text: Title: TabularPredictor. Shuffle=False??
  Body: Hi everyone,
  
  First of all, thank you for the well-documented library.
  I have a question regarding the use of TabularPredictor for creating a stacking ensemble model. I’m unsure how AutoGluon handles hyperparameter tuning when both tuning_data and train_data are provided. Specifically, does AutoGluon perform hyperparameter tuning using K-Fold cross-validation? If so, is there a way to configure it to set shuffle=False? I’d appreciate any clarification on this point.