class_docstr: Applies a set of data transformations individually to an input time series.
  Stacks all of the results into a multivariate time series.
class_docstr_tok: ["Applies","a","set","of","data","transformations","individually","to","an","input","time","series",".","Stacks","all","of","the","results","into","a","multivariate","time","series","."]
docstr: A stacked transform is invertible if and only if at least one of the transforms comprising it are invertible.
docstr_tok: ["A","stacked","transform","is","invertible","if","and","only","if","at","least","one","of","the","transforms","comprising","it","are","invertible","."]
code: def proper_inversion(self):
      """
      A stacked transform is invertible if and only if at least one of the transforms comprising it are invertible.
      """
      return any(f.proper_inversion for f in self.transforms)
  
imports: from collections import OrderedDict
  import logging
  from typing import List
  from merlion.transform.base import TransformBase, InvertibleTransformBase, Identity
  from merlion.transform.factory import TransformFactory
  from merlion.utils import TimeSeries
label_desc: Use image node_id 2 for calling the TransformStack obj's underlying member method code with example usage: obj.proper_inversion() and returns: any