code: def test_unit(self, device, dtype):
      if dtype == torch.float16:
          pytest.skip("not work for half-precision")
  
      data = torch.tensor(
          [
              [
                  [
                      [
                          50.21928787,
                          23.29810143,
                          14.98279190,
                          62.50927353,
                          72.78904724,
                      ],
                      [
                          70.86846924,
                          68.75330353,
                          52.81696701,
                          76.17090607,
                          88.63134003,
                      ],
                      [
                          46.87160873,
                          72.38699341,
                          37.71450806,
                          82.57386780,
                          74.79967499,
                      ],
                      [
                          77.33016968,
                          47.39180374,
                          61.76217651,
                          90.83254242,
                          86.96239471,
                      ],
                  ],
                  [
                      [
                          65.81327057,
                          -3.69859719,
                          0.16971001,
                          14.86583614,
                          -65.54960632,
                      ],
                      [
                          -41.03258133,
                          -19.52661896,
                          64.16155243,
                          -58.53935242,
                          -71.78411102,
                      ],
                      [
                          112.05227661,
                          -60.13330460,
                          43.07910538,
                          -51.01456833,
                          -58.25787354,
                      ],
                      [
                          -62.37575531,
                          50.88882065,
                          -39.27450943,
                          17.00958824,
                          -24.93779755,
                      ],
                  ],
                  [
                      [
                          -69.53346252,
                          -73.34986877,
                          -11.47461891,
                          66.73863220,
                          70.43983459,
                      ],
                      [
                          51.92737579,
                          58.77009583,
                          45.97863388,
                          24.44452858,
                          98.81991577,
                      ],
                      [
                          -7.60597992,
                          78.97976685,
                          -69.31867218,
                          67.33953857,
                          14.28889370,
                      ],
                      [
                          92.31149292,
                          -85.91405487,
                          -32.83668518,
                          -23.45091820,
                          69.99038696,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # Reference output generated using skimage: lab2rgb(data)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.63513142,
                          0.0,
                          0.10660624,
                          0.79048697,
                          0.26823414,
                      ],
                      [
                          0.48903025,
                          0.64529494,
                          0.91140099,
                          0.15877841,
                          0.45987959,
                      ],
                      [1.0, 0.36069696, 0.29236125, 0.55744393, 0.0],
                      [
                          0.41710863,
                          0.3198324,
                          0.0,
                          0.94256868,
                          0.82748892,
                      ],
                  ],
                  [
                      [
                          0.28210726,
                          0.26080003,
                          0.15027717,
                          0.54540429,
                          0.80323837,
                      ],
                      [
                          0.748392,
                          0.68774842,
                          0.24204415,
                          0.83695682,
                          0.9902132,
                      ],
                      [
                          0.0,
                          0.79101603,
                          0.26633725,
                          0.89223337,
                          0.82301254,
                      ],
                      [
                          0.84857086,
                          0.34455393,
                          0.66555314,
                          0.86168397,
                          0.8948667,
                      ],
                  ],
                  [
                      [
                          0.94172458,
                          0.66390044,
                          0.21043296,
                          0.02453515,
                          0.04169043,
                      ],
                      [
                          0.28233233,
                          0.20235374,
                          0.19803933,
                          0.55069441,
                          0.0,
                      ],
                      [
                          0.50205101,
                          0.0,
                          0.79745394,
                          0.25376936,
                          0.6114783,
                      ],
                      [0.0, 1.0, 0.80867314, 1.0, 0.28778443],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      expected_unclipped = torch.tensor(
          [
              [
                  [
                      [
                          0.63513142,
                          -1.78708635,
                          0.10660624,
                          0.79048697,
                          0.26823414,
                      ],
                      [
                          0.48903025,
                          0.64529494,
                          0.91140099,
                          0.15877841,
                          0.45987959,
                      ],
                      [
                          1.01488435,
                          0.36069696,
                          0.29236125,
                          0.55744393,
                          -0.28090181,
                      ],
                      [
                          0.41710863,
                          0.3198324,
                          -1.81087917,
                          0.94256868,
                          0.82748892,
                      ],
                  ],
                  [
                      [
                          0.28210726,
                          0.26080003,
                          0.15027717,
                          0.54540429,
                          0.80323837,
                      ],
                      [
                          0.748392,
                          0.68774842,
                          0.24204415,
                          0.83695682,
                          0.9902132,
                      ],
                      [
                          -1.37862046,
                          0.79101603,
                          0.26633725,
                          0.89223337,
                          0.82301254,
                      ],
                      [
                          0.84857086,
                          0.34455393,
                          0.66555314,
                          0.86168397,
                          0.8948667,
                      ],
                  ],
                  [
                      [
                          0.94172458,
                          0.66390044,
                          0.21043296,
                          0.02453515,
                          0.04169043,
                      ],
                      [
                          0.28233233,
                          0.20235374,
                          0.19803933,
                          0.55069441,
                          -0.62707704,
                      ],
                      [
                          0.50205101,
                          -0.25005965,
                          0.79745394,
                          0.25376936,
                          0.6114783,
                      ],
                      [
                          -0.55802926,
                          1.0223477,
                          0.80867314,
                          1.07334156,
                          0.28778443,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      self.assert_close(kornia.color.lab_to_rgb(data), expected)
      self.assert_close(
          kornia.color.lab_to_rgb(data, clip=False), expected_unclipped
      )
  
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 TestLabToRgb obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types