code: def init_data_normalizer(config):
      """Initializes data normalizer."""
      normalizer = data_normalizer.registry[config["data_normalizer"]](
          config
      )
      if normalizer.exists():
          return
  
      if config["task"] == 0:
          tf.reset_default_graph()
          data_helper = data_helpers.registry[config["data_type"]](
              config
          )
          real_images, _ = data_helper.provide_data(batch_size=10)
  
          # Save normalizer.
          # Note if normalizer has been saved, save() is no-op. To regenerate the
          # normalizer, delete the normalizer file in train_root_dir/assets
          normalizer.save(real_images)
      else:
          while not normalizer.exists():
              time.sleep(5)
  
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 1 for calling a global function with example usage: init_data_normalizer(config) without return types