How to use the lineflow.Dataset function in lineflow

def setUp(self):
        self.data = Dataset(range(100))

def setUp(self):
        self.data = Dataset(range(100))
        window_size = 3
        expected = []
        it = iter(range(100))
        window = tuple(itertools.islice(it, window_size))
        while window:
            expected.append(window)
            window = tuple(itertools.islice(it, window_size))
        self.expected = expected
        self.window_size = window_size

return download.cache_or_load_file(pkl_path, creator, loader)


cached_get_text_classification_dataset = lru_cache()(get_text_classification_dataset)


class AgNews(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('ag_news')
        super(AgNews, self).__init__(raw[split])


class SogouNews(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('sogou_news')
        super(SogouNews, self).__init__(raw[split])


class Dbpedia(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('dbpedia')
        super(Dbpedia, self).__init__(raw[split])

with io.open(path, 'wb') as f:
            pickle.dump(dataset, f)
        return dataset

    def loader(path):
        with io.open(path, 'rb') as f:
            return pickle.load(f)

    pkl_path = os.path.join(root, 'ptb.pkl')
    return download.cache_or_load_file(pkl_path, creator, loader)


cached_get_penn_treebank = lru_cache()(get_penn_treebank)


class PennTreebank(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'dev', 'test'}:
            raise ValueError(f"only 'train', 'dev' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_penn_treebank()
        super(PennTreebank, self).__init__(raw[split])

with io.open(path, 'wb') as f:
            pickle.dump(dataset, f)
        return dataset

    def loader(path):
        with io.open(path, 'rb') as f:
            return pickle.load(f)

    pkl_path = os.path.join(root, 'enja.pkl')
    return download.cache_or_load_file(pkl_path, creator, loader)


cached_get_small_parallel_enja = lru_cache()(get_small_parallel_enja)


class SmallParallelEnJa(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'dev', 'test'}:
            raise ValueError(f"only 'train', 'dev' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_small_parallel_enja()
        super().__init__(raw[split])

assert key in urls

    if key in ('ag_news', 'dpbedia'):
        creator = list_creator
    else:
        creator = easyfile_creator

    pkl_path = os.path.join(root, f'{key}.pkl')
    return download.cache_or_load_file(pkl_path, creator, loader)


cached_get_text_classification_dataset = lru_cache()(get_text_classification_dataset)


class AgNews(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('ag_news')
        super(AgNews, self).__init__(raw[split])


class SogouNews(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('sogou_news')
        super(SogouNews, self).__init__(raw[split])

raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('ag_news')
        super(AgNews, self).__init__(raw[split])


class SogouNews(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('sogou_news')
        super(SogouNews, self).__init__(raw[split])


class Dbpedia(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('dbpedia')
        super(Dbpedia, self).__init__(raw[split])


class YelpReviewPolarity(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yelp_review_polarity')
        super(YelpReviewPolarity, self).__init__(raw[split])

from typing import List, Tuple, Any, Iterator
import random

from lineflow import Dataset


class SubDataset(Dataset):
    def __init__(self,
                 dataset: Dataset,
                 start: int,
                 end: int,
                 indices: List[int] = None) -> None:
        if start < 0 or end > len(dataset):
            raise ValueError('subset overruns the base dataset.')
        self._dataset = dataset
        self._start = start
        self._end = end
        self._size = end - start
        if indices is not None and len(indices) != len(dataset):
            msg = (f'indices option must have the same length as the base '
                   'dataset: len(indices) = {len(indices)} while len(dataset) = {len(dataset)}')
            raise ValueError(msg)
        self._indices = indices or list(range(len(dataset)))

raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yelp_review_polarity')
        super(YelpReviewPolarity, self).__init__(raw[split])


class YelpReviewFull(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yelp_review_full')
        super(YelpReviewFull, self).__init__(raw[split])


class YahooAnswers(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yahoo_answers')
        super(YahooAnswers, self).__init__(raw[split])


class AmazonReviewPolarity(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('amazon_review_polarity')
        super(AmazonReviewPolarity, self).__init__(raw[split])

raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yelp_review_full')
        super(YelpReviewFull, self).__init__(raw[split])


class YahooAnswers(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('yahoo_answers')
        super(YahooAnswers, self).__init__(raw[split])


class AmazonReviewPolarity(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('amazon_review_polarity')
        super(AmazonReviewPolarity, self).__init__(raw[split])


class AmazonReviewFull(Dataset):
    def __init__(self, split: str = 'train') -> None:
        if split not in {'train', 'test'}:
            raise ValueError(f"only 'train' and 'test' are valid for 'split', but '{split}' is given.")

        raw = cached_get_text_classification_dataset('amazon_review_full')
        super(AmazonReviewFull, self).__init__(raw[split])