code: def test_exception_1(self, fit_mock):
      # Check that training will not continue sampling if pyro raises an error
      class Dummy1:
          counter = 0
  
          def __call__(self):
              self.counter += 1
              raise RuntimeError("Unable to sample new cfgs")
  
      fit_mock.side_effect = Dummy1()
  
      rs = np.random.RandomState(1)
      X, Y, n_dims = get_cont_data(rs)
  
      model = get_gp(n_dims, rs)
      with self.assertRaises(RuntimeError):
          model._train(X[:10], Y[:10], do_optimize=True)
  
imports: import unittest.mock
  import numpy
  import pyro
  import torch
  from botorch.models.kernels.categorical import CategoricalKernel
  from gpytorch.constraints.constraints import Interval
  from gpytorch.kernels import MaternKernel, ProductKernel, ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.priors import HorseshoePrior, LogNormalPrior, UniformPrior
  from gpytorch.utils.errors import NotPSDError
  from smac.configspace import CategoricalHyperparameter, ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.gpytorch_gaussian_process import GPyTorchGaussianProcess
  from ._test_gp import TestGP, get_cat_data, get_cont_data
label_desc: Use image node_id 5 for calling the TestGPGPyTorch obj's underlying member method code with example usage: obj.test_exception_1(fit_mock) without return types