How to use the deepctr.layers.core.DNN function in deepctr

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

    att_input = concat_func(sparse_embedding_list, axis=1)

    for _ in range(att_layer_num):
        att_input = InteractingLayer(
            att_embedding_size, att_head_num, att_res)(att_input)
    att_output = tf.keras.layers.Flatten()(att_input)

    dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list)

    if len(dnn_hidden_units) > 0 and att_layer_num > 0:  # Deep & Interacting Layer
        deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout,
                       dnn_use_bn, seed)(dnn_input)
        stack_out = tf.keras.layers.Concatenate()([att_output, 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 att_layer_num > 0:  # Only Interacting Layer
        final_logit = tf.keras.layers.Dense(
            1, use_bias=False, activation=None)(att_output)
    else:  # Error
        raise NotImplementedError

    final_logit = add_func([final_logit, linear_logit])

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)
    bi_out = BiInteractionPooling()(fm_input)
    if bi_dropout:
        bi_out = tf.keras.layers.Dropout(bi_dropout)(bi_out, training=None)
    dnn_input = combined_dnn_input([bi_out], dense_value_list)
    dnn_output = 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_output)

    final_logit = add_func([linear_logit, dnn_logit])

    output = PredictionLayer(task)(final_logit)

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

"""
    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(
            1, use_bias=False, activation=None)(cross_out)
    else:  # Error
        raise NotImplementedError

: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)
    deep_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)(deep_out)
    final_logit = add_func([dnn_logit, linear_logit])

    output = PredictionLayer(task)(final_logit)

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

fg_input = concat_func(fg_deep_emb_list, axis=1)
    origin_input = concat_func(deep_emb_list, axis=1)

    if len(conv_filters) > 0:
        new_features = FGCNNLayer(
            conv_filters, conv_kernel_width, new_maps, pooling_width)(fg_input)
        combined_input = concat_func([origin_input, new_features], axis=1)
    else:
        combined_input = origin_input
    inner_product = tf.keras.layers.Flatten()(InnerProductLayer()(
        tf.keras.layers.Lambda(unstack, mask=[None] * int(combined_input.shape[1]))(combined_input)))
    linear_signal = tf.keras.layers.Flatten()(combined_input)
    dnn_input = tf.keras.layers.Concatenate()([linear_signal, inner_product])
    dnn_input = tf.keras.layers.Flatten()(dnn_input)

    final_logit = DNN(dnn_hidden_units, dropout_rate=dnn_dropout,
                      l2_reg=l2_reg_dnn)(dnn_input)
    final_logit = tf.keras.layers.Dense(1, use_bias=False)(final_logit)

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

    if use_negsampling:

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(len(sess_feature_list) * embedding_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])

    deep_input_emb = Concatenate()(
        [deep_input_emb, Flatten()(interest_attention_layer), Flatten()(lstm_attention_layer)])
    if len(dense_input) > 0:
        deep_input_emb = Concatenate()(
            [deep_input_emb] + list(dense_input.values()))

    output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn,
                 dnn_dropout, dnn_use_bn, seed)(deep_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_input_list = get_inputs_list([sparse_input, dense_input]) + sess_input_list + [
        user_sess_length]

    model = Model(inputs=model_input_list, outputs=output)

from .activation import Dice
from .core import DNN, LocalActivationUnit, PredictionLayer
from .interaction import (CIN, FM, AFMLayer, BiInteractionPooling, CrossNet,
                          InnerProductLayer, InteractingLayer,
                          OutterProductLayer, FGCNNLayer,SENETLayer,BilinearInteraction)
from .normalization import LayerNormalization
from .sequence import (AttentionSequencePoolingLayer, BiasEncoding, BiLSTM,
                       KMaxPooling, SequencePoolingLayer,WeightedSequenceLayer,
                       Transformer, DynamicGRU)
from .utils import NoMask, Hash,Linear,Add

custom_objects = {'tf': tf,
                  'InnerProductLayer': InnerProductLayer,
                  'OutterProductLayer': OutterProductLayer,
                  'DNN': DNN,
                  'PredictionLayer': PredictionLayer,
                  'FM': FM,
                  'AFMLayer': AFMLayer,
                  'CrossNet': CrossNet,
                  'BiInteractionPooling': BiInteractionPooling,
                  'LocalActivationUnit': LocalActivationUnit,
                  'Dice': Dice,
                  'SequencePoolingLayer': SequencePoolingLayer,
                  'AttentionSequencePoolingLayer': AttentionSequencePoolingLayer,
                  'CIN': CIN,
                  'InteractingLayer': InteractingLayer,
                  'LayerNormalization': LayerNormalization,
                  'BiLSTM': BiLSTM,
                  'Transformer': Transformer,
                  'NoMask': NoMask,
                  'BiasEncoding': BiasEncoding,