code: def main(args):
      os.environ["PADDLESEG_EXPORT_STAGE"] = "True"
      cfg = Config(args.cfg)
      net = cfg.model
  
      if args.model_path:
          para_state_dict = paddle.load(args.model_path)
          net.set_dict(para_state_dict)
          logger.info("Loaded trained params of model successfully.")
  
      if args.input_shape is None:
          shape = [None, 3, None, None]
      else:
          shape = args.input_shape
  
      new_net = net
      new_net.eval()
      new_net = paddle.jit.to_static(
          new_net,
          input_spec=[
              paddle.static.InputSpec(shape=shape, dtype="float32")
          ],
      )
      save_path = os.path.join(args.save_dir, "model")
      paddle.jit.save(new_net, save_path)
  
      yml_file = os.path.join(args.save_dir, "deploy.yaml")
      with open(yml_file, "w") as file:
          transforms = cfg.export_config.get(
              "transforms",
              [
                  {"type": "Resize", "target_size": [640, 368]},
                  {"type": "Normalize"},
              ],
          )
          data = {
              "Deploy": {
                  "transforms": transforms,
                  "model": "model.pdmodel",
                  "params": "model.pdiparams",
              }
          }
          yaml.dump(data, file)
  
      logger.info(f"Model is saved in {args.save_dir}.")
  
file_path: PaddleSeg/contrib/LaneSeg/export.py
imports: import argparse
  import os
  import paddle
  import yaml
  from paddleseg.cvlibs import Config
  from paddleseg.utils import logger
  from datasets import tusimple
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types