code: def test_mlp_loading_from_joblib_partial_fit(tmp_path):
      """Loading from MLP and partial fitting updates weights. Non-regression
      test for #19626."""
      pre_trained_estimator = MLPRegressor(
          hidden_layer_sizes=(42,),
          random_state=42,
          learning_rate_init=0.01,
          max_iter=200,
      )
      features, target = [[2]], [4]
  
      # Fit on x=2, y=4
      pre_trained_estimator.fit(features, target)
  
      # dump and load model
      pickled_file = tmp_path / "mlp.pkl"
      joblib.dump(pre_trained_estimator, pickled_file)
      load_estimator = joblib.load(pickled_file)
  
      # Train for a more epochs on point x=2, y=1
      fine_tune_features, fine_tune_target = [[2]], [1]
  
      for _ in range(200):
          load_estimator.partial_fit(
              fine_tune_features, fine_tune_target
          )
  
      # finetuned model learned the new target
      predicted_value = load_estimator.predict(fine_tune_features)
      assert_allclose(predicted_value, fine_tune_target, rtol=1e-4)
  
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 34 for calling a global function with example usage: test_mlp_loading_from_joblib_partial_fit(tmp_path) without return types