code: def test_corners_batch(self, device, dtype):
      inp = torch.tensor(
          [
              [
                  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
              ],
              [
                  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
              ],
          ],
          device=device,
          dtype=dtype,
      ).repeat(2, 1, 1, 1)
      expected = (
          torch.tensor(
              [
                  [
                      [
                          0.0415,
                          0.0541,
                          0.0346,
                          0.0058,
                          0.0346,
                          0.0541,
                          0.0415,
                      ],
                      [
                          0.0541,
                          0.0678,
                          0.0457,
                          0.0145,
                          0.0457,
                          0.0678,
                          0.0541,
                      ],
                      [
                          0.0346,
                          0.0457,
                          0.0335,
                          0.0139,
                          0.0335,
                          0.0457,
                          0.0346,
                      ],
                      [
                          0.0058,
                          0.0145,
                          0.0139,
                          0.0064,
                          0.0139,
                          0.0145,
                          0.0058,
                      ],
                      [
                          0.0346,
                          0.0457,
                          0.0335,
                          0.0139,
                          0.0335,
                          0.0457,
                          0.0346,
                      ],
                      [
                          0.0541,
                          0.0678,
                          0.0457,
                          0.0145,
                          0.0457,
                          0.0678,
                          0.0541,
                      ],
                      [
                          0.0415,
                          0.0541,
                          0.0346,
                          0.0058,
                          0.0346,
                          0.0541,
                          0.0415,
                      ],
                  ],
                  [
                      [
                          0.0415,
                          0.0547,
                          0.0447,
                          0.0440,
                          0.0490,
                          0.0182,
                          0.0053,
                      ],
                      [
                          0.0547,
                          0.0688,
                          0.0557,
                          0.0549,
                          0.0610,
                          0.0229,
                          0.0066,
                      ],
                      [
                          0.0447,
                          0.0557,
                          0.0444,
                          0.0437,
                          0.0489,
                          0.0168,
                          0.0035,
                      ],
                      [
                          0.0440,
                          0.0549,
                          0.0437,
                          0.0431,
                          0.0481,
                          0.0166,
                          0.0034,
                      ],
                      [
                          0.0490,
                          0.0610,
                          0.0489,
                          0.0481,
                          0.0541,
                          0.0205,
                          0.0060,
                      ],
                      [
                          0.0182,
                          0.0229,
                          0.0168,
                          0.0166,
                          0.0205,
                          0.0081,
                          0.0025,
                      ],
                      [
                          0.0053,
                          0.0066,
                          0.0035,
                          0.0034,
                          0.0060,
                          0.0025,
                          0.0008,
                      ],
                  ],
              ],
              device=device,
              dtype=dtype,
          ).repeat(2, 1, 1, 1)
          / 10.0
      )
      scores = kornia.feature.harris_response(inp, k=0.04)
      assert_close(scores, expected, atol=1e-4, rtol=1e-4)
  
imports: import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
label_desc: Use image node_id 4 for calling the TestCornerHarris obj's underlying member method code with example usage: obj.test_corners_batch(device, dtype) without return types