code: def call_servo(examples, serving_bundle):
    
    parsed_url = urlparse('http://' + serving_bundle.inference_address)
    channel = implementations.insecure_channel(parsed_url.hostname,
                                               parsed_url.port)
    stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
  
    if serving_bundle.use_predict:
      request = predict_pb2.PredictRequest()
    elif serving_bundle.model_type == 'classification':
      request = classification_pb2.ClassificationRequest()
    else:
      request = regression_pb2.RegressionRequest()
    request.model_spec.name = serving_bundle.model_name
    if serving_bundle.model_version is not None:
      request.model_spec.version.value = serving_bundle.model_version
    if serving_bundle.signature is not None:
      request.model_spec.signature_name = serving_bundle.signature
  
    if serving_bundle.use_predict:
      # tf.compat.v1 API used here to convert tf.example into proto. This
      # utility file is bundled in the witwidget pip package which has a dep
      # on TensorFlow.
      request.inputs[serving_bundle.predict_input_tensor].CopyFrom(
        tf.compat.v1.make_tensor_proto(
          values=[ex.SerializeToString() for ex in examples],
          dtype=types_pb2.DT_STRING))
    else:
      request.input.example_list.examples.extend(examples)
  
    if serving_bundle.use_predict:
      return common_utils.convert_predict_response(
        stub.Predict(request, 30.0), serving_bundle) # 30 secs timeout
    elif serving_bundle.model_type == 'classification':
      return stub.Classify(request, 30.0)  # 30 secs timeout
    else:
      return stub.Regress(request, 30.0)
docs: Send an RPC request to the Servomatic prediction service.
  
    Args:
      examples: A list of examples that matches the model spec.
      serving_bundle: A `ServingBundle` object that contains the information to
        make the serving request.
  
    Returns:
      A ClassificationResponse or RegressionResponse proto.