code: def test_wrong_inputs():
      with pytest.raises(
          ValueError, match=r"Beta should be a positive integer"
      ):
          Fbeta(0.0)
  
      with pytest.raises(
          ValueError,
          match=r"Input precision metric should have average=False",
      ):
          p = Precision(average="micro")
          Fbeta(1.0, precision=p)
  
      with pytest.raises(
          ValueError,
          match=r"Input recall metric should have average=False",
      ):
          r = Recall(average="samples")
          Fbeta(1.0, recall=r)
  
      with pytest.raises(
          ValueError,
          match=r"If precision argument is provided, output_transform should be None",
      ):
          p = Precision(average=False)
          Fbeta(1.0, precision=p, output_transform=lambda x: x)
  
      with pytest.raises(
          ValueError,
          match=r"If recall argument is provided, output_transform should be None",
      ):
          r = Recall(average=False)
          Fbeta(1.0, recall=r, output_transform=lambda x: x)
  
file_path: ignite/tests/ignite/metrics/test_fbeta.py
imports: import os
  import numpy
  import pytest
  import torch
  from sklearn.metrics import fbeta_score
  import ignite.distributed
  from ignite.engine import Engine
  from ignite.metrics import Fbeta, Precision, Recall
label_desc: Use image node_id 1 for calling a global function with example usage: test_wrong_inputs() without return types