def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,
        dnn_hidden_units=(256, 128, 64), dnn_activation='relu', att_hidden_size=(80, 40), att_activation="dice",
        att_weight_normalization=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, dnn_dropout=0, seed=1024,
        task='binary'):
    """Instantiates the Deep Interest Network architecture.

    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param history_feature_list: list,to indicate  sequence sparse field
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
    :param dnn_activation: Activation function to use in deep net
    :param att_hidden_size: list,list of positive integer , the layer number and units in each layer of attention net
    :param att_activation: Activation function to use in attention net
    :param att_weight_normalization: bool.Whether normalize the attention score of local activation unit.
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param seed: integer ,to use as random seed.
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.

    """

    features = build_input_features(dnn_feature_columns)

    sparse_feature_columns = list(
        filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if dnn_feature_columns else []
    dense_feature_columns = list(
        filter(lambda x: isinstance(x, DenseFeat), dnn_feature_columns)) if dnn_feature_columns else []
    varlen_sparse_feature_columns = list(
        filter(lambda x: isinstance(x, VarLenSparseFeat), dnn_feature_columns)) if dnn_feature_columns else []

    history_feature_columns = []
    sparse_varlen_feature_columns = []
    history_fc_names = list(map(lambda x: "hist_" + x, history_feature_list))
    for fc in varlen_sparse_feature_columns:
        feature_name = fc.name
        if feature_name in history_fc_names:
            history_feature_columns.append(fc)
        else:
            sparse_varlen_feature_columns.append(fc)

    inputs_list = list(features.values())

    embedding_dict = create_embedding_matrix(dnn_feature_columns, l2_reg_embedding, seed, prefix="")

    query_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns, history_feature_list,
                                      history_feature_list, to_list=True)
    keys_emb_list = embedding_lookup(embedding_dict, features, history_feature_columns, history_fc_names,
                                     history_fc_names, to_list=True)
    dnn_input_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns,
                                          mask_feat_list=history_feature_list, to_list=True)
    dense_value_list = get_dense_input(features, dense_feature_columns)

    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 = concat_func([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=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
Instantiates the Deep Interest Network architecture.

    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param history_feature_list: list,to indicate  sequence sparse field
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
    :param dnn_activation: Activation function to use in deep net
    :param att_hidden_size: list,list of positive integer , the layer number and units in each layer of attention net
    :param att_activation: Activation function to use in attention net
    :param att_weight_normalization: bool.Whether normalize the attention score of local activation unit.
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param seed: integer ,to use as random seed.
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.

    
deepctr/models/sequence/din.py