Machine Learning Projects (Classic)/Icr-identifying-age-related-conditions-end-to-end.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Use Machine Learning to detect conditions with measurements of anonymous characteristics\n\nThe goal of this competition is to predict if a person has any of three medical conditions. You are being asked to predict if the person has one or more of any of the three medical conditions (Class 1), or none of the three medical conditions (Class 0). You will create a model trained on measurements of health characteristics. \n\n**- InVitro Cell Research**\n\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np \nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2023-07-06T05:50:45.206313Z","iopub.execute_input":"2023-07-06T05:50:45.207065Z","iopub.status.idle":"2023-07-06T05:50:46.687014Z","shell.execute_reply.started":"2023-07-06T05:50:45.207040Z","shell.execute_reply":"2023-07-06T05:50:46.686232Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/train.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T05:52:48.105687Z","iopub.execute_input":"2023-07-06T05:52:48.106493Z","iopub.status.idle":"2023-07-06T05:52:48.138466Z","shell.execute_reply.started":"2023-07-06T05:52:48.106444Z","shell.execute_reply":"2023-07-06T05:52:48.137194Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"             Id        AB          AF          AH         AM        AR  \\\n0  000ff2bfdfe9  0.209377  3109.03329   85.200147  22.394407  8.138688   \n1  007255e47698  0.145282   978.76416   85.200147  36.968889  8.138688   \n2  013f2bd269f5  0.470030  2635.10654   85.200147  32.360553  8.138688   \n3  043ac50845d5  0.252107  3819.65177  120.201618  77.112203  8.138688   \n4  044fb8a146ec  0.380297  3733.04844   85.200147  14.103738  8.138688   \n\n         AX        AY         AZ          BC  ...        FL        FR  \\\n0  0.699861  0.025578   9.812214    5.555634  ...  7.298162   1.73855   \n1  3.632190  0.025578  13.517790    1.229900  ...  0.173229   0.49706   \n2  6.732840  0.025578  12.824570    1.229900  ...  7.709560   0.97556   \n3  3.685344  0.025578  11.053708    1.229900  ...  6.122162   0.49706   \n4  3.942255  0.054810   3.396778  102.151980  ...  8.153058  48.50134   \n\n         FS         GB          GE            GF         GH         GI  \\\n0  0.094822  11.339138   72.611063   2003.810319  22.136229  69.834944   \n1  0.568932   9.292698   72.611063  27981.562750  29.135430  32.131996   \n2  1.198821  37.077772   88.609437  13676.957810  28.022851  35.192676   \n3  0.284466  18.529584   82.416803   2094.262452  39.948656  90.493248   \n4  0.121914  16.408728  146.109943   8524.370502  45.381316  36.262628   \n\n          GL  Class  \n0   0.120343      1  \n1  21.978000      0  \n2   0.196941      0  \n3   0.155829      0  \n4   0.096614      1  \n\n[5 rows x 58 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>...</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000ff2bfdfe9</td>\n      <td>0.209377</td>\n      <td>3109.03329</td>\n      <td>85.200147</td>\n      <td>22.394407</td>\n      <td>8.138688</td>\n      <td>0.699861</td>\n      <td>0.025578</td>\n      <td>9.812214</td>\n      <td>5.555634</td>\n      <td>...</td>\n      <td>7.298162</td>\n      <td>1.73855</td>\n      <td>0.094822</td>\n      <td>11.339138</td>\n      <td>72.611063</td>\n      <td>2003.810319</td>\n      <td>22.136229</td>\n      <td>69.834944</td>\n      <td>0.120343</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>007255e47698</td>\n      <td>0.145282</td>\n      <td>978.76416</td>\n      <td>85.200147</td>\n      <td>36.968889</td>\n      <td>8.138688</td>\n      <td>3.632190</td>\n      <td>0.025578</td>\n      <td>13.517790</td>\n      <td>1.229900</td>\n      <td>...</td>\n      <td>0.173229</td>\n      <td>0.49706</td>\n      <td>0.568932</td>\n      <td>9.292698</td>\n      <td>72.611063</td>\n      <td>27981.562750</td>\n      <td>29.135430</td>\n      <td>32.131996</td>\n      <td>21.978000</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>013f2bd269f5</td>\n      <td>0.470030</td>\n      <td>2635.10654</td>\n      <td>85.200147</td>\n      <td>32.360553</td>\n      <td>8.138688</td>\n      <td>6.732840</td>\n      <td>0.025578</td>\n      <td>12.824570</td>\n      <td>1.229900</td>\n      <td>...</td>\n      <td>7.709560</td>\n      <td>0.97556</td>\n      <td>1.198821</td>\n      <td>37.077772</td>\n      <td>88.609437</td>\n      <td>13676.957810</td>\n      <td>28.022851</td>\n      <td>35.192676</td>\n      <td>0.196941</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>043ac50845d5</td>\n      <td>0.252107</td>\n      <td>3819.65177</td>\n      <td>120.201618</td>\n      <td>77.112203</td>\n      <td>8.138688</td>\n      <td>3.685344</td>\n      <td>0.025578</td>\n      <td>11.053708</td>\n      <td>1.229900</td>\n      <td>...</td>\n      <td>6.122162</td>\n      <td>0.49706</td>\n      <td>0.284466</td>\n      <td>18.529584</td>\n      <td>82.416803</td>\n      <td>2094.262452</td>\n      <td>39.948656</td>\n      <td>90.493248</td>\n      <td>0.155829</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>044fb8a146ec</td>\n      <td>0.380297</td>\n      <td>3733.04844</td>\n      <td>85.200147</td>\n      <td>14.103738</td>\n      <td>8.138688</td>\n      <td>3.942255</td>\n      <td>0.054810</td>\n      <td>3.396778</td>\n      <td>102.151980</td>\n      <td>...</td>\n      <td>8.153058</td>\n      <td>48.50134</td>\n      <td>0.121914</td>\n      <td>16.408728</td>\n      <td>146.109943</td>\n      <td>8524.370502</td>\n      <td>45.381316</td>\n      <td>36.262628</td>\n      <td>0.096614</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 58 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T05:53:04.641799Z","iopub.execute_input":"2023-07-06T05:53:04.642170Z","iopub.status.idle":"2023-07-06T05:53:04.665879Z","shell.execute_reply.started":"2023-07-06T05:53:04.642147Z","shell.execute_reply":"2023-07-06T05:53:04.664919Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 617 entries, 0 to 616\nData columns (total 58 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   Id      617 non-null    object \n 1   AB      617 non-null    float64\n 2   AF      617 non-null    float64\n 3   AH      617 non-null    float64\n 4   AM      617 non-null    float64\n 5   AR      617 non-null    float64\n 6   AX      617 non-null    float64\n 7   AY      617 non-null    float64\n 8   AZ      617 non-null    float64\n 9   BC      617 non-null    float64\n 10  BD      617 non-null    float64\n 11  BN      617 non-null    float64\n 12  BP      617 non-null    float64\n 13  BQ      557 non-null    float64\n 14  BR      617 non-null    float64\n 15  BZ      617 non-null    float64\n 16  CB      615 non-null    float64\n 17  CC      614 non-null    float64\n 18  CD      617 non-null    float64\n 19  CF      617 non-null    float64\n 20  CH      617 non-null    float64\n 21  CL      617 non-null    float64\n 22  CR      617 non-null    float64\n 23  CS      617 non-null    float64\n 24  CU      617 non-null    float64\n 25  CW      617 non-null    float64\n 26  DA      617 non-null    float64\n 27  DE      617 non-null    float64\n 28  DF      617 non-null    float64\n 29  DH      617 non-null    float64\n 30  DI      617 non-null    float64\n 31  DL      617 non-null    float64\n 32  DN      617 non-null    float64\n 33  DU      616 non-null    float64\n 34  DV      617 non-null    float64\n 35  DY      617 non-null    float64\n 36  EB      617 non-null    float64\n 37  EE      617 non-null    float64\n 38  EG      617 non-null    float64\n 39  EH      617 non-null    float64\n 40  EJ      617 non-null    object \n 41  EL      557 non-null    float64\n 42  EP      617 non-null    float64\n 43  EU      617 non-null    float64\n 44  FC      616 non-null    float64\n 45  FD      617 non-null    float64\n 46  FE      617 non-null    float64\n 47  FI      617 non-null    float64\n 48  FL      616 non-null    float64\n 49  FR      617 non-null    float64\n 50  FS      615 non-null    float64\n 51  GB      617 non-null    float64\n 52  GE      617 non-null    float64\n 53  GF      617 non-null    float64\n 54  GH      617 non-null    float64\n 55  GI      617 non-null    float64\n 56  GL      616 non-null    float64\n 57  Class   617 non-null    int64  \ndtypes: float64(55), int64(1), object(2)\nmemory usage: 279.7+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T05:56:42.166298Z","iopub.execute_input":"2023-07-06T05:56:42.166743Z","iopub.status.idle":"2023-07-06T05:56:42.175704Z","shell.execute_reply.started":"2023-07-06T05:56:42.166714Z","shell.execute_reply":"2023-07-06T05:56:42.174832Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"Id        0\nAB        0\nAF        0\nAH        0\nAM        0\nAR        0\nAX        0\nAY        0\nAZ        0\nBC        0\nBD        0\nBN        0\nBP        0\nBQ       60\nBR        0\nBZ        0\nCB        2\nCC        3\nCD        0\nCF        0\nCH        0\nCL        0\nCR        0\nCS        0\nCU        0\nCW        0\nDA        0\nDE        0\nDF        0\nDH        0\nDI        0\nDL        0\nDN        0\nDU        1\nDV        0\nDY        0\nEB        0\nEE        0\nEG        0\nEH        0\nEJ        0\nEL       60\nEP        0\nEU        0\nFC        1\nFD        0\nFE        0\nFI        0\nFL        1\nFR        0\nFS        2\nGB        0\nGE        0\nGF        0\nGH        0\nGI        0\nGL        1\nClass     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:01:30.855039Z","iopub.execute_input":"2023-07-06T06:01:30.855408Z","iopub.status.idle":"2023-07-06T06:01:30.984168Z","shell.execute_reply.started":"2023-07-06T06:01:30.855381Z","shell.execute_reply":"2023-07-06T06:01:30.983057Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"               AB            AF           AH          AM          AR  \\\ncount  617.000000    617.000000   617.000000  617.000000  617.000000   \nmean     0.477149   3502.013221   118.624513   38.968552   10.128242   \nstd      0.468388   2300.322717   127.838950   69.728226   10.518877   \nmin      0.081187    192.593280    85.200147    3.177522    8.138688   \n25%      0.252107   2197.345480    85.200147   12.270314    8.138688   \n50%      0.354659   3120.318960    85.200147   20.533110    8.138688   \n75%      0.559763   4361.637390   113.739540   39.139886    8.138688   \nmax      6.161666  28688.187660  1910.123198  630.518230  178.943634   \n\n               AX          AY          AZ           BC           BD   ...  \\\ncount  617.000000  617.000000  617.000000   617.000000    617.000000  ...   \nmean     5.545576    0.060320   10.566447     8.053012   5350.388655  ...   \nstd      2.551696    0.416817    4.350645    65.166943   3021.326641  ...   \nmin      0.699861    0.025578    3.396778     1.229900   1693.624320  ...   \n25%      4.128294    0.025578    8.129580     1.229900   4155.702870  ...   \n50%      5.031912    0.025578   10.461320     1.229900   4997.960730  ...   \n75%      6.431634    0.036845   12.969516     5.081244   6035.885700  ...   \nmax     38.270880   10.315851   38.971568  1463.693448  53060.599240  ...   \n\n               FL           FR          FS          GB           GE  \\\ncount  616.000000   617.000000  615.000000  617.000000   617.000000   \nmean     5.433199     3.533905    0.421501   20.724856   131.714987   \nstd     11.496257    50.181948    1.305365    9.991907   144.181524   \nmin      0.173229     0.497060    0.067730    4.102182    72.611063   \n25%      0.173229     0.497060    0.067730   14.036718    72.611063   \n50%      3.028141     1.131000    0.250601   18.771436    72.611063   \n75%      6.238814     1.512060    0.535067   25.608406   127.591671   \nmax    137.932739  1244.227020   31.365763  135.781294  1497.351958   \n\n                  GF          GH          GI          GL       Class  \ncount     617.000000  617.000000  617.000000  616.000000  617.000000  \nmean    14679.595398   31.489716   50.584437    8.530961    0.175041  \nstd     19352.959387    9.864239   36.266251   10.327010    0.380310  \nmin        13.038894    9.432735    0.897628    0.001129    0.000000  \n25%      2798.992584   25.034888   23.011684    0.124392    0.000000  \n50%      7838.273610   30.608946   41.007968    0.337827    0.000000  \n75%     19035.709240   36.863947   67.931664   21.978000    0.000000  \nmax    143790.071200   81.210825  191.194764   21.978000    1.000000  \n\n[8 rows x 56 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>BD</th>\n      <th>...</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>...</td>\n      <td>616.000000</td>\n      <td>617.000000</td>\n      <td>615.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>617.000000</td>\n      <td>616.000000</td>\n      <td>617.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.477149</td>\n      <td>3502.013221</td>\n      <td>118.624513</td>\n      <td>38.968552</td>\n      <td>10.128242</td>\n      <td>5.545576</td>\n      <td>0.060320</td>\n      <td>10.566447</td>\n      <td>8.053012</td>\n      <td>5350.388655</td>\n      <td>...</td>\n      <td>5.433199</td>\n      <td>3.533905</td>\n      <td>0.421501</td>\n      <td>20.724856</td>\n      <td>131.714987</td>\n      <td>14679.595398</td>\n      <td>31.489716</td>\n      <td>50.584437</td>\n      <td>8.530961</td>\n      <td>0.175041</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.468388</td>\n      <td>2300.322717</td>\n      <td>127.838950</td>\n      <td>69.728226</td>\n      <td>10.518877</td>\n      <td>2.551696</td>\n      <td>0.416817</td>\n      <td>4.350645</td>\n      <td>65.166943</td>\n      <td>3021.326641</td>\n      <td>...</td>\n      <td>11.496257</td>\n      <td>50.181948</td>\n      <td>1.305365</td>\n      <td>9.991907</td>\n      <td>144.181524</td>\n      <td>19352.959387</td>\n      <td>9.864239</td>\n      <td>36.266251</td>\n      <td>10.327010</td>\n      <td>0.380310</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.081187</td>\n      <td>192.593280</td>\n      <td>85.200147</td>\n      <td>3.177522</td>\n      <td>8.138688</td>\n      <td>0.699861</td>\n      <td>0.025578</td>\n      <td>3.396778</td>\n      <td>1.229900</td>\n      <td>1693.624320</td>\n      <td>...</td>\n      <td>0.173229</td>\n      <td>0.497060</td>\n      <td>0.067730</td>\n      <td>4.102182</td>\n      <td>72.611063</td>\n      <td>13.038894</td>\n      <td>9.432735</td>\n      <td>0.897628</td>\n      <td>0.001129</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.252107</td>\n      <td>2197.345480</td>\n      <td>85.200147</td>\n      <td>12.270314</td>\n      <td>8.138688</td>\n      <td>4.128294</td>\n      <td>0.025578</td>\n      <td>8.129580</td>\n      <td>1.229900</td>\n      <td>4155.702870</td>\n      <td>...</td>\n      <td>0.173229</td>\n      <td>0.497060</td>\n      <td>0.067730</td>\n      <td>14.036718</td>\n      <td>72.611063</td>\n      <td>2798.992584</td>\n      <td>25.034888</td>\n      <td>23.011684</td>\n      <td>0.124392</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.354659</td>\n      <td>3120.318960</td>\n      <td>85.200147</td>\n      <td>20.533110</td>\n      <td>8.138688</td>\n      <td>5.031912</td>\n      <td>0.025578</td>\n      <td>10.461320</td>\n      <td>1.229900</td>\n      <td>4997.960730</td>\n      <td>...</td>\n      <td>3.028141</td>\n      <td>1.131000</td>\n      <td>0.250601</td>\n      <td>18.771436</td>\n      <td>72.611063</td>\n      <td>7838.273610</td>\n      <td>30.608946</td>\n      <td>41.007968</td>\n      <td>0.337827</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.559763</td>\n      <td>4361.637390</td>\n      <td>113.739540</td>\n      <td>39.139886</td>\n      <td>8.138688</td>\n      <td>6.431634</td>\n      <td>0.036845</td>\n      <td>12.969516</td>\n      <td>5.081244</td>\n      <td>6035.885700</td>\n      <td>...</td>\n      <td>6.238814</td>\n      <td>1.512060</td>\n      <td>0.535067</td>\n      <td>25.608406</td>\n      <td>127.591671</td>\n      <td>19035.709240</td>\n      <td>36.863947</td>\n      <td>67.931664</td>\n      <td>21.978000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>6.161666</td>\n      <td>28688.187660</td>\n      <td>1910.123198</td>\n      <td>630.518230</td>\n      <td>178.943634</td>\n      <td>38.270880</td>\n      <td>10.315851</td>\n      <td>38.971568</td>\n      <td>1463.693448</td>\n      <td>53060.599240</td>\n      <td>...</td>\n      <td>137.932739</td>\n      <td>1244.227020</td>\n      <td>31.365763</td>\n      <td>135.781294</td>\n      <td>1497.351958</td>\n      <td>143790.071200</td>\n      <td>81.210825</td>\n      <td>191.194764</td>\n      <td>21.978000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 56 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:02:20.635120Z","iopub.execute_input":"2023-07-06T06:02:20.635585Z","iopub.status.idle":"2023-07-06T06:02:20.650106Z","shell.execute_reply.started":"2023-07-06T06:02:20.635547Z","shell.execute_reply":"2023-07-06T06:02:20.649101Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"df.hist(figsize=(20,15), bins=50)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:19:01.389707Z","iopub.execute_input":"2023-07-06T06:19:01.390128Z","iopub.status.idle":"2023-07-06T06:19:11.593528Z","shell.execute_reply.started":"2023-07-06T06:19:01.390103Z","shell.execute_reply":"2023-07-06T06:19:11.591694Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1500 with 56 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAABkkAAATDCAYAAADSotIyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdf1xUZd4//tcAwwgEJKj8SCJqsR+Oud5gKLWJCZi32Kpt1Gqbtt6trcrGomuZt3djESht6i4mra0LmEu0W1KWloy7CeuH3BBzEy2zjUxbiF1DUKFhxOv7h985McDADMyPc868no/HPOBc55oz7+vMvOecOdc559IIIQSIiIiIiIiIiIiIiIi8jI+nAyAiIiIiIiIiIiIiIvIEdpIQEREREREREREREZFXYicJERERERERERERERF5JXaSEBERERERERERERGRV2InCREREREREREREREReSV2khARERERERERERERkVdiJwkREREREREREREREXkldpIQEREREREREREREZFXYicJERERERERERERERF5JXaSeLHf/va30Gg00Ov1fc7XaDRWj6CgINx8881Yu3YtLl686OZoibyDo3lpeYwYMcLNkRKpnz35uGzZsj7nvfbaa9BoNNi/f78LIyTyHvZuHxcuXNjn/Kefflqq88UXX7guUCIv4uh+a0hICJKTk/HKK6+4OVIi79BfTlZVVcHHxwdPPvlkr3n//Oc/cdVVV+FHP/qRO8Ik8ip95eXSpUuh1Wpx+PDhXvU7Ozsxbtw4fO973+OxVzdjJ4kX+8Mf/gAAOHbsGP7+97/3WedHP/oR3n//fbz//vt488038aMf/QhPP/00HnroIXeGSuQ1HM1Ly2Pv3r3uDJPIK9iTj0TkHvbkY3BwMP785z/j/PnzVuVCCJSUlCAkJMTlcRJ5E0f2W2tqavDiiy+ira0N8+bNQ1lZmTtDJfIK/eXklClT8Itf/AIFBQX44IMPpPLLly9jwYIFCAwMRFFRkVvjJfIGfeXlc889h7i4OCxYsACdnZ1W9Q0GA44fP47S0lIEBQW5PV5vxk4SL3Xo0CH84x//wMyZMwEA27Zt67NeREQEJk2ahEmTJiE1NRXPPPMM5s+fjzfeeAPffvutO0MmUr3B5KXlkZCQ4M5QiVTP3nwkItezNx9/+MMfQgiB8vJyq/K//vWvaGhowP333+/yWIm8haP7rZMnT8a8efOwe/duAMDvfvc7t8VK5A3sycn8/HzccMMNWLBggXQ859e//jX+3//7f3jxxRcxcuRIt8ZMpHa28jIwMBClpaX4+OOP8dRTT0n1a2trUVBQgBUrVuD222/3SMzejJ0kXsqSmOvWrUNycjLKy8vR3t5u13NDQ0Oh0Wjg6+vryhCJvM5Q8pKInIv5SCQf9uZjaGgo5syZI52xZ/GHP/wBt99+O8aMGeOWeIm8wWC3k7GxsRg5ciS+/vprV4dI5FXsycmAgACUlJTg008/xZNPPon6+nr83//9H+bPn4+5c+d6ImwiVesvLydPnoxf/epXeO655/D3v/8dJpMJCxcuxM0334ynn37ak2F7LXaSeKGOjg688sormDhxIvR6PX7605/i/Pnz+POf/9yrrhACly5dwqVLl3Du3Dm8+eabKC0txQMPPACtVuuB6InUabB5aXkIITwQNZE6DTUfL126hMuXL3sgciL1cSQfAWDRokU4ePAgPv74YwDAuXPnsHPnTixatMidYROpmqN52V1rayu++eYbdloSOZEjOTl58mSsWLECv/nNb3DPPfcgPDwchYWFHoiaSN3sycu1a9di7NixWLhwIR5//HGcPHkS27dvh06n82Dk3oudJF7otddeQ2trq/Rj8f7778dVV13V5+WYW7ZsgVarhVarxfDhwzF79mzccccdvDyayMkGm5eWB28FROQ8Q81HrVbL2/oQOYkj+QgAU6dORVxcnHQ1SVlZGfz8/HDfffe5LWYitXMkLy0nE5jNZpw8eRIPPfQQAgMDrW4vQkRD4+i2cu3atQgODkZDQwN++9vfYvjw4e4Ml8gr2JOX/v7+2L59Oz7//HP85je/wf/93/9hwoQJngrZ67GTxAtt27YNAQEBeOCBBwAAV111Fe677z787W9/w8mTJ63qZmZmora2FrW1taiursZvf/tbHDp0CHfffTdMJpMnwidSpcHmpeUxe/ZsD0RNpE5Dzcfa2lqsX7/eE6ETqY4j+QgAGo0GCxcuxMsvv4xLly5h27ZtyMzMxFVXXeXu0IlUy5G8tJxM4O/vjzFjxuCdd97BK6+8wvH0iJzI0W1lcXExWltb4ePjA6PR6O5wibyCvXk5fvx4zJ07FwEBAVi1apWnwiWwk8TrfPbZZ6iursbMmTMhhMC5c+dw7tw5/OhHPwKAXvdwHjlyJBITE5GYmIgf/OAHyMrKwm9/+1scOHAAJSUlHmgBkfoMJS8tjxEjRngidCLVcUY+JiYm4vrrr/dE+ESq4mg+Wjz88MP497//jby8PBw+fJi32iJyIkfz0nIyQU1NDX73u98hODgYDzzwQJ8HbonIcY7m5Oeff45f/epXmDNnDtasWYPf/e532LdvnydCJ1ItR/NSp9PBx8eHYz97GDtJvMwf/vAHCCHw2muvYfjw4dJj5syZAIDS0lJ0dXX1u4xbb70VAPCPf/zD5fESeQNn5CUROQfzkUg+BpuPMTExSE1Nxdq1a3HjjTciOTnZ3aETqZajeWk5mWDy5Mn42c9+hjfeeAMXL17EL3/5S081gUhVHMlJIQQefvhhBAQE4MUXX8Tq1asxfvx4/M///A/Onz/vyWYQqQp/UyqTn6cDIPfp6upCaWkpbrjhBvz+97/vNf/tt9/G888/j3feeQcZGRk2l3PkyBEAwKhRo1wVKpHXcFZeEtHQMR+J5GOo+bh8+XIEBARwLBIiJ3LGdvIHP/gBHnroIZSWluL999/H5MmTXR02kWo5mpO/+c1vUF1djT//+c/S8ZySkhLcdttt+NWvfoUXX3zR3U0gUh3+plQudpJ4kXfeeQf/+te/sH79eqSkpPSar9frsXnzZmzbtk1K1K+//hoHDx4EAHz77bc4cuQIcnNzcfXVV+Phhx92Z/hEqjSYvCQi12A+EsnHUPMxPT0d6enpboiUyHs4azv5zDPP4NVXX8WaNWt4mx+iIXAkJ8eMGYMnn3wSDzzwgHTLHwD4/ve/jyeffBJr167Fj370I6SmprqxBUTqw9+UysXbbXmRbdu2wd/f32bnxogRIzBnzhy8/fbb+PrrrwEAr732GiZPnozJkycjPT0dhYWFuOeee3Do0CHExsa6M3wiVRpMXhKRazAfieSD+UgkP87Ky5iYGGRlZeEvf/kLqqurXRUukeo5kpM//OEPERoaihdeeKFXPd52i8h5uA+rXBohhPB0EERERERERERERERERO7GK0mIiIiIiIiIiIiIiMgrsZOEiIiIiIiIiIiIiIi8EjtJiIiIiIiIiIiIiIjIK7GThIiIiIiIiIiIiIiIvBI7SYiIiIiIiIiIiIiIyCuxk4SIiIiIiIiIiIiIiLySn6cDGIzLly/jX//6F4KDg6HRaDwdDpFDhBA4f/48oqOj4eOjjn5K5iQpGXOSSF6Yk0TywpwkkhfmJJG8MCeJ5GWwOanITpJ//etfiImJ8XQYRENy+vRpjB492tNhOAVzktSAOUkkL8xJInlhThLJC3OSSF6Yk0Ty4mhOKrKTJDg4GMCVxoaEhEjlZrMZlZWVSE9Ph1ar9VR4TsU2yZ+j7Wlra0NMTIz0OVYDWzkJqO/97knt7QPU38ZvvvkGcXFxqszJ3//+95g9e7Yq3zdPU3teeJKac9IbtpNqag/bcoWa912Vup1U6mdTqXED8opdzTnZfTspp3WuFlynruFt+64AP0uDxfXmuMGss8FuJxXZSWK51CskJKRXJ0lgYCBCQkJU82Fjm+RvsO1R0yWLtnISUN/73ZPa2weov41msxmAOnNSze+bp6k9LzxJzTnpDdtJNbWHbbGmxpxU6vur1M+mUuMG5Bm7GnOy+3ZSjutc6bhOXcPb9l0BfpYGi+vNcUNZZ47mpDpulkdERERERERERESkYF999RUefPBBhIeHIzAwEN///vdRV1cnzRdCwGAwIDo6GgEBAUhJScGxY8eslmEymZCVlYURI0YgKCgI99xzD86cOePuphApCjtJiIiIiIiIiIiIiDyopaUFt99+O7RaLd555x0cP34czz//PK6++mqpTkFBATZs2IDNmzejtrYWkZGRSEtLw/nz56U62dnZqKioQHl5OQ4cOIALFy4gIyMDXV1dHmgVkTIo8nZbRERERERERERERGqxfv16xMTEoLi4WCq77rrrpP+FENi0aRNWr16NuXPnAgBKS0sRERGBsrIyLF68GK2trdi2bRtefvllpKamAgB27NiBmJgY7Nu3D9OnT3drm4iUwms6Sa57Yrf0/xfrZnowEiLqD3OVyD265xrAfCPyNL1hL0xdV+6by3wk8jzukxKpC3OalGDXrl2YPn067rvvPlRVVeGaa67BkiVL8MgjjwAAGhoa0NTUhPT0dOk5Op0OU6ZMQU1NDRYvXoy6ujqYzWarOtHR0dDr9aipqXFqJ4ll/5U5RWrgNZ0kRERERERERERERHL0+eefo6ioCDk5OXjyySfxwQcf4Be/+AV0Oh0eeughNDU1AQAiIiKsnhcREYFTp04BAJqamuDv74/hw4f3qmN5fk8mkwkmk0mabmtrA3Bl0GzLwPTdWcp0PsJqmvpnWU9cX/YbzDob7PplJwkRERERERERERGRB12+fBmJiYnIy8sDAEyYMAHHjh1DUVERHnroIameRqOxep4QoldZT/3Vyc/Px9q1a3uVV1ZWIjAw0OYyn0m8DADYs2dPv69N1oxGo6dDUBxH1ll7e/ugXoOdJEREREREREREREQeFBUVhVtuucWq7Oabb8brr78OAIiMjARw5WqRqKgoqU5zc7N0dUlkZCQ6OzvR0tJidTVJc3MzkpOT+3zdVatWIScnR5pua2tDTEwM0tPTERIS0qu+2WyG0WjEmkM+MF3WoN7AcU7sYVlvaWlp0Gq1ng5HEQazzixXQjmKnSREREREREREREREHnT77bfjxIkTVmWffvopYmNjAQBxcXGIjIyE0WjEhAkTAACdnZ2oqqrC+vXrAQAJCQnQarUwGo3IzMwEADQ2NqK+vh4FBQV9vq5Op4NOp+tVrtVq+z0wbbqsgalLwwP+DhpovVJvjqyzwa5bdpIQERERERERERERedAvf/lLJCcnIy8vD5mZmfjggw+wdetWbN26FcCV22xlZ2cjLy8P8fHxiI+PR15eHgIDAzFv3jwAQGhoKBYtWoTly5cjPDwcYWFhWLFiBcaNG4fU1FRPNo9I1thJQkRERERERERERORBEydOREVFBVatWoWnn34acXFx2LRpE+bPny/VWblyJTo6OrBkyRK0tLQgKSkJlZWVCA4Olups3LgRfn5+yMzMREdHB6ZNm4aSkhL4+vp6ollEisBOEiKSreue2C39/8W6mR6MhIiIiIiIiIjItTIyMpCRkWFzvkajgcFggMFgsFln2LBhKCwsRGFhoQsiJFInH0efUF1djVmzZiE6OhoajQZvvPGG1fyFCxdCo9FYPSZNmmRVx2QyISsrCyNGjEBQUBDuuecenDlzZkgNISIiIiIiIiIiIiIicoTDnSQXL17E+PHjsXnzZpt17r77bjQ2NkqPPXv2WM3Pzs5GRUUFysvLceDAAVy4cAEZGRno6upyvAVERERERERERERERESD4PDttmbMmIEZM2b0W0en0yEyMrLPea2trdi2bRtefvllacCgHTt2ICYmBvv27cP06dMdDYmIiIiIiIiIiIiIiMhhDl9JYo/9+/dj1KhRGDNmDB555BE0NzdL8+rq6mA2m5Geni6VRUdHQ6/Xo6amxhXhEBERERERERERERER9eL0gdtnzJiB++67D7GxsWhoaMCaNWtw1113oa6uDjqdDk1NTfD398fw4cOtnhcREYGmpqY+l2kymWAymaTptrY2AIDZbIbZbJbKLf93L7PQ+Ype9ZSgvzYpldra5Gh71NJuIiIiIiIiIrm67ondng6BiIgUwumdJPfff7/0v16vR2JiImJjY7F7927MnTvX5vOEENBoNH3Oy8/Px9q1a3uVV1ZWIjAwsFe50WjsVVZw23f/9xwjRQn6apPSqa1N9ranvb3dxZEQERERERER9a+6uhrPPfcc6urq0NjYiIqKCsyePVuaL4TA2rVrsXXrVrS0tCApKQkvvPACxo4dK9UxmUxYsWIFXnnlFXR0dGDatGnYsmULRo8e7YEWERERDY7TO0l6ioqKQmxsLE6ePAkAiIyMRGdnJ1paWqyuJmlubkZycnKfy1i1ahVycnKk6ba2NsTExCA9PR0hISFSudlshtFoRFpaGrRardUy9Ia90v/1BuWMe9Jfm5RKbW1ytD2WK6GISP3y8/Px5JNP4rHHHsOmTZsA8McmkTvl5+dj586d+OSTTxAQEIDk5GSsX78eN954o1SHOUlERN7q4sWLGD9+PB5++GHce++9veYXFBRgw4YNKCkpwZgxY5Cbm4u0tDScOHECwcHBAIDs7Gy89dZbKC8vR3h4OJYvX46MjAzU1dXB19fX3U0iIiIaFJd3kpw9exanT59GVFQUACAhIQFarRZGoxGZmZkAgMbGRtTX16OgoKDPZeh0Ouh0ul7lWq22z4PSfZWbujRW85XGVluVTG1tsrc9amozEdlWW1uLrVu34tZbb7Uq549NIvepqqrC0qVLMXHiRFy6dAmrV69Geno6jh8/jqCgIADMSSIi8l4zZszAjBkz+pwnhMCmTZuwevVq6a4gpaWliIiIQFlZGRYvXozW1lZs27YNL7/8MlJTUwEAO3bsQExMDPbt24fp05VzgioREXk3hwduv3DhAo4cOYIjR44AABoaGnDkyBF8+eWXuHDhAlasWIH3338fX3zxBfbv349Zs2ZhxIgRmDNnDgAgNDQUixYtwvLly/GXv/wFH374IR588EGMGzdO2qgSEREp2YULFzB//ny89NJLVldN9vyxqdfrUVpaivb2dpSVlQGA9GPz+eefR2pqKiZMmIAdO3bg6NGj2Ldvn6eaRKRI7777LhYuXIixY8di/PjxKC4uxpdffom6ujoAzEkiIiJbGhoa0NTUhPT0dKlMp9NhypQpqKmpAQDU1dXBbDZb1YmOjoZer5fqEBERKYHDV5IcOnQIU6dOlaYtt8FasGABioqKcPToUWzfvh3nzp1DVFQUpk6dildffVU6Ew8ANm7cCD8/P2RmZkq3LCgpKeGZeEREpApLly7FzJkzkZqaitzcXKl8oB+bixcvHvDHZl9n5JlMJphMJmm6+239zGZznzHqfIXVtK161DfL+uJ6cz5XrtPW1lYAQFhYGAD356TZbO7VPsu0zkf0KlMiNeUG22L9XCLyLk1NTQCAiIgIq/KIiAicOnVKquPv7291UpCljuX5fbFnO+mM7+Ce+7sW3vq9pqbtmpxwfRKpg8OdJCkpKRCi7w0NAOzdu9fmPIthw4ahsLAQhYWFjr48ETnouuuuk3Ziu1uyZAleeOEFLFy4EKWlpVbzkpKScPDgQXeFSKQq5eXlOHz4MGpra3vNc9WPzfz8fKxdu7bPeUajsc/ygtusp/fs2dNnPeqfrfVLg9fe3u6S5QohkJOTgzvuuAN6vR6A+3OysrISgYGBfT7nmcTL0v9qyEc15Ya3t8VVOUlEyqDRaKymhRC9ynoaqI4j28mhfAf33N+1UMN2dijUtF2TA24nidTB5WOSEJFn1dbWoqurS5qur69HWloa7rvvPqns7rvvRnFxsTTt7+/v1hiJ1OL06dN47LHHUFlZiWHDhtms5+wfm6tWrZKu7ASunI0XExMDAEhLS+tzLCS9wfqkhnoD7xntCLPZDKPRaHP90uCdPXvWJctdtmwZPvroIxw4cKDXPHflZHp6OkJCQqzqWj5Law75wHT5yvKUnI9qyg225YruVycSkfeIjIwEcOVkAcsYswDQ3NwsnVwQGRmJzs5OtLS0WJ1M0NzcjOTkZJvLtmc76Yzv4J77uxZK3s4OhZq2a3Liqn1XInIvdpIQqdzIkSOtptetW4cbbrgBU6ZMkcp0Op20EyxX1z2x22r6i3UzPRQJkW11dXVobm5GQkKCVNbV1YXq6mps3rwZJ06cAOD8H5s6nQ46na7PeVqtts8fQaYuTa965Dhb65cGzxXrMysrC7t27UJ1dTVGjx4tlbvqAJCtnOzv82K6rJHyUg2fKTXlhre3RS1tJyLHxMXFITIyEkajERMmTAAAdHZ2oqqqCuvXrwcAJCQkQKvVwmg0IjMzEwDQ2NiI+vp6FBQU2Fy2I9vJoXwH99zf7b5Mb6am7ZoccF0SqYPDA7cTkXJ1dnZix44d+OlPf2p19uv+/fsxatQojBkzBo888giam5s9GCWRck2bNg1Hjx7FkSNHpEdiYiLmz5+PI0eO4Prrr5d+bFpYfmxaDrZ2/7FpYfmx2d8ZeUTUmxACy5Ytw86dO/HXv/4VcXFxVvO7HwCyYE4SEZG3uHDhgrTPClwZq+vIkSP48ssvodFokJ2djby8PFRUVKC+vh4LFy5EYGAg5s2bBwAIDQ3FokWLsHz5cvzlL3/Bhx9+iAcffBDjxo1DamqqB1tGRETkGF5JQuRF3njjDZw7dw4LFy6UymbMmIH77rsPsbGxaGhowJo1a3DXXXehrq7O5pnpgxmQ1t7BzGwNrteTXAZH84bB79TeRme2Kzg4WBrrwCIoKAjh4eFSueXHZnx8POLj45GXl2fzx2Z4eDjCwsKwYsUK/tgkGoSlS5eirKwMb775JoKDg6UxREJDQxEQEGB1AIg5SURE3ubQoUOYOnWqNG25BdaCBQtQUlKClStXoqOjA0uWLEFLSwuSkpJQWVmJ4OBg6TkbN26En58fMjMz0dHRgWnTpqGkpAS+vr5ubw8REdFgsZOEyIts27YNM2bMQHR0tFR2//33S//r9XokJiYiNjYWu3fvxty5c/tczmAGpLV3cDhbg+v1JLfB9rxh8Du1ttHdA+3xxyaR+xQVFQEAUlJSrMqLi4ulEwaYk0Sek5+fjyeffBKPPfYYNm3aBODKFWBr167F1q1bpZx84YUXMHbsWOl5JpMJK1aswCuvvCLl5JYtW6xup0dEA0tJSYEQtk9S02g0MBgMMBgMNusMGzYMhYWFKCwsdEGERERE7sFOEiIvcerUKezbtw87d+7st15UVBRiY2Nx8uRJm3UGMyCtvYPD2Rpcrye5DLbnDYPfqb2Nrh5ob//+/VbT/LFJ5D79HfixYE4SeUZtbS22bt2KW2+91aq8oKAAGzZsQElJCcaMGYPc3FykpaXhxIkTUudldnY23nrrLZSXlyM8PBzLly9HRkYG6urq2HlJRERERA5jJwmRlyguLsaoUaMwc2b/A56fPXsWp0+fthrAtqfBDEhr7+BwtgbX62t5cuINg9+ptY1qbBMREZGcXbhwAfPnz8dLL72E3NxcqVwIgU2bNmH16tXSFc2lpaWIiIhAWVkZFi9ejNbWVmzbtg0vv/yydMu7HTt2ICYmBvv27cP06fI4kYaIiIiIlIOdJERe4PLlyyguLsaCBQvg5/dd2l+4cAEGgwH33nsvoqKi8MUXX+DJJ5/EiBEjMGfOHA9GTERERERqtXTpUsycOROpqalWnSQNDQ1oampCenq6VKbT6TBlyhTU1NRg8eLFqKurg9lstqoTHR0NvV6PmpqaPjtJbI2nB9gem6z7OHlyG5dNqePFKTVuQF6xyyEGIiIitWEnCZEX2LdvH7788kv89Kc/tSr39fXF0aNHsX37dpw7dw5RUVGYOnUqXn31Vat7sRMREREROUN5eTkOHz6M2traXvOampoAABEREVblEREROHXqlFTH398fw4cP71XH8vyebI2nB9gec637OHlyGwvPQqnjxSk1bkAesbt7PD01uu6J3VbTX6zr/24LRESkfuwkIfIC6enpfd6XPSAgAHv32jcGCBERERHRUJw+fRqPPfYYKisrMWzYMJv1NBrr268KIXqV9dRfHVvj6QGwOeZa93Hy5DIWnoVSx4tTatyAvGLvfiUUEREROYdXdpJ0P2uAZwwQERERERG5Xl1dHZqbm5GQkCCVdXV1obq6Gps3b8aJEycAXLlapPv4eM3NzdLVJZGRkejs7ERLS4vV1STNzc1ITk7u83VtjacH2B5zrfs4eZ4+KG6LUseLU2rcgDxi9/TrExERqZGPpwMgIhqM657YLT2IiIiISP6mTZuGo0eP4siRI9IjMTER8+fPx5EjR3D99dcjMjLS6pZGnZ2dqKqqkjpAEhISoNVqreo0Njaivr7eZicJEREREVF/vPJKEiIiIiIiInKv4OBg6PV6q7KgoCCEh4dL5dnZ2cjLy0N8fDzi4+ORl5eHwMBAzJs3DwAQGhqKRYsWYfny5QgPD0dYWBhWrFiBcePGITU11e1tIiIiIiLlYycJERERERERycLKlSvR0dGBJUuWoKWlBUlJSaisrERwcLBUZ+PGjfDz80NmZiY6Ojowbdo0lJSUwNfX14ORExEREZFS8XZbRERERERE5BH79+/Hpk2bpGmNRgODwYDGxkZ8++23qKqq6nX1ybBhw1BYWIizZ8+ivb0db731ljQQOxERkVrk5+dDo9EgOztbKhNCwGAwIDo6GgEBAUhJScGxY8esnmcymZCVlYURI0YgKCgI99xzD86cOePm6ImUhZ0kRERERERERERERDJRW1uLrVu34tZbb7UqLygowIYNG7B582bU1tYiMjISaWlpOH/+vFQnOzsbFRUVKC8vx4EDB3DhwgVkZGSgq6vL3c0gUgx2khCRx3EQdiIiIiIiIiIi4MKFC5g/fz5eeuklDB8+XCoXQmDTpk1YvXo15s6dC71ej9LSUrS3t6OsrAwA0Nraim3btuH5559HamoqJkyYgB07duDo0aPYt2+fp5pEJHsck4SIiIiIiIiIiIhIBpYuXYqZM2ciNTUVubm5UnlDQwOampqQnp4ulel0OkyZMgU1NTVYvHgx6urqYDabrepER0dDr9ejpqYG06dP7/V6JpMJJpNJmm5rawMAmM1mmM3mXvUtZTofYTVN/bOsJ64v+w1mnQ12/bKThIiIiIiIiIiIiMjDysvLcfjwYdTW1vaa19TUBACIiIiwKo+IiMCpU6ekOv7+/lZXoFjqWJ7fU35+PtauXdurvLKyEoGBgTZjfSbxMgBgz549/bSIejIajZ4OQXEcWWft7e2Deg12khARERERERERERF50OnTp/HYY4+hsrISw4YNs1lPo9FYTQshepX11F+dVatWIScnR5pua2tDTEwM0tPTERIS0qu+2WyG0WjEmkM+MF3WoN7Q++oU6s2y3tLS0qDVaj0djiIMZp1ZroRyFDtJiIiIiIiIiIiIiDyorq4Ozc3NSEhIkMq6urpQXV2NzZs348SJEwCuXC0SFRUl1WlubpauLomMjERnZydaWlqsriZpbm5GcnJyn6+r0+mg0+l6lWu12n4PTJsua2Dq0vCAv4MGWq/UmyPrbLDr1uGB26urqzFr1ixER0dDo9HgjTfesJovhIDBYEB0dDQCAgKQkpKCY8eOWdUxmUzIysrCiBEjEBQUhHvuuQdnzpwZVAOIiIiIiIiIiIiIlGzatGk4evQojhw5Ij0SExMxf/58HDlyBNdffz0iIyOtbj3U2dmJqqoqqQMkISEBWq3Wqk5jYyPq6+ttdpIQ0SA6SS5evIjx48dj8+bNfc4vKCjAhg0bsHnzZtTW1iIyMhJpaWk4f/68VCc7OxsVFRUoLy/HgQMHcOHCBWRkZKCrq2vwLSEiIiIiIiIiIiJSoODgYOj1eqtHUFAQwsPDodfrodFokJ2djby8PFRUVKC+vh4LFy5EYGAg5s2bBwAIDQ3FokWLsHz5cvzlL3/Bhx9+iAcffBDjxo1Damqqh1tIJF8O325rxowZmDFjRp/zhBDYtGkTVq9ejblz5wIASktLERERgbKyMixevBitra3Ytm0bXn75ZSk5d+zYgZiYGOzbtw/Tp/M+dkRERERERERERETdrVy5Eh0dHViyZAlaWlqQlJSEyspKBAcHS3U2btwIPz8/ZGZmoqOjA9OmTUNJSQl8fX09GDmRvDl1TJKGhgY0NTUhPT1dKtPpdJgyZQpqamqwePFi1NXVwWw2W9WJjo6GXq9HTU0NO0mIiIiIyKtd98Ruq+kv1s30UCRERERE5En79++3mtZoNDAYDDAYDDafM2zYMBQWFqKwsNC1wRGpiFM7SZqamgBAGizIIiIiAqdOnZLq+Pv7Ww0eZKljeX5PJpMJJpNJmraMUm82m2E2m6Vyy//dyyx0vqLPZfdVV076a5NSqa1NjrZHLe0mIiIiIiIiIiIiUjqndpJYaDQaq2khRK+ynvqrk5+fj7Vr1/Yqr6ysRGBgYK/y7oMTWRTc1vfr7tmzp9+45KKvNimd2tpkb3va29tdHAkRERERERERERER2cOpnSSRkZEArlwtEhUVJZU3NzdLV5dERkais7MTLS0tVleTNDc3Izk5uc/lrlq1Cjk5OdJ0W1sbYmJikJ6ejpCQEKncbDbDaDQiLS0NWq3Wahl6w94+l11vkPftvfprk1KprU2OtsdyJRQREREREREREREReZZTO0ni4uIQGRkJo9GICRMmAAA6OztRVVWF9evXAwASEhKg1WphNBqRmZkJAGhsbER9fT0KCgr6XK5Op4NOp+tVrtVq+zwo3Ve5qavvq1SUcpDeVluVTG1tsrc97m6zwWDodSVW99vbCSGwdu1abN26VRr064UXXsDYsWPdGicRERERERERERGRuzncSXLhwgV89tln0nRDQwOOHDmCsLAwXHvttcjOzkZeXh7i4+MRHx+PvLw8BAYGYt68eQCA0NBQLFq0CMuXL0d4eDjCwsKwYsUKjBs3Dqmpqc5rGRFJxo4di3379knTvr6+0v8FBQXYsGEDSkpKMGbMGOTm5iItLQ0nTpxAcHCwJ8IlIiIiIiIiIiIicguHO0kOHTqEqVOnStOW22AtWLAAJSUlWLlyJTo6OrBkyRLprPTKykqrg60bN26En58fMjMz0dHRgWnTpqGkpMTqwC0ROY+fn590O7zuhBDYtGkTVq9ejblz5wIASktLERERgbKyMixevNjdoRIRERERERG5zXVP7Jb+/2LdTA9GQkREnuJwJ0lKSgqEEDbnazQaGAwGGAwGm3WGDRuGwsJCFBYWOvryRDQIJ0+eRHR0NHQ6HZKSkpCXl4frr78eDQ0NaGpqQnp6ulRXp9NhypQpqKmpsdlJYjKZYDKZpGnLOCtmsxlms9mqrmW6Z3l3Ol/b3yn26G/ZrmZP+5RO7W1Ua7uIiIiIiIiIiGhgTh2ThIjkJykpCdu3b8eYMWPw9ddfIzc3F8nJyTh27Jg0LklERITVcyIiInDq1Cmby8zPz+81zgkAVFZWIjAwsM/nGI1Gm8sruM2elti2Z8+eoS3ACfprn1qotY3t7e2eDoGIiIiIiJyg+1UhRERE9lJtJwk3jERXzJgxQ/p/3LhxmDx5Mm644QaUlpZi0qRJAK5cAdadEKJXWXerVq2SbrUHXLmSJCYmBunp6QgJCbGqazabYTQakZaWZnPQer1hr8Pt6q7eMH1Izx8Ke9qndGpv49mzZz0dAhEREREREREReYhqO0mIqG9BQUEYN24cTp48idmzZwMAmpqaEBUVJdVpbm7udXVJdzqdDjqdrle5Vqu1eRC9v3mmLtsdMvaQw4H7/tqnFmptoxrbRERERERERERE9vHxdACedt0Tu6UHkTcwmUz4+OOPERUVhbi4OERGRlrdRqmzsxNVVVVITk72YJRERERERERERERErscrSYhUbsWKFZg1axauvfZaNDc3Izc3F21tbViwYAE0Gg2ys7ORl5eH+Ph4xMfHIy8vD4GBgZg3b56nQyciIiIiIiIiIiJyKXaSEKncmTNn8OMf/xj/+c9/MHLkSEyaNAkHDx5EbGwsAGDlypXo6OjAkiVL0NLSgqSkJFRWViI4ONjDkRMRERERERERERG5FjtJiFSuvLy83/kajQYGgwEGg8E9AblA99vlfbFupgcjISIiIiIiIiIiIiXx+jFJiIiIiIiIiIiIiIjIO7GThIiIyEny8/MxceJEBAcHY9SoUZg9ezZOnDhhVUcIAYPBgOjoaAQEBCAlJQXHjh2zqmMymZCVlYURI0YgKCgI99xzD86cOePOphCpQnV1NWbNmoXo6GhoNBq88cYbVvOZj0RERP0zGAzQaDRWj8jISGm+PdtSIiIiuWMnCRERkZNUVVVh6dKlOHjwIIxGIy5duoT09HRcvHhRqlNQUIANGzZg8+bNqK2tRWRkJNLS0nD+/HmpTnZ2NioqKlBeXo4DBw7gwoULyMjIQFdXlyeaRaRYFy9exPjx47F58+Y+5zMfiYiIBjZ27Fg0NjZKj6NHj0rz7NmWEhERyR3HJCEiInKSd99912q6uLgYo0aNQl1dHe68804IIbBp0yasXr0ac+fOBQCUlpYiIiICZWVlWLx4MVpbW7Ft2za8/PLLSE1NBQDs2LEDMTEx2LdvH6ZPn+72dhEp1YwZMzBjxow+5zEfiYiI7OPn52d19YiFPdtSpeF4l0RE3omdJERERC7S2toKAAgLCwMANDQ0oKmpCenp6VIdnU6HKVOmoKamBosXL0ZdXR3MZrNVnejoaOj1etTU1PR5UNZkMsFkMknTbW1t0v9ms7nP2HS+wmraVj3qm2V9cb05n7vWqavyEbCdk2azuVf7LNM6H+uc7KuOEqgpN9gW6+cSkfc6efIkoqOjodPpkJSUhLy8PFx//fV2bUv7Ys92crDfWz33cYdCbd9/atquyQnXJ5E6sJOEiFSFZ/6QXAghkJOTgzvuuAN6vR4A0NTUBACIiIiwqhsREYFTp05Jdfz9/TF8+PBedSzP7yk/Px9r167tc57RaOyzvOA26+k9e/b03yDqk631S4PX3t7ultdxVT4CtnOysrISgYGBfT7nmcTLNpenxPxUU254e1ucmZP5+fnYuXMnPvnkEwQEBCA5ORnr16/HjTfeKNURQmDt2rXYunUrWlpakJSUhBdeeAFjx46V6phMJqxYsQKvvPIKOjo6MG3aNGzZsgWjR492WqxEdEVSUhK2b9+OMWPG4Ouvv0Zubi6Sk5Nx7Ngxu7alfXFkO+no91bPfdyhUOL21x5q2q7Jgbv2XYnItdhJQkRE5ALLli3DRx99hAMHDvSap9ForKaFEL3KeuqvzqpVq5CTkyNNt7W1ISYmBgCQlpYGrVbb6zl6w16r6XoDbxvkCLPZDKPRaHP90uCdPXvWra/n7HwEbOdkeno6QkJCrOpaPktrDvnAdLnvZSopP9WUG2zLFd2vThwqy9hdEydOxKVLl7B69Wqkp6fj+PHjCAoKAvDd+AYlJSUYM2YMcnNzkZaWhhMnTiA4OBjAlbGC3nrrLZSXlyM8PBzLly9HRkYG6urq4Ovr67R4iQhWt60cN24cJk+ejBtuuAGlpaWYNGkSAMe3pfZsJwf7vdVzH3colLT9tYeatmty4u59VyJyDXaSEBEROVlWVhZ27dqF6upqq7NaLfdybmpqQlRUlFTe3NwsnYEXGRmJzs5OtLS0WJ293tzcjOTk5D5fT6fTQafT9TlPq9X2+SPI1KXpVY8cZ2v90uC5a326Kh8B2znZ3+fFdFnTKy+7P09p1JQb3t4WZ7adY3cRKV9QUBDGjRuHkydPYvbs2QD635b2xZHtpKPfW7a2pYOhlu/+ntS0XZMDrksidfDxdABERERqIYTAsmXLsHPnTvz1r39FXFyc1fy4uDhERkZaXeLe2dmJqqoq6YBrQkICtFqtVZ3GxkbU19f3e1CWiBzDfCTyPEfH7gIw4FhBRORaJpMJH3/8MaKiouzalhIRESkBryQhIiJykqVLl6KsrAxvvvkmgoODpfs0h4aGIiAgABqNBtnZ2cjLy0N8fDzi4+ORl5eHwMBAzJs3T6q7aNEiLF++HOHh4QgLC8OKFSswbtw46YxZIrLPhQsX8Nlnn0nTDQ0NOHLkCMLCwnDttdcyH4k8yJ1jd9kaJBqwPeBu98Gf5TYor1IHX1Zq3IC8Ynd3DCtWrMCsWbNw7bXXorm5Gbm5uWhra8OCBQvs2rclIiJSAnaSEBEROUlRUREAICUlxaq8uLgYCxcuBACsXLkSHR0dWLJkiTQgbWVlpXSfdQDYuHEj/Pz8kJmZKQ1IW1JSwvusEzno0KFDmDp1qjRtuf/5ggULUFJSwnwk8iB3jt1la5BowPYAxt0Hf5br4M1KHXxZqXED8ojd3YNEnzlzBj/+8Y/xn//8ByNHjsSkSZNw8OBBxMbGArBv35aIiEju2ElCRETkJEKIAetoNBoYDAYYDAabdYYNG4bCwkIUFhY6MToi75OSktJvXjIfiTzD3WN32RokGoDNAYy7D/4st8GblTr4slLjBuQVe/crodyhvLy83/n2bEuJyD75+fnYuXMnPvnkEwQEBCA5ORnr16/HjTfeKNURQmDt2rXYunWr1DH5wgsvYOzYsVIdk8mEFStW4JVXXpFO8tmyZYvVNpeIrLGThIiIiIiIiFxOCIGsrCxUVFRg//79/Y7dNWHCBADfjW+wfv16ANZjBWVmZgL4bqyggoKCPl/X1iDRgO0BjLsP/uzpg+K2KHXwZaXGDcgjdk+/PhG5TlVVFZYuXYqJEyfi0qVLWL16NdLT03H8+HEEBQUBAAoKCrBhwwaUlJRgzJgxyM3NRVpaGk6cOCFdwZWdnY233noL5eXlCA8Px/Lly5GRkYG6ujpeDU1kAztJiIiIiIiIyOU4dhcREZFt7777rtV0cXExRo0ahbq6Otx5550QQmDTpk1YvXo15s6dCwAoLS1FREQEysrKsHjxYrS2tmLbtm14+eWXpe3ijh07EBMTg3379mH6dHldHUkkF07vJDEYDL3u99p9ED17LgsjIiIiIiIideHYXURERPZrbW0FAISFhQEAGhoa0NTUhPT0dKmOTqfDlClTUFNTg8WLF6Ourg5ms9mqTnR0NPR6PWpqavrsJDGZTDCZTNK05bZ+ZrMZZrO5V31Lmc5HWE1T/yzrievLfoNZZ4Ndvy65kmTs2LHYt2+fNN19Z9Wey8KIiJzhuid2S/9/sW6mByMhIiIaPG7PSC04dhcREZF9hBDIycnBHXfcAb1eDwDSCeiWcbosIiIicOrUKamOv7+/1bhdljqW5/eUn5/f64R3AKisrERgYKDNGJ9JvAwA2LNnj52tIgAwGo2eDkFxHFln7e3tg3oNl3SS+Pn5SYPudWfPZWFERERERERERERE3mrZsmX46KOPcODAgV7zNBqN1bQQoldZT/3VWbVqFXJycqTptrY2xMTEID09HSEhIb3qm81mGI1GrDnkA9NlDeoNvIWXPSzrLS0tjeNL2Wkw68xyJZSjXNJJcvLkSURHR0On0yEpKQl5eXm4/vrr7bosjIiIiIiIiIiIiMgbZWVlYdeuXaiursbo0aOlcssJ6U1NTYiKipLKm5ubpatLIiMj0dnZiZaWFqurSZqbm5GcnNzn6+l0Ouh0ul7lWq223wPTpssamLo0PODvoIHWK/XmyDob7Lp1eidJUlIStm/fjjFjxuDrr79Gbm4ukpOTcezYMbsuC+uLvffG636fMp3vwJdy93Tj6rel/+XSC6rG+9WprU2Otsfd7c7Pz8fOnTvxySefICAgAMnJyVi/fj1uvPFGqc7ChQtRWlpq9bykpCQcPHjQrbESEREREREREXkjIQSysrJQUVGB/fv3Iy4uzmp+XFwcIiMjYTQaMWHCBABAZ2cnqqqqsH79egBAQkICtFotjEYjMjMzAQCNjY2or69HQUGBextEpCBO7ySZMWOG9P+4ceMwefJk3HDDDSgtLcWkSZMAOH5ZmKP3xjMajSi4bbAtuEJu99NT4/3q1NYme9sz2HvjDVZVVRWWLl2KiRMn4tKlS1i9ejXS09Nx/PhxBAUFSfXuvvtuFBcXS9P+/v5ujZOIiIiIiIiIyFstXboUZWVlePPNNxEcHCydbB4aGoqAgABoNBpkZ2cjLy8P8fHxiI+PR15eHgIDAzFv3jyp7qJFi7B8+XKEh4cjLCwMK1aswLhx45CamurJ5hHJmktut9VdUFAQxo0bh5MnT2L27NkA+r8srC/23huv+33KJjz71yHFLacrSdR2vzq1tcnR9gz23niD9e6771pNFxcXY9SoUairq8Odd94plet0uj7HEnKV7oPQEhERERERERF5s6KiIgBASkqKVXlxcTEWLlwIAFi5ciU6OjqwZMkStLS0ICkpCZWVlQgODpbqb9y4EX5+fsjMzERHRwemTZuGkpIS+Pr6uqspRIrj8k4Sk8mEjz/+GD/4wQ/suiysL47eG0+r1cLU1f+ARQOR28F7Nd6vTm1tsrc9nm5za2srACAsLMyqfP/+/Rg1ahSuvvpqTJkyBc8++yxGjRrV5zLsvQWepaz7X4vB3BJvKFx1mzO13T6uL2pvo1rbRURERERERMohxMDHSTQaDQwGAwwGg806w4YNQ2FhIQoLC50YHZG6Ob2TZMWKFZg1axauvfZaNDc3Izc3F21tbViwYIFdl4URkesIIZCTk4M77rgDer1eKp8xYwbuu+8+xMbGoqGhAWvWrMFdd92Furq6PjsoHb0FHtD7dmRDvSWeo1x9Cz213T6uL2pto7tvgUdERERERERERPLh9E6SM2fO4Mc//jH+85//YOTIkZg0aRIOHjyI2NhYAPZdFkZErrFs2TJ89NFHOHDggFX5/fffL/2v1+uRmJiI2NhY7N69G3Pnzu21HHtvgQd8dzuyNYd8YLo8tCu8hsJVt9BT2+3j+qL2Np49e9bTIRARERERERERkYc4vZOkvLy83/n2XBZGRM6XlZWFXbt2obq6GqNHj+63blRUFGJjY3Hy5Mk+5zt6CzwAMF3WDPk2eEPh6oP7art9XF/U2kY1tomIiIiIiIiIiOzj8jFJlKrnoNJfrJvpoUiIhkYIgaysLFRUVGD//v2Ii4sb8Dlnz57F6dOnERUV5YYIiYiIiIiIiIiIiDzDx9MBEJFrLV26FDt27EBZWRmCg4PR1NSEpqYmdHR0AAAuXLiAFStW4P3338cXX3yB/fv3Y9asWRgxYgTmzJnj4eiJiIiIiIiIiIiIXIdXkhCpXFFREQAgJSXFqry4uBgLFy6Er68vjh49iu3bt+PcuXOIiorC1KlT8eqrr3KsICIiIiIiIvJK3e8wwruLEBGpGztJiFROCNHv/ICAAOzdu9dN0RARERERERERERHJB2+3RUREREREREREREREXolXkhARERERKQRv/UFERERERORc7CQhIiIiIiIiIiJF6n4CARER0WCwk4SIvELPHWeefUtERERERET24JWcRETqxjFJiIiIiIiIiIiIiIjIK7GThIiIiIiIiIiIiIiIvBJvt0VERES8hQAREREREREReSVeSUJERERERERERERERF6JnSREREREREREREREROSVeLstIvJ6vM0QEREpEbdfREREREREQ8dOEiLySt0PLBEREREREREREZF34u22iIiIiIiIiIiIiIjIK/FKEjvxdgZEREREJFc9r5Dk/ioREREREZF92ElCRERERERERERkB56YQESkPuwkISIiIiIiIrKBB0SJPI939yAiIldiJwkRERFZ4Y9QIiIiIiLXYMcrEZH8sJNkiHggicg7cEeWiIiUhPuoRERE7sFtLhGR8vl48sW3bNmCuLg4DBs2DAkJCfjb3/7myXDsdt0Tu6UHkZooNSediflNcsKcJJIX5iSRvDAnieTFXTnZ/Tcbf7cR9Y3bSCLHeKyT5NVXX0V2djZWr16NDz/8ED/4wQ8wY8YMfPnll54Kyam40SalUXtOEikNc5JIXpSck7b2SbmvSkrmyZxk7hD1puTtpKvwu4I8hflI5DiPdZJs2LABixYtwv/8z//g5ptvxqZNmxATE4OioiJPhUTk1ZiTjrHs7OoNez0dypBx512e5JKT/HwQXSGXnBwq5jRZKP2zoJacJFIL5uQVPDGB5ID5SOQ4j4xJ0tnZibq6OjzxxBNW5enp6aipqfFESE7R38bO1jx771epN+yFqUvj0HOI7KXWnByqwezA2rofrb1jmgz1+fQdJd8bWIk5qeT1TTQQJebkUDGnSc7klJND/Z1HpAZyykkib8d8JBocj3SS/Oc//0FXVxciIiKsyiMiItDU1NSrvslkgslkkqZbW1sBAN988w3MZrNUbjab0d7ejrNnz8Lv0kUXRe9c31vxJ+n/v6+aJv2flP8XAIDOR+B/J1yGn9kHXZc1vZ7TXffn28vyOs5Ylr26v09arXbAWFwdz1BZ2vP91Tth+v/fo/7iPX/+PABACOGW+OzhqpwEvls/3T/DauJ3WaC9/bL0ee7+3XP27Nnv6vX4Tuo+z2p5Q3z+YNh6TQtbOSt3A7XL4ptvvgGgzpzs730bzHbS1vYHsN6hGMx2qr9tgD3Pd1TP13N02c7Oi+7xyHmbZw9bbbG3jWrOSTlvJ3vmrT053RfLvqu9+0W2uDsn+vpOcHQfT056rr++vrPs3U6qed/V2dvJ7hzJGwt7P193rt8n5Vn146l91hnqds4VlLpPCbgu9sH8HldzTnbfTvZc50o5xjMYjv62s2e/Gej9OVJyDnqCUvddHc1HwLF9V6D3/mt/2zw5bH/kgjnouO7r7I5fV0vlLtlOCg/46quvBABRU1NjVZ6bmytuvPHGXvWfeuopAYAPPlT1OH36tLtSbkDMST74gPjnP//prpQbEHOSDz6Yk3zwIbcH91354ENeD+YkH3zI6yGXfVdH81EI5iQf6nw4up30yJUkI0aMgK+vb68ezObm5l49nQCwatUq5OTkSNOXL1/GN998g/DwcGg0351x19bWhpiYGJw+fRohISGua4AbsU3y52h7hBA4f/48oqOj3RCdfVyVk4D63u+e1N4+QP1tbG1txbXXXouwsDBPhyJxRk6eOnUK3//+91X7vnma2vPCk9Sak96ynVRTe9iWK9S676rk7aRSP5tKjRuQV+xqzcme20k5rXO14Dp1Dbntuzqaj4Bj+64AP0uDxfXmuMGss8FuJz3SSeLv74+EhAQYjUbMmTNHKjcajfjhD3/Yq75Op4NOp7Mqu/rqq20uPyQkRHUfNrZJ/hxpT2hoqIujcYyrcxJQ3/vdk9rbB6i/jT4+Pp4OQeKMnLS0R+3vm6dx/bqO2nLS27aTamoP26LOfVc1bCeVGrtS4wbkE7sac9LWdlIu61xNuE5dQy77ro7mIzC4fVeAn6XB4npznKPrbDDbSY90kgBATk4OfvKTnyAxMRGTJ0/G1q1b8eWXX+LRRx/1VEhEXo05SSQvzEkieWFOEskLc5JIXpiTRPLBfCRynMc6Se6//36cPXsWTz/9NBobG6HX67Fnzx7ExsZ6KiQir8acJJIX5iSRvDAnieSFOUkkL8xJIvlgPhI5zmOdJACwZMkSLFmyxGnL0+l0eOqpp3pdIqZkbJP8qak9zs5JQF3rpy9qbx+g/jbKuX1DyUk5t0sNuH5dR87rltvJgampPWyL/HnrdlKpsSs1bkDZsbuTM7eTXOfOx3XqGnJdr67Yb7WQa5vljuvNce5cZxohhHD5qxAREREREREREREREcmMPEYVIiIiIiIiIiIiIiIicjN2khARERERERERERERkVdiJwkREREREREREREREXklVXWSbNmyBXFxcRg2bBgSEhLwt7/9zdMhDUp+fj4mTpyI4OBgjBo1CrNnz8aJEyc8HZZT5efnQ6PRIDs729OhDMlXX32FBx98EOHh4QgMDMT3v/991NXVeTosWZFjXlZXV2PWrFmIjo6GRqPBG2+8YTVfCAGDwYDo6GgEBAQgJSUFx44ds6pjMpmQlZWFESNGICgoCPfccw/OnDljVaelpQU/+clPEBoaitDQUPzkJz/BuXPnrOp8+eWXmDVrFoKCgjBixAj84he/QGdn56DbZs/3h5LbBwBFRUW49dZbERISgpCQEEyePBnvvPOOatrnDHLMO7mR0/eAGsntu0jO5J6v7nwv3a2v/VEltWWg/VAltcXd5J53AGAwGKDRaKwekZGR0nx73l93cdc21ROxL1y4sNf7MGnSJFnErnZKyFO5cta2m2wb7D6EGjA3HTPQ9pyucMa+xJAJlSgvLxdarVa89NJL4vjx4+Kxxx4TQUFB4tSpU54OzWHTp08XxcXFor6+Xhw5ckTMnDlTXHvtteLChQueDs0pPvjgA3HdddeJW2+9VTz22GOeDmfQvvnmGxEbGysWLlwo/v73v4uGhgaxb98+8dlnn3k6NNmQa17u2bNHrF69Wrz++usCgKioqLCav27dOhEcHCxef/11cfToUXH//feLqKgo0dbWJtV59NFHxTXXXCOMRqM4fPiwmDp1qhg/fry4dOmSVOfuu+8Wer1e1NTUiJqaGqHX60VGRoY0/9KlS0Kv14upU6eKw4cPC6PRKKKjo8WyZcsG3TZ7vj+U3D4hhNi1a5fYvXu3OHHihDhx4oR48sknhVarFfX19apo31DJNe/kRi7fA2olp+8iOVNCvrrzvXQnW/ujSmmLPfuhSmmLuykh74QQ4qmnnhJjx44VjY2N0qO5uVmab8/76y7u2qZ6IvYFCxaIu+++2+p9OHv2rFUdb8wjV1NKnsqVs7bd1Leh7EMoHXPTcQNtz+kKZ+xLDJVqOkluu+028eijj1qV3XTTTeKJJ57wUETO09zcLACIqqoqT4cyZOfPnxfx8fHCaDSKKVOmKLqT5PHHHxd33HGHp8OQNSXkZc8v38uXL4vIyEixbt06qezbb78VoaGh4sUXXxRCCHHu3Dmh1WpFeXm5VOerr74SPj4+4t133xVCCHH8+HEBQBw8eFCq8/777wsA4pNPPhFCXNkI+Pj4iK+++kqq88orrwidTidaW1ud0r6e3x9qa5/F8OHDxe9//3vVts8RSsg7ufHk94C38OR3kZwpMV9d9V66k639USW1ZaD9UCW1xd2UkndPPfWUGD9+fJ/z7Hl/PcVV21RPxC7ElU6SH/7whzafI5fY1UYpeaoUg9l2U9+Gsg+hBsxNx/W3Pae+DWZfwhlUcbutzs5O1NXVIT093ao8PT0dNTU1HorKeVpbWwEAYWFhHo5k6JYuXYqZM2ciNTXV06EM2a5du5CYmIj77rsPo0aNwoQJE/DSSy95OizZUGpeNjQ0oKmpySpunU6HKVOmSHHX1dXBbDZb1YmOjoZer5fqvP/++wgNDUVSUpJUZ9KkSQgNDbWqo9frER0dLdWZPn06TCaT027b1vP7Q23t6+rqQnl5OS5evIjJkyerrn2OUmreyY07P0fewpPfRXKl1Hx11XvpTrb2R5XUloH2Q5XUFndSWt6dPHkS0dHRiIuLwwMPPIDPP/8cgH3vr1yo4bO4f/9+jBo1CmPGjMEjjzyC5uZmaZ7cY1cipeWpEgxm2019G8o+hNIxNwfP1vac7OOu/FJFJ8l//vMfdHV1ISIiwqo8IiICTU1NHorKOYQQyMnJwR133AG9Xu/UZf/2t7+FRqPpc7l93Xu15+O6665z6PXKy8tx+PBh5OfnO6kFnvX555+jqKgI8fHx2Lt3Lx599FH84he/wPbt2z0dmiwoNS8tsfUXd1NTE/z9/TF8+PB+64waNarX8keNGmVVJyIiAiUlJVJeWXZcf/jDHyIlJQVvv/12r2V8/vnnWLZsGcaMGYOAgAAEBgZi7Nix+N///V989dVXUr2+vj880b7uhg8fDn9//yF/Bo4ePYqrrroKOp0Ojz76KCoqKnDLLbeopn2DpdS8k4M5c+ZIeXjDDTcAuPIjqHsOWtajRqPBtGnTAAAjRozA8OHDMX78eCxevBg6nc6hz5E38PR3kVwpMV9d+V7a0n0baXmMHDmyz22kZf66detsLufZZ5+1uT/q6rY400D7oUpqizspKe+SkpKwfft27N27Fy+99BKampqQnJyMs2fP2vX+ulJ/edmTZZv6X//1X9BoNAgNDUVKSgpMJpMiPoszZszAH//4R/z1r3/F888/j9raWtx1110wmUwA5B27UikpT+VgoO1kz2332bNn8dRTTwEAkpOTpTEeX3zxRYwcOZLruB/9HdPy9PeyOzA37dMzJ9euXQs/Pz9cc801WLRokdX2vGfuhoSEIDk5Ga+88oqnmyEr7sovP6ctSQY0Go3VtBCiV5nSLFu2DB999BEOHDjg9GX/4Q9/AAAcO3YMf//7363OwlyzZg0effTRPp9XUlKC3/3ud5gzZ47dr3X69Gk89thjqKysxLBhw4YWuExcvnwZiYmJyMvLAwBMmDABx44dQ1FRER566CEPRycfSs3LwcTds05f9furU1xcjJtuugl33HEHHnnkERw5cgSzZs3Crl27MGvWLADA22+/jQceeAAjRozAsmXLMGHCBGg0Ghw9ehR/+MMfsHv3bnz44YcA+v/+8ET7HHmtgdx44404cuQIzp07h9dffx0LFixAVVWVzddVWvuGSql552mWHPzoo4+wePFi+Pr6WuVg9/V422234fDhw6iurkZbWxvq6+uxfft2fPTRR+jo6JCWKdfPiDvJ4btIzpSUr65+L/tjyU8hBJqamrB58+Ze20iLdevW4Wc/+1mfV2E///zzeO+99/rdH3V1W5zB3v1QJbTFE5SQdzNmzJD+HzduHCZPnowbbrgBpaWl0sDhnm5HX3kJALW1tZg9e7ZV3bfeegthYWH4/PPPkZubixMnTuC//uu/+l2+HN6X+++/X/pfr9cjMTERsbGx2L17N+bOnWvzeXKIXek8/flWGlvbyf/+7//GsWPHcODAAXzyySdIT0/HuXPnAAAvv/wyAgMD8fbbb2PZsmWIjIzEzTff7NmGyJS9x7S84XPrDW10hr5ycs2aNfjTn/6ErKwslJaWAgB+9KMfYfny5RBCoKGhAXl5eZg3bx6EEJg3b56HWyEvrv7sqeJKkhEjRsDX17dX71Fzc3OvXiYlycrKwq5du/Dee+9h9OjRTl32oUOH8I9//AMzZ84EAGzbts1q/g033IBJkyb1egBXEv3OO+/Ec889Z/fr1dXVobm5GQkJCfDz84Ofnx+qqqrw29/+Fn5+fujq6nJe49wkKioKt9xyi1XZzTffjC+//NJDEcmLUvMyMjISAPqNOzIyEp2dnWhpaem3ztdff91r+f/+97+t6nR/Hb1ejxtvvBFdXV3IyMjA22+/DZ1OJ51F0NDQgAceeABjxozBP/7xD6xYsQLTpk3DXXfdhcceewxHjhzBmjVrANj+/vBk+wCgpaUFZrN5yJ8Bf39/fO9730NiYiLy8/Mxfvx4/OY3v1FN+wZLqXknF3q9HpMmTZIuny8oKLDKwe7rMSoqCpcuXcJNN92E6dOnY/ny5Th8+DCGDx+O+vp6FBUV2fU5Ujs5fBfJldLy1dXv5UAs+Tl58mTMmTOn1zbSIjU1FRcvXsSzzz7b53JaWlps7o9aYnF1W5xhoP1Qd70vSqO0vOsuKCgI48aNw8mTJ+16f92hr7wEgL/97W+96l5zzTVITk7Ggw8+iN27dwMAGhsbASjrsxgVFYXY2FicPHkSgLJiVwol56kn9ZWPvr6+eO+99/Dee+8hKioK9957L9ra2lBRUQHgynGf//7v/8aWLVvwxz/+EV999RXOnDnj4ZbI00DHtOzZh1A65qZjbO27VlRUSNtz4MrVEJZ68+bNk7aRv/vd7zwZvqy4a79HFZ0k/v7+SEhIgNFotCo3Go1ITk72UFSDJ4TAsmXLsHPnTvz1r39FXFyc01/D0imybt06JCcno7y8HO3t7f0+p6mpCffeey9GjhyJP/3pT/Dzs/9CpGnTpuHo0aM4cuSI9EhMTMT8+fNx5MgR+Pr6Dqk9nnD77bfjxIkTVmWffvopYmNjPRSRvCg1L+Pi4hAZGWkVd2dnJ6qqqqS4ExISoNVqreo0Njaivr5eqjN58mS0trbigw8+kOr8/e9/R2trq1Wd+vp66UweAKisrIROp0NCQgKGDRsGf39/aLVaAMCGDRtw8eJFbNmyBaGhob1i12g0mDNnTr/fH55on+UHcM/2OZMQAiaTSbXts5dS805uLJ+j6upqKQd7fo5GjRrV63PU3NyMc+fOITQ0FM8995xdnyO1Gmhfxp25KldKyVd3vZeO6rmNtLjxxhuxaNEivPDCCzh16lSv55WXl9vcH73++us90pbBGGg/1FPvi9wpJe/6YjKZ8PHHHyMqKsqu99cTLGdX9/xtFxgYaBVrTEwMNBqNdKKckj6LZ8+exenTpxEVFQVAWbErhZLzVC6EEFixYgUuX76M6dOnIy4uDhUVFTh+/DieeOIJ3HXXXb2+Q+bMmQM/Pz989tln+Pe//+3B6OVpoGNa9uxDKB1zc2gs+64+Pj7S9rwvsbGxGDlyZJ8ngnkrt+33OG0IeA8rLy8XWq1WbNu2TRw/flxkZ2eLoKAg8cUXX3g6NIf9/Oc/F6GhoWL//v2isbFRerS3tztl+e3t7SI0NFRMnDhRCCHE73//ewFAlJSU2HxOZ2enuOOOO4S/v794//33nRLHlClTxGOPPeaUZXnCBx98IPz8/MSzzz4rTp48Kf74xz+KwMBAsWPHDk+HJhtyzcvz58+LDz/8UHz44YcCgNiwYYP48MMPxalTp4QQQqxbt06EhoaKnTt3iqNHj4of//jHIioqSrS1tUnLePTRR8Xo0aPFvn37xOHDh8Vdd90lxo8fLy5duiTVufvuu8Wtt94q3n//ffH++++LcePGiYyMDGn+pUuXhF6vFzfffLMAIAoLC8U111wjfv7zn4vTp0+LX/ziF8LHx0e8++67QgghxowZIyIiIvptmz3fH+5u37Rp08Thw4fFvn37xOjRo8WyZcscfcusrFq1SlRXV4uGhgbx0UcfiSeffFL4+PiIyspKVbRvqOSad3LT83sAgNi+fbv45z//KTo7O8WqVauEv7+/8PHxES+++KLV5wiAWLp0qc3P0f333y8AiNOnTw/4OVIrOX0XyZkS8tWd72VfiouLBQBx8OBBYTabRWdnZ5/bSCGElJuNjY0iMDBQ/OQnP+m1nNraWqvl99wfdWVbnMme/VCltMXdlJB3QgixfPlysX//fvH555+LgwcPioyMDBEcHCzFac/76yo98/Kbb74R7777rvjxj38sAIif/exn0r41ADF58mSrWO+9914BQCQlJUnL9NRnsb/fBefPnxfLly8XNTU1oqGhQbz33nti8uTJ4pprrvH6PHI1peSpHPS1nfzJT34i7ceWlZWJxsZG8eCDDwoA4uOPPxZC9P0dEhISIgCIP/3pTx5ulTIMZh9C6ZibA+uZk9nZ2eJPf/qTWLhwofDx8RG33XabtD237Lt2d+7cOeHr6ytmzZrloRZ4hjOO0w2VajpJhBDihRdeELGxscLf31/813/9l6iqqvJ0SINiOVjT81FcXOyU5W/fvl0AEC+++KIQ4soH8aqrrhI/+MEPbD5nyZIlVs9xBqV3kgghxFtvvSX0er3Q6XTipptuElu3bvV0SLIjx7x87733+syxBQsWCCGEuHz5snjqqadEZGSk0Ol04s477xRHjx61WkZHR4dYtmyZCAsLEwEBASIjI0N8+eWXVnXOnj0r5s+fL4KDg0VwcLCYP3++aGlpsapz6tQpceutt/YZj06nE1u2bJHqDhs2TEyaNKnfttnz/eHu9s2cOVMEBASIsLAwsWzZMvHtt9/224aB/PSnP5U+UyNHjhTTpk2TOkjU0D5nkGPeyY2t74HuD19fXxESEtLrc2TZmbX1OXr88ccFAPH3v//drs+RGsntu0jO5J6v7nwv+2L5oTnQNtISq+WH5urVq4WPj4/4xz/+YbWcgTpJXNkWZxtoP1RJbXE3ueedEELcf//9IioqSmi1WhEdHS3mzp0rjh07Js235/11FVt52de+NQDx85//XKxZs0ZEREQIrVYrhg8fLgCIF154QVqmpz6L/f0uaG9vF+np6WLkyJFCq9WKa6+9VixYsKBXXN6aR66mhDyVA3vz0fKw/Fbp6zukqKhIABDPPfech1ulDIPZh1AD5mb/+svJ0NBQq+05ALFkyRKpg/PTTz8V99xzjwgODhaHDh3ycEvcyxnH6YZKVZ0kZJ8pU6aIgIAAce7cOans4YcfFgDEp59+2qu+JcF/+tOfujNMIq9hybHt27eL2tpaUVtbK9555x3xs5/9TGg0GlFYWCiEsK+ThIgcZ28OWvR1xk93K1eulDpJiGhoHMnP7rnZ2toqRowYIe6++26r5fTsJCEixzmal30dJHr66ac92AIi9bA3H++++26rTpK+7NmzRwAQv/71r90VPpHqDHUbqdVqxdtvv+3BFngv+weVIFX47LPPUF1djXvvvRdCCGkshB/96EcoLi7GH/7wB+Tn50v1Dx06hJ///OdITEzEli1bPBQ1kXe4+eabkZiYKE3ffffdOHXqFFauXIkHH3wQ1157LRoaGjwYIZG6DZSDV199tV3LsYyDEB0d7YowibySo/kZEhKC//3f/0V2djbee+89N0dL5B3szcvMzEz86le/gkajQXBwMG644QZFjklJJGf2/JYEgIaGBtx00019LuOLL74AcGXcICIaGke3kWazGUePHsWqVavwwAMP4PDhw4iPj/dQ9N5JFQO3k/3+8Ic/QAiB1157DcOHD5ceM2fOBACUlpZKA+j9+9//xty5c3HVVVfh9ddfh06n82ToRF7p1ltvRUdHBz799FNMnz4dX3/9NQ4ePOjpsIi8RvcctEdHRwf27duHG264AaNHj3ZxdETebaD8/PnPf464uDg8/vjjEEK4OToi79RXXo4cORKJiYlISEjAmDFj2EFC5Cbd8zE9PR0A8MYbb9is/8Ybb8DPzw933nmnmyIk8i79bSMnT56Mn/3sZ3jjjTdw8eJF/PKXv/RgpN6JnSRepKurC6Wlpbjhhhvw3nvv9XosX74cjY2NeOedd3Dp0iXcd999+Ne//oVXX31VOuuAiNzryJEjAK5sOH/5y18iKCgIS5YsQWtra6+6QghUVFS4OUIideuegwPp6urCsmXLcPbsWTz++OMujoyIBspPf39/5Obmora2Fn/+85/dGBmR93Jku0lErtU9H2fPno1bbrkF69at6/PkgldffRWVlZW4//77ERkZ6eZIibyDPdvIH/zgB3jooYewe/duvP/++26KjACAt9vyIu+88w7+9a9/Yf369UhJSek1X6/XY/Pmzdi2bRv+8pe/oKqqCvPnz0dgYKDNM9cnTZrk4qiJvEd9fT0uXboEADh79ix27twJo9GIOXPmIC4uDgBQXl6O+++/H9///vexbNkyTJgwAQBw/Phx6UqxOXPmeKwNREpmTw5aWK7qEkLg/PnzqK+vx/bt2/GPf/wDv/zlL/HII494oglEquVIfnb34x//GL/+9a/xzjvvuCtUIq8x2LwkIuezJx9ff/11pKWlYfLkyVi+fDkmT54Mk8mEt956C1u3bsWtt96KoqIiTzaDSDWGso185pln8Oqrr2LNmjXYt2+fO8IlsJPEq2zbtg3+/v54+OGH+5w/YsQIzJkzB6+99hr8/K58NP74xz/ij3/8o81l8tYFRM7TPTdDQ0MRFxeHDRs2YMmSJVJ5RkYGjh49iueffx4vvvgiTp8+DR8fH8TFxeHuu+9GVlaWJ0InUgV7ctDitddew2uvvQYfHx9cddVViI2NxeTJk/Hiiy/yBAIiF3AkP7vTaDRYv369dJsRInKeweYlETmfPfl400034cMPP8Svf/1rvPzyy3j66adhMpkAAIsXL8bGjRsREBDg9tiJ1Ggo28iYmBhkZWXhueeeQ3V1NW+B5yYawaPcREREREREREREXuWrr77C5MmTERwcjKqqKowYMcLTIREReQTHJCEiIiIiIiIiIvIy11xzDfbu3Yumpiakp6f3OfYlEZE34JUkRERERERERERERETklXglCREREREREREREREReSV2khARERERERERERERkVdiJwkREREREREREREREXkldpIQEREREREREREREZFX8vN0AINx+fJl/Otf/0JwcDA0Go2nwyFyiBAC58+fR3R0NHx81NFPyZwkJWNOEskLc5JIXpiTRPLCnCSSF+YkkbwMNicV2Unyr3/9CzExMZ4Og2hITp8+jdGjR3s6DKdgTpIaMCeJ5IU5SSQvzEkieWFOEskLc5JIXhzNSUV2kgQHBwO40tiQkBAPR/Mds9mMyspKpKenQ6vVejocuzBm9+gec0dHB2JiYqTPsRoMlJNKfM/sxbYpT892tbW1qTonAwICVPk+DkStn197KL3tas9JW/uuSn/f5ITr0jks63Hy5MmIi4tzSk7m5+dj586d+OSTTxAQEIDk5GSsX78eN954o1RHCIG1a9di69ataGlpQVJSEl544QWMHTtWqmMymbBixQq88sor6OjowLRp07Blyxa7fwhzO2kb8+c7cl0X3ridlOt7MRClxg0wdkcwJ5Xx+WDMrieXeAebk4rsJLFc6hUSEiK7TpLAwECEhIQo4sMLMGZ36StmZ12y+NVXX+Hxxx/HO++8g46ODowZMwbbtm1DQkICAPf80BwoJ5X4ntmLbVMeW+1S02XE3XMyICBAle/jQNT6+bWHWtqu1pzsr5NEDe+bHHBdOodlPVp+YDojJ6uqqrB06VJMnDgRly5dwurVq5Geno7jx48jKCgIAFBQUIANGzagpKQEY8aMQW5uLtLS0nDixAkpluzsbLz11lsoLy9HeHg4li9fjoyMDNTV1cHX13fAOLidtI358x25rwtv2k7K/b2wRalxA4x9MHiMR94Ys+vJLV5Hc1IdN8sj8lItLS24/fbbodVq8c477+D48eN4/vnncfXVV0t1LD80N2/ejNraWkRGRiItLQ3nz5+X6mRnZ6OiogLl5eU4cOAALly4gIyMDHR1dXmgVURERESkRu+++y4WLlyIsWPHYvz48SguLsaXX36Juro6AFcO/GzatAmrV6/G3LlzodfrUVpaivb2dpSVlQEAWltbsW3bNjz//PNITU3FhAkTsGPHDhw9ehT79u3zZPOIiIiGhMd4iDxHkVeSENEV69evR0xMDIqLi6Wy6667Tvq/5w9NACgtLUVERATKysqwePFi6Yfmyy+/jNTUVADAjh07EBMTg3379mH69OlubRMREREReYfW1lYAQFhYGACgoaEBTU1NSE9Pl+rodDpMmTIFNTU1WLx4Merq6mA2m63qREdHQ6/Xo6amhvuuRESkWDzGQ+Q5qu0kue6J3VbTX6yb6aFIiFxn165dmD59Ou677z5UVVXhmmuuwZIlS/DII48AcN0PTZPJBJPJJE23tbUBuHJpndls7lXfUpbw9LswXb5yuVu9QR0bZkvb+mq30qm1bT3bpbb2OVv37Sm3pUSkVvzt4H5CCOTk5OCOO+6AXq8HADQ1NQEAIiIirOpGRETg1KlTUh1/f38MHz68Vx3L83vqb9/Vz89P+t9eesNe6X+17NNacP/oO3JdF3KLxxP4nU1qpdRjPErYFsr1O70/SotZLvEO9vVV20lC5A0+//xzFBUVIScnB08++SQ++OAD/OIXv4BOp8NDDz3ksh+a+fn5WLt2ba/yyspKBAYG2oz3mcTL0v979uyxr5EKYTQaPR2Cy6i1bZZ2tbe3ezgSIiIi77Ns2TJ89NFHOHDgQK95Pe8hLYQY8L7S/dWxZ9/Vkf2dgtu++19t+7QWat3/Gwy5rQvuuxKpl1KP8ShpWyi373R7KC1mT8c72O0kO0mIFOzy5ctITExEXl4eAGDChAk4duwYioqK8NBDD0n1nP1Dc9WqVcjJyZGm29raEBMTg/T0dJuDehmNRqw55KPKK0mMRiPS0tJkMTCVM6m1bT3bZTlLhoiIiNwjKysLu3btQnV1tdUgspGRkQCuHOCJioqSypubm6UDQpGRkejs7ERLS4vVAaDm5mYkJyf3+Xr97bsGBAQ4vL+j9itJ1Lj/NxhyXRfcdyVSL6Ue41HCtlCu3+n9UVrMcol3sNtJdpIQKVhUVBRuueUWq7Kbb74Zr7/+OgDX/dDU6XTQ6XS9yrVabb9fhKbLGpi6NFJdNRmo7Uqm1rZZ2qXGthEREcmREAJZWVmoqKjA/v37ERcXZzU/Li4OkZGRMBqNmDBhAgCgs7MTVVVVWL9+PQAgISEBWq0WRqMRmZmZAIDGxkbU19ejoKCgz9e1Z9/VkX0Cy/6s5XlqxH2k78htXcgpFiJyLqUe41HS95LcvtPtobSYPR3vYF/bx8lxEJEb3X777Thx4oRV2aefforY2FgA1j80LSw/NC0bx+4/NC0sPzRtbUCJiIiIiBy1dOlS7NixA2VlZQgODkZTUxOamprQ0dEB4MqZsdnZ2cjLy0NFRQXq6+uxcOFCBAYGYt68eQCA0NBQLFq0CMuXL8df/vIXfPjhh3jwwQcxbtw4aYBaInKN/Px8KU8thBAwGAyIjo5GQEAAUlJScOzYMc8FSaRgPMZD5Dm8koRIwX75y18iOTkZeXl5yMzMxAcffICtW7di69atAKx/aMbHxyM+Ph55eXk2f2iGh4cjLCwMK1as4A9NIiIiInKqoqIiAEBKSopVeXFxMRYuXAgAWLlyJTo6OrBkyRK0tLQgKSkJlZWVCA4Olupv3LgRfn5+yMzMREdHB6ZNm4aSkhL4+vq6qylEXqe2thZbt27FrbfealVeUFCADRs2oKSkBGPGjEFubi7S0tJw4sQJq7wlooHxGA+R57CThEjBJk6ciIqKCqxatQpPP/004uLisGnTJsyfP1+qwx+aRERE5M2ue2K39P8X62Z6MBISQgxYR6PRwGAwwGAw2KwzbNgwFBYWorCw0InREZEtFy5cwPz58/HSSy8hNzdXKhdCYNOmTVi9ejXmzp0LACgtLUVERATKysqwePFiT4VMpEg8xkPkOewkIVK4jIwMZGRk2JzPH5pEREREREQ0WEuXLsXMmTORmppq1UnS0NCApqYmpKenS2U6nQ5TpkxBTU2NzU4Sk8kEk8kkTVsG2TWbzTCbzb3qW8osf3W+os/5ctMzbiVh7I6/nrPwGA+RZ7CThIiIiKx0P+uaiIiIiLxXeXk5Dh8+jNra2l7zmpqaAEAaMNoiIiICp06dsrnM/Px8rF27tld5ZWUlAgMDbT7PMsZCwW3W5Xv27LH5HDnoPjaE0jD2gbW3t7vldYjItdhJQkREREREREREVk6fPo3HHnsMlZWVGDZsmM16Go3GaloI0ausu1WrViEnJ0eabmtrQ0xMDNLT0xESEtKrvtlshtFoRFpaGrRaLfSGvVbz6w3T7W2SW/WMW0kYu/0sV0IRkbKxk4SIiIiIiIiIiKzU1dWhubkZCQkJUllXVxeqq6uxefNmnDhxAsCVK0qioqKkOs3Nzb2uLulOp9NBp9P1Ktdqtf0e1LbMN3VpepXL2UDtkjPGbt/rEJHysZOEiIiIiIiIiIisTJs2DUePHrUqe/jhh3HTTTfh8ccfx/XXX4/IyEgYjUZMmDABANDZ2YmqqiqsX7/eEyETOU33WxB/sW6mByMhIndgJwkREREREREREVkJDg6GXq+3KgsKCkJ4eLhUnp2djby8PMTHxyM+Ph55eXkIDAzEvHnzPBEyERHRoPh4OgAiIiI1qa6uxqxZsxAdHQ2NRoM33njDav7ChQuh0WisHpMmTbKqYzKZkJWVhREjRiAoKAj33HMPzpw548ZWEBERERENbOXKlcjOzsaSJUuQmJiIr776CpWVlQgODvZ0aERERHZzSSfJV199hQcffBDh4eEIDAzE97//fdTV1UnzhRAwGAyIjo5GQEAAUlJScOzYMVeEQkRE5FYXL17E+PHjsXnzZpt17r77bjQ2NkqPPXv2WM3Pzs5GRUUFysvLceDAAVy4cAEZGRno6upydfhERERERDbt378fmzZtkqY1Gg0MBgMaGxvx7bffoqqqqtfVJ0RERHLn9NtttbS04Pbbb8fUqVPxzjvvYNSoUfjnP/+Jq6++WqpTUFCADRs2oKSkBGPGjEFubi7S0tJw4sQJnm1ARESKNmPGDMyYMaPfOjqdDpGRkX3Oa21txbZt2/Dyyy8jNTUVALBjxw7ExMRg3759mD59utNjJiIiIiIiIiLyVk7vJFm/fj1iYmJQXFwslV133XXS/0IIbNq0CatXr8bcuXMBAKWlpYiIiEBZWRkWL17s7JCIiIhkZf/+/Rg1ahSuvvpqTJkyBc8++yxGjRoFAKirq4PZbEZ6erpUPzo6Gnq9HjU1NewkISIiIiKygYNtExHRYDi9k2TXrl2YPn067rvvPlRVVeGaa67BkiVL8MgjjwAAGhoa0NTUZHXwR6fTYcqUKaipqWEnCRERqdqMGTNw3333ITY2Fg0NDVizZg3uuusu1NXVQafToampCf7+/hg+fLjV8yIiItDU1NTnMk0mE0wmkzTd1tYGADCbzfDz85P+t5fOV/RZ7sgyPM0Sq5Jidhalt93dcRcVFaGoqAhffPEFAGDs2LH4v//7P+mKMCEE1q5di61bt6KlpQVJSUl44YUXMHbsWLfGSURERERERK7h9E6Szz//HEVFRcjJycGTTz6JDz74AL/4xS+g0+nw0EMPSQd4IiIirJ4XERGBU6dO9bnM/g7+2Poh3fMAjzt+cCvxoARjdo/uMSspbiJyvvvvv1/6X6/XIzExEbGxsdi9e7d0hWVfhBDQaDR9zsvPz8fatWt7lVdWViIwMBAAYDQa7Y6x4La+y3uOnaIEjrRbbZTa9vb2dre+3ujRo7Fu3Tp873vfA3DlCucf/vCH+PDDDzF27FjeJlbFup9tTERERERE3svpnSSXL19GYmIi8vLyAAATJkzAsWPHUFRUhIceekiq1/NAz1AP/vTU8wCPOw/sKPGgBGN2D6PR6PaDP0Qkb1FRUYiNjcXJkycBAJGRkejs7ERLS4vV1STNzc1ITk7ucxmrVq1CTk6ONN3W1oaYmBikp6cjICAARqMRaWlp0Gq1dsWkN+zts7zeoJxbfZnNZofbrRZKb7vlZBh3mTVrltX0s88+i6KiIhw8eBC33HILbxNLRERERESkck7vJImKisItt9xiVXbzzTfj9ddfBwBpoNqmpiZERUVJdZqbm3tdXWLR38GfkJCQPp/T8wCPOw7sKPGgBGN2j+4xd3R0eDocIpKRs2fP4vTp09I2MSEhAVqtFkajEZmZmQCAxsZG1NfXo6CgoM9l6HQ66HS6XuVarVb6nuz+/0BMXX2ftKCU79zuHGm32ii17Z6MuaurC3/+859x8eJFTJ48edC3iR3MVdBKvFJWrnquy+5XmHdfv7ZuLdiznrfiZ5KIiIiIvIXTO0luv/12nDhxwqrs008/RWxsLAAgLi4OkZGRMBqNmDBhAgCgs7MTVVVVWL9+fZ/LtOfgT089D/C48we3Eg9KMGb30Gq1uHTpkqfDICIXunDhAj777DNpuqGhAUeOHEFYWBjCwsJgMBhw7733IioqCl988QWefPJJjBgxAnPmzAEAhIaGYtGiRVi+fDnCw8MRFhaGFStWYNy4cUhNTfVUs4hU7ejRo5g8eTK+/fZbXHXVVaioqMAtt9yCmpoaAI7dJhYY3FXQFkq8UlauLOuy+xXm3a8ut3VrwZ71vN17773n6RCIiAaFg7gTEZG9nN5J8stf/hLJycnIy8tDZmYmPvjgA2zduhVbt24FcOU2W9nZ2cjLy0N8fDzi4+ORl5eHwMBAzJs3z9nhEBERudWhQ4cwdepUadpyJeSCBQtQVFSEo0ePYvv27Th37hyioqIwdepUvPrqq1ZjG2zcuBF+fn7IzMxER0cHpk2bhpKSEvj6+rq9PUTe4MYbb8SRI0dw7tw5vP7661iwYAGqqqqk+Y7cJhYY3FXQSrxSVq56rktbtxDsjz1XoXdfrpJuR2gvy3rsvk0jIiIiIlIjp3eSTJw4ERUVFVi1ahWefvppxMXFYdOmTZg/f75UZ+XKlejo6MCSJUvQ0tKCpKQkVFZWcvBLIiJSvJSUFAhh+xYue/cOfLBu2LBhKCwsRGFhoTNDIyIb/P39pYHbExMTUVtbi9/85jd4/PHHATh2m1hgcFdBO1KH7GNZl7ZuITjQcwfSfblqfs/U3DYiIiIiIsAFnSQAkJGRgYyMDJvzNRoNDAYDDAaDK16eiIiIiGjQhBAwmUyDuk0suQ9vo0JERERERM7gkk4SIiIiIiIlePLJJzFjxgzExMTg/PnzKC8vx/79+/Huu+/yNrFERERERERegJ0kREREROS1vv76a/zkJz9BY2MjQkNDceutt+Ldd99FWloaAN4mloiIiIiISO3YSUJEREREXmvbtm39zudtYomIiIiIiNSNnSROpDfsRcFtV/6eeNb2mCxERERERERERDR03ceoIiIiGgwfTwdARERERERERERERETkCbyShIiIiIiIVEdv2AtTl8bTYRARERERkcyxk4SIiIiIiIiIiGSNnd9EROQqvN0WERERERERERERERF5JXaSEKlIfn4+NBoNsrOzpTIhBAwGA6KjoxEQEICUlBQcO3bM6nkmkwlZWVkYMWIEgoKCcM899+DMmTNujp6IiIi81XVP7LZ6DOX5esNep8bjDkNtPxERDR6/f0mueIyHyH3YSTJE3JiSXNTW1mLr1q249dZbrcoLCgqwYcMGbN68GbW1tYiMjERaWhrOnz8v1cnOzkZFRQXKy8tx4MABXLhwARkZGejq6nJ3M4iIiIiIiIiIvBqP8RC5FztJiFTgwoULmD9/Pl566SUMHz5cKhdCYNOmTVi9ejXmzp0LvV6P0tJStLe3o6ysDADQ2tqKbdu24fnnn0dqaiomTJiAHTt24OjRo9i3b5+nmkRERERERERE5HV4jIfI/ThwO5EKLF26FDNnzkRqaipyc3Ol8oaGBjQ1NSE9PV0q0+l0mDJlCmpqarB48WLU1dXBbDZb1YmOjoZer0dNTQ2mT5/e6/VMJhNMJpM03dbWBgAwm80wm8296lvKdD6iV5nSWdqhlvZ0p9a29WyX2tpHREREREREyqW0YzxK+E2txN//SotZLvEO9vXZSUKkcOXl5Th8+DBqa2t7zWtqagIAREREWJVHRETg1KlTUh1/f3+rsxMsdSzP7yk/Px9r167tVV5ZWYnAwECbsT6TeFn6f8+ePTbrKZHRaPR0CC6j1rZZ2tXe3u7hSIiIiEjOeGtlIiJyFyUe41HS8R0lHt9QWsyejnewx3jYSeIiPXekv1g300ORkJqdPn0ajz32GCorKzFs2DCb9TQajdW0EKJXWU/91Vm1ahVycnKk6ba2NsTExCA9PR0hISG96pvNZhiNRqw55APT5SvLrDf0PntBiSxtS0tLg1ar9XQ4TqXWtvVsl+UsGSIiIiIiUqfux2h4fIbkSqnHeJRwfEeJxzeUFrNc4h3sMR52khApWF1dHZqbm5GQkCCVdXV1obq6Gps3b8aJEycAXDmTICoqSqrT3NwsnXkQGRmJzs5OtLS0WJ1p0NzcjOTk5D5fV6fTQafT9SrXarX9fhGaLmtg6tJIddVkoLYrmVrbZmmXGttGRETyxIN0REREZItSj/Eo6Te1Eo8BKC1mT8c72NfmwO1ECjZt2jQcPXoUR44ckR6JiYmYP38+jhw5guuvvx6RkZFWl7p1dnaiqqpK2jgmJCRAq9Va1WlsbER9fb3NDSgRERERERERETkPj/EQeQ6vJCFSsODgYOj1equyoKAghIeHS+XZ2dnIy8tDfHw84uPjkZeXh8DAQMybNw8AEBoaikWLFmH58uUIDw9HWFgYVqxYgXHjxiE1NdXtbSIiIiIiIiJyFY4zRHLFYzxEnsNOEiKVW7lyJTo6OrBkyRK0tLQgKSkJlZWVCA4Olups3LgRfn5+yMzMREdHB6ZNm4aSkhL4+vp6MHIiIiIiebF1YI3jERIREZE78BgPkWuwk8RNeP9hcpf9+/dbTWs0GhgMBhgMBpvPGTZsGAoLC1FYWOja4IiIiIiIiIiIyC48xkPkHi4fkyQ/Px8ajQbZ2dlSmRACBoMB0dHRCAgIQEpKCo4dO+bqUJzmuid2Sw8iIqLuqqurMWvWLERHR0Oj0eCNN96wmm/PNtBkMiErKwsjRoxAUFAQ7rnnHpw5c8aNrSAiIiIiIiIi8g4u7SSpra3F1q1bceutt1qVFxQUYMOGDdi8eTNqa2sRGRmJtLQ0nD9/3pXhEBERudzFixcxfvx4bN68uc/59mwDs7OzUVFRgfLychw4cAAXLlxARkYGurq63NUMIiIiIiIiIiKv4LJOkgsXLmD+/Pl46aWXMHz4cKlcCIFNmzZh9erVmDt3LvR6PUpLS9He3o6ysjJXhSNbvCqFiEhdZsyYgdzcXMydO7fXPHu2ga2trdi2bRuef/55pKamYsKECdixYweOHj2Kffv2ubs5RKqXn5+PiRMnIjg4GKNGjcLs2bNx4sQJqzpKvwqaiIiIiIiIbHPZmCRLly7FzJkzkZqaitzcXKm8oaEBTU1NSE9Pl8p0Oh2mTJmCmpoaLF682FUhEREReZQ928C6ujqYzWarOtHR0dDr9aipqcH06dN7LddkMsFkMknTbW1tAACz2Qw/Pz/pf3vpfEWf5Y4sw9MssSopZmdRetvdHXdVVRWWLl2KiRMn4tKlS1i9ejXS09Nx/PhxBAUFAfjuCrCSkhKMGTMGubm5SEtLw4kTJ6wGySSy4HiERERERETK4ZJOkvLychw+fBi1tbW95jU1NQEAIiIirMojIiJw6tSpPpfX38EfWz+kex7gceYPblsHj3Q+wuqvLd1j6b4sTxzMUOKBFKXHrKS4ici57NkGNjU1wd/f3+oqTEsdy/N7ys/Px9q1a3uVV1ZWIjAwEABgNBrtjrPgtr7L9+zZY/cy5MKRdquNUtve3t7u1td79913raaLi4sxatQo1NXV4c477+x1BRgAlJaWIiIiAmVlZTzBh4iIiIiISOGc3kly+vRpPPbYY6isrMSwYcNs1tNoNFbTQoheZRb2HPzpqecBHmce2LF18MjimcTL/c7vHkv3ZXny4JMSD6QoNWZ3H/whIvlxZBtoT51Vq1YhJydHmm5ra0NMTAzS09MREBAAo9GItLQ0aLVau+LTG/YOWKfe0PuKFjkxm80Ot1stlN52y8kwntLa2goACAsLAzC4q6AHc4KPEk8CcabBnOA01BOX7GXrBKehPr+/5crhc+Dtn0kiIiIi8h5O7ySpq6tDc3MzEhISpLKuri5UV1dj8+bN0j2em5qaEBUVJdVpbm7udWatRX8Hf0JCQvp8Ts8DPM48mGPr4JHOR+CZxMtYc8gHpsu2D3Z1j6X7sjxxwEmJB1KUHnNHR4enwyEiD4mMjATQ/zYwMjISnZ2daGlpsbqapLm5GcnJyX0uV6fTQafT9SrXarXS92T3/wdi6uq/w8ayPCVwpN1qo9S2ezJmIQRycnJwxx13QK/XAxjcVdCDOcHHQokngTjDYE5wGuqJS/aydYLTUJ/f33LldOXee++95+kQiIiIiIhcyumdJNOmTcPRo0etyh5++GHcdNNNePzxx3H99dcjMjISRqMREyZMAAB0dnaiqqoK69ev73OZ9hz86annAR5n/uAe6OCR6bKm3zrdY+lez5MHBZR4IEWpMV+6dMnTYRCRh8TFxQ24DUxISIBWq4XRaERmZiYAoLGxEfX19SgoKPBY7ETeYNmyZfjoo49w4MCBXvMcuQJsMCf4KPEkEGcazAlOQz1xyV62TnAa6vP7W64crtazfCanTp3qtGVWV1fjueeeQ11dHRobG1FRUYHZs2dL84UQWLt2LbZu3YqWlhYkJSXhhRdewNixY6U6JpMJK1aswCuvvIKOjg5MmzYNW7ZswejRo50WJxERERF5F6d3kgQHB0tn3lkEBQUhPDxcKs/OzkZeXh7i4+MRHx+PvLw8BAYGYt68ec4Oh4iIyK0uXLiAzz77TJpuaGjAkSNHEBYWhmuvvXbAbWBoaCgWLVqE5cuXIzw8HGFhYVixYgXGjRuH1NRUTzWLSPWysrKwa9cuVFdXWx1stecKsJ4Gc4KPI3XUaDAnOA31xCV72TrBaajP72+5cvoMODOWixcvYvz48Xj44Ydx77339ppfUFCADRs2oKSkBGPGjEFubi7S0tJw4sQJBAcHA7jyW/Ktt95CeXk5wsPDsXz5cmRkZKCurg6+vr5Oi5WIiIiIvIdLBm4fyMqVK9HR0YElS5ZIZwhVVlZKO75ERERKdejQIauzbi1nky9YsAAlJSV2bQM3btwIPz8/ZGZmSmfJlpSU8OAPkQsIIZCVlYWKigrs378fcXFxVvPtuQKMiOwzY8YMzJgxo895Qghs2rQJq1evxty5cwEApaWliIiIQFlZGRYvXozW1lZs27YNL7/8snTiwI4dOxATE4N9+/Zh+nT3X4Fz3RO7pf+/WDfT7a9PREREREPnlk6S/fv3W01rNBoYDAYYDAZ3vDwREZHbpKSkQAjbA/vasw0cNmwYCgsLUVhY6IIIiai7pUuXoqysDG+++SaCg4OlMUhCQ0MREBAAjUbDq6CJ3KChoQFNTU1IT0+XynQ6HaZMmYKamhosXrwYdXV1MJvNVnWio6Oh1+tRU1Njs5PEZDLBZDJJ021tbQCu3FLMz89P+t8Wna/t7Xp3ahjk3tIGNbRlqOS6LtwdT35+Pnbu3IlPPvkEAQEBSE5Oxvr163HjjTdKdey5VR4REZGceeRKEiIiIiIiOSgqKgJwpYOzu+LiYixcuBAAr4ImcgdLB2XP29hFRETg1KlTUh1/f38MHz68Vx3L8/uSn5+PtWvX9iqvrKxEYGAgAMBoNNp8fsFt9rVhz5499lVUgP7Wh7eR27pob2936+tVVVVh6dKlmDhxIi5duoTVq1cjPT0dx48fR1BQEAD7bpVHREQkZ+wk8YDul2QTERERkef0d+WXBa+CJnIfjcZ6fBYhRK+yngaqs2rVKun2l8CVK0liYmKQnp6OgIAAGI1GpKWl2Rx/RW/Ya1fs9Qb33+7L2cxm84Drw1vIdV1YroRyl3fffddquri4GKNGjUJdXR3uvPNOu26VR0REJHfsJCEiIiIiIiKPioyMBHDlapGoqCipvLm5Wbq6JDIyEp2dnWhpabG6mqS5uRnJyck2l63T6aDT6XqVa7Va6eB39/97MnX130nTfXlq0d/68DZyWxeejqW1tRUAEBYWBsC+W+URERHJHTtJiIiIiIiIyKPi4uIQGRkJo9GICRMmAAA6OztRVVWF9evXAwASEhKg1WphNBqRmZkJAGhsbER9fT0KCgo8FjuRtxBCICcnB3fccQf0ej0A+26V11N/4wT1NeaKpUznY9/4QEPhzDFf5DqujT0Yu/V4VP0tS4nriIh6YycJERERERERudyFCxfw2WefSdMNDQ04cuQIwsLCcO211yI7Oxt5eXmIj49HfHw88vLyEBgYiHnz5gEAQkNDsWjRIixfvhzh4eEICwvDihUrMG7cOKSmpnqqWUReY9myZfjoo49w4MCBXvMcuVWePeME9eWZxMsORuw4V4wtJLdxbRzhzbF3H4+qv8+Fu8cJIiLXYCcJERERERERudyhQ4cwdepUadoyTsiCBQtQUlKClStXoqOjA0uWLEFLSwuSkpJQWVlpNfDzxo0b4efnh8zMTHR0dGDatGkoKSmBr6+v29tD5E2ysrKwa9cuVFdXY/To0VK5PbfK66m/cYJCQkJ61beMD7PmkA9Ml+27/d1gOXNsIbmOa2MPxm49HlV/nwt3jxNERK7BThI7cbB1IiIiIiKiwUtJSYEQtm+Xo9FoYDAYYDAYbNYZNmwYCgsLUVhY6IIIiagnIQSysrJQUVGB/fv3Iy4uzmq+PbfK68mecYL6YrqssXuMoMHq/vrdjwN9sW7mkJaptI4GC2+Ovftnrb/lKHX9EJE1dpIQEREREZEieOLEJZ4sRUTebOnSpSgrK8Obb76J4OBgaQyS0NBQBAQEQKPRDHirPCIiIrljJwkREREREREREfVSVFQE4MqVYN0VFxdj4cKFAGDXrfKIiIjkjJ0kRERExDOliYiIiKiX/m6RZ2HPrfKUzlm33iIiInliJwkREREREZGLsBOaiIiIiEje2ElCRERERERERERejx3bRETeiZ0kMsLLN4mIiIiIiIiIiIiI3IedJEREREREJCs8k5eIiIiIiNzFx9MBENHg5efnY+LEiQgODsaoUaMwe/ZsnDhxwqqOEAIGgwHR0dEICAhASkoKjh07ZlXHZDIhKysLI0aMQFBQEO655x6cOXPGnU0hIiIiIiIikr3rntht9XD0OUS28BgPkeewk4RIwaqqqrB06VIcPHgQRqMRly5dQnp6Oi5evCjVKSgowIYNG7B582bU1tYiMjISaWlpOH/+vFQnOzsbFRUVKC8vx4EDB3DhwgVkZGSgq6vLE80iIiIiIiIiIvIqPMZD5Dm83ZYN7N0nJXj33XetpouLizFq1CjU1dXhzjvvhBACmzZtwurVqzF37lwAQGlpKSIiIlBWVobFixejtbUV27Ztw8svv4zU1FQAwI4dOxATE4N9+/Zh+vTpbm8XERERERERkZLpDXtRcNuVv4DG0+GQAvAYD5HnsJOESEVaW1sBAGFhYQCAhoYGNDU1IT09Xaqj0+kwZcoU1NTUYPHixairq4PZbLaqEx0dDb1ej5qamj43oCaTCSaTSZpua2sDAJjNZpjN5l71LWU6H9GrTOks7VBLe7pTa9t6tktt7SMiIiIiIiLlU8oxHiX8plbi73+lxSyXeAf7+uwkIVIJIQRycnJwxx13QK/XAwCampoAABEREVZ1IyIicOrUKamOv78/hg8f3quO5fk95efnY+3atb3KKysrERgYaDPGZxIvS//v2bPHjlYph9Fo9HQILqPWtlna1d7e7uFIiIiIiIiIiL6jpGM8Sjq+o8TjG0qL2dPxDvYYDztJiFRi2bJl+Oijj3DgwIFe8zQa60t7hRC9ynrqr86qVauQk5MjTbe1tSEmJgbp6ekICQnpVd9sNsNoNGLNIR+YLl9ZZr1BHZd4WtqWlpYGrVbr6XCcSq1t69kuy1ky7mIwGHrtgHbfYRVCYO3atdi6dStaWlqQlJSEF154AWPHjnVrnEREREREROQZSjrGo4TjO0o8vqG0mOUS72CP8Ti9kyQ/Px87d+7EJ598goCAACQnJ2P9+vW48cYbpTo8AOS47mOkfLFupgcjITnKysrCrl27UF1djdGjR0vlkZGRAK6cSRAVFSWVNzc3S2ceREZGorOzEy0tLVZnGjQ3NyM5ObnP19PpdNDpdL3KtVptv1+EpssamLo0Ul01GajtSqbWtlna5Ym2jR07Fvv27ZOmfX19pf8tA/GVlJRgzJgxyM3NRVpaGk6cOIHg4GC3x0qkdtXV1XjuuedQV1eHxsZGVFRUYPbs2dJ87rcSkb34m42IiJxBacd4lHS8QInHN5QWs6fjHexr+zg5DlRVVWHp0qU4ePAgjEYjLl26hPT0dFy8eFGqYzkAtHnzZtTW1iIyMhJpaWk4f/68s8MhUjUhBJYtW4adO3fir3/9K+Li4qzmx8XFITIy0upSt87OTlRVVUkbx4SEBGi1Wqs6jY2NqK+vt7kBJaKh8fPzQ2RkpPQYOXIkAPQaiE+v16O0tBTt7e0oKyvzcNRE6nTx4kWMHz8emzdv7nM+91uJiIjIE657Yrf0IO/AYzxEnuP0K0neffddq+ni4mKMGjUKdXV1uPPOO3sdAAKA0tJSREREoKysDIsXL3Z2SESqtXTpUpSVleHNN99EcHCwdLue0NBQBAQEQKPRIDs7G3l5eYiPj0d8fDzy8vIQGBiIefPmSXUXLVqE5cuXIzw8HGFhYVixYgXGjRuH1NRUTzaPSLVOnjyJ6Oho6HQ6JCUlIS8vD9dff71dA/ERkXPNmDEDM2bM6HMe91uJiIiIyF14jIfIc1w+JklraysAICwsDAAGdQDIZDLBZDJJ05Z7i5nNZpsj1ut8hdW0oyPb93y+Xc/xEVZ/h+LG1W/3iOe7/x1tS38sy3LmMl1N6TE7M+6ioiIAQEpKilV5cXExFi5cCABYuXIlOjo6sGTJEuk2IZWVlVa37dm4cSP8/PyQmZmJjo4OTJs2DSUlJVa3ACIi50hKSsL27dsxZswYfP3118jNzUVycjKOHTtm10B8felvO+nn5yf93x9Ht3ty/w5W4rbCWZTedjnFzY5LIiIiInIXHuMh8hyXdpIIIZCTk4M77rgDer0eAAZ1ACg/P7/XILcAUFlZicDAwD6fU3Cb9fSePXscir3n8x3xTOLlwT/ZDo62xR7dL8NTCqXG3N7e7rTlCTHwQU2NRgODwQCDwWCzzrBhw1BYWIjCwkKnxUZEfet+xvq4ceMwefJk3HDDDSgtLcWkSZMAOD4Qnz3byYG+Mx3d7rliW+QKStxWOItS2+7M7eRQuaLj0lYnkNI7t4ZqMCco2VyWE09ccqXu7/VQT/ByBW//TBIRuQvHMyILHuMh8hyXdpIsW7YMH330EQ4cONBrniMHgFatWoWcnBxpuq2tDTExMUhPT0dISEifz9Eb9lpN1xumDxhvz+c4Sucj8EziZaw55APTZdsHs4bKnrbYy2w2w2g0Ii0tTTGDACk95o6ODk+HQ0QyEhQUhHHjxuHkyZPSYNH9DcTXl/62kwEBAXZ9Zw51G+jMbZMzKHFb4SxKb7ulQ0FOXNFxaYtSO7eGaignKNni6hOXhqp7Z/NQT/Bypffee8/TIRARERERuZTLOkmysrKwa9cuVFdXY/To0VJ5ZGQkAMcOAOl0Ouh0ul7lWq3W5o9/U5emV92B9HzOYJkua5y2rL644oBHf+tSrpQa86VLlzwdBhHJiMlkwscff4wf/OAHVgPxTZgwAcB3A/GtX7/e5jLs2U4O9J051O2WXL+PlbitcBaltl1OMQ9mvxUY3Ak+Su/cGqqhdtR2564Tl4aqe+fyYE7wcjXLZ3Lq1KmeDoWISNZ4JQgRkfI5vZNECIGsrCxUVFRg//79iIuLs5o/2ANA9B1ugImIlGvFihWYNWsWrr32WjQ3NyM3NxdtbW1YsGCBXQPxEZH7uLLj0haldm4NlStOMHL1iUtD1f19HswJXu4ip1jcqftvLiIiIiJSN6d3kixduhRlZWV48803ERwcLN3LOTQ0FAEBATwAREREXu3MmTP48Y9/jP/85z8YOXIkJk2ahIMHDyI2NhaAfQPxEZHzXLhwAZ999pk03dDQgCNHjiAsLAzXXnst91vJZXgQnoiIiIhIHpzeSVJUVAQASElJsSovLi7GwoULAfAAEBERea/y8vJ+59szEB8ROc+hQ4esbidkuU3WggULUFJSwv1WIiIi8jjeUYSIyLVccrutgfAAEBERERHJQUpKSr/7r9xvJSIiIiIiUjeXDdxORERERETEs1+Hxp7119+tu7jOiYiIiIj6x04SheOPTiIiIiIiIiIiz+t+jEbnO3AdIiKSBx9PB0BEREREREREREREROQJvJKEiIiIiIhIRuw5y5hnIhMRqc9Qv9tt3W2k53J5JxIiImu8koSIiIiIiIiIiIiIiLwSO0mIiIiIiIiIiIiIiMgr8XZbREREREReyNYtOYa6LFc+h4iIyNvx1llERM7HK0mIiIiIiIiIiIiIiMgr8UoSIiIiIiIiIiIiBeKVmUREQ8dOEiIiIiIiL+euW3fwQM7QDPW2ZrwlCxERERFRb+wkISIiIiIiInIidk4RERERKQc7SYiIiIiIiIiIiMiK3rAXpi5Nv5297BQmIjVgJwkREZEXcvYtb/jjiGho9Ia9KLjtyt8Tz2YMeXnuyEneOkt5+F1NRERERNSb13eSqOnHHX/0EBERERERERGRM9k7dhmPSxGRUnl9JwkREREREdlHTScYERERERERAYCPpwMgIiIiIiIiIiIiIiLyBK+5koRnvRERERERERERkTdw5XGwoS7bntty2Xvrrp6x6HwFCm4bUnhE5IW8ppPE29h7v0hbg4Ta+3wiIiIici457IfxnuLqx/fYfeSQ00RERERkGztJiIiIyKl44I2IiIiISL4GuhLEU1dj2IrL2Z3N/L1CRD15dEySLVu2IC4uDsOGDUNCQgL+9re/eTIcVbvuid3SQ07LInlhThLJC3OSSF6Yk0TywpwkkhfmJJF8MB+JHOOxK0leffVVZGdnY8uWLbj99tvxu9/9DjNmzMDx48dx7bXXeiosIq/FnCSSF+Ykkbx4MidtnZjS/cxHe09e4UkupBbcThLJC3OSSD6Yj0SO81gnyYYNG7Bo0SL8z//8DwBg06ZN2Lt3L4qKipCfn++psIi8FnOSSF7UkpO8lJ3UQi05SaQWSs5Jezo+iZRGyTlJnjWYEzjseY7esBemLg0AxwZ+VwPmI5HjPNJJ0tnZibq6OjzxxBNW5enp6aipqelV32QywWQySdOtra0AgG+++QZms7nP1/C7dNGJEdvH77JAe/tl+Jl90HVZ4/bXt9fZs2el//3MF6WYrcp7rL/vrfjTd/NslHf391XT7IolKf8vDj/nzvX78L8TLuP7q3ei+vFUu57jDv21xWw2o729HWfPnsW3334LABBCuDW+/rg6Jy3t754b3T879r73ctT9vdVqtZ4Ox6nU2rae7Tp//jwA9ebksGHD+nwfPbGd7L6dcTW1fn7tofS2qz0nbe67dtsns3c/sr99t8HUc9Xz3U0p++Ry0tf3s+W75JtvvgGg3pyU03bS1v5x998Z9uq5f23v7y6lb0OcSa7rwhu3k339nlQCJW+PnBF7f/v+rvyO7Sv2nrE4+vr9tUVuOeloPgJDz8mexwbleIxHrt/p/VFazHKJd9A5KTzgq6++EgDE//t//8+q/NlnnxVjxozpVf+pp54SAPjgQ1WP06dPuyvlBsSc5IMP5iQffMjtwZzkgw95PZiTfPAhrwdzkg8+5PWQS046mo9CMCf5UOfD0Zz02O22AECjse6NFkL0KgOAVatWIScnR5q+fPkyvvnmG4SHh/dZ31Pa2toQExOD06dPIyQkxNPh2IUxu0f3mIODg3H+/HlER0d7OqxeXJWTSnzP7MW2KU/PdgkhVJ2T58+fV+X7OBC1fn7tofS2qz0nbe27Kv19kxOuS+ewrMcvv/wSGo1GtTnprdtJW5g/35HruvDG7aRc34uBKDVugLE7Qq45aW8+At6Rk4zZ9eQS72Bz0iOdJCNGjICvry+ampqsypubmxEREdGrvk6ng06nsyq7+uqrXRnikISEhCjiw9sdY3YPS8yhoaGeDsWKu3JSie+Zvdg25eneLjXnpGWnVq3v40C8td2Astuu5pwciJLfN7nhunSO0NBQ2a1Hbiddj+vjO3JcF966nZTje2EPpcYNMHZ7ySknHc1HwLtykjG7nhziHUxO+rggjgH5+/sjISEBRqPRqtxoNCI5OdkTIRF5NeYkkbwwJ4nkhTlJJC/MSSJ5YU4SyQfzkWhwPHa7rZycHPzkJz9BYmIiJk+ejK1bt+LLL7/Eo48+6qmQiLza/8fe/cdFWeb743+NMAw/AhQUBhIRW+yHo2ZYqP0AQjA/YpmtWlpp67aaP5LUfpinbey4YLaZ52jZ2npEcw1PW7R1NGXcEvOgG2Kuoq1r38i0IFZDQKVhgOv7h2fuGGBgZpiZ+557Xs/H437o3HPNzPW+uN9z3XNd9w/mJJGyMCeJlIU5SaQszEkiZWFOEikH85HIebJNkkybNg0XLlzASy+9hKqqKhgMBuzatQuJiYlyVanHdDodXnzxxQ6nqCkZ6+wdvlBnT+akL8TvKsbme3wlLnflpK/E627+Gjfg37F7kqf3Xfl3cx+2pXsovR3ZT3oG2+NnbAvn8PdkR75ab4B193Xcb+2IdfY8X6tvexohhJC7EkRERERERERERERERN4myz1JiIiIiIiIiIiIiIiI5MZJEiIiIiIiIiIiIiIi8kucJCEiIiIiIiIiIiIiIr/ESRIiIiIiIiIiIiIiIvJLnCRxwf79+zFx4kTEx8dDo9Hggw8+sHleCAGj0Yj4+HiEhIQgPT0dJ06ckKeyAPLz83HrrbciPDwcMTExmDRpEk6dOmVTRml13rBhA4YNG4aIiAhERERg9OjR+PjjjxVb387k5+dDo9EgNzdXWucL9faEN954A0lJSQgODkZKSgo+++wzuavUJaPRCI1GY7Po9XrpeUf+jmazGQsXLkTfvn0RFhaGe++9F+fOnfNqHO74rnIkjtraWjzyyCOIjIxEZGQkHnnkEVy8eFHW2GbNmtXhbzhq1CifiM2dfCn33NVXuevv+u2332LixIkICwtD37598eSTT6Kpqckjsbflat/h63H7E1/bj1QqX9y/VSI17HP3lC/1lY5S8z6gM5S2b0GuUWKOeivH3M2bOeFO7uirlPD7XE28kZfeGpdx9TfSX//6V8X0tY78hjt+/DiGDx+OgIAABAQEQKPRoKioyKaMN8dRHK1zWloaQkJCcO211+Kll16CEAIeI8hpu3btEsuXLxfvvfeeACCKiopsnl+1apUIDw8X7733njh+/LiYNm2aiIuLE/X19bLUd9y4cWLz5s2ioqJCHD16VEyYMEEMGDBAXLp0SbF1/vDDD8XOnTvFqVOnxKlTp8Tzzz8vtFqtqKioUGR92/v888/FwIEDxbBhw8SiRYuk9UqvtycUFhYKrVYr3nrrLXHy5EmxaNEiERYWJs6cOSN31ex68cUXxZAhQ0RVVZW01NTUSM878necO3euuPbaa4XJZBJHjhwRGRkZYvjw4aK5udlrcbjju8qROO655x5hMBhEaWmpKC0tFQaDQeTk5Mga28yZM8U999xj8ze8cOGCTRmlxuYuvpZ77uqr3PF3bW5uFgaDQWRkZIgjR44Ik8kk4uPjxYIFCzzaBj3pO3w5bn/ja/uRSuWL+7dK5Ov73D3la32lo9S8D+gMJe1bkGuUmqPeyjF382ZOuJM7+iol/D5XC2/lpbfGZVz9jTRx4kRF9LWO/Iarq6sTsbGx4q677hKPP/64ePrppwUAMWvWLJs6e2scxZk6P/jgg+L48ePivffeE+Hh4eL3v/99J1uLe3CSpIfaJ0Jra6vQ6/Vi1apV0rqffvpJREZGijfffFOGGnZUU1MjAIiSkhIhhG/UWQgh+vTpI/74xz8qvr4NDQ0iOTlZmEwmkZaWJg10Kb3ennLbbbeJuXPn2qy74YYbxHPPPSdTjbr34osviuHDh3f6nCN/x4sXLwqtVisKCwulMt99953o1auX2L17t0frbo8r31WOxHHy5EkBQBw6dEgqc/DgQQFA/OMf//BwVFfZmyS577777L7GV2LrCV/MvbZc6avc9XfdtWuX6NWrl/juu++kMu+8847Q6XSirq7OI/H2pO/w5bj9nS/uRyqVr+7fKpGv7HO7g6/3lY5Q8z6gs+TctyDX+EKOeirHvMFTOeENzvRVSqmzWngrL70xLuOu30hy9rWO1O+NN94QkZGR4qeffpLKABB9+vQRra2t0jpvjaO4Wuf8/HwRHx9vU2d34uW23KyyshLV1dXIzs6W1ul0OqSlpaG0tFTGmv3sv/7rvwAAaWlp0Gg06NWrF6qrq/Huu+/if/7nfwBcrXNERATmzp3b4VSrtsvAgQM9Xt+WlhYUFhbi8uXLGD16tOLbeP78+ZgwYQLGjh1rs17p9faEpqYmlJeX28QMANnZ2YqP+fTp04iPj0dSUhIefPBBfP311wAc+zuWl5fDYrHYlImPj4fBYOgy7mPHjuGxxx6TTlu95pprcMstt2D16tX48ccfAQDp6ek2OajVajFw4EDMnj0bZ86ccTg+d8Vx8OBBREZGIjU1VSozatQoREZGyv433rdvH2JiYjB48GA8/vjjqKmpkZ7z9di648u5Z1VXVwcAiIqKAuDdbfbgwYMwGAw4f/68lJOzZs2C2WxGSkqKTU4CQGtrK95++22MHTsWffv2hVarRUxMDHJycvDRRx+htbW123h70ne4O+74+HipzLhx42A2m1FeXt5tDNRz/rivYI8jfSLwc78YExMDABg/fjx+8YtfYMKECaiurrbJKX9tS0f52j53T6mhr3SFu/uVkJAQKVfT09MBXG3DrvZf2y4VFRVeilzefQtynq/mqCd/L3bF0d+SBoNBek3bnDh//rw0NuTtujvDlb5K7jqribfz0tPjMp76jdS2ftbcvOGGG9DQ0IAFCxbglltuwdNPPy3Vz5qbnqrfwYMHkZaWBp1OZ1PP2tpafPPNNzbrvDGO4mqdx40bh++//75Dnd0l0CPv6seqq6sBALGxsTbrY2NjnRrA9BQhBAoLCwEAmzdvxg033IBjx45hzpw5CA4OxsSJE/Hhhx9i4sSJGDNmDCorK/Ef//EfHd6noKAAf/jDH3D//fd7rK7Hjx/H6NGj8dNPP+Gaa65BUVERbrrpJimplNjGhYWFOHLkCMrKyjo8p/RtwxPOnz+PlpaWTmO2tocSpaamYuvWrRg8eDB++OEHrFy5EmPGjMGJEycc+jtWV1cjKCgIffr06VDGXtxvvfUW5s2bh+uvvx5PP/00brrpJlgsFhw+fBhvvvkmDh48KF0vctCgQfjTn/4E4OpOSkVFBVasWAGTyYR//OMfCA0N7TZGd8VRXV0tDUy1FRMTI+vfePz48ZgyZQoSExNRWVmJF154AXfffTfKy8uh0+l8OjZH+GruWQkhsHjxYtxxxx3SDzlvbrPV1dWwWCxISUmxycmsrCykpKTY5ORPP/2ESZMmobi4GA8++CA2bNgAvV6Pf/3rX9i9ezemTJmCHTt24L777rMbb0/7DnfG3f5z+vTpg6CgIJ/YbtTAH/cVOuNMnwhc7Rfj4+NRX1+PNWvWoLKyEps3bwZwdQJyz549iIyMBOB/bekIX9zndgdf7ytd5c5+RafTdegrZ8yYgRtuuKHL/de2rrvuOrfH2Bm59y3Ieb6ao576vdgVZ/tNoGNOnD9/3qYe3qq7o3rSV8lVZzXyZl56Y1zGU7+RrOv37t2L559/XsrNXbt2obq6Gg888ABee+01aDQar9Svurra7kHu1dXVSEpKAuC9cRRX62x9Tds6uxMnSTxEo9HYPBZCdFgnhwULFuDs2bMAAIPBgJEjR0pHuG7duhU33XQT3nnnHUycOBHh4eHo06dPh5v0HDp0CJs3b8Zdd92FV155xWN1vf7663H06FFcvHgR7733HmbOnImSkhLpeaW18dmzZ7Fo0SIUFxcjODjYbjml1dsbfC3m8ePHS/8fOnQoRo8ejeuuuw5btmyR8sGVmOyVOXjwIJ544glkZWXhgw8+sJkpz8rKwpIlS7B7925pXUhIiE1e3nXXXQgODsbs2bNx4MCBDkd1dMUdcXRWXu6/8bRp06T/W7/rEhMTsXPnTkyePNnu63whNmf4Wu5ZLViwAMeOHcOBAwc6POeNbfaHH37AP/7xD4wbN84mJzUaDSZNmoStW7dKObl48WLs2bMHW7ZswaOPPmrznpMnT8bTTz+NxsZGu3XzZN/hyvbs69u8Wvhq7rqDs30iAFy8eBEtLS04cOAA+vfvDwC46aabcPvtt+Nvf/sbfvOb32DHjh0A/KstHeVr+9zupvb47Olpv/Ltt9+ipqYG99xzj02uhoaGIiMjAx999FGX+6/eJve+BbnOV3PUnb8Xu+JKvwl4PifczRN9la9sS0rkjbz01riMJ38jLVu2zCY3v/jiCzQ3N2PZsmW49tprMXv2bK/Vz15d26735jiKK2XE/9203VN5y8ttuZlerweADrOJNTU1HWbJvG3hwoX48MMP8eyzz9qst9b54sWLCAoKglarBdB5na0znv369cN///d/IzDQc/NsQUFB+MUvfoGRI0ciPz8fw4cPx3/8x38oto3Ly8tRU1ODlJQUBAYGIjAwECUlJfjP//xPBAYG2sx4tiV3vT2pb9++CAgI8PmYw8LCMHToUJw+fdqh7U+v16OpqQm1tbV2y7SVl5cHjUaDjRs3djj9EbiaC/fee2+XdbQeIWvN3+64Kw69Xo8ffvihw/v/61//UtTfOC4uDomJiTh9+jQAdcXWGV/OPWtf9emnn0qDnYB3t9kvv/wSQgibnKytrYXFYkFsbKyUk9XV1fjjH/+IcePGdZggsUpOTsawYcPsxuuOvsNdcev1+g6f0zZu8jyl7uN4k7N94unTp1FfX2/3O+OOO+7Au+++Kx1V6E9t6Shf2+d2F1/uK3vCXf3poUOHAKBDrlr7FUf2X71FCfsW5DxfzVFP/F7siiu/Je3lhJW36u6MnvRVctVZjeTMS0+My3jqN5K1fu1zs+1n9+/fH83NzV6pX2dlrLr6u3lqHMXVOlsv/eWpbY2TJG6WlJQEvV4Pk8kkrWtqakJJSQnGjBkjS52EEFiwYAHef/99fPLJJ+jXrx+Aq9dybG5uRv/+/dGvXz88+eSTuHz5MqZPn95pnS0WC6ZMmYLz58/jz3/+s9c7EyEEzGazItsYADIzM3H8+HEcPXpUWkaOHIkZM2bg6NGjGDRokCLr7UlBQUFISUmxiRkATCaTT8VsNpvx5ZdfIi4uzqHtLyUlBVqt1qZMVVUVKioqOsTd0tKCTz75BCkpKUhISHC4Ts3NzWhubsaVK1fw+eef46WXXsKgQYMcbld3xTF69GjU1dXh888/l8r87W9/Q11dnaL+xhcuXMDZs2cRFxcHQF2xdcYXc699X9X+9FlvbbMtLS349ttvAcDmQIDi4mLpsiJWn376KSwWCyZNmuRy3O7oO9y1PY8ePRoVFRWoqqrqMm7yHKXu43iLM32i9Tvj/PnzSEpKsvud0a9fPwgh8Nlnn/lVW/aE0ve53cUX+0p3cEd/2tLSgn/+85/Seqvu9pOs+6/WxZF7dvWEUvYtyDW+mqPu/r3YFVd+S1ZVVeG9995DcXExEhISpHxsaWkBcHUg2ht17yln+iql1FkN5MxLT4zLeOo30oABA6DRaBAXFyflppz1Gz16NPbv34+mpiabevbp06fLe017ahzF1ToXFxcjPj7ec/fHds/93/1LQ0OD+OKLL8QXX3whAIg1a9aIL774Qpw5c0YIIcSqVatEZGSkeP/998Xx48fFQw89JOLi4kR9fb0s9X3iiSdEZGSk2Ldvn6iqqhJr164VADpdfvOb39it87x58wQA8eabb3q8zsuWLRP79+8XlZWV4tixY+L5558XvXr1EsXFxUII5bWxPWlpaWLRokXSY1+ptzsVFhYKrVYrNm3aJE6ePClyc3NFWFiY+Oabb+Suml1LliwR+/btE19//bU4dOiQyMnJEeHh4VKdHfk7zp07V/Tv31/s3btXHDlyRNx9991i+PDhorm52eazqqurBQDx4IMPOlS3tLS0TnN38ODB4ssvv7Qp647vKkfiuOeee8SwYcPEwYMHxcGDB8XQoUNFTk6Oc43upK5ia2hoEEuWLBGlpaWisrJSfPrpp2L06NHi2muv9YnY3MXXcq99X2Vdrly5IpXxxjZrzcnIyEiRmZkpjhw5Ivbu3Sv69+8vFixYYFPnVatWCQBi9+7dbm0LV/oOd2zPzc3NwmAwdBs39Yyv7Ud6kzN9ovU7Y/jw4eL666+3+50RGhoqAIinnnrKr9rSUWrZ53aVr/WVjvL0PqA1V/V6vUP7Sfb2X2fMmOHRdlDKvgW5Tqk56q3fWd1x9rdkfHy83bEg65KZmemVujvDHX2Vt+usZt7KS2+Ny7j6G+k3v/mN3e8Ba25qtVqH6jds2DBx3XXXeew33MWLF0VsbKz45S9/Kd59913x6quvCgDi3nvvlWUcxZk6P/TQQ+L48ePi/fffFxEREeL3v/+98xuTgzhJ4oJPP/20085k5syZQgghWltbxYsvvij0er3Q6XTirrvuEsePH5etvvY6v8cff1yUlZWJsrIysWvXLnHLLbcIACIwMLBDnTdv3iwAiF/96ldeqfOvfvUrkZiYKIKCgkS/fv1EZmam1AEKobw2tqf9QJev1NvdXn/9denvecstt4iSkhK5q9SladOmibi4OKHVakV8fLyYPHmyOHHihPS8I3/HxsZGsWDBAhEVFSVCQkJETk6O+Pbbbzt8liuTJNddd52UuwcPHhTbt28Xw4cPF/Hx8eKf//ynVNYd31WOxHHhwgUxY8YMER4eLsLDw8WMGTNEbW2tQ/G4qqvYrly5IrKzs0W/fv2EVqsVAwYMEDNnzuxQb6XG5k6+lHv2+qrNmzdLZbyxzVpz8t577xUTJkwQISEhIioqSixYsED89NNPNu/jrUkSb+bqmTNnuo2besbX9iO9yZk+0dHvjOnTpwsAIiAgwK/a0lFq2efuCV/qKx3l6X1Aa65OnjzZof2k9vuv1uXrr7/2aDsoZd+CekaJOeqt31ndcfa3pL2c+O1vfyv27t0r/d8bdXeGO/oqb9dZ7byRl94al3H1N9KePXvsfg9Yc9NgMDhUv8DAQKHRaDz6G+7YsWNi6NChihlHcbTOd955p9DpdEKv1wuj0ShaW1uFp2iE+L+7npDfKCgowGOPPYaysjKMHDnS5rl77rkH+/fvx/fff4/evXsDAA4fPow777wTBoMBBw4c6PQ6l0TkmpaWFkRERGDo0KHStZ27kp6ejvPnz6OiosJmfVVVFa699lpMmzYN77zzjqeqS6R6zuTkO++8g+nTp2PDhg2YO3eul2pIpF7O9omA/X7R6s0338QTTzyBt99+Gw8//LA7q0vkt9y1/0pEPePOXDx//jz69euHF198EUaj0QO1JfIf7Cd9E+9JQjaGDRuGxsZG6Rqz//rXvzB58mRcc801eO+99zhBQuRmAQEByMzMRHl5Oc6dO+fy+8TFxaFv3774+9//7sbaEfkfZ3IyIyMDWq0WH3zwgXcqR6Ry7uoT2/rwww+h0Whw1113ueX9iMgzuUpEzmMuEikTc9M3cZKEbBw9ehQA0K9fPzQ3N2PKlCn4/vvvsWPHDgwYMEDeyhGp1LJlyyCEwOOPP97hRloAYLFY8NFHH3X5HufOncP58+cRExPjqWoS+Q1Hc1Kv1+PXv/419uzZg61bt3b6Xv/f//f/4dixY56uMpFquKNPtNq8eTM+/vhjPPTQQ9yPJXIzd+YqEbmOuUikTMxN3xModwVIPhUVFWhubgYAXLhwAe+//z5MJhPuv/9+JCUl4amnnkJJSQlmzJiB0NBQu6eIjRo1ypvVJlKd0aNHY8OGDZg3bx5SUlLwxBNPYMiQIbBYLPjiiy+wceNGGAwGTJw4EQDQ2Ngo5WNLSwsqKyuxevVqAEBubq5cYRCphjM5uWbNGnz99deYNWsW9uzZg/vvvx+xsbE4f/48TCYTNm/ejMLCQgwbNkzusIh8grN9ImDbLzY2NuLrr7/GBx98gP/5n/9BWloa3nzzTbnCIVItV3KViNyPuUikTM7mZn19Pf785z93eJ9+/fohLS3N29X3S7wniR+y3pOkrcjISCQlJeHRRx/FvHnzoNPpMHDgQJw5c6bb9+MmROQef//73/Haa6/h008/RXV1NbRaLQYPHoyJEydiwYIF6NevH9LT01FSUiK9plevXtDr9Rg+fDieffZZdp5EbuRITgJXJyv/9Kc/YcuWLTh69Cjq6+vRp08fjBw5Eo888gimTZuGXr148i6RMxzNv/b9YlhYGGJjY3HLLbdg2rRpmDx5MvOPyIMc3X/ltdaJPKunuch7khB5hivjPG2lpaVh37593q20n+IkCRERERERERERERER+SUeVkVERERERERERERERH6JkyREREREREREREREROSXOElCRERERERERERERER+iZMkRERERERERERERDL77rvv8PDDDyM6OhqhoaG4+eabUV5eLj0vhIDRaER8fDxCQkKQnp6OEydO2LyH2WzGwoUL0bdvX4SFheHee+/FuXPnvB0KkU/hJAkRERERERERERGRjGpra3H77bdDq9Xi448/xsmTJ/Hqq6+id+/eUpnVq1djzZo1WL9+PcrKyqDX65GVlYWGhgapTG5uLoqKilBYWIgDBw7g0qVLyMnJQUtLiwxREfkGjRBCyF0JIiIiIiIiIiIiIn/13HPP4X//93/x2Wefdfq8EALx8fHIzc3Fs88+C+DqWSOxsbF4+eWXMWfOHNTV1aFfv354++23MW3aNADA999/j4SEBOzatQvjxo3zWjxEvoRnkhCpSH5+PjQaDXJzc6V1PBWTiIiIiIiIiEjZPvzwQ4wcORJTpkxBTEwMRowYgbfeekt6vrKyEtXV1cjOzpbW6XQ6pKWlobS0FABQXl4Oi8ViUyY+Ph4Gg0EqQ0QdBcpdAVe0trbi+++/R3h4ODQajdzVIXKKEAINDQ2Ij49Hr17um6csKyvDxo0bMWzYMJv11lMxCwoKMHjwYKxcuRJZWVk4deoUwsPDAVw9FfOjjz5CYWEhoqOjsWTJEuTk5KC8vBwBAQHdfjZzknyZp3JSTsxJ8mXMSSJlYU4SKQtzkkhZ3JmTX3/9NTZs2IDFixfj+eefx+eff44nn3wSOp0Ojz76KKqrqwEAsbGxNq+LjY3FmTNnAADV1dUICgpCnz59OpSxvr49s9kMs9ksPW5tbcWPP/6I6Oho5iT5HJdzUvigs2fPCgBcuPj0cvbsWbflRENDg0hOThYmk0mkpaWJRYsWCSGEaG1tFXq9XqxatUoq+9NPP4nIyEjx5ptvCiGEuHjxotBqtaKwsFAq891334levXqJ3bt3Mye5+M3izpyUG3OSixoW5iQXLspamJNcuChrYU5y4aKsxR05qdVqxejRo23WLVy4UIwaNUoIIcT//u//CgDi+++/tynz61//WowbN04IIcSf/vQnERQU1OG9x44dK+bMmdPp57744ouytx8XLu5enM1JnzyTxHr0+9mzZxEREdHheYvFguLiYmRnZ0Or1Xq7erJj/MqOv76+HgkJCdJ27A7z58/HhAkTMHbsWKxcuVJa392pmHPmzOn2VExHrlfZVU4q/e8hJ7aNfd5sG0/kpNyssfzxj3/EpEmTuH31APO055xtQzXnZGVlJQ4ePKiq7UltOaK2eICex6TmnOTvSc9g+/VMd+3nTznJbalrbJ/ueaON3JmTcXFxuOmmm2zW3XjjjXjvvfcAAHq9HsDVs0Xi4uKkMjU1NdLZJXq9Hk1NTaitrbU5m6SmpgZjxozp9HOXLVuGxYsXS4/r6uowYMAAVFZWIjg4GJ9++ikyMjJUsZ1ZLBbGo3A9jamhoQFJSUlO56RPTpJYT/WKiIiwu1MbGhqKiIgI1WwgzmD8vhG/u05ZLCwsxJEjR1BWVtbhOW+ditnQ0AAACAkJQUhIiE3ZwMBAhIaGIiQkRNF/DzmwbezzZttYLBYA7stJJbDG4gvfhUrnK32KkrnahmrMyfDwcNVtT2rLEbXFA7gvJjXmJH9Pegbbr2ccbT9/yEluS11j+3TPm23kjpy8/fbbcerUKZt1//znP5GYmAgASEpKgl6vh8lkwogRIwAATU1NKCkpwcsvvwwASElJgVarhclkwtSpUwEAVVVVqKiowOrVqzv9XJ1OB51O12F9VFQUQkJCEBoaiujoaFVsZ9ZtgvEoV09jsr7G2Zz0yUkSIrrq7NmzWLRoEYqLixEcHGy3XPsvBiFEt18WXZXJz8/HihUrOqwvLi5GaGhop68xmUxdfp4/Y9vY5422uXLlisc/g4iIiIiIiKgrTz31FMaMGYO8vDxMnToVn3/+OTZu3IiNGzcCuDq2k5ubi7y8PCQnJyM5ORl5eXkIDQ3F9OnTAQCRkZGYPXs2lixZgujoaERFRWHp0qUYOnQoxo4dK2d4RIrGSRIiH1ZeXo6amhqkpKRI61paWrB//36sX79eOgLB06diWk8vzc7O7vRyWyaTCVlZWaqZ1XYXto193myb+vp6j74/ERERERERUXduvfVWFBUVYdmyZXjppZeQlJSEtWvXYsaMGVKZZ555Bo2NjZg3bx5qa2uRmpqK4uJim0sLvfbaawgMDMTUqVPR2NiIzMxMFBQUICAgQI6wiHyCqidJDMY9MLdcPRL+m1UTZK4NkftlZmbi+PHjNusee+wx3HDDDXj22WcxaNAgr56KqdVq7Q5oj/jdJ1I+AszJtrpqN3/njbZh27vHwOd2Sv9nfhN5DnONfNn+/fvxyiuvoLy8HFVVVSgqKsKkSZOk52fNmoUtW7bYvCY1NRWHDh2SHpvNZixduhTvvPOONPDzxhtvoH///h6rd9u8A5h7RL6I/Sf5ipycHOTk5Nh9XqPRwGg0wmg02i0THByMdevWYd26dR6o4VXMKVIbVU+SEKldeHg4DAaDzbqwsDBER0dL63kqJhEREREpweXLlzF8+HA89thjeOCBBzotc88992Dz5s3S46CgIJvnc3Nz8dFHH6GwsBDR0dFYsmQJcnJyUF5eziNkiYiIiMglnCQhUjmeiklERERESjB+/HiMHz++yzI6nQ56vb7T5+rq6rBp0ya8/fbb0sE827ZtQ0JCAvbu3Ytx48a5vc5EREREpH6cJCFSmX379tk8VsqpmERERERE3dm3bx9iYmLQu3dvpKWl4Xe/+x1iYmIAXL0fn8ViQXZ2tlQ+Pj4eBoMBpaWldidJzGYzzGaz9Nh6PzKLxQKLxdKhvHWd9V9dgOj0eepc+/Yj53TXfmxXIiIi9+MkCREREREREclu/PjxmDJlChITE1FZWYkXXngBd999N8rLy6HT6VBdXY2goCD06dPH5nWxsbGorq62+775+flYsWJFh/XFxcUIDQ21+zqTyQQAWH2b7fpdu3Y5EZX/srYfucZe+125csXLNfFd7e8nREREZA8nSYiIiIiIiEh206ZNk/5vMBgwcuRIJCYmYufOnZg8ebLd1wkhoNFo7D6/bNkyLF68WHpcX1+PhIQEZGdnIyIiokN5i8UCk8mErKwsaLVaGIx7bJ6vMPKyXl1p337knO7az3omFBEREbkPJ0mIiIiIiIhIceLi4pCYmIjTp08DAPR6PZqamlBbW2tzNklNTQ3GjBlj9310Oh10Ol2H9VqttstBfOvz5hbbCZjkF4ql/3+zaoLD