code: def test_special(self):
      for dtype in (torch.float32, torch.float64):
          for _a in (INF, -INF):
              a = torch.tensor(
                  _a,
                  dtype=dtype,
                  requires_grad=True,
                  device=self.device,
              )
              b = torch.tensor(
                  INF,
                  dtype=dtype,
                  requires_grad=True,
                  device=self.device,
              )
  
              out = self.safe_op(a, b)
              self.assertEqual(out, 0 if a != b else b)
  
              out.backward()
              self.assertEqual(a.grad, 0 if a != b else 1)
              self.assertEqual(b.grad, 0 if a != b else 1)
  
imports: from __future__ import annotations
  import itertools
  import math
  from abc import abstractmethod
  from itertools import combinations, product
  from typing import Callable
  import torch
  from botorch.exceptions import UnsupportedError
  from botorch.utils import safe_math
  from botorch.utils.constants import get_constants_like
  from botorch.utils.objective import compute_smoothed_feasibility_indicator
  from botorch.utils.safe_math import _pareto, cauchy, fatmax, fatmaximum, fatminimum, fatmoid, fatplus, log_fatmoid, log_fatplus, log_softplus, logdiffexp, logexpit, logmeanexp, logplusexp, sigmoid, smooth_amax
  from botorch.utils.testing import BotorchTestCase
  from torch import finfo, Tensor
  from torch.nn.functional import softplus
label_desc: Use image node_id 1 for calling the TestSafeAdd obj's underlying member method code with example usage: obj.test_special() without return types