code: def recsim_gym_wrapper(
      recsim_gym_env: gym.Env,
      convert_to_discrete_action_space: bool = False,
      wrap_for_bandits: bool = False,
  ) -> gym.Env:
      """Makes sure a RecSim gym.Env can ba handled by RLlib.
  
      In RecSim's observation spaces, the "doc" field is a dictionary keyed by
      document IDs. Those IDs are changing every step, thus generating a
      different observation space in each time. This causes issues for RLlib
      because it expects the observation space to remain the same across steps.
  
      Also, RecSim's reset() function returns an observation without the
      "response" field, breaking RLlib's check. This wrapper fixes that by
      assigning a random "response".
  
      Args:
          recsim_gym_env: The RecSim gym.Env instance. Usually resulting from a
              raw RecSim env having been passed through RecSim's utility function:
              `recsim.simulator.recsim_gym.RecSimGymEnv()`.
          convert_to_discrete_action_space: Optional bool indicating, whether
              the action space of the created env class should be Discrete
              (rather than MultiDiscrete, even if slate size > 1). This is useful
              for algorithms that don't support MultiDiscrete action spaces,
              such as RLlib's DQN. If None, `convert_to_discrete_action_space`
              may also be provided via the EnvContext (config) when creating an
              actual env instance.
          wrap_for_bandits: Bool indicating, whether this RecSim env should be
              wrapped for use with our Bandits agent.
  
      Returns:
          An RLlib-ready gym.Env instance.
      """
      env = RecSimResetWrapper(recsim_gym_env)
      env = RecSimObservationSpaceWrapper(env)
      if convert_to_discrete_action_space:
          env = MultiDiscreteToDiscreteActionWrapper(env)
      if wrap_for_bandits:
          env = RecSimObservationBanditWrapper(env)
      return env
  
imports: from collections import OrderedDict
  import gymnasium
  from gymnasium.spaces import Dict, Discrete, MultiDiscrete
  from gymnasium.wrappers import EnvCompatibility
  import numpy
  from recsim.document import AbstractDocumentSampler
  from recsim.simulator import environment, recsim_gym
  from recsim.user import AbstractUserModel, AbstractResponse
  from typing import Callable, List, Optional, Type
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.gym import convert_old_gym_space_to_gymnasium_space
  from ray.rllib.utils.spaces.space_utils import convert_element_to_space_type
label_desc: Use image node_id 1 for calling a global function with example usage: recsim_gym_wrapper(recsim_gym_env, convert_to_discrete_action_space, wrap_for_bandits) and returns: env