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",
      ):
          SaasFullyBayesianMultiTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(10, **tkwargs),
              train_Yvar=torch.rand(10, 1, **tkwargs),
              task_feature=4,
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_X to have shape n x d and train_Y to have shape n x 1",
      ):
          SaasFullyBayesianMultiTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(12, 1, **tkwargs),
              train_Yvar=torch.rand(12, 1, **tkwargs),
              task_feature=4,
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_X to have shape n x d and train_Y to have shape n x 1",
      ):
          SaasFullyBayesianMultiTaskGP(
              train_X=torch.rand(10, **tkwargs),
              train_Y=torch.randn(10, 1, **tkwargs),
              train_Yvar=torch.rand(10, 1, **tkwargs),
              task_feature=4,
          )
      with self.assertRaisesRegex(
          ValueError,
          "Expected train_Yvar to be None or have the same shape as train_Y",
      ):
          SaasFullyBayesianMultiTaskGP(
              train_X=torch.rand(10, 4, **tkwargs),
              train_Y=torch.randn(10, 1, **tkwargs),
              train_Yvar=torch.rand(10, **tkwargs),
              task_feature=4,
          )
      train_X, train_Y, train_Yvar, model = self._get_data_and_model(
          **tkwargs
      )
      sampler = IIDNormalSampler(sample_shape=torch.Size([2]))
      with self.assertRaisesRegex(
          NotImplementedError, "Fantasize is not implemented!"
      ):
          model.fantasize(
              X=torch.cat(
                  [
                      torch.rand(5, 4, **tkwargs),
                      torch.ones(5, 1, **tkwargs),
                  ],
                  dim=1,
              ),
              sampler=sampler,
          )
  
      # 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 typing import List, Optional
  import torch
  from botorch import fit_fully_bayesian_model_nuts
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL
  from botorch.models.fully_bayesian_multitask import MultitaskSaasPyroModel, SaasFullyBayesianMultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors import GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.test_helpers import gen_multi_task_dataset
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means import ConstantMean
label_desc: Use image node_id 4 for calling the TestFullyBayesianMultiTaskGP obj's underlying member method code with example usage: obj.test_raises() without return types