code: def topks_correct(preds, labels, ks):
      """
      Given the predictions, labels, and a list of top-k values, compute the
      number of correct predictions for each top-k value.
  
      Args:
          preds (array): array of predictions. Dimension is batchsize
              N x ClassNum.
          labels (array): array of labels. Dimension is batchsize N.
          ks (list): list of top-k values. For example, ks = [1, 5] correspods
              to top-1 and top-5.
  
      Returns:
          topks_correct (list): list of numbers, where the `i`-th entry
              corresponds to the number of top-`ks[i]` correct predictions.
      """
      assert preds.size(0) == labels.size(
          0
      ), "Batch dim of predictions and labels must match"
      # Find the top max_k predictions for each sample
      _top_max_k_vals, top_max_k_inds = torch.topk(
          preds, max(ks), dim=1, largest=True, sorted=True
      )
      # (batch_size, max_k) -> (max_k, batch_size).
      top_max_k_inds = top_max_k_inds.t()
      # (batch_size, ) -> (max_k, batch_size).
      rep_max_k_labels = labels.view(1, -1).expand_as(top_max_k_inds)
      # (i, j) = 1 if top i-th prediction for the j-th sample is correct.
      top_max_k_correct = top_max_k_inds.eq(rep_max_k_labels)
      # Compute the number of topk correct predictions for each k.
      topks_correct = [
          top_max_k_correct[:k, :].float().sum() for k in ks
      ]
      return topks_correct
  
label_desc: Use image node_id 1 for calling a global function with example usage: topks_correct(preds, labels, ks) and returns: topks_correct