PaftaLab.ipynb

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   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from neuralprophet import NeuralProphet\n",
    "from neuralprophet import set_random_seed\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import plotly.offline as pyoff\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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   "outputs": [],
   "source": [
    "# Import CSV File\n",
    "df = pd.read_csv('CHANGE-THIS')\n",
    "df.drop('store', axis=1, inplace=True)\n",
    "df.drop('item', axis=1, inplace=True)\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "df_day = pd.read_csv('CHANGE-THIS')\n",
    "df_day['ds'] = pd.to_datetime(df_day['ds'])\n",
    "df_day.dropna()\n",
    "df_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "pycharm": {
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   "outputs": [],
   "source": [
    "# Create Data Frame Based On EveryDay\n",
    "df_day= df.groupby(df['date'].dt.date).sum().reset_index()\n",
    "df_day['date'] = pd.to_datetime(df_day['date'])\n",
    "df_day.columns = ['ds','y']\n",
    "#df_day.to_csv('df_day.csv', index = False)\n",
    "df_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "pycharm": {
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   "outputs": [],
   "source": [
    "# Convert Previous Data Frame To EveryMonth\n",
    "df_month = df_day.groupby([df_day['ds'].dt.year.rename('year'),df_day['ds'].dt.month.rename('month')]).sum().reset_index()\n",
    "df_month['ds'] = pd.to_datetime(df_month['year'].astype(str) + df_month['month'].astype(str), format='%Y%m')\n",
    "df_month.drop('year', axis=1, inplace=True)\n",
    "df_month.drop('month', axis=1, inplace=True)\n",
    "df_month = df_month[[\"ds\", \"y\"]]\n",
    "df_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
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   "outputs": [
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     },
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   ],
   "source": [
    "# Plotly DF Before Train\n",
    "plot_data = [\n",
    "    go.Scatter(\n",
    "        x=df_day['ds'],\n",
    "        y=df_day['y'],\n",
    "    )\n",
    "]\n",
    "\n",
    "plot_layout = go.Layout(\n",
    "        title='Sales'\n",
    "    )\n",
    "fig = go.Figure(data=plot_data, layout=plot_layout)\n",
    "pyoff.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.796% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.796% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling daily seasonality. Run NeuralProphet with daily_seasonality=True to override this.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 16\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74e8a69242c744208a7f2c385b1a0729",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.utils_torch.lr_range_test) - lr-range-test results: steep: 1.06E-01, min: 7.32E-01\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e98782b1e1c643cc8191739f857d399d",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.utils_torch.lr_range_test) - lr-range-test results: steep: 1.26E-01, min: 2.10E+00\n",
      "INFO - (NP.forecaster._init_train_loader) - lr-range-test selected learning rate: 1.10E-01\n",
      "Epoch[256/256]: 100%|██████████| 256/256 [00:09<00:00, 26.82it/s, SmoothL1Loss=0.00662, MAE=3e+3, RMSE=3.65e+3, RegLoss=0]   \n"
     ]
    }
   ],
   "source": [
    "# Use NeuralProphet For Training\n",
    "m = NeuralProphet(epochs=256)\n",
    "df_train, df_val = m.split_df(df_day, freq='D', valid_p=0.2)\n",
    "metrics = m.fit(df_day, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 98.98% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.forecaster._evaluate) - Validation metrics:    SmoothL1Loss      MAE     RMSE\n",
      "1         0.004 2353.267 2811.617\n"
     ]
    }
   ],
   "source": [
    "test_metrics = m.test(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>29374.15234</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>32782.02344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>31971.91406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>33992.42969</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>2022-01-24</td>\n",
       "      <td>36654.32813</td>\n",
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       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>2022-01-25</td>\n",
       "      <td>36954.00391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
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       "      <td>36852.12109</td>\n",
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       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2022-01-27</td>\n",
       "      <td>37138.23438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>2022-01-28</td>\n",
       "      <td>37784.33203</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>393 rows × 2 columns</p>\n",
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       "            ds            y\n",
       "0   2021-01-01  29374.15234\n",
       "1   2021-01-02  32127.26758\n",
       "2   2021-01-03  32782.02344\n",
       "3   2021-01-04  31971.91406\n",
       "4   2021-01-05  33992.42969\n",
       "..         ...          ...\n",
       "388 2022-01-24  36654.32813\n",
       "389 2022-01-25  36954.00391\n",
       "390 2022-01-26  36852.12109\n",
       "391 2022-01-27  37138.23438\n",
       "392 2022-01-28  37784.33203\n",
       "\n",
       "[393 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train"
   ]
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     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.746% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.868% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.868% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n"
     ]
    }
   ],
   "source": [
    "# Forecast into the unknown future\n",
    "future = m.make_future_dataframe(df_train, periods=365, n_historic_predictions=True)\n",
    "forecast = m.predict(future)"
   ]
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       "            ds            y         yhat1    residual1         trend  \\\n",
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       "\n",
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   "source": [
    "# Mean Absolute Percentage Error (MAPE)\n",
    "start_day = pd.to_datetime(df_val['ds'][0].strftime(\"%Y-%m-%d\"))\n",
    "end_day = pd.to_datetime(df_val['ds'][len(df_val['ds'])-1].strftime(\"%Y-%m-%d\"))\n",
    "predicted_val = forecast[forecast['ds'].between(start_day, end_day)]['yhat1']\n",
    "\n",
    "mape = np.mean(np.abs((df_val['y'] - predicted_val.values) / df_val['y'])) * 100\n",
    "mape"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig_forecast = m.plot(forecast)\n",
    "fig = go.Figure()\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat1'],\n",
    "                    mode='lines',\n",
    "                    name='Forecast'))\n",
    "fig.add_trace(go.Scatter(x=df_val['ds'], y=df_val['y'],\n",
    "                    mode='markers',\n",
    "                    name='Actual',\n",
    "                    marker=dict(\n",
    "                        color='black',\n",
    "                        size=4\n",
    "                    )))\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7di06nQ5zc3MWLVoEgJ2dHW+++SYhISEAvPXWW1U37H355ZdVy7wNGjRIbtATQtQ7iqIwZ3sCgc5W9GphXyuv2cbZim5edny1O5EXenrLzUGiTsouLP2fXeP+aoT/d4WIFv/dPvl+P6f/7hp3rRFuJitENEg13iAvXrz4H49v3rz5b8c0Gg3z5s37x8dPmDCBCRMm/O14cHBw1frKQghRH+1KusLRtAK+Gdm2Vn9RT+7iwZgfD7EpPpP+vvpdc1kIfblcXP7XSPD/3CSXe/WvecHGRhq87MzxcbCgZwv7G9YKdrdpjLH8ESiuo/pNekIIIW5twcEMHCwa8UiHm99jUROGBzrzfMwJ5u+6IA2yUNXVsgrOZRXeOBKcda0RTr9ccsNj3W3MaNm0CaPaufx3JPjaUmmetuY00soKEeL2SIMshBAG7Hx2IRvP5fP6fT40NjGu1dc21RozIaQ5/956lqTcq7jbNq7V1xcNS1lFJYk5RX8fCc4qJCnvKsp12yc7WZri42DBYD/HG0aCW9ibY95IWhtx7+SrSAghDNgXOxIwNoKnu3iq8vpPdvbgo61n+XrvBd4d2EqVDKL+qPzvChFVI8HX3SSXkFNE+XUrRNj8d4WI7l52N+wa59P09leIEOJuSYMshBAG6nJxOQtjk3igpS0u1maqZPCyN2dQK0e+2XuRN/u1lE0MRLUURSHjcsmNI8H/bYjPZRVSXP7XChHmjYzxcbCgnasVD7d1vqERtrdoJDfHCdVIgyyEEAbq231JFBSXM6mDk6o5Jnfx5MEFscQcT2dEW1mzVVyTU1T6t62T/9xO+XJJedXjTIw16ByubZc8qJXjDSPBLlZm0gQLgyQNshBCGKDKSoXPdyQQ6mFLe2cLVbMMauWIh21j5u+6IA1yA3OlpJz4/xkJPp6Sy4X8o2QX/bVChJEGPO3MadnUgq6edtca4P/ODW5uKytEiLpHGmQhhDBAa09d4mxWIe8N9FU7CsZGGiJCPZi+7hSnMi7TyslS7UhCj4rLKjifXfS3keAzmVdIK7hxhQg3azOaW5kwoq3LXyPBDhZ421vIChGiXpEGWQghDNCcbedxtTZjeKAzmRnpasdhYqfm/N/G03y1+wKzH2qtdhxxh8orKknMvUp85pW/zQ2+kHvjChFNmzSipYMFA3wdbxgJ1jlcWyEiNTVVtkcW9Z40yEIIYWBOpF9mU3wWHw5uZTA3xTlZmjK8jTNR+5P5cHArWUrLQFVUKmw/n82pSzc2wuezb1whwtpMi09TC7p42DEu+M+R4GtLpdk0lhUihJCfcEIIYWDmbD+PmdaIiFAPtaPcYHIXT6IPpxJ9KJUJnZqrHUf8j4PJeTy57Cj7k/IBaGxihI9DE9o4WzE80Pmv9YIdLGjaRFaIEOJWpEEWQggDkl1Yyvf7k3k0yA17i0Zqx7lBd287AppZ8uWuRGmQDUhBcRlvrj/N3B0JODYx5dvwdvT1ccDFygwjuTlOiLsiDbIQQhiQr/dcoLi8kqndvdSO8jcajYbJnT149tfj7LuYR0hzG7UjNWiKovDL0TSmrjhB2uVinu7iyfuDWskUCSH0wDAmtwkhhKCsopK5OxPp6+NAa2crteP8o8eC3bBoZMz8XYlqR2nQzmcXcv83sTz83QGcLBuxd0p35g5rI82xEHqiaoP82WefERAQQOvWrRk9ejTFxcUkJCTQqVMndDodo0aNorS0FICSkhJGjRqFTqejU6dOJCYmVj3PjBkz0Ol0+Pr6smHDBpXORggh7s3yo2mk5Bcb5Ojxn6zMTBjTwZXowynkFpWqHafBKS2vZMbmeAI+2sr2hGxmDwkgdmp3Gc0XQs9Ua5BTUlL4/PPP2b9/P8ePH6eiooLo6GheffVVpk2bxtmzZ7G1tWXBggUALFiwAFtbW86ePcu0adN49dVXAYiLiyM6OpoTJ06wfv16nn76aSoqKtQ6LSGEuGtztifQwt6c+/3U3TmvOpO7eHK1rJKo/clqR2lQtp3Lpt2nf/D62lPc7+/EqVd7M7WHN1oDWelEiPpE1e+q8vJyrl69Snl5OUVFRTg7O7NlyxZGjBgBwLhx41ixYgUAMTExjBs3DoARI0awefNmFEUhJiaG8PBwTE1N8fLyQqfTERsbq9YpCSHEXYm9mMvuC7lM6e5l8DdWtXO1prOHLfN3JaJcv4CuqBFZV0qYEH2Ynl/u4mpZBasndmTZuGBcrRurHU2Ieku1BtnV1ZWXXnqJ5s2b4+zsjLW1NUFBQdjY2KDVXrt30M3NjZSUFODaiLO7uzsAWq0Wa2trsrOzbzj+v58jhBB1xZxtCViaahkf4l79gw3A5C4enMksZEt8ltpR6q3KSoWFey/iO+t3vj+QzL/66jjxci/u9zfsdxiEqA9UW8UiNzeXmJgYEhISsLGx4eGHH2b9+vU1+pqRkZFERkYCkJ6eTmpqao2+Xm3LzMxUO0K9IHXUH6nl7Um/UsrSIymMa+fIlZxMrvzPxw2xjl0dNdiaGfPpllP4NSlTO85tMcQ63szprKu8tukisSlX6OTahBn3+eDr0Ji8rEvkqZytLtXR0Ekt9aMm6qhag7xp0ya8vLxo2rQpAMOGDWPnzp3k5eVRXl6OVqslOTkZV1dX4NqIc1JSEm5ubpSXl5Ofn4+9vX3V8T9d/zn/KyIigoiICACCg4Pr5VaZ9fGc1CB11B+pZfXmrztFhQL/GtAaFweLf3yMIdZxYmgBn207DxZ2uFibqR3nthhiHa9XVFrOe7/F8/HWc1ibaVk4qi3jQ9wNblMPQ69jXSK11A9911G1KRbNmzdnz549FBUVoSgKmzdvxt/fn969e7Ns2TIAoqKiGDJkCABhYWFERUUBsGzZMvr06YNGoyEsLIzo6GhKSkpISEggPj6ejh07qnVaQghxR4rLKvhq9wUe9HeixU2aY0P1VGcPKioVvt5zQe0o9cKauAwC/r2VmVvOMjbYjVOv9ubxjs0NrjkWoiFQbQS5U6dOjBgxgg4dOqDVamnfvj0RERHcf//9hIeH88Ybb9C+fXsmTpwIwMSJE3nsscfQ6XTY2dkRHR0NQEBAACNHjsTf3x+tVsu8efMwNjZW67SEEOKO/HQwhazCUqZ291Y7yh1r4WDBAN+mfL33ItPv85HVFO5Sct5Vpq44zvJj6fg7NeGPp7vQo4W92rGEaNA0SgO9BTk4OJj9+/erHUOvUlNT5a0aPZA66o/U8tYURaHdJ9tQUDjyYs+bjhQach1jjqfz0KJ9LB8fzNA2zmrHuSVDq2P5fzeGeXP9KSoqFd7q15IXeragkdaw/9AwtDrWZVJL/bjbOt6qF5StpoUQQiV/nMvmaFoB34xsW2ffRr/fzxE3azPm70o0+AbZkMRezOWpZUc5lFLAoFaOzBvWBi97c7VjCSH+y7D/TBVCiHps9rbz2Jub8EiHf76xuC7QGhsR0dmD385kEZ/5v+tviP+Vd7WMZ345RujnO8i4XMqycUGsmdRRmmMhDIw0yEIIoYLz2YWsjMvgyc4eNDap2/dNTOrUHK2Rhq92y816N6MoCtGHUvCb9Ttf7U5kSjcvTr7ai+GBLnX23QMh6jNpkIUQQgVzdyRirNHwdFdPtaPcM2crM4a2acai2CSullWoHcfgnM0qZEDkHkb/cBA3GzP2Pd+d2Q+1xsrMRO1oQoibkAZZCCFq2eXichbEXuThti71ZrvgyV08yb1axtLD9WsDpntRUl7BuxvP0PrfW9l7MY+5Q1uzZ0p3OrjZqB1NCFENuUlPCCFq2bf7kigoLmdqdy+1o+hNrxb2tHJswvxdiYyrI9tl16Qt8VlM/uUoZzILCW/nwqdDAnC2qhubqQghZARZCCFqVWWlwhc7Egj1sKWTh63acfRGo9EwuYsHey/mcTA5T+04qrl0uYTHfjpI3692U1GpsP6JTix+LEiaYyHqGGmQhRCiFq07dYn4rMJ6NXr8p7HB7pg3Mmb+roZ3s15lpULk7gv4zvqdJYdTebOfD8de7sWAVo5qRxNC3IWbTrFYvnz5LT9x2LBheg8jhBD13ext53G1NmN4YP1bM9imsQmj27ny06EUPn7QH+vGDeMmtKOpBTy17Ci7L+TSq4U984e3oZWTpdqxhBD34KYN8qpVqwC4dOkSu3btok+fPgD8/vvvdOnSRRpkIYS4QyfSL7MpPosPBrXCpJ5uyzy5iwcLYi/y3f5knquHo+TXu1JSzjsbz/DZtvPYmZvw3eh2PBrkJsu2CVEP3LRBXrRoEQD9+/cnLi4OZ+drox1paWmMHz++VsIJIUR98vn285hpjYgIba52lBoT5G5Dx+Y2zN+dyLPdPOttsxhzPJ3nfj1GUl4xT4Q2Z+b9ftiZN1I7lhBCT6odwkhKSqpqjgGcnJy4ePFijYYSQoj6JruwlO/2J/NokBsOTUzVjlOjJnf25GTGFf44l612FL1LKyjmoYWxPLRoHzaNTdj5bFciH24rzbEQ9Uy1y7z17duXAQMGMHr0aACWLFnCfffdV+PBhBCiPvl6zwWKyyuZUs+nHQCMau/CCytPMH/XBXrpHNSOoze/HkvjiaVHKCqr4N8P+DO1h1e9nSojRENX7Xf23LlzefLJJzly5AhHjhwhIiKCL774Qi8vnpeXx4gRI2jVqhV+fn7s3r2bnJwc+vXrh4+PD/369SM3Nxe4tk3nlClT0Ol0BAYGcvDgwarniYqKwsfHBx8fH6KiovSSTQgh9KWsopJ5OxPp6+NAG2crtePUuMYmxowPcWf5sTTSC4rVjnPPrpSUM2nJEYZ9ux9PO3MOvdCTl3q3kOZYiHrstr67hw0bxmeffcZnn33G0KFD9fbiU6dOZeDAgZw6dYojR47g5+fHzJkz6du3L/Hx8fTt25eZM2cCsG7dOuLj44mPjycyMpLJkycDkJOTwzvvvMPevXuJjY3lnXfeqWqqhRDCEPx6LJ3k/OJ6ubTbzTzVxYPySoVv9tbtKXl7LuTS7pM/WLTvItPv82HXc93wdWyidiwhRA2rtkFevnw5Pj4+WFtbY2VlhaWlJVZW9z4Ckp+fz7Zt25g4cSIAjRo1wsbGhpiYGMaNGwfAuHHjWLFiBQAxMTGMHTsWjUZDaGgoeXl5pKWlsWHDBvr164ednR22trb069eP9evX33M+IYTQl9nbztPC3pz7/ZzUjlJrWjZtwn0+DkTuuUBFpaJ2nDtWXlHJ/204Tbe5O6lQFP54ugvvD2pFI62MGgvREFQ7B/mVV15h1apV+Pn56fWFExISaNq0KY8//jhHjhwhKCiIOXPmkJGRUXVTYLNmzcjIyAAgJSUFd/e/ti91c3MjJSXlpsf/SWRkJJGRkQCkp6eTmpqq13NSW2ZmptoR6gWpo/5ILeFweiG7L+Tybm930tPT7uo56modR/lZ8UR8Ft/vOEl/nY3acW67jgm5xTy3LpFDaYWM8LfjvT7NsTItqXe/M+5WXf16NERSS/2oiTpW2yA7OTnpvTkGKC8v5+DBg3zxxRd06tSJqVOnVk2n+JNGo9HrEkERERFEREQAEBwcjIuLi96e21DUx3NSg9RRfxp6LV/5/SCWplqm3heAldndb5xRF+s43qkZb29NZcnpAsb38Fc7DnDrOiqKwoK9F3k+5hSNjI1Y8lgQI9vVvbrXhrr49WiopJb6oe86VvteUXBwMKNGjWLx4sUsX7686n/3ys3NDTc3Nzp16gTAiBEjOHjwIE5OTqSlXRtlSUtLw9Hx2jadrq6uJCUlVX1+cnIyrq6uNz0uhBBqS80vZsnhVCZ0dL+n5riu0hob8URoczaczuRcVqHacW4p80oJQxft44mfjxLqYcvRl3pKcyxEA1Ztg1xQUIC5uTkbN25k1apVrFq1itWrV9/zCzdr1gx3d3dOnz4NwObNm/H39ycsLKxqJYqoqCiGDBkCQFhYGN999x2KorBnzx6sra1xdnZmwIABbNy4kdzcXHJzc9m4cSMDBgy453xCCHGv5u9KpEJReK5bw7k57389EdocI42G/+y+oHaUm1p3MoM2H//BulOZfBrmz8aIUNxsGqsdSwihomqnWPy5o15N+OKLLxgzZgylpaV4e3uzaNEiKisrGTlyJAsWLMDDw4OlS5cCMHjwYNauXYtOp8Pc3Lwql52dHW+++SYhISEAvPXWW9jZ2dVYZiGEuB3FZRX8Z88FHvR3ooWDhdpxVONq3ZghAU4sjL3IuwN9MTMxVjtSlatlFbyyKo65OxNp3cySjRGhBLrU/2X4hBDVq7ZBPnPmDJMnTyYjI4Pjx49z9OhRVq5cyRtvvHHPL96uXTv279//t+ObN2/+2zGNRsO8efP+8XkmTJjAhAkT7jmPEELoy+JDKWReKWVqd2+1o6huchdPlh9LZ9nRNB4NclM7DgCHkvMZ89NBTmZcYVoPbz4c3MqgmnchhLqqnWLxxBNPMGPGDExMrs2fCwwMJDo6usaDCSFEXaUoCrO3JdC6mSW9dfZqx1FdH50DPg4WzN+VqHYUKioVZm05S6fPt5N/tZzfngzl0yEB0hwLIW5QbYNcVFREx44dbzim1VY78CyEEA3WH+eyOZpWwPM9vPW6Ek9dZWSkYXIXD3Yl5nIkNV+1HMkFJfSZv4vX1pxkSEAzjr3ck/taNlUtjxDCcFXbIDs4OHDu3LmqH/LLli2rWqdYCCHE383ZnoC9uQmPdJAVdf40LsQdM60R83epc7PejweSuS8qjkMpBUSNbsfSsUHYmTdSJYsQwvBVOxQ8b948IiIiOHXqFK6urnh5efHjjz/WRjYhhKhzzmcXEnMinX/10dFY3ravYmfeiPD2rvxwIJmPHvCrtWXvcotKeWb5cRYfSiHExYIl40PxsjevldcWQtRdt2yQKyoq+PLLL9m0aROFhYVUVlZiaWlZW9mEEKLOmbsjEWONhqe7eqodxeBM7uLBt/uS+OFASq3U5/ezWYxbfIi0ghLeH+TLY60saC7NsRDiNtx0ikV5eTnGxsbs2LEDAAsLC2mOhRDiFi4Xl7Mg9iIPt3XB1VrW0f1fIe42BLlZM39XIoqi1NjrlJRfW76t71e7aWxizK7nujH9vpZojWQ+uBDi9tx0BLljx44cPHiQ9u3bExYWxsMPP4yFxV9reQ4bNqxWAgohRF3x7b4kCorLmdq94W4McisajYbJXTyZtPQIOxJy6O6t/xU+TqRfZsyPBzmSWsBTnT34+EF/LEzlxnIhxJ2p9qdGcXEx9vb2bNmyBY1Gg6IoaDQaaZCFEOI6lZUKX+xIoFNzGzp52Kodx2CFt3PhxZUnmL/rgl4b5MpKhbk7E3hl9UmszLSsmtiRB/yd9Pb8QoiG5aYN8qVLl/j0009p3bp1VWP8J1m2SAghbrTu1CXiswpZ/GgHtaMYNAtTLeNC3Jm/K5HZlwNwtDS95+dMzS9mwpLDbDidyQP+Tnwzsi1OenheIUTDddM5yBUVFVy5coUrV65w+fLlqv/+839CCCH+Mmf7eVytzRgeKMtgVmdyZw/KKhQWxl685+dafjSNwI+3su18Nl+NaMPKCSHSHAsh7tlNR5CdnZ156623ajOLEELUSSfSL/PbmSw+GNQKE+Nql5dv8Fo5Xdth8KvdF3i5tw7ju7h57nJxOc/HHGdhbBJBbtb8OKYDvo5NaiCtEKIhuulP8pq8w1gIIeqTz7efx0xrRERoc7Wj1BmTu3hyIfcq609duuPP3Z2YQ7tP/+DbfUlMv8+H3VO6SXMshNCrmzbImzdvrs0cQghRJ2UXlvL9gWQeDXLDoYm8tX+7HmrdjGaWpszflXjbn1NWUcnb60/Tbe5OKhWFP57uwvsyai+EqAE3/aliZ2dXKwEqKipo3749DzzwAAAJCQl06tQJnU7HqFGjKC0tBaCkpIRRo0ah0+no1KkTiYmJVc8xY8YMdDodvr6+bNiwoVZyCyEEwH92X+BqWSVTZGm3O2JibMSkTs1Ze+oSiTlF1T4+PvMK3ebu5N3fzvBYkBtHXuxJtxpYJk4IIeAWDXJtmTNnDn5+flX/fvXVV5k2bRpnz57F1taWBQsWALBgwQJsbW05e/Ys06ZN49VXXwUgLi6O6OhoTpw4wfr163n66aepqKhQ5VyEEA2Hoiisjsvgs23nGeDblDbOVmpHqnMiQj3QcO2PjJtRFIWv91yg3afbiM8sZOnYIL4d3b7WtqoWQjRMqjbIycnJrFmzhkmTJgHXfhBu2bKFESNGADBu3DhWrFgBQExMDOPGjQNgxIgRbN68GUVRiImJITw8HFNTU7y8vNDpdMTGxqpyPkKIhmFLfBZdvtjJgwtisTbT8tED/mpHqpPcbRvzoL8TC2IvUlL+94GNzCslPLRoHxE/H6WLhy3HXu7Jw21dVEgqhGhoVN1e6Pnnn+ejjz7i8uXLAGRnZ2NjY4NWey2Wm5sbKSkpAKSkpODu7g6AVqvF2tqa7OxsUlJSCA0NrXrO6z/nf0VGRhIZGQlAeno6qampNXZuasjMzFQ7Qr0gddSf+lbL/alX+GhnKjsvXsa5iQkf9WvOyAAHTLhCamrNLX9Z3+p4vZGtLIk5kcGCP07ykN9fU/s2n8/nxQ2J5JdU8HYvNyZ1cERTmEtqYe5dv1Z9rmNtkjrqj9RSP2qijqo1yKtXr8bR0ZGgoCC2bt1aK68ZERFBREQEAMHBwbi41L+RiPp4TmqQOupPfajl4ZR83lh3ijUnL+HYpBGzhwTwZGcPzEyMay1DfajjPwlv5sxbW1OJPpXP031bU1RazsurTvLlrkTaOFuyeUwHvU5fqa91rG1SR/2RWuqHvuuoWoO8c+dOVq5cydq1aykuLqagoICpU6eSl5dHeXk5Wq2W5ORkXF1dAXB1dSUpKQk3NzfKy8vJz8/H3t6+6vifrv8cIYS4F6cyLvPWhtP8fCQN28YmzBjciue6eWFhquqbb/WKkZGGpzp78vLqOL7bn8SMzWc5dekKL/T05oNBrWr1jxAhhPiTanOQZ8yYQXJyMomJiURHR9OnTx9+/PFHevfuzbJlywCIiopiyJAhAISFhREVFQXAsmXL6NOnDxqNhrCwMKKjoykpKSEhIYH4+Hg6duyo1mkJIeqBhOwixi8+RMC/t7Lu1CXe7OfD+el9ea2vjzTHNeDxju4YG2kYt/gwl0vK2fRkKJ+EBUhzLIRQjcH9pJ81axbh4eG88cYbtG/fnokTJwIwceJEHnvsMXQ6HXZ2dkRHRwMQEBDAyJEj8ff3R6vVMm/ePIyN5YeqEOLOpeRf5f3f4vlm70W0Rhqm9fDm1T46msr6xjXK3qIRXw1vw5HUAt4Z6IudeSO1IwkhGjiDaJB79epFr169APD29v7HVSjMzMz4+eef//Hzp0+fzvTp02syohCiHsu8UsLMLWf5cmciFYrCE6HNmX6fD67WjdWO1mBMCvVQO4IQQlQxiAZZCCHUkHe1jE+2nmP29vMUlVYwNtidt/q1xMveXO1oQgghVCQNshCiwSksKefzHQn8+/dz5F4tY2RbF94Z0JJWTpZqRxNCCGEApEEWQjQYxWUV/Gf3BT7cHM+lK6U84O/EewN9aedqrXY0IYQQBkQaZCFEvVdWUcm3+5J4d+MZkvOL6aNz4P1BvnT2tKv+k4UQQjQ40iALIeqtikqFxYdS+L8NpzmXXUSohy1Ro9vTx8dB7WhCCCEMmDTIQoh6R1EUfj2WzlsbTnMi/TJtXaxYNbEj9/s5otFo1I4nhBDCwEmDLISoNxRFYcPpTN5Yd4oDyfn4NrVgyWNBjAh0xshIGmMhhBC3RxpkIUS9sO1cNtPXnWJHQg6edo35NrwdYzq4ojVWbcNQIYQQdZQ0yEKIOm3fxTzeWHeKjWcycbEy48vhbZjYsTmNtNIYCyGEuDvSIAsh6qRjaQW8tf40K46n42DRiI8f9Ofprp40NpGt5oUQQtwbaZCFEHVKfOYV3t5whujDKViaanl3oC/Pd/fG0kx+nAkhhNAP+Y0ihKgTLuYW8e7GeL7dn4Sp1ojX+uh4qVcL7MwbqR1NCCFEPSMNshDCoKUXFPPh5rP8Z/cFAJ7t6sm/+vrgZGmqcjIhhBD1lWp3sSQlJdG7d2/8/f0JCAhgzpw5AOTk5NCvXz98fHzo168fubm5wLXlm6ZMmYJOpyMwMJCDBw9WPVdUVBQ+Pj74+PgQFRWlyvkIIfSrolLh3Y1n8P5wM1/uSmRciBtn/9WH2Q+1luZYCCFEjVKtQdZqtXzyySfExcWxZ88e5s2bR1xcHDNnzqRv377Ex8fTt29fZs6cCcC6deuIj48nPj6eyMhIJk+eDFxrqN955x327t1LbGws77zzTlVTLYQarpZVcCg5H0VR1I5SZxUUlzFkYSxvbzjNg/7NOPVqbyIfbou7bWO1owkhhGgAVGuQnZ2d6dChAwCWlpb4+fmRkpJCTEwM48aNA2DcuHGsWLECgJiYGMaOHYtGoyE0NJS8vDzS0tLYsGED/fr1w87ODltbW/r168f69evVOi3RwGVeKaH73J10+Gwbj/54iKLScrUj1Tnnsgrp/PkO1p/OZN6wNiwZG4TOwULtWEIIIRoQg5iDnJiYyKFDh+jUqRMZGRk4OzsD0KxZMzIyMgBISUnB3d296nPc3NxISUm56fF/EhkZSWRkJADp6emkpqbW1CmpIjMzU+0I9cLd1jGloJTwZWdIvVzKgBbWLD6UwpHkHL4Z0oLm1g1zSsCd1nLnxctErDoHwE/DdXRr3qjefZ/eDfne1g+po35IHfVHaqkfNVFH1RvkK1euMHz4cGbPno2VldUNH9NoNGg0+tseNiIigoiICACCg4NxcXHR23Mbirs9J0VRmL/rAjlFpbzaR4dJA9997E7rGJd+maFL93ClpILfnuxMN2971p7M4JEfDnL/T6dZ8lgQ97VsWkNpDdvt1nL+rkSm/BqPT1MLVk3oSAsZNb5Bffx5pQapo35IHfVHaqkf+q6jql1QWVkZw4cPZ8yYMQwbNgwAJycn0tLSAEhLS8PR0REAV1dXkpKSqj43OTkZV1fXmx4Xt+9KSTkjvzvAM8uP8eb60wz+ei95V8vUjlVn7L2QS/d5O6moVNj2TFe6edsDMNjPif3TeuBsZcaAyD18svWczEv+B2UVlTz9y1Ge/uUYA3ybsmdKN2mOhRBCqEq1BllRFCZOnIifnx8vvPBC1fGwsLCqlSiioqIYMmRI1fHvvvsORVHYs2cP1tbWODs7M2DAADZu3Ehubi65ubls3LiRAQMGqHJOddGf8z2XH0vj4wf9WTiqLVvPZdP1ix0kZBepHc/gbTx9ib5f7camsQk7nu1KoMuN74LoHCzY/Vw3hrZx5qVVcYyReck3yC4sZUDkHubvusArvVsQM6EjVmYmascSQgjRwKk2xWLnzp18//33tGnThnbt2gHw4Ycf8tprrzFy5EgWLFiAh4cHS5cuBWDw4MGsXbsWnU6Hubk5ixYtAsDOzo4333yTkJAQAN566y3s7OxUOae6ZsOpS4z+4SAaDax/IpR+vtemAHjamTPs2/2Efr6dlRM60snDVuWkhmnp4VQe/ekgfo6WrI/ohLOV2T8+ztJMy89jg5ix+SxvrD9FXMZlfh0fgpe9eS0nNiwn0i8TtjCW5LxivhvdjseC3av/JCGEEKIWaJQG+p5vcHAw+/fvVzuGXqWmpt7WHBxFUfjo93O8vvYkrZtZseLxvzdrpzIuc/+CWFLzi/n+kfaMaNtw5kjdTh3n70rkmeXH6Oppx6qJHbFpfHujnutOZvDIj4cw0tAg5iXfrJar467NzzZvZMyKx0MIlT/Cbul2v7fFrUkd9UPqqD9SS/242zreqhds2HdiNUCFJeWEf3+Q19ac5OG2Lux6rus/jmS2crJkz5RutHe15uHvDjBry1mZP8u1Py7e/+0MT/9yjPv9nNgQ0em2m2OAQX5O7Hu+e9W85I9/b1jzkhVF4aMtZwlbGItPUwv2P99dmmMhhBAGRxrkBuR8diFdvtjJsqOpzLrfj8WPdsDC9OazbJo2MWXL5M6MaufCa2tO8sTSo5RVVNZiYsNSWakwLeYEb64/zWNBbiwfH4x5ozufpaRzsGDPlG4Ma+PMy6sbzrzk4rIKxi0+zKtrTvJwoAvbn+mCm41s/CGEEMLwqL7Mm6gdv53OZNT3BwBY90Qn+vs63tbnmZkY89OYDugcLPhgUzyJuUUsGxd8R6Om9UFZRSUTlhzmhwMpPN/Di08eDMDI6O6XIGxiqmXp2CBmbjnL9HX1f15yWkExQxftY+/FPN4b6Mv0+3z0uoSjEEIIoU8yglzPKYrCx7+fY+DXe3CzMWPf891vuzn+k5GRhvcHtWLRqHZsO59Nlwa2wkVRaTlDF+3jhwMpfDCoFZ+G3Vtz/CeNRsO/+vqwZmJHLuReJXj2NjadqX+Lxh9IyiNk9naOpV/ml3HBvNGvpTTHQgghDJo0yPVYUWk5Y348xMur4xjWxpldz93b+rLjO7qzMSKUtIISOn2+nT0XcvWY1jDlXS2j/3/2sPbUJb4a0YbXa2Dksz7PS445lUP3eTsxNtKw67muDAt0VjuSEEIIUS1pkOuphOwiunyxk+jDKcwY3IqlY4Nocov5xrerl86BPVO6YWmqpfeXu/j5SP3dBjitoJie83YRm5THkseCeLKzZ4291v/OS37kh4MUltTdecmVlQpvrjvF02sSCHKzYd/U7rR1sVY7lhBCCHFbpEGuhzafySR49jYu5F5l7aROvNZXv6Oevo5N2DOlGx3crBn53QFmbo6vNyOefzqXVUi3uTs5l13ImomdeLgWlrn7c17yjMGtWHIklS5f7KyTU1mulJQz4rv9vL8pnvDW9mx6KhRHS1O1YwkhhBC3TRrkekRRFD794xz9I/fgbHVtvvHAVnc23/h2NW1iyuanOhPezoV/rT1Vr1a4OHGpiK5zd5J3tYwtkztXbaBSGzQaDa/19WHtpE5czLs2L/m303VnXvKFnCK6frGTmOPpzB4SwMf9PTDVGqsdSwghhLgj0iDXE0Wl5Ty3NpEXV8YxtI0zu5/rhu4e5hvfDjMTY34c04E37vNhQexFBkbuJe9qWY2+Zk3bfj6bEUvPYGKkYcezXenYXJ01ege2cmTf891xsTJj4Nd7+Pfvhr8O9Y7z2YTM2c6F3CLWTurE1B7ecjOeEEKIOkka5Hrgz1G7Fady+GBQK34eG4SlWe2s4GdkpOG9Qa34Nrwd2xPq9goXq+My6P+fPTQ117Lzua74OVmqmkfnYMHu/85LfmX1SUYb8LzkBXsv0uer3dg2NmHv1O4MqKF3LoQQQojaIA1yHbclPougz7aRkFNE1FBdjayycDvGhVxb4SL9vytc7E7MqfUM9+K7/Uk8tGgfrZ0t+TXcl+a2hrEecdV6yff7sfS/85LPZxeqHatKeUUlz684zqSlR+jd4toNnL6OTdSOJYQQQtwTaZDrKEVRmL3tPP0j9+Boacq+57vT11vdVQJ66RzYPaUbVqZaes/fzdLDdWOFi9nbzjNu8WF6etuz5aku2Jsb1iYoGo2GV/voWPfnvOTPthvEvOTcolLu/yaWOdsTeL6HF2smdcTWvJHasYQQQoh7Jg1yHXS1rIKxiw8xLeYEYQFO7J3SHZ+mhjFq9+cKF8Fu1oz6/gAzDHiFC0VReGPdKabFnGBYm2asfaJjrU1NuRsDWjmy//nuuFpfm5f80Rb15iWfvnSF0M938Pu5LL4Z2ZbPhrRGayw/ToQQQtQP9eY32vr16/H19UWn0zFz5ky149SYi7lFdJu7kx8PpvDeQF+WjQ02uKbOoYkpm57qzCPtXXl97SkmLT1CablhrXBRUakw+ZdjfLApnidCm7N0bHCdWG2hxX/nJQ8PdObVNerMS954+hKd5mwn92oZW57qzMROzWv19YUQQoiaZlid1V2qqKjgmWee4bfffsPNzY2QkBDCwsLw9/dXO5pebT2bxcPfHaC0opKVEzrygL+T2pFuyszEmB/GtEfnYMG7v50hMecqy8YFGcRb8CXlFTz64yGWHU3jX311fDCoVZ1abaGJqZYljwUR5HaOf609SVzGZVY8HoK3fc2uWqIoCnO2J/DiyhO0bmbFygkheNgZxlxtIYQQQp/qxQhybGwsOp0Ob29vGjVqRHh4ODExMWrH0htFUfh8+3nu+88eHCwaETu1u0E3x3/SaDS8M9CXqNF