class_docstr: Transformer base class.
class_docstr_tok: ["Transformer","base","class","."]
docstr: Get fitted parameters.
  
  State required:
      Requires state to be "fitted".
  
  Parameters
  ----------
  deep : bool, default=True
      Whether to return fitted parameters of components.
  
      * If True, will return a dict of parameter name : value for this object,
        including fitted parameters of fittable components
        (= BaseEstimator-valued parameters).
      * If False, will return a dict of parameter name : value for this object,
        but not include fitted parameters of components.
  
  Returns
  -------
  fitted_params : dict with str-valued keys
      Dictionary of fitted parameters, paramname : paramvalue
      keys-value pairs include:
  
      * always: all fitted parameters of this object, as via `get_param_names`
        values are fitted parameter value for that key, of this object
      * if `deep=True`, also contains keys/value pairs of component parameters
        parameters of components are indexed as `[componentname]__[paramname]`
        all parameters of `componentname` appear as `paramname` with its value
      * if `deep=True`, also contains arbitrary levels of component recursion,
        e.g., `[componentname]__[componentcomponentname]__[paramname]`, etc
code: def get_fitted_params(self, deep=True):
      """Get fitted parameters.
  
      State required:
          Requires state to be "fitted".
  
      Parameters
      ----------
      deep : bool, default=True
          Whether to return fitted parameters of components.
  
          * If True, will return a dict of parameter name : value for this object,
            including fitted parameters of fittable components
            (= BaseEstimator-valued parameters).
          * If False, will return a dict of parameter name : value for this object,
            but not include fitted parameters of components.
  
      Returns
      -------
      fitted_params : dict with str-valued keys
          Dictionary of fitted parameters, paramname : paramvalue
          keys-value pairs include:
  
          * always: all fitted parameters of this object, as via `get_param_names`
            values are fitted parameter value for that key, of this object
          * if `deep=True`, also contains keys/value pairs of component parameters
            parameters of components are indexed as `[componentname]__[paramname]`
            all parameters of `componentname` appear as `paramname` with its value
          * if `deep=True`, also contains arbitrary levels of component recursion,
            e.g., `[componentname]__[componentcomponentname]__[paramname]`, etc
      """
      # if self is not vectorized, run the default get_fitted_params
      if not getattr(self, "_is_vectorized", False):
          return super().get_fitted_params(deep=deep)
  
      # otherwise, we delegate to the instances' get_fitted_params
      # instances' parameters are returned at dataframe-slice-like keys
      fitted_params = {}
  
      # transformers contains a pd.DataFrame with the individual transformers
      transformers = self.transformers_
  
      # return transformers in the "transformers" param
      fitted_params["transformers"] = transformers
  
      def _to_str(x):
          if isinstance(x, str):
              x = f"'{x}'"
          return str(x)
  
      # populate fitted_params with transformers and their parameters
      for ix, col in product(transformers.index, transformers.columns):
          trafo = transformers.loc[ix, col]
          trafo_key = f"transformers.loc[{_to_str(ix)},{_to_str(col)}]"
          fitted_params[trafo_key] = trafo
          trafo_params = trafo.get_fitted_params(deep=deep)
          for key, val in trafo_params.items():
              fitted_params[f"{trafo_key}__{key}"] = val
  
      return fitted_params
  
imports: from itertools import product
  from typing import Union
  import numpy
  import pandas
  from sktime.base import BaseEstimator
  from sktime.datatypes import VectorizedDF, check_is_error_msg, check_is_mtype, check_is_scitype, convert, convert_to, mtype_to_scitype, update_data
  from sktime.datatypes._series_as_panel import convert_to_scitype
  from sktime.utils.sklearn import is_sklearn_classifier, is_sklearn_clusterer, is_sklearn_regressor, is_sklearn_transformer
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 15 for calling the BaseTransformer obj's underlying member method code with example usage: obj.get_fitted_params(deep) and returns: fitted_params, super, str