How to use the petastorm.utils.add_to_dataset_metadata function in petastorm

# which will read all the footers of the dataset in parallel and merge them.
            hadoop_config = sc._jsc.hadoopConfiguration()
            Path = sc._gateway.jvm.org.apache.hadoop.fs.Path
            parquet_output_committer = sc._gateway.jvm.org.apache.parquet.hadoop.ParquetOutputCommitter
            parquet_output_committer.writeMetaDataFile(hadoop_config, Path(dataset_url))

    spark.stop()

    if use_summary_metadata and arrow_metadata:
        # When calling writeMetaDataFile it will overwrite the _common_metadata file which could have schema information
        # or row group indexers. Therefore we want to retain this information and will add it to the new
        # _common_metadata file. If we were using the old legacy metadata method this file wont be deleted
        base_schema = arrow_metadata.schema.to_arrow_schema()
        metadata_dict = base_schema.metadata
        if ROW_GROUPS_PER_FILE_KEY in metadata_dict:
            add_to_dataset_metadata(dataset, ROW_GROUPS_PER_FILE_KEY, metadata_dict[ROW_GROUPS_PER_FILE_KEY])
        if ROWGROUPS_INDEX_KEY in metadata_dict:
            add_to_dataset_metadata(dataset, ROWGROUPS_INDEX_KEY, metadata_dict[ROWGROUPS_INDEX_KEY])

Path = sc._gateway.jvm.org.apache.hadoop.fs.Path
            parquet_output_committer = sc._gateway.jvm.org.apache.parquet.hadoop.ParquetOutputCommitter
            parquet_output_committer.writeMetaDataFile(hadoop_config, Path(dataset_url))

    spark.stop()

    if use_summary_metadata and arrow_metadata:
        # When calling writeMetaDataFile it will overwrite the _common_metadata file which could have schema information
        # or row group indexers. Therefore we want to retain this information and will add it to the new
        # _common_metadata file. If we were using the old legacy metadata method this file wont be deleted
        base_schema = arrow_metadata.schema.to_arrow_schema()
        metadata_dict = base_schema.metadata
        if ROW_GROUPS_PER_FILE_KEY in metadata_dict:
            add_to_dataset_metadata(dataset, ROW_GROUPS_PER_FILE_KEY, metadata_dict[ROW_GROUPS_PER_FILE_KEY])
        if ROWGROUPS_INDEX_KEY in metadata_dict:
            add_to_dataset_metadata(dataset, ROWGROUPS_INDEX_KEY, metadata_dict[ROWGROUPS_INDEX_KEY])

def _generate_unischema_metadata(dataset, schema):
    """
    Generates the serialized unischema and adds it to the dataset parquet metadata to be used upon reading.
    :param dataset: (ParquetDataset) Dataset to attach schema
    :param schema:  (Unischema) Schema to attach to dataset
    :return: None
    """
    # TODO(robbieg): Simply pickling unischema will break if the UnischemaField class is changed,
    #  or the codec classes are changed. We likely need something more robust.
    assert schema
    serialized_schema = pickle.dumps(schema)
    utils.add_to_dataset_metadata(dataset, UNISCHEMA_KEY, serialized_schema)