code: def step(self, action_dict):
      if self.actions_are_logits:
          action_dict = {
              k: np.random.choice([0, 1], p=v)
              for k, v in action_dict.items()
          }
  
      state_index = np.flatnonzero(self.state)
      if state_index == 0:
          action = action_dict[self.agent_1]
          assert action in [0, 1], action
          if action == 0:
              self.state = np.array([0, 1, 0])
          else:
              self.state = np.array([0, 0, 1])
          global_rew = 0
          terminated = False
      elif state_index == 1:
          global_rew = 7
          terminated = True
      else:
          if (
              action_dict[self.agent_1] == 0
              and action_dict[self.agent_2] == 0
          ):
              global_rew = 0
          elif (
              action_dict[self.agent_1] == 1
              and action_dict[self.agent_2] == 1
          ):
              global_rew = 8
          else:
              global_rew = 1
          terminated = True
  
      rewards = {
          self.agent_1: global_rew / 2.0,
          self.agent_2: global_rew / 2.0,
      }
      obs = self._obs()
      terminateds = {"__all__": terminated}
      truncateds = {"__all__": False}
      infos = {
          self.agent_1: {"done": terminateds["__all__"]},
          self.agent_2: {"done": terminateds["__all__"]},
      }
      return obs, rewards, terminateds, truncateds, infos
  
file_path: ray/python/ray/rllib/examples/env/two_step_game.py
imports: from gymnasium.spaces import Dict, Discrete, MultiDiscrete, Tuple
  import numpy
  from ray.rllib.env.multi_agent_env import MultiAgentEnv, ENV_STATE
label_desc: Use image node_id 3 for calling the TwoStepGame obj's underlying member method code with example usage: obj.step(action_dict) and returns: obs,  rewards,  terminateds,  truncateds,  infos