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.
code: def __init__(
      self,
      empty_target_action: str = "neg",
      ignore_index: Optional[int] = None,
      aggregation: Union[
          Literal["mean", "median", "min", "max"], Callable
      ] = "mean",
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      self.allow_non_binary_target = False
  
      empty_target_action_options = ("error", "skip", "neg", "pos")
      if empty_target_action not in empty_target_action_options:
          raise ValueError(
              f"Argument `empty_target_action` received a wrong value `{empty_target_action}`."
          )
      self.empty_target_action = empty_target_action
  
      if ignore_index is not None and not isinstance(ignore_index, int):
          raise ValueError(
              "Argument `ignore_index` must be an integer or None."
          )
      self.ignore_index = ignore_index
  
      if not (
          aggregation in ("mean", "median", "min", "max")
          or callable(aggregation)
      ):
          raise ValueError(
              "Argument `aggregation` must be one of `mean`, `median`, `min`, `max` or a custom callable function"
              f"which takes tensor of values, but got {aggregation}."
          )
      self.aggregation = aggregation
  
      self.add_state("indexes", default=[], dist_reduce_fx=None)
      self.add_state("preds", default=[], dist_reduce_fx=None)
      self.add_state("target", default=[], dist_reduce_fx=None)
  
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 1 to create a new RetrievalMetric object from inherited base classes: Metric, ABC with example: obj = RetrievalMetric(empty_target_action, ignore_index, aggregation)