code: def test_metric_sum_reduction(self, device, dtype):
      sample = torch.ones(4, 4, 2, device=device, dtype=dtype)
      expected = (
          torch.tensor(1.4142, device=device, dtype=dtype) * 4**2
      )
      actual = kornia.metrics.aepe(
          sample, 2.0 * sample, reduction="sum"
      )
      assert_close(actual, expected)
  
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 TestAepe obj's underlying member method code with example usage: obj.test_metric_sum_reduction(device, dtype) without return types