code: def test_tolerance():
      # Test tolerance.
      # It should force the solver to exit the loop when it converges.
      X = [[3, 2], [1, 6]]
      y = [1, 0]
      clf = MLPClassifier(tol=0.5, max_iter=3000, solver="sgd")
      clf.fit(X, y)
      assert clf.max_iter > clf.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 22 for calling a global function with example usage: test_tolerance() without return types