input: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      """ Transpile a given inference model from a source directory to a destination directory using Paddle Fluid.
      Create a CPUPlace object and an Executor object using Paddle Fluid. Load the inference program, feed target names, and fetch targets using fluid.io.load_inference_model(). Clone the inference program and apply the inference transpiler using fluid.InferenceTranspiler(). Save the optimized inference program to the destination directory. If the destination directory does not exist, create it using os.makedirs().
      >>> Transpile('/path/to/source', '/path/to/destination', 'model.pdmodel', 'model.pdparams')
      Loading inference_program from /path/to/source
      Saving the optimized inference_program to /path/to/destination
      """
  
  import os
  import sys
  import argparse
  import paddle.fluid as fluid
  
  
  def Transpile(src_dir, dst_dir, model_filename, params_filename):
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
  
  
  def main():
      parser = argparse.ArgumentParser()
      parser.add_argument('--src_dir', help='Source directory of inference model')
      parser.add_argument('--dst_dir', help='Destination directory of inference model')
      parser.add_argument('--model_filename', default=None, help='The name of model file')
      parser.add_argument('--params_filename', default=None, help='The name of params file')
      args = parser.parse_args()
      Transpile(args.src_dir, args.dst_dir, args.model_filename, args.params_filename)
  
  
  if __name__ == '__main__':
      main()
output:     
      place = fluid.CPUPlace()
      exe = fluid.Executor(place)
  
      inference_scope = fluid.core.Scope()
      with fluid.scope_guard(inference_scope):
          print("Loading inference_program from ", src_dir)
          [inference_program, feed_target_names,
           fetch_targets] = fluid.io.load_inference_model(src_dir, exe, model_filename, params_filename)
  
          inference_transpiler_program = inference_program.clone()
          t = fluid.InferenceTranspiler()
          t.transpile(inference_transpiler_program, place)
  
          print("Saving the optimized inference_program to ", dst_dir)
          if not os.path.isdir(dst_dir):
              os.makedirs(dst_dir)
          model_path = os.path.join(dst_dir, model_filename)
          with open(model_path, "wb") as f:
              f.write(inference_transpiler_program.desc.serialize_to_string())
          fluid.io.save_persistables(exe, dst_dir, inference_transpiler_program, params_filename)
  