code: def test_mixup(self, inp, random_apply, same_on_batch, device, dtype):
      inp = torch.as_tensor(inp, device=device, dtype=dtype)
      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.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
          K.RandomAffine(360, p=1.0),
          K.RandomMixUpV2(p=1.0),
          data_keys=["input"],
          random_apply=random_apply,
          same_on_batch=same_on_batch,
      )
      out = aug(inp)
      assert out.shape[-3:] == inp.shape[-3:]
      reproducibility_test(inp, 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 2 for calling the TestAugmentationSequential obj's underlying member method code with example usage: obj.test_mixup(inp, random_apply, same_on_batch, device, dtype) without return types