code: def multi_index_dataframe():
      """Return a multi-indexed DataFrame."""
      return pd.DataFrame(
          {
              "a": [1, 2, 3, 4],
              "b": [5, 6, 7, 8],
              "c": [9, 10, 11, 12],
              "d": [13, 14, 15, 16],
          },
          index=pd.MultiIndex.from_product(
              [["a", "b"], ["c", "d"]],
              names=["first", "second"],
          ),
      )
  
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 2 for calling a global function with example usage: multi_index_dataframe() and returns: pd