code: def test_pass_feature_importance_incorrect(iris_split_dataset):
      # Arrange
      check = DummyCheck()
      train, test = iris_split_dataset
  
      # Act & Assert
      assert_that(
          calling(check.run).with_args(
              train, test, feature_importance="wrong type"
          ),
          raises(
              DeepchecksValueError,
              "feature_importance must be given as a pandas.Series where the index is feature names and the "
              "value is the calculated importance",
          ),
      )
  
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 11 for calling a global function with example usage: test_pass_feature_importance_incorrect(iris_split_dataset) without return types