code: def main(args):
      imgs_list, _ = get_image_list(args.image_path)
  
      # collect dynamic shape by auto_tune
      if use_auto_tune(args):
          tune_img_nums = 10
          auto_tune(args, imgs_list, tune_img_nums)
  
      # create and run predictor
      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)
  
      if args.benchmark:
          predictor.autolog.report()
  
imports: import argparse
  import os
  import numpy
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from paddleseg.deploy.infer import DeployConfig
  from paddleseg.utils import get_image_list, logger
  from paddleseg.utils.visualize import get_pseudo_color_map
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) without return types