class_docstr: Area under the PR curve.
class_docstr_tok: ["Area","under","the","PR","curve","."]
docstr: First compute PR curve, then compute area under the curve.
  
  Returns:
      Value of the AUPR metric
docstr_tok: ["First","compute","PR","curve",",","then","compute","area","under","the","curve",".","Returns",":","Value","of","the","AUPR","metric"]
code: def compute(self) -> Tensor:
      """First compute PR curve, then compute area under the curve.
  
      Returns:
          Value of the AUPR metric
      """
      prec: Tensor
      rec: Tensor
  
      prec, rec = self._compute()
      return auc(rec, prec, reorder=True)
  
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 1 for calling the AUPR obj's underlying member method code with example usage: obj.compute() and returns: auc