class_docstr: Randomly choose one of the best 25 Sub-policies on SVHN.
  Example:
  >>> policy = SVHNPolicy()
  >>> transformed = policy(image)
  Example as a PyTorch Transform:
  >>> transform=transforms.Compose([
  >>>     transforms.Resize(256),
  >>>     SVHNPolicy(),
  >>>     transforms.ToTensor()])
class_docstr_tok: ["Randomly","choose","one","of","the","best","25","Sub-policies","on","SVHN",".","Example",":",">",">",">","policy","=","SVHNPolicy","(",")",">",">",">","transformed","=","policy","(","image",")","Example","as","a","PyTorch","Transform",":",">",">",">","transform=transforms.Compose","(","[",">",">",">","transforms.Resize","(","256",")",",",">",">",">","SVHNPolicy","(",")",",",">",">",">","transforms.ToTensor","(",")","]",")"]
code: def __call__(self, img):
      policy_idx = random.randint(0, len(self.policies) - 1)
      return self.policies[policy_idx](img)
  
imports: import math
  import random
  import numpy
  import torch
  import torch.distributed
  from PIL import Image, ImageEnhance, ImageOps
  from torch.utils.data import Sampler
label_desc: Use image node_id 2 for calling the SVHNPolicy obj's underlying member method code with example usage: obj.__call__(img) and returns: self