class_docstr: Borrow standard augmenter from image classification.
  Which is good once you know label won't be affected after this augmenter.
  
  Parameters
  ----------
  augmenter : mx.image.Augmenter
      The borrowed standard augmenter which has no effect on label
class_docstr_tok: ["Borrow","standard","augmenter","from","image","classification",".","Which","is","good","once","you","know","label","wo","n't","be","affected","after","this","augmenter",".","Parameters","--","--","--","--","--","augmenter",":","mx.image.Augmenter","The","borrowed","standard","augmenter","which","has","no","effect","on","label"]
docstr: Override the default one to avoid duplicate dump.
docstr_tok: ["Override","the","default","one","to","avoid","duplicate","dump","."]
code: def dumps(self):
      """Override the default one to avoid duplicate dump."""
      return [self.__class__.__name__.lower(), self.augmenter.dumps()]
  
imports: import json
  import logging
  import random
  import warnings
  import numpy
  from ..base import numeric_types
  from ..None import ndarray
  from ..ndarray._internal import _cvcopyMakeBorder
  from ..None import io
  from .image import RandomOrderAug, ColorJitterAug, LightingAug, ColorNormalizeAug
  from .image import ResizeAug, ForceResizeAug, CastAug, HueJitterAug, RandomGrayAug
  from .image import fixed_crop, ImageIter, Augmenter
  from ..util import is_np_array
  from ..None import numpy
label_desc: Use image node_id 2 for calling the DetBorrowAug obj's underlying member method code with example usage: obj.dumps() and returns: list