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_

    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.
    
art/utils.py