def filter_by_size(indices, dataset, max_positions, raise_exception=False):
    '\n    [deprecated] Filter indices based on their size.\n    Use `FairseqDataset::filter_indices_by_size` instead.\n\n    Args:\n        indices (List[int]): ordered list of dataset indices\n        dataset (FairseqDataset): fairseq dataset instance\n        max_positions (tuple): filter elements larger than this size.\n            Comparisons are done component-wise.\n        raise_exception (bool, optional): if ``True``, raise an exception if\n            any elements are filtered (default: False).\n    '
    warnings.warn('data_utils.filter_by_size is deprecated. Use `FairseqDataset::filter_indices_by_size` instead.', stacklevel=2)
    if (isinstance(max_positions, float) or isinstance(max_positions, int)):
        if (hasattr(dataset, 'sizes') and isinstance(dataset.sizes, np.ndarray)):
            ignored = indices[(dataset.sizes[indices] > max_positions)].tolist()
            indices = indices[(dataset.sizes[indices] <= max_positions)]
        elif (hasattr(dataset, 'sizes') and isinstance(dataset.sizes, list) and (len(dataset.sizes) == 1)):
            ignored = indices[(dataset.sizes[0][indices] > max_positions)].tolist()
            indices = indices[(dataset.sizes[0][indices] <= max_positions)]
        else:
            (indices, ignored) = _filter_by_size_dynamic(indices, dataset.size, max_positions)
    else:
        (indices, ignored) = _filter_by_size_dynamic(indices, dataset.size, max_positions)
    if ((len(ignored) > 0) and raise_exception):
        raise Exception('Size of sample #{} is invalid (={}) since max_positions={}, skip this example with --skip-invalid-size-inputs-valid-test'.format(ignored[0], dataset.size(ignored[0]), max_positions))
    if (len(ignored) > 0):
        logger.warning('{} samples have invalid sizes and will be skipped, max_positions={}, first few sample ids={}'.format(len(ignored), max_positions, ignored[:10]))
    return indices
[deprecated] Filter indices based on their size.
Use `FairseqDataset::filter_indices_by_size` instead.

Args:
    indices (List[int]): ordered list of dataset indices
    dataset (FairseqDataset): fairseq dataset instance
    max_positions (tuple): filter elements larger than this size.
        Comparisons are done component-wise.
    raise_exception (bool, optional): if ``True``, raise an exception if
        any elements are filtered (default: False).
def filter_by_size(indices, dataset, max_positions, raise_exception=False):
    '\n    [deprecated] Filter indices based on their size.\n    Use `FairseqDataset::filter_indices_by_size` instead.\n\n    Args:\n        indices (List[int]): ordered list of dataset indices\n        dataset (FairseqDataset): fairseq dataset instance\n        max_positions (tuple): filter elements larger than this size.\n            Comparisons are done component-wise.\n        raise_exception (bool, optional): if ``True``, raise an exception if\n            any elements are filtered (default: False).\n    '
    warnings.warn('data_utils.filter_by_size is deprecated. Use `FairseqDataset::filter_indices_by_size` instead.', stacklevel=2)
    if (isinstance(max_positions, float) or isinstance(max_positions, int)):
        if (hasattr(dataset, 'sizes') and isinstance(dataset.sizes, np.ndarray)):
            ignored = indices[(dataset.sizes[indices] > max_positions)].tolist()
            indices = indices[(dataset.sizes[indices] <= max_positions)]
        elif (hasattr(dataset, 'sizes') and isinstance(dataset.sizes, list) and (len(dataset.sizes) == 1)):
            ignored = indices[(dataset.sizes[0][indices] > max_positions)].tolist()
            indices = indices[(dataset.sizes[0][indices] <= max_positions)]
        else:
            (indices, ignored) = _filter_by_size_dynamic(indices, dataset.size, max_positions)
    else:
        (indices, ignored) = _filter_by_size_dynamic(indices, dataset.size, max_positions)
    if ((len(ignored) > 0) and raise_exception):
        raise Exception('Size of sample #{} is invalid (={}) since max_positions={}, skip this example with --skip-invalid-size-inputs-valid-test'.format(ignored[0], dataset.size(ignored[0]), max_positions))
    if (len(ignored) > 0):
        logger.warning('{} samples have invalid sizes and will be skipped, max_positions={}, first few sample ids={}'.format(len(ignored), max_positions, ignored[:10]))
    return indices