tensorflow_addons/optimizers/adabelief.py [308:326]:
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        )

        updates = [var_update, m_t, v_t]
        if self.amsgrad:
            updates.append(vhat_t)
        return tf.group(*updates)

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "learning_rate": self._serialize_hyperparameter("learning_rate"),
                "beta_1": self._serialize_hyperparameter("beta_1"),
                "beta_2": self._serialize_hyperparameter("beta_2"),
                "decay": self._serialize_hyperparameter("decay"),
                "weight_decay": self._serialize_hyperparameter("weight_decay"),
                "sma_threshold": self._serialize_hyperparameter("sma_threshold"),
                "epsilon": self.epsilon,
                "amsgrad": self.amsgrad,
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tensorflow_addons/optimizers/rectified_adam.py [302:320]:
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            )

        updates = [var_update, m_t, v_t]
        if self.amsgrad:
            updates.append(vhat_t)
        return tf.group(*updates)

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "learning_rate": self._serialize_hyperparameter("learning_rate"),
                "beta_1": self._serialize_hyperparameter("beta_1"),
                "beta_2": self._serialize_hyperparameter("beta_2"),
                "decay": self._serialize_hyperparameter("decay"),
                "weight_decay": self._serialize_hyperparameter("weight_decay"),
                "sma_threshold": self._serialize_hyperparameter("sma_threshold"),
                "epsilon": self.epsilon,
                "amsgrad": self.amsgrad,
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