code: def test_dynamo(self, device, dtype, torch_optimizer):
      # Define script
      op = kornia.geometry.transform.crop_by_boxes3d
      op_script = torch_optimizer(op)
      # Define input
      inp = torch.randn((1, 1, 7, 7, 7), device=device, dtype=dtype)
      src_box = torch.tensor(
          [
              [
                  [1.0, 1.0, 1.0],
                  [3.0, 1.0, 1.0],
                  [3.0, 3.0, 1.0],
                  [1.0, 3.0, 1.0],
                  [1.0, 1.0, 2.0],
                  [3.0, 1.0, 2.0],
                  [3.0, 3.0, 2.0],
                  [1.0, 3.0, 2.0],
              ]
          ],
          device=device,
          dtype=dtype,
      )  # 1x8x3
      dst_box = torch.tensor(
          [
              [
                  [0.0, 0.0, 0.0],
                  [1.0, 0.0, 0.0],
                  [1.0, 1.0, 0.0],
                  [0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0],
                  [1.0, 0.0, 1.0],
                  [1.0, 1.0, 1.0],
                  [0.0, 1.0, 1.0],
              ]
          ],
          device=device,
          dtype=dtype,
      )  # 1x8x3
  
      actual = op_script(inp, src_box, dst_box, align_corners=True)
      expected = op(inp, src_box, dst_box, align_corners=True)
      assert_close(actual, expected, rtol=1e-4, atol=1e-4)
  
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 3 for calling the TestCropByBoxes3D obj's underlying member method code with example usage: obj.test_dynamo(device, dtype, torch_optimizer) without return types