code: def parse_infer_args(*args, **kwargs):
      parser = argparse.ArgumentParser(description="Model inference")
      parser.add_argument(
          "--config",
          dest="cfg",
          help="Config file.",
          default=None,
          type=str,
          required=True,
      )
      parser.add_argument(
          "--image_path",
          help="Directory or path or file list of the images to be predicted.",
          type=str,
          default=None,
          required=True,
      )
      parser.add_argument(
          "--batch_size",
          help="Mini batch size on each GPU (or on CPU).",
          type=int,
          default=1,
      )
      parser.add_argument(
          "--save_dir",
          help="Directory to save the predicted results.",
          type=str,
          default="./output",
      )
      parser.add_argument(
          "--device",
          choices=["cpu", "gpu", "xpu", "npu"],
          default="gpu",
          help="Select which device to perform inference with. Defaults to gpu.",
      )
      parser.add_argument(
          "--use_trt",
          default=False,
          type=literal_eval,
          choices=[True, False],
          help="Whether or not to use NVIDIA TensorRT to accelerate prediction.",
      )
      parser.add_argument(
          "--precision",
          default="fp32",
          type=str,
          choices=["fp32", "fp16", "int8"],
          help="TensorRT precision.",
      )
      parser.add_argument(
          "--min_subgraph_size",
          default=3,
          type=int,
          help="Minimum subgraph size in TensorRT optimization.",
      )
      parser.add_argument(
          "--enable_auto_tune",
          default=False,
          type=literal_eval,
          choices=[True, False],
          help="Whether or not to enable tuned dynamic shape. We uses some images to collect "
          "the dynamic shape for trt sub graph, which avoids setting dynamic shape manually.",
      )
      parser.add_argument(
          "--auto_tuned_shape_file",
          type=str,
          default="auto_tune_tmp.pbtxt",
          help="Path of the temp file to save tuned dynamic shape.",
      )
  
      parser.add_argument(
          "--cpu_threads",
          default=10,
          type=int,
          help="Number of threads to use when using CPU.",
      )
      parser.add_argument(
          "--enable_mkldnn",
          default=False,
          type=literal_eval,
          choices=[True, False],
          help="Whether or not to enable the use of mkldnn when using CPU.",
      )
  
      parser.add_argument(
          "--benchmark",
          type=literal_eval,
          default=False,
          choices=[True, False],
          help="Whether or not to print information about environment, model, configuration and performance.",
      )
      parser.add_argument(
          "--model_name",
          default="",
          type=str,
          help="When `--benchmark` is True, the specified model name is displayed.",
      )
      parser.add_argument(
          "--print_detail",
          default=True,
          type=literal_eval,
          choices=[True, False],
          help="Whether or not to print GLOG information of Paddle Inference.",
      )
  
      return parser.parse_args(*args, **kwargs)
  
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 1 for calling a global function with example usage: parse_infer_args() and returns: parser