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: gplite_plot.m
    path of file: ./repos/vbmc/gplite
    
    the code of the file until where you have to start completion: function gplite_plot(gp,x0,lb,ub,sigma,qflag)
%GPLITE_PLOT Profile plot of GP for lite Gaussian process regression.
%   GPLITE_PLOT(GP,X0) plot the Gaussian process GP profile centered around 
%   a given point X0. The plot is a D-by-D panel matrix, in which panels on 
%   the diagonal show the profile of the GP prediction (mean and +/- 1 SD) 
%   by varying one dimension at a time, whereas off-diagonal panels show 
%   2-D contour plots of the GP mean and standard deviation (respectively, 
%   above and below diagonal). In each panel, black lines indicate the 
%   location of the reference point X0. X0 can be a vector, or 'max' or 
%   'min', in which case the maximum (resp., minimum) of the GP training 
%   input is used as reference. If X0 is left empty, the default is 'max'.
%
%   GPLITE_PLOT(GP,X0,DELTAY) for each dimension, chooses the range of the 
%   plot such that the plotted predictive GP mean approximately brackets 
%   [Y0-DELTAY,Y0+DELTAY], where Y0 is the predictive GP mean at X0.
%
%   GPLITE_PLOT(GP,X0,LB,UB) for each dimension, sets lower bounds LB and
%   upper bounds UB for the range of the plots.
%
%   See also GPLITE_PRED, GPLITE_TRAIN.

% Luigi Acerbi 2019

if nargin < 2; x0 = []; end
if nargin < 3; lb = []; end
if nargin < 4; ub = []; end
if nargin < 5; sigma = []; end
if nargin < 6 || isempty(qflag); qflag = false; end


    
function gplite_plot(gp,x0,lb,ub,sigma,qflag)
%GPLITE_PLOT Profile plot of GP for lite Gaussian process regression.
%   GPLITE_PLOT(GP,X0) plot the Gaussian process GP profile centered around 
%   a given point X0. The plot is a D-by-D panel matrix, in which panels on 
%   the diagonal show the profile of the GP prediction (mean and +/- 1 SD) 
%   by varying one dimension at a time, whereas off-diagonal panels show 
%   2-D contour plots of the GP mean and standard deviation (respectively, 
%   above and below diagonal). In each panel, black lines indicate the 
%   location of the reference point X0. X0 can be a vector, or 'max' or 
%   'min', in which case the maximum (resp., minimum) of the GP training 
%   input is used as reference. If X0 is left empty, the default is 'max'.
%
%   GPLITE_PLOT(GP,X0,DELTAY) for each dimension, chooses the range of the 
%   plot such that the plotted predictive GP mean approximately brackets 
%   [Y0-DELTAY,Y0+DELTAY], where Y0 is the predictive GP mean at X0.
%
%   GPLITE_PLOT(GP,X0,LB,UB) for each dimension, sets lower bounds LB and
%   upper bounds UB for the range of the plots.
%
%   See also GPLITE_PRED, GPLITE_TRAIN.

% Luigi Acerbi 2019

if nargin < 2; x0 = []; end
if nargin < 3; lb = []; end
if nargin < 4; ub = []; end
if nargin < 5; sigma = []; end
if nargin < 6 || isempty(qflag); qflag = false; end

deltay = [];
if isscalar(lb) && isempty(ub)
    deltay = lb;
    lb = [];
end

[N,D] = size(gp.X);            % Number of training points and dimension
Ns = numel(gp.post);           % Hyperparameter samples
Nx = 100;                      % # grid points per visualization

% Loop over hyperparameter samples
ell = zeros(D,Ns);
for s = 1:Ns
    hyp = gp.post(s).hyp;
    ell(:,s) = exp(hyp(1:D));       % Extract length scales from HYP
end