class_docstr: Applies a series of data transformations sequentially.
class_docstr_tok: ["Applies","a","series","of","data","transformations","sequentially","."]
code: def __init__(self, transforms: List[TransformBase]):
      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.extend(self._extract_nontrivial_transforms(t))
  
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 TransformSequence object from inherited base classes: InvertibleTransformBase with example: obj = TransformSequence(transforms)