code: def main(args):
      os.environ["PADDLESEG_EXPORT_STAGE"] = "True"
      cfg = Config(args.config)
      builder = SegBuilder(cfg)
  
      net = builder.model
  
      skip_quant(net)
      quantizer = QAT(config=quant_config)
      quantizer.quantize(net)
      logger.info("Quantize the model successfully")
  
      if args.model_path is not None:
          utils.load_entire_model(net, args.model_path)
          logger.info("Loaded trained params of model successfully")
  
      output_op = args.output_op
      if args.without_argmax:
          logger.warning(
              "`--without_argmax` will be deprecated. Please use `--output_op`."
          )
          output_op = "none"
      if args.with_softmax:
          logger.warning(
              "`--with_softmax` will be deprecated. Please use `--output_op`."
          )
          output_op = "softmax"
  
      new_net = (
          net if output_op == "none" else WrappedModel(net, output_op)
      )
  
      new_net.eval()
      if args.for_fd:
          save_name = "inference"
          yaml_name = "inference.yml"
      else:
          save_name = "model"
          yaml_name = "deploy.yaml"
      save_path = os.path.join(args.save_dir, save_name)
      shape = (
          [None, 3, None, None]
          if args.input_shape is None
          else args.input_shape
      )
      input_spec = [
          paddle.static.InputSpec(shape=shape, dtype="float32")
      ]
      quantizer.save_quantized_model(
          new_net, save_path, input_spec=input_spec
      )
  
      yml_file = os.path.join(args.save_dir, yaml_name)
      with open(yml_file, "w") as file:
          transforms = cfg.val_dataset_cfg.get(
              "transforms", [{"type": "Normalize"}]
          )
          data = {
              "Deploy": {
                  "transforms": transforms,
                  "model": save_name + ".pdmodel",
                  "params": save_name + ".pdiparams",
              }
          }
          yaml.dump(data, file)
  
      logger.info(
          f"The quantized inference model is saved in {args.save_dir}."
      )
  
file_path: PaddleSeg/deploy/slim/quant/qat_export.py
imports: import argparse
  import os
  import yaml
  import paddle
  from paddleslim import QAT
  from paddleseg.cvlibs import Config, SegBuilder
  from paddleseg.utils import logger, utils
  from paddleseg.deploy.export import WrappedModel
  from qat_config import quant_config
  from qat_train import skip_quant
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types