code: def retrieval_hit_rate(
      preds: Tensor, target: Tensor, top_k: Optional[int] = None
  ) -> Tensor:
      """Compute the hit rate for information retrieval.
  
      The hit rate is 1.0 if there is at least one relevant document among all the top `k` retrieved 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 HitRate@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.
          top_k: consider only the top k elements (default: `None`, which considers them all)
  
      Returns:
          A single-value tensor with the hit rate (at ``top_k``) of the predictions ``preds`` w.r.t. the labels
            ``target``.
  
      Raises:
          ValueError:
              If ``top_k`` parameter is not `None` or an integer larger than 0
  
      Example:
          >>> from torch import tensor
          >>> preds = tensor([0.2, 0.3, 0.5])
          >>> target = tensor([True, False, True])
          >>> retrieval_hit_rate(preds, target, top_k=2)
          tensor(1.)
  
      """
      preds, target = _check_retrieval_functional_inputs(preds, target)
  
      if top_k is None:
          top_k = 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"
          )
  
      relevant = target[torch.argsort(preds, dim=-1, descending=True)][
          :top_k
      ].sum()
      return (relevant > 0).float()
  
imports: from typing import Optional
  import torch
  from torch import 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_hit_rate(preds, target, top_k) and returns: unknown