code: def test_ard_accuracy_on_easy_problem(
      global_random_seed, n_samples, n_features
  ):
      # Check that ARD converges with reasonable accuracy on an easy problem
      # (Github issue #14055)
      X = np.random.RandomState(global_random_seed).normal(
          size=(250, 3)
      )
      y = X[:, 1]
  
      regressor = ARDRegression()
      regressor.fit(X, y)
  
      abs_coef_error = np.abs(1 - regressor.coef_[1])
      assert abs_coef_error < 1e-10
  
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 11 for calling a global function with example usage: test_ard_accuracy_on_easy_problem(global_random_seed, n_samples, n_features) without return types