code: def test_n_iter_no_change():
      # test n_iter_no_change using binary data set
      # the classifying fitting process is not prone to loss curve fluctuations
      X = X_digits_binary[:100]
      y = y_digits_binary[:100]
      tol = 0.01
      max_iter = 3000
  
      # test multiple n_iter_no_change
      for n_iter_no_change in [2, 5, 10, 50, 100]:
          clf = MLPClassifier(
              tol=tol,
              max_iter=max_iter,
              solver="sgd",
              n_iter_no_change=n_iter_no_change,
          )
          clf.fit(X, y)
  
          # validate n_iter_no_change
          assert clf._no_improvement_count == n_iter_no_change + 1
          assert 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 28 for calling a global function with example usage: test_n_iter_no_change() without return types