code: def knn_predict(
      feature: Tensor,
      feature_bank: Tensor,
      feature_labels: Tensor,
      num_classes: int,
      knn_k: int = 200,
      knn_t: float = 0.1,
  ) -> Tensor:
      """Run kNN predictions on features based on a feature bank
  
      This method is commonly used to monitor performance of self-supervised
      learning methods.
  
      The default parameters are the ones
      used in https://arxiv.org/pdf/1805.01978v1.pdf.
  
      Args:
          feature:
              Tensor with shape (B, D) for which you want predictions.
          feature_bank:
              Tensor of shape (D, N) of a database of features used for kNN.
          feature_labels:
              Labels with shape (N,) for the features in the feature_bank.
          num_classes:
              Number of classes (e.g. `10` for CIFAR-10).
          knn_k:
              Number of k neighbors used for kNN.
          knn_t:
              Temperature parameter to reweights similarities for kNN.
  
      Returns:
          A tensor containing the kNN predictions
  
      Examples:
          >>> images, targets, _ = batch
          >>> feature = backbone(images).squeeze()
          >>> # we recommend to normalize the features
          >>> feature = F.normalize(feature, dim=1)
          >>> pred_labels = knn_predict(
          >>>     feature,
          >>>     feature_bank,
          >>>     targets_bank,
          >>>     num_classes=10,
          >>> )
      """
      # compute cos similarity between each feature vector and feature bank ---> (B, N)
      sim_matrix = torch.mm(feature, feature_bank)
      # (B, K)
      sim_weight, sim_indices = sim_matrix.topk(k=knn_k, dim=-1)
      # (B, K)
      sim_labels = torch.gather(
          feature_labels.expand(feature.size(0), -1),
          dim=-1,
          index=sim_indices,
      )
      # we do a reweighting of the similarities
      sim_weight = (sim_weight / knn_t).exp()
      # counts for each class
      one_hot_label = torch.zeros(
          feature.size(0) * knn_k, num_classes, device=sim_labels.device
      )
      # (B*K, C)
      one_hot_label = one_hot_label.scatter(
          dim=-1, index=sim_labels.view(-1, 1), value=1.0
      )
      # weighted score ---> (B, C)
      pred_scores = torch.sum(
          one_hot_label.view(feature.size(0), -1, num_classes)
          * sim_weight.unsqueeze(dim=-1),
          dim=1,
      )
      pred_labels = pred_scores.argsort(dim=-1, descending=True)
      return pred_labels
  
label_desc: Use image node_id 1 for calling a global function with example usage: knn_predict(feature, feature_bank, feature_labels, num_classes, knn_k, knn_t) and returns: pred_labels