class_docstr: Compute the average precision (AP) score for binary 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, ...)`` 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:
  
  - ``bap`` (:class:`~torch.Tensor`): A single scalar with the average precision 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:
      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 BinaryAveragePrecision
      >>> preds = tensor([0, 0.5, 0.7, 0.8])
      >>> target = tensor([0, 1, 1, 0])
      >>> metric = BinaryAveragePrecision(thresholds=None)
      >>> metric(preds, target)
      tensor(0.5833)
      >>> bap = BinaryAveragePrecision(thresholds=5)
      >>> bap(preds, target)
      tensor(0.6667)
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docstr: Compute metric.
docstr_tok: ["Compute","metric","."]
code: def compute(self) -> Tensor:  # type: ignore[override]
      """Compute metric."""
      state = (
          (dim_zero_cat(self.preds), dim_zero_cat(self.target))
          if self.thresholds is None
          else self.confmat
      )
      return _binary_average_precision_compute(state, self.thresholds)
  
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 1 for calling the BinaryAveragePrecision obj's underlying member method code with example usage: obj.compute() and returns: _binary_average_precision_compute