spotify-popularity-analysis-and-a-simple-modeling.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","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":"code","source":"# Modules\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sb\nfrom sklearn.pipeline import make_pipeline, Pipeline\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler\nfrom category_encoders import TargetEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import cross_val_score,GridSearchCV\nfrom scipy import stats\nimport joblib","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-04-18T13:45:40.643646Z","iopub.execute_input":"2023-04-18T13:45:40.644207Z","iopub.status.idle":"2023-04-18T13:45:43.820724Z","shell.execute_reply.started":"2023-04-18T13:45:40.644158Z","shell.execute_reply":"2023-04-18T13:45:43.819252Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"## Exploratory analysis:","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/spotify-tracks-datasets/tracks.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:43.823900Z","iopub.execute_input":"2023-04-18T13:45:43.824927Z","iopub.status.idle":"2023-04-18T13:45:49.329390Z","shell.execute_reply.started":"2023-04-18T13:45:43.824862Z","shell.execute_reply":"2023-04-18T13:45:49.327310Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"                       id                                 name  popularity  \\\n0  35iwgR4jXetI318WEWsa1Q                                Carve           6   \n1  021ht4sdgPcrDgSk7JTbKY  Capítulo 2.16 - Banquero Anarquista           0   \n2  07A5yehtSnoedViJAZkNnc   Vivo para Quererte - Remasterizado           0   \n3  08FmqUhxtyLTn6pAh6bk45        El Prisionero - Remasterizado           0   \n4  08y9GfoqCWfOGsKdwojr5e                  Lady of the Evening           0   \n\n   duration_ms  explicit              artists                  id_artists  \\\n0       126903         0              ['Uli']  ['45tIt06XoI0Iio4LBEVpls']   \n1        98200         0  ['Fernando Pessoa']  ['14jtPCOoNZwquk5wd9DxrY']   \n2       181640         0  ['Ignacio Corsini']  ['5LiOoJbxVSAMkBS2fUm3X2']   \n3       176907         0  ['Ignacio Corsini']  ['5LiOoJbxVSAMkBS2fUm3X2']   \n4       163080         0      ['Dick Haymes']  ['3BiJGZsyX9sJchTqcSA7Su']   \n\n  release_date  danceability  energy  key  loudness  mode  speechiness  \\\n0   1922-02-22         0.645  0.4450    0   -13.338     1       0.4510   \n1   1922-06-01         0.695  0.2630    0   -22.136     1       0.9570   \n2   1922-03-21         0.434  0.1770    1   -21.180     1       0.0512   \n3   1922-03-21         0.321  0.0946    7   -27.961     1       0.0504   \n4         1922         0.402  0.1580    3   -16.900     0       0.0390   \n\n   acousticness  instrumentalness  liveness  valence    tempo  time_signature  \n0         0.674            0.7440     0.151    0.127  104.851               3  \n1         0.797            0.0000     0.148    0.655  102.009               1  \n2         0.994            0.0218     0.212    0.457  130.418               5  \n3         0.995            0.9180     0.104    0.397  169.980               3  \n4         0.989            0.1300     0.311    0.196  103.220               4  ","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>name</th>\n      <th>popularity</th>\n      <th>duration_ms</th>\n      <th>explicit</th>\n      <th>artists</th>\n      <th>id_artists</th>\n      <th>release_date</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>35iwgR4jXetI318WEWsa1Q</td>\n      <td>Carve</td>\n      <td>6</td>\n      <td>126903</td>\n      <td>0</td>\n      <td>['Uli']</td>\n      <td>['45tIt06XoI0Iio4LBEVpls']</td>\n      <td>1922-02-22</td>\n      <td>0.645</td>\n      <td>0.4450</td>\n      <td>0</td>\n      <td>-13.338</td>\n      <td>1</td>\n      <td>0.4510</td>\n      <td>0.674</td>\n      <td>0.7440</td>\n      <td>0.151</td>\n      <td>0.127</td>\n      <td>104.851</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>021ht4sdgPcrDgSk7JTbKY</td>\n      <td>Capítulo 2.16 - Banquero Anarquista</td>\n      <td>0</td>\n      <td>98200</td>\n      <td>0</td>\n      <td>['Fernando Pessoa']</td>\n      <td>['14jtPCOoNZwquk5wd9DxrY']</td>\n      <td>1922-06-01</td>\n      <td>0.695</td>\n      <td>0.2630</td>\n      <td>0</td>\n      <td>-22.136</td>\n      <td>1</td>\n      <td>0.9570</td>\n      <td>0.797</td>\n      <td>0.0000</td>\n      <td>0.148</td>\n      <td>0.655</td>\n      <td>102.009</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>07A5yehtSnoedViJAZkNnc</td>\n      <td>Vivo para Quererte - Remasterizado</td>\n      <td>0</td>\n      <td>181640</td>\n      <td>0</td>\n      <td>['Ignacio Corsini']</td>\n      <td>['5LiOoJbxVSAMkBS2fUm3X2']</td>\n      <td>1922-03-21</td>\n      <td>0.434</td>\n      <td>0.1770</td>\n      <td>1</td>\n      <td>-21.180</td>\n      <td>1</td>\n      <td>0.0512</td>\n      <td>0.994</td>\n      <td>0.0218</td>\n      <td>0.212</td>\n      <td>0.457</td>\n      <td>130.418</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>08FmqUhxtyLTn6pAh6bk45</td>\n      <td>El Prisionero - Remasterizado</td>\n      <td>0</td>\n      <td>176907</td>\n      <td>0</td>\n      <td>['Ignacio Corsini']</td>\n      <td>['5LiOoJbxVSAMkBS2fUm3X2']</td>\n      <td>1922-03-21</td>\n      <td>0.321</td>\n      <td>0.0946</td>\n      <td>7</td>\n      <td>-27.961</td>\n      <td>1</td>\n      <td>0.0504</td>\n      <td>0.995</td>\n      <td>0.9180</td>\n      <td>0.104</td>\n      <td>0.397</td>\n      <td>169.980</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>08y9GfoqCWfOGsKdwojr5e</td>\n      <td>Lady of the Evening</td>\n      <td>0</td>\n      <td>163080</td>\n      <td>0</td>\n      <td>['Dick Haymes']</td>\n      <td>['3BiJGZsyX9sJchTqcSA7Su']</td>\n      <td>1922</td>\n      <td>0.402</td>\n      <td>0.1580</td>\n      <td>3</td>\n      <td>-16.900</td>\n      <td>0</td>\n      <td>0.0390</td>\n      <td>0.989</td>\n      <td>0.1300</td>\n      <td>0.311</td>\n      <td>0.196</td>\n      <td>103.220</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:49.331203Z","iopub.execute_input":"2023-04-18T13:45:49.331620Z","iopub.status.idle":"2023-04-18T13:45:49.540138Z","shell.execute_reply.started":"2023-04-18T13:45:49.331583Z","shell.execute_reply":"2023-04-18T13:45:49.538624Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 586672 entries, 0 to 586671\nData columns (total 20 columns):\n #   Column            Non-Null Count   Dtype  \n---  ------            --------------   -----  \n 0   id                586672 non-null  object \n 1   name              586601 non-null  object \n 2   popularity        586672 non-null  int64  \n 3   duration_ms       586672 non-null  int64  \n 4   explicit          586672 non-null  int64  \n 5   artists           586672 non-null  object \n 6   id_artists        586672 non-null  object \n 7   release_date      586672 non-null  object \n 8   danceability      586672 non-null  float64\n 9   energy            586672 non-null  float64\n 10  key               586672 non-null  int64  \n 11  loudness          586672 non-null  float64\n 12  mode              586672 non-null  int64  \n 13  speechiness       586672 non-null  float64\n 14  acousticness      586672 non-null  float64\n 15  instrumentalness  586672 non-null  float64\n 16  liveness          586672 non-null  float64\n 17  valence           586672 non-null  float64\n 18  tempo             586672 non-null  float64\n 19  time_signature    586672 non-null  int64  \ndtypes: float64(9), int64(6), object(5)\nmemory usage: 89.5+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:49.543417Z","iopub.execute_input":"2023-04-18T13:45:49.543844Z","iopub.status.idle":"2023-04-18T13:45:50.073825Z","shell.execute_reply.started":"2023-04-18T13:45:49.543804Z","shell.execute_reply":"2023-04-18T13:45:50.072050Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"          popularity   duration_ms       explicit   danceability  \\\ncount  586672.000000  5.866720e+05  586672.000000  586672.000000   \nmean       27.570053  2.300512e+05       0.044086       0.563594   \nstd        18.370642  1.265261e+05       0.205286       0.166103   \nmin         0.000000  3.344000e+03       0.000000       0.000000   \n25%        13.000000  1.750930e+05       0.000000       0.453000   \n50%        27.000000  2.148930e+05       0.000000       0.577000   \n75%        41.000000  2.638670e+05       0.000000       0.686000   \nmax       100.000000  5.621218e+06       1.000000       0.991000   \n\n              energy            key       loudness           mode  \\\ncount  586672.000000  586672.000000  586672.000000  586672.000000   \nmean        0.542036       5.221603     -10.206067       0.658797   \nstd         0.251923       3.519423       5.089328       0.474114   \nmin         0.000000       0.000000     -60.000000       0.000000   \n25%         0.343000       2.000000     -12.891000       0.000000   \n50%         0.549000       5.000000      -9.243000       1.000000   \n75%         0.748000       8.000000      -6.482000       1.000000   \nmax         1.000000      11.000000       5.376000       1.000000   \n\n         speechiness   acousticness  instrumentalness       liveness  \\\ncount  586672.000000  586672.000000     586672.000000  586672.000000   \nmean        0.104864       0.449863          0.113451       0.213935   \nstd         0.179893       0.348837          0.266868       0.184326   \nmin         0.000000       0.000000          0.000000       0.000000   \n25%         0.034000       0.096900          0.000000       0.098300   \n50%         0.044300       0.422000          0.000024       0.139000   \n75%         0.076300       0.785000          0.009550       0.278000   \nmax         0.971000       0.996000          1.000000       1.000000   \n\n             valence          tempo  time_signature  \ncount  586672.000000  586672.000000   586672.000000  \nmean        0.552292     118.464857        3.873382  \nstd         0.257671      29.764108        0.473162  \nmin         0.000000       0.000000        0.000000  \n25%         0.346000      95.600000        4.000000  \n50%         0.564000     117.384000        4.000000  \n75%         0.769000     136.321000        4.000000  \nmax         1.000000     246.381000        5.000000  ","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>popularity</th>\n      <th>duration_ms</th>\n      <th>explicit</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>586672.000000</td>\n      <td>5.866720e+05</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n      <td>586672.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>27.570053</td>\n      <td>2.300512e+05</td>\n      <td>0.044086</td>\n      <td>0.563594</td>\n      <td>0.542036</td>\n      <td>5.221603</td>\n      <td>-10.206067</td>\n      <td>0.658797</td>\n      <td>0.104864</td>\n      <td>0.449863</td>\n      <td>0.113451</td>\n      <td>0.213935</td>\n      <td>0.552292</td>\n      <td>118.464857</td>\n      <td>3.873382</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>18.370642</td>\n      <td>1.265261e+05</td>\n      <td>0.205286</td>\n      <td>0.166103</td>\n      <td>0.251923</td>\n      <td>3.519423</td>\n      <td>5.089328</td>\n      <td>0.474114</td>\n      <td>0.179893</td>\n      <td>0.348837</td>\n      <td>0.266868</td>\n      <td>0.184326</td>\n      <td>0.257671</td>\n      <td>29.764108</td>\n      <td>0.473162</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>3.344000e+03</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-60.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>13.000000</td>\n      <td>1.750930e+05</td>\n      <td>0.000000</td>\n      <td>0.453000</td>\n      <td>0.343000</td>\n      <td>2.000000</td>\n      <td>-12.891000</td>\n      <td>0.000000</td>\n      <td>0.034000</td>\n      <td>0.096900</td>\n      <td>0.000000</td>\n      <td>0.098300</td>\n      <td>0.346000</td>\n      <td>95.600000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>27.000000</td>\n      <td>2.148930e+05</td>\n      <td>0.000000</td>\n      <td>0.577000</td>\n      <td>0.549000</td>\n      <td>5.000000</td>\n      <td>-9.243000</td>\n      <td>1.000000</td>\n      <td>0.044300</td>\n      <td>0.422000</td>\n      <td>0.000024</td>\n      <td>0.139000</td>\n      <td>0.564000</td>\n      <td>117.384000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>41.000000</td>\n      <td>2.638670e+05</td>\n      <td>0.000000</td>\n      <td>0.686000</td>\n      <td>0.748000</td>\n      <td>8.000000</td>\n      <td>-6.482000</td>\n      <td>1.000000</td>\n      <td>0.076300</td>\n      <td>0.785000</td>\n      <td>0.009550</td>\n      <td>0.278000</td>\n      <td>0.769000</td>\n      <td>136.321000</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>100.000000</td>\n      <td>5.621218e+06</td>\n      <td>1.000000</td>\n      <td>0.991000</td>\n      <td>1.000000</td>\n      <td>11.000000</td>\n      <td>5.376000</td>\n      <td>1.000000</td>\n      <td>0.971000</td>\n      <td>0.996000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>246.381000</td>\n      <td>5.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sum(df.duplicated()) # Are there duplicates?","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:50.075807Z","iopub.execute_input":"2023-04-18T13:45:50.076270Z","iopub.status.idle":"2023-04-18T13:45:51.619836Z","shell.execute_reply.started":"2023-04-18T13:45:50.076229Z","shell.execute_reply":"2023-04-18T13:45:51.618212Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"numeric_columns = [col for col in df if pd.api.types.is_numeric_dtype(df[col])]\nprint(numeric_columns)\nno_interest = ['track_number','disc_number','explicit','time_signature', 'year']\nfeatures = [f for f in numeric_columns if f not in no_interest]\nfeatures","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:51.621658Z","iopub.execute_input":"2023-04-18T13:45:51.622054Z","iopub.status.idle":"2023-04-18T13:45:51.634215Z","shell.execute_reply.started":"2023-04-18T13:45:51.622018Z","shell.execute_reply":"2023-04-18T13:45:51.632392Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"['popularity', 'duration_ms', 'explicit', 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']\n","output_type":"stream"},{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"['popularity',\n 'duration_ms',\n 'danceability',\n 'energy',\n 'key',\n 'loudness',\n 'mode',\n 'speechiness',\n 'acousticness',\n 'instrumentalness',\n 'liveness',\n 'valence',\n 'tempo']"},"metadata":{}}]},{"cell_type":"code","source":"df_features = df[features]\ndf_features","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:51.636360Z","iopub.execute_input":"2023-04-18T13:45:51.636901Z","iopub.status.idle":"2023-04-18T13:45:51.708418Z","shell.execute_reply.started":"2023-04-18T13:45:51.636856Z","shell.execute_reply":"2023-04-18T13:45:51.706843Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"        popularity  duration_ms  danceability  energy  key  loudness  mode  \\\n0                6       126903         0.645  0.4450    0   -13.338     1   \n1                0        98200         0.695  0.2630    0   -22.136     1   \n2                0       181640         0.434  0.1770    1   -21.180     1   \n3                0       176907         0.321  0.0946    7   -27.961     1   \n4                0       163080         0.402  0.1580    3   -16.900     0   \n...            ...          ...           ...     ...  ...       ...   ...   \n586667          50       258267         0.560  0.5180    0    -7.471     0   \n586668          72       153293         0.765  0.6630    0    -5.223     1   \n586669          70       187601         0.535  0.3140    7   -12.823     0   \n586670          58       142003         0.696  0.6150   10    -6.212     1   \n586671          38       214360         0.686  0.7230    6    -7.067     1   \n\n        speechiness  acousticness  instrumentalness  liveness  valence  \\\n0            0.4510         0.674          0.744000    0.1510   0.1270   \n1            0.9570         0.797          0.000000    0.1480   0.6550   \n2            0.0512         0.994          0.021800    0.2120   0.4570   \n3            0.0504         0.995          0.918000    0.1040   0.3970   \n4            0.0390         0.989          0.130000    0.3110   0.1960   \n...             ...           ...               ...       ...      ...   \n586667       0.0292         0.785          0.000000    0.0648   0.2110   \n586668       0.0652         0.141          0.000297    0.0924   0.6860   \n586669       0.0408         0.895          0.000150    0.0874   0.0663   \n586670       0.0345         0.206          0.000003    0.3050   0.4380   \n586671       0.0363         0.105          0.000000    0.2640   0.9750   \n\n          tempo  \n0       104.851  \n1       102.009  \n2       130.418  \n3       169.980  \n4       103.220  \n...         ...  \n586667  131.896  \n586668  150.091  \n586669  145.095  \n586670   90.029  \n586671  112.204  \n\n[586672 rows x 13 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>popularity</th>\n      <th>duration_ms</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>126903</td>\n      <td>0.645</td>\n      <td>0.4450</td>\n      <td>0</td>\n      <td>-13.338</td>\n      <td>1</td>\n      <td>0.4510</td>\n      <td>0.674</td>\n      <td>0.744000</td>\n      <td>0.1510</td>\n      <td>0.1270</td>\n      <td>104.851</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>98200</td>\n      <td>0.695</td>\n      <td>0.2630</td>\n      <td>0</td>\n      <td>-22.136</td>\n      <td>1</td>\n      <td>0.9570</td>\n      <td>0.797</td>\n      <td>0.000000</td>\n      <td>0.1480</td>\n      <td>0.6550</td>\n      <td>102.009</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>181640</td>\n      <td>0.434</td>\n      <td>0.1770</td>\n      <td>1</td>\n      <td>-21.180</td>\n      <td>1</td>\n      <td>0.0512</td>\n      <td>0.994</td>\n      <td>0.021800</td>\n      <td>0.2120</td>\n      <td>0.4570</td>\n      <td>130.418</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>176907</td>\n      <td>0.321</td>\n      <td>0.0946</td>\n      <td>7</td>\n      <td>-27.961</td>\n      <td>1</td>\n      <td>0.0504</td>\n      <td>0.995</td>\n      <td>0.918000</td>\n      <td>0.1040</td>\n      <td>0.3970</td>\n      <td>169.980</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>163080</td>\n      <td>0.402</td>\n      <td>0.1580</td>\n      <td>3</td>\n      <td>-16.900</td>\n      <td>0</td>\n      <td>0.0390</td>\n      <td>0.989</td>\n      <td>0.130000</td>\n      <td>0.3110</td>\n      <td>0.1960</td>\n      <td>103.220</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    </tr>\n    <tr>\n      <th>586667</th>\n      <td>50</td>\n      <td>258267</td>\n      <td>0.560</td>\n      <td>0.5180</td>\n      <td>0</td>\n      <td>-7.471</td>\n      <td>0</td>\n      <td>0.0292</td>\n      <td>0.785</td>\n      <td>0.000000</td>\n      <td>0.0648</td>\n      <td>0.2110</td>\n      <td>131.896</td>\n    </tr>\n    <tr>\n      <th>586668</th>\n      <td>72</td>\n      <td>153293</td>\n      <td>0.765</td>\n      <td>0.6630</td>\n      <td>0</td>\n      <td>-5.223</td>\n      <td>1</td>\n      <td>0.0652</td>\n      <td>0.141</td>\n      <td>0.000297</td>\n      <td>0.0924</td>\n      <td>0.6860</td>\n      <td>150.091</td>\n    </tr>\n    <tr>\n      <th>586669</th>\n      <td>70</td>\n      <td>187601</td>\n      <td>0.535</td>\n      <td>0.3140</td>\n      <td>7</td>\n      <td>-12.823</td>\n      <td>0</td>\n      <td>0.0408</td>\n      <td>0.895</td>\n      <td>0.000150</td>\n      <td>0.0874</td>\n      <td>0.0663</td>\n      <td>145.095</td>\n    </tr>\n    <tr>\n      <th>586670</th>\n      <td>58</td>\n      <td>142003</td>\n      <td>0.696</td>\n      <td>0.6150</td>\n      <td>10</td>\n      <td>-6.212</td>\n      <td>1</td>\n      <td>0.0345</td>\n      <td>0.206</td>\n      <td>0.000003</td>\n      <td>0.3050</td>\n      <td>0.4380</td>\n      <td>90.029</td>\n    </tr>\n    <tr>\n      <th>586671</th>\n      <td>38</td>\n      <td>214360</td>\n      <td>0.686</td>\n      <td>0.7230</td>\n      <td>6</td>\n      <td>-7.067</td>\n      <td>1</td>\n      <td>0.0363</td>\n      <td>0.105</td>\n      <td>0.000000</td>\n      <td>0.2640</td>\n      <td>0.9750</td>\n      <td>112.204</td>\n    </tr>\n  </tbody>\n</table>\n<p>586672 rows × 13 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"sb.heatmap(df_features.corr().round(2), annot=True, cmap=\"magma\",\n           annot_kws={\"size\": 8},linewidths=.5)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:51.710864Z","iopub.execute_input":"2023-04-18T13:45:51.711377Z","iopub.status.idle":"2023-04-18T13:45:53.430622Z","shell.execute_reply.started":"2023-04-18T13:45:51.711330Z","shell.execute_reply":"2023-04-18T13:45:53.429366Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"df_features.corr().round(2)['popularity'].sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:53.432334Z","iopub.execute_input":"2023-04-18T13:45:53.433103Z","iopub.status.idle":"2023-04-18T13:45:53.802948Z","shell.execute_reply.started":"2023-04-18T13:45:53.433054Z","shell.execute_reply":"2023-04-18T13:45:53.801331Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"popularity          1.00\nloudness            0.33\nenergy              0.30\ndanceability        0.19\ntempo               0.07\nduration_ms         0.03\nkey                 0.02\nvalence             0.00\nmode               -0.03\nspeechiness        -0.05\nliveness           -0.05\ninstrumentalness   -0.24\nacousticness       -0.37\nName: popularity, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"df_features.hist(bins=50,figsize = (15,10))","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:53.807421Z","iopub.execute_input":"2023-04-18T13:45:53.807923Z","iopub.status.idle":"2023-04-18T13:45:57.562695Z","shell.execute_reply.started":"2023-04-18T13:45:53.807877Z","shell.execute_reply":"2023-04-18T13:45:57.561256Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"array([[<AxesSubplot:title={'center':'popularity'}>,\n        <AxesSubplot:title={'center':'duration_ms'}>,\n        <AxesSubplot:title={'center':'danceability'}>,\n        <AxesSubplot:title={'center':'energy'}>],\n       [<AxesSubplot:title={'center':'key'}>,\n        <AxesSubplot:title={'center':'loudness'}>,\n        <AxesSubplot:title={'center':'mode'}>,\n        <AxesSubplot:title={'center':'speechiness'}>],\n       [<AxesSubplot:title={'center':'acousticness'}>,\n        <AxesSubplot:title={'center':'instrumentalness'}>,\n        <AxesSubplot:title={'center':'liveness'}>,\n        <AxesSubplot:title={'center':'valence'}>],\n       [<AxesSubplot:title={'center':'tempo'}>, <AxesSubplot:>,\n        <AxesSubplot:>, <AxesSubplot:>]], dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 16 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAABN8AAANCCAYAAABWMGquAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzde1xU1f4//tfIZbgIIxe5jBcgU7yg5pFUNANSQALMrKxIgo6HLK8E/vLSx8SO4t08H7yUZmKp0ekoHW8HwRvkB1BEKVFTK68FYoqgqICwfn/4nX0YhqsyMDO8no8Hj5q937P3WnvD2zVr1tpLJoQQICIiIiIiIiIiombXrrULQEREREREREREZKjY+UZERERERERERKQl7HwjIiIiIiIiIiLSEna+ERERERERERERaQk734iIiIiIiIiIiLSEnW9ERERERERERERaws43IiIiIiIiIiIiLWHnGxERERERERERkZaw842IiIiIiIiIiEhL2PlGBis2NhYymazZjxsREQFXV1e1bXFxcfj++++b/VxEpB3ayg+NcebMGcTGxuLSpUsa+2rLL0REDWnNnKZNERERaN++faNiXV1dERERIb2+dOkSZDIZEhISpG0JCQmQyWRq+Xfbtm1YtWpV8xSYiIioDux8I2qiuXPnIikpSW0bO9+IqLHOnDmD+fPn19r5Vlt+ISKihiUlJWHu3Ln1xgQFBSEzMxPOzs7SNna+ERFRSzBu7QIQ6Yt79+7BwsIC3bp1a+2iEJEOUeWG5sD8QkT0eAYMGNBgTMeOHdGxY8cWKA0R0SPN2U4k/caRb/RYVNMbTp48ibFjx8La2hoKhQLjx4/HjRs3pLiqqiosXboUPXv2hFwuh4ODA95++21cu3ZN7Xg+Pj7w8PDADz/8gCFDhsDc3BydOnXC3LlzUVlZKcUdPnwYMpkMhw8fVnt/bVMLavPtt9/C398fzs7OMDc3R69evTBr1iyUlpaqxammOZw6dQr+/v6wsrLCiBEjpH3Vp4XJZDKUlpZi8+bNkMlkkMlk8PHxwaVLl2BsbIxFixZplCM9PR0ymQzfffddveUloie3Z88ePPPMM5DL5XBzc8Py5cvV9teXP2QyGWJjY6XXqtx34sQJvPrqq7CxsZE6zI4fP4433ngDrq6uMDc3h6urK958801cvnxZen9CQgJee+01AICvr6+UM1Tnrm3a6YMHDzB79my4ubnB1NQUnTp1wuTJk3H79m21OFdXVwQHByM5ORl/+ctfYG5ujp49e+LLL79s8jWTyWSYMmUKNm3aBHd3d5ibm8PT0xNZWVkQQmDZsmVwc3ND+/bt8cILL+CXX35Re//JkycRHBwMBwcHyOVyKJVKBAUFaeR+Imq6hnIaAKxZswbPP/88HBwcYGlpib59+2Lp0qWoqKhQi1O1v7KzszF8+HBYWFjgqaeewuLFi1FVVaUWe/v2bcTExOCpp56S2nQvvvgifv75ZymmvLwcCxYskNp9HTt2xDvvvKPWNgQa3x5TOX36NEaMGAFLS0t07NgRU6ZMwb1799Riak47rU3Naac+Pj7Ys2cPLl++LOVjmUwGIQS6d++OgIAAjWPcvXsXCoUCkydPrvdcRNS6Lly4gNDQUKkt0qtXL6xZs0bar/pc+c033+Cjjz6CUqmEtbU1Ro4ciXPnzmkcb//+/RgxYgSsra1hYWGBYcOG4cCBA2ox9bUTy8rKEBMTAycnJ1hYWOD5559HTk6OWu7i50fDxpFv9ERefvlljBs3Du+99x5Onz6NuXPn4syZMzh69ChMTEzw/vvvY/369ZgyZQqCg4Nx6dIlzJ07F4cPH8aJEydgb28vHaugoABvvPEGZs2ahU8++QR79uzBggULUFRUhNWrVzdLeS9cuIAXX3wRUVFRsLS0xM8//4wlS5bg2LFjOHjwoFpseXk5Ro8ejYkTJ2LWrFl4+PBhrcfMzMzECy+8AF9fX2m6g7W1NVxdXTF69Gh89tln+PDDD2FkZCS9Z/Xq1VAqlXj55ZebpV5EVLsDBw7gpZdegpeXFxITE1FZWYmlS5fi+vXrT3TcsWPH4o033sB7770nfVi8dOkS3N3d8cYbb8DW1hb5+flYt24dnn32WZw5cwb29vYICgpCXFwc5syZgzVr1uAvf/kLgLpHvAkhMGbMGBw4cACzZ8/G8OHD8dNPP2HevHnIzMxEZmYm5HK5FP/jjz8iJiYGs2bNgqOjI7744gtMmDABTz/9NJ5//vkm1XH37t04efIkFi9eDJlMhpkzZyIoKAjh4eH47bffsHr1ahQXFyM6OhqvvPIKcnNzpS8j/Pz84ObmhjVr1sDR0REFBQU4dOgQ7ty585hXnIiAxue0X3/9FaGhoVKn/Y8//oiFCxfi559/1uiQLygowFtvvYWYmBjMmzcPSUlJmD17NpRKJd5++20AwJ07d/Dcc8/h0qVLmDlzJgYPHoy7d+8iPT0d+fn56NmzJ6qqqvDSSy/hhx9+wIcffoihQ4fi8uXLmDdvHnx8fHD8+HGYm5sDaFp7rKKiAi+++KLUHsvIyMCCBQtw+fJl7Nq164mu59q1a/Huu+/i119/VZvyL5PJMHXqVERFReHChQvo3r27tO+rr75CSUkJO9+IdNiZM2cwdOhQdO3aFStWrICTkxP27duHadOm4c8//8S8efOk2Dlz5mDYsGH44osvUFJSgpkzZyIkJARnz56VPr9t2bIFb7/9Nl566SVs3rwZJiYm+PzzzxEQEIB9+/ZJgzRUamsnvvPOO/j222/x4Ycf4oUXXsCZM2fw8ssvo6SkRHofPz8aOEH0GObNmycAiA8++EBt+9atWwUAsWXLFnH27FkBQEyaNEkt5ujRowKAmDNnjrTN29tbABD//ve/1WIjIyNFu3btxOXLl4UQQhw6dEgAEIcOHVKLu3jxogAgNm3apFHGulRVVYmKigqRlpYmAIgff/xR2hceHi4AiC+//FLjfeHh4cLFxUVtm6WlpQgPD9eIVZU3KSlJ2vb7778LY2NjMX/+/DrLRkTNY/DgwUKpVIr79+9L20pKSoStra2UH2rLHyoAxLx586TXqrzy8ccfN3juhw8firt37wpLS0vxj3/8Q9r+3Xff1ZrHhNDML8nJyQKAWLp0qVrct99+KwCI9evXS9tcXFyEmZmZlC+FEOL+/fvC1tZWTJw4scHyVgdAODk5ibt370rbvv/+ewFAPPPMM6KqqkravmrVKgFA/PTTT0IIIY4fPy4AiO+//75J5ySihjUmp9VUWVkpKioqxFdffSWMjIzErVu3pH2q9tfRo0fV3tO7d28REBAgvf7kk08EAJGamlpn2b755hsBQGzfvl1te3Z2tgAg1q5dW+v7GtMeq55DhRBi4cKFAoA4cuSItM3FxUWtLVZbbt+0aZMAIC5evChtCwoK0mjXCfHoulpZWYnp06erbe/du7fw9fWt4yoQkS4ICAgQnTt3FsXFxWrbp0yZIszMzMStW7ekz2kvvviiWsw///lPAUBkZmYKIYQoLS0Vtra2IiQkRC2usrJS9O/fXwwaNEjaVlc78fTp0wKAmDlzptp2Vd6snrv4+dFwcdopPZG33npL7fW4ceNgbGyMQ4cO4dChQwCgMQVg0KBB6NWrl8YwXSsrK4wePVptW2hoKKqqqpCent4s5f3tt98QGhoKJycnGBkZwcTEBN7e3gCAs2fPasS/8sorT3Q+Hx8f9O/fX22I82effQaZTIZ33333iY5NRPUrLS1FdnY2xo4dCzMzM2m7lZUVQkJCnujYteWGu3fvYubMmXj66adhbGwMY2NjtG/fHqWlpbXml8ZQjQCpmUdfe+01WFpaauTRZ555Bl27dpVem5mZoUePHmpTXxvL19cXlpaW0utevXoBAAIDA9VWVVRtV53j6aefho2NDWbOnInPPvsMZ86cafK5iUhTU3LayZMnMXr0aNjZ2UntnbfffhuVlZU4f/68WqyTkxMGDRqktq1fv35qeeM///kPevTogZEjR9ZZvt27d6NDhw4ICQnBw4cPpZ9nnnkGTk5Oao8MaWp7rGZ7MzQ0FACktqY2WFlZ4Z133kFCQoI0cuXgwYM4c+YMpkyZorXzEtGTefDgAQ4cOICXX34ZFhYWavnoxRdfxIMHD5CVlSXF1/z82a9fPwD/bddkZGTg1q1bCA8PVztWVVUVRo0ahezsbI0p8zXbiWlpaQAefVau7tVXX4WxsfpkRH5+NFzsfKMn4uTkpPba2NgYdnZ2uHnzJm7evAkAaitKqSiVSmm/iqOjY53Hrxn7OO7evYvhw4fj6NGjWLBgAQ4fPozs7Gzs2LEDAHD//n21eAsLC1hbWz/xeadNm4YDBw7g3LlzqKiowIYNG/Dqq69qXDsial5FRUWoqqqq9W/tSf/+astroaGhWL16Nf72t79h3759OHbsGLKzs9GxY0eN/NJYN2/ehLGxscYDwmUyGZycnDRyo52dncYx5HL5Y53f1tZW7bWpqWm92x88eAAAUCgUSEtLwzPPPIM5c+agT58+UCqVmDdvnsbzpoio8Rqb065cuYLhw4fj999/xz/+8Q/88MMPyM7Olj7I1cwHjckbN27cQOfOnest3/Xr13H79m2YmprCxMRE7aegoAB//vkngKa3x1Rty9rq2xztw/pMnToVd+7cwdatWwE8mvbVuXNnvPTSS1o9LxE9vps3b+Lhw4eIj4/XyEUvvvgiAEj5CNDMgarHeahykWpa/6uvvqpxvCVLlkAIgVu3bqkdo2Y7UZWran7erS2/Afz8aKj4zDd6IgUFBejUqZP0+uHDh7h58ybs7OykRJKfn6/RYPvjjz/UnvcGoNZnMBUUFAD4b1JUfdNbVlamFlc9gdbl4MGD+OOPP3D48GHp21UAGg8tV6k+suNJhIaGYubMmVizZg2GDBmCgoICPieEqAXY2NhAJpNJeaS66tvqyiv1fairmR+Ki4uxe/duzJs3D7NmzZK2l5WVaTTImsLOzg4PHz7EjRs31DrghBAoKCjAs88++9jH1qa+ffsiMTERQgj89NNPSEhIwCeffAJzc3O160NEjdfYnPb999+jtLQUO3bsgIuLi7Q9Nzf3sc/dsWPHBhdMsbe3h52dHZKTk2vdb2VlBaDp7bHqbUuVmu1DbXn66acRGBiINWvWIDAwEDt37sT8+fPVnsNERLrFxsYGRkZGCAsLq/Mzl5ubG06dOtWo46k+s8bHx2PIkCG1xtTsVKvZTlTlquvXr9f62bkmfn40TBz5Rk9E9U2gyj//+U88fPgQPj4+eOGFFwA8ekBlddnZ2Th79qzGgynv3LmDnTt3qm3btm0b2rVrJz0oXLUK4E8//aQWV/N9tVElweoPJweAzz//vMH3NqS+kSVmZmZ49913sXnzZqxcuRLPPPMMhg0b9sTnJKL6WVpaYtCgQdixY4c0Kgt4lGuqP6Tb0dERZmZmGnnl3//+d6PPpVodr2Z++eKLL9RWbAY0v1GtjypP1syj27dvR2lpqUYe1TUymQz9+/fHp59+ig4dOuDEiROtXSQivdXYnFZbe0cIgQ0bNjz2uQMDA3H+/HmNxRCqCw4Oxs2bN1FZWQlPT0+NH3d39zrLB9TfHqvZ3ty2bRuAR9OznlRDo4OnT5+On376CeHh4TAyMkJkZOQTn5OItMfCwgK+vr44efIk+vXrV2s+akrH/bBhw9ChQwecOXOm1mN5enpKswDqovos++2336pt/9e//lXron78/GiYOPKNnsiOHTtgbGwMPz8/abXT/v37Y9y4cTA1NcW7776L+Ph4tGvXDoGBgdJqp126dMEHH3ygdiw7Ozu8//77uHLlCnr06IG9e/diw4YNeP/996VnGDk5OWHkyJFYtGgRbGxs4OLiggMHDkhTFeozdOhQ2NjY4L333sO8efNgYmKCrVu34scff3zi69C3b18cPnwYu3btgrOzM6ysrKRGJgBMmjQJS5cuRU5ODr744osnPh8RNc7f//53jBo1Cn5+foiJiUFlZSWWLFkCS0tLaUSaTCbD+PHj8eWXX6Jbt27o378/jh07Jn24awxra2s8//zzWLZsGezt7eHq6oq0tDRs3LgRHTp0UIv18PAAAKxfvx5WVlYwMzODm5tbrQ1BPz8/BAQEYObMmSgpKcGwYcOk1U4HDBiAsLCwx784WrJ7926sXbsWY8aMwVNPPQUhBHbs2IHbt2/Dz8+vtYtHpNcak9P8/PxgamqKN998Ex9++CEePHiAdevWoaio6LHPGxUVhW+//RYvvfQSZs2ahUGDBuH+/ftIS0tDcHAwfH198cYbb2Dr1q148cUXMX36dAwaNAgmJia4du0aDh06hJdeegkvv/xyk9tjpqamWLFiBe7evYtnn31WWu00MDAQzz333GPXSaVv377YsWMH1q1bh4EDB6Jdu3bw9PSU9vv5+aF37944dOgQxo8fDwcHhyc+JxFp1z/+8Q8899xzGD58ON5//324urrizp07+OWXX7Br1656v0ioqX379oiPj0d4eDhu3bqFV199FQ4ODrhx4wZ+/PFH3LhxA+vWrav3GH369MGbb76JFStWwMjICC+88AJOnz6NFStWQKFQoF07zTFR/PxogFpztQfSX6qVXHJyckRISIho3769sLKyEm+++aa4fv26FFdZWSmWLFkievToIUxMTIS9vb0YP368uHr1qtrxvL29RZ8+fcThw4eFp6enkMvlwtnZWcyZM0dUVFSoxebn54tXX31V2NraCoVCIcaPHy+trtfQaqcZGRnCy8tLWFhYiI4dO4q//e1v4sSJExrvDQ8PF5aWlrXWvbbVTnNzc8WwYcOEhYWFACC8vb013ufj4yNsbW3FvXv36rmyRNTcdu7cKfr16ydMTU1F165dxeLFizXyQ3Fxsfjb3/4mHB0dhaWlpQgJCRGXLl2qc7XTGzduaJzn2rVr4pVXXhE2NjbCyspKjBo1SuTl5WmswCfEoxVC3dzchJGRkVr+qS2/3L9/X8ycOVO4uLgIExMT4ezsLN5//31RVFSkFufi4iKCgoI0yuXt7V1rTqoPADF58mS1baqVA5ctW6a2XbUq13fffSeEEOLnn38Wb775pujWrZswNzcXCoVCDBo0SCQkJDSpDERUu8bktF27don+/fsLMzMz0alTJ/H//X//n/jPf/6jsdKyqv1VU225qKioSEyfPl107dpVmJiYCAcHBxEUFCR+/vlnKaaiokIsX75cOnf79u1Fz549xcSJE8WFCxekuKa2x3766Sfh4+MjzM3Nha2trXj//ffVVmMW4vFXO71165Z49dVXRYcOHYRMJqt11djY2FgBQGRlZWnsIyLddPHiRfHXv/5VdOrUSZiYmIiOHTuKoUOHigULFgghNNsv1d9XM3cIIURaWpoICgoStra2wsTERHTq1EkEBQWpvb++duKDBw9EdHS0cHBwEGZmZmLIkCEiMzNTKBQK8cEHH9RaB35+NCwyIYRoyc4+MgyxsbGYP38+bty4ofHstsfh4+ODP//8E3l5ec1QOt1TWFgIFxcXTJ06FUuXLm3t4hARERFRI3l6ekImkyE7O7u1i0JEBiQjIwPDhg3D1q1bpVWcVfj50fBw2imRFl27dg2//fYbli1bhnbt2mH69OmtXSQiIiIiakBJSQny8vKwe/du5OTkICkpqbWLRER6LDU1FZmZmRg4cCDMzc3x448/YvHixejevTvGjh0rxfHzo+Fi5xuRFn3xxRf45JNP4Orqiq1bt6qtbkNE1NJqe6hvde3atav1uSNERG3NiRMn4OvrCzs7O8ybNw9jxoxp7SIRkR6ztrZGSkoKVq1ahTt37sDe3h6BgYFYtGgRzMzMpDh+fjRcnHZKRETURqhWGaxLeHg4EhISWqYwRERERERtBEe+ERERtRENPa+oOZ7hSURERERE6jjyjYiIiIiIiIiISEv4YBciIiIiIiIiIiItadPTTquqqvDHH3/AysqqwefgEFHrE0Lgzp07UCqVfCh8LZjTiPQLc1rdmM+I9AvzWd2Yz4j0jzZyWpvufPvjjz/QpUuX1i4GETXR1atX0blz59Yuhs5hTiPST8xpmpjPiPQT85km5jMi/dWcOa1Nd75ZWVkBeHRBra2t642tqKhASkoK/P39YWJi0hLFaxGsl35p6/UqKSlBly5dpL9dUtfYnGaov0faxGvWdLxmDWNOqxvbaIZbL8Bw69aW68V8VjfmM9ZL37Be2slpbbrzTTXs19raulGJ0MLCAtbW1gb3C8h66Q/W65HGDNlft24d1q1bh0uXLgEA+vTpg48//hiBgYEAHg0lnj9/PtavX4+ioiIMHjwYa9asQZ8+faRjlJWVYcaMGfjmm29w//59jBgxAmvXrlX79qOoqAjTpk3Dzp07AQCjR49GfHw8OnToIMVcuXIFkydPxsGDB2Fubo7Q0FAsX74cpqamUsypU6cwZcoUHDt2DLa2tpg4cSLmzp3bpOkJjc1phvp7pE28Zk3Ha9Z4nIakiW00w60XYLh1Y72Yz2rDfMZ66RvW67+aM6dxQj4RGaTOnTtj8eLFOH78OI4fP44XXngBL730Ek6fPg0AWLp0KVauXInVq1cjOzsbTk5O8PPzw507d6RjREVFISkpCYmJiThy5Aju3r2L4OBgVFZWSjGhoaHIzc1FcnIykpOTkZubi7CwMGl/ZWUlgoKCUFpaiiNHjiAxMRHbt29HTEyMFFNSUgI/Pz8olUpkZ2cjPj4ey5cvx8qVK1vgShEREREREZE2temRb0RkuEJCQtReL1y4EOvWrUNWVhZ69+6NVatW4aOPPsLYsWMBAJs3b4ajoyO2bduGiRMnori4GBs3bsTXX3+NkSNHAgC2bNmCLl26YP/+/QgICMDZs2eRnJyMrKwsDB48GACwYcMGeHl54dy5c3B3d0dKSgrOnDmDq1evQqlUAgBWrFiBiIgILFy4ENbW1ti6dSsePHiAhIQEyOVyeHh44Pz581i5ciWio6P5LTIREREREZEe48g3IjJ4lZWVSExMRGlpKby8vHDx4kUUFBTA399fipHL5fD29kZGRgYAICcnBxUVFWoxSqUSHh4eUkxmZiYUCoXU8QYAQ4YMgUKhUIvx8PCQOt4AICAgAGVlZcjJyZFivL29IZfL1WL++OMPadosERERERER6SeOfCMig3Xq1Cl4eXnhwYMHaN++PZKSktC7d2+pY8zR0VEt3tHREZcvXwYAFBQUwNTUFDY2NhoxBQUFUoyDg4PGeR0cHNRiap7HxsYGpqamajGurq4a51Htc3Nzq7V+ZWVlKCsrk16XlJQAePQ8g4qKijquCqR99cWQOl6zpuM1axivDREREVHbwM43IjJY7u7uyM3Nxe3bt7F9+3aEh4cjLS1N2l9zOqcQosEpnjVjaotvjhghRJ3vVVm0aBHmz5+vsT0lJQUWFhb11OKR1NTUBmNIHa9Z0/Ga1e3evXutXQQiIiIiagHsfGsij9h9KKt89GH40uKgVi4NEdXH1NQUTz/9NADA09MT2dnZ+Mc//oGZM2cCeDSqzNnZWYovLCyURpw5OTmhvLwcRUVFaqPfCgsLMXToUCnm+vXrGue9ceOG2nGOHj2qtr+oqAgVFRVqMapRcNXPA2iOzqtu9uzZiI6Oll6rlsT29/dvcLXT1NRU+Pn5qa304xG7Ty0uLzagzmO0NXVdM6obr1nDVKNViaj5uc7ao7GNbXci0jbmHqoLO9+IqM0QQqCsrAxubm5wcnJCamoqBgwYAAAoLy9HWloalixZAgAYOHAgTExMkJqainHjxgEA8vPzkZeXh6VLlwIAvLy8UFxcjGPHjmHQoEEAgKNHj6K4uFjqoPPy8sLChQuRn58vdfSlpKRALpdj4MCBUsycOXNQXl4OU1NTKUapVGpMR61OLperPSdOxcTEpFGdHTXjVF8sVN9P6hp7bem/eM3qxutC1Hxq+8BLRESkK9j5RkQGac6cOQgMDESXLl1w584dJCYm4vDhw0hOToZMJkNUVBTi4uLQvXt3dO/eHXFxcbCwsEBoaCgAQKFQYMKECYiJiYGdnR1sbW0xY8YM9O3bV1r9tFevXhg1ahQiIyPx+eefAwDeffddBAcHw93dHQDg7++P3r17IywsDMuWLcOtW7cwY8YMREZGSqPTQkNDMX/+fERERGDOnDm4cOEC4uLi8PHHH3OlUyIiIiIiHVG9o19uJLB0UCsWhvQKVzslIoN0/fp1hIWFwd3dHSNGjMDRo0eRnJwMPz8/AMCHH36IqKgoTJo0CZ6envj999+RkpICKysr6RiffvopxowZg3HjxmHYsGGwsLDArl27YGRkJMVs3boVffv2hb+/P/z9/dGvXz98/fXX0n4jIyPs2bMHZmZmGDZsGMaNG4cxY8Zg+fLlUoxCoUBqaiquXbsGT09PTJo0CdHR0WpTSomIiIjaivT0dISEhECpVEImk+H7779X2x8REQGZTKb2M2TIELWYsrIyTJ06Ffb29rC0tMTo0aNx7do1tZiioiKEhYVBoVBAoVAgLCwMt2/fVou5cuUKQkJCYGlpCXt7e0ybNg3l5eXaqDa1Ea6z9qj9UNvAkW9EZJA2btxY736ZTIbY2FjExsbWGWNmZob4+HjEx8fXGWNra4stW7bUe66uXbti9+7d9cb07dsX6enp9cYQERERtQWlpaXo378/3nnnHbzyyiu1xowaNQqbNm2SXqse3aESFRWFXbt2ITExEXZ2doiJiUFwcDBycnKkL1JDQ0Nx7do1JCcnA3g0gyEsLAy7du0CAFRWViIoKAgdO3bEkSNHcPPmTYSHh0MIUW/7kKilNaYTj8+ea13sfCMiIiIiIr3yOKNFar6HH0R1V2BgIAIDA+uNkcvlcHJyqnVfcXExNm7ciK+//lp6XMiWLVvQpUsX7N+/HwEBATh79iySk5ORlZWFwYMHAwA2bNgALy8vnDt3Du7u7khJScGZM2dw9epVKJVKAMCKFSsQERGBhQsX1rvAFekf5gjSJna+ERERERGRzuK0LKrN4cOH4eDggA4dOsDb2xsLFy6Eg4MDACAnJwcVFRXw9/eX4pVKJTw8PJCRkYGAgABkZmZCoVBIHW8AMGTIECgUCmRkZMDd3R2ZmZnw8PCQOt4AICAgAGVlZcjJyYGvr69GucrKylBWVia9Vq1sXVFRgYqKinrrpNrfUJy+0Zd6yY2E2uvayls9Rt5OaMTVPEZtx2nMeZqqtvM2VI6G4nT9fjVVU+qljbqz842IiIiIiIj0RmBgIF577TW4uLjg4sWLmDt3Ll544QXk5ORALpejoKAApqamsLGxUXufo6MjCgoKAAAFBQVSZ111Dg4OajGOjo5q+21sbGBqairF1LRo0SLMnz9fY3tKSgosLCwaVb/U1NRGxekbXa9XzcUT9u7d22AMoF6v2vbXPE5jztNUjVn4oann0fX79bgaU6979+41+3nZ+UZERERERER64/XXX5f+38PDA56ennBxccGePXswduzYOt8nhFBbSb62VeUfJ6a62bNnqy2aVVJSgi5dusDf37/BaaoVFRVITU2Fn58fTExM6o3VJ/pSL4/YfWqv82ID6o2RtxP4u2eVWr1qHqMxajtPU8vWmPM25j15sQF6c7+aqin1Uo1YbU7sfCMiIiIiIiK95ezsDBcXF1y4cAEA4OTkhPLychQVFamNfissLMTQoUOlmOvXr2sc68aNG9JoNycnJxw9elRtf1FRESoqKjRGxKnI5XLI5XKN7SYmJo3uyGhKrD7R9XqVVap3qNZW1poxqjhVbG37G9KYa1LzuN3nptSIaPi8Nc9TV12q/78u36/H1Zh6aaPe7Zr9iEREREREREQt5ObNm7h69SqcnZ0BAAMHDoSJiYna9LL8/Hzk5eVJnW9eXl4oLi7GsWPHpJijR4+iuLhYLSYvLw/5+flSTEpKCuRyOQYOHNgSVSMiA8GRb0RERERERKQz7t69i19++UV6ffHiReTm5sLW1ha2traIjY3FK6+8AmdnZ1y6dAlz5syBvb09Xn75ZQCAQqHAhAkTEBMTAzs7O9ja2mLGjBno27evtPppr169MGrUKERGRuLzzz8HALz77rsIDg6Gu7s7AMDf3x+9e/dGWFgYli1bhlu3bmHGjBmIjIzkSqdE1CTsfCMiIiIiIiKdcfz4cbWVRFXPUAsPD8e6detw6tQpfPXVV7h9+zacnZ3h6+uLb7/9FlZWVtJ7Pv30UxgbG2PcuHG4f/8+RowYgYSEBBgZGUkxW7duxbRp06RVUUePHo3Vq1dL+42MjLBnzx5MmjQJw4YNg7m5OUJDQ7F8+XJtXwLSAVxpmZoTO9+IiIiIiKjNq+2D9qXFQa1QEvLx8YEQos79+/Y1/HB5MzMzxMfHIz4+vs4YW1tbbNmypd7jdO3aFbt3727wfESPi7mnbWDnGxERERERERHRY/CI3fdYCy1Q28LONyIiIiIianMaM6WsZgxHoxAR0eNg5xsRERERERERGSxO7dQefknROO1auwBERERERERERESGiiPfiIiIiIio1XDUBBGR9nH11tbFzjciIiIiItIZ/IBIRC2BuUaT66w9kBsJLB30aCGJcwuDW7tIBoOdb0RERERERERkMPS9Y03fy0+a+Mw3IiIiIiIiIiIiLeHINyIiIiIiIiIiqhdH5D0+jnwjIiIiIiIiIiLSEo58IyIiIiIiIiKdxBWRDU9bvKcc+UZERERERERERKQlHPlGREREREQtxiN2H5YOevTfskpZaxeHiIhI69j5RkRERERERER6iwsB6C7em0fY+UZERERERNQIbfE5RURETcHOttrxmW9ERERERERERERawpFvRERERERERKQTOHJKd/BeNB+OfCMiIiIiIiIiItISdr4RERERERERERFpCaedEhERERGRVtQ2ZUlu1AoFISIiakUc+UZERERERERERKQlHPlGRERERERERK2CD/Wnmr8DlxYHtVJJtIedb0REREREREREpBNq65DV9w45dr4RERERERERkV7gSLm2Sd9Hx/GZb0RERER6btGiRXj22WdhZWUFBwcHjBkzBufOnVOLEUIgNjYWSqUS5ubm8PHxwenTp9ViysrKMHXqVNjb28PS0hKjR4/GtWvX1GKKiooQFhYGhUIBhUKBsLAw3L59Wy3mypUrCAkJgaWlJezt7TFt2jSUl5drpe5EREREuu6JOt8WLVoEmUyGqKgoaRsbdkREREQtKy0tDZMnT0ZWVhZSU1Px8OFD+Pv7o7S0VIpZunQpVq5cidWrVyM7OxtOTk7w8/PDnTt3pJioqCgkJSUhMTERR44cwd27dxEcHIzKykopJjQ0FLm5uUhOTkZycjJyc3MRFhYm7a+srERQUBBKS0tx5MgRJCYmYvv27YiJiWmZi0FERDrLddYejR+ituCxp51mZ2dj/fr16Nevn9p2VcMuISEBPXr0wIIFC+Dn54dz587BysoKwKOG3a5du5CYmAg7OzvExMQgODgYOTk5MDJ6tPZ4aGgorl27huTkZADAu+++i7CwMOzatQvAfxt2HTt2xJEjR3Dz5k2Eh4dDCIH4+PjHrRYRERGR3lG1l1Q2bdoEBwcH5OTk4Pnnn4cQAqtWrcJHH32EsWPHAgA2b94MR0dHbNu2DRMnTkRxcTE2btyIr7/+GiNHjgQAbNmyBV26dMH+/fsREBCAs2fPIjk5GVlZWRg8eDAAYMOGDfDy8sK5c+fg7u6OlJQUnDlzBlevXoVSqQQArFixAhEREVi4cCGsra1b8MpQS+MHaSIiIk2P1fl29+5dvPXWW9iwYQMWLFggbWfDjoiIiKj1FRcXAwBsbW0BABcvXkRBQQH8/f2lGLlcDm9vb2RkZGDixInIyclBRUWFWoxSqYSHhwcyMjIQEBCAzMxMKBQKqX0GAEOGDIFCoUBGRgbc3d2RmZkJDw8PqX0GAAEBASgrK0NOTg58fX01yltWVoaysjLpdUlJCQCgoqICFRUV9dZVtb+hOH2jr/WSG4mGY9oJtf/qs+r3R1/vWUMaUy9DqzMRUXN7rM63yZMnIygoCCNHjlTrfNP1hh0RERGRoRNCIDo6Gs899xw8PDwAAAUFBQAAR0dHtVhHR0dcvnxZijE1NYWNjY1GjOr9BQUFcHBw0Ding4ODWkzN89jY2MDU1FSKqWnRokWYP3++xvaUlBRYWFg0WGcASE1NbVScvtG3ei0d1PjYv3tWaa8gLWTv3r0a2/TtnjVWffW6d+9eC5aEiEj/NLnzLTExESdOnEB2drbGPl1v2DXHt6rVv6EzhG942vI3dPqordfL0OpNRKQNU6ZMwU8//YQjR45o7JPJZGqvhRAa22qqGVNb/OPEVDd79mxER0dLr0tKStClSxf4+/s3OJuhoqICqamp8PPzg4mJSb2x+kRf6+URu6/BGHk7gb97VmHu8XYoq6r/90/X5cUGSP+vr/esIY2pl+pzFRER1a5JnW9Xr17F9OnTkZKSAjMzszrjdLVh1xzfqlb/hq62b7r0VVv8hk6ftdV68VtVIqL6TZ06FTt37kR6ejo6d+4sbXdycgLw6MtLZ2dnaXthYaH0ZaaTkxPKy8tRVFSk9iVpYWEhhg4dKsVcv35d47w3btxQO87Ro0fV9hcVFaGiokLji1MVuVwOuVyusd3ExKTRnRhNidUn+lavssrGd6aVVcmaFK+Lars3+nbPGqu+ehlifYmImlOTOt9ycnJQWFiIgQMHStsqKyuRnp6O1atXS0va62rDrjm+Va3+DV31b7r0VVv+hk4ftfV6NeVb1UWLFmHHjh34+eefYW5ujqFDh2LJkiVwd3eXYoQQmD9/PtavX4+ioiIMHjwYa9asQZ8+faSYsrIyzJgxA9988w3u37+PESNGYO3atWofaouKijBt2jTs3LkTADB69GjEx8ejQ4cOUsyVK1cwefJkHDx4EObm5ggNDcXy5cthamoqxZw6dQpTpkzBsWPHYGtri4kTJ2Lu3LkNfnlBRCSEwNSpU5GUlITDhw/Dzc1Nbb+bmxucnJyQmpqKAQMGAADKy8uRlpaGJUuWAAAGDhwIExMTpKamYty4cQCA/Px85OXlYenSpQAALy8vFBcX49ixYxg06NH8wqNHj6K4uFhqx3l5eWHhwoXIz8+X2oMpKSmQy+VqbUgiIjJ8rrP2QG4ksHSQamQs27XUNjWp823EiBE4deqU2rZ33nkHPXv2xMyZM/HUU0/pdMOuOb5Vrf4NnSF1frTFb+j0WVutV1PqnJaWhsmTJ+PZZ5/Fw4cP8dFHH8Hf3x9nzpyBpaUlAN1anbmkpAR+fn7w9fVFdnY2zp8/j4iICFhaWiImJqbpF5OI2pTJkydj27Zt+Pe//w0rKyvpERwKhQLm5uaQyWSIiopCXFwcunfvju7duyMuLg4WFhYIDQ2VYidMmICYmBjY2dnB1tYWM2bMQN++faVFsnr16oVRo0YhMjISn3/+OYBHOS84OFj6csPf3x+9e/dGWFgYli1bhlu3bmHGjBmIjIzkglhERETUJjWp883Kykp6cK+KpaUl7OzspO1s2BGRLlB1hKls2rQJDg4OyMnJwfPPP69zqzNv3boVDx48QEJCAuRyOTw8PHD+/HmsXLkS0dHRHP1GRPVat24dAMDHx0dt+6ZNmxAREQEA+PDDD3H//n1MmjRJGu2bkpIifdkAAJ9++imMjY0xbtw4abRvQkKC9GUDAGzduhXTpk2TFs8aPXo0Vq9eLe03MjLCnj17MGnSJAwbNkxttC8RERFRW/RYq53Whw07ItJFxcXFAABbW1sAurc6c2ZmJry9vdVG5wYEBGD27Nm4dOmSxhQy4PEXkalrgQu5kVB77f7RbrXXhjDV/nEZ6mIn2sRr1rDmvDZCiAZjZDIZYmNjERsbW2eMmZkZ4uPjpVG5tbG1tcWWLVvqPVfXrl2xe/fuemOIiIiI2oon7nw7fPiw2ms27IhI1wghEB0djeeee04apatrqzMXFBTA1dVV4zyqfbV1vj3pIjI1F7hYOqj+eENaZOZxGepiJ9rEa1Y3LiJDpP9cZ+2R/l/1XCsiIqKamn3kGxGRrpkyZQp++uknHDlyRGOfLq3OXFtZ6nov8PiLyNS1wMWjh+DWra2PfDPExU60idesYU1ZRIaIqC1JT0/HsmXLkJOTg/z8fCQlJWHMmDHSfl1bNIuIqCHsfCMigzZ16lTs3LkT6enpao0tJycnALqzOrOTk5M0Cq76eQDN0XkqT7qITM041WIy9cW3dYa62Ik28ZrVjdeFiKh2paWl6N+/P9555x288sorGvt1adEsUld9NCgR/Ve71i4AEZE2CCEwZcoU7NixAwcPHtSYtunm5iatzqyiWp1Z1bFWfXVmFdXqzNVXXlatzqxS2+rMeXl5yM/Pl2Jqrs7s5eWF9PR0lJeXq8UolUqN6ahEREREhiwwMBALFiyQFsWqruaiWR4eHti8eTPu3buHbdu2AYC0aNaKFSswcuRIDBgwAFu2bMGpU6ewf/9+AJAWzfriiy/g5eUFLy8vbNiwAbt378a5c+cAQFo0a8uWLRgwYABGjhyJFStWYMOGDRy9TERNws43IjJIkydPxpYtW7Bt2zZYWVmhoKAABQUFuH//PoBHUzlVqzMnJSUhLy8PERERda7OfODAAZw8eRLjx4+vc3XmrKwsZGVlITIyss7VmU+ePIkDBw5orM4cGhoKuVyOiIgI5OXlISkpCXFxcVzplIiIiKiahhbNAtDgolkAGlw0SxVT36JZRESNxWmnRGSQ1q1bBwDw8fFR275p0yZEREQA0K3VmRUKBVJTUzF58mR4enrCxsYG0dHRas90IyIiImrrdG3RrJoedzV6VUz1/+ojuZHm6tvydkLtv4aC9WpdTf07acrflzb+Btn5RkQGSbVYQX10bXXmvn37Ij09vd4YIiIiXcLnO1Fr0aVFs6p70tXoAf1eKby+FX//7lnVcgVpQaxX69i7d+9jva8xf1/aWJGenW9ERERERNQgdrSRLtC1RbNqetzV6AHDWCncI3afxjZ5O4G/e1Zh7vF2KKsynMepsF6tKy82oEnxTfn70sYzHdn5RkRERERERHqh+qJZAwYMAPDfRbOWLFkCQH3RrHHjxgH476JZS5cuBaC+aNagQY+Ga9W2aNbChQuRn58vdfTVXDSrpiddjb6psa2p9g75ujtryqpkKKvU3c6cx8V6tY7H/RtpzN+XNv7+2PlGREREREREOuPu3bv45ZdfpNcXL15Ebm4ubG1t0bVrV2nRrO7du6N79+6Ii4urc9EsOzs72NraYsaMGXUumvX5558DAN599906F81atmwZbt26pbFoFhG1jpqdv5cWB7VSSRqHnW9ERERERESkM44fPw5fX1/ptWoaZ3h4OBISEnRq0ay2hFPPiR4fO9+IiIiIiIhIZ/j4+NS7eJauLZpFRNSQdq1dACIiIiIiIiIiIkPFzjciIiIiIiIiIiItYecbERERERERERGRlrDzjYiIiIiIiIiISEvY+UZERERERERERKQlXO2UiIiIiIg0uM7a09pFICIiMggc+UZERERERERERKQl7HwjIiIiIiIiIiLSEk47JSIiIiIiaiYesftQVimTXl9aHNSKpSEiIl3AzjciIiIiIiKiNozPeCTSLna+ERERERERERGR3qrZgaxro475zDciIiIiIiIiIiIt4cg3IiIiIiLitDMiIiIt4cg3IiIiIiIiIiIiLWHnGxERERERERERkZZw2ikRERERERFRG8Jp5kQti51vREREREREWqLrK/AREZH2cdopERERERERERGRlnDkGxERERFRG8MpZ0RERC2HI9+IiIiIiIiIiIi0hJ1vREREREREREREWsLONyIiIiIiIiIiIi1h5xsREREREREREZGWsPONiIiIiIiIiIhIS9j5RkREREREREREpCXsfCMiIiIiIiIiItIS49YuABkW11l7NLZdWhzUCiUhIiIiIiIiImp97HxrI9gpRkRERNR21dYWpNbBdjkRUdvDaadERERERERERERawpFvpHU1v93jN3tERERERERE1FZw5Fsb5jprDzxi9wEAPGL3wXXWngZ/iIiISPekp6cjJCQESqUSMpkM33//vdp+IQRiY2OhVCphbm4OHx8fnD59Wi2mrKwMU6dOhb29PSwtLTF69Ghcu3ZNLaaoqAhhYWFQKBRQKBQICwvD7du31WKuXLmCkJAQWFpawt7eHtOmTUN5ebk2qk1ERI3Ez3VEratJnW+LFi3Cs88+CysrKzg4OGDMmDE4d+6cWgwbd20LkzgREVHrKy0tRf/+/bF69epa9y9duhQrV67E6tWrkZ2dDScnJ/j5+eHOnTtSTFRUFJKSkpCYmIgjR47g7t27CA4ORmVlpRQTGhqK3NxcJCcnIzk5Gbm5uQgLC5P2V1ZWIigoCKWlpThy5AgSExOxfft2xMTEaK/yRERERDquSZ1vaWlpmDx5MrKyspCamoqHDx/C398fpaWlUgwbd0REREQtKzAwEAsWLMDYsWM19gkhsGrVKnz00UcYO3YsPDw8sHnzZty7dw/btm0DABQXF2Pjxo1YsWIFRo4ciQEDBmDLli04deoU9u/fDwA4e/YskpOT8cUXX8DLywteXl7YsGEDdu/eLX0Zm5KSgjNnzmDLli0YMGAARo4ciRUrVmDDhg0oKSlpuQtCREREpEOa9My35ORktdebNm2Cg4MDcnJy8Pzzz2s07gBg8+bNcHR0xLZt2zBx4kSpcff1119j5MiRAIAtW7agS5cu2L9/PwICAqTGXVZWFgYPHgwA2LBhA7y8vHDu3Dm4u7tLjburV69CqVQCAFasWIGIiAgsXLgQ1tbWT3xxiIiIiPTdxYsXUVBQAH9/f2mbXC6Ht7c3MjIyMHHiROTk5KCiokItRqlUwsPDAxkZGQgICEBmZiYUCoXUNgOAIUOGQKFQICMjA+7u7sjMzISHh4fUNgOAgIAAlJWVIScnB76+vi1TaSIiImrTas7MkxsJLB3USoXBEy64UFxcDACwtbUFwMadLtHWFFBOLSV9kZ6ejmXLliEnJwf5+flISkrCmDFjpP1CCMyfPx/r169HUVERBg8ejDVr1qBPnz5STFlZGWbMmIFvvvkG9+/fx4gRI7B27Vp07txZiikqKsK0adOwc+dOAMDo0aMRHx+PDh06SDFXrlzB5MmTcfDgQZibmyM0NBTLly+HqampFHPq1ClMmTIFx44dg62tLSZOnIi5c+dCJpNp7yIRUZtQUFAAAHB0dFTb7ujoiMuXL0sxpqamsLGx0YhRvb+goAAODg4ax3dwcFCLqXkeGxsbmJqaSjG1KSsrQ1lZmfRaNUquoqICFRUV9dZPtb+hOH3T3PWSG4lmOU5zkLcTav81FE9SL13+/W3M76Iul78t4mc2It3z2J1vQghER0fjueeeg4eHBwDdb9w1R8Ou+j+muvyPTGMbWK3R+HH/aLfa67zYgGY/Bxvi+qWx9WpKvVXPP3rnnXfwyiuvaOxXTZFPSEhAjx49sGDBAvj5+eHcuXOwsrIC8GiK/K5du5CYmAg7OzvExMQgODgYOTk5MDIyAvBoivy1a9ekkcHvvvsuwsLCsGvXLgD/nSLfsWNHHDlyBDdv3kR4eDiEEIiPjwfwKBf5+fnB19cX2dnZOH/+PCIiImBpacmp9ETUbGp25gshGuzgrxlTW/zjxNS0aNEizJ8/X2N7SkoKLCws6i2jSmpqaqPi9E1z1as1v+2vy989q1q7CFrxOPXau3evFkrSvOr7Xbx3714LloSISP88dufblClT8NNPP+HIkSMa+3S1cdccDbvq/5jq8j+STW1gtWbjR5vXkQ1x/dJQvZrSsAsMDERgYGCt+3RtivzWrVvx4MEDJCQkQC6Xw8PDA+fPn8fKlSsRHR3N0W9E9EScnJwAPPri0tnZWdpeWFgofZHp5OSE8vJyFBUVqX1BWlhYiKFDh0ox169f1zj+jRs31I5z9OhRtf1FRUWoqKjQ+NK0utmzZyM6Olp6XVJSgi5dusDf37/BR4lUVFQgNTUVfn5+MDExqTdWnzxpvVQr2usieTuBv3tWYe7xdiirMpx/456kXtr4Mrq5NOZ3kc90JCKq32N1vk2dOhU7d+5Eenq62vQrXW/cNUfDrvo/prr0j+TjNrB0sfHTHNeVDXH90th6NVfDTtemyGdmZsLb2xtyuVwtZvbs2bh06RLc3Nxqrcfjjuata6RhQyNmDW3EZVMY6qhTbeI1a1hLXRs3Nzc4OTkhNTUVAwYMAACUl5cjLS0NS5YsAQAMHDgQJiYmSE1Nxbhx4wAA+fn5yMvLw9KlSwEAXl5eKC4uxrFjxzBo0KNv+Y4ePYri4mKpDefl5YWFCxciPz9fagumpKRALpdj4MCBdZZRLper5UAVExOTRv9715RYffK49Sqr1I12XX3KqmR6Uc6mepx66cPvbn2/iy1d/tjYWI1BFdVnUrXk40WIiBqjSZ1vQghMnToVSUlJOHz4sMYHQl1v3DVHw676P6a69I/kkzZcdKnx05zXlQ1x/dJQvZqrzro2Rb6goACurq4a51Htq6vz7UlH89YcadjQiFldHu3bUgx11Kk28ZrVrTmnad29exe//PKL9PrixYvIzc2Fra0tunbtiqioKMTFxaF79+7o3r074uLiYGFhgdDQUACAQqHAhAkTEBMTAzs7O9ja2mLGjBno27evNPq3V69eGDVqFCIjI/H5558DeDTVPjg4GO7u7gAAf39/9O7dG2FhYVi2bBlu3bqFGTNmIDIykothEVGz6tOnj7QaMwDpkSBAyz1eRFfwGW9Euq9JnW+TJ0/Gtm3b8O9//xtWVlbSB0eFQgFzc3PIZDI27ohIb+jSFPnaylLXe1UedzRvXSMNGxpBq0ujfVuaoY461SZes4Y15zSt48ePqy02pcoN4eHhSEhIwIcffoj79+9j0qRJ0iiQlJQU6UMoAHz66acwNjbGuHHjpFEgCQkJah9ot27dimnTpkmjgkePHo3Vq1dL+42MjLBnzx5MmjQJw4YNU1tkhrSHH7ypLTI2NpZmXlXXko8XISJqrCZ1vq1btw4A4OPjo7Z906ZNiIiIAAA27loAG1hET0bXpsg7OTlpLBRTWFgIQHN0XnVPOpq3ZlxDo1/ZgWK4o061idesbs15XXx8fKRO+9rIZDLExsYiNja2zhgzMzPEx8dLi8HUxtbWFlu2bKm3LF27dsXu3bvrjSEielIXLlyAUqmEXC7H4MGDERcXh6eeeqpFHy9Sm9ZYvVmXVjOuDVc41i+GXq/G/H1p49EgTZ522pC21LirrRPs0uKgViiJYeF1JW3TtSnyXl5emDNnDsrLy2FqairFKJVKjemoREREZHhqtn/Z9q3f4MGD8dVXX6FHjx64fv06FixYgKFDh+L06dMt+niR2rTG6s26uJpxbbjCsX4x1Ho15u9LGys4P/Zqp9RyONKNqOn06flHoaGhmD9/PiIiIjBnzhxcuHABcXFx+Pjjj7nSKREREVEN1Ve079u3L7y8vNCtWzds3rwZQ4YMAdByjxepqTVWb9bl1Y0B3VzkrzmwXvpFVa/G/H1pYwVndr4RkUHSp+cfKRQKpKamYvLkyfD09ISNjQ2io6PVGm5EREREVDtLS0v07dsXFy5cwJgxYwC0zONFatMSqzdrDs7Qjw4SXVrkrzmxXvqlMX+L2nhkCjvfiMgg6dvzj/r27Yv09PR6Y4iIiIhIU1lZGc6ePYvhw4e36ONFiIgai51vOojTTDXxORhERERERAQAM2bMQEhICLp27YrCwkIsWLAAJSUlCA8Ph0wma7HHixARNRY731oYO9aIiIiI6EmwPUlt3bVr1/Dmm2/izz//RMeOHTFkyBBkZWXBxcUFAFrs8SJERI3FzjciIiIiIiLSG4mJifXub8nHixARNQY735oZv4kkIiIiIiIiIiIVdr4RERERERER6SAO7iAyDOx8I73EBRiIiIiIiIiISB+w842IiIiIiEiH8ItmIiLDws43IiIiIiIdxmlnRERE+o2db2QQajZK5UYCSwe1UmGIiIiIiIiIiP6fdq1dACIiIiIiIiIiIkPFzjciIiIiIiIiIiIt4bRTMmgesftQVikDwAfVEhEREREREVHL48g3IiIiIiIiIiIiLeHINyIiIiIiHaFaREq1eJRH7D4AstYtFBERET0Rdr4RERERERER6Yjqj84hIsPAzjdqM1TfJKvwGXBEREREREREpG3sfCMiIiIiItJhNb9EBvhFMhGRPuGCC0RERERERERERFrCkW/UZvEbRCIiIiIiIiLSNna+ERERERG1ktq+DCQiIiLDwmmnREREREREREREWsLONyIiIiIiIiIiIi3htFOiampO/eAz4IiIiKg5cZopERFR28PONyKiNsojdh/KKmWtXQwiIiIiIiKDxs43IiIiIiIiPcMZG0RE+oOdb0T1YKOGiIiIiIiIiJ4EO9+IiIiIiLSEz3gjIiIidr4REVGj1PYBkqNBiYiIiIiI6sfON6ImYOcDERER1YWj3IiIiKg27HwjIiIiIiLSc3xWMRGR7mLnG9ETYkOHiIiIiIiIiOrSrrULQEREREREREREZKjY+UZERERERERERKQlnHZK1Mw4DZWIiKht4AILRERE1BjsfCMiIiIiIjIw/EKYiEh3sPONSMtq+1acjR8iIiIiIiKitoHPfCMiIiIiIiIiItISjnwjagWcBkBERKR/+Iw3IiIiehzsfCPSAeyMIyIiIiJt4qNQiIhaj953vq1duxbLli1Dfn4++vTpg1WrVmH48OGtXSyiJ8LOuLZL33Iaf1eJqC76ns+IiKrTt5xGRLpFrzvfvv32W0RFRWHt2rUYNmwYPv/8cwQGBuLMmTPo2rVraxePqNmoPhDIjQSWDgI8Yvfh3MLgVi4VNTfmNCIyFPqQz9jZRsQv0RpLH3IaEek2vV5wYeXKlZgwYQL+9re/oVevXli1ahW6dOmCdevWtXbRiLTOddYetR/Sf8xpRGQomM+IyJAwpxHRk9LbkW/l5eXIycnBrFmz1Lb7+/sjIyOj1veUlZWhrKxMel1cXAwAuHXrFioqKuo9X0VFBe7duwfjinaorJI9Yel1h3GVwL17VayXnqivXk/P+GeD7z86e4S2ivZEVH9fN2/ehImJSZ1xd+7cAQAIIVqqaC2mJXOaNvNZzd9DXf2da6rG/o7Sf/GaNcxQc1prtdGe+WgHyv5fTmtM7jF+WNpgTGsy1LYMYLh1M4R63bx5U2NbY/K5oeYzoOk5jZ85NRnC30ZtWC/9oqpXY9qm2shpetv59ueff6KyshKOjo5q2x0dHVFQUFDrexYtWoT58+drbHdzc9NKGfVFaGsXQEtYL032K5qtGK3qzp07UCgUrV2MZmWoOc1QfueItMnQcpou5DNDyT2G2pYBDLdu+l6vJ/3bMbR8BjQ9p+lD+6w16PvfRl1YL/3S1Ho1Z07T2843FZlMvSdWCKGxTWX27NmIjo6WXldVVeHWrVuws7Or8z0qJSUl6NKlC65evQpra+snL7iOYL30S1uvlxACd+7cgVKpbMHStayWyGmG+nukTbxmTcdr1jBDz2lsoz0ZQ60XYLh1a8v1MvR8BjQ+pzGfaWK99AvrpZ2cpredb/b29jAyMtL4tqGwsFDjWwkVuVwOuVyutq1Dhw5NOq+1tbVB/QKqsF76pS3Xy9C+TVVpjZxmqL9H2sRr1nS8ZvUzxJzGNlrzMtR6AYZbt7ZaL0PMZ0DTcxrzWd1YL/3S1uvV3DlNbxdcMDU1xcCBA5Gamqq2PTU1FUOHDm2lUhERPR7mNCIyFMxnRGRImNOIqDno7cg3AIiOjkZYWBg8PT3h5eWF9evX48qVK3jvvfdau2hERE3GnEZEhoL5jIgMCXMaET0pve58e/3113Hz5k188sknyM/Ph4eHB/bu3QsXF5dmP5dcLse8efM0hhDrO9ZLv7Behq2lchqvd9PxmjUdr1nbxjbakzPUegGGWzfWy3CxjfZkWC/9wnpph0wY4nrQREREREREREREOkBvn/lGRERERERERESk69j5RkREREREREREpCXsfCMiIiIiIiIiItISdr4RERERERERERFpCTvfGmHt2rVwc3ODmZkZBg4ciB9++KG1i9QkixYtwrPPPgsrKys4ODhgzJgxOHfunFpMREQEZDKZ2s+QIUNaqcSNExsbq1FmJycnab8QArGxsVAqlTA3N4ePjw9Onz7diiVuHFdXV416yWQyTJ48GYD+3Kv09HSEhIRAqVRCJpPh+++/V9vfmPtTVlaGqVOnwt7eHpaWlhg9ejSuXbvWgrUwTPqe01pSY/In1W3RokWQyWSIiopq7aKQHmlqjkpLS8PAgQNhZmaGp556Cp999plGzPbt29G7d2/I5XL07t0bSUlJ2ip+vZpStx07dsDPzw8dO3aEtbU1vLy8sG/fPrWYhISEWtsMDx480HZV1DSlXocPH661zD///LNanC7cs6bUq7b2mUwmQ58+faQYXbhfDbXPaqNPf2O6yBBzGnMZc1lr3y+9zGWC6pWYmChMTEzEhg0bxJkzZ8T06dOFpaWluHz5cmsXrdECAgLEpk2bRF5ensjNzRVBQUGia9eu4u7du1JMeHi4GDVqlMjPz5d+bt682Yqlbti8efNEnz591MpcWFgo7V+8eLGwsrIS27dvF6dOnRKvv/66cHZ2FiUlJa1Y6oYVFhaq1Sk1NVUAEIcOHRJC6M+92rt3r/joo4/E9u3bBQCRlJSktr8x9+e9994TnTp1EqmpqeLEiRPC19dX9O/fXzx8+LCFa2M4DCGntaTG5E+q3bFjx4Srq6vo16+fmD59emsXh/REU3PUb7/9JiwsLMT06dPFmTNnxIYNG4SJiYn417/+JcVkZGQIIyMjERcXJ86ePSvi4uKEsbGxyMrKaqlqCSGaXrfp06eLJUuWiGPHjonz58+L2bNnCxMTE3HixAkpZtOmTcLa2lqtTZCfn99SVRJCNL1ehw4dEgDEuXPn1Mpc/d92XbhnTa3X7du31epz9epVYWtrK+bNmyfF6ML9aqh9VpM+/Y3pIkPMacxljzCXMZc1FTvfGjBo0CDx3nvvqW3r2bOnmDVrViuV6MkVFhYKACItLU3aFh4eLl566aXWK9RjmDdvnujfv3+t+6qqqoSTk5NYvHixtO3BgwdCoVCIzz77rIVK2DymT58uunXrJqqqqoQQ+nmvaibExtyf27dvCxMTE5GYmCjF/P7776Jdu3YiOTm5xcpuaAwxp7Wk2vInabpz547o3r27SE1NFd7e3ux8o0Zrao768MMPRc+ePdW2TZw4UQwZMkR6PW7cODFq1Ci1mICAAPHGG280U6kbpznyb+/evcX8+fOl15s2bRIKhaK5ivhYmlov1QfWoqKiOo+pC/fsSe9XUlKSkMlk4tKlS9I2Xbhf1TXmA6s+/Y3pIkPMacxljzCXKZqzmE9EX3IZp53Wo7y8HDk5OfD391fb7u/vj4yMjFYq1ZMrLi4GANja2qptP3z4MBwcHNCjRw9ERkaisLCwNYrXJBcuXIBSqYSbmxveeOMN/PbbbwCAixcvoqCgQO3eyeVyeHt769W9Ky8vx5YtW/DXv/4VMplM2q6P96q6xtyfnJwcVFRUqMUolUp4eHjo1T3UJYaa01pSXfmT1E2ePBlBQUEYOXJkaxeF9Mjj5KjMzEyN+ICAABw/fhwVFRX1xrRk3muO/FtVVYU7d+5o5J+7d+/CxcUFnTt3RnBwME6ePNls5W7Ik9RrwIABcHZ2xogRI3Do0CG1fa19z5rjfm3cuBEjR46Ei4uL2vbWvF+PQ1/+xnSRIeY05jJNzGXMZY3Fzrd6/Pnnn6isrISjo6PadkdHRxQUFLRSqZ6MEALR0dF47rnn4OHhIW0PDAzE1q1bcfDgQaxYsQLZ2dl44YUXUFZW1oqlrd/gwYPx1VdfYd++fdiwYQMKCgowdOhQ3Lx5U7o/+n7vvv/+e9y+fRsRERHSNn28VzU15v4UFBTA1NQUNjY2dcZQ0xhiTmtJdeVPUpeYmIgTJ05g0aJFrV0U0jOPk6MKCgpqjX/48CH+/PPPemNaMu81R/5dsWIFSktLMW7cOGlbz549kZCQgJ07d+Kbb76BmZkZhg0bhgsXLjRr+evyOPVydnbG+vXrsX37duzYsQPu7u4YMWIE0tPTpZjWvmdPer/y8/Pxn//8B3/729/Utrf2/Xoc+vI3posMMacxl/0XcxlzWVMZN8tRDFz1EUfAow9gNbfpiylTpuCnn37CkSNH1La//vrr0v97eHjA09MTLi4u2LNnD8aOHdvSxWyUwMBA6f/79u0LLy8vdOvWDZs3b5YWIND3e7dx40YEBgZCqVRK2/TxXtXlce6Pvt1DXaTvfxetpa78Sf919epVTJ8+HSkpKTAzM2vt4pCeamqOqi2+5nZdyXuPW45vvvkGsbGx+Pe//w0HBwdp+5AhQ9QWXRo2bBj+8pe/ID4+Hv/7v//bfAVvQFPq5e7uDnd3d+m1l5cXrl69iuXLl+P5559/rGNqy+OWISEhAR06dMCYMWPUtuvK/Woqffob00WGmNOYy5jLdOF+NVVr/21x5Fs97O3tYWRkpNHTWVhYqNEjqg+mTp2KnTt34tChQ+jcuXO9sc7OznBxcdHp3uuaLC0t0bdvX1y4cEFa9VSf793ly5exf/9+jW8aatLHe9WY++Pk5ITy8nIUFRXVGUNNY2g5rSU1JX+2ZTk5OSgsLMTAgQNhbGwMY2NjpKWl4X//939hbGyMysrK1i4i6bDHyVFOTk61xhsbG8POzq7emJbMe0+Sf7/99ltMmDAB//znPxucyt2uXTs8++yzLdYmaK5/V4YMGaJW5ta+Z09SLyEEvvzyS4SFhcHU1LTe2Ja+X49DX/7GdJEh5jTmsvoxlzGX1Yedb/UwNTXFwIEDkZqaqrY9NTUVQ4cObaVSNZ0QAlOmTMGOHTtw8OBBuLm5Nfiemzdv4urVq3B2dm6BEjaPsrIynD17Fs7OznBzc4OTk5PavSsvL0daWpre3LtNmzbBwcEBQUFB9cbp471qzP0ZOHAgTExM1GLy8/ORl5enN/dQ1xhKTmtJj5M/27IRI0bg1KlTyM3NlX48PT3x1ltvITc3F0ZGRq1dRNJhj5OjvLy8NOJTUlLg6ekJExOTemNaMu89bv795ptvEBERgW3btjXYHgAe5azc3NwWaxM0178rJ0+eVCtza9+zJ6lXWloafvnlF0yYMKHB87T0/Xoc+vI3posMMacxl9WPuYy5rF7NsmyDAVMtzbtx40Zx5swZERUVJSwtLdVW+9B177//vlAoFOLw4cNqSwHfu3dPCPFoVbqYmBiRkZEhLl68KA4dOiS8vLxEp06dRElJSSuXvm4xMTHi8OHD4rfffhNZWVkiODhYWFlZSfdm8eLFQqFQiB07dohTp06JN998Uzg7O+t0nVQqKytF165dxcyZM9W269O9unPnjjh58qQ4efKkACBWrlwpTp48KS1r3Zj7895774nOnTuL/fv3ixMnTogXXnhB9O/fX20Jb2oaQ8hpLamh/EkN42qn1BQN5ahZs2aJsLAwKf63334TFhYW4oMPPhBnzpwRGzduFCYmJuJf//qXFPN///d/wsjISCxevFicPXtWLF68WBgbG4usrCydrtu2bduEsbGxWLNmjVr+uX37thQTGxsrkpOTxa+//ipOnjwp3nnnHWFsbCyOHj2qs/X69NNPRVJSkjh//rzIy8sTs2bNEgDE9u3bpRhduGdNrZfK+PHjxeDBg2s9pi7cr4baZ/r8N6aLDDGnMZc9wlzGXNZU7HxrhDVr1ggXFxdhamoq/vKXv4i0tLTWLlKTAKj1Z9OmTUIIIe7duyf8/f1Fx44dhYmJiejatasIDw8XV65cad2CN+D1118Xzs7OwsTERCiVSjF27Fhx+vRpaX9VVZWYN2+ecHJyEnK5XDz//PPi1KlTrVjixtu3b58AIM6dO6e2XZ/ulWr57Zo/4eHhQojG3Z/79++LKVOmCFtbW2Fubi6Cg4N1sq76Rt9zWktqKH9Sw9j5Rk1VX44KDw8X3t7eavGHDx8WAwYMEKampsLV1VWsW7dO45jfffedcHd3FyYmJqJnz55qH45aUlPq5u3tXe+/o0IIERUVJbp27SpMTU1Fx44dhb+/v8jIyGjBGj3SlHotWbJEdOvWTZiZmQkbGxvx3HPPiT179mgcUxfuWVN/F2/fvi3Mzc3F+vXraz2eLtyvhtpn+v43posMMacxlzGXtfb90sdcJhPi/z1ljoiIiIiIiIiIiJoVn/lGRERERERERESkJex8IyIiIiIiIiIi0hJ2vhEREREREREREWkJO9+IiIiIiIiIiIi0hJ1vREREREREREREWsLON9ILsbGxkMlk+PPPP1u7KETURiQkJEAmk+HSpUstet5Lly5BJpMhISGhRc9LRKQrmAeJSB805TOqq6srIiIitF8o0lnGrV0AIiIiIiIiIiJDlZSUBGtr69YuBrUijnwjIiIiqkV6ejpCQkKgVCohk8nw/fffN/kYQggsX74cPXr0gFwuR5cuXRAXF9f8hSUiIiKdNWDAAHTr1q21i0GtiJ1vpLd+/vlnPPXUUxg8eDAKCwtRUFCAiRMnonPnzjA1NYWbmxvmz5+Phw8fAnj0Aah79+4ICAjQONbdu3ehUCgwefLklq4GEemRL7/8Ev3794eZmRlsbW3x8ssv4+zZs2oxPj4+8PHx0XhvREQEXF1d1bb98ccfGDduHKysrKBQKPD666+joKCg1ve2b98ev/zyC1588UW0b98eXbp0QUxMDMrKytRiy8vLsWDBAvTs2RNyuRwdO3bEO++8gxs3bqjFHTx4ED4+PrCzs4O5uTm6du2KV155Bffu3ZNi1q1bh/79+6N9+/awsrJCz549MWfOnCZeNf1VWlqK/v37Y/Xq1Y99jOnTp+OLL77A8uXL8fPPP2PXrl0YNGhQM5aSSDtU06l++uknvPbaa1AoFLC1tUV0dDQePnyIc+fOYdSoUbCysoKrqyuWLl2q9v4rV65g/PjxcHBwgFwuR69evbBixQpUVVWpxTU2DwLA8ePHMXr0aNja2sLMzAwDBgzAP//5T61dAyLSrhs3buDdd99Fly5dpDbLsGHDsH//fgCP2lQeHh744YcfMGTIEJibm6NTp06YO3cuKisr1Y7V2PYPAHz77bfw8vKCpaUl2rdvj4CAAJw8eVIj7ujRowgJCYGdnR3MzMzQrVs3REVFacRdv34db775JhQKBRwdHfHXv/4VxcXFajE1p50ePnwYMpkM33zzDT766CMolUpYW1tj5MiROHfunMY59u/fjxEjRsDa2hoWFhYYNmwYDhw40KTrCQAnT55EcHCwlJuVSiWCgoJw7do1zRtEzYrTTkkvpaWl4eWXX8bzzz+Pbdu2oaSkBIMGDUK7du3w8ccfo1u3bsjMzMSCBQtw6dIlbNq0CTKZDFOnTkVUVBQuXLiA7t27S8f76quvUFJSws43IqrTokWLMGfOHLz55ptYtGgRbt68idjYWHh5eSE7O1stpzTG/fv3MXLkSPzxxx9YtGgRevTogT179uD111+vNb6iogKjR4/GhAkTEBMTg/T0dPz973+HQqHAxx9/DACoqqrCSy+9hB9++AEffvghhg4disuXL2PevHnw8fHB8ePHYW5ujkuXLiEoKAjDhw/Hl19+iQ4dOuD3339HcnIyysvLYWFhgcTEREyaNAlTp07F8uXL0a5dO/zyyy84c+bME19LfREYGIjAwMA695eXl+N//ud/sHXrVty+fRseHh5YsmSJ1Pl69uxZrFu3Dnl5eXB3d2+hUhM1r3HjxmH8+PGYOHEiUlNTsXTpUlRUVGD//v2YNGkSZsyYgW3btmHmzJl4+umnMXbsWNy4cQNDhw5FeXk5/v73v8PV1RW7d+/GjBkz8Ouvv2Lt2rUAmpYHDx06hFGjRmHw4MH47LPPoFAokJiYiNdffx337t3js5SI9FBYWBhOnDiBhQsXokePHrh9+zZOnDiBmzdvSjEFBQV44403MGvWLHzyySfYs2cPFixYgKKiIunLsca2fwAgLi4O//M//4N33nkH//M//4Py8nIsW7YMw4cPx7Fjx9C7d28AwL59+xASEoJevXph5cqV6Nq1Ky5duoSUlBSNerzyyit4/fXXMWHCBJw6dQqzZ88G8OhL24bMmTMHw4YNwxdffIGSkhLMnDkTISEhOHv2LIyMjAAAW7Zswdtvv42XXnoJmzdvhomJCT7//HMEBARg3759GDFiRKOuZ2lpKfz8/ODm5oY1a9bA0dERBQUFOHToEO7cufO4t5EaSxDpgXnz5gkA4saNG+Lrr78WpqamYtq0aaKyslIIIcTEiRNF+/btxeXLl9Xet3z5cgFAnD59WgghRElJibCyshLTp09Xi+vdu7fw9fVtkboQkX7YtGmTACAuXrwoioqKhLm5uXjxxRfVYq5cuSLkcrkIDQ2Vtnl7ewtvb2+N44WHhwsXFxfp9bp16wQA8e9//1stLjIyUgAQmzZtUnsvAPHPf/5TLfbFF18U7u7u0utvvvlGABDbt29Xi8vOzhYAxNq1a4UQQvzrX/8SAERubm6d9Z8yZYro0KFDnfvbGgAiKSlJbVtoaKgYOnSoSE9PF7/88otYtmyZkMvl4vz580IIIZYsWSJ69Oghli9fLlxdXYWLi4uYMGGCuHnzZivUgKhpVG2vFStWqG1/5plnBACxY8cOaVtFRYXo2LGjGDt2rBBCiFmzZgkA4ujRo2rvff/994VMJhPnzp0TQjQtD/bs2VMMGDBAVFRUqMUGBwcLZ2dnqU1IRPqjffv2Iioqqs793t7edeaIdu3aSZ/9Gtv+uXLlijA2NhZTp05Vi7tz545wcnIS48aNk7Z169ZNdOvWTdy/f7/O8qny5NKlS9W2T5o0SZiZmYmqqippm4uLiwgPD5deHzp0SADQaFv+85//FABEZmamEEKI0tJSYWtrK0JCQtTiKisrRf/+/cWgQYOkbQ1dz+PHjwsA4vvvv68zhrSH005JryxcuBARERFYvHgx/vGPf6Bdu0e/wrt374avry+USiUePnwo/ahGLKSlpQEArKys8M477yAhIQGlpaUAHk29OnPmDKZMmdI6lSIinZeZmYn79+9rjKzo0qULXnjhBY1h/41x6NAhWFlZYfTo0WrbQ0NDa42XyWQICQlR29avXz9cvnxZer1792506NABISEharnwmWeegZOTEw4fPgwAeOaZZ2Bqaop3330Xmzdvxm+//aZxvkGDBuH27dt488038e9//5urTdfw66+/4ptvvsF3332H4cOHo1u3bpgxYwaee+45bNq0CQDw22+/4fLly/juu+/w1VdfISEhATk5OXj11VdbufREjRccHKz2ulevXpDJZGqjQo2NjfH0009L+ejgwYPo3bu3xhTriIgICCFw8OBBAI3Pg7/88gt+/vlnvPXWWwCglt9efPFF5Ofn1zpNi4h026BBg5CQkIAFCxYgKysLFRUVGjF15Yiqqiqkp6cDaHz7Z9++fXj48CHefvtttTgzMzN4e3tLcefPn8evv/6KCRMmwMzMrMF61Cxfv3798ODBAxQWFj7WewFI+TQjIwO3bt1CeHi4WpmrqqowatQoZGdnS59rG7qeTz/9NGxsbDBz5kx89tlnbWo2gy5g5xvplS1btqBTp05444031LZfv34du3btgomJidpPnz59AEDtQ+PUqVNx584dbN26FQCwevVqdO7cGS+99FLLVYSI9IpquL6zs7PGPqVSqTY9oinHdHR01Nju5ORUa7yFhYVGA1Aul+PBgwfS6+vXr+P27dswNTXVyIcFBQVSLuzWrRv2798PBwcHTJ48Gd26dUO3bt3wj3/8QzpWWFgYvvzyS1y+fBmvvPIKHBwcMHjwYKSmpja5roboxIkTEEKgR48eaN++vfSTlpaGX3/9FcCjaTBlZWX46quvMHz4cPj4+GDjxo04dOgQOwpIb9ja2qq9NjU1rTUfmZqaSvno5s2bdeZL1X7VfxuTB69fvw4AmDFjhkZumzRpEgDwCwIiPfTtt98iPDwcX3zxBby8vGBra4u3335b7bmP9eUIVS5pbPtHlUueffZZjbhvv/1WilM9J65z586NqoednZ3aa7lcDuDR1Ponfa+qzK+++qpGmZcsWQIhBG7dugWg4eupUCiQlpaGZ555BnPmzEGfPn2gVCoxb968Wjs+qXnxmW+kV5KTk/H6669j+PDhOHDgAFxcXAAA9vb26NevHxYuXFjr+1SNPeBRj39gYCDWrFmDwMBA7Ny5E/Pnz5fm1BMR1aRqGOXn52vs++OPP2Bvby+9NjMz03jILqD5wdDOzg7Hjh3TiKvrQeONYW9vDzs7OyQnJ9e638rKSvr/4cOHY/jw4aisrMTx48cRHx+PqKgoODo6Sl9wvPPOO3jnnXdQWlqK9PR0zJs3D8HBwTh//ryUf9uqqqoqGBkZIScnR+Pfj/bt2wN41FlrbGyMHj16SPt69eoF4NHD6PkcODJUdnZ2deZLAFLObGweVMXPnj0bY8eOrfWc/Hsi0j/29vZYtWoVVq1ahStXrmDnzp2YNWsWCgsLpbaMqvOpOlWOULXPGtv+UeWSf/3rX/W2Yzp27AgAOrEIgarM8fHxGDJkSK0xqg7KxlzPvn37IjExEUII/PTTT0hISMAnn3wCc3NzzJo1q2Uq1Uax8430iouLC3744QeMHDlS6oDr3r07goODsXfvXnTr1g02NjYNHmf69Onw9/dHeHg4jIyMEBkZ2QKlJyJ95eXlBXNzc2zZsgWvvfaatP3atWs4ePCg2jRCV1dXfPfddygrK5O+vbx58yYyMjJgbW0txfn6+uKf//wndu7cqTblYNu2bY9dzuDgYCQmJqKyshKDBw9u1HuMjIwwePBg9OzZE1u3bsWJEyc0RhdbWloiMDAQ5eXlGDNmDE6fPt3mO98GDBiAyspKFBYWYvjw4bXGDBs2DA8fPsSvv/6Kbt26AXg0lQVAm79+ZNhGjBiBRYsW4cSJE/jLX/4ibf/qq68gk8ng6+sLoPF50N3dHd27d8ePP/6IuLi4lqkEEbWorl27YsqUKThw4AD+7//+T9p+586dWnNEu3bt8PzzzwNofPsnICAAxsbG+PXXX/HKK6/UGdejRw9069YNX375JaKjo6X2XGsYNmwYOnTo0OTHJNV1PVVkMhn69++PTz/9FAkJCThx4kRzFptqwc430jvOzs5IS0tDQEAAnn/+eaSmpuKTTz5Bamoqhg4dimnTpsHd3R0PHjzApUuXsHfvXnz22Wdqw4b9/PzQu3dvHDp0COPHj4eDg0Mr1oiIdF2HDh0wd+5czJkzB2+//TbefPNN3Lx5E/Pnz4eZmRnmzZsnxYaFheHzzz/H+PHjERkZiZs3b2Lp0qVqHW8A8Pbbb+PTTz/F22+/jYULF6J79+7Yu3cv9u3b99jlfOONN7B161a8+OKLmD59OgYNGgQTExNcu3YNhw4dwksvvYSXX34Zn332GQ4ePIigoCB07doVDx48kFbkGjlyJAAgMjIS5ubmGDZsGJydnVFQUIBFixZBoVDg2Weffewy6pO7d+/il19+kV5fvHgRubm5sLW1RY8ePfDWW2/h7bffxooVKzBgwAD8+eefOHjwIPr27YsXX3wRI0eOxF/+8hf89a9/xapVq1BVVYXJkyfDz89PbTQckaH54IMP8NVXXyEoKAiffPIJXFxcsGfPHqxduxbvv/++9PvflDz4+eefIzAwEAEBAYiIiECnTp1w69YtnD17FidOnMB3333X0tUkoidQXFwMX19fhIaGomfPnrCyskJ2djaSk5PVRrja2dnh/fffx5UrV9CjRw/s3bsXGzZswPvvv4+uXbsCaHz7x9XVFZ988gk++ugj/Pbbbxg1ahRsbGxw/fp1HDt2DJaWlpg/fz4AYM2aNQgJCcGQIUPwwQcfoGvXrrhy5Qr27dsnPb6oJbRv3x7x8fEIDw/HrVu38Oqrr8LBwQE3btzAjz/+iBs3bmDdunWNup67d+/G2rVrMWbMGDz11FMQQmDHjh24ffs2/Pz8WqxObVarLvdA1EjVVztVuX37thg2bJiwtbUV2dnZ4saNG2LatGnCzc1NmJiYCFtbWzFw4EDx0Ucfibt372ocMzY2VgAQWVlZLVkVItIT1Vc7Vfniiy9Ev379hKmpqVAoFOKll16SVlOubvPmzaJXr17CzMxM9O7dW3z77bcaq50KIcS1a9fEK6+8Itq3by+srKzEK6+8IjIyMmpd7dTS0lLjPKrcWF1FRYVYvny56N+/vzAzMxPt27cXPXv2FBMnThQXLlwQQgiRmZkpXn75ZeHi4iLkcrmws7MT3t7eYufOnWp18PX1FY6OjsLU1FQolUoxbtw48dNPPz3G1dRPqpXIav6oVisrLy8XH3/8sXB1dRUmJibCyclJvPzyy2rX6Pfffxdjx44V7du3F46OjiIiIoKrnZJeqK3tJUTd+cjb21v06dNHen358mURGhoq7OzshImJiXB3dxfLli3TWJW0sXlQCCF+/PFHMW7cOOHg4CD9zb3wwgvis88+a76KE1GLePDggXjvvfdEv379hLW1tTA3Nxfu7u5i3rx5orS0VAjx37xy+PBh4enpKeRyuXB2dhZz5szRWPm4Me0fle+//174+voKa2trIZfLhYuLi3j11VfF/v371eIyMzNFYGCgUCgUQi6Xi27duokPPvhA2l9XnqytDVnXaqffffed2nsvXrxYa/5LS0sTQUFBwtbWVpiYmIhOnTqJoKAg6f2NuZ4///yzePPNN0W3bt2Eubm5UCgUYtCgQSIhIaGBu0XNQSaEEC3f5UfU+jw9PSGTyZCdnd3aRSEiIiIiIqJqfHx88OeffyIvL6+1i0L0xDjtlNqUkpIS5OXlYffu3cjJyUFSUlJrF4mIiIiIiIiIDBg736hNOXHiBHx9fWFnZ4d58+ZhzJgxrV0kIiIiIiIiIjJgnHZKRERERERERESkJe1auwBERERERERERESGip1vREREREREREREWsLONyIiIiI9smjRIshkMkRFRUnbhBCIjY2FUqmEubk5fHx8cPr0abX3lZWVYerUqbC3t4elpSVGjx6Na9euqcUUFRUhLCwMCoUCCoUCYWFhuH37tlrMlStXEBISAktLS9jb22PatGkoLy9Xizl16hS8vb1hbm6OTp064ZNPPgGfdEJERERtFTvfiIiIiPREdnY21q9fj379+qltX7p0KVauXInVq1cjOzsbTk5O8PPzw507d6SYqKgoJCUlITExEUeOHMHdu3cRHByMyspKKSY0NBS5ublITk5GcnIycnNzERYWJu2vrKxEUFAQSktLceTIESQmJmL79u2IiYmRYkpKSuDn5welUons7GzEx8dj+fLlWLlypRavDBEREZHuatMLLlRVVeGPP/6AlZUVZDJZaxeHiBoghMCdO3egVCrRrh2/O6iJOY1IvzQ1p929exd/+ctfsHbtWixYsADPPPMMVq1aBSEElEoloqKiMHPmTACPRrk5OjpiyZIlmDhxIoqLi9GxY0d8/fXXeP311wEAf/zxB7p06YK9e/ciICAAZ8+eRe/evZGVlYXBgwcDALKysuDl5YWff/4Z7u7u+M9//oPg4GBcvXoVSqUSAJCYmIiIiAgUFhbC2toa69atw+zZs3H9+nXI5XIAwOLFixEfH49r1641Kj8xnxHpF7bR6sZ8RqR/tJHTjJvlKHpK1egkIv1y9epVdO7cubWLoXOY04j0U2Nz2uTJkxEUFISRI0diwYIF0vaLFy+ioKAA/v7+0ja5XA5vb29kZGRg4sSJyMnJQUVFhVqMUqmEh4cHMjIyEBAQgMzMTCgUCqnjDQCGDBkChUKBjIwMuLu7IzMzEx4eHlLHGwAEBASgrKwMOTk58PX1RWZmJry9vaWON1XM7NmzcenSJbi5uTVYV+YzIv3ENpom5jMi/dWcOa1Nd75ZWVkBeHRBra2t642tqKhASkoK/P39YWJi0hLFa3asg25gHR5fSUkJunTpIv3tkrrG5jRD+B1sDrwOj/A6PNIa16EpOS0xMREnTpxAdna2xr6CggIAgKOjo9p2R0dHXL58WYoxNTWFjY2NRozq/QUFBXBwcNA4voODg1pMzfPY2NjA1NRULcbV1VXjPKp9tXW+lZWVoaysTHqtmphx8eLFBq9PRUUFDh06BF9fX4P6HWa99Ich1gloWr3u3LkDNzc3ttFq0dY+c9bHkOtnyHUDDLt+tdVNG58723Tnm2rYr7W1daMSoYWFBaytrfX2l4110A2sw5PjkP3aNTantfb90xW8Do/wOjzSmtehoZx29epVTJ8+HSkpKTAzM2v0cYQQDR67Zkxt8c0Ro+pMq6s8ixYtwvz58zW2Z2ZmwsLCot46AICFhQWOHj3aYJy+Yb30hyHWCWh8ve7duweAbbTatLXPnPUx5PoZct0Aw65ffXVrzpzWpjvfiIiIiHRdTk4OCgsLMXDgQGlbZWUl0tPTsXr1apw7dw7Ao1Flzs7OUkxhYaE04szJyQnl5eUoKipSG/1WWFiIoUOHSjHXr1/XOP+NGzfUjlPzg3hRUREqKirUYlSj4KqfB9Acnacye/ZsREdHS69V3zj7+/s36sNqamoq/Pz8DOoDAeulPwyxTkDT6lVSUtJCpSIi0k/sfCMiIiLSYSNGjMCpU6fUtr3zzjvo2bMnZs6ciaeeegpOTk5ITU3FgAEDAADl5eVIS0vDkiVLAAADBw6EiYkJUlNTMW7cOABAfn4+8vLysHTpUgCAl5cXiouLcezYMQwaNAgAcPToURQXF0sddF5eXli4cCHy8/Oljr6UlBTI5XKpc9DLywtz5sxBeXk5TE1NpRilUqkxHVVFLperPSNOxcTEpNGdGU2J1Sesl/4wxDoBjauXIdabiKg5sfONiIiISIdZWVnBw8NDbZulpSXs7Oyk7VFRUYiLi0P37t3RvXt3xMXFwcLCAqGhoQAAhUKBCRMmICYmBnZ2drC1tcWMGTPQt29fjBw5EgDQq1cvjBo1CpGRkfj8888BAO+++y6Cg4Ph7u4OAPD390fv3r0RFhaGZcuW4datW5gxYwYiIyOlEWqhoaGYP38+IiIiMGfOHFy4cAFxcXH4+OOPOSWNiIiI2iR2vhERERHpuQ8//BD379/HpEmTUFRUhMGDByMlJUXtQcGffvopjI2NMW7cONy/fx8jRoxAQkICjIyMpJitW7di2rRp0qqoo0ePxurVq6X9RkZG2LNnDyZNmoRhw4bB3NwcoaGhWL58uRSjUCiQmpqKyZMnw9PTEzY2NoiOjlabVkpERETUlrDzrYk8YvehrPLRt7aXFge1cmmIiIjaBtdZe9Ret/V/gw8fPqz2WiaTITY2FrGxsXW+x8zMDPHx8YiPj68zxtbWFlu2bKn33F27dsXu3bvrjenbty/S09PrjSEi3VM918qNBJYOasXCEADNf/8A/htIpI/atXYBiIiIiIiIiIiIDBVHvhE9IY7GICIiIiIiIqK6cOQbERERERERERGRlrDzjYiIiIiIiIiISEvY+UZERERERERERKQl7HwjIiIiIiIiIiLSEna+ERERERERERERaQk734iIiIiIiIiIiLSEnW9ERERERERERERaYtzaBSAiIiJqKtdZe9ReX1oc1EolISIiIiKqH0e+ERERERERERERaQlHvhEREZHOqTmyjYiIiIhIX3HkGxERERERERERkZaw842IiIiIiIi0btGiRXj22WdhZWUFBwcHjBkzBufOnVOLEUIgNjYWSqUS5ubm8PHxwenTp9ViysrKMHXqVNjb28PS0hKjR4/GtWvX1GKKiooQFhYGhUIBhUKBsLAw3L59Wy3mypUrCAkJgaWlJezt7TFt2jSUl5erxZw6dQre3t4wNzdHp06d8Mknn0AI0XwXhYjaBHa+ERERERERkdalpaVh8uTJyMrKQmpqKh4+fAh/f3+UlpZKMUuXLsXKlSuxevVqZGdnw8nJCX5+frhz544UExUVhaSkJCQmJuLIkSO4e/cugoODUVlZKcWEhoYiNzcXycnJSE5ORm5uLsLCwqT9lZWVCAoKQmlpKY4cOYLExERs374dMTExUkxJSQn8/PygVCqRnZ2N+Ph4LF++HCtXrtTylSIiQ8NnvhEREREREZHWJScnq73etGkTHBwckJOTg+effx5CCKxatQofffQRxo4dCwDYvHkzHB0dsW3bNkycOBHFxcXYuHEjvv76a4wcORIAsGXLFnTp0gX79+9HQEAAzp49i+TkZGRlZWHw4MEAgA0bNsDLywvnzp2Du7s7UlJScObMGVy9ehVKpRIAsGLFCkRERGDhwoWwtrbG1q1b8eDBAyQkJEAul8PDwwPnz5/HypUrER0dDZlM1oJXj4j0GTvfDETNB1NfWhzUSiUhIiIiIiJqWHFxMQDA1tYWAHDx4kUUFBTA399fipHL5fD29kZGRgYmTpyInJwcVFRUqMUolUp4eHggIyMDAQEByMzMhEKhkDreAGDIkCFQKBTIyMiAu7s7MjMz4eHhIXW8AUBAQADKysqQk5MDX19fZGZmwtvbG3K5XC1m9uzZuHTpEtzc3DTqVFZWhrKyMul1SUkJAKCiogIVFRX1Xg/V/upxciPNKa4NHUdX1VY/Q2HIdQMMu3611U0b9WTnGxEREREREbUoIQSio6Px3HPPwcPDAwBQUFAAAHB0dFSLdXR0xOXLl6UYU1NT2NjYaMSo3l9QUAAHBweNczo4OKjF1DyPjY0NTE1N1WJcXV01zqPaV1vn26JFizB//nyN7SkpKbCwsKjlSmhKTU2V/n/pIM39e/fubdRxdFX1+hkaQ64bYNj1q163e/fuNfvx2flGRERERERELWrKlCn46aefcOTIEY19NadzCiEanOJZM6a2+OaIUS22UFd5Zs+ejejoaOl1SUkJunTpAn9/f1hbW9dbh4qKCqSmpsLPzw8mJiYAAI/YfRpxebEB9R5HV9VWP0NhyHUDDLt+tdVNNWK1ObHzjchA1ZyKDHA6MhERERG1vqlTp2Lnzp1IT09H586dpe1OTk4AHo0qc3Z2lrYXFhZKI86cnJxQXl6OoqIitdFvhYWFGDp0qBRz/fp1jfPeuHFD7ThHjx5V219UVISKigq1GNUouOrnATRH56nI5XK1aaoqJiYmje60qB5bVqnZyafvnR9NuRb6xpDrBhh2/arXTRt15GqnREREREREpHVCCEyZMgU7duzAwYMHNaZturm5wcnJSW36V3l5OdLS0qSOtYEDB8LExEQtJj8/H3l5eVKMl5cXiouLcezYMSnm6NGjKC4uVovJy8tDfn6+FJOSkgK5XI6BAwdKMenp6SgvL1eLUSqVGtNRiYjqw843Imqzfv/9d4wfPx52dnawsLDAM888g5ycHGm/EAKxsbFQKpUwNzeHj48PTp8+rXaMsrIyTJ06Ffb29rC0tMTo0aNx7do1tZiioiKEhYVBoVBAoVAgLCwMt2/fVou5cuUKQkJCYGlpCXt7e0ybNk2toUdERESk7yZPnowtW7Zg27ZtsLKyQkFBAQoKCnD//n0Aj6ZyRkVFIS4uDklJScjLy0NERAQsLCwQGhoKAFAoFJgwYQJiYmJw4MABnDx5EuPHj0ffvn2l1U979eqFUaNGITIyEllZWcjKykJkZCSCg4Ph7u4OAPD390fv3r0RFhaGkydP4sCBA5gxYwYiIyOl6aGhoaGQy+WIiIhAXl4ekpKSEBcXx5VOiajJ2PlGRG1SUVERhg0bBhMTE/znP//BmTNnsGLFCnTo0EGKWbp0KVauXInVq1cjOzsbTk5O8PPzw507d6SYqKgoJCUlITExEUeOHMHdu3cRHByMyspKKSY0NBS5ublITk5GcnIycnNzERYWJu2vrKxEUFAQSktLceTIESQmJmL79u2IiYlpkWtBRERE1BLWrVuH4uJi+Pj4wNnZWfr59ttvpZgPP/wQUVFRmDRpEjw9PfH7778jJSUFVlZWUsynn36KMWPGYNy4cRg2bBgsLCywa9cuGBkZSTFbt25F37594e/vD39/f/Tr1w9ff/21tN/IyAh79uyBmZkZhg0bhnHjxmHMmDFYvny5FKNQKJCamopr167B09MTkyZNQnR0tNoz3YiIGoPPfCOiNmnJkiXo0qULNm3aJG2rPn1ACIFVq1bho48+wtixYwEAmzdvhqOjI7Zt24aJEyeiuLgYGzduxNdffy1907plyxZ06dIF+/fvR0BAAM6ePYvk5GRkZWVJy91v2LABXl5eOHfuHNzd3ZGSkoIzZ87g6tWr0nL3K1asQEREBBYuXNjgw3mJiIiI9IFqsYL6yGQyxMbGIjY2ts4YMzMzxMfHIz4+vs4YW1tbbNmypd5zde3aFbt37643pm/fvkhPT683hoioIU0e+cZpWkRkCHbu3AlPT0+89tprcHBwwIABA7BhwwZp/8WLF1FQUAB/f39pm1wuh7e3NzIyMgAAOTk5qKioUItRKpXw8PCQYjIzM6FQKKSONwAYMmQIFAqFWoyHh4fU8QYAAQEBKCsrU8uvREREREREpH+aNPJNNU3L19cX//nPf+Dg4IBff/211mlaCQkJ6NGjBxYsWAA/Pz+cO3dOGiocFRWFXbt2ITExEXZ2doiJiUFwcDBycnKkocKhoaG4du0akpOTAQDvvvsuwsLCsGvXLgD/nabVsWNHHDlyBDdv3kR4eDiEEPV+A0JEBAC//fYb1q1bh+joaMyZMwfHjh3DtGnTIJfL8fbbb0srW9VcycrR0RGXL18G8GglLlNTU7WVtlQxqvcXFBTAwcFB4/wODg5qMTXPY2NjA1NTU40VtqorKytDWVmZ9Fq1JHZFRQUqKirqfJ9qX30xbQGvwyO6eh3kRg2PjqjuScvfGtdB1645EREREWlHkzrfOE2LiAxFVVUVPD09ERcXBwAYMGAATp8+jXXr1uHtt9+W4mo+TFcI0eADdmvG1Bb/ODE1LVq0CPPnz9fYnpKSAgsLi3rLCEBtlbC2jNfhEV27DksHNS1+7969zXLelrwO9+7da7FzEREREVHraVLn286dOxEQEIDXXnsNaWlp6NSpEyZNmoTIyEgADU/TmjhxYoPTtAICAhqcpuXu7t7gNC1fX9/HvihEZPicnZ3Ru3dvtW29evXC9u3bAQBOTk4AHo1Kc3Z2lmIKCwulUWpOTk4oLy9HUVGR2ui3wsJCaRl7JycnXL9+XeP8N27cUDvO0aNH1fYXFRWhoqJCY0RcdbNnz1Z74G9JSQm6dOkCf3//er+AqKioQGpqKvz8/GBiYlJnnKHjdXhEF66DR+y+Jz5GXmzAE72/Na6DarQqERERERm2JnW+6fs0rcedoqWKAQB5O6GxTRfUnJ5TW9l0dWpRU+hiHRpz7Wvbr+061DZlq7nO2Vr3oTnPN2zYMJw7d05t2/nz5+Hi4gIAcHNzg5OTE1JTUzFgwAAAQHl5OdLS0rBkyRIAwMCBA2FiYoLU1FSMGzcOAJCfn4+8vDwsXboUAODl5YXi4mIcO3YMgwY9Gspz9OhRFBcXSx10Xl5eWLhwIfLz86WOvpSUFMjlcgwcOLDOOsjlcsjlco3tJiYmjeo8aGycoeN1eKQ1r0NZZf2jSRujucrektehsedZt24d1q1bh0uXLgEA+vTpg48//hiBgYEAHo2SnT9/PtavX4+ioiIMHjwYa9asQZ8+faRjlJWVYcaMGfjmm29w//59jBgxAmvXrkXnzp2lmKKiIkybNg07d+4EAIwePRrx8fFqjxe5cuUKJk+ejIMHD8Lc3ByhoaFYvnw5TE1NpZhTp05hypQpOHbsGGxtbTFx4kTMnTu3wVHDRERERIaqSZ1v+j5N60mnaAHA3z2rpP9vrikuzaHm9Jz6yqZrU4sehy7VoSnXvjpt16G2KVvN/Tvb0vehOadoffDBBxg6dCji4uIwbtw4HDt2DOvXr8f69esBPMovUVFRiIuLQ/fu3dG9e3fExcXBwsICoaGhAB4tPz9hwgTExMTAzs4Otra2mDFjBvr27StNq+/VqxdGjRqFyMhIfP755wAePcMyODgY7u7uAAB/f3/07t0bYWFhWLZsGW7duoUZM2YgMjKSU+iJCJ07d8bixYvx9NNPA3j0SI+XXnoJJ0+eRJ8+fXTqebslJSXw8/ODr68vsrOzcf78eURERMDS0hIxMTEtfemIiIiIdEKTOt/0fZrW407RAv47HWXu8XYoq3rUufekU1yaU80pO7WVTRemFj0pXaxDY659dS1Vh9qmcTXX72xr3YfmnKL17LPPIikpCbNnz8Ynn3wCNzc3rFq1Cm+99ZYU8+GHH+L+/fuYNGmSNJokJSVF+jALAJ9++imMjY0xbtw4aTRJQkKC9GEWALZu3Ypp06ZJ0+1Hjx6N1atXS/uNjIywZ88eTJo0CcOGDVMbTUJEFBISovZ64cKFWLduHbKystC7d2+det7u1q1b8eDBAyQkJEAul8PDwwPnz5/HypUrER0dzdFvRERE1CY1qfNN36dpPekULQAoq5JJ02N0pfMH0JyyU1/ZDGGKlS7VoSnXvmacNutQ2zSu5j5fS9+H5j5XcHAwgoOD69wvk8kQGxuL2NjYOmPMzMwQHx9f7yrLtra22LJlS71l6dq1K3bv3t1gmYmobausrMR3332H0tJSeHl56dzzdjMzM+Ht7a3W3goICMDs2bNx6dIluLm5afkKEREREemeJnW+cZoWERERUcs7deoUvLy88ODBA7Rv3x5JSUno3bs3MjIyAOjO83YLCgrg6uqqcR7Vvro635rjuby69DzY5sB66Q9DqlP1ZwarnnXdmHoZQt2JiLSpSZ1vnKZFRERE1PLc3d2Rm5uL27dvY/v27QgPD0daWpq0X5eet1tbWep6r0pzPJdXl54H25xYL/1hCHWq7ZnBjalXcz6Xl4jIEDWp8w3gNC0iIiKilmZqaiotuODp6Yns7Gz84x//wMyZMwHozvN2nZycNFadLywsBKA5Oq+65ngury49D7Y5sF76w5DqVP2ZwfJ2An/3rGpUvZrzubxERIaoyZ1vRERERNS6hBAoKyvTueftenl5Yc6cOSgvL4epqakUo1QqNaajVtccz+XVpefBNifWS38YQp3qemZwQ/XS93oTEWlbu9YuABERERHVbc6cOfjhhx9w6dIlnDp1Ch999BEOHz6Mt956S+15u0lJScjLy0NERESdz9s9cOAATp48ifHjx9f5vN2srCxkZWUhMjKyzuftnjx5EgcOHNB43m5oaCjkcjkiIiKQl5eHpKQkxMXFcaVTIiIiatM48o2IiIhIh12/fh1hYWHIz8+HQqFAv379kJycDD8/PwC69bxdhUKB1NRUTJ48GZ6enrCxsUF0dLTalFIiIiKitoadb0REREQ6bOPGjfXu17Xn7fbt2xfp6en1xhARERG1Jex8IyIiohblOmtPaxeBiIiIiKjF8JlvREREREREREREWsLONyIiIiIiIiIiIi3htFPSC6opSnIjgaWDWrkwRERERERERESNxJFvREREREREREREWsKRb0RERERERER6oubCRZcWB7VSSYiosTjyjYiIiIiIiFpEeno6QkJCoFQqIZPJ8P3336vtj4iIgEwmU/sZMmSIWkxZWRmmTp0Ke3t7WFpaYvTo0bh27ZpaTFFREcLCwqBQKKBQKBAWFobbt2+rxVy5cgUhISGwtLSEvb09pk2bhvLycrWYU6dOwdvbG+bm5ujUqRM++eQTCCGa7XoQUdvAzjciIiIiIiJqEaWlpejfvz9Wr15dZ8yoUaOQn58v/ezdu1dtf1RUFJKSkpCYmIgjR47g7t27CA4ORmVlpRQTGhqK3NxcJCcnIzk5Gbm5uQgLC5P2V1ZWIigoCKWlpThy5AgSExOxfft2xMTESDElJSXw8/ODUqlEdnY24uPjsXz5cqxcubIZrwgRtQWcdkpEREREREQtIjAwEIGBgfXGyOVyODk51bqvuLgYGzduxNdff42RI0cCALZs2YIuXbpg//79CAgIwNmzZ5GcnIysrCwMHjwYALBhwwZ4eXnh3LlzcHd3R0pKCs6cOYOrV69CqVQCAFasWIGIiAgsXLgQ1tbW2Lp1Kx48eICEhATI5XJ4eHjg/PnzWLlyJaKjoyGTyZrxyhCRIePINyIiIiIiItIZhw8fhoODA3r06IHIyEgUFhZK+3JyclBRUQF/f39pm1KphIeHBzIyMgAAmZmZUCgUUscbAAwZMgQKhUItxsPDQ+p4A4CAgACUlZUhJydHivH29oZcLleL+eOPP3Dp0iWt1J2IDBNHvhEREREREZFOCAwMxGuvvQYXFxdcvHgRc+fOxQsvvICcnBzI5XIUFBTA1NQUNjY2au9zdHREQUEBAKCgoAAODg4ax3ZwcFCLcXR0VNtvY2MDU1NTtRhXV1eN86j2ubm5aZyjrKwMZWVl0uuSkhIAQEVFBSoqKuqtu2p/9Ti5UcPPl2vouLqitvoZCkOuG2DY9autbtqoJzvfiIiIiIiISCe8/vrr0v97eHjA09MTLi4u2LNnD8aOHVvn+4QQatNAa5sS2hwxqsUW6ppyumjRIsyfP19je0pKCiwsLOosf3WpqanS/y8d1HB8zWfi6brq9TM0hlw3wLDrV71u9+7da/bjs/ONiIiI9J7rrD0a2y4tDmqFkhARUXNydnaGi4sLLly4AABwcnJCeXk5ioqK1Ea/FRYWYujQoVLM9evXNY5148YNaeSak5MTjh49qra/qKgIFRUVajGqUXDVzwNAY9ScyuzZsxEdHS29LikpQZcuXeDv7w9ra+t661pRUYHU1FT4+fnBxMQEAOARu6/e9wBAXmxAgzG6oLb6GQpDrhtg2PWrrW6qEavNiZ1vpHU1PxDxwxARERERETXGzZs3cfXqVTg7OwMABg4cCBMTE6SmpmLcuHEAgPz8fOTl5WHp0qUAAC8vLxQXF+PYsWMYNOjR0LGjR4+iuLhY6qDz8vLCwoULkZ+fLx07JSUFcrkcAwcOlGLmzJmD8vJymJqaSjFKpVJjOqqKXC5Xe0aciomJSaM7LarHllU2vKiDvnWGNOVa6BtDrhtg2PWrXjdt1JELLhAREREREVGLuHv3LnJzc5GbmwsAuHjxInJzc3HlyhXcvXsXM2bMQGZmJi5duoTDhw8jJCQE9vb2ePnllwEACoUCEyZMQExMDA4cOICTJ09i/Pjx6Nu3r7T6aa9evTBq1ChERkYiKysLWVlZiIyMRHBwMNzd3QEA/v7+6N27N8LCwnDy5EkcOHAAM2bMQGRkpDRCLTQ0FHK5HBEREcjLy0NSUhLi4uK40ikRNRlHvhEREZFW1TYllIiI2qbjx4/D19dXeq2aohkeHo5169bh1KlT+Oqrr3D79m04OzvD19cX3377LaysrKT3fPrppzA2Nsa4ceNw//59jBgxAgkJCTAyMpJitm7dimnTpkmroo4ePRqrV6+W9hsZGWHPnj2YNGkShg0bBnNzc4SGhmL58uVSjEKhQGpqKiZPngxPT0/Y2NggOjpabVopEVFjsPONiIiIiIiIWoSPj4+0aEFt9u1r+BlnZmZmiI+PR3x8fJ0xtra22LJlS73H6dq1K3bv3l1vTN++fZGent5gmYiI6sNpp0RERERERERERFrCzjciIiIiIiIiIiItYecbERERERERERGRlrDzjYiIiIiIiIiISEvY+UZERERERERERKQl7HwjIiIiIiIiIiLSEna+EREBWLRoEWQyGaKioqRtQgjExsZCqVTC3NwcPj4+OH36tNr7ysrKMHXqVNjb28PS0hKjR4/GtWvX1GKKiooQFhYGhUIBhUKBsLAw3L59Wy3mypUrCAkJgaWlJezt7TFt2jSUl5drq7pERERERETUQtj5RkRtXnZ2NtavX49+/fqpbV+6dClWrlyJ1atXIzs7G05OTvDz88OdO3ekmKioKCQlJSExMRFHjhzB3bt3ERwcjMrKSikmNDQUubm5SE5ORnJy8v/P3r3HRVXn/wN/jQgjEI5cBBxFxTK8gOWi4mgbugroCta6u+5GkbRm9gVFFviWly3BVUhTcxfL0lxxRaNt1fIWgpWwfPGCKCVqaJvXAjFF8DqM8Pn9wW9ODMNlBhmYGV7Px8NHzTnvOfN5nzPz4Xw+53POB0VFRYiIiJDW19TUYMqUKbhz5w7y8vKQkZGB7du3Iz4+3vTJExERERERkUk9VOcbR4oQkaW7ffs2nn/+eWzYsAHOzs7SciEE1qxZg0WLFmHatGnw9fXF5s2bcffuXWzbtg0AUFlZiY0bN2LVqlWYOHEihg8fjvT0dJw8eRIHDhwAAJw5cwaZmZn48MMPoVKpoFKpsGHDBuzZswclJSUAgKysLJw+fRrp6ekYPnw4Jk6ciFWrVmHDhg2oqqpq/51CREREREREbabVnW8cKdI6/efv1flHRB0rOjoaU6ZMwcSJE3WWnz9/HmVlZQgODpaWyeVyBAYGIj8/HwBQWFgIjUajE6NUKuHr6yvFHDp0CAqFAgEBAVLM6NGjoVAodGJ8fX2hVCqlmJCQEKjVahQWFrZ90kRERERERNRuurbmTfVHiixdulRa3nCkCABs3rwZHh4e2LZtG2bPni2NFNmyZYvU2E1PT4eXlxcOHDiAkJAQaaTI4cOHpQbrhg0boFKpUFJSAh8fH2mkyOXLl6UG66pVqxAZGYlly5ahe/fuD7VjiMj6ZWRk4Pjx4ygoKNBbV1ZWBgDw8PDQWe7h4YGLFy9KMXZ2djoj5rQx2veXlZXB3d1db/vu7u46MQ0/x9nZGXZ2dlJMY9RqNdRqtfRaO0pOo9FAo9E0+T7tuuZiOgPuhzrtsR/kNsJk226OMTl1xPehs3/3iIiIiDqLVnW+1R8pUr/zraWRIrNnz25xpEhISEiLI0V8fHxaHCkyfvz41qRGRJ3E5cuXMW/ePGRlZaFbt25NxslkMp3XQgi9ZQ01jGksvjUxDaWkpCApKUlveVZWFhwcHJotIwBkZ2e3GNMZcD/UMeV+WDHKZJtu1r59+4x+T3t+H+7evWtQXEpKCnbs2IFvv/0W9vb2GDNmDJYvXw4fHx8pRgiBpKQkrF+/HhUVFQgICMC7776LoUOHSjFqtRoJCQn46KOPcO/ePUyYMAHvvfce+vTpI8VUVFQgJiYGu3btAgBMnToVqamp6NGjhxRz6dIlREdH48svv4S9vT3Cw8OxcuVK2NnZSTEnT57EnDlzcPToUbi4uGD27Nl44403Wqw/iYiIiKyR0Z1vljxSpLWjRLQxACDvIvSWGaPh1f+2uuptyHY7apRHW+Ss3YZ2/5vTaAFj82uv49DYSJO2+syO+i615ecVFhaivLwc/v7+0rKamhrk5uZi7dq10vPYysrK0KtXLymmvLxcqns8PT1RXV2NiooKnTqtvLwcY8aMkWKuXr2q9/nXrl3T2c6RI0d01ldUVECj0ejVc/UtWLAAcXFx0uuqqip4eXkhODi42dG/Go0G2dnZCAoKgq2tbZNx1o77oU577AffxP0m2W5LihNDDI7tiO+Doc90zMnJQXR0NEaOHIkHDx5g0aJFCA4OxunTp+Ho6Ajg58d+pKWl4fHHH8fSpUsRFBSEkpISODk5Aah77Mfu3buRkZEBV1dXxMfHIzQ0FIWFhbCxsQFQ99iPK1euIDMzEwDwyiuvICIiArt37wbw82M/evbsiby8PFy/fh0zZsyAEAKpqalSXkFBQRg/fjwKCgpw9uxZREZGwtHR0ewfD0JERERkCkZ1vln6SJGHHSUCAH8dUSv9f2uuqDe8+t+abTzsdtt7lEdb5NxwG+Y0UqW1+Zk6h8ZGmrTV902rvY+DoaNEDDFhwgScPHlSZ9lLL72EQYMG4fXXX8eAAQPg6emJ7OxsDB8+HABQXV2NnJwcLF++HADg7+8PW1tbZGdnY/r06QCA0tJSFBcXY8WKFQAAlUqFyspKHD16FKNG1R2UI0eOoLKyUuqgU6lUWLZsGUpLS6WOvqysLMjlcp3OwYbkcjnkcrnecltbW4M6DwyNs3bcD3VMuR/UNR0z2qk1+bTn98HQz9F2hGlt2rQJ7u7uKCwsxNNPP212j/3YunUr7t+/j7S0NMjlcvj6+uLs2bNYvXo14uLiOPqNiIiIOh2jOt8sfaRIa0eJAD9fEX/jWBeoa+tOGo25oq7V8Op/a7bR2u121CiPtshZuw15F4G/jqg1q5EqxubXXsehsZEmbfV966jvUlvO/Onk5ARfX1+dZY6OjnB1dZWWx8bGIjk5GQMHDsTAgQORnJwMBwcHhIeHAwAUCgVmzpyJ+Ph4uLq6wsXFBQkJCfDz85Mat4MHD8akSZMwa9YsfPDBBwDqRpKEhoZKt4wFBwdjyJAhiIiIwNtvv40bN24gISEBs2bN4vMriUhPZWUlAMDFxQWA+T3249ChQwgMDNS5OBASEoIFCxbgwoUL8Pb21supLe5OMKdR8W2BeVkOa8qp/p0TxtxxYg25ExGZklGdb5Y+UuRhR4kAgLpWJl3Bb02nQ8Or/23VcWHMdtt7lEdb5NzYNsyl8621+Zk6h8ZGmrT157U2h8Zm+r3w1hSDPq89vfbaa7h37x6ioqKkZyhlZWVJt3ABwDvvvIOuXbti+vTp0jOU0tLSpFu4AGDr1q2IiYmRGr1Tp07F2rVrpfU2NjbYu3cvoqKiMHbsWJ1nKBER1SeEQFxcHJ566inpQoG5PfajrKwM/fv31/sc7brGOt/a4u4EcxoV35aYl+Wwhpwau3PCkLza8u4EIiJrZFTnG0eKEJE1O3jwoM5rmUyGxMREJCYmNvmebt26ITU1VXrWUWNcXFyQnp7e7Gf37dsXe/bsMaa4RNQJzZkzB9988w3y8vL01pnTYz8aK0tT7wXM4+4Ec2Otz6W0xrysKaf6d04Yc8dJW96dQERkjVo122lzOFKEyLr1n78XchuBFaPqTtBKloV2dJGIyIw0NrKV2sbcuXOxa9cu5Obm6sxQ6unpCcB8Hvvh6empN/lVeXk5AP3ReVrmcHeCuTKn0f5tyRrzsoacmrpzoqW8LD1vIiJT6/KwGzh48CDWrFkjvdaOFCktLcX9+/eRk5OjN1pOO1Lk+vXruHv3Lnbv3g0vLy+dGO1IkaqqKlRVVSE9PV1nmnvg55Eid+/exfXr15GamtroiRsRERGRpRJCYM6cOdixYwe+/PJLvds2vb29pcd+aGkf+6HtWKv/2A8t7WM/6j/SQ/vYD63GHvtRXFyM0tJSKabhYz9UKhVyc3NRXV2tE6NUKvVuRyUiIiLqDB66842IiIiITCc6Ohrp6enYtm0bnJycUFZWhrKyMty7dw9A3YVP7WM/du7cieLiYkRGRjb52I8vvvgCJ06cwAsvvNDkYz8OHz6Mw4cPY9asWU0+9uPEiRP44osv9B77ER4eDrlcjsjISBQXF2Pnzp1ITk7mTKdERETUabX5badERERE1HbWrVsHABg3bpzO8k2bNiEyMhKAeT32Q6FQIDs7G9HR0RgxYgScnZ0RFxen80w3IiIios6EnW9EREREZkw7WUFzzG2CGD8/P+Tm5jYbQ0RERNRZ8LZTIiIiIiIiahe5ubkICwuDUqmETCbDp59+qrNeCIHExEQolUrY29tj3LhxOHXqlE6MWq3G3Llz4ebmBkdHR0ydOhVXrlzRiamoqEBERAQUCgUUCgUiIiJw8+ZNnZhLly4hLCwMjo6OcHNzQ0xMjM7zKgHg5MmTCAwMhL29PXr37o0lS5YYdFGEiKg+dr4RERERERFRu7hz5w6eeOIJnVva61uxYgVWr16NtWvXoqCgAJ6enggKCsKtW7ekmNjYWOzcuRMZGRnIy8vD7du3ERoaipqaGikmPDwcRUVFyMzMRGZmJoqKihARESGtr6mpwZQpU3Dnzh3k5eUhIyMD27dvR3x8vBRTVVWFoKAgKJVKFBQUIDU1FStXrsTq1atNsGeIyJrxtlMiIiIiIiJqF5MnT8bkyZMbXSeEwJo1a7Bo0SJMmzYNALB582Z4eHhg27ZtmD17NiorK7Fx40Zs2bJFmjAmPT0dXl5eOHDgAEJCQnDmzBlkZmbi8OHDCAgIAABs2LABKpUKJSUl8PHxQVZWFk6fPo3Lly9DqVQCAFatWoXIyEgsW7YM3bt3x9atW3H//n2kpaVBLpfD19cXZ8+exerVqzmJDBEZhSPfiIiIiIiIqMOdP38eZWVl0qQvACCXyxEYGIj8/HwAQGFhITQajU6MUqmEr6+vFHPo0CEoFAqp4w0ARo8eDYVCoRPj6+srdbwBQEhICNRqNQoLC6WYwMBAyOVynZgff/wRFy5caPsdQERWiyPfiMxQ//l7dV5feGtKB5WEiIiIiKh9lJWVAQA8PDx0lnt4eODixYtSjJ2dHZydnfVitO8vKyuDu7u73vbd3d11Yhp+jrOzM+zs7HRi+vfvr/c52nXe3t56n6FWq6FWq6XXVVVVAACNRgONRtNM9pDW14+T27T8fLmWtmsuGsvPWlhzboB159dYbqbIk51vREREREREZDYa3s4phGjxFs+GMY3Ft0WMdrKFpsqTkpKCpKQkveVZWVlwcHBoNget7Oxs6f9XjGo5ft++fQZt11zUz8/aWHNugHXnVz+3u3fvtvn22flGREREREREHc7T0xNA3aiyXr16ScvLy8ulEWeenp6orq5GRUWFzui38vJyjBkzRoq5evWq3vavXbums50jR47orK+oqIBGo9GJ0Y6Cq/85gP7oPK0FCxYgLi5Oel1VVQUvLy8EBweje/fuzeav0WiQnZ2NoKAg2NraAgB8E/c3+x4AKE4MaTHGHDSWn7Ww5twA686vsdy0I1bbEjvfiIiIiIiIqMN5e3vD09MT2dnZGD58OACguroaOTk5WL58OQDA398ftra2yM7OxvTp0wEApaWlKC4uxooVKwAAKpUKlZWVOHr0KEaNqhs6duTIEVRWVkoddCqVCsuWLUNpaanU0ZeVlQW5XA5/f38pZuHChaiuroadnZ0Uo1Qq9W5H1ZLL5TrPiNOytbU1uNOifqy6puVJHSytM8SYfWFprDk3wLrzq5+bKXLkhAtERERERETULm7fvo2ioiIUFRUBqJtkoaioCJcuXYJMJkNsbCySk5Oxc+dOFBcXIzIyEg4ODggPDwcAKBQKzJw5E/Hx8fjiiy9w4sQJvPDCC/Dz85NmPx08eDAmTZqEWbNm4fDhwzh8+DBmzZqF0NBQ+Pj4AACCg4MxZMgQRERE4MSJE/jiiy+QkJCAWbNmSSPUwsPDIZfLERkZieLiYuzcuRPJycmc6ZSIjMaRb9RpNZzUAODEBkREREREpnTs2DGMHz9eeq29RXPGjBlIS0vDa6+9hnv37iEqKgoVFRUICAhAVlYWnJycpPe888476Nq1K6ZPn4579+5hwoQJSEtLg42NjRSzdetWxMTESLOiTp06FWvXrpXW29jYYO/evYiKisLYsWNhb2+P8PBwrFy5UopRKBTIzs5GdHQ0RowYAWdnZ8TFxencVkpEZAh2vhEREREREVG7GDdunDRpQWNkMhkSExORmJjYZEy3bt2QmpqK1NTUJmNcXFyQnp7ebFn69u2LPXv2NBvj5+eH3NzcZmOIiFrC206JiIiIiIiIiIhMhJ1vREREREREREREJsLbTomIiKjVGnt+JhERERER/Ywj34iIiIiIiIiIiEyEnW9EREREREREREQmws43IiIiIiIiIiIiE2HnGxERERERERERkYmw842IiIiIiIiIiMhE2PlGRERERERERERkIux8IyIiIiIiIiIiMhF2vhEREREREREREZkIO9+IiIiIiIiIiIhMhJ1vREREREREREREJsLONyIiIiIiIiIiIhNh5xsRdUopKSkYOXIknJyc4O7ujmeffRYlJSU6MUIIJCYmQqlUwt7eHuPGjcOpU6d0YtRqNebOnQs3Nzc4Ojpi6tSpuHLlik5MRUUFIiIioFAooFAoEBERgZs3b+rEXLp0CWFhYXB0dISbmxtiYmJQXV1tktyJiIiIiIio/RjV+cbGKhFZi5ycHERHR+Pw4cPIzs7GgwcPEBwcjDt37kgxK1aswOrVq7F27VoUFBTA09MTQUFBuHXrlhQTGxuLnTt3IiMjA3l5ebh9+zZCQ0NRU1MjxYSHh6OoqAiZmZnIzMxEUVERIiIipPU1NTWYMmUK7ty5g7y8PGRkZGD79u2Ij49vn51BREREREREJmNU5xsbq0RkLTIzMxEZGYmhQ4fiiSeewKZNm3Dp0iUUFhYCqLuQsGbNGixatAjTpk2Dr68vNm/ejLt372Lbtm0AgMrKSmzcuBGrVq3CxIkTMXz4cKSnp+PkyZM4cOAAAODMmTPIzMzEhx9+CJVKBZVKhQ0bNmDPnj3SxYusrCycPn0a6enpGD58OCZOnIhVq1Zhw4YNqKqq6pgdRERERERERG3CqM43NlatW//5e3X+EXUmlZWVAAAXFxcAwPnz51FWVobg4GApRi6XIzAwEPn5+QCAwsJCaDQanRilUglfX18p5tChQ1AoFAgICJBiRo8eDYVCoRPj6+sLpVIpxYSEhECtVkv1K5G54N+KjpGbm4uwsDAolUrIZDJ8+umnOuvN7c6DkydPIjAwEPb29ujduzeWLFkCIUSb7Q8iIiIiS9L1Yd5sbGN19uzZLTZWQ0JCWmys+vj4tNhYHT9+/MOkRkSdiBACcXFxeOqpp+Dr6wsAKCsrAwB4eHjoxHp4eODixYtSjJ2dHZydnfVitO8vKyuDu7u73me6u7vrxDT8HGdnZ9jZ2UkxjVGr1VCr1dJr7YUHjUYDjUbT5Pu065qL6Qy4H+oYux/kNpbTgeKzaI/O6+LEkCZjO+L7YMxn3blzB0888QReeukl/Pa3v9Vbr73zIC0tDY8//jiWLl2KoKAglJSUwMnJCUDdnQe7d+9GRkYGXF1dER8fj9DQUBQWFsLGxgZA3Z0HV65cQWZmJgDglVdeQUREBHbv3g3g5zsPevbsiby8PFy/fh0zZsyAEAKpqakA6uqioKAgjB8/HgUFBTh79iwiIyPh6OjIOxSIiIioU2p155slNlZb21DVxgCAvIvQW2aMho2WtjrJN2S7LTUsOrJshm5Du//bomyNNSDb45ga0sBry332sNtpbLva4yDvItq1bKZqFM+ZMwfffPMN8vLy9NbJZDKd10IIvWUNNYxpLL41MQ2lpKQgKSlJb3lWVhYcHByaLSMAZGdntxjTGXA/1DF0P6wYZeKCmNC+fftajGnP78Pdu3cNjp08eTImT57c6LqGdx4AwObNm+Hh4YFt27Zh9uzZ0p0HW7ZswcSJEwEA6enp8PLywoEDBxASEiLdeXD48GHpAuiGDRugUqlQUlICHx8f6c6Dy5cvSxdAV61ahcjISCxbtgzdu3fH1q1bcf/+faSlpUEul8PX1xdnz57F6tWrERcX12IdSkRERGRtWt35ZomN1YdtqALAX0fUSv9vyEl8Qw0bLa3ZxsNut6mGhTmUzdBttEXjqLEGZHse0+ZyMMU+a+12mtvuX0fUtmvZjGmoGmru3LnYtWsXcnNz0adPH2m5p6cngLqO/l69eknLy8vLpY5/T09PVFdXo6KiQueCQnl5OcaMGSPFXL16Ve9zr127prOdI0eO6KyvqKiARqPRu8hQ34IFCxAXFye9rqqqgpeXF4KDg9G9e/cm36fRaJCdnY2goCDY2to2GWftuB/qGLsffBP3t0OpTKOlkW/t/X1oq8dkmNudB4cOHUJgYCDkcrlOzIIFC3DhwgV4e3vr5WAOF0jNjbWOzrXGvKwpp/oXSI256G0NuRMRmVKrOt8stbHa2oYq8PNJ+RvHukBdW9e519xJfFMaNlpas43WbrelhkVHls3Qbci7CPx1RG2bNI4aa0C2xzE1pIHXlvvsYbfT2Ha1x+GNY11Q+OakditbWz7PUQiBuXPnYufOnTh48KBeY9Db2xuenp7Izs7G8OHDAQDV1dXIycnB8uXLAQD+/v6wtbVFdnY2pk+fDgAoLS1FcXExVqxYAQBQqVSorKzE0aNHMWpUXa/jkSNHUFlZKdV5KpUKy5YtQ2lpqVR3ZmVlQS6Xw9/fv8kc5HK5TuNWy9bW1qDfh6Fx1o77oY6h+0FdY7mjlsztd9FWn2Nudx6UlZWhf//+ep+jXddY55s5XCA1V9Y6Otca87KGnBq7QGpIXqa4QEpEZE2M6nyz9MbqwzZUAUBdK5MaHq05aW7YaGmrE29jtttUvuZQNmO28bDla6wB2Z7HtLkcTLHPWrud5rarrpW1a9naskEcHR2Nbdu24bPPPoOTk5PUaFQoFLC3t4dMJkNsbCySk5MxcOBADBw4EMnJyXBwcEB4eLgUO3PmTMTHx8PV1RUuLi5ISEiAn5+fdFvX4MGDMWnSJMyaNQsffPABgLpnKIWGhsLHxwcAEBwcjCFDhiAiIgJvv/02bty4gYSEBMyaNavFCwNERFrmdOdBY2Vp6r2AeVwgNTfWOjrXGvOyppzqXyA15qJ3W14gTUxM1OuMr3+hQAiBpKQkrF+/HhUVFQgICMC7776LoUOHSvFqtRoJCQn46KOPcO/ePUyYMAHvvfeezsCRiooKxMTEYNeuXQCAqVOnIjU1FT169JBiLl26hOjoaHz55Zewt7dHeHg4Vq5cCTs7uzbLl4g6B6M639hYJSJrsW7dOgDAuHHjdJZv2rQJkZGRAIDXXnsN9+7dQ1RUlHRyl5WVJT28HADeeecddO3aFdOnT5dO7tLS0qSHlwPA1q1bERMTI93uNXXqVKxdu1Zab2Njg7179yIqKgpjx47VObkjImqJud154Onpqff83fLycgD6o/O0zOECqbmy1tG51piXNeTU1AXSlvJq67yHDh2KAwcOSK/rn1eZ0wQz5qLh7OMX3prSQSUhoqYY1fnGxioRWQvtKIzmyGQyJCYmIjExscmYbt26ITU1tdmTMBcXF6Snpzf7WX379sWePXuajSEiaoy53XmgUqmwcOFCVFdXS6NDsrKyoFQq9W5HJSJqTNeuXaULC/WZ2wQzRESGMvq205awsUpERETUtm7fvo3vvvtOen3+/HkUFRXBxcUFffv2Nas7D8LDw5GUlITIyEgsXLgQ586dQ3JyMt58803OdEpEBjl37hyUSiXkcjkCAgKQnJyMAQMGmN0EM0REhmr1bKdERERE1D6OHTum09DTPh9txowZSEtLM6s7DxQKBbKzsxEdHY0RI0bA2dkZcXFxOs90IyJqSkBAAP75z3/i8ccfx9WrV7F06VKMGTMGp06dMrsJZhrTFrM314+rPwOtocx19llrmhm4IWvODbDu/BrLzRR5svONiIiIyMyNGzeu2TsQzO3OAz8/P+Tm5jYbQ0TUmMmTJ0v/7+fnB5VKhUcffRSbN2/G6NGjAZjXBDMNtcXszfVnmG1sBtqWmPuMz9YwM3BTrDk3wLrzq5+bKWZwZucbERERNarhA5yJiIjam6OjI/z8/HDu3Dk8++yzAMxngpnGtMXszfVnmK0/A62hzHXGZ2uaGbgha84NsO78GsutLWdw1mLnGxEREREREZkltVqNM2fO4Je//KXZTTDTmLaYvbl+bGMz0BryfnNmDTMDN8WacwOsO7/6uZkiR3a+ERERERERkVlISEhAWFgY+vbti/LycixduhRVVVWYMWMGZDKZWU0wQ0RkKHa+ERERERERkVm4cuUKnnvuOfz000/o2bMnRo8ejcOHD6Nfv34AYFYTzBARGYqdb0RERERERGQWMjIyml1vbhPMEBEZoktHF4CIiIiIiIiIiMhasfONiIiIiIiIiIjIRNj5RkREREREREREZCJ85hsREREBAPrP39vRRSAiIiIisjoc+UZERERERERERGQi7HwjIiIiIiIiIiIyEXa+ERERERERERERmQg734iIiIiIiIiIiEyEEy4QERERERERWYnGJlC68NaUDigJEWmx842IiKiT6j9/L+Q2AitGAb6J+wHIOrpIRERERERWh7edEhERERERERERmQg734iIiIiIiIiIiEyEnW9EREREREREREQmwme+ERERdQKNPXyZiIiIiIhMjyPfiIiIiIiIiIiITIQj34iIiIiIiIisWMMR8BfemtJBJSHqnDjyjYiIiIiIiIiIyEQ48o2IiMgK8RlvRERERETmgZ1vRERE1CnwlhsiIiIi6gjsfCMiIiIiIiLqRHhBiqh9sfONiIjICvA2UyIiIiIi88TONyIiIiIiIqJOrLGLeBwNR9R2LH620/feew/e3t7o1q0b/P398Z///Keji0RE1Gqs08gQ/efv1ftHZG5YnxGRNemMdRrPNciSmPv31aJHvn388ceIjY3Fe++9h7Fjx+KDDz7A5MmTcfr0afTt27eji0dEZBTWadQUczyBIGoO6zMisias0+oYcj7C0XJkLsxtNKdFd76tXr0aM2fOxMsvvwwAWLNmDfbv349169YhJSWlg0tHRGQc1mmkxc42snSsz4jImrBOM1xz5zByG4EVowDfxP1Q18iajGMHHhnC0s6XLbbzrbq6GoWFhZg/f77O8uDgYOTn53dQqYiIWod1WudhaScK1qz+sdA2COjhsT4jImvCOq39tcW5EjvwyNxYbOfbTz/9hJqaGnh4eOgs9/DwQFlZWaPvUavVUKvV0uvKykoAwI0bN6DRaJr9PI1Gg7t376Krpgtqaut66a9fv250ubs+uKPzujXbaO12tTlcv34dtra2ZlU2Q7fRtVbg7t3aJnN4mHI9bNkM3UZLx6E12zRkG63dTmPb1R6Hrpou7Vq2W7duAQCEEEZ/prlrzzrNkO9gZ9BwPwSkfNEun2tuf3jr/561f986I+1+eHLRDqjr7YcjCyaY7DOttU6z1HM0c2OtdbU15mVNOdU/RzPmvNta6zPA+DqtLeqz+n+LzO284WG05znHYwn/Mun2G5J3EfjLcP3ziLZiyvMRQzRXz7XXOXRDrfltGNpPYoo6zeJ/yzKZ7hdbCKG3TCslJQVJSUl6y729vVv12W6rWvW2Nt+GqbZrrmULb5tiNMpc91tbbbMty6Y9Dm5vt832jCnbrVu3oFAo2uaDzUxH1mnUeZmyXrUkje0HU/0trM9a6zRLP0cjIuP/PlhrfQYYXqfx/Kx51nzOYe7tVDJ+P7ZlnWaxnW9ubm6wsbHRu9pQXl6ud1VCa8GCBYiLi5Ne19bW4saNG3B1dW3yZFCrqqoKXl5euHz5Mrp37/7wCXQA5mAemEPrCSFw69YtKJXKdvvM9tKedZo1fAfbAvdDHe6HOh2xH6y1TuM5WttgXpbDGnMCjMvLWuszwPg6jfVZ06w5P2vODbDu/BrLzRR1msV2vtnZ2cHf3x/Z2dn4zW9+Iy3Pzs7GM8880+h75HI55HK5zrIePXoY9bndu3e3+C8bczAPzKF1rPVqakfUadbwHWwL3A91uB/qtPd+sMY6jedobYt5WQ5rzAkwPC9rrM8A4+s01mcts+b8rDk3wLrza5hbW9dpFtv5BgBxcXGIiIjAiBEjoFKpsH79ely6dAmvvvpqRxeNiMhorNOIyFqwPiMia8I6jYgelkV3vv3hD3/A9evXsWTJEpSWlsLX1xf79u1Dv379OrpoRERGY51GRNaC9RkRWRPWaUT0sCy68w0AoqKiEBUVZfLPkcvlWLx4sd4QYkvCHMwDc6DmtEedxuNXh/uhDvdDHe6HtsdztIfDvCyHNeYEWG9ercVztIdnzflZc26AdefXXrnJhDXOB01ERERERERERGQGunR0AYiIiIiIiIiIiKwVO9+IiIiIiIiIiIhMhJ1vREREREREREREJsLOt3ree+89eHt7o1u3bvD398d//vOfZuNzcnLg7++Pbt26YcCAAXj//ffbqaT6UlJSMHLkSDg5OcHd3R3PPvssSkpKmn3PwYMHIZPJ9P59++237VRqXYmJiXpl8fT0bPY95nQMAKB///6N7tPo6OhG483hGOTm5iIsLAxKpRIymQyffvqpznohBBITE6FUKmFvb49x48bh1KlTLW53+/btGDJkCORyOYYMGYKdO3eaKAMy1t69exEQEAB7e3u4ublh2rRpOusvXbqEsLAwODo6ws3NDTExMaiuru6g0pqWWq3Gk08+CZlMhqKiIp111r4fLly4gJkzZ8Lb2xv29vZ49NFHsXjxYr0crX0/aBl7DkCmY4rzMXP4m2RMXjt27EBQUBB69uyJ7t27Q6VSYf/+/ToxaWlpjZ5D3L9/39Sp6DAmL0PPeyzteEVGRjaa19ChQ6WYjj5eLZ3vNcZSflvmzlrrNMB66zUta63fAOur47TMuq4TJIQQIiMjQ9ja2ooNGzaI06dPi3nz5glHR0dx8eLFRuO///574eDgIObNmydOnz4tNmzYIGxtbcW///3vdi55nZCQELFp0yZRXFwsioqKxJQpU0Tfvn3F7du3m3zPV199JQCIkpISUVpaKv178OBBO5b8Z4sXLxZDhw7VKUt5eXmT8eZ2DIQQory8XKf82dnZAoD46quvGo03h2Owb98+sWjRIrF9+3YBQOzcuVNn/VtvvSWcnJzE9u3bxcmTJ8Uf/vAH0atXL1FVVdXkNvPz84WNjY1ITk4WZ86cEcnJyaJr167i8OHDJs6GWvLvf/9bODs7i3Xr1omSkhLx7bffik8++URa/+DBA+Hr6yvGjx8vjh8/LrKzs4VSqRRz5szpwFKbTkxMjJg8ebIAIE6cOCEt7wz74fPPPxeRkZFi//794r///a/47LPPhLu7u4iPj5diOsN+EML4cwAyHVOcj5nD3yRj85o3b55Yvny5OHr0qDh79qxYsGCBsLW1FcePH5diNm3aJLp3765z/lBaWtpeKQkhjM/LkPMeSzxeN2/e1Mnn8uXLwsXFRSxevFiK6ejj1dL5XkOW8tsyd9ZapwlhvfWalrXWb0JYZx2nZc51HTvf/r9Ro0aJV199VWfZoEGDxPz58xuNf+2118SgQYN0ls2ePVuMHj3aZGU0Rnl5uQAgcnJymozRVhAVFRXtV7BmLF68WDzxxBMGx5v7MRCi7o/Mo48+Kmpraxtdb27HoGEFVVtbKzw9PcVbb70lLbt//75QKBTi/fffb3I706dPF5MmTdJZFhISIv74xz+2eZnJcBqNRvTu3Vt8+OGHTcbs27dPdOnSRfzwww/Sso8++kjI5XJRWVnZHsVsN/v27RODBg0Sp06d0ut860z7ob4VK1YIb29v6XVn2Q/GngOQ6ZjifMwc/ia1xXdsyJAhIikpSXq9adMmoVAo2qqIrWJsXoac91jD8dq5c6eQyWTiwoUL0jJzOF5ahjRILeW3Ze6stU4TwnrrNS1rrd+EsP46Tsvc6jredgqguroahYWFCA4O1lkeHByM/Pz8Rt9z6NAhvfiQkBAcO3YMGo3GZGU1VGVlJQDAxcWlxdjhw4ejV69emDBhAr766itTF61Z586dg1KphLe3N/74xz/i+++/bzLW3I9BdXU10tPT8ac//QkymazZWHM6BvWdP38eZWVlOvtZLpcjMDCwyd8G0PSxae49ZHrHjx/HDz/8gC5dukjfucmTJ+vcRnzo0CH4+vpCqVRKy0JCQqBWq1FYWNgRxTaJq1evYtasWdiyZQscHBz01neW/dBQZWWlzt+NzrAfWnMOQKZhqvOxjv6b1BbfsdraWty6dUvvvO727dvo168f+vTpg9DQUJw4caLNyt2Sh8mrufMeazheGzduxMSJE9GvXz+d5R15vIxlCb8tc2etdRpgvfWalrXWbwDruIba8zfHzjcAP/30E2pqauDh4aGz3MPDA2VlZY2+p6ysrNH4Bw8e4KeffjJZWQ0hhEBcXByeeuop+Pr6NhnXq1cvrF+/Htu3b8eOHTvg4+ODCRMmIDc3tx1L+7OAgAD885//xP79+7FhwwaUlZVhzJgxuH79eqPx5nwMAODTTz/FzZs3ERkZ2WSMuR2DhrTff2N+G9r3GfseMj1tZ3ZiYiL+8pe/YM+ePXB2dkZgYCBu3LgBoPFj5+zsDDs7O6s5fkIIREZG4tVXX8WIESMajekM+6Gh//73v0hNTcWrr74qLesM+6E15wBkGqY6H+vov0lt8R1btWoV7ty5g+nTp0vLBg0ahLS0NOzatQsfffQRunXrhrFjx+LcuXNtWv6mtCYvQ857LP14lZaW4vPPP8fLL7+ss7yjj5exLOG3Ze6stU4DrLde07LW+g1gHddQe/7muj5cUa1Lw9FJQohmRyw1Ft/Y8vY2Z84cfPPNN8jLy2s2zsfHBz4+PtJrlUqFy5cvY+XKlXj66adNXUw9kydPlv7fz88PKpUKjz76KDZv3oy4uLhG32OuxwCouyIwefJknREjDZnbMWiKsb+N1r6HWicxMRFJSUnNxhQUFKC2thYAsGjRIvz2t78FAGzatAl9+vTBJ598gtmzZwNo/PdjCcfP0P2Qn5+PqqoqLFiwoNlYa98P9Tsef/zxR0yaNAm///3v9U6mLHU/GIt1lvkwxfmYORzf1pbho48+QmJiIj777DO4u7tLy0ePHo3Ro0dLr8eOHYtf/OIXSE1Nxd///ve2K3gLjMnL0PMeSz5eaWlp6NGjB5599lmd5eZyvIxhKb8tc2etddrDlMPc6zUta63fHqYc1lTHabXXb46dbwDc3NxgY2Oj13NZXl6u18Op5enp2Wh8165d4erqarKytmTu3LnYtWsXcnNz0adPH6PfP3r0aKSnp5ugZMZzdHSEn59fk73l5noMAODixYs4cOAAduzYYfR7zekYaGebLSsrQ69evaTlzf02tO8z5vdED2fOnDn44x//2GxM//79cevWLQDAkCFDpOVyuRwDBgzApUuXANQduyNHjui8t6KiAhqNxuyPn6H7YenSpTh8+DDkcrnOuhEjRuD555/H5s2bO8V+0Prxxx8xfvx4qFQqrF+/XifOkveDoVpzDkCmYarzsY7+m/Qw37GPP/4YM2fOxCeffIKJEyc2G9ulSxeMHDmy3UYZtNVvp+F5jyUfLyEE/vGPfyAiIgJ2dnbNxrb38TKWJfy2zJ211mmA9dZrWtZavwGs4xpqz98cbzsFYGdnB39/f2RnZ+ssz87OxpgxYxp9j0ql0ovPysrCiBEjYGtra7KyNkUIgTlz5mDHjh348ssv4e3t3artnDhxQqeTpSOp1WqcOXOmyfKY2zGob9OmTXB3d8eUKVOMfq85HQNvb294enrq7Ofq6mrk5OQ0+dsAmj42zb2HWs/NzQ2DBg1q9p92GnG5XI6SkhLpvRqNBhcuXJCe2aBSqVBcXIzS0lIpJisrC3K5HP7+/u2emzEM3Q9///vf8fXXX6OoqAhFRUXYt28fgLqTwWXLlgHoHPsBAH744QeMGzcOv/jFL7Bp0yZ06aJ7WmDJ+8FQrTkHINMw1flYR/9Nau137KOPPkJkZCS2bdtm0PmEEAJFRUXtdg7RVr+dhuc9lnq8ACAnJwffffcdZs6c2eLntPfxMpYl/LbMnbXWaYD11mta1lq/AazjGmrX35xR0zNYMe10uxs3bhSnT58WsbGxwtHRUZrBY/78+SIiIkKK105J++c//1mcPn1abNy4UW9K2vb0P//zP0KhUIiDBw/qTO179+5dKaZhDu+8847YuXOnOHv2rCguLhbz588XAMT27ds7IgURHx8vDh48KL7//ntx+PBhERoaKpycnCzmGGjV1NSIvn37itdff11vnTkeg1u3bokTJ06IEydOCABi9erV4sSJE9JU02+99ZZQKBRix44d4uTJk+K5554TvXr1ElVVVdI2IiIidGbH+b//+z9hY2Mj3nrrLXHmzBnx1ltvcep5MzFv3jzRu3dvsX//fvHtt9+KmTNnCnd3d3Hjxg0hhBAPHjwQvr6+YsKECeL48ePiwIEDok+fPmLOnDkdXHLTOX/+vN5sp51hP/zwww/iscceE7/61a/ElStXGp0WvjPsByFaPgeg9mOK8zFz+JtkbF7btm0TXbt2Fe+++67Ob/PmzZtSTGJiosjMzBT//e9/xYkTJ8RLL70kunbtKo4cOWK2eRly3mOJx0vrhRdeEAEBAY1us6OPV0vne5b62zJ31lqntSY3S6nXWpufpdRvrclNy5zrOC1zruvY+VbPu+++K/r16yfs7OzEL37xC5GTkyOtmzFjhggMDNSJP3jwoBg+fLiws7MT/fv3F+vWrWvnEv8MQKP/Nm3aJMU0zGH58uXi0UcfFd26dRPOzs7iqaeeEnv37m3/wv9/f/jDH0SvXr2Era2tUCqVYtq0aeLUqVPSenM/Blr79+8XAERJSYneOnM8BtppsRv+mzFjhhBCiNraWrF48WLh6ekp5HK5ePrpp8XJkyd1thEYGCjFa33yySfCx8dH2NraikGDBnVYpy7pqq6uFvHx8cLd3V04OTmJiRMniuLiYp2YixcviilTpgh7e3vh4uIi5syZI+7fv99BJTa9xjrfhLD+/bBp06Ym/3bUZ+37Qau5cwBqX6Y4HzOHv0nG5BUYGNjs32YhhIiNjRV9+/YVdnZ2omfPniI4OFjk5+e3Y0Z1jMnL0PMeSzteQghx8+ZNYW9vL9avX9/o9jr6eLV0vmfJvy1zZ611mhDWW69pWWv9JoT11XFa5lzXyYT4/0+TIyIiIiIiIiIiojbFZ74RERERERERERGZCDvfiIiIiIiIiIiITISdb0RERERERERERCbCzjciIiIiIiIiIiITYecbERERERERERGRibDzjYiIiIiIiIiIyETY+UZWY9u2bVizZk2j62QyGRITE9u1PERknLS0NMhkMly4cKHNt52fn4/ExETcvHmzzbdtrtoi58TERMhksrYrFBF1uIZ1bWRkJPr379+hZSIiMhcHDx6ETCbDwYMHO7ooZGXY+UZWo7nOt0OHDuHll19u3wIRkVGmTJmCQ4cOoVevXm2+7fz8fCQlJXW6zrfOljMRGe+NN97Azp07O7oYREREVq1rRxeAqD2MHj26o4tARC3o2bMnevbs2dHFAADcu3cP9vb2HV0MIiKTe/TRRzu6CERERFaPI98IAPDdd9/hpZdewsCBA+Hg4IDevXsjLCwMJ0+e1Iu9efMm4uPjMWDAAMjlcri7u+PXv/41vv32Wynmxo0biIqKQu/evWFnZ4cBAwZg0aJFUKvVUsyFCxcgk8mQlpam9xkNbxO9du0aXnnlFXh5eUEul6Nnz54YO3YsDhw4AAAYN24c9u7di4sXL0Imk0n/mtoeAPzwww/SNu3s7KBUKvG73/0OV69eBfDzkOOPPvoIixYtglKpRPfu3TFx4kSUlJTolfnAgQOYMGECunfvDgcHB4wdOxZffPGFTkxLeQDAiRMnEBoaCnd3d8jlciiVSkyZMgVXrlxp5MgRWY+Gt0KNGzcOvr6+KCgowC9/+Us4ODhgwIABeOutt1BbWyu9r7a2FkuXLoWPjw/s7e3Ro0cPDBs2DH/7298A1N06+b//+78AAG9vb6l+0N5O0L9/f4SGhmLHjh0YPnw4unXrhqSkJKPqKO3tmd988w1+//vfQ6FQwMXFBXFxcXjw4AFKSkowadIkODk5oX///lixYoXeNquqqpCQkABvb2/Y2dmhd+/eiI2NxZ07d/Q+e86cOdiyZQsGDx4MBwcHPPHEE9izZ49OeZrL+eOPP0ZwcDB69eoFe3t7DB48GPPnz9f7rMZo91dmZiZ+8YtfwN7eHoMGDcI//vEPvdiysjLMnj0bffr0gZ2dHby9vZGUlIQHDx7oxK1btw5PPPEEHnnkETg5OWHQoEFYuHChtP7u3bvSvunWrRtcXFwwYsQIfPTRRy2Wl4ia1/C20+HDh+OXv/ylXlxNTQ169+6NadOmScuqq6uxdOlSDBo0SDqveemll3Dt2jWd97LeICJT+fTTTyGTyfTaXUBdPaE9Pzt27Bj++Mc/on///rC3t0f//v3x3HPP4eLFiwZ9zrFjxzB16lS4uLigW7duGD58OP71r3/pxGjPZb/66iv8z//8D9zc3ODq6opp06bhxx9/1Nvmtm3boFKp8Mgjj+CRRx7Bk08+iY0bN+rEGNLGJMvAkW8EAPjxxx/h6uqKt956Cz179sSNGzewefNmBAQE4MSJE/Dx8QEA3Lp1C0899RQuXLiA119/HQEBAbh9+zZyc3NRWlqKQYMG4f79+xg/fjz++9//IikpCcOGDcN//vMfpKSkoKioCHv37jW6fBERETh+/DiWLVuGxx9/HDdv3sTx48dx/fp1AMB7772HV155Bf/9738NunXihx9+wMiRI6HRaLBw4UIMGzYM169fx/79+1FRUQEPDw8pduHChRg7diw+/PBDVFVV4fXXX0dYWBjOnDkDGxsbAEB6ejpefPFFPPPMM9i8eTNsbW3xwQcfICQkBPv378eECRMMyuPOnTsICgqCt7c33n33XXh4eKCsrAxfffUVbt26ZfR+I7J0ZWVleP755xEfH4/Fixdj586dWLBgAZRKJV588UUAwIoVK5CYmIi//OUvePrpp6HRaPDtt99Kt1u+/PLLuHHjBlJTU7Fjxw7pttYhQ4ZIn3P8+HGcOXMGf/nLX+Dt7Q1HR8dWlXf69Ol44YUXMHv2bGRnZ2PFihXQaDQ4cOAAoqKikJCQgG3btuH111/HY489JjVi7969i8DAQFy5ckWqk06dOoU333wTJ0+exIEDB3QuKOzduxcFBQVYsmQJHnnkEaxYsQK/+c1vUFJSggEDBrSY87lz5/DrX/8asbGxcHR0xLfffovly5fj6NGj+PLLL1vM8+uvv0Z8fDzmz58PDw8PfPjhh5g5cyYee+wxPP3009KxGzVqFLp06YI333wTjz76KA4dOoSlS5fiwoUL2LRpEwAgIyMDUVFRmDt3LlauXIkuXbrgu+++w+nTp6XPi4uLw5YtW7B06VIMHz4cd+7cQXFxsVR3ElHbeemllzBv3jycO3cOAwcOlJZnZWXhxx9/xEsvvQSg7sLHM888g//85z947bXXMGbMGFy8eBGLFy/GuHHjcOzYMZ0RxKw3iMgUtIMWNm3aJLW5tNLS0vCLX/wCw4YNw7///W/4+Pjgj3/8I1xcXFBaWop169Zh5MiROH36NNzc3Jr8jK+++gqTJk1CQEAA3n//fSgUCmRkZOAPf/gD7t69i8jISJ34l19+GVOmTMG2bdtw+fJl/O///i9eeOEFnXOsN998E3/9618xbdo0xMfHQ6FQoLi4WKcz0NA2JlkIQdSIBw8eiOrqajFw4EDx5z//WVq+ZMkSAUBkZ2c3+d73339fABD/+te/dJYvX75cABBZWVlCCCHOnz8vAIhNmzbpbQOAWLx4sfT6kUceEbGxsc2WecqUKaJfv36Nrmu4vT/96U/C1tZWnD59usntffXVVwKA+PWvf62z/F//+pcAIA4dOiSEEOLOnTvCxcVFhIWF6cTV1NSIJ554QowaNcrgPI4dOyYAiE8//bTJGCJrtWnTJgFAnD9/XgghRGBgoAAgjhw5ohM3ZMgQERISIr0ODQ0VTz75ZLPbfvvtt3W2XV+/fv2EjY2NKCkp0VluTB21ePFiAUCsWrVKJ+7JJ58UAMSOHTukZRqNRvTs2VNMmzZNWpaSkiK6dOkiCgoKdN7/73//WwAQ+/bt0/lsDw8PUVVVJS0rKysTXbp0ESkpKQblXF9tba3QaDQiJydHABBff/21Xl719evXT3Tr1k1cvHhRWnbv3j3h4uIiZs+eLS2bPXu2eOSRR3TihBBi5cqVAoA4deqUEEKIOXPmiB49ejRbRl9fX/Hss882G0NEhmlY186YMUPn/Omnn34SdnZ2YuHChTrvmz59uvDw8BAajUYIIcRHH30kAIjt27frxBUUFAgA4r333pOWsd4gIlOKi4sT9vb24ubNm9Ky06dPCwAiNTW10fc8ePBA3L59Wzg6Ooq//e1v0nJtG/Crr76Slg0aNEgMHz5cqv+0QkNDRa9evURNTY0Q4uf6NSoqSiduxYoVAoAoLS0VQgjx/fffCxsbG/H88883mZMxbUyyDLztlAAADx48QHJyMoYMGQI7Ozt07doVdnZ2OHfuHM6cOSPFff7553j88ccxceLEJrf15ZdfwtHREb/73e90lmuvCLRmmOyoUaOQlpaGpUuX4vDhw9BoNEZvo77PP/8c48ePx+DBg1uMnTp1qs7rYcOGAYB0VSI/Px83btzAjBkz8ODBA+lfbW0tJk2ahIKCAulWrpbyeOyxx+Ds7IzXX38d77//vs4VXKLOyNPTE6NGjdJZNmzYMJ2rgqNGjcLXX3+NqKgo7N+/H1VVVUZ/zrBhw/D4448/dHlDQ0N1Xg8ePBgymQyTJ0+WlnXt2hWPPfaYTg579uyBr68vnnzySZ16JCQkpNEZt8aPHw8nJyfptYeHB9zd3Q2+deL7779HeHg4PD09YWNjA1tbWwQGBgKATp3flCeffBJ9+/aVXnfr1g2PP/64Xk7jx4+HUqnUyUm7L3JycgDUHb+bN2/iueeew2effYaffvpJ7/NGjRqFzz//HPPnz8fBgwdx7949g/IkIuO5uroiLCwMmzdvlm7xr6iowGeffYYXX3wRXbvW3TizZ88e9OjRA2FhYTq/8SeffBKenp569RbrDSIylT/96U+4d+8ePv74Y2nZpk2bIJfLER4eDgC4ffu2dOdB165d0bVrVzzyyCO4c+dOs+c+3333Hb799ls8//zzAKBTN/36179GaWmp3iOJWmo/Zmdno6amBtHR0U1+rjFtTLIM7HwjAHVD89944w08++yz2L17N44cOYKCggI88cQTOicr165dQ58+fZrd1vXr1+Hp6alzixQAuLu7o2vXrq0a7v/xxx9jxowZ+PDDD6FSqeDi4oIXX3wRZWVlRm8LMCwPLVdXV53XcrkcAKT9on1G3O9+9zvY2trq/Fu+fDmEELhx44ZBeSgUCuTk5ODJJ5/EwoULMXToUCiVSixevPihOxyJLFHD3x9Q9xusXy8tWLAAK1euxOHDhzF58mS4urpiwoQJOHbsmMGf01YzrLq4uOi8trOzg4ODA7p166a3/P79+9Lrq1ev4ptvvtGrQ5ycnCCE0GtYGrJfmnL79m388pe/xJEjR7B06VIcPHgQBQUF2LFjBwAYtA1DPv/q1avYvXu3Xk5Dhw4FACmniIgI/OMf/8DFixfx29/+Fu7u7ggICEB2dra0rb///e94/fXX8emnn2L8+PFwcXHBs88+i3PnzrVYViIy3p/+9Cf88MMP0u/wo48+glqt1rm16urVq7h58ybs7Oz0fudlZWWtqrdYbxBRawwdOhQjR46Ubk2vqalBeno6nnnmGencLDw8HGvXrsXLL7+M/fv34+jRoygoKEDPnj2bPffRtvUSEhL06qaoqCgAaLG+a9h+1D4Xs7n2qDFtTLIMfOYbAfj5fvLk5GSd5T/99BN69Oghve7Zs2eLD/53dXXFkSNHIITQ6YArLy/HgwcPpPvptY3R+pMwAGi0c87NzQ1r1qzBmjVrcOnSJezatQvz589HeXk5MjMzjcrV0DwMpc0nNTW1yVlVtc+QMyQPPz8/ZGRkQAiBb775BmlpaViyZAns7e0xf/78NikzkTXp2rUr4uLiEBcXh5s3b+LAgQNYuHAhQkJCcPnyZTg4OLS4jYYXCwDj6qiH5ebmBnt7+0YfPq5d31a+/PJL/Pjjjzh48KA02g2A9Iy8tuLm5oZhw4Zh2bJlja5XKpXS/7/00kt46aWXcOfOHeTm5mLx4sUIDQ3F2bNn0a9fPzg6OiIpKQlJSUm4evWqNJolLCxMZ7IfImobISEhUCqV2LRpE0JCQrBp0yYEBAToPCtT+yDxps7D6o/ONRTrDSJqrZdeeglRUVE4c+YMvv/+e5SWlkrPqKysrMSePXuwePFinfaUWq1usQNLew62YMECnQln6tM+H91QPXv2BABcuXIFXl5ezX6uIW1MsgzsfCMAdQ1PbY+81t69e/HDDz/gsccek5ZNnjwZb775Jr788kv86le/anRbEyZMwL/+9S98+umn+M1vfiMt/+c//ymtB+oqi27duuGbb77Ref9nn33WbFn79u2LOXPm4IsvvsD//d//ScsNHfWhzWPLli0oKSkxurJsaOzYsejRowdOnz6NOXPmGPy+pvLQkslkeOKJJ/DOO+8gLS0Nx48ff6hyEnUGPXr0wO9+9zv88MMPiI2NxYULFzBkyBC9K46GaG0d1RqhoaFITk6Gq6srvL2922SbTeWs7WhsWOd/8MEHbfK5WqGhodi3bx8effRRODs7G/QeR0dHTJ48GdXV1Xj22Wdx6tQp9OvXTyfGw8MDkZGR+Prrr7FmzRrcvXvXoA5WIjKcjY0NIiIisGbNGvznP//BsWPH9OqI0NBQZGRkoKamBgEBAW3yuaw3iKi1nnvuOcTFxSEtLQ3ff/89evfujeDgYAB15z5CCL1znw8//BA1NTXNbtfHxwcDBw7E119/rTdQpbWCg4NhY2ODdevWQaVSNRrT2jYmmS92vhGAupOdtLQ0DBo0CMOGDUNhYSHefvttvaGwsbGx+Pjjj/HMM89g/vz5GDVqFO7du4ecnByEhoZi/PjxePHFF/Huu+9ixowZuHDhAvz8/JCXl4fk5GT8+te/lp4XJ5PJ8MILL+Af//gHHn30UTzxxBM4evQotm3bpvOZlZWVGD9+PMLDwzFo0CA4OTmhoKAAmZmZOlcf/Pz8sGPHDqxbtw7+/v7o0qULRowY0Wi+S5Ysweeff46nn34aCxcuhJ+fH27evInMzEzExcVh0KBBBu+7Rx55BKmpqZgxYwZu3LiB3/3ud3B3d8e1a9fw9ddf49q1a1i3bp1BeezZswfvvfcenn32WQwYMABCCOzYsQM3b95EUFCQwWUi6kzCwsLg6+uLESNGoGfPnrh48SLWrFmDfv36STP1+fn5AQD+9re/YcaMGbC1tYWPj0+zIzMMraPaQmxsLLZv346nn34af/7znzFs2DDU1tbi0qVLyMrKQnx8vNGN26ZyHjNmDJydnfHqq69i8eLFsLW1xdatW/H111+3aU5LlixBdnY2xowZg5iYGPj4+OD+/fu4cOEC9u3bh/fffx99+vTBrFmzYG9vj7Fjx6JXr14oKytDSkoKFAoFRo4cCQAICAhAaGgohg0bBmdnZ5w5cwZbtmyBSqViA5rIRP70pz9h+fLlCA8Ph729Pf7whz/orP/jH/+IrVu34te//jXmzZuHUaNGwdbWFleuXMFXX32FZ555RucirCFYbxBRa/Xo0QO/+c1vkJaWhps3byIhIQFdutQ9Zat79+54+umn8fbbb8PNzQ39+/dHTk4ONm7cqHOXV1M++OADTJ48GSEhIYiMjETv3r1x48YNnDlzBsePH8cnn3xiVFn79++PhQsX4q9//Svu3buH5557DgqFAqdPn8ZPP/2EpKQkg9uYZEE6cLIHMiMVFRVi5syZwt3dXTg4OIinnnpK/Oc//xGBgYEiMDBQL3bevHmib9++wtbWVri7u4spU6aIb7/9Voq5fv26ePXVV0WvXr1E165dRb9+/cSCBQvE/fv3dbZVWVkpXn75ZeHh4SEcHR1FWFiYuHDhgs5Mgvfv3xevvvqqGDZsmOjevbuwt7cXPj4+YvHixeLOnTvStm7cuCF+97vfiR49egiZTKYzQx8azEwohBCXL18Wf/rTn4Snp6ewtbUVSqVSTJ8+XVy9elUI8fNMN5988onO+5qaATEnJ0dMmTJFuLi4CFtbW9G7d28xZcoU6f2G5PHtt9+K5557Tjz66KPC3t5eKBQKMWrUKJGWlmbYgSSyYI3Ndjp06FC9uIYz861atUqMGTNGuLm5CTs7O9G3b18xc+ZMceHCBZ33LViwQCiVStGlSxedWaz69esnpkyZ0miZDKmjhPh5VtBr167pldXR0VFvu43ldvv2bfGXv/xF+Pj4CDs7O6FQKISfn5/485//LMrKyqQ4ACI6Olpvm/369RMzZswwKOf8/HyhUqmEg4OD6Nmzp3j55ZfF8ePH9eq2pmY7bWx/Nfb34tq1ayImJkZ4e3sLW1tb4eLiIvz9/cWiRYvE7du3hRBCbN68WYwfP154eHgIOzs7qS7+5ptvpO3Mnz9fjBgxQjg7Owu5XC4GDBgg/vznP4uffvpJrxxE1LyWZjutb8yYMQJAkzPyaTQasXLlSvHEE0+Ibt26iUceeUQMGjRIzJ49W5w7d06KY71BRO0hKytLABAAxNmzZ3XWXblyRfz2t78Vzs7OwsnJSUyaNEkUFxfrnT81NtupEEJ8/fXXYvr06cLd3V3Y2toKT09P8atf/Uq8//77Uoy2fm04e31T2/znP/8pRo4cKdWfw4cPN7qNSZZDJoQQ7dvdR0RERERERERE1DlwtlMiIiIiIiIiIiITYecbERERERERERGRibDzjYiIiIiIiIiIyETY+UZERERERERERGQi7HwjIiIiIiIiIiIyEXa+ERERERERERERmUjXji5AR6qtrcWPP/4IJycnyGSyji4OEbVACIFbt25BqVSiSxdeO2iIdRqRZWGd1jTWZ0SWhfVZ01ifEVkeU9Rpnbrz7ccff4SXl1dHF4OIjHT58mX06dOno4thdlinEVkm1mn6WJ8RWSbWZ/pYnxFZrras0zp155uTkxOAuh3avXv3ZmM1Gg2ysrIQHBwMW1vb9iheu2BelqWz51VVVQUvLy/pt0u6DK3TOvv3yNIwL8tiTF6s05rGczTmZUmsMSeA9VlbYX3GvCwN8zJNndapO9+0w367d+9uUEXo4OCA7t27W90XkHlZDuZVh0P2G2doncbvkWVhXpalNXmxTtPHczTmZUmsMSeA9VlbYX3GvCwN8/pZW9ZpvCGfiIiIiIiIiIjIRNj5RkREREREREREZCLsfCMiIiIiIiIiIjIRdr4RERERERERERGZSKeecKE1fBP3Q11T99C9C29N6eDSEBG1Xv36DGCdRkRUX//5e/WWsZ4kIiIyTw3/bpvb32yOfCMiIiIiIiIiIjIRdr4RERERERERERGZCDvfiIiIiIiIiIiITISdb0RERERERERERCbCzjciIiIiIiIiIiITYecbERERERERERGRibDzjYiIiIiIiIiIyETY+UZERERERERERGQi7HwjIiIiIiIis5CSkoKRI0fCyckJ7u7uePbZZ1FSUqITI4RAYmIilEol7O3tMW7cOJw6dUonRq1WY+7cuXBzc4OjoyOmTp2KK1eu6MRUVFQgIiICCoUCCoUCERERuHnzpk7MpUuXEBYWBkdHR7i5uSEmJgbV1dUmyZ2IrBc734iIiIgs3IMHD/CXv/wF3t7esLe3x4ABA7BkyRLU1tZKMWysEpElyMnJQXR0NA4fPozs7Gw8ePAAwcHBuHPnjhSzYsUKrF69GmvXrkVBQQE8PT0RFBSEW7duSTGxsbHYuXMnMjIykJeXh9u3byM0NBQ1NTVSTHh4OIqKipCZmYnMzEwUFRUhIiJCWl9TU4MpU6bgzp07yMvLQ0ZGBrZv3474+Pj22RlEZDW6dnQBiIiIiOjhLF++HO+//z42b96MoUOH4tixY3jppZegUCgwb948AD83VtPS0vD4449j6dKlCAoKQklJCZycnADUNVZ3796NjIwMuLq6Ij4+HqGhoSgsLISNjQ2AusbqlStXkJmZCQB45ZVXEBERgd27dwP4ubHas2dP5OXl4fr165gxYwaEEEhNTe2AvUNElkRbt2ht2rQJ7u7uKCwsxNNPPw0hBNasWYNFixZh2rRpAIDNmzfDw8MD27Ztw+zZs1FZWYmNGzdiy5YtmDhxIgAgPT0dXl5eOHDgAEJCQnDmzBlkZmbi8OHDCAgIAABs2LABKpUKJSUl8PHxQVZWFk6fPo3Lly9DqVQCAFatWoXIyEgsW7YM3bt3b8c9Q0SWjJ1vRERERBbu0KFDeOaZZzBlyhQAQP/+/fHRRx/h2LFjAMDGKhFZrMrKSgCAi4sLAOD8+fMoKytDcHCwFCOXyxEYGIj8/HzMnj0bhYWF0Gg0OjFKpRK+vr7Iz89HSEgIDh06BIVCIdVlADB69GgoFArk5+fDx8cHhw4dgq+vr1SXAUBISAjUajUKCwsxfvx4vfKq1Wqo1WrpdVVVFQBAo9FAo9E0m6t2fUtxloZ5WRZLzUtuI3ReNyy/MXmZInd2vhERERFZuKeeegrvv/8+zp49i8cffxxff/018vLysGbNGgBsrLZGw5N4U3xGcyy18dMSa8zLGnMCOr6hCtRdOIiLi8NTTz0FX19fAEBZWRkAwMPDQyfWw8MDFy9elGLs7Ozg7OysF6N9f1lZGdzd3fU+093dXSem4ec4OzvDzs5OimkoJSUFSUlJesuzsrLg4ODQYs4AkJ2dbVCcpWFelsXS8loxSvf1vn37Go0zJK+7d++2RZF0sPONiKxeSkoKFi5ciHnz5kkNUSEEkpKSsH79elRUVCAgIADvvvsuhg4dKr1PrVYjISEBH330Ee7du4cJEybgvffeQ58+faSYiooKxMTEYNeuXQCAqVOnIjU1FT169JBiLl26hOjoaHz55Zewt7dHeHg4Vq5cCTs7Oynm5MmTmDNnDo4ePQoXFxfMnj0bb7zxBmQymWl3DhFZhddffx2VlZUYNGgQbGxsUFNTg2XLluG5554DwMZqazQ8iQeaPpE3JUtr/BjKGvOyxpyAjmuoAsCcOXPwzTffIC8vT29dw3MkIUSL500NYxqLb01MfQsWLEBcXJz0uqqqCl5eXggODm5x5K9Go0F2djaCgoJga2vbbKwlYV6WxVLz8k3cr/O6ODFE57UxeWkvArYldr4RkVUrKCjA+vXrMWzYMJ3l5vTso6qqKgQFBWH8+PEoKCjA2bNnERkZCUdHRz7Ql4gM8vHHHyM9PR3btm3D0KFDUVRUhNjYWCiVSsyYMUOKY2PVcA1P4gH9E3lTstTGT0usMS9rzAno+Ibq3LlzsWvXLuTm5upc+PT09ARQ19Hfq1cvaXl5ebnU8e/p6Ynq6mpUVFToXFAoLy/HmDFjpJirV6/qfe61a9d0tnPkyBGd9RUVFdBoNHoXGbTkcjnkcrnecltbW4O/H8bEWhLmZVksLS91je45RlNlNyQvU+TNzjcislq3b9/G888/jw0bNmDp0qXScnN79tHWrVtx//59pKWlQS6Xw9fXF2fPnsXq1asRFxfH0W9E1KL//d//xfz58/HHP/4RAODn54eLFy8iJSUFM2bMYGO1FRqexGs/o71ZWuPHUNaYlzXmBLR/Q1UIgblz52Lnzp04ePAgvL29ddZ7e3vD09MT2dnZGD58OACguroaOTk5WL58OQDA398ftra2yM7OxvTp0wEApaWlKC4uxooVKwAAKpUKlZWVOHr0KEaNqhvqeuTIEVRWVkp1nkqlwrJly1BaWirVnVlZWZDL5fD392+znInI+rHzjYisVnR0NKZMmYKJEyfqdL6Z27OPDh06hMDAQJ2GZ0hICBYsWIALFy7onXRqtfYZSdp18i7NP5TU0vCZO5aFebVt7nfv3kWXLl10ltnY2KC2thYAG6tEZDmio6Oxbds2fPbZZ3BycpJuV1coFLC3t4dMJkNsbCySk5MxcOBADBw4EMnJyXBwcEB4eLgUO3PmTMTHx8PV1RUuLi5ISEiAn5+fdFF18ODBmDRpEmbNmoUPPvgAQN0dDKGhofDx8QEABAcHY8iQIYiIiMDbb7+NGzduICEhAbNmzeLkMURkFHa+EZFVysjIwPHjx1FQUKC3ztyefVRWVob+/fvrfY52XVOdbw/7jKS/jqjVed0RzzIyhc78zB1L1JnzastnJIWFhWHZsmXo27cvhg4dihMnTmD16tX405/+BABsrBKRxVi3bh0AYNy4cTrLN23ahMjISADAa6+9hnv37iEqKkp6dm9WVpb06BAAeOedd9C1a1dMnz5denZvWlqa9OgQANi6dStiYmKki61Tp07F2rVrpfU2NjbYu3cvoqKiMHbsWJ1n9xIRGYOdb0RkdS5fvox58+YhKysL3bp1azLOnJ591FhZmnqvVmufkaR9hssbx7pAXfvz9tvzWUamwGfuWBbm1bbPSEpNTcUbb7yBqKgolJeXQ6lUYvbs2XjzzTelGDZWicgSaM+BmiOTyZCYmIjExMQmY7p164bU1FTpGbuNcXFxQXp6erOf1bdvX+zZs6fFMhERNYedb0RkdQoLC1FeXq5ze1NNTQ1yc3Oxdu1alJSUADCfZx95enrqzQBYXl4OQH90Xn0P+4wkda1M55lG1tIB0pmfuWOJOnNebZm3k5MT1qxZI83o3Bg2VomIiIg6RpeWQ4iILMuECRNw8uRJFBUVSf9GjBiB559/HkVFRRgwYID07CMt7bOPtB1r9Z99pKV99lH95xppn32k1dizj4qLi1FaWirFNHz2kUqlQm5uLqqrq3VilEql3u2oREREREREZFk48o2IrI6TkxN8fX11ljk6OsLV1VVabk7PPgoPD0dSUhIiIyOxcOFCnDt3DsnJyXjzzTc50ykREREREZGFY+cbEXVK5vTsI4VCgezsbERHR2PEiBFwdnZGXFyczvPciIiIiIiIyDKx842IOoWDBw/qvDa3Zx/5+fkhNze32RgiIiIiIiKyPOx8IyIiIqJOr//8vR1dBCIiIrJSnHCBiIiIiIiIiIjIRNj5RkREREREREREZCJGd7798MMPeOGFF+Dq6goHBwc8+eSTKCwslNYLIZCYmAilUgl7e3uMGzcOp06d0tmGWq3G3Llz4ebmBkdHR0ydOhVXrlzRiamoqEBERAQUCgUUCgUiIiJw8+ZNnZhLly4hLCwMjo6OcHNzQ0xMDKqrq41NiYiIiIiIiIiIyCSM6nyrqKjA2LFjYWtri88//xynT5/GqlWr0KNHDylmxYoVWL16NdauXYuCggJ4enoiKCgIt27dkmJiY2Oxc+dOZGRkIC8vD7dv30ZoaChqamqkmPDwcBQVFSEzMxOZmZkoKipCRESEtL6mpgZTpkzBnTt3kJeXh4yMDGzfvh3x8fEPsTuIiIiIiIiIiIjajlETLixfvhxeXl7YtGmTtKx///7S/wshsGbNGixatAjTpk0DAGzevBkeHh7Ytm0bZs+ejcrKSmzcuBFbtmzBxIkTAQDp6enw8vLCgQMHEBISgjNnziAzMxOHDx9GQEAAAGDDhg1QqVQoKSmBj48PsrKycPr0aVy+fBlKpRIAsGrVKkRGRmLZsmXo3r37Q+0YIiIiIiIiIiKih2VU59uuXbsQEhKC3//+98jJyUHv3r0RFRWFWbNmAQDOnz+PsrIyBAcHS++Ry+UIDAxEfn4+Zs+ejcLCQmg0Gp0YpVIJX19f5OfnIyQkBIcOHYJCoZA63gBg9OjRUCgUyM/Ph4+PDw4dOgRfX1+p4w0AQkJCoFarUVhYiPHjx+uVX61WQ61WS6+rqqoAABqNBhqNptnctevlXYTeMkumzcEacqmPeVkWQ/OytryJiIiIiIjI+hnV+fb9999j3bp1iIuLw8KFC3H06FHExMRALpfjxRdfRFlZGQDAw8ND530eHh64ePEiAKCsrAx2dnZwdnbWi9G+v6ysDO7u7nqf7+7urhPT8HOcnZ1hZ2cnxTSUkpKCpKQkveVZWVlwcHAwZBfgryNqpf/ft2+fQe+xBNnZ2R1dBJNgXpalpbzu3r3bTiUhIiIiIiIiahtGdb7V1tZixIgRSE5OBgAMHz4cp06dwrp16/Diiy9KcTKZTOd9Qgi9ZQ01jGksvjUx9S1YsABxcXHS66qqKnh5eSE4OLjF21Q1Gg2ys7PxxrEuUNfWbb84MaTZ91gCbV5BQUGwtbXt6OK0GeZlWQzNSztalYiIiIiIiMhSGNX51qtXLwwZMkRn2eDBg7F9+3YAgKenJ4C6UWm9evWSYsrLy6VRap6enqiurkZFRYXO6Lfy8nKMGTNGirl69are51+7dk1nO0eOHNFZX1FRAY1GozciTksul0Mul+stt7W1NbgjQ10rg7pGJr3PWhizDywJ87IsLeVljTkTERERERGRdTNqttOxY8eipKREZ9nZs2fRr18/AIC3tzc8PT11bh2rrq5GTk6O1LHm7+8PW1tbnZjS0lIUFxdLMSqVCpWVlTh69KgUc+TIEVRWVurEFBcXo7S0VIrJysqCXC6Hv7+/MWkRERERERERERGZhFEj3/785z9jzJgxSE5OxvTp03H06FGsX78e69evB1B3G2hsbCySk5MxcOBADBw4EMnJyXBwcEB4eDgAQKFQYObMmYiPj4erqytcXFyQkJAAPz8/afbTwYMHY9KkSZg1axY++OADAMArr7yC0NBQ+Pj4AACCg4MxZMgQRERE4O2338aNGzeQkJCAWbNmcaZTIiIiIiIiIiIyC0Z1vo0cORI7d+7EggULsGTJEnh7e2PNmjV4/vnnpZjXXnsN9+7dQ1RUFCoqKhAQEICsrCw4OTlJMe+88w66du2K6dOn4969e5gwYQLS0tJgY2MjxWzduhUxMTHSrKhTp07F2rVrpfU2NjbYu3cvoqKiMHbsWNjb2yM8PBwrV65s9c4gIiIiIiIiIiJqS0Z1vgFAaGgoQkNDm1wvk8mQmJiIxMTEJmO6deuG1NRUpKamNhnj4uKC9PT0ZsvSt29f7Nmzp8UyExERERERERERdQSjnvlGREREREREREREhmPnGxERERERERERkYmw842IiIiIiIiIiMhE2PlGRERERERERERkIux8IyIiIiIiIrORm5uLsLAwKJVKyGQyfPrppzrrIyMjIZPJdP6NHj1aJ0atVmPu3Llwc3ODo6Mjpk6diitXrujEVFRUICIiAgqFAgqFAhEREbh586ZOzKVLlxAWFgZHR0e4ubkhJiYG1dXVpkibiKwYO9+IiIiIrMAPP/yAF154Aa6urnBwcMCTTz6JwsJCab0QAomJiVAqlbC3t8e4ceNw6tQpnW2wsUpE5uDOnTt44oknsHbt2iZjJk2ahNLSUunfvn37dNbHxsZi586dyMjIQF5eHm7fvo3Q0FDU1NRIMeHh4SgqKkJmZiYyMzNRVFSEiIgIaX1NTQ2mTJmCO3fuIC8vDxkZGdi+fTvi4+PbPmmiNtZ//l6df9SxunZ0AYiIiIjo4VRUVGDs2LEYP348Pv/8c7i7u+O///0vevToIcWsWLECq1evRlpaGh5//HEsXboUQUFBKCkpgZOTE4C6xuru3buRkZEBV1dXxMfHIzQ0FIWFhbCxsQFQ11i9cuUKMjMzAQCvvPIKIiIisHv3bgA/N1Z79uyJvLw8XL9+HTNmzIAQAqmpqe27Y4jIIk2ePBmTJ09uNkYul8PT07PRdZWVldi4cSO2bNmCiRMnAgDS09Ph5eWFAwcOICQkBGfOnEFmZiYOHz6MgIAAAMCGDRugUqlQUlICHx8fZGVl4fTp07h8+TKUSiUAYNWqVYiMjMSyZcvQvXv3NsyaiKwZO9+IiIiILNzy5cvh5eWFTZs2Scv69+8v/b8QAmvWrMGiRYswbdo0AMDmzZvh4eGBbdu2Yfbs2WysEpFFOXjwINzd3dGjRw8EBgZi2bJlcHd3BwAUFhZCo9EgODhYilcqlfD19UV+fj5CQkJw6NAhKBQKqS4DgNGjR0OhUCA/Px8+Pj44dOgQfH19pboMAEJCQqBWq1FYWIjx48frlUutVkOtVkuvq6qqAAAajQYajabZnLTrW4qzNMyrY8hthM5rQ8tp7nk1paV8jcnLFLmz842IrNK6deuwbt06XLhwAQAwdOhQvPnmm9JVVCEEkpKSsH79elRUVCAgIADvvvsuhg4dKm1DrVYjISEBH330Ee7du4cJEybgvffeQ58+faSYiooKxMTEYNeuXQCAqVOnIjU1VWe0yaVLlxAdHY0vv/wS9vb2CA8Px8qVK2FnZyfFnDx5EnPmzMHRo0fh4uKC2bNn44033oBMJjPhXiIia7Fr1y6EhITg97//PXJyctC7d29ERUVh1qxZAIDz58+jrKxMpyEql8sRGBiI/Px8zJ49u9M3VhuetDf3We3BUhs/LbHGvKwxJ6DjG6rNmTx5Mn7/+9+jX79+OH/+PN544w386le/QmFhIeRyOcrKymBnZwdnZ2ed93l4eKCsrAwAUFZWJnXW1efu7q4T4+HhobPe2dkZdnZ2UkxDKSkpSEpK0luelZUFBwcHg/LLzs42KM7SMK/2tWKU7uuGt2a3xFzzaoqh+RqS1927d9uiSDrY+UZEVqlPnz5466238NhjjwGoG+HxzDPP4MSJExg6dKhZ3X5VVVWFoKAgjB8/HgUFBTh79iwiIyPh6OjIZ4oQkUG+//57rFu3DnFxcVi4cCGOHj2KmJgYyOVyvPjii1IjsWEj0sPDAxcvXgSATt9YbXjS3hhjGy5twdIaP4ayxrysMSeg4xqqzfnDH/4g/b+vry9GjBiBfv36Ye/evdLo3sYIIXQubDZ2kbM1MfUtWLAAcXFx0uuqqip4eXkhODi4xZG/Go0G2dnZCAoKgq2tbbOxlqSz5+WbuF/ndXFiiKmL9lCfa6nHq6V8jclLexGwLbHzjYisUlhYmM7rZcuWYd26dTh8+DCGDBliVrdfbd26Fffv30daWhrkcjl8fX1x9uxZrF69GnFxcRz9RkQtqq2txYgRI5CcnAwAGD58OE6dOoV169bhxRdflOIa1ifNNSCbirHWxmrDk/bGtFeDCbDcxk9LrDEva8wJ6PiGqjF69eqFfv364dy5cwAAT09PVFdXo6KiQueCQnl5OcaMGSPFXL16VW9b165dky4geHp64siRIzrrKyoqoNFo9C4yaMnlcsjlcr3ltra2Bn8/jIm1JJ01L3WN7t++ttgHDSdQuPDWlDb/XEOPlyFlaQ+G5mtIXqb4nrLzjYisXk1NDT755BPcuXMHKpXK7G6/OnToEAIDA3VO1EJCQrBgwQJcuHAB3t7ejebV2tu0tOvkXVr3HAhzxdt+LAvzatvce/XqhSFDhugsGzx4MLZv3w4A0kPJy8rK0KtXLymmvLxcp5HZmRurDU/am/qM9tZZG6uWyBpzAjquoWqM69ev4/Lly1L95u/vD1tbW2RnZ2P69OkAgNLSUhQXF2PFihUAAJVKhcrKShw9ehSjRtUNfT1y5AgqKyulOk+lUmHZsmUoLS2Vtp2VlQW5XA5/f//2TpOILBg734jIap08eRIqlQr379/HI488gp07d2LIkCHIz88HYD63X5WVlek8GL1+2crKyprsfHvY27T+OqJW53VH3E5lCp35th9L1JnzasvbtMaOHYuSkhKdZWfPnkW/fv0AAN7e3vD09ER2djaGDx8OAKiurkZOTg6WL18OgI1VIjIft2/fxnfffSe9Pn/+PIqKiuDi4gIXFxckJibit7/9LXr16oULFy5g4cKFcHNzw29+8xsAgEKhwMyZMxEfHw9XV1e4uLggISEBfn5+0h0NgwcPxqRJkzBr1ix88MEHAOoeHxIaGgofHx8AQHBwMIYMGYKIiAi8/fbbuHHjBhISEjBr1ixOHkONajgKzNo/lwzHzjcislo+Pj4oKirCzZs3sX37dsyYMQM5OTnSenO6/aqxsjT1Xq3W3qalvY3kjWNdoK79efvteTuVKfC2H8vCvNr2Nq0///nPGDNmDJKTkzF9+nQcPXoU69evx/r16wHU1SWxsbFITk7GwIEDMXDgQCQnJ8PBwQHh4eEA2FglIvNx7NgxnclZtOc7M2bMwLp163Dy5En885//xM2bN9GrVy+MHz8eH3/8sfTcXgB455130LVrV0yfPl2aOCstLU16bi8AbN26FTExMdKdDlOnTsXatWul9TY2Nti7dy+ioqIwduxYnYmziIiMwc43IrJadnZ20oQLI0aMQEFBAf72t7/h9ddfB2A+t195enrqPYS8vLwcgP7ovPoe9jYtda1M5zYra+kA6cy3/ViizpxXW+Y9cuRI7Ny5EwsWLMCSJUvg7e2NNWvW4Pnnn5diXnvtNdy7dw9RUVHSLM9ZWVlsrBKR2Rk3bpx0IbIx+/e3/IzGbt26ITU1VZrgqjEuLi5IT09vdjt9+/bFnj17Wvw8IkMZMkqtPZ6b1lg5Gn6uuTzPzRqw842IOg0hBNRqtdndfqVSqbBw4UJUV1fDzs5OilEqlXq3oxIRNSU0NBShoaFNrpfJZEhMTERiYmKTMWysEhERGcc3cb9Bzw01hik6vcz51tTO0MnHzjciskoLFy7E5MmT4eXlhVu3biEjIwMHDx5EZmam2d1+FR4ejqSkJERGRmLhwoU4d+4ckpOT8eabb3KmUyIiIiKiegwZsUWt03/+XshtBFaM0s4CLtNb31EsvYOOnW9EZJWuXr2KiIgIlJaWQqFQYNiwYcjMzERQUBAA87r9SqFQIDs7G9HR0RgxYgScnZ0RFxen8zw3IiIiIiIyjKV31DRkzqPWyDDsfCMiq7Rx48Zm15vb7Vd+fn7Izc1tNoaIiIiIiDpO/U4w7QgxIkOw842IiIiIiIiIiNocbxOu06WjC0BERERERERERGStOPKNiIiIiMgA1vYMISIia8FnopG548g3IiIiIiIiIiIiE+HINyIiIiIiIiIySxzVRtaAnW9ERERERERE1Kmxk49MiZ1vRERERERERNSpsLPNNLhfG8dnvhEREREREREREZkIR74RERERERERkdXiaCzz0hmPx0ONfEtJSYFMJkNsbKy0TAiBxMREKJVK2NvbY9y4cTh16pTO+9RqNebOnQs3Nzc4Ojpi6tSpuHLlik5MRUUFIiIioFAooFAoEBERgZs3b+rEXLp0CWFhYXB0dISbmxtiYmJQXV39MCkRERERUSfQf/5enX9ERERkHhr+jbaGv9Ot7nwrKCjA+vXrMWzYMJ3lK1aswOrVq7F27VoUFBTA09MTQUFBuHXrlhQTGxuLnTt3IiMjA3l5ebh9+zZCQ0NRU1MjxYSHh6OoqAiZmZnIzMxEUVERIiIipPU1NTWYMmUK7ty5g7y8PGRkZGD79u2Ij49vbUpERERERERERERtqlW3nd6+fRvPP/88NmzYgKVLl0rLhRBYs2YNFi1ahGnTpgEANm/eDA8PD2zbtg2zZ89GZWUlNm7ciC1btmDixIkAgPT0dHh5eeHAgQMICQnBmTNnkJmZicOHDyMgIAAAsGHDBqhUKpSUlMDHxwdZWVk4ffo0Ll++DKVSCQBYtWoVIiMjsWzZMnTv3v2hdgwRERERERERtS9rGOVE1FCrOt+io6MxZcoUTJw4Uafz7fz58ygrK0NwcLC0TC6XIzAwEPn5+Zg9ezYKCwuh0Wh0YpRKJXx9fZGfn4+QkBAcOnQICoVC6ngDgNGjR0OhUCA/Px8+Pj44dOgQfH19pY43AAgJCYFarUZhYSHGjx+vV261Wg21Wi29rqqqAgBoNBpoNJpmc9aul3cResssmTYHa8ilPuZlWQzNy9ryJiIiIiIiIutndOdbRkYGjh8/joKCAr11ZWVlAAAPDw+d5R4eHrh48aIUY2dnB2dnZ70Y7fvLysrg7u6ut313d3edmIaf4+zsDDs7OymmoZSUFCQlJektz8rKgoODQ6PvaeivI2ql/9+3b59B77EE2dnZHV0Ek2BelqWlvO7evdtOJSEiIiIiIiJqG0Z1vl2+fBnz5s1DVlYWunXr1mScTCbTeS2E0FvWUMOYxuJbE1PfggULEBcXJ72uqqqCl5cXgoODW7xNVaPRIDs7G28c6wJ1bd32ixNDmn2PJdDmFRQUBFtb244uTpthXpbF0Ly0o1WJiIiIiIiILIVRnW+FhYUoLy+Hv7+/tKympga5ublYu3YtSkpKANSNSuvVq5cUU15eLo1S8/T0RHV1NSoqKnRGv5WXl2PMmDFSzNWrV/U+/9q1azrbOXLkiM76iooKaDQavRFxWnK5HHK5XG+5ra2twR0Z6loZ1DUy6X3Wwph9YEmYl2VpKS9rzJmIiIiIqDPjM96oMzBqttMJEybg5MmTKCoqkv6NGDECzz//PIqKijBgwAB4enrq3DpWXV2NnJwcqWPN398ftra2OjGlpaUoLi6WYlQqFSorK3H06FEp5siRI6isrNSJKS4uRmlpqRSTlZUFuVyu0zlIRERERERERETUUYwa+ebk5ARfX1+dZY6OjnB1dZWWx8bGIjk5GQMHDsTAgQORnJwMBwcHhIeHAwAUCgVmzpyJ+Ph4uLq6wsXFBQkJCfDz85NmPx08eDAmTZqEWbNm4YMPPgAAvPLKKwgNDYWPjw8AIDg4GEOGDEFERATefvtt3LhxAwkJCZg1axZnOiUiIiIiIiJqZw1HsV14a0oHlYTIvLRqttPmvPbaa7h37x6ioqJQUVGBgIAAZGVlwcnJSYp555130LVrV0yfPh337t3DhAkTkJaWBhsbGylm69atiImJkWZFnTp1KtauXSutt7Gxwd69exEVFYWxY8fC3t4e4eHhWLlyZVunREREREREREQPibeYUmdl1G2njTl48CDWrFkjvZbJZEhMTERpaSnu37+PnJwcvdFy3bp1Q2pqKq5fv467d+9i9+7d8PLy0olxcXFBeno6qqqqUFVVhfT0dPTo0UMnpm/fvtizZw/u3r2L69evIzU1tdFnuhGReek/f6/OP9/E/R1dJCIiIiIyE7m5uQgLC4NSqYRMJsOnn36qs14IgcTERCiVStjb22PcuHE4deqUToxarcbcuXPh5uYGR0dHTJ06FVeuXNGJqaioQEREBBQKBRQKBSIiInDz5k2dmEuXLiEsLAyOjo5wc3NDTEwMqqurTZE2EVmxh+58IyIiIiLzkpKSAplMhtjYWGkZG6tEZCnu3LmDJ554QufOp/pWrFiB1atXY+3atSgoKICnpyeCgoJw69YtKSY2NhY7d+5ERkYG8vLycPv2bYSGhqKmpkaKCQ8PR1FRETIzM5GZmYmioiJERERI62tqajBlyhTcuXMHeXl5yMjIwPbt2xEfH2+65InIKrHzjYiIiMiKFBQUYP369Rg2bJjOcjZWichSTJ48GUuXLsW0adP01gkhsGbNGixatAjTpk2Dr68vNm/ejLt372Lbtm0AgMrKSmzcuBGrVq3CxIkTMXz4cKSnp+PkyZM4cOAAAODMmTPIzMzEhx9+CJVKBZVKhQ0bNmDPnj0oKSkBUDeh3+nTp5Geno7hw4dj4sSJWLVqFTZs2ICqqqr22yFEZPHY+UZEViklJQUjR46Ek5MT3N3d8eyzz0onUlrmNgrk5MmTCAwMhL29PXr37o0lS5ZACNF2O4WIrN7t27fx/PPPY8OGDXB2dpaWs7FKRNbi/PnzKCsrk54NDgByuRyBgYHIz88HABQWFkKj0ejEKJVK+Pr6SjGHDh2CQqFAQECAFDN69GgoFAqdGF9fXyiVSikmJCQEarUahYWFJs3THDV8dAwRGa7NJ1wgIjIHOTk5iI6OxsiRI/HgwQMsWrQIwcHBOH36NBwdHQH8PAokLS0Njz/+OJYuXYqgoCCUlJRIk8TExsZi9+7dyMjIgKurK+Lj4xEaGorCwkJpkpjw8HBcuXIFmZmZAOpmZ46IiMDu3bsB/DwKpGfPnsjLy8P169cxY8YMCCGQmpoKAKiqqkJQUBDGjx+PgoICnD17FpGRkXB0dORoESIyWHR0NKZMmYKJEydi6dKl0vKWGquzZ89usbEaEhLSYmPVx8enxcbq+PHj9cqtVquhVqul19pOOo1GA41G02zO2vUtxTUkt3n4ixvGfmZrtm3Kz+gI1piXNeYEGJdXe+ZeVlYGAPDw8NBZ7uHhgYsXL0oxdnZ2OhchtDHa95eVlcHd3V1v++7u7joxDT/H2dkZdnZ2UkxDHVGftZeG9abPoj2NxEAvRt5F4K8jAP8lmZDbyExZxHYl7yJ0/mstLCWvhr+Tht/Phus7uk5j5xsRWSVtR5jWpk2b4O7ujsLCQjz99NN6o0AAYPPmzfDw8MC2bdswe/ZsaRTIli1bMHHiRABAeno6vLy8cODAAYSEhEijQA4fPiw1Rjds2ACVSoWSkhL4+PhIo0AuX74sNUZXrVqFyMhILFu2DN27d8fWrVtx//59pKWlQS6Xw9fXF2fPnsXq1asRFxcHmcx6TlSIyDQyMjJw/PhxFBQU6K0z98ZqSkoKkpKS9JZnZWXBwcGh0fc0lJ2dbVCc1opRRoU3at++fQ+/kRYYm5elsMa8rDEnwLC87t692w4l0dXw3EgI0eL5UsOYxuJbE1NfR9Rn7eVh682/jqhtm4KYGebVMRr+DW74/Wzqb3RH1WnsfCOiTqGyshJA3UzKgPmNAjl06BACAwN1ZmwOCQnBggULcOHCBXh7e+vl1Norq9p1Da9mmetVVkOZ+9Xi1mJelqWjrqpevnwZ8+bNQ1ZWFrp169ZknLk2VhcsWIC4uDjpdVVVFby8vBAcHIzu3bs3Wz6NRoPs7GwEBQXB1ta22dj62mKm7eLEkIfeRlNam5e5s8a8rDEnwLi82vOWck9PTwB1Hf29evWSlpeXl0sd/56enqiurkZFRYXOBYXy8nKMGTNGirl69are9q9du6aznSNHjuisr6iogEaj0bvIoNUR9Vl7aW29WTfyrRZvHOsCda31XFBmXh2r4d/ght/Phus7uk5j5xsRWT0hBOLi4vDUU0/B19cXgPmNAikrK0P//v31Pke7rrHOt4e9strwalZ7jOBoD+Z6tfhhMS/L0t5XVQsLC1FeXg5/f39pWU1NDXJzc7F27VrpeWzm2liVy+U6Fx+0bG1tDW6AGhMLAOqah29QtEfj2Ni8LIU15mWNOQGG5dWeeXt7e8PT0xPZ2dkYPnw4AKC6uho5OTlYvnw5AMDf3x+2trbIzs7G9OnTAQClpaUoLi7GihUrAAAqlQqVlZU4evQoRo2qGzJz5MgRVFZWSnWeSqXCsmXLUFpaKtWdWVlZkMvlOvVtfR1Rn7WXh6031bWyNql7zQ3z6hgD38hqsES3rE39hjqqTmPnGxFZvTlz5uCbb75BXl6e3jpzGgXSWFmaei/Q+iur2qs+Da9mmXIER3sw96vFrcW8LEtHXVWdMGECTp48qbPspZdewqBBg/D6669jwIABZt1YJSKq7/bt2/juu++k1+fPn0dRURFcXFzQt29fxMbGIjk5GQMHDsTAgQORnJwMBwcHhIeHAwAUCgVmzpyJ+Ph4uLq6wsXFBQkJCfDz85MeJTJ48GBMmjQJs2bNwgcffACg7rm9oaGh8PHxAQAEBwdjyJAhiIiIwNtvv40bN24gISEBs2bNanEUGxFRfex8IyKrNnfuXOzatQu5ubno06ePtNzcblnw9PTUexZSeXk5AP3ReVoPe2W14dUsa+kAMderxQ+LeVmW9r6q6uTkJI3s1XJ0dISrq6u0nI1VIrIUx44d05mcRXuxccaMGUhLS8Nrr72Ge/fuISoqChUVFQgICEBWVpY0YRYAvPPOO+jatSumT5+Oe/fuYcKECUhLS5MmzAKArVu3IiYmRnrEyNSpU7F27VppvY2NDfbu3YuoqCiMHTsW9vb2CA8Px8qVK029C4jIyrDzjYiskhACc+fOxc6dO3Hw4EG92zbN7ZYFlUqFhQsXorq6GnZ2dlKMUqnUux2ViKg12FglIksxbtw46Q6AxshkMiQmJiIxMbHJmG7duiE1NVWaWb4xLi4uSE9Pb7Ysffv2xZ49+rN6EhEZg51vRGSVoqOjsW3bNnz22WdwcnKSRpUpFArY29tDJpOZ1SiQ8PBwJCUlITIyEgsXLsS5c+eQnJyMN998kzOdElGrHDx4UOc1G6tERNSU/vP3dnQRiKwaO9+IyCqtW7cOQN2V0/o2bdqEyMhIAOY1CkShUCA7OxvR0dEYMWIEnJ2dERcXp/NMNyIiMi8NG6sX3prSQSUhIiIic8bONyKySs3dqqBlbqNA/Pz8kJub22wMERERERERWZYuHV0AIiIiIiIiIiIia8XONyIiIiIiIiIiIhNh5xsREREREREREZGJsPONiIiIiIiIiIjIRNj5RkREREREREREZCLsfCMiIiIiIiIiIjIRdr4RERERERERERGZCDvfiIiIiIiIiIiITKRrRxeAiIiIiIiIiNpP//l7O7oIRJ0KR74RERERERERERGZCDvfiIiIiIiIiIiITIS3nT6ExobqXnhrSgeUhIiIiIiIiIiIzBFHvhEREREREREREZkIR74RERERERERWTFOsEDUsdj5RkRERETUBvhIEiIiImqMUbedpqSkYOTIkXBycoK7uzueffZZlJSU6MQIIZCYmAilUgl7e3uMGzcOp06d0olRq9WYO3cu3Nzc4OjoiKlTp+LKlSs6MRUVFYiIiIBCoYBCoUBERARu3rypE3Pp0iWEhYXB0dERbm5uiImJQXV1tTEpERERERERERERmYxRnW85OTmIjo7G4cOHkZ2djQcPHiA4OBh37tyRYlasWIHVq1dj7dq1KCgogKenJ4KCgnDr1i0pJjY2Fjt37kRGRgby8vJw+/ZthIaGoqamRooJDw9HUVERMjMzkZmZiaKiIkREREjra2pqMGXKFNy5cwd5eXnIyMjA9u3bER8f/zD7g4iIiIiIiMhi9J+/V+cfEZkfo247zczM1Hm9adMmuLu7o7CwEE8//TSEEFizZg0WLVqEadOmAQA2b94MDw8PbNu2DbNnz0ZlZSU2btyILVu2YOLEiQCA9PR0eHl54cCBAwgJCcGZM2eQmZmJw4cPIyAgAACwYcMGqFQqlJSUwMfHB1lZWTh9+jQuX74MpVIJAFi1ahUiIyOxbNkydO/e/aF3DhERERERERER0cN4qGe+VVZWAgBcXFwAAOfPn0dZWRmCg4OlGLlcjsDAQOTn52P27NkoLCyERqPRiVEqlfD19UV+fj5CQkJw6NAhKBQKqeMNAEaPHg2FQoH8/Hz4+Pjg0KFD8PX1lTreACAkJARqtRqFhYUYP378w6RGRERERFaCI0GIiIioI7W6800Igbi4ODz11FPw9fUFAJSVlQEAPDw8dGI9PDxw8eJFKcbOzg7Ozs56Mdr3l5WVwd3dXe8z3d3ddWIafo6zszPs7OykmIbUajXUarX0uqqqCgCg0Wig0WiazVe7Xt5FGBRnKbTltbRyt4R5mTe5je7vSPu7MvR3SERERERERGQpWt35NmfOHHzzzTfIy8vTWyeTyXReCyH0ljXUMKax+NbE1JeSkoKkpCS95VlZWXBwcGi2fFp/HVHb7Pp9+/YZtB1zk52d3dFFMAnmZZ5WjGp8eUt53b171wSlISIiIiJLkpiYqNeuqz+YQwiBpKQkrF+/HhUVFQgICMC7776LoUOHSvFqtRoJCQn46KOPcO/ePUyYMAHvvfce+vTpI8VUVFQgJiYGu3btAgBMnToVqamp6NGjh+mT/P84izKRdWhV59vcuXOxa9cu5Obm6lROnp6eAOpGpfXq1UtaXl5eLo1S8/T0RHV1NSoqKnRGv5WXl2PMmDFSzNWrV/U+99q1azrbOXLkiM76iooKaDQavRFxWgsWrPXBGgAAPwFJREFULEBcXJz0uqqqCl5eXggODm7xGXEajQbZ2dl441gXqGub7kgsTgxpdjvmRptXUFAQbG1tO7o4bYZ5mTffxP06r+VdBP46orbFvLSjVYmISFdKSgp27NiBb7/9Fvb29hgzZgyWL18OHx8fKaY9G6OXLl1CdHQ0vvzyS9jb2yM8PBwrV66EnZ2d6XcGEXUKQ4cOxYEDB6TXNjY20v9rJwFMS0vD448/jqVLlyIoKAglJSVwcnICUDcJ4O7du5GRkQFXV1fEx8cjNDQUhYWF0rbCw8Nx5coV6dnnr7zyCiIiIrB79+52zJSIrIFRs50KITBnzhzs2LEDX375Jby9vXXWe3t7w9PTU2f0SnV1NXJycqSONX9/f9ja2urElJaWori4WIpRqVSorKzE0aNHpZgjR46gsrJSJ6a4uBilpaVSTFZWFuRyOfz9/Rstv1wuR/fu3XX+AYCtra1B/wBAXSuDuqbpf4Zuy5z+GbMPLOkf8zLff3q/nf/foW1o/obIzc1FWFgYlEolZDIZPv30U531QggkJiZCqVTC3t4e48aNw6lTp3Ri1Go15s6dCzc3Nzg6OmLq1Km4cuWKTkxFRQUiIiKgUCigUCgQERGBmzdv6sRcunQJYWFhcHR0hJubG2JiYlBdXa0Tc/LkSQQGBsLe3h69e/fGkiVLIETzt7kTEWlxRnoi6my6du0KT09P6V/Pnj0BQG8SQF9fX2zevBl3797Ftm3bAECaBHDVqlWYOHEihg8fjvT0dJw8eVLq0NNOAvjhhx9CpVJBpVJhw4YN2LNnD0pKSjosbyKyTEaNfIuOjsa2bdvw2WefwcnJSRrWq1AoYG9vD5lMhtjYWCQnJ2PgwIEYOHAgkpOT4eDggPDwcCl25syZiI+Ph6urK1xcXJCQkAA/Pz9p9tPBgwdj0qRJmDVrFj744AMAdVcZQkNDpSu4wcHBGDJkCCIiIvD222/jxo0bSEhIwKxZszp0ptOGw4I5JJioY9y5cwdPPPEEXnrpJfz2t7/VW99eV0S1jdCePXsiLy8P169fx4wZMyCEQGpqKoC6EX1BQUEYP348CgoKcPbsWURGRsLR0ZGNVSIyCGekN188NyQyjXPnzkGpVEIulyMgIADJyckYMGBAu04CaK44yQyR+TGq823dunUAgHHjxuks37RpEyIjIwEAr732Gu7du4eoqCjploasrCypMQsA77zzDrp27Yrp06dLtzSkpaXpDBXeunUrYmJipApx6tSpWLt2rbTexsYGe/fuRVRUFMaOHatzSwMR0eTJkzF58uRG15lbI3Tr1q24f/8+0tLSIJfL4evri7Nnz2L16tWIi4tr8ZmZREQNWdqM9G0xKVZzcQ0n+ulIhk4eZC2TLDVkjXlZY06AcXm1d+4BAQH45z//iccffxxXr17F0qVLMWbMGJw6dapdJwFsTFvXZ43VXw23Y051XGO0k6u1NHmhpWFe5q3h76Sj6zSjOt8MuQVKJpMhMTERiYmJTcZ069YNqamp0qiPxri4uCA9Pb3Zz+rbty/27NnTYpmIiOozt0booUOHEBgYCLlcrhOzYMECXLhwQe8Wf63Wntw1NXuzpTca2PixLMzLdLlb4oz0bTEpVnOT9jQ10U9HMHZyLkufZKkp1piXNeYEGJZXe0+KVf8Cq5+fH1QqFR599FFs3rwZo0ePBtB+kwA21Nb1WWP1V8N6xJzquOa0NHmhpWJe5qmpv7cdVae1erZTIiJDmdvQd3NrhJaVlaF///56n6Nd11Tn28Oe3DX8g2qpszU31JkbP5aoM+dlqsaqJc5I3xaTYjU3aU/DiX46kqGTc1nLJEsNWWNe1pgTYFxeHT0plqOjI/z8/HDu3Dk8++yzANpnEsDGtHV91lj91bAeMac6rjHaydVamrzQ0jAv89bwd9LRdRo734io0zKnRmhjZWnqvVqtPblravZmS5utuSE2fiwL8zLNiZ2lzkgvl8t1Rv9qGTPZTnOx6hrzaTwY+303dsIhS2GNeVljToBheXV03mq1GmfOnMEvf/lLnUkAhw8fDuDnSQCXL18OQHcSwOnTpwP4eRLAFStWANCdBHDUqLqhZQ0nAWxMW9dnjdVfDbdjTnVcc7STF1ob5mWemvq9dVSdxs43E+NDdonMj7k1Qj09PfVuxSovLwegPzqvvoc9uWv4B7WjT5zbSmdu/FiizpxXW+YthMDcuXOxc+dOHDx4sNkZ6U3dGFWpVFi2bBlKS0ulOralGek7E54bEj28hIQEhIWFoW/fvigvL8fSpUtRVVWFGTNmtOskgEREhurS0QUgImpv9RuhWtpGqLbxWL8RqqVthNZvYGoboVqNNUKLi4tRWloqxTRshKpUKuTm5qK6ulonRqlU6t2OSkTUmOjoaKSnp2Pbtm3SjPRlZWW4d+8eAOg0Rnfu3Ini4mJERkY22Rj94osvcOLECbzwwgtNNkYPHz6Mw4cPY9asWU3OSH/ixAl88cUXZjEjPRFZjytXruC5556Dj48Ppk2bBjs7Oxw+fBj9+vUDUDcJYGxsLKKiojBixAj88MMPjU4C+Oyzz2L69OkYO3YsHBwcsHv3br1JAP38/BAcHIzg4GAMGzYMW7Zsafd8icjyceQbEVml27dv47vvvpNenz9/HkVFRXBxcUHfvn3b7Ypo/Ubo22+/jRs3bug1QsPDw5GUlITIyEgsXLgQ586dQ3JyMt58803OdEpEBuGM9ETUmWRkZDS7vj0nASQiMgQ734jIKh07dgzjx4+XXmufjTZjxgykpaWZVSNUoVAgOzsb0dHRGDFiBJydnREXF6fzPDciouZwRnoios7D3CYzI6KWsfOtnfE5H0TtY9y4cc02Rs2tEern54fc3NxmY4iIyPqY6tyQ55xERETmg51vRNTmeDWOiIio/fDvLhERkXlj51sHa+xkiVcmiYiIiDon7bmh3EZgxSjAN3E/SpaFNhrTmu1q8XyTiIio/bDzjYiIiIjIjHFkGxERkWVj55sZ4pVJsiRsEBARERERERE1rUtHF4CIiIiIiIiIiMhaceSbBeBIODInHOlGREREREREZDh2vlkgTtJAhmAnGREREREREVHHY+ebleDouM6HnWtERETUWryYS0RE1H7Y+Wal2Bln2Xj8iIiIiIiIiKwDO986CV7d7DhtMUJNuw25jcCKUYBv4n4AsofeLhERERERERGZFjvfOrH+8/fqdOaoa1ruzGGHnT7e/klERERERERETWHnGxmlPTqaGuvga+lzDekUZCcZEREREREREbU3dr6R2WlNJ1nD9xg7oo+IiIiIiIiIyBTY+UZERERERJzwiYiIyES6dHQBiIiIiIiIiIiIrBVHvhERERGRVeFzXomIiMiccOQbERERERERERGRiXDkGxERERER6eEz4IiIiNoGR74RERERERERERGZCEe+ERERERFRixp7lh5HwxEREbWMnW9ERERERNQqvDWViIjMUcO/T3IbgRWjOqgwYOcbERERERG1kZZmmu3oxk9zOLKPiIhMxeI739577z28/fbbKC0txdChQ7FmzRr88pe/7OhiERG1SkfWaRy9QERtiedo1BzfxP1Q18g6uhgtsva/jdaeX1tqzzrNUn4fRGQ4i+58+/jjjxEbG4v33nsPY8eOxQcffIDJkyfj9OnT6Nu3b0cXj4jIKKzTiMhasD4ja9XY6DjtaD5th4m5dGC1NAqRDMc6jYgelkV3vq1evRozZ87Eyy+/DABYs2YN9u/fj3Xr1iElJaWDS0dEZBxzq9MMOWk3lwYGkakamfyOt4651WdE7cmSO73ql92cbxFub6zTiOhhWWznW3V1NQoLCzF//nyd5cHBwcjPz2/0PWq1Gmq1WnpdWVkJALhx4wY0Gk2zn6fRaHD37l101XRBTa31DAHuWitw924t87IQ1p7X9evXYWtr22TcrVu3AABCiPYqWrtpzzqtLeuzxxL+9VDvb0vyLgJ/GV6LJxftgLqRvI4smNABpXp42uPV0u+jowSkfNGq97V0vIxlqhMaQ77j9b9bxhwva63TOuocrf53yWJPcOux9r/51pSXNeYEGH5+BlhvfQYYX6exzanP2n8jzMsydHSdZrHnJj/99BNqamrg4eGhs9zDwwNlZWWNviclJQVJSUl6y729vU1SRksR3tEFMBHmZVmMyevWrVtQKBQmK0tHYJ3WNpr7HrmtardikIGspT572O+WtdVprM/ajrX8RhqyxrysMSfA+LysrT4DjK/TWJ81jr8Ry8K86rRlnWaxnW9aMpluT6wQQm+Z1oIFCxAXFye9rq2txY0bN+Dq6trke7Sqqqrg5eWFy5cvo3v37g9fcDPBvCxLZ89LCIFbt25BqVS2Y+naV3vUaZ39e2RpmJdlMSYva6/TeI72cJiX5bDGnADWZw0ZWqexPtPHvCwL8zJNnWaxnW9ubm6wsbHRu9pQXl6ud1VCSy6XQy6X6yzr0aOHUZ/bvXt3q/oCajEvy9KZ87K2q6laHVGndebvkSViXpbF0LyssU7jOVrbYl6WwxpzAjp3fQYYX6exPmsa87IsnT2vtq7TurTp1tqRnZ0d/P39kZ2drbM8OzsbY8aM6aBSERG1Dus0IrIWrM+IyJqwTiOitmCxI98AIC4uDhERERgxYgRUKhXWr1+PS5cu4dVXX+3oohERGY11GhFZC9ZnRGRNWKcR0cOy6M63P/zhD7h+/TqWLFmC0tJS+Pr6Yt++fejXr1+bf5ZcLsfixYv1hhBbOuZlWZiXdWuvOs1a9zfzsizMy7rxHO3hMS/LYY05AdabV2vwHO3hMC/LwrxMQyascT5oIiIiIiIiIiIiM2Cxz3wjIiIiIiIiIiIyd+x8IyIiIiIiIiIiMhF2vhEREREREREREZkIO9+IiIiIiIiIiIhMpNN2vr333nvw9vZGt27d4O/vj//85z/Nxufk5MDf3x/dunXDgAED8P777+vFbN++HUOGDIFcLseQIUOwc+dOUxW/ScbktWPHDgQFBaFnz57o3r07VCoV9u/frxOTlpYGmUym9+/+/fumTkWPMbkdPHiw0XJ/++23OnGWdswiIyMbzWvo0KFSTEcfs9zcXISFhUGpVEImk+HTTz9t8T2W8vsyZ6zTLKtOs8b6zNrqMoD1WUdhfcb6zNKOlyXUaazPOo411mmsz1ifdfTxssg6TXRCGRkZwtbWVmzYsEGcPn1azJs3Tzg6OoqLFy82Gv/9998LBwcHMW/ePHH69GmxYcMGYWtrK/79739LMfn5+cLGxkYkJyeLM2fOiOTkZNG1a1dx+PDh9krL6LzmzZsnli9fLo4ePSrOnj0rFixYIGxtbcXx48elmE2bNonu3buL0tJSnX/tzdjcvvrqKwFAlJSU6JT7wYMHUowlHrObN2/q5HP58mXh4uIiFi9eLMV09DHbt2+fWLRokdi+fbsAIHbu3NlsvKX8vswZ67Q6llKnWWN9Zo11mRCszzoC67M6rM8s63hZQp3G+qxjWGOdxvqsDusznqMZq1N2vv2/9u49uqryzv/455DLIYnJMSEm4YwR6ZhGMegPg4aACgyQgFzG0im2cVJwEHFQaAYoBVldDVYSRG5KWpYy1LC4CO0g1QqNiVPFYRIupmQpwjC1RS7LhCCEJEB6cgj79weTbQ8JgROyORfer7VYWfvZ37P38zx77+9qvz7n7Iceesh49tlnPdruvvtuY+7cue3Gz5kzx7j77rs92qZOnWoMGDDA3J4wYYIxcuRIj5js7Gzj+9//fhf1+uq8HVd7+vTpYyxYsMDcfvPNNw2Hw9FVXew0b8fWmgzr6uqueMxguGZbt241bDab8eWXX5pt/nLNDMO4pkQYKM+XPyOnXZk/5rRgzGfBnssMg3x2o5DProx8FjjXy99zGvnsxgnGnEY+u4R85ujKbl6XQMlpN93XTpubm1VZWamsrCyP9qysLJWXl7f7mYqKijbx2dnZ+uSTT+R2uzuMudIxu1pnxnW5ixcvqrGxUXFxcR7tZ8+eVa9evXT77bdrzJgx2rdvX5f1+1pcz9j69eunnj17atiwYfrwww899gXDNVuzZo2GDx+uXr16ebT7+pp5IxCeL39GTrsyf8xpwZjPyGXfCIRny5+Rz66MfBZY1ysYclogPFv+LhhzGvmsLfKZ/+czyT+erZuu+Pb111+rpaVFiYmJHu2JiYmqqalp9zM1NTXtxl+4cEFff/11hzFXOmZX68y4Lrd06VKdO3dOEyZMMNvuvvtuFRcX691339Vbb72l7t27a9CgQfrTn/7Upf3vSGfG1rNnT73xxhvasmWL3n77baWmpmrYsGH6+OOPzZhAv2bV1dX6/e9/r6efftqj3R+umTcC4fnyZ+S0K/PHnBaM+Yxc9o1AeLb8GfnsyshngXO9giWnBcKz5e+CMaeRz75BPgucfCb5x7MV2iVHCUA2m81j2zCMNm1Xi7+83dtjWqGzfXjrrbeUn5+vd955RwkJCWb7gAEDNGDAAHN70KBBeuCBB7Ry5Uq99tprXdfxa+DN2FJTU5WammpuZ2Zm6tixY1qyZIkeffTRTh3TKp3tQ3FxsW699VY9/vjjHu3+dM2uVaA8X/6MnObJ33NaMOYzctklgfJs+TPymSfyWWBdr2DKaYHybPm7YMxp5DPymT9cL2/5+tm66Va+xcfHKyQkpE31sra2tk2Vs1VSUlK78aGhoerRo0eHMVc6ZlfrzLhabd68WZMnT9avf/1rDR8+vMPYbt266cEHH7yhVe3rGdvfGjBggEe/A/maGYahX/3qV8rNzVV4eHiHsb64Zt4IhOfLn5HT2vLnnBaM+Yxc9o1AeLb8GfmsLfJZ5455PchplwTCs+XvgjGnkc86Rj7zz3wm+cezddMV38LDw5Wenq6ysjKP9rKyMg0cOLDdz2RmZraJLy0tVf/+/RUWFtZhzJWO2dU6My7p0n99mDRpkjZu3KjRo0df9TyGYaiqqko9e/a87j5fq86O7XL79u3z6HegXjPp0muSv/jiC02ePPmq5/HFNfNGIDxf/oyc5snfc1ow5jNy2TcC4dnyZ+QzT+SzbwTC9ZKCK6cFwrPl74Ixp5HPOkY+8898JvnJs9Ulr20IMK2v212zZo1x4MABIy8vz4iKijLf4DF37lwjNzfXjG99Le2//du/GQcOHDDWrFnT5rW0//3f/22EhIQYixYtMg4ePGgsWrTIZ68RvtZxbdy40QgNDTV+8YtfeLwi+MyZM2ZMfn6+UVJSYvz5z3829u3bZzz11FNGaGiosXv37hs2rs6Mbfny5cbWrVuN//3f/zX2799vzJ0715BkbNmyxYwJxGvW6p//+Z+NjIyMdo/p62vW2Nho7Nu3z9i3b58hyVi2bJmxb98+83XWgfp8+TNy2iWBktOCMZ8FYy4zDPKZL5DPLiGfBdb1auXPOY185hvBmNPIZ5eQz/jfaN66KYtvhmEYv/jFL4xevXoZ4eHhxgMPPGDs2LHD3Ddx4kRj8ODBHvEfffSR0a9fPyM8PNy48847jVWrVrU55m9+8xsjNTXVCAsLM+6++26PB+9G8WZcgwcPNiS1+Tdx4kQzJi8vz7jjjjuM8PBw47bbbjOysrKM8vLyGziib3gztpdfftn4+7//e6N79+5GbGys8fDDDxvbtm1rc8xAu2aGYRhnzpwxIiIijDfeeKPd4/n6mrW+dvtK91UgP1/+jJwWWDktGPNZsOUywyCf+Qr5jHwWaNfLMPw/p5HPfCcYcxr5jHzm6+sViDnNZhj/9ytzAAAAAAAAALrUTfebbwAAAAAAAMCNQvENAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACxC8Q03XHl5ufLz83XmzBlfdwUAAAAAAMBSFN9ww5WXl2vBggUU3wAAAAAAQNCj+AYAAAAAAABYhOIbbqj8/Hz9+Mc/liT17t1bNptNNptNH330kSRp8+bNyszMVFRUlG655RZlZ2dr3759HseYNGmSbrnlFv3P//yPsrOzFRUVpZ49e2rRokWSpF27dunhhx9WVFSUvv3tb2vt2rUeny8uLpbNZlNZWZmeeuopxcXFKSoqSmPHjtVf/vKXNn3+1a9+pfvvv1/du3dXXFycvvOd7+jgwYMWzA4AAAAAAAg2FN9wQz399NOaPn26JOntt99WRUWFKioq9MADD6igoEA/+MEP1KdPH/3617/WunXr1NjYqEceeUQHDhzwOI7b7db48eM1evRovfPOOxo1apTmzZunF154QRMnTtS//Mu/aOvWrUpNTdWkSZNUWVnZpi+TJ09Wt27dtHHjRq1YsUJ79uzRkCFDPL4OW1hYqMmTJ+vee+/V22+/rVdffVWffvqpMjMz9ac//cnSuQIAAAAAAIHPZhiG4etO4OayZMkS/fjHP9bhw4d15513SpKOHTumb33rW/rXf/1Xvfbaa2bs2bNnlZKSokcffVSbN2+WdGnl29q1a7VlyxaNHz9eknThwgU5nU6dPHlSf/zjH9WvXz9J0unTp5WQkKAf/ehHWrp0qaRLK9+eeuopfec739Hbb79tnqu8vFyDBg3SSy+9pPnz5+vMmTNyOp0aOnSotm3bZsYdO3ZMKSkp+u53v6sNGzZYOlcAAAAAACCwsfINfuH999/XhQsX9MMf/lAXLlww/3Xv3l2DBw82v5baymaz6bHHHjO3Q0NDddddd6lnz55m4U2S4uLilJCQoCNHjrQ555NPPumxPXDgQPXq1UsffvihJKmiokJNTU2aNGmSR1xycrL+4R/+Qf/5n/95naMGAAAAAADBLtTXHQAk6cSJE5KkBx98sN393bp51okjIyPVvXt3j7bw8HDFxcW1+Wx4eLj++te/tmlPSkpqt+3UqVOSZP7t2bNnmzin06mysrJ2+woAAAAAANCK4hv8Qnx8vCTpP/7jP9SrV68bcs6ampp22+666y5JUo8ePSRJ1dXVbeK++uors88AAAAAAABXQvENN5zdbpckNTU1mW3Z2dkKDQ3Vn//8Z333u9+9If3YsGGDx7nKy8t15MgRPf3005KkzMxMRUREaP369fre975nxh0/flx/+MMf9E//9E83pJ8AAAAAACBwUXzDDde3b19J0quvvqqJEycqLCxMqampevHFFzV//nz95S9/0ciRIxUbG6sTJ05oz549ioqK0oIFC7q0H5988omefvppfe9739OxY8c0f/58/d3f/Z2mTZsmSbr11lv105/+VC+88IJ++MMf6gc/+IFOnTqlBQsWqHv37vrZz37Wpf0BAAAAAADBh+IbbrghQ4Zo3rx5Wrt2rVavXq2LFy/qww8/1Lx589SnTx+9+uqreuutt+RyuZSUlKQHH3xQzz77bJf3Y82aNVq3bp2+//3vy+VyaejQoXr11Vc9fjdu3rx5SkhI0GuvvabNmzcrIiJCQ4YMUUFBgVJSUrq8TwAAAAAAILjYDMMwfN0J4EYqLi7WU089pb1796p///6+7g4AAAAAAAhi3a4eAgAAAAAAAKAzKL4BAAAAAAAAFuFrpwAAAAAAAIBFWPkGAAAAAAAAWITiGwAAAAAAAGARim8AAAAAAACARUJ93QFfunjxor766itFR0fLZrP5ujsArsIwDDU2NsrpdKpbN/7bAQAAAADA/93UxbevvvpKycnJvu4GAC8dO3ZMt99+u6+7AQAAAADAVd3Uxbfo6GhJl/6PfExMTIexbrdbpaWlysrKUlhY2I3o3k2DubVWMM1vQ0ODkpOTzWcXAAAAAAB/d1MX31q/ahoTE3NNxbfIyEjFxMQEfAHD3zC31grG+eVr4gAAAACAQMGPJgEAAAAAAAAWofgGAAAAAAAAWITiGwAAAAAAAGARim8AAAAAAACARSi+AQAAAAAAABa5qd92iuB259xtHttfLhrto54AAAAAAICbFSvfAAAAAAAAAItQfAMAAAAAAAAsQvENAAAAAAAAsMh1Fd8KCwtls9mUl5dnthmGofz8fDmdTkVERGjIkCH6/PPPPT7ncrk0ffp0xcfHKyoqSuPGjdPx48c9Yurq6pSbmyuHwyGHw6Hc3FydOXPGI+bo0aMaO3asoqKiFB8frxkzZqi5ufl6hgQAAAAAAAB0mU4X3/bu3as33nhD9913n0f74sWLtWzZMhUVFWnv3r1KSkrSiBEj1NjYaMbk5eVp69at2rRpk3bu3KmzZ89qzJgxamlpMWNycnJUVVWlkpISlZSUqKqqSrm5ueb+lpYWjR49WufOndPOnTu1adMmbdmyRbNmzerskAAAAAAAAIAu1ani29mzZ/Xkk09q9erVio2NNdsNw9CKFSs0f/58jR8/XmlpaVq7dq3Onz+vjRs3SpLq6+u1Zs0aLV26VMOHD1e/fv20fv16ffbZZ/rggw8kSQcPHlRJSYn+/d//XZmZmcrMzNTq1av13nvv6dChQ5Kk0tJSHThwQOvXr1e/fv00fPhwLV26VKtXr1ZDQ8P1zgsAAAAAAABw3UI786HnnntOo0eP1vDhw/XSSy+Z7YcPH1ZNTY2ysrLMNrvdrsGDB6u8vFxTp05VZWWl3G63R4zT6VRaWprKy8uVnZ2tiooKORwOZWRkmDEDBgyQw+FQeXm5UlNTVVFRobS0NDmdTjMmOztbLpdLlZWVGjp0aJt+u1wuuVwuc7u1SOd2u+V2uzscc+v+q8XBe1bNrT3EaPc8N5tguneDYQwAAAAAgJuL18W3TZs26Y9//KP27t3bZl9NTY0kKTEx0aM9MTFRR44cMWPCw8M9Vsy1xrR+vqamRgkJCW2On5CQ4BFz+XliY2MVHh5uxlyusLBQCxYsaNNeWlqqyMjIdj9zubKysmuKg/e6em4XP+S5vX379i49fqAJhnv3/Pnzvu4CAAAAAABe8ar4duzYMf3oRz9SaWmpunfvfsU4m83msW0YRpu2y10e0158Z2L+1rx58zRz5kxzu6GhQcnJycrKylJMTEyH/XO73SorK9OIESMUFhbWYSy8Y9XcpuW/77G9Pz+7y44dSILp3uUr5QAAAACAQONV8a2yslK1tbVKT08321paWvTxxx+rqKjI/D22mpoa9ezZ04ypra01V6klJSWpublZdXV1HqvfamtrNXDgQDPmxIkTbc5/8uRJj+Ps3r3bY39dXZ3cbnebFXGt7Ha77HZ7m/awsLBrLkp4EwvvdPXculo8i7A3+3ULhns30PsPAAAAALj5ePXChWHDhumzzz5TVVWV+a9///568sknVVVVpW9961tKSkry+Hpbc3OzduzYYRbW0tPTFRYW5hFTXV2t/fv3mzGZmZmqr6/Xnj17zJjdu3ervr7eI2b//v2qrq42Y0pLS2W32z2KgwAAAAAAAICveLXyLTo6WmlpaR5tUVFR6tGjh9mel5engoICpaSkKCUlRQUFBYqMjFROTo4kyeFwaPLkyZo1a5Z69OihuLg4zZ49W3379tXw4cMlSffcc49GjhypKVOm6PXXX5ckPfPMMxozZoxSU1MlSVlZWerTp49yc3P1yiuv6PTp05o9e7amTJly1a+QAgAAAAAAADdCp9522pE5c+aoqalJ06ZNU11dnTIyMlRaWqro6GgzZvny5QoNDdWECRPU1NSkYcOGqbi4WCEhIWbMhg0bNGPGDPOtqOPGjVNRUZG5PyQkRNu2bdO0adM0aNAgRUREKCcnR0uWLOnqIQEAAAAAAACdYjMMw/B1J3yloaFBDodD9fX11/TChe3bt+uxxx7jd6e6mFVze+fcbR7bXy4a3WXHDiTBdO9688wCAAAAAOAPunzlG3A1lxfF/vTzrC4/JgAAAAAAgD/w6oULAAAAAAAAAK4dK99w0+BrqAAAAAAA4EZj5RsAAAAAAABgEYpvAAAAAAAAgEUovgEAAAAAAAAWofgGAAAAAAAAWITiGwAAAAAAAGARim8AAAAAAACARSi+AQAAAAAAABah+AYAAAAAAABYhOIbAAAAAAAAYBGKbwAAAAAAAIBFKL4BAAAAAAAAFqH4BgAAAAAAAFiE4hsAAAAAAABgEYpvAAAAAAAAgEVCfd0BBL87524LiGMCAAAAAAB0NVa+wefS8t83/1JUAwAAAAAAwYTiGwAAAAAAAGARvnYKv9Pe6rcvF432QU8AAAAAAACuD8U3BAS+jgoAAAAAAAIRXzsFAAAAAAAALELxDQAAAAAAALCIV8W3VatW6b777lNMTIxiYmKUmZmp3//+9+Z+wzCUn58vp9OpiIgIDRkyRJ9//rnHMVwul6ZPn674+HhFRUVp3LhxOn78uEdMXV2dcnNz5XA45HA4lJubqzNnznjEHD16VGPHjlVUVJTi4+M1Y8YMNTc3ezl8AAAAAAAAwDpe/ebb7bffrkWLFumuu+6SJK1du1b/+I//qH379unee+/V4sWLtWzZMhUXF+vb3/62XnrpJY0YMUKHDh1SdHS0JCkvL0+/+93vtGnTJvXo0UOzZs3SmDFjVFlZqZCQEElSTk6Ojh8/rpKSEknSM888o9zcXP3ud7+TJLW0tGj06NG67bbbtHPnTp06dUoTJ06UYRhauXJll00OghsvdgAAAAAAAFbzqvg2duxYj+2FCxdq1apV2rVrl/r06aMVK1Zo/vz5Gj9+vKRLxbnExERt3LhRU6dOVX19vdasWaN169Zp+PDhkqT169crOTlZH3zwgbKzs3Xw4EGVlJRo165dysjIkCStXr1amZmZOnTokFJTU1VaWqoDBw7o2LFjcjqdkqSlS5dq0qRJWrhwoWJiYq57YgAAAAAAAIDr1em3nba0tOg3v/mNzp07p8zMTB0+fFg1NTXKysoyY+x2uwYPHqzy8nJNnTpVlZWVcrvdHjFOp1NpaWkqLy9Xdna2Kioq5HA4zMKbJA0YMEAOh0Pl5eVKTU1VRUWF0tLSzMKbJGVnZ8vlcqmyslJDhw5tt88ul0sul8vcbmhokCS53W653e4Ox9u6/2pxaMseYnS8v5vh8deXgvH6BtO9GwxjAAAAAADcXLwuvn322WfKzMzUX//6V91yyy3aunWr+vTpo/LycklSYmKiR3xiYqKOHDkiSaqpqVF4eLhiY2PbxNTU1JgxCQkJbc6bkJDgEXP5eWJjYxUeHm7GtKewsFALFixo015aWqrIyMirDV2SVFZWdk1x+Mbih64t7uf9L1rbkWuwfft2X3fBMsFw754/f97XXQAAAAAAwCteF99SU1NVVVWlM2fOaMuWLZo4caJ27Nhh7rfZbB7xhmG0abvc5THtxXcm5nLz5s3TzJkzze2GhgYlJycrKyvrql9VdbvdKisr04gRIxQWFtZh7M0kLf99j+39+dlXjbmcvZuhn/e/qJ9+0k2uix3fK1Zrr/+BLpju3dbVqgAAAAAABAqvi2/h4eHmCxf69++vvXv36tVXX9VPfvITSZdWpfXs2dOMr62tNVepJSUlqbm5WXV1dR6r32prazVw4EAz5sSJE23Oe/LkSY/j7N6922N/XV2d3G53mxVxf8tut8tut7dpDwsLu+aihDexNwNXi2exrL25uTzmise6aLvmWKsE87UNhns30PsPAAAAALj5dLveAxiGIZfLpd69eyspKcnjq23Nzc3asWOHWVhLT09XWFiYR0x1dbX2799vxmRmZqq+vl579uwxY3bv3q36+nqPmP3796u6utqMKS0tld1uV3p6+vUOCQAAAAAAAOgSXq18e+GFFzRq1CglJyersbFRmzZt0kcffaSSkhLZbDbl5eWpoKBAKSkpSklJUUFBgSIjI5WTkyNJcjgcmjx5smbNmqUePXooLi5Os2fPVt++fc23n95zzz0aOXKkpkyZotdff12S9Mwzz2jMmDFKTU2VJGVlZalPnz7Kzc3VK6+8otOnT2v27NmaMmUKbzoFAAAAAACA3/Cq+HbixAnl5uaqurpaDodD9913n0pKSjRixAhJ0pw5c9TU1KRp06aprq5OGRkZKi0tVXR0tHmM5cuXKzQ0VBMmTFBTU5OGDRum4uJihYSEmDEbNmzQjBkzzLeijhs3TkVFReb+kJAQbdu2TdOmTdOgQYMUERGhnJwcLVmy5LomA7hz7jaP7S8XjfZRTwAAAAAAQDDwqvi2Zs2aDvfbbDbl5+crPz//ijHdu3fXypUrtXLlyivGxMXFaf369R2e64477tB7773XYQwAAAAAAADgS9f9m28AAAAAAAAA2kfxDQAAAAAAALAIxTcAAAAAAADAIhTfAAAAAAAAAItQfAMAAAAAAAAsQvENAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACxC8Q0AAAAAAACwCMU3AAAAAAAAwCIU3wAAAAAAAACLhPq6Awgud87d5usuAAAAAAAA+A1WvgEAAAAAAAAWofgGAAAAAAAAWITiGwAAAAAAAGARim8AAAAAAACARSi+AQAAAAAAABah+AYAAAAAAABYhOIbAAAAAAAAYBGKbwAAAAAAAIBFKL4BAAAAAAAAFqH4BgAAAAAAAFgk1NcdQGC5c+42X3cBAAAAAAAgYHi18q2wsFAPPvigoqOjlZCQoMcff1yHDh3yiDEMQ/n5+XI6nYqIiNCQIUP0+eefe8S4XC5Nnz5d8fHxioqK0rhx43T8+HGPmLq6OuXm5srhcMjhcCg3N1dnzpzxiDl69KjGjh2rqKgoxcfHa8aMGWpubvZmSAAAAAAAAIBlvCq+7dixQ88995x27dqlsrIyXbhwQVlZWTp37pwZs3jxYi1btkxFRUXau3evkpKSNGLECDU2NpoxeXl52rp1qzZt2qSdO3fq7NmzGjNmjFpaWsyYnJwcVVVVqaSkRCUlJaqqqlJubq65v6WlRaNHj9a5c+e0c+dObdq0SVu2bNGsWbOuZz4AAAAAAACALuPV105LSko8tt98800lJCSosrJSjz76qAzD0IoVKzR//nyNHz9ekrR27VolJiZq48aNmjp1qurr67VmzRqtW7dOw4cPlyStX79eycnJ+uCDD5Sdna2DBw+qpKREu3btUkZGhiRp9erVyszM1KFDh5SamqrS0lIdOHBAx44dk9PplCQtXbpUkyZN0sKFCxUTE3PdkwMAAAAAAABcj+t64UJ9fb0kKS4uTpJ0+PBh1dTUKCsry4yx2+0aPHiwysvLJUmVlZVyu90eMU6nU2lpaWZMRUWFHA6HWXiTpAEDBsjhcHjEpKWlmYU3ScrOzpbL5VJlZeX1DAsAAAAAAADoEp1+4YJhGJo5c6YefvhhpaWlSZJqamokSYmJiR6xiYmJOnLkiBkTHh6u2NjYNjGtn6+pqVFCQkKbcyYkJHjEXH6e2NhYhYeHmzGXc7lccrlc5nZDQ4Mkye12y+12dzje1v1Xiwt29hCj64/ZzfD460+C4XoH070bDGMAAAAAANxcOl18e/755/Xpp59q586dbfbZbDaPbcMw2rRd7vKY9uI7E/O3CgsLtWDBgjbtpaWlioyM7LB/rcrKyq4pLlgtfsi6Y/+8/0XrDt5J27dv93UXukww3Lvnz5/3dRcAAAAAAPBKp4pv06dP17vvvquPP/5Yt99+u9melJQk6dKqtJ49e5rttbW15iq1pKQkNTc3q66uzmP1W21trQYOHGjGnDhxos15T5486XGc3bt3e+yvq6uT2+1usyKu1bx58zRz5kxzu6GhQcnJycrKyrrqb8S53W6VlZVpxIgRCgsL6zA2mKXlv9/lx7R3M/Tz/hf100+6yXWx4yLtjbY/P9vXXbhuwXTvtq5WBQAAAAAgUHhVfDMMQ9OnT9fWrVv10UcfqXfv3h77e/furaSkJJWVlalfv36SpObmZu3YsUMvv/yyJCk9PV1hYWEqKyvThAkTJEnV1dXav3+/Fi9eLEnKzMxUfX299uzZo4ceurTUavfu3aqvrzcLdJmZmVq4cKGqq6vNQl9paansdrvS09Pb7b/dbpfdbm/THhYWds1FCW9ig5GrxbrimOuizdLjd0bKT0s9tr9cNNpHPbl+wXDvBnr/AQAAAAA3H6+Kb88995w2btyod955R9HR0eZvqzkcDkVERMhmsykvL08FBQVKSUlRSkqKCgoKFBkZqZycHDN28uTJmjVrlnr06KG4uDjNnj1bffv2Nd9+es8992jkyJGaMmWKXn/9dUnSM888ozFjxig1NVWSlJWVpT59+ig3N1evvPKKTp8+rdmzZ2vKlCm86RQAAAAAAAB+wavi26pVqyRJQ4YM8Wh/8803NWnSJEnSnDlz1NTUpGnTpqmurk4ZGRkqLS1VdHS0Gb98+XKFhoZqwoQJampq0rBhw1RcXKyQkBAzZsOGDZoxY4b5VtRx48apqKjI3B8SEqJt27Zp2rRpGjRokCIiIpSTk6MlS5Z4NQEAAAAAAACAVbz+2unV2Gw25efnKz8//4ox3bt318qVK7Vy5corxsTFxWn9+vUdnuuOO+7Qe++9d9U+AQAAAAAAAL7QzdcdAAAAAAAAAIIVxTcAAAAAAADAIhTfAAAAAAAAAItQfAMAAAAAAAAsQvENAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACxC8Q0AAAAAAACwCMU3AAAAAAAAwCKhvu4AEEjunLutTduXi0b7oCcAAAAAACAQsPINAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACzCCxcAH7j8xQ28tAEAAAAAgODEyjcAAAAAAADAIhTfAAAAAAAAAItQfAMAAAAAAAAsQvENAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACxC8Q0AAAAAAACwCMU3AAAAAAAAwCJeF98+/vhjjR07Vk6nUzabTb/97W899huGofz8fDmdTkVERGjIkCH6/PPPPWJcLpemT5+u+Ph4RUVFady4cTp+/LhHTF1dnXJzc+VwOORwOJSbm6szZ854xBw9elRjx45VVFSU4uPjNWPGDDU3N3s7JAAAAAAAAMASXhffzp07p/vvv19FRUXt7l+8eLGWLVumoqIi7d27V0lJSRoxYoQaGxvNmLy8PG3dulWbNm3Szp07dfbsWY0ZM0YtLS1mTE5OjqqqqlRSUqKSkhJVVVUpNzfX3N/S0qLRo0fr3Llz2rlzpzZt2qQtW7Zo1qxZ3g4J8Lk7527z+AcAAAAAAIJDqLcfGDVqlEaNGtXuPsMwtGLFCs2fP1/jx4+XJK1du1aJiYnauHGjpk6dqvr6eq1Zs0br1q3T8OHDJUnr169XcnKyPvjgA2VnZ+vgwYMqKSnRrl27lJGRIUlavXq1MjMzdejQIaWmpqq0tFQHDhzQsWPH5HQ6JUlLly7VpEmTtHDhQsXExHRqQgAAAAAAAICu4nXxrSOHDx9WTU2NsrKyzDa73a7BgwervLxcU6dOVWVlpdxut0eM0+lUWlqaysvLlZ2drYqKCjkcDrPwJkkDBgyQw+FQeXm5UlNTVVFRobS0NLPwJknZ2dlyuVyqrKzU0KFD2/TP5XLJ5XKZ2w0NDZIkt9stt9vd4dha918tLtjZQ4yuP2Y3w+NvoOnMPXG1eezK+yyY7t1gGAMAAAAA4ObSpcW3mpoaSVJiYqJHe2Jioo4cOWLGhIeHKzY2tk1M6+dramqUkJDQ5vgJCQkeMZefJzY2VuHh4WbM5QoLC7VgwYI27aWlpYqMjLyWIaqsrOya4oLV4oesO/bP+1+07uAW2r59u9efudo8duaYVxMM9+758+d93QUAAAAAALzSpcW3VjabzWPbMIw2bZe7PKa9+M7E/K158+Zp5syZ5nZDQ4OSk5OVlZV11a+put1ulZWVacSIEQoLC+swNlik5b9/Q85j72bo5/0v6qefdJPrYsf3SSDan5/dpu1qc9veZzormO7d1tWqAAAAAAAEii4tviUlJUm6tCqtZ8+eZnttba25Si0pKUnNzc2qq6vzWP1WW1urgQMHmjEnTpxoc/yTJ096HGf37t0e++vq6uR2u9usiGtlt9tlt9vbtIeFhV1zUcKb2EDnarmxhTDXRdsNP+eN0N79crVxWnGPBcO9G+j9BwAAAADcfLx+22lHevfuraSkJI+vtzU3N2vHjh1mYS09PV1hYWEeMdXV1dq/f78Zk5mZqfr6eu3Zs8eM2b17t+rr6z1i9u/fr+rqajOmtLRUdrtd6enpXTksAAAAAAAAoFO8Xvl29uxZffHFF+b24cOHVVVVpbi4ON1xxx3Ky8tTQUGBUlJSlJKSooKCAkVGRionJ0eS5HA4NHnyZM2aNUs9evRQXFycZs+erb59+5pvP73nnns0cuRITZkyRa+//rok6ZlnntGYMWOUmpoqScrKylKfPn2Um5urV155RadPn9bs2bM1ZcoU3nQKv3Ln3G2+7gIAAAAAAPARr4tvn3zyicebRFt/Q23ixIkqLi7WnDlz1NTUpGnTpqmurk4ZGRkqLS1VdHS0+Znly5crNDRUEyZMUFNTk4YNG6bi4mKFhISYMRs2bNCMGTPMt6KOGzdORUVF5v6QkBBt27ZN06ZN06BBgxQREaGcnBwtWbLE+1kAAAAAAAAALGAzDMPwdSd8paGhQQ6HQ/X19df0woXt27frscceu2l+d+pGrdiyhxha/FCL5uwJCcrffOsKXy4a3enPBtO9680zCwAAAACAP+jS33wDAAAAAAAA8A2KbwAAAAAAAIBFKL4BAAAAAAAAFqH4BgAAAAAAAFiE4hsAAAAAAABgEYpvAAAAAAAAgEUovgEAAAAAAAAWofgGAAAAAAAAWCTU1x2A/7hz7jZfdwEAAAAAACCosPINAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACzCCxeAANDeyzC+XDTaBz0BAAAAAADeYOUbAAAAAAAAYBGKbwAAAAAAAIBFKL4BAAAAAAAAFuE3325i7f2OGAAAAAAAALoOK98AAAAAAAAAi7DyDQhQl69c5O2nAAAAAAD4H4pvQJBqLc7ZQwwtfkhKy39fhxaO8XGvAAAAAAC4uVB8A24irJYDAAAAAODG4jffAAAAAAAAAItQfAMAAAAAAAAsEvBfO/3lL3+pV155RdXV1br33nu1YsUKPfLII77ult+5/OuGCD5cYwAAAAAA/E9Ar3zbvHmz8vLyNH/+fO3bt0+PPPKIRo0apaNHj/q6awAAAAAAAEBgr3xbtmyZJk+erKefflqStGLFCr3//vtatWqVCgsLfdw732IVFK4FL2AAAAAAAMBaAVt8a25uVmVlpebOnevRnpWVpfLy8nY/43K55HK5zO36+npJ0unTp+V2uzs8n9vt1vnz5/X/5r8t10WbJGn3vGHXM4QulVH4nx7bgXRhQy8aOn/+okLd3dTyf3OLruPN/J46deoG9apzGhsbJUmGYfi4JwAAAAAAXJtAqtF4+Prrr9XS0qLExESP9sTERNXU1LT7mcLCQi1YsKBNe+/evTvVh/ilnfoY2pHj6w4EuWud30C5pxsbG+VwOHzdDQAAAAAAripgi2+tbDbPlTyGYbRpazVv3jzNnDnT3L548aJOnz6tHj16XPEzrRoaGpScnKxjx44pJibm+jsOE3NrrWCaX8Mw1NjYKKfT6euuAAAAAABwTQK2+BYfH6+QkJA2q9xqa2vbrIZrZbfbZbfbPdpuvfVWr84bExMT8AUMf8XcWitY5pcVbwAAAACAQBKwbzsNDw9Xenq6ysrKPNrLyso0cOBAH/UKAAAAAAAA+EbArnyTpJkzZyo3N1f9+/dXZmam3njjDR09elTPPvusr7sGAAAAAAAABHbx7YknntCpU6f04osvqrq6Wmlpadq+fbt69erV5eey2+362c9+1uZrq7h+zK21mF8AAAAAAHzHZhiG4etOAAAAAAAAAMEoYH/zDQAAAAAAAPB3FN8AAAAAAAAAi1B8AwAAAAAAACxC8Q0AAAAAAACwCMW3a/DLX/5SvXv3Vvfu3ZWenq7/+q//8nWXAsLHH3+ssWPHyul0ymaz6be//a3HfsMwlJ+fL6fTqYiICA0ZMkSff/65R4zL5dL06dMVHx+vqKgojRs3TsePH7+Bo/BPhYWFevDBBxUdHa2EhAQ9/vjjOnTokEcM8wsAAAAAgO9RfLuKzZs3Ky8vT/Pnz9e+ffv0yCOPaNSoUTp69Kivu+b3zp07p/vvv19FRUXt7l+8eLGWLVumoqIi7d27V0lJSRoxYoQaGxvNmLy8PG3dulWbNm3Szp07dfbsWY0ZM0YtLS03ahh+aceOHXruuee0a9culZWV6cKFC8rKytK5c+fMGOYXAAAAAADfsxmGYfi6E/4sIyNDDzzwgFatWmW23XPPPXr88cdVWFjow54FFpvNpq1bt+rxxx+XdGlVltPpVF5enn7yk59IurQKKzExUS+//LKmTp2q+vp63XbbbVq3bp2eeOIJSdJXX32l5ORkbd++XdnZ2b4ajt85efKkEhIStGPHDj366KPMLwAAAAAAfoKVbx1obm5WZWWlsrKyPNqzsrJUXl7uo14Fh8OHD6umpsZjbu12uwYPHmzObWVlpdxut0eM0+lUWloa83+Z+vp6SVJcXJwk5hcAAAAAAH9B8a0DX3/9tVpaWpSYmOjRnpiYqJqaGh/1Kji0zl9Hc1tTU6Pw8HDFxsZeMQaXVhHOnDlTDz/8sNLS0iQxvwAAAAAA+ItQX3cgENhsNo9twzDatKFzOjO3zL+n559/Xp9++ql27tzZZh/zCwAAAACAb7HyrQPx8fEKCQlpswqotra2zYoieCcpKUmSOpzbpKQkNTc3q66u7ooxN7vp06fr3Xff1Ycffqjbb7/dbGd+AQAAAADwDxTfOhAeHq709HSVlZV5tJeVlWngwIE+6lVw6N27t5KSkjzmtrm5WTt27DDnNj09XWFhYR4x1dXV2r9//00//4Zh6Pnnn9fbb7+tP/zhD+rdu7fHfuYXAAAAAAD/wNdOr2LmzJnKzc1V//79lZmZqTfeeENHjx7Vs88+6+uu+b2zZ8/qiy++MLcPHz6sqqoqxcXF6Y477lBeXp4KCgqUkpKilJQUFRQUKDIyUjk5OZIkh8OhyZMna9asWerRo4fi4uI0e/Zs9e3bV8OHD/fVsPzCc889p40bN+qdd95RdHS0ucLN4XAoIiJCNpuN+QUAAAAAwA9QfLuKJ554QqdOndKLL76o6upqpaWlafv27erVq5evu+b3PvnkEw0dOtTcnjlzpiRp4sSJKi4u1pw5c9TU1KRp06aprq5OGRkZKi0tVXR0tPmZ5cuXKzQ0VBMmTFBTU5OGDRum4uJihYSE3PDx+JNVq1ZJkoYMGeLR/uabb2rSpEmSxPwCAAAAAOAHbIZhGL7uBAAAAAAAABCM+M03AAAAAAAAwCIU3wAAAAAAAACLUHwDAAAAAAAALELxDQAAAAAAALAIxTcAAAAAAADAIhTfAAAAAAAAAItQfAMAAAAAAAAsQvENAAAAAAAAsAjFNwAAAAAAAMAiFN8AAAAAAAAAi1B8AwAAAAAAACxC8Q0AAAAAAACwyP8HEj439t3WQH8AAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"sb.barplot(data=df_features,x='key',y='popularity')","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:45:57.564291Z","iopub.execute_input":"2023-04-18T13:45:57.564727Z","iopub.status.idle":"2023-04-18T13:46:04.804229Z","shell.execute_reply.started":"2023-04-18T13:45:57.564663Z","shell.execute_reply":"2023-04-18T13:46:04.800610Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='key', ylabel='popularity'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"sb.boxplot(data=df_features,x='key',y='popularity')","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:04.806416Z","iopub.execute_input":"2023-04-18T13:46:04.808034Z","iopub.status.idle":"2023-04-18T13:46:05.551513Z","shell.execute_reply.started":"2023-04-18T13:46:04.807955Z","shell.execute_reply":"2023-04-18T13:46:05.550217Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='key', ylabel='popularity'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"sb.lineplot(data=df_features,x='valence',y='popularity')","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:05.553526Z","iopub.execute_input":"2023-04-18T13:46:05.554096Z","iopub.status.idle":"2023-04-18T13:46:55.324784Z","shell.execute_reply.started":"2023-04-18T13:46:05.554027Z","shell.execute_reply":"2023-04-18T13:46:55.323413Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<AxesSubplot:xlabel='valence', ylabel='popularity'>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"## Modeling