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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'ProdTaken'}) and 29 missing columns ({'OwnCar', 'NumberOfChildrenVisiting', 'TypeofContact_Self Enquiry', 'ProductPitched_Deluxe', 'Gender_Male', 'ProductPitched_Super Deluxe', 'Designation_VP', 'NumberOfFollowups', 'Age', 'Designation_Executive', 'NumberOfPersonVisiting', 'ProductPitched_King', 'Occupation_Large Business', 'PreferredPropertyStar', 'MaritalStatus_Married', 'MonthlyIncome', 'PitchSatisfactionScore', 'Occupation_Salaried', 'ProductPitched_Standard', 'Occupation_Small Business', 'Passport', 'CityTier', 'DurationOfPitch', 'NumberOfTrips', 'Gender_Female', 'Designation_Manager', 'MaritalStatus_Unmarried', 'MaritalStatus_Single', 'Designation_Senior Manager'}).

This happened while the csv dataset builder was generating data using

hf://datasets/HF-Sum/tourism-package-prediction/processed_data/ytest.csv (at revision 2b5de582c0775bec51fdee19705860f31db9b70d), [/tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtest.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtrain.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytest.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytrain.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytrain.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/tourism.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/tourism.csv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1893, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              ProdTaken: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 376
              to
              {'Age': Value('float64'), 'CityTier': Value('int64'), 'DurationOfPitch': Value('float64'), 'NumberOfPersonVisiting': Value('int64'), 'NumberOfFollowups': Value('float64'), 'PreferredPropertyStar': Value('float64'), 'NumberOfTrips': Value('float64'), 'Passport': Value('int64'), 'PitchSatisfactionScore': Value('int64'), 'OwnCar': Value('int64'), 'NumberOfChildrenVisiting': Value('float64'), 'MonthlyIncome': Value('float64'), 'TypeofContact_Self Enquiry': Value('bool'), 'Occupation_Large Business': Value('bool'), 'Occupation_Salaried': Value('bool'), 'Occupation_Small Business': Value('bool'), 'Gender_Female': Value('bool'), 'Gender_Male': Value('bool'), 'ProductPitched_Deluxe': Value('bool'), 'ProductPitched_King': Value('bool'), 'ProductPitched_Standard': Value('bool'), 'ProductPitched_Super Deluxe': Value('bool'), 'MaritalStatus_Married': Value('bool'), 'MaritalStatus_Single': Value('bool'), 'MaritalStatus_Unmarried': Value('bool'), 'Designation_Executive': Value('bool'), 'Designation_Manager': Value('bool'), 'Designation_Senior Manager': Value('bool'), 'Designation_VP': Value('bool')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1895, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'ProdTaken'}) and 29 missing columns ({'OwnCar', 'NumberOfChildrenVisiting', 'TypeofContact_Self Enquiry', 'ProductPitched_Deluxe', 'Gender_Male', 'ProductPitched_Super Deluxe', 'Designation_VP', 'NumberOfFollowups', 'Age', 'Designation_Executive', 'NumberOfPersonVisiting', 'ProductPitched_King', 'Occupation_Large Business', 'PreferredPropertyStar', 'MaritalStatus_Married', 'MonthlyIncome', 'PitchSatisfactionScore', 'Occupation_Salaried', 'ProductPitched_Standard', 'Occupation_Small Business', 'Passport', 'CityTier', 'DurationOfPitch', 'NumberOfTrips', 'Gender_Female', 'Designation_Manager', 'MaritalStatus_Unmarried', 'MaritalStatus_Single', 'Designation_Senior Manager'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/HF-Sum/tourism-package-prediction/processed_data/ytest.csv (at revision 2b5de582c0775bec51fdee19705860f31db9b70d), [/tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtest.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtest.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtrain.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/Xtrain.