code: def test_partial_fit_classification():
      # Test partial_fit on classification.
      # `partial_fit` should yield the same results as 'fit' for binary and
      # multi-class classification.
      for X, y in classification_datasets:
          mlp = MLPClassifier(
              solver="sgd",
              max_iter=100,
              random_state=1,
              tol=0,
              alpha=1e-5,
              learning_rate_init=0.2,
          )
  
          with ignore_warnings(category=ConvergenceWarning):
              mlp.fit(X, y)
          pred1 = mlp.predict(X)
          mlp = MLPClassifier(
              solver="sgd",
              random_state=1,
              alpha=1e-5,
              learning_rate_init=0.2,
          )
          for i in range(100):
              mlp.partial_fit(X, y, classes=np.unique(y))
          pred2 = mlp.predict(X)
          assert_array_equal(pred1, pred2)
          assert mlp.score(X, y) > 0.95
  
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 12 for calling a global function with example usage: test_partial_fit_classification() without return types