parameters: (name, key, prefix="", module_dict=None)
docstring: Creates accessor functions for list of bytes features.
  
    The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and
    clear_${NAME}.
  
    Example:
      example = tensorflow.train.SequenceExample()
      set_clip_label_string(["dog", "cat"], example)
      if has_clip_label_string(example):
        clip_label_string = get_clip_label_string(example)
        clear_clip_label_string(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.
docstring_summary: Creates accessor functions for list of bytes features.
docstring_tokens: Creates accessor functions for list of bytes features .
function: def create_bytes_list_context_feature(name, key, prefix="", module_dict=None):
    """Creates accessor functions for list of bytes features.
  
    The provided functions are has_${NAME}, get_${NAME}, set_${NAME} and
    clear_${NAME}.
  
    Example:
      example = tensorflow.train.SequenceExample()
      set_clip_label_string(["dog", "cat"], example)
      if has_clip_label_string(example):
        clip_label_string = get_clip_label_string(example)
        clear_clip_label_string(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_list(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_list(key, value, sequence_example, prefix=prefix)
  
    def _get_key(prefix=prefix):
      return merge_prefix(prefix, key)
  
    def _get_default_parser():
      return tf.io.VarLenFeature(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)
function_tokens: def create_bytes_list_context_feature ( name , key , prefix = "" , module_dict = None ) : def _has ( sequence_example , prefix = prefix ) : return has_context ( key , sequence_example , prefix = prefix ) def _get ( sequence_example , prefix = prefix ) : return get_context_bytes_list ( 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_list ( key , value , sequence_example , prefix = prefix ) def _get_key ( prefix = prefix ) : return merge_prefix ( prefix , key ) def _get_default_parser ( ) : return tf . io . VarLenFeature ( 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 )