code: def main(args):
      if not os.path.exists(args.save_dir):
          os.makedirs(args.save_dir)
  
      if use_auto_tune(args):
          if args.resize_width == 0 and args.resize_height == 0:
              auto_tune(args, args.image_path, 1)
          else:
              img = np.random.rand(
                  1, 3, args.resize_height, args.resize_width
              ).astype("float32")
              auto_tune(args, img, 1)
  
      predictor = PredictorBenchmark(args)
      predictor.run(args.image_path)
  
      if use_auto_tune(args) and os.path.exists(
          args.auto_tuned_shape_file
      ):
          os.remove(args.auto_tuned_shape_file)
  
file_path: PaddleSeg/deploy/python/infer_benchmark.py
imports: import argparse
  import codecs
  import os
  import time
  import yaml
  import numpy
  from paddleseg.deploy.infer import DeployConfig
  from paddleseg.utils import logger
  from paddleseg.utils.visualize import get_pseudo_color_map
  from infer import auto_tune, use_auto_tune, Predictor
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types