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: covEye.m
    path of file: ./repos/bads/gpml-matlab-v3.6-2015-07-07/cov
    
    the code of the file until where you have to start completion: function K = covEye(hyp, x, z, i)

% Independent covariance function, i.e. "white noise", with unit variance.
% The covariance function is specified as:
%
% k(x^p,x^q) = \delta(p,q)
%
% \delta(p,q) is a Kronecker delta function which is 1 iff p=q and zero
% otherwise in mode 1).
% In cross covariance mode 2) two data points x_p and z_q a
    
function is specified as:
%
% k(x^p,x^q) = \delta(p,q)
%
% \delta(p,q) is a Kronecker delta function which is 1 iff p=q and zero
% otherwise in mode 1).
% In cross covariance mode 2) two data points x_p and z_q are considered equal
% if their difference norm |x_p-z_q| is less than eps, the machine precision.
% The hyperparameters are:
%
% hyp = [ ]
%
% For more help on design of covariance functions, try "help covFunctions".
%
% Copyright (c) by Hannes Nickisch, 2014-09-05.
%
% See also COVFUNCTIONS.M.

tol = eps;   % threshold on the norm when two vectors are considered to be equal
if nargin<2, K = '0'; return; end                  % report number of parameters
if nargin<3, z = []; end                                   % make sure, z exists
dg = strcmp(z,'diag');                                          % determine mode

n = size(x,1);

% precompute raw
if dg                                                               % vector kxx
  K = ones(n,1);
else
  if isempty(z)                                           % symmetric matrix Kxx
    K = eye(n);
  else                                                   % cross covariances Kxz
    K = double(sq_dist(x',z')<tol*tol);
  end