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: gp.m
    path of file: ./repos/hctsa/Toolboxes/gpml
    
    the code of the file until where you have to start completion: function [varargout] = gp(hyp, inf, mean, cov, lik, x, y, xs, ys)
% Gaussian Process inference and prediction. The gp function provides a
% flexible framework for Bayesian inference and prediction with Gaussian
% processes for scalar targets, i.e. both regression and binary
% classification. The prior is Gaussian process, defined through specification
% of its mean and covariance function. The likelihood function is also
% specified. Both the prior and the likelihood may have hyperparameters
% associated with them.
%
% Two modes are possible: training or prediction: if no test cases are
% supplied, then the negative log marginal likelihood and its partial
% derivatives w.r.t. the hyperparameters is computed; this mode is used to fit
% the hyperparameters. If test cases are given, then the test set predictive
% probabilities are returned. Usage:
%
%   training: [nlZ dnlZ          ] = gp(hyp, inf, mean, cov, lik, x, y);
% prediction: [ymu ys2 fmu fs2   ] = gp(hyp, inf, mean, cov, lik, x, y, xs);
%         or: [ymu ys2 fmu fs2 lp] = gp(hyp, inf, mean, cov, lik, x, y, xs, ys);
%
% where:
%
%   hyp      struct of column vectors of mean/cov/lik hyperparameters
%   inf      function specifying the inference method 
%   mean     prior mean function
%   cov      prior covariance function
%   lik      likelihood function
%   x        n by D matrix of training inputs
%   y        column vector of length n of training targets
%   xs       ns by D matrix of test inputs
%   ys       column vector of length nn of test targets
%
%   nlZ      returned value of the negative log marginal likelihood
%   dnlZ     struct of column vectors of partial derivatives of the negative
%               log marginal likelihood w.r.t. mean/cov/lik hyperparameters
%   ymu      column vector (of length ns) of predictive output means
%   ys2      column vector (of length ns) of predictive output variances
%   fmu      column vector (of length ns) of predictive latent means
%   fs2      column vector (of length ns) of predictive latent variances

    
function [varargout] = gp(hyp, inf, mean, cov, lik, x, y, xs, ys)
% Gaussian Process inference and prediction. The gp function provides a
% flexible framework for Bayesian inference and prediction with Gaussian
% processes for scalar targets, i.e. both regression and binary
% classification. The prior is Gaussian process, defined through specification
% of its mean and covariance function. The likelihood function is also
% specified. Both the prior and the likelihood may have hyperparameters
% associated with them.
%
% Two modes are possible: training or prediction: if no test cases are
% supplied, then the negative log marginal likelihood and its partial
% derivatives w.r.t. the hyperparameters is computed; this mode is used to fit
% the hyperparameters. If test cases are given, then the test set predictive
% probabilities are returned. Usage:
%
%   training: [nlZ dnlZ          ] = gp(hyp, inf, mean, cov, lik, x, y);
% prediction: [ymu ys2 fmu fs2   ] = gp(hyp, inf, mean, cov, lik, x, y, xs);
%         or: [ymu ys2 fmu fs2 lp] = gp(hyp, inf, mean, cov, lik, x, y, xs, ys);
%
% where:
%
%   hyp      struct of column vectors of mean/cov/lik hyperparameters
%   inf      function specifying the inference method 
%   mean     prior mean function
%   cov      prior covariance function
%   lik      likelihood function
%   x        n by D matrix of training inputs
%   y        column vector of length n of training targets
%   xs       ns by D matrix of test inputs
%   ys       column vector of length nn of test targets
%
%   nlZ      returned value of the negative log marginal likelihood
%   dnlZ     struct of column vectors of partial derivatives of the negative
%               log marginal likelihood w.r.t. mean/cov/lik hyperparameters
%   ymu      column vector (of length ns) of predictive output means
%   ys2      column vector (of length ns) of predictive output variances
%   fmu      column vector (of length ns) of predictive latent means
%   fs2      column vector (of length ns) of predictive latent variances
%   lp       column vector (of length ns) of log predictive probabilities
%
%   post     struct representation of the (approximate) posterior
%            3rd output in training mode or 6th output in prediction mode
%            can be reused in prediction mode gp(.., cov, lik, x, post, xs,..)
%
% See also infMethods.m, meanFunctions.m, covFunctions.m, likFunctions.m.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2014-12-08.
%                                      File automatically generated using noweb.
if nargin<7 || nargin>9
  disp('Usage: [nlZ dnlZ          ] = gp(hyp, inf, mean, cov, lik, x, y);')
  disp('   or: [ymu ys2 fmu fs2   ] = gp(hyp, inf, mean, cov, lik, x, y, xs);')
  disp('   or: [ymu ys2 fmu fs2 lp] = gp(hyp, inf, mean, cov, lik, x, y, xs, ys);')
  return
end