how to use pyCaret for Semi-supervised hybrid model

Question

I am having a semi-supervised model consisting of Random Forest Classifier and LOF that can be used for anomaly detection. I want to make use of PyCaret to implement this. I see, there is a way to combine multiple classification models using stack_models(), however unable to find similar thing for hybrid models. Is there an approach I can follow in order to have a semi supervised model consisting of models in Classification and Anomaly Detection categories using pyCaret functions?

I tried to use stack_models for RF classifier and LOF, but encountered the following error-

stack_models(rf_classifier, lof_model)

raise ValueError(
ValueError: 'final_estimator' parameter should be a classifier. Got LOF(algorithm='auto', contamination=0.1, leaf_size=30, metric='minkowski',
  metric_params=None, n_jobs=-1, n_neighbors=20, novelty=True, p=2)

Apparently, it is only for classifiers and expects the same.

Also, I want to incorporate dashboard() function available for Classification models into the hybrid models.