content: from dataloaders.datasets import cityscapes, coco, combine_dbs, pascal, sbd
  from torch.utils.data import DataLoader
  
  def make_data_loader(args, **kwargs):
  
      if args.dataset == 'pascal':
          train_set = pascal.VOCSegmentation(args, split='train')
          val_set = pascal.VOCSegmentation(args, split='val')
          if args.use_sbd:
              sbd_train = sbd.SBDSegmentation(args, split=['train', 'val'])
              train_set = combine_dbs.CombineDBs([train_set, sbd_train], excluded=[val_set])
  
          num_class = train_set.NUM_CLASSES
          train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
          val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, **kwargs)
          test_loader = None
  
          return train_loader, val_loader, test_loader, num_class
  
      elif args.dataset == 'cityscapes':
          train_set = cityscapes.CityscapesSegmentation(args, split='train')
          val_set = cityscapes.CityscapesSegmentation(args, split='val')
          test_set = cityscapes.CityscapesSegmentation(args, split='test')
          num_class = train_set.NUM_CLASSES
          train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
          val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, **kwargs)
          test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, **kwargs)
  
          return train_loader, val_loader, test_loader, num_class
  
      elif args.dataset == 'coco':
          train_set = coco.COCOSegmentation(args, split='train')
          val_set = coco.COCOSegmentation(args, split='val')
          num_class = train_set.NUM_CLASSES
          train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, **kwargs)
          val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False, **kwargs)
          test_loader = None
          return train_loader, val_loader, test_loader, num_class
  
      else:
          raise NotImplementedError
  
  