code: def infer_with_args(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 osp.exists(args.auto_tuned_shape_file):
          os.remove(args.auto_tuned_shape_file)
  
      if args.benchmark:
          predictor.autolog.report()
  
imports: import os
  import os.path
  import argparse
  from ast import literal_eval
  import numpy
  from PIL import Image
  from paddle.inference import create_predictor, PrecisionType
  from paddle.inference import Config
  from paddleseg.utils import logger, get_image_list
  from paddleseg.deploy.infer import DeployConfig
  from paddlepanseg.cvlibs.info_dicts import build_info_dict
  from paddlepanseg.transforms.functional import id2rgb
label_desc: Use image node_id 4 for calling a global function with example usage: infer_with_args(args) without return types