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: train_coco_Rankloss_shift_hard_vgg19.m
    path of file: ./repos/Image-Text-Embedding
    
    the code of the file until where you have to start completion: function train_id_net_vgg16(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------

imdb = load('./dataset/MSCOCO-prepare/url_data.mat');
imdb = imdb.imdb;
load('./dataset/MSCOCO-prepare/coco_word2.mat');
%sort row
[imdb.images.label2,index] = sort(imdb.images.label2);
wordcnn = wordcnn(:,index);
imdb.charcnn = wordcnn; 
%imdb.charmean = mean(imdb.charcnn(:,:,:,imdb.images.set==1),4);
% -------------------------------------------------------------------------
% Part 4.2: initialize a CNN architecture
% -------------------------------------------------------------------------
net = coco_word2_Rankloss_vgg19();
net.conserveMemory = true;
im_mean = imdb.rgbMean;
net.meta.normalization.averageImage = im_mean;
%net.meta.normalization.charmean = imdb.charmean;
% -------------------------------------------------------------------------
% Part 4.3: train and evaluate the CNN

% -------------------------------------------------------------------------
opts.train.averageImage = net.meta.normalization.averageImage;
opts.train.batchSize = 32;
opts.train.continue = true;
opts.train.gpus = 1;
opts.train.prefetch = false ;
opts.train.nesterovUpdate = true ;
opts.train.expDir = './data/vgg19_coco_batch32_Rankloss_2:1:0.1_margin1_both_drop0.5_hard_256';
opts.train.derOutputs = {'objective_f',2,'objective_img',1,'objective_txt',0.1} ;
%opts.train.gamma = 0.9;
opts.train.candidate = 256;

    
function train_id_net_vgg16(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------

imdb = load('./dataset/MSCOCO-prepare/url_data.mat');
imdb = imdb.imdb;
load('./dataset/MSCOCO-prepare/coco_word2.mat');
%sort row
[imdb.images.label2,index] = sort(imdb.images.label2);
wordcnn = wordcnn(:,index);
imdb.charcnn = wordcnn; 
%imdb.charmean = mean(imdb.charcnn(:,:,:,imdb.images.set==1),4);
% -------------------------------------------------------------------------
% Part 4.2: initialize a CNN architecture
% -------------------------------------------------------------------------
net = coco_word2_Rankloss_vgg19();
net.conserveMemory = true;
im_mean = imdb.rgbMean;
net.meta.normalization.averageImage = im_mean;
%net.meta.normalization.charmean = imdb.charmean;
% -------------------------------------------------------------------------
% Part 4.3: train and evaluate the CNN

% -------------------------------------------------------------------------
opts.train.averageImage = net.meta.normalization.averageImage;
opts.train.batchSize = 32;
opts.train.continue = true;
opts.train.gpus = 1;
opts.train.prefetch = false ;
opts.train.nesterovUpdate = true ;
opts.train.expDir = './data/vgg19_coco_batch32_Rankloss_2:1:0.1_margin1_both_drop0.5_hard_256';
opts.train.derOutputs = {'objective_f',2,'objective_img',1,'objective_txt',0.1} ;
%opts.train.gamma = 0.9;
opts.train.candidate = 256;
opts.train.momentum = 0.9;
%opts.train.constraint = 100;
opts.train.learningRate = [0.1*ones(1,15),0.01*ones(1,5)] ;
opts.train.weightDecay = 0.0001;
opts.train.numEpochs = numel(opts.train.learningRate) ;
[opts, ~] = vl_argparse(opts.train, varargin) ;
% Call training function in MatConvNet
[net,info] = cnn_train_dag_batchsize_net(net, imdb, @getBatch,opts) ;

% --------------------------------------------------------------------
function inputs = getBatch(imdb,batch,net,opts)
% --------------------------------------------------------------------
%-- img data
im_url = imdb.images.data(batch) ;
im = vl_imreadjpeg(im_url,'Pack','Resize',[224,224],'Flip',...
    'CropLocation','random','CropSize',[0.8,1],...
    'Interpolation', 'bicubic','NumThreads',16,... %'Brightness', double(0.1*imdb.rgbCovariance),...
    'SubtractAverage',imdb.rgbMean,...
    'CropAnisotropy',[3/4,4/3]);
oim = im{1}; %bsxfun(@minus,im{1},opts.averageImage);
label_img =  imdb.images.label(batch);
label_txt = label_img;
% ------ first half img feature
net.mode = 'test';
%net.vars(net.getVarIndex('fc2bn_n')).value = [];
%net.vars(net.getVarIndex('x0')).value = [];
%net.vars(net.getVarIndex('data2')).value = [];
%net.vars(net.getVarIndex('label_txt')).value = [];
%net.vars(net.getVarIndex('label_img')).value = [];
reset(net);
batchsize = numel(batch);
half = batchsize/2;
f_img = getFeature2(net,oim(:,:,:,1:half),[],'x0','fc1bn_n');
f_img = reshape(f_img,[],half)';
%reset(net);

% ------- rand txt input  ---different with flickr30k
neg_label = imdb.images.label;
neg_label(batch(1:half))=[]; %remove same label
neg_label(neg_label==0) = []; % remove test and val images
neg_label = neg_label(randi(numel(neg_label), opts.candidate,1)); % random select 256 image sample
for i=1:opts.candidate
  rand_ind(i) = rand_same_class_coco(imdb,neg_label(i)); % neg txt index
end