code: def get_config(model, nr_tower):
      batch = TOTAL_BATCH_SIZE // nr_tower
  
      logger.info(
          "Running on {} towers. Batch size per tower: {}".format(
              nr_tower, batch
          )
      )
      dataset_train = get_data("train", batch)
      dataset_val = get_data("val", batch)
  
      step_size = 1280000 // TOTAL_BATCH_SIZE
      max_iter = 3 * 10**5
      max_epoch = (max_iter // step_size) + 1
      callbacks = [
          ModelSaver(),
          ScheduledHyperParamSetter(
              "learning_rate",
              [(0, 0.5), (max_iter, 0)],
              interp="linear",
              step_based=True,
          ),
          EstimatedTimeLeft(),
      ]
      infs = [
          ClassificationError("wrong-top1", "val-error-top1"),
          ClassificationError("wrong-top5", "val-error-top5"),
      ]
      if nr_tower == 1:
          # single-GPU inference with queue prefetch
          callbacks.append(
              InferenceRunner(QueueInput(dataset_val), infs)
          )
      else:
          # multi-GPU inference (with mandatory queue prefetch)
          callbacks.append(
              DataParallelInferenceRunner(
                  dataset_val, infs, list(range(nr_tower))
              )
          )
  
      return TrainConfig(
          model=model,
          dataflow=dataset_train,
          callbacks=callbacks,
          steps_per_epoch=step_size,
          max_epoch=max_epoch,
      )
  
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 7 for calling a global function with example usage: get_config(model, nr_tower) and returns: TrainConfig