You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: kafbox_gpr.m
    path of file: ./repos/kafbox/lib/util/gpml
    
    the code of the file until where you have to start completion: function [out1, out2] = kafbox_gpr(logtheta, covfunc, x, y, xstar)

% gpr - Gaussian process regression, with a named covariance function. Two
% modes are possible: training and prediction: if no test data are given, the
% function returns minus the log likelihood and its partial derivatives with
% respect to the hyperparameters; this mode is used to fit the hyperparameters.
% If test data are given, then (marginal) Gaussian predictions are computed,

    
function [out1, out2] = kafbox_gpr(logtheta, covfunc, x, y, xstar)

% gpr - Gaussian process regression, with a named covariance function. Two
% modes are possible: training and prediction: if no test data are given, the
% function returns minus the log likelihood and its partial derivatives with
% respect to the hyperparameters; this mode is used to fit the hyperparameters.
% If test data are given, then (marginal) Gaussian predictions are computed,
% whose mean and variance are returned. Note that in cases where the covariance
% function has noise contributions, the variance returned in S2 is for noisy
% test targets; if you want the variance of the noise-free latent function, you
% must substract the noise variance.
%
% usage: [nlml dnlml] = gpr(logtheta, covfunc, x, y)
%    or: [mu S2]  = gpr(logtheta, covfunc, x, y, xstar)
%
% where:
%
%   logtheta is a (column) vector of log hyperparameters
%   covfunc  is the covariance function
%   x        is a n by D matrix of training inputs
%   y        is a (column) vector (of size n) of targets
%   xstar    is a nn by D matrix of test inputs
%   nlml     is the returned value of the negative log marginal likelihood
%   dnlml    is a (column) vector of partial derivatives of the negative
%                 log marginal likelihood wrt each log hyperparameter
%   mu       is a (column) vector (of size nn) of prediced means
%   S2       is a (column) vector (of size nn) of predicted variances
%
% For more help on covariance functions, see "help covFunctions".
%
% (C) copyright 2006 by Carl Edward Rasmussen (2006-03-20).

if ischar(covfunc), covfunc = cellstr(covfunc); end