class_docstr: Compute the label ranking loss for multilabel data [1].
  
  The score is corresponds to the average number of label pairs that are incorrectly ordered given some predictions
  weighted by the size of the label set and the number of labels not in the label set. The best score is 0.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor
    containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider
    the input to be logits and will auto apply sigmoid per element.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
    containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).
  
  .. note::
     Additional dimension ``...`` will be flattened into the batch dimension.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mlrl`` (:class:`~torch.Tensor`): A tensor containing the multilabel ranking loss.
  
  Args:
      preds: Tensor with predictions
      target: Tensor with true labels
      num_labels: Integer specifying the number of labels
      ignore_index:
          Specifies a target value that is ignored and does not contribute to the metric calculation
      validate_args: bool indicating if input arguments and tensors should be validated for correctness.
          Set to ``False`` for faster computations.
  
  Example:
      >>> from torchmetrics.classification import MultilabelRankingLoss
      >>> _ = torch.manual_seed(42)
      >>> preds = torch.rand(10, 5)
      >>> target = torch.randint(2, (10, 5))
      >>> mlrl = MultilabelRankingLoss(num_labels=5)
      >>> mlrl(preds, target)
      tensor(0.4167)
docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:
      """Compute metric."""
      return _ranking_reduce(self.measure, self.total)
  
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor
  from torchmetrics.functional.classification.ranking import _multilabel_confusion_matrix_arg_validation, _multilabel_confusion_matrix_format, _multilabel_coverage_error_update, _multilabel_ranking_average_precision_update, _multilabel_ranking_loss_update, _multilabel_ranking_tensor_validation, _ranking_reduce
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 3 for calling the MultilabelRankingLoss obj's underlying member method code with example usage: obj.compute() and returns: _ranking_reduce