code: def main(unused_argv):
      tf.logging.set_verbosity(FLAGS.log)
  
      if not tf.gfile.Exists(FLAGS.logdir):
          tf.gfile.MakeDirs(FLAGS.logdir)
  
      with tf.Graph().as_default():
          # If ps_tasks is 0, the local device is used. When using multiple
          # (non-local) replicas, the ReplicaDeviceSetter distributes the variables
          # across the different devices.
          model = utils.get_module("baseline.models.%s" % FLAGS.model)
          hparams = model.get_hparams(FLAGS.config)
  
          # Run the Reader on the CPU
          if FLAGS.ps_tasks:
              cpu_device = "/job:worker/cpu:0"
          else:
              cpu_device = "/job:localhost/replica:0/task:0/cpu:0"
  
          with tf.device(cpu_device):
              with tf.name_scope("Reader"):
                  batch = reader.NSynthDataset(
                      FLAGS.train_path, is_training=True
                  ).get_baseline_batch(hparams)
  
          with tf.device(
              tf.train.replica_device_setter(ps_tasks=FLAGS.ps_tasks)
          ):
              train_op = model.train_op(batch, hparams, FLAGS.config)
  
              # Run training
              slim.learning.train(
                  train_op=train_op,
                  logdir=FLAGS.logdir,
                  master=FLAGS.master,
                  is_chief=FLAGS.task == 0,
                  number_of_steps=hparams.max_steps,
                  save_summaries_secs=FLAGS.save_summaries_secs,
                  save_interval_secs=FLAGS.save_interval_secs,
              )
  
imports: from magenta.models.nsynth import reader
  from magenta.models.nsynth import utils
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 1 for calling a global function with example usage: main(unused_argv) without return types