code: def test_with_transforms(self):
      dim = 2
      bounds = torch.stack([torch.zeros(dim), torch.ones(dim) * 3])
      intf = Normalize(d=dim, bounds=bounds)
      octf = Standardize(m=1)
      # update octf state with dummy data
      octf(torch.rand(5, 1) * 7)
      octf.eval()
      model = DummyDeterministicModel(octf, intf)
      # check that the posterior output agrees with the manually transformed one
      test_X = torch.rand(3, dim)
      expected_Y, _ = octf.untransform(model.forward(intf(test_X)))
      with warnings.catch_warnings(record=True) as ws:
          posterior = model.posterior(test_X)
          msg = "does not have a `train_inputs` attribute"
          self.assertTrue(any(msg in str(w.message) for w in ws))
      self.assertAllClose(expected_Y, posterior.mean)
      # check that model.train/eval works and raises the warning
      model.train()
      with self.assertWarns(RuntimeWarning):
          model.eval()
  
imports: import warnings
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.deterministic import AffineDeterministicModel, DeterministicModel, FixedSingleSampleModel, GenericDeterministicModel, PosteriorMeanModel
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors.ensemble import EnsemblePosterior
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 4 for calling the TestDeterministicModels obj's underlying member method code with example usage: obj.test_with_transforms() without return types