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"]
code: def __init__(self, aug_list, skip_prob=0):
      super(DetRandomSelectAug, self).__init__(skip_prob=skip_prob)
      if not isinstance(aug_list, (list, tuple)):
          aug_list = [aug_list]
      for aug in aug_list:
          if not isinstance(aug, DetAugmenter):
              raise ValueError("Allow DetAugmenter in list only")
      if not aug_list:
          skip_prob = 1  # disabled
  
      self.aug_list = aug_list
      self.skip_prob = skip_prob
  
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 1 to create a new DetRandomSelectAug object from inherited base classes: DetAugmenter with example: obj = DetRandomSelectAug(aug_list, skip_prob)