code: def main(pargs):
      config = (
          PPOConfig()
          .environment(
              "ALE/Breakout-v5",
              clip_rewards=True,
              env_config={
                  "frameskip": 1,
                  "full_action_space": False,
                  "repeat_action_probability": 0.0,
              },
          )
          .training(
              lambda_=0.95,
              kl_coeff=0.5,
              vf_clip_param=10.0,
              entropy_coeff=0.01,
              train_batch_size=32 if pargs.smoke_test else 16000,
              sgd_minibatch_size=1 if pargs.smoke_test else 2000,
              num_sgd_iter=1 if pargs.smoke_test else 10,
              vf_loss_coeff=0.01,
              clip_param=0.1,
              lr=0.0001,
              grad_clip=100,
              grad_clip_by="global_norm",
          )
          .rollouts(
              num_rollout_workers=1 if pargs.smoke_test else 64,
              num_envs_per_worker=1,
              batch_mode="truncate_episodes",
              rollout_fragment_length="auto",
              create_env_on_local_worker=True,
          )
          .framework(
              "torch",
              torch_compile_worker=tune.grid_search([True, False]),
              torch_compile_worker_dynamo_backend=pargs.backend,
              torch_compile_worker_dynamo_mode=pargs.mode,
          )
          .resources(
              num_learner_workers=1,
              num_gpus_per_learner_worker=0 if pargs.smoke_test else 1,
          )
      )
  
      tuner = tune.Tuner(
          "PPO",
          run_config=air.RunConfig(
              stop={
                  "training_iteration": 1
                  if pargs.smoke_test
                  else pargs.num_iters
              },
          ),
          param_space=config,
      )
  
      results = tuner.fit()
  
      compiled_throughput = results[0].metrics[
          "num_env_steps_sampled_throughput_per_sec"
      ]
      eager_throughput = results[1].metrics[
          "num_env_steps_sampled_throughput_per_sec"
      ]
      print(
          f"Speed up (%): {100 * (compiled_throughput / eager_throughput - 1)}"
      )
  
imports: import argparse
  from ray import tune, air
  from ray.rllib.algorithms.ppo import PPOConfig
label_desc: Use image node_id 2 for calling a global function with example usage: main(pargs) without return types