umojahackcrypt23_b.ipynb

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     "text": [
      "/kaggle/input/umojahack-cryptojacking/SampleSubmission.csv\n",
      "/kaggle/input/umojahack-cryptojacking/Cryptojacking_Detection_Starter_Notebook.ipynb\n",
      "/kaggle/input/umojahack-cryptojacking/Train.csv\n",
      "/kaggle/input/umojahack-cryptojacking/Test.csv\n",
      "/kaggle/input/umojahack-cryptojacking/Variable_Definitions.csv\n"
     ]
    }
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gc\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "import plotly.express as px\n",
    "from pylab import rcParams\n",
    "\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "sns.set_style('darkgrid')\n",
    "rcParams['figure.figsize'] = 8,8\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline \n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import catboost as catt\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import log_loss,roc_auc_score,accuracy_score\n",
    "\n",
    "# from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "#import datasets\n",
    "train = pd.read_csv(\"../input/umojahack-cryptojacking/Train.csv\")\n",
    "test = pd.read_csv(\"../input/umojahack-cryptojacking/Test.csv\")\n",
    "SampleSubmission = pd.read_csv(\"../input/umojahack-cryptojacking/SampleSubmission.csv\")"
   ]
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    "ntrain= train.shape[0]\n",
    "ntest = test.shape[0]\n",
    "train_set = pd.concat((train, test)).reset_index(drop=True)"
   ]
  },
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       "      <td>8908.0</td>\n",
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       "      <td>15.667814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022229</td>\n",
       "      <td>8.315039e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bytes Sent/sent</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>559.546628</td>\n",
       "      <td>1939.745234</td>\n",
       "      <td>1.197890</td>\n",
       "      <td>33.843800</td>\n",
       "      <td>67.366295</td>\n",
       "      <td>221.314810</td>\n",
       "      <td>9.809778e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Received Bytes (HTTP)</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>16558.044068</td>\n",
       "      <td>90428.025210</td>\n",
       "      <td>1.554938</td>\n",
       "      <td>27.377705</td>\n",
       "      <td>89.057696</td>\n",
       "      <td>561.381397</td>\n",
       "      <td>4.812144e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets sent</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>4.795485</td>\n",
       "      <td>20.797585</td>\n",
       "      <td>0.022183</td>\n",
       "      <td>0.399999</td>\n",
       "      <td>0.710898</td>\n",
       "      <td>1.555063</td>\n",
       "      <td>1.118878e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets received</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>12.625792</td>\n",
       "      <td>61.246541</td>\n",
       "      <td>0.022183</td>\n",
       "      <td>0.333225</td>\n",
       "      <td>0.635705</td>\n",
       "      <td>1.711155</td>\n",
       "      <td>3.183701e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Read/sec</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>0.988036</td>\n",
       "      <td>12.495768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022222</td>\n",
       "      <td>0.088887</td>\n",
       "      <td>4.750311e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Input/sec</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>0.004177</td>\n",
       "      <td>0.082923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.955622e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Errors/sec</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>2779.178084</td>\n",
       "      <td>4996.003166</td>\n",
       "      <td>16.143987</td>\n",
       "      <td>351.620535</td>\n",
       "      <td>682.943866</td>\n",
       "      <td>1356.673217</td>\n",
       "      <td>9.803141e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmed byte radius</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>28.436192</td>\n",
       "      <td>3.970140</td>\n",
       "      <td>18.057942</td>\n",
       "      <td>25.266134</td>\n",
       "      <td>28.543826</td>\n",
       "      <td>30.969542</td>\n",
       "      <td>4.678480e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Label</th>\n",
       "      <td>8908.0</td>\n",
       "      <td>0.269870</td>\n",
       "      <td>0.443917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count           mean            std        min  \\\n",
       "I/O Data Operations       8908.0      36.429205      75.884647   0.000000   \n",
       " I/O Data Bytes           8908.0  113556.435854  489684.616342   0.000000   \n",
       "Number of subprocesses    8908.0      29.811900       5.691411   1.000000   \n",
       "Time on processor         8908.0       0.496405       1.593626   0.000000   \n",
       "Disk Reading/sec          8908.0       5.353260      19.286690   0.044459   \n",
       "Disc Writing/sec          8908.0       1.046153      15.667814   0.000000   \n",
       "Bytes Sent/sent           8908.0     559.546628    1939.745234   1.197890   \n",
       "Received Bytes (HTTP)     8908.0   16558.044068   90428.025210   1.554938   \n",
       "Network packets sent      8908.0       4.795485      20.797585   0.022183   \n",
       "Network packets received  8908.0      12.625792      61.246541   0.022183   \n",
       "Pages Read/sec            8908.0       0.988036      12.495768   0.000000   \n",
       "Pages Input/sec           8908.0       0.004177       0.082923   0.000000   \n",
       "Page Errors/sec           8908.0    2779.178084    4996.003166  16.143987   \n",
       "Confirmed byte radius     8908.0      28.436192       3.970140  18.057942   \n",
       "Label                     8908.0       0.269870       0.443917   0.000000   \n",
       "\n",
       "                                 25%          50%           75%           max  \n",
       "I/O Data Operations         1.394404    11.613803     34.494953  1.795316e+03  \n",
       " I/O Data Bytes           813.631825  4930.481627  39403.570772  4.677280e+06  \n",
       "Number of subprocesses     27.000000    28.000000     30.000000  5.100000e+01  \n",
       "Time on processor           0.000000     0.034734      0.451350  2.524615e+01  \n",
       "Disk Reading/sec            0.755629     1.711077      5.845619  1.064819e+03  \n",
       "Disc Writing/sec            0.000000     0.000000      0.022229  8.315039e+02  \n",
       "Bytes Sent/sent            33.843800    67.366295    221.314810  9.809778e+04  \n",
       "Received Bytes (HTTP)      27.377705    89.057696    561.381397  4.812144e+06  \n",
       "Network packets sent        0.399999     0.710898      1.555063  1.118878e+03  \n",
       "Network packets received    0.333225     0.635705      1.711155  3.183701e+03  \n",
       "Pages Read/sec              0.000000     0.022222      0.088887  4.750311e+02  \n",
       "Pages Input/sec             0.000000     0.000000      0.000000  2.955622e+00  \n",
       "Page Errors/sec           351.620535   682.943866   1356.673217  9.803141e+04  \n",
       "Confirmed byte radius      25.266134    28.543826     30.969542  4.678480e+01  \n",
       "Label                       0.000000     0.000000      1.000000  1.000000e+00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43d95ecf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.529347Z",
     "iopub.status.busy": "2023-03-19T00:35:10.528570Z",
     "iopub.status.idle": "2023-03-19T00:35:10.536867Z",
     "shell.execute_reply": "2023-03-19T00:35:10.535898Z"
    },
    "papermill": {
     "duration": 0.024,
     "end_time": "2023-03-19T00:35:10.539345",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.515345",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace all 0.000 values with NaN\n",
    "train.replace(0.000, np.nan, inplace=True)\n",
    "test.replace(0.000, np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16a985d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.565839Z",
     "iopub.status.busy": "2023-03-19T00:35:10.565163Z",
     "iopub.status.idle": "2023-03-19T00:35:10.585291Z",
     "shell.execute_reply": "2023-03-19T00:35:10.584417Z"
    },
    "papermill": {
     "duration": 0.036242,
     "end_time": "2023-03-19T00:35:10.587540",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.551298",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>ID_4W8AP96UO6</td>\n",
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       "      <td>0.621206</td>\n",
       "      <td>0.521813</td>\n",
       "      <td>24.624612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.533430</td>\n",
       "      <td>27.190843</td>\n",
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       "      <th>1</th>\n",
       "      <td>ID_UD3TM0ZYND</td>\n",
       "      <td>14.154240</td>\n",
       "      <td>5182.451722</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.138876</td>\n",
       "      <td>5.399498</td>\n",
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       "      <td>29.508369</td>\n",
       "      <td>0.422183</td>\n",
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       "      <td>0.044440</td>\n",
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       "      <td>1089.121009</td>\n",
       "      <td>30.150941</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>ID_XAG1HC0HWM</td>\n",
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       "      <td>1.577765</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>322.464081</td>\n",
       "      <td>28.163287</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_CEXD05IR09</td>\n",
       "      <td>31.369916</td>\n",
       "      <td>127228.251100</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>15.981107</td>\n",
       "      <td>5.356671</td>\n",
       "      <td>602.125436</td>\n",
       "      <td>6976.986795</td>\n",
       "      <td>3.311801</td>\n",
       "      <td>5.756755</td>\n",
       "      <td>5.623394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12015.169690</td>\n",
       "      <td>34.204404</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_X6E97FT8IF</td>\n",
       "      <td>5.046181</td>\n",
       "      <td>1091.620117</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.069468</td>\n",
       "      <td>1.356022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.138887</td>\n",
       "      <td>13.271234</td>\n",
       "      <td>0.155609</td>\n",
       "      <td>0.133379</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>550.989619</td>\n",
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       "              ID  I/O Data Operations   I/O Data Bytes  \\\n",
       "0  ID_4W8AP96UO6           114.798900      3790.450939   \n",
       "1  ID_UD3TM0ZYND            14.154240      5182.451722   \n",
       "2  ID_XAG1HC0HWM             0.044444       120.821253   \n",
       "3  ID_CEXD05IR09            31.369916    127228.251100   \n",
       "4  ID_X6E97FT8IF             5.046181      1091.620117   \n",
       "\n",
       "   Number of subprocesses  Time on processor  Disk Reading/sec  \\\n",
       "0                    28.0           0.427078          6.162365   \n",
       "1                    28.0           0.138876          5.399498   \n",
       "2                    28.0                NaN          1.577765   \n",
       "3                    31.0           0.523750         15.981107   \n",
       "4                    25.0           0.069468          1.356022   \n",
       "\n",
       "   Disc Writing/sec  Bytes Sent/sent  Received Bytes (HTTP)  \\\n",
       "0         21.220403        58.492773              63.959387   \n",
       "1               NaN        22.886762              29.508369   \n",
       "2               NaN        11.733239              12.888786   \n",
       "3          5.356671       602.125436            6976.986795   \n",
       "4               NaN        16.138887              13.271234   \n",
       "\n",
       "   Network packets sent  Network packets received  Pages Read/sec  \\\n",
       "0              0.621206                  0.521813       24.624612   \n",
       "1              0.422183                  0.444403        0.044440   \n",
       "2              0.177776                  0.155554        0.044444   \n",
       "3              3.311801                  5.756755        5.623394   \n",
       "4              0.155609                  0.133379             NaN   \n",
       "\n",
       "   Pages Input/sec  Page Errors/sec  Confirmed byte radius  Label  \n",
       "0              NaN      1001.533430              27.190843    1.0  \n",
       "1              NaN      1089.121009              30.150941    NaN  \n",
       "2              NaN       322.464081              28.163287    NaN  \n",
       "3              NaN     12015.169690              34.204404    NaN  \n",
       "4              NaN       550.989619              31.466889    1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee3d2b1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.614033Z",
     "iopub.status.busy": "2023-03-19T00:35:10.613597Z",
     "iopub.status.idle": "2023-03-19T00:35:10.642042Z",
     "shell.execute_reply": "2023-03-19T00:35:10.640820Z"
    },
    "papermill": {
     "duration": 0.044914,
     "end_time": "2023-03-19T00:35:10.644870",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.599956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "train.duplicated().any(), test.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a2e3b90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.672528Z",
     "iopub.status.busy": "2023-03-19T00:35:10.672080Z",
     "iopub.status.idle": "2023-03-19T00:35:10.684417Z",
     "shell.execute_reply": "2023-03-19T00:35:10.683198Z"
    },
    "papermill": {
     "duration": 0.029189,
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     "exception": false,
     "start_time": "2023-03-19T00:35:10.657683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "train.isnull().sum().any(), test.isnull().sum().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a636b91",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.714395Z",
     "iopub.status.busy": "2023-03-19T00:35:10.713630Z",
     "iopub.status.idle": "2023-03-19T00:35:10.729439Z",
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    "tags": []
   },
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of subprocesses</th>\n",
       "      <td>Number of subprocesses</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time on processor</th>\n",
       "      <td>Time on processor</td>\n",
       "      <td>41.097890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disk Reading/sec</th>\n",
       "      <td>Disk Reading/sec</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disc Writing/sec</th>\n",
       "      <td>Disc Writing/sec</td>\n",
       "      <td>69.802425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bytes Sent/sent</th>\n",
       "      <td>Bytes Sent/sent</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Received Bytes (HTTP)</th>\n",
       "      <td>Received Bytes (HTTP)</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets sent</th>\n",
       "      <td>Network packets sent</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets received</th>\n",
       "      <td>Network packets received</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Read/sec</th>\n",
       "      <td>Pages Read/sec</td>\n",
       "      <td>40.895824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Input/sec</th>\n",
       "      <td>Pages Input/sec</td>\n",
       "      <td>99.472384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Errors/sec</th>\n",
       "      <td>Page Errors/sec</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmed byte radius</th>\n",
       "      <td>Confirmed byte radius</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Label</th>\n",
       "      <td>Label</td>\n",
       "      <td>73.013022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       column_name  percent_missing\n",
       "ID                                              ID         0.000000\n",
       "I/O Data Operations            I/O Data Operations         9.418500\n",
       " I/O Data Bytes                     I/O Data Bytes         9.418500\n",
       "Number of subprocesses      Number of subprocesses         0.000000\n",
       "Time on processor                Time on processor        41.097890\n",
       "Disk Reading/sec                  Disk Reading/sec         0.000000\n",
       "Disc Writing/sec                  Disc Writing/sec        69.802425\n",
       "Bytes Sent/sent                    Bytes Sent/sent         0.000000\n",
       "Received Bytes (HTTP)        Received Bytes (HTTP)         0.000000\n",
       "Network packets sent          Network packets sent         0.000000\n",
       "Network packets received  Network packets received         0.000000\n",
       "Pages Read/sec                      Pages Read/sec        40.895824\n",
       "Pages Input/sec                    Pages Input/sec        99.472384\n",
       "Page Errors/sec                    Page Errors/sec         0.000000\n",
       "Confirmed byte radius        Confirmed byte radius         0.000000\n",
       "Label                                        Label        73.013022"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = train.isnull().sum() * 100 / len(train)\n",
    "missing_values_train = pd.DataFrame({'column_name': train.columns,\n",
    "                                 'percent_missing': percent_missing})\n",
    "missing_values_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "36bcf3d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.759691Z",
     "iopub.status.busy": "2023-03-19T00:35:10.758754Z",
     "iopub.status.idle": "2023-03-19T00:35:10.772514Z",
     "shell.execute_reply": "2023-03-19T00:35:10.771522Z"
    },
    "papermill": {
     "duration": 0.030314,
     "end_time": "2023-03-19T00:35:10.775094",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.744780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train['I/O Data Operations'] = train['I/O Data Operations'].fillna(train['I/O Data Operations'].mean())\n",
    "test['I/O Data Operations'] = test['I/O Data Operations'].fillna(test['I/O Data Operations'].mean())\n",
    "\n",
    "train[' I/O Data Bytes'] = train[' I/O Data Bytes'].fillna(train[' I/O Data Bytes'].mean())\n",
    "test[' I/O Data Bytes'] = test[' I/O Data Bytes'].fillna(test[' I/O Data Bytes'].mean())\n",
    "\n",
    "\n",
    "train['Pages Read/sec'] = train['Pages Read/sec'].fillna(train['Pages Read/sec'].mean())\n",
    "test['Pages Read/sec'] = test['Pages Read/sec'].fillna(test['Pages Read/sec'].mean())\n",
    "\n",
    "train['Time on processor'] = train['Time on processor'].fillna(train['Time on processor'].mean())\n",
    "test['Time on processor'] = test['Time on processor'].fillna(test['Time on processor'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f9a137e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.802576Z",
     "iopub.status.busy": "2023-03-19T00:35:10.801775Z",
     "iopub.status.idle": "2023-03-19T00:35:10.817238Z",
     "shell.execute_reply": "2023-03-19T00:35:10.816011Z"
    },
    "papermill": {
     "duration": 0.031943,
     "end_time": "2023-03-19T00:35:10.819699",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.787756",
     "status": "completed"
    },
    "tags": []
   },
   "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>column_name</th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>ID</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I/O Data Operations</th>\n",
       "      <td>I/O Data Operations</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I/O Data Bytes</th>\n",
       "      <td>I/O Data Bytes</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of subprocesses</th>\n",
       "      <td>Number of subprocesses</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time on processor</th>\n",
       "      <td>Time on processor</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disk Reading/sec</th>\n",
       "      <td>Disk Reading/sec</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Disc Writing/sec</th>\n",
       "      <td>Disc Writing/sec</td>\n",
       "      <td>70.350969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bytes Sent/sent</th>\n",
       "      <td>Bytes Sent/sent</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Received Bytes (HTTP)</th>\n",
       "      <td>Received Bytes (HTTP)</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets sent</th>\n",
       "      <td>Network packets sent</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Network packets received</th>\n",
       "      <td>Network packets received</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Read/sec</th>\n",
       "      <td>Pages Read/sec</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pages Input/sec</th>\n",
       "      <td>Pages Input/sec</td>\n",
       "      <td>99.319015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Page Errors/sec</th>\n",
       "      <td>Page Errors/sec</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmed byte radius</th>\n",
       "      <td>Confirmed byte radius</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       column_name  percent_missing\n",
       "ID                                              ID         0.000000\n",
       "I/O Data Operations            I/O Data Operations         0.000000\n",
       " I/O Data Bytes                     I/O Data Bytes         0.000000\n",
       "Number of subprocesses      Number of subprocesses         0.000000\n",
       "Time on processor                Time on processor         0.000000\n",
       "Disk Reading/sec                  Disk Reading/sec         0.000000\n",
       "Disc Writing/sec                  Disc Writing/sec        70.350969\n",
       "Bytes Sent/sent                    Bytes Sent/sent         0.000000\n",
       "Received Bytes (HTTP)        Received Bytes (HTTP)         0.000000\n",
       "Network packets sent          Network packets sent         0.000000\n",
       "Network packets received  Network packets received         0.000000\n",
       "Pages Read/sec                      Pages Read/sec         0.000000\n",
       "Pages Input/sec                    Pages Input/sec        99.319015\n",
       "Page Errors/sec                    Page Errors/sec         0.000000\n",
       "Confirmed byte radius        Confirmed byte radius         0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = test.isnull().sum() * 100 / len(test)\n",
    "missing_values_test = pd.DataFrame({'column_name': test.columns,\n",
    "                                 'percent_missing': percent_missing})\n",
    "missing_values_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42c8d72b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.847889Z",
     "iopub.status.busy": "2023-03-19T00:35:10.846712Z",
     "iopub.status.idle": "2023-03-19T00:35:10.855705Z",
     "shell.execute_reply": "2023-03-19T00:35:10.854541Z"
    },
    "papermill": {
     "duration": 0.025597,
     "end_time": "2023-03-19T00:35:10.858191",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.832594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#cols to drop\n",
    "high_miss_val =['Disc Writing/sec', \"Pages Input/sec\"]\n",
    "train = train.drop(high_miss_val, axis=1)\n",
    "test = test.drop(high_miss_val,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30cfb590",
   "metadata": {
    "papermill": {
     "duration": 0.012543,
     "end_time": "2023-03-19T00:35:10.883624",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.871081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "675fc25c",
   "metadata": {
    "papermill": {
     "duration": 0.012555,
     "end_time": "2023-03-19T00:35:10.909023",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.896468",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Explore Data(EDA)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "458fc765",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:10.936804Z",
     "iopub.status.busy": "2023-03-19T00:35:10.936305Z",
     "iopub.status.idle": "2023-03-19T00:35:11.156301Z",
     "shell.execute_reply": "2023-03-19T00:35:11.155319Z"
    },
    "papermill": {
     "duration": 0.236913,
     "end_time": "2023-03-19T00:35:11.158759",
     "exception": false,
     "start_time": "2023-03-19T00:35:10.921846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Target value distribution\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(x = 'Label', data = train)\n",
    "plt.title('Target Variable Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f87cba6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:11.188301Z",
     "iopub.status.busy": "2023-03-19T00:35:11.187201Z",
     "iopub.status.idle": "2023-03-19T00:35:11.196031Z",
     "shell.execute_reply": "2023-03-19T00:35:11.194529Z"
    },
    "papermill": {
     "duration": 0.026623,
     "end_time": "2023-03-19T00:35:11.198804",
     "exception": false,
     "start_time": "2023-03-19T00:35:11.172181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The skewness of the I/O D_O feature is:  7.435873440152207\n",
      "The skewness of the I/O D_B feature is:  5.960240914134111\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew \n",
    "print(\"The skewness of the I/O D_O feature is: \", train['I/O Data Operations'].skew())\n",
    "print(\"The skewness of the I/O D_B feature is: \", train[' I/O Data Bytes'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6d3b6b74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:11.228295Z",
     "iopub.status.busy": "2023-03-19T00:35:11.227828Z",
     "iopub.status.idle": "2023-03-19T00:35:11.235893Z",
     "shell.execute_reply": "2023-03-19T00:35:11.234648Z"
    },
    "papermill": {
     "duration": 0.026191,
     "end_time": "2023-03-19T00:35:11.238418",
     "exception": false,
     "start_time": "2023-03-19T00:35:11.212227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def diagnostic_plots(df, variable):\n",
    "    # function takes a dataframe (df) and\n",
    "    # the variable of interest as arguments\n",
    "\n",
    "    # define figure size\n",
    "    plt.figure(figsize=(16, 4))\n",
    "\n",
    "    # histogram\n",
    "    plt.subplot(1, 3, 1)\n",
    "    sns.histplot(df[variable], bins=30)\n",
    "    plt.title('Histogram')\n",
    "\n",
    "    # Q-Q plot\n",
    "    plt.subplot(1, 3, 2)\n",
    "    stats.probplot(df[variable], dist=\"norm\", plot=plt)\n",
    "    plt.ylabel('Variable quantiles')\n",
    "\n",
    "    # boxplot\n",
    "    plt.subplot(1, 3, 3)\n",
    "    sns.boxplot(y=df[variable])\n",
    "    plt.title('Boxplot')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d44e6804",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:11.267473Z",
     "iopub.status.busy": "2023-03-19T00:35:11.266648Z",
     "iopub.status.idle": "2023-03-19T00:35:11.926865Z",
     "shell.execute_reply": "2023-03-19T00:35:11.925922Z"
    },
    "papermill": {
     "duration": 0.677849,
     "end_time": "2023-03-19T00:35:11.929645",
     "exception": false,
