code: def test_crop_batch(self, device, dtype):
      inp = torch.tensor(
          [
              [
                  [
                      [1.0, 2.0, 3.0, 4.0],
                      [5.0, 6.0, 7.0, 8.0],
                      [9.0, 10.0, 11.0, 12.0],
                      [13.0, 14.0, 15.0, 16.0],
                  ]
              ],
              [
                  [
                      [1.0, 5.0, 9.0, 13.0],
                      [2.0, 6.0, 10.0, 14.0],
                      [3.0, 7.0, 11.0, 15.0],
                      [4.0, 8.0, 12.0, 16.0],
                  ]
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      expected = torch.tensor(
          [[[[6.0, 7.0], [10.0, 11.0]]], [[[7.0, 15.0], [8.0, 16.0]]]],
          device=device,
          dtype=dtype,
      )
  
      boxes = torch.tensor(
          [
              [[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]],
              [[1.0, 2.0], [3.0, 2.0], [3.0, 3.0], [1.0, 3.0]],
          ],
          device=device,
          dtype=dtype,
      )  # 2x4x2
  
      patches = kornia.geometry.transform.crop_and_resize(
          inp, boxes, (2, 2)
      )
      assert_close(patches, expected, rtol=1e-4, atol=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 TestCropAndResize obj's underlying member method code with example usage: obj.test_crop_batch(device, dtype) without return types