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: Check if the estimator has been fitted.
  
  Raises
  ------
  NotFittedError
      If the estimator has not been fitted yet.
docstr_tok: ["Check","if","the","estimator","has","been","fitted",".","Raises","--","--","--","NotFittedError","If","the","estimator","has","not","been","fitted","yet","."]
code: def check_is_fitted(self):
      """Check if the estimator has been fitted.
  
      Raises
      ------
      NotFittedError
          If the estimator has not been fitted yet.
      """
      if not self.is_fitted:
          raise NotFittedError(
              f"This instance of {self.__class__.__name__} has not "
              f"been fitted yet; please call `fit` first."
          )
  
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 3 for calling the BaseEstimator obj's underlying member method code with example usage: obj.check_is_fitted() without return types