code: def test_transform_multiple_boxes(self, device, dtype):
      # Define boxes in XYZXYZ format for simplicity.
      boxes_xyzxyz = torch.tensor(
          [
              [
                  139.2640,
                  103.0150,
                  283.162,
                  398.3120,
                  411.5225,
                  454.185,
              ],
              [1.0240, 80.5547, 469.50, 513.0000, 513.0000, 513.0],
              [165.2053, 262.1440, 42.98, 511.6347, 509.9280, 785.443],
              [119.8080, 144.2067, 234.21, 258.0240, 411.1292, 387.14],
          ],
          device=device,
          dtype=dtype,
      ).repeat(
          2, 1, 1
      )  # 2 x 4 x 4 two images 4 boxes each
  
      expected_boxes_xyzxyz = torch.tensor(
          [
              [
                  [
                      372.7360,
                      103.0150,
                      567.324,
                      115.6880,
                      411.5225,
                      908.37,
                  ],
                  [510.9760, 80.5547, 940.0, 1.0000, 513.0000, 1026.0],
                  [
                      346.7947,
                      262.1440,
                      86.96,
                      2.3653,
                      509.9280,
                      1570.886,
                  ],
                  [
                      392.1920,
                      144.2067,
                      469.42,
                      255.9760,
                      411.1292,
                      774.28,
                  ],
              ],
              [
                  [
                      139.2640,
                      103.0150,
                      283.162,
                      398.3120,
                      411.5225,
                      454.185,
                  ],
                  [1.0240, 80.5547, 469.50, 513.0000, 513.0000, 513.0],
                  [
                      165.2053,
                      262.1440,
                      42.98,
                      511.6347,
                      509.9280,
                      785.443,
                  ],
                  [
                      119.8080,
                      144.2067,
                      234.21,
                      258.0240,
                      411.1292,
                      387.14,
                  ],
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      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],
              ],
              [
                  [1.0, 0.0, 0.0, 0.0],
                  [0.0, 1.0, 0.0, 0.0],
                  [0.0, 0.0, 1.0, 0.0],
                  [0.0, 0.0, 0.0, 1.0],
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      boxes = Boxes3D.from_tensor(boxes_xyzxyz)
      expected_boxes = Boxes3D.from_tensor(
          expected_boxes_xyzxyz, validate_boxes=False
      )
  
      out = boxes.transform_boxes(trans_mat)
      assert_close(out.data, expected_boxes.data, atol=1e-4, rtol=1e-4)
  
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 3 for calling the TestTransformBoxes3D obj's underlying member method code with example usage: obj.test_transform_multiple_boxes(device, dtype) without return types