code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train parall face network"
      )
      # general
      parser.add_argument(
          "--dataset", default=default.dataset, help="dataset config"
      )
      parser.add_argument(
          "--network", default=default.network, help="network config"
      )
      parser.add_argument(
          "--loss", default=default.loss, help="loss config"
      )
      args, rest = parser.parse_known_args()
      generate_config(args.network, args.dataset, args.loss)
      parser.add_argument(
          "--models-root",
          default=default.models_root,
          help="root directory to save model.",
      )
      parser.add_argument(
          "--pretrained",
          default=default.pretrained,
          help="pretrained model to load",
      )
      parser.add_argument(
          "--pretrained-epoch",
          type=int,
          default=default.pretrained_epoch,
          help="pretrained epoch to load",
      )
      parser.add_argument(
          "--ckpt",
          type=int,
          default=default.ckpt,
          help="checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save",
      )
      parser.add_argument(
          "--verbose",
          type=int,
          default=default.verbose,
          help="do verification testing and model saving every verbose batches",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=default.lr,
          help="start learning rate",
      )
      parser.add_argument(
          "--lr-steps",
          type=str,
          default=default.lr_steps,
          help="steps of lr changing",
      )
      parser.add_argument(
          "--wd", type=float, default=default.wd, help="weight decay"
      )
      parser.add_argument(
          "--mom", type=float, default=default.mom, help="momentum"
      )
      parser.add_argument(
          "--frequent", type=int, default=default.frequent, help=""
      )
      parser.add_argument(
          "--per-batch-size",
          type=int,
          default=default.per_batch_size,
          help="batch size in each context",
      )
      parser.add_argument(
          "--kvstore",
          type=str,
          default=default.kvstore,
          help="kvstore setting",
      )
      parser.add_argument(
          "--worker-id",
          type=int,
          default=0,
          help="worker id for dist training, starts from 0",
      )
      parser.add_argument(
          "--extra-model-name",
          type=str,
          default="",
          help="extra model name",
      )
      args = parser.parse_args()
      return args
  
imports: import os
  import sys
  import math
  import random
  import logging
  import pickle
  import sklearn
  import numpy
  from image_iter import FaceImageIter
  import mxnet
  from mxnet import ndarray
  import argparse
  import mxnet.optimizer
  from config import config, default, generate_config
  import verification
  import fresnet
  import fmobilefacenet
  import fmobilenet
  import fmnasnet
  import fdensenet
  import vargfacenet
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args