code: def test_unfitted(iris_dataset):
      clf = AdaBoostClassifier()
      assert_that(
          calling(_calculate_feature_importance).with_args(
              clf,
              iris_dataset,
              model_classes=None,
              observed_classes=None,
              task_type=None,
          ),
          raises(
              ModelValidationError,
              "Got error when trying to predict with model on dataset: "
              "This AdaBoostClassifier instance is not fitted yet. "
              "Call 'fit' with appropriate arguments before using this estimator.",
          ),
      )
  
imports: import pandas
  import pytest
  from hamcrest import any_of, assert_that, calling, close_to, contains_exactly, contains_string, equal_to, has_length, is_, none, not_none, raises
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.neural_network import MLPClassifier, MLPRegressor
  from sklearn.pipeline import Pipeline
  from deepchecks.core.errors import DeepchecksTimeoutError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.utils.feature_importance import _calculate_feature_importance, calculate_feature_importance_or_none, column_importance_sorter_df, column_importance_sorter_dict, validate_feature_importance
  from deepchecks.tabular.utils.task_inference import get_all_labels, infer_classes_from_model, infer_task_type_by_class_number, infer_task_type_by_labels
  from deepchecks.tabular.utils.task_type import TaskType
label_desc: Use image node_id 3 for calling a global function with example usage: test_unfitted(iris_dataset) without return types