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: Check shape, check and convert dtypes, flatten and add to accumulators.
docstr_tok: ["Check","shape",",","check","and","convert","dtypes",",","flatten","and","add","to","accumulators","."]
code: def update(
      self, preds: Tensor, target: Tensor, indexes: Tensor
  ) -> None:
      """Check shape, check and convert dtypes, flatten and add to accumulators."""
      if indexes is None:
          raise ValueError("Argument `indexes` cannot be None")
  
      indexes, preds, target = _check_retrieval_inputs(
          indexes,
          preds,
          target,
          allow_non_binary_target=self.allow_non_binary_target,
          ignore_index=self.ignore_index,
      )
  
      self.indexes.append(indexes)
      self.preds.append(preds)
      self.target.append(target)
  
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 2 for calling the RetrievalMetric obj's underlying member method code with example usage: obj.update(preds, target, indexes) without return types