code: def get_args(add_help=True):
      """
      parse args
      """
      import argparse
  
      def str2bool(v):
          return v.lower() in ("true", "t", "1")
  
      parser = argparse.ArgumentParser(
          description="PaddlePaddle Classification Training",
          add_help=add_help,
      )
  
      parser.add_argument(
          "--model_dir", default=None, help="inference model dir"
      )
      parser.add_argument("--batch_size", type=int, default=1)
      parser.add_argument(
          "--short_size", default=1024, type=int, help="short size"
      )
      parser.add_argument("--img_path", default="./images/demo.jpg")
  
      parser.add_argument(
          "--benchmark", default=False, type=str2bool, help="benchmark"
      )
      parser.add_argument(
          "--warmup", default=0, type=int, help="warmup iter"
      )
      parser.add_argument(
          "--polygon", action="store_true", help="output polygon or box"
      )
  
      parser.add_argument("--use_gpu", type=str2bool, default=True)
      parser.add_argument(
          "--use_tensorrt", type=str2bool, default=False
      )
      parser.add_argument("--precision", type=str, default="fp32")
      parser.add_argument("--gpu_mem", type=int, default=500)
      parser.add_argument("--gpu_id", type=int, default=0)
      parser.add_argument(
          "--enable_mkldnn", type=str2bool, default=False
      )
      parser.add_argument("--cpu_threads", type=int, default=10)
  
      args = parser.parse_args()
      return args
  
imports: import os
  import sys
  import pathlib
  import cv2
  import paddle
  from paddle import inference
  import numpy
  from PIL import Image
  from paddle.vision import transforms
  from tools.predict import resize_image
  from post_processing import get_post_processing
  from utils.util import draw_bbox, save_result
label_desc: Use image node_id 1 for calling a global function with example usage: get_args(add_help) and returns: args, unknown