code: def convert_prediction_values(values, serving_bundle, model_spec=None):
    
    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
docs: 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.