class_docstr: Precision-Recall Area under the curve.
  
  Args:
      num_classes (int): Number of classes
      n_bins (int): number of confidence threshold to evaluate on.
      average (bool): If true will return the mean AuC of all classes.
class_docstr_tok: ["Precision-Recall","Area","under","the","curve",".","Args",":","num_classes","(","int",")",":","Number","of","classes","n_bins","(","int",")",":","number","of","confidence","threshold","to","evaluate","on",".","average","(","bool",")",":","If","true","will","return","the","mean","AuC","of","all","classes","."]
code: def _make_report(self, output, target, threshold):
      output = (output > threshold).astype(np.int8)
      output = output.reshape([-1])
      target = target.reshape([-1])
      _, fp, fn, tp = confusion_matrix(
          target, output, labels=[0, 1]
      ).ravel()
      return Report(tp=tp, fp=fp, fn=fn)
  
imports: import dataclasses
  import math
  import warnings
  from collections import defaultdict
  import numpy
  import torch
  from sklearn.metrics import confusion_matrix, auc
  from baal.utils.array_utils import to_prob
label_desc: Use image node_id 4 for calling the PRAuC obj's underlying member method code with example usage: obj._make_report(output, target, threshold) and returns: Report