code: def test_torch_to_tensor_v2_augmentations_with_transpose_3d_mask(
      image,
  ):
      aug = ToTensorV2(transpose_mask=True)
      mask = np.random.randint(
          low=0, high=256, size=(100, 100, 4), dtype=np.uint8
      )
      data = aug(image=image, mask=mask, force_apply=True)
      image_height, image_width, image_num_channels = image.shape
      mask_height, mask_width, mask_num_channels = mask.shape
      assert isinstance(data["image"], torch.Tensor) and data[
          "image"
      ].shape == (
          image_num_channels,
          image_height,
          image_width,
      )
      assert isinstance(data["mask"], torch.Tensor) and data[
          "mask"
      ].shape == (
          mask_num_channels,
          mask_height,
          mask_width,
      )
      assert data["image"].dtype == torch.uint8
      assert data["mask"].dtype == torch.uint8
  
file_path: albumentations/tests/test_pytorch.py
imports: import pytest
  import numpy
  import torch
  from PIL import Image
  from torchvision.transforms import ColorJitter
  import albumentations
  from albumentations.pytorch.transforms import ToTensor, ToTensorV2
label_desc: Use image node_id 3 for calling a global function with example usage: test_torch_to_tensor_v2_augmentations_with_transpose_3d_mask(image) without return types