def METHOD_NAME(self, config):
    sparse_feature_number = config.get(
        "hyper_parameters.sparse_feature_number")
    sparse_feature_dim = config.get("hyper_parameters.sparse_feature_dim")
    fc_sizes = config.get("hyper_parameters.fc_sizes")
    dense_feature_dim = config.get('hyper_parameters.dense_input_dim')
    sparse_input_slot = config.get('hyper_parameters.sparse_inputs_slots')
    cross_num = config.get("hyper_parameters.cross_num")
    l2_reg_cross = config.get("hyper_parameters.l2_reg_cross", None)
    clip_by_norm = config.get("hyper_parameters.clip_by_norm", None)
    is_Stacked = config.get("hyper_parameters.is_Stacked", None)
    use_low_rank_mixture = config.get(
        "hyper_parameters.use_low_rank_mixture", None)
    low_rank = config.get("hyper_parameters.low_rank", 32)
    num_experts = config.get("hyper_parameters.num_experts", 4)
    dnn_use_bn = config.get("hyper_parameters.dnn_use_bn", None)
    dcn_v2_model = net.DCN_V2Layer(
        sparse_feature_number, sparse_feature_dim, dense_feature_dim,
        sparse_input_slot - 1, fc_sizes, cross_num, is_Stacked,
        use_low_rank_mixture, low_rank, num_experts)
    return dcn_v2_model