code: def test_four_classes_2d_perfect(self, device, dtype):
      batch_size = 1
      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, 0, 1, 1], [2, 2, 3, 3], [2, 2, 3, 3]]],
          device=device,
          dtype=torch.long,
      )
  
      mean_iou = kornia.metrics.mean_iou(predicted, actual, num_classes)
      mean_iou_real = torch.tensor(
          [[1.0, 1.0, 1.0, 1.0]], device=device, dtype=torch.float32
      )
      assert mean_iou.shape == (batch_size, num_classes)
      assert_close(mean_iou, mean_iou_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 4 for calling the TestMeanIoU obj's underlying member method code with example usage: obj.test_four_classes_2d_perfect(device, dtype) without return types