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: bramila_glminit.m
    path of file: ./repos/bspm/thirdparty/bramila
    
    the code of the file until where you have to start completion: % Submission script for triton cluster
% See wiki for detailed instructions
clear all
close all
addpath(genpath('/triton/becs/scratch/braindata/shared/toolboxes/bramila/bramila'));
%% Parameters will be stored in single struct to simplify function call
% The data should be structured in a way /dataroot/subject/session/inputfile
dataroot = '/triton/becs/scratch/braindata/shared/2pnhyperEMO'; % where subject folders are located
outdirname = '1stlvl'; % Where would the output go
cfg.TR = 1.7; % TR, get from protocol
cfg.AR = 'none'; % or 'AR(1)' for autoregression correction
cfg.sesses = {'run1','run2','run3','run4','run5'}; % sessions, or runs (see preprocessing guidelines)
cfg.hpf = 512; % high-pass filter, you can play with cutoffcalc in fsl to find a good estimate
cfg.incmoves = 0; % include or not include motion params?
cfg.inputfile = 'epi_preprocessed.nii'; % how the input epi file will be called
cfg.motionfile = 'epi_MCF.nii.par'; % how the input motion parameters file is called
cfg.cnames = {'story'}; % names of conditions or tasks that you model
cfg.regressor_unit = 'secs'; % 'scans' or 'secs', be VERY CAREFUL ABOUT IT
%% Parametric modulations
cfg.pmod = 0; % set to 0 if no parametric modulations, set to 1 if you have some
cfg.pmodname = {'valence','arousal'}; % leave empty if no pmods
% Subject folders
subjects = {
'Emo_listening_4';
};
% Example of how the input files would look like, examine it in case something is wrong
fprintf('Example of input file: %s/%s/%s/%s',dataroot,subjects{1},cfg.sesses{1},cfg.inputfile);
disp('If not fine, check variables: dataroot, subjects, cfg.sesses and cfg.inputfile');
%% Predefine contrasts
% If you want to have your own contrasts, define them here
% Contrasts should be of size(cname), don't include motion and constant
% By default, if not predefined contrasts, you will get main effect (1 0) and one vs other (1 -1; -1 1) contrasts
% cfg.predefined_contrast = []; % if no predefined contrasts
% Example of how to define contrasts
cfg.predefined_contrast{1} = [1 0 0];
cfg.predefined_contrast_name{1} = 'story';
cfg.predefined_contrast{1} = [0 1 0];
cfg.predefined_contrast_name{1} = 'arousal';
cfg.predefined_contrast{1} = [0 0 1];
cfg.predefined_contrast_name{1} = 'valence';
%% Main
% Which function to run on cluster
runfile = 'bramila_glm1st'; % how the function that will be executed is called
for s = 1:length(subjects)
    subject = subjects{s};
    %% Build regressor, varies from person to person, but logic is following
    % cfg.onset should be cell array of size(sesses).
    % each cell contains onsets for each condition in separate cell
    % i.e. if you have 3 tasks or conditions, each cell of cfg.onset contains
    % three ce
    
function call
% The data should be structured in a way /dataroot/subject/session/inputfile
dataroot = '/triton/becs/scratch/braindata/shared/2pnhyperEMO'; % where subject folders are located
outdirname = '1stlvl'; % Where would the output go
cfg.TR = 1.7; % TR, get from protocol
cfg.AR = 'none'; % or 'AR(1)' for autoregression correction
cfg.sesses = {'run1','run2','run3','run4','run5'}; % sessions, or runs (see preprocessing guidelines)
cfg.hpf = 512; % high-pass filter, you can play with cutoffcalc in fsl to find a good estimate
cfg.incmoves = 0; % include or not include motion params?
cfg.inputfile = 'epi_preprocessed.nii'; % how the input epi file will be called
cfg.motionfile = 'epi_MCF.nii.par'; % how the input motion parameters file is called
cfg.cnames = {'story'}; % names of conditions or tasks that you model
cfg.regressor_unit = 'secs'; % 'scans' or 'secs', be VERY CAREFUL ABOUT IT
%% Parametric modulations
cfg.pmod = 0; % set to 0 if no parametric modulations, set to 1 if you have some
cfg.pmodname = {'valence','arousal'}; % leave empty if no pmods
% Subject folders
subjects = {
'Emo_listening_4';
};
% Example of how the input files would look like, examine it in case something is wrong
fprintf('Example of input file: %s/%s/%s/%s',dataroot,subjects{1},cfg.sesses{1},cfg.inputfile);
disp('If not fine, check variables: dataroot, subjects, cfg.sesses and cfg.inputfile');
%% Predefine contrasts
% If you want to have your own contrasts, define them here
% Contrasts should be of size(cname), don't include motion and constant
% By default, if not predefined contrasts, you will get main effect (1 0) and one vs other (1 -1; -1 1) contrasts
% cfg.predefined_contrast = []; % if no predefined contrasts
% Example of how to define contrasts
cfg.predefined_contrast{1} = [1 0 0];
cfg.predefined_contrast_name{1} = 'story';
cfg.predefined_contrast{1} = [0 1 0];
cfg.predefined_contrast_name{1} = 'arousal';
cfg.predefined_contrast{1} = [0 0 1];
cfg.predefined_contrast_name{1} = 'valence';
%% Main
% Which function to run on cluster
runfile = 'bramila_glm1st'; % how the function that will be executed is called
for s = 1:length(subjects)
    subject = subjects{s};
    %% Build regressor, varies from person to person, but logic is following
    % cfg.onset should be cell array of size(sesses).
    % each cell contains onsets for each condition in separate cell
    % i.e. if you have 3 tasks or conditions, each cell of cfg.onset contains
    % three cells where onsets of those conditions are specified.
    % Same logic applies to cfg.durations
    % Run this bit to see it as an example
    load(sprintf('%s/%s/Ratings/regressor.mat',dataroot,subject));
    % 1 - onset
    % 2 - duration
    % 3 - boxcar model for speech
    % 4 - arousal
    % 5 - valence
    % 6 - story number (gist)    
    for r = 1:length(cfg.sesses) % for each run        
        for c = 1:length(cfg.cnames)
            cfg.onsets{r}{c} = regressor{r}(:,1);
            cfg.durations{r}{c} = regressor{r}(:,2);
            if cfg.pmod == 1
                cfg.pmodparam{r}{c}{1} = regressor{r}(:,4);
                cfg.pmodparam{r}{c}{2} = regressor{r}(:,5);
            end
        end
    end
    % save the cfgfile
    save(sprintf('%s/%s/%s.mat',dataroot,subject,outdirname),'cfg');
    % job filename
    filename = fullfile(dataroot,subject,[outdirname 'job']);
    % log filename
    logfile = fullfile(dataroot,subject,[outdirname 'logfile']);
    %load the modules
    dlmwrite(filename, '#!/bin/sh', '');
    % Handle the long jobs
    dlmwrite(filename, '#SBATCH -p batch','-append