code: def test_posterior_transform(self):
      tkwargs = {"device": self.device, "dtype": torch.double}
      m1 = GenericDeterministicModel(
          lambda X: X.sum(dim=-1, keepdims=True), num_outputs=1
      )
      m2 = GenericDeterministicModel(
          lambda X: X.prod(dim=-1, keepdims=True), num_outputs=1
      )
      model = ModelList(m1, m2)
      X = torch.rand(5, 3, **tkwargs)
      posterior_tf = model.posterior(
          X, posterior_transform=DummyPosteriorTransform()
      )
      self.assertTrue(
          torch.allclose(
              posterior_tf.mean,
              torch.cat((2 * m1(X) + 1, 2 * m2(X) + 1), dim=-1),
          )
      )
  
imports: from unittest.mock import patch
  import torch
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.exceptions.errors import InputDataError
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.model import Model, ModelDict, ModelList
  from botorch.models.utils import parse_training_data
  from botorch.posteriors.deterministic import DeterministicPosterior
  from botorch.posteriors.posterior_list import PosteriorList
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
label_desc: Use image node_id 5 for calling the TestBaseModel obj's underlying member method code with example usage: obj.test_posterior_transform() without return types