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_ADEM_M_set.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function M = spm_ADEM_M_set(M)
% Set indices and perform checks on hierarchical action models
% FORMAT M = spm_ADEM_M_set(M)
%
% for each level (i); required fields
%
%   M(i).g  = y(t)  = g(x,v,a,P)    {inline function, string or m-file}
%   M(i).f  = dx/dt = f(x,v,a,P)    {inline function, string or m-file}
%
% and
%
%   M(i).m  = number of inputs v(i + 1);
%   M(i).n  = number of states x(i);
%   M(i).l  = number of output v(i);
%   M(i).k  = number of action a(i);
%
% or
%
%   M(i).x  = hidden states;
%   M(i).v  = causal states;
%   M(i).a  = action states;
%
% for each level (i); optional fields

    
function M = spm_ADEM_M_set(M)
% Set indices and perform checks on hierarchical action models
% FORMAT M = spm_ADEM_M_set(M)
%
% for each level (i); required fields
%
%   M(i).g  = y(t)  = g(x,v,a,P)    {inline function, string or m-file}
%   M(i).f  = dx/dt = f(x,v,a,P)    {inline function, string or m-file}
%
% and
%
%   M(i).m  = number of inputs v(i + 1);
%   M(i).n  = number of states x(i);
%   M(i).l  = number of output v(i);
%   M(i).k  = number of action a(i);
%
% or
%
%   M(i).x  = hidden states;
%   M(i).v  = causal states;
%   M(i).a  = action states;
%
% for each level (i); optional fields
%
%   M(i).pE = prior expectation of p model-parameters
%   M(i).V  = precision (input noise)
%   M(i).W  = precision (state noise)
%   M(i).U  = precision (action)
%
%
% sets fields, checks internal consistency of model specification and sets
% estimation parameters.  If (V,W) are not specified infinite precision is
% assumed.
%--------------------------------------------------------------------------
%
%   M(1).E.s;     = smoothness (s.d. in time bins)
%   M(1).E.d;     = embedding order q(v)  (i.e., number of derivatives)
%   M(1).E.n;     = embedding order q(x)
%
% If the highest level involves any dynamic or static transformation
% of its inputs a further level is added with flat priors
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% order
%--------------------------------------------------------------------------
g      = length(M);
 
% set missing fields
%==========================================================================
 
% check for specification of hidden states
%--------------------------------------------------------------------------
if isfield(M,'f') && ~isfield(M,'n') && ~isfield(M,'x')
    msgbox('please specify hidden states or their number')
    error(' ')
end