code: def iris_split_dataset(iris_clean) -> Tuple[Dataset, Dataset]:
      """Return Iris train and val datasets and trained AdaBoostClassifier model."""
      train, test = train_test_split(
          iris_clean.frame, test_size=0.33, random_state=42
      )
      train_ds = Dataset(train, label="target")
      test_ds = Dataset(test, label="target")
      return train_ds, test_ds
  
imports: import logging
  import random
  from typing import Tuple
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from sklearn.datasets import load_diabetes, load_iris
  from sklearn.ensemble import AdaBoostClassifier, GradientBoostingRegressor, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import KBinsDiscretizer
  from deepchecks.core.check_result import CheckResult
  from deepchecks.tabular import Context, Dataset, TrainTestCheck
  from deepchecks.tabular.datasets.classification import lending_club
  from deepchecks.utils.logger import set_verbosity
label_desc: Use image node_id 15 for calling a global function with example usage: iris_split_dataset(iris_clean) and returns: train_ds,  test_ds