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: bspm_clinical_segment_normalize.m
    path of file: ./repos/bspm/prepoc
    
    the code of the file until where you have to start completion: function matlabbatch = bspm_clinical_segment_normalize(t1, lesion, varargin)
% BSPM_CLINICAL_SEGMENT_NORMALIZE
%
%   USAGE: matlabbatch = bspm_clinical_segment_normalize(t1, lesion, varargin)
% ____________________________________________________________________________
% REQUIRED ARGUMENTS
%   t1:       T1-weighted image filename(s)
%   lesion:   lesion map(s) [registered with t1 image(s)]
% ____________________________________________________________________________
% OPTIONAL ARGUMENTS (name-value pairs)
%   template:           template to normalize to ('older' or 'younger')
%   cleanuplevel:       tissue cleanup (0=none, 1=light, 2=thorough)
%   voxelsize:          resolution for resampling normalized images
%   boundingbox:        bounding box for normalized images
%   skullstripthresh:   prob threshold for skull stripping
%   delintermediate:    delete intermediate images (0 or 1)

    
function matlabbatch = bspm_clinical_segment_normalize(t1, lesion, varargin)
% BSPM_CLINICAL_SEGMENT_NORMALIZE
%
%   USAGE: matlabbatch = bspm_clinical_segment_normalize(t1, lesion, varargin)
% ____________________________________________________________________________
% REQUIRED ARGUMENTS
%   t1:       T1-weighted image filename(s)
%   lesion:   lesion map(s) [registered with t1 image(s)]
% ____________________________________________________________________________
% OPTIONAL ARGUMENTS (name-value pairs)
%   template:           template to normalize to ('older' or 'younger')
%   cleanuplevel:       tissue cleanup (0=none, 1=light, 2=thorough)
%   voxelsize:          resolution for resampling normalized images
%   boundingbox:        bounding box for normalized images
%   skullstripthresh:   prob threshold for skull stripping
%   delintermediate:    delete intermediate images (0 or 1)
%   enantiomorphic:     enantiomorphic normalization (see below)
%   autosetorigin:      attempt to auto-orient to ac/pc origin
% ____________________________________________________________________________
% If you choose 'Enantiomorphic normalization' the lesion will be replaced
% by tissue from the intact hemisphere, if you set this to false a
% traditional lesion-masked normalization will occur. In general
% enantiomorphic is better for large unilateral lesions, lesion masking is
% required for bilateral lesions and these methods perform similarly for
% smaller lesions (see Nachev et al., 2008).
% 

% ---------------------------------- Copyright (C) 2015 ----------------------------------
%	Author: Bob Spunt
%	Affilitation: Caltech
%	Email: spunt@caltech.edu
%
%	$Revision Date: May_26_2015
def = { 'template',             'older'                     , ...
        'cleanuplevel',         2                           , ...
        'voxelsize',            [1 1 1]                     , ...
        'boundingbox',          [-78 -112 -50; 78 76 85]    , ...
        'skullstripthresh'      0.005                       , ...                                                                                         
        'delintermediate',      1                           , ...
        'enantiomorphic',       1                           , ...
        'autosetorigin',        1                           , ...
        };
vals = setargs(def, varargin);
if nargin < 2, mfile_showhelp; fprintf('\t= DEFAULT SETTINGS =\n'); disp(vals); return; end