code: def test_dim_args(self, dim, device, dtype):
      if "xla" in device.type:
          pytest.skip("buggy with XLA devices.")
  
      if dtype == torch.float16:
          pytest.skip("not work for half-precision")
  
      data = torch.tensor(
          [[0, 1], [1, 0], [-1, 0], [0, -1]], device=device, dtype=dtype
      )
  
      if dim == 1:
          expected = torch.tensor(
              [
                  [-0.35360718, 0.35360718],
                  [0.35351562, -0.35351562],
                  [-0.35353088, 0.35353088],
                  [0.35353088, -0.35353088],
              ],
              device=device,
              dtype=dtype,
          )
      elif dim == 0:
          expected = torch.tensor(
              [
                  [0.0, 1.2247448],
                  [1.2247448, 0.0],
                  [-1.2247448, 0.0],
                  [0.0, -1.2247448],
              ],
              device=device,
              dtype=dtype,
          )
  
      zca = kornia.enhance.ZCAWhitening(dim=dim)
      actual = zca(data, True)
  
      self.assert_close(actual, expected, low_tolerance=True)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import BaseTester
label_desc: Use image node_id 2 for calling the TestZCA obj's underlying member method code with example usage: obj.test_dim_args(dim, device, dtype) without return types