class_docstr: Randomly select one augmenter to apply, with chance to skip all.
  
  Parameters
  ----------
  aug_list : list of DetAugmenter
      The random selection will be applied to one of the augmenters
  skip_prob : float
      The probability to skip all augmenters and return input directly
class_docstr_tok: ["Randomly","select","one","augmenter","to","apply",",","with","chance","to","skip","all",".","Parameters","--","--","--","--","--","aug_list",":","list","of","DetAugmenter","The","random","selection","will","be","applied","to","one","of","the","augmenters","skip_prob",":","float","The","probability","to","skip","all","augmenters","and","return","input","directly"]
docstr: Override default.
docstr_tok: ["Override","default","."]
code: def dumps(self):
      """Override default."""
      return [
          self.__class__.__name__.lower(),
          [x.dumps() for x in self.aug_list],
      ]
  
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 DetRandomSelectAug obj's underlying member method code with example usage: obj.dumps() and returns: list