How to use the gym.Wrapper.__init__ function in gym

def __init__(self, env, max_random_steps, on_startup=True, every_episode=False):
        gym.Wrapper.__init__(self, env)
        self.on_startup = on_startup
        self.every_episode = every_episode
        self.random_steps = max_random_steps
        self.last_obs = None
        if on_startup:
            self.some_random_steps()

def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype=np.uint8)
        self._skip       = skip

def __init__(self, env, HIRO_agent):
        Wrapper.__init__(self, env)
        self.env = env
        self.HIRO_agent = HIRO_agent
        self.action_space = self.observation_space

def __init__(self, env):
        """Take action on reset for environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

def __init__(self, env):
        """Take action on reset for environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = deque(maxlen=2)
        self._skip       = skip

def __init__(self, env):
        """Take action on reset for environments that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

def __init__(self, env, noop_max=30):
        """
        Sample initial states by taking random number of no-ops on reset.
        No-op is assumed to be action 0.
        """
        gym.Wrapper.__init__(self, env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = 0
        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'

def __init__(self, env):
        """Take action on reset for env that are fixed until firing."""
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.fire_action = env.unwrapped.get_action_meanings().index('FIRE')
        assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
        assert len(env.unwrapped.get_action_meanings()) >= 3

def __init__(self, env, skip=4):
        """Return only every `skip`-th frame"""
        gym.Wrapper.__init__(self, env)
        # most recent raw observations (for max pooling across time steps)
        self._obs_buffer = np.zeros((2,)+env.observation_space.shape, dtype='uint8')
        self._skip       = skip