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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code: def __init__(self, forecaster, param_names, n_jobs=None):
      self.forecaster = forecaster
      self.param_names = param_names
      self.n_jobs = n_jobs
      super().__init__()
  
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 1 to create a new FittedParamExtractor object from inherited base classes: BaseTransformer with example: obj = FittedParamExtractor(forecaster, param_names, n_jobs)