code: def test_model_wrong_input(iris_labeled_dataset):
      bad_model = "wrong_input"
      # Act & Assert
      assert_that(
          calling(TrainTestPerformance().run).with_args(
              iris_labeled_dataset, iris_labeled_dataset, bad_model
          ),
          raises(
              ModelValidationError,
              r"Model supplied does not meets the minimal interface requirements. Read more about .*",
          ),
      )
  
imports: import re
  from typing import List
  import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_items, has_length, instance_of, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.metrics import jaccard_score, make_scorer
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks.model_evaluation import TrainTestPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_REGRESSION_SCORERS, MULTICLASS_SCORERS_NON_AVERAGE
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 3 for calling a global function with example usage: test_model_wrong_input(iris_labeled_dataset) without return types