code: def accuracy(output, target, topk=(1,)):
      """Computes the accuracy over the k top predictions for the specified values of k"""
      with torch.inference_mode():
          maxk = max(topk)
          batch_size = target.size(0)
          if target.ndim == 2:
              target = target.max(dim=1)[1]
  
          _, pred = output.topk(maxk, 1, True, True)
          pred = pred.t()
          correct = pred.eq(target[None])
  
          res = []
          for k in topk:
              correct_k = correct[:k].flatten().sum(dtype=torch.float32)
              res.append(correct_k * (100.0 / batch_size))
          return res
  
imports: import copy
  import datetime
  import errno
  import hashlib
  import os
  import time
  from collections import defaultdict, deque, OrderedDict
  from typing import List, Optional, Tuple
  import torch
  import torch.distributed
label_desc: Use image node_id 1 for calling a global function with example usage: accuracy(output, target, topk) and returns: res