class_docstr: Compute the Receiver Operating Characteristic (ROC) for binary tasks.
  
  The curve consist of multiple pairs of true positive rate (TPR) and false positive rate (FPR) values evaluated at
  different thresholds, such that the tradeoff between the two values can be 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 a tuple of either 3 tensors or 3 lists containing
  
  - ``fpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of
    size ``(n_thresholds+1, )`` with false positive rate values (length may differ between classes). If `thresholds`
    is set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with false positive rate
    values is returned.
  - ``tpr`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of
    size ``(n_thresholds+1, )`` with true positive rate values (length may differ between classes). If `thresholds` is
    set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with true positive rate
    values is returned.
  - ``thresholds`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d
    tensor of size ``(n_thresholds, )`` with decreasing threshold values (length may differ between classes). If
    `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )`` is returned with shared
    threshold values for all classes.
  
  .. 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).
  
  .. note::
     Note that outputted thresholds will be in reversed order to ensure that they corresponds to both fpr
     and tpr which are sorted in reversed order during their calculation, such that they are monotome increasing.
  
  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 an 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 torch import tensor
      >>> from torchmetrics.classification import MulticlassROC
      >>> preds = 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 = tensor([0, 1, 3, 2])
      >>> metric = MulticlassROC(num_classes=5, thresholds=None)
      >>> fpr, tpr, thresholds = metric(preds, target)
      >>> fpr  # doctest: +NORMALIZE_WHITESPACE
      [tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]),
       tensor([0.0000, 0.3333, 1.0000]), tensor([0., 1.])]
      >>> tpr
      [tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0., 0.])]
      >>> thresholds  # doctest: +NORMALIZE_WHITESPACE
      [tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]),
       tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.7500, 0.0500]), tensor([1.0000, 0.0500])]
      >>> mcroc = MulticlassROC(num_classes=5, thresholds=5)
      >>> mcroc(preds, target)  # doctest: +NORMALIZE_WHITESPACE
      (tensor([[0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
               [0.0000, 0.0000, 0.0000, 0.0000, 1.0000],
               [0.0000, 0.3333, 0.3333, 0.3333, 1.0000],
               [0.0000, 0.3333, 0.3333, 0.3333, 1.0000],
               [0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]),
       tensor([[0., 1., 1., 1., 1.],
               [0., 1., 1., 1., 1.],
               [0., 0., 0., 0., 1.],
               [0., 0., 0., 0., 1.],
               [0., 0., 0., 0., 0.]]),
       tensor([1.0000, 0.7500, 0.5000, 0.2500, 0.0000]))
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docstr: Plot a single or multiple values from the metric.
  
  Args:
      curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will
          automatically call `metric.compute` and plot that result.
      score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided,
          will automatically compute the score.
      ax: An matplotlib axis object. If provided will add plot to that axis
  
  Returns:
      Figure and Axes object
  
  Raises:
      ModuleNotFoundError:
          If `matplotlib` is not installed
  
  .. plot::
      :scale: 75
  
      >>> from torch import randn, randint
      >>> from torchmetrics.classification import MulticlassROC
      >>> preds = randn(20, 3).softmax(dim=-1)
      >>> target = randint(3, (20,))
      >>> metric = MulticlassROC(num_classes=3)
      >>> metric.update(preds, target)
      >>> fig_, ax_ = metric.plot(score=True)
docstr_tok: ["Plot","a","single","or","multiple","values","from","the","metric",".","Args",":","curve",":","the","output","of","either","`","metric.compute","`","or","`","metric.forward","`",".","If","no","value","is","provided",",","will","automatically","call","`","metric.compute","`","and","plot","that","result",".","score",":","Provide","a","area-under-the-curve","score","to","be","displayed","on","the","plot",".","If","`","True","`","and","no","curve","is","provided",",","will","automatically","compute","the","score",".","ax",":","An","matplotlib","axis","object",".","If","provided","will","add","plot","to","that","axis","Returns",":","Figure","and","Axes","object","Raises",":","ModuleNotFoundError",":","If","`","matplotlib","`","is","not","installed","..","plot",":",":",":","scale",":","75",">",">",">","from","torch","import","randn",",","randint",">",">",">","from","torchmetrics.classification","import","MulticlassROC",">",">",">","preds","=","randn","(","20",",","3",")",".softmax","(","dim=-1",")",">",">",">","target","=","randint","(","3",",","(","20",",",")",")",">",">",">","metric","=","MulticlassROC","(","num_classes=3",")",">",">",">","metric.update","(","preds",",","target",")",">",">",">","fig_",",","ax_","=","metric.plot","(","score=True",")"]
code: def plot(
      self,
      curve: Optional[
          Union[
              Tuple[Tensor, Tensor, Tensor],
              Tuple[List[Tensor], List[Tensor], List[Tensor]],
          ]
      ] = None,
      score: Optional[Union[Tensor, bool]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will
              automatically call `metric.compute` and plot that result.
          score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided,
              will automatically compute the score.
          ax: An matplotlib axis object. If provided will add plot to that axis
  
      Returns:
          Figure and Axes object
  
      Raises:
          ModuleNotFoundError:
              If `matplotlib` is not installed
  
      .. plot::
          :scale: 75
  
          >>> from torch import randn, randint
          >>> from torchmetrics.classification import MulticlassROC
          >>> preds = randn(20, 3).softmax(dim=-1)
          >>> target = randint(3, (20,))
          >>> metric = MulticlassROC(num_classes=3)
          >>> metric.update(preds, target)
          >>> fig_, ax_ = metric.plot(score=True)
  
      """
      curve_computed = curve or self.compute()
      score = (
          _reduce_auroc(
              curve_computed[0], curve_computed[1], average=None
          )
          if not curve and score is True
          else None
      )
      return plot_curve(
          curve_computed,
          score=score,
          ax=ax,
          label_names=("False positive rate", "True positive rate"),
          name=self.__class__.__name__,
      )
  
imports: from typing import Any, List, Optional, Tuple, Type, Union
  from torch import Tensor
  from typing_extensions import Literal
  from torchmetrics.classification.base import _ClassificationTaskWrapper
  from torchmetrics.classification.precision_recall_curve import BinaryPrecisionRecallCurve, MulticlassPrecisionRecallCurve, MultilabelPrecisionRecallCurve
  from torchmetrics.functional.classification.auroc import _reduce_auroc
  from torchmetrics.functional.classification.roc import _binary_roc_compute, _multiclass_roc_compute, _multilabel_roc_compute
  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 2 for calling the MulticlassROC obj's underlying member method code with example usage: obj.plot(curve, score, ax) and returns: plot_curve