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_MDP_factor_graph.m
    path of file: ./repos/spm12/toolbox/DEM
    
    the code of the file until where you have to start completion: function spm_MDP_factor_graph(MDP)
% Draws a factor graph corresponding to MDP
% FORMAT spm_MDP_factor_graph(MDP)
%
% MDP.A{G}(O,N1,...,NF) - likelihood of O outcomes given hidden states
% MDP.B{F}(NF,NF,MF)    - transitions among states under MF control states
%
% This routine draws a simplified (normal style) factor graph  based upon
% the size of likelihood and prior probability matrices (and labels). The
% resulting graph can either be interpreted in terms of a factor graph with
% factors corresponding to white boxes. Alternatively, it can be

    
function spm_MDP_factor_graph(MDP)
% Draws a factor graph corresponding to MDP
% FORMAT spm_MDP_factor_graph(MDP)
%
% MDP.A{G}(O,N1,...,NF) - likelihood of O outcomes given hidden states
% MDP.B{F}(NF,NF,MF)    - transitions among states under MF control states
%
% This routine draws a simplified (normal style) factor graph  based upon
% the size of likelihood and prior probability matrices (and labels). The
% resulting graph can either be interpreted in terms of a factor graph with
% factors corresponding to white boxes. Alternatively, it can be
% interpreted as a graphical model with coloured boxes corresponding to
% random variables. The magenta boxes denote outcomes (at intermediate
% levels of deep models, if specified). The cyan boxes denote hidden states
% and the puce boxes represent policies. If a hidden state is controllable
% (i.e., has more than one control dependent probability transition matrix)
% it is labelled in blue (and the hidden states are shown as a stack of
% boxes to indicate they are conditioned on several policies). Key message
% passing is illustrated with three arrows, corresponding to ascending
% likelihoods, forward and backward messages (1, 2 and 3,respectively).
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging

% Karl Friston
% $Id: spm_MDP_factor_graph.m 7651 2019-08-03 12:35:15Z karl $


% deal with a sequence of trials
%==========================================================================
spm_figure('GetWin','Factor Graph'); clf


% options
%--------------------------------------------------------------------------
MDP = spm_MDP_check(MDP);

if isfield(MDP,'link')
    
    % deep model
    %----------------------------------------------------------------------
    [hg,af] = spm_MDP_factor_level(MDP,1/2);
    [ag,hf] = spm_MDP_factor_level(MDP.MDP,1/8);
    
    % link if levels
    %----------------------------------------------------------------------
    for g = 1:size(MDP.link,1)
        for f = 1:size(MDP.link,2)
            if ~isempty(MDP.link{g,f})
                pf = get(hg(f),'Position');
                pg = get(hf(g),'Position');
                annotation('line',[pg(1)+pg(3)/2 pf(1)+pf(3)/2],[pg(2)+pg(4) pf(2)]);
            end