class_docstr: Filter a times series using the Christiano Fitzgerald filter.
  
  This is a wrapper around the `cffilter` function from `statsmodels`.
  (see `statsmodels.tsa.filters.cf_filter.cffilter`).
  
  Parameters
  ----------
  low : float, optional, default = 6.0
      Minimum period of oscillations. Features below low periodicity
      are filtered out. For quarterly data, the default of 6 gives
      1.5 years periodicity.
  
  high : float, optional, default = 32.0
      Maximum period of oscillations. Features above high periodicity
      are filtered out. For quarterly data, the default of 32 gives
      8 year periodicity.
  
  drift : bool, optional, default = True
      Whether or not to subtract a trend from the data.
      The trend is estimated as np.arange(nobs)*(x[-1] -x[0])/(len(x)-1).
      > X : argument of CFFilter._transform()
      > x : If X is 1d, X=x. If 2d, x is assumed to be in columns.
      > nobs : len(x)
  
  Examples
  --------
  >>> from sktime.transformations.series.cffilter import CFFilter # doctest: +SKIP
  >>> import pandas as pd # doctest: +SKIP
  >>> import statsmodels.api as sm # doctest: +SKIP
  >>> dta = sm.datasets.macrodata.load_pandas().data # doctest: +SKIP
  >>> index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q') # doctest: +SKIP
  >>> dta.set_index(index, inplace=True) # doctest: +SKIP
  >>> cf = CFFilter(6, 24, True) # doctest: +SKIP
  >>> cycles = cf.fit_transform(X=dta[['realinv']]) # doctest: +SKIP
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.
      There are currently no reserved values for transformers.
  
  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.
          There are currently no reserved values for transformers.
  
      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`
      """
      params1 = {"low": 8, "high": 26, "drift": False}
      params2 = {}
      return [params1, params2]
  
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the CFFilter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list