     "start_time": "2023-03-19T00:35:11.251796",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvomvry8tW7YEwN/fH4A333yTYcOG4eDgwM0330zr1q0ZMWIEb731FnZ2duZpCosXL8bb25tHH30UKHriNmbMGFasWEHDhg3Nu4S/9tpreHp6ljripjR9+/bl9ddf59VXX6Vbt278/PPPvP766/j5+VXpGmMiIlJ1hg4dyttvv82zzz5LWloa/v7+/Pvf/2bFihX06dOHnj17WrRv3LgxjzzyCE8++aR5l/CjR48SHx9vblPe64W9vb15tkFhYSGrVq3i3LlzFhvSVVSLFi1wcXHhgw8+oE2bNtSvX5+mTZta7AA+evRopkyZgsFg4KGHHrrhc16LnZ0d06dPZ9q0aUyYMIERI0aQn5/P6tWrOXv2LFOnTjW3vdr1XxvUiYhUvZSUFPM168yZM3z66afs3LmTgQMH0rx58zJfP99//322bdvGggULaNeuHe3atePhhx8mLi6O22+/3WLdSUdHR9auXcuFCxe47bbbzLuE9+7dm65du1411kcffZStW7cyYcIEnn76aXx8fPjHP/7BO++8w5///GfzJqsNGjTA19eXL774guDgYNzc3GjcuLE2dhObUsJSpJrq3r07n3zyCe+//z7nzp3D09OTnj178uSTT5rXduzevTsTJkxgy5YtvP/++xQWFrJu3Tq6d+/O7Nmzad68OZs2beKdd96hQYMGhIaGMnXqVIuh/1OmTKFevXq89957JCYmmndwjY+PL/PUs/DwcC5evMimTZtISEigbdu2zJ49m88//5w9e/ZUSf+IiEjVcnZ2Zt26dcTHx5OQkMDp06fx8vJi7NixTJw4sUT7/v3707ZtW1555RWOHz9O8+bNiYuLY8iQIeY25b1e/OUvfyEvL4/o6GiysrJo164dK1asuO7GdWVRr149XnzxRZYuXcq4ceMwGo1MnDjRIhk6YMAAnJyc6N69O61atbrhc17PPffcQ7169Vi5ciVTpkzB3t6egIAA1q1bx+23325ud63rv4iIVK2ZM2eaf3Z1dcXPz4+ZM2eaH2yV5fp5+PBhoqOjGTZsmHkzHoBnn32W5ORkJk+ezNatW833Y46Ojixfvpzo6GiWLVuGi4sLDzzwADNmzLhmrE2aNOG9997jpZde4qWXXuL8+fP4+fkxffp0xowZY9F2/vz5LFy4kCeeeIL8/HyGDRtGTExMpfSZSEUYTJfPORERAX777TcGDx7MxIkTCQ8Pt3U4IiIiNvHll1/yxBNPsHLlSvr06WPrcEREpA567rnn+OSTT0hOTrZ1KCJWpRGWInXcoUOHSEpKIigoiAYNGvDzzz+TkJBAgwYNuP/++20dnoiIiNX99NNPpKWlERsby6233krv3r1tHZKIiIhInaKEpUgdV69ePQ4cOMCmTZvIycmhQYMGdO/encmTJ+Ph4WHr8ERERKxuzpw5/Oc//6FDhw7ExMSUeU1nEREREakcmhIuIiIiIiIiIiIi1YadrQMQERERERERERERKaaEpYiIiIiIiIiIiFQbSliKiIiIiIiIiIhItaGEpYiIiIiIiIiIiFQbSliKiIiIiIiIiIhIteFg6wBqmqysHCqyr7rBAO7urhX+vC3V1NgVt3UpbuuqqXFD1cdefHy5vtL+DGry71ZNoP6tWurfqqO+rVrX6l9d18pOv58iItVfWa9rSliWk8nEDV0Eb/TztlRTY1fc1qW4raumxg01O/ba4lp/BvrzqVrq36ql/q066tuqpf69Meo/EZHaQ1PCRUREREREREREpNpQwlJERERERERERESqDSUsRUREREREREREpNpQwlJERERERERERESqDSUsRUREREREREREpNpQwlJERERERERERESqDSUsRUREREREqkBBAezcaU9iogM7d9pTUGDriESkpvj22z088cRYvv12j61DEbEJB1sHICIiIiIiUtskJTkQEeFMevofY0R8fAqJjs4jLOySDSMTkeouLy+XVate59SpLFatep3bbuuMs7OLrcMSsSqNsBQREREREalESUkOjBvnQnq6waL8+HED48a5kJSkcSMicnVbtmzi9OlTAJw+fYotWzbZOCIR61PCUkREREREpJIUFEBEhDMmE4BlwtJkKnofEeGs6eEiUqrjx9PZunUTpqJ/RDCZTGzduonjx9NtHJmIdSlhaWUGQ9leIiIiIiJS8+zebf+/aeCl/6feZDKQnm7H7t321g1MRKo9k8nE6tXLzcnK65WL1Gaai2BFxoJC3N1dy9Q231hA9pkLVRyRiIiIiIhUphMnyjb6oKztRKTuSEtLZd++5BLlhYWF7NuXTFpaKn5+zW0QmYj1KWFpJQYDONrb8djaPVw0Xnv+Rz1HexLG3IHBAHqAIiIiIiJSc3h5le0/8GVtJyJ1h6+vHwEBQXz//T4KCwvN5XZ2dnTuHIivr58NoxOxLk0Jt7KLxgJyr/O6XkJTRESqp7179xIeHk5ISAjt27fn888/t6hv3759qa+EhARzm1GjRpWonzJlisVxsrOzmT59Ol26dKFLly5Mnz6ds2fPWuU7iojItfXoUYCPTyEGQ+kJSYPBhI9PIT166P/8ImLJYDAwblw4hivWibtauUhtphGWIiIileTChQu0b9+e++67j0mTJpWo/+qrryze/+tf/+L555/nrrvusih/8MEHefrpp83vXVxcLOqnTp3KiRMnzInOqKgoZsyYwfLlyyvrq4iISAXZ20N0dB7jxrlgMJjMG+0A5iRmdHQe9tV8Ccu9e/eyevVqDhw4QEZGBq+99hoDBgww17dv377Uz02fPp3HHnsMKHoIt2fPHov6IUOGEB8fb36fnZ1NdHQ0X375JQD9+/cnMjKShg0bVvZXEqkRvL19GDr0fhITN2IymTAYDAwdej/NmnnbOjQRq1LCUkREpJL06dOHPn36XLXe09PT4v0XX3xB9+7dad7cci0iFxeXEm2LHTlyhB07drBx40YCAgIAmDdvHiNGjODo0aO0bt36Br+FiIjcqLCwS6xenUtEhDPp6X8kLL29TURH5xEWdsmG0ZWNHsKJ2M6wYfezffvnnDqVRZMmTRg27H5bhyRidUpYioiI2EBmZib//Oc/iYmJKVH3wQcfsG3bNjw8POjduzdPPfUUDRo0ACA5ORlXV1dzshIgMDAQV1dXkpOTy5WwLG1WUXGZZhxVDfVv1VL/Vh31bfndc88lhgy5xO7d9pw4YcDLy0SPHgWljqy8Vv/aqs/1EE7EdpydXRg//klWr17OuHHhODu7XP9DIrWMEpYiIiI2sGXLFm666SYGDRpkUX7PPffg5+eHh4cHKSkpvPTSSxw6dIi1a9cCRYlOd3f3Esdzd3cnMzOzXDG4u7tWqE5unPq3aql/q476tvz+9Keyt62p/VsdHsKBEupSu3Trdgfdut1h6zBEKl1Z/61WwlJERMQGNm/ezD333IOzs7NF+YMPPmj+2d/fn5YtWzJ8+HD++9//0rFjx6ser3iNo/LIysrBdMWeEAZD0Q1zaXVy49S/VUv9W3XUt1XrWv1bXFedVYeHcEWfq979JCIiZaeEpYiIiJV9++23/Pzzz7zyyivXbduxY0ccHR359ddf6dixIx4eHmRlZZVod+rUqVJv+q7FZOKqiYdr1cmNU/9WLfVv1VHfVq2a2r/V4SEclP4gTkREqpeyPohTwlJERMTKNm3aRMeOHbnllluu2zYlJQWj0Whe/ysoKIicnBz2799P586dAdi3bx85OTkEBQVVadwiIiJXqi4P4aDmJnxFRKQkO1sHICIiUlucP3+egwcPcvDgQQBSU1M5ePAg6enp5jbnzp3j448/5oEHHijx+WPHjrF06VK+//57UlNT+ec//8lf//pXOnTowO233w5AmzZtCA0NJSIigu+++47vvvuOiIgI+vXrp80JRETE6irrIVwxPYQTERHQCEsREZFKc+DAAUaPHm1+v2DBAgCGDRtm3ojgww8/xGQyERYWVuLzjo6O7N69m/Xr13P+/Hm8vb3p06cPEydOxP6ybWXj4uKIjo5m7NixAPTv35+oqKiq/GoiIlLHnD9/nmPHjpnfFz+Ec3Nzw8fHB/jjIdyzzz5b4vPHjh1j27Zt9OnTh8aNG3PkyBFiYmKu+hBu7ty5AERGRuohnIiIKGEpIiJSWbp3787hw4ev2WbEiBGMGDGi1Dpvb2/eeuut656nUaNGxMXFVShGERGRstBDOBERsSUlLEVERERERMSCHsKJiIgtaQ1LERERERERERERqTaUsBQREREREREREZFqQwlLERERERERERERqTaUsBQREREREREREZFqw6YJy/79+9O+ffsSrzlz5gBgMplYsmQJISEhdO7cmVGjRpGSkmJxjPz8fObNm0f37t0JDAwkPDyc33//3aJNdnY206dPp0uXLnTp0oXp06dz9uxZq31PERERERERERERKRubJiw3bdrEV199ZX6tXbsWgLvvvhuAVatWsXbtWqKioti0aRMeHh6MGTOGc+fOmY8xf/58PvvsM+Lj43nnnXe4cOECEyZMoKCgwNxm6tSpHDp0iISEBBISEjh06BAzZsyw7pcVERERERERERGR67JpwrJJkyZ4enqaX9u3b6dFixbccccdmEwm1q1bR3h4OIMGDcLf35/Y2Fhyc3NJSkoCICcnh82bN/Pcc8/Rs2dPOnTowKJFi/jxxx/ZtWsXAEeOHGHHjh1ER0cTFBREUFAQ8+bNY/v27Rw9etSWX19ERERERERERESu4GDrAIrl5+ezbds2xowZg8Fg4LfffiMjI4OQkBBzGycnJ7p160ZycjIjR47kwIEDGI1GevXqZW7j5eVFu3btSE5OJjQ0lOTkZFxdXQkICDC3CQwMxNXVleTkZFq3bl2uOA2GG/+u1fFc11IcR3WJp6wUt3UpbuuqqXFD1cdeE/tERERERERE/lBtEpaff/45OTk5DBs2DICMjAwA3N3dLdp5eHiQnp4OQGZmJo6Ojri5uZVok5mZaW5z5TGKj1vcpjzc3V3L/ZnLOTraUVCGNpVxrspW3eIpK8VtXYrbumpq3FCzYxcREREREZGqU20Slps3b6Z37954eXlZlBuuGCpjMpmue6yytrny2GWRlZVDGQ5fgsFQdHNuNBZiNF47ZWl/g+eqbMWxV5d4ykpxW5fitq6aGjdUfezFxxcREREREZGaqVokLNPS0ti1axdLliwxl3l6egJFIySbNm1qLs/KysLDwwMoGklpNBrJzs62GGWZlZVFUFCQuU1WVlaJc546darUkZfXYzJR4YSltc5VVapbPGWluK1LcVtXTY0banbsIiIiIiIiUnVsuulOscTERNzd3enbt6+5zM/PD09PT3bu3Gkuy8/PZ+/eveZkZKdOnXB0dLRoc/LkSVJSUsxtgoKCyMnJYf/+/eY2+/btIycnx9xGREREREREREREqgebj7AsLCwkMTGRoUOH4uDwRzgGg4HRo0ezYsUKWrVqRcuWLVmxYgUuLi6EhYUB4OrqyvDhw4mNjaVx48a4ubkRGxuLv78/PXv2BKBNmzaEhoYSERHB3LlzAYiMjKRfv37l3nBHREREREREREREqpbNE5a7du0iPT2d4cOHl6gbP348eXl5zJkzh+zsbAICAlizZg0NGjQwt5k1axYODg5MnjyZ3NxcgoODiYmJwd7e3twmLi6O6Ohoxo4dC0D//v2Jioqq+i8nIiIiIiIiIiIi5WLzhGVISAiHDx8utc5gMDBp0iQmTZp01c87OzsTGRlJZGTkVds0atSIuLi4G45VREREREREREREqla1WMNSREREREREREREBJSwFBERERERERERkWpECUsRERERERERERGpNpSwFBERERERERERkWpDCUsRERERERERERGpNpSwFBERERERERERkWpDCUsRERERERERERGpNpSwFBERERERERERkWpDCUsRERERERERERGpNpSwFBERqSR79+4lPDyckJAQ2rdvz+eff25R/9xzz9G+fXuL14MPPmjRJj8/n3nz5tG9e3cCAwMJDw/n999/t2iTnZ3N9OnT6dKlC126dGH69OmcPXu2yr+fiIiIiIiINShhKSIiUkkuXLhA+/btiYqKumqb0NBQvvrqK/Nr5cqVFvXz58/ns88+Iz4+nnfeeYcLFy4wYcIECgoKzG2mTp3KoUOHSEhIICEhgUOHDjFjxowq+14iIiIiIiLW5GDrAERERGqLPn360KdPn2u2cXJywtPTs9S6nJwcNm/ezMKFC+nZsycAixYtom/fvuzatYvQ0FCOHDnCjh072LhxIwEBAQDMmzePESNGcPToUVq3bl25X0pERERERMTKlLAUERGxoj179hAcHEzDhg3p1q0bU6ZMwd3dHYADBw5gNBrp1auXub2Xlxft2rUjOTmZ0NBQkpOTcXV1NScrAQIDA3F1dSU5OblcCUuD4eplpdXJjVP/Vi31b9VR31ata/Wv+lxEROoiJSxFRESspHfv3tx99934+PiQmprK4sWLeeSRR0hMTMTJyYnMzEwcHR1xc3Oz+JyHhweZmZkAZGZmmhOcl3N3dze3KSt3d9cK1cmNU/9WLfVv1VHfFikogB074Phx8PaG0FCwt7/x41an/t27dy+rV6/mwIEDZGRk8NprrzFgwABz/XPPPceWLVssPhMQEMDGjRvN7/Pz84mNjSUpKYm8vDx69OjB7NmzadasmblNdnY20dHRfPnllwD079+fyMhIGjZsWMXfUEREqjMlLEVERKxkyJAh5p/9/f3p1KkT/fv35x//+AeDBg266udMJtN1j20ymTCUcxhOVlYOVx7aYCi6YS6tTm6c+rdqqX+rjvr2D0lJDjz/vDPp6X9sB+DjU8j8+XmEhV2q0DGv1b/FddZWvC7zfffdx6RJk0ptExoayoIFC8zvHR0dLernz5/P9u3biY+Pp1GjRsTExDBhwgQSExOx/1+Gd+rUqZw4cYKEhAQAoqKimDFjBsuXL6+ibyYiIjWBEpYiIiI20rRpU3x8fPjll1+AopGURqOR7Oxsi1GWWVlZBAUFmdtkZWWVONapU6dKHXl5LSYTV008XKtObpz6t2qpf6tOXe/bpCQHxo1zKdEHx48bGDvWhdWrcyuctITq1b9al1lERGxJu4SLiIjYyOnTpzl+/DhNmzYFoFOnTjg6OrJz505zm5MnT5KSkmJOWAYFBZGTk8P+/fvNbfbt20dOTo65jYiIVL6CAoiIcP5fQtFyRLvJVPQ+IsKZggLrx2Yrxesy33XXXURERFg8ULveuszAdddlLi+DQS+99NJLr5rwKguNsBQREakk58+f59ixY+b3qampHDx4EDc3N9zc3Fi6dCmDBg3C09OTtLQ04uPjady4sXlNMFdXV4YPH05sbCyNGzfGzc2N2NhY/P39zaNT2rRpQ2hoKBEREcydOxeAyMhI+vXrp5EoIiJVaPdue4tp4FcymQykpxvYvdueXr1qf9ayuq3LXPS56rMGqMiN2rVrF4sXL+avf/2r+f+BInWJEpYiIiKV5MCBA4wePdr8vnhdr2HDhjF79mx+/PFHtm7dSk5ODp6ennTv3p34+HgaNGhg/sysWbNwcHBg8uTJ5ObmEhwcTExMjHmtL4C4uDiio6MZO3YsULRBQVRUlJW+pYhI3XTiRNmGhJS1XU1X3dZlhtLXZhapifLycomLe4lTp7KIi3uJJUva4ezsYuuwRCqFwVC2B0xKWIqIiFSS7t27c/jw4avWr169+rrHcHZ2JjIyksjIyKu2adSoEXFxcRWKUUREKsbLq2yZsLK2q21svS4zVK81QEVuRGLiJk6fPgXA6dOnSEzcxMiRD9s4KhHr0hqWIiIiIiIi19GjRwE+PoUYDKVnxAwGEz4+hfToUfung5dG6zKLVI7jx9PZunWTeTSyyWRi69ZNHD+ebuPIRKxLCUsREREREZHrsLeH6Og8gBJJy+L30dF5XLaCR412/vx5Dh48yMGDB4E/1mVOT0/n/PnzxMbGkpycTGpqKt988w1PPPHEVddl/vrrr/nhhx+YPn36Vddl/u677/juu++IiIjQusxSZ5lMJlavXl5i6YSrlYvUZpoSLiIiIiIiUgZhYZdYvTqXiAhn0tP/WGPR29tEdHQeYWGXbBhd5dK6zCLWl5aWyr59ySXKCwsL2bcvmbS0VPz8mtsgMhHrU8JSRERERESkjMLCLjF48CV277bnxAkDXl4mevQoqDUjK4tpXWYR6/P19SMgIIjvv99HYWGhudzOzo7OnQPx9fWzYXQi1qUp4SIiIiIiIuVgbw+9ehVw332X6NWr9iUrRcQ2DAYD48aFYzAYylQuUpspYSkiIiIiIiIiUg14e/swdOj95uSkwWBg6ND7adbM28aRiViXEpYiIiIiIiIiItXEsGH307hxEwCaNGnCsGH32zgiEetTwlJEREREREREpJpwdnZh/Pgn8fDw5LHHnsTZ2cXWIYlYnc0TlidOnGDatGl0796dgIAA/vSnP3HgwAFzvclkYsmSJYSEhNC5c2dGjRpFSkqKxTHy8/OZN28e3bt3JzAwkPDwcH7//XeLNtnZ2UyfPp0uXbrQpUsXpk+fztmzZ63yHUVEREREREREyqpr1ztYtmwNXbveYetQRGzCpgnL7Oxs/vznP+Po6MiqVav48MMPee6552jYsKG5zapVq1i7di1RUVFs2rQJDw8PxowZw7lz58xt5s+fz2effUZ8fDzvvPMOFy5cYMKECRQUFJjbTJ06lUOHDpGQkEBCQgKHDh1ixowZVv2+IiIiIiIiIiIicm0Otjz5qlWraNasGQsWLDCX+fn5mX82mUysW7eO8PBwBg0aBEBsbCw9e/YkKSmJkSNHkpOTw+bNm1m4cCE9e/YEYNGiRfTt25ddu3YRGhrKkSNH2LFjBxs3biQgIACAefPmMWLECI4ePUrr1q2t+K1FRERERERERETkamw6wvLLL7+kU6dOPP300wQHBzN06FA2btxork9NTSUjI4OQkBBzmZOTE926dSM5ORmAAwcOYDQa6dWrl7mNl5cX7dq1M7dJTk7G1dXVnKwECAwMxNXV1dymrAyGir0qoqLnqopXdYtHcVfPl+JW3NUldhEREREREam5bDrC8rfffuPdd99lzJgxhIeHs3//fqKjo3FycmLo0KFkZGQA4O7ubvE5Dw8P0tPTAcjMzMTR0RE3N7cSbTIzM81trjxG8XGL25SVu7trudpfydHRjoIytKmMc1W26hZPWSlu61Lc1lVT44aaHbuIiIiIiIhUHZsmLE0mE506deKZZ54BoEOHDvz000+8++67DB061NzOcMVwGZPJVKZjl6XNlce+nqysHMpw6BIMhqKbc6OxEKPx2ilL+xs8V2Urjr26xFNWitu6FLd11dS4oepjLz6+iIiIiIiI1Ew2TVh6enrSpk0bi7LWrVvzySefmOuhaIRk06ZNzW2ysrLw8PAAikZSGo1GsrOzLUZZZmVlERQUZG6TlZVV4vynTp0qdeTltZhMVDhhWV4VPVdVqW7xlJXiti7FbV01NW6o2bGLiIiIiIhI1bHpGpa33347P//8s0XZL7/8gq+vL1C0AY+npyc7d+401+fn57N3715zMrJTp044OjpatDl58iQpKSnmNkFBQeTk5LB//35zm3379pGTk2NuIyIiIiIiIiIiIrZn0xGWjzzyCH/+859Zvnw5gwcPZv/+/WzcuJG5c+cCRVPBR48ezYoVK2jVqhUtW7ZkxYoVuLi4EBYWBoCrqyvDhw8nNjaWxo0b4+bmRmxsLP7+/uZdw9u0aUNoaCgRERHmY0dGRtKvXz/tEC4iIiIiIiIiIlKN2DRh2blzZ5YuXcrLL7/Ma6+9hp+fH7NmzeLee+81txk/fjx5eXnMmTOH7OxsAgICWLNmDQ0aNDC3mTVrFg4ODkyePJnc3FyCg4OJiYnB3t7e3CYuLo7o6GjGjh0LQP/+/YmKirLelxUREREREREREZHrsmnCEqBfv37069fvqvUGg4FJkyYxadKkq7ZxdnYmMjKSyMjIq7Zp1KgRcXFxNxSriIiIiIiIiIiIVC2brmEpIiIiIiIiIiIicjklLEVERERERERERKTasPmUcBERERERkeqooAB277bnxAkDXl4mevQo4LJl8kVERKSKKGEpIiIiIiJyhaQkByIinElP/2NSmo9PIdHReYSFXbJhZCIiIrWfpoSLiIiIiIhcJinJgXHjXEhPN1iUHz9uYNw4F5KSque4j9zcXC5evGh+n5aWxhtvvMFXX31lw6hERETKTwlLERERERGR/ykogIgIZ0wmAMuEpclU9D4iwpmCAuvHdj1PPvkkW7duBeDs2bM8+OCDrF27lieffJJ33nnHtsGJiIiUgxKWIiIilWTv3r2Eh4cTEhJC+/bt+fzzz811RqORRYsWcc899xAYGEhISAgzZszgxIkTFscYNWoU7du3t3hNmTLFok12djbTp0+nS5cudOnShenTp3P27FmrfEcRkdpu9277/00DN5RabzIZSE+3Y/fu6reY5X//+1+6du0KwCeffIK7uzvbt28nNjaW9evX2zg6ERGRsquecxlERERqoAsXLtC+fXvuu+8+Jk2aZFGXm5vLDz/8wBNPPMEtt9zC2bNnefHFF3niiSdITEy0aPvggw/y9NNPm9+7uLhY1E+dOpUTJ06QkJAAQFRUFDNmzGD58uVV9M1EROqOEydKT1RWtJ015ebmctNNNwHw1VdfMWjQIOzs7AgMDCQ9Pd3G0YmIiJSdEpYiIiKVpE+fPvTp06fUOldXV9auXWtRFhERwQMPPEB6ejo+Pj7mchcXFzw9PUs9zpEjR9ixYwcbN24kICAAgHnz5jFixAiOHj1K69atK+nbiIjUPQUFkJFRtkSkl5epiqMpvxYtWvD5558zcOBAvvrqKx599FEAsrKyaNCggW2DE5Fy+fbbPaxevZxx48Lp2vUOW4cjYnVKWIqIiNjIuXPnMBgMNGzY0KL8gw8+YNu2bXh4eNC7d2+eeuop841mcnIyrq6u5mQlQGBgIK6uriQnJ5crYWko5Z68uKy0Orlx6t+qpf6tOnWhb5OSHHj+ectdwUtjMJjw8TERHFxQaf1xrf4tzzmeeuoppk2bxoIFCwgODiYoKAiAnTt3cuutt1ZCpCJiDXl5uaxa9TqnTmWxatXr3HZbZ5ydXa7/QZFaRAlLERERG8jLyyMuLo6wsDCLUS/33HMPfn5+eHh4kJKSwksvvcShQ4fMozMzMzNxd3cvcTx3d3cyMzPLFYO7u2uF6uTGqX+rlvq36tTWvk1MhLFj+d9GO1dXlDw08OqrBry8Kr8vbrR/7777brp06UJGRga33HKLuTw4OJgBAwbcaHgiYiVbtmzi9OlTAJw+fYotWzYxcuTDNo5KxLqUsBQREaFoN9UrRzpWFaPRyJQpUzCZTMyePdui7sEHHzT/7O/vT8uWLRk+fDj//e9/6dix41WPaTKZMJRzqE9WVk6Jm3ODoeiGubQ6uXHq36ql/q06tblv8/Ph8cdv+t8O4Nf+d9THp5Do6Dx6975EOZ8RXdO1+re4rqw8PT1LLCvSuXPnyghTRKzg+PF0tm7dhOl//xiYTCa2bt1Enz798fb2uc6nRWoPJSxFRKTOWblyJX5+fgwZMgSAv/71r3z66ad4eHiwatUqi1Eplc1oNDJ58mRSU1N58803r7umWMeOHXF0dOTXX3+lY8eOeHh4kJWVVaLdqVOnSh15eS0m09VHE12rTm6c+rdqqX+rTm3r26QkB6ZPdyYr69rTwAHmzcvlsceM2NtXXR/caP9euHCBlStXsnv3brKysigsLLSo/+KLL24wQhGpSiaTidWrl5uTlVeWP//8nHI/oBapqZSwFBGROmfDhg0sWrQIKFrXa9euXaxatYqPPvqIhQsXsmbNmio5b3Gy8tdff2XdunU0btz4up9JSUnBaDSaR8sEBQWRk5PD/v37zSNm9u3bR05OjnmtMhERub6kJAfGjXMpc4LQ09OEvX3VxnSjIiIi2LNnD3/605/w9PS8ocTG3r17Wb16NQcOHCAjI4PXXnvNPK3caDTyyiuv8K9//YvffvuNBg0a0LNnT6ZOnYqXl5f5GKNGjWLPnj0Wxx0yZAjx8fHm99nZ2URHR/Pll18C0L9/fyIjI60260GkOklLS2XfvuQS5YWFhezbl0xaWip+fs1tEJmI9SlhKSIidU5GRgbe3t4AbN++ncGDBxMSEoKvr6/FlOzyOn/+PMeOHTO/T01N5eDBg7i5udG0aVOefvppfvjhB1asWEFBQQEZGRkAuLm54eTkxLFjx9i2bRt9+vShcePGHDlyhJiYGDp06MDtt98OQJs2bQgNDSUiIoK5c+cCEBkZSb9+/bRDuIhIGRUUQESE8/+SlTV3V/Ar/etf/2LFihV06dLlho914cIF2rdvz3333cekSZMs6nJzc/nhhx944oknuOWWWzh79iwvvvgiTzzxBImJiRZtH3zwQZ5++mnzexcXy41Dpk6dyokTJ0hISAAgKiqKGTNmsHz58hv+DiI1ja+vHwEBQXz//T6LEdJ2dnZ07hyIr6+fDaMTsS4lLEVEpM5p2LAhx48fx9vbmx07djB58mSgaLpNQUFBhY974MABRo8ebX6/YMECAIYNG8bEiRPNo0f+9Kc/WXxu3bp1dO/eHUdHR3bv3s369es5f/483t7e9OnTh4kTJ2J/2bCeuLg4oqOjGTt2LFA0GiUqKqrCcYuI1DW7d9tfdzfwYgaDCW9vEz16VPz6YC0NGzakUaNGlXKsPn360KdPn1LrXF1dzZvBFYuIiOCBBx4gPT0dH58/1tlzcXEpsaZmsSNHjrBjxw42btxIQEAAAPPmzWPEiBEcPXpUD+KkzjEYDIwbF86UKU+WWq7p4FKXKGEpIiJ1zqBBg5g2bRotW7bkzJkz9O7dG4BDhw7RsmXLCh+3e/fuHD58+Kr116oD8Pb25q233rrueRo1akRcXFy54xMRkSInTpT1pr9oVGV0dF61nw4ORWsyL168mNjYWOrVq2fVc587dw6DwVBiKvcHH3zAtm3b8PDwoHfv3jz11FPm9ZuTk5NxdXU1JysBAgMDcXV1JTk5udwJS+VypDbw8fEhLGwof/vbZnPZPfcMNc8OEqnpyvpvtRKWIiJS58ycORNfX1+OHz/O9OnTuemmmwA4efIkDz30kI2jExGRqlbW6d0eHiYWLswjLOxSFUdUOdauXcuxY8fo2bMnfn5+ODhY3u5t2bKlSs6bl5dHXFwcYWFhFpvJ3XPPPfj5+eHh4UFKSgovvfQShw4dMo/OzMzMLHXDOHd3dzIrsA17eXZTF6nO6td3uuK9Mx4e+v2WukUJSxERqXMcHR0ZN25cifJHH33U+sGIiIjV9ehRgI9PIcePGzCZShvqYcLd3cR3353HyamU6mqqeFMcazIajUyZMgWTycTs2bMt6i5fF9rf35+WLVsyfPhw/vvf/9KxY8erHtNkMlVo6mtWVk6t2sVe6qbjx9PZuHGjRdmGDRsIDu6Dt7fPVT4lUnMYDGV7wKSEpYiI1Elbt25lw4YN/Pbbb2zYsAFfX1/eeOMN/Pz8bHLDJyIi1mNvXzTNe9w4FwwGk0XS0mAoyngtWpRXo5KVABMnTrTq+YxGI5MnTyY1NZU333zTYnRlaTp27IijoyO//vorHTt2xMPDg6ysrBLtTp06VerIy+sxmVDCUmo0k8lEQsJyTFf8IheXP//8HK1jKXVG2VaaFhERqUXeeecdYmJi6N27Nzk5OeZdGBs2bMibb75p4+hERMQawsIusXp1Lt7elokBb28Tq1fn1php4KU5cOAAf/vb39i2bRs//PBDlZyjOFn566+/8sYbb9C4cePrfiYlJQWj0WjehCcoKIicnBz2799vbrNv3z5ycnIICgqqkrhFqrO0tFT27Uu22CEcoLCwkH37kklLS7VRZCLWpxGWIiJS57z11ltER0czYMAAVq5caS7v1KkTsbGxNoxMRESsKSzsEoMHX2L3bntOnDDg5VW0G3hN2GCnNFlZWUyZMoU9e/bQsGFDTCYTOTk5dO/enfj4eJo0aVLmY50/f55jx46Z36empnLw4EHc3Nxo2rQpTz/9ND/88AMrVqygoKCAjIwMANzc3HBycuLYsWNs27aNPn360LhxY44cOUJMTAwdOnTg9ttvB6BNmzaEhoYSERHB3LlzAYiMjKRfv37aIVzqJF9fP265pQOHDpV80HDrrR3x9fWzQVQitqGEpYiI1DmpqanceuutJcqdnJy4ePGiDSISERFbsbeHXr0KbB1GpZg3bx7nzp3jww8/pE2bNgD89NNPPPvss0RHR/Pyyy+X+VgHDhxg9OjR5vcLFiwAYNiwYUycOJEvv/wSgD/96U8Wn1u3bh3du3fH0dGR3bt3s379es6fP4+3tzd9+vRh4sSJ2F+WEY6LiyM6OpqxY8cC0L9/f6KioirWASK12JXTxEVqOyUsRUSkzvHz8+PgwYP4+vpalP/rX/+ibdu2NopKRETkxuzYsYO1a9eak5UAbdu25YUXXjAnBMuqe/fuHD58+Kr116oD8Pb25q233rrueRo1akRcXFy5YhOprdLSUksdXQlw6NAPpKWl4ufX3MpRidiGEpYiIlLnjBs3jrlz55Kfnw/A/v37SUpKYuXKlURHR9s4OhERkYopLCzE0dGxRLmDg0OJNfFEpPrx9fUjICCI/fu/sxhRaTAYCAgI0pRwqVO06Y6IiNQ5w4cPZ+LEiSxatIiLFy8ydepU3nvvPWbNmsX//d//2To8ERGRCunRowfz58/nxIkT5rITJ06wYMECgoODbRiZiJSFwWBg3Lhw7OwsUzV2dnaMGxeuHcKlTtEISxERqZMefPBBHnzwQU6dOoXJZMLd3d3WIYmIiNyQqKgonnzySe68806aNWuGwWDg+PHj+Pv7s2jRIluHJyJl4O3tw9Ch95OYuBGTyYTBYGDo0Ptp1szb1qGJWJUSliIiUqeVZ8dUERGR6szb25stW7awc+dOjh49islkom3btvTs2dPWoYlIOQwbdj/bt3/OqVNZNGnShGHD7rd1SCJWZ9OE5ZIlS1i6dKlFmYeHBzt37gSKdsFaunQpGzZs4OzZswQEBBAVFUW7du3M7fPz84mNjSUpKYm8vDx69OjB7NmzadasmblNdnY20dHR5p3s+vfvT2RkJA0bNrTCtxQRkepg6NChZZ5Gs2XLliqORkREpOr06tWLXr162ToMEakgZ2cXxo9/ktWrlzNuXDjOzi62DknE6mw+wrJdu3asXbvW/N7e3t7886pVq1i7di0xMTG0atWKZcuWMWbMGD7++GMaNGgAwPz589m+fTvx8fE0atSImJgYJkyYQGJiovlYU6dO5cSJEyQkJABFUyVmzJjB8uXLrfhNRUTElgYMGGDrEERERCrdunXrGDFiBM7Ozqxbt+6abUePHm2lqETkRnXtegddu95h6zBEbMbmCUt7e3s8PT1LlJtMJtatW0d4eDiDBg0CIDY2lp49e5KUlMTIkSPJyclh8+bNLFy40DzNYdGiRfTt25ddu3YRGhrKkSNH2LFjBxs3biQgIACAefPmMWLECI4ePUrr1q2t92VFRMRmJk6caOsQREREKt0bb7zBPffcg7OzM2+88cZV2xkMBiUsRUSkxrB5wvLXX38lJCQEJycnAgICeOaZZ2jevDmpqalkZGQQEhJibuvk5ES3bt1ITk5m5MiRHDhwAKPRaDHdwcvLi3bt2pGcnExoaCjJycm4urqak5UAgYGBuLq6kpycXO6EpTU35aouG4AVx1Fd4ikrxW1ditu6amrcUPWx18Q+ERERqajiZa+u/FlERKQms2nCsnPnzsTGxtKqVSuysrJYtmwZI0eOJCkpiYyMDIASu7Z6eHiQnp4OQGZmJo6Ojri5uZVok5mZaW5T2s6v7u7u5jbl4e7uWu7PXM7R0Y6CMrSpjHNVtuoWT1kpbutS3NZVU+MG68d+xx138PHHH9OkSRO6det2zfUs9+zZY8XIREREKsfSpUsZN24c9erVsyjPzc0lISFBsw1ERKTGsGnCsk+fPhbvAwMDGThwIFu3bjWPiLzyhtJkMl33uGVtU9bNFy6XlZVDGQ5fgsFQdHNuNBZiNF47ZVm8imdFz1XZimOvLvGUleK2LsVtXTU1bqj62IuPf6WZM2ea1z+eOXNmha4BIiIi1dlrr73Gn//85xIJy4sXL/Laa68pYSkiIjWGzaeEX65+/fr4+/vzyy+/mDdHyMzMpGnTpuY2WVlZeHh4AEUjKY1GI9nZ2RajLLOysggKCjK3ycrKKnGuU6dOlTry8npMJiqcsLTWuapKdYunrBS3dSlu66qpcYP1Yx82bJj55/vuu896JxYREbGSqw3KOHToUIlZaSIiItWZna0DuFx+fj5HjhzB09MTPz8/PD092blzp0X93r17zcnITp064ejoaNHm5MmTpKSkmNsEBQWRk5PD/v37zW327dtHTk6OuY2IiNQtt956a6kPs06fPs2tt95qg4hEREQqrlu3btxxxx0YDAbuuusu7rjjDvOrS5cujB07lsGDB9s6TBERkTKz6QjL2NhY+vXrh7e3N6dOnWLZsmWcO3eOYcOGmXexW7FiBa1ataJly5asWLECFxcXwsLCAHB1dWX48OHExsbSuHFj3NzciI2Nxd/f37xreJs2bQgNDSUiIoK5c+cCEBkZSb9+/bRDuIhIHXW1pUPy8/NxdHS0cjQiIiI3ZtasWZhMJmbNmsWkSZNwdf1jaRRHR0d8fX01WENERGoUmyYsf//9d5555hnOnDlD48aNCQwMZOPGjfj6+gIwfvx48vLymDNnDtnZ2QQEBLBmzRrzGmRQdHF2cHBg8uTJ5ObmEhwcTExMDPb29uY2cXFxREdHM3bsWAD69+9PVFSUdb+siIjY3Lp164Ci9ZHff/996tevb64rLCxk7969epglIlJHFBTA7t32nDhhwMvLRI8eBVx2C1GjFC974ufnR1BQkB6+iYhIjWcwlWWHGjHLzKzYJhF2dkWbQPxl5dfkXmfTHRdHe95+PLjC56psBgN4eLhWm3jKSnFbl+K2rpoaN1R97MXHL03//v0BSE9Pp1mzZtjZ/bEyiqOjI35+fjz99NPmjd9qu9L+DGry71ZNoP6tWurfqlPb+jYpyYGICGfS0/+4Dvj4FBIdnUdY2CWrx3Ot/r3Wde1acnNzuXTJ8rtcPvCjNqotv58iIrVZWa9r1WrTHRERkar05ZdfAjBq1CiWLl2qDQhEROqgpCQHxo1zKZHYOn7cwLhxLqxenWuTpGVluHjxIosWLeKjjz7izJkzJeoPHjxo/aBEREQqoFptuiMiImIN69evV7JSRKQOKiiAiAjn/yUrLXfTNpmK3kdEOFNw7QlR1dbChQvZvXs3L7zwAk5OTkRHRzNp0iSaNm1KbGysrcMTEREpM42wFBGROqegoIDExER2795NVlYWhYWFFvXFa12KiEjtsnu3vcU08CuZTAbS0w3s3m1Pr141L2u5fft2YmNj6d69O7NmzaJr1660bNkSHx8fPvjgA+69915bhygiIlImGmEpIiJ1zvz583nxxRcpKCigXbt23HLLLRavitq7dy/h4eGEhITQvn17Pv/8c4t6k8nEkiVLCAkJoXPnzowaNYqUlBSLNvn5+cybN4/u3bsTGBhIeHg4v//+u0Wb7Oxspk+fTpcuXejSpQvTp0/n7NmzFY5bRKSuOHHCcP1G5WhX3WRnZ+Pn5wcUrVeZnZ0NQJcuXfj2229tGZqIiEi5aISliIjUOR9++CGvvPIKffr0qdTjXrhwgfbt23PfffcxadKkEvWrVq1i7dq1xMTE0KpVK5YtW8aYMWP4+OOPzRshzJ8/n+3btxMfH0+jRo2IiYlhwoQJJCYmYv+/7WunTp3KiRMnSEhIACAqKooZM2awfPnySv0+IiK1jZdX2XZkKWu76sbPz4+0tDR8fX1p27YtH330EZ07d2b79u24upZ/4x4RERFb0QhLERGpcxwdHWnRokWlH7dPnz5MmTKFQYMGlagzmUysW7eO8PBwBg0ahL+/P7GxseTm5pKUlARATk4Omzdv5rnnnqNnz5506NCBRYsW8eOPP7Jr1y4Ajhw5wo4dO4iOjiYoKIigoCDmzZvH9u3bOXr0aKV/JxGR2qRHjwK8vQuB0hOSBoMJH59CevSoedPBAYYPH86hQ4cAePzxx3nnnXfo1KkTCxYsYNy4cTaOTkREpOw0wlJEROqcsWPHsm7dOqKiojAYrDPtLzU1lYyMDEJCQsxlTk5OdOvWjeTkZEaOHMmBAwcwGo306tXL3MbLy4t27dqRnJxMaGgoycnJuLq6EhAQYG4TGBiIq6srycnJtG7duswxlfbVi8us1C11jvq3aql/q05t6duPP3YgNxeu3HAHipKVAPPn5+Fg5buka/Vvefr80UcfNf/co0cPPvroIw4cOECLFi1uaMkTERERa1PCUkRE6px///vffPPNN/zrX/+iXbt2OFxxZ7p06dJKP2dGRgYA7u7uFuUeHh6kp6cDkJmZiaOjY4kdzD08PMjMzDS3ufIYxcctblNW7u5Xnx54rTq5cerfqqX+rTo1uW8TE2HsWP63Q3hJTZoYWLkS7ruvnnUDu8yN9K/RaGTs2LHMnTuXm2++GQAfHx98fHwqKzwRERGrUcJSRETqnIYNGzJw4ECbnPvKEZ2mq905V6BNeUeLZmXllLhxNxiKbphLq5Mbp/6tWurfqlPT+7agACZNugmTyUBpoyvBhLOziV69zlPOZz+V4lr9W1x3PY6OjqSkpFht5oCIVK1vv93D6tXLGTcunK5d77B1OCJWp4SliIjUOQsWLLD6OT09PYGiEZJNmzY1l2dlZeHh4QEUjaQ0Go1kZ2dbjLLMysoiKCjI3CYrK6vE8U+dOlXqyMtrMZmuPtLoWnVy49S/VUv9W3Vqat9+/bU96enXWr7fQHq6ga+/tqdXL9utX3mj/Tt06FA2bdrEtGnTKi8oEbG6vLxcVq16nVOnsli16nVuu60zzs4utg5LxKqUsBQREbECPz8/PD092blzJx06dAAgPz+fvXv3mm8sO3XqhKOjIzt37mTIkCEAnDx5kpSUFKZPnw5AUFAQOTk57N+/n86dOwOwb98+cnJyzElNERGx9NFHZbvtOXGiZo9ONBqNvP/+++zatYtOnTpRr57l9PaZM2faKDIRKY8tWzZx+vQpAE6fPsWWLZsYOfJhG0clYl1KWIqISJ308ccf89FHH3H8+HGMRqNF3ZYtWyp0zPPnz3Ps2DHz+9TUVA4ePIibmxs+Pj6MHj2aFStW0KpVK1q2bMmKFStwcXEhLCwMAFdXV4YPH05sbCyNGzfGzc2N2NhY/P396dmzJwBt2rQhNDSUiIgI5s6dC0BkZCT9+vUr14Y7IiJ1RVKSAytXOpaprZdXDRw+epkff/zR/FDs559/tqjTVHGRmuH48XS2bt1kXhLIZDKxdesm+vTpj7e31qSVukMJSxERqXPWrVtHfHw8w4YN44svvuC+++7jt99+4/vvv+cvf/lLhY974MABRo8ebX5fPPV82LBhxMTEMH78ePLy8pgzZw7Z2dkEBASwZs0aGjRoYP7MrFmzcHBwYPLkyeTm5hIcHExMTAz29vbmNnFxcURHRzN27FgA+vfvT1RUVIXjFhGprQoKICLC+brtDAYT3t4mevSw3XTwyrB+/XpbhyAiN8BkMrF69fIS65cXlz///Bw9fJA6QwlLERGpc9555x3mzZtHWFgYW7ZsYfz48TRv3pzFixeTnZ1d4eN2796dw4cPX7XeYDAwadIkJk2adNU2zs7OREZGEhkZedU2jRo1Ii4ursJxiojUFbt3X2/tyiImE0RH53HZs6Ea7ddff+XYsWN069YNFxeXCm3MtnfvXlavXs2BAwfIyMjgtddeY8CAAeZ6k8nE0qVL2bBhA2fPniUgIICoqCjatWtnbpOfn09sbCxJSUnk5eXRo0cPZs+eTbNmzcxtsrOziY6O5ssvvwSKHsJFRkbSsGHDG+wFkZonLS2VffuSS5QXFhayb18yaWmp+Pk1t0FkItZ3/au3iIhILXP8+HHzeo8uLi6cP38egD/96U98+OGHtgxNREQqUVnXpJwwIZ+wsEtVHE3VO336NI888gh33XUXjz/+OBkZGQA8//zzxMTElOtYFy5coH379lcdwb9q1SrWrl1LVFQUmzZtwsPDgzFjxnDu3Dlzm/nz5/PZZ58RHx/PO++8w4ULF5gwYQIFBX+MZJ06dSqHDh0iISGBhIQEDh06xIwZMyrw7UVqPl9fPwICgrCzs0zV2NnZERh4O76+fjaKTMT6KpSwvPPOOzl9+nSJ8rNnz3LnnXfecFAiIiJVycPDgzNnzgDg4+PDd999BxStOXnlFBwREam5yrom5d131+yp4MUWLFiAg4MD//jHP3Bx+WNH4SFDhrBjx45yHatPnz5MmTKFQYMGlagzmUysW7eO8PBwBg0ahL+/P7GxseTm5pKUlARATk4Omzdv5rnnnqNnz5506NCBRYsW8eOPP7Jr1y4Ajhw5wo4dO4iOjiYoKIigoCDmzZvH9u3bOXr06A30hEjNZDAYGDcuvMSI6KuVi9RmFZoSnpaWRmFhYYny/Px8Tpw4ccNBiYiIVKUePXqwfft2OnbsyP3338+CBQv45JNPOHDgAAMHDrR1eCIiUkl69CjAx6eQ48cNmEwlb/Rry9qVxXbu3Mnq1astplwDtGzZkvT09Eo7T2pqKhkZGYSEhJjLnJyc6NatG8nJyYwcOZIDBw5gNBrp1auXuY2Xlxft2rUjOTmZ0NBQkpOTcXV1JSAgwNwmMDAQV1dXkpOTy72ZnHI5Uhv4+Phwzz1D2bp1s7ns3nuH4u3tbcOoRCpPWf+tLlfC8osvvjD/vGPHDlxdXc3vCwsL+frrr/H19S3PIUVERKxu3rx55gdvf/7zn3Fzc+M///kP/fr1Y+TIkTaOTkREKou9fdHalOPGuQAm4I+7JIOhaPRlbVq78sKFCxYjK4udPn0aJyenSjtP8VRzd3d3i3IPDw9zYjQzMxNHR0fc3NxKtMnMzDS3ufIYxcctblMe7u6u128kUgPUq+d0xXtnPDz0+y11S7kSlk899RRQNBz5ueeeszyQgwO+vr4lykVERKobOzs7i7WBhgwZwpAhQ2wYkYiIVIWCAjh0yI569UxcuGC5Glbjxibi4vJqxdqVxbp168bWrVuZPHmyuaywsJDVq1fTvXv3Sj/fldNTy7KsSlnbVGTqa1ZWDlrZRWq648fT2bhxo0XZhg0bCA7ug7e3j42iEqk8BkPZHjCVK2F56NAhoGjntk2bNtGkSZOKRSciImJDe/fuvWZ9t27drBSJiIhUlaQkB6ZOdeb06dKW7Tdx6lTtmz88Y8YMRo0aZZ6OvWjRIn766Seys7N59913K+08np6eQNEIyaZNm5rLs7Ky8PDwAIpGUhqNRrKzsy1GWWZlZZk3vvPw8CArK6vE8U+dOlXqyMvrMZlQwlJqNJPJRELC8hKJ/eLy55+fo3Uspc6o0BqWX375ZWXHISIiYjWjRo0qUXb5f/4OHjxozXBERKSSJSU5MHZsyanRfzBgMJiIiHBm8OBLtWZKeNu2bdm2bRvvvvsu9vb2XLx4kYEDB/KXv/zFIrF4o/z8/PD09GTnzp106NABKNrPYO/evUybNg2ATp064ejoyM6dO82zGE6ePElKSgrTp08HICgoiJycHPbv30/nzp0B2LdvHzk5OeakpkhdkpaWyr59ySXKCwsL2bcvmbS0VPz8mtsgMhHrq1DCEuDrr7/m66+/Jisrq8QGPAsWLLjhwERERKrKlSMsjUYjBw8eZPHixUyZMsVGUYmISGUoKICpU53/9+7qI5FMJgPp6QZ277anV6/asekOFI1+fPrpp2/4OOfPn+fYsWPm96mpqRw8eBA3Nzd8fHwYPXo0K1asoFWrVrRs2ZIVK1bg4uJCWFgYAK6urgwfPpzY2FgaN26Mm5sbsbGx+Pv707NnTwDatGlDaGgoERERzJ07F4DIyEj69etX7g13RGoDX18/AgKC+P77fRZ5Fjs7Ozp3DsTX18+G0YlYV4USlkuXLuW1116jU6dOeHp6akiyiIjUKJdvGlesV69eODk5sWDBAhITE20QlYiIVIb4eKerTAMv3YkTteteJjs7m02bNnHkyBEMBgNt2rThvvvuo1GjRuU6zoEDBxg9erT5ffGglGHDhhETE8P48ePJy8tjzpw5ZGdnExAQwJo1a2jQoIH5M7NmzcLBwYHJkyeTm5tLcHAwMTEx2F82pDUuLo7o6GjGjh0LFC0/FhUVdQM9IFJzGQwGxo0L569/DbcoN5lMjBsXrtyL1CkVSli+9957LFiwgKFDh1ZyOCIiIrbTpEkTfv75Z1uHISIiFVRQACtXOpbrM15etWfRwz179vDkk0/SoEEDOnXqBMD69et57bXXWLZsGXfccUeZj9W9e3cOHz581XqDwcCkSZOYNGnSVds4OzsTGRlJZGTkVds0atSIuLi4MsclUleVZcMqkdqkQglLo9HI7bffXtmxiIiIWEXxJnKXO3nyJKtWraJ9+/Y2iEhERCrD7t32nDlT1tGVJnx8TPToUXumg8+dO5fBgwcze/Zs8yjGgoIC5syZw9y5c0lKSrJxhCJyLSaTidWrl5dat3q1Nt2RuqVCCcv777+fDz74gKeeeqqy4xEREalyQ4cOxWAwlHhSHRgYyPz5820UlYiI3KiyT+82YTBAdHRerdlwB+DYsWO8+uqrFlOu7e3tefTRR9m6davtAhORMrnapjsmk0mb7kidU6GEZV5eHhs3buTrr7+mffv2ODhYHmbmzJmVEpyIiEhV+OKLLyze29nZ0aRJE5ydna/yCRERqQnKOr3b1RUWL84lLOxSFUdkXR06dODo0aMlNqw5evQot956q42iEpGy0qY7In+oUMLy8OHD3HLLLQD8+OOPFnUaniwiItWdr6+vrUMQEZEqkJVlAIqTlqXdl5ho0MDEwYPncXKyYmBWMnr0aObPn8+vv/5KQEAAAPv27ePtt99m2rRpFkuiFN/PiUj1UbzpzpQpT5ZarnyL1CUVSliuX7++suNgxYoVvPzyy4wePZrnn38eKBr2vHTpUjZs2MDZs2cJCAggKiqKdu3amT+Xn59PbGwsSUlJ5OXl0aNHD2bPnk2zZs3MbbKzs4mOjubLL78Einaei4yMpGHDhpX+PUREpPpbt25dmdtevkOqiIhUXwUFEBVVPFK+9GQlwCuv5NXKZCXAM888A8CiRYtKrSteDsVgMHDw4EFrhyciZeDt7cPQofezefMGc9nQoffTrJm3DaMSsb4KJSwr2/79+9mwYUOJjQ5WrVrF2rVriYmJoVWrVixbtowxY8bw8ccf06BBAwDmz5/P9u3biY+Pp1GjRsTExDBhwgQSExPNa7dMnTqVEydOkJCQAEBUVBQzZsxg+fLSF7MVEZHa7Y033uD06dNcvHjR/PDq7Nmz1KtXj8aNG5vbGQwGJSxFRGqI3bvtSU+/1oY7RUlMd/fau9PulUueiEjNNGTIPRYJyyFD7rFhNCK2UaGE5ahRo645FLk8I1fOnz/P9OnTiY6OZtmyZeZyk8nEunXrCA8PZ9CgQQDExsbSs2dPkpKSGDlyJDk5OWzevJmFCxfSs2dPoOhpYt++fdm1axehoaEcOXKEHTt2sHHjRvO0iHnz5jFixIhS13e5HmuOwK4uo72L46gu8ZSV4rYuxW1dNTVuqPrYy3LcKVOm8M477zB//nzzdeDo0aNERkYyYsQI7r333qoJTkREqkxZN9wp+8Y8NY+WPBGpHbZt21Li/cMPP2qbYERspEIJyysXbL506RIHDx4kJSWFoUOHlutYc+fOpU+fPvTs2dMiYZmamkpGRgYhISHmMicnJ7p160ZycjIjR47kwIEDGI1GevXqZW7j5eVFu3btSE5OJjQ0lOTkZFxdXc3JSijaBdbV1ZXk5ORyJyzd3V3L1f5Kjo52FJShTWWcq7JVt3jKSnFbl+K2rpoaN9g29sWLF/Pqq69aXANat27NzJkzefrpp5WwFBGpgcq64U5Z29VEH330ER9++CG//PILBoOBli1bEhYWxt13323r0ESkjI4fT2fbtkSLsm3bErnzzkF4e/vYKCoR66tQwnLWrFmlli9ZsoQLFy6U+TgffvghP/zwA5s2bSpRl5GRAYC7u7tFuYeHB+np6QBkZmbi6OiIm5tbiTaZmZnmNlceo/i4xW3KIysrB1MF/o9jMBTdnBuNhRiN105Z2t/guSpbcezVJZ6yUtzWpbitq6bGDVUfe/HxryUjI4NLl0ruDFtYWEhWVlblByUiIlWuR48CfHwKOX7cgMlUchSlwWDC29tEjx7XGz5Q8xQWFvLMM8/w8ccf06pVK1q3bo3JZCIlJYUpU6bwySef8PLLL2vDDpFqzmQy8frrizFd8Z/k4vK5c2P091jqjEpdw/Lee+/lgQce4Nlnn71u2+PHjzN//nzWrFmDs7PzVdtd+Zfxyr+4pSlrm4r8RTeZqHDC0lrnqirVLZ6yUtzWpbitq6bGDbaNPTg4mIiICObPn0+nTp0wGAx8//33REVFERwcbJugRETkhtjbQ3R0HuPGuWAwmCySlgZD0QUnOjoPe/urHaHmevPNN/n6669ZtmwZ/fr1s6j74osvmDVrFm+++SaPPvqobQIUkTJJTf2NQ4d+KLXu0KEfSE39jebNW1g5KhHbuNaq1OWWnJyMUxm33Pvvf/9LVlYW9913Hx06dKBDhw7s2bOH9evX06FDBzw8PABKjILMysoy13l4eGA0GsnOzr5mm9JGy5w6darUkZciIlL7vfjii3h5efHAAw9w22230alTJx588EE8PT2ZP3++rcMTEZEKCgu7xOrVuXh7Wz4R8/Y2sXp1LmFhJUfX1waJiYlMnz69RLIS4M4772TatGls3rzZBpGJiIhUTIVGWE6cONHivclkIiMjgwMHDvDkk0+W6Rg9evTggw8+sCibOXMmrVu3Zvz48TRv3hxPT0927txJhw4dAMjPz2fv3r1MmzYNgE6dOuHo6MjOnTsZMmQIACdPniQlJYXp06cDEBQURE5ODvv376dz584A7Nu3j5ycHIKCgiry9UVEpIZr0qQJq1at4pdffuHIkSOYTCbatGnDzTffbOvQRESkggoKYOdOe/bts6Nnz0uYTNC8uYnQ0AJ69iyolSMri/3666/mTUhL07NnT+bNm2fFiESkIvz8mtO6dVuOHv2pRF2bNu3w82tug6hEbKNCCUtXV8u1wQwGAzfffDNPP/20xSY519KgQQP8/f0tyurXr0+jRo3M5aNHj2bFihW0atWKli1bsmLFClxcXAgLCzPHMXz4cGJjY2ncuDFubm7Exsbi7+9vvmC3adOG0NBQIiIimDt3LgCRkZH069ev3BvuiIhI7dKqVStatWpl6zBEROQGFBTASy85sXSpE7m5JddhevPNQl56Ka/Wjq4EcHFx4ezZs/j4lL4hx7lz53BxcbFyVCJSEVebtero6GjlSERsq0IJywULFlR2HKUaP348eXl5zJkzh+zsbAICAlizZg0NGjQwt5k1axYODg5MnjyZ3NxcgoODiYmJwf6yR6hxcXFER0czduxYAPr3709UVJRVvoOIiIiIiFSNpCQHJk1y5vz5q690dfq0gbFjXVizpvZOCQ8MDOTdd99lzpw5pda//fbbBAYGWjcoESm3tLTUa65hmZaWqlGWUmfc0KY7Bw4c4MiRIxgMBtq2bWueul1R69evt3hvMBiYNGkSkyZNuupnnJ2diYyMJDIy8qptGjVqRFxc3A3FJiIiIiIi1UdSkgNjx5Zl1KABMBER4czgwZdq5dTw8PBwRo8ezZkzZxg3bpx5l/AjR46wZs0avvzyS958801bhyki1+Hr60dAQBD79iWXqAsMvB1fXz8bRCViGxVKWGZlZTFlyhT27NlDw4YNMZlM5OTk0L17d+Lj42nSpEllxykiIlLj9e/fn7S0tBLlDz30EC+88ALPPfccW7ZssagLCAhg48aN5vf5+fnExsaSlJREXl4ePXr0YPbs2TRr1qzK4xcRqS4KCuD5553/967kNPCSDKSnG9i9255evQqqMjSbuP3224mPjycyMpJPP/3Uoq5hw4a89NJLdOnSxUbRiUhZGQwG7r33vlITlvfccx8GQ1n+vROpHSqUsJw3bx7nzp3jww8/pE2bNgD89NNPPPvss0RHR/Pyyy9XapAiIiK1waZNmygo+ONGOSUlhTFjxnD33Xeby0JDQy2WXrlyvaL58+ezfft24uPjadSoETExMUyYMIHExESL5VBERGqz3bvtOX786tPAr+bEidp7sz9w4EBCQkL46quv+OWXXwC4+eab6dWrF/Xq1bNtcCJSJiaTiW3bEkut27ZtM7fd1llJS6kzKpSw3LFjB2vXrjUnKwHatm3LCy+8YF4nUkREpDr79ttvee+99/jtt9949dVX8fLyYuvWrfj5+dG1a9cqOeeVMxBWrlxJixYtuOOOO8xlTk5OeHp6lvr5nJwcNm/ezMKFC82byy1atIi+ffuya9cuQkNDqyRuEZHqpqKJRy8vUyVHUr3Uq1ePgQMH2joMEamgtLTUUkdXAuzbl6w1LKVOKf9jSaCwsLDUHaocHBwoLCy84aBERESq0ieffMK4ceNwcXHhhx9+ID8/H4Dz58+zYsUKq8SQn5/Ptm3bGD58uMWT8j179hAcHMxdd91FREQEWVlZ5roDBw5gNBrp1auXuczLy4t27dqRnFz6f26vxWAo/XWtOr1u/KX+Vf/W1Fd16ttmzcqbeDTh41NIcHCBzWOvSP+KSN1QvIalnZ1lqsbOzk5rWEqdU6ERlj169GD+/Pm89NJLeHl5AXDixAkWLFhAcHBwpQYoIiJS2ZYtW8acOXMYOnQoH374obn89ttv5/XXX7dKDJ9//jk5OTkMGzbMXNa7d2/uvvtufHx8SE1NZfHixTzyyCMkJibi5OREZmYmjo6OuLm5WRzLw8ODzMzMcsfg7u5aoTq5cerfqqX+rTrVpW//95ypHAwsWWLAy6t6xH811aV/RcQ2DAYD48aFM2XKk6WWazq41CUVSlhGRUXx5JNPcuedd9KsWTMMBgPHjx/H39+fRYsWVXaMIiIilernn38uddp3gwYNOHv2rFVi2Lx5M7179zY/+AMYMmSI+Wd/f386depE//79+cc//sGgQYOueiyTqWJTHLOycrjyowZD0Q1zaXVy49S/VUv9W3WqU98WFMDkyTcBhv+9rq1Jk0JeeimP3r0vUYFnO1Zxrf4trhORusHb24ehQ+8nMXEjJpMJg8HA0KH306yZt61DE7GqCiUsvb292bJlCzt37uTo0aOYTCbatm1rXk9LRESkOvP09OTYsWP4+VlOq/n3v/9N8+ZVvy5QWloau3btYsmSJdds17RpU3x8fMybJ3h4eGA0GsnOzrYYZZmVlUVQUFC54zCZuGri4Vp1cuPUv1VL/Vt1qkPffv21Penp11/ZqkePS0yfnk/PngXY29s+7rKoDv0rIrY3bNj9bN/+OadOZdGkSROGDbvf1iGJWF251rD8+uuvGTJkCOfOnQOgV69ejBo1itGjR3Pbbbfxf//3f3z77bdVEqiIiEhlGTFiBPPnz2ffvn0YDAZOnDjBtm3biI2N5aGHHqry8ycmJuLu7k7fvn2v2e706dMcP36cpk2bAtCpUyccHR3ZuXOnuc3JkydJSUmpUMJSRKQmKuuGO48+aiQ0tChZWRfl5uZy7tw5i5eI1AzOzi6MH/8kHh6ePPbYkzg7u9g6JBGrK9cIyzfffJMHH3yQBg0alKhzdXVlxIgRrF27tsp2VxUREakM48eP59y5c4wePZq8vDwefvhhnJycGDt2LA8//HCVnruwsJDExESGDh2Kg8Mfl+Hz58+zdOlSBg0ahKenJ2lpacTHx9O4cWMGDBgAFF1rhw8fTmxsLI0bN8bNzY3Y2Fj8/f01y0FE6oyy7vRd23cEL83FixdZtGgRH330EWfOnClRf/DgQesHJSIV8tNPP3LqVBY//fQjXbveYetwRKyuXAnLw4cPM3369KvW9+rVizVr1txwUCIiIlVtypQphIeH89NPP2EymWjTpg033XRTlZ93165dpKenM3z4cItye3t7fvzxR7Zu3UpOTg6enp50796d+Ph4iweFs2bNwsHBgcmTJ5Obm0twcDAxMTHY19UhRCJS5/ToUYCPTyHHjxswmUqOtjQYTHh7m+jRo8AG0dnWwoUL+eabb3jhhRd49tlniYqK4sSJE2zYsIGpU6faOjwRKaOzZ7PNa1gmJm5kyJB7aNjQ7fofFKlFypWwzMzMtBgNUuJgDg6cOnXqhoMSERGxhnr16nHbbbdZ9ZwhISEcPny4RLmLiwurV6++7uednZ2JjIwkMjKyKsITEan27O0hOjqPceNcMBhMFklLg6FoVGV0dF6dnAq+fft2YmNj6d69O7NmzaJr1660bNkSHx8fPvjgA+69915bhygiZRAbG23eVNFkMrFwYTTR0drgWOqWciUsvby8+PHHH2nZsmWp9YcPH8bT07NSAhMREalMEydOLHPbpUuXVmEkIiJyo8LCLrF6dS4REc6kp/+RsPT2NhEdnUdY2CUbRmc72dnZ5g3lGjRoQHZ2NgBdunRhzpw5tgxNRMpo//7v+PHHQxZlhw8fYv/+7+jcOdA2QYnYQLkSln369OHVV1+ld+/eODs7W9Tl5uayZMkS+vXrV6kBioiIVAZXV1dbhyAiIpUoLOwSgwdfYvdue06cMODlVTQNvC6OrCzm5+dHWloavr6+tG3blo8++ojOnTuzfft2XQdFaoDCwkJeeimm1LqXXoph7dp3sLMr197JIjVWuRKWTzzxBJ9++il33XUXf/nLX7j55psxGAwcOXKEd955h4KCAsLDw6sqVhERkQpbsGCBrUMQEZFKZm8PvXrVvbUqr2b48OEcOnSIO+64g8cff5wJEyawfv16CgoKeO655yr9fP379yctLa1E+UMPPcQLL7zAc889x5YtWyzqAgIC2Lhxo/l9fn4+sbGxJCUlkZeXR48ePZg9ezbNmjWr9HhFqrv//OdbLlw4X2rdhQvn+c9/vtUGPFJnlCth6eHhwXvvvcfs2bN5+eWXzWsqGAwGQkJCeOGFF