code: def _check_quantiles(quantiles):
      """Validate quantiles.
  
      Parameters
      ----------
      quantiles : str, list, tuple or None
          Either a string or list/tuple of strings indicating the pandas summary
          functions ("mean", "min", "max", "median", "sum", "skew", "kurtosis",
          "var", "std", "mad", "sem", "nunique", "count") that is used to summarize
          each column of the dataset.
  
      Returns
      -------
      quantiles : list or tuple
          The validated quantiles that will be used to summarize the dataset.
      """
      msg = """`quantiles` must be None, int, float or a list or tuple made up of
            int and float values that are between 0 and 1.
            """
      if isinstance(quantiles, (int, float)):
          if not 0.0 <= quantiles <= 1.0:
              raise ValueError(msg)
          quantiles = [quantiles]
      elif isinstance(quantiles, (list, tuple)):
          if len(quantiles) == 0 or not all(
              [
                  isinstance(q, (int, float)) and 0.0 <= q <= 1.0
                  for q in quantiles
              ]
          ):
              raise ValueError(msg)
      elif quantiles is not None:
          raise ValueError(msg)
      return quantiles
  
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 4 for calling a global function with example usage: _check_quantiles(quantiles) and returns: quantiles