code: def _test_sequential(augment_method, device, dtype):
      inp = torch.rand(1, 3, 1000, 500, device=device, dtype=dtype)
      bbox = torch.tensor(
          [[[355, 10], [660, 10], [660, 250], [355, 250]]],
          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 = AugmentationSequential(
          augment_method,
          data_keys=["input", "mask", "bbox", "keypoints"],
      )
      out = aug(inp, mask, bbox, keypoints)
      assert out[0].shape == inp.shape
      assert out[1].shape == mask.shape
      assert out[2].shape == bbox.shape
      assert out[3].shape == keypoints.shape
      assert set(out[1].unique().tolist()).issubset(
          set(mask.unique().tolist())
      )
  
      out_inv = aug.inverse(*out)
      assert out_inv[0].shape == inp.shape
      assert out_inv[1].shape == mask.shape
      assert out_inv[2].shape == bbox.shape
      assert out_inv[3].shape == keypoints.shape
      assert set(out_inv[1].unique().tolist()).issubset(
          set(mask.unique().tolist())
      )
  
      reproducibility_test((inp, mask, bbox, keypoints), aug)
  
imports: import inspect
  from typing import List
  import pytest
  import torch
  from kornia.augmentation.auto.autoaugment import AutoAugment
  from kornia.augmentation.auto.operations import OperationBase, ops
  from kornia.augmentation.auto.rand_augment.rand_augment import RandAugment
  from kornia.augmentation.auto.rand_augment.rand_augment import default_policy
  from kornia.augmentation.auto.trivial_augment import TrivialAugment
  from kornia.augmentation.container import AugmentationSequential
  from kornia.geometry.bbox import bbox_to_mask
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version
  from test.augmentation.test_container import reproducibility_test
label_desc: Use image node_id 2 for calling a global function with example usage: _test_sequential(augment_method, device, dtype) without return types