How to use the gdown.cached_download function in gdown

def list_creator(path):
        dataset = {}
        archive_path = gdown.cached_download(url)

        maxsize = sys.maxsize
        while True:
            try:
                csv.field_size_limit(maxsize)
                break
            except OverflowError:
                maxsize = int(maxsize / 10)
        csv.field_size_limit(maxsize)

        with tarfile.open(archive_path, 'r') as archive:
            for split in ('train', 'test'):
                filename = f'{key}_csv/{split}.csv'
                print(f'Processing {filename}...')
                reader = csv.reader(
                    io.TextIOWrapper(archive.extractfile(filename), encoding='utf-8'))

def creator(path):
        archive_path = gdown.cached_download(url)
        target_path = os.path.join(root, 'raw')
        with tarfile.open(archive_path, 'r') as archive:
            print(f'Extracting to {target_path}')
            archive.extractall(target_path)

        split2filename = {'train': 'train.tok.clean.bpe.32000',
                          'dev': 'newstest2013.tok.bpe.32000',
                          'test': 'newstest2014.tok.bpe.32000'}
        dataset = {}
        for split, filename in split2filename.items():
            src_path = f'{filename}.en'
            tgt_path = f'{filename}.de'
            dataset[split] = (
                easyfile.TextFile(os.path.join(target_path, src_path)),
                easyfile.TextFile(os.path.join(target_path, tgt_path))
            )

def easyfile_creator(path):
        dataset = {}
        archive_path = gdown.cached_download(url)

        with tarfile.open(archive_path, 'r') as archive:
            print(f'Extracting to {root}...')
            archive.extractall(root)

        dataset = {}
        for split in ('train', 'test'):
            filename = f'{key}_csv/{split}.csv'
            dataset[split] = easyfile.CsvFile(os.path.join(root, filename))

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