code: def build_tf_example_serving_input_receiver_fn(
      context_feature_spec,
      example_feature_spec,
      size_feature_name=None,
      mask_feature_name=None,
      default_batch_size=None,
  ):
      """Builds a serving input fn for `tensorflow.training.Example`.
  
      Args:
        context_feature_spec: (dict) Map from feature keys to `FixedLenFeature`,
          `VarLenFeature` or `RaggedFeature` values.
        example_feature_spec: (dict) Map from  feature keys to `FixedLenFeature`,
          `VarLenFeature` or `RaggedFeature` values.
        size_feature_name: (str) Name of feature for example list sizes. Populates
          the feature dictionary with a `tf.int32` Tensor of value 1, and 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.
        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 = functools.partial(
          parse_from_tf_example,
          **{
              "context_feature_spec": context_feature_spec,
              "example_feature_spec": example_feature_spec,
              "size_feature_name": size_feature_name,
              "mask_feature_name": mask_feature_name,
          },
      )
      return build_ranking_serving_input_receiver_fn_with_parsing_fn(
          parsing_fn=parsing_fn,
          receiver_name="input_ranking_data",
          default_batch_size=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 16 for calling a global function with example usage: build_tf_example_serving_input_receiver_fn(context_feature_spec, example_feature_spec, size_feature_name, mask_feature_name, default_batch_size) and returns: build_ranking_serving_input_receiver_fn_with_parsing_fn