code: def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
      hparams = hparams or _HPARAMS_DEFAULT
      self.aug_fn = NAME_TO_OP[name]
      self.level_fn = LEVEL_TO_ARG[name]
      self.prob = prob
      self.magnitude = magnitude
      self.hparams = hparams.copy()
      self.kwargs = {
          "fillcolor": hparams["img_mean"]
          if "img_mean" in hparams
          else _FILL,
          "resample": hparams["interpolation"]
          if "interpolation" in hparams
          else _RANDOM_INTERPOLATION,
      }
  
      # If magnitude_std is > 0, we introduce some randomness
      # in the usually fixed policy and sample magnitude from a normal distribution
      # with mean `magnitude` and std-dev of `magnitude_std`.
      self.magnitude_std = self.hparams.get("magnitude_std", 0)
  
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 AugmentOp object  with example: obj = AugmentOp(name, prob, magnitude, hparams)