input: Write a python function to transpile and save a PaddlePaddle inference model.
  assert os.path.isfile('dst_dir/model')==True
  assert os.path.isfile('dst_dir/params')==True
  assert Transpile('src_dir', 'dst_dir', 'model', 'params')==None
output: def Transpile(src_dir, dst_dir, model_filename, params_filename):
      import os
      import paddle
      from paddle.static import InputSpec
  
      # Define the input shape and data type
      input_spec = [InputSpec(shape=[None, 3, 224, 224], dtype='float32')]
  
      # Load the PaddlePaddle model
      model = paddle.jit.load(src_dir)
  
      # Save the inference model
      paddle.jit.save(model, os.path.join(dst_dir, model_filename), input_spec=input_spec)
  
      # Ensure the model and params files are saved
      assert os.path.isfile(os.path.join(dst_dir, model_filename)) == True
      assert os.path.isfile(os.path.join(dst_dir, params_filename)) == True