code: def make_parsing_fn(
      data_format,
      list_size=None,
      context_feature_spec=None,
      example_feature_spec=None,
      size_feature_name=None,
      mask_feature_name=None,
      shuffle_examples=False,
      seed=None,
  ):
      """Returns a parsing fn for a standard data format.
  
      Args:
        data_format: (string) See RankingDataFormat.
        list_size: (int) The number of examples to keep per ranking instance. If
          specified, truncation or padding may happen. Otherwise, the output Tensors
          have a dynamic list size.
        context_feature_spec: (dict) A mapping from feature keys to
          `FixedLenFeature` or `VarLenFeature` values for context.
        example_feature_spec: (dict) A mapping from feature keys to
          `FixedLenFeature` or `VarLenFeature` values for the list of examples.
        size_feature_name: (str) Name of feature for example list sizes. Populates
          the feature dictionary with a `tf.int32` Tensor of shape [batch_size] for
          this feature name. If None, which is default, this feature is not
          generated.
        mask_feature_name: (str) Name of feature for example list masks. Populates
          the feature dictionary with a `tf.bool` Tensor of shape [batch_size,
          list_size] for this feature name. If None, which is default, this feature
          is not generated.
        shuffle_examples: (bool) A boolean to indicate whether examples within a
          list are shuffled before the list is trimmed down to list_size elements
          (when list has more than list_size elements).
        seed: (int) A seed passed onto random_ops.uniform() to shuffle examples.
  
      Returns:
        A parsing function with signature parsing_fn(serialized), where serialized
        is a string Tensor representing the serialized data in the specified
        `data_format` and the function returns a feature map.
      """
      kwargs = {
          "list_size": list_size,
          "context_feature_spec": context_feature_spec,
          "example_feature_spec": example_feature_spec,
          "size_feature_name": size_feature_name,
          "mask_feature_name": mask_feature_name,
          "shuffle_examples": shuffle_examples,
          "seed": seed,
      }
      fns_dict = {
          EIE: parse_from_example_in_example,
          SEQ: parse_from_sequence_example,
          ELWC: parse_from_example_list,
      }
      if data_format in fns_dict:
          return functools.partial(fns_dict[data_format], **kwargs)
      else:
          raise ValueError(
              "Format {} is not supported.".format(data_format)
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import functools
  import six
  import tensorflow
  from tensorflow import estimator
  from google.protobuf import descriptor_pb2
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 8 for calling a global function with example usage: make_parsing_fn(data_format, list_size, context_feature_spec, example_feature_spec, size_feature_name, mask_feature_name, shuffle_examples, seed) and returns: functools