class_docstr: Handles parameter management for estimators composed of named estimators.
  
  Partly adapted from sklearn utils.metaestimator.py.
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
      """
      fitted_params = self._get_fitted_params_default()
  
      steps = self._steps_fitted_attr
      steps_params = self._get_params(steps, fitted=True)
  
      fitted_params.update(steps_params)
  
      return fitted_params
  
imports: from inspect import isclass
  import numpy
  import pandas
  from sktime.base import BaseEstimator
label_desc: Use image node_id 3 for calling the _HeterogenousMetaEstimator obj's underlying member method code with example usage: obj._get_fitted_params() and returns: fitted_params