code: def test_mix_sized_filter_padding_same(self, padding, device, dtype):
      kernel = torch.ones(1, 5, 6, device=device, dtype=dtype)
      sample_ = torch.tensor(
          [
              [
                  [
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      expected_same = torch.tensor(
          [
              [
                  [
                      [2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
                      [3.0, 3.0, 3.0, 3.0, 3.0, 0.0],
                      [3.0, 3.0, 3.0, 3.0, 3.0, 0.0],
                      [3.0, 3.0, 3.0, 3.0, 3.0, 0.0],
                      [2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      actual = filter2d(
          sample_, kernel, padding="same", border_type="constant"
      )
      self.assert_close(actual, expected_same)
  
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 9 for calling the TestFilter2D obj's underlying member method code with example usage: obj.test_mix_sized_filter_padding_same(padding, device, dtype) without return types