How to use the deepctr.layers.sequence.WeightedSequenceLayer function in deepctr

def get_varlen_pooling_list(embedding_dict, features, varlen_sparse_feature_columns, to_list=False):
    pooling_vec_list = defaultdict(list)
    for fc in varlen_sparse_feature_columns:
        feature_name = fc.name
        combiner = fc.combiner
        feature_length_name = fc.length_name
        if feature_length_name is not None:
            if fc.weight_name is not None:
                seq_input = WeightedSequenceLayer()(
                    [embedding_dict[feature_name], features[feature_length_name], features[fc.weight_name]])
            else:
                seq_input = embedding_dict[feature_name]
            vec = SequencePoolingLayer(combiner, supports_masking=False)(
                [seq_input, features[feature_length_name]])
        else:
            if fc.weight_name is not None:
                seq_input = WeightedSequenceLayer(supports_masking=True)(
                    [embedding_dict[feature_name], features[fc.weight_name]])
            else:
                seq_input = embedding_dict[feature_name]
            vec = SequencePoolingLayer(combiner, supports_masking=True)(
                seq_input)
        pooling_vec_list[fc.group_name].append(vec)
        if to_list:
            return chain.from_iterable(pooling_vec_list.values())

pooling_vec_list = defaultdict(list)
    for fc in varlen_sparse_feature_columns:
        feature_name = fc.name
        combiner = fc.combiner
        feature_length_name = fc.length_name
        if feature_length_name is not None:
            if fc.weight_name is not None:
                seq_input = WeightedSequenceLayer()(
                    [embedding_dict[feature_name], features[feature_length_name], features[fc.weight_name]])
            else:
                seq_input = embedding_dict[feature_name]
            vec = SequencePoolingLayer(combiner, supports_masking=False)(
                [seq_input, features[feature_length_name]])
        else:
            if fc.weight_name is not None:
                seq_input = WeightedSequenceLayer(supports_masking=True)(
                    [embedding_dict[feature_name], features[fc.weight_name]])
            else:
                seq_input = embedding_dict[feature_name]
            vec = SequencePoolingLayer(combiner, supports_masking=True)(
                seq_input)
        pooling_vec_list[fc.group_name].append(vec)
        if to_list:
            return chain.from_iterable(pooling_vec_list.values())
    return pooling_vec_list

def get_config(self, ):
        config = {'weight_normalization':self.weight_normalization,'supports_masking': self.supports_masking}
        base_config = super(WeightedSequenceLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))