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_eeg_invert_classic_mix.m
    path of file: ./repos/bspm/thirdparty/spm12
    
    the code of the file until where you have to start completion: function [D] = spm_eeg_invert_classic_mix(D,val,Qpriors,surfind,ugainfiles)
%function [D] = spm_eeg_invert_classic_mix(D,val,Qpriors,surfind,ugainfiles)
%%
% ReML inversion of multiple posterior current variances from previous
% iterations spm_eeg_invert_classic or spm_eeg_invert
% ReML estimation of regularisation hyperparameters using the
% spatiotemporal hierarchy implicit in EEG/MEG data
%
% D contains the data and inversion parameters (see
% spm_eeg_invert.m/spm_eeg_invert_classic.m)
% val the inversion index 
% Qpriors is N solutions of rows by Nd variance estimates
% surfind is N solutions long and contains indices into ugainfiles to these priors with different lead field structures
%% ugainfiles are the SPMgain matrices for the different surfaces
%
% Output D will have a solution which is optimal REML mixture of Qpriors
%
% Created by:   
%               Gareth Barnes - g.barnes@ucl.ac.uk
%
% This version is for single subject single modality analysis and therefore
% contains none of the associated scaling factors.
%
% $Id: spm_eeg_invert_classic_mix.m 6077 2014-06-30 16:55:03Z spm $


    
function [D] = spm_eeg_invert_classic_mix(D,val,Qpriors,surfind,ugainfiles)
%function [D] = spm_eeg_invert_classic_mix(D,val,Qpriors,surfind,ugainfiles)
%%
% ReML inversion of multiple posterior current variances from previous
% iterations spm_eeg_invert_classic or spm_eeg_invert
% ReML estimation of regularisation hyperparameters using the
% spatiotemporal hierarchy implicit in EEG/MEG data
%
% D contains the data and inversion parameters (see
% spm_eeg_invert.m/spm_eeg_invert_classic.m)
% val the inversion index 
% Qpriors is N solutions of rows by Nd variance estimates
% surfind is N solutions long and contains indices into ugainfiles to these priors with different lead field structures
%% ugainfiles are the SPMgain matrices for the different surfaces
%
% Output D will have a solution which is optimal REML mixture of Qpriors
%
% Created by:   
%               Gareth Barnes - g.barnes@ucl.ac.uk
%
% This version is for single subject single modality analysis and therefore
% contains none of the associated scaling factors.
%
% $Id: spm_eeg_invert_classic_mix.m 6077 2014-06-30 16:55:03Z spm $



Nl = length(D);



if Nl>1,
    error('function only defined for a single subject');
end