code: def train(
      model: models.ActorCritic,
      config: ml_collections.ConfigDict,
      model_dir: str,
  ):
      """Main training loop.
  
      Args:
        model: the actor-critic model
        config: object holding hyperparameters and the training information
        model_dir: path to dictionary where checkpoints and logging info are stored
  
      Returns:
        optimizer: the trained optimizer
      """
  
      game = config.game + "NoFrameskip-v4"
      simulators = [
          agent.RemoteSimulator(game) for _ in range(config.num_agents)
      ]
      summary_writer = tensorboard.SummaryWriter(model_dir)
      summary_writer.hparams(dict(config))
      loop_steps = config.total_frames // (
          config.num_agents * config.actor_steps
      )
      log_frequency = 40
      checkpoint_frequency = 500
      # train_step does multiple steps per call for better performance
      # compute number of steps per call here to convert between the number of
      # train steps and the inner number of optimizer steps
      iterations_per_step = (
          config.num_agents * config.actor_steps // config.batch_size
      )
  
      initial_params = get_initial_params(jax.random.key(0), model)
      state = create_train_state(
          initial_params,
          model,
          config,
          loop_steps * config.num_epochs * iterations_per_step,
      )
      del initial_params
      state = checkpoints.restore_checkpoint(model_dir, state)
      # number of train iterations done by each train_step
  
      start_step = (
          int(state.step) // config.num_epochs // iterations_per_step
      )
      logging.info("Start training from step: %s", start_step)
  
      for step in range(start_step, loop_steps):
          # Bookkeeping and testing.
          if step % log_frequency == 0:
              score = test_episodes.policy_test(
                  1, state.apply_fn, state.params, game
              )
              frames = step * config.num_agents * config.actor_steps
              summary_writer.scalar("game_score", score, frames)
              logging.info(
                  "Step %s:\nframes seen %s\nscore %s\n\n",
                  step,
                  frames,
                  score,
              )
  
          # Core training code.
          alpha = (
              1.0 - step / loop_steps
              if config.decaying_lr_and_clip_param
              else 1.0
          )
          all_experiences = get_experience(
              state, simulators, config.actor_steps
          )
          trajectories = process_experience(
              all_experiences,
              config.actor_steps,
              config.num_agents,
              config.gamma,
              config.lambda_,
          )
          clip_param = config.clip_param * alpha
          for _ in range(config.num_epochs):
              permutation = np.random.permutation(
                  config.num_agents * config.actor_steps
              )
              trajectories = tuple(x[permutation] for x in trajectories)
              state, _ = train_step(
                  state,
                  trajectories,
                  config.batch_size,
                  clip_param=clip_param,
                  vf_coeff=config.vf_coeff,
                  entropy_coeff=config.entropy_coeff,
              )
          if (step + 1) % checkpoint_frequency == 0:
              checkpoints.save_checkpoint(model_dir, state, step + 1)
      return state
  
imports: import functools
  from typing import Any, Callable
  from absl import logging
  import flax
  from flax import linen
  import agent
  import models
  import test_episodes
  from flax.metrics import tensorboard
  from flax.training import checkpoints
  from flax.training import train_state
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  import optax
label_desc: Use image node_id 8 for calling a global function with example usage: train(model, config, model_dir) and returns: state