmlo8VCp7/zNclZaAh0SADQKe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIm7vBQAAAKDJdH9oQ63zPlk0vhkjAZoPe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIm7vBQAAgGZT162eACBcsEcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgImavNBetGiRLBaLZs2a5ZlWUVGhrKwsdejQQW3btlVGRoZKSkqaOhQAAAAAAJpckxbau3fv1rPPPqv+/ft7TZ89e7Zee+01rVmzRtu2bdPx48c1ceLEpgwFAAAAAIBm0WSF9rlz5zR58mQ9//zzat++vWf6mTNntHLlSj311FMaPXq0Bg8erPz8fO3YsUM7d+5sqnAAAECQ4yg4AEC4aLL7aGdlZWn8+PFKTU3VY4895pleVFQkl8ul1NRUz7RevXqpa9euKiws1PDhw6uty+l0yul0ep6XlZVJklwul1wul19x2iKNRi9b9dr+xtBS0X/+of/8Q//5J1z6L9TjDyd1HQW3YcMGrVmzRrGxsZoxY4YmTpyod999N0CRoiG4VzaAlq5JCu2XXnpJ77//vnbv3l1tXnFxsaKiohQXF+c1PSEhQcXFxTWuLzc3VwsWLKg2fcuWLYqJifEr1sVDG79sQUGB179oHPrPP/Sff+g//4R6/5WXlwc6BMj7KLhvbpyvOgpu9erVGj16tCQpPz9fvXv31s6dO2vcOA8AQDAwvdD+7LPPdO+996qgoEDR0dGmrDMnJ0fZ2dme52VlZUpOTlZaWprsdrtf6+47f3Ojl/3g/0aroKBAY8eOldVq9SuOlsjlctF/fqD//EP/+Sdc+q/qCCkElplHwUmNOxIuHI7SCKYc/Dli0BZheP0bikI1h29+di7+PNX3ntb1uatr2ab+vAbT96KxwiEHKTzy8CV20wvtoqIinTx5UldddZVnWmVlpbZv365f/epX2rx5sy5cuKDS0lKvvdolJSVKTEyscZ02m002m63adKvV6vcAz1lpafSyVa9tRhwtGf3nH/rPP/Sff0K9/0I59nBh9lFwkn9HwoX6URpScOTgzxGDVR4d4vZ/JQEWajls3Lix2rSqz1N972lNy1apa9m6ljNTMHwv/BUOOUihnYcvR8KZXmiPGTNGH374ode0KVOmqFevXnrwwQeVnJwsq9WqrVu3KiMjQ5J08OBBHT16VA6Hw+xwAABAkGqKo+Ckxh0JFw5HaQRTDv4cMWiLMPToELfm7ImQ0934HSKBFKo57Juf7vn/xZ+n+t7Tby57sbqWrWs5MwTT96KxwiEHKTzy8OVIONML7Xbt2qlv375e09q0aaMOHTp4pk+dOlXZ2dmKj4+X3W7XzJkz5XA4ONcKAIAWpCmOgpP8OxIu1I/SkIIjB3+OGPSsw20xZT2BFGo51PS5qfo81ZdHXZ+5upZtrs9qMHwv/BUOOUihnYcvcTfZVcfr8vTTTysiIkIZGRlyOp1KT0/XsmXLAhEKAAAIEI6CAwCEq2YptN9++22v59HR0crLy1NeXl5zvDwAAAhCHAUHhA9u6QZ4C8gebQAAgIbgKDgAQCii0AYAAEGDo+AAAOEgItABAAAAAAAQTii0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATNQq0AEAAAAACLzuD23w/N8WaWjxUKnv/M1yVloCGBUQmtijDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAm4hxtAAAA+OSb5/ICAKpjjzYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmMj0Qnv58uXq37+/7Ha77Ha7HA6HXn/9dc/8iooKZWVlqUOHDmrbtq0yMjJUUlJidhgAAAAAAASE6YV2ly5dtGjRIhUVFWnPnj0aPXq0brnlFu3fv1+SNHv2bL322mtas2aNtm3bpuPHj2vixIlmhwEAAEIAG+gBAOHI9EJ7woQJuummm9SjRw9dccUVevzxx9W2bVvt3LlTZ86c0cqVK/XUU09p9OjRGjx4sPLz87Vjxw7t3LnT7FAAAECQYwM9ACActWrKlVdWVmrNmjU6f/68HA6HioqK5HK5lJqa6mnTq1cvde3aVYWFhRo+fHiN63E6nXI6nZ7nZWVlkiSXyyWXy+VXjLZIo9HLVr22vzG0VPSff+g//9B//gmX/gv1+MPBhAkTvJ4//vjjWr58uXbu3KkuXbpo5cqVWr16tUaPHi1Jys/PV+/evbVz585axw0AAARakxTaH374oRwOhyoqKtS2bVutXbtWffr00d69exUVFaW4uDiv9gkJCSouLq51fbm5uVqwYEG16Vu2bFFMTIxfsS4e2vhlCwoKvP5F49B//qH//EP/+SfU+6+8vDzQIeAbzNpADwBAoDVJod2zZ0/t3btXZ86c0csvv6zMzExt27at0evLyclRdna253lZWZmSk5OVlpYmu93uV6x9529u9LIf/N9oFRQUaOzYsbJarX7F0RK5XC76zw/0n3/oP/+ES/9VHSGFwDJ7A31jjoQLh6M0mjMHf44IrHfdEYbXv6GIHBquqT+vfLeDRzjk4UvsTVJoR0VF6fLLL5ckDR48WLt379Yvf/lLffe739WFCxdUWlrq9aNZUlKixMTEWtdns9lks9mqTbdarX4P8JyVlkYvW/XaZsTRktF//qH//EP/+SfU+y+UYw8nZm+g9+dIuFA/SkNqnhz8OSKwoR4d4m76F2li5FC/jRs3Nun6q/DdDh6hnIcvR8I16TnaVdxut5xOpwYPHiyr1aqtW7cqIyNDknTw4EEdPXpUDoejOUIBAABBxuwN9I05Ei4cjtJozhz8OSKwPrYIQ48OcWvOngg53Y3fIRJI5NBw++anN9m6Jb7bwSQc8vDlSDjTC+2cnByNGzdOXbt21dmzZ7V69Wq9/fbb2rx5s2JjYzV16lRlZ2crPj5edrtdM2fOlMPhCMnzrPrO36zFQ7/6t6Y9458sGh+AqIDA6/7QhmZ/zbq+bzXFY4s06vz++vuaABrP3w30/hwJF+pHaUjNk4M/RwQ2+DXclmZ5naZEDvVrru8b3+3gEcp5+BK36YX2yZMn9YMf/EAnTpxQbGys+vfvr82bN2vs2LGSpKeffloRERHKyMiQ0+lUenq6li1bZnYYAAAgBLSkDfQAgJbD9EJ75cqVdc6Pjo5WXl6e8vLyzH5pAAAQYthADwAIR81yjjYAAEBN2EAPAAhHFNpNKBDnqQYC56kiGATj982fmPheAQAAhC4KbQAAgBaqrg2CbPADgMaLCHQAAAAAAACEE/Zow2/1HR5b3xbxxt5eqSVtaa/r9lS+LgcAAACgabFHGwAAAAAAE7FHGwAAANVwVBSag79HRgLBij3aAAAAAACYiD3aCFlNtaWdLacIBny+AQAAQheFNgAAAICQw+3pEMw4dBwAAAAAABNRaAMAAAAAYCIOHQcuwlVWEc4a+/nmEDwAAICGY482AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNxMTQAAAAAYaXq4p+2SEOLh0p952+Ws9Limc9FPtHU2KMNAAAAAICJ2KMNAKjXxbcFq20PgdnY4wAAAEIRe7QBAAAAADARhTYAAAAAACai0AYAAAAAwEScow0ACFoXnxv+TZy/DQAAghWFNgAAQAir6zZGbJACgMDg0HEAAAAAAExkeqGdm5urq6++Wu3atVOnTp1066236uDBg15tKioqlJWVpQ4dOqht27bKyMhQSUmJ2aEAAAAAANDsTC+0t23bpqysLO3cuVMFBQVyuVxKS0vT+fPnPW1mz56t1157TWvWrNG2bdt0/PhxTZw40exQAABAkGMDPQAgHJleaG/atEl33XWXrrzySg0YMECrVq3S0aNHVVRUJEk6c+aMVq5cqaeeekqjR4/W4MGDlZ+frx07dmjnzp1mhwMAAIIYG+gBAOGoyS+GdubMGUlSfHy8JKmoqEgul0upqameNr169VLXrl1VWFio4cOHV1uH0+mU0+n0PC8rK5MkuVwuuVwuv+KzRRqNXzbC8PoXNavtPaqaTv81TlW/1fUd8OfzHe7M+P7W9/cnnPs/GP7++fv336x1wD+bNm3yer5q1Sp16tRJRUVFuu666zwb6FevXq3Ro0dLkvLz89W7d2/t3LmzxnEDAACB1qSFttvt1qxZszRixAj17dtXklRcXKyoqCjFxcV5tU1ISFBxcXGN68nNzdWCBQuqTd+yZYtiYmL8inHxUL8WlyQ9OsTt/0rC2MaNG+ucT//5p6CgoNZ5Zny+w50/n7/6Ptstof8D+f2tr/8bory83IRIYKZAbaCvmh6KG1+qNurVtAEsFDcIBsOGPH+Rg3n83aFQWx6h9F0P5b9P3xQOefgSu8UwjCb79kyfPl2vv/663nnnHXXp0kWStHr1ak2ZMsXrB1CShg4dqhtuuEFPPvlktfXU9IOZnJys//73v7Lb7X7F2Hf+5kYva4sw9OgQt+bsiZDTbfErjnC2b356jdNdLpcKCgrov0aq+vyNHTtWVqu1xjb+fL7DnRnf39o+21XCuf+D4e9fff3fEGVlZbrkkkt05swZv39P4D+3261vf/vbKi0t1TvvvCOpceOG+fPn17iBfvXq1X5voAcAtFzl5eX6/ve/36BxQ5Pt0Z4xY4bWr1+v7du3e4psSUpMTNSFCxdUWlrqtVe7pKREiYmJNa7LZrPJZrNVm261WmstMBqq6j6Tfq3DbTFlPeGqvveI/vNPXd8D+rV+/nz+6v1st4D+D+T319+//2atA+bJysrSvn37PEV2Y+Xk5Cg7O9vzvGoDfVpaWq0Do6qNv3VtvAxWVRv1atoAFoobBINhQ56/yME8dX2GG/L5rS0PMzbWNpdQ/vv0TeGQR9URUg1heqFtGIZmzpyptWvX6u2331ZKSorX/MGDB8tqtWrr1q3KyMiQJB08eFBHjx6Vw+EwOxwAABACgmUDvRkb8ZvbxRu7vrkBLJQ3CIbDhnhy8F9dn2Ff4ro4j1D7nkuh+fepJqGchy9xm15oZ2VlafXq1XrllVfUrl07z3nXsbGxat26tWJjYzV16lRlZ2crPj5edrtdM2fOlMPh4IImAAC0MGygBwCEI9ML7eXLl0uSRo0a5TU9Pz9fd911lyTp6aefVkREhDIyMuR0OpWenq5ly5aZHQoAAAhybKAHUJfuD20IdAhAozTJoeP1iY6OVl5envLy8sx+eQAAEELYQA8ACEdNfh9tAACA2rCBHgAQjiICHQAAAAAAAOGEQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYKJWgQ4AAAAATaP7QxsCHQIAtEjs0QYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE3GONgAAAAD8f3Vd2+CTReObMRKEMvZoAwAAAABgIgptAAAAAABMRKENAAAAAICJOEcbAAAAQIvCPebR1NijDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJjI9EJ7+/btmjBhgpKSkmSxWLRu3Tqv+YZhaO7cuercubNat26t1NRUHTp0yOwwAABACGDcAAAIR6bf3uv8+fMaMGCAfvjDH2rixInV5i9evFhLly7VCy+8oJSUFM2ZM0fp6ek6cOCAoqOjzQ4HAAAEMcYNAEJJfbcF+2TR+GaKBMHO9EJ73LhxGjduXI3zDMPQkiVL9Mgjj+iWW26RJP32t79VQkKC1q1bp0mTJpkdDgAACGKMGwAA4cj0QrsuR44cUXFxsVJTUz3TYmNjNWzYMBUWFtb6g+l0OuV0Oj3Py8rKJEkul0sul8uvmGyRRuOXjTC8/kXNanuPqqbTf41T1W91fQf8+XyHOzO+v/X9/Qnn/g+Gv3/+/v03ax1oOo0dNwAAEGjNWmgXFxdLkhISErymJyQkeObVJDc3VwsWLKg2fcuWLYqJifErpsVD/VpckvToELf/KwljGzdurHM+/eefgoKCWueZ8fkOd/58/ur7bLeE/g/k97e+/m+I8vJyEyJBU2nsuKExG+irpofixpeqjXrBsAHMDOGQBzkEj+bOoyn+hoTy36dvCoc8fIm9WQvtxsrJyVF2drbneVlZmZKTk5WWlia73e7XuvvO39zoZW0Rhh4d4tacPRFyui1+xRHO9s1Pr3G6y+VSQUEB/ddIVZ+/sWPHymq11tjGn893uDPj+1vbZ7tKOPd/MPz9q6//G6KqAEN48WcDfV0bL4PVxRv1wmUDdjjkQQ7Bo7nyMGMjcG1C8e9TTUI5D1820DdroZ2YmChJKikpUefOnT3TS0pKNHDgwFqXs9lsstls1aZbrdZaC4yGclb6P0B0ui2mrCdc1fce0X/+qet7QL/Wz5/PX72f7RbQ/4H8/vr799+sdaDpNHbc0JgN9FUbf+vaeBmsqjbqBcMGMDOEQx7kEDyaOw8zNgJfLJT/Pn1TOOThywb6Zi20U1JSlJiYqK1bt3p+IMvKyrRr1y5Nnz69OUMBAABBrrHjBn820JuxEb+5XbyxK1w2YIdDHuQQPJorj6b8+xGKf59qEsp5+BK36YX2uXPndPjwYc/zI0eOaO/evYqPj1fXrl01a9YsPfbYY+rRo4fnNh1JSUm69dZbzQ4FAAAEOcYNAIBwZHqhvWfPHt1www2e51WHbmVmZmrVqlV64IEHdP78eU2bNk2lpaUaOXKkNm3axL0wAQBogRg3AADCkemF9qhRo2QYtV/Vz2KxaOHChVq4cKHZLw0AAEIM4wYAQDiKCHQAAAAAAACEk5C4vRcAAECw6/7Qhjrnf7JofJMsCyB41PVd5nvcsrBHGwAAAAAAE1FoAwAAAABgIgptAAAAAABMxDnaAAAAzaC+87ABAOGDPdoAAAAAAJiIQhsAAAAAABNx6DgAAECQ47BzILzV9R23RRpaPLQZg4Ep2KMNAAAAAICJKLQBAAAAADARhTYAAAAAACbiHG0AAAAACFN1nf/9yaLxzRhJy8IebQAAAAAATEShDQAAAACAiSi0AQAAAAAwEedoAwAAAEATq+tc6UDh/O2mwx5tAAAAAABMRKENAAAAAICJOHQcAAAAAIJc3/mb5ay0BDoMNBB7tAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARJyjDQAAAAAIafXdPq25b1fGHm0AAAAAAExEoQ0AAAAAgIkCeuh4Xl6efvazn6m4uFgDBgzQM888o6FDhwYyJAAAEISCZcxQ36GJABAu/Pl7V9dh2vXdpqy5D/FuKgHbo/3HP/5R2dnZmjdvnt5//30NGDBA6enpOnnyZKBCAgAAQYgxAwAg1ASs0H7qqad09913a8qUKerTp49WrFihmJgY/eY3vwlUSAAAIAgxZgAAhJqAHDp+4cIFFRUVKScnxzMtIiJCqampKiwsrNbe6XTK6XR6np85c0aSdPr0ablcLr9iafXl+cYv6zZUXu5WK1eEKt21H/7Q0p06darG6S6XS+Xl5fRfI1V9/k6dOiWr1VpzGz8+3+HOjO9vbZ9tz2uEcf8Hw9+/+vq/Ic6ePStJMgzD73Whafg6ZpAaN26o+k2q62+qFNzf62D4XpohHPIgh+ARDnmEYg41/UY3dOzf2N/3+v4+N/u4wQiAY8eOGZKMHTt2eE2///77jaFDh1ZrP2/ePEMSDx48ePDg0SSPzz77rLl+AuEjX8cMhsG4gQcPHjx4NO2jIeOGkLiPdk5OjrKzsz3P3W63Tp8+rQ4dOshiCdxWnbKyMiUnJ+uzzz6T3W4PWByhiv7zD/3nH/rPP+HSf4Zh6OzZs0pKSgp0KDBRY8YN4fCZDoccpPDIgxyCRzjkEQ45SOGRhy/jhoAU2pdccokiIyNVUlLiNb2kpESJiYnV2ttsNtlsNq9pcXFxTRmiT+x2e8h+WIIB/ecf+s8/9J9/wqH/YmNjAx0C6uDrmEHyb9wQDp/pcMhBCo88yCF4hEMe4ZCDFPp5NHTcEJCLoUVFRWnw4MHaunWrZ5rb7dbWrVvlcDgCERIAAAhCjBkAAKEoYIeOZ2dnKzMzU0OGDNHQoUO1ZMkSnT9/XlOmTAlUSAAAIAgxZgAAhJqAFdrf/e539Z///Edz585VcXGxBg4cqE2bNikhISFQIfnMZrNp3rx51Q5PQ8PQf/6h//xD//mH/kNzao4xQzh8psMhByk88iCH4BEOeYRDDlL45NFQFsPgniYAAAAAAJglIOdoAwAAAAAQrii0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoNkJubq6uvvlrt2rVTp06ddOutt+rgwYNebSoqKpSVlaUOHTqobdu2ysjIUElJSYAiDi7Lly9X//79PTendzgcev311z3z6buGW7RokSwWi2bNmuWZRv/Vbf78+bJYLF6PXr16eebTf3U7duyY7rjjDnXo0EGtW7dWv379tGfPHs98wzA0d+5cde7cWa1bt1ZqaqoOHToUwIiB2m3fvl0TJkxQUlKSLBaL1q1b5zX/rrvuqvb34sYbbwxMsLUIlzFJQ/IYNWpUtffjxz/+cYAiri5cxjf15RHs70NNwmG8VFMOofBeMO76GoV2A2zbtk1ZWVnauXOnCgoK5HK5lJaWpvPnz3vazJ49W6+99prWrFmjbdu26fjx45o4cWIAow4eXbp00aJFi1RUVKQ9e/Zo9OjRuuWWW7R//35J9F1D7d69W88++6z69+/vNZ3+q9+VV16pEydOeB7vvPOOZx79V7vPP/9cI0aMkNVq1euvv64DBw7oF7/4hdq3b+9ps3jxYi1dulQrVqzQrl271KZNG6Wnp6uioiKAkQM1O3/+vAYMGKC8vLxa29x4441efy/+8Ic/NGOE9QuXMUlD8pCku+++2+v9WLx4cYAiri5cxjf15SEF9/twsXAYL9WWgxQa7wXjrv/PgM9OnjxpSDK2bdtmGIZhlJaWGlar1VizZo2nzUcffWRIMgoLCwMVZlBr37698etf/5q+a6CzZ88aPXr0MAoKCozrr7/euPfeew3D4LPXEPPmzTMGDBhQ4zz6r24PPvigMXLkyFrnu91uIzEx0fjZz37mmVZaWmrYbDbjD3/4Q3OECDSaJGPt2rVe0zIzM41bbrklIPE0VriMSS7OwzAMr9+7UBEu45uqPAwjtN6HcBgv1ZaDYYTGe8G462vs0W6EM2fOSJLi4+MlSUVFRXK5XEpNTfW06dWrl7p27arCwsKAxBisKisr9dJLL+n8+fNyOBz0XQNlZWVp/PjxXv0k8dlrqEOHDikpKUnf+ta3NHnyZB09elQS/VefV199VUOGDNF3vvMdderUSYMGDdLzzz/vmX/kyBEVFxd79V9sbKyGDRtG/yFkvf322+rUqZN69uyp6dOn69SpU4EOqU7hMia5OI8qL774oi655BL17dtXOTk5Ki8vD0R49QqX8c3FeVQJlfchHMZLteVQJRTeC8ZdX2kV6ABCjdvt1qxZszRixAj17dtXklRcXKyoqCjFxcV5tU1ISFBxcXEAogw+H374oRwOhyoqKtS2bVutXbtWffr00d69e+m7erz00kt6//33tXv37mrz+OzVb9iwYVq1apV69uypEydOaMGCBbr22mu1b98++q8eH3/8sZYvX67s7Gw9/PDD2r17t+655x5FRUUpMzPT00cJCQley9F/CFU33nijJk6cqJSUFP3rX//Sww8/rHHjxqmwsFCRkZGBDq+acBmT1JSHJH3/+99Xt27dlJSUpL///e968MEHdfDgQf3lL38JYLTewmV8U1seUmi8D1J4jJfqykEKjfeCcdfXKLR9lJWVpX379nmda4D69ezZU3v37tWZM2f08ssvKzMzU9u2bQt0WEHvs88+07333quCggJFR0cHOpyQNG7cOM//+/fvr2HDhqlbt27605/+pNatWwcwsuDndrs1ZMgQPfHEE5KkQYMGad++fVqxYoUyMzMDHB1gvkmTJnn+369fP/Xv31+XXXaZ3n77bY0ZMyaAkdUsXMYkteUxbdo0z//79eunzp07a8yYMfrXv/6lyy67rLnDrFG4jG9qy6NPnz4h8T6Ew3ipITmEwnvBuOtrHDrugxkzZmj9+vV666231KVLF8/0xMREXbhwQaWlpV7tS0pKlJiY2MxRBqeoqChdfvnlGjx4sHJzczVgwAD98pe/pO/qUVRUpJMnT+qqq65Sq1at1KpVK23btk1Lly5Vq1atlJCQQP/5KC4uTldccYUOHz7M568enTt39uzRqNK7d2/PIWBVfXTx1ULpP4SLb33rW7rkkkt0+PDhQIdSTbiMSWrLoybDhg2TpKB6P8JlfFNbHjUJxvchHMZL9eVQWVlZbZlgfC8u1pLHXRTaDWAYhmbMmKG1a9fqzTffVEpKitf8wYMHy2q1auvWrZ5pBw8e1NGjR73Ob8HX3G63nE4nfVePMWPG6MMPP9TevXs9jyFDhmjy5Mme/9N/vjl37pz+9a9/qXPnznz+6jFixIhqt9v55z//qW7dukmSUlJSlJiY6NV/ZWVl2rVrF/2HsPDvf/9bp06dUufOnQMdike4jEnqy6Mme/fulaSgej8uFi7jm6o8ahKM70M4jJfqy6Gm01eC8b24WIsedwX4YmwhYfr06UZsbKzx9ttvGydOnPA8ysvLPW1+/OMfG127djXefPNNY8+ePYbD4TAcDkcAow4eDz30kLFt2zbjyJEjxt///nfjoYceMiwWi7FlyxbDMOg7X118xUn6r2733Xef8fbbbxtHjhwx3n33XSM1NdW45JJLjJMnTxqGQf/V5b333jNatWplPP7448ahQ4eMF1980YiJiTF+//vfe9osWrTIiIuLM1555RXj73//u3HLLbcYKSkpxhdffBHAyIGanT171vjggw+MDz74wJBkPPXUU8YHH3xgfPrpp8bZs2eNn/70p0ZhYaFx5MgR44033jCuuuoqo0ePHkZFRUWgQ/cIlzFJfXkcPnzYWLhwobFnzx7jyJEjxiuvvGJ861vfMq677roAR/61cBnf1JVHKLwPtQmH8dI3cwiV94Jx19cotBtAUo2P/Px8T5svvvjC+MlPfmK0b9/eiImJMW677TbjxIkTgQs6iPzwhz80unXrZkRFRRkdO3Y0xowZ4/kRMgz6zlcX/3DQf3X77ne/a3Tu3NmIiooyLr30UuO73/2ucfjwYc98+q9ur732mtG3b1/DZrMZvXr1Mp577jmv+W6325gzZ46RkJBg2Gw2Y8yYMcbBgwcDFC1Qt7feeqvG3/PMzEyjvLzcSEtLMzp27GhYrVajW7duxt13320UFxcHOmwv4TImqS+Po0ePGtddd50RHx9v2Gw24/LLLzfuv/9+48yZM4EN/BvCZXxTVx6h8D7UJhzGS9/MIVTeC8ZdX7MYhmE03/5zAAAAAADCG+doAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYAAAAAACai0AYAAAAAwEQU2gAAAAAAmIhCGwAAAAAAE1FoAwAAAABgIgptAAAAAABMRKENAAAAAICJKLQBAAAAADARhTYQYKtWrZLFYtGePXvqbTtq1CiNGjXK8/yTTz6RxWLRqlWr6l32rrvuUvfu3RsfKAAACDhfxg0AAodCGwAAAAAAE7UKdAAAGm7Lli2BDgEAAABAPdijDYSQqKgoRUVFNdn6v/zyS124cKHJ1g8AAEJDeXl5oEMAQhqFNtAMjh07pqlTpyopKUk2m00pKSmaPn26V1HrdDqVnZ2tjh07qk2bNrrtttv0n//8x2s9F5+jXZt169apb9++io6OVt++fbV27dpqbarO7/75z3+uJUuW6LLLLpPNZtOBAwckSf/4xz/0P//zP4qPj1d0dLSGDBmiV1991WsdVeeJvfvuu/XGDgAAGsasccMrr7yi8ePHe9Zz2WWX6dFHH1VlZaVXu1GjRqlv374qKirSddddp5iYGD388MOSpFOnTunOO++U3W5XXFycMjMz9be//a3Ga8Q0ZOzgcrm0YMEC9ejRQ9HR0erQoYNGjhypgoICE3sQCDwOHQea2PHjxzV06FCVlpZq2rRp6tWrl44dO6aXX37Za2vxzJkz1b59e82bN0+ffPKJlixZohkzZuiPf/yjT6+3ZcsWZWRkqE+fPsrNzdWpU6c0ZcoUdenSpcb2+fn5qqio0LRp02Sz2RQfH6/9+/drxIgRuvTSS/XQQw+pTZs2+tOf/qRbb71Vf/7zn3Xbbbd5rcOs2AEAaOnMHDesWrVKbdu2VXZ2ttq2bas333xTc+fOVVlZmX72s595ve6pU6c0btw4TZo0SXfccYcSEhLkdrs1YcIEvffee5o+fbp69eqlV155RZmZmdXibujYYf78+crNzdX//u//aujQoSorK9OePXv0/vvva+zYsU3Uq0AAGACa1A9+8AMjIiLC2L17d7V5brfbyM/PNyQZqamphtvt9sybPXu2ERkZaZSWlnqmXX/99cb111/veX7kyBFDkpGfn++ZNnDgQKNz585ey23ZssWQZHTr1q3asna73Th58qRXXGPGjDH69etnVFRUeMV6zTXXGD169PBM8yV2AABQPzPHDeXl5dXW8aMf/ciIiYnx+o2//vrrDUnGihUrvNr++c9/NiQZS5Ys8UyrrKw0Ro8eXW380dCxw4ABA4zx48c3sDeA0MWh40ATcrvdWrdunSZMmKAhQ4ZUm2+xWDz/nzZtmtfza6+9VpWVlfr0008b/HonTpzQ3r17lZmZqdjYWM/0sWPHqk+fPjUuk5GRoY4dO3qenz59Wm+++aZuv/12nT17Vv/973/13//+V6dOnVJ6eroOHTqkY8eOea3DjNgBAGjpzB43tG7d2vP/qt/0a6+9VuXl5frHP/7htW6bzaYpU6Z4Tdu0aZOsVqvuvvtuz7SIiAhlZWV5tfNl7BAXF6f9+/fr0KFDvnQNEHIotIEm9J///EdlZWXq27dvvW27du3q9bx9+/aSpM8//7zBr1f149qjR49q83r27FnjMikpKV7PDx8+LMMwNGfOHHXs2NHrMW/ePEnSyZMnTY8dAICWzuxxw/79+3XbbbcpNjZWdrtdHTt21B133CFJOnPmjNfyl156abULrn766afq3LmzYmJivKZffvnlXs99GTssXLhQpaWluuKKK9SvXz/df//9+vvf/15vvkCo4RxtIEhERkbWON0wjCZ93W9u7Za+2pouST/96U+Vnp5e4zIX/8AGKnYAAFqq+n57S0tLdf3118tut2vhwoW67LLLFB0drffff18PPvig5/e+ysXjAV/4Mna47rrr9K9//UuvvPKKtmzZol//+td6+umntWLFCv3v//5vo2MAgg2FNtCEOnbsKLvdrn379jXL63Xr1k2Sajwc6+DBgw1ax7e+9S1JktVqVWpqqnnBAQCAOpk5bnj77bd16tQp/eUvf9F1113nmX7kyJEGr6Nbt2566623VF5e7rVX+/Dhw17tfB07xMfHa8qUKZoyZYrOnTun6667TvPnz6fQRljh0HGgCUVEROjWW2/Va6+9pj179lSbb/Ye386dO2vgwIF64YUXvA4JKygo8Ny2qz6dOnXSqFGj9Oyzz+rEiRPV5nPbLgAAmoaZ44aqPd7fXObChQtatmxZg9eRnp4ul8ul559/3jPN7XYrLy/Pq50vY4dTp055zWvbtq0uv/xyOZ3OBscFhAL2aANN7IknntCWLVt0/fXXa9q0aerdu7dOnDihNWvW6J133jH99XJzczV+/HiNHDlSP/zhD3X69Gk988wzuvLKK3Xu3LkGrSMvL08jR45Uv379dPfdd+tb3/qWSkpKVFhYqH//+9/629/+ZnrcAADAvHHDNddco/bt2yszM1P33HOPLBaLfve73/lUrN96660aOnSo7rvvPh0+fFi9evXSq6++qtOnT0vyvjhbQ8cOffr00ahRozR48GDFx8drz549evnllzVjxowGxwWEAgptoIldeuml2rVrl+bMmaMXX3xRZWVluvTSSzVu3LhqFxcxw4033qg1a9bokUceUU5Oji677DLl5+frlVde0dtvv92gdfTp00d79uzRggULtGrVKp06dUqdOnXSoEGDNHfuXNNjBgAAXzFr3NChQwetX79e9913nx555BG1b99ed9xxh8aMGVPredQXi4yM1IYNG3TvvffqhRdeUEREhG677TbNmzdPI0aMUHR0tKdtQ8cO99xzj1599VVt2bJFTqdT3bp102OPPab777+/4Z0EhACLwdWKAAAAADTQunXrdNttt+mdd97RiBEjAh0OEJQotAEAAADU6IsvvvC6InllZaXS0tK0Z88eFRcX+3W1ciCcceg4AAAAgBrNnDlTX3zxhRwOh5xOp/7yl79ox44deuKJJyiygTqwRxsAAABAjVavXq1f/OIXOnz4sCoqKnT55Zdr+vTpXLwMqAeFNgAAAAAAJvL5PtrHjh3THXfcoQ4dOqh169bq16+f133+DMPQ3Llz1blzZ7Vu3Vqpqak6dOiQ1zpOnz6tyZMny263Ky4uTlOnTm3wbYcAAAAAAAhmPhXan3/+uUaMGCGr1arXX39dBw4c0C9+8Qu1b9/e02bx4sVaunSpVqxYoV27dqlNmzZKT09XRUWFp83kyZO1f/9+FRQUaP369dq+fbumTZtmXlYAAAAAAASIT4eOP/TQQ3r33Xf117/+tcb5hmEoKSlJ9913n376059Kks6cOaOEhAStWrVKkyZN0kcffaQ+ffpo9+7dGjJkiCRp06ZNuummm/Tvf/9bSUlJ9cbhdrt1/PhxtWvXThaLpaHhAwDgxTAMnT17VklJSYqI8PkgL4QIxg0AADP4Mm7w6arjr776qtLT0/Wd73xH27Zt06WXXqqf/OQnuvvuuyVJR44cUXFxsVJTUz3LxMbGatiwYSosLNSkSZNUWFiouLg4T5EtSampqYqIiNCuXbt022231RvH8ePHlZyc7EvoAADU6rPPPlOXLl0CHQaaCOMGAICZGjJu8KnQ/vjjj7V8+XJlZ2fr4Ycf1u7du3XPPfcoKipKmZmZKi4uliQlJCR4LZeQkOCZV1xcrE6dOnkH0aqV4uPjPW0u5nQ65XQ6Pc+rdsIfOXJE7dq18yUFLy6XS2+99ZZuuOEGWa3WRq8n3NFPDUM/1Y8+ahj6qX5m9dHZs2eVkpLi128Jard9+3b97Gc/U1FRkU6cOKG1a9fq1ltvrbHtj3/8Yz377LN6+umnNWvWLM/006dPa+bMmXrttdcUERGhjIwM/fKXv1Tbtm0bHEfV+/vZZ5/JbrfX297lcmnLli1KS0sL6+9gS8lTItdw1FLylMg1mJSVlSk5OblB4wafCm23260hQ4boiSeekCQNGjRI+/bt04oVK5SZmdm4aBsgNzdXCxYsqDa9sLBQMTExfq07JiZGu3bt8msdLQH91DD0U/3oo4ahn+pnRh+Vl5dLEocTN5Hz589rwIAB+uEPf6iJEyfW2m7t2rXauXNnjaePTZ48WSdOnFBBQYFcLpemTJmiadOmafXq1Q2Oo+r9tdvtDS60Y2JiZLfbg3KgZ5aWkqdEruGopeQpkWswasi4wadCu3PnzurTp4/XtN69e+vPf/6zJCkxMVGSVFJSos6dO3valJSUaODAgZ42J0+e9FrHl19+qdOnT3uWv1hOTo6ys7M9z6u2JKSlpTXoB7M2LpdLBQUFGjt2bFC/kYFGPzUM/VQ/+qhh6Kf6mdVHZWVlJkaFi40bN07jxo2rs82xY8c0c+ZMbd68WePHj/ea99FHH2nTpk1e13V55plndNNNN+nnP/95g67rAgBAIPhUaI8YMUIHDx70mvbPf/5T3bp1kySlpKQoMTFRW7du9RTWZWVl2rVrl6ZPny5JcjgcKi0tVVFRkQYPHixJevPNN+V2uzVs2LAaX9dms8lms1WbbrVaTRmEmrWecEc/NQz9VD/6qGHop/r520f0b2C53W7deeeduv/++3XllVdWm2/GdV0AAAgEnwrt2bNn65prrtETTzyh22+/Xe+9956ee+45Pffcc5K+2oU+a9YsPfbYY+rRo4dSUlI0Z84cJSUlec7J6t27t2688UbdfffdWrFihVwul2bMmKFJkyaxZRoAgBbkySefVKtWrXTPPffUOL8x13WRql/bperIBZfLJZfLVW9cVW0a0jaUtZQ8JXINRy0lT4lcg4kvcflUaF999dVau3atcnJytHDhQqWkpGjJkiWaPHmyp80DDzyg8+fPa9q0aSotLdXIkSO1adMmRUdHe9q8+OKLmjFjhsaMGeO5sMnSpUt9CQUAAISwoqIi/fKXv9T7779v+jnytV3bZcuWLT5d26WgoMDMsIJWS8lTItdw1FLylMg1GFRd26UhfCq0Jenmm2/WzTffXOt8i8WihQsXauHChbW2iY+P9+kiJgAAILz89a9/1cmTJ9W1a1fPtMrKSt13331asmSJPvnkk0Zd10Xy/9ouLeU6CS0lT4lcw1FLyVMi12Diy7VdfC60AQAA/HXnnXcqNTXVa1p6erruvPNOTZkyRVLjrusimXdtl5ZynYSWkqdEruGopeQpkWsw8CUmCm0AANAkzp07p8OHD3ueHzlyRHv37lV8fLy6du2qDh06eLW3Wq1KTExUz549JXFdFwBA6IoIdAAAACA87dmzR4MGDdKgQYMkSdnZ2Ro0aJDmzp3b4HW8+OKL6tWrl8aMGaObbrpJI0eO9FyEFQCAYMUebQAA0CRGjRolwzAa3P6TTz6pNo3rugAAQhF7tAEAAAAAMBF7tCX1nb9Zzsqaby3yyaLxzRwNAAAIVt0f2lDrPMYMAIAq7NEGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAKBJbN++XRMmTFBSUpIsFovWrVvnmedyufTggw+qX79+atOmjZKSkvSDH/xAx48f91rH6dOnNXnyZNntdsXFxWnq1Kk6d+5cM2cCAIBvKLQBAECTOH/+vAYMGKC8vLxq88rLy/X+++9rzpw5ev/99/WXv/xFBw8e1Le//W2vdpMnT9b+/ftVUFCg9evXa/v27Zo2bVpzpQAAQKO0CnQAAAAgPI0bN07jxo2rcV5sbKwKCgq8pv3qV7/S0KFDdfToUXXt2lUfffSRNm3apN27d2vIkCGSpGeeeUY33XSTfv7znyspKanJcwAAoDF8KrTnz5+vBQsWeE3r2bOn/vGPf0iSKioqdN999+mll16S0+lUenq6li1bpoSEBE/7o0ePavr06XrrrbfUtm1bZWZmKjc3V61aUfMDANCSnTlzRhaLRXFxcZKkwsJCxcXFeYpsSUpNTVVERIR27dql2267rcb1OJ1OOZ1Oz/OysjJJXx2u7nK56o2jqk1NbW2RRr3LhYq68gw35Bp+WkqeErkGE1/i8rm6vfLKK/XGG298vYJvFMizZ8/Whg0btGbNGsXGxmrGjBmaOHGi3n33XUlSZWWlxo8fr8TERO3YsUMnTpzQD37wA1mtVj3xxBO+hgIAAMJERUWFHnzwQX3ve9+T3W6XJBUXF6tTp05e7Vq1aqX4+HgVFxfXuq7c3NxqOwYkacuWLYqJiWlwTBfvcZekxUNrb79x48YGrzuY1JRnuCLX8NNS8pTINRiUl5c3uK3PhXarVq2UmJhYbfqZM2e0cuVKrV69WqNHj5Yk5efnq3fv3tq5c6eGDx+uLVu26MCBA3rjjTeUkJCggQMH6tFHH9WDDz6o+fPnKyoqytdwAABAiHO5XLr99ttlGIaWL1/u9/pycnKUnZ3teV5WVqbk5GSlpaV5ivj64ikoKNDYsWNltVq95vWdv7nW5fbNT2980AFQV57hhlzDT0vJUyLXYFJ1hFRD+FxoHzp0SElJSYqOjpbD4VBubq66du2qoqIiuVwupaametr26tVLXbt2VWFhoYYPH67CwkL169fP61Dy9PR0TZ8+Xfv379egQYNqfE1/DwGrTdWytojwOQysKQT7IRzBgn6qH33UMPRT/czqI/o48KqK7E8//VRvvvmmVyGcmJiokydPerX/8ssvdfr06Ro3+lex2Wyy2WzVplutVp8GbjW1d1Za6mwfinztl1BGruGnpeQpkWsw8CUmnwrtYcOGadWqVerZs6dOnDihBQsW6Nprr9W+fftUXFysqKgoz3lVVRISEjyHdxUXF3sV2VXzq+bVxqxDwGrz6BB3rfNC9TCwphCsh3AEG/qpfvRRw9BP9fO3j3w5BAzmqyqyDx06pLfeeksdOnTwmu9wOFRaWqqioiINHjxYkvTmm2/K7XZr2LBhgQgZAIAG8anQ/uaVQ/v3769hw4apW7du+tOf/qTWrVubHlwVfw8Bq03VoQlz9kTI6a55C3WoHQbWFIL9EI5gQT/Vjz5qGPqpfmb1kS+HgMF3586d0+HDhz3Pjxw5or179yo+Pl6dO3fW//zP/+j999/X+vXrVVlZ6dnoHh8fr6ioKPXu3Vs33nij7r77bq1YsUIul0szZszQpEmTuOI4ACCo+XWp77i4OF1xxRU6fPiwxo4dqwsXLqi0tNRrr3ZJSYnn8K7ExES99957XusoKSnxzKuNWYeA1cbpttR6KBiD3K8F6yEcwYZ+qh991DD0U/387SP6t2nt2bNHN9xwg+d51UbzzMxMzZ8/X6+++qokaeDAgV7LvfXWWxo1apQk6cUXX9SMGTM0ZswYRUREKCMjQ0uXLm2W+AEAaCy/Cu1z587pX//6l+68804NHjxYVqtVW7duVUZGhiTp4MGDOnr0qBwOh6SvDgF7/PHHdfLkSc9VRAsKCmS329WnTx8/UwEAAMFk1KhRMozar4NS17wq8fHxWr16tZlhAQDQ5HwqtH/6059qwoQJ6tatm44fP6558+YpMjJS3/ve9xQbG6upU6cqOztb8fHxstvtmjlzphwOh4YPHy5JSktLU58+fXTnnXdq8eLFKi4u1iOPPKKsrKwa91gDAAAAABBqfCq0//3vf+t73/ueTp06pY4dO2rkyJHauXOnOnbsKEl6+umnPYd1OZ1Opaena9myZZ7lIyMjtX79ek2fPl0Oh0Nt2rRRZmamFi5caG5WAAAAAAAEiE+F9ksvvVTn/OjoaOXl5SkvL6/WNt26deNK3gAAAACAsBUR6AAAAAAAAAgnFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmahXoAAAAAMJB94c2NHrZTxaNNzESAECgsUcbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAATWL79u2aMGGCkpKSZLFYtG7dOq/5hmFo7ty56ty5s1q3bq3U1FQdOnTIq83p06c1efJk2e12xcXFaerUqTp37lwzZgEAgO8otAEAQJM4f/68BgwYoLy8vBrnL168WEuXLtWKFSu0a9cutWnTRunp6aqoqPC0mTx5svbv36+CggKtX79e27dv17Rp05orBQAAGqVVoAMAAADhady4cRo3blyN8wzD0JIlS/TII4/olltukST99re/VUJCgtatW6dJkybpo48+0qZNm7R7924NGTJEkvTMM8/opptu0s9//nMlJSU1Wy4AAPiCPdoAAKDZHTlyRMXFxUpNTfVMi42N1bBhw1RYWChJKiwsVFxcnKfIlqTU1FRFRERo165dzR4zAAANxR5tAADQ7IqLiyVJCQkJXtMTEhI884qLi9WpUyev+a1atVJ8fLynTU2cTqecTqfneVlZmSTJ5XLJ5XLVG1tVm5ra2iKNepdvjIbE1VSvGYjXbm7kGn5aSp4SuQYTX+Ki0AYAAGElNzdXCxYsqDZ9y5YtiomJafB6CgoKqk1bPNSv0Gq1cePGpllxA9SUZ7gi1/DTUvKUyDUYlJeXN7gthTYAAGh2iYmJkqSSkhJ17tzZM72kpEQDBw70tDl58qTXcl9++aVOnz7tWb4mOTk5ys7O9jwvKytTcnKy0tLSZLfb643N5XKpoKBAY8eOldVq9ZrXd/7mepdvjH3z05tkvXWpK89wQ67hp6XkKZFrMKk6QqohKLQBAECzS0lJUWJiorZu3eoprMvKyrRr1y5Nnz5dkuRwOFRaWqqioiINHjxYkvTmm2/K7XZr2LBhta7bZrPJZrNVm261Wn0auNXU3llpafDyvgjkgNLXfgll5Bp+WkqeErkGA19iotAGAABN4ty5czp8+LDn+ZEjR7R3717Fx8era9eumjVrlh577DH16NFDKSkpmjNnjpKSknTrrbdKknr37q0bb7xRd999t1asWCGXy6UZM2Zo0qRJLe6K490f2lDrvE8WjW/GSAAADUGhDQAAmsSePXt0ww03eJ5XHc6dmZmpVatW6YEHHtD58+c1bdo0lZaWauTIkdq0aZOio6M9y7z44ouaMWOGxowZo4iICGVkZGjp0qXNngsAAL7w6/ZeixYtksVi0axZszzTKioqlJWVpQ4dOqht27bKyMhQSUmJ13JHjx7V+PHjFRMTo06dOun+++/Xl19+6U8oAAAgyIwaNUqGYVR7rFq1SpJksVi0cOFCFRcXq6KiQm+88YauuOIKr3XEx8dr9erVOnv2rM6cOaPf/OY3atu2bQCyAQCg4RpdaO/evVvPPvus+vfv7zV99uzZeu2117RmzRpt27ZNx48f18SJEz3zKysrNX78eF24cEE7duzQCy+8oFWrVmnu3LmNzwIAAAAAgCDRqEL73Llzmjx5sp5//nm1b9/eM/3MmTNauXKlnnrqKY0ePVqDBw9Wfn6+duzYoZ07d0r66tYaBw4c0O9//3sNHDhQ48aN06OPPqq8vDxduHDBnKwAAAAAAAiQRhXaWVlZGj9+vFJTU72mFxUVyeVyeU3v1auXunbtqsLCQklSYWGh+vXrp4SEBE+b9PR0lZWVaf/+/Y0JBwAAAACAoOHzxdBeeuklvf/++9q9e3e1ecXFxYqKilJcXJzX9ISEBBUXF3vafLPIrppfNa8mTqdTTqfT87zq/mUul0sul8vXFDyqlrVFGPW2acmq+oC+qBv9VD/6qGHop/qZ1Uf0MQAAaAo+FdqfffaZ7r33XhUUFHhdEbSp5ebmasGCBdWmb9myRTExMX6v/9Eh7lrnbdy40e/1h4uCgoJAhxAS6Kf60UcNQz/Vz98+Ki8vNykSAACAr/lUaBcVFenkyZO66qqrPNMqKyu1fft2/epXv9LmzZt14cIFlZaWeu3VLikpUWJioiQpMTFR7733ntd6q65KXtXmYjk5OZ5bgkhf7dFOTk5WWlqa7Ha7Lyl4cblcKigo0Jw9EXK6LTW22Tc/vdHrDxdV/TR27NigvHF8sKCf6kcfNQz9VD+z+qjqCCkAAAAz+VRojxkzRh9++KHXtClTpqhXr1568MEHlZycLKvVqq1btyojI0OSdPDgQR09elQOh0OS5HA49Pjjj+vkyZPq1KmTpK/2SNjtdvXp06fG17XZbLLZbNWmW61WUwahTrdFzsqaC20GuV8zq7/DHf1UP/qoYein+vnbR/QvAABoCj4V2u3atVPfvn29prVp00YdOnTwTJ86daqys7MVHx8vu92umTNnyuFwaPjw4ZKktLQ09enTR3feeacWL16s4uJiPfLII8rKyqqxmAYAAAAAIJT4fDG0+jz99NOKiIhQRkaGnE6n0tPTtWzZMs/8yMhIrV+/XtOnT5fD4VCbNm2UmZmphQsXmh0KAAAAAADNzu9C++233/Z6Hh0drby8POXl5dW6TLdu3bjIGAAAAAAgLDXqPtoAAAAAAKBmFNoAAAAAAJjI9HO0AQAA4JvuD21okmU/WTS+0esFADQee7QBAAAAADARhTYAAAAAACai0AYAAAAAwEScow0AAIBqOPcbABqPPdoAAAAAAJiIQhsAAAAAABNRaAMAAAAAYCIKbQAAAAAATEShDQAAAACAiSi0AQAAAAAwEYU2AAAAAAAmotAGAAAAAMBEFNoAAAAAAJiIQhsAAAAAABO1CnQAAACgZaqsrNT8+fP1+9//XsXFxUpKStJdd92lRx55RBaLRZJkGIbmzZun559/XqWlpRoxYoSWL1+uHj16BDj60ND9oQ21zrNFGlo8tBmDAYAWhD3aAAAgIJ588kktX75cv/rVr/TRRx/pySef1OLFi/XMM8942ixevFhLly7VihUrtGvXLrVp00bp6emqqKgIYOQAANSNPdoAACAgduzYoVtuuUXjx4+XJHXv3l1/+MMf9N5770n6am/2kiVL9Mgjj+iWW26RJP32t79VQkKC1q1bp0mTJgUsdgAA6kKhDQAAAuKaa67Rc889p3/+85+64oor9Le//U3vvPOOnnrqKUnSkSNHVFxcrNTUVM8ysbGxGjZsmAoLC2sttJ1Op5xOp+d5WVmZJMnlcsnlctUbV1WbmtraIo2GJxjkbBFf5VJbn9SVa0P6MZjU9Z6Gm5aSa0vJUyLXYOJLXBTaAAAgIB566CGVlZWpV69eioyMVGVlpR5//HFNnjxZklRcXCxJSkhI8FouISHBM68mubm5WrBgQbXpW7ZsUUxMTIPjKygoqDYtHM9prilPqe5cN27c2ETRNK3acg1HLSXXlpKnRK7BoLy8vMFtKbQBAEBA/OlPf9KLL76o1atX68orr9TevXs1a9YsJSUlKTMzs9HrzcnJUXZ2tud5WVmZkpOTlZaWJrvdXu/yLpdLBQUFGjt2rKxWq9e8vvM3NzquYGOLMPToEHeNeUp157pvfnpThma6ut7TcNNScm0peUrkGkyqjpBqCAptAAAQEPfff78eeughzyHg/fr106effqrc3FxlZmYqMTFRklRSUqLOnTt7lispKdHAgQNrXa/NZpPNZqs23Wq1+jRwq6m9s9LS4OVDRW39UleuwTgAbghfPwOhrKXk2lLylMg1GPgSE1cdBwAAAVFeXq6ICO+hSGRkpNxutyQpJSVFiYmJ2rp1q2d+WVmZdu3aJYfD0ayxAgDgC/ZoAwCAgJgwYYIef/xxde3aVVdeeaU++OADPfXUU/rhD38oSbJYLJo1a5Yee+wx9ejRQykpKZozZ46SkpJ06623BjZ4AADqQKENAAAC4plnntGcOXP0k5/8RCdPnlRSUpJ+9KMfae7cuZ42DzzwgM6fP69p06aptLRUI0eO1KZNmxQdHR3AyAEAqBuFNhqs7/zNNZ6v9cmi8QGIBgAQ6tq1a6clS5ZoyZIltbaxWCxauHChFi5c2HyBAQDgJ87RBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIl8KrSXL1+u/v37y263y263y+Fw6PXXX/fMr6ioUFZWljp06KC2bdsqIyNDJSUlXus4evSoxo8fr5iYGHXq1En333+/vvzyS3OyAQAAgE/6zt+s7g9tqPYAADSeT4V2ly5dtGjRIhUVFWnPnj0aPXq0brnlFu3fv1+SNHv2bL322mtas2aNtm3bpuPHj2vixIme5SsrKzV+/HhduHBBO3bs0AsvvKBVq1Zp7ty55mYFAAAAAECAtPKl8YQJE7yeP/7441q+fLl27typLl26aOXKlVq9erVGjx4tScrPz1fv3r21c+dODR8+XFu2bNGBAwf0xhtvKCEhQQMHDtSjjz6qBx98UPPnz1dUVJR5mQEAAAAAEAA+FdrfVFlZqTVr1uj8+fNyOBwqKiqSy+VSamqqp02vXr3UtWtXFRYWavjw4SosLFS/fv2UkJDgaZOenq7p06dr//79GjRoUI2v5XQ65XQ6Pc/LysokSS6XSy6Xq7EpeJa1RRj1tmnJ6usn+ugrVf1Af9SOPmoY+ql+ZvURfQwAAJqCz4X2hx9+KIfDoYqKCrVt21Zr165Vnz59tHfvXkVFRSkuLs6rfUJCgoqLiyVJxcXFXkV21fyqebXJzc3VggULqk3fsmWLYmJifE2hmkeHuGudt3HjRr/XHy5q6yf6yFtBQUGgQwh69FHD0E/187ePysvLTYoEAADgaz4X2j179tTevXt15swZvfzyy8rMzNS2bduaIjaPnJwcZWdne56XlZUpOTlZaWlpstvtjV6vy+VSQUGB5uyJkNNtqbHNvvnpjV5/uKivn+ijr1T109ixY2W1WgMdTlCijxqGfqqfWX1UdYQUAACAmXwutKOionT55ZdLkgYPHqzdu3frl7/8pb773e/qwoULKi0t9dqrXVJSosTERElSYmKi3nvvPa/1VV2VvKpNTWw2m2w2W7XpVqvVlEGo022Rs7LmQptB7tdq6yf6yJtZn8twRh81DP1UP3/7iP4FAABNwe/7aLvdbjmdTg0ePFhWq1Vbt271zDt48KCOHj0qh8MhSXI4HPrwww918uRJT5uCggLZ7Xb16dPH31AAAAAAAAg4n/Zo5+TkaNy4ceratavOnj2r1atX6+2339bmzZsVGxurqVOnKjs7W/Hx8bLb7Zo5c6YcDoeGDx8uSUpLS1OfPn105513avHixSouLtYjjzyirKysGvdYAwAAAAAQanwqtE+ePKkf/OAHOnHihGJjY9W/f39t3rxZY8eOlSQ9/fTTioiIUEZGhpxOp9LT07Vs2TLP8pGRkVq/fr2mT58uh8OhNm3aKDMzUwsXLjQ3KwAAAAAAAsSnQnvlypV1zo+OjlZeXp7y8vJqbdOtWzeuUg0AAAAACFt+n6MNAAAAAAC+RqENAAAAAICJKLQBAAAAADARhTYAAAAAACby6WJoAAAAQPeHNtQ5/5NF45spEgAITuzRBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AABAwx44d0x133KEOHTqodevW6tevn/bs2eOZbxiG5s6dq86dO6t169ZKTU3VoUOHAhgxAAD1o9AGAAAB8fnnn2vEiBGyWq16/fXXdeDAAf3iF79Q+/btPW0WL16spUuXasWKFdq1a5fatGmj9PR0VVRUBDByAADqxu29AABAQDz55JNKTk5Wfn6+Z1pKSorn/4ZhaMmSJXrkkUd0yy23SJJ++9vfKiEhQevWrdOkSZOaPWYAABqCPdoAACAgXn31VQ0ZMkTf+c531KlTJw0aNEjPP/+8Z/6RI0dUXFys1NRUz7TY2FgNGzZMhYWFgQgZAIAGYY82AAAIiI8//ljLly9Xdna2Hn74Ye3evVv33HOPoqKilJmZqeLiYklSQkKC13IJCQmeeTVxOp1yOp2e52VlZZIkl8sll8tVb1xVbWpqa4s06k8sRNgiDK9/zdSQfm5Odb2n4aal5NpS8pTINZj4EheFNgAACAi3260hQ4boiSeekCQNGjRI+/bt04oVK5SZmdno9ebm5mrBggXVpm/ZskUxMTENXk9BQUG1aYuHNjqsoPXoELfp69y4caPp6zRDTe9puGopubaUPCVyDQbl5eUNbkuhDQAAAqJz587q06eP17TevXvrz3/+syQpMTFRklRSUqLOnTt72pSUlGjgwIG1rjcnJ0fZ2dme52VlZUpOTlZaWprsdnu9cblcLhUUFGjs2LGyWq1e8/rO31zv8qHCFmHo0SFuzdkTIafbYuq6981PN3V9/qrrPQ03LSXXlpKnRK7BpOoIqYag0AYAAAExYsQIHTx40GvaP//5T3Xr1k3SVxdGS0xM1NatWz2FdVlZmXbt2qXp06fXul6bzSabzVZtutVq9WngVlN7Z6W5BWkwcLotpucVjANkyffPQChrKbm2lDwlcg0GvsREoQ0AAAJi9uzZuuaaa/TEE0/o9ttv13vvvafnnntOzz33nCTJYrFo1qxZeuyxx9SjRw+lpKRozpw5SkpK0q233hrY4AEAqAOFNgAACIirr75aa9euVU5OjhYuXKiUlBQtWbJEkydP9rR54IEHdP78eU2bNk2lpaUaOXKkNm3apOjo6ABGDgBA3Si0AQBAwNx88826+eaba51vsVi0cOFCLVy4sBmjAgDAPxTagMn6zt9c47lunywaH4BoAAAAADS3iEAHAAAAAABAOKHQBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARBTaAAAAAACYiEIbAAAAAAAT+VRo5+bm6uqrr1a7du3UqVMn3XrrrTp48KBXm4qKCmVlZalDhw5q27atMjIyVFJS4tXm6NGjGj9+vGJiYtSpUyfdf//9+vLLL/3PBgAAAACAAPOp0N62bZuysrK0c+dOFRQUyOVyKS0tTefPn/e0mT17tl577TWtWbNG27Zt0/HjxzVx4kTP/MrKSo0fP14XLlzQjh079MILL2jVqlWaO3eueVkBAAAAABAgrXxpvGnTJq/nq1atUqdOnVRUVKTrrrtOZ86c0cqVK7V69WqNHj1akpSfn6/evXtr586dGj58uLZs2aIDBw7ojTfeUEJCggYOHKhHH31UDz74oObPn6+oqCjzsgMAAAAAoJn5VGhf7MyZM5Kk+Ph4SVJRUZFcLpdSU1M9bXr16qWuXbuqsLBQw4cPV2Fhofr166eEhARPm/T0dE2fPl379+/XoEGDqr2O0+mU0+n0PC8rK5MkuVwuuVyuRsdftawtwqi3TUtWXz/RR1+hn+pX1Qf0Rd3op/qZ1Uf0MQAAaAqNLrTdbrdmzZqlESNGqG/fvpKk4uJiRUVFKS4uzqttQkKCiouLPW2+WWRXza+aV5Pc3FwtWLCg2vQtW7YoJiamsSl4PDrEXeu8jRs3+r3+cFFbP9FH3uin+hUUFAQ6hJBAP9XP3z4qLy83KRIAAICvNbrQzsrK0r59+/TOO++YGU+NcnJylJ2d7XleVlam5ORkpaWlyW63N3q9LpdLBQUFmrMnQk63pcY2++anN3r94aK+fqKPvkI/1a+qj8aOHSur1RrocIIW/VQ/s/qo6ggpAAAAMzWq0J4xY4bWr1+v7du3q0uXLp7piYmJunDhgkpLS732apeUlCgxMdHT5r333vNaX9VVyavaXMxms8lms1WbbrVaTRmEOt0WOStrLrQZ5H6ttn6ij7zRT/Uz67sb7uin+vnbR/QvAABoCj5dddwwDM2YMUNr167Vm2++qZSUFK/5gwcPltVq1datWz3TDh48qKNHj8rhcEiSHA6HPvzwQ508edLTpqCgQHa7XX369PEnFwAAAAAAAs6nPdpZWVlavXq1XnnlFbVr185zTnVsbKxat26t2NhYTZ06VdnZ2YqPj5fdbtfMmTPlcDg0fPhwSVJaWpr69OmjO++8U4sXL1ZxcbEeeeQRZWVl1bjXGgAAAACAUOJTob18+XJJ0qhRo7ym5+fn66677pIkPf3004qIiFBGRoacTqfS09O1bNkyT9vIyEitX79e06dPl8PhUJs2bZSZmamFCxf6lwkAAACCQveHNtQ675NF45sxEgAIDJ8KbcOo/TZYVaKjo5WXl6e8vLxa23Tr1o0rMAMAAAAAwpJP52gDAAAAAIC6UWgDAAAAAGAiCm0AAAAAAExEoQ0AAILCokWLZLFYNGvWLM+0iooKZWVlqUOHDmrbtq0yMjJUUlISuCABAGgACm0AABBwu3fv1rPPPqv+/ft7TZ89e7Zee+01rVmzRtu2bdPx48c1ceLEAEUJAEDDUGgDAICAOnfunCZPnqznn39e7du390w/c+aMVq5cqaeeekqjR4/W4MGDlZ+frx07dmjnzp0BjBgAgLr5dHsvAAAAs2VlZWn8+PFKTU3VY4895pleVFQkl8ul1NRUz7RevXqpa9euKiws1PDhw2tcn9PplNPp9DwvKyuTJLlcLrlcrnrjqWpTU1tbZP23Og0VtgjD69/m0pD3oKleMxCv3dxaSq4tJU+JXIOJL3FRaAMAgIB56aWX9P7772v37t3V5hUXFysqKkpxcXFe0xMSElRcXFzrOnNzc7VgwYJq07ds2aKYmJgGx1ZQUFBt2uKhDV48ZDw6xN2sr7dx48Zmfb1vquk9DVctJdeWkqdErsGgvLy8wW0ptAEAQEB89tlnuvfee1VQUKDo6GjT1puTk6Ps7GzP87KyMiUnJystLU12u73e5V0ulwoKCjR27FhZrVaveX3nbzYtzkCzRRh6dIhbc/ZEyOm2NNvr7puf3myvVaWu9zTctJRcW0qeErkGk6ojpBqCQhsAAAREUVGRTp48qauuusozrbKyUtu3b9evfvUrbd68WRcuXFBpaanXXu2SkhIlJibWul6bzSabzVZtutVq9WngVlN7Z2XzFaTNxem2NGtegRw8+/oZCGUtJdeWkqdErsHAl5gotAEAQECMGTNGH374ode0KVOmqFevXnrwwQeVnJwsq9WqrVu3KiMjQ5J08OBBHT16VA6HIxAhAwDQIBTaAAAgINq1a6e+fft6TWvTpo06dOjgmT516lRlZ2crPj5edrtdM2fOlMPhqPVCaAAABAMKbQAAELSefvppRUREKCMjQ06nU+np6Vq2bFmgwwIAoE4U2gCaXd/5m2s8H/CTReMDEA2AYPL22297PY+OjlZeXp7y8vICExAAAI0QEegAAAAAAAAIJxTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJqLQBgAAAADARBTaAAAAAACYiEIbAAAAAAATUWgDAAAAAGAiCm0AAAAAAExEoQ0AAAAAgIkotAEAAAAAMBGFNgAAAAAAJmoV6AAAAAAAf3V/aEOt8w49mtaMkQAAe7QBAAAAADAVhTYAAAAAACbyudDevn27JkyYoKSkJFksFq1bt85rvmEYmjt3rjp37qzWrVsrNTVVhw4d8mpz+vRpTZ48WXa7XXFxcZo6darOnTvnVyIAAAAAAAQDnwvt8+fPa8CAAcrLy6tx/uLFi7V06VKtWLFCu3btUps2bZSenq6KigpPm8mTJ2v//v0qKCjQ+vXrtX37dk2bNq3xWQAAAAAAECR8vhjauHHjNG7cuBrnGYahJUuW6JFHHtEtt9wiSfrtb3+rhIQErVu3TpMmTdJHH32kTZs2affu3RoyZIgk6ZlnntFNN92kn//850pKSvIjHQAAAAAAAsvUc7SPHDmi4uJipaameqbFxsZq2LBhKiwslCQVFhYqLi7OU2RLUmpqqiIiIrRr1y4zwwEAAAAAoNmZenuv4uJiSVJCQoLX9ISEBM+84uJiderUyTuIVq0UHx/vaXMxp9Mpp9PpeV5WViZJcrlccrlcjY63allbhFFvm5asvn6ij75CP9WPPmqYqn6gP2pnVh/RxwAAoCmExH20c3NztWDBgmrTt2zZopiYGL/X/+gQd63zNm7c6Pf6w0Vt/UQfeaOf6kcfNUxBQUGgQwh6/vZReXm5SZEAAAB8zdRCOzExUZJUUlKizp07e6aXlJRo4MCBnjYnT570Wu7LL7/U6dOnPctfLCcnR9nZ2Z7nZWVlSk5OVlpamux2e6PjdblcKigo0Jw9EXK6LTW22Tc/vdHrDxf19RN99BX6qX70UcNU9dPYsWNltVoDHU5QMquPqo6QAgAAMJOphXZKSooSExO1detWT2FdVlamXbt2afr06ZIkh8Oh0tJSFRUVafDgwZKkN998U263W8OGDatxvTabTTabrdp0q9VqyiDU6bbIWVlzoc0g92u19RN95I1+qh991DBm/Y0LZ/72Ef0beLm5ufrLX/6if/zjH2rdurWuueYaPfnkk+rZs6enTUVFhe677z699NJLcjqdSk9P17Jly6qdqgYAQLDwudA+d+6cDh8+7Hl+5MgR7d27V/Hx8eratatmzZqlxx57TD169FBKSormzJmjpKQk3XrrrZKk3r1768Ybb9Tdd9+tFStWyOVyacaMGZo0aRJXHAcAoIXZtm2bsrKydPXVV+vLL7/Uww8/rLS0NB04cEBt2rSRJM2ePVsbNmzQmjVrFBsbqxkzZmjixIl69913Axw9wkX3hzbUOu+TReObMRIA4cLnQnvPnj264YYbPM+rDunOzMzUqlWr9MADD+j8+fOaNm2aSktLNXLkSG3atEnR0dGeZV588UXNmDFDY8aMUUREhDIyMrR06VIT0gEAAKFk06ZNXs9XrVqlTp06qaioSNddd53OnDmjlStXavXq1Ro9erQkKT8/X71799bOnTs1fPjwQIQNAECdfC60R40aJcOo/SrdFotFCxcu1MKFC2ttEx8fr9WrV/v60gAAIMydOXNG0ldjBUkqKiqSy+XyunVor1691LVrVxUWFlJoAwCCUkhcdRwAAIQ/t9utWbNmacSIEerbt6+kr24LGhUVpbi4OK+237x16MX8vS1oXbePs0XWvrMh1FTdarGu25w2haa6rV5d7019twRsyLKhoqXcIrKl5CmRazDxJS4KbQAAEBSysrK0b98+vfPOO36tx6zbgtZ0+7jFQ/0KLSjVdZvTptBUt3Ks672pei9ruyVgXcuG6q0nW8otIltKnhK5BgNfbgtKoQ0AAAJuxowZWr9+vbZv364uXbp4picmJurChQsqLS312qtdUlLSZLcFrev2cX3nb/Yxs+BlizD06BB3nbc5bQr+3Mqxsf3vT66hduvJlnKLyJaSp0SuwcSX24JSaAMAgIAxDEMzZ87U2rVr9fbbbyslJcVr/uDBg2W1WrV161ZlZGRIkg4ePKijR4/K4XDUuE6zbgtaU/vabgcayuq6zWlTqOs9qOvq31/xL87G5OpPvIG8YnlLuUVkS8lTItdg4EtMFNoAACBgsrKytHr1ar3yyitq166d57zr2NhYtW7dWrGxsZo6daqys7MVHx8vu92umTNnyuFwcCE0AEDQotAGAAABs3z5cklf3dXkm/Lz83XXXXdJkp5++mnP7UCdTqfS09O1bNmyZo4UAICGo9AGAAABU9ctQ6tER0crLy9PeXl5zRARmlr9h4cDQOiLCHQAAAAAAACEEwptAAAAAABMRKENAAAAAICJOEcbAAAAQJ0CdSuz2l7XFmlo8dAmeUnAFOzRBgAAAADAROzRBgAAABAQXIUe4Yo92gAAAAAAmIg92gAAAEAt2OMa3PrO3yxnpaXa9KY6ZxxoKPZoAwAAAABgIvZoAwAAAE2grr3h4bbHNZz2/Lek9w1Nhz3aAAAAAACYiD3aAAAAQDML1H2p0bTYG44q7NEGAAAAAMBE7NEGAAAAEFY4YgCBRqENAAAABBkOQQZCG4eOAwAAAABgIvZoAwAAAEAD+HMbM45SaFnYow0AAAAAgInYow0AAACEkLr2jB56NK0ZIwld/uyZBhqCPdoAAAAAAJiIPdoAEKT6zt8sZ6Wl2nTO4wIA1Kbv/M1aPLRxvyHs5QXMwx5tAAAAAABMRKENAAAAAICJOHQcABC0ajuM0RZpaPHQZg4GAACggSi0AQAAACCA6jo/no3LoYlDxwEAAAAAMBGFNgAAAAAAJgpooZ2Xl6fu3bsrOjpaw4YN03vvvRfIcAAAQJBizAAACCUBO0f7j3/8o7Kzs7VixQoNGzZMS5YsUXp6ug4ePKhOnToFKiwAABBkGDMA5uFe2QhX9X2267qHfFMIWKH91FNP6e6779aUKVMkSStWrNCGDRv0m9/8Rg899FCgwgIAAEGGMQMASH3nb5az0lJten0FZF0FaF3L+lO4NvY1qzQ212ASkEL7woULKioqUk5OjmdaRESEUlNTVVhYWK290+mU0+n0PD9z5owk6fTp03K5XI2Ow+Vyqby8XK1cEap0V38jJenUqVONXn+4qK+f6KOv0E/1o48ahn76Wqsvz9c83W2ovNytU6dOyWq1Nnr9Z8+elSQZhtHodaBp+TpmkPwfN1R9B2v6fNX2mQxFVd+jusZB4YJcw09LyVOqP9f6xgV1/d2qa9n6/t41dtm6lvNnDORPvA3l07jBCIBjx44ZkowdO3Z4Tb///vuNoUOHVms/b948QxIPHjx48ODRJI/PPvusuX4C4SNfxwyGwbiBBw8ePHg07aMh44aQuI92Tk6OsrOzPc/dbrdOnz6tDh06yGJp/BassrIyJScn67PPPpPdbjcj1LBEPzUM/VQ/+qhh6Kf6mdVHhmHo7NmzSkpKMjE6BJq/44aW8h1sKXlK5BqOWkqeErkGE1/GDQEptC+55BJFRkaqpKTEa3pJSYkSExOrtbfZbLLZbF7T4uLiTIvHbrcH5RsZbOinhqGf6kcfNQz9VD8z+ig2NtakaNAUfB0zSOaNG1rKd7Cl5CmRazhqKXlK5BosGjpuCMjtvaKiojR48GBt3brVM83tdmvr1q1yOByBCAkAAAQhxgwAgFAUsEPHs7OzlZmZqSFDhmjo0KFasmSJzp8/77miKAAAgMSYAQAQegJWaH/3u9/Vf/7zH82dO1fFxcUaOHCgNm3apISEhGaLwWazad68edUOL4M3+qlh6Kf60UcNQz/Vjz5qWZp7zNBSPl8tJU+JXMNRS8lTItdQZTEM7mkCAAAAAIBZAnKONgAAAAAA4YpCGwAAAAAAE1FoAwAAAABgIgptAAAAAABM1KIL7by8PHXv3l3R0dEaNmyY3nvvvUCHFFS2b9+uCRMmKCkpSRaLRevWrQt0SEEnNzdXV199tdq1a6dOnTrp1ltv1cGDBwMdVtBZvny5+vfvL7vdLrvdLofDoddffz3QYQW1RYsWyWKxaNasWYEOJajMnz9fFovF69GrV69Ah4UwEuxjg/p+mw3D0Ny5c9W5c2e1bt1aqampOnTokFeb06dPa/LkybLb7YqLi9PUqVN17tw5rzZ///vfde211yo6OlrJyclavHhxtVjWrFmjXr16KTo6Wv369dPGjRtNy7Mhv68VFRXKyspShw4d1LZtW2VkZKikpMSrzdGjRzV+/HjFxMSoU6dOuv/++/Xll196tXn77bd11VVXyWaz6fLLL9eqVauqxdOUn4v6fiPDJc+L1fQ7Fy651vdbFS55Vjl27JjuuOMOdejQQa1bt1a/fv20Z88ez/xw+bvkM6OFeumll4yoqCjjN7/5jbF//37j7rvvNuLi4oySkpJAhxY0Nm7caPzf//2f8Ze//MWQZKxduzbQIQWd9PR0Iz8/39i3b5+xd+9e46abbjK6du1qnDt3LtChBZVXX33V2LBhg/HPf/7TOHjwoPHwww8bVqvV2LdvX6BDC0rvvfee0b17d6N///7GvffeG+hwgsq8efOMK6+80jhx4oTn8Z///CfQYSFMhMLYoL7f5kWLFhmxsbHGunXrjL/97W/Gt7/9bSMlJcX44osvPG1uvPFGY8CAAcbOnTuNv/71r8bll19ufO973/PMP3PmjJGQkGBMnjzZ2Ldvn/GHP/zBaN26tfHss8962rz77rtGZGSksXjxYuPAgQPGI488YlitVuPDDz80Jc+G/L7++Mc/NpKTk42tW7cae/bsMYYPH25cc801nvlffvml0bdvXyM1NdX44IMPjI0bNxqXXHKJkZOT42nz8ccfGzExMUZ2drZx4MAB45lnnjEiIyONTZs2edo09eeivt/IcMnzm2r7nQuXXOv7rQqXPA3DME6fPm1069bNuOuuu4xdu3YZH3/8sbF582bj8OHDnjbh8nfJVy220B46dKiRlZXleV5ZWWkkJSUZubm5AYwqeFFoN8zJkycNSca2bdsCHUrQa9++vfHrX/860GEEnbNnzxo9evQwCgoKjOuvv55C+yLz5s0zBgwYEOgwEKZCbWxw8W+z2+02EhMTjZ/97GeeaaWlpYbNZjP+8Ic/GIZhGAcOHDAkGbt37/a0ef311w2LxWIcO3bMMAzDWLZsmdG+fXvD6XR62jz44INGz549Pc9vv/12Y/z48V7xDBs2zPjRj35kao5VLv59LS0tNaxWq7FmzRpPm48++siQZBQWFhqG8dVGiYiICKO4uNjTZvny5Ybdbvfk9sADDxhXXnml12t997vfNdLT0z3PA/G5qPqNDMc8a/udC6dc6/qtCqc8DeOrvw0jR46sdX44/12qT4s8dPzChQsqKipSamqqZ1pERIRSU1NVWFgYwMgQ6s6cOSNJio+PD3AkwauyslIvvfSSzp8/L4fDEehwgk5WVpbGjx/v9fcJ3g4dOqSkpCR961vf0uTJk3X06NFAh4QwEA5jgyNHjqi4uNgrh9jYWA0bNsyTQ2FhoeLi4jRkyBBPm9TUVEVERGjXrl2eNtddd52ioqI8bdLT03Xw4EF9/vnnnjYX/51KT09vsr66+Pe1qKhILpfLK4ZevXqpa9euXrn269dPCQkJXjGWlZVp//79DcqjuT8XF/9GhmOetf3OhVuutf1WhVuer776qoYMGaLvfOc76tSpkwYNGqTnn3/eMz+c/y7Vp0UW2v/9739VWVnp9eGVpISEBBUXFwcoKoQ6t9utWbNmacSIEerbt2+gwwk6H374odq2bSubzaYf//jHWrt2rfr06RPosILKSy+9pPfff1+5ubmBDiVoDRs2TKtWrdKmTZu0fPlyHTlyRNdee63Onj0b6NAQ4sJhbFAVZ105FBcXq1OnTl7zW7Vqpfj4eK82Na3jm69RW5um6Kuafl+Li4sVFRWluLi4WmPwJ4+ysjJ98cUXzfa5qO03MtzyrOt3Lpxyreu3KpzylKSPP/5Yy5cvV48ePbR582ZNnz5d99xzj1544QWveMPt71JDtArIqwJhKCsrS/v27dM777wT6FCCUs+ePbV3716dOXNGL7/8sjIzM7Vt2zaK7f/vs88+07333quCggJFR0cHOpygNW7cOM//+/fvr2HDhqlbt27605/+pKlTpwYwMgBNpSX8vtb2GxlOWtLvXF2/Va1btw5gZOZzu90aMmSInnjiCUnSoEGDtG/fPq1YsUKZmZkBji6wWuQe7UsuuUSRkZHVru5XUlKixMTEAEWFUDZjxgytX79eb731lrp06RLocIJSVFSULr/8cg0ePFi5ubkaMGCAfvnLXwY6rKBRVFSkkydP6qqrrlKrVq3UqlUrbdu2TUuXLlWrVq1UWVkZ6BCDUlxcnK644godPnw40KEgxIXD2KAqzrpySExM1MmTJ73mf/nllzp9+rRXm5rW8c3XqK2N2X1V2+9rYmKiLly4oNLS0lpj8CcPu92u1q1bN9vnorbfyHDKs77fuYSEhLDJ9WLf/K0Kp/dUkjp37lxtp0nv3r09h8qH49+lhmqRhXZUVJQGDx6srVu3eqa53W5t3bqVc0bhE8MwNGPGDK1du1ZvvvmmUlJSAh1SyHC73XI6nYEOI2iMGTNGH374ofbu3et5DBkyRJMnT9bevXsVGRkZ6BCD0rlz5/Svf/1LnTt3DnQoCHHhMDZISUlRYmKiVw5lZWXatWuXJweHw6HS0lIVFRV52rz55ptyu90aNmyYp8327dvlcrk8bQoKCtSzZ8//1979hMLzx3EcH8VsNvlTq01bu6VIcSBSG7eVcpLT5iBxEK6bcpGT2oNc5OLCgdrcJCXKrqIotf5lWw6Li1JKKQ7K63v4Rd9la+k3fru/8XzUnubT7Ps9O8173k07b6OiouJ9zd/f87bGqmOVrb42NzcbRUVFaTEkk0nj5uYmLdfT09O0G/itrS2jtLT0vTHIlkeuzou3GmmnPLPVuZaWFtvk+tHftcpOv6lhGEZbW9un0XsXFxeGz+czDMNe16Vvy8kr2PJAJBKRw+HQ4uKizs/PNTQ0pPLy8rS3+/12j4+PisfjisfjMgxDMzMzisfjur6+znVoeWNkZERlZWWKxWJpIxyenp5yHVpeGR8f187OjlKplE5OTjQ+Pq6CggJtbm7mOrS8xlvHPwuFQorFYkqlUtrb21NHR4dcLpfu7u5yHRps4P9wb5CtNofDYZWXl2t1dVUnJyfq7u7OOEanqalJBwcH2t3dVU1NTdoYnYeHB7ndbvX19ens7EyRSEROp/PTGJ3CwkJNT08rkUhocnLS0jE6X6mvw8PD8nq92t7e1uHhofx+v/x+//v2txFJnZ2dOjo60sbGhiorKzOOSBobG1MikdDc3FzGEUk/eV5kq5F2yTOTj3XOLrlmq1V2yVP6Z1RbYWGhpqamdHl5qeXlZTmdTi0tLb2vsct16bt+baMtSbOzs/J6vTJNU62trdrf3891SHklGo3KMIxPn/7+/lyHljcyHR/DMLSwsJDr0PLK4OCgfD6fTNNUZWWlAoEATfYX0Gh/FgwGVVVVJdM05fF4FAwG02Z1Av9Wvt8bZKvNr6+vmpiYkNvtlsPhUCAQUDKZTNvH/f29ent7VVJSotLSUg0MDOjx8TFtzfHxsdrb2+VwOOTxeBQOhz/FsrKyotraWpmmqfr6eq2vr1uW51fq6/Pzs0ZHR1VRUSGn06menh7d3t6m7efq6kpdXV0qLi6Wy+VSKBTSy8tL2ppoNKrGxkaZpqnq6uqMNfwnz4tsNdIueWbysc7ZJddstcoueb5ZW1tTQ0ODHA6H6urqND8/n7bdLtel7yqQpP/u+TkAAAAAAPb2K/+jDQAAAADAT6HRBgAAAADAQjTaAAAAAABYiEYbAAAAAAAL0WgDAAAAAGAhGm0AAAAAACxEow0AAAAAgIVotAEAAAAAsBCNNgAAAAAAFqLRBgAAAADAQjTaAAAAAABYiEYbAAAAAAAL/QFOL05pke9GeQAAAABJRU5ErkJggg==","text/plain":["<Figure