code: def argsparser():
      parser = argparse.ArgumentParser(description=__doc__)
      parser.add_argument(
          "--model_dir",
          type=str,
          default=None,
          help=(
              "Directory include:'model.pdiparams', 'model.pdmodel', "
              "'infer_cfg.yml', created by tools/export_model.py."
          ),
          required=True,
      )
      parser.add_argument(
          "--image_file",
          type=str,
          default=None,
          help="Path of image file.",
      )
      parser.add_argument(
          "--image_dir",
          type=str,
          default=None,
          help="Dir of image file, `image_file` has a higher priority.",
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="batch_size for inference.",
      )
      parser.add_argument(
          "--video_file",
          type=str,
          default=None,
          help="Path of video file, `video_file` or `camera_id` has a highest priority.",
      )
      parser.add_argument(
          "--camera_id",
          type=int,
          default=-1,
          help="device id of camera to predict.",
      )
      parser.add_argument(
          "--threshold",
          type=float,
          default=0.5,
          help="Threshold of score.",
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="output",
          help="Directory of output visualization files.",
      )
      parser.add_argument(
          "--run_mode",
          type=str,
          default="paddle",
          help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)",
      )
      parser.add_argument(
          "--device",
          type=str,
          default="cpu",
          help="Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU.",
      )
      parser.add_argument(
          "--use_gpu",
          type=ast.literal_eval,
          default=False,
          help="Deprecated, please use `--device`.",
      )
      parser.add_argument(
          "--run_benchmark",
          type=ast.literal_eval,
          default=False,
          help="Whether to predict a image_file repeatedly for benchmark",
      )
      parser.add_argument(
          "--enable_mkldnn",
          type=ast.literal_eval,
          default=False,
          help="Whether use mkldnn with CPU.",
      )
      parser.add_argument(
          "--enable_mkldnn_bfloat16",
          type=ast.literal_eval,
          default=False,
          help="Whether use mkldnn bfloat16 inference with CPU.",
      )
      parser.add_argument(
          "--cpu_threads",
          type=int,
          default=1,
          help="Num of threads with CPU.",
      )
      parser.add_argument(
          "--trt_min_shape",
          type=int,
          default=1,
          help="min_shape for TensorRT.",
      )
      parser.add_argument(
          "--trt_max_shape",
          type=int,
          default=1280,
          help="max_shape for TensorRT.",
      )
      parser.add_argument(
          "--trt_opt_shape",
          type=int,
          default=640,
          help="opt_shape for TensorRT.",
      )
      parser.add_argument(
          "--trt_calib_mode",
          type=bool,
          default=False,
          help="If the model is produced by TRT offline quantitative "
          "calibration, trt_calib_mode need to set True.",
      )
      parser.add_argument(
          "--save_images",
          type=ast.literal_eval,
          default=True,
          help="Save visualization image results.",
      )
      parser.add_argument(
          "--save_mot_txts",
          action="store_true",
          help="Save tracking results (txt).",
      )
      parser.add_argument(
          "--save_mot_txt_per_img",
          action="store_true",
          help="Save tracking results (txt) for each image.",
      )
      parser.add_argument(
          "--scaled",
          type=bool,
          default=False,
          help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
          "True in general detector.",
      )
      parser.add_argument(
          "--tracker_config",
          type=str,
          default=None,
          help=("tracker donfig"),
      )
      parser.add_argument(
          "--reid_model_dir",
          type=str,
          default=None,
          help=(
              "Directory include:'model.pdiparams', 'model.pdmodel', "
              "'infer_cfg.yml', created by tools/export_model.py."
          ),
      )
      parser.add_argument(
          "--reid_batch_size",
          type=int,
          default=50,
          help="max batch_size for reid model inference.",
      )
      parser.add_argument(
          "--use_dark",
          type=ast.literal_eval,
          default=True,
          help="whether to use darkpose to get better keypoint position predict ",
      )
      parser.add_argument(
          "--action_file",
          type=str,
          default=None,
          help="Path of input file for action recognition.",
      )
      parser.add_argument(
          "--window_size",
          type=int,
          default=50,
          help="Temporal size of skeleton feature for action recognition.",
      )
      parser.add_argument(
          "--random_pad",
          type=ast.literal_eval,
          default=False,
          help="Whether do random padding for action recognition.",
      )
      parser.add_argument(
          "--save_results",
          action="store_true",
          default=False,
          help="Whether save detection result to file using coco format",
      )
      parser.add_argument(
          "--use_coco_category",
          action="store_true",
          default=False,
          help="Whether to use the coco format dictionary `clsid2catid`",
      )
      parser.add_argument(
          "--slice_infer",
          action="store_true",
          help="Whether to slice the image and merge the inference results for small object detection.",
      )
      parser.add_argument(
          "--slice_size",
          nargs="+",
          type=int,
          default=[640, 640],
          help="Height of the sliced image.",
      )
      parser.add_argument(
          "--overlap_ratio",
          nargs="+",
          type=float,
          default=[0.25, 0.25],
          help="Overlap height ratio of the sliced image.",
      )
      parser.add_argument(
          "--combine_method",
          type=str,
          default="nms",
          help="Combine method of the sliced images' detection results, choose in ['nms', 'nmm', 'concat'].",
      )
      parser.add_argument(
          "--match_threshold",
          type=float,
          default=0.6,
          help="Combine method matching threshold.",
      )
      parser.add_argument(
          "--match_metric",
          type=str,
          default="ios",
          help="Combine method matching metric, choose in ['iou', 'ios'].",
      )
      parser.add_argument(
          "--collect_trt_shape_info",
          action="store_true",
          default=False,
          help="Whether to collect dynamic shape before using tensorrt.",
      )
      parser.add_argument(
          "--tuned_trt_shape_file",
          type=str,
          default="shape_range_info.pbtxt",
          help="Path of a dynamic shape file for tensorrt.",
      )
      parser.add_argument("--use_fd_format", action="store_true")
      return parser
  
imports: import time
  import os
  import ast
  import argparse
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: argsparser() and returns: parser