code: def test_multilabel_wrong_inputs():
      pr = Precision(is_multilabel=True)
      assert pr._updated is False
  
      with pytest.raises(ValueError):
          # incompatible shapes
          pr.update(
              (
                  torch.randint(0, 2, size=(10,)),
                  torch.randint(0, 2, size=(10,)).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError):
          # incompatible y_pred
          pr.update(
              (
                  torch.rand(10, 5),
                  torch.randint(0, 2, size=(10, 5)).long(),
              )
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError):
          # incompatible y
          pr.update(
              (torch.randint(0, 5, size=(10, 5, 6)), torch.rand(10))
          )
      assert pr._updated is False
  
      with pytest.raises(ValueError):
          # incompatible shapes between two updates
          pr.update(
              (
                  torch.randint(0, 2, size=(20, 5)),
                  torch.randint(0, 2, size=(20, 5)).long(),
              )
          )
          pr.update(
              (
                  torch.randint(0, 2, size=(20, 6)),
                  torch.randint(0, 2, size=(20, 6)).long(),
              )
          )
      assert pr._updated is True
  
imports: import warnings
  import pytest
  import torch
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import precision_score
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Precision
label_desc: Use image node_id 8 for calling a global function with example usage: test_multilabel_wrong_inputs() without return types