code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [
                          0.5513626,
                          0.8487718,
                          0.1822479,
                          0.2851745,
                          0.2669488,
                      ],
                      [
                          0.7596772,
                          0.4565057,
                          0.6181599,
                          0.3852497,
                          0.7746902,
                      ],
                      [
                          0.5742747,
                          0.1957062,
                          0.7530835,
                          0.2104362,
                          0.9449323,
                      ],
                      [
                          0.9918052,
                          0.2437515,
                          0.4718738,
                          0.8502576,
                          0.1675640,
                      ],
                      [
                          0.9210159,
                          0.0538564,
                          0.5801026,
                          0.6110542,
                          0.3768399,
                      ],
                  ],
                  [
                      [
                          0.4111853,
                          0.0183454,
                          0.7832276,
                          0.2975794,
                          0.1139528,
                      ],
                      [
                          0.6207729,
                          0.1073406,
                          0.8335325,
                          0.5700451,
                          0.2594557,
                      ],
                      [
                          0.7520493,
                          0.5097187,
                          0.4719872,
                          0.9477938,
                          0.1640292,
                      ],
                      [
                          0.8973427,
                          0.6455371,
                          0.7567374,
                          0.3159562,
                          0.8135307,
                      ],
                      [
                          0.0855004,
                          0.6645504,
                          0.9923756,
                          0.6209313,
                          0.2356791,
                      ],
                  ],
                  [
                      [
                          0.4734681,
                          0.0422099,
                          0.7405791,
                          0.9671807,
                          0.1793800,
                      ],
                      [
                          0.8221875,
                          0.7219887,
                          0.3627397,
                          0.4403201,
                          0.0024084,
                      ],
                      [
                          0.0803350,
                          0.9432759,
                          0.0241543,
                          0.8292291,
                          0.7745832,
                      ],
                      [
                          0.3707901,
                          0.0851424,
                          0.5805428,
                          0.1098685,
                          0.4238486,
                      ],
                      [
                          0.1058410,
                          0.0816052,
                          0.5792874,
                          0.9578886,
                          0.6281684,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      data[:, 0] = 2 * math.pi * data[:, 0]
  
      # OpenCV
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.21650219,
                          0.01911971,
                          0.91374826,
                          0.17609520,
                          0.10979544,
                      ],
                      [
                          0.65698080,
                          0.02984191,
                          0.77314806,
                          0.38072730,
                          0.25964087,
                      ],
                      [
                          0.73213010,
                          0.81102980,
                          0.47240910,
                          0.96834683,
                          0.29108350,
                      ],
                      [
                          0.93540700,
                          0.64780010,
                          0.61551300,
                          0.35066980,
                          0.89171433,
                      ],
                      [
                          0.09454980,
                          0.69192480,
                          0.98795897,
                          0.25782573,
                          0.08763295,
                      ],
                  ],
                  [
                      [
                          0.48587522,
                          0.01757100,
                          0.94376480,
                          0.58539250,
                          0.13439366,
                      ],
                      [
                          0.30897713,
                          0.18483935,
                          0.80829670,
                          0.75936294,
                          0.25883088,
                      ],
                      [
                          0.75421450,
                          0.97218925,
                          0.46058673,
                          0.99108470,
                          0.03697497,
                      ],
                      [
                          0.85927840,
                          0.67571700,
                          0.89796180,
                          0.28124255,
                          0.89256540,
                      ],
                      [
                          0.07645091,
                          0.65486740,
                          0.99254686,
                          0.50014400,
                          0.38372523,
                      ],
                  ],
                  [
                      [
                          0.60586834,
                          0.01897625,
                          0.62269044,
                          0.00976634,
                          0.09351197,
                      ],
                      [
                          0.93256867,
                          0.14439031,
                          0.89391685,
                          0.49867177,
                          0.26008060,
                      ],
                      [
                          0.77196840,
                          0.04724807,
                          0.48338777,
                          0.90450300,
                          0.12093388,
                      ],
                      [
                          0.86302150,
                          0.61535730,
                          0.85029656,
                          0.34361976,
                          0.73449594,
                      ],
                      [
                          0.08502806,
                          0.63717590,
                          0.99679226,
                          0.98403690,
                          0.16492467,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      f = kornia.color.hls_to_rgb
      self.assert_close(f(data), expected, low_tolerance=True)
  
      data[:, 0] += 2 * math.pi
      self.assert_close(f(data), expected, low_tolerance=True)
  
      data[:, 0] -= 4 * math.pi
      self.assert_close(f(data), expected, low_tolerance=True)
  
imports: import math
  import warnings
  import pytest
  import torch
  from packaging import version
  from torch.autograd import gradcheck
  import kornia
  from kornia.testing import BaseTester
label_desc: Use image node_id 4 for calling the TestHlsToRgb obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types