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

if gru_type not in ["GRU", "AIGRU", "AGRU", "AUGRU"]:
        raise ValueError("gru_type error ")
    aux_loss_1 = None
    embedding_size = None
    rnn_outputs = DynamicGRU(embedding_size, return_sequence=True,
                             name="gru1")([concat_behavior, user_behavior_length])

    if gru_type == "AUGRU" and use_neg:
        aux_loss_1 = auxiliary_loss(rnn_outputs[:, :-1, :], concat_behavior[:, 1:, :],

                                    neg_concat_behavior[:, 1:, :],

                                    tf.subtract(user_behavior_length, 1), stag="gru")  # [:, 1:]

    if gru_type == "GRU":
        rnn_outputs2 = DynamicGRU(embedding_size, return_sequence=True,
                                  name="gru2")([rnn_outputs, user_behavior_length])
        # attention_score = AttentionSequencePoolingLayer(hidden_size=att_hidden_size, activation=att_activation, weight_normalization=att_weight_normalization, return_score=True)([
        #     deep_input_item, rnn_outputs2, user_behavior_length])
        # outputs = Lambda(lambda x: tf.matmul(x[0], x[1]))(
        #     [attention_score, rnn_outputs2])
        # hist = outputs
        hist = AttentionSequencePoolingLayer(att_hidden_units=att_hidden_size, att_activation=att_activation,
                                             weight_normalization=att_weight_normalization, return_score=False)([
            deep_input_item, rnn_outputs2, user_behavior_length])

    else:  # AIGRU AGRU AUGRU

        scores = AttentionSequencePoolingLayer(att_hidden_units=att_hidden_size, att_activation=att_activation,
                                               weight_normalization=att_weight_normalization, return_score=True)([
            deep_input_item, rnn_outputs, user_behavior_length])

hist = AttentionSequencePoolingLayer(att_hidden_units=att_hidden_size, att_activation=att_activation,
                                             weight_normalization=att_weight_normalization, return_score=False)([
            deep_input_item, rnn_outputs2, user_behavior_length])

    else:  # AIGRU AGRU AUGRU

        scores = AttentionSequencePoolingLayer(att_hidden_units=att_hidden_size, att_activation=att_activation,
                                               weight_normalization=att_weight_normalization, return_score=True)([
            deep_input_item, rnn_outputs, user_behavior_length])

        if gru_type == "AIGRU":
            hist = multiply([rnn_outputs, Permute([2, 1])(scores)])
            final_state2 = DynamicGRU(embedding_size, gru_type="GRU", return_sequence=False, name='gru2')(
                [hist, user_behavior_length])
        else:  # AGRU AUGRU
            final_state2 = DynamicGRU(embedding_size, gru_type=gru_type, return_sequence=False,
                                      name='gru2')([rnn_outputs, user_behavior_length, Permute([2, 1])(scores)])
        hist = final_state2
    return hist, aux_loss_1

def get_config(self, ):
        config = {'num_units': self.num_units, 'gru_type': self.gru_type, 'return_sequence': self.return_sequence}
        base_config = super(DynamicGRU, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

def interest_evolution(concat_behavior, deep_input_item, user_behavior_length, gru_type="GRU", use_neg=False,
                       neg_concat_behavior=None,att_hidden_size=(64, 16), att_activation='sigmoid',
                       att_weight_normalization=False, ):
    if gru_type not in ["GRU", "AIGRU", "AGRU", "AUGRU"]:
        raise ValueError("gru_type error ")
    aux_loss_1 = None
    embedding_size = None
    rnn_outputs = DynamicGRU(embedding_size, return_sequence=True,
                             name="gru1")([concat_behavior, user_behavior_length])

    if gru_type == "AUGRU" and use_neg:
        aux_loss_1 = auxiliary_loss(rnn_outputs[:, :-1, :], concat_behavior[:, 1:, :],

                                    neg_concat_behavior[:, 1:, :],

                                    tf.subtract(user_behavior_length, 1), stag="gru")  # [:, 1:]

    if gru_type == "GRU":
        rnn_outputs2 = DynamicGRU(embedding_size, return_sequence=True,
                                  name="gru2")([rnn_outputs, user_behavior_length])
        # attention_score = AttentionSequencePoolingLayer(hidden_size=att_hidden_size, activation=att_activation, weight_normalization=att_weight_normalization, return_score=True)([
        #     deep_input_item, rnn_outputs2, user_behavior_length])
        # outputs = Lambda(lambda x: tf.matmul(x[0], x[1]))(
        #     [attention_score, rnn_outputs2])

'LocalActivationUnit': LocalActivationUnit,
                  'Dice': Dice,
                  'SequencePoolingLayer': SequencePoolingLayer,
                  'AttentionSequencePoolingLayer': AttentionSequencePoolingLayer,
                  'CIN': CIN,
                  'InteractingLayer': InteractingLayer,
                  'LayerNormalization': LayerNormalization,
                  'BiLSTM': BiLSTM,
                  'Transformer': Transformer,
                  'NoMask': NoMask,
                  'BiasEncoding': BiasEncoding,
                  'KMaxPooling': KMaxPooling,
                  'FGCNNLayer': FGCNNLayer,
                  'Hash': Hash,
                  'Linear':Linear,
                  'DynamicGRU': DynamicGRU,
                  'SENETLayer':SENETLayer,
                  'BilinearInteraction':BilinearInteraction,
                  'WeightedSequenceLayer':WeightedSequenceLayer,
                  'Add':Add
                  }