/Dw8KiSQEVERCpbVlYWP//8MwA333wz7u7uNo5IRESk4h599FHzzz169OCjjz7iwIEDtGjRgltuuaXSz7dp0yYKCv5IGKekpDBmzBjuvvtuc1loaKjFQ0NHR0eLY8yfP5/t27cTHx9Po0aNiImJYcKECSQmJmpDOalzPDyuvcTe9epFapNyJSwBfH19WbVqFdnZ2fz6668AtGzZEjc37VglIiI1w7lz55gzZw5///vfzTda9vb2DB48mBdeeEHT5kREpEbaunUrQ4YMwcnJCQAfHx98fHzIz89n69atDB06tFLP16RJE4v3K1eupEWLFtxxxx8jwJycnK66z0FOTg6bN29m4cKF9OzZE4BFixbRt29fdu3aRWhoaKXGK1LdZWZmXLe+VaubrRSNiG2VO2FZzM3Njc6dO1dmLCIiIlbx/PPPc+jQIZYvX05QUBAAycnJzJ8/n4iICBYvXmzjCEVERMpv5syZhIaGlpgxcP78eWbOnFnpCcvL5efns23bNsaMGYPB8MdGSHv27CE4OJiGDRvSrVs3pkyZYo7vwIEDGI1GevXqZW7v5eVFu3btSE5OLnfC8rLTitRIXbp0pX79m0qdFn7TTTfRpUtX/Z5LjVfW3+EKJyxFRERqqn/+858kJCTQtWtXc1loaCjR0dE89thjNoxMRETKqqAAbbhzBZPJZJEsLHbixIkqnz3w+eefk5OTw7Bhw8xlvXv35u6778bHx4fU1FQWL17MI488QmJiIk5OTmRmZuLo6Fhitp6HhweZmZnljsHdXTMkpOabO3cO06ZNK1E+b948mjbVzFapO5SwFBGROqdRo0al3rg1aNCAhg0b2iAiEREpj6QkB55/3pnjx//YLdfbu5D58/MIC7tkw8hsY+jQoRgMBgwGA4888ggODn/c5hUUFJCamlrl06s3b95M79698fLyMpcNGTLE/LO/vz+dOnWif//+/OMf/2DQoEFXPVbxXgnllZWVQwU/KlJttGrVniZN3Dl1Kstc5u7uTosW7cjMzLFhZCKVw2Ao2wMmJSxFRKTOeeKJJ4iJiSE2NpamTZsCkJGRwaJFi3jyySdtHJ2IiFzLtm0OPPaYS4ny48cNjB3rwpo1uXUuaTlgwAAADh48SEhICDfddJO5ztHREV9f32smCG9UWloau3btYsmSJdds17RpU3x8fPjll1+AopGURqOR7Oxsi1GWWVlZ5iVbysNkQglLqfGOH0/nzJnTFmWnT58mPT0db28fG0UlYn1KWIqISJ1QPPqk2C+//EL//v3x9vYG4Pjx4zg6OnLq1ClGjhxpqzBFROQatm1zYPx4F6C0BbAMgIlp05wZPPhSnZoePnHiRKBog9QhQ4bg7Oxs1fMnJibi7u5O3759r9nu9OnTHD9+3PywsFOnTjg6OrJz507zaMyTJ0+SkpLC9OnTqzpskWrHZDKxevXyUutWr17O88/PKXXZB5HaSAlLERGpE4pHn4iISM2UlFQ8svJaN+sGTp0ysGuXPaGhBdYKrdq4fP1IayksLCQxMZGhQ4daTEU/f/48S5cuZdCgQXh6epKWlkZ8fDyNGzc2X5NdXV0ZPnw4sbGxNG7cGDc3N2JjY/H39zfvGi5Sl6SlpbJvX3KJ8sLCQvbtSyYtLRU/v+Y2iEzE+pSwFBGROqF49ImIiNQ8Fy/CX/9achr41ezcWTcTlgUFBbzxxht89NFHHD9+HKPRaFG/Z8+eSj/nrl27SE9PZ/jw4Rbl9vb2/Pjjj2zdupWcnBw8PT3p3r078fHxNGjQwNxu1qxZODg4MHnyZHJzcwkODiYmJgb7ujREVuR/fH39CAgI4vvv91FYWGgut7Ozo3PnQHx9/WwYnYh1KWEpIiIiIiLV1pw5Trz2mhPXHllpqa6uY7h06VLef/99xowZw+LFiwkPDyctLY3PP/+cp556qkrOGRISwuHDh0uUu7i4sHr16ut+3tnZmcjISCIjI6siPJEaxWAwMG5cOFOmPFlquaaDS12ihKWIiNQ5thiBIiIi5fdHsrJ8evWqe6MrAT744AOio6Pp27cvS5cuJSwsjBYtWtC+fXv27dtn6/BEpAy8vX0YOvR+EhM3YjKZMBgMDB16P82aeds6NBGrsrN1ACIiIta2dOlS1q5dy+DBg8nJyeHRRx9l4MCBGAwGTR0XEakm8vO5LFlZ9lFFrq6FdTZhmZmZib+/PwA33XQTOTk5APTr149//OMfNoxMRMpj2LD7ady4CQBNmjRh2LD7bRyRiPUpYSkiInVO8QiUcePGYW9vT1hYGPPnz+epp57SCBQRkWpi7VpHihKV5ZkCaSI+Pq9O7RB+OS8vLzIyMgBo0aIFO3fuBOD777/Hyan8I1VFxDacnV0YP/5JPDw8eeyxJ3F2LvsaviK1hU0Tlu+88w733HMPt99+O7fffjsjRozgn//8p7neZDKxZMkSQkJC6Ny5M6NGjSIlJcXiGPn5+cybN4/u3bsTGBhIeHg4v//+u0Wb7Oxspk+fTpcuXejSpQvTp0/n7NmzVvmOIiJS/WgEiohI9ffLL+W/VRk69BL33nupCqKpGQYOHMjXX38NwOjRo1m8eDGDBg1ixowZJTbFEZHqrWvXO1i2bA1du95h61BEbMKma1g2a9aMadOm0aJFCwC2bt3KU089xZYtW2jXrh2rVq1i7dq1xMTE0KpVK5YtW8aYMWP4+OOPzTvLzZ8/n+3btxMfH0+jRo2IiYlhwoQJJCYmmneWmzp1KidOnCAhIQGAqKgoZsyYwfLly23zxUVExKaKR6D4+PiYR6B07NhRI1BERKqR8+fL175Bg0KWLcutmmBqiGnTppl/vvvuu2nWrBnJycm0aNGCO++804aRiYiIlI9NE5b9+/e3eD9lyhTeffddvvvuO9q2bcu6desIDw9n0KBBAMTGxtKzZ0+SkpIYOXIkOTk5bN68mYULF9KzZ08AFi1aRN++fdm1axehoaEcOXKEHTt2sHHjRgICAgCYN28eI0aM4OjRo7Ru3dq6X1pERGyueARKQEAAo0ePZurUqWzatIn09HQeffRRW4cnIlLnFRTA3/7mCJi4/pTwoi3BX3ml7k4Fv5rAwEACAwNtHYaIiEi5VZtdwgsKCvj444+5cOECQUFBpKamkpGRQUhIiLmNk5MT3bp1Izk5mZEjR3LgwAGMRiO9evUyt/Hy8qJdu3YkJycTGhpKcnIyrq6u5mQlFF24XV1dSU5OLnfC0lCeJXRukDXPdS3FcVSXeMpKcVuX4raumho3VH3sZTmuRqCIiFRvTzzhzMWLZb9QPPVUfp2eCl7s66+/5rPPPiMtLQ2DwYCfnx933XUX3bp1s3VoIiIi5WLzhOXhw4cZOXIkeXl51K9fn9dee422bdvyn//8BwB3d3eL9h4eHqSnpwNFa5A5Ojri5uZWok1mZqa5zZXHKD5ucZvycHd3LfdnLufoaMf19ix0dLSrlHNVtuoWT1kpbutS3NZVU+OG6hW7RqCIiFQf+fmwdatjmdo6OppYtixXyUqKlr3auHEjbm5utGrVCpPJRHJyMm+//TYPPfQQkZGRtg5RRESkzGyesLz55pvZunUrZ8+e5dNPP+XZZ5/lrbfeMtcbrhgqYzKZrnvMsra58thlkZWVQxkOX4LBUHRzbjQWYjReO2VZPJOloueqbMWxV5d4ykpxW5fitq6aGjdUfezFx7/SF198Qe/evXF0dOSLL7645jE0ylJExHamTXOmrDuDR0TkKVkJfPbZZyQmJvLiiy8ybNgw831OYWEhiYmJzJ49m549e+r6JlKDfPvtHlavXs64ceHaeEfqJJsnLJ2cnGjZsiUAt912G99//z3r1q1j/PjxQNEIyaZNm5rbZ2Vl4eHhARSNpDQajWRnZ1uMsszKyiIoKMjcJisrq8R5T506VerIy+sxmahwwtJa56oq1S2eslLc1qW4raumxg3Wj/2pp55i586duLu789RTT121ncFg4ODBg9YLTEREzAoKYMuWst+ieHrW0ItgJdu8eTNjxozhvvvusyi3s7Pj/vvv5+eff2bTpk1KWIrUEHl5uSxeHEdu7kUWL44jIWEdzs4utg5LxKrsbB3AlUwmE/n5+fj5+eHp6cnOnTvNdfn5+ezdu9ecjOzUqROOjo4WbU6ePElKSoq5TVBQEDk5Oezfv9/cZt++feTk5JjbiIhI7Xfo0CHzg6pDhw5d9aVkpYiI7cTHO5GXV/ZbFG9vJSwBfvjhBwYOHHjV+rvuuov//ve/VoxIRG7Eu+++RW7uRQBycy/y7rtvXecTIrWPTROWL7/8Mt9++y2pqakcPnyY+Ph49uzZwz333IPBYGD06NGsWLGCzz77jB9//JGZM2fi4uJCWFgYAK6urgwfPpzY2Fi+/vprfvjhB6ZPn46/v7951/A2bdoQGhpKREQE3333Hd999x0RERH069dPO4SLiNRBRqORUaNG8fPPP9s6FBERuUxBAaxcWba1K8GEj08hPXpcb3X4uuH06dN4eXldtd7Ly4szZ85YLyARqbDjx9P58MO/WZR9+OHfOH483UYRidiGTaeEZ2ZmMmPGDE6ePImrqyvt27cnISHBvOv3+PHjycvLY86cOWRnZxMQEMCaNWto0KCB+RizZs3CwcGByZMnk5ubS3BwMDExMdjb25vbxMXFER0dzdixYwHo378/UVFR1v2yIiJSLTg6OpKSklKhdYxv1JIlS1i6dKlFmYeHh3mmgMlkYunSpWzYsIGzZ88SEBBAVFQU7dq1M7fPz88nNjaWpKQk8vLy6NGjB7Nnz6ZZs2ZW/S4iIpVt9257zpwp+3iK6Og8Lvsvf51mNBpxdLx6stfe3h6j0WjFiESkIkwmE6+8sqjUuldeWURMzMs2+T+siC3YNGH54osvXrPeYDAwadIkJk2adNU2zs7OREZGXnPXu0aNGhEXF1fhOEVEpHYZOnQomzZtYtq0aVY/d7t27Vi7dq35/eUP2FatWsXatWuJiYmhVatWLFu2jDFjxvDxxx+bH9bNnz+f7du3Ex8fT6NGjYiJiWHChAkkJiZaHEtEpKY5frzsN+Hjx+cTFqbNdi73yiuvUK9evVLrLl68aOVoRKQifvvtGEeP/lRq3dGjP/Hbb8do0aKllaMSsQ2bb7ojIiJibUajkffff59du3bRqVOnEjd4M2fOrLJz29vb4+npWaLcZDKxbt06wsPDGTRoEACxsbH07NmTpKQkRo4cSU5ODps3b2bhwoXmpU8WLVpE37592bVrF6GhoVUWt4hIVcvKKnvCcsgQTQW/XLdu3a671EnXrl2tFI2IVNSJE79ft14JS6krlLAUEZE658cff6RDhw4AJW7wqnqaza+//kpISAhOTk4EBATwzDPP0Lx5c1JTU8nIyCAkJMTc1snJiW7dupGcnMzIkSM5cOAARqPRvHQKFK1L1q5dO5KTk8udsCztqxaXabZR1VD/Vi31b9WxRt96eJRtA53GjQsJDi6oVX/O1+rfsnzP9evXV25AImITXl7XXuLnevUitYkSliIiUufY6sauc+fOxMbG0qpVK7Kysli2bBkjR44kKSmJjIwMAPNO5sU8PDxITy9aZD0zMxNHR0fc3NxKtMnMzCx3PO7urhWqkxun/q1a6t+qU5V9e8stZWs3ebIdXl61889Yv7sidZufX3McHBy4dKnkkhcODg74+TW3QVQitqGEpYiIiJX06dPH4n1gYCADBw5k69atBAQEACVHeJpM1x9xVJY2pcnKyuHKjxoMRTfMpdXJjVP/Vi31b9WxRt8ePeqAweCCyXS1IYUmmjQxMWHCeSrwjKZau1b/FteJSO137NivpSYrAS5dusSxY7/SqtXNVo5KxDaUsBQRkTpp//79fPzxxxw/frzEzqlX7uRdVerXr4+/vz+//PILAwYMAIpGUTZt2tTcJisrCw8PD6BoJKXRaCQ7O9tilGVWVhZBQUHlPr/JxFUTD9eqkxun/q1a6t+qU1V9m5TkwLhxLtc6MwB//rMRO7va++er312Ruu2TT/5+3foJE56yUjQitmVn6wBERESs7cMPP+Shhx7iyJEjfPbZZ1y6dImffvqJ3bt34+pqvVEs+fn5HDlyBE9PT/z8/PD09GTnzp0W9Xv37jUnIzt16oSjo6NFm5MnT5KSklKhhKWISHVQUABTpjj/793VRlcWlW/Z4kiB9tsRkVpq7NjHb6hepDbRCEsREalzli9fzsyZM/nLX/5CUFAQzz//PH5+fkRFRZW6g3dliY2NpV+/fnh7e3Pq1CmWLVvGuXPnGDZsGAaDgdGjR7NixQpatWpFy5YtWbFiBS4uLoSFhQHg6urK8OHDiY2NpXHjxri5uREbG4u/v79513ARkZpm1y57srPLMo7CQHq6gd277enVS1lLEal9HB0d6d9/EF9++WmJugED7sLR0dEGUYnYhhKWIiJS5/z222/m9SSdnJy4cOECBoOBRx99lEceeYSnn366Ss77+++/88wzz3DmzBkaN25MYGAgGzduxNfXF4Dx48eTl5fHnDlzyM7OJiAggDVr1tCgQQPzMWbNmoWDgwOTJ08mNzeX4OBgYmJisLe3r5KYRUSq2s6d5fv368SJWrQ9eCXbv38/H374Ib/88gsGg4GWLVsSFhbGbbfdZuvQRKQMTCYTWVkZpdZlZJzEZDKVWO9cpLZSwlJEROocNzc3zp8/D4CXlxcpKSm0b9+es2fPcvHixSo7b3x8/DXrDQYDkyZNYtKkSVdt4+zsTGRkJJGRkZUdnoiITZR3zUYvLy3yWJqFCxeyZs0a6tevT/PmzTGZTHzzzTesW7eOsWPHMn36dFuHKCLXkZaWyr59yaXW7duXTFpaqnYKlzpDCUsREalzunbtyq5du2jfvj2DBw9m/vz57N69m127dhEcHGzr8ERE6pR//ausIyxN+PiY6NFD08GvtGXLFt566y0iIiIYMWKEedqo0Wjk3XffJS4ujnbt2jF06FDbBioi19SsmfcN1YvUJkpYiohInXHw4EFuvfVWIiMjycvLA2DChAk4ODjw73//m4EDB/Lkk0/aOEoRkbqhoAB69qzPzz+XZf3KolGV0dF5aAWMkt5++22eeeYZHn74YYtyR0dHRo8eTUFBAW+99ZYSliLV3BdffHLd+rvu+j8rRSNiW9olXERE6oxhw4Zx33338fe//5369esDYGdnx/jx480b8bi5udk4ShGR2m/bNge8vW/i55/tufrO4JczMGNGPmFhl6o6tBrpp59+4s4777xq/YABA/jpp5+sGJGIVMSdd951Q/UitYkSliIiUme8++67dOjQgZdeeomQkBCmTZvG7t27bR2WiEidMmeOE4895kJ5b0Vaty6smoBqATs7O/Lz869abzQasbPTrZ9Idff778dvqF6kNtFVS0RE6oygoCCio6PZuXMns2fP5sSJE4wZM4YBAwawbNkyfv/9d1uHKCJSq23b5sBrrzlV6LPabOfqOnbsyAcffHDV+r/97W907NjRihGJSEX4+vrRunXbUuvatGmLr6+flSMSsR0lLEVEpM5xcXFh2LBhrF+/nk8++YT/+7//Y8OGDdx5552MHz/e1uGJiNRKBQXw5JMuFE0BL8s08D80bKjNdq5l7NixrFy5koULF5KZmWkuz8jIIDY2loSEBMaOHVvp512yZAnt27e3ePXq1ctcbzKZWLJkCSEhIXTu3JlRo0aRkpJicYz8/HzmzZtH9+7dCQwMJDw8XA8Qpc4ymUykp6eVWpeWlobJpAc3Undo0x0REanTWrRoweOPP463tzfx8fF89dVXtg5JRKRW2rHDnvz88iUqi5iIi8vVZjvX0K9fP2bOnElsbCxr167F1dUVgJycHOzt7Zk+fTr9+vWrknO3a9eOtWvXmt/bX/YHtWrVKtauXUtMTAytWrVi2bJljBkzho8//pgGDRoAMH/+fLZv3058fDyNGjUiJiaGCRMmkJiYaHEskbrgP//ZS27uxVLrcnMv8p//7KVr1+5WjkrENpSwFBGROmvPnj1s3ryZTz/9FDs7OwYPHsz9999v67BERGql99+vyK2HibvuusTQodps53pGjRrFgAED+OSTT/jll18AaNWqFXfddRfe3t5Vdl57e3s8PT1LlJtMJtatW0d4eDiDBg0CIDY2lp49e5KUlMTIkSPJyclh8+bNLFy4kJ49ewKwaNEi+vbty65duwgNDa2yuEWqo6ZNm91QvUhtooSliIjUKcePHycxMZEtW7aQmppKUFAQERERDB482LxzuIiIVL6tW8t762Fi0KBLrF+fWyXx1CYvv/wyAwYMoHPnzjz66KNWPfevv/5KSEgITk5OBAQE8Mwzz9C8eXNSU1PJyMggJCTE3NbJyYlu3bqRnJzMyJEjOXDgAEaj0WIauZeXF+3atSM5ObncCUtDRQbwilQjLVq0oE2bthw58lOJujZt2tGiRQv9nkuNV9bfYSUsRUSkzhgzZgzffPMNTZo04U9/+hPDhw+ndevWtg5LRKTWu/32+hiN5bnLNjFhQj7z5l1952v5Q0ZGBhMmTMDe3p5+/foxYMAAgoODcXKq2AZHZdW5c2diY2Np1aoVWVlZLFu2jJEjR5KUlERGRgYA7u7uFp/x8PAgPT0dgMzMTBwdHXFzcyvR5vK1OMvK3d21gt9EpPqYM2c2Dz/8cInyuXNn4+nZ0OrxiNiKEpYiIlJnODs78+qrr9KvXz+tiyUiYiW3316f1FQ7yr7RjomEhFzuvVfTwMtqwYIFmEwm/v3vf/Pll1+yYMECTpw4Qa9evejfvz99+/alSZMmlX7ePn36WLwPDAxk4MCBbN26lYCAAAAMVwylKcumIRXdWCQrKwftSSI1nYuLG4MGDebTTz8yl91112CcnRuSmZljw8hEKofBULYHTEpYiohInbF8+XJbhyAiUmdcvAitW9enoKA8yUrYtOkivXtrR/DyMhgMdO3ala5duzJjxgyOHDnCl19+ycaNG4mKiiIgIID+/fsTFhaGl5dXlcRQv359/P39+eWXXxgwYABQNIqyadOm5jZZWVl4eHgARSMpjUYj2dnZFqMss7KyCAoKKvf5TSaUsJRa4aefUkq81++21DV2tg5ARERERERql4cfdqFlywYUFNhTnmSls7OJXr2UrKwMbdq0Yfz48bz33nv885//ZOjQoXz77bckJSVV2Tnz8/M5cuQInp6e+Pn54enpyc6dOy3q9+7da05GdurUCUdHR4s2J0+eJCUlpUIJS5HaYP/+7zh61HINyyNHfmL//u9sE5CIjWiEpYiIiIiIVJo77qjPL7+Ub1RlsSefzEcrdlQ+d3d3HnjgAR544IFKPW5sbCz9+vXD29ubU6dOsWzZMs6dO8ewYcMwGAyMHj2aFStW0KpVK1q2bMmKFStwcXEhLCwMAFdXV4YPH05sbCyNGzfGzc2N2NhY/P39zbuGi9QlhYWFLFr0Yql1ixa9yJtvvoedncadSd2ghKWIiIiIiFSK5593+l+ysvzs7EzMmKFNdipi4sSJZWq3dOnSSj3v77//zjPPPMOZM2do3LgxgYGBbNy4EV9fXwDGjx9PXl4ec+bMITs7m4CAANasWUODBg3Mx5g1axYODg5MnjyZ3NxcgoODiYmJ0VrTUif9+997yc29WGpdbu5F/v3vvXTr1t3KUYnYhhKWIiIiIiJyw/LzYdUqJyoyshJMLF+eq9GVFeTqapvdsePj469ZbzAYmDRpEpMmTbpqG2dnZyIjI4mMjKzs8ERqnOttOFXRDalEaiIlLEVERERE5IatXu1IRZOVgwZdYuhQ7QpeUQsWLLB1CCJSCZo1876hepHaRIsfiIiIiIjIDVuzxrECnzLRuXMBb72VW+nxiIjUNM2bt6B167al1rVp047mzVtYOSIR21HCUkREREREbkh+Pvz6a3lvLYqSlZ9/Xvp6bSIidY3BYCA4OKTUuuDgXhgMFRnFLlIz2TRhuWLFCoYPH05QUBDBwcE8+eSTHD161KKNyWRiyZIlhISE0LlzZ0aNGkVKSopFm/z8fObNm0f37t0JDAwkPDyc33//3aJNdnY206dPp0uXLnTp0oXp06dz9uzZKv+OIiIiIiK13c0316d808GLpoErWSki8oeCggLefvuNUuveeusNCgoKrBuQiA3ZNGG5Z88e/vKXv7Bx40bWrl1LQUEB48aN48KFC+Y2q1atYu3atURFRbFp0yY8PDwYM2YM586dM7eZP38+n332GfHx8bzzzjtcuHCBCRMmWPxlnjp1KocOHSIhIYGEhAQOHTrEjBkzrPp9RURERERqm2efdcJoLM9thYmVK3M1DVxE5AqffPL3G6oXqU1suunO6tWrLd4vWLCA4OBg/vvf/9KtWzdMJhPr1q0jPDycQYMGARAbG0vPnj1JSkpi5MiR5OTksHnzZhYuXEjPnj0BWLRoEX379mXXrl2EhoZy5MgRduzYwcaNGwkICABg3rx5jBgxgqNHj9K6dWvrfnERERERkVogPx/Wri3PzuAmjh49R4MGVRmViEjNVFhYeEP1IrVJtdolPCcnBwA3NzcAUlNTycjIICTkjzUcnJyc6NatG8nJyYwcOZIDBw5gNBrp1auXuY2Xlxft2rUjOTmZ0NBQkpOTcXV1NScrAQIDA3F1dSU5OblcCUtrLhlRXZanKI6jusRTVorbuhS3ddXUuKHqY6+JfSIiUlOVd2dwT89CJStFRK7ielO+NSVc6pJqk7A0mUwsWLCALl264O/vD0BGRgYA7u7uFm3/v707D4uy3P8H/h6GAUQRkUVFTdwYEBRwyRUXXI9p/ZTscI5hqVnZyXLfMQUVUE+c0jQs5aSl6VGjQr8dTasTWmpFmgiIKCKKbBKCCDPMPL8/Rh4ZZ1iFWeD9ui4uZu77fp75zM04z8zHe3FycsLt27cBAHl5eZDJZGKSs3KbvLw8sc3j56g4b0Wb2nJ0tKtT+8fJZBao6S1GJrNokMdqaKYWT20xbsNi3IZlrnED5h07ERFphIVZ1aG1gLNnS2puRkTUTF29mvJE9URNickkLENDQ3HlyhXs27dPp+7xnbAEQajxfLVtU9ddtvLzi1CLU+uQSDRfzpVKNZTK6lOW0id8rIZWEbupxFNbjNuwGLdhmWvcQOPHXnF+IiJqXH372qK8vPafpTt04OhKIqLqDBw4FD//fKbaeqLmwiQSlmFhYTh16hQ+/fRTtG/fXix3dnYGoBkh6eLiIpbn5+fDyckJgGYkpVKpRGFhodYoy/z8fPj5+Ylt8vPzdR737t27ekdeVkcQUO+EZV3V97Eai6nFU1uM27AYt2GZa9yAecdeX9HR0Th+/DiuXbsGGxsb+Pn5YfHixVpLkyxfvhxffPGF1nE+Pj44ePCgeF+hUCAyMhJxcXEoKyvDoEGDsHbtWq1rKBFRY+rb1xaZmRaoy9qV589zdCURUXWKi4ueqJ6oKTHqLuGCICA0NBTHjx/HJ598gs6dO2vVd+rUCc7Ozjh9+rRYplAocP78eTEZ6e3tDZlMptUmJycHqampYhs/Pz8UFRXh4sWLYpsLFy6gqKhIbENERNTYzp07h+nTp+PgwYOIiYmBSqXC7NmzUVKi/SXe398f8fHx4s/OnTu16jds2IATJ04gKioK+/btQ0lJCV577TWua0REBuHmVtdkJeDmpoZVXWaPExE1Q2PHTniieqKmxKgjLNetW4e4uDhs374dLVu2FNestLOzg42NDSQSCWbMmIHo6Gi4ubmhS5cuiI6Oho2NDSZNmiS2DQwMRGRkJBwcHGBvb4/IyEi4u7uLu4Z3794d/v7+WL16NUJDQwEAISEhGDVqFHcIJyIig9m1a5fW/fDwcAwePBiJiYkYMGCAWG5lZSXOMnhcUVERDh8+jE2bNonXuc2bN2PkyJE4c+YM/P39G+8JEFGzdvcu4OxsC82Yh7pMHxJw6hRHVxIR1aSmpe1qs/QdUVNh1ITl/v37AQDBwcFa5eHh4Zg6dSoAYM6cOSgrK8O6detQWFgIHx8f7N69G60qLYCzcuVKWFpaYv78+SgtLcXgwYMREREBqVQqttmyZQvWr1+PWbNmAQACAgKwZs2axn6KREREVSoq0kzreXzjuHPnzmHw4MFo3bo1BgwYgAULFohLmFy6dAlKpRJDhz5aw6hdu3bo2bMnEhIS6pSw1LdciTnvQG8O2L+Ni/3beDw8bKFZYUlaU1MdrVurYcelhatV3WuXr2ei5uPQoc9rrA8KetFA0RAZl1ETlikpNe9wJZFIMG/ePMybN6/KNtbW1ggJCUFISEiVbdq0aYMtW7bUK04iIqKGJggCwsPD0a9fP7i7u4vlw4cPx4QJE+Dq6orMzEy89957eOmll3DkyBFYWVkhLy8PMplMJ8np5OSEvLy8OsVQ3eZE3LiocbF/Gxf7t2Hk5gK9egF1fGvRceuWFK1aNdO/iSAApaVAixa1as7XLlHz1rnzU09UT9SUmMSmO0RERM1NaGgorly5gn379mmVT5w4Ubzt7u4Ob29vBAQE4Pvvv8e4ceOqPF99pgjp26ndnHegNwfs38bF/q2/P/4AAgJsoT3VW4K6Tf1+nIA+fVQoLX2A0tIni8/kCQIk2dmwTEmCNDkJlinJkCYnQZqSDEnRPRT/6wOU/b3qUVHVvXYr6oio6UtPv15j/dChww0UDZFxMWFJRERkYGFhYTh16hQ+/fTTGnf2dnFxgaurK9LT0wFoRlIqlUoUFhZqjbLMz8+v80Zy1e3U3hx3cTck9m/jYv/WztWrwJAhFUnKJ01OPk6ATKbGt98+aHJ/C0luLiyTL0OakgTL5GRNkjIlCRZ//qm3vWBtDXUbh1r1A1+7RM1b69b2T1RP1JQwYUlERGQggiAgLCwMJ06cwN69e9G5c+cajykoKEBWVhZcXFwAAN7e3pDJZDh9+rQ4GjMnJwepqalYsmRJo8ZPRE3Dr78Cf/lLfTbPqS0BgBq3bpn3RjuS/PxKIyY1oyUtU5JgoVnMU4dgYQGVW1eo5J4o9/DQ/JZ7QtWjJ2BtbeDoicgcZWSkP1E9UVPChCUREZGBrFu3DnFxcdi+fTtatmyJ3NxcAICdnR1sbGxw//59bNu2DePGjYOzszNu3bqFqKgoODg4YMyYMWLbwMBAREZGwsHBAfb29oiMjIS7u7u4azgRUVVcXBozUQlUJCtzcswnWSkpuCtO4RYTk8lJsMjL1dtekEig7uKGcg/Ph0lJj0eJyVquVUlEpI9CUf5E9URNCROWREREBrJ//34AQHBwsFZ5eHg4pk6dCqlUiitXriA2NhZFRUVwdnbGwIEDERUVhVatWontV65cCUtLS8yfPx+lpaUYPHgwIiIiIJXWffdeImo+mnuyUnKvENJKU7gtkzS/pTnZVR6jeqoLyuUeYmJS5dkL5T3cAVtbA0ZORM3FzZvVr2FZUz1RU8KEJRERkYGkpKRUW29jY4Ndu3bVeB5ra2uEhIQgJCSkoUIjoibuUbKysQjo2FGNhATjJyslRfceTt/WHjUpzbpd5TGqTp0fJSY9PKGSe6C8pxyo9J9FRESNrVu3Hrh5M6PaeqLmgglLIiIiIqIm7M4doLHXq0xNLYG9ofeCKC6GZWqKJilZOTF5K7PKQ1QdXKHyeLi2pMfDUZNyDwituAs3ERnfxInP4ocfTlVbT9RcMGFJRERERNSE9elTsRN4QxNgaanG7duNPKqypORRYjIlWTOdOyUZ0owbVR6iatded/MbuRyCfZvGjZWI6Ank5ubUWN+tW3cDRUNkXExYEhERERE1URkZQONMBRcwZkw59u0rbbhTPngAy6tXtBOTyUmwyLgBiSDoPUTt7IJycaRkpcSkQ9uGi4uIyEDy8/OeqJ6oKWHCkoiIiIioierfv6FHV6oRGFiOd98tq/+G2GVlkF5NfbT5TXIypMmXIb2RDolarf9RHR0fJiM9Kk3n9oTg6Fj/p0JEZGLsaxgFXlM9UVPChCURERERURPzww/AtGn13WhHePjziLe3GvHxllAq76OKwY66FApI065qEpOVRk1Kr1+DRKXSe4jaweFhYlJ7Orfg7FyP50FEZF4uXPitxvqhQ/0NFA2RcTFhSURERERk5uLjgalTK4+mlKDuIys1G+gcP14CX1/tGokEsLe3Q56+2YhKJaTX0sQp3GJi8loaJOXleh9J3dpeM1rSoxdUHppRk+VyTwguLpoHIyJqhiQ1vP/VVE/UlDBhSURERERkxlxcKkZSPskXWQHJycVoW93Sj+XlkF5NhUVS0qPp3CnJkF5NhUSp1HuIupXdw8Sk9nRudfsOTEwSET1mwoRJOHXqRLX1RM0FE5ZERERERGZEpQI2bpRi61ZrPBpJ+WTJv2efVT5KVqpUkN64DmlyslZiEqlX4KBQ6D1e3bIVVHK5znRutWtHJiaJiGpJqGHNjZrqiZoSJiyJiIiIiMzEunWW+OADGzTERjoSqOGGdHjhEvb0SoD1Gw/Xmrx6BZJS/bt/C7a2KO8przRaUjOtW92xE2DRGLuRkymLjo7G8ePHce3aNdjY2MDPzw+LFy9Gt27dxDbLly/HF198oXWcj48PDh48KN5XKBSIjIxEXFwcysrKMGjQIKxduxbt27c32HMhMgWHDx+osX7JkpUGiobIuJiwJCIiIiIyA/Wd+i2BGk8hA15I1PrxRBJaokTTKEL7GMHG5lFi0sMTag9PtB7cH/mtHCFImJgkjXPnzmH69Ono3bs3VCoVoqKiMHv2bBw9ehS2trZiO39/f4SHh4v3ZTKZ1nk2bNiA7777DlFRUWjTpg0iIiLw2muv4ciRI5BKpQZ7PkTG5uLS7onqiZoSJiyJiIiIiExc7ZKVAjohE15IhDcuiYnJXriMVriv94gyWEHay12zxqSH58PNbzyg7uIGVEoUSSQAnOyAvKLHNxCnZmzXrl1a98PDwzF48GAkJiZiwIABYrmVlRWcq9jpvaioCIcPH8amTZswZMgQAMDmzZsxcuRInDlzBv7+3BGZiKg5YsKSiIiIiMjE3LkD9OlT1a7fAlxxW2fEZC9chj3u6T2fAjKkQK7VOhGeiL/dAbDkVwJqGEVFRQAAe3t7rfJz585h8ODBaN26NQYMGIAFCxbA0dERAHDp0iUolUoMHTpUbN+uXTv07NkTCQkJdUpYcrlUMndXr16psZ6vczJ3tX0N89MJEREREZEJ6dLFFg8eaKZdt8cdncSkFxLRBoV6j1XCElfgrnPEVfRAOSqm4QoA1MjJKTHME6JmQRAEhIeHo1+/fnB3dxfLhw8fjgkTJsDV1RWZmZl477338NJLL+HIkSOwsrJCXl4eZDKZTpLTyckJeXl5dYrB0dGuQZ4LkbE8eKB/NHzleicnvs6peWDCkoiIiIjIyCS5ubBMScI7z6fhXXUivHAZXkhEWxTobV8OKa6ih05i8grcoYRVNY/EZCU1jtDQUFy5cgX79u3TKp84caJ4293dHd7e3ggICMD333+PcePGVXm++uyGnJ9fBG6iTObMysq6xvq8vCIDRUPUOCSS2v0HExOWREREREQGIsnPh2XKw924U5KAy8koOZ8MR7VmJNl7j7VXwQJp6K6TmEyBHApU/8VWmyZRefx4CXx9G+jJED0UFhaGU6dO4dNPP61xZ28XFxe4uroiPT0dgGYkpVKpRGFhodYoy/z8fPj5+dUpDkEAE5Zk1u7cya6xnq9xai6YsCQiIiIiamCSgruwTEkWE5PSlGRYJifBIi9Xp20LAGpIcB1dkQgvXIK3VmKyFC3qEYGAit1xRo8ux+7dZWhRn9MQVUMQBISFheHEiRPYu3cvOnfuXOMxBQUFyMrKgouLCwDA29sbMpkMp0+fFkdj5uTkIDU1FUuWLGnU+IlMzf37xU9UT9SUMGFJRERERFRPknuFkCYnP0xKJsEyORnSlCRIs+9Uecx1uOmMmEyCJx7AtoGiEjBqVDkOHChtoPMR6bdu3TrExcVh+/btaNmyJXJzNQl5Ozs72NjY4P79+9i2bRvGjRsHZ2dn3Lp1C1FRUXBwcMCYMWPEtoGBgYiMjISDgwPs7e0RGRkJd3d3cddwoubCwsICarWq2nqi5oIJSyIiIiKiGkiK7mlGST42alKadbvKY27gqYe7cVfsyu2NJPTCfbRq4OgejaYcObIcn3zC0ZRkGPv37wcABAcHa5WHh4dj6tSpkEqluHLlCmJjY1FUVARnZ2cMHDgQUVFRaNXq0b+DlStXwtLSEvPnz0dpaSkGDx6MiIgISKVSgz4fImMrL1c+UT1RU8KEJRERERHRQyvmFePCgRsPN73RbHzjjUQ8hZtVHpOJjpUSk964BG8kwRNFaG2AiLmJDhlPSkpKtfU2NjbYtWtXjeextrZGSEgIQkJCGio0IiIyc0xYEhEREVGTFBwM/Pe/tgAkOnUtUAJPJD2ckH1Z/P0x0qs832100JnKfRm9UIg2jfYcqsdkJRERETVNTFgSERERkdmKiADefffxpGTLh78lsEEpPJD8WJoxEV1xHRbQv9VqNlx0Nr+5jF4oQNvGfjp1IMDNTYVz5x4YOxAiIiKiBseEJRERERGZvFmzgLg4faMlJQAksEIZ5EjRSUx2RxqkUOs9Zy6cdEZMJsIL+XBq7KdTD4L4u1cvFeLiStGqoZfCJCIiIjIRRk1Ynj9/Hrt27cKlS5eQm5uLDz74QNwtDgAEQcC2bdtw4MAB3Lt3Dz4+PlizZg169uwptlEoFIiMjERcXBzKysowaNAgrF27Fu3btxfbFBYWYv369Th16hQAICAgACEhIWjd2hDrChERERFRbSxfDuzerX8Kd0ViUgYF3HFFJ83YA1dhCf07q+ajrd7EZC5cGvPpNLjdu0sxaVK5scMgIqJG4uDgiIKC/GrriZoLoyYsS0pKIJfLMXXqVMybN0+n/qOPPkJMTAwiIiLg5uaGHTt2YObMmfjmm2/EXeU2bNiA7777DlFRUWjTpg0iIiLw2muv4ciRI+KucosWLUJ2djY+/vhjAMCaNWuwdOlSfPjhh4Z7skREREQGlJEB9O9fVfLPVElQEa8llOiBq2J60RuX4IVE9EQqZNCftPsT9noTk3fQHubVD5UJGDpUgUOHFOCGyURETVtZWekT1RM1JUZNWI4YMQIjRozQWycIAvbs2YPXX38d48aNAwBERkZiyJAhiIuLQ1BQEIqKinD48GFs2rQJQ4YMAQBs3rwZI0eOxJkzZ+Dv74+0tDT8+OOPOHjwIHx8fAAAYWFh+Otf/4pr166hW7duhnmyRERETYRKBfz8sxTZ2RK0aydg0CCV3kSKvnaAdtmAASqcP6+57+QkQCIBcnMf3c7OliA3V4KCAk2yyd5eQGHho9sFBRLcvi2ButKMX0EA8vIkKCmRQKEArKyAsjKgrEyCkhLtdmVlQHm5JlaVCigv15xLrdbUN56WNTd5Yo+Sf6ZMinJ0R5pOmlGOFFhBqfeYe7DTm5i8DVeYw3OunoCK6d8zZpQiMlL/vy8iImp67Oxao6TkfrX1RM2Fya5hmZmZidzcXAwbNkwss7KywoABA5CQkICgoCBcunQJSqUSQ4cOFdu0a9cOPXv2REJCAvz9/ZGQkAA7OzsxWQkAvr6+sLOzQ0JCQp0TlhIDfgY25GNVpyIOU4mnthi3YTFuwzLXuIHGj90c+6S+PvvsM+zatQu5ubno2bMnVq5cif79+zfqY8bFWWL1amvcvm0hlrm6qrF+fZnWVFV97RwcNFnFgoJHZRYWAtTqZvRHE1nU3KSJsYAKXXFdHClZOTFpgzK9xxSjJS6jl84GOJnoBPNPTD5Os+P32rUleOMNY8dCRETG0KKF7RPVEzUlJpuwzM3NBQA4Omqv0eDk5ITbt28DAPLy8iCTyWBvb6/TJi8vT2zz+DkqzlvRpi4cHe3qfExlMplFFasrabdpiMdqaKYWT20xbsNi3IZlrnED5h27KTh27BjCw8PxzjvvoG/fvvj8888xZ84cHD16FK6uro3ymHFxlpg920Zn5GFWlgSzZ9tg1y7N+npVtasYJVmZWv9eKGTGJFDDDek64x89kYQW0D+VrQQtxMRk5Z8MPAWhSSd3NaMp+/Qpx9dfl6FFC2PHQ0RExpSTc+eJ6omaEpNNWFaQPDZURqjF/Kzatnn83LWRn19UryliEonmy7lSqYZSWX3KsmLWT30fq6FVxG4q8dQW4zYsxm1Y5ho30PixV5y/qYuJiUFgYCCmTZsGAFi1ahXi4+Oxf/9+LFq0qMEfT6UCVq+2fvg3e/zaLIFEImD1amuMG1deZbuqN1IhcySBGk8hQ29isiVK9B7zADZIgqdOYjIdbk00Mfloere+ui1bSjBjhiHjISIiU6ZQ6J9xUNt6oqbEZBOWzs7OADQjJF1cHu3gmJ+fDycnJwCakZRKpRKFhYVaoyzz8/Ph5+cntsnP191l6+7du3pHXtZEEOq3plV9pijW97Eai6nFU1uM27AYt2GZa9yAecdubAqFAomJiXj11Ve1yocOHYqEhIRan0fftamqKftnz0q1pnc/ThA0a0nGxMiqbUfmSEAnZGqlGL1xCb1wGa2gf52tMlghGR5aSclL8MZ1dIUa5rYgY3VJx+qPmzu3BKGhDR2PcZjzUiTmoLr+ZZ8TNR/l5fo3lattPVFTYrIJy06dOsHZ2RmnT59Gr169AGi+oJ0/fx6LFy8GAHh7e0Mmk+H06dOYOHEiACAnJwepqalYsmQJAMDPzw9FRUW4ePEi+vTpAwC4cOECioqKxKQmERGROSkoKIBKpdK7bErFkiq1Ud1I1MfrSvQPmNORnW1T68cnUyPAFbd1Rkz2wmXY457eIxSQIQVynRGTaegOlel+zKyTxEQJevWqb8ao6Y32bg4j2I2J/UtERKRh1E+S9+/fR0ZGhng/MzMTSUlJsLe3h6urK2bMmIHo6Gi4ubmhS5cuiI6Oho2NDSZNmgQAsLOzQ2BgICIjI+Hg4AB7e3tERkbC3d1d3DW8e/fu8Pf3x+rVqxH68L+4Q0JCMGrUKO4QTkREZk3fsil1We5E37T8qqbs29pKAdS80Hu7dqUAmLQ0bQLa447exKQD/tR7hBKWSEVPnc1vrqIHyiEzbPi1ors4qpOTCufPl6JVq7qfrR7Lnjc55rwUiTmorn+by1InRERElRk1YXnp0iXMqLRwT3h4OABgypQpiIiIwJw5c1BWVoZ169ahsLAQPj4+2L17N1pV+qS5cuVKWFpaYv78+SgtLcXgwYMREREBqfTRdKMtW7Zg/fr1mDVrFgAgICAAa9asMdCzJCIialgODg6QSqU6m8dVXjalNqqblv943cCBKri6qpGVJYEg6CZFJRIBHToImDlTiR07rKpsR4bljBydxKQXEuGIu3rbl0OKq+ihc8QVuEMJKwNHXxN9U7UF7NkjxV/+cr/a1zbVH5fzaFzsXyIiIg2jJiwHDhyIlJSUKuslEgnmzZuHefPmVdnG2toaISEhCAkJqbJNmzZtsGXLlieKlYiIyFRYWVnBy8sLp0+fxtixY8XyM2fOYPTo0Y3ymFIpsH59GWbPtoFEImglIyUSzbfr9evLYGVVdbtHyaXHy5jYfFKOyNObmHSG/qGBakj0JiZTIIcC1gaOvjr614+UyQT8+msJ2rfXLpdIACcnO46IJCIiIjJzTWNxISIiomZm5syZWLp0Kby9veHn54cDBw4gKysLQUFBjfaYkyaVY9euUqxebY3btx8lGTt0ELB+fRkmTSqvtp2DgybxVFDwqMzCAlDrzt6lKrRBgc7mN15IRDvk6G2vhgTX0VWc9K358UIyPFCKFlU8iqn8QQQcOVKCYcOMHQcRERERGRoTlkRERGZo4sSJKCgowPbt25GTkwN3d3fs3LkTHTt2bNTHnTSpHH/5Szl+/lmK7GwJ2rUTMGiQClJp7doB0CobMECF8+c1952cBEgkQG7uo9vZ2RLk5krEJKe9vYDCwke3Cwo0u5NXTnoKApCXJ0FJiQQKBWBlBZSVAWVlEq3NgwRBU15eDqhUmp/ycs251OrGnJZpgZqSgq1RWCkxeVn87YqsKo+5DjcxIVnxOwmeeABbAAI+/7wErwRUtFYDVezwTURE1NgEQUBZWZmxwzBLpaWlxg7BpFhbW9dpDXcyH0xYEhERmanp06dj+vTpBn9cqRQYOlRV73aPl9XmXE3FoynLmjUWJcVFkKYkwzI5CdLkJFimJEGakgxp1u0qz6Hq2AnlHp5QyT0f/vZAeU85WrVqhYEABmq1FsDEJBERmRJBEBASsgwpKUnGDsUsBQdPM3YIJkUu90RYWCSTlk0QE5ZEREREja24GJapKbBMSQYy0tA64YImMZl5s8pDVB1cNclIuSdUHp4ol3tAJfeAYNfagIETERERERkeE5YmrLb/QcCdBImIiExESQksU1MejpZMhjTl4e+MG1rNKu+3rWrX/uFoSQ/Nb7knVHI5BPs2Bg2diIjIECQSCcLCIjklXI/r169jzZqlVdaHhm5C165dDRiR6eOU8KaLCUsTZGkhgVKlhqOjXa3aK5QqFP5ZUnNDIiIiahilpZCmXoFlRUIy+TIsk5NgkXEDkir+J1Ht5IxyD09Y+fZBcZfuKJdrRk0KDm0NHDwREZFxSSQS2NjYGDsMk+Pp6flE9URNCROWJshSKoFMaoFXYs7hgbL6db1ayKT4eObTkEg40pKIiKjBlZVBejX14dqSSbBM1oyalKZfh6SK7c3Vjo4PR0lWns7tCcHRUVzDsjSviNdtIiIi0vGf/3yNadMm6y0nak6YsDRhD5QqlNaQsCQiIqIGoFBAmnZVk5isNJ1bev0aJCr912J1mzbiFO5yT0/xtuDsbODgiYiIqCnp3r0n0tJSxfs9e8qNGA2RcTBhSURERM2HUgnp9WsPR0tqduS2TEmCNO0qJOXleg9Rt7bXjJb00B41qXZpV/sFp4mIiIhqae3ajVq7gW/cuMWI0RAZBxOWRERE1PSUl0Oafv3haMmkR5vfXE2FRKnUe4i6lZ3+xGT7DkxMEhFRrQmCwA1l6ImUlpaKtz/6aK/WfaL6MMfNiZiwJCIiIvOlUsHiRjosK0ZKJj8cOZmWCkkVXxYF25Yol8t1pnOrXTsyMUlERE+srKxMa3Qc0ZOYMyfY2CFQE7B373/MbqMrJiyJiIjI9KnVsMi4Ia4tKU7nTk2BpIpRB0KLFih396g0WlLzW92pM2BhYeAnQEREREREtcWEJREREZkOQYBF5k1YJl+GNPnhqMmKxGRJif5DbGxQ3sNdsxu3R8XmNx5QP9WFiUkiIjKq4t7TIFhIjR0GmSPh4W9O/qB6kqhVaPXHf4wdRr0xYUlERESGJwiwuH3r4WjJh6MmU5IgTUmBxf1i/YdYWUHVwx3lHh7idG6VhwdUXboCUn4ZJCIi0yAIgnjbnJMFRNR0VH5fMhdMWBIREVHjEQRY3MmqtPlNsmY695UUWBTd03+ITAZVj54ol1dOTHpC5dYVsORHFyKipuizzz7Drl27kJubi549e2LlypXo37+/scMiIiIj4ad+IiIienKCAElODiwrRkomJ2umdV9JgUXhn/oPsbSEqlt3cQp3xXRuVbfugExm2PiJiMhojh07hvDwcLzzzjvo27cvPv/8c8yZMwdHjx6Fq6urscOrs8o78XJKONUbp4TTE6o8JdzcdggHmLAkIiKierBIvw6rk8e1pnNbFBTobStIpVB17SYmJlUeD0dNdu8BWFkZOHIiIjI1MTExCAwMxLRpmp21V61ahfj4eOzfvx+LFi0ycnRPhlPCiYjqhwlLIiIiqhtBgMOEUbC4e1e7WCKByq2rJjHp6floOnePnoC1tZGCJSIiU6ZQKJCYmIhXX31Vq3zo0KFISEio07lMZQCRqcRBRFRBIjGd96baxsGEJREREdWNRIIHc+bC8kICVO4ej0ZN9nAHWrQwdnRERGRGCgoKoFKp4OjoqFXu5OSE3NzcOp3L0dGuIUOrN0Fohf/7v/8zdhgmSxAELF68GJcvXzZ2KNQE9OrVC1u2bDHLKc+GZGNjY3Z9xIQlERER1VnJomXGDoGIiJqQx79IC4JQ5y/X+flFMMONcJuldesiUFZWZuwwTFrFrs7mlmQyNGtra5SUqIwdhsm7f7/Y2CGIJJLa/QcTE5ZNRF3ew3gRJyIiIiIiU+Dg4ACpVIq8vDyt8vz8fDg5OdXpXILA7zrmQwJraxtjB0FNBP/dN01MWJo5SwsJlCp1naY/KJQq3CssqVVb/sMnIiIiIqLGYmVlBS8vL5w+fRpjx44Vy8+cOYPRo0cbMTIiIjImJizNnKVUApnUAq/EnMMDZc3DoO2sLbH1xX5wcqpdgrMuyU0iIiIiIqK6mjlzJpYuXQpvb2/4+fnhwIEDyMrKQlBQkLFDIyIiI2HCsol4oFShtBYJSxuZRa0TnC1kUnw882mT2UmKiIiIiIianokTJ6KgoADbt29HTk4O3N3dsXPnTnTs2NHYoRERkZEwYdlM1TbBSURERERE1NimT5+O6dOnGzsMIiIyERbGDoCIiIiIiIiIiIioAhOWREREREREREREZDKYsCQiIiIiIiIiIiKT0awSlp999hkCAgLQu3dvTJ06Fb/88ouxQyIiomYiMzMTK1euREBAAPr06YMxY8bg/fffh0Kh0Gonl8t1fvbv36/VJiUlBS+++CL69OkDf39/bNu2DYIgGPLpEBERERERNZpms+nOsWPHEB4ejnfeeQd9+/bF559/jjlz5uDo0aNwdXU1dnhmoabdwgWh5jaV2xIRNSfXrl2DIAgIDQ1Fly5dcOXKFYSEhODBgwdYtmyZVtvw8HD4+/uL9+3s7MTbxcXFmDVrFgYOHIhDhw4hPT0dy5cvh62tLWbNmmWw50NERERERNRYmk3CMiYmBoGBgZg2bRoAYNWqVYiPj8f+/fuxaNEiI0dnmiwtJFCq1HB01HxRrvhdFaVKDZm0doN2FUoV7hWW1KptYyZCTSHB+ngMFfcfL2eSl8i8DR8+HMOHDxfvd+7cGdevX8f+/ft1EpatW7eGs7Oz3vN89dVXKCsrQ0REBKysrODu7o709HTExMRg5syZkNT2jY2IiIiIiMhENYuEpUKhQGJiIl599VWt8qFDhyIhIaFO57KwqF/iqOL7o4OtDKVKabVt7VtYNkrb+pxbJrXA4gO/QQVAqVRX2ba1jSU2Pu+LxQd+Q2k17QCgpZUU6wN94ORUfQK0Ql0TocVF2olQiyoObWVnCytZzX1W1XmrUpcEa3UxPJ4gbqwYGrptRX8bM4b6tK3qdfKk561r+7qeu7Zxm0IfP962utifJEFvTvm6oqIi2Nvb65SHhoZi1apV6NSpE55//nn89a9/hcXDDvv9998xYMAAWFlZie2HDRuGf/7zn8jMzETnzp1r/fj6rmkV/Vff6x1Vj/3buNi/jYd927iq619zuq4ZG/uKiMj01fa9ulkkLAsKCqBSqeDo6KhV7uTkhNzc3Dqdq23b2iXZqrLtxf5Gb1vX9lv+2rdR2tZWbZOVAGAlk+r8jZ70b1bVeQ3NFGKoDXOIUR/GbXjmHHtDyMjIwKefforly5drlb/99tsYPHgwbGxs8NNPPyEyMhIFBQV44403AAB5eXno2LGj1jEV17e8vLw6JSyr+xs0979PY2P/Ni72b+Nh3zYu9u+TqWlGGBERmY9mkbCs8Pg0OUEQOHWOiIieyNatW7Ft27Zq2xw6dAi9e/cW72dnZ+OVV17BhAkTxKVKKlQkJgHA09MTAPDBBx9olVd17eI1jYiIiIiImoJmkbB0cHCAVCpFXl6eVnl+fj6cnJyMFBURETUF06dPx8SJE6tt06lTJ/F2dnY2ZsyYAV9fX4SFhdV4fh8fHxQXFyMvLw9OTk56Zwfk5+cDgM5MAiIiIiIiInPULBKWVlZW8PLywunTpzF27Fix/MyZMxg9erQRIyMiInPXtm1btG3btlZtK5KVXl5eCA8PF9elrE5SUhKsra3RunVrAICvry+ioqKgUCjEdSzj4+Ph4uKilRglIiIiIiIyV80iYQkAM2fOxNKlS+Ht7Q0/Pz8cOHAAWVlZCAoKMnZoRETUDGRnZyM4OBgdOnTAsmXLcPfuXbGuYkfwU6dOIS8vD76+vrCxscHZs2cRFRWFF154QUxOTp48GR988AFWrFiB1157DTdu3EB0dDT+8Y9/cEo4ERERERE1CRJBaD77/H322WfYtWsXcnJy4O7ujhUrVmDAgAHGDouIiJqBI0eOYMWKFXrrUlJSAAD/+9//8O677+LGjRsQBAGdO3fG888/j+nTp8PS0lKrfWhoKC5evAh7e3sEBQUxYUlERERERE1Gs0pYEhERERERERERkWmrefEsIiIiIiIiIiIiIgNhwpKIiIiIiIiIiIhMBhOWREREREREREREZDKYsCQiIiIiIiIiIiKTwYSlgXz22WcICAhA7969MXXqVPzyyy9GiyU6OhqBgYHw8/PD4MGD8cYbb+DatWtabZYvXw65XK7188ILL2i1USgUCAsLw8CBA+Hr64vXX38dd+7cabS4t27dqhPT0KFDxXpBELB161YMGzYMffr0QXBwMFJTU40ac4WAgACd2OVyOdatWwfAdPr7/PnzeP311zFs2DDI5XJ8++23WvUN1ceFhYVYsmQJ+vXrh379+mHJkiW4d+9eo8StVCqxefNmTJ48Gb6+vhg2bBiWLl2K7OxsrXMEBwfr/A0WLFhgtLiBhntdGDpufa91uVyOjz/+WGxjjP6mJ6NQKPDcc89BLpcjKSnJ2OGYvczMTKxcuRIBAQHo06cPxowZg/fffx8KhcLYoZktU/qs1ZTU5nMjNYzo6GjI5XJs2LDB2KEQEREZHROWBnDs2DGEh4dj7ty5iI2NRb9+/TBnzhzcvn3bKPGcO3cO06dPx8GDBxETEwOVSoXZs2ejpKREq52/vz/i4+PFn507d2rVb9iwASdOnEBUVBT27duHkpISvPbaa1CpVI0We8+ePbVi+vrrr8W6jz76CDExMVizZg0OHToEJycnzJw5E8XFxUaNGQAOHTqkFXdMTAwAYMKECWIbU+jvkpISyOVyrFmzRm99Q/XxokWLkJycjI8//hgff/wxkpOTsXTp0kaJu7S0FJcvX8bcuXNx5MgRbNu2Denp6Zg7d65O2xdeeEHrbxAaGqpVb8i4KzTE68LQcVeONz4+Hhs3boREIsH48eO12hm6v+nJbNq0CS4uLsYOo8m4du0aBEFAaGgojh49ihUrVuDzzz9HVFSUsUMzS6b2Waspqe3nRnoyFy9exIEDByCXy40dChERkWkQqNE9//zzwpo1a7TKJkyYIGzZssVIEWnLz88X3N3dhXPnzolly5YtE+bOnVvlMffu3RO8vLyEo0ePimV37twRPDw8hP/973+NEuf7778vPPvss3rr1Gq1MHToUCE6OlosKysrE/r16yfs37/faDFXZf369cKYMWMEtVotCIJp9re7u7tw4sQJ8X5D9fHVq1cFd3d34ffffxfbJCQkCO7u7kJaWlqDx63PhQsXBHd3d+HWrVti2YsvviisX7++ymOMEXdDvC5Mob/nzp0rzJgxQ6vM2P1NdfP9998LEyZMEFJTUwV3d3fh8uXLxg6pSfroo4+EgIAAY4dhlkz9s1ZTou9zIz2Z4uJiYdy4ccLp06drvD4SERE1Fxxh2cgUCgUSExMxbNgwrfKhQ4ciISHBSFFpKyoqAgDY29trlZ87dw6DBw/G+PHjsXr1auTn54t1ly5dglKp1JqS3a5dO/Ts2bNRn9eNGzcwbNgwBAQEYMGCBbh58yYAzdS63NxcrX62srLCgAEDxHiMFfPjFAoFvvrqKwQGBkIikYjlptjflTVUHyckJMDOzg4+Pj5iG19fX9jZ2RnsuRQXF0MikaB169Za5V9//TUGDhyIZ555BpGRkVojR40V95O+Lozd33l5efjhhx/w/PPP69SZYn+Trry8PISEhGDTpk2wsbExdjhNWlFRkc61mGpmDp+1mpKqPjdS/YWGhmLEiBEYMmSIsUMhIiIyGZbGDqCpKygogEqlgqOjo1a5k5MTcnNzjRTVI4IgIDw8HP369YO7u7tYPnz4cEyYMAGurq7IzMzEe++9h5deeglHjhyBlZUV8vLyIJPJdD6sOjk5IS8vr1Fi7dOnDyIjI+Hm5ob8/Hzs2LEDQUFBiIuLE/tSXz9XTAczRsz6fPvttygqKsKUKVPEMlPs78c1VB/n5eXpnKPivIZ4LmVlZdiyZQsmTZqEVq1aieWTJ09Gp06d4OTkhNTUVPzzn/9EcnKyOH3fGHE3xOvC2P39xRdfoGXLlhg3bpxWuSn2N+kSBAHLly9HUFAQevfujczMTGOH1GRlZGTg008/xfLly40ditkx9c9aTUlVnxup/o4ePYrLly/j0KFDxg6FiIjIpDBhaSCVR9IBmg98j5cZQ2hoKK5cuYJ9+/ZplU+cOFG87e7uDm9vbwQEBOD777/XSTxUJghCo8U6YsQIrfu+vr4YO3YsYmNjxVFY+vq5Jo0Zsz6HDx/G8OHD0a5dO7HMFPu7Ko3Vx4b4N6FUKrFgwQIIgoC1a9dq1VXezMbd3R1dunRBYGAgEhMT4eXlVeU5GzPuxnxdGOo96PDhw5g8eTKsra21yk2xv5uTrVu3Ytu2bdW2OXToEBISElBcXIzXXnvNQJGZv9r2be/evcX72dnZeOWVVzBhwgRMmzatsUNsskz1s1ZTUtXnRqqfrKwsbNiwAbt379a5ThIRETV3TFg2MgcHB0ilUp0RQfn5+XBycjJSVBphYWE4deoUPv30U7Rv377ati4uLnB1dUV6ejoAzagFpVKJwsJCrdFd+fn58PPza8ywRba2tnB3d0d6ejrGjBkDQDMqq/KmEJX72RRivnXrFs6cOYOtW7dW284U+9vZ2RnAk/exk5OT1rTmCnfv3tU7oq6hKJVKzJ8/H5mZmfjkk0+0Rlfq4+XlBZlMhhs3bsDLy8tocVdWn9eFMeP+5ZdfcP36dfzrX/+qsa0p9ndTNn36dK2EuD6dOnXCjh07cOHCBa3kGgAEBgZi8uTJiIyMbMwwzVJt+7ZCdnY2ZsyYAV9fX4SFhTV2eE2SKX/Wakrq8rmRaicxMRH5+fmYOnWqWKZSqXD+/Hl89tln+OOPPyCVSo0YIRERkfFwDctGZmVlBS8vL5w+fVqr/MyZMwZLND1OeLgr6fHjx/HJJ5+gc+fONR5TUFCArKwsMVHl7e0NmUym9bxycnKQmppqsOelUCiQlpYGZ2dndOrUCc7OzlrxKBQKnD9/XozHFGI+cuQIHB0dMXLkyGrbmWJ/N1Qf+/n5oaioCBcvXhTbXLhwAUVFRY32XCqSlTdu3MC///1vODg41HhMamoqlEqlmKg1RtyPq8/rwphxHzp0CF5eXvDw8KixrSn2d1PWtm1bdO/evdofa2trrF69Gl9++SViY2MRGxsr7lIfFRWFBQsWGPlZmKba9i3wKFnp5eWF8PBwWFjwY1l9mOJnraakPp8bqXYGDRqEr7/+WnyPjY2Nhbe3NyZPnozY2FgmK4mIqFnjCEsDmDlzJpYuXQpvb2/4+fnhwIEDyMrKQlBQkFHiWbduHeLi4rB9+3a0bNlSXN/Jzs4ONjY2uH//PrZt24Zx48bB2dkZt27dQlRUFBwcHMSRjHZ2dggMDERkZCQcHBxgb2+PyMhIuLu7N9qC4ZGRkRg1ahQ6dOiAu3fvYseOHSguLsaUKVMgkUgwY8YMREdHw83NDV26dEF0dDRsbGwwadIko8VcmVqtxpEjR/D//t//g6Xlo396ptTf9+/fR0ZGhng/MzMTSUlJsLe3h6ura4P0cffu3eHv74/Vq1cjNDQUABASEoJRo0ahW7duDR63i4sL3nrrLVy+fBnR0dFQqVTia97e3h5WVlbIyMjAV199hREjRsDBwQFpaWmIiIhAr1690LdvX6PEbW9v3yCvC0PH7erqCkCzsdE333yDZcuW6RxvrP6muqv4e1awtbUFADz11FMcYfWEsrOzERwcjA4dOmDZsmW4e/euWFeRuKfaM7XPWk1JTZ8bqf5atWqlsxaora0t2rRpwzVCiYio2ZMIxlgErxn67LPPsGvXLuTk5MDd3R0rVqzAgAEDjBKLXC7XWx4eHo6pU6eitLQU//jHP3D58mUUFRXB2dkZAwcOxNtvv40OHTqI7cvKyrBp0ybExcWhtLQUgwcPxjvvvKPVpiEtWLAA58+fx59//gkHBwf4+vri7bffRo8ePQBoRgBs27YNBw4cQGFhIXx8fLBmzRqtD3yGjrmy+Ph4zJ49G9988w26du0qlptSf589exYzZszQKZ8yZQoiIiIarI///PNPrF+/HqdOnQIABAQEYM2aNTq7djdE3G+++SZGjx6t97g9e/Zg4MCByMrKwpIlS5Camor79++jQ4cOGDFiBN588020adPGKHGvXbu2wV4Xhow7IiICAHDgwAFs3LgR8fHxsLOz02pnrP6mJ5eZmYnRo0cjNjYWnp6exg7HrB05cgQrVqzQW5eSkmLgaJoGU/qs1ZTU9LmRGlZwcDA8PDywatUqY4dCRERkVExYEhERERERERERkcngYklERERERERERERkMpiwJCIiIiIiIiIiIpPBhCURERERERERERGZDCYsiYiIiIiIiIiIyGQwYUlEREREREREREQmgwlLIiIiIiIiIiIiMhlMWBIREREREREREZHJYMKSiIiIiIiIiIiITAYTlkRkFgICAvDvf//b2GEQEZm8zMxMyOVyJCUlGTuUOmno9/ng4GBs2LChwc5nDHK5HN9++y0A8/27EhEREdWHpbEDIDK05cuX4969e9i+fbtOuZOTExYvXgwA+O6777B7925cunQJarUaPXr0wPTp0zF16tRqzx8cHIxz584BAGQyGRwcHODl5YWpU6di3LhxdYp169at+Pbbb/Hll1/W6biq/Pbbb9ixYwd+//13lJaWws3NDVOmTMFLL70EqVTaII/xpI4cOYKNGzfil19+0So/dOgQWrRoYaSoiIhMg1wur7Z+ypQpePPNNw0UTf3wfV5XVdf7+Ph42NvbGykqIiIiIuNhwpIIgFqtxvfff48dO3YAAPbu3YuNGzdizpw5eOeddyCTyXDy5Em88847SE1NxbJly6o93wsvvIC33noL5eXlyM7OxokTJ7Bw4UJMmTIFYWFhhnhKOk6cOIH58+dj6tSp2LNnD+zs7PDTTz9h8+bN+P333/Hee+9BIpE02uMrFApYWVnV+/i2bds2YDREROYpPj5evH3s2DG8//77+Oabb8QyGxsbFBYWGiM0qFQqSCQSWFjUbwIP3+d1OTs7GzsEIiIiIqPglHAiaEYeWlhYwMfHB1lZWYiMjMRLL72EhQsXokePHujSpQtmzZqFpUuXYvfu3bhw4UK157OxsYGzszM6dOgAX19fLFmyBOvWrcPBgwdx5swZsd3mzZsxfvx4+Pj4YPTo0fjXv/4FpVIJQDMCZdu2bUhOToZcLodcLseRI0cAADExMZg8eTJ8fX0xYsQIrF27Fvfv368ynpKSEqxevRoBAQEICwuDp6cnOnXqhGnTpiEiIgL//e9/8X//938AHk05O3r0KIKCgtC7d28888wzOHv2rNY5r169ijlz5sDPzw9DhgzBkiVLcPfuXbE+ODgYoaGhCA8Px8CBAzFr1qwaYz979ixWrFiBoqIi8Tlv3boVgO5Uwdu3b2Pu3Lnw8/ND37598fbbbyMvL0+s37p1K5577jnExsYiICAA/fr1w4IFC1BcXCy2+eabbzB58mT06dMHAwcOxMsvv4ySkpJq/7ZERMbk7Ows/tjZ2UEikeiUVbh58yaCg4Ph4+ODZ599FgkJCVrn+u233zB9+nT06dMHI0aMwPr167XeAwsLC7F06VIMGDAAPj4+eOWVV5Ceni7WHzlyBP3798d3332HiRMnonfv3rh16xYUCgU2bdoEf39/+Pr6Ytq0aeI1pC7v8/fu3UNISAiGDBmC3r17Y9KkSfjuu+8AAAUFBVi4cCGGDx8OHx8fTJ48GXFxcXXuz507d2LIkCHw8/PDypUrsWXLFjz33HNivb5p5W+88QaWL18u3v/yyy8xdepU+Pn5YejQoVi0aBHy8/PF+rNnz0Iul+Onn37C1KlT4ePjg6CgIFy7dk3sx6qu95WnhOtT07WY1zkiIiIyV0xYEgE4efIkRo0aBQsLC/z3v/+FUqkUE2yV/fWvf4WtrW29vhRNmTIF9vb2OH78uFjWsmVLhIeH4+jRo1i1ahX+85//iF/WJk6ciFmzZqFnz56Ij49HfHw8Jk6cCACQSCRYtWoVvv76a0RERODnn3/G5s2bq3zs06dP488//9T7nAICAuDm5qbznDZt2oSZM2ciNjYWfn5+mDt3LgoKCgAAOTk5ePHFF+Hp6YlDhw7h448/Rn5+PubPn691ji+++AJSqRT79+/HunXraoy94gtjq1atxOesL2ZBEPCPf/wDhYWF2Lt3L2JiYnDz5k0sWLBAq11GRgZOnjyJDz/8ENHR0Th//jw++ugj8TksWrQIgYGBOHbsGPbs2YOxY8dCEIQq+5GIyJxERUVh9uzZiI2NhZubGxYtWoTy8nIAQEpKCmbPno2xY8fiq6++QlRUFH799VetWQDLly/HpUuXsGPHDhw4cACCIODVV18V/2MNAEpLSxEdHY3169cjLi4Ojo6OWLFiBX777TdERUXhq6++woQJE8RkZ23f59VqNebMmYOEhARs3rwZx44dw6JFi8TRmwqFAl5eXoiOjkZcXBxeeOEFLF26tMb/UKysYoTqggULcPjwYTg7O2Pfvn117melUom3334bX331FT744ANkZmZqJTQrREVFYfny5Th8+DCkUilWrlwJoPrrfXVquhbzOkdERETmjFPCiQCcOnVKnOZ9/fp12NnZwcXFRaedlZUVOnfurDXCpLYsLCzg5uaGW7duiWVvvPGGeLtTp064du0ajh07hjlz5sDGxga2traQSqU6U8Jefvll8Xbnzp3x9ttvY+3atVi7dq3ex75+/ToAoHv37nrru3XrpvOcpk+fjvHjxwMA1q5dix9//BGHDh3CnDlzsH//fnh5eWHhwoVi+40bN2LEiBG4fv06unbtCgDo0qULli5dWuvYraystEYMVeXMmTNISUnByZMn0aFDBwCaBOszzzyDixcvok+fPgA0ic3w8HC0atUKAPDss8/ip59+woIFC5Cbm4vy8nKMHTsWHTt2BFDz2nBEROZk1qxZGDlyJADgrbfewjPPPIMbN26ge/fu2LVrFyZPniy+J7u5uWHVqlUIDg7G2rVrkZWVhVOnTmH//v3o27cvAGDLli0YOXIkvv32W/zlL38BoEnWrV27Fh4eHgA0/1F09OhR/PDDD2jXrh0AYPbs2fjxxx9x5MgRLFy4sNbv8xcvXsSxY8fEa0rnzp3F+nbt2mH27Nni/eDgYPz444/45ptv4OPjU6v+2bNnDwIDAzFt2jQAwIIFC/DTTz+hrKysVsdXeP7558XbnTt3xqpVqzBt2jTcv38fLVu2FOsWLFiAp59+GgDw6quv4tVXX0VZWVm11/vq1HQtLikp4XWOiIiIzBYTltTspaWlITs7G0OGDKlVe0EQ6r3W4+PHfvPNN/jkk0+QkZEhfrGoSK5V5+eff0Z0dDSuXr2K4uJiqFQqlJWVoaSkBLa2ttU+fm3iAjSjHStYWlrC29tbnL6WmJiIs2fParWpkJGRIX659Pb2brDYK0tLS0P79u3FZCUA9OjRA61bt8a1a9fEhGXHjh21+tPFxUWcpufh4YHBgwdj8uTJGDZsGIYNG4bx48dzcwMiajIqJ6cqEmF3795F9+7dkZiYiBs3buDrr78W2wiCALVajczMTKSnp8PS0lIr+efg4ICuXbsiLS1NLJPJZFqPk5iYCEEQMGHCBK1YFAoF2rRpU+vYk5KS0L59e/F68jiVSoWdO3fi2LFjyMnJgUKhgEKhqNOmPWlpaQgKCtIq8/X11VkCpSaXL1/G1q1bkZycjD///FO81mZlZaFHjx5iO31/j/z8fLi6utbp8SrUdC0eNmwYr3NERERktpiwpGbv5MmTGDJkCGxsbAAAXbt2RVFREbKzs8XRIRUUCgUyMzMxaNCgOj+OSqXCjRs30Lt3bwDA77//joULF2LevHkYNmwY7OzscPToUcTExFR7nlu3buHVV19FUFAQ3n77bdjb2+PXX3/FqlWrxKl+j6v4wpeWliaOlKns+vXrVY6+1EetVmPUqFHijuqVVR4d8vgXx/rErk9VSePHE7KWlrpvcRVtpFIpYmJi8Ntvv+H06dPYu3cvoqKicPDgQa1RPERE5komk4m3K94z1Wq1+DsoKAjBwcE6x3Xo0EEcmf+4x99/bWxstO4LggCpVCpOe66stv8pVXHe6uzevRv//ve/sXLlSsjlcrRo0QIbN27Umq7eECQSic61pfL1qqSkBLNmzcLQoUOxefNmODg4ICsrC7Nnz9aJpfI16fG/R33UdC3mdY6IiIjMGdewpGbv1KlTCAgIEO+PGzcOlpaWehOHn3/+OUpKSjBp0qQ6P84XX3yBwsJCjBs3DoBmswNXV1fMnTsXvXv3hpubG27fvq11jEwm0/kyc+nSJahUKixfvhy+vr7o2rUrcnJyqn3soUOHok2bNnqf08mTJ5Genq7znH7//Xfxdnl5ORITE9GtWzcAgJeXF1JTU9GxY0d06dJF66e6L6S1iV0mk0GlUlX7fHr06IGsrCxkZWWJZVevXkVRUVGdEq8SiQT9+vXDW2+9hdjYWMhksmo3NyAiaip69eqF1NRUnffwLl26wMrKCj169EB5ebnWmpAFBQVIT0+v9n3W09MTKpUKd+/e1TlvxX9o1eZ9Xi6X486dO1UmTn/99VeMHj0azz33HDw8POq1XEv37t21rnUAdNbAbNu2LXJzc8X7KpUKqamp4v1r166hoKAAixcvRv/+/dG9e3etDXdqS9/1via1uRbzOkdERETmiglLatby8/Pxxx9/YNSoUWKZq6srlixZgk8++QRRUVFIS0tDRkYGYmJisHnzZsyaNavG9bFKS0uRm5uLO3fu4MKFC9i8eTPWrl2Lv/3tb+LozKeeegpZWVk4evQoMjIysGfPHp0vER07dkRmZiaSkpJw9+5dKBQKPPXUUygvL8fevXtx8+ZNxMbG4vPPP682HltbW6xbtw4nT55ESEgIkpOTkZmZif/85z9YsWIFxo8fL65HVmHfvn04ceIE0tLSEBoaisLCQgQGBgIA/v73v6OwsBALFy7ExYsXcfPmTcTHx2PFihXVfgmtTewdO3ZESUkJfvrpJ9y9excPHjzQOc+QIUMgl8uxePFiJCYm4uLFi1i6dCmefvppcQRrTS5cuIAPP/wQf/zxB27fvo3jx4/j7t27YlKWiKgpmzNnDn7//XesW7cOSUlJSE9Px8mTJ8VNd9zc3DB69GiEhITgl19+QXJyMpYsWYJ27dph9OjRVZ63a9eumDx5MpYuXYrjx4/j5s2buHjxInbu3IkffvgBQO3e559++mn0798fb731Fk6fPo2bN2/ihx9+wP/+9z8AmuvJmTNn8NtvvyEtLQ1r1qxBXl5enfpgxowZOHz4MA4dOoTr16/j/fff10pGAsCgQYPwww8/4Pvvv0daWhrWrVuHe/fuifWurq6QyWTide3kyZPYvn17neKo6JPHr/c1qelazOscERERmTNOCadm7dSpU+jTpw8cHR21yl9++WV07twZu3fvxp49e6BSqdCjRw+sXbtWTNpV5+DBgzh48CBkMhnatGkDb29vREVFYezYsWKbMWPG4KWXXkJoaCgUCgVGjhyJuXPnYtu2bWKb8ePH48SJE5gxYwbu3buH8PBwTJ06FStWrMBHH32Ed999F/3798fChQvFTYOqMmHCBDg5OeHDDz/Eiy++iNLSUnTp0gWvv/46XnrpJZ0p1osWLcJHH32Ey5cv46mnnsL27dvRtm1bAJrNDvbv348tW7Zg9uzZUCgUcHV1hb+/v7iDqz6enp41xt63b18EBQVh/vz5+PPPP/Hmm29i3rx5WueRSCT44IMPEBYWhhdffBESiQT+/v4ICQmp8W9ToVWrVjh//jw++eQTFBcXw9XVFcuXL8eIESNqfQ4iInPl4eGBvXv34l//+hf+/ve/A9BsGFN5d+rw8HBs2LABr7/+OpRKJfr374+dO3dqTTXXJzw8HDt27EBERARycnLQpk0b+Pr6iu+vtXmfB4CtW7ciMjISCxcuxIMHD9ClSxcsWrQIgGbTuszMTMyePRstWrTACy+8gDFjxqCoqKjWfTBx4kRkZGRgy5YtKCsrw/jx4/G3v/0N8fHxYpvAwEAkJydj2bJlkEqlePnllzFw4ECxvm3btoiIiMC7776LvXv3wsvLC8uWLcPcuXNrHQdQ9fW+OjVdi3mdIyIiInMmEarahYOoGXj99dfRr18/zJkzx9ihmIzMzEyMHj0asbGx8PT0NHY4REREBrN161Z8++23+PLLL40dChEREVGzxinh1Kz169evXutREhERERERERFR4+CUcGrWOLKSiIiIiIiIiMi0cEo4ERERERERERERmQxOCSciIiIiIiIiIiKTwYQlERERERERERERmQwmLImIiIiIiIiIiMhkMGFJREREREREREREJoMJSyIiIiIiIiIiIjIZTFgSERERERERERGRyWDCkoiIiIiIiIiIiEwGE5ZERERERERERERkMv4/DiITB3RkWRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diagnostic_plots(train, \"I/O Data Operations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96873e89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:11.961919Z",
     "iopub.status.busy": "2023-03-19T00:35:11.960549Z",
     "iopub.status.idle": "2023-03-19T00:35:11.966103Z",
     "shell.execute_reply": "2023-03-19T00:35:11.965251Z"
    },
    "papermill": {
     "duration": 0.024358,
     "end_time": "2023-03-19T00:35:11.968532",
     "exception": false,
     "start_time": "2023-03-19T00:35:11.944174",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#from the above plots we can see the data is skewed, and has a lot of outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786a5043",
   "metadata": {
    "papermill": {
     "duration": 0.013894,
     "end_time": "2023-03-19T00:35:11.996615",
     "exception": false,
     "start_time": "2023-03-19T00:35:11.982721",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Feature Eng**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7dad09b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:12.027949Z",
     "iopub.status.busy": "2023-03-19T00:35:12.027080Z",
     "iopub.status.idle": "2023-03-19T00:35:12.035135Z",
     "shell.execute_reply": "2023-03-19T00:35:12.034063Z"
    },
    "papermill": {
     "duration": 0.026697,
     "end_time": "2023-03-19T00:35:12.037906",
     "exception": false,
     "start_time": "2023-03-19T00:35:12.011209",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train[\"I/O_bytes\"] = train[\"I/O Data Operations\"]/train[\" I/O Data Bytes\"]\n",
    "test[\"I/O_bytes\"] = test[\"I/O Data Operations\"]/test[\" I/O Data Bytes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "44926052",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:12.069534Z",
     "iopub.status.busy": "2023-03-19T00:35:12.068354Z",
     "iopub.status.idle": "2023-03-19T00:35:12.076305Z",
     "shell.execute_reply": "2023-03-19T00:35:12.074898Z"
    },
    "papermill": {
     "duration": 0.026413,
     "end_time": "2023-03-19T00:35:12.078925",
     "exception": false,
     "start_time": "2023-03-19T00:35:12.052512",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import norm\n",
    "def distribution_plot(df, variable):\n",
    "    plt.figure(figsize=(16, 8))\n",
    "\n",
    "    # boxplot\n",
    "    plt.subplot(1, 4, 1)\n",
    "    sns.boxplot(df[variable])\n",
    "    plt.title('Boxplot')\n",
    "\n",
    "    # distplot\n",
    "    plt.subplot(1, 4, 2)\n",
    "    sns.distplot(df[variable], fit=norm)\n",
    "    plt.title('distplot')\n",
    "\n",
    "    \n",
    "#      # boxplot\n",
    "#     plt.subplot(1, 4, 3)\n",
    "#     sns.boxplot(np.log(df[variable]))\n",
    "#     plt.title('Boxplot')\n",
    "\n",
    "#     # distplot\n",
    "#     plt.subplot(1, 4, 4)\n",
    "#     sns.distplot(np.log(df[variable]))\n",
    "#     plt.title('distplot')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883a6b6e",
   "metadata": {
    "papermill": {
     "duration": 0.014329,
     "end_time": "2023-03-19T00:35:12.107737",
     "exception": false,
     "start_time": "2023-03-19T00:35:12.093408",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "30765810",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:12.138798Z",
     "iopub.status.busy": "2023-03-19T00:35:12.138040Z",
     "iopub.status.idle": "2023-03-19T00:35:12.653128Z",
     "shell.execute_reply": "2023-03-19T00:35:12.651941Z"
    },
    "papermill": {
     "duration": 0.534045,
     "end_time": "2023-03-19T00:35:12.656085",
     "exception": false,
     "start_time": "2023-03-19T00:35:12.122040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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li1Rm7ty5VueXLVtmcZ2qqio8+uijGDFiBEaMGIFHH30Uly9fdvFtEhEREVFXc3qyTV1dHWJjY5GWloYHH3zQ6vzBgwctfv6///s/rFq1CpMnT7Y4Pnv2bPz5z3+Wfvb3t5w9u3z5cly4cEEKoKtXr8aKFSvw+uuvO1tlIiIiIuoGTgfJCRMmYMKECXbPh4WFWfz86aefYsyYMRgwYIDFcX9/f6uyZkVFRfjyyy+xZ88eDBs2DACwdu1a/O53v8Pp06dx3XXXOVttIiIiIupi3br8j1arxf/+7/9i3bp1Vufee+897N+/HxqNBuPHj8d//dd/ITAwEABQUFCAoKAgKUQCQHx8PIKCglBQUOBUkOTuXUTUUfy8ICJyTrcGyX379iEgIACTJk2yOH7nnXciOjoaGo0GhYWFWL9+PU6ePInt27cDMAVQtVptdT21Wg2tVutUHdTqINffABERERHZ1a1Bcu/evbjzzjvh5+dncXz27NnSn2NiYnDNNddgxowZ+PHHH3HTTTfZvZ4oitI+yR2l01VDFJ2rNxHJkyDwl08iImd0W5A8cuQI/vOf/+DFF19st+xNN90ElUqFs2fP4qabboJGo4FOp7MqV1FRYbOl0hFRBIMkERERUTfotnUks7OzcdNNN+GGG25ot2xhYSH0er00+SYhIQHV1dX4/vvvpTLHjh1DdXU1EhISuqvKREREROQEp1ska2tr8euvv0o/FxcX48SJEwgODkZkZCQAoKamBv/+97/x17/+1er5v/76K/bv348JEyYgJCQERUVFWLduHW688UYMHz4cADBo0CAkJyfj8ccfx5NPPgkAeOKJJ3DrrbdyxjYRERGRh3A6SB4/fhzz5s2Tfs7IyAAApKamSrOz33//fYiiiGnTplk9X6VS4euvv8Zbb72F2tpaREREYMKECXjggQegVCqlcs8//zyeeuopLFy4EACQkpKC1atXO1tdIiIiIuomgih69whCrZaTbYioYwQB0Gi8d7JNZz4PFQoBVxQKfPpDKer1RvirFBg7SINQPx94+deIxPz3Q+7fK7wP3n8PnPks5F7bREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElEBCAn513MnHkXhgwZgrS0NBw5csRh+fz8fKSlpWHIkCGYOHEidu3aZXG+sLAQDz74IFJSUhAbG4s33njD6hrmc20fa9askcqsXLnS6nzr3cGIiNypW7dIJCLqCT799CO8/PJ6LF++EhMmjMPu3buxePFivP/++9L6uK2dO3cO9913H2bNmoXnnnsO3333HdasWYPQ0FBMnjwZAHDlyhVER0djypQp0jJpbWVnZ8NgMEg/FxYWYsGCBZgyZYpFueTkZItrqFSqrnjbRESdxiBJRLK3e/dOTJt2N+66azo0miCsWrUKBw8exK5du7B8+XIb5XcjIiICq1atAmDaROGHH37Atm3bpCA5dOhQDB06FACwfv16m68bGhpq8XNWVhZ+85vfYPTo0RbHfX19pZ2/iIg8CYMkEcmaXq/HqVMn8cc//snieGJiIgoKCmw+5+jRo0hMTLQ4lpycjL1790Kv17vUYtjY2Ij9+/djwYIFEATB4lx+fj7Gjh2LPn36YNSoUVi2bBnUarXTr9Hmsi49VxAA82WETl6zp2l9D+SM98H774Ez74tBkohkrarqEgwGg1XroEajQXl5uc3naLVaaDQai2NqtRpNTU2orKxEeHi40/X45JNPUF1djdTUVIvj48ePx5QpUxAZGYni4mK89NJLmD9/PnJycuDr6+vUa6jVnVtsva6yDgG9/eHja4SfjwK9evtCHdK7U9fsiTp7H70F7wPvAcAgSUQEAFatgKIoWh1rr7yt4x21d+9ejB8/Hv369bM4PnXqVOnPMTExiIuLQ0pKCr744gtMmjTJqdfQ6Tq3sw0EBWrr6lHfaIRepcCVukbojEZZ7WyjVgd16j56A94H778H5vfXEQySRCRrwcF9oVQqodPpLI7rdDqrVkczW62VFRUV8PHxQd++fZ2uQ0lJCfLy8rBx48Z2y4aHhyMyMhJnzpxx+nVEES5/6YkiAKH5GuZjnbxmTyXH92wL7wPvAcDlf4hI5lQqFWJibsDhw99YHM/Ly0NCQoLN58THxyMvL8/i2MGDBxEXF+fS+MicnByo1Wrccsst7ZatrKxEWVmZS93nRERdjUGSiGRvzpx7cOBALg4c+BeKiorwzDPPoKysDHPmzAFgmnW9YsWKVuXnoLS0FBkZGSgqKkJ2djb27t2LhQsXSmUaGxtx4sQJnDhxAo2Njbhw4QJOnDiBs2fPWry20WhETk4Opk+fDh8fy06i2tpaZGZmoqCgAMXFxfjmm29w//33IyQkBLfddls33hEioo5h1zYRyd7EiZNQVVWF7du34Pnn1yEmJgZZWVmIiooCAJSXl6OsrEwqP2DAAGRlZSEjIwM7d+5EeHg4Vq1aJS39AwAXL17E9OnTpZ+3bduGbdu2YfTo0Xjrrbek43l5eSgtLcWMGTOs6qVUKnHq1Cnk5uaiuroaYWFhGDNmDDZs2IDAwMBuuBNERM4RRC8fJa3VeudAWCLqeoIAaDTeOwuzM5+HCoWAKwoFPv2hFPV6I/xVCowdpEGon4+sJttoNEGy/17hffD+e+DMZyG7tomIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5hEGSiIiIiFzCIElERERELmGQJCIiIiKXMEgSERERkUsYJImIiIjIJQySREREROQSBkkiIiIicgmDJBERERG5xOkgefjwYaSnpyMpKQmxsbH45JNPLM6vXLkSsbGxFo/Zs2dblGlsbMTatWsxZswYxMfHIz09HefPn7coU1VVhUcffRQjRozAiBEj8Oijj+Ly5csuvEUiIiIi6g5OB8m6ujrExsZi9erVdsskJyfj4MGD0iMrK8vi/NNPP42PP/4YGzZswDvvvIO6ujosWbIEBoNBKrN8+XKcPHkSW7ZswZYtW3Dy5EmsWLHC2eoSERERUTfxcfYJEyZMwIQJExyW8fX1RVhYmM1z1dXV2Lt3L5599lmMGzcOAPDcc8/hlltuQV5eHpKTk1FUVIQvv/wSe/bswbBhwwAAa9euxe9+9zucPn0a1113nbPVJiIiIqIu5nSQ7Ij8/HyMHTsWffr0wahRo7Bs2TKo1WoAwPHjx6HX65GYmCiV79evHwYPHoyCggIkJyejoKAAQUFBUogEgPj4eAQFBaGgoMCpICkIXfe+iMi78fOCiMg5XR4kx48fjylTpiAyMhLFxcV46aWXMH/+fOTk5MDX1xdarRYqlQrBwcEWz9NoNNBqtQAArVYrBc/W1Gq1VKaj1Oog198MEREREdnV5UFy6tSp0p9jYmIQFxeHlJQUfPHFF5g0aZLd54mi2O61RVGE4GSTgU5XjQ5cmogIgsBfPomInNEtXduthYeHIzIyEmfOnAFgannU6/WoqqqyaJXU6XRISEiQyuh0OqtrVVRU2GypdEQUwSBJRERE1A26fR3JyspKlJWVITw8HAAQFxcHlUqFQ4cOSWUuXryIwsJCKUgmJCSguroa33//vVTm2LFjqK6ulsoQERERkXs53SJZW1uLX3/9Vfq5uLgYJ06cQHBwMIKDg/HKK69g0qRJCAsLQ0lJCTZs2ICQkBDcdtttAICgoCDMmDEDmZmZCAkJQXBwMDIzMxETEyPN4h40aBCSk5Px+OOP48knnwQAPPHEE7j11ls5Y5s67ciRfGzd+joWLUrHyJGj3V0dIiKiHsvpIHn8+HHMmzdP+jkjIwMAkJqain/84x84deoUcnNzUV1djbCwMIwZMwYbNmxAYGCg9JzHHnsMPj4+ePjhh1FfX4+xY8di3bp1UCqVUpnnn38eTz31FBYuXAgASElJcbh2JVFHNDTUY/PmV1FRocPmza9iyJCh8PPzd3e1iHqEsqorKK2qR2hvX3dXhYg8hCB2ZJZLD6bVcrINtdi9+23k5OyRJm6lpc3GnDl/dHe1yEMIAqDReO9km858HioUAkY8978AgPuTBqJ/Hz+MHaRBqJ9PhyZLegPz3w+5f6/wPnj/PXDms5B7bZNslJWVIjc3W/rSE0URubnZKCsrdXPNiHoWbU2ju6tARB6CQZJkQRRFbN36ulXLib3jRGSp9b8RBb85iKgZPw5IFkpKinHsWAGMRqPFcaPRiGPHClBSUuymmhH1DA1NLf92FNwCiIiaMUiSLERFRWPYsAQo2jSlKBQKxMcPR1RUtJtqRtQzMEgSkS0MkiQLgiBg0aJ0q52R7B0nIkutg6RXzi4gIpcwSJJsREREYvr0mVJoFAQB06fPRP/+EW6uGZHnax0kDcyRRNSMQZJkJTV1JkJCQgEAoaGhSE2d6eYakafIyXkXM2fehSFDhiAtLQ1HjhxxWD4/Px9paWkYMmQIJk6ciF27dlmcLywsxIMPPoiUlBTExsbijTfesLrGxo0bERsba/FITEy0KCOKIjZu3IikpCQMHToUc+fORWFhYaffr7PqmwzSn41skSSiZgySJCt+fv5YvHgpNJow3HvvUi5GTgCATz/9CC+/vB7z5i1Ebm4uRowYgcWLF6O01PbSUOfOncN9992HESNGIDc3F+np6Xj66afx4YcfSmWuXLmC6OhoLF++HGFhYXZfe/DgwTh48KD0eO+99yzOb968Gdu3b8fq1auRnZ0NjUaDBQsWoKampmvefAc1tmqRNBoZJInIxOmdbYh6upEjR3NrRLKwe/dOTJt2N+66azo0miCsWrUKBw8exK5du7B8+XIb5XcjIiICq1atAmDa1vWHH37Atm3bMHnyZADA0KFDMXToUADA+vXr7b62Uqm0GzRFUcSbb76J9PR0TJo0CQCQmZmJcePG4cCBA5gzZ06n3rczLLu2GSSJyIRBkohkTa/X49Spk/jjH/9kcTwxMREFBQU2n3P06FGrLujk5GTs3bsXer0eKpWqw69/9uxZJCUlwdfXF8OGDcMjjzyCAQMGAACKi4tRXl6OpKQkqbyvry9GjRqFgoICp4NkZ+aUNRhatUg250ihk9fsaczvVU7v2RbeB++/B868LwZJIpK1qqpLMBgMCA0NtTiu0WhQXl5u8zlarRYajcbimFqtRlNTEyorKxEeHt6h1x46dCgyMzMxcOBA6HQ6vPbaa5gzZw4OHDiAkJAQ6fXVarVV3ex1uzuiVru+/aPqQq30Z19fHwQG+KNXb1+oQ3q7fM2eqjP30ZvwPvAeAAySREQAYLUElHk/dmfK2zruyIQJEyx+jo+Px+23347c3FwsWLCg3ddylk7n+r7Auso66c91VxpRU1uPK3WN0BmNstkZShBMwaEz99Eb8D54/z0wv7+OYJAkIlkLDu4LpVIJnU5ncVyn01m1OprZaq2sqKiAj48P+vbt63JdevfujZiYGJw5cwYApLGTWq3WopXTUd0cEUXXl4BsPWvbvPyP2Mlr9lRyfM+28D7wHgCctU1EMqdSqRATcwMOH/7G4nheXh4SEhJsPic+Ph55eXkWxw4ePIi4uDinxke21djYiKKiIilARkdHIywsDIcOHbIoc/jwYbt16y4N+laTbThrm4iasUWSiGRvzpx7sHbtavz2t79FcvJY/POf/0RZWZk0mWX9+vW4cOECnn322ebyc7Bz505kZGRg9uzZKCgowN69ey1mZ5tDofnPFy5cwIkTJ9C7d29cc801AEwzsG+99VZERESgoqICr732GmpqapCamgrA1KU9b948bNq0CQMHDsQ111yDTZs2wd/fH9OmTbuatwj1rZf/kXsTDBFJGCSJSPYmTpyEqqoqbN++Bc8/vw4xMTHIyspCVFQUAKC8vBxlZWVS+QEDBiArKwsZGRnYuXMnwsPDsWrVKmnpHwC4ePEipk+fLv28bds2bNu2DaNHj8Zbb70FADh//jweeeQRXLp0CSEhIYiPj8eePXuk1wWAxYsXo6GhAWvWrEFVVRWGDRuGbdu2ITAwsJvviqUGBkkiskEQvXyUtFbrnQNhiajrCQKg0XjvLMzOfB6+evA/2P7NOQDA+EFqTIzVYOwgDUL9fGQ12UajCZL99wrvg/ffA2c+CzlGkoiI2sUWSSKyhUGSiIjaxSBJRLYwSBIRUbtaT7ZptckNEckcgyQREbWLLZJEZAuDJBERtavBYkFyBkkiMmGQJCKidrVekNzIBcmJqBmDJBERtcuya9uNFSEij8IgSURE7bKYbMOubSJqxiBJRETtsmiRZJMkETVjkCQiona1nmzDWdtEZMYgSURE7WrgOpJEZAODJBERtYvrSBKRLQySRETUrnoGSSKygUGSiIgcajKKMLSaYMOubSIyY5AkIiKHWk+0AdgiSUQtGCSJiMih1uMjAa4jSUQtGCSJiMihtkGS60gSkRmDJBEROWQVJJkjiagZgyQRETnU1CY5smubiMwYJImIyLE2uZFd20RkxiBJREQOtZ2lzRxJRGYMkkRE5FDbnmx2bRORGYMkERE5ZGzTt82ubSIyY5AkIiKH2uZGLkhORGYMkkRE5FhzcBSafzQwRxJRMwZJIiJyyNwiqfIxfWWwa5uIzBgkiYjIIXNXtq/S9JUhgt3bRGTCIElERA6ZM6NKKUjH2CpJRACDJBERtcMcGVXKlq8MjpMkIoBBkoiI2mHuxm4dJNm1TUQAgyQREbXDVte2gV3bRAQGSSIiaoe59VEQBCgEy2NEJG8MkkRE5JA5MyoEQCmYkqTB6MYKEZHHYJAkIiKHRLRqkWxukmSLJBEBDJJERNQOo80WSQZJImKQJCKidpgbH00tkqY/s0WSiAAGSSIiaoex1V7bHCNJRK0xSBIRkUPmtkdBAMdIEpEFBkkiInJIbA6NCkFoaZFkkCQiMEgSEVE7Wk+2kdaR5GQbIgKDJBERtUOUWh8FKBXmFkn31YeIPAeDJBEROWTOjKYWyeYxkmyRJCIwSJIMHTmSj/vvX4gjR/LdXRWiHsHYavmflhZJBkkiYpAkmWloqMfmza9Cqy3H5s2voqGh3t1VIvJ4LZNtOEaSiCwxSJKs7NuXjcrKCgBAZWUF9u3LdnONiDwfWySJyB4GSZKNsrJS5OZmS60roigiNzcbZWWlbq4ZkWdrvSB5yxhJN1aIiDwGgyTJgiiK2Lr19VazTx0fJyJrCgHwaW6RbGLXNhGBQZJkoqSkGMeOFcDYphnFaDTi2LEClJQUu6lmRJ5PapFs1bXNIElEAIMkyURUVDSGDUuAQmH5V16hUCA+fjiioqLdVDMiz9d6QXJzi6SBfdtEBAZJkglBELBoUTqE5vFd7R0nohZiqxZJdm0TUWtOB8nDhw8jPT0dSUlJiI2NxSeffCKd0+v1eO6553DnnXciPj4eSUlJWLFiBS5cuGBxjblz5yI2NtbisWzZMosyVVVVePTRRzFixAiMGDECjz76KC5fvuzi2yQCIiIiMX36TCk0CoKA6dNnon//CDfXjMizidKsbUCpZJAkohZOB8m6ujrExsZi9erVVufq6+vx008/4f7770dOTg5eeeUVnDlzBvfff79V2dmzZ+PgwYPS48knn7Q4v3z5cpw8eRJbtmzBli1bcPLkSaxYscLZ6hJZSE2diZCQUABAaGgoUlNnurlGRJ7P3ImtaNUiaWCQJCIAPs4+YcKECZgwYYLNc0FBQdi+fbvFsccffxyzZs1CaWkpIiMjpeP+/v4ICwuzeZ2ioiJ8+eWX2LNnD4YNGwYAWLt2LX73u9/h9OnTuO6665ytNhEAwM/PH4sXL8XWra9j0aJ0+Pn5u7tKRB6vpWsbUDaPM2aLJBEBLgRJZ9XU1EAQBPTp08fi+HvvvYf9+/dDo9Fg/Pjx+K//+i8EBgYCAAoKChAUFCSFSACIj49HUFAQCgoKnAqSHPpGbY0aNRqjRo12dzXIA/HzwjZpsg0EmOerMUgSEdDNQbKhoQHPP/88pk2bJoVEALjzzjsRHR0NjUaDwsJCrF+/HidPnpRaM7VaLdRqtdX11Go1tFqtU3VQq4M69yaIiGROWmZVAHwEdm0TUYtuC5J6vR7Lli2DKIr4xz/+YXFu9uzZ0p9jYmJwzTXXYMaMGfjxxx9x00032b2mKIpOz67V6arBtaaptSNH8qWu7ZEj2TJJLQSBv3za0rLXtgAlWySJqJVuCZJ6vR4PP/wwiouLsWPHDovWSFtuuukmqFQqnD17FjfddBM0Gg10Op1VuYqKCpstlY6IIhgkSdLQUI+srFdRUaFDVtariIsbynGSRO0wT7YRWu9sY+AHKxF1wzqS5hB59uxZvPHGGwgJCWn3OYWFhdDr9dLkm4SEBFRXV+P777+Xyhw7dgzV1dVISEjo6iqTjOzbl43KygoAQGVlBfbty3ZzjYg8X+sWSS5ITkStOd0iWVtbi19//VX6ubi4GCdOnEBwcDDCw8Px5z//GT/99BM2bdoEg8GA8vJyAEBwcDB8fX3x66+/Yv/+/ZgwYQJCQkJQVFSEdevW4cYbb8Tw4cMBAIMGDUJycjIef/xxaVmgJ554ArfeeitnbJPLyspKkZubLX0piqKI3NxsTJiQgoiIyHaeTSRf5l5sAdxrm4gsOR0kjx8/jnnz5kk/Z2RkAABSU1PxwAMP4LPPPgMA3H333RbPe/PNNzFmzBioVCp8/fXXeOutt1BbW4uIiAhMmDABDzzwAJRKpVT++eefx1NPPYWFCxcCAFJSUmyuXUnUEaIoYuvW16UQ2fb4qlVruLsNkR2Wy/9wsg0RtXA6SI4ZMwY///yz3fOOzgFAREQE3n777XZfp2/fvnj++eedrR6RTSUlxTh2rMDquNFoxLFjBSgpKUZ09AA31IzI84nSXtvcIpGILHGvbZKFqKhoDBuWAIXC8q+8QqFAfPxwREVFu6lmRJ7P2KpF0kfJBcmJqAWDJMmCIAhYtCjdqvva3nEiatGyjKTArm0issAgSbIRERGJwYNjLY4NHhyL/v0j3FQjop6hpWubk22IyBKDJMlGWVkpTp06aXHs1KmTKCsrdVONiHqGlq7tlhZJBkkiAhgkSSbMs7NtsTWbm4ha2GqRZNc2EQEMkiQT5lnbxjaLKLeetU1EtrVukWTXNhG1xiBJssBZ20Sua71FYuvJNmzJJyIGSZIFztom6gRpi8SWrm0A0HO/bSLZY5Ak2YiIiMT06TOl0CgIAqZPn8lZ2wQAyMl5FzNn3oUhQ4YgLS0NR44ccVg+Pz8faWlpGDJkCCZOnIhdu3ZZnC8sLMSDDz6IlJQUxMbG4o033rC6xqZNmzBjxgwkJCRg7NixWLp0KU6fPm1RZuXKlYiNjbV4zJ49u9Pv1xktWyQKbYIk99smkjsGSZKV1NSZCAkJBQCEhoYiNXWmm2tEnuDTTz/Cyy+vx7x5C5Gbm4sRI0Zg8eLFKC21PaP/3LlzuO+++zBixAjk5uYiPT0dTz/9ND788EOpzJUrVxAdHY3ly5cjLCzM5nXy8/Nxzz33YM+ePdi+fTsMBgMWLVqEuro6i3LJyck4ePCg9MjKyuq6N98BxlaTbZStgmQjgySR7Dm9RSJRT+bn54/Fi5di69bXsWhROvz8/N1dJfIAu3fvxLRpd+Ouu6ZDownCqlWrcPDgQezatQvLly+3UX43IiIisGrVKgDAoEGD8MMPP2Dbtm2YPHkyAGDo0KEYOnQoAGD9+vU2X3fr1q0WP2dkZGDs2LH48ccfMWrUKOm4r6+v3TDqDFdHcIjmJckFAQqFAKUgwCCKaDKILl+zJzK/Vzm9Z1t4H7z/HjjzvhgkSXZGjhyNkSNHu7sa5CH0ej1OnTqJP/7xTxbHExMTUVBgvT87ABw9ehSJiYkWx5KTk7F3717o9XqoVCqX6lJdXQ0ACA4Otjien5+PsWPHok+fPhg1ahSWLVsGtVrt9PXV6iCX6tWrly8AU4tkQG9/+CgFGJpEKFRKqNWBLl2zJ3P1Pnob3gfeA4BBkohkrqrqEgwGA0JDQy2OazQalJeX23yOVquFRqOxOKZWq9HU1ITKykqEh4c7XQ9RFJGRkYERI0YgJiZGOj5+/HhMmTIFkZGRKC4uxksvvYT58+cjJycHvr6+Tr2GTlcNVyZa19Y2ADCNK66tq4eyubmiurYBOl2NbGZvC4IpOLh6H70F74P33wPz++sIBkkiIsBq5r4oig5n89sqb+t4Rz355JM4deoU3nnnHYvjU6dOlf4cExODuLg4pKSk4IsvvsCkSZOceg1RhEtfeq3HSIpiyzjJRoPo8jV7Mjm+Z1t4H3gPAAZJIpK54OC+UCqV0Ol0Fsd1Op1Vq6OZrdbKiooK+Pj4oG/fvk7XYe3atfjss8/w9ttvo3///g7LhoeHIzIyEmfOnHH6dVxlbBOSzUFS38TJNkRyx1nbRCRrKpUKMTE34PDhbyyO5+XlISEhweZz4uPjkZeXZ3Hs4MGDiIuLc2p8pCiKePLJJ/HRRx9hx44dGDBgQLvPqaysRFlZmUvd551lbmv1kVokGSSJ5I5Bkohkb86ce3DgQC4OHPgXioqK8Mwzz6CsrAxz5swBYJp1vWLFilbl56C0tBQZGRkoKipCdnY29u7di4ULF0plGhsbceLECZw4cQKNjY24cOECTpw4gbNnz0pl1qxZg/3792P9+vUICAhAeXk5ysvLUV9fDwCora1FZmYmCgoKUFxcjG+++Qb3338/QkJCcNttt12lu2PZtQ20BEkuSE5E7NomItmbOHESqqqqsH37Fjz//DrExMQgKysLUVFRAIDy8nKUlZVJ5QcMGICsrCxkZGRg586dCA8Px6pVq6SlfwDg4sWLmD59uvTztm3bsG3bNowePRpvvfUWAEiLmM+dO9eiPhkZGUhLS4NSqcSpU6eQm5uL6upqhIWFYcyYMdiwYQMCA6/ebGm7XdtskSSSPUH08ul2Wq13zqgioq4nCIBG473Lebj6eZjxcSFyvi/DoqSB+E0fP2z96lf8WnkFf5scgxlDImQ1a1ujCZL99wrvg/ffA2c+C9m1TUREDkktkmjbIumF36BE5BQGSSIicsgcFwWrMZLs2iaSOwZJIiJyyNx1rWhOkgySRGTGIElERA6ZZ22bWySVXP6HiJoxSBIRkUP2WyQ5RpJI7hgkiYjIobZxkcv/EJEZgyQRETnUdkFyqWubWyQSyR6DJBEROcSubSKyh0GSiIgcaplsw51tiMgSgyQRETkkSlskmn5miyQRmTFIEhGRQ20XJOfyP0RkxiBJREQOtUy2ae7aFti1TUQmDJJEROSQVde20vTVwa5tImKQJCIih6QWSbBFkogsMUgSEZFD9ibbcIwkETFIEhGRQy2Tbbj8DxFZYpAkIiKHjNKC5KafufwPEZkxSBIRkUNckJyI7GGQJCIix8xBsvlH7rVNRGYMkkRE5BC7tonIHgZJIiJyyNzuyK5tImqLQZKIiBwS7bRIcvkfImKQJCIih+xPthGlkElE8sQgSUREjpkXJG/+0RwkAaDJyCBJJGcMkkRE5JC0RWJzi6RPqyDJ7m0ieWOQJCIih1q6tk3/tQiSXAKISNYYJImIyCER5sk2pgApCII08aaRSwARyRqDJBEROSTNp2lpiGy1liRbJInkjEGSiIgcarsgOQD4KExfH+zaJpI3BkkiInJIlLZIbEmSSq4lSURgkCQionbYbpFkkCQiBkkiImqHNERSsNEi2cTJNkRyxiBJREQOiW2W/wEAHyUn2xARgyQREbWjpWu7JUmau7YbGCSJZI1BkoiIHLLVIintt81Z20SyxiBJREQOOWqR5ILkRPLGIElERA7ZWI+cs7aJCACDJBERtUNsbpG01bXNBcmJ5I1BkoiIHDI2N0na6trmrG0ieWOQJNk5ciQf99+/EEeO5Lu7KkQ9gu0WyeYtEhkkiWSNQZJkpaGhHps3vwqtthybN7+KhoZ6d1eJyOO1jJG0MdmGXdtEssYgSbKyb182KisrAACVlRXYty/bzTUi8nwtXdstxzhrm4gABkmSkbKyUuTmZkvddKIoIjc3G2VlpW6uGZFna+natt4ikWMkieSNQZJkQRRFbN36uvSF2N5xImrhuEWSQZJIzhgkSRZKSopx7FgBjEbLLz2j0YhjxwpQUlLsppoReT6jjRZJ817bHCNJJG8MkiQLUVHRGDYsAQqF5V95hUKB+PjhiIqKdlPNiHoOm+tIcowkkawxSJIsCIKARYvSLVpUHB0nohbmrm2bs7bZtU0kawySJBsREZGYPn2mFBoFQcD06TPRv3+Em2tG5NlEaa/tlmOcbENEgAtB8vDhw0hPT0dSUhJiY2PxySefWJwXRREbN25EUlIShg4dirlz56KwsNCiTGNjI9auXYsxY8YgPj4e6enpOH/+vEWZqqoqPProoxgxYgRGjBiBRx99FJcvX3bhLRK1SE2diZCQUABAaGgoUlNnurlGRJ7P1s42KvOC5BwjSSRrTgfJuro6xMbGYvXq1TbPb968Gdu3b8fq1auRnZ0NjUaDBQsWoKamRirz9NNP4+OPP8aGDRvwzjvvoK6uDkuWLIHBYJDKLF++HCdPnsSWLVuwZcsWnDx5EitWrHDhLRK18PPzx+LFS6HRhOHee5fCz8/f3VUi8ngipL5tiZJd20QEwMfZJ0yYMAETJkyweU4URbz55ptIT0/HpEmTAACZmZkYN24cDhw4gDlz5qC6uhp79+7Fs88+i3HjxgEAnnvuOdxyyy3Iy8tDcnIyioqK8OWXX2LPnj0YNmwYAGDt2rX43e9+h9OnT+O6665z9f0SYeTI0Rg5crS7q0HUY4hckJyI7OjSMZLFxcUoLy9HUlKSdMzX1xejRo1CQUEBAOD48ePQ6/VITEyUyvTr1w+DBw+WyhQUFCAoKEgKkQAQHx+PoKAgqUxHCQIffPDBR8cfZM3W8j9KbpFIRHChRdKR8vJyAIBarbY4rtFoUFpq2j1Eq9VCpVIhODjYqoxWq5XKtL2G+brmMh2lVgc5VZ6IiCxxQXIisqdLg6RZ26VUOrJrSEfLOLtMi05XDW5aQq0dOZKPrVtfx6JF6eziJguCwF8+HWn9+WsOlUZ+wBLJWpcGybCwMACmFsXw8HDpuE6ng0ajAWBqedTr9aiqqrJoldTpdEhISJDK6HQ6q+tXVFTYbKl0RBTBIEmShoZ6ZGW9iooKHbKyXkVc3FBOuCFqh9S13eqYojlJGtkgSSRrXTpGMjo6GmFhYTh06JB0rLGxEYcPH5ZCYlxcHFQqlUWZixcvorCwUCqTkJCA6upqfP/991KZY8eOobq6WipD5Ip9+7JRWVkBAKisrMC+fdlurhGR57O1/I95cXIDf1MnkjWnWyRra2vx66+/Sj8XFxfjxIkTCA4ORmRkJObNm4dNmzZh4MCBuOaaa7Bp0yb4+/tj2rRpAICgoCDMmDEDmZmZCAkJQXBwMDIzMxETEyPN4h40aBCSk5Px+OOP48knnwQAPPHEE7j11ls5Y5tcVlZWitzcbGkYhSiKyM3NxoQJKYiIiHRz7Yg8lyhNtmk5Zu7aNhgZJInkzOkgefz4ccybN0/6OSMjAwCQmpqKdevWYfHixWhoaMCaNWtQVVWFYcOGYdu2bQgMDJSe89hjj8HHxwcPP/ww6uvrMXbsWKxbtw5KpVIq8/zzz+Opp57CwoULAQApKSl2164kao8oiti69XWrsbjm46tWrXF6/C2RXNhqkTT/mWMkieRNEDsyy6UH02o52YaA4uJzWLZsqd3zGza8iujoAVexRuSJBAHQaLx3so0rn4eiKGL0C18CAN57IBHfndaiXm/E5Xo9/vvLMwj298En/zWuG2rrecx/P+T+vcL74P33wJnPQu61TbIQFRWNYcMSoFBY/pVXKBSIjx+OqKhoN9WMyLO1/o607NrmGEkiYpAkmRAEAYsWpVt1X9s7TkQmrYdAKmwt/8NZ20SyxiBJshEREYnp02dKoVEQBEyfPhP9+0e4uWbkqtLSEndXweu1Hv3U+vctgS2SRAQGSZKZ1NSZCAkJBQCEhoYiNXWmm2tEnTFnTioefHAJPvzwAzQ0NLi7Ol5JbKdFkrO2ieSNQZJkxc/PH7feehsUCgVuueU2Lkbew73xxjuIiYnFK6+8iLvvnoxnn30aP/103N3V8iqtZ2VbLEjOWdtEBAZJkpmGhnp8/vknMBqN+PzzT9DQUO/uKlEnXHfd9XjwwUewb98H+Nvf/o6KCh2WLr0Xf/zjbOze/TYqKyvdXcUez3KyTasFyaUtEju2xS0ReScGSZIV7mzjnXx8fDBhwq148sl1uP/+B1FaWoz//u+XkJY2FWvXroZWq3V3FXus1i2OChuztgHAwBxJJFsMkiQb9na2KSsrdXPNqLNOnvwJzz+/DnffPQX//Oc7mDPnj/jnP3Px0kuvQastx9/+9oi7q9hjtW5sFGyMkQQAI8dJEsmW0zvbEPVE3NnGO+3e/TY++OA9/PrrWYwdm4jHH1+DsWMTpfVCIyOj8Oijj+GeezipylWWQbL1n1u3SDJIEskVWyRJFkpKinHsWAGMbRa9MxqNOHasACUlxW6qGXVGbu5e3H77FOzdewAZGeuRmJhsteh8v379sXLlE+1eKyfnXcyceReGDBmCtLQ0HDlyxGH5/Px8pKWlYciQIZg4cSJ27dplcb6wsBAPPvggUlJSEBsbizfeeMPmdXbu3ImUlBS7ryuKIjZu3IikpCQMHToUc+fORWFhYbvvp6sY7Sz/07pFkjO3ieSLQZJkgTvbeKcNG/4b99wzH2q1xuK4KIo4f/48AEClUuGOO6Y5vM6nn36El19ej3nzFiI3NxcjRozA4sWLUVpqe9jDuXPncN9992HEiBHIzc1Feno6nn76aXz44YdSmStXriA6OhrLly9HWFiYzet88MEHyMjIwP3332/3dTdv3ozt27dj9erVyM7OhkajwYIFC1BTU9Ohe9RZ9pf/afkzZ24TyReDJMkCd7bxTr/73XRUVV2yOn75chVmz76rw9fZvXsnpk27G3fdNR2DBg3CqlWr0L9/f6tWxpbyuxEREYFVq1Zh0KBBmDVrFtLS0rBt2zapzNChQ/HXv/4V/+///T/4+vravM727dsxY8YMzJo1y+briqKIN998E+np6Zg0aRJiYmKQmZmJ+vp6HDhwoMPvrzOMsL38j8AWSSICgyTJCHe28T72lp25cuWK3fDWll6vx6lTJzFq1M0WxxMTE1FQUGDzOUePHkViYqLFseTkZBw/fhx6vb5Dr9vY2Igff/wRSUlJdl+3uLgY5eXlFmV8fX0xatQou3VzRBCcf1g+XzAdR5sWSRev3RMfrt5Hb3vwPnj/PegoTrYhWUlNnYnPP/8EFRU67mzTg23c+AIAQBAEbNnyOvz9WxaWNxiM+Omn4xg8OKZD16qqugSDwYDQ0FCL4xqNBuXl5Tafo9VqodFYdqer1Wo0NTWhsrIS4eHh7b5uZWUlDAYD1Gq13dc1/9dWGXvd7o6o1UFOP8foa1pr1TwmMqC3P3x8jfDzUUCpEGAwiujbNwCaYPks7u/KffRGvA+8BwCDJMmMn58/Fi9eiq1bX8eiRenc2aaHOnXqZwCmFsnTp3+Bj49KOqdSqXD99YPx+9/PdeqabYc3iKJoday98raOd8Xr2nstZ+l01XD2qdpq09aT5hbI2rp61DcaoVcpoBAAA4BybTV8OtgS25MJgik4uHIfvQnvg/ffA/P76wgGSZKdkSNHY+TI0e6uBnXCxo2bAADPPLMGDz20HAEBgS5fKzi4L5RKJXQ6ncVxnU5n1epoZqu1sqKiAj4+Pujbt2+HXjckJARKpdJqsfTWr2uepKPVai1aOR3VzRFRhNNfeubxj+YsK4otu92YwqWIJqPolV+m9rhyH70R7wPvAcAxkkTUgz322N87FSIBUwtmTMwNOHz4G4vjeXl5SEhIsPmc+Ph45OXlWRw7ePAg4uLioFKpbD6nLV9fX9x00004dOiQ3deNjo5GWFiYRZnGxkYcPnzYbt26i8JGS6uyub+bc22I5IstkkTUozz22KNYtcoUIB977FGHZZ955rkOXXPOnHuwdu1q/Pa3v0Vy8lj885//RFlZGebMmQMAWL9+PS5cuIBnn322ufwc7Ny5ExkZGZg9ezYKCgqwd+9erF+/XrpmY2MjioqKpD9fuHABJ06cQO/evXHNNdcAABYsWIAVK1YgLi4OCQkJVq8rCALmzZuHTZs2YeDAgbjmmmuwadMm+Pv7Y9o0x0sadRVHIVHZHC45a5tIvhgkiahHCQwMlMYMBgZ2rjXSbOLESaiqqsL27Vvw/PPrEBMTg6ysLERFRQEwTXopKyuTyg8YMABZWVnIyMjAzp07ER4ejlWrVmHy5MlSmYsXL2L69OnSz9u2bcO2bdswevRovPXWWwCAqVOnorKyEq+++iouXrxo9boAsHjxYjQ0NGDNmjWoqqrCsGHDsG3bti577+0xrxGpsDH0U9F8kDvbEMmXILo6aruH0Gq9cyAsue7IkXxpsg3HSlJrggBoNN47C9OVz8NzlVeQtu0wAnyV+HDZeHz6Qynq9Ub4qxR46YvTqKjTY+fc4YgJvzrB1p3Mfz/k/r3C++D998CZz0KOkSRZaWiox+bNr0KrLcfmza+ioaHe3VWiTmhoqEd9fcv/h+fPl2HPnneQn/+1G2vlXYyi5WSb1szjJtkiSSRfDJIkK/v2ZaOysgIAUFlZgX37st1cI+qMlSuX49//Nu3wUl1djcWL52P37p1YuXI5/7/tIuaIKMDBZBuOkSSSLQZJko2yslLk5mZLa/CJoojc3GyUlTm/sDN5hlOnTmLoUNPs5S+++BShoWpkZ7+Hxx9fg+zs3W6unXcwNzbaHCMptUhexQoRkUdhkCRZEEURW7e+brWQs73j1DPU19ejd+8AAEB+/teYMOFWKBQK3HRTHM6fL2vn2dQRLV3b1klS0fwNwlnbRPLFIEmyUFJSjGPHCmA0Gi2OG41GHDtWgJKSYjfVjDojOnoAvvzyC1y4cB75+V9h9GjTftmVlZWdXl+STBy1SJqX/zHyFzEi2WKQJFmIiorGsGH2Fpcejqio6KtcI+oKf/rTvfjv/34Rs2bdhRtvjENc3FAAwOHDX2Pw4Fg31847iHDUIsl1JInkjutIkiwIgoC77krDsWMFVufuuivN6f2RyTPceuttGDo0HjqdFtdfHyMdHzFiFMaPv9WNNfMe5oxo618IZ20TEYMkyYIoiti/PweCIFiMhxQEAf/6117ExQ1lmOyh1GoN1GrLfadvvDHOTbXxPqKDBcmVzX1abUaMEJGMMEiSLJjHSLYliqI0RjI6eoAbakadceXKFbz99hv49tvDqKyssFqG5t13/+WmmnkPqUXSVtc2WySJZI9BkmTBPEbyhx+OWUy4USgUGDo0nmMke6h169bi6NHvMHnyVGg0GtjugKXOcNgiyb22iWSPQZJkQRAELFqUjoceSrc4LooiFi1KZ7d2D/XNN3l49tkXMXRovLur4rWkBckdTLbhrG0i+eKsbZI9riHZcwUF9UGfPsHuroZXczTZRtl8kC2SRPLFIEmyYF543BYuSN5z3XtvOrZsed1iv23qWi1d2xwjSUTW2LVNssDJNt5p9+63UVJSgjvvnISIiAj4+Fh+pG3bttNNNfMeRkcLkkt7bV/FChGRR2GQJFngZBvvlJx8i7ur4PUcLkjOFkki2WOQJFkwT7ZZtmypzeOcbNMzLVx4n7ur4PVEafkf63Pca5uIOEaSZCMiIhLTp8+UQqMgCJg+fSb6949wc82oM6qrq/Hee7l4/fVXcPlyFQDg559Porz8optr5h2MHVj+h7O2ieSLQZJkJTV1JgICAgEAgYGBSE2d6eYaUWf88kshfv/7NOzcuQO7dr2F6upqAMD//d/neP31V9xcO+/QMmvb0V7bV7NGRORJGCRJttiI0vNt3LgBU6dOw+7d++Dr6ycdv/nmcTYnV5ELHEy24RhJImKQJFnZty8btbU1AIDa2hrs25ft5hpRZ5w8+SPuvnuG1fGwsHBUVOjcUCPvY3Qw2aZl1jaDJJFcMUiSbJSVlSI3N1taF08UReTmZqOsrNTNNSNX+fr6Sb8YtPbrr2fRt2+IG2rkfYyOJtsI5jIMkkRyxSBJsmBekLztwuP2jlPPkJQ0Adu3b0FTUxMAU6vZ+fPn8frrGzFhQoqba+cdHC1Ibp5s08QWSSLZYpAkWTAvSG5ss3Ky0WiUFiSnnueBBx7CpUuVmDbtdjQ2NuCBB+7DnDnT0bt3AO67b2n7F6B2OWyR5F7bRLLHdSRJFrgguXcKCAjEa69txXffHcHJkycgikbExNyAUaPGuLtqXsOcEW21OkjL/3DWNpFsMUiSLHBBcu9jNBrxwQfv4f/+73OUlZVCEAREREQiNFQNURT5/2kXMXdt297ZxvTfJrZIEskWu7ZJNrggufcQRRErVz6CzMynUF5+Eddddz2uvfY6nD9/Hs88swaPPfYXd1fRa5gbG23lcs7aJiK2SJKspKbOxOeff4KKCh1CQ0O5IHkP9cEH7+Ho0QK89NJrGD58pMW5b789jL/97S/4n/85gDvumOamGnoPR5NtFNzZhkj22CJJsuLn54/Fi5dCownDvfcuhZ+fv7urRC745JMPMXfuAqsQCQAjRozCH/84Hx9//G831Mz7iI4WJFdw1jaR3LFFkmRn5MjRGDlytLurQZ1QVPQLli79s93zN988DtnZ/7yKNfJejlobW/bavlq1ISJPwxZJIupxLl+uQkiI2u750FA1qqsvX8Uaea+WFklbe22b/ssxkkTyxSBJRD2O0WiEUqm0e16hUMBgMFzFGnkv8xaJ3GubiGxh1zYR9TiiKOKZZ/4BlcrX5nm9vvEq18h7idKC5Pb32jawRZJIthgkiajH6chs7ClT/t9VqIn3k4KkjXOctU1EDJJE1OM89tjf3V0F2TA63Gvb9F+2SBLJF8dIEhGRXWIH9to2MEcSyRaDJBER2eW4RZI72xDJHYMkERHZZY6INlskOWubSPYYJImIyC7zFom2Jtsom79BOEaSSL4YJImIyC6jowXJubMNkewxSJLs7N79Nn73u7uxe/fb7q4KkcczOphsw3UkiYhBkmTl8uUq5OTsgdFoRE7OHly+XOXuKhF5OPuTbThGkogYJElWMjOfksZ8iaKIZ599ys01IvJsjlokudc2ETFIkmx8//1RnDp10uLYzz+fxPffH3VPhYh6APPyPza3SGSLJJHsMUiSLBiNRqxfv87mufXr18FoNF7lGhH1DOaMaOvLwrwgOVskieSLQZJk4bvvjqCurtbmubq6Wnz33ZGrXCOinsHRguQtYySvapWIyIMwSJIshIf369R5IrnjrG0isoVBkmQhKira5hgvwDT2Kyoq+irXiKhncLj8j2AuwyBJJFddHiRTUlIQGxtr9VizZg0AYOXKlVbnZs+ebXGNxsZGrF27FmPGjEF8fDzS09Nx/vz5rq4qycjRo99Ks7XbEkURR49+e5VrRNQzdKhrmy2SRLLl09UXzM7OhsFgkH4uLCzEggULMGXKFOlYcnIyMjIypJ9VKpXFNZ5++ml8/vnn2LBhA/r27Yt169ZhyZIlyMnJgVKp7OoqkwwkJIxEYGAQamqqrc4FBfVBQsJIN9SKyPOJHVmQnC2SRLLV5S2SoaGhCAsLkx6ff/45fvOb32D06NFSGV9fX4syffv2lc5VV1dj7969WLlyJcaNG4cbb7wRzz33HE6dOoW8vLyuri7JhEKhwLJlK2yeW7ZsBRQKjvIgskU0L0huY7dtaYtELnpAJFtd3iLZWmNjI/bv348FCxZYjE/Lz8/H2LFj0adPH4waNQrLli2DWq0GABw/fhx6vR6JiYlS+X79+mHw4MEoKChAcnKyU3WwMyyOZGjYsHjccMONOHnyJ+nYb397I4YOHebGWpEn4eeFNYcLkjcfY4skkXx1a5D85JNPUF1djdTUVOnY+PHjMWXKFERGRqK4uBgvvfQS5s+fj5ycHPj6+kKr1UKlUiE4ONjiWhqNBlqt1uk6qNVBnX4f5D3WrXsGaWlpMBqNUCgUyMh4Bn378u8IkT2iowXJOWubSPa6NUju3bsX48ePR79+LUurTJ06VfpzTEwM4uLikJKSgi+++AKTJk2yey17EyXao9NVg78sUwslUlNnYd++d5GaOgtNTUpotdbjJkmeBIG/fLZlzogKmy2SzV3b/JAlkq1uC5IlJSXIy8vDxo0bHZYLDw9HZGQkzpw5A8DU8qjX61FVVWXRKqnT6ZCQkOB0PUQRDJJk4frrYxAaqsb118fw7wZROxy1SCrYIkkke902wyAnJwdqtRq33HKLw3KVlZUoKytDeHg4ACAuLg4qlQqHDh2Syly8eBGFhYUuBUmi1hoa6rF586vQasuxefOraGiod3eViDyaOSLaapFUcmcbItnrliBpNBqRk5OD6dOnw8enpdGztrYWmZmZKCgoQHFxMb755hvcf//9CAkJwW233QYACAoKwowZM5CZmYmvvvoKP/30Ex599FHExMRg3Lhx3VFdkpF9+7JRWVkBAKisrMC+fdlurhGRZ5Mm29g4Z17sgHttE8lXt3Rt5+XlobS0FDNmzLA4rlQqcerUKeTm5qK6uhphYWEYM2YMNmzYgMDAQKncY489Bh8fHzz88MOor6/H2LFjsW7dOq4hSZ1SVlaK3NxsqatOFEXk5mZjwoQUREREurl2RJ7J4WQbjpEkkr1uCZJJSUn4+eefrY77+/tj69at7T7fz88PTzzxBJ544onuqB7JkCiK2Lr1datJW+bjq1atsbuFIpGcOZpsY5613cQWSSLZ4irMJAslJcU4dqwAxjYrJxuNRhw7VoCSkmI31YzIs4lSkHSwIDlbJIlki0GSZCEqKhrDhiVY7WCjUCgQHz8cUVHRbqoZkWcTYT8kmlsp2SBJJF8MkiQLgiBg0aJ0q+5re8eJyIRd20TkCIMkyUZERCSmT58phUZBEDB9+kz07x/h5poReS7zuGKHXdsMkkSyxSBJspKaOhMhIaEAgNDQUKSmznRzjYg8m3n4o829tptbJEVwnCSRXDFIkqz4+flj8eKl0GjCcO+9S+Hn5+/uKhF5NKODFkllq2NslSSSp27da5vIE40cORojR452dzWIegRzPHS0IDlg2t2GXyhE8sMWSSIissvoaK/t1i2S7NomkiUGSSIisqsjs7YBwMCubSJZYpAkIiL7pMk2jsdIMkgSyRODJBER2dXStW19rnUrJbu2ieSJQZKICEBOzruYOfMuDBkyBGlpaThy5IjD8vn5+UhLS8OQIUMwceJE7Nq1y6rMhx9+iKlTpyIuLg5Tp07Fxx9/bHE+JSUFsbGxVo81a9ZIZVauXGl1fvbs2V3zpjvAvKmorS8LQRCkSTgG5kgiWeIkOyKSvU8//Qgvv7wey5evxIQJ47B7924sXrwY77//PiIjI63Knzt3Dvfddx9mzZqF5557Dt999x3WrFmD0NBQTJ48GQBQUFCAZcuW4aGHHsJtt92GTz75BA8//DDeeecdDBs2DACQnZ0Ng8EgXbewsBALFizAlClTLF4vOTkZGRkZ0s8qlao7boNNooPJNoBpLUmDUeTyP0QyxRZJIpK93bt3Ytq0u3HXXdMxaNAgrFq1Cv3797fZymgqvxsRERFYtWoVBg0ahFmzZiEtLQ3btm2TyuzYsQPjxo3DkiVLMGjQICxZsgQ333wzduzYIZUJDQ1FWFiY9Pj888/xm9/8BqNHWy5P5evra1Gub9++3XIfbBEdTLYBAKW03zaDJJEcMUgSkazp9XqcOnUSo0bdbHE8MTERBQUFNp9z9OhRJCYmWhxLTk7G8ePHodfrpTJJSUlWZexds7GxEfv378eMGTOsWv/y8/MxduxYTJ48GY8//jh0Op1T79FMEJx/iLBskRSEljUlBbTsbmMURZeu39Mert5Hb3vwPnj/Pegodm0TkaxVVV2CwWBAaGioxXGNRoPy8nKbz9FqtdBoNBbH1Go1mpqaUFlZifDwcGi1WqjVaqsy9q75ySefoLq6GqmpqRbHx48fjylTpiAyMhLFxcV46aWXMH/+fOTk5MDX19ep96pWBzlVHgBUvqZu9MAAPwBAQG9/+Pga4eejQK/evvBRKAAYEdw3ABpNgNPX74lcuY/eiPeB9wBgkCQiAgC0bQUURdHqWHvl2x535pp79+7F+PHj0a9fP4vjU6dOlf4cExODuLg4pKSk4IsvvsCkSZMcvCNrOl01nO2Brq83tbDWX2kEANTW1aO+0Qi9SoErdY1S66S2ogaB0tQc7yQIpuDgyn30JrwP3n8PzO+vIxgkiUjWgoP7QqlUWnUX63Q6q1ZHM1utlRUVFfDx8ZHGL2o0Gmi1Wqsytq5ZUlKCvLw8bNy4sd36hoeHIzIyEmfOnGm3bFuiCKe/9KSA3Poa5j+jZZvEJoPolV+otrhyH70R7wPvAcAxkkQkcyqVCjExN+Dw4W8sjufl5SEhIcHmc+Lj45GXl2dx7ODBg4iLi5NmVMfHx+PQoUNWZWxdMycnB2q1Grfccku79a2srERZWRnCw8PbLdsVjA4WJAcAn+bjnGxDJE8MkkQke3Pm3IMDB3Jx4MC/UFRUhGeeeQZlZWWYM2cOAGD9+vVYsWJFq/JzUFpaioyMDBQVFSE7Oxt79+7FwoULpTLz5s3DoUOHkJWVhaKiImRlZeGrr77C/PnzLV7baDQiJycH06dPh4+PZSdRbW0tMjMzUVBQgOLiYnzzzTe4//77ERISgttuu60b70ir+jlYkBxoNdnGu3u1icgOdm0TkexNnDgJVVVV2L59C55/fh1iYmKQlZWFqKgoAEB5eTnKysqk8gMGDEBWVhYyMjKwc+dOhIeHY9WqVdIakgAwfPhwvPDCC3jxxRfx8ssvY8CAAdiwYYO0hqRZXl4eSktLMWPGDKt6KZVKnDp1Crm5uaiurkZYWBjGjBmDDRs2IDAwsJvuhqX2lv9RNCdMA1skiWRJEEXv/tev1XrnQFgi6nqCAGg03jsL05XPw0f2HceXpyvw+OQYTImPwqc/lKJeb4S/SoGxgzT4045vUXq5Htv/EI+4iD7dU3EPYf77IffvFd4H778HznwWsmubiIjsMn9H2m2RbP4WMXBnGyJZYpAkIiK7zK0tLbtqW1JIk22uVo2IyJMwSBIRkV3tTbZRctY2kawxSBIRkV0tk23stEg293mza5tInhgkiYjIrpa9tm2fVzYfZ4skkTwxSBIRkV3GjrZIMkcSyRKDJMnOkSP5uP/+hThyJN/dVSHyeG23SGxLmmzDrm0iWWKQJFlpaKjH5s2vQqstx+bNr6Khod7dVSLyaC1bJNo+z65tInljkCRZ2bcvGxUVOgBARYUO+/Zlu7lGRJ7N3CLJrm0isoVBkmSjrKwU+/a9a3Fs3753UVZW6qYaEXm+9hYkV7Jrm0jWGCRJFkRRxNatr8NoNFocNxqN2Lr1dXj5TqFELmvJh+0tSM5/Q0RyxCBJslBSUoxjxwpsnjt2rAAlJcVXuUZEPUNL17bt89IWiQySRLLEIEmyEBkZhd69A2yeCwgIQGRk1FWuEVHP0LL8j+3zLV3bV6lCRORRGCRJFkpKilFXV2vzXG1tLVskiewwtzMK9ibbCObJNmyRJJIjBkmShfbGQHKMJJFt7Xdtc7INkZwxSJIs2GtN6eh5IrlqWUfS9r8RriNJJG8MkiQL0dEDcMMNN9o899vf3oTo6AFXuUZEPYOxgzvbNHGMJJEsMUiSLAiCgKVLH7J5bunSh9giSdQOewuSKxVc/odIzhgkSfY4PpLIPqlF0t6sbQZJIlljkCRZEEURa9c+YfPc2rVPMEwS2dHeXtvSrG1OtiGSJQZJkoWzZ8+gvPyizXPl5Rdx9uyZq1shoh6ivb22WybbXK0aEZEnYZAkWfjwww86dZ5IrtpbkFzBrm0iWWOQJFlYuPC+Tp0nkjuhnb222bVNJE8MkiQLKpUKd92VZvPc9OkzoFKprnKNiHoGYzsLkktbJLJFkkiWGCRJNubOXQClUmlxTKlU4p57/uSeChH1AO0tSK5o/hYxMEcSyRKDJMnKmjUZDn8mIktie8v/CNwikUjOGCRJVgoKvnX4MxFZEqXJNo7HSLJrm0ieGCRJNsrKSpGbm21xLDc3G2VlpW6qEZHna29Bcqlrm1skEskSgyTJgiiK2Lr1dauFx+0dJyIT878Me5uIcrINkbwxSJIslJQU49ixAhiNls0mRqMRx44VoKSk2E01I/Jsxva6trmOJJGsMUiSLERFRWPYsASb5+LjhyMqKvoq14ioZ+joZBuuI0kkTwySJAuCICApaYLNc4mJE+wubUIkd+1PtjH9lzmSSJ4YJEkWjEYjtm/fbPPc9u1ZVl3eRGQiTbaxc97ctW1g1zaRLDFIkix8991h1NXV2jxXV1eL7747fJVrRNQzSJNt7LRIch1JInljkCRZCA/v36nzRHLV3haJXEeSSN4YJEkWBgz4Da677nqb5wYNGowBA35zlWtE1DO0O0aSWyQSyRqDJMmCIAi45575Ns/dc898TrYhskOU9tq2fZ5d20TyxiBJsiCKIvbvz7EKjIIg4F//2ssFyYnsaHeyjcDJNkRyxiBJsmBekNzWzjZckJzIPvO/GHtd20oF15EkkjMGSZIF84LkCoXlX3mFQsEFyYkcaHdBcmlnm6tVIyLyJAySJAuCIGDRonSbWyQuWpTOMZJEdrS7RaK0IDmTJJEcMUiSbJSXX7R5/OLFC1e5JkQ9h6MWSUFo1SJpFPkLGZEMMUiSLBiNRmzY8KzNcxs2PMudbYjsMLdICm2m2/goBAiCgCtNpn879QYRFQ1N0F/tChKRWzFIkiwUFBxBTU21zXM1NdUoKDhylWtE1DO0TLaxPK5UCKjTG3FWVwcAqKxrxLdnK1HTaGDLJJGMMEiSLCQkjETv3gE2zwUEBCIhYeRVrhGR52u9yoG9cGgQTS2SBqOIxibDVakXEXmOLg+SGzduRGxsrMUjMTFROi+KIjZu3IikpCQMHToUc+fORWFhocU1GhsbsXbtWowZMwbx8fFIT0/H+fPnu7qqJCOCIKB//wib5/r3788WFCIbWs/EtrtFIrhFIpGcdUuL5ODBg3Hw4EHp8d5770nnNm/ejO3bt2P16tXIzs6GRqPBggULUFNTI5V5+umn8fHHH2PDhg145513UFdXhyVLlsBg4G+75JqSkmKcPv2LzXNFRb9wHUkiGyxbJG2XMR9njiSSp24JkkqlEmFhYdIjNDQUgOlD6c0330R6ejomTZqEmJgYZGZmor6+HgcOHAAAVFdXY+/evVi5ciXGjRuHG2+8Ec899xxOnTqFvLy87qguyQDXkSRyXusWSXut9uZlgdgiSSRP3RIkz549i6SkJKSkpGDZsmU4d+4cAKC4uBjl5eVISkqSyvr6+mLUqFEoKCgAABw/fhx6vd6iO7xfv34YPHiwVMYZgsAHH4BCIeDee63XixQE03GFQnB7HfnwjAe1aB0N7XVtm+8ZcySRPPl09QWHDh2KzMxMDBw4EDqdDq+99hrmzJmDAwcOoLy8HACgVqstnqPRaFBaWgoA0Gq1UKlUCA4Otiqj1Wqdro9aHeTiOyFvo9HE4g9/+APefvttiKJpzbt77rkHcXEx7q4akUdq3bWtsLPbtrlFkjmSSJ66PEhOmDDB4uf4+HjcfvvtyM3NxbBhwwAAbVuF2u5/bEtHytii01XzN2WSTJlyF95//wNUVOgQGhqKyZPvhFZre1kgkh9B4C+frVlse9hOiyS7tonkqduX/+nduzdiYmJw5swZhIWFAYBVy6JOp4NGowFgannU6/WoqqqyW8YZosgHHy0PX19/LF68FBpNGO69dyl8ff3dXic+POtBLVqHQ/tbJDa3SPLeEclStwfJxsZGFBUVISwsDNHR0QgLC8OhQ4cszh8+fBgJCQkAgLi4OKhUKosyFy9eRGFhoVSGqDNGjhyN117bhpEjR7u7KkQerXU4bG+MJFskieSpy7u2MzMzceuttyIiIgIVFRV47bXXUFNTg9TUVAiCgHnz5mHTpk0YOHAgrrnmGmzatAn+/v6YNm0aACAoKAgzZsxAZmYmQkJCEBwcjMzMTMTExGDcuHFdXV0iIrJDbDXy0e6sbXNZ5kgiWeryIHn+/Hk88sgjuHTpEkJCQhAfH489e/YgKioKALB48WI0NDRgzZo1qKqqwrBhw7Bt2zYEBgZK13jsscfg4+ODhx9+GPX19Rg7dizWrVsHpVLZ1dUlIiI7OrIguSBNtmGSJJKjLg+SGzZscHheEAQ8+OCDePDBB+2W8fPzwxNPPIEnnniiq6tHhN2738a+fe8iNXUW5sz5o7urQ+SxLBYkt1NGIXVtd399iMjzcK9tkpXLl6uwd+8/YTQasXfvP3H5clX7TyKSqY4sSC5wsg2RrDFIkqxkZDxp8fO6dU/aKUlE5mxor1u79TlOtiGSJwZJko3vvz+KX345ZXGssPAUvv/+qHsqROThzF3b9lojW59j1zaRPDFIkiwYjUY899wzNs8999wzMBqNV7lGRJ7PHA4d7RypkLZIZJIkkiMGSZKFb789jPr6KzbP1ddfwbffHr7KNSLyfOZw6Lhrm1skEskZgyQREdlkDof2urbLL5ShqdH0CxrHSBLJU5cv/0PkiYYPHwmFQmGzC1uhUGD48JFuqBWRZzM6aJEs+rEAT730d/j2CoRiyGyI/W+4yrUjIk/AFkmShbKyUrvjII1GI8rKSq9yjYg8n7mRse0+2/W11fjXtg0wGAy4UlMF32+2w3iFS2kRyRGDJMlCVFQ0hg2zvVd7fPxwREVFX+UaEXk+ezOxj/3fB6i+pENk9G+gib4OgrEJiuJjV7dyROQRGCRJFgRBwKJF6VAoLLfZVCqVWLQo3eHyJkRy1dK1bfnv4z8/HgEATJv5B/x2TAoAQFlSwJnbRDLEIEmyERERiTvvnG5x7M47p6N//wj3VIjI00ld2y2HrtRcxvmzhQCA+JE3I3Z4EkQIUFT+ioqLHCJCJDcMkiQrTU1NFj/r9U12ShKREdYLkv/npwJAFBEePRDqsHAEBPeFUXMdAODMye/dUk8ich8GSZKNsrJSfPDBfotjH3ywnxNtiOywtSD56Z++AwBcHzcCgKnb2xhyDQCg7IzlzlFE5P0YJEkWRFHEq6++ZDWGy95xkp+cnHcxc+ZdGDJkCNLS0nDkyBGH5fPz85GWloYhQ4Zg4sSJ2LVrl1WZDz/8EFOnTkVcXBymTp2Kjz/+2OL8xo0bERsba/FITEy0KCOKIjZu3IikpCQMHToUc+fORWFhYeffcAe0bJHYcuz8GdNrXxMTJ50z9h0AALhwtuiq1IuIPAeDJMlCcfE5nDz5k81zJ0/+hOLic1e5RuRJPv30I7z88nrMm7cQubm5GDFiBBYvXozSUtut1efOncN9992HESNGIDc3F+np6Xj66afx4YcfSmUKCgqwbNky3H333fjXv/6Fu+++Gw8//DCOHbOc3Tx48GAcPHhQerz33nsW5zdv3ozt27dj9erVyM7OhkajwYIFC1BTU9P1N6KNtsv/NDY0QHehBADQf4CpO1sBwBhiCpLasrNoqK/v9noRkedgkCRZaK/FkS2S8rZ7905Mm3Y37rprOgYNGoRVq1ahf//+NlsZTeV3IyIiAqtWrcKgQYMwa9YspKWlYdu2bVKZHTt2YNy4cViyZAkGDRqEJUuW4Oabb8aOHTssrqVUKhEWFiY9QkNDpXOiKOLNN99Eeno6Jk2ahJiYGGRmZqK+vh4HDhzonpvRithmss2Z/5yGaDTCv3cQgkLUAJrHT/oHQ/QLgmg04nQRu7eJ5IRBkohkTa/X49Spkxg16maL44mJiSgoKLD5nKNHj1p1QScnJ+P48ePQ6/VSmaSkJKsyba959uxZJCUlISUlBcuWLcO5cy2t48XFxSgvL7e4jq+vL0aNGmW3bo4IgnMPsdVkG0EATv9iColh0QOlCThKhenC5lbJX34+4fTr9KSHK/fRGx+8D95/DzqKWySSLLBFkuypqroEg8Fg0RIIABqNBuXl5Tafo9VqodFoLI6p1Wo0NTWhsrIS4eHh0Gq1UKvVVmVaX3Po0KHIzMzEwIEDodPp8Nprr2HOnDk4cOAAQkJCpLJtr6PRaOx2uzuiVgc5Vb7PFQMAwEepQGhoIIoKTUEycuD1COjtD5VKicCAXhAAGIOjoDz/E4rP/QdqdaDTdetJnL2P3or3gfcAYJAkmbh48UK756+5ZuDVqQx5pLaL0ouiaHWsvfJtj7d3zQkTJlicj4+Px+23347c3FwsWLCg3ddylk5XDWeeWnmp1vR6RiMqKmpw+hfTRJvg8AGorauHXt8LtXX1EARADAwHAPz6nzPQ6Wq88pczQTAFB2fvo7fhffD+e2B+fx3BIEmyoNGEdeo8ea/g4L5QKpXQ6XQWx3U6nVWro5mt1sqKigr4+Pigb9++UhmtVmtVxt41AaB3796IiYnBmTNnAABhYaa/l1qtFuHh4R2qmyOiCKe+9Mzb0wuCAFEE/lP0CwBAE3WtxTUFQYAx0FTXkuJfnX6dnsbb319H8T7wHgAcI0kyceLE8U6dJ++lUqkQE3MDDh/+xuJ4Xl4eEhISbD4nPj4eeXl5FscOHjyIuLg4qFQqqcyhQ4esyti7JgA0NjaiqKhICpDR0dEICwuzuE5jYyMOHz7s8DpdpWWLRODy5SpUX64CAISER1qUUwAQA0zBtupSJaqrq7u9bkTkGRgkSRYqKys7dZ6825w59+DAgVwcOPAvFBUV4ZlnnkFZWRnmzJkDAFi/fj1WrFjRqvwclJaWIiMjA0VFRcjOzsbevXuxcOFCqcy8efNw6NAhZGVloaioCFlZWfjqq68wf/58qUxmZiby8/Nx7tw5HDt2DH/+859RU1OD1NRUAKaWvnnz5mHTpk34+OOPcerUKfztb3+Dv78/pk2b1u33xdzSIggCSkqKAQCBwaFQ+flblBMEAVD5o3cf0zjTX3892+11IyLPwK5tkoVx45KRm5vt8DzJ18SJk1BVVYXt27fg+efXISYmBllZWYiKigIAlJeXo6ysTCo/YMAAZGVlISMjAzt37kR4eDhWrVqFyZMnS2WGDx+OF154AS+++CJefvllDBgwABs2bMCwYcOkMufPn8cjjzyCS5cuISQkBPHx8dizZ4/0ugCwePFiNDQ0YM2aNaiqqsKwYcOwbds2BAZ2/4QWaYtEmGaQA0DfMOu96c3LAwWHR6LucgXOnTuLG2+8qdvrR0TuxyBJsqDVXmz3/LXXXneVakOeKC1tFmbMmAWNxnqA+bp166yOjR49Gvv27XN4zSlTpmDKlCl2z2/YsKHdegmCgAcffBAPPvhgu2W7WusFyUtKTMsShYRbB0nzXKDgsEiU/XKcLZJEMsKubZKFfv36d+o8kRy1dG23tEiG2GiRNM8qDwozjZ08d+7Xq1NBInI7BkmSifZWV3Vi9VUimTBKSxrBYYukuWu7j9r0C1lxMYMkkVwwSJIsOFoPsCPnieSodde21CJpM0ia/v30DjEtUXT+/PmrU0EicjsGSZKF6OgBuOGGG22e++1vb0J09ICrXCMiz2feIlFsrEdlZQUA25NtzL+GBYSYlgCqqalGTU3NVakjEbkXgyTJgiAImDXr9zbPzZr1e7ZIEtlgbG6RNNaYFmvvExwM/14BVuXM/34UKn/0Ce4LALhwocyqHBF5HwZJkgVRFPHuu7tsntuz5x2v3M6NqLPM/ywMtabWyH4RkTbLmcdIiqKIsOaJa62XSyIi78UgSbJQXHwOJ0/+ZPPcyZM/obj43FWuEZHnM0+2MdSYgmS4ndUNzC2SRhEI72fq+j5/nkGSSA4YJEkWjOZNg108TyRHxjYtkvaCZOsWyX79TWUYJInkgUGSZKG83PGC5O2dJ5InU5JsqjFtIWq3RbL5v0YRCAtniySRnDBIkiwMHRrfqfNEcmRukWyqNk22sd8iaYqSoiginC2SRLLCIEmykJOzp1PnieTIPAmtqXmMZL/+1kv/AC1bJLYeI8lZ20TywCBJspCWNrtT54nkyCgCMBrQVFsFAFJrY1stk21EhIWbylRVVeHKlStXpZ5E5D4MkiQLx44VdOo8kRyJAIT6KgAifH19Edw3xGY5ZasWyd4BAejVqxcAQKstvzoVJSK3YZAkWeCsbSLniaIIoa55ok14PygUtr8yFIqWFklBEKBWhwEAdDrt1akoEbkNgyTJRHsLjnNBcqK2jCIgXLkEwBQk7TFPtjE0z84JCzMFSa6GQOT9GCRJFioqKjp1nkiOjKII4Urz+EgHQVJpDpLNk3M0GlOQZNc2kfdjkCRZiIm5oVPnieTKNEaypZXRFnOPt3mECIMkkXwwSJIs5Od/1anzRHJkFEUpSJrDoS1tWyTNoZNBksj7MUiSLIwadXOnzhPJkWmMpLlFMtxuOfMYSaPR3LVtKlteziBJ5O0YJEkWPv743506TyRHoihCqL8MoGNd22yRJJIfBkmShYCAwE6dJ5KjJoMBaKgG4LhFUiktSG762dwNrtNppd1xiMg7MUiSLISE2F5IuaPnieSo9vIlCKIREASEhITaLdd2+R+1WgMA0Ov1qKq61O31JCL3YZAkWfDx8enUeSI5qr6kAwCoAoKhVCrtlmu9IDkAqFQqKXiye5vIuzFIkixMmnSHtB9wW4KgwKRJd1zlGhF5PnOQ9At03GJv3iLR3CIJABqNqVWSE26IvBuDJMmCUqnEnXem2jx3992pDltbiOSqpjlI+rYTJBVtxkgCXEuSSC4YJEkWjEYj/v3v922e++CDA9xrm8iGmkumHZ/8gtoJkgrLMZJAyxJADJJE3o1BkmThyJF8NDY22DzX2NiAI0fyr3KNiDxfbVXHgmTLrG3rrm0GSSLvxiBJslBRoevUeSI5qr1cCaD9MZIKG2MkuZYkkTwwSJIs3H77FAeTbQTcfvuUq1wjIs9XJwXJYIfllApbLZLs2iaSAwZJkgVBEKBQ2P7rrlAo7IZMIjkzB0n/oL4Oy0nrSFpMtuGsbSI5YJAkWTh8+BsYDAab5wwGAw4f/uYq14jIs+n1etTXmrZH7GjXttHGZJtLlyqh1+u7p5JE5HYMkiQLRUWFnTpPJDeVlaaJNqKggF87W4iau7YNrbq2g4ODoVKpAJi2SiQi78QgSbJw7bWDOnWeSG50uuYJaH5BUCgcr7MqrSPZqkVSEARpq0R2bxN5LwZJkgV74yM7ep5IbsytiKJ/ENobQWxrjCQAhIWFN1+LQZLIW/Hbk2Shvck0nGxDZKklSPaRgqI9yuZvktYtkkDL7jZskSTyXgySJAv9+0d06jyR3EhB0q8P2vs9q6VFsm2Q5KLkRN6OQZJkITp6AHr16mXzXK9evRAdPeAq14jIs5kX6Rf9g9ptkbQ1RhKANEayooKTbYi8FYMkyUJpaQmuXLli89yVK1dQWlpylWtE5NnMk21Ev/bHSJq7ttuOkTQHSWniDhF5HQZJkoV+/fp36jyR3LQeI9nRrm2jaLtFksv/EHkvBkmShXff3dWp80Ry49xkm+YxklZd22qLaxGR92GQJFkYNOj6Tp0nkhOj0SiNkUQXtEhevnwZjY2NXV5PInI/BkmShfDwfp06TyQnly9flrYUFf0CIbQzStK8RaLBaHk8KKgPfH19AbBVkshbMUiSLHz11cFOnSeSE3Po8+0dBCh8pKBoj7lru22LpCAICA1l9zaRN+vyILlp0ybMmDEDCQkJGDt2LJYuXYrTp09blFm5ciViY2MtHrNnz7Yo09jYiLVr12LMmDGIj49Heno6zp8/39XVJZkICurTqfNEcmIOff6BfQG0v2C/tI5kmzGSAGduE3m7Lg+S+fn5uOeee7Bnzx5s374dBoMBixYtQl1dnUW55ORkHDx4UHpkZWVZnH/66afx8ccfY8OGDXjnnXdQV1eHJUuWSN0tRM6oqqrq1HkiOTEHSb+gvgDQboukvTGSAGduE3k7n66+4NatWy1+zsjIwNixY/Hjjz9i1KhR0nFfX1+EhYXZvEZ1dTX27t2LZ599FuPGjQMAPPfcc7jllluQl5eH5OTkrq42eTlz95qr54nkxNx66NfcItl+17bpv23XkQQYJIm8XZcHybaqq6sBAMHBwRbH8/PzMXbsWPTp0wejRo3CsmXLpKUijh8/Dr1ej8TERKl8v379MHjwYBQUFDgVJLmFMgHAxIm3Y/v2LIfn+XeF+HfAxDxj29y13d6NsbezDcAlgIi8XbcGSVEUkZGRgREjRiAmJkY6Pn78eEyZMgWRkZEoLi7GSy+9hPnz5yMnJwe+vr7QarVQqVRW4VOj0UCrde7DSK0O6pL3Qj3bs8++5vD8O+9sx4oVK65SbYg8mzTZxtwi2U55c5AUYWstSbZIEnmzbg2STz75JE6dOoV33nnH4vjUqVOlP8fExCAuLg4pKSn44osvMGnSJLvXE22Mv2mPTlcNF55GXqa8vKLd81pt9VWqDXkqQeAvn0DrIBkMNKEDe223/JlBkkheui1Irl27Fp999hnefvtt9O/vePu58PBwREZG4syZMwBMLY96vR5VVVUWrZI6nQ4JCQlO1UMUwSBJiIm5AYcPf+3wPP+eEJlIYyQD+gJV7Xf5K1slySaj5WKSGg1nbRN5sy6ftS2KIp588kl89NFH2LFjBwYMGNDucyorK1FWVobw8HAAQFxcHFQqFQ4dOiSVuXjxIgoLC50OkkQAUF19uVPnieSkosLUeqiSlv9xXL51i2WTnRbJS5cq0dTU1HWVJCKP0OUtkmvWrMGBAwfw6quvIiAgAOXl5QCAoKAg+Pv7o7a2Fq+88gomTZqEsLAwlJSUYMOGDQgJCcFtt90mlZ0xYwYyMzMREhKC4OBgZGZmIiYmRprFTeSMykrHXdvtnSeSi7q6Oly5cgUA4BvQF8ClTnVtBwf3hVKphMFgQGVlBcLCwru4xkTkTl0eJHft2gUAmDt3rsXxjIwMpKWlQalU4tSpU8jNzUV1dTXCwsIwZswYbNiwAYGBgVL5xx57DD4+Pnj44YdRX1+PsWPHYt26dVAqlV1dZZKB9iZpOTuJi8hb9erVCzfccCNCQkKg9PUDgHY2SDQtWK4QAKNo3SKpUCgQGqpGeflF6HRaBkkiL9PlQfLnn392eN7f399qrUlb/Pz88MQTT+CJJ57oqqqRjDU01HfqPJFcCIKATZu2QxAErPm36fO8vRZJcxmjKKLJxmKSarU5SHKcJJG34V7bJAujR9/cqfNEcmLeEtG8UkZH1tc0T7ixtU1iaKhpnKRWW95FNSQiT8EgSbLw/fdHO3WeSI7M3dTK9ra2Qcs4ybZd2wCXACLyZgySJAvtrUHqyhqlRN7O0LySj0+HgqS5RdJodY5LABF5LwZJkgWOkSRynkHseIukuQxbJInkhUGSZOH06aJOnSeSI/N4R2UHJ9sAjoOkeX1KIvIeDJJERGSTwYUxkrYm26jVagDs2ibyRgySRERkkzNBUuratrn8j7lFUgejjTGURNRzMUgSEZFNTc1jJJ2ZbGOrazskJBSCIMBgMODSpcqurSQRuRWDJBER2eRa17Z1i6OPjw9CQkIAsHubyNswSBIRkU3OTLZxNGsb4MxtIm/FIElERDY51yLpOEiad7dhkCTyLgySRERkkytB0tasbaD1zG0GSSJvwiBJshAe3q9T54nkyLkFyU3/bb9rm2MkibwJgyTJwsiRYzp1nkiOzEv5dHbWNtASJLXa8i6qHRF5AgZJkgWNJqxT54nkSGqR7MhkG3PXto11JAHubkPkrRgkSRZ++OFop86T98vJeRczZ96FIUOGIC0tDUeOHHFYPj8/H2lpaRgyZAgmTpyIXbt2WZX58MMPMXXqVMTFxWHq1Kn4+OOPLc5v2rQJM2bMQEJCAsaOHYulS5fi9OnTFmVWrlyJ2NhYi8fs2bM7/4Y7oMmZMZJS17btBcc1GnZtE3kjBkmShfa609jdJm+ffvoRXn55PebNW4jc3FyMGDECixcvRmlpqc3y586dw3333YcRI0YgNzcX6enpePrpp/Hhhx9KZQoKCrBs2TLcfffd+Ne//oW7774bDz/8MI4dOyaVyc/Pxz333IM9e/Zg+/btMBgMWLRoEerq6ixeLzk5GQcPHpQeWVlZ3XMj2ujayTYts7ZF0XYZIup5GCRJFq699vpOnSfvtnv3Tkybdjfuums6Bg0ahFWrVqF///42WxlN5XcjIiICq1atwqBBgzBr1iykpaVh27ZtUpkdO3Zg3LhxWLJkCQYNGoQlS5bg5ptvxo4dO6QyW7duRVpaGgYPHowbbrgBGRkZKC0txY8//mjxer6+vggLC5Meffv27Zb70JZTWyS2u/yPada2Xq9HdfXlLqohEbkbgyTJwh13TOvUefJeer0ep06dxKhRN1scT0xMREFBgc3nHD16FImJiRbHkpOTcfz4cej1eqlMUlKSVRl71wSA6upqAEBwcLDF8fz8fIwdOxaTJ0/G448/7nL3sCA49zAHSZVSgHmYpCAAQttrAlA0h02jUbR5LT8/X/Tp0weAaZyks3XxlIcr99EbH7wP3n8POsrHpU8joh7m559/avf89dcPvkq1IU9SVXUJBoMBoaGhFsc1Gg3Ky20PedBqtdKYPzO1Wo2mpiZUVlYiPDwcWq1WWjuxdRl71xRFERkZGRgxYgRiYmKk4+PHj8eUKVMQGRmJ4uJivPTSS5g/fz5ycnLg6+vr1HtVq4OcKm8e7ahRByI0NBB1lXUI6O0PH18jAvx8oFIppZ/9fZu/TpQKqNWBNq8XHh6Oy5cvQ6+vg0bjXF08ibP30VvxPvAeAAySJBNFRb906jx5P6HNr+CiKFoda6982+POXPPJJ5/EqVOn8M4771gcnzp1qvTnmJgYxMXFISUlBV988QUmTZrk4B1Z0+mq4czwxCaDKUpWV9WhQglAUKC2rh71jUYoDD7Q63tJPxsNBgBAfUMTdLoam+Mg+/Y1hfXTp39FTMwQp+ruCQTBFBycvY/ehvfB+++B+f11BIMkycIvv5zq1HnyXsHBfaFUKq26i3U6nVWro5mt1sqKigr4+PhI4xc1Gg20Wq1VGVvXXLt2LT777DO8/fbb6N+/v8P6hoeHIzIyEmfOnGnnnVkTRTgXJJu7thWCYHqe0HyNttcE4NM8bbteb7D7OuYWWq1W26O/fJ29j96K94H3AOAYSZKJe+9d2qnz5L1UKhViYm7A4cPfWBzPy8tDQkKCzefEx8cjLy/P4tjBgwcRFxcHlUollTl06JBVmdbXFEURTz75JD766CPs2LEDAwYMaLe+lZWVKCsrQ3h4eIfeX2dIk206MGDK18dU5ore9vI/AKBWm9Zr5RJARN6DQZJkYevWVzt1nrzbnDn34MCBXBw48C8UFRXhmWeeQVlZGebMmQMAWL9+PVasWNGq/ByUlpYiIyMDRUVFyM7Oxt69e7Fw4UKpzLx583Do0CFkZWWhqKgIWVlZ+OqrrzB//nypzJo1a7B//36sX78eAQEBKC8vR3l5Oerr6wEAtbW1yMzMREFBAYqLi/HNN9/g/vvvR0hICG677bZuvy/OzNpWNe+RWN9ksFum9RJAROQd2LVNspCUdCv27Nnp8DzJ18SJk1BVVYXt27fg+efXISYmBllZWYiKigIAlJeXo6ysTCo/YMAAZGVlISMjAzt37kR4eDhWrVqFyZMnS2WGDx+OF154AS+++CJefvllDBgwABs2bMCwYcOkMublhebOnWtRn4yMDKSlpUGpVOLUqVPIzc1FdXU1wsLCMGbMGGzYsAGBgbYntHQVoyhKXdjOBMkrjY6CpKlrm0GSyHswSJIs/PDDsXbPz5o15yrVhjxRWtoszJgxy+Zs4nXr1lkdGz16NPbt2+fwmlOmTMGUKVPsnv/5558dPt/f3x9bt251WKa7tF5YvCN7bfsqTWXqHXZtc3cbIm/Drm2ShYoKx19c7Z0nkpvWQdKpFkl9+13bFRXc3YbIWzBIkixcuFDWqfNEctN6h5oOTbZxYozklStXUFdX28kaEpEnYJAkIiIrzrZI+vq03yLZq1cv9O4dAIDd20TegkGSiIisGFp1PXcgR0LVXKi+0f4YSQDSOpqccEPkHRgkiYjISuulfxzt8GNmbpGsbzI4HP/IJYCIvAuDJBERWTEHyY7M2AZaJtsYRaChqf2Z2/b2HCeinoVBkoiIrDQ5sasNAKiULeUcjZMMD+8HALh48UInakdEnoJBkoiIrDizqw1g2o/bPE7S0TaJYWGmrR3Lyy92soZE5AkYJImIyIp5sk1HgyTQMk6yzsHuNmyRJPIuDJJERGTF2RZJoGUtScdd26YWSQZJIu/AIElERFakINnxHCmNk+zIGMnKygro9XrXK0hEHoFBkoiIrDg7axto1bXtIEj27RsClUoFURSh1XLmNlFPxyBJsuDr69up80Ry09SZrm0HYyQFQeCEGyIvwiBJsuDv79+p80Ry05nJNo66tgFOuCHyJj7urgB5LlEU0dDQ4O5qdInLl6vbPV9fX3+VatN9/Pz8OrQLCVF7mgyutEiayjrq2gZalgBikCTq+RgkySZRFPHEE3/Fzz+fcHdVrhIRc+fOcnclOi029rdYuzaTYZI6TWqRdOLvUke6tgG2SBJ5E3ZtExGRFVeW/1FJXdv2FyQHGCSJvAlbJMkmQRCwdm2m13RtNzQ04N57/2j3/JYtb8PPz+8q1qh7sGubuopLs7Y7sI4kAPTvHwEAOH++zMXaEZGnYJAkuwRB8JpJKP7+/rj55iR8/fVBq3OJickIDg52Q62IPJdLC5J3YGcbAIiIYJAk8hbs2ibZWL78rzaPP/zwiqtcEyLP59ryP+0vSA4A/fqZgmRNTQ2qqx1PhCMiz8YgSbKycuVqi59Xr37KTTUh8mydmmzTTpDs1asXgoP7AgAuXGCrJFFPxiBJsnLTTUOkPwcEBGLIkGFurA2R55LGSDqxR6K5a7u2na5toKV7u6yMQZKoJ2OQJNl6/fXt7q4Ckcdq2Wu740Ey0FcJANDVNrZb1ty9feHCeRdqR0SegkGSiIisuDLZJsjfNH+zvKYBYnPXuD0tM7dLXawhEXkCBkkiIrLS1LwUpDPL/wT5mYJko0FEVX2Tw7LmIMmubaKejUGSiIisuLLXto9SgeBWrZKOcC1JIu/AIElERFZc6doGAHWgaWH/izWOx0lGREQCAMrKStrtBiciz8UgSUREVlyZbAMA6gBfAIC2nRbJyMgoAKa1JC9frnKhhkTkCRgkiYjIiqstkprmINlei6S/vz/CwsIBACUlxS7UkIg8AYMkERFZcb1r2xQk2xsjCQBRUdEAGCSJejLutd1JoiiioaH9D0zyDPX19Tb/TJ7Pz88PgpPdrOS6puZxi87M2gZaWiTL22mRBExB8ujR7xgkiXowBslOamhowNy5s9xdDXLB4sVz3V0FcsJbb70Lf39/d1dDNlxukXQySAJASck5J2tHRJ6CXdtERGTF1ck2Ghe6touL2SJJ1FOxRbIL1cT/HqKCt9TjmZcaYTepxxOMTQg8usvd1ZAlV1skw5qX/6mo0+OK3oBeKqXdslFRAwAApaUlLtaSiNyNqacLiQofQKlydzWIvAZXF3QfV4Nkn14qaAJ8oa1tRGF5LYZG9rFbNirKtARQZWUFamtrEBAQ6HqFicgt2LVNRERWXNnZxiwm3BQIf75Y47BcQEAgQkPVAICzZ886/TpE5H5skexKBr27a0DkXfhvym2ajK7N2gaA2PBA5P2nAqdaBcm2M+7Nu9lcc81AVFTocPbsf3DjjTd1osZE5A4Mkp3UemuvoGO73VgTIu/GbfSuLlcn2wCmIAm0tEjqAdQ0NFmUCfRVQgVg4MDrUFDwLc6ePdOZ6hKRmzBIEhGRFVfHSAItQbJIWwuDUURNkxHfnq1EY5MBAODro8SIa0IQ6ueDa64ZCAA4c+Y/XVNxIrqqGCQ7qXV3TfWwOZxsQ9SVDHqppZ+LkV9dnQmSUX39EeCrRG2jAScv1kAT7I9TF6rRS6VEcC/Lz0hzkGSLJFHPxCDZlZQqBkki8gpNnQiSCkHA2IEh+OSUFn/aWYAgPx9UNzShj78P0hMHWpS95pprAQBlZSVoaGiAn59fp+tORFePxwfJnTt3YuvWrSgvL8fgwYPx2GOPYeTIke6ulk2CsYnLlfQEXEeyxxCMTe0Xom7haoukIJge9yddi09OaQEA1c3jIy/XN6FIW4uhUS1LAqnVagQGBqGmphrFxb9i0KDBXfQOiOhq8Ogg+cEHHyAjIwN///vfMXz4cOzevRuLFy/G+++/j8jISHdXzwoXTiYib2Fe/sfHiV+4fBQCBEFARX0TggJ8cc+oaOw8XIy7hkag/HI9vjpTie9LL1sESUEQMHDgtTh+/HucPl3EIEnUw3j0OpLbt2/HjBkzMGvWLAwaNAirVq1C//79sWsXAxsRUXdypWtbqRBQpzdNrPmqSIuYsAA8Ne23uDfpWgwfEAwA+EVbi9pGy5bm6683hcdffjnVRbUnoqvFY1skGxsb8eOPP+K+++6zOJ6YmIiCgoIOX6e7ey/9/f3w9tvvdu+LuIEoinjyySdQWPizu6tCToiJicUTT6z1yokpfn5+3f7v2Qtvm8s6M9mmocmAer0RABDcy9ReER7kh5DeKlTW6XH+suU+3NdfHwMAKCws7EyVicgNPDZIVlZWwmAwQK1WWxzXaDQoLy/v8HXU6qCurposiKIIPz9OHOppfH1ViI4O88ogSVdXZ4KkPWEBvqis06O8ptHi+ODBsQCAwsKfIYoi//4S9SAeGyTNbO2G4MyHjE5XDa5j7Jq///0ZNDQ0tF+whzEvbO2NX1Z+fn7Q6RxvS0f2CQJ/+TTrjiCpDvAFymtRXmP5uXLddYOgVCpRVXUJ5eUXER7er8tek4i6l8cGyZCQECiVSmi1WovjOp0OGo2mw9cRRTBIukyAn5+/uytBTuLfd+oK0l7bXfgLlzrAFwCgbdMi6efnh9/8ZiD+858i/PLLKQZJoh7EYyfb+Pr64qabbsKhQ4csjufl5SEhIcFNtSIikgdDJ/batkcTaAqSbbu2AWDwYNM4yVOnOC6bqCfx2CAJAAsWLEB2djays7NRVFSEZ555BmVlZZgzZ467q0ZE5NVaZm133TXNLZK1jQZcvqK3OHfDDTcCAH788XjXvSARdTuP7doGgKlTp6KyshKvvvoqLl68iJiYGGRlZSEqKsrdVSMi8motLZJdlyR9lQr08ffB5fom/Fp5BQP79pLOxcUNAQD8+OMPMBqNUHTh6xJR9/HoIAkA99xzD+655x53V4OISFa6Y7INAGgCfE1BsqIOuDZUOn799THw9/dHTU01XnrvKwy/6QZMuF7t4EpE5An4Kx8REVnRG0zrQHblGEkACO1t6t4uvnTF4riPjw9+M+gGAMA/PzmEv773Ey7X662eT0SehUGSiIisVDcYAABBfl3bcRUaYFqftuRSvcVxPQBF2LUAAEXFGRiMIg4XV3XpaxNR12OQJCIiCwajiOoG0zaGfXp1cZDsbQ6SLS2SgiCgptGAcj/T+HeF9hdAFPHNmUqvXO+VyJswSBIRkQVziASAPl3dItnctV1aVS+NwwRM4fWiXyREhQ8UVy5BqLmIY2yRJPJ4DJJERGShut4UJHurlPDpyvV/APTx94GPQkCTUUTZ5Zbu7dPaWjQKvoDmOgCA4uLPOK2rw6U6jpMk8mQMkkREZME8yaWPf9cv7CEIgtS9/WtlS/f28bLLptccaFoGqJfuFADgaAlbJYk8GYMkERFZuNzctR3UDUESaFmYvHWQ/LHUFCQH3mjaucx48RdAX4/vm48TkWdikCQiIguXr5iCZHA3B8mzFXUAAFEUpRbJ668bhJDwSIiGJijP/4jvS9kiSeTJGCSJiMhCVfMYyT7+qm65fkQfPwCQWhtLqupRUauHUgAig/0RO3ICAEBZXICfzldLa1oSkedhkCQiIgvVDaYxkt3VtX2NujcA4NTFGlRd0UvjICOC/aFSKhA7IhkAoCw/hca6yzh1saZb6kFEnccgSUREFi7Xd2/XdpCfDwaE9III02SaYyWmlsnfhJj23g7pF4WIgTGAaITPmW/w7Tl2bxN5KgZJIiKyYA6SXb2rTWvDooIBAP9XpMNnp8oBAANDe0vnR6bcCQDw+c8h/M+PJd1WDyLqHAZJIiKyYA6SfXp1zxhJABgWbQqS+49fQFV9E6L7+uP6sADp/A0jkxCiDoPQUI3/fPd/+Jnd20QeiUGSiIgsmNeR7K6ubQAYc20IBmlaguMfR/8GSkXLdohKpQ/SZv8eAKA6+SHePXya2yUSeSAGSSIistDdXds+CgG9fX3wwow43D00AncPjUDyYI1Vmf83fRbU/aIgNFTjg3d34H9P68B9bog8C4MkERFZaJls0z1d20qFgDq9ESfKqjE8ug/GX6+GQbQuo4cSU36/BADgc/pLrM7agx9KL7NlksiDMEgSEZGF6gbzGMnu69oGgIYmA+r1RuibDHbLDPhtAobdYpp4Y/j6LTz8ag5yfyiDKIp2nwOYFjm/oje0W46IOodBkogIQE7Ou5g58y4MGTIEaWlpOHLkiMPy+fn5SEtLw5AhQzBx4kTs2rXLqsyHH36IqVOnIi4uDlOnTsXHH39sVWbnzp1ISUmx+7qiKGLjxo1ISkrC0KFDMXfuXBQWFnbuzTpQrzegocm0AHh3ztp2xvjUP2HADQkQDHqIBzch49UtWLDzO3x44iJOXazBf3R1OH+5HnqDEWWX67H9m1+RuvUwxr98CONfPoS//OtHfPDTBdQ0B2Qzoyii6ooe9Xr7QZaIHPOMTwkiIjf69NOP8PLL67F8+UpMmDAOu3fvxuLFi/H+++8jMjLSqvy5c+dw3333YdasWXjuuefw3XffYc2aNQgNDcXkyZMBAAUFBVi2bBkeeugh3Hbbbfjkk0/w8MMP45133sGwYcMAAB988AEyMjLw97//HcOHD7f5ups3b8b27duxbt06DBw4EK+99hoWLFiAf//73wgMDOyW++Hno0CArxIBvspuub6zlEof3L3kMXz05gs4VfAVfI/vxy9nvsbfv06Csf+NEP2DATvd3fVNRvzvLzr87y86+CgE3DwwBMOjg1GkrcUXv+hQ22iAn48CydepMWd4JIZG9umyrvMmo4hLdY0I7qWCSsl2G/JOgujl7f5abTW8+x0SUWctXjwfsbE34NFH/waNJggAcMcdd+C2227D8uXLrco/99xz+Oyzz/A///M/0rHVq1fj559/xj//+U8AwMMPP4yamhps2bJFKrNo0SIEBwfjhRdeAADMmjULN954I9asWSOVaf26oigiOTkZ8+bNw3333QcAaGxsxLhx4/CXv/wFc+bMcep9dvTz8OSFavj7KDFQ3bKuo0Ih4IpCgU9/KEW93ojgXj64MToEBWd0qNebWjBdPdbR5wX4KlDx4//hza2vo7amuqXC/n1gDI6C0b8P4BeEsJA+iOgbgH59A9AoKnCxzogL1Y3Q1jbafL8iWoJj314q9FIp0di8LWPfXiqEBvgitLcpDIpKJS7XNqCxyYj6JiMamoxo1BugN4oQBEAhCFAIAmobmqCtbZTGfgb6KtHH3wd9e6mgDvBFaIAKvkoFlAoBSkGAQgCMoukhQoTRKEIEYDSKaDCIaDQY0dj8enV6A3S1jdA3GdHbV4nQAF+oe/uiby8fKBUCRJhass3XM4im6xlF03ERptZYiIARgEIA/H2U8Fe1hF3T3xMRzcWs+PmrcOWKHmi+HmAa12p+tI7NIlpdo9XFLK8ttj0No1FEk1GE3iiatskUATTfYwGAIAgQBEBA8zGhuZu1+X62LqNAc9nm44DpvZtf0Gh+v63eu7FVZcz3EWLz+xSAgABf6Bua0PrXDrH1f9vcOPO/PdHqfy3PS+fs3Hu755r/vzA/BJgmrZn/P0kePhQJsdfZuaIlQYD0Wdger2+R5JhsInJEr9fj1KmTmDv3TxafF4mJiSgoKLD5nKNHjyIxMdHiWHJyMvbu3Qu9Xg+VSoWjR4/iT3/6k1WZHTt2ADAFwh9//FEKiLZet7i4GOXl5UhKSpLO+/r6YtSoUSgoKHA6SCoU6FCQvDHC+gvE/AUc6O8DldKA3r4+UAgtPwNw+VhHnxfop0L8/0tFRNw4HP7yY/z87SGU/fofGI0GoOZXoHmpycYS4CxMj9ZCOnifGlr9ubL5UdTB57YW3ObnegDnmx9dpar58Z8uvCZ5p//Z3Rvv5+6Hj7L9ngZnspPXB0m1umOJmojk6cKFCzAYDLj22miLzwuNRoPy8nKbz9FqtdBoLJerUavVaGpqQmVlJcLDw6HVaqFWq63KmK9ZWVkJg8FgVab165r/a6tMaWmp0+81NLTzn4czRg+0+DlugHU8c/WYM8+blXQDgAfbqS0RdTcO2iAiAqzGxYmi6HCsnK3ybY935JquliEi8gQMkkQkayEhIVAqldBqtRbHdTqdVaujma3WyoqKCvj4+KBv375SmbbXrKiokK7ZkdcNCwsDAKfqRkR0NTFIEpGs+fr64qabbsKhQ4csjufl5SEhIcHmc+Lj45GXl2dx7ODB/9/evQfVmP9xAH8fOS1tSVQoEXJiumuxKWwHZdOawQ7tomi1W+7lfksuK2G1616sFuMe2iV2JWt2S8gl1m2XXKNVQhcNR6fv7w/j+e3RRc5mi/N+zZyZc77P9/k+n893emY+fZ/zPCcF9vb2kMvlUp+Xx0xJSZHGrMpxmzdvDjMzM40+KpUK6enpFcZGRPRfYiFJRDpv+PDhiI+PR3x8PDIzM7FgwQJkZ2dLN7N88803mDx5stTfz88Pd+/eRWRkJDIzMxEfH49du3YhMDBQ6uPv74/U1FTExsYiMzMTsbGxSEtLQ0BAQJWPK5PJ4O/vj5iYGCQlJeGvv/7CtGnTUK9ePfj6+v5Hs0NEVLF3/mYbIqJX8fHxwcOHD7Fq1Srk5ORAoVAgNjYWlpaWAJ7f9JKdnS31t7KyQmxsLCIjI7F582aYm5tjxowZ0jMkAaBDhw5YunQpvv32WyxbtgxWVlaIjo6WniFZleMCQFBQEJ4+fYo5c+YgPz8fTk5OWL9+/Rt7hiQR0et4558jSURERERvBi9tExEREZFWWEgSERERkVZYSBIRERGRVlhIEhEREZFWWEiSTtm8eTOUSiUcHBzQv39/nDx5sqZDIqr13uXzZvny5bC1tdV4/fN31IUQWL58OTw8PODo6IihQ4fiypUrGmOoVCrMmzcPnTt3hrOzM4KDg/H339X5i9rVLz09HcHBwfDw8ICtrS0OHTqksb268s7Pz8ekSZPg6uoKV1dXTJo0CQUFBW88v6p41RxMnTq1zN/GwIEDNfq87XNQHVhIks7Yv38/IiMjERISgoSEBLi6uiIoKEir3ywm0hW6cN60bdsWKSkp0mvv3r3StrVr1yIuLg7h4eGIj4+Hqakphg8fjqKiIqnP119/jaSkJERHR2PLli0oLi7GV199BbVaXRPpVElxcTFsbW0RHh5e7vbqynvChAm4fPky1q1bh3Xr1uHy5csaz2StSa+aAwDo2rWrxt9GbGysxva3fQ6qhSDSEZ9++qkIDw/XaOvdu7dYsmRJDUVEVPu96+fNsmXLRN++fcvdVlpaKtzd3UVMTIzU9vTpU+Hq6iq2bt0qhBCioKBA2NnZicTERKnP33//Ldq1ayd+++23Nxt8NVEoFCIpKUn6XF15X716VSgUCpGRkSH1OXPmjFAoFCIzM/NNp/VaXp4DIYSYMmWKCAkJqXCfd20OtMUVSdIJKpUKFy5cgIeHh0a7u7s7zpw5U0NREdVuunLe3Lx5Ex4eHlAqlQgNDcXt27cBAFlZWcjNzdXIX19fHx07dpTyP3/+PJ49e6ZxObxJkyZo27btWztH1ZX3mTNnYGRkpPEQfmdnZxgZGb01c3PixAm4ubnB29sbM2fORF5enrRNV+bgVfjLNqQTHj58CLVajcaNG2u0m5qaIjc3t4aiIqrddOG8cXR0RFRUFKytrZGXl4fVq1fDz88P+/btk3IsL/8Xl/bv378PuVwOY2PjMn3u37//3yRRzaor7/v375cZ48W4b8PcdOvWDb1794aFhQWysrLw3XffISAgALt374a+vr5OzEFVsJAknSKTyTQ+CyHKtBGRpnf5vOnevbvGZ2dnZ/Tq1QsJCQnSKlJ5+b9KVfrUdm8q77fl78fHx0d6r1AoYG9vD6VSiSNHjsDLy6vC/d6lOagKXtomnWBiYgI9Pb0y/wHm5eXB1NS0hqIiqt108bwxMDCAQqHAjRs3YGZmBgCV5m9qaopnz54hPz+/wj5vm+rK29TUVONS8AsPHjwod5WutjM3N4eFhQVu3LgBQDfnoDwsJEkn6Ovrw87ODqmpqRrtR48ehYuLSw1FRVS76eJ5o1KpkJmZCTMzMzRv3hxmZmYa+atUKqSnp0v529vbQy6Xa/TJycnBlStX3to5qq68XVxcUFhYiHPnzkl9zp49i8LCwrdybh4+fIjs7GyYm5sD0M05KA8vbZPOGD58OCZPngx7e3u4uLhg+/btyM7Ohp+fX02HRlRrvevnTVRUFDw9PdGsWTM8ePAAq1evRlFREfr16weZTAZ/f3/ExMTA2toaLVu2RExMDOrVqwdfX18AgJGREQYMGICoqCiYmJjA2NgYUVFRUCgU6NKlSw1nV7HHjx/j1q1b0uesrCxcunQJxsbGsLCwqJa827Rpg65du2LmzJmYO3cuAGDWrFnw9PRE69at//ukX1LZHBgbG2PFihXw8vKCmZkZ7ty5g+joaJiYmKBnz54A3o05qA4y8S58kYOoijZv3ozvv/8eOTk5UCgUmDZtGjp27FjTYRHVau/yeRMaGor09HQ8evQIJiYmcHZ2xrhx42BjYwPg+XfZVqxYge3btyM/Px9OTk4IDw+HQqGQxnj69CkWLVqEffv24cmTJ3Bzc8Ps2bPRrFmzmkrrlY4fPw5/f/8y7f369cPChQurLe9Hjx5h/vz5OHz4MABAqVQiPDwcDRo0ePNJvkJlcxAREYFRo0bh4sWLKCwshJmZGTp37oxx48Zp5Pe2z0F1YCFJRERERFrhdySJiIiISCssJImIiIhIKywkiYiIiEgrLCSJiIiISCssJImIiIhIKywkiYiIiEgrLCSJiIiISCssJImIiIhIKywkiYiIqFZTKpX44YcfajoMKgd/2YaIiHTG1KlTUVBQgFWrVpVpNzU1xcSJEwEAv/76K9avX4/z58+jtLQUNjY2GDx4MPr371/p+EOHDsWJEycAAHK5HCYmJrCzs0P//v3h5eX1WrEuX74chw4dwo8//vha+1Xk9OnTWL16NTIyMvDkyRNYW1ujX79+CAgIgJ6eXrUc49/avXs3FixYgJMnT2q0P3jwAPXr10f9+vVrKDKqCFckiYhIp5WWluLIkSPo0aMHAGDTpk0YOXIkXFxcsHPnTvz000/o06cPZs+ejaioqFeON3DgQKSkpCApKQnLly9HmzZtEBYWhlmzZr3pVCqUlJSEoUOHomnTpti4cSMOHDgAf39/rFmzBqGhoXjTa0oqlepf7d+oUSMWkbWVICIi0hFTpkwRISEhGm3p6enCzc1NqNVqcffuXWFnZyciIyPL7Ltx40ahUChERkZGheMPGTJEzJ8/v0x7fHy8UCgUIjU1VWpbtGiR8PLyEo6OjkKpVIro6GihUqmEEELs2rVLKBQKjdeuXbuEEEKsX79e+Pr6CicnJ9GtWzcxe/ZsUVRUVGFMjx8/Fp06dRKjR48usy05OVkoFAqRmJgohBDi9u3bQqFQiH379olBgwYJe3t74ePjI44dO6ax35UrV8SIESOEs7OzcHNzExMnThR5eXka8zBnzhyxYMEC0alTJzF48OBXxn7s2LEyOS9btkwIIYSnp6eIi4uTxr9z544IDg4Wzs7OwsXFRYwdO1bk5uZK25ctWyb69u0r9uzZIzw9PUWHDh3E+PHjRWFhodTnwIEDwtfXVzg4OIhOnTqJgIAA8fjx4wrnkcrHFUkiItJpycnJ8PT0RJ06dfDLL7/g2bNnCAwMLNNv0KBBMDAwwL59+177GP369YOxsTEOHjwotb3//vuIjIxEYmIiZsyYgZ07d0rfA/Tx8UFgYCDatm2LlJQUpKSkwMfHBwAgk8kwY8YM7N27FwsXLsSxY8ewePHiCo+dmpqKR48elZuTUqmEtbV1mZwWLVqE4cOHIyEhAS4uLggJCcHDhw8BADk5ORgyZAjat2+P+Ph4rFu3Dnl5eRg/frzGGHv27IGenh62bt2KOXPmvDJ2FxcXTJ8+HYaGhlLO5cUshMCoUaOQn5+PTZs2IS4uDrdv30ZoaKhGv1u3biE5ORlr1qxBTEwM0tPTsXbtWimHCRMmYMCAAdi/fz82btyIXr16vfGV2XdR3ZoOgIiIqCYdPnwYU6ZMAQBcv34dRkZGMDc3L9NPX18fVlZWuHHjxmsfo06dOrC2tsadO3ektpEjR0rvmzdvjmvXrmH//v0ICgpCvXr1YGBgAD09PZiZmWmMNWzYMOm9lZUVxo0bh4iICERERJR77OvXrwMA2rRpU+721q1bl8lp8ODB8Pb2BgBERETg999/R3x8PIKCgrB161bY2dkhLCxM6r9gwQJ0794d169fR6tWrQAALVu2xOTJk6scu76+PoyMjCCTycrk/E9Hjx7Fn3/+ieTkZDRr1gzA88K3T58+OHfuHBwdHQE8LzgjIyNhaGgIAOjbty/S0tIQGhqK3NxclJSUoFevXrC0tAQA2NraVnhMqhgLSSIi0lmZmZm4d+8eunTpUqX+QgjIZDKtjvXyvj///DM2bNiAW7duobi4GCUlJVLRU5ljx44hJiYGV69eRVFREdRqNZ4+fYri4mIYGBhUevyqxAU8Xx18oW7durC3t8e1a9cAABcuXMDx48c1+rxw69YtqZC0t7evttj/KTMzE02bNpWKSACwsbFBgwYNcO3aNamQtLS01JhPc3Nz5OXlAQDatWsHNzc3fPLJJ/Dw8ICHhwe8vb1hbGxcpRjo/1hIEhGRzkpOTkaXLl1Qr149AECrVq1QWFiIe/fuoUmTJhp9VSoVsrKy8OGHH772cdRqNW7evAkHBwcAQEZGBsLCwjBmzBh4eHjAyMgIiYmJiIuLq3ScO3fu4Msvv4Sfnx/GjRsHY2NjnDp1CjNmzEBJSUm5+7wo7DIzM9GhQ4cy269fv17hamV5SktL4enpKd3h/k//XEl8+eYYbWIvT0XF/MuFct26ZUucF3309PQQFxeH06dPIzU1FZs2bUJ0dDR27NgBKyurKsdCvGubiIh02OHDh6FUKqXPXl5eqFu3brkF3bZt21BcXAxfX9/XPs6ePXuQn58vPQLo9OnTsLCwQEhICBwcHGBtbY27d+9q7COXy1FaWqrRdv78eajVakydOhXOzs5o1aoVcnJyKj22u7s7GjZsWG5OycnJuHHjRpmcMjIypPclJSW4cOECWrduDQCws7PDlStXYGlpiZYtW2q8KltVrErscrkcarW60nxsbGyQnZ2N7Oxsqe3q1asoLCx8rYJYJpPB1dUVY8eORUJCAuRyOQ4dOlTl/ek5rkgSEZFOysvLwx9//IGVK1dKbRYWFpg0aRKioqLw3nvvoW/fvpDL5UhOTsbSpUsRGBgIJyenSsd98uQJcnNzoVarce/ePRw8eBAbNmzAZ599Jq1mtmjRAtnZ2UhMTISDgwOOHDlSpoixtLREVlYWLl26hCZNmsDQ0BAtWrRASUkJNm3aBKVSiVOnTmHbtm2VxmNgYIA5c+ZIjyAaPHgwDA0NkZaWhsWLF8Pb2xsff/yxxj5btmyBtbU1WrdujQ0bNiA/Px8DBgwAAHz++efYsWMHwsLCMGLECJiYmODmzZtITEzE/PnzK3wmZVVit7S0RHFxMdLS0mBra1vusyO7dOkCW1tbTJw4EdOnT4darUZERAQ6deokrfi+ytmzZ5GWlgZ3d3c0btwYZ8+exYMHD6RimaqOhSQREemkw4cPw9HREY0bN9ZoHzZsGKysrLB+/Xps3LgRarUaNjY2iIiIkIqpyuzYsQM7duyAXC5Hw4YNYW9vj+joaPTq1Uvq07NnTwQEBGDu3LlQqVT46KOPEBISghUrVkh9vL29kZSUBH9/fxQUFCAyMhL9+/fHtGnTsHbtWixduhQffPABwsLCpJuFKtK7d2+YmppizZo1GDJkCJ48eYKWLVsiODgYAQEBZS4VT5gwAWvXrsXFixfRokULrFq1Co0aNQIANGnSBFu3bsWSJUvwxRdfQKVSwcLCAl27dkWdOhVf6Gzfvv0rY+/QoQP8/Pwwfvx4PHr0CKNHj8aYMWM0xpHJZFi5ciXmzZuHIUOGQCaToWvXrq/1nE5DQ0Okp6djw4YNKCoqgoWFBaZOnYru3btXeQx6jr9sQ0REOik4OBiurq4ICgqq6VBqjaysLPTo0QMJCQlo3759TYdDbwF+R5KIiHSSq6urVt93JKL/46VtIiLSSVyJJPr3eGmbiIiIiLTCS9tEREREpBUWkkRERESkFRaSRERERKQVFpJEREREpBUWkkRERESkFRaSRERERKQVFpJEREREpBUWkkRERESklf8B/yKC9lxNDVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(train, 'I/O Data Operations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "776ec197",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:12.688659Z",
