<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def _player_step_tuple(self, envs_step_tuples):
 """Construct observation, return usual step tuple. Args: envs_step_tuples: tuples. Returns: Step tuple: ob, reward, done, info ob: concatenated images [simulated observation, real observation, difference], with additional informations in header. reward: real environment reward done: True iff. envs_step_tuples['real_env'][2] is True info: real environment info """

    ob_real, reward_real, _, _ = envs_step_tuples["real_env"]
    ob_sim, reward_sim, _, _ = envs_step_tuples["sim_env"]
    ob_err = absolute_hinge_difference(ob_sim, ob_real)

    ob_real_aug = self._augment_observation(ob_real, reward_real,
                                            self.cumulative_real_reward)
    ob_sim_aug = self._augment_observation(ob_sim, reward_sim,
                                           self.cumulative_sim_reward)
    ob_err_aug = self._augment_observation(
        ob_err, reward_sim - reward_real,
        self.cumulative_sim_reward - self.cumulative_real_reward
    )
    ob = np.concatenate([ob_sim_aug, ob_real_aug, ob_err_aug], axis=1)
    _, reward, done, info = envs_step_tuples["real_env"]
    return ob, reward, done, info