code: import argparse
  import os, sys
  from abc import ABC, abstractmethod
  
  import torch
  import models
  import datasets
  
  
  class BaseOptions(ABC):
      """This class is an abstract base class (ABC) for options.
      To create a subclass, you need to implement the following five functions:
          -- <__init__>:                      initialize the class; first call BaseOptions.__init__(self, opt).
          -- <initialize>:                    initialize the option's arguments.
          -- <parse>:                         parse the option's arguments.
      """
      def __init__(self):
          pass
  
      @abstractmethod
      def initialize(self, parser):
          pass
  
      @abstractmethod
      def parse(self):
          pass
  
  
  class BasicOptions(BaseOptions):
      """This class defines options used during both training and test time.
  
      It also implements several helper functions such as parsing, printing, and saving the options.
      It also gathers additional options defined in <modify_commandline_options> functions in both dataset class and model class.
      """
  
      def __init__(self):
          """Reset the class; indicates the class hasn't been initailized"""
          self.initialized = False
  
      def initialize(self, parser):
          """Define the common options that are used in both training and test."""
          # basic parameters
          parser.add_argument('--data_root', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
          parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
          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('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
          # model parameters
          parser.add_argument('--model', type=str, default='cycle_gan', help='chooses which model to use. [cycle_gan | pix2pix | recyc_gan]')
          parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
          parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
          parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
          parser.add_argument('--ndf', type=int, default=64, help='# of discrim 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_9bs', help='specify generator architecture [resnet_9bs | resnet_6bs | unet_256 | unet_128]')
          parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
          parser.add_argument('--norm', type=str, default='instance', help='instance normalization or batch normalization [instance | batch | none]')
          parser.add_argument('--init_type', type=str, default='normal', help='network initialization [normal | xavier | kaiming | orthogonal]')
          parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
          parser.add_argument('--no_dropout', action='store_true', help='no dropout for the generator')
          # vgg parameters for perceptrual loss
          parser.add_argument('--vgg', type=float, default=0, help='use perceptrual loss')
          parser.add_argument('--vgg_mean', action='store_true', help='substract mean in vgg loss')
          parser.add_argument('--vgg_choose', type=str, default='relu5_3', help='choose layer for vgg')
          parser.add_argument('--no_vgg_instance', action='store_true', help='vgg instance normalization')
          parser.add_argument('--vgg_maxpooling', action='store_true', help='normalize attention map')
          # dataset parameters
          parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | degraded]')
          parser.add_argument('--degraded_mode', type=str, default='colorization', help='chooses how datasets are loaded. [colorization | super_resolution | denoising | restoration]')
          parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
          parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
          parser.add_argument('--num_threads', default=0 if sys.platform.startswith('win') else 4, type=int, help='# threads for loading data')
          parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
          parser.add_argument('--load_size', type=int, default=286, help='scale images to this size')
          parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
          parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
          parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
          parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
          parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
          # additional parameters
          parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
          parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]')
          parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
          parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
  
          self.initialized = True
          return parser
  
      def gather_options(self):
          """Initialize our parser with basic options(only once).
          """
          if not self.initialized:  # check if it has been initialized
              parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
              parser = self.initialize(parser)
  
          # get the basic options
          opt, _ = parser.parse_known_args()
  
          # modify model-related parser options
          model_name = opt.model
          model_option_setter = models.get_option_setter(model_name)
          parser = model_option_setter(parser, self.is_train)
          opt, _ = parser.parse_known_args()  # parse again with new defaults
  
          # modify dataset-related parser options
          dataset_name = opt.dataset_mode
          dataset_option_setter = datasets.get_option_setter(dataset_name)
          parser = dataset_option_setter(parser, self.is_train)
          opt, _ = parser.parse_known_args()  # parse again with new defaults
  
          # save and return the parser
          self.parser = parser
          return parser.parse_args()
  
      def print_options(self, opt):
          """Print and save options
  
          It will print both current options and default values(if different).
          It will save options into a text file / [checkpoints_dir] / opt.txt
          """
          message = ''
          message += '----------------- Options ---------------\n'
          for k, v in sorted(vars(opt).items()):
              comment = ''
              default = self.parser.get_default(k)
              if v != default:
                  comment = '\t[default: %s]' % str(default)
              message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
          message += '----------------- End -------------------'
          print(message)
  
          # save to the disk
          expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
          os.makedirs(expr_dir, exist_ok=True)
          file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
          with open(file_name, 'wt') as opt_file:
              opt_file.write(message)
              opt_file.write('\n')
  
      def parse(self):
          """Parse our options, create checkpoints directory suffix, and set up gpu device."""
          opt = self.gather_options()
          opt.is_train = self.is_train   # train or test
  
          # process opt.suffix
          if opt.suffix:
              suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
              opt.name = opt.name + suffix
  
          self.print_options(opt)
  
          # set gpu ids
          str_ids = opt.gpu_ids.split(',')
          opt.gpu_ids = []
          for str_id in str_ids:
              id = int(str_id)
              if id >= 0:
                  opt.gpu_ids.append(id)
          if len(opt.gpu_ids) > 0:
              torch.cuda.set_device(opt.gpu_ids[0])
  
          self.opt = opt
          return self.opt
  
apis: torch.cuda.set_device