code: def test_special(self):
      for dtype in (torch.float32, torch.float64):
          x = torch.zeros(
              [], dtype=dtype, requires_grad=True, device=self.device
          )
          y = self.safe_op(x)
          self.assertEqual(y, math.log(finfo(dtype).tiny))
  
          y.backward()
          self.assertEqual(x.grad, 0)
  
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 TestSafeLog obj's underlying member method code with example usage: obj.test_special() without return types