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: CBIG_LiGSR_LRR_workflowGSP.m
    path of file: ./repos/CBIG/stable_projects/preprocessing/Li2019_GSR/LinearRidgeRegression/GSP/scripts
    
    the code of the file until where you have to start completion: function CBIG_LiGSR_LRR_workflowGSP( data_csv, subject_list, FC_name, y_name, covariate_list, ...

    
function CBIG_LiGSR_LRR_workflowGSP( data_csv, subject_list, FC_name, y_name, covariate_list, ...
    seed, num_test_folds, num_inner_folds, FD_file, DVARS_file, gpso_dir, outdir, eval, tree, varargin )

% CBIG_LiGSR_LRR_workflowGSP( data_csv, subject_list, FC_name, y_name, covariate_list, ...
%     seed, num_test_folds, num_inner_folds, FD_file, DVARS_file, gpso_dir, outdir, eval, tree, varargin )
% 
% This function performs the whole linear ridge regression procedure for
% the Brain Genomics Superstruct Project (GSP) dataset, given a target
% trait to be predicted (y_name). It first splits the data into
% cross-validation folds, then read in the traits and covariates, and calls
% "CBIG_LRR_workflow_1measure.m" to run linear ridge regression algorithm.
% The prediction accuracy using the optimal hyperparameters will be saved as
% fullfile(outdir, 'results', 'optimal_acc', [y_name '.mat']).
% 
% Inputs:
%   - data_csv
%     Full path of the CSV file containing behavioral and demographic
%     information from the GSP dataset.
% 
%   - subject_list
%     Full path of the subject ID list. Each line in this list corresponds
%     to one subject.
%  
%   - FC_name
%     Full path of the resting-state functional connectivity (RSFC) matrix.
%  
%   - y_name
%     A string, the trait name, i.e. the behavioral (or demographic)
%     measure (target measure to be predicted using linear ridge
%     regression). The name should correspond to the headers in "data_csv".
% 
%   - covariate_list
%     Full path of a text list of covariate names (e.g. age, sex, FD) that
%     need to be regressed from y (i.e. the measures to be predicted).
%     Each line in this text file corresponds to one covariate name. The
%     covariate names stated in this list, except for 'FD' and 'DVARS',
%     should correspond to the headers in "data_csv".
% 
%   - seed
%     A string or scalar, the random seed used to split the data into
%     training-test cross-validation folds.
% 
%   - num_test_folds
%     A string or scalar, the number of training-test cross-validation
%     folds.
% 
%   - num_inner_folds
%     A string or scalar.
%     To select optimal hyperparameters, each training fold will be split
%     randomly into "num_inner_folds" inner-loop cross-validation folds.
% 
%   - FD_file (optional)
%     If there is a need to regress FD (framewise displacement) from the
%     behavioral (or demographic) measures, the user should include 'FD' in
%     the "covariate_list". In this case, "FD_file" is the full path of a
%     text file containing the mean FD of all subjects. The number of lines
%     in "FD_file" should be the same as the number of lines in "subject_list". 
%     If the user does not need to regress FD from y, then the input
%     variable "FD_file" is not required and the user can pass in 'NONE' to
%     the function.
%     If "covariate_list" does not contain FD, this argument will be
%     ignored.
% 
%   - DVARS_file (optional)
%     If there is a need to regress 'DVARS' from the behavioral
%     (demographic) measures, y, the user must include the covariate
%     'DVARS' (or 'DV') in the 'covariate_list'. In this case, "DVARS_file"
%     is the full path of a text file containing the mean DVARS of all
%     subjects. The number of lines in "DVARS_file" should be the same as
%     the number of lines in "subject_list". 
%     If the user does not need to regress DVARS from y, then the input
%     variable 'DVARS_file' is not required and the user can pass in 'NONE'
%     to the function.
%     If "covariate_list" does not contain DV (or DVARS), this argument
%     will be ignored.
% 
%   - gpso_dir
%     A string, the full path of the directory to store the cloned git
%     repository for Gaussian process code. Current script needs the
%     Gaussian-Process Surrogate Optimisation (GPSO;
%     https://github.com/jhadida/gpso) package to optimize the objective
%     function. This git repository and its dependency "deck"
%     (https://github.com/jhadida/deck) need to be both cloned into the
%     same folder, which is passed into current script through gpso_dir.
% 
%   - outdir
%     The full path of output directory. A subfolder 
%     [outdir '/randseed_' seed] will be created to save all output files of
%     current random seed.
% 
%   - eval (optional)
%     A string or scalar, the maximal evaluation times of the objective
%     function. Default is 15. If it is too large, the runtime would be too
%     long; if it is too small, the objective function may not be able to
%     reach its optimal value. The users need to consider this trade-off
%     when setting this variable. For more information, please refer to: 
%     https://github.com/jhadida/gpso
% 
%   - tree (optional)
%     A string or scalar, the depth of the partition tree used to explore
%     hyperparameters. Default is 3. For more information, please refer to:
%     https://github.com/jhadida/gpso
% 
%   - varargin (optional)
%     One or more strings. 
%     Some demographic variables (e.g. age) are usually treated as
%     covariates when predicting other behavioral variables (e.g. IQ). In
%     this case, age values are saved together with other covariates in a
%     mat file called 'covariates.mat'.
%     However, sometimes these variables could also be treated as target
%     variables. For example, we want to predict age so that age cannot be
%     included in covariates files anymore. In this case, this script
%     automatically save the covariate file as 'covariate_Age_Bin.mat', where
%     '_Age_Bin' file stem indicates that this covariate file is used to
%     predict age.
%     To achieve this, this script claims a sets of such variable names
%     that could be used as either covariate or target. The default set of
%     such variables include 'Sex', 'FD', 'DVARS', 'Age_Bin', 'Race_Ethn',
%     'Educ'. If the user have other possible variables, he/she can pass
%     them in through varargin. This script will concatenate varargin with
%     the variables aforementioned.
% 
% Written by Jingwei Li and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md

if(ischar(seed))
    seed = str2num(seed);
end