code: def test_torch_to_tensor_v2_augmentations(image, mask):
      aug = ToTensorV2()
      data = aug(image=image, mask=mask, force_apply=True)
      height, width, num_channels = image.shape
      assert isinstance(data["image"], torch.Tensor) and data[
          "image"
      ].shape == (num_channels, height, width)
      assert (
          isinstance(data["mask"], torch.Tensor)
          and data["mask"].shape == mask.shape
      )
      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 1 for calling a global function with example usage: test_torch_to_tensor_v2_augmentations(image, mask) without return types