code: def iris_split_dataset_and_model_rf(
      iris_split_dataset,
  ) -> Tuple[Dataset, Dataset, RandomForestClassifier]:
      """Return Iris train and val datasets and trained RF model."""
      train_ds, test_ds = iris_split_dataset
      clf = RandomForestClassifier(
          random_state=0, n_estimators=10, max_depth=2
      )
      clf.fit(train_ds.features_columns, train_ds.label_col)
      return train_ds, test_ds, clf
  
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 19 for calling a global function with example usage: iris_split_dataset_and_model_rf(iris_split_dataset) and returns: train_ds,  test_ds,  clf