text: import os
  import torch
  from torch.autograd import Variable
  #from pdb import set_trace as st
  
  class BaseModel():
      def __init__(self):
          pass;
          
      def name(self):
          return 'BaseModel'
  
      def initialize(self, use_gpu=True, gpu_ids=[0]):
          self.use_gpu = use_gpu
          self.gpu_ids = gpu_ids
  
      def forward(self):
          pass
  
      def get_image_paths(self):
          pass
  
      def optimize_parameters(self):
          pass
  
      def get_current_visuals(self):
          return self.input
  
      def get_current_errors(self):
          return {}
  
      def save(self, label):
          pass
  
      # helper saving function that can be used by subclasses
      def save_network(self, network, path, network_label, epoch_label):
          save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
          save_path = os.path.join(path, save_filename)
          torch.save(network.state_dict(), save_path)
  
      # helper loading function that can be used by subclasses
      def load_network(self, network, network_label, epoch_label):
          save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
          save_path = os.path.join(self.save_dir, save_filename)
          print('Loading network from %s'%save_path)
          network.load_state_dict(torch.load(save_path))
  
      def update_learning_rate():
          pass
  
      def get_image_paths(self):
          return self.image_paths
  
      def save_done(self, flag=False):
          np.save(os.path.join(self.save_dir, 'done_flag'),flag)
          np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i')