code: def test_predict_proba_multiclass():
      # Test that predict_proba works as expected for multi class.
      X = X_digits_multi[:10]
      y = y_digits_multi[:10]
  
      clf = MLPClassifier(hidden_layer_sizes=5)
      with ignore_warnings(category=ConvergenceWarning):
          clf.fit(X, y)
      y_proba = clf.predict_proba(X)
      y_log_proba = clf.predict_log_proba(X)
  
      (n_samples, n_classes) = y.shape[0], np.unique(y).size
  
      proba_max = y_proba.argmax(axis=1)
      proba_log_max = y_log_proba.argmax(axis=1)
  
      assert y_proba.shape == (n_samples, n_classes)
      assert_array_equal(proba_max, proba_log_max)
      assert_allclose(y_log_proba, np.log(y_proba))
  
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 18 for calling a global function with example usage: test_predict_proba_multiclass() without return types