code: def test_model_wrong_input(iris_labeled_dataset):
      bad_model = "wrong_input"
      # Act & Assert
      assert_that(
          calling(SingleDatasetPerformance().run).with_args(
              iris_labeled_dataset, bad_model
          ),
          raises(
              ModelValidationError,
              r"Model supplied does not meets the minimal interface requirements. Read more about .*",
          ),
      )
  
imports: from typing import List
  from hamcrest import assert_that, calling, close_to, equal_to, has_entries, has_items, has_length, instance_of, none, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks import SingleDatasetPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_MULTICLASS_SCORERS, DEFAULT_REGRESSION_SCORERS
  from tests.base.utils import equal_condition_result
  from tests.common import is_nan
label_desc: Use image node_id 2 for calling a global function with example usage: test_model_wrong_input(iris_labeled_dataset) without return types