void getPrototype(ServerInterface &srvInterface, ColumnTypes &argTypes, ColumnTypes &returnType)
    {
        // This function supports any argument data type
        argTypes.addAny();

        // Output data type will be the same as the argument data type
        // We will specify that in getReturnType()
        returnType.addAny();
    }

    void getReturnType(ServerInterface &srvInterface, const SizedColumnTypes &inputTypes,
                       SizedColumnTypes &outputTypes)
    {
        // This function accepts only one argument
        // Complain if we find a different number
        std::vector<size_t> argCols;
        inputTypes.getArgumentColumns(argCols); // get argument column indices

        if (argCols.size() != 1)
        {
            vt_report_error(0, "Only one argument is expected but %s provided",
                            argCols.size()? std::to_string(argCols.size()).c_str() : "none");
        }

        // Define output type the same as argument type
        outputTypes.addArg(inputTypes.getColumnType(argCols[0]), inputTypes.getColumnName(argCols[0]));
    }

    void processPartition(ServerInterface &srvInterface, AnalyticPartitionReader &inputReader,
                          AnalyticPartitionWriter &outputWriter)
    {
        try {
            const SizedColumnTypes &inTypes = inputReader.getTypeMetaData();
            std::vector<size_t> argCols; // Argument column indexes.
            inTypes.getArgumentColumns(argCols);

            vint currentRow = 1;
            bool nthRowExists = false;

            // Find the value of the n-th row
            do {
                if (currentRow == this->n) {
                    nthRowExists = true;
                    break;
                } else {
                    currentRow++;
                }
            } while (inputReader.next());

            if (nthRowExists) {
                do {
                    // Return n-th value
                    outputWriter.copyFromInput(0 /*dest column*/, inputReader,
                                               argCols[0] /*source column*/);
                } while (outputWriter.next());
            } else {
                // The partition has less than n rows
                // Return NULL value
                do {
                    outputWriter.setNull(0);
                } while (outputWriter.next());
            }
        } catch(std::exception& e) {
            // Standard exception. Quit.
            vt_report_error(0, "Exception while processing partition: [%s]", e.what());
        }
    }
};

        @Override
        public void processBlock(ServerInterface srvInterface,
                                 BlockReader arg_reader,
                                 BlockWriter res_writer)
                    throws UdfException, DestroyInvocation
        {
        SizedColumnTypes inTypes = arg_reader.getTypeMetaData();
        ArrayList<Integer> argCols = new ArrayList<Integer>(); // Argument column indexes.
        inTypes.getArgumentColumns(argCols);
        // While we have inputs to process
            do {
        long sum = 0;
        for (int i = 0; i < argCols.size(); ++i){
            long a = arg_reader.getLong(i);
            sum += a;
        }
                res_writer.setLong(sum);
                res_writer.next();
            } while (arg_reader.next());
        }
    }

    @Override
    public void getPrototype(ServerInterface srvInterface,
                             ColumnTypes argTypes,
                             ColumnTypes returnType)
    {
    argTypes.addAny();
        returnType.addInt();
    }

kmeansPoly <- function(v.data.frame,v.param.list) {
  # Computes clusters using the kmeans algorithm.
  #
  # Input: A dataframe and a list of parameters.
  # Output: A dataframe with one column that tells the cluster to which each data
  #         point belongs.
  # Args:
  #  v.data.frame: The data from Vertica cast as an R data frame.
  #  v.param.list: List of function parameters.
  #
  # Returns:
  #  The cluster associated with each data point.
  # Ensure k is not null.
  if(!is.null(v.param.list[['k']])) {
     number_of_clusters <- as.numeric(v.param.list[['k']])
  } else {
    stop("k cannot be NULL! Please use a valid value.")
  }
  # Run the kmeans algorithm.
  kmeans_clusters <- kmeans(v.data.frame, number_of_clusters)
  final.output <- data.frame(kmeans_clusters$cluster)
  return(final.output)
}

kmeansFactoryPoly <- function() {
  # This function tells Vertica the name of the R function,
  # and the polymorphic parameters.
  list(name=kmeansPoly, udxtype=c("transform"), intype=c("any"),
       outtype=c("int"), parametertypecallback=kmeansParameters)
}

kmeansParameters <- function() {
  # Callback function for the parameter types.
  function.parameters <- data.frame(datatype=rep(NA, 1), length=rep(NA,1),
                                    scale=rep(NA,1), name=rep(NA,1))
  function.parameters[1,1] = "int"
  function.parameters[1,4] = "k"
  return(function.parameters)
}