code: def step(self, actions):
      actions_dict = dict()
      for i, act in enumerate(actions):
          agent = self.agents_name[i]
          if self.continuous_actions:
              assert np.all(
                  ((act <= 1.0 + 1e-3), (act >= -1.0 - 1e-3))
              ), "the action should be in range [-1.0, 1.0], but got {}".format(
                  act
              )
              high = self.action_space[i].high
              low = self.action_space[i].low
              mapped_action = low + (act - (-1.0)) * (
                  (high - low) / 2.0
              )
              mapped_action = np.clip(mapped_action, low, high)
              actions_dict[agent] = mapped_action
          else:
              actions_dict[agent] = np.argmax(act)
      obs, reward, done, info = self.env.step(actions_dict)
      return (
          list(obs.values()),
          list(reward.values()),
          list(done.values()),
          list(info.values()),
      )
  
file_path: PARL/parl/env/multiagent_env.py
imports: import numpy
label_desc: Use image node_id 4 for calling the mpe_wrapper_for_pettingzoo obj's underlying member method code with example usage: obj.step(actions) and returns: list,  list,  list,  list