code: def _inner_input_fn():
      context_feature_spec = tf.feature_column.make_parse_example_spec(  # pylint: disable=g-deprecated-tf-checker
          list(context_feature_column().values())
      )
      label_column = tf.feature_column.numeric_column(  # pylint: disable=g-deprecated-tf-checker
          _LABEL_FEATURE, default_value=_PADDING_LABEL
      )
      example_feature_spec = tf.feature_column.make_parse_example_spec(  # pylint: disable=g-deprecated-tf-checker
          list(example_feature_columns().values()) + [label_column]
      )
      dataset = data.build_ranking_dataset(
          file_pattern=DATA_FILE,
          data_format=data.ELWC,
          batch_size=10,
          context_feature_spec=context_feature_spec,
          example_feature_spec=example_feature_spec,
          list_size=2,
          reader=tf.data.TFRecordDataset,
      )
      features = tf.compat.v1.data.make_one_shot_iterator(
          dataset
      ).get_next()
      label = tf.squeeze(features.pop(_LABEL_FEATURE), axis=2)
      return features, label
  
imports: import os
  from absl.testing import parameterized
  import six
  import tensorflow
  from tensorflow import estimator
  from google.protobuf import text_format
  from tensorflow_ranking.python import data
  from tensorflow_ranking.python import estimator
  from tensorflow_ranking.python import feature
  from tensorflow_serving.apis import input_pb2
label_desc: Use image node_id 3 for calling a global function with example usage: _inner_input_fn() and returns: features,  label