code: def test_classification_report():
      met = ClassificationReport(num_classes=3)
      pred = torch.FloatTensor([[0.4, 0.5, 0.1], [0.1, 0.8, 0.1]])
      target = torch.LongTensor([2, 1])
      met.update(pred, target)
      pred = torch.FloatTensor([[0.1, 0.5, 0.4], [0.8, 0.1, 0.1]])
      target = torch.LongTensor([1, 3])
      met.update(pred, target)
      assert np.allclose(
          met.value["accuracy"], [1.0, 0.666666666, 0.666666666]
      )
  
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 13 for calling a global function with example usage: test_classification_report() without return types