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: ADEM_mountaincar_loss.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function ADEM_mountaincar_loss
% This demo re-visits the mountain car problem to show that adaptive
% (desired) behaviour can be prescribed in terms of loss-functions (i.e.
% reward functions of state-space).
% It exploits the fact that under the free-energy formulation, loss is
% divergence. This means that priors can be used to make certain parts of
% state-space costly (i.e. with high divergence) and others rewarding (low
% divergence). Active inference under these priors will lead to sampling of
% low cost states and (apparent) attractiveness of those states.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
% generative process (mountain car terrain)
%==========================================================================                        % switch for demo
P       = struct;
M       = struct;
G       = struct;
DEMO    = 0;
 
% range of position for later plotting
%--------------------------------------------------------------------------
dx      = 1/64;
x       = linspace(-2,2,1/dx);
xx      = x.^2;
 
% parameters of generative process
%--------------------------------------------------------------------------
P.a     = 0;
P.b     = [0 0];
P.c     = [0 0 0 0];
P.d     = 0;                                % action off
 
% level 1
%--------------------------------------------------------------------------
G(1).x  = [0; 0];
G(1).f  = inline('spm_fx_mountaincar(x,v,a,P)/2','x','v','a','P');
G(1).g  = inline('x','x','v','a','P');
G(1).pE = P;
G(1).V  = exp(16);                          % error precision
G(1).W  = exp(16);                          % error precision
G(1).U  = exp(5);                           % error precision
 
% level 2
%--------------------------------------------------------------------------
G(2).a  = 0;                                % action
G(2).v  = 0;                                % inputs
G(2).V  = exp(16);
G       = spm_ADEM_M_set(G);
 

% generative model
%==========================================================================
clear P
M(1).E.n = 4;
M(1).E.d = 2;
 
% parameters and equations of motion
%--------------------------------------------------------------------------
P.x     =  0;
P.k     =  1;
P.p     =  -1/2;
P.q     =  0;
 
% level 1
%--------------------------------------------------------------------------
M(1).x  = [0; 0];
M(1).f  = inline('spm_mc_fx(x,v,P)/2','x','v','P');
M(1).g  = inline('x','x','v','P');
M(1).pE = P;
M(1).V  = exp(8);                           % error precision
M(1).W  = exp(8);                           % error precision
 
% level 2
%--------------------------------------------------------------------------
M(2).v  = 0;                                % inputs
M(2).V  = exp(16);
M       = spm_DEM_M_set(M);
 
 
% learn gradients with a flat loss-functions (priors on divergence)
%==========================================================================
DEM.U   = sparse(128,1);
DEM.C   = sparse(128,1);
DEM.G   = G;
DEM.M   = M;
 
if DEMO
    
    % enable learning by relaxing priors on parameters
    %----------------------------------------------------------------------
    DEM.M(1).pC = diag([ones(1,2) zeros(1,2)])/128;
    
    % initialise states (randomly) and integrate

    
function ADEM_mountaincar_loss
% This demo re-visits the mountain car problem to show that adaptive
% (desired) behaviour can be prescribed in terms of loss-functions (i.e.
% reward functions of state-space).
% It exploits the fact that under the free-energy formulation, loss is
% divergence. This means that priors can be used to make certain parts of
% state-space costly (i.e. with high divergence) and others rewarding (low
% divergence). Active inference under these priors will lead to sampling of
% low cost states and (apparent) attractiveness of those states.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
% generative process (mountain car terrain)
%==========================================================================                        % switch for demo
P       = struct;
M       = struct;
G       = struct;
DEMO    = 0;
 
% range of position for later plotting
%--------------------------------------------------------------------------
dx      = 1/64;
x       = linspace(-2,2,1/dx);
xx      = x.^2;
 
% parameters of generative process
%--------------------------------------------------------------------------
P.a     = 0;
P.b     = [0 0];
P.c     = [0 0 0 0];
P.d     = 0;                                % action off
 
% level 1
%--------------------------------------------------------------------------
G(1).x  = [0; 0];
G(1).f  = inline('spm_fx_mountaincar(x,v,a,P)/2','x','v','a','P');
G(1).g  = inline('x','x','v','a','P');
G(1).pE = P;
G(1).V  = exp(16);                          % error precision
G(1).W  = exp(16);                          % error precision
G(1).U  = exp(5);                           % error precision
 
% level 2
%--------------------------------------------------------------------------
G(2).a  = 0;                                % action
G(2).v  = 0;                                % inputs
G(2).V  = exp(16);
G       = spm_ADEM_M_set(G);
 

% generative model
%==========================================================================
clear P
M(1).E.n = 4;
M(1).E.d = 2;
 
% parameters and equations of motion
%--------------------------------------------------------------------------
P.x     =  0;
P.k     =  1;
P.p     =  -1/2;
P.q     =  0;
 
% level 1
%--------------------------------------------------------------------------
M(1).x  = [0; 0];
M(1).f  = inline('spm_mc_fx(x,v,P)/2','x','v','P');
M(1).g  = inline('x','x','v','P');
M(1).pE = P;
M(1).V  = exp(8);                           % error precision
M(1).W  = exp(8);                           % error precision
 
% level 2
%--------------------------------------------------------------------------
M(2).v  = 0;                                % inputs
M(2).V  = exp(16);
M       = spm_DEM_M_set(M);
 
 
% learn gradients with a flat loss-functions (priors on divergence)
%==========================================================================
DEM.U   = sparse(128,1);
DEM.C   = sparse(128,1);
DEM.G   = G;
DEM.M   = M;
 
if DEMO
    
    % enable learning by relaxing priors on parameters
    %----------------------------------------------------------------------
    DEM.M(1).pC = diag([ones(1,2) zeros(1,2)])/128;
    
    % initialise states (randomly) and integrate
    %----------------------------------------------------------------------
    for i = 1:8
        DEM.G(1).x  = 4*rand(2,1) - 2;
        DEM.M(1).x  = DEM.G(1).x;
        
        DEM         = spm_ADEM(DEM);
        DEM.M(1).pE = DEM.qP.P{1};
    end