code: def test_multioutput_regression():
      # Test that multi-output regression works as expected
      X, y = make_regression(n_samples=200, n_targets=5)
      mlp = MLPRegressor(
          solver="lbfgs",
          hidden_layer_sizes=50,
          max_iter=200,
          random_state=1,
      )
      mlp.fit(X, y)
      assert mlp.score(X, y) > 0.9
  
imports: import re
  import sys
  import warnings
  from io import StringIO
  import joblib
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equal
  from sklearn.datasets import load_digits, load_iris, make_multilabel_classification, make_regression
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.metrics import roc_auc_score
  from sklearn.neural_network import MLPClassifier, MLPRegressor
  from sklearn.preprocessing import LabelBinarizer, MinMaxScaler, scale
  from sklearn.utils._testing import ignore_warnings
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 10 for calling a global function with example usage: test_multioutput_regression() without return types