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_DEM_qC.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [qP] = spm_DEM_qC(M)
% returns the conditional precision over hidden states
% FORMAT [qP] = spm_DEM_qC(M)
%
% M  - recognition  model
%   M(1).x    = Conditional expectation of hidden states
%   M(1).v    = Conditional expectation of causal states
%
% qP     - conditional precision, evaluated at M.x, M.v

    
function [qP] = spm_DEM_qC(M)
% returns the conditional precision over hidden states
% FORMAT [qP] = spm_DEM_qC(M)
%
% M  - recognition  model
%   M(1).x    = Conditional expectation of hidden states
%   M(1).v    = Conditional expectation of causal states
%
% qP     - conditional precision, evaluated at M.x, M.v
%__________________________________________________________________________
%
% see spm_DEM and spm_ADEM for details.
%
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging
 
 
% order parameters (d = n = 1 for static models) and checks
%==========================================================================
d    = M(1).E.d + 1;                   % embedding order of q(v)
n    = M(1).E.n + 1;                   % embedding order of q(x) (n >= d)
s    = M(1).E.s;                       % smoothness - s.d. of kernel (bins)
 
% number of states and parameters - recognition model
%--------------------------------------------------------------------------
nl   = size(M,2);                      % number of levels
nv   = sum(spm_vec(M.m));              % number of v (causal states)
nx   = sum(spm_vec(M.n));              % number of x (hidden states)
ny   = M(1).l;                         % number of y (inputs)
nc   = M(end