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)
code: def __init__(
      self,
      summary_function=("mean", "std", "min", "max"),
      quantiles=(0.1, 0.25, 0.5, 0.75, 0.9),
      flatten_transform_index=True,
  ):
      self.summary_function = summary_function
      self.quantiles = quantiles
      self.flatten_transform_index = flatten_transform_index
  
      super().__init__()
  
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 1 to create a new SummaryTransformer object from inherited base classes: BaseTransformer with example: obj = SummaryTransformer(summary_function, quantiles, flatten_transform_index)