code: def test_scale_and_stability(Est, X, Y):
      """scale=True is equivalent to scale=False on centered/scaled data
      This allows to check numerical stability over platforms as well"""
  
      X_s, Y_s, *_ = _center_scale_xy(X, Y)
  
      X_score, Y_score = Est(scale=True).fit_transform(X, Y)
      X_s_score, Y_s_score = Est(scale=False).fit_transform(X_s, Y_s)
  
      assert_allclose(X_s_score, X_score, atol=1e-4)
      assert_allclose(Y_s_score, Y_score, atol=1e-4)
  
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 12 for calling a global function with example usage: test_scale_and_stability(Est, X, Y) without return types