How to use the ludwig.utils.data_utils.save_hdf5 function in ludwig

logger.info(
            'Using full raw csv, no hdf5 and json file '
            'with the same name have been found'
        )
        logger.info('Building dataset (it may take a while)')
        data, train_set_metadata = build_dataset(
            data_csv,
            features,
            preprocessing_params,
            train_set_metadata=train_set_metadata,
            random_seed=random_seed
        )
        if not skip_save_processed_input:
            logger.info('Writing dataset')
            data_hdf5_fp = replace_file_extension(data_csv, 'hdf5')
            data_utils.save_hdf5(data_hdf5_fp, data, train_set_metadata)
            train_set_metadata[DATA_TRAIN_HDF5_FP] = data_hdf5_fp
            logger.info('Writing train set metadata with vocabulary')

            train_set_metadata_json_fp = replace_file_extension(
                data_csv,
                'json'
            )
            data_utils.save_json(
                train_set_metadata_json_fp, train_set_metadata)

        training_set, test_set, validation_set = split_dataset_tvt(
            data,
            data['split']
        )

    elif data_train_csv is not None:

if validation_set is not None:
                data_validation_hdf5_fp = replace_file_extension(
                    data_validation_csv,
                    'hdf5'
                )
                data_utils.save_hdf5(
                    data_validation_hdf5_fp,
                    validation_set,
                    train_set_metadata
                )
                train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp

            if test_set is not None:
                data_test_hdf5_fp = replace_file_extension(data_test_csv,
                                                           'hdf5')
                data_utils.save_hdf5(
                    data_test_hdf5_fp,
                    test_set,
                    train_set_metadata
                )
                train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp

            logger.info('Writing train set metadata with vocabulary')
            train_set_metadata_json_fp = replace_file_extension(data_train_csv,
                                                                'json')
            data_utils.save_json(
                train_set_metadata_json_fp,
                train_set_metadata,
            )

    return training_set, test_set, validation_set, train_set_metadata

concatenated_df.csv = data_train_csv
        data, train_set_metadata = build_dataset_df(
            concatenated_df,
            features,
            preprocessing_params,
            train_set_metadata=train_set_metadata,
            random_seed=random_seed
        )
        training_set, test_set, validation_set = split_dataset_tvt(
            data,
            data['split']
        )
        if not skip_save_processed_input:
            logger.info('Writing dataset')
            data_train_hdf5_fp = replace_file_extension(data_train_csv, 'hdf5')
            data_utils.save_hdf5(
                data_train_hdf5_fp,
                training_set,
                train_set_metadata
            )
            train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp
            if validation_set is not None:
                data_validation_hdf5_fp = replace_file_extension(
                    data_validation_csv,
                    'hdf5'
                )
                data_utils.save_hdf5(
                    data_validation_hdf5_fp,
                    validation_set,
                    train_set_metadata
                )
                train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp

)
        if not skip_save_processed_input:
            logger.info('Writing dataset')
            data_train_hdf5_fp = replace_file_extension(data_train_csv, 'hdf5')
            data_utils.save_hdf5(
                data_train_hdf5_fp,
                training_set,
                train_set_metadata
            )
            train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp
            if validation_set is not None:
                data_validation_hdf5_fp = replace_file_extension(
                    data_validation_csv,
                    'hdf5'
                )
                data_utils.save_hdf5(
                    data_validation_hdf5_fp,
                    validation_set,
                    train_set_metadata
                )
                train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp

            if test_set is not None:
                data_test_hdf5_fp = replace_file_extension(data_test_csv,
                                                           'hdf5')
                data_utils.save_hdf5(
                    data_test_hdf5_fp,
                    test_set,
                    train_set_metadata
                )
                train_set_metadata[DATA_TRAIN_HDF5_FP] = data_train_hdf5_fp