code: def test_multiclass_overflow():
      """Test that multiclass computations does not overflow even on byte inputs."""
      preds = torch.randint(20, (100,)).byte()
      target = torch.randint(20, (100,)).byte()
  
      m = MulticlassConfusionMatrix(num_classes=20)
      res = m(preds, target)
  
      compare = sk_confusion_matrix(target, preds)
      assert torch.allclose(res, torch.tensor(compare))
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from torchmetrics.classification.confusion_matrix import BinaryConfusionMatrix, ConfusionMatrix, MulticlassConfusionMatrix, MultilabelConfusionMatrix
  from torchmetrics.functional.classification.confusion_matrix import binary_confusion_matrix, multiclass_confusion_matrix, multilabel_confusion_matrix
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 3 for calling a global function with example usage: test_multiclass_overflow() without return types