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_compute_PFM_general.m
    path of file: ./repos/CBIG/utilities/matlab/predictive_models/PFM
    
    the code of the file until where you have to start completion: function [PFM] = CBIG_compute_PFM_general(feature_file, y_pred_train_file)

% [PFM] = CBIG_compute_PFM_general(feature_file, y_pred_train_file)
% 
% This function computes the predictive network features for general purpose
% by computing the covariance between the features and the predicted behavior
% (Haufe et al. 2014).
%
% Inputs:
%   - feature_file
%     The feature file used in predictive model (dim: #features x #subjects).
%     The variable storing the #features x #subjects matrix can be any name.
%

    
function [PFM] = CBIG_compute_PFM_general(feature_file, y_pred_train_file)

% [PFM] = CBIG_compute_PFM_general(feature_file, y_pred_train_file)
% 
% This function computes the predictive network features for general purpose
% by computing the covariance between the features and the predicted behavior
% (Haufe et al. 2014).
%
% Inputs:
%   - feature_file
%     The feature file used in predictive model (dim: #features x #subjects).
%     The variable storing the #features x #subjects matrix can be any name.
%
%   - y_pred_train_file
%     The predicted y for training subjects from the predictive model
%     (dim: #subjects x 1). The variable storing the #subjects x 1 vector 
%     can be any name.
%
% Outputs:
%   - PFM
%      The predictive-feature matrix (dim: #features x 1).
%
% Written by Naren Wulan and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md

%% load features
FC = CBIG_PFM_load_mat(feature_file);
FC = normalize(FC,'center');
FC = normalize(FC,'norm',2);

%% load predicted y
y_predicted_train = CBIG_PFM_load_mat(y_pred_train_file);

%% compute predictive feature matrices
PFM = CBIG_PFM_cov_matrix(FC',y_predicted_train)/std(y_predicted_train);
end