def get_dir_from_zipfile(zip_file, fallback=None):
    """Return the name of the root folder in a zip file.

    :param zip_file: a :class:`zipfile.ZipFile` instance.
    :param fallback: if `None`, the name of the zip file is used. This is
        returned if the zip file contains more than one top-level directory,
        or none at all.
    """
    fallback = fallback or zip_file.filename
    directories = [name for name in zip_file.namelist() if name.endswith("/")
                   and len(PurePath(name).parts) == 1]
    return fallback if len(directories) > 1 else directories[0]
def test_sgpr_fast_pred_var(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        train_x, train_y, test_x, test_y = make_data()
        likelihood = GaussianLikelihood()
        gp_model = GPRegressionModel(train_x, train_y, likelihood)
        mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

        # Optimize the model
        gp_model.train()
        likelihood.train()

        optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
        for _ in range(50):
            optimizer.zero_grad()
            output = gp_model(train_x)
            loss = -mll(output, train_y)
            loss.backward()
            optimizer.step()

        for param in gp_model.parameters():
            self.assertTrue(param.grad is not None)
            self.assertGreater(param.grad.norm().item(), 0)

        # Test the model
        gp_model.eval()
        likelihood.eval()

        with gpytorch.settings.max_preconditioner_size(5), gpytorch.settings.max_cg_iterations(50):
            with gpytorch.settings.fast_pred_var(True):
                fast_var = gp_model(test_x).variance
                fast_var_cache = gp_model(test_x).variance
                self.assertLess(torch.max((fast_var_cache - fast_var).abs()), 1e-3)

            with gpytorch.settings.fast_pred_var(False):
                slow_var = gp_model(test_x).variance

        self.assertLess(torch.max((fast_var_cache - slow_var).abs()), 1e-3)