code: def test_gradcheck(self, device):
      # input data
      img = torch.rand(1, 1, 5, 4, 3, device=device)
  
      # evaluate function gradient
      img = utils.tensor_to_gradcheck_var(img)  # to var
  
      # TODO: review method since it needs `nondet_tol` in cuda sometimes.
      assert gradcheck(
          kornia.losses.ssim3d_loss,
          (img, img, 3),
          raise_exception=True,
          nondet_tol=1e-8,
          fast_mode=True,
      )
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester, assert_close
label_desc: Use image node_id 4 for calling the TestSSIM3DLoss obj's underlying member method code with example usage: obj.test_gradcheck(device) without return types