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: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      from sktime.forecasting.exp_smoothing import ExponentialSmoothing
      from sktime.forecasting.trend import TrendForecaster
      from sktime.utils.validation._dependencies import (
          _check_estimator_deps,
      )
  
      # accessing a nested parameter
      params = [
          {
              "forecaster": TrendForecaster(),
              "param_names": ["regressor__intercept"],
          }
      ]
  
      # ExponentialSmoothing requires statsmodels
      if _check_estimator_deps(ExponentialSmoothing, severity="none"):
          # accessing a top level parameter
          params = params + [
              {
                  "forecaster": ExponentialSmoothing(),
                  "param_names": ["initial_level"],
              }
          ]
  
      return params
  
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 4 for calling the FittedParamExtractor obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params