code: def test_image_mask_pipeline_to_torch_normalization(
      img_3x3_rgb, mask_3x3, mean, std
  ):
      ppl = slc.Stream(
          [
              slt.Rotate(angle_range=(90, 90), p=1),
              slt.Rotate(angle_range=(90, 90), p=1),
          ],
      )
      img, mask = ppl(
          {"image": img_3x3_rgb, "mask": mask_3x3},
          as_dict=False,
          mean=mean,
          std=std,
      )
  
      if mean is None:
          np.testing.assert_almost_equal(
              img[0, :, :].max().item(), 0.515 / 0.229
          )
      else:
          assert img.max() == 1
      assert mask.max() == 1
      assert isinstance(img, torch.FloatTensor)
      assert isinstance(mask, torch.FloatTensor)
  
imports: import itertools
  import random
  import cv2
  import numpy
  import pytest
  import torch
  import solt.core
  import solt.transforms
  import solt.utils
  from .fixtures import *
label_desc: Use image node_id 45 for calling a global function with example usage: test_image_mask_pipeline_to_torch_normalization(img_3x3_rgb, mask_3x3, mean, std) without return types