class_docstr: Compute the precision-recall curve for multiclass 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.
  
  For multiclass the metric is calculated by iteratively treating each class as the positive class and all other
  classes as the negative, which is referred to as the one-vs-rest approach. One-vs-one is currently not supported by
  this metric.
  
  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 softmax per sample.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
    ground truth labels, and therefore only contain values in the [0, n_classes-1] range (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:
  
  - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with precision values
  - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with recall values
  - ``thresholds`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds, )`` with increasing threshold values
  
  .. 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_{classes})` (constant memory).
  
  Args:
      num_classes: Integer specifying the number of classes
      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 a 1D `tensor` of floats, will use the indicated thresholds in the tensor as
            bins for the calculation.
  
      average:
          If aggregation of curves should be applied. By default, the curves are not aggregated and a curve for
          each class is returned. If `average` is set to ``"micro"``, the metric will aggregate the curves by one hot
          encoding the targets and flattening the predictions, considering all classes jointly as a binary problem.
          If `average` is set to ``"macro"``, the metric will aggregate the curves by first interpolating the curves
          from each class at a combined set of thresholds and then average over the classwise interpolated curves.
          See `averaging curve objects`_ for more info on the different averaging methods.
      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.
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example:
      >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve
      >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
      ...                       [0.05, 0.75, 0.05, 0.05, 0.05],
      ...                       [0.05, 0.05, 0.75, 0.05, 0.05],
      ...                       [0.05, 0.05, 0.05, 0.75, 0.05]])
      >>> target = torch.tensor([0, 1, 3, 2])
      >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None)
      >>> precision, recall, thresholds = mcprc(preds, target)
      >>> precision  # doctest: +NORMALIZE_WHITESPACE
      [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]),
       tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
      >>> recall
      [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
      >>> thresholds
      [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]),
       tensor(0.0500)]
      >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5)
      >>> mcprc(preds, target)  # doctest: +NORMALIZE_WHITESPACE
      (tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
               [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000],
               [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
               [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
               [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
       tensor([[1., 1., 1., 1., 0., 0.],
               [1., 1., 1., 1., 0., 0.],
               [1., 0., 0., 0., 0., 0.],
               [1., 0., 0., 0., 0., 0.],
               [0., 0., 0., 0., 0., 0.]]),
       tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]))
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code: def __init__(
      self,
      num_classes: int,
      thresholds: Optional[Union[int, List[float], Tensor]] = None,
      average: Optional[Literal["micro", "macro"]] = None,
      ignore_index: Optional[int] = None,
      validate_args: bool = True,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
      if validate_args:
          _multiclass_precision_recall_curve_arg_validation(
              num_classes, thresholds, ignore_index, average
          )
  
      self.num_classes = num_classes
      self.average = average
      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_classes, 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 MulticlassPrecisionRecallCurve object from inherited base classes: Metric with example: obj = MulticlassPrecisionRecallCurve(num_classes, thresholds, average, ignore_index, validate_args)