code: def test_raises(self):
      tkwargs = {"device": self.device, "dtype": torch.double}
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_X to have shape n x d and train_Y to have shape n x 1",
      ):
          SaasFullyBayesianSingleTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(10, **tkwargs),
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_X to have shape n x d and train_Y to have shape n x 1",
      ):
          SaasFullyBayesianSingleTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(12, 1, **tkwargs),
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_X to have shape n x d and train_Y to have shape n x 1",
      ):
          SaasFullyBayesianSingleTaskGP(
              train_X=torch.rand(10, **tkwargs),
              train_Y=torch.randn(10, 1, **tkwargs),
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_Yvar to be None or have the same shape as train_Y",
      ):
          SaasFullyBayesianSingleTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(10, 1, **tkwargs),
              train_Yvar=torch.rand(10, **tkwargs),
          )
      train_X, train_Y, train_Yvar, model = self._get_data_and_model(
          infer_noise=True, **tkwargs
      )
      # Make sure an exception is raised if the model has not been fitted
      not_fitted_error_msg = (
          "Model has not been fitted. You need to call "
          "`fit_fully_bayesian_model_nuts` to fit the model."
      )
      with self.assertRaisesRegex(RuntimeError, not_fitted_error_msg):
          model.num_mcmc_samples
      with self.assertRaisesRegex(RuntimeError, not_fitted_error_msg):
          model.median_lengthscale
      with self.assertRaisesRegex(RuntimeError, not_fitted_error_msg):
          model.forward(torch.rand(1, 4, **tkwargs))
      with self.assertRaisesRegex(RuntimeError, not_fitted_error_msg):
          model.posterior(torch.rand(1, 4, **tkwargs))
  
imports: import itertools
  from unittest import mock
  from unittest.mock import patch
  import pyro
  import torch
  from botorch import fit_fully_bayesian_model_nuts, utils
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import prune_inferior_points_multi_objective, qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.logei import qLogExpectedHypervolumeImprovement, qLogNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.utils import prune_inferior_points
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL, PyroModel, SaasFullyBayesianSingleTaskGP, SaasPyroModel
  from botorch.models.transforms import Normalize, Standardize
  from botorch.posteriors.fully_bayesian import batched_bisect, GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.safe_math import logmeanexp
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from linear_operator.operators import to_linear_operator
  from pyro.ops.integrator import _EXCEPTION_HANDLERS, potential_grad, register_exception_handler
label_desc: Use image node_id 5 for calling the TestFullyBayesianSingleTaskGP obj's underlying member method code with example usage: obj.test_raises() without return types