bayesopt of patternet in classification problems

I would like to optimize the variables of patternet for a classification problem using bayesopt. I found online the example below which optimizes the number of hidden layers and the learning rate:

% Define a train/validation split to use inside the objective function

cv = cvpartition(numel(YTrain), 'Holdout', 1/3);

% Define hyperparameters to optimize

vars = [optimizableVariable('hiddenLayerSize', [1,20], 'Type', 'integer');

optimizableVariable('lr', [1e-3 1], 'Transform', 'log')];

% Optimize

minfn = @(T)kfoldLoss(XTrain', YTrain', cv, T.hiddenLayerSize, T.lr);

results = bayesopt(minfn, vars,'IsObjectiveDeterministic', false, ...

'AcquisitionFunctionName', 'probability-of-improvement', ...

'MaxObjectiveEvaluations',20);

function rmse = kfoldLoss(x, y, cv, numHid, lr)

% Train net.

net = patternnet(numHid, 'traingd');

net.trainParam.lr = lr;

net = train(net, x(:,cv.training), y(:,cv.training));

% Evaluate on validation set and compute rmse

ypred = net(x(:, cv.test));

rmse = sqrt(mean((ypred - y(cv.test)).^2));

end

%

Which other variables of patternet can I optimize? Can I optimize them all? Is there an official Matlab documentation for patternet like there is one for fitcensemble to optimize the hyperparameters in classification problems using bayesopt? I can’t find in the official Matlab documentation the variables of patternet that I can actually optimize, and then how to implement them in the code above. I found epochs, goal and others but only in some examples online.

Thanks