code: def main():
      args = parser.parse_args()
      ray.init()
  
      env_config = {
          "num_candidates": args.env_num_candidates,
          "resample_documents": not args.env_dont_resample_documents,
          "slate_size": args.env_slate_size,
          "seed": 0,
          "convert_to_discrete_action_space": False,
      }
  
      config = (
          SlateQConfig()
          .environment(
              InterestEvolutionRecSimEnv
              if args.env == "interest-evolution"
              else InterestExplorationRecSimEnv
              if args.env == "interest-exploration"
              else LongTermSatisfactionRecSimEnv,
              env_config=env_config,
          )
          .framework(args.framework)
          .rollouts(num_rollout_workers=7)
          .resources()
      )
  
      config.num_steps_sampled_before_learning_starts = (
          args.num_steps_sampled_before_learning_starts
      )
  
      # Perform a test run on the env with a random agent to see, what
      # the random baseline reward is.
      if args.random_test_episodes:
          print(
              f"Running {args.random_test_episodes} episodes to get a random "
              "agent's baseline reward ..."
          )
          env = config["env"](config=env_config)
          env.reset()
          num_episodes = 0
          episode_rewards = []
          episode_reward = 0.0
          while num_episodes < args.random_test_episodes:
              action = env.action_space.sample()
              _, r, d, _, _ = env.step(action)
              episode_reward += r
              if d:
                  num_episodes += 1
                  episode_rewards.append(episode_reward)
                  episode_reward = 0.0
                  env.reset()
          print(
              f"Ran {args.random_test_episodes} episodes with a random agent "
              "reaching a mean episode return of "
              f"{np.mean(episode_rewards)}+/-{sem(episode_rewards)}."
          )
  
      if args.run_as_test:
          stop = {"training_iteration": 1}
      else:
          stop = {
              "training_iteration": 200,
              "timesteps_total": 150000,
              "episode_reward_mean": 160,
          }
  
      tune.Tuner(
          SlateQ,
          run_config=air.RunConfig(
              stop=stop,
          ),
          param_space=config,
      ).fit()
  
file_path: ray/rllib_contrib/slate_q/examples/recommender_system_with_recsim_and_slateq.py
imports: import argparse
  import numpy
  from rllib_slate_q.slate_q import SlateQ, SlateQConfig
  from scipy.stats import sem
  import ray
  from ray import air, tune
  from ray.rllib.examples.env.recommender_system_envs_with_recsim import InterestEvolutionRecSimEnv, InterestExplorationRecSimEnv, LongTermSatisfactionRecSimEnv
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types