code: def retrieval_auroc(
      preds: Tensor,
      target: Tensor,
      top_k: Optional[int] = None,
      max_fpr: Optional[float] = None,
  ) -> Tensor:
      """Compute area under the receiver operating characteristic curve (AUROC) for information retrieval.
  
      ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``,
      ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be ``float``,
      otherwise an error is raised.
  
      Args:
          preds: estimated probabilities of each document to be relevant.
          target: ground truth about each document being relevant or not.
          top_k: consider only the top k elements (default: ``None``, which considers them all)
          max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``.
  
      Return:
          a single-value tensor with the auroc value of the predictions ``preds`` w.r.t. the labels ``target``.
  
      Raises:
          ValueError:
              If ``top_k`` is not ``None`` or an integer larger than 0.
  
      Example:
          >>> from torchmetrics.functional.retrieval import retrieval_auroc
          >>> preds = tensor([0.2, 0.3, 0.5])
          >>> target = tensor([True, False, True])
          >>> retrieval_auroc(preds, target)
          tensor(0.5000)
  
      """
      preds, target = _check_retrieval_functional_inputs(preds, target)
  
      top_k = top_k or preds.shape[-1]
      if not (isinstance(top_k, int) and top_k > 0):
          raise ValueError(
              "`top_k` has to be a positive integer or None"
          )
  
      top_k_idx = preds.topk(
          min(top_k, preds.shape[-1]), sorted=True, dim=-1
      )[1]
      target = target[top_k_idx]
      if (0 not in target) or (1 not in target):
          return tensor(0.0, device=preds.device, dtype=preds.dtype)
  
      preds = preds[top_k_idx]
      return binary_auroc(preds, target.int(), max_fpr=max_fpr)
  
imports: from typing import Optional
  from torch import Tensor, tensor
  from torchmetrics.functional.classification.auroc import binary_auroc
  from torchmetrics.utilities.checks import _check_retrieval_functional_inputs
label_desc: Use image node_id 1 for calling a global function with example usage: retrieval_auroc(preds, target, top_k, max_fpr) and returns: binary_auroc, tensor