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

invalid_column_names = predicate_column_names - all_schema_names
        if invalid_column_names:
            raise ValueError('At least some column names requested by the predicate ({}) '
                             'are not valid schema names: ({})'.format(', '.join(invalid_column_names),
                                                                       ', '.join(all_schema_names)))

        other_column_names = all_schema_names - predicate_column_names - self._dataset.partitions.partition_names

        # Read columns needed for the predicate
        predicate_rows = self._read_with_shuffle_row_drop(piece, pq_file, predicate_column_names,
                                                          shuffle_row_drop_partition)

        # Decode values
        decoded_predicate_rows = [
            utils.decode_row(_select_cols(row, predicate_column_names), self._schema)
            for row in predicate_rows]

        # Use the predicate to filter
        match_predicate_mask = [worker_predicate.do_include(row) for row in decoded_predicate_rows]

        # Don't have anything left after filtering? Exit early.
        if not any(match_predicate_mask):
            return []

        # Remove rows that were filtered out by the predicate
        filtered_decoded_predicate_rows = [row for i, row in enumerate(decoded_predicate_rows) if
                                           match_predicate_mask[i]]

        if other_column_names:
            # Read remaining columns
            other_rows = self._read_with_shuffle_row_drop(piece, pq_file, other_column_names,