code: def test_mlp_warm_start_no_convergence(MLPEstimator, solver):
      """Check that we stop the number of iteration at `max_iter` when warm starting.
  
      Non-regression test for:
      https://github.com/scikit-learn/scikit-learn/issues/24764
      """
      model = MLPEstimator(
          solver=solver,
          warm_start=True,
          early_stopping=False,
          max_iter=10,
          n_iter_no_change=np.inf,
          random_state=0,
      )
  
      with pytest.warns(ConvergenceWarning):
          model.fit(X_iris, y_iris)
      assert model.n_iter_ == 10
  
      model.set_params(max_iter=20)
      with pytest.warns(ConvergenceWarning):
          model.fit(X_iris, y_iris)
      assert model.n_iter_ == 20
  
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 37 for calling a global function with example usage: test_mlp_warm_start_no_convergence(MLPEstimator, solver) without return types