code: def test_alpha():
      # Test that larger alpha yields weights closer to zero
      X = X_digits_binary[:100]
      y = y_digits_binary[:100]
  
      alpha_vectors = []
      alpha_values = np.arange(2)
      absolute_sum = lambda x: np.sum(np.abs(x))
  
      for alpha in alpha_values:
          mlp = MLPClassifier(
              hidden_layer_sizes=10, alpha=alpha, random_state=1
          )
          with ignore_warnings(category=ConvergenceWarning):
              mlp.fit(X, y)
          alpha_vectors.append(
              np.array(
                  [
                      absolute_sum(mlp.coefs_[0]),
                      absolute_sum(mlp.coefs_[1]),
                  ]
              )
          )
  
      for i in range(len(alpha_values) - 1):
          assert (alpha_vectors[i] > alpha_vectors[i + 1]).all()
  
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 1 for calling a global function with example usage: test_alpha() without return types