code: def test_crop(self, device, dtype):
      inp = torch.arange(0.0, 64.0, device=device, dtype=dtype).view(
          1, 1, 4, 4, 4
      )
  
      depth, height, width = 2, 2, 2
      expected = torch.tensor(
          [
              [
                  [
                      [[25.1667, 27.1667], [30.5000, 32.5000]],
                      [[46.5000, 48.5000], [51.8333, 53.8333]],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      )
  
      boxes = torch.tensor(
          [
              [
                  [0, 0, 1],
                  [3, 0, 1],
                  [3, 2, 1],
                  [0, 2, 1],
                  [0, 0, 3],
                  [3, 0, 3],
                  [3, 2, 3],
                  [0, 2, 3],
              ]
          ],
          device=device,
          dtype=dtype,
      )  # 1x8x3
  
      patches = kornia.geometry.transform.crop_and_resize3d(
          inp, boxes, (depth, height, width)
      )
      assert_close(patches, expected)
  
imports: import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version
label_desc: Use image node_id 1 for calling the TestCropAndResize3D obj's underlying member method code with example usage: obj.test_crop(device, dtype) without return types