class_docstr: Compute the average precision (AP) score for multiclass tasks.
  
  The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
  difference in recall from the previous threshold as weight:
  
  .. math::
      AP = \sum_{n} (R_n - R_{n-1}) P_n
  
  where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
  equivalent to the area under the precision-recall curve (AUPRC).
  
  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. By default the reported metric is then the average over all classes, but this behavior can be changed
  by setting the ``average`` argument.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)`` 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, ...)`` containing ground truth labels, and
    therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified).
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mcap`` (:class:`~torch.Tensor`): If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be
    returned with AP score per class. If `average="macro"|"weighted"` then a single scalar is returned.
  
  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} \times n_{classes})` (constant memory).
  
  Args:
      num_classes: Integer specifying the number of classes
      average:
          Defines the reduction that is applied over classes. Should be one of the following:
  
          - ``macro``: Calculate score for each class and average them
          - ``weighted``: calculates score for each class and computes weighted average using their support
          - ``"none"`` or ``None``: calculates score for each class and applies no reduction
      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 MulticlassAveragePrecision
      >>> 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 = MulticlassAveragePrecision(num_classes=5, average="macro", thresholds=None)
      >>> metric(preds, target)
      tensor(0.6250)
      >>> mcap = MulticlassAveragePrecision(num_classes=5, average=None, thresholds=None)
      >>> mcap(preds, target)
      tensor([1.0000, 1.0000, 0.2500, 0.2500,    nan])
      >>> mcap = MulticlassAveragePrecision(num_classes=5, average="macro", thresholds=5)
      >>> mcap(preds, target)
      tensor(0.5000)
      >>> mcap = MulticlassAveragePrecision(num_classes=5, average=None, thresholds=5)
      >>> mcap(preds, target)
      tensor([1.0000, 1.0000, 0.2500, 0.2500, -0.0000])
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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 MulticlassAveragePrecision
      >>> metric = MulticlassAveragePrecision(num_classes=3)
      >>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.classification import MulticlassAveragePrecision
      >>> metric = MulticlassAveragePrecision(num_classes=3)
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (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","MulticlassAveragePrecision",">",">",">","metric","=","MulticlassAveragePrecision","(","num_classes=3",")",">",">",">","metric.update","(","torch.randn","(","20",",","3",")",",","torch.randint","(","3",",","(","20",",",")",")",")",">",">",">","fig_",",","ax_","=","metric.plot","(",")","..","plot",":",":",":","scale",":","75",">",">",">","#","Example","plotting","multiple","values",">",">",">","import","torch",">",">",">","from","torchmetrics.classification","import","MulticlassAveragePrecision",">",">",">","metric","=","MulticlassAveragePrecision","(","num_classes=3",")",">",">",">","values","=","[","]",">",">",">","for","_","in","range","(","10",")",":","...","values.append","(","metric","(","torch.randn","(","20",",","3",")",",","torch.randint","(","3",",","(","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 MulticlassAveragePrecision
          >>> metric = MulticlassAveragePrecision(num_classes=3)
          >>> metric.update(torch.randn(20, 3), torch.randint(3,(20,)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.classification import MulticlassAveragePrecision
          >>> metric = MulticlassAveragePrecision(num_classes=3)
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.randn(20, 3), torch.randint(3, (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.average_precision import _binary_average_precision_compute, _multiclass_average_precision_arg_validation, _multiclass_average_precision_compute, _multilabel_average_precision_arg_validation, _multilabel_average_precision_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 MulticlassAveragePrecision obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self