def install_bower(self, package, save=True, save_dev=False):
        """Installs a bower package.

        :param save: if `True`, pins the package to the Hydrogen requirements
            YAML file.
        :param save_dev: if `True`, pins the package as a development
            dependency to the Hydrogen requirements YAML file.
        :param return: a list of tuples, containing all installed package names
            and versions, including any dependencies.
        """
        requirement = Requirement.coerce(package)
        url = Bower.get_package_url(requirement.package)

        installed = []
        for name, _ in self.get_bower_package(url):
            installed.append(Requirement(name, requirement.version))

        for requirement in installed:
            if save:
                self.requirements["bower"].add(requirement, replace=True)
            if save_dev:
                self.requirements["bower-dev"].add(requirement, replace=True)
            success("installed {}".format(str(requirement)))
        if save or save_dev:
            self.requirements.save()
        return installed
def test_sgpr_mean_abs_error(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(30):
            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()

        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(), 0.05)