code: def get_config():
      logger.auto_set_dir()
      dataset_train = get_data("train")
      dataset_val = get_data("val")
  
      return TrainConfig(
          dataflow=dataset_train,
          callbacks=[
              ModelSaver(),
              InferenceRunner(
                  dataset_val,
                  [
                      ClassificationError(
                          "wrong-top1", "val-top1-error"
                      ),
                      ClassificationError(
                          "wrong-top5", "val-top5-error"
                      ),
                  ],
              ),
              ScheduledHyperParamSetter(
                  "learning_rate",
                  [
                      (8, 0.03),
                      (14, 0.02),
                      (17, 5e-3),
                      (19, 3e-3),
                      (24, 1e-3),
                      (26, 2e-4),
                      (30, 5e-5),
                  ],
              ),
          ],
          model=Model(),
          steps_per_epoch=5000,
          max_epoch=80,
      )
  
file_path: tensorpack/examples/ImageNetModels/inception-bn.py
imports: import argparse
  import os
  from tensorpack import tfv1
  from tensorpack import *
  from tensorpack.dataflow import dataset
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.utils.gpu import get_num_gpu
  from imagenet_utils import 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