class_docstr: Create summary values of a time series' splits.
  
  A series-to-series transformer that applies the series-to-primitives transformer
  ``transformer`` to each train split created using the splitter ``splitter``.
  
  The i-th row of the resulting series is equivalent to
  ``transformer.fit_transform(splitter.split_series(X)[i][0])``,
  where ``X`` is the data seen in ``transform``, if ``remember_data=False``; or
  where ``X`` is all data seen in ``fit``, ``transform``, if ``remember_data=True``
  
  The output series aims to provide a summarization of the input series based on the
  given transformer and splitter.
  
  Parameters
  ----------
  transformer : `sktime` transformer inheriting from `BaseTransformer`
  series-to-primitives transformer used to convert series to primitives.
  
  splitter : `sktime` splitter inheriting from `BaseSplitter`, optional (default=None)
  splitter used to divide the series.
  If None, it takes `ExpandingWindowSplitter` with `start_with_window=False`
  and otherwise default parameters.
  
  index : str, optional (default="last")
  Determines the indexing approach for the resulting series.
  If "last", the latest index of the split is used.
  If anything else, the row's number becomes the index.
  
  remember_data : bool, optional (default=True)
  if True, memorizes data seen in ``fit``, ``update``, uses it in ``transform``
  if False, only uses data seen in ``transform`` for splits and summaries.
  
  Methods
  -------
  transform(X) : Transforms the series according to the specified
      series-to-primitives transformer and splitter.
  
  See Also
  --------
  SummaryTransformer: Calculates summary value of a time series.
  
  Examples
  --------
  >>> from sktime.transformations.series.summarize import SplitterSummarizer
  >>> from sktime.transformations.series.summarize import SummaryTransformer
  >>> from sktime.split import ExpandingWindowSplitter
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = SplitterSummarizer(
  ...     transformer=SummaryTransformer(),
  ...     splitter=ExpandingWindowSplitter())
  >>> y_splitsummarized = transformer.fit_transform(y)
docstr: Return testing parameter settings for the transformer.
  
  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`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the transformer.
  
      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 = {
          "transformer": SummaryTransformer(),
          "splitter": ExpandingWindowSplitter(initial_window=3),
      }
  
      params2 = {
          "transformer": SummaryTransformer(
              summary_function=["mad"], quantiles=(0.7,)
          ),
          "splitter": SlidingWindowSplitter(
              window_length=3, step_length=2
          ),
          "index": None,
      }
  
      params3 = {
          "transformer": SummaryTransformer(),
          "splitter": SlidingWindowSplitter(
              window_length=3, step_length=2
          ),
          "remember_data": True,
      }
  
      params4 = {
          "transformer": SummaryTransformer(),
          "splitter": SlidingWindowSplitter(
              window_length=3,
              step_length=2,
              fh=1,
              start_with_window=True,
          ),
          "index": "last",
      }
  
      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 SplitterSummarizer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list