code: def test_unit(self, device, dtype):
      data = torch.tensor(
          [
              [
                  [
                      [
                          3.5433271,
                          5.6390061,
                          1.3766849,
                          2.5384088,
                          4.6848912,
                      ],
                      [
                          5.7209363,
                          5.3262630,
                          6.2059994,
                          4.1164689,
                          2.3872600,
                      ],
                      [
                          0.6370091,
                          3.6186798,
                          5.9170871,
                          2.8275447,
                          5.4289737,
                      ],
                      [
                          0.2751994,
                          1.6632686,
                          1.0049511,
                          0.7046204,
                          1.3791083,
                      ],
                      [
                          0.7863123,
                          4.4852505,
                          4.3064494,
                          2.5573561,
                          5.9083076,
                      ],
                  ],
                  [
                      [
                          0.5026655,
                          0.9453601,
                          0.5929778,
                          0.2632897,
                          0.4590443,
                      ],
                      [
                          0.6201433,
                          0.5610679,
                          0.9653260,
                          0.0830478,
                          0.5000827,
                      ],
                      [
                          0.6067343,
                          0.6422323,
                          0.6777940,
                          0.7705711,
                          0.6050767,
                      ],
                      [
                          0.5495264,
                          0.5573426,
                          0.4683768,
                          0.2268902,
                          0.2116482,
                      ],
                      [
                          0.6525245,
                          0.0022379,
                          0.4909980,
                          0.1682271,
                          0.6327152,
                      ],
                  ],
                  [
                      [
                          0.8471680,
                          0.9302199,
                          0.3265766,
                          0.7944570,
                          0.7038843,
                      ],
                      [
                          0.4833369,
                          0.2088473,
                          0.1169234,
                          0.4966302,
                          0.6448684,
                      ],
                      [
                          0.2713015,
                          0.5893380,
                          0.6015301,
                          0.6801558,
                          0.2322258,
                      ],
                      [
                          0.5704236,
                          0.6797268,
                          0.4755683,
                          0.4811209,
                          0.5317836,
                      ],
                      [
                          0.3236262,
                          0.0999796,
                          0.3614958,
                          0.5117705,
                          0.8194097,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      # OpenCV
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.42132590,
                          0.93021995,
                          0.26564622,
                          0.58528465,
                          0.53384290,
                      ],
                      [
                          0.48333693,
                          0.20884734,
                          0.11692339,
                          0.45538613,
                          0.32238087,
                      ],
                      [
                          0.27130150,
                          0.21084610,
                          0.60153013,
                          0.15604737,
                          0.23222584,
                      ],
                      [
                          0.57042360,
                          0.45685310,
                          0.47556830,
                          0.48112088,
                          0.49611038,
                      ],
                      [
                          0.32362622,
                          0.09981924,
                          0.20394461,
                          0.42567685,
                          0.81940967,
                      ],
                  ],
                  [
                      [
                          0.68380290,
                          0.05082710,
                          0.32657660,
                          0.79445700,
                          0.38077020,
                      ],
                      [
                          0.18359877,
                          0.09166980,
                          0.00405421,
                          0.45823452,
                          0.64486840,
                      ],
                      [
                          0.20682439,
                          0.41690278,
                          0.19381660,
                          0.68015575,
                          0.09171140,
                      ],
                      [
                          0.33933756,
                          0.67972680,
                          0.46658220,
                          0.44541004,
                          0.53178360,
                      ],
                      [
                          0.27101707,
                          0.09975589,
                          0.18400209,
                          0.51177055,
                          0.30095676,
                      ],
                  ],
                  [
                      [
                          0.84716797,
                          0.59178180,
                          0.13292392,
                          0.67397410,
                          0.70388430,
                      ],
                      [
                          0.34453064,
                          0.19874583,
                          0.01237347,
                          0.49663020,
                          0.41256943,
                      ],
                      [
                          0.10669357,
                          0.58933800,
                          0.33635240,
                          0.52297890,
                          0.20633064,
                      ],
                      [
                          0.25696078,
                          0.30088606,
                          0.25282317,
                          0.37195927,
                          0.41923255,
                      ],
                      [
                          0.11245217,
                          0.09997964,
                          0.36149580,
                          0.46373847,
                          0.48655340,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      f = kornia.color.hsv_to_rgb
      self.assert_close(f(data), expected)
  
      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 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 TestHsvToRgb obj's underlying member method code with example usage: obj.test_unit(device, dtype) without return types