code: def parse_args():
      parser = argparse.ArgumentParser(description="Train RetinaFace")
      # general
      parser.add_argument(
          "--network",
          help="network name",
          default=default.network,
          type=str,
      )
      parser.add_argument(
          "--dataset",
          help="dataset name",
          default=default.dataset,
          type=str,
      )
      args, rest = parser.parse_known_args()
      generate_config(args.network, args.dataset)
      parser.add_argument(
          "--image_set",
          help="image_set name",
          default=default.image_set,
          type=str,
      )
      parser.add_argument(
          "--root_path",
          help="output data folder",
          default=default.root_path,
          type=str,
      )
      parser.add_argument(
          "--dataset_path",
          help="dataset path",
          default=default.dataset_path,
          type=str,
      )
      # training
      parser.add_argument(
          "--frequent",
          help="frequency of logging",
          default=default.frequent,
          type=int,
      )
      parser.add_argument(
          "--kvstore",
          help="the kv-store type",
          default=default.kvstore,
          type=str,
      )
      parser.add_argument(
          "--work_load_list",
          help="work load for different devices",
          default=None,
          type=list,
      )
      parser.add_argument(
          "--no_flip", help="disable flip images", action="store_true"
      )
      parser.add_argument(
          "--no_shuffle",
          help="disable random shuffle",
          action="store_true",
      )
      # e2e
      # parser.add_argument('--gpus', help='GPU device to train with', default='0,1,2,3', type=str)
      parser.add_argument(
          "--pretrained",
          help="pretrained model prefix",
          default=default.pretrained,
          type=str,
      )
      parser.add_argument(
          "--pretrained_epoch",
          help="pretrained model epoch",
          default=default.pretrained_epoch,
          type=int,
      )
      parser.add_argument(
          "--prefix",
          help="new model prefix",
          default=default.prefix,
          type=str,
      )
      parser.add_argument(
          "--begin_epoch",
          help="begin epoch of training, use with resume",
          default=0,
          type=int,
      )
      parser.add_argument(
          "--end_epoch",
          help="end epoch of training",
          default=default.end_epoch,
          type=int,
      )
      parser.add_argument(
          "--lr",
          help="base learning rate",
          default=default.lr,
          type=float,
      )
      parser.add_argument(
          "--lr_step",
          help="learning rate steps (in epoch)",
          default=default.lr_step,
          type=str,
      )
      parser.add_argument(
          "--wd", help="weight decay", default=default.wd, type=float
      )
      args = parser.parse_args()
      return args
  
imports: from __future__ import print_function
  import sys
  import argparse
  import os
  import pprint
  import re
  import mxnet
  import numpy
  from mxnet.module import Module
  import mxnet.optimizer
  from rcnn.logger import logger
  from rcnn.config import config, default, generate_config
  from rcnn.symbol import *
  from rcnn.core import callback, metric
  from rcnn.core.loader import CropLoader, CropLoader2
  from rcnn.core.module import MutableModule
  from rcnn.utils.load_data import load_gt_roidb, merge_roidb, filter_roidb
  from rcnn.utils.load_model import load_param
label_desc: Use image node_id 3 for calling a global function with example usage: parse_args() and returns: args