code: def load_diabetes(
      raw: bool = False, test_set: float = 0.3
  ) -> DATASET_TYPE:
      """
      Loads the Diabetes Regression dataset from sklearn.
  
      :param raw: `True` if no preprocessing should be applied to the data. Otherwise, data is normalized to 1.
      :param test_set: Proportion of the data to use as validation split. The value should be between 0 and 1.
      :return: Entire dataset and labels.
      """
      from sklearn.datasets import load_diabetes as load_diabetes_sk
  
      diabetes = load_diabetes_sk()
      data = diabetes.data
      if not raw:
          data /= np.amax(data, axis=0)
      targets = diabetes.target
  
      min_, max_ = np.amin(data), np.amax(data)
  
      # Shuffle data set
      random_indices = np.random.permutation(len(data))
      data, targets = data[random_indices], targets[random_indices]
  
      # Split training and test sets
      split_index = int((1 - test_set) * len(data))
      x_train = data[:split_index]
      y_train = targets[:split_index]
      x_test = data[split_index:]
      y_test = targets[split_index:]
  
      return (x_train, y_train), (x_test, y_test), min_, max_
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import math
  import os
  import shutil
  import sys
  import tarfile
  import warnings
  import zipfile
  from functools import wraps
  from inspect import signature
  from typing import TYPE_CHECKING, Callable, List, Dict, Optional, Tuple, Union
  import numpy
  import six
  from scipy.special import gammainc
  from tqdm.auto import tqdm
  from art import config
label_desc: Use image node_id 32 for calling a global function with example usage: load_diabetes(raw, test_set) and returns: x_train,  y_train,  x_test,  y_test,  min_,  max_