code: def test_partial_fit_regression():
      # Test partial_fit on regression.
      # `partial_fit` should yield the same results as 'fit' for regression.
      X = X_reg
      y = y_reg
  
      for momentum in [0, 0.9]:
          mlp = MLPRegressor(
              solver="sgd",
              max_iter=100,
              activation="relu",
              random_state=1,
              learning_rate_init=0.01,
              batch_size=X.shape[0],
              momentum=momentum,
          )
          with warnings.catch_warnings(record=True):
              # catch convergence warning
              mlp.fit(X, y)
          pred1 = mlp.predict(X)
          mlp = MLPRegressor(
              solver="sgd",
              activation="relu",
              learning_rate_init=0.01,
              random_state=1,
              batch_size=X.shape[0],
              momentum=momentum,
          )
          for i in range(100):
              mlp.partial_fit(X, y)
  
          pred2 = mlp.predict(X)
          assert_allclose(pred1, pred2)
          score = mlp.score(X, y)
          assert score > 0.65
  
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 14 for calling a global function with example usage: test_partial_fit_regression() without return types