code: def create_bytes_context_feature(
      name, key, prefix="", module_dict=None
  ):
      """Creates accessor functions for single bytes features.
  
      The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and
      clear_${NAME}.
  
      Example:
        example = tensorflow.train.SequenceExample()
        set_data_path("path_to_a_file", example)
        if has_data_path(example):
          path = get_data_path(example)
          clear_data_path(example)
  
      Args:
        name: the name of the feature to use in function names.
        key: the key for this feature in the SequenceExample.
        prefix: a prefix to append to the key in the SequenceExample
        module_dict: adds the functions to the corresponding module dict.
      """
  
      def _has(sequence_example, prefix=prefix):
          return has_context(key, sequence_example, prefix=prefix)
  
      def _get(sequence_example, prefix=prefix):
          return get_context_bytes(key, sequence_example, prefix=prefix)
  
      def _clear(sequence_example, prefix=prefix):
          clear_context(key, sequence_example, prefix=prefix)
  
      def _set(value, sequence_example, prefix=prefix):
          set_context_bytes(key, value, sequence_example, prefix=prefix)
  
      def _get_key(prefix=prefix):
          return merge_prefix(prefix, key)
  
      def _get_default_parser():
          return tf.io.FixedLenFeature((), tf.string)
  
      function_dict = {
          "has_" + name: _has,
          "get_" + name: _get,
          "clear_" + name: _clear,
          "set_" + name: _set,
          "get_" + name + "_key": _get_key,
          "get_" + name + "_default_parser": _get_default_parser,
      }
      add_functions_to_module(function_dict, module_dict)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import types
  import tensorflow.compat.v1
label_desc: Use image node_id 36 for calling a global function with example usage: create_bytes_context_feature(name, key, prefix, module_dict) and returns: has_context, get_context_bytes, merge_prefix, tf