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 reset(self):
      self._data.clear()
  
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 2 for calling the PRAuC obj's underlying member method code with example usage: obj.reset() without return types