code: def test_posterior_transform(self):
      def f(X):
          return X
  
      model = GenericDeterministicModel(f)
      test_X = torch.rand(3, 2)
      post_tf = ScalarizedPosteriorTransform(weights=torch.rand(2))
      # expect error due to post_tf expecting an MVN
      with self.assertRaises(NotImplementedError):
          model.posterior(test_X, posterior_transform=post_tf)
  
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 5 for calling the TestDeterministicModels obj's underlying member method code with example usage: obj.test_posterior_transform() and returns: X