code: def test_incorrect_type(average):
      # Tests changing of type during training
  
      re = Recall(average=average)
      assert re._updated is False
  
      y_pred = torch.softmax(torch.rand(4, 4), dim=1)
      y = torch.ones(4).long()
      re.update((y_pred, y))
      assert re._updated is True
  
      y_pred = torch.zeros(4)
      y = torch.ones(4).long()
  
      with pytest.raises(RuntimeError):
          re.update((y_pred, y))
  
      assert re._updated is True
  
imports: import os
  import warnings
  import pytest
  import torch
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import recall_score
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Recall
label_desc: Use image node_id 11 for calling a global function with example usage: test_incorrect_type(average) without return types