code: def test_transforms(self):
      for infer_noise in [True, False]:
          tkwargs = {"device": self.device, "dtype": torch.double}
          (
              train_X,
              train_Y,
              train_Yvar,
              test_X,
          ) = self._get_unnormalized_data(
              infer_noise=infer_noise, **tkwargs
          )
          n, d = train_X.shape
  
          lb, ub = train_X.min(dim=0).values, train_X.max(dim=0).values
          mu, sigma = train_Y.mean(), train_Y.std()
  
          # Fit without transforms
          with torch.random.fork_rng():
              torch.manual_seed(0)
              gp1 = SaasFullyBayesianSingleTaskGP(
                  train_X=(train_X - lb) / (ub - lb),
                  train_Y=(train_Y - mu) / sigma,
                  train_Yvar=train_Yvar / sigma**2
                  if train_Yvar is not None
                  else train_Yvar,
              )
              fit_fully_bayesian_model_nuts(
                  gp1,
                  warmup_steps=8,
                  num_samples=5,
                  thinning=2,
                  disable_progbar=True,
              )
              posterior1 = gp1.posterior((test_X - lb) / (ub - lb))
              pred_mean1 = mu + sigma * posterior1.mean
              pred_var1 = (sigma**2) * posterior1.variance
  
          # Fit with transforms
          with torch.random.fork_rng():
              torch.manual_seed(0)
              gp2 = SaasFullyBayesianSingleTaskGP(
                  train_X=train_X,
                  train_Y=train_Y,
                  train_Yvar=train_Yvar,
                  input_transform=Normalize(d=train_X.shape[-1]),
                  outcome_transform=Standardize(m=1),
              )
              fit_fully_bayesian_model_nuts(
                  gp2,
                  warmup_steps=8,
                  num_samples=5,
                  thinning=2,
                  disable_progbar=True,
              )
              posterior2 = gp2.posterior(test_X)
              pred_mean2, pred_var2 = (
                  posterior2.mean,
                  posterior2.variance,
              )
  
          self.assertAllClose(pred_mean1, pred_mean2)
          self.assertAllClose(pred_var1, pred_var2)
  
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 7 for calling the TestFullyBayesianSingleTaskGP obj's underlying member method code with example usage: obj.test_transforms() without return types