def test_set_subset_obsm(adata, subset_func):
    init_hash = joblib.hash(adata)
    orig_obsm_val = adata.obsm["o"].copy()

    while True:
        subset_idx = slice_subset(adata.obs_names)
        if len(adata[subset_idx, :]) > 2:
            break
    subset = adata[subset_idx, :]

    internal_idx = _normalize_index(
        subset_func(np.arange(subset.obsm["o"].shape[0])), subset.obs_names
    )

    assert subset.is_view
    subset.obsm["o"][internal_idx] = 1
    assert not subset.is_view
    assert np.all(adata.obsm["o"] == orig_obsm_val)

    assert init_hash == joblib.hash(adata)
def test_sgpr_mean_abs_error_cuda(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        if not torch.cuda.is_available():
            return
        with least_used_cuda_device():
            train_x, train_y, test_x, test_y = make_data(cuda=True)
            likelihood = GaussianLikelihood().cuda()
            gp_model = GPRegressionModel(train_x, train_y, likelihood).cuda()
            mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

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

            optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
            optimizer.n_iter = 0
            for _ in range(25):
                optimizer.zero_grad()
                output = gp_model(train_x)
                loss = -mll(output, train_y)
                loss.backward()
                optimizer.n_iter += 1
                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()
            test_preds = likelihood(gp_model(test_x)).mean
            mean_abs_error = torch.mean(torch.abs(test_y - test_preds))

            self.assertLess(mean_abs_error.squeeze().item(), 0.02)