code: def test_multi_label_without_drift(
      dummy_multilabel_textdata_train_test,
  ):
      # Arrange
      train, test = dummy_multilabel_textdata_train_test
      check = LabelDrift(
          min_samples=20
      ).add_condition_drift_score_less_than()
      # Act
      result = check.run(train_dataset=train, test_dataset=test)
      condition_result = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          has_items(
              equal_condition_result(
                  is_pass=True,
                  details="Label's drift score Cramer's V is 0.02",
                  name="Label drift score < 0.15",
              )
          ),
      )
      assert_that(result.value["Drift score"], close_to(0.02, 0.01))
  
file_path: deepchecks/tests/nlp/checks/train_test_validation/label_drift_test.py
imports: from hamcrest import assert_that, close_to, has_items
  from deepchecks.nlp.checks import LabelDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 4 for calling a global function with example usage: test_multi_label_without_drift(dummy_multilabel_textdata_train_test) without return types