class_docstr: Apply a list of transforms sequentially.
class_docstr_tok: ["Apply","a","list","of","transforms","sequentially","."]
docstr: Args:
      tfms (list[Transform]):
docstr_tok: ["Args",":","tfms","(","list","[","Transform","]",")",":"]
code: def __init__(self, tfms):
      """
      Args:
          tfms (list[Transform]):
      """
      for t in tfms:
          assert isinstance(t, BaseTransform), t
      self.tfms = tfms
  
imports: import numpy
  import cv2
  from ...utils.argtools import log_once
  from .base import ImageAugmentor, _default_repr
label_desc: Use image node_id 1 to create a new TransformList object from inherited base classes: BaseTransform with example: obj = TransformList(tfms)