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytest.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytest.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytrain.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/processed_data/ytrain.csv), /tmp/hf-datasets-cache/medium/datasets/58214860859266-config-parquet-and-info-HF-Sum-tourism-package-pr-50c8f733/hub/datasets--HF-Sum--tourism-package-prediction/snapshots/2b5de582c0775bec51fdee19705860f31db9b70d/tourism.csv (origin=hf://datasets/HF-Sum/tourism-package-prediction@2b5de582c0775bec51fdee19705860f31db9b70d/tourism.csv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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Age float64	CityTier int64	DurationOfPitch float64	NumberOfPersonVisiting int64	NumberOfFollowups float64	PreferredPropertyStar float64	NumberOfTrips float64	Passport int64	PitchSatisfactionScore int64	OwnCar int64	NumberOfChildrenVisiting float64	MonthlyIncome float64	TypeofContact_Self Enquiry bool	Occupation_Large Business bool	Occupation_Salaried bool	Occupation_Small Business bool	Gender_Female bool	Gender_Male bool	ProductPitched_Deluxe bool	ProductPitched_King bool	ProductPitched_Standard bool	ProductPitched_Super Deluxe bool	MaritalStatus_Married bool	MaritalStatus_Single bool	MaritalStatus_Unmarried bool	Designation_Executive bool	Designation_Manager bool	Designation_Senior Manager bool	Designation_VP bool
34	1	9	2	4	3	4	0	1	0	0	17,979	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
32	1	6	3	3	4	2	0	3	0	0	21,220	true	false	true	false	false	true	true	false	false	false	false	false	false	false	true	false	false
30	3	11	2	3	3	3	0	4	1	1	24,419	true	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true	false
39	3	9	3	4	4	2	0	4	1	2	26,029	true	false	false	true	false	true	false	false	true	false	false	false	true	false	false	true	false
37	1	31	3	4	4	2	0	3	1	2	24,352	false	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
34	1	9	3	4	3	2	0	3	0	2	21,178	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
27	1	7	4	6	3	5	0	4	1	3	23,042	false	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
30	3	6	3	4	5	2	0	4	1	1	24,714	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
53	1	32	3	5	3	5	0	5	0	2	32,504	false	false	false	true	true	false	false	false	false	true	true	false	false	false	false	false	false
55	1	7	3	4	3	2	0	5	1	2	29,180	false	false	true	false	true	false	false	false	true	false	true	false	false	false	false	true	false
46	1	6	2	4	5	3	1	2	1	1	25,673	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	true	false
39	1	19	2	5	5	4	0	5	1	1	24,966	false	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
54	2	32	1	2	3	3	1	3	1	0	32,328	false	false	true	false	true	false	false	false	false	true	false	true	false	false	false	false	false
42	1	19	3	1	5	6	0	4	1	0	20,538	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
33	1	12	3	2	3	5	0	5	1	2	21,990	true	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
35	1	6	1	4	3	2	0	4	1	0	17,859	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
39	1	16	3	3	3	1	0	3	1	0	28,464	true	false	false	true	false	true	false	false	true	false	false	false	true	false	false	true	false
29	1	17	3	4	3	5	0	4	1	2	22,338	true	false	true	false	true	false	true	false	false	false	false	false	true	false	true	false	false
23	1	11	3	5	3	7	0	5	1	1	22,572	false	true	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false
37	1	15	2	3	3	2	1	2	0	0	17,326	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
33	1	10	4	4	5	3	0	1	1	1	25,403	true	false	false	true	true	false	true	false	false	false	true	false	false	false	true	false	false
33	1	7	4	4	5	3	0	1	0	2	21,634	true	false	true	false	false	true	false	false	false	false	false	false	true	true	false	false	false
50	1	25	4	4	3	3	1	1	0	1	25,482	false	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
42	1	6	2	4	3	1	1	3	0	0	21,062	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
