code: def accuracy(pred, target, topk=1, thresh=None):
      """Calculate accuracy according to the prediction and target.
  
      Args:
          pred (torch.Tensor): The model prediction, shape (N, num_class)
          target (torch.Tensor): The target of each prediction, shape (N, )
          topk (int | tuple[int], optional): If the predictions in ``topk``
              matches the target, the predictions will be regarded as
              correct ones. Defaults to 1.
          thresh (float, optional): If not None, predictions with scores under
              this threshold are considered incorrect. Default to None.
  
      Returns:
          float | tuple[float]: If the input ``topk`` is a single integer,
              the function will return a single float as accuracy. If
              ``topk`` is a tuple containing multiple integers, the
              function will return a tuple containing accuracies of
              each ``topk`` number.
      """
      assert isinstance(topk, (int, tuple))
      if isinstance(topk, int):
          topk = (topk,)
          return_single = True
      else:
          return_single = False
  
      maxk = max(topk)
      if pred.shape[0] == 0:
          accu = [pred.new_tensor(0.0) for i in range(len(topk))]
          return accu[0] if return_single else accu
      assert pred.ndim == 2 and target.ndim == 1
      assert pred.shape[0] == target.shape[0]
      assert (
          maxk <= pred.shape[1]
      ), f"maxk {maxk} exceeds pred dimension {pred.shape[1]}"
      pred_value, pred_label = pred.topk(maxk, axis=1)
      pred_label = pred_label.t()  # transpose to shape (maxk, N)
      correct = pred_label.equal(
          target.reshape([1, -1]).expand_as(pred_label)
      )
      if thresh is not None:
          # Only prediction values larger than thresh are counted as correct
          correct = correct & (pred_value > thresh).t()
      res = []
      for k in topk:
          correct_k = (
              correct[:k]
              .reshape([-1])
              .cast("float32")
              .sum(0, keepdim=True)
          )
          correct_k = correct_k * (100.0 / pred.shape[0])
          res.append(correct_k)
      return res[0] if return_single else res
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.modeling.initializer import constant_
  from paddle.nn.initializer import KaimingNormal
  import paddle.nn
label_desc: Use image node_id 2 for calling a global function with example usage: accuracy(pred, target, topk, thresh) and returns: unknown, unknown