8fib7tqXumav/dimRERE7tdL7goQERERERERtXfhwgWcPXsWcXFxAICUlBRotVqbyxBVVVWhoqKiy0kSIiIiIqKu8EwSIiIiIiIi8rhLly7hq6++kh5XVlbi6NGjiIqKQlRUFIxGIx544AHExcXhm2++wfPPP4++ffvi/vvvBwBERkZi9uzZWLJkCaKjoxEVFYWlS5di6NChGDt2rFxhEREREZGP4yQJERERERERedzhw4eRkZEhPbbeJ2TmzJnYsGEDjh8/jq1bt+LixYuIi4tDRkYGduzYgfDwcOk1r732GgIDAzF16lQ0NjYiMzMTBQUFCAgI8Ho8RERERKQOnCQhIiIiIiIij0tPT4cQwu7ze/bssfucVXBwMNatW4d169a5s2pERERE5Md4TxIiIiIiIiIiIiIiIvJLnCQhIiIiIiIiIiIiIiK/xEkSIiIiIiIiIiIiIiLyS7wnCRERERGRDxr43E7p/9+smiBjTYiIiIiIiHwXzyQhIiIiIiIiIiIiIiK/xEkSIiIiIiIiIiIiIiLyS5wkISIiIiIiIiIiIiIiv8R7khAREZHT2t4LgYiIiIiIiIjIV/FMEiIiIiIiIiIiIiIi8ks8k4SIiIiIiIioHYNxD8wtGrmrQUREREQexjNJiIiIiEiV9u/fj4kTJyI+Ph4ajQYffPCBzfNCCBiNRsTHxyMkJATp6ek4ceKETRmz2YyFCxeib9++CAsLw7333otz5855MQoiIiIiIiLyJE6SEBEREZEqXb58GcOHD8f69es7fX716tVYs2YN1q9fj7KyMuj1emRlZaGhoUEqk5ubi6KiIhQWFuLAgQO4dOkScnJy0NLS4q0wiIiIiIiIyIM4SUJERORGPHKdSDnGjx+PlStXYvLkyR2eE0Jg7dq1WL58OSZPngyDwYAtW7bgypUr2L59OwCgrq4OmzZtwquvvoqxY8dixIgR2LZtG44fP469e/d6OxwiIiIiIiLyAE6SEBERuRGPXCfyDZWVlaiurkZ2dra0TqfTIS0tDaWlpQCA8vJyWCwWmzLx8fEwGAxSGSIiIiIiIvJtvHE7ERGRG40fPx7jx4/v9Ln2R64DwJYtWxAbG4vt27djzpw50pHrb7/9NsaOHQsA2LZtGxISErB3716MGzfOa7EQqVl1dTUAIDY21mZ9bGwszpw5I5UJCgpCnz59OpSxvr4zZrMZZrNZelxfXw8AsFgsNv+6QhcgOl3fk/fsCXfEpCRqiwfoeUxqagsiIiIios5wkoSIiMhLujtyfc6cOd0euc5JEiL30mg0No+FEB3Wtdddmfz8fKxYsaLD+k8//RShoaEwmUyuVRbA6ts6X79r1y6X39MdehKTEqktHsD1mK5cueLmmhCRUuzfvx+vvPIKysvLUVVVhaKiIkyaNEl6XgiBFStWYOPGjaitrUVqaipef/11DBkyRCpjNpuxdOlSvPPOO2hsbERmZibeeOMN9O/fX4aIiIiIXNOjSZL8/Hw8//zzWLRoEdauXQuAnSgREZE9njpy3d5R64DnjgC2dzS7Jz9TDmo8qtzbnG1Db7W1Xq8HcDXn4uLipPU1NTVSjur1ejQ1NaG2ttYmJ2tqajBmzBi7771s2TIsXrxYelxfX4+EhARkZGTgb3/7G7KysqDVal2qt8G4p9P1FUZ5JlAtFgtMJlOPYlIStcUD9Dymtn0KEamL9TKxjz32GB544IEOz1svE1tQUIDBgwdj5cqVyMrKwqlTpxAeHg7g6mViP/roIxQWFiI6OhpLlixBTk4OysvLERAQ4O2QiIiIXOLyJElZWRk2btyIYcOG2axnJ0pERNQ1dx+5bu+odcBzR0PbO5odkP+Idk9Q41Hl3uZoG3rrqPWkpCTo9XqYTCaMGDECANDU1ISSkhK8/PLLAICUlBRotVqYTCZMnToVAFBVVYWKigqsXr3a7nvrdDrodLoO660D1Fqt1uUBeHNL598Dcg/o9yQmJVJbPIDrMamtHYjoZ7xMLJGy8eB0Iu9xaZLk0qVLmDFjBt566y2sXLlSWs9OlIiIyD5PHblu76h1AB47Gtre0eyAfEe0e4Iajyr3Nmfb0J1HrV+6dAlfffWV9LiyshJHjx5FVFQUBgwYgNzcXOTl5SE5ORnJycnIy8tDaGgopk+fDgCIjIzE7NmzsWTJEkRHRyMqKgpLly7F0KFDpf1YIiIiNeJlYonkxYPTibzLpUmS+fPnY8KECRg7dqzNJImnOtGubn7Z2SUZrOt0vUSHdf7A3y8NovT4lVovIvI8Tx25bu+odcBzR0PbO5rd+plqo8ajyr3N0TZ0ZzsfPnwYGRkZ0mPrZOLMmTNRUFCAZ555Bo2NjZg3b550JF5xcbH0AxMAXnvtNQQGBmLq1KnSkXgFBQX8gUlERKrmqcvEAo6P8fT0t729y8Nev/x/bB776gE+Sh/7UAJvtJEn3psHpxN5n9OTJIWFhThy5AjKyso6POepTtTeZUSKi4sRGhpqt67/PrJV+r8aL/3RHX+/NIhS4+fNL4nUjUeuEylHeno6hLB//xyNRgOj0Qij0Wi3THBwMNatW4d169Z5oIZERETK5u7LxALOj/G4+tu+q8vDtuXr40VKHftQEk+2kSfGeLx9cDoROTlJcvbsWSxatAjFxcUIDg62W87dnai9y4hkZ2cjIiKiQ3nrZR1eONwL5tar7+mrRwa4wt8vDaL0+HnzSyJ145HrRCSHgc/ttHn8zaoJMtWEiIjUwFOXiQUcH+Pp6W/7ri4P25avjhcpfexDCbzRRu4e45Hj4PSuzu4KDAyU/t9W2zO1fOlsJrWdgaW2eICex+Tq65yaJCkvL0dNTQ1SUlKkdS0tLdi/fz/Wr1+PU6dOAXB/J9rVzS+7+pIzt2qky4H4Y4fh75cGUWr8SqwTEbkPj1wnIiIiIl/nqcvEAs6P8bj6276ry8O2f39fptSxDyXxZBu5833lOjjdkbO72p+N0/ZMLV88G0ttZ2CpLR7A9ZhcPbvLqUmSzMxMHD9+3GbdY489hhtuuAHPPvssBg0a5LFOlIiIiIiIiIiI3IOXiSVSFrkOTu/q7K6QkJBOz8Zpe6aWL52NpbYzsNQWD9DzmFw9u8upSZLw8HAYDAabdWFhYYiOjpbWsxMlIiIiIiKi9vbv349XXnkF5eXlqKqqQlFRESZNmiQ9L4TAihUrsHHjRumSlK+//jqGDBkilTGbzVi6dCneeecd6ZKUb7zxBvr37y9DRES+jZeJJVIWuQ5Od+TsrvZn47Q9U8sXB+fVdgaW2uIBXI/J1XZw+sbt3WEnSkRERERERO1dvnwZw4cPx2OPPYYHHnigw/OrV6/GmjVrUFBQgMGDB2PlypXIysrCqVOnpN+Tubm5+Oijj1BYWIjo6GgsWbIEOTk5KC8v5+9JIifxMrFEysKD04nk0+NJkn379tk8ZidKRERERERE7Y0fPx7jx4/v9DkhBNauXYvly5dj8uTJAIAtW7YgNjYW27dvx5w5c1BXV4dNmzbh7bfflgZ6tm3bhoSEBOzduxfjxvnO5T6IiIhcwYPTiTzD7WeSEBERERERETmjsrIS1dXVyM7OltbpdDqkpaWhtLQUc+bMQXl5OSwWi02Z+Ph4GAwGlJaW2p0kMZvNMJvN0mPrtaotFgssFkuH8tZ1ul72j7BvX5Z+Zm0Tto1rums/tiuRf+HB6UTewUkSIiIiIiIiklV1dTUASDeetYqNjcWZM2ekMkFBQTY3orWWsb6+M/n5+VixYkWH9cXFxQgNDbX7un8f2dptvXft2tVtGX9lMpnkroJPs9d+V65c8XJNiIiI1I+TJERERERERKQIGo3G5rEQosO69rors2zZMumG1MDVM0kSEhKQnZ2NiIiIDuUtFgtMJhNeONwL5tauP7vCyEt8tWdtv6ysLNXdRNYbums/65lQRERE5D6cJCEiIiIiIiJZ6fV6AFfPFomLi5PW19TUSGeX6PV6NDU1oba21uZskpqaGowZM8bue+t0Ouh0ug7rtVptl4P45lYNzC1dT5JwEsC+7tqXumav/dimRERE7tdL7goQERGRugx8bqe0EBEROSIpKQl6vd7mEkNNTU0oKSmRJkBSUlKg1WptylRVVaGioqLLSRIiIiIioq7wTBIiIiJyCCc9iIioJy5duoSvvvpKelxZWYmjR48iKioKAwYMQG5uLvLy8pCcnIzk5GTk5eUhNDQU06dPBwBERkZi9uzZWLJkCaKjoxEVFYWlS5di6NChGDt2rCwxte0bv1k1QZY6EBEREVHPcJKEiIiIiIiIPO7w4cPIyMiQHlvvEzJz5kwUFBTgmWeeQWNjI+bNm4fa2lqkpqaiuLgY4eHh0mtee+01BAYGYurUqWhsbERmZiYKCgoQEBDg9XiIiIiISB04SUJEREREREQel56eDiGE3ec1Gg2MRiOMRqPdMsHBwVi3bh3WrVvngRoSERERkT/iPUmIiIiIiIiIiIiIiMgvcZKEiIiIiIiIiIiIiIj8EidJiIiIiIiIiIiIiIjIL3GShMiH5efn49Zbb0V4eDhiYmIwadIknDp1yqaMEAJGoxHx8fEICQlBeno6Tpw4YVPGbDZj4cKF6Nu3L8LCwnDvvffi3Llz3gyFiIiIiIiIiIiIyOs4SULkw0pKSjB//nwcOnQIJpMJzc3NyM7OxuXLl6Uyq1evxpo1a7B+/XqUlZVBr9cjKysLDQ0NUpnc3FwUFRWhsLAQBw4cwKVLl5CTk4OWlhY5wiIiIiIiIiIiIiLyikC5K0BErtu9e7fN482bNyMmJgbl5eW46667IITA2rVrsXz5ckyePBkAsGXLFsTGxmL79u2YM2cO6urqsGnTJrz99tsYO3YsAGDbtm1ISEjA3r17MW7cOK/HRUREREREREREROQNnCQhUpG6ujoAQFRUFACgsrIS1dXVyM7OlsrodDqkpaWhtLQUc+bMQXl5OSwWi02Z+Ph4GAwGlJaWdjpJYjabYTabpcf19fUAAIvFAovFYlPW+ljXS3S63p9Z24Bt0ZE324btT0RKN/C5nXJXgYiIiIiISLU4SUKkEkIILF68GHfccQcMBgMAoLq6GgAQGxtrUzY2NhZnzpyRygQFBaFPnz4dylhf315+fj5WrFjRYX1xcTFCQ0M7fc2/j2y1ebxr1y4HovIPJpNJ7iooljfa5sqVKx7/DCIiIiIiIiIiUiZOkhCpxIIFC3Ds2DEcOHCgw3MajcbmsRCiw7r2uiqzbNkyLF68WHpcX1+PhIQEZGdnIyIiwqasxWKByWTCC4d7wdz68/tVGHkZL2vbZGVlQavVyl0dRfFm21jPhCIiIiIiIiIiIv/DSRIiFVi4cCE+/PBD7N+/H/3795fW6/V6AFfPFomLi5PW19TUSGeX6PV6NDU1oba21uZskpqaGowZM6bTz9PpdNDpdB3Wa7VauwPa5lYNzC0am7J0VVft5u+80TZseyIiIiIiIiIi/9VL7goQkeuEEFiwYAHef/99fPLJJ0hKSrJ5PikpCXq93uaSRU1NTSgpKZEmQFJSUqDVam3KVFVVoaKiwu4kCREREREREZEaDHxup7QQEZF/4pkkRD5s/vz52L59O/7yl78gPDxcuodIZGQkQkJCoNFokJubi7y8PCQnJyM5ORl5eXkIDQ3F9OnTpbKzZ8/GkiVLEB0djaioKCxduhRDhw7F2LFj5QyPiIiIiIiIiIiIyKM4SULkwzZs2AAASE9Pt1m/efNmzJo1CwDwzDPPoLGxEfPmzUNtbS1SU1NRXFyM8PBwqfxrr72GwMBATJ06FY2NjcjMzERBQQECAgK8FQoRERERERERERGR13GShMiHCSG6LaPRaGA0GmE0Gu2WCQ4Oxrp167Bu3To31o6IiIiIiIiIiIhI2XhPEiIiIiIiIiIiIiIi8kucJCEiIiIiIiIiIiIiIr/Ey20REREREREREZHPG/jcTrmrQEREPohnkhARERGR3zIajdBoNDaLXq+XnhdCwGg0Ij4+HiEhIUhPT8eJEydkrDERERERERG5EydJiIiIvIgDskTKM2TIEFRVVUnL8ePHpedWr16NNWvWYP369SgrK4Ner0dWVhYaGhpkrDERERERERG5CydJiIiIvIwDskTKEhgYCL1eLy39+vUDcHXScu3atVi+fDkmT54Mg8GALVu24MqVK9i+fbvMtSYiIvI8HuBDRET+wKlJkvz8fNx6660IDw9HTEwMJk2ahFOnTtmUcaSDNJvNWLhwIfr27YuwsDDce++9OHfuXM+jISIi8gEckCVSltOnTyM+Ph5JSUl48MEH8fXXXwMAKisrUV1djezsbKmsTqdDWloaSktL7b6f2WxGfX29zQIAFotF+teZRRcgnF6c/YyeLK7EpORFbfG4IyYi8m88wIfIOzjuSiQfp27cXlJSgvnz5+PWW29Fc3Mzli9fjuzsbJw8eRJhYWEAfu4gCwoKMHjwYKxcuRJZWVk4deoUwsPDAQC5ubn46KOPUFhYiOjoaCxZsgQ5OTkoLy9HQECA+6MkIiJSEOuArE6nQ2pqKvLy8jBo0KBuB2TnzJnT6fuZzWaYzWbpsXVAFoBbB7d0AcLp1/j64FrbwUVyjbNt6O22Tk1NxdatWzF48GD88MMPWLlyJcaMGYMTJ06guroaABAbG2vzmtjYWJw5c8bue+bn52PFihUd1n/66acIDQ2FyWRyqo6rb3OqOABg165dzr+oB5yNSenUFg/gekxXrlxxc02IyNdYD/Bpr/0BPgCwZcsWxMbGYvv27Xb3XYmocxx3JZKPU5Mku3fvtnm8efNmxMTEoLy8HHfddZdDHWRdXR02bdqEt99+G2PHjgUAbNu2DQkJCdi7dy/GjRvnptCIiIiUx5sDsoB7B/p8YaDWU9Q4YOptjrahtwdkx48fL/1/6NChGD16NK677jps2bIFo0aNAgBoNBqb1wghOqxra9myZVi8eLH0uL6+HgkJCcjIyMDf/vY3ZGVlQavVOlxHg3GPw2WtKoze2ae2WCwwmUxOx6RUaosH6HlMbSfeicg/ufsAHyLqHMddieTj1CRJe3V1dQCAqKgoAN1fkmDOnDkoLy+HxWKxKRMfHw+DwYDS0tJOk9XeEbL2Tv+2rtP1Eh3W+QN/P+pV6fErtV5E5B3eHJAF0OOBPlcGZ9vy1kCtp6hxwNTbnG1DuQdkw8LCMHToUJw+fRqTJk0CAFRXVyMuLk4qU1NT02Eysy2dTgedTtdhvTV+rVbr1PZkbrGf//Z4e3t1NialU1s8gOsxqa0diMg5njjAx9ExHld+27ty5nNbvvR7XeljH0rgjTby5Ht7a9yViHowSSKEwOLFi3HHHXfAYDAAgEMdZHV1NYKCgtCnT58OZayvb8/eEbLFxcUIDQ21W8d/H9kq/V8tR7I6w9+PelVq/LxkARG15ckBWaDnA32uDM62/3w1UOOAqbc52oZyt7PZbMaXX36JO++8E0lJSdDr9TCZTBgxYgQAoKmpCSUlJXj55ZdlrSeRWhmNxg6//dr+VhRCYMWKFdi4cSNqa2uRmpqK119/HUOGDJGjukSq54kDfJwd43Hmt70rZz635YtjR0od+1AST7aRp8Z4vDnu2tXEZWBgoPT/ttpOSPrSRJ3aJhfVFg/Q85hcfZ3LkyQLFizAsWPHcODAgQ7POdtBdlfG3hGy2dnZiIiI6FDeesTiC4d7wdx69T19/UhWZ/j7Ua9Kj1/uI2SJSFk4IEskr6VLl2LixIkYMGAAampqsHLlStTX12PmzJnQaDTIzc1FXl4ekpOTkZycjLy8PISGhmL69OlyV71LA5/bKf3/m1UTZKwJkfOGDBmCvXv3So/bXj/dkWuxE5HnuOMAH0fHeFz5be9PZ0ErfexDCbzRRp4a4/HmuKsjE5ftJ5raTkhyclF+aosH8P799FyaJFm4cCE+/PBD7N+/H/3795fWW2/k1VUHqdfr0dTUhNraWptZzZqaGowZM6bTz+vqkgVdfcmZWzXSEbD+2GH4+1GvSo1fiXUiIu9R64Aska86d+4cHnroIZw/fx79+vXDqFGjcOjQISQmJgIAnnnmGTQ2NmLevHnSUevFxcUcjCXyIN4kmki53HGAj7NjPM78tvfHs6CVOvahJJ5sI0+8r7fHXbuauAwJCel0oqnthCQnF+WjtngA+e6n59QkiRACCxcuRFFREfbt24ekpCSb5x3pIFNSUqDVamEymTB16lQAQFVVFSoqKrB69WqXgiAiIvIVHJAlUpbCwsIun9doNDAajTAajd6pEBG5/SbR7rjHpSPUdKmLnlDjpT+8qbv283a78gAfIu+Ra9zVkYnL9hNNbSckfXFwXm2Ti2qLB/D+/fScmiSZP38+tm/fjr/85S8IDw+XrmUXGRmJkJAQhzrIyMhIzJ49G0uWLEF0dDSioqKwdOlSDB06FGPHjnUpCCLyPbwMCPkrDsgSERHZ54mbRLvjHpeO8MXLjXiSGi/94U322s/b97jkAT5E3sNxVyL5ODVJsmHDBgBAenq6zfrNmzdj1qxZABzrIF977TUEBgZi6tSpaGxsRGZmJgoKCmyuNUtERERERET+xRM3iXbHPS4d4UuXG/EkNV76w5u6az9v3+OSB/gQeQ/HXYnk4/TltrrjSAcZHByMdevWYd26dc58PBEREfkYnjVGREQ94Y6bRLvjHpeO4ISALTVe+sOburo/BxGpE8ddieTTS+4KEBEREREREXXGepPouLg4m2uxW1mvxW7vZrRERERERN1x6kwSIiIiIiIiIk/hTaKJiIiIyNs4SUJERERERESKwJtEExEREZG3cZKEiIiIiIiIFIE3iSYiIiIib+M9SYiIiIiIiIiIiIiIyC/xTBIiIiIiIhUb+NxO6f/frJogY02IiIiUjX0mEZF/4iQJEREReQV/dBIRERERERGR0vByW0RERERERERERERE5Jc4SUJERERERERERERERH6Jl9siIiIiIpIZL0dHREREREQkD06SEBERERH5CU7GEBERERER2eLltoiIiIiIiIiIiIiIyC/xTBIiIiIiIiKiHuKZWkRERES+iWeSEBERERERERERERGRX+KZJERERGSj7ZGwRERERERERERqxkkSIiIiIiIiIiKiNngJPSIi/8HLbRERERERERERERERkV/imSREREREREREbsQj0Im8h5eKJSKinuIkCREREXld+x+zHEAi8j4O4hIREREREXGShIgUgIM0REREREREREREJAfek4SIiIiIiIiIiIiIiPwSJ0mIiIiIiIiIiIiIiMgv8XJbREREJDtedo/oZ7wBLRERkbJwX5WISN04SUJERERE5OfaT8xwAIjIczjYSkRERKQsfjNJwh1RIiIi+3jkOhERERERERH5I7+ZJCEiIiIiIiLyNh6IQKQuPPuSiEh9OElCREREisKzP4mUhTlJRERERERqxkkSIiIiIiIiIhlwEpKIiIhIfr3k/PA33ngDSUlJCA4ORkpKCj777DM5q0Pk95SQkwOf29npQuSPlJCTRPQzf8pJR/rggc/thMG4x4u1IrLlTzlJ5AuYk471n0Te4M185PgNqYFsZ5Ls2LEDubm5eOONN3D77bfjD3/4A8aPH4+TJ09iwIABclWLyG8xJ4mUhTlJpCzMya4ZjHtgbtFIj+0dEe/oD2ceUU/dYU4SKQtzkkg5mI9EzpNtkmTNmjWYPXs2fv3rXwMA1q5diz179mDDhg3Iz8/36GfzlGaijuTMSSLqyNM56YtH+Djaf7OfJ09gP+ldzGPqjhpz0l7fzBwgX+DNfVel5YSz+9W88Tt5mhr7SCJPk2WSpKmpCeXl5Xjuueds1mdnZ6O0tLRDebPZDLPZLD2uq6sDAPz444+wWCwdylssFly5cgWBll5oadV0eL6tXyz9b+n/f1uW6VQcSmWN/8KFC9BqtXJXx+uUHn9DQwMAQAghc01+5smcdCYfu9I2VwHbfE3N/2unr3GkTPtyjmr7fq5+dyh9W5WTN9tGzTnZVRsGNl92c609o23uB9pZ3569cq7kattt8Y7f7+/Re8nNHd9brrC24c3L34f5//qBrj5fzTn5448/2s1LX83JwFaBK1daO/Tz9nLPlTgvXLjgWmU70d0+gyf6H7lyz8peTI7WS+052dPfk97QVZ8n135sV7iP6xrr30XXS+DfRrTabT9/ysm221Lb/qN9v6DUPtRe7rYfiHN1f7W7XOsq1+Xumxwh929uX+0nnc1HoOucDA4O7rQNu8q7rvotq67Gazy5Taqtj1JbPEDPY3I5J4UMvvvuOwFA/O///q/N+t/97ndi8ODBHcq/+OKLAgAXLqpazp49662U6xZzkgsX5iQXLkpbmJNcuChrYU5y4aKshTnJhYuyFqXkpLP5KARzkos6F2dzUrbLbQGARmN7VI4QosM6AFi2bBkWL14sPW5tbcWPP/6I6OjoTsvX19cjISEBZ8+eRUREhPsrrnCMX9nxCyHQ0NCA+Ph4uavSgSdyUul/DzmxbezzZtuoNSfPnDmDm2++mdtXDzFPe87ZNlRrTv7444/QarUYMGCAqrYnteWI2uIBeh6TmnOSvyc9g+3XM921nz/lJLelrrF9uueNNlJqTjqaj0DXOdnQ0KCq7UxteaO2eAD59l1lmSTp27cvAgICUF1dbbO+pqYGsbGxHcrrdDrodDqbdb179+72cyIiIlSzgbiC8Ss3/sjISLmrYMMbOankv4fc2Db2eatt1JiTvXr1AsDty13Yjj3nTBuqMSd79+6N+vp6AOrcntQWk9riAXoWk1pzsjtq3A68ie3XM121n7/lJLelrrF9uufpNlJSTjqbj0DXOWmdWFHbdsZ4lM/b+669XPqkHgoKCkJKSgpMJpPNepPJhDFjxshRJSK/xpwkUhbmJJGyMCeJlIU5SaQszEki5WA+ErlGtsttLV68GI888ghGjhyJ0aNHY+PGjfj2228xd+5cuapE5NeYk0TKwpwkUhbmJJGyMCeJlIU5SaQczEci58k2STJt2jRcuHABL730EqqqqmAwGLBr1y4kJib2+L11Oh1efPHFDqeK+QvG79/xu8pTOcm/h31sG/vYNj3PSbahe7Ade04tbeiOflItbdGW2mJSWzyAOmMC+HtSydh+PeOr7eeJnPTVtvAWtk/3/LWN3JmPamtDxqN8csWkEUIIr34iERERERERERERERGRAshyTxIiIiIiIiIiIiIiIiK5cZKEiIiIiIiIiIiIiIj8EidJiIiIiIiIiIiIiIjIL3GShIiIiIiIiIiIiIiI/JLqJkneeOMNJCUlITg4GCkpKfjss8/krlK38vPzceuttyI8PBwxMTGYNGkSTp06ZVNGCAGj0Yj4+HiEhIQgPT0dJ06csCljNpuxcOFC9O3bF2FhYbj33ntx7tw5mzK1tbV45JFHEBkZicjISDzyyCO4ePGiTZlvv/0WEydORFhYGPr27Ysnn3wSTU1NHom9M/n5+dBoNMjNzZXW+VP8auOLOemM/fv3Y+LEiYiPj4dGo8EHH3xg87w3t10lUdr3mj9Se+55kiPbLzmns77dH/lCXnrz+1sOntzP9KbvvvsODz/8MKKjoxEaGoqbb74Z5eXl0vO+GJNS+EKeysFb+7xqpPbv1Z5yNudKSkqQkpKC4OBgDBo0CG+++aaXaioPZ9qnqqoK06dPx/XXX49evXr5zX6XM230/vvvIysrC/369UNERARGjx6NPXv2eLG2vsWX+0R39FtK4a5+REk2bNiAYcOGISIiQsrFjz/+WHpelniEihQWFgqtViveeustcfLkSbFo0SIRFhYmzpw5I3fVujRu3DixefNmUVFRIY4ePSomTJggBgwYIC5duiSVWbVqlQgPDxfvvfeeOH78uJg2bZqIi4sT9fX1Upm5c+eKa6+9VphMJnHkyBGRkZEhhg8fLpqbm6Uy99xzjzAYDKK0tFSUlpYKg8EgcnJypOebm5uFwWAQGRkZ4siRI8JkMon4+HixYMECr7TF559/LgYOHCiGDRsmFi1a5Hfxq42v5qQzdu3aJZYvXy7ee+89AUAUFRXZPO+tbVdplPS95o/8Ifc8yZHtlxxnr2/3N76Sl978/vY2T+9nesuPP/4oEhMTxaxZs8Tf/vY3UVlZKfbu3Su++uorn41JKXwlT+XgrX1eNVLz92pPOZtzX3/9tQgNDRWLFi0SJ0+eFG+99ZbQarXiz3/+s5dr7h3Otk9lZaV48sknxZYtW8TNN9/sF/tdzrbRokWLxMsvvyw+//xz8c9//lMsW7ZMaLVaceTIES/XXPl8vU90R7+lFO7qR5Tkww8/FDt37hSnTp0Sp06dEs8//7zQarWioqJCCCFPPKqaJLntttvE3LlzbdbdcMMN4rnnnpOpRq6pqakRAERJSYkQQojW1lah1+vFqlWrpDI//fSTiIyMFG+++aYQQoiLFy8KrVYrCgsLpTLfffed6NWrl9i9e7cQQoiTJ08KAOLQoUNSmYMHDwoA4h//+IcQ4uqXSK9evcR3330nlXnnnXeETqcTdXV1ngtaCNHQ0CCSk5OFyWQSaWlpUofuL/GrkVpy0lHtO15vbrtKJ+f3mj/yt9zztPbbLznOXt/uj3w1Lz31/e1tnt7P9KZnn31W3HHHHXaf98WYlMJX89TbPLXP6y/U8r3qDs7m3DPPPCNuuOEGm3Vz5swRo0aN8lgd5dST7yR/2e9yx/f2TTfdJFasWOHuqvk8NfWJrvRbSuZKP+IL+vTpI/74xz/KFo9qLrfV1NSE8vJyZGdn26zPzs5GaWmpTLVyTV1dHQAgKioKAFBZWYnq6mqb2HQ6HdLS0qTYysvLYbFYbMrEx8fDYDBIZQ4ePIjIyEikpqYCAI4dO4Y//OEP0Gg0MBgMuOaaa/CrX/0KMTExCA4OBgCkp6fjoYcegtlsRmRkJIKDg3HTTTdh5cqVbr8E1fz58zFhwgSMHTvWZr2n4geAUaNGITIy0qaMwWBAfHy8VGbcuHEwm802lzCg7qkpJ13l7LZ77NgxPPbYY7j99tshhMDEiRNxyy234Le//S3Cw8ORmpoKi8WCP/zhD1i4cCE0Gg2GDRuGxMRE3HfffSgqKpIr1G5563sN6JjX/oa55x7WfExKSkL//v0BAHPnzsXq1avx448/AgAGDhyInJycTl9/+PBhaDQaFBQUeKvKimSvb/c3vpyXnvr+7krb/AsODsY111yDW265xSb/cnJyEB4ejubmZpvXfvHFF9BoNIiLi7NZP3/+fNx8883IysqyuTyNN+Jxtw8//BAjR47ElClTEBMTgxEjRuCtt96SnvfFmJTAl/PUm6z92v333w+NRoPg4GDExMSguroa586dQ01NDQD729yDDz6I3//+9wD8d5uT43tViVzJuYMHD3YoP27cOBw+fBgWi8VjdZWDo+1TUFAAjUYjLcHBwdDr9Th69CjKysqknLQyGo025dsv33zzjTfCcwt3fG+3traioaFByke6Su19oiPfuz1hLy8zMjKQn5/vVF6uX7++w/u70o8oWUtLCwoLC3H58mWMHj1atnhUM0ly/vx5tLS0IDY21mZ9bGwsqqurZaqV84QQWLx4Me644w4YDAYAkOrfVWzV1dUICgpCnz59uiwTExMDAHjrrbeQkpKCsrIy9O3bF7NmzUJRURESEhJQV1eH2bNnS+8xaNAgBAYGYsWKFXj33XeRnJyMF154AQsWLHBb3IWFhThy5Ajy8/M7POeJ+Nuy7tRby7T/nD59+iAoKMintiMlUEtO9oQz2+6f//xnKSeffvpp3HLLLcjKysKUKVPw17/+Fa2trQCARx55BAsXLkRGRgbi4uLw8MMP49/+7d8QGBio2Gupeut7ra22ee1vmHs917aPXLp0KVJSUjBkyBA88sgjePPNN236SLKvq77d3/hqXnry+9uetvn39NNPY/fu3SgqKsKUKVNs8i8jIwOXLl3C4cOHbV6/b98+hIWFobq6Gv/4xz8A/Lwt3njjjQCA3r17S+U9HY8nfP3119iwYQOSk5OxZ88ezJ07F08++SS2bt0q1ddaP3v1VVpMSuCreSqXhQsX4uDBgzCZTNJ9D7Zu3Yobb7wRe/fuBWDbdocOHZJeu2nTJun//ta+cnyvKpUrOdfZ7/XY2Fg0Nzfj/PnzHqurHJxtn82bN0s5+frrr+Oaa67B4cOHbXKyrd27d+PgwYMdlvYHGSiZO763X331VVy+fBlTp071RBV9ltr7REe+d92hfV7efPPNePnll53KyylTptiUcbUfUaLjx4/jmmuugU6nw9y5c1FUVISbbrpJtngCPfbOMtFoNDaPhRAd1inZggULcOzYMRw4cKDDc67E1r6MRqPBwYMH8cQTTyArKwsffPABDAYDrrvuOmRlZeHdd99FZGQkHnvsMek1ISEh0Gg0GDx4MCZOnIjx48fjpptuwpYtW/Cf//mf0lknrjp79iwWLVqE4uLiLt/LXfG7oww5ztdz0h26a4PW1labnNTpdNKk5bJly6SzRyorK7Fjxw789re/xYoVK1BUVITk5GQ8/vjjePzxx6WJFKXxxvdad2X8EXPPNe37yMWLF+P777/HgQMH0L9/fyxZsgS7d++Wu5qK52jf7m98LS89/f3dXvv80+l00nNZWVk2+ZeRkQHg6qTIqFGjpHL79u3Dfffdh08//RSffvopwsLCpG1x8eLF0g2PvRGPp7S2tmLkyJHIy8sDAIwYMQInTpzAhg0b8Oijj0rlfCkmJfG1PJXLgAEDpNwLCAgAAOzduxcPPPAAJk+ejNOnT9u03b59+wAAEyZMwM6dO1FaWooxY8b4Xft6+3vVFzgbd2flO1uvFo62j8FgwMiRI6XH69atQ3Z2Nvbu3SvlZNsBx5SUFPTt29dzFfciV7+333nnHRiNRvzlL3/p9MA7Un+f6On42uflAw88gKeeegp33HGHy3np7n5ETtdffz2OHj2Kixcv4r333sPMmTNRUlIiPe/teFRzJknfvn0REBDQYUappqamw8yTUi1cuBAffvghPv30U+nSHgCg1+sBoMvY9Ho9mpqaUFtb22WZH374AXl5edBoNNi4cSN0Oh3+9a9/2ZSpqanBvffeK71HS0sLLBaLVCYwMBA333wzmpqacPHixR7HXV5ejpqaGqSkpCAwMBCBgYEoKSnBf/7nfyIwMFD6XHfF3177+Nt/Tm1trU385Bg15GRPOZq7zc3NNjnZvkz//v1x+fJlXLhwAQCko3vabrsA0KuX8r7SvfW91l77tvEnzL2eadtHLl26tMP2GxQUZNNHUue669tbWlrkrqJX+WJeevr7uzPt91Hba5t/N998M/r06SMNvAJXJw8+++wzpKenIy0tDZ9++qm0Ld5yyy345JNPcP78eezfv9/t+5neFBcXh5tuuslm3Y033ohvv/0WgGf/Rmrmi3mqFNZtLjAwEK+++ioaGhrwhz/8QWq7n376STqTxGg0AgD+67/+C4B/ta8c36tK5krOdfZ7vaamBoGBgYiOjvZYXeXgju+kiIgIm5xUm5600Y4dOzB79mz893//t99fFrYzau8THfne9ZQBAwa4nJc96UeUKCgoCL/4xS8wcuRI5OfnY/jw4fiP//gP2eJR3oiai4KCgpCSkgKTyWSz3mQyYcyYMTLVyjFCCCxYsADvv/8+PvnkEyQlJdk8n5SUBL1ebxNbU1MTSkpKpNhSUlKg1WptylRVVaGiokIqM3r0aNTV1WHv3r1ISUlBQkIC/va3v6Gurs6mTEVFBaqqqqT3uXTpEnQ6HVJSUqR1lZWV6N27N/r169fj+DMzM3H8+HEcPXpUWkaOHIkZM2bg6NGjGDRokFvj//zzz6UyjsRfXFzcIX7qni/npLs4krs333wzgKv3NkhISABgf9u9fPkyevfujRUrVmDZsmU2267SePt7rau89jfMPde1tLTgk08+QUpKCl5++WW72y91r7u+3XrUsb/wpbz01vd3e23zz9ofdqVXr1646667cODAAem+JEePHkVtbS3S0tKQlpaGkpISaVu0Xt5n+fLlHtnP9Kbbb78dp06dsln3z3/+E4mJiQA89zdSO1/KU6Vpu839v//3/xAQEIB9+/ZJ29z777+PhoYG9OrVC19//TXuuOMO7NixA1999ZVfbHNyfa8qnSs5N3r06A7li4uLMXLkSGi1Wo/VVQ7u+k6y5uT+/ftt1re0tKC5udlm8bWDWFxto3feeQezZs3C9u3bMWHCBE9X0yepvU905HvXkxzNS2tOuqMf8QVCCJjNZvni8cjt4GVSWFgotFqt2LRpkzh58qTIzc0VYWFh4ptvvpG7al164oknRGRkpNi3b5+oqqqSlitXrkhlVq1aJSIjI8X7778vjh8/Lh566CERFxcn6uvrpTJz584V/fv3F3v37hVHjhwRd999txg+fLhobm6WymRkZAgAIisrSxw8eFAMHTpU5OTkSM83NzcLg8EgMjMzxZEjR8SwYcNEYGCgmDdvnrBYLKKqqkr89re/FQDEm2++6bE2SUtLE4sWLXJ7/Pfcc48YNmyYOHjwoEPx7927V/Tv318sWLDAY7Gqma/mpDMaGhrEF198Ib744gsBQKxZs0Z88cUX4syZM0KI7rfd6upqAUCEhIQ4tO2++uqronfv3gKAACCio6PFlClTxIcffihL/PZ483utu7z2R/6Qe55gzcdf/OIX3W6/QgiRmJgoJkyY0Ol7lZWVCQBi8+bNXqi5b2jft/sbX8lLb35/t2XNvwcffNDhuq5du1YAEKWlpUIIIV599VURFxcnhBDi5MmTAoCoqKgQQgixYsUKAUCcPHnSY/uZ3vL555+LwMBA8bvf/U6cPn1a/OlPfxKhoaFi27ZtPhuTUvhKnsrBus9rzaWnnnrK7j5vdHS0iIiIkLa5u+++WwQHB4vHHntM9O/fXzz99NMCgLjhhhv8YpuT63vVF3SXc88995x45JFHpPJff/21CA0NFU899ZQ4efKk2LRpk9BqteLPf/6zXCF4lCPtM2bMGAFAlJWVCSGE9Ns0JSVFTJ8+XXzxxRciOjpa3HjjjUIIIV588UXpt2T75brrrpMtVlc5uw1t375dBAYGitdff90mHy9evChXCIrl631iT8dqemLz5s02edmZ2NjYbvPy2muvFUK4rx9RkmXLlon9+/eLyspKcezYMfH888+LXr16ieLiYiGEPPGoapJECCFef/11kZiYKIKCgsQtt9wiSkpK5K5St+x1UG0HVlpbW8WLL74o9Hq90Ol04q677hLHjx+3eZ/GxkaxYMECERUVJUJCQkROTo749ttvbcp8+eWXAoAIDAwU4eHhYsaMGaK2ttamzJkzZ8SECRNESEiICAwM7LRuy5Yt81RzCCE6DqS4K/4LFy6IGTNmiPDwcIfij4qKEgsWLBA//fSTp0JVPV/MSWd8+umnnebIzJkzhRDdb7vWQaHk5GSHt91rrrlG3HXXXWLBggXirrvuElqtVgAQ8+fP92boXfLm95ojee2P1J57nmDNR0e2XyE4SeIsf58kEcI38tKb399tuTJJ8ve//10AEHl5eUIIISZOnGjz+piYGLFu3TohxNUDhWJjY4UQntvP9KaPPvpIGAwGodPpxA033CA2btxo87wvxqQUvpCncnBmnxeACA0NFcePHxdff/210Gg0Yvr06dI216dPHwFA9OnTxy+2Obm+V31FVzk3c+ZMkZaWZlN+3759YsSIESIoKEgMHDhQbNiwwcs19q7u2uf666+3GYztbFvr1atXh8HYvXv3irKyMpul/TbnK5zZhtLS0rr8LiNbvtwn9nSspiccmSSJiYnpNi///ve/CyHc148oya9+9Stp2+rXr5/IzMyUJkiEkCce1U2SUNeam5tFaGioSE1Ndah8WlqauO6660RZWZn4/PPPxbvvviuGDx8uAIh33nnHw7UlUj9nc7IzZ86cETfddJPNEbNE5Dxn8/G6664T99xzT6fPHTx4UACwObKbiOxzpT9sbW0Vffv2FVlZWaKlpUX07t3bZrDsl7/8pXjggQfETz/9JIKDg52agCGiznU38HPp0iUREBAgMjMzhRBC/Nu//ZsAIN5//31RW1srLTNmzBAAxJdffunN6hOpjrM5aR2M/de//uXNahL5Fealb1LNPUnIMQEBAcjMzER5eTnOnTvn0GuCg4MxcuRI3HrrrfjlL3+Jv/71r4iNjUVubi4uXbrk4RoTqZsrOdnegAED8Jvf/AYAcOLECXdWj8ivOJuPsbGx+O677zp9zrpeyTfKI1ISV/pDjUaDtLQ0lJaW4vPPP8fFixeRlpYmPZ+WloZ9+/bh4MGD+Omnn5CRkeGp6hPR/9m5cydaWlqQnp6O1tZWFBQUAAAmT56MPn36SMuf/vQnAD/fwJ2IPKNtThKRMjAvlYmTJH5o2bJlEELg8ccfR1NTU4fnLRYLPvroI7uvj46OxqpVq/DDDz9g3bp1nqwqkV9wNCcbGhrsTkx++eWXAID4+HiP1pVI7ZzpI8eOHYuKigqcPHmyQ7n//u//xjXXXIPU1FSP15lILVzZR83IyMDly5fxyiuvICYmBjfeeKP0XFpaGi5cuCDtr3KShMizvv32WyxduhSRkZGYM2cO9uzZg3PnzmH+/Pn49NNPOyxDhgzB1q1b0dzcLHfViVSpfU4SkfyYl8oVKHcFyPtGjx6NDRs2YN68eUhJScETTzyBIUOGwGKx4IsvvsDGjRthMBgwceJEu+/x6KOPYs2aNfj973+P+fPnIyIiwosREKmLozkZFxeHcePG4cEHH0RaWhri4uJQW1uLnTt3YuPGjUhPT8eYMWPkDofIpznTRy5atAhbt25Feno6nn/+eQwdOhS1tbXYsWMH/vznP2PNmjUIDw+XOyQin+HKPqp14qOoqAi//OUvbd7PYDAgOjoaRUVFuPbaa5GcnOzVeIjUrKKiAs3NzWhubkZNTQ0+++wzbN68GQEBASgqKkK/fv2wadMmBAYG4vnnn+/0QJ45c+bgySefxM6dO3HffffJEAWRejiSk22Vl5cjMjKyw/vcdNNNHN8hchNn85JkJu/VvkhOR48eFTNnzhQDBgwQQUFBIiwsTIwYMUL89re/FTU1NUKIq/ckGTJkSKev37lzpwAgVqxY4c1qE6lWdzlZW1srVq5cKe6++25x7bXXSmVuvvlmsXLlSnHlyhW5QyBSDUf6SCGu3mz6iSeeEAMGDBCBgYEiPDxc3HHHHeLdd9+VsfZEvs3R/LOy3ih6/fr1HZ6bNGmSACBmzJjhjaoTqZ71OuvWJSgoSMTExIi0tDSRl5cn5ei//vUvERQUJCZNmmT3vWpra0VISIiYOHGit6pPpDqO5qSV9d4H9haTySRTJETq4Wpe8p4k8tIIIYR3p2WIiIiIiIiIiIiIiIjkx3uSEBERERERERERERGRX+IkCRERERERERERERER+SVOkhARERERERERERERkV/iJAkREREREREREREREfklTpIQEREREREREREREZFf4iQJERERERERERERERH5pUC5K+CK1tZWfP/99wgPD4dGo5G7OkROEUKgoaEB8fHx6NVLHfOUzEnyZcxJImVhThIpC3OSSFmYk0TKwpwkUhZXc9InJ0m+//57JCQkyF0Noh45e/Ys+vfvL3c13II5SWrAnCRSFuYkkbIwJ4mUhTlJpCzMSSJlcTYnfXKSJDw8HMDVYCMiIgAAFosFxcXFyM7OhlarlbN6bqXGuNQYE+B4XPX19UhISJC2YzXoLCf9hVq3Z0eoJXZ/yEm1/K16iu3wMyW3hT/kpNIpeftwB8bnHLXnZEhIiKq3B09Sey55Sk/bTe05qfYxHk9juzmPOdlRV/uu3Mbcj23qXq7mpE9OklhP9YqIiLDpQENDQxEREaGqDUqNcakxJsD5uNR0ymJnOekv1Lo9O0Jtsas5J9X2t3IV2+FnvtAWas5JpfOF7aMnGJ9r1JqTISEhqt4ePEntueQp7mo3teak2sd4PI3t5jzmZEdd7btyG3M/tqlnOJuT6rhYHhERERERERERERERkZM4SUJERERERERERERERH6JkyREREREREREREREROSXfPKeJL5o4HM7pf9/s2qCjDUh8h3MGyLi9wD5C27rRMTvAaKeYx4ReYfBuAfmlqv3fGCukRrwTBIiIiIiIiIiIiIiIvJLnCQhIiIiIiIiIiIiIiK/xEkSIh+3f/9+TJw4EfHx8dBoNPjggw9snp81axY0Go3NMmrUKJsyZrMZCxcuRN++fREWFoZ7770X586d82IURERERERERERERN7HSRIiH3f58mUMHz4c69evt1vmnnvuQVVVlbTs2rXL5vnc3FwUFRWhsLAQBw4cwKVLl5CTk4OWlhZPV5/IL3333Xd4+OGHER0djdDQUNx8880oLy+XnhdCwGg0Ij4+HiEhIUhPT8eJEydkrDERERER+SvuuxIRkdpxkoTIx40fPx4rV67E5MmT7ZbR6XTQ6/XSEhUVJT1XV1eHTZs24dVXX8XYsWMxYsQIbNu2DcePH8fevXu9EQKRX6mtrcXtt98OrVaLjz/+GCdPnsSrr76K3r17S2VWr16NNWvWYP369SgrK4Ner0dWVhYaGhrkqzgREZGXcECWSDm470pERP6AkyREfmDfvn2IiYnB4MGD8fjjj6OmpkZ6rry8HBaLBdnZ2dK6+Ph4GAwGlJaWylFdIlV7+eWXkZCQgM2bN+O2227DwIEDkZmZieuuuw7A1YGftWvXYvny5Zg8eTIMBgO2bNmCK1euYPv27TLXnoiIyLM4IEukLNx3JSIifxAodwWIyLPGjx+PKVOmIDExEZWVlXjhhRdw9913o7y8HDqdDtXV1QgKCkKfPn1sXhcbG4vq6upO39NsNsNsNkuP6+vrAQAWiwUWi8VtddcFCOn/7nxfd7LWS6n18yS1xO7t+n/44YcYN24cpkyZgpKSElx77bWYN28eHn/8cQBAZWUlqqurbSYudTod0tLSUFpaijlz5nR4z+5y0pf/Vu78HvDldnA3JbeFEutERN7TdkDWauDAgdL/2w/IAsCWLVsQGxuL7du3d9pPEpHr5Nh3tf6/7b+O8oXfkJ6k5H08peppm7GtidSBkyREKjdt2jTp/waDASNHjkRiYiJ27tzZ5SW6hBDQaDSdPpefn48VK1Z0WF9cXIzQ0NCeV/r/rL7t5/+3v4+K0phMJrmrIBtfj/3KlSte/byvv/4aGzZswOLFi/H888/j888/x5NPPgmdTodHH31UmpyMjY21eV1sbCzOnDnT6Xs6mpO++LfyxPeAL7aDpyixLbydk0SkLJ4YkCUi18m57wo4v6/iS78hPUmJ+3hK52qbcd+VSB04SULkZ+Li4pCYmIjTp08DAPR6PZqamlBbW2tzNklNTQ3GjBnT6XssW7YMixcvlh7X19cjISEB2dnZiIiIcFtdDcY90v8rjOPc9r7uZLFYYDKZkJWVBa1WK3d1vEotsVuPXPOW1tZWjBw5Enl5eQCAESNG4MSJE9iwYQMeffRRqVz7ScquJi67y0lf/lu583vAl9vB3ZTcFt7OSSJSFk8MyHZ11HpgYKD0f7n52hHwPGLdNb521Loc+66A6/sqvvAb0pOUvI+nVD1tM+67EqkDJ0mI/MyFCxdw9uxZxMXFAQBSUlKg1WphMpkwdepUAEBVVRUqKiqwevXqTt9Dp9NBp9N1WK/Vat26I2Zu+XmnWuk7eO6O3Zf4euzerntcXBxuuukmm3U33ngj3nvvPQBXJy4BoLq6WspT4OrEZfsBIStHc9IX/1ae+B7wxXbwFCW2hdLqQ0Te5YkBWUeOWlfCUde+egS8EtrOF/nKUety7rvaW9cVX/oN6UlK3MdTOlfbjO1MpA5unyQZOHBgp0fwzJs3D6+//jpmzZqFLVu22DyXmpqKQ4cOubsqRH7h0qVL+Oqrr6THlZWVOHr0KKKiohAVFQWj0YgHHngAcXFx+Oabb/D888+jb9++uP/++wEAkZGRmD17NpYsWYLo6GhERUVh6dKlGDp0KMaOHStXWESqdfvtt+PUqVM26/75z38iMTERAJCUlAS9Xg+TyYQRI0YAAJqamlBSUoKXX37Z6/WVw8DndspdBSIikoknBmS7Omo9JCREMUdd+9oR8Dxi3TW+dtS6L+y7ct+RiIh6yu2TJGVlZWhpaZEeV1RUICsrC1OmTJHW3XPPPTY34gsKCnJ3NYj8xuHDh5GRkSE9tv4AnDlzJjZs2IDjx49j69atuHjxIuLi4pCRkYEdO3YgPDxces1rr72GwMBATJ06FY2NjcjMzERBQQECAgK8Hg93cEntnnrqKYwZMwZ5eXmYOnUqPv/8c2zcuBEbN24EcPXI2NzcXOTl5SE5ORnJycnIy8tDaGgopk+fLnPtiYiIPMsTA7KOHLWuhKOuffUIeCW0nS/ylaPWue9KRET+wO2TJP369bN5vGrVKlx33XVIS0uT1ul0OukIILXiQC95S3p6OoQQdp/fs2eP3eesgoODsW7dOqxbt86dVSOiTtx6660oKirCsmXL8NJLLyEpKQlr167FjBkzpDLPPPMMGhsbMW/ePNTW1iI1NRXFxcU2k5tERERqxAFZImXx5X3XtuMy36yaIGNNiIhI6Tx6T5KmpiZs27YNixcvtrk+7L59+xATE4PevXsjLS0Nv/vd7xATE+PJqhARESlGTk4OcnJy7D6v0WhgNBphNBq9VykiIiIF8OUBWSK14r4rERGpnUcnST744ANcvHgRs2bNktaNHz8eU6ZMQWJiIiorK/HCCy/g7rvvRnl5eaenQAOA2WyG2WyWHluvwWmxWGCxWKT/t/1XbroA+0f2O1NHpcXlDmqMCXA8LrXFTURERETkThyQ5RHwRERERN7k0UmSTZs2Yfz48YiPj5fWTZs2Tfq/wWDAyJEjkZiYiJ07d2Ly5Mmdvk9+fj5WrFjRYX1xcTFCQ0Nt1plMJjfVvmdW32b/uV27djn9fkqJy53UGBPQfVxXrlzxUk2IiIiIiEhtOIFCRERE5F4emyQ5c+YM9u7di/fff7/LcnFxcUhMTMTp06ftllm2bJl0M2rg6pkkCQkJyM7ORkREBICrR+ebTCZkZWUp4qZxBqP9+0BUGMc5/D5Ki8sd1BgT4Hhc1jOhiIiIiIiIiIiIiEheHpsk2bx5M2JiYjBhQtdHtly4cAFnz55FXFyc3TI6na7TS3FptdoOg9GdrZODuUVj9zlX6qeUuNxJjTEB3celxpiJiIiUaP/+/XjllVdQXl6OqqoqFBUVYdKkSdLzs2bNwpYtW2xek5qaikOHDkmPzWYzli5dinfeeQeNjY3IzMzEG2+8gf79+3srDCIiIiIiIvKgXp5409bWVmzevBkzZ85EYODP8zCXLl3C0qVLcfDgQXzzzTfYt28fJk6ciL59++L+++/3RFWIiIiIyE9dvnwZw4cPx/r16+2Wueeee1BVVSUt7S+Lmpubi6KiIhQWFuLAgQO4dOkScnJy0NLS4unqExERERERkRd45EySvXv34ttvv8WvfvUrm/UBAQE4fvw4tm7diosXLyIuLg4ZGRnYsWMHwsPDPVEVIiIi8gFtr69O5C7jx4/H+PHjuyyj0+mg1+s7fa6urg6bNm3C22+/jbFjxwIAtm3bhoSEBOzduxfjxjl+CVUiIiIiIiJSJo9MkmRnZ0MI0WF9SEgI9uyxf68OIiIiIiJv2rdvH2JiYtC7d2+kpaXhd7/7HWJiYgAA5eXlsFgsyM7OlsrHx8fDYDCgtLSUkyREREREREQq4LF7khARERERKdn48eMxZcoUJCYmorKyEi+88ALuvvtulJeXQ6fTobq6GkFBQejTp4/N62JjY1FdXW33fc1mM8xms/S4vr4eAGCxWGCxWDp9jS7g5wOM7JXxFuvny10PT2F8rr0fEREREZFacZKEiIiIiPzStGnTpP8bDAaMHDkSiYmJ2LlzJyZPnmz3dUIIaDQau8/n5+djxYoVHdYXFxcjNDS009esvu3n/7e/L4pcTCaT3FXwKMbnmCtXrrjlfYiI5NT20q7frJogY02IHJefn4/nn38eixYtwtq1awFc3Q9dsWIFNm7ciNraWqSmpuL111/HkCFDpNeZzWYsXboU77zzDhobG5GZmYk33ngD/fv3lykSIuXjJAkREREREYC4uDgkJibi9OnTAAC9Xo+mpibU1tbanE1SU1ODMWPG2H2fZcuWYfHixdLj+vp6JCQkIDs7GxEREZ2+xmD8+ZK0FUZ5L+NlsVhgMpmQlZUFrVYra108gfE5x3omFBEREXlPWVkZNm7ciGHDhtmsX716NdasWYOCggIMHjwYK1euRFZWFk6dOiXd7zk3NxcfffQRCgsLER0djSVLliAnJwfl5eUICAiQIxwixeMkCREREXkdb9ROSnThwgWcPXsWcXFxAICUlBRotVqYTCZMnToVAFBVVYWKigqsXr3a7vvodDrodLoO67Vard1Ba3OLxqacEnRVXzVgfI6/DxEREXnPpUuXMGPGDLz11ltYuXKltF4IgbVr12L58uXSWc9btmxBbGwstm/fjjlz5qCurg6bNm3C22+/jbFjxwIAtm3bhoSEBOzdu5f31COyo5fcFSAiIiIi8oRLly7h6NGjOHr0KACgsrISR48exbfffotLly5h6dKlOHjwIL755hvs27cPEydORN++fXH//fcDACIjIzF79mwsWbIEf/3rX/HFF1/g4YcfxtChQ6UfnUREchr43E5pISIidZg/fz4mTJjQYX+zsrIS1dXVyM7OltbpdDqkpaWhtLQUAFBeXg6LxWJTJj4+HgaDQSpDRB3xTBIiIiIiUqXDhw8jIyNDemy9BNbMmTOxYcMGHD9+HFu3bsXFixcRFxeHjIwM7NixQ7pUAQC89tprCAwMxNSpU6VrOhcUFPBSBURERD6q/aQi71FCSlJYWIgjR46grKysw3PV1dUAgNjYWJv1sbGxOHPmjFQmKCjI5lKx1jLW17dnNpthNpulx9ZLbVosFlgsFpuy1se6XqLDOnKNtf3Yju7hajtykoSIiIiIVCk9PR1CCLvP79mzx+5zVsHBwVi3bh3WrVvnzqoREREREdk4e/YsFi1ahOLiYgQHB9stp9FobB4LITqsa6+rMvn5+VixYkWH9cXFxQgNDe30Nf8+slX6/65du7r8bHKMyWSSuwqqcOXKFZdex0kSIiIiIiIiIiIiIhmVl5ejpqYGKSkp0rqWlhbs378f69evx6lTpwBcPVvEeg89AKipqZHOLtHr9WhqakJtba3N2SQ1NTUYM2ZMp5+7bNky6Yxr4OqZJAkJCcjOzkZERIRNWYvFApPJhBcO94K59eqkS4WR9znpCWubZmVl8V5wbmA9E8pZnCQhIiIir+D10omIiIiIiDqXmZmJ48eP26x77LHHcMMNN+DZZ5/FoEGDoNfrYTKZMGLECABAU1MTSkpK8PLLLwMAUlJSoNVqYTKZMHXqVABAVVUVKioqsHr16k4/V6fTQafTdViv1WrtDtqbWzUwt2ikctRzXbU3Oc7VNuQkCREREREREZFC8SADIiL/EB4eDoPBYLMuLCwM0dHR0vrc3Fzk5eUhOTkZycnJyMvLQ2hoKKZPnw4AiIyMxOzZs7FkyRJER0cjKioKS5cuxdChQzvcCJ6IfsZJEiIiIiIiIiIiIiKFe+aZZ9DY2Ih58+ahtrYWqampKC4uRnh4uFTmtddeQ2BgIKZOnYrGxkZkZmaioKAAAQEBMtacSNk4SUJERERERERERESkMPv27bN5rNFoYDQaYTQa7b4mODgY69atw7p16zxbOSIV6SV3BYiIiIiIiIiIiIiIiOTASRIiIiIiIiIiIiIiIvJLvNwWERERERERkQx4U3YiIiIi+fFMkh4a+NxOaSGSw/79+zFx4kTEx8dDo9Hggw8+sHleCAGj0Yj4+HiEhIQgPT0dJ06csCljNpuxcOFC9O3bF2FhYbj33ntx7tw5L0ZB5L/y8/Oh0WiQm5srrXMkb4mIiIiIvIn7rUREpFacJCHycZcvX8bw4cOxfv36Tp9fvXo11qxZg/Xr16OsrAx6vR5ZWVloaGiQyuTm5qKoqAiFhYU4cOAALl26hJycHLS0tHgrDCK/VFZWho0bN2LYsGE26x3JWyIiIrVT64AsD7QjX8T9ViIiUjNOksiAO8XkTuPHj8fKlSsxefLkDs8JIbB27VosX74ckydPhsFgwJYtW3DlyhVs374dAFBXV4dNmzbh1VdfxdixYzFixAhs27YNx48fx969e70dDpHfuHTpEmbMmIG33noLffr0kdY7krdERERqxwFZIuXgfisREakd70lCpGKVlZWorq5Gdna2tE6n0yEtLQ2lpaWYM2cOysvLYbFYbMrEx8fDYDCgtLQU48aN6/C+ZrMZZrNZelxfXw8AsFgssFgsPaqzLkB0ur6n7+sp1noptX6epJbY5ar//PnzMWHCBIwdOxYrV66U1juSt9TxGu7frJogU02IiMjd2g7Itu0j2w/IAsCWLVsQGxuL7du3s48k8hDutxIRkdpxkoRIxaqrqwEAsbGxNutjY2Nx5swZqUxQUJDNEUHWMtbXt5efn48VK1Z0WF9cXIzQ0NAe1Xn1bZ2v37VrV4/e19NMJpPcVZCNr8d+5coVr39mYWEhjhw5grKysg7POZK37XU3camUCS17k6COalv/9u/lSGxKaQclUHJbKLFORORdHJAlUg5377cCjh1058y+ijv3MX2dkvfxlKqnbca2JlIHt0+SGI3GDoOnbQdbhRBYsWIFNm7ciNraWqSmpuL111/HkCFD3F0VIvo/Go3G5rEQosO69roqs2zZMixevFh6XF9fj4SEBGRnZyMiIqJHdTUY93S6vsLY8YwWJbBYLDCZTMjKyoJWq5W7Ol6lltitP8q85ezZs1i0aBGKi4sRHBxst5wzeevoxKXcE1r2JkEd1XaytP17OTORKnc7KIkS20KOiUsiUg5vD8gGBgZK//eWng7o2uPtgToOxrrGlwZkPbHfCjh30J0j+yru3MdUCyXu4ymdq23GfVcidfDImSRDhgyxuZdBQECA9H/rNWQLCgowePBgrFy5EllZWTh16hTCw8M9UR1Fa3u5EF4qhNxNr9cDuPqDMi4uTlpfU1Mj/bjU6/VoampCbW2tzdkkNTU1GDNmTKfvq9PpoNPpOqzXarU9Hiw3t3S+M630QXh3xO6rfD12b9e9vLwcNTU1SElJkda1tLRg//79WL9+PU6dOgWg67xtr7uJS6VMaNmbBHVU28nS9u/lyESqUtpBCZTcFt6euCQi5ZBzQNabA4o9HdC1R66BXg7GusYXBmQ9sd8KOHbQXVf7Kj3dp2xPqQfkuULJ+3hK1dM2474rkTp4ZJIkMDBQGpxtS+3XkOWN2ElpkpKSoNfrYTKZMGLECABAU1MTSkpK8PLLLwMAUlJSoNVqYTKZMHXqVABAVVUVKioqsHr1atnqTqRWmZmZOH78uM26xx57DDfccAOeffZZDBo0qNu8bc/RiUu5J7TsTYI6qm3d27+XM3HJ3Q5KosS2UFp9iMh75BiQDQkJ8fqAorsHeK28PdDLwVjX+NKArCf2WwHnDrrrbF1P9yk7+1y1UeI+ntK52mZsZyJ18MgkyenTpxEfHw+dTofU1FTk5eVh0KBBvIYskQdcunQJX331lfS4srISR48eRVRUFAYMGIDc3Fzk5eUhOTkZycnJyMvLQ2hoKKZPnw4AiIyMxOzZs7FkyRJER0cjKioKS5cuxdChQzF27Fi5wiJSrfDwcBgMBpt1YWFhiI6OltZ3l7dERERqJOeArDcHFN09wGsl10AdB2Nd4wsDstxvJSIif+H2SZLU1FRs3boVgwcPxg8//ICVK1dizJgxOHHihGJu6uVO7ryebGd1V+N1XtUYE+B4XO6O+/Dhw8jIyJAeW4+SmzlzJgoKCvDMM8+gsbER8+bNk+4DVFxcbHN5u9deew2BgYGYOnUqGhsbkZmZiYKCAptL5RGR9ziSt0RERGrDAVki38P9ViIiUgO3T5KMHz9e+v/QoUMxevRoXHfdddiyZQtGjRoFQP6bermTO68n29U1ZNV4nVc1xgR0H5e7ryGbnp4OIexP1mk0GhiNRhiNRrtlgoODsW7dOqxbt86tdSMix+zbt8/msSN5S0Td279/P1555RWUl5ejqqoKRUVFmDRpkvS8EAIrVqzAxo0bpYGd119/HUOGDJHKmM1mLF26FO+88450IMEbb7yB/v37yxAR1sB43QABAABJREFUEXFAlkhe3G8lIiI18sjlttoKCwvD0KFDcfr0aelHqbdv6uVJ7ryebGfXkFXjdV7VGBPgeFy8qRcREZF3XL58GcOHD8djjz2GBx54oMPzq1evxpo1a1BQUIDBgwdj5cqVyMrKwqlTp6QB19zcXHz00UcoLCxEdHQ0lixZgpycHJSXl/OMSwVpf2/Ab1ZNkKkm5G4ckCUiT2vbh7D/ICLyTx6fJDGbzfjyyy9x5513OnQT6c709KZenuTO68l2VW81XudVjTEB3celxpiJiIiUaPz48TZnObclhMDatWuxfPlyTJ48GQCwZcsWxMbGYvv27ZgzZw7q6uqwadMmvP3229J9urZt24aEhATs3bsX48Z59ybJROSb2k/iEREREZGyuH2SZOnSpZg4cSIGDBiAmpoarFy5EvX19Zg5cyY0Gg2vIUtERORH3DkwxEEmcqfKykpUV1cjOztbWqfT6ZCWlobS0lLMmTMH5eXlsFgsNmXi4+NhMBhQWlpqd5LEkfvptdf2Pndy37fNF+8f1/4+gV3V3Rfjc4a741NrOxERERERWbl9kuTcuXN46KGHcP78efTr1w+jRo3CoUOHkJiYCIDXkCUiIiIi+VVXVwNAh0u+xsbG4syZM1KZoKAg9OnTp0MZ6+s748z99Kza3ueuq/vUeZMv3T+u/X0CHWlDX4rPFe6Kz9330yMiIiIiUhq3T5IUFhZ2+TyvIUtERERESqHR2F46VQjRYV173ZVx5H567bW9z11n96nzJl+8f1z7+wR21Ya+GJ8z3B0f76dHRERERGrn8XuSEBF1h5fQISIib9Pr9QCuni0SFxcnra+pqZHOLtHr9WhqakJtba3N2SQ1NTUYM2aM3fd25n56Vm3vc6eUgXtfun9c+/sEOlJvX4rPFe6KT81tREREREQEAL3krgARkSMGPrfTZiEiIuqJpKQk6PV6m0sSNTU1oaSkRJoASUlJgVartSlTVVWFioqKLidJiIiIiIiIyHfwTBIiIiIiUqVLly7hq6++kh5XVlbi6NGjiIqKwoABA5Cbm4u8vDwkJycjOTkZeXl5CA0NxfTp0wEAkZGRmD17NpYsWYLo6GhERUVh6dKlGDp0KMaOHStXWERERERERORGnCQhIiIiIlU6fPgwMjIypMfW+4TMnDkTBQUFeOaZZ9DY2Ih58+ahtrYWqampKC4uRnh4uPSa1157DYGBgZg6dSoaGxuRmZmJgoICBAQEeD0eIiIiIiIicj9OkhARERGRKqWnp0MIYfd5jUYDo9EIo9Fot0xwcDDWrVuHdevWeaCGRKRWvDwsERERke/gJAkRERH5vLaDUd+smiBjTYiIiIhIDbh/SUTkP3jjdiIiIiIiIiIiIiIi8ks8k8QFPHWaiIhIuXjUHxERERERERE5ipMkRERERERERCrS/sA+HjRAREREZB8nSYiIiIiIiIiIyO/xyiFERP6JkyQKwsuDEBERERH1HPeriYiIiIjIUbxxOxEREREREREREZGM8vPzceuttyI8PBwxMTGYNGkSTp06ZVNGCAGj0Yj4+HiEhIQgPT0dJ06csCljNpuxcOFC9O3bF2FhYbj33ntx7tw5b4ZC5HM4SUJEREREREREREQko5KSEsyfPx+HDh2CyWRCc3MzsrOzcfnyZanM6tWrsWbNGqxfvx5lZWXQ6/XIyspCQ0ODVCY3NxdFRUUoLCzEgQMHcOnSJeTk5KClpUWOsIh8Ai+3RURERERERERERCSj3bt32zzevHkzYmJiUF5ejrvuugtCCKxduxbLly/H5MmTAQBbtmxBbGwstm/fjjlz5qCurg6bNm3C22+/jbFjxwIAtm3bhoSEBOzduxfjxo3zelxEvoCTJEREREREREQ+jjecJiJSl7q6OgBAVFQUAKCyshLV1dXIzs6Wyuh0OqSlpaG0tBRz5sxBeXk5LBaLTZn4+HgYDAaUlpZ2OkliNpthNpulx/X19QAAi8UCi8ViU9b6WNdLdFhHrrG2H9vRPVxtR06SEBERERERERER2dF2EvKbVRNkrAn5CyEEFi9ejDvuuAMGgwEAUF1dDQCIjY21KRsbG4szZ85IZYKCgtCnT58OZayvby8/Px8rVqzosL64uBihoaGdvubfR7ZK/9+1a5eDUVFXTCaT3FVQhStXrrj0Ok6SEBERERERERERESnEggULcOzYMRw4cKDDcxqNxuaxEKLDuva6KrNs2TIsXrxYelxfX4+EhARkZ2cjIiLCpqzFYoHJZMILh3vB3Hr1/SqMvIRXT1jbNCsrC1qtVu7q+DzrmVDO4iQJERERERERERERkQIsXLgQH374Ifbv34/+/ftL6/V6PYCrZ4vExcVJ62tqaqSzS/R6PZqamlBbW2tzNklNTQ3GjBnT6efpdDrodLoO67Vard1Be3OrBuYWjVSOeq6r9ibHudqGvdxcDyJSGKPRCI1GY7NYO1bg6tEERqMR8fHxCAkJQXp6Ok6cOCFjjYnULT8/H7feeivCw8MRExODSZMm4dSpUzZlmJdEROSv2E8SKQtzksh7hBBYsGAB3n//fXzyySdISkqyeT4pKQl6vd7mskxNTU0oKSmRJkBSUlKg1WptylRVVaGiosLuJAkRcZKEyC8MGTIEVVVV0nL8+HHpudWrV2PNmjVYv349ysrKoNfrkZWVhYaGBhlrTKReJSUlmD9/Pg4dOgSTyYTm5mZkZ2fj8uXLUhnmJRER+Sv2k0TKwpwk8p758+dj27Zt2L59O8LDw1FdXY3q6mo0NjYCuHqZrdzcXOTl5aGoqAgVFRWYNWsWQkNDMX36dABAZGQkZs+ejSVLluCvf/0rvvjiCzz88MMYOnQoxo4dK2d4RIrm9stt5efn4/3338c//vEPhISEYMyYMXj55Zdx/fXXS2VmzZqFLVu22LwuNTUVhw4dcnd1fJb1pmC6AIHVt8lcGfJ5gYGBNmePWAkhsHbtWixfvhyTJ08GAGzZsgWxsbHYvn075syZ4+2qEqne7t27bR5v3rwZMTExKC8vx1133cW8JCIiv8Z+kkhZmJPd403dyV02bNgAAEhPT7dZv3nzZsyaNQsA8Mwzz6CxsRHz5s1DbW0tUlNTUVxcjPDwcKn8a6+9hsDAQEydOhWNjY3IzMxEQUEBAgICvBUKkc9x+ySJ9SiDW2+9Fc3NzVi+fDmys7Nx8uRJhIWFSeXuuecebN68WXocFBTk7qoQ0f85ffo04uPjodPpkJqairy8PAwaNAiVlZWorq5Gdna2VFan0yEtLQ2lpaV2d2jNZjPMZrP02HpTJIvFAovF4nT9dAHC6de48jmeYK2HUurjTWqJXe7619XVAQCioqIAwKW87C4n5fxbuZLf7tQ2ZrVss+6g5LZQYp2ISD7u6CeJyH28se9q/X/bf9tS0v4lYFsfufdjlLyPp1Q9bTN3trUQ3W/bGo0GRqMRRqPRbpng4GCsW7cO69atc1vdiNTO7ZMk3R1lYKXT6To9sp2I3Cs1NRVbt27F4MGD8cMPP2DlypUYM2YMTpw4gerqagCQbvBlFRsbizNnzth9z/z8fKxYsaLD+uLiYoSGhjpdR1fOltq1a5fzL/Kgttf79De+HvuVK1dk+2whBBYvXow77rgDBoMBAFzKS0dzUo6/ldxnQ3b2XeHr26w7KbEt5MxJIlIWd/WTXQ3IBgYGSv93J7kHcdvzxIApB2Ndo6QBWWd5e98V6HxfRWn7l23ro5TfqUrcx1M6V9uM+65E6uD2SZL22h9lYLVv3z7ExMSgd+/eSEtLw+9+9zvExMR4ujoua3v6JJEvGT9+vPT/oUOHYvTo0bjuuuuwZcsWjBo1CsDVIxHaEkJ0WNfWsmXLsHjxYulxfX09EhISkJ2djYiICKfraDDucfo1FcZxTr/GEywWC0wmE7KysqDVauWujlepJXbrQIkcFixYgGPHjuHAgQMdnnMmL7vLSTn/Vq7ktzu1/a5QyzbrDkpuC2/npNFo7DBQExsbKw36CCGwYsUKbNy4Ubqkweuvv44hQ4Z4tZ5E/shd/aQjA7LuHlCUexC3PU8O3HIw1jW+OCDrrX1XoOt9Fbn3L7si9+9UJe/jKVVP20zO35NE5D4enSTp7CgD4Oqg7ZQpU5CYmIjKykq88MILuPvuu1FeXg6dTtfhfXp6KqY7yHUkkK7X1c9V09E5aj3iyNG45I47LCwMQ4cOxenTpzFp0iQAV4/+iYuLk8rU1NR0OBKoLZ1O12muarVal3YqzC32J2TsUdoOn6uxq4Gvxy5X3RcuXIgPP/wQ+/fvR//+/aX11rMsnclLR3NSjr+VK/ntTp3F6+vbrDspsS3kqM+QIUOwd+9e6XHb6zVbb0ZbUFCAwYMHY+XKlcjKysKpU6dsrv1MRO7lzn6yqwHZkJAQtw0o+tvALQdjXeOrA7Jy7LvaWyf3/mVXlJILStzHUzpX24ztTKQOHp0ksXeUwbRp06T/GwwGjBw5EomJidi5c6d0o6+2enoqpjvIfSSQGo/OUWNMQPdxyX0qptlsxpdffok777wTSUlJ0Ov1MJlMGDFiBACgqakJJSUlePnll2WtJ5FaCSGwcOFCFBUVYd++fUhKSrJ5Xg15qaSzL9vW5fS/Z3dRkvxZYGBgp5eB5c1oibzPE/2kIwOy7hhQ9NeBWw7GusZXBmT9Yd/VneztB/OG7kREyuaxSRJ7Rxl0Ji4uDomJiTh9+nSnz/f0VEx3kOuoIF0vgX8f2aqqo3PUesSRo3F5+8ifpUuXYuLEiRgwYABqamqwcuVK1NfXY+bMmdBoNMjNzUVeXh6Sk5ORnJyMvLw8hIaGYvr06V6tJ5G/mD9/PrZv346//OUvCA8Ply7pExkZiZCQEOYlkQxOnz6N+Ph46HQ6pKamIi8vD4MGDXL5BtGOnAXdHm/62jNdnfXdPg5fjM8Z7o7P2+3ka/2kkg4M6ErbenKwlpzhazlJRETkCrdPknR3lEFnLly4gLNnz9qcmtlWT0/FdAe5jwpS49E5aowJ6D4ub8d87tw5PPTQQzh//jz69euHUaNG4dChQ0hMTAQAPPPMM2hsbMS8efOka60XFxfzEiJEHrJhwwYAQHp6us36zZs3Y9asWQCYl97QflCLA0b+KzU1FVu3bsXgwYPxww8/YOXKlRgzZgxOnDjh0s1oAefOgrbiTV97pquzvu21py/F5wp3xefts6DZTxIpC3OSiIj8gdsnSbo7yuDSpUswGo144IEHEBcXh2+++QbPP/88+vbti/vvv9/d1SHye4WFhV0+r9FoYDQaYTQavVMhIj8nRPf3uGJeEnnP+PHjpf8PHToUo0ePxnXXXYctW7Zg1KhRAJy7GS3g2FnQ7bU9a5k3fXWeo2d9VxjH+WR8znB3fN4+C5r9JJGyMCeJiMgfuH2SpLujDAICAnD8+HFs3boVFy9eRFxcHDIyMrBjxw4eZUBEROSjfOFyIwbjHqy+zTqYqtzrxpO8wsLCMHToUJw+fRqTJk0C4NzNaAHnzoK2anvWslIG7n3prF9Hz/puG48vxecKd8Wn5jYiIiIiIgI8dLmtroSEhGDPHnnu70FERERE1BWz2Ywvv/wSd955J29GS0RERERE5Ac8duN2IiJP4s0niYjIHZYuXYqJEydiwIABqKmpwcqVK1FfX4+ZM2fyZrRERERERER+gJMkREREROS3zp07h4ceegjnz59Hv379MGrUKBw6dAiJiYkAeDNaIiIiIiIiteMkiR2+cG11IiIiIuqZwsLCLp/nzWiJiIiIiIjUjZMkRERERERERH6Cl60lX8QDWYmIyJN6yV0BIiIiIiIiIiIiIiIiOfBMEh9hMO6BuUUDgEf7kG9qf+QPt2Mi38CjTYmIiIiIiIhIzThJQkREREREROQgXvaHiJzFA4+IiJSNl9siIiIiIiIiIiIiIiK/xDNJiIiIyGk8ipaIiEi9eKlcIiIi8iecJCEiIiIiIrvUdImQgc/thC5AYPVtcteEiIiIiIiUgpMkbfCoWCLvYb4RKZe9/GTeEhEREREREZHacJKEiHyemo5wJZILJ0CIiIg6xz7SlrU9eFYWkWt4OTsiIuXhJIkP4oAwEREREREREREREVHPqXaShDPzRERERERERESkZDwQlohIfqqdJCEi/8QdTCIiIiIiIiIiInIUJ0mIiIiI2uBkK5F/MBj3wNyiAcBcJyIi5bG3T+rKlVN4tRUioq5xkkSl2AESEVFXeBNaW/bagxMmRET+yR/7SX+MmUhpepqHBuMerL7t6r+nfpfjploREakfJ0mIiIiIiMjnuHNAlwcYEREREbmG+1GkBpwk8RM8EpaIiMh9+EOASB480p3IvTyVU+wnidyrq1xt+5wuwBu1ISJSH06SEBERERERtcEDjIiIiIiI/EcvOT/8jTfeQFJSEoKDg5GSkoLPPvvMK5878LmdnS6+yNfrT8oiV056ir1cZ76Qr1BbThL5OuYk9z39PX6lYU4SKQtz0vc52s95qj9kP+s+zEci58h2JsmOHTuQm5uLN954A7fffjv+8Ic/YPz48Th58iQGDBggV7WI/Ja/5SSPECWl87ec9HWO/JBr+13j6A8/fj8pB3PSszgYQs5iTnqXIznKPPZvzEnf4mxOd7VP2tPf1vzucD+585HjLeSLZDuTZM2aNZg9ezZ+/etf48Ybb8TatWuRkJCADRs2yFUln8cZd3kZjHts/vU1zEkiZWFOEimLP+akwbiH+5Vd8OQZq9yv754/5iSRkjEniZSD+UjkPFnOJGlqakJ5eTmee+45m/XZ2dkoLS31yGfyB8bP7M3o9rSNejo77M6jDzhT7Rw5cpKI7GNOKlNP+0l3vt4X+zlfrr8v5qS97c3evp+7/yaOvLev7J/LXU9fzh1P8cWcVDODcQ/MLRqvfZ4jv2flzhVXblyvpPo7izmpTO7svxx9L0fHm9y5jbuSO21fc/rfsztd35Yv5aTS8lENbUr+QZZJkvPnz6OlpQWxsbE262NjY1FdXd2hvNlshtlslh7X1dUBAH788UdYLBYAgMViwZUrV3DhwgVotVoENl/2YATeE9gqcOVKKwItvdDS6v4dzwsXLvz8WT1ss7bv1ZX2f6vOPt/R92qrp6/vqUDLZelv1dXnNzQ0AACEEN6qWrc8kZPtKTknf7H0vztd/7dlmd2+1t727Gmp+X+V/u9IPT1Brti70rZd2uqqjfwhJzv7Wyk5Jz3F032qJ9jrp3vaz8mRv47W3x9ysjPu3o+xl+Nt+7xAO+t1vQT+bUT3udL2Ne2/Zx2JR67vIW98Fzi7b9G+/7L3I82RbcPR/HZ0X0LtORkcHNztbxPqnDO55M7fZ/a+X+T+PdhW++2nbX16+ntY7Tmp9jEeT1PS/m5X402OjEU5mseu5H7711i3NVfqorScdDYfAef2Xa252dNtrKv9SFd+1zv7eneOqbT/PGffT4ljK0rk8X1XIYPvvvtOABClpaU261euXCmuv/76DuVffPFFAYALF1UtZ8+e9VbKdYs5yYULc5ILF6UtzEkuXJS1MCe5cFHWwpzkwkVZi1Jy0tl8FII5yUWdi7M5KcuZJH379kVAQECHGcyampoOM50AsGzZMixevFh63Nraih9//BHR0dHQaK7OWtbX1yMhIQFnz55FRESEZwPwIjXGpcaYAMfjEkKgoaEB8fHxXqxd1zyRk/5CrduzI9QSuz/kpFr+Vj3FdviZktvCH3JS6ZS8fbgD43OO2nOyoaFB1duDJ6k9lzylp+2m9pxU+xiPp7HdnKe2nHQ2HwHn9l25jbkf29S9XM1JWSZJgoKCkJKSApPJhPvvv19abzKZcN9993Uor9PpoNPpbNb17t270/eOiIhQ5QalxrjUGBPgWFyRkZFeqo1jPJmT/kKt27Mj1BC7v+SkGv5W7sB2+JlS28JfclLplLp9uAvjc5yac9I6+KP27cGT2Hau6Um7qTkn2+P25Rq2m/PUkpPO5iPg2r4rtzH3Y5u6jys5KcskCQAsXrwYjzzyCEaOHInRo0dj48aN+PbbbzF37ly5qkTk15iTRMrCnCRSFuYkkbIwJ4mUhTlJpBzMRyLnyTZJMm3aNFy4cAEvvfQSqqqqYDAYsGvXLiQmJspVJSK/xpwkUhbmJJGyMCeJlIU5SaQszEki5WA+EjlPtkkSAJg3bx7mzZvnlvfS6XR48cUXO5we5uvUGJcaYwLUEZc7c9JfqOHv7ip/jt1b3JWT/FtdxXb4GdvCNf7ST6p9+2B86uGOnPSn9nI3tp1r1NxuHOORH9vNeWptM0/tt6q1veTENlUGjRBCyF0JIiIiIiIiIiIiIiIib+sldwWIiIiIiIiIiIiIiIjkwEkSIiIiIiIiIiIiIiLyS5wkISIiIiIiIiIiIiIiv6SKSZI33ngDSUlJCA4ORkpKCj777DO5q9Rj+/fvx8SJExEfHw+NRoMPPvhA7ir1WH5+Pm699VaEh4cjJiYGkyZNwqlTp+SuVo9t2LABw4YNQ0REBCIiIjB69Gh8/PHHcleLeqC7/BNCwGg0Ij4+HiEhIUhPT8eJEydsypjNZixcuBB9+/ZFWFgY7r33Xpw7d86LUTjPkRxVa+xqpsY+0llq7X96Kj8/HxqNBrm5uXJXhWTiru99X9DZ9u7rsX333Xd4+OGHER0djdDQUNx8880oLy+Xnvf1+LyBfWRH/rof3FPcj3Y/f85Pb+VhbW0tHnnkEURGRiIyMhKPPPIILl686OHoPMObOaimdnOFP+dmT/jTfrev8vlJkh07diA3NxfLly/HF198gTvvvBPjx4/Ht99+K3fVeuTy5csYPnw41q9fL3dV3KakpATz58/HoUOHYDKZ0NzcjOzsbFy+fFnuqvVI//79sWrVKhw+fBiHDx/G3Xffjfvuu49fZD6su/xbvXo11qxZg/Xr16OsrAx6vR5ZWVloaGiQyuTm5qKoqAiFhYU4cOAALl26hJycHLS0tHgrDKc5kqNqjV2t1NpHOkut/U9PlJWVYePGjRg2bJjcVSEZuet7X+nsbe++HFttbS1uv/12aLVafPzxxzh58iReffVV9O7dWyrjy/F5A/vIzvnrfnBPcT/avfw9P72Vh9OnT8fRo0exe/du7N69G0ePHsUjjzzi8fg8wZs5qKZ2c5a/52ZP+Mt+t08TPu62224Tc+fOtVl3ww03iP+fvfuPa/K898f/ihDCjwIqCoFKlXbYWqOWgbWiK1ohrkfW0487tZuu1c72Y+ePStFj/fHZaWgdVLYqO/7acB5wdZbtrGXVU9sSN8U6aqVYT0VX2q3UWgejsxSw0BDh+v7hN3cJEAzhTnLfd17PxyMPzZUryfW+cr+5r+S67+tev369n1okPwCivLzc382QXVNTkwAgKisr/d0U2Y0YMUL86le/8nczSAa986+7u1sYjUbx3HPPSWVfffWViI6OFr/4xS+EEEJ88cUXQq/Xi7KyMqnOpUuXxLBhw8Trr7/us7YPVe8cDaTYtSIQ9pGe0PL+xx1tbW0iOTlZWK1WkZGRIVavXu3vJpFCePJ3X+lcbe9qj+2pp54SM2fOdPm42uPzBe4jry+Qx8FDxXH00DA/v+atPDx//rwAIE6ePCnVeeuttwQA8f7773s5Ku/zVg5qvd+uh7kpHy2Ou9VO1WeSdHZ2oqamBmaz2ancbDajqqrKT60id7W0tAAARo4c6eeWyKerqwtlZWX48ssvMX36dH83h7ygvr4ejY2NTn93DAYDMjIypL87NTU1sNvtTnUSEhJgMplU9bepd44GUuxawH2ka1rc/wzGihUrMG/ePGRmZvq7KaQwnvzdVzpX27vaYzt48CDS0tLwwAMPIDY2FikpKdizZ4/0uNrj8zbuIz3DsaD7OI72HPNzYHJtS2+99Raio6Mxbdo0qc5dd92F6OhoTfSzt3JQ6/02EOamvLQ47lY7VU+S/POf/0RXVxfi4uKcyuPi4tDY2OinVilXaWkpdDqddAsNDYXRaMTs2bNRUFCApqYmp/oWiwU6nQ7//Oc/+309k8mEWbNmedQWIQRyc3Mxc+ZMmEwmj15DSc6ePYsbbrgBBoMBjz/+OMrLy3H77bf7u1nkBY6/LQP93WlsbERISAhGjBjhsk5/