code: def accuracy(
      pred: Tensor, target: Tensor, topk: Tuple[int, ...] = (1,)
  ) -> List[Tensor]:
      """Computes the accuracy over the k top predictions for the specified values of k.
  
      Args:
          pred: the input tensor with the logits to evaluate.
          target: the tensor containing the ground truth.
          topk: the expected topk ranking.
  
      Example:
          >>> logits = torch.tensor([[0, 1, 0]])
          >>> target = torch.tensor([[1]])
          >>> accuracy(logits, target)
          [tensor(100.)]
      """
      maxk = min(max(topk), pred.size()[1])
      batch_size = target.size(0)
      _, pred = pred.topk(maxk, 1, True, True)
      pred = pred.t()
      correct = pred.eq(target.reshape(1, -1).expand_as(pred))
      return [
          correct[: min(k, maxk)].reshape(-1).float().sum(0)
          * 100.0
          / batch_size
          for k in topk
      ]
  
label_desc: Use image node_id 1 for calling a global function with example usage: accuracy(pred, target, topk) and returns: unknown