/rnCh/g1mV0rKeeCbWJYdTePjB/35cLBfnWqO/3T9vOTkvGKCP9vOxtOXauz1SsormLT0CNNiTjCkdTN2PtdVmmMhhBD1Vr0YQU5JScHd3b3q325ubuzdu/dvj4uMjCQyMhKA9PR0UlMN/yayq2WVvLbpAsvichjQwpo5g7ywLC8gNbXgb4/NzFT/xq1/cp+LMYuH+zBp5Tk6fraNhQ+1INil9udM5xSVM/bXeI5mFPHZQE9Gtmz8j18DhlrHf9LGClY/4sukmHMM+nov/+rmyuQQJ702/VlFZTyx8jyxKVd4PtSZF7s4U5B9ib9/Bf5dXaqlIZM66ofUUT+kjvojtdSPmqhjvWiQb1dERAQREREABAcH4+JS89sH34uk3KuMWrKPA8n5/F//lrzZryVGRrdufAz1nIa7QGsvV+7/Zi8jf44nKrwdo9q71trrJ+Ve5eHv95CYU8zy8SGEtW52y8cbah3/iYsL7GvhzoQlR/hgewrxBQoLR7XFwvTev72PpOYTFr2PS5dLiH60w11ds7pUS0MmddQPqaN+SB31R2qpH/quY71okF1dXUlKSqr6d3JyMq6utdd81YRt57IZ8d1+issqiXm8+oauLvhzhYuh3+4n/IeDnMsu4l99dTU+xeFUxmX6R+4hv7icDRGh9GhhX6OvpwYLUy3Rj3UgyM2af609yclL9z4v+ddjaTz60yFsG5uw49muBLnb6C+wEEIIYcDqxRzkkJAQ4uPjSUhIoLS0lOjoaMLCwtSOdVcURWHujgT6frUbu8YmxE7tVi+a4z9dW+EilEfauzJ93SkmLqnZFS72Xcyj29ydlJRX8sfTXeplc/wnjUbDK310rHvir3nJG07d+bxkRVF477czDPt2P22aWbLv+e7SHAshhGhQ6kWDrNVqmTt3LgMGDMDPz4+RI0cSEBCgdqw7VlxWwYQlR3ju1+MMbOXI3qndaaXydsc1wVR7bYWLt/u3ZNG+JAZ+vYfcolK9v87mM5n0+WoXlmZadj7XjXau6m6kUlv6+zqy7/nuuNmYMfibvcy6g/WSi0rLCf/+IG+tP81jQW5sfboLzlZmNZxYCCGEMCz1YooFwODBgxk8eLDaMe5act5Vhn27n31JebzVryVv969+vnFdptFo+L8BvrSwN2fi0iN0+WInayZ11NtSZb8cTeWRHw7RsqkFGyJCcbFuWE1eCwcLdj/XjQlLjvDampMcSM5j4ah2NLnFvOTkvKsMWbSPQyn5zLrfj5d7t6iTK3wIIYQQ96reNMh12fbz2YyI2k9RWQW/jg/moTbOakeqNY8Fu+Nha87Qb/fRac4OYh4PoYuX3T0959d7LvDUsqOEetiyemLD3f74f+cln7p0hV/Hh9DC4e9/hOy5kMvQRfsoLK0w+DW2hRBCiJpWL6ZY1FWKovDlzkT6zN+NtZkJe6d0b1DN8Z96tLBn95Ru2DQ2oc9Xu4k+lHJXz6MoCjM3xxPx81EG+DqyMSK0wTbHf/rfeckhs/8+L/n7/Un0+nIX5o2M2T2lmzTHQgghGjxpkFVSUl7BE0uP8szyYwzwbUrs893xb1b/5hvfrpZNm7D7ua6EuNsw+oeDfLDpzG3PmwWorFR4aVUc/1p7ikfauxIzIUQvy5zVF/19Hdk/7cZ5yeUVlbyyKo6xiw/TxdOW2KndCWjAX4NCCCHEn6SDUEFK/lWGf7ufvRfzeOM+H94Z4Fuv5xvfrj9XuJi45AhvrDvN2awi/jMikEbaW/8dV15RyaSlR4jan8xz3byYPSRA6vkPvO2vzUueuPTavOTX1pwE4Okunsx+KAATY/l7WQghhABpkGvdzoQchkftp7C0nF/GBTMssOFNqbgVU60x3z/SHh8HC/5v4xku5Bbxy7jgm06VuFpWQfj3B1h5IoN3B/ryxn0+cmPZLViYaln86LV5yR/9fo4PBrUiorOH2rGEEEIIgyINci36z+5Envv1OB625mx+qrO8nX0TGo2Gtwf40sLBgolLjtD58x2smdTpbzeX5V8tI2xhLNsTcvhyeBsmd/FUJ3Ado9FoeLm3jpd6ySoVQgghxD+R91RrQUWlQsTPR3hq2THu82lK7NRu0hzfhkeD3Nj0VCiZhaWEfr6DnQk5VR/LuFxCry93sftCLovHdJDm+C5IcyyEEEL8M2mQa4GR5lqT/K++OlY14GXH7kZ3b3v2TOmGbWMT+n61m8UHU0jILqLb3J2cySpk1YSOjGpft7cVF0IIIYRhkSkWtUCj0fDNyLYyYneXfJo2YfeUbgz7dh+P/HgQm8YmaIDNT3Um1MNW7XhCCCGEqGdkBLmWSHN8b+wtGrHxyVAeD3HHqUkjdj7XVZpjIYQQQtQIGUEWdYap1piF4e3UjiGEEEKIek5GkIUQQgghhLiONMhCCCGEEEJcR5UG+eWXX6ZVq1YEBgYydOhQ8vLyqj42Y8YMdDodvr6+bNiwoer4+vXr8fX1RafTMXPmzKrjCQkJdOrUCZ1Ox6hRoygtLa3NUxFCCCGEEPWMKg1yv379OH78OEePHqVly5bMmDEDgLi4OKKjozlx4gTr16/n6aefpqKigoqKCp555hnWrVtHXFwcixcvJi4uDoBXX32VadOmcfbsWWxtbVmwYIEapySEEEIIIeoJVRrk/v37o9Veuz8wNDSU5ORkAGJiYggPD8fU1BQvLy90Oh2xsbHExsai0+nw9vamUaNGhIeHExMTg6IobNmyhREjRgAwbtw4VqxYocYpCSGEEEKIekL1VSwWLlzIqFGjAEhJSSE0NLTqY25ubqSkpADg7u5+w/G9e/eSnZ2NjY1NVbN9/eP/SWRkJJGRkQCkp6eTmpqq9/NRU2ZmptoR6gWpo/5ILfVD6qgfUkf9kDrqj9RSP2qijjXWIN93332kp6f/7fgHH3zAkCFDqv5bq9UyZsyYmopxg4iICCIiIgBwcHAgLCysVl63tmRmZtK0aVO1Y9R5Ukf9kVrqh9RRP6SO+iF11B+ppX7cbR0TExNv+rEaa5A3bdp0y49/++23rF69ms2bN1dtouHq6kpSUlLVY5KTk3F1vbaN8D8dt7e3Jy8vj/LycrRa7Q2Pr05WVtadnpLBCw4OZv/+/WrHqPOkjvojtdQPqaN+SB31Q+qoP1JL/aiJOqoyB3n9+vV89NFHrFy5EnNz86rjYWFhREdHU1JSQkJCAvHx8XTs2JGQkBDi4+NJSEigtLSU6OhowsLC0Gg09O7dm2XLlgEQFRVVNTothBBCCCHE3VBlDvKzzz5LSUkJ/fr1A67dqPfVV18REBDAyJEj8ff3R6vVMm/ePIyNjQGYO3cuAwYMoKKiggkTJhAQEADArFmzCA8P54033qB9+/ZMnDhRjVMSQgghhBD1hCoN8tmzZ2/6senTpzN9+vS/HR88eDCDBw/+23Fvb29iY2P1mq+u+nN+tbg3Ukf9kVrqh9RRP6SO+iF11B+ppX7URB01iqIoen9WIYQQQggh6ijZaloIIYQQQojrSIMshBBCCCHEdaRBNmBJSUn07t0bf39/AgICmDNnDgA5OTn069cPHx8f+vXrR25uLgCnTp2ic+fOmJqa8vHHH9/wXJ6enrRp04Z27doRHBxc6+eiJn3WMS8vjxEjRtCqVSv8/PzYvXt3rZ+PmvRVy9OnT9OuXbuq/1lZWTF79mw1TkkV+vya/OyzzwgICKB169aMHj2a4uLiWj8fteizjnPmzKF169YEBAQ0qK9FuPM6/vjjjwQGBtKmTRu6dOnCkSNHqp5r/fr1+Pr6otPpmDlzpirnoyZ91nLChAk4OjrSunVrVc5FTfqq482e57YowmClpqYqBw4cUBRFUQoKChQfHx/lxIkTyssvv6zMmDFDURRFmTFjhvLKK68oiqIoGRkZSmxsrPL6668r//73v294Lg8PDyUzM7N2T8BA6LOOY8eOVb7++mtFURSlpKREyc3Nrb0TMQD6rOWfysvLFScnJyUxMbF2TsIA6KuOycnJiqenp1JUVKQoiqI8/PDDyqJFi2r3ZFSkrzoeO3ZMCQgIUAoLC5WysjKlb9++Snx8fO2fkErutI47d+5UcnJyFEVRlLVr1yodO3ZUFOXa97K3t7dy7tw5paSkRAkMDFROnDihwhmpR1+1VBRF+eOPP5QDBw4oAQEBtXwW6tNXHW/2PLdDGuQ6JCwsTNm4caPSsmVLJTU1VVGUaxe/ZcuWNzzu7bfflgb5Fu62jnl5eYqnp6dSWVlZq3kN2b18Tf5pw4YNSpcuXWo8qyG72zomJycrbm5uSnZ2tlJWVqbcf//9yoYNG2o1uyG52zouXbpUmTBhQtW/3333XWXWrFm1E9oA3W4dFUVRcnJyFBcXF0VRFGXXrl1K//79qz724YcfKh9++GHthDZQd1vLPyUkJDTIBvl/3Wsd//d5bodMsagjEhMTOXToEJ06dSIjIwNnZ2cAmjVrRkZGRrWfr9Fo6N+/P0FBQURGRtZ0XIN1L3VMSEigadOmPP7447Rv355JkyZRWFhYG7EN0r1+Tf4pOjqa0aNH11RMg3cvdXR1deWll16iefPmODs7Y21tTf/+/WsjtsG5lzq2bt2a7du3k52dTVFREWvXrr1h99aG5E7ruGDBAgYNGgRASkoK7u7uVR9zc3MjJSWldoIboHuppfiLvup4/fPcDmmQ64ArV64wfPhwZs+ejZWV1Q0f02g0VVt138qOHTs4ePAg69atY968eWzbtq2m4hqse61jeXk5Bw8eZPLkyRw6dAgLC4sGOccO9PM1CVBaWsrKlSt5+OGHayKmwbvXOubm5hITE0NCQgKpqakUFhbyww8/1GRkg3SvdfTz8+PVV1+lf//+DBw4kHbt2lVtUtWQ3Gkdf//9dxYsWMCsWbNqM2adILXUD33V8VbPczPSIBu4srIyhg8fzpgxYxg2bBgATk5OpKWlAZCWloajo2O1z+Pq6gqAo6MjQ4cObXCbq+ijjm5ubri5uVX99TlixAgOHjxYs8ENkL6+JgHWrVtHhw4dcHJyqrG8hkofddy0aRNeXl40bdoUExMThg0bxq5du2o8uyHR19fjxIkTOXDgANu2bcPW1paWLVvWaG5Dc6d1PHr0KJMmTSImJgZ7e3vg2u+Z60fek5OTq373NCT6qKXQXx3/6XluhzTIBkxRFCZOnIifnx8vvPBC1fGwsDCioqIAiIqKYsiQIbd8nsLCQi5fvlz13xs3bmxQd8Xqq47NmjXD3d2d06dPA7B582b8/f1rLrgB0lct/7R48eIGOb1CX3Vs3rw5e/bsoaioCEVR2Lx5M35+fjWa3ZDo8+vx0qVLAFy8eJHly5fzyCOP1ExoA3Sndbx48SLDhg3j+++/v+EPiZCQEOLj40lISKC0tJTo6GjCwsJq92RUpq9aNnT6quPNnud2QwgDtX37dgVQ2rRpo7Rt21Zp27atsmbNGiUrK0vp06ePotPplL59+yrZ2dmKoihKWlqa4urqqlhaWirW1taKq6urkp+fr5w7d04JDAxUAgMDFX9/f+X9999X+cxql77qqCiKcujQISUoKEhp06aNMmTIkKq7ZhsKfdbyypUrip2dnZKXl6fmKalCn3V86623FF9fXyUgIEB59NFHleLiYjVPrVbps47dunVT/Pz8lMDAQGXTpk1qnlatu9M6Tpw4UbGxsal6bFBQUNVzrVmzRvHx8VG8vb0b3O8aRdFvLcPDw5VmzZopWq1WcXV1Vb755hu1TqvW6auON3ue2yFbTQshhBBCCHEdmWIhhBBCCCHEdaRBFkIIIYQQ4jrSIAshhBBCCHEdaZCFEEIIIYS4jjTIQgghhBBCXEcaZCGEaGD+7//+j48//ljtGEIIYbCkQRZCCCGEEOI60iALIUQD8MEHH9CyZUu6detWtRvk559/jr+/P4GBgYSHh6ucUAghDIdW7QBCCCFq1oEDB4iOjubw4cOUl5fToUMHgoKCmDlzJgkJCZiampKXl6d2TCGEMBgygiyEEPXc9u3bGTp0KObm5lhZWREWFgZAYGAgY8aM4YcffkCrlfESIYT4kzTIQgjRQK1Zs4ZnnnmGgwcPEhISQnl5udqRhBDCIEiDLIQQ9VyPHj1YsWIFV69e5fLly6xatYrKykqSkpLo3bs3s2bNIj8/nytXrqgdVQghDIK8pyaEEPVchw4dGDVqFG3btsXR0ZGQkBA0Gg2PPvoo+fn5KIrClClTsLGxUTuqEEIYBI2iKIraIYQQQgghhDAUMsVCCCGEEEKI60iDLIQQQgghxHWkQRZCCCGEEOI60iALIYQQQghxHWmQhRBCCCGEuI40yEIIIYQQQlxHGmQhhBBCCCGuIw2yEEIIIYQQ15EGWQghhBBCiOtIgyyEEEIIIcR1pEEWQgghhBDiOlq1A6jFwcEBT0/PWn3NsrIyTExMavU1xTVSe8Mg18EwyHUwHHItDIdcC8NQm9chMTGRrKysf/xYg22QPT092b9/f62+ZmpqKi4uLrX6muIaqb1hkOtgGOQ6GA65FoZDroVhqM3rEBwcfNOPyRQLIYQQQgghriMNshBCCCGEENeRBlkIIYQQQojrSIMshBBCCCHEdRrsTXpCCCGEPuRdLSOnqJSKSoWKSoVGWiPcrBvTSCtjUELUVdIgCyGEELcpp6iUzfFZ/HYmk+Npl4nPKiSrsPRvj9NooJmlKd525nTxtKNHC3u6edlh01iWEROiLpAGWQghhLiFguIyfjqYQtT+ZPZezEVRwNpMS3tXa4a2aYaPgwWOTUwxNtJgrNFwtayCpLyrXMi9yunMK8zefp5/bz2HkQb66Bx4LNiNYW2caWIqv4KFMFTy3SmEEEL8g5MZl/lk63kWH06hqLSCNs6WvNWvJQN8mxLiboPW+PamUFwtq2DPhVw2nclk8aFUxi0+zORfjjGmgyuP+VkiS+8KYXikQRZCCCGuE595hXd/O8NPB1MwMzFmdDtXIjo3J8TdBo1Gc8fP19jEmN46B3rrHHh/UCt2JuQQtT+Z7/cn882eSoa2yeb1vj4Eudvo/2SEEHdFGmQhhBACyC0qZfq6U0TuuYip1oiXerXg5V4tcGhiqrfX0Gg0dPO2p5u3Pe8PasUH647y/dFslh9LZ3R7Vz4c3ApPO3O9vZ4Q4u5IgyyEEKJBUxSF7/Yn8/LqOHKKyni6iyfT7/PByVJ/jfE/cbI05bVurrwf1o6Pfj/Hp3+c45ejaUzp7sVb/VpiaSa/ooVQi3z3CSGEaLASc4p4PPowW89l09nDlvlPtqGti3WtZrAyM+H9Qa14qrMHb64/zcdbz7HkcApfDg/kAX+nWs0ihLhGFmkUQgjR4CiKwrexSQR+/AcHkvOJfDiQHc92rfXm+HpuNo1ZFN6Onc92xcrMhAcXxDLyu/2kFRSrlkmIhkoaZCGEEA1KblEpw6P28/iSw3Rws+boSz15ItQDI6M7vwGvJnTxsuPgtB68P8iXlScy8Jv1O5G7L6AoitrRhGgwpEEWQgjRYMRezKX9p9tYHZfBvx/wZ8tTnQ3yprhGWiOm39eSYy/1pIObNU8uO0r//+zhQk6R2tGEaBCkQRZCCFHvKYrCF9sT6DZ3JwA7nu3KS71bGMyo8c34NG3C5qc6M394G/ZczKXNx3/w9R4ZTRaipkmDLIQQol4rLqtg7OJDTFlxnAG+jhx8oQcdm9uqHeu2aTQanuriybGXehHibkPEz0cZGLmXi7kymixETZEGWQghRL2Vml9Mzy938cOBFN4d6EvM4yHYmTdSO9Zd8bQz57cnQ5k3rA07E3No/e8/WLD3oowmC1EDpEEWQghRL8VezCV49jZOpF9m+fhg3uzX0uCnVFTHyEjD012vjSYHuVkzaekRBn+zl+S8q2pHE6JekQZZCCFEvfPDgWR6zNuFqdaI3VO6MbSNs9qR9MrL3pzNT3Vm7tDWbDufQ8C/t7JQRpOF0BtpkIUQQtQbFZUKr6yK47GfDhHqYcu+qd1p42yldqwaYWSk4ZluXhx9sSftXKyYuPQI938TS0q+jCYLca+kQRZCCFEv5F8tI2xhLP/eeo7JXTz47clQHJrU7HbRhqCFgwW/T+7C5w+1Zuu5LAI+2sq3sUkymizEPZAGWQghRJ0Xn3mF0M93sPF0Jl8Ob8OXwwMxMW44v+KMjDQ8192Loy/1ItDFiseXHObBBbGk5ssufELcjYbz00MIIUS99NvpTDrO2UHmlRJ+ezKUyV081Y6kGp2DBVsnd2H2kAC2nM0i4N9bmbsjgbKKSrWjCVGnSIMshBCiTlIUhc+3n2fQN3txszZj3/M96KVzUDuW6oyMNEzt4c2RF3vSwdWa5349TuDHf7DuZIba0YSoM6RBFkIIUeeUllcS8fNRpq44wf1+jux6rhte9oa3ZbSafJo2YdNToax4PITySoXB38TS+8td/H42S+YnC1ENrZovPmHCBFavXo2joyPHjx8HICcnh1GjRpGYmIinpydLly7F1tYWRVGYOnUqa9euxdzcnG+//ZYOHToAEBUVxfvvvw/AG2+8wbhx41Q7JyGEEDUr80oJw6P2s/18Dv/qq+P9ga3q/PrGNUWj0TCkdTMGtXLkq92JzNxylj7zd9PNy47X+ugY2MoRYwOpXUWlQkr+VRJzrpKQU0RqQTG5RWXkXi3jckk5GkBrrEFrZERTi0a4WJvhYmVGK8cm+Dk1aVBzzkXNU7VBHj9+PM8++yxjx46tOjZz5kz69u3La6+9xsyZM5k5cyazZs1i3bp1xMfHEx8fz969e5k8eTJ79+4lJyeHd955h/3796PRaAgKCiIsLAxb27qzjagQQojbczS1gLCFsWRcLuHHMe15pIOb2pHqhEZaI6Z09yYi1IMFey8yc8tZHlgQi6ddYyJCPXg8xJ1mVma1ludKSTnH0go4klrA4dQCDqfkcyz9MkWlFTc8zkxrhK25CZamWhQFyisVyioqySwspaS88obHBbpY0dXTjgf8nejubScNs7gnqjbIPXr0IDEx8YZjMTExbN26FYBx48bRq1cvZs2aRUxMDGPHjkWj0RAaGkpeXh5paWls3bqVfv36YWdnB0C/fv1Yv349o0ePruWzEUIIUZO+35/Ek8uOYtPYhG3PdCWkuY3akeocMxNjnunmxROhHqw4ns5XuxN5fe0p3lh3iq5edjzUuhlhAc1oYW+ORnPvI8uKopCUd5Xj6Zc5nFLA4dR8jqQWEJ9VyJ+zPGwbm9DWxYqI0Ob4OVriZWeOl705rtZmNDYxvunz5l4tIzmvmOPpBRxIzudAcj5f7krks23nsTbTcr+fE5NCm9Orhb1ezkU0LKo2yP8kIyMDZ+drOx41a9aMjIxrNxWkpKTg7u5e9Tg3NzdSUlJuevyfREZGEhkZCUB6ejqpqak1dRr/KDMzs1ZfT/xFam8Y5DoYhrp2HUrKK3l7axLfH8ki1K0J8x/wxlFbRGpqkdrR7pma16KbI3Qb4snZnGasOJnD+nN5vLgyjhdXxtHUXEsHZwvaOVvgbWuGm1Uj3KwaYWOmRXvdlAxFUbhaXklecQVpl0tJvVxGSkEJZ7KLOZN9lTPZxRSW/TXS62HdiABHc8J8nAloak6Aozkulib/08CWQWk+uZn55FZzDg5Ar2ZG9GpmC8G2FJa6s/3CZX47n8fak+n8dCgFL1tTHg1sypg2Dlia/nPDDXXv+6K+MpTrYHAN8vU0Go1e/+qLiIggIiICgODgYFxcXPT23LdLjdcU10jtDYNcB8NQV67DhZwiRn13gH1JebzcqwUfDm6Ftp69da72tXBxgR6tvfkUSMguYt2pS+y9mMueC7lsOPf3gSRjIw2NTYxQFCgqq+Cf7vdzbNKIgGaWPN7CkYBmlgQ0syTQ2QrrxiY1fj4+njChJ1wtq2DZkVT+s/sC7/2RzNzYDF7o6c1z3bxumkPtayGuMYTrYHANspOTE2lpaTg7O5OWloajoyMArq6uJCUlVT0uOTkZV1dXXF1dq6Zk/Hm8V69etZxaCCGEvq2Jy2Ds4kOUVyosHx/M0DbOakeq97zszXm6qydPd/UEru1OmJBTxIXcq1zILSK/uJyrZRUU/3dUuImpMU0aabFurMXNujFuNma42zTGzryRimdxTWMTYx4LduexYHf2J+Xx7sYzvLn+NJ/8cZ7pfX2Y0t2LRtr69ceW0B+Da5DDwsKIioritddeIyoqiiFDhlQdnzt3LuHh4ezduxdra2ucnZ0ZMGAAr7/+Orm5196I2bhxIzNmzFDzFIQQQtyDwpJyXloVx1e7LxDobMWycUH4NG2idqwGybqxCe1crWnnaq12lHsS7G7DyokdOZicx5vrT/Py6ji+3nuB2UMCGOTnpHY8YYBUbZBHjx7N1q1bycrKws3NjXfeeYfXXnuNkSNHsmDBAjw8PFi6dCkAgwcPZu3ateh0OszNzVm0aBEAdnZ2vPnmm4SEhADw1ltvVd2wJ4QQom7ZdzGPMT8e5Gx2IS/1asH7g3wx1d583qgQd6KDmw1rJnVi7ckMpsWcYPA3sQwJcOLL4YFqRxMGRqM00NXCg4OD2b9/f62+ZmpqqkHMq2mIpPaGQa6DYTDE61BeUcmMLWd5Z+MZXKxMiRrdnt4NYFc8Q7wWDUVpeSWzt53n7Q2nMdUa8VYPV6b1byMrXqisNr8nbtULGtwUCyGEEA3Lvot5RPx8hMOpBTzS3pV5w9tgUws3c4mGrZHWiFf66BjaphkTlx7hxY0X2HihiEXh7XCuxTWhhWGS2elCCCFUUVBcxpRfj9Pp8+1culLKL+OC+fHRDtIci1rl07QJWyd34YM+7mw7n03