code: def main(pargs):
      use_cpu = pargs.cpu or pargs.smoke_test
  
      if not torch.cuda.is_available() and not use_cpu:
          raise RuntimeError(
              "CUDA is required for this benchmark. Please run with --cpu flag."
          )
  
      # create the output directory and save the config
      exp_name_pargs = {
          "bs": pargs.batch_size,
          "backend": pargs.backend,
          "mode": pargs.mode,
      }
  
      suffix_list = [f"{k}-{v}" for k, v in exp_name_pargs.items()]
      if pargs.cpu:
          suffix_list.append("cpu")
  
      suffix = "-".join(suffix_list)
      output = Path(pargs.output) / f"inference_{suffix}"
      output.mkdir(parents=True, exist_ok=True)
  
      config = vars(pargs)
      with open(output / "args.json", "w") as f:
          json.dump(config, f)
  
      # create the environment
      env = wrap_deepmind(
          gym.make("GymV26Environment-v0", env_id="ALE/Breakout-v5")
      )
  
      # setup RLModule
      model_cfg = MODEL_DEFAULTS.copy()
      spec = SingleAgentRLModuleSpec(
          module_class=PPOTorchRLModule,
          observation_space=env.observation_space,
          action_space=env.action_space,
          catalog_class=PPOCatalog,
          model_config_dict=model_cfg,
      )
      device = torch.device("cuda" if not use_cpu else "cpu")
  
      eager_module = spec.build().to(device)
      compiled_module = spec.build().to(device)
      compile_config = TorchCompileConfig(
          torch_dynamo_backend=pargs.backend,
          torch_dynamo_mode=pargs.mode,
      )
      compiled_module = compiled_module.compile(compile_config)
  
      batch = get_ppo_batch_for_env(env, batch_size=pargs.batch_size)
      batch = convert_to_torch_tensor(batch, device=device)
  
      burn_in = 2 if pargs.smoke_test else pargs.burn_in
      num_iters = 10 if pargs.smoke_test else pargs.num_iters
  
      # Burn-in
      print("Burn-in...")
      for _ in tqdm.tqdm(range(burn_in)):
          with torch.no_grad():
              eager_module.forward_exploration(batch)
              compiled_module.forward_exploration(batch)
      print("Burn-in done.")
  
      # Eager
      print("Eager...")
      eager_times = []
      for _ in tqdm.tqdm(range(num_iters)):
          _, t = timed(
              lambda: eager_module.forward_exploration(batch),
              use_cuda=not use_cpu,
          )
          eager_times.append(t)
      eager_throughputs = 1 / np.array(eager_times)
      print("Eager done.")
  
      # Compiled
      print("Compiled...")
      compiled_times = []
      for _ in tqdm.tqdm(range(num_iters)):
          _, t = timed(
              lambda: compiled_module.forward_exploration(batch),
              use_cuda=not use_cpu,
          )
          compiled_times.append(t)
          pass
      compiled_throughputs = 1 / np.array(compiled_times)
      print("Compiled done.")
  
      # Results
      eager_throughput_median = np.median(eager_throughputs)
      compiled_throughput_median = np.median(compiled_throughputs)
      speedup = compiled_throughput_median / eager_throughput_median - 1
      results = {
          "eager_throughput_median": eager_throughput_median,
          "compiled_throughput_median": compiled_throughput_median,
          "speedup": speedup,
          "eager_throughputs": eager_throughputs.tolist(),
          "compiled_throughputs": compiled_throughputs.tolist(),
      }
  
      with open(output / "results.json", "w") as f:
          json.dump(results, f)
  
      # Plot
      plot_results(results=results, output=output, config=config)
  
file_path: ray/python/ray/rllib/benchmarks/torch_compile/run_inference_bm.py
imports: import argparse
  import matplotlib.pyplot
  import gymnasium
  from pathlib import Path
  import numpy
  import json
  import tqdm
  import torch
  from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.benchmarks.torch_compile.utils import get_ppo_batch_for_env, timed
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.torch.torch_rl_module import TorchCompileConfig
  from ray.rllib.env.wrappers.atari_wrappers import wrap_deepmind
  from ray.rllib.models.catalog import MODEL_DEFAULTS
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 3 for calling a global function with example usage: main(pargs) without return types