class_docstr: Compute `Normalized Discounted Cumulative Gain`_.
  
  Works with binary or positive integer 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
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``ndcg@k`` (:class:`~torch.Tensor`): A single-value tensor with the nDCG of the predictions
    ``preds`` w.r.t. the labels ``target``
  
  All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
  so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
  ``indexes`` and then will be computed as the mean of the metric over each query.
  
  
  Args:
      empty_target_action:
          Specify what to do with queries that do not have at least a positive ``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.
      top_k: Consider only the top k elements for each query (default: ``None``, which considers them all)
      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.
      ValueError:
          If ``top_k`` is not ``None`` or not an integer greater than 0.
  
  Example:
      >>> from torch import tensor
      >>> from torchmetrics.retrieval import RetrievalNormalizedDCG
      >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
      >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
      >>> target = tensor([False, False, True, False, True, False, True])
      >>> ndcg = RetrievalNormalizedDCG()
      >>> ndcg(preds, target, indexes=indexes)
      tensor(0.8467)
code: def __init__(
      self,
      empty_target_action: str = "neg",
      ignore_index: Optional[int] = None,
      top_k: Optional[int] = None,
      aggregation: Union[
          Literal["mean", "median", "min", "max"], Callable
      ] = "mean",
      **kwargs: Any,
  ) -> None:
      super().__init__(
          empty_target_action=empty_target_action,
          ignore_index=ignore_index,
          aggregation=aggregation,
          **kwargs,
      )
  
      if top_k is not None and not (
          isinstance(top_k, int) and top_k > 0
      ):
          raise ValueError(
              "`top_k` has to be a positive integer or None"
          )
      self.top_k = top_k
      self.allow_non_binary_target = True
  
file_path: torchmetrics/src/torchmetrics/retrieval/ndcg.py
imports: from typing import Any, Callable, Optional, Sequence, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.functional.retrieval.ndcg import retrieval_normalized_dcg
  from torchmetrics.retrieval.base import RetrievalMetric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 to create a new RetrievalNormalizedDCG object from inherited base classes: RetrievalMetric with example: obj = RetrievalNormalizedDCG(empty_target_action, ignore_index, top_k, aggregation)