code: def retrieval_fall_out(
      preds: Tensor, target: Tensor, top_k: Optional[int] = None
  ) -> Tensor:
      """Compute the Fall-out for information retrieval, as explained in `IR Fall-out`_.
  
      Fall-out is the fraction of non-relevant documents retrieved among all the non-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 Fall-out@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 fall-out (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  torchmetrics.functional import retrieval_fall_out
          >>> preds = tensor([0.2, 0.3, 0.5])
          >>> target = tensor([True, False, True])
          >>> retrieval_fall_out(preds, target, top_k=2)
          tensor(1.)
  
      """
      preds, target = _check_retrieval_functional_inputs(preds, target)
  
      top_k = preds.shape[-1] if top_k is None else top_k
  
      if not (isinstance(top_k, int) and top_k > 0):
          raise ValueError(
              "`top_k` has to be a positive integer or None"
          )
  
      target = (
          1 - target
      )  # we want to compute the probability of getting a non-relevant doc among all non-relevant docs
  
      if not target.sum():
          return tensor(0.0, device=preds.device)
  
      relevant = (
          target[torch.argsort(preds, dim=-1, descending=True)][:top_k]
          .sum()
          .float()
      )
      return relevant / target.sum()
  
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_fall_out(preds, target, top_k) and returns: unknown, tensor