class_docstr: Wrap an existing transformer to tune whether to include it in a pipeline.
  
  Allows tuning the implicit hyperparameter whether or not to use a
  particular transformer inside a pipeline (e.g. TransformedTargetForecaster)
  or not. This is achieved by the hyperparameter `passthrough`
  which can be added to a tuning grid then (see example).
  
  Parameters
  ----------
  transformer : Estimator
      scikit-learn-like or sktime-like transformer to fit and apply to series.
      this is a "blueprint" transformer, state does not change when `fit` is called
  passthrough : bool, default=False
     Whether to apply the given transformer or to just
      passthrough the data (identity transformation). If, True the transformer
      is not applied and the OptionalPassthrough uses the identity
      transformation.
  
  Attributes
  ----------
  transformer_: transformer,
      this clone is fitted when `fit` is called and provides `transform` and inverse
      if passthrough = False, a clone of `transformer`passed
      if passthrough = True, the identity transformer `Id`
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.transformations.compose import OptionalPassthrough
  >>> from sktime.transformations.series.detrend import Deseasonalizer
  >>> from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  >>> from sktime.forecasting.compose import TransformedTargetForecaster
  >>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
  >>> from sktime.split import SlidingWindowSplitter
  >>> from sklearn.preprocessing import StandardScaler
  >>> # create pipeline
  >>> pipe = TransformedTargetForecaster(steps=[
  ...     ("deseasonalizer", OptionalPassthrough(Deseasonalizer())),
  ...     ("scaler", OptionalPassthrough(TabularToSeriesAdaptor(StandardScaler()))),
  ...     ("forecaster", NaiveForecaster())])  # doctest: +SKIP
  >>> # putting it all together in a grid search
  >>> cv = SlidingWindowSplitter(
  ...     initial_window=60,
  ...     window_length=24,
  ...     start_with_window=True,
  ...     step_length=48)  # doctest: +SKIP
  >>> param_grid = {
  ...     "deseasonalizer__passthrough" : [True, False],
  ...     "scaler__transformer__transformer__with_mean": [True, False],
  ...     "scaler__passthrough" : [True, False],
  ...     "forecaster__strategy": ["drift", "mean", "last"]}  # doctest: +SKIP
  >>> gscv = ForecastingGridSearchCV(
  ...     forecaster=pipe,
  ...     param_grid=param_grid,
  ...     cv=cv,
  ...     n_jobs=-1)  # doctest: +SKIP
  >>> gscv_fitted = gscv.fit(load_airline())  # 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.
  
  
  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`
docstr_tok: ["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","`"]
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.transformations.series.boxcox import BoxCoxTransformer
  
      return {"transformer": BoxCoxTransformer(), "passthrough": False}
  
imports: from sktime.transformations._delegate import _DelegatedTransformer
  from sktime.transformations.compose._common import CORE_MTYPES
  from sktime.transformations.compose._id import Id
label_desc: Use image node_id 2 for calling the OptionalPassthrough obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict