docstr: Get fitted parameters.
  
  Returns
  -------
  fitted_params : dict
      A dict containing the best hyper parameters and the parameters of
      the best estimator (if available), merged together with the former
      taking precedence.
code: def _get_fitted_params(self):
      """Get fitted parameters.
  
      Returns
      -------
      fitted_params : dict
          A dict containing the best hyper parameters and the parameters of
          the best estimator (if available), merged together with the former
          taking precedence.
      """
      fitted_params = {}
      try:
          fitted_params = self.best_forecaster_.get_fitted_params()
      except NotImplementedError:
          pass
      fitted_params = {**fitted_params, **self.best_params_}
      fitted_params.update(self._get_fitted_params_default())
  
      return fitted_params
  
imports: from collections.abc import Sequence
  from typing import Dict, List, Optional, Union
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from sklearn.model_selection import ParameterGrid, ParameterSampler, check_cv
  from sktime.datatypes import mtype_to_scitype
  from sktime.exceptions import NotFittedError
  from sktime.forecasting.base._delegate import _DelegatedForecaster
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.performance_metrics.base import BaseMetric
  from sktime.split.base import BaseSplitter
  from sktime.utils.parallel import parallelize
  from sktime.utils.validation.forecasting import check_scoring
  from sktime.utils.warnings import warn
label_desc: Use image node_id 3 for calling the BaseGridSearch obj's underlying member method code with example usage: obj._get_fitted_params() and returns: fitted_params