code: def diabetes(diabetes_df) -> Tuple[Dataset, Dataset]:
      """Return diabetes dataset split to train and test as Datasets."""
      train_df, test_df = train_test_split(
          diabetes_df, test_size=0.33, random_state=42
      )
      train = Dataset(train_df, label="target", cat_features=["sex"])
      test = Dataset(test_df, label="target", cat_features=["sex"])
      return train, test
  
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 4 for calling a global function with example usage: diabetes(diabetes_df) and returns: train,  test