class_docstr: Create a RandAugment transform.
  :param magnitude: integer magnitude of rand augment
  :param magnitude_std: standard deviation of magnitude. If > 0, introduces
  random variability in the augmentation magnitude.
  :param num_layers: integer number of transforms
  :param increasing_severity: boolean that indicates whether to use
  augmentations that increase severity w/ increasing magnitude. Some
  augmentations do this by default.
  :param weight_choice: Index of pre-determined probability distribution
  over augmentations. Currently only one such distribution available (i.e.
  no valid values other than 0 or None), unclear if beneficial. Default =
  None.
code: def __init__(
      self,
      magnitude=10,
      magnitude_std=0,
      num_layers=2,
      increasing_severity=False,
      weight_choice=None,
      **kwargs,
  ):
      hparams = kwargs
      hparams.update(_HPARAMS_DEFAULT)
      hparams["magnitude_std"] = magnitude_std
      if increasing_severity:
          transforms = _RAND_INCREASING_TRANSFORMS
      else:
          transforms = _RAND_TRANSFORMS
      self.num_layers = num_layers
      self.choice_weights = (
          None
          if weight_choice is None
          else _select_rand_weights(weight_choice)
      )
      self.ops = rand_augment_ops(
          magnitude=magnitude, hparams=hparams, transforms=transforms
      )
  
imports: import math
  import random
  import re
  import numpy
  import PIL
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from PIL import Image, ImageEnhance, ImageOps
label_desc: Use image node_id 1 to create a new RandAugment object from inherited base classes: ClassyTransform with example: obj = RandAugment(magnitude, magnitude_std, num_layers, increasing_severity, weight_choice)