code: def main(args):
      imgs_list = get_image_list(
          args.image_path
      )  # get image list from image path
  
      # support autotune to collect dynamic shape, works only with trt on.
      if use_auto_tune(args):
          tune_img_nums = 10
          auto_tune(args, imgs_list, tune_img_nums)
  
      # infer with paddle inference.
      predictor = Predictor(args)
      predictor.run(imgs_list)
  
      if use_auto_tune(args) and os.path.exists(
          args.auto_tuned_shape_file
      ):
          os.remove(args.auto_tuned_shape_file)
  
      # test the speed.
      if args.benchmark:
          predictor.autolog.report()
  
imports: import os
  import sys
  import codecs
  import warnings
  import argparse
  import yaml
  import functools
  import numpy
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  import paddle
  import medicalseg.transforms
  from medicalseg.cvlibs import manager
  from medicalseg.utils import get_sys_env, logger, get_image_list
  from medicalseg.utils.visualize import get_pseudo_color_map
  from medicalseg.core.infer import sliding_window_inference
  from tools import HUnorm, resample
  from tools import Prep
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) without return types