code: def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      with torch.no_grad():
          maxk = max(topk)
          batch_size = target.size(0)
  
          _, pred = output.topk(maxk, 1, True, True)
          pred = pred.t()
          correct = pred.eq(target.reshape(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 collections
  import torch
  from torch import Tensor
  import torch.nn
  import torch.backends.cudnn
  import subprocess
  import os
  import time
  import shutil
  from datetime import datetime
  import torch.optim
  from torch.optim import lr_scheduler
  import sys
  import models
  import numpy
  import torch.nn
  import math
label_desc: Use image node_id 6 for calling a global function with example usage: accuracy(output, target, topk) and returns: res