code: def retrieval_r_precision(preds: Tensor, target: Tensor) -> Tensor:
      """Compute the r-precision metric for information retrieval.
  
      R-Precision is the fraction of relevant documents among all the top ``k`` retrieved documents where ``k`` is equal
      to the total number of relevant documents.
  
      ``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. If you want to measure Precision@K, ``top_k`` must be a positive integer.
  
      Args:
          preds: estimated probabilities of each document to be relevant.
          target: ground truth about each document being relevant or not.
  
      Returns:
          A single-value tensor with the r-precision of the predictions ``preds`` w.r.t. the labels ``target``.
  
      Example:
          >>> preds = tensor([0.2, 0.3, 0.5])
          >>> target = tensor([True, False, True])
          >>> retrieval_r_precision(preds, target)
          tensor(0.5000)
  
      """
      preds, target = _check_retrieval_functional_inputs(preds, target)
  
      relevant_number = target.sum()
      if not relevant_number:
          return tensor(0.0, device=preds.device)
  
      relevant = (
          target[torch.argsort(preds, dim=-1, descending=True)][
              :relevant_number
          ]
          .sum()
          .float()
      )
      return relevant / relevant_number
  
imports: import torch
  from torch import Tensor, tensor
  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_r_precision(preds, target) and returns: unknown, tensor