import tensorflow as tf

            self.outputs = prev_layer.outputs
            # self.all_layers = list(layer.all_layers)
            # self.all_params = list(layer.all_params)
            # self.all_drop = dict(layer.all_drop)
        else:
            self.inputs = prev_layer.outputs
            logging.info("GaussianNoiseLayer %s: mean:%f stddev:%f" % (self.name, mean, stddev))
            with tf.variable_scope(name):
                # noise = np.random.normal(0.0 , sigma , tf.to_int64(self.inputs).get_shape())
                noise = tf.random_normal(shape=self.inputs.get_shape(), mean=mean, stddev=stddev, seed=seed)
                self.outputs = self.inputs + noise
            # self.all_layers = list(layer.all_layers)
            # self.all_params = list(layer.all_params)
            # self.all_drop = dict(layer.all_drop)
