class_docstr: Remove seasonal components from a time series.
  
  Applies `statsmodels.tsa.seasonal.seasonal_compose` and removes the `seasonal`
  component in `transform`. Adds seasonal component back again in `inverse_transform`.
  Seasonality removal can be additive or multiplicative.
  
  `fit` computes :term:`seasonal components <Seasonality>` and
  stores them in `seasonal_` attribute.
  
  `transform` aligns seasonal components stored in `seasonal_` with
  the time index of the passed :term:`series <Time series>` and then
  subtracts them ("additive" model) from the passed :term:`series <Time series>`
  or divides the passed series by them ("multiplicative" model).
  
  Parameters
  ----------
  sp : int, default=1
      Seasonal periodicity.
  model : {"additive", "multiplicative"}, default="additive"
      Model to use for estimating seasonal component.
  
  Attributes
  ----------
  seasonal_ : array of length sp
      Seasonal components computed in seasonal decomposition.
  
  See Also
  --------
  ConditionalDeseasonalizer
  
  Notes
  -----
  For further explanation on seasonal components and additive vs.
  multiplicative models see
  `Forecasting: Principles and Practice <https://otexts.com/fpp3/components.html>`_.
  Seasonal decomposition is computed using `statsmodels
  <https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html>`_.
  
  Examples
  --------
  >>> from sktime.transformations.series.detrend import Deseasonalizer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()  # doctest: +SKIP
  >>> transformer = Deseasonalizer()  # doctest: +SKIP
  >>> y_hat = transformer.fit_transform(y)  # doctest: +SKIP
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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.
      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`
      """
      params = {}
  
      params2 = {"sp": 2}
  
      return [params, params2]
  
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 9 for calling the Deseasonalizer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list