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,
      ).expand(2, 2, 7, 7)
      expected = torch.tensor(
          [
              [
                  [
                      [
                          0.1684,
                          0.0856,
                          -0.0118,
                          0.0059,
                          0.0842,
                          0.0956,
                          0.0734,
                      ],
                      [
                          0.0856,
                          -0.0203,
                          -0.1385,
                          -0.0931,
                          0.0428,
                          0.0888,
                          0.0733,
                      ],
                      [
                          -0.0118,
                          -0.1385,
                          -0.2741,
                          -0.1998,
                          -0.0059,
                          0.0768,
                          0.0695,
                      ],
                      [
                          0.0059,
                          -0.0931,
                          -0.1998,
                          -0.1443,
                          0.0030,
                          0.0639,
                          0.0567,
                      ],
                      [
                          0.0842,
                          0.0428,
                          -0.0059,
                          0.0030,
                          0.0421,
                          0.0478,
                          0.0367,
                      ],
                      [
                          0.0956,
                          0.0888,
                          0.0768,
                          0.0639,
                          0.0478,
                          0.0289,
                          0.0189,
                      ],
                      [
                          0.0734,
                          0.0733,
                          0.0695,
                          0.0567,
                          0.0367,
                          0.0189,
                          0.0117,
                      ],
                  ],
                  [
                      [
                          0.0725,
                          -0.0023,
                          -0.0226,
                          0.0810,
                          0.1114,
                          0.0699,
                          0.0471,
                      ],
                      [
                          -0.0023,
                          -0.0868,
                          -0.1010,
                          0.0361,
                          0.0974,
                          0.0758,
                          0.0598,
                      ],
                      [
                          -0.0226,
                          -0.1010,
                          -0.1408,
                          -0.0652,
                          0.0030,
                          0.0559,
                          0.0777,
                      ],
                      [
                          0.0810,
                          0.0361,
                          -0.0652,
                          -0.1802,
                          -0.1625,
                          -0.0049,
                          0.0764,
                      ],
                      [
                          0.1114,
                          0.0974,
                          0.0030,
                          -0.1625,
                          -0.1710,
                          -0.0092,
                          0.0761,
                      ],
                      [
                          0.0699,
                          0.0758,
                          0.0559,
                          -0.0049,
                          -0.0092,
                          0.0531,
                          0.0844,
                      ],
                      [
                          0.0471,
                          0.0598,
                          0.0777,
                          0.0764,
                          0.0761,
                          0.0844,
                          0.0865,
                      ],
                  ],
              ]
          ],
          device=device,
          dtype=dtype,
      ).expand(2, 2, 7, 7)
      det = kornia.feature.BlobDoGSingle(1.0, 1.6).to(device)
      scores = det(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 TestBlobDoGSingle obj's underlying member method code with example usage: obj.test_blobs_batch(device, dtype) without return types