5M7RoegvR70ZO8mP+8j+ubP/UVIuyq2srAynT59GQUGBv5tCCuPp331v8jQXY2Nj0dbW1md7P3nyJF555RVFxDZUH330EXbv3o3k5GS88cYbePzxx/HEE0/g17/+NQD1x+dt3Ed6Zu/evQCAb37zm045WV1djaqqKjQ1NTmNBZcsWYIbbrgBQP99e8MNN2DJkiW+DsPrOI4eGubnwHpvS4595cGDB/HrX/8aoaGhmD9/PnQ6HX7xi19I+8rPPvsMZ8+exf79+6XXiY2NdXrt1tZWtLa2orCw0IcRyc+bOdhfvwFAbGys5rdP5qb7rjeG/cc//oHc3FyMGDECM2fOxMWLF/tso59//jni4+Px9ttvo6GhwZ/hBAxVT5I46HQ6p/tCiD5l9LWSkhK89dZbsFqt2LlzJ+644w5s2bIFEyZMwJEjR3zShpUrV+K9997Diy++6JP387Zbb70VZ86cwcmTJ/GjH/0Iixcvxvnz5/3dLPIiT/7uuPu3Sek56s3YSX7cRzobzP5HCbkop4sXL2L16tXYv38/QkND/d0cUhi5/+7LabC5+Nlnn+H//b//59b27u/YPNXd3Y1vfvObyM/PR0pKCpYtW4bHHnsMu3fvdqqn1vh8hf3jmW3btjnlZExMDD7++GNMmDABtbW1/T4nkPqW42h5MD8H1rsv7r77bkybNg1WqxVLliyBTqdz2leOHj0ao0ePxoULF9Dc3Nzva5SVlUEIgalTp/osDm/wdg72Vz+Qtk/mpvtcjWHHjh2Lt99+G0eOHEFwcDAeffRR6TmOvly5ciXa2tpw9913Y9gwTfx8r3iq7uVRo0YhKCioz4xlU1NTn5lN+prJZMJdd92Fb33rW/jud7+Lbdu24b333kNERATmz5+Pf/zjH159/1WrVuHgwYM4evQoxowZ49X38pWQkBB84xvfQFpaGgoKCjBlyhT8/Oc/93ezyAuMRiMADPh3x2g0orOzUxp89ldnIErNUV/ETvLhPrKvwe5//J2LcqupqUFTUxNSU1MRHByM4OBgVFZW4j//8z8RHBwccBeFpa8N5e++Lww2F7/97W+juLi4z/Zus9nw8ccfIzg4WGq/v2PzVHx8fJ+zlidMmCBdOFUpn51ScR/pmejoaADA6NGjnXLy5ptvxn333YeIiAj8/Oc/D+ixIMfRQ8f8HJirbQm4dvDmt771Ldx3333o7u7Gm2++6bSvDA8PR1dXF37zm9/AaDT22X/+13/9F3Q6He6++26fxOIN3s7B/voNuHaAhta3T+bm4PU3hr3//vtx9epVtLe348Ybb8SuXbtQUVGBP/3pTwCubaPl5eV48cUX8dOf/hQ2m4396yOqniQJCQlBamoqrFarU7nVakV6erqfWqVON910E55//nm0tbXhl7/8pVfeQwiBlStX4uWXX8af/vQnJCUleeV9lEAIAZvN5u9mkBckJSXBaDQ6/d3p7OxEZWWl9HcnNTUVer3eqU5DQwNqa2s9/tukhBz1V+zkGe4jvybn/scXuegtc+bMwdmzZ3HmzBnplpaWhkWLFuHMmTMICgrydxPJx+T4u+8vA+Xi5s2bIYTAAw884LS9h4SE4MYbb8SZM2dw8803KzY2d8yYMQN1dXVOZR988AHGjh0LQNmfnRJwH+mZ0aNHAwDefvttqcyxXZnNZjz//PNob2/HsGHDAm4syHG0fJifA+tvWwKuHQzTe1v6y1/+Iu0rf/azn+HChQsYNWoUSkpKMH36dLS0tODUqVMAgL/85S94++23IYTAt771LZ/HNVS+ysHe/QZc+5vY0tKi+e2TuTk0jm3UarVi69atuHLlCn75y19iwYIF+N73voctW7Zg1KhR+MMf/oDHH38cWVlZWLp0KcduvuTlC8N7XVlZmdDr9WLv3r3i/PnzIicnR0RERIiPP/7Y300bkra2NvHuu++Kd999VwAQW7duFe+++664cOGCx69ZUlIiAIjq6up+H79y5YoICgoSc+bMEUII8fTTTwsAorGxUdjt9j63iRMnioyMDLff/0c/+pGIjo4Wx44dEw0NDdKtvb3d45iUYMOGDeL48eOivr5evPfee2Ljxo1i2LBhoqKiwt9NIw9dL/+ee+45ER0dLV5++WVx9uxZ8f3vf1/Ex8eL1tZW6TUef/xxMWbMGHHkyBFx+vRpcc8994gpU6aIq1evunxfT3P0s88+kyVud3LUW7GTd2h1HzlYg93/+DsXfSkjI0OsXr3a380gP5Hr7763DCUXn3zySREcHCzq6uqk+gaDQYwbN06678/YhurUqVMiODhY/OQnPxEffvih+M1vfiPCw8PF/v37pTpqjs8XuI/s30DjYEdORkRE9LtdOXLyxhtvFGPGjBFms1mEhYW5HAtGRESIxYsX+ydQmXEcLa9Az8/BfB999tlnBQARExPT77Z06NAhERQUJEaMGCGmTJkiNm7cKACIM2fOiG9/+9ti8uTJ4q233hKLFi0SAMSsWbP8FfaQ+DIHe/bbW2+9JSZNmiSys7N9Gq+/BHpuuqu/MWzPbfRvf/ubCAoKEt/61rdEe3u7uHz5soiPjxc333yz0Ov1IiIiQlitVo7dfEz1kyRCCLFz504xduxYERISIr75zW+KyspKfzdpyI4ePSoA9LkNZRB5vS+aQggRFxcnJkyYIIT4+ovmQLfBTJK4eo2SkhKPY1KCH/7wh9L2N3r0aDFnzhxOkKjc9fKvu7tbPP3008JoNAqDwSDuvvtucfbsWafX6OjoECtXrhQjR44UYWFhIjs7W3zyyScDvq+nOSrXD7Pu5Ki3Yifv0eI+crAGu//xdy76EidJAptcf/e9ZSi5+M9//lNER0eL7373u1Ld3pMk/oxNDocOHRImk0kYDAZx2223ieLiYqfH1R6fL3Af2ddA42BHTj722GMut6u4uDhx2223iZUrV4qQkBABwOVYUEuTJBxHyy+Q83Mw30eDg4MFAFFWVub0Gj23JQDihhtuEJ988on46KOPhE6nE0888YS4fPmyWLRokbjhhhuETqcTo0ePFs3Nzb4PWAa+zEFHv0VGRorIyEixaNEi1fabJwI5N93V3xj2etvo4cOHpbLo6GiO3fxAE5Mk5B53vmjGxsb2+aJ55MgRUV1d3ed2yy23DGqShIgG5mmOqvGHWSIlYy4SKcNQczE/P18AECdPnhRCCDF27Fgxb9487zecSKMGm5OLFy8WERERLutqaZKEyF8Gm5dCCHHPPfeImJgYYbPZhBBCHDx4UAAQe/fu9Xp7iQKBJ3kphBB33XWXSE5O9nbzyAVVX5OE5PXll1/i8uXLSEhIcCqfMmUK0tLS+txCQ0P91FKiwOQqR4nIt5iLRMpwvVzMyclBQkIC1q1b5+OWEQWm3jkZHByMrq4ul/WvXr0KvV7vq+YRBaT+9pVLly7F5cuXcfDgQQBASUkJbrjhBixYsMBfzSQKKK7GsAaDASEhIX5qFXGShCSvvvoqurq6MGvWLH83hYj6wRwlUgbmIpEyXC8Xw8LCYLFYcPz4cbz66qu+bRxRAOqdk3Fxcfjqq6/w+eef96l7+fJl2Gw2xMXF+biVRIGlv33l/PnzMWLECPzXf/0XPvvsM/zP//wPHnzwQdxwww3+ayhRAOH3SWXiJAkBAD755BOsXbsW0dHRWLZsmb+bQ0S9MEeJlIG5SKQM7ubiD3/4Q0yYMAHr169Hd3e3D1tIFFj6y8nMzEwAwG9/+9s+9X/3u9851SEi+bnaV4aGhmLhwoWoqKjAli1bYLfb8cMf/tCPLSUKHPw+qVzB/m4A+V5tbS2uXr2Kq1evoqmpCW+++SZKSkoQFBSE8vJyjB492t9NJApozFEiZRhsLh46dAiRkZF9Xuff/u3ffNVkIk0ayn4xKCgI+fn5+D//5/8AACZPnuyrZhNplrs5OXv2bNx3331YvXo1Pv74Y2RkZEAIgePHj2Pbtm247777eBQtkUwGu69cunQpdu7cia1bt+K2225Denq6n1pOpF38bUddOEkSgB555BEAQEhICIYPH44JEybgqaeewqOPPsoEJVIA5iiRMgw2F10dgSeE8Go7ibRuqPvF+++/H+np6aiqqvJ2U4kCwmBy8ve//z1+9rOf4Te/+Q1+/vOfAwC+8Y1vIC8vD2vXrvV524m0arD7ypSUFKSkpODdd9/lWSREXsLfdtRFJ/jNnYiIiIiIiIiIiIiIAhCvSUJERERERERERERERAGJkyRERERERERERERERBSQOElCREREREREREREREQBiZMkREREREREREREREQUkDhJQkREREREREREREREAYmTJEREREREREREREREFJCC/d0AT3R3d+Pvf/87IiMjodPp/N0cokERQqCtrQ0JCQkYNkwb85TMSVIz5iSRsjAniZSFOUmkLHLn5KVLl/DUU0/htddeQ0dHB8aPH4+9e/ciNTVVer+8vDwUFxejubkZ06ZNw86dOzFx4kTpNWw2G9auXYsXX3wRHR0dmDNnDnbt2oUxY8a41QbmJKkZ95NEyuJpTqpykuTvf/87EhMT/d0MoiG5ePGi24NGpWNOkhYwJ4mUhTlJpCzMSSJlkSMnm5ubMWPGDMyePRuvvfYaYmNj8be//Q3Dhw+X6hQWFmLr1q0oLS3F+PHjsXnzZmRlZaGurg6RkZEAgJycHBw6dAhlZWWIiYnBmjVrkJ2djZqaGgQFBV23HcxJ0gLuJ4mUZbA5qRNCCC+2xytaWlowfPhw1NfX46233oLZbIZer/d3s1TDbrejoqKC/eYBOfqutbUViYmJ+OKLLxAdHS1zC/3DkZMXL15EVFSUv5ujOszJoRlq/2k9J8PCwjSzfWklV7QSB+CdWLSek2rbT2ppe3VF6zFyP9nX9XJSK9uEFuLQQgyAvHHImZPr16/Hn//8Z7z55pv9Pi6EQEJCAnJycvDUU08BuHbWSFxcHLZs2YJly5ahpaUFo0ePxgsvvIAHH3wQwNc/sB4+fBhz5869bjvUvJ+Ui1a2dbmoqT8CbT+pps/GXYxJHdyNydOcVOWZJI5TvSIjIxEeHo6oqCjNfOC+YLfb2W8ekrPvtHTKoiOWqKiogB3UDgVzcmjk6j+t5mRYWJhmti+t5IpW4gC8G4tWc1Jt+0ktba+uaD1G7if7ul5OamWb0EIcWogB8E4ccuTkwYMHMXfuXDzwwAOorKzEjTfeiOXLl+Oxxx4DANTX16OxsRFms1l6jsFgQEZGBqqqqrBs2TLU1NTAbrc71UlISIDJZEJVVVW/kyQ2mw02m02639bWBgAICwtDWFjYkONSo+DgYISHhyMsLEzV27pc1NQfdrsdQODsJ7Xyd7knxqQOg41psDmpykkSIiIiIiIiIiLy3EcffYTdu3cjNzcXGzduxKlTp/DEE0/AYDDg4YcfRmNjIwAgLi7O6XlxcXG4cOECAKCxsREhISEYMWJEnzqO5/dWUFCAvLy8PuUVFRUIDw+XIzTVslqt/m6CoqihP9rb2/3dBCKSASdJiIiIiIiIiIgCTHd3N9LS0pCfnw8ASElJwblz57B79248/PDDUr3eR+MKIa57hO5AdTZs2IDc3FzpvmNpFLPZrLozLuVit9thtVqRlZWlmaO+h0JN/dHa2urvJhCRDDhJolDj1r/qdP/j5+b5qSVEvtVz2+d2T0SucD9JpCzMSdIik+UN2Lp03J5Js+Lj43H77bc7lU2YMAEvvfQSAMBoNAK4drZIfHy8VKepqUk6u8RoNKKzsxPNzc1OZ5M0NTUhPT293/c1GAwwGAx9yvV6vcsfxAPle+JAfRCI1NAfSm+fkgRKHpM6DfN3A4iIiIiIiIiIyLdmzJiBuro6p7IPPvgAY8eOBQAkJSXBaDQ6LXnU2dmJyspKaQIkNTUVer3eqU5DQwNqa2tdTpIQEREpDc8kISIiIiIiIiIKME8++STS09ORn5+PBQsW4NSpUyguLkZxcTGAa8ts5eTkID8/H8nJyUhOTkZ+fj7Cw8OxcOFCAEB0dDSWLl2KNWvWICYmBiNHjsTatWsxadIkZGZm+jM8IiIit3nlTJJLly7hBz/4AWJiYhAeHo477rgDNTU10uNCCFgsFiQkJCAsLAyzZs3CuXPnvNEUIlKBcetflW5ERERERETkfVOnTkV5eTlefPFFmEwmPPvssygqKsKiRYukOuvWrUNOTg6WL1+OtLQ0XLp0CRUVFYiMjJTqbNu2Dffffz8WLFiAGTNmIDw8HIcOHUJQUJA/wiIiIho02c8kaW5uxowZMzB79my89tpriI2Nxd/+9jcMHz5cqlNYWIitW7eitLQU48ePx+bNm5GVlYW6ujqnHS0REREREREREXlHdnY2srOzXT6u0+lgsVhgsVhc1gkNDcX27duxfft2L7SQiIjI+2SfJNmyZQsSExNRUlIilY0bN076vxACRUVF2LRpE+bPnw8A2LdvH+Li4nDgwAEsW7ZM7iYRERERERERERERERH1IfskycGDBzF37lw88MADqKysxI033ojly5fjscceAwDU19ejsbERZrNZeo7BYEBGRgaqqqr6nSSx2Wyw2WzS/dbWVgCA3W53+ldLDEHC6b6cMWq537xNjr6Tu9+PHz+On/70p6ipqUFDQwPKy8tx//33S48LIZCXl4fi4mI0Nzdj2rRp2LlzJyZOnCjVsdlsWLt2LV588UV0dHRgzpw52LVrF8aMGSNrW4mIiIiIiIiIiIiURPZJko8++gi7d+9Gbm4uNm7ciFOnTuGJJ56AwWDAww8/jMbGRgBAXFyc0/Pi4uJw4cKFfl+zoKAAeXl5fcqPHj2K8PBwWK1WucPwu8I7ne8fPnxY9vfQYr/5ylD6rr29XcaWAF9++SWmTJmCRx55BN/97nf7PO7O8nY5OTk4dOgQysrKEBMTgzVr1iA7Oxs1NTVcR5aIiIiIiIiIiIg0S/ZJku7ubqSlpSE/Px8AkJKSgnPnzmH37t14+OGHpXo6nc7peUKIPmUOGzZsQG5urnS/tbUViYmJmD17Nt5++21kZWVBr9fLHYpfmSxvON2vtcyV7bXtdjusVqsm+83b5Og7x5lQcrn33ntx77339vuYO8vbtbS0YO/evXjhhReQmZkJANi/fz8SExNx5MgRzJ0r37ZHREREREREREREpCSyT5LEx8fj9ttvdyqbMGECXnrpJQCA0WgEADQ2NiI+Pl6q09TU1OfsEgeDwQCDwdCn3PEjtV6v19yP/bYu5wkjb8SnxX7zlaH0nS/73J3l7WpqamC3253qJCQkwGQyoaqqqt9JkoGWwPNkObHey8s5BMqScFwCb2iG2n/sdyIiIiIiIiL/GLf+Vaf7Hz83z08toUAm+yTJjBkzUFdX51T2wQcfYOzYsQCApKQkGI1GWK1WpKSkAAA6OztRWVmJLVu2yN0cooDmzvJ2jY2NCAkJwYgRI/rUcTy/N1dL4FVUVCA8PHzQ7ey9vJyDN5aZUzIugTc0nvaf3EvgERERERERERGResg+SfLkk08iPT0d+fn5WLBgAU6dOoXi4mIUFxcDuLbMVk5ODvLz85GcnIzk5GTk5+cjPDwcCxculLs5mtFzVpUzqjRYg1nezp06rpbAM5vNiIqKGnT7ei8v5yDnMnNKxiXwhmao/Sf3EnhERERERERERKQesk+STJ06FeXl5diwYQOeeeYZJCUloaioCIsWLZLqrFu3Dh0dHVi+fDmam5sxbdo0VFRUSBeRJiJ5uLO8ndFoRGdnJ5qbm53OJmlqakJ6enq/rzvQEnie/Ejde3m5nq8XSLgE3tB42n/scyIiIiIiIiLf6b3EFpG/DfPGi2ZnZ+Ps2bP46quv8Je//AWPPfaY0+M6nQ4WiwUNDQ346quvUFlZCZPJ5I2mEAW0nsvbOTiWt3NMgKSmpkKv1zvVaWhoQG1trctJEiIiIiIiIiIiIiItkP1MEiLyrStXruCvf/2rdL++vh5nzpzByJEjcdNNN113ebvo6GgsXboUa9asQUxMDEaOHIm1a9di0qRJyMzM9FdYRERERERERERERF7nlTNJiMh33nnnHaSkpCAlJQUAkJubi5SUFPzHf/wHgGvL2+Xk5GD58uVIS0vDpUuX+ixvt23bNtx///1YsGABZsyYgfDwcBw6dAhBQUF+iYmIyGHc+lelGxERERERUaC7dOkSfvCDHyAmJgbh4eG44447UFNTIz0uhIDFYkFCQgLCwsIwa9YsnDt3zo8tJlI+TpIQqdysWbMghOhzKy0tBeDe8nahoaHYvn07Ll++jPb2dhw6dAiJiYl+iIaIiMh/CgoKoNPpkJOTI5XxSyYRERERKUVzczNmzJgBvV6P1157DefPn8fzzz+P4cOHS3UKCwuxdetW7NixA9XV1TAajcjKykJbW5v/Gk6kcJwkISIiIqKAV11djeLiYkyePNmpnF8yiYiIiEgptmzZgsTERJSUlODOO+/EuHHjMGfOHNxyyy0Arh3gU1RUhE2bNmH+/PkwmUzYt28f2tvbceDAAT+3nki5OElCRETkYzw9mkhZrly5gkWLFmHPnj0YMWKEVM4vmURERESkJAcPHkRaWhoeeOABxMbGIiUlBXv27JEer6+vR2NjI8xms1RmMBiQkZGBqqoqfzSZSBV44XYiIiIfcpwePXv2bLz22muIjY3F3/72t35Pjy4tLcX48eOxefNmZGVloa6uzul6QkQkjxUrVmDevHnIzMzE5s2bpfLrfclctmxZv69ns9lgs9mk+62trQAAu90Ou93upSi8w9He67XbECT6fZ4auBujWg01Pq32CxERkRp99NFH2L17N3Jzc7Fx40acOnUKTzzxBAwGAx5++GE0NjYCAOLi4pyeFxcXhwsXLvT7moMZuw5lXNF7vOiKr8ceWhwLBnJMnsbMSRIiIiIf6nl6tMO4ceOk//c+ch0A9u3bh7i4OBw4cMDlj7JE5JmysjKcPn0a1dXVfR7z5EsmcO3aJnl5eX3KKyoqEB4ePsQW+4fVah3w8cI7ne8fPnzYi63xjuvFqHaextfe3i5zS4iIiMhT3d3dSEtLQ35+PgAgJSUF586dw+7du/Hwww9L9XQ6ndPzhBB9yhw8Gbt6Mq7oPV50xV/jSC2OBQMxJk/Hrpwk8bNx61+V/v/xc/P82BIiIvKFgwcPYu7cuXjggQdQWVmJG2+8EcuXL8djjz0GwLMj1wc68ic4OFj6v1qYLG9I/zcEfV3e88gRHrWuDN6IxZf9cvHiRaxevRoVFRUIDQ11WW8wXzIBYMOGDcjNzZXut7a2IjExEWazGVFRUUNvuA/Z7XZYrVZkZWVBr9e7rNczbwGg1jL3uvVc1fE1d2NUq6HG59inEBERkf/Fx8fj9ttvdyqbMGECXnrpJQCA0WgEcO1gn/j4eKlOU1NTnwN/HAYzdh3KuKL3eNEVX48RtTgWDOSYPB27cpKEiIjIh7xxerQ7R/6o6QgSV0cYOWKwWq08al1h5IzFl0et19TUoKmpCampqVJZV1cXjh8/jh07dqCurg7A4L5kAtcmNg0GQ59yvV6v2i8p12u7rct50shV3Z71lNYXav583OFpfFruEyIiIrWZMWOGNEZ1+OCDDzB27FgAQFJSEoxGI6xWK1JSUgAAnZ2dqKysxJYtW/p9TU/Grp6MK3qPF13x19hDi2PBQIzJ03g5SUJERORD3jg9eqAjf8LCwlR3BImrI4ze3XSPFEvKT/7k9JhSjkh3h5aO6vFGLL48an3OnDk4e/asU9kjjzyC2267DU899RRuvvnmQX/J1CqT5Q3YunROZz73PCOaiIiIiLzvySefRHp6OvLz87FgwQKcOnUKxcXFKC4uBnDte2ROTg7y8/ORnJyM5ORk5OfnIzw8HAsXLvRz64mUi5MkRKRYXI6OtMgbp0e7c+SPmo4gcXWEUc9Y3D1qXcnU9Jlcj5yx+LJPIiMjYTKZnMoiIiIQExMjlfNLJhEREREpxdSpU1FeXo4NGzbgmWeeQVJSEoqKirBo0SKpzrp169DR0YHly5ejubkZ06ZNQ0VFBSIjI/3YciJlG+bvBhAREQWSwZwe7eA4cj09Pd2nbSWia18yc3JysHz5cqSlpeHSpUv8kumGcetflW5ErhQUFEhHvDoIIWCxWJCQkICwsDDMmjUL586dc3qezWbDqlWrMGrUKEREROC+++7Dp59+6uPWExER+Ud2djbOnj2Lr776Cn/5y1+k61s66HQ6WCwWNDQ04KuvvkJlZWWfA4OIyBknSYiIiHzoySefxMmTJ5Gfn4+//vWvOHDgAIqLi7FixQoAzqdHl5eXo7a2FkuWLOGR60Q+cuzYMRQVFUn3+SWTyDuqq6tRXFyMyZMnO5UXFhZi69at2LFjB6qrq2E0GpGVlYW2tjapTk5ODsrLy1FWVoYTJ07gypUryM7ORldXl6/DICIiIiIN4CQJERGRDzlOj37xxRdhMpnw7LPP9nt6NI9cJyIirbpy5QoWLVqEPXv2YMSIEVK5EAJFRUXYtGkT5s+fD5PJhH379qG9vR0HDhwAALS0tGDv3r14/vnnkZmZiZSUFOzfvx9nz57FkSNH/BUSEREREakYr0lCRETkY9nZ2cjOznb5uOPIdYvF4rtGERER+ciKFSswb948ZGZmYvPmzVJ5fX09GhsbYTabpTKDwYCMjAxUVVVh2bJlqKmpgd1ud6qTkJAAk8mEqqoqzJ07t8/72Ww22Gw26X5raysAwG63w26396nvKDMME0731cbRbrW2H9BGDIC8cai9L4iIiJSIkyQqxItZExFRIDJZ3kDhndf+Bfq/uDsRqQfHtIGprKwMp0+fRnV1dZ/HGhsbAQBxcXFO5XFxcbhw4YJUJyQkxOkMFEcdx/N7KygoQF5eXp/yiooKhIeHu2zrs2ndAIDDhw8PEJHy9bzOmVppIQZAnjja29tlaAkRERH1xEkSIiIiIiIi8rqLFy9i9erVqKioQGhoqMt6Op3zRLgQok9ZbwPV2bBhA3Jzc6X7ra2tSExMhNlsRlRUVJ/6drsdVqsVP35nGGzdOtRa+p6dogaOOLKysqDX6/3dHI9oIQZA3jgcZ0IRERGRfDhJQkRERKrHI9KJiJSvpqYGTU1NSE1Nlcq6urpw/Phx7NixA3V1dQCunS0SHx8v1WlqapLOLjEajejs7ERzc7PT2SRNTU1IT0/v930NBgMMBkOfcr1eP+AP1rZuHWxdOlX/OA9cP0410EIMgDxxeLMfCgoKsHHjRqxevRpFRUUArk1A5uXlobi4GM3NzZg2bRp27tyJiRMnSs+z2WxYu3YtXnzxRXR0dGDOnDnYtWsXxowZ47W2EhERyYkXbicivxi3/lXpRkRERETaN2fOHJw9exZnzpyRbmlpaVi0aBHOnDmDm2++GUaj0WlJos7OTlRWVkoTIKmpqdDr9U51GhoaUFtb63KShIiur7q6GsXFxZg8ebJTeWFhIbZu3YodO3aguroaRqMRWVlZaGtrk+rk5OSgvLwcZWVlOHHiBK5cuYLs7Gx0dXX5OgwiIiKP8EwSIiIiIiIi8rrIyEiYTCansoiICMTExEjlOTk5yM/PR3JyMpKTk5Gfn4/w8HAsXLgQABAdHY2lS5dizZo1iImJwciRI7F27VpMmjQJmZmZPo+JSAuuXLmCRYsWYc+ePdi8ebNULoRAUVERNm3ahPnz5wMA9u3bh7i4OBw4cADLli1DS0sL9u7dixdeeEHKwf379yMxMRFHjhzB3LnqXK6OiIgCCydJiIiIiIjIa3jWKA3GunXr0NHRgeXLl0tL+1RUVCAyMlKqs23bNgQHB2PBggXS0j6lpaUICgryY8uJ1GvFihWYN28eMjMznSZJ6uvr0djYCLPZLJUZDAZkZGSgqqoKy5YtQ01NDex2u1OdhIQEmEwmVFVV9TtJYrPZYLPZpPuO66zY7XbY7fZ+22gIEtL/XdVRM0dMWozNE2rqDzW0kYiuj5MkRERE5Hf8EZWIKDAdO3bM6b5Op4PFYoHFYnH5nNDQUGzfvh3bt2/3buOIAkBZWRlOnz6N6urqPo81NjYCgHRNIIe4uDhcuHBBqhMSEuJ0jSBHHcfzeysoKEBeXl6f8oqKCoSHh/f7nMI7v/7/4cOHXQekcj2XEiR19Ed7e7u/m0BEMuAkCREREREROXFMXBqChNMPU0REpB0XL17E6tWrUVFRgdDQUJf1dDqd030hRJ+y3gaqs2HDBuTm5kr3W1tbkZiYCLPZjKioqH6fY7K8If2/1qK9JbzsdjusViuysrKg1+v93Ry/U1N/OM6Eov7xYDhSC69PkhQUFGDjxo1YvXo1ioqKAFzbWebl5aG4uFg6hXrnzp2YOHGit5tDRERERERERBTwampq0NTUhNTUVKmsq6sLx48fx44dO1BXVwfg2tki8fHxUp2mpibp7BKj0YjOzk40Nzc7nU3S1NSE9PT0ft/XYDDAYDD0Kdfr9S5/ELd16ZzqadVAfRCI1NAfSm8fEblnmDdfvLq6GsXFxZg8ebJTeWFhIbZu3YodO3aguroaRqMRWVlZaGtr82ZziIiIiIiIiIgIwJw5c3D27FmcOXNGuqWlpWHRokU4c+YMbr75ZhiNRqcljzo7O1FZWSlNgKSmpkKv1zvVaWhoQG1trctJEiIiIqXx2pkkV65cwaJFi7Bnzx6nC38JIVBUVIRNmzZh/vz5AIB9+/YhLi4OBw4cwLJly7zVJCIiIiIiIiIiAhAZGQmTyeRUFhERgZiYGKk8JycH+fn5SE5ORnJyMvLz8xEeHo6FCxcCAKKjo7F06VKsWbMGMTExGDlyJNauXYtJkyYhMzPT5zERERF5wmuTJCtWrMC8efOQmZnpNElSX1+PxsZGmM1mqcxgMCAjIwNVVVX9TpLYbDbYbDbpvmO9P7vd7vSvGhmChPT/nnH0LB+IJ7Frod/8RY6+Y78TERGRWnFdaSKiwLJu3Tp0dHRg+fLl0nLpFRUViIyMlOps27YNwcHBWLBgATo6OjBnzhyUlpYiKCjIjy0nIiJyn1cmScrKynD69GlUV1f3eayxsREApPUrHeLi4nDhwoV+X6+goAB5eXl9yo8ePYrw8HCn0zrVpueFMA8fPtxv+UB6Pmew1Nxv/jaUvmtvb5exJURE6sQfWomIiIiU59ixY073dTodLBYLLBaLy+eEhoZi+/bt2L59u3cbR0RE5CWyT5JcvHgRq1evRkVFBUJDQ13W0+l0TveFEH3KHDZs2IDc3FzpfmtrKxITEzF79my8/fbbyMrKUu2FkkyWN4b0/FrL3EE/x263w2q1qrrf/EWOvnOcCUVERERERERERET963lw3cfPzfNjS0jrZJ8kqampQVNTE1JTU6Wyrq4uHD9+HDt27EBdXR2Aa2eUxMfHS3Wampr6nF3iYDAYYDAY+pQ7fqTW6/Wq/bHf1tX/xJC7hhK3mvvN34bSd+xzIiIiIvfxyzEREREREXnTMLlfcM6cOTh79izOnDkj3dLS0rBo0SKcOXMGN998M4xGo9NyRZ2dnaisrER6errczSEiIiIiIiIiIiIiIuqX7JMkkZGRMJlMTreIiAjExMTAZDJBp9MhJycH+fn5KC8vR21tLZYsWYLw8HAsXLhQ7uYQERERERERERERaUpBQYH0O6uDEAIWiwUJCQkICwvDrFmzcO7cOf81kkglvHLh9utZt24dOjo6sHz5cjQ3N2PatGmoqKhAZGSkP5pDRD7AizQTEREREREREQ1ddXU1iouLMXnyZKfywsJCbN26FaWlpRg/fjw2b96MrKws1NXV8XdXogHIfiZJf44dO4aioiLpvk6ng8ViQUNDA7766itUVlbCZDL5oilERESKwqN/iIiIiIiIyF1XrlzBokWLsGfPHowYMUIqF0KgqKgImzZtwvz582EymbBv3z60t7fjwIEDfmwxkfL5ZJKEiIiI+rre0T87duxAdXU1jEYjsrKy0NbW5qeWEhERERERkRKsWLEC8+bNQ2ZmplN5fX09GhsbYTabpTKDwYCMjAxUVVX5upkeG7f+VelG5Ct+WW6LiIgo0PU8+mfz5s1See+jfwBg3759iIuLw4EDB7Bs2TJ/NZmIiIiIiIj8qKysDKdPn0Z1dXWfxxobGwEAcXFxTuVxcXG4cOGCy9e02Wyw2WzS/dbWVgCA3W6H3W53quu437vcFUOQcKueO9x9T09f11uv7w+BHJOnMXOShIiIyA96Hv3Tc5Lkekf/9DdJMtCgNjg4WPq/kngyWDYME07/uqK0WHvT0oDVG7FooV+IiIiIiOR28eJFrF69GhUVFQgNDXVZT6fTOd0XQvQp66mgoAB5eXl9yisqKhAeHt7vc6xWq1ttLrzTrWpuOXz4sHwv1g93Y1KTQIypvb3do9flJImPeOsUsZ6v+/Fz87zyHkREJC+5j/5xZ1CrtMHRUAbLz6Z1D/i4twfPclHaZzIUcsbi6aCW1K33WJnjWiIiIiJnNTU1aGpqQmpqqlTW1dWF48ePY8eOHairqwNw7TtlfHy8VKepqanP98ueNmzYgNzcXOl+a2srEhMTYTabERUV5VTXbrfDarUiKysLer3+um02Wd5wO77rqbXMle21ehpsTGoQyDE5DhodLE6SEJEq8McT0gpvHP0z0KA2LCxMkYMjTwbLhmECz6Z148fvDIOt2/WRUD15ayA9FFoasHojFk8HtUREREREWjZnzhycPXvWqeyRRx7Bbbfdhqeeego333wzjEYjrFYrUlJSAACdnZ2orKzEli1bXL6uwWCAwWDoU67X612O8Qd6rCdbl3vf29zh7e9O7sakJoEYk6fxcpKEiIjIh7xx9I87g1qlDY6GMli2devcfr6SYu5NaZ/JUMgZi1b6hIiIiIhITpGRkTCZTE5lERERiImJkcpzcnKQn5+P5ORkJCcnIz8/H+Hh4Vi4cKE/miwrrqZD3sRJEiIiIh/y1tE/REREREREFNjWrVuHjo4OLF++HM3NzZg2bRoqKioQGRnp76YRKdowfzeAiLzLYrFAp9M53YxGo/S4EAIWiwUJCQkICwvDrFmzcO7cOT+2mEjbHEf/9Lz1PPpHp9NJR/+Ul5ejtrYWS5Ys0czRP0RERERERCSPY8eOoaioSLqv0+lgsVjQ0NCAr776CpWVlX3OPiGivngmCVEAmDhxIo4cOSLdDwoKkv5fWFiIrVu3orS0FOPHj8fmzZuRlZWFuro6HmlA5Cc8+oeIiIiIiIiIyDc4SaJRvMg19RQcHOx09oiDEAJFRUXYtGkT5s+fDwDYt28f4uLicODAASxbtszXTSUKSMeOHXO67zj6x2Kx+KU9RERERERERESBgsttEQWADz/8EAkJCUhKSsL3vvc9fPTRRwCA+vp6NDY2wmw2S3UNBgMyMjJQVVXlr+YSERERERERERER+QTPJCHSuGnTpuHXv/41xo8fj3/84x/YvHkz0tPTce7cOTQ2NgIA4uLinJ4TFxeHCxcuuHxNm80Gm80m3W9tbQUA2O122O32fp9jCBJDDcWJq/dRI0csWorJl4baf+x3IiIiIiIiIqLAxUkSIo279957pf9PmjQJ06dPxy233IJ9+/bhrrvuAnBtaZ+ehBB9ynoqKChAXl5en/KKigqEh4f3+5zCOz1pvWuHDx+W9wUVwGq1+rsJquZp/7W3t8vcEiJSk4KCArz88st4//33ERYWhvT0dGzZsgW33nqrVEcIgby8PBQXF0vXCdq5cycmTpzox5ZrW++lY4mIiIiIiLyFkyREASYiIgKTJk3Chx9+iPvvvx8A0NjYiPj4eKlOU1NTn7NLetqwYQNyc3Ol+62trUhMTITZbEZUVFS/zzFZ3pAngP9frWWurK/nT3a7HVarFVlZWdDr9f5ujuoMtf8cZ0IRUWCqrKzEihUrMHXqVFy9ehWbNm2C2WzG+fPnERERAQAoLCzE1q1bUVpaivHjx2Pz5s3IyspCXV0dIiMj/RwBERERERERDQUnSYgCjM1mw1/+8hd861vfQlJSEoxGI6xWK1JSUgAAnZ2dqKysxJYtW1y+hsFggMFg6FOu1+td/kht63J9ZoontDiZMFD/0fV52n/sc6LA9vrrrzvdLykpQWxsLGpqanD33XdDCIGioiJs2rQJ8+fPBwDs27cPcXFxOHDgAJYtW+aPZhMREREREZFMOEmiIVyWgPqzdu1afOc738FNN92EpqYmbN68Ga2trVi8eDF0Oh1ycnKQn5+P5ORkJCcnIz8/H+Hh4Vi4cKG/m05ERORzLS0tAICRI0cCAOrr69HY2Aiz2SzVMRgMyMjIQFVVFSdJiIiIiIiIVI6TJEQa9+mnn+L73/8+/vnPf2L06NG46667cPLkSYwdOxYAsG7dOnR0dGD58uXSOusVFRVcPoSIZMfJfFI6IQRyc3Mxc+ZMmEwmANeWpATQZxnKuLg4XLhwod/XsdlssNls0n3Hsn52ux12u90bTZedIUhc+3eY87/+5o3+c7ymWj6bwRpqfFrtFyIiIiIiB06SEGlcWVnZgI/rdDpYLBZYLBbfNIiIiEihVq5ciffeew8nTpzo85hO57xspBCiT5lDQUEB8vLy+pRXVFQgPDxcnsZ6WeGdzvefTev2T0N6OXz4sNde22q1eu21lcDT+Nrb22VrQ0FBAV5++WW8//77CAsLQ3p6OrZs2YJbb71VqiOEQF5eHoqLi6UDeHbu3ImJEydKdWw2G9auXYsXX3wRHR0dmDNnDnbt2oUxY8bI1lYiIiIiChycJCEiIiKv4dkjpBarVq3CwYMHcfz4cacfWo1GI4BrZ5TEx8dL5U1NTX3OLnHYsGEDcnNzpfutra1ITEyE2WxGVFSUlyKQl8nyBoBrZ5A8m9aNH78zDLZuea8v5olay1zZX9Nut8NqtSIrK0uT16kaanyOM6HkUFlZiRUrVmDq1Km4evUqNm3aBLPZjPPnzyMiIgIAUFhYiK1bt6K0tBTjx4/H5s2bkZWVhbq6OulM55ycHBw6dAhlZWWIiYnBmjVrkJ2djZqaGgQFBcnWXiIiIiIKDJwk8RL+KERERESkfEIIrFq1CuXl5Th27BiSkpKcHk9KSoLRaITVakVKSgoAoLOzE5WVldiyZUu/r2kwGGAwGPqU6/V61fwIb+tynhCxdev6lPmDN/tPTZ+PJzyNT84+ef31153ul5SUIDY2FjU1Nbj77rshhEBRURE2bdqE+fPnAwD27duHuLg4HDhwAMuWLUNLSwv27t2LF154AZmZmQCA/fv3IzExEUeOHMHcufJPpBERERGRtnGSREacGCEiIiJSlxUrVuDAgQN45ZVXEBkZKV2DJDo6GmFhYdDpdMjJyUF+fj6Sk5ORnJyM/Px8hIeHY+HChX5uPZG6tbS0AABGjhwJAKivr0djYyPMZrNUx2AwICMjA1VVVVi2bBlqampgt9ud6iQkJMBkMqGqqqrfSZLBXifIUea4Fo9ar8uihevtaCEGQN445OwLLoFHRER0DSdJiIiIiChg7d69GwAwa9Ysp/KSkhIsWbIEALBu3Tp0dHRg+fLl0g9EFRUV0tI/WsCDfcjXhBDIzc3FzJkzYTKZAECapOy9lF1cXBwuXLgg1QkJCcGIESP61HE8vzdPrxPkuBaPN6+F4wtauN6OFmIA5IlDzusEcQk8ItKC3uPYj5+b56eWkJpxkiRA9PyD8eGz5gFqEhEREQUOIcR16+h0OlgsFlgsFu83iChArFy5Eu+99x5OnDjR5zGdznlpNyFEn7LeBqoz2OsEOa7j4rgWjzeuheMLWrjejhZiAOSNQ87rBHEJPCLSop6/gXLChNwl+ySJXKdrEhERERERkfasWrUKBw8exPHjx52W4zEajQCunS0SHx8vlTc1NUlnlxiNRnR2dqK5udnpbJKmpiakp6f3+36eXifIcS0eNf84D2jjejtaiAGQJw5v9oNSl8ADAEPQ1wc1qH35tf5oZWk5uaipP9TQRiK6PtknSeQ6XZOIiIiIiIi0QwiBVatWoby8HMeOHUNSUpLT40lJSTAajbBarUhJSQEAdHZ2orKyElu2bAEApKamQq/Xw2q1YsGCBQCAhoYG1NbWorCw0LcBEWmI0pfAK7zz6/+rfQm8gWhlaTm5qKE/5FwCj4j8R/ZJEjlO1yQiIiIiIiJtWbFiBQ4cOIBXXnkFkZGR0g+o0dHRCAsLg06nQ05ODvLz85GcnIzk5GTk5+cjPDwcCxculOouXboUa9asQUxMDEaOHIm1a9di0qRJ0lI/RDR4Sl4CDwBMljek/6t1CbyBaGVpObmoqT/kXAKPiPzH69ck8eR0zd4GOhWz57/+1vP0TyVTWr+piRx9x34nIiIiJeDF2snXdu/eDQCYNWuWU3lJSQmWLFkCAFi3bh06OjqwfPlyaWnmiooKpxUHtm3bhuDgYCxYsAAdHR2YM2cOSktLeYFoIg+pYQk8W5fOqZ5WaWVpObmooT+U3j4ico9XJ0k8PV2zN1enYh49ehTh4eGKOf2u5+mfSuboL6X0mxoNpe94KiYRERERBSIhrn9QmU6ng8VigcVicVknNDQU27dvx/bt22VsHVHg4RJ4ROqjhmtB++JAHB7sQ3Lz6iSJXKdrujoVc/bs2Xj77bf9evpdz1M+1eLdTfeo5rRFpZHjlE+eikkU2NQwqCUiUpveX5Q/fm6en1pCRKQeXAKPSH2Uci1ok+UN6QwvjrtIC7w2STKU0zV7G+hUTMe//vqxv+cpn2qhhH5Tu6H0HftcHj1/DOEOmdREKYNaIiIiIgpsXAKPSH14LWgi75B9kkSO0zWJiIi0ioNaIiIiIlICLoFHpH7evhZ07+vqOu4bhok+ZdL7Keiaze5cF1iL124O5Jg8jVn2SRI5Ttck3+HR+ERE/uXtQW1wcLD0f3+Qc4DsGIj3HJBfz62b/kf6f61lrmxtGQotDVi9EYsW+oWIiIiIyNu8fS3oiooKhIeH9/ucZ9O6pf8fPnzY6TElXbO5d9sGosVrNwdiTJ5eC1r2SRK5TtckIiLSOl8Oav01OPLGALnngHwwBjNA9gUtDVjljMXTQS05c3UgjNovcqn29hMRERHJxdvXgjabzYiKinKq67hW74/fGQZb97XX630wmpKu3+zOgXJyXH9YaQI5Jk+vBe2V5baux53TNYmIiLTOF4PasLAwnw+OvDUoNgwTeDat22lAPhhKOpNEKwNWb8Ti6aCWiIiIiChQ+Opa0K7G+LZunXSd5t51lHT95sF8R9HitZsDMSZP4/XahduJiIjINV8Pan05OPL2oLjngHwwlDY41NKAVc5YtNInRERERERy47WgibyDkyREREQ+xEEtEfkal6giIiIi0gZeC5rIOzhJQkRE5EMc1BIREREREZEnlHgtaCUfkOPqOn1EvXGShIiIyIeUOKiVg5IHxkREDv39rTIECRTe6YfGEBEREQ0SrwVN5B2cJCEiIvIhDmqJiIiIiIiIiJSDkyQByGR5A4V3XvsX8O7FdYmIiIiI1MBkeQO2rmtjYy7HQEREREQUODhJQkSqxzUmiYiIiIiIiIiIyBPD/N0AIiIiIiIiIiIiIiIif+CZJERERERERERERESkWT1XIenpw2fNPm4JKRHPJCEiIiIiIiIiIiKigHPtms3X/nU1kULaxzNJiIiIiIhUjl/onLE/iIiIiIjIXTyThIiIiIiIiIiIiIiIAhLPJCEiTel55OjHz83zY0uIiIiIiIiIiIhI6XgmCRERERERERERERERBSSeSeKmQF/XmEfnExGR2nFfRkRERERERES9cZLEhUCfFCEiIiIiIiIiIiIi0jpOkhAREREREfXAM8+IiIiIiAIHJ0lI4smXQX6BJLXofXYYt1eiwMa/CURERERERNQbf+sMTJwkISIiIiIicoFflImIiIiItI2TJD3wOiRDwy+QpDTMaSJyF/dhRETkCs8+JCIiItI2TpJQvwb6cdmdH575RYKISNmG+neak5BERERERESkZa4Ophvo+7CrevxtVNk4SUJERERu4cQIkbIwJ/2LX3qJiIiIqDeO0dWJkyREFJD4wwYREREREREREREN8+eb79q1C0lJSQgNDUVqairefPNNfzaHKOAxJ52NW/+qdCPyByXkJPOA6GvezEnmmjq4+px6ll9v2Vp/fs7+fn+5KWE/SURfY04SKQfzUX5qHEeZLG+ors3+4rczSX77298iJycHu3btwowZM/DLX/4S9957L86fP4+bbrppyK/Pa2IQDY63c1LJhnpWCc9KIW/wZ05yAOUbJssbsHXpAPBvhxoE8n6SSImYk0TKwpwkUg7mo/LJ+TsSfwOXh98mSbZu3YqlS5fi0UcfBQAUFRXhjTfewO7du1FQUOCvZhEFLObkNfxxmJSCOUmkLP7MSU7Gq5s7Ywt3v9y6uy30rPfhs+YhvZZSMSeJlMWXOckfBIkGxu+SRIPnl0mSzs5O1NTUYP369U7lZrMZVVVVferbbDbYbDbpfktLCwDg888/R3t7Oy5fvgy9Xu/0nOCrXzrd/8ba30n/f3vDHOn/0wr++PVzPIhFjYK7BdrbuxFsH4aubp1P3vPy5cvS/3v2ec/PoqeedQaq5y533tMddrvd5Tbn7nu0tbUBAIQQHrdDbnLmpN1u7/c9euek2vTchnvrGVvPvzUD6bmN9NyuZv7seL91eueEq9dyxd2c8iRX5MovT7nKS+bktZwMDQ3tt38G2k8qdX/oq/2Xq3wfaJsaTB44ttmecbjznj0N5f3lNNB+sTfmpPv7yd7bg6vHfLFv9ce40de8EaM7n9NAdVz9TRhoOxmoniNP3WlLb4GYk/39nXbF1djPH2Oi3gbzN1qptBADIG8cgZiTwMD7vIH+hqmFVrZ1uaipP5SWk4PNR2BwOTmYfaRa+ON75kBjOlffm1x9N+z9/f3y5cuD+s45EH//3tPT3VuO4P+ldOOOTS/j+FOZLut5nJPCDy5duiQAiD//+c9O5T/5yU/E+PHj+9R/+umnBQDeeNPU7eLFi75KuetiTvLGG3OSN96UdmNO8sabsm7MSd54U9aNOckbb8q6KSUnB5uPQjAnedPmbbA56deDRXU659k5IUSfMgDYsGEDcnNzpfvd3d34/PPPodfrcdNNN+HixYuIioryenu1orW1FYmJiew3D8jRd0IItLW1ISEhQebWDd1QczImJqbf+jQw5uTQDLX/tJ6TbW1tmtm+tJIrWokD8E4sWs9Jte0ntbS9uqL1GLmfHHxOamWb0EIcWogBkDeOQMzJQKCVbV0uauoPpeaku/kIDC4n1fTZuIsxqYO7MXmak36ZJBk1ahSCgoLQ2NjoVN7U1IS4uLg+9Q0GAwwGg1PZ8OHD0draCgCIiorSzAfuS+w3zw2176Kjo2VszdDJlZM0NMzJoRlK/2k5Jx2DWi1tX1qJRStxAPLHouWcVCstba+uaD1G7icHn5Na2Sa0EIcWYgDkiyNQczIQaGVbl4ta+kNJOTnYfAQ8y0m1fDaDwZjUwZ2YPMnJYZ42aChCQkKQmpoKq9XqVG61WpGenu6PJhEFNOYkkbIwJ4mUhTlJpCzMSSJlYU4SKQfzkcgzfltuKzc3Fw899BDS0tIwffp0FBcX45NPPsHjjz/uryYRBTTmJJGyMCeJlIU5SaQszEkiZWFOEikH85Fo8Pw2SfLggw/i8uXLeOaZZ9DQ0ACTyYTDhw9j7Nixbr+GwWDA008/3eeUMBoY+81zWu47OXKSPKPl7coXtNp/cuWklvpHK7FoJQ5AW7FcT6DuJwPhM9Z6jFqNz5s5qZU+00IcWogB0E4cAwnU/aRcAmEbGQz2x9BwHzk4jEkdvB2TTgghvPLKRERERERERERERERECuaXa5IQERERERERERERERH5GydJiIiIiIiIiIiIiIgoIHGShIiIiIiIiIiIiIiIAhInSYiIiIiIiIiIiIiIKCApfpJk165dSEpKQmhoKFJTU/Hmm28OWL+yshKpqakIDQ3FzTffjF/84hc+aqmyDKbfjh07Bp1O1+f2/vvv+7DF/nf8+HF85zvfQUJCAnQ6Hf7whz9c9znc3qg/FoulTz4ZjUbpcSEELBYLEhISEBYWhlmzZuHcuXNOr2Gz2bBq1SqMGjUKERERuO+++/Dpp5/6OhSfuV7+ydVnzc3NeOihhxAdHY3o6Gg89NBD+OKLL7wcnX8Ndj/qa7767L2toKAAU6dORWRkJGJjY3H//fejrq7OqY5aYtm9ezcmT56MqKgoREVFYfr06XjttddUFwe5T67tV00KCgqg0+mQk5MjlWkhxkuXLuEHP/gBYmJiEB4ejjvuuAM1NTXS41qI0ReUvu/sSY5xpz9oYf9/vRiWLFnS57O56667FBUD+ZYc272WBOL4QwvUtI/sSa37y560sO/sTUn7UkVPkvz2t79FTk4ONm3ahHfffRff+ta3cO+99+KTTz7pt359fT3+5V/+Bd/61rfw7rvvYuPGjXjiiSfw0ksv+bjl/jXYfnOoq6tDQ0ODdEtOTvZRi5Xhyy+/xJQpU7Bjxw636nN7o4FMnDjRKZ/Onj0rPVZYWIitW7dix44dqK6uhtFoRFZWFtra2qQ6OTk5KC8vR1lZGU6cOIErV64gOzsbXV1d/gjH666Xf3L12cKFC3HmzBm8/vrreP3113HmzBk89NBDXo/PXzzdH/iSrz57b6usrMSKFStw8uRJWK1WXL16FWazGV9++aXqYhkzZgyee+45vPPOO3jnnXdwzz334F//9V+lAbZa4iD3ybX9qkV1dTWKi4sxefJkp3K1x9jc3IwZM2ZAr9fjtddew/nz5/H8889j+PDhUh21x+gLath39jbUcac/aGH/7873x29/+9tOn83hw4edHvd3DORbcmz3WhJo4w8tUOM+sic17i970sK+szdF7UuFgt15553i8ccfdyq77bbbxPr16/utv27dOnHbbbc5lS1btkzcddddXmujEg22344ePSoAiObmZh+0Th0AiPLy8gHrcHsjV55++mkxZcqUfh/r7u4WRqNRPPfcc1LZV199JaKjo8UvfvELIYQQX3zxhdDr9aKsrEyqc+nSJTFs2DDx+uuve7XtStA7/+Tqs/PnzwsA4uTJk1Kdt956SwAQ77//vpej8o/B7g/8zVufvT80NTUJAKKyslIIoe5YhBBixIgR4le/+pXq4yD3eLL9qkVbW5tITk4WVqtVZGRkiNWrVwshtBHjU089JWbOnOnycS3E6Atq23cOddypBFrY//f3/XHx4sXiX//1X10+R2kxkG95st1rnZbHH1qhtn1kT1rYX/akhX1nb/7elyr2TJLOzk7U1NTAbDY7lZvNZlRVVfX7nLfeeqtP/blz5+Kdd96B3W73WluVxJ1+Ky0tdTpNafbs2QCAESNGQKfT4Zvf/CaOHj2KcePGOdULDQ3FN77xDeTm5uKf//ynz2NTGm5vNJAPP/wQCQkJSEpKwve+9z189NFHAK6dgdTY2Oi07RgMBiQlJeHxxx+HTqfD8OHDYbfb8b3vfU/Kvw8++AAmkwkPPvhgv8vj6XQ6zJo1y0/ReperPsvIyJD+rtXU1MButzvVSUhIgMlkkuq89dZbiI6OxrRp06Q6d911F6Kjo13uV9TMk/2o0sj12Q9G731k79uxY8ekujabDTt37kRGRgZiYmKg1+sRExODWbNmScsvjhw50m+xyKGrqwtlZWX48ssvMX36dNXGQYPT0tICYHDbr7e4m5Pjxo1DdnZ2v6/xzjvvQKfTobS0FCtWrMC8efOQmZnpVMefMcrl4MGDSEtLwwMPPIDY2FikpKRgz5490uNaiNHb1LrvHOy4U47PXI7cdEVL+5pjx44hNjYW48ePx2OPPYampibpMbXEQL4x1HwdTE72LL/hhhswbdo0/PrXv/ZWaG5T0viD+lLrPrInX+0v3c1HnU6HlStX9vsav//97/t8/xyIlvadvflqXxosW4tl9s9//hNdXV2Ii4tzKo+Li0NjY2O/z2lsbOy3/tWrV/HPf/4T8fHxXmuvUgym30pKSnDbbbfhwoULOHPmDG677TZ0dnbi7bffxpw5cxAXF4cZM2bgZz/7GQCgo6MD77zzDiwWC44fP4533nnHZ3EpEbc3csUxyBw/fjz+8Y9/YPPmzUhPT8e5c+ekPOy97URHRwO4lpcXLlzA5s2bndb2vP322xEXF4ePPvrIKS97ioqK8mJU/uOqz+Li4nDhwgWpTkhICEaMGNGnjuP5jY2NiI2N7fP6sbGxLvcraubJflRp5PrsPeHYR/Z2++23AwA+++wzfPvb30ZtbS0WL16MJ554ArGxsbh8+TL++Mc/4plnnsHo0aNhMpn8Hosnzp49i+nTp+Orr77CDTfcgPLyctx+++3SQFMtcdDgCSGQm5uLmTNnDmr79bbr5aQ73n77bZw+fRrV1dV9HlNCjEP10UcfYffu3cjNzcXGjRtx6tQpPPHEEzAYDHj44Yc1EaO3qXHf6cm4U87PXI7c7E1t+0xX7r33XjzwwAMYO3Ys6uvr8eMf/xj33HMPampqYDAYVBED+Y5c+epOTvb8Pvnpp5/iZz/7GRYvXowvv/wSP/rRjzxp/pApdfxBX1PjPrInf+wvvbGPdEUr+87efLkvVewkiYNOp3O6L4ToU3a9+v2Va507/WYymZCWloa77roLDz74oFT+2GOP4R//+Af++Mc/Yvjw4U4XxJk9ezba2trw7LPP4oMPPsD48eO9G4jCcXuj/tx7773S/ydNmoTp06fjlltuwb59+6R8crXtmEwmhISEYNiwYX0uRuWo0zsvA8Vg9wf91emvvjuvo2ae9JvSyPHZD5ZjH+nKD37wA5w9exZHjhzB3Xff7fSY1WqF0WjEv//7v/d5nj9i8cStt96KM2fO4IsvvsBLL72ExYsXo7KyUnpcLXHQ4K1cuRLvvfceTpw40ecxf/49uV5OuuM3v/kN3nzzTYSGhrqso+a/md3d3UhLS0N+fj4AICUlBefOncPu3bvx8MMPS/XUHKOvqKmPPB13yhWPHLnpitr3NT2/Yzv6aezYsXj11Vcxf/58l89TUgzke0PNV3dysvf3yczMTIwdOxZbt2712ySJUscf1JdaPw9/7C+9uY90Re37zt58uS9V7HJbo0aNQlBQUJ9Zn6ampj6zYg5Go7Hf+sHBwYiJifFaW5XEk37rz1133eVyySjHEe96vd7zhmoAtzdyV0REBCZNmoQPP/wQRqMRAPpsO62trdL/jUYjOjs70dzc7FSnqakJQUFB3m+wwrjqs55/1wbqs551/vGPf/R5/c8++2xQfx/VQq79gT/J9dnLrbq6GhUVFfi///f/9pkgWbVqFQ4ePIjjx4/jiSeekMqVGosrISEh+MY3voG0tDQUFBRgypQp+PnPf666OGhwHNvv0aNHMWbMGKncnc9dDdra2pCamorg4GAEBwejsrIS//mf/4ng4GApDjXHGB8f3+fIxAkTJkgXU9XK5+hNWth3ujPuVHo8Wt3XxMfHY+zYsfjwww8BqDMG8h5/5uvw4cNx6623+u3sDK2PP7RCC/vInrSwv+xJq/vO3ry5L1XsJElISAhSU1NhtVqdyq1WK9LT0/t9zvTp0/vUr6ioQFpaWsD8oD+Yfuvq6sLVq1edbl1dXQCAd999F8HBwRBCSI9duXIFR48eRVFREWbMmIGkpCSfxaVE3N7IXTabDX/5y18QHx+PpKQkGI1Gp22ns7MTdXV1AK7l5ZQpUxAcHIzXX39dysuGhgbU1tbCYDA45WXPm+NME61x1WeVlZXS37XU1FTo9XqnOo4+c9SZPn06WlpacOrUKanO22+/jZaWFpf7FTXzZD+qNHJ99p4YaB/peK/77rtPqi+EwMqVK/Hyyy/jT3/6U599pD9jkYMQAjabTfVxUP/k2H69baCc7BlHf/tHR71nn30WZ86ckW5paWlYtGgRzpw5g5tvvtnvMQ7VjBkzpPGEwwcffICxY8cCUMbnqHRa2He6M+6U8zN3JzcHS6v7msuXL+PixYvSssxqjIG8R6589SQn7XY7Lly4gNGjR3vcfk+oYfxBX9PCPrInX+wvvbGPdEWr+87evLovHdRl3n2srKxM6PV6sXfvXnH+/HmRk5MjIiIixMcffyyEEGL9+vXioYcekup/9NFHIjw8XDz55JPi/PnzYu/evUKv14vf//73/grBL67Xb//yL/8iAPR7CwoKEuvXrxcAxOjRo/utc+edd4qGhgY/Rym/trY28e6774p3331XABBbt24V7777rrhw4YIQgtsbuW/NmjXi2LFj4qOPPhInT54U2dnZIjIyUsrB5557TkRHR4uXX35ZnD17Vnz/+98X0dHRA+blPffcI6ZMmSLGjh3rst6zzz7r58g9d73866/P4uPjRWtrq/Qajz/+uBgzZow4cuSIOH36tNRnV69elep8+9vfFpMnTxZvvfWWeOutt8SkSZNEdna2z+P1levtD5TAV5+9u0pKSgbMRcf7ARDvv/++9Lwf/ehHIioqShw5ckRcvHhRXLx4UXz66aeivb1dquPrWDy1YcMGcfz4cVFfXy/ee+89sXHjRjFs2DBRUVGhqjjIfT/60Y9EdHS0OHbsmGhoaJBug91+vcGdnBRCDLh/dNxKSkqcXjsjI0OsXr3a7zHK5dSpUyI4OFj85Cc/ER9++KH4zW9+I8LDw8X+/fulOmqP0RfUsO/syZNxpxyf+WByc968eX2er7T9vycGiqGtrU2sWbNGVFVVifr6enH06FExffp0ceONNyoqBvItObZ7VwaTk//yL/8i7Ha7sNvtor6+XixevFgAEP/+7//utdj7o+TxB/VPbfvInny5v3Q3HwGIFStW9Psa//3f/y0AiKNHj0plWth39qakfamiJ0mEEGLnzp1i7NixIiQkRHzzm98UlZWV0mOLFy8WGRkZTvWPHTsmUlJSREhIiBg3bpzYvXu3j1usDAP124wZMwQA8etf/1pUV1eLVatWiRtvvFGEhISIyMhIMXPmTPHqq6+KsWPHipkzZ4rq6mpRXV0t/vznP4u9e/eKpKQkMWHCBPHZZ5/5MUL5HT16tN8/YIsXLxZCcHsj9z344IMiPj5e6PV6kZCQIObPny/OnTsnPd7d3S2efvppYTQahcFgEHfffbd49tlnnfLyxIkT4oEHHhBRUVHCYDCI7Oxs8cknn/TJy563v//9736Memiul3/99dnZs2edXqOjo0OsXLlSjBw5UoSFhUl91tPly5fFokWLRGRkpIiMjBSLFi0Szc3NPorSPwbaHyiBrz57dzkGtI5c7Hl75513hBD9T5K486Osr2Px1A9/+ENpmxk9erSYM2eONEGipjjIfXJtv97gTk4KIQbcP/761792a5LEXzHK6dChQ8JkMgmDwSBuu+02UVxc7PS4FmL0BaXvO3vyZNwpx2c+mNzsb5JEaft/TwwUQ3t7uzCbzWL06NFCr9eLm266SSxevLhP+/wdA/mWHNu9K4PJyd7vHxYWJlatWiVsNps3wnZJyeMPck1N+8iefLm/dDcfBztJooV9Z29K2pfqhNDo+izkUmlpKR555BFUV1cPeAGhcePGwWQy4X/+53+cyk+ePInp06dj/fr1KCgo8HZziQLCUPOSiOThTi4WFBRg48aNeOONN2A2m6XyL774An/9618BAHl5eTh69CiuXLnik3YTaZUc+8d33nkHU6dORUlJCZYsWeLF1hIFDo5diZRlMDmZmJiIbdu2QafTITw8HLfccgtCQkJ82FoibXM3H4ODg/HYY49h9+7dfR4rKyvD97//fZw4cQIzZszwZnPp/6fYa5KQck2ePBkA8L//+79+bgkREZHvZWVlAQAOHjzoVD58+HCkpaUhLS0NMTEx/mgaEREREdGAoqOjkZaWhtTUVEyYMIETJER+EhcXh0uXLvX7mKNcLRdU1wJOktCgnTlzBgAQGxvr34YQERH5QVpaGsxmM/bs2YM333zT380hIiIiIiIilcnMzMTRo0fx2WefOZULIfDf//3fGDduHL7xjW/4qXWBJ9jfDSD/qa2txdWrV/uU33LLLRg9ejSAa0uHnDx5EgBgt9vxl7/8Bfn5+TAYDFixYoVP20sUCAablz0ZDAakpKR4vY1EgeB6ubh//37MnTsXmZmZWLJkCebOnYvY2Fi0trbivffew5EjRxAVFeWHlhNpkzv7RyLyPXdys7GxEb///e/71Bk3btyAy5AQ0eBxf0mkHNfLx//4j//AoUOHMG3aNKxfvx7JyclobGzEnj17UF1djd/97nd+aHXg4iRJAHvkkUf6Ld+zZw8effRRAMCf//xnTJ8+HQAQFBSEG2+8EXfeeSc2bdqEO+64w1dNJQoYg83Lnm688UZ8+umnXm0fUaC4Xi6OHj0ab731Fvbs2YPf/va3+N3vfocrV64gKioKEydOxBNPPCHlLBENnTv7RyLyPXdys6amBg888ECfOosXL0Zpaak3m0cUcLi/JFKO6+XjLbfcglOnTiEvLw8WiwWfffYZbrjhBtx5552wWq245557fNziwMYLtxMRERERERERERERUUDiNUmIiIiIiIiIiIiIiCggcZKEiIiIiIiIiIiIiIgCEidJiIiIiIiIiIiIiIgoIHGShIiIiIiIiIiIiIiIAhInSYiIiIiIiIiIiIiIKCBxkoSIiIiINOvSpUv4wQ9+gJiYGISHh+OOO+5ATU2N9LgQAhaLBQkJCQgLC8OsWbNw7tw5p9ew2WxYtWoVRo0ahYiICNx333349NNPfR0KEREREREReQEnSYiIiIhIk5qbmzFjxgzo9Xq89tprOH/+PJ5//nkMHz5cqlNYWIitW7dix44dqK6uhtFoRFZWFtra2qQ6OTk5KC8vR1lZGU6cOIErV64gOzsbXV1dfoiKiIiIiLSKB/gQ+YdOCCHcrVxQUICXX34Z77//PsLCwpCeno4tW7bg1ltvleoIIZCXl4fi4mI0Nzdj2rRp2LlzJyZOnCjVsdlsWLt2LV588UV0dHRgzpw52LVrF8aMGeNWO7q7u/H3v/8dkZGR0Ol0gwiXyP+EEGhra0NCQgKGDdPGPCVzktSMOUmkLHLm5Pr16/HnP/8Zb775psv3SkhIQE5ODp566ikA18apcXFx2LJlC5YtW4aWlhaMHj0aL7zwAh588EEAwN///nckJibi8OHDmDt37nXbwZwkNeN+kkhZmJNEyiJnTjY3NyMlJQWzZ8/Gj370I8TGxuJvf/sbxo0bh1tuuQUAsGXLFvzkJz9BaWkpxo8fj82bN+P48eOoq6tDZGQkAOBHP/oRDh06hNLSUsTExGDNmjX4/PPPUVNTg6CgoOu2gzlJauZxTopBmDt3rigpKRG1tbXizJkzYt68eeKmm24SV65ckeo899xzIjIyUrz00kvi7Nmz4sEHHxTx8fGitbVVqvP444+LG2+8UVitVnH69Gkxe/ZsMWXKFHH16lW32nHx4kUBgDfeVH27ePHiYNJP0ZiTvGnhxpzkjTdl3eTIyQkTJoicnBzxb//2b2L06NHijjvuEMXFxdLjf/vb3wQAcfr0aafn3XfffeLhhx8WQgjxxz/+UQAQn3/+uVOdyZMni//4j//o932/+uor0dLSIt3Onz/v9/7kjbeh3rif5I03Zd2Yk7zxpqybHDn51FNPiZkzZ7p8vLu7WxiNRvHcc89JZV999ZWIjo4Wv/jFL4QQQnzxxRdCr9eLsrIyqc6lS5fEsGHDxOuvv+5WO5iTvGnhNticDMYgvP766073S0pKEBsbi5qaGtx9990QQqCoqAibNm3C/PnzAQD79u1DXFwcDhw4IB2Nt3fvXrzwwgvIzMwEAOzfvx+JiYk4cuSIW0fjOWZGL168iKioqMGE4Bd2ux0VFRUwm83Q6/X+bo5b1NZmNbW3tbUViYmJ0nasBa5yUk2fC6C+9gLqa7MS2xtIOQko8zOQgxbjCtSY5MzJjz76CLt370Zubi42btyIU6dO4YknnoDBYMDDDz+MxsZGAEBcXJzT8+Li4nDhwgUAQGNjI0JCQjBixIg+dRzP762goAB5eXl9yn/1q18hPDx8yHER+VJ7ezseffTRgNlPUv+0uE/yNm/1WaCNXal/zMmhk6sP5czJgwcPYu7cuXjggQdQWVmJG2+8EcuXL8djjz0GAKivr0djYyPMZrP0HIPBgIyMDFRVVWHZsmWoqamB3W53qpOQkACTyYSqqipZfnfVwvanhRgAbcQhdwye5uSgJkl6a2lpAQCMHDkSgO+S1XGqV1RUlCp2oHa7HeHh4YiKilLNBqu2NqutvQA0dcqiq5xU2+eitvYC6muzktsbCDkJKPszGAotxhXoMcmRk93d3UhLS0N+fj4AICUlBefOncPu3bvx8MMPu3wvIcR133+gOhs2bEBubq503zFQv//++11+0bRarcjKytLMZ+1N7C/3ydFXra2tePTRRwNmP0n90+I+ydu83WfMycDGnBw6uftQjpz01wE+NpsNNptNuu+4Nl9YWBjCwsL61A8ODkZ4eDjCwsJUu/1pIQZAG3HIHYPdbgcw+Jz0eJJECIHc3FzMnDkTJpMJAHyWrK2trQCuBe0IXMkcbVRDWx3U1mY1tVcNbSQiItKC+Ph43H777U5lEyZMwEsvvQQAMBqNAK6NT+Pj46U6TU1N0njWaDSis7MTzc3NTuPXpqYmpKen9/u+BoMBBoOhT7lerx9w4H+9x8kZ+8t9Q+kr9jFR4CgoKMDGjRuxevVqFBUVAYBPrjtLRNf46wAfV2dBV1RUDHgWtNVqHfA91UALMQDaiEOuGNrb2z16nseTJCtXrsR7772HEydO9HlMKcmqNGrcYNXWZjW019NkHYpLly7hqaeewmuvvYaOjg6MHz8ee/fuRWpqKgD3Br5ERERqM2PGDNTV1TmVffDBBxg7diwAICkpCUajEVarFSkpKQCAzs5OVFZWYsuWLQCA1NRU6PV6WK1WLFiwAADQ0NCA2tpaFBYW+jAaIiIi76murkZxcTEmT57sVF5YWIitW7c6XSQ6KyvL6SLROTk5OHToEMrKyqSLRGdnZ7t9kWgiusZfB/i4OgvabDZr9ixoLcQAaCMOuWNwnFwxWB5NkqxatQoHDx7E8ePHnY4MUEqyAoDJ8obT/VrL9Zfx8hY1brBqa7Oa2utpsnqqubkZM2bMwOzZs/Haa68hNjYWf/vb3zB8+HCpjjsDX38bt/5Vp/sfPzfPTy0h0r6e+cZcIzV78sknkZ6ejvz8fCxYsACnTp1CcXExiouLAVw7sCcnJwf5+flITk5GcnIy8vPzER4ejoULFwIAoqOjsXTpUqxZswYxMTEYOXIk1q5di0mTJknX15OLyfIGbF065h0RUQDy5/jrypUrWLRoEfbs2YPNmzdL5b667qw7OD6lQOCvA3w8PQs65Sd/Uv3YVStnJmshDrli8PQ1BjVJIoTAqlWrUF5ejmPHjiEpKcnpcSUlq61L16euv6lxg1Vbm9XQXl+3b8uWLUhMTERJSYlUNm7cOOn/7gx8iYiI1Gjq1KkoLy/Hhg0b8MwzzyApKQlFRUVYtGiRVGfdunXo6OjA8uXLpbMpKyoqnA4S2LZtG4KDg7FgwQJpGZHS0lIeHUtERJqwYsUKzJs3D5mZmU6TJN667qwnS6obgoT0fy5hra4lx5VKrj6U8zNQ2wE+RFoyqEmSFStW4MCBA3jllVcQGRkpXUMkOjoaYWFhTFYiBTp48CDmzp2LBx54AJWVlbjxxhuxfPlyPPbYYwDcG/gqEY8kIiIid2RnZyM7O9vl4zqdDhaLBRaLxWWd0NBQbN++Hdu3b/dCC4mIiPynrKwMp0+fRnV1dZ/HvHXdWU+WVC+88+v/Hz582HVAAUYNS44r3VD7UM4l1XmAD5H/DGqSZPfu3QCAWbNmOZWXlJRgyZIlAJisRErz0UcfYffu3cjNzcXGjRtx6tQpPPHEEzAYDHj44YfdGvj25u6RP3Ie3dLzyKHe5DpyQ41H46itzUpsr5LaQkRERETkKxcvXsTq1atRUVGB0NBQl/Xkvu7sUJdU9+dy6kqhpiXHlUquPpR7SXUe4EPkH4Nebut6mKxEytLd3Y20tDTk5+cDAFJSUnDu3Dns3r0bDz/8sFRvMAPfwR75I8fRLT2PHOpN7iOJ1Hg0jtrarKT2ynnkDxERERGRWtTU1KCpqQmpqalSWVdXF44fP44dO3ZI10aQ+7qzQ11SnZMCX1PDkuNKN9Q+ZP8TaYNHF24nIvWIj4/H7bff7lQ2YcIEvPTSSwCuDWqBgQe+vbl75I+cR7f0PHKoN7mOJFLj0Thqa7MS2yvnkT8FBQV4+eWX8f777yMsLAzp6enYsmULbr31VqmOEAJ5eXkoLi6WzrjcuXMnJk6cKNWx2WxYu3YtXnzxRemMy127dmHMmDGytZWIiIiIAtucOXNw9uxZp7JHHnkEt912G5566incfPPNXrnuLBERkdJwkoRI42bMmCEdAeTwwQcfYOzYsQCApKSk6w58exvskT9yHN3S88ih/l5fTmo8GkdtbVZSe+VsR2VlJVasWIGpU6fi6tWr2LRpE8xmM86fP4+IiAgAQGFhIbZu3YrS0lKMHz8emzdvRlZWFurq6qSlKXNycnDo0CGUlZUhJiYGa9asQXZ2Nmpqarg0JRERERHJIjIyEiaTyaksIiICMTExUjmvO0tERIGAkyREGvfkk08iPT0d+fn5WLBgAU6dOoXi4mIUFxcDuLbM1vUGvr7EC7KTmr3++utO90tKShAbG4uamhrcfffdEEKgqKgImzZtwvz58wEA+/btQ1xcHA4cOIBly5ahpaUFe/fuxQsvvCB9sdy/fz8SExNx5MgRzJ3rnTWYe+YeERERERHA684SEVFg4CQJkcZNnToV5eXl2LBhA5555hkkJSWhqKgIixYtkuq4M/AlosFraWkBAIwcORIAUF9fj8bGRpjNZqmOwWBARkYGqqqqsGzZMtTU1MButzvVSUhIgMlkQlVVVb+TJDabDTabTbrvWELMbrf3uTC9437vckNQ/9cdU8uF7V3FpWaBGpOW4iUiIlKbY8eOOd3ndWeJiCgQcJKEKABkZ2cjOzvb5ePuDHy9iUewkxYJIZCbm4uZM2dKyxU0NjYCQJ/r/cTFxeHChQtSnZCQEKcLXzrqOJ7fW0FBAfLy8vqUV1RUIDw8vN/nWK1Wp/uFd/Yfx+HDh/t/QKF6x6UFgRZTe3u7D1tCREREREREgY6TJERERF6wcuVKvPfeezhx4kSfx3Q652vsCCH6lPU2UJ0NGzYgNzdXut/a2orExESYzWZERUU51bXb7bBarcjKynK6HovJ8ka/r11r8c7yXnJzFZeaBWpMjjOhiIiIiIiIiHyBkyREREQyW7VqFQ4ePIjjx49jzJgxUrnRaARw7WyR+Ph4qbypqUk6u8RoNKKzsxPNzc1OZ5M0NTUhPT293/czGAwwGAx9yvV6vcsfons/ZuvqfwJGbT/ODxSzWgVaTFqLlYiIiIiIiJRtmL8bQEREpBVCCKxcuRIvv/wy/vSnPyEpKcnp8aSkJBiNRqelhjo7O1FZWSlNgKSmpkKv1zvVaWhoQG1trctJEiIiIiIiIiIi8gzPJCEiIpLJihUrcODAAbzyyiuIjIyUriESHR2NsLAw6HQ65OTkID8/H8nJyUhOTkZ+fj7Cw8OxcOFCqe7SpUuxZs0axMTEYOTIkVi7di0mTZqEzMxMf4ZHRERERERERKQ5nCQhIiKSye7duwEAs2bNciovKSnBkiVLAADr1q1DR0cHli9fjubmZkybNg0VFRWIjIyU6m/btg3BwcFYsGABOjo6MGfOHJSWliIoKMhXoRARERERERERBQROkhAREclECHHdOjqdDhaLBRaLxWWd0NBQbN++Hdu3b5exdURERERERERE1BuvSUJEREREREReV1BQgKlTpyIyMhKxsbG4//77UVdX51RHCAGLxYKEhASEhYVh1qxZOHfunFMdm82GVatWYdSoUYiIiMB9992HTz/91JehEBEREZGGcJKEiIiIiIiIvK6yshIrVqzAyZMnYbVacfXqVZjNZnz55ZdSncLCQmzduhU7duxAdXU1jEYjsrKy0NbWJtXJyclBeXk5ysrKcOLECVy5cgXZ2dno6uryR1hEREREpHJcbouIiIiIiIi87vXXX3e6X1JSgtjYWNTU1ODuu++GEAJFRUXYtGkT5s+fDwDYt28f4uLicODAASxbtgwtLS3Yu3cvXnjhBWRmZgIA9u/fj8TERBw5cgRz5871eVxEREREpG48k4SIiIiIiIh8rqWlBQAwcuRIAEB9fT0aGxthNpulOgaDARkZGaiqqgIA1NTUwG63O9VJSEiAyWSS6hARERERDQbPJCEiIiIiIiKfEkIgNzcXM2fOhMlkAgA0NjYCAOLi4pzqxsXF4cKFC1KdkJAQjBgxok8dx/N7s9lssNls0v3W1lYAgN1uh91ulycgjXP0E/vLfYPtM0OQ6PPcgV6XiIiI5MNJEiJSrHHrX/V3E4iIiIjIC1auXIn33nsPJ06c6POYTqdzui+E6FPW20B1CgoKkJeX16e8oqIC4eHhg2g1Wa1WfzdBddzts8I7v/7/4cOHXdZrb28fapOIiIioF06SEBERERERkc+sWrUKBw8exPHjxzFmzBip3Gg0Arh2tkh8fLxU3tTUJJ1dYjQa0dnZiebmZqezSZqampCent7v+23YsAG5ubnS/dbWViQmJsJsNiMqKkrW2LTKbrfDarUiKysLer3e381RhcH2mcnyhvT/Wovra+s4zoQiIiIi+XCShIiIiIiIiLxOCIFVq1ahvLwcx44dQ1JSktPjSUlJMBqNsFqtSElJAQB0dnaisrISW7ZsAQCkpqZCr9fDarViwYIFAICGhgbU1taisLCw3/c1GAwwGAx9yvV6PX/wHyT22eC522e2rq/PhBqoPvufiIhIfpwkISIiIiIiIq9bsWIFDhw4gFdeeQWRkZHSNUSio6MRFhYGnU6HnJwc5OfnIzk5GcnJycjPz0d4eDgWLlwo1V26dCnWrFmDmJgYjBw5EmvXrsWkSZOQmZnpz/CIiIiISKU4SUJERERERERet3v3bgDArFmznMpLSkqwZMkSAMC6devQ0dGB5cuXo7m5GdOmTUNFRQUiIyOl+tu2bUNwcDAWLFiAjo4OzJkzB6WlpQgKCvJVKERERESkIZwkISIiIiIiIq8TQly3jk6ng8VigcVicVknNDQU27dvx/bt22VsHREREREFqmH+bgAREREREREREREREZE/8EwSItKUcetflf7/8XPz/NgSIiIiIiIiIiIiUjqeSUJERERERERERERERAGJkyREREREFBAKCgqg0+mQk5MjlQkhYLFYkJCQgLCwMMyaNQvnzp1zep7NZsOqVaswatQoRERE4L777sOnn37q49YTERERUSDh2JXIdwJmkmTc+lelGxEREREFlurqahQXF2Py5MlO5YWFhdi6dSt27NiB6upqGI1GZGVloa2tTaqTk5OD8vJylJWV4cSJE7hy5Qqys7PR1dXl6zCIiIiIKABw7ErkWwEzSUJEREREgenKlStYtGgR9uzZgxEjRkjlQggUFRVh06ZNmD9/PkwmE/bt24f29nYcOHAAANDS0oK9e/fi+eefR2ZmJlJSUrB//36cPXsWR44c8VdIRERERKRRHLsS+R4v3E5EREREmrZixQrMmzcPmZmZ2Lx5s1ReX1+PxsZGmM1mqcxgMCAjIwNVVVVYtmwZampqYLfbneokJCTAZDKhqqoKc+fO7fN+NpsNNptNut/a2goAsNvtsNvtfeo7ygzDhNN96p+jf9hP1ydHX7GfiYiIfMvXY1ci4iQJUcApKCjAxo0bsXr1ahQVFQG4djRCXl4eiouL0dzcjGnTpmHnzp2YOHGifxtLREQ0RGVlZTh9+jSqq6v7PNbY2AgAiIuLcyqPi4vDhQsXpDohISFOR/E56jie31tBQQHy8vL6lFdUVCA8PNxlW59N6wYAHD58eICIyMFqtfq7CaoxlL5qb2+XsSVEREQ0EH+MXQPxAB+tHHSjhTjkjsHT1+EkCVEAud6alqWlpRg/fjw2b96MrKws1NXVITIy0k+tJSIiGpqLFy9i9erVqKioQGhoqMt6Op3O6b4Qok9ZbwPV2bBhA3Jzc6X7ra2tSExMhNlsRlRUVJ/6drsdVqsVP35nGGzdOtRaeITfQBz9lZWVBb1e7+/mKJocfeX4oYSIiIi8y19j10A+wEcrB91oIQ65YvD0AB9OkhAFiJ5rWvY8XbP3mpYAsG/fPsTFxeHAgQNYtmyZv5pMREQ0JDU1NWhqakJqaqpU1tXVhePHj2PHjh2oq6sDcO2Iu/j4eKlOU1OTdISe0WhEZ2cnmpubnY7Ia2pqQnp6er/vazAYYDAY+pTr9foBf6i2detg69Lxh383Xa8/6WtD6Sv2MRERkW/4a+waiAf4aOWgGy3EIXcMnh7gw0kSogAxlDUtiYiI1GjOnDk4e/asU9kjjzyC2267DU899RRuvvlmGI1GWK1WpKSkAAA6OztRWVmJLVu2AABSU1Oh1+thtVqxYMECAEBDQwNqa2tRWFjo24CIiIiISLP8NXYN5AN8tHLQjRbikCsGT1+DkyREAWCoa1r25u56le6uK2gIEm5E4VrP1+/5WoNdh1CNazmqrc1KbK+S2kJE8oqMjITJZHIqi4iIQExMjFSek5OD/Px8JCcnIzk5Gfn5+QgPD8fChQsBANHR0Vi6dCnWrFmDmJgYjBw5EmvXrsWkSZOQmZnp85iIiIiISJs4diXyH06SEGmcN9a0HOx6lddbV7DwzgEfvq6e61/2fC1P18VU41qOamuzktrLC9ISBbZ169aho6MDy5cvR3NzM6ZNm4aKigqna3Jt27YNwcHBWLBgATo6OjBnzhyUlpYiKCjIjy0nIiIiokDDsSuRd3CShEjj5FjTsjd316t0d11Bk+UNj+MbyGDXxVTjWo5qa7MS28sL0hIFlmPHjjnd1+l0sFgssFgsLp8TGhqK7du3Y/v27d5tHBERERFRDxy7EvkGJ0mINE6ONS17G+x6ldddx7Kr/zNWhmooFyhVyg/47lJbm5XUXqW0g4iIiIiIiIiIfI+TJEQaJ8ealkRERERERERERERaxEkSInJrTUsiIiIiIiIiIiIireEkCVEA8mRNSyIiIiIiIiIiIiKtGebvBhAREREREREREREREflDQJ5JMm79q9L/P35unh9bQkRERERERERERERE/sIzSYiIiIiIiIiIiIiIKCBxkoSIiIiIiIiIiIiIiAJSQC63RUSBgUvrERERERERERER0UAGfSbJ8ePH8Z3vfAcJCQnQ6XT4wx/+4PS4EAIWiwUJCQkICwvDrFmzcO7cOac6NpsNq1atwqhRoxAREYH77rsPn3766ZACISIiIiIiIiIiIiIiGoxBT5J8+eWXmDJlCnbs2NHv44WFhdi6dSt27NiB6upqGI1GZGVloa2tTaqTk5OD8vJylJWV4cSJE7hy5Qqys7PR1dXleSREREREREREROSWgoICTJ06FZGRkYiNjcX999+Puro6pzo8EJaIiALBoCdJ7r33XmzevBnz58/v85gQAkVFRdi0aRPmz58Pk8mEffv2ob29HQcOHAAAtLS0YO/evXj++eeRmZmJlJQU7N+/H2fPnsWRI0eGHhEREREREREREQ2osrISK1aswMmTJ2G1WnH16lWYzWZ8+eWXUh0eCEtERIFA1muS1NfXo7GxEWazWSozGAzIyMhAVVUVli1bhpqaGtjtdqc6CQkJMJlMqKqqwty5c/u8rs1mg81mk+63trYCAOx2O+x2e79tMQQJt9rs6vlycryHL95LLmprs5raq4Y2EhERERERkba9/vrrTvdLSkoQGxuLmpoa3H333X0OhAWAffv2IS4uDgcOHMCyZcukA2FfeOEFZGZmAgD279+PxMREHDlypN/feIiIiJRG1kmSxsZGAEBcXJxTeVxcHC5cuCDVCQkJwYgRI/rUcTy/t4KCAuTl5fUpr6ioQHh4eL/PKbzTvTYfPnzYvYoysFqtPnsvuaitzWpob3t7u7+bQEREREREROSkpaUFADBy5EgA3jsQloiISGlknSRx0Ol0TveFEH3KehuozoYNG5Cbmyvdb21tRWJiIsxmM6Kiovp9jsnyhlttrbV4f4dtt9thtVqRlZUFvV7v9feTg9rarKb2Os6EIiJtOn78OH7605+ipqYGDQ0NKC8vx/333y89LoRAXl4eiouL0dzcjGnTpmHnzp2YOHGiVMdms2Ht2rV48cUX0dHRgTlz5mDXrl0YM2aMHyIiIiIiIq0TQiA3NxczZ86EyWQC4L0DYYe6WghXZ1DXahpKJVcf8jMg0gZZJ0mMRiOAazvJ+Ph4qbypqUnaqRqNRnR2dqK5udlpJ9rU1IT09PR+X9dgMMBgMPQp1+v1Ln8Qt3UNPCnT8zV8ZaD2KpXa2qyG9iq9fUQ0NF9++SWmTJmCRx55BN/97nf7PO5Y17m0tBTjx4/H5s2bkZWVhbq6OkRGRgK4tq7zoUOHUFZWhpiYGKxZswbZ2dmoqalBUFCQr0MiIiIiIo1buXIl3nvvPZw4caLPY3IfCDvU1UJ8uSKI0qlhNQ2lG2ofcrUQIm2QdZIkKSkJRqMRVqsVKSkpAIDOzk5UVlZiy5YtAIDU1FTo9XpYrVYsWLAAANDQ0IDa2loUFhbK2RwiIiKfu/fee3Hvvff2+xjXdSYiIiIipVm1ahUOHjyI48ePO5257K0DYYe6WogvVgRROjWtpqFUcvUhVwsh0oZBT5JcuXIFf/3rX6X79fX1OHPmDEaOHImbbroJOTk5yM/PR3JyMpKTk5Gfn4/w8HAsXLgQABAdHY2lS5dizZo1iImJwciRI7F27VpMmjRJ+jGIiLRv3PpX/d0EIp/z1rrOg1mywNVp5T2XMOivvtJpccmBQI1JS/ESEREpmRACq1atQnl5OY4dO4akpCSnx711IOxQVwvhpMDX1LCahtINtQ/Z/0TaMOhJknfeeQezZ8+W7jtm/xcvXozS0lKsW7cOHR0dWL58ubTWekVFhbSECABs27YNwcHBWLBggbTWemlpKZcQISIiTfPWus6eLFnQ+7TynksY9KS25Qy0uORAoMXEJQuIiIh8Y8WKFThw4ABeeeUVREZGSmPN6OhohIWFQafT8UBYIiIKCIOeJJk1axaE6P9oU+DaWpUWiwUWi8VlndDQUGzfvh3bt28f7NsTERGpntzrOg9myQJXp5X3XMKgJ7UsZ6DFJQcCNSYuWUCkXcePH8dPf/pT1NTUoKGhAeXl5bj//vulx4UQyMvLQ3FxsXTA3c6dOzFx4kSpjs1mw9q1a/Hiiy9KB9zt2rXLaYkgInLP7t27AVz7naenkpISLFmyBAB4ICwREQUEWa9JQkRERK55a11nT5Ys6P1YzyUMetdTEy0uORBoMWktViL62pdffokpU6bgkUcewXe/+90+jxcWFmLr1q0oLS3F+PHjsXnzZmRlZaGurk76QTYnJweHDh1CWVkZYmJisGbNGmRnZ6OmpoY/yBIN0kAHwDrwQFgiIgoEw/zdACIiokDRc11nB8e6zo4JkJ7rOjs41nV2NUlCRESkBvfeey82b96M+fPn93lMCIGioiJs2rQJ8+fPh8lkwr59+9De3o4DBw4AAFpaWrB37148//zzyMzMREpKCvbv34+zZ8/iyJEjvg6HiIiIiDSCZ5IQUUDofaH4j5+b56eWkNZduXIFf/3rX6X79fX1OHPmDEaOHImbbrqJ6zoTERH1o76+Ho2NjTCbzVKZwWBARkYGqqqqsGzZMtTU1MButzvVSUhIgMlkQlVVFebOVccSkURERESkLJwkISIiktE777yD2bNnS/cd1wpZvHgxSktLVbeuc88JRk4uEhGRtzguGO1YftIhLi4OFy5ckOqEhIQ4LUfpqON4fn9sNhtsNpt033HtI7vdDrvdLkv7tc7RT+wv9w22zwxBXy99NdBz+BkQERHJj5MkROQTvc/kINKqWbNmDbi+M9d1JiIick2nc75GlhCiT1lv16tTUFCAvLy8PuUVFRUIDw/3rKEBqudyoOQed/us8M6v/3/48GGX9drb24faJCIiIuqFkyRERERERETkV0ajEcC1s0Xi4+Ol8qamJunsEqPRiM7OTjQ3NzudTdLU1DTgdbs2bNggndkJXDuTJDExEWazGVFRUXKHokl2ux1WqxVZWVnQ6/X+bo4qDLbPTJY3pP/XWlwvHec4E4qIiIjkwwu3E1FAGrf+VelGRETaVFBQgKlTpyIyMhKxsbG4//77UVdX51RHCAGLxYKEhASEhYVh1qxZOHfunFMdm82GVatWYdSoUYiIiMB9992HTz/91JehEGleUlISjEaj01H3nZ2dqKyslCZAUlNTodfrneo0NDSgtrZ2wEkSg8GAqKgopxsA6PV63gZxY595t89sXTrp5s7rEpH2cOxK5D+cJCEiIiIiTaqsrMSKFStw8uRJWK1WXL16FWazGV9++aVUp7CwEFu3bsWOHTtQXV0No9GIrKwstLW1SXVycnJQXl6OsrIynDhxAleuXEF2dja6urr8ERaRal25cgVnzpzBmTNnAFy7WPuZM2fwySefQKfTIScnB/n5+SgvL0dtbS2WLFmC8PBwLFy4EAAQHR2NpUuXYs2aNfjjH/+Id999Fz/4wQ8wadIkZGZm+jEyIiKioePYlch/An65LV6QlrSuoKAAL7/8Mt5//32EhYUhPT0dW7Zswa233irVEUIgLy8PxcXF0oWkd+7ciYkTJ/qx5UREREPz+uuvO90vKSlBbGwsampqcPfdd0MIgaKiImzatAnz588HAOzbtw9xcXE4cOAAli1bhpaWFuzduxcvvPCC9CPs/v37kZiYiCNHjmDuXNdLohCRs3feeQezZ8+W7juWwFq8eDFKS0uxbt06dHR0YPny5dKYtKKiApGRkdJztm3bhuDgYCxYsAAdHR2YM2cOSktLERQU5PN4iIiI5MSxK5H/BPwkCZHWOY5EmDp1Kq5evYpNmzbBbDbj/PnziIiIAPD1kQilpaUYP348Nm/ejKysLNTV1Tl9KSUiIlKzlpYWAMDIkSMBXDuKvbGxEWazWapjMBiQkZGBqqoqLFu2DDU1NbDb7U51EhISYDKZUFVV1e8XTZvNBpvNJt13rB9vt9tht9v71HeUGYYJp/vUP0f/sJ+uT46+krOfZ82aBSGEy8d1Oh0sFgssFovLOqGhodi+fTu2b98uW7uIiIiUyFdjVyLiJAmR5slxJAIREZHaCSGQm5uLmTNnwmQyAbh2gWgA0kWhHeLi4nDhwgWpTkhIiNNFoh11HM/vraCgAHl5eX3KKyoqEB4e7rKNz6Z1AwAOHz7sZlSBred1KWhgQ+mr9vZ2GVtCRERE7vDl2DUQD/DRykE3WohD7hg8fR1OkhAFGE+ORCAiIlK7lStX4r333sOJEyf6PKbT6ZzuCyH6lPU2UJ0NGzZIywgB175oJiYmwmw2SxeM7slut8NqteLH7wyDrVuHWguP8BuIo7+ysrJ4AePrkKOvHD+UEBERke/4cuwayAf4aOWgGy3EIVcMnh7gw0kSogDi6ZEIvbl7lEHP2WBDkOulFfyt96y1mmbg1dZmJbZXSW0hIu9YtWoVDh48iOPHj2PMmDFSudFoBHBtXxgfHy+VNzU1SftFo9GIzs5ONDc3Ox2R19TUhPT09H7fz2AwwGAw9CnX6/UD/lBt69bB1qXjD/9uul5/0teG0lfsYyIiIt/y9dg1EA/w0cpBN1qIQ+4YPD3Ah5MkRAFEriMRBnuUgdVqReGdHjbaB3of9aDGGXi1tVlJ7eUyIkTaJYTAqlWrUF5ejmPHjiEpKcnp8aSkJBiNRlitVqSkpAAAOjs7UVlZiS1btgAAUlNTodfrYbVasWDBAgBAQ0MDamtrUVhY6NuAiIiIiEiz/DV2DeQDfLRy0I0W4pArBk9fg5MkRAFiKEci9ObuUQY9Z4NTfvInuUOSjeOoBzUeDaG2owaU2F4uI0KkXStWrMCBAwfwyiuvIDIyUjp7Mjo6GmFhYdDpdMjJyUF+fj6Sk5ORnJyM/Px8hIeHY+HChVLdpUuXYs2aNYiJicHIkSOxdu1aTJo0CZmZmf4Mj4iIiIg0hGNXIv/hJAmRxslxJEJvgz3KQK/Xw9Y18PqY/tS7zWo8GkJtRw0oqb1KaQcRyW/37t0AgFmzZjmVl5SUYMmSJQCAdevWoaOjA8uXL0dzczOmTZuGiooKREZGSvW3bduG4OBgLFiwAB0dHZgzZw5KS0sRFBTkq1CIiIiISOM4diXyH06SEGmcHEciEBERqZEQ178elk6ng8VigcVicVknNDQU27dvx/bt22VsHRERERHR1zh2JfIfTpIQaZxcRyIQERERERERERERaQ0nSYg0Tq4jEbRs3PpXAQCGIKHoC8wTERERERERERGRvDhJQkREFIBMljcUfa0gIiIiIiIiIiJfGObvBhAREREREREREREREfkDJ0mIiIiIiIiIiIiIiCggcbktIiI3OK5b4vDxc/P81BIiIiIiIiIiIiKSC88kISIiIiIiIiIiIiKigMQzSXroeaQ4jxInIiIiIiIiIiIiItI2nklCREREREREREREREQBiZMkREREREREREREREQUkLjcFhERERERERERqULPpdIBLpdORERDxzNJiIiIiIiIiIiIiIgoIHGShIiIiIiIiIiIiIiIAhInSYiIiIiIiIiIiIiIKCBxkoSIiIiIiIiIiIiIiAISJ0mIiIiIiIiIiIiIiCggcZKEiIiIiIiIiIiIiIgCUrC/G0BEpFTj1r/q7yYQERERERHRAHp+b/v4uXl+bAkREakVJ0mIiIjILfwCSkRERERERERaw0kSIiIPuHOWCX9EJiIiIiIiIiIiUjZek4SIiIiIiIiIiIiIiAISzyQhIiIiIiIi8jMua0lERETkH5wkcaH3UjocpBIRERERERERERERaQsnSYiIiIiIiIgUaqBr4fFgPiJnPCOLiIg8wUkSIiIv4QCdiIiISNuGOt4baAJkKLgyAhEREZH7OEniJv7YSURERERERK4o+Tujq8kYpbWTiIiIyB84SUJE5AMDHc2n5C/URETkezwCnEj9hrpElidnmHjrrBQiteL3LCIictcwf775rl27kJSUhNDQUKSmpuLNN9/0Z3OIAh5z0nfGrX9Vug22vifPIXViThIpC3OSSFnUmpNqH6Opvf3kPWrNSSItYj4SDY7fziT57W9/i5ycHOzatQszZszAL3/5S9x77704f/48brrpJn81yy08GoG0SM05SaRFzEkiZWFOEikLc/KaoU5UuPpu2/t1P3zW7PFrDbVdcrweeR9zkkg5mI9Eg+e3SZKtW7di6dKlePTRRwEARUVFeOONN7B7924UFBT4q1my6TmoMwQJFN7px8YQuUHrOakWgzlLBPj674vJ8gZsXTq3v0C684XYV19GvfWeap/QVnpODnUZESK1UXpOEgUaf+akVs+gGCguk+UNacwJ6Ab9WnKON93p/6GOiWnwlL6fdDdvuR2QFvg6H/ndkLTAL5MknZ2dqKmpwfr1653KzWYzqqqq+tS32Wyw2WzS/ZaWFgDA559/Drvd3u97BF/9UsYWu/aNtb/r//17/r9boL29G3dsehm2bucB5dsb5vT7/GkFf3SrnitDfb7dbkd7ezsuX74MvV4/qOf6gy/b27Nve/arq/Le2traAABCCC+0zjPeysmen4uvcnIoHLkabB+Gru7rf/lTgt5tvnz58teP9ejznuUDPTbQc+TQX6566z3dfd1Aykng68/Am9u5q32jq7+ZntTr/Xd2sPsBT/aTnrR5sPvfntS2L76eaQV/hGGYwP9L6R4wpkDLSeD6eemNv8dqprXc8CY5+ioQcxIY+G9+IF7Y09U4uec+f6B+cVWv59+3nn3e+7XcfZ/+Xrf3a/fU87VcjV8Az/bng80/jl2V8RuPO/vcof7eIgfuC4dOrj5UWk4ONh8B+ceuPfX82+qN70ye0koOaSEOuWPwOCeFH1y6dEkAEH/+85+dyn/yk5+I8ePH96n/9NNPCwC88aap28WLF32VctfFnOSNN+Ykb7wp7cac5I03Zd2Yk7zxpqwbc5I33pR1U0pODjYfhWBO8qbN22Bz0q8Hweh0zrONQog+ZQCwYcMG5ObmSve7u7vx+eefIyYmpt/6StPa2orExERcvHgRUVFR/m6OW9TWZjW1VwiBtrY2JCQk+Lspfcidk2r6XAD1tRdQX5uV2N5AyklAmZ+BHLQYV6DGFGg5CWjzs/Ym9pf75OirQMxJ6ot5N3je6jPmJAHMSTnI1YdKzUl38xEIzLGrFmIAtBGH3DF4mpN+mSQZNWoUgoKC0NjY6FTe1NSEuLi4PvUNBgMMBoNT2fDhw73ZRK+IiopS3Qartjarpb3R0dH+boITb+ekWj4XB7W1F1Bfm5XW3kDLSUB5n4FctBhXIMYUiDkJaPOz9ib2l/uG2leBmpPUF/Nu8LzRZ8xJcmBODp0cfaiknBxsPgKBPXbVQgyANuKQMwZPcnKYLO88SCEhIUhNTYXVanUqt1qtSE9P90eTiAIac5JIWZiTRMrCnCRSFuYkkbIwJ4mUg/lI5Bm/LbeVm5uLhx56CGlpaZg+fTqKi4vxySef4PHHH/dXk4gCGnOSSFmYk0TKwpwkUhbmJJGyMCeJlIP5SDR4fpskefDBB3H58mU888wzaGhogMlkwuHDhzF27Fh/NclrDAYDnn766T6nrimZ2tqstvYqkTdyUm2fi9raC6ivzWprrz95az+p1c9Ai3ExJmXx5thVzf3iD+wv92m5rwLp+6QSaHlb8pZA6zPmpG8F2vblDVruQ2/noxb6TgsxANqIQykx6IQQwq8tICIiIiIiIiIiIiIi8gO/XJOEiIiIiIiIiIiIiIjI3zhJQkREREREREREREREAYmTJEREREREREREREREFJA4SUJERERERERERERERAGJkyQyKSgowNSpUxEZGYnY2Fjcf//9qKurc6qzZMkS6HQ6p9tdd93lpxYDFoulT3uMRqP0uBACFosFCQkJCAsLw6xZs3Du3Dm/tXfcuHF92qvT6bBixQoAyuvfQLdr1y4kJSUhNDQUqampePPNN/3dJJeulwv+dvz4cXznO99BQkICdDod/vCHPzg9rrRcBa7fZuar/yglN+XYrm02G1atWoVRo0YhIiIC9913Hz799FOnOs3NzXjooYcQHR2N6OhoPPTQQ/jiiy+c6nzyySf4zne+g4iICIwaNQpPPPEEOjs7Bx2TO2MBtcW1e/duTJ48GVFRUYiKisL06dPx2muvqTYepVJKXiqJXPkUiAoKCqDT6ZCTkyOVsa/IHWr7fugvvhrDUOCRIwcDbdtS0neKQKKUsavSvn/JFZMn4zh/x3Dp0iX84Ac/QExMDMLDw3HHHXegpqZGVTFAkCzmzp0rSkpKRG1trThz5oyYN2+euOmmm8SVK1ekOosXLxbf/va3RUNDg3S7fPmy39r89NNPi4kTJzq1p6mpSXr8ueeeE5GRkeKll14SZ8+eFQ8++KCIj48Xra2tfmlvU1OTU1utVqsAII4ePSqEUF7/BrKysjKh1+vFnj17xPnz58Xq1atFRESEuHDhgr+b1q/r5YK/HT58WGzatEm89NJLAoAoLy93elxpuSrE9dvMfPUPJeWmHNv1448/Lm688UZhtVrF6dOnxezZs8WUKVPE1atXpTrf/va3hclkElVVVaKqqkqYTCaRnZ0tPX716lVhMpnE7NmzxenTp4XVahUJCQli5cqVg47JnbGA2uI6ePCgePXVV0VdXZ2oq6sTGzduFHq9XtTW1qoyHiVSUl4qiVz5FGhOnTolxo0bJyZPnixWr14tlbOvyB1q+37oL74aw1DgkSMHA23bUsp3ikCipLGrkr5/yWEo4zh/xvD555+LsWPHiiVLloi3335b1NfXiyNHjoi//vWvqolBCCE4SeIlTU1NAoCorKyUyhYvXiz+9V//1X+N6uXpp58WU6ZM6fex7u5uYTQaxXPPPSeVffXVVyI6Olr84he/8FELB7Z69Wpxyy23iO7ubiGE8vo3kN15553i8ccfdyq77bbbxPr16/3UooENlAtK03vgp4ZcdTVJwnz1PaXmpifb9RdffCH0er0oKyuT6ly6dEkMGzZMvP7660IIIc6fPy8AiJMnT0p13nrrLQFAvP/++0KIa1+shg0bJi5duiTVefHFF4XBYBAtLS1Diqv3WEArcY0YMUL86le/0kw8/qbUvFQaT/Ip0LS1tYnk5GRhtVpFRkaG9OWafUXuUvv3Q3/w1hiGAtNQczDQty1/fqcIJEoeu/rz+9dQDWUc5+8YnnrqKTFz5kyXj6shBiGE4HJbXtLS0gIAGDlypFP5sWPHEBsbi/Hjx+Oxxx5DU1OTz9tWWloKnU6HvLw8/O///q/TqZyPPvooAKC+vh6NjY34/PPPMXPmTIwYMQKRkZGw2WwoKChAZWWlz9vdU2dnJ/bv348f/vCH0Ol0UrkS+jfQdXZ2oqamBmaz2ancbDajqqrKT626vg8//BAJCQlISkrC9773PXz00UdefT9HHvZ3W7t2rVSv5zJzw4Zd+5O9cuVKPPzww6ioqJBytWd/GwwGZGRkKLq/Aearr6kpN93ZrmtqamC3253qJCQkwGQySXXeeustREdHY9q0aVKdu+66C9HR0U51TCYTEhISpDpz586FzWZDdHT0gDnqahlInU6HWbNm9RkLKCWunqc9D0ZXVxfKysrw5ZdfYvr06aqPRwnUlJf+1jOfSktLMWzYMDQ2NmL9+vVS3oWGhmLUqFGoqqrCuHHjkJ2d7edW+9aKFSswb948ZGZmOpWreaxAvudqTNzfdvTiiy+ipaUFjz/++ID7ykDKRbn2jRS4BpODjm3r17/+NXQ6HYYPHw673Y7vfe97Ui5u3boVJpMJDz74oFOORkRE4Jvf/CZ27NgBIYS/wvUqX45VA4XSx67+/P7Vmzu/+Vy8eBHLly/H+PHjMXz4cHzyySd48sknUVdXh7a2Nr/H4K6DBw8iLS0NDzzwAGJjY5GSkoI9e/ZIj6shBgAIHvIrUB9CCOTm5mLmzJkwmUxS+b333osHHngAY8eORX19PX784x/jnnvuQU1NDQwGg8/b+eSTT2L06NFITEzE559/jtLSUrzyyivYsmUL3n//fQDAtm3b8MMf/hD/H3v3Hxdlme+P/zXAMPwISCAYSCRqsU6OugZG0A8xBGPV1twTbrqtlKdjK7Jy0GOZp09jGSi7qXu07NhxkXKJdjcxXV1jqMB1yQ0xv4KWxzb8VRAnQlChYYTr+4dn7mX4OcD8uOee1/PxmAfc11wzc73vud9z/7ju+7r//d//HcHBwdDr9Th16hRSUlJQU1ODyZMnO7zdALBnzx5cunQJmZmZUpnc5q+7+vbbb9HV1YXw8HCL8vDwcDQ2NjqpVYNLSEjAm2++ifHjx+Obb77BunXrkJSUhJMnTyIkJMSun11YWIg77rjDoqznQUAAuPfee/HrX/8aAJCYmIgf/ehH+OyzzzBz5kwkJycDQL/z+9y5c/Zr+CgxXx3PlXLT3J7BluvGxkZ4e3tjzJgxfeqYX9/Y2IiwsLA+7x8WFmZRp/fnjBkzBp6enujq6hoyR3vmZ08BAQF9tgXkEJe3t/ewv+/a2lokJibi+++/xw033IDS0lLceeed0oaoq8UjJ66Ul87Ue9v66NGj0nN79+7FTTfdJE3/53/+p1vOu5KSEhw7dgzV1dV9nrPmt4cIGHybeKDlCAAmTJiA//7v/7Yo67096y5sta4n9zSSHAwPD8ff//53AMC//uu/4re//a3F/SEiIyNx4sQJfPnllxbbrV9//TU2btyI7OxstLW14bnnnnNQlI7jyG1vdyHnbdf+jsXKYRkYaH/y4sWLuOuuu3DjjTfigQcegMlkwoYNG/DFF18gLy9P6vCRQwxD+fLLL7Ft2zbk5ubiueeewyeffIJf/vKX0Gg0+PnPf+4SMQDsJLGLZcuW4cSJEzh8+LBF+fz586X/dTod4uPjER0djf3792PevHmObiYWLFiA+Ph4aXrx4sW47bbbUFRUhD/84Q8AgN/97nd49NFHpTq33nor1Go1XnrppT4LriPt2LED6enpFhvfcpu/7q7nFT7A9RVW7zK5SE9Pl/6fOHEiEhMTpVzIzc2162ebl9XB3HjjjRY3Nf/Rj36E7du3Q6/XY+3atQBca34DzFdncqVlZSRt7V2nv/rW1DEbKkd756dZVlZWv9sC/X2eI+Mayfd9++234/jx47h06RLeffddLFq0yOKKUleLR45cKS+dYaBtawCIj49HRESENL1jxw589913jmye0124cAHLly9HWVkZfHx8BqzH5YyGMtg2sXld198yExAQ0O+60J3ZYt1I7mckOdhzuYmKioKHh0effDRfKdJ7u3XGjBkYN24c/uu//kuRnSRmjtpWdSdy3KYYbHvRmcvAQPuTL7zwAr799lvs2bMH8+bNQ1lZmXQiellZGW6++WbZxDCU7u5uxMfHIy8vDwAwZcoUnDx5Etu2bcPPf/7zAT9fTjEAAIfbsrHs7Gzs3bsXH330EcaOHTto3YiICERHR+PMmTMOat3g/P39MXHiRFRVVeHIkSMAgB/84AcWdZqamhAeHo6pU6di3Lhxzmgmzp07h/LycmlosIHIbf66i9DQUHh6evbpxTUvO67AnAtyX3b0ej1iY2MBoM+ZoK40vwHmqyO4Um5qtVoAGLStWq0WnZ2daGlpGbTON9980+f9//d//9eiTu/PaWlpQVdX14jbP9C2gBziMplMw/6+vb298YMf/ADx8fHIz8/H5MmT8Zvf/MZl45ETV8pLZxlq25rz7vrwBE1NTYiLi4OXlxe8vLxQWVmJ//zP/4SXl5c0PzivaLh6bhMP9JsP9B1i2p3Zat1IBFiXg01NTQgMDAQAhISEDLhseXp69nn/wMBA6aoVJXLktqq7kOu2qxz2v4arubkZHh4euHDhQr/bcVu2bLF6O87Zy3FERATuvPNOi7J/+qd/wvnz56XPlnsMADtJbEYIgWXLlmH37t348MMPERMTM+RrmpubceHCBYuz3xypq6sL165dkx5Xr17FZ599hkuXLgG4PoyFwWCQ6nd2dqKyshJJSUlOaa9ZYWEhwsLCMGvWrEHrOXv+uitvb2/ExcVZLDsAYDAYnL7sWMtoNOKzzz5zyLLTOw+vXbs2rNebr7ooKiqSyuSSq8PBfLU/V8rNmJgYaLXaQddBcXFxUKvVFnUaGhpQV1cn1UlMTERrays++eQTqc7f/vY3tLa2WtSpq6tDQ0ODVKesrAxeXtcvth0qR4UQUrnJZMLSpUvx7rvv4oMPPuizLSCHuDQaDeLi4ob4BgYnhIDRaFRMPM7kSnnpaNZsWwcHB+PgwYNSDra3t7vc+s8WUlJSUFtbi+PHj0uP+Ph4LFy4EMePH8ett946ZK4S9afnNnF/v/nmdeLEiRNHtT2rJLZaNxIBQ+egedkyn9w6fvx4eHl5WawbzctWf0MaX7t2DRcuXMD48eMdFpMjOXJb1V3Ibdt1qO1FOSwDA+1PJiYmoru7G2+88QZef/11HD58eMTbcc5eju+9916cPn3aoux//ud/EB0dDUAe34NVRn3rdxJCCPGLX/xCBAUFiYqKCtHQ0CA92tvbhRBCXL58WaxYsUJUVVWJ+vp68dFHH4nExERx8803i7a2Noe2tbCwUADo93HDDTeIhQsXCgBixYoVIigoSOzevVvU1taKxx57TERERDi8vT11dXWJcePGiWeeecaiXE7zl4QoKSkRarVa7NixQ5w6dUrk5OQIf39/cfbsWWc3rV8rVqwQFRUV4ssvvxRHjhwRs2fPFgEBAXZt72B5aDKZpHrR0dFi5syZ4tNPPxWffvqpACA2btwoPv30U3Hu3Dmxbds2AUD4+vrKKlcvX748YJuZr84jp9wcbBkRQoj169cPuQ56+umnxdixY0V5ebk4duyYePDBB8XkyZPFtWvXpDoPPfSQmDRpkvj444/Fxx9/LCZOnChmz54tPX/t2jWh0+lESkqKOHbsmCgvLxdjx44VKSkpQ+ZodHT0gHVWrVrVZ1tADnEtW7ZsWN/T6tWrxaFDh0R9fb04ceKEeO6554SHh4coKytzyXjkSE55KSeDbVsPtg7VarWira1NREdHi1mzZjk7DKeZNm2aWL58uTRtTa4SDbVN3Hs5SkhIsGpdqbRcdNQ2DLmf4eagedky7xP290hOThaTJ08W0dHR4kc/+pEwmUzCZDKJc+fOiaeeekqo1Wrxpz/9ycmRj5xc9inciZy2XYc6FiuE85aBoY75dHd3iyVLlggPDw8BQKhUKvFP//RP4t/+7d9EQkLCsLfjnLkcf/LJJ8LLy0u8/PLL4syZM+J3v/ud8PPzE7t27XKZGIQQgp0kNjLQgl9YWCiEEKK9vV2kpaWJm266SajVajFu3DixaNEicf78eYe31ZyoCQkJIiQkRHh6eorQ0FAxffp0cfLkSfH0008LAOKzzz4TL7zwgtBqtUKj0YgHHnhA1NbWOry9Pb3//vsCgDh9+rRFuZzmL1336quviujoaOHt7S3uuusuUVlZ6ewmDWj+/PkiIiJCqNVqERkZKebNmydOnjxp18805+Gbb74pqqurLR49RUdHi3vuuaff35dFixaJ1157TQAQ//zP/yyrXP3oo48GbDPz1bnkkpuDLSNCCNHd3T3kOqijo0MsW7ZMBAcHC19fXzF79uw+y1Fzc7NYuHChCAgIEAEBAWLhwoWipaXFos65c+fErFmzhK+vrwgODhbLli0T27dvHzJHo6OjxX333SeVD7UtIIe4vv/++2F8S0I8+eST0vJy0003iZSUFKmDxBXjkSu55KWcDJZP5nVoUVGReOqpp0RISIhQq9ViypQp0vKnxAOzw9G7k8SaXCUaapu493I0fvx4q9aVSstFR23DkPsZbg6al62e+5aHDx8Wjz76qAgMDBQajUZatgY6uef11193YsSjJ6d9Cncil21Xue1/9WTtMZ+zZ8+K1157TTz55JPiBz/4gQAgPDw8xE9+8hOnxzAc+/btEzqdTmg0GnHHHXeI7du3WzzvCjGohPi/OziR29i5cyeeeOIJVFdX93vzoPz8fDz33HP485//jIceesgJLSRSvqHy0OyWW26BTqfDn/70p36ff/bZZ7Fhwwb85S9/wX333Wev5hK5HWtydKj8JCL7YH4SyQNzkUgerM3FqKgobNq0CV1dXThz5gyef/55XLx4ER999BH3JYlszNpjPr39/ve/x2OPPYa4uDiLYaXI/nhPEupj5syZAIA9e/Y4tyFENCghBPbt2wd/f/9hrXSJiIiIiIjIvQQFBSE+Ph4JCQn42c9+hrKyMqjVaixduhTd3d3Obh4RAcjIyMCkSZNQV1fn7Ka4HXaSUB933XUX0tPTsWPHDnz44Yf91jl69CjOnz/v4JYRUU9r167FqVOnsHz5cvj4+Di7OUREREREROQiYmNjsWrVKtTW1uKdd95xdnOI3EpDQ0O/5VeuXMGFCxcQGRnp4BaRl7MbQPL05ptv4qGHHkJ6ejqefPJJpKenY8yYMWhoaMC+ffvw9ttvo6amBuPGjXN2U4kU79KlSzhy5AgA4OrVqzh9+jRKSkrwl7/8BRkZGVi7dq2TW0jkvnrmZ08ajQZTpkxxQouIiIjkpbGxEX/84x/7lN9yyy28GprIyVauXInXX38da9euRUZGBjw9PZ3dJCK38PLLL+Ovf/0r5s+fjx/+8Ifw9fVFfX09tm7diubmZvzqV79ydhPdDjtJqF+hoaE4fPgw3njjDbz99tsoLi5Ge3s7wsLCcM8992Dv3r2YPHmys5tJ5Bb++te/IjExESqVCv7+/rj55ptx99134z/+4z+Qlpbm7OYRuTVzfvZ288034+LFi05oERERkbzU1NTg0Ucf7VO+aNEi7Ny50/ENIiLJDTfcgP/3//4fsrKy8Lvf/Q4///nPnd0kIrfw+OOPAwBKSkrwq1/9Cq2trQgODkZcXBwOHDiA9PR0J7fQ/fDG7URERERERERERERE5JZ4TxIiIiIiIiIiIiIiInJL7CQhIiIiIiIiIiIiIiK3xE4SIiIiIiIiIiIiIiJyS+wkISIiIiIiIiIiIiIit8ROEiIiIiIiIiIiIiIickvsJCEiIiIiIiIiIiIiIrfk5ewGjER3dze+/vprBAQEQKVSObs5RMMihMDly5cRGRkJDw9l9FMyJ8mVMSeJ5IU5SSQvzEkieWFOEskLc5JIXkaaky7ZSfL1118jKirK2c0gGpULFy5g7Nixzm6GTTAnSQmYk0TywpwkkhfmJJG8MCeJ5IU5SSQvw81Jl+wkCQgIAHA92MDAwD7Pm0wmlJWVIS0tDWq12tHNswvG5BqsiamtrQ1RUVHScqwE7pSTjEV+RhuHu+WkUr73gSg9PkD5MX733XeIiYlxm5yUA6UsU0qIQ44xuNt6EpDn92APjNM1MSdd/zscCXefB3KOnzkpr+9juBiLPI0mlpHmpEt2kpgv9QoMDBwwWf38/BAYGOjyC4UZY3INw4lJSZcsulNOMhb5sVUctsjJ/Px87N69G59//jl8fX2RlJSEDRs24Pbbb5fqCCGwdu1abN++HS0tLUhISMCrr76KCRMmSHWMRiNWrlyJt99+Gx0dHUhJScFrr71m9VkQg+WkUr73gSg9PkD5MZpMJgDutZ50NqUsU0qIQ84x2CInt23bhm3btuHs2bMAgAkTJuD//b//h/T0dACOWUf2jMUdtl0HwzhdmzutJ5X6HQ6Hu88DV4ifOemaGIs82SKW4eakMgbLI3JjX331FX72s58hJCQEfn5++OEPf4iamhrpeSEE9Ho9IiMj4evri+TkZJw8edLiPYxGI7KzsxEaGgp/f388/PDDuHjxoqNDIXJ5lZWVyMrKwpEjR2AwGHDt2jWkpaXh6tWrUp2CggJs3LgRW7duRXV1NbRaLVJTU3H58mWpTk5ODkpLS1FSUoLDhw/jypUrmD17Nrq6upwRFhERkU2MHTsW69evx9GjR3H06FE8+OCD+PGPfyxtm3IdSeR43J8kIiJiJwmRS2tpacG9994LtVqNP//5zzh16hReeeUV3HjjjVId7mwSOc7BgweRmZmJCRMmYPLkySgsLMT58+elHU0hBDZv3ow1a9Zg3rx50Ol0KCoqQnt7O4qLiwEAra2t2LFjB1555RXMmDEDU6ZMwa5du1BbW4vy8nJnhkdERDQqc+bMwY9+9COMHz8e48ePx8svv4wbbrgBR44c4TqSyAm4P0lERHSdSw63RUTXbdiwAVFRUSgsLJTKbrnlFun/3jubAFBUVITw8HAUFxdjyZIl0s7mW2+9hRkzZgAAdu3ahaioKJSXl2PmzJkOjYlISVpbWwEAwcHBAID6+no0NjYiLS1NqqPRaDBt2jRUVVVhyZIlqKmpgclksqgTGRkJnU6Hqqoq5iQRESlCV1cX/vCHP+Dq1atITEy06zrSaDTCaDRK021tbQCuD+VgHuKvJ3NZf88pCeN0TbaMg/uTRERE1ym6k0Snfx/Gruvjj51dP8vJrSGyvb1792LmzJl49NFHUVlZiZtvvhlLly7FU089BUB+B2SZk+ROhBDIzc3FfffdB51OBwBobGwEAISHh1vUDQ8Px7lz56Q63t7eGDNmTJ865tf3NpyDPwMdKNDp35f+r9O77s6s0g6E9EfpMSo1Ljm65dn9AACNp0DB3U5uDLmN2tpaJCYm4vvvv8cNN9yA0tJS3HnnnaiqqgJg+3UkcP2eYWvXru1TXlZWBj8/vwFfZzAYrI7LlTFO19Le3m6z93LV/UnuSxLJA3OSlETRnSRESvfll19i27ZtyM3NxXPPPYdPPvkEv/zlL6HRaPDzn/9cFgdkzeUAoPEQfcpcjZIOTiolltHGYa/4ly1bhhMnTuDw4cN9nut9AzEhxJA3FRuszkgO/vQ+UNDzAOmBAwcGbYsrUMqBkMEoNUZbHvwhIvm5/fbbcfz4cVy6dAnvvvsuFi1ahMrKSul5W68jAWD16tXIzc2Vptva2hAVFYW0tLQBb0hrMBjw/FEPGLtVLn3ywGDMcaamprr8DV4Ho7Q4zftetuCq+5Ouvv8yEkrZdxspOccvxzYR0fCxk4TIhXV3dyM+Ph55eXkAgClTpuDkyZPYtm0bfv7zn0v15HBAFgBeiu+W/nf1g7BKOjiplFhGGoc9DshmZ2dj7969OHToEMaOHSuVa7VaANd3JiMiIqTypqYmaedTq9Wis7MTLS0tFjubTU1NSEpK6vfzhnPwZ6ADBUq6kkRJB0L6o/QYm5ubnd0EIrIjb29v/OAHPwAAxMfHo7q6Gr/5zW/wzDPPALD9OhK4fua7RqPpU65Wqwf9HTV2q2DsUinyt7anoeaDUiglTlvG4Kr7k66+LzkaStl3Gyk5xs8TfIiUgZ0kRC4sIiICd955p0XZP/3TP+Hdd98FII8DskDfs/EA1z0Iq6SDk0qJZbRx2PJsPCEEsrOzUVpaioqKCsTExFg8HxMTA61WC4PBgClTpgAAOjs7UVlZiQ0bNgAA4uLioFarYTAYkJGRAQBoaGhAXV0dCgoK+v3ckRz86f2ceSg883OuTikHQgaj1BiVGBMRDUwIAaPRaLd1JBENzFX3J111X3I0lLLvNlJyjt+W+5NE5DzsJCFyYffeey9Onz5tUfY///M/iI6OBiCvA7LAP87GM9d1ZUo6OKmUWEYahy1jz8rKQnFxMd577z0EBARIQwwEBQXB19cXKpUKOTk5yMvLQ2xsLGJjY5GXlwc/Pz8sWLBAqrt48WKsWLECISEhCA4OxsqVKzFx4kTpZphERESu6LnnnkN6ejqioqJw+fJllJSUoKKiAgcPHuQ6ksgJXHV/Ugn7LiOllH23kZJj/HJrDxGNDDtJiFzYv/3bvyEpKQl5eXnIyMjAJ598gu3bt2P79u0AwJ1NIgfbtm0bACA5OdmivLCwEJmZmQCAVatWoaOjA0uXLkVLSwsSEhJQVlaGgIAAqf6mTZvg5eWFjIwMdHR0ICUlBTt37oSnp6ejQiEiIrK5b775Bo8//jgaGhoQFBSESZMm4eDBg0hNTQXAdSSRo3F/koiI6Dp2khC5sKlTp6K0tBSrV6/Giy++iJiYGGzevBkLFy6U6nBnk8hxhBBD1lGpVNDr9dDr9QPW8fHxwZYtW7BlyxYbto6IiMi5duzYMejzXEcSORb3J4mIiK5jJwmRi5s9ezZmz5494PPc2SQiIiIiIqL+cH+SiIgI8HB2A4iIiIiIiIiIiIiIiJyBnSREREREREREREREROSW2ElCRERERERERERERERuiZ0kREREROS2brnlFqhUqj6PrKwsAEBmZmaf5+655x4nt5qIiIiIiIhshTduJyIiIiK3VV1dja6uLmm6rq4OqampePTRR6Wyhx56CIWFhdK0t7e3Q9tIRERERERE9sNOEiIiIiJyWzfddJPF9Pr163Hbbbdh2rRpUplGo4FWq3V004iIiIiIiMgBONwWERERERGAzs5O7Nq1C08++SRUKpVUXlFRgbCwMIwfPx5PPfUUmpqanNhKIiIiIiIisiVeSUJEREREBGDPnj24dOkSMjMzpbL09HQ8+uijiI6ORn19PZ5//nk8+OCDqKmpgUaj6fd9jEYjjEajNN3W1gYAMJlMMJlMdo1hODSe4vpfj+t/5dS2kTC335XjkGMMcmoLEREREZE9jKqTJD8/H8899xyWL1+OzZs3AwCEEFi7di22b9+OlpYWJCQk4NVXX8WECROk1xmNRqxcuRJvv/02Ojo6kJKSgtdeew1jx44dVTBERERERCO1Y8cOpKenIzIyUiqbP3++9L9Op0N8fDyio6Oxf/9+zJs3r9/3yc/Px9q1a/uUl5WVwc/Pz/YNH6GCuy2nDQaDcxpiY0qIQ04xtLe3O7sJRERERER2NeJOkurqamzfvh2TJk2yKC8oKMDGjRuxc+dOjB8/HuvWrUNqaipOnz6NgIAAAEBOTg727duHkpIShISEYMWKFZg9ezZqamrg6ek5uoiIiIiIiIbp3LlzKC8vx+7duwetFxERgejoaJw5c2bAOqtXr0Zubq403dbWhqioKKSlpSEwMNBmbR4tnf59ANevJHkpvhupqalQq9VObtXImUwmGAwGl45DjjGYr4QiIiIiIlKqEXWSXLlyBQsXLsQbb7yBdevWSeVCCGzevBlr1qyRzqwrKipCeHg4iouLsWTJErS2tmLHjh146623MGPGDADArl27EBUVhfLycsycOdMGYRERERERWa+wsBBhYWGYNWvWoPWam5tx4cIFREREDFhHo9H0OxSXWq2WzYFvADB2qSym5da+kVJCHHKKQS7tICIiIiKylxHduD0rKwuzZs2SOjnM6uvr0djYiLS0NKlMo9Fg2rRpqKqqAgDU1NTAZDJZ1ImMjIROp5Pq9GY0GtHW1mbxAP4xrnN/D+D6WXEaz+uPweq6ymOomF3x4a4xERERkXx0d3ejsLAQixYtgpfXP84hunLlClauXImPP/4YZ8+eRUVFBebMmYPQ0FA88sgjTmwxERERESldfn4+VCoVcnJypDIhBPR6PSIjI+Hr64vk5GScPHnS4nVGoxHZ2dkIDQ2Fv78/Hn74YVy8eNHBrSdyLcO+kqSkpATHjh1DdXV1n+caGxsBAOHh4Rbl4eHhOHfunFTH29sbY8aM6VPH/PreRjqu80vx3dL/Bw4cGLCeK5HT+MS24m4xcVxnIiIieSkvL8f58+fx5JNPWpR7enqitrYWb775Ji5duoSIiAhMnz4d77zzjjSMLBERERGRrfE2B0SONaxOkgsXLmD58uUoKyuDj4/PgPVUKstL94UQfcp6G6zOcMd1Npmuj+X7/FEPGLuvv2ed3rWH8TLHJKfxiUfLXWPiuM5ERETykpaWBiFEn3JfX1+8//77TmgREREREbkr3uaAyPGG1UlSU1ODpqYmxMXFSWVdXV04dOgQtm7ditOnTwO4frVIz3Gam5qapKtLtFotOjs70dLSYnE1SVNTE5KSkvr93JGO62zsVkljLSvlILycxie2FXeLSWmxEhERERERERGRbfS8zUHPTpKhbnOwZMmSIW9z0F8nidFohNFolKZ73+agN3OZxkNYTLuinsPmuzrGYvna4RpWJ0lKSgpqa2styp544gnccccdeOaZZ3DrrbdCq9XCYDBgypQpAIDOzk5UVlZiw4YNAIC4uDio1WoYDAZkZGQAABoaGlBXV4eCgoIRBUFERERERERERETkylzxNgdKuMWBkm4F4O6xjPQ2B8PqJAkICIBOp7Mo8/f3R0hIiFSek5ODvLw8xMbGIjY2Fnl5efDz88OCBQsAAEFBQVi8eDFWrFiBkJAQBAcHY+XKlZg4cWKfG8ETERERERERERERKZ2r3ubAlW9xoKRbATCW60Z6m4Nh37h9KKtWrUJHRweWLl2KlpYWJCQkoKyszOLmlps2bYKXlxcyMjLQ0dGBlJQU7Ny5kzcPIiIiIiIiIiIiIrfjqrc5cPUD8oCybgXg7rGMNHaPEb2qh4qKCmzevFmaVqlU0Ov1aGhowPfff4/Kyso+V5/4+Phgy5YtaG5uRnt7O/bt24eoqKjRNoWIiIiIiIiIiIjI5Zhvc3D8+HHpER8fj4ULF+L48eMWtzkwM9/mwNwB0vM2B2bm2xwM1ElCRHa4koSIiIiIiIiIiIiIrMfbHBA5DztJiIiIiIgIOv37MHb1Hav67PpZTmgNEREREfXG2xwQ2Qc7SYiIiMjCLc/ul/7nwVEiIiIiIiLnqKiosJg23+ZAr9cP+BrzbQ62bNli38YRKcio70lCRERERERERERERETkithJQkREREREREREREREbomdJEREREREbuKWZ/dLDyJHy8/Px9SpUxEQEICwsDDMnTsXp0+ftqgjhIBer0dkZCR8fX2RnJyMkydPWtQxGo3Izs5GaGgo/P398fDDD+PixYuODIWIiIiIFISdJEQKkp+fD5VKhZycHKmMO5pEREREJAeVlZXIysrCkSNHYDAYcO3aNaSlpeHq1atSnYKCAmzcuBFbt25FdXU1tFotUlNTcfnyZalOTk4OSktLUVJSgsOHD+PKlSuYPXs2urq6nBEWkWJwf5KIiNwVO0mIFKK6uhrbt2/HpEmTLMq5o0lEREREcnDw4EFkZmZiwoQJmDx5MgoLC3H+/HnU1NQAuH4wdvPmzVizZg3mzZsHnU6HoqIitLe3o7i4GADQ2tqKHTt24JVXXsGMGTMwZcoU7Nq1C7W1tSgvL3dmeEQujfuTRETkzryc3QAiGr0rV65g4cKFeOONN7Bu3TqpvPeOJgAUFRUhPDwcxcXFWLJkibSj+dZbb2HGjBkAgF27diEqKgrl5eWYOXOmU2IiIiIiImVrbW0FAAQHBwMA6uvr0djYiLS0NKmORqPBtGnTUFVVhSVLlqCmpgYmk8miTmRkJHQ6HaqqqvrddjUajTAajdJ0W1sbAMBkMsFkMvWpby7TeAiLaaUxx6XU+MyUFqc94uD+JBERuTt2khApQFZWFmbNmoUZM2ZYbNTKdUezZ5mrUdJOllJiGW0crh4/ERGRKxJCIDc3F/fddx90Oh0AoLGxEQAQHh5uUTc8PBznzp2T6nh7e2PMmDF96phf31t+fj7Wrl3bp7ysrAx+fn4DtvGl+G4AwIEDB6yMyjUZDAZnN8EhlBJne3u7zd/T1fYn3XH7XSn7biMl5/jl2CYiGj52khC5uJKSEhw7dgzV1dV9npPrjibg+jubStnJApQTy0jjsMeOJhEREQ1u2bJlOHHiBA4fPtznOZVKZTEthOhT1ttgdVavXo3c3Fxpuq2tDVFRUUhLS0NgYGCf+iaTCQaDAc8f9YCxW4U6vTLPhDfHmZqaCrVa7ezm2I3S4jR3KNiKK+5Puvq+5GgoZd9tpOQYP/cniZSBnSRELuzChQtYvnw5ysrK4OPjM2A9ue1oAnDZnU0l7WQpJZbRxmHrHU0iIiIaXHZ2Nvbu3YtDhw5h7NixUrlWqwVw/aBrRESEVN7U1CQdpNVqtejs7ERLS4vFQdmmpiYkJSX1+3kajQYajaZPuVqtHnTbwditgrFL5dLbSdYYaj4ohVLitGUMrro/6ar7kqOhlH23kZJz/NyfJFIGdpIQubCamho0NTUhLi5OKuvq6sKhQ4ewdetWnD59GoD8djTNdV2ZUnayAOXEMtI4lBA7EdFgbnl2v7ObQATg+kHT7OxslJaWoqKiAjExMRbPx8TEQKvVwmAwYMqUKQCAzs5OVFZWYsOGDQCAuLg4qNVqGAwGZGRkAAAaGhpQV1eHgoICxwZE5OJcdX/SnbfflbLvNlJyjF9u7SGikfFwdgOIaORSUlJQW1uL48ePS4/4+HgsXLgQx48fx6233irtaJqZdzTNG6w9dzTNzDuaA23UEhERERENV1ZWFnbt2oXi4mIEBASgsbERjY2N6OjoAHD9bPWcnBzk5eWhtLQUdXV1yMzMhJ+fHxYsWAAACAoKwuLFi7FixQp88MEH+PTTT/Gzn/0MEydOlG4aTUTW4f4kERHRdbyShMiFBQQESDe6NPP390dISIhUbt7RjI2NRWxsLPLy8gbc0QwJCUFwcDBWrlzJHU0iIiIisqlt27YBAJKTky3KCwsLkZmZCQBYtWoVOjo6sHTpUrS0tCAhIQFlZWUICAiQ6m/atAleXl7IyMhAR0cHUlJSsHPnTnh6ejoqFCJF4P4kERHRdewkIVI47mgSERENTK/X97l5bM+bzQohsHbtWmzfvl1aj7766quYMGGCM5pL5NKEEEPWUalU0Ov10Ov1A9bx8fHBli1bsGXLFhu2joj6w/1JIiJyB+wkIVKYiooKi2nuaBIREQ1uwoQJKC8vl6Z7HtQpKCjAxo0bsXPnTowfPx7r1q1DamoqTp8+bXGAiIiISAm4P0lERO6I9yQhIiIiIrfm5eUFrVYrPW666SYA189637x5M9asWYN58+ZBp9OhqKgI7e3tKC4udnKriYiIiIiIyBbYSUJEREREbu3MmTOIjIxETEwMfvrTn+LLL78EANTX16OxsRFpaWlSXY1Gg2nTpqGqqspZzSUiIiIiIiIb4nBbREREROS2EhIS8Oabb2L8+PH45ptvsG7dOiQlJeHkyZPSfUnCw8MtXhMeHo5z584N+J5GoxFGo1GabmtrAwCYTCaYTCY7RDE4jefg94HQeAiLv705o80jYW6nq7S3P3KMQU5tISIiIiKyB3aSEBEREZHbSk9Pl/6fOHEiEhMTcdttt6GoqAj33HMPgOvjsfckhOhT1lN+fn6fm8EDQFlZGfz8/GzUcusV3G1dvZfiu/stP3DggA1bY38Gg8HZTRg1OcXQ3t7u7CYQEREREdkVO0mIiIiIiP6Pv78/Jk6ciDNnzmDu3LkAgMbGRkREREh1mpqa+lxd0tPq1auRm5srTbe1tSEqKgppaWkIDAy0W9sHotO/P+jzGg+Bl+K78fxRDxi7+3b+1Oln2qtpNmUymWAwGJCamgq1Wu3s5oyIHGMwXwlFRERERKRU7CQhIiIiIvo/RqMRn332Ge6//37ExMRAq9XCYDBgypQpAIDOzk5UVlZiw4YNA76HRqOBRqPpU65Wq51y4NvYNfBVLxb1ulX91pXLwXprOWs+25KcYpBLO4iIiIiI7IU3biciIrKhQ4cOYc6cOYiMjIRKpcKePXssnhdCQK/XIzIyEr6+vkhOTsbJkyct6hiNRmRnZyM0NBT+/v54+OGHcfHiRQdGQeQ+Vq5cicrKStTX1+Nvf/sb/vmf/xltbW1YtGgRVCoVcnJykJeXh9LSUtTV1SEzMxN+fn5YsGCBs5tORERERERENsBOEiIiIhu6evUqJk+ejK1bt/b7fEFBATZu3IitW7eiuroaWq0WqampuHz5slQnJycHpaWlKCkpweHDh3HlyhXMnj0bXV1djgqDyG1cvHgRjz32GG6//XbMmzcP3t7eOHLkCKKjowEAq1atQk5ODpYuXYr4+Hh89dVXKCsrQ0BAgJNbTkRERERERLbA4baIiIhsKD093eJG0D0JIbB582asWbMG8+bNAwAUFRUhPDwcxcXFWLJkCVpbW7Fjxw689dZbmDFjBgBg165diIqKQnl5OWbOdI17AxC5ipKSkkGfV6lU0Ov10Ov1jmkQERERERERORSvJCEiInKQ+vp6NDY2Ii0tTSrTaDSYNm0aqqqqAAA1NTUwmUwWdSIjI6HT6aQ6RERERERERERkG7yShIiIyEEaGxsBAOHh4Rbl4eHhOHfunFTH29sbY8aM6VPH/PrejEYjjEajNN3W1gYAMJlMMJlMFnXN073LNZ6i3/fuXU/uBopPSZQeo1LjIiIiIiIiInliJwkREZGDqVQqi2khRJ+y3gark5+fj7Vr1/YpLysrg5+fX7+vMRgMFtMFd/f/uQcOHBi0XXLVOz4lUmqM7e3tzm4CERERERERuRF2khARETmIVqsFcP1qkYiICKm8qalJurpEq9Wis7MTLS0tFleTNDU1ISkpqd/3Xb16NXJzc6XptrY2REVFIS0tDYGBgRZ1TSYTDAYDnj/qAWP34B0zAFCnd617oJjjS01NhVqtdnZz7ELpMTY3Nzu7CURERERERORG2ElCRETkIDExMdBqtTAYDJgyZQoAoLOzE5WVldiwYQMAIC4uDmq1GgaDARkZGQCAhoYG1NXVoaCgoN/31Wg00Gg0fcrVavWAB9GN3SoYu4buJHHVg/CDxa4USo1RiTERERERERGRfLGThIiIyIauXLmCL774Qpqur6/H8ePHERwcjHHjxiEnJwd5eXmIjY1FbGws8vLy4OfnhwULFgAAgoKCsHjxYqxYsQIhISEIDg7GypUrMXHiRMyYMcNZYRGRG7vl2f3S/2fXz3JiS4iIiIiIiGyPnSREREQ2dPToUUyfPl2aNg+DtWjRIuzcuROrVq1CR0cHli5dipaWFiQkJKCsrAwBAQHSazZt2gQvLy9kZGSgo6MDKSkp2LlzJzw9PR0eDxERkbvr2VEIsLOQiIiISGk8hlM5Pz8fU6dORUBAAMLCwjB37lycPn3aoo4QAnq9HpGRkfD19UVycjJOnjxpUcdoNCI7OxuhoaHw9/fHww8/jIsXL44+GiIiIidLTk6GEKLPY+fOnQCu37Rdr9ejoaEB33//PSorK6HT6Szew8fHB1u2bEFzczPa29uxb98+REVFOSEaIiJLtzy7X3oQEREREREpwbA6SSorK5GVlYUjR47AYDDg2rVrSEtLw9WrV6U6BQUF2LhxI7Zu3Yrq6mpotVqkpqbi8uXLUp2cnByUlpaipKQEhw8fxpUrVzB79mx0dXXZLjIiIiIiIiIiIiIiF8CT04mcZ1idJAcPHkRmZiYmTJiAyZMno7CwEOfPn0dNTQ2A64m6efNmrFmzBvPmzYNOp0NRURHa29tRXFwMAGhtbcWOHTvwyiuvYMaMGZgyZQp27dqF2tpalJeX2z5CIiIiIiIiIiIiIhnjyelEzjOsTpLeWltbAQDBwcEArt+ctrGxEWlpaVIdjUaDadOmoaqqCgBQU1MDk8lkUScyMhI6nU6qQ0REREREREREROQueHI6kfOM+MbtQgjk5ubivvvuk8ZSb2xsBACEh4db1A0PD8e5c+ekOt7e3hgzZkyfOubX92Y0GmE0GqXptrY2AIDJZILJZOpT31ym8RB9ylyVuf2uHkdP7hqTkuIlIiIieeI9Q4iIiIhc23BPTl+yZMmQJ6fPnDnTsUEQuYgRd5IsW7YMJ06cwOHDh/s8p1KpLKaFEH3KehusTn5+PtauXdunvKysDH5+fgO+50vx3dL/Bw4cGPTzXYXBYHB2E2zO3WJqb293YEuIiIiIiIiIiMiVuNLJ6a58MrCSTuBmLJavHa4RdZJkZ2dj7969OHToEMaOHSuVa7VaANcTMiIiQipvamqSElir1aKzsxMtLS0WCdvU1ISkpKR+P2/16tXIzc2Vptva2hAVFYW0tDQEBgb2qW8ymWAwGPD8UQ8Yu693vNTpXbun1BxTamoq1Gq1s5tjE+4ak3llQ0RERERERERE1JsrnZyuhBPTlXQCt7vHMtKT04fVSSKEQHZ2NkpLS1FRUYGYmBiL52NiYqDVamEwGDBlyhQAQGdnJyorK7FhwwYAQFxcHNRqNQwGAzIyMgAADQ0NqKurQ0FBQb+fq9FooNFo+pSr1epBD64bu1UwdqmkukowVMyuyN1iUlqsRERERERERERkG652crorn5iupBO4Gct1Iz05fVidJFlZWSguLsZ7772HgIAA6TKtoKAg+Pr6QqVSIScnB3l5eYiNjUVsbCzy8vLg5+eHBQsWSHUXL16MFStWICQkBMHBwVi5ciUmTpyIGTNmjCgIIiIiIiIikrdDhw7hV7/6FWpqatDQ0IDS0lLMnTtXel4IgbVr12L79u1oaWlBQkICXn31VUyYMEGqYzQasXLlSrz99tvo6OhASkoKXnvtNYuDSERERK7IVU9Od/UD8oCyTuB291hGGrvHcCpv27YNra2tSE5ORkREhPR45513pDqrVq1CTk4Oli5divj4eHz11VcoKytDQECAVGfTpk2YO3cuMjIycO+998LPzw/79u2Dp6fniIIgclf5+fmYOnUqAgICEBYWhrlz5+L06dMWdYQQ0Ov1iIyMhK+vL5KTk3Hy5EmLOkajEdnZ2QgNDYW/vz8efvhhXLx40ZGhEBEREZHCXb16FZMnT8bWrVv7fb6goAAbN27E1q1bUV1dDa1Wi9TUVFy+fFmqk5OTg9LSUpSUlODw4cO4cuUKZs+eja6uLkeFQaQY3J8kkpesrCzs2rULxcXF0snpjY2N6OjoAACLk9NLS0tRV1eHzMzMAU9O/+CDD/Dpp5/iZz/7GU9OJxrCsDpJhBD9PjIzM6U6KpUKer0eDQ0N+P7771FZWSndYMjMx8cHW7ZsQXNzM9rb27Fv3z5ERUXZJCAid1JZWYmsrCwcOXIEBoMB165dQ1paGq5evSrV4c4mEREREclBeno61q1bh3nz5vV5TgiBzZs3Y82aNZg3bx50Oh2KiorQ3t6O4uJiAEBrayt27NiBV155BTNmzMCUKVOwa9cu1NbWory83NHhELk87k8SyQtPTidynhHduJ2I5OHgwYMW04WFhQgLC0NNTQ0eeOCBPjubAFBUVITw8HAUFxdjyZIl0s7mW2+9JZ1VsGvXLkRFRaG8vBwzZ7ru2JJERERE5Brq6+vR2NiItLQ0qUyj0WDatGmoqqrCkiVLUFNTA5PJZFEnMjISOp0OVVVVA263Go1GGI1Gado8VrXJZILJZOpT31ym8RD9vl9/r3FF5jiUEs9AlBanLePg/iSRvAjR/3qnJ/PJ6Xq9fsA65pPTt2zZYsPWESkbO0mIFKS1tRUAEBwcDMB+O5u22NF01Z0UJe1kKSWW0cbh6vETEREpgfl+l+Ybz5qFh4fj3LlzUh1vb2+LG9Ga65hf35/8/HysXbu2T3lZWRn8/PwGfN1L8d39lh84cGDA17gig8Hg7CY4hFLibG9vt9t7u8r+pDtuvytl322k5By/HNtERMPHThIihRBCIDc3F/fdd580xJ29djZtsaPp6juXStnJApQTy0jjsOeOJhHJX35+Pnbv3o3PP/8cvr6+SEpKwoYNG3D77bdLdTIzM1FUVGTxuoSEBBw5csTRzSVSPJVKZTEthOhT1ttQdVavXo3c3Fxpuq2tDVFRUUhLS0NgYGCf+iaTCQaDAc8f9YCxu+/71umVcWa8Oc7U1FTF3OC1P0qL09yhYGuutD/p6vuSo6GUfbeRkmP83J8kUgZ2khApxLJly3DixAkcPny4z3O23tm0xY6mq+5cKmknSymxjDYOe+1oEpFrMI/HPnXqVFy7dg1r1qxBWloaTp06BX9/f6neQw89hMLCQmna29vbGc0lUiytVgvg+gHXiIgIqbypqUk6QKvVatHZ2YmWlhaLA7JNTU1ISkoa8L01Gg00Gk2fcrVaPei2g7FbBWNX3+1hV95u6s9Q80EplBKnvWJwpf1JV92XHA2l7LuNlJzj5/4kkTKwk4RIAbKzs7F3714cOnQIY8eOlcrttbNpix1NuW3YDJdSdrIA5cQy0jiUEDsRjdxQ47GbaTQaab0qR7c8u9/ZTSAalZiYGGi1WhgMBkyZMgUA0NnZicrKSmzYsAEAEBcXB7VaDYPBgIyMDABAQ0MD6urqUFBQ4LS2E7k6V9ufdOftd6Xsu42UHOOXW3uIaGQ8nN0AIho5IQSWLVuG3bt348MPP0RMTIzF8z13Ns3MO5vmDdaeO5tm5p3Nwc7IIyIiUqLe47GbVVRUICwsDOPHj8dTTz2FpqYmZzSPyKVduXIFx48fx/HjxwFcv9/B8ePHcf78eahUKuTk5CAvLw+lpaWoq6tDZmYm/Pz8sGDBAgBAUFAQFi9ejBUrVuCDDz7Ap59+ip/97GeYOHGidMNoIrIe9yeJiIiu45UkRC4sKysLxcXFeO+99xAQECCN+RoUFARfX1+Lnc3Y2FjExsYiLy9vwJ3NkJAQBAcHY+XKldzZJCIit9PfeOwAkJ6ejkcffRTR0dGor6/H888/jwcffBA1NTX9ngk73BvS2oLGU4z8tf93I1zzX2vJ7Ualcr6pq7XkGIMt23L06FFMnz5dmjYPt7No0SLs3LkTq1atQkdHB5YuXYqWlhYkJCSgrKwMAQEB0ms2bdoELy8vZGRkoKOjAykpKdi5cyc8PT1t1k4id8H9SSIiouvYSULkwrZt2wYASE5OtigvLCxEZmYmAHBnk4iIyEoDjcc+f/586X+dTof4+HhER0dj//79mDdvXp/3GekNaUej4O7Rv4f5hrjWkuuNc+V4U9fhklMMtrwhbXJyMoQYuDNOpVJBr9dDr9cPWMfHxwdbtmzBli1bbNau4eo5vN3Z9bOc1g6i0eL+JBER0XXsJCFyYYPtZJq5ys4mEckTDwSRuxhoPPb+REREIDo6GmfOnOn3+eHekBYAdPr3pf9HckPanq8fLo2HwEvx3dINca0ltxvnyvmmrtaSYwy8IS2RcnF/koiI6Dp2khARERGR2xJCIDs7G6WlpaioqOgzHnt/mpubceHCBYub2PY0khvSGrtUFvWGq+frR8p8Q1xrxT5fJv0vp05UOd7UdbjkFINc2kFEREREZC+8cTsRERERua2srCzs2rULxcXF0njsjY2N6OjoAHD9RtMrV67Exx9/jLNnz6KiogJz5sxBaGgoHnnkESe3noiIiIiIiEaLV5IQERERkdsaajx2T09P1NbW4s0338SlS5cQERGB6dOn45133rEYj52IiIiIiIhcEztJiIiIiMhtDTUeu6+vL95/f+T3+yAiIiIiIiJ543BbRERERERERERERETklthJQkREREREREREREREbomdJERERERERERERERE5JbYSUJERERERERERERERG6JnSREREREREREREREROSWvJzdACIiInINtzy7X/r/7PpZTmwJEQGWOUlEREREREQjw04SIiIiIiIiIiIiIhq23ifu8IQ6ckUcbouIiIiIiIiIiIiIiNwSO0mIiIiIiIiIiIiIiMgtcbgtIiIiIiIaFQ6zQEREREREroqdJEREREREZDc9O1DYeUJERERERHLjNp0k3DkjIiIiInKM3leWECkV9zOJiIiIXB/vSUJERERERERERERERG7Jba4kISIiItvh/QeIiIiIiIiISAl4JQkREREREREREREREbklXklCREREROQieK8PIiIiIiIi22InCRERERERORxveE1ERERERHLA4baIiIiIiIiIiIiIiMgtsZOEiIiIiIiIiIiIiIjcklsOt8VL+4mIiIiIiMiWuJ9JRERE5JqceiXJa6+9hpiYGPj4+CAuLg5/+ctfnNkcIrfHnCSSF1fNyVue3d/vg8jVOSsnlZRHA8XC3wsaCVddTxIpFXOSSD6Yj0TD47ROknfeeQc5OTlYs2YNPv30U9x///1IT0/H+fPnndUkIrfGnCSSF1fLSWsOblp7AJQHSkmOXC0nXd1ofwf4O6J8zEkieWFOEsmHs/OR22Hkipw23NbGjRuxePFi/Mu//AsAYPPmzXj//fexbds25OfnO6wdvCSa6Dq55CQRXaf0nOy9wcx1MMmdI3OSO5REQ5P7enKwPO65zuP+KCmF3HOSyJ0wH4mGzymdJJ2dnaipqcGzzz5rUZ6WloaqqipnNInIrTEnieTFHXOSB4VJztwxJ+VkoIPI1h5c5kHo/rnyfHH1nOQ6j5TG1XOSSEmYj0Qj45ROkm+//RZdXV0IDw+3KA8PD0djY2Of+kajEUajUZpubW0FAHz33XcwmUx96ptMJrS3t8PL5IGubpXV7frByt9bXdfsb6tTpP8T8j8Y1esH88CGcvzHlG78cM1uGHvEZO3re+rZzpG83lbM31NzczPUarXT2mErCfkfQOMh8B9TugeN6fLlywAAIYQjmzcoZ+TkQPk20DLZO7+sqWeP5VtJy61SYhkoDmuXBXfLyZGuI53F2t8K8/dt/h02ry8HWk8PtkwMVM/a9by9182umrvWxvjdd98BcJ+cNPO6dtWGLR4er26B9vZul/hdaG5ulv7vOc+am5stcmOg+dnz9XLkyPzuPf8G4m7rScB560pr90dHu24z673vMth7WfM5o13PjtZA62lr9tHs2S5rcdvV+pwcLFeceYzDnlx1+89WnBG/q+bkcPMRsO96crjHfhxNSbml9FjsnpPCCb766isBQFRVVVmUr1u3Ttx+++196r/wwgsCAB98KOpx4cIFR6XckJiTfPDBnOSDD7k9/v73vzsq5YbEnOSDD64n+eBDbg/mJB98yOshl5wcbj4KwZzkQ5mP4eakU64kCQ0NhaenZ58ezKampj49nQCwevVq5ObmStPd3d347rvvEBISApWqb49lW1sboqKicOHCBQQGBto+ACdgTK7BmpiEELh8+TIiIyMd3LqBMSetx1jkZ7RxuFtOKuV7H4jS4wOUH2NrayvGjRuH4OBgZzdFYu/1pLMpZZlSQhxyjMHd1pOAPL8He2Ccrok56frf4Ui4+zyQc/xyy8nh5iPg3jnJWORpNLGMNCed0kni7e2NuLg4GAwGPPLII1K5wWDAj3/84z71NRoNNBqNRdmNN9445OcEBga6/ELRG2NyDUPFFBQU5MDWDI05OXyMRX5GE4c75qRSvveBKD0+QPkxenh4OLsJEketJ51NKcuUEuKQWwzuuJ4E5Pc92AvjdD3MSffl7vNArvHLKSeHm48AcxJgLHI10lhGkpNO6SQBgNzcXDz++OOIj49HYmIitm/fjvPnz+Ppp592VpOI3BpzkkhemJNE8sKcJJIX5iSRvDAnieSD+Ug0fE7rJJk/fz6am5vx4osvoqGhATqdDgcOHEB0dLSzmkTk1piTRPLCnCSSF+YkkbwwJ4nkhTlJJB/MR6Lhc1onCQAsXboUS5cutfn7ajQavPDCC30uFXNljMk1uHpMzMmhMRb5UUoc/bFHTip5fgHKjw9Qfoxyjs9e60lnk/M8Hw4lxKGEGByJ266jwzjJ1piT9uPu88Dd4x8Je263Kun7YCzy5IxYVEII4bBPIyIiIiIiIiIiIiIikgn53BGTiIiIiIiIiIiIiIjIgdhJQkREREREREREREREbomdJERERERERERERERE5JYU10ny2muvISYmBj4+PoiLi8Nf/vIXZzdJcujQIcyZMweRkZFQqVTYs2ePxfNCCOj1ekRGRsLX1xfJyck4efKkRR2j0Yjs7GyEhobC398fDz/8MC5evGhRp6WlBY8//jiCgoIQFBSExx9/HJcuXbJ5PPn5+Zg6dSoCAgIQFhaGuXPn4vTp0y4d07Zt2zBp0iQEBgYiMDAQiYmJ+POf/+yy8ciFnPMSAPR6PVQqlcVDq9VKz9vqe7cHpfyuDBVHZmZmn+/onnvukV0ccifHXHRU/lnz3Z8/fx5z5syBv78/QkND8ctf/hKdnZ3DjklOeWlNTLW1tZg2bRp8fX1x880348UXX8Rgt6yTU77aIz4ls8Wy6Wy22v50Nltsc5J9yHFdORq2WM/KlaPWt+R8SstLwLX3QUdKTtvINHKumo9K2A4GlLMtbCarbWKhICUlJUKtVos33nhDnDp1Sixfvlz4+/uLc+fOObtpQgghDhw4INasWSPeffddAUCUlpZaPL9+/XoREBAg3n33XVFbWyvmz58vIiIiRFtbm1Tn6aefFjfffLMwGAzi2LFjYvr06WLy5Mni2rVrUp2HHnpI6HQ6UVVVJaqqqoROpxOzZ8+2eTwzZ84UhYWFoq6uThw/flzMmjVLjBs3Tly5csVlY9q7d6/Yv3+/OH36tDh9+rR47rnnhFqtFnV1dS4ZjxzIPS+FEOKFF14QEyZMEA0NDdKjqalJet5W37s9KOV3Zag4Fi1aJB566CGL76i5udmijhzikDO55qKj8m+o7/7atWtCp9OJ6dOni2PHjgmDwSAiIyPFsmXLhh2TXPLSmphaW1tFeHi4+OlPfypqa2vFu+++KwICAsSvf/3rEcfnqHy1V3xKZotl09lstf3pbLbY5iTbk+u6cjRssZ6VK0etb8m5lJiXQrj2PuhIyWUbmUbOlfNRCdvBQihnW9hMTtvEiuokufvuu8XTTz9tUXbHHXeIZ5991kktGljvhOzu7hZarVasX79eKvv+++9FUFCQeP3114UQQly6dEmo1WpRUlIi1fnqq6+Eh4eHOHjwoBBCiFOnTgkA4siRI1Kdjz/+WAAQn3/+uV1jampqEgBEZWWlYmISQogxY8aI//7v/1ZMPI7mCnn5wgsviMmTJ/f7nK2+d0dQyu/KQAddf/zjHw/4GjnGITdyzUVH5J813/2BAweEh4eH+Oqrr6Q6b7/9ttBoNKK1tXXE8TkzL62J6bXXXhNBQUHi+++/l+rk5+eLyMhI0d3dPez4hHBcvjoiPiUbybIpRyPZ/pSr4Wxzkn3IdV05GqNdz7oKe61vyfmUmJdCKGcfdKSUsu/qbpSSj0rZDhZCWdvCZs7aJlbMcFudnZ2oqalBWlqaRXlaWhqqqqqc1Crr1dfXo7Gx0aL9Go0G06ZNk9pfU1MDk8lkUScyMhI6nU6q8/HHHyMoKAgJCQlSnXvuuQdBQUF2nw+tra0AgODgYEXE1NXVhZKSEly9ehWJiYkuH48zuFJenjlzBpGRkYiJicFPf/pTfPnllwBstxw7g9KW2YqKCoSFhWH8+PF46qmn0NTUJD3nSnE4g9xz0d75Z813//HHH0On0yEyMlKqM3PmTBiNRtTU1NgsVrnF9PHHH2PatGnQaDQWdb7++mucPXt2xHE6Il+dGZ8SWbNsytFItj/lZiTbnGR7cl9XjsZo1rOuypW34ekflJyXgDL3QUdKafuuSqTkfHTl9aEStoXNnL1NrJhOkm+//RZdXV0IDw+3KA8PD0djY6OTWmU9cxsHa39jYyO8vb0xZsyYQeuEhYX1ef+wsDCL+XDixAksXrwYt912G3x9feHr64vY2FgsWbIER48eler1HifTw8MDERER+NGPfoS//vWvUj0hBHJzc3HfffdBp9M5JSZbqa2txQ033ACNRoOnn34apaWluPPOO102HmdylbxMSEjAm2++iffffx9vvPEGGhsbkZSUhObmZpt979YYaV6aHwDwyCOPSAf+lLTMpqen43e/+x0+/PBDvPLKK6iursaDDz4Io9EotdEV4nAWOeeiI/LPmu++sbGxz+dcuHABHh4e+OlPfzqinDTnZU/OjmnMmDHw9vYetI55eqTLhqPy1VnxKZU1y6YzDbSOTElJweTJk6Xtz5dffhkAoFarLV5vjkOn0yE5OdnRze/XaLY5yfbkvK4cjdGuZ60x3G3Yb7/91jbBDcKR2/BkP0rNS8A+28AnTpzAt99+i02bNskmF62lpH1XpVJyPg5nfXjixAk88cQT0n1ZbrjhBtx1110oKCjAd999BwBITk52yPbmSI/Fyo1ctom9bP6OTtb7gIQQot+DFHI1kvb3rtNf/Z51/uu//gvLli3D7bffjuXLl2PChAlQqVT47LPP8Pbbb2Pq1Kn44osvcNttt0mvP3jwIIKCgtDd3Y3z58+joKAAycnJ+Nvf/oa77roLy5Ytw4kTJ3D48GGnxGRLt99+O44fP45Lly7h3XffxaJFi1BZWTlgW+QejxzIPS/T09Ol/ydOnIjExETcdtttKCoqkm42bIvvfTCjyUuzxMRErF+/HhERERbvrYRldv78+dL/Op0O8fHxiI6Oxv79+zFv3rwBXye3OJxNjrnoqPyz5rvv+b85J7u7u5Geno7HH3982DkJXM/L/jgjJmvriP+7qflIlw1H5qsz4lM6Of5ODLSO1Ov1+OSTT3D+/Hn8/e9/t8jH3uQQR2/22Oak0VPafLfXetZsJNuwjmTvbXhyDKXlJWD73DTnokajweTJk/Hiiy/KKhetpYR9V6VTYj6aDRXbG2+8gaVLl+L222/Hv//7v+POO++EyWTC0aNH8frrr+Pjjz9GaWmpw9pr62OxziKXbWLFdJKEhobC09OzT09SU1NTnx4nOdJqtQCu9/j1PMDZs/1arRadnZ1oaWmx6DlvampCUlKSVOebb77p8/7/+7//i/DwcPz1r3/F0qVLMWvWLPzxj3+Et7e3VOfBBx9EVlYW/vCHP8DX19fi9XFxcQgNDQUAJCUl4e6778Ztt92GP/7xjygsLMTevXtx6NAhjB071uEx2Zq3tzd+8IMfAADi4+NRXV2N3/zmN3jmmWdcMh5nctW89Pf3x8SJE3HmzBnMnTsXwOi/98HYIi/Nbr/9dmlYGVfNQWtEREQgOjoaZ86cAeC6cTiKK+WiPfLPmu9eq9Xib3/7G4B/5GRaWhoOHjyIRYsWYfr06QCGn5O9OTIve8Zk1tLSApPJZFGnv+UC6HvGzkjZK1/lEp9SWLNsOsNA68js7GzU19fjs88+w9GjR6V8vOGGG6R292SO4+LFi44NYBCj2eYk23OldeVoDHc9O5iRbsM6gq3Wt+Rc7pKXwOi2gU+dOiXlYn19PWbMmIGUlBQAzs9Fayl531UplJyP1ix/H3/8MX7xi18gNTUVe/bssRjONzU1FStWrMDBgwcd1ubs7OwRH4uVG7lsEytmuC1vb2/ExcXBYDBYlBsMBpfYwImJiYFWq7Vof2dnJyorK6X2x8XFQa1WW9RpaGhAXV2dVCcxMRGtra345JNPpDp/+9vf0NraiqSkJOTl5cHT0xP/9V//ZbER29Ojjz5qMb53f8xnyhoMBuzevRsffvghYmJinBKTvQkhYDQaFROPI7lqXhqNRnz22WeIiIiw2fc+GFvlZW9KXmabm5tx4cIFaUXpqnE4iivloj3yz5rvPjExEXV1dWhoaJBy8sc//jE0Gg3i4uL6tHMkOQk4Ni97xmRWVlZmEVNiYiIOHTqEzs5OizqRkZG45ZZbhh1ff+yVr3KJTymsWTadofc6UgiBZcuWWWx/9szHG2+8EQAszj6TQxzWGM42J9meK60rR2O469nB2Gsb1hYcsQ1P9ucueQmMbhvYnIsvvvgiTp061e+8cVYuWkvJ+65KoeR8tGb5y8vLg0qlwvbt2y06SMy8vb3x8MMPD/gZa9euRUJCAoKDgxEYGIi77roLO3bskK5yN/vwww+RnJyMkJAQ+Pr6Yty4cfjJT36C9vZ2ANe3F5OTk/H666/ju+++w6RJk3DHHXfgueeeszoWuXPaNrENbwLvdCUlJUKtVosdO3aIU6dOiZycHOHv7y/Onj3r7KYJIYS4fPmy+PTTT8Wnn34qAIiNGzeKTz/9VJw7d04IIcT69etFUFCQ2L17t6itrRWPPfaYiIiIEG1tbdJ7PP3002Ls2LGivLxcHDt2TDz44INi8uTJ4tq1a1Kdhx56SEyaNEl8/PHH4uOPPxYTJ04Us2fPFteuXRO+vr4iMTHR6ja/8MILAoBobGwUJpNJGI1GcebMGTF//nzh6ekpbrjhBlFRUSEaGhqkR3t7u/R6e8dka6tXrxaHDh0S9fX14sSJE+K5554THh4eoqyszCXjkQO556UQQqxYsUJUVFSIL7/8Uhw5ckTMnj1bBAQESG201ffen9HmZUtLi6iurhbV1dUCgPj1r3/t0N8VWxns9/Hy5ctixYoVoqqqStTX14uPPvpIJCYmiptvvll2cciZXHPRUfk31Hd/7do1odPpxIMPPih8fHzEnXfeKcaOHSuWLVs2ZAy915XmvDx69KhT1ve9Y0pJSRHHjh0T5eXlfWK6dOmSCA8PF4899piora0Vu3fvFoGBgeLXv/71gPHKJV/tFZ+S2WJb1JH6W0f+4he/EEFBQQNuf5rzMSAgQPzhD38Qn376qZg/f77QarXiu+++ExMmTBDTpk1zSjw92WKbk2xPruvK0bDFerY/o9mG/d///d9RxWTmqP1rci4l5qUQttsGvvnmm4VGoxGTJk2yevm1dS5ay9nHxGj0XDkfR7P8Xbt2Tfj5+YmEhASrPmvatGl9tjczMzPFjh07hMFgEAaDQbz00kvC19dXrF27VqpTX18vfHx8RGpqqtizZ4+oqKgQv/vd78Tjjz8uWlpahBBCzJgxQwAQ8+bNEyUlJeL3v/+92LBhg1i6dKn0Pq60HSmnbWJFdZIIIcSrr74qoqOjhbe3t7jrrrtEZWWls5sk+eijjwSAPo9FixYJIYTo7u4WL7zwgtBqtUKj0YgHHnhA1NbWWrxHR0eHWLZsmQgODha+vr5i9uzZ4vz58xZ1mpubxcKFC0VAQIAICAgQCxcuFC0tLaKxsVEAED/96U/7tO3atWvCZDJJj+7ubiHEP1aevR+BgYH9lgMQhYWF0vvaOyZbe/LJJ6Xl56abbhIpKSlSYrpiPHIh57wUQoj58+eLiIgIoVarRWRkpJg3b544efKk9Lytvvf+2DIvnfG7YiuD/T62t7eLtLQ0cdNNNwm1Wi3GjRsnFi1a1KeNcohD7uSYi47KP2u++3Pnzkkbnd7e3mLZsmXi+++/l54fTU46Ky/PnTsnZs2aJXx9fUVwcHCfmIQQ4sSJE+L+++8XGo1GaLVaodfrpdj6I6d8tUd8SmaLbVFH6m8dOVB+/fa3vxVCWJePcugkscU2J9mHHNeVo2GL9Wx/RrMNa6sDs47avybnU1peCmG7beAnn3xSABCenp59ll9H5aK1nH1MjGzDVfNxNMvfYOu8/vTXSdJTV1eXMJlM4sUXXxQhISFSbv7xj38UAMTx48cHfK2tjsXKhZy2iRXXSUIDGyypJ0+ebJFcv/rVr4QQ/1h5lpeXi+rqavHJJ5+IP/3pT2LevHnC29tb7N6929FhECmKLfKy50OuKz4iV2HLnPz//r//z9HNJ1IUW68jq6urxW233SaLThIiVzea/HT0gVkiJWMuEtmfLTpJPvjgA5GSktLvSeeNjY1CCCG++OIL4e3tLe6++26xc+dO8fe//73Pe7/55ptSW/bs2cM8tiHF3LidhhYaGgpfX1+cO3euz3PFxcVob29HQ0NDv2PoTZ482eJmtOnp6Zg4cSKysrLwyCOP2LXdREpmy7wkotFjThLJhz3y0cfHxy5tJXI3o8lPIrId5iKR/YWGhsLPzw/19fUjev0nn3yCtLQ0JCcn44033sDYsWPh7e2NPXv24OWXX0ZHRwcA4LbbbkN5eTkKCgqQlZWFq1ev4tZbb8Uvf/lLLF++HADw+OOP49q1a3jjjTfwk5/8BN3d3Zg6dSrWrVuH1NRUm8XsjhRz43YamqenJx588EEcPXrU4ianAHDnnXciPj4eEydOtOq9PDw8MGHCBDQ0NKCpqckezSVyC7bMSyIaPeYkkXwwH4nki/lJJA/MRSL78/T0REpKCmpqanDx4sVhv76kpARqtRp/+tOfkJGRgaSkJMTHx/db9/7778e+ffvQ2tqKI0eOIDExETk5OSgpKZHqPPHEE6iqqkJrayv2798PIQRmz57db2cpWY+dJG5m9erV6OrqwtNPPw2TyTTi9+nq6kJtbS00Gg0CAwNt2EIi92OrvCQi22BOEskH85FIvpifRPLAXCSyv9WrV0MIgaeeegqdnZ19njeZTNi3b1+/r1WpVPDy8oKnp6dU1tHRgbfeemvAz/P09ERCQgJeffVVAMCxY8f61PH390d6ejrWrFmDzs5OnDx5crhhUQ8cbsvN3HvvvXj11VeRnZ2Nu+66C//6r/+KCRMmwMPDAw0NDXj33XcBoE/HR01NDYKCggAA33zzDX7729/i888/x7/9279x2AKiUbJFXvZ05513svOSaBRGmpNEZHvMRyL5Yn4SycNIc3Hfvn0ICAjo837//M//7JB2E7mSxMREbNu2DUuXLkVcXBx+8YtfYMKECTCZTPj000+xfft26HQ6zJkzp89rZ82ahY0bN2LBggX413/9VzQ3N+PXv/41NBqNRb3XX38dH374IWbNmoVx48bh+++/x29/+1sAwIwZMwAATz31FHx9fXHvvfciIiICjY2NyM/PR1BQEKZOnWr/GaFg7CRxQ08//TQSExPxm9/8Bps2bcLXX38NlUqFsWPHIikpCR988AEefPBBi9c89NBD0v/BwcGIjY3Fb3/7WyxatMjRzSdSpNHmZU8Gg0FagRLRyIwkJ4nIPpiPRPLF/CSSh5Hk4pNPPtnvewkhHNFkIpfz1FNP4e6778amTZuwYcMGNDY2Qq1WY/z48ViwYAGWLVvW7+sefPBB/Pa3v8WGDRswZ84c3HzzzXjqqacQFhaGxYsXS/V++MMfoqysDC+88AIaGxtxww03QKfTYe/evUhLSwNwfTiunTt34ve//z1aWloQGhqK++67D2+++SZuuukmh8wHpVIJ/voREREREREREREREZEb4j1JiIiIiIiIiIiIiIjILbGThIiIiIiIiIiIiIiI3BI7SYiIiIiIiIiIiIiIyC2xk4SIiIiIiIiIiIiIiNwSO0mIiIiIiIiIiIiIiMgtsZOEiIiIiIiIiIiIiIjckpezGzAS3d3d+PrrrxEQEACVSuXs5hANixACly9fRmRkJDw8lNFPyZwkV8acJJIX5iSRvDAnieSFOUkkL8xJInkZaU66ZCfJ119/jaioKGc3g2hULly4gLFjxzq7GTbBnCQlYE4SyQtzkkhemJNE8sKcJJIX5iSRvAw3J12ykyQgIADA9WB9fX1RVlaGtLQ0qNVqJ7fMMUwmE2N2YW1tbYiKipKWY1vKz8/Hc889h+XLl2Pz5s0Arvegrl27Ftu3b0dLSwsSEhLw6quvYsKECdLrjEYjVq5cibfffhsdHR1ISUnBa6+9ZvWPSc+cDAwMtHlczqak5W8gSo9xsPjsmZPOMlROuvP3rQRKjs9kMmHPnj34l3/5F7fKSXtztWXG1doLKLvNXE+6xvcpZ5yftvXdd98hJiaGOUkWOI+GZq95xPUkl7fh4vwbnaHm30hz0iU7ScyXegUGBsLX1xd+fn4IDAx0mwXLZDIxZgWw9SWL1dXV2L59OyZNmmRRXlBQgI0bN2Lnzp0YP3481q1bh9TUVJw+fVr6wcjJycG+fftQUlKCkJAQrFixArNnz0ZNTQ08PT2tjiUwMFCxnSRKW/56U3qM1sSnpMuIh8pJft+uTcnxmWMD3Csn7c3VlhlXay/gHm12p5x0xe9Tzjg/bctkMgFgTpIlzqOh2XseMSfJWpx/o2Pt/BtuTipjsDwiN3flyhUsXLgQb7zxBsaMGSOVCyGwefNmrFmzBvPmzYNOp0NRURHa29tRXFwMAGhtbcWOHTvwyiuvYMaMGZgyZQp27dqF2tpalJeXOyskIiIiIiIiIiIiIrtjJwmRAmRlZWHWrFmYMWOGRXl9fT0aGxuRlpYmlWk0GkybNg1VVVUAgJqaGphMJos6kZGR0Ol0Uh0iIiIiIkfLz8+HSqVCTk6OVCaEgF6vR2RkJHx9fZGcnIyTJ086r5FERERE5PJccrgtR7jl2f0W02fXz3JSS4gGV1JSgmPHjqG6urrPc42NjQCA8PBwi/Lw8HCcO3dOquPt7W1xBYq5jvn1vRmNRhiNRmm6ra0NwPVL3syXf/em079vMV2nnzlYWLJijmmg2JRA6TEOFp9SY7aGTv8+jF3XL0Hleo7IvXHbl+RmNNg5JPQAAEAZSURBVEPJOkvPPGIOEdmeeduV+UUkD8xJUhJ2khC5sAsXLmD58uUoKyuDj4/PgPV6j8MnhBhybL7B6uTn52Pt2rV9ysvKyqSx5HsruNty+sCBA4N+vhwZDAZnN8HulB5jf/G1t7c7oSVEREQ0kJ5Dya5bt04q7z2ULAAUFRUhPDwcxcXFWLJkibOaTEREREQujJ0kRC6spqYGTU1NiIuLk8q6urpw6NAhbN26FadPnwZw/WqRiIgIqU5TU5N0dYlWq0VnZydaWlosriZpampCUlJSv5+7evVq5ObmStNtbW2IiopCWlragDekdfUrSQwGA1JTUxV7Uy2lxzhYfOYroYiIiEgeeg4l27OTZKihZAfqJBnuVdAjvcJW4yn6vAcp/4plR+N8JCIisj12kvTQe5gBIrlLSUlBbW2tRdkTTzyBO+64A8888wxuvfVWaLVaGAwGTJkyBQDQ2dmJyspKbNiwAQAQFxcHtVoNg8GAjIwMAEBDQwPq6upQUFDQ7+dqNBpoNJo+5Wq1esAD7OYhfXrWdTWDxacUSo+xv/hsHe+hQ4fwq1/9CjU1NWhoaEBpaSnmzp0rPZ+ZmYmioiKL1yQkJODIkSPStNFoxMqVK/H222+jo6MDKSkpeO211zB27FibtpWIiEhuRjuUbH9GchU0MPwrbHteOe2KV03bm9KvWHYUXgVNRERke+wkIXJhAQEB0Ol0FmX+/v4ICQmRynNycpCXl4fY2FjExsYiLy8Pfn5+WLBgAQAgKCgIixcvxooVKxASEoLg4GCsXLkSEydO7HMjeCIa2tWrVzF58mQ88cQT+MlPftJvnYceegiFhYXStLe3t8XzOTk52LdvH0pKShASEoIVK1Zg9uzZqKmpgaenp13bT0RE5Cz2Gkp2uFdBj/QK295XTpu50hXU9qD0K5Ydrbm52dlNICIiUhx2khAp3KpVq9DR0YGlS5eipaUFCQkJKCsrs7ix5aZNm+Dl5YWMjAzprPWdO3fyYCzRCKSnpyM9PX3QOhqNBlqttt/nWltbsWPHDrz11ltSR+WuXbsQFRWF8vJyzJzp3gdaiMgxeANqcgZbDCXbn5FcBW3N8731vnK65/uQ8q9YdhTOQyIiItvzcHYDiMi2KioqsHnzZmlapVJBr9ejoaEB33//PSorK/tcfeLj44MtW7agubkZ7e3t2LdvH6KiohzcciL3UVFRgbCwMIwfPx5PPfUUmpqapOdqampgMpksxluPjIyETqdDVVVVv+9nNBrR1tZm8QD+MdZ6fw8A0HgIaDyvPwar64qPoeJ39YeS43Om/Px8qFQq5OTkSGVCCOj1ekRGRsLX1xfJyck4efKk8xpJpGDmoWSPHz8uPeLj47Fw4UIcP37cYihZM/NQsgPdS4+IBrZt2zZMmjQJgYGBCAwMRGJiIv785z9Lz1uzDjQajcjOzkZoaCj8/f3x8MMP4+LFi44OhYiIaFR4JQkREZEDpaen49FHH0V0dDTq6+vx/PPP48EHH0RNTQ00Gg0aGxvh7e2NMWPGWLwuPDxcGou9t5GOtf5SfLf0vxLHTlf62OdKj8/RqqursX37dkyaNMmivKCgABs3bsTOnTsxfvx4rFu3DqmpqTh9+rTFVZlENHq2GEqWiKw3duxYrF+/Hj/4wQ8AAEVFRfjxj3+MTz/9FBMmTLBqHchhYomISAncvpOEN2snIiJHmj9/vvS/TqdDfHw8oqOjsX//fsybN2/A1w023vpIx1p//qgHjN3X31NJ46Wb41Pq2OdKjs9kMuG9995z+OdeuXIFCxcuxBtvvIF169ZJ5UIIbN68GWvWrJHys6ioCOHh4SguLsaSJUsc3lYid2fNULJEZJ05c+ZYTL/88svYtm0bjhw5gjvvvHPIdSCHiSUiIqWweSfJLbfcgnPnzvUpX7p0KV599VVkZmaiqKjI4rmEhAQcOXLE1k0hIiKSvYiICERHR+PMmTMAAK1Wi87OTrS0tFhcTdLU1DTgUCIjHWvd2K2Sxk9X2sF2QPljnys9PkfKysrCrFmzMGPGDItOkvr6ejQ2NloMf6fRaDBt2jRUVVUN2EliNBphNBql6d5D4DlazyHaBqLxFEO+3lGsaa/cKLnNzo6poqLCYto8lKxer3dKe4iUqqurC3/4wx9w9epVJCYmWrUOHGqY2IE6SYa7njSXaTyExTT9gyuuhxzNXvOI85xIGWzeSVJdXY2uri5puq6uDqmpqXj00UelsoceegiFhYXStLe3t62bQURE5BKam5tx4cIF6Qa0cXFxUKvVMBgMyMjIAAA0NDSgrq4OBQUFzmwqkSKVlJTg2LFjqK6u7vOceYi73jeEDg8P7/ekILORDoFnb4MN0VZw98Cvc9ZwfK44pJwS29ze3u6glhCRM9TW1iIxMRHff/89brjhBpSWluLOO++U7oU32DpwJMPEAqMfKlaJw8TaiiuuhxzN1vOI60kiZbB5J8lNN91kMb1+/XrcdtttmDZtmlSm0Wig1Wpt/dFEREROd+XKFXzxxRfSdH19PY4fP47g4GAEBwdDr9fjJz/5CSIiInD27Fk899xzCA0NxSOPPAIACAoKwuLFi7FixQqEhIQgODgYK1euxMSJE6VhDIjINi5cuIDly5ejrKwMPj4+A9brPdTdYMPfAcMfAs/erBmiTad/f8DXO3o4PlccUk7JbTaf4U1EynT77bfj+PHjuHTpEt59910sWrQIlZWV0vPDXQdaU2e0Q8UqaZhYW3HF9ZCj2WsecT1JpAx2vSdJZ2cndu3ahdzcXIsVZEVFBcLCwnDjjTdi2rRpePnllxEWFjbg+wx2KaaXl5f0/0gMNrRAT3K6fM4dL6NUUsxKiIGIBnb06FFMnz5dmjbvAC5atAjbtm1DbW0t3nzzTVy6dAkRERGYPn063nnnHYux1Ddt2gQvLy9kZGSgo6MDKSkp2LlzJ29+SWRjNTU1aGpqQlxcnFTW1dWFQ4cOYevWrTh9+jSA62fKmq/2Aq4Pf9f7zNqeRjoEnr0N9vnmofcGep0zOHt+jYQS2+xq8RDR8Hh7e0s3bo+Pj0d1dTV+85vf4JlnngEw+DpwJMPEAqMfKpa/SwNzxfWQo9l6HnF+EymDXTtJ9uzZg0uXLiEzM1MqS09Px6OPPoro6GjU19fj+eefx4MPPoiampp+V5KAdZdijvRyucGGFuhJjpdzuuNllEqImZdiEilbcnIyhBi4A/799wc+W9vMx8cHW7ZswZYtW2zZNCLqJSUlBbW1tRZlTzzxBO644w4888wzuPXWW6HVamEwGDBlyhQA108CqqysxIYNG5zRZNm65dn9FtNn18/q97me5UREJD9CCBiNRsTExAy5DuQwsUREpBR27STZsWMH0tPTERkZKZXNnz9f+l+n0yE+Ph7R0dHYv38/5s2b1+/7DHYppq+v76gulxtsaIGe5HQ5pzteRqmkmHkpJhERkTwEBARAp9NZlPn7+yMkJEQqz8nJQV5eHmJjYxEbG4u8vDz4+flhwYIFzmgyESkMOxHJmZ577jmkp6cjKioKly9fRklJCSoqKnDw4EGoVKoh14EcJpaIiJTCbp0k586dQ3l5OXbv3j1ovYiICERHR+PMmTMD1rHmUsyRXi432NACvT9LbtzxMkolxOzq7SciInInq1atQkdHB5YuXYqWlhYkJCSgrKzMYog8IiIiV/TNN9/g8ccfR0NDA4KCgjBp0iQcPHgQqampAKxbB3KYWCIiUgK7dZIUFhYiLCwMs2YNfjZMc3MzLly4YDHGJRERERGRM1RUVFhMq1Qq6PV66PV6p7SHiIjIXnbs2DHo89asAzlMLBERKYGHPd60u7sbhYWFWLRokXRjdQC4cuUKVq5ciY8//hhnz55FRUUF5syZg9DQUDzyyCP2aAoREREREREREREREVG/7HIlSXl5Oc6fP48nn3zSotzT0xO1tbV48803cenSJURERGD69Ol45513OGQBEREREZEM9b4puxnvn0BEREREREpglytJ0tLSIITA+PHjLcp9fX3x/vvvo6mpCZ2dnTh37hx27tyJqKgoezSDiIiIiIiISFZueXa/9CAiIhpIfn4+VCoVcnJypDIhBPR6PSIjI+Hr64vk5GScPHnS4nVGoxHZ2dkIDQ2Fv78/Hn74YVy8eNHBrSdyLXbpJCEiIiIiIvfFg8BEREREI1ddXY3t27dj0qRJFuUFBQXYuHEjtm7diurqami1WqSmpuLy5ctSnZycHJSWlqKkpASHDx/GlStXMHv2bHR1dTk6DCKXwU4SIiIiIiIiIiIiIhm4cuUKFi5ciDfeeANjxoyRyoUQ2Lx5M9asWYN58+ZBp9OhqKgI7e3tKC4uBgC0trZix44deOWVVzBjxgxMmTIFu3btQm1tLcrLy50VEpHssZOEiIiIiIiIyMmsvQKLV2oRESlbVlYWZs2ahRkzZliU19fXo7GxEWlpaVKZRqPBtGnTUFVVBQCoqamByWSyqBMZGQmdTifVIaK+7HLjdiIiIiIiIiIiIiKyXklJCY4dO4bq6uo+zzU2NgIAwsPDLcrDw8Nx7tw5qY63t7fFFSjmOubX92Y0GmE0GqXptrY2AIDJZILJZOpT31ym8RAW02Qd8/zifBuZoebfSOcrO0mIiIiIiIiIZKTnVSJn189yYkuIiMhRLly4gOXLl6OsrAw+Pj4D1lOpVBbTQog+Zb0NVic/Px9r167tU15WVgY/P78B3/Ol+G4AwIEDBwb9bOqfwWBwdhNc2kDzr729fUTvx04SIiIiIiIaNg71Q0RERGQ7NTU1aGpqQlxcnFTW1dWFQ4cOYevWrTh9+jSA61eLRERESHWampqkq0u0Wi06OzvR0tJicTVJU1MTkpKS+v3c1atXIzc3V5pua2tDVFQU0tLSEBgY2Ke+yWSCwWDA80c9YOxWoU4/c3SBuxnz/EtNTYVarXZ2c1zOUPPPfCXUcLGThIiIiIiIiIiIiMiJUlJSUFtba1H2xBNP4I477sAzzzyDW2+9FVqtFgaDAVOmTAEAdHZ2orKyEhs2bAAAxMXFQa1Ww2AwICMjAwDQ0NCAuro6FBQU9Pu5Go0GGo2mT7larR70IL6xWwVjl4oH+kdoqPlLgxto/o10nrKThIiIiIiIiIiIiMiJAgICoNPpLMr8/f0REhIilefk5CAvLw+xsbGIjY1FXl4e/Pz8sGDBAgBAUFAQFi9ejBUrViAkJATBwcFYuXIlJk6c2OdG8ET0D+wkISIiIiIiIiIiIpK5VatWoaOjA0uXLkVLSwsSEhJQVlaGgIAAqc6mTZvg5eWFjIwMdHR0ICUlBTt37oSnp6cTW04kb+wkISIiIiIiIiIiIpKZiooKi2mVSgW9Xg+9Xj/ga3x8fLBlyxZs2bLFvo0jUhAPZzeAiIiIiIiIiIiIiIjIGdhJQkREREREREREREREbonDbVnplmf3S/+fXT/LiS0hIiIiIiIiOei5nziaOkRERETkPLyShIiIiIiIiIiIiIiI3BI7SYiIiIiIiIiIiIiIyC1xuC0iIiIiIiIiIiKi/8Nh94ncC68kISIiIiIiIiIiIiIit8ROEiIiIhs6dOgQ5syZg8jISKhUKuzZs8fieSEE9Ho9IiMj4evri+TkZJw8edKijtFoRHZ2NkJDQ+Hv74+HH34YFy9edGAURET2ccuz+6XHQOU6/ftOah0REREREbkjdpIQERHZ0NWrVzF58mRs3bq13+cLCgqwceNGbN26FdXV1dBqtUhNTcXly5elOjk5OSgtLUVJSQkOHz6MK1euYPbs2ejq6nJUGERERDSAgTr7iIiIiMg1sZOEiIjIhtLT07Fu3TrMmzevz3NCCGzevBlr1qzBvHnzoNPpUFRUhPb2dhQXFwMAWltbsWPHDrzyyiuYMWMGpkyZgl27dqG2thbl5eWODoeIiIiIFCo/Px9Tp05FQEAAwsLCMHfuXJw+fdqiDq+CJiIid+CWN27nGT9EROQM9fX1aGxsRFpamlSm0Wgwbdo0VFVVYcmSJaipqYHJZLKoExkZCZ1Oh6qqKsycObPP+xqNRhiNRmm6ra0NAGAymWAymfrUN5dpPESfMiUwx6KkmHpScnxKjImIiEiuKisrkZWVhalTp+LatWtYs2YN0tLScOrUKfj7+wP4x1XQO3fuxPjx47Fu3Tqkpqbi9OnTCAgIAHD9Kuh9+/ahpKQEISEhWLFiBWbPno2amhp4eno6M0QiIiKr2LyTRK/XY+3atRZl4eHhaGxsBHD9LIS1a9di+/btaGlpQUJCAl599VVMmDDB1k2xm56dLGfXz3JiS4iIyJWY14Xh4eEW5eHh4Th37pxUx9vbG2PGjOlTx/z63vLz8/usewGgrKwMfn5+A7bnpfhu6f8DBw5YF4QLMRgMzm6CXSk9PiIiIrKvgwcPWkwXFhYiLCwMNTU1eOCBB/pcBQ0ARUVFCA8PR3FxMZYsWSJdBf3WW29hxowZAIBdu3YhKioK5eXl/Z7gQ0REJDd2uZJkwoQJFkOC9DxzwJqzEIiIiJRMpVJZTAsh+pT1Nlid1atXIzc3V5pua2tDVFQU0tLSEBgY2Ke+yWSCwWDA80c9YOy+/p51euXswJrjS01NhVqtdnZzbE7J8ZlMJrz33nvObgYREZFbam1tBQAEBwcDkO9V0LzytC8lX2lsK8OdRxpP66665zwnUga7dJJ4eXlBq9X2KbfmLAQiIiKlMq8bGxsbERERIZU3NTVJV5dotVp0dnaipaXF4mqSpqYmJCUl9fu+Go0GGo2mT7larR70ILqxWwVjl0qqqzRDxe/qlB4fEREROY4QArm5ubjvvvug0+kAyPcqaCVeAW0rvNJ4aNbOo4K7//H/YMtce3v7aJtERDJgl06SM2fOIDIyEhqNBgkJCcjLy8Ott95q1VkI/RnsLAMvLy/pf2v17A0eLWf0GLvjGQJKilkJMRDRyMTExECr1cJgMGDKlCkAgM7OTlRWVmLDhg0AgLi4OKjVahgMBmRkZAAAGhoaUFdXh4KCAqe1nYiIiIiUa9myZThx4gQOHz7c5zm5XQWtpCugbUXJVxrbynDnkU7/vvT/YMuc+RglEbk2m3eSJCQk4M0338T48ePxzTffYN26dUhKSsLJkyetOguhP9acZTCc3vKevcGj5cwzGNzxDAElxMyzDIiU7cqVK/jiiy+k6fr6ehw/fhzBwcEYN24ccnJykJeXh9jYWMTGxiIvLw9+fn5YsGABACAoKAiLFy/GihUrEBISguDgYKxcuRITJ06UxnkmIiIiIrKV7Oxs7N27F4cOHcLYsWOlcrleBc1OgIHxSuOhWTuPzFfcm18z2PsRkeuzeSdJenq69P/EiRORmJiI2267DUVFRbjnnnsADP8shMHOMvD19R12b3nP3mBbctTZDO54hoCSYuZZBkTKdvToUUyfPl2aNq+/Fi1ahJ07d2LVqlXo6OjA0qVL0dLSgoSEBJSVlVncl2vTpk3w8vJCRkYGOjo6kJKSgp07d1rc44uIiIiIaDSEEMjOzkZpaSkqKioQExNj8TyvgiYiIndhl+G2evL398fEiRNx5swZzJ07F8DgZyH0x5qzDIbTW96zN9iWHH3w3h3PEFBCzK7efiIaXHJyMoQYeFhHlUoFvV4PvV4/YB0fHx9s2bIFW7ZssUML+3fLs/ul/8+un+WwzyUix2KuExGRWVZWFoqLi/Hee+8hICBAGv0jKCgIvr6+UKlUvAqaiIjcgt07SYxGIz777DPcf//9Vp2FQERERERERETDw05QGq5t27YBuH6ST0+FhYXIzMwEAF4FTUREbsHmnSQrV67EnDlzMG7cODQ1NWHdunVoa2vDokWLrDoLgYiIiIiIiIiI7Guwq5/N5HoVNBERkS3ZvJPk4sWLeOyxx/Dtt9/ipptuwj333IMjR44gOjoagHVnIRAREREREREREREREdmbh63fsKSkBF9//TU6Ozvx1Vdf4d1338Wdd94pPW8+C6GhoQHff/89KisrodPpbN0MIreQn5+PqVOnIiAgAGFhYZg7dy5Onz5tUUcIAb1ej8jISPj6+iI5ORknT560qGM0GpGdnY3Q0FD4+/vj4YcfxsWLFx0ZChEREREREREREZHD2byThIgcp7KyEllZWThy5AgMBgOuXbuGtLQ0XL16VapTUFCAjRs3YuvWraiuroZWq0VqaiouX74s1cnJyUFpaSlKSkpw+PBhXLlyBbNnz0ZXV5czwiIiIiIb0unft/hrdsuz+6WHMzj780mebHUSEBERERGRtdhJQuTCDh48iMzMTEyYMAGTJ09GYWEhzp8/j5qaGgDXdyA3b96MNWvWYN68edDpdCgqKkJ7ezuKi4sBAK2trdixYwdeeeUVzJgxA1OmTMGuXbtQW1uL8vJyZ4ZHRE7CA5dEROQstjoJiIiIiIjIWja/JwkROU9raysAIDg4GABQX1+PxsZGpKWlSXU0Gg2mTZuGqqoqLFmyBDU1NTCZTBZ1IiMjodPpUFVVhZkzZ/b5HKPRCKPRKE23tbUBAEwmE0wmU79t03ha3hRwoHpyZG6rK7V5uJQe42DxKTVmIiIiV3Tw4EGL6cLCQoSFhaGmpgYPPPBAn5OAAKCoqAjh4eEoLi7GkiVLnNFsIiIiInJh7CQhUgghBHJzc3HfffdJ9/lpbGwEAISHh1vUDQ8Px7lz56Q63t7eGDNmTJ865tf3lp+fj7Vr1/YpLysrg5+fX7+vKbjbcvrAgQNDByUzBoPB2U2wO6XH2F987e3tTmgJEclFfn4+du/ejc8//xy+vr5ISkrChg0bcPvtt0t1hBBYu3Yttm/fjpaWFiQkJODVV1/FhAkTnNhyIvcwkpOAehvuCT5DnTzS++Qfe+vdjp6f3/O5gcqdTekn4zga5yMREZHtsZOESCGWLVuGEydO4PDhw32eU6lUFtNCiD5lvQ1WZ/Xq1cjNzZWm29raEBUVhbS0NAQGBvb7mt7joNfp+16hIlcmkwkGgwGpqalQq9XObo5dKD3GweIzHyghIvdkHtpn6tSpuHbtGtasWYO0tDScOnUK/v7+AP4xtM/OnTsxfvx4rFu3DqmpqTh9+jQCAgKcHAGRco30JKDeRnKCDzDwySO9T/6xt94nF/X8/J7PDVQuF0o/GcdReIIPERGR7bGThEgBsrOzsXfvXhw6dAhjx46VyrVaLYDrO5MRERFSeVNTk7RjqdVq0dnZiZaWFourSZqampCUlNTv52k0Gmg0mj7larV6wAPsxi5Vn7quZrD4lELpMfYXn5LjJaKhcWgfIvmy1UlAwz3BZ6iTR3qf/GNvvU8usubzB3uNo09WUvrJOI7W3Nzs7CYQEREpDjtJiFyYEALZ2dkoLS1FRUUFYmJiLJ6PiYmBVquFwWDAlClTAACdnZ2orKzEhg0bAABxcXFQq9UwGAzIyMgAADQ0NKCurg4FBQWODYiIiMjJbDG0DxGN3mhOAuptJCf4DPZ875N/7K13G6z5/MFe46yOCqWfjOMonIdERES2x04SIheWlZWF4uJivPfeewgICJCGHwgKCoKvry9UKhVycnKQl5eH2NhYxMbGIi8vD35+fliwYIFUd/HixVixYgVCQkIQHByMlStXYuLEiZgxY4YzwyMiInIoWw3tM9z7H9ibxkNY/JU7cztdadx9V7zngrVtdnRMtjgJiIiIiIhoONhJQuTCtm3bBgBITk62KC8sLERmZiYAYNWqVejo6MDSpUulm82WlZVZjKG+adMmeHl5ISMjAx0dHUhJScHOnTvh6enpqFCIiIiczlZD+4z0/gf28lK8+W+3wz97NFzx/gVKbLOj739gi5OAiIiIiIiGg50kRC5MiKHPCFWpVNDr9dDr9QPW8fHxwZYtW7BlyxYbto6IiMh12HJon+He/8De4l48iJfiu/H8UQ8Yux07TNBIaDwEXorvdqn7F7jiPResbbP5SihHsdVJQEpyy7P77fZ+Z9fPsul7ExEREbkidpIQERERkduyx9A+I73/gb2YO0aM3SqH30thNFzx/gVKbLOj47HVSUBERESuJj8/H7t378bnn38OX19fJCUlYcOGDbj99tulOkIIrF27Ftu3b5dOFHj11VcxYcIEqY7RaMTKlSvx9ttvS6OFvPbaaxYnAhGRJQ9nN4CIiIiIyFmysrKwa9cuFBcXS0P7NDY2oqOjAwAshvYpLS1FXV0dMjMzObQPEREREdlUZWUlsrKycOTIERgMBly7dg1paWm4evWqVKegoAAbN27E1q1bUV1dDa1Wi9TUVFy+fFmqk5OTg9LSUpSUlODw4cO4cuUKZs+eja6uLmeEReQSeCUJEREREbktDu1DRERERHJw8OBBi+nCwkKEhYWhpqYGDzzwAIQQ2Lx5M9asWYN58+YBAIqKihAeHo7i4mIsWbIEra2t2LFjB9566y3MmDEDALBr1y5ERUWhvLwcM2fOdHhcRK6AnSRERERE5LY4tI986fTvw9il4j0TiIiIyC21trYCAIKDgwEA9fX1aGxsRFpamlRHo9Fg2rRpqKqqwpIlS1BTUwOTyWRRJzIyEjqdDlVVVf12khiNRhiNRmnafD8yk8kEk8nUp765TOMhLKbJOub5xfk2MkPNv5HOV3aSEBEREREREREREcmEEAK5ubm47777oNPpAACNjY0AgPDwcIu64eHhOHfunFTH29sbY8aM6VPH/Pre8vPzsXbt2j7lZWVl8PPzG7CNL8V3AwAOHDhgZVTUk8FgcHYTXNpA86+9vX1E78dOEiIiIiIiIiIiIiKZWLZsGU6cOIHDhw/3eU6lUllMCyH6lPU2WJ3Vq1cjNzdXmm5ra0NUVBTS0tIQGBjYp77JZILBYMDzRz1g7FahTs8hvIbDPP9SU1OhVqud3RyXM9T8M18JNVzsJCEiIiIiIiIiIiKSgezsbOzduxeHDh3C2LFjpXKtVgvg+tUiERERUnlTU5N0dYlWq0VnZydaWlosriZpampCUlJSv5+n0Wig0Wj6lKvV6kEP4hu7VTB2qXigf4SGmr80uIHm30jnqcdoG0REREREREREREREIyeEwLJly7B79258+OGHiImJsXg+JiYGWq3WYpihzs5OVFZWSh0gcXFxUKvVFnUaGhpQV1c3YCcJEbnRlSS3PLvf2U0gIiIiIiIiIiIi6iMrKwvFxcV47733EBAQIN1DJCgoCL6+vlCpVMjJyUFeXh5iY2MRGxuLvLw8+Pn5YcGCBVLdxYsXY8WKFQgJCUFwcDBWrlyJiRMnYsaMGc4Mj0jWeCUJERGRA+n1eqhUKouH+bJp4PrZQ3q9HpGRkfD19UVycjJOnjzpxBYTERERERGRvW3btg2tra1ITk5GRESE9HjnnXekOqtWrUJOTg6WLl2K+Ph4fPXVVygrK0NAQIBUZ9OmTZg7dy4yMjJw7733ws/PD/v27YOnp6czwiJyCewksZNbnt1v8SAiIjKbMGECGhoapEdtba30XEFBATZu3IitW7eiuroaWq0WqampuHz5shNbTERE5L64b0dKdejQIcyZMweRkZFQqVTYs2ePxfPWnLxjNBqRnZ2N0NBQ+Pv74+GHH8bFixcdGAWRcggh+n1kZmZKdVQqFfR6PRoaGvD999+jsrISOp3O4n18fHywZcsWNDc3o729Hfv27UNUVJSDoyFyLTbvJMnPz8fUqVMREBCAsLAwzJ07F6dPn7aok5mZ2ecs2nvuucfWTSEiIpIlLy8vaLVa6XHTTTcBuL5RvHnzZqxZswbz5s2DTqdDUVER2tvbUVxc7ORWExEREZGSXL16FZMnT8bWrVv7fd6ak3dycnJQWlqKkpISHD58GFeuXMHs2bPR1dXlqDCIiIhGzeb3JKmsrERWVhamTp2Ka9euYc2aNUhLS8OpU6fg7+8v1XvooYdQWFgoTXt7e9u6KURERLJ05swZREZGQqPRICEhAXl5ebj11ltRX1+PxsZGpKWlSXU1Gg2mTZuGqqoqLFmypN/3MxqNMBqN0nRbWxsAwGQywWQy9alvLtN4iCHb2t/r5c7cZldsuzWUHJ8SYyIiIpKr9PR0pKen9/tc75N3AKCoqAjh4eEoLi7GkiVL0Nraih07duCtt96S7nWwa9cuREVFoby8HDNnznRYLERERKNh806SgwcPWkwXFhYiLCwMNTU1eOCBB6RyjUZjMQY7ERGRO0hISMCbb76J8ePH45tvvsG6deuQlJSEkydPSjfmCw8Pt3hNeHg4zp07N+B75ufnY+3atX3Ky8rK4OfnN+DrXorvHrK9Bw4cGLKOXBkMBmc3wa6UHh+RWc/hjc6un+XElhApjyPzS6d/H8YuFfPYRVhz8k5NTQ1MJpNFncjISOh0OlRVVQ3YSTLaE3x4UkVfSj6JxlaGO480nv84oWyw13CeEymDzTtJemttbQUABAcHW5RXVFQgLCwMN954I6ZNm4aXX34ZYWFh/b7HYCtQLy8v6f/B9Pxxs5eebej9ebb80XTHlZ+SYlZCDEQ0cj3P1ps4cSISExNx2223oaioSBp6UqVSWbxGCNGnrKfVq1cjNzdXmm5ra0NUVBTS0tIQGBjYp77JZILBYMDzRz1g7B74fQGgTu96ZwCa40tNTYVarXZ2c2xOyfGZTCa89957zm4GERGR27Pm5J3GxkZ4e3tjzJgxfeqYX9+f0Z7g48on8dgbT6IZmrXzqODuf/w/2DLX3t4+2iYRkQzYtZNECIHc3Fzcd999FjcRSk9Px6OPPoro6GjU19fj+eefx4MPPoiamhpoNJo+72PNCnSoH7meP2720vNHs/fn2WMl7o4rPyXEzBUoEfXk7++PiRMn4syZM5g7dy6A6zucERERUp2mpqY+O6g9aTSaftefarV60IPoxm4VjF2Dd5K48kH4oeJ3dUqPj4Y22BngPZ/TeDqsSQ412A20eaY6ubuR3GDe2t8U5pd7Ge7JO9bUGe0JPq54Eo+9KfkkGlsZ7jzS6d+X/h9smTOfyE1Ers2unSTLli3DiRMncPjwYYvy+fPnS//rdDrEx8cjOjoa+/fvl8a67GmwFaivr69VP3I9f9zspeePZu/Ps+VK3B1XfkqKmStQIurJaDTis88+w/3334+YmBhotVoYDAZMmTIFANDZ2YnKykps2LDBKe3jQREiIiIi92MeHn2wk3e0Wi06OzvR0tJicTVJU1MTkpKSBnzv0Z7g4+rHBOyJJ9EMzdp51PNkssHqc34TKYPdOkmys7Oxd+9eHDp0CGPHjh20bkREBKKjo3HmzJl+n7dmBTrkynSIM2VtIfb5sh5Tlp9njx9Nd1z5KSFmV28/EY3OypUrMWfOHIwbNw5NTU1Yt24d2trasGjRIqhUKuTk5CAvLw+xsbGIjY1FXl4e/Pz8sGDBAmc3nYiIiIjchDUn78TFxUGtVsNgMCAjIwMA0NDQgLq6OhQUFDit7URERMNl804SIQSys7NRWlqKiooKxMTEDPma5uZmXLhwweLsBCIiIiW6ePEiHnvsMXz77be46aabcM899+DIkSOIjo4GAKxatQodHR1YunQpWlpakJCQgLKyMgQEBDi55URERO7FfKNxIqW6cuUKvvjiC2m6vr4ex48fR3BwMMaNGzfkyTtBQUFYvHgxVqxYgZCQEAQHB2PlypWYOHEiZsyY4aywiIiIhs3mnSRZWVkoLi7Ge++9h4CAAOlmXUFBQfD19cWVK1eg1+vxk5/8BBERETh79iyee+45hIaG4pFHHrF1c4iIiGSlpKRk0OdVKhX0ej30er1jGkREREREbuno0aOYPn26NG0e5nzRokXYuXOnVSfvbNq0CV5eXsjIyEBHRwdSUlKwc+dOeHoq9IZYRESkSDbvJNm2bRsAIDk52aK8sLAQmZmZ8PT0RG1tLd58801cunQJERERmD59Ot555x2eJUtERERERAMayc2oiYiof8nJyRBCDPi8NSfv+Pj4YMuWLdiyZYsdWkhEROQYdhluazC+vr54/33730SdiIiIbIs3cSciIqKBcDuBiIiIXJXdbtxOlrjBSEREREREREREREQkL+wkISIiIiJycRyGiohcHU8sJCIiImdhJwkREREREREREREREcmS+WQKjadAwd22f392khAREdGw9T5rnWd8EhEREREREZErYicJEREREREREVnFmmGxetYZyRmfPBmDiIiIHImdJERERERE5NZ4QJaIiIiIyH15OLsBREREREREREREREREzsArSYiIiIiIiIhI0vvqKiIiIiIlYycJETmFNWMZExEREREREREREdkTO0mcgAeHiYiIiIiIyB1xf5iIiIjkhvckISIiIiIiIiIiIiIit8QrSYiIiMhuBhrTnGeOEhEREREREZEcsJPEyXipMRERKQ1v9kpEREREREREroKdJEREREREMsaTamyH85KIiIiIiHpTbCcJz2IlIiJyHK53iYiIiIiIiMgVKbaThIhcB8/qJCIisg47JK1jzXzivCQaPUflkSPuccZ9EiIiIvfFThIZ4UYZEREREREREREREZHjsJOEiIiIiIiIiBxutFeiOOpEQ0dcyUJERETOw04SIpItXl1FpFyDHRTpme/8HSB3waGfiIiIiIiInIOdJDLVe0eZB4aIiMhd8GAxERERERERETkKO0mIiIjI5fBkAiKyp4GuYrP26jZXuApOp38fBXdf/3v65dnObg6RTQ22nWBNfvKEDXnjdiAREdmahzM//LXXXkNMTAx8fHwQFxeHv/zlL85sDpHbk3NO3vLsfosHkTuQc046w0h+A/i7QbbEnCSSF+YkkbwwJ4nkg/lINDxO6yR55513kJOTgzVr1uDTTz/F/fffj/T0dJw/f95ZTSJya3LJSR7QJLpOLjnpKvjbQfbGnCSSF+YkkbwwJ4nkg/lINHxOG25r48aNWLx4Mf7lX/4FALB582a8//772LZtG/Lz853VLCK35Wo5OdCBUF5qTUrhajnpajhMAw0Xc5IG03PoKmOXasB61m6/uMJwXc7GnKTeBjtRYqDnRntyxWiH4FPSPo3cc5K/q+RO5J6PRHLklE6Szs5O1NTU4Nlnn7UoT0tLQ1VVVZ/6RqMRRqNRmm5tbQUAfPfdd/Dx8UF7ezuam5uhVqulOl7Xrtqp9c7R3Nws/f/AhnL8x5Ru/HDNbhx6ZoZUnpD/gfT/31anWLy+53M99a4nVyaTqd/vWU4Gm/89Xb58GQAghLB7m6xly5w0mUz9foajcvIHK38/qtf39925wvI3WkqPcbD43DEnzfPDy+SBru6BD+a5qh+u2S2tJ716xNfz96H3BlDP9exo15nWrg9Gyh75au82W8scG+BeOQnYdz3p1S3Q3t7tMjkvt/b2/H3o+T1ZlJuuDtjmgV4/UJ3BPseWerZ5sM/getL5y6Crk1tO28tgeTTgb4eVvwk9fffddwCYkz313MbruR3Tc/um57bfYPN3tNtE1ry+97bmaLe9EvI/gMZD4D+mdCt2f84WhrsNbe26WG7ryeHmIzD6nLTXtopSKf34i72Yc9K8XTHQ/BtxTgon+OqrrwQA8de//tWi/OWXXxbjx4/vU/+FF14QAPjgQ1GPCxcuOCrlhsSc5IMP5iQffMjtwZzkgw95PZiTfPAhr8ff//53R6XckJiTfPAhn/XkcPNRCOYkH8p8DDcnnTbcFgCoVJZnAAgh+pQBwOrVq5GbmytNd3d347vvvkNISAguX76MqKgoXLhwAYGBgXZvsxy0tbUxZhcmhMDly5cRGRnp7Kb0YYuc7K++q1PS8jcQpcc4WHzumJPu/H0rgZLjM8d26tQpt8pJe3O1ZcbV2gsou81cT7rG9ylnnJ+21drainHjxiE4ONjZTemDOek8nEdDs9c8kut60tp8BJiTjsb5NzpDzb+R5qRTOklCQ0Ph6emJxsZGi/KmpiaEh4f3qa/RaKDRaCzKbrzxRgD/SPrAwEC3W7AYs+sKCgpydhMs2DInlUwpy99glB7jQPG5a0666/etFEqO7+abb4aHh4ezmyFRynrS1ZYZV2svoNw2cz1JtsD5aVvuuJ7kMjQ0zqOh2WMeyWk9Odx8BJiTzsL5NzqDzb+R5KRT1qre3t6Ii4uDwWCwKDcYDEhKSnJGk4jcGnOSSF6Yk0TywpwkkhfmJJG8MCeJ5IP5SDQyThtuKzc3F48//jji4+ORmJiI7du34/z583j66aed1SQit8acJJIX5iSRvDAnieSFOUkkL8xJIvlgPhINn9M6SebPn4/m5ma8+OKLaGhogE6nw4EDBxAdHT2s99FoNHjhhRf6XBamZIyZ7MFWOalE7rD8KT1GV4zPnjnpivNjOBif65JzbK68npTzfO2Pq7UXYJudgetJ18H5aVtynZ/MSefiPBqaO80je2+3utO8tAfOv9Gx1/xTCSGETd+RiIiIiIiIiIiIiIjIBcjnTl9EREREREREREREREQOxE4SIiIiIiIiIiIiIiJyS+wkISIiIiIiIiIiIiIit8ROEiIiIiIiIiIiIiIicksu30ny2muvISYmBj4+PoiLi8Nf/vIXZzfJJvR6PVQqlcVDq9VKzwshoNfrERkZCV9fXyQnJ+PkyZNObPHwHTp0CHPmzEFkZCRUKhX27Nlj8bw1MRqNRmRnZyM0NBT+/v54+OGHcfHiRQdGQa7KFjkmp+XPUfnU0tKCxx9/HEFBQQgKCsLjjz+OS5cu2Tm664aKMTMzs893es8991jUkXuMjiK3daej8tGa7/b8+fOYM2cO/P39ERoail/+8pfo7OwcVjxyykdr4qmtrcW0adPg6+uLm2++GS+++CKEECOKzZF5aOvYXF1+fj6mTp2KgIAAhIWFYe7cuTh9+rRFHTlvP+bn50OlUiEnJ0cqk2N7v/rqK/zsZz9DSEgI/Pz88MMf/hA1NTXS83Jr87Vr1/Af//EfiImJga+vL2699Va8+OKL6O7ulm2bnU1u60i54r6c7djq91vJ85N5+Q+2yD0lc/XtITkZbt5VVlYiLi4OPj4+uPXWW/H66687qKXyNJz5V1FR0WcfSqVS4fPPP3dgi+VjqN+5/thk+RMurKSkRKjVavHGG2+IU6dOieXLlwt/f39x7tw5Zzdt1F544QUxYcIE0dDQID2ampqk59evXy8CAgLEu+++K2pra8X8+fNFRESEaGtrc2Krh+fAgQNizZo14t133xUARGlpqcXz1sT49NNPi5tvvlkYDAZx7NgxMX36dDF58mRx7do1B0dDrsYWOSan5c9R+fTQQw8JnU4nqqqqRFVVldDpdGL27NmyiHHRokXioYcesvhOm5ubLerIPUZHkOO601H5ONR3e+3aNaHT6cT06dPFsWPHhMFgEJGRkWLZsmXDikcu+WhNPK2trSI8PFz89Kc/FbW1teLdd98VAQEB4te//vWIYnNUHtojNlc3c+ZMUVhYKOrq6sTx48fFrFmzxLhx48SVK1ekOnLdfvzkk0/ELbfcIiZNmiSWL18ulcutvd99952Ijo4WmZmZ4m9/+5uor68X5eXl4osvvpBtm9etWydCQkLEn/70J1FfXy/+8Ic/iBtuuEFs3rxZtm12JjmuI+WK+3K2Y6vfb6XOT+alJVvknpK58vaQnAw377788kvh5+cnli9fLk6dOiXeeOMNoVarxR//+EcHt1wehjv/PvroIwFAnD592mI/ytV/v0dqqN+53my1/Ll0J8ndd98tnn76aYuyO+74/9u7/5io6z8O4E/xjkMd3SCDu5OJ6AyqI0ewyRnJJg0z2NrYmjIytraWLQrMpWz9Yf+UtlltbUXTuZbToi1ps2wJJuCcZxY/EqSCDVRmXCRDZCN+da/+6Nun74XkHXzu7v358HxsbPL5vIHX6/15v97v9/nmR4ZUV1dHKSL97N27V9atW3fbe36/XxwOh+zfv1+7Nj4+Lna7XT744IMIRaivfw/6YHK8efOmWK1Wqa2t1dpcv35dYmJi5Ouvv45Y7GRM860xlcdfuOqpq6tLAMiFCxe0Nl6vVwDITz/9FOasAs32n7NPPPHErB9jtBzDRcW1MxL1GMyz/eqrryQmJkauX7+utfnkk0/EZrPJyMjInHKLZj0Gk8/7778vdrtdxsfHtTb79u0Tl8slfr8/pNxEIleH4c7NDAYHBwWANDc3i4i6+8fR0VFZu3atNDQ0SH5+vnZIomK8e/bskby8vFnvqxhzUVGRPPPMMwHXSkpK5KmnnhIRNWOOJhXXSCPgazl9zWX+NnN/si5nN5faW2iMsh9STah1t3v3bsnIyAi49txzz0lubm7YYlRZqP339yHJ8PBwBKIzlmAOSfQaf4b9dVuTk5NoaWlBYWFhwPXCwkKcP38+SlHpq6enBy6XC2lpadi2bRt6e3sBAH19ffD5fAG522w25Ofnmyb3YHJsaWnB1NRUQBuXywW3222afqDwmk+NGWn86ZWP1+uF3W7H+vXrtTa5ubmw2+3K5NzU1ISkpCTce++9ePbZZzE4OKjdM0uO86Hy2hnuegzm2Xq9XrjdbrhcLq3N5s2bMTExEfDrdOZDtXy8Xi/y8/Nhs9kC2vzyyy+4cuXKnHKMRB1GKzcjGRkZAQAkJiYCUHf/+MILL6CoqAiPPvpowHUV4z1x4gRycnLw5JNPIikpCVlZWTh06JB2X8WY8/Ly8M0336C7uxsA8MMPP+DcuXN4/PHHlY05WlReI43GbHvpSJvL/G3W/mRdhoZz+kxG2Q+pZC515/V6Z7TfvHkzvv/+e0xNTYUtVhXNZ97KysqC0+lEQUEBGhsbwxmmqeg1/gx7SHLjxg388ccfSE5ODrienJwMn88Xpaj0s379ehw5cgSnTp3CoUOH4PP5sGHDBgwNDWn5mTV3AEHl6PP5EBsbi4SEhFnbEM1mvjVmpPGnVz4+nw9JSUkzPn9SUpISOW/ZsgXHjh3DmTNn8NZbb+G7777Dpk2bMDExAcAcOc6XqmtnJOoxmGfr8/lmfJ2EhATExsbq1j+q5XO7Nn+/P5ecI1WH0cjNSEQEL7/8MvLy8uB2uwEEN/Yirba2Fq2trdi3b9+MeyrG29vbi5qaGqxduxanTp3Cjh078NJLL+HIkSMA1Ix5z549KC0tRUZGBqxWK7KyslBVVYXS0lIAasYcLaqukUZktr10JM11/jZrf7IuQ8M5PZBR9kOqmUvdzbbvnp6exo0bN8IWq4rm0n9OpxMHDx7E8ePHUVdXh/T0dBQUFODs2bORCNnw9Bp/Fr0Di7RFixYFvC8iM64Z0ZYtW7R/Z2ZmwuPxYM2aNfjoo4+0P4Bq1tz/31xyNGM/kP7CVWMqjz898rlde1Vy3rp1q/Zvt9uNnJwcpKam4uTJkygpKZn144yUo15UWz8iVY/BPNtIPX+V8rldLLN97J1Esg4jnZuRVFRU4NKlSzh37tyMe6rUf39/PyorK1FfX4+4uLhZ26kSLwD4/X7k5OTgjTfeAPDXd/tdvnwZNTU1ePrpp7V2KsX86aef4ujRo/j444/xwAMPoL29HVVVVXC5XCgvL9faqRRztLEv9GO2vXQk6D1/m6U/WZehYX/9xQj7IZWF2kcLdd89m1D6Lz09Henp6dr7Ho8H/f39OHDgADZu3BjWOM1Cj/Fn2J8kWb58ORYvXjzjFG5wcHDG6ZEZLFu2DJmZmejp6YHD4QAw87sgzZR7MDk6HA5MTk5ieHh41jZEwQq1xow0/vTKx+Fw4Ndff53x+X/77Tflcgb++m6M1NRU9PT0ADBnjqEyytoZjnoM5tk6HI4ZX2d4eBhTU1O69Y9q+dyuzd+/HkuPnMNVhyrkpqoXX3wRJ06cQGNjI1JSUrTrqu0fW1paMDg4iOzsbFgsFlgsFjQ3N+Pdd9+FxWKZ9ad+ojlfOZ1O3H///QHX7rvvPly7dg2Aen0MAK+88gqqq6uxbds2ZGZmYvv27di5c6f20zsqxhwtRlkjjcBse+lImc/8bdb+ZF2GhnP6P4yyH1LRXOputn23xWLB3XffHbZYVaTXvJWbm6u9hqL/ptf4M+whSWxsLLKzs9HQ0BBwvaGhARs2bIhSVOEzMTGBH3/8EU6nE2lpaXA4HAG5T05Oorm52TS5B5NjdnY2rFZrQJuBgQF0dnaaph8ockKtMSONP73y8Xg8GBkZwcWLF7U23377LUZGRpTLGQCGhobQ398Pp9MJwJw5hsooa2c46jGYZ+vxeNDZ2YmBgQGtTX19PWw2G7Kzs3XJTbV8PB4Pzp49i8nJyYA2LpcLq1atmne+4apDFXJTjYigoqICdXV1OHPmDNLS0gLuq7Z/LCgoQEdHB9rb27W3nJwclJWVob29HatXr1YqXgB4+OGH8fPPPwdc6+7uRmpqKgD1+hgAxsbGEBMT+JJv8eLF8Pv9ANSMOVqMskYagdn20uGmx/xt1v5kXYaGc7rx9kMqmkvdeTyeGe3r6+uRk5MDq9UatlhVpNe81dbWpr2Gov+m2/gL6c+8K6a2tlasVqscPnxYurq6pKqqSpYtWyZXrlyJdmjztmvXLmlqapLe3l65cOGCFBcXS3x8vJbb/v37xW63S11dnXR0dEhpaak4nU65detWlCMP3ujoqLS1tUlbW5sAkLffflva2trk6tWrIhJcjjt27JCUlBQ5ffq0tLa2yqZNm2TdunUyPT0drbTIIPSoMZXGX6Tq6bHHHpMHH3xQvF6veL1eyczMlOLi4qjnODo6Krt27ZLz589LX1+fNDY2isfjkRUrVhgqx0hQce2MVD3e6dlOT0+L2+2WgoICaW1tldOnT0tKSopUVFSElI8q9RhMPjdv3pTk5GQpLS2Vjo4Oqaurk7vuuksOHDgQcm6RrMNw5GZ0zz//vNjtdmlqapKBgQHtbWxsTGuj+v4xPz9fKisrtfdVi/fixYtisVjk9ddfl56eHjl27JgsXbpUjh49qmzM5eXlsmLFCvnyyy+lr69P6urqZPny5bJ7925lY44mFddIVfG1nH70mr/N2p+sy0B61J6ZmWE/pII71V11dbVs375da9/b2ytLly6VnTt3SldXlxw+fFisVqt89tln0UohqkLtv3feeUc+//xz6e7uls7OTqmurhYAcvz48WilEFV3mufCNf4MfUgiIvLee+9JamqqxMbGykMPPSTNzc3RDkkXW7duFafTKVarVVwul5SUlMjly5e1+36/X/bu3SsOh0NsNpts3LhROjo6ohhx6BobGwXAjLfy8nIRCS7H33//XSoqKiQxMVGWLFkixcXFcu3atShkQ0ajR42pNP4iVU9DQ0NSVlYm8fHxEh8fL2VlZTI8PBz1HMfGxqSwsFDuuecesVqtsnLlSikvL58Rv+o5Ropqa2ek6jGYZ3v16lUpKiqSJUuWSGJiolRUVMj4+HhI+ahUj8Hkc+nSJXnkkUfEZrOJw+GQ1157Tfx+f8i5RboO9c7N6G73XADIhx9+qLVRff/470MSFeP94osvxO12i81mk4yMDDl48GDAfdVivnXrllRWVsrKlSslLi5OVq9eLa+++qpMTEwoG3O0qbZGqoqv5fSj1/xt5v5kXf5Dj9ozMzPsh1TxX3VXXl4u+fn5Ae2bmpokKytLYmNjZdWqVVJTUxPhiNUSSv+9+eabsmbNGomLi5OEhATJy8uTkydPRiFqNdxpngvX+Fsk8r+/ZEJERERERERERERERLSAGPZvkhAREREREREREREREc0HD0mIiIiIiIiIiIiIiGhB4iEJEREREREREREREREtSDwkISIiIiIiIiIiIiKiBYmHJEREREREREREREREtCDxkISIiIiIiIiIiIiIiBYkHpIQEREREREREREREdGCxEMSIiIiIiIiIiIiIiJakHhIQkRERERERERERERECxIPSYiIiIiIiIiIiIiIaEHiIQkRERERERERERERES1IPCQhIiIiIiIiIiIiIqIF6U/zYUpaxwXtDwAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"# checking for outliers\nfrom scipy import stats\ndf_z = df.drop([\"Id\", \"EJ\", \"Class\"], axis=1)\nz = np.abs(stats.zscore(df_z))\nprint(z)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:29:58.919178Z","iopub.execute_input":"2023-07-06T06:29:58.919503Z","iopub.status.idle":"2023-07-06T06:29:58.938544Z","shell.execute_reply.started":"2023-07-06T06:29:58.919482Z","shell.execute_reply":"2023-07-06T06:29:58.937278Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"           AB        AF        AH        AM        AR        AX        AY  \\\n0    0.572153  0.170975  0.261669  0.237889  0.189295  1.900558  0.083417   \n1    0.709105  1.097801  0.261669  0.028701  0.189295  0.750457  0.083417   \n2    0.015212  0.377169  0.261669  0.094845  0.189295  0.465662  0.083417   \n3    0.480851  0.138196  0.012347  0.547477  0.189295  0.729610  0.083417   \n4    0.206946  0.100517  0.261669  0.356885  0.189295  0.628845  0.013229   \n..        ...       ...       ...       ...       ...       ...       ...   \n612  0.699975  0.161828  0.040232  0.422762  0.275215  0.802577  0.040875   \n613  0.088253  0.852755  0.261669  0.108831  0.556117  0.170319  0.082686   \n614  0.106514  0.453742  0.090140  0.235206  0.011673  0.990330  0.083417   \n615  0.243466  0.973904  0.261669  0.219353  0.189295  0.955584  0.083417   \n616  0.012178  0.360885  3.350987  1.048310  0.189295  0.920714  0.135921   \n\n           AZ        BC       BD   ...        FI  FL        FR  FS        GB  \\\n0    0.173502  0.038354  0.405383  ...  2.226608 NaN  0.035806 NaN  0.940094   \n1    0.678919  0.104787  0.048541  ...  0.084542 NaN  0.060566 NaN  1.145070   \n2    0.519453  0.104787  0.071089  ...  0.517060 NaN  0.051023 NaN  1.637944   \n3    0.112088  0.104787  0.391109  ...  1.617160 NaN  0.060566 NaN  0.219883   \n4    1.649292  1.445139  0.125327  ...  1.212850 NaN  0.896815 NaN  0.432313   \n..        ...       ...       ...  ...       ...  ..       ...  ..       ...   \n612  0.464891  0.080610  0.395082  ...  0.079062 NaN  0.045332 NaN  1.177680   \n613  0.473063  0.065661  0.100596  ...  0.272594 NaN  0.045702 NaN  1.519617   \n614  1.113828  0.104787  0.178374  ...  0.654338 NaN  0.060566 NaN  0.076400   \n615  0.699741  0.104787  0.275772  ...  0.710915 NaN  0.054771 NaN  0.387591   \n616  1.649292  0.001602  0.838845  ...  0.806821 NaN  0.047645 NaN  0.706235   \n\n           GE        GF        GH        GI  GL  \n0    0.410260  0.655511  0.948991  0.531241 NaN  \n1    0.410260  0.687893  0.238862  0.509218 NaN  \n2    0.299210  0.051850  0.351743  0.424754 NaN  \n3    0.342195  0.650833  0.858232  1.101332 NaN  \n4    0.099920  0.318309  1.409422  0.395228 NaN  \n..        ...       ...       ...       ...  ..  \n612  0.593022  0.340465  0.694915  0.513497 NaN  \n613  2.535523  0.599582  0.186764  2.048314 NaN  \n614  0.019561  0.424307  0.540129  1.903449 NaN  \n615  0.410260  0.657500  0.646592  0.370599 NaN  \n616  0.410260  0.404872  1.446419  1.773276 NaN  \n\n[617 rows x 55 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"# maintaining a threshold to check for outliers\nthreshold = 3 \nnp.where(z > 3)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:41:31.474719Z","iopub.execute_input":"2023-07-06T06:41:31.475083Z","iopub.status.idle":"2023-07-06T06:41:31.484443Z","shell.execute_reply.started":"2023-07-06T06:41:31.475059Z","shell.execute_reply":"2023-07-06T06:41:31.483283Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"(array([  3,   5,  10,  10,  10,  10,  10,  10,  13,  14,  15,  19,  20,\n         23,  26,  29,  32,  32,  33,  33,  34,  36,  45,  52,  52,  52,\n         52,  52,  52,  52,  52,  52,  52,  52,  55,  56,  57,  62,  63,\n         63,  64,  68,  75,  75,  76,  79,  91, 103, 103, 103, 103, 105,\n        107, 109, 109, 113, 113, 115, 116, 116, 118, 118, 119, 127, 130,\n        130, 131, 134, 134, 134, 135, 135, 140, 144, 150, 150, 150, 151,\n        153, 154, 155, 155, 156, 156, 156, 160, 161, 165, 165, 169, 184,\n        184, 184, 187, 189, 194, 194, 194, 194, 201, 203, 204, 205, 207,\n        209, 217, 227, 229, 229, 229, 231, 232, 232, 232, 232, 232, 232,\n        232, 232, 234, 237, 237, 240, 249, 249, 258, 258, 258, 262, 262,\n        263, 265, 271, 278, 279, 280, 284, 291, 292, 293, 295, 301, 302,\n        303, 308, 310, 310, 315, 321, 324, 325, 325, 330, 330, 330, 330,\n        330, 330, 330, 330, 331, 331, 331, 331, 331, 331, 331, 331, 331,\n        331, 331, 335, 336, 336, 337, 337, 338, 344, 344, 353, 355, 356,\n        356, 356, 356, 356, 356, 357, 357, 363, 365, 368, 372, 373, 374,\n        378, 378, 378, 378, 378, 378, 378, 378, 378, 378, 380, 380, 380,\n        381, 384, 385, 385, 385, 387, 387, 387, 387, 390, 393, 394, 408,\n        412, 413, 414, 415, 432, 432, 438, 442, 445, 445, 452, 470, 470,\n        473, 475, 477, 486, 486, 488, 490, 495, 500, 504, 511, 511, 519,\n        523, 525, 526, 526, 526, 527, 530, 530, 530, 530, 531, 533, 539,\n        540, 547, 547, 548, 548, 548, 548, 549, 554, 554, 562, 565, 565,\n        565, 572, 573, 578, 580, 580, 581, 581, 581, 582, 585, 585, 585,\n        585, 592, 593, 600, 607, 608, 614, 616, 616, 616, 616]),\n array([37, 10,  6,  8,  9, 29, 45, 52, 34, 40, 17, 10, 34, 37, 51, 17, 36,\n        38, 41, 50, 26, 44, 21,  2,  4, 19, 20, 22, 25, 28, 33, 35, 36, 40,\n         5, 21, 26, 51, 41, 50, 23, 10,  0,  3,  3, 34, 23, 20, 33, 50, 53,\n        23, 28, 17, 29, 26, 36,  0, 31, 36,  1, 29, 17, 26, 40, 44, 35,  0,\n         5, 11, 49, 53, 44, 27,  8,  9, 52, 50, 37, 26,  0, 13, 22, 34, 40,\n        47, 51,  3, 53, 36,  2, 33, 40, 51, 44,  1,  5,  7, 29, 53, 18, 50,\n        53, 45, 51, 34, 26, 17, 29, 51, 26,  0,  1,  5,  7, 11, 21, 31, 52,\n        50, 20, 33, 50,  2, 29, 11, 29, 44, 38, 44, 23, 44,  1, 17, 17, 53,\n        36, 44, 36, 37, 50, 44, 34, 23, 26, 17, 21, 21, 10, 29, 30, 45,  0,\n         1,  5, 11, 17, 19, 31, 35,  2,  4, 11, 19, 20, 22, 25, 33, 35, 36,\n        40, 17,  0, 11,  7, 30, 30, 20, 33, 34, 10,  0,  3,  4, 19, 27, 29,\n         3, 17, 36, 23, 17, 44, 10, 53,  0,  2,  3,  8,  9, 11, 14, 18, 35,\n        49, 11, 28, 29, 50, 34,  7, 27, 34,  1, 38, 43, 52, 50, 45, 26, 33,\n        27, 17, 52, 50, 10, 25, 50, 27,  5, 37, 26,  3, 29, 44, 28, 50,  7,\n        49, 26, 44, 30, 49, 37, 38, 43, 11, 11, 25,  3, 11, 18, 35,  0,  3,\n         4, 29, 51, 45, 34, 31, 27, 51,  1, 23, 31, 52, 31, 45, 51, 36, 41,\n        50, 53, 37, 23, 51, 13, 52,  2, 19, 21, 26,  0,  2, 17, 19, 26, 30,\n        37, 51, 29, 30,  2, 17, 19, 21]))"},"metadata":{}}]},{"cell_type":"code","source":"# another way to check for outliers to confirm \nQ1 = df_z.quantile(0.25)\nQ3 = df_z.quantile(0.75)\nIQR = Q3 - Q1\nprint(IQR)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:45:33.501072Z","iopub.execute_input":"2023-07-06T06:45:33.501785Z","iopub.status.idle":"2023-07-06T06:45:33.526833Z","shell.execute_reply.started":"2023-07-06T06:45:33.501755Z","shell.execute_reply":"2023-07-06T06:45:33.526088Z"},"trusted":true},"execution_count":63,"outputs":[{"name":"stdout","text":"AB         0.307656\nAF      2164.291910\nAH        28.539393\nAM        26.869572\nAR         0.000000\nAX         2.303340\nAY         0.011267\nAZ         4.839936\nBC         3.851344\nBD      1880.182830\nBN         4.237200\nBP        90.956223\nBQ       106.174590\nBR       550.658617\nBZ         0.000000\nCB        53.992530\nCC         0.208518\nCD        35.089328\nCF         8.499595\nCH         0.010945\nCL         0.178220\nCR         0.269775\nCS        10.746934\nCU         0.590319\nCW        30.905192\nDA        23.466240\nDE       319.080510\nDF         0.000000\nDH         0.131184\nDI        63.133402\nDL        32.408440\nDN         9.655960\nDU         1.053172\nDV         0.000000\nDY        19.342552\nEB         4.537656\nEE         2.261391\nEG       794.540850\nEH         0.234234\nEL        78.197691\nEP        34.239686\nEU        44.760696\nFC        30.899064\nFD         4.583364\nFE      5483.285390\nFI         2.993559\nFL         6.065585\nFR         1.015000\nFS         0.467337\nGB        11.571688\nGE        54.980608\nGF     16236.716656\nGH        11.829059\nGI        44.919980\nGL        21.853608\ndtype: float64\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Once we have IQR we can check for outliers. Anything outside the range. ","metadata":{}},{"cell_type":"code","source":"df_z < (Q1 - 1.5 * IQR)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:40:09.749479Z","iopub.execute_input":"2023-07-06T06:40:09.749848Z","iopub.status.idle":"2023-07-06T06:40:09.774994Z","shell.execute_reply.started":"2023-07-06T06:40:09.749821Z","shell.execute_reply":"2023-07-06T06:40:09.773815Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"        AB     AF     AH     AM     AR     AX     AY     AZ     BC    BD   \\\n0    False  False  False  False  False  False  False  False  False  False   \n1    False  False  False  False  False  False  False  False  False  False   \n2    False  False  False  False  False  False  False  False  False  False   \n3    False  False  False  False  False  False  False  False  False  False   \n4    False  False  False  False  False  False  False  False  False  False   \n..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n612  False  False  False  False  False  False  False  False  False  False   \n613  False  False  False  False  False  False  False  False  False  False   \n614  False  False  False  False  False  False  False  False  False  False   \n615  False  False  False  False  False  False  False  False  False  False   \n616  False  False  False  False  False  False  False  False  False  False   \n\n     ...     FI     FL     FR     FS     GB     GE     GF     GH     GI     GL  \n0    ...   True  False  False  False  False  False  False  False  False  False  \n1    ...  False  False  False  False  False  False  False  False  False  False  \n2    ...  False  False  False  False  False  False  False  False  False  False  \n3    ...  False  False  False  False  False  False  False  False  False  False  \n4    ...  False  False  False  False  False  False  False  False  False  False  \n..   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n612  ...  False  False  False  False  False  False  False  False  False  False  \n613  ...  False  False  False  False  False  False  False  False  False  False  \n614  ...  False  False  False  False  False  False  False  False  False  False  \n615  ...  False  False  False  False  False  False  False  False  False  False  \n616  ...  False  False  False  False  False  False  False  False  False  False  \n\n[617 rows x 55 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>BD</th>\n      <th>...</th>\n      <th>FI</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>612</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>613</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>615</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>616</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>617 rows × 55 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df_z > (Q3 + 1.5 * IQR)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T06:45:44.229609Z","iopub.execute_input":"2023-07-06T06:45:44.229938Z","iopub.status.idle":"2023-07-06T06:45:44.255378Z","shell.execute_reply.started":"2023-07-06T06:45:44.229915Z","shell.execute_reply":"2023-07-06T06:45:44.253831Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"        AB     AF     AH     AM     AR     AX     AY     AZ     BC    BD   \\\n0    False  False  False  False  False  False  False  False  False  False   \n1    False  False  False  False  False  False  False  False  False  False   \n2    False  False  False  False  False  False  False  False  False  False   \n3    False  False  False  False  False  False  False  False  False  False   \n4    False  False  False  False  False  False   True  False   True  False   \n..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n612  False  False  False  False   True  False   True  False  False  False   \n613  False  False  False  False   True  False  False  False  False  False   \n614  False  False  False  False   True  False  False  False  False  False   \n615  False  False  False  False  False  False  False  False  False  False   \n616  False  False   True   True  False  False   True  False  False  False   \n\n     ...     FI     FL     FR     FS     GB     GE     GF     GH     GI     GL  \n0    ...  False  False  False  False  False  False  False  False  False  False  \n1    ...  False  False  False  False  False  False  False  False  False  False  \n2    ...  False  False  False  False  False  False  False  False  False  False  \n3    ...  False  False  False  False  False  False  False  False  False  False  \n4    ...  False  False   True  False  False  False  False  False  False  False  \n..   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n612  ...  False  False  False  False  False   True  False  False  False  False  \n613  ...  False  False  False  False  False   True  False  False  False  False  \n614  ...  False  False  False  False  False  False  False  False  False  False  \n615  ...  False  False  False  False  False  False  False  False  False  False  \n616  ...  False  False  False  False  False  False  False  False  False  False  \n\n[617 rows x 55 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>BD</th>\n      <th>...</th>\n      <th>FI</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>612</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>613</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>615</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>616</th>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>...</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>617 rows × 55 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# let's remove the outliers\noutlier_df = df_z[((df_z < (Q1 - 1.5 * IQR)) |(df_z > (Q3 + 1.5 * IQR))).any(axis=1)]","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:21:01.889415Z","iopub.execute_input":"2023-07-06T07:21:01.890492Z","iopub.status.idle":"2023-07-06T07:21:01.898978Z","shell.execute_reply.started":"2023-07-06T07:21:01.890446Z","shell.execute_reply":"2023-07-06T07:21:01.897644Z"},"trusted":true},"execution_count":111,"outputs":[]},{"cell_type":"code","source":"outlier_df.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:21:10.084097Z","iopub.execute_input":"2023-07-06T07:21:10.084449Z","iopub.status.idle":"2023-07-06T07:21:10.091707Z","shell.execute_reply.started":"2023-07-06T07:21:10.084427Z","shell.execute_reply":"2023-07-06T07:21:10.090487Z"},"trusted":true},"execution_count":112,"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":"(551, 55)"},"metadata":{}}]},{"cell_type":"code","source":"# adding the dropped columns to create a new dataset after modification. We are not adding ID here\noutlier_df.insert(1, \"EJ\", df[\"EJ\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:22:12.224490Z","iopub.execute_input":"2023-07-06T07:22:12.224882Z","iopub.status.idle":"2023-07-06T07:22:12.232220Z","shell.execute_reply.started":"2023-07-06T07:22:12.224857Z","shell.execute_reply":"2023-07-06T07:22:12.230682Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"code","source":"outlier_df.insert(1, \"Class\", df[\"Class\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:23:00.084229Z","iopub.execute_input":"2023-07-06T07:23:00.084611Z","iopub.status.idle":"2023-07-06T07:23:00.091006Z","shell.execute_reply.started":"2023-07-06T07:23:00.084588Z","shell.execute_reply":"2023-07-06T07:23:00.089832Z"},"trusted":true},"execution_count":117,"outputs":[]},{"cell_type":"code","source":"outlier_df","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:23:06.940598Z","iopub.execute_input":"2023-07-06T07:23:06.941093Z","iopub.status.idle":"2023-07-06T07:23:06.971937Z","shell.execute_reply.started":"2023-07-06T07:23:06.941065Z","shell.execute_reply":"2023-07-06T07:23:06.969894Z"},"trusted":true},"execution_count":118,"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":"           AB  Class EJ          AF          AH          AM         AR  \\\n0    0.209377      1  B  3109.03329   85.200147   22.394407   8.138688   \n2    0.470030      0  B  2635.10654   85.200147   32.360553   8.138688   \n3    0.252107      0  B  3819.65177  120.201618   77.112203   8.138688   \n4    0.380297      1  B  3733.04844   85.200147   14.103738   8.138688   \n5    0.209377      0  A  2615.81430   85.200147    8.541526   8.138688   \n..        ...    ... ..         ...         ...         ...        ...   \n612  0.149555      0  A  3130.05946  123.763599    9.513984  13.020852   \n613  0.435846      0  B  5462.03438   85.200147   46.551007  15.973224   \n614  0.427300      0  A  2459.10720  130.138587   55.355778  10.005552   \n615  0.363205      0  B  1263.53524   85.200147   23.685856   8.138688   \n616  0.482849      0  A  2672.53426  546.663930  112.006102   8.138688   \n\n           AX        AY         AZ  ...         FI         FL        FR  \\\n0    0.699861  0.025578   9.812214  ...   3.583450   7.298162   1.73855   \n2    6.732840  0.025578  12.824570  ...  11.626917   7.709560   0.97556   \n3    3.685344  0.025578  11.053708  ...  14.852022   6.122162   0.49706   \n4    3.942255  0.054810   3.396778  ...  13.666727   8.153058  48.50134   \n5    4.013127  0.025578  12.547282  ...  10.981896   0.173229   0.49706   \n..        ...       ...        ...  ...        ...        ...       ...   \n612  3.499305  0.077343   8.545512  ...   9.879296   0.173229   1.26092   \n613  5.979825  0.025882  12.622906  ...  10.910227  10.223150   1.24236   \n614  8.070549  0.025578  15.408390  ...  12.029366   0.173229   0.49706   \n615  7.981959  0.025578   7.524588  ...   8.026928   9.256996   0.78764   \n616  3.198099  0.116928   3.396778  ...   7.745765   0.173229   1.14492   \n\n           FS         GB          GE            GF         GH          GI  \\\n0    0.094822  11.339138   72.611063   2003.810319  22.136229   69.834944   \n2    1.198821  37.077772   88.609437  13676.957810  28.022851   35.192676   \n3    0.284466  18.529584   82.416803   2094.262452  39.948656   90.493248   \n4    0.121914  16.408728  146.109943   8524.370502  45.381316   36.262628   \n5    1.164956  21.915512   72.611063  24177.595550  28.525186   82.527764   \n..        ...        ...         ...           ...        ...         ...   \n612  0.067730   8.967128  217.148554   8095.932828  24.640462   69.191944   \n613  0.426699  35.896418  496.994214   3085.308063  29.648928  124.808872   \n614  0.067730  19.962092  128.896894   6474.652866  26.166072  119.559420   \n615  0.670527  24.594488   72.611063   1965.343176  25.116750   37.155112   \n616  0.149006  13.673940   72.611063   6850.484442  45.745974  114.842372   \n\n            GL  \n0     0.120343  \n2     0.196941  \n3     0.155829  \n4     0.096614  \n5    21.978000  \n..         ...  \n612  21.978000  \n613   0.145340  \n614  21.978000  \n615   0.184622  \n616  21.978000  \n\n[551 rows x 57 columns]","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>AB</th>\n      <th>Class</th>\n      <th>EJ</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>...</th>\n      <th>FI</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.209377</td>\n      <td>1</td>\n      <td>B</td>\n      <td>3109.03329</td>\n      <td>85.200147</td>\n      <td>22.394407</td>\n      <td>8.138688</td>\n      <td>0.699861</td>\n      <td>0.025578</td>\n      <td>9.812214</td>\n      <td>...</td>\n      <td>3.583450</td>\n      <td>7.298162</td>\n      <td>1.73855</td>\n      <td>0.094822</td>\n      <td>11.339138</td>\n      <td>72.611063</td>\n      <td>2003.810319</td>\n      <td>22.136229</td>\n      <td>69.834944</td>\n      <td>0.120343</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.470030</td>\n      <td>0</td>\n      <td>B</td>\n      <td>2635.10654</td>\n      <td>85.200147</td>\n      <td>32.360553</td>\n      <td>8.138688</td>\n      <td>6.732840</td>\n      <td>0.025578</td>\n      <td>12.824570</td>\n      <td>...</td>\n      <td>11.626917</td>\n      <td>7.709560</td>\n      <td>0.97556</td>\n      <td>1.198821</td>\n      <td>37.077772</td>\n      <td>88.609437</td>\n      <td>13676.957810</td>\n      <td>28.022851</td>\n      <td>35.192676</td>\n      <td>0.196941</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.252107</td>\n      <td>0</td>\n      <td>B</td>\n      <td>3819.65177</td>\n      <td>120.201618</td>\n      <td>77.112203</td>\n      <td>8.138688</td>\n      <td>3.685344</td>\n      <td>0.025578</td>\n      <td>11.053708</td>\n      <td>...</td>\n      <td>14.852022</td>\n      <td>6.122162</td>\n      <td>0.49706</td>\n      <td>0.284466</td>\n      <td>18.529584</td>\n      <td>82.416803</td>\n      <td>2094.262452</td>\n      <td>39.948656</td>\n      <td>90.493248</td>\n      <td>0.155829</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.380297</td>\n      <td>1</td>\n      <td>B</td>\n      <td>3733.04844</td>\n      <td>85.200147</td>\n      <td>14.103738</td>\n      <td>8.138688</td>\n      <td>3.942255</td>\n      <td>0.054810</td>\n      <td>3.396778</td>\n      <td>...</td>\n      <td>13.666727</td>\n      <td>8.153058</td>\n      <td>48.50134</td>\n      <td>0.121914</td>\n      <td>16.408728</td>\n      <td>146.109943</td>\n      <td>8524.370502</td>\n      <td>45.381316</td>\n      <td>36.262628</td>\n      <td>0.096614</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.209377</td>\n      <td>0</td>\n      <td>A</td>\n      <td>2615.81430</td>\n      <td>85.200147</td>\n      <td>8.541526</td>\n      <td>8.138688</td>\n      <td>4.013127</td>\n      <td>0.025578</td>\n      <td>12.547282</td>\n      <td>...</td>\n      <td>10.981896</td>\n      <td>0.173229</td>\n      <td>0.49706</td>\n      <td>1.164956</td>\n      <td>21.915512</td>\n      <td>72.611063</td>\n      <td>24177.595550</td>\n      <td>28.525186</td>\n      <td>82.527764</td>\n      <td>21.978000</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>612</th>\n      <td>0.149555</td>\n      <td>0</td>\n      <td>A</td>\n      <td>3130.05946</td>\n      <td>123.763599</td>\n      <td>9.513984</td>\n      <td>13.020852</td>\n      <td>3.499305</td>\n      <td>0.077343</td>\n      <td>8.545512</td>\n      <td>...</td>\n      <td>9.879296</td>\n      <td>0.173229</td>\n      <td>1.26092</td>\n      <td>0.067730</td>\n      <td>8.967128</td>\n      <td>217.148554</td>\n      <td>8095.932828</td>\n      <td>24.640462</td>\n      <td>69.191944</td>\n      <td>21.978000</td>\n    </tr>\n    <tr>\n      <th>613</th>\n      <td>0.435846</td>\n      <td>0</td>\n      <td>B</td>\n      <td>5462.03438</td>\n      <td>85.200147</td>\n      <td>46.551007</td>\n      <td>15.973224</td>\n      <td>5.979825</td>\n      <td>0.025882</td>\n      <td>12.622906</td>\n      <td>...</td>\n      <td>10.910227</td>\n      <td>10.223150</td>\n      <td>1.24236</td>\n      <td>0.426699</td>\n      <td>35.896418</td>\n      <td>496.994214</td>\n      <td>3085.308063</td>\n      <td>29.648928</td>\n      <td>124.808872</td>\n      <td>0.145340</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>0.427300</td>\n      <td>0</td>\n      <td>A</td>\n      <td>2459.10720</td>\n      <td>130.138587</td>\n      <td>55.355778</td>\n      <td>10.005552</td>\n      <td>8.070549</td>\n      <td>0.025578</td>\n      <td>15.408390</td>\n      <td>...</td>\n      <td>12.029366</td>\n      <td>0.173229</td>\n      <td>0.49706</td>\n      <td>0.067730</td>\n      <td>19.962092</td>\n      <td>128.896894</td>\n      <td>6474.652866</td>\n      <td>26.166072</td>\n      <td>119.559420</td>\n      <td>21.978000</td>\n    </tr>\n    <tr>\n      <th>615</th>\n      <td>0.363205</td>\n      <td>0</td>\n      <td>B</td>\n      <td>1263.53524</td>\n      <td>85.200147</td>\n      <td>23.685856</td>\n      <td>8.138688</td>\n      <td>7.981959</td>\n      <td>0.025578</td>\n      <td>7.524588</td>\n      <td>...</td>\n      <td>8.026928</td>\n      <td>9.256996</td>\n      <td>0.78764</td>\n      <td>0.670527</td>\n      <td>24.594488</td>\n      <td>72.611063</td>\n      <td>1965.343176</td>\n      <td>25.116750</td>\n      <td>37.155112</td>\n      <td>0.184622</td>\n    </tr>\n    <tr>\n      <th>616</th>\n      <td>0.482849</td>\n      <td>0</td>\n      <td>A</td>\n      <td>2672.53426</td>\n      <td>546.663930</td>\n      <td>112.006102</td>\n      <td>8.138688</td>\n      <td>3.198099</td>\n      <td>0.116928</td>\n      <td>3.396778</td>\n      <td>...</td>\n      <td>7.745765</td>\n      <td>0.173229</td>\n      <td>1.14492</td>\n      <td>0.149006</td>\n      <td>13.673940</td>\n      <td>72.611063</td>\n      <td>6850.484442</td>\n      <td>45.745974</td>\n      <td>114.842372</td>\n      <td>21.978000</td>\n    </tr>\n  </tbody>\n</table>\n<p>551 rows × 57 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"outlier_df.columns.duplicated()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:25:13.766031Z","iopub.execute_input":"2023-07-06T07:25:13.766393Z","iopub.status.idle":"2023-07-06T07:25:13.774328Z","shell.execute_reply.started":"2023-07-06T07:25:13.766368Z","shell.execute_reply":"2023-07-06T07:25:13.772822Z"},"trusted":true},"execution_count":125,"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":"array([False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False])"},"metadata":{}}]},{"cell_type":"code","source":"new_df = outlier_df.copy()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:26:38.158997Z","iopub.execute_input":"2023-07-06T07:26:38.159321Z","iopub.status.idle":"2023-07-06T07:26:38.163584Z","shell.execute_reply.started":"2023-07-06T07:26:38.159300Z","shell.execute_reply":"2023-07-06T07:26:38.162807Z"},"trusted":true},"execution_count":126,"outputs":[]},{"cell_type":"code","source":"new_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:26:59.799200Z","iopub.execute_input":"2023-07-06T07:26:59.799511Z","iopub.status.idle":"2023-07-06T07:26:59.809900Z","shell.execute_reply.started":"2023-07-06T07:26:59.799490Z","shell.execute_reply":"2023-07-06T07:26:59.808316Z"},"trusted":true},"execution_count":128,"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":"AB        0\nClass     0\nEJ        0\nAF        0\nAH        0\nAM        0\nAR        0\nAX        0\nAY        0\nAZ        0\nBC        0\nBD        0\nBN        0\nBP        0\nBQ       52\nBR        0\nBZ        0\nCB        1\nCC        2\nCD        0\nCF        0\nCH        0\nCL        0\nCR        0\nCS        0\nCU        0\nCW        0\nDA        0\nDE        0\nDF        0\nDH        0\nDI        0\nDL        0\nDN        0\nDU        1\nDV        0\nDY        0\nEB        0\nEE        0\nEG        0\nEH        0\nEL       52\nEP        0\nEU        0\nFC        1\nFD        0\nFE        0\nFI        0\nFL        1\nFR        0\nFS        2\nGB        0\nGE        0\nGF        0\nGH        0\nGI        0\nGL        1\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"new_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:27:33.779233Z","iopub.execute_input":"2023-07-06T07:27:33.779622Z","iopub.status.idle":"2023-07-06T07:27:33.787326Z","shell.execute_reply.started":"2023-07-06T07:27:33.779596Z","shell.execute_reply":"2023-07-06T07:27:33.786673Z"},"trusted":true},"execution_count":130,"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":"AB       float64\nClass      int64\nEJ        object\nAF       float64\nAH       float64\nAM       float64\nAR       float64\nAX       float64\nAY       float64\nAZ       float64\nBC       float64\nBD       float64\nBN       float64\nBP       float64\nBQ       float64\nBR       float64\nBZ       float64\nCB       float64\nCC       float64\nCD       float64\nCF       float64\nCH       float64\nCL       float64\nCR       float64\nCS       float64\nCU       float64\nCW       float64\nDA       float64\nDE       float64\nDF       float64\nDH       float64\nDI       float64\nDL       float64\nDN       float64\nDU       float64\nDV       float64\nDY       float64\nEB       float64\nEE       float64\nEG       float64\nEH       float64\nEL       float64\nEP       float64\nEU       float64\nFC       float64\nFD       float64\nFE       float64\nFI       float64\nFL       float64\nFR       float64\nFS       float64\nGB       float64\nGE       float64\nGF       float64\nGH       float64\nGI       float64\nGL       float64\ndtype: object"},"metadata":{}}]},{"cell_type":"markdown","source":"We can skip the exploratory data analysis part as we don't know the meaning of features and it wil be reduntant in a way. Let's look at class distribution","metadata":{}},{"cell_type":"code","source":"new_df.Class.value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:39:20.075804Z","iopub.execute_input":"2023-07-06T07:39:20.076214Z","iopub.status.idle":"2023-07-06T07:39:20.083708Z","shell.execute_reply.started":"2023-07-06T07:39:20.076185Z","shell.execute_reply":"2023-07-06T07:39:20.082587Z"},"trusted":true},"execution_count":147,"outputs":[{"execution_count":147,"output_type":"execute_result","data":{"text/plain":"0    443\n1    108\nName: Class, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see we have class imbalance here","metadata":{}},{"cell_type":"code","source":"new_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:43:28.508356Z","iopub.execute_input":"2023-07-06T07:43:28.508725Z","iopub.status.idle":"2023-07-06T07:43:28.517832Z","shell.execute_reply.started":"2023-07-06T07:43:28.508701Z","shell.execute_reply":"2023-07-06T07:43:28.516620Z"},"trusted":true},"execution_count":148,"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":"AB        0\nClass     0\nEJ        0\nAF        0\nAH        0\nAM        0\nAR        0\nAX        0\nAY        0\nAZ        0\nBC        0\nBD        0\nBN        0\nBP        0\nBQ       52\nBR        0\nBZ        0\nCB        1\nCC        2\nCD        0\nCF        0\nCH        0\nCL        0\nCR        0\nCS        0\nCU        0\nCW        0\nDA        0\nDE        0\nDF        0\nDH        0\nDI        0\nDL        0\nDN        0\nDU        1\nDV        0\nDY        0\nEB        0\nEE        0\nEG        0\nEH        0\nEL       52\nEP        0\nEU        0\nFC        1\nFD        0\nFE        0\nFI        0\nFL        1\nFR        0\nFS        2\nGB        0\nGE        0\nGF        0\nGH        0\nGI        0\nGL        1\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"# let's first get rid of null values from the columns\nnew_df.fillna({\n     \n     \"BQ\": new_df[\"BQ\"].median(),\n     \"CB\": new_df[\"CB\"].median(),\n     \"CC\": new_df[\"CC\"].median(),\n     \"DU\": new_df[\"DU\"].median(),\n     \"EL\": new_df[\"EL\"].median(),\n     \"FC\": new_df[\"FC\"].median(),\n     \"FL\": new_df[\"FL\"].median(),\n     \"FS\": new_df[\"FS\"].median(),\n     \"GL\": new_df[\"GL\"].median(),\n }, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:48:26.302410Z","iopub.execute_input":"2023-07-06T07:48:26.302772Z","iopub.status.idle":"2023-07-06T07:48:26.317827Z","shell.execute_reply.started":"2023-07-06T07:48:26.302749Z","shell.execute_reply":"2023-07-06T07:48:26.316754Z"},"trusted":true},"execution_count":151,"outputs":[]},{"cell_type":"code","source":"new_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:48:34.660395Z","iopub.execute_input":"2023-07-06T07:48:34.660744Z","iopub.status.idle":"2023-07-06T07:48:34.674027Z","shell.execute_reply.started":"2023-07-06T07:48:34.660721Z","shell.execute_reply":"2023-07-06T07:48:34.672461Z"},"trusted":true},"execution_count":152,"outputs":[{"execution_count":152,"output_type":"execute_result","data":{"text/plain":"AB       0\nClass    0\nEJ       0\nAF       0\nAH       0\nAM       0\nAR       0\nAX       0\nAY       0\nAZ       0\nBC       0\nBD       0\nBN       0\nBP       0\nBQ       0\nBR       0\nBZ       0\nCB       0\nCC       0\nCD       0\nCF       0\nCH       0\nCL       0\nCR       0\nCS       0\nCU       0\nCW       0\nDA       0\nDE       0\nDF       0\nDH       0\nDI       0\nDL       0\nDN       0\nDU       0\nDV       0\nDY       0\nEB       0\nEE       0\nEG       0\nEH       0\nEL       0\nEP       0\nEU       0\nFC       0\nFD       0\nFE       0\nFI       0\nFL       0\nFR       0\nFS       0\nGB       0\nGE       0\nGF       0\nGH       0\nGI       0\nGL       0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"new_df[\"EJ\"]","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:49:03.682469Z","iopub.execute_input":"2023-07-06T07:49:03.682799Z","iopub.status.idle":"2023-07-06T07:49:03.691039Z","shell.execute_reply.started":"2023-07-06T07:49:03.682777Z","shell.execute_reply":"2023-07-06T07:49:03.689735Z"},"trusted":true},"execution_count":153,"outputs":[{"execution_count":153,"output_type":"execute_result","data":{"text/plain":"0      B\n2      B\n3      B\n4      B\n5      A\n      ..\n612    A\n613    B\n614    A\n615    B\n616    A\nName: EJ, Length: 551, dtype: object"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nnew_df[\"EJ\"] = LabelEncoder().fit_transform(new_df[\"EJ\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:50:48.409071Z","iopub.execute_input":"2023-07-06T07:50:48.409438Z","iopub.status.idle":"2023-07-06T07:50:48.415571Z","shell.execute_reply.started":"2023-07-06T07:50:48.409411Z","shell.execute_reply":"2023-07-06T07:50:48.414262Z"},"trusted":true},"execution_count":154,"outputs":[]},{"cell_type":"code","source":"new_df[\"EJ\"]","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:51:21.657381Z","iopub.execute_input":"2023-07-06T07:51:21.657810Z","iopub.status.idle":"2023-07-06T07:51:21.665892Z","shell.execute_reply.started":"2023-07-06T07:51:21.657723Z","shell.execute_reply":"2023-07-06T07:51:21.664641Z"},"trusted":true},"execution_count":156,"outputs":[{"execution_count":156,"output_type":"execute_result","data":{"text/plain":"0      1\n2      1\n3      1\n4      1\n5      0\n      ..\n612    0\n613    1\n614    0\n615    1\n616    0\nName: EJ, Length: 551, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"new_df.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T07:51:30.648907Z","iopub.execute_input":"2023-07-06T07:51:30.649299Z","iopub.status.idle":"2023-07-06T07:51:30.655084Z","shell.execute_reply.started":"2023-07-06T07:51:30.649268Z","shell.execute_reply":"2023-07-06T07:51:30.654398Z"},"trusted":true},"execution_count":157,"outputs":[{"execution_count":157,"output_type":"execute_result","data":{"text/plain":"(551, 57)"},"metadata":{}}]},{"cell_type":"code","source":"X = new_df.drop([\"Class\"], axis = 1)\ny = new_df[\"Class\"]","metadata":{"execution":{"iopub.status.busy":"2023-07-06T08:18:16.749723Z","iopub.execute_input":"2023-07-06T08:18:16.750090Z","iopub.status.idle":"2023-07-06T08:18:16.755640Z","shell.execute_reply.started":"2023-07-06T08:18:16.750062Z","shell.execute_reply":"2023-07-06T08:18:16.754788Z"},"trusted":true},"execution_count":159,"outputs":[]},{"cell_type":"code","source":"X.shape, y.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T08:18:25.802944Z","iopub.execute_input":"2023-07-06T08:18:25.803286Z","iopub.status.idle":"2023-07-06T08:18:25.812317Z","shell.execute_reply.started":"2023-07-06T08:18:25.803261Z","shell.execute_reply":"2023-07-06T08:18:25.811474Z"},"trusted":true},"execution_count":160,"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":"((551, 56), (551,))"},"metadata":{}}]},{"cell_type":"markdown","source":"Let's create training and validation set first","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.1, random_state = 42)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T08:32:57.611102Z","iopub.execute_input":"2023-07-06T08:32:57.611457Z","iopub.status.idle":"2023-07-06T08:32:57.745346Z","shell.execute_reply.started":"2023-07-06T08:32:57.611431Z","shell.execute_reply":"2023-07-06T08:32:57.744534Z"},"trusted":true},"execution_count":169,"outputs":[]},{"cell_type":"code","source":"X_train.shape, X_val.shape, y_train.shape, y_val.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T09:25:44.551400Z","iopub.execute_input":"2023-07-06T09:25:44.552573Z","iopub.status.idle":"2023-07-06T09:25:44.558748Z","shell.execute_reply.started":"2023-07-06T09:25:44.552543Z","shell.execute_reply":"2023-07-06T09:25:44.557852Z"},"trusted":true},"execution_count":173,"outputs":[{"execution_count":173,"output_type":"execute_result","data":{"text/plain":"((495, 56), (56, 56), (495,), (56,))"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nX_train = StandardScaler().fit_transform(X_train)\nX_val = StandardScaler().fit_transform(X_val)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T09:58:08.378670Z","iopub.execute_input":"2023-07-06T09:58:08.379006Z","iopub.status.idle":"2023-07-06T09:58:08.385568Z","shell.execute_reply.started":"2023-07-06T09:58:08.378983Z","shell.execute_reply":"2023-07-06T09:58:08.384774Z"},"trusted":true},"execution_count":187,"outputs":[]},{"cell_type":"code","source":"X_modified = StandardScaler().fit_transform(X)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:19:40.783734Z","iopub.execute_input":"2023-07-06T10:19:40.784074Z","iopub.status.idle":"2023-07-06T10:19:40.795656Z","shell.execute_reply.started":"2023-07-06T10:19:40.784050Z","shell.execute_reply":"2023-07-06T10:19:40.794141Z"},"trusted":true},"execution_count":205,"outputs":[]},{"cell_type":"markdown","source":"## Training Our Model","metadata":{}},{"cell_type":"markdown","source":"### Logistic Regression","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\n\nlog_reg = LogisticRegression(class_weight = \"balanced\")\n\nmodel1 = log_reg.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T09:58:11.953661Z","iopub.execute_input":"2023-07-06T09:58:11.953993Z","iopub.status.idle":"2023-07-06T09:58:11.976413Z","shell.execute_reply.started":"2023-07-06T09:58:11.953971Z","shell.execute_reply":"2023-07-06T09:58:11.975334Z"},"trusted":true},"execution_count":188,"outputs":[]},{"cell_type":"code","source":"y_pred_1 = model1.predict(X_val)\ny_pred_1","metadata":{"execution":{"iopub.status.busy":"2023-07-06T09:59:46.264134Z","iopub.execute_input":"2023-07-06T09:59:46.264450Z","iopub.status.idle":"2023-07-06T09:59:46.272661Z","shell.execute_reply.started":"2023-07-06T09:59:46.264428Z","shell.execute_reply":"2023-07-06T09:59:46.270875Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1])"},"metadata":{}}]},{"cell_type":"code","source":"score_1 = model1.score(X_val, y_val)\nscore_1","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:01:43.157848Z","iopub.execute_input":"2023-07-06T10:01:43.158200Z","iopub.status.idle":"2023-07-06T10:01:43.166018Z","shell.execute_reply.started":"2023-07-06T10:01:43.158176Z","shell.execute_reply":"2023-07-06T10:01:43.165082Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":"0.8035714285714286"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_val, y_pred_1))\nroc_auc_score(y_val, y_pred_1)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:48:50.609079Z","iopub.execute_input":"2023-07-06T11:48:50.609424Z","iopub.status.idle":"2023-07-06T11:48:50.628325Z","shell.execute_reply.started":"2023-07-06T11:48:50.609401Z","shell.execute_reply":"2023-07-06T11:48:50.627217Z"},"trusted":true},"execution_count":276,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.97      0.80      0.88        49\n           1       0.38      0.86      0.52         7\n\n    accuracy                           0.80        56\n   macro avg       0.68      0.83      0.70        56\nweighted avg       0.90      0.80      0.83        56\n\n","output_type":"stream"},{"execution_count":276,"output_type":"execute_result","data":{"text/plain":"0.826530612244898"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\ncm = confusion_matrix(y_val, y_pred_1)\nConfusionMatrixDisplay(cm, display_labels = [False, True]).plot();","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:29:52.948984Z","iopub.execute_input":"2023-07-06T12:29:52.949850Z","iopub.status.idle":"2023-07-06T12:29:53.165944Z","shell.execute_reply.started":"2023-07-06T12:29:52.949825Z","shell.execute_reply":"2023-07-06T12:29:53.164888Z"},"trusted":true},"execution_count":301,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Cross - val","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import cross_val_score\nscores = cross_val_score(log_reg, X_modified, y, cv = 5, scoring='roc_auc')\nscores.mean()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:01.114434Z","iopub.execute_input":"2023-07-06T12:05:01.114807Z","iopub.status.idle":"2023-07-06T12:05:01.208098Z","shell.execute_reply.started":"2023-07-06T12:05:01.114783Z","shell.execute_reply":"2023-07-06T12:05:01.207026Z"},"trusted":true},"execution_count":281,"outputs":[{"execution_count":281,"output_type":"execute_result","data":{"text/plain":"0.8845265179151799"},"metadata":{}}]},{"cell_type":"markdown","source":"### Support Vector Machine","metadata":{}},{"cell_type":"code","source":"from sklearn.svm import SVC\n\nsvc = SVC(class_weight = \"balanced\", probability=True)\n\nmodel2 = svc.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:53:21.549667Z","iopub.execute_input":"2023-07-06T12:53:21.550242Z","iopub.status.idle":"2023-07-06T12:53:21.599146Z","shell.execute_reply.started":"2023-07-06T12:53:21.550213Z","shell.execute_reply":"2023-07-06T12:53:21.598213Z"},"trusted":true},"execution_count":325,"outputs":[]},{"cell_type":"code","source":"y_pred_2 = model2.predict(X_val)\ny_pred_2","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:53:24.959840Z","iopub.execute_input":"2023-07-06T12:53:24.960363Z","iopub.status.idle":"2023-07-06T12:53:24.969505Z","shell.execute_reply.started":"2023-07-06T12:53:24.960336Z","shell.execute_reply":"2023-07-06T12:53:24.967955Z"},"trusted":true},"execution_count":326,"outputs":[{"execution_count":326,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0,\n       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1])"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_val, y_pred_2))\n\nroc_auc_score(y_val, y_pred_2)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:53:28.069480Z","iopub.execute_input":"2023-07-06T12:53:28.069906Z","iopub.status.idle":"2023-07-06T12:53:28.088458Z","shell.execute_reply.started":"2023-07-06T12:53:28.069875Z","shell.execute_reply":"2023-07-06T12:53:28.086922Z"},"trusted":true},"execution_count":327,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.98      0.82      0.89        49\n           1       0.40      0.86      0.55         7\n\n    accuracy                           0.82        56\n   macro avg       0.69      0.84      0.72        56\nweighted avg       0.90      0.82      0.85        56\n\n","output_type":"stream"},{"execution_count":327,"output_type":"execute_result","data":{"text/plain":"0.8367346938775511"},"metadata":{}}]},{"cell_type":"code","source":"cm_2 = confusion_matrix(y_val, y_pred_2)\nConfusionMatrixDisplay(cm_2, display_labels = [False, True]).plot();","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:53:32.919877Z","iopub.execute_input":"2023-07-06T12:53:32.920224Z","iopub.status.idle":"2023-07-06T12:53:33.150771Z","shell.execute_reply.started":"2023-07-06T12:53:32.920196Z","shell.execute_reply":"2023-07-06T12:53:33.149599Z"},"trusted":true},"execution_count":328,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"In additon to AUC score it is also performing well.","metadata":{}},{"cell_type":"code","source":"score_2 = model2.score(X_val, y_val)\nscore_2","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:53:42.879608Z","iopub.execute_input":"2023-07-06T12:53:42.879976Z","iopub.status.idle":"2023-07-06T12:53:42.889703Z","shell.execute_reply.started":"2023-07-06T12:53:42.879952Z","shell.execute_reply":"2023-07-06T12:53:42.888272Z"},"trusted":true},"execution_count":329,"outputs":[{"execution_count":329,"output_type":"execute_result","data":{"text/plain":"0.8214285714285714"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Cross Val","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(svc, X_modified, y, cv =5, scoring='roc_auc')","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:15.273354Z","iopub.execute_input":"2023-07-06T12:05:15.274117Z","iopub.status.idle":"2023-07-06T12:05:15.339827Z","shell.execute_reply.started":"2023-07-06T12:05:15.274064Z","shell.execute_reply":"2023-07-06T12:05:15.338203Z"},"trusted":true},"execution_count":282,"outputs":[]},{"cell_type":"code","source":"scores.mean()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:18.782010Z","iopub.execute_input":"2023-07-06T12:05:18.782331Z","iopub.status.idle":"2023-07-06T12:05:18.789453Z","shell.execute_reply.started":"2023-07-06T12:05:18.782308Z","shell.execute_reply":"2023-07-06T12:05:18.788498Z"},"trusted":true},"execution_count":283,"outputs":[{"execution_count":283,"output_type":"execute_result","data":{"text/plain":"0.9223725287310579"},"metadata":{}}]},{"cell_type":"markdown","source":"## SGD ","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import SGDClassifier\n\nsgd = SGDClassifier(class_weight = \"balanced\")\n\nmodel3 = sgd.fit(X_train, y_train)\ny_pred_3 = model3.predict(X_val)\ny_pred_3","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:30:53.578604Z","iopub.execute_input":"2023-07-06T10:30:53.578938Z","iopub.status.idle":"2023-07-06T10:30:53.591915Z","shell.execute_reply.started":"2023-07-06T10:30:53.578914Z","shell.execute_reply":"2023-07-06T10:30:53.591073Z"},"trusted":true},"execution_count":220,"outputs":[{"execution_count":220,"output_type":"execute_result","data":{"text/plain":"array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n       1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1])"},"metadata":{}}]},{"cell_type":"code","source":"score_3 = model3.score(X_val, y_val)\nscore_3","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:31:18.544598Z","iopub.execute_input":"2023-07-06T10:31:18.545102Z","iopub.status.idle":"2023-07-06T10:31:18.550576Z","shell.execute_reply.started":"2023-07-06T10:31:18.545079Z","shell.execute_reply":"2023-07-06T10:31:18.549978Z"},"trusted":true},"execution_count":221,"outputs":[{"execution_count":221,"output_type":"execute_result","data":{"text/plain":"0.75"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_val, y_pred_3))\n\nroc_auc_score(y_val, y_pred_3)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:50:07.798976Z","iopub.execute_input":"2023-07-06T11:50:07.799315Z","iopub.status.idle":"2023-07-06T11:50:07.823132Z","shell.execute_reply.started":"2023-07-06T11:50:07.799291Z","shell.execute_reply":"2023-07-06T11:50:07.821273Z"},"trusted":true},"execution_count":278,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.93      0.88      0.91        49\n           1       0.40      0.57      0.47         7\n\n    accuracy                           0.84        56\n   macro avg       0.67      0.72      0.69        56\nweighted avg       0.87      0.84      0.85        56\n\n","output_type":"stream"},{"execution_count":278,"output_type":"execute_result","data":{"text/plain":"0.7244897959183673"},"metadata":{}}]},{"cell_type":"code","source":"cm_3 = confusion_matrix(y_val, y_pred_3)\nConfusionMatrixDisplay(cm_3, display_labels = [False, True]).plot();","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:32:28.590697Z","iopub.execute_input":"2023-07-06T12:32:28.591032Z","iopub.status.idle":"2023-07-06T12:32:28.811222Z","shell.execute_reply.started":"2023-07-06T12:32:28.591008Z","shell.execute_reply":"2023-07-06T12:32:28.810378Z"},"trusted":true},"execution_count":303,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Cross Val","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(sgd, X_modified, y, cv = 5, scoring='roc_auc')\nscores.mean()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:32.754735Z","iopub.execute_input":"2023-07-06T12:05:32.755264Z","iopub.status.idle":"2023-07-06T12:05:32.788396Z","shell.execute_reply.started":"2023-07-06T12:05:32.755235Z","shell.execute_reply":"2023-07-06T12:05:32.787399Z"},"trusted":true},"execution_count":284,"outputs":[{"execution_count":284,"output_type":"execute_result","data":{"text/plain":"0.8601226403831103"},"metadata":{}}]},{"cell_type":"markdown","source":"### Decision Tree Classifier","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeClassifier\n\ndt = DecisionTreeClassifier(class_weight = \"balanced\")\n\nmodel4 = dt.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:34:24.600398Z","iopub.execute_input":"2023-07-06T10:34:24.600741Z","iopub.status.idle":"2023-07-06T10:34:24.789044Z","shell.execute_reply.started":"2023-07-06T10:34:24.600719Z","shell.execute_reply":"2023-07-06T10:34:24.787925Z"},"trusted":true},"execution_count":224,"outputs":[{"execution_count":224,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_4 = model4.predict(X_val)\ny_pred_4","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:34:46.664024Z","iopub.execute_input":"2023-07-06T10:34:46.664342Z","iopub.status.idle":"2023-07-06T10:34:46.672096Z","shell.execute_reply.started":"2023-07-06T10:34:46.664320Z","shell.execute_reply":"2023-07-06T10:34:46.670765Z"},"trusted":true},"execution_count":225,"outputs":[{"execution_count":225,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_val, y_pred_4))\n\nroc_auc_score(y_val, y_pred_4)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:50:22.840583Z","iopub.execute_input":"2023-07-06T11:50:22.840989Z","iopub.status.idle":"2023-07-06T11:50:22.858689Z","shell.execute_reply.started":"2023-07-06T11:50:22.840961Z","shell.execute_reply":"2023-07-06T11:50:22.857814Z"},"trusted":true},"execution_count":279,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.93      0.88      0.91        49\n           1       0.40      0.57      0.47         7\n\n    accuracy                           0.84        56\n   macro avg       0.67      0.72      0.69        56\nweighted avg       0.87      0.84      0.85        56\n\n","output_type":"stream"},{"execution_count":279,"output_type":"execute_result","data":{"text/plain":"0.7244897959183673"},"metadata":{}}]},{"cell_type":"code","source":"cm_4 = confusion_matrix(y_val, y_pred_4)\nConfusionMatrixDisplay(cm_4, display_labels = [False, True]).plot();","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:33:07.868348Z","iopub.execute_input":"2023-07-06T12:33:07.868941Z","iopub.status.idle":"2023-07-06T12:33:08.091263Z","shell.execute_reply.started":"2023-07-06T12:33:07.868917Z","shell.execute_reply":"2023-07-06T12:33:08.090444Z"},"trusted":true},"execution_count":305,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"score_4 = model4.score(X_val, y_val)\nscore_4","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:35:11.980073Z","iopub.execute_input":"2023-07-06T10:35:11.980460Z","iopub.status.idle":"2023-07-06T10:35:11.989102Z","shell.execute_reply.started":"2023-07-06T10:35:11.980429Z","shell.execute_reply":"2023-07-06T10:35:11.987746Z"},"trusted":true},"execution_count":227,"outputs":[{"execution_count":227,"output_type":"execute_result","data":{"text/plain":"0.8392857142857143"},"metadata":{}}]},{"cell_type":"markdown","source":"## Cross Val","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(dt, X_modified, y, cv = 5, scoring='roc_auc')\nscores.mean()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:45.209196Z","iopub.execute_input":"2023-07-06T12:05:45.209822Z","iopub.status.idle":"2023-07-06T12:05:45.293696Z","shell.execute_reply.started":"2023-07-06T12:05:45.209796Z","shell.execute_reply":"2023-07-06T12:05:45.293060Z"},"trusted":true},"execution_count":285,"outputs":[{"execution_count":285,"output_type":"execute_result","data":{"text/plain":"0.7478908264020623"},"metadata":{}}]},{"cell_type":"markdown","source":"### RandomForestClassifier","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\n\nrfc = RandomForestClassifier(class_weight = \"balanced\")\n\nmodel5 = rfc.fit(X_train, y_train)\n\ny_pred_5 = model5.predict(X_val)\ny_pred_5","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:38:03.839436Z","iopub.execute_input":"2023-07-06T10:38:03.839805Z","iopub.status.idle":"2023-07-06T10:38:04.207309Z","shell.execute_reply.started":"2023-07-06T10:38:03.839782Z","shell.execute_reply":"2023-07-06T10:38:04.206252Z"},"trusted":true},"execution_count":229,"outputs":[{"execution_count":229,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"score_5 = model5.score(X_val, y_val)\nscore_5","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:38:30.763904Z","iopub.execute_input":"2023-07-06T10:38:30.764919Z","iopub.status.idle":"2023-07-06T10:38:30.787237Z","shell.execute_reply.started":"2023-07-06T10:38:30.764893Z","shell.execute_reply":"2023-07-06T10:38:30.786232Z"},"trusted":true},"execution_count":231,"outputs":[{"execution_count":231,"output_type":"execute_result","data":{"text/plain":"0.875"},"metadata":{}}]},{"cell_type":"code","source":"print(classification_report(y_val, y_pred_5))\n\nroc_auc_score(y_val, y_pred_5)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:50:49.599268Z","iopub.execute_input":"2023-07-06T11:50:49.599620Z","iopub.status.idle":"2023-07-06T11:50:49.618282Z","shell.execute_reply.started":"2023-07-06T11:50:49.599588Z","shell.execute_reply":"2023-07-06T11:50:49.616812Z"},"trusted":true},"execution_count":280,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.94      0.92      0.93        49\n           1       0.50      0.57      0.53         7\n\n    accuracy                           0.88        56\n   macro avg       0.72      0.74      0.73        56\nweighted avg       0.88      0.88      0.88        56\n\n","output_type":"stream"},{"execution_count":280,"output_type":"execute_result","data":{"text/plain":"0.7448979591836735"},"metadata":{}}]},{"cell_type":"code","source":"cm_5 = confusion_matrix(y_val, y_pred_5)\nConfusionMatrixDisplay(cm_5, display_labels = [False, True]).plot();","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:34:01.439855Z","iopub.execute_input":"2023-07-06T12:34:01.440397Z","iopub.status.idle":"2023-07-06T12:34:01.666996Z","shell.execute_reply.started":"2023-07-06T12:34:01.440364Z","shell.execute_reply":"2023-07-06T12:34:01.665973Z"},"trusted":true},"execution_count":306,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAgwAAAG2CAYAAAATP96BAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5NElEQVR4nO3dfVxUdd7/8ffgzYDIYKTcmISk4k14l5Q3W4mVKZU/EytLr01bdTM1Y93UTS91XFdIW827lczdRWp11b263UzTXVMvcy0gLVNyNdFok3RdDUFBYM7vD3KuJrBhmAMM+Ho+Hufx4HzPOd/zGdPmw+f7Pd9jMQzDEAAAwI/wq+sAAACA7yNhAAAAbpEwAAAAt0gYAACAWyQMAADALRIGAADgFgkDAABwi4QBAAC4RcIAAADcImEAAABukTAAANBApKSkyGKxKCkpydk2ZswYWSwWl61Pnz4e993YxDgBAEAdycjI0Msvv6xu3bpVODZ48GClpaU595s2bepx/1QYAACo5woKCjRq1CitWbNG1113XYXjVqtV4eHhzi0kJMTje1BhqAKHw6Gvv/5aQUFBslgsdR0OAMBDhmHowoULat26tfz8au535aKiIl2+fNnrfgzDqPB9Y7VaZbVaKz1/0qRJuv/++3XPPffoN7/5TYXjO3fuVGhoqFq0aKH+/ftrwYIFCg0N9SgmEoYq+PrrrxUZGVnXYQAAvJSbm6s2bdrUSN9FRUWKjmquvNNlXvfVvHlzFRQUuLTNnTtXdru9wrkbNmzQxx9/rIyMjEr7SkhI0MMPP6yoqCjl5ORo9uzZuuuuu5SVlXXVBKQyJAxVEBQUJEk6+XFb2ZozioOGaXi3uLoOAagxpUaJdhe/4fz/eU24fPmy8k6X6WRWW9mCqv9dkX/BoaheJ5SbmyubzeZsr+zLPTc3V88884y2bdsmf3//SvsbMWKE8+fY2FjFxcUpKipKmzdvVmJiYpXjImGogitlIVtzP6/+EgC+rLHF80lQQH1TG8PKzYMsah5U/fs49N13js3mkjBUJisrS6dPn1avXr2cbWVlZdq9e7dWrlyp4uJiNWrUyOWaiIgIRUVF6ejRox7FRcIAAICJygyHygzvrq+qu+++WwcPHnRpe+KJJ9SpUyfNmDGjQrIgSWfPnlVubq4iIiI8iouEAQAAEzlkyKHqZwyeXBsUFKTY2FiXtsDAQF1//fWKjY1VQUGB7Ha7hg8froiICJ04cUIzZ85Uy5YtNWzYMI/iImEAAKCBatSokQ4ePKhXXnlF58+fV0REhAYMGKCNGzd6PJ+DhAEAABM55FDVBxUqv94bO3fudP4cEBCg9957z6v+riBhAADARGWGoTKj+kMS3lxbk5jyDwAA3KLCAACAiWpz0mNtImEAAMBEDhkqa4AJA0MSAADALSoMAACYiCEJAADgFk9JAACAaxYVBgAATOT4bvPmel9EwgAAgInKvHxKwptraxIJAwAAJioz5OXbKs2LxUzMYQAAAG5RYQAAwETMYQAAAG45ZFGZLF5d74sYkgAAAG5RYQAAwEQOo3zz5npfRMIAAICJyrwckvDm2prEkAQAAHCLCgMAACZqqBUGEgYAAEzkMCxyGF48JeHFtTWJIQkAAOAWFQYAAEzEkAQAAHCrTH4q86KAX2ZiLGYiYQAAwESGl3MYDOYwAACA+ooKAwAAJmIOAwAAcKvM8FOZ4cUcBh9dGpohCQAA4BYVBgAATOSQRQ4vfh93yDdLDCQMAACYqKHOYWBIAgAAuEWFAQAAE3k/6ZEhCQAAGrzyOQxevHyKIQkAAFCTUlJSZLFYlJSU5GwzDEN2u12tW7dWQECA4uPjdejQIY/7JmEAAMBEju/eJVHdrbpPWGRkZOjll19Wt27dXNoXLVqkJUuWaOXKlcrIyFB4eLgGDhyoCxcueNQ/CQMAACa6MofBm81TBQUFGjVqlNasWaPrrrvO2W4YhpYuXapZs2YpMTFRsbGxSk9P18WLF7V+/XqP7kHCAACAiRzfVQm82SQpPz/fZSsuLr7qPSdNmqT7779f99xzj0t7Tk6O8vLydO+99zrbrFar+vfvr71793r0uUgYAADwQZGRkQoODnZuKSkplZ63YcMGffzxx5Uez8vLkySFhYW5tIeFhTmPVRVPSQAAYKIyw6IyL15RfeXa3Nxc2Ww2Z7vVaq1wbm5urp555hlt27ZN/v7+V+3TYnGNxzCMCm3ukDAAAGCiK5MXq399+ToMNpvNJWGoTFZWlk6fPq1evXr93/VlZdq9e7dWrlypI0eOSCqvNERERDjPOX36dIWqgzsMSQAAUE/dfffdOnjwoA4cOODc4uLiNGrUKB04cEA33XSTwsPDtX37duc1ly9f1q5du9SvXz+P7kWFAQAAEzkMPzm8WOnR4cFKj0FBQYqNjXVpCwwM1PXXX+9sT0pKUnJysjp06KAOHTooOTlZzZo108iRIz2Ki4QBAAATmTUkYZbp06fr0qVLmjhxos6dO6fevXtr27ZtCgoK8qgfEgYAABqQnTt3uuxbLBbZ7XbZ7Xav+iVhAADARA7Jq6ckHOaFYioSBgAATOTwYnnnK9f7It+MCgAA+BQqDAAAmKi674P4/vW+iIQBAAATOWSRQ97MYaj+tTWJhAEAABM11AqDb0YFAAB8ChUGAABM5P3CTb75uzwJAwAAJnIYFjm8WYfBi2trkm+mMQAAwKdQYQAAwEQOL4ckfHXhJhIGAABM5P3bKn0zYfDNqAAAgE+hwgAAgInKZFGZF4sveXNtTSJhAADARAxJAACAaxYVBgAATFQm74YVyswLxVQkDAAAmKihDkmQMAAAYCJePgUAAK5ZVBgAADCRIYscXsxhMHisEgCAho8hCQAAcM2iwgAAgIka6uutSRgAADBRmZdvq/Tm2prkm1EBAACfQoUBAAATMSQBAADccshPDi8K+N5cW5N8MyoAAOBTqDAAAGCiMsOiMi+GFby5tiaRMAAAYCLmMAAAALcML99WabDSIwAAqK9IGAAAMFGZLF5vnkhNTVW3bt1ks9lks9nUt29fbdmyxXl8zJgxslgsLlufPn08/lwMSQAAYCKH4d08BIfh2flt2rTR888/r/bt20uS0tPTNXToUO3fv18333yzJGnw4MFKS0tzXtO0aVOP4yJhAACgHhsyZIjL/oIFC5Samqp9+/Y5Ewar1arw8HCv7lMvhyTWrl2rFi1a1HUYMNGGFaEa1LqHUufc4Gz7bdKNGtS6h8v2zAMd6jBKwFyPPPUvbTn+oZ6cfbKuQ4GJHN9NevRmk6T8/HyXrbi42O29y8rKtGHDBhUWFqpv377O9p07dyo0NFQxMTEaP368Tp8+7fHnqtMKw5gxY5Senl6h/ejRo87SChq+IwcC9O6frld0l0sVjsUNyNcvX/zSud+4iYe1OsBHxXQrUMKjZ3Q8u1ldhwKTOWSRw8N5CD+8XpIiIyNd2ufOnSu73V7pNQcPHlTfvn1VVFSk5s2b64033lCXLl0kSQkJCXr44YcVFRWlnJwczZ49W3fddZeysrJktVqrHFedD0n8cFxFklq1alVH0aC2XSr008LJUUp6IVd/XlaxXNakqaGQ0NI6iAyoOf7NyjTtxS+0bGa0Hpv0r7oOBz4qNzdXNpvNuf9jX+4dO3bUgQMHdP78eb322msaPXq0du3apS5dumjEiBHO82JjYxUXF6eoqCht3rxZiYmJVY6nzockroyrfH9btmyZunbtqsDAQEVGRmrixIkqKCi4ah+ffPKJBgwYoKCgINlsNvXq1UuZmZnO43v37tWdd96pgIAARUZGasqUKSosLKyNjwc3Vs5so9vuztctd1b+3/fTfzTXI11v1s9u76QXn43U+X/XeY4LeG3SvBPKeL+FDnwQXNehoAZcWenRm02S86mHK9uPJQxNmzZV+/btFRcXp5SUFHXv3l3Lli2r9NyIiAhFRUXp6NGjHn2uOk8YKuPn56fly5frs88+U3p6unbs2KHp06df9fxRo0apTZs2ysjIUFZWln71q1+pSZMmksrLNIMGDVJiYqI+/fRTbdy4UXv27NHkyZNr6+PgKna+2ULHDgboZ8+dqvR43IB8zVh5Uov+8oV+Pudr/fNAM01/uJ0uF/vmKmhAVfR/4KzaxRYqbVGk+5NRL5k1h8EbhmFcdc7D2bNnlZubq4iICI/6rPNf19555x01b97cuZ+QkKC//OUvzv3o6GjNnz9fTz31lFatWlVpH19++aWmTZumTp06SZI6dPi/iXEvvPCCRo4cqaSkJOex5cuXq3///kpNTZW/v3+F/oqLi13+oPPz8736jKjo9L+aKHXODUr+8xdq6l/5vIT4oeedP7ftVKQO3S/q8du66KO/23T7fd/WUqSAeVpGFOvJOSc06/FOKrnsk7+voR6aOXOmEhISFBkZqQsXLmjDhg3auXOntm7dqoKCAtntdg0fPlwRERE6ceKEZs6cqZYtW2rYsGEe3afOE4YBAwYoNTXVuR8YGKj3339fycnJOnz4sPLz81VaWqqioiIVFhYqMDCwQh9Tp07VuHHj9Oqrr+qee+7Rww8/rHbt2kmSsrKydOzYMa1bt855vmEYcjgcysnJUefOnSv0l5KSonnz5tXAp8UVxz5tpvP/bqLJgzs62xxlFh3cF6i301rqnROfqFEj12uuDytVaJsS/et41SfpAL6kQ2yhrmtZqhVvf+Zsa9RYir3tgob8NE//r9NtcjiooNV3Dnn5LgkPJ0x+8803+ulPf6pTp04pODhY3bp109atWzVw4EBdunRJBw8e1CuvvKLz588rIiJCAwYM0MaNGxUUFOTRfeo8YQgMDHR5IuLkyZO67777NGHCBM2fP18hISHas2ePxo4dq5KSkkr7sNvtGjlypDZv3qwtW7Zo7ty52rBhg4YNGyaHw6Enn3xSU6ZMqXDdjTfeWGl/zz33nKZOnercz8/PrzBbFd7pcccFrd7xuUvb4l/cqMj2RXpk0ukKyYIk5f+nkc583UQhYZX/PQB83YG9wZowuKtL29RFx5X7hb/+sro1yUIDYXj5lITh4bV/+MMfrnosICBA7733XrVj+b46Txh+KDMzU6WlpVq8eLH8/MpLdps2bXJ7XUxMjGJiYvSLX/xCjz32mNLS0jRs2DDdcsstOnTokEePaVqtVo8eNYHnmjV3qG2nIpc2/2YOBV1XpradinSp0E+v/jZct99/XiFhpfomt6nSUiIUHFKqnyQwHIH66VJhI538p+tjlEUX/XThfJMK7ai/eFtlLWnXrp1KS0u1YsUKDRkyRB988IFeeumlq55/6dIlTZs2TQ899JCio6P11VdfKSMjQ8OHD5ckzZgxQ3369NGkSZM0fvx4BQYGKjs7W9u3b9eKFStq62PBQ35+hk587q+//U+0CvMbKSS0VN1/UqCZL51Qs+aOug4PAK45Ppcw9OjRQ0uWLNHChQv13HPP6c4771RKSooef/zxSs9v1KiRzp49q8cff1zffPONWrZsqcTEROcchG7dumnXrl2aNWuW7rjjDhmGoXbt2rk8lwrf8MJrx5w/WwMMJf/5eB1GA9SOGSO71HUIMJm3TzqY8ZRETbAYhsHSeW7k5+crODhY5/55k2xBvvkfEvBWwk2ev70OqC9KjcvaUbRJ3377rctiSGa68l0xdNvP1CTQ85c7XVFSeFlv3fvHGo21Ovj2AwAAbvnckAQAAPWZWe+S8DUkDAAAmKihPiXBkAQAAHCLCgMAACZqqBUGEgYAAEzUUBMGhiQAAIBbVBgAADBRQ60wkDAAAGAiQ949GumrqymSMAAAYKKGWmFgDgMAAHCLCgMAACZqqBUGEgYAAEzUUBMGhiQAAIBbVBgAADBRQ60wkDAAAGAiw7DI8OJL35traxJDEgAAwC0qDAAAmMghi1cLN3lzbU0iYQAAwEQNdQ4DQxIAAMAtKgwAAJiooU56JGEAAMBEDXVIgoQBAAATNdQKA3MYAACAW1QYAAAwkeHlkISvVhhIGAAAMJEhyTC8u94XMSQBAADcosIAAICJHLLIwkqPAADgx/CUBAAA8Dmpqanq1q2bbDabbDab+vbtqy1btjiPG4Yhu92u1q1bKyAgQPHx8Tp06JDH9yFhAADARFcWbvJm80SbNm30/PPPKzMzU5mZmbrrrrs0dOhQZ1KwaNEiLVmyRCtXrlRGRobCw8M1cOBAXbhwwaP7kDAAAGAiw/B+88SQIUN03333KSYmRjExMVqwYIGaN2+uffv2yTAMLV26VLNmzVJiYqJiY2OVnp6uixcvav369R7dh4QBAAAflJ+f77IVFxe7vaasrEwbNmxQYWGh+vbtq5ycHOXl5enee+91nmO1WtW/f3/t3bvXo3hIGAAAMNGVSY/ebJIUGRmp4OBg55aSknLVex48eFDNmzeX1WrVhAkT9MYbb6hLly7Ky8uTJIWFhbmcHxYW5jxWVTwlAQCAicx6SiI3N1c2m83ZbrVar3pNx44ddeDAAZ0/f16vvfaaRo8erV27djmPWyyu8RiGUaHNHRIGAABM5DAsspjwtsorTz1URdOmTdW+fXtJUlxcnDIyMrRs2TLNmDFDkpSXl6eIiAjn+adPn65QdXCHIQkAABoYwzBUXFys6OhohYeHa/v27c5jly9f1q5du9SvXz+P+qTCAACAiarzpMMPr/fEzJkzlZCQoMjISF24cEEbNmzQzp07tXXrVlksFiUlJSk5OVkdOnRQhw4dlJycrGbNmmnkyJEe3YeEAQAAE5UnDN7MYfDs/G+++UY//elPderUKQUHB6tbt27aunWrBg4cKEmaPn26Ll26pIkTJ+rcuXPq3bu3tm3bpqCgII/uQ8IAAEA99oc//OFHj1ssFtntdtntdq/uQ8IAAICJGuq7JEgYAAAwkfHd5s31voinJAAAgFtUGAAAMBFDEgAAwL0GOiZBwgAAgJm8rDDIRysMzGEAAABuUWEAAMBEtb3SY20hYQAAwEQNddIjQxIAAMAtKgwAAJjJsHg3cdFHKwwkDAAAmKihzmFgSAIAALhFhQEAADNdyws3LV++vModTpkypdrBAABQ3zXUpySqlDC8+OKLVerMYrGQMAAA0ABVKWHIycmp6TgAAGg4fHRYwRvVnvR4+fJlHTlyRKWlpWbGAwBAvXZlSMKbzRd5nDBcvHhRY8eOVbNmzXTzzTfryy+/lFQ+d+H55583PUAAAOoVw4TNB3mcMDz33HP65JNPtHPnTvn7+zvb77nnHm3cuNHU4AAAgG/w+LHKN998Uxs3blSfPn1ksfxf2aRLly764osvTA0OAID6x/Ld5s31vsfjhOHMmTMKDQ2t0F5YWOiSQAAAcE1qoOsweDwkceutt2rz5s3O/StJwpo1a9S3b1/zIgMAAD7D4wpDSkqKBg8erMOHD6u0tFTLli3ToUOH9I9//EO7du2qiRgBAKg/qDCU69evnz744ANdvHhR7dq107Zt2xQWFqZ//OMf6tWrV03ECABA/XHlbZXebD6oWu+S6Nq1q9LT082OBQAA+KhqJQxlZWV64403lJ2dLYvFos6dO2vo0KFq3Jh3WQEArm0N9fXWHn/Df/bZZxo6dKjy8vLUsWNHSdI///lPtWrVSm+//ba6du1qepAAANQbzGEoN27cON1888366quv9PHHH+vjjz9Wbm6uunXrpp///Oc1ESMAAKhjHlcYPvnkE2VmZuq6665ztl133XVasGCBbr31VlODAwCg3vF24qKPTnr0uMLQsWNHffPNNxXaT58+rfbt25sSFAAA9ZXF8H7zRVWqMOTn5zt/Tk5O1pQpU2S329WnTx9J0r59+/TrX/9aCxcurJkoAQCoLxroHIYqJQwtWrRwWfbZMAw98sgjzjbjuymdQ4YMUVlZWQ2ECQAA6lKVEob333+/puMAAKBhqOU5DCkpKXr99df1+eefKyAgQP369dPChQudTzJK0pgxYyqsn9S7d2/t27evyvepUsLQv3//KncIAMA1rZaHJHbt2qVJkybp1ltvVWlpqWbNmqV7771Xhw8fVmBgoPO8wYMHKy0tzbnftGlTj+5T7ZWWLl68qC+//FKXL192ae/WrVt1uwQAAB7aunWry35aWppCQ0OVlZWlO++809lutVoVHh5e7ftU6/XWTzzxhLZs2VLpceYwAACuaSZVGL7/wIFU/oVvtVrdXv7tt99KkkJCQlzad+7cqdDQULVo0UL9+/fXggULFBoaWuWwPH6sMikpSefOndO+ffsUEBCgrVu3Kj09XR06dNDbb7/taXcAADQshgmbpMjISAUHBzu3lJQU97c2DE2dOlW33367YmNjne0JCQlat26dduzYocWLFysjI0N33XWXiouLq/yxPK4w7NixQ2+99ZZuvfVW+fn5KSoqSgMHDpTNZlNKSoruv/9+T7sEAAA/kJubK5vN5tyvSnVh8uTJ+vTTT7Vnzx6X9hEjRjh/jo2NVVxcnKKiorR582YlJiZWKR6PE4bCwkJnCSMkJERnzpxRTEyMunbtqo8//tjT7gAAaFhMekrCZrO5JAzuPP3003r77be1e/dutWnT5kfPjYiIUFRUlI4ePVrl/qu10uORI0ckST169NDq1av1r3/9Sy+99JIiIiI87Q4AgAaltld6NAxDkydP1uuvv64dO3YoOjra7TVnz55Vbm6uR9/bHlcYkpKSdOrUKUnS3LlzNWjQIK1bt05NmzbV2rVrPe0OAAB4YdKkSVq/fr3eeustBQUFKS8vT5IUHBysgIAAFRQUyG63a/jw4YqIiNCJEyc0c+ZMtWzZUsOGDavyfTxOGEaNGuX8uWfPnjpx4oQ+//xz3XjjjWrZsqWn3QEA0LDU8joMqampkqT4+HiX9rS0NI0ZM0aNGjXSwYMH9corr+j8+fOKiIjQgAEDtHHjRgUFBVX5PtVeh+GKZs2a6ZZbbvG2GwAAUA1XXs9wNQEBAXrvvfe8vk+VEoapU6dWucMlS5ZUOxgAAOo7i7x746Rvvty6ignD/v37q9TZ919QBQAAGg5ePuWBYTFd1djSpK7DAGqEn39dRwA0ELX88qna4vUcBgAA8D21POmxtni8DgMAALj2UGEAAMBMDbTCQMIAAICJqrNa4w+v90UMSQAAALeqlTC8+uqr+slPfqLWrVvr5MmTkqSlS5fqrbfeMjU4AADqHZNeb+1rPE4YUlNTNXXqVN133306f/68ysrKJEktWrTQ0qVLzY4PAID6hYSh3IoVK7RmzRrNmjVLjRo1crbHxcXp4MGDpgYHAAB8g8eTHnNyctSzZ88K7VarVYWFhaYEBQBAfcWkx+9ER0frwIEDFdq3bNmiLl26mBETAAD115WVHr3ZfJDHFYZp06Zp0qRJKioqkmEY+uijj/TnP/9ZKSkp+v3vf18TMQIAUH+wDkO5J554QqWlpZo+fbouXryokSNH6oYbbtCyZcv06KOP1kSMAACgjlVr4abx48dr/Pjx+ve//y2Hw6HQ0FCz4wIAoF5qqHMYvFrpsWXLlmbFAQBAw8CQRLno6GhZLFefkHH8+HGvAgIAAL7H44QhKSnJZb+kpET79+/X1q1bNW3aNLPiAgCgfvJySKLBVBieeeaZStt/97vfKTMz0+uAAACo1xrokIRpL59KSEjQa6+9ZlZ3AADAh5j2euv/+Z//UUhIiFndAQBQPzXQCoPHCUPPnj1dJj0ahqG8vDydOXNGq1atMjU4AADqGx6r/M6DDz7osu/n56dWrVopPj5enTp1MisuAADgQzxKGEpLS9W2bVsNGjRI4eHhNRUTAADwMR5NemzcuLGeeuopFRcX11Q8AADUb4YJmw/y+CmJ3r17a//+/TURCwAA9d6VOQzebL7I4zkMEydO1C9/+Ut99dVX6tWrlwIDA12Od+vWzbTgAACAb6hywvCzn/1MS5cu1YgRIyRJU6ZMcR6zWCwyDEMWi0VlZWXmRwkAQH3io1UCb1Q5YUhPT9fzzz+vnJycmowHAID67Vpfh8Ewyj9BVFRUjQUDAAB8k0dzGH7sLZUAAICFmyRJMTExbpOG//znP14FBABAvXatD0lI0rx58xQcHFxTsQAAAA+lpKTo9ddf1+eff66AgAD169dPCxcuVMeOHZ3nGIahefPm6eWXX9a5c+fUu3dv/e53v9PNN99c5ft4lDA8+uijCg0N9eQSAACuKbU9JLFr1y5NmjRJt956q0pLSzVr1izde++9Onz4sHPpg0WLFmnJkiVau3atYmJi9Jvf/EYDBw7UkSNHFBQUVKX7VDlhYP4CAABVUMtDElu3bnXZT0tLU2hoqLKysnTnnXfKMAwtXbpUs2bNUmJioqTyJx/DwsK0fv16Pfnkk1W6T5VXerzylAQAAKh5+fn5LltVX8vw7bffSpJCQkIkSTk5OcrLy9O9997rPMdqtap///7au3dvleOpcsLgcDgYjgAAwB2T3iURGRmp4OBg55aSkuL+1oahqVOn6vbbb1dsbKwkKS8vT5IUFhbmcm5YWJjzWFV4vDQ0AAC4OrPmMOTm5spmsznbrVar22snT56sTz/9VHv27KnY7w+mFlxZobmqSBgAADCTSXMYbDabS8LgztNPP623335bu3fvVps2bZzt4eHhksorDREREc7206dPV6g6/BiP31YJAAB8h2EYmjx5sl5//XXt2LFD0dHRLsejo6MVHh6u7du3O9suX76sXbt2qV+/flW+DxUGAADMVMtPSUyaNEnr16/XW2+9paCgIOe8hODgYAUEBMhisSgpKUnJycnq0KGDOnTooOTkZDVr1kwjR46s8n1IGAAAMFFtr8OQmpoqSYqPj3dpT0tL05gxYyRJ06dP16VLlzRx4kTnwk3btm2r8hoMEgkDAAD1WlWWPbBYLLLb7bLb7dW+DwkDAABm4l0SAADAnYb6tkqekgAAAG5RYQAAwEwMSQAAALcaaMLAkAQAAHCLCgMAACayfLd5c70vImEAAMBMDXRIgoQBAAAT8VglAAC4ZlFhAADATAxJAACAKvHRL31vMCQBAADcosIAAICJGuqkRxIGAADM1EDnMDAkAQAA3KLCAACAiRiSAAAA7jEkAQAArlVUGAAAMBFDEgAAwL0GOiRBwgAAgJkaaMLAHAYAAOAWFQYAAEzEHAYAAOAeQxIAAOBaRYUBAAATWQxDFqP6ZQJvrq1JJAwAAJiJIQkAAHCtosIAAICJeEoCAAC4x5AEAAC4VlFhAADARA11SIIKAwAAZjJM2Dywe/duDRkyRK1bt5bFYtGbb77pcnzMmDGyWCwuW58+fTz+WCQMAACY6EqFwZvNE4WFherevbtWrlx51XMGDx6sU6dOObd3333X48/FkAQAAPVYQkKCEhISfvQcq9Wq8PBwr+5DhQEAADOZNCSRn5/vshUXF1c7pJ07dyo0NFQxMTEaP368Tp8+7XEfJAwAAJjMjOGIyMhIBQcHO7eUlJRqxZKQkKB169Zpx44dWrx4sTIyMnTXXXd5nIAwJAEAgA/Kzc2VzWZz7lut1mr1M2LECOfPsbGxiouLU1RUlDZv3qzExMQq90PCAACAmQyjfPPmekk2m80lYTBLRESEoqKidPToUY+uI2EAAMBEvr4Ow9mzZ5Wbm6uIiAiPriNhAACgHisoKNCxY8ec+zk5OTpw4IBCQkIUEhIiu92u4cOHKyIiQidOnNDMmTPVsmVLDRs2zKP7kDAAAGCmWn6XRGZmpgYMGODcnzp1qiRp9OjRSk1N1cGDB/XKK6/o/PnzioiI0IABA7Rx40YFBQV5dB8SBgAATGRxlG/eXO+J+Ph4GT8yZ+K9996rfjDfw2OVAADALRIG+IwHHv+3Uv92RK8fOajXjxzUi28fVdyA/LoOC6gxjzz1L205/qGenH2yrkOBmWr5XRK1xacShh++HOOH25gxY+o6RNSgM6ea6I/JEXo6IUZPJ8Tokw+ay552QlExRXUdGmC6mG4FSnj0jI5nN6vrUGCy2n6XRG3xqTkMp06dcv68ceNGzZkzR0eOHHG2BQQEuJxfUlKiJk2a1Fp8qFkfbg922V+7MEIPPH5WnXoV6uQ//esoKsB8/s3KNO3FL7RsZrQem/Svug4HZjNpHQZf41MVhvDwcOcWHBwsi8Xi3C8qKlKLFi20adMmxcfHy9/fX3/6059kt9vVo0cPl36WLl2qtm3burSlpaWpc+fO8vf3V6dOnbRq1ara+2DwmJ+fof5Dz8nazKHszMC6Dgcw1aR5J5Txfgsd+CDY/cmAj/CpCkNVzJgxQ4sXL1ZaWpqsVqtefvllt9esWbNGc+fO1cqVK9WzZ0/t379f48ePV2BgoEaPHl3h/OLiYpc1tvPzGUevLW07XdLSvx5TU6tDlwr99OuxbfXlUaoLaDj6P3BW7WIL9czQ2LoOBTXE1xduqq56lzAkJSV5tPa1JM2fP1+LFy92XhcdHa3Dhw9r9erVlSYMKSkpmjdvninxwjNffWHVxIExCrSV6fb7v9Wzy77UtMT2JA1oEFpGFOvJOSc06/FOKrnsUwVemKmW12GoLfUuYYiLi/Po/DNnzig3N1djx47V+PHjne2lpaUKDq68HPjcc885F76QyisMkZGR1QsYHikt8dPXJ8pfsHL002bq2OOiHhx3Rstn8OeP+q9DbKGua1mqFW9/5mxr1FiKve2Chvw0T/+v021yOCx1GCFwdfUuYQgMdB3P9vPzq7BgRUlJifNnh6N8BYw1a9aod+/eLuc1atSo0ntYrdZqvxUM5mvS1EfTbcBDB/YGa8Lgri5tUxcdV+4X/vrL6tYkCw0EQxI+qlWrVsrLy5NhGLJYyv+xHThwwHk8LCxMN9xwg44fP65Ro0bVUZSoiid+dUoZO4J05uumCmhepvih59WtX4H+e9RNdR0aYIpLhY108p+uj1EWXfTThfNNKrSjHmugT0nU+4QhPj5eZ86c0aJFi/TQQw9p69at2rJli8srQe12u6ZMmSKbzaaEhAQVFxcrMzNT586dcxl6QN1q0apU01Z8qZDQUl280Eg52f7671E36ePdnq13DgAwX71PGDp37qxVq1YpOTlZ8+fP1/Dhw/Xss8+6PD0xbtw4NWvWTC+88IKmT5+uwMBAde3aVUlJSXUXOCp48ZfMU8C1Z8bILnUdAkzWUIckLMaPvbECksonPQYHByteQ9XYwkJRaJj8/HkSBQ1XqXFZO4o26dtvv3WpQJvpyndF38G/VuMm1f/3VFpSpH9snVOjsVYHz/UAAAC36v2QBAAAvqShDkmQMAAAYCaHUb55c70PImEAAMBMDXSlR+YwAAAAt6gwAABgIou8nMNgWiTmImEAAMBMDXSlR4YkAACAW1QYAAAwEY9VAgAA93hKAgAAXKuoMAAAYCKLYcjixcRFb66tSSQMAACYyfHd5s31PoghCQAA4BYVBgAATMSQBAAAcK+BPiVBwgAAgJlY6REAAFyrqDAAAGAiVnoEAADuMSQBAAB8ze7duzVkyBC1bt1aFotFb775pstxwzBkt9vVunVrBQQEKD4+XocOHfL4PiQMAACYyOLwfvNEYWGhunfvrpUrV1Z6fNGiRVqyZIlWrlypjIwMhYeHa+DAgbpw4YJH92FIAgAAM9XykERCQoISEhKu0pWhpUuXatasWUpMTJQkpaenKywsTOvXr9eTTz5Z5ftQYQAAwAfl5+e7bMXFxR73kZOTo7y8PN17773ONqvVqv79+2vv3r0e9UXCAACAmQwTNkmRkZEKDg52bikpKR6HkpeXJ0kKCwtzaQ8LC3MeqyqGJAAAMJFZS0Pn5ubKZrM5261Wa/X7tFhc9g3DqNDmDgkDAAA+yGazuSQM1REeHi6pvNIQERHhbD99+nSFqoM7DEkAAGCmK5MevdlMEh0drfDwcG3fvt3ZdvnyZe3atUv9+vXzqC8qDAAAmMmQ5OGjkRWu90BBQYGOHTvm3M/JydGBAwcUEhKiG2+8UUlJSUpOTlaHDh3UoUMHJScnq1mzZho5cqRH9yFhAADARLX9euvMzEwNGDDAuT916lRJ0ujRo7V27VpNnz5dly5d0sSJE3Xu3Dn17t1b27ZtU1BQkEf3IWEAAKAei4+Pl/EjSYbFYpHdbpfdbvfqPiQMAACYyZCXCzeZFompSBgAADATL58CAADXKioMAACYySHJszWRKl7vg0gYAAAwUW0/JVFbGJIAAABuUWEAAMBMDXTSIwkDAABmaqAJA0MSAADALSoMAACYqYFWGEgYAAAwE49VAgAAd3isEgAAXLOoMAAAYCbmMAAAALcchmTx4kvf4ZsJA0MSAADALSoMAACYiSEJAADgnpcJg3wzYWBIAgAAuEWFAQAAMzEkAQAA3HIY8mpYgackAABAfUWFAQAAMxmO8s2b630QCQMAAGZiDgMAAHCLOQwAAOBaRYUBAAAzMSQBAADcMuRlwmBaJKZiSAIAALhFhQEAADMxJAEAANxyOCR5sZaCwzfXYWBIAgAAuEWFAQAAMzXQIQkqDAAAmOlKwuDN5gG73S6LxeKyhYeHm/6xqDAAAFDP3Xzzzfrb3/7m3G/UqJHp9yBhAADATHWwNHTjxo1rpKrwfQxJAABgIsNweL1JUn5+vstWXFx81XsePXpUrVu3VnR0tB599FEdP37c9M9FwgAAgJkMo7xKUN3tuzkMkZGRCg4Odm4pKSmV3q5379565ZVX9N5772nNmjXKy8tTv379dPbsWVM/FkMSAAD4oNzcXNlsNue+1Wqt9LyEhATnz127dlXfvn3Vrl07paena+rUqabFQ8IAAICZDC/nMHxXYbDZbC4JQ1UFBgaqa9euOnr0aPVjqARDEgAAmMnh8H7zQnFxsbKzsxUREWHSBypHwgAAQD327LPPateuXcrJydGHH36ohx56SPn5+Ro9erSp92FIAgAAM5k0JFFVX331lR577DH9+9//VqtWrdSnTx/t27dPUVFR1Y+hEiQMAACYyHA4ZFiqP6xw5bHKqtqwYUO17+UJhiQAAIBbVBgAADBTLQ9J1BYSBgAAzOQwJEvDSxgYkgAAAG5RYQAAwEyGIcmLtRR8tMJAwgAAgIkMhyHDiyEJg4QBAIBrgOGQdxUG71Z6rCnMYQAAAG5RYQAAwEQMSQAAAPca6JAECUMVXMn2SlXi1VocgC/zMxihRMNVapRIqp3f3r39rihViXnBmIiEoQouXLggSdqjd+s4EqAGFdV1AEDNu3DhgoKDg2uk76ZNmyo8PFx78rz/rggPD1fTpk1NiMo8FsNXB0t8iMPh0Ndff62goCBZLJa6DueakJ+fr8jISOXm5spms9V1OICp+Ptd+wzD0IULF9S6dWv5+dVcNa2oqEiXL1/2up+mTZvK39/fhIjMQ4WhCvz8/NSmTZu6DuOaZLPZ+B8qGiz+fteumqosfJ+/v7/PfdGbhUFLAADgFgkDAABwi4QBPslqtWru3LmyWq11HQpgOv5+oz5i0iMAAHCLCgMAAHCLhAEAALhFwgAAANwiYYBPWbt2rVq0aFHXYQAAfoCEATVizJgxslgsFbZjx47VdWiAqSr7e/79bcyYMXUdImAKVnpEjRk8eLDS0tJc2lq1alVH0QA149SpU86fN27cqDlz5ujIkSPOtoCAAJfzS0pK1KRJk1qLDzALFQbUGKvVqvDwcJdt2bJl6tq1qwIDAxUZGamJEyeqoKDgqn188sknGjBggIKCgmSz2dSrVy9lZmY6j+/du1d33nmnAgICFBkZqSlTpqiwsLA2Ph4gSS5/v4ODg2WxWJz7RUVFatGihTZt2qT4+Hj5+/vrT3/6k+x2u3r06OHSz9KlS9W2bVuXtrS0NHXu3Fn+/v7q1KmTVq1aVXsfDPgBEgbUKj8/Py1fvlyfffaZ0tPTtWPHDk2fPv2q548aNUpt2rRRRkaGsrKy9Ktf/cr529nBgwc1aNAgJSYm6tNPP9XGjRu1Z88eTZ48ubY+DlAlM2bM0JQpU5Sdna1BgwZV6Zo1a9Zo1qxZWrBggbKzs5WcnKzZs2crPT29hqMFKseQBGrMO++8o+bNmzv3ExIS9Je//MW5Hx0drfnz5+upp5666m9OX375paZNm6ZOnTpJkjp06OA89sILL2jkyJFKSkpyHlu+fLn69++v1NTUBvsCGNQ/SUlJSkxM9Oia+fPna/Hixc7roqOjdfjwYa1evVqjR4+uiTCBH0XCgBozYMAApaamOvcDAwP1/vvvKzk5WYcPH1Z+fr5KS0tVVFSkwsJCBQYGVuhj6tSpGjdunF599VXdc889evjhh9WuXTtJUlZWlo4dO6Z169Y5zzcMQw6HQzk5OercuXPNf0igCuLi4jw6/8yZM8rNzdXYsWM1fvx4Z3tpaWmtvHERqAwJA2pMYGCg2rdv79w/efKk7rvvPk2YMEHz589XSEiI9uzZo7Fjx6qkpKTSPux2u0aOHKnNmzdry5Ytmjt3rjZs2KBhw4bJ4XDoySef1JQpUypcd+ONN9bY5wI89cNk2M/PTz9clf/7/wYcDoek8mGJ3r17u5zXqFGjGooS+HEkDKg1mZmZKi0t1eLFi+XnVz59ZtOmTW6vi4mJUUxMjH7xi1/oscceU1pamoYNG6ZbbrlFhw4dcklKgPqgVatWysvLk2EYslgskqQDBw44j4eFhemGG27Q8ePHNWrUqDqKEnBFwoBa065dO5WWlmrFihUaMmSIPvjgA7300ktXPf/SpUuaNm2aHnroIUVHR+urr75SRkaGhg8fLql8IlmfPn00adIkjR8/XoGBgcrOztb27du1YsWK2vpYgMfi4+N15swZLVq0SA899JC2bt2qLVu2yGazOc+x2+2aMmWKbDabEhISVFxcrMzMTJ07d05Tp06tw+hxreIpCdSaHj16aMmSJVq4cKFiY2O1bt06paSkXPX8Ro0a6ezZs3r88ccVExOjRx55RAkJCZo3b54kqVu3btq1a5eOHj2qO+64Qz179tTs2bMVERFRWx8JqJbOnTtr1apV+t3vfqfu3bvro48+0rPPPutyzrhx4/T73/9ea9euVdeuXdW/f3+tXbtW0dHRdRQ1rnW83hoAALhFhQEAALhFwgAAANwiYQAAAG6RMAAAALdIGAAAgFskDAAAwC0SBgAA4BYJA1BP2O129ejRw7k/ZswYPfjgg7Uex4kTJ2SxWFyWMv6htm3baunSpVXuc+3atWrRooXXsVksFr355pte9wOgIhIGwAtjxoyRxWKRxWJRkyZNdNNNN+nZZ59VYWFhjd972bJlWrt2bZXOrcqXPAD8GN4lAXhp8ODBSktLU0lJif73f/9X48aNU2Fhocurva8oKSlRkyZNTLkvrzkGUJuoMABeslqtCg8PV2RkpEaOHKlRo0Y5y+JXhhH++Mc/6qabbpLVapVhGPr222/185//XKGhobLZbLrrrrv0ySefuPT7/PPPKywsTEFBQRo7dqyKiopcjv9wSMLhcGjhwoVq3769rFarbrzxRi1YsECSnO8f6NmzpywWi+Lj453XpaWlqXPnzvL391enTp20atUql/t89NFH6tmzp/z9/RUXF6f9+/d7/Ge0ZMkSde3aVYGBgYqMjNTEiRNVUFBQ4bw333xTMTEx8vf318CBA5Wbm+ty/K9//at69eolf39/3XTTTZo3b55KS0s9jgeA50gYAJMFBASopKTEuX/s2DFt2rRJr732mnNI4P7771deXp7effddZWVl6ZZbbtHdd9+t//znP5LKX/s9d+5cLViwQJmZmYqIiKjwRf5Dzz33nBYuXKjZs2fr8OHDWr9+vcLCwiSVf+lL0t/+9jedOnVKr7/+uiRpzZo1mjVrlhYsWKDs7GwlJydr9uzZSk9PlyQVFhbqgQceUMeOHZWVlSW73V7hJUlV4efnp+XLl+uzzz5Tenq6duzYoenTp7ucc/HiRS1YsEDp6en64IMPlJ+fr0cffdR5/L333tN//dd/acqUKTp8+LBWr16ttWvXOpMiADXMAFBto0ePNoYOHerc//DDD43rr7/eeOSRRwzDMIy5c+caTZo0MU6fPu085+9//7ths9mMoqIil77atWtnrF692jAMw+jbt68xYcIEl+O9e/c2unfvXum98/PzDavVaqxZs6bSOHNycgxJxv79+13aIyMjjfXr17u0zZ8/3+jbt69hGIaxevVqIyQkxCgsLHQeT01NrbSv74uKijJefPHFqx7ftGmTcf311zv309LSDEnGvn37nG3Z2dmGJOPDDz80DMMw7rjjDiM5Odmln1dffdWIiIhw7ksy3njjjaveF0D1MYcB8NI777yj5s2bq7S0VCUlJRo6dKhWrFjhPB4VFaVWrVo597OyslRQUKDrr7/epZ9Lly7piy++kCRlZ2drwoQJLsf79u2r999/v9IYsrOzVVxcrLvvvrvKcZ85c0a5ubkaO3asxo8f72wvLS11zo/Izs5W9+7d1axZM5c4PPX+++8rOTlZhw8fVn5+vkpLS1VUVKTCwkIFBgZKkho3bqy4uDjnNZ06dVKLFi2UnZ2t2267TVlZWcrIyHCpKJSVlamoqEgXL150iRGA+UgYAC8NGDBAqampatKkiVq3bl1hUuOVL8QrHA6HIiIitHPnzgp9VffRwoCAAI+vcTgcksqHJXr37u1yrFGjRpIkwzCqFc/3nTx5Uvfdd58mTJig+fPnKyQkRHv27NHYsWNdhm6k8scif+hKm8Ph0Lx585SYmFjhHH9/f6/jBPDjSBgALwUGBqp9+/ZVPv+WW25RXl6eGjdurLZt21Z6TufOnbVv3z49/vjjzrZ9+/Zdtc8OHTooICBAf//73zVu3LgKx5s2bSqp/DfyK8LCwnTDDTfo+PHjGjVqVKX9dunSRa+++qouXbrkTEp+LI7KZGZmqrS0VIsXL5afX/m0qU2bNlU4r7S0VJmZmbrtttskSUeOHNH58+fVqVMnSeV/bkeOHPHozxqAeUgYgFp2zz33qG/fvnrwwQe1cOFCdezYUV9//bXeffddPfjgg4qLi9Mzzzyj0aNHKy4uTrfffrvWrVunQ4cO6aabbqq0T39/f82YMUPTp09X06ZN9ZOf/ERnzpzRoUOHNHbsWIWGhiogIEBbt25VmzZt5O/vr+DgYNntdk2ZMkU2m00JCQkqLi5WZmamzp07p6lTp2rkyJGaNWuWxo4dq//+7//WiRMn9Nvf/tajz9uuXTuVlpZqxYoVGjJkiD744AO99NJLFc5r0qSJnn76aS1fvlxNmjTR5MmT1adPH2cCMWfOHD3wwAOKjIzUww8/LD8/P3366ac6ePCgfvOb33j+HwKAR3hKAqhlFotF7777ru6880797Gc/U0xMjB599FGdOHHC+VTDiBEjNGfOHM2YMUO9evXSyZMn9dRTT/1ov7Nnz9Yvf/lLzZkzR507d9aIESN0+vRpSeXzA5YvX67Vq1erdevWGjp0qCRp3Lhx+v3vf6+1a9eqa9eu6t+/v9auXet8DLN58+b661//qsOHD6tnz56aNWuWFi5c6NHn7dGjh5YsWaKFCxcqNjZW69atU0pKSoXzmjVrphkzZmjkyJHq27evAgICtGHDBufxQYMG6Z133tH27dt16623qk+fPlqyZImioqI8igdA9VgMMwYpAQBAg0aFAQAAuEXCAAAA3CJhAAAAbpEwAAAAt0gYAACAWyQMAADALRIGAADgFgkDAABwi4QBAAC4RcIAAADcImEAAABukTAAAAC3/j9Gf9sVjdbN+wAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"markdown","source":"#### Cross Val","metadata":{}},{"cell_type":"code","source":"scores = cross_val_score(rfc, X_modified, y, cv = 5, scoring='roc_auc')\nscores.mean()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:05:54.772101Z","iopub.execute_input":"2023-07-06T12:05:54.772452Z","iopub.status.idle":"2023-07-06T12:05:55.951629Z","shell.execute_reply.started":"2023-07-06T12:05:54.772427Z","shell.execute_reply":"2023-07-06T12:05:55.950560Z"},"trusted":true},"execution_count":286,"outputs":[{"execution_count":286,"output_type":"execute_result","data":{"text/plain":"0.9525003426944181"},"metadata":{}}]},{"cell_type":"markdown","source":"### K-Nearest Neighbors ","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\n\nknc = KNeighborsClassifier(weights = \"uniform\")\n\nmodel6 = knc.fit(X_train, y_train)\ny_pred_6 = model6.predict(X_val)\ny_pred_6","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:44:19.899106Z","iopub.execute_input":"2023-07-06T10:44:19.899765Z","iopub.status.idle":"2023-07-06T10:44:19.956740Z","shell.execute_reply.started":"2023-07-06T10:44:19.899735Z","shell.execute_reply":"2023-07-06T10:44:19.955843Z"},"trusted":true},"execution_count":236,"outputs":[{"execution_count":236,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0])"},"metadata":{}}]},{"cell_type":"code","source":"score_6 = model6.score(X_val, y_val)\nscore_6","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:44:45.798843Z","iopub.execute_input":"2023-07-06T10:44:45.799706Z","iopub.status.idle":"2023-07-06T10:44:45.813723Z","shell.execute_reply.started":"2023-07-06T10:44:45.799672Z","shell.execute_reply":"2023-07-06T10:44:45.812461Z"},"trusted":true},"execution_count":237,"outputs":[{"execution_count":237,"output_type":"execute_result","data":{"text/plain":"0.8571428571428571"},"metadata":{}}]},{"cell_type":"markdown","source":"Let's check different parameter","metadata":{}},{"cell_type":"code","source":"train_scores = []\nval_scores = []\n\nneighbors = range(1, 21)\n\nknn = KNeighborsClassifier(weights = 'uniform')\n\nfor i in neighbors:\n    \n    knn.set_params(n_neighbors = i)\n    knn.fit(X_train, y_train)\n    train_scores.append(knn.score(X_train, y_train))\n    val_scores.append(knn.score(X_val, y_val))\n","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:49:12.927145Z","iopub.execute_input":"2023-07-06T10:49:12.927501Z","iopub.status.idle":"2023-07-06T10:49:13.326465Z","shell.execute_reply.started":"2023-07-06T10:49:12.927478Z","shell.execute_reply":"2023-07-06T10:49:13.325562Z"},"trusted":true},"execution_count":241,"outputs":[]},{"cell_type":"code","source":"max(val_scores), max(train_scores)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:49:57.559607Z","iopub.execute_input":"2023-07-06T10:49:57.560251Z","iopub.status.idle":"2023-07-06T10:49:57.566900Z","shell.execute_reply.started":"2023-07-06T10:49:57.560208Z","shell.execute_reply":"2023-07-06T10:49:57.565823Z"},"trusted":true},"execution_count":244,"outputs":[{"execution_count":244,"output_type":"execute_result","data":{"text/plain":"(0.8928571428571429, 1.0)"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(neighbors, train_scores, label =\"Train Score\")\nplt.plot(neighbors, val_scores, label = \"Val Score\")\nplt.xticks(np.arange(1, 21, 1))\nplt.xlabel(\"Number of Neighbors\")\nplt.ylabel(\"Model Score\")\nplt.legend()\n\nprint(f\"Max score on validation set is: {max(val_scores):.4f}%\")","metadata":{"execution":{"iopub.status.busy":"2023-07-06T10:58:49.953880Z","iopub.execute_input":"2023-07-06T10:58:49.954677Z","iopub.status.idle":"2023-07-06T10:58:50.260465Z","shell.execute_reply.started":"2023-07-06T10:58:49.954651Z","shell.execute_reply":"2023-07-06T10:58:50.259331Z"},"trusted":true},"execution_count":246,"outputs":[{"name":"stdout","text":"Max score on validation set is: 0.8929%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Seems like our ideal neighbors is 8 from the graph. \nknn.set_params(n_neighbors = 8)\nknn.fit(X_train, y_train)\nknn.score(X_val, y_val)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:13:30.209439Z","iopub.execute_input":"2023-07-06T11:13:30.209832Z","iopub.status.idle":"2023-07-06T11:13:30.222131Z","shell.execute_reply.started":"2023-07-06T11:13:30.209802Z","shell.execute_reply":"2023-07-06T11:13:30.221476Z"},"trusted":true},"execution_count":252,"outputs":[{"execution_count":252,"output_type":"execute_result","data":{"text/plain":"0.8928571428571429"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_6_1 = knn.predict(X_val)\ny_pred_6_1","metadata":{"execution":{"iopub.status.busy":"2023-07-06T11:27:09.214752Z","iopub.execute_input":"2023-07-06T11:27:09.215190Z","iopub.status.idle":"2023-07-06T11:27:09.228829Z","shell.execute_reply.started":"2023-07-06T11:27:09.215160Z","shell.execute_reply":"2023-07-06T11:27:09.227505Z"},"trusted":true},"execution_count":257,"outputs":[{"execution_count":257,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0])"},"metadata":{}}]},{"cell_type":"markdown","source":"This has beaten our RandomForest score","metadata":{}},{"cell_type":"markdown","source":"#### Cross Val ","metadata":{}},{"cell_type":"code","source":"kn = KNeighborsClassifier(n_neighbors = 8, weights = 'uniform')\nscores = cross_val_score(kn, X_modified, y, cv = 5, scoring='roc_auc')\nscores.mean() ","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:06:10.353415Z","iopub.execute_input":"2023-07-06T12:06:10.353784Z","iopub.status.idle":"2023-07-06T12:06:10.394709Z","shell.execute_reply.started":"2023-07-06T12:06:10.353761Z","shell.execute_reply":"2023-07-06T12:06:10.392691Z"},"trusted":true},"execution_count":287,"outputs":[{"execution_count":287,"output_type":"execute_result","data":{"text/plain":"0.8540977685066039"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import classification_report, roc_auc_score\n\nprint(classification_report(y_val, y_pred_6_1))\n\nroc_auc_score(y_val, y_pred_6_1)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:06:24.585482Z","iopub.execute_input":"2023-07-06T12:06:24.585921Z","iopub.status.idle":"2023-07-06T12:06:24.604292Z","shell.execute_reply.started":"2023-07-06T12:06:24.585890Z","shell.execute_reply":"2023-07-06T12:06:24.602613Z"},"trusted":true},"execution_count":288,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.92      0.96      0.94        49\n           1       0.60      0.43      0.50         7\n\n    accuracy                           0.89        56\n   macro avg       0.76      0.69      0.72        56\nweighted avg       0.88      0.89      0.89        56\n\n","output_type":"stream"},{"execution_count":288,"output_type":"execute_result","data":{"text/plain":"0.6938775510204082"},"metadata":{}}]},{"cell_type":"markdown","source":"Based on the above scores we can see that Support Vector Machine is doing better as compared to others because it has better roc_auc score than others and it also has better prediction rate. We can also further tune the hyperparameter of both the best performing models Logistic regression and SVM ","metadata":{}},{"cell_type":"markdown","source":"Let's make the prediction on test set and see it's performance","metadata":{}},{"cell_type":"code","source":"test_df = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/test.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:42:09.234159Z","iopub.execute_input":"2023-07-06T12:42:09.234568Z","iopub.status.idle":"2023-07-06T12:42:09.253777Z","shell.execute_reply.started":"2023-07-06T12:42:09.234534Z","shell.execute_reply":"2023-07-06T12:42:09.252493Z"},"trusted":true},"execution_count":307,"outputs":[]},{"cell_type":"code","source":"test_df.tail()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:47:34.464805Z","iopub.execute_input":"2023-07-06T12:47:34.465142Z","iopub.status.idle":"2023-07-06T12:47:34.493507Z","shell.execute_reply.started":"2023-07-06T12:47:34.465117Z","shell.execute_reply":"2023-07-06T12:47:34.492275Z"},"trusted":true},"execution_count":321,"outputs":[{"execution_count":321,"output_type":"execute_result","data":{"text/plain":"    AB   AF   AH   AM   AR   AX   AY   AZ   BC  BD   ...   FI   FL   FR   FS  \\\n0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n\n    GB   GE   GF   GH   GI   GL  \n0  0.0  0.0  0.0  0.0  0.0  0.0  \n1  0.0  0.0  0.0  0.0  0.0  0.0  \n2  0.0  0.0  0.0  0.0  0.0  0.0  \n3  0.0  0.0  0.0  0.0  0.0  0.0  \n4  0.0  0.0  0.0  0.0  0.0  0.0  \n\n[5 rows x 56 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>BD</th>\n      <th>...</th>\n      <th>FI</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 56 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:42:29.039187Z","iopub.execute_input":"2023-07-06T12:42:29.039536Z","iopub.status.idle":"2023-07-06T12:42:29.048021Z","shell.execute_reply.started":"2023-07-06T12:42:29.039494Z","shell.execute_reply":"2023-07-06T12:42:29.046822Z"},"trusted":true},"execution_count":309,"outputs":[{"execution_count":309,"output_type":"execute_result","data":{"text/plain":"Id      object\nAB     float64\nAF     float64\nAH     float64\nAM     float64\nAR     float64\nAX     float64\nAY     float64\nAZ     float64\nBC     float64\nBD     float64\nBN     float64\nBP     float64\nBQ     float64\nBR     float64\nBZ     float64\nCB     float64\nCC     float64\nCD     float64\nCF     float64\nCH     float64\nCL     float64\nCR     float64\nCS     float64\nCU     float64\nCW     float64\nDA     float64\nDE     float64\nDF     float64\nDH     float64\nDI     float64\nDL     float64\nDN     float64\nDU     float64\nDV     float64\nDY     float64\nEB     float64\nEE     float64\nEG     float64\nEH     float64\nEJ      object\nEL     float64\nEP     float64\nEU     float64\nFC     float64\nFD     float64\nFE     float64\nFI     float64\nFL     float64\nFR     float64\nFS     float64\nGB     float64\nGE     float64\nGF     float64\nGH     float64\nGI     float64\nGL     float64\ndtype: object"},"metadata":{}}]},{"cell_type":"code","source":"test_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:42:47.553370Z","iopub.execute_input":"2023-07-06T12:42:47.553800Z","iopub.status.idle":"2023-07-06T12:42:47.563587Z","shell.execute_reply.started":"2023-07-06T12:42:47.553776Z","shell.execute_reply":"2023-07-06T12:42:47.562177Z"},"trusted":true},"execution_count":310,"outputs":[{"execution_count":310,"output_type":"execute_result","data":{"text/plain":"Id     0\nAB     0\nAF     0\nAH     0\nAM     0\nAR     0\nAX     0\nAY     0\nAZ     0\nBC     0\nBD     0\nBN     0\nBP     0\nBQ     0\nBR     0\nBZ     0\nCB     0\nCC     0\nCD     0\nCF     0\nCH     0\nCL     0\nCR     0\nCS     0\nCU     0\nCW     0\nDA     0\nDE     0\nDF     0\nDH     0\nDI     0\nDL     0\nDN     0\nDU     0\nDV     0\nDY     0\nEB     0\nEE     0\nEG     0\nEH     0\nEJ     0\nEL     0\nEP     0\nEU     0\nFC     0\nFD     0\nFE     0\nFI     0\nFL     0\nFR     0\nFS     0\nGB     0\nGE     0\nGF     0\nGH     0\nGI     0\nGL     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"test_df.drop([\"Id\"], axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:43:34.483334Z","iopub.execute_input":"2023-07-06T12:43:34.483688Z","iopub.status.idle":"2023-07-06T12:43:34.489624Z","shell.execute_reply.started":"2023-07-06T12:43:34.483657Z","shell.execute_reply":"2023-07-06T12:43:34.488466Z"},"trusted":true},"execution_count":311,"outputs":[]},{"cell_type":"code","source":"test_df.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:43:40.928085Z","iopub.execute_input":"2023-07-06T12:43:40.929071Z","iopub.status.idle":"2023-07-06T12:43:40.934743Z","shell.execute_reply.started":"2023-07-06T12:43:40.929045Z","shell.execute_reply":"2023-07-06T12:43:40.933617Z"},"trusted":true},"execution_count":312,"outputs":[{"execution_count":312,"output_type":"execute_result","data":{"text/plain":"(5, 56)"},"metadata":{}}]},{"cell_type":"code","source":"test_df[\"EJ\"] = LabelEncoder().fit_transform(test_df[\"EJ\"])","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:44:53.613737Z","iopub.execute_input":"2023-07-06T12:44:53.614422Z","iopub.status.idle":"2023-07-06T12:44:53.618756Z","shell.execute_reply.started":"2023-07-06T12:44:53.614398Z","shell.execute_reply":"2023-07-06T12:44:53.617928Z"},"trusted":true},"execution_count":313,"outputs":[]},{"cell_type":"code","source":"X_test = StandardScaler().fit_transform(test_df)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:45:37.359616Z","iopub.execute_input":"2023-07-06T12:45:37.359947Z","iopub.status.idle":"2023-07-06T12:45:37.369560Z","shell.execute_reply.started":"2023-07-06T12:45:37.359925Z","shell.execute_reply":"2023-07-06T12:45:37.368543Z"},"trusted":true},"execution_count":314,"outputs":[]},{"cell_type":"code","source":"X_test.shape","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:46:21.559934Z","iopub.execute_input":"2023-07-06T12:46:21.560293Z","iopub.status.idle":"2023-07-06T12:46:21.568750Z","shell.execute_reply.started":"2023-07-06T12:46:21.560266Z","shell.execute_reply":"2023-07-06T12:46:21.567067Z"},"trusted":true},"execution_count":318,"outputs":[{"execution_count":318,"output_type":"execute_result","data":{"text/plain":"(5, 56)"},"metadata":{}}]},{"cell_type":"code","source":"y_pred_test = model2.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:45:55.604951Z","iopub.execute_input":"2023-07-06T12:45:55.606024Z","iopub.status.idle":"2023-07-06T12:45:55.611304Z","shell.execute_reply.started":"2023-07-06T12:45:55.605940Z","shell.execute_reply":"2023-07-06T12:45:55.609654Z"},"trusted":true},"execution_count":315,"outputs":[]},{"cell_type":"code","source":"y_pred_test","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:46:01.704608Z","iopub.execute_input":"2023-07-06T12:46:01.705412Z","iopub.status.idle":"2023-07-06T12:46:01.711414Z","shell.execute_reply.started":"2023-07-06T12:46:01.705388Z","shell.execute_reply":"2023-07-06T12:46:01.710410Z"},"trusted":true},"execution_count":316,"outputs":[{"execution_count":316,"output_type":"execute_result","data":{"text/plain":"array([0, 0, 0, 0, 0])"},"metadata":{}}]},{"cell_type":"code","source":"y_prob = model2.predict_proba(X_test)\ny_prob","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:54:22.320240Z","iopub.execute_input":"2023-07-06T12:54:22.320577Z","iopub.status.idle":"2023-07-06T12:54:22.327476Z","shell.execute_reply.started":"2023-07-06T12:54:22.320554Z","shell.execute_reply":"2023-07-06T12:54:22.326612Z"},"trusted":true},"execution_count":330,"outputs":[{"execution_count":330,"output_type":"execute_result","data":{"text/plain":"array([[0.95264517, 0.04735483],\n       [0.95264517, 0.04735483],\n       [0.95264517, 0.04735483],\n       [0.95264517, 0.04735483],\n       [0.95264517, 0.04735483]])"},"metadata":{}}]},{"cell_type":"code","source":"test_data = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/test.csv\")\ntest_data.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:56:09.291585Z","iopub.execute_input":"2023-07-06T12:56:09.291913Z","iopub.status.idle":"2023-07-06T12:56:09.329114Z","shell.execute_reply.started":"2023-07-06T12:56:09.291890Z","shell.execute_reply":"2023-07-06T12:56:09.327686Z"},"trusted":true},"execution_count":331,"outputs":[{"execution_count":331,"output_type":"execute_result","data":{"text/plain":"             Id   AB   AF   AH   AM   AR   AX   AY   AZ   BC  ...   FI   FL  \\\n0  00eed32682bb  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n1  010ebe33f668  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n2  02fa521e1838  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n3  040e15f562a2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n4  046e85c7cc7f  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n\n    FR   FS   GB   GE   GF   GH   GI   GL  \n0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n\n[5 rows x 57 columns]","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>AB</th>\n      <th>AF</th>\n      <th>AH</th>\n      <th>AM</th>\n      <th>AR</th>\n      <th>AX</th>\n      <th>AY</th>\n      <th>AZ</th>\n      <th>BC</th>\n      <th>...</th>\n      <th>FI</th>\n      <th>FL</th>\n      <th>FR</th>\n      <th>FS</th>\n      <th>GB</th>\n      <th>GE</th>\n      <th>GF</th>\n      <th>GH</th>\n      <th>GI</th>\n      <th>GL</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00eed32682bb</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>010ebe33f668</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>02fa521e1838</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>040e15f562a2</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>046e85c7cc7f</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 57 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sample_submission = pd.read_csv(\"/kaggle/input/icr-identify-age-related-conditions/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:59:29.300779Z","iopub.execute_input":"2023-07-06T12:59:29.301149Z","iopub.status.idle":"2023-07-06T12:59:29.310366Z","shell.execute_reply.started":"2023-07-06T12:59:29.301124Z","shell.execute_reply":"2023-07-06T12:59:29.309202Z"},"trusted":true},"execution_count":333,"outputs":[]},{"cell_type":"code","source":"sample_submission","metadata":{"execution":{"iopub.status.busy":"2023-07-06T12:59:39.330107Z","iopub.execute_input":"2023-07-06T12:59:39.330429Z","iopub.status.idle":"2023-07-06T12:59:39.341509Z","shell.execute_reply.started":"2023-07-06T12:59:39.330405Z","shell.execute_reply":"2023-07-06T12:59:39.340096Z"},"trusted":true},"execution_count":334,"outputs":[{"execution_count":334,"output_type":"execute_result","data":{"text/plain":"             Id  class_0  class_1\n0  00eed32682bb      0.5      0.5\n1  010ebe33f668      0.5      0.5\n2  02fa521e1838      0.5      0.5\n3  040e15f562a2      0.5      0.5\n4  046e85c7cc7f      0.5      0.5","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>class_0</th>\n      <th>class_1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00eed32682bb</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>010ebe33f668</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>02fa521e1838</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>040e15f562a2</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>046e85c7cc7f</td>\n      <td>0.5</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sample_submission[['class_0', 'class_1']] = y_prob\nsample_submission.to_csv('/kaggle/working/submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T13:00:45.834762Z","iopub.execute_input":"2023-07-06T13:00:45.835153Z","iopub.status.idle":"2023-07-06T13:00:45.847897Z","shell.execute_reply.started":"2023-07-06T13:00:45.835117Z","shell.execute_reply":"2023-07-06T13:00:45.846808Z"},"trusted":true},"execution_count":336,"outputs":[]}]}