code: def add_int64(
      parser_builder: builders.BaseParserBuilder,
      decoder_builder: builders.DecoderBuilder,
      feature_name: str = "clip/label/index",
      output_name: str = "label_index",
  ):
      """Adds functions to process integer feature to builders.
  
      This function expects the input to be either a `tf.train.SequenceExample`
      (with the features in the context) or a `tf.train.Example`. The expected
      structure is (or equivalent for `tf.train.Example`):
      ```
      context {
        feature {
          key: input_label_index_feature_name
          value {
            int64_list {
              value: 42
              ...
            }
          }
        }
      }
      ```
  
      The corresponding `builders.ExampleParserBuilder` or
      `builders.SequenceExampleParserBuilder` has to be given as parameter.
  
      Args:
        parser_builder: An instance of a `builders.BaseParserBuilder`.
        decoder_builder: An instance of a `builders.DecoderBuilder`.
        feature_name: Name of the label index feature in the input
          `tf.train.Example` or `tf.train.SequenceExample`. Exposing this as an
          argument allows using this function for different label features within a
          single dataset.
        output_name: Name of the label index feature in the output features
          dictionary. Exposing this as an argument allows using this function for
          different label features within a single dataset.
      """
  
      # Parse label.
      if isinstance(
          parser_builder, builders.SequenceExampleParserBuilder
      ):
          parser_builder.parse_feature(
              feature_name=feature_name,
              feature_type=tf.io.VarLenFeature(dtype=tf.int64),
              output_name=output_name,
              is_context=True,
          )
      elif isinstance(parser_builder, builders.ExampleParserBuilder):
          parser_builder.parse_feature(
              feature_name=feature_name,
              feature_type=tf.io.VarLenFeature(dtype=tf.int64),
              output_name=output_name,
          )
      else:
          raise ValueError("`parser_builder` has an unexpected type.")
  
      # Densify tensor
      decoder_builder.add_fn(
          fn=tf.sparse.to_dense,
          feature_name=output_name,
          fn_name=f"{output_name}_sparse_to_dense",
      )
  
imports: import functools
  from typing import Any, Dict, Optional, Sequence
  from absl import logging
  from dmvr import builders
  from dmvr import processors
  from dmvr import tokenizers
  import numpy
  from scenic.projects.mbt.datasets.dataset_utils import _decode_spectrogram
  from scenic.projects.mbt.datasets.dataset_utils import apply_specaugment
  import tensorflow
label_desc: Use image node_id 15 for calling a global function with example usage: add_int64(parser_builder, decoder_builder, feature_name, output_name) without return types