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: mpcg.m
    path of file: ./repos/imtsl/libs/tensorlab
    
    the code of the file until where you have to start completion: function [x,flag,relres,iter] = mpcg(A,b,tol,maxit,M,~,x0)
%MPCG Modified preconditioned conjugate gradients method.
%   x = mpcg(A,b) attempts to solve the system of linear equations A*x = b
%   for x. The n-by-n coefficient matrix A must be symmetric and positive
%   definite. The column vector b must have length n. A can be a function
%   handle afun such that afun(x) returns A*x. MATLAB's pcg.m can in some
%   cases return x0 (which is often equal to 0) if it is the iterate with
%   the smallest residual. The only difference between mpcg and MATLAB's
%   pcg method is that this implementation returns the last iterate,
%   regardless of its residual.

    
function [x,flag,relres,iter] = mpcg(A,b,tol,maxit,M,~,x0)
%MPCG Modified preconditioned conjugate gradients method.
%   x = mpcg(A,b) attempts to solve the system of linear equations A*x = b
%   for x. The n-by-n coefficient matrix A must be symmetric and positive
%   definite. The column vector b must have length n. A can be a function
%   handle afun such that afun(x) returns A*x. MATLAB's pcg.m can in some
%   cases return x0 (which is often equal to 0) if it is the iterate with
%   the smallest residual. The only difference between mpcg and MATLAB's
%   pcg method is that this implementation returns the last iterate,
%   regardless of its residual.
%
%   mpcg(A,b,tol) specifies the tolerance of the method. If tol is [], then
%   mpcg uses the default, 1e-6.
%
%   mpcg(A,b,tol,maxit) specifies the maximum number of iterations. If
%   maxit is [], then mpcg uses the default, min(n,20).
%
%   mpcg(A,b,tol,maxit,M) uses a symmetric positive definite preconditioner
%   M and effectively solve the system inv(M)*A*x = inv(M)*b for x. If M is
%   [] then mpcg applies no preconditioner. M can be a function handle mfun
%   such that mfun(x) returns M\x. If M is 'SSOR', then mpcg applies a
%   Symmetric Successive Over-Relaxation preconditioner.
%
%   mpcg(A,b,tol,maxit,M,[],x0) specifies the initial guess. If x0 is [],
%   then mpcg uses the default, an all-zero vector.
%
%   [x,flag,relres,iter] = mpcg(A,b,...) also returns a convergence flag,
%   which is 0 if mpcg converged to the desired tolerance within maxit
%   iterations and 1 otherwise, the relative residual norm(A*x-b)/norm(b)
%   and the number of iterations, respectively.
%
%   See also bicgstab, cgs, gmres, lsqsr, qmr.

%   Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be)
%            Marc Van Barel (Marc.VanBarel@cs.kuleuven.be)
%            Lieven De Lathauwer (Lieven.DeLathauwer@kuleuven-kulak.be)
%
%   References:
%   [1] J. Nocedal and S. J. Wright, "Numerical Optimization," Springer
%       Series in Operations Research, Springer, second ed., 2006.

% Check the options.
if nargin < 3 || isempty(tol), tol = 1e-6; end