def __init__(
        self,
        root: Optional[Union[str, Path]] = None,
        *,
        data_name: str = "clear10",
        download: bool = True,
        verbose: bool = False,
    ):
        """
        Creates an instance of the CLEAR dataset.
        This base class simply download and unzip the CLEAR dataset.

        This serves as a base class for _CLEARImage/_CLEARFeature dataset.

        :param root: The directory where the dataset can be found or downloaded.
            Defaults to None, which means that the default location for
            str(data_name) will be used.
        :param data_name: Data module name with the google drive url and md5
        :param download: If True, the dataset will be downloaded if needed.
        """

        if root is None:
            root = default_dataset_location(data_name)

        assert data_name in _CLEAR_DATA_SPLITS
        self.data_name = data_name
        self.module = clear_data
        self._paths_and_targets: List[List[Tuple[str, int]]] = []

        super(CLEARDataset, self).__init__(root, download=download, verbose=True)
        self._load_dataset()

        Creates an instance of the CLEAR dataset.
        This base class simply download and unzip the CLEAR dataset.

        This serves as a base class for _CLEARImage/_CLEARFeature dataset.

        :param root: The directory where the dataset can be found or downloaded.
            Defaults to None, which means that the default location for
            str(data_name) will be used.
        :param data_name: Data module name with the google drive url and md5
        :param download: If True, the dataset will be downloaded if needed.
        
avalanche/benchmarks/datasets/clear/clear.py