class_docstr: Area under the PR curve.
class_docstr_tok: ["Area","under","the","PR","curve","."]
docstr: Compute prec/rec value pairs.
  
  Returns:
      Tuple containing Tensors for rec and prec
docstr_tok: ["Compute","prec\/rec","value","pairs",".","Returns",":","Tuple","containing","Tensors","for","rec","and","prec"]
code: def _compute(self) -> tuple[Tensor, Tensor]:
      """Compute prec/rec value pairs.
  
      Returns:
          Tuple containing Tensors for rec and prec
      """
      prec: Tensor
      rec: Tensor
      prec, rec, _ = super().compute()
      return (prec, rec)
  
file_path: anomalib/src/anomalib/utils/metrics/aupr.py
imports: from __future__ import annotations
  from matplotlib.figure import Figure
  from torch import Tensor
  from torchmetrics import PrecisionRecallCurve
  from torchmetrics.functional import auc
  from torchmetrics.utilities.data import dim_zero_cat
  from .plotting_utils import plot_figure
label_desc: Use image node_id 3 for calling the AUPR obj's underlying member method code with example usage: obj._compute() and returns: prec,  rec