code: def test_random_flips(self, device, dtype):
      inp = torch.randn(1, 3, 510, 1020, device=device, dtype=dtype)
      bbox = torch.tensor(
          [[[355, 10], [660, 10], [660, 250], [355, 250]]],
          device=device,
          dtype=dtype,
      )
  
      expected_bbox_vertical_flip = torch.tensor(
          [[[355, 259], [660, 259], [660, 499], [355, 499]]],
          device=device,
          dtype=dtype,
      )
      expected_bbox_horizontal_flip = torch.tensor(
          [[[359, 10], [664, 10], [664, 250], [359, 250]]],
          device=device,
          dtype=dtype,
      )
  
      aug_ver = K.AugmentationSequential(
          K.RandomVerticalFlip(p=1.0),
          data_keys=["input", "bbox"],
          same_on_batch=False,
      )
  
      aug_hor = K.AugmentationSequential(
          K.RandomHorizontalFlip(p=1.0),
          data_keys=["image", "bbox"],
          same_on_batch=False,
      )
  
      out_ver = aug_ver(inp.clone(), bbox.clone())
      out_hor = aug_hor(inp.clone(), bbox.clone())
  
      assert_close(out_ver[1], expected_bbox_vertical_flip)
      assert_close(out_hor[1], expected_bbox_horizontal_flip)
  
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 5 for calling the TestAugmentationSequential obj's underlying member method code with example usage: obj.test_random_flips(device, dtype) without return types