code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [
                          0.6315228,
                          0.4196397,
                          0.33123854,
                          0.3277169,
                          0.20501322,
                      ],
                      [
                          0.44133404,
                          0.4823162,
                          0.528561,
                          0.51644784,
                          0.28001237,
                      ],
                      [
                          0.31131047,
                          0.8453884,
                          0.4486181,
                          0.6015828,
                          0.42048606,
                      ],
                      [
                          0.5472367,
                          0.48154795,
                          0.36668795,
                          0.39913517,
                          0.40271503,
                      ],
                  ],
                  [
                      [
                          0.79137707,
                          0.501063,
                          0.3700857,
                          0.57410157,
                          0.15295872,
                      ],
                      [
                          0.570678,
                          0.76664513,
                          0.48567873,
                          0.47680324,
                          0.2583247,
                      ],
                      [
                          0.38080955,
                          0.9315215,
                          0.4404478,
                          0.50659215,
                          0.5984908,
                      ],
                      [
                          0.5388581,
                          0.76993656,
                          0.4027568,
                          0.5952581,
                          0.68663263,
                      ],
                  ],
                  [
                      [
                          0.86013114,
                          0.17629854,
                          0.83010703,
                          0.27881518,
                          0.30543375,
                      ],
                      [
                          0.17009716,
                          0.61201245,
                          0.33521807,
                          0.15526368,
                          0.7401195,
                      ],
                      [
                          0.34011865,
                          0.6541383,
                          0.96909684,
                          0.43090558,
                          0.70467836,
                      ],
                      [
                          0.6738866,
                          0.47461915,
                          0.91508406,
                          0.44147202,
                          0.14099535,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # Reference output generated using OpenCV: cv2.cvtColor(data, cv2.COLOR_RGB2XYZ)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.4011656,
                          0.5017336,
                          0.09065688,
                          0.04048038,
                          0.2769511,
                      ],
                      [
                          0.46811658,
                          0.07937729,
                          0.79911184,
                          0.8632158,
                          0.1413149,
                      ],
                      [
                          0.2538725,
                          0.98146427,
                          0.29357457,
                          0.95588684,
                          0.09129933,
                      ],
                      [
                          0.6090472,
                          0.14032376,
                          0.11294556,
                          0.15829873,
                          0.1792411,
                      ],
                  ],
                  [
                      [
                          0.90825266,
                          0.54057753,
                          0.40771842,
                          0.7709542,
                          0.1009315,
                      ],
                      [
                          0.64988965,
                          0.99616426,
                          0.41274834,
                          0.40036052,
                          0.24396756,
                      ],
                      [
                          0.42678973,
                          0.95531154,
                          0.43172216,
                          0.3851813,
                          0.7444883,
                      ],
                      [
                          0.5084846,
                          0.99737406,
                          0.4381809,
                          0.7481805,
                          0.9036418,
                      ],
                  ],
                  [
                      [
                          0.78309417,
                          0.10751611,
                          0.82060075,
                          0.19588977,
                          0.3031369,
                      ],
                      [
                          0.08796211,
                          0.51749885,
                          0.28474382,
                          0.09561294,
                          0.74540937,
                      ],
                      [
                          0.2992335,
                          0.5486014,
                          0.95973116,
                          0.38571155,
                          0.64634556,
                      ],
                      [
                          0.6330101,
                          0.3715171,
                          0.90575427,
                          0.36752605,
                          0.03138365,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(
          kornia.color.xyz_to_rgb(data), expected, low_tolerance=True
      )
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  from kornia.testing import BaseTester
label_desc: Use image node_id 4 for calling the TestXyzToRgb obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types