new_contents: import shutil, torch
  def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
      torch.save(state, filename)
      if is_best:
          shutil.copyfile(filename, 'model_best.pth.tar')
          
  save_checkpoint({
      'epoch': epoch + 1,
      'arch': args.arch,
      'state_dict': model.state_dict(),
      'best_prec1': best_prec1,
      'optimizer' : optimizer.state_dict(),
  }, is_best)
  
  '''
  best_prec1 = <whatever>
  while training:
      prec1 = validate(val_loader, model, criterion)
  
      is_best = prec1 > best_prec1
      best_prec1 = max(prec1, best_prec1)
  '''
  
subject: Create save_checkpoint.py