code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Convert MMDetection models to ONNX"
      )
      parser.add_argument("config", help="test config file path")
      parser.add_argument("checkpoint", help="checkpoint file")
      parser.add_argument(
          "--input-img", type=str, help="Images for input"
      )
      parser.add_argument(
          "--show", action="store_true", help="show onnx graph"
      )
      parser.add_argument("--output-file", type=str, default="")
      parser.add_argument("--opset-version", type=int, default=11)
      parser.add_argument(
          "--test-img", type=str, default=None, help="Images for test"
      )
      parser.add_argument(
          "--dataset", type=str, default="coco", help="Dataset name"
      )
      parser.add_argument(
          "--verify",
          action="store_true",
          help="verify the onnx model output against pytorch output",
      )
      parser.add_argument(
          "--shape",
          type=int,
          nargs="+",
          # default=[640, 640],
          # default=[384, 384],
          default=[-1, -1],
          help="input image size",
      )
      parser.add_argument(
          "--mean",
          type=float,
          nargs="+",
          default=[127.5, 127.5, 127.5],
          help="mean value used for preprocess input data",
      )
      parser.add_argument(
          "--std",
          type=float,
          nargs="+",
          default=[128.0, 128.0, 128.0],
          help="variance value used for preprocess input data",
      )
      args = parser.parse_args()
      return args
  
file_path: insightface/detection/scrfd/tools/scrfd2onnx.py
imports: import argparse
  import os.path
  import numpy
  import onnx
  import os
  import torch
  from mmdet.core import build_model_from_cfg, generate_inputs_and_wrap_model, preprocess_example_input
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args