code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [0.9637, 0.0586, 0.6470, 0.6212, 0.9622],
                      [0.8293, 0.4858, 0.8953, 0.2607, 0.3250],
                      [0.5314, 0.4189, 0.8388, 0.8065, 0.2211],
                      [0.9682, 0.2928, 0.4118, 0.2533, 0.0455],
                  ],
                  [
                      [0.6936, 0.3457, 0.9466, 0.9937, 0.2692],
                      [0.7485, 0.7320, 0.8323, 0.6889, 0.4831],
                      [0.1865, 0.7439, 0.1366, 0.8858, 0.2077],
                      [0.6227, 0.6140, 0.3936, 0.5024, 0.4157],
                  ],
                  [
                      [0.6477, 0.9269, 0.7531, 0.7349, 0.9485],
                      [0.4264, 0.8539, 0.9830, 0.2269, 0.1138],
                      [0.3988, 0.1605, 0.6220, 0.0546, 0.1106],
                      [0.2128, 0.5673, 0.0781, 0.1431, 0.3310],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # Reference output generated using OpenCV: cv2.cvtColor(data, cv2.COLOR_RGB2XYZ)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.7623584,
                          0.31501925,
                          0.7412189,
                          0.7441359,
                          0.66425407,
                      ],
                      [
                          0.6866283,
                          0.61618143,
                          0.84423876,
                          0.39480132,
                          0.32732624,
                      ],
                      [
                          0.3578189,
                          0.4677382,
                          0.50703406,
                          0.6592388,
                          0.18541752,
                      ],
                      [
                          0.6603961,
                          0.44267434,
                          0.32468265,
                          0.30994105,
                          0.22713262,
                      ],
                  ],
                  [
                      [
                          0.7477299,
                          0.32658678,
                          0.86891913,
                          0.89580274,
                          0.4656054,
                      ],
                      [
                          0.7424382,
                          0.6884378,
                          0.8565741,
                          0.5644922,
                          0.4228247,
                      ],
                      [
                          0.2751717,
                          0.63267857,
                          0.3209684,
                          0.8089483,
                          0.20354219,
                      ],
                      [
                          0.6665957,
                          0.5423198,
                          0.37470126,
                          0.42349333,
                          0.33085644,
                      ],
                  ],
                  [
                      [
                          0.7167665,
                          0.92310345,
                          0.8409531,
                          0.82877415,
                          0.95198023,
                      ],
                      [
                          0.51042646,
                          0.9080406,
                          1.0505873,
                          0.30275896,
                          0.17200153,
                      ],
                      [
                          0.4114541,
                          0.24927814,
                          0.62354034,
                          0.17305644,
                          0.13412625,
                      ],
                      [
                          0.29514894,
                          0.6179093,
                          0.12908883,
                          0.20075734,
                          0.3649534,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(kornia.color.rgb_to_xyz(data), expected)
  
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 TestRgbToXyz obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types