code: def _get_unnormalized_data(self, infer_noise: bool, **tkwargs):
      with torch.random.fork_rng():
          torch.manual_seed(0)
          train_X = 5 + 5 * torch.rand(10, 4, **tkwargs)
          train_Y = 10 + torch.sin(train_X[:, :1])
          test_X = 5 + 5 * torch.rand(5, 4, **tkwargs)
          train_Yvar = (
              None
              if infer_noise
              else 0.1 * torch.arange(10, **tkwargs).unsqueeze(-1)
          )
      return train_X, train_Y, train_Yvar, test_X
  
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 2 for calling the TestFullyBayesianSingleTaskGP obj's underlying member method code with example usage: obj._get_unnormalized_data(infer_noise) and returns: train_X,  train_Y,  train_Yvar,  test_X