def METHOD_NAME(model_path, x_values):
  # Create the tflm interpreter
  tflm_interpreter = runtime.Interpreter.from_file(model_path)
  input_shape = np.array(tflm_interpreter.get_input_details(0).get('shape'))
  y_predictions = np.empty(x_values.size, dtype=np.float32)
  for i, x_value in enumerate(x_values):
    y_predictions[i] = invoke_tflm_interpreter(input_shape,
                                               tflm_interpreter,
                                               x_value,
                                               input_index=0,
                                               output_index=0)
  return y_predictions