code: def test_mean_filter(self, device, dtype):
      kernel = torch.ones(1, 3, 3, 3, device=device, dtype=dtype)
      sample = torch.tensor(
          [
              [
                  [
                      [
                          [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, 5.0, 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],
                      ],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      expected = torch.tensor(
          [
              [
                  [
                      [
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                      ],
                      [
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                      ],
                      [
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 5.0, 5.0, 5.0, 0.0],
                          [0.0, 0.0, 0.0, 0.0, 0.0],
                      ],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      actual = filter3d(sample, kernel)
      self.assert_close(actual, expected)
  
imports: import pytest
  import torch
  from kornia.filters import DexiNed, filter2d, filter2d_separable, filter3d
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
  from kornia.utils._compat import torch_version_le
label_desc: Use image node_id 4 for calling the TestFilter3D obj's underlying member method code with example usage: obj.test_mean_filter(device, dtype) without return types