class_docstr: Calculate summary value of a time series.
  
  For :term:`univariate time series` a combination of summary functions and
  quantiles of the input series are calculated. If the input is a
  :term:`multivariate time series` then the summary functions and quantiles
  are calculated separately for each column.
  
  Parameters
  ----------
  summary_function : str, list, tuple, or None, default=("mean", "std", "min", "max")
      If not None, a string, or list or tuple of strings indicating the pandas
      summary functions that are used to summarize each column of the dataset.
      Must be one of ("mean", "min", "max", "median", "sum", "skew", "kurt",
      "var", "std", "mad", "sem", "nunique", "count").
      If None, no summaries are calculated, and quantiles must be non-None.
  quantiles : str, list, tuple or None, default=(0.1, 0.25, 0.5, 0.75, 0.9)
      Optional list of series quantiles to calculate. If None, no quantiles
      are calculated, and summary_function must be non-None.
  flatten_transform_index : bool, optional (default=True)
      if True, columns of return DataFrame are flat, by "variablename__feature"
      if False, columns are MultiIndex (variablename__feature)
      has no effect if return mtype is one without column names
  
  See Also
  --------
  WindowSummarizer:
      Extracting features across (shifted) windows from series
  
  Notes
  -----
  This provides a wrapper around pandas DataFrame and Series agg and
  quantile methods.
  
  Examples
  --------
  >>> from sktime.transformations.series.summarize import SummaryTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = SummaryTransformer()
  >>> y_mean = transformer.fit_transform(y)
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`
      """
      params1 = {}
      params2 = {
          "summary_function": ["mean", "mad", "skew"],
          "quantiles": None,
      }
      params3 = {"summary_function": ["mad"], "quantiles": (0.7,)}
      params4 = {
          "summary_function": None,
          "quantiles": (0.1, 0.2, 0.25),
      }
  
      return [params1, params2, params3, params4]
  
imports: import pandas
  from joblib import Parallel, delayed
  from sktime.split import ExpandingWindowSplitter, SlidingWindowSplitter
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.multiindex import flatten_multiindex
label_desc: Use image node_id 3 for calling the SummaryTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list