code: def test_video(self, device, dtype):
      input = torch.randn(2, 3, 5, 6, device=device, dtype=dtype)[None]
      bbox = torch.tensor(
          [[[1.0, 1.0], [2.0, 1.0], [2.0, 2.0], [1.0, 2.0]]],
          device=device,
          dtype=dtype,
      ).expand(2, 1, -1, -1)[None]
      points = torch.tensor(
          [[[1.0, 1.0]]], device=device, dtype=dtype
      ).expand(2, -1, -1)[None]
      aug_list = K.AugmentationSequential(
          K.VideoSequential(
              kornia.augmentation.ColorJiggle(
                  0.1, 0.1, 0.1, 0.1, p=1.0
              ),
              kornia.augmentation.RandomAffine(360, p=1.0),
          ),
          data_keys=["input", "mask", "bbox", "keypoints"],
      )
      out = aug_list(input, input, bbox, points)
      assert out[0].shape == input.shape
      assert out[1].shape == input.shape
      assert out[2].shape == bbox.shape
      assert out[3].shape == points.shape
  
      out_inv = aug_list.inverse(*out)
      assert out_inv[0].shape == input.shape
      assert out_inv[1].shape == input.shape
      assert out_inv[2].shape == bbox.shape
      assert out_inv[3].shape == points.shape
  
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 3 for calling the TestAugmentationSequential obj's underlying member method code with example usage: obj.test_video(device, dtype) without return types