code: def test_inverse_and_forward_return_transform(
      self, random_apply, device, dtype
  ):
      inp = torch.randn(1, 3, 1000, 500, device=device, dtype=dtype)
      bbox = torch.tensor(
          [[[355, 10], [660, 10], [660, 250], [355, 250]]],
          device=device,
          dtype=dtype,
      )
      bbox_2 = [
          # torch.tensor([[[355, 10], [660, 10], [660, 250], [355, 250]]], device=device, dtype=dtype),
          torch.tensor(
              [
                  [[355, 10], [660, 10], [660, 250], [355, 250]],
                  [[355, 10], [660, 10], [660, 250], [355, 250]],
              ],
              device=device,
              dtype=dtype,
          )
      ]
      bbox_wh = torch.tensor(
          [[[30, 40, 100, 100]]], device=device, dtype=dtype
      )
      bbox_wh_2 = [
          # torch.tensor([[30, 40, 100, 100]], device=device, dtype=dtype),
          torch.tensor(
              [[30, 40, 100, 100], [30, 40, 100, 100]],
              device=device,
              dtype=dtype,
          )
      ]
      keypoints = torch.tensor(
          [[[465, 115], [545, 116]]], device=device, dtype=dtype
      )
      mask = bbox_to_mask(
          torch.tensor(
              [[[155, 0], [900, 0], [900, 400], [155, 400]]],
              device=device,
              dtype=dtype,
          ),
          1000,
          500,
      )[:, None]
      aug = K.AugmentationSequential(
          K.ImageSequential(
              K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
              K.RandomAffine(360, p=1.0),
          ),
          K.AugmentationSequential(
              K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
              K.RandomAffine(360, p=1.0),
          ),
          K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
          K.RandomAffine(360, p=1.0),
          data_keys=[
              "input",
              "mask",
              "bbox",
              "keypoints",
              "bbox",
              "BBOX_XYWH",
              "BBOX_XYWH",
          ],
          random_apply=random_apply,
      )
      with pytest.raises(
          Exception
      ):  # No parameters available for inversing.
          aug.inverse(
              inp, mask, bbox, keypoints, bbox_2, bbox_wh, bbox_wh_2
          )
  
      out = aug(inp, mask, bbox, keypoints, bbox_2, bbox_wh, bbox_wh_2)
      assert out[0].shape == inp.shape
      assert out[1].shape == mask.shape
      assert out[2].shape == bbox.shape
      assert out[3].shape == keypoints.shape
  
      if random_apply is False:
          reproducibility_test(
              (inp, mask, bbox, keypoints, bbox_2, bbox_wh, bbox_wh_2),
              aug,
          )
  
imports: from functools import partial
  from unittest.mock import patch
  import pytest
  import torch
  import kornia
  import kornia.augmentation
  from kornia.augmentation.container.base import ParamItem
  from kornia.constants import BorderType
  from kornia.geometry.bbox import bbox_to_mask
  from kornia.testing import assert_close
label_desc: Use image node_id 17 for calling the TestAugmentationSequential obj's underlying member method code with example usage: obj.test_inverse_and_forward_return_transform(random_apply, device, dtype) without return types