code: def _parse_args():
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--batch-size", "-bs", type=int, default=1, help="Batch size"
      )
      parser.add_argument(
          "--num-iters",
          "-n",
          type=int,
          default=5000,
          help="Number of iterations",
      )
      parser.add_argument(
          "--backend",
          type=str,
          default="cudagraphs",
          help="torch dynamo backend",
      )
      parser.add_argument(
          "--mode", type=str, default=None, help="torch dynamo mode"
      )
      parser.add_argument(
          "--output",
          type=str,
          default="./outputs",
          help="output directory",
      )
      parser.add_argument(
          "--burn-in", type=int, default=500, help="burn-in iterations"
      )
      parser.add_argument("--cpu", action="store_true", help="use CPU")
      parser.add_argument(
          "--smoke-test", action="store_true", help="smoke test"
      )
  
      return parser.parse_args()
  
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 1 for calling a global function with example usage: _parse_args() and returns: parser