size 1200x800 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# show histograms for the numerical columns by using the hist() method on medical\n","\n","#I changed the bins and figsize because it was hard to see\n","\n","import matplotlib.pyplot as plt\n","medical.hist(bins=50, figsize=(12, 8))\n","#plt.show()\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ZSFTdPiKk5lI"},"source":["Briefly write here what you observe from these histograms.\n","\n","an insurance charge of 60,000 is rare, while charges ranging in the 0-17,000 range are more common, maybe because people in the database are generally young. The bmi histogram makes an approxitametly normal curve, skewed slightly right which implies that the mean is greater than the median, which is approximately bmi = 30. This implies that people in the database are being estimated to be obease, but this many not necessairly be true if you take median into account. People in the database range from 15-66? And have 0-4 children, with most having 0 children. (probably becuase most people in the database are children themselves)."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"v-LGH0uemOcX"},"source":["## Create a Test Set"]},{"cell_type":"code","execution_count":578,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":916,"status":"ok","timestamp":1682900858120,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"z6gfJ134UViY","outputId":"75e9e020-d782-42dc-a31e-191c5e43b02f"},"outputs":[],"source":["# use train_test_split() to split the data into training and test sets\n","from sklearn.model_selection import train_test_split\n","\n","train_set, test_set = train_test_split(medical, test_size=0.2, random_state=42)\n","#len(train_set), len(test_set)\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"QnUCgxdgAVUO"},"source":["# Explore and Visualize the Data to Gain Insights\n","\n","So far you have only taken a quick glance at the data to get a general\n","understanding of the kind of data you are manipulating. Now the goal is to\n","go into a little more depth.\n","\n","First, make sure you have put the test set aside and you are only\n","exploring the training set."]},{"cell_type":"code","execution_count":579,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1682900858487,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"BPeWC-Xq_wG1"},"outputs":[],"source":["# make a copy of the train set and save it to a variable called medical\n","medical = train_set.copy()\n"]},{"cell_type":"code","execution_count":580,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"executionInfo":{"elapsed":543,"status":"ok","timestamp":1682900859019,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"9i9iKNVq9_K5","outputId":"da3d1b9f-459c-4df5-cbf2-578ca9c8e6a3"},"outputs":[{"data":{"text/plain":["array([[<Axes: title={'center': 'charges'}>]], dtype=object)"]},"execution_count":580,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# build a histogram of the charges column\n","medical.hist(['charges'],bins=50, figsize=(12, 8))"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"wtM3hsM-FDNX"},"source":["This distribution is right-skewed. To make it closer to normal we can apply natural log"]},{"cell_type":"code","execution_count":581,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1682900859021,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"emyr8lPuFErr","outputId":"d6efea10-5f62-4dea-868e-580f497af160"},"outputs":[{"data":{"text/plain":["<Axes: >"]},"execution_count":581,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 1200x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# apply a log transformation to the charges column using the np.log10() function\n","# build a histogram of the transformed column\n","import numpy as np\n","\n","\n","log_transformed = np.log10(medical['charges'])\n","log_transformed.hist(bins=50, figsize=(12, 8))\n","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"27jWlXuUFTxF"},"source":["Now let's look at the mean charges by region"]},{"cell_type":"code","execution_count":582,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":525},"executionInfo":{"elapsed":358,"status":"ok","timestamp":1682900859369,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"CcTz-ca8FVY3","outputId":"d15da936-0639-4df2-ed28-76287370e083"},"outputs":[{"name":"stdout","output_type":"stream","text":["southeast total =  14698.242993074204\n","southwest total =  12611.500972651515\n","northwest total =  12622.514245976563\n","northeast total =  13484.520254242425\n","southwest    12611.500973\n","northwest    12622.514246\n","northeast    13333.008791\n","southeast    14698.242993\n","dtype: float64\n"]},{"data":{"text/plain":["<Axes: >"]},"execution_count":582,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA09UlEQVR4nO3dfVxX9d3H8fcPkRvNH4gmyCJ0qxTvTZvi/SYTb3J6zXllMvVqpFcNSrNpeV0OUSuXpuLdcral1nQ5t3SlDSWcYoqoKKlopIU3S4GVAmEJKN/rjx6cy1/elIUhX17Px+M8Hv7O93O+53vO19+PN4dzwGWMMQIAALCMV3UPAAAA4GYg5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArORd3QOoThUVFTp9+rQaNGggl8tV3cMBAABfgzFGn376qUJDQ+Xlde3rNbU65Jw+fVphYWHVPQwAAPANnDp1Snfcccc122t1yGnQoIGkL06S2+2u5tEAAICvo7i4WGFhYc7X8Wup1SGn8kdUbrebkAMAQA3zVbeacOMxAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJW8q3sAAADUdM2e3ljdQ6gWx387qLqHcF1cyQEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASjccctLS0jR48GCFhobK5XJp/fr116x95JFH5HK5lJSU5LH+7NmziomJkdvtVmBgoGJjY1VSUuJRc+DAAfXs2VN+fn4KCwvT7Nmzr+h/7dq1atmypfz8/NS2bVu99dZbN3o4AADAUjcccs6fP6/27dtryZIl161bt26ddu3apdDQ0CvaYmJilJ2drZSUFG3YsEFpaWkaN26c015cXKx+/fopPDxcmZmZmjNnjhITE7Vs2TKnZufOnXrwwQcVGxur/fv3a+jQoRo6dKgOHTp0o4cEAAAs5DLGmG+8sculdevWaejQoR7rP/roI3Xp0kWbNm3SoEGDNGHCBE2YMEGSdOTIEbVq1Up79uxR586dJUnJyckaOHCg/vWvfyk0NFQvvvii/vd//1d5eXny8fGRJD399NNav3693nvvPUnSAw88oPPnz2vDhg3Ofrt27aoOHTpo6dKlX2v8xcXFCggIUFFRkdxu9zc9DQCAWq7Z0xurewjV4vhvB1XLfr/u1+8qvyenoqJCo0aN0qRJk9S6desr2tPT0xUYGOgEHEmKioqSl5eXMjIynJpevXo5AUeSoqOjlZOTo3Pnzjk1UVFRHn1HR0crPT39mmMrLS1VcXGxxwIAAOxU5SHn+eefl7e3tx5//PGrtufl5alJkyYe67y9vRUUFKS8vDynJjg42KOm8vVX1VS2X82sWbMUEBDgLGFhYTd2cAAAoMao0pCTmZmpBQsWaMWKFXK5XFXZdZWYMmWKioqKnOXUqVPVPSQAAHCTVGnI2b59uwoKCnTnnXfK29tb3t7eOnHihJ588kk1a9ZMkhQSEqKCggKP7S5evKizZ88qJCTEqcnPz/eoqXz9VTWV7Vfj6+srt9vtsQAAADtVacgZNWqUDhw4oKysLGcJDQ3VpEmTtGnTJklSZGSkCgsLlZmZ6Wy3ZcsWVVRUqEuXLk5NWlqaysvLnZqUlBS1aNFCDRs2dGpSU1M99p+SkqLIyMiqPCQAAFBDed/oBiUlJTp27JjzOjc3V1lZWQoKCtKdd96pRo0aedTXrVtXISEhatGihSQpIiJC/fv319ixY7V06VKVl5crPj5eI0aMcB43HzlypKZPn67Y2Fg99dRTOnTokBYsWKD58+c7/Y4fP169e/fW3LlzNWjQIL322mvau3evx2PmAACg9rrhKzl79+5Vx44d1bFjR0nSxIkT1bFjRyUkJHztPlatWqWWLVuqb9++GjhwoHr06OERTgICArR582bl5uaqU6dOevLJJ5WQkODxu3S6deum1atXa9myZWrfvr3++te/av369WrTps2NHhIAALDQt/o9OTUdvycHAFAV+D05361q+z05AAAAtwJCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgpRv+21UAgK/Gb8AFqh9XcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAAreVf3AGqzZk9vrO4hVIvjvx1U3UOoFsw3AHy3uJIDAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFa64ZCTlpamwYMHKzQ0VC6XS+vXr3faysvL9dRTT6lt27aqX7++QkNDNXr0aJ0+fdqjj7NnzyomJkZut1uBgYGKjY1VSUmJR82BAwfUs2dP+fn5KSwsTLNnz75iLGvXrlXLli3l5+entm3b6q233rrRwwEAAJa64ZBz/vx5tW/fXkuWLLmi7bPPPtO+ffv0m9/8Rvv27dPrr7+unJwc/fSnP/Woi4mJUXZ2tlJSUrRhwwalpaVp3LhxTntxcbH69eun8PBwZWZmas6cOUpMTNSyZcucmp07d+rBBx9UbGys9u/fr6FDh2ro0KE6dOjQjR4SAACw0A3/gc4BAwZowIABV20LCAhQSkqKx7rFixfrhz/8oU6ePKk777xTR44cUXJysvbs2aPOnTtLkhYtWqSBAwfqhRdeUGhoqFatWqWysjK9/PLL8vHxUevWrZWVlaV58+Y5YWjBggXq37+/Jk2aJEmaOXOmUlJStHjxYi1duvRGDwsAAFjmpt+TU1RUJJfLpcDAQElSenq6AgMDnYAjSVFRUfLy8lJGRoZT06tXL/n4+Dg10dHRysnJ0blz55yaqKgoj31FR0crPT39mmMpLS1VcXGxxwIAAOx0U0POhQsX9NRTT+nBBx+U2+2WJOXl5alJkyYedd7e3goKClJeXp5TExwc7FFT+fqrairbr2bWrFkKCAhwlrCwsG93gAAA4JZ100JOeXm5/vM//1PGGL344os3azc3ZMqUKSoqKnKWU6dOVfeQAADATXLD9+R8HZUB58SJE9qyZYtzFUeSQkJCVFBQ4FF/8eJFnT17ViEhIU5Nfn6+R03l66+qqWy/Gl9fX/n6+n7zAwMAADVGlV/JqQw4R48e1dtvv61GjRp5tEdGRqqwsFCZmZnOui1btqiiokJdunRxatLS0lReXu7UpKSkqEWLFmrYsKFTk5qa6tF3SkqKIiMjq/qQAABADXTDIaekpERZWVnKysqSJOXm5iorK0snT55UeXm5fv7zn2vv3r1atWqVLl26pLy8POXl5amsrEySFBERof79+2vs2LHavXu3duzYofj4eI0YMUKhoaGSpJEjR8rHx0exsbHKzs7WmjVrtGDBAk2cONEZx/jx45WcnKy5c+fqvffeU2Jiovbu3av4+PgqOC0AAKCmu+GQs3fvXnXs2FEdO3aUJE2cOFEdO3ZUQkKCPvroI73xxhv617/+pQ4dOqhp06bOsnPnTqePVatWqWXLlurbt68GDhyoHj16ePwOnICAAG3evFm5ubnq1KmTnnzySSUkJHj8Lp1u3bpp9erVWrZsmdq3b6+//vWvWr9+vdq0afNtzgcAALDEDd+T06dPHxljrtl+vbZKQUFBWr169XVr2rVrp+3bt1+3Zvjw4Ro+fPhX7g8AANQ+/O0qAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFa64ZCTlpamwYMHKzQ0VC6XS+vXr/doN8YoISFBTZs2lb+/v6KionT06FGPmrNnzyomJkZut1uBgYGKjY1VSUmJR82BAwfUs2dP+fn5KSwsTLNnz75iLGvXrlXLli3l5+entm3b6q233rrRwwEAAJa64ZBz/vx5tW/fXkuWLLlq++zZs7Vw4UItXbpUGRkZql+/vqKjo3XhwgWnJiYmRtnZ2UpJSdGGDRuUlpamcePGOe3FxcXq16+fwsPDlZmZqTlz5igxMVHLli1zanbu3KkHH3xQsbGx2r9/v4YOHaqhQ4fq0KFDN3pIAADAQt43usGAAQM0YMCAq7YZY5SUlKSpU6dqyJAhkqRXXnlFwcHBWr9+vUaMGKEjR44oOTlZe/bsUefOnSVJixYt0sCBA/XCCy8oNDRUq1atUllZmV5++WX5+PiodevWysrK0rx585wwtGDBAvXv31+TJk2SJM2cOVMpKSlavHixli5d+o1OBgAAsEeV3pOTm5urvLw8RUVFOesCAgLUpUsXpaenS5LS09MVGBjoBBxJioqKkpeXlzIyMpyaXr16ycfHx6mJjo5WTk6Ozp0759Rcvp/Kmsr9XE1paamKi4s9FgAAYKcqDTl5eXmSpODgYI/1wcHBTlteXp6aNGni0e7t7a2goCCPmqv1cfk+rlVT2X41s2bNUkBAgLOEhYXd6CECAIAaolY9XTVlyhQVFRU5y6lTp6p7SAAA4Cap0pATEhIiScrPz/dYn5+f77SFhISooKDAo/3ixYs6e/asR83V+rh8H9eqqWy/Gl9fX7ndbo8FAADYqUpDTvPmzRUSEqLU1FRnXXFxsTIyMhQZGSlJioyMVGFhoTIzM52aLVu2qKKiQl26dHFq0tLSVF5e7tSkpKSoRYsWatiwoVNz+X4qayr3AwAAarcbDjklJSXKyspSVlaWpC9uNs7KytLJkyflcrk0YcIEPfPMM3rjjTd08OBBjR49WqGhoRo6dKgkKSIiQv3799fYsWO1e/du7dixQ/Hx8RoxYoRCQ0MlSSNHjpSPj49iY2OVnZ2tNWvWaMGCBZo4caIzjvHjxys5OVlz587Ve++9p8TERO3du1fx8fHf/qwAAIAa74YfId+7d69+9KMfOa8rg8eYMWO0YsUKTZ48WefPn9e4ceNUWFioHj16KDk5WX5+fs42q1atUnx8vPr27SsvLy8NGzZMCxcudNoDAgK0efNmxcXFqVOnTmrcuLESEhI8fpdOt27dtHr1ak2dOlX/8z//o7vvvlvr169XmzZtvtGJAAAAdrnhkNOnTx8ZY67Z7nK5NGPGDM2YMeOaNUFBQVq9evV199OuXTtt3779ujXDhw/X8OHDrz9gAABQK9Wqp6sAAEDtQcgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEpVHnIuXbqk3/zmN2revLn8/f31gx/8QDNnzpQxxqkxxighIUFNmzaVv7+/oqKidPToUY9+zp49q5iYGLndbgUGBio2NlYlJSUeNQcOHFDPnj3l5+ensLAwzZ49u6oPBwAA1FBVHnKef/55vfjii1q8eLGOHDmi559/XrNnz9aiRYucmtmzZ2vhwoVaunSpMjIyVL9+fUVHR+vChQtOTUxMjLKzs5WSkqINGzYoLS1N48aNc9qLi4vVr18/hYeHKzMzU3PmzFFiYqKWLVtW1YcEAABqIO+q7nDnzp0aMmSIBg0aJElq1qyZ/vznP2v37t2SvriKk5SUpKlTp2rIkCGSpFdeeUXBwcFav369RowYoSNHjig5OVl79uxR586dJUmLFi3SwIED9cILLyg0NFSrVq1SWVmZXn75Zfn4+Kh169bKysrSvHnzPMIQAAConar8Sk63bt2Umpqq999/X5L07rvv6p133tGAAQMkSbm5ucrLy1NUVJSzTUBAgLp06aL09HRJUnp6ugIDA52AI0lRUVHy8vJSRkaGU9OrVy/5+Pg4NdHR0crJydG5c+euOrbS0lIVFxd7LAAAwE5VfiXn6aefVnFxsVq2bKk6dero0qVLevbZZxUTEyNJysvLkyQFBwd7bBccHOy05eXlqUmTJp4D9fZWUFCQR03z5s2v6KOyrWHDhleMbdasWZo+fXoVHCUAALjVVfmVnL/85S9atWqVVq9erX379mnlypV64YUXtHLlyqre1Q2bMmWKioqKnOXUqVPVPSQAAHCTVPmVnEmTJunpp5/WiBEjJElt27bViRMnNGvWLI0ZM0YhISGSpPz8fDVt2tTZLj8/Xx06dJAkhYSEqKCgwKPfixcv6uzZs872ISEhys/P96ipfF1Z82W+vr7y9fX99gcJAABueVV+Jeezzz6Tl5dnt3Xq1FFFRYUkqXnz5goJCVFqaqrTXlxcrIyMDEVGRkqSIiMjVVhYqMzMTKdmy5YtqqioUJcuXZyatLQ0lZeXOzUpKSlq0aLFVX9UBQAAapcqDzmDBw/Ws88+q40bN+r48eNat26d5s2bp//4j/+QJLlcLk2YMEHPPPOM3njjDR08eFCjR49WaGiohg4dKkmKiIhQ//79NXbsWO3evVs7duxQfHy8RowYodDQUEnSyJEj5ePjo9jYWGVnZ2vNmjVasGCBJk6cWNWHBAAAaqAq/3HVokWL9Jvf/Ea/+tWvVFBQoNDQUP33f/+3EhISnJrJkyfr/PnzGjdunAoLC9WjRw8lJyfLz8/PqVm1apXi4+PVt29feXl5adiwYVq4cKHTHhAQoM2bNysuLk6dOnVS48aNlZCQwOPjAABA0k0IOQ0aNFBSUpKSkpKuWeNyuTRjxgzNmDHjmjVBQUFavXr1dffVrl07bd++/ZsOFQAAWIy/XQUAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlW5KyPnoo4/0i1/8Qo0aNZK/v7/atm2rvXv3Ou3GGCUkJKhp06by9/dXVFSUjh496tHH2bNnFRMTI7fbrcDAQMXGxqqkpMSj5sCBA+rZs6f8/PwUFham2bNn34zDAQAANVCVh5xz586pe/fuqlu3rv7xj3/o8OHDmjt3rho2bOjUzJ49WwsXLtTSpUuVkZGh+vXrKzo6WhcuXHBqYmJilJ2drZSUFG3YsEFpaWkaN26c015cXKx+/fopPDxcmZmZmjNnjhITE7Vs2bKqPiQAAFADeVd1h88//7zCwsK0fPlyZ13z5s2dfxtjlJSUpKlTp2rIkCGSpFdeeUXBwcFav369RowYoSNHjig5OVl79uxR586dJUmLFi3SwIED9cILLyg0NFSrVq1SWVmZXn75Zfn4+Kh169bKysrSvHnzPMIQAAConar8Ss4bb7yhzp07a/jw4WrSpIk6duyol156yWnPzc1VXl6eoqKinHUBAQHq0qWL0tPTJUnp6ekKDAx0Ao4kRUVFycvLSxkZGU5Nr1695OPj49RER0crJydH586dq+rDAgAANUyVh5wPP/xQL774ou6++25t2rRJjz76qB5//HGtXLlSkpSXlydJCg4O9tguODjYacvLy1OTJk082r29vRUUFORRc7U+Lt/Hl5WWlqq4uNhjAQAAdqryH1dVVFSoc+fOeu655yRJHTt21KFDh7R06VKNGTOmqnd3Q2bNmqXp06dX6xgAAMB3o8qv5DRt2lStWrXyWBcREaGTJ09KkkJCQiRJ+fn5HjX5+flOW0hIiAoKCjzaL168qLNnz3rUXK2Py/fxZVOmTFFRUZGznDp16pscIgAAqAGqPOR0795dOTk5Huvef/99hYeHS/riJuSQkBClpqY67cXFxcrIyFBkZKQkKTIyUoWFhcrMzHRqtmzZooqKCnXp0sWpSUtLU3l5uVOTkpKiFi1aeDzJdTlfX1+53W6PBQAA2KnKQ84TTzyhXbt26bnnntOxY8e0evVqLVu2THFxcZIkl8ulCRMm6JlnntEbb7yhgwcPavTo0QoNDdXQoUMlfXHlp3///ho7dqx2796tHTt2KD4+XiNGjFBoaKgkaeTIkfLx8VFsbKyys7O1Zs0aLViwQBMnTqzqQwIAADVQld+Tc99992ndunWaMmWKZsyYoebNmyspKUkxMTFOzeTJk3X+/HmNGzdOhYWF6tGjh5KTk+Xn5+fUrFq1SvHx8erbt6+8vLw0bNgwLVy40GkPCAjQ5s2bFRcXp06dOqlx48ZKSEjg8XEAACDpJoQcSbr//vt1//33X7Pd5XJpxowZmjFjxjVrgoKCtHr16uvup127dtq+ffs3HicAALAXf7sKAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJVuesj57W9/K5fLpQkTJjjrLly4oLi4ODVq1Ei33Xabhg0bpvz8fI/tTp48qUGDBqlevXpq0qSJJk2apIsXL3rUbN26Vffee698fX111113acWKFTf7cAAAQA1xU0POnj179Pvf/17t2rXzWP/EE0/ozTff1Nq1a7Vt2zadPn1aP/vZz5z2S5cuadCgQSorK9POnTu1cuVKrVixQgkJCU5Nbm6uBg0apB/96EfKysrShAkT9PDDD2vTpk0385AAAEANcdNCTklJiWJiYvTSSy+pYcOGzvqioiL98Y9/1Lx58/TjH/9YnTp10vLly7Vz507t2rVLkrR582YdPnxYf/rTn9ShQwcNGDBAM2fO1JIlS1RWViZJWrp0qZo3b665c+cqIiJC8fHx+vnPf6758+ffrEMCAAA1yE0LOXFxcRo0aJCioqI81mdmZqq8vNxjfcuWLXXnnXcqPT1dkpSenq62bdsqODjYqYmOjlZxcbGys7Odmi/3HR0d7fRxNaWlpSouLvZYAACAnbxvRqevvfaa9u3bpz179lzRlpeXJx8fHwUGBnqsDw4OVl5enlNzecCpbK9su15NcXGxPv/8c/n7+1+x71mzZmn69Onf+LgAAEDNUeVXck6dOqXx48dr1apV8vPzq+ruv5UpU6aoqKjIWU6dOlXdQwIAADdJlYeczMxMFRQU6N5775W3t7e8vb21bds2LVy4UN7e3goODlZZWZkKCws9tsvPz1dISIgkKSQk5IqnrSpff1WN2+2+6lUcSfL19ZXb7fZYAACAnao85PTt21cHDx5UVlaWs3Tu3FkxMTHOv+vWravU1FRnm5ycHJ08eVKRkZGSpMjISB08eFAFBQVOTUpKitxut1q1auXUXN5HZU1lHwAAoHar8ntyGjRooDZt2nisq1+/vho1auSsj42N1cSJExUUFCS3263HHntMkZGR6tq1qySpX79+atWqlUaNGqXZs2crLy9PU6dOVVxcnHx9fSVJjzzyiBYvXqzJkyfrl7/8pbZs2aK//OUv2rhxY1UfEgAAqIFuyo3HX2X+/Pny8vLSsGHDVFpaqujoaP3ud79z2uvUqaMNGzbo0UcfVWRkpOrXr68xY8ZoxowZTk3z5s21ceNGPfHEE1qwYIHuuOMO/eEPf1B0dHR1HBIAALjFfCchZ+vWrR6v/fz8tGTJEi1ZsuSa24SHh+utt966br99+vTR/v37q2KIAADAMvztKgAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWqvKQM2vWLN13331q0KCBmjRpoqFDhyonJ8ej5sKFC4qLi1OjRo102223adiwYcrPz/eoOXnypAYNGqR69eqpSZMmmjRpki5evOhRs3XrVt17773y9fXVXXfdpRUrVlT14QAAgBqqykPOtm3bFBcXp127diklJUXl5eXq16+fzp8/79Q88cQTevPNN7V27Vpt27ZNp0+f1s9+9jOn/dKlSxo0aJDKysq0c+dOrVy5UitWrFBCQoJTk5ubq0GDBulHP/qRsrKyNGHCBD388MPatGlTVR8SAACogbyrusPk5GSP1ytWrFCTJk2UmZmpXr16qaioSH/84x+1evVq/fjHP5YkLV++XBEREdq1a5e6du2qzZs36/Dhw3r77bcVHBysDh06aObMmXrqqaeUmJgoHx8fLV26VM2bN9fcuXMlSREREXrnnXc0f/58RUdHV/VhAQCAGuam35NTVFQkSQoKCpIkZWZmqry8XFFRUU5Ny5Ytdeeddyo9PV2SlJ6errZt2yo4ONipiY6OVnFxsbKzs52ay/uorKns42pKS0tVXFzssQAAADvd1JBTUVGhCRMmqHv37mrTpo0kKS8vTz4+PgoMDPSoDQ4OVl5enlNzecCpbK9su15NcXGxPv/886uOZ9asWQoICHCWsLCwb32MAADg1nRTQ05cXJwOHTqk11577Wbu5mubMmWKioqKnOXUqVPVPSQAAHCTVPk9OZXi4+O1YcMGpaWl6Y477nDWh4SEqKysTIWFhR5Xc/Lz8xUSEuLU7N6926O/yqevLq/58hNZ+fn5crvd8vf3v+qYfH195evr+62PDQAA3Pqq/EqOMUbx8fFat26dtmzZoubNm3u0d+rUSXXr1lVqaqqzLicnRydPnlRkZKQkKTIyUgcPHlRBQYFTk5KSIrfbrVatWjk1l/dRWVPZBwAAqN2q/EpOXFycVq9erb///e9q0KCBcw9NQECA/P39FRAQoNjYWE2cOFFBQUFyu9167LHHFBkZqa5du0qS+vXrp1atWmnUqFGaPXu28vLyNHXqVMXFxTlXYh555BEtXrxYkydP1i9/+Utt2bJFf/nLX7Rx48aqPiQAAFADVfmVnBdffFFFRUXq06ePmjZt6ixr1qxxaubPn6/7779fw4YNU69evRQSEqLXX3/daa9Tp442bNigOnXqKDIyUr/4xS80evRozZgxw6lp3ry5Nm7cqJSUFLVv315z587VH/7wBx4fBwAAkm7ClRxjzFfW+Pn5acmSJVqyZMk1a8LDw/XWW29dt58+ffpo//79NzxGAABgP/52FQAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAAr1fiQs2TJEjVr1kx+fn7q0qWLdu/eXd1DAgAAt4AaHXLWrFmjiRMnatq0adq3b5/at2+v6OhoFRQUVPfQAABANavRIWfevHkaO3asHnroIbVq1UpLly5VvXr19PLLL1f30AAAQDXzru4BfFNlZWXKzMzUlClTnHVeXl6KiopSenr6VbcpLS1VaWmp87qoqEiSVFxcfHMHew0VpZ9Vy36rW3Wd7+rGfNcuzHftwnxXz36NMdetq7Eh5+OPP9alS5cUHBzssT44OFjvvffeVbeZNWuWpk+ffsX6sLCwmzJGXF1AUnWPAN8l5rt2Yb5rl+qe708//VQBAQHXbK+xIeebmDJliiZOnOi8rqio0NmzZ9WoUSO5XK5qHNl3q7i4WGFhYTp16pTcbnd1Dwc3GfNduzDftUttnW9jjD799FOFhoZet67GhpzGjRurTp06ys/P91ifn5+vkJCQq27j6+srX19fj3WBgYE3a4i3PLfbXaveFLUd8127MN+1S22c7+tdwalUY2889vHxUadOnZSamuqsq6ioUGpqqiIjI6txZAAA4FZQY6/kSNLEiRM1ZswYde7cWT/84Q+VlJSk8+fP66GHHqruoQEAgGpWo0POAw88oH//+99KSEhQXl6eOnTooOTk5CtuRoYnX19fTZs27Yof3cFOzHftwnzXLsz39bnMVz1/BQAAUAPV2HtyAAAAroeQAwAArETIAQAAViLk1ALNmjVTUlJSdQ8DN9nWrVvlcrlUWFhY3UPBd4Q5r534TP/6CDkWWbFixS3/yw2PHz8ul8ulrKys6h5KjdanTx9NmDChuodxXYmJierQoUN1D8Mat+qcE7Runlv5M72mBC1CDlCDlJWVVfcQ8B1jzoFvweA7tXbtWtOmTRvj5+dngoKCTN++fU1JSYm5dOmSmT59uvne975nfHx8TPv27c0//vEPZ7t//vOfRpI5d+6cs27//v1GksnNzXXaL1+mTZtmjDEmPDzcPPvss+ahhx4yt912mwkLCzO///3vnX6GDRtm4uLinNfjx483ksyRI0eMMcaUlpaaevXqmZSUFGOMMZcuXTLPPfecadasmfHz8zPt2rUza9eudbY/e/asGTlypGncuLHx8/Mzd911l3n55ZeNMeaKMfbu3buqT/EtpXfv3uaxxx4zkyZNMg0bNjTBwcHOvBhjzIkTJ8xPf/pTU79+fdOgQQMzfPhwk5eX57RPmzbNtG/f3rz00kumWbNmxuVymTFjxlxxHi//P/D222+bTp06GX9/fxMZGWnee+89Y4wxhYWFxsvLy+zZs8cY88U8NmzY0HTp0sXZ36uvvmruuOMO5/XJkyfN8OHDTUBAgGnYsKH56U9/anJzc532f/7zn+a+++4z9erVMwEBAaZbt27m+PHjZvny5VeMcfny5TfnJN9ibqU5r7R+/XrTsWNH4+vra5o3b24SExNNeXm50z537lzTpk0bU69ePXPHHXeYRx991Hz66adO+/Hjx839999vAgMDTb169UyrVq3Mxo0bTW5u7hXjGjNmzE07t7eiW/Ez3Zivfu/u3r3bREVFmUaNGhm322169eplMjMznfaKigozbdo0ExYWZnx8fEzTpk3NY489Zoz54v/4l8d2q7p1R2ah06dPG29vbzNv3jyTm5trDhw4YJYsWWI+/fRTM2/ePON2u82f//xn895775nJkyebunXrmvfff98Y89VviNLSUpOUlGTcbrc5c+aMOXPmjPMhFR4eboKCgsySJUvM0aNHzaxZs4yXl5fzQbhw4ULTunVrp98OHTqYxo0bmxdffNEYY8w777xj6tata86fP2+MMeaZZ54xLVu2NMnJyeaDDz4wy5cvN76+vmbr1q3GGGPi4uJMhw4dzJ49e0xubq5JSUkxb7zxhjHmizdW5YfymTNnzCeffHJzT3o16927t3G73SYxMdG8//77ZuXKlcblcpnNmzebS5cumQ4dOpgePXqYvXv3ml27dplOnTp5BL9p06aZ+vXrm/79+5t9+/aZd9991xQWFprIyEgzduxYZ64vXrzo/B/p0qWL2bp1q8nOzjY9e/Y03bp1c/q79957zZw5c4wxxmRlZZmgoCDj4+Pj/F95+OGHTUxMjDHGmLKyMhMREWF++ctfmgMHDpjDhw+bkSNHmhYtWpjS0lJTXl5uAgICzK9//Wtz7Ngxc/jwYbNixQpz4sQJ89lnn5knn3zStG7d2hnjZ5999t2d+Gp0q815WlqacbvdZsWKFeaDDz4wmzdvNs2aNTOJiYlOzfz5882WLVtMbm6uSU1NNS1atDCPPvqo0z5o0CDzk5/8xBw4cMB88MEH5s033zTbtm0zFy9eNH/729+MJJOTk2POnDljCgsLv5PzfCu4VT/Tv+q9a4wxqamp5tVXXzVHjhwxhw8fNrGxsSY4ONgUFxcbY74Ib26327z11lvmxIkTJiMjwyxbtswYY8wnn3xi7rjjDjNjxgxnbLcqQs53KDMz00gyx48fv6ItNDTUPPvssx7r7rvvPvOrX/3KGPPVbwhjjFm+fLkJCAi4ou/w8HDzi1/8wnldUVFhmjRp4oSYAwcOGJfLZQoKCszZs2eNj4+PmTlzpnnggQeMMV+EmsoPzQsXLph69eqZnTt3euwjNjbWPPjgg8YYYwYPHmweeuihq56Dyu/89u/ff42zZJfevXubHj16eKy77777zFNPPWU2b95s6tSpY06ePOm0ZWdnG0lm9+7dxpgvvuDVrVvXFBQUXNHv+PHjPdZd/l19pY0bNxpJ5vPPPzfGGDNx4kQzaNAgY4wxSUlJ5oEHHvD4DvOuu+5yPsheffVV06JFC1NRUeH0V1paavz9/c2mTZvMJ598YiQ54fbLKq9I1Da32pz37dvXPPfccx7bvfrqq6Zp06bXPIa1a9eaRo0aOa/btm3rEYquNobLP5tqi1v1M/2r3rtXc+nSJdOgQQPz5ptvGmO+uLp3zz33mLKysqvWh4eHm/nz51+17VbCPTnfofbt26tv375q27athg8frpdeeknnzp1TcXGxTp8+re7du3vUd+/eXUeOHKmSfbdr1875t8vlUkhIiAoKCiRJbdq0UVBQkLZt26bt27erY8eOuv/++7Vt2zZJ0rZt29SnTx9J0rFjx/TZZ5/pJz/5iW677TZneeWVV/TBBx9Ikh599FG99tpr6tChgyZPnqydO3dWyTHUVJefe0lq2rSpCgoKdOTIEYWFhSksLMxpa9WqlQIDAz3mPTw8XLfffvs32l/Tpk0lyZnr3r1765133tGlS5ecee3Tp4+2bt2q06dP69ixY85cv/vuuzp27JgaNGjgzHNQUJAuXLigDz74QEFBQfqv//ovRUdHa/DgwVqwYIHOnDlzw+fHRrfSnL/77ruaMWOGx/t17NixOnPmjD777DNJ0ttvv62+ffvqe9/7nho0aKBRo0bpk08+cdoff/xxPfPMM+revbumTZumAwcO3OAZsdOt+pn+Ve9dScrPz9fYsWN19913KyAgQG63WyUlJTp58qQkafjw4fr888/1/e9/X2PHjtW6det08eLFKhn7d4mQ8x2qU6eOUlJS9I9//EOtWrXSokWL1KJFC+Xm5n7ltl5eX0yVueyvcJSXl3/tfdetW9fjtcvlUkVFhfPvXr16aevWrc4Xvnbt2qm0tFSHDh3Szp071bt3b0lSSUmJJGnjxo3KyspylsOHD+uvf/2rJGnAgAE6ceKEnnjiCZ0+fVp9+/bVr3/96689Vttc79x/HfXr1//G+3O5XJLk7K9Xr1769NNPtW/fPqWlpXmEnG3btik0NFR33323pC/mulOnTh7znJWVpffff18jR46UJC1fvlzp6enq1q2b1qxZo3vuuUe7du26ofHa6Faa85KSEk2fPt1jDg8ePKijR4/Kz89Px48f1/3336927drpb3/7mzIzM7VkyRJJ/3/T88MPP6wPP/xQo0aN0sGDB9W5c2ctWrTohsZoo1v1M/3rvHfHjBmjrKwsLViwQDt37lRWVpYaNWrkzHlYWJhycnL0u9/9Tv7+/vrVr36lXr163dAYbwWEnO+Yy+VS9+7dNX36dO3fv18+Pj5KTU1VaGioduzY4VG7Y8cOtWrVSpKc7+ou/075y49h+/j46NKlS99oXL1799bWrVu1detW9enTR15eXurVq5fmzJmj0tJS5zuSVq1aydfXVydPntRdd93lsVz+3entt9+uMWPG6E9/+pOSkpK0bNkyZ4ySvvE4bRIREaFTp07p1KlTzrrDhw+rsLDQmfdr+aZzHRgYqHbt2mnx4sWqW7euWrZsqV69emn//v3asGGDE2Yl6d5779XRo0fVpEmTK+Y6ICDAqevYsaOmTJminTt3qk2bNlq9evW3GqPNqmPO7733XuXk5Fwxh3fddZe8vLyUmZmpiooKzZ07V127dtU999yj06dPX9FPWFiYHnnkEb3++ut68skn9dJLLznjkmrve/pW/Ez/Ou/dHTt26PHHH9fAgQPVunVr+fr66uOPP/box9/fX4MHD9bChQu1detWpaen6+DBg99qbN81Qs53KCMjQ88995z27t2rkydP6vXXX9e///1vRUREaNKkSXr++ee1Zs0a5eTk6Omnn1ZWVpbGjx8vSU6ISExM1NGjR7Vx40bNnTvXo/9mzZqppKREqamp+vjjj51LzV9Hnz59dPjwYWVnZ6tHjx7OulWrVqlz587Od5YNGjTQr3/9az3xxBNauXKlPvjgA+3bt0+LFi3SypUrJUkJCQn6+9//rmPHjik7O1sbNmxQRESEJKlJkyby9/dXcnKy8vPzVVRU9K3Pa00VFRWltm3bKiYmRvv27dPu3bs1evRo9e7dW507d77uts2aNVNGRoaOHz+ujz/++IauElTOa2WgCQoKUkREhNasWeMRcmJiYtS4cWMNGTJE27dvV25urrZu3arHH39c//rXv5Sbm6spU6YoPT1dJ06c0ObNm3X06FFnrps1a6bc3FxlZWXp448/Vmlp6Tc4S3apjjlPSEjQK6+8ounTpys7O1tHjhzRa6+9pqlTp0r64rOlvLxcixYt0ocffqhXX31VS5cu9ehjwoQJ2rRpk3Jzc7Vv3z7985//dOY5PDxcLpdLGzZs0L///W/nam9tcKt+pn/Ve1eS7r77br366qs6cuSIMjIyFBMTI39/f6ePFStW6I9//KMOHTqkDz/8UH/605/k7++v8PBwZ2xpaWn66KOPrghHt5TqvimoNjl8+LCJjo42t99+u/H19TX33HOPWbRokTHmi5u+EhMTzfe+9z1Tt27dKx43NOaLp5zatm1r/Pz8TM+ePc3atWs9blIzxphHHnnENGrU6IrHDb98g1j79u09Hmu92uPElTfBPf300x7bVlRUmKSkJNOiRQtTt25dc/vtt5vo6Gizbds2Y4wxM2fONBEREcbf398EBQWZIUOGmA8//NDZ/qWXXjJhYWHGy8urVjxC/uWbRYcMGeI8Zvt1Hyf+spycHNO1a1fj7+9/xSOn17uR0Rhj1q1bZyQ5Nyka8/+/NuDLjx6fOXPGjB492jRu3Nj4+vqa73//+2bs2LGmqKjI5OXlmaFDh5qmTZsaHx8fEx4ebhISEsylS5eMMV/cpD5s2DATGBhY6x4hv9XmPDk52XTr1s34+/sbt9ttfvjDHzo3mBtjzLx580zTpk2Nv7+/iY6ONq+88opHv/Hx8eYHP/iB8fX1NbfffrsZNWqU+fjjj53tZ8yYYUJCQpzH3WuLW/kz/XrvXWOM2bdvn+ncubPx8/Mzd999t1m7dq1Hv+vWrTNdunQxbrfb1K9f33Tt2tXjBvf09HTTrl074+vre0s/Qu4y5rIfCAIAAFiCH1cBAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYKX/A5tSBKwbgSRBAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# compute the average insurance cost for each region\n","# sort the charges_by_region Series from the lowest to highest cost\n","# plot the sorted Series using the plot.bar() method\n","\n","region_charges=medical[['region','charges']].sort_values('region')\n","total_patients_by_region = medical[\"region\"].value_counts()\n","\n","southeast_total = total_patients_by_region[\"southeast\"]\n","southeast = region_charges[region_charges[\"region\"] == 'southeast']\n","sesum = southeast['charges'].sum()\n","print(\"southeast total = \" , sesum/southeast_total)\n","\n","southwest = region_charges[region_charges[\"region\"] == 'soutwest']\n","southwest_total = total_patients_by_region[\"southwest\"]\n","southwest = region_charges[region_charges[\"region\"] == 'southwest']\n","swsum = southwest['charges'].sum()\n","print(\"southwest total = \" , swsum/southwest_total)\n","\n","\n","northwest = region_charges[region_charges[\"region\"] == 'northwest']\n","northwest_total = total_patients_by_region[\"northwest\"]\n","northwest = region_charges[region_charges[\"region\"] == 'northwest']\n","nwsum = northwest['charges'].sum()\n","print(\"northwest total = \" , nwsum/northwest_total)\n","\n","\n","northeast = region_charges[region_charges[\"region\"] == 'northeast']\n","northeast_total = total_patients_by_region[\"northeast\"]\n","northeast = region_charges[region_charges[\"region\"] == 'northeast']\n","nesum = northeast['charges'].sum()\n","print(\"northeast total = \" , nesum/southwest_total)\n","\n","d = {'southeast':sesum/southeast_total, 'southwest':swsum/southwest_total, 'northwest':nwsum/northwest_total, 'northeast':nesum/northeast_total}\n","charges_by_region = pd.Series(data=d,index=['southeast','southwest','northeast','northwest']).sort_values()\n","\n","print(charges_by_region)\n","\n","charges_by_region.plot.bar(rot=0)\n","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"DfKEMo4VHAVn"},"source":["Overall the highest medical charges are in the Southeast and the lowest are in the Southwest. Taking into account certain factors (sex, smoking, having children) let's see how it changes by region.\n","\n","Now, create three grouped barcharts for average charges by region grouped by sex, smoking, and number of children. \n","\n","### How to create grouped barcharts?\n","\n","Creating grouped bar charts with Seaborn is a bit more intuitive compared to Matplotlib. \n","You can use the catplot function with kind='bar' to create grouped bar charts. Here is an example on the tips datasets that comes with Seaborn. \n","The tips dataset contains information about the total bill and tip amount for different meals, along with additional information such as the sex of the individual paying for the meal, whether they are a smoker, the day and time of the meal, and the size of the party.\n","\n","We will create a grouped bar chart showing the average total bill for each day, grouped by whether the meal took place at lunch or dinner. \n","\n","\n","\n","```\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","# Load the 'tips' dataset\n","tips = sns.load_dataset(\"tips\")\n","\n","# Create a grouped bar chart\n","sns.catplot(data=tips, x=\"day\", y=\"total_bill\", hue=\"time\", kind=\"bar\")\n","\n","plt.show()\n","```\n","In this plot, the height of the bars represents the average total bill for meals on each day, with separate bars for lunch and dinner. \n","\n","The `catplot` function is a flexible function that can create a variety of different plot types. By setting `kind='bar'`, we specify that we want a bar chart. The x and y arguments specify the data for the x and y axes, and the `hue` argument specifies a third variable that is used to group the data.\n","\n","In the context of Seaborn and many other statistical visualization libraries, error bars commonly represent one standard deviation or standard error of the mean."]