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: covMaterniso.m
    path of file: ./repos/hctsa/Toolboxes/gpml/cov
    
    the code of the file until where you have to start completion: function K = covMaterniso(d, hyp, x, z, i)

% Matern covariance function with nu = d/2 and isotropic distance measure. For
% d=1 the function is also known as the exponential covariance function or the 
% Ornstein-Uhlenbeck covariance in 1d. The covariance function is:
%
%   k(x^p,x^q) = sf^2 * f( sqrt(d)*r ) * exp(-sqrt(d)*r)

    
function K = covMaterniso(d, hyp, x, z, i)

% Matern covariance function with nu = d/2 and isotropic distance measure. For
% d=1 the function is also known as the exponential covariance function or the 
% Ornstein-Uhlenbeck covariance in 1d. The covariance function is:
%
%   k(x^p,x^q) = sf^2 * f( sqrt(d)*r ) * exp(-sqrt(d)*r)
%
% with f(t)=1 for d=1, f(t)=1+t for d=3 and f(t)=1+t+tÂ²/3 for d=5.
% Here r is the distance sqrt((x^p-x^q)'*inv(P)*(x^p-x^q)), P is ell times
% the unit matrix and sf2 is the signal variance. The hyperparameters are:
%
% hyp = [ log(ell)
%         log(sf) ]
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-09-10.
%
% See also COVFUNCTIONS.M.

if nargin<3, K = '2'; return; end