code: def evaluate(
      checkpoint: str = cli.Checkpoint,
      algo: str = cli.Algo,
      env: str = cli.Env,
      local_mode: bool = cli.LocalMode,
      render: bool = cli.Render,
      steps: int = cli.Steps,
      episodes: int = cli.Episodes,
      out: str = cli.Out,
      config: str = cli.Config,
      save_info: bool = cli.SaveInfo,
      use_shelve: bool = cli.UseShelve,
      track_progress: bool = cli.TrackProgress,
  ):
      """Roll out a reinforcement learning agent given a checkpoint argument.
      You have to provide an environment ("--env") an an RLlib algorithm ("--algo") to
      evaluate your checkpoint.
  
      Example usage:\n\n
  
          rllib evaluate /tmp/ray/checkpoint_dir/checkpoint-0 --algo DQN --env CartPole-v1
          --steps 1000000 --out rollouts.pkl
      """
      from ray.rllib import evaluate as evaluate_module
  
      evaluate_module.run(
          checkpoint=checkpoint,
          algo=algo,
          env=env,
          local_mode=local_mode,
          render=render,
          steps=steps,
          episodes=episodes,
          out=out,
          config=config,
          save_info=save_info,
          use_shelve=use_shelve,
          track_progress=track_progress,
      )
  
file_path: ray/rllib/scripts.py
imports: import collections
  from rich.console import Console
  from rich.table import Table
  import typer
  from ray.rllib import train
  from ray.rllib.common import CLIArguments
  from ray.rllib.common import EXAMPLES, FrameworkEnum, example_help, download_example_file
label_desc: Use image node_id 7 for calling a global function with example usage: evaluate(checkpoint, algo, env, local_mode, render, steps, episodes, out, config, save_info, use_shelve, track_progress) without return types