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: Obtain fitted params of object, per sklearn convention.
  
  Extracts a dict with {paramstr : paramvalue} contents,
  where paramstr are all string names of "fitted parameters".
  
  A "fitted attribute" of obj is one that ends in "_" but does not start with "_".
  "fitted parameters" are names of fitted attributes, minus the "_" at the end.
  
  Parameters
  ----------
  obj : any object, optional, default=self
  
  Returns
  -------
  fitted_params : dict with str keys
      fitted parameters, keyed by names of fitted parameter
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code: def _get_fitted_params_default(self, obj=None):
      """Obtain fitted params of object, per sklearn convention.
  
      Extracts a dict with {paramstr : paramvalue} contents,
      where paramstr are all string names of "fitted parameters".
  
      A "fitted attribute" of obj is one that ends in "_" but does not start with "_".
      "fitted parameters" are names of fitted attributes, minus the "_" at the end.
  
      Parameters
      ----------
      obj : any object, optional, default=self
  
      Returns
      -------
      fitted_params : dict with str keys
          fitted parameters, keyed by names of fitted parameter
      """
      obj = obj if obj else self
  
      # default retrieves all self attributes ending in "_"
      # and returns them with keys that have the "_" removed
      fitted_params = {
          attr[:-1]: getattr(obj, attr)
          for attr in dir(obj)
          if attr.endswith("_")
          and not attr.startswith("_")
          and hasattr(obj, attr)
      }
      return fitted_params
  
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 5 for calling the BaseEstimator obj's underlying member method code with example usage: obj._get_fitted_params_default(obj) and returns: fitted_params