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: spm_bireduce.m
    path of file: ./repos/spm12
    
    the code of the file until where you have to start completion: function [M0,M1,L1,L2] = spm_bireduce(M,P)
% reduction of a fully nonlinear MIMO system to Bilinear form
% FORMAT [M0,M1,L1,L2] = spm_bireduce(M,P);
%
% M   - model specification structure
% Required fields:
%   M.f   - dx/dt    = f(x,u,P,M)                 {function string or m-file}
%   M.g   - y(t)     = g(x,u,P,M)                 {function string or m-file}
%   M.bi  - bilinear form [M0,M1,L1,L2] = bi(M,P) {function string or m-file}
%   M.m   - m inputs
%   M.n   - n states
%   M.l   - l outputs
%   M.x   - (n x 1) = x(0) = expansion point: defaults to x = 0;
%   M.u   - (m x 1) = u    = expansion point: defaults to u = 0;
%
%   M.D   - delay operator df/dx -> D*df/dx [optional]
%
% P   - model parameters
%
% A Bilinear approximation is returned where the states are
%
%        q(t) = [1; x(t) - x(0)]
%
%___________________________________________________________________________
% Returns Matrix operators for the Bilinear approximation to the MIMO
% system described by
%
%       dx/dt = f(x,u,P)
%        y(t) = g(x,u,P)
%
% evaluated at x(0) = x and u = 0
%
%       dq/dt = M0*q + u(1)*M1{1}*q + u(2)*M1{2}*q + ....
%        y(i) = L1(i,:)*q + q'*L2{i}*q/2;
%
%--------------------------------------------------------------------------
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_bireduce.m 6856 2016-08-10 17:55:05Z karl $


% set up
%==========================================================================

% create inline functions
%--------------------------------------------------------------------------
try
    funx = fcnchk(M.f,'x','u','P','M');

    
function [M0,M1,L1,L2] = spm_bireduce(M,P)
% reduction of a fully nonlinear MIMO system to Bilinear form
% FORMAT [M0,M1,L1,L2] = spm_bireduce(M,P);
%
% M   - model specification structure
% Required fields:
%   M.f   - dx/dt    = f(x,u,P,M)                 {function string or m-file}
%   M.g   - y(t)     = g(x,u,P,M)                 {function string or m-file}
%   M.bi  - bilinear form [M0,M1,L1,L2] = bi(M,P) {function string or m-file}
%   M.m   - m inputs
%   M.n   - n states
%   M.l   - l outputs
%   M.x   - (n x 1) = x(0) = expansion point: defaults to x = 0;
%   M.u   - (m x 1) = u    = expansion point: defaults to u = 0;
%
%   M.D   - delay operator df/dx -> D*df/dx [optional]
%
% P   - model parameters
%
% A Bilinear approximation is returned where the states are
%
%        q(t) = [1; x(t) - x(0)]
%
%___________________________________________________________________________
% Returns Matrix operators for the Bilinear approximation to the MIMO
% system described by
%
%       dx/dt = f(x,u,P)
%        y(t) = g(x,u,P)
%
% evaluated at x(0) = x and u = 0
%
%       dq/dt = M0*q + u(1)*M1{1}*q + u(2)*M1{2}*q + ....
%        y(i) = L1(i,:)*q + q'*L2{i}*q/2;
%
%--------------------------------------------------------------------------
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_bireduce.m 6856 2016-08-10 17:55:05Z karl $


% set up
%==========================================================================

% create inline functions
%--------------------------------------------------------------------------
try
    funx = fcnchk(M.f,'x','u','P','M');
catch
    M.f  = inline('sparse(0,1)','x','u','P','M');
    M.n  = 0;
    M.x  = sparse(0,0);
    funx = fcnchk(M.f,'x','u','P','M');
end