code: def test_PosteriorMeanModel(self):
      train_X = torch.rand(2, 3)
      train_Y = torch.rand(2, 2)
      model = SingleTaskGP(train_X=train_X, train_Y=train_Y)
      mean_model = PosteriorMeanModel(model=model)
  
      test_X = torch.rand(2, 3)
      post = model.posterior(test_X)
      mean_post = mean_model.posterior(test_X)
      self.assertTrue((mean_post.variance == 0).all())
      self.assertTrue(torch.equal(post.mean, mean_post.mean))
  
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 6 for calling the TestDeterministicModels obj's underlying member method code with example usage: obj.test_PosteriorMeanModel() without return types