code: def test_inference(self, device, dtype, data):
      model = DexiNed(pretrained=False)
      model.load_state_dict(data, strict=True)
      model = model.to(device, dtype)
      model.eval()
  
      img = torch.tensor(
          [[[[0.0, 255.0, 0.0], [0.0, 255.0, 0.0], [0.0, 255.0, 0.0]]]],
          device=device,
          dtype=dtype,
      )
      img = img.repeat(1, 3, 1, 1)
  
      expect = torch.tensor(
          [
              [
                  [
                      [-0.3709, 0.0519, -0.2839],
                      [0.0627, 0.6587, -0.1276],
                      [-0.1840, -0.3917, -0.8240],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      out = model(img)[-1]
      assert_close(out, expect, atol=3e-4, rtol=3e-4)
  
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 2 for calling the TestDexiNed obj's underlying member method code with example usage: obj.test_inference(device, dtype, data) without return types