code: def test_GenericDeterministicModel(self):
      def f(X):
          return X.mean(dim=-1, keepdim=True)
  
      model = GenericDeterministicModel(f)
      self.assertEqual(model.num_outputs, 1)
      X = torch.rand(3, 2)
      # basic test
      p = model.posterior(X)
      self.assertIsInstance(p, EnsemblePosterior)
      self.assertEqual(p.ensemble_size, 1)
      self.assertTrue(torch.equal(p.mean, f(X)))
      # check that observation noise doesn't change things
      p_noisy = model.posterior(X, observation_noise=True)
      self.assertTrue(torch.equal(p_noisy.mean, f(X)))
      # test proper error on explicit observation noise
      with self.assertRaises(UnsupportedError):
          model.posterior(X, observation_noise=X[..., :-1])
      # check output indices
      model = GenericDeterministicModel(lambda X: X, num_outputs=2)
      self.assertEqual(model.num_outputs, 2)
      p = model.posterior(X, output_indices=[0])
      self.assertTrue(torch.equal(p.mean, X[..., [0]]))
      # test subset output
      subset_model = model.subset_output([0])
      self.assertIsInstance(subset_model, GenericDeterministicModel)
      p_sub = subset_model.posterior(X)
      self.assertTrue(torch.equal(p_sub.mean, X[..., [0]]))
  
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 2 for calling the TestDeterministicModels obj's underlying member method code with example usage: obj.test_GenericDeterministicModel() and returns: X