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

def parse_fn(sequence_example):
      """Parses a Kinetics example."""
      context_features = {
          ms.get_example_id_key(): ms.get_example_id_default_parser(),
          ms.get_clip_label_string_key(): tf.FixedLenFeature((), tf.string),
          ms.get_clip_label_index_key(): tf.FixedLenFeature((), tf.int64),
      }

      sequence_features = {
          ms.get_image_encoded_key(): ms.get_image_encoded_default_parser(),
          ms.get_forward_flow_encoded_key():
              ms.get_forward_flow_encoded_default_parser(),
      }
      parsed_context, parsed_sequence = tf.io.parse_single_sequence_example(
          sequence_example, context_features, sequence_features)

      target = tf.one_hot(parsed_context[ms.get_clip_label_index_key()], 700)

      images = tf.image.convert_image_dtype(
          tf.map_fn(tf.image.decode_jpeg,

def parse_fn(sequence_example):
      """Parses a clip classification example."""
      context_features = {
          ms.get_example_id_key():
              ms.get_example_id_default_parser(),
          ms.get_clip_label_index_key():
              ms.get_clip_label_index_default_parser(),
          ms.get_clip_label_string_key():
              ms.get_clip_label_string_default_parser()
      }
      sequence_features = {
          ms.get_image_encoded_key(): ms.get_image_encoded_default_parser(),
      }
      parsed_context, parsed_sequence = tf.io.parse_single_sequence_example(
          sequence_example, context_features, sequence_features)
      example_id = parsed_context[ms.get_example_id_key()]
      classification_target = tf.one_hot(
          tf.sparse_tensor_to_dense(
              parsed_context[ms.get_clip_label_index_key()]), NUM_CLASSES)
      images = tf.map_fn(
          tf.image.decode_jpeg,
          parsed_sequence[ms.get_image_encoded_key()],
          back_prop=False,
          dtype=tf.uint8)
      return {
          "id": example_id,
          "labels": classification_target,
          "images": images,
      }

segments_matrix,
          tf.sparse_tensor_to_dense(
              parsed_context[ms.get_segment_label_index_key()]
              ) + CLASS_LABEL_OFFSET,
          NUM_CLASSES + CLASS_LABEL_OFFSET)

      # [segments, 2] start and end time in seconds.
      gt_segment_seconds = tf.to_float(tf.concat(
          [tf.expand_dims(tf.sparse_tensor_to_dense(parsed_context[
              ms.get_segment_start_timestamp_key()]), 1),
           tf.expand_dims(tf.sparse_tensor_to_dense(parsed_context[
               ms.get_segment_end_timestamp_key()]), 1)],
          1)) / float(SECONDS_TO_MICROSECONDS)
      gt_segment_classes = tf.sparse_tensor_to_dense(parsed_context[
          ms.get_segment_label_index_key()]) + CLASS_LABEL_OFFSET
      example_id = parsed_context[ms.get_example_id_key()]
      sampling_rate = parsed_context[ms.get_image_frame_rate_key()]

      images = tf.map_fn(tf.image.decode_jpeg,
                         parsed_sequence[ms.get_image_encoded_key()],
                         back_prop=False,
                         dtype=tf.uint8)

      output_dict = {
          "segment_matrix": segments_matrix,
          "indicator_matrix": indicator,
          "classification_target": classification_target,
          "example_id": example_id,
          "sampling_rate": sampling_rate,
          "gt_segment_seconds": gt_segment_seconds,
          "gt_segment_classes": gt_segment_classes,
          "num_segments": num_segments,