code: def my_train_fn(config):
      iterations = config.pop("train-iterations", 10)
  
      config = (
          PPOConfig()
          .update_from_dict(config)
          .environment("CartPole-v1")
      )
  
      # Train for n iterations with high LR.
      config.lr = 0.01
      agent1 = config.build()
      for _ in range(iterations):
          result = agent1.train()
          result["phase"] = 1
          train.report(result)
          phase1_time = result["timesteps_total"]
      state = agent1.save()
      agent1.stop()
  
      # Train for n iterations with low LR
      config.lr = 0.0001
      agent2 = config.build()
      agent2.restore(state)
      for _ in range(iterations):
          result = agent2.train()
          result["phase"] = 2
          result[
              "timesteps_total"
          ] += phase1_time  # keep time moving forward
          train.report(result)
      agent2.stop()
  
imports: import argparse
  import os
  import ray
  from ray import train, tune
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
label_desc: Use image node_id 1 for calling a global function with example usage: my_train_fn(config) without return types