Does h2o4gpu handle categorical features like sklearn or like h2o?

I understand that sklearn requires categorical features to be encoded to dummy variables or one-hot encoded when running the sklearn.ensemble.RandomForestRegressor method, and that XGBoost requires the same, but h2o permitted raw categorical features to be used in its h2o.estimators.random_forest.H2ORandomForestEstimator method. Since h2o4gpu's implementation of random forest is built on top of XGBoost, does this mean support for raw categorical features is not included?

There is no native support for categorical columns in h2o4gpu (at least yet), so you will have to one-hot encode (or label encode) your categorical columns like you do in sklearn and xgboost.