def __init__(self, location: str=dataset_dir('MSLR10K'), split: str='train', fold: int=1, normalize: bool=True, filter_queries: Optional[bool]=None, download: bool=True, validate_checksums: bool=True):
    '\n        Args:\n            location: Directory where the dataset is located.\n            split: The data split to load ("train", "test" or "vali")\n            fold: Which data fold to load (1...5)\n            normalize: Whether to perform query-level feature\n                normalization.\n            filter_queries: Whether to filter out queries that\n                have no relevant items. If not given this will filter queries\n                for the test set but not the train set.\n            download: Whether to download the dataset if it does not\n                exist.\n            validate_checksums: Whether to validate the dataset files\n                via sha256.\n        '
    if (split not in MSLR10K.splits.keys()):
        raise ValueError(("unrecognized data split '%s'" % str(split)))
    if (fold not in MSLR10K.per_fold_expected_files.keys()):
        raise ValueError(("unrecognized data fold '%s'" % str(fold)))
    validate_and_download(location=location, expected_files=MSLR10K.per_fold_expected_files[fold], downloader=(MSLR10K.downloader if download else None), validate_checksums=validate_checksums)
    if (filter_queries is None):
        filter_queries = (False if (split == 'train') else True)
    datafile = os.path.join(location, ('Fold%d' % fold), MSLR10K.splits[split])
    super().__init__(file=datafile, sparse=False, normalize=normalize, filter_queries=filter_queries, zero_based='auto')
Args:
    location: Directory where the dataset is located.
    split: The data split to load ("train", "test" or "vali")
    fold: Which data fold to load (1...5)
    normalize: Whether to perform query-level feature
        normalization.
    filter_queries: Whether to filter out queries that
        have no relevant items. If not given this will filter queries
        for the test set but not the train set.
    download: Whether to download the dataset if it does not
        exist.
    validate_checksums: Whether to validate the dataset files
        via sha256.
def __init__(self, location: str=dataset_dir('MSLR10K'), split: str='train', fold: int=1, normalize: bool=True, filter_queries: Optional[bool]=None, download: bool=True, validate_checksums: bool=True):
    '\n        Args:\n            location: Directory where the dataset is located.\n            split: The data split to load ("train", "test" or "vali")\n            fold: Which data fold to load (1...5)\n            normalize: Whether to perform query-level feature\n                normalization.\n            filter_queries: Whether to filter out queries that\n                have no relevant items. If not given this will filter queries\n                for the test set but not the train set.\n            download: Whether to download the dataset if it does not\n                exist.\n            validate_checksums: Whether to validate the dataset files\n                via sha256.\n        '
    if (split not in MSLR10K.splits.keys()):
        raise ValueError(("unrecognized data split '%s'" % str(split)))
    if (fold not in MSLR10K.per_fold_expected_files.keys()):
        raise ValueError(("unrecognized data fold '%s'" % str(fold)))
    validate_and_download(location=location, expected_files=MSLR10K.per_fold_expected_files[fold], downloader=(MSLR10K.downloader if download else None), validate_checksums=validate_checksums)
    if (filter_queries is None):
        filter_queries = (False if (split == 'train') else True)
    datafile = os.path.join(location, ('Fold%d' % fold), MSLR10K.splits[split])
    super().__init__(file=datafile, sparse=False, normalize=normalize, filter_queries=filter_queries, zero_based='auto')