code: def test_ssim(
      self, device, dtype, batch_shape, window_size, reduction_type
  ):
      if device.type == "xla":
          pytest.skip("test highly unstable with tpu")
  
      # input data
      img = torch.rand(batch_shape, device=device, dtype=dtype)
  
      loss = kornia.losses.ssim_loss(
          img, img, window_size, reduction=reduction_type
      )
  
      tol_val: float = utils._get_precision_by_name(
          device, "xla", 1e-1, 1e-4
      )
      if reduction_type == "none":
          expected = torch.zeros_like(img)
      else:
          expected = torch.tensor(0.0, device=device, dtype=dtype)
  
      assert_close(loss, expected, rtol=tol_val, atol=tol_val)
  
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 TestSSIMLoss obj's underlying member method code with example usage: obj.test_ssim(device, dtype, batch_shape, window_size, reduction_type) without return types