code: def test_lbfgs_classification(X, y):
      # Test lbfgs on classification.
      # It should achieve a score higher than 0.95 for the binary and multi-class
      # versions of the digits dataset.
      X_train = X[:150]
      y_train = y[:150]
      X_test = X[150:]
      expected_shape_dtype = (X_test.shape[0], y_train.dtype.kind)
  
      for activation in ACTIVATION_TYPES:
          mlp = MLPClassifier(
              solver="lbfgs",
              hidden_layer_sizes=50,
              max_iter=150,
              shuffle=True,
              random_state=1,
              activation=activation,
          )
          mlp.fit(X_train, y_train)
          y_predict = mlp.predict(X_test)
          assert mlp.score(X_train, y_train) > 0.95
          assert (
              y_predict.shape[0],
              y_predict.dtype.kind,
          ) == expected_shape_dtype
  
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 4 for calling a global function with example usage: test_lbfgs_classification(X, y) without return types