text: from .base_model import BaseModel
  from . import networks
  
  import torch
  from visda.utils.serialization import load_checkpoint, save_checkpoint, copy_state_dict
  
  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'.
          """
          parser.set_defaults(no_dropout=True)
          parser.add_argument('--model_suffix', type=str, default='_A', help='In checkpoints_dir, [epoch]_net_G[model_suffix].pth will be loaded as the generator.')
  
          return parser
  
      def __init__(self, opt):
          """Initialize the pix2pix class.
  
          Parameters:
              opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
          """
          # assert(not opt.isTrain)
          BaseModel.__init__(self, opt)
          # 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 = ['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_A']  # only generator is needed.
          self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                          not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
  
  
      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.norm_img(self.netG_A(self.real))  # G_A(A)
  
      def norm_img(self, imgs, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
          mean = torch.Tensor(mean).view(1,3,1,1).expand_as(imgs).cuda()
          std = torch.Tensor(std).view(1,3,1,1).expand_as(imgs).cuda()
          return (imgs - mean) / std
  
      def optimize_parameters(self):
          """No optimization for test model."""
          pass
  