},{"cell_type":"code","execution_count":583,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2289,"status":"ok","timestamp":1682900861653,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"4flq9AoqHF1p","outputId":"a949728c-ade6-45e3-f5ba-658fc70306af"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkIAAAHpCAYAAABulFOwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABI8UlEQVR4nO3deVgW5eL/8c/jwqYsrixFqFmKCohahppSmmhmeo5HS03NQ3YqM5cyj307RFpietxSy+zkUuk3s1NWWiqSu+SO5kaKGJ5foqUJogYq9+8Pv87pCZdE4AHn/bquua5n5r7nfu6ZQfg4c8+MwxhjBAAAYEPlXN0BAAAAVyEIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIFRFjjLKzs8VjmQAAKDsIQkXk1KlT8vX11alTp1zdFQAA8AcRhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG0RhAAAgG1VcHUHAOBmZIzR6dOnrflKlSrJ4XC4sEcALocgBADF4PTp0+rSpYs1//nnn6ty5cou7BGAy+HSGAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2XBqE1a9aoc+fOCgoKksPh0KJFi5zKHQ7HZafx48dbdWrVqlWgfOzYsU7t7Ny5U/fee688PDwUHByscePGFejLwoULVb9+fXl4eCgsLExfffVVsWwzAAAoPVwahE6fPq2IiAhNnz79suVHjhxxmmbNmiWHw6Fu3bo51Rs1apRTvUGDBlll2dnZat++vUJCQrR161aNHz9e8fHxmjlzplVnw4YN6tmzp2JjY7V9+3Z17dpVXbt21a5du4pnwwEAQKlQwZVf3rFjR3Xs2PGK5QEBAU7zn3/+ue677z7VqVPHabm3t3eBupfMmzdPeXl5mjVrltzc3NSwYUOlpKRo4sSJevLJJyVJU6ZMUYcOHTR8+HBJ0ujRo5WYmKhp06ZpxowZN7KJAACgFCszY4SOHj2qJUuWKDY2tkDZ2LFjVa1aNUVGRmr8+PE6f/68VZacnKzWrVvLzc3NWhYTE6PU1FT98ssvVp127do5tRkTE6Pk5OQr9ic3N1fZ2dlOEwAAKFtcekboesydO1fe3t7685//7LT8ueeeU5MmTVS1alVt2LBBI0eO1JEjRzRx4kRJUmZmpmrXru20jr+/v1VWpUoVZWZmWst+WyczM/OK/UlISNCrr75aFJsGAABcpMwEoVmzZql3797y8PBwWj5s2DDrc3h4uNzc3PS3v/1NCQkJcnd3L7b+jBw50um7s7OzFRwcXGzfBwAAil6ZCEJr165VamqqFixYcM26zZs31/nz53Xo0CHVq1dPAQEBOnr0qFOdS/OXxhVdqc6Vxh1Jkru7e7EGLQAAUPzKxBih9957T02bNlVERMQ166akpKhcuXKqWbOmJCkqKkpr1qzRuXPnrDqJiYmqV6+eqlSpYtVJSkpyaicxMVFRUVFFuBUAAKC0cWkQysnJUUpKilJSUiRJ6enpSklJUUZGhlUnOztbCxcu1BNPPFFg/eTkZE2ePFk7duzQwYMHNW/ePA0dOlSPPfaYFXJ69eolNzc3xcbGavfu3VqwYIGmTJnidFlr8ODBWrp0qSZMmKB9+/YpPj5eW7Zs0bPPPlu8OwAAALiWcaGVK1caSQWmfv36WXXeeecd4+npaU6ePFlg/a1bt5rmzZsbX19f4+HhYUJDQ82YMWPMr7/+6lRvx44dplWrVsbd3d3ccsstZuzYsQXa+vjjj82dd95p3NzcTMOGDc2SJUuua1uysrKMJJOVlXVd6wG4OZ06dcrcf//91nTq1ClXdwnAZTiMMcaVQexmkZ2dLV9fX2VlZcnHx8fV3QHgYjk5OerSpYs1//nnn6ty5cou7BGAyykTY4QAAACKA0EIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYVgVXdwAASpuMUWE33MaZ8w5JNaz5/4xrIa8K5obbvS3uuxtuA8B/cUYIAADYFkEIAADYFkEIAADYFkEIAADYFkEIAADYFneNAQBuWsYYnT592pqvVKmSHA6HC3uE0oYgBNyE+OUPXHT69Gl16dLFmv/8889VuXJlF/YIpQ1BCLgJ8csfAP4YxggBAADbIggBAADb4tIYgDKN8VAAbgRBCECZxngoADeCS2MAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2XBqE1qxZo86dOysoKEgOh0OLFi1yKn/88cflcDicpg4dOjjVOXHihHr37i0fHx/5+fkpNjZWOTk5TnV27type++9Vx4eHgoODta4ceMK9GXhwoWqX7++PDw8FBYWpq+++qrItxcAAJQuLg1Cp0+fVkREhKZPn37FOh06dNCRI0es6X//93+dynv37q3du3crMTFRixcv1po1a/Tkk09a5dnZ2Wrfvr1CQkK0detWjR8/XvHx8Zo5c6ZVZ8OGDerZs6diY2O1fft2de3aVV27dtWuXbuKfqMBAECp4dInS3fs2FEdO3a8ah13d3cFBARctmzv3r1aunSpNm/erGbNmkmSpk6dqgcffFD//Oc/FRQUpHnz5ikvL0+zZs2Sm5ubGjZsqJSUFE2cONEKTFOmTFGHDh00fPhwSdLo0aOVmJioadOmacaMGZf97tzcXOXm5lrz2dnZ1739AADAtUr9GKFVq1apZs2aqlevnp5++mkdP37cKktOTpafn58VgiSpXbt2KleunDZu3GjVad26tdzc3Kw6MTExSk1N1S+//GLVadeundP3xsTEKDk5+Yr9SkhIkK+vrzUFBwcXyfYCAICSU6qDUIcOHfT+++8rKSlJb7zxhlavXq2OHTvqwoULkqTMzEzVrFnTaZ0KFSqoatWqyszMtOr4+/s71bk0f606l8ovZ+TIkcrKyrKmw4cP39jGAgCAEleqX7r66KOPWp/DwsIUHh6u22+/XatWrVLbtm1d2LOLl+zc3d1d2ofSyi5vA7fLdgLAzaxUB6Hfq1OnjqpXr64DBw6obdu2CggI0LFjx5zqnD9/XidOnLDGFQUEBOjo0aNOdS7NX6vOlcYm4ers8jZwu2wnANzMSvWlsd/7z3/+o+PHjyswMFCSFBUVpZMnT2rr1q1WnW+++Ub5+flq3ry5VWfNmjU6d+6cVScxMVH16tVTlSpVrDpJSUlO35WYmKioqKji3iQAAOBCLg1COTk5SklJUUpKiiQpPT1dKSkpysjIUE5OjoYPH65vv/1Whw4dUlJSkrp06aK6desqJiZGkhQaGqoOHTpowIAB2rRpk9avX69nn31Wjz76qIKCgiRJvXr1kpubm2JjY7V7924tWLBAU6ZM0bBhw6x+DB48WEuXLtWECRO0b98+xcfHa8uWLXr22WdLfJ8AAICS49IgtGXLFkVGRioyMlKSNGzYMEVGRiouLk7ly5fXzp079fDDD+vOO+9UbGysmjZtqrVr1zqNzZk3b57q16+vtm3b6sEHH1SrVq2cnhHk6+ur5cuXKz09XU2bNtXzzz+vuLg4p2cNtWjRQvPnz9fMmTMVERGhTz75RIsWLVKjRo1KbmcAAIAS59IxQtHR0TLGXLF82bJl12yjatWqmj9//lXrhIeHa+3atVet0717d3Xv3v2a3wcAAG4eZWqMEAAAQFEiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsqU+8aw3/xwk8AAG4cQaiM4oWfAADcOC6NAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2yIIAQAA2+JdYwBQDDzLG01v+ZPT/M2Kl0CjLCMIAUAxcDgkrwo3b/j5LV4CjbKMIASUMhmjwm64jTPnHZJqWPP/GdeiSP4o3xb33Q23AQClCWOEAACAbRGEAACAbRGEAACAbRGEAACAbRGEAACAbXHXGACg1CmKuyel4rmDkrsnby4EIQAu0XT4+0XSjuN8nnx/Mx/9j49kKrjdUJufed9YnwCUHVwaAwAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtsVzhGBLRfEMm+J4fo3EM2wAoCRxRggAANgWQQgAANgWl8bgpCje71Mc7/aReL8PAKDocUYIAADYFkEIAADYFkEIAADYlkuD0Jo1a9S5c2cFBQXJ4XBo0aJFVtm5c+c0YsQIhYWFqVKlSgoKClLfvn31448/OrVRq1YtORwOp2ns2LFOdXbu3Kl7771XHh4eCg4O1rhx4wr0ZeHChapfv748PDwUFhamr776qli2GQAAlB4uDUKnT59WRESEpk+fXqDszJkz2rZtm/7xj39o27Zt+vTTT5WamqqHH364QN1Ro0bpyJEj1jRo0CCrLDs7W+3bt1dISIi2bt2q8ePHKz4+XjNnzrTqbNiwQT179lRsbKy2b9+url27qmvXrtq1a1fxbDgAACgVXHrXWMeOHdWxY8fLlvn6+ioxMdFp2bRp03T33XcrIyNDt912m7Xc29tbAQEBl21n3rx5ysvL06xZs+Tm5qaGDRsqJSVFEydO1JNPPilJmjJlijp06KDhw4dLkkaPHq3ExERNmzZNM2bMuGy7ubm5ys3Nteazs7P/+IYDAIBSoUyNEcrKypLD4ZCfn5/T8rFjx6patWqKjIzU+PHjdf78eassOTlZrVu3lpvbf5/4GxMTo9TUVP3yyy9WnXbt2jm1GRMTo+Tk5Cv2JSEhQb6+vtYUHBxcBFsIAABKUpkJQr/++qtGjBihnj17ysfHx1r+3HPP6aOPPtLKlSv1t7/9TWPGjNGLL75olWdmZsrf39+prUvzmZmZV61zqfxyRo4cqaysLGs6fPjwDW8jAAAoWWXigYrnzp1Tjx49ZIzR22+/7VQ2bNgw63N4eLjc3Nz0t7/9TQkJCXJ3dy+2Prm7uxdr+wAAoPiV+iB0KQT98MMP+uabb5zOBl1O8+bNdf78eR06dEj16tVTQECAjh496lTn0vylcUVXqnOlcUc3ihd+AgBQOpTqS2OXQtD+/fu1YsUKVatW7ZrrpKSkqFy5cqpZs6YkKSoqSmvWrNG5c+esOomJiapXr56qVKli1UlKSnJqJzExUVFRUUW4NQAAoLRx6RmhnJwcHThwwJpPT09XSkqKqlatqsDAQP3lL3/Rtm3btHjxYl24cMEas1O1alW5ubkpOTlZGzdu1H333Sdvb28lJydr6NCheuyxx6yQ06tXL7366quKjY3ViBEjtGvXLk2ZMkWTJk2yvnfw4MFq06aNJkyYoE6dOumjjz7Sli1bnG6xBwAANx+XBqEtW7bovvvus+Yvjffp16+f4uPj9cUXX0iSGjdu7LTeypUrFR0dLXd3d3300UeKj49Xbm6uateuraFDhzqNG/L19dXy5cs1cOBANW3aVNWrV1dcXJx167wktWjRQvPnz9fLL7+sl156SXfccYcWLVqkRo0aFePWAwAAV3NpEIqOjpYxV34r+dXKJKlJkyb69ttvr/k94eHhWrt27VXrdO/eXd27d79mWwAA4OZRqscIAQAAFCeCEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsK1S/64xAEDxKIr3HkrF8+5D3nuIksIZIQAAYFucEQJuQp7ljaa3/MlpHgBQEEEIuAk5HJJXBcIPAFwLl8YAAIBtEYQAAIBtEYQAAIBtEYQAAIBtEYQAAIBtEYQAAIBtEYQAAIBtEYQAAIBt8UBFAGWaKV9RWeE9neYB4I8iCAEo2xyOG37BJwD74tIYAACwLc4IAQBuWryAGNdCEAIA3LR4ATGuhUtjAADAtjgjBBQSdysBQNlHEAIKi7uVAKDM49IYAACwLc4IochxlwYAoKwgCKHIcZcGAKCs4NIYAACwrSIJQtnZ2Vq0aJH27t1bFM0BAACUiEIFoR49emjatGmSpLNnz6pZs2bq0aOHwsPD9e9//7tIOwgAAFBcChWE1qxZo3vvvVeS9Nlnn8kYo5MnT+rNN9/Ua6+9VqQdBAAAKC6FGiydlZWlqlWrSpKWLl2qbt26ycvLS506ddLw4cOLtIO4PB7mBwDAjSvUGaHg4GAlJyfr9OnTWrp0qdq3by9J+uWXX+Th4VGkHcQV/N/D/C5Ncjhc3SMAAMqcQp0RGjJkiHr37q3KlSvrtttuU3R0tKSLl8zCwsKKsn8AAADFplBB6JlnntHdd9+tw4cP64EHHlC5chdPLNWpU4cxQgAAoMwo9AMVmzVrpvDwcKWnp+v2229XhQoV1KlTp6LsGwAAQLEq1BihM2fOKDY2Vl5eXmrYsKEyMjIkSYMGDdLYsWOLtIMAAADFpVBBaOTIkdqxY4dWrVrlNDi6Xbt2WrBgQZF1DgAAoDgV6tLYokWLtGDBAt1zzz1y/OZupYYNGyotLa3IOgcAAFCcCnVG6KefflLNmjULLD99+rRTMAIAACjNChWEmjVrpiVLlljzl8LPv/71L0VFRRVNzwAAAIpZoYLQmDFj9NJLL+npp5/W+fPnNWXKFLVv316zZ8/W66+//ofbWbNmjTp37qygoCA5HA4tWrTIqdwYo7i4OAUGBsrT01Pt2rXT/v37neqcOHFCvXv3lo+Pj/z8/BQbG6ucnBynOjt37tS9994rDw8PBQcHa9y4cQX6snDhQtWvX18eHh4KCwvTV1999cd3CAAAKJMKFYRatWqllJQUnT9/XmFhYVq+fLlq1qyp5ORkNW3a9A+3c/r0aUVERGj69OmXLR83bpzefPNNzZgxQxs3blSlSpUUExOjX3/91arTu3dv7d69W4mJiVq8eLHWrFmjJ5980irPzs5W+/btFRISoq1bt2r8+PGKj4/XzJkzrTobNmxQz549FRsbq+3bt6tr167q2rWrdu3aVYi9AwAAyopCP0fo9ttv17vvvntDX96xY0d17NjxsmXGGE2ePFkvv/yyunTpIkl6//335e/vr0WLFunRRx/V3r17tXTpUm3evFnNmjWTJE2dOlUPPvig/vnPfyooKEjz5s1TXl6eZs2aJTc3NzVs2FApKSmaOHGiFZimTJmiDh06WO9JGz16tBITEzVt2jTNmDHjhrYRAACUXoU6I5SdnX3Z6dSpU8rLyyuSjqWnpyszM1Pt2rWzlvn6+qp58+ZKTk6WJCUnJ8vPz88KQdLFW/jLlSunjRs3WnVat24tNzc3q05MTIxSU1P1yy+/WHV++z2X6lz6nsvJzc0tsP0AYEeXXgJ9aeIl0ChLChWE/Pz8VKVKlQKTn5+fPD09FRISoldeeUX5+fmF7lhmZqYkyd/f32m5v7+/VZaZmVng7rUKFSqoatWqTnUu18Zvv+NKdS6VX05CQoJ8fX2tKTg4+Ho3EQBuDrwEGmVYoYLQnDlzFBQUpJdeekmLFi3SokWL9NJLL+mWW27R22+/rSeffFJvvvnmTf2U6ZEjRyorK8uaDh8+7OouAQCA61SoMUJz587VhAkT1KNHD2tZ586dFRYWpnfeeUdJSUm67bbb9Prrr+ull14qVMcCAgIkSUePHlVgYKC1/OjRo2rcuLFV59ixY07rnT9/XidOnLDWDwgI0NGjR53qXJq/Vp1L5Zfj7u4ud3f3QmwZAAAoLQp1RmjDhg2KjIwssDwyMtIaV9OqVSvrHWSFUbt2bQUEBCgpKclalp2drY0bN1rPKoqKitLJkye1detWq84333yj/Px8NW/e3KqzZs0anTt3zqqTmJioevXqqUqVKlad337PpTo8EwkAgJtboYJQcHCw3nvvvQLL33vvPWuszPHjx62gcSU5OTlKSUlRSkqKpIsDpFNSUpSRkSGHw6EhQ4botdde0xdffKHvvvtOffv2VVBQkLp27SpJCg0NVYcOHTRgwABt2rRJ69ev17PPPqtHH31UQUFBkqRevXrJzc1NsbGx2r17txYsWKApU6Zo2LBhVj8GDx6spUuXasKECdq3b5/i4+O1ZcsWPfvss4XZPQAAoIwoVBD65z//qUmTJikiIkJPPPGEnnjiCTVu3FiTJ0/WhAkTJEmbN2/WI488ctV2tmzZosjISOvs0rBhwxQZGam4uDhJ0osvvqhBgwbpySef1F133aWcnBwtXbrU6UWv8+bNU/369dW2bVs9+OCDatWqldMzgnx9fbV8+XKlp6eradOmev755xUXF+f0rKEWLVpo/vz5mjlzpiIiIvTJJ59o0aJFatSoUWF2DwAAZcInn3yisLAweXp6qlq1amrXrp1Onz4t6eLbIkJDQ+Xh4aH69evrrbfestb761//qvDwcOXm5kqS8vLyFBkZqb59+7pkO25EocYIPfzww0pNTdU777yj1NRUSRefCbRo0SLVqlVLkvT0009fs53o6GgZY65Y7nA4NGrUKI0aNeqKdapWrar58+df9XvCw8O1du3aq9bp3r27unfvfvUOAwBwkzhy5Ih69uypcePG6U9/+pNOnTqltWvXyhijefPmKS4uTtOmTVNkZKS2b9+uAQMGqFKlSurXr5/efPNNRURE6O9//7smTZqk//mf/9HJkyc1bdo0V2/WdbvuIHTu3Dl16NBBM2bMUEJCQnH0CQAAFLMjR47o/Pnz+vOf/6yQkBBJUlhYmCTplVde0YQJE/TnP/9Z0sVxu3v27NE777yjfv36qXLlyvrwww/Vpk0beXt7a/LkyVq5cqV8fHxctj2Fdd1BqGLFitq5c2dx9AUAAJSQiIgItW3bVmFhYYqJiVH79u31l7/8RW5ubkpLS1NsbKwGDBhg1T9//rx8fX2t+aioKL3wwgsaPXq0RowYoVatWrliM25YocYIPfbYY5cdLA0AAMqG8uXLKzExUV9//bUaNGigqVOnql69etZ7Nt99913rhqaUlBTt2rVL3377rbV+fn6+1q9fr/Lly+vAgQOu2owbVqgxQufPn9esWbO0YsUKNW3aVJUqVXIqnzhxYpF0DgAAFB+Hw6GWLVuqZcuWiouLU0hIiNavX6+goCAdPHhQvXv3vuK648eP1759+7R69WrFxMRo9uzZ6t+/fwn2vmgUKgjt2rVLTZo0kSR9//33TmUOHq0OAECpt3HjRiUlJal9+/aqWbOmNm7cqJ9++kmhoaF69dVX9dxzz8nX11cdOnRQbm6utmzZol9++UXDhg3T9u3bFRcXp08++UQtW7bUxIkTNXjwYLVp00Z16tRx9aZdl0IFoZUrVxZ1PwAAQAny8fHRmjVrNHnyZGVnZyskJEQTJkxQx44dJUleXl4aP368hg8frkqVKiksLExDhgzRr7/+qscee0yPP/64OnfuLEl68skntWTJEvXp00dr1qxR+fLlXblp16VQQQgAAJRtoaGhWrp06RXLe/XqpV69el22bPfu3QWWff7550XWt5JU6CC0ZcsWffzxx8rIyFBeXp5T2aeffnrDHQMAAChuhbpr7KOPPlKLFi20d+9effbZZzp37px2796tb775xunWOgAAgNKsUEFozJgxmjRpkr788ku5ublpypQp2rdvn3r06KHbbrutqPsIAABQLAoVhNLS0tSpUydJkpubm06fPi2Hw6GhQ4c6vecLAACgNCtUEKpSpYpOnTolSbrlllushy+dPHlSZ86cKbreAQAAFKNCDZZu3bq1EhMTFRYWpu7du2vw4MH65ptvlJiYqLZt2xZ1HwEAAIpFoYLQtGnT9Ouvv0qS/ud//kcVK1bUhg0b1K1bN7388stF2kEAAIDiUqggVLVqVetzuXLl9Pe//73IOgQAAFBSCv0cofz8fB04cEDHjh1Tfn6+U1nr1q1vuGMAAADFrVBB6Ntvv1WvXr30ww8/yBjjVOZwOHThwoUi6RwAADejpsPfL9Hv2zq+73WvY4zR3/72N33yySf65ZdftH37djVu3LjoO3cNhw4dUu3atYvt+wsVhJ566ik1a9ZMS5YsUWBgIC9aBQDgJrN06VLNmTNHq1atUp06dVS9enVXd6lYFCoI7d+/X5988onq1q1b1P0BAAClQFpamgIDA9WiRQtXd6VYFeo5Qs2bN9eBAweKui8AAKAUePzxxzVo0CBlZGTI4XCoVq1ays/PV0JCgmrXri1PT09FRETok08+sdZZtWqVHA6Hli1bpsjISHl6eur+++/XsWPH9PXXXys0NFQ+Pj7q1auX0zMHly5dqlatWsnPz0/VqlXTQw89pLS0tKv2b9euXerYsaMqV64sf39/9enTRz///HOhtvUPnxHauXOn9XnQoEF6/vnnlZmZqbCwMFWsWNGpbnh4eKE6AwAAXG/KlCm6/fbbNXPmTG3evFnly5dXQkKCPvzwQ82YMUN33HGH1qxZo8cee0w1atRQmzZtrHXj4+M1bdo0eXl5qUePHurRo4fc3d01f/585eTk6E9/+pOmTp2qESNGSJJOnz6tYcOGKTw8XDk5OYqLi9Of/vQnpaSkqFy5gudrTp48qfvvv19PPPGEJk2apLNnz2rEiBHq0aOHvvnmm+ve1j8chBo3biyHw+E0OPqvf/2r9flSGYOlAQAo23x9feXt7a3y5csrICBAubm5GjNmjFasWKGoqChJUp06dbRu3Tq98847TkHotddeU8uWLSVJsbGxGjlypNLS0lSnTh1J0l/+8hetXLnSCkLdunVz+u5Zs2apRo0a2rNnjxo1alSgb9OmTVNkZKTGjBnjtE5wcLC+//573Xnnnde1rX84CKWnp19XwwAA4OZw4MABnTlzRg888IDT8ry8PEVGRjot++1VIX9/f3l5eVkh6NKyTZs2WfP79+9XXFycNm7cqJ9//tl6JE9GRsZlg9COHTu0cuVKVa5cuUBZWlpa8QWhkJAQ63NCQoL8/f2dzghJFxPZTz/9ZKU8AABQ9uXk5EiSlixZoltuucWpzN3d3Wn+t8NlHA5HgeEzDofD6fmDnTt3VkhIiN59910FBQUpPz9fjRo1Ul5e3hX70rlzZ73xxhsFygIDA69vw1TIu8beeecdzZ8/v8Dyhg0b6tFHHyUIAQBwE2nQoIHc3d2VkZHhdBnsRh0/flypqal69913de+990qS1q1bd9V1mjRpon//+9+qVauWKlQo9HOhLYW6aywzM/OyqatGjRo6cuTIDXcKAACUHt7e3nrhhRc0dOhQzZ07V2lpadq2bZumTp2quXPnFrrdKlWqqFq1apo5c6YOHDigb775RsOGDbvqOgMHDtSJEyfUs2dPbd68WWlpaVq2bJn69+9fqDHKhYpSwcHBWr9+vWrXru20fP369QoKCipMkwAA2EZhnvTsaqNHj1aNGjWUkJCggwcPys/PT02aNNFLL71U6DbLlSunjz76SM8995waNWqkevXq6c0331R0dPQV1wkKCtL69es1YsQItW/fXrm5uQoJCVGHDh0ue5fZtTjM79+R8QeMGzdO48aN0/jx43X//fdLkpKSkvTiiy/q+eef18iRI6+7I2Vddna2fH19lZWVJR8fn6vWLelHq1+Pz7zHu7oLV3Rb3HdF1hbHoHA4Bq7HMXC9ojwGcL1CnREaPny4jh8/rmeeecYazOTh4aERI0bYMgQBAICyqVBByOFw6I033tA//vEP7d27V56enrrjjjsKjBwHAAAozW5ouHXlypV11113FVVfAAAASlSh7hoDAAC4GRCEAACAbRGEAACAbRGEAACAbRGEAACAbRGEAACAbd3428oAAMB1yRgVVqLfV1qehv3444/r5MmTWrRokau7YuGMEAAAsC2CEAAAsC2CEAAAKCA6OlqDBg3SkCFDVKVKFfn7++vdd9/V6dOn1b9/f3l7e6tu3br6+uuvJUkXLlxQbGysateuLU9PT9WrV09Tpky56nfk5+crISHBWiciIkKffPJJSWyehSAEAAAua+7cuapevbo2bdqkQYMG6emnn1b37t3VokULbdu2Te3bt1efPn105swZ5efn69Zbb9XChQu1Z88excXF6aWXXtLHH398xfYTEhL0/vvva8aMGdq9e7eGDh2qxx57TKtXry6xbWSwNAAAuKyIiAi9/PLLkqSRI0dq7Nixql69ugYMGCBJiouL09tvv62dO3fqnnvu0auvvmqtW7t2bSUnJ+vjjz9Wjx49CrSdm5urMWPGaMWKFYqKipIk1alTR+vWrdM777yjNm3alMAWEoQAAMAVhIeHW5/Lly+vatWqKSzsv3e8+fv7S5KOHTsmSZo+fbpmzZqljIwMnT17Vnl5eWrcuPFl2z5w4IDOnDmjBx54wGl5Xl6eIiMji3hLrqzUXxqrVauWHA5HgWngwIGSLl7D/H3ZU0895dRGRkaGOnXqJC8vL9WsWVPDhw/X+fPnneqsWrVKTZo0kbu7u+rWras5c+aU1CYCAFAqVaxY0Wne4XA4LXM4HJIujvX56KOP9MILLyg2NlbLly9XSkqK+vfvr7y8vMu2nZOTI0lasmSJUlJSrGnPnj0lOk6o1J8R2rx5sy5cuGDN79q1Sw888IC6d+9uLRswYIBGjRplzXt5eVmfL1y4oE6dOikgIEAbNmzQkSNH1LdvX1WsWFFjxoyRJKWnp6tTp0566qmnNG/ePCUlJemJJ55QYGCgYmJiSmArAQAo29avX68WLVromWeesZalpaVdsX6DBg3k7u6ujIyMErsMdjmlPgjVqFHDaX7s2LG6/fbbnXaal5eXAgICLrv+8uXLtWfPHq1YsUL+/v5q3LixRo8erREjRig+Pl5ubm6aMWOGateurQkTJkiSQkNDtW7dOk2aNIkgBADAH3DHHXfo/fff17Jly1S7dm198MEH2rx5s2rXrn3Z+t7e3nrhhRc0dOhQ5efnq1WrVsrKytL69evl4+Ojfv36lUi/S30Q+q28vDx9+OGHGjZsmHU6TpLmzZunDz/8UAEBAercubP+8Y9/WGeFkpOTFRYWZl3HlKSYmBg9/fTT2r17tyIjI5WcnKx27do5fVdMTIyGDBlyxb7k5uYqNzfXms/Ozi6irQQA3OxKy5Oei9Lf/vY3bd++XY888ogcDod69uypZ555xrq9/nJGjx6tGjVqKCEhQQcPHpSfn5+aNGmil156qcT6XaaC0KJFi3Ty5Ek9/vjj1rJevXopJCREQUFB2rlzp0aMGKHU1FR9+umnkqTMzEynECT9d3BXZmbmVetkZ2fr7Nmz8vT0LNCXhIQEp9HxAADcTFatWlVg2aFDhwosM8ZYn2fPnq3Zs2c7lSckJFiffz/+1uFwaPDgwRo8ePAN9fVGlKkg9N5776ljx44KCgqylj355JPW57CwMAUGBqpt27ZKS0vT7bffXmx9GTlypIYNG2bNZ2dnKzg4uNi+DwAAFL0yE4R++OEHrVixwjrTcyXNmzeXdPG2vNtvv10BAQHatGmTU52jR49KkjWuKCAgwFr22zo+Pj6XPRskSe7u7nJ3dy/UtgAAgNKh1N8+f8ns2bNVs2ZNderU6ar1UlJSJEmBgYGSpKioKH333XfWMw4kKTExUT4+PmrQoIFVJykpyamdxMRE6wFPAADg5lQmglB+fr5mz56tfv36qUKF/57ESktL0+jRo7V161YdOnRIX3zxhfr27avWrVtbD4Fq3769GjRooD59+mjHjh1atmyZXn75ZQ0cONA6o/PUU0/p4MGDevHFF7Vv3z699dZb+vjjjzV06FCXbC8AACgZZSIIrVixQhkZGfrrX//qtNzNzU0rVqxQ+/btVb9+fT3//PPq1q2bvvzyS6tO+fLltXjxYpUvX15RUVF67LHH1LdvX6fnDtWuXVtLlixRYmKiIiIiNGHCBP3rX//i1nkAAG5yZWKMUPv27Z1GpV8SHBz8h17MFhISoq+++uqqdaKjo7V9+/ZC9xEAAJQ9ZeKMEAAAQHEgCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsiCAEAANsq1UEoPj5eDofDaapfv75V/uuvv2rgwIGqVq2aKleurG7duuno0aNObWRkZKhTp07y8vJSzZo1NXz4cJ0/f96pzqpVq9SkSRO5u7urbt26mjNnTklsHgAAcLFSHYQkqWHDhjpy5Ig1rVu3ziobOnSovvzySy1cuFCrV6/Wjz/+qD//+c9W+YULF9SpUyfl5eVpw4YNmjt3rubMmaO4uDirTnp6ujp16qT77rtPKSkpGjJkiJ544gktW7asRLcTAACUvAqu7sC1VKhQQQEBAQWWZ2Vl6b333tP8+fN1//33S5Jmz56t0NBQffvtt7rnnnu0fPly7dmzRytWrJC/v78aN26s0aNHa8SIEYqPj5ebm5tmzJih2rVra8KECZKk0NBQrVu3TpMmTVJMTMwV+5Wbm6vc3FxrPjs7u4i3HAAAFLdSf0Zo//79CgoKUp06ddS7d29lZGRIkrZu3apz586pXbt2Vt369evrtttuU3JysiQpOTlZYWFh8vf3t+rExMQoOztbu3fvtur8to1LdS61cSUJCQny9fW1puDg4CLZXgAAUHJKdRBq3ry55syZo6VLl+rtt99Wenq67r33Xp06dUqZmZlyc3OTn5+f0zr+/v7KzMyUJGVmZjqFoEvll8quVic7O1tnz569Yt9GjhyprKwsazp8+PCNbi4AAChhpfrSWMeOHa3P4eHhat68uUJCQvTxxx/L09PThT2T3N3d5e7u7tI+AACAG1Oqzwj9np+fn+68804dOHBAAQEBysvL08mTJ53qHD161BpTFBAQUOAuskvz16rj4+Pj8rAFAACKV5kKQjk5OUpLS1NgYKCaNm2qihUrKikpySpPTU1VRkaGoqKiJElRUVH67rvvdOzYMatOYmKifHx81KBBA6vOb9u4VOdSGwAA4OZVqoPQCy+8oNWrV+vQoUPasGGD/vSnP6l8+fLq2bOnfH19FRsbq2HDhmnlypXaunWr+vfvr6ioKN1zzz2SpPbt26tBgwbq06ePduzYoWXLlunll1/WwIEDrctaTz31lA4ePKgXX3xR+/bt01tvvaWPP/5YQ4cOdeWmAwCAElCqxwj95z//Uc+ePXX8+HHVqFFDrVq10rfffqsaNWpIkiZNmqRy5cqpW7duys3NVUxMjN566y1r/fLly2vx4sV6+umnFRUVpUqVKqlfv34aNWqUVad27dpasmSJhg4dqilTpujWW2/Vv/71r6veOg8AAG4OpToIffTRR1ct9/Dw0PTp0zV9+vQr1gkJCdFXX3111Xaio6O1ffv2QvURAACUXaX60hgAAEBxIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbKtVBKCEhQXfddZe8vb1Vs2ZNde3aVampqU51oqOj5XA4nKannnrKqU5GRoY6deokLy8v1axZU8OHD9f58+ed6qxatUpNmjSRu7u76tatqzlz5hT35gEAABcr1UFo9erVGjhwoL799lslJibq3Llzat++vU6fPu1Ub8CAATpy5Ig1jRs3ziq7cOGCOnXqpLy8PG3YsEFz587VnDlzFBcXZ9VJT09Xp06ddN999yklJUVDhgzRE088oWXLlpXYtgIAgJJXwdUduJqlS5c6zc+ZM0c1a9bU1q1b1bp1a2u5l5eXAgICLtvG8uXLtWfPHq1YsUL+/v5q3LixRo8erREjRig+Pl5ubm6aMWOGateurQkTJkiSQkNDtW7dOk2aNEkxMTGXbTc3N1e5ubnWfHZ29o1uLgAAKGGl+ozQ72VlZUmSqlat6rR83rx5ql69uho1aqSRI0fqzJkzVllycrLCwsLk7+9vLYuJiVF2drZ2795t1WnXrp1TmzExMUpOTr5iXxISEuTr62tNwcHBN7x9AACgZJXqM0K/lZ+fryFDhqhly5Zq1KiRtbxXr14KCQlRUFCQdu7cqREjRig1NVWffvqpJCkzM9MpBEmy5jMzM69aJzs7W2fPnpWnp2eB/owcOVLDhg2z5rOzswlDAACUMWUmCA0cOFC7du3SunXrnJY/+eST1uewsDAFBgaqbdu2SktL0+23315s/XF3d5e7u3uxtQ8AAIpfmbg09uyzz2rx4sVauXKlbr311qvWbd68uSTpwIEDkqSAgAAdPXrUqc6l+Uvjiq5Ux8fH57JngwAAwM2hVAchY4yeffZZffbZZ/rmm29Uu3bta66TkpIiSQoMDJQkRUVF6bvvvtOxY8esOomJifLx8VGDBg2sOklJSU7tJCYmKioqqoi2BAAAlEalOggNHDhQH374oebPny9vb29lZmYqMzNTZ8+elSSlpaVp9OjR2rp1qw4dOqQvvvhCffv2VevWrRUeHi5Jat++vRo0aKA+ffpox44dWrZsmV5++WUNHDjQurT11FNP6eDBg3rxxRe1b98+vfXWW/r44481dOhQl207AAAofqU6CL399tvKyspSdHS0AgMDrWnBggWSJDc3N61YsULt27dX/fr19fzzz6tbt2768ssvrTbKly+vxYsXq3z58oqKitJjjz2mvn37atSoUVad2rVra8mSJUpMTFRERIQmTJigf/3rX1e8dR4AANwcSvVgaWPMVcuDg4O1evXqa7YTEhKir7766qp1oqOjtX379uvqHwAAKNtK9RkhAACA4kQQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQ+p3p06erVq1a8vDwUPPmzbVp0yZXdwkAABQTgtBvLFiwQMOGDdMrr7yibdu2KSIiQjExMTp27JiruwYAAIoBQeg3Jk6cqAEDBqh///5q0KCBZsyYIS8vL82aNcvVXQMAAMWggqs7UFrk5eVp69atGjlypLWsXLlyateunZKTkwvUz83NVW5urjWflZUlScrOzr7md13IPVsEPS4epypecHUXruiP7Ns/imNQOBwD1+MYuN71HANvb285HI5i7A1uFEHo//z888+6cOGC/P39nZb7+/tr3759BeonJCTo1VdfLbA8ODi42PpYEhq5ugNXk+Dr6h6UCI6B63EMXO9mOQZZWVny8fEpxs7gRhGECmnkyJEaNmyYNZ+fn68TJ06oWrVqZTb9Z2dnKzg4WIcPH+YfrotwDFyPY+B6N9Mx8Pb2dnUXcA0Eof9TvXp1lS9fXkePHnVafvToUQUEBBSo7+7uLnd3d6dlfn5+xdnFEuPj41Pmf/mUdRwD1+MYuB7HACWBwdL/x83NTU2bNlVSUpK1LD8/X0lJSYqKinJhzwAAQHHhjNBvDBs2TP369VOzZs109913a/LkyTp9+rT69+/v6q4BAIBiQBD6jUceeUQ//fST4uLilJmZqcaNG2vp0qUFBlDfrNzd3fXKK68UuOSHksMxcD2OgetxDFCSHMYY4+pOAAAAuAJjhAAAgG0RhAAAgG0RhAAAgG0RhGxo1apVcjgcOnnypKu7Ynsci9KjVq1amjx5squ7YUvse7gSQegmFx0drSFDhri6G1cVHx+vxo0bu7obxa60Hgu7hbE5c+aU2oef3uyBoDTv+0sOHTokh8OhlJQUV3cFJYQgdJPKy8tzdRfwfzgWAFCKGZQKbdq0MYMGDTLDhw83VapUMf7+/uaVV16xyn/44Qfz8MMPm0qVKhlvb2/TvXt3k5mZaZW/8sorJiIiwrz77rumVq1axuFwmH79+hlJTlN6erpZuXKlkWRWrFhhmjZtajw9PU1UVJTZt2+fMcaYkydPmnLlypnNmzcbY4y5cOGCqVKlimnevLn1fR988IG59dZbrfmMjAzTvXt34+vra6pUqWIefvhhk56ebpWvXLnS3HXXXcbLy8v4+vqaFi1amEOHDpnZs2cX6OPs2bOLZyf/QaXpWFyyaNEiExkZadzd3U3t2rVNfHy8OXfunFU+YcIE06hRI+Pl5WVuvfVW8/TTT5tTp05Z5YcOHTIPPfSQ8fPzM15eXqZBgwZmyZIlJj09vUC/+vXrV2z79o9auHChadSokfHw8DBVq1Y1bdu2NTk5OebChQvm1VdfNbfccotxc3MzERER5uuvv7bWu7Q/f/nlF2vZ9u3bC+zv306Xjm1ISIh5/fXXTf/+/U3lypVNcHCweeedd5z6da2f802bNpl27dqZatWqGR8fH9O6dWuzdetWqzw/P9+88sorJjg42Li5uZnAwEAzaNAgY8zFn7vf980VSuO+79atmxk4cKA1P3jwYCPJ7N271xhjTG5urvHy8jKJiYnGmIu/s8aMGWNq1aplPDw8THh4uFm4cKG1/okTJ0yvXr1M9erVjYeHh6lbt66ZNWuWMcYU6GObNm2KehejlCEIlRJt2rQxPj4+Jj4+3nz//fdm7ty5xuFwmOXLl5sLFy6Yxo0bm1atWpktW7aYb7/91jRt2tTpH+grr7xiKlWqZDp06GC2bdtmduzYYU6ePGmioqLMgAEDzJEjR8yRI0fM+fPnrV9IzZs3N6tWrTK7d+829957r2nRooXVXpMmTcz48eONMcakpKSYqlWrGjc3N+uP6xNPPGF69+5tjDEmLy/PhIaGmr/+9a9m586dZs+ePaZXr16mXr16Jjc315w7d874+vqaF154wRw4cMDs2bPHzJkzx/zwww/mzJkz5vnnnzcNGza0+njmzJmS2/GXUdqOxZo1a4yPj4+ZM2eOSUtLM8uXLze1atUy8fHxVp1JkyaZb775xqSnp5ukpCRTr1498/TTT1vlnTp1Mg888IDZuXOnSUtLM19++aVZvXq1OX/+vPn3v/9tJJnU1FRz5MgRc/LkyRLZz1fy448/mgoVKpiJEyea9PR0s3PnTjN9+nRz6tQpM3HiROPj42P+93//1+zbt8+8+OKLpmLFiub77783xlz7j3Fubq6ZPHmy8fHxsY7DpZ/pkJAQU7VqVTN9+nSzf/9+k5CQYMqVK2eF0mv9nBtjTFJSkvnggw/M3r17zZ49e0xsbKzx9/c32dnZxpiLIcPHx8d89dVX5ocffjAbN240M2fONMYYc/z4cXPrrbeaUaNGWX0raaV137/55pumYcOGVruNGzc21atXN2+//bYxxph169aZihUrmtOnTxtjjHnttddM/fr1zdKlS01aWpqZPXu2cXd3N6tWrTLGGDNw4EDTuHFjs3nzZpOenm4SExPNF198YYy5GGYv/efkyJEj5vjx48W70+FyBKFSok2bNqZVq1ZOy+666y4zYsQIs3z5clO+fHmTkZFhle3evdtIMps2bTLGXPzjW7FiRXPs2LEC7Q4ePNhp2W/PQlyyZMkSI8mcPXvWGGPMsGHDTKdOnYwxxkyePNk88sgjTv8DrFu3rvUL/IMPPjD16tUz+fn5Vnu5ubnG09PTLFu2zBw/ftxIsn4J/d6lMyilRWk7Fm3btjVjxoxxWu+DDz4wgYGBV9yGhQsXmmrVqlnzYWFhTsHpcn347R8wV9q6dauRZA4dOlSgLCgoyLz++utOy+666y7zzDPPGGOu/cfYGGNmz55tfH19C7QdEhJiHnvsMWs+Pz/f1KxZ0/pje62f88u5cOGC8fb2Nl9++aUx5uKZuzvvvNPk5eVdtn5ISIiZNGnSZctKQmnd9zt37jQOh8McO3bMnDhxwri5uZnRo0ebRx55xBhzMfhc+s/Dr7/+ary8vMyGDRucviM2Ntb07NnTGGNM586dTf/+/S+7Dy6dJd2+ffsV9hJuNowRKkXCw8Od5gMDA3Xs2DHt3btXwcHBCg4OtsoaNGggPz8/7d2711oWEhKiGjVqFOr7AgMDJUnHjh2TJLVp00br1q3ThQsXtHr1akVHRys6OlqrVq3Sjz/+qAMHDig6OlqStGPHDh04cEDe3t6qXLmyKleurKpVq+rXX39VWlqaqlatqscff1wxMTHq3LmzpkyZoiNHjlz3/ilJpelY7NixQ6NGjbL2beXKlTVgwAAdOXJEZ86ckSStWLFCbdu21S233CJvb2/16dNHx48ft8qfe+45vfbaa2rZsqVeeeUV7dy58zr3SMmJiIhQ27ZtFRYWpu7du+vdd9/VL7/8ouzsbP34449q2bKlU/2WLVs67fsb8dvj4HA4FBAQ4HQcrvZzLklHjx7VgAEDdMcdd8jX11c+Pj7KyclRRkaGJKl79+46e/as6tSpowEDBuizzz7T+fPni6TvRaG07vtGjRqpatWqWr16tdauXavIyEg99NBDWr16tSRZv6Mk6cCBAzpz5oweeOABp38z77//vnWcnn76aX300Udq3LixXnzxRW3YsKFItgFlE0GoFKlYsaLTvMPhUH5+/h9ev1KlSoX+PofDIUnW97Vu3VqnTp3Stm3btGbNGqcgtHr1agUFBemOO+6QJOXk5Khp06ZKSUlxmr7//nv16tVLkjR79mwlJyerRYsWWrBgge688059++2319XfklSajkVOTo5effVVp3373Xffaf/+/fLw8NChQ4f00EMPKTw8XP/+97+1detWTZ8+XdJ/B2o/8cQTOnjwoPr06aPvvvtOzZo109SpU6+rjyWlfPnySkxM1Ndff60GDRpo6tSpqlevntLT06+5brlyF3+lmd+8OejcuXN/+Luvdtz/yM95v379lJKSoilTpmjDhg1KSUlRtWrVrOMQHBys1NRUvfXWW/L09NQzzzyj1q1bX1cfi1Np3fcOh0OtW7e2fv9ER0crPDxcubm52rVrlzZs2KA2bdpIunicJGnJkiVOx2nPnj365JNPJEkdO3bUDz/8oKFDh+rHH39U27Zt9cILL/zhvuLmQhAqA0JDQ3X48GEdPnzYWrZnzx6dPHlSDRo0uOq6bm5uunDhwnV/p5+fn8LDwzVt2jRVrFhR9evXV+vWrbV9+3YtXrzY+qUjSU2aNNH+/ftVs2ZN1a1b12ny9fW16kVGRmrkyJHasGGDGjVqpPnz599QH13BFceiSZMmSk1NLbBv69atq3Llymnr1q3Kz8/XhAkTdM899+jOO+/Ujz/+WKCd4OBgPfXUU/r000/1/PPP691337X6JalUHQOHw6GWLVvq1Vdf1fbt2+Xm5qakpCQFBQVp/fr1TnXXr19v7ftLZ+F+e8bx97dB38hxuNbP+fr16/Xcc8/pwQcfVMOGDeXu7q6ff/7ZqR1PT0917txZb775platWqXk5GR99913N9S3olQa97108Sz1qlWrtGrVKkVHR6tcuXJq3bq1xo8fr9zcXOtsVYMGDeTu7q6MjIwCx+m3Z3Jr1Kihfv366cMPP9TkyZM1c+ZMq49S6fr3gOJFECoD2rVrp7CwMPXu3Vvbtm3Tpk2b1LdvX7Vp00bNmjW76rq1atXSxo0bdejQIf3888/XdVYjOjpa8+bNs0JP1apVFRoaqgULFjgFod69e6t69erq0qWL1q5dq/T0dK1atUrPPfec/vOf/yg9PV0jR45UcnKyfvjhBy1fvlz79+9XaGio1cf09HSlpKTo559/Vm5ubiH2UslwxbGIi4vT+++/r1dffVW7d+/W3r179dFHH+nll1+WJNWtW1fnzp3T1KlTdfDgQX3wwQeaMWOGUxtDhgzRsmXLlJ6erm3btmnlypXW/g8JCZHD4dDixYv1008/Wf+jdpWNGzdqzJgx2rJlizIyMvTpp5/qp59+UmhoqIYPH6433nhDCxYsUGpqqv7+978rJSVFgwcPliTrj118fLz279+vJUuWaMKECU7t16pVSzk5OUpKStLPP/9sXT68lmv9nEvSHXfcoQ8++EB79+7Vxo0b1bt3b3l6elptzJkzR++995527dqlgwcP6sMPP5Snp6dCQkKsvq1Zs0b/7//9vwIBqiSU1n0vXfx9tGfPHu3evVutWrWyls2bN0/NmjWzzsJ6e3vrhRde0NChQzV37lylpaVp27Ztmjp1qubOnSvp4r+pzz//XAcOHNDu3bu1ePFi699DzZo15enpqaVLl+ro0aPKysq64f2KUs7Vg5Rw0eUG0nbp0sW6lfmP3rL9e6mpqeaee+4xnp6eBW5jvdqgRmOM+eyzz4wka8CiMf+9bfX3t3cfOXLE9O3b11SvXt24u7ubOnXqmAEDBpisrCyTmZlpunbtagIDA42bm5sJCQkxcXFx5sKFC8aYi4Mbu3XrZvz8/ErN7fOl7VgsXbrUtGjRwnh6ehofHx9z9913W4PVjTFm4sSJJjAw0Hh6epqYmBjz/vvvO7X77LPPmttvv924u7ubGjVqmD59+piff/7ZWn/UqFEmICDAutXflfbs2WNiYmJMjRo1jLu7u7nzzjvN1KlTjTEXBx/Hx8ebW265xVSsWLHALdzGXLyDKCwszHh4eJh7773XLFy4sMD+fOqpp0y1atUK3ML9+4HKERERTo9OuNrPuTHGbNu2zTRr1sx4eHiYO+64wyxcuNCp3c8++8w0b97c+Pj4mEqVKpl77rnHaaB8cnKyCQ8PN+7u7i65fb407/vLPcbj0r+Vv//9707r5ufnm8mTJ5t69eqZihUrmho1apiYmBizevVqY4wxo0ePNqGhocbT09NUrVrVdOnSxRw8eNBa/9133zXBwcGmXLly3D5vAw5jfnNBFwAAwEa4NAYAAGyLIAQAAGyLIAQAAGyLIAQAAGyLIAQAAGyLIAQAAGyLIAQAAGyLIAQAAGyLIASgWNSqVUuTJ092dTcA4Kp4sjSAYvHTTz+pUqVK8vLycnVXAOCKCEIAnOTl5Vlv4AaAmx2XxgCbi46O1rPPPqshQ4aoevXqiomJ0a5du9SxY0dVrlxZ/v7+6tOnj9Pb0E+dOqXevXurUqVKCgwM1KRJkxQdHa0hQ4ZYdX5/aSwjI0NdunRR5cqV5ePjox49eujo0aNWeXx8vBo3bqwPPvhAtWrVkq+vrx599FGdOnWqJHYDAJsiCAHQ3Llz5ebmpvXr12vs2LG6//77FRkZqS1btmjp0qU6evSoevToYdUfNmyY1q9fry+++EKJiYlau3attm3bdsX28/Pz1aVLF504cUKrV69WYmKiDh48qEceecSpXlpamhYtWqTFixdr8eLFWr16tcaOHVts2w0AFVzdAQCud8cdd2jcuHGSpNdee02RkZEaM2aMVT5r1iwFBwfr+++/V2BgoObOnav58+erbdu2kqTZs2crKCjoiu0nJSXpu+++U3p6uoKDgyVJ77//vho2bKjNmzfrrrvuknQxMM2ZM0fe3t6SpD59+igpKUmvv/56sWw3ABCEAKhp06bW5x07dmjlypWqXLlygXppaWk6e/aszp07p7vvvtta7uvrq3r16l2x/b179yo4ONgKQZLUoEED+fn5ae/evVYQqlWrlhWCJCkwMFDHjh27oW0DgKshCAFQpUqVrM85OTnq3Lmz3njjjQL1AgMDdeDAgWLrR8WKFZ3mHQ6H8vPzi+37AIAxQgCcNGnSRLt371atWrVUt25dp6lSpUqqU6eOKlasqM2bN1vrZGVl6fvvv79im6GhoTp8+LAOHz5sLduzZ49OnjypBg0aFOv2AMDVEIQAOBk4cKBOnDihnj17avPmzUpLS9OyZcvUv39/XbhwQd7e3urXr5+GDx+ulStXavfu3YqNjVW5cuXkcDgu22a7du0UFham3r17a9u2bdq0aZP69u2rNm3aqFmzZiW8hQDwXwQhAE6CgoK0fv16XbhwQe3bt1dYWJiGDBkiPz8/lSt38VfGxIkTFRUVpYceekjt2rVTy5YtFRoaKg8Pj8u26XA49Pnnn6tKlSpq3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size 598x500 with 1 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","text/plain":["<Figure size 573.875x500 with 1 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","text/plain":["<Figure size 566.861x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot grouped bar charts of region and insurance costs hue by sex, smoker, and number of children (three separate charts)\n","# use the catplot() function to create the bar charts\n","# set the kind parameter to \"bar\" and the data parameter to medical\n","\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","#medical = sns.load_dataset('medical')\n","sns.catplot(data=medical, kind='bar', x='region', y='charges', hue='sex')\n","sns.catplot(data=medical, kind='bar', x='region', y='charges', hue='smoker')\n","sns.catplot(data=medical, kind='bar', x='region', y='charges', hue='children')\n","\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"7DA2GOsiMrv9"},"source":["**What do you observe?** Briefly write what you observe from the charts. \n","\n","\n","I notice males tend to have higher insurance charges than females in every region, which might point to the fact that males are more likely take risks and get injured. This would be consistent, but google searches tend to suggest that females actually pay more. Smokers pay an astounding amount in medical charges in every region because well, smoking causes a plethora of health issues. Having 5 children is actually cheaper than having 0-4, maybe because there are subsidies in place to help large families?\n","\n","\n","Now let's analyze the medical charges by age, bmi and children according to the smoking factor."]},{"cell_type":"code","execution_count":584,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2778,"status":"ok","timestamp":1682901196627,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"Cl6tfQLDL9Gk","outputId":"4a4547cf-6b07-4dff-bf46-30333a169ed3"},"outputs":[{"name":"stdout","output_type":"stream","text":["      age     sex     bmi  children smoker     region      charges\n","560    46  female  19.950         2     no  northwest   9193.83850\n","1285   47  female  24.320         0     no  northeast   8534.67180\n","1142   52  female  24.860         0     no  southeast  27117.99378\n","969    39  female  34.320         5     no  southeast   8596.82780\n","486    54  female  21.470         3     no  northwest  12475.35130\n","...   ...     ...     ...       ...    ...        ...          ...\n","1095   18  female  31.350         4     no  northeast   4561.18850\n","1130   39  female  23.870         5     no  southeast   8582.30230\n","1294   58    male  25.175         0     no  northeast  11931.12525\n","860    37  female  47.600         2    yes  southwest  46113.51100\n","1126   55    male  29.900         0     no  southwest  10214.63600\n","\n","[1070 rows x 7 columns]\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAi4AAAHpCAYAAAClT7dOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAADWt0lEQVR4nOydd5xU9bn/3+dM3w4LW2AXpEmX3sQWg2IumqhojNFEjSUqmghJLDHXdqMm/hLBXGNJTNRc9dquJZFEgxBQgQiiIiCiFF3aFmD7Tj/n98d36u60nZ3Zmdn9vl8vmJ05Z2bOzJyZ8zlP+TyKrus6EolEIpFIJDmAmukNkEgkEolEIkkUKVwkEolEIpHkDFK4SCQSiUQiyRmkcJFIJBKJRJIzSOEikUgkEokkZ5DCRSKRSCQSSc4ghYtEIpFIJJKcQQqXFKHrOi0tLUhbHIlEIpFI0ocULimitbWV4uJiWltbM70pEolEIpH0WaRwkUgkEolEkjNI4SKRSCQSiSRnkMJFIpFIJBJJziCFi0QikUgkkpxBCheJRCKRSCQ5gxQuEolEIpFIcgYpXCQSiUQikeQMUrhIJBKJRCLJGaRwkUgkEolEkjNI4SKRSCQSiSRnkMJFIpFIJBJJziCFi0QikUgkkpxBCheJRCKRSCQ5gzHTGyCRSCQpRdOgdit0HIW8UqiYAqo8R5NI+gpSuEgkkr7D3nXw3nI48gVoblBNMGgMnLQURp6a6a2TSCQpQJ6GSCSSvsHedfDGTVC3A8z5UFAuLut2iNv3rsv0FkokkhQghYtEIsl9NE1EWpxtUFgJJhsoqrgsrBS3v7dcrCeRSHIaKVwkEknuU7tVpIdsA0BRwpcpirj9yBdiPYlEktNI4SKRSHKfjqOipsVoibzcaBHLO4727nZJJJKUI4WLRCLJffJKRSGuxxl5uccplueV9u52SSSSlCOFi0QiyX0qpojuIXsj6Hr4Ml0Xtw8aI9aTSCQ5jRQuEokk91FV0fJsKYDWw+C2g66Jy9bDYCkUy6Wfi0SS88hvsUQi6RuMPBXOXgHlE8HVDm114rJ8Ipy9XPq4SCR9BEXXO8dVJcnQ0tJCcXExzc3NFBUVZXpzJJL+i3TOlUj6NNI5VyKR9C1UFYZMy/RWSCSSNCFPQyQSiUQikeQMUrhIJBKJRCLJGaRwkUgkEolEkjNI4SKRSCQSiSRnkMJFIpFIJBJJziC7iiQSiSQXkG3eEgmQBRGXgwcPcumll1JaWorNZmPy5Ml88MEHgeW6rnPHHXdQWVmJzWZjwYIFfPHFF2GPcezYMS655BKKioooKSnhyiuvpK2tLWydTz75hJNPPhmr1Up1dTUPPPBAl2156aWXGDduHFarlcmTJ/P3v/89PS9aIpFIusPedfDM+fD8pfDa9eLymfPF7RJJPyOjwqWxsZH58+djMpn4xz/+waeffspvf/tbBgwYEFjngQce4He/+x2PPfYY77//Pvn5+SxcuBCHwxFY55JLLmHHjh2sWrWKN954g3feeYdrrrkmsLylpYUzzzyT4cOHs2XLFv7f//t/3HXXXfzhD38IrLNhwwYuvvhirrzySj766CPOPfdczj33XLZv3947b4ZEIpFEYu86eOMmqNsB5nwoKBeXdTvE7VK8SPoZGXXOvfXWW1m/fj3vvvtuxOW6rjNkyBB+8pOf8NOf/hSA5uZmysvLeeqpp/jOd77Dzp07mTBhAps3b2bmzJkAvPnmm/zHf/wHBw4cYMiQITz66KPcfvvt1NbWYjabA8/92muv8dlnnwFw0UUX0d7ezhtvvBF4/rlz5zJ16lQee+yxuK9FOudKJJKUo2kislK3AworQVGCy3RdzGEqnwiXviLTRpJ+Q0b39L/+9a/MnDmTCy+8kLKyMqZNm8Yf//jHwPJ9+/ZRW1vLggULArcVFxczZ84cNm7cCMDGjRspKSkJiBaABQsWoKoq77//fmCdU045JSBaABYuXMiuXbtobGwMrBP6PP51/M/TGafTSUtLS9g/iUQiSSm1W+HIF2AbEC5aQFy3DRDLa7dmZvskkgyQUeGyd+9eHn30UcaMGcNbb73Fddddx49+9COefvppAGprawEoLy8Pu195eXlgWW1tLWVlZWHLjUYjAwcODFsn0mOEPke0dfzLO3P//fdTXFwc+FddXd3t1y+RSCQx6TgKmhuMlsjLjRaxvONo726XRJJBMipcNE1j+vTp3HfffUybNo1rrrmGq6++OqHUTKa57bbbaG5uDvzbv39/pjdJIpH0NfJKQTWBxxl5uccplueV9u52SSQZJKPCpbKykgkTJoTdNn78eGpqagCoqKgAoK6uLmydurq6wLKKigrq6+vDlns8Ho4dOxa2TqTHCH2OaOv4l3fGYrFQVFQU9k8ikUhSSsUUGDQG7I2ipiUUXRe3Dxoj1pNI+gkZFS7z589n165dYbd9/vnnDB8+HIARI0ZQUVHB6tWrA8tbWlp4//33mTdvHgDz5s2jqamJLVu2BNZZs2YNmqYxZ86cwDrvvPMObrc7sM6qVasYO3ZsoINp3rx5Yc/jX8f/PBKJRNLrqCqctBQsBaIQ120HXROXrYfBUiiWy8JcSX9CzyCbNm3SjUajfu+99+pffPGF/uyzz+p5eXn6M888E1jnV7/6lV5SUqK//vrr+ieffKJ/61vf0keMGKHb7fbAOmeddZY+bdo0/f3339ffe+89fcyYMfrFF18cWN7U1KSXl5fr3/ve9/Tt27frzz//vJ6Xl6c//vjjgXXWr1+vG41G/Te/+Y2+c+dO/c4779RNJpO+bdu2hF5Lc3OzDujNzc0peGckWYPXq+sHP9T1L1aJS68301sk6Y/sWavrT39L1387Qdf/3xhx+fS3xO0SST8jo8JF13X9b3/7mz5p0iTdYrHo48aN0//whz+ELdc0Tf/P//xPvby8XLdYLPrXv/51fdeuXWHrHD16VL/44ov1goICvaioSL/iiiv01tbWsHW2bt2qn3TSSbrFYtGHDh2q/+pXv+qyLS+++KJ+/PHH62azWZ84caK+cuXKhF+HFC59EHmwkGQTUkRLJLqu63pGfVz6EtLHpY/hN/1ytomWU6NFFELaG0XY/uwVMPLUTG+lRCKR9DtkYlQi6YymwXvLhWgprASTDRRVXBZWitvfWy7Wk0gkEkmvIoWLRNIZafolkUgkWYsULhJJZ6Tpl0QikWQtUrhIJJ2Rpl8SiUSStUjhIpF0Rpp+SSQSSdYihYtE0hlp+iWRSCRZi/zllUgiMfJU0fJcPhFc7dBWJy7LJ8LZy2UrtEQikWQI6eOSIqSPSx9F00T3UMdRUdNSMUVGWiQSiSSDGDO9ARJJVqOqMGRaprdCIpFIJD7kqaNEIpFIJJKcQQoXiUQikUgkOYMULhKJRCKRSHIGKVwkEolEIpHkDFK4SCQSiUQiyRmkcJFIJBKJRJIzSOEikUgkEokkZ5DCRSKRSCQSSc4ghYtEIpFIJJKcQTrnSiTpRI4MkEgkkpQihYtEki72roP3lsORL0Bzg2qCQWPEZGk5pFEikUiSQp76SSTpYO86eOMmqNsB5nwoKBeXdTvE7XvXZXoLJRKJJCeRwkUiSTWaJiItzjYorASTDRRVXBZWitvfWy7Wk0gkEkm3kMJFIkk1tVtFesg2ABQlfJmiiNuPfCHWk0gkEkm3kMJFIkk1HUdFTYvREnm50SKWdxzt3e2SSCSSPoAULhJJqskrFYW4Hmfk5R6nWJ5X2rvbJZFIJH0AKVwkklRTMUV0D9kbQdfDl+m6uH3QGLGeRCKRSLqFFC4SSapRVdHybCmA1sPgtoOuicvWw2ApFMuln4tEIpF0G/nLKZGkg5GnwtkroHwiuNqhrU5clk+Es5dLHxeJRCJJEkXXO8eyJcnQ0tJCcXExzc3NFBUVZXpzJNmCdM6VSCSSlCKdcyWSdKKqMGRaprdCIpFI+gzy1E8ikUgkEknOIIWLRCKRSCSSnEEKF4lEIpFIJDmDFC4SiUQikUhyBilcJBKJRCKR5Ayyq0gikUgk2Yu0FJB0QgoXiUQikWQne9fBe8vFNHXNLWZ8DRojnKeliWO/RcpWiUQikWQfe9fBGzdB3Q4w50NBubis2yFu37su01soyRBSuEgkEokku9A0EWlxtkFhJZhsoKjisrBS3P7ecrGepN8hhYtEIpFIsovarSI9ZBsAihK+TFHE7Ue+EOtJ+h1SuEgkEokku+g4KmpajJbIy40WsbzjaO9ulyQrkMJFIpFIJNlFXqkoxPU4Iy/3OMXyvNLe3S5JViCFi0QikUiyi4oponvI3gi6Hr5M18Xtg8aI9ST9DilcJLmPpsGhj2D32+JSFuxJJLmNqoqWZ0sBtB4Gtx10TVy2HgZLoVgu/Vz6JYqud5azkmRoaWmhuLiY5uZmioqKMr05/Qfp8yCR9F3k91sSASlcUoQULhnA7/PgbBNdBkaLyH3bG8WZ2tkr5I+bRJLrSOdcSSekc64kN+ns8+BvmTTZwGgV4eT3lsNxJ8sfOYkkl1FVGDIt01shySLkL7okN5E+DxKJRNIvkcJFkptInweJRCLpl0jhIslNpM+DRCKR9EukcJHkJtLnQSKRSPolUrhIchPp8yCRSCT9koz+qt91110oihL2b9y4cYHlDoeDJUuWUFpaSkFBAYsXL6auri7sMWpqali0aBF5eXmUlZXxs5/9DI/HE7bO2rVrmT59OhaLhdGjR/PUU0912Zbf//73HHfccVitVubMmcOmTZvS8polKWTkqaLluXwiuNqhrU5clk+Es5fLVmiJRCLpg2S8HXrixIm8/fbbgetGY3CTli5dysqVK3nppZcoLi7mhhtu4Pzzz2f9+vUAeL1eFi1aREVFBRs2bODw4cN8//vfx2Qycd999wGwb98+Fi1axLXXXsuzzz7L6tWrueqqq6isrGThwoUAvPDCCyxbtozHHnuMOXPmsGLFChYuXMiuXbsoKyvrxXdD0m1GnipanqXPg0QikfQP9Axy55136lOmTIm4rKmpSTeZTPpLL70UuG3nzp06oG/cuFHXdV3/+9//rquqqtfW1gbWefTRR/WioiLd6XTquq7rN998sz5x4sSwx77ooov0hQsXBq7Pnj1bX7JkSeC61+vVhwwZot9///0Jv5bm5mYd0JubmxO+j0QikUiyGK9X1w9+qOtfrBKXXm+mt0ii63rGT0u/+OILhgwZwsiRI7nkkkuoqakBYMuWLbjdbhYsWBBYd9y4cQwbNoyNGzcCsHHjRiZPnkx5eXlgnYULF9LS0sKOHTsC64Q+hn8d/2O4XC62bNkSto6qqixYsCCwTiScTictLS1h/yQSiUTSR9i7Dp45H56/FF67Xlw+c764XZJRMipc5syZw1NPPcWbb77Jo48+yr59+zj55JNpbW2ltrYWs9lMSUlJ2H3Ky8upra0FoLa2Nky0+Jf7l8Vap6WlBbvdzpEjR/B6vRHX8T9GJO6//36Ki4sD/6qrq5N6DyQSiUSSZfjHidTtAHM+FJSLy7od4nYpXjJKRmtcvvGNbwT+PuGEE5gzZw7Dhw/nxRdfxGazZXDL4nPbbbexbNmywPWWlhYpXiQSiSQZsmkekRwnkvVkvDg3lJKSEo4//nh2797NGWecgcvloqmpKSzqUldXR0VFBQAVFRVdun/8XUeh63TuRKqrq6OoqAibzYbBYMBgMERcx/8YkbBYLFgsUVxbJRJJ+simg5yk52TbBOjujBORM5QyQlZ929va2tizZw+VlZXMmDEDk8nE6tWrA8t37dpFTU0N8+bNA2DevHls27aN+vr6wDqrVq2iqKiICRMmBNYJfQz/Ov7HMJvNzJgxI2wdTdNYvXp1YB2JRJIlyLqD7ETT4NBHsPttcalpid0vG1MycpxI1pPRiMtPf/pTzjnnHIYPH86hQ4e48847MRgMXHzxxRQXF3PllVeybNkyBg4cSFFRETfeeCPz5s1j7ty5AJx55plMmDCB733vezzwwAPU1tbyi1/8giVLlgSiIddeey0PP/wwN998Mz/4wQ9Ys2YNL774IitXrgxsx7Jly7jsssuYOXMms2fPZsWKFbS3t3PFFVdk5H2RSCQR8B/knG3irNdoEaMd/Ae5s1dI755YpCtSlWzEJFUpmVS/rtBxIqYIJQtynEjGyahwOXDgABdffDFHjx5l8ODBnHTSSfz73/9m8ODBACxfvhxVVVm8eDFOp5OFCxfyyCOPBO5vMBh44403uO6665g3bx75+flcdtll3HPPPYF1RowYwcqVK1m6dCkPPfQQVVVVPPHEEwEPF4CLLrqIhoYG7rjjDmpra5k6dSpvvvlml4JdiUSSIWTdQc9IVzqmJ2IyFSmZdLwu/ziRuh1i3wrdNv84kfKJcpxIBlF0vfOgF0kytLS0UFxcTHNzM0VFRZneHImkb3HoI5EWMudHPgt224Vr8neekXUHnYkmLuyNYmRGspEqTRNpurod4WISxAG+9bA4wF/6SmQxufttke4rKAclwnJdE27Y3/w9FAzqGlFJ1+uCOI9dKJ25M0xWFedKJBJJRBKpO3A0ybqDzqQzUtXTiEkiKRlNg3W/gtba8IjK/B/D+ofSF4HzjxPxR3McTeK5yydmrmhYEkAKF4lEkv3IuoPkSGeHTE/FZGhKxmABrwM0D6hGMFhFtMXjhMavIG9geBrq9SXgcYnb09X5M/JUGD4ftr8MzfuhuBomXQAGedjMNPITkEgk2Y+sO0iOdEaqeiom/RPeX/0hNOwEQqsWFPHPnAdFQ7pGVBq/BI8diipT/7r8RKqf2fq8jLhkAbKKTSKRZD/+g5ylQKQB3HZRA+G2i+uWQrFcFuaGEyouItGTSJVfTNobhXgMxS8mB41JTEwqCN2i6z79ooPuAXNBlIhKCWhecDRHfryeRuCysU1bEkB+yyUSSW7grzsonygKcdvqxGX5xL5RLJmsF0osUikuOtMdMRnptfnrbzQvDJ4ApSNhwHHisrhabJ+jqet2A1iKQDUI4aLr4O4AZ6u47Onr6lwXZLKJ4mGTTVx3tvm2OwWfjyQpZKpIIpHkDiNPFQWXfc05N5G23mT8Svzi4o2bhJiI1CHTk0hVIkWs0V7b+HPC629MeSEP3AGKATwOkRIKWwZ4XUK8ADR8CpqOCNUooCqQNyjx19X5fdW14HaBEEP+2hujTTrnZgFSuEgkktxCVfvWASMRLxRI3q8k3R0yscRkrNdWv1P8nTew62Oa8kQti6cDvG4whSzzR1SKq0TUzadXfP+Fl8rEI5KoyisVkTyDGVoOCfHkfxKjFfIHS+fcDCN9XFKE9HGRSCTdJhEvlKIhIiXiau+ZX0lvz3iK99qaakQ0Y8BxkYt7O45C6yEwF0FBWfjrNheAtUgIi4KKCB1JtbE9ZCC6qGpvEOIORWyzooCugKL70lYKWIvh0pf7loDOIXI8viqRSCQ5TCLtyvU7wd7U83oLf6Rq9AJxme70WrzXli8c0mlviFx/43FC+WSonNK1punEG6D9iHhsVRURGkuRuFTV8HROJGLVsRRX+YqEvUIMeT2guXyXvtt0r9g2SUaQqSKJRCLJFPHalXWvWG7Oz71JxfFem8nqSwmZo9ffnHFP5DTU3jXBx9Z1UQcTWocS2g4dKdIUS1R5HATbnPz41/HdpmtQty373vN+ghQuEolEkinieaG4HeLSZI18/2x2DE7E58WcDycvg51/i11/01kg+B+745hIo3WuQ7EWi+WNX4l0VefaoJGnRhdVmgfwR7BCBYsiojI6onOq7UjP3yNJUkjhIpFIJJkinrGeu0McbKNl9bPZMThR08AZV4h/3am/qZgC+YOgdpvQFqqRQJTEY4dWX+3MxocjFwYf+VykfSKJKs0T/Ntg9j2s7tt+VfjLaF6wZ6FY7CdI4SKR9FV6uxizt+hLr6tzu7K/1sLvh2IrgZJhogjVZMstx+DutmJHS7tE+rxDCWR0fMJF9/1rqw/WAkWaZaR7RcQm1JkXRBt2KIoaEnjRxWejGiJ3Q0l6BSlcJJK+SCK+ILlIX3xdI08VEYf3HoTmowTSEtYicXvllPT5sKSbnrZix/KAaT8iRIe9yZcq8gI+Pxhznlgea5ZRx7HI9TWOZt+0akWIGx0CokjzAqooBPYXF0t6HdkOnSJkO7Qka4jW5tnd9tlMES2ikuuvKxr+1+VoFQdcf8TF1QHWwp77uGQDyUTJYn3eiir+LqkWf7s7hKhQDUK4OJqh6SsoGS7qXTqja6JLac51sG9d1/fV3ijmIWke37gEf/2MRaSlhkyL3WotSSsy4iKR9CU6t3lGCpG/t1x0amTjj260M+z5P4b1D2X+daU6TRX6eXVOWVhLgq/r0ldy2zG4u6aB8fZjvweMv0als7OuriEiJr4i287Cxl8bNOo0sW91fl+/fDcoJvNKO4nJouyOcvUDpHCRSPoSifiCZGv7bCyX1deXgMcVO/Sf7teVjjRVdz+vbPvM0kUiHjBNXwkPmOLqrrU/foHhaIb2o+ANiZoYLGDwRU384q/z+9o5xaU5xOddMSl3olx9GClcJJK+RDzvjGxtn413ht34pegWKaqMfP90v65EbPmTOZjl6ueVbnrqAWMtghGnwEf/IyItBiPicOcFdzt4DTDq9NhRk746F6sPID8BiaQvEeqdEYlsbZ+NG3koEQcgR3Pk+6fzdaVzWnCufl7pJpH3xZwPp9wceVr4ogeF2DUXiPV0RBuzjrhuLoA9a+J/Zr3tNixJCBlxkUj6Eol6Z2Rb+2y8M2xLkahPcDSL2o/efF3pTL/l6ueVbkLfF4Ol6yyieB4w/s+soNw3rLGTs67Hkb0pU0lcpHyUSPoSfu8MS4EIobvtQU+Q1sPZ2z4b7wzb6xLixZzf+68rkXROstOCc/XzSjf+90U1QMNOOLZXFOQe2yuuq4bg+xIpKhL6mSlK+CwjRenZZybJOP3s2yCR9AP8hYWRQuhnL8/OwkL/Gba9MfLAPXsjVEyGbz3S+68r3emcXPy8ehOfhYowfyNoBhcLmYLr08hUkUTSF8m1wsJEXVZHniqKLnvzdfVGOmfkqTB8Pmx/GZr3i06ZSRf4ikr7If66Is0Lgyf4Uj2+dmajLX77u0zB9WmkAV2KkAZ0EkkKyFZn3Jjmd4U9j4z4X3fdDpEWM5gTd5dNBdk2RuHQR/D8pSI1GHH4pF1Epb7zTPQalXR/ZpKMIYVLipDCRSJJEdl2EPWTLlG1dx28+kPoOAKaTiAfoiqQNwjOezw1B9hYjsTZJhZ3vw2vXS+Ka5UIn73f+fbcR0RtSzSy8bVJeowULilCCheJpB+QDufcP35NTDkGX2pIBTTw+qYUV0yGq//Vs+eJdgAfdTpseTL7xiikIuLiJ1uFsCRp+mkCVSLJAuQPau7RXev6eBz6GOp3ir8N5pDCU1Vc97rE8kMfQ9X05J4jlnlezb/F9ZLhsccoQO7WFaX6M5NkHClcJJJMkIoQthQ+uc/Bzb7P39i1W0ZBFKNqbrFeMsIlliOxrokJyp29aSDcn2bLk/DpX6H+02D9TdkEOHlZ+qIxiRZry/29XyKFi0TS26TCPl7m7vsY0Xp8E+n9jUEs8zxdE7d53aJrp/OgQqMF2uth9X+Bqw1Re+Pjq/VwZFfq6m8i0XlekKNJ7Oe9WbQsyUqkcJFIepNUTG9O19wcSe9TNVMcjDUPKKauKRHNI5ZXzUzu8WOZ56kGAhOUNU/X5R6n2Md0r9gu1UjAVEXzQls9rLqj5/U3sci1tn5JryA/fYmkN+mOfXwk0jk3R9L7VE6DsvHib80jRISuh4uJsvFivXhomihq3f22uNS02EZspjyR9kEHxRC+TNeFMNE8iA4nk9jPFEVcqr5z3vqdcPijZF99Ysh5QZJOyIiLRNKb9HQacDrn5kh6H1WFM+4R7dDtR0QkI4ACBYPF8ngH62ipw/k/jl3kavQJF3uTECShdSSqT8yoxsj7mmoUz3XgAxg6IwVvhkSSGFK6SiS9SU+tyNM5N0eSGUaeKmpFhs8H20Axudg2UFxPpIbEnzqs2yHahwvKxWXdDli5TLQ8R5uFlFcKX/s5VEzqOm5g0vk+wRLNMUM6aUgyg4y4SCS9SU/bPEOFTyR/CzmDJbuJ1gmWbC1HIjVTe9bAouWwfkX0Itd5N3R97kMfw5anffU3hvA6YR2fBb8Jhs5Ky1slkURDCheJpDfpaZunnMESn2xtE4/XCZaM30iiqUNbMVz6SvT3JdJzD5kq6mtqt/naoCOY45WNF+tJJL2IFC4SSW/TkzZP6W8Rm2xtE09XJ1h3aqa6K4xC6286joDXC/iLdVUxjiCR+huJJMVIy/8UIS3/Jd2mJ5GBbD1AZ5KYQ/UyaF+vafDM+UKkhKZzQETJWg8L0XrpK90XAamyxo+1L2Z6AKRE0gkZcZFIMkVPrMilv0U4qfDH8T9Oqt/TdHaCpSJ1GE8Ey31NkmVI4SJJP9lac5DryBksQbojDiqm9O6U5J62wMeiO6nDSN/DL99NLIXVH/c1r0c4BrvawVok3ktJViCFiyS9yJRGdtLXxGSi4mDvWnj77sSnJKfCjbhzJ5i7w9eRYxAmcD3tBEukZira99De2PMoVV/C4/KJlTbxtx8pWrIKWeOSImSNSwSyteagv9MXxWQitR4dR337oAvMecJwTdfA1SEOVEYLFA8Dr8NntW8EgxXaapOvQYFgjcuhj8RZvNeJ6CdWwGAR3TpDpiX/+KHPEy2SFOl72N4AjmYhWvIHRX7PEqmPyXXcDvE6XW1iblMkCspE1EWSFciIiyQ9pKrmQJJa+uqco3i1Hh3HxMwdV7sQJY4mguLBBB6HEDHH9oYLC6MVrMU9cyNWVRHR+fJdEWkxGBE/vV5wt4PXIJb39HsQKZ0T63toLREnEY4mIXQ6p9h6ksLKdtx28Z642jq5FUtyAXnEkKSHns7kkaSevjznyF/rEc0h1mgRr8vVLkSK6pu3o6o+F2NdCBaPvdMyu5iQ7GpP/gCuacIEzlwgIkI6oHvEpTlf3L5nTXre91jfQ4NRGMt5HOJ1dqavmRl6nNB+FBq/hOaDItokRUtOIoWLJD1Ia/rso6+LSX+tR/nErvb1M3/gOzhrvtk7oQMDQwcMKl2HCWpeUZdiG5Dcdvnf94JyGDgKBo6AkmHicuAocXu63vdY30NTnogo6VrXFIm/I2nQmNw1M9R1335QD8f2QdN+8Zq8ESZhS3IKmSqSpAdpTZ99pLO7JZX0pHA4Wuvuthd9BbFqZNEW6W9xQ/BPvfOyBAl93xVFCIZQ0vm+x/se5g2AVrsYsmgw576ZoaaJ9JvL90+WcPZJpHCRJEZ3Dya5Yk3f17prYpELYnLvOnj3Qaj/NGh2VjYBTl6WeO1NpFoPW6mIrGheoUVCNUjowc0vbvzW9ppXpFPMeeA4ltz+ksn3Pd730OOE8smi3uXo7u65OGcLXk9QrLjtUqz0A6RwkcQnmS6UXLCm74vdNbHIdjG5d52wl28/Qtjk4a/Ww5FdiU1KjkbBILAUg6NRfNZKyH7nr3NQVNFFpLkBL6I41wa2EvH+NH4luoO6u79k8n1P5Ht4xj25ZzDn9YCr1SdWHJneGkkvI9uhU0SfbYfuaUtztoqD/tqqHfN1F8LZyzNni//Hr4mBfgqitgQF0IWw0HWomAxX/6tnLckHNov2Z0ILYUNqWgaP97VD+3xWjDZxwC8aAs6Wnn0PMvm+Z+v3sDt43aILyNnmK6juRWQ7dFYhhUuK6JPCJVUzVrItHZPO2THZRCxfj2w7iB3cAn/+hmhVNpi6fiZetxAzP/gHDJ2R3HOs/x2svjsoSkKFkaKKtmfV2FVYmAvEQavlUM/2l0y/79n2PUyETIqVUKRwySpkqkgSnVTNWMk2u/B0zo7JFuIdJNOdGujuQfLAB8HtjPSZqEax/MAHyQmX0JZkzRNsgUYRLcmqUXT6RKr1GH+OqLvp6f6S6Zk/2fY9jEY091qJxIcULpLo5EoXSnfpq6/LT2hawmQTEQxd62oyl+y04ESeP+nIQrQAcA8Dw6EtyUaraI32u+MabcLLpP0ILFoOqhL+uveuSd3+kiviobdJxL1WIvEhhYskOrnQhZIMufK6khEPfpO5jmOigNHeSJi9vNcV37E4EeHRXXv5eM68Q2eJ59E8oosnrOsHX3rHJNZLhkRbkh3HYPSC8GW5sr/kGv6RAs5WaQQn6RZZk+D81a9+haIo3HTTTYHbHA4HS5YsobS0lIKCAhYvXkxdXV3Y/Wpqali0aBF5eXmUlZXxs5/9DI8n3GBo7dq1TJ8+HYvFwujRo3nqqae6PP/vf/97jjvuOKxWK3PmzGHTpk3peJm5hb8bwt7YtcUwlw2qcuF17V0H/3MePPttePlKcfk/54nbY1G7FWq3izNXdweg+4IVurjuahPLo5md+YVH3Q6RQikoF5d+4bF3nfj3zPliNtBr14vLZ86HPWuTd+YdMhXKxou/vS4RIdLxmaP50gVl48V6yRAqPiIRS3zkwv6SC4QZwu0V7rX2JilaJN0mK4TL5s2befzxxznhhBPCbl+6dCl/+9vfeOmll1i3bh2HDh3i/PPPDyz3er0sWrQIl8vFhg0bePrpp3nqqae44447Auvs27ePRYsW8bWvfY2PP/6Ym266iauuuoq33norsM4LL7zAsmXLuPPOO/nwww+ZMmUKCxcupL6+Pv0vvjtomhjUtvttcZlua/Z4Nuq91dKc6tedLa8rGv624K/Wg/2YEBv2Y+L6qz+MLV7ajoCz2Xcw0EQEQ/eIS78vibNZrNeZREYCrLojurB5/XrRFZSMM6+qirbcgjLxt9crBIvX56tSUCaWJ/KZRNpfeiI+urO/9PZ3NNvRNBFRaa0VYqXlMDhacut90XWx3x76ONNbIvGR8a6itrY2pk+fziOPPMIvf/lLpk6dyooVK2hubmbw4ME899xzXHDBBQB89tlnjB8/no0bNzJ37lz+8Y9/cPbZZ3Po0CHKy8sBeOyxx7jllltoaGjAbDZzyy23sHLlSrZv3x54zu985zs0NTXx5ptvAjBnzhxmzZrFww8/DICmaVRXV3PjjTdy6623JvQ60t5VlMmOhL763Jnu8ohET9uCtz4Pr15L7JoQBc57DKZ8J/zmeBOWXR3Q9JUwYyse1rW7pukrEdUZNFZ07bg7gh08pjxxsG+rg3Mf6ZqO8eP/TOp2BA3oumOGFuszhZ61JMfbX7Jxf8oEmjdYr5KrhnAth0TrvP9fxxEY+TX4/muZ3jIJWVDjsmTJEhYtWsSCBQv45S9/Gbh9y5YtuN1uFiwI/sCNGzeOYcOGBYTLxo0bmTx5ckC0ACxcuJDrrruOHTt2MG3aNDZu3Bj2GP51/Ckpl8vFli1buO222wLLVVVlwYIFbNy4Mep2O51OnM5g2LmlpSXp9yAumZ7om6luiHS/7kx3eUTi8EdQv1P8HdZho4iiUa9bLD/8UeTuGmsR8QtZ9citnfGKltHEclN+5IiKtVicXbfXC5HjcRA2ZdlWEr8WpCefSSL7y9krguKiuy6xsbYt09/RTOP1+DqBfO61uUb7ETj4QVCotBzsuk7Nv8VnGvX7IektMipcnn/+eT788EM2b97cZVltbS1ms5mSkpKw28vLy6mtrQ2sEypa/Mv9y2Kt09LSgt1up7GxEa/XG3Gdzz77LOq233///dx9992JvdCeEGssvdEqQtXxii1TQW93Q2TL6+5tetoWfHhbYs9zeBuM/Y/w2+IVofodSk3WyI9pLRY/+G0NYlsNRgLW+e4O8a9icvxakGT2tUT3l0tf6ZlYjbRt/XVfDbQtt2fWYyUZHC1waItPqHwAx/bEv0/paPFZDjgu7ZsniU3GhMv+/fv58Y9/zKpVq7Bao/wQZjG33XYby5YtC1xvaWmhuro69U/UHzxHItEbrzurQ/vJtgUnemCMsF48a3pXu09QGbreF3wHL999AvfVg9dTlTKI1NHU3f0lld+V/vQdzdW2ZbddpEIPfgD7N0HDZ8T9LpUMh6pZ4t/xZ8HA43pjSyUJkDHhsmXLFurr65k+fXrgNq/XyzvvvMPDDz/MW2+9hcvloqmpKSzqUldXR0VFBQAVFRVdun/8XUeh63TuRKqrq6OoqAibzYbBYMBgMERcx/8YkbBYLFgsvRAy7OueI9FI9+vO1tB+T9uCB1SLglo9RvGjoor1OhNvro1tAAwYLvL/kYRNe4N47MIyYY8fliqyiYhM+5H0iM2Rp2bue9LXv6Mep/ieuFpFSigX8LpE99yBzXBgE9Rt9xWox6CgHKpmQ9VMIVYKQqLweQPTu72SbpEx4fL1r3+dbdvCw9pXXHEF48aN45ZbbqG6uhqTycTq1atZvHgxALt27aKmpoZ58+YBMG/ePO69917q6+spKysDYNWqVRQVFTFhwoTAOn//+9/DnmfVqlWBxzCbzcyYMYPVq1dz7rnnAqI4d/Xq1dxwww1pe/0RiXQm2V89JNL5urM5tO9vC67d5itODUm3+A8asdqCJ10AK38qDjLRMOWL9SIx8tTYdSAQXdgYLULA5JdC/qCuJm/oojg3HWLzyOdC1GXie9IXv6MBsZIjkRXNCw27hEg5sFnUgMVLX1lLfBGV2eKyuKprxEySlWRMuBQWFjJp0qSw2/Lz8yktLQ3cfuWVV7Js2TIGDhxIUVERN954I/PmzWPu3LkAnHnmmUyYMIHvfe97PPDAA9TW1vKLX/yCJUuWBKIh1157LQ8//DA333wzP/jBD1izZg0vvvgiK1euDDzvsmXLuOyyy5g5cyazZ89mxYoVtLe3c8UVV/TSu0H0M8n5P87uib7pIp0TdbM5tO9vC371h6KTwesFPIjiXBXyBsVuC1Z8rcPHYgiXgrLw6cidiVcgG03Y+K3x/QfwziZvbkd6xabuFcZ7RUN693uS7VO3E8Vfs+JszX6xouuivdovVA5uEdseC3M+DJkeFCqlo2J/DyRZS8a7imKxfPlyVFVl8eLFOJ1OFi5cyCOPPBJYbjAYeOONN7juuuuYN28e+fn5XHbZZdxzzz2BdUaMGMHKlStZunQpDz30EFVVVTzxxBMsXLgwsM5FF11EQ0MDd9xxB7W1tUydOpU333yzS8Fu2oh1JrlyGcy4QrSaRhtLH+ohkU0dMj0hXtqiJ14r2R7aH3kqnPd4/LbgaLUeHqcQOPZGcTD3oxjE++hx9my+VDRhA7Dzb5kTmx3HwGhO/f4Sj3Tuq+nG4wzWrGTzXCBdh5YDwWLaA5uFt1EsDBYRmfTXqQwe57MXkOQ6Gfdx6Ssk7eOS6KTi+TfB+hX9z0MiHa8rnl+J34r8O89ktpgylhCNVevx70dFfl4HnE3igGQ0g6VE1MzE81LpCdFEeKhXSjJdPbvfFi69BeWRz5L9HjFzroN96/qe51Cq0HWfg3K7+JfNrrVtDXBwc7BOpbU29vqqAcon+2pUZosONoM5Ndsip0NnFVJ+ZppE0xa2YtHKma0eEumK9qTDayVXQvvRoh7dqfWwdSoqdNvTW2+RSI3MM+d3/+CeaB3JqNNEejUTkcds9AWCYEeYv3U5W89V7U0i5eMXK41fxrmDIqIofqFSOVWYI0r6PFK4ZJrupC2y1UMi3WeaqfaQyeXQfjbXeviJdgD/8t3kBXZ3xGYmJzBny/RnTQN3ezCyko1ixdUuop9+07cjnxO3RXngSBjq6/oZOlNGQfopUrhkmp52JGS60DQboj3JEC8ykI3bDOGfN/hs9UM6dzJZ6xFK5wN4TwV2LovN3kDXRTTNbRf7RDYawnmcUPtJUKjU7QivwYpE4RConiXa/6tmiW41Sb9HCpdM09O0RSYLTbMh2tMTsjW0Hwv/5+11Cz+Vzrb6+YPF9s+6OljrkQ2iLBUCO1fFZrrI9qiK5hHjKfxC5fBW8MYRVHmlwYhK9WwoGto72yrJKaRwyTQ9PZMMjdgYrV29MxL1kEimRiXT0Z5UkC2h/UTJKxU1LM37AV0UJPqHMHrs4nZrcWZrPSKRKoGdi2IzlXg9Qqw424RozSaxomtwdHdQqBz8UGxrLCyF4amfgSOll4okLlK4ZAM9OZP0R2wOfSQEi8dJ8AzcIgTMkGmxaxqSrVHJ9rbivkj5ZBFe1zyi3TN0CKOiiDNa3SvWyyZRlkqTtmx6Xb1Bts4E0nVoqgnp/PlAfN9jYbT6vFR8QmWwb5K4RNINpHDJFpI9k1RVGHW6KHzUvD6nVSPgGy2vGsTyaI8TWqNisoHBJM6cEqlR6S3H0Fz2p0n1ttdtA8UoPlfdA7qBgLOu7hVCVTGK9bLp4J4rnVzZQrY617bWBiMqBzaLSeCxUE1QcULQRr98kviNkUh6gBQu2USyU3H3rAFzgQgje50EnFZN+ULI7FkD827oesD016h0HBP3tTcSiNYYLML4LFaNSm8cjNLdsZROUZSObfd3l5UMEz4XHgfgJTAPqGCwEKzZFuWSxbXx8fsHudqyZyZQxzExmNAvVJr3x15fUcVICn8xbeWUyCc1ndB0nS/q22i2uym2mRhTVoAqU0aSKEjhkkvEmopbUC5+INwdvkF8BmG57rZHrzOp3SoGkfmtstWQs3evQxwPa7dHr1FJ98Eo3R1Le9cJi/r6T4PutGUT4ORlPRdF6dp2f5RLNQnL8kifd7bOxZHFteH4/VXcHeI7qMUYjNlbuNpEbYrf9O3o7vj3GTgqWEw7ZLr43neDD2sa+d9NNdQc7cCj6RhVhWGleVw8exjThw1I8oVI+jJSuOQKiU7F7TwfJladSdsRcDaLH1CDOWQSsSrEhtcllrcdib5d6ToYpbtjae86MQ+o/Qhh3hFfrYcju4TlfjZue+coV+jnnQspF1lcG+wEctszX1zrtoe3KNfvjN+iXDRUGL5Vz4ahM3okkj+saeTBVbvocHkpspowGVTcXo09De08uGoXy84YK8WLpAtSuOQC6ZqKaz/qO1tXQ0SLDwXf/COvWC8W6TgYpbNjSdNg1R3QVu97nUYCnTmaV9y+6g64+l/JvYZ0bntfSLn0u+LaLJoH5HVD/Y6QFuVPxIlPLPIHB+f9DJ0FRZUp2RRN1/nfTTV0uLwMKrCg+L4rFtXAIKPKkTYX/7uphqnVJTHTRilPMym+xgajNXgp63KyCilcsolIqSAInr0XVIgUjqtNHGwLKqCtNnmn1LyBviJPr1i38311TSzPG9j1vp1J9cEonR1Lhz8SZ5YgRF1oZ46q+H7cd4r1hs7Irm0HmXLJBdyOkHqVDBbXal5xchMQKh+JKEssrMXBFuWqWVAyPC0tyl/Ut1FztIMiqykgWvwoikKh1UjN0Q6+qG9jbHnk9FOP00yKIkRJqEiJ9r2VZA1SuGQL0eotJnxTHJwMZmjcG/RuUHyGY9YScT0Zp9T8wWApAkezz/slxBNE8wKqWJ4/uGdFrMncN1UdS5Ge+8AHwXRbpIiIahTLD3yQnHDpjW6r/p5yyTb8zrV+sZKp4YW6Lmb8+GtUDm4BZ0vs+5jyxElH1WwhVAaNiTzIMsU02914NB2TIfJzmQ0qbbqHZntk4dftNJM/kmKwiN9Lg0Vcl0XAOYcULtlArHqLw1vFGZvXIcSFHx1f6NkB5kKYtaT7TqkVU8QE1YgeMFZxAK+YDPbm4ARrv6hK9Ow+kc6aSOIiFR1L0Z570JiQNzESPaw76K3W3/6Wcsk23A5RWOu2Z9YMruVQUKgc2Bw/kmcwixbl6tki9VM2PiOpkGKbCaOq4PZqWCJ4ubi8GkZFodjWddvipZkOtWn8+YMjTB07CtXkEysGebjrK8hPMtPEq7dwNAOh3QahZwe6EByuNhh5SvedUkPrJRyt4j6KKlJErg4xwGzU6fDatdBxBDSdgLD56j1o+Cx2EWsinTUQXdj0pJYjXl2QYhDvr2Lo8paKuh+T+FFPhr5QhyLpir+w1j8PKFNdQO1HwluUWw7GXl8xiOht1Swx96fiBCGoM8yYsgKGleaxp6GdQUY1LF2k6zqtDg+jBuczpqygy339aaZ8mxWnasWDEbdiwo0JD0Yc+RofHvWwo9HA5Kr83nxZkl5ACpdME6/eQgst5lM6HWR9Akf3iLO9ZM7AO9dLaA6fadQkIYTevkuIKvCdsfjbpT2xi1gT6axZdYcIY8cSNsnUcsR77pZD4tLd4YsgdXpdIM5Ch0zt3nsZ632VdSi5ib9Wxd2eucJaRwsc2hJ0pz22J/59Bh0vhHf1LPGbYO568M80qqJw8exhPLhqF0faXBRajZgNKi6vRqvDQ55Z5eLZw4KFtkZzoAalrsHKPq2cwSYbqto11WMxqDRrOsc6MlwMLUkLUrhkmnj1Fv4ICCBESufwAOK2g1uEO2UyjDwVhs0TB9nGfTBghDi41m4PiqrO7dIGszjo1++EQx9D1fTwx4zXWWMtEfc150HxsOgtw5e+0v1ajnjPnTdQPJbRItq9vV4Cpn2qCnmD4Ix7eh4RiVeHksuOwH2VwODCDnGZiaiK2y7St/6oSsNnIb8BUSgZ7iumnSkKa/3Tw7Oc6cMGsOyMsYEC2zZdzFmrKivl0vmjmT66UvzWGMxh340BhWA0GnB5NawR0kxOr4ZJVRiYZ+7NlyPpJaRwyRri1Vv4Wpb1kHSNonYSNkmy/nfw3oPizM7/2O8/CsNP9IkqY5R2aYNYfnBzV+ESr7MGTSw35SfWMtydSFIiXT2qAeZeJ1JKydTuJEq0KFi6HYElieNxBcVKJmpVvC5xknBgs/gu1W4Lr2eLREG5r5jW1/1TUN4725pKfMWy08eOYOr4sXxa7+CoAwbmW5g4pChiJMXPxCFFjCorYOfhViqKuqaZmjrcjK8sZOKQot54JZJeRgqXTDN0ljhoaZ7I9RahP6IGs7jRpy3A17qrmpKPtqz/Hay+O2TOkQHwgr0Jdv3DJ4qi/YDEqMaP11njdohLU5Rce09ahhPt6hl5GpzYzbqgVJBuR2BJfAKDCzPgraJ5RRTFX1B7+OP4wxNtA4KDCatmQ3FVbnXDKIr4/Qq0HFtF6seHCkwanrjIUFWF604dxc9f3UZti5OSPBMWg4rTq9HU4abAYuC6U0fFFD+S3EUKl0wzZKqop6jdFrneQlHAYBM/bP6WZcVfvBtSj1GZRHeJ1yMiLZpXVN0HvuQ+AeP1/Zh63aCYu3bHaJ6gaOqc9iifHLuzxtXuS49FmQzbk5bh7nT19HZnTrodgSXRU3BeNzhbe1+s6LqoS/EX0x7cEhyzEQ1zvk+kzBQnN6Wj0ypUUmLi5rcSMPhGUhiMwTRPGrqWThw9iPvOm8yj6/awp76NZk3HpCqMryzkulNHceLoQSl/Tkl2kBLh0tLSwpo1axg7dizjx49PxUP2H1RV1FO8+kPRuROp3mLu9fDvR0Q3QZg/hCKG6iVbj7H9ZZEeMhhDRIt/uxTQfX4mfpHUxecFIZocraJdunPaY9Tp0PRV5M4a2wAYMDxYKJvKluFs7upJp6uupGsKTjHCwBEw44qeFVt3B10XnT4HNokatgObwX4s9n0MFrF9/qhK2Xhfh2H6SdrELaRYNlOeKCeOHsTckaXsONTCsQ4XA/PMcdNMktwnqW/Gt7/9bU455RRuuOEG7HY7M2fO5Msvv0TXdZ5//nkWL16c6u3s24w8VbQVv7c8er1F5RR497eiaM/r9P3QTYOTf5J8WqF5PyLvFCXqoRgAjzj78zgji6ZJi2Hl0shpj6avxAFjz5rInTWQmLjweoTIat4PxdUw6YL4ngzZ2tWTblfd/szedfC3H4uoirUY1HxfAfmnsPpO+NrtIs2SDtoaRH2KP6rSejj2+qpBRCX97rQVk32p4N4lYRM3v+GlPzJotGZNRFBVFSZXFWd6MyS9SFLC5Z133uH2228H4NVXXxXFUE1NPP300/zyl7+UwiUZRp4Kw+dHP0Af3ir+OdsAHTzu4G3JHoSLqxERFC+RdwWvKP6dfTUc/Kirq+9JN8H6h2KnPfasgUtehrptketI4omLSIXDb94KJy2D+T+K/55mm7tsb7jq9if8jrXONvjXfcL3KL8sWH5ltIjr7fXwwVMimpEKV1h7k0j5+MVK45dx7qDA4HFBoVI5VXTUZZBoJm5m1UiR0crBNp3HtrTy2KTJqObM+75IJH6SEi7Nzc0MHCjm17z55pssXryYvLw8Fi1axM9+9rOUbmC/IVKXydbnxQH88NbIBbSOZnE7xD+IR2LSBUIE2JvEY4aGVzVdRDpsJfC1X4gf+84CoHPaw93hM28zCBtxf9qjblv0tEcscRGrcDjR151t7rK95arbl/G6g461rnbxvtV/Csf2iUhLpA44a7EQFw2fCdHdXVztItrpT/8c+Zy4DssDRgQ7f4bOENuQRXxR38ZXRzuw2vKxqxZcmHEpZjyKqEfR871sP+JhR72TyVVSuEiyh6SES3V1NRs3bmTgwIG8+eabPP/88wA0NjZitcodvNuEdpmYbKKQTdfEwe1vN4mzxVgFtO89KOpgumtpbTCKyMXqu32FuCHiwOuraTlpWfBxOwsAf9pDc8ORg6KVFA1QxUG5sEwsi5f2iCQuEikcTvZ1Z5Jsrr/JVjSvECqujqA47oyjSRgxRku3GMygt4j1EsHjhNpPgqmfuh1iGGksCocIw7ehPj+V/MGJPVdvYjSD0QYmK3UNNvbplZSZLNLETZJTJPWLf9NNN3HJJZdQUFDAsGHDOO200wCRQpo8eXIqt6/v4+8y6TgmDtb2RgIpEYNFdB+4O0ShXqQCWowijbL9ZZjyne4/vz9iEUjHeMVz20rC0zGROjXySsXtjV928pLRwN0GjR1gK00u7RGvcLinrzuTZGv9TTbhcfoGFrbHbxUGYWioGEUqM1L9kNcllltLIt9f8whDRP+8n8NbxX1ikVcqUk/VvuGERUPjb2dvoqpCpIS2IIeZuGmYDIo0cZPkHEkJl+uvv57Zs2ezf/9+zjjjDFTfl2HkyJH88pe/TOkG9nlqtwrzKX97pGog2A7tCHHujHYG7ouQNO9Pfhvm/wjmXNvVOdfvsxDNLO3EH4HHHt0AT9fE8rKJIszenTqTeIXDqXjdmSQb628yiab50j8dQqx0d7ry4HEw4Dg4+kV4jQuI3cjRDKVjxHog9s2ju4VI2b9J7J/u9tjPYSkUKR//FOUBI7LHS8Xvk2IKESpxWpCliZskV0k6xj5z5kxOOOEE9u3bx6hRozAajSxatCiV29Y/aDsibOd1vautvqqK2UEgwuARD+K+CElxdfLbEEmY1GwM7/yJ1DX0+hJRZxCg89ECsfzPZ0FbXfccYhMpHO7p68402VZ/09t4XEGh0lPHWkWFmZfDv+4VqVVrcXAshaNZ1FyN+wbseCU48yde2shoFZ+PX6gMOt53YpEFGIyBlI8QKeZuiyhp4ibJVRRd7/6vRUdHBzfeeCNPP/00AJ9//jkjR47kxhtvZOjQodx6660p39Bsp6WlheLiYpqbmykq6sYZytbn4bXrxUFMNYozQb8zrqKKELbXLdY1WCMU0DpFWuenu5Or9Yjm4mpvFG3Q1mLhtRLaNQRBUy1XW8h2h44jUABVCC5zgQijh9VyFMR2iPV64DejRSFuWI1Lil63pPcJmwMUpValpxzYJLqHGr8U+4imicih7vWlYWOgmsTkZL+NfvmktBinJYXBKMSX0SouU7jPb9h9JGDi5vaZuI0qK5AmbpKsJam9/7bbbmPr1q2sXbuWs846K3D7ggULuOuuu/qlcEmavIG+mT9ekRoCQjz9ffOIDEIYxCug7e7Qvngurk01QrQMOC6yWZopz5fi8tXj4BMvftHi8b0eS1Gw9TdRh9juFg5LspPAHCBfrUo65wB1HBNCt2gINH0ZjKhEK1VRVGH05rfRr5wSuUU9E4QU0WK0pXU/lyZuklwjqW/Da6+9xgsvvMDcuXPD8qITJ05kz54ERq5LguQPFj9MzuZOC3w/8LoXLMUw9WL45AUR9tZ9owBCC2iTGdoXz8XVnO9LY0U5M7YWBTueVKNPZPmWaRoBAWYp6PrYiTjEJlo4LMkeNK8QrP7pyt44wwJ7grMVDn0Y7Pw5ujv+fUpHBwtqh0wXdSuZRlVD5vdkxtxNmrhJcomkhEtDQwNlZWVdbm9vbw8TMpIEKJ8c8iOl4pusiC9XBGhi+ZgzoW6naNEMOOueIM4Skx3aF8/F1T8A0e0Q6Z4uqL4Bkd4Ic5Z86S2j74yxM4k6xM7/kWh57q5zrqR3CC2qdduDn3s6cNvF/r9/kzB+q98ZfzJ6cVWwRmXoTBHhzCQGo4hOhg4clPuyRNItkvrGzJw5k5UrV3LjjTcCBMTKE088wbx581K3df2Bum0iFaT6vFtUQ1C3aBooJnH51xvFQaGgLChMGnYKi3NrcXJD++K5uPqFibsjJAXkQ9eF8CifKApvI81Z0n3PEUnMdsch1mDMvZbnvoqui4iK2y7+9bSoNhZeN9TvCEZUDn/im50Vg/zBQqgM9dWpFFWmZ9vi4bfIN4aIlCQKaCUSSVeSEi733Xcf3/jGN/j000/xeDw89NBDfPrpp2zYsIF169alehv7Nh1HhVgprob2Bl9diA66r4YkfzC0HBDrhtaaJFqHEislE8/F1dEkagCcLdHN0s64R6zfZc7SBFFv0HIosuiRDrG5geZrafcLFa+r50JF14SDraNJ+KoMHucrRPcKR9oDm+HgByINFNa1FgFrcVCkVM2CkuGZEQeqwVc46yuejRbFlEgkPSYp4XLSSSfx8ccf86tf/YrJkyfzz3/+k+nTp7Nx40ZpQNdd/FEPgwkGjhQHCc0jakaMNvHjrnlFXUcydSixUjKJuLiGCpNYZmmRPEm+fFc6xOYiHlfQ+DDVBbWhXT+am0ANlG0AHNsrRHIsTHmiNsUvVAaNSc3soe4QiKZYZbpHIskASbVDS7qSdDu0psEz54toRaSW46avxAFk0FhxVtd5HpCrTfzgFw6B/Aiti/55Lt95JnoRbCKFvd3tWOrOY0syS6BOxZ7egtoDm0SXmKNVpELdjvg2+gazqOOq8lnpl43v/RZlgykYTfGLFYlEkjGSEi4tLZHPihRFwWKxYDb3P4vopIULxPZSMYTUoNibgqkkfGd91mKRYjLni3RTZ+HTelhERy59JX5rdLpcXNP52JLuE0j/OIJRlXTRfkSkffZvgs/f9LW2x0AxiP3VH1GpOKH3hYLfM8VkE5fZYjonkUiAJIWLqqoxu4eqqqq4/PLLufPOOwPjAPo6PRIuED0yMf8mePtOqN0m1gvr3PGdGQ84DtAjCx9LIZy9XEY3+jOd61TSKVScrUKo+Atqj+2Nfx+DxSdOVPiPB4Stfm+hKMF6MYNZpn0kkhwgqW/oU089xe23387ll1/O7NmzAdi0aRNPP/00v/jFL2hoaOA3v/kNFouFn//85ynd4D5LtNk1oQTEoh68rutCnCy4G9avkEP7JL0bUXHbxZwfv1hp+Cx+i7LBLCIZ5jxfRMMo9uOOI/EjMoCm63xR30az3U2xzcSYsgLU7hTkGs1gyhfP37koXSKRZD1JCZenn36a3/72t3z7298O3HbOOecwefJkHn/8cVavXs2wYcO49957pXDpDpFm1xz6SITbCyuF+VxYqsjmSxUdAVuxSAfJlEz/w+MU+4X/0utOY4uySwwFPbBZ1KzUbRfF5LEoKBdzfg5tFfupOT/y48aa3uzjw5pG/ndTDTVHO/BoOkZVYVhpHhfPHsb0YQMi38lo8aV+fJ5C8jshkcTl8ssvp6mpiddeey3Tm9KFpITLhg0beOyxx7rcPm3aNDZu3AiIzqOampqebZ0kaBJXUC7ESOeuI3Sfj8pRObSvP6BpPpHiSL+PCohC8IbPgkLl8MfxIzi2AUEb/apZwgQOHV6/QUxvNuXHn94cgQ9rGnlw1S46XF6KrCZMBhW3V2NPQzsPrtrFsjPGCvESGEBoEyJJ1qhIJH2KpIRLdXU1f/rTn/jVr34Vdvuf/vQnqqvFtN6jR48yYECUMyBJ4nQ2iTPlhS93OxI3cpPkHl63T6Q4hGj1RBu8kyL8wzMPbBITlA9u8c2jioE5H4bMgOpZQqwMHBUh/aLEnt5szhfLo7Q2a7rO/26qocPlZVCBJVBjZ1JNFJps7G+F32/p4I8nTEM1yhoVSRBN0+UcpizA5XKlrHEnqZjpb37zG5YvX86UKVO46qqruOqqq5g6dSorVqzgt7/9LQCbN2/moosuSslG9mv8JnH2xq5n1n4jt0FjpJFbX0Dzitb1jmPQchiO7YPGr6C1zpcmTINo0XUxSmH7K/DWbfDnM+B/L4J3fwv71kUWLUYLVM+BeTfAhX+Bq9aIAvAp3xWzgKLVjFTNhq/dLiIrrg5R0+LqENe/9nOxPApf1LdRc7SDApsFh5pHk1JCnVpOraGSY4bBqPkD2XnEw47a9hS9MZK+wIbdR7jsyU388H8+4KcvbuWH//MBlz25iQ27j2R607rNyy+/zOTJk7HZbJSWlrJgwQLa29u5/PLLOffcc7nvvvsoLy+npKSEe+65B4/Hw89+9jMGDhxIVVUVTz75ZNjjbdu2jdNPPz3weNdccw1tbdFPUjZv3szgwYP59a9/DUBTUxNXXXUVgwcPpqioiNNPP52tW7cG1r/rrruYOnUqTzzxBCNGjMBqtabsvUjq1OSb3/wmu3bt4vHHH2fXrl0AfOMb3+C1117juOOOA+C6665L2Ub2axIxiZNGbrmJxxns8vHY0zuQMJS2+mCL8sHN0Fobe33VIGZqVfkiKhWTRLQkGfx2/JGccyPh6/o5oukc0EopMRVGPFu2GFSaNZ1jHWmOSElyhg27j/DzV7fR5vQwIM+M2aDi8mrsPNzKz1/dxn3nTebE0RG8r7KQw4cPc/HFF/PAAw9w3nnn0drayrvvvou/KXjNmjVUVVXxzjvvsH79eq688ko2bNjAKaecwvvvv88LL7zAD3/4Q8444wyqqqpob29n4cKFzJs3j82bN1NfX89VV13FDTfcwFNPPdXl+desWcP555/PAw88wDXXXAPAhRdeiM1m4x//+AfFxcU8/vjjfP3rX+fzzz9n4EAxE2z37t383//9H6+88goGQ+pStt1uh3a73Zx11lk89thjjBkzJmUbkuv0uB06HtLILffxesKN3rQ43Tepwt4kUj4HNwux0vRVnDsoQkxU+epUKqeKDpzeILQ92W+dryhsO9DMD//nA/ItRqymrj+AdreXDqeHx783U045lqBpOpc9uYmdh1uoKLKG2Xfouk5ti5PxlYU8fcXsnEgbffjhh8yYMYMvv/yS4cOHhy27/PLLWbt2LXv37g3Yj4wbN46ysjLeeecdALxeL8XFxTzxxBN85zvf4Y9//CO33HIL+/fvJz9fFMv//e9/55xzzuHQoUOUl5cHinMvu+wyvv/97/PEE08EsijvvfceixYtor6+Hosl6LM0evRobr75Zq655hruuusu7rvvPg4ePMjgwYNT+n50O+JiMpn45JNPUroRkgSI1i4tIy3Zi8cVUkTbixEVV7voRvN7qRz5nEALfTQGjhTOtFUzhY+KtZcO/p2FiilyOHnikCJGlRWw83ArFUVqlwNRU4eb8ZWFTByShpMGSXYTweByx6FW9tS3MSDP3MVzTFEUSvJM7KlvY8ehlpwQulOmTOHrX/86kydPZuHChZx55plccMEFgTrSiRMnhnmmlZeXM2nSpMB1g8FAaWkp9fX1AOzcuZMpU6YERAvA/Pnz0TSNXbt2UV5eDsD777/PG2+8wcsvv8y5554bWHfr1q20tbVRWhpeW2m329mzZ0/g+vDhw1MuWiDJVNGll14asThXkmZk11D2Etrt4//XWxEVjxNqPwkKlbod8a30i4b6RIpvQGF+6n9cIqIoIe3JtoR9VFRV4bpTR/HzV7dR2+KkJM+ExaDi9Go0dbgpsBi47tRROXH2LEkhUSLR2nE/wO3Nw2yIfGKXa6lFg8HAqlWr2LBhA//85z/57//+b26//Xbef/99QAQUQlEUJeJtWjd/k0aNGkVpaSl//vOfWbRoUeAx29raqKysZO3atV3uU1JSEvg7VBilkqSEi8fj4c9//jNvv/02M2bM6LJxDz74YEo2TiLJWryeoMlbb3T7hKJ5oH6nr/NnMxzeKrpzYpFXGrTRr5olhEtv4Bcq/vZkky1pw7cTRw/ivvMm8+i6Peypb6NZ0zGpCuMrC7nu1FE5U68gSRHRRqXU7WDckf9krnIFn3mnYY3QDu/0aphUhYF5uTOeRlEU5s+fz/z587njjjsYPnw4r776alKPNX78eJ566ina29sDx+/169ejqipjx44NrDdo0CBeeeUVTjvtNL797W/z4osvYjKZmD59OrW1tRiNxkBda2+SlHDZvn0706dPB+Dzzz8PWxZrFIBEkpPoeojJm+9fb6V9QDjRHvkiGFE59JGokYmFpUikfPxCZcCI3nGITaFQicSJowcxd2SpbG/t72iaiLQ428KH0/qieObWw1yjvs4P2idQXmzL+dTi+++/z+rVqznzzDMpKyvj/fffp6GhgfHjxydVunHJJZdw5513ctlll3HXXXfR0NDAjTfeyPe+971AmshPWVkZa9as4Wtf+xoXX3wxzz//PAsWLGDevHmce+65PPDAAxx//PEcOnSIlStXct555zFz5sxUvfSIJCVc/vWvf6V6OySS7EHXg+ZuvWHyFun5m2qCxbQHPxDt0LEwWmHI9KBQGXR87xivKb5hn4E6ldQKlUioqpITdQmRkJ4iKaJ2qxDztgFd9zdFQbEN4PiOWqYpX/Fhy4icTy0WFRXxzjvvsGLFClpaWhg+fDi//e1v+cY3vsELL7zQ7cfLy8vjrbfe4sc//jGzZs0iLy+PxYsXR82WVFRUsGbNGk477TQuueQSnnvuOf7+979z++23c8UVV9DQ0EBFRQWnnHJKF+GTDpIasijpStq7iiTpw+sJd6P1unpXqIBoSfZHVA5+INyQY6GaxOTkKl+NSvkkMUk83aiqr9vHL1Z6eXJzDrNh95FAmsvt1TEZFEaVFcg0F3R/gvzut+G164WjeKRWel2Dtjo+nf0r7v9iqHjPfanF7rznUmhmJ0lbTH7wwQe8+OKL1NTU4HKF59dfeeWVHm+YRJIWAmkfe9BHRYtTyJoO7I0+ofKBqFVp3h97fUWFweODEZXKKUI4pBtV9aV9QtqTJd0m2z1FMnqATsbqobOjeGc8TlBNTBg9gqdPmprUa5NCM3tJSrg8//zzfP/732fhwoX885//5Mwzz+Tzzz+nrq6O8847L9XbKJEkj+btZJnv7P1oCoCz1dei7CuoPbo7/n0GjoJq37yfIdOF2WC68bcn+8dLSKHSYzRN59F1e2hzesI8RayqgYoildoWJ4+u28PckaUZOZvP6AE6RoEtb9wEZ6+ILF78juJ1O7p2pvkdxcsnQsWUpFKL2S40+ztJmYDcd999LF++nL/97W+YzWYeeughPvvsM7797W8zbNiwhB/n0Ucf5YQTTqCoqIiioiLmzZvHP/7xj8Byh8PBkiVLKC0tpaCggMWLF1NXFx5Cr6mpYdGiReTl5VFWVsbPfvYzPJ7wwsm1a9cyffp0LBYLo0ePjugM+Pvf/57jjjsOq9XKnDlz2LRpU/feFEl24PHNvmmtE3b5x/YJ+3x7oxAvvSVa3HbY/z5s+G946fvwxOmwcils/d/ooqW4GiaeDwvvhx+sgu++CCf/FEacmj7RohrAUgD5g6CkGkpHQdGQ4EFE0mN2HGpJ2FOkt/EfoHcebiHfYqSs0EK+xRg4QKfVGr9zga3JJiKLJpu47mwTyyO18PodxS0FwlHcbRfpIbddXO+Bo3hnoWk1GVBVBavJQEWRhTanl0fX7UHTZJVFpkgq4rJnzx4WLVoEgNlspr29HUVRWLp0Kaeffjp33313Qo9TVVXFr371K8aMGYOu6zz99NN861vf4qOPPmLixIksXbqUlStX8tJLL1FcXMwNN9zA+eefz/r16wHhBrho0SIqKirYsGEDhw8f5vvf/z4mk4n77rsPgH379rFo0SKuvfZann32WVavXs1VV11FZWUlCxcuBOCFF15g2bJlPPbYY8yZM4cVK1awcOFCdu3aRVlZWTJvkaQ3CIumOHq/iDYUrxvqd4hoyv7NwldFc8e+T/7g8Bblwsr0b6eiBD1UTHlgzJ120FzlWIcLt1fPOk+RjEeC4hTYYhsgltdujexfNfJUEZHxp5kcTSJ9VD6xR47i3RGauVoknuskJVwGDBhAa2srAEOHDmX79u1MnjyZpqYmOjo6En6cc845J+z6vffey6OPPsq///1vqqqq+NOf/sRzzz3H6aefDsCTTz7J+PHj+fe//83cuXP55z//yaeffsrbb79NeXk5U6dO5b/+67+45ZZbuOuuuzCbzTz22GOMGDEiMPxx/PjxvPfeeyxfvjwgXB588EGuvvpqrrjiCgAee+wxVq5cyZ///GduvfXWiNvudDpxOp2B6y0tvX+21K8INXjzujJXmxLYHq9wpPUX0x76UGxTLKzFPsM3n5V+yfD0tygnafgmSR0D88yYDAour5ZVniIZP0B3HBXiPlpkz2gRYqTjaPTHSIOjeLYKTUmQpITLKaecwqpVq5g8eTIXXnghP/7xj1mzZg2rVq3i61//elIb4vV6eemll2hvb2fevHls2bIFt9vNggULAuuMGzeOYcOGsXHjRubOncvGjRuZPHlyWPvVwoULue6669ixYwfTpk1j48aNYY/hX+emm24CxKjtLVu2cNtttwWWq6rKggUL2LhxY9Ttvf/++xOOLEm6SZhviu/SGyd60Rvb1Pilr6B2k5j944wjVk15vhZln1AZNCb6MMFUYjCJ5zbnicJaORYio/TWuILuFthm/ACdYIEteaVdl4WSYkfxbBWakiBJCZeHH34Yh8MBwO23347JZGLDhg0sXryYX/ziF916rG3btjFv3jwcDgcFBQW8+uqrTJgwgY8//hiz2RxmHwxiBkNtrZhmW1tb26Vn3H893jotLS3Y7XYaGxvxer0R1/nss8+ibvdtt93GsmXLAtdbWlqorq7u1muX+PC6gyIlU+3IkWg5FCymPbA59pkfiInJlVNE2mfoLCgb3zstygZTsKDWaAND0s2CkjTQG+MKkimwzfgBuhsFtjHpbit1HORcrOwnqV84/8hqENGJaOmURBg7diwff/wxzc3NvPzyy1x22WWsW7cu6cfrLSwWS9hUTEmC+OtSQp1oe2umTzzaG0R78kFfm3LLwdjrKwbxw1rlG05YcYL4AU43qgqm/GCdihQqWU86xxUk2wGT8QO0v8D2jZtEQW1oV5G9MVBgq6Gw40Bz5EiSv5W6boc44TGYu1/j0kn4qBVT5FysLCfpXzxN09i9ezf19fVdBjedcsopCT+O2Wxm9OjRAMyYMYPNmzfz0EMPcdFFF+FyuWhqagqLutTV1VFRUQEIN7/O3T/+rqPQdTp3ItXV1VFUVITNZsNgMGAwGCKu438MSZJ4PeB1hqd9MlmX0hlHs0j5+MXKsb3x7zNobND0bch0MKdniFgXjBbxXDEmKEuym3SMK+hJgW1WDK6MU2C7QZvIo09uihxJUnfAqz+EjiOg6YgJ6Ap89R40fAbnPR5fvETxkDnxpKVyLlYWk5Rw+fe//813v/tdvvrqKzob7yqKgteb/MFJ0zScTiczZszAZDKxevVqFi9eDMCuXbuoqalh3rx5AMybN497772X+vr6QPfPqlWrKCoqYsKECYF1/v73v4c9x6pVqwKPYTabmTFjBqtXrw6M7dY0jdWrV3PDDTck/Tr6HZq3a11KNokUEGmoQx8FUz8NnyF+7GJQMjzY9TN0hjgr7A0MRp9I8f2TdSp9glSPK+hpgW1WDK6MUmC7Ye+xqJGk21/Zyl+td1DYVi8ew2BEuHto4oSprR5W3QFX/yv6dyeOh8yJZ69g7hWnSOfcLCQp4XLttdcyc+ZMVq5cSWVlZdKDFW+77Ta+8Y1vMGzYMFpbW3nuuedYu3Ytb731FsXFxVx55ZUsW7aMgQMHUlRUxI033si8efOYO3cuAGeeeSYTJkzge9/7Hg888AC1tbX84he/YMmSJYE0zrXXXsvDDz/MzTffzA9+8APWrFnDiy++yMqVKwPbsWzZMi677DJmzpzJ7NmzWbFiBe3t7YEuI0knPC4RlvW6xBfd6+zdoYOJ4nVB7fZgQW3ddjFZORYF5aKQ1i9WCnqpHd7fpmz2CZXeqI2RdJtss4BPRYFtVgyu7FRgGy+SNKBpO5aOXegKKAYzBDZVFekir0tMUD/0MVRN7/p8cYY00noY3luOetzJWd3ynG37Y2+RlHD54osvePnllwMpnmSpr6/n+9//PocPH6a4uJgTTjiBt956izPOOAOA5cuXo6oqixcvxul0snDhQh555JHA/Q0GA2+88QbXXXcd8+bNIz8/n8suu4x77rknsM6IESNYuXIlS5cu5aGHHqKqqoonnngi0AoNcNFFF9HQ0MAdd9xBbW0tU6dO5c033+yVYVFZj8fVqbsnSwpnI6F5RRTFL1QOfyy2Oxa2AcFi2qpZUFzVu1OUA1EVmf5JFen6Mc9GC/hUFdhmenBl589M0/WYkaRZpr0YnB50xdT166ogjBU1t0gBRxIuPfWQyQKycX/sLZIasnj66adz8803c9ZZZ6Vjm3KSPjFk0S9SvCFiJVtFCohtO7YnZDjhFnC1xb6POT/opTJ0FpSO7j1fE4MxvKhWpn9STrp+zKMVwDb6akEyZQGvaTqXPbnJV2Br6VJgW9viZHxlIU9fMTtrz8QjfWYD8s0cOGanaoAt4nb/R8dfucb+B1BNGAwRpqBrmhAuZ/0K5vyw6/IEhzRy7iMwekHX5RkmU/vjaaedxgknnIDVauWJJ57AbDZz7bXXctdddwHCyf7GG29k9erVqKrKWWedxX//93+nPAiQcMTlk08+Cfx944038pOf/ITa2lomT56MyRQe1j7hhBNSt4WS9BCYiBwycDCbRQqI7Ws5IJxp/Z0/9mOx72O0QOVUX+pnNgweC2ovdeEE5v7kCcEk0z9pJV3zZTLuMBuDrCiw7QHRPrODTXbaXB6a7C4G5nft3tymjMGDEbPuBV3t2kqteUShbdXMyE+cKg+ZDJDp/fHpp59m2bJlvP/++2zcuJHLL7+c+fPn8/Wvf51vfetbFBQUsG7dOjweD0uWLOGiiy5i7dq1Kd2GhH/Bp06diqIoYcW4P/jBDwJ/+5f1tDhXkjxRQ+Qel6+7x3+ZRS3I8WirD4mobIbW2tjrqwYonxysU6mYJHLevYXB5Ov+8QkW6VLbK6TzxzzjDrNxyIoC2ySI9ZkNLbHyeV0b9a1OSmwm1JDopK7rbHYO56DpOEZ49vpEigGRI9KDTQFl46FyWmSfl1R5yGSATO+PJ5xwAnfeeScAY8aM4eGHH2b16tWA8GXbt29fwNPsL3/5CxMnTmTz5s3MmjUrZduQsHDZt29fyp5Ukno27D7Co2t3s7++Ebwu8g0expSa+f6sCqYP66VOmFRgb/K1KPuM35q+inMHBQaPE1OUh84U+ehIZ1DpQhbVZgXp/DHPuMNsAmRFgW03ifWZqYpKWaGV2hYHB5scDCq0hEeSrCY65t2B8v7N0H6kUwejAgWD4Yx74Mt3I7Y7c9LShDxksjGdm+n9sXNGpbKykvr6enbu3El1dXWYEeuECRMoKSlh586dmREuw4cPD/x9//33U15eHhZxAfjzn/9MQ0MDt9xyS8o2UBKFEFv8zbsP89DfP8HlcnKc1YjJquL2atTUt/PgqmaWnTE2e8WLqz28RfnI58RtUR440lenMktcWnu5pkg1gLkgKFZkVCXjpPPHPOMOswmS0QLbJNxr431mJTYTbU4PVQNsHGt3dYkkTRo9CIYUw7sPQv2nQQO6sglwss/VPEa7M2evSMuQxnST6f2xc2mIoihdvNzSTVLJ/scff5znnnuuy+0TJ07kO9/5jhQuqUbTgimeQAGtmN2j6Tovrv8Ut8vB4IJgcZ5FNTDIqHKkzcX/bqphanUJajYcYD1OMTnZL1TqdoAeJ7VYOASq/Z0/M8VU5d7GZA3WqkQbCtdN+msrYzpI5495xh1mEyTe/pS2/S2KiVu8g38in1m+2cC9501GVZTI2x1tyCLAM+fHbXfm0ldSPqQx3WTr/jh+/Hj279/P/v37A1GXTz/9lKampoCvWqpISrjU1tZSWVnZ5fbBgwdz+PDhHm9Uv8fjEgWzbgfxBgx+Ud9GzdEOiqymiCHyQquRmqMdfFHfxtjywnRveVc0j/BTCLQobxVnRrHIKw36qFTNgqKhvbOtoShK0KnWnO/LoaeO/tzKmA7S+WOeLQWwsYRHvP0pbfub38TN0SoikAaT6Mip3R6MakQRL4l+ZpOHFsd+byMNWTz0UffanbO05TkS2bI/dmbBggVMnjyZSy65hBUrVuDxeLj++us59dRTmTkzSpF0kiQlXKqrq1m/fj0jRowIu339+vUMGTIkJRvWr2nen3CHT7PdjUfTMUUJt5oNKm26h2Z7L01X1jXxg+CPqBz6CNztse9jKRKutP7OnwHHZSb9EkgB+Ypr07QN6ep+6c+k+8c80wWwsYQHEHN/umTOMJ59vyb1+5vfxK3jmDhBcTQRsN03WqDDLZYfd3LECEZaP7OOoyL6Y7SgA3a3F6+mY1AVbCYDitEitjfe4NQUD3BMFZneHyOhKAqvv/46N954I6ecckpYO3SqSUq4XH311dx000243W5OP/10AFavXs3NN9/MT37yk5RuoCQ2xTYTRlXB7dWwRIgKuLwaRkWh2JamolFdFwW0gc6fD8QMoFgYrWLOjz+iMuj4lEc0EkZVwVwIloJeKerNdCtjXybdP+aZKoCNJXRve+UTiny1IJH3Jwe/X7sHgwKVxbbU7m+1W6F2m6hTQw/v7PE4xN+122KauKXtM/O1O3d0dFBrV3B6NHRdnItYjCoVNp28eO3OSabAeotM7I+R2ppfe+21wN/Dhg3j9ddfT9vz+0lKuPzsZz/j6NGjXH/99bhcIuxvtVq55ZZbuO2221K6gZLYjCkrYFhpHnsa2hlk7BpubXV4GDU4nzFlBal70tbaoFA5sBna62Ovr5rE5GT/cMLySZntvvEbwWWguDbTrYx9nXT/mPd2AWw8oXug0U5ti5PqgbaI+5PNZOBQk50hJZGX92h/a28AZwugie944PEVUBVxsHe2iPVikJbPrGIKjXnHodVux8EAjKoBRRXnWQ63F7v7GM6KSQyI1u4cZ45RrBRYb5Jpx+NMkZRwURSFX//61/znf/4nO3fuxGazMWbMmMB8IEnvoSoKF88exoOrdnGkzUWh1Rg4I2t1eMgzq1w8e1jPCnM7jolIyoEPRJ1K8/7Y6yuq8FAYOlOkfiqn9G6Lcpft8bUsm2wZN4LLdCtjf6Av/ZiHCl0Au8uLR9MwqipWk4rNbKDF4Y5qy6QoInkT7fvfo/2t45hoQ1bVyHUkiiqWd8QxiST1n5mGwmPec7iE3VQojbRSiFs3YcbNIKWVNt3Gs95zuAWFLt/EBOcYRUuBSdJPjyxECwoKUtqbLUmO6cMGsOyMsfzvphpqjnbQpnswKgqjBudz8exh3W+FdrbCoQ+DEZWju+Pfp3R0MPUzZLrwQcgkBqNIAZnzs2oOUKZbGSU9o7c7wfxC1+XRONxs75LyKLQIEe5we8m3dP0513WRvNGi1Mz1aH+zlfpmAnnFk4S+DTpCAKgGsV4vs+NQC39rGcOxvCV81/0KQ70HKNTb8ChGvjKO4FnT+WxoGcPZkSJNfWCOUV+nl7zPJelm+rABTK0u4Yv6NprtboptJsaUFSQWaXHbRbeP3522fqcoso1FcXVQqAydCXkDU/NCeoLftdZckFViJZRsbWWUCHrSuRPv/skgBg5qHGp2oelgVJVAFMXu1nC4naiKQofby0Cfc7kfXdexu70U2kx0uLwU27ou79H+VjAILMXgaPTVgBgAFdB8YkYVywt6v1DUL/g+zZvGndapjPTuoUhroUUtYq9hFF5dwd3mjBxpCinsjUiihb2StCGFSx9CRWessh/UJlBKgHH4T4M0XQ+KGovCGP1L1AO+eT+1n4gvaizyy0JalGeKEGqm8c8C8rct54Brbba2Mkp61rlz33mTAVLecjy+ohCvDh6vjtmoBE5EFMCo6rg8OvkWA0VWY5T9yRjoKurR/hbNNr9ikugc9HqE1xResXUGq4h6VkzKiG1+WGTTZGCPcUzYcqfHGz3SlMNzjPoLSU2HlnQlpdOhj+7p/sDDA5vgg6eg8UvQPaAYRVvxzMv50DuSdzZsoLRxKxO1XUxgD1bi5LStxeERlZLh2eEQGxArBeJfjuaYww6Svk4K6eOSOWJN2803qxTZTBxudoQVyEJwAnNlsYUWu5t2lzel03q3HWjm8ic3BewMDP6Iiw5eTfxGFNtMLD3jeN7aURt1f+rR/haruwaCRawmm4iy6JqI4loK4ezlGSli7dHUbE0T5nV1O8JrXMSdRY1L+URhXpejvz+5jhQuKSKjwuXAJvjXveDqEIJDNQnvFHsjXk3D5dWx4Yz9GKa8YIty9WxRsxJp3Hsm8JvBmfNFN1Af+bGQzrnZQfAg1xJRmBxotGN3e6keaMNm6hqktrs97D9mx2ZSqRqQF/MgCXTrM1/3eQM/fXEreRYDR9tcOD3ekBoXA6UFZjpcXn5z4RROHj0o9c650bpr7I3CQuDsFWK9nrYNp8EvJShGvREjTTHFZMzXnTlBJhHIVFGuo2si0uJoFWc8HUeFaPENHTMAkfp53Bj5jBEcKJjEwoXnoJZNyK5Ui6qGmMH1zXlAfan7JZeJ16Ier3NH08Dt1SjNj93i/tymmmBUJMFUkj/lYTaoHDcoD4dLC3YVmVUcbg2TqjEwz5z6/SnR7pqe2uanyS+lRx4xI0/NyTlG/QUpXHKV9iOiRfmLVeJS94IzuvGbhsKXhhF8ZhzHZ8Zx7DGOos2j4nB6GamOZGw2iJZQ51pzXqa3RtJPiNeibjOJ7q9onTsOtzhJsJoimyhaDCoNLi//veYLvJreLffa8GJuCzazAXE60r3i2g27j/DY2i8w1G2jwNtMm6EYb/lkrj1tTPQDeHe7a5LpsEmzX0qPPGKizUHqIxHfXEYKl1zB0RL0Ujm4GY7tjXsXr2qmVTNjxMtf8i5jq3l62HKzQe/dcQCRMJpDzOAy6PUi6beEFnLaFKVLB4qigFFVo3budLi9GFWVaMdCh8eL3eVFAaoGdM+9NhXF3Bt2H+Gll5/lesfLjFAOY8KD22Nk34EhvPDyYrjgksjiJd3dNb3kl9KjSFSkOUiSjCOFS7bitotqfb+XSsNniCbIGBjMvqGAwhHWpam0NjZjw0Wj2rVdOZXjAMK6lmK1YvvN4Mx5QrAY5C4oySz+qEb+wQ1co75GlXYQo+7Boxg5oA7lD9q55FXMpMXhiSgeSmwmqgfYONzsxGoydBE2R9tcKAoMKkzOLbknKQ9N01n31sssdTxCoeKgRSmkFRMm3BzPVyx1PMKzb1mYO/KHXcVPurtrpF9Kz8nSWUrpRh41soma92HPavj8LajbLgaXxaKgXLQm1+0E+zEoqAgzgbIqUKzY2asPoUatDrtrKscBfFjTGDC/82g6RlVhWGle0PzO76/id6/tg/Uq6UAW7/YOqqpwy9g6Sg48jtXTgUux4sECus4wzz5+oTxO07RRtFScGFU8AFGjIhajig5YDNFTSfHca08cPYi5IwawZ9sG7E312ErKGDV5JmroY0Y4iO042MzpR56jAAdHlNLAd8+FhaOYGagf5fQjz7Hj4EVMru5kVFkxRdSa1O0QEZDO3TX2RlHzkWy7s/RL6RlZPkspnUjhkk2s+7UQLtGwDQjxUpkFRVXix8TfVdReL7qKDGbwulAczZjzCvm7+0wa2j0UWkn5OIAPaxp5cNUuOlxeiqwmTAYVt1dne4OH/3y7jtvPrebE4yuSfvz+SiJmZ5IUoWlM2vtn3KYO3C4XxbpvaCAKbkwUm6Bq75/hxLNj1ktEi4osnFjBI//a3TO35L3rUN9bzpjQg9QnIQepKAcxy8CvMUg/SKtaGDGq0aYWMkw7yKGDH0P118KXq6p4/DduEmmbSN01Jy1N/gxf+qUkTwZmKf3lL39h6dKlHDp0KGy8z7nnnkthYSH/8z//w+uvv87dd9/Np59+ypAhQ7jsssu4/fbbMRqN6LrO3XffzZ///Gfq6uooLS3lggsu4He/+123t0W2Q6eIlLRDv7cC3r4zeN2c75v345v5M3BU9GhFLB8XbUwwIqLrGJVOEZEk0XSdW/7vE/Y0tDGgIA+XasGh2HBiQUOJ7ZUQ+jgyshBGLE+RnniCSARd9jf2oj53ITga0QFdMaCjoKCj6KI2BesAuPSluCmLSPsykLynCIQfpLp4pRTAjCtgy5MRW3dduoK9o516pSyiwNA1jYH6MVrP+m9GzP1m5BeVrjN76ZeSHBl63+x2O5WVlfzxj3/kwgsvBKC+vp6hQ4fyz3/+E6PRyNlnn83vfvc7Tj75ZPbs2cM111zD5Zdfzp133snLL7/MlVdeyfPPP8/EiROpra1l69atXH311d3eFhlxySZGfQ32rhXTk6tmweCxoCb4EVXNFiKn4TMRXrWWwOBxoKhMh+THAcTg86NuPjlmwJA3lHpjuMW+AglNnpWRhXDiTQOOVcgpiU+k/e28wk/5iaMJVddRDGYUNCEMFAUUE3hdomOv7Ujcx49WCOovsK1rtjPTup9BSitH9EI+cFRTYDVFL7D1F7B2HBPutPZG/NEgDBaxbe89KE5UioZ0KXA1NdUATlTdhYYV4Vkn7q8qYNBdYDAxvKq663P7SVd3TYoiOv3uxCdDtUE2m43vfve7PPnkkwHh8swzzzBs2DBOO+00zjjjDG699VYuu+wyAEaOHMl//dd/cfPNN3PnnXdSU1NDRUUFCxYswGQyMWzYMGbPnp3Utkjhkk1UToHvv5accy74pjJPiLhIVRTGlqdg8KHJGnCtrTvWyDHtEGVRctTxcvfRIgvxWkT7MvE8RRIRg5LIRNvfmo/Vo2teNEVF9Tp93z2fOFAUwDfl2J58rcWJowfx8LxW9HeXM7i9BiMePBhpsAxDmbeUSbFakmu3g6tNXA+dB+R1gAfxd3F1xIOYkj8Yg6udUq2J/e5BdJ6EWKm2YRg8CXVInDqVdHXX9NAvpV+e+GSwNujqq69m1qxZHDx4kKFDh/LUU09x+eWXoygKW7duZf369dx7772B9b1eLw6Hg46ODi688EJWrFjByJEjOeuss/iP//gPzjnnHIzG7ssQKVwksQl0Afmca0Ny9D2ZdCwjC5GJ5ymSSCGnpCux9jeDbQBaOxh1j0+uKAQO8LoGaCLymTcwfhdHtOV71zHpwzvRjW04bAPwKCbydTfl7gMoH94JQ4ojH6Tbjohoj66L2rXAV0EVj+txiu2L5nJtsqIZbLg0lQqlkSa9ACdGLHgoUdroII+WST9kQJpTMTGjIklGdPrtiU8Ga4OmTZvGlClT+Mtf/sKZZ57Jjh07WLlyJQBtbW3cfffdnH/++V3uZ7Vaqa6uZteuXbz99tusWrWK66+/nv/3//4f69atw2TqXmerFC45RMItxz0lwXlAPZl0LCMLkemJGJREJ9b+1mIoRu8UieiKAo1fidqCaLUe0WpB5t8E61eAsw2lsBJb4PnNYM0L+pUMnw9128IP3vajItqjquHBEt8moao+6167KMzvhO5x0qyZeUI9l7PUzQz1HsRIBx6M7DeM4A/aubTvKufpE/W0nSAkFBXpZkSnX5/4pLvbKw5XXXUVK1as4ODBgyxYsIDqapFmnD59Ort27WL06NFR72uz2TjnnHM455xzWLJkCePGjWPbtm1Mnz496n0iIYVLjhC35binJDG8sCfmWDKyEJmeiEFJdGLvbwo6Kl40DCgI4eJLFYWqhfeWg9cduYsjtEDWZBMWALomlr9+vVg3rzR6TULtdvjzQmitDRc9x50sopy6l8CQIj+67ktrKUK4RFjuaT/GHm0IawsW8W/TN7uY63V4dDrSeIKQrqhIvz7xSXe3Vxy++93v8tOf/pQ//vGP/OUvfwncfscdd3D22WczbNgwLrjgAlRVZevWrWzfvp1f/vKXPPXUU3i9XubMmUNeXh7PPPMMNpuN4cOHd3sbZKl2DuBvOd7T0IbNbGBgvhmb2cCehnYeXLWLD2sak3tgRRFGcAWDRQdS0RCwFnVrh/ebY42vLKTD6aG+zUmH08P4ysKYP0qhkYVI9NfIgl8MFlgM1LY4sbu9aJqO3e2ltsWZkFOqpCux9rdCvdXXCSe6iTAYQTWLS0UBxQDoos6ksDLY2WOyieuOVlEg23FMDDptOQTN+8Wlq0PUGzhbRKonEppb+DAd+UKkYwvKxWXdDvjgT2C0IepsPEIM6bq41DxiOyyForuo9bBPwPgiMK2HcRvzeEo5D5PRiK6o7DGO4SPzDPYYx6ArKhaDijtNJwidoyJWkwFVVbCaDFQUWWhzenl03R40rfv1fImc+KTrdWUF/tqg8ongaoe2OnFZPjHtAyCLi4tZvHgxBQUFnHvuuYHbFy5cyBtvvME///lPZs2axdy5c1m+fHlAmJSUlPDHP/6R+fPnc8IJJ/D222/zt7/9jdLS7qe0ZMQly9F0nf/dVEOHy8uggmArpUU1MMiocqTNxf9uqmFqdUliaaOwmpXEIivxSGYeiIwsRKdHw+FSRF/r1Ii1vzUrRbRjxWnIY5DBDh4HoIGuBF2e24+ITj0Ad4cQDapRiApznhAq+NI5nQtodcTfzhZxdtyZllpAh4KyYM2C3/a+5ZBv4Gi+eE6Pk0A0yGgV2zBkWjAd1anAtXbcNWxdbSE/A6nHdEZFZEqVjM5SOnjwIJdcckmYnwsI8bJw4cKI9zn33HPDhE5PkMIly/mivo2aox0UWU0Rv/yFViM1Rzv4or4teteQqvrmAfn+paEuprvzQFIxg8VPXzvIQg+Hw/WQvtipEWt/29A+lANqFVNMB1BKRoLH7qsrMQhh0vil+FsxiBlhHgdh4sGc7yviRbQody6g9bp8tQdNQvyEfv9c7eLxTFYhkkJRFFEQ3HEUTBbwuHzpJp+Pi6tDREj9NTYjTulyEDsOhVHbNmXkBCGd6WB54uOjl2cpNTY2snbtWtauXcsjjzzSa8/bGSlcspxmuxuPpmOK8uU3G9TIgxJVQzCqkmGb/WjCIhWRhb54kPXTo+FwSdKXOzWi7W/jhhRjG/sTzB/eGawZMOeL6EbrYZGKAV9URfdFVHy1MB67iMBAnAJaXQwU7VyT0N4gvpsFFZG/o0aLeL7ZV4vi3yNfgOYQEZWKSeEtwxEOYiqk7AShu6QzKpLKEx9J4kybNo3GxkZ+/etfM3bs2Ixth3TOTREpcc71E+LjsquulTte247NbMBi6vrld7i9OFxe7jl3EmOHDPAV1+ZnzaTlRIRFshET6TCbWjRN9zm8toR1akCCDq85QtT9be86ePdBqP9UREkMZuGLNP9H8H9XioiJwULYGGhNF+kgEBEZg6lrAa3mFstOuxX2vRvedVRYISI6eaWRv7Nuu4jKfOcZkQZIMi0Q9j30CbZ0C/zg/pSkY3ACZOJ1STKPjLhkOWPKChhWmseehnYGGbuGRI85oKq8gjFjxoPZGuORep9Ez96TiSz063bINNFfOjVi7m+Run4avxTOtKpBjNPQQ2pYdK9YpnvFuponPCKjecW6liIYdTrMXxouPsonw7MXJNba2oO0QCZSj70RFclkSlWSOaRwyXJUReHi2cN4cNUujrS5KLQawWilWbNwuMOAzWrhe6dPRs0y0ZJuYdFfDrK9Sb9uUY81tK5+pxAkJcOgrcFX4+JF1LjYIH8QtBwUERRFiVxAWzE5uvjopdbWTKQee6PQPBOvS5JZpHDJAaYPH8iPvzGNP7xfx84GD06XiklVOH5I9oZE0y0s+vVBNk30204N/zwgZ1v40Dp/Z09TjahjUcugdJSvq8hXvGvK8w07LBY1LLEKaKOJjx7a3mc7MioiSTVSuGQrihIsrjXnM6tUYcakCRn78ne3DiXdwqLfHmTTSGinRmUhjNL2BszK9qgjwzs14lnf5xLxhtblD4amr0QhbXF1ePePP51TMQnm/xjWPxS7gDYaGWxtTYgeft4yKiJJJVK4ZCN+A6pOP6KZ+vIn07mTbmEh2yFTj78m4aWXn+Wixv9jhHIIEx7cGNmnD+EF62IuPPUS1C/fiWxtn6vRgXhD6/ytypG6gkLTOSNPhRGnZq/4SJZoowxy9fOW5DyyqyhFpLSrKItItnOntzoKxLZ5Ixb+ya6iJNi7DvurN+Jsb+GYlo8TExbcDFTbseQXYZt7VdDavssBvECkPHLtYHboI3j+0ujdeP7OnpOXwc6/pecAnq3iIFrtTy5/3pKcRwqXFNEXhUtP22N7Q1hkeztkTpnjaZoYIli3A72wErtHw6vpGFQFm1FFaT3sa+01ivEQnTtgWg+LuoxLX+lZlKG301AhrzusxgW6vi5I/bZlqzjozvuS61ElSU4hU0WSqPS0wLY3OgqyufAv58zxQmo9FEUhr7NvkMkGzUehuCr6sMAjX4jHSdbN0x95qNsR9FJJd5Fqd4fWpdKpNF5hsH9y9HEn9744iFf7k4rP20dOCXxJxpHCRRKV0AJbXddxuDU8moZRVbGa1IQKbHtDWGRj4V9OOtB2rvXo3D2jqIDuu4yA0SI6YjqOJvf8e9fBqz+EjiPC2M3fUvzVe9DwGZz3eGrES6SITqY6e3pRHHSbeLU/Pf28feScwJdkHClcJFHxF9g22V002904PRq6Ln5PLUaVIpspoQLbbBQWKSPCQVBDyU1zvLxScbC2NwqX2C4zeWzib/9cns54nOL+ed2f9oqmwao7oK1eXDcYCQ4q9IjbV90BV/+rZ5GHeLUkvd3Z00viICn8+4PHGbn2pyeft4+cFPiSjCOFSxaRbeHSiUOKKC0ws+NQC4oCRlVFUcShzO720uHyMnFIUf/t3IlyEPxy3DXsqbfknjlexRRhpla7TVwPFQ/uDvHPlOfzJimJ7fIaC68Htr8sZv8UV8OkC+DwJ8LoDUR6KPDQqrjudYnlhz6GqunJvb5YJnNv3BSsJYkR2dC8XvZs24C9qR5bSRmjJp+IaujaNZcwvSAOkqZiihB1ibj6JoF0v5YkixQuWULWh0v9Jdw+J/PA9f5KjINgRcOtTHFfQU3ezIh3zQlzPMUXWdF1UHxhNl2HgjJAT97ldf3v4L0HwdFCIJrz5q0w/ESf+DNGGVRoEMsPbo4vXCKlgqDHtSTb3/sr+rvLGeysYQAePBjZ8Y9hKCcvZdJJ34z/nkYizeKgR3S39qebSPdrSbLIUvAswB8u3Xm4hXyLkbJCC/kWYyBcumH3kYxs145DLRxtc1FZbMNmNqLpOh6vjqbr2MxGKottHG1zseNQS0a2L2N0Lqg02UTdh8kGhZWYPB1crr+K2+OJePeEPWw0TbTq7n5bXGpRUjSponYrtB+BvIHioOl1g+YSl7oubvc4Yd4N4mDqaoe2OnFZPhHOXh67FmT972D13SINpaqgmsWlvQl2/cOXgop2Zp3gGffedaIT5vlL4bXrxeUz54sW7kRrSSKw/b2/UrL6Ziqde3AoNprUgTgUG5XOvZSsvpnt7/01se3rjF8cWAqEOHDbxfvgtgcnU6fI8j8p/LU/yXzecUjEpNKd7QJfkhFkxCXDZHO41P/DUlZoZkC+CYcrpDjXrKJrUN/mzO0flmRab+MUVBrzBzLKdYiyjs9pMk1IzhwvEV+PVLcNdxwVByVXm7huMIGuiIiLpgmBYSmAAcNFC2x3ntvrEZEWzdtpwrIRMIDX6VvPBYolwoRlj3gPqiJHsYDYqaCGneB2CvEVCV8tidZ2lB0HmsPStega+rvLseodNKqDAtvmViw06mYGaEfR312ONm9RcmmjLLD8j5mmTlPtj3S/liSLFC4ZJpvDpZ1/WGxmAxD8gXF4vbn9w5Ks6VecgkrFaKHIBJV6O58lMxU3kVoMSL1hmXWgqGPRtU51Joi/vS5ffcvA7k8q3v6ySA8ZjCGixYeqAEYR2YEoE5aBsvFQGeU547UVN+/31ek4wJzX9f4eJ07NwC9XH2Z10wdh6dqLq44x3VlDm1IUUai2KYUMdtawZ9sGxkw9OfH3JJQMWv5v2H2ER9bu5rPaVtweHZNRYVxFIdefNjqYpo7zeSdTnyfdryXJIoVLhsnmYYF9+ocl0ULNSCRQUGk2W7h0/jRqt+d1z8MmEV+PVXeAo1lER7q77bFQQguXfPUnYdcjrZcgzft9jxEtImEAPMK91uMMihXxhFAwGM64J/qBPJF5Q652MW/INKxLRMfVdpRPXFW86SyjJN8Y1t3y5qHtzMKDRzFFfGqPYsKot2Jvqk/svYhGd8VgJ5IRDxt2H2Hpix9zrN1FwIrUBe/vO8YX9R+z/NtT49bYJVuf5x8x8fNXt1GbjMCX9FukcMkw2Rwu7bM/LD01/UqwoHLKrFN5epbSvYNJIr4e9TtFd0/JsJjbrul69zpg7I2+acftkaMefj8Xe2Mi73I4xdW+x/IS+WfHK+qEZl8NBz+C+k+DBnRlE4Tdfg+iYBgtItJisHQpNNXtjRzzWPgT36K8OK9LurbhWAFu3YBBd+FRrF0fWnfjwYitpKy770rKSEY8aJrO/f/YSUOrM9g1iJCXHk2jodXJ/f/YyetLToq6z/a0nbk3TColfQ8pXDJMSqMaabBK75M/LD01/epGt4UK3UvxxTOB071ieYQhnKHbvufN/6Zj62vd64DJK/VNJM8P8XHRAFWIIltJcL3uMukC0T1kbwIM4ekiTRc1MLYS+NovhIDp7n6cSFuxKT983pCvlqStZCz31i9gT8E0rBHStQ0FY9nbMoRxeg2Netf6mwK9lcOWkUycfGL335cUkKx42HawmV21bSiASQ3+9vivu70au2rb2HawmSnVJV3un6r6vGx2v5ZkJ1K4ZJiURTXSOKStz/2wpML0KxUFlZGEZjwTOP82m7qe+fu33d1cS9GmFRTjpU0p8qUy3FQ69+JYfTPbIbJ48UeSDn0kjl4KvuJc39+uNiHkkmnNNRjhpGWiq8jrBD1CNOekZT7vGLqfMkm0rXjGFeJfyPv+YesQ/v3SNsqipWtNJp5Wz+M/+QMDtKO0KYWB97RAb8Wu2FBOXtozP5ck6Yl4+Gh/kyi2NygR6+sMBgWPV+Oj/U0RhUsq6/P6tEmlJOVI4ZIF9Diq0ZN6jQTp0Q9Lbw/Ni0eqTL96UlAZTWjOvym2CZyr3ZfCifwcutuB7u7AhIUGtbx7HTCqCqNOhy/f9XX/GMVARbzB5x11evKf3fwfwbG98NH/iFRU4HmNMO17Yjkkt7/0YN7QwAPNcdO1OyxT2XXCf1HxyaMMdtZg1FvxYOSwZWTPfFx6SE/EQ6BUqXM5EyG3h67XiWyuz5P0bTLq43L//fcza9YsCgsLKSsr49xzz2XXrl1h6zgcDpYsWUJpaSkFBQUsXryYurq6sHVqampYtGgReXl5lJWV8bOf/QxPJw+NtWvXMn36dCwWC6NHj+app57qsj2///3vOe6447BarcyZM4dNmzal/DVH48TRg3j6itk8/r2Z/ObCKTz+vZk8fcXs+KIljqcIzjaxPN0eINGI5quxd13KnkLTdLYdaGbd5w1sO9CMpsUpHvWfndsbofNwdP/Z+aAxiUUW/AWVoxeIy0RFyxs3CWFpzoeCcnFZtwNWLgVnq1gvcCDSg9cVghGhCNvuaatHB5qUkrgdMF3QNNizBswFIq2iA7pHXJryxe171iS/L+1dB/vWgaUECirE6y6oENf3rRPLe7K/JOk54k/XNna40Tu9p/507aiyAr7+jQuZePPbtJ77FIdOe5DWc59i4s1vd0u0dHtfjUNPvFCmDivBZFDxajp6J0dJHR2vpmMyqEwdVhLxsUPr8yIh25kl6SKjEZd169axZMkSZs2ahcfj4ec//zlnnnkmn376Kfn5+QAsXbqUlStX8tJLL1FcXMwNN9zA+eefz/r16wHwer0sWrSIiooKNmzYwOHDh/n+97+PyWTivvvuA2Dfvn0sWrSIa6+9lmeffZbVq1dz1VVXUVlZycKFCwF44YUXWLZsGY899hhz5sxhxYoVLFy4kF27dlFW1jtFd0lFNbJ5SFsvRIKS6mjofHbuF3t+4690mn4l0rbraoeCCnRnC7rHgX9AlGK0oViLRfrIYIoYWfCqZpx48CjiYGHBiUH34lUMOLHE7oDx70sF5WJ7OtfXuO3J70uhr7t4KHjswcc22oLdUs6W+PtLrIhMElGw7qVrDUm3PKfDHbsnxf2ThxZzfHkBOw614PZoGA1qwCDZ49XQgePLC5g8NPJvUp/uOpRkNYre+RQjgzQ0NFBWVsa6des45ZRTaG5uZvDgwTz33HNccMEFAHz22WeMHz+ejRs3MnfuXP7xj39w9tlnc+jQIcrLywF47LHHuOWWW2hoaMBsNnPLLbewcuVKtm/fHniu73znOzQ1NfHmm28CMGfOHGbNmsXDDz8MgKZpVFdXc+ONN3Lrrbd22Van04nT6Qxcb2lpobq6mubmZoqKevGLuvttcWZaUB55aq+uiTPPcx8RUYHeQtPEmXLdjvADNIhfxtbD4kz40leSFgjRihIbfQeb+86bHLs2J5L9vLVI1Fr0JG0Ri0MfiSiCOT9ymqr9CLQeoqNgOHV2A2ZPCyY8uDHiMhYxuNBMgfsozL1OCMNOqaYDlQswrl8OikIRbZh0Nwo6OgpuxUQLBaDrtJ77VNcDcHf2pZGnd+998b9uRYlcu2MtFq3K5jwo7tquHNhf5v8Y1j+UllquMGHhS9emauxGIvtqMs+haTqXPbnJJx4sXcRDbYuT8ZWFPH3F7Ig1aeHt0HpgiKqiKJTmm3kwTjt08HV5Iwo+OSRRkg6yqsalubkZgIEDhbvlli1bcLvdLFgQPOCOGzeOYcOGBYTLxo0bmTx5ckC0ACxcuJDrrruOHTt2MG3aNDZu3Bj2GP51brrpJgBcLhdbtmzhtttuCyxXVZUFCxawcePGiNt6//33c/fdd6fkdfeIbB3SluZIUCJFiff/YyfFNhN7G9q7nuGqO4QNvGKE4qpgxMXVIW6v9KWJ3lsuxJe/NbenbqbxCoNNVnQdHK3HGIwLC0Hh4fS00tZYgCnPhGXkaXDij7uIhyG6zpebnmKYew8AXgzoqCjomHUng3BSYxrFqEgdMInuS41fCVHaHfEQ5sqrh9TpaCL60moHvGAaHH1/qd0Ory8RRnVpiOClqwg9ne7YPS3uP3H0IJZ/eyqPrN3DrtpWXF4Ns0FlbEUh158WX7D1ya5DSdaTNcJF0zRuuukm5s+fz6RJkwCora3FbDZTUlIStm55eTm1tbWBdUJFi3+5f1msdVpaWrDb7TQ2NuL1eiOu89lnn0Xc3ttuu41ly5YFrvsjLr1Otg5pS0XnTgziFSVajCqfHm6lyKwwL/8QpaZWjuqFbD5Uze2vbOWVogcZ4GyDoiHh75m1JJi2aKuDjiOiXdcfHfjqPWj4DM57PLnOIevAmOJAVwx4UCmiBVDChIcVJ1ac1LhHcVz5CagRDMtUTWNwgQV85Tuar3pBQUdFvNTBBRbUzuIAuu5LndM59kbxfm18uPvpv6iuvKqI1Hic4j02WQPTx72ajkFVsJkMKEYLOJvBa4MBxyU1JDER0tHdkm537J6Kh54Ktj7XdSjJerJGuCxZsoTt27fz3nvvZXpTEsJisWCxRDko9yZpnuCaKF1cO62lqCmKBEVyBI1VlKij09ThYg7bWaquZLj9IEbdg0cxckAdypuO2Xidu9AHDARF6XqQtJaIA7HmFQfI0M4erwfa6oWwufpfsd/XSJ1DpaNF11DLoYjiwNN+FCcmbHjRI4xJVtBxur3iIFc9oOtz1m7F7DxGPQMppC0kYgMOzLRSQKnzWORIl39fevWH0PBpuGBTFbD5PqtkjPsSdOW1OxwcblFwerRA2sJiVKk027FpXuH1km21XHHoje6bnoqHngo22c4s6U2yQrjccMMNvPHGG7zzzjtUVVUFbq+oqMDlctHU1BQWdamrq6OioiKwTufuH3/XUeg6nTuR6urqKCoqwmazYTAYMBgMEdfxP0Y2ENXSO8ND2iIVHY4enMdDeccxoPXzHkWCohU0LpxYEbUo0eHSmOb9hF8a/0SR5qBVLcStmDDh5jjvl1xDDSbNyTFnKc3N7V0OkhUWN3maG1C7RgcMZpE2qt8Jhz6GqumRNzxaYXL9p0KkaJ6I4kA3FuLERLtSQIHehhk3CppIFWGmTSmgRG/h0MGPofprXZ5WazuK3WGnmWLaDcWYdCcGvHgx4FYseDSNPEczprajsVsKA9pCCV7XPNB8UAwq7K54SMCVV9MV3O2NODCRp7gxKGK7O9wm3O5GLKqKaolSP9bDCF466S13bCkeJP2FjAoXXde58cYbefXVV1m7di0jRowIWz5jxgxMJhOrV69m8eLFAOzatYuamhrmzZsHwLx587j33nupr68PdP+sWrWKoqIiJkyYEFjn73//e9hjr1q1KvAYZrOZGTNmsHr1as4991xApK5Wr17NDTfckLbX3x3idiRkaEibvziv3eFilvVAMCVzuJp7TGdyn2k/tiQjQbEcQWuOdVBaYOZws7NLR4PH4+Ya9a8UKHaOhkzzdWHhKGbKtHqsuDjY2ooDi7A6V4Wecrg17O5GbICiGrr6Wyj4hIcbDm6OLFzidQ41fSVqafyPFyIOVHSMeDlGIS1qkegK8gkPJxZ0XWcgxxhIa8TC4T0dFgo1AzbFi1sx4lasuEM2zaZ4cGgqDR0WxkTbbs0Lg8eD1+ETGUYw+LbbY4eiysgfWCzxEOrK62juVJxrQ7cW09rWikfXGa3XoIT0DOgotCr5tJJHkdeFomZRLVcCyO4biSS1ZFS4LFmyhOeee47XX3+dwsLCQE1KcXExNpuN4uJirrzySpYtW8bAgQMpKirixhtvZN68ecydOxeAM888kwkTJvC9732PBx54gNraWn7xi1+wZMmSQCrn2muv5eGHH+bmm2/mBz/4AWvWrOHFF19k5cqVgW1ZtmwZl112GTNnzmT27NmsWLGC9vZ2rrjiit5/YzqRsKV3D4e0dRd/0eE4+0dco75OVaeUzB9c57Ki8HpuLXwTpZuRoEQKGousRvLNapeixErHF4xSD9GqFEaMDDQqxVRoDgYozRxRywO6QVHApIJZE91iuqJG9OWK7NYVQqzCZBARG80DA0eKzyxEHBhaDmDDhao70VQbTsIdcg2aEwwmhqsNEQtkO4ZfzlGGcjxfcVQ3d4l0FdHK58pwnLbjuwqX0O1WVVA7TVG2lUBzmxAetghpqljiIbR+ZsCILqLI03KYA1oppVpjuJhDyBu3ZqCWARzffgxT8ZCkI3iZoM/O/JJIMkRGhcujjz4KwGmnnRZ2+5NPPsnll18OwPLly1FVlcWLF+N0Olm4cCGPPPJIYF2DwcAbb7zBddddx7x588jPz+eyyy7jnnvuCawzYsQIVq5cydKlS3nooYeoqqriiSeeCHi4AFx00UU0NDRwxx13UFtby9SpU3nzzTe7FOymlQhn0BpK2joSerptOw61UlK7kVv0P5DvtdOihKdkfsHj/LrpWrZ/80kmG77sViQotKBRRWekZzdFWgstahF7DaMoyTNxtM3F9V8bzVs7asOKEqeWatiOarTopog7eIduxIEZD2ZK9aO0Uogbsd2Feisd5GPBjUnzgGLqepDUPOIAXTUz8sbHKkz22EVXjOJLk5jCxYGSPxiDs51SrZlDXgsGVUVVfON8NI1y2jAXVaH+O3KB7Lgjd/IHw6lUafVRXpuNZ43nc01+hJEB8QqqLUUi2uRoFkXM3REPobVYbbViu80FYrvbanGrNjRNx6BofE41VtwY8eLBgAMTZUojmqbjVm2YMljLlSyy+0YiSR1Z5eOSy7S0tFBcXJy8j0sUC/i9467h0tUW8i1GrKau+XG720uH08Pj35uZvvx2lG3bOvxy7P96kOOVrziqlHY5kJXqR/mc4Ti/8xKnju2eAFz3eQM/fXErC6yf8W3nywz1HghEcw4aqnjRcgFvO8fxmwuncPLoQeG1P+zF/dx3OWRXsetm0UHjL6fQday4sOHgZdu3Ocmzsctjv2Q+n0vbn2Sc8iWqonatx9B1qJgcvTg3lleLs0W0E6NA6cguwgVdg+b9ODFhd7o5puXjxIQFNwPVdix5RdgKB4ji3gj+OHrrYbZ7qnjEfTZXq3+lSusUBdPOpX3oiZF9PeJ5zLjtQtwYLV1bkv3iIYZDLRB1X/oo/2TKtv6edqy4lK7CyaI7ycNB/ZQlTGt/Ny0+Lr1B1Do1iUSSMFlRnNvvieEwW9FwK1PcV1CTF/nsPu3zQEK3zWQTjq26BnU7GF93G2200UJxxJRMi17IcfpBWu2fA90TLgPzzMxVtrOk/THyCY/mDPfsY4nnYdqM1zIwb1bXokRtCpaKsZQf+IR9LhNurx6oNVVVKDN2sMNbzd+MZ7LK9g1GeveERXM6PDqtpsv4rekRzI5jQqwEXxgUDIYz7ol+dh+aFjFYwtMiqIAubu8sWiAwxdhy8jLMO/9GXt0udG8bisGEqXwqyvhz4N0Ho/rjKLYBHN9Ri1cp4ofu25lp288gpZUjeiEfOKrJt5m4L1paIpHW+orJYp7S+hXJFYJHqcVS33kVk+LBpUf+SXJipFjxopYeB+ctjVnLlc3iQBbQSiQ9RwqXTBOnkNPUfIjL9Vf5T89UrCZDl4Os06unbx6If9s6jok2YHsjgYJKgwWTolOIkzp9YMQdya4bKVE1BtscbDvQ3K0DycTKAq5RX8eqd3AkQoHtAO0o16ivM7Hyxq53VlW2j/wBJftuppxjtKqFuBQTZt1NIa00evP4a+G3OdbhpcJkZI8xWO0hiiVdNFXOw3jaVPH66z8NGtCVTYCTl8U+QIe1Fe8ktN0XHSFgjOaAlX/Ik4dNMVZmXIG58wF675q4/jgWtYmb5pZy/xfFbK03BFxgxw3p5iiEaOmYkafCiFOSLwSPUIuVV1KGVzFh1j049a4FrFY8eBUjeSVlMWu50mGrL5FIsgspXDJNHIdZY/5ARrkOcVr73zlL3RQ19J+WjoTarcKt1NUmroe6nXodKDoY0Siig2ZvIQZVCanH0EVLq8nMXWtqWd30QbcOJGrdJxxvqOWQUohbA4NKyGNDm1LA8YZa1LpPxEEz5CCqlZ/Ar3eVk69cwzWG16nSDlKst+NRjdSoI/iDdi4HTVPIt3hiF0uOGhT3AB337F4Rb1eYH4q5QAiCbk4xBhJ2t50wegRPnzS1+5GHRFvrU1wIPmryiez4xzAqnXuo1QeGDTtUFBigtHHYMoqJkRx/fSRcxC6RSHIaKVwyTZyCSMVooVh1cbX7ZRTNi0ux4sECus4wzz5+oTxO01hf6D/Vc3Xajgi3Ul2P7HbqdaGgU2ay49SLcHp1vLpYzWpUKFPt7HBX8+aRMkryjd07kHQcxaJ6GVxSQn2bG6dHCz62SWVQQREW91HYsxbevjus5qG9cAQl9Qv4rGAGdxlnRkwFdUQp7O1SLBnn7P6xtV9gqNtGgbeZNkMx3vLJXHvqKE7c6G8rnhB5oGBBedD3pDvplm44JSedlshAa71qMKCcvBTH6pup4BitSniUzK7koZy8FNXQtc4L0murL5FIsgspXDJNvDNotwOT5mCA0YjLo1GstwWH5mGm2ARVe/8MlYWpHz5nP+o74KpR/ExU0HRMZisjaMFhKsKjmDHqLqzuFuqcVv7Etygvzuv+gcT3vhQYvOQPyu/qbuu2g1ODzX/sUihqPrqTn7m/4FHLjWxXpoalggAsBp1mTad6YB5PXz6TPds2YG+qx1ZSxqjJM6MeHEPZsPsIL738LNc7XmaEclgMQvQY2XdgCOtfmscM4y4seb4oWudaFtsAMUzx7AfFjKTuiIPeckru5dZ6gEknfZPtgP7ucgY7ayjU2/Bg5LBlFMrJS5l00jej3jfdtvoSiSR7kMIl08Qq5DRYxcRcXcOoCSOy0HoJIw4UtxsOfZie4XN5A0WUQPdGrsfQNbGdc66DL9/FULcL1duMYjDRWjyWexsWsKdgGtZkDiQh74titJIX2lHljyzoHvAQPm/IZMObX0Ge4wCL7S+xw3wCeqdJx36n0uqmzajP/oExoWLvk/hiT9N01r31Mksdj1CoOGhRCmn1tRwfz1eMdNXgdnswFw2K7PjiN2qzNyY3sTvDTslA6qN7Piad9E20eYvCxOTEySfGFZO9YasvkUiyAylcMk2sQk4UMBcKgaB7fQfB0EOhLg7ejhZxt1QPn8sfLLw7HM2RbdpRwVLEVst0fus6EYMrmDJpsI9lv9tJVUGSB5J4kQV/d1ME+3mb2cgxYxFDPQcY4dnDXlPn4ls3iwfsZsS/H+/+sEBgx8EmTj/yHAU4OBLSBu4vHB6s12HSnTgcdmy2/K4PkAqX1ww5JQNRW5pTJZpUg4ExU0/u1n16y1ZfIpFknux0a+qv+DSBiGb4riNESxA95F/IbSZb/Pkx3aViimh/NecLEaRpQsBomrhuzqexaBw3rdP4tLaNA7ax7C2eywHbWA40O2lzeWiyRxYmCR1IfJEFvXwiLnsrzqbDuOyt6OUTYfbVvpqRrrVBClBSWIBZ8eJtO4Ld7UXTdOxuL7UtTgrNCtca/obi7+Qy2UTKxmQT151tPut7LeJmuQ5+zDD9IK1qZGfeZqUEAGNHg/gsQ/FHiwaN6bnLqz+dM3qBuOwt0fLGTULgmfNFrY45Pyj49q5L/zZEwG+r39jhprM1lV+sjiorkLb6EkkfQEZcMk3YfJgIhZxHd/fs8XsyfC406uFoFWf1iioiHa4OdGshj3nPodWldSmIHFpi5fO6NupbnZTYTKghB9XuzGfZoE3kMdctYdEcr2syPzE5mBKjNqjA4MVkszGwoJzqxl0UaM20qcUMqZjMTyY7GLD+y6idXPEmDQ+kFRMe2nRTRPd/J2YcmMkzWoRRnDkv7H3DWpTVLq9RiTeDqSfRvR4ibfUlkv6DFC6ZpnM7dOdCTv9E3XjokaMD/rSEZi1lRze9VICu9RSaQ6QGKiaxb9w1/G21hQF5xi4FkaqiUlZopbbFwcEmB4MKLd0+kIS3t44NdCU11rZz0zGFV4piT5+2FA/hbuv/4W7/HN3rFiZu5rEorlPjeqHEEnvDq6o5ZjChel1odLXON+gu3IY8DJPOh23PQ/MBAiE0axHMuDwnXF67EKd1P57gSzfSVl8i6R9I4ZJp4s2HMUW5vTMeR1RDs8bC4/nxmw52N3TPSyVAlHqK/buP4vZujVoQWWIz0eb0UDXAxrF2V7cOJKHtrZWFZkZpeynyiJbmPYUjOdzq5jHvOdxqeRwlUg2MaoC2OpSWQ5g717Ac+VxED+J4oUSrQVGHTMEweCwltdup85qjzxPa/ZZ4nOKqYMTFbYctT0LllNwTL/H21Z5E91LEiaMHMXdkadY650pyi2x2Ye7PSOGSaeIaiiXYBWEwRUxL2A353NN0Jp962npmyhWhPTaRgsh8s4F7z5uMqijd+vL721u/Zt7Jd9te6TJP6Fnz+fytZSIXff1eRn72h67dNfbGrvN8/CmNlkOiqNneGNcLJdp7MeDMm7G/eiND2hvD5gmVq+1Y8oqxWY3Q0tZ1npC1JKMplR6RoPldj4qOU4C01ZekAunCnL1I4ZJp4hmKuTsSeBAFhs2FrzaEpSV0axGvqmfwrmdCWky5/AWROw+3UlHU1abdX8cyeWhxtx//WIeLKe5PuMn9B/L0ji6zipZ6H6VduYb9JZcy8tKzwqNBugYvfF9EYQDsx8DjBqMJrANEJ1LHUSH2kvVCGXkqtvP+G+t7y7s9TyjTKZWk6Yb5nUSSywTS1A4PBoOCoghzzZ2HW6QLcxYghUumiWsoVgSudsK7iDqjw6GPu6QlPM52Tm5+g4/yhvGZMj3sHqkw5UpnQeRAm5HL9Vex6R0cUbu2HA/UjnK58ip5tsu7RoN2vy1SGs4WUdwc2pWlHISCMpFKmn216IJJ1gtl5Kkox52c1DyhTKdUkqK3zO8kkgziT1M3tDpFR2LIT6+qgN3llS7MGUYKl2wglqHYuEXw5m3iQBgVRURmioeFnQU7lAJs9oNc7Po/7rJO7WLElgpTroQKIpMwK5uofMkx9RCN3oKIUYsmChilHmKg8iUwIHx5Xim4HWJcQWd0rzjoWoph5Glw4o975oUSyWE2F1IqyRrIZYP5nUSSRnYcamHr/ibaXeKEJ/TXR9Oh3eVl6/4m6cKcQaRwyRZGngrD58P2l6F5PxRXw6QLYN9aMZTP1So8VDqjGERqxJTf5QBvMKi0UMBQ70FGevd0sb5PlSlXzILIJM3KVMdRikxwTDPh8epdBjgaFRNFJieqI0LUomxieCdW55QGiOVlE9NjbZ/tKZWeGshl0vxOIukm8QpsOy+vb7bT6hC/tUrgP4His9hqdXhoaHUAUrhkAilcsoVIB5Otz8P4c4TBlzkf7E2iewgNUMVB0WQVNRymrm25NpMBxWjB4GmjyNvEKL4IDBvco45M2EslESIWRPrNypJwpyWvFLPZQpXZQK1d6TJkscKmYMYSOWqx45Xw9vBIWTZdE+tN+U5Srzcm2ZxS6clnEkoGZhlJJN0lXoFtpOVGgxL8yeicCfKZhOrA9oMtnD6+vFdfj0QghUs2EOtg0vQV5A8SnTADR3adZdS8X4gcpWtXjwKU5yk4WnQubHuWMqVJDAPEyD59CC9YF3PhqZekJ0/bU7MyX9Qir24HI0orsXu04JBFoypaoKNFLZr3i0vVFJyz5PdRUVRflMobXC8dZGNKJYsN5CSSVBPuA9W1o/KSOcN49v2aLssPHAs2RERymPCjKLHqDiXpRAqXTJPIwcRSJCIubbVC2JgLhLDxXx8wXAibCGmJPNcRLAYnqtbAMb0AJwVYcDNOreE+05+wqVOBNBxEe2pWFhK1UFoPk2cbAGafoGuNE7UorkaIFEC1IMYm+H+BVNEKrSu+9dJItqVUstxATiJJFaE+UJE7Kh38fu0eDApUFFlpdnhocbgxGVQGFpg52OQIPFbnqR0gZuVUDYgwh0zSK0jhkmkSOZi0H4GTl8HOv0U+e4foaQmPE4PJSnHJcMxhUYtSEbVI1xl2KszKQqMWdTvA6wKDOX7UYtIF8OatIrVmMIjX5n9rNR28HrCViPXSTTalVHLAQE4iSQV+H6gBeWbwdQJ5NA2jqmI1q9hMBg412SmwGPmsrg1vSOuQQnBsnP8ydJkOFOWZOOeEyt58SZIQpHDJNIkeTAYMh0tfiX72HiktUTIMGr+EvFIURSHP1CmdlM4z7FR21kQaVBgLgxFOWgar7wavE7GbGwCvEC2qQSw39LPdPxe6nSSSFHCsw4Xbq+PyahxuduD0eANBV4vRQL7ZgKZDc2gRrg89yt/+6wYFlpw2CqNRplMzRT/75c5CunMwiXX2HqkryVYCf/1R75xhd26vLZ/c886aaLU/9Z/GLySd/yNx+d6D4GgBvIAi3pOTlgWX9yeyvdtJ0m9JtbX+wDwzmq5zsNEOgEFVhL2VDg63F4fbE1OgxKLIZmLiENlNlEmkcMk0qTqYROpKKqwQk6ZTcIYd84clWnvtqNNFcXG8zppIniLQ80LS+T+Cudd3bTHvb5EWP9nc7RSCnA/Tv0iHtf74ikK8uo5X0zEbFdTA76qOqui4vDHvHqBzqshmUvBqSAO6DNNPf8GziM4HE5MtfCBfIgeTaJGJphrhuqt5oGR40qIo5g+LuiN2R9SMK2DPmuidNdFEz/hzUlNIajCmp+U5BWTkAJ2N3U4hyPkwuUmy+3Jo54/NZBCtyCmw1t9Z24pBAYMKbq+OUdVQEEYSXq2rIIlG53X+f3t3HidXWSZ6/HfOqa33Nb2RfYHsBBOIAQwqGRaRK+CwyThMdGTAgCLOHXAQ0KuA4IJyBQJ4rzAjDooKCnMFI5BEJAIJZl9MOiFrV3cn3dVVXd1VdZb3/nG6Kl3pfUmqq/v5fj79SdKnlrdOqvs89b7P87ztpiJumWw/0iIN6DJIApeRYOoFsHA56q0fQMux4xUwgUK0hf/U+8Wkr6okO+EGEuEj7h49A/yE3VtJ4d2/2cRvCn9ASW+zIrVvwA2/gvotXXNzeisDb9wBZtwdc3eyPJE0oxfokVbt1KGv8lXZH2ZkGux7OVn509yWwLIVLe1m6lefz9AxbadfMxuW5fDy5joOh9qoLAiwbFYFe4+2opRGRUGAUFuChO2kUuN8hoamabSbTo+P2RtHQXObydFofFD3F0MngctIsHcN7X/5CfGYolWV4aChK0V+LIH/Lz8hp/rMnoOXvqqS8iuhrclN7o0EB/QJu6+SwuKW7diNu1AlpWkbLKaeOzkrUr+l66xIXwFXy0F3GwMz5u54faIsTiQdERfokVTtRH/KV4e2Iag4OYbyXt52JMz2I2GicTtVwQMdeSiWg2bB9iPhHmc2lFI8sXoPK9fupbXdSrblJC/g4e9mV+LRQdc0CnM8hNosTFu5y0dWsq/T4DkKjkYkcMkUCVwyzXFo/sPDOJEW6inBoxtomvvDG3YcKiNNxP7wMCU3fcS9/YmfkvtTlaTrcMFdkF8+oE/YnUsKTwxMNE1jvL8N2kzalYduQoveZ0X6CrjyxrnLXNFG8E4cNYmkcoHuXl/vtaFuCCqG31Dfy8da44RjJrajug0jNCAcM2mMxNhyCBpb4+T5DKaW55FwFD9b9wFPrd2LrXCDFNxfDZGYxYvvH0bX3WWhk2VnXfjkPbjolQQuGeYc2YTduIsW8vEaBn7iGMrG1gzihp+QnU9R4y6c9f8Xfecr3eeC9KcqKb98wJ+wkyWFPqP7AKfdKMbEgzLj4PP2/NzdzYr0J+Dy5YLhH9GJpAM1Vi7QA8156Ou9NhwbgorhNdT3cnObiWV3H7SAOydi2opv//d2WuPujIlX15hQlsffLzyN/1i3H1u5AYvldL3vyQxaAN7b33xyn0D0SAKXDNt/6CAFtolfMyh1DuNVJhoKhYapeWnSivDa7VirH8anqa65IM0fHN8SYJhLXEtzfXgNjYTtENC7bimwnckc0E7jQ+YRUHkDe+7+lIF783pvvJeFOxGPhQv0YHIe+nqvDdeGoGL4DPW9XBAw+rVgUxdqoyw/B0NzaOvYmfn9/c2p+57k+KRHPmPszIiONBK4ZFgTBRTiUKmOoqGw0VHoaCh8KkElR1EoMD1QNqn7BNhAEfjzh31mYk5NIdMq8tlRF6GqUE/7VKWUornd5o3yz3CO/eTAn7u/ZeALl7tfIyyRdLBG+wV6sDkPfb3XhnNDUDFw3c2gDeW9nLAc/nog1K/njttwKNQ+1Jcw7M6dln35daOFBC4Z5quZj4GDgU0CL8kUNYWGhYaPBAqNRG754LcEGOTMhK5r3HLBNP79xS0Ew3GKc734DZ247RBqM8n3G1xw8d+j6bMGXl470J4iIyiRdChG8wV6KDkP/Xmv3XLBtDGV9zNS9DSD9i9Lp6bey5WFGnFTpdrq+71a6r08u7qAmGkTNx3aTZu4ZWM7ijazf81UnP5My2TABWdUZHoIY5YELhk2Rz9ARDewHB0PdseMi4aGwsDBQUfDIdDT/1R/twQYpHOnl/PAlfN4fHUtu4IREraDz9CZWVXAFz+anPofZHntCO8pcjKM5gv0UHMeku+15EWyxXFzGmZVF0gfl5Osp5yk3mbQvv7SVm5YPJG/1Uf4W30rSqlUybGmubOL1ywcz4HmdlTHgdaYxY5gmJ11Edbubhz0eL06DLKaeVgEPDqt8X52sRPDTgKXDNNjx8jx+aiPjaOEEH5MdBQOGjG8RMmjgmY0M+buCn2i/m4JMGTKXbJS7t+6lBMO9rlHaE+Rk2m0XqCHI3/n3OnlfHhqmXTOPYV6m1F5cu1eWuMWlQV+4pYimrDw6DqVBT7qIwl+t+kIjlJpQUuS5TjUHm1lT2MrO+oi7KgLc7B5eJZ8Mhm0FAYM8v3erF3OHQ0kcMm03DJ8Pj9FyoI4oJJZ9srdWsfnQbO9bk+TZHempFNQFtzTJ66dwdbh6zkywnqKnAqj8QI9XPk7uq71WlElWwIMn95mVP7nrzaTsBwCXp39TW3ELQfHUR0bFerk+b3srIsQ8BpMKs2lPeHQbtlYtsK0HZraLH74xz2ZfolDUuTX8Xu9qZnmsjwPjVGLaRX5WbmcO1pI4JJpVWcSMYrJi29HAZZmpJaKAphoiUba8ieS69NPeVnwQHIWALmYDFBfF+hscyryd2RLgOHT18/3gaZ2WuMmuubmmeja8SZxbQmH9kQcB9Asmw+a2rH7mYxSGPAws7qQWVUF7KwL884HI7esOGYpxhUdX85tjGb3cu5oIYFLhjlK0dgaZ2LHv7W0P93Zl2Dcy+QrHkZ/+9FTmgvS35yFn797gNe2BeViMsad7PydEdFxOIslEjYr1+5lf1OUSaV5nD+9LPXzjQbtCRvTttE1HZ9XI8erEY4dT45NxSUq7Q8Sdu8By4SSHBZOKqEk10d5gY+pZfnMqMrHthSXPPqnk/Jah0tlUYC2uDVqlnNHCwlcMqx2y9sUmM0c1coopLWjj4vjVhJpfsLkk2s2U9vqZcZJSL7tTX9yFhoTNv/7jd3YjpKLiThp+TvScXho7v7NZn6x/hBWp1mRR/6Iu3OyrnEo1EY84aSCEa+hYQzDecz16nx64XjW7GzgD/X1btWRoTO3uoCaktwhNt4/uTy6xh++tJTdR6MykzzCSOCSYe2hBkqwCGmlRLSi9M65+NGUQ7Fqoj3UcMpzQfrKWYhZNu0JGw0YX5IjFxMBnJz8nbHScfhkuPs3m3nu3YNdvq+AuKU41E3CbMJW0MdMSn/ETIenVu+hzTr+WAnb5t39IfT9oSE//sl07aLxBAIeeT+NQKO3dCNL5BRXYOHBo0wA2pWfVnJoV24rfI8ysfCQU3zqewYkcxaa28xUOWOSUopjrQk0DcoL+r6YiLElmb9zwenjmDe+aMiBa39m/8ws7zg83OKWzbFwjOff6xq0nCoOpAUtJx7LpOJcL9edPR7PCe9Nj65xwzkTuP+q+RkameiLzLhk2LR557Lt9xOpjtcStErTSgo1DUq0MHX+acyZd+4pH1tfOQt+j44C/EbX2RgYHe3rTyapjum/0d5xeKgcRxGNWfx20xEONkcpywtw5vginuzYhHCs0nEDpM7d+d1NGTVWfHQaX1g6jf91+dy03J+bl07F5+v+d5oYGSRwyTDdMDgw6yYK3v8GFVoTLeST0Dz4lEURrUScHA7Muol5PQQHJ1tvOQsXz6ni8Tf3yMVkEKQ6ZmCypePwqQpGLdshZjnETZuY5fDMW3v52bsHiSXsEZ03cirpGlw2v5o/7T5KpN1E4RY9FOd6U0ELgM9n8KVlMzI6VjEwErhkmOMonj82Fb9+E8vVi0zWjlCEhal52M1EfqpfSfzYVC51VMY+jfeUswDw2rbgiL+YjDRSHTNw2dBxeKjBaE9Bj1KKhO0Qbbd4eXMdB5qiGLpGcY6PXfUR3q49SmOrzGp2pgGFOV4euXoBAC9vruNwqI3TinO5fH41Ho9kSWQzTZ2YvCAGJRwOU1RUREtLC4WF/b9QbznUwr/853o0TSMUbWe6vZdiIoQoYI8xleK8HJRSPPnZRSMySez4Rdju9mIy1i/CJ16MZlUVsPzZ99hRF06rjgE32AuG48yqLuDZ5efIslE30oKDjtm/kTBT1VMw2nzCz0FvrfWTrythOXgMjcnledxwzkQmlOby5Jq9/HFnPeZYXvc5gaEDyi3TNnQ3WFGA7YCha9x5yRmpWRUxusiMS4Y1tSWIJmxaYya2gk1MPX7QgVhLO/kB74jNExmt7euHQ3efwCsKAxxsilKa55fqmEEYiR2H+1uq7SjFk2v3dpmROXdqKf/17kFa4xYFAQ+6phNN2Ly/v5n3Pmjud2O3saQw4CHf7+HiuVW8+NfDRNpNtxkeUHTCUpAYfSRwybDiHC9tcavHBDpbQVvcojjHe2oHNgAj8WKSaT19At/X2EokbpHv9xLwds0LkoTmvmWy47BlOV2WHXYEI32Wam8/Eu5ooW9TnOujQNeImTYbD4R4u/Youqaha5pbwZeRVzZyTSwJELNUqu1+eZ6XhlaTaRX53HPZbO6+dJYsBY0xErhkmKNUWlOoE7ciArAchZPhFb2+kg5HW/v6oejtE3h5vp9I3KIxEqcg4OlyoZOE5pHr6bW1PLa6NvXpXge++co2LplT2W2pturYd8zQoKU9gd/QKcr1EmoziZk2MdNOfWCxu9u4dIzTcH8fhmM25QX+1DJ0Q2t6TpOua1z5odMyPVxxCkngkmGbDrWkf6O7312ae7uzJpackjGdSCpgBqa3Zmk5fgO/x0g178v1H/8RlITmkevptbU89OoubEdh6O4vTgW0tJm8sOEweT6DuGXj8xgopbAdRcyyiSUcwnETy3F3S462xPv9nB5dQ9f6bqmf7Tw6+Dw6cdNJVf74vTpeQ2diaS5FOV72NkZlGVqkSOCSYVrHhs9Gx0ZmSpH64dU1UhucaRn63SUVMAPXW7M0DY2KQj+HmtpobE1QoWsjrjpGpLMsh8dW16ZyTWwn/fOF7ShaYxaW7eDz6B1lys6g5k+S/UY0zZ25s4Y8+pFP0zRyfR7K8gw0rWPTe9Mm3+/ha5fOkmVo0YUELhm2YGIxXkNP/dJTHcGLprlfpuXgNXQWTCw+5WOT/WEGp69maV5DpzjXx4TSXBrCMfkkOYJ0tyT68uY6WvrIPXGANtOhzRxaP9jk5EqmPqhkwoLxxQR8Rlql2KzqwrSfBVmGFp1J4JJh804r4vTKfLYdCWNa7gZkuu4GL6blfmo7vTKfeaed+h9c2R9mcPrTLG12TSE/vfFsdgQj8klyhEguie6pj2DaCo+hMbE0F6+hDyn7ZFJZDoea27EHENOMobiFZ288m0DAI7Mqot8kcMkwXdf42qWz+MovN9IUTWA7TmrGRdc1yvJ8fO3SWRn5Ie7P/jBSAdNVf5uleTy6BHwZcOKsyumV+by1+yjfeHkbLe0JdE3DtBWJmEMw3P+clJ7sP9Z1E8OeJHuRdP5pH81BzFkTisjNdSsm5WdB9JcELiPAudPLeeSaBTy+upZdwUiq7O+MqgK++NHMLR3I/jCDJ/1tRqY/727ksdW17G1oJW45KBR5fg8t7SbReGbb5Sfz3JJ8Hg3LVlm915BH15hbU8DGQ103Wj1rQhEvrjg/A6MS2S6jnXPXrl3Ld7/7XTZs2EBdXR0vvvgiV1xxReq4Uor77ruPp59+mlAoxHnnnccTTzzBjBnH95Voamritttu4+WXX0bXdT796U/zox/9iPz8/NRtNm/ezIoVK3jvvfcYN24ct912G//2b/+WNpYXXniBe+65hw8++IAZM2bw0EMP8YlPfKLfr2WwnXM7G2mb7jmO4safvtux5OGXLq+DMNL+T8eCzr1WKgsCLJtVQcJR/GbDIZ5cW0tbx34+0oX25EvustzWZnLHrzdzoCnKxNI8fvDp+amZFiEGKqMzLtFolDPPPJPPfe5zXHXVVV2OP/zwwzz66KM8++yzTJkyhXvuuYeLL76Y7du3EwgEALjhhhuoq6tj1apVmKbJ8uXLuemmm/j5z38OuAHFRRddxLJly1i5ciVbtmzhc5/7HMXFxdx0000AvP3221x//fU8+OCDfPKTn+TnP/85V1xxBe+//z5z5849ZedjpPVCyYb9YUa6kfZ/Opo5jmLlmj2sXLuXSLuVmj0xOsqKBxKoJHuI6BpYQ8u3HfUWTylhw/5QWj8qj65x7aLx3H/VfAByc72s/OzCTA1RjDIjZq8iTdPSZlyUUtTU1PDVr36Vf/3XfwWgpaWFyspKnnnmGa677jp27NjB7Nmzee+991i0aBEAr776Kp/4xCc4dOgQNTU1PPHEE9x9990Eg0F8PndJ46677uKll15i586dAFx77bVEo1FeeeWV1Hg+/OEPs2DBAlauXNnteOPxOPH48fXvcDjMhAkThjTjMlKN1P1hxNiWsBzilk2ozWTL4RD/uW4/f9nbNODlnmReSXff1zoOSuzSvTy/waZ7LnKDxrV72d8UZVJpHjcvnYrPl5kd7cXoN2JzXPbt20cwGGTZsmWp7xUVFbF48WLWrVvHddddx7p16yguLk4FLQDLli1D13XeeecdrrzyStatW8fSpUtTQQvAxRdfzEMPPURzczMlJSWsW7eOO+64I+35L774Yl566aUex/fggw/yzW9+c/he8AgmLf3FQA33EpnjKOKWQ7tp8bf6VjYdDLHtSJgddWH2Ho0OaT+fnu6pON69eizSteOvv7vToGtw+4UzUu31v7RsRje3EmL4jdjAJRgMAlBZWZn2/crKytSxYDBIRUVF2nGPx0NpaWnabaZMmdLlMZLHSkpKCAaDvT5Pd772ta+lBTvJGZfRSpY8RH8NR6dl03aImTbBlhh/PdjMlkMt7KiLsDMYoTXe/7ZsuubO5jqOGtXVOUPl0d3zlKpoxO3YW5Tr5ealU3liTW1q+U0DCnI83Pqx6bKRociIERu4jHR+vx+/35/pYQhxUgx2xmQwnZaVcmdTwu0mWw63sLHTbEpdS6xf4/XoGpaj0DUwNC0VsID7p4XNEHvDjWq2Ax4DDN2tbErYCkPXUrss//P5U2UjQzFijNjApaqqCoD6+nqqq6tT36+vr2fBggWp2zQ0NKTdz7IsmpqaUvevqqqivr4+7TbJf/d1m+RxIcaSwc6Y9LfT8sJJJcQtm9rGKH890MyWw26QUtvY2u8EWkPXMDQNv0dncnku50wu5ad//gBD1/DoXS+oY72AyG+AobvbESRnVQIeHa9HpzTPR3PUJBIzsXFnVIpyvamgBcDj0WUjQzFijNjAZcqUKVRVVfH666+nApVwOMw777zDLbfcAsCSJUsIhUJs2LCBhQvdjPU33ngDx3FYvHhx6jZ33303pmni9brld6tWreKMM86gpKQkdZvXX3+d22+/PfX8q1atYsmSJafo1QoxMiRnTCIxsyMAUNgObD/S0ufeVN11WlZKuV2gbQdDg/f3N3PVE29zsKmNcKx/Sz4luV5mVRdSmuvjnX3HsByHsjw/fo9Bwnaoa4nzxx0N5PgN2uI2ODYO7syB3rHsMYQUmFEhL+BF1zTK8rvuB3T/FfM4Z3KpzKiIrJHRwKW1tZU9e/ak/r1v3z42btxIaWkpEydO5Pbbb+fb3/42M2bMSJVD19TUpCqPZs2axSWXXMIXvvAFVq5ciWma3HrrrVx33XXU1NQA8JnPfIZvfvObfP7zn+fOO+9k69at/OhHP+KRRx5JPe+Xv/xlLrjgAr7//e9z2WWX8fzzz7N+/XqeeuqpU3o+hMik5IxJYyRGu+mkXex1DWKm3eveVMeicRKWQ4EfwrEEkXaLdtPGtFVaqey2I12bkSV5dHcWJdHRH9/v0TmjIp9bLpjKU3/ahwJOK87tdjanosDPvngbZqdx2wrsoZ2WrKbh7o11+7LTWbW9vtf9gGRGRWSLjJZDr169mo997GNdvn/jjTfyzDPPpBrQPfXUU4RCIc4//3wef/xxTj/99NRtm5qauPXWW9Ma0D366KM9NqArLy/ntttu484770x7zhdeeIGvf/3rqQZ0Dz/88ClvQCdEJm051MINP/lLrzMhhQEPz/3zh5lTU8imgyGCkRixhEO4PcHa3UdZ+7dGzAFMb0woyWF2TRHzxxfh0zV+/OZuWmLpoYYG5AcMfIZBaZ4Pv0cnZjpYjoNH1wl43SWQg8eixMfompAG6HpHaXfHUlCyKmpOTSG/7ehQK5WBYjQYMX1csp0ELiLbvbGjns8/uz5VfdN5X83OvyU+Oa+K9w+GaIzEseyBVevk+QyuO3sCZ00s4axJJVQW+PEYOo6juPAHq9l3tK3H++qaG+gcbU3QbtqpC3SO16A018PB0ND3Fco2ybgj3+9JnZMkTYPSPB+PXLNA+i2JUWXE5rgIIU6trYfDaUFITx9pXtnSc5uAEyWbuCnlzgBMKc/l7stmd/mkv+lQiA86BS3dBU2OggNN7V3GGE3YRBOjf0HIqwOdSpZRCstxE2kfvW4BT63dy85gJJVQPbOqgC9+dLoELWLUkcBFiDEubtnELYf61v6VHvdGx+2m2tqxYWFyucLQoCTXR1PUZNuRcJeeQK9uCXY705P8d2+N0Ea7ZPBnnVCybDmkSpaXnl7B+dPHyVKQGBMkcBFiDHEcRcyyaY4m2HgwxKaDLWyvC7MjGOZYa6Lfj5O8mGokm7yBabut8dsTtlvN0+mi6TiKlnaTXL+HupY2nn/3AB80RZlcmsc9n5hF1Dw+YyKL167kVgTJJnDuHkw9lyxLk0gxVkiOyzCRHBcx0iTb5LclLP4WjLDxUIith90g5YOj0SGVCCdDkmQn1eQsiaPcf/u9OhrHAxeFwrTcSqXunnZ2dT7b61oHP6BRwqODhobqKEM3dI07LzmDLyydlrbrtZQsi7FMApdhIoGLyCSlFAnbIWY6HA61sfFAiC2HW3h/f4gPjkWJn8ItjjXA59HcICXZQl6DuNX7r5qeNjscC3TNTbCNxju31U+fURFCuGSpSIgsZNoO0ZjFrzccZtPhZqJxG9N22BmM0BDpf3WNobm9UzyGhq5pROL9T3LtnD2hTvh7X0FKd7I9aNEBNLr0v9E78lN6c/3ZE/jm/5grMypC9IPMuAwTmXERJ4vtKOKWTXvCZneDuzPybzceYWcwPKDlHr3T+k5311GPBoOIN0Qnfo8GdKr8QZGw3M0KL5lTya82HE5rxufRNa5dNJ77r5qfqSELkXUkcBkmEriMToPdbHCwkhsOxk2HYLidjQdDbD3cwva6CDuDYaL9nBHpXIac/LfPoxHLgsgk4NExbYVCoaHhNTRip3CpqzuGBrk+o9sZqcKAO3EdjVs4io7Zq47Kn47NCpN5KomEzcq1e9nfFGVSaR43L52Kz2ec6pcjRFaTwGWYSOAy+gx2s8HOekuodBzFxoMhGiJxfIZGwnbYeiTM9gHujJykd1qm0DXZn2c4aEBxrpfxJTnUtcSpyPdyNGqSsB18hk55npeGVpNZ1QV8ZHoZj69xK38kT0WIk0cCl2EigcvoktxssDVuUZLrw2e4++c0t5nk+41eNxtMenptLY+9WUs4ZqZmPgoDHpafN4nCHB//+ZcDBFvaSdgKW6KMEWH6uDyOtiZSgcmcmkJWfGw6QMf7waY414vf0InbDqET3g9S+SPEySeByzCRwGX0cBzFjT99lx11YaoKA6kN/cBdygmG48yqLuDZ5ecA3e//8uTqPTz06q5uc0kGojDgYXxJDjOrCvn4zAq2HA7x5Np9Q3xU0V1isaFrPPWPC6nID3S7NJg2A9exUeFAZ+CEEEMnVUVCnGDbkTC1Da2U5PrSghYATdMozvVS29DKz989wGvbguypd9use3SN8WU5nDe1jMdX7x1w0OJulKdRnufj0nlVbDoY4kionYZwnFDbMRpb48yuLhi21zkWJZfQTvy0ZugaxTkeKvIDPTZxO3d6OR+eWibdaYXIMAlchDhBU1sC01b4jK5T/Eq5n7QjcYtHVu3CtJXbjt12+6gEI3HWfxAa9HMXBQwsR/HatnpM20lbptpRF2HroeYhvLLRzaNraRU7Jwp4dby6jtORAK2UQtM0/B4dQ9eZXVPEnJreZ0ulO60QmSeLr0KcoDTXh9fQiFs2jnLzT0zboT1hE2pLcCTUTmvM4ljUJByzaGm3iCZsTHtoq64KaGqzaG5LEI1bVBb4OzYRtFAKKgt8tJtjd2VXw63s6U5Jrpc7LzmDklxvj8e/+nenU5bvI+A1qCoKML4kl6qiAAGvQWmel1sumCazJ0JkAZlxEaNaf8uZO5chVxT4qSkOsKu+lRyvTsx0O9Im7FNTkusodyfgD461ETOdVFlwwKtjjJELa085KI/fcBZPr93HtrowpuXg9ejMqXYTaM+dXs6cmiIee3NPr8eTeSpxx8Gra8yuKZQ8FSGyiCTnDhNJzs2cnoKTnsqZb146lYWTSzta5NskLIfDze3sqAu7Gw7WRdgVDJMYwAyKu9Gghq4pzP43n+1RT+3vx3KZc2HA4Ll/XsKcmsJeg9G+gtVT3ZtHCDG8JHAZJhK4ZEZPwcnSGeU8984BIjGT4lwfXl0nbtuE2iwCXo0rzzoNy1Zsrwuz5XCYlnazX89naBrjCvwEwzF0Dby6lkrgTf6ZsGyGuGo0ZhUEPLTFrbTzZ2hQlu9HAd+7+kwuOH1cxsYnhMg8WSoSWatzr5Ucr4HH0NxP04dDvLvvGD5Dp7LQj2k7tMYsYqZNu+XQ3Obw+Oq9fT6+rkHAY2B2LBH5DI3ZNQWcN30cP35zjzsjorkzI0qB5v4N+SgweDlenYklBbS0W5i2g9fQKcrxELcVbXGL0lxfpocohMgwCVxEVnIcxWOr99AUjWPZilBb4nh7+45N7RK2TfRo24CWVjQgx6dzyZwq1vytkZb2483j4jZsPBSm9mgbuT4P0YTdsZlg+hNoY3mb4yFqjdtUFGiU5B0PUJRy/39nVRf0WfUjhBj9JHAZRUbz2r1pOyQs9ytuOWw+FGLroRZaY/bxmY9ksNDxp+qmX0d3DB0MAM0tp21POPy/rUHipjvToqceW2E70Bw18Xt1nB4iIplxGTi9I9jL9RkEw/Fuu9NK1Y8QAiRwGTXe3nOUx1fvYWcwgmkpvB6NmVUFfPGj0zNeLTHQgMoNTtyk2YTtEEvY7KiLUNsY4WirSVNbnPf3N9MSs47faRDBgoa7/KNpx/NUdE11JO0eryCyTwiIgLTjIp2h0WuOj0cDTe+0g7JSWA4U5Xp55OoFPPXWXmobWmnp6E47q7pAqn6EECkSuIwCb+85yld+uZGm6PHlEhLwzr4mdjds5JFrFpz0X/oDrey55YJpLJlWRqKjP8qWQy00tsbJ83mYVpFHpN1iRzDMmzsbeGdfE5GYNeyrL15DQ9M1bEehHLeRnKFrDOQz/YlVPiOp6mewK1a65t73xARZn0envR8Bm99jMKHYx5Hw8c0Iawq9HAwlaDfdxGUP7kyXo9xlPUPXWPHRaXzkjHGcN6N81M4cCiGGTqqKhslwVBUNZqnHcRSfeuwtth0Jo2ng0fXUBctyHJSCOTWF/HbF+UP65d/b2Pqq7Ekmz4LCUdCesMn1GXzl784AFD/7ywH2NESIW8rtWaJpJKzBzWgM5GI9mlNRks3aOneIVUr1q9rJZ2hMH5dHOGanEmQLAwYHQjFaY1aPwZnRkahcmucDtG6Xe86bVsZ/bw3KDspCiEGTwGWYDDVw6W1morfZkk0HQ1y9ch22415gTtwQ0LQdDF3nhZuXcOaE4sG8tF7HBnS7i/Kx1jjRhIWhubU2pu2kZoM8hjvGHK9BJG4PaGdkn6ET8OrELQfLchiGlimjUnLfI0/HDFIykO1PD72JJTm0mU6XwCPPp3MsmiAat/F5NHfPn47lHl2DhKUoyvXy6HULePpP+3rcjFB2UBZCDIUELsNkKIFL57Lezhf/5o5PqQ9cOa/H4OWZtz/gf728DY+hYWhdf/nbysGyFfdePod/OnfygF9Xb2PL8+kUBDzUtcQYl+8nbilM20bXdRzlcKg5BrgXUU3rf7JsZzlenVyfgaHpGAb4DIMcrzuGvUfbBvx6xgodd2nnxEA21sdM1lkTivifF8/scRfkbUdaeOjVXdiOwmNoqdkXy1YYusadl5zBF5ZOG9WJ4kKIzJIclwxzHMUTa2ppjVtUFQZSF5qAblBVqBMMx3liTS0fnlrW7S9+rXPiaHfXBXXC7XoZx4kXGiBtbMmH8xo6ZXle6kIxjrTEKMn1cqCprWNZglRvk85D6G94rOF+ek8uaeT7PUTjFjHTXVrQNbf1fVkPe9IIl67T7c7WXkPrcU+lsyYU8eKK8wF63AU5GUA/trqWSLuJjft/VpSbvtwjmxEKIU4WCVwybNuRMLUNrZTk+rq90BTneqltaGXbkXC3F4IFE4vxGjqW7aDr7p42SQp3g0CvoXPm+CK2HGrpMUflxIqkMyrzWTa7it31EQr8Hkxb0XlyTin3E7cZVzRGEukzKUOYw1OkJ4UebU1/bFspogmbaEIWiXrjOKCMru8Hx3GX+n587QJe3FTHgaYoE0vz+MGn55PbKRjsLfD4wtJpLD93iiz3CCEyQgKXDGtqS2DaCp/R/S99v6HT4iia2hLdHp93WhGnV+az7Yi7qZzH0FPLMpbtoICa4gDfW7WLvY3RbnNUbv/FX9MrkuJuRdKWw2Ecx531sGyHmGV3bDjo/ml33OFkrjXKOuYgacnlm+PLdLbjJukWBrzUlOax8rMLB/3wHo/OlR86bRgHLIQQ/SOBS4aV5vrwGhoJ28Gv68QSDpbj4NF1Aj43MdKraz22Otd1ja9dOitVDm13VBJpmnss3++hLWGzMxihJNeHV9eI2w7bjoT5t19vRsOdMelcCuwot9FaOGahAR8cs7EGkECb/IwvQcfJ190uyuAu53l0932lHPf94PfoeAx3N2TpQCuEyFYSuGTYnJpCplXks/lQCMt23JJglbzQaHgMnfnji3u90Jw7vZxHrlnA46tr2RWMpHpnnF6ZT6g9QV1LjIp8PwoNSykMTaMsz0uwJU4k7jZx03E/kZ+YQOtWowwsBJGAZegWTylhw/5Q2rn36BqLJpfw3r4mtI6k2OT/VzI3SNfcZaKCgPujXer1pWZc2k2bfL9HOtAKIbKaBC4ZpusaS2eU8/aeo+k9NhRYCYWhOSydUd7nhebc6eUsnFTCxoMhGiJuI7eEZXPvb7dS4PdiK0U4ZmJaNgq3GiRmHc8T6e9uxp1nUzTc4CphKaSP7PC669JZzKkqZOXavexvijKpNI+bl05F1zUWPfBHWtpMfB4N6NSBFpUqSe7cgdZMdaAtlA60QoisJ4FLhjmO4nebjvTYbdVR8LtNR/j8+VPTghfbUcQtm7jp7t0Tt9xmYc1Rk7aEhVfXaYomiFkO0UR8SMmshub2XrE6ohtNc5civLpGNGHjAF7dDWYcdfxTvwQzXQO9/sSHeT6DcMzC5zP40rIZXY6v+Og0Hnp1FwlL4THoUpIsHWiFEKOZBC4ZtuVwC7uCreiauxSAduIeLoqddRHWf9DE9Ip8Nne0xi/we5lemYfeUYn01wPNPPv2fvYfixK33MRZ21HD0n5+XIGf4hwv4biFZTv4PAbFOV5a2k1aE+1oQOdO8COl5X2meXQNhXL/jzoiF8c53r32xEBGB0ryvPg9Ro85TUCq5FhKkoUQY5EELhn214MhNxnX0NDQOnqedMxs4PbjsGzFz989wP6jreyqd7vXenQYX5LDzOoi9je1se1wS7+XewbjUChGzLQ7giqzo5OqMaimcmOBrkFFoZ9wu+n2t8H9/wz4DExLYTkOk0pzMG3S2uo3tJpMq8jvM3lWSpKFEGOVBC4ZlmwM5zgKS6UnxyYTLhXwyqYjWJ0iBNOB3Y1t7G48ed1jk4sKDZF4eoCiIBq3aIsP/8aHp5JHx62yslRazg5otPdjr6Rcr0Gb2XUJLt9nMGVcHnUtcSaV5hLvCFQ8uo7fo3Eo1E7cgnDMpiTPR5HhJW47NLS6nZL7mzwrJclCiLFIApcMWzCxGEPTMLtZX+ncjM0aRISgM7Q8k1RT3m6eW5H9My2Ffg+arlNWYGBoGrZStCdsd4knZhLrZSfkPJ/BrR+fwVu7G9l8qCVVyTV/fBErPjYdcPdwqo8kKM71kufzELcd6iMJSnJ93LB4Imt3H6W2oZWWVPJsgSTPCiFEHyRwybA51YX4vTpmfPg7wUpybO+uPnsC2+si1Da0Enfcfjmzawq5eE4Vj7+5h4Tl0BRNpJ1HHXf3Y59H5/zp5fzL0qk9JsA+cOW81J4/3QUnnz+/5/sKIYTongQuGbYjGMHvMWg9CYGL6F1BwMuzy8/pdo+m17YF2VEXYWZ1PuF2+3geSo5BQ+R4HkpvCbDnTi/vcc8fkORZIYQYDAlcMqypLdFjO39xcuiam89yWnFuj8HDLRdM499f3EJDxKQ413s8DyUysDwUCU6EEGJ4SQlChnlR/d45WQycRwOvoeHR3Z2Rvbqbs1OQ4+Xy+dU93u/c6eU8cOU8ZlUX0Ba3aGiN0xa3mFVdwANXzpM8FCGEyBCZccmwf39pc6aHkNUMDfxenXbTSfVKyfHq2DYkbAdbuW9yQ+9o0uaQatLWV+lwX0s9QgghTj0JXDLsUCie6SFklKH1f7uB7uT4DLdhW156ZVBBgYfzppXx31uDvTZp64ss9QghxMgigUuGeQwdyxl79T8a7kaABQEPoTa39Fih0NAIeHUKAx7qwn0HdY9ev4Cf/nl/l8qgZOXON//HXGnSJoQQo4gELhl23sQcXq+NZnoYJ5Wupe9iXBjwsOJj0/jTnmPsqIswuaxrkza334mX5jazx8e9cOY4Pj6zio+eXtnjco40aRNCiNFFApcMG+1BC8C1i8ZzzpTyLrMec08r7rFJW77f4IErF/B/3trL6zsbuzzmhTPH8X/+6RxAlnOEEGIskcBF9MuJGwL2d6djgPsum0Mg0PWtlqzc6a1J27nTy4nFLL71/3bwQVOUyaV53POJWd0+nhBCiNFPfvuLHrcG8OkaiY6tCBQdO1Z36G8J94Uzx/UaZPSncicQ8HD/VfP694RCCCFGNQlcMqw6B+raMzsGv89gckkOh1tixG0Hv6FzWlGAxqiJR4dgR5Jsd8HKDedMIBiO9bmc0xtZ6hFCCNFfErhkWJvyANaQHyfg0TBtlVZarNP3ZoiGBuV5XhqjJhWFAfyGTtx2aIyaHXkm8/jvzUf4xfpDWJ02gvToGtcuGs/9V80HkOUcIYQQp4SmlPRtHQ7hcJiioiJaWlooLCzs9/2WfX8Nexpbh/z81y48jW99ah4r1+5lf1OUSaV53Lx0Kt98ZRvPvXuwx/vdcM4ELptfk8ozMTvyTKZV5KftVJxI2F0e2+czhjxuIYQQYiAkcBkmgw1cQpEYC+5/fcjPv/MbF/c4w3H3bzb3OWPiOEo6xAohhBjxZC4/w4oLApTleTkW7blfiVcHs5cedX0lwN5/1Xzu++ScXmdMJM9ECCFENpAWoid47LHHmDx5MoFAgMWLF/Puu++e9OfccM9FlOV5uz1Wludl9wOXceHMcd0e728CrM9n8KVlM/j+NQv40rIZsswjhBAiK8lSUSe/+MUv+Md//EdWrlzJ4sWL+eEPf8gLL7zArl27qKio6PW+g10q6iwUifH3T71DYyTGuIIAv7ppMcUFgdRxSYAVQggx1kng0snixYs5++yz+fGPfwyA4zhMmDCB2267jbvuuivttvF4nHj8+F464XCYCRMmDClwEUIIIUTvZKmoQyKRYMOGDSxbtiz1PV3XWbZsGevWrety+wcffJCioqLU14QJE07lcIUQQogxSQKXDkePHsW2bSorK9O+X1lZSTAY7HL7r33ta7S0tKS+Dh7sueRYCCGEEMNDEiQGye/34/f7Mz0MIYQQYkyRGZcO5eXlGIZBfX192vfr6+upqqrK0KiEEEII0ZkELh18Ph8LFy7k9dePN4NzHIfXX3+dJUuWZHBkQgghhEiSpaJO7rjjDm688UYWLVrEOeecww9/+EOi0SjLly/P9NCEEEIIgQQuaa699loaGxu59957CQaDLFiwgFdffbVLwq4QQgghMkP6uAyT4WhAJ4QQQojeSY6LEEIIIbKGBC5CCCGEyBoSuAghhBAia0jgIoQQQoisIVVFwySZ4xwOhzM8EiGEECNVQUEBmqZlehhZTQKXYRKJRABks0UhhBA9ksrToZNy6GHiOA5HjhzJeDQdDoeZMGECBw8elB+OfpJzNjhy3gZHztvAjaZzlulrxGggMy7DRNd1xo8fn+lhpBQWFmb9D/ipJudscOS8DY6ct4GTcyZAknOFEEIIkUUkcBFCCCFE1pDAZZTx+/3cd999+P3+TA8la8g5Gxw5b4Mj523g5JyJziQ5VwghhBBZQ2ZchBBCCJE1JHARQgghRNaQwEUIIYQQWUMCFyGEEEJkDQlcstCDDz7I2WefTUFBARUVFVxxxRXs2rUr7TaxWIwVK1ZQVlZGfn4+n/70p6mvr8/QiEeGJ554gvnz56eaWC1ZsoTf//73qeNyzvr2ne98B03TuP3221Pfk/PW1Te+8Q00TUv7mjlzZuq4nLPuHT58mH/4h3+grKyMnJwc5s2bx/r161PHlVLce++9VFdXk5OTw7Jly9i9e3cGRywyQQKXLLRmzRpWrFjBX/7yF1atWoVpmlx00UVEo9HUbb7yla/w8ssv88ILL7BmzRqOHDnCVVddlcFRZ9748eP5zne+w4YNG1i/fj0f//jH+dSnPsW2bdsAOWd9ee+993jyySeZP39+2vflvHVvzpw51NXVpb7eeuut1DE5Z101Nzdz3nnn4fV6+f3vf8/27dv5/ve/T0lJSeo2Dz/8MI8++igrV67knXfeIS8vj4svvphYLJbBkYtTToms19DQoAC1Zs0apZRSoVBIeb1e9cILL6Rus2PHDgWodevWZWqYI1JJSYn6yU9+IuesD5FIRM2YMUOtWrVKXXDBBerLX/6yUkreaz2577771JlnntntMTln3bvzzjvV+eef3+Nxx3FUVVWV+u53v5v6XigUUn6/X/3Xf/3XqRiiGCFkxmUUaGlpAaC0tBSADRs2YJomy5YtS91m5syZTJw4kXXr1mVkjCONbds8//zzRKNRlixZIuesDytWrOCyyy5LOz8g77Xe7N69m5qaGqZOncoNN9zAgQMHADlnPfnd737HokWLuPrqq6moqOCss87i6aefTh3ft28fwWAw7bwVFRWxePHiMX3exiIJXLKc4zjcfvvtnHfeecydOxeAYDCIz+ejuLg47baVlZUEg8EMjHLk2LJlC/n5+fj9fm6++WZefPFFZs+eLeesF88//zzvv/8+Dz74YJdjct66t3jxYp555hleffVVnnjiCfbt28dHPvIRIpGInLMe7N27lyeeeIIZM2bw2muvccstt/ClL32JZ599FiB1biorK9PuN9bP21gku0NnuRUrVrB169a09XPRszPOOIONGzfS0tLCr371K2688UbWrFmT6WGNWAcPHuTLX/4yq1atIhAIZHo4WePSSy9N/X3+/PksXryYSZMm8ctf/pKcnJwMjmzkchyHRYsW8cADDwBw1llnsXXrVlauXMmNN96Y4dGJkURmXLLYrbfeyiuvvMKbb77J+PHjU9+vqqoikUgQCoXSbl9fX09VVdUpHuXI4vP5mD59OgsXLuTBBx/kzDPP5Ec/+pGcsx5s2LCBhoYGPvShD+HxePB4PKxZs4ZHH30Uj8dDZWWlnLd+KC4u5vTTT2fPnj3yXutBdXU1s2fPTvverFmzUktsyXNzYvXVWD9vY5EELllIKcWtt97Kiy++yBtvvMGUKVPSji9cuBCv18vrr7+e+t6uXbs4cOAAS5YsOdXDHdEcxyEej8s568GFF17Ili1b2LhxY+pr0aJF3HDDDam/y3nrW2trK7W1tVRXV8t7rQfnnXdel7YOf/vb35g0aRIAU6ZMoaqqKu28hcNh3nnnnTF93sakTGcHi4G75ZZbVFFRkVq9erWqq6tLfbW1taVuc/PNN6uJEyeqN954Q61fv14tWbJELVmyJIOjzry77rpLrVmzRu3bt09t3rxZ3XXXXUrTNPWHP/xBKSXnrL86VxUpJeetO1/96lfV6tWr1b59+9Sf//xntWzZMlVeXq4aGhqUUnLOuvPuu+8qj8ej7r//frV792713HPPqdzcXPWzn/0sdZvvfOc7qri4WP32t79VmzdvVp/61KfUlClTVHt7ewZHLk41CVyyENDt109/+tPUbdrb29UXv/hFVVJSonJzc9WVV16p6urqMjfoEeBzn/ucmjRpkvL5fGrcuHHqwgsvTAUtSsk5668TAxc5b11de+21qrq6Wvl8PnXaaaepa6+9Vu3Zsyd1XM5Z915++WU1d+5c5ff71cyZM9VTTz2VdtxxHHXPPfeoyspK5ff71YUXXqh27dqVodGKTNGUUiqTMz5CCCGEEP0lOS5CCCGEyBoSuAghhBAia0jgIoQQQoisIYGLEEIIIbKGBC5CCCGEyBoSuAghhBAia0jgIoQQQoisIYGLEEIIIbKGBC5CCCGEyBoSuAghhBAia0jgIoQQQoisIYGLEGJAXn31Vc4//3yKi4spKyvjk5/8JLW1tanjb7/9NgsWLCAQCLBo0SJeeuklNE1j48aNqdts3bqVSy+9lPz8fCorK/nsZz/L0aNHM/BqhBDZRgIXIcSARKNR7rjjDtavX8/rr7+OrutceeWVOI5DOBzm8ssvZ968ebz//vt861vf4s4770y7fygU4uMf/zhnnXUW69ev59VXX6W+vp5rrrkmQ69ICJFNZHdoIcSQHD16lHHjxrFlyxbeeustvv71r3Po0CECgQAAP/nJT/jCF77AX//6VxYsWMC3v/1t/vSnP/Haa6+lHuPQoUNMmDCBXbt2cfrpp2fqpQghsoDMuAghBmT37t1cf/31TJ06lcLCQiZPngzAgQMH2LVrF/Pnz08FLQDnnHNO2v03bdrEm2++SX5+fupr5syZAGlLTkII0R1PpgcghMgul19+OZMmTeLpp5+mpqYGx3GYO3cuiUSiX/dvbW3l8ssv56GHHupyrLq6eriHK4QYZSRwEUL027Fjx9i1axdPP/00H/nIRwB46623UsfPOOMMfvaznxGPx/H7/QC89957aY/xoQ99iF//+tdMnjwZj0d+BQkhBkaWioQQ/VZSUkJZWRlPPfUUe/bs4Y033uCOO+5IHf/MZz6D4zjcdNNN7Nixg9dee43vfe97AGiaBsCKFStoamri+uuv57333qO2tpbXXnuN5cuXY9t2Rl6XECJ7SOAihOg3Xdd5/vnn2bBhA3PnzuUrX/kK3/3ud1PHCwsLefnll9m4cSMLFizg7rvv5t577wVI5b3U1NTw5z//Gdu2ueiii5g3bx633347xcXF6Lr8ShJC9E6qioQQJ9Vzzz3H8uXLaWlpIScnJ9PDEUJkOVlgFkIMq//4j/9g6tSpnHbaaWzatIk777yTa665RoIWIcSwkMBFCDGsgsEg9957L8FgkOrqaq6++mruv//+TA9LCDFKyFKREEIIIbKGZMIJIYQQImtI4CKEEEKIrCGBixBCCCGyhgQuQgghhMgaErgIIYQQImtI4CKEEEKIrCGBixBCCCGyhgQuQgghhMga/x9X5UIBkV1feAAAAABJRU5ErkJggg==","text/plain":["<Figure 