code: def iris(iris_clean) -> t.Tuple[Dataset, Dataset, AdaBoostClassifier]:
      # note: to run classification suite successfully we need to modify iris dataframe
      # according to suite needs
      df = t.cast(pd.DataFrame, iris_clean.frame.copy())
      df["index"] = range(len(df))
      df["date"] = datetime.now()
  
      train, test = t.cast(
          t.Tuple[pd.DataFrame, pd.DataFrame],
          train_test_split(df, test_size=0.33, random_state=42),
      )
  
      train, test = (
          Dataset(
              train,
              label="target",
              datetime_name="date",
              index_name="index",
          ),
          Dataset(
              test,
              label="target",
              datetime_name="date",
              index_name="index",
          ),
      )
  
      model = AdaBoostClassifier(random_state=0)
      model.fit(
          train.data[train.features], train.data[train.label_name]
      )
  
      return train, test, model
  
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 1 for calling a global function with example usage: iris(iris_clean) and returns: train,  test,  model