code: def test_gen_candidates_scipy_nan_handling(self):
      for dtype, expected_regex in [
          (torch.float, "Consider using"),
          (torch.double, "gradient array"),
      ]:
          ckwargs = {"dtype": dtype, "device": self.device}
  
          test_ics = torch.rand(3, 1, **ckwargs)
          test_grad = torch.tensor([0.5, 0.2, float("nan")], **ckwargs)
          # test NaN in grad
          with mock.patch(
              "torch.autograd.grad", return_value=[test_grad]
          ):
              with self.assertRaisesRegex(RuntimeError, expected_regex):
                  gen_candidates_scipy(
                      initial_conditions=test_ics,
                      acquisition_function=mock.Mock(
                          return_value=test_ics
                      ),
                  )
  
          # test NaN in `x`
          test_ics = torch.tensor([0.0, 0.0, float("nan")], **ckwargs)
          with self.assertRaisesRegex(
              RuntimeError, "array `x` are NaN."
          ):
              gen_candidates_scipy(
                  initial_conditions=test_ics,
                  acquisition_function=mock.Mock(),
              )
  
imports: import math
  import warnings
  from unittest import mock
  import torch
  from botorch.acquisition import qExpectedImprovement, qKnowledgeGradient
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.generation.gen import gen_candidates_scipy, gen_candidates_torch, get_best_candidates
  from botorch.models import SingleTaskGP
  from botorch.utils.testing import BotorchTestCase, MockAcquisitionFunction
  from gpytorch import settings
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from scipy.optimize import OptimizeResult
label_desc: Use image node_id 15 for calling the TestGenCandidates obj's underlying member method code with example usage: obj.test_gen_candidates_scipy_nan_handling() without return types