code: def testPPOLearnerRunTPU(self):
      if tf.config.list_logical_devices("TPU"):
          tpu_strategy = self._get_tpu_strategy()
      else:
          logging.info(
              "TPU hardware is not available, TPU strategy test skipped."
          )
          return
  
      batch_size = 1
      minibatch_size = 5
      num_epochs = 3
      n_time_steps = 10
      num_replicas = tpu_strategy.num_replicas_in_sync
  
      # Create a dataset with 10 element of length 10. This simulates a Reverb
      # dataset.
      num_collected_episodes = 20
      traj = ppo_learner_test_utils.create_trajectories(
          n_time_steps=n_time_steps, batch_size=batch_size
      )
      info = ()
  
      def dataset_fn():
          return tf.data.Dataset.from_tensors(
              (traj, info),
          ).repeat(num_collected_episodes)
  
      with tpu_strategy.scope():
          print(
              "Number of devices for the strategy: {}".format(
                  tpu_strategy.num_replicas_in_sync
              )
          )
          fake_agent = ppo_learner_test_utils.FakePPOAgent(tpu_strategy)
  
      learner = ppo_learner.PPOLearner(
          root_dir=FLAGS.test_tmpdir,
          train_step=tf.Variable(0, dtype=tf.int32),
          agent=fake_agent,
          experience_dataset_fn=dataset_fn,
          normalization_dataset_fn=dataset_fn,
          num_samples=num_collected_episodes,
          num_epochs=num_epochs,
          minibatch_size=minibatch_size,
          # Disable shuffling to have deterministic input into agent.train.
          shuffle_buffer_size=1,
          triggers=None,
          strategy=tpu_strategy,
      )
  
      learner.run()
  
      # Check that fake agent was called the expected number of times.
      num_train_frames = (
          num_collected_episodes
          * batch_size
          * n_time_steps
          * num_epochs
      )
      num_minibatches = num_train_frames / minibatch_size
      num_minibatches_per_replica = int(num_minibatches / num_replicas)
      self.assertEqual(
          fake_agent.train_called_times.numpy(),
          num_minibatches_per_replica,
      )
  
      # Check that fake agent was called the expected number of times the second
      # time it is called.
      fake_agent.reset()
      learner.run()
      self.assertEqual(
          fake_agent.train_called_times.numpy(),
          num_minibatches_per_replica,
      )
  
file_path: agents/tf_agents/train/tpu_ppo_learner_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import flags
  from absl import logging
  import tensorflow
  from tf_agents.system import system_multiprocessing
  from tf_agents.train import ppo_learner
  from tf_agents.train import ppo_learner_test_utils
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the DistributedPpoLearnerTest obj's underlying member method code with example usage: obj.testPPOLearnerRunTPU() and returns: tf