code: def data(request):
      """
      Creates a fixture of train and test splits for the sklearn digits dataset
      For ease of use returns a Dataset named tuple composed of two Split tuples.
      """
      X, y = make_regression(
          n_samples=500,
          n_features=22,
          n_informative=8,
          random_state=42,
          noise=0.2,
          bias=0.2,
      )
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, random_state=11
      )
  
      # Set a class attribute for digits
      request.cls.data = Dataset(
          Split(X_train, X_test), Split(y_train, y_test)
      )
  
imports: import sys
  import pytest
  import matplotlib
  import matplotlib.pyplot
  from yellowbrick.datasets import load_energy
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.regressor.residuals import ResidualsPlot, residuals_plot
  from unittest import mock
  from tests.fixtures import Dataset, Split
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from sklearn.datasets import make_regression
  from sklearn.linear_model import Ridge, Lasso
  from sklearn.neural_network import MLPRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 1 for calling a global function with example usage: data(request) without return types