     "iopub.status.busy": "2023-03-19T00:35:12.687815Z",
     "iopub.status.idle": "2023-03-19T00:35:13.175999Z",
     "shell.execute_reply": "2023-03-19T00:35:13.174660Z"
    },
    "papermill": {
     "duration": 0.507626,
     "end_time": "2023-03-19T00:35:13.178917",
     "exception": false,
     "start_time": "2023-03-19T00:35:12.671291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(train, \"Number of subprocesses\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad306fba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:13.214045Z",
     "iopub.status.busy": "2023-03-19T00:35:13.213592Z",
     "iopub.status.idle": "2023-03-19T00:35:13.218765Z",
     "shell.execute_reply": "2023-03-19T00:35:13.217391Z"
    },
    "papermill": {
     "duration": 0.025589,
     "end_time": "2023-03-19T00:35:13.221270",
     "exception": false,
     "start_time": "2023-03-19T00:35:13.195681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train[\"I/O Data Operations\"] = np.log1p(train[\"I/O Data Operations\"])\n",
    "# test[\"I/O Data Operations\"] = np.log(test[\"I/O Data Operations\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5ddf1d2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:13.256544Z",
     "iopub.status.busy": "2023-03-19T00:35:13.255817Z",
     "iopub.status.idle": "2023-03-19T00:35:13.770933Z",
     "shell.execute_reply": "2023-03-19T00:35:13.769629Z"
    },
    "papermill": {
     "duration": 0.535736,
     "end_time": "2023-03-19T00:35:13.773487",
     "exception": false,
     "start_time": "2023-03-19T00:35:13.237751",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RVDms8M033ygnJ0fdunXTqFGjNG3aNL377rvavn27vvzySz3xxBP6z3/+I0kaOnSojjvuON1xxx3auHGjvv76a911110aNmyYb5gWAJygru+Hgbr88suVmZmpRYsW6aefftIbb7yh5cuX+6ZbAHA2Y8Oyn376qd99zrxzn8rLy7Vw4UKlp6drx44datWqlQYMGOA74kqq3IRz5syZWrNmjWJiYnTKKafotttuO+x2GQDQ2ATj/TBQ//73vzV37lz9+OOPSkpK0oQJE6qdSAPAmRrFnDsAAAAER6Pd5w4AAACBI9wBAACEEMIdAABACCHcAQAAhBBj+9zl5+8RSzkAHIllSW3bxpkuo97U13uh9+vmxPdaJ9cuObt+J9cuObv+mtRe0/dDY+HOtuW4LzwABFt9vxc6+b3WybVLzq7fybVLzq4/GLUzLAsAABBCCHcAAAAhhHAHAAAQQgh3AAAAIYRwBwAAEEIIdwAAACGEcAcAABBCCHcAAAAhhHAHAAAQQgh3AAAAIYRwBwABqqio0Lx58zRixAj169dPp512mubPny+Px2O6NAAwd7YsADjV4sWL9dJLL2nOnDk69thj9fXXX2v69OmKi4vT5Zdfbro8AE0c4Q4AAvTFF1/otNNO0+9+9ztJUlJSkt566y19/fXXZgsDABHuACBggwYN0ksvvaQffvhB3bp106ZNm/TZZ5/pjjvuCPhellUPBR503/q6f31ycu2Ss+t3cu2Ss+uvSe01/XdZtm3bdS8pcDt37pGZVwbgJJYlxcfHmS6jCtu2NXfuXC1evFhhYWFyu9266aabdPXVV5suDQDouQOAQL399tt6/fXX9dBDD+nYY4/VN998o9mzZ6tdu3YaM2ZMQPfKz6+fP3QtS2rbNq7e7l+fnFy75Oz6nVy75Oz6a1K79zFHQrgDgAA98MADuuqqq/T73/9ekpScnKycnBw9/vjjAYc721a9/hKq7/vXJyfXLjm7fifXLjm7/mDUzlYoABCgkpISWb+Z/BIWFiZDs1wAoAp67gAgQKeeeqoWLVqkTp06+YZlly5dqgsuuMB0aQBAuAOAQP31r3/VI488onvuuUf5+flq166d/vjHP+raa681XRoAEO7QOKxfv1ZLlizSxImTNXjwCabLAQ4rNjZWd955p+68807TpQBANcy5g3GlpSVavHiBdu7M0+LFC1RaWmK6JAAAHItwB+NWrkxTYWGBJKmwsEArV6YZrggAAOci3MGo3Nwcpaen+VYZ2rat9PQ05ebmGK4MAABnItzBGNu2tWTJomrbRxzqOgAAODLCHYzJzs5SZmaGPB5Plesej0eZmRnKzs4yVBkQOizL8n0AaBoIdzAmMTFJ/funVPulY1mWBgwYqMTEJEOVAaHh133lKiipUEFp5Ue56YIANAjCHYyxLEsTJ072Oyw7ceJkehqAOrAsS3tKyvXZT4X65Pud+uynQhWVufm5ApoA9rlDo8R8OyA4SivcKin3HPmBAEIGPXcwxrZtPfzwg37bHn74QQIeAAC1QLiDMdu3b9PWrd/5bdu69Ttt376tgSsCAMD5CHcwZseOn+vUDgAAqiPcwZiUlMFyufx/C7pcLqWkDG7gigAAcD7CHYzJzc2ptsedl8fj4ZQKAABqgXAHY7z73PnDPncAANQO4Q7GWJal884b67ftvPPGsh8XAAC1QLiDMbZt64UXnvHb9vzzT7MVCgAAtUC4gzFshQIAQPAR7mAMW6EAABB8hDsYM3Dg8YqJae63rXnzWA0ceHwDVwQAgPMR7mCMy+XSLbfc7rftlltuP+QeeAAA4ND47Qmj+vUboB49ela51qNHT/Xt299QRQAAOBvhDsbddttfff9vWVaVzwEAQGAIdzCuRYuWuuCCP8rlcmns2D+oRYuWpksCAMCxwk0XAEjS+PF/0vjxfzJdBgAAjkfPHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3KFRWL9+ra655gqtX7/WdCkAADga4Q7GlZaWaPHiBdq5M0+LFy9QaWmJ6ZIAAHAswh2MW7kyTYWFBZKkwsICrVyZZrgiAACci3AHo3Jzc5SenibbtiVJtm0rPT1Nubk5hisDAMCZCHcwxrZtLVmyyBfsjnQdAAAcGeEOxmRnZykzM0Mej6fKdY/Ho8zMDGVnZxmqDAAA5yLcwZjExCT1758il6vqt6HL5dKAAQOVmJhkqDIAAJyLcAdjLMvSxImTZVlWja4DAIAjI9zBqI4dO2n06At9Qc6yLI0efaE6dOhouDIAAJyJcAfjxoy5UK1bt5EktWnTRmPGXGi4IgAAnItwB+OioqI1adIUxccn6MorpygqKtp0SQAAOFa46QIASRo8+AQNHnyC6TIAAHA8eu4AAABCCOEOjcL69Wt1zTVXaP36taZLAQDA0Qh3MK60tESLFy/Qzp15Wrx4gUpLS0yXBACAYxHuYNzKlWkqLCyQJBUWFmjlyjTDFQEA4FyEOxiVm5uj9PQ03zmytm0rPT1Nubk5hisDAMCZCHcwxrZtLVmyyBfsjnQdAAAcGeEOxmRnZykzM0Mej6fKdY/Ho8zMDGVnZxmqDAAA5yLcwZjExCT1758il6vqt6HL5dKAAQOVmJhkqDIAAJyLcAdjLMvSxImTfefKHuk6AAA4MsIdjOrYsZNGj77QF+Qsy9Lo0ReqQ4eOhisDDm/EiBFKTk6u9nHPPfeYLg1AE8fxYzBuzJgL9a9/va2ioj2KjY3VmDEXmi4JOKK0tDS53W7f51u2bNGECRN01llnGawKAAh3aGRYIAunaNOmTZXPn3jiCXXp0kUnnMAZyQDMItzBuJUr07R3b5Ekae/eIq1cmabx4/9kuCqg5srKyvT6669rwoQJAc8Vra+ppd77WpbkfQmrHl8vmA6u3YmcXL+Ta5ecXX9Naq/pv4twB6MOtYlxauoIdezYyXB1QM2sWrVKe/bs0ZgxYwJ+btu2cfVQUaXiwmI1j4lWeKRHUeEuNYuJVNvWMfX2esFWn1+bhuDk+p1cu+Ts+oNRO+EOxhxpE+M777yHFbNwhFdffVWnnHKK2rdvH/Bz8/P31Mt0BJfLkiyX9haXqKTMo/IIl/YVlynf42n0G4RbVuUvuPr62tQ3J9fv5NolZ9dfk9q9jzkSwh2M8W5i/FsHb2KclNTZQGVAzWVnZ+vjjz/Wo48+Wqvn23b9zDW1bUnW/vt7r9Xj69UHJ9Xqj5Prd3LtkrPrD0btbIUCY9jEGKFgxYoVatu2rX73u9+ZLgUAJBHuYBCbGMPpPB6PVqxYodGjRys8nIEQAI0D4Q5GsYkxnOzjjz9WTk6OLrjgAtOlAIAP4Q7GjRlzoVq3rtwzrE2bNmxiDMcYPny4Nm/erG7dupkuBQB8CHcwLioqWpMmTVF8fIKuvHKKoqKiTZcEAIBjMUkEjcLgwSdo8GB29gcAoK7ouQMAAAghhDsAAIAQQrgDAAAIIYQ7AACAEFKncPf4448rOTlZs2bNClY9AAAAqINah7svv/xSL7/8spKTk4NZDwAAAOqgVuFu7969mjp1qmbOnKmWLVsGuyYAAADUUq32uZsxY4ZSU1M1dOhQLVy4sFYvzLGhAGqC9woACEzA4e6tt97Sxo0blZaWVqcXbts2rk7PBwAAQHUBhbvc3FzNmjVLTz31lKKiour0wvn5e2TbdboFQsj69Wu1ZMkiTZw4mZMqUIVl8ccgAAQioHC3YcMG5efna+zYsb5rbrdb69at0wsvvKCvvvpKYWFhNbqXbYtwB0lSaWmJnnhigQoK8vXEEwvUp08/zpcFAKCWAgp3J510kt54440q16ZPn66jjz5akyZNqnGwAw62cmWaCgsLJEmFhQVauTJN48f/yXBVAAA4U0DhLjY2Vj169KhyLSYmRq1atap2HaiJ3Nwcpaenyd7fjWvbttLT05SaOkIdO3YyXB0AAM7DCRUwxrZtLVmyyBfsjnQdAAAcWa22QjnYc889F4w60ARlZ2cpMzOj2nWPx6PMzAxlZ2cpKamzgcoAAHAueu5gTGJikvr3T5HLVfXb0OVyacCAgUpMTDJUGQAAzkW4gzGWZWnixMmyfrNL7aGuAwCAIyPcwaiOHTtp9OgLfUHOsiyNHn2hOnToaLgyAACciXAH48aMuVCtW7eRJLVp00ZjxlxouCIAAJyLcAfjoqKiNWnSFMXHJ+jKK6ewgTEAAHVQ59WyQDAMHnwCx44BABAE9NwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHRqF9evX6pprrtD69WtNlwIAgKMR7mBcaWmJFi9eoJ0787R48QKVlpaYLgkAAMci3MG4lSvTVFhYIEkqLCzQypVphisCAMC5CHcwKjc3R+npabJtW5Jk27bS09OUm5tjuDIAAJyJcAdjbNvWkiWLfMHuSNcBAMCREe5gTHZ2ljIzM+TxeKpc93g8yszMUHZ2lqHKAABwLsIdjElMTFL//ilyuap+G7pcLg0YMFCJiUmGKgMAwLkIdzDGsixNnDhZlmXV6DoAADgywh2M6tixk0aPvtAX5CzL0ujRF6pDh46GKwMAwJkIdzBuzJgL1bp1G0lSmzZtNGbMhYYrAgDAuQh3MC4qKlqTJk1RfHyCrrxyiqKiok2XBACAY4WbLgCQpMGDT9DgwSeYLgMAAMej5w6NAmfLAgAQHIQ7GFdaWqLHHntYO3fm6bHHHuZsWQAA6oBwB+NeeeUlFRXtkSQVFe3RK6+8ZLgiAACci3AHo3Jzc/T66yuqXHv99RWcLQsAQC0R7mCMbdtasOARv2fL+rsOAACOjHAHY7KytmvTpo1+2zZt2qisrO0NXBEAAM5HuAMAAAghhDsYk5TUWT179vbb1qvXcUpK6tzAFQEA4HyEOxhjWZamTLnBd67ska4DAIAjI9zBqI4dO+m888ZWuXb++WPVoUNHQxUBAOBshDsYN27ceMXGxkmS4uLidOGF4w1XBACAcxHuYFxUVLSuvfZGxccnaMqUGxUVFW26JAAAHCvcdAGAJA0efIIGDz7BdBkAADgePXcAAAAhhHAHALXwyy+/6NZbb9WJJ56o/v376/zzz9fXX39tuiwAYFgWAAL166+/6qKLLtKJJ56oxYsXq02bNtq+fbtatGhhujQAINwBQKAWL16sDh06aPbs2b5rSUlJBisCgAMIdwAQoNWrV2v48OG6/vrrtW7dOrVv314XX3yx/vCHPwR8r/raq9t7X8uSvC9h1ePrBdPBtTuRk+t3cu2Ss+uvSe01/XcR7gAgQNu3b9eyZcs0YcIETZ48WV9++aVmzpypyMhIjR49OqB7tW0bVz9FSiouLFbzmGiFR3oUFe5Ss5hItW0dU2+vF2z1+bVpCE6u38m1S86uPxi1E+7QKKxfv1ZLlizSxImT2RIFjZ5t2+rTp49uvvlmSVLv3r313XffadmyZQGHu/z8PbLt4NfoclmS5dLe4hKVlHlUHuHSvuIy5Xs8suvjBYPIsip/wdXX16a+Obl+J9cuObv+mtTufcyREO5gXGlpiRYvXqCCgnwtXrxAffv2YyNjNGoJCQk65phjqlw7+uij9a9//Svge9m26uWXkG1Lsvbf33utHl+vPjipVn+cXL+Ta5ecXX8wamcrFBi3cmWaCgsLJEmFhQVauTLNcEXA4Q0cOFA//PBDlWs//vijEhMTDVUEAAcQ7mBUbm6O0tPTfMNEtm0rPT1Nubk5hisDDu3yyy9XZmamFi1apJ9++klvvPGGli9frosvvth0aQBAuIM5tm1ryZJF1eb/HOo60Fj069dP8+fP11tvvaVzzz1XCxYs0B133KHzzjvPdGkAwJw7mJOdnaXMzIxq1z0ejzIzM5SdnaWkpM4GKgOO7NRTT9Wpp55qugwAqIaeOxiTmJik/v1TZP1m4x7LsjRgwEAlJrIpLAAAgSLcwRjLsjRx4mS/w7ITJ06uFvoAAMCREe7QKDHfDgCA2iHcwRjbtrVgwSN+2xYseISABwBALRDuYExW1nZt2rTRb9umTRuVlbW9gSsCAMD5CHcAAAAhhHAHY5KSOqtz5y5+27p06co2KAAA1ALhDsbYtq38/Hy/bTt37mTOHQAAtUC4gzEZGetVXLzXb1tx8V5lZKxv4IoAAHA+wh2MSUkZrNjYOL9tcXEtlJIyuIErAgDA+Qh3MMblcun88y/w2zZ69AVyufj2BAAgUPz2hDEej0evvfaq37aVK9Pk8XgauCIAAJyPcAdjMjLWq6hoj9+2oqI9zLkDAKAWCHcwZsCAQYccenW5XBowYFADVwQAgPMR7mBMdnbWIYdePR6PsrOzGrgiAACcj3AHY3bs+LlO7QAAoDrCHYzp339gndoBAEB1hDsY8/7779apHQAAVEe4gzG9eh1Xp3YAAFAd4Q7GdOqUWKd2AABQHeEOxrz66st1agcAANUR7mDM6NEX1qkdAABUR7iDMenpaXVqBwAA1RHuYMyFF46vUzsAAKiOcAdjXC6XwsLC/LaFhYUd8mgyAABwaPz2hDHr16+V2+322+Z2u7V+/doGrggAAOcj3MGYvLwddWoHAADVEe5gjGVZdWoHAADVEe5gTM+evevUDgAAqiPcwZhPP/24Tu0AAKA6wh2MGTz4xDq1AwCA6gh3MObf/15Vp3YAAFAd4Q7G/O53p9WpHQAAVEe4gzGrVr1Tp3YAAFAd4Q7G/Prrr3VqBwAA1RHuYMyuXYV1agcAANUR7mBM79596tQOAACqI9zBmNjYuDq1AwCA6gh3MObdd9+uUzsAAKiOcAdjdu/eXad2AABQHeEOxvTseVyd2gEAQHWEOxiTk5Ndp3YAAFAd4Q7GFBTk1akdAABUR7gDAAAIIYQ7GPPYY0vq1A4AAKoj3MGYdu3aybIsv22WZaldu3YNXBEAAM5HuIMxHo9HzZvH+m1r3jxWHo+ngSsCAMD5CHcwJiNjvYqK9vhtKyrao4yM9Q1cEQAAzke4gzEDBgxSWFiY37awsDANGDCogSsCAMD5CHcwJjc3R26322+b2+1Wbm5OA1cEAIDzEe5gTGJikjp37uq3rUuXrkpMTGrgigAAcL7wQB784osvatmyZcrOrjw5oHv37poyZYpSU1PrpTiENo/Ho+zs7X7bsrK2y+PxHHLYFgAA+BdQz12HDh1066236tVXX9Wrr76qk046Sddee622bNlSX/UhhK1a9c4hV8R6PB6tWvVOA1cEAIDzBRTuRowYodTUVHXr1k3dunXTTTfdpJiYGH3xxRf1VB5C2ciRZx12QcXIkWc1cEUAADhfQMOyB3O73XrnnXdUXFyslJSUgJ9/iL1r0YSEh4fpmmv+ovnzH67WNmXK9QoPZ0gWvFcAQKACDnebN2/W+PHjVVpaqpiYGD322GM69thjA37htm3jAn4OQs+FF47Wyy+/oLy8PN+1du3a6YILzjdYFQAAzhVwuOvWrZvS09O1e/duvfvuu7rtttv0/PPPBxzw8vP3yLYDfXWEohkz7tc110z0fX7PPbO1c6f/zY3R9FgWfwwCQCACDneRkZHq2rVy+4q+ffvqq6++0rPPPqsZM2YEdB/bFuEOkqT4+HYaMmS4PvlkjYYMGa74+HZ8bwAAUEt13ufOtm2VlZUFoxY0Yaeccqri4xN0yimnmi4FAABHCyjczZ07V+vXr1dWVpY2b96sefPmae3atRo1alR91YcmoLS0RIsXL9DOnXlavHiBSktLTJcEAIBjBTQsu3PnTk2bNk07duxQXFyckpOT9eSTT2rYsGH1VR+agJUr01RYWCBJKiws0MqVaRo//k+GqwIAwJkCCnf33XdffdWBJio3N0fp6Wmy90+ys21b6elpSk0doY4dOxmuDgAA5+FsWRhj27aWLFnkC3ZHug4AAI6McAdjsrOzlJmZUe0IMo/Ho8zMDGVnZxmqDAAA5yLcwZjExCT17Nnbb1uvXscpMTGpgSsCau7RRx9VcnJylQ/mHwNoDGp9/BgQDIfaRqe0tLSBKwEC1717dy1dutT3+aHOSgaAhkS4gzFZWdu1det3ftu2bv1OWVnb1blzlwauCqi5sLAwJSQkmC4DAKog3AFALf30008aPny4IiMj1b9/f918883q3LlzQPewrPqpzXtfy5K8L2HV4+sF08G1O5GT63dy7ZKz669J7TX9dxHuYExSUmf17NlbmzZtrNbWq9dxSkoK7Jck0JD69eunOXPm6KijjlJ+fr4WLlyo8ePH680331Tr1q1rfJ/6PDe3uLBYzWOiFR7pUVS4S81iItW2dUy9vV6wOf1MYSfX7+TaJWfXH4zaLdvQfhM7d+7h/FAoNzdHN9wwucq2J5Zl6Z//fFwdOnQ0WBkaC8uS4uMb/xt1cXGxTj/9dF155ZWaMGFCjZ+Xn18/74Uul6Viy6XVX+eopMyjqAiXhh4TrzbR4Y1+myHLqvwFV19fm/rm5PqdXLvk7PprUrv3MUdCzx2M6tixk847b6xee+1V37Xzzx9LsIPjxMTEqEePHvrxxx8Dep5tq15+Cdm2JGv//b3X6vH16oOTavXHyfU7uXbJ2fUHo3a2QoFx48aNV2xs5V8icXFxuvDC8YYrAgJXVlam77//ngUWAIwj3MG4qKhoXXvtjYqPT9CUKTcqKiradEnAEc2ZM0dr167V9u3blZmZqeuvv15FRUUaM2aM6dIANHEMywJALfz888+6+eabtWvXLrVu3VoDBgzQ8uXLlZiYaLo0AE0c4Q7GlZaWaPHiBSooyNfixQvUt28/eu/Q6M2bN890CQDgF8OyMG7lyjQVFhZIkgoLC7RyZZrhigAAcC7CHYzKzc1Renqab2sG27aVnp6m3Nwcw5UBAOBMhDsYY9u2lixZVG3PrUNdBwAAR0a4gzHZ2VnKzMyQx+Opct3j8SgzM0PZ2VmGKgMAwLkIdzAmMTFJPXv29tvWq9dxSkxMauCKAABwPsIdGiWGZAEAqB3CHYzJzs7Spk0b/bZt2rSRYVkAAGqBcAdjEhOTdPTRx/ptO+aYYxmWBQCgFgh3MMa2beXk+O+dy87OYmgWAIBaINzBmM8+W6eSkhK/bSUlJfrss3UNXBEAAM5HuAMAAAghhDsYM2jQ8YqObua3rVmzZho06PgGrggAAOcj3MEYl8ulCy74o9+2Cy4YL5eLb08AAALFb08Y4/F49Nprr/ptS09Pq3ZyBQAAODLCHYzJyFivoqI9ftuKivYoI2N9A1cEAIDzEe5gTP/+A+vUDgAAqiPcwZgvvvi8Tu0AAKA6wh2MadeufZ3aAQBAdYQ7GNO5c5fDHD/WXZ07d2ngigAAcD7CHYyxLEs33jjVb9uNN06VZVkNXBEAAM5HuEOjxLmyAADUDuEOxti2rSVLFlXbrNjlcmnJkkUEPAAAaoFwB2Oys7OUmZlRbbNij8ejzMwMZWdnGaoMAADnItzBmMTEJPXvn+K3527AgIFKTEwyVBkAAM5FuIMxlmVp4sTJ1RZOHOo6AAA4MsIdjOrYsZNGj76wyrXRoy9Uhw4dDVUEAICzEe5g3DnnjPL10lmWpXPOGWW4IgAAnItwB+PefvsNeRfG2nbl5wAAoHYIdzAqNzdH6elpkrzbnthKT09Tbm6OybIAAHAswh2M8e5z528rFPa5AwCgdgh3MMa7z91vQ5xt2+xzBwBALRHuYExiYpJ69uztt61Xr97scwcAQC0Q7tAoMSILAEDtEO5gTHZ2ljZt2ui3bdOmjQzLAgBQC4Q7GMOwLAAAwUe4g1FlZWV+r5eW+r8OAAAOj3AHY7Kytmvr1u/8tm3d+p2ysrY3cEVoCrZv5/sKQGgj3AFoUs444wxdeumleu2111RaWmq6HAAIOsIdjElK6qzOnbv6bevSpauSkjo3cEVoCl577TX17t1bc+bM0bBhw3TXXXfpyy+/NF0WAAQN4Q7G2Lat/Pydftt27tzJCRWoFz169ND06dP13//+V7Nnz1ZeXp4uvvhi/f73v9fSpUtVUFBgukQAqBPCHYzJyFiv4uK9ftuKi/cqI2N9A1eEpiQ8PFynn366HnnkEd16663atm2b5syZo1NOOUXTpk3Tjh07TJcIALVCuIMxKSmDFRsb57ctLq6FUlIGN3BFaEq++uor/f3vf9fw4cO1dOlSXXHFFXrvvff0zDPP6JdfftGUKVNMlwgAtRJuugA0XS6XSzfdNE333vu3am033TRNLhd/eyD4li5dqhUrVuiHH37QKaecojlz5ig1NdX3/da5c2fNmDFDZ599tuFKAaB2CHcwKiGhnd/r8fEJDVwJmoply5bpggsu0NixY5WQ4P/7rGPHjpo1a1YDVwYAwUHXCIyxbVsLFjzit23BgkdYUIF68dRTT2nSpEnVgp1t28rJyZEkRUZGasyYMSbKA4A6I9zBmKys7Yc9W5ZNjFEfTj/9dBUWFla7vmvXLp122mkGKgKA4CLcAWhSDtUjXFxcrKioqAauBgCCjzl3MCYpqbN69uztt/euV6/j2MQYQTV79mxJkmVZeuSRR9SsWTNfm9vt1pdffqmePXuaKg8AgoZwB2Msy9K4cRf5XS07btxFsizLQFUIVRs3Vv4RYdu2vv32W0VERPjaIiMj1bNnT11xxRWmygOAoCHcwRjbtvX66ytkWVaVoTLLsvTaa6+qT59+BDwEzXPPPSdJmj59uu68807FxsYarggA6gdz7mBMdnaWMjMzqs2Bsm1bmZkZys7OMlQZQtns2bMJdgBCGj13MCYxMUn9+6foq68y5fF4fNddLpf69RugxMQkg9UhlFx33XW6//77FRsbq+uuu+6wj50/f34DVQUA9YOeOxhjWZYmTpxcbej1UNeB2oqLi6vy/4f7AACno+cORnXs2EmjR1+oFSuWy7ZtWZal0aMvVIcOHU2XhhDiXSn72/8HgFBEzx2MGzPmQrVu3UaS1KZNG40Zc6HhihDKSkpKtG/fPt/n2dnZevrpp7VmzRqDVQFA8BDuYFxUVLQmTZqi+PgEXXnlFEVFRZsuCSFsypQpSk9PlyTt3r1b48aN09KlSzVlyhS9+OKLZosDgCAg3KFRGDz4BC1c+JQGDz7BdCkIcRs2bNDgwYMlSf/6178UHx+vf//735ozZ45vuxQAcDLCHYAmpaSkRM2bN5ckrVmzRmeccYZcLpcGDBignJwcw9UBQN0R7gA0KV26dNGqVauUm5urNWvWaNiwYZKk/Px89r8DEBIIdwCalGuvvVYPPPCARowYof79+yslJUWS9NFHH6lXr16GqwOAumMrFABNyllnnaVBgwYpLy9PPXv29F0fMmSIRo4cabAyAAgOwh2AJichIUEJCQlVrvXr189QNQAQXIQ7AE1KcXGxnnjiCf3vf/9Tfn5+laPvJOn99983VBkABAfhDkCT8te//lVr167V+eefr4SEhJA+5q6otEK2bZsuA0ADI9wBaFL++9//6vHHH9egQYOCds/HH39cc+fO1WWXXaY777wzaPeti4ysXbrqpUwNO7qNhh/d1nQ5ABoQq2UBNCktWrRQq1atgna/L7/8Ui+//LKSk5ODds9gWPzxT/LY0offF5guBUADI9yhUXjppef1xz+er5deet50KQhxN9xwgx555JEq58vW1t69ezV16lTNnDlTLVu2rNU9LKt+PlpER/hew/ZUDs1a9fh6wf6oz68N9Ydu7U6vvya11wTDsjBu9+5ftWLFctm2rRUrluucc0apRYva/aIEjmTp0qXatm2bhg4dqqSkJIWHV30bXLlyZY3vNWPGDKWmpmro0KFauHBhrepp2zauVs87kjZxB85oLpGlhObRahYTqbatY+rl9epDfX1tGoqT63dy7ZKz6w9G7YQ7GPfgg/f5Jn3btq0HH7xP9947x3BVCFXB2svurbfe0saNG5WWllan++Tn71F9rHnI232gZ/LHHXvUOsqlfcVlyvd4Gv0iC8uq/AVXX1+b+ubk+p1cu+Ts+mtSu/cxR0K4g1FffvmFNm3aWOXapk0b9eWXX6hfvwFmikJIu+666+p8j9zcXM2aNUtPPfWUoqKi6nQv21a9/BL6dV+57/93FJVVvlY9vl59cFKt/ji5fifXLjm7/mDUzpw7GOPxeDRv3gN+2+bNe6Da/mNAsOzevVuvvPKKHnroIe3atUuStGHDBv3yyy81ev6GDRuUn5+vsWPHqnfv3urdu7fWrl2r5557Tr1795bb7a7H6mtm10HhLq+o1GAlABoaPXcwJiNjvYqK9vhtKyrao4yM9Ro06IQGrgqhbtOmTZowYYLi4uKUnZ2tP/zhD2rVqpXee+895eTk6IEH/P/BcbCTTjpJb7zxRpVr06dP19FHH61JkyYpLCysvsqvsV37Knz/n7e/5w5A00DPHYxJSRms2Fj/cwfi4looJWVwA1eEpuD+++/XmDFj9O677yoyMtJ3/ZRTTtH69etrdI/Y2Fj16NGjykdMTIxatWqlHj161FfpNWbbdpVh2eIy8z2JABoO4Q7GuFwu3XTTNL9tN900TS4X354Ivq+++krjx4+vdr19+/bKy8szUFHw7S1zq8JzYNJOmbvxL6IAEDwMy8Kofv0GqGfP3lUWVfTs2Vt9+/Y3WBVCWVRUlIqKiqpd/+GHH9SmTZta3/e5556rS1lB9WtJebVr5R7CHdBU0DUC46ZOvUPe8z1dLpemTr3DcEUIZaeddpoee+wxlZcfCEA5OTl66KGHdMYZZxisLHh+3T/fLj72wLBzWQULlICmgnAH41q0aKmxY/8gl8ulMWPGsYEx6tVtt92mgoICDR06VKWlpbr00kt1xhlnqHnz5rrppptMlxcU3pWyrWIiFRFW+YdTuZtwBzQVDMuiURg//k8aP/5PpstAExAbG6tly5bpf//7nzZs2CCPx6PjjjtOQ4cONV1a0HiHZVs2C1dkmEvlbrfKKhiWBZoKwh2AJsPj8WjFihV67733lJ2dLcuylJiYqISEBNm27Zse4HTeYdkW0RGKDHdpb5lbZfTcAU0Gw7JoFObOnaNx40Zp7lyOHUP9sG1b11xzjf7617/ql19+UY8ePXTssccqJydHt99+u6699lrTJQaNd1i2ZUyEIsMq3+aZcwc0HfTcwbi8vB365JM1kqRPPlmjvLwJSkhoZ7gqhJoVK1Zo3bp1evrpp3XSSSdVafvkk0907bXXKj09XaNHjzZTYBB597hrGR2hyPDK3kh67oCmg547GPe3v9122M+BYHjrrbc0efLkasFOkoYMGaKrrrqq2qkTTlW0f9Pi2OjwAz13hDugySDcwagPPnhf+fk7q1zLz9+pDz5431BFCFWbN2/WySeffMj2U045RZs2bWrAiupP6f4h2OiIMF+4K2VYFmgyCHcwxu12a9GiR/22LVr0aKM4fB2h49dff1Xbtm0P2d62bVv9+uuvDVhR/fHOr4sMcyky3Ntzx2pZoKkg3MGYVaveOWSAc7vdWrXqnQauCKHM7XYrPPzQ04zDwsJC5g+K0v1DsJHhLhZUAE0QCypgzMiRZ2np0sV+f6GGhYVp5MizDFSFUGXbtm6//XZFRkb6bS8rK2vgiupPaXllkIsKP7jnjnAHNBWEOxgTFhamyZP/oscee7ha2zXXXK+wsLCGLwoha8yYMUd8TCislJUOBLnKnrv9q2XpuQOaDMIdjPrd707TSy89X2VRRdu28UpNHWGwKoSi2bNnmy6hwfjm3NFzBzRJzLmDcffeO+ewnwMITGlF5VSHqCpz7lhQATQVhDsYl5DQTkOGDJckDRkynA2MgToq3b8yNjI8TFH03AFNDsOyaBRuvvk2SWxeDASDd1g2KozVskBTRM8dGgXOlgWCxzssGxnBnDugKSLcwbjqZ8vuMFwR4Fy2bfs2LK7suWO1LNDUEO5g3F//Ou2wnwOouYNPomC1LNA0Ee5g1AcfvK+Cgvwq1woK8jlbFqgl75Cs9JvVshw/BjQZAYW7xx9/XBdccIFSUlI0ZMgQTZkyRVu3bq2v2hDi3G63Fi78p9+2hQv/GTJHQQENyTv86rKkMJfl67lze2yV03sHNAkBhbu1a9fqkksu0fLly7V06VK53W5NnDhRxcXF9VUfQth77/2fPB7/v2w8Ho/ee+//GrgiwPkOPlfWsixFhB14my8pJ9wBTUFAW6EsWbKkyuezZ8/WkCFDtGHDBh1//PFBLQyhr2fP3nVqB1BdacWBc2UlKdxlKcyy5LZt7SunNxxoCuq0z92ePXskSS1btgz4uZZVl1dGKDj4yLFDtXfrdnQDVYPGiveKwBy8x51XRJgld4WtEsId0CTUOtzZtq3Zs2dr0KBB6tGjR8DPb9s2rrYvjRBxxhmnav78eSoqKqrWFhsbqzPOOFUuF2t+gECUHnSurFe4qzIhM+cOaBpqHe5mzJihb7/9Vi+++GKtnp+fv0c2i7eavJtvvk0zZvyt2vVbbrldBQV7DVSExsay+GMwEAeGZcN818LDXJLcrJgFmohahbt7771Xq1ev1vPPP68OHTrU6oVtW4Q7KD7e/zmybdsm8P0B1IJ3P7soPz13bGQMNA0BjXnZtq0ZM2bo3Xff1TPPPKPOnTvXV11oAmzb1oIFj/htW7DgEdmkOyBg3gDnPZlCOhDuSgl3QJMQULi755579Prrr+uhhx5S8+bNlZeXp7y8PJWUlNRXfQhhWVnbtWnTRr9tmzZtVFbW9gauCHC+Ej/DsmHenjvm3AFNQkDDssuWLZMkXXrppVWuz549W2PHjg1eVQCAWimr8DMsG8aCCqApCSjcbd68ub7qQBOUmJikmJjmKi6uvnCiefPmSkxMMlAV4Gxl7kOvlmXOHdA0sM8EjMnJyfYb7CRp7969ysnJbuCKAOf77SbGkhTu8p4vS7gDmgLCHYzp2LGTrEPsUGtZljp27NTAFQHOV+pnE2N67oCmhXAHYz77bN0hV8Tatq3PPlvXwBUBzsewLADCHYwpKMivUzuA6vwNy4aFsVoWaEoIdzBm5Mgz69QOoDr/c+684Y69I4GmgHAHY37+ObdO7QCqKzvM2bIMywJNA+EOxiQmJql//xS/bQMGDGQrFKAW/B8/xmpZoCkh3MEYy7J03nn+N78eNWrsIVfSAji0w62WLafnDmgSCHcwxrZtvf76Cr9tr7/+KmfLArVQ6m9Ydv+CilJ67oAmgXAHY7Kzs5SZmeG3LTMzQ9nZWQ1cEeB8/odlmXMHNCWEOxjTqVOiYmPj/LbFxcWpU6fEBq4IcD6/W6G42AoFaEoIdzAmJydbRUV7/Lbt2bOH48eAWvANy/qdc8dUB6ApINzBmMTEJPXs2dtvW69evVktC9SCd+g1OoLVskBTRbhDo8RaCqB2yvcHOG+gkw4sqCDcAU0D4Q7GZGdnadOmjX7bNm3ayIIKoBbKPZV/GfkblmVBBdA0EO5gjHcTY5er6rehy+ViE2Oglsr3HzEWEXZgn0jCHdC0EO5gjGVZmjhxcrXNig91HcCReYdlI/z13DEsCzQJhDsY1bFjJ40efaEvyFmWpdGjL1SHDh0NVwY4U8X+Ydnwg3ruwlhQATQphDsYN2bMhWrduo0kqU2bNhoz5kLDFQHO5eu5O2i6g3eIlmFZoGkg3MG4qKhoTZo0RfHxCbryyimKioo2XRLgSG6Prf0dd7/puav8f499oGcPQOgi3AFAiCg/aNjV34IKSSqtcDdoTQAaHuEOxpWWlmjx4gXauTNPixcvUGlpiemSAEc6uFfu4GHZg8MdQ7NA6CPcwbiVK9NUWFggSSosLNDKlWmGKwKc6eCeu4OHZS3L8g3NlhLugJBHuINRubk5Sk9Pk73/SArbtpWenqbc3BzDlQHO4+25C3NZcv1mKyG2QwGaDsIdjLFtW0uWLJLHU/WXjcfj0ZIli3yBD0DN+DYwdlXfIzKCjYyBJoNwB2Oys7OUmZlRLcTZtq3MzAyOHwMC5G8DYy/vMC3DskDoI9zBmMTEJPXs2dtvW69evTl+DAiQ91zZg1fKeoXvX2BBuANCH+EOjRIjskDgKvb33IX7GZZlzh3QdBDuYEx2dpY2bdrot23Tpo0MywIB8s25Y1gWaNIIdzAmMTFJ/funyOWq+m3ocrk0YMBAhmWBAJV7atBzR7gDQh7hDsZYlqWJEyfL+s2WDYe6DuDwDt9zx5w7oKkg3MGojh076dxzR1e5NmrUaHXo0NFMQYCDVbgPt6CCOXdAUxFuugDgt1hMASd48cUXtWzZMmVnZ0uSunfvrilTpig1NdVYTeW+BRXV/24P84U7fsCAUEfPHYzKzc3Rm2+mV7n25pvpnFCBRq9Dhw669dZb9eqrr+rVV1/VSSedpGuvvVZbtmwxVlPFYbdCYc4d0FQQ7mCM94QKf5sYc0IFGrsRI0YoNTVV3bp1U7du3XTTTTcpJiZGX3zxhbGavAsqGJYFmjaGZWGM94SK3/J4PL4TKpKSOhuoDAiM2+3WO++8o+LiYqWkpAT03GCuG6o4aEGF976WJVk6MCxb7vEE9TXrw8G1O5GT63dy7ZKz669J7TX9dxHuYExiYpKOPvpYbd36XbW2Y445lq1Q0Oht3rxZ48ePV2lpqWJiYvTYY4/p2GOPDegebdvGBa2eqJgCSVLzZhFq0yZWxYXFah4TrfBIj5pFRUiSwiLCFR8fvNesT8H82pjg5PqdXLvk7PqDUTvhDsbYtq2ff87125abmyvbttkOBY1at27dlJ6ert27d+vdd9/Vbbfdpueffz6ggJefvydoi4gKf90nSfJUeFRQUCRZLu0tLlFJmUe22y1J+rWoVDt37gnOC9YTy6r8BRfMr01DcnL9Tq5dcnb9Nand+5gjIdzBmIyM9Sou3uu3rbh4rzIy1mvQoBMauCqg5iIjI9W1a1dJUt++ffXVV1/p2Wef1YwZM2p8D9sO3grx8oO2QrFtSdb+++ug1bIVHsf80gvm18YEJ9fv5NolZ9cfjNpZUAFjBgwYVO10Cq/KUyoGNXBFQN3Ytq2ysjJjr19eg7Nly1lQAYQ8eu5gTE5Otjwe/79oPB6PcnKy1blzlwauCqiZuXPn6pRTTlGHDh20d+9evf3221q7dq2efPJJYzVVcEIFABHuAKBWdu7cqWnTpmnHjh2Ki4tTcnKynnzySQ0bNsxYTYc7WzaMnjugySDcwZikpM7q3Lmrtm//qVpbly5d2QYFjdp9991nuoRqDnu2LCdUAE0Gc+5gjG3bysvb4bdtx44dbGIMBIgTKgBIhDsY9Pnn61VSss9vW0nJPn3++foGrghwNu+Qa4SfhUqcUAE0HYQ7GNOuXfs6tQOoyrugItxPzx1z7oCmg3AHYzp37qKjj/a/2esxx3RnpSwQoANnyx66547VskDoI9zBGMuydOONU/223XjjVE6nAALkW1Bx2H3umMsKhDrCHYzq2LGTfv/786tcO/fc89WhQ0dDFQHO5Ztz53dYtvLtnjl3QOgj3MG4iy76k6Kjm0mSmjVrpvHj/2S4IsCZvKtlww+3oIJhWSDkEe5gXFRUtG644VbFxyfo+utvVVRUtOmSAEfyHT/mbyuUMFbLAk0FmxijURg8+AQNHnyC6TIARzvcJsaslgWaDnru0CisX79W11xzhdavX2u6FMCxarKgosxts0E4EOIIdzCutLREixcv0M6deVq8eIFKS0tMlwQ4UkUNtkKRWDELhDrCHYxbuTJNhYUFkqTCwgKtXJlmuCLAmXwLKg6zibHEvDsg1BHuYFRubo7S09N8w0S2bSs9PU25uTmGKwOc5/BboVjVHgcgNBHuYIxt21qyZFG1+T+Hug7g8A7Muav+1u6yLN9cPE6pAEIb4Q7GZGdnKTMzQx5P1V80Ho9HmZkZys7OMlQZ4EyH67mrvO7a/zj+cAJCGeEOxiQmJqlnz95+23r1Ok6JiUkNXBHgbAfm3Pl/a49grzugSSDcoVFiSBYInLdHLtzPVijSgZ47wh0Q2gh3MCY7O0ubNm3027Zp00aGZYEAHWlYNjJ8f7hjzh0Q0gh3MObww7K9GZYFAlTuOfSCCulA6GPOHRDaCHcwqqysLKDrAPyzbVtub7g7woKKUoZlgZBGuIMxWVnbtXXrd37bvv/+O2VlbW/gigDn8i6mkPyfUCFJkd7VsgzLAiGNcAcAIeDgodZDL6hgtSzQFBDuYExSUmd17tzFb1uXLl2UlNS5gSsCnOvgUycO1XPHPndA00C4gzG2bWvnzjy/bXl5eWyHAgTAu5jCZVU9auxgzLkDmgbCHYz5/PP12rdvn9+2ffv26fPP1zdwRYBzVfi2QTn023qkd7Usc+6AkEa4gzHt2rWvUzuAA460gbHEJsZAU0G4gzGdO3fR0Ucf67ftmGO6H3I+HoDqyvef0XzYcBdOuAOaAsIdjLEsSzfeONVv2403TpVlHfqXFICqvD13hx+W9YY75rMCoYxwh0aJxRRAYCqOcPTYwW3MuQNCG+EOxti2rQcfnOW37cEHZxHwgADUpOeOOXdA00C4gzHbtv2k7du3+W3bvn2btm37qYErApzLe0IFCyoAEO5gzDffbKhTO4ADvAsqarIVCnPugNBGuIMxp59+llwu/9+CLpdLp59+VgNXBDjXgWHZI/fcMecOCG2EOxjjcrmUmJjkty0xsfMhgx+A6nwLKtgKBWjy+O0JY7Kzsw4z5+4nZWdnNXBFgHP5NjGu0bAs4Q4IZYQ7GJOYmKT+/VP8tg0YMPCQvXoAqjsw564mCyqYcweEMsIdjLEsSxMnTvbbNnHiZDYxBgJw4PixGmyFwpw7IKQR7tAosccdEJiaLKjwDsuWMywLhDTCHYyxbVv33XeP37b77ruHgAcEoKImZ8uyzx3QJBDuYMy2bT/q559z/Lb9/HOOtm37sWELAhwsoBMqGJYFQhrhDsZ88smaOrUDOKCiBgsqIsMPbGJsWRbzWoEQRbiDMRdcML5O7QAO8PXcHWJBRbjLUmR4mCSptMKjgtIKFZRWqLzBKgTQUAh3MOaXX36uUzuAAw7sc+e/Ny7MZcm7A8q+crc++X6nPvupUEVlbnrwgBBDuIMx7HMHBE9Fjc6Wde1/rK2Sco/KKtwNUhuAhkW4gzHefe5+e8xYWFgY+9wBATowLHvonxtvr57bY7MaHQhhhDsY1bFjJ40ZM67KtdGjL1SHDh0NVQQ4k3fvupqslpUkD9kOCFmEOxh3zjmjfL10luXSOeeMMlwR4DzlHu8JFYfbxPjAW753GBdA6CHcwbi3337DN0Rk2x69/fYbhisCnKdif8/doRZU/LbNTdcdELIIdzAqNzdHK1Ysr3JtxYrlys31v7kxAP9qsomxy7LkzXcVhDsgZBHuYIxt21qyZFG1id2Hug7g0Hxz7g4zLCtJ4QetmAUQmgh3MCY7O0uZmRl+2zIzM5SdndXAFQHO5Q1rh+u5kyr3u5MYlgVCGeEOxnTs2ElhYWF+28LCwtSxY6cGrghwrnJfuDtCzx3hDgh5hDsY88UXn8nt9r+Jqtvt1hdffNbAFQHOdWBBRc167hiWBUIX4Q7G9O8/sE7tAA6oySbGEj13QFNAuIMxmZmf16kdwAEHNjGuWbij5w4IXYQ7GJOSMlgxMc39tjVv3lwpKYMbuCLAucpZUAFgP8IdjLEs65DHjLVv35GzZYEA+Obc1XBYlp47IHQR7mBMdnaWtm79zm/b1q3fsRUKEADvnLsjLahgzh0Q+gh3MKZTp0TFxsb5bYuLi1OnTokNXBHgXL5h2SP03IW5Kt/2CXdA6CLcwZicnGwVFe3x27Znzx7l5GQ3cEWAc1X4FlQcoecujGFZINQR7mBMYmKSevbs7betV6/eSkxMauCKAOeqYBNjAPsR7mBUWVmZ3+ulpf6vA/CvvIYLKtjEGAh9hDsYk5W1/bALKrKytjdwRYAz2bZ9YBPjGi6oINwBoYtwB2M8Hk+d2gFUctuSN6oxLAuAcAdj8vJ21KkdMOXxxx/XBRdcoJSUFA0ZMkRTpkzR1q1bjdXjXUwhsYkxAMIdDOrfP6VO7YApa9eu1SWXXKLly5dr6dKlcrvdmjhxooqLi43U4x2SlWp+tmwFPeNAyAo43K1bt06TJ0/W8OHDlZycrFWrVtVHXWgCVq9+t07tgClLlizR2LFj1b17d/Xs2VOzZ89WTk6ONmzYYKSe8oOCWlgNwx09d0DoCjjcFRcXKzk5WXfddVd91IMm5LTTzqxTO9BY7NlTuV9jy5YtA36uZdX9w7s4ItxlyeWyfNd99z/o9bwnWHjDnRWkGoL9EayvDfU3rdqdXn9Naq+J8EDfiFJTU5Wamhro04Bqfv4594jtSUmdG6gaoHZs29bs2bM1aNAg9ejRI+Dnt23r/5SWQBRbYZKkyHCX4uMP3K+4sFjNY6IVHulR86hwRUSEKSY6orLR5VJs82g1i4lU29Yxda6hPgTja2OSk+t3cu2Ss+sPRu0Bh7tgqWn6ROhKSkpS//4pyszMqNY2YMBAJSUl8X2CRv89MGPGDH377bd68cUXa/X8/Pw9sus4Qrojv3KuX7jL0s6dlb2ILpclWS7tLS5RSZlHLne4ysubyVPhliSVllWoaG+J9hWXKd/jkV3XIoLIsip/wQXja2OCk+t3cu2Ss+uvSe3exxyJsXDn5FSN4Ln00kv8hrtLL71ECQktDFQE1Ny9996r1atX6/nnn1eHDh1qdQ/bVp1/CXkXVIS7LN+9bFuStf/+Bz3Wt4nx/gfaQaqhPjTWumrKyfU7uXbJ2fUHo3Zj4c6JqRrBZdu2nnvuBb9tzz77vLp06S6rsXfboN7V9C/VhmTbtu6991699957eu6559S5s9npA94FFUfaBkViKxSgKTAW7pycqhEcWVlZfnvtJCkzM0NZWVnMuUOjdM899+jNN9/UggUL1Lx5c+Xl5UmS4uLiFB0d3eD1HDid4sh/DPm2QnHzBgyEKmPhDujQoWOd2gFTli1bJkm69NJLq1yfPXu2xo4d2+D1eM+VjXAduefOtxUKf10DISvgcLd3715t27bN93lWVpa++eYbtWzZUp06dQpqcQhtq1b964jtZ531+waqBqi5zZs3my6hCm8vXDg9dwBUi3D39ddf67LLLvN9Pnv2bEnSmDFjdP/99wevMoS8Xr2Oq1M7gEqBzLnz7nNXwZw7IGQFHO5OPPHERvdXK5ypS5eu6tCho9/97jp27KQuXboaqApwnoNXyx7JgePHCHdAqOJsWRjVqlVrv9dbtmzVsIUADuabcxfAsKybs2WBkEW4gzHZ2VnatGmj37ZNmzYqOzurgSsCnOnAalmGZQEQ7mBQYmLlCRWu36zwc7lcGjBgoBITkwxVBjjLgdWyNe+589jsdQeEKsIdjLEsSxMnTq527JFt25o4cTIbGAM1VL4/pEWG16Tn7sDPFb13QGgi3KFRakznXAKNnbfnLpAFFZJUwbw7ICQR7mCMbdtasmSR37YlSxYR8IAaCmTOncuy5M137HUHhCbCHYzJzq48fszfsGxmZgYLKoAaKtvfcxdZg3AnHQiBDMsCoYlwB2O8Cyr8YUEFUHMVAWyFIklh7HUHhDTCHYyxLEvnnef/HM7zzhvLggqghgIZlpUOPoKMOXdAKCLcwRjbtvX66yuqhTjLsvTaa68y5w6oobIAe+44pQIIbYQ7GMOcOyA4vCEtwhVozx3hDghFhDsY451z56/njjl3QM2VVQTYc8eCCiCkEe5gjHfOnb+eu1GjmHMH1JR3E+OA59wR7oCQRLiDMbZt65VXlvlte+WVF5lzB9TQgdWyNXtL966WLWdBBRCSCHcwJitruzZt2ui3bdOmjcrK2t7AFQHOVOZbLcuCCgCEOwBwvHJWywI4COEOxiQmJikmprnftubNm7OgAqih8gBPqGCfOyC0Ee5gTE5OtoqL9/pt27t3r3Jyshu4IsCZvJsYh9c03LFaFghphDsYk5iYpJ49e/tt69XrOHrugBryrpaNDHBYtpx97oCQRLhDo8RKWaDmfHPuAt3EmJ47ICQR7mBMdnbWYVfLckIFUDPecBdew567MF+4Y84dEIoIdzCmY8dOch2ip8Hlcqljx04NXBHgTN6tUAJfUEHPHRCKCHcw5vPP18tziJ4Dj8ejzz9f38AVAc5UEehWKCyoAEIa4Q4AHK7czfFjAA4g3MGYQYOOV3R0M79tzZo106BBxzdwRYAzlQV4/Bj73AGhjXAHY1wul6ZOvcNv29Spdx5yPh6Aqrw9cJxQAUAi3MGwfv0G6Nhje1S5duyxPdS3b39DFQHOYtu2yipq2XNHuANCEuEOxk2fftdhPwdwaG5b8ka0CFdNF1SwWhYIZYQ7GBcVFeWbexcd3UxRUVGGKwKc4+B5c5HhNe25q3xcOfvcASGJcAfjVq5MU2lpiSSptLREK1emGa4IcI6yg8JdTXvuwtjnDghphDsYlZubo/T0NN9xY7ZtKz09Tbm5OYYrA5zh4PNhw2o6LMucOyCkEe5gjG3bWrJkUbVzZA91HUB13qPHIsMsWRbhDgDhDgZlZ2cpMzOj2ikVHo9HmZkZnC0L1ECgGxhLBy2oINwBIYlwB2MSE5PUv39Ktf3sXC6XBgwYqMTEJEOVAc7hXRQRULjb/zPn9thyE/CAkEO4gzGWZWnixMl+h2UnTpxc4yEmoCkrrwhsA2PpwLCsdGBYF0DoINyhUWK+HVAzvp67Gi6mkKqGO+8GyABCB+EOxngXTvjDggqgZgI9V1aSXC5L3nxXSs8dEHIIdzDGu6DC37AsCyqAmqnNgoqDH0/PHRB6CHcwJjExSZ07d/Xb1qVLVxZUADVQ4Q58zp10YGi2pJxwB4Qawh2M8Xg82r79J79t27b9VG2LFADV1WZY9uDHl1a4g14TALMIdzDmnXferFM7gAOrXQPtufM+vpRhWSDkEO5gzK5du+rUDqD2c+68w7KEOyD0EO5gTKtWrevUDuCgnrsAtkKRDh6WJdwBoYZwB2P69OlXp3YAUvn+EyYiw2vbc8ecOyDUEO5gTJcuXdW5c5dDtnXp4n8lLYADvD134QH33LFaFghVhDsYY1mWpk6902/b1Kl3cvwYUAO1nnPHPndAyCLcwbjfhjjLsjidAqgh71YokYFuheLd545wB4Qcwh2M4fgxoO4qar0VCvvcAaGKcAdjOH4MqLu6boXCsCwQegh3MCYxMUkdOnTy29axYyeOHwNqoKyOPXcMywKhh3AHY9xut37+OcdvW25ujtxuhouAI/HuUxcV4FYonFABhC7CHYxJS3upTu0Aar+gwrfPXTl/RAGhhnAHY046aVid2gEcmDMXFR4W0PPCOaECCFmEOxjTtetRcrn8fwu6XC517XpUwxYEOFCJL9zVbhNjwh0Qegh3MKa4uFgej/9fLB6PR8XFxQ1cEeA83p67QI8fi3DRcweEKsIdjJk69fo6tQM4eEFFYMOyB3rumHMHhBrCHYw544xz6tQO4MCCiqjaLqig5w4IOYQ7GDNq1Og6tQM4EM4iA55zx7AsEKoIdzDqt+fKHuk6gKpqOywb7h2WLSfcAaGGcAdjVq1655Dnx9q2rVWr3mngigDnqe2wrHdBRQlz7oCQQ7iDMaeddmad2gHUfrWst+fOY0sVbnrvgFBCuIMxOTnZdWoHcPA+d7U7fuzgewAIDYQ7GLNjx891agdMWrdunSZPnqzhw4crOTlZq1atavAabNuudc9dmGXJG+9KOIIMCCmEOxiTkjL4sCdUpKQMbuCKgJorLi5WcnKy7rrrLmM1VHhseWetBjrnzrIsX+8dPXdAaAk3XQCartzcnMOeUJGbm6OkpM4NXBVQM6mpqUpNTTVaw8HbmAQ6LCtVbodS5narhBWzQEgh3MGYxMQk9e+foszMjGptAwYMVGJikoGqgIZVl11/yg5aCBEZblW5l/f/LUs6+CUO/tx3SoXbXac6gu3g2p3IyfU7uXbJ2fXXpPaa/rsIdzDGsixNnDhZ119/dbW2iRMns9cdmoS2beNq/dySsMrzl6PCXUpIaFGtvbiwWM1johUe6VHzqHBFRIT5Pq98XpikCkU2i1J8fO3rqC91+do0Bk6u38m1S86uPxi1E+5gVF7eDr/Xd+z4RR06dGzgaoCGl5+/R4fY7vGIfs4/EO527txTpc3lsiTLpb3FJSop88jlDld5eTPf55K0/wQy7Sgo0s6WkbX+NwSbZVX+gqvL18YkJ9fv5NolZ9dfk9q9jzkSwh2M8Xg8euih+/22PfTQ/Vq69MVDLrgAQoVtq9a/hLwLISLDXNXuYduSrP33/+3r7f//yP2LMPaVeRrlL8K6fG0aAyfX7+TaJWfXH4za+c0JYz7/fJ2Ki/f6bSsu3qvPP1/XwBUBzlLbbVC8vM8rLmMrFCCU0HMHY9q1a1+ndsCkvXv3atu2bb7Ps7Ky9M0336hly5bq1KlTg9RQWssNjL184a68Img1ATCPcAdjPJ7D9zsfqR0w6euvv9Zll13m+3z27NmSpDFjxuj++/1PNwi20lqeK+vlfR49d0BoIdzBmK+//vKI7Ucd1a2BqgECc+KJJ2rz5s1Ga2BYFoA/zLmDMdu3/1SndqCpC96wLOEOCCWEOxiTlNSlTu1AU1dW13C3fxNjeu6A0MKwrMPYtq3S0lLTZQRFixYtj9heUlLSQNXUn6ioKDZkRr3wzrmLrO2cO4ZlgZBEuHMQ27b1t7/dps2bvzFdSoOYP3+u5s83XUXdJSf30r33ziHgIejqPCwbxrAsEIoYlgUAh2JBBQB/6LlzEMuydO+9c0JmWFaS1qz5QI8//li161dffZ2GD081UFHwMSyL+lLTrVDcFeXK/mGr8r7J1+Ztv8gKj1SrhE4Ka5koiXAHhBrCncNYlqXo6GjTZQTNyJFnafnyZSosLPBda9OmrUaOPNNgVYAzlJYfflg2O2u7/vXC49q4/kOVFhdVaw8Lj1BEu17a0/8MSYPqs1QADYhwB+Puumumbrppiu/zmTMfMFgN4Bxlbv/DssXFe/XUU08o7dXl8rgre+WaNY/TsT16qiIsWvv2FWtn9o8q2pWv8JwvtTvnS/2t+HNdf/3NSkho1+D/DgDBRbiDcfHxCb7/P+GEIfxyAWqotKIyuB3cc/ftt5t19913KDt7uyTp6D6DNODU83VcvwHq0yVeGT/mq6TcI9u2tf2HLVqetlxh2z/Tf/6zWhkZ6zV9+t0aNuxkI/8eAMHBggo0Kn/5y82mSwAc47erZdeu/UTXXTdJ2dnb1a5de82eN19/+Mvf1SW5v1yusCrPtSxLSd26qzzljypNvVHJyb20e/du3XHHrVq5Mq3B/y0AgodwBwAOVeauPH85MsylVav+pdtuu1klJSUaPPhEPf30Cxp84pDDPt+7ibHdspPmzF2k884bI9u2NW/eA1qy5HHZNuc7A07EsCwAOJR3WPb7jA/11tKHJEmnnXaG7rjjbkVFRWrfEZ4fHuZSuMtShcdWmW3plltuV3x8gp566gk988wSeTxuTZo05Qh3AdDY0HMHAA5VVuGRK2+L/u/ZRyRJo0dfoL/9bYYiIiJqfI9mEZXDtfvKPbIsS3/+85W66aZpkqTnnntaK1a8EvzCAdQrwh0AONTun7cpcu3T8rgrNGLE6brxxqlyuQJ7W28WWRnuDj6lYsyYCzVx4tWSpEce+Yc++GB18IoGUO8IdwDgQLt27VLO/82XVVGqY3r10x133B1wsJOkZhHeUyoqqly/7LIrfHPwZs26W99//11Q6gZQ/wh3AOAwbrdb9977N7n3FsrTPF5X3zpDkZGRtbqXd1i2uMxT5bplWbrppmk64YSTVFpaqr/97TYVFVXfCBlA40O4AwCHeeaZJVq37lMpLEJlJ1yu+NYta30vX7grr6jWFhYWpr/+dYbat++grKztmj17BitoAQcg3AGAg3z22To988wSSZJn4DjZLToqJiLsCM86tGjvgopDnC/bqlUrzZhxvyIiIvThhx/o1VeX1/q1ADQMwh0AOMSePbt13333yLZt/f7c81XaaaAkKSay9uHO+9y9hwh3ktSrV29de+2NkqRFi+brhx++r/XrAah/hDsAcIi5cx9QXt4OJSV11sSrrvddr0vP3YGtUA4d7qTKFbQnnjhUZWWlmjnzbpWXl9f6NQHUL8IdADjA6tXv6f333/XNg3OHVS6gCHdZigyv/Vu5t+euqPTw4c6yLN1++1/VsmUrbdnyrZYsebzWrwmgfhHuAMABPvtsnaTKLUp69z7ON0euLkOykhQbVXlQ0e7S6gsqfqtt23hNm3aHJOmll57XN99srNNrA6gfhDsAcIBJk6boH//4p/785yslSXv3D6PWZUhWkmKjK8PdnpIjhztJOvnk32nkyDPl8Xg0Z85MhmeBRihkz5a1bVulpaWmy0ANlJSU+P1/NH5RUVGyLMt0GU1Cq1atdMIJJ/k+9/bcNatjz12LKG+4q3lIu/76W7Ru3afauvU7vfDCM77ACaBxCNlwV1paqksvHWe6DARo0qRLTZeAADz33CuKjo42XUaT5F3d2jxIw7K/1rDnTqoMmjfeeKvuueevevbZp5Saeqq6dTumTnUACB6GZQHAgbyrW5sFa1i2BnPuDjZixOkaOvRkVVRUaM6cWXK7D78gA0DDCdmeu4MVDbhItqtJ/FOdy7vrPUN8jZ7lqVDsF8tMl9Hkec+CreucuzjvgooAeu6kytWzt9xymzIzP9fGjV/r1Vdf1h/+cHGdagEQHE0i8diucCkswnQZQEjg8KnGobi88izYuq6Wjdvfc1da4VFphUdRAWyrkpDQTlOm3KAHH7xPTz65SCef/Dt17NipTvUAqDuGZQHAgXw9d3UMdzGRYfL2lweyqMLr978/T/37p6ikpERz587h7FmgESDcAYADFZft77mr47Csy7LUIrrme91Ve77LpalT71BkZKQ+/fQTrVr1rzrVA6DuCHcA4EDF5ZVBrK5boUgHhmZ37ws83ElSly5dddllV0iS/vnPudq1a1edawJQe4Q7AHCg4iBthSJJLaIq5yTXpufO66KLLlW3bsfo11936bHHHq5zTQBqj3AHAA7kDXd13QpFklo0C+yUCn8iIiJ02213yrIs/etfb2vduk/rXBeA2iHcAYADefe5C0bPXZxvI+O6HSXWu3cfjR1buXn8Qw/dz4kzgCGEOwBwoL3B7LkL8HzZw5k06Rq1a9deOTnZWrr0iTrfD0DgCHcA4EDenru6boUiSS2iK+fcBXpKhT8xMc11883TJEnLly/T5s2b6nxPAIGpVbh74YUXNGLECPXt21djx47V+vXrg10XAOAwvHPughPuAj9f9nCGDj1ZI0acLrfbrQcemKWKiuDcF0DNBHxCxdtvv63Zs2fr7rvv1sCBA/XSSy9p0qRJeuutt9SpUyPdmdxdt3kkAA7Cz1OjUOztuQvCsGxcEIdlva6//matW/eptmzZrFdeeUkXXfSnoN0bwOEFHO6WLl2qCy64QOPGVU6avfPOO7VmzRotW7ZMt9xyS9ALrK2Dd0mPy3zJYCVA6OI0AjNs2w5qz12r/cOydV1QcbA2bdpqypTrNWfOTD311ONKTT1VnTolBu3+AA4toGHZsrIybdiwQcOHD69yfdiwYcrIyAjohS2r/j8A1K+G+DnmZ7m6feUeefbn6tiouh8RHh8bKUnaWVRW53sd7JxzRiklZZBKS0v1j3/czx8DQAMJ6F2hsLBQbrdbbdu2rXI9Pj5eeXl5Ab1w27ZxAT0+UPv21f0ND8DhxcfHqVmzZqbLaHIK91WGsKhwV1BWy8Y3rwx3eXvL5LFtuYKUqC3L0q23TteECZdo/fpP9frrK3X++WODcm8Ah1arBGT95gfftu1q144kP3+P6vOPONu29fzzr9TfCxhi27ZKS0tNl4EAREVFBfzz4RRFReXau7d+J8tbVv3/Meg0hcWVw6etm0UE5X7ecOf22Nq1r1xtYiKDcl9J6ty5i6666hrNn/+wHnvsYQ0cOEidO3cN2v0BVBdQuGvdurXCwsK0c+fOKtfz8/MVHx8f0Avbtuo13EmWoqKi6/MFjImOpqcEjQcjbQ3PF+5ighPuwsNcahMToYLicuUVlQU13EnShReO1yeffKTPPlune++9WwsWPKnwcEZXgPoS0Jy7yMhIHXfccfroo4+qXP/444+VkpIS1MIAAP4V7qsMd62C1HMnHei9C/a8O0lyuVyaPv0uxcW10KZNG/XMM0uC/hoADgh4n7sJEyYoLS1NaWlp+v7773XfffcpNzdX48ePr4/6AAC/sSvIPXeS1C4uSpKUV1Q/0z7atWuvW265TZL03HNL9fXXX9bL6wCoxZy7c845R4WFhVqwYIF27NihHj166IknnlBiIkvcAaAh1GfPXd7e4PfceY0Ycbo++WSN/vWv/9OMGX/TkiXPKS6uRb29HtBU1WrSwyWXXKJLLrkk2LUAAGqgsLgygAVzblxCPW2H8ls33DBVX331pXJysjVr1t91333/kMvFSZhAMPETBQAO4+25C9ZqWUmKj60clt1RT8OyXrGxsZox435FRkbq44/X6MUXn63X1wOaIsIdADiMd7VsqyDOuUuoxwUVv9WjR7JuvHGqJOnJJxdp7dpP6v01gaaEcAcADrOrHnruvMOy9Tnn7mDnnnu+zjlnlDwej+6++w798MP3DfK6QFNAuAMAhwn2PnfSgdWyBXvLVFrhCdp9D+fmm29Tv34DtHfvXt12283atauwQV4XCHWEOwBwkH3lbpXsD1/BXC3bulmE4qLCZUv6qaA4aPc9nMjISM2c+YASE5P088+5uu22m1VcvLdBXhsIZYQ7AHAQb69dRJil5pF1P1fWy7IsHd02RpK0Nb9hwp0ktWrVSnPmzFPLli31zTcbNH36rRyxCNQR4Q4AHOTglbLBPrP46HhvuGvY3rMuXbrqwQcfUUxMc2VkfKa77pqusrKGmfsHhCLCHQA4yM+7SyRJCfu3Lgmmo9s2lyRt3dlwPXdePXv21v33P6TIyCh9/PEaXXvttdq3b1+D1wGEAsIdADjID/uHTI/aP4QaTAeGZc3MexswYKDuv/8hRUdHa82aNbrlluu1Z88eI7UATka4AwAH+XH/YoejWjcL+r294S5rV4lKyt1Bv39NDB58gubOna8WLVroq68yNWXKldq+fZuRWgCnItwBgIP8WFA5VNmtHnru2jaPVIvoyhWzDbmo4rf69u2nZ555RvHxCfrppx909dUT2OjYMMuyqnygcSPcAYBDeGzbt03JUW2CH+4sy1K/Ti0kSZ/+ZHbPuZ49e2rx4md03HF9VFS0R1On3qiFCx9loYUB5ZIKSiuqfJSbLgqHRbgDAIf4ZU+pSio8CndZSmwV/GFZSTr56DaSpA+/z6+X+wciPj5ejzyySKNGjZZt21q27DlNnjxB33672XRpTYZlWSoqc+uznwr1yfc79cn3O/XZT4UqKnPTg9eIEe4AwCG8iyk6t26mcFf9/GIdfnRbSdLXuXtUUGy+lywyMlJTp96hmTPnqGXLVvruuy266qrLNXfuHP366y7T5TUZZRVulZR7VFLuUVmFmfmYqDnCHQA4xPc7K1exdquHIVmvdnFRSm4XK1vSB1t21tvrBOqUU07V00+/qBEjTpfH41F6+qu66KILtHTpYu3Zs9t0eSHN7bG1vXCfvsz+VZt+2aM9JRWmS8IRhJsuAABQM//dP1TaP7FFvb7OWb3aafOOIi353zad3bu9mkUE7ySMumjbNl5///ssnX/+WD3yyEPauvU7LV26WMuXv6izzx6l884bo6OO6ma6zJBh27be3PCLFn38o37ZfeDUEEvSh1sLdN3wburZPtZcgTgkeu4AwAF+3l2iL7J3y5I0skdC0O5rWd6PA6sg/5CSqE4torWjqEyPffiDbNsO2usFQ0rKIC1Z8pz+/vdZ6tbtGO3du1dpaS/pssv+qClTrtQrr7ykHTt+MV2mo+X+WqLr0r7SPe9s1i+7S9UswqWj2jRT+7go2ZI+/bFQlz7/uaa+tkHf5XEecGNDzx0A1NILL7ygJUuWKC8vT927d9cdd9yhwYMH18trrfq2coh0QFJLtYsLzukU4a7KQFdQUiFblSEvOiJM+9weXTnsKM34v016OSNHO4rKdPnxSUpuH1dvc/0CFRYWphEjTtfvfnea1q79n15/fYU+/niNvv76S3399Zd69NG56tq1m1JSBmrAgIHq27e/4uMTWARwBLZt68VPt2nWWxu1t8ytqHCXLj2hszrERcrtqXxMUWmFNv5SpNWb8/TBd/n6z3f5GpmcoEuPT1LPdrF8jRsBwh0A1MLbb7+t2bNn6+6779bAgQP10ksvadKkSXrrrbfUqVOnoL/ed/vn253VM3i9dmEuS8XlHm3M2qXSCrdioyPUvUMLbczapTDL1uh+HfX6V7n695ad+veWnYoIs9QmJlKtm0WoTUyE2jSPVEJspOKbRyo+NkoJzSPVtnmk2sREKLqBhnJdLpdOOmmoTjppqPLydug///m3PvjgfX31VaZ++ukH/fTTD0pPf1WS1KJFCx199LHq1u0YJSV1Vvv27dW+fQe1b99BcXEtFBbWOIafTSit8OijHwr0/Lrt+iq38lSQ/p1a6O6zkxUbE6lPvt8pt6cy3cXHRur2AT00eWhXPf7RT1r1bZ7e21z5kdgyWqnHtlW/Ti3Vs31zdWwRLRdhr8FZtqH+9p0796iR9fQDaIQsS4qPjzNdRjXjxo1T7969dc899/iunX322Ro5cqRuueWWGt+npu+FP+8u0dptu/T73u0VVoPeM5fL0j6XS+9/laOSco9aNgtX76TWyvgxXyXllb+kf3vtt59HR7iU0CJar32RozVbC7S3rOarJJtFuNQ6JlJR4S6FuyzfR5jLki3J47HlsSv37nN7bNmSyt0elbttlbk9qvDYKi33qMztkS0pzJJcVuXzXZYll0sKs6yDrqnK/6usWBU7vldZ7rcqyf1W5QXZOvwX2lJYdIwiomMV0ay5IprFKiwiUmHhkQqLiFB4RJTCwiOksEgpPFwehckjyWNbcsuSR5bc9v7PbUtyWXJZLkWGhykizKWIcJciXJYiwlwKd7kUGb7/axLmkkuW9v9XOTxeWY48duViBo+9/2vlseU+6GtX4fEcuF7lsbbcHsm9/3mS5LLk+9qFWZWhOMxV+Rq791Xo5z0lqtjfMxcRZunsXu007Oi2CnNJe8o82vzzbpW7K28WHeFS9/Zxvi/nz3v26ePvC7Xplz2+x3iFu6SW0RFqFROhltHhigoPU1S4S5H7vx7eXj7L++/f/zWQVTlvzNvu/Zb3fY32f27blb2N3le1JUVFhWvfvnJ59j/Atiuv2795vO95tvY/VrJlV32M7znee1T+j+egx7r3v8bBX+Pw/d+j4S6X73s1bP/3v/fnIMyy1D6+jS44bZjCw8J873WHe0+o6fuhsZ47gjyAmmiM7xVlZWXasGGDrrrqqirXhw0bpoyMjIDu5XIdIXPs16lVtEa36lDj+3p/WcZGhysizK2YyHC5rAOfS6p2zd/nyR3iNO3MHrrVYyuvqEx7SiqUX1ymzbl79GtJuYrLK8NXwd4yFewt06595b5f8LvruKrS5bIU7arem2ZLleHF9+vXnwipdc/Kj95SmLtcVlGeXEW/yNr9i6x9hbKKd8kq+VVW6UHn15YXyVNepNLdwZmzZ34zmZr57RKdNV9Jaw7z+P/zc+1QkcOWVLj/A9XtLZ6mK8ee7XuvO9x7Qk3fD42Fu7ZtG99f4gBQE4WFhXK73Wrbtm2V6/Hx8crLywvoXm3a1O974QUnHFXl8z6dW1d7zG+v+XuMJHUPWlUADiUY7wmslgWAWvrtxHHbtplMDsA4wh0ABKh169YKCwvTzp1VN/nNz89XfHy8oaoAoBLhDgACFBkZqeOOO04fffRRlesff/yxUlJSDFUFAJXYCgUAamHChAmaNm2a+vTpo5SUFL388svKzc3V+PHjTZcGoIkj3AFALZxzzjkqLCzUggULtGPHDvXo0UNPPPGEEhMTTZcGoIkzts8dAAAAgo85dwAAACGEcAcAABBCCHcAAAAhhHAHAAAQQgh3aBReeOEFjRgxQn379tXYsWO1fv160yUBjuXUn6d169Zp8uTJGj58uJKTk7Vq1SrTJdXY448/rgsuuEApKSkaMmSIpkyZoq1bt5ouq8ZefPFFjRo1SgMHDtTAgQP1xz/+Uf/5z39Ml1Urjz/+uJKTkzVr1izTpdTIo48+quTk5Cofw4YNq9M9CXcw7u2339bs2bN1zTXXKD09XYMGDdKkSZOUk5NjujTAcZz881RcXKzk5GTdddddpksJ2Nq1a3XJJZdo+fLlWrp0qdxutyZOnKji4mLTpdVIhw4ddOutt+rVV1/Vq6++qpNOOknXXnuttmzZYrq0gHz55Zd6+eWXlZycbLqUgHTv3l1r1qzxfbzxxht1uh9bocC4cePGqXfv3rrnnnt8184++2yNHDlSt9xyi8HKAOcJlZ+n5ORkPfbYYxo5cqTpUmqloKBAQ4YM0fPPP6/jjz/edDm1csIJJ2jq1KkaN26c6VJqZO/evRo7dqzuvvtuLVy4UD179tSdd95puqwjevTRR7Vq1Sq99tprQbsnPXcwqqysTBs2bNDw4cOrXB82bJgyMjIMVQU4Ez9PjceePXskSS1btjRcSeDcbrfeeustFRcXO+o4vRkzZig1NVVDhw41XUrAfvrpJw0fPlwjRozQTTfdpO3bt9fpfpxQAaMKCwvldrvVtm3bKtfj4+OVl5dnqCrAmfh5ahxs29bs2bM1aNAg9ejRw3Q5NbZ582aNHz9epaWliomJ0WOPPaZjjz3WdFk18tZbb2njxo1KS0szXUrA+vXrpzlz5uioo45Sfn6+Fi5cqPHjx+vNN99U69ata3VPwh0aBcuyqnxu23a1awBqhp8ns2bMmKFvv/1WL774oulSAtKtWzelp6dr9+7devfdd3Xbbbfp+eefb/QBLzc3V7NmzdJTTz2lqKgo0+UELDU1tcrnAwYM0Omnn6709HRNmDChVvck3MGo1q1bKywsTDt37qxyPT8/X/Hx8YaqApyJnyfz7r33Xq1evVrPP/+8OnToYLqcgERGRqpr166SpL59++qrr77Ss88+qxkzZhiu7PA2bNig/Px8jR071nfN7XZr3bp1euGFF/TVV18pLCzMYIWBiYmJUY8ePfTjjz/W+h7MuYNRkZGROu644/TRRx9Vuf7xxx87aq4H0Bjw82SObduaMWOG3n33XT3zzDPq3Lmz6ZLqzLZtlZWVmS7jiE466SS98cYbSk9P93306dNHo0aNUnp6uqOCnVQ5d/b7779XQkJCre9Bzx2MmzBhgqZNm6Y+ffooJSVFL7/8snJzczV+/HjTpQGO4+Sfp71792rbtm2+z7OysvTNN9+oZcuW6tSpk8HKjuyee+7Rm2++qQULFqh58+a+OY5xcXGKjo42XN2RzZ07V6eccoo6dOigvXv36u2339batWv15JNPmi7tiGJjY6vNbYyJiVGrVq0cMedxzpw5OvXUU9WxY0cVFBRo4cKFKioq0pgxY2p9T8IdjDvnnHNUWFioBQsWaMeOHerRo4eeeOIJJSYmmi4NcBwn/zx9/fXXuuyyy3yfz549W5I0ZswY3X///abKqpFly5ZJki699NIq12fPnl1luLCx2rlzp6ZNm6YdO3YoLi5OycnJevLJJ+u8mS6O7Oeff9bNN9+sXbt2qXXr1howYICWL19ep59Z9rkDAAAIIcy5AwAACCGEOwAAgBBCuAMAAAghhDsAAIAQQrgDAAAIIYQ7AACAEEK4AwAACCGEOwAAgCBYt26dJk+erOHDhys5OVmrVq0K+B62bWvJkiU688wz1adPH6WmpmrRokUB3YMTKgAAAIKguLhYycnJGjt2rP7yl7/U6h6zZs3SmjVrNG3aNPXo0UNFRUUqLCwM6B703AEAQs6nn36q5ORk7d69u9r14cOHy3s4U25uru644w4NHz5cffr00amnnqqZM2ce8ZfpihUrlJycrOTkZPXq1UvHH3+8xo0bp/nz52vPnj0B1ZqVlaXk5GR98803gf0j/Xj00Ud9dSUnJ2vQoEG6+OKLtXbt2oDuc+mll2rWrFl1rqepSU1N1U033aQzzjjDb3tZWZkeeOABnXzyyRowYIDGjRunTz/91Nf+/fffa9myZVqwYIFOO+00de7cWb169dLQoUMDqoOeOwBAk7F69WqNGDFClmVp+/bt+uMf/6ijjjpKc+fOVVJSkrZs2aIHH3xQH374oV5++WW1atXqkPeKjY3VO++8I9u2tWfPHn3++ed64okntGLFCi1btkzt27dvuH/YQbp3766lS5dKkn799VctWbJEV199tf773/8qLi7OSE2oNH36dGVnZ2vevHlq166d3nvvPV155ZV64403dNRRR2n16tVKSkrSBx98oCuvvFKSNGTIEE2dOvWw34u/Rc8dAKDJ8IY7SbrnnnsUERGhp556SieccII6deqk1NRULV26VL/88ovmzZt32HtZlqWEhAS1a9dOxxxzjMaNG6eXXnpJxcXFevDBB32P++9//6uLLrpIgwcP1oknnqirr75a27Zt87WfdtppkqTRo0crOTlZl156qSTpyy+/1IQJE3TiiSdq0KBB+tOf/qQNGzYc8d8YFhamhIQEJSQk6Nhjj9X111+v4uJi/fDDD5IqA8bVV19d5TkVFRUaNmyY0tLSdPvtt2vt2rV69tlnfT2AWVlZkqTvvvtOkyZNUkpKioYOHaqpU6eqoKDAd5933nlHo0aNUr9+/XTiiSfqz3/+s4qLi49Yc1Owbds2vfXWW3rkkUc0ePBgdenSRRMnTtSgQYO0YsUKSdL27duVk5Ojd955Rw888IBmz56tDRs26Prrrw/otQh3AIAmYcuWLdq5c6eGDBmiXbt2ac2aNbr44osVHR1d5XEJCQkaNWqU/u///s83fFtTbdu21ahRo7R69Wq53W5J0r59+zRhwgSlpaXp6aeflmVZuvbaa+XxeCRJr7zyiiTp6aef1po1a/Too49Kkvbu3avRo0frxRdf1PLly9W1a1ddddVVKioqqnE9ZWVlWrFihVq0aKGjjz5akjRu3Dh9+OGH2rFjh+9x//nPf1RcXKyzzz5bd955p1JSUvSHP/xBa9as0Zo1a9SxY0ft2LFDf/rTn9SrVy+lpaXpySefVH5+vm688UZJ0o4dO3TLLbfoggsu0Ntvv61nn31Wp59+esBfw1C1YcMG2bats846SykpKb6PdevW+cK+bdsqKyvTnDlzfH8MzJo1S59++qm2bt1a49diWBYA0CS8//77Gj58uKKiorRp0ybZtq1jjjnG72OPOeYY/frrryooKFDbtm0Dep2jjz5ae/fu1a5du9S2bVudeeaZVdrvu+8+DRkyRN9995169OihNm3aSJJatWqlhIQE3+OGDBlS5XkzZszQ8ccfr3Xr1unUU0895Ot/++23SklJkVQZLJs3b6558+YpNjZWkjRw4EB169ZNr732miZNmiSpcg7hWWedpebNm0uSIiIiFB0dXaWeZcuW6bjjjtPNN99c5d+SmpqqH374QcXFxaqoqNDpp5+uxMRESVJycnJAX7tQZtu2wsLC9OqrryosLKxKW0xMjKTKPyzCw8PVrVs3X5v3ezQ3N9cX0I+EcAcAaBLef/99XXzxxTV6rLe3ybKsgF/nt8/dtm2bHnnkEX3xxRcqLCysspijR48eh7xPfn6+HnnkEX366afauXOnPB6P9u3bp5ycnMO+frdu3bRw4UJJlb1/b7/9tm644QY9++yz6tu3r6TK3ruXX35ZkyZNUn5+vv7zn//o6aefPux9N2zYoE8//dQXHA+2bds2DR8+XEOGDNGoUaM0fPhwDR8+XGeeeaZatmx52Ps2Fb169ZLb7VZBQYEGDx7s9zEDBw5URUWFtm3bpi5dukiSfvzxR0lSp06davxahDsAQMjLy8vTxo0blZqaKknq0qWLLMvSd999p5EjR1Z7/NatW9WyZUu1bt064NfaunWrYmNjfRPgJ0+erI4dO2rmzJlq166dPB6Pzj33XJWXlx/2PrfffrsKCgp0xx13qFOnToqMjNQf//jHIz4vIiJCXbt29X3eu3dvrVq1Ss8884z+8Y9/SJLOP/98/eMf/1BGRoa++OILJSYmHjJweHk8Hp166qm69dZbq7UlJCQoLCxMS5cu1eeff66PPvpIzz33nObNm6fly5erc+fOh713qNi7d2+V+ZRZWVn65ptv1LJlS3Xr1k2jRo3StGnTdPvtt6tXr14qLCzU//73PyUnJys1NVVDhw7VcccdpzvuuEN33HGHPB6PZsyYoWHDhlXpzTsS5twBAELe6tWrNWDAAN8QaOvWrTVs2DC9+OKLKikpqfLYvLw8vfHGGzr77LMD7rnLz8/XG2+8oZEjR8rlcqmwsFDff/+9rrnmGg0ZMsQ33HuwiIgISfLN0fNav369Lr30UqWmpqp79+6KjIwMeL8zr7CwMJWWlvo+b926tUaOHKkVK1ZoxYoVGjt2bLWavHMCvY477jht2bJFiYmJ6tq1a5UP77CiZVkaNGiQrr/+eqWnpysiIqJWG/k61ddff63Ro0dr9OjRkqTZs2dr9OjR+uc//1nl8/vvv19nn322pkyZoi+//FIdOnSQJLlcLi1cuFCtW7fWJZdcoquvvlrHHHOM5s6dG1Ad9NwBAELe6tWrfatSvf72t79p/Pjxmjhxom688cYqW6G0b99eN91002Hvadu28vLyfFuhZGRk6PHHH1dcXJxuueUWSVLLli3VqlUrvfzyy0pISFBOTo4eeuihKvdp27atoqOj9eGHH6pDhw6KiopSXFycunbtqtdff119+/ZVUVGRHnjggWqLP/xxu93Ky8uTdGBY1rvK9WDjxo3T1VdfLY/H4wsjXomJicrMzFRWVpZiYmLUqlUrXXzxxVq+fLluvvlmXXnllWrdurV++uknvfXWW5o5c6a+/vprffLJJxo2bJjatm2rzMxMFRQU1HieWCg48cQTtXnz5kO2R0RE6Prrrz/s6tf27dv7FtXUFuEOABDSiouL9cknn2j69OlVrh911FF69dVXNX/+fN10003atWuX4uPjNXLkSF177bVH3FesqKhIw4cPl2VZio2NVbdu3TR69GhdfvnlvsULLpdL8+bN08yZM3XuueeqW7du+utf/+rb7kSSwsPD9de//lWPPfaY/vnPf2rw4MF67rnndN999+lvf/ubRo8erU6dOummm27SAw88cMR/75YtWzR8+HBJUrNmzdS5c2f9/e9/rxbghg4dqnbt2unYY4+ttiffFVdcodtvv12///3vVVJSovfff19JSUlatmyZ/vGPf2jixIkqKytTp06ddPLJJ8vlcik2Nlbr1q3TM888o6KiInXq1Em33367bygcDceyWaMMAAhh7777rh5++GG9/fbbpktpVPbt26eTTz5Z99133yFPVIAz0XMHAAhpMTExfhcBNFUej0d5eXlaunSp4uLifJs6I3TQcwcAQBOSlZWl0047TR06dND9999fbT89OB/hDgAAIISwFQoAAEAIIdwBAACEEMIdAABACCHcAQAAhBDCHQAAQAgh3AEAAIQQwh0AAEAIIdwBAACEkP8Hj9PcD8DCJQMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(train, ' I/O Data Bytes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c4f4c9bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:13.808517Z",
     "iopub.status.busy": "2023-03-19T00:35:13.808113Z",
     "iopub.status.idle": "2023-03-19T00:35:13.814324Z",
     "shell.execute_reply": "2023-03-19T00:35:13.813448Z"
    },
    "papermill": {
     "duration": 0.026254,
     "end_time": "2023-03-19T00:35:13.816461",
     "exception": false,
     "start_time": "2023-03-19T00:35:13.790207",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def find_skewed_boundaries(df, variable, distance):\n",
    "\n",
    "    # Let's calculate the boundaries outside which sit the outliers\n",
    "    # for skewed distributions\n",
    "\n",
    "    # distance passed as an argument, gives us the option to\n",
    "    # estimate 1.5 times or 3 times the IQR to calculate\n",
    "    # the boundaries.\n",
    "\n",
    "    IQR = df[variable].quantile(0.75) - df[variable].quantile(0.25)\n",
    "\n",
    "    lower_boundary = df[variable].quantile(0.25) - (IQR * distance)\n",
    "    upper_boundary = df[variable].quantile(0.75) + (IQR * distance)\n",
    "\n",
    "    return upper_boundary, lower_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b27626c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:13.851680Z",
     "iopub.status.busy": "2023-03-19T00:35:13.850612Z",
     "iopub.status.idle": "2023-03-19T00:35:15.347457Z",
     "shell.execute_reply": "2023-03-19T00:35:15.346216Z"
    },
    "papermill": {
     "duration": 1.517263,
     "end_time": "2023-03-19T00:35:15.350163",
     "exception": false,
     "start_time": "2023-03-19T00:35:13.832900",
     "status": "completed"
    },
    "tags": []
   },
   "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>ID</th>\n",
       "      <th>I/O Data Operations</th>\n",
       "      <th>I/O Data Bytes</th>\n",
       "      <th>Number of subprocesses</th>\n",
       "      <th>Time on processor</th>\n",
       "      <th>Disk Reading/sec</th>\n",
       "      <th>Bytes Sent/sent</th>\n",
       "      <th>Received Bytes (HTTP)</th>\n",
       "      <th>Network packets sent</th>\n",
       "      <th>Network packets received</th>\n",
       "      <th>Pages Read/sec</th>\n",
       "      <th>Page Errors/sec</th>\n",
       "      <th>Confirmed byte radius</th>\n",
       "      <th>Label</th>\n",
       "      <th>I/O_bytes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_UD3TM0ZYND</td>\n",
       "      <td>14.154240</td>\n",
       "      <td>5182.451722</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.138876</td>\n",
       "      <td>5.399498</td>\n",
       "      <td>22.886762</td>\n",
       "      <td>29.508369</td>\n",
       "      <td>0.422183</td>\n",
       "      <td>0.444403</td>\n",
       "      <td>0.044440</td>\n",
       "      <td>1089.121009</td>\n",
       "      <td>30.150941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_XAG1HC0HWM</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>120.821253</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>1.577765</td>\n",
       "      <td>11.733239</td>\n",
       "      <td>12.888786</td>\n",
       "      <td>0.177776</td>\n",
       "      <td>0.155554</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>322.464081</td>\n",
       "      <td>28.163287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_CEXD05IR09</td>\n",
       "      <td>31.369916</td>\n",
       "      <td>127228.251100</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>15.981107</td>\n",
       "      <td>602.125436</td>\n",
       "      <td>6976.986795</td>\n",
       "      <td>3.311801</td>\n",
       "      <td>5.756755</td>\n",
       "      <td>5.623394</td>\n",
       "      <td>12015.169690</td>\n",
       "      <td>34.204404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_X6E97FT8IF</td>\n",
       "      <td>5.046181</td>\n",
       "      <td>1091.620117</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.069468</td>\n",
       "      <td>1.356022</td>\n",
       "      <td>16.138887</td>\n",
       "      <td>13.271234</td>\n",
       "      <td>0.155609</td>\n",
       "      <td>0.133379</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>550.989619</td>\n",
       "      <td>31.466889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ID_J1YVGQ3RS4</td>\n",
       "      <td>6.891100</td>\n",
       "      <td>1694.254741</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>1.022550</td>\n",
       "      <td>87.717034</td>\n",
       "      <td>94.385841</td>\n",
       "      <td>0.733569</td>\n",
       "      <td>0.733569</td>\n",
       "      <td>0.022229</td>\n",
       "      <td>320.636215</td>\n",
       "      <td>27.690737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8903</th>\n",
       "      <td>ID_XJWM9B7X4L</td>\n",
       "      <td>26.769658</td>\n",
       "      <td>6965.281426</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.155955</td>\n",
       "      <td>1.497184</td>\n",
       "      <td>252.525113</td>\n",
       "      <td>589.072219</td>\n",
       "      <td>0.858386</td>\n",
       "      <td>0.758573</td>\n",
       "      <td>0.019962</td>\n",
       "      <td>1177.825039</td>\n",
       "      <td>30.875026</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8904</th>\n",
       "      <td>ID_YIC5VRPLUD</td>\n",
       "      <td>40.217048</td>\n",
       "      <td>125363.828305</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>28.089513</td>\n",
       "      <td>27.267273</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>0.355563</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>221.249363</td>\n",
       "      <td>23.503856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>ID_T3RF72SEG1</td>\n",
       "      <td>0.866827</td>\n",
       "      <td>853.957842</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>4.378587</td>\n",
       "      <td>20.559356</td>\n",
       "      <td>26.093712</td>\n",
       "      <td>0.333395</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.022226</td>\n",
       "      <td>262.270699</td>\n",
       "      <td>21.060478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8906</th>\n",
       "      <td>ID_2AUQFJYWKV</td>\n",
       "      <td>0.044430</td>\n",
       "      <td>16.527885</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.888596</td>\n",
       "      <td>74.375484</td>\n",
       "      <td>116.650437</td>\n",
       "      <td>0.555372</td>\n",
       "      <td>0.377653</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>268.222697</td>\n",
       "      <td>24.775056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>ID_LVH8ZOJXLJ</td>\n",
       "      <td>1.155529</td>\n",
       "      <td>192.706713</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.555543</td>\n",
       "      <td>82.064794</td>\n",
       "      <td>61.909698</td>\n",
       "      <td>1.044421</td>\n",
       "      <td>0.866647</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>289.482282</td>\n",
       "      <td>31.735209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7946 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  I/O Data Operations   I/O Data Bytes  \\\n",
       "1     ID_UD3TM0ZYND            14.154240      5182.451722   \n",
       "2     ID_XAG1HC0HWM             0.044444       120.821253   \n",
       "3     ID_CEXD05IR09            31.369916    127228.251100   \n",
       "4     ID_X6E97FT8IF             5.046181      1091.620117   \n",
       "5     ID_J1YVGQ3RS4             6.891100      1694.254741   \n",
       "...             ...                  ...              ...   \n",
       "8903  ID_XJWM9B7X4L            26.769658      6965.281426   \n",
       "8904  ID_YIC5VRPLUD            40.217048    125363.828305   \n",
       "8905  ID_T3RF72SEG1             0.866827       853.957842   \n",
       "8906  ID_2AUQFJYWKV             0.044430        16.527885   \n",
       "8907  ID_LVH8ZOJXLJ             1.155529       192.706713   \n",
       "\n",
       "      Number of subprocesses  Time on processor  Disk Reading/sec  \\\n",
       "1                       28.0           0.138876          5.399498   \n",
       "2                       28.0           0.842762          1.577765   \n",
       "3                       31.0           0.523750         15.981107   \n",
       "4                       25.0           0.069468          1.356022   \n",
       "5                       27.0           0.842762          1.022550   \n",
       "...                      ...                ...               ...   \n",
       "8903                    25.0           0.155955          1.497184   \n",
       "8904                    27.0           0.842762          0.400009   \n",
       "8905                    29.0           0.842762          4.378587   \n",
       "8906                    28.0           0.842762          0.888596   \n",
       "8907                    27.0           0.842762          0.555543   \n",
       "\n",
       "      Bytes Sent/sent  Received Bytes (HTTP)  Network packets sent  \\\n",
       "1           22.886762              29.508369              0.422183   \n",
       "2           11.733239              12.888786              0.177776   \n",
       "3          602.125436            6976.986795              3.311801   \n",
       "4           16.138887              13.271234              0.155609   \n",
       "5           87.717034              94.385841              0.733569   \n",
       "...               ...                    ...                   ...   \n",
       "8903       252.525113             589.072219              0.858386   \n",
       "8904        28.089513              27.267273              0.400009   \n",
       "8905        20.559356              26.093712              0.333395   \n",
       "8906        74.375484             116.650437              0.555372   \n",
       "8907        82.064794              61.909698              1.044421   \n",
       "\n",
       "      Network packets received  Pages Read/sec  Page Errors/sec  \\\n",
       "1                     0.444403        0.044440      1089.121009   \n",
       "2                     0.155554        0.044444       322.464081   \n",
       "3                     5.756755        5.623394     12015.169690   \n",
       "4                     0.133379        1.671685       550.989619   \n",
       "5                     0.733569        0.022229       320.636215   \n",
       "...                        ...             ...              ...   \n",
       "8903                  0.758573        0.019962      1177.825039   \n",
       "8904                  0.355563        1.671685       221.249363   \n",
       "8905                  0.355621        0.022226       262.270699   \n",
       "8906                  0.377653        1.671685       268.222697   \n",
       "8907                  0.866647        1.671685       289.482282   \n",
       "\n",
       "      Confirmed byte radius  Label  I/O_bytes  \n",
       "1                 30.150941    NaN   0.002731  \n",
       "2                 28.163287    NaN   0.000368  \n",
       "3                 34.204404    NaN   0.000247  \n",
       "4                 31.466889    1.0   0.004623  \n",
       "5                 27.690737    NaN   0.004067  \n",
       "...                     ...    ...        ...  \n",
       "8903              30.875026    1.0   0.003843  \n",
       "8904              23.503856    NaN   0.000321  \n",
       "8905              21.060478    NaN   0.001015  \n",
       "8906              24.775056    NaN   0.002688  \n",
       "8907              31.735209    NaN   0.005996  \n",
       "\n",
       "[7946 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_outliers(df, threshold=3):\n",
    "    # Calculate Z-score for each column in the DataFrame\n",
    "    z_scores = np.abs((df - df.mean()) / df.std())\n",
    "    \n",
    "    # Identify rows with any Z-score greater than the threshold\n",
    "    outliers = z_scores.apply(lambda x: np.any(x > threshold), axis=1)\n",
    "    \n",
    "    # Remove outliers from DataFrame\n",
    "    df = df[~outliers]\n",
    "    \n",
    "    return df\n",
    "remove_outliers(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f745714",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:15.387235Z",
     "iopub.status.busy": "2023-03-19T00:35:15.385950Z",
     "iopub.status.idle": "2023-03-19T00:35:15.758951Z",
     "shell.execute_reply": "2023-03-19T00:35:15.757838Z"
    },
    "papermill": {
     "duration": 0.394417,
     "end_time": "2023-03-19T00:35:15.761623",
     "exception": false,
     "start_time": "2023-03-19T00:35:15.367206",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def detect_and_plot_outliers(df, column_name, threshold=1):\n",
    "\n",
    "\n",
    "    # Calculate the Z-scores for the column\n",
    "    z_scores = (df[column_name] - df[column_name].mean()) / df[column_name].std()\n",
    "\n",
    "    # Identify the outliers using the threshold\n",
    "    outliers = df[np.abs(z_scores) > threshold]\n",
    "\n",
    "    # Remove the outliers from the dataset\n",
    "    df = df[np.abs(z_scores) <= threshold]\n",
    "\n",
    "    # Plot the original and modified columns\n",
    "    fig, ax = plt.subplots(1, 2, figsize=(10,5))\n",
    "\n",
    "    ax[0].boxplot(df[column_name], vert=False)\n",
    "    ax[0].set_title(\"Original\")\n",
    "\n",
    "    ax[1].boxplot(df[column_name], vert=False)\n",
    "    ax[1].set_title(\"Outliers Removed\")\n",
    "\n",
    "    plt.show()\n",
    "detect_and_plot_outliers(train, \"I/O Data Operations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f1437e9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:15.799251Z",
     "iopub.status.busy": "2023-03-19T00:35:15.798125Z",
     "iopub.status.idle": "2023-03-19T00:35:16.312512Z",
     "shell.execute_reply": "2023-03-19T00:35:16.311224Z"
    },
    "papermill": {
     "duration": 0.536227,
     "end_time": "2023-03-19T00:35:16.315213",
     "exception": false,
     "start_time": "2023-03-19T00:35:15.778986",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_plot(train, 'I/O Data Operations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "20423423",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:16.354011Z",
     "iopub.status.busy": "2023-03-19T00:35:16.353266Z",
     "iopub.status.idle": "2023-03-19T00:35:16.361192Z",
     "shell.execute_reply": "2023-03-19T00:35:16.359686Z"
    },
    "papermill": {
     "duration": 0.030181,
     "end_time": "2023-03-19T00:35:16.363557",
     "exception": false,
     "start_time": "2023-03-19T00:35:16.333376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['I/O Data Operations', ' I/O Data Bytes', 'Number of subprocesses', 'Time on processor', 'Disk Reading/sec', 'Bytes Sent/sent', 'Received Bytes (HTTP)', 'Network packets sent', 'Network packets received', 'Pages Read/sec', 'Page Errors/sec', 'Confirmed byte radius', 'I/O_bytes']\n"
     ]
    }
   ],
   "source": [
    "unwanted = [\"ID\", \"Label\"]\n",
    "columns = [col for col in train.columns if col not in unwanted]\n",
    "print(columns)\n",
    "from sklearn.preprocessing import FunctionTransformer, PowerTransformer\n",
    "transformer = FunctionTransformer(np.log, validate=True)\n",
    "# train= transformer.transform(train[columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "65f56eca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:16.404695Z",
     "iopub.status.busy": "2023-03-19T00:35:16.403380Z",
     "iopub.status.idle": "2023-03-19T00:35:16.430914Z",
