code: def shufflenet_stage(input, channel, num_blocks, group):
      l = input
      for i in range(num_blocks):
          name = "block{}".format(i)
          if args.v2:
              l = shufflenet_unit_v2(
                  name, l, channel, 2 if i == 0 else 1
              )
          else:
              l = shufflenet_unit(
                  name, l, channel, group, 2 if i == 0 else 1
              )
      return l
  
imports: import argparse
  import math
  import numpy
  import os
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import imgaug
  from tensorpack.tfutils import argscope, SmartInit, model_utils
  from tensorpack.tfutils.scope_utils import under_name_scope
  from tensorpack.utils import logger
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow
label_desc: Use image node_id 5 for calling a global function with example usage: shufflenet_stage(input, channel, num_blocks, group) and returns: l