code: def run(config):
      """Entry point to run training."""
      init_data_normalizer(config)
  
      stage_ids = train_util.get_stage_ids(**config)
      if not config["train_progressive"]:
          stage_ids = list(stage_ids)[-1:]
  
      # Train one stage at a time
      for stage_id in stage_ids:
          batch_size = train_util.get_batch_size(stage_id, **config)
          tf.reset_default_graph()
          with tf.device(
              tf.train.replica_device_setter(config["ps_tasks"])
          ):
              model = lib_model.Model(stage_id, batch_size, config)
              model.add_summaries()
              print("Variables:")
              for v in tf.global_variables():
                  print("\t", v.name, v.get_shape().as_list())
              logging.info("Calling train.train")
              train_util.train(model, **config)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import importlib
  import json
  import os
  import time
  from absl import logging
  import absl.flags
  from magenta.models.gansynth.lib import data_helpers
  from magenta.models.gansynth.lib import data_normalizer
  from magenta.models.gansynth.lib import flags
  from magenta.models.gansynth.lib import model
  from magenta.models.gansynth.lib import train_util
  from magenta.models.gansynth.lib import util
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling a global function with example usage: run(config) without return types