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_cfg_normalise.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8/config
    
    the code of the file until where you have to start completion: function normalise = spm_cfg_normalise
% SPM Configuration file
% automatically generated by the MATLABBATCH utility function GENCODE
%_______________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% $Id: spm_cfg_normalise.m 3804 2010-03-31 16:16:21Z ged $

rev = '$Rev: 3804 $';
% ---------------------------------------------------------------------
% source Source Image
% ---------------------------------------------------------------------
source         = cfg_files;
source.tag     = 'source';
source.name    = 'Source Image';
source.help    = {'The image that is warped to match the template(s).  The result is a set of warps, which can be applied to this image, or any other image that is in register with it.'};
source.filter = 'image';
source.ufilter = '.*';
source.num     = [1 1];
% ---------------------------------------------------------------------
% wtsrc Source Weighting Image
% ---------------------------------------------------------------------
wtsrc         = cfg_files;
wtsrc.tag     = 'wtsrc';
wtsrc.name    = 'Source Weighting Image';
wtsrc.val     = {''};
wtsrc.help    = {'Optional weighting images (consisting of pixel values between the range of zero to one) to be used for registering abnormal or lesioned brains.  These images should match the dimensions of the image from which the parameters are estimated, and should contain zeros corresponding to regions of abnormal tissue.'};
wtsrc.filter = 'image';
wtsrc.ufilter = '.*
    
function (derived from the residual squared difference) and the prior function (which is based on the probability of obtaining a particular set of zooms and shears).'
                     ''
                     'The affine registration is followed by estimating nonlinear deformations, whereby the deformations are defined by a linear combination of three dimensional discrete cosine transform (DCT) basis functions /* \cite{ashburner99a} */.  The default options result in each of the deformation fields being described by 1176parameters, where these represent the coefficients of the deformations in three orthogonal directions.  The matching involved simultaneously minimising the membrane energies of the deformation fields and the residual squared difference between the images and template(s).'
                     ''
                     'The primarily use is for stereotactic normalisation to facilitate inter-subject averaging and precise characterisation of functional anatomy /* \cite{ashburner97bir} */.  It is not necessary to spatially normalise the data (this is only a pre-requisite  for  inter-subject averaging or reporting in the Talairach space).  If you wish to circumnavigate this step  (e.g. if you have single slice data or do not have an appropriate high resolution MRI scan) simply specify where you think the  anterior commissure  is  with  the  ORIGIN in the header of the first scan (using the ''Display'' facility) and proceed directly  to ''Smoothing''or ''Statistics''.'
                     ''
                     'All normalised *.img scans are written to the same subdirectory as the original *.img, prefixed with a ''w'' (i.e. w*.img).  The details of the transformations are displayed in the results window, and the parameters are saved in the "*_sn.mat" file.'
}';
normalise.values  = {est write estwrite };
%normalise.num     = [1 Inf];
%------------------------------------------------------------------------
 
%------------------------------------------------------------------------
function dep = vout_estimate(job)
for k=1:numel(job.subj)
    dep(k)            = cfg_dep;
    dep(k).sname      = sprintf('Norm Params File (Subj %d)',k);
    dep(k).src_output = substruct('()',{k},'.','params');
    dep(k).tgt_spec   = cfg_findspec({{'filter','mat','strtype','e'}});
end