code: def get_data():
      # probably not the best dataset
      ds = dataset.BSDS500("train", shuffle=True)
      ds = AugmentImageComponent(
          ds,
          [
              imgaug.Grayscale(keepdims=False),
              imgaug.Resize((SHAPE, SHAPE)),
          ],
      )
      ds = ThetaImages(ds)
      ds = RepeatedData(ds, 50)  # just pretend this dataset is bigger
      # this pre-computation is pretty heavy
      ds = MultiProcessRunnerZMQ(
          ds, min(20, multiprocessing.cpu_count())
      )
      ds = BatchData(ds, BATCH)
      return ds
  
imports: import argparse
  import multiprocessing
  import numpy
  import cv2
  import tensorflow
  from scipy.signal import convolve2d
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.utils import logger
  from tensorpack.utils.gpu import change_gpu
  from tensorpack.utils.argtools import shape2d, shape4d
  from tensorpack.utils.viz import *
label_desc: Use image node_id 2 for calling a global function with example usage: get_data() and returns: ds