43	1	33	3	4	5	5	1	3	0	1	31,869	false	false	false	true	true	false	false	false	true	false	true	false	false	false	false	true	false
36	1	15	3	1	4	2	0	5	1	0	17,810	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
27	3	8	2	1	3	1	0	1	0	1	21,500	true	false	false	true	true	false	true	false	false	false	false	false	true	false	true	false	false
29	3	16	4	4	3	3	0	3	1	2	23,931	true	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
34	1	12	4	5	3	3	0	2	0	3	21,589	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false
41	3	21	3	4	5	3	0	3	0	2	23,317	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
32	3	20	4	5	5	7	1	1	1	1	20,980	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
50	2	9	3	3	4	2	0	1	1	2	33,200	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true
24	3	30	2	3	3	1	0	4	1	1	17,400	false	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
43	1	7	3	5	3	2	1	3	0	1	24,740	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
39	1	16	3	3	5	3	0	5	1	2	20,377	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
55	1	6	2	3	5	1	1	1	1	1	34,045	true	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	true
33	1	10	3	4	3	3	0	4	1	1	24,887	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	false
34	3	23	4	4	5	4	1	5	0	1	27,242	true	false	true	false	false	false	false	false	true	false	false	false	true	false	false	true	false
25	1	25	3	4	3	2	0	4	0	1	21,452	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
30	1	24	3	3	3	2	0	1	1	2	17,632	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
32	3	12	3	4	4	3	0	3	0	1	21,467	false	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
34	1	12	4	4	4	8	0	3	1	3	30,556	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true	false
50	1	30	3	3	3	4	1	4	1	2	28,973	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
33	1	6	3	4	5	4	1	4	0	0	17,799	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
36	3	18	3	4	3	3	0	5	0	1	23,646	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
50	1	25	4	4	3	3	1	2	0	2	25,482	false	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
49	3	14	4	4	3	4	1	4	1	2	21,333	false	false	false	true	true	false	false	false	false	false	true	false	false	true	false	false	false
37	3	14	3	2	5	4	0	1	1	1	23,317	false	false	false	true	true	false	true	false	false	false	false	false	false	false	true	false	false
30	1	24	3	3	3	2	0	2	1	0	17,632	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
23	1	7	4	4	3	2	0	3	0	3	22,053	true	false	true	false	false	true	false	false	false	false	false	false	true	true	false	false	false
34	1	33	3	3	4	3	0	3	0	0	17,311	true	false	false	true	true	false	false	false	false	false	false	true	false	true	false	false	false
52	3	28	4	4	3	2	1	5	0	3	24,119	true	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
27	3	36	4	6	5	2	0	3	0	1	23,647	false	false	false	true	false	true	true	false	false	false	false	false	true	false	true	false	false
40	3	30	3	1	4	5	1	3	1	2	28,194	false	false	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false
44	1	8	3	1	3	2	0	4	1	0	17,011	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false
27	1	9	3	4	5	8	1	5	0	1	20,720	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
42	1	12	4	5	5	8	0	3	1	1	20,785	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
28	3	9	3	4	5	2	0	5	0	2	21,719	true	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
59	1	12	3	5	4	4	1	5	1	2	29,230	true	true	false	false	true	false	false	false	true	false	true	false	false	false	false	true	false
40	3	28	3	5	3	5	1	1	0	2	24,798	true	false	true	false	false	true	true	false	false	false	false	false	false	false	true	false	false
29	2	7	3	4	3	3	0	4	0	2	21,384	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
35	1	15	3	4	5	5	0	5	1	1	23,799	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
34	2	15	2	3	3	2	0	1	1	0	17,742	true	true	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false
36	1	10	2	4	3	2	0	5	1	1	20,810	true	false	true	false	false	true	true	false	false	false	false	true	false	false	true	false	false
