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: g_fullDCM4fmri.m
    path of file: ./repos/VBA-toolbox/demos/_models
    
    the code of the file until where you have to start completion: function [gx,dG_dX,dG_dPhi] = g_fullDCM4fmri(Xt,Phi,ut,inG)
% full DCM observation function (embedding dynamic Ballon model)

% function [gx,dG_dX,dG_dPhi] = g_fullDCM4fmri(Xt,Phi,ut,inG)
% This function evaluates the observation function of the neuronal level in
% DCM for fMRI. Note that this "generalized" observation function includes
% the HRF convolution model (balloon model).


persistent xh dxhdx dxhdp ph ti ko
% These variables are described bellow:
%   - xh{i}: current hemodynamic states for region i.
%   - dxhdx{i}: derivatives of the hemodynamic states of region i w.r.t.
%   neuronal state of region i.

    
function [gx,dG_dX,dG_dPhi] = g_fullDCM4fmri(Xt,Phi,ut,inG)
% full DCM observation function (embedding dynamic Ballon model)

% function [gx,dG_dX,dG_dPhi] = g_fullDCM4fmri(Xt,Phi,ut,inG)
% This function evaluates the observation function of the neuronal level in
% DCM for fMRI. Note that this "generalized" observation function includes
% the HRF convolution model (balloon model).


persistent xh dxhdx dxhdp ph ti ko
% These variables are described bellow:
%   - xh{i}: current hemodynamic states for region i.
%   - dxhdx{i}: derivatives of the hemodynamic states of region i w.r.t.
%   neuronal state of region i.
%   - dxhdp{i}: derivatives of the hemodynamic states of region i w.r.t.
%   hemodynamic parameters of region i.
%   - ph: the last remembered hemodynamic parameters. This is only used
%   when this function is called from f_fullDCM4fmri.m. It is updated each
%   time this function is called outside f_fullDCM4fmri.m.
%   - ti: the index within the time series. This is used to reinitialize
%   the persistent variables.
% These are reinitialized at the beginning of the time series.

try % just to get lastly updated phi
    
    inG.getPhi;
    ph = Phi;
    
catch

    n = size(Xt,1);

    % Check whether the system is at initial state
    if isempty(xh)
        ti = 0;
        xh = kron(ones(n,1),[0;0;0;0]);
        dxhdx = zeros(n,n*4);
        dxhdp = zeros(size(inG.Phi,1),n*4);
        ko = kron(eye(n),[1 0 0 0]);
    end