code: def test_ssim(
      self, device, dtype, shape, window_size, reduction_type
  ):
      if device.type == "xla":
          pytest.skip("test highly unstable with tpu")
  
      # Sanity test
      img = torch.rand(shape, device=device, dtype=dtype)
      actual = kornia.losses.ssim3d_loss(
          img, img, window_size, reduction=reduction_type
      )
      if reduction_type == "none":
          expected = torch.zeros_like(img)
      else:
          expected = torch.tensor(0.0, device=device, dtype=dtype)
  
      self.assert_close(actual, expected)
  
      # Check loss computation
      img1 = torch.ones(shape, device=device, dtype=dtype)
      img2 = torch.zeros(shape, device=device, dtype=dtype)
  
      actual = kornia.losses.ssim3d_loss(
          img1, img2, window_size, reduction=reduction_type
      )
  
      if reduction_type == "mean":
          expected = torch.tensor(0.9999, device=device, dtype=dtype)
      elif reduction_type == "sum":
          expected = (
              torch.ones_like(img1, device=device, dtype=dtype) * 0.9999
          ).sum()
      elif reduction_type == "none":
          expected = (
              torch.ones_like(img1, device=device, dtype=dtype) * 0.9999
          )
  
      self.assert_close(actual, expected)
  
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 2 for calling the TestSSIM3DLoss obj's underlying member method code with example usage: obj.test_ssim(device, dtype, shape, window_size, reduction_type) without return types