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(
          [
              [
                  [
                      0.0379,
                      0.0456,
                      0.0283,
                      0.0121,
                      0.0283,
                      0.0456,
                      0.0379,
                  ],
                  [
                      0.0456,
                      0.0598,
                      0.0402,
                      0.0168,
                      0.0402,
                      0.0598,
                      0.0456,
                  ],
                  [
                      0.0283,
                      0.0402,
                      0.0545,
                      0.0245,
                      0.0545,
                      0.0402,
                      0.0283,
                  ],
                  [
                      0.0121,
                      0.0168,
                      0.0245,
                      0.0276,
                      0.0245,
                      0.0168,
                      0.0121,
                  ],
                  [
                      0.0283,
                      0.0402,
                      0.0545,
                      0.0245,
                      0.0545,
                      0.0402,
                      0.0283,
                  ],
                  [
                      0.0456,
                      0.0598,
                      0.0402,
                      0.0168,
                      0.0402,
                      0.0598,
                      0.0456,
                  ],
                  [
                      0.0379,
                      0.0456,
                      0.0283,
                      0.0121,
                      0.0283,
                      0.0456,
                      0.0379,
                  ],
              ],
              [
                  [
                      0.0379,
                      0.0462,
                      0.0349,
                      0.0345,
                      0.0443,
                      0.0248,
                      0.0112,
                  ],
                  [
                      0.0462,
                      0.0608,
                      0.0488,
                      0.0483,
                      0.0581,
                      0.0274,
                      0.0119,
                  ],
                  [
                      0.0349,
                      0.0488,
                      0.0669,
                      0.0664,
                      0.0460,
                      0.0191,
                      0.0084,
                  ],
                  [
                      0.0345,
                      0.0483,
                      0.0664,
                      0.0660,
                      0.0455,
                      0.0189,
                      0.0083,
                  ],
                  [
                      0.0443,
                      0.0581,
                      0.0460,
                      0.0455,
                      0.0555,
                      0.0262,
                      0.0114,
                  ],
                  [
                      0.0248,
                      0.0274,
                      0.0191,
                      0.0189,
                      0.0262,
                      0.0172,
                      0.0084,
                  ],
                  [
                      0.0112,
                      0.0119,
                      0.0084,
                      0.0083,
                      0.0114,
                      0.0084,
                      0.0046,
                  ],
              ],
          ],
          device=device,
          dtype=dtype,
      ).repeat(2, 1, 1, 1)
      shi_tomasi = kornia.feature.CornerGFTT().to(device)
      scores = shi_tomasi(inp)
      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 TestCornerGFTT obj's underlying member method code with example usage: obj.test_corners_batch(device, dtype) without return types