code: def build_ranking_serving_input_receiver_fn(
      data_format,
      context_feature_spec,
      example_feature_spec,
      list_size=None,
      size_feature_name=None,
      mask_feature_name=None,
      receiver_name="input_ranking_data",
      default_batch_size=None,
  ):
      """Returns a serving input receiver fn for a standard data format.
  
      Args:
        data_format: (string) See RankingDataFormat.
        context_feature_spec: (dict) Map from feature keys to `FixedLenFeature` or
          `VarLenFeature` values.
        example_feature_spec: (dict) Map from  feature keys to `FixedLenFeature` or
          `VarLenFeature` values.
        list_size: (int) The number of examples to keep. If specified, truncation or
          padding may happen. Otherwise, set it to None to allow dynamic list size
          (recommended).
        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.
        receiver_name: (string) The name for the receiver tensor.
        default_batch_size: (int) Number of instances expected per batch. Leave
          unset for variable batch size (recommended).
  
      Returns:
        A `tf.estimator.export.ServingInputReceiver` object, which packages the
        placeholders and the resulting feature Tensors together.
      """
  
      parsing_fn = make_parsing_fn(
          data_format,
          list_size=list_size,
          size_feature_name=size_feature_name,
          mask_feature_name=mask_feature_name,
          context_feature_spec=context_feature_spec,
          example_feature_spec=example_feature_spec,
      )
      return build_ranking_serving_input_receiver_fn_with_parsing_fn(
          parsing_fn, receiver_name, default_batch_size
      )
  
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 12 for calling a global function with example usage: build_ranking_serving_input_receiver_fn(data_format, context_feature_spec, example_feature_spec, list_size, size_feature_name, mask_feature_name, receiver_name, default_batch_size) and returns: build_ranking_serving_input_receiver_fn_with_parsing_fn