code: def parse_args():
      # Settings that may be overrided by parameters from nni
      parser = argparse.ArgumentParser(
          description="PyTorch Pix2pix Example"
      )
      parser.add_argument(
          "--ngf",
          type=int,
          default=64,
          help="# of generator filters in the last conv layer",
      )
      parser.add_argument(
          "--ndf",
          type=int,
          default=64,
          help="# of discriminator filters in the first conv layer",
      )
      parser.add_argument(
          "--netD",
          type=str,
          default="basic",
          help="specify discriminator architecture [basic | n_layers | pixel]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator",
      )
      parser.add_argument(
          "--netG",
          type=str,
          default="resnet_9blocks",
          help="specify generator architecture [resnet_9blocks | resnet_6blocks | unet_256 | unet_128]",
      )
      parser.add_argument(
          "--init_type",
          type=str,
          default="normal",
          help="network initialization [normal | xavier | kaiming | orthogonal]",
      )
      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(
          "--lr_policy",
          type=str,
          default="linear",
          help="learning rate policy. [linear | step | plateau | cosine]",
      )
      parser.add_argument(
          "--gan_mode",
          type=str,
          default="lsgan",
          help="the type of GAN objective. [vanilla| lsgan | wgangp]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.",
      )
      parser.add_argument(
          "--norm",
          type=str,
          default="instance",
          help="instance normalization or batch normalization [instance | batch | none]",
      )
      parser.add_argument(
          "--lambda_L1",
          type=float,
          default=100,
          help="weight of L1 loss in the generator objective",
      )
  
      # Additional training settings
      parser.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="input batch size for training (default: 1)",
      )
      parser.add_argument(
          "--n_epochs",
          type=int,
          default=100,
          help="number of epochs with the initial learning rate",
      )
      parser.add_argument(
          "--n_epochs_decay",
          type=int,
          default=100,
          help="number of epochs to linearly decay learning rate to zero",
      )
  
      args, _ = parser.parse_known_args()
      return args
  
imports: import sys
  import os
  import pathlib
  import logging
  import time
  import argparse
  from collections import namedtuple
  import numpy
  import torch
  import torch.utils.data
  import nni
  from nni.utils import merge_parameter
  from pix2pixlib.data.aligned_dataset import AlignedDataset
  from pix2pixlib.models.pix2pix_model import Pix2PixModel
  from base_params import get_base_params
label_desc: Use image node_id 3 for calling a global function with example usage: parse_args() and returns: args