code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [0.9892, 0.2620, 0.4184, 0.5286, 0.2793],
                      [0.0722, 0.8828, 0.8714, 0.0657, 0.7798],
                      [0.8118, 0.7522, 0.0260, 0.8811, 0.5226],
                      [0.0644, 0.3648, 0.4448, 0.4202, 0.7316],
                      [0.9138, 0.1956, 0.4257, 0.6381, 0.1353],
                  ],
                  [
                      [0.7408, 0.8529, 0.5119, 0.0220, 0.0226],
                      [0.8963, 0.5652, 0.9568, 0.6977, 0.8221],
                      [0.4645, 0.0478, 0.4952, 0.5492, 0.4861],
                      [0.9980, 0.9978, 0.0281, 0.5283, 0.8146],
                      [0.7789, 0.2663, 0.6437, 0.6926, 0.5627],
                  ],
                  [
                      [0.7377, 0.7152, 0.3080, 0.8515, 0.4841],
                      [0.7192, 0.3297, 0.7337, 0.0230, 0.2464],
                      [0.6399, 0.8998, 0.3838, 0.3043, 0.3774],
                      [0.1281, 0.6731, 0.4218, 0.3963, 0.8541],
                      [0.2245, 0.2413, 0.2351, 0.9522, 0.8158],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # Reference output generated using OpenCV: cv2.cvtColor(data, cv2.COLOR_RGB2XYZ)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          1.0000,
                          0.5639256,
                          0.14902398,
                          1.0000,
                          0.2569923,
                      ],
                      [
                          0.37973762,
                          0.64386904,
                          1.0000,
                          0.0000,
                          0.4239992,
                      ],
                      [1.0000, 1.0000, 0.0000, 0.60653293, 0.3505922],
                      [
                          0.0000,
                          0.6076593,
                          0.33508536,
                          0.27470887,
                          1.0000,
                      ],
                      [
                          0.52727354,
                          0.0000,
                          0.05404532,
                          1.0000,
                          0.5783674,
                      ],
                  ],
                  [
                      [
                          0.736647,
                          0.0000,
                          0.55139434,
                          0.44206098,
                          0.4548782,
                      ],
                      [
                          0.0000,
                          0.98196536,
                          0.54739904,
                          0.33826917,
                          0.850068,
                      ],
                      [
                          0.72412336,
                          0.6222996,
                          0.110618,
                          1.0000,
                          0.614918,
                      ],
                      [
                          0.15862459,
                          0.0699634,
                          0.66296846,
                          0.4845066,
                          0.3705502,
                      ],
                      [
                          1.0000,
                          0.46070462,
                          0.5654058,
                          0.24897486,
                          0.0000,
                      ],
                  ],
                  [
                      [1.0000, 0.88769174, 0.4394987, 0.0000, 0.0000],
                      [
                          0.77483994,
                          0.99839956,
                          1.0000,
                          0.41622213,
                          1.0000,
                      ],
                      [
                          0.7488585,
                          0.0000,
                          0.01748962,
                          0.9683316,
                          0.49795526,
                      ],
                      [0.9473541, 1.0000, 0.0000, 0.47037587, 1.0000],
                      [
                          1.0000,
                          0.0000,
                          0.6804801,
                          0.9795798,
                          0.24646705,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(kornia.color.ycbcr_to_rgb(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 TestYcbcrToRgb obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types