How to use the mediapipe.util.sequence.media_sequence.set_clip_label_index function in mediapipe

row = dict(zip(["video", "start", "end", "split"],
                         csv_row))
        metadata = tf.train.SequenceExample()
        ms.set_example_id(bytes23(row["video"] + "_" + row["start"]),
                          metadata)
        ms.set_clip_media_id(bytes23(row["video"]), metadata)
        ms.set_clip_alternative_media_id(bytes23(row["split"]), metadata)
        if video_path_format_string:
          filepath = video_path_format_string.format(**row)
          ms.set_clip_data_path(bytes23(filepath), metadata)
        assert row["start"].isdigit(), "Invalid row: %s" % str(row)
        assert row["end"].isdigit(), "Invalid row: %s" % str(row)
        if "label_name" in row:
          ms.set_clip_label_string([bytes23(row["label_name"])], metadata)
          if label_map:
            ms.set_clip_label_index([label_map[row["label_name"]]], metadata)
        yield metadata

basename = url.split("/")[-1]
        local_path = os.path.join(self.path_to_data, basename)
        if not tf.io.gfile.exists(local_path):
          urlretrieve(url, local_path)

        for start_time in range(0, int(row["duration"]), SECONDS_PER_EXAMPLE):
          metadata = tf.train.SequenceExample()
          ms.set_example_id(bytes23(basename + "_" + str(start_time)),
                            metadata)
          ms.set_clip_data_path(bytes23(local_path), metadata)
          ms.set_clip_start_timestamp(start_time * MICROSECONDS_PER_SECOND,
                                      metadata)
          ms.set_clip_end_timestamp(
              (start_time + SECONDS_PER_EXAMPLE) * MICROSECONDS_PER_SECOND,
              metadata)
          ms.set_clip_label_index((int(row["label index"]),), metadata)
          ms.set_clip_label_string((bytes23(row["label string"]),),
                                   metadata)
          all_metadata.append(metadata)
      random.seed(47)
      random.shuffle(all_metadata)
      shard_names = [self._indexed_shard(split, i) for i in range(NUM_SHARDS)]
      writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
      with _close_on_exit(writers) as writers:
        for i, seq_ex in enumerate(all_metadata):
          for graph in GRAPHS:
            graph_path = os.path.join(path_to_graph_directory, graph)
            seq_ex = self._run_mediapipe(path_to_mediapipe_binary, seq_ex,
                                         graph_path)
          writers[i % len(writers)].write(seq_ex.SerializeToString())