code: def test_p(self, p, device, dtype):
      torch.manual_seed(76)
      f = RandomMosaic(
          output_size=(300, 300), p=p, data_keys=["input", "bbox_xyxy"]
      )
  
      input = torch.randn((2, 3, 224, 224), device=device, dtype=dtype)
      boxes = torch.tensor(
          [
              # image 1
              [
                  [70.0, 5, 150, 100],
                  [60, 180, 175, 220],
              ],  # head  # feet
              # image 2
              [[75, 30, 175, 140], [0, 0, 0, 0]],  # head  # placeholder
          ],
          device=device,
          dtype=dtype,
      )
  
      f(input, boxes)
  
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 3 for calling the TestRandomMosaic obj's underlying member method code with example usage: obj.test_p(p, device, dtype) without return types