code: def test_pass_feature_importance_correct(iris_split_dataset):
      # Arrange
      check = DummyCheck()
      train, test = iris_split_dataset
      feature_importance = pd.Series(
          data=np.random.rand(len(train.features)), index=train.features
      )
  
      # Act
      result = check.run(
          train, test, feature_importance=feature_importance
      )
      context: Context = result.value
  
      # Assert
      assert_that(context._calculated_importance, is_(True))
      assert_that(context._feature_importance is not None)
  
imports: import numpy
  import pandas
  from hamcrest import *
  from deepchecks import __version__
  from deepchecks.core import BaseCheck, CheckResult, ConditionResult
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular import Context, TrainTestCheck
label_desc: Use image node_id 12 for calling a global function with example usage: test_pass_feature_importance_correct(iris_split_dataset) without return types