code: def evaluate_policy(
      model: "BaseRLModel",
      env: Union[gym.Env, VecEnv],
      n_eval_episodes: int = 10,
      deterministic: bool = True,
      render: bool = False,
      callback: Optional[Callable] = None,
      reward_threshold: Optional[float] = None,
      return_episode_rewards: bool = False,
  ) -> Union[Tuple[float, float], Tuple[List[float], List[int]]]:
      """
      Runs policy for ``n_eval_episodes`` episodes and returns average reward.
      This is made to work only with one env.
  
      :param model: (BaseRLModel) The RL agent you want to evaluate.
      :param env: (gym.Env or VecEnv) The gym environment. In the case of a ``VecEnv``
          this must contain only one environment.
      :param n_eval_episodes: (int) Number of episode to evaluate the agent
      :param deterministic: (bool) Whether to use deterministic or stochastic actions
      :param render: (bool) Whether to render the environment or not
      :param callback: (callable) callback function to do additional checks,
          called after each step.
      :param reward_threshold: (float) Minimum expected reward per episode,
          this will raise an error if the performance is not met
      :param return_episode_rewards: (Optional[float]) If True, a list of reward per episode
          will be returned instead of the mean.
      :return: (float, float) Mean reward per episode, std of reward per episode
          returns ([float], [int]) when ``return_episode_rewards`` is True
      """
      if isinstance(env, VecEnv):
          assert (
              env.num_envs == 1
          ), "You must pass only one environment when using this function"
  
      is_recurrent = model.policy.recurrent
  
      episode_rewards, episode_lengths = [], []
      for i in range(n_eval_episodes):
          # Avoid double reset, as VecEnv are reset automatically
          if not isinstance(env, VecEnv) or i == 0:
              obs = env.reset()
              # Because recurrent policies need the same observation space during training and evaluation, we need to pad
              # observation to match training shape. See https://github.com/hill-a/stable-baselines/issues/1015
              if is_recurrent:
                  zero_completed_obs = np.zeros(
                      (model.n_envs,) + model.observation_space.shape
                  )
                  zero_completed_obs[0, :] = obs
                  obs = zero_completed_obs
          done, state = False, None
          episode_reward = 0.0
          episode_length = 0
          while not done:
              action, state = model.predict(
                  obs, state=state, deterministic=deterministic
              )
              new_obs, reward, done, _info = env.step(action)
              if is_recurrent:
                  obs[0, :] = new_obs
              else:
                  obs = new_obs
              episode_reward += reward
              if callback is not None:
                  callback(locals(), globals())
              episode_length += 1
              if render:
                  env.render()
          episode_rewards.append(episode_reward)
          episode_lengths.append(episode_length)
      mean_reward = np.mean(episode_rewards)
      std_reward = np.std(episode_rewards)
      if reward_threshold is not None:
          assert (
              mean_reward > reward_threshold
          ), "Mean reward below threshold: {:.2f} < {:.2f}".format(
              mean_reward, reward_threshold
          )
      if return_episode_rewards:
          return episode_rewards, episode_lengths
      return mean_reward, std_reward
  
imports: import typing
  from typing import Callable, List, Optional, Tuple, Union
  import gym
  import numpy
  from stable_baselines.common.vec_env import VecEnv
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_policy(model, env, n_eval_episodes, deterministic, render, callback, reward_threshold, return_episode_rewards) and returns: mean_reward,  std_reward, episode_rewards,  episode_lengths