class_docstr: Compute the precision-recall curve for multilabel tasks.
  
  The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the
  tradeoff between the two values can been seen.
  
  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 a tuple of either 3 tensors or
  3 lists containing:
  
  - ``precision`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned
    with an 1d tensor of size ``(n_thresholds+1, )`` with precision values (length may differ between labels). If
    `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with
    precision values is returned.
  - ``recall`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned
    with an 1d tensor of size ``(n_thresholds+1, )`` with recall values (length may differ between labels). If
    `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with recall
    values is returned.
  - ``thresholds`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is
    returned with an 1d tensor of size ``(n_thresholds, )`` with increasing threshold values (length may differ
    between labels). If `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )``
    is returned with shared threshold values for all labels.
  
  .. note::
     The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
     that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the
     non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
     argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
     size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).
  
  Args:
      preds: Tensor with predictions
      target: Tensor with true labels
      num_labels: Integer specifying the number of labels
      thresholds:
          Can be one of:
  
          - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from
            all the data. Most accurate but also most memory consuming approach.
          - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
            0 to 1 as bins for the calculation.
          - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation
          - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as
            bins for the calculation.
  
      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 MultilabelPrecisionRecallCurve
      >>> preds = torch.tensor([[0.75, 0.05, 0.35],
      ...                       [0.45, 0.75, 0.05],
      ...                       [0.05, 0.55, 0.75],
      ...                       [0.05, 0.65, 0.05]])
      >>> target = torch.tensor([[1, 0, 1],
      ...                        [0, 0, 0],
      ...                        [0, 1, 1],
      ...                        [1, 1, 1]])
      >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None)
      >>> precision, recall, thresholds = mlprc(preds, target)
      >>> precision  # doctest: +NORMALIZE_WHITESPACE
      [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]),
       tensor([0.7500, 1.0000, 1.0000, 1.0000])]
      >>> recall  # doctest: +NORMALIZE_WHITESPACE
      [tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]),
       tensor([1.0000, 0.6667, 0.3333, 0.0000])]
      >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
      [tensor([0.0500, 0.4500, 0.7500]), tensor([0.0500, 0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])]
      >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5)
      >>> mlprc(preds, target)  # doctest: +NORMALIZE_WHITESPACE
      (tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000],
               [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000],
               [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]),
       tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000],
               [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000],
               [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]),
       tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))
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code: def __init__(
      self,
      num_labels: int,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      if validate_args:
          _multilabel_precision_recall_curve_arg_validation(
              num_labels, thresholds, ignore_index
          )
  
      self.num_labels = num_labels
      self.ignore_index = ignore_index
      self.validate_args = validate_args
  
      thresholds = _adjust_threshold_arg(thresholds)
      if thresholds is None:
          self.thresholds = thresholds
          self.add_state("preds", default=[], dist_reduce_fx="cat")
          self.add_state("target", default=[], dist_reduce_fx="cat")
      else:
          self.register_buffer(
              "thresholds", thresholds, persistent=False
          )
          self.add_state(
              "confmat",
              default=torch.zeros(
                  len(thresholds), num_labels, 2, 2, dtype=torch.long
              ),
              dist_reduce_fx="sum",
          )
  
imports: from typing import Any, List, Optional, Tuple, Type, Union
  import torch
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.functional.classification.auroc import _reduce_auroc
  from torchmetrics.functional.classification.precision_recall_curve import _adjust_threshold_arg, _binary_precision_recall_curve_arg_validation, _binary_precision_recall_curve_compute, _binary_precision_recall_curve_format, _binary_precision_recall_curve_tensor_validation, _binary_precision_recall_curve_update, _multiclass_precision_recall_curve_arg_validation, _multiclass_precision_recall_curve_compute, _multiclass_precision_recall_curve_format, _multiclass_precision_recall_curve_tensor_validation, _multiclass_precision_recall_curve_update, _multilabel_precision_recall_curve_arg_validation, _multilabel_precision_recall_curve_compute, _multilabel_precision_recall_curve_format, _multilabel_precision_recall_curve_tensor_validation, _multilabel_precision_recall_curve_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.compute import _auc_compute_without_check
  from torchmetrics.utilities.data import dim_zero_cat
  from torchmetrics.utilities.enums import ClassificationTask
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_curve
label_desc: Use image node_id 1 to create a new MultilabelPrecisionRecallCurve object from inherited base classes: Metric with example: obj = MultilabelPrecisionRecallCurve(num_labels, thresholds, ignore_index, validate_args)