gx3+w6kS62rGEyqptkCsqKqp7iBBCCHHbKioVvtlzAZ8ZW5i7M4Gnu3gS90ovhgXKKhVCHUZGGsa3d+TAtB64WpsRtnAfz/xyjKtl0gPdjuKyCgpLyim9bnfDuq7aKRY+Pj4MHz6cxx9/HH9//9rIJIQQoh5SFIXN8Vm8tCqOI6kFdPG0ZfXETrIjnjAYfk6W7J3ajdfXnuLTP86z9VwWix8NItDFSu1oBqGkvII/zmWz50Iex9MLOJ5+mfPZRTds+23T2AQP28a0cbakq6cd/Vo2pYWDhYqp7061DfKRI0eIjo5m0qRJVFZWMmHCBMLDw7Gyki8WIYQQt+ePc1m8tf40287n4GHbmCWPBfFwW2e5IUoYHFOtMZ+EBTDAtynjFh+m45ztfDYkgKc6ezTIr9fisgp+OZrG8mNpbDyTyZWSCjQa8LYzp3UzS+73c8K2sQkmxhqKyyvJuFzCuexCNp3J4ocD13Y27uBmzfhgd8aFuGFlVjemUFXbIFtaWvLEE0/wxBNP8Mcff/DII48wbdo0RowYwZtvvolOp6uNnEIIIeqYykqFdacu8ckf5/j9bDbOVqZ8MbQ1kzo1x8xElm4Thq2/ryNHXuzJ2MWHePqXY2yOz+KbkW0bzBz5+Mwr/Gf3BRbtSyKnqAwXKzPGdHDjQX8nerWwx8L01i2koijEZxWyOi6DxYdSmLLiONPXnWJKdy9e7OmNrQFsJnMr1TbIFRUVrFmzhkWLFpGYmMiLL77ImDFj2L59O4MHD+bMmTO1kVMIIUQdUVhSzncHkpmz7TynMwtxtTbj0zB/nuriSWNpjEUd4mhpytpJnfh46zmmrzvFgeQ8oh8NopOHrdrRaszR1ALe++0MvxxLw1ijYWibZjzV2ZNeLewxMrr9EXSNRkPLpk14oWcTXujZgn0X8/j31rN8sCmer3Yl8umQAB4LcjPYUfnbmoPcu3dvXn75Zbp06VJ1fMSIEWzbtq1GwwkhhKg74tIv8+2+JL7Ze5Hcq2UEu1vz45j2PNzWBRNjWTRJ1E1GRhpe6aOjRwt7wr8/QLe5O/lwcCte7NnijhpGQ3c8rYA3159mxfF0LE21/KuPjme7eeltybuQ5jYsHRvMkdR8Ji87xrjFh/l+fzJfjQg0yDnK1TbIR48epUmTf97i8/PPP9d7ICGEEHVHblEp0YdT+XZfErEX8zA20jAkwIlpPbzp6mVnsKNDQtypUA9bDr3Qg0lLj/DK6pP8fjabqNHtaNrEVO1o9yStoJi31p9mYexFLE21vN2/JVO7e9XYFIi2LtbseLYr/9lzgVdXn6TNx1v5clgg4zu618jr3a2bNsjPPffcLX+wSXMshBANU25RKavjMlh+LJ21Jy9RWlFJG2dLPg3z55EObjhZ1u2GQYibsTVvxLJxwczfdYEXVp6g7Sd/8OOYDnVy18crJeV8svUcH209R1lFJVO6e/HGfS2xt6j5ucFGRhomd/EkLMCJx346xONLDrP7Qg7/ebhtjb/27bppgxwcHFybOeq9UxmXKS4sQzYUFULURRmXS4g5ns7yY2lsjs+ivFLB1dqMp7p4MD7YnXauVjJaLBoEjUbD01096eJpy6jvD9D3q928eV9L3uzng7YOTCWqqFSI2pfEG+tPkVZQwsNtnZkx2E+VaQ6u1o357cnOvPfbGYP7w/qmDfK4ceOoqKjg1Vdf5eOPP67NTPVOeUUlQxbto7SsnK3PNMXDzlztSEIIUa0LOUX8+t+meEdCDooCLezNeaGnN8PaOBPiblOv5mAKcSfauVpzYFoPnll+jHd/O8P605f4Nrwdfk6Wake7qa1ns5gWc4LDqQWEetjyy7hgOnvaqZrJ2EjD/w3wVTXDP7nlHGRjY2N27txZW1nqLa2xEVGj2zPwP7vpNncnm57qjK/jP8/rFkIINZ2+dIXlx9L45WgaB5LzAQh0tuLtfi0ZFuhM62aWMlIsxH81MdUSNbo9g1o58szyY7T/dBvvDfTlhZ4tMDagPx7PZhXy8qo4VhxPp7ltYxY/2oFR7Vzke/kWqr1Jr127doSFhfHwww9jYfHX8PuwYcNqNFh9E+phy88jW/Lor+foPm8nGyNCaedqrXYsIYTgVMZlfj6axs9HUjmWdhm49jProwf8GNrGGZ0B3mEuhCEJb+9Kb50Dk385yiurTxJ9OJV5w9oQqvJycLlFpby/KZ4vdiRgqjXig0GtmNbTW5ZbvA3VNsjFxcXY29uzZcuWqmMajUYa5LsQ4GjO9me7ct9Xu+k9fzd/PN1Ftq8UQqjiz6Z46eFUjqdfRqOBrp52zHkogGFtnHGzaax2RCHqFCdLU34ZF8zSw6m8sDKOzp/vYEJHd2YM9sOxlufXFhSXMWd7Ap9sPUdBSTkTOzbnvYG+NNPTkm0NQbUN8qJFi2ojR4PRsmkT/ni6K93n7aR/5B62PdOFlk1luoUQoublFpWy+FAqkTvPcSSjqKop/vyh1gwPdMbFWn55CnEvNBoNo9q7MtjPifd+O8Nn286z9EgqU7p58WKvFtjV8O5x2YWl/Gf3BT754xw5RWUMCXDi3YGtZDDuLtzWCPKCBQs4ceIExcXFVccXLlxYo8HqMy97czY9GUr3ebu476vd7Hi2K81t5cY9IYT+VVYqbIrPZGFsEiuOp1NSXol/08Z8NiSAkW1dpCkWogZYmmn56EF/JnR05/82nmHGlrN8sSORZ7t58mSoh95v1j+ZcZnPtycQtT+Jq2WVDGrlyLsDfQl2t9Hr6zQk1TbIjz32GK1atWLDhg289dZb/Pjjj/j5+dVGtnqtlZMlGyNC6T1/F32/2sOu57rW+cXGhRCG43JxOd/uS+KLHQnEZxViZ25CRKgHj4e442RUiIuLLDopRE1r5WRJ9GNBvHGfD+/+doaZW84yc8tZBvg2ZVKn5gzwdaSJabWt2D9Kyb/KksOpLD6Uwv6kfEy1RjzawY3ne3jR2llGjO9VtVfl7Nmz/Pzzz8TExDBu3DgeeeQRunfvXhvZ6r32btasndSJvl/tJmzhPrZM7iwT54UQ9+RsViFzdySwMDaJyyXlhHrY8n8DWjI80BlT7bWfL6mphSqnFKJhae1sxdKxwVzIKWJhbBILYi8yIuoAJsYaunra0a9lU9q5WuHjYIGnnfkNW7MrikJOURkXc69y8tJltp/PYdv5bOIyrgAQ5GbNxw/681iQW63Pda7Pqm2QTUxMALCxseH48eM0a9aMS5cu1XiwhqKLlx0/junAiO/28+iPB/l5bLCsKyqEuGPH0wr4cPNZlhxOwdhIw6h2LjzXzYuOzdW9i14I8RcPO3PeGejLm/182HY+h42nM9lw+hLT152qeozWSIOlqRZjIw3GRhqulJRTWFpR9XFLUy1dvWx5NMiN4YHOch9TDam2QY6IiCA3N5f33nuPsLAwrly5wrvvvlsb2RqMYYHOfPKgPy+sjOOV1XF8HBagdiQhRB1xICmPDzbH8+uxdJqYGvNSrxY838MbZ7lbXQiDpTU2oo+PA318HJj5gB/ZhaWcvnSF+KxC4rMKKSgup6JSobyyEvNGxnjYmtPcpjHe9ua0cbYyqDWW66tqG+RJkyYB0LNnT86fP1/jgRqq53t4k5BzlU/+OI9PUwue7OypdiQhhAE7llbA62tPsTouA5vGJrzVryVTe3jV+F3yQgj9s7doRBcvO7p4qburnfhLtQ1yRkYGr7/+Oqmpqaxbt464uDh2797NxIkTayNfg6HRaPhsSABnswp5dvlx/Bwt6dHCXu1YQggDk5hTxFvrT/PDwWSszUz4YFArnunqiXVjE7WjCSFEvWFU3QPGjx/PgAEDSE1NBaBly5bMnj27pnM1SMZGGn56tAMt7M0ZHrWfCzlFakcSQhiIrCslPL/iOL4zf+fnI6m83KsF51/vw+v3+UhzLIQQelZtg5yVlcXIkSMxMrr2UK1Wi7GxrLRQU2wamxAzoSNlFZU8tGgfhSXlakcSQqiorKKSOdvO4zPzd77YkcBjQW7E/6sPsx7wx1amUwghRI2otkG2sLAgOzsbjebahPA9e/ZgbW1d48EaMl/HJix+tANH0gp4fMlhFEVRO5IQQgW/nc6k3Sd/8HzMCULcrTn6Ui++GdVWtoEWQogaVu0c5E8++YSwsDDOnTtH165dyczMZNmyZbWRrUEb5OfEzMF+vLrmJG1d4pl+X0u1Iwkhasm5rEJeXHmCmBMZeNubs+LxEMICnKoGKoQQQtSsahvkoKAg/vjjD06fPo2iKPj6+latjSxq1su9W3AktYA31p0m0NmKBwOaqR1JCFGDrpSU8+HmeD7Zeh4TYw0zBrdiWk/vqg0+hBBC1I5qp1gEBQURGRmJi4sLrVu3NtjmeP369fj6+qLT6Zg5c6bacfRCo9Hwzai2dHCz5tGfDnH60hW1IwkhaoCiKPxwIBnfmb8zY/NZRrVz4cxrfXitr480x0IIoYJqG+QlS5aQkpJCSEgI4eHhbNiwweDmxFZUVPDMM89ULUO3ePFi4uLi1I6lF41NjPl1fDCNjI14aNE+CorL1I4khNCj/Ul5dP1iJ4/9dAhXazN2T+nGd4+0x8VaNvoQQgi1VNsg63Q6PvjgA86cOcMjjzzChAkT8PDw4O233yYnJ6c2MlYrNjYWnU6Ht7c3jRo1Ijw8nJiYGLVj6U1zW3N+HhtEfFYhj/10iMpKw/oDRQhx59ILipm45DAd52znfE4RC0e1Zc+UboR6yNbQQgihtmrnIAMcPXqURYsWsXbtWoYPH86YMWPYsWMHffr04fDhwzUcsXopKSm4u7tX/dvNzY29e/f+7XGRkZFERkYCkJ6eXrW2c23JzMy8689taQ5v93Tjrd+TeGX5AV7o4qLHZPXfvdRe6I9cBygur+SbA5f4fG8apRUKTwU5MTXUGUtTY9LT02olg1wHwyHXwnDItTAMhnIdbusmPRsbGyZOnMjMmTMxNTUFoFOnTuzcubPGA+pTREQEERERAAQHB+PiUvtN5r285hvOzpy7rPDJ7mS6t3JjSGu5ae9OqHG9xd811OugKAq/HkvnpVWnScgpYkiAEx+HBaBzsFAlT0O9DoZIroXhkGthGAzhOlTbIP/88894e3v/48eWL1+u90B3w9XVlaSkpKp/Jycn4+rqqmKimqHRaPhqRCAnMi7z2E+HiJ3ajVZOlmrHEkJU43BKPtNiTrD1XDatm1my6clQ+rZsqnYsIYQQN1HtHOSbNceGJCQkhPj4eBISEigtLSU6OpqwsDC1Y9UIMxNjlo8LobGJEUMW7SP/qty0J4ShSswpYvziQ3T4bBvH0gr4cngbDr3QQ5pjIYQwcLc1B9nQabVa5s6dy4ABA6ioqGDChAkEBASoHavGuNs2Ztm4YPrM382jPx0i5vEQjIxkAwEhDMWlyyV8sDme+bsSMdZoeLFnC17vq5OtoYUQoo6oFw0ywODBgxk8eLDaMWpNd297Zg8J4Nlfj/N/G0/z7sBWakcSosHLLixl9rbzfLbtPMXllUzo6M5b/VrK1tBCCFHH3FaDnJ6eTrNmzW76b6GOp7t6cjAln/d+i6e9qzVD2zirHUmIBik1v5hP/zjHV7svUFhawci2Lrw3yJeWTZuoHU0IIcRdqHYOMsDEiRNv+W+hDo1Gw7xhbejY3Iaxiw8Rl35Z7UhCNChnswp58ucjeH2wmdnbExjaphnHXurJkrFB0hwLIUQdVu0I8qpVq1i1atUNx9asWVNjgcSdMTMxZvn4YII+207Ywlj2TOmGQxNTtWMJUW9VVCqsO3WJL3cmsv70JUyMjJjQ0Z2Xe7fA216dJduEEELo121tNe3j48Mrr7zCqVOnaiOTuEOu1o1Z8XgIKfnFPLRoH8VlFWpHEqLeybhcwszN8bT4cDMPLojlcGo+b/VrSeIbfZk/IlCaYyGEqEeqHUH+4YcfyM/PJzo6mvHjx6PRaHj88ccZPXo0lpayBq+hCPWw5btH2jPyuwM8Hn2YH8d0kJUthLhHl4vL+fV4Gj8dTGFTfBYVlQp9dA58EuZPWEAzTIxva5aaEEKIOua2btKztrZmxIgRXL16ldmzZ/Prr7/y73//mylTpvDcc8/VdEZxmx5u68LM+4t4bc1JWjhY8P4gWdlCiDuVf7WMjWcy+eVoGitPpHO1rBIvO3Ne66Pj0Q6usjmPEEI0ANU2yDExMXz77becPXuWsWPHEhsbi6OjI0VFRfj7+0uDbGBe6d2Cs1mFfLApHhcrM57u6ql2JCEMmqIonMy4wpqTGaw5eYmdCTmUVyo4WDRiQsfmPNLelc6etmg08o6MEEI0FNU2yMuXL2fatGn06NHjhuPm5uYsWLCgxoKJu6PRaJg/vA2XrpTw7K/HsDM3Ibx9/dt2W4h7cbWsgt/PZrEm7hJrT2WQmHMVgLYuVrzSuwWD/ZwI9bDFWKYpCSFEg1Rtg9ysWbO/Ncevvvoqs2bNom/fvjUWTNw9rbER0Y8FMTByD2MXH8K2sQkDWjmqHUsIVV3IKWLNyUusOZnBlvgsissrsWhkzH0+Dvyrjw+D/RxlQw8hhBDAbTTIv/32G7Nmzbrh2Lp16/52TBiWxibGrJzQkZ5f7mJY1H7WTepEjxb2ascSotZUVirsT85jxfF0Vp7I4MR/1wlvYW9ORGcP7vdzpGcLe0y1xionFUIIYWhu2iDPnz+fL7/8knPnzhEYGFh1/PLly3Tt2rVWwol7Y93YhA0RofT6cheDvtnLmokd6aVzUDuWEDWmvKKS389ms+J4OjEn0knJL8bYSENPb3smhrlzv78TPg4WMp9YCCHELd20QX7kkUcYNGgQ//rXv5g5c2bVcUtLS+zs7GolnLh3Tpam/D65M32/2s3gb/ayemIn+vhIkyzql2NpBXy3P5kfDiSTfrkE80bGDPRtykOtm3G/vxN25o3UjiiEEKIOuWmDrNFo8PT0ZN68eX/7WE5OjjTJdUgzKzN+n9yFvl/t5v5v9rJ8fDCD/JzUjiXEPSmrqGT50TTmbE9g94VctEYa7vdz5LFgNwa1csS80W2tYimEEEL8zS1HkFevXk1QUBAajQZFUao+ptFoOH/+fK0EFPrhaGnKlsmd6f+fPTy4cB//GRHIxE7N1Y4lxB0rKi3ny50XmL39PCn5xegcLPhsSABjOrjSVLZZF0IIoQc3bZBXr14NQEJCQq2FETWraRNTtj3TlYe/28+kpUe4kFvEOwN8ZT6mqBOKyyr4z+4LzNhylozLJfT1ceCrEYEMbuUou0YKIYTQq5s2yAcPHrzlJ3bo0EHvYUTNszTTsmpiR55adpT3fosnPrOQr0e2pYmpvB0tDJOiKMQcT+f5mBNcyL1Kb509y8YG0c1bVmURQghRM27aFb344os3/SSNRsOWLVtqJJCoeSbGRnwzsi06BwveWHeKw6kFLBsXTEAz2UJXGJbz2YVM+fU4a05eonUzSzY9GUrflk3VjiWEEKKeu2mD/Pvvv9dmDlHLNBoN/+rrQ6fmtjzy40E6ztnOvKFtGBfiJlMuhOoUReGr3Rd4ceUJjI00fBLmz3PdvDAxNlI7mhBCiAbgtt5XP378OHFxcRQXF1cdGzt2bI2FErWnj48Dh17owegfDvL4ksNEH07hqxGBeNqZqx1NNFCZV0qYuOQIq+IyGODblG9GtpUd7oQQQtSqahvkd955h61btxIXF8fgwYNZt24d3bp1kwa5HnG2MmPzU535cmcir687ScC/t/LeQF+e7epFI62M2Inas/18NiO/O0BOURmfDQlgSjcvuQFPCCFErau2+1m2bBmbN2+mWbNmLFq0iCNHjpCfn18b2UQtMjbS8Fx3L0683IveLex5cWUcvrO28N3+JCoqleqfQIh7tGDvRfp+tRsrMy2xz3fj+R7e0hwLIYRQRbUNcuPGjTEyMkKr1VJQUICjoyNJSUm1kU2ooLmtOasmdmTdE52wbWzCuMWHCfx4Kwv2XqSotFzteKIeKq+o5IWYE0xaeoTeLRzYM6UbbV2s1Y4lhBCiAat2ikVwcDB5eXk88cQTBAUF0aRJEzp37lwb2YRKNBoNA1s50r9lU5YfS+OdjWeYtPQIL6+K4/GO7owPcad1M0u5mU/cs5LyCsK/P8iK4+lM6e7FJw/6o5Ub8YQQQqis2gb5yy+/BOCpp55i4MCBFBQUEBgYWOPBhPqMjDSMaOvC8EBntp3PZt7ORD7fnsCnf5zHx8GC4YHOPOjvRLC7jcxVFnesqLScoYv2s/FMJrOHBDC1h7fakYQQQgjgNlexSElJ4cKFC5SXX3uLfdu2bfTo0aNGgwnDodFo6NnCgZ4tHMi4XMKK42ksO5LGv7eeY+aWs5g3Mqabpx3dvO3o4GpNO1crXKzMZIRZ3FRBcRkPLIhlZ0IOC0a2ZYJsey6EEMKAVNsgv/rqqyxZsgR/f3+MjY2Baw2TNMgNk5OlKU929uTJzp5kF5ay9VwWW89m88f5bN5af7rqcU2bNKKdixXtXKxp42xJoIsVrRybYKo1VjG9MARXSsoZELmX/Ul5LH40iJHtXNSOJIQQQtyg2gZ5xYoVnD59GlNT09rII+oQe4tGDA90YXjgtQanoLiMo6kFHEop4FBKPodS8pmzPYHSikrg2koZvk0tCHS2oo2zFW2cLQlys8HF2kzN0xC1qKS8kocW7WNfUh4/jw1iaBtntSMJIYQQf1Ntg+zt7U1ZWZk0yKJaVmYmdPO2p5u3fdWxsopK4jMLOZZWwNG0Ao6lXWbPxVyiD6dWPaaFvTk9vO3prbNnsJ8T9haN1Igvalh5RSXPrElg89k8oka3k+ZYCCGEwaq2QTY3N6ddu3b07dv3hib5888/v+sX/fnnn/m///s/Tp48SWxsLMHBwVUfmzFjBgsWLMDY2JjPP/+cAQMGALB+/XqmTp1KRUUFkyZN4rXXXgMgISGB8PBwsrOzCQoK4vvvv6dRI2mwDIWJsRH+zSzxb2bJqPauVccLiss4nnaZvRdz2XY+h5gT6Szal4SxkYYe3nYMbe3MIx1cpVmuJxRFIeLno6w7m8echwIYG+yudiQhhBDipqptkMPCwggLC9Pri7Zu3Zrly5fz5JNP3nA8Li6O6OhoTpw4QWpqKvfddx9nzpwB4JlnnuG3337Dzc2NkJAQwsLC8Pf359VXX2XatGmEh4fz1FNPsWDBAiZPnqzXvEL/rMxM6OJlRxcvO6b1bEFlpcLBlHx+PZZ2bcmvFcd5eXUcI9u6MLmLB6EetnLTXx324eZ4Fu1L4oXOzkzpLqtVCCGEMGzVNsjjxo2jtLS0qlH19fXFxMTknl7Uz8/vH4/HxMQQHh6OqakpXl5e6HQ6YmNjAdDpdHh7X/vFGh4eTkxMDH5+fmzZsoWffvqpKuv//d//SYNcBxkZaQh2tyHY3YYPBvtxLK2Ar3Zd4PsDyXx/IJnu3nZ8MKgV3a+bviHqhp+PpPLGutM8GuTKC52d1I4jhBBCVKvaBnnr1q2MGzcOT09PFEUhKSmJqKioGlnFIiUlhdDQ0Kp/u7m5kZKSAoC7u/sNx/fu3Ut2djY2NjZotdq/Pf6fREZGEhkZCUB6ejqpqak3fWxNyMzMrNXXq8vsgemd7ZkaZMPPJ7L5Ym86PebtopenFf/q7kprR/M7ej6pvToOpxcydslpQlwseLebI1lZWfJOgAGQ7wfDIdfCcMi1MAyGch2qbZBffPFFNm7ciK+vLwBnzpxh9OjRHDhw4Jafd99995Genv634x988AFDhgy5y7j3JiIigoiICODaDoEuLrW/vJQar1nXTfd0Z1q/cr7ceYGZW+IZ/OMpXurZgrcHtKSxye0vGye1r10p+VeZuPI4zlaNWfNkV5o2MSVVayTXwUDIdTAcci0Mh1wLw2AI16HaBrmsrKyqOQZo2bIlZWVl1T7xpk2b7jiMq6srSUlJVf9OTk7G1fXajV3/dNze3p68vDzKy8vRarU3PF7UL+aNtLzUuwUTO7nz8qqTzPr9LL8cS+PrhwPppXNQO574H2UVlYz87gCXS8rZ9FRnmjaRVXCEEELUHdXuDxwcHMykSZPYunUrW7duZdKkSTesOqFPYWFhREdHU1JSQkJCAvHx8XTs2JGQkBDi4+NJSEigtLSU6OhowsLC0Gg09O7dm2XLlgEQFRWl2ui0qB225o34ZlRbNj/VGUVR6PPVbt7/7QyVlYra0cR1Xlkdx67EXL4Z2ZaAZpZqxxFCCCHuSLUN8vz58/H39+fzzz/n888/JyAggPnz59/Ti/7666+4ubmxe/du7r///qql3AICAhg5ciT+/v4MHDiQefPmYWxsjFarZe7cuQwYMAA/Pz9GjhxJQEAAALNmzeLTTz9Fp9ORnZ3NxIkT7ymbqBv6+Dhw5MWePNLelTfXn+bBhbHkFJWqHUsASw+nMntbAlO6exHeXt7REUIIUfdoFEW57aG3nJwckpOTCQwMrMlMtSI4OJj9+/fX6mumpqYaxLya+kRRFL7afYHnV5zAxdqUtZM64ef09xFLqX3tOJVxmZA522nTzIqtT3ehkfbGv8HlOhgGuQ6GQ66F4ZBrYRhq8zrcqhesdgS5V69eFBQUkJOTQ1BQEE888QTTpk3Te0gh7oZGo2FyF0+2P9uFq2WVdP1iJ7sSctSO1SCVlFcw+oeDmBobsXRs0N+aYyGEEKKuqPY3WH5+PlZWVixfvpyxY8eyd+9eNm/eXBvZhLhtHZvbsuu5rjhYNKLvV7tZefzvK6iImvXmutMcTi1gwah2uNk0VjuOEEIIcdeqbZDLy8tJS0tj6dKlPPDAA7WRSYi74m1vwc7nutLG2Yqh3+7jp4PJakdqMDafyeTfW8/xZGcPhrRupnYcIYQQ4p5U2yC/9dZbDBgwAJ1OR0hICOfPn8fHx6c2sglxx5o2MWXL5M708LbnsZ8O8fOR2t0MpiHKLixlXPRhfJta8MmD/mrHEUIIIe5ZtesgP/zwwzz88MNV//b29uaXX36p0VBC3IsmplpWTezIoK/3MvqHg2iNNHSSHaprhKIoPLnsKJeulLBySjcsTKv9kSKEEEIYvGp/mxUXF7NgwQJOnDhBcXFx1fGFCxfWaDAh7kUTUy1rJ3Wif+QeRn1/gAVhLXhM7k7Wu4WxSfxyNI2PHvCjg5uN2nGEEEIIvah2isVjjz1Geno6GzZsoGfPniQnJ2NpKQv/C8NnaaZl/ROdaONsxZOrzrPvYp7akeqVc1mFTFlxnD46B17s2ULtOEIIIYTeVNsgnz17lvfeew8LCwvGjRvHmjVr2Lt3b21kE+KeWTc2Yc3EjjiYa3lgwV7OZxeqHaleqKxUmLj0CFojDVGj22FkpFE7khBCCKE31TbIJiYmANjY2HD8+HHy8/O5dOlSjQcTQl+aWZnxw3AfyisVBkbuJetKidqR6ryvdl/gj3PZfBoWIEu6CSGEqHeqbZAjIiLIzc3lvffeIywsDH9/f1555ZXayCaE3ujszFg5oSMX867y0KJ9lJZXqh2pzkrMKeKV1XH0b9mUCR3d1Y4jhBBC6F21N+lNmjQJgJ49e3L+/PkaDyRETenqZUdUeDvCfzjIs78e4z8jAtFoZGrAnVAUhSeWHkGjgciHpX5CCCHqp2pHkDMyMpg4cSKDBg0CIC4ujgULFtR4MCFqwqj2rvyrr46v91zkq90X1I5T5yzYe5FN8Vl89IA/HnbmascRQgghakS1DfL48eMZMGAAqanXNlxo2bIls2fPrulcQtSY9we24gF/J6b8epw/zmWpHafOSM67your4ujVwp4nQz3UjiOEEELUmGob5KysLEaOHImR0bWHarVajI2NazyYEDXFyEjDD4+0R+dgwYioAyTnXVU7ksH7c0OQ8kqFBaPayqoVQggh6rVqG2QLCwuys7Or5hru2bMHa2vrGg8mRE2ybmzCr+ODKS6vIPz7A5RVyE17t/L9gWTWnrzEjMGt8La3UDuOEEIIUaOqbZA//fRTwsLCOHfuHF27dmXs2LF88cUXtZFNiBrVysmSrx9uy87EXF5fe0rtOAYrraCYqStO0NXTlme7eqkdRwghhKhx1a5i0aFDB/744w9Onz6Noij4+vpWrY0sRF0X3t6V7edz+HjrObp52TGkdTO1IxkURVGYvOwoxWUVLAyXDUGEEEI0DDcdQd63bx/p6enAtXnHBw4cYPr06bz44ovk5OTUWkAhatqnQ/wJcrNm3OJDstPe/1hyOJWYExm8N7AVLZs2UTuOEEIIUStu2iA/+eSTNGrUCIBt27bx2muvMXbsWKytrYmIiKi1gELUNFOtMT+PDUaj0fDwdwcoLqtQO5JBuHS5hGeXH6Njcxum9fRWO44QQghRa27aIFdUVGBnZwfAkiVLiIiIYPjw4bz33nucPXu21gIKURu87M2JCm/HweR8Xlh5Qu04BuG5X49zuaSCRaPaYSxTK4QQQjQgt2yQy8vLAdi8eTN9+vSp+tifx4WoT8JaN+OlXi2Yv+sCSw+nqh1HVcuPprH0SCpv92+JfzNLteMIIYQQteqmN+mNHj2anj174uDgQOPGjenevTsAZ8+elWXeRL314eBW7EjIYdLSIwS5WdPCoeEtaZZdWMrkX47Swc2al3u3UDuOEEIIUetuOoI8ffp0PvnkE8aPH8+OHTuq1kGurKyUZd5EvWVibET0ox3QGmkY+f0BSsob3nzkqSuOk1NUxsJRbTExrnYlSCGEEKLeueUyb6GhoX871rJlyxoLI4Qh8LAzZ1F4Ox5atI+XV53k86Gt1Y5Ua1adSOfHgym83b8lbV3knSIhhBANkwwPCfEPhrRuxvM9vPhiRwLLj6apHadWZBeWEvHzUQKdrXi9r4/acYQQQgjVSIMsxE3Mut+fEHcbJiw5TEJ2kdpxatyzy4+RXVTKd4+0o5FWfjQIIYRouOS3oBA30UhrxJLHggAY9f0BSssrVU5Uc5YdSSX6cCpv9ZOpFUIIIYQ0yELcgpe9OQtHtWNfUh6vrTmpdpwakXG5hMm/HCPY3ZrX+ujUjiOEEEKoTpUG+eWXX6ZVq1YEBgYydOhQ8vLyqj42Y8YMdDodvr6+bNiwoer4+vXr8fX1RafTMXPmzKrjCQkJdOrUCZ1Ox6hRoygtLa3NUxENwLBAZ57t6sln286z8ni62nH0SlEUnlp2lMsl5USFt0crq1YIIYQQ6jTI/fr14/jx4xw9epSWLVsyY8YMAOLi4oiOjubEiROsX7+ep59+moqKCioqKnjmmWdYt24dcXFxLF68mLi4OABeffVVpk2bxtmzZ7G1tWXBggVqnJKo5z4O86eDmzXjow9zIaf+zEf+8WAKK46n8/7AVrIhiBBCCPFfqjTI/fv3R6u9tsJcaGgoycnJAMTExBAeHo6pqSleXl7odDpiY2OJjY1Fp9Ph7e1No0aNCA8PJyYmBkVR2LJlCyNGjABg3LhxrFixQo1TEvWcqdaYJY8FUV6pEP7DQcoq6v585JT8qzz363G6etoyrae32nGEEEIIg3HLdZBrw8KFCxk1ahQAKSkpN6y97ObmRkpKCgDu7u43HN+7dy/Z2dnY2NhUNdvXP/6fREZGEhkZCUB6ejqpqbW7nXBmZmatvp74iz5qbw581M+dyasTmLp0P2/0dLv3YCqpqFQYtewMpeUVzOzjQkZ67SxlJ98DhkGug+GQa2E45FoYBkO5DjXWIN93332kp/99vuYHH3zAkCFDqv5bq9UyZsyYmopxg4iICCIiIgAIDg7GxcWlVl73emq8prhGH7V/ysWFI9mVzN99gfvbenC/v5MektW+DzadYXfSFb4Nb0e3APfqP0GP5HvAMMh1MBxyLQyHXAvDYAjXocYa5E2bNt3y499++y2rV69m8+bNVdtYu7q6kpSUVPWY5ORkXF1dAf7xuL29PXl5eZSXl6PVam94vBA15bMhAey+kMu4xYc4/GJP3Gwaqx3pjuxOzOHtDWcY3d6VscF1dxRcCCGEqCmqzEFev349H330EStXrsTc3LzqeFhYGNHR0ZSUlJCQkEB8fDwdO3YkJCSE+Ph4EhISKC0tJTo6mrCwMDQaDb1792bZsmUAREVFVY1OC1FTzEyMWTo2iJKKSsLr2PrIeVfLGP3DQZrbNGb+8DZVf5wKIYQQ4i+qNMjPPvssly9fpl+/frRr146nnnoKgICAAEaOHIm/vz8DBw5k3rx5GBsbo9VqmTt3LgMGDMDPz4+RI0cSEBAAwKxZs/j000/R6XRkZ2czceJENU5JNDAtmzbh64fbsjMxl6krjqsd57ZUVio8Hn2Y5Pxifnq0A9aNTdSOJIQQQhgkVW7SO3v27E0/Nn36dKZPn/6344MHD2bw4MF/O+7t7U1sbKxe8wlxO8Lbu3I4pYBZv5+ljbMVT3f1VDvSLc3YEs+K4+l8NiSAUA9bteMIIYQQBkt2BRDiHnwwuBX3+zkyZcVxtsRnqR3nptafusSb60/zSHtXpnb3UjuOEEIIYdCkQRbiHhgbafjp0Q60bGrBw9/t50zmFbUj/c357EIe+eEggc5WfD0yUOYdCyGEENWQBlmIe2RlZsLKCR0x0mjo/589pOYXqx2pSk5RKQ8uuDYFafn4YMwbqb70uRBCCGHwpEEWQg90DhasndSJrMJSBn69h7yrZWpH4mpZBUMW7uNsVhG/jA/G295C7UhCCCFEnSANshB6EtLchl//v737jorq6v4G/h16B5EiTYr0XkVUEEHFWLDFXmOLsZdoNMaoiSY+sbdoSFTsvcXeEVEUQcGCFCkW6gDSQcqc9w9/mVdiiWWGOzPsz1qu5bR79uw7c2dz7rnnjPJBUn45QrfGoKq2nrNY6gUMQ3ffwfXMIuwc4oGO1nqcxUIIIYRIGyqQCRGhznb62DHYA9fSi9BrawwqXtY1egyMMUw5eh9H7+didagTBrhzvyIRIYQQIk2oQCZExAZ5mGDbQHdcSi1A1z9vobS68YZb1AsYxh+8h003nuC7jtaYFmDVaG0TQgghsoIKZELEYFRrM+wZ6ombT14geHM0iiprxN5mbb0AQ3ffwV+3nuKHTjb4tbu92NskhBBCZBEVyISIyUAPExwZ5Y172WVouy4KqWKcAq7iZR36hsdif3w2/tfdAT9/YU/TuRFCCCGfiApkQsSop1MLnP/61ewWvmujcOJhrsjbSC+sgN/6KJx+lIff+7lgTpC1yNsghBBCmhIqkAkRsw6t9BAz3R/mzVQRuvU2ph59gHIRXLzHGMOuuOfwWBWJZ8XVOD3WF9+0tfj8gAkhhJAmjgpkQhqBVXN1RE9tj6n+llgflQHn5RE4fC8bjLFP2l4Kvxzd/4rB8D134WqkhTszAhBibyDiqAkhhJCmiZbVIqSRqCjKY21vZ/R3NcLXh+7hy+1x8DbTxqwOrdDXxQhKCv/992p8VgnWRKZj150sqCvJY3UvJ0xpbwl5ORpvTAghhIgKFciENLL2Vs2RMKsDdsY9x9KLqRi86w6aqSqih6Mh2lvqwqmFJlpoKkNejofS6jo8LqjAzScvcDY5H/dzyqCqKIep7S0xp2MrtNBS4frtEEIIITKHCmRCOKAgL4evWrfESG8znEvOx/74bJxMzMPOuOdvfb6iPA9+5s2wsa8LBnkYQ1dNqZEjJoQQQpoOKpAJ4ZCcHA9fOBjiCwdDMMaQUVSJ5Pxy5JfXgDFAXVkerZqrwd5AA2pK9HUlhBBCGgP94hIiIXg8Hqyaq8OquTrXoRBCCCFNGs1iQQghhBBCyGuoQCaEEEIIIeQ1VCATQgghhBDyGiqQCSGEEEIIeQ0VyIQQQgghhLyGxz51rVspp6enBwsLi0Ztk8/nQ19fv1HbJK9Q7iUD7QfJQPtBctC+kBy0LyRDY+6HzMxMFBQUvPWxJlsgc8Hb2xuxsbFch9EkUe4lA+0HyUD7QXLQvpActC8kg6TsBxpiQQghhBBCyGuoQCaEEEIIIeQ1VCA3ovHjx3MdQpNFuZcMtB8kA+0HyUH7QnLQvpAMkrIfaAwyIYQQQgghr6EeZEIIIYQQQl5DBTIhhBBCCCGvoQL5LTQ0NLgO4b1Gjx4NAwMDODs7cx2KWPF4PAwbNkx4u66uDvr6+ujRo4dItv8h+zkpKQl+fn5QVlbGihUrRNKuLBDnviksLETHjh2hoaGByZMnf/b2moL/+iwHBgZ+1LRJ8+fPh5mZmcQfC0Vl6dKlcHJygqurK9zd3XHr1q1P2k5ERARu3LghsrgsLCzeOUfrp+jatSt0dHREdgwVFx6Ph1mzZglvr1ixAosWLeIkFlF+B2Tp2CbpxwZR1ElUIEuIurq6D37uqFGjcPbsWTFGIxnU1dXx4MEDVFVVAQAuXLgAExOTj9rGx+T1bXR1dbFu3Tp8++23n7UdWSOKffMuKioq+Pnnn+kPEg717NkTMTExXIfRKKKjo3Hy5EncuXMH9+7dw8WLF2FmZvZJ2xJ1gfw53nbsmz17Nnbu3MlBNB9HWVkZR44cEekfB1z49z6gY9vnaew6iQrkdygvL0dwcDA8PT3h4uKC48ePA3i16oqDgwPGjRsHJycndOnSRVgkvN5LU1BQIFypLzMzE/7+/vD09ISnp6fwABoREQF/f3+EhobC0dERP/74I9asWSOMYf78+Vi7du0bsQUEBEBXV1eM715ydOvWDadOnQIA7N27F4MHDxY+FhMTAz8/P3h4eKBt27ZITk4GAISHhyM0NBRBQUEIDg5GeXk5vvrqK7i4uMDV1RWHDx8WbmP+/Plwc3NDmzZtkJeX90b7BgYG8PHxgaKiopjfqfT5lH0TEBCA+Ph44fPat2+PhISEBttVV1dH+/btoaKiIv43IUMiIiIa9AxOnjwZ4eHhDZ6zdetWTJ8+XXj7zz//xIwZM97YVps2bWBkZCSuUCVKTk4O9PT0oKysDODVKqvGxsYAgLi4OHTo0AFeXl4ICQlBTk4OgFfH+mnTpsHd3R3Ozs6IiYlBZmYmNm/ejNWrV8Pd3R3Xrl0Dn89Hv3794OPjAx8fH1y/fh0AsGjRIowcORL+/v4wNzfHkSNHMGfOHLi4uKBr166ora0Vxvfbb7/BxcUFrVu3xuPHjwHgvdsdPnw42rVrh+HDh7/xXoODg6GpqSm+ZIqIgoICxo8fj9WrV7/xWGZmJoKCguDq6org4GA8ffoUJSUlMDc3h0AgAABUVFTAzMwMtbW1SEtLQ9euXeHl5QV/f38kJSUBeFVAffPNN2jTpg2srKwQERGB0aNHw8HBAaNGjWrQ5owZM+Dk5ITg4GDw+XwAeO92J0yYAF9fX8yZM6fBdmTt2CbzdRIjb1BXV2e1tbWspKSEMcYYn89nrVq1YgKBgGVkZDB5eXl29+5dxhhj/fv3Zzt37mSMMdahQwd2+/Zt4WvMzc0ZY4xVVFSwqqoqxhhjKSkpzMvLizHG2JUrV5iamhpLT09njDGWkZHBPDw8GGOM1dfXMysrK1ZQUPDWGDMyMpiTk5Po37wEUVdXZwkJCaxfv36sqqqKubm5sStXrrDu3bszxhgrKSlhtbW1jDHGLly4wPr27csYY2zbtm3MxMSEFRYWMsYYmzNnDps2bZpwu0VFRYwxxgCwv//+mzHG2OzZs9nPP//8zlgWLlzIli9fLvL3KK0+dd+Eh4cL90VycrLwu/A227ZtY5MmTRLvG5ER6urqDfLPGGOTJk1i27ZtY4z9/2NTWVkZs7KyYjU1NYwxxvz8/Ni9e/feu11ZV1ZWxtzc3JiNjQ375ptvWEREBGOMsZqaGubn58fy8/MZY4zt27ePffXVV4yxV/kcO3YsY4yxq1evCo/F/z5ODB48mF27do0xxtiTJ0+Yvb298Hnt2rVjNTU1LD4+nqmqqrLTp08zxhjr3bs3O3r0KGOMMXNzc7ZkyRLGGGPbt28X7t/3bdfT05NVVla+8/3++3MiidTV1VlJSQkzNzdnxcXFbPny5WzhwoWMMcZ69OjBwsPDGWOMbdmyhfXq1YsxxlhoaCi7fPkyY+zVvhozZgxjjLGgoCCWkpLCGGPs5s2brGPHjowxxkaOHMkGDhzIBAIBO3bsGNPU1GT37t1j9fX1zNPTU/gbD4Dt2rWLMcbY4sWLhcek9223e/furK6u7p3vTxaObU2hTlL4vPJadjHG8P333yMyMhJycnLIysoS9jBaWlrC3d0dAODl5YXMzMz3bqu2thaTJ09GfHw85OXlkZKSInysdevWsLS0BPBqvFnz5s1x9+5d5OXlwcPDA82bNxfL+5MWrq6uyMzMxN69e9GtW7cGj5WUlGDkyJFITU0Fj8dr0OvSuXNn4V+PFy9exL59+4SPNWvWDACgpKQk7HHz8vLChQsXxP12ZMqn7Jv+/fvj559/xvLly7F169Y3emqIeGloaCAoKAgnT56Eg4MDamtr4eLiwnVYnNLQ0EBcXByuXbuGK1euYODAgVi2bBm8vb3x4MEDdO7cGQBQX1/foFf9nzMmAQEBKC0tRXFx8RvbvnjxIhITE4W3S0tLUV5eDgD44osvoKioCBcXF9TX16Nr164AABcXlwa/Kf+0M3jwYGFv//u2GxoaClVV1c9NC+e0tLQwYsQIrFu3rsH7iY6OxpEjRwAAw4cPF/bSDhw4EPv370fHjh2xb98+TJw4EeXl5bhx4wb69+8vfP3Lly+F/+/Zsyd4PB5cXFxgaGgo/C44OTkhMzMT7u7ukJOTw8CBAwEAw4YNQ9++ff9zu/3794e8vLwYsiJZZL1OogL5HXbv3g0+n4+4uDgoKirCwsIC1dXVACA8FQcA8vLywlMHCgoKwlM8/zwXAFavXg1DQ0MkJCRAIBA0OL2irq7eoN2xY8ciPDwcubm5GD16tNjenzQJDQ3Ft99+i4iICBQWFgrvX7BgATp27IijR48iMzMTgYGBwsf+nde3UVRUBI/HA/BqP37ueOWm6GP3jZqaGjp37ozjx4/jwIEDiIuL4yhy2fP68QdoeAx63dixY/HLL7/A3t4eX331VWOFJ9Hk5eURGBiIwMBAuLi4YPv27fDy8oKTkxOio6Pf+pp/jh3vug0AAoEAN2/efOsp9X9+R+Tk5Boci+Tk5Boci17f7j//f992P+TYJy2mT58OT0/PD/qchoaG4vvvv0dRURHi4uIQFBSEiooK6OjoNBjW9brX98Hrv+v/3gev4/F4EAgE792uLO2D95H1OonGIL9DSUkJDAwMoKioiCtXruDJkyf/+RoLCwvhD/6hQ4cabMvIyAhycnLYuXMn6uvr37mNPn364OzZs7h9+zZCQkI+/43IgNGjR2PhwoVv9HSVlJQILwz791jL13Xu3BkbN24U3n7x4oVY4myKPmXfjB07FlOnToWPj4+wN598PnNzcyQmJuLly5coLi7GpUuX3vo8X19fPHv2DHv27GkwbrypSk5ORmpqqvB2fHw8zM3NYWdnBz6fLyyQa2tr8fDhQ+Hz9u/fDwCIioqCtrY2tLW1oampibKyMuFzunTpgvXr1zfY9sf6p539+/fDz89PZNuVBrq6uhgwYAC2bNkivK9t27bCM4K7d++Gv78/gFdnAnx8fDBt2jT06NED8vLy0NLSgqWlJQ4ePAjgVY/nv695+C8CgUD4e75nzx60b99eJNuVBbJeJ1GB/C91dXVQVlbG0KFDERsbCxcXF+zYsQP29vb/+dpvv/0WmzZtgoeHR4OrbydOnIjt27fDzc0NSUlJ7/3rUklJCR07dsSAAQPeeYpm8ODB8PPzQ3JyMkxNTRscPGSRqakppk6d+sb9c+bMwbx58+Dh4fHe3t8ffvgBL168gLOzM9zc3HDlypUPbjs3NxempqZYtWoVlixZAlNTU5SWln7S+5BFn7JvvLy8oKWl9d5eIQsLC8ycORPh4eEwNTVtcDqZNPTPMcvMzAwDBgyAs7MzBgwYAA8Pj3e+ZsCAAWjXrt07/0CZM2cOTE1NUVlZCVNTU86m2GoM5eXlGDlyJBwdHeHq6orExEQsWrQISkpKOHToEL777ju4ubnB3d29wQwVKioq8PDwwIQJE4TH4J49e+Lo0aPCi/TWrVuH2NhYuLq6wtHREZs3b/7o+F68eAFXV1esXbtWeNHap27X398f/fv3x6VLl2Bqaopz5859dDyNbdasWQ1+T9evX49t27bB1dUVO3fubHCB1sCBA7Fr1y7hkAjgVRG9ZcsWuLm5wcnJSXgh2YdSV1dHTEwMnJ2dcfnyZfz444+ftV1ZOLY1lTqJlpr+l4SEBIwbN46zKY4EAgE8PT1x8OBB2NjYcBIDIeKUnZ2NwMBAJCUlQU6O/kb/XJ9yzOrRowdmzJiB4OBgMUYmuwIDA7FixQp4e3tzHQohja6p1En06/SazZs3Y/DgwViyZAkn7ScmJsLa2hrBwcFUHBOZtGPHDvj6+mLp0qVUHIvAxx6ziouLYWtrC1VVVSqOCSEfrSnVSdSDTAghhBBCyGuoC4cQQgghhJDXUIFMCCGEEELIa6hAJoQQQggh5DVUIBNCiISQl5eHu7s7nJyc4ObmhpUrVzZY/EMcZs+eDScnJ8yePVus7RBCiDShi/QIIURCaGhoCJcMzs/Px5AhQ9CuXTssXrxYbG1qa2ujqKioUZbGraurg4ICLeBKCJF81INMCCESyMDAAGFhYdiwYQMYY8jMzIS/vz88PT3h6ekpXLRixIgROHbsmPB1Q4cOfWPRAsYYZs+eDWdnZ7i4uAhXZwsNDUV5eTm8vLyE9wGv5hm1sbEBn88X3ra2tgafzwefz0e/fv3g4+MDHx8fXL9+HQAQExMDPz8/eHh4oG3btkhOTgbwaiXF0NBQBAUF0dRyhBDpwQghhEgEdXX1N+7T1tZmubm5rKKiglVVVTHGGEtJSWFeXl6MMcYiIiJYr169GGOMFRcXMwsLC1ZbW9tgG4cOHWKdOnVidXV1LDc3l5mZmbHs7Ox3tskYY4sWLWKrV69mjDF27tw51rdvX8YYY4MHD2bXrl1jjDH25MkTZm9vzxhjrKSkRNjuhQsXhM/ftm0bMzExYYWFhZ+UE0II4QKd6yKEEClQW1uLyZMnIz4+HvLy8khJSQEAdOjQARMnTgSfz8fhw4fRr1+/N4YxREVFYfDgwZCXl4ehoSE6dOiA27dvIzQ09J3tjR49Gr169cL06dOxdetW4dLgFy9ebLA8bmlpKcrLy1FSUoKRI0ciNTUVPB4PtbW1wud07twZurq6okwHIYSIFRXIhBAiodLT0yEvLw8DAwMsXrwYhoaGSEhIgEAggIqKivB5I0aMwK5du7Bv3z5s27ZNJG2bmZnB0NAQly9fRkxMDHbv3g3g1XCLmzdvNmgfACZPnoyOHTvi6NGjyMzMRGBgoPAxdXV1kcRECCGNhcYgE0KIBOLz+ZgwYQImT54MHo+HkpISGBkZQU5ODjt37kR9fb3wuaNGjcKaNWsAAI6Ojm9sy9/fH/v370d9fT34fD4iIyPRunXr/4xh7NixGDZsGPr37y+8iK9Lly5Yv3698Dnx8fEAgJKSEpiYmAB4Ne6YEEKkGRXIhBAiIaqqqoTTvHXq1AldunTBwoULAQATJ07E9u3b4ebmhqSkpAa9soaGhnBwcBAOg/i3Pn36wNXVFW5ubggKCsJvv/2GFi1a/Gc8/1zE9/p2161