docstr: Args:
      actions: array of action
  
  Returns:
      obs: array of next obs of env
      reward: array of return reward of env
      done: array of done of env
      info: array of info of env
docstr_tok: ["Args",":","actions",":","array","of","action","Returns",":","obs",":","array","of","next","obs","of","env","reward",":","array","of","return","reward","of","env","done",":","array","of","done","of","env","info",":","array","of","info","of","env"]
code: def step(self, actions):
      """
      Args:
          actions: array of action
  
      Returns:
          obs: array of next obs of env
          reward: array of return reward of env
          done: array of done of env
          info: array of info of env
      """
      agents_actions_list = []
      for agent_id in range(len(self.observation_space)):
          if (
              self.action_space[agent_id].__class__.__name__
              == "MultiDiscrete"
          ):
              for action_id in range(self.action_space[agent_id].shape):
                  action_label = actions[agent_id][action_id]
                  max_action = (
                      self.action_space[agent_id].high[action_id] + 1
                  )
                  action_one_hot = np.squeeze(
                      np.eye(max_action)[action_label]
                  )
                  if action_id == 0:
                      action_env = action_one_hot
                  else:
                      action_env = np.concatenate(
                          (action_env, action_one_hot)
                      )
              agents_actions_list.append(action_env)
          else:
              action_label = actions[agent_id]
              max_action = self.action_space[agent_id].n
              action_one_hot = np.squeeze(
                  np.eye(max_action)[action_label]
              )
              agents_actions_list.append(action_one_hot)
  
      results = self.env.step(agents_actions_list)
      obs, rews, dones, infos = map(np.array, results)
  
      return obs, rews, dones, infos
  
file_path: PARL/benchmark/torch/mappo/env_wrappers.py
imports: import numpy
  from multiagent.environment import MultiAgentEnv
  from multiagent.scenarios import load
label_desc: Use image node_id 3 for calling the MPEEnv obj's underlying member method code with example usage: obj.step(actions) and returns: obs,  rews,  dones,  infos