code: def test_preprocess_data_multioutput(setup):
      n_samples = 200
      n_features = 3
      n_outputs = 2
      X = rng.rand(n_samples, n_features)
      y = rng.rand(n_samples, n_outputs)
      expected_y_mean = np.mean(y, axis=0)
  
      # case 1
      _, yt, _, y_mean, _ = _preprocess_data(
          X, y, fit_intercept=False, normalize=False
      )
      assert_array_almost_equal(y_mean, np.zeros(n_outputs))
      assert_array_almost_equal(yt, y)
  
      _, yt, _, y_mean, _ = _preprocess_data(
          X, y, fit_intercept=True, normalize=False
      )
      assert_array_almost_equal(y_mean, expected_y_mean)
      assert_array_almost_equal(yt, y - y_mean)
  
      _, yt, _, y_mean, _ = _preprocess_data(
          X, y, fit_intercept=True, normalize=True
      )
      assert_array_almost_equal(y_mean, expected_y_mean)
      assert_array_almost_equal(yt, y - y_mean)
  
      # case 2
      X = sparse.csc_matrix(X)
      error_msg = "Does not support sparse input!"
      with pytest.raises(NotImplementedError, match=error_msg):
          _, yt, _, y_mean, _ = _preprocess_data(
              X, y, fit_intercept=False, normalize=False
          )
  
      with pytest.raises(NotImplementedError, match=error_msg):
          _, yt, _, y_mean, _ = _preprocess_data(
              X, y, fit_intercept=True, normalize=False
          )
  
      with pytest.raises(NotImplementedError, match=error_msg):
          _, yt, _, y_mean, _ = _preprocess_data(
              X, y, fit_intercept=True, normalize=True
          )
  
imports: import re
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal, assert_almost_equal, assert_allclose
  from scipy import sparse
  from scipy import linalg
  from sklearn.datasets import make_sparse_uncorrelated, make_regression, load_iris
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model._base import make_dataset
  from sklearn.utils import check_random_state
  from ..None import LinearRegression
  from .._base import _preprocess_data, _rescale_data
label_desc: Use image node_id 13 for calling a global function with example usage: test_preprocess_data_multioutput(setup) without return types