code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [0.7925, 0.8704, 0.2771, 0.3279, 0.0193],
                      [0.2483, 0.6212, 0.9859, 0.5044, 0.5621],
                      [0.5762, 0.3959, 0.2931, 0.2669, 0.0243],
                      [0.6989, 0.0529, 0.8344, 0.6523, 0.8980],
                      [0.5181, 0.9341, 0.2172, 0.0520, 0.7266],
                  ],
                  [
                      [0.8413, 0.0284, 0.3625, 0.8864, 0.5595],
                      [0.3791, 0.0235, 0.4251, 0.0619, 0.5270],
                      [0.3516, 0.8005, 0.9571, 0.4113, 0.6119],
                      [0.0632, 0.8836, 0.0261, 0.1550, 0.4923],
                      [0.2332, 0.7044, 0.9514, 0.2443, 0.2818],
                  ],
                  [
                      [0.6899, 0.2063, 0.3179, 0.8989, 0.4378],
                      [0.0384, 0.5230, 0.6416, 0.9749, 0.7863],
                      [0.8577, 0.3115, 0.2375, 0.5446, 0.9837],
                      [0.3213, 0.6618, 0.5977, 0.3999, 0.4962],
                      [0.1385, 0.5831, 0.9756, 0.8714, 0.8017],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # Reference output generated using OpenCV: cv2.cvtColor(data, cv2.COLOR_RGB2XYZ)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.8094629,
                          0.30041942,
                          0.33188093,
                          0.7208117,
                          0.38409105,
                      ],
                      [
                          0.30114722,
                          0.2591345,
                          0.6174494,
                          0.29830942,
                          0.5670594,
                      ],
                      [
                          0.47643244,
                          0.623786,
                          0.6765313,
                          0.38332558,
                          0.47857922,
                      ],
                      [
                          0.2826805,
                          0.60994685,
                          0.33298355,
                          0.3316514,
                          0.6140681,
                      ],
                      [
                          0.30756146,
                          0.75926465,
                          0.7346588,
                          0.25828302,
                          0.4740529,
                      ],
                  ],
                  [
                      [
                          0.43258172,
                          0.4469004,
                          0.492101,
                          0.60042214,
                          0.53028214,
                      ],
                      [
                          0.35180065,
                          0.64882994,
                          0.51362985,
                          0.8815981,
                          0.62366176,
                      ],
                      [
                          0.71505237,
                          0.32387614,
                          0.2523859,
                          0.5909421,
                          0.78490996,
                      ],
                      [
                          0.52178127,
                          0.529264,
                          0.64931786,
                          0.53848785,
                          0.43353173,
                      ],
                      [
                          0.4046721,
                          0.40065458,
                          0.6358658,
                          0.8458253,
                          0.6847739,
                      ],
                  ],
                  [
                      [
                          0.48791525,
                          0.9063825,
                          0.46094114,
                          0.21983841,
                          0.23989305,
                      ],
                      [
                          0.46231723,
                          0.7581379,
                          0.7626976,
                          0.6469568,
                          0.496467,
                      ],
                      [
                          0.5711212,
                          0.33752257,
                          0.22661471,
                          0.4169921,
                          0.17608929,
                      ],
                      [
                          0.7967522,
                          0.10283372,
                          0.85753804,
                          0.72865105,
                          0.70245713,
                      ],
                      [
                          0.65009415,
                          0.6246666,
                          0.13107029,
                          0.35291404,
                          0.6800583,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(
          kornia.color.rgb_to_ycbcr(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 5 for calling the TestRgbToYcbcr obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types