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 _check_param_names(param_names):
      if isinstance(param_names, str):
          param_names = [param_names]
      elif isinstance(param_names, (list, tuple)):
          for param in param_names:
              if not isinstance(param, str):
                  raise ValueError(
                      f"All elements of `param_names` must be strings, "
                      f"but found: {type(param)}"
                  )
      else:
          raise ValueError(
              f"`param_names` must be str, or a list or tuple of strings, "
              f"but found: {type(param_names)}"
          )
      return 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 3 for calling the FittedParamExtractor obj's underlying member method code with example usage: obj._check_param_names(param_names) and returns: param_names