code: def test_numerical(self, device, dtype):
      torch.manual_seed(76)
      f = RandomMosaic(p=1.0, data_keys=["input", "bbox_xyxy"])
  
      input = torch.stack(
          [
              torch.ones(1, 8, 8, device=device, dtype=dtype),
              torch.zeros(1, 8, 8, device=device, dtype=dtype),
          ]
      )
      boxes = torch.tensor(
          [[[4, 5, 6, 7], [1, 2, 3, 4]], [[2, 2, 6, 6], [0, 0, 0, 0]]],
          device=device,
          dtype=dtype,
      )
  
      out_image, out_box = f(input, boxes)
  
      expected = torch.tensor(
          [
              [
                  [
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0],
                  ]
              ],
              [
                  [
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                  ]
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      expected_box = torch.tensor(
          [
              [
                  [0.7074, 0.7099, 2.7074, 2.7099],
                  [0.0000, 0.0000, 1.0000, 1.0000],
                  [0.0000, 5.7099, 2.7074, 8.0000],
                  [0.0000, 2.7099, 1.0000, 4.7099],
                  [7.0000, 0.7099, 8.0000, 2.7099],
                  [5.7074, 0.0000, 7.7074, 1.0000],
                  [7.0000, 7.0000, 8.0000, 8.0000],
                  [5.7074, 5.7099, 7.7074, 7.7099],
              ],
              [
                  [0.0000, 0.0000, 1.0000, 2.8313],
                  [0.0000, 0.0000, 1.0000, 1.0000],
                  [0.0000, 7.0000, 1.0000, 8.0000],
                  [0.0000, 6.8313, 1.0000, 8.0000],
                  [4.5036, 0.0000, 8.0000, 2.8313],
                  [1.5036, 0.0000, 3.5036, 1.0000],
                  [4.5036, 6.8313, 8.0000, 8.0000],
                  [1.5036, 3.8313, 3.5036, 5.8313],
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      assert_close(out_image, expected, rtol=1e-4, atol=1e-4)
      assert_close(out_box, expected_box, rtol=1e-4, atol=1e-4)
  
imports: import copy
  import pytest
  import torch
  from kornia.augmentation import AugmentationSequential, RandomCutMixV2, RandomJigsaw, RandomMixUpV2, RandomMosaic, RandomTransplantation, RandomTransplantation3D
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 2 for calling the TestRandomMosaic obj's underlying member method code with example usage: obj.test_numerical(device, dtype) without return types