code: def test_incorrect_type():
      acc = Accuracy()
  
      # Start as binary data
      y_pred = torch.randint(0, 2, size=(4,))
      y = torch.ones(4).long()
      acc.update((y_pred, y))
  
      # And add a multiclass data
      y_pred = torch.rand(4, 4)
      y = torch.ones(4).long()
  
      with pytest.raises(RuntimeError):
          acc.update((y_pred, y))
  
imports: import os
  import pytest
  import torch
  from packaging.version import Version
  from sklearn.metrics import accuracy_score
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Accuracy
label_desc: Use image node_id 14 for calling a global function with example usage: test_incorrect_type() without return types