code: def test_train_continue_on_linalg_error():
      """Checks that training does not stop on a linalg error, but that uncertainty is increased!"""
  
      class Dummy:
          counter = 0
  
          def __call__(self, X, y):
              if self.counter >= 10:
                  return None
  
              self.counter += 1
              raise np.linalg.LinAlgError
  
      with patch.object(
          sklearn.gaussian_process.GaussianProcessRegressor,
          "fit",
          Dummy().__call__,
      ):
          seed = 1
          rs = np.random.RandomState(seed)
          X, Y, n_dims = get_cont_data(rs)
  
          model = get_gp(n_dims, seed)
          fixture = np.exp(model._kernel.theta[-1])
          model._train(X[:10], Y[:10], optimize_hyperparameters=False)
          assert (
              pytest.approx(np.exp(model._kernel.theta[-1]))
              == fixture + 10
          )
  
imports: from unittest.mock import patch
  import numpy
  import pytest
  import scipy.optimize
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import CategoricalHyperparameter, ConfigurationSpace, EqualsCondition, UniformFloatHyperparameter
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.utils.configspace import convert_configurations_to_array
label_desc: Use image node_id 8 for calling a global function with example usage: test_train_continue_on_linalg_error() and returns: None