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","text/plain":["<Figure 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26kuLh4AN8NRUrGz5Epz6rGjUKhUCiEhdx6661i2rRpKeeampqEy+USzzzzTHxs/fr1AhCrVq0SQgjxz3/+U+i6LmpqauI2S5YsEbm5uSIYDAohhLjuuutERUVF0nOfc845Yt68efHHRx55pFi4cGH8sWEYory8XNx5551p/y7Nzc0CEM3NzWn/jELRbxiGELs/FGLTMnk0DKtXpFAoFAcEyy9ZN23aRHl5OePHj+eCCy5gx44dAKxevZpwOMzcuXPjtocccgijR49m1apVAKxatYqpU6dSUlISt5k3bx6BQIDKysq4TeJzxGxizxEKhVi9enWSja7rzJ07N26TimAwSCAQSLopFJawdQU8fhY8+R144Up5fPwsOa5QKBRDDEuFy1FHHcWjjz7KK6+8wpIlS9i2bRvHH388LS0t1NTU4Ha7yc/PT/qZkpISampqAKipqUkSLbH52Ny+bAKBAB0dHezZswfDMFLaxJ4jFXfeeSd5eXnx26hRo/r0HigUX4pYS4TaSnBnQXaJPNZWynElXhQKxRDD0hiX0047LX7/sMMO46ijjmLMmDE8/fTT+Hw+C1e2f2644QYWLVoUfxwIBJR4OZCoPk49US0RFAqFDbE8ODeR/Px8DjroIDZv3swpp5xCKBSiqakpyetSW1tLaWkpAKWlpT2yf2JZR4k23TORamtryc3Nxefz4XA4cDgcKW1iz5EKj8eDx9NLRde+ok7OqVHdoVPzRVoiqB5YCoViiJBRZ8XW1la2bNlCWVkZM2fOxOVysXz58vj8xo0b2bFjB7NnzwZg9uzZrF27lrq6urjNsmXLyM3NZcqUKXGbxOeI2cSew+12M3PmzCQb0zRZvnx53GZAUHEKqYlthdSsA90BLr881qxTWyGqJYJCobAhlgqXH//4x6xYsYLt27ezcuVKvvnNb+JwODjvvPPIy8vj0ksvZdGiRfz73/9m9erVXHLJJcyePZujjz4agFNPPZUpU6Zw4YUXsmbNGl599VVuvvlmFi5cGPeGXH755WzdupXrrruODRs2cP/99/P0009z7bXXxtexaNEiHnroIR577DHWr1/PFVdcQVtbG5dccsnAvBEqTiE1sa2Q9gYIt0Pzbmj6XB7D7XL8rcXSzo6olgjpYZpQ9RFsfk0e7fp5USiGCJZuFe3atYvzzjuPvXv3Mnz4cI477jjeeecdhg8fDsDixYvRdZ0FCxYQDAaZN28e999/f/znHQ4HL730EldccQWzZ88mKyuLiy66iJ///Odxm3HjxrF06VKuvfZa7r33XkaOHMnDDz8cr+ECcM4551BfX88tt9xCTU0N06dP55VXXukRsHtAUHEKvVOzBmrWQrAFhJEwYUI4AhGHnLfrVkisJUJtpfysJG4XxVoilFTYuyWC2mZUKIYcmhDdm8Ao+kIgECAvL4/m5mZyc3PT/8Gqj+S2kDtLipVwO5hG17ZIuENWij33cfudnDf9C/7vPDAjvdvoTjjv/2DSqQO3rkwi5q0LtsqYFqdHelo6GmVLBDtXF97ne5MtKzLb9b1RKAYxNruEz0BicQpmGPZugYZtcjukYZt8HJuzY5xC255k0aJpXbcYZkTa2RXVEiE13T2ZLh9oujzmlMlxO28zKhSDmIzKKrIl/iL55dkkC++hO5B60oRIhxz3FtgzTqEzsaifBkm+QY34QKfNi/+plgg9URlXCsWQRQkXqymZCiIit4ccnoQvWV3eN4JyvmSqpcu0BE2jS6D0tqOp9zwx2RFdVyfgRNLJuOpssqcnU6EY5Nj4kixDqF0LmkPGaogICFMGVgpTPtadcr52rdUrHXhGHBH1QO0DXZd2CkUiKuNKoRiyKI+L1bTvlSfnvFHQVg+RTqR3QQOnD7KGQ7jNnleGpYdKEbcvhJB2dseIwLpnoXmn/Cwd+i1w2PjPW2VcKRRDFht/s2UIsStDhwsKx8u4FjPqaXH6pJAxbHplWPlcesKl8jmYdu7ArCkTeft38J+75dZHTPS+/FM4fhEc+0OLF2cRui5Tnl+6RpYUSJVxddy19o4DUigGKeqv1mpiV4YdjfKxyw+eXHkEOT5skj2vDJt30ntsSwwRtbMpb/8Olt0CnY10vVdCPl52i5y3KyrjSqEYkiiPi9V0vzKMpW0KU9ZwsfOVYe5I0hIuuSMHYjWZhxGB139B7++RkPNHX2nfbSOVcaVQDDnUX28mMH4OzLxEZkE074qWtd8lH8+82L5XhkWTkFlFUVLVcUGL2tmQNU+CEdq3jRGSdnYmlnE1ca48KtGiUAxq1F9wJrB1Baz+M+CQwbixGw45btdeRcEmcGV1PRai6xbDnSXt7Mjap/vXbqiiehUpFEMKm/qPM4jERoJmBIJBurKKPNAesW+vIn8R+PLB7ZPvT2K/Is0B/kJZ+8aOgcvQe6pvX+2GIqpXkUIx5LDZmTADiTUSDLXJDCJdlxlFui4fh9q6GgnajVjgMhoUV0D+aMguk8fiCjlu18BlgPEn9q/dUEN1XVcohiRKuFhNWz0EA4AZLTYXrQSrRQUMppxvq7d6pQNPLHDZkw2tNTI9PHu4PLbW2DtwGeD4/2b/f8J61M5mJPYqyi6VZQZa6+Uxu1T1KlIoBjE2/cbPINobZLn/mGBJJCZgTEPa2RGV0to7TjfMvGjfNjMvknZ2I9arCAF1lbLnV2u1PNZVyvFYryKFQjGoUDEuVuMrkpVzTSOaQBMt+a9pgC6vCHWHtLMrKqW1dwrHk9RwMgktOm9D2vdCRxOEW3vOCUN6MN3Z9qxIrVAMcpRwsZrsYeDJk4XmIh095zWnnM8eNvBryyRUE8GeGBF462553+GVwaexwG7dJRt0vnW3Peu4ePJlq4wY3Uv+g/TcefIHclUKhaIfUJesVlM6DfJGJGfMJCIMOW/XAFRF76x7FjoDMsPKDEUbc0ZvZkiOdwaknd3YG90mApJqASU9FlE7hUIxmLDZZViGEmxhn9VPgy0DuRrFYKF5pxQpSSfo6ElZmIApH9uxJUJgF11baAJEonhJeL8CuwZ8aQqF4suhPC5WU/2RrJKLHr3FvmC1rrHmXdJOoUgkqSWC1qVbNEjyKtixJULeqGjAezRTD4i/V4njeaMsW6JCoegbSrhYza4PZGyCI9oh2uGUadAOZ9eYGZZ2CkUiSS0RunvsEgSNHVsiHPot8ObKrVbdLTOrHNGj7pbj3lxpp1AoBhVKuGQKwpACxYjICrpGRD7uLfZFoVAtEXrH4YTjFsmMPCMY3TqLNi81gnL8uEX2C1pWKIYA6q/WakYcEQ2uDHebiJ2ETJkhMuIIS5anyGBUS4R9c+wP5fGtu2WQMgagyffsuEVd8wqFYlChhIvVlB0GTi+EYsIlRRCh0yvtFIpEYi0RaiuheIqsTWKE5JZI1nBZrM/OLRFAipOjr5SZVc07ZUzLod9SnhaFYhCj/nqtpnatFCbhjugVc2KsgiavnJ1eaafqmCgSibVEeP6/YM8GMKMZNGjQsRf8w+zdEiGGwwnTzrV6FQqFop+w+TdaBtC+V55YvLmkDLD05sp5VeFTsS/ioiV6M3tLr1coFIrBjRIuVuMvkl2gOxJ7ESVsF3U0yHm7xikoeifWSDDcAS5vtDqsJo+uqBdPNRJUKBRDDLVVZDXF0eaBcRLruCSUJi+uGOCFZRimqXoVdadmDdSsjX5+hMyUiX1uIp3yfs1aaae2GRUKxRBBCRerqXyu2xVxChe/aUo7u+7Tb10hPQd7NsnsK90lg06Pu9be3aHb6iEYAEzQXMhMNDPaVdwJIizn2+qtXqm1KNGrUAwplHCxmuad9F7uP4awZ9l2kKLlpWsg2Aq+AnB6IBKUmTQvXQOn32Nf8dLeILuKo4EIReu3RINzY93FhSHt7IoSvQrFkENddlhNUtn23rBp2fZYDEewFXLKwOWTZdpdPvk42GrvGA5fkRQoIhItsAZJvYpERM77bBofFRO9tZWyEF92iTzGRO/WFVavUKFQ9AElXKwmqWx7b9i0bHvNGnml7CuIehAS0DQ5vmeTtLMj/iLS+uzYMbBbiV6FYsiihIvVdC/bngq7lm1v3yvd+05P6nmnR87bNVVcE9GGgYnNORObLkbnNBumRivRq1AMWZRwsRp/UTSVtZf/Ck2XBejseNXsL5IxCZFg6vlIUM7b8b0B6GgEl1825dQd0Qad7ujRIcddfmlnN5ToVSiGLEq4WE3JVBlAKQQ4vLJcu+6SR4c32q/IkHZ2I1bSvqMxuXEgyMcdjfYuae8vkt64rGIpbmOfFSHk46xiOW9HYadEr0IxZFHCxWpq18rUVd3RFUypO7uCLnWnnK9da/VKB55YSXtPNrRUR9simPLYUg2eHHuXtI8Ju1Brcmfo2C3Ual9hp0SvQjFksek3fgYRK/mfPxqcPhksaIbl0emD/FH2Lvk/fo5MeS6JFuprrZXHkgo4fbG9U1p1HSacBMEWCLdFe12Z8hhuk+MTTrKnsFOiV6EYsqg6LlYTc2nrLigaL68EjTA4XDKAMNypXNrj58DY41URse6YJqz7W0+PQgwh5Pzsq+z5XsVEb6yOS2eT/FsqqVB1XBSKQYwSLlYTc2lXfQRGBIwg8SJibQ0y0LL8cOXSVvSk6mOoWy+3FR0+pLdFdBWfM0JyvupjGDnD2rVahRK9CsWQQwkXq4m5+7f/R1ZBTew3E24Dw2Ffd3+MrSvgP3dD3afyZOxwQ/EUOH6Rva+ad78frQbby5+x7pDzu9+3r3AB+bejejUpFEMGJVysxjRhy+vg8IDoBDPSNac55PiW1+3r7t+6Ap7/L2jbk1AdFvj8bdizEb75oL3FC8j3xYz0LPnfW4q93VC9ihSKIYUSLlYT6/BrhJAdfp1d7n4h5LhdO/yaJiy7RQbkim4VToUhx5fdApf9254nopGzpLg1w9EBjeSS/6aM6Rg5y6oVWo/qVaRQDDls+G2fYcQ6/IpofIJpyJOyaXSdfOza4bf6I9lXprtoiSFMOV/90cCuK1MondatwJpIuEVxeuwbH6V6FSkUQxIlXKymvSHq5jdIKtcO8rEw5LwdO/zufC/Bm9ALZlja2ZHatTJlvteqyw45b8caQKpXkUIxZFHCxWp8hQnprFqXt1+DLre/kHZ2o3l3/9oNNWI1gArGgitbFirUdHl0ZUPBGPvWAFK9ihSKIYuKcbGajoaueBYEiMQv2aig0TRpp1AkklgDaNhECLd3Zaa5/LLYml1rAKXTq6izyZ6iTqEY5CiPi9X4C6Nl/R3RgW5xClq0WZ7fhh6XvJH9azfU6F7W3uWXFWFdflXWPrFXkWlK4d9SI4+mqXoVKRSDGCVcrCZrOHhy923jyZV2dmPkkb3XKImhO6WdHVFl7XsnJuqad0FdJTTtgNYaeayrlON2FXUKxSDHht9oGUbptKjHoJey7Qg5b8cv2LLD5FXxvtBd0s6uqF5OqYnF/sR7OCUQ6+VUMNaeok6hGOSoGJeMQQMcoCfEuJi9iRmbULMmuSBfKsyItBsxc2DWlImosvY9MSKw9tl926x9Fr72G9lWQ6FQDBps/M2WIdSskVVhfYVSu5iRrpuGHG/bY8/sh10fyKtj3UXPj6oux4Uh7exOrKz9xLnyaGfRArD2GQi1RB9oKW7I+bXPWLI8hULRd2z+7ZYBtO+Vrv3OpmizPHfXTdPkeLjN5tkP3Yqq9TpmY0xTNurc/Jo82r0+ya73u+73olt62NkV9dlRDDKUj9RqvIUyjVWYUbGSMCccsuR/qF3a2Y0RR6QoaR9DSK+U7pJ2dkY1oeyJOyv5caLG1fZhZzdUSwTFICRjPC6/+tWv0DSNa665Jj7W2dnJwoULKSoqIjs7mwULFlBbW5v0czt27GD+/Pn4/X6Ki4v5yU9+QiSSHBfxxhtvMGPGDDweDxMnTuTRRx/t8fr33XcfY8eOxev1ctRRR/HeewNUjVVL/EZN5VVIZWcTyg4DpzdhIFVJe6+9g3NjTSg/f1t65YIt8vj523LcrmXtp5xBUgHHxM9OYsHHKWdYsryMQLVEUAxSMkK4vP/++zz44IMcdljyCejaa6/lH//4B8888wwrVqygqqqKs846Kz5vGAbz588nFAqxcuVKHnvsMR599FFuueWWuM22bduYP38+X/nKV/j444+55ppr+P73v8+rr74at3nqqadYtGgRt956Kx9++CHTpk1j3rx51NXVHfhfvqNR1t3QHWCEIRKCcFAejXBXMbGOxgO/lkyjdm20gJjWi4Em5+1Y0h6Sm1CaYRnvE7uZ4a4mlHZ0/ZfPhJzSfdvklEo7O6JaIigGMZYLl9bWVi644AIeeughCgoK4uPNzc088sgj3H333Zx00knMnDmTP//5z6xcuZJ33nkHgH/96198+umnPP7440yfPp3TTjuNX/ziF9x3332EQiEAHnjgAcaNG8dvf/tbJk+ezFVXXcW3vvUtFi9eHH+tu+++m8suu4xLLrmEKVOm8MADD+D3+/nTn/504N8Af5G8ytGdXScdzK77ulPO27FQVvteCHeyz1TxcKd94396NKHsFsRh9yaU2SXsU/RmlwzkajIL1RJBMYixXLgsXLiQ+fPnM3fu3KTx1atXEw6Hk8YPOeQQRo8ezapVqwBYtWoVU6dOpaSk6wto3rx5BAIBKisr4zbdn3vevHnx5wiFQqxevTrJRtd15s6dG7dJRTAYJBAIJN36ROm0aF2J9tTz4XY5b8c6Lt48GZi8L8Jt0s6OJDWh7KXPlV2bUMay9fxF9BQvmhy3a7YepNcSwQzb96JAkdFYKlyefPJJPvzwQ+68884eczU1NbjdbvLz85PGS0pKqKmpidskipbYfGxuXzaBQICOjg727NmDYRgpbWLPkYo777yTvLy8+G3UqFHp/dLdMSPQup8tqda6/dczGYrs2cT+M4dE1M6GBKqSH6cIAUppZwdi2XrBgNwCcUR7Ojlc8nEwIOftemJObImQCtUSQZHBWCZcdu7cyY9+9CP++te/4vV69/8DGcYNN9xAc3Nz/LZz586+PdFbi3tW9uyOMKSd3Ui3Potd67jkjkh4IFLcUtnZBF9BV9NJh0tuuTqc0aNLjofbpZ0d6d7nKhG797lSZDyWCZfVq1dTV1fHjBkzcDqdOJ1OVqxYwe9+9zucTiclJSWEQiGampqSfq62tpbSUhl0V1pa2iPLKPZ4fza5ubn4fD6GDRuGw+FIaRN7jlR4PB5yc3OTbn2icVvXfa1bsYnEvedEO7uQbqqqXVNaRx2R0JyzFzSHtLMbSV3WU2wVpbSzEarPlWIQY9mn8uSTT2bt2rV8/PHH8dusWbO44IIL4vddLhfLly+P/8zGjRvZsWMHs2fPBmD27NmsXbs2Kftn2bJl5ObmMmXKlLhN4nPEbGLP4Xa7mTlzZpKNaZosX748bnNAKRjXv3ZDicSU1l6xcUpr6TRZs2VfONz2vGrubJDZeJoezbgyo44oUz7WHOD2Szu7ovpcKQYplhWgy8nJ4dBDD00ay8rKoqioKD5+6aWXsmjRIgoLC8nNzeXqq69m9uzZHH300QCceuqpTJkyhQsvvJC77rqLmpoabr75ZhYuXIjHI4POLr/8cv7whz9w3XXX8b3vfY/XX3+dp59+mqVLl8Zfd9GiRVx00UXMmjWLI488knvuuYe2tjYuueSSA/9GHHctvHmXjGHp4bKNHnWntLMbZYfLIMFIZ+82To+0syPVn6TXy6n6Exg5Y2DWlCnEsvU82dDRFP0MGcgUeh/48uXfm91jOFSfK8UgJKMr5y5evBhd11mwYAHBYJB58+Zx//33x+cdDgcvvfQSV1xxBbNnzyYrK4uLLrqIn//853GbcePGsXTpUq699lruvfdeRo4cycMPP8y8efPiNueccw719fXccsst1NTUMH36dF555ZUeAbsHBKcbJpwMm17t3WbCydLObtSuBXe2rGlDqnoSupyvXSv789iN3e/L+CctmkrfvTys5pDju9+3n3CJxXDUVkLheIh0yLgW3SGFS0u19CzY0RvVnVifK4VikKAJ0f0yX9EXAoEAeXl5NDc3f7F4F9OEx8+Cz1eBkcKz4PDCmNnwnefsdxW0+TV44UpwZUGgJvr+mIAu35fcUpkOfeb9srmg3Xj3QXjlp4AOIoXnRXMCJnz1V3DUfw306qwnVhk22CqDcJ0emS3T0ShjONR2iEIxKLHZmTADiRWCKhgDxVPBVwSubHksnirH7VoIKpayaYTAoUvhpjnk0aHLcTunbI6cBWipRQtEx7WonQ1RMRyKvmKasrhlsAXaG6KFMBWZQkZvFdmCWCEoMyzrbUSilWKNoDwxZw+3byGo0mmQNQxq1soYXd2JvCPk+9RSDaVT7evuL5lKWnVuSqYOxGoyExXDodgXRlh+zxrRFiuxx2a3EhXZTnANvrIdQxUlXKzGXyTVfdMOGSyo6yB02VQx3C7HvQX29SrEiJ+fo8JF7XBC5XPp200798CuJZNRMRz2xjQSxEmk636qhAjFoEAJF6spmSpd+mYE0OUfFoJ4LRdhyHk7XjXHyrbnlvfMDHH5ZWZIrGy7HU9MzdGih7or+vnpFpwb63/V3MfiiArFYEGIXjwnYdUocgiihIvV1K5NcEsm/oElVD81DXtmzsS20bJLpMcpVgk11jFbmDJuwY7baAB5o4j3KHJ4AEN+gWsa4Ig+1qJ2NsY01VbRUCFpayfRe7Kf6uOKIYUSLlbTVi89CZojoctvzOOCLKAV6ZR2diOxn4rLJ8VKInbvp3Lot2RWUUcj0hMVE7ta9LEps2kO/ZaVq7SWrStku4w9m6QI1l0yTfq4a1VwbqYS39oJJwsVtbWjiKKEi9W0N0S9CDrgkttCwpSCRXMChpxvt2GFz8RaHE5vz1ocHY32rsXhcMKUM2H1n+nVWzflTGlnR3pLh66tlOOn36PEi1UIkSIwVm3tKNLDpt9oGYSvSJ6IjTAQ7hoXJhBNZ3W4pJ3diPVTef6/oP5TMBM8CroG/mH27qdimtC4HdxZiFAHyeJFR3P75bxp2u89Mk3paQm2Qk5ZV98vl0+K4JZqOT/2ePu9NwNJYuxJJJQsVBSKPqKEi9VkD+uqVZISIeezhw3osjKO+O6Z1vXY7kRrALV7y6gV4AkHcBIhgpOgK5cSL/hjNYDsFh8Vq4/kK0huVgrysa+gqz6S3d6bA4ESKIoBRAkXqymu2HcvHpDzxRUDs55MInbVbBowfLKsnGtGZLaMwwutNfa+am7fSygUZFfQRURohPVcNE2eQyJhwa6IyVhPELcdg5djgd1OT+p5pwc6m+wb2N1XumfvRIJKoCgGHCVcrGbd36J9ZvaBMKTd9PMGZk2ZQuJVs66D3i041+ZXzaa3iEAYHCKM5ugqjqVp4HJo6EaYQBgKvUX2K5HdPbC7O3YP7E7ANAWVVQEa2kMU+t1UlOeia0TjToJd8SeRoAqQVWQESrhYzc5307ezm3BRV837pFKMpd0sZyKf02C68WghHBgYOAgKN/m0stkcQ7UYi+2qAHUP7E7cLhJCBXZHWbmpnj+u2MiuuiYwwvgcESYWubnwiHJmjC6wenkKRUpsdyGWcTTv7l+7oUTiVXMqbH7V3NAR4VHtm5iagzFiB+VmFaVmLeVmFWPEDkzNwaP6N2no6KWX0VAmFtjtyZatNDoaobNZHgNVssminQK7hZCxJ8FWmaHYUsPqjz/mvuf+RaB6CyNdAcZlBSlyhfm8rpm7l23kwx2NVq9aoUiJ8rhYTe6I/rUbSqir5n1S6HfjdGi4wmGcREgMQdUxcYkwTl2j0O+2bI2WMn4OzLwE3robmncRj/D25sLMi4dmKnRS1dhuBdoStnhMIXjynS10hCIMy/agRf+2PLqDYU6dPa0h/u+9HUwflY/ePbhZobAYm1xuZDCjj+xfu6FE4lVzSzWEO2SaeLhDPrbbVXM3KsqyuZbHySVA91OLBuQS4Foep6Is24rlWc/WFbLGje6CvJGQP0YedZcc37rC6hX2DSMCoXbpQWrbC4FqaPwc9m6Rx0CVbIXR0STtjJ5xKZvqWtmxt51crysuWmJomkaO18mOve1sqmsdwF9MoUgP5XGxmoIx/Ws31Bg/RxYKi1U/7WySJ56SCttXP9Wr1zA2srWHaImhAWMjW9Gr18DIGQO5NOvprY4LgDc/s+u4mIb0mpjhLg+KGYkWZzP6JTi2uSNMxBS4HKl/d7dDp1VEaO5Q2UKKzEMJF6tZ/Zf07cYce2DXkqmMnyNPMKrfTDK73kEXxj5L2ujCgF3v2E+4JGakQbTPVTSV3unLjIy0pNL2oYTMnQPfdyfP58Kpa4QNE4/u6DEfMkycmkaez3XA16JQfFGUcLGajjQD4NK1G6roui1TnvdJ1RpAelZSiRetm52tiGWkGWG5dRLpJB7j4vRC1nA5fyAy0mK1Tsxo13cz2rZDRI+xcQvTiicVZzO6yM+W+jaGOfWk7SIhBC2dESYMz2JSsU23GRUZjRIuVjNiBmx6NT07hSKRhKaT+wyf7N6c0g74i6RIaN6JrD7tIC7xIh1y3Jv35TLSYgIlVoQtdhwEnYp1TeO8I0dz97KN7GkNkeN14nbohAyTls4IfrfOeUeOVoG5iozE5r72DOCYH/av3RDFNAVrdzWz4rN61u5qxjRVESxGpRmwna7dUKJkatTDEZHNSjVdxrnEmpeaETlfso8KN7FGgOEOCLZIr2fbHunBadwug2GbdkJrnQyEDXcMCtESY8boAhadcjAThmfRGTJoaA/RGTKYMDyLRaccrOq4KDKWfvG4BAIBXn/9dQ4++GAmT57cH09pH9b/PX27aece2LVkKCs372HJii1sqWslbAhcDo0JxdlcMWcCx0y0cQ+nqWfDP38CoZbebdw50s5u1K6VAkV3RDuuO5DXaaYULJoDcMCu92Sgd2wLJxYIKwxbdCmeMbqA6aPy2VTXSnNHmDyfi0nF2crTosho+uRx+fa3v80f/vAHADo6Opg1axbf/va3Oeyww/jb3/7Wrwsc8jTv7F+7IcbKzXu48fm1bKhqYrpzO1/1rmO6czsbqpq58fm1rNy8x+olWofDCXOui56EU6A55LzDRjvCpiG3bAK7oznhI8DhASMWCGvIx7mloAnYuxla62VRts6A9JoYYVuIlhi6pnFwSQ5Hji3k4JIcJVoUGU+fvtHefPNNbrrpJgCef/55hBA0NTXx2GOP8T//8z8sWLCgXxc5pMkp61+7IYRpCpas2MIhHR/xA/1FRnbsxikiRDQnu/QR/LHjTJas8HP0+CJ03aZftsdGtxDfulvW9RBCbol48+C4RV3zQ4HY1o0RigbeRhICYCOyxk8s4NU0AV1uDeWPksG5piE9ME6vFDdaWKZGKxSKQUWfhEtzczOFhYUAvPLKKyxYsAC/38/8+fP5yU9+0q8LHPJoaTq90rUbQlRWBcivWcX14o9kGR0EtBzCmgsXYcYa27mZB/l1jUZl1SFMHZln9XKt49gfwtFXwrpnpWcubxQc+q3B52kRIpoinNBxuK9bN8MPgYKxsHcTZBVLsRJ/HaTIK5ok7RQKxaCiT99so0aNYtWqVRQWFvLKK6/w5JNPAtDY2IjX693PTyuSaNrRv3ZDiIa2Ti4IP4df62CPVhQvIhbCw17cFIm9XBB5joa28wAbCxeQwnb4wZA1TGbKZJLQ7e4Z6Z4aHLvfn+nBmg6zLoZ//xLa6qQHyuGWgqizGdxZcj6T3ieFQpEWfRIu11xzDRdccAHZ2dmMHj2aE088EZBbSFOn2q4P7Zejem3/2g0hyjs+I4fdBMhJrnwKoGkERA5jxW5aOj4DSixZY0awdUVXZWEzLCsLD5s0sJWFTVN6SiIJ3pJ+rPTaJ0YeCV+5CT54VGYBiYAM2C2aJEXLSBtmWykUQ4A+CZcrr7ySI488kp07d3LKKaegRyuYjh8/nv/5n//p1wUOeSKd/Ws3hJjgDxLQDRpNZ8oPaodwkq+bFPt76R5tB7augJeukaXtfQXg9EjxUFspx0+/p3/FS2KcSbx2SVB6VTKRkUfCiFlQv0G2i/Dmy+0h5WlRKAYtfd4EnzVrFocddhjbtm1jwoQJOJ1O5s+f359rswnpBpXaL/hUzy7C5/Xh7QjTYeg4dA1dA1OAYQr8Whif14ue/SWKiA1meuvH4/LJmI4v2o8nvnVjpq70Go83GWQ1dDQdiqdYvQqFQtFP9Omyo729nUsvvRS/309FRQU7dsj4i6uvvppf/epX/brAIc+wif1rN5QonYan9GBGeDrxOjVMIQibAlMIvE6Nck8nntKDZd8iO5LYjyfFVlpSPx6IeksiXQXV2htk8bTmXdCwFRq2yYJqzbuhpSY5TTjULoXLYBMtCoViyNEn4XLDDTewZs0a3njjjaRg3Llz5/LUU0/12+Jsgcoq6h1dh+Ouxe3PZZwnwNg8jVH5HsbmaYzzBHD782Qch12bLcb68Tg98rEQXd4SMyI/M5FOuW20d4u8NW6PCpPahNolnbaqW6JQKAY3fdoqeuGFF3jqqac4+uijk5pzVVRUsGXLln5bnC1oS7OAWrp2Q43xc+D0e9DeWoxvzyYwW2TwaUnFwAafWolpdgW6JtYsAUCXW0VODz1aLUaCsgidJ1d5ShQKxZChT8Klvr6e4uLiHuNtbW1JQkaRBu40G+ClazcUGT9HxmnUrJFeBn+R3B4arJ6W7t2CE9OB4/fN/XcRzhsN+aO7apUk/umpWiUKhWKI0ifhMmvWLJYuXcrVV18NEBcrDz/8MLNnz+6/1dmB7DTTeNO1G6roOpQfbvUqekeIrgDWRPEREyDxwFaz/7wfqlaJQqGwIX0SLnfccQennXYan376KZFIhHvvvZdPP/2UlStXsmLFiv5e49DGm9u/dor+x0zwfiQWUYvft7C3japVsn+EqdKhFakRptxSjXRGb6GuekSRzugxKLdiPdkw9VtWr1hBH4XLcccdx8cff8yvfvUrpk6dyr/+9S9mzJjBqlWrVAG6L0pLTXp2jZ/LWAZNj2aQaF1fvknbcwn3NS3BXtGDJPGRKhU4MjgyaVStkt7Z9V6CqItIUVcwVom6TCTmtYwJhriACKYYSxAVX2bMDKe/vqxiJVwyhD7XcZkwYQIPPfRQf67FnuSWp2fnyU5f5HQnJlx0R1TIxI6JNy1B6HS7oSWIJS3hRC6i90XXmDDlST+2TaI5oq8bfc3utj2eI+EYE2Hx1xTJIqK7oEgUaIn28dcxkwVKpguSL4KqVdKTXe/JbbRQe/I22t5NcvwrNynxsi+E6CYeoif9fQqKfYmMRBERSvByJNiKDM5us2ER0EylT8IlEAikHNc0DY/Hg9vt/lKLshWjjpRZMvtS/poTSg/r+2vETtCZWt1UoehvhCk9LaH25MBlp0c+bquT8yNmDR7PlGl0neTDnWCk8iZ0EwiRzq4Kx4lbIvsVJNHntBu6Axxe+TlJvHlyVSfxDKJPwiU/P3+f2UMjR47k4osv5tZbb423A1D0QtnhMjOkYR9p5Lll6mpaofgi1G+Q20PevJ5FpzXkeON2adeXvy0h5MVGD6EQExHdx7o9TjXW4+c6k21N48u/L4MNh1sKB0dMRHi7HWNz3cRG2mOJz+MGhyv1OrKLVZxhBtEn4fLoo49y0003cfHFF3PkkdLV+t577/HYY49x8803U19fz29+8xs8Hg833nhjvy54SOLJQdYCTOUm1WR2iEKxD0wh2FTXSnNHmDyfi0nF2eh2im2KB1lGBcCez6Inew+E2hK2Ds2u7cNQK6x5Qnpg9rX1kbTlkSAmutfNGepoek8R4XB3EwPebjbpjnUXItHbYPGGKQaUPgmXxx57jN/+9rd8+9vfjo99/etfZ+rUqTz44IMsX76c0aNH88tf/lIJl/1Rs0YWl8sth44m+cUoDCD6JeHJgfbGvl8ZKoY8H+5o5P/e28GOve1ETIFT1xhd5Oe8I0czY3SBNYsyIyk8B32Jj0hzzAilXkdn877XufHl/v/dBwrdmSAgum1tpBpzeMHVi4hIEiCpRIRXvp6dxLDNufjii2lqauKFF16weik96JNwWblyJQ888ECP8cMPP5xVq1YBMvMo1sNIsQ9iZduzS2RhtUiH7CUT22tFQPsemS2iUHTjwx2N3L1sI+0hg1yvC5dDJ2yYbKlv4+5lG1l0ysHMGJWfHOfQY9sizZiHlHEQvWyB2HFbI771kLC94fL2jJlI5WXovm3R61iCINEdVv/GCoUl9Em4jBo1ikceeaRHQ8VHHnmEUaNGAbB3714KCiy62htM+ItkcG4kKLv6uvwyAyfmho6EZHCuzQPDhtRWSFLtiH2kasZjHlLFQgQR4U5cO+pYFOwgy2Hg7gzhEmFchHGLEM6OEJ6/RxCE0ey4reFwyXR2SMhqQ3o0daf0YOaU7VtQJI31sp0R81gM1s+jQjEAhEKhfkvc6ZNw+c1vfsPZZ5/Nyy+/zBFHHAHABx98wIYNG3j22WcBeP/99znnnHP6ZZFDmtJpMGySbITn9HZL6UWVbecAb4XEK96myspIzMjoltI5ULUj9oEGxKsmZbqDQ3d98fiGmFiIBWimCs7sLQgzFmSp6rgohgjPPvsst99+O5s3b8bv93P44Yfz4osvsnDhQpqamjjyyCO59957CQaDLFq0iBtvvJEbbriBRx55BL/fzy9+8QsuueSS+POtXbuWH/3oR6xatQq/38+CBQu4++67yc7OTvn677//Pl/72tf48Y9/zPXXX09TUxM//vGPefHFFwkGg8yaNYvFixczbdo0AG677TZeeOEFrrrqKn75y1/y+eefY/ZToc4+CZczzjiDjRs38uCDD7Jx40YATjvtNF544QXGjh0LwBVXXNEvCxzyRDsg89I10FINvuiJ2K5l27vVjqjcUcfTb29ECweZ7jbxuSLoRgijtpPVL0cYfkgBo3IcXyw2Ip5OGh3L5NoRBwStl1gId4JYSBUzkSIQM+lnfb17I6za1lDF+RRDgOrqas477zzuuusuvvnNb9LS0sJ//vMfRLTUxeuvv87IkSN58803efvtt7n00ktZuXIlJ5xwAu+++y5PPfUU//Vf/8Upp5zCyJEjaWtrY968ecyePZv333+furo6vv/973PVVVfx6KOP9nj9119/nbPOOou77rqLH/zgBwCcffbZ+Hw+Xn75ZfLy8njwwQc5+eST+eyzzygsLARg8+bN/O1vf+O5557D4ei/7wBNiC9WhSscDvPVr36VBx54gEmTJvXbQgY7gUCAvLw8mpubyc3tQ9rc1hXw1mLYs0nGuWTKlWFi7Yi+pHgmjaURW6FqR/Qe39DNu7CnU+Pl9Q3g9GA6PIQ1NyFchDU3Yc1Fa8RBa8TBladUML6kKOF51LaGQvGFsDgd+sMPP2TmzJls376dMWPGJM1dfPHFvPHGG2zdujVefuSQQw6huLiYN998EwDDMMjLy+Phhx/m3HPP5aGHHuL6669n586dZGXJrNV//vOffP3rX6eqqoqSkpJ4cO5FF13Ed7/7XR5++OH4Lspbb73F/Pnzqaurw+PxxNcyceJErrvuOn7wgx9w2223cccdd7B7926GDx/er+/HF/a4uFwuPvnkk35dhILkDsg1a1NfGSaVxN5PDYh00jl7FSQJY6YNi9bFtyZSeBm+UBxEb6mi3QSJ3rcC1oVC8MneT9hS38YwnzuptpIQgj0dISYMz2Ls+IPtLVRUryLFIGfatGmcfPLJTJ06lXnz5nHqqafyrW99Kx5HWlFRkVQzraSkhEMPPTT+2OFwUFRURF1dHQDr169n2rRpcdECcOyxx2KaJhs3bqSkpASAd999l5deeolnn32WM888M267Zs0aWltbKSoqSlpnR0cHW7Z01SQbM2ZMv4sW6ONW0Xe+852UwbmKL8kHj8CnL8ov2EjidkZsyyNku20NE40wLkKaO+kY8y60mQ7GlRRSmJfbS52IFGmeQ6R2hK5pnHfkaO5etpE9rSFyvE7cDp2QYdLSGcHv1jnvyNGDN4i5P1AxLoohgMPhYNmyZaxcuZJ//etf/P73v+emm27i3XffBaRDIRFN01KOfdEYkwkTJlBUVMSf/vQn5s+fH3/O1tZWysrKeOONN3r8TH5+fvx+ojDqT/okXCKRCH/605947bXXmDlzZo/F3X333f2yONvRsBW2/8fqVewb3bGfOIj9pXPuJ8UzYeyzvSF+9veN+DxOPK6e+6OdYYPOkMHPjz+UwpIcC94M65kxuoBFpxwcD15uFRGcmsaE4VnW1nHJBFSvIsUQQtM0jj32WI499lhuueUWxowZw/PPP9+n55o8eTKPPvoobW1t8fP322+/ja7rHHzwwXG7YcOG8dxzz3HiiSfy7W9/m6effhqXy8WMGTOoqanB6XTG41oHkj4Jl3Xr1jFjxgwAPvvss6S5fbUCUOwHl++L/0wPodBLlkX3bYx9CovuzxETFu4+b2v0hYllgtHDsuRWiFPvsRXS0hlhwvAsJhWnjoK3CzNGFzB9VP7QSRfvD4ZiryKFbXn33XdZvnw5p556KsXFxbz77rvU19czefLkPoVuXHDBBdx6661cdNFF3HbbbdTX13P11Vdz4YUXxreJYhQXF/P666/zla98hfPOO48nn3ySuXPnMnv2bM4880zuuusuDjroIKqqqli6dCnf/OY3mTVrVn/96inp01no3//+d3+vQwFwyOnSjd0Z6CW2orsgcQ/pL121FaLoMwe6V5FCMYDk5uby5ptvcs899xAIBBgzZgy//e1vOe2003jqqae+8PP5/X5effVVfvSjH3HEEUckpUOnorS0lNdff50TTzyRCy64gCeeeIJ//vOf3HTTTVxyySXU19dTWlrKCSec0EP4HAi+cFaRIjVfOqsokb1bujo6K/hwRyNPvPs5W/e0ETEETofG+GFZnH/UGHtvhUTJyJL/VrNjJbx2G/iHycDkSLSab2yrU0QrUs+9DUYfY/VqFZmOarKYUfTZ7//BBx/w9NNPs2PHDkKh5D4hzz333JdemEKRjIaGBojoUXlZIM2S/3YUL958GYjb2YwItiAiQSlWNA3N6UHz5KiK1ArFIKVP+wxPPvkkxxxzDOvXr+f5558nHA5TWVnJ66+/Tl5eXn+vUWFjYifmrXtayfE6Kcn1kuN1snWPPDF/uKPR6iVahikE//feDtpDBsOyPXhcDnRdw+NyMCzbTXvI5P/e24FpR+/d8EPAX4BorcUItxMREEInIsAItyNaa8FfYOuK1ArFYKVPwuWOO+5g8eLF/OMf/8DtdnPvvfeyYcMGvv3tbzN69Oi0n2fJkiUcdthh5Obmkpuby+zZs3n55Zfj852dnSxcuJCioiKys7NZsGABtbW1Sc+xY8cO5s+fj9/vp7i4mJ/85CdEIsm1R9544w1mzJiBx+Nh4sSJKSsD3nfffYwdOxav18tRRx3Fe++998XeFEW/k9EnZk1Lvul6tJCci3iTPVdCN16nW845oqXvXT5ZFdmdBW5/183lS77F57LAk51029hgsrHBwOvLIhJNE4/gxEDH1Bxk+dxsb+hgU117z/XG1jyE44NagwYy+VNL+FfeM6PzCoVi8NGnraItW7Ywf/58ANxuN21tbWiaxrXXXstJJ53E7bffntbzjBw5kl/96ldMmjQJIQSPPfYY3/jGN/joo4+oqKjg2muvZenSpTzzzDPk5eVx1VVXcdZZZ/H2228Dshrg/PnzKS0tZeXKlVRXV/Pd734Xl8vFHXfcAcC2bduYP38+l19+OX/9619Zvnw53//+9ykrK2PevHkAPPXUUyxatIgHHniAo446invuuYd58+axceNGiouL+/IWKfqBTXWt7NjbTq7X1SNbTdM0crxOtjd08Fl9B4eU5UVPyDoQPSbedD3aaC+6zZT0fIn3RVd8UaJt/Lkc8rkygDrNQbVZSLHLQ4veU4CYmqAuGKTGVc7BRfsoAiWEjP+IFRuMvzfd3ydktk7izTS6jQnke2gmxGmJFDFbKcRmzEYY8v6XEKRm3QZCrXtoI58c2nERQcdEoBHCRQt+XK17MOs2oJeo4FyFYjDRJ+FSUFBAS0sLACNGjGDdunVMnTqVpqYm2tvb036er3/960mPf/nLX7JkyRLeeecdRo4cySOPPMITTzzBSSedBMCf//xnJk+ezDvvvMPRRx/Nv/71Lz799FNee+01SkpKmD59Or/4xS+4/vrrue2223C73TzwwAOMGzeO3/72t4DMX3/rrbdYvHhxXLjcfffdXHbZZfEGVA888ABLly7lT3/6Ez/96U9Trj0YDBIMdpWnDwQCaf/eigRiokB3dBMbDvaYglozj0KXD13XEFGBYaJjohNxadSFQtQ6yzikoP+rM2Y6hX43LodGyDDxpugFFDRMXLpGoX8/HVk1DRxOecskzJgYMqICyUgWS3HRlDgvBc/ummqyzAgd5NGhZeMmhAMTA50QboQQ5JnN7K6pZpQSLgrFoKJP31QnnHACy5YtY+rUqZx99tn86Ec/4vXXX2fZsmWcfPLJfVqIYRg888wztLW1MXv2bFavXk04HGbu3Llxm0MOOYTRo0ezatUqjj76aFatWsXUqVOT0q/mzZvHFVdcQWVlJYcffjirVq1Keo6YzTXXXAPIVturV6/mhhtuiM/rus7cuXNZtWpVr+u988470/YsDUk0TdZ00Z3dvBgJHorEK/a418IRFSnR4z6a7+UWuAk5d9BiOvGmaNAVNI30TsxDlIryXCYUZ7O+uoWSXI1gWBAxTZy6jsel0dQeZnJZDhXlgzQbQteRu9lf8GvKNKnzbaccN7omCGluDFwkenncBAkLJ416AaMczi5PEXxpb49CoTiw9Em4/OEPf6CzsxOAm266CZfLxcqVK1mwYAE333zzF3qutWvXMnv2bDo7O8nOzub5559nypQpfPzxx7jd7qTywSB7MNTU1ABQU1PTI2c89nh/NoFAgI6ODhobGzEMI6XNhg0bel33DTfcwKJFi+KPA4EAo0aN+kK/ew9Mc9+9igaSmDBxuLoEisMFejROYwC6/SaemEtzexagG/Qn5i+JrmtcMWcC1z79MZ/VtiKEiCXOoGkaRVlurpgzAT3FNtKQRtdxj5rJDm0kE8XnBDUvInHLSwj8op3N2hj8E0+BghRZV0J02/6C+JaXMIlvh5mRrm222H1hyL9lhUJxQOiTcIm1rAbpnehtOyUdDj74YD7++GOam5t59tlnueiii1ixYkWfn2+g8Hg8SV0xvzRbVyDeuptIdaXsDq27cA6fhDbrkgNTllzXuwSJRcJk/0uUJ+Ybn19LbXMHs7w7Gaa1sEfk8EHnKLK9LnuemHtFSwpJsbPPoGJEPr8edj5l9YspEntpIYcwLlyEyREttOLj9WHnc/2I/NRPoGnSK8iX+DswowIHugmeBFGUFA+U4OlJepx47G7b7ZgktBSKoUmfN7VN02Tz5s3U1dX1aNx0wgknpP08brebiRMnAjBz5kzef/997r33Xs455xxCoRBNTU1JXpfa2lpKS0sBWc2ve/ZPLOso0aZ7JlJtbS25ubn4fD4cDgcOhyOlTew5DjhbVxB85jL0jr3ILzoT0IjsWo2o34L7tP/54uIlluHicHcddWfCVs3gONkfM3EYf5jdgvjPYoa37cBJhAhO6j2j0WZfy6ETh1m9RMswTcGSFVswTMFBxdkEIwlbRU6N2pYQS1Zs4ejxRbYTd7quMWfet1j8bJBzOv/GOKrIoZUwTj4TY3jKu4Cz533rwL4vVgZxx+OAEj1CMW9QondICR3F4KNPwuWdd97h/PPP5/PPP6d74V1N0zCMvqcZmqZJMBhk5syZuFwuli9fzoIFCwDYuHEjO3bsYPbs2QDMnj2bX/7yl9TV1cWzf5YtW0Zubi5TpkyJ2/zzn/9Meo1ly5bFn8PtdjNz5kyWL18eb9ttmibLly/nqquu6vPv8QV+YVpeuhF/Rz0mYOIAdGQoqoEj2EDrv+8m+ztPJG8baVo0RiRRnCR4TAaJMNkvW1dw6Ie3IpytdPoKiGguskSYkvAutA9vhfI8GD/H6lVaQmVVgC11rRT43ei6js8NiR6CfL+LLXWtVFYFmDrSfvWVjpk4DL51Afe/cSSO2rVkm8206nkYJVO5/MRJcn6ooke9RQ7Xfk2ThE1M1KTaBlMCR5Eh9Em4XH755cyaNYulS5dSVlbW58aKN9xwA6eddhqjR4+mpaWFJ554gjfeeINXX32VvLw8Lr30UhYtWkRhYSG5ublcffXVzJ49m6OPPhqAU089lSlTpnDhhRdy1113UVNTw80338zChQvj2ziXX345f/jDH7juuuv43ve+x+uvv87TTz/N0qVL4+tYtGgRF110EbNmzeLII4/knnvuoa2tLZ5ldCAxqz7C07ARE6IBhLLKhIhmzkAYd/M2zObd6CNndm3ppPOFNNgxTXhrMQRbIacMETHBFAiHF7x+aKmW82OPz5gU5YGkoT1E2BC4Hal/d49Dp9kUNLSHUs7bgWMmDuPo8UVUVk2hoT1Eod9NRXmu7TxQ+2Q/QfJxhOjmuUmI54lld3XP9lKBzgcU0xRUVgVs99nuk3DZtGkTzz77bHyLp6/U1dXx3e9+l+rqavLy8jjssMN49dVXOeWUUwBYvHgxuq6zYMECgsEg8+bN4/7774//vMPh4KWXXuKKK65g9uzZZGVlcdFFF/Hzn/88bjNu3DiWLl3Ktddey7333svIkSN5+OGH46nQAOeccw719fXccsst1NTUMH36dF555ZUBaRZVXfkfSokQSbhSFgkl7U0cODGo3rKGERO/csDXk1HUrIE9m2h35FKzt52OsBEPPvW5HJT6cvHv2STtyg+3erUDTr+lQw9xdF2zpcep39G0vl00xeoE9RA3UfFjhJO3rxRpsXLzHpas2MKWulbChsDl0JhQnM0VcyYMbW8ifWyyeNJJJ3Hdddfx1a9+9UCsaVDS1yaLm/7xW8av/gURHIgUgYAaBk4Mts78GZO+/t/9ueTMZ/NrhJ69nC2dWYTNnlcRLl0wwduG+1sPwMS5KZ5gaGOagov+/F4068rTI+uqJhBkclkOj11ypC2uwhRDgLhXJ9JN0CTeLMjYyrAmiys37+HG59fSGoxQ4HfjduiEDJPG9jDZHgd3fHPqAREvJ554Iocddhher5eHH34Yt9vN5Zdfzm233QbISvZXX301y5cvR9d1vvrVr/L73/++350AaXtcPvnkk/j9q6++mv/+7/+mpqaGqVOn4nIlK/DDDjus/1Y41Bk5i8hqJw4MIkRrn8QRODCJ4ISRs6xaoWWY3iIagwKHGSZMT6+BwwzTGBQM9xb1rXfFICcx66omECTf78Lj0AkaJk3RLzCVdaUYVCR6dVy+1Dam2XuQcfeChKYx5Lw4saD81mCE0lxv/ILFqzsozdWpCQQPaFD+Y489xqJFi3j33XdZtWoVF198Mcceeywnn3wy3/jGN8jOzmbFihVEIhEWLlzIOeecwxtvvNGva0jb46Lrso5Gb+axuS8bnDtY6avHxTQMtv/qKEaHtyCjW7o+aDoyzXGHawJjf/oueooibEOZj7fvJfDINzhE30GNKKC7qCvVGtlgjib30heZPrbIqmVaTpLL2BS4dPu4jBWKtDBiAcYJLSpSZV71VoMngzwua3c181//7wOyPE68rp7nhI6wQXswwoMXzur3LdITTzwRwzD4z3/+Ex878sgjOemkkzj55JM57bTT2LZtW7ym2aeffkpFRQXvvfceRxxxRL+tI22Py7Zt2/rtRRVd6A4H7XNuofW1K8mlJWmzSADN5NA+5xbbiRaAlz+t4xPjDO7QHqZUa6RJZBPEiYcI+VorbcLHEuMMDvu0ztbCpSsA1X5BegpFWnyRlhaJQcgxMePsx5pdXxKrg/K776iUlZVRV1fH+vXrGTVqVFIh1ilTppCfn8/69eutES5jxoyJ37/zzjspKSnhe9/7XpLNn/70J+rr67n++uv7bYF24NDyPII+P0ZHO7ITj4h6XnT8Pj+HltszsLAtFGGVWcGNke9zhePvTNCqyNcihIWTDeZolhhnsMqsYEIosv8nG+KoAFSFop/oaxDyAGF1UH730BBN03rUcjvQ9Cmr6MEHH+SJJ57oMV5RUcG5556rhMsXIZry6xFBhMuLiARBmDg0Hc3pQRNB26b8ThuZz+PsYJVZwTvmZCq07RRqLTSIHCrFWEQ0smXayHxrF5oB2DUtMh3Ue6MYSmRqK5TJkyezc+dOdu7cmbRV1NTUFK+r1l/0SbjU1NRQVlbWY3z48OFUV1d/6UXZipo1ULMOM9iKKaSnRaCjCdDDneiRIHrNOlum/E4szo7fF+isE+NT1rFPtLMjdk6L3B/qvVEMNTI1KH/u3LlMnTqVCy64gHvuuYdIJMKVV17JnDlzmDWrf5NL+nQJP2rUKN5+++0e42+//Tbl5eVfelG2onUPZmcTpjDQhIFTRHCJCE4RQRMGpjAwO5ugdY/VKx1wAp0Rstz7ju3JcjsIdNp3qyiWFrm+OkCWx0lxjocsj5P11S3c+PxaVm623+cmhnpvFEOVYyYO445vTmVyWQ7twQh1rUHagxEml+UcsFTo/aFpGi+++CIFBQWccMIJzJ07l/Hjx/PUU0/1+2v1yeNy2WWXcc011xAOhznppJMAWL58Oddddx3//d82qzXyJTHb9yLMCLoQdNfHGtL1J8wIZvte26X8Fvrd5PlcOHQtpTjJ9TrJ9jhtW2DN6rTITEa9N4qhjhVB+anSml944YX4/dGjR/Piiy8esNeP0Sfh8pOf/IS9e/dy5ZVXEgrJyGWv18v111/PDTfc0K8LHOpsb3MxJoVoiRETL9vbXIwfyIVlABXluRRlu6kJdOLQY++FjJ0TQGswwpgi/4Dv5WYKib2Kurfd0DTN1r2K1HujsAN2Dcrvk3DRNI1f//rX/OxnP2P9+vX4fD4mTZoU7w+kSJ9dVbsZtx8bPWpnN+GShACHQ5eiRUDEsKB6ZoaRmBYphKAzbMa7Q3tduq17FVmdMqpQKA4cfRIuMbKzs/s1N9uOuCKtCOjV4wLSu+CKtA7QijKHyqoAe1tDlOX5aO4I0Rk2u3oVuR3k+dzsbQ3Z9qo5lhbZ1BGiuSNMMNL1/nicOrk+l217FVmdMqpQKA4cdgubyDhGFfoRaNEaub3dNEYV+i1cpTXErpqdDvn+mELEbwJwOjTCNr5qjm2lVTd30hE20DUNp66haxodYYOa5k6Kst223EqLpYw2tod7VPuOpYxOKM625XujUAx2lHCxmPKK4zE0ZzQNWvpdYt4XEW0BYGhOyiuOt26RFlHod2MKwc6GdtqDYaawleP1NUxhK+3BMDsb2jFNoa6aiVYuFwIjJuxsvpMWSxnN9jioCQTpCBuYppCCLhBUfZwUikHMl9oqUnx59PLDCRYejL+hEi0hSFcepd8/WHgwbpvVcAGYXJpDWyjCUVolVzhl5VxXtHLuFlHOEuMMPgpNZXJpjtVLtYTYVlqh301j1DsVw6FBgd/eW2mxlNFYHZfmaB+nyWU5qo6LQjGIUcLFanSdnJnnYL52GxqGjHcRILSoeNHkvN2q5gKs293M4cZa7nA+TJbWKXsVCdmr6BB9B3doD3Nj5Pus230008cUWL3cAaehPURb0KAjHAENXHo03UqT3pdAZxif22nbrTRQfZwUiqGI/c6GmYZpwpbXMXUPJnpUrEjRYqJj6h7Y8nrqjqVDnJfXVXGF4+9kaZ3UiAI6cSPQ6cRNjSggS+vkCsffeXldldVLtYR8n4uOsIFhCly6jlPXcTrk0aXrGKagI2SQ78vMnisDRSxldM5Bw5k6Mk+JFoVikKOEi9XUrCG0+xOEEUQAYZzxmwCEESS0+xNZ8t9m5DV/ygStiiaRTc+8K40mkc0ErYq85k+tWF5GoCXvLSZMdJu3M6YJVR/B5tfk0YYXAQrFUEJtFVmM2VKPFmxGQxDBSeIZyETHiQHBZsyWetupzMMKDFxahKBI/TEN4iRfi3BYgTHAK8sMmjrCeF0O2k1BxBCySF+0zo1hCnRNw+ty0NQRtnqp1rF1hWxSWlsJRggcbiipgOOuhfFzrF6dQqHoA3Y7F2Ycu6p3o2FiopPqstlEQ8NkV/VuK5ZnKUdNmUQ4GtOSCg8yUPeoKZMGeGWZQaHfTZbbQXGuF6/LgSmkgDGFwOuS41luh32zrraugOf/Cz5/C9obINgij5+/Jce3rrB6hQrFl8Om3kTlcbGYJnIoRUfHxBB6UvNjDdA1KWqayGG0VYu0iA36eFr0EUwUn1MjCkgWdoJ8rZXN2hga9PFMtWqRFpLY3n5MoY/mzghhw8Tl0MnzOqltCVnS3j4jME1Ydgu01pFc4VGAIeT4slvgsn/bMvBdMQSIeRP3bAIzDLoLhk2yhTdR/cVajC+/hBb8mELDiYEereaiI3BiYAqNFvz48kusXuqA09AR4f/pZ9Gp+SjVGvESQsPES4hSrZFOzc//c5xFQ4c9u0PHapU4dPisrpWa5g72tASpae7gs7pWnDr2rVVS9THUrQdhAIY8xm6xx3XrpZ1CMdjYugJeukZugbqzILtEHmsr5fgB8Cb+5S9/oaioiGAwmDR+5plncuGFFwLw4osvMmPGDLxeL+PHj+f2228nEpHfz0IIbrvtNkaPHo3H46G8vJwf/vCHfVqLEi4WM65iNp8xlna8dOJCx4wKGJNOXLTj5TPGMq5ittVLHXAK/W7WuA7j9/6F7HKNI9cRpExvJtcRZJdrHL/zX8ka52H23QpJQMOkQtvGCfonVGjb0DAR+/+xocvu92VMy74wQtLO5pimYO2uZlZ8Vs/aXc2Ypq0/OZmPaUpPS7AVcsrA5QNNl8ecMjn+1uJ+3zY6++yzMQyDv//97/Gxuro6li5dyve+9z3+85//8N3vfpcf/ehHfPrppzz44IM8+uij/PKXvwTgb3/7G4sXL+bBBx9k06ZNvPDCC0yd2jdfudoqspj1tW08pp/J9ZE/kqV10CByEOhomPi0EG3Cx2OOM8mubbNdEbHYVsi/q6ewMXsqJ0T+Q7FRR52jmDedx1PVajC5zL5l201TsGTFFg6PfMIPfC8yxtiOS0QIa04+d4zlj5EzWbIih6PHF9nP62JEYL/STUTt7MvKzXviBfrChsDl0JhQnK0K9GUyNWvk9pCvoGfaoKbJ8T2bpF0/Fi71+Xycf/75/PnPf+bss88G4PHHH2f06NGceOKJnHLKKfz0pz/loosuAmD8+PH84he/4LrrruPWW29lx44dlJaWMnfuXFwuF6NHj+bII4/s01qUx8ViGtpDrBJTuSlyKRvM0Tgx8WudODHZYI7mpsilrBJTbVlELLYVcryzkhubbuXCtr9weudLXNj2F25supXjnZX23QpBVs7Nr1nFL8TvmRpZR65owU87uaKFqZF1/EL8jvyaVVRWBaxe6sCz57P+tRuCrNy8hxufX8v66gBZHifFOR6yPE7WV7dw4/NrWbl5j9VLVKSifa+MaXF6Us87PXK+fW+/v/Rll13Gv/71L3bvlskijz76KBdffDGaprFmzRp+/vOfk52dHb9ddtllVFdX097eztlnn01HRwfjx4/nsssu4/nnn49vI31RlMfFYvJ9LlqDEVaaFawyJ1OhbadQa6FB5FApxiLQcQQjti0idoxeyeGuRwiGAjSYWQTJxkNYVs51PYJPnw4M7UC03mho6+SK8GMU0oQADBwINDQEDgwKRRNXhB+jvu08wF7eOiKd/Ws3xIh561qDEUpzvWjRK3ev7qA0V6cmEGTJii329NZlOv4iGYgbCcrtoe5EgnLeX9TvL3344Yczbdo0/vKXv3DqqadSWVnJ0qVLAWhtbeX222/nrLPO6vFzXq+XUaNGsXHjRl577TWWLVvGlVdeyf/+7/+yYsUKXK4vdn5TwsViIqaJEd1TFuisE+N7eLgNUxCxSZpbEtG9XJ/Zgbd4NO6IfK8cuobPWYTWUi33csceb8vMkPL2jYxmJwKI4ELDREfEHzsJM4GduNo3AjYL7i4c3792Q4zKqgBb6lop8LvjoiWGpmnk+11sqWu1bZ+rjKZ0msweqq0Epzd5u0gI6GiUtYpKpx2Ql//+97/PPffcw+7du5k7dy6jRo0CYMaMGWzcuJGJEyf2+rM+n4+vf/3rfP3rX2fhwoUccsghrF27lhkzZnyhNdjv2z7DeLWytl/thhQJe7mapuF3OcjxOPG7HPLLNnEv14ZMCK7HSQQTDZcI4RLhhFsIE5mpNiG43uqlDjzHXcv+v970qJ39aIg25XQ7Ur9HHodO2BS23KLOePTo59aTDS3VEO6Q7eHDHfKxJ0fOH6CLufPPP59du3bx0EMP8b3vfS8+fsstt/CXv/yF22+/ncrKStavX8+TTz7JzTffDMhtpUceeYR169axdetWHn/8cXw+H2PGjPnCa1DCxWLagunt8aVrN6SwcC93MKBHe1o5RVcaPdCVTi8MNAS29PRrOjj241B2OKWdDSn0u3E5NEJGak9u0DBx6ZrK2MtUxs+B0++RnpVQG7TWymNJBZy++IDWccnLy2PBggVkZ2dz5plnxsfnzZvHSy+9xL/+9S+OOOIIjj76aBYvXhwXJvn5+Tz00EMce+yxHHbYYbz22mv84x//oKjoi29pqa0iixme08tJuY92Q4rEvVynFyIdYEZAd4LTd0D3cgcDZvkMQEhPcYoEGk0DgcAsn2G/K5R1z4JpIK/NUp2cdTm/7lmYdu4AL856EosXlubqSdtFQgia2sP2LV44WBg/R26T16yRF2/+Irk9NADb5rt37+aCCy7A40k+L82bN4958+al/JkzzzwzSeh8GZRwsZh0949tuc8c28ut+ghhRhCRoNzD1TQ0pwdNd8p0vwO0l5vpbKlvZTw9G0UkIoCt9a1MGjVAi8oUmncCQvYm0jTpmYt+dtBd8r4ZitrZj1jG3o3Pr6UmECTf78Lj0AkaJk3tYbI9Dltn7A0adL1fU573R2NjI2+88QZvvPEG999//4C9bndsdyGWaRRne8n1OvZpk+t1UJztHaAVZRC6DhNOwgy2IkJtRExBUGhETIEItWEGW2HCSbYMzAVg94eAPAenIj4etbMVeaOQks6QB4cLnG551JDjaFE7e3LMxGHc8c2pTC7LoT0Yoa41SHswwuSyHO745lRVx0XRg8MPP5yLL76YX//61xx88MGWrUN5XCymojyXaaMKWL29kWAkzGS60qHXMxaP08W0UQX2dNmaJo1rX8UhPOgYeIjgwkSg0Y4HUzgw1r5KweyrbClefG4HCI0IGg7Mbp2cwEDHIYS0sxuHfgte+Sl0NAEOkgJ9zGjhOV++tLMxx0wcxtHji6isCtDQHqLQ76aiPFd5WhQp2b59u9VLAJRwsZyYy/aZ+r/y7c5nGUsVLiKEcbKdcp72fouz51xgyy8Ss2oNRv1GGijAdHjxEMQhDAzNQRAPutFJXv1GzKo16CMHzl2aKbQPP5wwThwYBHGhRQN0BSDQcGEQxkn7cPu9NziccNwiWH47GEHkV50DMKRo0R1yfn8BvDZA1zV7bkUrBi32u0zNQI7RK7nD9QiT9Z104KWeAjrwMlnfyR2uRzhGr7R6iZbw+a6dYIQxNZnZEMRDu+YniAwIMzQ3GGFpZ0OqfAezSYxEQ+DCQItWctGiokUDNomRVPmsc+layrE/hJNvlZ4V05QxLaYpH598q5xXKBSDDnW5YTWqyFqvNJCDFyduLUyInllVHi1MWDhpIIdxFqzPaho7DB6OnM9vXPdTQAAHBrFNIRONRnL5deR8FnQYlq7TUo79IRx9pcweat4pY1oO/ZbytCgUgxj112s1iUXWAD9BIIL8r/EdsIZZgwH3iOns0EYw0fycRt1FNm04RZiI5qKVLLLNFjZrY/CPmG71Ui0hP8vFu9qh/DhyJVc4XuQgbWd8m/EzMYolxjd4VzuUS7Ps2S4ijsNpy5RnhWKoooSL1cSKrBlhCFRFe6cIQJO1S7KG27bIWsWIfH497HwOqv8fxptbu4JPBRQDzeTw+rDzuX5EvnWLtJBhWR5yvU5WdRzK26Gefa503UG+z8mwLBvWAFIoFEMWJVysxl8kC2HF6k7oDoiFWEY65Lg3z5ZF1nRd49xRjeTWt/aoVaIBubRy7qhGWwYug8xIK8/30dAWSNnnyjQF5fk+e2akKRSKIYu9giYykZKpIAwwIwjNiYGOIWQqq9CcslKsMKSd3TAijFv3B3RiTSi7biDL2o9b9weZJWJjNA0cmEx3bOMrzk+Y7tgm06PtqecUCsUQR3lcrKZ2LWhOTM2BaYSJRLvOaAicmOiaA11zSjubxbiw9mkItcZTfLVulUo0gFCrtJt+viVLtJLKqgB7W0PMz97EuaG/MZbdXan0zhE86V7A6tapqsOvQqEYUiiPi9W07yVkwi4xjA7hRhembJonTDqEm11iGCETW8a4sOuD+F0NonVKRLxeSSo7O9HQHmJa+BN+ajzIFH0HId1Pk15ISPczWd/BT40HmRb5RHX4NU2o+gg2vyaPZurGggqFYnCgPC4WY3qLaApBp+lgG6X4COLEJIJOBx68IkRTyGCYt8h+KtOd1b92Q4xCn5OLxfP4RDt79CLZwwmI4GCvcFNo7uVi7Xn8voutXqp1bF0hywns2SSD3HWX7H913LUHtIPuoMI0LWnUp1D0FSVcLGatOYaWSCkHaTuoFfl4COMmQggnnbjI11r5LDKaanMMtmslOPkMWPm79OxsSIW2nQa9ikYjGwCP6MSBgYGsLNxENhP0Kgq17UCBpWu1hK0r4KVrINgqywo4PbKjeG2lHD/9HiVelLBTDEKUrLaYD3c2c1/kDByYVGifM0qrp0RrZJRWT4X2OQ5M7oucwYc7m61e6sCTbnCpTYNQ9c695LrATYQys4pys5pSs5Zys5oyswo3EXJd0s52RAs7EmyFnDJw+UDT5TGnTI6/tdje20YxYVdbKb2W2SXyGBN2W1dYvUKFIiVKuFhMTXMnFdp28mhL6DMTr+RCHm1UaNupae60dJ2WsPtD9q9KNHt2PwbwF+HWBSO0evx0oGOgY6Jj4KeDEVo9bl3YMpU+sbBjj/QqTUsu7GhHlLBTDGKUcLGY4Vk6VzpfREPQgZMgTkLRYwdONARXOl9keJb6r1J0o2QqRILowogGL0ti93VhyK0RO6bSxwo7Oj0gBITbIRiQRyHkuE0LOwJK2CkGNSrGxWJG7nqZXNqJ4IBoxZKEGmJEgFzaGbnrZcBmzfJGzIzX4usVLWpnR6o/kUUK2YdfKtIh7UbOGLBlZQT+Ihmv0d4Anc09K1J78+S8Hb1RkCzsUuH0QGeTfYWdIqNRl/EWkxuqRUNEe/n2xIjWdMkN1Q7wyjIATWP/H1G95xWjXdj1nixQuC/MiLSzG6XTIGsYtFQjIu0Ymo6BA0PTEZEOaKmW86W2C3mXxIRdJJh6PhK0t7BTZDRKuFiMo2AUAg1HL24FB0LOF4wa4JVlAO179y9KNM2+V4WB3QkPtBS3VHb2QgiBYQoihkkoejRMEyH25cazAaXTZPZQR6PcOktECDk+bJJ9hZ0io1HCxWK2lc4jgB8nBmDixMBNJOlxAD/bSudZvdSBp70h+qXa28dUl/PtDQO5qswhtzzhgeimWUQvdjahZg3B5jpqKaITDzoCl2ykQSceaiki2Fxn3xgOXZcpz55s6X0Kd4Aw5bGlGjw5cl7Vc1FkIOpTaTErNjdxf+QbAPiI4MLAgYkLAx9yG+D+yDdYsbnJwlVahK8o6nHpLbPBjAYS2tSdPfIo6c6PIUTXLYbuknY2w2zdS0dnB41kUe0YQbWjjFq9hGpHGdWOETSSTUdnJ2arTb11IOu0nH4PlFRAqA1aa+WxpAJOX6zquCgyFhWcazVCMFariTcS7I6OnN9tR9d2ViFppUNnFQ7EajKP8unyJFP9CanFnS7ny6cP7LoygC3tHnJMBz7NICwcxIsMCHn0aRE6TZ36dg+TLF6rpYyfA2OPV5VzFYMKSz+dd955J0cccQQ5OTkUFxdz5plnsnHjxiSbzs5OFi5cSFFREdnZ2SxYsIDa2uRA1R07djB//nz8fj/FxcX85Cc/IRJJDlp84403mDFjBh6Ph4kTJ/Loo4/2WM99993H2LFj8Xq9HHXUUbz33oEPajxouIdzHf/ep825jn9z0PBeov+HMiLNoNt07YYaug6n/Bx8+annffly3oYnoSrfQWxnBIWigXKzijKzhhKzjjKzhnKzikLRwHZtBFW+g6xeqvXoumzgOnGuPNrw86IYXFj6CV2xYgULFy7knXfeYdmyZYTDYU499VTa2triNtdeey3/+Mc/eOaZZ1ixYgVVVVWcddZZ8XnDMJg/fz6hUIiVK1fy2GOP8eijj3LLLbfEbbZt28b8+fP5yle+wscff8w111zD97//fV599dW4zVNPPcWiRYu49dZb+fDDD5k2bRrz5s2jrq7ugL4HZ7Q8haPXrRCJA5MzWp46oOvISNr3su9caOS8XYNzYzg94HCBJlPq0RzycW+prjagMMvL+47p+OjEKzoxBYSFjinAKzrx0cn7+nQKs7xWL9VyTFOwdlczKz6rZ+2uZkzTht5dxaBCExkUXl9fX09xcTErVqzghBNOoLm5meHDh/PEE0/wrW99C4ANGzYwefJkVq1axdFHH83LL7/M6aefTlVVFSUlJQA88MADXH/99dTX1+N2u7n++utZunQp69ati7/WueeeS1NTE6+88goARx11FEcccQR/+MMfADBNk1GjRnH11Vfz05/+tMdag8EgwWBXKmEgEGDUqFE0NzeTm5ub9u/c/PBZ5O5cvl+7wKiTyfv+c2k/75BgzZPwwpVR934vWyGaBmfeD9POHejVWY9pwuNnQW0lIqeUUGsjGCFwuHFnF6C11Mitou88Z7uraNMwWPOrkxgX2oSDCF4iaNEMvU6cGDjZ5p7EtJ++ju5wWL1cy1i5eQ9LVmxhS10rYUPgcmhMKM7mijkTOGbiMKuXp1CkJKO+zZqbZT+ewkIZs7B69WrC4TBz586N2xxyyCGMHj2aVatWAbBq1SqmTp0aFy0A8+bNIxAIUFlZGbdJfI6YTew5QqEQq1evTrLRdZ25c+fGbbpz5513kpeXF7+NGtW3dOXsgmFphXFkF9jwS8Qfi13ZR3Bukp3NiFY/7RQuOmo3o7dWo3fsQW+tpqN2M53CZd/qp9WfMMLYTYuIeVQEWkJ5x1bhZYSxOxofZE9Wbt7Djc+vZX11gCyPk+IcD1keJ+urW7jx+bWs3LzH6iUqFCnJGOFimibXXHMNxx57LIceeigANTU1uN1u8vPzk2xLSkqoqamJ2ySKlth8bG5fNoFAgI6ODvbs2YNhGCltYs/RnRtuuIHm5ub4befOnX36vbeOPrtf7YYUvkIQxr5thCHt7Ej7XsKdLejttbhEEBMtmpOm4RJB9PZawp0tttxK+3zXTjxmB6V6I1kEo++KwIFJFkFK9EY8Zgef7+rb3+1gxzQFS1ZsoTUYoTTXi9flQNc1vC4HpbkeWoMGS1ZsUdtGpglVH8Hm1+RR9W7KCDImq2jhwoWsW7eOt956y+qlpIXH48Hj+fIxBJG9W76A3Slf+vUGFXs+i99N9fWpJdqNnDUQK8ooTG8BItSOhklEyL5WGiYCjQgOnFoEEWrH9BZkzhXKANEgshhBB05M0JI/PxrgxMRHBw0ii3FWLdJCKqsCbKlrpcDvRutW5FHTNPL9LrbUtVJZFWDqyDyLVmkxW1fIRpN7Nsn2CLpLFuU77lqVKm4xGfF9dtVVV/HSSy/x73//m5EjR8bHS0tLCYVCNDU1JdnX1tZSWloat+meZRR7vD+b3NxcfD4fw4YNw+FwpLSJPceBYljTurTiT4c1rduP0RAkWvE1/vYkts5OHLdpZdgt9a2y+44QuAnhJoybSPQYQhcCEbWzG7kePR70Lj8nXdX5Yp8bBya5noz4ChxwGtpDhA2B25H69/c4dMKmoKE9NMAryxC2roCXroHaSnBnQXaJPNZWyvGtK6xeoa2x9K9WCMFVV13F888/z+uvv864ccnXPjNnzsTlcrF8eVfw6saNG9mxYwezZ88GYPbs2axduzYp+2fZsmXk5uYyZcqUuE3ic8RsYs/hdruZOXNmko1pmixfvjxuc6AwXX6gd+0iutnZipxy+ft37zyZMCaidnakvWkPIeHAgUAnuTu0jqwBFBIO2pvsF6swIfRZV39OAQgRLeEi4p8bLWpnRwr9blwOjZCReusjaJi4dI1Cv3uAV5YBmKb0tARbIacMXD7QdHnMKZPjby1W20YWYqlwWbhwIY8//jhPPPEEOTk51NTUUFNTQ0eH7Hibl5fHpZdeyqJFi/j3v//N6tWrueSSS5g9ezZHH300AKeeeipTpkzhwgsvZM2aNbz66qvcfPPNLFy4ML6Vc/nll7N161auu+46NmzYwP3338/TTz/NtddeG1/LokWLeOihh3jsscdYv349V1xxBW1tbVxyySUH9D3YUToXE63X+FxZN1ZjR+ncXiyGLpE099fTtRtqNJhZeAjv08ZDmAYza4BWlDnoGoBGRDgQ0b+umK9FRMdBi9rZj4ryXCYUZ9PYHu7Rt0kIQVN7mAnF2VSUp58hOWSIBr3jK+jZK03T5Lhdg94zBEuFy5IlS2hububEE0+krKwsfnvqqa6aJYsXL+b0009nwYIFnHDCCZSWlvLcc11pwQ6Hg5deegmHw8Hs2bP5zne+w3e/+11+/vOfx23GjRvH0qVLWbZsGdOmTeO3v/0tDz/8MPPmdfX/Oeecc/jNb37DLbfcwvTp0/n444955ZVXegTs9je7vJOpFfn7tKkV+ezyTj6g68hENm76rEf/t+4IIe3sSLZHx8G+g5cdGGTbcDvEHDGLCA40TRDCRRgnBg7COAnhQtMEERyYI+wXGwWg6xpXzJlAtsdBTSBIR9jANAUdYYOaQJBsj4Mr5kxAt6Oya98rY1p6q4Pk9Mh5Gwa9ZwqWBuemU0LG6/Vy3333cd999/VqM2bMGP75z3/u83lOPPFEPvroo33aXHXVVVx11VX7XVN/UuB3sZd8SmhMqSJNYC/5FPhdKWaHNrtFEQejAyapKm3IU7bOblFExYCuLDNwV3+03ysPPWoHpw7AijKHSjEepzaKg8Q2PFooyaMpdxk1PtNGERHjmWrVIi3mmInDuOObU+N1XJpNgUvXmFyWY+86Lv4iGYgbCcrtoe5EgnLeb9MeaRlAxmQV2ZURwU0U6nsxhY6G2eML1kSnXN+LN7gJOLCBwplG26Qz6dhwB9l0Aj0zQxxAK27aJp1pweqsJ9+QcV2xeI3uxMZjdnaioSPCRxzHJLajI+LvRexoovGqdhyHd0T2/URDnGMmDuPo8UVUVgVoaA9R6HdTUZ5rT09LjNJpMnuothKc3uTtIiGgo1EWdiydZt0abY79fMgZxgRfB7m0o8d34ruQQZaCXNqZ4OuwYnmW8vVpI9irFXQbTX6X9moFfH3aiIFbVAbhLBgJWteJuKvEmtYVN6VF7WxGoc/JbNbQjo92fJg4MNExcdCOjzZ8zBZrKPSpazdd15g6Mo85Bw1n6sg8e4sWkFWmj7sWPNnQUg3hDhCmPLZUgydHztusGnUmod55i9E7G3Bg7LM7tAMDvbNhgFdmPc76tRR5BHvIxUCPJ7NqgIHOHnIp8gic9WutXqollEw+nnB0Ey0mWUTUrxALRA3joGTy8dYt0iIqtO1M0Kuop4AqrYx6rYhGLZ96rYgqrYw9FDBBr6JC2271UhWZyPg5cPo90rMSaoPWWnksqYDTF6s6LhajLjcsxvTkJVSWSI2GwPTk2U9ltu8lxyVo0UrY0F7IMJpxa2FCwsUe8ij0uxjuDNg2SG69NgEHYziYbfHPUOJnyURjM2MwtAm2i+PQO/eS64IOI0KB2IuHcLxXUQ4tNJJHrkvaKRQpGT8Hxh4vs4fa98qYltJpytOSAaj/AYup3fhuWgXoaje+OyDrySj8RQRNB3pnE2P1Wor0FnK0Dor0FsbqteidTQRNh22D5Bo6Ivxe/y579QKZIROVLWa0cu5evYDfO75Lgx3jOPxFuHXBCG0vXkIYaIRxYKDhJcQIbS9uXdj2s6NIE12H8sNh4lx5VKIlI1D/CxbTVLerX+2GEmbJYewOZzGMvXgJ0qXwBF6CDGMvu8NZmCWHWblMyyj0u1njOozn3N+gU/OjabJcu6ZBp+bnOfc3WOM8zJ5FxEqmgjDQRQSHw4XL6cTt0HE5nTgcLnQRkX2uSuzmi+qJaQrW7mpmxWf1rN3VrPoTKTIetVVkMRGnd/9GX8BuKFFZFcAZNqKByz2/TAUawbAh+6mM6h7EO/SpKM/l67mb+Fr9yxjo1GnFCDQ0BB7RydeCL9M4/BAqyk+yeqkDT+1a0JygO9BEBIfmkNVPMaVg0Z1yvnatvJK2KSs374mnQ4cNgcuhMaE4297p0IqMR3lcLGZYQXqdjdO1G0qEdn/MCGp6jQHSEIyghtDujwd2YRmCjuByxz/IopNqighoObTp2QS0HGooIotOLnf8o9fA7yFN+17p1s8fDU6fLM9uhuXR6YP8UXLepvFRIEXLjc+vZX11gCyPk+IcD1keJ+urW7jx+bWs3Gy/VhGKwYHyuFhMid7SVWCiN7Sonc0oNJvIRqaBp6pVqGmQTQeFZtPALixTqFlDQft22nOH4W0HLdKJjoGJA+H04vMPw9++XQYX2s2rECsiprugaAKE28E0QHeAyy9TW21cRMw0BUtWbKE1GKE01xvvEO3VHZTm6tQEgixZsYWjxxep9GhFxqGEi8XoniypWXoTL1qXnd0YE/pMZoKIXgqsCdA0wRibNsqLlSb3u9yM0+sRemfsTUHTvWiO4RC2aWny7kXEEpuUqiJiVFYF2FLXSoHfHRctMTRNI9/vYktdq9yGHZln0SoVitSorSKrGX5I10k5VQW62GH4IQO2pIxBxJrjpUbrZmc7/EXSi9C8Ey3Sga7r6A4nuq6jRTqgeaect6NXIaGImAhU0dmyh/aWBjpb9iACVbYvItbQHiJsCNyO1L+/x6ETNgUN7aEBXplCsX/s+VebSYRakx0tWsItioja2Y1dFHV5o1KhyfdmFzY8MUM8cwYzIgNNNV3un2m6fGzaPHNm/By2jf02rR2dOFur8LTuxNlaRWtHJ9vGnm3rImKFfjcuh0bIMFPOBw0Tl67ZMyNNkfEo4WIxsVLt+yJWwt1u1HtGdz3QUtxS2dmJhMwZISKYpolhCkzTRIhIcuaMDVn31t9xffQYQaFTy3CqKaGW4QSFjuujx1j31t+tXqJlVJTnMqE4m8b2cI9mt0IImtrDTCjOpqI816IVKhS9o4SLxWzxHEJkP/8NEXS2eOy3VVSotWHu570x0SnU2gZoRRlGNHOmwz+CDuHGMAxMI4xhGHQINx3+cttmzpiGgfjPYryinRpRSIPpp9H00WD6qRGFeEU74j+LMQ3D6qVagq5rXDFnAtkeBzWBIB1hA9MUdIQNagJBsj0OrpgzQQXmKjISJVwspsp3UFoelyrfQQO0osxhjK8z3pcoFbH+RWN8nQO6rowhWlm4ps1guyilSi+lTi+hSi9luyilts2wbWXhLWtXMiy4gwYzOxqvrMVvQkCjmc2w4A62rF1p9VIt45iJw7jjm1OZXJZDezBCXWuQ9mCEyWU53PHNqaqOiyJjUVlFFlPevhEX+y7J7iJCeftGoGxgFpUh6FmFmLoDYZp04sQB8X4zBuBCoOk6epb9atyArCz8mVFKmdhCRB9GSPMSC6V06YJss5XPjPFUlBxmuyuU9sY68kSYEFlRsdK1HaJpGkHhJE+00d5YZ+EqreeYicM4enwRlVUBGtpDFPrdVJTnKk+LIqOx2/dZxjGhfvl+o1e0qJ3tyBqO7s1D13RcmPE+PAJwYaJrOro3D7KGW71SS6isbuWPpiz3XyT24hZBNGHiFkGKxF46NR9/NL9BZbX9ArsbRA5h4cRNBFOIaO9seTOFwEOEsHDQIHIsXqn16LrG1JF5zDloOFNH5inRosh4lHCxGD3NbKF07YYUpdOgdCq6041D03ATid8cmobudEPpVNvW4mhoD/GOOJT7sq7ic+c4fHRSKBrx0cnnznHcl3UV73CoPVNayw9jqygnX2ulZ4EkQZ7WylZRDuX27HOlUAxm1FaR1ZhmOoVzZalyu6HrMOEk2P4fNGHKLJnYu2UaYATlvE1rccRSWj/Qp1KZcxjjjS3kmgECei5bHRNojwhcesSWKa0FPg+/Ns7gDu1hSrVGmkQ2QZx4iJCvtdImfNxvnMH1Po/VS1UoFF8Qe37jZxJ6mkXW7Oi+NU3Y8jq4s8GVRVd5YU0+dmfLeTuKOpJTWk1hEgybdIQMgmETU5i2TmndsqeNVWYFN0a+zwZzNFl0UqI1kUUnG8zR3Bi5lFVmBVv22DQjTaEYxCiPi9V45B57bx6X6Gk6bmcratbAnk2QXQIuX+p+M3s22bMXD10prY8/+f84f+/fGKdV4SJCGCfbWsv5P/cCLphzoS1jFqqbZY+rVWYF75iTqdC2U6i10CByqBRjEdFrtpidQqEYPCjhYjVTvoFY+Xv2tVkk0NCmfGPg1pQpRHvx4Iy68xP7zYAc72yyZZ2SGLk1K/lp5AF8WgeNIpsQMiD1YG0H10ceIFAzASaeYfUyB5zSXG/8vkBnnRif8k8s0U6hUAwO1FaRxZhlh1PjKJcPRIobUOMoxyyzn0ch3uE3EkQAje1h6lqCstonQCRo7w6/0SJrfjpo0otw6Rq5egiXrtGkF+Gn07ZF1vYVM9YXO4U9MU3B2l3NrPisnrW7mjFN9YnJBJTHxWIqq1vZaExmgdid2kDA28ZkDq5utV+X1miH3/Zdn7AtlEtiW5WqZhjnDuAfeZhts4q2rF3J8OAOwrgoM6txE4pHAYWEm4CWxfBokbVJ04+3erkDSm0gvaKE6doNZUxTqDouKVi5eQ9LVmxhS10rYUPgcmhMKM7mijkTVHE+i1HCxWL2tLRxkninK5alGwI4SbzDmpY2wGbCRddZmnsOUzo/ZbjWQBMJmSGildpOH5/mnsN8m2YVdTTVMZwOfCKIjkkEXW4rIvAQokiE6dA81DXZr8haWZ6vX+2GKurknJqVm/dw4/NraQ1GKPC7cTt0QobJ+uoWbnx+raosbDH2/MbPIHwbnycPmdnQy04RebTh2/i8NQu0kEjE5L9X5+8zM+S/V+cTidgzq8ibOwyvCOHAJIwjGnCqIdAJo+PAxCtCeHPt9wU7YVhWeoUdh2UNxHIyktjJeX11gCyPk+IcD1keZ/zkvHLzHquXaAmmKViyYgutwQiluV68Lge6ruF1OSjN9dAaNFiyYovaNrIQ5XGxmLodn6ETq+ypRb9spf8l9q+OoG7HZ1Yu0xJe+Hg3nWGTVewjM8Q0eeHj3Xxr1iirl2sZ8uuz+2las3X8RlNHGF0DYx9vgq5JOzvS/eSsafLz49UdlObq1ASCLFmxhaPHF9lu26iyKsCWulYK/O74+xJD0zTy/S621LVSWRWw3/Z9hqA8Lhazq7E9fl86+mXLxZhgSWVnF17f0LXFEcsMedOcxjoxPp7O2t3OTrQ31dOJGxMNF0b886IjcGFgotGBm/ameotXOvDI2jb7tjGFtLMjX+TkbDca2kOEDYHbkfr06HHohE1hz4rUGYISLhazzhjba3wLdJVcW2eMHbA1ZQqmSM9nkK7dUGN9wE2b8FIn8unEjY4ZFTAmnbipF/m0Cy/rA/arnJvjdezX4ySidnZEnZx7J1aROmSk3oIOGiYuXbNlRepMQQkXi2lzFmDuZzfeRKPNWTBAK8ocDi5Nr+heunZDjT3ZB7FFlOPRImwTJXwuStglhvO5KGGbKMGtRdgiytmTfZDVSx1w1qXpKUjXbqihTs69k1iRWnS7KBJC2LoidaaghIvFeBwirSBCj8N+XoXDytPbP07XbqgxsjCHB40zaBNeSrVGBBqteBBolGqNtAkfDxpnMLLQfsKuPhDsV7uhhjo5906sInW2x0FNIEhH2MA0BR1hg5pAkGyPgyvmTLBd7E8moYSLxYwPb4Z4cC6YCbeuzCIRtbMXgaCRlqgLBO1XYA3g64eVUemdvs+sq0rvdL5+WJnVSx1wsrzp5R2kazfUUCfnfXPMxGHc8c2pTC7LoT0Yoa41SHswwuSyHJUKnQHY8682gwgbBugaYTSc0eDcGAKNSHQ8bMPqp/lZLnQdevFmA7IxdH6Wa+AWlUE4nTrzDy3lr+9V8K55MF/XVzFS28MuMYx/mLMxcXLBoaU4nfa7PplXUcIf39yalp1diZ2c739jCxtrWggZJm6HziGlOVx5or3ruIB8f44eX6SK82UgSrhYzOrIBMJuJ04MgjiiGUVd3hY3JmGcrI5MsHahFpDu/rod9+FBprTuaOzgBGcl39f+zgStCpcWISycLBBv8bA4gx2NwzBNYbsvW6euo2vsM7NI16SdQkifr0j0/SpAeqZUynPmof5qLWatGM9n5kgAXEjXQixYN/b4M3Mka8V4axZoIcIUmPupLWea0s6OVFYF8Ox8i9v1hzlE30EbXmpFPm14OUTfwe36w3h2vmXLlNbGthC6tm+xpmsajW32y5qJEStAt6GmhQK/m5EFPgr8bjbUtNq6AJ0i81HCxWJMdO40zqde5GFGt4VcGDgRmGjUizzuNM7HtOF/1Ue7mtJKaf1oV9MArCbzqA+0813jebK0TmpEAZ24Eeh04qZGFJCldfJd43nqA/arARQLOu1tl8ypyyBUu9ZxUdVhFYMZ+50NMwxdg1VmBYsiV/KuOZlGkU0bXhpFNu+ak1kUuZJVZgU28/QDUN2cXgO8dO2GGrWfvc8ErYomkU2qyrlNIpsJWhW1n71vxfIsRcZHafTWDSJiym0Au8ZHqQJ0isGMinGxGFe0LPkqcx9l7aN2dqM0x9OvdkMNT6gBlxYhKFL/GQdxkq9F8IQaBnhl1jMsy7P/UA0RtbMh6RSga7ZpATpF5qOEi8UkXhDGytqn+sK1YxvBvDSDbtO1G2psCHg4Sshu2Z30fA88yEDdDQH7nZwnFPoJ72ebI2wKJhT6B2hFmUViATqv3rN6sJ0L0CkyH7VVpMhYirLc+90i0zVpZ0fqfRPZIsrJ11rpqXYF+VorW0Q59b6JVizPUn75yoZ+tRtqqAJ0+0cIQcQwCUYMDBXrk1Eoj4vFOHWNUBp/FE4bBrnk+1xpNcrL99kzTqGmzWCJcQZ3aA9TqjXSJLIJIj0w+VorbcLHEuMMaLNfDaBte1v71W6oEStAd+Pza6kJBMn3u/A4dIKGSVN7eMgWoDNMEb9FTBPTBEMk3zej86aQt821baDBiHyfquOSISjhYjEeJ7RH0rOzGxtq0wsM3FAbYPoY+/VyGp7l4O9mBTdGvs8VDlnHJT9ax2WDOZolxhmsMis4I8t+jQT3l0b/Re2GIkOlAF13EZIoTkwhiJgCwxAYQvTwLu2Lj3Y08sR7O9m5tw1DgMepM6E4myvmDJ73Zqhiw9NhZmGKWLm5dOzsxd8/rkrb7twjxxzg1WQeta0ylXd/gd0xOzsxtsjHO9vSs7M7QgjChknYkBWkvsjJ/UBgmiJZgES9IBFTxOcihhQlB2oL56Mdjdy97DPaQwa5Xhc+lwNDCNZXt3Dj82tV2X+LUcLFYtrD6V3ypWs3lEg3o8GumQ8RI7E9RO+B3Yl2diGU5u5YunZDkZWb93Dt0x/T0BZCCIEQEIoYvLe9gc1Pf8zib0/vl5OzELGtmS6xkShK4t6RqMfEcuEkBE+8t5P2kMGwbDcaGrqu4dJ1SnN1agJBlqzYwtHji9S2kUUo4WIx6eoRG+oWfK70tjjStRtquNPsQZSu3VBiclkOfJSmnQ0xTcGdL6+nviWIEODQidZzkd6M+pYgd768nhcXHpfy5Ly/7ZnujwcTm2vb2Lm3jVyvC41917hR7QCsQQkXRcZS6E/v45mu3VBj5pgCVm3df42WmTaM/2npTG97LF27ocba3c18VtsaD36XhfoSPHgCNta08J9N9Uwuy+0hToYyzZ0hwqYg15Ham6Jq3FiPPb/xFYOC3c3BfrUbauR60sumStduKPFZXVu/2g02EuNEUm3PvLmxnmBvZYWjhAzBB9sbGWWzWjd+lxOHBu0hA6euSY+RpqFrGnk+l6pxkwEo4aLIWFxp7h+nazfUWLk1vSZ4K7fu4Qcn2qu7eLqfiMH0yekRGxLNlEnaskkze6YzkkYqI2CKwbNHLYSgI2zQFjRoC0VoC0ZoCxq0hyK0Bg35OBShPTrfGozQHjK62UUIx2PCkr1xTl0j1+ukqT3M5LIcW9e4sRolXBQZS7p744NtD72/CKS5zZGu3VDi5EOKeXldTVp2VtFb9kyqwNX+3p5pDaYXlZyu3ZclYpi0xUWEFBStwUh8rD0kxUVbwliynRQkB/KbwBCCmkBwyNa4GUwo4WIxLj29wFuX/eIrVZzCftDS/JZO124occZh5fz42U/SsusvEoNRY/VDzCRR8sW8IgeSdL9O9mcnhCAYMXsIiraQQXswQmviWNTTkTge83jsb9vKCjRA1zQEAk3TcGhwSGk2V544UaVCW4wSLhbjcaQnXDw2TJxJt3aNHWvcABxUks3qnc1p2dmNypr0ihdW1gQ4fHTP4OV43RCRwhuSIEJM88DWEzlwpPc3s3pHEw3tm6JbL0bSVktMoGTi7+5yaGR7nPjdTrI8DrI8TrJi96NHOeckO34/2c7ncmCa8PrGOlqDYcYPy+Hrh5XhtGGWXqahhIvF5PrctIb3H52e67NfIFiex8GuNO3syPBcb7/aDSX+ubY6Lbu/rd7FiHxfVyBrhtQS2ReyYJyQWyQJXovW7qIift+IbrXI+62hCC2d6cW4fLK7mU92718c9yd+d5e4kEIievQ45Vw3EeL3OMiOjvmjY/1RAiBV5dznPtqlKudmAJYKlzfffJP//d//ZfXq1VRXV/P8889z5plnxueFENx666089NBDNDU1ceyxx7JkyRImTZoUt2loaODqq6/mH//4B7qus2DBAu69916ys7uuMj/55BMWLlzI+++/z/Dhw7n66qu57rrrktbyzDPP8LOf/Yzt27czadIkfv3rX/O1r33tgL8HwUh6e8jp2g0lhuV6oXb/WR/DbHhiBpn10J92mY4QAlMQ344RoquGiBDJ3pDPalrSes7P97bRGkzvJN4fmELQHt1GiW+t7CN+IzbfGhcfciySgV4Op65FBYb0YGTHhUWC+OgmQrK7CRK/24GuWe9BjVXObQtG8LoceBxSCH1aFVCVczMAS4VLW1sb06ZN43vf+x5nnXVWj/m77rqL3/3udzz22GOMGzeOn/3sZ8ybN49PP/0Ur1eerC644AKqq6tZtmwZ4XCYSy65hB/84Ac88cQTAAQCAU499VTmzp3LAw88wNq1a/ne975Hfn4+P/jBDwBYuXIl5513HnfeeSenn346TzzxBGeeeSYffvghhx566AF9D1SFz97JT7Prc7p2Q40dDeml8qZrN9AkBqcKIU/q3cu7R7oVNUuXjnC6FwTpx1ZEDDPusUgWHolxHD1jPRKzVtpDBzaAtD/RgFljChg7LCvu/YhtqWQneDdigsTt1KNF7AY3scq5Te1hTCE9W0LIxpRuh07YEKpyrsVYKlxOO+00TjvttJRzQgjuuecebr75Zr7xjW8A8Je//IWSkhJeeOEFzj33XNavX88rr7zC+++/z6xZswD4/e9/z9e+9jV+85vfUF5ezl//+ldCoRB/+tOfcLvdVFRU8PHHH3P33XfHhcu9997LV7/6VX7yk58A8Itf/IJly5bxhz/8gQceeCDl+oLBIMFgV/2QQCC9PfXuONKMnEzXbiihm+mdfNK1G2rsTbMHUbp2X5ZUpd0TK6smBqxGzAO7HeN2pndCaWgL8swHO+OCJMnT0S2WI5SBAaS6RnzrxJ8QvxEXFD1iOqQA2dsS4t7lm2gLRjCBxFprppDPm+N1celx4zmo1F4xUptr29hS10IwYmAKcOgyMBdNozNiomsmn1Y1q8q5FpKxMS7btm2jpqaGuXPnxsfy8vI46qijWLVqFeeeey6rVq0iPz8/LloA5s6di67rvPvuu3zzm99k1apVnHDCCbjdXVfl8+bN49e//jWNjY0UFBSwatUqFi1alPT68+bN44UXXuh1fXfeeSe33377l/49vU4HsP8vRGlnL17/bG+/2g01crzp/fmma5dI960XI5op01uA6oEq7W6YIqn+Rmu3+hxtQSO5Hkd0LF0v0+b6djav2Nrv604Hj1PvitlI2EaJjWV3CyJNFCGxn/H20cthCsHLlTVsrAlgmLLJYrTOGl6njkPXmFCczcSSrAPwm2c2jR0hWoMRKVqib21MzDk1iAhBoDPCnjZ7Fr7MBDJWuNTUyBoMJSUlSeMlJSXxuZqaGoqLk+swOJ1OCgsLk2zGjRvX4zlicwUFBdTU1OzzdVJxww03JImdQCDAqFGjvsivCIDfnV4QWbp2Qwm7xXB8UaaPyueNz/ZfhG76qHyE6LntYhxAERJLk43X2egRSBqL80gOIk32eETozEAvhwY9tkl6i9nIShXfEfWOuBzW/U3rmsb5R46Kd0DO87nQNFnqPxgx8bsdnH/kqIyIN+kNTZOdhDQNNDR51OS4Hh3TNUCTv28svTlmI38u2V7TwKlpmKb8uYgJIrq5p5ki/hqmKWhqs2cZhkwgY4VLpuPxePB4PF/6eTrSjAtM124oke65M4MTQA4IMWFx1PgiWL55v/YTS7LZtif9OJdYAGmPIl+J2SlJgiQhcDTD02S7U+B3MaU8t0dWSnIgaSzYVAoSX4YEkH5ZDh9dwKJTDopnzoSFwKVpjB+ezflHjkqZJt4b3U/+cXFAgliAJBGR9DN6ahEhnzvFzxzA9394jgdNB6ObZhZEv2uiTSkL/PZrpZEpZKxwKS0tBaC2tpaysrL4eG1tLdOnT4/b1NXVJf1cJBKhoaEh/vOlpaXU1tYm2cQe788mNn8gSffvbwh8T35hPA5IpzL5YM2GTvR+JGbIxGuD7KdeyJb61rReZ+Xmvexs6EgZLNoW7JaxEg0gzUR8LkfPFNkEsZHt6br/5Hs72NHYsd/nHJnv4xffOLAB+Aea7gJBjqUQE9BDOJw8uYSTDilhQ00LTR0hCv1uJpfl4nRoPcSD/PGu50r0XgwlCrLc6GjsK4xaR6PApkkBmUDGCpdx48ZRWlrK8uXL40IlEAjw7rvvcsUVVwAwe/ZsmpqaWL16NTNnzgTg9ddfxzRNjjrqqLjNTTfdRDgcxuWSCnnZsmUcfPDBFBQUxG2WL1/ONddcE3/9ZcuWMXv27AP+e/rSjF1J124oIdAgjRwMYXHHmR49ZKKBp7HUXSGSs2dSbcWYQtAZ7bPS2luJ8259VjZUp5fy+8R7Ow/Er502Dl1LKTRSxWz0sIvGevjcDhxfIIPjw88b0xIuJQcglV6PNuSLnc91PXlLI7Z9EbufKDzQunsYiHt3tFQ/009ZLbMnFPXL8wwZ9ve2Di2tNuiwVLi0trayeXOXq3vbtm18/PHHFBYWMnr0aK655hr+53/+h0mTJsXTocvLy+O1XiZPnsxXv/pVLrvsMh544AHC4TBXXXUV5557LuXlspT3+eefz+23386ll17K9ddfz7p167j33ntZvHhx/HV/9KMfMWfOHH77298yf/58nnzyST744AP++Mc/HvD3IMebniBJ124o4XXptKdRv8b7JfshxERGd++HKQSiW2CqKXp6QPraZ6W7IMnEjRWvU+9Z/Kub8PB7nGR3CyxNFCEeC9Jk508r5bUNdfu1O2tmOX63M8kj0V0cdI+JSBQmMWGhD8AWhmJgaGwL7Xf7WQhpp7AGS4XLBx98wFe+8pX441iw60UXXcSjjz7KddddR1tbGz/4wQ9oamriuOOO45VXXonXcAH461//ylVXXcXJJ58cL0D3u9/9Lj6fl5fHv/71LxYuXMjMmTMZNmwYt9xySzwVGuCYY47hiSee4Oabb+b/t3fvwVGV5x/Av+ecvWWTzY1ALhBACSigSeSWYi2gUijjMGBnlB91WqSdzrQNFiZ1QOoIlNaKdPr70RmodXqR9g9GO52Bts4Iw1CFqqAQGgoRUaLWaBKSELK57+Wc8/tjL9lNSPJSF87Zc76fmR2yu0/Cm0PYffK+z/u8P/7xjzF9+nQcOnTopvdwAcRf6Oz4gpjhcgD9IomLgkBYjfcCiSUhuha7HzsbJuH56JJMX0hFz0BoWH1G8nkqCYlHmpyzksjrUjBtfGZCx9HBQtKkZCQ6u5FY53Ejsxyjib3RJ80eXKcGYsSiyYQaCABJMxjXq4Wo/c81oXH1B3UU5dizgSFd37W+0Jj1Waqm41ofi3ONYmjismTJklF7OUiShJ07d2Lnzp0jxuTn58ebzY2kvLwc//znP0eNeeSRR/DII4+MPuCboF/koKIbiDO7681uxJILTY88H3ssINhErDeo4rXzzcJH2A9unTVnAen1zlmJ71hJeOzvdZ/jc//YWzLLCrzY8z/3XPe5YcmDNMaMAq6/nJFYlHm9HRy3WtO1PqG4z0zanO9W0jQd9U1d6OiL1LjMLsm2dWM1n0cZc/ZThz1nwc3CtDUudqGqYm/OonGpFFsOic9gDEkyEHscg8/rCfdjMxu6HtlSGLmvJZ2zktyfI/lMla6A2PfsH1Cx6ZVzN/NSCBM9Z2Wkg928Q85ZGVpgKcuD9//2L7Halau9A5iYl3HT6iPMqL5J8JBFwTirevtyO1443oCG1h6EVB1OJdK/xc7n8bzXJFY79l5TN5bOuvkbOGg4Ji4G6xTc5zw0Tk+YodCiiUEkcRj8OJZU6Npg4hArFk1KNhBJMIDkBGWoxG2yQxt+2eKclSFbZBOXXWJ/OmR5cFkkmmQkLpMkzlxIMuL3B5dLhtwfZbaiLyg2C9cb0OC2WXH3p1cFj0MQjLOity+348cHz6MnEEae1wWXIiOoarjY3G3v83hES+bs11rLNJi4GCykir2JD4Q0fHq1Lz7jceN/jzZKcjFyYWm6nLMyLss1/MTYhI6jid1HE+s4sjxOZLkVeBxKJNGIJhtKtNAiMdmIPDdyonGrZzBEd1MZvevKCAOi/68E46xG0yLn7fQEwijK9sQTZI+soChbRktXwLbn8ZTmeiFLkW65QPIGothPiyxF4sgYTFwM5lDEXhRkGWho6x7WZXRoK3QrnLMSq+84fKFJ6HDJHLeMV5+4D4okxZdWFHnwY6vuAhEtnE1VgW06uavEh0+ujl3ncleJ7xaMxnzqm7rQ0NqDPG+kF0l/UEVY0+CQZXicMnK9TjS09tjyPJ6V5cX4yav16OwLxZMWHdGC8OjH2RlOrCwvHvFr0M3FxMVgRT432gVaR/sHVKzff+YWjEiMO7pNdvBcleTTY69b35HwXJbbCa9LhiJHzkWJzFgMJhWKJOHk5Ta0CVwbj9OB4pyMW/Bdm0tBphMdAjsbCjLt1+Ez0y32PYvGWU1HXxAhVUcwrKHZ349AePCsIrdDxrhMN0Kajo4++235dThkVC+ZhucPX4Kq6VDkyMyqDh2qFllWrl4yDQ4H14qMwsTFYLdPyMSFFrEOqKkg4frnrFzvkLexzllJmtGQI8lG4pKKIkXqOBRJGkxOEmZDxnJXsQ+vX+4YM26WzU6vjZlRlIUP2saeVbDb6b4AEBLo/3MjcVaT73VB0zU0+YPRN2cpPrvQF1QRCPcj2+NAvtee3WG/u2gaAGDfGw3o7g9Biy645nidqF4yLf48GYOJi8HGZ4vPFDgVaXBGY9jx9cn3E3eteF2DicdI56wMznpEE5BowhF/TE5edlGij91M55v9KY2zmkBYrD5DNM5K3C6xlzbROKuZWeSDqkdq7CQAYVWPL4cAkcdVPRJnV99dNA3r770Nf/93Mz7v7MPEXC9WlhdzpsUE7Pm/1kQ0weLAVeXF2PjVGWPGxZKKxCLTwWQkmnAkLMvEHjcjv0DzuRuJsxqenj2yqQVihZOicVZzsaU7vpMw8RUo8WNN03Gxpdt2NS6JZFlC2YQs5Ge5kO91mfa10m6YuBjs7YZ2objzn3ciy+MYXHZJWI6RE5Zj0rnYdCgpsbR/rDgbEu1/Zcc+WRcF+7OIxllNe28A/SEVsjR4unrijIskAf0hFe29Yzc4tKq3L7dj3+sfor6pC8GwDpdDwuySbFTfP92e28RNhImLwdp6xF4YOvtDmOCzV2vyiT4HPr42dvHpRJ89f4wvCDbKEo2zkiv+gZTGWU1nbwhatLZFkaV4D6jYzrvYKeWdAsXxVvT25XZUHziLzr5QfBaqLwS83dCB95rPYt835jB5MRAX6wzmFVxjF42zFFlwqkA0zmI6BwSbFwrGWYkki720icZZTZ7XCVmW4stFckIBPRBZJpJlCXle++260jQdTx86j2sJSUuMjshZRk8fOh+/dnTr2fN/rYncMzkvpXFW0tEjthVTNM5qPIJFgqJxVjJtgljtimic1YzLciPb44QkSQir0ZPQEfkzrOqQJAnZHifGZbmNHuotd+6zTnzSPrhbLzYLlbgK/0l7H8591nnrB0cAmLgYrqJUrPBNNM5KRHvmmbC33i0xMUfsTUU0zkouNYstj4nGWc3skmzMKslGpluB2yFB1XSEwpGDT90OCZluBbNKsjG7JNvood5yr11oic+0DC0ZjN3Xo3FkDCYuBvN5xKZiReOsJKyJZSSicVbTGxA/q8huPhXomnsjcVYjyxK+v3gaPE4FQTV6fln0FlR1ZDgVfH/xNFvuoukXPNxVNI5Sj4mLwT5oEfuNTzTOSkRfMu330hrRFxLcDi0YZyUDIbG6HtE4K9P15EN5Yoew2lXl5NyUxlHqMXEx2On/XEtpnJW4BM9xEo2zmpDgTJNonJXkZIh1fBWNs5rYIYv9QRVuhwJJinTOlSQJboeC/qCGF4432LIAdVVFCbLckYJ/XUdkXSh6i+V4WW4FqypKjBqi7TFxMZgMsTcV0TgryRDcSSUaZzUep9huKtE4K/EJJiSicVZT39SF95r86A+pGAhrUGQJTiVybthAWEN/KIz3mvyot2GfG4dDxsYHp0NOqGeJ3YDI4bAbH5zODroG4pU3WFiwc65onJWILnDYbyEkokyw66tonJWIHixpxwMogUgDuq6BMDRdh1OOnZ4e+dMpS9B0HV0DYds2oJtdkoOcDOewZWgJQE6GE7NL7LdZwkyYuBhMtNOtlTriihqXKfbbsGic1eQLNiQUjbOSkjyxZE00zmpiDehiCUuiWAJj1wZ0sWU0RZYwsygLk3IzUOhzY1JuBmYWZUGRZdsuo5kFExejidYf2LBOYWax2AFvonFWExQ8PFE0zkpYYDm6xAZ0Q0txdei2bkBX39SFhtYe5HldkBUZHqeCDJcCj1OBrMjI9TrR0Npjy2U0s7BncYCJtPaI/UYjGmcltZ90pDTOatwOsVk40TgrUVWxRF80zmpiDej8/SGEVB2ypEOCFG1CB1s3oOvoCyKk6giqGpo6+xEIq9D0SG2L26GgwOdGSNPR0WfPxpdmwBkXg3UFxLZjisZZSWu32AuDaJzliE6k2G/CBX8715zSOKuJNaBzO2Touh5/ow5Fe7q4HbJtG9Dle13QdB2NHX3oDaoIa5GzXsMa0BtU0djRB03Tke+15xK1GTBxMZjPLTbpJRpnJbrgG65onNX0BsRm4UTjrKSzX+x7Fo2zGlmWsGh6AQJhLTKbgMGbpgOBsIZF0wts2YBuZpEPA9FZluvRdGAgrGJmkT2XqM2AiYvBvlKWn9I4K8l2i/14isZZzeV2sa6vonFWMmNCZkrjrEbTdJz4sB2xHb1awg0AHDJw4sN2Wxag1jd3IRgaXEKUEm4xwZCG+mbWuBjFnq/4JuJ2iM2kiMZZSb/gIUSicZajC/42LBpnIVW3j0tpnNXUN3XhXGMnBkYo3B4I6zjX2GnLAtR/NXZC1XU4ZCmplwsQqXNxyBJUXce/GjuNGqLt2e/d0GR6BVuOi8ZZiWhZjw3LfwAAkiSWsInGWclHbb0pjbOatu4BdA8M/sdJ3BEdW3rtHgijrXsAgL16lkjR71+WAIciQ492zI2dEK1p0QJm+01GmQZnXAzW3iNWWCoaZyWscRndtV6xnwnROCu5KHjqs2ic1Vz4vEvoBOQLn9tvxqVyci6cigw1ukwmSxKUaJM+AFA1HU5Ftu1WejNg4mKw8591pjTOSjyC84GicVYTFJxIEY2zkizBuifROMtJ/LaHJv76CHE2cffEHMwozIIOIBTWoOl6dJu4jlBYgw5gRmEW7p5or5koM7Hhj6W5BAT7SIjGWYlTsK5HNM5q8jPEvm/ROCvJ84r1HxGNs5rSXG9y/UbiQYLRGFmKxNmNLEvYumImxvvckGUJqqYhFNagahpkWcIEnxtbV8y05Y4rs2DiYrAMwYO6ROOsJKSJnUIkGmc143xib7qicVYiekKGDU/SAACsLC9GdkakK27sEsSXjqJ/Zmc4sbK8+FYPzRTuLSvA/z1aiarbxiHP60aWx4k8rxtVt43D/z5aiXvLCoweoq3Z71cxk/E4HcDA2DUIHqf9/qmcsgyRIxQjcfZzVfAcGdE4K2nxix0OKBpnNQ6HjOol0/D84UtQNR2KjHjnXFWL7JypXjLN1icg31tWgC/dPg71TV3o6Asi3+vC7JJszrSYgP3eDU2mOyBWOCkaZyXjfW50Dozdg2S8DWcUAMAl+AIqGmclRTliPxOicVb03UXTAAD73mhAd38IGvTI6cdeJ6qXTIs/b2eyLOHuSaxlMRsmLkbTJQj1ZLdhLw5V8GBJ0TiryfYoKY2zkgnZYidii8ZZ1XcXTcP6e2/D3//djM87+zAx14uV5cW2nmkh82PiYjCHIgOhsZdDHIr9XkiuCm7jFY2zGkUWS0hE46wkJ0PsVGPROCtzOGQ8PGei0cMgEma/d0OT8Q+IFZaKxlmJaENcuzbOFf227Xh5FEnGWHOUUjSOiNIL/9eSaeUJnr4qGmc1bsFZONE4K6mcnAuXY+TkRQLgcrCJGFE6st8rGqWNOwQPwBONsx7RlsH2ay0cayImSYPny8RucrR1O5uIEaUnJi5kWl63WP2BaJzVuJ1itSuicVaS2ERMkSMF8Loe6bCmsIkYUVpj4kKmdbm1J6VxVpPhEqutF42zGjYRI7Ime76iUVrwCzTmu5E4q5mUl5HSOCtiEzEi62HiQqalCp7PJBpnNXcUZac0zqrYRIzIWrhURKYlC/YfEY2zmgKfG2NNHMhSJI6IyCqYuJBp5XvFim5F46ymINONPK9zxORFloA8rxMFmUxciMg6mLiQaYl2Hbdrd/LZJdmYVZKDLLcTGQ4JihRrqgZkOCRkuZ2YVZKD2SX2XioiImux6Us+pQP/QDilcVYjyxK+v3ga8jOd8LqdKM7NwOR8L4pzM+B1O5Gf6cT3F09jISoRWQoTFzKtDMH+LKJxVnRvWQF+/vDdmFWSDU3T0RdSoWk6ZpVk4+cP380tv0RkOdxVRKa1dGYhLjZ3C8XZGbf8EpGdMHEh03piSRn2/uPyqA3rpWic3XHLLxHZBZeKyLRcLgXfWFA6asw3FpTC5bLndmgiIjti4mKwTJfYP4FonNU8+/VyPLagFMqQVQ9FAh5bUIpnv15uzMCIiMgQkh45eYy+oK6uLuTk5MDv9yM7W3z7acu1Hnzp+eNjxp3ashhFeVlfZIhpLRhU8ZsTH+E/Hb2Ykp+J7y26nTMtREQ2xBoXgxXlZcEpA6FRutY7Zdg6aQEiy0Y/XDrd6GEQEZHB7Ln+MIp9+/Zh6tSp8Hg8qKqqwrvvvnvT/84Pf/4QnCP8SzjlyPNERETExCXJK6+8gpqaGmzfvh1nz55FRUUFli9fjtbW1pv+d3/484dwasti+NwOKBLgcztwastiJi1EREQJWOOSoKqqCvPnz8fevXsBAJqmobS0FE888QSeeuqppNhAIIBAIBC/39XVhdLS0huucSEiIiJxnHGJCgaDqK2txdKlS+OPybKMpUuX4uTJk8Pin3vuOeTk5MRvpaWjb9slIiKiL46JS1R7eztUVUVhYXIX1sLCQrS0tAyL37p1K/x+f/zW2Nh4q4ZKRERkW9xV9F9yu91wu91GD4OIiMhWOOMSVVBQAEVRcOXKlaTHr1y5gqKiIoNGRURERImYuES5XC7MnTsXx44diz+maRqOHTuGhQsXGjgyIiIiiuFSUYKamhqsW7cO8+bNw4IFC7Bnzx709vZi/fr1Rg+NiIiIwMQlyZo1a9DW1oZt27ahpaUFlZWVOHz48LCCXSIiIjIG+7ikyH97VhERERGJY40LERERpQ0mLkRERJQ2mLgQERFR2mDiQkRERGmDu4pSJFbj3NXVZfBIiIjIrHw+HyRJMnoYaY2JS4p0d3cDAA9bJCKiEXHn6RfH7dApomkampqavnA23dXVhdLSUjQ2NvKHewhem9Hx+oyM12Z0vD4jS/W14YzLF8cZlxSRZRmTJk1K2dfLzs7mC8gIeG1Gx+szMl6b0fH6jIzXxjxYnEtERERpg4kLERERpQ0mLibjdruxfft2uN1uo4diOrw2o+P1GRmvzeh4fUbGa2M+LM4lIiKitMEZFyIiIkobTFyIiIgobTBxISIiorTBxIWIiIjSBhMXk9m3bx+mTp0Kj8eDqqoqvPvuu0YPyRROnDiBlStXoqSkBJIk4dChQ0YPyTSee+45zJ8/Hz6fDxMmTMDq1atx6dIlo4dlCi+88ALKy8vjzcMWLlyI1157zehhmdKuXbsgSRI2bdpk9FBMYceOHZAkKel25513Gj0sAhMXU3nllVdQU1OD7du34+zZs6ioqMDy5cvR2tpq9NAM19vbi4qKCuzbt8/ooZjO8ePHUV1djVOnTuHo0aMIhUJYtmwZent7jR6a4SZNmoRdu3ahtrYWZ86cwQMPPIBVq1ahvr7e6KGZyunTp/Hiiy+ivLzc6KGYyuzZs9Hc3By/vfnmm0YPicDt0KZSVVWF+fPnY+/evQAi5x+VlpbiiSeewFNPPWXw6MxDkiQcPHgQq1evNnooptTW1oYJEybg+PHjWLRokdHDMZ38/Hz84he/wHe+8x2jh2IKPT09mDNnDn7961/jZz/7GSorK7Fnzx6jh2W4HTt24NChQ6irqzN6KDQEZ1xMIhgMora2FkuXLo0/Jssyli5dipMnTxo4Mko3fr8fQOQNmgapqoqXX34Zvb29WLhwodHDMY3q6mo89NBDSa89FPHhhx+ipKQEt99+Ox577DF8+umnRg+JwEMWTaO9vR2qqqKwsDDp8cLCQrz//vsGjYrSjaZp2LRpE7785S/jrrvuMno4pnD+/HksXLgQAwMDyMrKwsGDBzFr1iyjh2UKL7/8Ms6ePYvTp08bPRTTqaqqwv79+3HHHXegubkZP/nJT/CVr3wFFy5cgM/nM3p4tsbEhchCqqurceHCBa7FJ7jjjjtQV1cHv9+Pv/zlL1i3bh2OHz9u++SlsbERGzduxNGjR+HxeIwejumsWLEi/nF5eTmqqqowZcoU/PnPf+Yyo8GYuJhEQUEBFEXBlStXkh6/cuUKioqKDBoVpZMNGzbg1VdfxYkTJzBp0iSjh2MaLpcLZWVlAIC5c+fi9OnT+NWvfoUXX3zR4JEZq7a2Fq2trZgzZ078MVVVceLECezduxeBQACKohg4QnPJzc3FjBkzcPnyZaOHYnuscTEJl8uFuXPn4tixY/HHNE3DsWPHuB5Po9J1HRs2bMDBgwfxj3/8A7fddpvRQzI1TdMQCASMHobhHnzwQZw/fx51dXXx27x58/DYY4+hrq6OScsQPT09aGhoQHFxsdFDsT3OuJhITU0N1q1bh3nz5mHBggXYs2cPent7sX79eqOHZrienp6k33Q+/vhj1NXVIT8/H5MnTzZwZMarrq7GgQMH8Ne//hU+nw8tLS0AgJycHGRkZBg8OmNt3boVK1aswOTJk9Hd3Y0DBw7gjTfewJEjR4wemuF8Pt+wOqjMzEyMGzeO9VEAnnzySaxcuRJTpkxBU1MTtm/fDkVRsHbtWqOHZntMXExkzZo1aGtrw7Zt29DS0oLKykocPnx4WMGuHZ05cwb3339//H5NTQ0AYN26ddi/f79BozKHF154AQCwZMmSpMdfeuklPP7447d+QCbS2tqKb33rW2hubkZOTg7Ky8tx5MgRfPWrXzV6aGRyn332GdauXYurV69i/PjxuO+++3Dq1CmMHz/e6KHZHvu4EBERUdpgjQsRERGlDSYuRERElDaYuBAREVHaYOJCREREaYOJCxEREaUNJi5ERESUNpi4EBERUdpg4kJERERpg4kLEcV98sknkCQJdXV1I8bs378fubm58fs7duxAZWXlqF/38ccfx+rVq1MyRiKyNyYuRHRD1qxZgw8++MDoYRCRTfGsIiK6IRkZGSk/vDEYDMLlcqX0axKRNXHGhciGNE3D7t27UVZWBrfbjcmTJ+PZZ5+NP//RRx/h/vvvh9frRUVFBU6ePBl/buhS0VCqqqKmpga5ubkYN24cNm/ejKFHoi1ZsgQbNmzApk2bUFBQgOXLlwMALly4gBUrViArKwuFhYX45je/ifb29qTP++EPf4jNmzcjPz8fRUVF2LFjR2ouChGlBSYuRDa0detW7Nq1C8888wzee+89HDhwIOkU8qeffhpPPvkk6urqMGPGDKxduxbhcFjoa//yl7/E/v378Yc//AFvvvkmOjo6cPDgwWFxf/zjH+FyufDWW2/hN7/5DTo7O/HAAw/gnnvuwZkzZ3D48GFcuXIFjz766LDPy8zMxDvvvIPdu3dj586dOHr06Be7IESUPnQispWuri7d7Xbrv/3tb4c99/HHH+sA9N/97nfxx+rr63UA+sWLF3Vd1/WXXnpJz8nJiT+/fft2vaKiIn6/uLhY3717d/x+KBTSJ02apK9atSr+2OLFi/V77rkn6e/+6U9/qi9btizpscbGRh2AfunSpfjn3XfffUkx8+fP17ds2SL2zRNR2uOMC5HNXLx4EYFAAA8++OCIMeXl5fGPi4uLAQCtra1jfm2/34/m5mZUVVXFH3M4HJg3b96w2Llz5ybdP3fuHF5//XVkZWXFb3feeScAoKGh4bpji41PZGxEZA0sziWyGZHCWqfTGf9YkiQAkbqYVMrMzEy639PTg5UrV+L5558fFhtLnoaOLTa+VI+NiMyLMy5ENjN9+nRkZGTg2LFjKf/aOTk5KC4uxjvvvBN/LBwOo7a2dszPnTNnDurr6zF16lSUlZUl3YYmOURkX0xciGzG4/Fgy5Yt2Lx5M/70pz+hoaEBp06dwu9///uUfP2NGzdi165dOHToEN5//3384Ac/QGdn55ifV11djY6ODqxduxanT59GQ0MDjhw5gvXr10NV1ZSMjYjSH5eKiGzomWeegcPhwLZt29DU1ITi4mJ873vfS8nX/tGPfoTm5masW7cOsizj29/+Nh5++GH4/f5RP6+kpARvvfUWtmzZgmXLliEQCGDKlCn42te+Blnm71hEFCHp+pAGC0REREQmxV9jiIiIKG0wcSEiIqK0wcSFiIiI0gYTFyIiIkobTFyIiIgobTBxISIiorTBxIWIiIjSBhMXIiIiShtMXIiIiChtMHEhIiKitMHEhYiIiNLG/wMVdGTxMC5z3QAAAABJRU5ErkJggg==","text/plain":["<Figure size 573.875x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import seaborn as sns\n","# using the lmplot() function of seaborn, build a scatter plot of age and insurance costs, hue by smoker\n","# build a second scatter plot of bmi and insurance costs, hue by smoker\n","# build a third scatter plot of children and insurance costs, hue by smoker\n","\n","print(medical)\n","\n","a = sns.lmplot(data=medical, x='age', y='charges', hue='smoker')\n","plt.show()\n","b = sns.lmplot(data=medical, x='bmi', y='charges', hue='smoker')\n","plt.show()\n","c = sns.lmplot(data=medical, x='children', y='charges', hue='smoker')\n","plt.show()\n","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ZwPUt5YuvILu"},"source":["Describe in a one-liner what you obeserve from the charts.\n","\n","smoking is certainly bad, and smoking paired with obeasity increases medical charges almost linearly, smoking paired with children also increases charges."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"mZAx137X6YLu"},"source":["### Look for Correlations"]},{"cell_type":"code","execution_count":585,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":231},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1682901509164,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"EfLpDaDSEc6p","outputId":"e28685d4-5c18-416d-a1d3-67ab69a7ac6f"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>bmi</th>\n","      <th>children</th>\n","      <th>charges</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>1.000000</td>\n","      <td>0.118274</td>\n","      <td>0.060999</td>\n","      <td>0.281721</td>\n","    </tr>\n","    <tr>\n","      <th>bmi</th>\n","      <td>0.118274</td>\n","      <td>1.000000</td>\n","      <td>-0.005040</td>\n","      <td>0.197316</td>\n","    </tr>\n","    <tr>\n","      <th>children</th>\n","      <td>0.060999</td>\n","      <td>-0.005040</td>\n","      <td>1.000000</td>\n","      <td>0.071885</td>\n","    </tr>\n","    <tr>\n","      <th>charges</th>\n","      <td>0.281721</td>\n","      <td>0.197316</td>\n","      <td>0.071885</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               age       bmi  children   charges\n","age       1.000000  0.118274  0.060999  0.281721\n","bmi       0.118274  1.000000 -0.005040  0.197316\n","children  0.060999 -0.005040  1.000000  0.071885\n","charges   0.281721  0.197316  0.071885  1.000000"]},"execution_count":585,"metadata":{},"output_type":"execute_result"}],"source":["#compute pairwise correlation of columns using the corr() method\n","correlation_matrix = medical.corr(numeric_only=True)\n","\n","correlation_matrix\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"cna9S2v76j_F"},"source":["The correlation coefficient ranges from –1 to 1. When it is close to 1, it\n","means that there is a strong positive correlation. \n","Finally, coefficients close to 0 mean that there is no linear correlation.\n","\n","Another way to check for correlation between attributes is to use the\n","Pandas `scatter_matrix()` function, which plots every numerical\n","attribute against every other numerical attribute."]},{"cell_type":"code","execution_count":586,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":470},"executionInfo":{"elapsed":4486,"status":"ok","timestamp":1682901607934,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"OGRXLj_cFOjz","outputId":"edb76521-28f2-4400-9748-4ca90f23d362"},"outputs":[{"data":{"text/plain":["array([[<Axes: xlabel='age', ylabel='age'>,\n","        <Axes: xlabel='bmi', ylabel='age'>,\n","        <Axes: xlabel='children', ylabel='age'>,\n","        <Axes: xlabel='charges', ylabel='age'>],\n","       [<Axes: xlabel='age', ylabel='bmi'>,\n","        <Axes: xlabel='bmi', ylabel='bmi'>,\n","        <Axes: xlabel='children', ylabel='bmi'>,\n","        <Axes: xlabel='charges', ylabel='bmi'>],\n","       [<Axes: xlabel='age', ylabel='children'>,\n","        <Axes: xlabel='bmi', ylabel='children'>,\n","        <Axes: xlabel='children', ylabel='children'>,\n","        <Axes: xlabel='charges', ylabel='children'>],\n","       [<Axes: xlabel='age', ylabel='charges'>,\n","        <Axes: xlabel='bmi', ylabel='charges'>,\n","        <Axes: xlabel='children', ylabel='charges'>,\n","        <Axes: xlabel='charges', ylabel='charges'>]], dtype=object)"]},"execution_count":586,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 16 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot correlation matrix using scatter_matrix() function from pandas.plotting\n","import pandas as pd\n","pd.plotting.scatter_matrix(medical)\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"G2QwlPxaGNKK"},"source":["The main diagonal would be full of straight lines if Pandas plotted each\n","variable against itself, which would not be very useful. So instead, the\n","Pandas displays a histogram of each attribute.\n","\n","Looking at the correlation scatterplots, it seems like the most promising\n","attribute to predict the charge value is bmi, so let's\n","zoom in on their scatterplot."]},{"cell_type":"code","execution_count":587,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"executionInfo":{"elapsed":483,"status":"ok","timestamp":1682901835397,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"OWnocrWUGQmK","outputId":"1b7d2d95-9cb3-4294-a2b2-7aa6ddc616c6"},"outputs":[{"data":{"text/plain":["<Axes: xlabel='bmi', ylabel='charges'>"]},"execution_count":587,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# plot a scatter plot of bmi vs. insurance costs using the medical.plot() method, use the alpha parameter to set the opacity of the points to 0.1\n","\n","medical.plot(kind='scatter',x='bmi',y='charges', alpha=0.1)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"g1J0yXbkGt3w"},"source":["The correlation is somewhat visible; you can clearly see the upward trend."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"DNY99csK6lSQ"},"source":["# Prepare the data for ML"]},{"cell_type":"code","execution_count":588,"metadata":{"executionInfo":{"elapsed":137,"status":"ok","timestamp":1682905801586,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"3Py1Masi6oKx"},"outputs":[],"source":["# drop the charges column from the train_set and save the resulting dataset to a variable called `medical`\n","# create a copy of the train_set labels and save it to a variable called `medical_labels`\n","# replace None with the correct code\n","\n","\n","medical = train_set.drop('charges',axis=1) #axis=1 drops column\n","medical_labels = train_set['charges'].copy()\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ID5Olve_74qE"},"source":["# Transformation Pipelines\n","\n","As you can see, there are many data transformation steps that need to be\n","executed in the right order. Fortunately, Scikit-Learn provides the\n","Pipeline class to help with such sequences of transformations."]},{"cell_type":"code","execution_count":589,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1682905801852,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"i83AU1UOF3Qz","outputId":"a55b7618-0391-4c4e-c1d0-8d3f802901ac"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1070, 11)\n","['num__age' 'num__bmi' 'num__children' 'cat__sex_female' 'cat__sex_male'\n"," 'cat__smoker_no' 'cat__smoker_yes' 'cat__region_northeast'\n"," 'cat__region_northwest' 'cat__region_southeast' 'cat__region_southwest']\n"]}],"source":["# uncomment the following code to create a pipeline for preprocessing the data\n","\n","from sklearn.compose import ColumnTransformer\n","from sklearn.impute import SimpleImputer\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.preprocessing import OneHotEncoder\n","from sklearn.pipeline import make_pipeline\n","\n","num_attribs = [\"age\", \"bmi\", \"children\"]\n","cat_attribs = [\"sex\", \"smoker\", \"region\"]\n","\n","num_pipeline = make_pipeline(\n","    SimpleImputer(strategy=\"median\"),   #fill missing vals with median\n","    StandardScaler())               #standardize; compute z-score\n","\n","cat_pipeline = make_pipeline(\n","    SimpleImputer(strategy=\"most_frequent\"),\n","    OneHotEncoder(handle_unknown=\"ignore\"))  #represent categorical vars as numerical values\n","\n","preprocessing = ColumnTransformer([     #applying data transformers to different columns in dataset\n","    (\"num\", num_pipeline, num_attribs),\n","    (\"cat\", cat_pipeline, cat_attribs)])\n","\n","medical_prepared = preprocessing.fit_transform(medical) #idk but basicaalyu apply pipeline\n","\n","print(medical_prepared.shape)\n","print(preprocessing.get_feature_names_out())"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"BjwLVq7REdE1"},"source":["# Select and Train a Model\n","\n","At last! You framed the problem, you got the data and explored it, you\n","sampled a training set and a test set, and you wrote a preprocessing\n","pipeline to automatically clean up and prepare your data for machine\n","learning algorithms. You are now ready to select and train a machine\n","learning model.