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_fx_mountaincar.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8/toolbox/DEM
    
    the code of the file until where you have to start completion: function f = spm_fx_mountaincar(x,v,varargin)
% state equations for mountain car problem
% FORMAT f = spm_fx_mountaincar(x,v,P)
% FORMAT f = spm_fx_mountaincar(x,v,a,P)
% FORMAT f = spm_fx_mountaincar(x,v,P,M)
% x    - [x, x']
% v    - exogenous force
% a    - action
%
% P.a  - 0th order coefficients of force
% P.b  - 1st order coefficients of force
% P.c  – 2nd order coefficients of force
% P.d  - action coefficient
%
% M    - model structure
%
% f    - flow dx/dt
%
% see:
% Gaussian Processes in Reinforcement Learning
% Carl Edward Rasmussen and Malte Kuss
% Max Planck Institute for Biological Cybernetics
% Spemannstraße 38, 72076 T¨ubingen, Germany
% {carl,malte.kuss}@tuebingen.mpg.de
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_fx_mountaincar.m 4339 2011-06-02 13:04:49Z karl $
 
 
% determine controlled forces (a)
%==========================================================================
 
% spm_fx_mountaincar(x,v,P) - recognition model (no action)
%--------------------------------------------------------------------------
if length(varargin) == 1
    
    a    = 0;
    P    = varargin{1};
    
elseif length(varargin) > 1
    
    % spm_fx_mountaincar(x,v,P,M) - generative model (no action)
    %----------------------------------------------------------------------
    try
        varargin{2}.f;
        varargin{2}.g;
        a    = 0;
        P    = varargin{1};
 
    % spm_fx_mountaincar(x,v,a,P) - generative model (action)
    %----------------------------------------------------------------------

    
function f = spm_fx_mountaincar(x,v,varargin)
% state equations for mountain car problem
% FORMAT f = spm_fx_mountaincar(x,v,P)
% FORMAT f = spm_fx_mountaincar(x,v,a,P)
% FORMAT f = spm_fx_mountaincar(x,v,P,M)
% x    - [x, x']
% v    - exogenous force
% a    - action
%
% P.a  - 0th order coefficients of force
% P.b  - 1st order coefficients of force
% P.c  – 2nd order coefficients of force
% P.d  - action coefficient
%
% M    - model structure
%
% f    - flow dx/dt
%
% see:
% Gaussian Processes in Reinforcement Learning
% Carl Edward Rasmussen and Malte Kuss
% Max Planck Institute for Biological Cybernetics
% Spemannstraße 38, 72076 T¨ubingen, Germany
% {carl,malte.kuss}@tuebingen.mpg.de
%__________________________________________________________________________
% Copyright (C) 2005 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_fx_mountaincar.m 4339 2011-06-02 13:04:49Z karl $
 
 
% determine controlled forces (a)
%==========================================================================
 
% spm_fx_mountaincar(x,v,P) - recognition model (no action)
%--------------------------------------------------------------------------
if length(varargin) == 1
    
    a    = 0;
    P    = varargin{1};
    
elseif length(varargin) > 1
    
    % spm_fx_mountaincar(x,v,P,M) - generative model (no action)
    %----------------------------------------------------------------------
    try
        varargin{2}.f;
        varargin{2}.g;
        a    = 0;
        P    = varargin{1};
 
    % spm_fx_mountaincar(x,v,a,P) - generative model (action)
    %----------------------------------------------------------------------
    catch
        a    = varargin{1};
        P    = varargin{2};
    end