How to use the lineflow.core.MapDataset function in lineflow

for i, y in enumerate(lines):
            self.assertEqual(data[i], y)

        self.assertEqual(len(data), len(lines))
        self.assertEqual(data._length, len(lines))
        # check if length is cached
        self.assertEqual(len(data), len(lines))

        self.assertIsInstance(data._dataset, easyfile.TextFile)

        data = data.map(str.split)

        for x, y in zip(data, lines):
            self.assertEqual(x, y.split())

        self.assertIsInstance(data, lineflow.core.MapDataset)
        self.assertIsInstance(data._dataset, TextDataset)

def __init__(self,
                 dataset: DatasetMixin,
                 map_func: Callable[[Any], Any]) -> None:
        assert callable(map_func)

        self._map_func = map_func

        super(MapDataset, self).__init__(dataset)

def map(self, map_func: Callable[[Any], Any]) -> 'MapDataset':
        """Applies a function across the examples of this dataset.

        Args:
            map_func (Callable[[Any], Any]): A function to apply.

        Returns ('MapDataset'):
            The dataset applied the function.
        """
        return MapDataset(self, map_func)

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


cached_get_imdb = lru_cache()(get_imdb)


def _imdb_loader(path: str) -> Tuple[str, int]:
    with io.open(path, 'rt', encoding='utf-8') as f:
        string = f.read()
    label = 0 if 'pos' in path else 1
    return (string, label)


class Imdb(MapDataset):
    def __init__(self, split: str = 'train', loader=_imdb_loader) -> 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_imdb()

        super().__init__(raw[split], loader)