code: def test_image_transforms_runtime(verbose=1):
      ### MAKE TRANSFORMS ###
      tforms = {}
      tforms.update(ToTensor_setup())
      # tforms.update(ToFile_setup())
      tforms.update(ChannelsLast_setup())
      tforms.update(ChannelsFirst_setup())
      tforms.update(TypeCast_setup())
      tforms.update(AddChannel_setup())
      tforms.update(Transpose_setup())
      tforms.update(RangeNormalize_setup())
      tforms.update(StdNormalize_setup())
      tforms.update(RandomCrop_setup())
      tforms.update(SpecialCrop_setup())
      tforms.update(Pad_setup())
      tforms.update(RandomFlip_setup())
      tforms.update(RandomOrder_setup())
  
      ### MAKE DATA
      images = {}
      images.update(gray2d_setup())
      images.update(multi_gray2d_setup())
      images.update(color2d_setup())
      images.update(multi_color2d_setup())
  
      successes = []
      failures = []
      for im_key, im_val in images.items():
          for tf_key, tf_val in tforms.items():
              try:
                  if isinstance(im_val, (tuple, list)):
                      tf_val(*im_val)
                  else:
                      tf_val(im_val)
                  successes.append((im_key, tf_key))
              except:
                  failures.append((im_key, tf_key))
  
      if verbose > 0:
          for k, v in failures:
              print("%s - %s" % (k, v))
  
      print("# SUCCESSES: ", len(successes))
      print("# FAILURES: ", len(failures))
  
imports: import torch
  from pywick.transforms import ToTensor, ToFile, ChannelsLast, HWC, ChannelsFirst, CHW, TypeCast, AddChannel, Transpose, RangeNormalize, StdNormalize, RandomCrop, SpecialCrop, Pad, RandomFlip, RandomOrder
label_desc: Use image node_id 19 for calling a global function with example usage: test_image_transforms_runtime(verbose) without return types