class_docstr: Works with binary target data. Accepts float predictions from a model output.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``
  - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)``
  - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
    prediction belongs
  
  .. note:: ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten
  to single dimension once provided.
  
  .. note::
      Predictions will be first grouped by ``indexes`` and then the real metric, defined by overriding
      the `_metric` method, will be computed as the mean of the scores over each query.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``metric`` (:class:`~torch.Tensor`): A tensor as computed by ``_metric`` if the number of positive targets is
    at least 1, otherwise behave as specified by ``self.empty_target_action``.
  
  Args:
      empty_target_action:
          Specify what to do with queries that do not have at least a positive
          or negative (depend on metric) target. Choose from:
  
          - ``'neg'``: those queries count as ``0.0`` (default)
          - ``'pos'``: those queries count as ``1.0``
          - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned
          - ``'error'``: raise a ``ValueError``
  
      ignore_index:
          Ignore predictions where the target is equal to this number.
      aggregation:
          Specify how to aggregate over indexes. Can either a custom callable function that takes in a single tensor
          and returns a scalar value or one of the following strings:
  
          - ``'mean'``: average value is returned
          - ``'median'``: median value is returned
          - ``'max'``: max value is returned
          - ``'min'``: min value is returned
  
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Raises:
      ValueError:
          If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``.
      ValueError:
          If ``ignore_index`` is not `None` or an integer.
docstr: First concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists.
  
  After that, compute list of groups that will help in keeping together predictions about the same query. Finally,
  for each group compute the ``_metric`` if the number of positive targets is at least 1, otherwise behave as
  specified by ``self.empty_target_action``.
docstr_tok: ["First","concat","state","``","indexes","``",",","``","preds","``","and","``","target","``","since","they","were","stored","as","lists",".","After","that",",","compute","list","of","groups","that","will","help","in","keeping","together","predictions","about","the","same","query",".","Finally",",","for","each","group","compute","the","``","_metric","``","if","the","number","of","positive","targets","is","at","least","1",",","otherwise","behave","as","specified","by","``","self.empty_target_action","``","."]
code: def compute(self) -> Tensor:
      """First concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists.
  
      After that, compute list of groups that will help in keeping together predictions about the same query. Finally,
      for each group compute the ``_metric`` if the number of positive targets is at least 1, otherwise behave as
      specified by ``self.empty_target_action``.
  
      """
      indexes = dim_zero_cat(self.indexes)
      preds = dim_zero_cat(self.preds)
      target = dim_zero_cat(self.target)
  
      indexes, indices = torch.sort(indexes)
      preds = preds[indices]
      target = target[indices]
  
      split_sizes = _flexible_bincount(indexes).detach().cpu().tolist()
  
      res = []
      for mini_preds, mini_target in zip(
          torch.split(preds, split_sizes, dim=0),
          torch.split(target, split_sizes, dim=0),
      ):
          if not mini_target.sum():
              if self.empty_target_action == "error":
                  raise ValueError(
                      "`compute` method was provided with a query with no positive target."
                  )
              if self.empty_target_action == "pos":
                  res.append(tensor(1.0))
              elif self.empty_target_action == "neg":
                  res.append(tensor(0.0))
          else:
              # ensure list contains only float tensors
              res.append(self._metric(mini_preds, mini_target))
  
      if res:
          return _retrieval_aggregate(
              torch.stack([x.to(preds) for x in res]), self.aggregation
          )
      return tensor(0.0).to(preds)
  
file_path: torchmetrics/src/torchmetrics/retrieval/base.py
imports: from abc import ABC, abstractmethod
  from typing import Any, Callable, List, Optional, Union
  import torch
  from torch import Tensor, tensor
  from typing_extensions import Literal
  from torchmetrics import Metric
  from torchmetrics.utilities.checks import _check_retrieval_inputs
  from torchmetrics.utilities.data import _flexible_bincount, dim_zero_cat
label_desc: Use image node_id 3 for calling the RetrievalMetric obj's underlying member method code with example usage: obj.compute() and returns: tensor, _retrieval_aggregate