code: def step(self, action_dict):
      obs, rew, terminated, truncated, info = {}, {}, {}, {}, {}
      for i, action in action_dict.items():
          (
              obs[i],
              rew[i],
              terminated[i],
              truncated[i],
              info[i],
          ) = self.agents[i].step(action)
          obs[i] = obs[i] + i
          rew[i] = rew[i] + i
          info[i]["timestep"] = info[i]["timestep"] + i
          if terminated[i]:
              self.terminateds.add(i)
          if truncated[i]:
              self.truncateds.add(i)
      terminated["__all__"] = len(self.terminateds) == len(self.agents)
      truncated["__all__"] = len(self.truncateds) == len(self.agents)
      return obs, rew, terminated, truncated, info
  
imports: import ray
  import unittest
  from typing import Dict, List, Optional, Union, Tuple
  import numpy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.examples.env.mock_env import MockEnv3
  from ray.rllib.policy import Policy
  from ray.rllib.utils import override
  from ray.rllib.utils.typing import TensorStructType, TensorType
label_desc: Use image node_id 3 for calling the EpisodeEnv obj's underlying member method code with example usage: obj.step(action_dict) and returns: obs,  rew,  terminated,  truncated,  info