class_docstr: BaseEnv that executes its sub environments as @ray.remote actors.
  
  This provides dynamic batching of inference as observations are returned
  from the remote simulator actors. Both single and multi-agent child envs
  are supported, and envs can be stepped synchronously or asynchronously.
  
  NOTE: This class implicitly assumes that the remote envs are gym.Env's
  
  You shouldn't need to instantiate this class directly. It's automatically
  inserted when you use the `remote_worker_envs=True` option in your
  Algorithm's config.
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code: def try_reset(
      self,
      env_id: Optional[EnvID] = None,
      *,
      seed: Optional[int] = None,
      options: Optional[dict] = None,
  ) -> Tuple[MultiEnvDict, MultiEnvDict]:
      actor = self.actors[env_id]
      obj_ref = actor.reset.remote(seed=seed, options=options)
  
      self.pending[obj_ref] = actor
      # Because this env type does not support synchronous reset requests (with
      # immediate return value), we return ASYNC_RESET_RETURN here to indicate
      # that the reset results will be available via the next `poll()` call.
      return ASYNC_RESET_RETURN, ASYNC_RESET_RETURN
  
imports: import gymnasium
  import logging
  from typing import Callable, Dict, List, Optional, Set, Tuple, TYPE_CHECKING
  import ray
  from ray.util import log_once
  from ray.rllib.env.base_env import BaseEnv, _DUMMY_AGENT_ID, ASYNC_RESET_RETURN
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.typing import AgentID, EnvID, EnvType, MultiEnvDict
label_desc: Use image node_id 4 for calling the RemoteBaseEnv obj's underlying member method code with example usage: obj.try_reset(env_id) and returns: ASYNC_RESET_RETURN,  ASYNC_RESET_RETURN