code: def _validate_supported_estimator_type(self, estimator_type):
      supported_estimator_types = (
          self._model.get_supported_estimator_types()
      )
      if estimator_type not in supported_estimator_types:
          msg_template = "Model class: {} is incompatible with Estimator: {}.  Estimator Support: {}"
          model_class = type(self._model)
          estimator_type = estimator_type
          raise ForecasterException(
              msg_template.format(
                  model_class,
                  estimator_type,
                  str(supported_estimator_types),
              )
          )
  
imports: from copy import deepcopy
  import numpy
  import pandas
  import warnings
  from enum import Enum
  from typing import Dict, Any
  from ..exceptions import ForecasterException, AbstractMethodException, IllegalArgument
  from ..utils.general import is_ordered_datetime, is_even_gap_datetime
  from ..template.model_template import ModelTemplate
  from ..estimators.stan_estimator import StanEstimatorMCMC
  from ..constants.constants import TrainingMetaKeys, PredictionMetaKeys
label_desc: Use image node_id 2 for calling the Forecaster obj's underlying member method code with example usage: obj._validate_supported_estimator_type(estimator_type) without return types