code: def test_shuffle():
      # Test that the shuffle parameter affects the training process (it should)
      X, y = make_regression(
          n_samples=50, n_features=5, n_targets=1, random_state=0
      )
  
      # The coefficients will be identical if both do or do not shuffle
      for shuffle in [True, False]:
          mlp1 = MLPRegressor(
              hidden_layer_sizes=1,
              max_iter=1,
              batch_size=1,
              random_state=0,
              shuffle=shuffle,
          )
          mlp2 = MLPRegressor(
              hidden_layer_sizes=1,
              max_iter=1,
              batch_size=1,
              random_state=0,
              shuffle=shuffle,
          )
          mlp1.fit(X, y)
          mlp2.fit(X, y)
  
          assert np.array_equal(mlp1.coefs_[0], mlp2.coefs_[0])
  
      # The coefficients will be slightly different if shuffle=True
      mlp1 = MLPRegressor(
          hidden_layer_sizes=1,
          max_iter=1,
          batch_size=1,
          random_state=0,
          shuffle=True,
      )
      mlp2 = MLPRegressor(
          hidden_layer_sizes=1,
          max_iter=1,
          batch_size=1,
          random_state=0,
          shuffle=False,
      )
      mlp1.fit(X, y)
      mlp2.fit(X, y)
  
      assert not np.array_equal(mlp1.coefs_[0], mlp2.coefs_[0])
  
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 20 for calling a global function with example usage: test_shuffle() without return types