code: def test_two_classes_batch2(self, device, dtype):
      batch_size = 2
      num_classes = 2
      actual = torch.tensor(
          [[1, 1, 1, 1, 0, 0, 0, 0]], device=device, dtype=torch.long
      ).repeat(batch_size, 1)
      predicted = torch.tensor(
          [[1, 1, 1, 1, 0, 0, 0, 1]], device=device, dtype=torch.long
      ).repeat(batch_size, 1)
  
      conf_mat = kornia.metrics.confusion_matrix(
          predicted, actual, num_classes
      )
      conf_mat_real = torch.tensor(
          [[[3, 1], [0, 4]], [[3, 1], [0, 4]]],
          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 2 for calling the TestConfusionMatrix obj's underlying member method code with example usage: obj.test_two_classes_batch2(device, dtype) without return types