class_docstr: A simple multi-agent example environment where agents have different spaces.
  
  agent0: obs=(10,), act=Discrete(2)
  agent1: obs=(20,), act=Discrete(3)
  
  The logic of the env doesn't really matter for this example. The point of this env
  is to show how one can use multi-agent envs, in which the different agents utilize
  different obs- and action spaces.
class_docstr_tok: ["A","simple","multi-agent","example","environment","where","agents","have","different","spaces",".","agent0",":","obs=","(","10",",",")",",","act=Discrete","(","2",")","agent1",":","obs=","(","20",",",")",",","act=Discrete","(","3",")","The","logic","of","the","env","does","n't","really","matter","for","this","example",".","The","point","of","this","env","is","to","show","how","one","can","use","multi-agent","envs",",","in","which","the","different","agents","utilize","different","obs-","and","action","spaces","."]
code: def step(self, action_dict):
      obs, rew, terminated, truncated, info = {}, {}, {}, {}, {}
      for i, action in action_dict.items():
          obs[i] = self.observation_space[i].sample()
          rew[i] = 0.0
          terminated[i] = False
          truncated[i] = False
          info[i] = {}
      terminated["__all__"] = len(self.terminateds) == len(self.agents)
      truncated["__all__"] = len(self.truncateds) == len(self.agents)
      return obs, rew, terminated, truncated, info
  
file_path: ray/python/ray/rllib/examples/multi_agent_different_spaces_for_agents.py
imports: import argparse
  import gymnasium
  import os
  import ray
  from ray import air, tune
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.tune.registry import get_trainable_cls
label_desc: Use image node_id 3 for calling the BasicMultiAgentMultiSpaces obj's underlying member method code with example usage: obj.step(action_dict) and returns: obs,  rew,  terminated,  truncated,  info