code: def test_four_classes_2d_one_class_nonperfect(self, device, dtype):
      num_classes = 4
      actual = torch.tensor(
          [[[0, 0, 1, 1], [0, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]]],
          device=device,
          dtype=torch.long,
      )
      predicted = torch.tensor(
          [[[0, 0, 1, 1], [0, 3, 0, 1], [2, 2, 1, 3], [2, 2, 3, 3]]],
          device=device,
          dtype=torch.long,
      )
  
      conf_mat = kornia.metrics.confusion_matrix(
          predicted, actual, num_classes
      )
      conf_mat_real = torch.tensor(
          [[[3, 0, 0, 1], [1, 3, 0, 0], [0, 0, 4, 0], [0, 1, 0, 3]]],
          device=device,
          dtype=torch.float32,
      )
      assert_close(conf_mat, conf_mat_real)
  
imports: import pytest
  import torch
  import kornia
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 7 for calling the TestConfusionMatrix obj's underlying member method code with example usage: obj.test_four_classes_2d_one_class_nonperfect(device, dtype) without return types