How to use the deepctr.inputs.combined_dnn_input function in deepctr

:param dnn_activation: Activation function to use in DNN
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.

    """
    if len(dnn_hidden_units) == 0 and cross_num == 0:
        raise ValueError("Either hidden_layer or cross layer must > 0")

    features = build_input_features(dnn_feature_columns)
    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                         l2_reg_embedding, init_std, seed)
    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)
    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)

    if len(dnn_hidden_units) > 0 and cross_num > 0:  # Deep & Cross
        deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                       dnn_use_bn, seed)(dnn_input)
        cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
        stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out])
        final_logit = tf.keras.layers.Dense(
            1, use_bias=False, activation=None)(stack_out)
    elif len(dnn_hidden_units) > 0:  # Only Deep
        deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                       dnn_use_bn, seed)(dnn_input)
        final_logit = tf.keras.layers.Dense(
            1, use_bias=False, activation=None)(deep_out)
    elif cross_num > 0:  # Only Cross
        cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input)
        final_logit = tf.keras.layers.Dense(

"""

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding,
                                                                        init_std, seed, support_group=True)

    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)
    fm_logit = add_func([FM()(concat_func(v, axis=1))
                         for k, v in group_embedding_dict.items() if k in fm_group])

    dnn_input = combined_dnn_input(list(chain.from_iterable(
        group_embedding_dict.values())), dense_value_list)
    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                     dnn_use_bn, seed)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(
        1, use_bias=False, activation=None)(dnn_output)

    final_logit = add_func([linear_logit, fm_logit, dnn_logit])

    output = PredictionLayer(task)(final_logit)
    model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
    return model

sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                         l2_reg_embedding, init_std, seed)

    senet_embedding_list = SENETLayer(
        reduction_ratio, seed)(sparse_embedding_list)

    senet_bilinear_out = BilinearInteraction(
        bilinear_type=bilinear_type, seed=seed)(senet_embedding_list)
    bilinear_out = BilinearInteraction(
        bilinear_type=bilinear_type, seed=seed)(sparse_embedding_list)

    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)

    dnn_input = combined_dnn_input(
        [Flatten()(concat_func([senet_bilinear_out, bilinear_out]))], dense_value_list)
    dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                  False, seed)(dnn_input)
    dnn_logit = Dense(
        1, use_bias=False, activation=None)(dnn_out)

    final_logit = add_func([linear_logit,dnn_logit])
    output = PredictionLayer(task)(final_logit)

    model = Model(inputs=inputs_list, outputs=output)
    return model

:param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                         l2_reg_embedding, init_std, seed)

    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)

    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
    dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                  False, seed)(dnn_input)
    dnn_logit = Dense(
        1, use_bias=False, activation=None)(dnn_out)

    final_logit = add_func([dnn_logit, linear_logit])

    output = PredictionLayer(task)(final_logit)

    model = Model(inputs=inputs_list, outputs=output)
    return model

"""

    features = build_input_features(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns,
                                                                         l2_reg_embedding, init_std, seed)

    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed, prefix='linear',
                                    l2_reg=l2_reg_linear)

    fm_input = concat_func(sparse_embedding_list, axis=1)

    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)
    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                     dnn_use_bn, seed)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(
        1, use_bias=False, activation=None)(dnn_output)

    final_logit = add_func([linear_logit, dnn_logit])

    if len(cin_layer_size) > 0:
        exFM_out = CIN(cin_layer_size, cin_activation,
                       cin_split_half, l2_reg_cin, seed)(fm_input)
        exFM_logit = tf.keras.layers.Dense(1, activation=None, )(exFM_out)
        final_logit = add_func([final_logit, exFM_logit])

    output = PredictionLayer(task)(final_logit)

    model = tf.keras.models.Model(inputs=inputs_list, outputs=output)

linear_signal = tf.keras.layers.Reshape(
        [len(sparse_embedding_list) * embedding_size])(concat_func(sparse_embedding_list))

    if use_inner and use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product, outter_product])
    elif use_inner:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, inner_product])
    elif use_outter:
        deep_input = tf.keras.layers.Concatenate()(
            [linear_signal, outter_product])
    else:
        deep_input = linear_signal

    dnn_input = combined_dnn_input([deep_input], dense_value_list)
    dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                  False, seed)(dnn_input)
    dnn_logit = tf.keras.layers.Dense(
        1, use_bias=False, activation=None)(dnn_out)

    output = PredictionLayer(task)(dnn_logit)

    model = tf.keras.models.Model(inputs=inputs_list,
                                  outputs=output)
    return model

neg_concat_behavior = concat_func(neg_uiseq_embed_list)

    else:
        neg_concat_behavior = None
    hist, aux_loss_1 = interest_evolution(keys_emb, query_emb, user_behavior_length, gru_type=gru_type,
                                          use_neg=use_negsampling, neg_concat_behavior=neg_concat_behavior,
                                          att_hidden_size=att_hidden_units,
                                          att_activation=att_activation,
                                          att_weight_normalization=att_weight_normalization, )

    deep_input_emb = Concatenate()([deep_input_emb, hist])

    deep_input_emb = tf.keras.layers.Flatten()(deep_input_emb)

    dnn_input = combined_dnn_input([deep_input_emb], dense_value_list)
    output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                 dnn_dropout, use_bn, seed)(dnn_input)
    final_logit = Dense(1, use_bias=False)(output)
    output = PredictionLayer(task)(final_logit)

    #model_input_list = get_inputs_list(
    #    [sparse_input, dense_input, user_behavior_input])
    model_input_list = inputs_list

    #if use_negsampling:
    #    model_input_list += list(neg_user_behavior_input.values())

    model_input_list += [user_behavior_length]

    model = tf.keras.models.Model(inputs=model_input_list, outputs=output)

sess_fea = sess_interest_extractor(
        tr_input, sess_max_count, Self_Attention)

    interest_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16), weight_normalization=True,
                                                             supports_masking=False)(
        [query_emb, sess_fea, user_sess_length])

    lstm_outputs = BiLSTM(hist_emb_size,
                          layers=2, res_layers=0, dropout_rate=0.2, )(sess_fea)
    lstm_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16), weight_normalization=True)(
        [query_emb, lstm_outputs, user_sess_length])

    dnn_input_emb = Concatenate()(
        [dnn_input_emb, Flatten()(interest_attention_layer), Flatten()(lstm_attention_layer)])

    dnn_input_emb = combined_dnn_input([dnn_input_emb], dense_value_list)
    output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                 dnn_dropout, dnn_use_bn, seed)(dnn_input_emb)
    output = Dense(1, use_bias=False, activation=None)(output)
    output = PredictionLayer(task)(output)

    sess_input_list = []
    # sess_input_length_list = []
    for i in range(sess_max_count):
        sess_name = "sess_" + str(i)
        sess_input_list.extend(get_inputs_list(
            [user_behavior_input_dict[sess_name]]))
        # sess_input_length_list.append(user_behavior_length_dict[sess_name])

    model = Model(inputs=inputs_list + [user_sess_length], outputs=output)

    return model

if fc_j.use_hash:
            j_input = Hash(fc_j.vocabulary_size)(j_input)

        fc_i_embedding = feature_embedding(fc_i, fc_j, sparse_embedding, i_input)
        fc_j_embedding = feature_embedding(fc_j, fc_i, sparse_embedding, j_input)

        element_wise_prod = multiply([fc_i_embedding, fc_j_embedding])
        if reduce_sum:
            element_wise_prod = Lambda(lambda element_wise_prod: K.sum(
                element_wise_prod, axis=-1))(element_wise_prod)
        embed_list.append(element_wise_prod)

    ffm_out = tf.keras.layers.Flatten()(concat_func(embed_list, axis=1))
    if use_bn:
        ffm_out = tf.keras.layers.BatchNormalization()(ffm_out)
    dnn_input = combined_dnn_input([ffm_out], dense_value_list)
    dnn_out = DNN(dnn_hidden_units, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout)(dnn_input)
    dnn_logit = Dense(1, use_bias=False)(dnn_out)


    final_logit = add_func([dnn_logit,linear_logit])

    output = PredictionLayer(task)(final_logit)

    model = Model(inputs=inputs_list, outputs=output)
    return model

sequence_embed_dict = varlen_embedding_lookup(embedding_dict,features,sparse_varlen_feature_columns)
    sequence_embed_list = get_varlen_pooling_list(sequence_embed_dict, features, sparse_varlen_feature_columns,to_list=True)

    dnn_input_emb_list += sequence_embed_list


    keys_emb = concat_func(keys_emb_list, mask=True)
    deep_input_emb = concat_func(dnn_input_emb_list)
    query_emb = concat_func(query_emb_list, mask=True)
    hist = AttentionSequencePoolingLayer(att_hidden_size, att_activation,
                                         weight_normalization=att_weight_normalization, supports_masking=True)([
        query_emb, keys_emb])

    deep_input_emb = Concatenate()([NoMask()(deep_input_emb), hist])
    deep_input_emb = Flatten()(deep_input_emb)
    dnn_input = combined_dnn_input([deep_input_emb],dense_value_list)
    output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                 dnn_dropout, dnn_use_bn, seed)(dnn_input)
    final_logit = Dense(1, use_bias=False)(output)

    output = PredictionLayer(task)(final_logit)

    model = Model(inputs=inputs_list, outputs=output)
    return model