code: def test_set_transformed_inputs(self):
      # This intended to catch https://github.com/pytorch/botorch/issues/1078.
      # More general testing of _set_transformed_inputs is done under ModelListGP.
      X = torch.rand(5, 2)
      Y = X**2
      for tf_class in [Normalize, InputStandardize]:
          intf = tf_class(d=2)
          model = SingleTaskGP(X, Y, input_transform=intf)
          mll = ExactMarginalLogLikelihood(model.likelihood, model)
          fit_gpytorch_mll(
              mll, optimizer_kwargs={"options": {"maxiter": 2}}
          )
          tf_X = intf(X)
          self.assertEqual(X.shape, tf_X.shape)
  
imports: import itertools
  import warnings
  import torch
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import FixedNoiseGP, HeteroskedasticSingleTaskGP, SingleTaskGP
  from botorch.models.transforms import Normalize, Standardize
  from botorch.models.transforms.input import InputStandardize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.sampling import manual_seed
  from botorch.utils.test_helpers import get_pvar_expected
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase, FixedNoiseGaussianLikelihood, GaussianLikelihood, HeteroskedasticNoise
  from gpytorch.means import ConstantMean, ZeroMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.noise_model_added_loss_term import NoiseModelAddedLossTerm
  from gpytorch.priors import GammaPrior
label_desc: Use image node_id 9 for calling the TestSingleTaskGP obj's underlying member method code with example usage: obj.test_set_transformed_inputs() without return types