code: def test_auc(y_ypred):
      predicted, true = y_ypred
      num_classes = predicted.shape[1]
      met = PRAuC(num_classes=num_classes, n_bins=10, average=False)
      met.update(predicted, true)
      met.update(predicted, true)
      assert len(met.value) == num_classes
  
      met = PRAuC(num_classes=num_classes, n_bins=10, average=True)
      met.update(predicted, true)
      met.update(predicted, true)
      assert isinstance(met.value, float)
  
imports: import os
  from pathlib import Path
  import numpy
  import pytest
  import shutil
  import torch
  from hypothesis import given
  from torch_hypothesis import classification_logits_and_labels
  from baal.utils.metrics import Loss, Accuracy, Precision, ECE, ClassificationReport, PRAuC, ECE_PerCLs
label_desc: Use image node_id 8 for calling a global function with example usage: test_auc(y_ypred) without return types