code: def _train_using_tf(output_dir):
      worker_cpu = tf_init_tpu()
      with tf.device(worker_cpu):
          if trainer_lib.num_devices() == 1:
              # TF's device priority is GPU > CPU > TPU, so we need to explicitly make
              # the TPU core the default device here.
              with tf.device("/device:TPU:0"):
                  trainer_lib.train(output_dir=output_dir)
          else:
              trainer_lib.train(output_dir=output_dir)
  
imports: import atexit
  import datetime
  import functools
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import jax
  from jax.lib import xla_extension
  import tensorflow.compat.v2
  from trax import fastmath
  from trax import trainer_flags
  from trax.supervised import trainer_lib
  from trax.tf_numpy import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: _train_using_tf(output_dir) without return types