docstr: Test the branch of NdarrayOptimizationClosure that handles errors.
docstr_tok: ["Test","the","branch","of","NdarrayOptimizationClosure","that","handles","errors","."]
code: def test_fit_with_nans(self) -> None:
      """Test the branch of NdarrayOptimizationClosure that handles errors."""
  
      from botorch.optim.closures import NdarrayOptimizationClosure
  
      def closure():
          raise RuntimeError("singular")
  
      for dtype in [torch.float32, torch.float64]:
          parameters = {"x": torch.tensor([0.0], dtype=dtype)}
  
          wrapper = NdarrayOptimizationClosure(
              closure=closure, parameters=parameters
          )
  
          def _assert_np_array_is_float64_type(array) -> bool:
              # e.g. "float32" in "torch.float32"
              self.assertEqual(str(array.dtype), "float64")
  
          _assert_np_array_is_float64_type(wrapper()[0])
          _assert_np_array_is_float64_type(wrapper()[1])
          _assert_np_array_is_float64_type(wrapper.state)
          _assert_np_array_is_float64_type(
              wrapper._get_gradient_ndarray()
          )
  
          # Any mll will do
          mll = next(iter(self.mlls.values()))
          # will error if dtypes are wrong
          fit.fit_gpytorch_mll_scipy(
              mll, closure=wrapper, parameters=parameters
          )
  
imports: import math
  from unittest.mock import MagicMock, patch
  from warnings import catch_warnings
  import torch
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.models import SingleTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.optim import core, fit
  from botorch.optim.core import OptimizationResult
  from botorch.settings import debug
  from botorch.utils.context_managers import module_rollback_ctx, TensorCheckpoint
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from scipy.optimize import OptimizeResult
label_desc: Use image node_id 4 for calling the TestFitGPyTorchMLLScipy obj's underlying member method code with example usage: obj.test_fit_with_nans() without return types