code: def test_update_damping(self):
      # test error when old and new covariance are not positive semi-definite
      tkwargs = {"device": self.device}
  
      for dtype in (torch.float, torch.double):
          tkwargs["dtype"] = dtype
          cov_old = torch.ones(1, 2, 2, **tkwargs)
          cov_new = torch.ones(1, 2, 2, **tkwargs)
          damping_factor = torch.ones(1, **tkwargs)
          jitter = 0.0
  
          with self.assertRaises(ValueError):
              _update_damping(
                  nat_cov=cov_old,
                  nat_cov_new=cov_new,
                  damping_factor=damping_factor,
                  jitter=jitter,
              )
  
file_path: botorch/test/acquisition/multi_objective/test_predictive_entropy_search.py
imports: from itertools import product
  import torch
  from botorch.acquisition.multi_objective.predictive_entropy_search import _safe_update_omega, _update_damping, qMultiObjectivePredictiveEntropySearch
  from botorch.exceptions import UnsupportedError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.testing import BotorchTestCase
label_desc: Use image node_id 6 for calling the TestQMultiObjectivePredictiveEntropySearch obj's underlying member method code with example usage: obj.test_update_damping() without return types