code: def test_simple_2D_input():
      # Tests for 2D inputs with normalized = True and False
  
      num_iters = 5
      num_samples = 100
      num_classes = 10
      torch.manual_seed(0)
      for _ in range(num_iters):
          target = torch.randint(0, 2, size=(num_samples, num_classes))
          prediction = torch.randint(
              0, 2, size=(num_samples, num_classes)
          )
          sklearn_CM = multilabel_confusion_matrix(
              target.numpy(), prediction.numpy()
          )
          mlcm = MultiLabelConfusionMatrix(
              num_classes, normalized=False
          )
          mlcm.update([prediction, target])
          ignite_CM = mlcm.compute().numpy()
          assert np.all(
              sklearn_CM.astype(np.int64) == ignite_CM.astype(np.int64)
          )
          mlcm = MultiLabelConfusionMatrix(num_classes, normalized=True)
          mlcm.update([prediction, target])
          ignite_CM_normalized = mlcm.compute().numpy()
          sklearn_CM_normalized = (
              sklearn_CM / sklearn_CM.sum(axis=(1, 2))[:, None, None]
          )
          assert np.allclose(
              sklearn_CM_normalized, ignite_CM_normalized
          )
  
file_path: ignite/tests/ignite/metrics/test_multilabel_confusion_matrix.py
imports: import numpy
  import pytest
  import torch
  from sklearn.metrics import multilabel_confusion_matrix
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics.multilabel_confusion_matrix import MultiLabelConfusionMatrix
label_desc: Use image node_id 8 for calling a global function with example usage: test_simple_2D_input() without return types