input: Write a function to create a Python function named "Transpile" that takes 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 that the function can be called with valid inputs and outputs correctly.
  
  assert Transpile("path/to/src", "path/to/dst", "model.pdmodel", "model.pdparams")==None
output: 
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      import paddle.fluid as fluid
      import os
      
      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
      )
  