class_docstr: Remove seasonal components from a time-series using STL.
  
  Interfaces ``statsmodels.tsa.seasonal.STL`` as an sktime transformer.
  
  ``STLTransformer`` can be used to perform deseasonalization or decomposition:
  
  If ``return_components=False``, it will return the deseasonalized series, i.e.,
  the trend component from ``statsmodels`` ``STL``.
  
  If ``return_components=True``, it will transform the series into a decomposition
  of component, returning the trend, seasonal, and residual components.
  
  ``STLTransformer`` performs ``inverse_transform`` by summing any components,
  and can be used for pipelining in a ``TransformedTargetForecaster``.
  
  Important: for separate forecasts of trend and seasonality, and an
  inverse transform that respects seasonality, ensure
  that ``return_components=True`` is set, otherwise the inverse will just
  return the trend component.
  
  An alternative for pipeline-style composition is ``STLForecaster``.
  
  Parameters
  ----------
  sp : int, default=1
      Seasonal periodicity.
  seasonal : int, default=7
      Length of the seasonal smoother. Must be an odd integer, and should
      normally be >= 7 (default).
  trend : {int, default=None}
      Length of the trend smoother. Must be an odd integer. If not provided
      uses the smallest odd integer greater than
      1.5 * period / (1 - 1.5 / seasonal), following the suggestion in
      the original implementation.
  low_pass : {int, default=None}
      Length of the low-pass filter. Must be an odd integer >=3. If not
      provided, uses the smallest odd integer > period.
  seasonal_deg : int, default=1
      Degree of seasonal LOESS. 0 (constant) or 1 (constant and trend).
  trend_deg : int, default=1
      Degree of trend LOESS. 0 (constant) or 1 (constant and trend).
  low_pass_deg : int, default=1
      Degree of low pass LOESS. 0 (constant) or 1 (constant and trend).
  robust : bool, default False
      Flag indicating whether to use a weighted version that is robust to
      some forms of outliers.
  seasonal_jump : int, default=1
      Positive integer determining the linear interpolation step. If larger
      than 1, the LOESS is used every seasonal_jump points and linear
      interpolation is between fitted points. Higher values reduce
      estimation time.
  trend_jump : int, default=1
      Positive integer determining the linear interpolation step. If larger
      than 1, the LOESS is used every trend_jump points and values between
      the two are linearly interpolated. Higher values reduce estimation
      time.
  low_pass_jump : int, default=1
      Positive integer determining the linear interpolation step. If larger
      than 1, the LOESS is used every low_pass_jump points and values between
      the two are linearly interpolated. Higher values reduce estimation
      time.
  return_components : bool, default=False
      if False, will return only the trend component
      if True, will return the transformed series, as well as three components
          as variables in the returned multivariate series (DataFrame cols)
          "transformed" - the transformed series
          "seasonal" - the seasonal component
          "trend" - the trend component
          "resid" - the residuals after de-trending, de-seasonalizing
  
  Attributes
  ----------
  trend_ : pd.Series
      Trend component of series seen in fit.
  seasonal_ : pd.Series
      Seasonal components of series seen in fit.
  resid_ : pd.Series
      Residuals component of series seen in fit.
  
  See Also
  --------
  Detrender
  Deseasonalizer
  STLForecaster
  
  References
  ----------
  .. [1] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.transformations.series.detrend import STLTransformer
  >>> X = load_airline()  # doctest: +SKIP
  >>> transformer = STLTransformer(sp=12)  # doctest: +SKIP
  >>> Xt = transformer.fit_transform(X)  # doctest: +SKIP
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docstr: Return testing parameter settings for the estimator.
  
  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):
      """Return testing parameter settings for the estimator.
  
      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`
      """
      # test case 1: all default parameters
      params1 = {}
  
      # test case 2: return all components
      params2 = {"return_components": True}
  
      # test case 3: seasonality parameter set, from the skipped doctest
      params3 = {"sp": 12}
  
      return [params1, params2, params3]
  
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.datetime import _get_duration, _get_freq
  from sktime.utils.seasonality import autocorrelation_seasonality_test
  from sktime.utils.validation.forecasting import check_sp
label_desc: Use image node_id 6 for calling the STLTransformer obj's underlying member method code with example usage: obj.get_test_params(cls) and returns: list