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_dcm_optimise.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function [varargout] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
% Optimise the priors of a model (under Laplace approximation)
% FORMAT [rE,rC] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
%
% qE,qC    - posterior expectation and covariance of model
% pE,pC    - prior expectation and covariance of model
% priorfun - inline function that returns priors
%            {rE rC} = priorfun(varargin{:})
%
% rE,rC    - optimal priors defining a reduced model
%
%--------------------------------------------------------------------------
% This routine optimizes the prior covariance on the free parameters of any 
% model (DCM) under the Laplace approximation. In other words, it assumes 
% that the prior means are fixed and will maximise model evidence with 
% respect to the hyperparameters of a function that returns the prior 
% covariance. This optimization uses the reduced free-energy, based upon 
% the posterior and prior densities of the full model supplied. If the 
% prior covariance function is not specified, this routine will assume a 
% simple diagonal form with a single hyperparameter. In principle, this 
% routine can be used in a flexible and powerful way to emulate 
% hierarchical modeling by using suitable prior covariance functions with 
% unknown hyperparameters. The outputs are the prior moments (mean and 
% covariance) of the optimum model.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


    
function [varargout] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
% Optimise the priors of a model (under Laplace approximation)
% FORMAT [rE,rC] = spm_dcm_optimise(qE,qC,pE,pC,priorfun,varargin)
%
% qE,qC    - posterior expectation and covariance of model
% pE,pC    - prior expectation and covariance of model
% priorfun - inline function that returns priors
%            {rE rC} = priorfun(varargin{:})
%
% rE,rC    - optimal priors defining a reduced model
%
%--------------------------------------------------------------------------
% This routine optimizes the prior covariance on the free parameters of any 
% model (DCM) under the Laplace approximation. In other words, it assumes 
% that the prior means are fixed and will maximise model evidence with 
% respect to the hyperparameters of a function that returns the prior 
% covariance. This optimization uses the reduced free-energy, based upon 
% the posterior and prior densities of the full model supplied. If the 
% prior covariance function is not specified, this routine will assume a 
% simple diagonal form with a single hyperparameter. In principle, this 
% routine can be used in a flexible and powerful way to emulate 
% hierarchical modeling by using suitable prior covariance functions with 
% unknown hyperparameters. The outputs are the prior moments (mean and 
% covariance) of the optimum model.
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging


% Compute reduced log-evidence
%==========================================================================

% default prior function
%--------------------------------------------------------------------------
if nargin == 4
    priorfun    = inline('{v2, diag(exp(v1))}','v1','v2');
    varargin{1} = log(diag(pC));
    varargin{2} = pE;
end