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: Transform X and return a transformed version.
  
  private _transform containing the core logic, called from transform
  
  Parameters
  ----------
  X : pd.Series or pd.DataFrame
      Data to be transformed
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  Xt : pd.DataFrame
      The transformed Data
docstr_tok: ["Transform","X","and","return","a","transformed","version",".","private","_transform","containing","the","core","logic",",","called","from","transform","Parameters","--","--","--","--","--","X",":","pd.Series","or","pd.DataFrame","Data","to","be","transformed","y",":","ignored","argument","for","interface","compatibility","Additional","data",",","e.g.",",","labels","for","transformation","Returns","--","--","--","-","Xt",":","pd.DataFrame","The","transformed","Data"]
code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing the core logic, called from transform
  
      Parameters
      ----------
      X : pd.Series or pd.DataFrame
          Data to be transformed
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      Xt : pd.DataFrame
          The transformed Data
      """
      if self.remember_data:
          X = X.combine_first(self._X)
  
      transformed_series = []
      splits = self._splitter.split_series(X)
  
      for split in splits:
          tf = self.transformer.clone()
          transformed_split = tf.fit_transform(split[0])
          transformed_split.index = [split[0].index[-1]]
          transformed_series.append(transformed_split)
  
      Xt = pd.concat(transformed_series)
  
      if self.index != "last":
          Xt = Xt.reset_index(drop=True)
  
      return Xt
  
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 2 for calling the SplitterSummarizer obj's underlying member method code with example usage: obj._transform(X, y) and returns: Xt