\n","\n","## Train and Evaluate on the Training Set\n","\n","The good news is that thanks to all these previous steps, things are now\n","going to be easy! You decide to train a very basic linear regression model\n","to get started:"]},{"cell_type":"code","execution_count":590,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226},"executionInfo":{"elapsed":752,"status":"ok","timestamp":1682905802599,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"7vD9DaxcEqzu","outputId":"7586deb9-e82b-45e0-e8ad-d201c99ee955"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-30 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-30 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-30 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-30 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-30 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-30 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-30 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-30 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-30 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-30 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-30 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-30 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-30 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-30 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-30 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-30 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-30 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-30 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-30 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-30 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-30 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-30 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-30 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-30 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-30 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-30 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-30 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-30 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-30 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-30 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-30 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-30 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-30 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-30 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-30 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-30 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-30 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-30 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-30 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-30 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-30 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-30 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-30\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n","                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                                  (&#x27;standardscaler&#x27;,\n","                                                                   StandardScaler())]),\n","                                                  [&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]),\n","                                                 (&#x27;cat&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                                  (&#x27;onehotencoder&#x27;,\n","                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                                  [&#x27;sex&#x27;, &#x27;smoker&#x27;,\n","                                                   &#x27;region&#x27;])])),\n","                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-275\" type=\"checkbox\" ><label for=\"sk-estimator-id-275\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n","                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                                  (&#x27;standardscaler&#x27;,\n","                                                                   StandardScaler())]),\n","                                                  [&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]),\n","                                                 (&#x27;cat&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                                  (&#x27;onehotencoder&#x27;,\n","                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                                  [&#x27;sex&#x27;, &#x27;smoker&#x27;,\n","                                                   &#x27;region&#x27;])])),\n","                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-276\" type=\"checkbox\" ><label for=\"sk-estimator-id-276\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;columntransformer: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for columntransformer: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                 (&#x27;standardscaler&#x27;,\n","                                                  StandardScaler())]),\n","                                 [&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]),\n","                                (&#x27;cat&#x27;,\n","                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                 (&#x27;onehotencoder&#x27;,\n","                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                 [&#x27;sex&#x27;, &#x27;smoker&#x27;, &#x27;region&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-277\" type=\"checkbox\" ><label for=\"sk-estimator-id-277\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-278\" type=\"checkbox\" ><label for=\"sk-estimator-id-278\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-279\" type=\"checkbox\" ><label for=\"sk-estimator-id-279\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-280\" type=\"checkbox\" ><label for=\"sk-estimator-id-280\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;sex&#x27;, &#x27;smoker&#x27;, &#x27;region&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-281\" type=\"checkbox\" ><label for=\"sk-estimator-id-281\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-282\" type=\"checkbox\" ><label for=\"sk-estimator-id-282\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-283\" type=\"checkbox\" ><label for=\"sk-estimator-id-283\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"],"text/plain":["Pipeline(steps=[('columntransformer',\n","                 ColumnTransformer(transformers=[('num',\n","                                                  Pipeline(steps=[('simpleimputer',\n","                                                                   SimpleImputer(strategy='median')),\n","                                                                  ('standardscaler',\n","                                                                   StandardScaler())]),\n","                                                  ['age', 'bmi', 'children']),\n","                                                 ('cat',\n","                                                  Pipeline(steps=[('simpleimputer',\n","                                                                   SimpleImputer(strategy='most_frequent')),\n","                                                                  ('onehotencoder',\n","                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n","                                                  ['sex', 'smoker',\n","                                                   'region'])])),\n","                ('linearregression', LinearRegression())])"]},"execution_count":590,"metadata":{},"output_type":"execute_result"}],"source":["# create a pipeline for preprocessing the data and fitting a linear regression model\n","# lin_reg = ...\n","\n","# housing_labels is the column we want to predict\n","# uncomment the following line to fit the model\n","\n","# lin_reg.fit(medical, medical_labels)\n","\n","from sklearn.linear_model import LinearRegression\n","lin_reg = make_pipeline(preprocessing, LinearRegression())\n","lin_reg.fit(medical, medical_labels)\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"NoVA0iaTdXEF"},"source":["Try out the model on the training set, look at the first five predictions and compare them to\n","the labels:"]},{"cell_type":"code","execution_count":591,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":32,"status":"ok","timestamp":1682905802600,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"vaWBK5cqdc31","outputId":"a55a2a32-0d18-484b-8a48-6d99a4ec817c"},"outputs":[{"data":{"text/plain":["array([ 7094.54007011,  8344.72998713,  9153.77419778, ...,\n","       11441.08519155, 37314.37460682, 11453.12102783])"]},"execution_count":591,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following line to make predictions\n","\n","medical_predictions = lin_reg.predict(medical)\n","medical_predictions"]},{"cell_type":"code","execution_count":592,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1682905802601,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"XtoJRmbXd2v4","outputId":"c1b86f0c-2d7a-4c6f-d416-ef63d34f8a0f"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\naumh\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n","  warnings.warn(\n"]},{"data":{"text/plain":["6105.545160099847"]},"execution_count":592,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following lines to compute the RMSE\n","\n","from sklearn.metrics import mean_squared_error\n","\n","lin_rmse = mean_squared_error(medical_labels, medical_predictions, squared=False)\n","lin_rmse"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"jeN1MIMleM1a"},"source":["Now try `DecisionTreeRegressor`, as this is a fairly powerful\n","model capable of finding complex nonlinear relationships in the data\n","(decision trees are covered later in the course):"]},{"cell_type":"code","execution_count":593,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1682905802601,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"WaysGUTleakU","outputId":"a30636c0-d908-47ab-fbb7-230d6933dd20"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\naumh\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:483: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n","  warnings.warn(\n"]},{"data":{"text/plain":["494.20598375812835"]},"execution_count":593,"metadata":{},"output_type":"execute_result"}],"source":["# use DecisionTreeRegressor to train the model\n","# use the make_pipeline() function to create a pipeline for preprocessing and model training\n","# use the preprocessing object you created earlier\n","# make predictions on the training set and compute the RMSE\n","\n","from sklearn.tree import DecisionTreeRegressor   #still unsure about what this does\n","\n","tree_reg = make_pipeline(preprocessing, DecisionTreeRegressor())\n","\n","tree_reg.fit(medical, medical_labels)\n","\n","medical_predictions = tree_reg.predict(medical)\n","tree_rsme = mean_squared_error(medical_labels, medical_predictions,squared=False)\n","tree_rsme\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"799A6PT4fJ1A"},"source":["# Better Evaluation Using Cross-Validation\n","\n","The following code randomly splits the training set into 10 nonoverlapping\n","subsets called folds, then it trains and evaluates the decision tree model\n","10 times, picking a different fold for evaluation every time and using the\n","other 9 folds for training. The result is an array containing the 10\n","evaluation scores:"]},{"cell_type":"code","execution_count":594,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1682905802602,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"4iPF0XfifqAx","outputId":"79b2f7d4-7cd1-4a75-c9ed-6ee077c677cd"},"outputs":[{"data":{"text/plain":["array([6319.56347264, 6708.44435978, 6412.87171148, 6718.5592829 ,\n","       7098.7921269 , 6154.25490847, 7634.9457189 , 7226.03064722,\n","       7176.75079223, 5206.11673843])"]},"execution_count":594,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following lines to train the model and make predictions\n","\n","from sklearn.model_selection import cross_val_score\n","\n","tree_rmses = -cross_val_score(tree_reg, \n","                              medical,medical_labels,\n","                              scoring=\"neg_root_mean_squared_error\", \n","                              cv=10)\n","tree_rmses"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"bAoH2lLrgFxM"},"source":["**Warning**. Scikit-Learn’s cross-validation features expect a utility function (greater is better) rather than a cost function (lower is better), so the scoring function is actually the opposite of the RMSE. It’s a negative value, so you need to switch the sign of the output to get the RMSE scores."]},{"cell_type":"code","execution_count":595,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1682905802602,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"PZL0xTOxgcdy","outputId":"e251f880-389e-4e8f-8a45-a6fba855a4b9"},"outputs":[{"data":{"text/plain":["6665.63297589507"]},"execution_count":595,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following line to compute the mean of the RMSEs\n","\n","np.mean(tree_rmses)"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"KqVoYfjwgvZI"},"source":["Let’s try one last model now: the RandomForestRegressor. As you will\n","see later in the course, random forests work by training many decision trees on\n","random subsets of the features, then averaging out their predictions."]},{"cell_type":"code","execution_count":596,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4184,"status":"ok","timestamp":1682905806774,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"ujkiHEQxgwbr","outputId":"65a881f0-1079-4037-fab9-3615b49142ab"},"outputs":[{"data":{"text/plain":["6688.988418530002"]},"execution_count":596,"metadata":{},"output_type":"execute_result"}],"source":["# use RandomForestRegressor to train the model\n","# use the make_pipeline() function to create a pipeline for preprocessing and model training\n","# use the preprocessing object you created earlier\n","# make predictions on the training set and compute the RMSEs using cross-validation\n","# compute the mean of the RMSEs\n","\n","\n","from sklearn.ensemble import RandomForestRegressor\n","\n","forest_reg = make_pipeline(preprocessing,RandomForestRegressor())  #pipeline for preprocessing and model training\n","\n","forest_reg.fit(medical, medical_labels)\n","\n","medical_predictions = forest_reg.predict(medical)\n","\n","\n","forest_rmses = -cross_val_score(tree_reg, \n","                             medical,medical_labels,\n","                             scoring=\"neg_root_mean_squared_error\", \n","                             cv=10)\n","np.mean(forest_rmses)\n","\n","\n","\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"q0ZoN4y3iJ07"},"source":["# Fine-Tune Your Model\n","\n","Let’s assume that you now have a shortlist of promising models. You now\n","need to fine-tune them.\n"]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"KLGLIF7bxQEI"},"source":["## Randomized Search for Good Hyperparameters\n"]},{"cell_type":"code","execution_count":597,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":252},"executionInfo":{"elapsed":10644,"status":"ok","timestamp":1682905817405,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"ZRGmHfvxxWNP","outputId":"ad7e21b9-c884-41e3-d883-2143c73a7244"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-31 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-31 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-31 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-31 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-31 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-31 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-31 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-31 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-31 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-31 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-31 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-31 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-31 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-31 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-31 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-31 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-31 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-31 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-31 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-31 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-31 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-31 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-31 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-31 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-31 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-31 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-31 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-31 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-31 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-31 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-31 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-31 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-31 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-31 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-31 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-31 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-31 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-31 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-31 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-31 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-31 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-31 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-31\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3,\n","                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n","                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                                                               (&#x27;standardscaler&#x27;,\n","                                                                                                StandardScaler())]),\n","                                                                               [&#x27;age&#x27;,\n","                                                                                &#x27;bmi&#x27;,\n","                                                                                &#x27;children&#x27;]),\n","                                                                              (&#x27;cat&#x27;,\n","                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                                                SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                                                               (&#x27;onehotencoder&#x27;,\n","                                                                                                OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                                                               [&#x27;sex&#x27;,\n","                                                                                &#x27;smoker&#x27;,\n","                                                                                &#x27;region&#x27;])])),\n","                                             (&#x27;random_forest&#x27;,\n","                                              RandomForestRegressor(random_state=42))]),\n","                   param_distributions={&#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001C52DA6D040&gt;},\n","                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-284\" type=\"checkbox\" ><label for=\"sk-estimator-id-284\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=3,\n","                   estimator=Pipeline(steps=[(&#x27;preprocessing&#x27;,\n","                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                                                SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                                                               (&#x27;standardscaler&#x27;,\n","                                                                                                StandardScaler())]),\n","                                                                               [&#x27;age&#x27;,\n","                                                                                &#x27;bmi&#x27;,\n","                                                                                &#x27;children&#x27;]),\n","                                                                              (&#x27;cat&#x27;,\n","                                                                               Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                                                SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                                                               (&#x27;onehotencoder&#x27;,\n","                                                                                                OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                                                               [&#x27;sex&#x27;,\n","                                                                                &#x27;smoker&#x27;,\n","                                                                                &#x27;region&#x27;])])),\n","                                             (&#x27;random_forest&#x27;,\n","                                              RandomForestRegressor(random_state=42))]),\n","                   param_distributions={&#x27;random_forest__max_features&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001C52DA6D040&gt;},\n","                   random_state=42, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-285\" type=\"checkbox\" ><label for=\"sk-estimator-id-285\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n","                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                                  (&#x27;standardscaler&#x27;,\n","                                                                   StandardScaler())]),\n","                                                  [&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]),\n","                                                 (&#x27;cat&#x27;,\n","                                                  Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                                  (&#x27;onehotencoder&#x27;,\n","                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                                  [&#x27;sex&#x27;, &#x27;smoker&#x27;,\n","                                                   &#x27;region&#x27;])])),\n","                (&#x27;random_forest&#x27;, RandomForestRegressor(random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-286\" type=\"checkbox\" ><label for=\"sk-estimator-id-286\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessing: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessing: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n","                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n","                                                 (&#x27;standardscaler&#x27;,\n","                                                  StandardScaler())]),\n","                                 [&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]),\n","                                (&#x27;cat&#x27;,\n","                                 Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n","                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n","                                                 (&#x27;onehotencoder&#x27;,\n","                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n","                                 [&#x27;sex&#x27;, &#x27;smoker&#x27;, &#x27;region&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-287\" type=\"checkbox\" ><label for=\"sk-estimator-id-287\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;age&#x27;, &#x27;bmi&#x27;, &#x27;children&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-288\" type=\"checkbox\" ><label for=\"sk-estimator-id-288\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-289\" type=\"checkbox\" ><label for=\"sk-estimator-id-289\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-290\" type=\"checkbox\" ><label for=\"sk-estimator-id-290\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;sex&#x27;, &#x27;smoker&#x27;, &#x27;region&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-291\" type=\"checkbox\" ><label for=\"sk-estimator-id-291\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-292\" type=\"checkbox\" ><label for=\"sk-estimator-id-292\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-293\" type=\"checkbox\" ><label for=\"sk-estimator-id-293\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["RandomizedSearchCV(cv=3,\n","                   estimator=Pipeline(steps=[('preprocessing',\n","                                              ColumnTransformer(transformers=[('num',\n","                                                                               Pipeline(steps=[('simpleimputer',\n","                                                                                                SimpleImputer(strategy='median')),\n","                                                                                               ('standardscaler',\n","                                                                                                StandardScaler())]),\n","                                                                               ['age',\n","                                                                                'bmi',\n","                                                                                'children']),\n","                                                                              ('cat',\n","                                                                               Pipeline(steps=[('simpleimputer',\n","                                                                                                SimpleImputer(strategy='most_frequent')),\n","                                                                                               ('onehotencoder',\n","                                                                                                OneHotEncoder(handle_unknown='ignore'))]),\n","                                                                               ['sex',\n","                                                                                'smoker',\n","                                                                                'region'])])),\n","                                             ('random_forest',\n","                                              RandomForestRegressor(random_state=42))]),\n","                   param_distributions={'random_forest__max_features': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001C52DA6D040>},\n","                   random_state=42, scoring='neg_root_mean_squared_error')"]},"execution_count":597,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following lines to search for the best hyperparameters\n","\n","from sklearn.model_selection import RandomizedSearchCV\n","from scipy.stats import randint\n","from sklearn.pipeline import Pipeline\n","\n","full_pipeline = Pipeline([(\"preprocessing\", preprocessing),\n","                          (\"random_forest\", RandomForestRegressor(random_state=42)),\n","                          ])\n","\n","param_distribs = {'random_forest__max_features': randint(low=2,high=20)}\n","\n","rnd_search = RandomizedSearchCV(full_pipeline, \n","                                param_distributions=param_distribs,\n","                                n_iter=10, \n","                                cv=3,\n","                                scoring='neg_root_mean_squared_error', \n","                                random_state=42)\n","\n","rnd_search.fit(medical, medical_labels)"]},{"cell_type":"code","execution_count":598,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1682905817406,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"ByENu7OMz7Uh","outputId":"552dddb2-9f16-4f26-e469-749c1c2775d5"},"outputs":[{"data":{"text/plain":["7   -4810.698561\n","9   -4861.373750\n","0   -4912.976290\n","4   -4912.976290\n","3   -4976.129581\n","Name: mean_test_score, dtype: float64"]},"execution_count":598,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following lines to print the best search scores\n","\n","rn_res = pd.DataFrame(rnd_search.cv_results_)\n","rn_res.sort_values(by=\"mean_test_score\", ascending=False, inplace=True)\n","rn_res.head(5)[\"mean_test_score\"]"]},{"cell_type":"code","execution_count":599,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1682905817407,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"A3gE266DyrFS","outputId":"b09c26ba-a591-413a-c822-9ad247b1bb43"},"outputs":[{"data":{"text/plain":["array([0.14032922, 0.18831699, 0.0230048 , 0.00493845, 0.00454404,\n","       0.28737838, 0.33010198, 0.00580561, 0.00521445, 0.00654471,\n","       0.00382136])"]},"execution_count":599,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following lines to print the feature importances\n","\n","final_model = rnd_search.best_estimator_ # includes preprocessing\n","feature_importances = final_model[\"random_forest\"].feature_importances_\n","final_model\n","feature_importances"]},{"cell_type":"code","execution_count":600,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1682905817408,"user":{"displayName":"Alex Thomo","userId":"08504196803322236588"},"user_tz":420},"id":"Cc2X3ol1zriq","outputId":"0233f397-1f44-4606-fe5b-ed2b493daf25"},"outputs":[{"data":{"text/plain":["[(0.3301019845385152, 'cat__smoker_yes'),\n"," (0.2873783836848969, 'cat__smoker_no'),\n"," (0.18831699212058217, 'num__bmi'),\n"," (0.1403292169242826, 'num__age'),\n"," (0.023004795671815754, 'num__children'),\n"," (0.006544713122635331, 'cat__region_southeast'),\n"," (0.005805609883995696, 'cat__region_northeast'),\n"," (0.0052144512790023795, 'cat__region_northwest'),\n"," (0.0049384524581677835, 'cat__sex_female'),\n"," (0.004544038586538503, 'cat__sex_male'),\n"," (0.003821361729567661, 'cat__region_southwest')]"]},"execution_count":600,"metadata":{},"output_type":"execute_result"}],"source":["# uncomment the following line to print the feature importances with the feature names\n","\n","sorted(zip(feature_importances, final_model[\"preprocessing\"].get_feature_names_out()),reverse=True)"]},{"cell_type":"code","execution_count":601,"metadata":{},"outputs":[{"data":{"text/plain":["4692.401642760105"]},"execution_count":601,"metadata":{},"output_type":"execute_result"}],"source":["#now that you have a final model, evaluate it on the test set (find rmse)\n","\n","#medical = train_set.drop('charges',axis=1) #axis=1 drops column\n","#medical_labels = train_set['charges'].copy()\n","\n","ft = test_set.drop('charges',axis=1)\n","ft_labels = test_set['charges'].copy()\n","\n","ft_rmses = -cross_val_score(final_model,\n","                            ft, ft_labels,\n","                            scoring=\"neg_root_mean_squared_error\",\n","                            cv=10)\n","\n","ft_rmses\n","\n","np.mean(ft_rmses)\n","\n","\n","#tree_rmses = -cross_val_score(tree_reg, \n","#                              ft, f_labels \n","#                              scoring=\"neg_root_mean_squared_error\", \n","#                              cv=10)\n","#tree_rmses\n","\n"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyNBEAT/qEAF28JU/iWUQQSi","provenance":[{"file_id":"1COCqu5BSOfHDBbeQAzka1L4RBUE-hxIp","timestamp":1682870378102}]},"kernelspec":{"display_name":"Python 3","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":"3.12.1"}},"nbformat":4,"nbformat_minor":0}