code: def compile(program, loss=None):
      """transfer the program into a new program that runs in multi-cpus or multi-gpus.
      This function uses the `fluid.compiler.CompiledProgram` to transfer the program.
      For more detail about speeding the program, please visit
      "https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/user_guides/howto/training/single_node.html#id7"
  
      Args:
          program(fluid.Program): a normal fluid program.
          loss_name(str): Optional. The loss tensor of a trainable program. Set it to None if you are transferring a prediction or evaluation program.
      """
      loss_name = None
      if loss is not None:
          assert isinstance(
              loss, fluid.framework.Variable
          ), "type of loss is expected to be a fluid tensor"
          loss_name = loss.name
      # TODO: after solving the learning rate issue that occurs in training A2C algorithm, set it to 3.
      os.environ["CPU_NUM"] = "1"
      exec_strategy = fluid.ExecutionStrategy()
      exec_strategy.num_threads = 3 * 4
  
      build_strategy = fluid.BuildStrategy()
      build_strategy.reduce_strategy = (
          fluid.BuildStrategy.ReduceStrategy.Reduce
      )
  
      compiled_program = fluid.compiler.CompiledProgram(
          program
      ).with_data_parallel(
          loss_name=loss_name,
          exec_strategy=exec_strategy,
          build_strategy=build_strategy,
      )
      compiled_program._init_program = program
      return compiled_program
  
label_desc: Use image node_id 1 for calling a global function with example usage: compile(program, loss) and returns: compiled_program