code: def _select_rand_weights(weight_idx=0, transforms=None):
      transforms = transforms or _RAND_TRANSFORMS
      assert weight_idx == 0  # only one set of weights currently
      rand_weights = _RAND_CHOICE_WEIGHTS_0
      probs = [rand_weights[k] for k in transforms]
      probs /= np.sum(probs)
      return probs
  
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 39 for calling a global function with example usage: _select_rand_weights(weight_idx, transforms) and returns: probs