code: def test_loadings_converges(global_random_seed):
      """Test that CCA converges. Non-regression test for #19549."""
      X, y = make_regression(
          n_samples=200,
          n_features=20,
          n_targets=20,
          random_state=global_random_seed,
      )
  
      cca = CCA(n_components=10, max_iter=500)
  
      with warnings.catch_warnings():
          warnings.simplefilter("error", ConvergenceWarning)
  
          cca.fit(X, y)
  
      # Loadings converges to reasonable values
      assert np.all(np.abs(cca.x_loadings_) < 1)
  
imports: import warnings
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.cross_decomposition import CCA, PLSSVD, PLSCanonical, PLSRegression
  from sklearn.cross_decomposition._pls import _center_scale_xy, _get_first_singular_vectors_power_method, _get_first_singular_vectors_svd, _svd_flip_1d
  from sklearn.datasets import load_linnerud, make_regression
  from sklearn.ensemble import VotingRegressor
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression
  from sklearn.utils import check_random_state
  from sklearn.utils.extmath import svd_flip
label_desc: Use image node_id 17 for calling a global function with example usage: test_loadings_converges(global_random_seed) without return types