code: def test_early_stopping_stratified():
      # Make sure data splitting for early stopping is stratified
      X = [[1, 2], [2, 3], [3, 4], [4, 5]]
      y = [0, 0, 0, 1]
  
      mlp = MLPClassifier(early_stopping=True)
      with pytest.raises(
          ValueError,
          match="The least populated class in y has only 1 member",
      ):
          mlp.fit(X, y)
  
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 30 for calling a global function with example usage: test_early_stopping_stratified() without return types