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: cascadeface_48net2.m
    path of file: ./repos/2015_Face_Detection
    
    the code of the file until where you have to start completion: function cascadeface_48net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------

% Load character dataset
imdb = load('../../aflw/matlab/imdb48_v1.mat') ;
imdb = imdb.imdb;
imdb.meta.sets=['train','val'];
ss = size(imdb.images.label);
imdb.images.set = ones(1,ss(2));
imdb.images.set(ceil(rand(1,ceil(ss(2)/5))*ss(2))) = 2;
% -------------------------------------------------------------------------
% Part 4.2: initialize a CNN architecture
% -------------------------------------------------------------------------

net = f48net() ;

    
function cascadeface_48net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------

% Load character dataset
imdb = load('../../aflw/matlab/imdb48_v1.mat') ;
imdb = imdb.imdb;
imdb.meta.sets=['train','val'];
ss = size(imdb.images.label);
imdb.images.set = ones(1,ss(2));
imdb.images.set(ceil(rand(1,ceil(ss(2)/5))*ss(2))) = 2;
% -------------------------------------------------------------------------
% Part 4.2: initialize a CNN architecture
% -------------------------------------------------------------------------

net = f48net() ;

% -------------------------------------------------------------------------
% Part 4.3: train and evaluate the CNN
% -------------------------------------------------------------------------

opts.train.batchSize = 256 ;
%opts.train.numSubBatches = 1 ;
opts.train.continue = false;
opts.train.gpus = 3 ;
%opts.train.prefetch = true ;
opts.train.sync = false ;
opts.train.errorFunction = 'binary' ;
opts.train.expDir = 'data/48net-v1.0-4-dropout/' ;
opts.train.learningRate = [0.05*ones(1,15) 0.005*ones(1,10) 0.0005*ones(1,5)]  ;
opts.train.weightDecay = 0.0001 ;
opts.train.numEpochs = numel(opts.train.learningRate) ;
[opts, ~] = vl_argparse(opts.train, varargin) ;

% Take the average image out
imageMean = mean(imdb.images.data(:)) ;
imdb.images.data = imdb.images.data - imageMean ;
net.imageMean = imageMean ;

global net48;
net48 = net;
global net24;
net24 = load('./data/24net-v2.0/f24net.mat');
global net12;
net12 = load('./data/12netv2-v2.0/f12net.mat');
%net12 = vl_simplenn_move(net12,'gpu');
%net24 = vl_simplenn_move(net24,'gpu');
% Call training function in MatConvNet
[net,info] = cnn_train(net, imdb, @getBatch,opts) ;

% Save the result for later use
net.layers(end