code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--dataset",
          required=True,
          help="cifar10 | lsun | imagenet | folder | lfw ",
      )
      parser.add_argument(
          "--dataroot", required=True, help="path to dataset"
      )
      parser.add_argument(
          "--workers",
          type=int,
          help="number of data loading workers",
          default=2,
      )
      parser.add_argument(
          "--batchSize", type=int, default=64, help="input batch size"
      )
      parser.add_argument(
          "--imageSize",
          type=int,
          default=64,
          help="the height / width of the input image to network",
      )
      parser.add_argument(
          "--nc", type=int, default=3, help="input image channels"
      )
      parser.add_argument(
          "--nz",
          type=int,
          default=100,
          help="size of the latent z vector",
      )
      parser.add_argument("--ngf", type=int, default=64)
      parser.add_argument("--ndf", type=int, default=64)
      parser.add_argument(
          "--niter",
          type=int,
          default=25,
          help="number of epochs to train for",
      )
      parser.add_argument(
          "--lrD",
          type=float,
          default=0.00005,
          help="learning rate for Critic, default=0.00005",
      )
      parser.add_argument(
          "--lrG",
          type=float,
          default=0.00005,
          help="learning rate for Generator, default=0.00005",
      )
      parser.add_argument(
          "--beta1",
          type=float,
          default=0.5,
          help="beta1 for adam. default=0.5",
      )
      parser.add_argument(
          "--cuda", action="store_true", help="enables cuda"
      )
      parser.add_argument(
          "--ngpu", type=int, default=1, help="number of GPUs to use"
      )
      parser.add_argument(
          "--netG",
          default="",
          help="path to netG (to continue training)",
      )
      parser.add_argument(
          "--netD",
          default="",
          help="path to netD (to continue training)",
      )
      parser.add_argument("--clamp_lower", type=float, default=-0.01)
      parser.add_argument("--clamp_upper", type=float, default=0.01)
      parser.add_argument(
          "--Diters",
          type=int,
          default=5,
          help="number of D iters per each G iter",
      )
      parser.add_argument(
          "--noBN",
          action="store_true",
          help="use batchnorm or not (only for DCGAN)",
      )
      parser.add_argument(
          "--mlp_G", action="store_true", help="use MLP for G"
      )
      parser.add_argument(
          "--mlp_D", action="store_true", help="use MLP for D"
      )
      parser.add_argument(
          "--n_extra_layers",
          type=int,
          default=0,
          help="Number of extra layers on gen and disc",
      )
      parser.add_argument(
          "--experiment",
          default=None,
          help="Where to store samples and models",
      )
      parser.add_argument(
          "--adam",
          action="store_true",
          help="Whether to use adam (default is rmsprop)",
      )
      opt = parser.parse_args()
      print(opt)
      return opt
  
imports: from __future__ import print_function
  import argparse
  from mxnet.image import image
  from mxnet.gluon.data.vision import transforms, CIFAR10, ImageFolderDataset
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import Trainer, nn
  from mxnet.base import numeric_types
  from mxnet.initializer import *
  from mxnet import autograd
  import mxnet
  import mxnet.gluon
  import numpy
  import math
  import os
  from mxboard import SummaryWriter
  from lsun import LSUN
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: opt