code: def test_lbfgs_regression_maxfun(X, y):
      # Test lbfgs parameter max_fun.
      # It should independently limit the number of iterations for lbfgs.
      max_fun = 10
      # regression tests
      for activation in ACTIVATION_TYPES:
          mlp = MLPRegressor(
              solver="lbfgs",
              hidden_layer_sizes=50,
              tol=0.0,
              max_iter=150,
              max_fun=max_fun,
              shuffle=True,
              random_state=1,
              activation=activation,
          )
          with pytest.warns(ConvergenceWarning):
              mlp.fit(X, y)
              assert max_fun >= mlp.n_iter_
  
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 7 for calling a global function with example usage: test_lbfgs_regression_maxfun(X, y) without return types