def METHOD_NAME(model, model_kwargs, training_loop):
    """Test training models with PyTorch Lightning."""
    # some models require inverse relations
    create_inverse_triples = model is not models.RGCN
    dataset = get_dataset(dataset="nations", dataset_kwargs=dict(create_inverse_triples=create_inverse_triples))
    # some model require access to the training triples
    if "triples_factory" in model_kwargs:
        model_kwargs["triples_factory"] = dataset.training
    # inductive models require an inductive mode to be set, and an inference factory to be passed
    if issubclass(model, models.InductiveNodePiece):
        # fake an inference factory
        model_kwargs["inference_factory"] = dataset.training
        mode = TRAINING
    else:
        mode = None
    lit_pipeline(
        training_loop=training_loop,
        training_loop_kwargs=dict(
            model=model,
            dataset=dataset,
            model_kwargs=model_kwargs,
            batch_size=8,
            mode=mode,
        ),
        trainer_kwargs=dict(
            # automatically choose accelerator
            accelerator="auto",
            # defaults to TensorBoard; explicitly disabled here
            logger=False,
            # disable checkpointing
            enable_checkpointing=False,
            # fast run
            max_epochs=2,
        ),
    )