code: def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      maxk = max(topk)
      batch_size = target.size(0)
  
      _, pred = output.topk(maxk, 1, True, True)
      pred = pred.t()
      correct = pred.eq(target.view(1, -1).expand_as(pred))
  
      res = []
      for k in topk:
          correct_k = (
              correct[:k].reshape(-1).float().sum(0, keepdim=True)
          )
          res.append(correct_k.mul_(100.0 / batch_size))
      return res
  
imports: import os
  import sys
  import time
  import functools
  import logging
  from contextlib import contextmanager
  from functools import wraps
  import numpy
  import torch.nn
  import torch
label_desc: Use image node_id 5 for calling a global function with example usage: accuracy(output, target, topk) and returns: res