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_log_evidence.m
    path of file: ./repos/spm
    
    the code of the file until where you have to start completion: function [F,sE,sC] = spm_log_evidence(varargin)
% Return the log-evidence of a reduced model (under Laplace approximation)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC,rE,rC)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC,priorfun,varargin)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC)
%
% qE,qC    - posterior expectation and covariance of full model
% pE,pC    - prior expectation and covariance of full model
% rE,rC    - prior expectation and covariance of reduced model
% or 
% priorfun - inline function that returns prior moments
%            {rE rC} = priorfun(varargin{:})
%
% or (if omitted) rE = 0 and rC = 0;
%
% F        - reduced log-evidence: ln p(y|reduced model) - ln p(y|full model)
% [sE,sC]  - posterior expectation and covariance of reduced model
%
%--------------------------------------------------------------------------
% This routine assumes the reduced model is nested within a full model and
% that the posteriors (and priors) are Gaussian. Nested here means that the
% prior precision of the reduced model, minus the prior precision of the
% full model is positive definite. We additionally assume that the prior
% means are unchanged. The two input argument formats are for use with
% spm_argmax.
%
% See also: spm_log_evidence_reduce
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2005-2022 Wellcome Centre for Human Neuroimaging


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

    
function [F,sE,sC] = spm_log_evidence(varargin)
% Return the log-evidence of a reduced model (under Laplace approximation)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC,rE,rC)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC,priorfun,varargin)
% FORMAT [F,sE,sC] = spm_log_evidence(qE,qC,pE,pC)
%
% qE,qC    - posterior expectation and covariance of full model
% pE,pC    - prior expectation and covariance of full model
% rE,rC    - prior expectation and covariance of reduced model
% or 
% priorfun - inline function that returns prior moments
%            {rE rC} = priorfun(varargin{:})
%
% or (if omitted) rE = 0 and rC = 0;
%
% F        - reduced log-evidence: ln p(y|reduced model) - ln p(y|full model)
% [sE,sC]  - posterior expectation and covariance of reduced model
%
%--------------------------------------------------------------------------
% This routine assumes the reduced model is nested within a full model and
% that the posteriors (and priors) are Gaussian. Nested here means that the
% prior precision of the reduced model, minus the prior precision of the
% full model is positive definite. We additionally assume that the prior
% means are unchanged. The two input argument formats are for use with
% spm_argmax.
%
% See also: spm_log_evidence_reduce
%__________________________________________________________________________
 
% Karl Friston
% Copyright (C) 2005-2022 Wellcome Centre for Human Neuroimaging


% Compute reduced log-evidence
%==========================================================================
 
% check to see if priors are specified by a function
%--------------------------------------------------------------------------
qE = varargin{1};
qC = varargin{2};
pE = varargin{3};
pC = varargin{4};

try
    priors = varargin{5}(varargin{6:end