code: def test_toy_bayesian_ridge_object():
      # Test BayesianRidge on toy
      X = np.array([[1], [2], [6], [8], [10]])
      Y = np.array([1, 2, 6, 8, 10])
      clf = BayesianRidge(compute_score=True)
      clf.fit(X, Y)
  
      # Check that the model could approximately learn the identity function
      test = [[1], [3], [4]]
      assert_array_almost_equal(clf.predict(test), [1, 3, 4], 2)
  
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 5 for calling a global function with example usage: test_toy_bayesian_ridge_object() without return types