class_docstr: Applies a series of data transformations sequentially.
class_docstr_tok: ["Applies","a","series","of","data","transformations","sequentially","."]
code: def train(self, time_series: TimeSeries):
      for f in self.transforms:
          f.train(time_series)
          time_series = f(time_series)
  
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 9 for calling the TransformSequence obj's underlying member method code with example usage: obj.train(time_series) without return types