code: def get_data(name, batch):
      isTrain = name == "train"
      if isTrain:
          augmentors = [
              imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
              imgaug.RandomCrop(224),
              imgaug.Lighting(
                  0.1,
                  eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1])
                  * 255.0,
                  eigvec=np.array(
                      [
                          [-0.5675, 0.7192, 0.4009],
                          [-0.5808, -0.0045, -0.8140],
                          [-0.5836, -0.6948, 0.4203],
                      ],
                      dtype="float32",
                  )[::-1, ::-1],
              ),
              imgaug.Flip(horiz=True),
          ]
      else:
          augmentors = [
              imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
              imgaug.CenterCrop((224, 224)),
          ]
      return get_imagenet_dataflow(args.data, name, batch, augmentors)
  
file_path: tensorpack/examples/ImageNetModels/alexnet.py
imports: import argparse
  import numpy
  import os
  import cv2
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import imgaug
  from tensorpack.tfutils import argscope
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import ImageNetModel, get_imagenet_dataflow
label_desc: Use image node_id 2 for calling a global function with example usage: get_data(name, batch) and returns: get_imagenet_dataflow