text: import time
  from options.train_options import TrainOptions
  from data.data_loader import CreateDataLoader
  from models.models import create_model
  from util.visualizer import Visualizer
  
  print ("ET, phone Home")
  
  opt = TrainOptions().parse()
  data_loader = CreateDataLoader(opt)
  dataset = data_loader.load_data()
  dataset_size = len(data_loader)
  
  # print ("opt.epoch_count", opt.epoch_count)
  # r = opt.niter + opt.niter_decay + 1
  # print ("opt.niter + opt.niter_decay + 1:", r)
  
  print('#training images = %d' % dataset_size)
  
  model = create_model(opt)
  visualizer = Visualizer(opt)
  total_steps = 0
  
  
  
  for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
  
      epoch_start_time = time.time()
      epoch_iter = 0
  
      print ("Num epoch = ", epoch)
      print ("epoch_start_time = ", epoch_start_time)
  
      for i, data in enumerate(dataset):
          iter_start_time = time.time()
          visualizer.reset()
          total_steps += opt.batchSize
          epoch_iter += opt.batchSize
          model.set_input(data)
          model.optimize_parameters()
  
          if total_steps % opt.display_freq == 0:
              save_result = total_steps % opt.update_html_freq == 0
              visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
  
          if total_steps % opt.print_freq == 0:
              errors = model.get_current_errors()
              t = (time.time() - iter_start_time) / opt.batchSize
              visualizer.print_current_errors(epoch, epoch_iter, errors, t)
              if opt.display_id > 0:
                  visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
  
          if total_steps % opt.save_latest_freq == 0:
              print('saving the latest model (epoch %d, total_steps %d)' %
                    (epoch, total_steps))
              model.save('latest')
  
      if epoch % opt.save_epoch_freq == 0:
          print('saving the model at the end of epoch %d, iters %d' %
                (epoch, total_steps))
          model.save('latest')
          model.save(epoch)
  
      print('End of epoch %d / %d \t Time Taken: %d sec' %
            (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
      model.update_learning_rate()
  