cvpartition with different selection of input features per fold

I am currently utilizing cvpartition to perform k-fold cross validation.

A simplified code I am using looks like below:

% create cross validation object
cvo = cvpartition(y, 'KFold', 5); % 5-fold cross validation
% perform cross validation using logistic regression
logisticRegression = fitglm(x, y, 'CVPartition', cvo, 'ClassNames', classOrder);

The issue I have is that I now want to perform some feature selection only using the training fold of each cross validation split to prevent data leakage (using some statistics from the training data in order to select which features to use for model training).

Procedure (very rough pseudo code) for what I am describing above looks like below:

create cvo

for each fold f:

perform feature selection using training portion of the fold (using some stats calculated based on the training portion of the fold)

train the model with reduced set of features

evaluate (validate) the model using the validation portion of the fold

end

Is there a way achieve above (using different feature subsets for each fold) with cvo? I have already built a pretty extensive pipeline around using cvo, so I'd like to keep using it to prevent myself from re-doing most of the work.

Any help will be greatly appreciated. Thank you,