class_docstr: Fitted parameter extractor.
  
  Extract parameters of a fitted forecaster as features for a subsequent
  tabular learning task.
  This class first fits a forecaster to the given time series and then
  returns the fitted parameters.
  The fitted parameters can be used as features for a tabular estimator
  (e.g. classification).
  
  Parameters
  ----------
  forecaster : estimator object
      sktime estimator to extract features from
  param_names : str
      Name of parameters to extract from the forecaster.
  n_jobs : int, optional (default=None)
      Number of jobs to run in parallel.
      None means 1 unless in a joblib.parallel_backend context.
      -1 means using all processors.
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docstr: Transform X.
  
  Parameters
  ----------
  X : pd.DataFrame
      Univariate time series to transform.
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  Xt : pd.DataFrame
      Extracted parameters; columns are parameter values
docstr_tok: ["Transform","X.","Parameters","--","--","--","--","--","X",":","pd.DataFrame","Univariate","time","series","to","transform",".","y",":","ignored","argument","for","interface","compatibility","Additional","data",",","e.g.",",","labels","for","transformation","Returns","--","--","--","-","Xt",":","pd.DataFrame","Extracted","parameters",";","columns","are","parameter","values"]
code: def _transform(self, X, y=None):
      """Transform X.
  
      Parameters
      ----------
      X : pd.DataFrame
          Univariate time series to transform.
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      Xt : pd.DataFrame
          Extracted parameters; columns are parameter values
      """
      param_names = self._check_param_names(self.param_names)
      n_instances = X.shape[0]
  
      def _fit_extract(forecaster, x, param_names):
          forecaster.fit(x)
          params = forecaster.get_fitted_params()
          return np.hstack([params[name] for name in param_names])
  
      def _get_instance(X, key):
          # assuming univariate data
          if isinstance(X, pd.DataFrame):
              return X.iloc[key, 0]
          else:
              return pd.Series(X[key, 0])
  
      # iterate over rows
      extracted_params = Parallel(n_jobs=self.n_jobs)(
          delayed(_fit_extract)(
              self.forecaster.clone(), _get_instance(X, i), param_names
          )
          for i in range(n_instances)
      )
  
      return pd.DataFrame(extracted_params, columns=param_names)
  
imports: import numpy
  import pandas
  from joblib import Parallel, delayed
  from sktime.datatypes import convert_to
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.segment import RandomIntervalSegmenter
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 2 for calling the FittedParamExtractor obj's underlying member method code with example usage: obj._transform(X, y) and returns: pd, np, X, pd