class EnsembleDetector:

    ...

    def save(self, folder):
        '''Saves the EnsembleDetector (as multiple files) in a given folder.'''
        # Save TF-based AutoEncoders in separate sub-directories (they don't pickle)
        tf_models = {}   # {index for self.models: model} 
        for i, model in enumerate(self.models):
            if 'AutoEncoder' in str(type(model)):
                model.model_.save(Path(folder)/str(i))
                tf_models[i] = model.model_
                model.model_ = None  # Remove non-pickleable TF models from self so we can pickle self
            if 'VAE' in str(type(model)):
                raise Exception('VAE is not supported when saving the ensemble yet, since it uses a Lambda layer.')

        # Pickle the entire EnsembleDetector after the TF models are removed
        Path(folder).mkdir(parents=True, exist_ok=True)
        joblib.dump(self, Path(folder)/'ensemble_detector.joblib')

        # Add the TF model objects back into self
        for i in tf_models: self.models[i].model_ = tf_models[i]

    @staticmethod
    def load(folder):
        '''Loads the EnsembleDetector (from multiple files) in a given folder.'''
        # Unpickle the EnsembleDetector object
        ed = joblib.load(Path(folder)/'ensemble_detector.joblib')

        # Load TF-based AutoEncoders from separate sub-directories (they don't pickle)
        for i, model in enumerate(ed.models):
            if 'AutoEncoder' in str(type(model)):
                model.model_ = keras.models.load_model(Path(folder)/str(i))

        return ed