def METHOD_NAME(test_model, num_classes, url, multimodal):
    x = []
    adj_t = []
    for modality in range(test_model.num_modalities):
        data = test_model.dataset.get(modality)
        x.append(data.x[data.train_idx])
        adj_t.append(data.adj_t_train)
    outputs = test_model.forward(x, adj_t, multimodal)
    assert isinstance(outputs, list) != multimodal
    if not multimodal:
        assert len(outputs) == test_model.num_modalities
        for modality in range(test_model.num_modalities):
            assert outputs[modality].shape == (test_model.dataset.get(modality).num_train, test_model.num_classes)
    else:
        assert isinstance(outputs, torch.Tensor)
        assert outputs.shape == (test_model.dataset.get(0).num_train, test_model.num_classes)
        test_model.multimodal_decoder = None
        with pytest.raises(TypeError):
            _ = test_model.forward(x, adj_t, multimodal)