     "shell.execute_reply": "2023-03-19T00:35:16.429567Z"
    },
    "papermill": {
     "duration": 0.051383,
     "end_time": "2023-03-19T00:35:16.433536",
     "exception": false,
     "start_time": "2023-03-19T00:35:16.382153",
     "status": "completed"
    },
    "tags": []
   },
   "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>ID</th>\n",
       "      <th>I/O Data Operations</th>\n",
       "      <th>I/O Data Bytes</th>\n",
       "      <th>Number of subprocesses</th>\n",
       "      <th>Time on processor</th>\n",
       "      <th>Disk Reading/sec</th>\n",
       "      <th>Bytes Sent/sent</th>\n",
       "      <th>Received Bytes (HTTP)</th>\n",
       "      <th>Network packets sent</th>\n",
       "      <th>Network packets received</th>\n",
       "      <th>Pages Read/sec</th>\n",
       "      <th>Page Errors/sec</th>\n",
       "      <th>Confirmed byte radius</th>\n",
       "      <th>Label</th>\n",
       "      <th>I/O_bytes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_4W8AP96UO6</td>\n",
       "      <td>114.798900</td>\n",
       "      <td>3790.450939</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.427078</td>\n",
       "      <td>6.162365</td>\n",
       "      <td>58.492773</td>\n",
       "      <td>63.959387</td>\n",
       "      <td>0.621206</td>\n",
       "      <td>0.521813</td>\n",
       "      <td>24.624612</td>\n",
       "      <td>1001.533430</td>\n",
       "      <td>27.190843</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_UD3TM0ZYND</td>\n",
       "      <td>14.154240</td>\n",
       "      <td>5182.451722</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.138876</td>\n",
       "      <td>5.399498</td>\n",
       "      <td>22.886762</td>\n",
       "      <td>29.508369</td>\n",
       "      <td>0.422183</td>\n",
       "      <td>0.444403</td>\n",
       "      <td>0.044440</td>\n",
       "      <td>1089.121009</td>\n",
       "      <td>30.150941</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_XAG1HC0HWM</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>120.821253</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>1.577765</td>\n",
       "      <td>11.733239</td>\n",
       "      <td>12.888786</td>\n",
       "      <td>0.177776</td>\n",
       "      <td>0.155554</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>322.464081</td>\n",
       "      <td>28.163287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_CEXD05IR09</td>\n",
       "      <td>31.369916</td>\n",
       "      <td>127228.251100</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>15.981107</td>\n",
       "      <td>602.125436</td>\n",
       "      <td>6976.986795</td>\n",
       "      <td>3.311801</td>\n",
       "      <td>5.756755</td>\n",
       "      <td>5.623394</td>\n",
       "      <td>12015.169690</td>\n",
       "      <td>34.204404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_X6E97FT8IF</td>\n",
       "      <td>5.046181</td>\n",
       "      <td>1091.620117</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.069468</td>\n",
       "      <td>1.356022</td>\n",
       "      <td>16.138887</td>\n",
       "      <td>13.271234</td>\n",
       "      <td>0.155609</td>\n",
       "      <td>0.133379</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>550.989619</td>\n",
       "      <td>31.466889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8903</th>\n",
       "      <td>ID_XJWM9B7X4L</td>\n",
       "      <td>26.769658</td>\n",
       "      <td>6965.281426</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.155955</td>\n",
       "      <td>1.497184</td>\n",
       "      <td>252.525113</td>\n",
       "      <td>589.072219</td>\n",
       "      <td>0.858386</td>\n",
       "      <td>0.758573</td>\n",
       "      <td>0.019962</td>\n",
       "      <td>1177.825039</td>\n",
       "      <td>30.875026</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8904</th>\n",
       "      <td>ID_YIC5VRPLUD</td>\n",
       "      <td>40.217048</td>\n",
       "      <td>125363.828305</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>28.089513</td>\n",
       "      <td>27.267273</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>0.355563</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>221.249363</td>\n",
       "      <td>23.503856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>ID_T3RF72SEG1</td>\n",
       "      <td>0.866827</td>\n",
       "      <td>853.957842</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>4.378587</td>\n",
       "      <td>20.559356</td>\n",
       "      <td>26.093712</td>\n",
       "      <td>0.333395</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.022226</td>\n",
       "      <td>262.270699</td>\n",
       "      <td>21.060478</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8906</th>\n",
       "      <td>ID_2AUQFJYWKV</td>\n",
       "      <td>0.044430</td>\n",
       "      <td>16.527885</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.888596</td>\n",
       "      <td>74.375484</td>\n",
       "      <td>116.650437</td>\n",
       "      <td>0.555372</td>\n",
       "      <td>0.377653</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>268.222697</td>\n",
       "      <td>24.775056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>ID_LVH8ZOJXLJ</td>\n",
       "      <td>1.155529</td>\n",
       "      <td>192.706713</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.555543</td>\n",
       "      <td>82.064794</td>\n",
       "      <td>61.909698</td>\n",
       "      <td>1.044421</td>\n",
       "      <td>0.866647</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>289.482282</td>\n",
       "      <td>31.735209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8908 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  I/O Data Operations   I/O Data Bytes  \\\n",
       "0     ID_4W8AP96UO6           114.798900      3790.450939   \n",
       "1     ID_UD3TM0ZYND            14.154240      5182.451722   \n",
       "2     ID_XAG1HC0HWM             0.044444       120.821253   \n",
       "3     ID_CEXD05IR09            31.369916    127228.251100   \n",
       "4     ID_X6E97FT8IF             5.046181      1091.620117   \n",
       "...             ...                  ...              ...   \n",
       "8903  ID_XJWM9B7X4L            26.769658      6965.281426   \n",
       "8904  ID_YIC5VRPLUD            40.217048    125363.828305   \n",
       "8905  ID_T3RF72SEG1             0.866827       853.957842   \n",
       "8906  ID_2AUQFJYWKV             0.044430        16.527885   \n",
       "8907  ID_LVH8ZOJXLJ             1.155529       192.706713   \n",
       "\n",
       "      Number of subprocesses  Time on processor  Disk Reading/sec  \\\n",
       "0                       28.0           0.427078          6.162365   \n",
       "1                       28.0           0.138876          5.399498   \n",
       "2                       28.0           0.842762          1.577765   \n",
       "3                       31.0           0.523750         15.981107   \n",
       "4                       25.0           0.069468          1.356022   \n",
       "...                      ...                ...               ...   \n",
       "8903                    25.0           0.155955          1.497184   \n",
       "8904                    27.0           0.842762          0.400009   \n",
       "8905                    29.0           0.842762          4.378587   \n",
       "8906                    28.0           0.842762          0.888596   \n",
       "8907                    27.0           0.842762          0.555543   \n",
       "\n",
       "      Bytes Sent/sent  Received Bytes (HTTP)  Network packets sent  \\\n",
       "0           58.492773              63.959387              0.621206   \n",
       "1           22.886762              29.508369              0.422183   \n",
       "2           11.733239              12.888786              0.177776   \n",
       "3          602.125436            6976.986795              3.311801   \n",
       "4           16.138887              13.271234              0.155609   \n",
       "...               ...                    ...                   ...   \n",
       "8903       252.525113             589.072219              0.858386   \n",
       "8904        28.089513              27.267273              0.400009   \n",
       "8905        20.559356              26.093712              0.333395   \n",
       "8906        74.375484             116.650437              0.555372   \n",
       "8907        82.064794              61.909698              1.044421   \n",
       "\n",
       "      Network packets received  Pages Read/sec  Page Errors/sec  \\\n",
       "0                     0.521813       24.624612      1001.533430   \n",
       "1                     0.444403        0.044440      1089.121009   \n",
       "2                     0.155554        0.044444       322.464081   \n",
       "3                     5.756755        5.623394     12015.169690   \n",
       "4                     0.133379        1.671685       550.989619   \n",
       "...                        ...             ...              ...   \n",
       "8903                  0.758573        0.019962      1177.825039   \n",
       "8904                  0.355563        1.671685       221.249363   \n",
       "8905                  0.355621        0.022226       262.270699   \n",
       "8906                  0.377653        1.671685       268.222697   \n",
       "8907                  0.866647        1.671685       289.482282   \n",
       "\n",
       "      Confirmed byte radius  Label  I/O_bytes  \n",
       "0                 27.190843    1.0   0.030286  \n",
       "1                 30.150941    NaN   0.002731  \n",
       "2                 28.163287    NaN   0.000368  \n",
       "3                 34.204404    NaN   0.000247  \n",
       "4                 31.466889    1.0   0.004623  \n",
       "...                     ...    ...        ...  \n",
       "8903              30.875026    1.0   0.003843  \n",
       "8904              23.503856    NaN   0.000321  \n",
       "8905              21.060478    NaN   0.001015  \n",
       "8906              24.775056    NaN   0.002688  \n",
       "8907              31.735209    NaN   0.005996  \n",
       "\n",
       "[8908 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a5b202c",
   "metadata": {
    "papermill": {
     "duration": 0.018565,
     "end_time": "2023-03-19T00:35:16.470713",
     "exception": false,
     "start_time": "2023-03-19T00:35:16.452148",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# ***MODELLING***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5dafe888",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:16.511504Z",
     "iopub.status.busy": "2023-03-19T00:35:16.510719Z",
     "iopub.status.idle": "2023-03-19T00:35:16.519533Z",
     "shell.execute_reply": "2023-03-19T00:35:16.518612Z"
    },
    "papermill": {
     "duration": 0.032434,
     "end_time": "2023-03-19T00:35:16.522098",
     "exception": false,
     "start_time": "2023-03-19T00:35:16.489664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train[\"Label\"] = train[\"Label\"].fillna(0)\n",
    "features = train.drop(columns=['ID','Label']).columns\n",
    "target = 'Label'\n",
    "\n",
    "# transform data\n",
    "X = train[features]\n",
    "y = train[\"Label\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "915959d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:16.564656Z",
     "iopub.status.busy": "2023-03-19T00:35:16.563823Z",
     "iopub.status.idle": "2023-03-19T00:35:16.587759Z",
     "shell.execute_reply": "2023-03-19T00:35:16.586362Z"
    },
    "papermill": {
     "duration": 0.049605,
     "end_time": "2023-03-19T00:35:16.590590",
     "exception": false,
     "start_time": "2023-03-19T00:35:16.540985",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>I/O Data Operations</th>\n",
       "      <th>I/O Data Bytes</th>\n",
       "      <th>Number of subprocesses</th>\n",
       "      <th>Time on processor</th>\n",
       "      <th>Disk Reading/sec</th>\n",
       "      <th>Bytes Sent/sent</th>\n",
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       "      <th>Confirmed byte radius</th>\n",
       "      <th>I/O_bytes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114.798900</td>\n",
       "      <td>3790.450939</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.427078</td>\n",
       "      <td>6.162365</td>\n",
       "      <td>58.492773</td>\n",
       "      <td>63.959387</td>\n",
       "      <td>0.621206</td>\n",
       "      <td>0.521813</td>\n",
       "      <td>24.624612</td>\n",
       "      <td>1001.533430</td>\n",
       "      <td>27.190843</td>\n",
       "      <td>0.030286</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.154240</td>\n",
       "      <td>5182.451722</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.138876</td>\n",
       "      <td>5.399498</td>\n",
       "      <td>22.886762</td>\n",
       "      <td>29.508369</td>\n",
       "      <td>0.422183</td>\n",
       "      <td>0.444403</td>\n",
       "      <td>0.044440</td>\n",
       "      <td>1089.121009</td>\n",
       "      <td>30.150941</td>\n",
       "      <td>0.002731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.044444</td>\n",
       "      <td>120.821253</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>1.577765</td>\n",
       "      <td>11.733239</td>\n",
       "      <td>12.888786</td>\n",
       "      <td>0.177776</td>\n",
       "      <td>0.155554</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>322.464081</td>\n",
       "      <td>28.163287</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.369916</td>\n",
       "      <td>127228.251100</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.523750</td>\n",
       "      <td>15.981107</td>\n",
       "      <td>602.125436</td>\n",
       "      <td>6976.986795</td>\n",
       "      <td>3.311801</td>\n",
       "      <td>5.756755</td>\n",
       "      <td>5.623394</td>\n",
       "      <td>12015.169690</td>\n",
       "      <td>34.204404</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.046181</td>\n",
       "      <td>1091.620117</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.069468</td>\n",
       "      <td>1.356022</td>\n",
       "      <td>16.138887</td>\n",
       "      <td>13.271234</td>\n",
       "      <td>0.155609</td>\n",
       "      <td>0.133379</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>550.989619</td>\n",
       "      <td>31.466889</td>\n",
       "      <td>0.004623</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>8903</th>\n",
       "      <td>26.769658</td>\n",
       "      <td>6965.281426</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.155955</td>\n",
       "      <td>1.497184</td>\n",
       "      <td>252.525113</td>\n",
       "      <td>589.072219</td>\n",
       "      <td>0.858386</td>\n",
       "      <td>0.758573</td>\n",
       "      <td>0.019962</td>\n",
       "      <td>1177.825039</td>\n",
       "      <td>30.875026</td>\n",
       "      <td>0.003843</td>\n",
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       "    <tr>\n",
       "      <th>8904</th>\n",
       "      <td>40.217048</td>\n",
       "      <td>125363.828305</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>28.089513</td>\n",
       "      <td>27.267273</td>\n",
       "      <td>0.400009</td>\n",
       "      <td>0.355563</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>221.249363</td>\n",
       "      <td>23.503856</td>\n",
       "      <td>0.000321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8905</th>\n",
       "      <td>0.866827</td>\n",
       "      <td>853.957842</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>4.378587</td>\n",
       "      <td>20.559356</td>\n",
       "      <td>26.093712</td>\n",
       "      <td>0.333395</td>\n",
       "      <td>0.355621</td>\n",
       "      <td>0.022226</td>\n",
       "      <td>262.270699</td>\n",
       "      <td>21.060478</td>\n",
       "      <td>0.001015</td>\n",
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       "    <tr>\n",
       "      <th>8906</th>\n",
       "      <td>0.044430</td>\n",
       "      <td>16.527885</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.888596</td>\n",
       "      <td>74.375484</td>\n",
       "      <td>116.650437</td>\n",
       "      <td>0.555372</td>\n",
       "      <td>0.377653</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>268.222697</td>\n",
       "      <td>24.775056</td>\n",
       "      <td>0.002688</td>\n",
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       "    <tr>\n",
       "      <th>8907</th>\n",
       "      <td>1.155529</td>\n",
       "      <td>192.706713</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.842762</td>\n",
       "      <td>0.555543</td>\n",
       "      <td>82.064794</td>\n",
       "      <td>61.909698</td>\n",
       "      <td>1.044421</td>\n",
       "      <td>0.866647</td>\n",
       "      <td>1.671685</td>\n",
       "      <td>289.482282</td>\n",
       "      <td>31.735209</td>\n",
       "      <td>0.005996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8908 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      I/O Data Operations   I/O Data Bytes  Number of subprocesses  \\\n",
       "0              114.798900      3790.450939                    28.0   \n",
       "1               14.154240      5182.451722                    28.0   \n",
       "2                0.044444       120.821253                    28.0   \n",
       "3               31.369916    127228.251100                    31.0   \n",
       "4                5.046181      1091.620117                    25.0   \n",
       "...                   ...              ...                     ...   \n",
       "8903            26.769658      6965.281426                    25.0   \n",
       "8904            40.217048    125363.828305                    27.0   \n",
       "8905             0.866827       853.957842                    29.0   \n",
       "8906             0.044430        16.527885                    28.0   \n",
       "8907             1.155529       192.706713                    27.0   \n",
       "\n",
       "      Time on processor  Disk Reading/sec  Bytes Sent/sent  \\\n",
       "0              0.427078          6.162365        58.492773   \n",
       "1              0.138876          5.399498        22.886762   \n",
       "2              0.842762          1.577765        11.733239   \n",
       "3              0.523750         15.981107       602.125436   \n",
       "4              0.069468          1.356022        16.138887   \n",
       "...                 ...               ...              ...   \n",
       "8903           0.155955          1.497184       252.525113   \n",
       "8904           0.842762          0.400009        28.089513   \n",
       "8905           0.842762          4.378587        20.559356   \n",
       "8906           0.842762          0.888596        74.375484   \n",
       "8907           0.842762          0.555543        82.064794   \n",
       "\n",
       "      Received Bytes (HTTP)  Network packets sent  Network packets received  \\\n",
       "0                 63.959387              0.621206                  0.521813   \n",
       "1                 29.508369              0.422183                  0.444403   \n",
       "2                 12.888786              0.177776                  0.155554   \n",
       "3               6976.986795              3.311801                  5.756755   \n",
       "4                 13.271234              0.155609                  0.133379   \n",
       "...                     ...                   ...                       ...   \n",
       "8903             589.072219              0.858386                  0.758573   \n",
       "8904              27.267273              0.400009                  0.355563   \n",
       "8905              26.093712              0.333395                  0.355621   \n",
       "8906             116.650437              0.555372                  0.377653   \n",
       "8907              61.909698              1.044421                  0.866647   \n",
       "\n",
       "      Pages Read/sec  Page Errors/sec  Confirmed byte radius  I/O_bytes  \n",
       "0          24.624612      1001.533430              27.190843   0.030286  \n",
       "1           0.044440      1089.121009              30.150941   0.002731  \n",
       "2           0.044444       322.464081              28.163287   0.000368  \n",
       "3           5.623394     12015.169690              34.204404   0.000247  \n",
       "4           1.671685       550.989619              31.466889   0.004623  \n",
       "...              ...              ...                    ...        ...  \n",
       "8903        0.019962      1177.825039              30.875026   0.003843  \n",
       "8904        1.671685       221.249363              23.503856   0.000321  \n",
       "8905        0.022226       262.270699              21.060478   0.001015  \n",
       "8906        1.671685       268.222697              24.775056   0.002688  \n",
       "8907        1.671685       289.482282              31.735209   0.005996  \n",
       "\n",
       "[8908 rows x 13 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7c0e73f6",
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    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.0\n",
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       "3       0.0\n",
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       "Name: Label, Length: 8908, dtype: float64"
      ]
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   "source": [
    "y"
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  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6faa28b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:35:16.685308Z",
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     "start_time": "2023-03-19T00:35:16.662324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "err:  0.06733639261631376\n",
      "err:  0.043526088385475586\n",
      "err:  0.0516814224791182\n",
      "err:  0.05784822276520606\n",
      "err:  0.06792472335495978\n",
      "err:  0.05336595097907002\n",
      "err:  0.05547616102197391\n",
      "err:  0.04504456193616686\n",
      "err:  0.05182822435639733\n",
      "err:  0.06064417815863642\n",
      "0.055467592605331795\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, KFold\n",
    "from imblearn.over_sampling import  RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "fold_pred=[]\n",
    "oof_pred = []\n",
    "\n",
    "params =     params = {\n",
    "        'iterations':10000,\n",
    "        'random_state':42,\n",
    "        'max_depth': 7,\n",
    "        'learning_rate': 0.008436034013684798,\n",
    "        'l2_leaf_reg': 0.8908564261285311,\n",
    "         }\n",
    "\n",
    "fold= KFold(n_splits=10)#15#5#10\n",
    "i=1\n",
    "for train_index, test_index in fold.split(X,y):     \n",
    "  \n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "    sm = SMOTE(random_state = 42)\n",
    "    X_train_s, y_train_s = sm.fit_resample(X_train, y_train)\n",
    "   \n",
    "\n",
    "    model = catt.CatBoostClassifier(bootstrap_type='Bernoulli', task_type=\"CPU\", **params)\n",
    "\n",
    "    model.fit(X_train,y_train,eval_set=[(X_test, y_test)], early_stopping_rounds=100, verbose = False)#erly100\n",
    "\n",
    "    preds= model.predict_proba(X_test)\n",
    "    print(\"err: \",(log_loss(y_test,preds)))  \n",
    "    oof_pred.append(log_loss(y_test,preds))\n",
    "    p2 = model.predict_proba(test[X.columns])\n",
    "    fold_pred.append(p2)\n",
    "    \n",
    "print(np.mean(oof_pred)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f7df0237",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:40:32.677396Z",
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     "shell.execute_reply": "2023-03-19T00:40:32.682291Z"
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     "end_time": "2023-03-19T00:40:32.685841",
     "exception": false,
     "start_time": "2023-03-19T00:40:32.655905",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3818"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fold_pred[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "49b0eaf0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:40:32.727394Z",
     "iopub.status.busy": "2023-03-19T00:40:32.726693Z",
     "iopub.status.idle": "2023-03-19T00:40:32.741159Z",
     "shell.execute_reply": "2023-03-19T00:40:32.739835Z"
    },
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     "duration": 0.038032,
     "end_time": "2023-03-19T00:40:32.743467",
     "exception": false,
     "start_time": "2023-03-19T00:40:32.705435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
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       "              ID  Label\n",
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   "source": [
    "submission_df = test[[\"ID\"]].copy()\n",
    "submission_df[\"Label\"]=np.round(np.mean([pred[:, 1] for pred in fold_pred], axis=0))\n",
    "submission_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea98a233",
   "metadata": {
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c77a96aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:40:32.823459Z",
     "iopub.status.busy": "2023-03-19T00:40:32.822531Z",
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   "outputs": [],
   "source": [
    "submission_df.to_csv(\"baseline13.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "db393158",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:40:32.880528Z",
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       "  <thead>\n",
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       "      <th>9</th>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                feature_name varimp (%)\n",
       "0        I/O Data Operations  22.419943\n",
       "12                 I/O_bytes  16.173175\n",
       "1             I/O Data Bytes  10.810908\n",
       "10           Page Errors/sec    8.38935\n",
       "4           Disk Reading/sec   6.764749\n",
       "3          Time on processor   6.386677\n",
       "11     Confirmed byte radius    6.34546\n",
       "2     Number of subprocesses   5.744773\n",
       "6      Received Bytes (HTTP)   5.313996\n",
       "8   Network packets received   3.712472\n",
       "5            Bytes Sent/sent   3.410136\n",
       "7       Network packets sent   2.531528\n",
       "9             Pages Read/sec   1.996833"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 2200\n",
    "#define a function to create variable importance dataframe\n",
    "def get_catt_varimp(model, train_columns, max_vars=350):\n",
    "   \n",
    "    feature_importances_perc = (model.feature_importances_ / sum(model.feature_importances_)) * 100\n",
    "    cv_varimp_df = pd.DataFrame([train_columns, feature_importances_perc]).T\n",
    "\n",
    "    cv_varimp_df.columns = ['feature_name', 'varimp (%)']\n",
    "\n",
    "    cv_varimp_df.sort_values(by='varimp (%)', ascending=False, inplace=True)\n",
    "\n",
    "    cv_varimp_df = cv_varimp_df.iloc[0:max_vars]   \n",
    "\n",
    "    return cv_varimp_df\n",
    "#\n",
    "get_catt_varimp(model, X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3949abfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-19T00:40:32.942648Z",
     "iopub.status.busy": "2023-03-19T00:40:32.941458Z",
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     "exception": false,
     "start_time": "2023-03-19T00:40:32.920553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    2842\n",
       "1.0     976\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df[\"Label\"].value_counts()"
   ]
  },
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   "execution_count": null,
   "id": "24dd033c",
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    "tags": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "df39cd32",
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   "source": []
  }
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