code: def test_bayesian_ridge_parameter():
      # Test correctness of lambda_ and alpha_ parameters (GitHub issue #8224)
      X = np.array(
          [[1, 1], [3, 4], [5, 7], [4, 1], [2, 6], [3, 10], [3, 2]]
      )
      y = np.array([1, 2, 3, 2, 0, 4, 5]).T
  
      # A Ridge regression model using an alpha value equal to the ratio of
      # lambda_ and alpha_ from the Bayesian Ridge model must be identical
      br_model = BayesianRidge(compute_score=True).fit(X, y)
      rr_model = Ridge(alpha=br_model.lambda_ / br_model.alpha_).fit(
          X, y
      )
      assert_array_almost_equal(rr_model.coef_, br_model.coef_)
      assert_almost_equal(rr_model.intercept_, br_model.intercept_)
  
imports: from math import log
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.linear_model import ARDRegression, BayesianRidge, Ridge
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_less
  from sklearn.utils.extmath import fast_logdet
label_desc: Use image node_id 3 for calling a global function with example usage: test_bayesian_ridge_parameter() without return types