code: def test_logistic_regression():
      train_df = pd.DataFrame(
          [[23, True], [19, False], [15, False], [5, True]],
          columns=["age", "smoking"],
          index=[0, 1, 2, 3],
      )
      train_y = pd.Series([1, 1, 0, 0])
  
      logreg = LogisticRegression()
      logreg.fit(train_df, train_y)
  
      ds_train = Dataset(df=train_df, label=train_y)
  
      feature_importances, fi_type = run_fi_calculation(
          logreg, ds_train
      )
      assert_that(feature_importances.sum(), close_to(1, 0.000001))
      assert_that(fi_type, is_("coef_"))
  
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 6 for calling a global function with example usage: test_logistic_regression() without return types