class_docstr: Compute the average precision (AP) score for multilabel 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).
  
  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 sigmoid per element.
  - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)`` containing ground truth labels, and
    therefore only contain {0,1} values (except if `ignore_index` is specified).
  
  As output to ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mlap`` (: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="micro|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_{labels})` (constant memory).
  
  Args:
      num_labels: Integer specifying the number of labels
      average:
          Defines the reduction that is applied over labels. Should be one of the following:
  
          - ``micro``: Sum score over all labels
          - ``macro``: Calculate score for each label and average them
          - ``weighted``: calculates score for each label and computes weighted average using their support
          - ``"none"`` or ``None``: calculates score for each label 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 MultilabelAveragePrecision
      >>> preds = 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 = tensor([[1, 0, 1],
      ...                  [0, 0, 0],
      ...                  [0, 1, 1],
      ...                  [1, 1, 1]])
      >>> metric = MultilabelAveragePrecision(num_labels=3, average="macro", thresholds=None)
      >>> metric(preds, target)
      tensor(0.7500)
      >>> mlap = MultilabelAveragePrecision(num_labels=3, average=None, thresholds=None)
      >>> mlap(preds, target)
      tensor([0.7500, 0.5833, 0.9167])
      >>> mlap = MultilabelAveragePrecision(num_labels=3, average="macro", thresholds=5)
      >>> mlap(preds, target)
      tensor(0.7778)
      >>> mlap = MultilabelAveragePrecision(num_labels=3, average=None, thresholds=5)
      >>> mlap(preds, target)
      tensor([0.7500, 0.6667, 0.9167])
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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 MultilabelAveragePrecision
      >>> metric = MultilabelAveragePrecision(num_labels=3)
      >>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
      >>> fig_, ax_ = metric.plot()
  
  .. plot::
      :scale: 75
  
      >>> # Example plotting multiple values
      >>> import torch
      >>> from torchmetrics.classification import MultilabelAveragePrecision
      >>> metric = MultilabelAveragePrecision(num_labels=3)
      >>> values = [ ]
      >>> for _ in range(10):
      ...     values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
      >>> fig_, ax_ = metric.plot(values)
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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 MultilabelAveragePrecision
          >>> metric = MultilabelAveragePrecision(num_labels=3)
          >>> metric.update(torch.rand(20,3), torch.randint(2, (20,3)))
          >>> fig_, ax_ = metric.plot()
  
      .. plot::
          :scale: 75
  
          >>> # Example plotting multiple values
          >>> import torch
          >>> from torchmetrics.classification import MultilabelAveragePrecision
          >>> metric = MultilabelAveragePrecision(num_labels=3)
          >>> values = [ ]
          >>> for _ in range(10):
          ...     values.append(metric(torch.rand(20,3), torch.randint(2, (20,3))))
          >>> 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 MultilabelAveragePrecision obj's underlying member method code with example usage: obj.plot(val, ax) and returns: self