code: def test_generic_boost(
      iris_split_dataset_and_model_cat: t.Tuple[
          Dataset, Dataset, CatBoostClassifier
      ],
      iris_split_dataset_and_model_xgb: t.Tuple[
          Dataset, Dataset, XGBClassifier
      ],
      iris_split_dataset_and_model_lgbm: t.Tuple[
          Dataset, Dataset, LGBMClassifier
      ],
      diabetes_split_dataset_and_model_xgb: t.Tuple[
          Dataset, Dataset, CatBoostRegressor
      ],
      diabetes_split_dataset_and_model_lgbm: t.Tuple[
          Dataset, Dataset, XGBRegressor
      ],
      diabetes_split_dataset_and_model_cat: t.Tuple[
          Dataset, Dataset, LGBMRegressor
      ],
  ):
      (
          iris_cat_train,
          iris_cat_test,
          iris_cat_model,
      ) = iris_split_dataset_and_model_cat
      (
          iris_xgb_train,
          iris_xgb_test,
          iris_xgb_model,
      ) = iris_split_dataset_and_model_xgb
      (
          iris_lgbm_train,
          iris_lgbm_test,
          iris_lgbm_model,
      ) = iris_split_dataset_and_model_lgbm
  
      (
          diabetes_cat_train,
          diabetes_cat_test,
          diabetes_cat_model,
      ) = diabetes_split_dataset_and_model_cat
      (
          diabetes_xgb_train,
          diabetes_xgb_test,
          diabetes_xgb_model,
      ) = diabetes_split_dataset_and_model_xgb
      (
          diabetes_lgbm_train,
          diabetes_lgbm_test,
          diabetes_lgbm_model,
      ) = diabetes_split_dataset_and_model_lgbm
  
      suite = suites.full_suite()
  
      arguments = (
          dict(
              train_dataset=iris_cat_train,
              test_dataset=iris_cat_test,
              model=iris_cat_model,
          ),
          dict(
              train_dataset=iris_xgb_train,
              test_dataset=iris_xgb_test,
              model=iris_xgb_model,
          ),
          dict(
              train_dataset=iris_lgbm_train,
              test_dataset=iris_lgbm_test,
              model=iris_lgbm_model,
          ),
          dict(
              train_dataset=diabetes_cat_train,
              test_dataset=diabetes_cat_test,
              model=diabetes_cat_model,
          ),
          dict(
              train_dataset=diabetes_xgb_train,
              test_dataset=diabetes_xgb_test,
              model=diabetes_xgb_model,
          ),
          dict(
              train_dataset=diabetes_lgbm_train,
              test_dataset=diabetes_lgbm_test,
              model=diabetes_lgbm_model,
          ),
      )
  
      for args in arguments:
          result = suite.run(**args)
          length = get_expected_results_length(suite, args)
          validate_suite_result(result, length)
  
imports: import typing
  from datetime import datetime
  import pandas
  import pytest
  from catboost import CatBoostClassifier, CatBoostRegressor
  from hamcrest import assert_that, contains_exactly, has_length
  from lightgbm import LGBMClassifier, LGBMRegressor
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.model_selection import train_test_split
  from xgboost import XGBClassifier, XGBRegressor
  from deepchecks.tabular import Dataset, suites
  from tests.common import get_expected_results_length, validate_suite_result
label_desc: Use image node_id 16 for calling a global function with example usage: test_generic_boost(iris_split_dataset_and_model_cat, iris_split_dataset_and_model_xgb, iris_split_dataset_and_model_lgbm, diabetes_split_dataset_and_model_xgb, diabetes_split_dataset_and_model_lgbm, diabetes_split_dataset_and_model_cat) without return types