def __repr__(self):
        return "<NamedRequirements({}{})>".format(
            self.name,
            ", filename='{}'".format(self.filename.name) if self.filename
            else "")
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)