class_docstr: Transform by instance or panel.
  
  Used to apply multiple copies of `transformer` by instance or by panel.
  
  If `groupby="global"`, behaves like `transformer`.
  If `groupby="local"`, fits a clone of `transformer` per time series instance.
  If `groupby="panel"`, fits a clone of `transformer` by panel (first non-time level).
  
  The fitted transformers can be accessed in the `transformers_` attribute,
  if more than one clone is fitted, otherwise in the `transformer_` attribute.
  
  Parameters
  ----------
  transformer : sktime transformer used in TransformByLevel
      A "blueprint" transformer, 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 `transformer`
      "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
  raise_warnings : bool, optional, default=True
      whether to warn the user if `transformer` is instance-wise
      in this case wrapping the `transformer` om `TransformByLevel` does not change
      the estimator logic, compared to not wrapping it.
      Wrapping this way can make sense in some cases of tuning,
      in which case `warn=False` can be set to suppress the warning raised.
  
  Attributes
  ----------
  transformer_ : sktime transformer, present only if `groupby` is "global"
      clone of `transformer` used for fitting and transformation
  transformers_ : pd.DataFrame of sktime transformer, present otherwise
      entries are clones of `transformer` used for fitting and transformation
  
  Examples
  --------
  >>> from sktime.transformations.hierarchical.reconcile import Reconciler
  >>> from sktime.utils._testing.hierarchical import _make_hierarchical
  >>> X = _make_hierarchical()
  >>> f = TransformByLevel(Reconciler(), groupby="panel")
  >>> f.fit(X)
  TransformByLevel(...)
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.transformations.series.time_since import TimeSince
  
      groupbys = ["local", "panel", "global"]
  
      t = TimeSince()
  
      params = [{"transformer": t, "groupby": g} for g in groupbys]
  
      return params
  
imports: from sktime.datatypes import ALL_TIME_SERIES_MTYPES, mtype_to_scitype
  from sktime.transformations._delegate import _DelegatedTransformer
  from sktime.utils.warnings import warn
label_desc: Use image node_id 2 for calling the TransformByLevel obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params