How to apply the learned model using boosting technique

I have no clue on how to apply the learned model using adaptive boosting method since there is not model as such that is returned similar to the one returned by say the SVM classifier's fitcsvm() function. I am using the implementation given https://www.mathworks.com/matlabcentral/fileexchange/63162-adaboost?focused=7722161&tab=function

This method uses 5 base classifiers and so the number of learning cycles =5. Variable ada_test contains the labels from each independent run, itr of the boosting module.

QUESTION: For the boosting method once the training is done which includes the training set and prediction on the validation set, which variable should be used to predict on the unseen data set for testing? In the following code,

for itr=1:maxItr
    [~,ada_test(:,itr)]= adaboost(X,Y, Xtest);
    fm_=[fm_; confusion_mat(Ytest, ada_test(:,itr))];
end

the variable ada_test(:,1)=sign(Htest*alpha'); are the predicted labels. In the function, adaboost() there is the line ada_train(:,1)=sign(H*alpha'); Why ada_train variable is never used and how to test the learned adaboost model on unseen data? I cannot understand the procedure to predict on a new unseen test data because I cannot follow which variable or what is the learned model. Please help.