class_docstr: Wrapper for deprecated import.
  
  >>> from torch import tensor
  >>> 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)
class_docstr_tok: ["Wrapper","for","deprecated","import",".",">",">",">","from","torch","import","tensor",">",">",">","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,
      **kwargs: Any,
  ) -> None:
      _deprecated_root_import_class(
          "RetrievalNormalizedDCG", "retrieval"
      )
      super().__init__(
          empty_target_action=empty_target_action,
          ignore_index=ignore_index,
          top_k=top_k,
          **kwargs,
      )
  
imports: from typing import Any, Optional
  from torchmetrics.retrieval.average_precision import RetrievalMAP
  from torchmetrics.retrieval.fall_out import RetrievalFallOut
  from torchmetrics.retrieval.hit_rate import RetrievalHitRate
  from torchmetrics.retrieval.ndcg import RetrievalNormalizedDCG
  from torchmetrics.retrieval.precision import RetrievalPrecision
  from torchmetrics.retrieval.precision_recall_curve import RetrievalPrecisionRecallCurve, RetrievalRecallAtFixedPrecision
  from torchmetrics.retrieval.r_precision import RetrievalRPrecision
  from torchmetrics.retrieval.recall import RetrievalRecall
  from torchmetrics.retrieval.reciprocal_rank import RetrievalMRR
  from torchmetrics.utilities.prints import _deprecated_root_import_class
label_desc: Use image node_id 1 to create a new _RetrievalNormalizedDCG object from inherited base classes: RetrievalNormalizedDCG with example: obj = _RetrievalNormalizedDCG(empty_target_action, ignore_index, top_k)