code: def slow_test_vs_open_cv():
      batch_size = 10
      for device in ["cpu", "gpu"]:
          # they are independent parameters, it's just not to go overboard with test cases
          for normalize, grayscale in ((True, False), (False, True)):
              for in_type, out_type in (
                  (types.UINT8, types.FLOAT),
                  (types.FLOAT, None),
              ):
                  for window_size in [1] + list(
                      range(3, max_window_size + 2, 4)
                  ):
                      yield (
                          _test_vs_open_cv,
                          device,
                          batch_size,
                          window_size,
                          in_type,
                          out_type,
                          normalize,
                          grayscale,
                      )
  
imports: from nvidia.dali.pipeline import pipeline_def
  import nvidia.dali.types
  import nvidia.dali.fn
  import numpy
  import cv2
  from scipy.ndimage import convolve1d, filters
  import os
  from nose_utils import assert_raises
  from nose.plugins.attrib import attr
  from sequences_test_utils import video_suite_helper, ArgCb
  from test_utils import get_dali_extra_path, check_batch, RandomlyShapedDataIterator
label_desc: Use image node_id 9 for calling a global function with example usage: slow_test_vs_open_cv() without return types