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","."]
code: def __init__(self, transforms, *, check_aligned=True):
      super().__init__()
      self.transforms = []
      for t in transforms:
          assert isinstance(
              t, (TransformBase, dict)
          ), f"Expected all transforms to be instances of TransformBase, or dict, but got {transforms}"
          if isinstance(t, dict):
              t = TransformFactory.create(**t)
          self.transforms.append(t)
      self.check_aligned = check_aligned
  
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 1 to create a new TransformStack object from inherited base classes: InvertibleTransformBase with example: obj = TransformStack(transforms)