code: def test_linear_regression(diabetes):
      ds, _ = diabetes
      clf = LinearRegression()
      clf.fit(ds.data[ds.features], ds.data[ds.label_name])
      feature_importances, fi_type = run_fi_calculation(clf, ds)
      assert_that(
          feature_importances.max(),
          close_to(0.225374532399, 0.0000000001),
      )
      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 4 for calling a global function with example usage: test_linear_regression(diabetes) without return types