class_docstr: Forecast by instance or panel.
  
  Used to apply multiple copies of `forecaster` by instance or by panel.
  
  If `groupby="global"`, behaves like `forecaster`.
  If `groupby="local"`, fits a clone of `forecaster` per time series instance.
  If `groupby="panel"`, fits a clone of `forecaster` per panel (first non-time level).
  
  The fitted forecasters can be accessed in the `forecasters_` attribute,
  if more than one clone is fitted, otherwise in the `forecaster_` attribute.
  
  Parameters
  ----------
  forecaster : sktime forecaster used in ForecastByLevel
      A "blueprint" forecaster, state does not change when `fit` is called.
  groupby : str, one of ["local", "global", "panel"], optional, default="local"
      level on which data are grouped to fit clones of `forecaster`
      "local" = unit/instance level, one reduced model per lowest hierarchy level
      "global" = top level, one reduced model overall, on pooled data ignoring levels
      "panel" = second lowest level, one reduced model per panel level (-2)
      if there are 2 or less levels, "global" and "panel" result in the same
      if there is only 1 level (single time series), all three settings agree
  
  Attributes
  ----------
  forecaster_ : sktime forecaster, present only if `groupby` is "global"
      clone of `forecaster` used for fitting and forecasting
  forecasters_ : pd.DataFrame of sktime forecaster, present otherwise
      entries are clones of `forecaster` used for fitting and forecasting
  
  Examples
  --------
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.forecasting.compose import ForecastByLevel
  >>> from sktime.utils._testing.hierarchical import _make_hierarchical
  >>> y = _make_hierarchical()
  >>> f = ForecastByLevel(NaiveForecaster(), groupby="local")
  >>> f.fit(y)
  ForecastByLevel(...)
  >>> fitted_forecasters = f.forecasters_
  >>> fitted_forecasters_alt = f.get_fitted_params()["forecasters"]
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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.
  
  Returns
  -------
  params : dict or list of dict
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"]
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
      """
      from sktime.forecasting.naive import NaiveForecaster
  
      groupbys = ["local", "panel", "global"]
  
      f = NaiveForecaster()
  
      params = [{"forecaster": f, "groupby": g} for g in groupbys]
  
      return params
  
imports: from sktime.datatypes import ALL_TIME_SERIES_MTYPES, mtype_to_scitype
  from sktime.forecasting.base._delegate import _DelegatedForecaster
label_desc: Use image node_id 2 for calling the ForecastByLevel obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params