41	1	16	3	4	5	5	0	2	1	0	32,181	false	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
46	1	6	2	4	5	3	1	1	1	1	25,673	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
27	3	36	3	4	3	7	0	5	1	1	22,984	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
32	3	27	4	2	3	2	0	5	1	1	21,469	false	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
38	1	26	4	4	4	6	0	4	0	2	21,700	true	false	true	false	false	true	false	false	false	false	true	false	false	true	false	false	false
34	3	29	4	4	4	2	0	1	0	1	24,824	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
51	2	11	2	3	4	2	1	3	1	1	29,026	true	false	true	false	false	true	false	false	false	true	true	false	false	false	false	false	false
40	1	8	2	4	3	1	1	3	1	1	17,342	true	false	false	true	true	false	false	false	false	false	false	true	false	true	false	false	false
49	1	13	2	4	3	1	0	1	1	0	25,965	true	false	true	false	false	true	false	false	true	false	false	false	true	false	false	true	false
48	1	16	4	4	3	6	0	3	1	1	20,783	true	false	true	false	true	false	false	false	false	false	false	true	false	true	false	false	false
29	3	26	2	3	3	3	0	1	1	0	21,931	true	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false
25	3	31	3	4	3	2	0	4	1	2	21,078	false	false	false	true	false	true	false	false	false	false	true	false	false	true	false	false	false
35	3	23	3	3	5	4	1	3	0	2	23,966	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
30	3	17	3	5	4	3	1	5	1	1	26,946	true	false	false	true	true	false	true	false	false	false	true	false	false	false	true	false	false
35	1	29	2	4	3	4	1	4	1	0	20,916	true	false	true	false	false	true	true	false	false	false	true	false	false	false	true	false	false
36	1	8	3	3	3	5	0	5	1	0	17,543	true	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
50	3	5	2	3	3	5	1	5	0	1	34,331	true	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true
44	3	32	4	5	3	7	0	4	1	2	29,476	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
38	3	8	2	3	4	1	0	4	1	0	22,351	true	false	false	true	false	true	false	false	true	false	false	false	true	false	false	true	false
37	1	14	4	4	4	4	0	1	0	3	20,691	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
32	2	9	4	5	5	5	0	3	0	2	25,088	true	false	true	false	false	true	true	false	false	false	false	false	false	false	true	false	false
42	3	17	3	4	3	2	0	2	0	2	24,908	false	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
50	1	34	3	2	3	2	1	2	1	2	18,221	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
25	1	14	3	4	3	3	1	4	0	1	21,564	false	false	true	false	true	false	false	false	false	false	true	false	false	true	false	false	false
19	1	15	2	3	5	2	0	3	0	0	17,552	true	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false	false
41	3	17	4	5	4	4	0	4	0	1	28,383	true	false	false	true	false	true	false	false	true	false	true	false	false	false	false	true	false
47	1	25	3	4	3	7	0	3	1	1	29,205	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	true	false
32	3	27	3	4	3	3	0	2	1	1	25,610	false	false	false	true	true	false	true	false	false	false	false	false	false	false	true	false	false
44	3	34	2	1	3	4	1	2	1	1	28,320	true	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	false
51	3	15	3	4	4	2	0	2	1	1	22,553	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false
37	1	7	2	4	3	2	0	1	0	0	21,474	true	false	true	false	true	false	true	false	false	false	true	false	false	false	true	false	false
36	1	7	4	5	5	3	0	1	0	3	21,128	true	false	false	true	false	true	false	false	false	false	false	true	false	true	false	false	false
30	1	15	4	6	5	3	1	3	1	2	20,797	true	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false
43	3	21	4	5	3	2	0	3	1	1	24,922	true	false	false	true	false	false	true	false	false	false	false	false	true	false	true	false	false
28	3	9	4	4	3	3	1	4	0	2	23,156	true	false	true	false	false	true	true	false	false	false	false	false	true	false	true	false	false
33	1	9	3	5	5	6	0	4	0	2	20,854	true	true	false	false	false	true	true	false	false	false	false	true	false	false	true	false	false

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