class_docstr: Base class for defining estimators in sktime.
  
  Extends sktime's BaseObject to include basic functionality for fittable estimators.
class_docstr_tok: ["Base","class","for","defining","estimators","in","sktime",".","Extends","sktime","'s","BaseObject","to","include","basic","functionality","for","fittable","estimators","."]
docstr: Get fitted parameters.
  
  private _get_fitted_params, called from get_fitted_params
  
  State required:
      Requires state to be "fitted".
  
  Returns
  -------
  fitted_params : dict with str keys
      fitted parameters, keyed by names of fitted parameter
docstr_tok: ["Get","fitted","parameters",".","private","_get_fitted_params",",","called","from","get_fitted_params","State","required",":","Requires","state","to","be","``","fitted","''",".","Returns","--","--","--","-","fitted_params",":","dict","with","str","keys","fitted","parameters",",","keyed","by","names","of","fitted","parameter"]
code: def _get_fitted_params(self):
      """Get fitted parameters.
  
      private _get_fitted_params, called from get_fitted_params
  
      State required:
          Requires state to be "fitted".
  
      Returns
      -------
      fitted_params : dict with str keys
          fitted parameters, keyed by names of fitted parameter
      """
      return self._get_fitted_params_default()
  
imports: import warnings
  from copy import deepcopy
  from skbase.base import BaseObject
  from sklearn import clone
  from sklearn.base import BaseEstimator
  from sktime.exceptions import NotFittedError
  from sktime.utils.random_state import set_random_state
label_desc: Use image node_id 6 for calling the BaseEstimator obj's underlying member method code with example usage: obj._get_fitted_params() and returns: self