code: def test_selective_stream_returns_torch_when_asked(img_5x5):
      img = img_5x5 * 255
      dc = slc.DataContainer((img,), "I")
  
      ppl = slc.SelectiveStream(
          [
              slt.Rotate(angle_range=(90, 90), p=0),
              slt.Rotate(angle_range=(-90, -90), p=0),
          ]
      )
  
      res = ppl(dc, normalize=False)
      res_img = (res["image"] * 255).numpy().astype(np.uint8)
      assert isinstance(res, dict)
      assert tuple(res) == ("image",)
      # We can do squeeze here because it is a grayscale image!
      assert np.array_equal(img.squeeze(), res_img.squeeze())
  
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 49 for calling a global function with example usage: test_selective_stream_returns_torch_when_asked(img_5x5) without return types