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_mfx.m
    path of file: ./repos/spm12
    
    the code of the file until where you have to start completion: function [SPM] = spm_mfx(SPM,c)

    
function [SPM] = spm_mfx(SPM,c)
% Convert a 1st-level design specification into a MFX specification
% FORMAT [SPM] = spm_mfx(SPM,c)
% SPM {in} - design and estimation structure after a 1st-level analysis
% c        - contrast used to define 2nd level design matrix. If this is
%            not specified spm_mfx will (1) suggest the ones(n,1) contrast
%            where n is the number of sessions/subjects, (2) call
%            spm_conman to allow this contrast to be modified interactively
%
%            Note: the specification of a contrast that is not ones(n,1) allows, 
%            for example, specified sessions/subjects to be ignored.
%            
% SPM {out} is saved in fullfile(SPM.swd,'mfx','SPM.mat')
%
% spm_mfx takes the SPM.mat of a 1st-level estimation of a repeated-measure
% multi-session study and produces the SPM design specification for a
% full mixed-effects (MFX) analysis. The 1st-level design (X1) must have
% the same number of parameters for each session. These are assumed to
% represent session-specific realisations of 2nd-level effects.
%
% spm_mfx prompts for a 2nd-level design matrix (X2) in the form of an
% F-contrast. This is expanded using the Kronecker tensor product to
% model the effects of each 2nd-level parameter separately. A new
% SPM.mat structure is saved in a subdirectory of the 1st-level results
% directory and can be estimated in the usual way. 2nd-level contrasts
% can then be used to test specific hypotheses at the 2nd-level in terms
% of compounds of 1st-level parameters specified by X2 (e.g. their
% mean).
%
% spm_mfx is a full mixed effects analysis in the sense that it allows
% for unbalanced designs at the 1st-level and different 1st-level error
% covariances. Operationally, ReML estimates of the 1st and 2nd-level
% covariance components are computed by projecting the 2nd-level effects
% down to the 1st-level and partitioning the covariance of the data in
% observation space. The 2nd-level parameter estimates are then computed
% as linear mixtures of the 1st-level estimates, using the appropriate
% non-sphericity. This non-sphericity is a mixture of 1st- and 2nd-level
% components that rend