class_docstr: A custom ranking pipeline for dasalc model.
class_docstr_tok: ["A","custom","ranking","pipeline","for","dasalc","model","."]
docstr: Returns `Estimator` `input_fn` for serving the model.
docstr_tok: ["Returns","`","Estimator","`","`","input_fn","`","for","serving","the","model","."]
code: def _make_serving_input_fn(self):
      """Returns `Estimator` `input_fn` for serving the model."""
      context_feature_spec = tf.feature_column.make_parse_example_spec(
          context_feature_columns().values()
      )
      example_feature_spec = tf.feature_column.make_parse_example_spec(
          example_feature_columns().values()
      )
  
      serving_input_receiver_fn = (
          tfr.data.build_ranking_serving_input_receiver_fn(
              data_format="example_list_with_context",
              context_feature_spec=context_feature_spec,
              example_feature_spec=example_feature_spec,
              mask_feature_name=_MASK,
          )
      )
  
      return serving_input_receiver_fn
  
file_path: ranking/tensorflow_ranking/research/dasalc.py
imports: from absl import flags
  import tensorflow
  from tensorflow import estimator
  from tensorflow.python.estimator.canned import optimizers
  import tensorflow_ranking
label_desc: Use image node_id 1 for calling the DASALCPipeline obj's underlying member method code with example usage: obj._make_serving_input_fn() and returns: serving_input_receiver_fn