class_docstr: An environment that hosts multiple independent agents.
  
  Agents are identified by (string) agent ids. Note that these "agents" here
  are not to be confused with RLlib Algorithms, which are also sometimes
  referred to as "agents" or "RL agents".
  
  The preferred format for action- and observation space is a mapping from agent
  ids to their individual spaces. If that is not provided, the respective methods'
  observation_space_contains(), action_space_contains(),
  action_space_sample() and observation_space_sample() have to be overwritten.
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docstr: Resets the env and returns observations from ready agents.
  
  Args:
      seed: An optional seed to use for the new episode.
  
  Returns:
      New observations for each ready agent.
  
  .. testcode::
      :skipif: True
  
      from ray.rllib.env.multi_agent_env import MultiAgentEnv
      class MyMultiAgentEnv(MultiAgentEnv):
          # Define your env here.
      env = MyMultiAgentEnv()
      obs, infos = env.reset(seed=42, options={})
      print(obs)
  
  .. testoutput::
  
      {
          "car_0": [2.4, 1.6],
          "car_1": [3.4, -3.2],
          "traffic_light_1": [0, 3, 5, 1],
      }
docstr_tok: ["Resets","the","env","and","returns","observations","from","ready","agents",".","Args",":","seed",":","An","optional","seed","to","use","for","the","new","episode",".","Returns",":","New","observations","for","each","ready","agent",".","..","testcode",":",":",":","skipif",":","True","from","ray.rllib.env.multi_agent_env","import","MultiAgentEnv","class","MyMultiAgentEnv","(","MultiAgentEnv",")",":","#","Define","your","env","here",".","env","=","MyMultiAgentEnv","(",")","obs",",","infos","=","env.reset","(","seed=42",",","options=","{","}",")","print","(","obs",")","..","testoutput",":",":","{","``","car_0","''",":","[","2.4",",","1.6","]",",","``","car_1","''",":","[","3.4",",","-3.2","]",",","``","traffic_light_1","''",":","[","0",",","3",",","5",",","1","]",",","}"]
code: def reset(
      self,
      *,
      seed: Optional[int] = None,
      options: Optional[dict] = None,
  ) -> Tuple[MultiAgentDict, MultiAgentDict]:
      """Resets the env and returns observations from ready agents.
  
      Args:
          seed: An optional seed to use for the new episode.
  
      Returns:
          New observations for each ready agent.
  
      .. testcode::
          :skipif: True
  
          from ray.rllib.env.multi_agent_env import MultiAgentEnv
          class MyMultiAgentEnv(MultiAgentEnv):
              # Define your env here.
          env = MyMultiAgentEnv()
          obs, infos = env.reset(seed=42, options={})
          print(obs)
  
      .. testoutput::
  
          {
              "car_0": [2.4, 1.6],
              "car_1": [3.4, -3.2],
              "traffic_light_1": [0, 3, 5, 1],
          }
      """
      # Call super's `reset()` method to (maybe) set the given `seed`.
      super().reset(seed=seed, options=options)
  
imports: import gymnasium
  import logging
  from typing import Callable, Dict, List, Tuple, Optional, Union, Set, Type
  from ray.rllib.env.base_env import BaseEnv
  from ray.rllib.env.env_context import EnvContext
  from ray.rllib.utils.annotations import ExperimentalAPI, override, PublicAPI, DeveloperAPI
  from ray.rllib.utils.typing import AgentID, EnvCreator, EnvID, EnvType, MultiAgentDict, MultiEnvDict
  from ray.util import log_once
label_desc: Use image node_id 2 for calling the MultiAgentEnv obj's underlying member method code with example usage: obj.reset() without return types