text: import argparse
  import pytorch_lightning as pl
  from ..models.multnat_model import MultNatModel
  from .utils import add_dataset_specific_args, load_custom_data
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('checkpoint')
      parser.add_argument('--classification_threshold', type=float, default=None)
      parser.add_argument('--gpus', type=int, default=1)
      add_dataset_specific_args(parser)
      args = parser.parse_args()
  
      model = MultNatModel.load_from_checkpoint(args.checkpoint)
  
      if args.classification_threshold is not None:
          model.set_classification_threshold(args.classification_threshold)
  
      dataloader = load_custom_data(args, model)
  
      trainer = pl.Trainer(gpus=args.gpus, logger=False)
      trainer.test(model, test_dataloaders=dataloader)
  