bh9jYWLi6usLR0RGbN28GAMyZMwfz5s2Dh4cH6urqPicNhBDCOZrmjRBCpFxlZSVcXFxw584daGtri2y7sbGxmDFjBq5duyaybRJCiDSgHmRCCJFiFy9ehIODA6ZMmSLS4njZsmXo168ffv31V5FtkxBCpAX1IBNCCCGEEPIa6kEmhBBCCCHkNVQgE0IIIYQQ8hoqkAkhhBBCCHkNFciEEEIIIYS8hgpkQgghhBBCXkMFMiGEEEIIIa+hApkQQgghhJDXUIFMCCGEEELIa6hAJoQQQggh5DUKXAfAFT09PVhYWDRqm7W1tVBUVGzUNmUZ5VO0KJ+iRfkULcqnaFE+RYvyKVqNlc/MzEwUFBS89bEmWyBbWFggNja2UdvMzs6GsbFxo7YpyyifokX5FC3Kp2hRPkWL8ilalE/Raqx8ent7v/MxqRpi8ezZM3Ts2BGOjo5wcnLC2rVrAQBFRUXo3LkzbGxs0LlzZ7x48YLjSAkhhBBCiLSSqgJZQUEBK1euRGJiIm7evImNGzciMTERy5YtQ3BwMFJTUxEcHIxly5ZxHSohhBBCCJFSUlUgGxkZwdPTEwCgqakJBwcHZGVl4fjx4xg5ciQAYOTIkTh27BiHURJCCCGEEGkmtWOQMzMzcffuXfj6+iIvLw9GRkYAgBYtWiAvL++trwkLC0NYWBgAIDc3F9nZ2Y0WLwDw+fxGbU/WUT5Fi/IpWpRP0aJ8ihblU7Qon6IlCfmUygK5vLwc/fr1w5o1a6ClpdXgMR6PBx6P99bXjR8/HuPHjwfwamA2FwPqaRC/aFE+RYvyKVqUT9GifIoW5VO0KJ+ixXU+pa5Arq2tRb9+/TB06FD07dsXAGBoaIicnBwYGRkhJycHBgYGHEdJJE12STWupRciMr0IT15UorpOgOraetQJGGz01eFqpAVXIy20bqmDZmpKXIdLCCGEEA5JVYHMGMOYMWPg4OCAmTNnCu8PDQ3F9u3bMXfuXGzfvh29evXiMEoiKcqq6/DnrSf4I/oJUvgVAAANZXnY6mtAVUEOKoryAIArjwuxKy4LAKAkL4c+Li0wurUZOtnoQ07u7WcjCCGEECK7pKpAvn79Onbu3AkXFxe4u7sDAH755RfMnTsXAwYMwJYtW2Bubo4DBw5wGyjhVG5pNdZEZmBzdCZKquvgb6WLr/3MEWDVHO7GWlCQf/Pa1MKKGiRkl+LYg1zsinuO/fHZMG+mikVd7DDC25QKZUIIIaQJkaoCuX379mCMvfWxS5cuNXI0RNIwxrAj9jmmHXuAspd16OdqhNmB1vBpqfOfr22uroQgGz0E2ejhtx4O+PthHlZdTcNX++OxKToT63o7w9e8mfjfBCGEEEI4J1UFMiHvkl1SjfEHE3DqUT7aW+rirwFusDPQ+KRtqSjKY4C7Mb50NcLuO8/x3alHaLMuCuPbtMSa3s5Q/b+hGYQQQgiRTVQgE6l3IZmPATvj8LKuHqt7OWFqe0uRDImQk+NhuLcZejsbYfH5ZKy8mo6Yp8U4NNIbrfTURRA5IYQQQiSRVC0UQsi/bYt5im5/3YKZjgoSZnXA9AArkY8X1lRRwIpQJ5wa2xpPXlTBa3Ukjj/IFWkbhBBCCJEcVCATqcQYw8ob2Ri9PwGBrZrj2qR2sNH/tCEVH6qbgyHuzAyAtZ46em+7jVVX08TaHiGEEEK4QQUykTqMMUw4dA+ronMwyscMp8f5QltVsVHattBVQ9TkdvjS1Qiz/k7E4nPJ77xwlBBCCCHSicYgE6nCGMPsE4kIu/kUk1u3wLoBbu9cOVFcVBTlsXeYJzQO3sOi8ykoe1mH5T0dGz0OQgghhIgHFchEqiy7/Bgrr6ZjcjsLzPXV5awoVZCXw5YBbtBQksfKq+moqhVgQ19nKpIJIYQQGUAFMpEam29k4vvTSRjqaYK1vZ2Rm5vDaTxycjys6/Nq2rflEWkw0FDCwhA7TmMihBBCyOejAplIhRMPczHxyH30cDTEtkHuErOyHY/Hw/96OIBfUYNF51NgpKWC8X7mXIdFCCGEkM9ABTKReKn8cgzbcxceJto4MMILim9ZKppLPB4PYf1dkV/+Et8cvgdDTWX0cm7BdViEEEII+USSVWkQ8i/lL+vQJzwWinI8HBnpLbGr2CnKy+HAcC94m+lg0M443HryguuQCCGEEPKJqEAmEosxhjH7E/Aorwz7hnvBXFeN65DeS11ZASfHtIaRlgr6bY9FXtlLrkMihBBCyCegAplIrNWR6TiQkI1fuzmgk60+1+F8EH0NZRwZ5Y2iyhoM2BGL2noB1yERQggh5CNRgUwkUnxWCeaeeoQ+Li0wu2MrrsP5KO4m2gjr74bI9CLMPpHIdTiEEEII+UhSVSCPHj0aBgYGcHZ2Ft63aNEimJiYwN3dHe7u7jh9+jSHERJRqK6tx/A9d6GnroQ/+zf+QiCiMMzLFFP9LbH2WgZ2xT3nOhxCCJF5dfUCpBVU4MyjPGy59RQHE7JxIZmP2GfFKKuu4zo8ImWkahaLUaNGYfLkyRgxYkSD+2fMmIFvv/2Wo6iIqP1wJgkPcstwemxrNFdX4jqcT7aipyPuZpVgwqF7aN1SB7b6GlyHRAghMiW3tBp772Zh791sJGSXouYdw9oU5XloZ6GLrvYG6O5gAGcjrUaOlEgbqSqQAwICkJmZyXUYRIyuphVgVWQ6JviZ4wsHQ67D+SyK8nLYM9QTriuuYsiuO7gxpT2UFKTqpA0hhEikq2kFWHb5Mc4n8yFggLeZNmYEWMHOQB12+how0VZB2cs6FFfVorCyFtGZL3A2OR9zTz3C3FOP0NVeHz92toWfhS7Xb4VIKKkqkN9lw4YN2LFjB7y9vbFy5Uo0a9bsrc8LCwtDWFgYACA3NxfZ2dmNGSb4fH6jtidtyl7WY9iuRJhrK2OWT7P/3D/SkE85AL91NsO4v9Mx41As5geYch3SO0lDPqUJ5VO0KJ+iJa35fFL8Eksin+N0ajFaaChiUusW+NKhOaybq7z2rGqguhq6AHRVACsVwEdXG1M9tZFbXoPDiUXYHJuHtuuvw7+lJr4PMIGrofpnxSWt+ZRUkpBPHmOMcR3Ex8jMzESPHj3w4MEDAEBeXh709PTA4/GwYMEC5OTkYOvWrf+5HW9vb8TGxoo73Aays7NhbGzcqG1Kk6lHH2DD9Qxcn9zug/6ql6Z8jj+YgL9uPcWF8W0QLKEzckhTPqUB5VO0KJ+iJW35rK0XYPH5FCy/kgYFeR7mBVljVmCrT54bv+JlHTZHP8FvVx6jqLIWi0JsMTfIBvKfuEqrtOVT0jVWPt9XC0r9+V5DQ0PIy8tDTk4O48aNQ0xMDNchkU8Q+6wYG69nYGJbC5k85bU61Am2euoYsTcehRU1XIdDCCFSI7ukGkGborH0YioGuBshZW5H/NDZ9rMWjlJXVsCswFZI+q4j+roY4Yczyeiw8ToyCitFGDmRZlJfIOfk5Aj/f/To0QYzXBDpUC9gmHDoHgw0lLH0C3uuwxELdWUF7B3mhfzyl5h69AHX4RBCiFS48rgAHquu4k5WCXYP9cDOIZ4w0VYV2fabqSlh33BP7Bzigfu5ZXBbeRWXUrg/vU+4J1UF8uDBg+Hn54fk5GSYmppiy5YtmDNnDlxcXODq6oorV65g9erVXIdJPtLG6xmIe16CNb2coK2qyHU4YuNhqo0fOtlgz90sHH+Qy3U4hBAi0f6IzkSnzdHQVVPC7Wn+GOIpnms4eDwehnmZImFWB5g3U8UXf93C/rtZYmmLSA+pukhv7969b9w3ZswYDiIhopJVUoUfziQjxE4fA9xlf/zWvGAbHH2QiwmH7sHfShe6atI7jR0hhIjLqqtpmPV3Iro7GGDvMC9oqoi/XLHQVUPkpLbote02Bu++g/zyGkzxtxR7u0QySVUPMpE9M48norZegN/7uUjlgiAfS0lBDuGD3FFQUYPpxx5yHQ4hhEicJRdSMOvvRPR3M8KRUT6NUhz/o5maEs6Nb4NQR0NMPfYAP19IabS2iWShAplw5lp6IQ4kZGNukDWsmn/eFDvSxN1EG98H22Bn3HOcTMzjOhxCCJEYP5xJwoKzyRjuZYo9Qz05mTteVVEeh0Z6Y7iXKX48m4xNNzIbPQbCPSqQCScEAoYZxx/CVFsFszu24jqcRje/kw1cjDQx4dA9WgKVEEIArI1Mx9KLqRjXpiXCB7lDQZ67EkVBXg5bB7qhh6MhJh25j4MJjbtuAuEeFciEE7vuPEfc8xL82t0BakpSNRReJJQU5PBnfzdkl1ZjwdkkrsMhhBBOHb6XjRl/P0QflxbY1M8Vcp84H7EoKcjLYf9wT7Q1b4Zhu+/S7BZNjFgKZC8vL2zcuBEvXrwQx+aJlKt4WYd5p5LQuqUOhniYcB0OZ3zNm+EbPwusj8pA3LNirsMhhBBO3MgowrDdd9GmZTPsHur5yYt1iIOakgJOjGkNW3119A6/jcTcMq5DIo1ELAXy/v37kZ2dDR8fHwwaNAjnzp2DlC3YR8TotytpyC6txupQJ4noJeDSL93sYaChjK8P3UO9gL4jhJCmJZVfjtCtMTDVUcXfo30+a/EPcWmmpoSz432hrqSAPuG3UVpdy3VIpBGIpUC2trbG0qVLkZKSgiFDhmD06NEwNzfHwoULUVRUJI4miZR4XlyF5RGPMdDdGG0tZW/FvI+lraqItb2dEfe8BBuvZ3AdDiGENJrKmjr02/5qmd8z43yhp6HMcUTvZqKtiv3DPZFWWIlR++Kp068JENsY5Hv37mHWrFmYPXs2+vXrh4MHD0JLSwtBQUHiapJIgcXnU1AvAJZ1d+A6FInR380IXe31Mf9MEp4XV3EdDiGEiB1jDN8cvo8HuWXYPdQT1nqSP5NRh1Z6WN7DAUfv5+K3K2lch0PETCxXR3l5eUFHRwdjxozBsmXLoKz86q9CX19fXL9+XRxNEimQwi/HttvPMKmdBSx01bgOR2LweDxs7OsCp98iMOfkI+wZ5sl1SIQQIlZbbj3FjtjnWNjFFiH2BlyH88GmB1jh1tNifH/6EbxNtRFsq891SERMxNKDfPDgQVy6dAlDhgwRFsf/OHLkiDiaJFJgwZlkqCjIYX6wDdehSByr5uqY09Eae+9mISq9kOtwCCFEbO4+L8Hkow/QxVYfCzrbch3OR+HxePhrgBvsDDQwfO9dFFbUcB0SEROR9iCvWrXqvY/PnDlTlM0RKXLneTEOJGRjQWcbGGhK7jgzLn0X1Arbbj/FlKMPEDsjQKKu5CaEEFEoq65D/x2x0FdXwq6hHlJ5nNNQVsDuIZ7wXXcNEw/fx77hdNZPFom0B7msrOy9/0jTNf9MEnTVFDGrQ9NbFORDqSkpYEVPJ8Rnl+KvW0+4DocQQkTu2xMPkV5Uib3DPKEvwRfl/RcPU20s6mKHAwnZ2Hs3i+twiBiItAd54cKFAICioiLo6jacoSAjg67Qb6oi0wpxNomP5T0coa2qyHU4Eq2/mxF+v9Ec808nYYCbMZqpKXEdEiGEiMSpxDyE3XyK7zpao71Vc67D+WxzOrbCycQ8TDryABeG28OY64CISIllDHLPnj1RWloqvP3o0SP07Nnzs7c7evRoGBgYwNnZWXhfUVEROnfuDBsbG3Tu3JkWJ5EwjDHMP5MEYy0VTGpvwXU4Eo/H42Ftbye8qKrFwnMpXIdDCCEiUVD+EmMOJMDFSBOLu0rXuON3UZCXw44hHqitF2Dm2UwIaC57mSKWAvn7779Hz549UV5ejri4OHz55ZfYtWvXZ2931KhROHv2bIP7li1bhuDgYKSmpiI4OBjLli377HaI6FxOLUBURhHmd7KRyAngJZGbsTYm+Fng9xuZeJBT+t8vIIQQCfbPlG5FlTXYOcQDygqy81tgraeOlaGOuPa0DNtuP+M6HCJCYimQu3fvjhkzZqBLly4YNWoUjh49Cnd398/ebkBAwBtDN44fP46RI0cCAEaOHIljx459djtENBhjWHQ+BSbaKhjja8Z1OFLlp6520FJWwLRjD2lCekKIVNsfn41D93LwU4gd3Iy1uQ5H5Ma3MUcbUw3MPpGI/LKXXIdDRESkY5CnTJkCHu/VFamMMZSUlKBVq1bYsGEDAGDdunWibA4AkJeXByMjIwBAixYtkJeX987nhoWFISwsDACQm5uL7OxskcfzPnw+v1Hb41rU01JEZRRhaZAZCvPfvV8+lazn89u2LTD/0jP8dTUR3W2bib09Wc9nY6N8ihblU7QaK58vquow+fBDeLRQw1A7tUb/3W0sczw1MfBkBb7ZH4v13Sy5DkfqScL3XaQFsre393tvixuPxxMW6G8zfvx4jB8/HsCr2IyNG39IPRdtcoExhg1HM2CirYKZXVygIqbhFbKczzmGLbAvsRhLo3IwrJ19owxRkeV8coHyKVqUT9FqjHwu2B+P4pf1uDzUB2bGWmJvjyu+AOYGa+DnC6mY4G+Lzna0gMjn4vr7LtIC+Z+hDgBQVVWFp0+fws7OTpRNvMHQ0BA5OTkwMjJCTk4ODAykZ0UeWRaRVohr6UVY38dZbMWxrFOQl8O63s7ouCkay6+k4ccusnFhCyGkaYh4XICtMc/wXUdruMpwcfyP74NtsO9uNr45fA/3ZwfSdTdSTixjkE+cOAF3d3d07doVABAfH4/Q0FBxNIXQ0FBs374dALB9+3b06tVLLO2Qj7PoXDKMtVQw1rcl16FItUBrPfR3M8Kyy6l4+qKS63AIIeSDVNfW4+tD92DVXA0/dmkaq6eqKMpj85euSCusxC8XU7kOh3wmsRTIixYtQkxMDHR0dAAA7u7uSE9P/+ztDh48GH5+fkhOToapqSm2bNmCuXPn4sKFC7CxscHFixcxd+7cz26HfJ6raQWITC/C3CBr6j0WgRU9HcEYMO9UEtehEELIB/n10mOk8CuwuZ8r1JREerJaogXZ6GGopwmWR6Qho5A6NaSZWD61ioqK0NZueKWqnNzn1+J79+596/2XLl367G0T0fnl4mMYaipjbBvqPRaFls3UMCuwFZZeTMVUf0v4mov/gj1CCPlUqfxyLLv8GMO8TJrkWNz/9XDA0Qe5+PbEQxwe5cN1OOQTiaUH2cnJCXv27EF9fT1SU1MxZcoUtG3bVhxNEQkT96wY51P4mBlgReOvRGhukDVaaCpjxnGa9o0QItmmH38IZQU5LO/hyHUonDDRVsX3wdY4cj8Xl1MLuA6HfCKxFMjr16/Hw4cPoaysjCFDhkBbWxtr1qwRR1NEwvx6+TF0VBUxoa0516HIFA1lBSz9wh7RT17gQLxsTpNECJF+JxPzcPpRPhaF2KKFlgrX4XBmZodWsNBVxfTjD1BXL+A6HPIJxFIgq6mpYenSpbh69Spu376NJUuWQEWl6X5RmopHeWU4cj8Hk9tZQEtFketwZM5IHzO4G2vhu1OPUF1bz3U4hBDSwMu6ekw/9gD2BhqY3K5pzwWsqiiPFT0dcT+nDH/eesp1OOQTiKVAvnHjBhwdHWFvbw8ASEhIwMSJE8XRFJEg/7v8GKqK8pjm37QPjOIiL8fDql5OePKiCmsiP/+iV0IIEaVVV9ORVliJdb2doaQglvJCqvR1MUJgq+b44UwSXlTWcB0O+Uhi+QTPmDED586dQ/PmzQEAbm5uiIyMFEdTREI8KarE7jtZGN+mJfQ0lLkOR2Z1tNZDLydD/HLpMfJoSVNCiIR4XlyFJRdT0celRZO8MO9teDwe1vR2wouqWiy7/JjrcMhHEtufeGZmZg1uy8vTBVuybEVEGng8YFaHVlyHIvN+6+mIqtp6LDhL074RQiTDvNOPUC9gWBXqxHUoEsXNWBvDPE2x9loGnr2o4joc8hHEUiCbmZnhxo0b4PF4qK2txYoVK+Dg4CCOpogEKKyowZaYpxjmaQpTHVWuw5F5tvoamNzeAltuPcW97FKuwyGENHFxz4qxKy4LMwKsYKGrxnU4EufnrnZgDPjxXDLXoZCPIJYCefPmzdi4cSOysrJgYmKC+Ph4bNy4URxNEQmwOToTVbUCzAqk3uPGsqCzLbRVFDHrb5r2jRDCHcYYZp1IhL6GEuYFW3MdjkQy11XDlPYW2B77DPdzqFNDWoilQNbQ0MDu3buRl5eH/Px87Nq1SzgemciWl3X12BCVia72+nBqocl1OE2GrpoSFoXY4mJqAU49yuc6HEJIE/X3wzxcTSvE4hA7mr3oPb7vZANtFUXMPfWI61DIBxJLgezs7Ix27dph7ty5OHXqFEpKSsTRDJEAe+5kIbfsJY095sA3bS1gq6+Ob/9+iFqaZ5MQ0shq6wWYczIRDoYaGOdLK6e+j66aEuYFWeP0o3xEPKbFQ6SBWArkx48fY+/evXBxccGpU6fg5uYGd3d3cTRFOMQYw6qr6XA10kKwjR7X4TQ5ivJyWNHTEcn8Cmy+8YTrcAghTcwf0U+Qwq/A8h6OUJCnad3+yxR/S5hqq2De6SQaGicFxPKJfv78Oa5fv45r167h7t27cHJywsCBA8XRFOHQ+WQ+HuSWYWYHK/B4PK7DaZJ6OBoi2EYPi84n0zybhJBGU1pdi8XnUxBkrYduDgZchyMVVBXl8WMXW9x88gKnaWicxBNLgdyyZUusWbMGX3zxBaKjo3Hq1CnMmzdPHE0RDq28mgYjLWUM9jDhOpQmi8fjYWWoI15U1eLnC6lch0MIaSKWX0lDQUUNlvd0oA6SjzDKxwytmqvhhzNJEAioF1mSiaVAvnv3LkaMGIE9e/bAz88PI0aMwJYtW8TRFOHI/ZxSXEgpwJT2lrRiEsfcjLUxpnVLbLiegVR+OdfhEEJkXG5pNVZFpmOguzE8TXW4DkeqKMrLYVGIHeKzS3Hkfg7X4ZD3EEtl4+bmhpEjR+Krr75CUFAQrl69ip9++kkcTQlZWFjAxcUF7u7u8Pb2Fmtb5NWSompK8vjaz5zrUAhezbOprCCHOSfpCmlCiHj9dCEFNXUCLPnCnutQpNJgDxM4GGrgx3PJqKdeZIkllgLZ29sbfn5+OHr0KBwcHBAZGYknT8R/EdGVK1cQHx+P2NhYsbfVlOWUVmP3necY7WMGXTUlrsMhAFpoqWBekA2OPcjFFbpCmhAiJo8LKvDnzacY38Yc1nrqXIcjleTlePgpxA6P8sqx924W1+GQd1AQx0bPnDkDfX1ai11WbYjKQJ2AYXqAFdehkNfM6GCFP24+wczjDxE7IwDycjQukBAiWj+cSYKSghwWdLbhOhSp1tfFCO7GWlh4LhkD3Y2hSLOASByxFMhcFMc8Hg9dunQBj8fD119/jfHjx7/xnLCwMISFhQEAcnNzkZ2d3agx8vn8Rm1PHCpr6/H79Qx8Ya0D1ZoSZGdzN8e1LORT1L7za4FJpzOw7uJ9DHT+uKn3KJ+iRfkULcqnaH1KPu/lVWB/fDamtWkBQXkRsumSB6FPyee01vr46lgaNlx68NHHa1knCd93sRTIXIiKioKJiQny8/PRuXNn2NvbIyAgoMFzxo8fLyycvb29YWxs3OhxctGmKG2MykBxdT3mhzjB2FiX63CkPp+i9o2REXY+LMZvN3IxroMjNJQ/7itO+RQtyqdoUT5F62PzOebUTeiqKWJxD3doq9Kqef/2sfkcaWSEdbf52BjLx5RgZ5pL+l+4/r7LzN4wMXk11ZiBgQH69OmDmJgYjiOSPfUChtWR6Whj3gxtLbkvjsmbeDweVvdyQm7ZS/x25THX4RBCZMT1jCKcTeLju47WVByLCI/Hw4+dbZFWWIl98Y17Rpv8N7EVyLm5ue+9LUoVFRUoKysT/v/8+fNwdnYWW3tN1d8Pc5FWWIlZHWjssSRrY94Mg9yNsSIiDc9eVHEdDiFEBiw4mwQDDSVMamfBdSgyJdSpBVyNtLDkQgrNaCFhxFYgjxkz5r23RSkvLw/t27eHm5sbWrduje7du6Nr165ia6+pWhmRBktdNfRxMeI6FPIflnV3gIAB35+had8IIZ/ncmoBrjwuxPfBNlD/yGFb5P3k5HhY0NkGyfwKHEygXmRJIpZP+okTJ3DixIkG9506dUocTQEArKyskJCQILbtE+DWkxe4nvkCa3s70ewIUsBcVw0zO1jh10uPMaW9JVq3bMZ1SIQQKcQYw4KzSTDRVqF578Wkr4sRHA018POFFAxwM4Yc/cZKBLH0IO/fvx82NjaYM2cOkpKSxNEEaWQrr6ZBW0UBX/m05DoU8oHmBdnAUFMZM48/BGN06o4Q8vHOJfNxI/MFfuhkAxVFea7DkUmvepFtkZhXTqvrSRCxFMi7du3CnTt30KpVK4waNQp+fn4ICwsTjhMm0iWjsBKH7+Xgaz9zaKrQ6TVpoamigCVd7XA98wUO3aODLiHk4zDG8MOZJFjoqmJ0a+ocEaf+bsaw01fHzxdSIaCxyBJBbGOQtbW18eWXX2LQoEHIycnB0aNH4enpifXr14urSSIma6+lQ47Hw5T2llyHQj7SV61bwtVIC3NOJqK6tp7rcAghUuT4g1zEPS/Bj51toaQgM5NeSSR5OR5+6GyLezml+Puh+CY1IB9OLJ/448ePo0+fPggMDERtbS1iYmJw5swZJCQkYOXKleJokohJcVUttsQ8xSAPY5jqqHIdDvlI8nI8rAp1RGZRFdZey+A6HEKIlBAIGH48lwwbPXUM9zLlOpwmYZC7Maz11PHThRQaFicBxFIgHzlyBDNmzMD9+/cxe/ZsGBgYAADU1NSwZcsWcTRJxOTPm09Q/rIeszq04joU8omCbfXR09EQSy+mIr/sJdfhEEKkwMGEbNzPKcOiEFtawKKRKMjLYX6wDe5mleLUo3yuw2nyxPKpb9GixRur2H333XcAgODgYHE0ScSgtl6AddcyEGStB3cTba7DIZ9heU9HVNXW48dzyVyHQgiRcHX1Aiw8lwynFpoY6G7CdThNylAvE1jqquFn6kXmnFgK5AsXLrxx35kzZ8TRFBGjA/HZeF5SjVmBtDCItLMz0MCkdhYIu/kEcc+KuQ6HECLB9tzNQjK/AotDbGlaz0amKC+H74OtEfO0GOeT+VyH06SJtEDetGkTXFxckJSUBFdXV+E/S0tLuLq6irIpImaMMay8mgYHQw10tTPgOhwiAotC7GCgoYxJR+7TVdKEkLeqrRdg8fkUeJhooY8zLQrFhRHeZmjZTBWLz1MvMpdEWiAPGTIEJ06cQK9evYSLhZw4cQJxcXHYtWuXKJsiYhaRVoi7WaWYGWBFk5bLCB1VRfzWwwG3nhZj2+1nXIdDCJFA4befIb2wEj91tadjP0eUFOQwN8ga0U9e4HJqAdfhNFkiLZB5PB4sLCywceNGaGpqCv8BQFFRkSibImK2MiINBhpKGEZXL8uU4V6maG+pi7mnHqGosobrcAghEuRlXT1+vpAC35Y66O5AZw659JWPGYy0lLH0UirXoTRZIu9BBgAvLy94e3vDy8tL+M/b21uUTRExepRXhlOP8jGpnSWtnCRjeDweNvR1RlFlDX44Q6tcEkL+vz9vPsWz4mr83NUePB71HnNJRVEe3wa2wpXHhYjOpA5GLoi0QD558iQAICMjA+np6cjIyBD+S09PF2VTRIxWR6ZDRUEO37Q15zoUIgZuxtqY3N4Sm6OfIJYu2COEAKisqcPSi6kIsNJFJ1s9rsMhAL5uY47maopYepF6kbkg0nWD79y5897HPT09RdkcEYP8spfYEfsco3zMoK+hzHU4REx+CrHDgfhsjD+YgJhp/jTPKSFN3KYbT5Bb9hL7h3tR77GEUFdWwIwOVvjhTDLuPi+BhylNt9qYRFogz5o1652P8Xg8XL58WZTNNXD27FlMmzYN9fX1GDt2LObOnSu2tmTZ7zcy8bJOgBkBNLWbLNNWVcS6Ps4YsCMO66IyMJMWgiGkySqrrsOyy4/R2VYPAa2acx0Oec2kdpb47UoafrmUioMjaahqYxJpgXzlyhVRbu6D1dfXY9KkSbhw4QJMTU3h4+OD0NBQODo6chKPtKqqrcfG65no4WgIOwMNrsMhYvalqxG6Oxhgwdlk9HMxgiLXARFCOLE+KgMFFTX4uas916GQf9FRVcTkdhb49fJjPMorg4OhJtchNRliO6/64MEDHDhwADt27BD+E5eYmBhYW1vDysoKSkpKGDRoEI4fPy629mTVztjnKKiowawO1HvcFPB4PGzs6wIAmHTkPs23SUgTVFxVi+URaejhaAhf82Zch0PeYnqAFVQV5bHs8mOuQ2lSRNqD/I/FixcjIiICiYmJ6NatG86cOYP27dtjxIgR4mgOWVlZMDMzE942NTXFrVu3xNKWrBIIGFZdTYOnqTY60Cm2JsNcVw1Lutph5t+JONlKA1+b0LKykkggYHiQW4Y7z0uQVliBtMJKZBRVoqKmDjV1AtTUMyjI8WCoqYwWmsow0lKBi5EmfFs2g1MLTVoNjbzT6qvpKK6qxU8hdlyHQt5BX0MZX7cxx7qoDCzsYgur5upch9QkiKVAPnToEBISEuDh4YFt27YhLy8Pw4YNE0dTHyUsLAxhYWEAgNzcXGRnZzdq+3y+5C4beSGtGMn8CmzoZomcnByuw/kgkpxPadKvlQrCDdUw/2Im2rXUhK6qWA4LTc7nfj6zy2pwJrUY15+W4lZWOYqr6wEAcjzAVEsJLbWVYaouD0V5BSjK81Bbz8CvrMHdZxU4U16LiloBAEBNUQ5eRuroZtsM3Wx0oKcmnYNp6PsuWnw+H0WVdVh19TG62ejAUK4C2dkVXIcltcT9+RzmoI6N14GFJ+/hf51lf4YpSfi+i+WXUFVVFXJyclBQUEBpaSkMDAzw7Jn4Vu4yMTFpsP3nz5/D5C09YePHj8f48eMBAN7e3jA2NhZbTO/CRZsfYvvxTJhqq2B8oCMUpWhGA0nNp7TZMUwD3qsj8Wt0AXYPo9lmROVjP58vKmuwPz4be+9m4VpGERgDWjVXQ19XY3Ro1RxtzJvBUlftP7+jjDE8LqhAzNNi3HpajHPJ+Zh38SnmX3qKjtZ6GOfbEv1cjaRu9hL6vovW2jvFqKgV4Lfe7jBuQWNbP5c4P5/GAEa3LsXWmGdY1tsdJtqqYmtLUnD9fRdLgezt7Y3i4mKMGzcOXl5e0NDQgJ+fnziaAgD4+PggNTUVGRkZMDExwb59+7Bnzx6xtSdr7j4vwZXHhVjeQ7qKYyI6bsbamOZrhJXRWejvZoTeLkZch9SkJOeXY921DITHPkNlTT3sDTSwqIsdBnkYw1b/4y+Y5fF4sNHXgI2+BoZ6mYIxhvs5ZTiQ8Kr4HrTrDsybqWJ6gBXGtG4JTRU6a9DU5FfUYn1UBga7m8CJimOpMKejNf689RQrI9KxqpcT1+HIPLEcFX///XcAwIQJE9C1a1eUlpbC1dVVHE0BABQUFLBhwwaEhISgvr4eo0ePhpMTfXg+1MqradBUVsC4Ni25DoVwaIqvES49qcCEw/fhb9UczdWVuA5J5sU+K8bi8yk4mZgHJXk5DPU0waR2FvA01RbpXLQ8Hg+uxlpwNdbCTyF2OJGYhxURaZhx/CF+vpCCRV3sMKGtOf2B3IRsiMlFTT3DwhBbrkMhH8iyuRqGeppgc3Qm5gVb01oFYia2o2FWVhZu3LiBp0+fori4GJGRkeJqCgDQrVs3pKSkIC0tDfPnzxdrW7LkeXEV9sdnY6xvS2irSufYRCIaivI8hA92R2FFDaYefcB1ODLtYW4Z+obfhs+aa4jOLMKiLrZ4uqATtg5yh5eZjlgXapCT46GXcwtcm9wON6e2h4eJNqYeewCX5RE4mZhHs5k0Ac+Lq7ArgY8RXqafdIaCcGdekDWq6wRYE0mrE4ubWHqQv/vuO+zfvx+Ojo6Ql5cH8KoHIyAgQBzNkc+wOjIdDMA0f0uuQyESwM1YGws622LhuWT0cWmBL91ozKco5ZW9xLxTjxAe+wyaygpYHGKH6QGW0FLh5o9TX/NmuPB1G5xMzMO3JxLRc0sMQp0M8ceXrmihpcJJTET8llxMhYABCzpT77G0sTfUxJeuRthwPROzO1pDhzq2xEYsBfKxY8eQnJwMZWXq/pdkLyprEHbzCQa5G8NcV43rcIiEmBdsjZOJeRh/8B58WzaDWTPZvxhE3GrrBdgQlYFF51NQVVuPWR1aYW6QtUQMY+HxeOjp1AJd7Q2wJjIdC84mw2l5BDb0ccEgD2NadljGpPLL8detpxjuqgfL5nTcl0bfB9vgYEIONkRl4Af6I0dsxDLEwsrKCrW1teLYNBGh329kovxlPeZ0tOY6FCJBFOXlsGeYJ2oFAgzfexf1Ajrl/jluZBTBY1UkZv6diLYWzXD/20As7+koEcXx6xTl5TC7ozXiZwbARk8dQ3bfwYAdcSiuomO5LPnxbDKUFeQwrQ1diCut3E200d3h1R+05S/ruA5HZomlQFZTU4O7uzu+/vprTJ06VfiPSI6q2nqsvZaBL+wN4GqsxXU4RMJY66ljQx8XXE0rxP9o9aZPUlFTj6lHH6D9xusoe1mH41/54PRYX4lfxt3eUBNRk9thWXcHHHuQC+/VkUjILuE6LCIC8Vkl2BefjWn+ljBQp1Pz0mx+JxsUVtYi7OYTrkORWWIZYhEaGorQ0FBxbJqISPjtZ+CX1+C7oFZch0Ik1AhvU5xNyseP55IRbKNHy9B+hIspfIzak4js8hpMbmeJpV/YS9VUagrycvguyBrtLXUxYEcc2qyNwqZ+rhjV2uy/X0wk1vwzSdBRVcTswFaoKi7gOhzyGfwsdBFkrYcVEWmY2NYCKoryXIckc8RyxB45ciRqamqQkpICALCzs4OiIv21Kinq6gVYEZGG1i11EGBFy0qTt+PxeNj0pSuin7zAoF1xiJsRAF01yRoWIGle1tXj+9NJWHU1Hda6Koia1A5tLXW5DuuTtbPUxZ2ZARi8Kw5f7Y9H3PNirO7lJHULjBAgKr0Qpx/lY1l3BzRTU0JVMdcRkc81v5MNgjdHY9vtZ/imrQXX4cgcsRzlIiIiYGNjg0mTJmHixImwtbUV+zRv5MMduZ+L9MJKfNfRmi7AIe+lo6qI/cO9kFVSjaG770BA45HfKTG3DK3XRGHV1XRMbmeBs8McpLo4/oehpjLOj2+DmR2ssOF6Jnpvu03jHqUMYwxzTz2CkZYyprS34DocIiIdrV+trvm/y49RWy/gOhyZI5YCedasWTh//jyuXr2KyMhInDt3DjNmzBBHU+QjMcbwvyuPYauvjl7OLbgOh0gBX/NmWNfbGWeT+Fh8PoXrcCQOYwy/X8+E1+pI5JRV4+SY1ljf1wWqirLTy6ogL4eVoU74vZ8LziTlI2DjdWSVVHEdFvlApx/l43rmCyzobAs1JekZ6kPej8fj4YdONnjyogq747K4DkfmiOUIXltbCzs7O+FtW1tbmtVCQlxKLcCd5yWYHdgK8nLUe0w+zNd+5hjlY4afLrxa9Y28kl/2Ej23xGDSkfvoaK2H+98GorujIddhic03bS1wYkxrpBZUoM3aKCTllXEdEvkPAgHD/DNJsGquhjGtabVUWdPNwQDuxlpYeikVddSLLFJiKZC9vb0xduxYREREICIiAmPHjoW3t7c4miIf6X+XH6OFpjKGe5tyHQqRIjweD7/3c4GnqTaG7b6DVH451yFx7syjPLisiMDF1AKs6+2MU2Nbw1BT9ud+7+ZgiGuT2qGmXoCA32/g7nOa4UKS7Y/PRkJ2KX4KsYOSguyc1SCv8Hg8LAqxw+OCCuy+Q73IoiSWb8umTZvg6OiIdevWYd26dXBycsKmTZvE0RT5CHHPinExtQDTA6ygrEBXvJKPo6ooj8MjvaEoL4duf8WgoPwl1yFxoqr21fRt3f6KgaGmMmKn+2OKv2WTGs/vbqKNa5PbQUVBDh033cD1jCKuQyJvUVsvwIKzSXA10sJgDxOuwyFiEupkCE9Tbfx8IYXGIouQWApkZWVlzJw5E0eOHMFff/2F4OBgWlVPAvx2JQ1aKgqY4GfOdShESlnoquHv0T54XlyF0K23UVVbz3VIjep+Tilar7mG9VEZmB5giZhp/nA2aprziNvqayBqcjsYaCij8x/RuJjC5zok8i9bY54irbASS7vZQ46G1MksHo+HRV1skVZYiZ2xz7kOR2aIpUAODAxEaWkpioqK4OXlhXHjxtFFehxLK6jAoXvZmOBnDm1au518Bj8LXewa6oGbT19g+J67TWJmC4GAYW1kOnzWXAO/ogZnxvlidS/nJj/3aMtmarg2uR2s9dTRc0sMLlGRLDEqa+rw0/lUtLVohu4OBlyHQ8Ssh6MhvM20seRiKvUii4hYCuSSkhJoaWnhyJEjGDFiBG7duoVLly6JoynygVZEpEFBTg7TA6y4DoXIgH6uxljR0xGH7+Vg1omHYEx2i+Tc0mp0++sWph9/iC62+rj/bQd0taeC4x+Gmsq4NMHvVZG8NQaXU2kBCkmwPioT2aXV+LWbQ5Ma/tNUvepFtkNGUSW2337GdTgyQSwFcl1dHXJycnDgwAH06NFDHE0ILVq0CCYmJnB3d4e7uztOnz4t1vakUV7ZS2y7/QwjvE1hpKXCdThERswIsMI0f0usiczA/DNJMlkk//0gFy4rriIyvRCb+rng+Ggf6GvQcLF/09d4VSRbNVdHjy23cOUxFclc4pe/xC+XUtHdwQABrWgxqKaim4MBWrfUwZKLqaipo17kzyWWAvnHH39ESEgIrK2t4ePjg/T0dNjY2IijKQDAjBkzEB8fj/j4eHTr1k1s7UirVVfTUFMvwLeBtKw0ER0ej4dVoU742s8cv156jB/PJstMkVxcVYtRe++i17bbMNVWQdyMAExoa0E9ce9hoKmMyxP8YKmrhh5bYhCdSRfucWXx+RRU1NRjeU9HrkMhjeifschPXlQhnHqRP5tYCuT+/fvj3r17+P333wEAVlZWOHz4sDiaIv+hoPwlNl7PxCB3E9gZaHAdDpExcnI8/N7XBWN9W2LJxVSZWEjkYgofLssjsOtOFhZ0tsGtaf5wMNTkOiypYPB/wy2MNJXR/a8YPMgp5TqkJic5vxybo59gfJuW9LltgrraG8C3pQ6WXEzBy7qmdRG1qIllSZ3q6mps2bIFDx8+RHV1tfD+rVu3iqM5bNiwATt27IC3tzdWrlyJZs2avfV5YWFhCAsLAwDk5uYiOztbLPG8C5/f+BewLIvKQmVNPca5ajf6+xU3LvIpyz4nnwvb6aG8ogKLz6cgt7AEP3QwgZyU9bZW1NRjaWQWtifwYa2rguOD7OBhpI6C/NxP2l5T/nzu6mOFPvuS0WnTDRwdZAdznc8fltKU8/kxph57DFUFHia4vf+YT/kULUnK5zQffQw5nIpV5x9gpLs+1+F8EknIJ4+J4Zxo//79YW9vjz179uDHH3/E7t274eDggLVr137S9jp16oTc3Dd/pJYuXYo2bdpAT08PPB4PCxYsQE5OzgcV4t7e3oiNjf2keD5VdnY2jI2NG629osoaWCy5hK72+jgwQvYWamnsfMq6z82nQMAw7dgDbLieiX6uRtg5xAOqUjLLQ1R6IUbti0d6USVmBFhhyRf2nx17U/98PswtQ8DG69BRVUTU5Hafff1DU8/nh4h4XICOm6LxSzd7zAt+/7BGyqdoSVI+GWNov+E6nr6owuPvg6Ry3YPGyuf7akGx9CA/fvwYBw8exPHjxzFy5EgMGTIE/v7+n7y9ixcvftDzxo0bJ/aLAqXJumsZKHtZhx862XIdCmkC5OR4WNfHGVbN1TDrRCKyNlXjbwm/qK20uhY/nk3GuqgMWDRTQ8Q3bemiJhFxaqGJ02N9Ebw5GiFhN3F1Yls0U1PiOiyZJRAwfHsiEWY6KjRbURPH4/GwOMQOnf+4ib9uPsWk9pZchySVxDIGWVHx1Ty7Ojo6ePDgAUpKSpCfny+OppCTkyP8/9GjR+Hs7CyWdqRNSVUt1kSmo49LC7gaN82FDEjj4/F4mNGhFQ6N8EZ8Vgl81lzDDQlcZY0xhn13s2D/vytYF5WBCX7muPdtByqORczXvBmOfeWD5PwK9NgSg4qXdVyHJLPCbz9D3PMS/NrNQWrO3BDxCbbRQ3tLXfxy6TGqm9iCTqIilgJ5/PjxePHiBX7++WeEhobC0dERc+bMEUdTmDNnDlxcXODq6oorV65g9erVYmlH2qyPykBJdR1+7Ey9x6Tx9XU1QuSkdpDj8RDw+w38fCEF9RKyoEhCdgk6/3ETg3fdgbGWCm5N9cfv/VyhoSyWE2pNXidbfewZ5oGbT16g3/ZYmn5KDIqrajH39CO0tWiGIZ60pDT5/73I2aXVCLv5hOtwpJJYfhHGjh0LAOjQoQPS09PF0YTQzp07xbp9aVRcVYuVV9MR6mQIdxNtrsMhTZRPSx3cnRmAiYfv48ezybiYwseWge6w1lPnJJ7MokosOJuE3XeyoKOiiA19nDGhrQXkaQlesevnaoyw/nUYeyABI/bexZ6hnrT0sQj9dD4FBRU1ODvOl6YiJEIdrZsjsFVzLL2Yiq98WkJThToBPoZYepDz8vIwZswYfPHFFwCAxMREbNmyRRxNkbdYEZGG4qpa/NTVjutQSBOnraqI3cM8sWOwO+5mlcLxtyuYfSIRJVW1jRZDWkEFJh2+D7tlV3AoIQdzAq2RPj8Yk9pbUnHciMb4tsRvPRywPz4b04/L9uqLjSkxtwzrozIwzrclPE11uA6HSBAej4dl3R2QX16DVVfTuA5H6oilQB41ahRCQkKEU8zY2tpizZo14miK/Et+2UusiUzHQHdjuBlT7zGRDMO9zZA8tyOGeZpi5dU02C67jI1RGWIbk8oYw60nL9B/eyxsl13GX7eeYqSPKVLnBWFZDwfoqCqKpV3yfrM7WmNmByusj8rA/y4/5jocqccYw/TjD6CuJI8lX9hzHQ6RQL7mzdDP1QgrrqYhv+wl1+FIFbEUyAUFBRgwYADk5F5tXkFBAfLydNFAY/j1ciqq6wTUe0wkjpGWCrYOcsftaf6wM9DA5KMPYPrzRcz6+yHSCipE0saTokr8eikVzssj0GZdFC6k8DGnozUyfwhGWH83mOqoiqQd8umW93DEEA8TzDudhPAYWu3rcxx/kIsLKQX4qaudRM8WQ7i19At7VNUKsORiKtehSBWxDEhRV1dHYWGhcCzUzZs3oa1NvZni9uxFFX6//gQjvU1hq0+r5hHJ5GWmg6sT2+JG5gusj8rAumsZWHU1HZ6m2gix00eInT78zHWhpPDff7+XVtciKqMIVx4X4srjAsQ9LwEAtLfUxaZ+LhjqaUrj7iSMnBwP2wa5g1/xEmMPJsBAUwndHAy5DkvqlFXXYcrRB3BuoYlv2lpwHQ6RYHYGGhjT2gybozMxzd8SrTi6DkTaiOWXY9WqVQgNDUVaWhratWsHPp+PQ4cOiaMp8pqfL75a5pdmriCSjsfjoZ2lLtpZ6iK7pBrbY5/hTFI+fruShl8vPYa8HA8tdVRh1VwNlrpqUFaQwz9DVkuqa5FeWImMokrk/t8pQyV5ObQx18GSL+wwxMMUls3VOHx35L8oKcjh8EgfBG66gf474nB5gh98zd++Aip5u4XnkvG8pBoHRnhBUV4sJ4OJDFnYxQ47455jwdlk7BnmyXU4UkEsBbKnpyeuXr2K5ORkMMZgZ2cnnBuZiEcqvxxbY55hYlsLmOtScUCkh7G2CuYF22BesA1Kq2txObUAsc9LkF5YibTCCpxIzENd/aupwXg8HjSU5WGpq4buDoawaq6GNubN4GfRjOZ+lTKaKgo4PdYX7dZHoftft3B9SnvYGdCZrw9x53kx1l5LxwQ/c/hZ6HIdDpECxtoqmBFghV8uPcaMACv4tNThOiSJJ9IC+fbt2zAzM0OLFi2goKCAuLg4HD58GObm5li0aBF0demLLC7fnXoEFQU5fB9szXUohHwyLRVF9HYxQm8XI65DIY3AUFMZ58a3Qdv1UQgJu4kbU9rDWPvzlqSWdfUChvEH70FfQxm/dnfgOhwiRb4LssZft55i+vEHiJrcjqYE/A8iPS/z9ddfQ0np1VKikZGRmDt3LkaMGAFtbW2MHz9elE2R10SmFeLo/VzMDbJGCy36cSGESI9Weuo4PdYXhZU1+OLPW406BaA02ng9A3HPS7CmlxPNxkI+ipaKIpZ+YY8bmS+wPz6b63AknkgL5Pr6emEv8f79+zF+/Hj069cPP//8Mx4/pil9xEEgYJh14iFMtVUws4MV1+EQQshH8zLTwZGRPniUX4Ze227T0rjvkFlUiflnkhBip4+B7sZch0Ok0FetW8LdWAtzTiaisoaWfn8fkRfIdXWvEn7p0iUEBQUJH/vnfiJae+9mIfZZCX7pZg81JbpanxAinTrb6SN8kDuuphVi+J67ErM0uaQQCBjG7E8AAGz+0pVOj5NPIi/Hw9reznhWXI0VEeJd6VjaibRAHjx4MDp06IBevXpBVVUV/v7+AIDHjx/TNG9iUFVbj3mnH8HLVBtDPU25DocQQj7LEE9TrAx1xKF7OZh27AGttveaTTcycflxAVb2dIIFXYhNPkNAq+b40tUI/7vyGM+Lq7gOR2KJtMtx/vz5CA4ORk5ODrp06SL8C1cgEGD9+vWibIoAWH01Hc+Kq7FziAfkaMlcQogMmNmhFXJKX2JFRBqMtJQxvxNNW/m4oAJzTj1CiJ0+xrVpyXU4RAYs7+mIE4l5mH0iEXuHe3EdjkQS+Tn5Nm3avHGfrS0d4ETt2YsqLL2Uit7OLdChlR7X4RBCiMj8r7sDcsuq8cOZZBhpqmC0b9MtCgUChq/2xUNRjoe/BrjR0AoiEha6apgbZI3F51PwVWszdLEz4DokiSM1s4sfPHgQTk5OkJOTQ2xsbIPHfv31V1hbW8POzg7nzp3jKMLGNe34q9OPa3o5cR0KIYSIlJwcD1sGuKOLrT7GH7qHk4l5XIfEmVVX0xGVUYQ1vZxpqXQiUnODrGGjp46Jh++jii6MfYPUFMjOzs44cuQIAgICGtyfmJiIffv24eHDhzh79iwmTpyI+nrZ3tGnEvNw9H4ufuxsS4uCEEJkkpKCHA6P8oaHiRYG7IhFbHY51yE1ultPXmDe6Ufo49ICI33oOhMiWiqK8tj8pSvSCivxy8VUrsOROFJTIDs4OMDOzu6N+48fP45BgwZBWVkZlpaWsLa2RkxMDAcRNo7KmjpMOfoADoYamNmhFdfhEEKI2GgoK+DUGF+YaKti5NHHuJddynVIjeZFZQ0G7oyDibYKttDQCiImQTZ6GOZlgv9deYxHeWVchyNRpKZAfpesrCyYmZkJb5uamiIrK4vDiMTrl0uPkVFUid/7ukBJQep3HyGEvJeBpjLOj28DFQU5dPojGklN4EecMYaxBxKQVVKNfcO90ExNieuQiAxb2dMJGkoKmHDoHs0c8xqJmji3U6dOyM3NfeP+pUuXolevXp+9/bCwMISFhQEAcnNzkZ3duCvJ8Pn8z3p9amEVfrv8GP0cdWGrVtPo8Uuaz80naYjyKVqUT9FRBvBHJz2MPc9Hx9+v4/BAO1joKHMdltiEx+fjyP1cLAgwQUvFKmRni34qLvp8ipa05/P79saYfeEJlp25h5Hu+lyHIxH5lKgC+eLFix/9GhMTEzx79kx4+/nz5zAxMXnrc8ePHy9c8trb2xvGxo2/EtGntllXL0DvA9ehqaKAjQO8Yagpuz8OH4OLfSjLKJ+iRfkUHW8AlyfaIvD3Gxh8JA2RE9vK5DUYNzKKsDgiC90cDLCop3in8KTPp2hJcz5nGRnh4tNKLLmWhf6trWGtp851SJznU+rP0YeGhmLfvn14+fIlMjIykJqaitatW3Mdlsgtu/wYt58VY1M/VyqOCSFNkrORFs5/3Qal1XUI3HQDGYWVXIckUk+KKtE7/DZaNlOl+e1Jo+LxeNgy0A1K8nIYQStZApCiAvno0aMwNTVFdHQ0unfvjpCQEACAk5MTBgwYAEdHR3Tt2hUbN26EvLw8x9GKVnxWCRafT8Egd2MMcJfev1AJIeRzeZrq4NKEV0VywMbrSOXLxuwW5S/rELr1NmrqBDgx2ge6NO6YNDITbVVs7OuM6CcvsPzKY67D4ZzUFMh9+vTB8+fP8fLlS+Tl5TWY73j+/PlIS0tDcnIyvvjiCw6jFL2XdfUYvucu9NSVsKGvC9fhEEII5zxNdXD5Gz9U1wnQ4fcbUn/hnkDAMHzPXTzILcWBEV6wN9TkOiTSRA32MEF/NyP8eC4ZCdklXIfDKakpkJuqH88m40FuGf4a4Ibm6tSjQAghAOBmrI2IiW0hYEDA7zcQ96yY65A+CWMMs048xLEHuVjdy4lWNCOc4vF4+L2vC5qrKWHQzjsoq67jOiTOUIEswU4m5uG3K2kY16Ylujsach0OIYRIFKcWmoic1BZqivLo8PsNnEvK5zqkj/bj2WSsiczANH9LTGlvyXU4hEBPQxm7h3oihV+OMQfim+zUb1QgS6i0ggoM230HHiZaWNvbmetwCCFEItnqa+DGlPaw1lNHjy0x2Bn77L9fJCGWXUrFkoupGOvbEqt7OdFiIERiBNno4ZduDjiYkIM1kelch8MJKpAlUGVNHfptj4Ucj4fDI32gqihbFx0SQogoGWur4OrEtgiwao4Re+Px0/kUCCT8Kvz11zIw73QShniYYPOXrlQcE4kzp2Mr9HZugdknH+FaeiHX4TQ6KpAlDGMM3xy+j3s5pdg91AOWzWVvnk9CCBE1bVVFnB7XGsO9TLHwXDL6bY9FaXUt12G9gTGGxeeSMfXYA/RyMkT4YHfI03RuRALxeDyED3KHla4aBuyIw7MXol+wRpJRgSxhll9Jw47Y51jY2RZfONC4Y0II+VDKCvLYPtgdq3s54URiHtqsi0KKBE0DV1cvwPiD97DofApGeJvi4EhvKMrTzzCRXNqqijgyyhuVtfUI+fMmiipruA6p0dA3U4JsufUU3516hEHuxljQ2ZbrcAghROrweDxMD7DCha/bgF9eA+/V1xAe84zzC43KX9ah17bb+OvWU8zvZIPwQe5UHBOp4Gykhb9H+yC9sBI9/opBZU3TmNmCvp0S4uj9HIw/mIAQO31sH0wrKBFCyOfoaK2HuBn+8DDRwlf749Fveyz45S85ieXO82J4r47E2aR8bP7SBUu+sKcxx0SqdGilhz1DPXHr6Qv03xGH2noB1yGJHRXIEuByagEG7byD1i2b4fBIbygp0G4hhJDP1bKZGq580xYrejriVGI+nJdHYO+drEbrTRYIGFZcSUObdVEor6nHxQl++NrPolHaJkTU+roa4fd+Ljj9KB/Ddt9FTZ1sF8lUiXHsyL0cdP/rFmz01XFqbGuoKytwHRIhhMgMOTkeZgW2QuwMf5jqqGLI7jtot/46bj15IdZ272WXotMf0Zh9MhE9HQ2RMKsDOlrribVNQsTtaz8L/NbDAQcSstH9r1syvZAIFcgcWhuZji93xMLdRBtXvvGDrhqtlEcIIeLgYqSFmGn+2DLADRlFlWizLgqDdsaJfAW+Zy+q8NW+eLivuoq7WaUI6++KQyO9aSVUIjNmd7TGtoHuuJJWiI6bbiC/jJuhS+JG3ZUcqBcwfHviIdZEZqCPSwvsHupJcx0TQoiYycvxMNq3Jfq7GWPZ5VSsi8rA/vhstLNohqn+VujlbAhlhY8/FjPGcD2jCOG3n2P3necQMGBWh1aYF2xNHR9EJo1qbQZ9DSX03xGLtuujcHCENzxMtbkOS6SoQG5kibllGL0/HreeFmOavyVWhjrRHJiEENKINFUUsLSbA+Z0tEb47WdYH5WBgTvjoK4kjyBrPXzhYIAgaz1Y6qq985qQ3NJqxD4vwa0nL7D3bhbSCiuhriSPoZ6mWNDZBua6NIc9kW3dHQ1x+Zu26Bt+G63XXsPiEDvM6dgKCjIyOwsVyI2krl6A9bdysCr6LjSU5bF7qAeGeJpyHRYhhDRZ2qqKmBZghcntLXEhhY+/H+biTFI+TiTmAQDkeICJtgosddWgICeH6rp6VNXWI6+sBtml1QAAHg/oYNUcP3axRV8XI2jQdSSkCWlj3gwPZgfim0P3Mf9MEk4m5mHrQDfYG2pyHdpnk5pv8sGDB7Fo0SI8evQIMTEx8Pb2BgBkZmbCwcEBdnZ2AIA2bdpg8+bNXIb6hnoBQ4ffb+BG5gv0czXCxr4uMNRU5josQggheDX0oqu9AbraG4AxhhR+BaIzXyCjqBLpRRXILKpCdV09VBXloaumBOcWWvA01Ya3qTbcTbSpKCZNmq6aEvYN90QvZ0NMOvIATssj8KWrMeYFW8PdRHqHXUjNt9rZ2RlHjhzB119//cZjrVq1Qnx8fOMH9YHk5XgY6G6MEc7N8HVHJ67DIYQQ8g48Hg92BhqwM9DgOhRCpAaPx8MQT1ME2+hjTWQ6Nl7PxIGEbHSx1UeokyECrfXgaKjxxvzfAgFDakEFbj8rhraKAno6teDoHbxJagpkBwcHrkP4LFP9rZCdnc11GIQQQgghYmGoqYxfuzvguyBr/H49E3/cfILzR/kAAH0NJVg3V4eSghwU5XioqRcgPrsUpf83VVxXe30qkEUtIyMDHh4e0NLSwpIlS+Dv7//W54WFhSEsLAwAkJub2+gFK5/Pb9T2ZB3lU7Qon6JF+RQtyqdoUT5Fi/L5plGO6hjp4IBnpTW48bQMN56XIb+8Fi9fMpTXM/B4QC87HbgbqsOthTpsmqsI6zJJyKdEFcidOnVCbm7uG/cvXboUvXr1eutrjIyM8PTpUzRv3hxxcXHo3bs3Hj58CC0trTeeO378eIwfPx4A4O3tDWNjY9G+gQ/ARZuyjPIpWpRP0aJ8ihblU7Qon6JF+Xw7ExOgjQMw8yNfx3U+JapAvnjx4ke/RllZGcrKry548/LyQqtWrZCSkiK8iI8QQgghhJCPIfWT1fH5fNTX1wMA0tPTkZqaCisrK46jIoQQQggh0kpqCuSjR4/C1NQU0dHR6N69O0JCQgAAkZGRcHV1hbu7O7788kts3rwZurq6HEdLCCGEEEKklUQNsXifPn36oE+fPm/c369fP/Tr14+DiAghhBBCiCySmh5kQgghhBBCGgMVyIQQQgghhLyGxxhjXAfBBT09PVhYWDRqm3w+H/r6+o3apiyjfIoW5VO0KJ+iRfkULcqnaFE+Raux8pmZmYmCgoK3PtZkC2QueHt7IzY2luswZAblU7Qon6JF+RQtyqdoUT5Fi/IpWpKQTxpiQQghhBBCyGuoQCaEEEIIIeQ1VCA3on+WuSaiQfkULcqnaFE+RYvyKVqUT9GifIqWJOSTxiATQgghhBDyGupBJoQQQggh5DVUIBNCCCGEEPIaKpA/0NKlS+Hk5ARXV1e4u7vj1q1bn73NRYsWYcWKFSKITnrweDwMGzZMeLuurg76+vro0aOHSLbflHJaWFgId3d3uLu7o0WLFjAxMRHerqmpEVk7ERERIts/XJoxYwbWrFkjvB0SEoKxY8cKb8+aNQurVq36z+1kZmbC2dlZHCEKaWhoiHX74vKuz6SOjg4cHR3F3n54eDgmT54s9nYkiby8vDDn7u7uyMzMfOM53bp1Q3Fx8Rv3N6Xj5es+5vc8PDwc2dnZn92mhYXFO+fblTWyUi8pNGprUio6OhonT57EnTt3oKysjIKCApEWIE2Juro6Hjx4gKqqKqiqquLChQswMTHhOiyp1Lx5c8THxwN4dfDQ0NDAt99+y21QEqxdu3Y4cOAApk+fDoFAgIKCApSWlgofv3HjBlavXs1hhNLvXZ/JzMzMz/ojq66uDgoK9HP1NqqqqsKc/xtjDIwxnD59unGDkmAf+3seHh4OZ2dnGBsbf3AbTfnzKkv1EvUgf4CcnBzo6elBWVkZwKtV+IyNjRv8RRgbG4vAwEAAr34YRo8ejcDAQFhZWWHdunXCbS1duhS2trZo3749kpOThff/+eef8PHxgZubG/r164fKykqUlZXB0tIStbW1AIDS0tIGt6VVt27dcOrUKQDA3r17MXjwYOFjRUVF6N27N1xdXdGmTRvcu3cPAOX0Q40aNQqHDh0S3n69J3L58uXw8fGBq6srFi5cCACoqKhA9+7d4ebmBmdnZ+zfvx8AcPbsWdjb28PT0xNHjhwRbiMmJgZ+fn7w8PBA27ZthfkOCAho8CPdvn17JCQkiPOtfrS2bdsiOjoaAPDw4UM4OztDU1MTL168wMuXL/Ho0SPweDx06NABXl5eCAkJQU5ODgAgLi4Obm5ucHNzw8aNG4XbDA8PR9++fdG1a1fY2Nhgzpw5wsfOnz8PPz8/eHp6on///igvLwcAzJ07F46OjnB1dRX+QZORkQE/Pz+4uLjghx9+EG6jvLwcwcHB8PT0hIuLC44fPw4A+PHHHxv0hs+fPx9r164VT+JEpL6+HuPGjYOTkxO6dOmCqqoqAEBgYKBwQYCCggLhCqfh4eEIDQ1FUFAQgoODkZOTg4CAALi7u8PZ2RnXrl0DAGzbtg22trZo3bo1rl+/LmzvxIkT8PX1hYeHBzp16oS8vDwIBALY2NiAz+cDAAQCAaytrYW3ZUFmZibs7OwwYsQIODs749mzZw1+q5r68fJdv+c//fQTfHx84OzsjPHjx4MxhkOHDiE2NhZDhw6Fu7s7qqqq3vu7P3z4cLRr1w7Dhw9HYWEhunTpAicnJ4wdOxavz4fQu3dveHl5wcnJCWFhYQCArVu3Yvr06cLn/Pnnn5gxY0bjJEWEZKpeYuQ/lZWVMTc3N2ZjY8O++eYbFhERwRhjzNzcnPH5fMYYY7dv32YdOnRgjDG2cOFC5ufnx6qrqxmfz2e6urqspqaGxcbGMmdnZ1ZRUcFKSkpYq1at2PLlyxljjBUUFAjbmz9/Plu3bh1jjLFRo0axo0ePMsYY++OPP9jMmTMb6V2Lh7q6OktISGD9+vVjVVVVzM3NjV25coV1796dMcbY5MmT2aJFixhjjF26dIm5ubkxxiin/2XhwoVs+fLlbOTIkezgwYPC+9XV1RljjJ07d46NGzeOCQQCVl9fz7p3786uXr3KDh06xMaOHSt8fnFxMauqqmKmpqYsJSWFCQQC1r9/f+H+KSkpYbW1tYwxxi5cuMD69u3LGGMsPDycTZs2jTHGWHJyMvPy8mqMt/3RLCws2JMnT9jmzZvZpk2b2A8//MBOnTrFoqKiWJs2bZifnx/Lz89njDG2b98+9tVXXzHGGHNxcWFXr15ljDH27bffMicnJ8YYY9u2bWOWlpbCvLVs2ZI9ffqU8fl85u/vz8rLyxljjC1btowtXryYFRQUMFtbWyYQCBhjjL148YIxxljPnj3Z9u3bGWOMbdiwQbjfamtrWUlJCWOMMT6fz1q1asUEAgHLyMhgHh4ejDHG6uvrmZWVVYPPuyT45zPJGGMZGRlMXl6e3b17lzHGWP/+/dnOnTsZY4x16NCB3b59mzH26j2am5szxl7l1sTEhBUWFjLGGFuxYgVbsmQJY4yxuro6VlpayrKzs5mZmRnLz89nL1++ZG3btmWTJk1ijDFWVFQkzPOff/4p/J4vWrSIrV69mjH26nvxz2dYWsnJyTE3Nzfm5ubGevfuzTIyMhiPx2PR0dHC5/zzW0XHy3f/nv/zOWOMsWHDhrG///6bMdbw88nY+3/3PT09WWVlJWOMsSlTprDFixczxhg7efIkAyB83T9tVVZWMicnJ1ZQUMDKysqYlZUVq6mpYYwx5ufnx+7duyeuNIiNLNVL1IP8ATQ0NBAXF4ewsDDo6+tj4MCBCA8Pf+9runfvDmVlZejp6cHAwAB5eXm4du0a+vTpAzU1NWhpaSE0NFT4/AcPHsDf3x8uLi7YvXs3Hj58CAAYO3Ystm3bBuBVT8lXX30ltvfZWFxdXZGZmYm9e/eiW7duDR6LiorC8OHDAQBBQUEoLCwUngannH668+fP4/z58/Dw8ICnpyeSkpKQmpoKFxcXXLhwAd999x2uXbsGbW1tJCUlwdLSEjY2Nm+MGS8pKUH//v3h7OyMGTNmCHPav39/nDx5ErW1tdi6dStGjRrF0Tt9v7Zt2+LGjRu4ceMG/Pz84OfnJ7xtYmKCBw8eoHPnznB3d8eSJUvw/PlzFBcXo7i4GAEBAQAg/Hz+Izg4GNra2lBRUYGjoyOePHmCmzdvIjExEe3atYO7uzu2b9+OJ0+eCJ83ZswYHDlyBGpqagCA69evC8+kvL59xhi+//57uLq6olOnTsjKykJeXh4sLCzQvHlz3L17V7hfmzdv3khZ/DSWlpZwd3cHAHh5eb11rOy/de7cGbq6ugAAHx8fbNu2DYsWLcL9+/ehqamJW7duITAwEPr6+lBSUsLAgQOFr33+/DlCQkLg4uKC5cuXCz+ro0ePxo4dOwC86rWT9u//P0Ms4uPjcfToUQCAubk52rRp88Zz6Xj57t/zK1euwNfXFy4uLrh8+bLw/X+M0NBQqKqqAgAiIyOFx87u3bujWbNmwuetW7cObm5uaNOmDZ49e4bU1FRoaGggKCgIJ0+eRFJSEmpra+Hi4iKaN92IZKleapqDZD6BvLw8AgMDERgYCBcXF2zfvh0KCgoQCAQAgOrq6gbP/+f0wj+vraure+/2R40ahWPHjsHNzQ3h4eGIiIgA8GrcZGZmJiIiIlBfXy/2i4MaS2hoKL799ltERESgsLDwg15DOf1vr38mBQKBcOwXYwzz5s3D119//cZr7ty5g9OnT+OHH35AcHBwgwPRvy1YsAAdO3bE0aNHkZmZKTxNpqamhs6dO+P48eM4cOAA4uLiRP/mRKBdu3a4ceMG7t+/D2dnZ5iZmWHlypXQ0tJCYGAgsrKyhMMw/vG2i5te97bPJWMMnTt3xt69e994fkxMDC5duoRDhw5hw4YNuHz5MoBXF7D+2+7du8Hn8xEXFwdFRUVYWFgIjzVjx45FeHg4cnNzMXr06I9NRaP7d57+GWLxvuOourq68P8BAQGIjIzEqVOnMGrUKMycORNaWlrvbG/KlCmYOXMmQkNDERERgUWLFgEAzMzMYGhoiMuXLyMmJga7d+8W1VuUGK/n7UM1pePlv3/P//jjD9y7dw+xsbEwMzPDokWL3vgs/uNDP6/vEhERgYsXLyI6OhpqamoIDAxs8J3+5ZdfYG9vL9V/iMhKvUQ9yB8gOTkZqampwtvx8fEwNzeHhYWFsBA4fPjwf24nICAAx44dQ1VVFcrKynDixAnhY2VlZTAyMkJtbe0bB+wRI0ZgyJAhUv2F+bfRo0dj4cKFb/yF7O/vL3z/ERER0NPTe++PIOW0odc/k3///bdw/FVISAi2bt0qHAeblZWF/Px8ZGdnQ01NDcOGDcPs2bNx584d2NvbIzMzE2lpaQDQoMgrKSkRXlT5716BsWPHYurUqfDx8WnQWyJJ2rZti5MnT0JXVxfy8vLQ1dVFcXExoqOjMXjwYPD5fGGBXFtbi4cPH0JHRwc6OjqIiooCgA8qqNq0aYPr16/j8ePHAF6N9U5JSUF5eTlKSkrQrVs3rF69WjhOu127dti3b98b2y8pKYGBgQEUFRVx5coVPHnyRPhYnz59cPbsWdy+fRshISGiSRAHXv/Mvj5+/t+ePHkCQ0NDjBs3DmPHjsWdO3fg6+uLq1evorCwELW1tTh48KDw+a9/Vrdv395gW2PHjsWwYcPQv39/yMvLi+FdSSY6Xr7999zOzg7Aq/Gy5eXlDT6HmpqaKCsrE97+0N/9gIAA7NmzBwBw5swZvHjxAsCrz2WzZs2gpqaGpKQk3Lx5U/gaX19fPHv2DHv27GlwbY40kaV6iXqQP0B5eTmmTJmC4uJiKCgowNraGmFhYXj06BHGjBmDBQsWCHvS3sfT0xMDBw6Em5sbDAwM4OPjI3zs559/hq+vL/T19eHr69vgCzl06FD88MMPUvuFeRtTU1NMnTr1jfv/GbDv6uoKNTW1N37Y/o1y2tC4cePQq1cvuLm5oWvXrsIejS5duuDRo0fw8/MD8Oo02K5du/D48WPMnj0bcnJyUFRUxKZNm6CiooKwsDB0794dampq8Pf3F+Zuzpw5GDlyJJYsWYLu3bs3aNvLywtaWloS/SPq4uKCgoICDBkypMF95eXlMDAwwKFDhzB16lSUlJSgrq4O06dPh5OTE7Zt24bRo0eDx+OhS5cu/9mOvr4+wsPDMXjwYLx8+RIAsGTJEmhqaqJXr16orq4GY0w4rdzatWsxZMgQ/O9//0OvXr2E2xk6dCh69uwJFxcXeHt7w97eXviYkpISOnbsCB0dHaku8r799lsMGDBA+Jl7l4iICCxfvhyKiorQ0NDAjh07YGRkhEWLFsHPzw86OjrCIRzAq2NJ//790axZMwQFBSEjI0P4WGhoKL766iuJ/qyKAx0v3/17rqOjA2dnZ7Ro0aJBXkaNGoUJEyZAVVUV0dHRWLhw4Qf97i9cuBCDBw+Gk5MT2rZti5YtWwIAunbtis2bN8PBwQF2dnZvDIUZMGAA4uPjJbaT4b/IUr1ES01LgUOHDuH48ePYuXMn16HIDMqp6GVnZyMwMBBJSUmQk6OTU+ImEAjg6emJgwcPwsbGhutwpEpsbCxmzJghnAmDvB8dLxtPjx49MGPGDAQHB3MdilQS5WeVepAl3JQpU3DmzBmax1KEKKeit2PHDsyfPx+rVq2i4rgRJCYmokePHujTpw8Vxx9p2bJl2LRpk0yOPRYHOl42juLiYrRu3Rpubm5UHH8iUX9WqQeZEEIIIYSQ11BXDyGEEEIIIa+hApkQQgghhJDXUIFMCCGEEELIa6hAJoQQjsnLy8Pd3R1OTk5wc3PDypUrhZPqi8vs2bPh5OSE2bNni7WdzMxMmVhcghDStNAsFoQQwrF/lgsGgPz8fAwZMgSlpaVYvHix2NoMCwtDUVGRVM+hTAgh4kI9yIQQIkEMDAwQFhaGDRs2gDGGzMxM+Pv7w9PTE56enrhx4waAVytGHTt2TPi6oUOH4vjx4w22xRjD7Nmz4ezsDBcXF+zfvx/Aq4UyysvL4eXlJbzvHy4uLiguLgZjDM2bN8eOHTuE7V24cAH19fWYPXs2fHx84Orqij/++EP42uXLlwvvX7hw4RvvLT09HR4eHrh9+7ZIckUIIeJCPciEECJhrKysUF9fj/z8fBgYGODChQtQUVFBamoqBg8ejNjYWIwZMwarV69G7969UVJSghs3bryx8uSRI0cQHx+PhIQEFBQUwMfHBwEBAfj777+hoaEh7LV+Xbt27XD9+nWYm5vDysoK165dw4gRIxAdHY1NmzZhy5Yt0NbWxu3bt/Hy5Uu0a9cOXbp0QWpqKlJTUxETEwPGGEJDQxEZGSlcQSw5ORmDBg1CeHg43NzcGiONhBDyyahAJoQQCVZbW4vJkycjPj4e8vLySElJAQB06NABEydOBJ/Px+HDh9GvXz8oKDQ8pEdFRWHw4MGQl5eHoaEhOnTogNu3byM0NPSd7fn7+yMyMhLm5ub45ptvEBYWhqysLDRr1gzq6uo4f/487t27h0OHDgEASkpKkJqaivPnz+P8+fPw8PAA8GrJ2dTUVLRs2RJ8Ph+9evXCkSNH4OjoKKZMEUKI6FCBTAghEiY9PR3y8vIwMDDA4sWLYWhoiISEBAgEAqioqAifN2LECOzatQv79u3Dtm3bRNJ2QEAANm7ciKdPn2Lp0qU4evQoDh06BH9/fwCvhm2sX78eISEhDV537tw5zJs3D19//XWD+zMzM6GtrY2WLVsiKiqKCmRCiFSgMciEECJB+Hw+JkyYgMmTJ4PH46GkpARGRkaQk5PDzp07UV9fL3zuqFGjsGbNGgB4a+Hp7++P/fv3o76+Hnw+H5GRkWjduvV72zczM0NBQQFSU1NhZWWF9u3bY8WKFQgICAAAhISEYNOmTaitrQUApKSkoKKiAiEhIdi6dSvKy8sBAFlZWcjPzwcAKCkp4ejRo9ixYwf27Nnz2TkihBBxox5kQgjhWFVVFdzd3VFbWwsFBQUMHz4cM2fOBABMnDgR/fr1w44dO9C1a1eoq6sLX2doaAgHBwf07t37rdvt06cPoqOj4ebmBh6Ph99++w0tWrT4z3h8fX2Fhbi/vz/mzZuH9u3bAwDGjh2LzMxMeHp6gjEGfX19HDt2DF26dMGjR4/g5+cHANDQ0MCuXbuEs2Soq6vj5MmT6Ny5MzQ0NN47zIMQQrjGY4wxroMghBDy8SorK+Hi4oI7d+5AW1ub63AIIURm0BALQgiRQhcvXoSDgwOmTJlCxTEhhIgY9SATQgghhBDyGupBJoQQQggh5DVUIBNCCCGEEPIaKpAJIYQQQgh5DRXIhBBCCCGEvIYKZEIIIYQQQl7z/wC7uk29Xidv6AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}