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")
    sparse_fea_num = config.get('hyper_parameters.sparse_fea_num')
    dense_feature_dim = config.get('hyper_parameters.dense_input_dim')
    sparse_input_slot = config.get('hyper_parameters.sparse_inputs_slots')
    use_embedding_gate = config.get('hyper_parameters.use_embedding_gate')
    use_hidden_gate = config.get('hyper_parameters.use_hidden_gate')
    gate_dnn_model = net.GateDNNLayer(
        sparse_feature_number, sparse_feature_dim, dense_feature_dim,
        sparse_input_slot - 1, fc_sizes, use_embedding_gate,
        use_hidden_gate)
    return gate_dnn_model