code: def parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--dataroot",
          required=True,
          help="path to images (should have subfolders trainA, trainB, valA, valB, etc)",
      )
      parser.add_argument(
          "--batchSize", type=int, default=1, help="input batch size"
      )
      parser.add_argument(
          "--loadSize",
          type=int,
          default=286,
          help="scale images to this size",
      )
      parser.add_argument(
          "--fineSize",
          type=int,
          default=256,
          help="then crop to this size",
      )
      parser.add_argument(
          "--output_nc",
          type=int,
          default=3,
          help="# of output image channels",
      )
      parser.add_argument(
          "--ngf",
          type=int,
          default=64,
          help="# of gen filters in first conv layer",
      )
      parser.add_argument(
          "--ndf",
          type=int,
          default=64,
          help="# of discrim filters in first conv layer",
      )
      parser.add_argument(
          "--which_model_netD",
          type=str,
          default="basic",
          help="selects model to use for netD",
      )
      parser.add_argument(
          "--which_model_netG",
          type=str,
          default="resnet_9blocks",
          help="selects model to use for netG",
      )
      parser.add_argument(
          "--n_layers_D",
          type=int,
          default=3,
          help="only used if which_model_netD==n_layers",
      )
      parser.add_argument(
          "--gpu_ids",
          type=str,
          default="0",
          help="gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU",
      )
      parser.add_argument(
          "--no_dropout",
          action="store_false",
          help="no dropout for the generator",
      )
      parser.add_argument(
          "--epoch_count",
          type=int,
          default=1,
          help="the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...",
      )
      parser.add_argument(
          "--lambda_A",
          type=float,
          default=10.0,
          help="weight for cycle loss (A -> B -> A)",
      )
      parser.add_argument(
          "--lambda_B",
          type=float,
          default=10.0,
          help="weight for cycle loss (B -> A -> B)",
      )
      parser.add_argument(
          "--lambda_idt",
          type=float,
          default=0.5,
          help="use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1",
      )
      parser.add_argument(
          "--beta1",
          type=float,
          default=0.5,
          help="momentum term of adam",
      )
      parser.add_argument(
          "--lr",
          type=float,
          default=0.0002,
          help="initial learning rate for adam",
      )
      parser.add_argument(
          "--pool_size",
          type=int,
          default=50,
          help="the size of image buffer that stores previously generated images",
      )
      parser.add_argument(
          "--workers",
          type=int,
          help="number of data loading workers",
          default=4,
      )
      parser.add_argument(
          "--niter",
          type=int,
          default=100,
          help="# of iter at starting learning rate",
      )
      parser.add_argument(
          "--niter_decay",
          type=int,
          default=100,
          help="# of iter to linearly decay learning rate to zero",
      )
      parser.add_argument(
          "--experiment", default=None, help="Where to store models"
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=233,
          help="Random seed to be fixed.",
      )
  
      opt = parser.parse_args()
      print(opt)
      return opt
  
imports: import argparse
  import random
  from mxnet import gluon, image, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.base import numeric_types
  import gluoncv
  from gluoncv import utils
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import nn
  import mxnet.ndarray
  import mxnet
  import os
  import numpy
  from mxboard import SummaryWriter
label_desc: Use image node_id 6 for calling a global function with example usage: parse_args() and returns: opt