code: def preload(self):
      try:
          self.next_input, self.next_target = next(self.loader)
      except StopIteration:
          self.next_input = None
          self.next_target = None
          return
      with torch.cuda.stream(self.stream):
          self.next_input = self.next_input.cuda(non_blocking=True)
          self.next_target = self.next_target.cuda(non_blocking=True)
          self.next_input = self.next_input.float()
          self.next_input = self.next_input.sub_(self.mean).div_(
              self.std
          )
  
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 data_prefetcher obj's underlying member method code with example usage: obj.preload() without return types