code: def test_precision_recall_f1_no_labels_check_warnings(setup, average):
      y_true = mt.zeros((20, 3))
      y_pred = mt.zeros_like(y_true)
  
      func = precision_recall_fscore_support
      with pytest.warns(UndefinedMetricWarning):
          p, r, f, s = func(y_true, y_pred, average=average, beta=1.0)
          p, r, f = fetch(execute(p, r, f))
  
      assert_almost_equal(p, 0)
      assert_almost_equal(r, 0)
      assert_almost_equal(f, 0)
      assert s is None
  
      with pytest.warns(UndefinedMetricWarning):
          fbeta = fetch(
              execute(
                  fbeta_score(y_true, y_pred, average=average, beta=1.0)
              )
          )
  
      assert_almost_equal(fbeta, 0)
  
imports: import numpy
  import pytest
  from sklearn import datasets, svm
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_equal, assert_array_almost_equal
  from sklearn.utils._mocking import MockDataFrame
  from ....None import execute, fetch
  from ....None import tensor
  from ....lib.sparse import SparseNDArray
  from ..None import accuracy_score, log_loss
  from .._classification import _check_targets, multilabel_confusion_matrix, precision_recall_fscore_support, precision_score, recall_score, f1_score, fbeta_score
label_desc: Use image node_id 10 for calling a global function with example usage: test_precision_recall_f1_no_labels_check_warnings(setup, average) without return types