code: def test_gradcheck(self, device):
      # Define boxes in XYZXYZ format for simplicity.
      boxes_xyzxyz = torch.tensor(
          [
              [
                  139.2640,
                  103.0150,
                  283.162,
                  397.3120,
                  410.5225,
                  453.185,
              ],
              [1.0240, 80.5547, 469.50, 512.0000, 512.0000, 512.0],
              [165.2053, 262.1440, 42.98, 510.6347, 508.9280, 784.443],
              [119.8080, 144.2067, 234.21, 257.0240, 410.1292, 386.14],
          ],
          device=device,
      )
      boxes = Boxes3D.from_tensor(boxes_xyzxyz)
  
      trans_mat = torch.tensor(
          [
              [
                  [-1.0, 0.0, 0.0, 512.0],
                  [0.0, 1.0, 0.0, 0.0],
                  [0.0, 0.0, 2.0, 1.0],
                  [0.0, 0.0, 0.0, 1.0],
              ]
          ],
          device=device,
      )
  
      trans_mat = tensor_to_gradcheck_var(trans_mat)
      t_boxes = tensor_to_gradcheck_var(boxes.data)
  
      def _wrapper_transform_boxes(hexahedrons, M):
          boxes = Boxes3D(hexahedrons)
          boxes = boxes.transform_boxes(M)
          return boxes.data
  
      gradcheck(_wrapper_transform_boxes, (t_boxes, trans_mat))
  
imports: from functools import partial
  import pytest
  import torch
  from kornia.geometry.boxes import Boxes, Boxes3D
  from kornia.testing import assert_close, gradcheck, tensor_to_gradcheck_var
label_desc: Use image node_id 4 for calling the TestTransformBoxes3D obj's underlying member method code with example usage: obj.test_gradcheck(device) and returns: boxes