hunk.code_window:         ):
               self._spaces_in_preferred_format = (
                   self._check_if_space_maps_agent_id_to_sub_space()
               )
           if self._spaces_in_preferred_format:
               return self.observation_space.contains(x)
           logger.warning("observation_space_contains() has not been implemented")
           return True
       @ExperimentalAPI
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:             for key, agent_obs in x.items():
                   if not self.observation_space[key].contains(agent_obs):
                       return False
               if not all(k in self.observation_space for k in x):
                   if log_once("possibly_bad_multi_agent_dict_missing_agent_observations"):
                       logger.warning(
                           "You environment returns observations that are "
                           "MultiAgentDicts with incomplete information. "
                           "Meaning that they only contain information on a subset of"
                           " participating agents. Ignore this warning if this is "
                           "intended, for example if your environment is a turn-based "
                           "simulation."
                       )
               return True
  
hunk.file_path: rllib/env/multi_agent_env.py
hunk.type: replace
file: Out-of-band Communication
  =========================
  
  Typically, Ray actor communication is done through actor method calls and data is shared through the distributed object store.
  However, in some use cases out-of-band communication can be useful.
  
  Wrapping Library Processes
  --------------------------
  Many libraries already have mature, high-performance internal communication stacks and
  they leverage Ray as a language-integrated actor scheduler.
  The actual communication between actors is mostly done out-of-band using existing communication stacks.
  For example, Horovod-on-Ray uses NCCL or MPI-based collective communications, and RayDP uses Spark's internal RPC and object manager.
  See `Ray Distributed Library Patterns <https://www.anyscale.com/blog/ray-distributed-library-patterns>`_ for more details.
  
  Ray Collective
  --------------
  Ray's collective communication library (\ ``ray.util.collective``\ ) allows efficient out-of-band collective and point-to-point communication between distributed CPUs or GPUs.
  See :ref:`Ray Collective <ray-collective>` for more details.
  
  HTTP Server
  -----------
  You can start a http server inside the actor and expose http endpoints to clients
  so users outside of the ray cluster can communicate with the actor.
  
  .. tabbed:: Python
  
      .. literalinclude:: ../doc_code/actor-http-server.py
  
  Similarly, you can expose other types of servers as well (e.g., gRPC servers).
  
  Limitations
  -----------
  
  When using out-of-band communication with Ray actors, keep in mind that Ray does not manage the calls between actors. This means that functionality like distributed reference counting will not work with out-of-band communication, so you should take care not to pass object references in this way.
  