code: def auc(
      targets: csr_matrix,
      user_ids: Union[np.array, torch.tensor],
      preds: Union[np.array, torch.tensor],
      k: Optional[Any] = None,
  ) -> float:
      """
      Calculate the area under the ROC curve (AUC) for each user and average the results.
  
      Parameters
      ----------
      targets: scipy.sparse.csr_matrix
          Interaction matrix containing user and item IDs
      user_ids: np.array or torch.tensor
          Users corresponding to the recommendations in the top k predictions
      preds: torch.tensor
          Tensor of shape (n_users x n_items) with each user's scores for each item
      k: Any
          Ignored, included only for compatibility with ``mapk``
  
      Returns
      -------
      auc_score: float
  
      """
      agg = 0
      for i, user_id in enumerate(user_ids):
          target_tensor = torch.tensor(
              targets[user_id].toarray(),
              device=preds.device,
              dtype=torch.long,
          ).view(-1)
          # many models' ``preds`` may be unbounded if a final activation layer is not applied
          # we have to normalize ``preds`` here to avoid a ``ValueError`` stating that ``preds``
          # should be probabilities, but values were detected outside of [0,1] range
          try:
              auc = auroc(
                  torch.sigmoid(preds[i, :]),
                  target=target_tensor,
                  pos_label=1,
              )
          except (
              TypeError
          ):  # compatible with old ``aucroc`` API used in versions prior to ``1.6``
              auc = auroc(
                  torch.sigmoid(preds[i, :]),
                  target=target_tensor,
                  task="binary",
              )
          agg += auc
  
      return (agg / len(user_ids)).item()
  
imports: from typing import Any, Callable, Iterable, List, Optional, Tuple, Union
  import warnings
  import numpy
  from scipy.sparse import csr_matrix
  import torch
  from torchmetrics import Metric
  from torchmetrics.functional import auroc
  from tqdm.auto import tqdm
  import collie
  from collie.interactions import ExplicitInteractions, Interactions, InteractionsDataLoader
  from collie.model import BasePipeline
label_desc: Use image node_id 6 for calling a global function with example usage: auc(targets, user_ids, preds, k) and returns: unknown