code: def step(
      self, action_dict: MultiAgentDict
  ) -> Tuple[
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
      MultiAgentDict,
  ]:
      # Increase the timestep by one.
      self.t += 1
      # The number of agents that are ready at this timestep.
      num_agents_step = self._np_random.integers(
          1, len(self._agents_alive)
      )
      # The agents that are ready.
      agents_step = self._np_random.choice(
          np.array(sorted(self._agents_alive)),
          num_agents_step,
          replace=False,
      )
      # Initialize observations.
      obs = {agent_id: self.t for agent_id in agents_step}
      info = {agent_id: {} for agent_id in agents_step}
      reward = {agent_id: 1.0 for agent_id in agents_step}
      # Add also agents without observations.
      reward.update(
          {
              agent_id: 1.0
              for agent_id in self._np_random.choice(
                  np.array(sorted(self._agents_alive)), 8, replace=False
              )
              if agent_id not in reward
          }
      )
  
      # Use tha last terminateds/truncateds.
      is_truncated = {"__all__": False}
      is_truncated.update({agent_id: False for agent_id in agents_step})
      is_terminated = {"__all__": False}
      is_terminated.update(
          {agent_id: False for agent_id in agents_step}
      )
  
      if self.t == 50:
          # Let agent 1 die.
          is_terminated["agent_1"] = True
          is_truncated["agent_1"] = False
          # Ensure that the set of alive agents is updated.
          self._agents_alive -= {"agent_1"}
          # Any terminated agent, terminates with an observation.
          obs.update({"agent_1": self.t})
          reward.update({"agent_1": 1.0})
          info.update({"agent_1": {}})
  
      if self.t == 100 and "agent_5":
          # Let agent 5 die.
          is_terminated["agent_5"] = True
          is_truncated["agent_5"] = False
          # Ensure that the set of alive agents is updated.
          self._agents_alive -= {"agent_5"}
          # Any terminated agent, terminates with an observation.
          obs.update({"agent_5": self.t})
          reward.update({"agent_5": 1.0})
          info.update({"agent_5": {}})
  
      # Truncate the episode if too long.
      if self.t >= 200 and self.truncate:
          is_truncated["__all__"] = True
          is_truncated.update(
              {agent_id: True for agent_id in agents_step}
          )
  
      return obs, reward, is_terminated, is_truncated, info
  
imports: import gymnasium
  import numpy
  import unittest
  from typing import List, Optional, Tuple
  import ray
  from ray.rllib.env.multi_agent_env import MultiAgentEnv
  from ray.rllib.env.multi_agent_episode import MultiAgentEpisode
  from ray.rllib.utils.typing import MultiAgentDict
label_desc: Use image node_id 3 for calling the MultiAgentTestEnv obj's underlying member method code with example usage: obj.step(action_dict) and returns: obs,  reward,  is_terminated,  is_truncated,  info