code: def test_pyro_catch_error(self):
      def potential_fn(z):
          mvn = pyro.distributions.MultivariateNormal(
              loc=torch.zeros(2),
              covariance_matrix=z["K"],
          )
          return mvn.log_prob(torch.zeros(2))
  
      # Test base case where everything is fine
      z = {"K": torch.eye(2)}
      grads, val = potential_grad(potential_fn, z)
      self.assertAllClose(grads["K"], -0.5 * torch.eye(2))
      norm_mvn = torch.distributions.Normal(0, 1)
      self.assertAllClose(val, 2 * norm_mvn.log_prob(torch.tensor(0.0)))
  
      # Default behavior should catch the ValueError when trying to instantiate
      # the MVN and return NaN instead
      z = {"K": torch.ones(2, 2)}
      _, val = potential_grad(potential_fn, z)
      self.assertTrue(torch.isnan(val))
  
      # Default behavior should catch the LinAlgError when peforming a
      # Cholesky decomposition and return NaN instead
      def potential_fn_chol(z):
          return torch.linalg.cholesky(z["K"])
  
      _, val = potential_grad(potential_fn_chol, z)
      self.assertTrue(torch.isnan(val))
  
      # Default behavior should not catch other errors
      def potential_fn_rterr_foo(z):
          raise RuntimeError("foo")
  
      with self.assertRaisesRegex(RuntimeError, "foo"):
          potential_grad(potential_fn_rterr_foo, z)
  
      # But once we register this specific error then it should
      def catch_runtime_error(e):
          return type(e) is RuntimeError and "foo" in str(e)
  
      register_exception_handler("foo_runtime", catch_runtime_error)
      _, val = potential_grad(potential_fn_rterr_foo, z)
      self.assertTrue(torch.isnan(val))
  
      # Unless the error message is different
      def potential_fn_rterr_bar(z):
          raise RuntimeError("bar")
  
      with self.assertRaisesRegex(RuntimeError, "bar"):
          potential_grad(potential_fn_rterr_bar, z)
  
imports: import itertools
  from unittest import mock
  from unittest.mock import patch
  import pyro
  import torch
  from botorch import fit_fully_bayesian_model_nuts, utils
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import prune_inferior_points_multi_objective, qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.logei import qLogExpectedHypervolumeImprovement, qLogNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.utils import prune_inferior_points
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL, PyroModel, SaasFullyBayesianSingleTaskGP, SaasPyroModel
  from botorch.models.transforms import Normalize, Standardize
  from botorch.posteriors.fully_bayesian import batched_bisect, GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.safe_math import logmeanexp
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from linear_operator.operators import to_linear_operator
  from pyro.ops.integrator import _EXCEPTION_HANDLERS, potential_grad, register_exception_handler
label_desc: Use image node_id 2 for calling the TestPyroCatchNumericalErrors obj's underlying member method code with example usage: obj.test_pyro_catch_error() and returns: mvn, torch, unknown