text: from .base_model import BaseModel
  from . import networks
  from util.util import show_image
  
  
  class TestModel(BaseModel):
      """ This TesteModel can be used to generate CycleGAN results for only one direction.
      This model will automatically set '--dataset_mode single', which only loads the images from one collection.
  
      See the test instruction for more details.
      """
      @staticmethod
      def modify_commandline_options(parser, is_train=True):
          """Add new dataset-specific options, and rewrite default values for existing options.
  
          Parameters:
              parser          -- original option parser
              is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
  
          Returns:
              the modified parser.
  
          The model can only be used during test time. It requires '--dataset_mode single'.
          You need to specify the network using the option '--model_suffix'.
          """
          assert not is_train, 'TestModel cannot be used during training time'
          parser.set_defaults(dataset_mode='single')
          parser.add_argument('--model_suffix', type=str, default='', help='In checkpoints_dir, [epoch]_net_G[model_suffix].pth will be loaded as the generator.')
  
          return parser
  
      def __init__(self, config):
          """Initialize the pix2pix class.
  
          Parameters:
              config (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
          """
          assert(not config.isTrain)
          BaseModel.__init__(self, config)
          # specify the training losses you want to print out. The training/test scripts  will call <BaseModel.get_current_losses>
          self.loss_names = []
          # specify the images you want to save/display. The training/test scripts  will call <BaseModel.get_current_visuals>
          self.visual_names = ['real', 'fake']
          # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>
          self.model_names = ['G' + config.model_suffix]  # only generator is needed.
          self.netG = networks.define_G(config.input_nc, config.output_nc, config.ngf, config.netG,
                                        config.norm, not config.no_dropout, config.init_type, config.init_gain, self.gpu_ids)
  
          # assigns the model to self.netG_[suffix] so that it can be loaded
          # please see <BaseModel.load_networks>
          setattr(self, 'netG' + config.model_suffix, self.netG)  # store netG in self.
  
      def set_input(self, input):
          """Unpack input data from the dataloader and perform necessary pre-processing steps.
  
          Parameters:
              input: a dictionary that contains the data itself and its metadata information.
  
          We need to use 'single_dataset' dataset mode. It only load images from one domain.
          """
          self.real = input['A'].to(self.device)
          self.image_paths = input['A_paths']
  
      def forward(self):
          """Run forward pass."""
          self.fake = self.netG(self.real)  # G(real)
  
      def optimize_parameters(self):
          """No optimization for test model."""
          pass
  