code: def get_config():
      data_train = get_data("train")
      data_test = get_data("val")
  
      return TrainConfig(
          dataflow=data_train,
          callbacks=[
              ModelSaver(),
              ScheduledHyperParamSetter(
                  "learning_rate", [(60, 4e-5), (75, 8e-6)]
              ),
              InferenceRunner(
                  data_test,
                  [
                      ClassificationError(
                          "wrong-top1", "val-error-top1"
                      ),
                      ClassificationError(
                          "wrong-top5", "val-error-top5"
                      ),
                  ],
              ),
          ],
          model=Model(),
          steps_per_epoch=1280000 // TOTAL_BATCH_SIZE,
          max_epoch=90,
      )
  
imports: import argparse
  import numpy
  import os
  import sys
  import cv2
  import tensorflow
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils.sessinit import SmartInit
  from tensorpack.tfutils.summary import add_param_summary
  from tensorpack.tfutils.varreplace import remap_variables
  from tensorpack.utils.gpu import get_num_gpu
  from dorefa import get_dorefa, ternarize
  from imagenet_utils import ImageNetModel, eval_classification, fbresnet_augmentor, get_imagenet_dataflow
label_desc: Use image node_id 2 for calling a global function with example usage: get_config() and returns: TrainConfig