def METHOD_NAME(self):
    train_x, train_y = train_data(cuda=False)
    likelihood = GaussianLikelihood()
    model = SVGPRegressionModel(torch.linspace(0, 1, 25), gpytorch.variational.CholeskyVariationalDistribution)
    data_file = Path(__file__).parent.joinpath("old_variational_strategy_model.pth").resolve()
    state_dicts = torch.load(data_file)
    likelihood.load_state_dict(state_dicts["likelihood"], strict=False)
    # Ensure we get a warning
    with warnings.catch_warnings(record=True) as ws:
        warnings.simplefilter("always", OldVersionWarning)
        model.load_state_dict(state_dicts["model"])
        self.assertTrue(any(issubclass(w.category, OldVersionWarning) for w in ws))
    with torch.no_grad():
        model.eval()
        likelihood.eval()
        test_preds = likelihood(model(train_x)).mean.squeeze()
        mean_abs_error = torch.mean(torch.abs(train_y - test_preds) / 2)
        self.assertLess(mean_abs_error.item(), 1e-1)