code: def set_regressor_interface_params(
      spec, features, output_features, training_features=None
  ):
      """
      Common utilities to set the regressor interface params.
      """
      if output_features is None:
          output_features = [("predicted_class", datatypes.Double())]
      else:
          output_features = _fm.process_or_validate_features(
              output_features, 1
          )
  
      if len(output_features) != 1:
          raise ValueError(
              "Provided output features for a regressor must be "
              "one Double feature."
          )
  
      if output_features[0][1] != datatypes.Double():
          raise ValueError(
              "Output type of a regressor must be a Double."
          )
  
      prediction_name = output_features[0][0]
      spec.description.predictedFeatureName = prediction_name
  
      # Normalize the features list.
      features = _fm.process_or_validate_features(features)
  
      # add input and output features
      for cur_input_name, feature_type in features:
          input_ = spec.description.input.add()
          input_.name = cur_input_name
          datatypes._set_datatype(input_.type, feature_type)
  
      # Add training features
      if training_features is not None:
          spec = set_training_features(spec, training_features)
  
      output_ = spec.description.output.add()
      output_.name = prediction_name
      datatypes._set_datatype(output_.type, "Double")
      return spec
  
imports: from ..proto import Model_pb2
  from .None import _feature_management
  from .None import datatypes
label_desc: Use image node_id 2 for calling a global function with example usage: set_regressor_interface_params(spec, features, output_features, training_features) and returns: spec