using important numerical features only:","metadata":{}},{"cell_type":"code","source":"sqrt_pipeline = make_pipeline(\nFunctionTransformer(np.sqrt),\nStandardScaler())\n\ndefault_pipeline = make_pipeline(StandardScaler())\n\npreprocessing = ColumnTransformer([\n    (\"sqrt\",sqrt_pipeline,[\"duration_ms\",\"speechiness\",\"acousticness\",\n                        \"instrumentalness\",\"liveness\",\"tempo\"])\n],\nremainder = default_pipeline )","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:55.326340Z","iopub.execute_input":"2023-04-18T13:46:55.326743Z","iopub.status.idle":"2023-04-18T13:46:55.333850Z","shell.execute_reply.started":"2023-04-18T13:46:55.326689Z","shell.execute_reply":"2023-04-18T13:46:55.332370Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"train_set, test_set = train_test_split(df_features,test_size=0.2,random_state=69)\nprint(\"Train:\",len(train_set),\"\",\"Test:\",len(test_set))","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:55.335746Z","iopub.execute_input":"2023-04-18T13:46:55.336217Z","iopub.status.idle":"2023-04-18T13:46:55.505735Z","shell.execute_reply.started":"2023-04-18T13:46:55.336165Z","shell.execute_reply":"2023-04-18T13:46:55.504176Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"Train: 469337  Test: 117335\n","output_type":"stream"}]},{"cell_type":"code","source":"tracks = train_set.drop(\"popularity\",axis=1)\ntracks_labels = train_set[\"popularity\"].copy()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:55.508703Z","iopub.execute_input":"2023-04-18T13:46:55.509158Z","iopub.status.idle":"2023-04-18T13:46:55.545511Z","shell.execute_reply.started":"2023-04-18T13:46:55.509117Z","shell.execute_reply":"2023-04-18T13:46:55.543640Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"lin_model = make_pipeline(preprocessing,LinearRegression())\nlin_model.fit(tracks,tracks_labels)\n\nlin_predictions = lin_model.predict(tracks)\nlin_rmse = mean_squared_error(tracks_labels,lin_predictions,squared=False)\nlin_rmse","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:55.547721Z","iopub.execute_input":"2023-04-18T13:46:55.548338Z","iopub.status.idle":"2023-04-18T13:46:56.239422Z","shell.execute_reply.started":"2023-04-18T13:46:55.548263Z","shell.execute_reply":"2023-04-18T13:46:56.237452Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"16.385463538078607"},"metadata":{}}]},{"cell_type":"code","source":"d_tree_model = make_pipeline(preprocessing, DecisionTreeRegressor(random_state=69))\nd_tree_model.fit(tracks,tracks_labels)\n\nd_tree_predictions = d_tree_model.predict(tracks)\nd_tree_rmse = mean_squared_error(tracks_labels,d_tree_predictions,squared=False)\nd_tree_rmse","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:46:56.242701Z","iopub.execute_input":"2023-04-18T13:46:56.244068Z","iopub.status.idle":"2023-04-18T13:47:09.620930Z","shell.execute_reply.started":"2023-04-18T13:46:56.243981Z","shell.execute_reply":"2023-04-18T13:47:09.619463Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"1.8463565076764248"},"metadata":{}}]},{"cell_type":"code","source":"r_forest_model = make_pipeline(preprocessing, RandomForestRegressor(random_state=69))\nr_forest_model.fit(tracks,tracks_labels)\n\nr_forest_predictions = r_forest_model.predict(tracks)\nr_forest_rmse = mean_squared_error(tracks_labels,r_forest_predictions,squared=False)\nr_forest_rmse","metadata":{"execution":{"iopub.status.busy":"2023-04-18T13:47:09.623094Z","iopub.execute_input":"2023-04-18T13:47:09.623496Z","iopub.status.idle":"2023-04-18T14:00:48.038437Z","shell.execute_reply.started":"2023-04-18T13:47:09.623456Z","shell.execute_reply":"2023-04-18T14:00:48.036907Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"5.619510641098086"},"metadata":{}}]},{"cell_type":"code","source":"line_rmses = -cross_val_score(lin_model,tracks,tracks_labels,\n                              scoring=\"neg_root_mean_squared_error\", cv=10)\nd_tree_rmses = -cross_val_score(d_tree_model,tracks,tracks_labels,\n                              scoring=\"neg_root_mean_squared_error\", cv=10)\nr_forest_rmses = -cross_val_score(r_forest_model,tracks,tracks_labels,\n                              scoring=\"neg_root_mean_squared_error\", cv=10)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T14:00:48.041090Z","iopub.execute_input":"2023-04-18T14:00:48.041891Z","iopub.status.idle":"2023-04-18T15:53:29.831508Z","shell.execute_reply.started":"2023-04-18T14:00:48.041840Z","shell.execute_reply":"2023-04-18T15:53:29.829950Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"pd.Series(line_rmses).describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T15:53:29.833915Z","iopub.execute_input":"2023-04-18T15:53:29.834379Z","iopub.status.idle":"2023-04-18T15:53:29.852480Z","shell.execute_reply.started":"2023-04-18T15:53:29.834337Z","shell.execute_reply":"2023-04-18T15:53:29.850790Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"count    10.000000\nmean     16.385725\nstd       0.067249\nmin      16.308421\n25%      16.328064\n50%      16.386118\n75%      16.421381\nmax      16.508147\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"pd.Series(d_tree_rmses).describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T15:53:29.854737Z","iopub.execute_input":"2023-04-18T15:53:29.856218Z","iopub.status.idle":"2023-04-18T15:53:29.868970Z","shell.execute_reply.started":"2023-04-18T15:53:29.856169Z","shell.execute_reply":"2023-04-18T15:53:29.867797Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"count    10.000000\nmean     20.550380\nstd       0.058405\nmin      20.449696\n25%      20.530232\n50%      20.564158\n75%      20.591014\nmax      20.621459\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"pd.Series(r_forest_rmses).describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T15:53:29.871499Z","iopub.execute_input":"2023-04-18T15:53:29.871987Z","iopub.status.idle":"2023-04-18T15:53:29.893079Z","shell.execute_reply.started":"2023-04-18T15:53:29.871943Z","shell.execute_reply":"2023-04-18T15:53:29.891778Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"count    10.000000\nmean     14.490471\nstd       0.059330\nmin      14.415643\n25%      14.446266\n50%      14.475503\n75%      14.538490\nmax      14.593188\ndtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"r_forest_model.n_features_in_","metadata":{"execution":{"iopub.status.busy":"2023-04-18T15:53:29.896089Z","iopub.execute_input":"2023-04-18T15:53:29.896803Z","iopub.status.idle":"2023-04-18T15:53:29.910572Z","shell.execute_reply.started":"2023-04-18T15:53:29.896741Z","shell.execute_reply":"2023-04-18T15:53:29.909259Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"12"},"metadata":{}}]},{"cell_type":"code","source":"full_pipeline = Pipeline([\n    (\"preprocessing\",preprocessing),\n    (\"random_forest\",RandomForestRegressor(random_state=69))\n])\n\nparam_grid = [{\"random_forest__max_features\":[1,2,3,4,5,8,12]}]\n\ngrid_search = GridSearchCV(full_pipeline,param_grid,cv=3,\n                          scoring=\"neg_root_mean_squared_error\")\n\ngrid_search.fit(tracks,tracks_labels)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T15:53:29.912943Z","iopub.execute_input":"2023-04-18T15:53:29.913589Z","iopub.status.idle":"2023-04-18T17:15:45.117588Z","shell.execute_reply.started":"2023-04-18T15:53:29.913530Z","shell.execute_reply":"2023-04-18T17:15:45.115830Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=3,\n             estimator=Pipeline(steps=[('preprocessing',\n                                        ColumnTransformer(remainder=Pipeline(steps=[('standardscaler',\n                                                                                     StandardScaler())]),\n                                                          transformers=[('sqrt',\n                                                                         Pipeline(steps=[('functiontransformer',\n                                                                                          FunctionTransformer(func=<ufunc 'sqrt'>)),\n                                                                                         ('standardscaler',\n                                                                                          StandardScaler())]),\n                                                                         ['duration_ms',\n                                                                          'speechiness',\n                                                                          'acousticness',\n                                                                          'instrumentalness',\n                                                                          'liveness',\n                                                                          'tempo'])])),\n                                       ('random_forest',\n                                        RandomForestRegressor(random_state=69))]),\n             param_grid=[{'random_forest__max_features': [1, 2, 3, 4, 5, 8,\n                                                          12]}],\n             scoring='neg_root_mean_squared_error')"},"metadata":{}}]},{"cell_type":"code","source":"df_res = pd.DataFrame(grid_search.cv_results_)\ndf_res.sort_values(by=\"mean_test_score\",ascending=False,inplace=True)\ndf_res","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:15:45.120077Z","iopub.execute_input":"2023-04-18T17:15:45.120593Z","iopub.status.idle":"2023-04-18T17:15:45.157662Z","shell.execute_reply.started":"2023-04-18T17:15:45.120544Z","shell.execute_reply":"2023-04-18T17:15:45.155630Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n3     173.131535      1.179906        12.088613        0.098630   \n2     137.504829      2.934283        12.074523        0.185188   \n4     209.666584      0.845817        12.333634        0.160952   \n5     321.561779      2.970904        12.378198        0.416038   \n1      99.362861      1.130771        12.348429        0.266969   \n6     462.553119      4.047779        12.092342        0.113411   \n0      61.614437      0.200493        12.051172        0.246589   \n\n  param_random_forest__max_features                               params  \\\n3                                 4   {'random_forest__max_features': 4}   \n2                                 3   {'random_forest__max_features': 3}   \n4                                 5   {'random_forest__max_features': 5}   \n5                                 8   {'random_forest__max_features': 8}   \n1                                 2   {'random_forest__max_features': 2}   \n6                                12  {'random_forest__max_features': 12}   \n0                                 1   {'random_forest__max_features': 1}   \n\n   split0_test_score  split1_test_score  split2_test_score  mean_test_score  \\\n3         -14.477408         -14.549281         -14.561320       -14.529336   \n2         -14.483575         -14.555302         -14.560202       -14.533027   \n4         -14.479864         -14.551939         -14.571757       -14.534520   \n5         -14.494804         -14.563047         -14.573873       -14.543908   \n1         -14.512246         -14.584863         -14.589628       -14.562246   \n6         -14.540372         -14.602703         -14.625480       -14.589518   \n0         -14.628051         -14.692222         -14.703872       -14.674715   \n\n   std_test_score  rank_test_score  \n3        0.037046                1  \n2        0.035024                2  \n4        0.039486                3  \n5        0.035002                4  \n1        0.035409                5  \n6        0.035974                6  \n0        0.033337                7  ","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>mean_fit_time</th>\n      <th>std_fit_time</th>\n      <th>mean_score_time</th>\n      <th>std_score_time</th>\n      <th>param_random_forest__max_features</th>\n      <th>params</th>\n      <th>split0_test_score</th>\n      <th>split1_test_score</th>\n      <th>split2_test_score</th>\n      <th>mean_test_score</th>\n      <th>std_test_score</th>\n      <th>rank_test_score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>173.131535</td>\n      <td>1.179906</td>\n      <td>12.088613</td>\n      <td>0.098630</td>\n      <td>4</td>\n      <td>{'random_forest__max_features': 4}</td>\n      <td>-14.477408</td>\n      <td>-14.549281</td>\n      <td>-14.561320</td>\n      <td>-14.529336</td>\n      <td>0.037046</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>137.504829</td>\n      <td>2.934283</td>\n      <td>12.074523</td>\n      <td>0.185188</td>\n      <td>3</td>\n      <td>{'random_forest__max_features': 3}</td>\n      <td>-14.483575</td>\n      <td>-14.555302</td>\n      <td>-14.560202</td>\n      <td>-14.533027</td>\n      <td>0.035024</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>209.666584</td>\n      <td>0.845817</td>\n      <td>12.333634</td>\n      <td>0.160952</td>\n      <td>5</td>\n      <td>{'random_forest__max_features': 5}</td>\n      <td>-14.479864</td>\n      <td>-14.551939</td>\n      <td>-14.571757</td>\n      <td>-14.534520</td>\n      <td>0.039486</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>321.561779</td>\n      <td>2.970904</td>\n      <td>12.378198</td>\n      <td>0.416038</td>\n      <td>8</td>\n      <td>{'random_forest__max_features': 8}</td>\n      <td>-14.494804</td>\n      <td>-14.563047</td>\n      <td>-14.573873</td>\n      <td>-14.543908</td>\n      <td>0.035002</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>99.362861</td>\n      <td>1.130771</td>\n      <td>12.348429</td>\n      <td>0.266969</td>\n      <td>2</td>\n      <td>{'random_forest__max_features': 2}</td>\n      <td>-14.512246</td>\n      <td>-14.584863</td>\n      <td>-14.589628</td>\n      <td>-14.562246</td>\n      <td>0.035409</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>462.553119</td>\n      <td>4.047779</td>\n      <td>12.092342</td>\n      <td>0.113411</td>\n      <td>12</td>\n      <td>{'random_forest__max_features': 12}</td>\n      <td>-14.540372</td>\n      <td>-14.602703</td>\n      <td>-14.625480</td>\n      <td>-14.589518</td>\n      <td>0.035974</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>61.614437</td>\n      <td>0.200493</td>\n      <td>12.051172</td>\n      <td>0.246589</td>\n      <td>1</td>\n      <td>{'random_forest__max_features': 1}</td>\n      <td>-14.628051</td>\n      <td>-14.692222</td>\n      <td>-14.703872</td>\n      <td>-14.674715</td>\n      <td>0.033337</td>\n      <td>7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"tracks_model = grid_search.best_estimator_\nfeature_importances = tracks_model['random_forest'].feature_importances_\nfeature_importances\n#sorted(zip(feature_importances,\n#           tracks_model[\"preprocessing\"].get_feature_names_out()),\n#      reverse=True)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:15:45.160012Z","iopub.execute_input":"2023-04-18T17:15:45.160556Z","iopub.status.idle":"2023-04-18T17:15:46.072709Z","shell.execute_reply.started":"2023-04-18T17:15:45.160505Z","shell.execute_reply":"2023-04-18T17:15:46.071215Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"array([0.09730521, 0.08009154, 0.17748336, 0.07547406, 0.06655683,\n       0.07149969, 0.08096146, 0.08966291, 0.03432453, 0.13515518,\n       0.00835668, 0.08312855])"},"metadata":{}}]},{"cell_type":"code","source":"in_test = test_set.drop(\"popularity\",axis=1)\nout_test = test_set[\"popularity\"].copy()\n\nfinal_predictions = tracks_model.predict(in_test)\n\nfinal_rmse = mean_squared_error(out_test,final_predictions,squared=False)\n\nprint(final_rmse)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:15:46.085106Z","iopub.execute_input":"2023-04-18T17:15:46.085596Z","iopub.status.idle":"2023-04-18T17:15:56.593544Z","shell.execute_reply.started":"2023-04-18T17:15:46.085554Z","shell.execute_reply":"2023-04-18T17:15:56.591655Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"14.421552566568895\n","output_type":"stream"}]},{"cell_type":"code","source":"confidence = 0.95\n\nsquared_errors = (final_predictions - out_test) ** 2\nrange_value = np.sqrt(stats.t.interval(confidence,len(squared_errors)-1,\n       loc = squared_errors.mean(),\n       scale = stats.sem(squared_errors)))\n\nrange_delta = range_value[1] - range_value[0]  \nprint(\"From:\",range_value[0],\"to\",range_value[1],\"with +-\",range_delta)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:15:56.595289Z","iopub.execute_input":"2023-04-18T17:15:56.595848Z","iopub.status.idle":"2023-04-18T17:15:56.615415Z","shell.execute_reply.started":"2023-04-18T17:15:56.595801Z","shell.execute_reply":"2023-04-18T17:15:56.613306Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"From: 14.358741285976754 to 14.484091464189474 with +- 0.1253501782127202\n","output_type":"stream"}]},{"cell_type":"code","source":"#joblib.dump(tracks_model,\"Spotify_tracks_model.pkl\")","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:15:56.617252Z","iopub.execute_input":"2023-04-18T17:15:56.617702Z","iopub.status.idle":"2023-04-18T17:16:19.283108Z","shell.execute_reply.started":"2023-04-18T17:15:56.617638Z","shell.execute_reply":"2023-04-18T17:16:19.281533Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"['Spotify_tracks_model.pkl']"},"metadata":{}}]},{"cell_type":"code","source":"bad_tracks = pd.read_csv(\"/kaggle/input/bad-bonny-songs/bad_bonny_tracks2.csv\")\nbad_tracks","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:43:26.863891Z","iopub.execute_input":"2023-04-18T19:43:26.865460Z","iopub.status.idle":"2023-04-18T19:43:26.899508Z","shell.execute_reply.started":"2023-04-18T19:43:26.865401Z","shell.execute_reply":"2023-04-18T19:43:26.898111Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"   popularity  duration_ms  danceability  energy  key  loudness  mode  \\\n0          77       172127         0.860   0.758   11    -5.160     1   \n1          82       230400         0.564   0.903    5    -3.221     1   \n2          92       178567         0.911   0.712    1    -5.105     0   \n3          90       243717         0.650   0.715    5    -5.198     0   \n4          78       250534         0.610   0.624    2    -4.773     1   \n5          75       300579         0.655   0.725    0    -5.497     1   \n6          76       201816         0.629   0.698    8    -4.485     1   \n7          79       295177         0.607   0.829    2    -4.074     1   \n8          80       148143         0.503   0.496    5    -9.123     0   \n\n   speechiness  acousticness  instrumentalness  liveness  valence    tempo  \\\n0       0.0695        0.0210          0.000065    0.3440    0.453   97.014   \n1       0.3070        0.3560          0.000000    0.1750    0.607   78.293   \n2       0.0817        0.0901          0.000027    0.0933    0.425   92.005   \n3       0.2530        0.0993          0.000291    0.1260    0.187  106.672   \n4       0.3090        0.6000          0.000002    0.2430    0.244  176.169   \n5       0.1880        0.0327          0.002640    0.0611    0.326  125.800   \n6       0.3080        0.0509          0.001660    0.0909    0.599  117.765   \n7       0.3790        0.0103          0.000000    0.1070    0.685   96.172   \n8       0.0556        0.6930          0.000000    0.1230    0.412  187.844   \n\n         artists  \n0  ['Bad Bunny']  \n1  ['Bad Bunny']  \n2  ['Bad Bunny']  \n3  ['Bad Bunny']  \n4  ['Bad Bunny']  \n5  ['Bad Bunny']  \n6  ['Bad Bunny']  \n7  ['Bad Bunny']  \n8  ['Bad Bunny']  ","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>popularity</th>\n      <th>duration_ms</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n      <th>artists</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>77</td>\n      <td>172127</td>\n      <td>0.860</td>\n      <td>0.758</td>\n      <td>11</td>\n      <td>-5.160</td>\n      <td>1</td>\n      <td>0.0695</td>\n      <td>0.0210</td>\n      <td>0.000065</td>\n      <td>0.3440</td>\n      <td>0.453</td>\n      <td>97.014</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>82</td>\n      <td>230400</td>\n      <td>0.564</td>\n      <td>0.903</td>\n      <td>5</td>\n      <td>-3.221</td>\n      <td>1</td>\n      <td>0.3070</td>\n      <td>0.3560</td>\n      <td>0.000000</td>\n      <td>0.1750</td>\n      <td>0.607</td>\n      <td>78.293</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>92</td>\n      <td>178567</td>\n      <td>0.911</td>\n      <td>0.712</td>\n      <td>1</td>\n      <td>-5.105</td>\n      <td>0</td>\n      <td>0.0817</td>\n      <td>0.0901</td>\n      <td>0.000027</td>\n      <td>0.0933</td>\n      <td>0.425</td>\n      <td>92.005</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>90</td>\n      <td>243717</td>\n      <td>0.650</td>\n      <td>0.715</td>\n      <td>5</td>\n      <td>-5.198</td>\n      <td>0</td>\n      <td>0.2530</td>\n      <td>0.0993</td>\n      <td>0.000291</td>\n      <td>0.1260</td>\n      <td>0.187</td>\n      <td>106.672</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>78</td>\n      <td>250534</td>\n      <td>0.610</td>\n      <td>0.624</td>\n      <td>2</td>\n      <td>-4.773</td>\n      <td>1</td>\n      <td>0.3090</td>\n      <td>0.6000</td>\n      <td>0.000002</td>\n      <td>0.2430</td>\n      <td>0.244</td>\n      <td>176.169</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>75</td>\n      <td>300579</td>\n      <td>0.655</td>\n      <td>0.725</td>\n      <td>0</td>\n      <td>-5.497</td>\n      <td>1</td>\n      <td>0.1880</td>\n      <td>0.0327</td>\n      <td>0.002640</td>\n      <td>0.0611</td>\n      <td>0.326</td>\n      <td>125.800</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>76</td>\n      <td>201816</td>\n      <td>0.629</td>\n      <td>0.698</td>\n      <td>8</td>\n      <td>-4.485</td>\n      <td>1</td>\n      <td>0.3080</td>\n      <td>0.0509</td>\n      <td>0.001660</td>\n      <td>0.0909</td>\n      <td>0.599</td>\n      <td>117.765</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>79</td>\n      <td>295177</td>\n      <td>0.607</td>\n      <td>0.829</td>\n      <td>2</td>\n      <td>-4.074</td>\n      <td>1</td>\n      <td>0.3790</td>\n      <td>0.0103</td>\n      <td>0.000000</td>\n      <td>0.1070</td>\n      <td>0.685</td>\n      <td>96.172</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>80</td>\n      <td>148143</td>\n      <td>0.503</td>\n      <td>0.496</td>\n      <td>5</td>\n      <td>-9.123</td>\n      <td>0</td>\n      <td>0.0556</td>\n      <td>0.6930</td>\n      <td>0.000000</td>\n      <td>0.1230</td>\n      <td>0.412</td>\n      <td>187.844</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"bad_features = bad_tracks.drop([\"popularity\",\"artists\"],axis=1)\nbad_labels = bad_tracks[\"popularity\"].copy()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:43:34.486647Z","iopub.execute_input":"2023-04-18T19:43:34.487664Z","iopub.status.idle":"2023-04-18T19:43:34.495971Z","shell.execute_reply.started":"2023-04-18T19:43:34.487604Z","shell.execute_reply":"2023-04-18T19:43:34.494352Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"bad_predictions = tracks_model.predict(bad_features)\n\nbad_rmse = mean_squared_error(bad_labels,bad_predictions,squared=False)\n\nprint(bad_rmse)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:43:37.489058Z","iopub.execute_input":"2023-04-18T19:43:37.489553Z","iopub.status.idle":"2023-04-18T19:43:37.620606Z","shell.execute_reply.started":"2023-04-18T19:43:37.489513Z","shell.execute_reply":"2023-04-18T19:43:37.619357Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"34.51516337575325\n","output_type":"stream"}]},{"cell_type":"code","source":"songs = [\n    \"Yo Perreo Sola\",\n    \"Después de la Playa\",\n    \"Me Porto Bonito\",\n    \"Tití Me Preguntó\",\n    \"Callaíta\", \n    \"La Romana\",\n    \"El Apagón\", \n    \"Safaera\",\n    \"Un Verano Sin Ti\",\n]\n\nfor i,song in enumerate(songs):\n    print(\"Prediction:\", round(bad_predictions[i],2),\n          \"\\t\\tReal Popularity:\",bad_labels.iloc[i],\"\\t\\tTrack:\",song)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:43:53.780458Z","iopub.execute_input":"2023-04-18T19:43:53.781003Z","iopub.status.idle":"2023-04-18T19:43:53.791233Z","shell.execute_reply.started":"2023-04-18T19:43:53.780959Z","shell.execute_reply":"2023-04-18T19:43:53.789509Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"Prediction: 69.22 \t\tReal Popularity: 77 \t\tTrack: Yo Perreo Sola\nPrediction: 44.77 \t\tReal Popularity: 82 \t\tTrack: Después de la Playa\nPrediction: 52.28 \t\tReal Popularity: 92 \t\tTrack: Me Porto Bonito\nPrediction: 49.18 \t\tReal Popularity: 90 \t\tTrack: Tití Me Preguntó\nPrediction: 70.91 \t\tReal Popularity: 78 \t\tTrack: Callaíta\nPrediction: 38.47 \t\tReal Popularity: 75 \t\tTrack: La Romana\nPrediction: 51.99 \t\tReal Popularity: 76 \t\tTrack: El Apagón\nPrediction: 43.54 \t\tReal Popularity: 79 \t\tTrack: Safaera\nPrediction: 26.96 \t\tReal Popularity: 80 \t\tTrack: Un Verano Sin Ti\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Modeling taking into consideration the artist (Target Encode)","metadata":{}},{"cell_type":"code","source":"df_features2 = df_features\ndf_features2[\"artists\"] = df[\"artists\"].copy()\ndf_features2.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:37:16.033966Z","iopub.execute_input":"2023-04-18T17:37:16.034718Z","iopub.status.idle":"2023-04-18T17:37:16.174814Z","shell.execute_reply.started":"2023-04-18T17:37:16.034648Z","shell.execute_reply":"2023-04-18T17:37:16.173175Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \n","output_type":"stream"},{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"   popularity  duration_ms  danceability  energy  key  loudness  mode  \\\n0           6       126903         0.645  0.4450    0   -13.338     1   \n1           0        98200         0.695  0.2630    0   -22.136     1   \n2           0       181640         0.434  0.1770    1   -21.180     1   \n3           0       176907         0.321  0.0946    7   -27.961     1   \n4           0       163080         0.402  0.1580    3   -16.900     0   \n\n   speechiness  acousticness  instrumentalness  liveness  valence    tempo  \\\n0       0.4510         0.674            0.7440     0.151    0.127  104.851   \n1       0.9570         0.797            0.0000     0.148    0.655  102.009   \n2       0.0512         0.994            0.0218     0.212    0.457  130.418   \n3       0.0504         0.995            0.9180     0.104    0.397  169.980   \n4       0.0390         0.989            0.1300     0.311    0.196  103.220   \n\n               artists  \n0              ['Uli']  \n1  ['Fernando Pessoa']  \n2  ['Ignacio Corsini']  \n3  ['Ignacio Corsini']  \n4      ['Dick Haymes']  ","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>popularity</th>\n      <th>duration_ms</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n      <th>artists</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>126903</td>\n      <td>0.645</td>\n      <td>0.4450</td>\n      <td>0</td>\n      <td>-13.338</td>\n      <td>1</td>\n      <td>0.4510</td>\n      <td>0.674</td>\n      <td>0.7440</td>\n      <td>0.151</td>\n      <td>0.127</td>\n      <td>104.851</td>\n      <td>['Uli']</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>98200</td>\n      <td>0.695</td>\n      <td>0.2630</td>\n      <td>0</td>\n      <td>-22.136</td>\n      <td>1</td>\n      <td>0.9570</td>\n      <td>0.797</td>\n      <td>0.0000</td>\n      <td>0.148</td>\n      <td>0.655</td>\n      <td>102.009</td>\n      <td>['Fernando Pessoa']</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>181640</td>\n      <td>0.434</td>\n      <td>0.1770</td>\n      <td>1</td>\n      <td>-21.180</td>\n      <td>1</td>\n      <td>0.0512</td>\n      <td>0.994</td>\n      <td>0.0218</td>\n      <td>0.212</td>\n      <td>0.457</td>\n      <td>130.418</td>\n      <td>['Ignacio Corsini']</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>176907</td>\n      <td>0.321</td>\n      <td>0.0946</td>\n      <td>7</td>\n      <td>-27.961</td>\n      <td>1</td>\n      <td>0.0504</td>\n      <td>0.995</td>\n      <td>0.9180</td>\n      <td>0.104</td>\n      <td>0.397</td>\n      <td>169.980</td>\n      <td>['Ignacio Corsini']</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>163080</td>\n      <td>0.402</td>\n      <td>0.1580</td>\n      <td>3</td>\n      <td>-16.900</td>\n      <td>0</td>\n      <td>0.0390</td>\n      <td>0.989</td>\n      <td>0.1300</td>\n      <td>0.311</td>\n      <td>0.196</td>\n      <td>103.220</td>\n      <td>['Dick Haymes']</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"cat_pipeline = make_pipeline(TargetEncoder(),StandardScaler())\n\npreprocessing2 = ColumnTransformer([\n    (\"sqrt\",sqrt_pipeline,[\"duration_ms\",\"speechiness\",\"acousticness\",\n                        \"instrumentalness\",\"liveness\",\"tempo\"]),\n    (\"cat\",cat_pipeline,[\"artists\"])\n],\nremainder = default_pipeline )","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:37:24.829213Z","iopub.execute_input":"2023-04-18T17:37:24.829742Z","iopub.status.idle":"2023-04-18T17:37:24.837610Z","shell.execute_reply.started":"2023-04-18T17:37:24.829701Z","shell.execute_reply":"2023-04-18T17:37:24.836494Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"train_set2, test_set2 = train_test_split(df_features2,test_size=0.2,random_state=69)\n\ntracks2 = train_set2.drop(\"popularity\",axis=1)\ntracks_labels = train_set2[\"popularity\"].copy()","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:37:29.059220Z","iopub.execute_input":"2023-04-18T17:37:29.059736Z","iopub.status.idle":"2023-04-18T17:37:29.595514Z","shell.execute_reply.started":"2023-04-18T17:37:29.059692Z","shell.execute_reply":"2023-04-18T17:37:29.594006Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"full_pipeline2 = Pipeline([\n    (\"preprocessing2\",preprocessing2),\n    (\"random_forest2\",RandomForestRegressor(random_state=69))\n])\n\nparam_grid2 = [{\"random_forest2__max_features\":[1,2,3,4,5,8,12]}]\n\ngrid_search2 = GridSearchCV(full_pipeline2,param_grid2,cv=3,\n                          scoring=\"neg_root_mean_squared_error\")\n\ngrid_search2.fit(tracks2,tracks_labels)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T17:37:33.185908Z","iopub.execute_input":"2023-04-18T17:37:33.186440Z","iopub.status.idle":"2023-04-18T19:08:52.365446Z","shell.execute_reply.started":"2023-04-18T17:37:33.186393Z","shell.execute_reply":"2023-04-18T19:08:52.363622Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"GridSearchCV(cv=3,\n             estimator=Pipeline(steps=[('preprocessing2',\n                                        ColumnTransformer(remainder=Pipeline(steps=[('standardscaler',\n                                                                                     StandardScaler())]),\n                                                          transformers=[('sqrt',\n                                                                         Pipeline(steps=[('functiontransformer',\n                                                                                          FunctionTransformer(func=<ufunc 'sqrt'>)),\n                                                                                         ('standardscaler',\n                                                                                          StandardScaler())]),\n                                                                         ['duration_ms',\n                                                                          'speechiness',\n                                                                          'acousticness',\n                                                                          'instrumentalness',\n                                                                          'liveness',\n                                                                          'tempo']),\n                                                                        ('cat',\n                                                                         Pipeline(steps=[('targetencoder',\n                                                                                          TargetEncoder()),\n                                                                                         ('standardscaler',\n                                                                                          StandardScaler())]),\n                                                                         ['artists'])])),\n                                       ('random_forest2',\n                                        RandomForestRegressor(random_state=69))]),\n             param_grid=[{'random_forest2__max_features': [1, 2, 3, 4, 5, 8,\n                                                           12]}],\n             scoring='neg_root_mean_squared_error')"},"metadata":{}}]},{"cell_type":"code","source":"tracks_model2 = grid_search2.best_estimator_\nfeature_importances2 = tracks_model2['random_forest2'].feature_importances_\nfeature_importances2","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:24:50.691406Z","iopub.execute_input":"2023-04-18T19:24:50.692005Z","iopub.status.idle":"2023-04-18T19:24:51.477628Z","shell.execute_reply.started":"2023-04-18T19:24:50.691961Z","shell.execute_reply":"2023-04-18T19:24:51.476241Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"array([0.03172808, 0.0300783 , 0.04925651, 0.02316393, 0.02582781,\n       0.02648526, 0.67062571, 0.03052651, 0.02610614, 0.01233772,\n       0.04163691, 0.00271731, 0.02950982])"},"metadata":{}}]},{"cell_type":"code","source":"in_test2 = test_set2.drop(\"popularity\",axis=1)\nout_test2 = test_set2[\"popularity\"].copy()\n\nfinal_predictions2 = tracks_model2.predict(in_test2)\n\nfinal_rmse2 = mean_squared_error(out_test2,final_predictions2,squared=False)\n\nprint(final_rmse2)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:24:58.678610Z","iopub.execute_input":"2023-04-18T19:24:58.679134Z","iopub.status.idle":"2023-04-18T19:25:08.446093Z","shell.execute_reply.started":"2023-04-18T19:24:58.679090Z","shell.execute_reply":"2023-04-18T19:25:08.444631Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"12.4124053952714\n","output_type":"stream"}]},{"cell_type":"code","source":"squared_errors2 = (final_predictions2 - out_test2) ** 2\nrange_value2 = np.sqrt(stats.t.interval(confidence,len(squared_errors2)-1,\n       loc = squared_errors2.mean(),\n       scale = stats.sem(squared_errors2)))\n\nrange_delta2 = range_value2[1] - range_value2[0]  \nprint(\"From:\",range_value2[0],\"to\",range_value2[1],\"with +-\",range_delta2)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:25:08.449192Z","iopub.execute_input":"2023-04-18T19:25:08.449787Z","iopub.status.idle":"2023-04-18T19:25:08.466224Z","shell.execute_reply.started":"2023-04-18T19:25:08.449730Z","shell.execute_reply":"2023-04-18T19:25:08.464704Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"From: 12.344485301679864 to 12.479955850472184 with +- 0.1354705487923198\n","output_type":"stream"}]},{"cell_type":"code","source":"bad_features2 = bad_tracks.drop(\"popularity\",axis=1)\nbad_labels2 = bad_tracks[\"popularity\"].copy()\nbad_features2","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:46:51.628976Z","iopub.execute_input":"2023-04-18T19:46:51.629485Z","iopub.status.idle":"2023-04-18T19:46:51.657481Z","shell.execute_reply.started":"2023-04-18T19:46:51.629447Z","shell.execute_reply":"2023-04-18T19:46:51.656172Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"   duration_ms  danceability  energy  key  loudness  mode  speechiness  \\\n0       172127         0.860   0.758   11    -5.160     1       0.0695   \n1       230400         0.564   0.903    5    -3.221     1       0.3070   \n2       178567         0.911   0.712    1    -5.105     0       0.0817   \n3       243717         0.650   0.715    5    -5.198     0       0.2530   \n4       250534         0.610   0.624    2    -4.773     1       0.3090   \n5       300579         0.655   0.725    0    -5.497     1       0.1880   \n6       201816         0.629   0.698    8    -4.485     1       0.3080   \n7       295177         0.607   0.829    2    -4.074     1       0.3790   \n8       148143         0.503   0.496    5    -9.123     0       0.0556   \n\n   acousticness  instrumentalness  liveness  valence    tempo        artists  \n0        0.0210          0.000065    0.3440    0.453   97.014  ['Bad Bunny']  \n1        0.3560          0.000000    0.1750    0.607   78.293  ['Bad Bunny']  \n2        0.0901          0.000027    0.0933    0.425   92.005  ['Bad Bunny']  \n3        0.0993          0.000291    0.1260    0.187  106.672  ['Bad Bunny']  \n4        0.6000          0.000002    0.2430    0.244  176.169  ['Bad Bunny']  \n5        0.0327          0.002640    0.0611    0.326  125.800  ['Bad Bunny']  \n6        0.0509          0.001660    0.0909    0.599  117.765  ['Bad Bunny']  \n7        0.0103          0.000000    0.1070    0.685   96.172  ['Bad Bunny']  \n8        0.6930          0.000000    0.1230    0.412  187.844  ['Bad Bunny']  ","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>duration_ms</th>\n      <th>danceability</th>\n      <th>energy</th>\n      <th>key</th>\n      <th>loudness</th>\n      <th>mode</th>\n      <th>speechiness</th>\n      <th>acousticness</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>valence</th>\n      <th>tempo</th>\n      <th>artists</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>172127</td>\n      <td>0.860</td>\n      <td>0.758</td>\n      <td>11</td>\n      <td>-5.160</td>\n      <td>1</td>\n      <td>0.0695</td>\n      <td>0.0210</td>\n      <td>0.000065</td>\n      <td>0.3440</td>\n      <td>0.453</td>\n      <td>97.014</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>230400</td>\n      <td>0.564</td>\n      <td>0.903</td>\n      <td>5</td>\n      <td>-3.221</td>\n      <td>1</td>\n      <td>0.3070</td>\n      <td>0.3560</td>\n      <td>0.000000</td>\n      <td>0.1750</td>\n      <td>0.607</td>\n      <td>78.293</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>178567</td>\n      <td>0.911</td>\n      <td>0.712</td>\n      <td>1</td>\n      <td>-5.105</td>\n      <td>0</td>\n      <td>0.0817</td>\n      <td>0.0901</td>\n      <td>0.000027</td>\n      <td>0.0933</td>\n      <td>0.425</td>\n      <td>92.005</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>243717</td>\n      <td>0.650</td>\n      <td>0.715</td>\n      <td>5</td>\n      <td>-5.198</td>\n      <td>0</td>\n      <td>0.2530</td>\n      <td>0.0993</td>\n      <td>0.000291</td>\n      <td>0.1260</td>\n      <td>0.187</td>\n      <td>106.672</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>250534</td>\n      <td>0.610</td>\n      <td>0.624</td>\n      <td>2</td>\n      <td>-4.773</td>\n      <td>1</td>\n      <td>0.3090</td>\n      <td>0.6000</td>\n      <td>0.000002</td>\n      <td>0.2430</td>\n      <td>0.244</td>\n      <td>176.169</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>300579</td>\n      <td>0.655</td>\n      <td>0.725</td>\n      <td>0</td>\n      <td>-5.497</td>\n      <td>1</td>\n      <td>0.1880</td>\n      <td>0.0327</td>\n      <td>0.002640</td>\n      <td>0.0611</td>\n      <td>0.326</td>\n      <td>125.800</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>201816</td>\n      <td>0.629</td>\n      <td>0.698</td>\n      <td>8</td>\n      <td>-4.485</td>\n      <td>1</td>\n      <td>0.3080</td>\n      <td>0.0509</td>\n      <td>0.001660</td>\n      <td>0.0909</td>\n      <td>0.599</td>\n      <td>117.765</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>295177</td>\n      <td>0.607</td>\n      <td>0.829</td>\n      <td>2</td>\n      <td>-4.074</td>\n      <td>1</td>\n      <td>0.3790</td>\n      <td>0.0103</td>\n      <td>0.000000</td>\n      <td>0.1070</td>\n      <td>0.685</td>\n      <td>96.172</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>148143</td>\n      <td>0.503</td>\n      <td>0.496</td>\n      <td>5</td>\n      <td>-9.123</td>\n      <td>0</td>\n      <td>0.0556</td>\n      <td>0.6930</td>\n      <td>0.000000</td>\n      <td>0.1230</td>\n      <td>0.412</td>\n      <td>187.844</td>\n      <td>['Bad Bunny']</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"bad_predictions2 = tracks_model2.predict(bad_features2)\n\nbad_rmse2 = mean_squared_error(bad_labels2,bad_predictions2,squared=False)\n\nprint(bad_rmse2)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:47:16.021317Z","iopub.execute_input":"2023-04-18T19:47:16.021824Z","iopub.status.idle":"2023-04-18T19:47:16.059512Z","shell.execute_reply.started":"2023-04-18T19:47:16.021784Z","shell.execute_reply":"2023-04-18T19:47:16.057878Z"},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"11.71378025731517\n","output_type":"stream"}]},{"cell_type":"code","source":"for i,song in enumerate(songs):\n    print(\"Prediction:\", round(bad_predictions2[i],2),\n          \"\\t\\tReal Popularity:\",bad_labels2.iloc[i],\"\\t\\tTrack:\",song)","metadata":{"execution":{"iopub.status.busy":"2023-04-18T19:47:50.669188Z","iopub.execute_input":"2023-04-18T19:47:50.669755Z","iopub.status.idle":"2023-04-18T19:47:50.677686Z","shell.execute_reply.started":"2023-04-18T19:47:50.669708Z","shell.execute_reply":"2023-04-18T19:47:50.676457Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"Prediction: 78.64 \t\tReal Popularity: 77 \t\tTrack: Yo Perreo Sola\nPrediction: 70.24 \t\tReal Popularity: 82 \t\tTrack: Después de la Playa\nPrediction: 76.76 \t\tReal Popularity: 92 \t\tTrack: Me Porto Bonito\nPrediction: 72.37 \t\tReal Popularity: 90 \t\tTrack: Tití Me Preguntó\nPrediction: 71.76 \t\tReal Popularity: 78 \t\tTrack: Callaíta\nPrediction: 66.53 \t\tReal Popularity: 75 \t\tTrack: La Romana\nPrediction: 72.17 \t\tReal Popularity: 76 \t\tTrack: El Apagón\nPrediction: 67.32 \t\tReal Popularity: 79 \t\tTrack: Safaera\nPrediction: 63.0 \t\tReal Popularity: 80 \t\tTrack: Un Verano Sin Ti\n","output_type":"stream"}]}]}