code: def train_rl(
      output_dir,
      n_epochs=10000,
      light_rl=True,
      light_rl_trainer=light_trainers.PolicyGradient,
  ):
      """Train the RL agent.
  
      Args:
        output_dir: Output directory.
        n_epochs: Number epochs to run the training for.
        light_rl: deprecated, always True, left out for old gin configs.
        light_rl_trainer: which light RL trainer to use (experimental).
      """
      del light_rl
      tf_np.set_allow_float64(FLAGS.tf_allow_float64)
      task = rl_task.RLTask()
      env_name = task.env_name
  
      if FLAGS.jax_debug_nans:
          config.update("jax_debug_nans", True)
  
      if FLAGS.use_tpu:
          config.update("jax_platform_name", "tpu")
      else:
          config.update("jax_platform_name", "")
  
      trainer = light_rl_trainer(task=task, output_dir=output_dir)
  
      def light_training_loop():
          """Run the trainer for n_epochs and call close on it."""
          try:
              logging.info(
                  "Starting RL training for %d epochs.", n_epochs
              )
              trainer.run(n_epochs, n_epochs_is_total_epochs=True)
              logging.info(
                  "Completed RL training for %d epochs.", n_epochs
              )
              trainer.close()
              logging.info("Trainer is now closed.")
          except Exception as e:
              raise e
          finally:
              logging.info(
                  "Encountered an exception, still calling trainer.close()"
              )
              trainer.close()
              logging.info("Trainer is now closed.")
  
      if FLAGS.jax_debug_nans or FLAGS.disable_jit:
          fastmath.disable_jit()
          with jax.disable_jit():
              light_training_loop()
      else:
          light_training_loop()
  
file_path: trax/trax/rl_trainer.py
imports: import faulthandler
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import jax
  from jax import config
  from trax import fastmath
  from trax import rl
  from trax import trainer_flags
  from trax.rl import task
  from trax.rl import training
  from trax.tf_numpy import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: train_rl(output_dir, n_epochs, light_rl, light_rl_trainer) without return types