class_docstr: Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for binary tasks.
  
  The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for
  multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5
  corresponds to random guessing.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` 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, ...)`` containing ground truth labels, and
    therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the
    positive class.
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``b_auroc`` (:class:`~torch.Tensor`): A single scalar with the auroc score.
  
  Additional dimension ``...`` will be flattened into the batch dimension.
  
  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})` (constant memory).
  
  Args:
      max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``.
      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.
  
      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 BinaryAUROC
      >>> preds = tensor([0, 0.5, 0.7, 0.8])
      >>> target = tensor([0, 1, 1, 0])
      >>> metric = BinaryAUROC(thresholds=None)
      >>> metric(preds, target)
      tensor(0.5000)
      >>> b_auroc = BinaryAUROC(thresholds=5)
      >>> b_auroc(preds, target)
      tensor(0.5000)
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docstr: Plot a single or multiple values from the metric.
  
  Args:
      val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
          If no value is provided, will automatically call `metric.compute` and plot that result.
      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
  
      >>> # Example plotting a single
      >>> import torch
      >>> from torchmetrics.classification import BinaryAUROC
      >>> metric = BinaryAUROC()
      >>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.classification import BinaryAUROC
      >>> metric = BinaryAUROC()
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
      >>> fig_, ax_ = metric.plot(values)
docstr_tok: ["Plot","a","single","or","multiple","values","from","the","metric",".","Args",":","val",":","Either","a","single","result","from","calling","`","metric.forward","`","or","`","metric.compute","`","or","a","list","of","these","results",".","If","no","value","is","provided",",","will","automatically","call","`","metric.compute","`","and","plot","that","result",".","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",">",">",">","#","Example","plotting","a","single",">",">",">","import","torch",">",">",">","from","torchmetrics.classification","import","BinaryAUROC",">",">",">","metric","=","BinaryAUROC","(",")",">",">",">","metric.update","(","torch.rand","(","20",",",")",",","torch.randint","(","2",",","(","20",",",")",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","multiple","values",">",">",">","import","torch",">",">",">","from","torchmetrics.classification","import","BinaryAUROC",">",">",">","metric","=","BinaryAUROC","(",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","torch.rand","(","20",",",")",",","torch.randint","(","2",",","(","20",",",")",")",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(","values",")"]
code: def plot(  # type: ignore[override]
      self,
      val: Optional[Union[Tensor, Sequence[Tensor]]] = None,
      ax: Optional[_AX_TYPE] = None,
  ) -> _PLOT_OUT_TYPE:
      """Plot a single or multiple values from the metric.
  
      Args:
          val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
              If no value is provided, will automatically call `metric.compute` and plot that result.
          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
  
          >>> # Example plotting a single
          >>> import torch
          >>> from torchmetrics.classification import BinaryAUROC
          >>> metric = BinaryAUROC()
          >>> metric.update(torch.rand(20,), torch.randint(2, (20,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.classification import BinaryAUROC
          >>> metric = BinaryAUROC()
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(20,), torch.randint(2, (20,))))
          >>> fig_, ax_ = metric.plot(values)
  
      """
      return self._plot(val, ax)
  
imports: from typing import Any, List, Optional, Sequence, 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 _binary_auroc_arg_validation, _binary_auroc_compute, _multiclass_auroc_arg_validation, _multiclass_auroc_compute, _multilabel_auroc_arg_validation, _multilabel_auroc_compute
  from torchmetrics.metric import Metric
  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
label_desc: Use image node_id 3 for calling the BinaryAUROC obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self