code: def test_mean_filter_2batch_2ch(self, padding, device, dtype):
      kernel = torch.ones(1, 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, 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],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      ).expand(2, 2, -1, -1)
  
      actual = filter2d(sample, kernel, padding=padding)
  
      if padding == "same":
          expected_same = 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],
                      ]
                  ]
              ],
              device=device,
              dtype=dtype,
          ).expand(2, 2, -1, -1)
  
          self.assert_close(actual, expected_same)
      else:
          expected_valid = torch.tensor(
              [[[[5.0, 5.0, 5.0], [5.0, 5.0, 5.0], [5.0, 5.0, 5.0]]]],
              device=device,
              dtype=dtype,
          ).expand(2, 2, -1, -1)
          self.assert_close(actual, expected_valid)
  
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 6 for calling the TestFilter2D obj's underlying member method code with example usage: obj.test_mean_filter_2batch_2ch(padding, device, dtype) without return types