code: def convert_prediction_values(
      values, serving_bundle, model_spec=None
  ):
      """Converts tensor values into ClassificationResponse or RegressionResponse.
  
      Args:
        values: For classification, a 2D list of numbers. The first dimension is for
          each example being predicted. The second dimension are the probabilities
          for each class ID in the prediction. For regression, a 1D list of numbers,
          with a regression score for each example being predicted.
        serving_bundle: A `ServingBundle` object that contains the information about
          the serving request that the response was generated by.
        model_spec: Optional model spec to put into the response.
  
      Returns:
        A ClassificationResponse or RegressionResponse.
      """
      if serving_bundle.model_type == "classification":
          response = classification_pb2.ClassificationResponse()
          for example_index in range(len(values)):
              classification = response.result.classifications.add()
              for class_index in range(len(values[example_index])):
                  class_score = classification.classes.add()
                  class_score.score = values[example_index][class_index]
                  class_score.label = str(class_index)
      else:
          response = regression_pb2.RegressionResponse()
          for example_index in range(len(values)):
              regression = response.result.regressions.add()
              regression.value = values[example_index]
      if model_spec:
          response.model_spec.CopyFrom(model_spec)
      return response
  
file_path: what-if-tool/utils/common_utils.py
imports: from tensorflow_serving.apis import classification_pb2
  from tensorflow_serving.apis import regression_pb2
label_desc: Use image node_id 2 for calling a global function with example usage: convert_prediction_values(values, serving_bundle, model_spec) and returns: response