code: def _gym_env_creator(
      env_context: EnvContext,
      env_descriptor: Union[str, Type[gym.Env]],
      auto_wrap_old_gym_envs: bool = True,
  ) -> gym.Env:
      """Tries to create a gym env given an EnvContext object and descriptor.
  
      Note: This function tries to construct the env from a string descriptor
      only using possibly installed RL env packages (such as gym, pybullet_envs,
      etc). These packages are no installation requirements for RLlib. In case
      you would like to support more such env packages, add the necessary imports
      and construction logic below.
  
      Args:
          env_context: The env context object to configure the env.
              Note that this is a config dict, plus the properties:
              `worker_index`, `vector_index`, and `remote`.
          env_descriptor: The env descriptor as a gym-registered string, e.g. CartPole-v1,
              ALE/MsPacman-v5, or CartPoleContinuousBulletEnv-v0.
              Alternatively, the gym.Env subclass to use.
          auto_wrap_old_gym_envs: Whether to auto-wrap old gym environments (using
              the pre 0.24 gym APIs, e.g. reset() returning single obs and no info
              dict). If True, RLlib will automatically wrap the given gym env class
              with the gym-provided compatibility wrapper (gym.wrappers.EnvCompatibility).
              If False, RLlib will produce a descriptive error on which steps to perform
              to upgrade to gymnasium (or to switch this flag to True).
  
      Returns:
          The actual gym environment object.
  
      Raises:
          gym.error.Error: If the env cannot be constructed.
      """
      # Allow for PyBullet or envs to be used as well (via string). This allows
      # for doing things like `env=CartPoleContinuousBulletEnv-v0`.
      try:
          import pybullet_envs
  
          pybullet_envs.getList()
      except (AttributeError, ModuleNotFoundError, ImportError):
          pass
  
      # Try creating a gym env. If this fails we can output a
      # decent error message.
      try:
          # If class provided, call constructor directly.
          if isinstance(env_descriptor, type):
              env = env_descriptor(env_context)
          else:
              env = gym.make(env_descriptor, **env_context)
          # If we are dealing with an old gym-env API, use the provided compatibility
          # wrapper.
          if auto_wrap_old_gym_envs:
              try:
                  # Call the env's reset() method to check for the env using the old
                  # gym (reset doesn't take `seed` and `options` args and returns only
                  # the initial observations) or new gymnasium APIs (reset takes `seed`
                  # and `options` AND returns observations and infos).
                  obs_and_infos = env.reset(seed=None, options={})
                  # Check return values for correct gymnasium .
                  check_old_gym_env(reset_results=obs_and_infos)
              # TypeError for `reset()` not accepting seed/options.
              # ValueError for `check_old_gym_env` raising error if return values
              # incorrect.
              except Exception:
                  if log_once("auto_wrap_gym_api"):
                      logger.warning(
                          "`config.auto_wrap_old_gym_envs` is activated AND you seem to "
                          "have provided an old gym-API environment. RLlib will therefore"
                          " try to auto-fix the following error. However, please "
                          "consider switching over to the new `gymnasium` APIs:\n"
                          + ERR_MSG_OLD_GYM_API
                      )
                  # Multi-agent case.
                  if isinstance(env, MultiAgentEnv):
                      env = MultiAgentEnvCompatibility(env)
                  # Single agent (gymnasium.Env) case.
                  else:
                      env = gym.wrappers.EnvCompatibility(env)
                  # Repeat the checks, now everything should work.
                  obs_and_infos = env.reset(seed=None, options={})
                  check_old_gym_env(reset_results=obs_and_infos)
      except gym.error.Error:
          raise EnvError(
              ERR_MSG_INVALID_ENV_DESCRIPTOR.format(env_descriptor)
          )
  
      return env
  
imports: import logging
  from typing import List, Optional, Type, Union
  import gymnasium
  import numpy
  import tree
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.env.wrappers.multi_agent_env_compatibility import MultiAgentEnvCompatibility
  from ray.rllib.utils.error import ERR_MSG_INVALID_ENV_DESCRIPTOR, ERR_MSG_OLD_GYM_API, EnvError
  from ray.rllib.utils.gym import check_old_gym_env
  from ray.rllib.utils.numpy import one_hot, one_hot_multidiscrete
  from ray.rllib.utils.spaces.space_utils import batch, get_dummy_batch_for_space, get_base_struct_from_space
  from ray.util import log_once
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 3 for calling a global function with example usage: _gym_env_creator(env_context, env_descriptor, auto_wrap_old_gym_envs) and returns: env