code: def _update_model(self, normalization_type='stats'):
          
          if self.num_acquisitions % self.model_update_interval == 0:
  
              # input that goes into the model (is unziped in case there are categorical variables)
              X_inmodel = self.space.unzip_inputs(self.X)
  
              # Y_inmodel is the output that goes into the model
              if self.normalize_Y:
                  Y_inmodel = normalize(self.Y, normalization_type)
              else:
                  Y_inmodel = self.Y
  
              self.model.updateModel(X_inmodel, Y_inmodel, None, None)
  
          # Save parameters of the model
          self._save_model_parameter_values()
docs: Updates the model (when more than one observation is available) and saves the parameters (if available).