code: def retrieval_average_precision(
      preds: Tensor, target: Tensor, top_k: Optional[int] = None
  ) -> Tensor:
      """Compute average precision (for information retrieval), as explained in `IR Average precision`_.
  
      ``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)
  
      Return:
          a single-value tensor with the average precision (AP) 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_average_precision
          >>> preds = tensor([0.2, 0.3, 0.5])
          >>> target = tensor([True, False, True])
          >>> retrieval_average_precision(preds, target)
          tensor(0.8333)
  
      """
      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(
              f"Argument ``top_k`` has to be a positive integer or None, but got {top_k}."
          )
  
      target = target[
          preds.topk(min(top_k, preds.shape[-1]), sorted=True, dim=-1)[
              1
          ]
      ]
      if not target.sum():
          return tensor(0.0, device=preds.device)
  
      positions = torch.arange(
          1, len(target) + 1, device=target.device, dtype=torch.float32
      )[target > 0]
      return torch.div(
          (
              torch.arange(
                  len(positions),
                  device=positions.device,
                  dtype=torch.float32,
              )
              + 1
          ),
          positions,
      ).mean()
  
imports: from typing import Optional
  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_average_precision(preds, target, top_k) and returns: torch, tensor