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

def get_region_score(features,feature_columns, region_number, l2_reg, init_std, seed,prefix='region_',seq_mask_zero=True):

    region_logit =concat_func([get_linear_logit(features, feature_columns, init_std=init_std, seed=seed + i,
                                                prefix=prefix + str(i + 1), l2_reg=l2_reg) for i in range(region_number)])
    return Activation('softmax')(region_logit)

:param afm_dropout: float in [0,1), Fraction of the attention net output units to dropout.
    :param init_std: float,to use as the initialize std of embedding vector
    :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(
        linear_feature_columns + dnn_feature_columns)

    inputs_list = list(features.values())

    group_embedding_dict, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
                                                         seed, support_dense=False, 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)

    if use_attention:
        fm_logit = add_func([AFMLayer(attention_factor, l2_reg_att, afm_dropout,
                                      seed)(list(v)) for k, v in group_embedding_dict.items() if k in fm_group])
    else:
        fm_logit = add_func([FM()(concat_func(v, axis=1))
                             for k, v in group_embedding_dict.items() if k in fm_group])

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

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

: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.
    """

    if len(conv_kernel_width) != len(conv_filters):
        raise ValueError(
            "conv_kernel_width must have same element with conv_filters")

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

    sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std,
                                                          seed, support_dense=False)
    linear_logit = get_linear_logit(features, linear_feature_columns, init_std=init_std, seed=seed,
                                    l2_reg=l2_reg_linear)

    n = len(sparse_embedding_list)
    l = len(conv_filters)

    conv_input = concat_func(sparse_embedding_list, axis=1)
    pooling_result = tf.keras.layers.Lambda(
        lambda x: tf.expand_dims(x, axis=3))(conv_input)

    for i in range(1, l + 1):
        filters = conv_filters[i - 1]
        width = conv_kernel_width[i - 1]
        k = max(1, int((1 - pow(i / l, l - i)) * n)) if i < l else 3

        conv_result = tf.keras.layers.Conv2D(filters=filters, kernel_size=(width, 1), strides=(1, 1), padding='same',
                                             activation='tanh', use_bias=True, )(pooling_result)

:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not DNN
    :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

:param init_std: float,to use as the initialize std of embedding vector
    :param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :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.
    """
    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

:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param init_std: float,to use as the initialize std of embedding vector
    :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.
    """

    if not (len(conv_kernel_width) == len(conv_filters) == len(new_maps) == len(pooling_width)):
        raise ValueError(
            "conv_kernel_width,conv_filters,new_maps  and pooling_width must have same length")

    features = build_input_features(dnn_feature_columns)

    inputs_list = list(features.values())

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

    deep_emb_list, _ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std, seed)
    fg_deep_emb_list,_ = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding, init_std, seed,
                                                    prefix='fg')


    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

:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :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())

    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

:param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :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)

    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)

:param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :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.
    """

    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