class_docstr: Apply a list of transforms sequentially.
class_docstr_tok: ["Apply","a","list","of","transforms","sequentially","."]
code: def __str__(self):
      repr_each_tfm = ",\n".join(["  " + repr(x) for x in self.tfms])
      return "imgaug.TransformList([\n{}])".format(repr_each_tfm)
  
imports: import numpy
  import cv2
  from ...utils.argtools import log_once
  from .base import ImageAugmentor, _default_repr
label_desc: Use image node_id 4 for calling the TransformList obj's underlying member method code with example usage: obj.__str__() and returns: str