def METHOD_NAME(self, config):
    user_size = config.get("hyper_parameters.user_size")
    cms_segid_size = config.get("hyper_parameters.cms_segid_size")
    cms_group_size = config.get("hyper_parameters.cms_group_size")
    final_gender_size = config.get("hyper_parameters.final_gender_size")
    age_level_size = config.get("hyper_parameters.age_level_size")
    pvalue_level_size = config.get("hyper_parameters.pvalue_level_size")
    shopping_level_size = config.get(
        "hyper_parameters.shopping_level_size")
    occupation_size = config.get("hyper_parameters.occupation_size")
    new_user_class_level_size = config.get(
        "hyper_parameters.new_user_class_level_size")
    adgroup_size = config.get("hyper_parameters.adgroup_size")
    cate_size = config.get("hyper_parameters.cate_size")
    campaign_size = config.get("hyper_parameters.campaign_size")
    customer_size = config.get("hyper_parameters.customer_size")
    brand_size = config.get("hyper_parameters.brand_size")
    pid_size = config.get("hyper_parameters.pid_size")
    feat_embed_size = config.get("hyper_parameters.feat_embed_size")
    dsin_model = net.DSIN_layer(
        user_size,
        adgroup_size,
        pid_size,
        cms_segid_size,
        cms_group_size,
        final_gender_size,
        age_level_size,
        pvalue_level_size,
        shopping_level_size,
        occupation_size,
        new_user_class_level_size,
        campaign_size,
        customer_size,
        cate_size,
        brand_size,
        sparse_embed_size=feat_embed_size,
        l2_reg_embedding=1e-6)
    return dsin_model