code: def test_blobs_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, 0.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0],
                  [0.0, 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, 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, 0.0, 0.0, 0.0, 0.0],
                  [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0],
                  [0.0, 0.0, 0.0, 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(
          [
              [
                  [
                      -0.0564,
                      -0.0759,
                      -0.0342,
                      -0.0759,
                      -0.0564,
                      -0.0057,
                      0.0000,
                  ],
                  [
                      -0.0759,
                      -0.0330,
                      0.0752,
                      -0.0330,
                      -0.0759,
                      -0.0096,
                      0.0000,
                  ],
                  [
                      -0.0342,
                      0.0752,
                      0.1914,
                      0.0752,
                      -0.0342,
                      -0.0068,
                      0.0000,
                  ],
                  [
                      -0.0759,
                      -0.0330,
                      0.0752,
                      -0.0330,
                      -0.0759,
                      -0.0096,
                      0.0000,
                  ],
                  [
                      -0.0564,
                      -0.0759,
                      -0.0342,
                      -0.0759,
                      -0.0564,
                      -0.0057,
                      0.0000,
                  ],
                  [
                      -0.0057,
                      -0.0096,
                      -0.0068,
                      -0.0096,
                      -0.0057,
                      -0.0005,
                      0.0000,
                  ],
                  [
                      0.0000,
                      0.0000,
                      0.0000,
                      0.0000,
                      0.0000,
                      0.0000,
                      0.0000,
                  ],
              ],
              [
                  [
                      -0.0564,
                      -0.0522,
                      -0.0522,
                      -0.0564,
                      -0.0057,
                      0.0000,
                      0.0000,
                  ],
                  [
                      -0.0522,
                      0.0688,
                      0.0688,
                      -0.0123,
                      0.0033,
                      -0.0057,
                      -0.0005,
                  ],
                  [
                      -0.0522,
                      0.0688,
                      -0.0755,
                      -0.1111,
                      -0.0123,
                      -0.0564,
                      -0.0057,
                  ],
                  [
                      -0.0564,
                      -0.0123,
                      -0.1111,
                      -0.0755,
                      0.0688,
                      -0.0522,
                      -0.0080,
                  ],
                  [
                      -0.0057,
                      0.0033,
                      -0.0123,
                      0.0688,
                      0.0688,
                      -0.0522,
                      -0.0080,
                  ],
                  [
                      0.0000,
                      -0.0057,
                      -0.0564,
                      -0.0522,
                      -0.0522,
                      -0.0564,
                      -0.0057,
                  ],
                  [
                      0.0000,
                      -0.0005,
                      -0.0057,
                      -0.0080,
                      -0.0080,
                      -0.0057,
                      -0.0005,
                  ],
              ],
          ],
          device=device,
          dtype=dtype,
      ).repeat(2, 1, 1, 1)
      shi_tomasi = kornia.feature.BlobHessian().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 3 for calling the TestBlobHessian obj's underlying member method code with example usage: obj.test_blobs_batch(device, dtype) without return types