code: def test_input_transform(self) -> None:
      train_X = torch.linspace(1, 3, 10, dtype=torch.double)[:, None]
      y = -3 * train_X + 5
  
      for input_transform in [None, Normalize(1)]:
          with self.subTest(input_transform=input_transform):
              with warnings.catch_warnings():
                  warnings.filterwarnings(
                      "ignore", message="Input data is not contained"
                  )
                  model = SingleTaskVariationalGP(
                      train_X=train_X,
                      train_Y=y,
                      input_transform=input_transform,
                  )
              mll = VariationalELBO(
                  model.likelihood,
                  model.model,
                  num_data=train_X.shape[-2],
              )
              fit_gpytorch_mll(mll)
              post = model.posterior(torch.tensor([train_X.mean()]))
              self.assertAllClose(
                  post.mean[0][0], y.mean(), atol=1e-3, rtol=1e-3
              )
  
imports: import itertools
  import warnings
  import torch
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.approximate_gp import _SingleTaskVariationalGP, ApproximateGPyTorchModel, SingleTaskVariationalGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Log
  from botorch.models.utils.inducing_point_allocators import GreedyImprovementReduction, GreedyVarianceReduction
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.likelihoods import GaussianLikelihood, MultitaskGaussianLikelihood
  from gpytorch.mlls import VariationalELBO
  from gpytorch.variational import IndependentMultitaskVariationalStrategy, VariationalStrategy
label_desc: Use image node_id 8 for calling the TestSingleTaskVariationalGP obj's underlying member method code with example usage: obj.test_input_transform() without return types