How to use the tf2rl.algos.policy_base.IRLPolicy.__init__ function in tf2rl

def __init__(
            self,
            state_shape,
            units=[32, 32],
            lr=0.001,
            enable_sn=False,
            name="GAIfO",
            **kwargs):
        IRLPolicy.__init__(self, name=name, n_training=1, **kwargs)
        self.disc = Discriminator(
            state_shape=state_shape,
            units=units, enable_sn=enable_sn)
        self.optimizer = tf.keras.optimizers.Adam(
            learning_rate=lr, beta_1=0.5)

units=[32, 32],
            lr=0.001,
            enable_sn=False,
            enable_gp=True,
            enable_gc=False,
            name="WGAIL",
            **kwargs):
        """
        :param enable_sn (bool): If true, add spectral normalization in Dense layer
        :param enable_gp (bool): If true, add gradient penalty to loss function
        :param enable_gc (bool): If true, apply gradient clipping while training
        """
        assert enable_gp and enable_gc, \
            "You must choose either Gradient Penalty or Gradient Clipping." \
            "Both at the same time is not supported."
        IRLPolicy.__init__(
            self, name=name, **kwargs)
        self.disc = Discriminator(
            state_shape=state_shape, action_dim=action_dim,
            units=units, enable_sn=enable_sn, output_activation="linear")
        self.optimizer = tf.keras.optimizers.Adam(
            learning_rate=lr, beta_1=0.5)
        self._enable_gp = enable_gp
        self._enable_gc = enable_gc

state_shape,
            action_dim,
            units=[32, 32],
            n_latent_unit=32,
            lr=5e-5,
            kl_target=0.5,
            reg_param=0.,
            enable_sn=False,
            enable_gp=False,
            name="VAIL",
            **kwargs):
        """
        :param enable_sn (bool): If true, add spectral normalization in Dense layer
        :param enable_gp (bool): If true, add gradient penalty to loss function
        """
        IRLPolicy.__init__(
            self, name=name, n_training=10, **kwargs)
        self.disc = Discriminator(
            state_shape=state_shape, action_dim=action_dim,
            units=units, n_latent_unit=n_latent_unit,
            enable_sn=enable_sn)
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
        self._kl_target = kl_target
        self._reg_param = tf.Variable(reg_param, dtype=tf.float32)
        self._step_reg_param = tf.constant(1e-5, dtype=tf.float32)
        self._enable_gp = enable_gp