Kedro

For the sake of completeness, here's my implementation of the` _load` method:
`def _load(self) -> tf.data.Dataset:
        logger = logging.getLogger('TensorFlowCSVDataSet')
        load_path = get_filepath_str(self._get_load_path(), self._protocol)
        logger.info(f'remote path: {load_path}')
        logger.info(self._fs.ls(load_path))
        
        logger.info(f'remote path contents: {self._fs.ls(load_path)}')
        self._tmp_data_dir =  tempfile.TemporaryDirectory(prefix=self._tmp_prefix)
        logger.info(f'local path: {self._tmp_data_dir.name}')
        self._fs.get(load_path + '/*.parquet', 
                     self._tmp_data_dir.name + '/', 
                     recursive=True)
        
        ds_dir = list(Path(self._tmp_data_dir.name).iterdir())
        logger.info(f'local path contents: {ds_dir}')
        ds = (tf.data.experimental
              .make_csv_dataset(file_pattern=f'{self._tmp_data_dir.name}/*.csv',
                                **self._load_args)).unbatch()
        
        return d`s