input: Write a function to transpile an inference model by taking in four arguments: "src_dir", "dst_dir", "model_filename", and "params_filename". Inside the function, create a CPUPlace object and an Executor object using Paddle Fluid. Then, create a Scope object using fluid.core.Scope(). Load the inference program, feed target names, and fetch targets using fluid.io.load_inference_model() with the "src_dir", "exe", "model_filename", and "params_filename" arguments. Clone the inference program and apply the inference transpiler using fluid.InferenceTranspiler() and t.transpile(). Save the optimized inference program to "dst_dir" using fluid.io.save_persistables() and the "inference_transpiler_program" and "params_filename" arguments. If "dst_dir" does not exist, create it using os.makedirs(). Finally, create a main function that takes in arguments using argparse.ArgumentParser() and calls the "Transpile" function with the parsed arguments.
  assert Transpile('/src', '/dst', 'model', 'params')==None
  assert Transpile('/src_empty', '/dst_empty', 'model_empty', 'params_empty')==None
output: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      import os
      import paddle.fluid as fluid
  
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
      scope = fluid.core.Scope()
  
      [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(
          dirname=src_dir,
          executor=exe,
          model_filename=model_filename,
          params_filename=params_filename
      )
  
      t = fluid.InferenceTranspiler()
      inference_transpiler_program = inference_program.clone()
      t.transpile(inference_transpiler_program, place)
  
      if not os.path.exists(dst_dir):
          os.makedirs(dst_dir)
  
      fluid.io.save_persistables(
          executor=exe,
          dirname=dst_dir,
          main_program=inference_transpiler_program,
          filename=params_filename
      )
  